diff --git a/test_bmad.ipynb b/test_bmad.ipynb
deleted file mode 100644
index 76e7a524..00000000
--- a/test_bmad.ipynb
+++ /dev/null
@@ -1,3172 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from copy import deepcopy\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "import cheetah"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PosixPath('$LCLS_LATTICE/bmad/models/cu_hxr/cu_hxr.lat.bmad')"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lattice_file_path = (\n",
-    "    Path(\"$LCLS_LATTICE\") / \"bmad\" / \"models\" / \"cu_hxr\" / \"cu_hxr.lat.bmad\"\n",
-    ")\n",
-    "lattice_file_path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lattice_dir = str((Path(\".\").absolute().parent / \"lcls-lattice\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Segment([Drift(length=-0.87, name=\"dl00\", device=\"cpu\"), Drift(length=0.87, name=\"loadlock\", device=\"cpu\"), Marker(name=beggun, device=\"cpu\"), Solenoid(length=0.00, k=0.00, misalignment=(0, 0), name=\"sol1bk\", device=\"cpu\"), Marker(name=dbmark80, device=\"cpu\"), Marker(name=cathode, device=\"cpu\"), Drift(length=0.10, name=\"dl01a\", device=\"cpu\"), Solenoid(length=0.20, k=0.00, misalignment=(0, 0), name=\"sol1\", device=\"cpu\"), Drift(length=0.08, name=\"dl01a1\", device=\"cpu\"), Marker(name=vv01, device=\"cpu\"), Drift(length=0.12, name=\"dl01a2\", device=\"cpu\"), Marker(name=am00, device=\"cpu\"), Drift(length=0.10, name=\"dl01a3\", device=\"cpu\"), Marker(name=am01, device=\"cpu\"), Drift(length=0.02, name=\"dl01a4\", device=\"cpu\"), Marker(name=yag01, device=\"cpu\"), Drift(length=0.01, name=\"dl01a5\", device=\"cpu\"), Marker(name=fc01, device=\"cpu\"), Drift(length=0.08, name=\"dl01b\", device=\"cpu\"), Marker(name=im01, device=\"cpu\"), Drift(length=0.13, name=\"dl01c\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc01\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc01\", device=\"cpu\"), Drift(length=0.06, name=\"dl01h\", device=\"cpu\"), Marker(name=bpm2, device=\"cpu\"), Drift(length=0.09, name=\"dl01d\", device=\"cpu\"), Marker(name=dbmark81, device=\"cpu\"), Marker(name=endgun, device=\"cpu\"), Marker(name=begl0, device=\"cpu\"), Drift(length=0.13, name=\"dbxg\", device=\"cpu\"), Drift(length=0.11, name=\"dl01e\", device=\"cpu\"), Marker(name=bpm3, device=\"cpu\"), Drift(length=0.14, name=\"dl01f\", device=\"cpu\"), Marker(name=cr01, device=\"cpu\"), Drift(length=0.03, name=\"dl01f2\", device=\"cpu\"), Marker(name=yag02, device=\"cpu\"), Drift(length=0.07, name=\"dl01g\", device=\"cpu\"), Marker(name=zlin00, device=\"cpu\"), Cavity(length=3.10, voltage=58010690.67, phase=1.10, frequency=2856000000.00, name=\"l0a\", device=\"cpu\"), Marker(name=l0awake, device=\"cpu\"), Marker(name=l0bbeg, device=\"cpu\"), Drift(length=0.06, name=\"dl02a1\", device=\"cpu\"), Marker(name=yag03, device=\"cpu\"), Drift(length=0.11, name=\"dl02a2\", device=\"cpu\"), Drift(length=0.07, name=\"dl02a3\", device=\"cpu\"), Quadrupole(length=0.11, k1=-9.879050939358796, misalignment=(0, 0), tilt=0.00, name=\"qa01\", device=\"cpu\"), Drift(length=0.09, name=\"dl02b1\", device=\"cpu\"), Marker(name=ph01, device=\"cpu\"), Drift(length=0.13, name=\"dl02b2\", device=\"cpu\"), Quadrupole(length=0.11, k1=10.77552715037701, misalignment=(0, 0), tilt=0.00, name=\"qa02\", device=\"cpu\"), Drift(length=0.07, name=\"dl02c\", device=\"cpu\"), Cavity(length=3.10, voltage=71013086.85, phase=1.10, frequency=2856000000.00, name=\"l0b\", device=\"cpu\"), Marker(name=endl0, device=\"cpu\"), Marker(name=begdl1_1, device=\"cpu\"), Marker(name=emat, device=\"cpu\"), Drift(length=0.02, name=\"de00\", device=\"cpu\"), Drift(length=0.02, name=\"de00a\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.2743176368428006, misalignment=(0, 0), tilt=0.00, name=\"qe01\", device=\"cpu\"), Drift(length=0.08, name=\"de01a\", device=\"cpu\"), Marker(name=im02, device=\"cpu\"), Drift(length=0.12, name=\"de01b\", device=\"cpu\"), Marker(name=vv02, device=\"cpu\"), Drift(length=0.09, name=\"de01c\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.7829366616960555, misalignment=(0, 0), tilt=0.00, name=\"qe02\", device=\"cpu\"), Drift(length=0.13, name=\"dh00\", device=\"cpu\"), Marker(name=lhbeg, device=\"cpu\"), Dipole(length=0.12, angle=-0.131709391614, e1=0.00,e2=-0.13,tilt=0.00,fringe_integral=0.40,fringe_integral_exit=0.40,gap=0.01,name=\"bxh1\", device=\"cpu\"), Drift(length=0.14, name=\"dh01\", device=\"cpu\"), Dipole(length=0.12, angle=0.131709391614, e1=0.13,e2=0.00,tilt=0.00,fringe_integral=0.40,fringe_integral_exit=0.40,gap=0.01,name=\"bxh2\", device=\"cpu\"), Drift(length=0.06, name=\"dh02a\", device=\"cpu\"), Marker(name=otrh1, device=\"cpu\"), Drift(length=0.08, name=\"dh03a\", device=\"cpu\"), Undulator(length=0.54, is_active=False, name=\"lh_und\", device=\"cpu\"), Drift(length=0.07, name=\"dh03b\", device=\"cpu\"), Marker(name=otrh2, device=\"cpu\"), Drift(length=0.09, name=\"dh02b\", device=\"cpu\"), Dipole(length=0.12, angle=0.131709391614, e1=0.00,e2=0.13,tilt=0.00,fringe_integral=0.40,fringe_integral_exit=0.40,gap=0.01,name=\"bxh3\", device=\"cpu\"), Drift(length=0.14, name=\"dh01\", device=\"cpu\"), Dipole(length=0.12, angle=-0.131709391614, e1=-0.13,e2=0.00,tilt=0.00,fringe_integral=0.40,fringe_integral_exit=0.40,gap=0.01,name=\"bxh4\", device=\"cpu\"), Marker(name=lhend, device=\"cpu\"), Drift(length=0.12, name=\"dh06\", device=\"cpu\"), Cavity(length=0.67, voltage=0.00, phase=0.00, frequency=2856000000.00, name=\"tcav0\", device=\"cpu\"), Drift(length=0.04, name=\"de02\", device=\"cpu\"), Quadrupole(length=0.11, k1=11.56804846753815, misalignment=(0, 0), tilt=0.00, name=\"qe03\", device=\"cpu\"), Drift(length=0.11, name=\"de03a\", device=\"cpu\"), Drift(length=0.05, name=\"de03b\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc07\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc07\", device=\"cpu\"), Drift(length=0.14, name=\"de03c\", device=\"cpu\"), Quadrupole(length=0.11, k1=-6.78990977461672, misalignment=(0, 0), tilt=0.00, name=\"qe04\", device=\"cpu\"), Drift(length=0.14, name=\"de04\", device=\"cpu\"), Marker(name=ws01, device=\"cpu\"), Drift(length=0.15, name=\"de05\", device=\"cpu\"), Marker(name=otr1, device=\"cpu\"), Drift(length=0.10, name=\"de05c\", device=\"cpu\"), Marker(name=vv03, device=\"cpu\"), Drift(length=1.41, name=\"de06a\", device=\"cpu\"), Marker(name=rst1, device=\"cpu\"), Drift(length=0.25, name=\"de06b\", device=\"cpu\"), Marker(name=ws02, device=\"cpu\"), Drift(length=0.08, name=\"de05a\", device=\"cpu\"), Marker(name=mrk0, device=\"cpu\"), Drift(length=0.08, name=\"de05a\", device=\"cpu\"), Marker(name=otr2, device=\"cpu\"), Drift(length=0.16, name=\"de06d\", device=\"cpu\"), Marker(name=bpm10, device=\"cpu\"), Drift(length=1.60, name=\"de06e\", device=\"cpu\"), Marker(name=ws03, device=\"cpu\"), Drift(length=0.15, name=\"de05\", device=\"cpu\"), Marker(name=otr3, device=\"cpu\"), Drift(length=0.15, name=\"de07\", device=\"cpu\"), Quadrupole(length=0.11, k1=15.082997416875, misalignment=(0, 0), tilt=0.00, name=\"qm01\", device=\"cpu\"), Drift(length=0.15, name=\"de08\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc08\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc08\", device=\"cpu\"), Drift(length=0.18, name=\"de08a\", device=\"cpu\"), Marker(name=vv04, device=\"cpu\"), Drift(length=0.12, name=\"de08b\", device=\"cpu\"), Quadrupole(length=0.11, k1=-11.969358129236, misalignment=(0, 0), tilt=0.00, name=\"qm02\", device=\"cpu\"), Drift(length=0.22, name=\"de09\", device=\"cpu\"), Marker(name=dbmark82, device=\"cpu\"), Marker(name=enddl1_1, device=\"cpu\"), Marker(name=begdl1_2, device=\"cpu\"), Dipole(length=0.20, angle=-0.30543261909900765, e1=-0.15,e2=-0.15,tilt=0.00,fringe_integral=0.45,fringe_integral_exit=0.45,gap=0.01,name=\"bx01\", device=\"cpu\"), Drift(length=0.40, name=\"db00a\", device=\"cpu\"), Marker(name=otr4, device=\"cpu\"), Drift(length=0.16, name=\"db00b\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc09\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc09\", device=\"cpu\"), Drift(length=0.17, name=\"db00c\", device=\"cpu\"), Quadrupole(length=0.11, k1=22.169715923062, misalignment=(0, 0), tilt=0.00, name=\"qb\", device=\"cpu\"), Drift(length=0.29, name=\"db00d\", device=\"cpu\"), Marker(name=ws04, device=\"cpu\"), Drift(length=0.44, name=\"db00e\", device=\"cpu\"), Dipole(length=0.20, angle=-0.30543261909900765, e1=-0.15,e2=-0.15,tilt=0.00,fringe_integral=0.45,fringe_integral_exit=0.45,gap=0.01,name=\"bx02\", device=\"cpu\"), Marker(name=cnt0, device=\"cpu\"), Marker(name=dbmark83, device=\"cpu\"), Drift(length=0.22, name=\"dm00\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc10\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc10\", device=\"cpu\"), Drift(length=0.14, name=\"dm00a\", device=\"cpu\"), Quadrupole(length=0.11, k1=-8.29494541292, misalignment=(0, 0), tilt=0.00, name=\"qm03\", device=\"cpu\"), Drift(length=0.09, name=\"dm01\", device=\"cpu\"), Drift(length=0.21, name=\"dm01a\", device=\"cpu\"), Quadrupole(length=0.11, k1=13.33787357734, misalignment=(0, 0), tilt=0.00, name=\"qm04\", device=\"cpu\"), Drift(length=0.14, name=\"dm02\", device=\"cpu\"), Marker(name=im03, device=\"cpu\"), Drift(length=0.16, name=\"dm02a\", device=\"cpu\"), Marker(name=enddl1_2, device=\"cpu\"), Marker(name=begl1, device=\"cpu\"), Marker(name=zlin01, device=\"cpu\"), Cavity(length=2.87, voltage=45397559.63, phase=21.50, frequency=2856000000.00, name=\"k21_1b\", device=\"cpu\"), Drift(length=0.04, name=\"daqa1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-3.77793667230024, misalignment=(0, 0), tilt=0.00, name=\"qa11\", device=\"cpu\"), Drift(length=0.03, name=\"daqa2\", device=\"cpu\"), Cavity(length=2.87, voltage=32100922.27, phase=21.50, frequency=2856000000.00, name=\"k21_1c\", device=\"cpu\"), Drift(length=0.03, name=\"daqa3\", device=\"cpu\"), Quadrupole(length=0.11, k1=1.8813942736818, misalignment=(0, 0), tilt=0.00, name=\"qa12\", device=\"cpu\"), Drift(length=0.03, name=\"daqa4\", device=\"cpu\"), Cavity(length=3.04, voltage=34057722.53, phase=21.50, frequency=2856000000.00, name=\"k21_1d\", device=\"cpu\"), Marker(name=endl1, device=\"cpu\"), Marker(name=begbc1, device=\"cpu\"), Drift(length=0.09, name=\"dl1xa\", device=\"cpu\"), Marker(name=vvx1, device=\"cpu\"), Drift(length=0.20, name=\"dl1xb\", device=\"cpu\"), Cavity(length=0.59, voltage=20000000.00, phase=160.00, frequency=11424000000.00, name=\"l1x\", device=\"cpu\"), Drift(length=0.21, name=\"dm10a\", device=\"cpu\"), Marker(name=vvx2, device=\"cpu\"), Drift(length=0.12, name=\"dm10c\", device=\"cpu\"), Quadrupole(length=0.11, k1=-9.33825405949416, misalignment=(0, 0), tilt=0.00, name=\"q21201\", device=\"cpu\"), Drift(length=0.09, name=\"dm10x\", device=\"cpu\"), Marker(name=imbc1i, device=\"cpu\"), Drift(length=0.28, name=\"dm11\", device=\"cpu\"), Quadrupole(length=0.11, k1=7.94906252387771, misalignment=(0, 0), tilt=0.00, name=\"qm11\", device=\"cpu\"), Drift(length=0.13, name=\"dm12\", device=\"cpu\"), Marker(name=bc1cbeg, device=\"cpu\"), Dipole(length=0.20, angle=-0.093575352547, e1=0.00,e2=-0.09,tilt=0.00,fringe_integral=0.39,fringe_integral_exit=0.39,gap=0.02,name=\"bx11\", device=\"cpu\"), Drift(length=0.25, name=\"dbq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0, misalignment=(0, 0), tilt=0.00, name=\"cq11\", device=\"cpu\"), Drift(length=2.09, name=\"d11o\", device=\"cpu\"), Dipole(length=0.20, angle=0.093575352547, e1=0.09,e2=0.00,tilt=0.00,fringe_integral=0.39,fringe_integral_exit=0.39,gap=0.02,name=\"bx12\", device=\"cpu\"), Drift(length=0.25, name=\"ddg1\", device=\"cpu\"), Marker(name=bpms11, device=\"cpu\"), Drift(length=0.16, name=\"ddg2\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=True, name=\"ce11\", device=\"cpu\"), Drift(length=0.18, name=\"ddg3\", device=\"cpu\"), Marker(name=otr11, device=\"cpu\"), Drift(length=0.23, name=\"ddg4\", device=\"cpu\"), Dipole(length=0.20, angle=0.093575352547, e1=0.00,e2=0.09,tilt=0.00,fringe_integral=0.39,fringe_integral_exit=0.39,gap=0.02,name=\"bx13\", device=\"cpu\"), Drift(length=0.26, name=\"d11oa\", device=\"cpu\"), Quadrupole(length=0.16, k1=0, misalignment=(0, 0), tilt=0.79, name=\"sq13\", device=\"cpu\"), Drift(length=1.67, name=\"d11ob\", device=\"cpu\"), Quadrupole(length=0.11, k1=0, misalignment=(0, 0), tilt=0.00, name=\"cq12\", device=\"cpu\"), Drift(length=0.25, name=\"dbq1\", device=\"cpu\"), Dipole(length=0.20, angle=-0.093575352547, e1=-0.09,e2=0.00,tilt=0.00,fringe_integral=0.39,fringe_integral_exit=0.39,gap=0.02,name=\"bx14\", device=\"cpu\"), Marker(name=cnt1, device=\"cpu\"), Marker(name=bc1cend, device=\"cpu\"), Drift(length=0.16, name=\"dm13a\", device=\"cpu\"), Marker(name=bl11, device=\"cpu\"), Drift(length=0.16, name=\"dm13b\", device=\"cpu\"), Quadrupole(length=0.11, k1=-8.326568628912, misalignment=(0, 0), tilt=0.00, name=\"qm12\", device=\"cpu\"), Drift(length=0.17, name=\"dm14a\", device=\"cpu\"), Drift(length=0.21, name=\"dm14b\", device=\"cpu\"), Marker(name=imbc1o, device=\"cpu\"), Drift(length=0.18, name=\"dm14c\", device=\"cpu\"), Quadrupole(length=0.11, k1=9.937676628969, misalignment=(0, 0), tilt=0.00, name=\"qm13\", device=\"cpu\"), Drift(length=0.10, name=\"dm15a\", device=\"cpu\"), Marker(name=bl12, device=\"cpu\"), Drift(length=0.20, name=\"dm15b\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc21275\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc21276\", device=\"cpu\"), Drift(length=0.24, name=\"dm15c\", device=\"cpu\"), Marker(name=ws11, device=\"cpu\"), Drift(length=1.66, name=\"dww1a\", device=\"cpu\"), Marker(name=ws12, device=\"cpu\"), Drift(length=0.29, name=\"dww1b\", device=\"cpu\"), Marker(name=otr12, device=\"cpu\"), Drift(length=0.42, name=\"dww1c1\", device=\"cpu\"), Marker(name=ph02, device=\"cpu\"), Drift(length=0.30, name=\"dww1c2\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc21302\", device=\"cpu\"), Drift(length=0.35, name=\"dww1d\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc21303\", device=\"cpu\"), Drift(length=0.30, name=\"dww1e\", device=\"cpu\"), Marker(name=ws13, device=\"cpu\"), Drift(length=0.26, name=\"dm16\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.1347, misalignment=(0, 0), tilt=0.00, name=\"q21301\", device=\"cpu\"), Drift(length=0.26, name=\"dm17a\", device=\"cpu\"), Drift(length=0.40, name=\"dm17b\", device=\"cpu\"), Marker(name=td11, device=\"cpu\"), Drift(length=0.39, name=\"dm17c\", device=\"cpu\"), Quadrupole(length=0.11, k1=7.054168937435, misalignment=(0, 0), tilt=0.00, name=\"qm14\", device=\"cpu\"), Drift(length=0.23, name=\"dm18a\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc21325\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc21325\", device=\"cpu\"), Drift(length=0.23, name=\"dm18b\", device=\"cpu\"), Quadrupole(length=0.11, k1=-6.723000446327, misalignment=(0, 0), tilt=0.00, name=\"qm15\", device=\"cpu\"), Marker(name=dbmark28, device=\"cpu\"), Drift(length=0.10, name=\"dm19\", device=\"cpu\"), Marker(name=endbc1, device=\"cpu\"), Marker(name=begl2, device=\"cpu\"), Marker(name=li21beg, device=\"cpu\"), Marker(name=zlin04, device=\"cpu\"), Cavity(length=3.04, voltage=72760505.13, phase=33.50, frequency=2856000000.00, name=\"k21_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=1.030123463078, misalignment=(0, 0), tilt=0.00, name=\"q21401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_4c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_4d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.820757556128, misalignment=(0, 0), tilt=0.00, name=\"q21501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_5c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.713914268545471, misalignment=(0, 0), tilt=0.00, name=\"q21601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_6d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.705827351725973, misalignment=(0, 0), tilt=0.00, name=\"q21701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_7c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_7d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.73569262480724, misalignment=(0, 0), tilt=0.00, name=\"q21801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_8d\", device=\"cpu\"), Drift(length=0.35, name=\"daq3\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.718205140809118, misalignment=(0, 0), tilt=0.00, name=\"q21901\", device=\"cpu\"), Drift(length=0.31, name=\"d219a\", device=\"cpu\"), Drift(length=0.30, name=\"bky21929\", device=\"cpu\"), Drift(length=0.24, name=\"d219b\", device=\"cpu\"), Drift(length=0.30, name=\"bkx21957\", device=\"cpu\"), Drift(length=1.40, name=\"d219c\", device=\"cpu\"), Marker(name=li21end, device=\"cpu\"), Marker(name=li22beg, device=\"cpu\"), Marker(name=zlin05, device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_1c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_1d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.714603191911, misalignment=(0, 0), tilt=0.00, name=\"q22201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.762188913757, misalignment=(0, 0), tilt=0.00, name=\"q22301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q22401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_4c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_4d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q22501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_5c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q22601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_6d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q22701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_7c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_7d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.747560469838, misalignment=(0, 0), tilt=0.00, name=\"q22801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_8d\", device=\"cpu\"), Drift(length=0.35, name=\"daq3\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.709980930665, misalignment=(0, 0), tilt=0.00, name=\"q22901\", device=\"cpu\"), Drift(length=0.31, name=\"d229a\", device=\"cpu\"), Drift(length=0.30, name=\"bky22929\", device=\"cpu\"), Drift(length=0.24, name=\"d229b\", device=\"cpu\"), Drift(length=0.30, name=\"bkx22957\", device=\"cpu\"), Drift(length=1.40, name=\"d229c\", device=\"cpu\"), Marker(name=li22end, device=\"cpu\"), Marker(name=li23beg, device=\"cpu\"), Marker(name=zlin06, device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_1c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_1d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.720791509747, misalignment=(0, 0), tilt=0.00, name=\"q23201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.741851024289, misalignment=(0, 0), tilt=0.00, name=\"q23301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q23401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_4c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_4d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q23501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_5c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q23601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_6d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q23701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_7c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_7d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.770666348011, misalignment=(0, 0), tilt=0.00, name=\"q23801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_8d\", device=\"cpu\"), Drift(length=0.35, name=\"daq3\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.726851638763, misalignment=(0, 0), tilt=0.00, name=\"q23901\", device=\"cpu\"), Drift(length=2.55, name=\"daq4\", device=\"cpu\"), Marker(name=li23end, device=\"cpu\"), Marker(name=li24beg, device=\"cpu\"), Marker(name=zlin07, device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_1c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_1d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.779293384791, misalignment=(0, 0), tilt=0.00, name=\"q24201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.856497177248, misalignment=(0, 0), tilt=0.00, name=\"q24301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=1.024817869713, misalignment=(0, 0), tilt=0.00, name=\"q24401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_4c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_4d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.954003584585, misalignment=(0, 0), tilt=0.00, name=\"q24501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_5c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.608135546584, misalignment=(0, 0), tilt=0.00, name=\"q24601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_6d\", device=\"cpu\"), Marker(name=li24term, device=\"cpu\"), Marker(name=endl2, device=\"cpu\"), Marker(name=begbc2, device=\"cpu\"), Drift(length=0.03, name=\"dm20\", device=\"cpu\"), Quadrupole(length=0.11, k1=-1.307122793026, misalignment=(0, 0), tilt=0.00, name=\"q24701a\", device=\"cpu\"), Drift(length=0.13, name=\"d10cma\", device=\"cpu\"), Quadrupole(length=0.11, k1=-1.307122793026, misalignment=(0, 0), tilt=0.00, name=\"q24701b\", device=\"cpu\"), Drift(length=0.06, name=\"dm21z\", device=\"cpu\"), Drift(length=0.32, name=\"dm21a\", device=\"cpu\"), Marker(name=ws24, device=\"cpu\"), Drift(length=0.19, name=\"dm21h\", device=\"cpu\"), Marker(name=imbc2i, device=\"cpu\"), Drift(length=0.63, name=\"dm21b\", device=\"cpu\"), Marker(name=vv21, device=\"cpu\"), Drift(length=0.32, name=\"dm21c\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.511895916574, misalignment=(0, 0), tilt=0.00, name=\"qm21\", device=\"cpu\"), Drift(length=0.14, name=\"dm21d\", device=\"cpu\"), Drift(length=0.20, name=\"dm21e\", device=\"cpu\"), Marker(name=bc2cbeg, device=\"cpu\"), Dipole(length=0.55, angle=-0.036971323962, e1=0.00,e2=-0.04,tilt=0.00,fringe_integral=0.63,fringe_integral_exit=0.63,gap=0.02,name=\"bx21\", device=\"cpu\"), Drift(length=1.96, name=\"dbq2a\", device=\"cpu\"), Quadrupole(length=0.11, k1=0, misalignment=(0, 0), tilt=0.00, name=\"cq21\", device=\"cpu\"), Drift(length=7.80, name=\"d21oa\", device=\"cpu\"), Dipole(length=0.55, angle=0.036971323962, e1=0.04,e2=0.00,tilt=0.00,fringe_integral=0.63,fringe_integral_exit=0.63,gap=0.02,name=\"bx22\", device=\"cpu\"), Drift(length=0.13, name=\"ddg0\", device=\"cpu\"), Drift(length=0.25, name=\"ddg1\", device=\"cpu\"), Marker(name=bpms21, device=\"cpu\"), Drift(length=0.16, name=\"ddg2\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=True, name=\"ce21\", device=\"cpu\"), Drift(length=0.18, name=\"ddg3\", device=\"cpu\"), Marker(name=otr21, device=\"cpu\"), Drift(length=0.23, name=\"ddg4\", device=\"cpu\"), Drift(length=0.13, name=\"ddga\", device=\"cpu\"), Dipole(length=0.55, angle=0.036971323962, e1=0.00,e2=0.04,tilt=0.00,fringe_integral=0.63,fringe_integral_exit=0.63,gap=0.02,name=\"bx23\", device=\"cpu\"), Drift(length=7.80, name=\"d21ob\", device=\"cpu\"), Quadrupole(length=0.11, k1=0, misalignment=(0, 0), tilt=0.00, name=\"cq22\", device=\"cpu\"), Drift(length=1.96, name=\"dbq2b\", device=\"cpu\"), Dipole(length=0.55, angle=-0.036971323962, e1=-0.04,e2=0.00,tilt=0.00,fringe_integral=0.63,fringe_integral_exit=0.63,gap=0.02,name=\"bx24\", device=\"cpu\"), Marker(name=cnt2, device=\"cpu\"), Marker(name=bc2cend, device=\"cpu\"), Drift(length=0.31, name=\"d21w\", device=\"cpu\"), Drift(length=0.21, name=\"d21x\", device=\"cpu\"), Marker(name=bl21, device=\"cpu\"), Drift(length=0.11, name=\"d21y\", device=\"cpu\"), Drift(length=0.05, name=\"dm23b\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.589455717616, misalignment=(0, 0), tilt=0.00, name=\"qm22\", device=\"cpu\"), Drift(length=0.13, name=\"dm24a\", device=\"cpu\"), Marker(name=vv22, device=\"cpu\"), Drift(length=0.18, name=\"dm24b\", device=\"cpu\"), Drift(length=0.07, name=\"dm24d\", device=\"cpu\"), Quadrupole(length=0.11, k1=1.081452709118, misalignment=(0, 0), tilt=0.00, name=\"q24901a\", device=\"cpu\"), Drift(length=0.11, name=\"dm24c\", device=\"cpu\"), Quadrupole(length=0.11, k1=1.081452709118, misalignment=(0, 0), tilt=0.00, name=\"q24901b\", device=\"cpu\"), Drift(length=0.16, name=\"dm25\", device=\"cpu\"), Marker(name=endbc2, device=\"cpu\"), Marker(name=begl3, device=\"cpu\"), Marker(name=li25beg, device=\"cpu\"), Marker(name=zlin09, device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_1b\", device=\"cpu\"), Drift(length=3.04, name=\"d25_1c\", device=\"cpu\"), Cavity(length=3.04, voltage=68162928.83, phase=-0.00, frequency=2856000000.00, name=\"k25_1d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.69835390233, misalignment=(0, 0), tilt=0.00, name=\"q25201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=68162928.83, phase=-0.00, frequency=2856000000.00, name=\"k25_2c\", device=\"cpu\"), Drift(length=0.06, name=\"d255a\", device=\"cpu\"), Marker(name=imbc2o, device=\"cpu\"), Drift(length=0.11, name=\"d255b\", device=\"cpu\"), Drift(length=0.15, name=\"d255c\", device=\"cpu\"), Cavity(length=2.44, voltage=0.00, phase=0.00, frequency=2856000000.00, name=\"tcav3\", device=\"cpu\"), Drift(length=0.31, name=\"d255d\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.479854531304, misalignment=(0, 0), tilt=0.00, name=\"q25301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=68162928.83, phase=-0.00, frequency=2856000000.00, name=\"k25_3c\", device=\"cpu\"), Drift(length=0.60, name=\"d256a\", device=\"cpu\"), Marker(name=ph03, device=\"cpu\"), Drift(length=0.56, name=\"d256b\", device=\"cpu\"), Marker(name=bl22, device=\"cpu\"), Drift(length=0.61, name=\"d256c\", device=\"cpu\"), Drift(length=1.06, name=\"bxkik\", device=\"cpu\"), Drift(length=0.21, name=\"d256d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.429832166012, misalignment=(0, 0), tilt=0.00, name=\"q25401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_4c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_4d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.400216279825, misalignment=(0, 0), tilt=0.00, name=\"q25501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_5c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q25601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_6d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q25701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_7c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_7d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.407524461523, misalignment=(0, 0), tilt=0.00, name=\"q25801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_8d\", device=\"cpu\"), Drift(length=0.27, name=\"daq7\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.38867870418, misalignment=(0, 0), tilt=0.00, name=\"q25901\", device=\"cpu\"), Drift(length=0.50, name=\"daq8a\", device=\"cpu\"), Marker(name=otr_tcav, device=\"cpu\"), Drift(length=2.13, name=\"daq8b\", device=\"cpu\"), Marker(name=li25end, device=\"cpu\"), Marker(name=li26beg, device=\"cpu\"), Marker(name=zlin10, device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_1c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_1d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.388844133085, misalignment=(0, 0), tilt=0.00, name=\"q26201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.405381176356, misalignment=(0, 0), tilt=0.00, name=\"q26301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q26401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_4c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_4d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q26501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_5c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q26601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_6d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q26701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_7c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_7d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.406007960598, misalignment=(0, 0), tilt=0.00, name=\"q26801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_8d\", device=\"cpu\"), Drift(length=0.27, name=\"daq7\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.388769548432, misalignment=(0, 0), tilt=0.00, name=\"q26901\", device=\"cpu\"), Drift(length=2.63, name=\"daq8\", device=\"cpu\"), Marker(name=li26end, device=\"cpu\"), Marker(name=li27beg, device=\"cpu\"), Marker(name=zlin11, device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_1c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_1d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.391071563294, misalignment=(0, 0), tilt=0.00, name=\"q27201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.407171874797, misalignment=(0, 0), tilt=0.00, name=\"q27301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q27401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_4c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_4d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q27501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_5c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q27601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_6c\", device=\"cpu\"), Drift(length=2.34, name=\"daq5a\", device=\"cpu\"), Marker(name=ws27644, device=\"cpu\"), Drift(length=0.73, name=\"daq6a\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q27701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_7c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_7d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.40682165023, misalignment=(0, 0), tilt=0.00, name=\"q27801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_8d\", device=\"cpu\"), Drift(length=0.27, name=\"daq7\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.389505138741, misalignment=(0, 0), tilt=0.00, name=\"q27901\", device=\"cpu\"), Drift(length=2.63, name=\"daq8\", device=\"cpu\"), Marker(name=li27end, device=\"cpu\"), Marker(name=li28beg, device=\"cpu\"), Marker(name=zlin12, device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_1c\", device=\"cpu\"), Drift(length=2.09, name=\"daq5b1\", device=\"cpu\"), Marker(name=xc29092, device=\"cpu\"), Drift(length=0.30, name=\"daq5b2\", device=\"cpu\"), Marker(name=yc29092, device=\"cpu\"), Drift(length=0.45, name=\"daq5b3\", device=\"cpu\"), Marker(name=ws28144, device=\"cpu\"), Drift(length=0.24, name=\"daq6b\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.390810721273, misalignment=(0, 0), tilt=0.00, name=\"q28201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.406510005482, misalignment=(0, 0), tilt=0.00, name=\"q28301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q28401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_4c\", device=\"cpu\"), Drift(length=2.08, name=\"daq5c1\", device=\"cpu\"), Marker(name=xc29095, device=\"cpu\"), Drift(length=0.29, name=\"daq5c2\", device=\"cpu\"), Marker(name=yc29095, device=\"cpu\"), Drift(length=0.47, name=\"daq5c3\", device=\"cpu\"), Marker(name=ws28444, device=\"cpu\"), Drift(length=0.24, name=\"daq6c\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q28501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_5c\", device=\"cpu\"), Drift(length=3.04, name=\"d28_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q28601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_6d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q28701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_7c\", device=\"cpu\"), Drift(length=2.34, name=\"daq5d\", device=\"cpu\"), Marker(name=ws28744, device=\"cpu\"), Drift(length=0.73, name=\"daq6d\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.406821689691, misalignment=(0, 0), tilt=0.00, name=\"q28801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_8d\", device=\"cpu\"), Drift(length=0.27, name=\"daq7\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.389505198882, misalignment=(0, 0), tilt=0.00, name=\"q28901\", device=\"cpu\"), Drift(length=0.34, name=\"daq8c\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=True, name=\"c29096\", device=\"cpu\"), Drift(length=0.74, name=\"daq8d1\", device=\"cpu\"), Drift(length=0.20, name=\"st28950\", device=\"cpu\"), Drift(length=0.22, name=\"daq8d2\", device=\"cpu\"), Drift(length=0.20, name=\"st28960\", device=\"cpu\"), Drift(length=0.93, name=\"daq8d3\", device=\"cpu\"), Marker(name=li28end, device=\"cpu\"), Marker(name=li29beg, device=\"cpu\"), Marker(name=zlin13, device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_1c\", device=\"cpu\"), Drift(length=2.82, name=\"d10ca\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=True, name=\"c29146\", device=\"cpu\"), Drift(length=0.23, name=\"d10cb\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.390810639876, misalignment=(0, 0), tilt=0.00, name=\"q29201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.40650981716, misalignment=(0, 0), tilt=0.00, name=\"q29301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q29401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_4c\", device=\"cpu\"), Drift(length=2.82, name=\"d10cc\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=True, name=\"c29446\", device=\"cpu\"), Drift(length=0.23, name=\"d10cd\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q29501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_5c\", device=\"cpu\"), Drift(length=2.82, name=\"d10ce\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=True, name=\"c29546\", device=\"cpu\"), Drift(length=0.23, name=\"d10cf\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q29601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_6d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q29701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_7c\", device=\"cpu\"), Cavity(length=2.87, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_7d\", device=\"cpu\"), Drift(length=0.08, name=\"daq9a\", device=\"cpu\"), Marker(name=pk297, device=\"cpu\"), Drift(length=0.13, name=\"daq9b\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.406747754958, misalignment=(0, 0), tilt=0.00, name=\"q29801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_8d\", device=\"cpu\"), Drift(length=0.35, name=\"daq3\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.389374252182, misalignment=(0, 0), tilt=0.00, name=\"q29901\", device=\"cpu\"), Drift(length=0.25, name=\"daq4a\", device=\"cpu\"), Marker(name=p30013, device=\"cpu\"), Drift(length=0.20, name=\"daq4b\", device=\"cpu\"), Marker(name=p30014, device=\"cpu\"), Drift(length=0.92, name=\"daq4c\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=True, name=\"c29956\", device=\"cpu\"), Drift(length=0.27, name=\"daq4d\", device=\"cpu\"), Marker(name=pk299, device=\"cpu\"), Drift(length=0.91, name=\"daq4e\", device=\"cpu\"), Marker(name=li29end, device=\"cpu\"), Marker(name=li30beg, device=\"cpu\"), Marker(name=zlin14, device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_1c\", device=\"cpu\"), Cavity(length=2.17, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_1d\", device=\"cpu\"), Drift(length=0.13, name=\"daq10a\", device=\"cpu\"), Marker(name=p30143, device=\"cpu\"), Drift(length=0.20, name=\"daq10b\", device=\"cpu\"), Marker(name=p30144, device=\"cpu\"), Drift(length=0.21, name=\"daq10c\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=True, name=\"c30146\", device=\"cpu\"), Drift(length=0.36, name=\"daq10d\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.390540048432, misalignment=(0, 0), tilt=0.00, name=\"q30201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.406220459812, misalignment=(0, 0), tilt=0.00, name=\"q30301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_3c\", device=\"cpu\"), Cavity(length=2.17, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_3d\", device=\"cpu\"), Drift(length=0.78, name=\"daq10e\", device=\"cpu\"), Marker(name=pk303, device=\"cpu\"), Drift(length=0.13, name=\"daq10f\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q30401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_4c\", device=\"cpu\"), Cavity(length=2.17, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_4d\", device=\"cpu\"), Drift(length=0.13, name=\"daq10g\", device=\"cpu\"), Marker(name=p30443, device=\"cpu\"), Drift(length=0.20, name=\"daq10h\", device=\"cpu\"), Marker(name=p30444, device=\"cpu\"), Drift(length=0.21, name=\"daq10i\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=True, name=\"c30446\", device=\"cpu\"), Drift(length=0.24, name=\"daq10j\", device=\"cpu\"), Marker(name=pk304, device=\"cpu\"), Drift(length=0.13, name=\"daq10k\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q30501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_5c\", device=\"cpu\"), Cavity(length=2.17, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_5d\", device=\"cpu\"), Drift(length=0.13, name=\"daq11a\", device=\"cpu\"), Marker(name=p30543, device=\"cpu\"), Drift(length=0.21, name=\"daq11b\", device=\"cpu\"), Marker(name=p30544, device=\"cpu\"), Drift(length=0.21, name=\"daq11c\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=True, name=\"c30546\", device=\"cpu\"), Drift(length=0.15, name=\"daq11d\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q30601\", device=\"cpu\"), Drift(length=0.23, name=\"daq12\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_6c\", device=\"cpu\"), Cavity(length=2.87, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_6d\", device=\"cpu\"), Drift(length=0.02, name=\"daq13\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.39623102008, misalignment=(0, 0), tilt=0.00, name=\"q30701\", device=\"cpu\"), Drift(length=0.21, name=\"daq14\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_7c\", device=\"cpu\"), Cavity(length=2.87, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_7d\", device=\"cpu\"), Drift(length=0.01, name=\"daq15\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.480592172677, misalignment=(0, 0), tilt=0.00, name=\"q30801\", device=\"cpu\"), Drift(length=0.23, name=\"daq16\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_8c\", device=\"cpu\"), Drift(length=0.06, name=\"daq17\", device=\"cpu\"), Marker(name=li30term, device=\"cpu\"), Marker(name=dbmark29, device=\"cpu\"), Marker(name=endl3, device=\"cpu\"), Marker(name=begclth_0, device=\"cpu\"), Marker(name=bsybeg, device=\"cpu\"), Marker(name=bsy1beg, device=\"cpu\"), Drift(length=0.73, name=\"dbsy01a\", device=\"cpu\"), Marker(name=imbsy1, device=\"cpu\"), Drift(length=0.25, name=\"dbsy01b\", device=\"cpu\"), Marker(name=imbsy2, device=\"cpu\"), Drift(length=0.25, name=\"dbsy01c\", device=\"cpu\"), Marker(name=imbsy3, device=\"cpu\"), Drift(length=0.41, name=\"dbsy01d\", device=\"cpu\"), Marker(name=imbsy34, device=\"cpu\"), Drift(length=2.90, name=\"dbsy01e\", device=\"cpu\"), Marker(name=imbsy1b, device=\"cpu\"), Drift(length=0.32, name=\"dbsy01f\", device=\"cpu\"), Marker(name=imbsy2b, device=\"cpu\"), Drift(length=0.32, name=\"dbsy01g\", device=\"cpu\"), Marker(name=imbsy3b, device=\"cpu\"), Drift(length=0.80, name=\"dbsy01h\", device=\"cpu\"), Marker(name=fftborgn, device=\"cpu\"), Drift(length=0.02, name=\"dbsy01i\", device=\"cpu\"), Marker(name=s100, device=\"cpu\"), Marker(name=zlin15, device=\"cpu\"), Drift(length=0.58, name=\"dbsy50a\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycbsyq1\", device=\"cpu\"), Drift(length=1.43, name=\"dbsy50b\", device=\"cpu\"), Quadrupole(length=0.26, k1=-0.231522298209, misalignment=(0, 0), tilt=0.00, name=\"q50q1\", device=\"cpu\"), Drift(length=0.24, name=\"dbsy02a\", device=\"cpu\"), Marker(name=wooddoor, device=\"cpu\"), Marker(name=endclth_0, device=\"cpu\"), Marker(name=begclth_1, device=\"cpu\"), Drift(length=0.22, name=\"dbsy02b\", device=\"cpu\"), Marker(name=bpmbsyq1, device=\"cpu\"), Drift(length=5.02, name=\"dbsy51a\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcbsyq2\", device=\"cpu\"), Drift(length=0.50, name=\"dbsy51b\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.102074330212, misalignment=(0, 0), tilt=0.00, name=\"q50q2\", device=\"cpu\"), Drift(length=0.08, name=\"dbsy52a\", device=\"cpu\"), Marker(name=bpmbsyq2, device=\"cpu\"), Drift(length=1.80, name=\"dbsy52b\", device=\"cpu\"), Marker(name=endclth_1, device=\"cpu\"), Marker(name=begclth_2, device=\"cpu\"), Drift(length=0.13, name=\"dbrcusdc1a\", device=\"cpu\"), Drift(length=0.13, name=\"dbrcusdc1b\", device=\"cpu\"), Drift(length=0.35, name=\"dckdc1\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrcusa\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrcusb\", device=\"cpu\"), Drift(length=0.35, name=\"dckdc2\", device=\"cpu\"), Drift(length=0.13, name=\"dbrcusdc2a\", device=\"cpu\"), Drift(length=0.13, name=\"dbrcusdc2b\", device=\"cpu\"), Drift(length=4.57, name=\"dcusblra\", device=\"cpu\"), Marker(name=bpmcus, device=\"cpu\"), Drift(length=10.95, name=\"dcusblrb\", device=\"cpu\"), Drift(length=0.50, name=\"dblrcusa\", device=\"cpu\"), Drift(length=0.50, name=\"dblrcusb\", device=\"cpu\"), Drift(length=32.68, name=\"dbsy52c\", device=\"cpu\"), Drift(length=0.50, name=\"dbxsp1ha\", device=\"cpu\"), Drift(length=0.50, name=\"dbxsp1hb\", device=\"cpu\"), Marker(name=bsy1end, device=\"cpu\"), Marker(name=endclth_2, device=\"cpu\"), Marker(name=begbsyh_1, device=\"cpu\"), Drift(length=0.50, name=\"dbsy52d\", device=\"cpu\"), Quadrupole(length=0.29, k1=0.384840836193, misalignment=(0, 0), tilt=0.00, name=\"q50q3\", device=\"cpu\"), Drift(length=0.09, name=\"dbsy53a\", device=\"cpu\"), Marker(name=bpmbsyq3, device=\"cpu\"), Drift(length=0.00, name=\"rfbbsyq3\", device=\"cpu\"), Drift(length=0.61, name=\"dbsy53b\", device=\"cpu\"), Drift(length=0.78, name=\"dbsy53c\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcbsyq3\", device=\"cpu\"), Drift(length=2.02, name=\"dbsy53d\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycbsyq4\", device=\"cpu\"), Drift(length=0.50, name=\"dbsy53f\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.219396715005, misalignment=(0, 0), tilt=0.00, name=\"q4\", device=\"cpu\"), Drift(length=0.91, name=\"dbsy53ga\", device=\"cpu\"), Drift(length=0.06, name=\"pcbsy3\", device=\"cpu\"), Drift(length=0.22, name=\"dbsy53gb\", device=\"cpu\"), Marker(name=mrgaline, device=\"cpu\"), Marker(name=endbsyh_1, device=\"cpu\"), Marker(name=begbsyh_2, device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm1a\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm1b\", device=\"cpu\"), Drift(length=0.21, name=\"dzapm1\", device=\"cpu\"), Aperture(x_max=0.04, y_max=0.01, shape=\"elliptical\", is_active=True, name=\"pcapm1\", device=\"cpu\"), Drift(length=0.21, name=\"dzapm1\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm2a\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm2b\", device=\"cpu\"), Drift(length=0.10, name=\"dzapm2a\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcapm2\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycapm2\", device=\"cpu\"), Drift(length=0.10, name=\"dzapm2b\", device=\"cpu\"), Aperture(x_max=0.04, y_max=0.01, shape=\"elliptical\", is_active=True, name=\"pcapm2\", device=\"cpu\"), Drift(length=0.21, name=\"dzapm2\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm3a\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm3b\", device=\"cpu\"), Drift(length=0.21, name=\"dzapm3\", device=\"cpu\"), Aperture(x_max=0.04, y_max=0.01, shape=\"elliptical\", is_active=True, name=\"pcapm3\", device=\"cpu\"), Drift(length=0.21, name=\"dzapm3\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm4a\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm4b\", device=\"cpu\"), Drift(length=0.21, name=\"dzapm4\", device=\"cpu\"), Aperture(x_max=0.04, y_max=0.01, shape=\"elliptical\", is_active=True, name=\"pcapm4\", device=\"cpu\"), Drift(length=0.20, name=\"dza01\", device=\"cpu\"), Drift(length=2.82, name=\"dza02\", device=\"cpu\"), Drift(length=0.45, name=\"pcbsyh\", device=\"cpu\"), Drift(length=23.39, name=\"dbsy53h\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.165708852383, misalignment=(0, 0), tilt=0.00, name=\"q5\", device=\"cpu\"), Drift(length=0.50, name=\"dbsy54a\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcbsyq5\", device=\"cpu\"), Drift(length=3.22, name=\"dbsy54b\", device=\"cpu\"), Drift(length=15.54, name=\"dbsy54c\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.113569101471, misalignment=(0, 0), tilt=0.00, name=\"q6\", device=\"cpu\"), Drift(length=0.20, name=\"drfb\", device=\"cpu\"), Drift(length=0.00, name=\"rfbbsyq6\", device=\"cpu\"), Drift(length=0.50, name=\"dbsy55a\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycbsyq6\", device=\"cpu\"), Drift(length=4.23, name=\"dbsy55b\", device=\"cpu\"), Aperture(x_max=0.01, y_max=0.01, shape=\"elliptical\", is_active=True, name=\"pc90\", device=\"cpu\"), Drift(length=8.02, name=\"dbsy55c\", device=\"cpu\"), Drift(length=0.06, name=\"pcbsy4\", device=\"cpu\"), Drift(length=6.47, name=\"dbsy55d\", device=\"cpu\"), Aperture(x_max=0.01, y_max=0.01, shape=\"elliptical\", is_active=True, name=\"pc119\", device=\"cpu\"), Drift(length=1.45, name=\"dbsy55e\", device=\"cpu\"), Drift(length=0.43, name=\"d2\", device=\"cpu\"), Drift(length=0.84, name=\"dm3\", device=\"cpu\"), Drift(length=0.76, name=\"st60\", device=\"cpu\"), Drift(length=0.44, name=\"dm4a\", device=\"cpu\"), Marker(name=dm60, device=\"cpu\"), Drift(length=5.19, name=\"dm4b\", device=\"cpu\"), Marker(name=cc31beg, device=\"cpu\"), Drift(length=0.35, name=\"bcx311\", device=\"cpu\"), Drift(length=0.20, name=\"dcc31o\", device=\"cpu\"), Drift(length=0.35, name=\"bcx312\", device=\"cpu\"), Drift(length=0.20, name=\"dcc31i\", device=\"cpu\"), Drift(length=0.35, name=\"bcx313\", device=\"cpu\"), Drift(length=0.20, name=\"dcc31o\", device=\"cpu\"), Drift(length=0.35, name=\"bcx314\", device=\"cpu\"), Marker(name=cc31end, device=\"cpu\"), Drift(length=0.30, name=\"dm4c\", device=\"cpu\"), Aperture(x_max=0.00, y_max=0.02, shape=\"rectangular\", is_active=True, name=\"cxq6\", device=\"cpu\"), Drift(length=0.28, name=\"dm4da\", device=\"cpu\"), Drift(length=0.06, name=\"pcbsy5\", device=\"cpu\"), Drift(length=0.80, name=\"dm4db\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xca0\", device=\"cpu\"), Drift(length=0.31, name=\"dxca0\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yca0\", device=\"cpu\"), Drift(length=0.51, name=\"dyca0\", device=\"cpu\"), Drift(length=0.76, name=\"st61\", device=\"cpu\"), Drift(length=0.79, name=\"dm5\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.109210100868, misalignment=(0, 0), tilt=0.00, name=\"qa0\", device=\"cpu\"), Drift(length=0.57, name=\"dm6\", device=\"cpu\"), Drift(length=0.01, name=\"dmoni\", device=\"cpu\"), Drift(length=0.01, name=\"dmoni\", device=\"cpu\"), Marker(name=muwall, device=\"cpu\"), Drift(length=1.84, name=\"dwalla\", device=\"cpu\"), Marker(name=dumpbsyh, device=\"cpu\"), Drift(length=14.92, name=\"dwallb\", device=\"cpu\"), Marker(name=bsyend, device=\"cpu\"), Marker(name=rwwake3h, device=\"cpu\"), Marker(name=endbsyh_2, device=\"cpu\"), Marker(name=begltuh, device=\"cpu\"), Drift(length=0.40, name=\"dycvm1\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycvm1\", device=\"cpu\"), Drift(length=0.34, name=\"dqvm1\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.337437273245, misalignment=(0, 0), tilt=0.00, name=\"qvm1\", device=\"cpu\"), Drift(length=0.25, name=\"dqvm2a\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcvm2\", device=\"cpu\"), Drift(length=0.25, name=\"dqvm2b\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.236577259392, misalignment=(0, 0), tilt=0.00, name=\"qvm2\", device=\"cpu\"), Drift(length=0.25, name=\"dwsvm2a\", device=\"cpu\"), Marker(name=wsvm2, device=\"cpu\"), Drift(length=0.25, name=\"dwsvm2b\", device=\"cpu\"), Marker(name=vbin, device=\"cpu\"), Dipole(length=1.02, angle=-0.002334257186083933, e1=-0.00,e2=-0.00,tilt=1.57,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.02,name=\"by1\", device=\"cpu\"), Drift(length=7.38, name=\"dvb1\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.42223036711, misalignment=(0, 0), tilt=0.00, name=\"qvb1\", device=\"cpu\"), Drift(length=0.40, name=\"d40cmc\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycvb1\", device=\"cpu\"), Drift(length=3.20, name=\"dvb2m80cm\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcvb2\", device=\"cpu\"), Drift(length=0.40, name=\"d40cmc\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.42223036711, misalignment=(0, 0), tilt=0.00, name=\"qvb2\", device=\"cpu\"), Drift(length=0.87, name=\"dvb2ha\", device=\"cpu\"), Drift(length=0.06, name=\"pc01\", device=\"cpu\"), Marker(name=btm01, device=\"cpu\"), Drift(length=3.07, name=\"dvb2hb\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.42223036711, misalignment=(0, 0), tilt=0.00, name=\"qvb3\", device=\"cpu\"), Drift(length=0.40, name=\"d40cmc\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycvb3\", device=\"cpu\"), Drift(length=6.97, name=\"dvb1m40cm\", device=\"cpu\"), Dipole(length=1.02, angle=-0.002334257186083933, e1=-0.00,e2=-0.00,tilt=1.57,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.02,name=\"by2\", device=\"cpu\"), Marker(name=cntlt1h, device=\"cpu\"), Marker(name=vbout, device=\"cpu\"), Drift(length=0.25, name=\"dvb25cmc\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcvm3\", device=\"cpu\"), Drift(length=0.25, name=\"d25cm\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.715150825176, misalignment=(0, 0), tilt=0.00, name=\"qvm3\", device=\"cpu\"), Drift(length=0.25, name=\"dvbem25cm\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycvm4\", device=\"cpu\"), Drift(length=0.25, name=\"d25cm\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.681650171006, misalignment=(0, 0), tilt=0.00, name=\"qvm4\", device=\"cpu\"), Drift(length=0.17, name=\"dvbem15cm\", device=\"cpu\"), Marker(name=im31, device=\"cpu\"), Drift(length=0.11, name=\"d10cmb\", device=\"cpu\"), Marker(name=imbcs1, device=\"cpu\"), Drift(length=0.22, name=\"d25cma\", device=\"cpu\"), Marker(name=mm1, device=\"cpu\"), Marker(name=dbmark34, device=\"cpu\"), Dipole(length=2.62, angle=0.008726643152447821, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=0.52,fringe_integral_exit=0.52,gap=0.01,name=\"bx31\", device=\"cpu\"), Drift(length=0.99, name=\"ddl10wa\", device=\"cpu\"), Drift(length=0.06, name=\"pc02\", device=\"cpu\"), Marker(name=btm02, device=\"cpu\"), Drift(length=10.94, name=\"ddl10wb\", device=\"cpu\"), Drift(length=0.10, name=\"dwsdl31a\", device=\"cpu\"), Marker(name=wsdl31, device=\"cpu\"), Drift(length=0.15, name=\"dwsdl31b\", device=\"cpu\"), Drift(length=0.13, name=\"ddl10x\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcdl1\", device=\"cpu\"), Drift(length=0.56, name=\"d31a\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.437183970154, misalignment=(0, 0), tilt=0.00, name=\"qdl31\", device=\"cpu\"), Drift(length=0.20, name=\"drfb\", device=\"cpu\"), Drift(length=0.00, name=\"rfbdl1\", device=\"cpu\"), Drift(length=0.36, name=\"d31b\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycdl1\", device=\"cpu\"), Drift(length=0.66, name=\"d32cmb\", device=\"cpu\"), Aperture(x_max=0.00, y_max=0.02, shape=\"rectangular\", is_active=True, name=\"cedl1\", device=\"cpu\"), Drift(length=0.50, name=\"d31c\", device=\"cpu\"), Drift(length=0.24, name=\"wig1h\", device=\"cpu\"), Drift(length=0.13, name=\"dwgh\", device=\"cpu\"), Drift(length=0.49, name=\"wig2h\", device=\"cpu\"), Drift(length=0.13, name=\"dwgh\", device=\"cpu\"), Drift(length=0.24, name=\"wig3h\", device=\"cpu\"), Marker(name=cntwigh, device=\"cpu\"), Drift(length=3.70, name=\"ddl10em80cma\", device=\"cpu\"), Drift(length=0.20, name=\"pc22\", device=\"cpu\"), Drift(length=5.12, name=\"ddl10em80cmb\", device=\"cpu\"), Aperture(x_max=0.02, y_max=0.00, shape=\"rectangular\", is_active=True, name=\"cybx32\", device=\"cpu\"), Drift(length=0.81, name=\"dcb32\", device=\"cpu\"), Dipole(length=2.62, angle=0.008726643152447821, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=0.52,fringe_integral_exit=0.52,gap=0.01,name=\"bx32\", device=\"cpu\"), Marker(name=cntlt2h, device=\"cpu\"), Drift(length=0.50, name=\"ddl20e\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt11\", device=\"cpu\"), Drift(length=0.51, name=\"ddl30em40cm\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcqt12\", device=\"cpu\"), Drift(length=0.49, name=\"d40cmb\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.839614778043, misalignment=(0, 0), tilt=0.00, name=\"qt12\", device=\"cpu\"), Drift(length=0.49, name=\"d40cmb\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycqt12\", device=\"cpu\"), Drift(length=0.51, name=\"ddl30em40cm\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt13\", device=\"cpu\"), Drift(length=0.50, name=\"ddl20e\", device=\"cpu\"), Marker(name=ss1, device=\"cpu\"), Drift(length=1.42, name=\"dx33a\", device=\"cpu\"), Marker(name=cc32beg, device=\"cpu\"), Dipole(length=0.35, angle=0.01008035696016623, e1=0.00,e2=0.01,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx321\", device=\"cpu\"), Drift(length=0.20, name=\"dcc32o\", device=\"cpu\"), Dipole(length=0.35, angle=-0.01008035696016623, e1=-0.01,e2=0.00,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx322\", device=\"cpu\"), Drift(length=0.20, name=\"dcc32i\", device=\"cpu\"), Dipole(length=0.35, angle=-0.01008035696016623, e1=0.00,e2=-0.01,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx323\", device=\"cpu\"), Drift(length=0.20, name=\"dcc32o\", device=\"cpu\"), Dipole(length=0.35, angle=0.01008035696016623, e1=0.01,e2=0.00,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx324\", device=\"cpu\"), Marker(name=cc32end, device=\"cpu\"), Drift(length=0.39, name=\"dx33b\", device=\"cpu\"), Drift(length=0.06, name=\"pc03\", device=\"cpu\"), Marker(name=btm03, device=\"cpu\"), Drift(length=4.05, name=\"ddl1ab\", device=\"cpu\"), Dipole(length=1.06, angle=0.0, e1=0.00,e2=0.00,tilt=1.57,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.03,name=\"bykik1\", device=\"cpu\"), Drift(length=0.08, name=\"ddl1d\", device=\"cpu\"), Drift(length=0.08, name=\"ddl1d\", device=\"cpu\"), Dipole(length=1.06, angle=0.0, e1=0.00,e2=0.00,tilt=1.57,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.03,name=\"bykik2\", device=\"cpu\"), Drift(length=4.89, name=\"ddl1e\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcdl2\", device=\"cpu\"), Drift(length=0.47, name=\"d32a\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.437183970154, misalignment=(0, 0), tilt=0.00, name=\"qdl32\", device=\"cpu\"), Drift(length=0.47, name=\"d32b\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycdl2\", device=\"cpu\"), Drift(length=0.43, name=\"dsplr\", device=\"cpu\"), Marker(name=spoiler, device=\"cpu\"), Drift(length=5.30, name=\"ddl1cm40cm\", device=\"cpu\"), Drift(length=0.61, name=\"tdkik\", device=\"cpu\"), Drift(length=0.26, name=\"d30cma\", device=\"cpu\"), Aperture(x_max=0.01, y_max=0.01, shape=\"elliptical\", is_active=True, name=\"pctdkik1\", device=\"cpu\"), Drift(length=0.27, name=\"dpc1\", device=\"cpu\"), Aperture(x_max=0.01, y_max=0.01, shape=\"elliptical\", is_active=True, name=\"pctdkik2\", device=\"cpu\"), Drift(length=0.27, name=\"dpc2\", device=\"cpu\"), Aperture(x_max=0.01, y_max=0.01, shape=\"elliptical\", is_active=True, name=\"pctdkik3\", device=\"cpu\"), Drift(length=0.27, name=\"dpc3\", device=\"cpu\"), Aperture(x_max=0.01, y_max=0.01, shape=\"elliptical\", is_active=True, name=\"pctdkik4\", device=\"cpu\"), Drift(length=0.20, name=\"dpc4\", device=\"cpu\"), Drift(length=0.75, name=\"dspontua\", device=\"cpu\"), Drift(length=0.75, name=\"dspontub\", device=\"cpu\"), Drift(length=0.12, name=\"ddl1dm30cm\", device=\"cpu\"), Drift(length=0.12, name=\"dx34a\", device=\"cpu\"), Marker(name=cc35beg, device=\"cpu\"), Dipole(length=0.35, angle=0.007127828404706885, e1=0.00,e2=0.01,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx351\", device=\"cpu\"), Drift(length=0.20, name=\"dcc35o\", device=\"cpu\"), Dipole(length=0.35, angle=-0.007127828404706885, e1=-0.01,e2=0.00,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx352\", device=\"cpu\"), Drift(length=0.20, name=\"dcc35i\", device=\"cpu\"), Dipole(length=0.35, angle=-0.007127828404706885, e1=0.00,e2=-0.01,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx353\", device=\"cpu\"), Drift(length=0.20, name=\"dcc35o\", device=\"cpu\"), Dipole(length=0.35, angle=0.007127828404706885, e1=0.01,e2=0.00,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx354\", device=\"cpu\"), Marker(name=cc35end, device=\"cpu\"), Drift(length=0.50, name=\"dx34b\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycqt21\", device=\"cpu\"), Drift(length=0.50, name=\"ddl20\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt21\", device=\"cpu\"), Drift(length=0.51, name=\"ddl30em40cm\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcqt22\", device=\"cpu\"), Drift(length=0.49, name=\"d40cmb\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.839614778043, misalignment=(0, 0), tilt=0.00, name=\"qt22\", device=\"cpu\"), Drift(length=0.47, name=\"d46cm\", device=\"cpu\"), Aperture(x_max=0.00, y_max=0.02, shape=\"rectangular\", is_active=True, name=\"cxqt22\", device=\"cpu\"), Drift(length=0.47, name=\"d46cm\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt23\", device=\"cpu\"), Drift(length=0.50, name=\"ddl20\", device=\"cpu\"), Marker(name=dl23beg, device=\"cpu\"), Dipole(length=2.62, angle=-0.008726643152447821, e1=-0.00,e2=-0.00,tilt=0.00,fringe_integral=0.52,fringe_integral_exit=0.52,gap=0.01,name=\"bx35\", device=\"cpu\"), Drift(length=1.35, name=\"dcq31aa\", device=\"cpu\"), Drift(length=0.06, name=\"pc04\", device=\"cpu\"), Marker(name=btm04, device=\"cpu\"), Drift(length=4.62, name=\"dcq31ab\", device=\"cpu\"), Quadrupole(length=0.11, k1=0, misalignment=(0, 0), tilt=0.00, name=\"cq31\", device=\"cpu\"), Drift(length=0.83, name=\"dcq31ba\", device=\"cpu\"), Drift(length=0.06, name=\"pc42\", device=\"cpu\"), Marker(name=btm42, device=\"cpu\"), Drift(length=4.92, name=\"dcq31bb\", device=\"cpu\"), Marker(name=otr30, device=\"cpu\"), Drift(length=0.58, name=\"d29cma\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcdl3\", device=\"cpu\"), Drift(length=0.39, name=\"d33a\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.437183970154, misalignment=(0, 0), tilt=0.00, name=\"qdl33\", device=\"cpu\"), Drift(length=0.32, name=\"d33b\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycdl3\", device=\"cpu\"), Drift(length=0.90, name=\"d32cmd\", device=\"cpu\"), Aperture(x_max=0.00, y_max=0.02, shape=\"rectangular\", is_active=True, name=\"cedl3\", device=\"cpu\"), Drift(length=5.48, name=\"dcq32a\", device=\"cpu\"), Quadrupole(length=0.11, k1=0, misalignment=(0, 0), tilt=0.00, name=\"cq32\", device=\"cpu\"), Drift(length=5.17, name=\"dcq32b\", device=\"cpu\"), Aperture(x_max=0.02, y_max=0.00, shape=\"rectangular\", is_active=True, name=\"cybx36\", device=\"cpu\"), Drift(length=0.81, name=\"dcb36\", device=\"cpu\"), Dipole(length=2.62, angle=-0.008726643152447821, e1=-0.00,e2=-0.00,tilt=0.00,fringe_integral=0.52,fringe_integral_exit=0.52,gap=0.01,name=\"bx36\", device=\"cpu\"), Marker(name=cntlt3h, device=\"cpu\"), Drift(length=0.50, name=\"ddl20e\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt31\", device=\"cpu\"), Drift(length=0.51, name=\"ddl30em40cma\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcqt32\", device=\"cpu\"), Drift(length=0.49, name=\"d40cmd\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.839614778043, misalignment=(0, 0), tilt=0.00, name=\"qt32\", device=\"cpu\"), Drift(length=0.50, name=\"d40cme\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycqt32\", device=\"cpu\"), Drift(length=0.50, name=\"ddl30em40cmb\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt33\", device=\"cpu\"), Drift(length=0.50, name=\"ddl20e\", device=\"cpu\"), Marker(name=ss3, device=\"cpu\"), Drift(length=0.27, name=\"d37a\", device=\"cpu\"), Marker(name=cc36beg, device=\"cpu\"), Dipole(length=0.35, angle=0.007127828404706885, e1=0.00,e2=0.01,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx361\", device=\"cpu\"), Drift(length=0.20, name=\"dcc36o\", device=\"cpu\"), Dipole(length=0.35, angle=-0.007127828404706885, e1=-0.01,e2=0.00,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx362\", device=\"cpu\"), Drift(length=0.20, name=\"dcc36i\", device=\"cpu\"), Dipole(length=0.35, angle=-0.007127828404706885, e1=0.00,e2=-0.01,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx363\", device=\"cpu\"), Drift(length=0.20, name=\"dcc36o\", device=\"cpu\"), Dipole(length=0.35, angle=0.007127828404706885, e1=0.01,e2=0.00,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx364\", device=\"cpu\"), Marker(name=cc36end, device=\"cpu\"), Drift(length=0.12, name=\"d37ba\", device=\"cpu\"), Drift(length=0.20, name=\"pc43\", device=\"cpu\"), Marker(name=btm43, device=\"cpu\"), Drift(length=0.32, name=\"d37bb\", device=\"cpu\"), Marker(name=imbcs2, device=\"cpu\"), Drift(length=1.26, name=\"d37c\", device=\"cpu\"), Drift(length=0.06, name=\"pc05\", device=\"cpu\"), Marker(name=btm05, device=\"cpu\"), Drift(length=7.70, name=\"d37d\", device=\"cpu\"), Marker(name=wsdl4, device=\"cpu\"), Drift(length=1.10, name=\"d37e\", device=\"cpu\"), Drift(length=2.00, name=\"dchirpv\", device=\"cpu\"), Drift(length=0.51, name=\"d37f\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.437183970154, misalignment=(0, 0), tilt=0.00, name=\"qdl34\", device=\"cpu\"), Drift(length=0.51, name=\"d38a\", device=\"cpu\"), Drift(length=2.00, name=\"dchirph\", device=\"cpu\"), Drift(length=0.61, name=\"d38b\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcdl4\", device=\"cpu\"), Drift(length=0.54, name=\"d38c\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycdl4\", device=\"cpu\"), Drift(length=7.15, name=\"d38da\", device=\"cpu\"), Drift(length=0.06, name=\"pc06\", device=\"cpu\"), Marker(name=btm06, device=\"cpu\"), Drift(length=4.68, name=\"d38db\", device=\"cpu\"), Drift(length=0.50, name=\"ddl20\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt41\", device=\"cpu\"), Drift(length=0.57, name=\"ddl30em40cmc\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcqt42\", device=\"cpu\"), Drift(length=0.43, name=\"d40cmf\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.839614778043, misalignment=(0, 0), tilt=0.00, name=\"qt42\", device=\"cpu\"), Drift(length=0.49, name=\"d40cmb\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycqt42\", device=\"cpu\"), Drift(length=0.51, name=\"ddl30em40cm\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt43\", device=\"cpu\"), Drift(length=0.50, name=\"ddl20\", device=\"cpu\"), Marker(name=mm2, device=\"cpu\"), Drift(length=0.26, name=\"d25cmb\", device=\"cpu\"), Marker(name=im36, device=\"cpu\"), Drift(length=0.24, name=\"d25cmc\", device=\"cpu\"), Drift(length=1.20, name=\"dmm1m90cm\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycem1\", device=\"cpu\"), Drift(length=0.37, name=\"dem1a\", device=\"cpu\"), Quadrupole(length=0.32, k1=-0.390827735953, misalignment=(0, 0), tilt=0.00, name=\"qem1\", device=\"cpu\"), Drift(length=4.14, name=\"dem1c\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.432215708114, misalignment=(0, 0), tilt=0.00, name=\"qem2\", device=\"cpu\"), Drift(length=0.50, name=\"dem2b\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcem2\", device=\"cpu\"), Drift(length=4.36, name=\"dmm3ma\", device=\"cpu\"), Drift(length=0.06, name=\"pc07\", device=\"cpu\"), Marker(name=btm07, device=\"cpu\"), Drift(length=6.85, name=\"dmm3mb\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycem3\", device=\"cpu\"), Drift(length=0.37, name=\"dem3a\", device=\"cpu\"), Quadrupole(length=0.32, k1=-0.593433950486, misalignment=(0, 0), tilt=0.00, name=\"qem3\", device=\"cpu\"), Drift(length=0.77, name=\"dem3b\", device=\"cpu\"), Quadrupole(length=0.11, k1=0, misalignment=(0, 0), tilt=0.00, name=\"qem3v\", device=\"cpu\"), Drift(length=2.76, name=\"dmm4m90cm\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcem4\", device=\"cpu\"), Drift(length=0.50, name=\"dem4a\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.420485259237, misalignment=(0, 0), tilt=0.00, name=\"qem4\", device=\"cpu\"), Drift(length=0.20, name=\"drfb\", device=\"cpu\"), Drift(length=0.00, name=\"rfbem4\", device=\"cpu\"), Drift(length=0.69, name=\"dmm5a\", device=\"cpu\"), Drift(length=0.06, name=\"pc08\", device=\"cpu\"), Marker(name=btm08, device=\"cpu\"), Drift(length=1.12, name=\"dmm5b\", device=\"cpu\"), Marker(name=dbmark36, device=\"cpu\"), Marker(name=ws31, device=\"cpu\"), Drift(length=0.40, name=\"d40cm\", device=\"cpu\"), Drift(length=5.43, name=\"de3ma\", device=\"cpu\"), Drift(length=0.06, name=\"pc09\", device=\"cpu\"), Marker(name=btm09, device=\"cpu\"), Drift(length=2.44, name=\"de3mb\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xce31\", device=\"cpu\"), Drift(length=0.43, name=\"dqea\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.402753198232, misalignment=(0, 0), tilt=0.00, name=\"qe31\", device=\"cpu\"), Drift(length=0.80, name=\"dqebx\", device=\"cpu\"), Drift(length=0.08, name=\"dcx31\", device=\"cpu\"), Drift(length=7.49, name=\"dqebx2\", device=\"cpu\"), Drift(length=8.73, name=\"de3a\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yce32\", device=\"cpu\"), Drift(length=0.44, name=\"dqeaa\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.402753198232, misalignment=(0, 0), tilt=0.00, name=\"qe32\", device=\"cpu\"), Drift(length=0.20, name=\"drfb\", device=\"cpu\"), Drift(length=0.00, name=\"rfbe32\", device=\"cpu\"), Drift(length=0.60, name=\"dqeby1\", device=\"cpu\"), Drift(length=0.08, name=\"dcy32\", device=\"cpu\"), Drift(length=7.89, name=\"dqeby2\", device=\"cpu\"), Marker(name=ws32, device=\"cpu\"), Drift(length=0.40, name=\"d40cm\", device=\"cpu\"), Drift(length=7.91, name=\"de3m80cmb\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xce33\", device=\"cpu\"), Drift(length=0.46, name=\"dqeab\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.402753198232, misalignment=(0, 0), tilt=0.00, name=\"qe33\", device=\"cpu\"), Drift(length=7.95, name=\"dqeca\", device=\"cpu\"), Marker(name=yagpsi, device=\"cpu\"), Drift(length=0.81, name=\"dqecb\", device=\"cpu\"), Marker(name=otr33, device=\"cpu\"), Drift(length=8.34, name=\"de3m40cm\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yce34\", device=\"cpu\"), Drift(length=0.43, name=\"dqea\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.402753198232, misalignment=(0, 0), tilt=0.00, name=\"qe34\", device=\"cpu\"), Drift(length=0.20, name=\"drfb\", device=\"cpu\"), Drift(length=0.00, name=\"rfbe34\", device=\"cpu\"), Drift(length=8.56, name=\"dqec1\", device=\"cpu\"), Marker(name=ws33, device=\"cpu\"), Drift(length=0.40, name=\"d40cm\", device=\"cpu\"), Drift(length=7.76, name=\"de3m80cm\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xce35\", device=\"cpu\"), Drift(length=0.61, name=\"dqeac\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.402753198232, misalignment=(0, 0), tilt=0.00, name=\"qe35\", device=\"cpu\"), Drift(length=0.80, name=\"dqebx\", device=\"cpu\"), Drift(length=0.08, name=\"dcx35\", device=\"cpu\"), Drift(length=7.49, name=\"dqebx2\", device=\"cpu\"), Drift(length=8.74, name=\"de3\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yce36\", device=\"cpu\"), Drift(length=0.43, name=\"dqea\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.402753198232, misalignment=(0, 0), tilt=0.00, name=\"qe36\", device=\"cpu\"), Drift(length=0.20, name=\"drfb\", device=\"cpu\"), Drift(length=0.00, name=\"rfbe36\", device=\"cpu\"), Drift(length=0.60, name=\"dqeby1\", device=\"cpu\"), Drift(length=0.08, name=\"dcy36\", device=\"cpu\"), Drift(length=7.89, name=\"dqeby2\", device=\"cpu\"), Marker(name=ws34, device=\"cpu\"), Drift(length=0.37, name=\"d40cmg\", device=\"cpu\"), Marker(name=dws35, device=\"cpu\"), Drift(length=0.03, name=\"d40cmh\", device=\"cpu\"), Drift(length=0.17, name=\"du1m80cma\", device=\"cpu\"), Marker(name=dws36, device=\"cpu\"), Drift(length=4.38, name=\"du1m80cmb\", device=\"cpu\"), Drift(length=0.08, name=\"dcx37\", device=\"cpu\"), Drift(length=0.30, name=\"d32cmc\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcum1\", device=\"cpu\"), Drift(length=0.49, name=\"dum1a\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.272741408897, misalignment=(0, 0), tilt=0.00, name=\"qum1\", device=\"cpu\"), Drift(length=0.47, name=\"dum1b\", device=\"cpu\"), Drift(length=0.32, name=\"d32cm\", device=\"cpu\"), Drift(length=4.73, name=\"du2m120cm\", device=\"cpu\"), Drift(length=0.08, name=\"dcy38\", device=\"cpu\"), Drift(length=0.42, name=\"d32cma\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycum2\", device=\"cpu\"), Drift(length=0.37, name=\"dum2a\", device=\"cpu\"), Quadrupole(length=0.32, k1=-0.270601268265, misalignment=(0, 0), tilt=0.00, name=\"qum2\", device=\"cpu\"), Drift(length=0.47, name=\"dum2b\", device=\"cpu\"), Drift(length=7.29, name=\"du3m80cm\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcum3\", device=\"cpu\"), Drift(length=0.38, name=\"dum3a\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.278762678373, misalignment=(0, 0), tilt=0.00, name=\"qum3\", device=\"cpu\"), Drift(length=0.47, name=\"dum3b\", device=\"cpu\"), Drift(length=1.81, name=\"d40cma\", device=\"cpu\"), Marker(name=eoblm, device=\"cpu\"), Drift(length=1.36, name=\"du4m120cm\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycum4\", device=\"cpu\"), Drift(length=0.50, name=\"dum4a\", device=\"cpu\"), Quadrupole(length=0.32, k1=-0.247470889897, misalignment=(0, 0), tilt=0.00, name=\"qum4\", device=\"cpu\"), Drift(length=0.60, name=\"dum4b\", device=\"cpu\"), Marker(name=rfb07, device=\"cpu\"), Drift(length=0.50, name=\"du5m80cm\", device=\"cpu\"), Marker(name=imundi, device=\"cpu\"), Drift(length=0.25, name=\"d40cmwa\", device=\"cpu\"), Marker(name=bltof, device=\"cpu\"), Drift(length=0.14, name=\"d40cmwb\", device=\"cpu\"), Marker(name=muhwall1, device=\"cpu\"), Drift(length=0.25, name=\"duhwall1\", device=\"cpu\"), Drift(length=1.61, name=\"duhvesta\", device=\"cpu\"), Marker(name=rfb08, device=\"cpu\"), Drift(length=0.40, name=\"duhvestb\", device=\"cpu\"), Marker(name=muhwall2, device=\"cpu\"), Drift(length=0.25, name=\"duhwall2\", device=\"cpu\"), Drift(length=0.20, name=\"dw2tdund\", device=\"cpu\"), Drift(length=0.86, name=\"dtdund1\", device=\"cpu\"), Marker(name=tdund, device=\"cpu\"), Drift(length=0.35, name=\"dtdund2\", device=\"cpu\"), Drift(length=0.03, name=\"dpcmuon\", device=\"cpu\"), Aperture(x_max=0.00, y_max=0.00, shape=\"elliptical\", is_active=True, name=\"pcmuon\", device=\"cpu\"), Drift(length=0.19, name=\"dmuon1\", device=\"cpu\"), Marker(name=vv999, device=\"cpu\"), Drift(length=0.02, name=\"dmuon2\", device=\"cpu\"), Marker(name=rwwake4h, device=\"cpu\"), Marker(name=endltuh, device=\"cpu\"), Marker(name=begundh, device=\"cpu\"), Drift(length=0.14, name=\"dmuon3\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx12\", device=\"cpu\"), Drift(length=0.07, name=\"dmuon4\", device=\"cpu\"), Marker(name=mm3, device=\"cpu\"), Marker(name=pfilt1, device=\"cpu\"), Marker(name=dbmark37, device=\"cpu\"), Drift(length=0.07, name=\"du0h\", device=\"cpu\"), Drift(length=0.03, name=\"du8h\", device=\"cpu\"), Marker(name=hxrstart, device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Marker(name=hxrcb1beg, device=\"cpu\"), Drift(length=0.04, name=\"d0cb1\", device=\"cpu\"), Dipole(length=0.37, angle=-0.0, e1=0.00,e2=-0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx11\", device=\"cpu\"), Drift(length=0.47, name=\"d1cb1a\", device=\"cpu\"), Marker(name=mcbx4, device=\"cpu\"), Drift(length=0.05, name=\"d1cb1b\", device=\"cpu\"), Marker(name=yagcbx42, device=\"cpu\"), Drift(length=0.06, name=\"d1cb1c\", device=\"cpu\"), Marker(name=bodcbx40, device=\"cpu\"), Drift(length=0.28, name=\"d1cb1d\", device=\"cpu\"), Dipole(length=0.37, angle=0.0, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx12\", device=\"cpu\"), Drift(length=0.08, name=\"dchcb1\", device=\"cpu\"), Marker(name=center1, device=\"cpu\"), Drift(length=0.08, name=\"dchcb1\", device=\"cpu\"), Dipole(length=0.37, angle=0.0, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx13\", device=\"cpu\"), Drift(length=0.86, name=\"d2cb\", device=\"cpu\"), Dipole(length=0.37, angle=-0.0, e1=-0.00,e2=0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx14\", device=\"cpu\"), Drift(length=-0.03, name=\"d3cb1\", device=\"cpu\"), Marker(name=hxrcb1end, device=\"cpu\"), Drift(length=0.08, name=\"du3hcb1a\", device=\"cpu\"), Marker(name=mblmh13, device=\"cpu\"), Drift(length=0.15, name=\"du3hcb1b\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh13\", device=\"cpu\"), Drift(length=0.06, name=\"du4hcb1\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx13\", device=\"cpu\"), Drift(length=-0.06, name=\"du5hcb1\", device=\"cpu\"), Drift(length=0.05, name=\"dpshx13\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh14\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh14, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh14\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx14\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh14\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Marker(name=vvhxu14, device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh15\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh15, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh15\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx15\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh15\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh16\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh16, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh16\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx16\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh16\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh17\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh17, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh17\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx17\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh17\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Marker(name=vvhxu17, device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh18\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh18, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh18\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx18\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh18\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh19\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh19, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh19\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx19\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh19\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh20\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh20, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh20\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx20\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh20\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Marker(name=vvhxu20, device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Marker(name=hxrcb2beg, device=\"cpu\"), Drift(length=0.04, name=\"d0cb2\", device=\"cpu\"), Dipole(length=0.37, angle=-0.0, e1=0.00,e2=-0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx21\", device=\"cpu\"), Drift(length=0.42, name=\"d1cb2a\", device=\"cpu\"), Marker(name=dsscbx11, device=\"cpu\"), Drift(length=0.05, name=\"d1cb2b\", device=\"cpu\"), Marker(name=mcbx1, device=\"cpu\"), Drift(length=0.39, name=\"d1cb2c\", device=\"cpu\"), Dipole(length=0.37, angle=0.0, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx22\", device=\"cpu\"), Drift(length=0.08, name=\"dchcb2\", device=\"cpu\"), Marker(name=center2, device=\"cpu\"), Drift(length=0.08, name=\"dchcb2\", device=\"cpu\"), Dipole(length=0.37, angle=0.0, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx23\", device=\"cpu\"), Drift(length=0.86, name=\"d2cb\", device=\"cpu\"), Dipole(length=0.37, angle=-0.0, e1=-0.00,e2=0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx24\", device=\"cpu\"), Drift(length=-0.03, name=\"d3cb2\", device=\"cpu\"), Marker(name=hxrcb2end, device=\"cpu\"), Drift(length=0.24, name=\"du3hcb2\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh21\", device=\"cpu\"), Drift(length=0.06, name=\"du4hcb2\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx21\", device=\"cpu\"), Drift(length=-0.06, name=\"du5hcb2\", device=\"cpu\"), Drift(length=0.05, name=\"dpshx21\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh22\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh22, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh22\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx22\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh22\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Marker(name=vvhxu22, device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh23\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh23, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh23\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx23\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh23\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh24\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh24, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh24\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx24\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh24\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Marker(name=vvhxu24, device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh25\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh25, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh25\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx25\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh25\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh26\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh26, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh26\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx26\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh26\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh27\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh27, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh27\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx27\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh27\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Marker(name=vvhxu27, device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Marker(name=hxrssbeg, device=\"cpu\"), Drift(length=0.08, name=\"dmono\", device=\"cpu\"), Dipole(length=0.36, angle=0.0, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.00,name=\"bcxhs1\", device=\"cpu\"), Drift(length=0.59, name=\"d1\", device=\"cpu\"), Dipole(length=0.36, angle=-0.0, e1=-0.00,e2=0.00,tilt=0.00,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.00,name=\"bcxhs2\", device=\"cpu\"), Drift(length=0.29, name=\"dch\", device=\"cpu\"), Marker(name=diamond, device=\"cpu\"), Drift(length=0.29, name=\"dch\", device=\"cpu\"), Dipole(length=0.36, angle=-0.0, e1=0.00,e2=-0.00,tilt=0.00,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.00,name=\"bcxhs3\", device=\"cpu\"), Drift(length=0.59, name=\"d1\", device=\"cpu\"), Dipole(length=0.36, angle=0.0, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.00,name=\"bcxhs4\", device=\"cpu\"), Drift(length=0.08, name=\"dmono\", device=\"cpu\"), Marker(name=hxrssend, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh28, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh28\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx28\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Drift(length=0.05, name=\"dpshx28\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh29\", 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Drift(length=0.05, name=\"dpshx50\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Marker(name=rwwake5h, device=\"cpu\"), Marker(name=hxrterm, device=\"cpu\"), Drift(length=2.80, name=\"due1a\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx51\", device=\"cpu\"), Drift(length=0.06, name=\"due1e\", device=\"cpu\"), Marker(name=endundh, device=\"cpu\"), Marker(name=begdmph_1, device=\"cpu\"), Marker(name=uebeg, device=\"cpu\"), Drift(length=0.10, name=\"due1d\", device=\"cpu\"), Marker(name=vv36, device=\"cpu\"), Drift(length=1.50, name=\"due1b\", device=\"cpu\"), Marker(name=mimundo, device=\"cpu\"), Drift(length=0.50, name=\"due1c\", device=\"cpu\"), Drift(length=0.50, name=\"due2a\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycue1\", device=\"cpu\"), Drift(length=0.63, name=\"due2b\", device=\"cpu\"), Marker(name=ph31, device=\"cpu\"), Drift(length=0.18, name=\"due2d\", device=\"cpu\"), Marker(name=ph32, device=\"cpu\"), Drift(length=0.18, name=\"due2d\", device=\"cpu\"), Marker(name=ph33, device=\"cpu\"), Drift(length=0.18, name=\"due2d\", device=\"cpu\"), Marker(name=ph34, device=\"cpu\"), Drift(length=0.58, name=\"due2e\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcue2\", device=\"cpu\"), Drift(length=0.55, name=\"due2c\", device=\"cpu\"), Quadrupole(length=0.55, k1=0.169109252491, misalignment=(0, 0), tilt=0.00, name=\"que1\", device=\"cpu\"), Drift(length=0.37, name=\"due3a\", device=\"cpu\"), Marker(name=bpmue1, device=\"cpu\"), Drift(length=1.21, name=\"due3b\", device=\"cpu\"), Drift(length=0.00, name=\"true1\", device=\"cpu\"), Drift(length=0.40, name=\"due3c\", device=\"cpu\"), Drift(length=1.00, name=\"dbkxdmph\", device=\"cpu\"), Drift(length=0.40, name=\"due3c\", device=\"cpu\"), Aperture(x_max=0.00, y_max=0.00, shape=\"elliptical\", is_active=True, name=\"pctcx\", device=\"cpu\"), Drift(length=0.40, name=\"dpcvv\", device=\"cpu\"), Marker(name=vvtcx, device=\"cpu\"), Drift(length=0.32, name=\"dvvtcx\", device=\"cpu\"), Marker(name=mtcx01, device=\"cpu\"), Cavity(length=1.00, voltage=0.00, phase=0.00, frequency=11424000000.00, name=\"tcx01\", device=\"cpu\"), Drift(length=0.19, name=\"dtcx12\", device=\"cpu\"), Marker(name=mtcx, device=\"cpu\"), Drift(length=0.19, name=\"dtcx12\", device=\"cpu\"), Cavity(length=1.00, voltage=0.00, phase=0.00, frequency=11424000000.00, name=\"tcx02\", device=\"cpu\"), Drift(length=0.38, name=\"dtcxsp\", device=\"cpu\"), Marker(name=sptcx, device=\"cpu\"), Drift(length=0.39, name=\"due4\", device=\"cpu\"), Quadrupole(length=0.55, k1=-0.137840816238, misalignment=(0, 0), tilt=0.00, name=\"que2\", device=\"cpu\"), Drift(length=0.37, name=\"due5a\", device=\"cpu\"), Marker(name=bpmue2, device=\"cpu\"), Drift(length=0.31, name=\"due5b\", device=\"cpu\"), Marker(name=btmque, device=\"cpu\"), Drift(length=0.27, name=\"due5c\", device=\"cpu\"), Aperture(x_max=0.02, y_max=0.02, shape=\"elliptical\", is_active=True, name=\"pcpm0\", device=\"cpu\"), Drift(length=0.05, name=\"due5f\", device=\"cpu\"), Marker(name=btm0, device=\"cpu\"), Drift(length=0.09, name=\"due5d\", device=\"cpu\"), Marker(name=mimbcs3, device=\"cpu\"), Drift(length=0.12, name=\"due5e\", device=\"cpu\"), Marker(name=mdlwall, device=\"cpu\"), Drift(length=0.25, name=\"ddlwall\", device=\"cpu\"), Marker(name=ueend, device=\"cpu\"), Marker(name=dlstart, device=\"cpu\"), Drift(length=0.26, name=\"dsb0a\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycd3\", device=\"cpu\"), Drift(length=0.34, name=\"dsb0b\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcd3\", device=\"cpu\"), Drift(length=0.33, name=\"dsb0c\", device=\"cpu\"), Marker(name=vv37, device=\"cpu\"), Drift(length=0.12, name=\"dsb0d\", device=\"cpu\"), Drift(length=0.11, name=\"dsb0e\", device=\"cpu\"), Marker(name=enddmph_1, device=\"cpu\"), Marker(name=begdmph_2, device=\"cpu\"), Drift(length=0.00, name=\"rodmp1h\", device=\"cpu\"), Dipole(length=0.50, angle=0.0006, e1=0.00,e2=0.00,tilt=1.57,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.02,name=\"bydsh\", device=\"cpu\"), Drift(length=0.32, name=\"ds1\", device=\"cpu\"), Dipole(length=1.45, angle=0.02240073511, e1=0.01,e2=0.01,tilt=1.57,fringe_integral=0.57,fringe_integral_exit=0.57,gap=0.02,name=\"byd1\", device=\"cpu\"), Drift(length=0.25, name=\"ds\", device=\"cpu\"), Dipole(length=1.45, angle=0.02240073511, e1=0.01,e2=0.01,tilt=1.57,fringe_integral=0.57,fringe_integral_exit=0.57,gap=0.02,name=\"byd2\", device=\"cpu\"), Drift(length=0.25, name=\"ds\", device=\"cpu\"), Dipole(length=1.45, angle=0.02240073511, e1=0.01,e2=0.01,tilt=1.57,fringe_integral=0.57,fringe_integral_exit=0.57,gap=0.02,name=\"byd3\", device=\"cpu\"), Drift(length=0.58, name=\"dd1a\", device=\"cpu\"), Aperture(x_max=0.01, y_max=0.01, shape=\"elliptical\", is_active=True, name=\"pcpm1l\", device=\"cpu\"), Marker(name=btm1l, device=\"cpu\"), Drift(length=1.00, name=\"dd1b\", device=\"cpu\"), Marker(name=mimdump, device=\"cpu\"), Drift(length=0.30, name=\"dd1c\", device=\"cpu\"), Marker(name=mimbcs4, device=\"cpu\"), Drift(length=8.82, name=\"dd1d\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycdd\", device=\"cpu\"), Drift(length=0.25, name=\"dd1e\", device=\"cpu\"), Aperture(x_max=0.04, y_max=0.04, shape=\"elliptical\", is_active=True, name=\"pcpm2l\", device=\"cpu\"), Marker(name=btm2l, device=\"cpu\"), Drift(length=0.41, name=\"dd1f\", device=\"cpu\"), Quadrupole(length=0.43, k1=-0.155212710055, misalignment=(0, 0), tilt=0.00, name=\"qdmp1\", device=\"cpu\"), Drift(length=0.30, name=\"dd12a\", device=\"cpu\"), Marker(name=bpmqd, device=\"cpu\"), Drift(length=0.01, name=\"dd12b\", device=\"cpu\"), Marker(name=mqdmp, device=\"cpu\"), Drift(length=0.31, name=\"dd12c\", device=\"cpu\"), Quadrupole(length=0.43, k1=-0.155212710055, misalignment=(0, 0), tilt=0.00, name=\"qdmp2\", device=\"cpu\"), Drift(length=0.47, name=\"dd2a\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcdd\", device=\"cpu\"), Drift(length=6.20, name=\"dd2b\", device=\"cpu\"), Drift(length=1.00, name=\"dd2c\", device=\"cpu\"), Drift(length=0.35, name=\"dd3a\", device=\"cpu\"), Marker(name=bpmdd, device=\"cpu\"), Drift(length=0.15, name=\"dd3b\", device=\"cpu\"), Marker(name=otrdmp, device=\"cpu\"), Drift(length=0.08, name=\"dwsdumpa1\", device=\"cpu\"), Aperture(x_max=0.03, y_max=0.03, shape=\"elliptical\", is_active=True, name=\"pcebd\", device=\"cpu\"), Drift(length=0.14, name=\"dwsdumpa2\", device=\"cpu\"), Drift(length=0.00, name=\"rfbdd\", device=\"cpu\"), Drift(length=0.22, name=\"dwsdumpb\", device=\"cpu\"), Drift(length=0.00, name=\"wsdump\", device=\"cpu\"), Drift(length=2.29, name=\"dwsdumpc\", device=\"cpu\"), Drift(length=0.00, name=\"rodmp2h\", device=\"cpu\"), Marker(name=dumpface, device=\"cpu\"), Drift(length=1.55, name=\"ddump\", device=\"cpu\"), Marker(name=dmpend, device=\"cpu\"), Marker(name=btmdump, device=\"cpu\"), Marker(name=dbmark38, device=\"cpu\"), Marker(name=enddmph_2, device=\"cpu\")])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "segment = cheetah.Segment.from_bmad(\n",
-    "    str(lattice_file_path), environment_variables={\"LCLS_LATTICE\": lattice_dir}\n",
-    ")\n",
-    "segment = segment.flattened()\n",
-    "segment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 2000x125 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(20, 1.25))\n",
-    "segment.plot(ax=plt.gca(), s=0.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jankaiser/Documents/DESY/cheetah/cheetah/accelerator.py:1915: UserWarning: The figure layout has changed to tight\n",
-      "  plt.tight_layout()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "beam = cheetah.ParameterBeam.from_twiss(\n",
-    "    beta_x=9.34799999999386,\n",
-    "    alpha_x=-1.69459999999829,\n",
-    "    emittance_x=3.494768647122823e-09,\n",
-    "    beta_y=9.48702794424903,\n",
-    "    alpha_y=-1.75970623484325,\n",
-    "    emittance_y=3.497810737006068e-09,\n",
-    ")\n",
-    "\n",
-    "mini_segment = cheetah.Segment(\n",
-    "    [\n",
-    "        cheetah.Drift(length=0.1),\n",
-    "        cheetah.Quadrupole(length=0.1, k1=2.0),\n",
-    "        cheetah.Drift(length=0.1),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "mini_segment.plot_twiss_over_lattice(beam)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "beam = cheetah.ParameterBeam.from_twiss(\n",
-    "    beta_x=10.0,\n",
-    "    alpha_x=0.0,\n",
-    "    emittance_x=3.494768647122823e-09,\n",
-    "    beta_y=10.0,\n",
-    "    alpha_y=0.0,\n",
-    "    emittance_y=3.497810737006068e-09,\n",
-    ")\n",
-    "segment = cheetah.Segment.from_bmad(\"test/bmad/bmad_tutorial_lattice.bmad\")\n",
-    "segment.plot_twiss_over_lattice(beam)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "beam = cheetah.ParameterBeam.from_twiss(\n",
-    "    beta_x=10.0,\n",
-    "    alpha_x=0.0,\n",
-    "    emittance_x=3.494768647122823e-09,\n",
-    "    beta_y=10.0,\n",
-    "    alpha_y=0.0,\n",
-    "    emittance_y=3.497810737006068e-09,\n",
-    ")\n",
-    "segment = cheetah.Segment.from_bmad(\"test/bmad/bmad_tutorial_lattice.bmad\")\n",
-    "split_segment = cheetah.Segment(segment.split(0.05))\n",
-    "split_segment.plot_twiss_over_lattice(beam)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "r12 = tensor(0.6725, dtype=torch.float64)\n",
-      "r12 = tensor(2.0584, dtype=torch.float64)\n",
-      "r12 = tensor(2.4919, dtype=torch.float64)\n",
-      "r12 = tensor(2.6500, dtype=torch.float64)\n",
-      "r12 = tensor(2.8312, dtype=torch.float64)\n",
-      "r12 = tensor(0.6194, dtype=torch.float64)\n",
-      "r12 = tensor(2.6836, dtype=torch.float64)\n",
-      "r12 = tensor(2.8310, dtype=torch.float64)\n",
-      "r12 = tensor(2.8572, dtype=torch.float64)\n",
-      "r12 = tensor(2.8776, dtype=torch.float64)\n",
-      "r12 = tensor(2.8941, dtype=torch.float64)\n",
-      "r12 = tensor(2.9075, dtype=torch.float64)\n",
-      "r12 = tensor(2.9188, dtype=torch.float64)\n",
-      "r12 = tensor(2.9283, dtype=torch.float64)\n",
-      "r12 = tensor(2.9365, dtype=torch.float64)\n",
-      "r12 = tensor(2.9436, dtype=torch.float64)\n",
-      "r12 = tensor(2.9498, dtype=torch.float64)\n",
-      "r12 = tensor(2.9553, dtype=torch.float64)\n",
-      "r12 = tensor(2.9602, dtype=torch.float64)\n",
-      "r12 = tensor(2.9646, dtype=torch.float64)\n",
-      "r12 = tensor(2.9686, dtype=torch.float64)\n",
-      "r12 = tensor(2.9721, dtype=torch.float64)\n",
-      "r12 = tensor(2.9754, dtype=torch.float64)\n",
-      "r12 = tensor(2.9783, dtype=torch.float64)\n",
-      "r12 = tensor(2.9811, dtype=torch.float64)\n",
-      "r12 = tensor(2.9836, dtype=torch.float64)\n",
-      "r12 = tensor(2.9859, dtype=torch.float64)\n",
-      "r12 = tensor(2.9880, dtype=torch.float64)\n",
-      "r12 = tensor(2.9900, dtype=torch.float64)\n",
-      "r12 = tensor(2.9919, dtype=torch.float64)\n",
-      "r12 = tensor(2.9936, dtype=torch.float64)\n",
-      "r12 = tensor(2.9952, dtype=torch.float64)\n",
-      "r12 = tensor(2.9968, dtype=torch.float64)\n",
-      "r12 = tensor(2.9982, dtype=torch.float64)\n",
-      "r12 = tensor(2.9995, dtype=torch.float64)\n",
-      "r12 = tensor(3.0008, dtype=torch.float64)\n",
-      "r12 = tensor(3.0020, dtype=torch.float64)\n",
-      "r12 = tensor(3.0031, dtype=torch.float64)\n",
-      "r12 = tensor(3.0042, dtype=torch.float64)\n",
-      "r12 = tensor(3.0052, dtype=torch.float64)\n",
-      "r12 = tensor(3.0062, dtype=torch.float64)\n",
-      "r12 = tensor(3.0071, dtype=torch.float64)\n",
-      "r12 = tensor(3.0080, dtype=torch.float64)\n",
-      "r12 = tensor(3.0088, dtype=torch.float64)\n",
-      "r12 = tensor(3.0096, dtype=torch.float64)\n",
-      "r12 = tensor(3.0104, dtype=torch.float64)\n",
-      "r12 = tensor(3.0111, dtype=torch.float64)\n",
-      "r12 = tensor(3.0118, dtype=torch.float64)\n",
-      "r12 = tensor(3.0125, dtype=torch.float64)\n",
-      "r12 = tensor(3.0131, dtype=torch.float64)\n",
-      "r12 = tensor(3.0137, dtype=torch.float64)\n",
-      "r12 = tensor(3.0143, dtype=torch.float64)\n",
-      "r12 = tensor(3.0149, dtype=torch.float64)\n",
-      "r12 = tensor(3.0155, dtype=torch.float64)\n",
-      "r12 = tensor(3.0160, dtype=torch.float64)\n",
-      "r12 = tensor(3.0165, dtype=torch.float64)\n",
-      "r12 = tensor(3.0170, dtype=torch.float64)\n",
-      "r12 = tensor(3.0175, dtype=torch.float64)\n",
-      "r12 = tensor(3.0179, dtype=torch.float64)\n",
-      "r12 = tensor(3.0184, dtype=torch.float64)\n",
-      "r12 = tensor(3.0188, dtype=torch.float64)\n",
-      "r12 = tensor(3.0192, dtype=torch.float64)\n",
-      "r12 = tensor(3.0196, dtype=torch.float64)\n",
-      "r12 = tensor(3.0200, dtype=torch.float64)\n",
-      "r12 = tensor(3.0204, dtype=torch.float64)\n",
-      "r12 = tensor(3.0207, dtype=torch.float64)\n",
-      "r12 = tensor(3.0211, dtype=torch.float64)\n",
-      "r12 = tensor(3.0214, dtype=torch.float64)\n",
-      "r12 = tensor(3.0218, dtype=torch.float64)\n",
-      "r12 = tensor(3.0221, dtype=torch.float64)\n",
-      "r12 = tensor(3.0224, dtype=torch.float64)\n",
-      "r12 = tensor(3.0227, dtype=torch.float64)\n",
-      "r12 = tensor(3.0230, dtype=torch.float64)\n",
-      "r12 = tensor(3.0233, dtype=torch.float64)\n",
-      "r12 = tensor(3.0236, dtype=torch.float64)\n",
-      "r12 = tensor(3.0238, dtype=torch.float64)\n",
-      "r12 = tensor(3.0241, dtype=torch.float64)\n",
-      "r12 = tensor(3.0244, dtype=torch.float64)\n",
-      "r12 = tensor(3.0246, dtype=torch.float64)\n",
-      "r12 = tensor(3.0249, dtype=torch.float64)\n",
-      "r12 = tensor(3.0251, dtype=torch.float64)\n",
-      "r12 = tensor(3.0253, dtype=torch.float64)\n",
-      "r12 = tensor(3.0256, dtype=torch.float64)\n",
-      "r12 = tensor(3.0258, dtype=torch.float64)\n",
-      "r12 = tensor(3.0260, dtype=torch.float64)\n",
-      "r12 = tensor(3.0262, dtype=torch.float64)\n",
-      "r12 = tensor(3.0264, dtype=torch.float64)\n",
-      "r12 = tensor(3.0266, dtype=torch.float64)\n",
-      "r12 = tensor(3.0268, dtype=torch.float64)\n",
-      "r12 = tensor(3.0270, dtype=torch.float64)\n",
-      "r12 = tensor(3.0272, dtype=torch.float64)\n",
-      "r12 = tensor(3.0274, dtype=torch.float64)\n",
-      "r12 = tensor(3.0276, dtype=torch.float64)\n",
-      "r12 = tensor(3.0278, dtype=torch.float64)\n",
-      "r12 = tensor(3.0279, dtype=torch.float64)\n",
-      "r12 = tensor(3.0281, dtype=torch.float64)\n",
-      "r12 = tensor(3.0283, dtype=torch.float64)\n",
-      "r12 = tensor(3.0284, dtype=torch.float64)\n",
-      "r12 = tensor(3.0286, dtype=torch.float64)\n",
-      "r12 = tensor(3.0287, dtype=torch.float64)\n",
-      "r12 = tensor(3.0289, dtype=torch.float64)\n",
-      "r12 = tensor(3.0291, dtype=torch.float64)\n",
-      "r12 = tensor(3.0292, dtype=torch.float64)\n",
-      "r12 = tensor(3.0293, dtype=torch.float64)\n",
-      "r12 = tensor(3.0295, dtype=torch.float64)\n",
-      "r12 = tensor(3.0296, dtype=torch.float64)\n",
-      "r12 = tensor(3.0298, dtype=torch.float64)\n",
-      "r12 = tensor(3.0299, dtype=torch.float64)\n",
-      "r12 = tensor(3.0300, dtype=torch.float64)\n",
-      "r12 = tensor(3.0302, dtype=torch.float64)\n",
-      "r12 = tensor(3.0303, dtype=torch.float64)\n",
-      "r12 = tensor(3.0304, dtype=torch.float64)\n",
-      "r12 = tensor(3.0305, dtype=torch.float64)\n",
-      "r12 = tensor(3.0306, dtype=torch.float64)\n",
-      "r12 = tensor(3.0308, dtype=torch.float64)\n",
-      "r12 = tensor(3.0309, dtype=torch.float64)\n",
-      "r12 = tensor(3.0310, dtype=torch.float64)\n",
-      "r12 = tensor(3.0295, dtype=torch.float64)\n",
-      "r12 = tensor(3.0297, dtype=torch.float64)\n",
-      "r12 = tensor(3.0239, dtype=torch.float64)\n",
-      "r12 = tensor(3.0300, dtype=torch.float64)\n",
-      "r12 = tensor(3.0301, dtype=torch.float64)\n",
-      "r12 = tensor(3.0245, dtype=torch.float64)\n",
-      "r12 = tensor(3.0304, dtype=torch.float64)\n",
-      "r12 = tensor(3.0305, dtype=torch.float64)\n",
-      "r12 = tensor(3.0251, dtype=torch.float64)\n",
-      "r12 = tensor(3.0308, dtype=torch.float64)\n",
-      "r12 = tensor(3.0309, dtype=torch.float64)\n",
-      "r12 = tensor(3.0310, dtype=torch.float64)\n",
-      "r12 = tensor(3.0312, dtype=torch.float64)\n",
-      "r12 = tensor(3.0313, dtype=torch.float64)\n",
-      "r12 = tensor(3.0314, dtype=torch.float64)\n",
-      "r12 = tensor(3.0315, dtype=torch.float64)\n",
-      "r12 = tensor(3.0316, dtype=torch.float64)\n",
-      "r12 = tensor(3.0317, dtype=torch.float64)\n",
-      "r12 = tensor(3.0318, dtype=torch.float64)\n",
-      "r12 = tensor(3.0319, dtype=torch.float64)\n",
-      "r12 = tensor(3.0320, dtype=torch.float64)\n",
-      "r12 = tensor(3.0321, dtype=torch.float64)\n",
-      "r12 = tensor(3.0322, dtype=torch.float64)\n",
-      "r12 = tensor(3.0323, dtype=torch.float64)\n",
-      "r12 = tensor(3.0324, dtype=torch.float64)\n",
-      "r12 = tensor(3.0324, dtype=torch.float64)\n",
-      "r12 = tensor(3.0325, dtype=torch.float64)\n",
-      "r12 = tensor(3.0326, dtype=torch.float64)\n",
-      "r12 = tensor(3.0327, dtype=torch.float64)\n",
-      "r12 = tensor(3.0328, dtype=torch.float64)\n",
-      "r12 = tensor(3.0329, dtype=torch.float64)\n",
-      "r12 = tensor(3.0330, dtype=torch.float64)\n",
-      "r12 = tensor(3.0330, dtype=torch.float64)\n",
-      "r12 = tensor(3.0331, dtype=torch.float64)\n",
-      "r12 = tensor(3.0332, dtype=torch.float64)\n",
-      "r12 = tensor(3.0333, dtype=torch.float64)\n",
-      "r12 = tensor(3.0334, dtype=torch.float64)\n",
-      "r12 = tensor(3.0334, dtype=torch.float64)\n",
-      "r12 = tensor(3.0335, dtype=torch.float64)\n",
-      "r12 = tensor(3.0336, dtype=torch.float64)\n",
-      "r12 = tensor(3.0336, dtype=torch.float64)\n",
-      "r12 = tensor(3.0337, dtype=torch.float64)\n",
-      "r12 = tensor(3.0338, dtype=torch.float64)\n",
-      "r12 = tensor(3.0339, dtype=torch.float64)\n",
-      "r12 = tensor(3.0339, dtype=torch.float64)\n",
-      "r12 = tensor(3.0340, dtype=torch.float64)\n",
-      "r12 = tensor(3.0341, dtype=torch.float64)\n",
-      "r12 = tensor(3.0341, dtype=torch.float64)\n",
-      "r12 = tensor(3.0342, dtype=torch.float64)\n",
-      "r12 = tensor(3.0343, dtype=torch.float64)\n",
-      "r12 = tensor(3.0343, dtype=torch.float64)\n",
-      "r12 = tensor(3.0344, dtype=torch.float64)\n",
-      "r12 = tensor(3.0344, dtype=torch.float64)\n",
-      "r12 = tensor(3.0345, dtype=torch.float64)\n",
-      "r12 = tensor(3.0346, dtype=torch.float64)\n",
-      "r12 = tensor(3.0346, dtype=torch.float64)\n",
-      "r12 = tensor(3.0347, dtype=torch.float64)\n",
-      "r12 = tensor(3.0347, dtype=torch.float64)\n",
-      "r12 = tensor(3.0348, dtype=torch.float64)\n",
-      "r12 = tensor(3.0349, dtype=torch.float64)\n",
-      "r12 = tensor(3.0349, dtype=torch.float64)\n",
-      "r12 = tensor(0.6725, dtype=torch.float64)\n",
-      "r12 = tensor(2.0584, dtype=torch.float64)\n",
-      "r12 = tensor(2.4919, dtype=torch.float64)\n",
-      "r12 = tensor(2.6500, dtype=torch.float64)\n",
-      "r12 = tensor(2.8312, dtype=torch.float64)\n",
-      "r12 = tensor(0.6194, dtype=torch.float64)\n",
-      "r12 = tensor(2.6836, dtype=torch.float64)\n",
-      "r12 = tensor(2.8310, dtype=torch.float64)\n",
-      "r12 = tensor(2.8572, dtype=torch.float64)\n",
-      "r12 = tensor(2.8776, dtype=torch.float64)\n",
-      "r12 = tensor(2.8941, dtype=torch.float64)\n",
-      "r12 = tensor(2.9075, dtype=torch.float64)\n",
-      "r12 = tensor(2.9188, dtype=torch.float64)\n",
-      "r12 = tensor(2.9283, dtype=torch.float64)\n",
-      "r12 = tensor(2.9365, dtype=torch.float64)\n",
-      "r12 = tensor(2.9436, dtype=torch.float64)\n",
-      "r12 = tensor(2.9498, dtype=torch.float64)\n",
-      "r12 = tensor(2.9553, dtype=torch.float64)\n",
-      "r12 = tensor(2.9602, dtype=torch.float64)\n",
-      "r12 = tensor(2.9646, dtype=torch.float64)\n",
-      "r12 = tensor(2.9686, dtype=torch.float64)\n",
-      "r12 = tensor(2.9721, dtype=torch.float64)\n",
-      "r12 = tensor(2.9754, dtype=torch.float64)\n",
-      "r12 = tensor(2.9783, dtype=torch.float64)\n",
-      "r12 = tensor(2.9811, dtype=torch.float64)\n",
-      "r12 = tensor(2.9836, dtype=torch.float64)\n",
-      "r12 = tensor(2.9859, dtype=torch.float64)\n",
-      "r12 = tensor(2.9880, dtype=torch.float64)\n",
-      "r12 = tensor(2.9900, dtype=torch.float64)\n",
-      "r12 = tensor(2.9919, dtype=torch.float64)\n",
-      "r12 = tensor(2.9936, dtype=torch.float64)\n",
-      "r12 = tensor(2.9952, dtype=torch.float64)\n",
-      "r12 = tensor(2.9968, dtype=torch.float64)\n",
-      "r12 = tensor(2.9982, dtype=torch.float64)\n",
-      "r12 = tensor(2.9995, dtype=torch.float64)\n",
-      "r12 = tensor(3.0008, dtype=torch.float64)\n",
-      "r12 = tensor(3.0020, dtype=torch.float64)\n",
-      "r12 = tensor(3.0031, dtype=torch.float64)\n",
-      "r12 = tensor(3.0042, dtype=torch.float64)\n",
-      "r12 = tensor(3.0052, dtype=torch.float64)\n",
-      "r12 = tensor(3.0062, dtype=torch.float64)\n",
-      "r12 = tensor(3.0071, dtype=torch.float64)\n",
-      "r12 = tensor(3.0080, dtype=torch.float64)\n",
-      "r12 = tensor(3.0088, dtype=torch.float64)\n",
-      "r12 = tensor(3.0096, dtype=torch.float64)\n",
-      "r12 = tensor(3.0104, dtype=torch.float64)\n",
-      "r12 = tensor(3.0111, dtype=torch.float64)\n",
-      "r12 = tensor(3.0118, dtype=torch.float64)\n",
-      "r12 = tensor(3.0125, dtype=torch.float64)\n",
-      "r12 = tensor(3.0131, dtype=torch.float64)\n",
-      "r12 = tensor(3.0137, dtype=torch.float64)\n",
-      "r12 = tensor(3.0143, dtype=torch.float64)\n",
-      "r12 = tensor(3.0149, dtype=torch.float64)\n",
-      "r12 = tensor(3.0155, dtype=torch.float64)\n",
-      "r12 = tensor(3.0160, dtype=torch.float64)\n",
-      "r12 = tensor(3.0165, dtype=torch.float64)\n",
-      "r12 = tensor(3.0170, dtype=torch.float64)\n",
-      "r12 = tensor(3.0175, dtype=torch.float64)\n",
-      "r12 = tensor(3.0179, dtype=torch.float64)\n",
-      "r12 = tensor(3.0184, dtype=torch.float64)\n",
-      "r12 = tensor(3.0188, dtype=torch.float64)\n",
-      "r12 = tensor(3.0192, dtype=torch.float64)\n",
-      "r12 = tensor(3.0196, dtype=torch.float64)\n",
-      "r12 = tensor(3.0200, dtype=torch.float64)\n",
-      "r12 = tensor(3.0204, dtype=torch.float64)\n",
-      "r12 = tensor(3.0207, dtype=torch.float64)\n",
-      "r12 = tensor(3.0211, dtype=torch.float64)\n",
-      "r12 = tensor(3.0214, dtype=torch.float64)\n",
-      "r12 = tensor(3.0218, dtype=torch.float64)\n",
-      "r12 = tensor(3.0221, dtype=torch.float64)\n",
-      "r12 = tensor(3.0224, dtype=torch.float64)\n",
-      "r12 = tensor(3.0227, dtype=torch.float64)\n",
-      "r12 = tensor(3.0230, dtype=torch.float64)\n",
-      "r12 = tensor(3.0233, dtype=torch.float64)\n",
-      "r12 = tensor(3.0236, dtype=torch.float64)\n",
-      "r12 = tensor(3.0238, dtype=torch.float64)\n",
-      "r12 = tensor(3.0241, dtype=torch.float64)\n",
-      "r12 = tensor(3.0244, dtype=torch.float64)\n",
-      "r12 = tensor(3.0246, dtype=torch.float64)\n",
-      "r12 = tensor(3.0249, dtype=torch.float64)\n",
-      "r12 = tensor(3.0251, dtype=torch.float64)\n",
-      "r12 = tensor(3.0253, dtype=torch.float64)\n",
-      "r12 = tensor(3.0256, dtype=torch.float64)\n",
-      "r12 = tensor(3.0258, dtype=torch.float64)\n",
-      "r12 = tensor(3.0260, dtype=torch.float64)\n",
-      "r12 = tensor(3.0262, dtype=torch.float64)\n",
-      "r12 = tensor(3.0264, dtype=torch.float64)\n",
-      "r12 = tensor(3.0266, dtype=torch.float64)\n",
-      "r12 = tensor(3.0268, dtype=torch.float64)\n",
-      "r12 = tensor(3.0270, dtype=torch.float64)\n",
-      "r12 = tensor(3.0272, dtype=torch.float64)\n",
-      "r12 = tensor(3.0274, dtype=torch.float64)\n",
-      "r12 = tensor(3.0276, dtype=torch.float64)\n",
-      "r12 = tensor(3.0278, dtype=torch.float64)\n",
-      "r12 = tensor(3.0279, dtype=torch.float64)\n",
-      "r12 = tensor(3.0281, dtype=torch.float64)\n",
-      "r12 = tensor(3.0283, dtype=torch.float64)\n",
-      "r12 = tensor(3.0284, dtype=torch.float64)\n",
-      "r12 = tensor(3.0286, dtype=torch.float64)\n",
-      "r12 = tensor(3.0287, dtype=torch.float64)\n",
-      "r12 = tensor(3.0289, dtype=torch.float64)\n",
-      "r12 = tensor(3.0291, dtype=torch.float64)\n",
-      "r12 = tensor(3.0292, dtype=torch.float64)\n",
-      "r12 = tensor(3.0293, dtype=torch.float64)\n",
-      "r12 = tensor(3.0295, dtype=torch.float64)\n",
-      "r12 = tensor(3.0296, dtype=torch.float64)\n",
-      "r12 = tensor(3.0298, dtype=torch.float64)\n",
-      "r12 = tensor(3.0299, dtype=torch.float64)\n",
-      "r12 = tensor(3.0300, dtype=torch.float64)\n",
-      "r12 = tensor(3.0302, dtype=torch.float64)\n",
-      "r12 = tensor(3.0303, dtype=torch.float64)\n",
-      "r12 = tensor(3.0304, dtype=torch.float64)\n",
-      "r12 = tensor(3.0305, dtype=torch.float64)\n",
-      "r12 = tensor(3.0306, dtype=torch.float64)\n",
-      "r12 = tensor(3.0308, dtype=torch.float64)\n",
-      "r12 = tensor(3.0309, dtype=torch.float64)\n",
-      "r12 = tensor(3.0310, dtype=torch.float64)\n",
-      "r12 = tensor(3.0295, dtype=torch.float64)\n",
-      "r12 = tensor(3.0297, dtype=torch.float64)\n",
-      "r12 = tensor(3.0239, dtype=torch.float64)\n",
-      "r12 = tensor(3.0300, dtype=torch.float64)\n",
-      "r12 = tensor(3.0301, dtype=torch.float64)\n",
-      "r12 = tensor(3.0245, dtype=torch.float64)\n",
-      "r12 = tensor(3.0304, dtype=torch.float64)\n",
-      "r12 = tensor(3.0305, dtype=torch.float64)\n",
-      "r12 = tensor(3.0251, dtype=torch.float64)\n",
-      "r12 = tensor(3.0308, dtype=torch.float64)\n",
-      "r12 = tensor(3.0309, dtype=torch.float64)\n",
-      "r12 = tensor(3.0310, dtype=torch.float64)\n",
-      "r12 = tensor(3.0312, dtype=torch.float64)\n",
-      "r12 = tensor(3.0313, dtype=torch.float64)\n",
-      "r12 = tensor(3.0314, dtype=torch.float64)\n",
-      "r12 = tensor(3.0315, dtype=torch.float64)\n",
-      "r12 = tensor(3.0316, dtype=torch.float64)\n",
-      "r12 = tensor(3.0317, dtype=torch.float64)\n",
-      "r12 = tensor(3.0318, dtype=torch.float64)\n",
-      "r12 = tensor(3.0319, dtype=torch.float64)\n",
-      "r12 = tensor(3.0320, dtype=torch.float64)\n",
-      "r12 = tensor(3.0321, dtype=torch.float64)\n",
-      "r12 = tensor(3.0322, dtype=torch.float64)\n",
-      "r12 = tensor(3.0323, dtype=torch.float64)\n",
-      "r12 = tensor(3.0324, dtype=torch.float64)\n",
-      "r12 = tensor(3.0324, dtype=torch.float64)\n",
-      "r12 = tensor(3.0325, dtype=torch.float64)\n",
-      "r12 = tensor(3.0326, dtype=torch.float64)\n",
-      "r12 = tensor(3.0327, dtype=torch.float64)\n",
-      "r12 = tensor(3.0328, dtype=torch.float64)\n",
-      "r12 = tensor(3.0329, dtype=torch.float64)\n",
-      "r12 = tensor(3.0330, dtype=torch.float64)\n",
-      "r12 = tensor(3.0330, dtype=torch.float64)\n",
-      "r12 = tensor(3.0331, dtype=torch.float64)\n",
-      "r12 = tensor(3.0332, dtype=torch.float64)\n",
-      "r12 = tensor(3.0333, dtype=torch.float64)\n",
-      "r12 = tensor(3.0334, dtype=torch.float64)\n",
-      "r12 = tensor(3.0334, dtype=torch.float64)\n",
-      "r12 = tensor(3.0335, dtype=torch.float64)\n",
-      "r12 = tensor(3.0336, dtype=torch.float64)\n",
-      "r12 = tensor(3.0336, dtype=torch.float64)\n",
-      "r12 = tensor(3.0337, dtype=torch.float64)\n",
-      "r12 = tensor(3.0338, dtype=torch.float64)\n",
-      "r12 = tensor(3.0339, dtype=torch.float64)\n",
-      "r12 = tensor(3.0339, dtype=torch.float64)\n",
-      "r12 = tensor(3.0340, dtype=torch.float64)\n",
-      "r12 = tensor(3.0341, dtype=torch.float64)\n",
-      "r12 = tensor(3.0341, dtype=torch.float64)\n",
-      "r12 = tensor(3.0342, dtype=torch.float64)\n",
-      "r12 = tensor(3.0343, dtype=torch.float64)\n",
-      "r12 = tensor(3.0343, dtype=torch.float64)\n",
-      "r12 = tensor(3.0344, dtype=torch.float64)\n",
-      "r12 = tensor(3.0344, dtype=torch.float64)\n",
-      "r12 = tensor(3.0345, dtype=torch.float64)\n",
-      "r12 = tensor(3.0346, dtype=torch.float64)\n",
-      "r12 = tensor(3.0346, dtype=torch.float64)\n",
-      "r12 = tensor(3.0347, dtype=torch.float64)\n",
-      "r12 = tensor(3.0347, dtype=torch.float64)\n",
-      "r12 = tensor(3.0348, dtype=torch.float64)\n",
-      "r12 = tensor(3.0349, dtype=torch.float64)\n",
-      "r12 = tensor(3.0349, dtype=torch.float64)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 2000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "beam = cheetah.ParameterBeam.from_twiss(\n",
-    "    beta_x=5.91253677,\n",
-    "    alpha_x=3.55631308,\n",
-    "    emittance_x=3.494768647122823e-09,\n",
-    "    beta_y=5.91253677,\n",
-    "    alpha_y=3.55631308,\n",
-    "    emittance_y=3.497810737006068e-09,\n",
-    "    energy=6e6,\n",
-    ")\n",
-    "\n",
-    "segment = cheetah.Segment.from_bmad(\n",
-    "    str(lattice_file_path), environment_variables={\"LCLS_LATTICE\": lattice_dir}\n",
-    ")\n",
-    "segment = segment.flattened()\n",
-    "# segment = cheetah.Segment([element for element in segment.elements[:100]])\n",
-    "\n",
-    "# outgoing = segment.track(beam)\n",
-    "\n",
-    "segment.plot_twiss_over_lattice(beam, figsize=(20, 5))\n",
-    "\n",
-    "# ------------------------------\n",
-    "\n",
-    "longitudinal_beams = [beam]\n",
-    "s_positions = [0.0]\n",
-    "for element in segment.elements:\n",
-    "    if not hasattr(element, \"length\") or element.length == 0:\n",
-    "        continue\n",
-    "\n",
-    "    outgoing = element.track(longitudinal_beams[-1])\n",
-    "\n",
-    "    longitudinal_beams.append(outgoing)\n",
-    "    s_positions.append(s_positions[-1] + element.length)\n",
-    "\n",
-    "sigma_s = [beam.sigma_s for beam in longitudinal_beams]\n",
-    "sigma_p = [beam.sigma_p for beam in longitudinal_beams]\n",
-    "energy = [beam.energy for beam in longitudinal_beams]\n",
-    "\n",
-    "plt.figure(figsize=(20, 4))\n",
-    "plt.title(\"sigma_s\")\n",
-    "plt.plot(s_positions, sigma_s)\n",
-    "plt.show()\n",
-    "\n",
-    "plt.figure(figsize=(20, 4))\n",
-    "plt.title(\"sigma_p\")\n",
-    "plt.plot(s_positions, sigma_p)\n",
-    "plt.show()\n",
-    "\n",
-    "plt.figure(figsize=(20, 4))\n",
-    "plt.title(\"energy\")\n",
-    "plt.plot(s_positions, energy)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Drift(length=-0.87, name=\"dl00\", device=\"cpu\")"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "segment.dl00"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "r12 = tensor(0.6725, dtype=torch.float64)\n",
-      "r12 = tensor(2.0584, dtype=torch.float64)\n",
-      "r12 = tensor(2.4919, dtype=torch.float64)\n",
-      "r12 = tensor(2.6500, dtype=torch.float64)\n",
-      "r12 = tensor(2.8312, dtype=torch.float64)\n",
-      "r12 = tensor(0.6194, dtype=torch.float64)\n",
-      "r12 = tensor(2.6836, dtype=torch.float64)\n",
-      "r12 = tensor(2.8310, dtype=torch.float64)\n",
-      "r12 = tensor(2.8572, dtype=torch.float64)\n",
-      "r12 = tensor(2.8776, dtype=torch.float64)\n",
-      "r12 = tensor(2.8941, dtype=torch.float64)\n",
-      "r12 = tensor(2.9075, dtype=torch.float64)\n",
-      "r12 = tensor(2.9188, dtype=torch.float64)\n",
-      "r12 = tensor(2.9283, dtype=torch.float64)\n",
-      "r12 = tensor(2.9365, dtype=torch.float64)\n",
-      "r12 = tensor(2.9436, dtype=torch.float64)\n",
-      "r12 = tensor(2.9498, dtype=torch.float64)\n",
-      "r12 = tensor(2.9553, dtype=torch.float64)\n",
-      "r12 = tensor(2.9602, dtype=torch.float64)\n",
-      "r12 = tensor(2.9646, dtype=torch.float64)\n",
-      "r12 = tensor(2.9686, dtype=torch.float64)\n",
-      "r12 = tensor(2.9721, dtype=torch.float64)\n",
-      "r12 = tensor(2.9754, dtype=torch.float64)\n",
-      "r12 = tensor(2.9783, dtype=torch.float64)\n",
-      "r12 = tensor(2.9811, dtype=torch.float64)\n",
-      "r12 = tensor(2.9836, dtype=torch.float64)\n",
-      "r12 = tensor(2.9859, dtype=torch.float64)\n",
-      "r12 = tensor(2.9880, dtype=torch.float64)\n",
-      "r12 = tensor(2.9900, dtype=torch.float64)\n",
-      "r12 = tensor(2.9919, dtype=torch.float64)\n",
-      "r12 = tensor(2.9936, dtype=torch.float64)\n",
-      "r12 = tensor(2.9952, dtype=torch.float64)\n",
-      "r12 = tensor(2.9968, dtype=torch.float64)\n",
-      "r12 = tensor(2.9982, dtype=torch.float64)\n",
-      "r12 = tensor(2.9995, dtype=torch.float64)\n",
-      "r12 = tensor(3.0008, dtype=torch.float64)\n",
-      "r12 = tensor(3.0020, dtype=torch.float64)\n",
-      "r12 = tensor(3.0031, dtype=torch.float64)\n",
-      "r12 = tensor(3.0042, dtype=torch.float64)\n",
-      "r12 = tensor(3.0052, dtype=torch.float64)\n",
-      "r12 = tensor(3.0062, dtype=torch.float64)\n",
-      "r12 = tensor(3.0071, dtype=torch.float64)\n",
-      "r12 = tensor(3.0080, dtype=torch.float64)\n",
-      "r12 = tensor(3.0088, dtype=torch.float64)\n",
-      "r12 = tensor(3.0096, dtype=torch.float64)\n",
-      "r12 = tensor(3.0104, dtype=torch.float64)\n",
-      "r12 = tensor(3.0111, dtype=torch.float64)\n",
-      "r12 = tensor(3.0118, dtype=torch.float64)\n",
-      "r12 = tensor(3.0125, dtype=torch.float64)\n",
-      "r12 = tensor(3.0131, dtype=torch.float64)\n",
-      "r12 = tensor(3.0137, dtype=torch.float64)\n",
-      "r12 = tensor(3.0143, dtype=torch.float64)\n",
-      "r12 = tensor(3.0149, dtype=torch.float64)\n",
-      "r12 = tensor(3.0155, dtype=torch.float64)\n",
-      "r12 = tensor(3.0160, dtype=torch.float64)\n",
-      "r12 = tensor(3.0165, dtype=torch.float64)\n",
-      "r12 = tensor(3.0170, dtype=torch.float64)\n",
-      "r12 = tensor(3.0175, dtype=torch.float64)\n",
-      "r12 = tensor(3.0179, dtype=torch.float64)\n",
-      "r12 = tensor(3.0184, dtype=torch.float64)\n",
-      "r12 = tensor(3.0188, dtype=torch.float64)\n",
-      "r12 = tensor(3.0192, dtype=torch.float64)\n",
-      "r12 = tensor(3.0196, dtype=torch.float64)\n",
-      "r12 = tensor(3.0200, dtype=torch.float64)\n",
-      "r12 = tensor(3.0204, dtype=torch.float64)\n",
-      "r12 = tensor(3.0207, dtype=torch.float64)\n",
-      "r12 = tensor(3.0211, dtype=torch.float64)\n",
-      "r12 = tensor(3.0214, dtype=torch.float64)\n",
-      "r12 = tensor(3.0218, dtype=torch.float64)\n",
-      "r12 = tensor(3.0221, dtype=torch.float64)\n",
-      "r12 = tensor(3.0224, dtype=torch.float64)\n",
-      "r12 = tensor(3.0227, dtype=torch.float64)\n",
-      "r12 = tensor(3.0230, dtype=torch.float64)\n",
-      "r12 = tensor(3.0233, dtype=torch.float64)\n",
-      "r12 = tensor(3.0236, dtype=torch.float64)\n",
-      "r12 = tensor(3.0238, dtype=torch.float64)\n",
-      "r12 = tensor(3.0241, dtype=torch.float64)\n",
-      "r12 = tensor(3.0244, dtype=torch.float64)\n",
-      "r12 = tensor(3.0246, dtype=torch.float64)\n",
-      "r12 = tensor(3.0249, dtype=torch.float64)\n",
-      "r12 = tensor(3.0251, dtype=torch.float64)\n",
-      "r12 = tensor(3.0253, dtype=torch.float64)\n",
-      "r12 = tensor(3.0256, dtype=torch.float64)\n",
-      "r12 = tensor(3.0258, dtype=torch.float64)\n",
-      "r12 = tensor(3.0260, dtype=torch.float64)\n",
-      "r12 = tensor(3.0262, dtype=torch.float64)\n",
-      "r12 = tensor(3.0264, dtype=torch.float64)\n",
-      "r12 = tensor(3.0266, dtype=torch.float64)\n",
-      "r12 = tensor(3.0268, dtype=torch.float64)\n",
-      "r12 = tensor(3.0270, dtype=torch.float64)\n",
-      "r12 = tensor(3.0272, dtype=torch.float64)\n",
-      "r12 = tensor(3.0274, dtype=torch.float64)\n",
-      "r12 = tensor(3.0276, dtype=torch.float64)\n",
-      "r12 = tensor(3.0278, dtype=torch.float64)\n",
-      "r12 = tensor(3.0279, dtype=torch.float64)\n",
-      "r12 = tensor(3.0281, dtype=torch.float64)\n",
-      "r12 = tensor(3.0283, dtype=torch.float64)\n",
-      "r12 = tensor(3.0284, dtype=torch.float64)\n",
-      "r12 = tensor(3.0286, dtype=torch.float64)\n",
-      "r12 = tensor(3.0287, dtype=torch.float64)\n",
-      "r12 = tensor(3.0289, dtype=torch.float64)\n",
-      "r12 = tensor(3.0291, dtype=torch.float64)\n",
-      "r12 = tensor(3.0292, dtype=torch.float64)\n",
-      "r12 = tensor(3.0293, dtype=torch.float64)\n",
-      "r12 = tensor(3.0295, dtype=torch.float64)\n",
-      "r12 = tensor(3.0296, dtype=torch.float64)\n",
-      "r12 = tensor(3.0298, dtype=torch.float64)\n",
-      "r12 = tensor(3.0299, dtype=torch.float64)\n",
-      "r12 = tensor(3.0300, dtype=torch.float64)\n",
-      "r12 = tensor(3.0302, dtype=torch.float64)\n",
-      "r12 = tensor(3.0303, dtype=torch.float64)\n",
-      "r12 = tensor(3.0304, dtype=torch.float64)\n",
-      "r12 = tensor(3.0305, dtype=torch.float64)\n",
-      "r12 = tensor(3.0306, dtype=torch.float64)\n",
-      "r12 = tensor(3.0308, dtype=torch.float64)\n",
-      "r12 = tensor(3.0309, dtype=torch.float64)\n",
-      "r12 = tensor(3.0310, dtype=torch.float64)\n",
-      "r12 = tensor(3.0295, dtype=torch.float64)\n",
-      "r12 = tensor(3.0297, dtype=torch.float64)\n",
-      "r12 = tensor(3.0239, dtype=torch.float64)\n",
-      "r12 = tensor(3.0300, dtype=torch.float64)\n",
-      "r12 = tensor(3.0301, dtype=torch.float64)\n",
-      "r12 = tensor(3.0245, dtype=torch.float64)\n",
-      "r12 = tensor(3.0304, dtype=torch.float64)\n",
-      "r12 = tensor(3.0305, dtype=torch.float64)\n",
-      "r12 = tensor(3.0251, dtype=torch.float64)\n",
-      "r12 = tensor(3.0308, dtype=torch.float64)\n",
-      "r12 = tensor(3.0309, dtype=torch.float64)\n",
-      "r12 = tensor(3.0310, dtype=torch.float64)\n",
-      "r12 = tensor(3.0312, dtype=torch.float64)\n",
-      "r12 = tensor(3.0313, dtype=torch.float64)\n",
-      "r12 = tensor(3.0314, dtype=torch.float64)\n",
-      "r12 = tensor(3.0315, dtype=torch.float64)\n",
-      "r12 = tensor(3.0316, dtype=torch.float64)\n",
-      "r12 = tensor(3.0317, dtype=torch.float64)\n",
-      "r12 = tensor(3.0318, dtype=torch.float64)\n",
-      "r12 = tensor(3.0319, dtype=torch.float64)\n",
-      "r12 = tensor(3.0320, dtype=torch.float64)\n",
-      "r12 = tensor(3.0321, dtype=torch.float64)\n",
-      "r12 = tensor(3.0322, dtype=torch.float64)\n",
-      "r12 = tensor(3.0323, dtype=torch.float64)\n",
-      "r12 = tensor(3.0324, dtype=torch.float64)\n",
-      "r12 = tensor(3.0324, dtype=torch.float64)\n",
-      "r12 = tensor(3.0325, dtype=torch.float64)\n",
-      "r12 = tensor(3.0326, dtype=torch.float64)\n",
-      "r12 = tensor(3.0327, dtype=torch.float64)\n",
-      "r12 = tensor(3.0328, dtype=torch.float64)\n",
-      "r12 = tensor(3.0329, dtype=torch.float64)\n",
-      "r12 = tensor(3.0330, dtype=torch.float64)\n",
-      "r12 = tensor(3.0330, dtype=torch.float64)\n",
-      "r12 = tensor(3.0331, dtype=torch.float64)\n",
-      "r12 = tensor(3.0332, dtype=torch.float64)\n",
-      "r12 = tensor(3.0333, dtype=torch.float64)\n",
-      "r12 = tensor(3.0334, dtype=torch.float64)\n",
-      "r12 = tensor(3.0334, dtype=torch.float64)\n",
-      "r12 = tensor(3.0335, dtype=torch.float64)\n",
-      "r12 = tensor(3.0336, dtype=torch.float64)\n",
-      "r12 = tensor(3.0336, dtype=torch.float64)\n",
-      "r12 = tensor(3.0337, dtype=torch.float64)\n",
-      "r12 = tensor(3.0338, dtype=torch.float64)\n",
-      "r12 = tensor(3.0339, dtype=torch.float64)\n",
-      "r12 = tensor(3.0339, dtype=torch.float64)\n",
-      "r12 = tensor(3.0340, dtype=torch.float64)\n",
-      "r12 = tensor(3.0341, dtype=torch.float64)\n",
-      "r12 = tensor(3.0341, dtype=torch.float64)\n",
-      "r12 = tensor(3.0342, dtype=torch.float64)\n",
-      "r12 = tensor(3.0343, dtype=torch.float64)\n",
-      "r12 = tensor(3.0343, dtype=torch.float64)\n",
-      "r12 = tensor(3.0344, dtype=torch.float64)\n",
-      "r12 = tensor(3.0344, dtype=torch.float64)\n",
-      "r12 = tensor(3.0345, dtype=torch.float64)\n",
-      "r12 = tensor(3.0346, dtype=torch.float64)\n",
-      "r12 = tensor(3.0346, dtype=torch.float64)\n",
-      "r12 = tensor(3.0347, dtype=torch.float64)\n",
-      "r12 = tensor(3.0347, dtype=torch.float64)\n",
-      "r12 = tensor(3.0348, dtype=torch.float64)\n",
-      "r12 = tensor(3.0349, dtype=torch.float64)\n",
-      "r12 = tensor(3.0349, dtype=torch.float64)\n",
-      "r12 = tensor(0.6725, dtype=torch.float64)\n",
-      "r12 = tensor(2.0584, dtype=torch.float64)\n",
-      "r12 = tensor(2.4919, dtype=torch.float64)\n",
-      "r12 = tensor(2.6500, dtype=torch.float64)\n",
-      "r12 = tensor(2.8312, dtype=torch.float64)\n",
-      "r12 = tensor(0.6194, dtype=torch.float64)\n",
-      "r12 = tensor(2.6836, dtype=torch.float64)\n",
-      "r12 = tensor(2.8310, dtype=torch.float64)\n",
-      "r12 = tensor(2.8572, dtype=torch.float64)\n",
-      "r12 = tensor(2.8776, dtype=torch.float64)\n",
-      "r12 = tensor(2.8941, dtype=torch.float64)\n",
-      "r12 = tensor(2.9075, dtype=torch.float64)\n",
-      "r12 = tensor(2.9188, dtype=torch.float64)\n",
-      "r12 = tensor(2.9283, dtype=torch.float64)\n",
-      "r12 = tensor(2.9365, dtype=torch.float64)\n",
-      "r12 = tensor(2.9436, dtype=torch.float64)\n",
-      "r12 = tensor(2.9498, dtype=torch.float64)\n",
-      "r12 = tensor(2.9553, dtype=torch.float64)\n",
-      "r12 = tensor(2.9602, dtype=torch.float64)\n",
-      "r12 = tensor(2.9646, dtype=torch.float64)\n",
-      "r12 = tensor(2.9686, dtype=torch.float64)\n",
-      "r12 = tensor(2.9721, dtype=torch.float64)\n",
-      "r12 = tensor(2.9754, dtype=torch.float64)\n",
-      "r12 = tensor(2.9783, dtype=torch.float64)\n",
-      "r12 = tensor(2.9811, dtype=torch.float64)\n",
-      "r12 = tensor(2.9836, dtype=torch.float64)\n",
-      "r12 = tensor(2.9859, dtype=torch.float64)\n",
-      "r12 = tensor(2.9880, dtype=torch.float64)\n",
-      "r12 = tensor(2.9900, dtype=torch.float64)\n",
-      "r12 = tensor(2.9919, dtype=torch.float64)\n",
-      "r12 = tensor(2.9936, dtype=torch.float64)\n",
-      "r12 = tensor(2.9952, dtype=torch.float64)\n",
-      "r12 = tensor(2.9968, dtype=torch.float64)\n",
-      "r12 = tensor(2.9982, dtype=torch.float64)\n",
-      "r12 = tensor(2.9995, dtype=torch.float64)\n",
-      "r12 = tensor(3.0008, dtype=torch.float64)\n",
-      "r12 = tensor(3.0020, dtype=torch.float64)\n",
-      "r12 = tensor(3.0031, dtype=torch.float64)\n",
-      "r12 = tensor(3.0042, dtype=torch.float64)\n",
-      "r12 = tensor(3.0052, dtype=torch.float64)\n",
-      "r12 = tensor(3.0062, dtype=torch.float64)\n",
-      "r12 = tensor(3.0071, dtype=torch.float64)\n",
-      "r12 = tensor(3.0080, dtype=torch.float64)\n",
-      "r12 = tensor(3.0088, dtype=torch.float64)\n",
-      "r12 = tensor(3.0096, dtype=torch.float64)\n",
-      "r12 = tensor(3.0104, dtype=torch.float64)\n",
-      "r12 = tensor(3.0111, dtype=torch.float64)\n",
-      "r12 = tensor(3.0118, dtype=torch.float64)\n",
-      "r12 = tensor(3.0125, dtype=torch.float64)\n",
-      "r12 = tensor(3.0131, dtype=torch.float64)\n",
-      "r12 = tensor(3.0137, dtype=torch.float64)\n",
-      "r12 = tensor(3.0143, dtype=torch.float64)\n",
-      "r12 = tensor(3.0149, dtype=torch.float64)\n",
-      "r12 = tensor(3.0155, dtype=torch.float64)\n",
-      "r12 = tensor(3.0160, dtype=torch.float64)\n",
-      "r12 = tensor(3.0165, dtype=torch.float64)\n",
-      "r12 = tensor(3.0170, dtype=torch.float64)\n",
-      "r12 = tensor(3.0175, dtype=torch.float64)\n",
-      "r12 = tensor(3.0179, dtype=torch.float64)\n",
-      "r12 = tensor(3.0184, dtype=torch.float64)\n",
-      "r12 = tensor(3.0188, dtype=torch.float64)\n",
-      "r12 = tensor(3.0192, dtype=torch.float64)\n",
-      "r12 = tensor(3.0196, dtype=torch.float64)\n",
-      "r12 = tensor(3.0200, dtype=torch.float64)\n",
-      "r12 = tensor(3.0204, dtype=torch.float64)\n",
-      "r12 = tensor(3.0207, dtype=torch.float64)\n",
-      "r12 = tensor(3.0211, dtype=torch.float64)\n",
-      "r12 = tensor(3.0214, dtype=torch.float64)\n",
-      "r12 = tensor(3.0218, dtype=torch.float64)\n",
-      "r12 = tensor(3.0221, dtype=torch.float64)\n",
-      "r12 = tensor(3.0224, dtype=torch.float64)\n",
-      "r12 = tensor(3.0227, dtype=torch.float64)\n",
-      "r12 = tensor(3.0230, dtype=torch.float64)\n",
-      "r12 = tensor(3.0233, dtype=torch.float64)\n",
-      "r12 = tensor(3.0236, dtype=torch.float64)\n",
-      "r12 = tensor(3.0238, dtype=torch.float64)\n",
-      "r12 = tensor(3.0241, dtype=torch.float64)\n",
-      "r12 = tensor(3.0244, dtype=torch.float64)\n",
-      "r12 = tensor(3.0246, dtype=torch.float64)\n",
-      "r12 = tensor(3.0249, dtype=torch.float64)\n",
-      "r12 = tensor(3.0251, dtype=torch.float64)\n",
-      "r12 = tensor(3.0253, dtype=torch.float64)\n",
-      "r12 = tensor(3.0256, dtype=torch.float64)\n",
-      "r12 = tensor(3.0258, dtype=torch.float64)\n",
-      "r12 = tensor(3.0260, dtype=torch.float64)\n",
-      "r12 = tensor(3.0262, dtype=torch.float64)\n",
-      "r12 = tensor(3.0264, dtype=torch.float64)\n",
-      "r12 = tensor(3.0266, dtype=torch.float64)\n",
-      "r12 = tensor(3.0268, dtype=torch.float64)\n",
-      "r12 = tensor(3.0270, dtype=torch.float64)\n",
-      "r12 = tensor(3.0272, dtype=torch.float64)\n",
-      "r12 = tensor(3.0274, dtype=torch.float64)\n",
-      "r12 = tensor(3.0276, dtype=torch.float64)\n",
-      "r12 = tensor(3.0278, dtype=torch.float64)\n",
-      "r12 = tensor(3.0279, dtype=torch.float64)\n",
-      "r12 = tensor(3.0281, dtype=torch.float64)\n",
-      "r12 = tensor(3.0283, dtype=torch.float64)\n",
-      "r12 = tensor(3.0284, dtype=torch.float64)\n",
-      "r12 = tensor(3.0286, dtype=torch.float64)\n",
-      "r12 = tensor(3.0287, dtype=torch.float64)\n",
-      "r12 = tensor(3.0289, dtype=torch.float64)\n",
-      "r12 = tensor(3.0291, dtype=torch.float64)\n",
-      "r12 = tensor(3.0292, dtype=torch.float64)\n",
-      "r12 = tensor(3.0293, dtype=torch.float64)\n",
-      "r12 = tensor(3.0295, dtype=torch.float64)\n",
-      "r12 = tensor(3.0296, dtype=torch.float64)\n",
-      "r12 = tensor(3.0298, dtype=torch.float64)\n",
-      "r12 = tensor(3.0299, dtype=torch.float64)\n",
-      "r12 = tensor(3.0300, dtype=torch.float64)\n",
-      "r12 = tensor(3.0302, dtype=torch.float64)\n",
-      "r12 = tensor(3.0303, dtype=torch.float64)\n",
-      "r12 = tensor(3.0304, dtype=torch.float64)\n",
-      "r12 = tensor(3.0305, dtype=torch.float64)\n",
-      "r12 = tensor(3.0306, dtype=torch.float64)\n",
-      "r12 = tensor(3.0308, dtype=torch.float64)\n",
-      "r12 = tensor(3.0309, dtype=torch.float64)\n",
-      "r12 = tensor(3.0310, dtype=torch.float64)\n",
-      "r12 = tensor(3.0295, dtype=torch.float64)\n",
-      "r12 = tensor(3.0297, dtype=torch.float64)\n",
-      "r12 = tensor(3.0239, dtype=torch.float64)\n",
-      "r12 = tensor(3.0300, dtype=torch.float64)\n",
-      "r12 = tensor(3.0301, dtype=torch.float64)\n",
-      "r12 = tensor(3.0245, dtype=torch.float64)\n",
-      "r12 = tensor(3.0304, dtype=torch.float64)\n",
-      "r12 = tensor(3.0305, dtype=torch.float64)\n",
-      "r12 = tensor(3.0251, dtype=torch.float64)\n",
-      "r12 = tensor(3.0308, dtype=torch.float64)\n",
-      "r12 = tensor(3.0309, dtype=torch.float64)\n",
-      "r12 = tensor(3.0310, dtype=torch.float64)\n",
-      "r12 = tensor(3.0312, dtype=torch.float64)\n",
-      "r12 = tensor(3.0313, dtype=torch.float64)\n",
-      "r12 = tensor(3.0314, dtype=torch.float64)\n",
-      "r12 = tensor(3.0315, dtype=torch.float64)\n",
-      "r12 = tensor(3.0316, dtype=torch.float64)\n",
-      "r12 = tensor(3.0317, dtype=torch.float64)\n",
-      "r12 = tensor(3.0318, dtype=torch.float64)\n",
-      "r12 = tensor(3.0319, dtype=torch.float64)\n",
-      "r12 = tensor(3.0320, dtype=torch.float64)\n",
-      "r12 = tensor(3.0321, dtype=torch.float64)\n",
-      "r12 = tensor(3.0322, dtype=torch.float64)\n",
-      "r12 = tensor(3.0323, dtype=torch.float64)\n",
-      "r12 = tensor(3.0324, dtype=torch.float64)\n",
-      "r12 = tensor(3.0324, dtype=torch.float64)\n",
-      "r12 = tensor(3.0325, dtype=torch.float64)\n",
-      "r12 = tensor(3.0326, dtype=torch.float64)\n",
-      "r12 = tensor(3.0327, dtype=torch.float64)\n",
-      "r12 = tensor(3.0328, dtype=torch.float64)\n",
-      "r12 = tensor(3.0329, dtype=torch.float64)\n",
-      "r12 = tensor(3.0330, dtype=torch.float64)\n",
-      "r12 = tensor(3.0330, dtype=torch.float64)\n",
-      "r12 = tensor(3.0331, dtype=torch.float64)\n",
-      "r12 = tensor(3.0332, dtype=torch.float64)\n",
-      "r12 = tensor(3.0333, dtype=torch.float64)\n",
-      "r12 = tensor(3.0334, dtype=torch.float64)\n",
-      "r12 = tensor(3.0334, dtype=torch.float64)\n",
-      "r12 = tensor(3.0335, dtype=torch.float64)\n",
-      "r12 = tensor(3.0336, dtype=torch.float64)\n",
-      "r12 = tensor(3.0336, dtype=torch.float64)\n",
-      "r12 = tensor(3.0337, dtype=torch.float64)\n",
-      "r12 = tensor(3.0338, dtype=torch.float64)\n",
-      "r12 = tensor(3.0339, dtype=torch.float64)\n",
-      "r12 = tensor(3.0339, dtype=torch.float64)\n",
-      "r12 = tensor(3.0340, dtype=torch.float64)\n",
-      "r12 = tensor(3.0341, dtype=torch.float64)\n",
-      "r12 = tensor(3.0341, dtype=torch.float64)\n",
-      "r12 = tensor(3.0342, dtype=torch.float64)\n",
-      "r12 = tensor(3.0343, dtype=torch.float64)\n",
-      "r12 = tensor(3.0343, dtype=torch.float64)\n",
-      "r12 = tensor(3.0344, dtype=torch.float64)\n",
-      "r12 = tensor(3.0344, dtype=torch.float64)\n",
-      "r12 = tensor(3.0345, dtype=torch.float64)\n",
-      "r12 = tensor(3.0346, dtype=torch.float64)\n",
-      "r12 = tensor(3.0346, dtype=torch.float64)\n",
-      "r12 = tensor(3.0347, dtype=torch.float64)\n",
-      "r12 = tensor(3.0347, dtype=torch.float64)\n",
-      "r12 = tensor(3.0348, dtype=torch.float64)\n",
-      "r12 = tensor(3.0349, dtype=torch.float64)\n",
-      "r12 = tensor(3.0349, dtype=torch.float64)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "<Figure size 2000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "<Figure size 2000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "beam = cheetah.ParticleBeam.from_twiss(\n",
-    "    num_particles=10_000,\n",
-    "    beta_x=5.91253677,\n",
-    "    alpha_x=3.55631308,\n",
-    "    emittance_x=3.494768647122823e-09,\n",
-    "    beta_y=5.91253677,\n",
-    "    alpha_y=3.55631308,\n",
-    "    emittance_y=3.497810737006068e-09,\n",
-    "    energy=6e6,\n",
-    ")\n",
-    "\n",
-    "segment = cheetah.Segment.from_bmad(\n",
-    "    str(lattice_file_path), environment_variables={\"LCLS_LATTICE\": lattice_dir}\n",
-    ")\n",
-    "segment = segment.flattened()\n",
-    "for element in segment.elements:\n",
-    "    if isinstance(element, cheetah.Aperture):\n",
-    "        element.is_active = False\n",
-    "# segment = cheetah.Segment([element for element in segment.elements[:100]])\n",
-    "\n",
-    "# outgoing = segment.track(beam)\n",
-    "\n",
-    "segment.plot_twiss_over_lattice(beam, figsize=(20, 5))\n",
-    "\n",
-    "# ------------------------------\n",
-    "\n",
-    "longitudinal_beams = [beam]\n",
-    "s_positions = [0.0]\n",
-    "for element in segment.elements:\n",
-    "    if not hasattr(element, \"length\") or element.length == 0:\n",
-    "        continue\n",
-    "\n",
-    "    outgoing = element.track(longitudinal_beams[-1])\n",
-    "\n",
-    "    longitudinal_beams.append(outgoing)\n",
-    "    s_positions.append(s_positions[-1] + element.length)\n",
-    "\n",
-    "sigma_s = [beam.sigma_s for beam in longitudinal_beams]\n",
-    "sigma_p = [beam.sigma_p for beam in longitudinal_beams]\n",
-    "energy = [beam.energy for beam in longitudinal_beams]\n",
-    "\n",
-    "plt.figure(figsize=(20, 4))\n",
-    "plt.title(\"sigma_s\")\n",
-    "plt.plot(s_positions, sigma_s)\n",
-    "plt.show()\n",
-    "\n",
-    "plt.figure(figsize=(20, 4))\n",
-    "plt.title(\"sigma_p\")\n",
-    "plt.plot(s_positions, sigma_p)\n",
-    "plt.show()\n",
-    "\n",
-    "plt.figure(figsize=(20, 4))\n",
-    "plt.title(\"energy\")\n",
-    "plt.plot(s_positions, energy)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "beam = cheetah.ParticleBeam.from_twiss(\n",
-    "    num_particles=10_000,\n",
-    "    beta_x=5.91253677,\n",
-    "    alpha_x=3.55631308,\n",
-    "    emittance_x=3.494768647122823e-09,\n",
-    "    beta_y=5.91253677,\n",
-    "    alpha_y=3.55631308,\n",
-    "    emittance_y=3.497810737006068e-09,\n",
-    "    energy=6e6,\n",
-    ")\n",
-    "\n",
-    "segment_copy = cheetah.Segment.from_bmad(\n",
-    "    str(lattice_file_path), environment_variables={\"LCLS_LATTICE\": lattice_dir}\n",
-    ")\n",
-    "segment_copy = segment_copy.flattened()\n",
-    "for element in segment_copy.elements:\n",
-    "    if isinstance(element, cheetah.Aperture):\n",
-    "        element.is_active = False\n",
-    "for element in segment_copy.elements:\n",
-    "    if isinstance(element, cheetah.Cavity):\n",
-    "        element.voltage = 0\n",
-    "# segment_copy = cheetah.Segment_copy([element for element in segment_copy.elements[:100]])\n",
-    "\n",
-    "# outgoing = segment_copy.track(beam)\n",
-    "\n",
-    "segment_copy.plot_twiss_over_lattice(beam, figsize=(20, 5))\n",
-    "\n",
-    "# ------------------------------\n",
-    "\n",
-    "longitudinal_beams = [beam]\n",
-    "s_positions = [0.0]\n",
-    "for element in segment_copy.elements:\n",
-    "    if not hasattr(element, \"length\") or element.length == 0:\n",
-    "        continue\n",
-    "\n",
-    "    outgoing = element.track(longitudinal_beams[-1])\n",
-    "\n",
-    "    longitudinal_beams.append(outgoing)\n",
-    "    s_positions.append(s_positions[-1] + element.length)\n",
-    "\n",
-    "sigma_s = [beam.sigma_s for beam in longitudinal_beams]\n",
-    "sigma_p = [beam.sigma_p for beam in longitudinal_beams]\n",
-    "energy = [beam.energy for beam in longitudinal_beams]\n",
-    "\n",
-    "plt.figure(figsize=(20, 4))\n",
-    "plt.title(\"sigma_s\")\n",
-    "plt.plot(s_positions, sigma_s)\n",
-    "plt.show()\n",
-    "\n",
-    "plt.figure(figsize=(20, 4))\n",
-    "plt.title(\"sigma_p\")\n",
-    "plt.plot(s_positions, sigma_p)\n",
-    "plt.show()\n",
-    "\n",
-    "plt.figure(figsize=(20, 4))\n",
-    "plt.title(\"energy\")\n",
-    "plt.plot(s_positions, energy)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Segment([Drift(length=-0.87, name=\"dl00\", device=\"cpu\"), Drift(length=0.87, name=\"loadlock\", device=\"cpu\"), Marker(name=beggun, device=\"cpu\"), Solenoid(length=0.00, k=0.00, misalignment=(0, 0), name=\"sol1bk\", device=\"cpu\"), Marker(name=dbmark80, device=\"cpu\"), Marker(name=cathode, device=\"cpu\"), Drift(length=0.10, name=\"dl01a\", device=\"cpu\"), Solenoid(length=0.20, k=0.00, misalignment=(0, 0), name=\"sol1\", device=\"cpu\"), Drift(length=0.08, name=\"dl01a1\", device=\"cpu\"), Marker(name=vv01, device=\"cpu\"), Drift(length=0.12, name=\"dl01a2\", device=\"cpu\"), Marker(name=am00, device=\"cpu\"), Drift(length=0.10, name=\"dl01a3\", device=\"cpu\"), Marker(name=am01, device=\"cpu\"), Drift(length=0.02, name=\"dl01a4\", device=\"cpu\"), Marker(name=yag01, device=\"cpu\"), Drift(length=0.01, name=\"dl01a5\", device=\"cpu\"), Marker(name=fc01, device=\"cpu\"), Drift(length=0.08, name=\"dl01b\", device=\"cpu\"), Marker(name=im01, device=\"cpu\"), Drift(length=0.13, name=\"dl01c\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc01\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc01\", device=\"cpu\"), Drift(length=0.06, name=\"dl01h\", device=\"cpu\"), Marker(name=bpm2, device=\"cpu\"), Drift(length=0.09, name=\"dl01d\", device=\"cpu\"), Marker(name=dbmark81, device=\"cpu\"), Marker(name=endgun, device=\"cpu\"), Marker(name=begl0, device=\"cpu\"), Drift(length=0.13, name=\"dbxg\", device=\"cpu\"), Drift(length=0.11, name=\"dl01e\", device=\"cpu\"), Marker(name=bpm3, device=\"cpu\"), Drift(length=0.14, name=\"dl01f\", device=\"cpu\"), Marker(name=cr01, device=\"cpu\"), Drift(length=0.03, name=\"dl01f2\", device=\"cpu\"), Marker(name=yag02, device=\"cpu\"), Drift(length=0.07, name=\"dl01g\", device=\"cpu\"), Marker(name=zlin00, device=\"cpu\"), Cavity(length=3.10, voltage=58010690.67, phase=1.10, frequency=2856000000.00, name=\"l0a\", device=\"cpu\"), Marker(name=l0awake, device=\"cpu\"), Marker(name=l0bbeg, device=\"cpu\"), Drift(length=0.06, name=\"dl02a1\", device=\"cpu\"), Marker(name=yag03, device=\"cpu\"), Drift(length=0.11, name=\"dl02a2\", device=\"cpu\"), Drift(length=0.07, name=\"dl02a3\", device=\"cpu\"), Quadrupole(length=0.11, k1=-9.879050939358796, misalignment=(0, 0), tilt=0.00, name=\"qa01\", device=\"cpu\"), Drift(length=0.09, name=\"dl02b1\", device=\"cpu\"), Marker(name=ph01, device=\"cpu\"), Drift(length=0.13, name=\"dl02b2\", device=\"cpu\"), Quadrupole(length=0.11, k1=10.77552715037701, misalignment=(0, 0), tilt=0.00, name=\"qa02\", device=\"cpu\"), Drift(length=0.07, name=\"dl02c\", device=\"cpu\"), Cavity(length=3.10, voltage=71013086.85, phase=1.10, frequency=2856000000.00, name=\"l0b\", device=\"cpu\"), Marker(name=endl0, device=\"cpu\"), Marker(name=begdl1_1, device=\"cpu\"), Marker(name=emat, device=\"cpu\"), Drift(length=0.02, name=\"de00\", device=\"cpu\"), Drift(length=0.02, name=\"de00a\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.2743176368428006, misalignment=(0, 0), tilt=0.00, name=\"qe01\", device=\"cpu\"), Drift(length=0.08, name=\"de01a\", device=\"cpu\"), Marker(name=im02, device=\"cpu\"), Drift(length=0.12, name=\"de01b\", device=\"cpu\"), Marker(name=vv02, device=\"cpu\"), Drift(length=0.09, name=\"de01c\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.7829366616960555, misalignment=(0, 0), tilt=0.00, name=\"qe02\", device=\"cpu\"), Drift(length=0.13, name=\"dh00\", device=\"cpu\"), Marker(name=lhbeg, device=\"cpu\"), Dipole(length=0.12, angle=-0.131709391614, e1=0.00,e2=-0.13,tilt=0.00,fringe_integral=0.40,fringe_integral_exit=0.40,gap=0.01,name=\"bxh1\", device=\"cpu\"), Drift(length=0.14, name=\"dh01\", device=\"cpu\"), Dipole(length=0.12, angle=0.131709391614, e1=0.13,e2=0.00,tilt=0.00,fringe_integral=0.40,fringe_integral_exit=0.40,gap=0.01,name=\"bxh2\", device=\"cpu\"), Drift(length=0.06, name=\"dh02a\", device=\"cpu\"), Marker(name=otrh1, device=\"cpu\"), Drift(length=0.08, name=\"dh03a\", device=\"cpu\"), Undulator(length=0.54, is_active=False, name=\"lh_und\", device=\"cpu\"), Drift(length=0.07, name=\"dh03b\", device=\"cpu\"), Marker(name=otrh2, device=\"cpu\"), Drift(length=0.09, name=\"dh02b\", device=\"cpu\"), Dipole(length=0.12, angle=0.131709391614, e1=0.00,e2=0.13,tilt=0.00,fringe_integral=0.40,fringe_integral_exit=0.40,gap=0.01,name=\"bxh3\", device=\"cpu\"), Drift(length=0.14, name=\"dh01\", device=\"cpu\"), Dipole(length=0.12, angle=-0.131709391614, e1=-0.13,e2=0.00,tilt=0.00,fringe_integral=0.40,fringe_integral_exit=0.40,gap=0.01,name=\"bxh4\", device=\"cpu\"), Marker(name=lhend, device=\"cpu\"), Drift(length=0.12, name=\"dh06\", device=\"cpu\"), Cavity(length=0.67, voltage=0.00, phase=0.00, frequency=2856000000.00, name=\"tcav0\", device=\"cpu\"), Drift(length=0.04, name=\"de02\", device=\"cpu\"), Quadrupole(length=0.11, k1=11.56804846753815, misalignment=(0, 0), tilt=0.00, name=\"qe03\", device=\"cpu\"), Drift(length=0.11, name=\"de03a\", device=\"cpu\"), Drift(length=0.05, name=\"de03b\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc07\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc07\", device=\"cpu\"), Drift(length=0.14, name=\"de03c\", device=\"cpu\"), Quadrupole(length=0.11, k1=-6.78990977461672, misalignment=(0, 0), tilt=0.00, name=\"qe04\", device=\"cpu\"), Drift(length=0.14, name=\"de04\", device=\"cpu\"), Marker(name=ws01, device=\"cpu\"), Drift(length=0.15, name=\"de05\", device=\"cpu\"), Marker(name=otr1, device=\"cpu\"), Drift(length=0.10, name=\"de05c\", device=\"cpu\"), Marker(name=vv03, device=\"cpu\"), Drift(length=1.41, name=\"de06a\", device=\"cpu\"), Marker(name=rst1, device=\"cpu\"), Drift(length=0.25, name=\"de06b\", device=\"cpu\"), Marker(name=ws02, device=\"cpu\"), Drift(length=0.08, name=\"de05a\", device=\"cpu\"), Marker(name=mrk0, device=\"cpu\"), Drift(length=0.08, name=\"de05a\", device=\"cpu\"), Marker(name=otr2, device=\"cpu\"), Drift(length=0.16, name=\"de06d\", device=\"cpu\"), Marker(name=bpm10, device=\"cpu\"), Drift(length=1.60, name=\"de06e\", device=\"cpu\"), Marker(name=ws03, device=\"cpu\"), Drift(length=0.15, name=\"de05\", device=\"cpu\"), Marker(name=otr3, device=\"cpu\"), Drift(length=0.15, name=\"de07\", device=\"cpu\"), Quadrupole(length=0.11, k1=15.082997416875, misalignment=(0, 0), tilt=0.00, name=\"qm01\", device=\"cpu\"), Drift(length=0.15, name=\"de08\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc08\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc08\", device=\"cpu\"), Drift(length=0.18, name=\"de08a\", device=\"cpu\"), Marker(name=vv04, device=\"cpu\"), Drift(length=0.12, name=\"de08b\", device=\"cpu\"), Quadrupole(length=0.11, k1=-11.969358129236, misalignment=(0, 0), tilt=0.00, name=\"qm02\", device=\"cpu\"), Drift(length=0.22, name=\"de09\", device=\"cpu\"), Marker(name=dbmark82, device=\"cpu\"), Marker(name=enddl1_1, device=\"cpu\"), Marker(name=begdl1_2, device=\"cpu\"), Dipole(length=0.20, angle=-0.30543261909900765, e1=-0.15,e2=-0.15,tilt=0.00,fringe_integral=0.45,fringe_integral_exit=0.45,gap=0.01,name=\"bx01\", device=\"cpu\"), Drift(length=0.40, name=\"db00a\", device=\"cpu\"), Marker(name=otr4, device=\"cpu\"), Drift(length=0.16, name=\"db00b\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc09\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc09\", device=\"cpu\"), Drift(length=0.17, name=\"db00c\", device=\"cpu\"), Quadrupole(length=0.11, k1=22.169715923062, misalignment=(0, 0), tilt=0.00, name=\"qb\", device=\"cpu\"), Drift(length=0.29, name=\"db00d\", device=\"cpu\"), Marker(name=ws04, device=\"cpu\"), Drift(length=0.44, name=\"db00e\", device=\"cpu\"), Dipole(length=0.20, angle=-0.30543261909900765, e1=-0.15,e2=-0.15,tilt=0.00,fringe_integral=0.45,fringe_integral_exit=0.45,gap=0.01,name=\"bx02\", device=\"cpu\"), Marker(name=cnt0, device=\"cpu\"), Marker(name=dbmark83, device=\"cpu\"), Drift(length=0.22, name=\"dm00\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc10\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc10\", device=\"cpu\"), Drift(length=0.14, name=\"dm00a\", device=\"cpu\"), Quadrupole(length=0.11, k1=-8.29494541292, misalignment=(0, 0), tilt=0.00, name=\"qm03\", device=\"cpu\"), Drift(length=0.09, name=\"dm01\", device=\"cpu\"), Drift(length=0.21, name=\"dm01a\", device=\"cpu\"), Quadrupole(length=0.11, k1=13.33787357734, misalignment=(0, 0), tilt=0.00, name=\"qm04\", device=\"cpu\"), Drift(length=0.14, name=\"dm02\", device=\"cpu\"), Marker(name=im03, device=\"cpu\"), Drift(length=0.16, name=\"dm02a\", device=\"cpu\"), Marker(name=enddl1_2, device=\"cpu\"), Marker(name=begl1, device=\"cpu\"), Marker(name=zlin01, device=\"cpu\"), Cavity(length=2.87, voltage=45397559.63, phase=21.50, frequency=2856000000.00, name=\"k21_1b\", device=\"cpu\"), Drift(length=0.04, name=\"daqa1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-3.77793667230024, misalignment=(0, 0), tilt=0.00, name=\"qa11\", device=\"cpu\"), Drift(length=0.03, name=\"daqa2\", device=\"cpu\"), Cavity(length=2.87, voltage=32100922.27, phase=21.50, frequency=2856000000.00, name=\"k21_1c\", device=\"cpu\"), Drift(length=0.03, name=\"daqa3\", device=\"cpu\"), Quadrupole(length=0.11, k1=1.8813942736818, misalignment=(0, 0), tilt=0.00, name=\"qa12\", device=\"cpu\"), Drift(length=0.03, name=\"daqa4\", device=\"cpu\"), Cavity(length=3.04, voltage=34057722.53, phase=21.50, frequency=2856000000.00, name=\"k21_1d\", device=\"cpu\"), Marker(name=endl1, device=\"cpu\"), Marker(name=begbc1, device=\"cpu\"), Drift(length=0.09, name=\"dl1xa\", device=\"cpu\"), Marker(name=vvx1, device=\"cpu\"), Drift(length=0.20, name=\"dl1xb\", device=\"cpu\"), Cavity(length=0.59, voltage=20000000.00, phase=160.00, frequency=11424000000.00, name=\"l1x\", device=\"cpu\"), Drift(length=0.21, name=\"dm10a\", device=\"cpu\"), Marker(name=vvx2, device=\"cpu\"), Drift(length=0.12, name=\"dm10c\", device=\"cpu\"), Quadrupole(length=0.11, k1=-9.33825405949416, misalignment=(0, 0), tilt=0.00, name=\"q21201\", device=\"cpu\"), Drift(length=0.09, name=\"dm10x\", device=\"cpu\"), Marker(name=imbc1i, device=\"cpu\"), Drift(length=0.28, name=\"dm11\", device=\"cpu\"), Quadrupole(length=0.11, k1=7.94906252387771, misalignment=(0, 0), tilt=0.00, name=\"qm11\", device=\"cpu\"), Drift(length=0.13, name=\"dm12\", device=\"cpu\"), Marker(name=bc1cbeg, device=\"cpu\"), Dipole(length=0.20, angle=-0.093575352547, e1=0.00,e2=-0.09,tilt=0.00,fringe_integral=0.39,fringe_integral_exit=0.39,gap=0.02,name=\"bx11\", device=\"cpu\"), Drift(length=0.25, name=\"dbq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0, misalignment=(0, 0), tilt=0.00, name=\"cq11\", device=\"cpu\"), Drift(length=2.09, name=\"d11o\", device=\"cpu\"), Dipole(length=0.20, angle=0.093575352547, e1=0.09,e2=0.00,tilt=0.00,fringe_integral=0.39,fringe_integral_exit=0.39,gap=0.02,name=\"bx12\", device=\"cpu\"), Drift(length=0.25, name=\"ddg1\", device=\"cpu\"), Marker(name=bpms11, device=\"cpu\"), Drift(length=0.16, name=\"ddg2\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=False, name=\"ce11\", device=\"cpu\"), Drift(length=0.18, name=\"ddg3\", device=\"cpu\"), Marker(name=otr11, device=\"cpu\"), Drift(length=0.23, name=\"ddg4\", device=\"cpu\"), Dipole(length=0.20, angle=0.093575352547, e1=0.00,e2=0.09,tilt=0.00,fringe_integral=0.39,fringe_integral_exit=0.39,gap=0.02,name=\"bx13\", device=\"cpu\"), Drift(length=0.26, name=\"d11oa\", device=\"cpu\"), Quadrupole(length=0.16, k1=0, misalignment=(0, 0), tilt=0.79, name=\"sq13\", device=\"cpu\"), Drift(length=1.67, name=\"d11ob\", device=\"cpu\"), Quadrupole(length=0.11, k1=0, misalignment=(0, 0), tilt=0.00, name=\"cq12\", device=\"cpu\"), Drift(length=0.25, name=\"dbq1\", device=\"cpu\"), Dipole(length=0.20, angle=-0.093575352547, e1=-0.09,e2=0.00,tilt=0.00,fringe_integral=0.39,fringe_integral_exit=0.39,gap=0.02,name=\"bx14\", device=\"cpu\"), Marker(name=cnt1, device=\"cpu\"), Marker(name=bc1cend, device=\"cpu\"), Drift(length=0.16, name=\"dm13a\", device=\"cpu\"), Marker(name=bl11, device=\"cpu\"), Drift(length=0.16, name=\"dm13b\", device=\"cpu\"), Quadrupole(length=0.11, k1=-8.326568628912, misalignment=(0, 0), tilt=0.00, name=\"qm12\", device=\"cpu\"), Drift(length=0.17, name=\"dm14a\", device=\"cpu\"), Drift(length=0.21, name=\"dm14b\", device=\"cpu\"), Marker(name=imbc1o, device=\"cpu\"), Drift(length=0.18, name=\"dm14c\", device=\"cpu\"), Quadrupole(length=0.11, k1=9.937676628969, misalignment=(0, 0), tilt=0.00, name=\"qm13\", device=\"cpu\"), Drift(length=0.10, name=\"dm15a\", device=\"cpu\"), Marker(name=bl12, device=\"cpu\"), Drift(length=0.20, name=\"dm15b\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc21275\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc21276\", device=\"cpu\"), Drift(length=0.24, name=\"dm15c\", device=\"cpu\"), Marker(name=ws11, device=\"cpu\"), Drift(length=1.66, name=\"dww1a\", device=\"cpu\"), Marker(name=ws12, device=\"cpu\"), Drift(length=0.29, name=\"dww1b\", device=\"cpu\"), Marker(name=otr12, device=\"cpu\"), Drift(length=0.42, name=\"dww1c1\", device=\"cpu\"), Marker(name=ph02, device=\"cpu\"), Drift(length=0.30, name=\"dww1c2\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc21302\", device=\"cpu\"), Drift(length=0.35, name=\"dww1d\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc21303\", device=\"cpu\"), Drift(length=0.30, name=\"dww1e\", device=\"cpu\"), Marker(name=ws13, device=\"cpu\"), Drift(length=0.26, name=\"dm16\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.1347, misalignment=(0, 0), tilt=0.00, name=\"q21301\", device=\"cpu\"), Drift(length=0.26, name=\"dm17a\", device=\"cpu\"), Drift(length=0.40, name=\"dm17b\", device=\"cpu\"), Marker(name=td11, device=\"cpu\"), Drift(length=0.39, name=\"dm17c\", device=\"cpu\"), Quadrupole(length=0.11, k1=7.054168937435, misalignment=(0, 0), tilt=0.00, name=\"qm14\", device=\"cpu\"), Drift(length=0.23, name=\"dm18a\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xc21325\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yc21325\", device=\"cpu\"), Drift(length=0.23, name=\"dm18b\", device=\"cpu\"), Quadrupole(length=0.11, k1=-6.723000446327, misalignment=(0, 0), tilt=0.00, name=\"qm15\", device=\"cpu\"), Marker(name=dbmark28, device=\"cpu\"), Drift(length=0.10, name=\"dm19\", device=\"cpu\"), Marker(name=endbc1, device=\"cpu\"), Marker(name=begl2, device=\"cpu\"), Marker(name=li21beg, device=\"cpu\"), Marker(name=zlin04, device=\"cpu\"), Cavity(length=3.04, voltage=72760505.13, phase=33.50, frequency=2856000000.00, name=\"k21_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=1.030123463078, misalignment=(0, 0), tilt=0.00, name=\"q21401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_4c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_4d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.820757556128, misalignment=(0, 0), tilt=0.00, name=\"q21501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_5c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.713914268545471, misalignment=(0, 0), tilt=0.00, name=\"q21601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_6d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.705827351725973, misalignment=(0, 0), tilt=0.00, name=\"q21701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_7c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_7d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.73569262480724, misalignment=(0, 0), tilt=0.00, name=\"q21801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k21_8d\", device=\"cpu\"), Drift(length=0.35, name=\"daq3\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.718205140809118, misalignment=(0, 0), tilt=0.00, name=\"q21901\", device=\"cpu\"), Drift(length=0.31, name=\"d219a\", device=\"cpu\"), Drift(length=0.30, name=\"bky21929\", device=\"cpu\"), Drift(length=0.24, name=\"d219b\", device=\"cpu\"), Drift(length=0.30, name=\"bkx21957\", device=\"cpu\"), Drift(length=1.40, name=\"d219c\", device=\"cpu\"), Marker(name=li21end, device=\"cpu\"), Marker(name=li22beg, device=\"cpu\"), Marker(name=zlin05, device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_1c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_1d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.714603191911, misalignment=(0, 0), tilt=0.00, name=\"q22201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.762188913757, misalignment=(0, 0), tilt=0.00, name=\"q22301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q22401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_4c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_4d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q22501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_5c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q22601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_6d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q22701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_7c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_7d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.747560469838, misalignment=(0, 0), tilt=0.00, name=\"q22801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k22_8d\", device=\"cpu\"), Drift(length=0.35, name=\"daq3\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.709980930665, misalignment=(0, 0), tilt=0.00, name=\"q22901\", device=\"cpu\"), Drift(length=0.31, name=\"d229a\", device=\"cpu\"), Drift(length=0.30, name=\"bky22929\", device=\"cpu\"), Drift(length=0.24, name=\"d229b\", device=\"cpu\"), Drift(length=0.30, name=\"bkx22957\", device=\"cpu\"), Drift(length=1.40, name=\"d229c\", device=\"cpu\"), Marker(name=li22end, device=\"cpu\"), Marker(name=li23beg, device=\"cpu\"), Marker(name=zlin06, device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_1c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_1d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.720791509747, misalignment=(0, 0), tilt=0.00, name=\"q23201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.741851024289, misalignment=(0, 0), tilt=0.00, name=\"q23301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q23401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_4c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_4d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q23501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_5c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q23601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_6d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.708388522907, misalignment=(0, 0), tilt=0.00, name=\"q23701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_7c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_7d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.770666348011, misalignment=(0, 0), tilt=0.00, name=\"q23801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k23_8d\", device=\"cpu\"), Drift(length=0.35, name=\"daq3\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.726851638763, misalignment=(0, 0), tilt=0.00, name=\"q23901\", device=\"cpu\"), Drift(length=2.55, name=\"daq4\", device=\"cpu\"), Marker(name=li23end, device=\"cpu\"), Marker(name=li24beg, device=\"cpu\"), Marker(name=zlin07, device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_1c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_1d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.779293384791, misalignment=(0, 0), tilt=0.00, name=\"q24201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.856497177248, misalignment=(0, 0), tilt=0.00, name=\"q24301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=1.024817869713, misalignment=(0, 0), tilt=0.00, name=\"q24401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_4c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_4d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.954003584585, misalignment=(0, 0), tilt=0.00, name=\"q24501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_5c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.608135546584, misalignment=(0, 0), tilt=0.00, name=\"q24601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=51449446.58, phase=33.50, frequency=2856000000.00, name=\"k24_6d\", device=\"cpu\"), Marker(name=li24term, device=\"cpu\"), Marker(name=endl2, device=\"cpu\"), Marker(name=begbc2, device=\"cpu\"), Drift(length=0.03, name=\"dm20\", device=\"cpu\"), Quadrupole(length=0.11, k1=-1.307122793026, misalignment=(0, 0), tilt=0.00, name=\"q24701a\", device=\"cpu\"), Drift(length=0.13, name=\"d10cma\", device=\"cpu\"), Quadrupole(length=0.11, k1=-1.307122793026, misalignment=(0, 0), tilt=0.00, name=\"q24701b\", device=\"cpu\"), Drift(length=0.06, name=\"dm21z\", device=\"cpu\"), Drift(length=0.32, name=\"dm21a\", device=\"cpu\"), Marker(name=ws24, device=\"cpu\"), Drift(length=0.19, name=\"dm21h\", device=\"cpu\"), Marker(name=imbc2i, device=\"cpu\"), Drift(length=0.63, name=\"dm21b\", device=\"cpu\"), Marker(name=vv21, device=\"cpu\"), Drift(length=0.32, name=\"dm21c\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.511895916574, misalignment=(0, 0), tilt=0.00, name=\"qm21\", device=\"cpu\"), Drift(length=0.14, name=\"dm21d\", device=\"cpu\"), Drift(length=0.20, name=\"dm21e\", device=\"cpu\"), Marker(name=bc2cbeg, device=\"cpu\"), Dipole(length=0.55, angle=-0.036971323962, e1=0.00,e2=-0.04,tilt=0.00,fringe_integral=0.63,fringe_integral_exit=0.63,gap=0.02,name=\"bx21\", device=\"cpu\"), Drift(length=1.96, name=\"dbq2a\", device=\"cpu\"), Quadrupole(length=0.11, k1=0, misalignment=(0, 0), tilt=0.00, name=\"cq21\", device=\"cpu\"), Drift(length=7.80, name=\"d21oa\", device=\"cpu\"), Dipole(length=0.55, angle=0.036971323962, e1=0.04,e2=0.00,tilt=0.00,fringe_integral=0.63,fringe_integral_exit=0.63,gap=0.02,name=\"bx22\", device=\"cpu\"), Drift(length=0.13, name=\"ddg0\", device=\"cpu\"), Drift(length=0.25, name=\"ddg1\", device=\"cpu\"), Marker(name=bpms21, device=\"cpu\"), Drift(length=0.16, name=\"ddg2\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=False, name=\"ce21\", device=\"cpu\"), Drift(length=0.18, name=\"ddg3\", device=\"cpu\"), Marker(name=otr21, device=\"cpu\"), Drift(length=0.23, name=\"ddg4\", device=\"cpu\"), Drift(length=0.13, name=\"ddga\", device=\"cpu\"), Dipole(length=0.55, angle=0.036971323962, e1=0.00,e2=0.04,tilt=0.00,fringe_integral=0.63,fringe_integral_exit=0.63,gap=0.02,name=\"bx23\", device=\"cpu\"), Drift(length=7.80, name=\"d21ob\", device=\"cpu\"), Quadrupole(length=0.11, k1=0, misalignment=(0, 0), tilt=0.00, name=\"cq22\", device=\"cpu\"), Drift(length=1.96, name=\"dbq2b\", device=\"cpu\"), Dipole(length=0.55, angle=-0.036971323962, e1=-0.04,e2=0.00,tilt=0.00,fringe_integral=0.63,fringe_integral_exit=0.63,gap=0.02,name=\"bx24\", device=\"cpu\"), Marker(name=cnt2, device=\"cpu\"), Marker(name=bc2cend, device=\"cpu\"), Drift(length=0.31, name=\"d21w\", device=\"cpu\"), Drift(length=0.21, name=\"d21x\", device=\"cpu\"), Marker(name=bl21, device=\"cpu\"), Drift(length=0.11, name=\"d21y\", device=\"cpu\"), Drift(length=0.05, name=\"dm23b\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.589455717616, misalignment=(0, 0), tilt=0.00, name=\"qm22\", device=\"cpu\"), Drift(length=0.13, name=\"dm24a\", device=\"cpu\"), Marker(name=vv22, device=\"cpu\"), Drift(length=0.18, name=\"dm24b\", device=\"cpu\"), Drift(length=0.07, name=\"dm24d\", device=\"cpu\"), Quadrupole(length=0.11, k1=1.081452709118, misalignment=(0, 0), tilt=0.00, name=\"q24901a\", device=\"cpu\"), Drift(length=0.11, name=\"dm24c\", device=\"cpu\"), Quadrupole(length=0.11, k1=1.081452709118, misalignment=(0, 0), tilt=0.00, name=\"q24901b\", device=\"cpu\"), Drift(length=0.16, name=\"dm25\", device=\"cpu\"), Marker(name=endbc2, device=\"cpu\"), Marker(name=begl3, device=\"cpu\"), Marker(name=li25beg, device=\"cpu\"), Marker(name=zlin09, device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_1b\", device=\"cpu\"), Drift(length=3.04, name=\"d25_1c\", device=\"cpu\"), Cavity(length=3.04, voltage=68162928.83, phase=-0.00, frequency=2856000000.00, name=\"k25_1d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.69835390233, misalignment=(0, 0), tilt=0.00, name=\"q25201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=68162928.83, phase=-0.00, frequency=2856000000.00, name=\"k25_2c\", device=\"cpu\"), Drift(length=0.06, name=\"d255a\", device=\"cpu\"), Marker(name=imbc2o, device=\"cpu\"), Drift(length=0.11, name=\"d255b\", device=\"cpu\"), Drift(length=0.15, name=\"d255c\", device=\"cpu\"), Cavity(length=2.44, voltage=0.00, phase=0.00, frequency=2856000000.00, name=\"tcav3\", device=\"cpu\"), Drift(length=0.31, name=\"d255d\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.479854531304, misalignment=(0, 0), tilt=0.00, name=\"q25301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=68162928.83, phase=-0.00, frequency=2856000000.00, name=\"k25_3c\", device=\"cpu\"), Drift(length=0.60, name=\"d256a\", device=\"cpu\"), Marker(name=ph03, device=\"cpu\"), Drift(length=0.56, name=\"d256b\", device=\"cpu\"), Marker(name=bl22, device=\"cpu\"), Drift(length=0.61, name=\"d256c\", device=\"cpu\"), Drift(length=1.06, name=\"bxkik\", device=\"cpu\"), Drift(length=0.21, name=\"d256d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.429832166012, misalignment=(0, 0), tilt=0.00, name=\"q25401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_4c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_4d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.400216279825, misalignment=(0, 0), tilt=0.00, name=\"q25501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_5c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q25601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_6d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q25701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_7c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_7d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.407524461523, misalignment=(0, 0), tilt=0.00, name=\"q25801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k25_8d\", device=\"cpu\"), Drift(length=0.27, name=\"daq7\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.38867870418, misalignment=(0, 0), tilt=0.00, name=\"q25901\", device=\"cpu\"), Drift(length=0.50, name=\"daq8a\", device=\"cpu\"), Marker(name=otr_tcav, device=\"cpu\"), Drift(length=2.13, name=\"daq8b\", device=\"cpu\"), Marker(name=li25end, device=\"cpu\"), Marker(name=li26beg, device=\"cpu\"), Marker(name=zlin10, device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_1c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_1d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.388844133085, misalignment=(0, 0), tilt=0.00, name=\"q26201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.405381176356, misalignment=(0, 0), tilt=0.00, name=\"q26301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q26401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_4c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_4d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q26501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_5c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q26601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_6d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q26701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_7c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_7d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.406007960598, misalignment=(0, 0), tilt=0.00, name=\"q26801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_8d\", device=\"cpu\"), Drift(length=0.27, name=\"daq7\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.388769548432, misalignment=(0, 0), tilt=0.00, name=\"q26901\", device=\"cpu\"), Drift(length=2.63, name=\"daq8\", device=\"cpu\"), Marker(name=li26end, device=\"cpu\"), Marker(name=li27beg, device=\"cpu\"), Marker(name=zlin11, device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_1c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_1d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.391071563294, misalignment=(0, 0), tilt=0.00, name=\"q27201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.407171874797, misalignment=(0, 0), tilt=0.00, name=\"q27301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q27401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_4c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_4d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q27501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_5c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q27601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_6c\", device=\"cpu\"), Drift(length=2.34, name=\"daq5a\", device=\"cpu\"), Marker(name=ws27644, device=\"cpu\"), Drift(length=0.73, name=\"daq6a\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q27701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_7c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_7d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.40682165023, misalignment=(0, 0), tilt=0.00, name=\"q27801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k27_8d\", device=\"cpu\"), Drift(length=0.27, name=\"daq7\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.389505138741, misalignment=(0, 0), tilt=0.00, name=\"q27901\", device=\"cpu\"), Drift(length=2.63, name=\"daq8\", device=\"cpu\"), Marker(name=li27end, device=\"cpu\"), Marker(name=li28beg, device=\"cpu\"), Marker(name=zlin12, device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_1c\", device=\"cpu\"), Drift(length=2.09, name=\"daq5b1\", device=\"cpu\"), Marker(name=xc29092, device=\"cpu\"), Drift(length=0.30, name=\"daq5b2\", device=\"cpu\"), Marker(name=yc29092, device=\"cpu\"), Drift(length=0.45, name=\"daq5b3\", device=\"cpu\"), Marker(name=ws28144, device=\"cpu\"), Drift(length=0.24, name=\"daq6b\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.390810721273, misalignment=(0, 0), tilt=0.00, name=\"q28201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.406510005482, misalignment=(0, 0), tilt=0.00, name=\"q28301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q28401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_4c\", device=\"cpu\"), Drift(length=2.08, name=\"daq5c1\", device=\"cpu\"), Marker(name=xc29095, device=\"cpu\"), Drift(length=0.29, name=\"daq5c2\", device=\"cpu\"), Marker(name=yc29095, device=\"cpu\"), Drift(length=0.47, name=\"daq5c3\", device=\"cpu\"), Marker(name=ws28444, device=\"cpu\"), Drift(length=0.24, name=\"daq6c\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q28501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_5c\", device=\"cpu\"), Drift(length=3.04, name=\"d28_5d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q28601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_6d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q28701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_7c\", device=\"cpu\"), Drift(length=2.34, name=\"daq5d\", device=\"cpu\"), Marker(name=ws28744, device=\"cpu\"), Drift(length=0.73, name=\"daq6d\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.406821689691, misalignment=(0, 0), tilt=0.00, name=\"q28801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k28_8d\", device=\"cpu\"), Drift(length=0.27, name=\"daq7\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.389505198882, misalignment=(0, 0), tilt=0.00, name=\"q28901\", device=\"cpu\"), Drift(length=0.34, name=\"daq8c\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=False, name=\"c29096\", device=\"cpu\"), Drift(length=0.74, name=\"daq8d1\", device=\"cpu\"), Drift(length=0.20, name=\"st28950\", device=\"cpu\"), Drift(length=0.22, name=\"daq8d2\", device=\"cpu\"), Drift(length=0.20, name=\"st28960\", device=\"cpu\"), Drift(length=0.93, name=\"daq8d3\", device=\"cpu\"), Marker(name=li28end, device=\"cpu\"), Marker(name=li29beg, device=\"cpu\"), Marker(name=zlin13, device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_1c\", device=\"cpu\"), Drift(length=2.82, name=\"d10ca\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=False, name=\"c29146\", device=\"cpu\"), Drift(length=0.23, name=\"d10cb\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.390810639876, misalignment=(0, 0), tilt=0.00, name=\"q29201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.40650981716, misalignment=(0, 0), tilt=0.00, name=\"q29301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_3c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_3d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q29401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_4c\", device=\"cpu\"), Drift(length=2.82, name=\"d10cc\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=False, name=\"c29446\", device=\"cpu\"), Drift(length=0.23, name=\"d10cd\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q29501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_5c\", device=\"cpu\"), Drift(length=2.82, name=\"d10ce\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=False, name=\"c29546\", device=\"cpu\"), Drift(length=0.23, name=\"d10cf\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q29601\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_6c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_6d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q29701\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_7c\", device=\"cpu\"), Cavity(length=2.87, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_7d\", device=\"cpu\"), Drift(length=0.08, name=\"daq9a\", device=\"cpu\"), Marker(name=pk297, device=\"cpu\"), Drift(length=0.13, name=\"daq9b\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.406747754958, misalignment=(0, 0), tilt=0.00, name=\"q29801\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_8c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k29_8d\", device=\"cpu\"), Drift(length=0.35, name=\"daq3\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.389374252182, misalignment=(0, 0), tilt=0.00, name=\"q29901\", device=\"cpu\"), Drift(length=0.25, name=\"daq4a\", device=\"cpu\"), Marker(name=p30013, device=\"cpu\"), Drift(length=0.20, name=\"daq4b\", device=\"cpu\"), Marker(name=p30014, device=\"cpu\"), Drift(length=0.92, name=\"daq4c\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=False, name=\"c29956\", device=\"cpu\"), Drift(length=0.27, name=\"daq4d\", device=\"cpu\"), Marker(name=pk299, device=\"cpu\"), Drift(length=0.91, name=\"daq4e\", device=\"cpu\"), Marker(name=li29end, device=\"cpu\"), Marker(name=li30beg, device=\"cpu\"), Marker(name=zlin14, device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_1a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_1b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_1c\", device=\"cpu\"), Cavity(length=2.17, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_1d\", device=\"cpu\"), Drift(length=0.13, name=\"daq10a\", device=\"cpu\"), Marker(name=p30143, device=\"cpu\"), Drift(length=0.20, name=\"daq10b\", device=\"cpu\"), Marker(name=p30144, device=\"cpu\"), Drift(length=0.21, name=\"daq10c\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=False, name=\"c30146\", device=\"cpu\"), Drift(length=0.36, name=\"daq10d\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.390540048432, misalignment=(0, 0), tilt=0.00, name=\"q30201\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_2a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_2b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_2c\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_2d\", device=\"cpu\"), Drift(length=0.03, name=\"daq1\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.406220459812, misalignment=(0, 0), tilt=0.00, name=\"q30301\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_3a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_3b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_3c\", device=\"cpu\"), Cavity(length=2.17, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_3d\", device=\"cpu\"), Drift(length=0.78, name=\"daq10e\", device=\"cpu\"), Marker(name=pk303, device=\"cpu\"), Drift(length=0.13, name=\"daq10f\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q30401\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_4a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_4b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_4c\", device=\"cpu\"), Cavity(length=2.17, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_4d\", device=\"cpu\"), Drift(length=0.13, name=\"daq10g\", device=\"cpu\"), Marker(name=p30443, device=\"cpu\"), Drift(length=0.20, name=\"daq10h\", device=\"cpu\"), Marker(name=p30444, device=\"cpu\"), Drift(length=0.21, name=\"daq10i\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=False, name=\"c30446\", device=\"cpu\"), Drift(length=0.24, name=\"daq10j\", device=\"cpu\"), Marker(name=pk304, device=\"cpu\"), Drift(length=0.13, name=\"daq10k\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.395649286346, misalignment=(0, 0), tilt=0.00, name=\"q30501\", device=\"cpu\"), Drift(length=0.03, name=\"daq2\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_5a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_5b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_5c\", device=\"cpu\"), Cavity(length=2.17, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_5d\", device=\"cpu\"), Drift(length=0.13, name=\"daq11a\", device=\"cpu\"), Marker(name=p30543, device=\"cpu\"), Drift(length=0.21, name=\"daq11b\", device=\"cpu\"), Marker(name=p30544, device=\"cpu\"), Drift(length=0.21, name=\"daq11c\", device=\"cpu\"), Aperture(x_max=inf, y_max=inf, shape=\"rectangular\", is_active=False, name=\"c30546\", device=\"cpu\"), Drift(length=0.15, name=\"daq11d\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.395798933782, misalignment=(0, 0), tilt=0.00, name=\"q30601\", device=\"cpu\"), Drift(length=0.23, name=\"daq12\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_6a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_6b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_6c\", device=\"cpu\"), Cavity(length=2.87, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_6d\", device=\"cpu\"), Drift(length=0.02, name=\"daq13\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.39623102008, misalignment=(0, 0), tilt=0.00, name=\"q30701\", device=\"cpu\"), Drift(length=0.21, name=\"daq14\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_7a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_7b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_7c\", device=\"cpu\"), Cavity(length=2.87, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_7d\", device=\"cpu\"), Drift(length=0.01, name=\"daq15\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.480592172677, misalignment=(0, 0), tilt=0.00, name=\"q30801\", device=\"cpu\"), Drift(length=0.23, name=\"daq16\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_8a\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_8b\", device=\"cpu\"), Cavity(length=3.04, voltage=0.00, phase=-0.00, frequency=2856000000.00, name=\"k30_8c\", device=\"cpu\"), Drift(length=0.06, name=\"daq17\", device=\"cpu\"), Marker(name=li30term, device=\"cpu\"), Marker(name=dbmark29, device=\"cpu\"), Marker(name=endl3, device=\"cpu\"), Marker(name=begclth_0, device=\"cpu\"), Marker(name=bsybeg, device=\"cpu\"), Marker(name=bsy1beg, device=\"cpu\"), Drift(length=0.73, name=\"dbsy01a\", device=\"cpu\"), Marker(name=imbsy1, device=\"cpu\"), Drift(length=0.25, name=\"dbsy01b\", device=\"cpu\"), Marker(name=imbsy2, device=\"cpu\"), Drift(length=0.25, name=\"dbsy01c\", device=\"cpu\"), Marker(name=imbsy3, device=\"cpu\"), Drift(length=0.41, name=\"dbsy01d\", device=\"cpu\"), Marker(name=imbsy34, device=\"cpu\"), Drift(length=2.90, name=\"dbsy01e\", device=\"cpu\"), Marker(name=imbsy1b, device=\"cpu\"), Drift(length=0.32, name=\"dbsy01f\", device=\"cpu\"), Marker(name=imbsy2b, device=\"cpu\"), Drift(length=0.32, name=\"dbsy01g\", device=\"cpu\"), Marker(name=imbsy3b, device=\"cpu\"), Drift(length=0.80, name=\"dbsy01h\", device=\"cpu\"), Marker(name=fftborgn, device=\"cpu\"), Drift(length=0.02, name=\"dbsy01i\", device=\"cpu\"), Marker(name=s100, device=\"cpu\"), Marker(name=zlin15, device=\"cpu\"), Drift(length=0.58, name=\"dbsy50a\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycbsyq1\", device=\"cpu\"), Drift(length=1.43, name=\"dbsy50b\", device=\"cpu\"), Quadrupole(length=0.26, k1=-0.231522298209, misalignment=(0, 0), tilt=0.00, name=\"q50q1\", device=\"cpu\"), Drift(length=0.24, name=\"dbsy02a\", device=\"cpu\"), Marker(name=wooddoor, device=\"cpu\"), Marker(name=endclth_0, device=\"cpu\"), Marker(name=begclth_1, device=\"cpu\"), Drift(length=0.22, name=\"dbsy02b\", device=\"cpu\"), Marker(name=bpmbsyq1, device=\"cpu\"), Drift(length=5.02, name=\"dbsy51a\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcbsyq2\", device=\"cpu\"), Drift(length=0.50, name=\"dbsy51b\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.102074330212, misalignment=(0, 0), tilt=0.00, name=\"q50q2\", device=\"cpu\"), Drift(length=0.08, name=\"dbsy52a\", device=\"cpu\"), Marker(name=bpmbsyq2, device=\"cpu\"), Drift(length=1.80, name=\"dbsy52b\", device=\"cpu\"), Marker(name=endclth_1, device=\"cpu\"), Marker(name=begclth_2, device=\"cpu\"), Drift(length=0.13, name=\"dbrcusdc1a\", device=\"cpu\"), Drift(length=0.13, name=\"dbrcusdc1b\", device=\"cpu\"), Drift(length=0.35, name=\"dckdc1\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrcusa\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrcusb\", device=\"cpu\"), Drift(length=0.35, name=\"dckdc2\", device=\"cpu\"), Drift(length=0.13, name=\"dbrcusdc2a\", device=\"cpu\"), Drift(length=0.13, name=\"dbrcusdc2b\", device=\"cpu\"), Drift(length=4.57, name=\"dcusblra\", device=\"cpu\"), Marker(name=bpmcus, device=\"cpu\"), Drift(length=10.95, name=\"dcusblrb\", device=\"cpu\"), Drift(length=0.50, name=\"dblrcusa\", device=\"cpu\"), Drift(length=0.50, name=\"dblrcusb\", device=\"cpu\"), Drift(length=32.68, name=\"dbsy52c\", device=\"cpu\"), Drift(length=0.50, name=\"dbxsp1ha\", device=\"cpu\"), Drift(length=0.50, name=\"dbxsp1hb\", device=\"cpu\"), Marker(name=bsy1end, device=\"cpu\"), Marker(name=endclth_2, device=\"cpu\"), Marker(name=begbsyh_1, device=\"cpu\"), Drift(length=0.50, name=\"dbsy52d\", device=\"cpu\"), Quadrupole(length=0.29, k1=0.384840836193, misalignment=(0, 0), tilt=0.00, name=\"q50q3\", device=\"cpu\"), Drift(length=0.09, name=\"dbsy53a\", device=\"cpu\"), Marker(name=bpmbsyq3, device=\"cpu\"), Drift(length=0.00, name=\"rfbbsyq3\", device=\"cpu\"), Drift(length=0.61, name=\"dbsy53b\", device=\"cpu\"), Drift(length=0.78, name=\"dbsy53c\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcbsyq3\", device=\"cpu\"), Drift(length=2.02, name=\"dbsy53d\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycbsyq4\", device=\"cpu\"), Drift(length=0.50, name=\"dbsy53f\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.219396715005, misalignment=(0, 0), tilt=0.00, name=\"q4\", device=\"cpu\"), Drift(length=0.91, name=\"dbsy53ga\", device=\"cpu\"), Drift(length=0.06, name=\"pcbsy3\", device=\"cpu\"), Drift(length=0.22, name=\"dbsy53gb\", device=\"cpu\"), Marker(name=mrgaline, device=\"cpu\"), Marker(name=endbsyh_1, device=\"cpu\"), Marker(name=begbsyh_2, device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm1a\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm1b\", device=\"cpu\"), Drift(length=0.21, name=\"dzapm1\", device=\"cpu\"), Aperture(x_max=0.04, y_max=0.01, shape=\"elliptical\", is_active=False, name=\"pcapm1\", device=\"cpu\"), Drift(length=0.21, name=\"dzapm1\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm2a\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm2b\", device=\"cpu\"), Drift(length=0.10, name=\"dzapm2a\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcapm2\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycapm2\", device=\"cpu\"), Drift(length=0.10, name=\"dzapm2b\", device=\"cpu\"), Aperture(x_max=0.04, y_max=0.01, shape=\"elliptical\", is_active=False, name=\"pcapm2\", device=\"cpu\"), Drift(length=0.21, name=\"dzapm2\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm3a\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm3b\", device=\"cpu\"), Drift(length=0.21, name=\"dzapm3\", device=\"cpu\"), Aperture(x_max=0.04, y_max=0.01, shape=\"elliptical\", is_active=False, name=\"pcapm3\", device=\"cpu\"), Drift(length=0.21, name=\"dzapm3\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm4a\", device=\"cpu\"), Drift(length=0.53, name=\"dbkrapm4b\", device=\"cpu\"), Drift(length=0.21, name=\"dzapm4\", device=\"cpu\"), Aperture(x_max=0.04, y_max=0.01, shape=\"elliptical\", is_active=False, name=\"pcapm4\", device=\"cpu\"), Drift(length=0.20, name=\"dza01\", device=\"cpu\"), Drift(length=2.82, name=\"dza02\", device=\"cpu\"), Drift(length=0.45, name=\"pcbsyh\", device=\"cpu\"), Drift(length=23.39, name=\"dbsy53h\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.165708852383, misalignment=(0, 0), tilt=0.00, name=\"q5\", device=\"cpu\"), Drift(length=0.50, name=\"dbsy54a\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcbsyq5\", device=\"cpu\"), Drift(length=3.22, name=\"dbsy54b\", device=\"cpu\"), Drift(length=15.54, name=\"dbsy54c\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.113569101471, misalignment=(0, 0), tilt=0.00, name=\"q6\", device=\"cpu\"), Drift(length=0.20, name=\"drfb\", device=\"cpu\"), Drift(length=0.00, name=\"rfbbsyq6\", device=\"cpu\"), Drift(length=0.50, name=\"dbsy55a\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycbsyq6\", device=\"cpu\"), Drift(length=4.23, name=\"dbsy55b\", device=\"cpu\"), Aperture(x_max=0.01, y_max=0.01, shape=\"elliptical\", is_active=False, name=\"pc90\", device=\"cpu\"), Drift(length=8.02, name=\"dbsy55c\", device=\"cpu\"), Drift(length=0.06, name=\"pcbsy4\", device=\"cpu\"), Drift(length=6.47, name=\"dbsy55d\", device=\"cpu\"), Aperture(x_max=0.01, y_max=0.01, shape=\"elliptical\", is_active=False, name=\"pc119\", device=\"cpu\"), Drift(length=1.45, name=\"dbsy55e\", device=\"cpu\"), Drift(length=0.43, name=\"d2\", device=\"cpu\"), Drift(length=0.84, name=\"dm3\", device=\"cpu\"), Drift(length=0.76, name=\"st60\", device=\"cpu\"), Drift(length=0.44, name=\"dm4a\", device=\"cpu\"), Marker(name=dm60, device=\"cpu\"), Drift(length=5.19, name=\"dm4b\", device=\"cpu\"), Marker(name=cc31beg, device=\"cpu\"), Drift(length=0.35, name=\"bcx311\", device=\"cpu\"), Drift(length=0.20, name=\"dcc31o\", device=\"cpu\"), Drift(length=0.35, name=\"bcx312\", device=\"cpu\"), Drift(length=0.20, name=\"dcc31i\", device=\"cpu\"), Drift(length=0.35, name=\"bcx313\", device=\"cpu\"), Drift(length=0.20, name=\"dcc31o\", device=\"cpu\"), Drift(length=0.35, name=\"bcx314\", device=\"cpu\"), Marker(name=cc31end, device=\"cpu\"), Drift(length=0.30, name=\"dm4c\", device=\"cpu\"), Aperture(x_max=0.00, y_max=0.02, shape=\"rectangular\", is_active=False, name=\"cxq6\", device=\"cpu\"), Drift(length=0.28, name=\"dm4da\", device=\"cpu\"), Drift(length=0.06, name=\"pcbsy5\", device=\"cpu\"), Drift(length=0.80, name=\"dm4db\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xca0\", device=\"cpu\"), Drift(length=0.31, name=\"dxca0\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yca0\", device=\"cpu\"), Drift(length=0.51, name=\"dyca0\", device=\"cpu\"), Drift(length=0.76, name=\"st61\", device=\"cpu\"), Drift(length=0.79, name=\"dm5\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.109210100868, misalignment=(0, 0), tilt=0.00, name=\"qa0\", device=\"cpu\"), Drift(length=0.57, name=\"dm6\", device=\"cpu\"), Drift(length=0.01, name=\"dmoni\", device=\"cpu\"), Drift(length=0.01, name=\"dmoni\", device=\"cpu\"), Marker(name=muwall, device=\"cpu\"), Drift(length=1.84, name=\"dwalla\", device=\"cpu\"), Marker(name=dumpbsyh, device=\"cpu\"), Drift(length=14.92, name=\"dwallb\", device=\"cpu\"), Marker(name=bsyend, device=\"cpu\"), Marker(name=rwwake3h, device=\"cpu\"), Marker(name=endbsyh_2, device=\"cpu\"), Marker(name=begltuh, device=\"cpu\"), Drift(length=0.40, name=\"dycvm1\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycvm1\", device=\"cpu\"), Drift(length=0.34, name=\"dqvm1\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.337437273245, misalignment=(0, 0), tilt=0.00, name=\"qvm1\", device=\"cpu\"), Drift(length=0.25, name=\"dqvm2a\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcvm2\", device=\"cpu\"), Drift(length=0.25, name=\"dqvm2b\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.236577259392, misalignment=(0, 0), tilt=0.00, name=\"qvm2\", device=\"cpu\"), Drift(length=0.25, name=\"dwsvm2a\", device=\"cpu\"), Marker(name=wsvm2, device=\"cpu\"), Drift(length=0.25, name=\"dwsvm2b\", device=\"cpu\"), Marker(name=vbin, device=\"cpu\"), Dipole(length=1.02, angle=-0.002334257186083933, e1=-0.00,e2=-0.00,tilt=1.57,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.02,name=\"by1\", device=\"cpu\"), Drift(length=7.38, name=\"dvb1\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.42223036711, misalignment=(0, 0), tilt=0.00, name=\"qvb1\", device=\"cpu\"), Drift(length=0.40, name=\"d40cmc\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycvb1\", device=\"cpu\"), Drift(length=3.20, name=\"dvb2m80cm\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcvb2\", device=\"cpu\"), Drift(length=0.40, name=\"d40cmc\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.42223036711, misalignment=(0, 0), tilt=0.00, name=\"qvb2\", device=\"cpu\"), Drift(length=0.87, name=\"dvb2ha\", device=\"cpu\"), Drift(length=0.06, name=\"pc01\", device=\"cpu\"), Marker(name=btm01, device=\"cpu\"), Drift(length=3.07, name=\"dvb2hb\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.42223036711, misalignment=(0, 0), tilt=0.00, name=\"qvb3\", device=\"cpu\"), Drift(length=0.40, name=\"d40cmc\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycvb3\", device=\"cpu\"), Drift(length=6.97, name=\"dvb1m40cm\", device=\"cpu\"), Dipole(length=1.02, angle=-0.002334257186083933, e1=-0.00,e2=-0.00,tilt=1.57,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.02,name=\"by2\", device=\"cpu\"), Marker(name=cntlt1h, device=\"cpu\"), Marker(name=vbout, device=\"cpu\"), Drift(length=0.25, name=\"dvb25cmc\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcvm3\", device=\"cpu\"), Drift(length=0.25, name=\"d25cm\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.715150825176, misalignment=(0, 0), tilt=0.00, name=\"qvm3\", device=\"cpu\"), Drift(length=0.25, name=\"dvbem25cm\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycvm4\", device=\"cpu\"), Drift(length=0.25, name=\"d25cm\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.681650171006, misalignment=(0, 0), tilt=0.00, name=\"qvm4\", device=\"cpu\"), Drift(length=0.17, name=\"dvbem15cm\", device=\"cpu\"), Marker(name=im31, device=\"cpu\"), Drift(length=0.11, name=\"d10cmb\", device=\"cpu\"), Marker(name=imbcs1, device=\"cpu\"), Drift(length=0.22, name=\"d25cma\", device=\"cpu\"), Marker(name=mm1, device=\"cpu\"), Marker(name=dbmark34, device=\"cpu\"), Dipole(length=2.62, angle=0.008726643152447821, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=0.52,fringe_integral_exit=0.52,gap=0.01,name=\"bx31\", device=\"cpu\"), Drift(length=0.99, name=\"ddl10wa\", device=\"cpu\"), Drift(length=0.06, name=\"pc02\", device=\"cpu\"), Marker(name=btm02, device=\"cpu\"), Drift(length=10.94, name=\"ddl10wb\", device=\"cpu\"), Drift(length=0.10, name=\"dwsdl31a\", device=\"cpu\"), Marker(name=wsdl31, device=\"cpu\"), Drift(length=0.15, name=\"dwsdl31b\", device=\"cpu\"), Drift(length=0.13, name=\"ddl10x\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcdl1\", device=\"cpu\"), Drift(length=0.56, name=\"d31a\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.437183970154, misalignment=(0, 0), tilt=0.00, name=\"qdl31\", device=\"cpu\"), Drift(length=0.20, name=\"drfb\", device=\"cpu\"), Drift(length=0.00, name=\"rfbdl1\", device=\"cpu\"), Drift(length=0.36, name=\"d31b\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycdl1\", device=\"cpu\"), Drift(length=0.66, name=\"d32cmb\", device=\"cpu\"), Aperture(x_max=0.00, y_max=0.02, shape=\"rectangular\", is_active=False, name=\"cedl1\", device=\"cpu\"), Drift(length=0.50, name=\"d31c\", device=\"cpu\"), Drift(length=0.24, name=\"wig1h\", device=\"cpu\"), Drift(length=0.13, name=\"dwgh\", device=\"cpu\"), Drift(length=0.49, name=\"wig2h\", device=\"cpu\"), Drift(length=0.13, name=\"dwgh\", device=\"cpu\"), Drift(length=0.24, name=\"wig3h\", device=\"cpu\"), Marker(name=cntwigh, device=\"cpu\"), Drift(length=3.70, name=\"ddl10em80cma\", device=\"cpu\"), Drift(length=0.20, name=\"pc22\", device=\"cpu\"), Drift(length=5.12, name=\"ddl10em80cmb\", device=\"cpu\"), Aperture(x_max=0.02, y_max=0.00, shape=\"rectangular\", is_active=False, name=\"cybx32\", device=\"cpu\"), Drift(length=0.81, name=\"dcb32\", device=\"cpu\"), Dipole(length=2.62, angle=0.008726643152447821, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=0.52,fringe_integral_exit=0.52,gap=0.01,name=\"bx32\", device=\"cpu\"), Marker(name=cntlt2h, device=\"cpu\"), Drift(length=0.50, name=\"ddl20e\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt11\", device=\"cpu\"), Drift(length=0.51, name=\"ddl30em40cm\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcqt12\", device=\"cpu\"), Drift(length=0.49, name=\"d40cmb\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.839614778043, misalignment=(0, 0), tilt=0.00, name=\"qt12\", device=\"cpu\"), Drift(length=0.49, name=\"d40cmb\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycqt12\", device=\"cpu\"), Drift(length=0.51, name=\"ddl30em40cm\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt13\", device=\"cpu\"), Drift(length=0.50, name=\"ddl20e\", device=\"cpu\"), Marker(name=ss1, device=\"cpu\"), Drift(length=1.42, name=\"dx33a\", device=\"cpu\"), Marker(name=cc32beg, device=\"cpu\"), Dipole(length=0.35, angle=0.01008035696016623, e1=0.00,e2=0.01,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx321\", device=\"cpu\"), Drift(length=0.20, name=\"dcc32o\", device=\"cpu\"), Dipole(length=0.35, angle=-0.01008035696016623, e1=-0.01,e2=0.00,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx322\", device=\"cpu\"), Drift(length=0.20, name=\"dcc32i\", device=\"cpu\"), Dipole(length=0.35, angle=-0.01008035696016623, e1=0.00,e2=-0.01,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx323\", device=\"cpu\"), Drift(length=0.20, name=\"dcc32o\", device=\"cpu\"), Dipole(length=0.35, angle=0.01008035696016623, e1=0.01,e2=0.00,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx324\", device=\"cpu\"), Marker(name=cc32end, device=\"cpu\"), Drift(length=0.39, name=\"dx33b\", device=\"cpu\"), Drift(length=0.06, name=\"pc03\", device=\"cpu\"), Marker(name=btm03, device=\"cpu\"), Drift(length=4.05, name=\"ddl1ab\", device=\"cpu\"), Dipole(length=1.06, angle=0.0, e1=0.00,e2=0.00,tilt=1.57,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.03,name=\"bykik1\", device=\"cpu\"), Drift(length=0.08, name=\"ddl1d\", device=\"cpu\"), Drift(length=0.08, name=\"ddl1d\", device=\"cpu\"), Dipole(length=1.06, angle=0.0, e1=0.00,e2=0.00,tilt=1.57,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.03,name=\"bykik2\", device=\"cpu\"), Drift(length=4.89, name=\"ddl1e\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcdl2\", device=\"cpu\"), Drift(length=0.47, name=\"d32a\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.437183970154, misalignment=(0, 0), tilt=0.00, name=\"qdl32\", device=\"cpu\"), Drift(length=0.47, name=\"d32b\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycdl2\", device=\"cpu\"), Drift(length=0.43, name=\"dsplr\", device=\"cpu\"), Marker(name=spoiler, device=\"cpu\"), Drift(length=5.30, name=\"ddl1cm40cm\", device=\"cpu\"), Drift(length=0.61, name=\"tdkik\", device=\"cpu\"), Drift(length=0.26, name=\"d30cma\", device=\"cpu\"), Aperture(x_max=0.01, y_max=0.01, shape=\"elliptical\", is_active=False, name=\"pctdkik1\", device=\"cpu\"), Drift(length=0.27, name=\"dpc1\", device=\"cpu\"), Aperture(x_max=0.01, y_max=0.01, shape=\"elliptical\", is_active=False, name=\"pctdkik2\", device=\"cpu\"), Drift(length=0.27, name=\"dpc2\", device=\"cpu\"), Aperture(x_max=0.01, y_max=0.01, shape=\"elliptical\", is_active=False, name=\"pctdkik3\", device=\"cpu\"), Drift(length=0.27, name=\"dpc3\", device=\"cpu\"), Aperture(x_max=0.01, y_max=0.01, shape=\"elliptical\", is_active=False, name=\"pctdkik4\", device=\"cpu\"), Drift(length=0.20, name=\"dpc4\", device=\"cpu\"), Drift(length=0.75, name=\"dspontua\", device=\"cpu\"), Drift(length=0.75, name=\"dspontub\", device=\"cpu\"), Drift(length=0.12, name=\"ddl1dm30cm\", device=\"cpu\"), Drift(length=0.12, name=\"dx34a\", device=\"cpu\"), Marker(name=cc35beg, device=\"cpu\"), Dipole(length=0.35, angle=0.007127828404706885, e1=0.00,e2=0.01,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx351\", device=\"cpu\"), Drift(length=0.20, name=\"dcc35o\", device=\"cpu\"), Dipole(length=0.35, angle=-0.007127828404706885, e1=-0.01,e2=0.00,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx352\", device=\"cpu\"), Drift(length=0.20, name=\"dcc35i\", device=\"cpu\"), Dipole(length=0.35, angle=-0.007127828404706885, e1=0.00,e2=-0.01,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx353\", device=\"cpu\"), Drift(length=0.20, name=\"dcc35o\", device=\"cpu\"), Dipole(length=0.35, angle=0.007127828404706885, e1=0.01,e2=0.00,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx354\", device=\"cpu\"), Marker(name=cc35end, device=\"cpu\"), Drift(length=0.50, name=\"dx34b\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycqt21\", device=\"cpu\"), Drift(length=0.50, name=\"ddl20\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt21\", device=\"cpu\"), Drift(length=0.51, name=\"ddl30em40cm\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcqt22\", device=\"cpu\"), Drift(length=0.49, name=\"d40cmb\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.839614778043, misalignment=(0, 0), tilt=0.00, name=\"qt22\", device=\"cpu\"), Drift(length=0.47, name=\"d46cm\", device=\"cpu\"), Aperture(x_max=0.00, y_max=0.02, shape=\"rectangular\", is_active=False, name=\"cxqt22\", device=\"cpu\"), Drift(length=0.47, name=\"d46cm\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt23\", device=\"cpu\"), Drift(length=0.50, name=\"ddl20\", device=\"cpu\"), Marker(name=dl23beg, device=\"cpu\"), Dipole(length=2.62, angle=-0.008726643152447821, e1=-0.00,e2=-0.00,tilt=0.00,fringe_integral=0.52,fringe_integral_exit=0.52,gap=0.01,name=\"bx35\", device=\"cpu\"), Drift(length=1.35, name=\"dcq31aa\", device=\"cpu\"), Drift(length=0.06, name=\"pc04\", device=\"cpu\"), Marker(name=btm04, device=\"cpu\"), Drift(length=4.62, name=\"dcq31ab\", device=\"cpu\"), Quadrupole(length=0.11, k1=0, misalignment=(0, 0), tilt=0.00, name=\"cq31\", device=\"cpu\"), Drift(length=0.83, name=\"dcq31ba\", device=\"cpu\"), Drift(length=0.06, name=\"pc42\", device=\"cpu\"), Marker(name=btm42, device=\"cpu\"), Drift(length=4.92, name=\"dcq31bb\", device=\"cpu\"), Marker(name=otr30, device=\"cpu\"), Drift(length=0.58, name=\"d29cma\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcdl3\", device=\"cpu\"), Drift(length=0.39, name=\"d33a\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.437183970154, misalignment=(0, 0), tilt=0.00, name=\"qdl33\", device=\"cpu\"), Drift(length=0.32, name=\"d33b\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycdl3\", device=\"cpu\"), Drift(length=0.90, name=\"d32cmd\", device=\"cpu\"), Aperture(x_max=0.00, y_max=0.02, shape=\"rectangular\", is_active=False, name=\"cedl3\", device=\"cpu\"), Drift(length=5.48, name=\"dcq32a\", device=\"cpu\"), Quadrupole(length=0.11, k1=0, misalignment=(0, 0), tilt=0.00, name=\"cq32\", device=\"cpu\"), Drift(length=5.17, name=\"dcq32b\", device=\"cpu\"), Aperture(x_max=0.02, y_max=0.00, shape=\"rectangular\", is_active=False, name=\"cybx36\", device=\"cpu\"), Drift(length=0.81, name=\"dcb36\", device=\"cpu\"), Dipole(length=2.62, angle=-0.008726643152447821, e1=-0.00,e2=-0.00,tilt=0.00,fringe_integral=0.52,fringe_integral_exit=0.52,gap=0.01,name=\"bx36\", device=\"cpu\"), Marker(name=cntlt3h, device=\"cpu\"), Drift(length=0.50, name=\"ddl20e\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt31\", device=\"cpu\"), Drift(length=0.51, name=\"ddl30em40cma\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcqt32\", device=\"cpu\"), Drift(length=0.49, name=\"d40cmd\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.839614778043, misalignment=(0, 0), tilt=0.00, name=\"qt32\", device=\"cpu\"), Drift(length=0.50, name=\"d40cme\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycqt32\", device=\"cpu\"), Drift(length=0.50, name=\"ddl30em40cmb\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt33\", device=\"cpu\"), Drift(length=0.50, name=\"ddl20e\", device=\"cpu\"), Marker(name=ss3, device=\"cpu\"), Drift(length=0.27, name=\"d37a\", device=\"cpu\"), Marker(name=cc36beg, device=\"cpu\"), Dipole(length=0.35, angle=0.007127828404706885, e1=0.00,e2=0.01,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx361\", device=\"cpu\"), Drift(length=0.20, name=\"dcc36o\", device=\"cpu\"), Dipole(length=0.35, angle=-0.007127828404706885, e1=-0.01,e2=0.00,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx362\", device=\"cpu\"), Drift(length=0.20, name=\"dcc36i\", device=\"cpu\"), Dipole(length=0.35, angle=-0.007127828404706885, e1=0.00,e2=-0.01,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx363\", device=\"cpu\"), Drift(length=0.20, name=\"dcc36o\", device=\"cpu\"), Dipole(length=0.35, angle=0.007127828404706885, e1=0.01,e2=0.00,tilt=0.00,fringe_integral=0.84,fringe_integral_exit=0.84,gap=0.02,name=\"bcx364\", device=\"cpu\"), Marker(name=cc36end, device=\"cpu\"), Drift(length=0.12, name=\"d37ba\", device=\"cpu\"), Drift(length=0.20, name=\"pc43\", device=\"cpu\"), Marker(name=btm43, device=\"cpu\"), Drift(length=0.32, name=\"d37bb\", device=\"cpu\"), Marker(name=imbcs2, device=\"cpu\"), Drift(length=1.26, name=\"d37c\", device=\"cpu\"), Drift(length=0.06, name=\"pc05\", device=\"cpu\"), Marker(name=btm05, device=\"cpu\"), Drift(length=7.70, name=\"d37d\", device=\"cpu\"), Marker(name=wsdl4, device=\"cpu\"), Drift(length=1.10, name=\"d37e\", device=\"cpu\"), Drift(length=2.00, name=\"dchirpv\", device=\"cpu\"), Drift(length=0.51, name=\"d37f\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.437183970154, misalignment=(0, 0), tilt=0.00, name=\"qdl34\", device=\"cpu\"), Drift(length=0.51, name=\"d38a\", device=\"cpu\"), Drift(length=2.00, name=\"dchirph\", device=\"cpu\"), Drift(length=0.61, name=\"d38b\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcdl4\", device=\"cpu\"), Drift(length=0.54, name=\"d38c\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycdl4\", device=\"cpu\"), Drift(length=7.15, name=\"d38da\", device=\"cpu\"), Drift(length=0.06, name=\"pc06\", device=\"cpu\"), Marker(name=btm06, device=\"cpu\"), Drift(length=4.68, name=\"d38db\", device=\"cpu\"), Drift(length=0.50, name=\"ddl20\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt41\", device=\"cpu\"), Drift(length=0.57, name=\"ddl30em40cmc\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcqt42\", device=\"cpu\"), Drift(length=0.43, name=\"d40cmf\", device=\"cpu\"), Quadrupole(length=0.46, k1=0.839614778043, misalignment=(0, 0), tilt=0.00, name=\"qt42\", device=\"cpu\"), Drift(length=0.49, name=\"d40cmb\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycqt42\", device=\"cpu\"), Drift(length=0.51, name=\"ddl30em40cm\", device=\"cpu\"), Quadrupole(length=0.46, k1=-0.420937827343, misalignment=(0, 0), tilt=0.00, name=\"qt43\", device=\"cpu\"), Drift(length=0.50, name=\"ddl20\", device=\"cpu\"), Marker(name=mm2, device=\"cpu\"), Drift(length=0.26, name=\"d25cmb\", device=\"cpu\"), Marker(name=im36, device=\"cpu\"), Drift(length=0.24, name=\"d25cmc\", device=\"cpu\"), Drift(length=1.20, name=\"dmm1m90cm\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycem1\", device=\"cpu\"), Drift(length=0.37, name=\"dem1a\", device=\"cpu\"), Quadrupole(length=0.32, k1=-0.390827735953, misalignment=(0, 0), tilt=0.00, name=\"qem1\", device=\"cpu\"), Drift(length=4.14, name=\"dem1c\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.432215708114, misalignment=(0, 0), tilt=0.00, name=\"qem2\", device=\"cpu\"), Drift(length=0.50, name=\"dem2b\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcem2\", device=\"cpu\"), Drift(length=4.36, name=\"dmm3ma\", device=\"cpu\"), Drift(length=0.06, name=\"pc07\", device=\"cpu\"), Marker(name=btm07, device=\"cpu\"), Drift(length=6.85, name=\"dmm3mb\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycem3\", device=\"cpu\"), Drift(length=0.37, name=\"dem3a\", device=\"cpu\"), Quadrupole(length=0.32, k1=-0.593433950486, misalignment=(0, 0), tilt=0.00, name=\"qem3\", device=\"cpu\"), Drift(length=0.77, name=\"dem3b\", device=\"cpu\"), Quadrupole(length=0.11, k1=0, misalignment=(0, 0), tilt=0.00, name=\"qem3v\", device=\"cpu\"), Drift(length=2.76, name=\"dmm4m90cm\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcem4\", device=\"cpu\"), Drift(length=0.50, name=\"dem4a\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.420485259237, misalignment=(0, 0), tilt=0.00, name=\"qem4\", device=\"cpu\"), Drift(length=0.20, name=\"drfb\", device=\"cpu\"), Drift(length=0.00, name=\"rfbem4\", device=\"cpu\"), Drift(length=0.69, name=\"dmm5a\", device=\"cpu\"), Drift(length=0.06, name=\"pc08\", device=\"cpu\"), Marker(name=btm08, device=\"cpu\"), Drift(length=1.12, name=\"dmm5b\", device=\"cpu\"), Marker(name=dbmark36, device=\"cpu\"), Marker(name=ws31, device=\"cpu\"), Drift(length=0.40, name=\"d40cm\", device=\"cpu\"), Drift(length=5.43, name=\"de3ma\", device=\"cpu\"), Drift(length=0.06, name=\"pc09\", device=\"cpu\"), Marker(name=btm09, device=\"cpu\"), Drift(length=2.44, name=\"de3mb\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xce31\", device=\"cpu\"), Drift(length=0.43, name=\"dqea\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.402753198232, misalignment=(0, 0), tilt=0.00, name=\"qe31\", device=\"cpu\"), Drift(length=0.80, name=\"dqebx\", device=\"cpu\"), Drift(length=0.08, name=\"dcx31\", device=\"cpu\"), Drift(length=7.49, name=\"dqebx2\", device=\"cpu\"), Drift(length=8.73, name=\"de3a\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yce32\", device=\"cpu\"), Drift(length=0.44, name=\"dqeaa\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.402753198232, misalignment=(0, 0), tilt=0.00, name=\"qe32\", device=\"cpu\"), Drift(length=0.20, name=\"drfb\", device=\"cpu\"), Drift(length=0.00, name=\"rfbe32\", device=\"cpu\"), Drift(length=0.60, name=\"dqeby1\", device=\"cpu\"), Drift(length=0.08, name=\"dcy32\", device=\"cpu\"), Drift(length=7.89, name=\"dqeby2\", device=\"cpu\"), Marker(name=ws32, device=\"cpu\"), Drift(length=0.40, name=\"d40cm\", device=\"cpu\"), Drift(length=7.91, name=\"de3m80cmb\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xce33\", device=\"cpu\"), Drift(length=0.46, name=\"dqeab\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.402753198232, misalignment=(0, 0), tilt=0.00, name=\"qe33\", device=\"cpu\"), Drift(length=7.95, name=\"dqeca\", device=\"cpu\"), Marker(name=yagpsi, device=\"cpu\"), Drift(length=0.81, name=\"dqecb\", device=\"cpu\"), Marker(name=otr33, device=\"cpu\"), Drift(length=8.34, name=\"de3m40cm\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yce34\", device=\"cpu\"), Drift(length=0.43, name=\"dqea\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.402753198232, misalignment=(0, 0), tilt=0.00, name=\"qe34\", device=\"cpu\"), Drift(length=0.20, name=\"drfb\", device=\"cpu\"), Drift(length=0.00, name=\"rfbe34\", device=\"cpu\"), Drift(length=8.56, name=\"dqec1\", device=\"cpu\"), Marker(name=ws33, device=\"cpu\"), Drift(length=0.40, name=\"d40cm\", device=\"cpu\"), Drift(length=7.76, name=\"de3m80cm\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xce35\", device=\"cpu\"), Drift(length=0.61, name=\"dqeac\", device=\"cpu\"), Quadrupole(length=0.11, k1=0.402753198232, misalignment=(0, 0), tilt=0.00, name=\"qe35\", device=\"cpu\"), Drift(length=0.80, name=\"dqebx\", device=\"cpu\"), Drift(length=0.08, name=\"dcx35\", device=\"cpu\"), Drift(length=7.49, name=\"dqebx2\", device=\"cpu\"), Drift(length=8.74, name=\"de3\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"yce36\", device=\"cpu\"), Drift(length=0.43, name=\"dqea\", device=\"cpu\"), Quadrupole(length=0.11, k1=-0.402753198232, misalignment=(0, 0), tilt=0.00, name=\"qe36\", device=\"cpu\"), Drift(length=0.20, name=\"drfb\", device=\"cpu\"), Drift(length=0.00, name=\"rfbe36\", device=\"cpu\"), Drift(length=0.60, name=\"dqeby1\", device=\"cpu\"), Drift(length=0.08, name=\"dcy36\", device=\"cpu\"), Drift(length=7.89, name=\"dqeby2\", device=\"cpu\"), Marker(name=ws34, device=\"cpu\"), Drift(length=0.37, name=\"d40cmg\", device=\"cpu\"), Marker(name=dws35, device=\"cpu\"), Drift(length=0.03, name=\"d40cmh\", device=\"cpu\"), Drift(length=0.17, name=\"du1m80cma\", device=\"cpu\"), Marker(name=dws36, device=\"cpu\"), Drift(length=4.38, name=\"du1m80cmb\", device=\"cpu\"), Drift(length=0.08, name=\"dcx37\", device=\"cpu\"), Drift(length=0.30, name=\"d32cmc\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcum1\", device=\"cpu\"), Drift(length=0.49, name=\"dum1a\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.272741408897, misalignment=(0, 0), tilt=0.00, name=\"qum1\", device=\"cpu\"), Drift(length=0.47, name=\"dum1b\", device=\"cpu\"), Drift(length=0.32, name=\"d32cm\", device=\"cpu\"), Drift(length=4.73, name=\"du2m120cm\", device=\"cpu\"), Drift(length=0.08, name=\"dcy38\", device=\"cpu\"), Drift(length=0.42, name=\"d32cma\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycum2\", device=\"cpu\"), Drift(length=0.37, name=\"dum2a\", device=\"cpu\"), Quadrupole(length=0.32, k1=-0.270601268265, misalignment=(0, 0), tilt=0.00, name=\"qum2\", device=\"cpu\"), Drift(length=0.47, name=\"dum2b\", device=\"cpu\"), Drift(length=7.29, name=\"du3m80cm\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcum3\", device=\"cpu\"), Drift(length=0.38, name=\"dum3a\", device=\"cpu\"), Quadrupole(length=0.32, k1=0.278762678373, misalignment=(0, 0), tilt=0.00, name=\"qum3\", device=\"cpu\"), Drift(length=0.47, name=\"dum3b\", device=\"cpu\"), Drift(length=1.81, name=\"d40cma\", device=\"cpu\"), Marker(name=eoblm, device=\"cpu\"), Drift(length=1.36, name=\"du4m120cm\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycum4\", device=\"cpu\"), Drift(length=0.50, name=\"dum4a\", device=\"cpu\"), Quadrupole(length=0.32, k1=-0.247470889897, misalignment=(0, 0), tilt=0.00, name=\"qum4\", device=\"cpu\"), Drift(length=0.60, name=\"dum4b\", device=\"cpu\"), Marker(name=rfb07, device=\"cpu\"), Drift(length=0.50, name=\"du5m80cm\", device=\"cpu\"), Marker(name=imundi, device=\"cpu\"), Drift(length=0.25, name=\"d40cmwa\", device=\"cpu\"), Marker(name=bltof, device=\"cpu\"), Drift(length=0.14, name=\"d40cmwb\", device=\"cpu\"), Marker(name=muhwall1, device=\"cpu\"), Drift(length=0.25, name=\"duhwall1\", device=\"cpu\"), Drift(length=1.61, name=\"duhvesta\", device=\"cpu\"), Marker(name=rfb08, device=\"cpu\"), Drift(length=0.40, name=\"duhvestb\", device=\"cpu\"), Marker(name=muhwall2, device=\"cpu\"), Drift(length=0.25, name=\"duhwall2\", device=\"cpu\"), Drift(length=0.20, name=\"dw2tdund\", device=\"cpu\"), Drift(length=0.86, name=\"dtdund1\", device=\"cpu\"), Marker(name=tdund, device=\"cpu\"), Drift(length=0.35, name=\"dtdund2\", device=\"cpu\"), Drift(length=0.03, name=\"dpcmuon\", device=\"cpu\"), Aperture(x_max=0.00, y_max=0.00, shape=\"elliptical\", is_active=False, name=\"pcmuon\", device=\"cpu\"), Drift(length=0.19, name=\"dmuon1\", device=\"cpu\"), Marker(name=vv999, device=\"cpu\"), Drift(length=0.02, name=\"dmuon2\", device=\"cpu\"), Marker(name=rwwake4h, device=\"cpu\"), Marker(name=endltuh, device=\"cpu\"), Marker(name=begundh, device=\"cpu\"), Drift(length=0.14, name=\"dmuon3\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx12\", device=\"cpu\"), Drift(length=0.07, name=\"dmuon4\", device=\"cpu\"), Marker(name=mm3, device=\"cpu\"), Marker(name=pfilt1, device=\"cpu\"), Marker(name=dbmark37, device=\"cpu\"), Drift(length=0.07, name=\"du0h\", device=\"cpu\"), Drift(length=0.03, name=\"du8h\", device=\"cpu\"), Marker(name=hxrstart, device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Marker(name=hxrcb1beg, device=\"cpu\"), Drift(length=0.04, name=\"d0cb1\", device=\"cpu\"), Dipole(length=0.37, angle=-0.0, e1=0.00,e2=-0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx11\", device=\"cpu\"), Drift(length=0.47, name=\"d1cb1a\", device=\"cpu\"), Marker(name=mcbx4, device=\"cpu\"), Drift(length=0.05, name=\"d1cb1b\", device=\"cpu\"), Marker(name=yagcbx42, device=\"cpu\"), Drift(length=0.06, name=\"d1cb1c\", device=\"cpu\"), Marker(name=bodcbx40, device=\"cpu\"), Drift(length=0.28, name=\"d1cb1d\", device=\"cpu\"), Dipole(length=0.37, angle=0.0, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx12\", device=\"cpu\"), Drift(length=0.08, name=\"dchcb1\", device=\"cpu\"), Marker(name=center1, device=\"cpu\"), Drift(length=0.08, name=\"dchcb1\", device=\"cpu\"), Dipole(length=0.37, angle=0.0, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx13\", device=\"cpu\"), Drift(length=0.86, name=\"d2cb\", device=\"cpu\"), Dipole(length=0.37, angle=-0.0, e1=-0.00,e2=0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx14\", device=\"cpu\"), Drift(length=-0.03, name=\"d3cb1\", device=\"cpu\"), Marker(name=hxrcb1end, device=\"cpu\"), Drift(length=0.08, name=\"du3hcb1a\", device=\"cpu\"), Marker(name=mblmh13, device=\"cpu\"), Drift(length=0.15, name=\"du3hcb1b\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh13\", device=\"cpu\"), Drift(length=0.06, name=\"du4hcb1\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx13\", device=\"cpu\"), Drift(length=-0.06, name=\"du5hcb1\", device=\"cpu\"), Drift(length=0.05, name=\"dpshx13\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh14\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh14, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh14\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx14\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh14\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Marker(name=vvhxu14, device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh15\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh15, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh15\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx15\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh15\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh16\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh16, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh16\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx16\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh16\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh17\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh17, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh17\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx17\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh17\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Marker(name=vvhxu17, device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh18\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh18, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh18\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx18\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh18\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh19\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh19, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh19\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx19\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh19\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh20\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh20, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh20\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx20\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh20\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Marker(name=vvhxu20, device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Marker(name=hxrcb2beg, device=\"cpu\"), Drift(length=0.04, name=\"d0cb2\", device=\"cpu\"), Dipole(length=0.37, angle=-0.0, e1=0.00,e2=-0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx21\", device=\"cpu\"), Drift(length=0.42, name=\"d1cb2a\", device=\"cpu\"), Marker(name=dsscbx11, device=\"cpu\"), Drift(length=0.05, name=\"d1cb2b\", device=\"cpu\"), Marker(name=mcbx1, device=\"cpu\"), Drift(length=0.39, name=\"d1cb2c\", device=\"cpu\"), Dipole(length=0.37, angle=0.0, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx22\", device=\"cpu\"), Drift(length=0.08, name=\"dchcb2\", device=\"cpu\"), Marker(name=center2, device=\"cpu\"), Drift(length=0.08, name=\"dchcb2\", device=\"cpu\"), Dipole(length=0.37, angle=0.0, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx23\", device=\"cpu\"), Drift(length=0.86, name=\"d2cb\", device=\"cpu\"), Dipole(length=0.37, angle=-0.0, e1=-0.00,e2=0.00,tilt=0.00,fringe_integral=1.04,fringe_integral_exit=1.04,gap=0.00,name=\"bcxcbx24\", device=\"cpu\"), Drift(length=-0.03, name=\"d3cb2\", device=\"cpu\"), Marker(name=hxrcb2end, device=\"cpu\"), Drift(length=0.24, name=\"du3hcb2\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh21\", device=\"cpu\"), Drift(length=0.06, name=\"du4hcb2\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx21\", device=\"cpu\"), Drift(length=-0.06, name=\"du5hcb2\", device=\"cpu\"), Drift(length=0.05, name=\"dpshx21\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh22\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh22, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh22\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx22\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh22\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Marker(name=vvhxu22, device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh23\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh23, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh23\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx23\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh23\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh24\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh24, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh24\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx24\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh24\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Marker(name=vvhxu24, device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh25\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh25, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh25\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx25\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh25\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh26\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh26, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh26\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx26\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh26\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh27\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh27, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh27\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx27\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh27\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Marker(name=vvhxu27, device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Marker(name=hxrssbeg, device=\"cpu\"), Drift(length=0.08, name=\"dmono\", device=\"cpu\"), Dipole(length=0.36, angle=0.0, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.00,name=\"bcxhs1\", device=\"cpu\"), Drift(length=0.59, name=\"d1\", device=\"cpu\"), Dipole(length=0.36, angle=-0.0, e1=-0.00,e2=0.00,tilt=0.00,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.00,name=\"bcxhs2\", device=\"cpu\"), Drift(length=0.29, name=\"dch\", device=\"cpu\"), Marker(name=diamond, device=\"cpu\"), Drift(length=0.29, name=\"dch\", device=\"cpu\"), Dipole(length=0.36, angle=-0.0, e1=0.00,e2=-0.00,tilt=0.00,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.00,name=\"bcxhs3\", device=\"cpu\"), Drift(length=0.59, name=\"d1\", device=\"cpu\"), Dipole(length=0.36, angle=0.0, e1=0.00,e2=0.00,tilt=0.00,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.00,name=\"bcxhs4\", device=\"cpu\"), Drift(length=0.08, name=\"dmono\", device=\"cpu\"), Marker(name=hxrssend, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh28, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh28\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx28\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Drift(length=0.05, name=\"dpshx28\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh29\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh29, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh29\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx29\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh29\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Marker(name=vvhxu29, device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh30\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh30, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=-1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh30\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx30\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh30\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh31\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh31, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, k1=1.3383591874999998, misalignment=(0, 0), tilt=0.00, name=\"qhxh31\", device=\"cpu\"), Drift(length=0.06, name=\"du4h\", device=\"cpu\"), Drift(length=0.05, name=\"rfbhx31\", device=\"cpu\"), Drift(length=0.08, name=\"du5h\", device=\"cpu\"), Undulator(length=0.05, is_active=False, name=\"pshxh31\", device=\"cpu\"), Drift(length=0.08, name=\"du6h\", device=\"cpu\"), Drift(length=0.00, name=\"dvvhxu\", device=\"cpu\"), Drift(length=0.09, name=\"du7h\", device=\"cpu\"), Drift(length=0.04, name=\"du1h\", device=\"cpu\"), Drift(length=0.00, name=\"du2h\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Undulator(length=3.35, is_active=False, name=\"umahxh32\", device=\"cpu\"), Drift(length=0.01, name=\"dthxu\", device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Marker(name=mblmh32, device=\"cpu\"), Drift(length=0.05, name=\"du3h\", device=\"cpu\"), Quadrupole(length=0.08, 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device=\"cpu\"), Marker(name=vvtcx, device=\"cpu\"), Drift(length=0.32, name=\"dvvtcx\", device=\"cpu\"), Marker(name=mtcx01, device=\"cpu\"), Cavity(length=1.00, voltage=0.00, phase=0.00, frequency=11424000000.00, name=\"tcx01\", device=\"cpu\"), Drift(length=0.19, name=\"dtcx12\", device=\"cpu\"), Marker(name=mtcx, device=\"cpu\"), Drift(length=0.19, name=\"dtcx12\", device=\"cpu\"), Cavity(length=1.00, voltage=0.00, phase=0.00, frequency=11424000000.00, name=\"tcx02\", device=\"cpu\"), Drift(length=0.38, name=\"dtcxsp\", device=\"cpu\"), Marker(name=sptcx, device=\"cpu\"), Drift(length=0.39, name=\"due4\", device=\"cpu\"), Quadrupole(length=0.55, k1=-0.137840816238, misalignment=(0, 0), tilt=0.00, name=\"que2\", device=\"cpu\"), Drift(length=0.37, name=\"due5a\", device=\"cpu\"), Marker(name=bpmue2, device=\"cpu\"), Drift(length=0.31, name=\"due5b\", device=\"cpu\"), Marker(name=btmque, device=\"cpu\"), Drift(length=0.27, name=\"due5c\", device=\"cpu\"), Aperture(x_max=0.02, y_max=0.02, shape=\"elliptical\", is_active=False, name=\"pcpm0\", device=\"cpu\"), Drift(length=0.05, name=\"due5f\", device=\"cpu\"), Marker(name=btm0, device=\"cpu\"), Drift(length=0.09, name=\"due5d\", device=\"cpu\"), Marker(name=mimbcs3, device=\"cpu\"), Drift(length=0.12, name=\"due5e\", device=\"cpu\"), Marker(name=mdlwall, device=\"cpu\"), Drift(length=0.25, name=\"ddlwall\", device=\"cpu\"), Marker(name=ueend, device=\"cpu\"), Marker(name=dlstart, device=\"cpu\"), Drift(length=0.26, name=\"dsb0a\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycd3\", device=\"cpu\"), Drift(length=0.34, name=\"dsb0b\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcd3\", device=\"cpu\"), Drift(length=0.33, name=\"dsb0c\", device=\"cpu\"), Marker(name=vv37, device=\"cpu\"), Drift(length=0.12, name=\"dsb0d\", device=\"cpu\"), Drift(length=0.11, name=\"dsb0e\", device=\"cpu\"), Marker(name=enddmph_1, device=\"cpu\"), Marker(name=begdmph_2, device=\"cpu\"), Drift(length=0.00, name=\"rodmp1h\", device=\"cpu\"), Dipole(length=0.50, angle=0.0006, e1=0.00,e2=0.00,tilt=1.57,fringe_integral=0.50,fringe_integral_exit=0.50,gap=0.02,name=\"bydsh\", device=\"cpu\"), Drift(length=0.32, name=\"ds1\", device=\"cpu\"), Dipole(length=1.45, angle=0.02240073511, e1=0.01,e2=0.01,tilt=1.57,fringe_integral=0.57,fringe_integral_exit=0.57,gap=0.02,name=\"byd1\", device=\"cpu\"), Drift(length=0.25, name=\"ds\", device=\"cpu\"), Dipole(length=1.45, angle=0.02240073511, e1=0.01,e2=0.01,tilt=1.57,fringe_integral=0.57,fringe_integral_exit=0.57,gap=0.02,name=\"byd2\", device=\"cpu\"), Drift(length=0.25, name=\"ds\", device=\"cpu\"), Dipole(length=1.45, angle=0.02240073511, e1=0.01,e2=0.01,tilt=1.57,fringe_integral=0.57,fringe_integral_exit=0.57,gap=0.02,name=\"byd3\", device=\"cpu\"), Drift(length=0.58, name=\"dd1a\", device=\"cpu\"), Aperture(x_max=0.01, y_max=0.01, shape=\"elliptical\", is_active=False, name=\"pcpm1l\", device=\"cpu\"), Marker(name=btm1l, device=\"cpu\"), Drift(length=1.00, name=\"dd1b\", device=\"cpu\"), Marker(name=mimdump, device=\"cpu\"), Drift(length=0.30, name=\"dd1c\", device=\"cpu\"), Marker(name=mimbcs4, device=\"cpu\"), Drift(length=8.82, name=\"dd1d\", device=\"cpu\"), VerticalCorrector(length=0.00, angle=0.0, name=\"ycdd\", device=\"cpu\"), Drift(length=0.25, name=\"dd1e\", device=\"cpu\"), Aperture(x_max=0.04, y_max=0.04, shape=\"elliptical\", is_active=False, name=\"pcpm2l\", device=\"cpu\"), Marker(name=btm2l, device=\"cpu\"), Drift(length=0.41, name=\"dd1f\", device=\"cpu\"), Quadrupole(length=0.43, k1=-0.155212710055, misalignment=(0, 0), tilt=0.00, name=\"qdmp1\", device=\"cpu\"), Drift(length=0.30, name=\"dd12a\", device=\"cpu\"), Marker(name=bpmqd, device=\"cpu\"), Drift(length=0.01, name=\"dd12b\", device=\"cpu\"), Marker(name=mqdmp, device=\"cpu\"), Drift(length=0.31, name=\"dd12c\", device=\"cpu\"), Quadrupole(length=0.43, k1=-0.155212710055, misalignment=(0, 0), tilt=0.00, name=\"qdmp2\", device=\"cpu\"), Drift(length=0.47, name=\"dd2a\", device=\"cpu\"), HorizontalCorrector(length=0.00, angle=0.0, name=\"xcdd\", device=\"cpu\"), Drift(length=6.20, name=\"dd2b\", device=\"cpu\"), Drift(length=1.00, name=\"dd2c\", device=\"cpu\"), Drift(length=0.35, name=\"dd3a\", device=\"cpu\"), Marker(name=bpmdd, device=\"cpu\"), Drift(length=0.15, name=\"dd3b\", device=\"cpu\"), Marker(name=otrdmp, device=\"cpu\"), Drift(length=0.08, name=\"dwsdumpa1\", device=\"cpu\"), Aperture(x_max=0.03, y_max=0.03, shape=\"elliptical\", is_active=False, name=\"pcebd\", device=\"cpu\"), Drift(length=0.14, name=\"dwsdumpa2\", device=\"cpu\"), Drift(length=0.00, name=\"rfbdd\", device=\"cpu\"), Drift(length=0.22, name=\"dwsdumpb\", device=\"cpu\"), Drift(length=0.00, name=\"wsdump\", device=\"cpu\"), Drift(length=2.29, name=\"dwsdumpc\", device=\"cpu\"), Drift(length=0.00, name=\"rodmp2h\", device=\"cpu\"), Marker(name=dumpface, device=\"cpu\"), Drift(length=1.55, name=\"ddump\", device=\"cpu\"), Marker(name=dmpend, device=\"cpu\"), Marker(name=btmdump, device=\"cpu\"), Marker(name=dbmark38, device=\"cpu\"), Marker(name=enddmph_2, device=\"cpu\")])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "segment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "beam.beta_x = tensor(5.8740)\n",
-      "beam.beta_y = tensor(5.9494)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"{beam.beta_x = }\")\n",
-    "print(f\"{beam.beta_y = }\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "element.name = 'zlin00'\n",
-      "beam_stepped.beta_x = tensor(0.4553)\n",
-      "beam_stepped.beta_y = tensor(0.4519)\n",
-      "beam_stepped.alpha_x = tensor(0.1958)\n",
-      "beam_stepped.alpha_y = tensor(0.2046)\n",
-      "beam_stepped.emittance_x = tensor(3.5122e-09)\n",
-      "beam_stepped.emittance_y = tensor(3.4921e-09)\n",
-      "beam_stepped.energy = 6000000.0\n",
-      "segment.l0a.phase = 1.0999999999999999\n"
-     ]
-    }
-   ],
-   "source": [
-    "beam_stepped = beam\n",
-    "for element in segment.elements[:38]:\n",
-    "    beam_stepped = element.track(beam_stepped)\n",
-    "\n",
-    "print(f\"{element.name = }\")\n",
-    "print(f\"{beam_stepped.beta_x = }\")\n",
-    "print(f\"{beam_stepped.beta_y = }\")\n",
-    "print(f\"{beam_stepped.alpha_x = }\")\n",
-    "print(f\"{beam_stepped.alpha_y = }\")\n",
-    "print(f\"{beam_stepped.emittance_x = }\")\n",
-    "print(f\"{beam_stepped.emittance_y = }\")\n",
-    "print(f\"{beam_stepped.energy = }\")\n",
-    "print(f\"{segment.l0a.phase = }\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Cavity(length=3.10, voltage=58010690.67, phase=1.10, frequency=2856000000.00, name=\"l0a\", device=\"cpu\")"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Minimal example of a cavity element from LCLS lattice\n",
-    "cavity_element = deepcopy(segment.l0a)\n",
-    "cavity_element"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "r12 = tensor(0.6725, dtype=torch.float64)\n",
-      "outgoing_beam.beta_x = tensor(13.1071)\n",
-      "outgoing_beam.beta_y = tensor(12.7197)\n",
-      "outgoing_beam.alpha_x = tensor(-3.5972)\n",
-      "outgoing_beam.alpha_y = tensor(-3.4858)\n",
-      "outgoing_beam.emittance_x = tensor(3.2032e-10)\n",
-      "outgoing_beam.emittance_y = tensor(3.3043e-10)\n",
-      "outgoing_beam.energy = tensor(64000000., dtype=torch.float64)\n"
-     ]
-    }
-   ],
-   "source": [
-    "incoming_beam = cheetah.ParticleBeam.from_twiss(\n",
-    "    num_particles=10_000,\n",
-    "    beta_x=0.44865302,\n",
-    "    beta_y=0.44865302,\n",
-    "    alpha_x=0.18863809,\n",
-    "    alpha_y=0.18863809,\n",
-    "    emittance_x=3.4008e-09,\n",
-    "    emittance_y=3.5380e-09,\n",
-    "    energy=6000000.0,\n",
-    ")\n",
-    "outgoing_beam = cavity_element.track(incoming_beam)\n",
-    "\n",
-    "print(f\"{outgoing_beam.beta_x = }\")\n",
-    "print(f\"{outgoing_beam.beta_y = }\")\n",
-    "print(f\"{outgoing_beam.alpha_x = }\")\n",
-    "print(f\"{outgoing_beam.alpha_y = }\")\n",
-    "print(f\"{outgoing_beam.emittance_x = }\")\n",
-    "print(f\"{outgoing_beam.emittance_y = }\")\n",
-    "print(f\"{outgoing_beam.energy = }\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\"),\n",
-       " Drift(length=0.03, name=\"daq1\", device=\"cpu\")]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "segment.daq1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i, element in enumerate(segment.elements):\n",
-    "    if element.name == \"q26201\":\n",
-    "        break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('k26_1c', 'k26_1d')"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "segment.elements[i - 3].name, segment.elements[i - 2].name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Cavity(length=3.04, voltage=48198469.20, phase=-0.00, frequency=2856000000.00, name=\"k26_1d\", device=\"cpu\")"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "segment.k26_1d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Quadrupole(length=0.11, k1=0.388844133085, misalignment=(0, 0), tilt=0.00, name=\"q26201\", device=\"cpu\")"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "segment.q26201"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quad_segment = segment.subcell(\"k26_1d\", \"begundh\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Quadrupole(length=0.11, k1=-0.405381176356, misalignment=(0, 0), tilt=0.00, name=\"q26301\", device=\"cpu\")"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "quad_segment.q26301"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for element in quad_segment.elements:\n",
-    "    if isinstance(element, cheetah.Cavity):\n",
-    "        element.voltage = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ZqbRx5g0j1uesvyoPlSsUa1w/UBosVXW4OoUVIVW6HIqHw2F94hOfkLMb4xkAAAAAAAD6C8MwdPyHP5Ik5Xz0GnnPmNTua1xZrYxPr299fLokXTDqAknSpuOb7OMAAGiLPT69jTHgLXeKtxyn3pTVKX6w+qAMw0hQhcmzr8oMxcfltOgUdzV2ilvj05t+HqxQ3LqhINHsneKdHZ8eIRTviOCWrZI6t0/c4srKUsaCBZKk6hUvJbSuRKhp6BL3nzdXTr+/2+fzTZ8uSQpt297tc/Vm1uj0An+BCjMKJUl7KvaksiSkSJcT7VtuuUV//etfE1kLAAAAAABAnxV49VUF331XDq9X+R1cK+ds2CnebHx6qFJS66H4yKyRmpA7QTEjpjWH13S7ZgBA/1YSKJFkjgRvTcvO8NONT7fC4ppIjSrqKxJUYXLE4jHtr9ovSRqf07xT3BqfHoqF7O7RnuwU72wobnWKN+10RduC28xQvDP7xJvKvnSZJKl6xYped/NHYPXrkro/Ot1i3TjQ3zvFj9SaofjwzOGaNMi8aXV3Re+bBIDk6/JO8Vgsph/+8Id66aWXNHPmTHk8nmbPP/TQQ90uDgAAAAAAoC8wIhGV/vh/JEl5t94iz9ChHXqdM9P8gXhHx6dL0gUjL9Ceyj1adWiVrhx/ZTeqBgD0Z7WRWpWHyiW13SneMgQ/3fh0r9urYRnDdLT2qA5UH1CeNy9xxSZYSaBE4XhY6a50FWUWNXvOCsDro6fvFD9Uc0iGYcjhcCS0trqoOT7d7+lYpy87xTsuHg6rfuf7kiRfFzrFJSnzggvMEep7zRHq3kntT/7pCdGKCgXfe0+SlHX++Qk5p2+G+TkKbduWlD/rvYXVKV6UWaRCf6HWHF6jDys+THFVSIUud4pv27ZNs2fPltPp1Pbt2/Xee+/ZvzZv3pzAEgEAAAAAAHq3ir//XeHiYrny8jT4c5/r8Os6Oz5dki4cdaEk6Y2SNxSOhbtYMQCgv7PGf+em5yorLavVY1qG4qfrFJcaR6gfqD6QgAqTZ2/lXknS2JyxcjldzZ6zPsZwPGwHzU07xa0bCGojtQnviI/FY/Y1O9spTijevvpdu2REInLl5sozcmSXzuHKylLGwoWSpJpeNEK9dt06KR5X+qRJ8gxv/SaXzkqfPFnyeBSrrFTkcHLWBfQG1sSMoowiu1OcUHxg6nKn+KpVqxJZBwAAAAAAQJ8UC9Sq7JcPS5KG3HWnXJmZHX6tsyEUj9cE7Mfa6xSfOniq8n35OhE8oQ1HN2jhiIVdrBwA0J8drDkoSRqR2frodKnzofjorNHacHSDDlYf7H6BSbS3ygzFW+4Tl5p3hVs3olkj1a3nC/wFKq0r1aGaQwntiK+NNq5LyfR07PsFe3x6lPHp7Qlubdwn3p2u5+xLlynw2muqXrFCQ+6+q1d0UAcaMrlEjU6XJGdamrxnnKHQ9u0KbdumtC7eSNDbHa09KsnsFLfHp1fu7tfd8WhdpzrFDx7s3D90JSUlnToeAAAAAACgrzn5v79TrLxcaWPGaND113fqta7sbElSrKZGhmHIMIx2O8WdDqcWj1wsSVp9aHUXq0ZLB6oP6LrnrtMFf7sgaXtkAaAnFVcVSzK7pdvSmfHpkjQ6e7QkaV/Vvu4Vl2TWPvHWQvGmH3NV2Pw3t2mnuJS8veJ1EXN0utvpbvdzbbFqo1O8faGGUNw3Y2a3zpN5wQVypKUpvG+f6j9M/e5pIxJRYO06SVLmBYsTem5rzHxwa//dK25NzSjKLNLonNFyO92qjdTau8YxcHQqFD/33HP1b//2b9q4cWObx1RVVem3v/2tpk+frqeeeqrbBQIAAAAAAPRWkePHVf77P0iS8u+9Rw6Pp1Ovt8anKxKREQqpNlKrqBGV1HanuGTuFZfMUDxuxDtdN071s3d/pl0Vu1QWLNPy/ctTXQ4AdFtxdbEkaUzOmDaPaRnMttcpPnHQREnS7orUB4WnY41PH587/pTn3E633A5ziK51I1rT7nEpeaF4bcTsFO/o6HRJ8nkYn95RVrDrm9W9UNyVmdk4Qv2lFd2uq7vq3n1P8ZoauQYNkm9m9z62lrzTG0Lx7f0zFI/FY/bf49HZo+VxejQ+x/y68GE5I9QHmk6F4jt37lRGRoYuvvhiFRYW6vLLL9fnP/953X333frUpz6ls846SwUFBXrsscf0wx/+UF/60peSVTcAAAAAAEDKnfjFL2SEQvKddZayLrqo0693+P2Sy9x1GquusUenp7vS7XGprZk7bK78br9Kg6XaeXJnl2pHo0g8ovVH1tvvN30bAPoqq1t6TPaYNo9p2SHdXveyNXr4UM0hu+u5t4kbcbuTfVzuqZ3iUuO4dOvf3ZafB2vkfElNYqfh2qG4u+OhOJ3iHROrqlJ4v/ln3jtjRrfPl71smSSpevkKGYbR7fN1R2D1aklS5qJFcjR835goVqd4aMdOGdFoQs/dGxyrO6ZIPCKP06NCf6EksVd8AOtUKD548GA99NBDOnr0qH75y19q4sSJKisr0+7d5l1hN910kzZt2qT169frsssuS0rBAAAAAAAAvUHoww9V9c+nJUkF932tSzsJHQ6HvYM8XlNtd6ydrktcMkOL+cPnS5JeO/hap6+L5jaXblYg0rjXffOJzb027AGAjjAMo0Pj0zvbKT7YN1iDvYNlyNCeyj3drjMZjtUeUzAalNvptju+W7I+zrY6xQd5B0mSqsPVCa3NCsX9Hn+HX2PdJEcofnrB7dslSZ5Ro+QeNKjb57NHqO/fr/oPUxue2qH4BYsTfu60sWPl9PtlBIOq39u71yJ0xcFqcy30iKwRcjnNGwrsiReVvXviBRKvU6G4xefz6brrrtNPf/pTPf3001qxYoX+9Kc/6d5779X06dMTXSMAAAAAAECvU/rjH0vxuLKWLpV/9uwun8dp7RVv0ine1j7xpqwR6qsOrerytWFac3iNJOnKcVeqKKNI0XhUm45vSnFVANB1J0MnVROpkUMOjcoe1eZxnd0pLkln5J0hqfd2WVqj08dkj5HH2fpaE6v7uq1O8aw0c71JTbgmobVZN1x1Zny6320G6KFoKKG19DeN+8S73yUuSa7MDGUsMkeoV69I3Qj1+n37zA54t1sZ8+cn/PwOl0vehlwvtG1rws+falYoPjprtP0YneIDV5dCcQAAAAAAgIGsdv161a5ZK7ndKrjnq906l7VXPF5T3alQfNGIRXI5XNpTuSfhO08HmrWH10oyP6fziuZJkt488mYqSwKAbrFGpxdlFp22+7vlc05H+5FBbw+UrNHpp+uQt8anV9ebneAtO8WtULzpFJFEsM6X6cns8Gus2oLRYMrHePdm9j7xmYkJxSUpe9mlkqSaFI5Qr3ruOUlSxvyP2N8zJpp3hhmKB7dtT8r5U+lAzQFJanZzkPU17ED1AW42GWAIxQEAAAAAADrBiMd1/Ec/kiQNuuEGpY0e3c4rTq+1TvH2xqdbx5w99GxJjFDvjpJAifZW7ZXL4dK8onn2WPp1JetSXBkAdF1xdbGk0wfDkuR2ujt97r4Sio/PHd/mMVZneChmBmJWSG5JVqd4d8anGzIUjocTWk9/YRiGgtvMUNw7c2bCzpu5eLE5Qr24WPW7diXsvB1lxON2KJ579dVJu45vhvk5C/bnTvHsxu/Xh/iGKDc9V3Ejbn+9wMBAKA4AAAAAANAJ1c8/r/qd78uZmakhd36x2+ezun5iTXaKd6RTXJKWjFoiSVp5YGW36xiorC7xWfmzlJOeo/OGnSe3w63i6mIdqqYDH0DfZO0TH5M95rTHORwO+9+cyXmTO3TupqF4b+xctsanj89pOxRv2SHvc/mavZ/lMf9tTvRO8bpo58enN+1iD0bYK96a6JEjipWVSW63vFOmJOy8rswMZZ6/SJL5/V9Pq9v4jqJHjsqZmanMCy9M2nV8DZ3i9R/uVjzUvzqnD1Sf2inucDh6/c09SA5CcQAAAAAAgA6K19er9Kc/lSQNvv12uQcN6vY5ndkN49M7uVNcagzFt5zYouO1x7tdy0C0tqRxdLpkdgfOHjq72XMA0Nd0tFNckn6w8Af61txv6fFLH+/QucfmjJXb4VZNuEbH63rXvz2GYWhfpdn5OS53XJvHtewMb6tTPBAOKG7EE1af1SnemVDc7XTbu9GDUULx1thd4mecIafX287RnZN95ZWSpKp/PS8jFkvoudtT9dyzZg2XLkv4x9WUu6hIrsGDpWhUofffT9p1elo0HtXhwGFJzXeKS71/4gWSg1AcAAAAAACggyr+9CdFjxyVu7BQeZ++OSHndGU1jE9vslO8I+PTJWloxlDNyp8lSXr14KsJqWcgCUVDevvo25KkhSMW2o8vGL5AEqE4gL7L2ineXqe4JM0fPl+fmPwJe0x3e9JcaRqTY563twVKJ4InVBOpkdPhPO3Hbo1Pb+t9KxQ3ZKguUpew+uzx6e6Oj0+XGkeoB2OE4q0JbjHHfnsTuE/ckrl4sZw5OYoeP666DRsSfv62xINB1ax4SZKUc9VVSb2Ww+GQb7rZLR7qR3vFj9YeVTQeVborXUMzhjZ7buKgiZKk3RW7U1EaUqRTofiLL76o0aNHKy8vT0uWLNGKFSskSffff78uv/xyPfDAAyotLU1KoQAAAAAAAKkUrahQ2a9/I0nK//KXE9axY3eK1wRUXW+Oae1op7gkXTz6YknSKwdfSUg9A8nGYxsVioVUmFGoibkT7ccXDl/Y+Hy0f40RBdD/hWNhlQRKJHWsU7wremuXpTU6fWTWSKW50to87pTx6S1uCEh3pdvd2YncK96VTnGpcYQ6neKts3ZhW7uxE8mZlqbsyy6VJFU+80zCz9+WmldfU7y2Vp7hw+U7++ykX8+6ocDquu8PrH3iI7NGyuloHof21q9hSK5OheJf+9rX9LGPfUx/+9vfNHv2bF1zzTW6/vrr9cMf/lCjRo3Sv/71L82ePVsffsgfIgAAAAAA0L+c/PVvFK+pUfrkycq56sqEnbe1TvHOhOIXjb5IkrTp+CaVh8oTVtdAsObwGklmCO5wOOzHJ+RO0FD/UNXH6rXx2MZUlQcAXXKo5pDiRlwZngwN8Q1JyjXsQKm8d2UB+6oaRqfntD06XWq+p1s6NSR3OBx2t3gi94pboXimJ7NTr7M6y7lR61RGNKrQjp2SJN+sxIfikpR7zTWSpJqXXlassjIp12ip6llzdHrO1VfJ4Uz+0GffDDMUD/WjUNzaJz4ya+Qpz43PHS+HHCoPlassWNbTpSFFOvU36cCBA/ryl7+siy66SD/+8Y/1y1/+Uv/85z/1X//1X3rkkUf05ptv6vrrr9c3v/nNZNULAAAAAADQ48KHDqn8iSckSQVf+5ocLlfCzu1qZad4R8enS9LwzOGaOniq4kZcrx18LWF19XeGYZyyT9zicDjsceqMUAfQ1xRXFUsyR6c3veEnkXprl6W1T3x87vjTHtcyBG8ZkktN9opHAgmqrsn4dE/nxqfTKd62+j17ZASDcmZmKm1sciYjeGfOVPqUKTLq61X59DNJuUZT0RMnVPvGG5KSPzrd4m0IxcPFxYpVJ+5GkFQ6WGN2io/OHn3Kcz63T6OyR0nqfV/HkDydCsXHjBmjt99+237/pptukmEYmj9/vv3YF7/4Ra1bty5xFQIAAAAAAKTYiZ/8RIpElDF/vjIXzG//BZ3gzDJ/6B6rqVFVfZWkznWKS40j1FceWJnQ2vqz/VX7VRIokcfp0ZzCOac8b41QX3t4rQzD6Onyki586JAO3fEFVfztb/3y4wMGsv3V5j7xZI1OlxpD8eLqYtXH6pN2nc7aW2WOT2+vU/yUUNzVSijuMf99TuT4dGs/eWfHp9s7xQnFTxHc2rBPfMb0pHVUOxwODbrxBklSxV+elBGPJ+U6lqrnX5DicfnOPFNpY8Yk9VoW96BB8owYIUkKbe8fe8WtTnEr/G7J+jrGXvGBo1NfIe677z7ddtttuv/++7Vx40a5XC6tW7dOkydPto+pq6tTbW1twgsFAAAAAABIheDWrap+cbnkcKjgvq8l/PyuhlA8XFNld6N1NhS/aJQ5Qv3to2/bwTpOz+oAP7fw3FY79uYOmyu3063DgcP2D1X7k7KHf6XA6tU69p3/p8BrTBgA+pP9VWYoPiZ7TNKuUeAvUE56jmJGzO7O7g062inesjP8dJ3ivWGnuBWKMz79VFYonox94k3lXHGFnJmZihw4qNo31yf1Wk1Hp/ck74zpkqTg1v4xQv1QzSFJ0uisUzvFJWnioImS6BQfSDoVit966636wx/+oJdfflnz5s1TVlaWvvCFL+juu+/WL37xC73yyiv60pe+pHnz5iWrXgAAAAAAgB5jGIZKf/gjSVLO1VfL26QxIFGc2eZO8aqIOarSocY9ph01JmeMJg6aqKgR1epDqxNcYf9k7RNvOTrdkuHJ0NlDz5bU/0aox0Mh1axsnCpQs2pVCqsBkGjF1cWSzH8bksXhcPS6EerloXJV1FdIav+GgJad4i3fl6TMNHPvd0J3ike7Fopbnex0ip8q1BDgJmufuMXp9yunYbd4xZNPJu06oV27VP/BB5LHo+xLL03adVpj3VgQ3N73Q/FoPKqSmhJJp+kUz6VTfKDp9CyJa6+9VuvWrVNlZaVee+013X777XK73Xr88cd11VVXad26ddqxY4euvfZafe9739PTTz/d4XOvWbNGV155pYqKiuRwOPTMM880e94wDH3nO9/RsGHD5PP5dNFFF2n37uZ/WMvLy3XTTTcpOztbubm5uu222xQIJG7nBwAAAAAAGDgCq1ap7p135EhPV/5XvpyUa1id4lVRsxMtOz1bLmfnd5ZbI9RXFK9IXHH9VCAc0LvH35XUOCa9NdZz60r616rAwOrXFW8y6TG46d0UVgMgkQzD6JFOcan37RW3usSHZw5vd2d303HpXpe31d3r2WnmTWuBcAJ3ioe72CnuYXx6a+K1tarfs0dS407sZLJGqAdWrVLkyJGkXKPq2eckSVmLF8uVm5uUa7TF19ApHtrWvfHp9bF6ff+t7+svH/wlEWV1yZHAEUWNqLwurwr8Ba0eY30N21u5V9F4tCfLQ4p0ecFCZmam5s+frzvvvFO//e1vtXHjRtXU1Gjbtm168MEHNW7cOK1bt0533HFHh89ZW1urWbNm6eGHH271+R/+8If6+c9/rl//+tfasGGDMjIytHTpUoVCjSNDbrrpJu3YsUMrV67U888/rzVr1uj222/v6ocJAAAAAAAGKCMaVemP/0eSlHfLLfIUFiblOlaneI3b/GFcZ0enW5aNWSZJeuvIW6oIVSSktv5q/dH1ihpRjcke02b3kNQYim88ttHeA9sfVL/wvCQp9/rrJEnh/fsVLS9PZUkAEqQ8VK6acI0ccmh0dusjgxOl14XiVWYo3t4+cUlKdzd2hrc2Ol1K/Ph0wzDoFE+w4I4dUjwud2GhPAWtB5+JlD5+vPxz50rxuCr+/veEn9+IRlX1LzMUz7nm6oSfvz3eqVMlp1PR48cVOV7a5fM8u+dZ/XXXX/VfG/5Lh2sOJ7DCjrNW34zMHimno/UodHjWcPncPoXjYR2sPtiT5SFFuhyKt8blcmnatGm66aab9KMf/UgrV67U8ePHO/z6Sy+9VN///vf10Y9+9JTnDMPQT3/6U33rW9/S1VdfrZkzZ+rxxx/XkSNH7I7y999/XytWrNDvfvc7zZ07VwsWLNAvfvEL/eUvf9GRJN21AwAAAAAA+qfKfzyl8L59cg0apMGf/1zSruP0+yWnUzU+s0stJz2nS+cZmzNWU/KmKGpEtfLAyvZfMICtPWyOQ18wfMFpjxubM1bDM4crEo9o47GNPVFa0sUqKxVY/bokadBNNyltgrl3t27TplSWBSBBrNHpRZlFbYa9idLbQvG9lXsltb9PXGreKd7a6HRJyvSY49NrIokJxUOxkOJGXFLXd4oTijcXsvaJz0zu6PSmBt14oySp8u//kBEOJ/TctevfUuxEmVy5ucpc2PYkm2RxZmQofbz59yfUjRHq64807lz/666/druurjhYY4bcbe0TlySnw8le8QEmoaF4Mu3fv1/Hjh3TRRddZD+Wk5OjuXPnav168y/Y+vXrlZubq3POOcc+5qKLLpLT6dSGDRvaPHd9fb2qq6ub/QIAAAAAAANXLFCrE7/8pSRpyBe/aI84TwaH0ylnVpYC5s+7u9wpLkmXjjV3Ty7fvzwBlfVPhmHYO8Lb2iducTgcdnDeX/aKV69YISMSUfoZZ8g7ebL8Z5s/R2OEOtA/FFcVS0r+6HTJDJ8dcqg8VK6yYFnSr9ee98vflyRNyJ3Q7rFNg3ArcG4p0Z3itZHGtRVtXbMt1vGhaKidIweWoLVPfGbyR6dbspZcKHd+vmJlZap55ZWEnrvq2WclSdmXXSZHWlpCz91R3obPpfW57axoPKoNxxrzuH/u/mdKbuawOsVPNxFIkibmEooPJH0mFD927JgkaejQoc0eHzp0qP3csWPHVNBiRIbb7VZeXp59TGseeOAB5eTk2L9GjhyZ4OoBAAAAAEBfUv7YY4qVlckzepQGfeLjSb+eKytLNQkIxa0R6puOb9Lx2o5P7xtI3i9/X2XBMvncPp099Ox2j7dGqK89vFaGYSS7vKSzdpXmXHWVJMnf0FxS9847KasJQOJY+8TH5oxN+rV8bp89oj3VgVIkHtHOkzslSTPy2w9Im45Pb6tTPNGhuLWGw+/2tznOuS10ircuuM0Mbr092Cnu8HiUe/31kqSKJ55M2HljgVo7ZE/F6HSLr2E3e2hb10Lx90rfU024RllpWSrKKFJ1uFr/2vuvRJbYIdY49PbWSFgTL3ZX7E56TUi9PhOKJ9M3vvENVVVV2b8OHTqU6pIAAAAAAECKREpLdfL3v5ckFXz1nh7p1HHl5HR7fLokDcscprMKzpIhQyuKVySqvH7FGp0+b9g8pbna/709t/BceZweHak9YodNfVX4wAEF33tPcjqVfcUVkiT/uWYoHtq5U7FAIJXloY+JxCL60mtf0u+2/S7VpaAJa3x6T3SKS7JHD6c6UNpTsUf1sXplebI69LE3HZ/e1pj57LRsSYnvFO/s6HRJ8nv8kqS6aF1CaukPIqWlih49Kjmd8k2b1qPXzv349ZLLpbp33lHow8TcEFLz8ssyQiGljR0r74ye63xvybp2cPv2Lt0M+NrB1yRJF4y8QJ+e9mlJ0mPbH1M0Hk1ckR1g7xTPOn0TbG9bA4Hk6jOheGFhoSSdsqP8+PHj9nOFhYUqLS1t9nw0GlV5ebl9TGvS09OVnZ3d7BcAAAAAABiYyn7xSxnBoHxnnqmspZf0yDVdubn2+PSctK6H4hIj1NuzpmSNJGnhiI7t6vR7/Dq38FxJfX+EetVzZqdWxrx58gw1py16CgvlGTlSisfNwBxoxUvFL53S6betbJtWHVqlX2/5tSKxSIoqQ0t2KJ4zpkeu11sCpW1lZlfrtCHTOtSF3bQ7vK1QPDOtYad4gkLxQMS88ahLobjbDMWbjmAf6KxO5vQJE+TM6PzntDs8hYXKuvBCSVLlXxKzM9sanZ5z9VVyOBwJOWdXeCdNkiMtTfHqakUOHOjUaw3DsEPxC0ddqI9N/JgGpQ9SSaCkR78vjcQiOlJ7RFL7neLWjT1Hao8k7O86eq8+E4qPHTtWhYWFevXVV+3HqqurtWHDBs2bN0+SNG/ePFVWVmrTpk32Ma+99pri8bjmzp3b4zUDAAAAAIC+pX73blU+9ZQkqeDr9/XYDyVdubkJGZ8uSZeMuUQuh0s7Tu6wu2RgqghVaNsJ84fo1q7wjrBHqPfhUNwwDFU91zA6vcVYVnuE+kZGqONUVfVV+trrX9N/rPsPVYYq7cdLAiWSpPpYfcoDUZgisYgO1xyW1DPj06XeE4pvPbFVkjRjSMc6bPO8ee0eY41Pt8Ls7rLGp3clFLdeY50DUnCL+Xvu7cF94k0N+uSNkswwOxbo3s0KkSNHVLfB3MOdc+WV3a6tOxwej7xTpkhqHE/fUR+Uf6AjtUfkdXn1kaKPyOf26eapN0uS/nfb/ypuxBNeb2sOBw4rbsTlc/uU78s/7bE56Tka6jfXNu+p3NMT5SGFelUoHggEtHnzZm3evFmStH//fm3evFkHDx6Uw+HQV77yFX3/+9/Xc889p23btunTn/60ioqKdM0110iSpkyZomXLlunzn/+83n77bb3xxhu66667dMMNN6ioqCh1HxgAAAAAAOgTSv/nISkeV9bFF8l/1lk9dl1XTo4C1vh0b/c6xfO8eTpv2HmS6BZvaV3JOhkydMagM1SY0fZUwZasAH3T8U19tksv+N57ihw6JIffr6wlS5o91xiKb0xFaejldpzcYb99OHDYfvtI4Ij99tayrT1aE1p3qOaQYkZMfre/3SAoUaxQfG/lXkXiqZsYYHWKz8zv2G7pcbnjdOu0WyVJZxec3eox1vj06nB1l8ZIt9Sd8enWa/rqv0HJENxmft3xzei5feJN+c87T2ljxypeW6vq57u3M7vqX8+b55wzR57hwxNRXrfYI9Q7GYq/dsjsEp8/fL58bvNuzxsm36BMT6b2Vu3VqoOrEltoG6x94qOyRnXoBlf75p5ybvDq73pVKP7OO+9o9uzZmj17tiTpnnvu0ezZs/Wd73xHkvT1r39dd999t26//Xade+65CgQCWrFihbzexvEmf/7znzV58mQtWbJEl112mRYsWKBHH300JR8PAAAAAADoO2o3vK3A6tWS2638e+7p0WsnslNckpaNXSbJDMUT8YP8/sLq9O7o6HTL6OzRGpk1UtF4VG8dfSsZpSVd1TPmWNbsiy+W0+9v9px/jjkePrh9u+LBYI/Xht5te9l2+22rC1mSjtYetd+2unSRWvur90syR6f31KSToswiZXgyFIlHdKAqNdNJasI12l9lfuwd7RSXpHvPuVfrb1yvO2bd0erzVqd4NB5VKBbqdp21UTPQtvaDd4YViieqa72vM+JxhbaZX5t8s1ITijscDg264ROSpIonnuzy91uGYTQZnX51O0f3DF9D931oa+dC8VcPmpOeLxx1of1YVlqWbpxsdtX/dttve+T70oM1DaF49qgOHW+NUE/1xAskX68KxRcvXizDME759Yc//EGS+UXm/vvv17FjxxQKhfTKK69o0qRJzc6Rl5enJ554QjU1NaqqqtJjjz2mzMzMFHw0AAAAAACgrzDicZX+8IeSpEEf/7jSx/bM2FlLokPxJaOWKM2Zpn1V+/gBX4NoPKo3St6QJC0asahTr3U4HHa3+LqSdQmvLdni9fWqXrFC0qmj0yXJM2KE3EOHSpGIPY4WsFgduFLzTnFrfLpEKN5bFFcVS5LGZI/psWs6HU5NzDUDpV0Vu3rsuk1tL9suQ4aGZw7XYN/gDr8uVlmp4K9/r/Deva0+73f77f3kgXD3w2jGpydOeP9+xQMBOXw+pU+YkLI6cq65Rg6vV/Uffqjge+916Ryh7dsV3rdPjvR0ZS29JMEVdo3VKR7aubPDN8sdqj6k3RW75XK4dP6I85s996mpn5LX5dWOkzu0/sj6hNfbkrU+qL194harU3x35e6k1YTeoVeF4gAAAAAAIDFi8ViqS+hTql9crtCOHXJmZGjInV/s8es7c3MUSGAonpWWZXdDv7j/xW6frz/YVrZN1eFqZadld6qT0GLvFT+8ts913wdWv654dbXcQ4fKP2fOKc87HA5GqKNVhmFo24nGULxpEN50fPrBmoOqCFX0aG04ldUt3VP7xC2p3ituTTOYPmR6h19jxOPau+xSlf3qVzrx8MOtHuNwOJTpMRvuasI13a7THp/uZnx6dwUbOpi906bK4XanrA5XTo6yr7hcktkt3hXWJJesiy6Sq5c0eKaNGSP3sGEywuEOf19gjU4/Z+g5yklvvgooz5un6yZdJ8nsFk+2puPTO6Lp17C+9j0eOodQHAAAAACAfuZrr39NF/79wmYjb9G2eDisEz/5iSRp8Oc/J/fgjneZJUp9jk8xV8NO8fTu7RS3XDr2UknSiv0r+AGfpDWH10gy91y6nZ3/Afq5hecq3ZWu43XHtadyT6LLSyp7LOtVV8rhcrV6jP9cc4R63Tvv9Fhd6P2O1x3XydBJ+31rfLphGDpWe0yS7NCwaUc5UqO4uliSOT69J6U6FLd22nfmhqf6Dz9UrLJSkhTcvKXN46wR6tXh6q4X2MAafZ6R1vVQPBwPKxJL3e723iK41fw9S9U+8aYG3WiOBq9+6SVFT55s5+jmjHBY1S+aNy+2NsklVRwOhzIXmBNyAms7NiGntdHpTd0y7Ra5nW69c/wdvVfata76jrLGp3e0U3xMzhi5nW7VRmp1pPZI+y9An0UoDgAAAABAP1ISKNFLxS+pPFSun737s1SX0ydU/OnPipSUyD10qPJuuSUlNQT85o9o0qKSz+1LyDnPH3G+/G6/jtQe0ZYTbf/Af6BYe7hhn/jwzu0Tt3jdXp1baAbH1m7yviBaVqbAGvOGgJyrrmrzOP+5Zqd4cPNmGeFwj9TWU0IffKDI8dKEnzceCmn/xz+hI9/8ZsLP3Vu0DLqtPeL1sXqF4+afk/OGnSeJEeqpZhhGY6d4dg93iuelLhRvOs1gZn7HA9JYVWPI7Rk6tM3jrFA8EZ3i9vj0LnSKN91DTrd4467rVO0Tb8o3bZq8s2ZKkYgq//FUp14bWLdOsYoKufKHKGPevCRV2DUZC81QvHZt+9/zlAXLtLl0s6S2Q/HCjEJdPd4M/n+7NXnd4uFY2P63qqM7xT1Oj8bljJMk7a5ghHp/RigOAAAAAEA/snz/cvvtXeW76BBuR6yyUmW//rUkKf9LX5LTl5hAurMCDT/rzuzY2sYO8bq99g8mm/65GIiO1x7XropdcqhxN3hXWIH664deT1RpSVf17LNSNCrvzJlKnzixzePSxo2TKy9PRn29gtv7z5SJ6uXLtf+aj+rQF+5I+LmDmzcrtHWrqv/1vCQpbsQTfo1Us0LxWfmzJDUGg01DOULx3qGivkLV4Wo55OhwEJQoE3LNnc6ldaWqDFX26LWP1h7VydBJuR1uTcmb0uHXxYONu7njdW3v6bZCcavLuzvs8eld2CnucXqU7ko3zxMd2KF4PBRSaJe5v943o/PrUJJh0A1mt3jFX/8iI9bxFUbW6PScy69I6Rj41mTMmye5XAoXFyt86NBpj119aLUMGZo2eJoKMwrbPO6z0z8rp8OptSVr9f7J9xNcselwzWHFjbj8br8Gezs+/SnVEy/QMwjFAQAAAADoR5ruj66or2i2/xWnKvvNo4pXVyt90qSUjq2sTjPDtKw6Q/H6+oSd1xqh/lLxS4rGowk7b19jdXbPyJ+hQd5BXT7PBSMvkCS9V/qejtceT0htyWQYht21lnvttac9ttle8bf7x15xwzBU8tV7JEn1O99P+E1CoYabB4xwWL/f/Ft95MmPJO2H/KlireGwgm8r1LP+63f7dWbBmZLMAL0/3hjQVxRXFUuShmUMS9jEkY7KSsvS8MzhkqTdlT3bZWmNTp84aKK8bm+HX2cEG+9CiwfbviMty5O4TvHuhOJNXzfQO8VD778vRaNyDR4sd1FRqsuRJGVfukyunBxFjxy1p7O0J1ZVpcCqVZJ61+h0iysrS77ZZ0qSatedfoS6NTp9yaglpz1uVPYoLR2zVJL0u22/636RrThQfUCSOTrd4XB0+HUTB5k3DhKK92+E4gAAAAAA9BMfVnyo3RW75XF67B167HhtW/jwYVX86U+SpIL7vtbmruWeUO0yxxBnBg17x2kizCuap9z0XJ0MnRzQ3eLdHZ1uGZY5TLMLZsuQoRXFKxJRWlIF33tP4f375fD5lH35Ze0e7z9vriSpZtVryS6tRxgtgq5E/t2SpOCOHfbbD235uWojtfr6mq8n9BqpFIvHtKPM/BitULw+Vq9ILGJ3zWZ6MjU+d7x8bp8CkYA9vhs9L1X7xC2pCpS6MjpdkuJ1TULxDnSKJ2KnuH0zSZNR6J1hheLWGPaBKrTVvBHCN3Nmp0LPZHJ6vcppuPms4sknO/Sa6uUrZEQiSj/jDHknT05meV2WucD8vul0e8UD4YA2HN0gqe3R6U19bsbnJEkrD6zUvqp9CaiyOWufeGcnZlid4oxP798IxQEAAAAA6Ces0HPh8IWaXzRfktnRitad+OnPZEQiyvjIPGUs6PpI7USoqq+SJGUFExvceZwe3TLN3JP+y/d+qfpY4rrQ+4pwLKz1R9dLkhaNWNTt81021gyXm05l6K2sLvHsZcvkysxs9/jsiy+WnE6FtmxV+PDhZJeXdC3/LkVPnEjo+UPbd5zymBVM9gf7q/arLlonn9vXLHCsjdQ2C/fcTremDZ4miRHqqWR1io/JHpOS66dq9LA1zWD6kOmdel3T7vAOjU8Pd398el20Yad4NzvFEzHKvS8LWvvEZ/aO0emWQTd8QpJUu3adwgcPtnt81bMNo9Ov7n1d4hZ7r/hbb8kIh1s9Zl3JOkXiEY3JHmPv5T6dSYMmafHIxTJk6LFtjyW0XqmxU3xUVtdC8eLq4gH5/fJAQSgOAAAAAEA/YBiGXtxnhnSXjbtMZw89W5K06fimVJbVawW3bVf1889LDocKvva1lHcaWaF4ZlCKVVQm9Nw3TblJBf4CHak9or9+8NeEnrsv2HR8k4LRoIb4hmhyXvc7sS4Zc4lcDpd2ntxph1C9USwQUPVy80aZ3OtOPzrd4s7Pl3/uHElS9Qu9P/RvT7Siotn7sbKyhJ07VlWlSBs7VvvLqgJr0si0wdOU5kqT12WOpg5EAnYonukxb7awQvMtJ7akoFJIsrv0x+aMTcn17VC8vOdC8Ug8op0nd0qSZg7pZKd4k53iRjDY5h5oKxRPxPh0K1i3/t50lt9tdpgP9PHpwYZOce/Mzv2eJ1vaqFHmTZaGoYq/nv77rfCBAwq+957kdCr7ist7qMLO806ZItfgwTLq6lT3bus32r520Jwuc+GoCzv8/fTnZ3xekvTCvhd0JHAkMcU2OFht3pBgTc3qqHxfvnLScxQ34tpbuTehNaH3IBQHAAAAAKAf2HJii47UHpHf7df5I87XWUPPkmSOALQCV5gMw1Dpj34kScq56kp5p05NcUVSZX2lpIZO8arE/n753D7deeadkqRHtz2akB/s9yVrDpu7PRcOXyino/s/Csvz5um8InOUdG8eSV/94osygkGljR0r31lndfh1OZdfbr++r2t5g0kiO8VDO5p3iXsdafbb/WX0qtWBO2OI2Y3ZdJ+xvRs5zXzMCsVZ2ZE6qR6fboXieyr3KBZvPWBOtD0VexSKhZTlyer0x91yvUI8GGr1uESG4lanOOPTuy5SWmrekORwyDejd3WKS9KgT94oSap66p+K17fdbVz13L8kSRkf+Yg8BQU9UltXOJxOZS4wp0/Vrlt7yvPhWFhrSszvszoyOt0yM3+m5g6bq6gR1e+3/z4xxTY4UNO4U7wzHA5HyiZeoOcQigMAAAAA0A/8a6/5w7Ulo5bI6/ZqiG+IxmSPkSFDm0s3p7a4Xibw+uuqe/ttOdLSlP/lL6e6HElNQ/HE7hS3XDX+Ko3LGaeq+io9tj3xoyp7s3Ul5h7MhSO6t0+8qcvHmsHxi/tflGEYCTtvIlU+ZY5Oz732Y52ahJB10UWSx6P6XbtUv2dPssrrEckcn950n3jUKYWMxrGy75a+m7DrpJIVcFtjqTPTzO7WigMfqvKoGTpkuBtC8YYu3T2VewZ8F2sqROIRHa4xVx6kanz6qKxRSnelKxQL6VBN61MUEs2eZjBkWqdvemq6U9x8v/U/t/ZO8Ujidop3dXy61WE+kP+OBd95R5KUPnmyXNnZKa7mVJnnny930TDFKitVs2JFq8cYhtEnRqdbMk6zV3zD0Q2qjdQq35dv30DVUbfPuF2S9M/d/1RZMDGTXELRkI7VHpPU+Z3iEnvFBwJCcQAAAAAA+rhwLKwVxeYP3q4cf6X9OCPUT2VEoyr98Y8lSXmfvlmeoqIUV2Syx6eHErtT3OJ2uvXls8wbAP608086Xns84dfojQ5WH1RxdbHcDrfmDZuXsPNeOOpCpbvSVVxdrPfL30/YeROlfvduhbZslVyuTv/A3ZWbq8z5ZldYX+8Wj7UYnx49kbjx6U33iQe8zZ97r7T1EbN9SSgaskOBlp3ie7/17zr06C8lNQbl+f58DcsYprgRp1s8BQ7VHFLUiMrn9mmof2hKanA5XZqQO0FSz3VZWjvsOxvGSc13ikundo5bsjyJ2SkejUftPcXWzSSdZXWY10QG1sSXpmrffluS5D/3nBRX0jqHy6VBHzd3i1c88WSrxwTffVeRw4fl9PuVddGSniyvSzIWzJccDtXv2qXI8dJmz712qHF0emdvTDm38FzNzJ+pcDysx3c+npBarZuDsjxZGpQ+qNOvp1O8/yMUBwAAAACgj1tzeI2qw9Uq8BdoTuEc+3FC8VNV/vOfCu/ZK1durgbffnuqy7E1G5+ehFBcki4YeYFmF8xWKBbSI1seSco1epu1Jeaoz7OGnmWHd4mQ4cnQ+SPOlyS9uK/3BccVf/u7JCnzgsVy5+d3+vXZ1gj1F3pvJ3xHJLNT3BqfnjZ2rAK+5s+9e/zdPv15k6QPyj9Q1IhqsHewCjMKJTV2qdalS8F0c/qAteNYkmYXzJYkvXe8798U0NcUVxVLMrvEOzMZItF6OlCybsCwxvd3hhFq2Sne+kjyRI1Pb9rd3dVO8TxvniSpIlTRzpH9k2EYqm3oVs44L3E3uiVa7nXXSh6Pglu2NJsqYql6xuwSz1q6VE6f75Tnexv3oEHyNoyqr13X2C0ei8e06uAqSdKFIzs+Ot3icDjsbvG/fvDXhKx7skanj8oe1aWvhRNzJ0oiFO/PCMUBAAAAAOjjrNHpV4y7Qi6ny378nKFmF83OkzsH9P5JS7yuTid+8QtJ0pAvfqFXjd1sNj49wTvFLQ6HQ189+6uSpKf3PK19lfuScp3exNonvmjEooSf+7Jxl0mSlhcvV9yIJ/z8XRULBFT1z39KkgZ94oYunSPrwgvk8HoVPnBAoZ07E1lej7I6xT2jzBGqiQrFoxUVihw2u9Ey5p1nd4oX+Arkdrp1InjC7lbrq5ruE7eCBSvIC6aZvyQ1u9nkrAJzd31/GR9vGIYefPtB/fn9P6e6lHalep+45Yy8MyT1TKBUE67R/qr9khpH/HfGqePTkxuKW9+HeZweeVyeLp1jsG+wJCVs1HRfE967V5GSEjnS0pRx3txUl9Mm95Ahyr74YklS5V/+0uy5eH29qhvGqveF0emWzAULJEmBJnvFt5Vt08nQSWV5snRu4bldOu+iEYs0adAk1UXr9MQHT3S7zoPVByWZ6xy6YnzueDnkUHmofMD+PevvCMUBAAAAAOjDKkOVWlNiBn9Xjruy2XPDModpWMYwRY2otpzYkoryepWTv/+9YifK5Bk5UoNu6FpYmCzloXJJUnZt8jrFJbOT84KRFyhuxPWzd3+WtOt0hRGLJfR8dZE6bTy2UZK0cHji9olbFg5fqCxPlkrrSnvVNIaqf/5T8dpapY0bZ4487QJnRoYyFy+W1LdHqMcqzVA8faLZ+RUtS9DO0oYbBTyjRskzfIQCPjM0LvAXaNrgaZL6fjDccp+41NgpHkw3f0nNO15nDzU7xbec2KJoPNpDlSbP9rLt+tP7f9IP3v5BQjoYk8nqFB+bMzaldfRkp/iOkztkyFBRRpGG+IZ0+vUtx6e3FYpbN350NxQPRMzx69bfo66wPs6TwZPdqqWvCrz+uiTJP2eOnH5/O0en1qBP3ihJqnr+BcWqG/fRB1atUrymRu5hw+Sf07UgORUyFpqheO2b62VEza/vrx58VZK0cMTCLt/o4XA49PkZn5ck/fn9P3f7Jt4D1Y2d4l3h9/jt17JXvH8iFAcAAAAAoA9bUbxC0XhUU/KmaMKgCac8zwh1U/TECZ3838ckSQX3fFWOtLQUV9SoLlKnYNT84XxOXXJDcUn6yllfkdPh1GuHXusVu4+NSESHv/JVfTj3PNXv35+w87597G1F4hENzxyelKAozZWmi0ZfJEl6cX/vCI6NWEzlf/yTJCnv0zd3a4xy9uVmJ3z1i8tlxHtPJ3xnRMvM4Mg7ebL5foI6xa194t5pU+XKzbHHp+ek5+isof2jW9oKxZvuarYC8Lp0h90p3jQUn5A7QVmeLAWjQe2q2NVzxSbAjzb+SJ984ZPNRlwfrT1qv73+yPpUlNVhVsf02OzUhuLW6OGSQEm3d3C3Z9uJhj+j+Z3fJy61EorXth7GZaeZU2VCsZAisUiXriU1jk+39oJ3hRWKD9QO1sBqMxTPPP/8FFfSPt/ZZyt94kQZwaA9Ll1qHJ2ec9VVcjj7TjznmzFDzpwcxauqFNy2TYZh2KH4klHd24t+8eiLNTp7tKrqq/T3D//erXMdrDE7xUdnj+7yORih3r/1nb91AAAAAADgFM/uMX+4duX4K1t9nlDcdOLhh2XU1ck7c6ayli1LdTnNWF3i6Y40ecPJD8XH5Y7TRyd8VJL0k00/Sfnu47Jf/0Y1K1YoHgio6p9PJ+y81uj0hcMXJm3H7qVjL5UkvVz8crfCkkQJvP66IocOyZmdrZyrrurWuTIXLZIzI0PRo0cV3Lw5MQX2MCsE906dIkmKBwKnBGFdYe0T902fLqffb49Pz07P1tkF5tfcd4/33VC8MlSpQzWHJEnThkyzH7c6ZkNpjZ3iTbtenQ6nziw4U1Lf2isejoX1+M7Hta1smx3ySLI/B1Lj15PeqreMT8/15qrAXyBJ2lO5J6nX2lq2VVLzGzc6Ix5sHoK31Sne9MaPmkjXu8WtDtiu7hOXBnYoHquuVt275tfVzMW9PxR3OBx2t3jFk0/KMAxFT55UYK05fjzn6u79G93THG63Mj5i7nGvXbtOeyr36FDNIaU507Rg+IJundvldOm26bdJkv5vx/+pPlbf5XN1t1Ncapx4Qad4/0QoDgAAAABAH7WrfJe2n9wut9Oty8dd3uoxVii+9cRWhWPhniyv16jft0+Vf/+HJGno1+9LWkDaVVYonufJkUPJD8Ul6QuzviCvy6v3St/T6kOrk369tsQCAZU//rj9fs3KlQkJ6Q3D0NoS8wfPC0ckfnS6ZU7hHA32DlZ1uFpvHnkzadfpqPLH/yhJyr3+um6PlnWmpyvrIrMTvvqF3tEJ31nWuPS0MWPk8JrJdXe7xQ3DUPA9M/D1Tp8hR3q66tLNrynZadl2KFxcXdxnRxxvP2nuEx+dPVo56Tn243aneJoUTDM/5pZdr32xU37nyZ32201/z5qG4utK1ikWT+yKh0SpCFWosr5SUtf36CZST4xQNwzD7hSfmT+za+do2CnuGmIGzW2F4m6n2/6z350R6rVRs1M8EaF4XbSuz3596araN96QYjGljRuntJEjU11Oh2RfeZWcfr/C+/erbsMG89/SWEzeGTOUPm5cqsvrtMwF5vdTgXXr7BuI5hXN69b0A8sV465QYUahTgRP2Df8dlYwGlRpXakkaXRW1zvFe3INBHoeoTgAAAAAAH3UPz40g94lo5Yoz5vX6jFjsscoz5uncDys7WXbe7K8XqP0x/8jxWLKXLJE/nPOSXU5p7B+sG39HsaqqpLevT00Y6g+NfVTkqSfvvvTlO3/rXjySXu3pjwehYuLVb+7+505uyt361jtMaW70jWncE4CKm2dy+myu8Vf2P9C0q7TEaFdH6rurbckp1N5n/xkQs6ZfYV5s031ihX2DtG+Ih4KKV5jBlju/Hy58/MldX+veLi4WNETJ+RIS5PvzFlypKcr7DafS3elKyc9RxNyzVUWvWE9QVe0tk9carFTPK35Y5azChpC8ePvpnwKRUdtObHFfntf1T777cM1h+23K+ormh3Xm1hd4sMyhiUknOqungiUjtUe08nQSbkdbk3Jm9Klc1hTI9x5eQ3vt73L2Ppz3p2R8IkYn57hybA/3lWHVnX5PH1RXxqdbnFlZii7oSO84sm/qOrZhtHpV1+dyrK6LGOB2REe2rZNr+1fKan7o9MtHpdHt067VZL02PbHuvR96cFqc3R6dlq2cr25Xa5l4iBzfPreyr0p+/4YyUMoDgAAAABAHxSMBvXCPjOEu3bitW0e53A4BvQI9bqNGxV47TXJ5VLBvfekupxWWZ3igzPN0E6xmOKB5O5ilaTPTP+MctJztK9qn57b+1zSr9dSPBRS+f+ZXeL5X/6SMhctkiRVL1/e7XOvPWx2ic8pnCOv29vt852OFYqvPrTaHo+bChV/MrvEsy66SJ7hwxNyzozzzpMrN1exkydV9/bbCTlnT7HCb0dampxZWY2heGn3OsXr3t4oSfLNmiVnerqc6ekKe8zn0l3mTPG+/jXXuoGq5Vhqq8M1mN76+HTJHLfucXp0MnSyWad1b9Y07N5budd+26p/eKb596m3hpDFVcWSzJvgeoOeCMWt0ekTB03s8td4OxQfMth8v41OcUnKSsuSJFWHq7t0LakxFM9wd71TXJIuGXOJJHNtx0BhxOP22PG+FIpL0qAbzBHqNS+9ZK7ecLuVffllKa6qazxDC5R+xhk6kWXo/aoP5XQ4df7IxP1+fGzix5TnzVNJoETL93f+e8FE7BOXpBFZI+Rz+xSOh+2gHf0HoTgAAAAAAH3Qy8UvqyZSo+GZwzV32NzTHtvXA5quMgxDx3/0Y0nmOOneOqryZMjsFB/sz7dHPPfECPXstGx9fsbnJUkPb35YwWj3dy13RtXTTytWViZPUZFyLr9c2Zea4XL1iy92u8PU2v+7aMSibtfZnhlDZmhE5ggFo8GUjaKPVlSo6rl/SZLybvl0ws7r8HiUtXSpJKnqxb41Qt0Kv935+XI4HHI3jEju7vh06+YA/xxzAkHTTnErnJtdMFtS3+wUNwzDDsXb7BRPc9id4i27XtNd6fbr+sIIdcMwtKW0eShuff05ETT/rNw42Qy1Urlq4nT2V++XlPp94pamoXiypgVYo9O7uk9cagzFXYPNrw3GaULx7LRsSd0cn94QimemZbZz5OldMtoMxd8+9rYqQhXdOldfEdq2TbHycjkzM+U/+6xUl9Mp3jMmyXfO2fb7mYsWyT1oUAor6p7MhQu0cZK5PmN2wew2J1V1hc/t081Tb5Yk/W7b7xQ34p16fSL2iUuS0+HUxFyzW/zDyvZv7onEI/rWum/p5+/+vNM1o+cRigMAAAAA0Ac9tfspSWaXuNNx+v+9P2eoOTJ884nNA2oMYM3y5Qpt3Sqn36/8u+5KdTltsjvFfYPlys2V1DOhuGSGPUUZRSqtK9Wf3/9zj1xTkoxoVCf/9zFJUt5nP2uGrxcslsPrVeTAQYV27Dz9CU6jqr7K7vxM5j5xi8Ph0GXjzK6vF/enJjiu/OvfZNTXyzt1qnxnJTYwsDraal5eqXg4nNBzJ1O0rCEUbwjD3QUF5uOlpV0+p2EYrYbikSbj06XGG5E+KP8gpdMDuuJI7RGVh8rldro1OW9ys+esMK/uNOPTpb51U8CR2iMqDTb+maiL1ikYDao+Vq9IPCJJWjZmmTxOj4qri5uNV+8trE7xsTljU1tIgzE5Y+R2ulUbqdWR2iNJuYY14n9GftdCcSMSkSLm7697cMc7xRMRivvd3RtxPyp7lCbnTVbMiNl7nfu7wOvm6PSM+fPl8HhSXE3nDbrxRvvt3I99NIWVdF/GgoV2KH7hyAsTfv5PnPEJZXmytK9qn147+FqnXmt1dXdnn7jFGqH+YXn7ofibJW/q2b3P6rfbfqtn9jzT7WsjuQjFAQAAAADoY/ZW7tV7pe/J5XDpmgnXtHv8hNwJykrLUm2kVrsqdiW/wF4gHg6r9KGfSJLyPnebHYz1RuVBMxTP8+bJlZMjSYpVVvXItdNcabprtnnDwGPbHlNlqLJHrlu9fLkihw/LlZen3Gs/JklyZmQo84LF5vPd6Epef2S9YkZM43PG22OPk+2ysWZw/EbJGz32ObQYkYgqnnxSkjTo0zfL4XAk9Pz+s8+Wu6BA8epq1a57I6HnTiarI9xdYI5N9wwbJkmKHD3a5XPa+8Q9HvlmzZQkc3x6i1C8MKNQRRlFihkxbT6xucvXSwUrbDxj0Bn2x2Pxy0zCKzMkw2n+ObNGqjfVdK94b2fVOGPIDLkd5m9kdbjaDj+dDqfy/fmaU2jeBLHqYO8bob6/qqFTvJeMT/c4PRqfM15SxwKlzorEI9p50rxxauaQmV06h9UlLjUZn157mp3iDTeEBCKNq00ix4+rZtUqGfGOdYba49Nb+TvTWUvHmBM8BsoI9b64T7yp7EsuUc7VVynnmmuUuXhxqsvplvppY7RzpBkrzq8fmfDzZ6Vl6cYp5k0Ev932205Nm7DGp3e3U1xqDMV3V+xu99iVB1bab284uqHb10ZyEYoDAAAAANDHWF3ii0YsUr4/v93jXU6XHVJsOjYwRqhXPvmkIocPy52fr8G33prqck7LGp+e583r8U5xyQx0Jw2apJpIjX637XdJv54Rj+vko7+VJOV9+tNy+nz2c9mXmeFy9fLlHQ4aWlpbYu4d7Ykuccv43PE6Y9AZihpRrTy4sv0XJFD1yy8revy4XEOG2J+/RHK4XMq+dJl5rT40Qt3aKe5quCHGU9T9ULzZPvGGVQeOVkJxSZo9tO90SzdljRJvbSy1N2IG4bU+87/OuDnutqUzC86UJBVXF+tk8GSSKk2MzaWbJZk1Z6ebI7Krw9X27ugMT4acDqcuGHmBpN43Qj0Sj+hwzWFJvadTXEruXvE9FXsUioWU5cnq8sh4OxR3ueRsuBnttJ3inlN3ih/9j2/q8Be+qOM/+EGHrmlNjUhEKD6QRqhHSksV2mneBJG5qOf+XU8kh8ejogcfVNEPHpDD7U51Od2y5vh6GU5pzDFDOW8n/u+3JH1qyqfkc/u08+ROvXnkzQ6/zuoUH5XV/VDc+hq2u/L0oXgkHtGqQ403S+04uaPb10ZyEYoDAAAAANCHhGNh/WuvuTv4uknXdfh1Zw1tCMUHwF7xWHW1yn71iCRpyJfultPfvVGlyZbK8emSedPEV8/+qiTpiQ+e0JFAcsbdWgKrX1f97t1yZmRo0CdvbPZc5qJFcmZkKHr0qIKbt7RxhrbFjbjWlayTJC0c3rM/PL90rLkTffn+5T163YrH/yhJGvSJT8iZlpaUa2Rffrkkqea115p1WPZmdqd4fstO8a7/+a7baIbi1uh0qUUo7mz8/PelbummrBDfGoHeVEak+Y+SveHWO/hy0nM0IXeCpMbQubd670Tjx2vtja6ub+wUtx47f6TZobrlxBaVBctSUGnrSmpKFDWi8rl9KvAXdOtcsUDAHCueAMkMxa1pBtOGTGt3fUxbrADc6fPZ3yOc7mtby/HpRjyu2jfMyRnW1+D2JLJTvOkI9c6OmO5rateskSR5Z8zo1VN/BgprZP+5u+MKrFublGsM8g6y/x/n0a2PdqhbvC5SpxNB89/9RHSKW1/DSgIlp12bsPHoRlWHq+1JIweqDzS7eQa9D6E4AAAAAAB9yKsHX1VlfaWG+odqftH8Dr/O2nH7bum7ihtd68DtK8p+8xvFqqqUPnGCcj/a+3c3Wp2UqeoUl6T5RfM1p3COIvGIHt78cNKuYxiGTj76qCRp0CdvlCs7u9nzzvR0ZV20RFLXupJ3lO1QeahcGZ4Mu1O3p1ih+DvH3tGx2mM9cs3g1q0KbtkieTwadMMnknYd74wZ8owYIaOuToHVq5N2nUSKnTCDS3uneFGRJCl6vFRGNNrp87W2T1wy/8xG3GbndFq88Uet1tfcrSe2KhJLTNCYbHWROn1Q/oGk1kNxb33zYMIXlow2gkT7poDS3ntTQHW4Wnsq9khqEYqHqxUIm2OyrTC0MKNQUwdPlSFDaw8nJwjqiuLqYknm6PSuBsSSecPHnvMX68BnPpOQunoiFG9tmkFHGaGQpBaheAd2ilt/LsIHDjR7viM3C9VGG3aKexJzo57VLf7ygf49Qj3wuhmK99XR6f1JXaRO64+slyTN2WUo+N5mxWraDoy745apt8jj9Ojd0nc7NKHDGp2em56rnPScbl8/Jz3HvtFoT+WeNo+zpgN9dOJH7ZU97598v9vXR/IQigMAAAAA0If848N/SDJ/+OJyujr8uql5U+Vz+1RZX6l9lfuSVV7KRUpKVPHHP0mS8u+9t9ePqYzGo6qsr5TUcqd4ZY/W4XA47G7xf+39l3aVJ2f3fN3GjQpu3ixHWpryPv3pVo+xR6ivWCEjFuvU+a3R6R8p+og8Tk/3iu2koswinVVwlgwZeqn4pR65ZnlDh2LOZZfZHdHJ4HA4Gn9f+sgI9Zad4u4hQySPR4rFFC0t7fT5IgcOKFpaau4TP3OW/bgjLc3uFG8aio/NGauc9ByFYiG9X943fkC+vWy7YkZMQ/1DNSxz2CnPp4WicsUag3FfvRSvrW31XH1hfPzWE1tlyNDIrJEa4huirPTGEdlWZ2CmJ9M+3hqh/tqh3tOZm6h94gdu/YzitbUKvpOYaTKT8sxQ/GDNQQWjiZ0use1E90NxK8R2+Hxy+s3O7Y6E4tafi/r3m/+djhxpfwJFbdj8u9L0z1R3XDLGDMU3HN2gylBlQs7Z2xjhsGrfNMdnE4qn3ptH3lR9rF4jMkdoQsZoKRZT7fr1SbnW0IyhumXaLZKkH73zI4Vj4dMef6DavFElEV3iFnuEeht7xWPxxkkNF4++WNMGT5Nk/luK3otQHAAAAACAPuJg9UG9fextOeTQRyd0rgPa4/JoZv5MSf17hHrpz34mIxyWf+7cPvED1Mr6Shky5JBDg9IHNXaKV1X1eC3Th0zX0jFLZcjQT9/9aVKuYe0Sz7n2Y22GuBnz5smVk6NYWZk9rrqj1hw2O8p6enS6xeoWf3F/8oPjyPHjql6xQpI06NM3J/162ZeboXjg9TVJ6wxLJGunuHuI+efM4XTKU1goqWt7xWsbusSb7hOXJIfbrbDn1E5xp8Npd1v3lRHqpxudLpld4b4muYQv3HaQaHWKv3/yfXuXcm9j/b5YH2/T8enW+FsrDJUaQ/G3jryV8KC3q+xO8S7u1pbMUeBqcgNSPHz68KkjBnsHK8+bp7gRT+iNeIFwQPuqzPPNyO9GKF5n/v51tlPc+nMRahmKl5S0e02rUzwR49MlaXT2aJ0x6AxzhHovulEjkeo2bVK8tlauIUPknTY11eUMeNbo9AtHXajMhYskSbVr1yXtep+b8Tnl+/J1qOaQ/rjz9GsKrH3io7NGJ+z6EwdNlNT2xIt3S99VeahcOek5OqfwHE0fMl0Se8V7O0JxAAAAAAD6iKd2PyVJ+sjwj6gos6jTr7fG+fbXUDy4Y4eqnzP3rRfcd58cDkeKK2qfNTp9kHeQXE5XysanW740+0tyO9xaV7JObx99O6HnDu7Yodp16ySXS4Nvu63N4xxpacq6xOyAq36h4+FyWbDM/kHkwhGpCcUvGXOJXA6Xdp7cqeKq4qReq+LJJ6VoVL6zz5Zv2rSkXkuS0idNUtqE8TLCYdW88mrSr9cdRiym6Enz75a7oPHmC3uveAe6Oluqe/vUfeKWcMNQgrQWgw3OLmj4mlvaN77mWvu1zyw4s9XnY7W18tU3vu8LG212ig/LGKah/qGKGlF73HVvs/nEZkmNH2+z8emR5uPTJbNrsCijSKFYSG8deatHa22L9XVmbM7YLp/DCojt9xNw04vD4Wg3UOqK7Se3y5ChoowiDfF1fb90PNhkp3hGx0Pxmoj5uQnt7EIoHkns+HSpsVt8xf4VCTtnbxJY/bokKXPRIjmcRFmpFIlH9Pph8/djyaglyly4QJIUWLeuQzu/uyLDk6GvnP0VSeZu8RN1J9o8Npmd4m19DVt5wBydfsHIC+RxeuxO8R1lhOK9GV9JAAAAAADoAyLxiJ7d86wk6fqJ13fpHOcMPUeSGYon6wdYqWIYhkp/9GNJUvYVV8g3PfkhYSJYofhg32BJSnkoPip7lK6bdJ0k6SebfpLQPycnf/s7SeZ49LQRI057rNWVXPPyyzIiHdvHvK7E7FaaOnhqt8KS7sjz5um8ovMkScv3L0/adeL19ar869/Ma96c/C5xqW+NUI9VVpqdrw6H3Hl59uOehr3ikSOd6xRvvk/83FOet3eKt/ij2vRGpFi8c6sAelokFrE7p61/K1oy6urkbxqK17cdJDocDvvjf/tYYm+wSYRIPGKP4Z6d36JTvMn49KahuMPh0OKRiyVJqw6t6sFq25aI8enx2kDz9xM0CSIZe8Xt0end6BKXzKkHkuT0d7BT3NO4U9wwDLtT3HfmmZI6FopbExMy3InpFJekZWOWSZI2HNugsmBZws7bWwRebwjF+8Dkn/7unWPvqCZcozxvnmblz5L/3HPlSEtT9OhRhffuTdp1rxh3hWYOmam6aJ1+9u7P2jzO2ik+OjtxneJNx6e3/H44bsT1yoFXJJmj0yXz+09JOlJ7ROWh8oTVgcQiFAcAAAAAoA94/dDrOvn/2TvrMKnKho3/zsTOzHYn3S2dEqIYWBgYGNiJCNLdIdIq2JgoKgpioaJ0SHctvd01sVPn++PsnN1la2pZfL+9r8vrfdk588yzszPnPOe5y5RJmDaMPnX7uDVG2/C2qBQq0oxpJOQneHmGNQv9tm0Ydu9GUKuJGDGipqfjNDJM0iZ2uFYicWuaFAd48YYX0al0HMs8xh+X/vDKmIUXLpC/UerZDnv+uSqP9+3SBWV4OLbcXKf7KrclSH3iNRWd7sCdDe8EpAj16hKf5P38M7bsbFSxMQTccnO1vEZ5CCoixfU7d2LNun43fB194sqQEAR1cbe8OrbIKZ7smlPccvlycZ/4DTeUeVzuFLeWTqdoGdYSf7U/+eZ8TmWfcuk1rzUOpx/GaDUSqg2VHb5Xw6bXl45Pr6RTHKB7jCQQuV5c1SVxOus0JpuJAJ8AGgU3AqomxQFuqidFqG9J2FLjQofcwlyyC7MBz4gge0FpUtxb9QjVQopneN4nDsXueEHnK5PiWCyIFUTHl+wUt6alYcvKAqUS/5ukz4M5oXJSXBRFDNYiUtxL8ekgCdnahrfFLtr59fz1LVZyFeZLlzBfvAhqNX69etb0dP7fwxGdflPdm1AqlCh0Ojk5paAaI9QVgoJxXccBsP7c+gr7uqvDKd4wsCEqQUWBpYBkfWkx3ZH0I6Qb0/FX+8vXOn8ff1mgVOsWv35RS4rXoha1qEUtalGLWtSiFrWoRS1q8R/A92e/B2BQk0GoFeoqji4fWpVW3kj+r8T5OgPRZpNd4iFPPIFPnbganpHzcDi7HM5mZXAQUDOd4g6E68J5qvVTACw/sByL3TmndmXI/PhjEEX8b7oJbbNmVR4vKJUE3nYb4FyEusVuYWfSTgD61HFPNOIt9K/XH41Sw8W8i5zIOuH18UVRJOtzqVszdMgQBJXK669REXwaNEDbujXYbOT/4R3BRHXAml7UJ35Vb73KEZ/uYqe4o09ce0M7FDpdmcfNKkn8cHV8ukqhkl3X3q4j8DZ2JUvik+4x3VEI5W8ZiwYDusJioUdlneIAPWJ7AFLkdW5hzZ3TykPJ/nTH7xuokUjxfHN+MSmuLk2Kd4rqRIA6gCxTFkcyjlzDGZfF6azTAMT6xXoUyX01KV4dTnFvCIREUZRJ8XYR7Tway24s0Sle4jvt+PnVcJDiBZYC9MclUk7TqCGapk0AMF++VOnrGa1G7KId8C4pDtK6EODH+B//p1KAHC5x306dUPr71/Bs/n/DLtrldIz+9frLP3dEqOu3bavW128X0Y57Gt8DwLx/58nfJQcKzAWyM7tegPdIcbVSTcNgqZrianGPIw2oX91++Ch95J+3Di+KUK/tFb9uUUuK16IWtahFLWpRi1rUoha1qEUtanGd41LeJXYk7kBA4IGmD3g01v9ir3juunUUnj2LIiiI8BdfqOnpuARHP2K4b2mnuD0/H9FqralpMbT1UEK1oVzOv8zaM2s9GsuSkkLu+p8ACHvheaefJ0eo//UX9sLCSo89lHaIAksBIZoQudOxpuCn9qNvHSnq9bfz3o9QN/y7l8LTpxF0OoIffNDr41cFOULdhb73aw2HU1wVXjpG3xGfbnWxU9zRJ+5XTp+41W7FVrTD6mOxl3m8a4z0nD0pe1x6zWsNh5vb4XgrD2Wc4ubKneLRftE0DmqMXbRfdxHq+1L2ARIp7oDsFC+s2CmuVqjltBZHn2xNwUHKexolbivjFC+o4EjX0Di4MQpBQU5hDunGiruAnUWKPoUMYwZKQUmL0BYejVWyU1xQq+VEiYpEHiU/B9mnpPdd07IlPg0lwsx84SKivez33wFHn7iAgE5VVljjCe5oeAdapZb4nPgaF2p4EyX7xGtRsziecZw0Qxq+Kl+6xXSTf+7XW0rmMezdW6lAyhsY0XEEvipfjqQf4Zfzv5R67FyuFN8ergsvc872FOUlXljtVjZelNKP7mh4R6nj24S1AWpJ8esZtaR4LWpRi1rUoha1qEUtalGLWtSiFtc5vjn1DQA3xt1I3cC6Ho3VMbIj8L9DitsNBtKXLQcg/KWXUAYF1fCMXIOjU1yOTw8MlB+rSbe4n9qPl254CYCVh1fKXajuIGvVp2Cx4NulC74dOlR5vAO69u1RxcRg1+sp2Lq10mMd0em94nqhVCjdnqu3MLCRRBz/dvG3Mo4mT5H1xecABN17jyyiuJYIvEPqsDXs24clNfWav74zsGaU7xRXxxR1iicmOe2oLN0nXpYUN9uKWWJ1Ydkxu0ZLzzmQegCLzfPUhepAbmEuxzIl96vD3V0eynaKi1USIY7xHEkO1wPsop19qRIp7vj7gHPx6VDcH/vnpT+9/v12BUfSJQK0XbiHrukC/VX/9o5TXKPUyFHCDle7J3AQvs1CmnlMLJfsFJf+t/JecR+lDxqlBoDMeIns0rZqhU/duqBWIxqNWCtJoHCQ4n5qPwRBqPA4dxDgE8CtDW4F4IezP3h17JqCXa/HsFcSI/n3q+0Tr2n8feVvAHrX6S1/DwB8GjZEHRuLaLHIf6/qQoRvBM+3k4SVS/cvLbUujc+OB6BpcPnVH56gZK+4A/+m/EumKZNgTXCZa6bsFK+NT79uUUuK16IWtahFLWpRi1rUoha1qEUtanEdw2AxsC5+HQBDWg7xeLz2ke1RCAqu5F8hVX99ElquIOuzz7CmpaGOiyPkMc/fn2sNh3suwlci7wSVCkURMV6TveIADzZ9kLoBdckyZfHZic/cGsOanU32d98BEPaCay5+QaEg8A7JgZO7bn2lx25LlEjxmo5Od6B3XG8C1AGkGdK8KkAxX7lCwSZpczr0iSe8Nq4rUMfGouvUCUSRvN+874T3BmSneMRVTvGYaEAivpyNiLZcvow1NbXCPnGTzVQ8vqVsx3TTkKaEaEIwWo0y8Xy9YW/KXuyinYZBDYn2i67wOJtej64kKV6FUxyKSfFdSbuum2jnM9lnyDPn4avypWVYS/nnjvj0qkjxXrG98FX5kqJPkeO8rzVEUSwmxT2NEr/aKZ7nHVIcvNsrfjTdO33iULJTXCLFBb/KSXEo/ixkX5J+F23LVggqFT71pbjmwvMXKnyu3ip9TzyJua8M9ze9H5AinR0E/H8Z+l27EC0W1HXrym78WtQcHH3iN9e7udTPBUGQ3eLV2SvuwBOtnqCOfx3SjGl8dPQj+efxORIp3iSkiddf00G0lzyHOaLTb61/a5lKqxahLVAICtKN6aQZ0rw+n1p4jlpSvBa1qEUt3IBotZI4ajRXXh1WJmaqFrVwF7mFufx07qdq71oTrVbMCYnV+hq1+P8Fs83Mj2d/5GLuxZqeSi1q4TKOph/lUNqhmp5GLWpRKdbFr6PAUkD9wPr0jO3p8XgBPgE0D2kOwIG0Ax6PV5OwZmaS+aG0KRYxciQKH58qnnH94epOcSiOUK9pUlytVDO843AAPj32qexqdwXZX36FaDCgadUSvxt7ufz84PvvA0GgYNMmTCfK7+dOKkgiPicehaDwynfEG/BR+nBL/VsA+PWC92LGs1Z9CqKIX69eaBo39tq4riJwoCRWyPv1OiXFMxykeGmnuEKnQxkSAoDFyQj1qvrEC60SS6yyigjmsk5whaCgS3QXoDii/HrDriSpT7xHTMUucZCc4vXTJGJbKQo0Tq7aKd45qjMqhYrEgkSu5F/xzoQ9xN4UydHYIapDKUKjVHy6RSKG/X3KdhlrVVr61pXcq39c/KO6p1sukvXJZJoyUSlUnkeJ66unUxy8TIoXCRA8jYuHkp3iEkktO8X1VZPiufnSdVvbUnrfNQ0bAWA+f67C5zpcrd7uE3egY2RHGgQ2wGg1yrHO/2U4+sT9+/b1urO+Fq7hfO55LuReQKVQcWPcjWUev1a94iClT4zpMgaAz45/Jl9TzuZILu7qdIpfyrtEoa2QQlshmy5JIoGro9MBdCodjYOl9dmxjOtTCPf/HbWkeC1qUYsagzU7m4z3P8B8+bJXx/3o6Ec8tOEhr0QzVYSsL78k75dfKNi0icz336+216nF/y+M2zaOSdsnMWbLmGpV0CdNnMi5W24h9+dfqj64BmHLzydp0iSyvvyqpqdSiyrwzqF3mLpzKs/98ZxH0aq1qEVlyFz1KcnTpmMr8J7z4VTWKZ747Qme/O3JaiXGRYuFrM8/x3i8NkKtFq7Darfy+QkpKvmxlo+hELxzG/+/0iue8e672A0GtG3ayCTdfw0OojlMFyb/zBEBX5Px6Q7cWv9WWoe1xmA18P4R1+597Ho9WV9+CUD4Cy+4tbGtadKEwDvvBJBj8q+GIzq9fUR7gjTXT3y+I0L9j4t/eCU225KWRs733wMQ9rzz3ezVgcDbbgOFAtORIx7d01/MvYjVbvXizCRU1CkOxb3ilqSKo45LwhEJW16fOBQ7xX2sYDcVlnuMg0jYeHHjdeOWLoldyUWkeCXR6SA5xXsfF/nKdxhr8x+ncUrVTnFfta/c2329RKg7SPGS0elQTIqb7WZZsBSoDqQ83Fb/NkCKUK+Jv6nDJd4ipAValdajscp0inspPh28R4pb7VZOZp0EPI+Lh5Kd4tJ7p/CVyOpKneJqiRQ3aEBdp45cd+LTWCLFK3WKO+LTVdVDiguCILvF155dWy2vca0giiIFW6TKFP++tdHpNY2/L0vpNN1iupWbnOHbvTuoVJgvXfL6Hn95uKnuTXSP6Y7ZbmbxvsVAcXx6k2DvO8UjfSMJ0gRhE22czznP9oTt5FvyifKNomNUx3Kf4+gVr6kkkVpUjlpSvBb/k8g0ZnIp71JNT8MpXMq7hNFq9OqYtvx8bF5UdVYXksdPIH3JEi4/9zx2s7nqJziB01mnWXZgGSezTjJ5x2Rs9rLRZZ7CkppKxvK35X9nff4Flkp6g2pR88gtzL0uNx5KYnvidnYk7gCkDYm/Lv9VLa9TsG07eT9tACBl6tQqNzBqEunL3yZ37Q+kzp6Nfs+/NT2dSpH5ySrib76FgmugjL3ekJCfwBfHvwAg1ZDKh0c/rOEZuY4Cc0G1XC/+CyjYtp34Abde9+ITw969pL35Jjlr1pDxdvmEiKsQRZGFexdiE22IiMz7d161fQ4yVq4kde48rrz0klfPuxa75f/tZ7e6cCrrVLUntriKPy7+QWJBIiGaEAY1GeS1cTtHdQb+26R44fkLZK/5FoDIMWMQFP+9LQ6T1SS7ESN0xY5W2SmenVMDsyoNhaBgZKeRAHx3+juu5Dnv9Mz+9jvsubn4NGhAwIABbs8h4rVhoFRSsGULhoMHyzzuiE7vXae3269RHegS1YVwXTh55jyvkIFZn6xCNJvRdeiAb7fyCdprBVV4OH7duwPuu8XXxa/j7nV38+ER768fbenld4oDqGNjALAkV+0Ul/rEJQLVt0uXco9xdIr7WEEsLJ8Uv7n+zfgofDiXe45TWaeq/gWuIRLyE7iSfwWVoJId7RVB1BsQgAYB9Qn2lYQ8lTlrHXAkOFwPpLhdtMvXvi5RpX9fP7UfSkEpHwflx6cD9IqTItST9ck1QnwcTj8MeMk1fXWneDXEp1/MvSh/V9xBfE48RqsRf7U/DYIaeDwv8ar4dNkpbqzaKa7XgrZlcey+plGRU/xcxU7xAoskPPDzqR5SHODuxnejElQcST9Sqv/4v4bCkyexpqUh6HT4dq38nFSL6oeDFO9ft3+5jyv9/fHtIAmfCrZXf4S6IAiM6zIOpaDkr8t/8fvF38k0SQJTh0Pb269XMkLdkf5zR8M7KhQr3xAhVa0cTCu7Zq1FzeO/d8dYi/8piFYrWZ9/IUeieAMF5gIe2vAQ96y7h81XNntt3OrAhnMbuOvHuxj621CvqMZBImzPDbiVcwNupfBCxQpFV7Hi0AoGbxjMiczy4+pcRcG27fLf3XL5Mjlff+3xmKIo8ta+t+R/n8o6xXdnvvN43KuROn8+doMBXfv26Dp3QiwsJL0ESe4Jvj39LU/+9qTX3meQ3Gl5f/6JNTvba2MmFyRz87c389CGh677rqJfzv9C7296s+TAkpqeSoWw2q0s2reo1M/e/PdNrztu7SYTKbNmFf/bYCDz44+9MvbelL088esT8mLZUxTGx5O9erX879S5cxFt3iF+fjz7IxO2TZA74jxF4blzpC1YgCUxkcTRY7xGeJ3KOsVDGx7ik2OfeGU8B0S73asikWUHlmEVi909nx3/rNqEaeaERDI/WeXVKNltCdu48ZsbefHPF71KLoqi6HUxzuzds7np25tkZ4unsBuNJE+ahOXKFVJnz6YwPt4r4+YW5jLinxEs3rfYK++BaLORMnee/O+s1V9jvuJ59ObWhK3sSdkj//tE5gl+iP/B43GvhunMGTJWrASkDfrMT1Z5ZdzEgkRu/vZm7v/p/uuOxL0axzOPM3jDYH6/8HtNT6VSbDi3gcEbBvP4r497tGl7NXJ//oX8v9wTu4miyKrj0mdmSMsh6FRlY3vdRYcoafMqPieebJP31onXEulLFoPNhn+/fvjVMEHoLhxORI1Sg7+6OKL3eolPd6BbTDd6xfXCKlpZftA5cZLdbCZrlfT5DXvuWQSl0u3X96lfX4pRB9KXLiv1mMlqYk+ydD7vHXd9keJKhZLbG9wOwC8XPEtpsmZlkb1mDQDhL790XcTJBt4pOeHzfnUvHn7KjikArDi8wmtzcsDhFFeW4xRXxRSR4k7Ep1uuXMGakgJqNbr27cs9RnaKW0A0l0+KB/oE0q9uPwA2nN9Q5eteSzhc4u0i2lUZ7exw0Sp8fYtJREPV9z8OB/relL1Y7N7Z/3IXFfWJg0R+XE2CV0RialVa+tapuQh1BxHvaZ84FHeKO0Qk3nSKR/tFE6AOwCpauZDr/j6lwxnfOry1V1JzKoxPrySVyvHZMGhA26r4s+PTSCLiKtuHlePTq8kpDlINiyPW/4ez3r+vuVZw7Bf79eiBQqOp4dn8/0aqPpWjGUcREOhfr3xSHJB7xfXXoFccpO7wh5s/DMCYLVKcepx/HL5q32p5PYe452DaQbYkSJ/PgQ0HVni84z7rWMYxr3E+tfAeaknxWtQosj79lNS5c7ny0ssYj3knTvKjox+RZkzDLtqZt2ceJqvJK+NakpPJ+vwLbHl5Xhkvz5zHwn0LATiZdZLVp1ZX8QznkLZwEbacHGw5OaTOnuOVzejD6YdZeXglp7JOMWn7JI+jzUSLhdT58wFQ+EmLwfQVKz2OBtySsIU9yXtQK9Q81fopAJYfWC5vNHkDBTt2kP/b76BQED1tKlFjxwKQu24dptOexbUn5CcwZ88cDqYdZPSW0V67aKa+uYDE14Zz+cknES3eGfOtfW+RZkzjZNZJ3j/svfh4URQR7XavjZdjymH8tvGIiKw6tsprkfr2wkIyP/pI7pbzFD+c/YH4nHiCNEFsGryJOP84Ug2pfHDkA6+M70DmBx9guXwZVVQUMXPnSj/7+BOnu/QqQqGtkInbJ3Io/RCv//O6x985URRJnTcfbDZ0nTuhCAqi8PRpcr7zXOQSnx3P1J1T+fn8z7x90HMxiyiKpM6ZI//bnptLxoeeO11sdhtTd0zlZNZJluxfIm8AeAq72cylR4dw7pYBXhHKHEo7xO8Xf0dA4Pu7v6dXbC8sdgvz/53vdUJYNJu58tKLpC1YQMKIkV4Z32KT5moTbexJ2eO1mDlbbi7nbr+dc7fdjiU11Stj7k7ezZrTa8gwZjB5+2QKbeVvuLqCzE8+wZqWJv87ZY531g1vH3ybTZc3ser4Ko8JAICctWspPHkSRUAAmlYtwWIhfclSj8a02C3yOuyZNs8wvut4QFo3eJNgFm02kqdMKfWzzE8+wZKaVsEznMeS/UvILszmfO55VhzyHplg2LuXlNlzvLbmtdgtDN80nFNZpxizdYxbfcTXAvnmfPkzcTHvIl+e/NIr4xZs20bS6NEkDHsN4+HDLj9/V9IuTmWdQqfS8WiLR70yJwdCtaE0DpI2cf+LveKG/fvJ//MvUCiIHD2qpqfjNkr2iZckOa83UhxgZMeRCAj8fvF3jmdUff+eu3491rQ0VFFRBN1zj8evH/7yywhqNYY9e9Dv2iX/fF/qPkw2E1G+UfKm5fUER9/k5iubPRK9Zn3yCaLRiLZ1a3nzuaYRcMstoFZTeOYMhWddcyUm5CeU+ndygffS1+x6vUzeqiIiyzzuiE+3OpH4Zii659O1K79PHIo7xdU26V6xItzV6C4AfrvwW7VExrsLR89599juVR7rEAAr/Pzk/RxnnOItQ1sSrAmmwFJQ4x2rDoFpx6iOqBSqMo87ItRB6oYt2Tl+NW5rIEWo/3bht2ua3mOxWTiZ6cUocQcpXpSiYM8vqOxwlyAIAk1Dil2W7kIWAXjh9wXJNACg8JW+18UJLRXfJ5ckxTUlneING0jPzcys8Lotx6dXU6e4A44I9Z/P/+xVkee1RMHm4j7xWtQs/rnyDyA5n8N1ZUVmDsi94nv2eC0Ntiq80v6VUpU51RGd7kCrsFaAVE1QaCukQWADWoS2qPD4hoENCdYEU2grlGsfanH9oJYUr0WNwXzpEulvvyP9QxQlF6CHG7GJBYl8ceIL+d9J+iSvOOxEq5UrL79C6ty5JLw23CsbxisPrSTLlFX878MrSTekezSm4cBB8jYUK471O3aQ/+efHo1ps9uYu2eu/O/4nHjWnF7j0ZjZ36zBfO4cypAQGv/1J5qmTSQi6X33CUCLzSI7bZ9o9QQjOo6gZWhL8i35LNnvHYew3Wwmdabksg15/DG0LVuia9eOgDtuB1EkbdGiKkaoHEsPLJXjua7kX/GKUMJ47DjZRR1+hWfjyfrK83jc3cm7+fNS8efqixNfcD73vMfjijYbl59+hjPdumM8dMjj8UB6T0ti7h7PzzMAGW+/TdrCRVx59jmP3YoF5gLePfQuAC/f8DKRvpGM6zIOgM9OfOaRkrokCs9fIOPDjwCImjiRoPsG4du5M2JhIWmLPfuOfHXyK1L0KfK/Pf3OFWzejH7HDgS1mti5c4kYNgyQXEGeiGdEUWTW7mKn/Lenv/W41yz/jz/R79yF4ONDZJFIJuuTVZgTEj0ad+3ZtaUWznP2zPHKJkvmRx9hPHwYS2IiGStXejRWyXSOQU0G0Ty0OeO6jkOlULE9cbusnvUWMj/5BHO8FEdn2L3b4+sbwJrTa7icX9x5tfzgcnJMOR6Pm778bSyXLmO5fFkSeHgIi93CvD3FTukkfRKfHvvUszFTUsj8SEqKiBg5EsHHB8Ou3eRv9MzlcjLzZKmUlsX7FnuUKGLLy5MdgRHDXiV2zhwQBPJ+/RXjUfdjKr87/R0X8y4Sqg3lubbP8XDzh2kS3IScwhyvCGYcyP7yS0yHj6Dw96fJls3o2rdHNBpJX76s6idXgkNph9h4caP87zWn13h8PgOwZmdz6elnyP7yS698dgG+PPElacZiEcCyA5797g4Y9u/n/H33ey1xasWhFaXW5u8ffp80g2fiBdFiKfU+lkxAcRaOe5kHmj5QLT3J/9VecVEUSVsgXYOCH3wQTZPq2wSrbpQkxUtCGXz9dIo70Dy0OXc2krq9l+xfUum6WrTZyCq6zoQ+/RSCj4/Hr6+OjSX4YckVlLZ0qfz6WxOk3tHedXpfF+7pq9E2vC11/OtgtBrdTrOzpKXJVSfhr7563fyeyqAg/IsI+lwX3eK/XyydHrI31TtJOADWDOl7Jeh0KPzKusbUMc53ijuE0JVF+JZyilfQKQ5Sr3iwJpgMY4acblDTsNltcnJOj5jK+8ShAqe4E0lZCkFB9xiJdK/pCHUHKV5RVHxJUryi6HQH+tTpQ7AmmDRj2jX9vU5nn8ZsNxOsCaZuQF2Px7PpJRJcHS2R4rZ874gjHfBGr/jRdGnt3zbc87h4AFHuFJdIcVWYVAfgOH+UBz9B6h/XawW0LVvJP1f4+ckJFBX1ijvuiarLyepAr9heRPpGklOYw99XvJPkdy1hzcrCeEQyBfj37VPDs6nFpsubACp1iQNoWrRAGRGOaDBgPHBtxLZBmiCGtR8m/9uRklAdcMShOzCw0cBK12KCINA+oj1QG6F+PaKWFK9FjUAURZKnT0csLMSnUSMErRbjgQNuR245sGz/Msx2M92iu/FWX2mT5JNjn5BY4Bk5kf3NGgpPSZ1Phj17yPv5Z4/GO5t9lq9PSXHh793yHm3D26K36MsQeK5AtNtJLXJ+Bj1wP+GvvAxA6rz58k2LO/gh/gdOZJ4gQB3AKze8AsC7h94ttWnoCqzZ2aS/I4khIl4fjiokhMgxUsxJ9hdfYE5IqOzpFWLN6TXy5vbzbZ9HqVAypfsUBAR+OveTVzb6sj7+GPOlS6giIogYPlz+eeSIEaBSod+6Df3u3W6N7djcFhB4vOXjgLQ564lQQrTbSZk5s9TPMt55t9IFflUoScwMaTGEvnX6YhWtzN/juSs0+5tvMOzejT0/n+QpUzx2tR9KOyS7Puf3no9WqeVA2gF+u+Be350DhefPk/nZ50DRZvebb3o03sfHPibLlEWDwAY81PwhAPrV7UfvuN5Y7Vbm7Znn8XsriqL0WbBY8OvTm4BbByAIApETxkvk0s8/uy1EyDRmyo52h4Ptp3M/cSjNvfFEs5m0+dJ7GvrUUHzq1SPkkYfxadIYW04O6e++69a4AOvPredA2gF0Kh1twtpgE20eCSXsRiOpb0pkR9hzzxL69FP4dusm/Q5vvVXFsytGjilHjiV9ru1z+Kv9OZF5gh/jf3R7TCj67K58T/539uqvParZ2HhpI0fSj6BT6RjWQboRaRjUkCdbPQnA/H/nY7QaPZqzA+aLF+X4aXW9egCkvblAVve7g9zCXFYelsac1G0STYKbkFuYyzuH3vForqbTp8kuUQmS//vvFHgYIbb65GrO554nVBvK5G6TASkZxxNHVdqixYhGI7pOnQh74XnCnnsOgNQ333R73SCKInP3zMUu2ulftz/1AuqRbkz3KFEkY8VKbFlZ+DRqRMiQIWhbtpTdhmkL3nLr+1vyb/9q+1cJ8AlApVAxsdtEQBLMOOOArArmhATSigj9yNGjUUdFETmuKGHmhx/dTpixi3YW7F0ASE6QAfUHYBNtXrlepL25AKyScy33xx/lDSl3kVyQLL/XDzR9AIAf43+UNzXdhd1o5MrzL1B48iRXXnwJW75nEZ/x2fGl1ubtItphsBpYvH+xR+Nmr16N+XyxcDDv199cWocdzzjOnpQ9qASVfG71Nv6rpHj+xj8wHj6M4OtL+LBXa3o6HiHVICWKlCHFg4KB68spDjCswzDUCjV7UvZUSgDl//EH5kuXUAYFETJ4sNdeP/zFFxC0WkyHj1Dwz2ZEUWRbQlGf+HUWne6AIAgMbCRFbDp6KF1F5vsfIJpMaG9oh/9N/bw3OS8gcGBxhLqz1yGLzcL3Z74v9TNP921KwnGuVUVElLtpLXeKV5GYVbJP3K9rxRUNpTrFK4hPB1Ar1bKz+Ofznu0reQunsk6RW5iLv9qfNuFtqjy+XKe4k2tHR6/4rqRdVRxZfSjZJ941uvy/aUkiPEBdOSmuVqplsdC6+HXemaQTkPvEw9t6RSTjiAxXR0dL//aiUxygWahnpHiBuUA2Y3ijQx3AbnDEpxeR4hHSddiaWfFazTdXuv80BmlQRUaUekzTsCEA5vPl94pfK6e4UqHkviZS3cgPZ/57Eer6bdtAFNG0aCF/HmtRM8gx5bAvZR9QNSkuCAL+vSS3eMG2bdU+NwcebPYg/er0o3tMdwY1HlRtr1M/sD4CxedaR/JLZWgf2R6oJcWvR9SS4rWoEeT+uA7Drt0IWi1131tJ+IsvAFL0t6PTxVUcTj/Mbxd/Q0BgdJfR3Fb/NrpGd6XQVshbe90nJ6yZmaQvkzY0Haq/1Plvuh0pKYoi8/6dh020cXO9m+kV14sJXScAnhFJuT+uw3TsGAo/PyJHjiTs+edRx8VhTU4m4z33NqNzTDmym+fVDq/yQrsXaBHagnxzPssPONcjdzUy3n4He24umubNCS7aHPHr3Ru/nj0QLRbS3XCsltzcHtZhGP4+Uhdf24i2PNBM2oCdvXu2R71V5oQE+X2MHDcOpX9x359P/fqEPPIIAGlvLXQ5/ruk2/K+pvcxpssY2oV7vhmb8933mI4cQeHnR5Mtm9G2aYO9oIC0Je67eL8++TXnc88TognhlfavMK7LONQKNbuSd3nUJW1NTy/VDVh4Np6sz7+o5BlVjGe3Mnv3bEBysN7Z6E6eaysRPov2LXI7tlAURVJnzwGLBXVcHCiVFPy1Cf1O9xThSQVJfH5cItjf6PSGHMsmCAITuk7AR+HDruRdpZz57iDv558x7N6NoNEQPWWKfNOsa92aoPukm6XUee4JG1YcWoHeoqdVWCvGdx0vR3XN3TPXLWdz1pdfSRuo4eGEvfgSAIJaTdQE6TyZ/dVqt3qPc0w5LN4nfZ9euuElFvVbhFapZX/qfreFEpkffog1KRlVbAxhzz+PIAhETZwACgX5Gze6Ha//zqF3yC3MpUlwE15t/yqvtJcEScsOLHM71lm020meOhXRYsGvbx/8+vYBq9XthAuzzczS/UsBeLr100T6FsdSvtjuRaL9okksSPRKBYAoiiTPmIFoNuPXqxeN1v2IKjoaS2Ki3FPqDt4/8j555jyaBDfhwWYPyoTod2e+41TWKbfnmjprNtjtBNx2G6FDJRIrZfasSqM0K0O6IV2+xr3e8XUeav4QnaI6YbKZ5KhnV2E8dEhKlhEEoiZMQBAEwp5/DnVsrLRu+MC9v9vP53/mUPohdCodE7pNYFxXKfXC3USRwvMXyCpKO4maMB5BLZ0jI14fLjnb9+51yyX84ZEPySnMoXFQY/mcBZJb6M5GdyIiMmPXDI/SGURRJGXqNESjEd/OnQl+SFrz+HboQMDtRQkzC9xbn/5+4XeOZhyVBCnthzG682i0Si37UveVco+7Cv2uXeSuWweCgE/Rpl7KzFmINvffB4c4pmNkR6b2mMo9jSVBg0M84S4yVqwotQHvSZy+KIpyjUL/uv3pFdeLiV0nIiDwy/lfOJDqntPBmpVF+juSkCt61kx0N9yAaLHIfcDOwOESv6PhHcT4x7g1j6rQMaojIJEinqQ6XEuIZjNpi6VretjTT6OOLBuN/F9CfI60rmkU1KjUz1WhIQDYstwTI1cX4vzjeKSFdO+zZP+Scr/LoiiS8YFUJxPyxBMyceYNqCIiCH1CEhGnL1/OhZzzJBQkoFaoZRfq9QhH7+SOxB0up9JYEhPJ/vZbACJHjrxuXOIOBNzUD0GrxXLpMiYna/F+jP+RxIJEwrRhcvWZp8l5JeHoE1eV0ycOJeLT09MRK4l5tSQkVNknDshCUB+riN1YuWjz7sZ3A5L7zpM4fW/B0SfeJbpLuVHiJSHa7cU9zC46xaG4V/xoxlHyzN51IjuL01mnyTPn4af2qzD6NlDjvFMcpD0HkGKGvZE65Qwc9Vre6BOH4vh0tRyf7r1OcfDcKX488zgiIrF+sZVGOLsCx2dZKOoUVxY5xW0ZFVf9aNOlz60xLKDMudincVGveAVOcYO1qFO8mklxKP5M7kreVaaq4nqBKIrlriEc93e10ek1j+/Pfo9VtNIqrBX1A+tXebyfI0L9GvWKA6gUKt6++W0+vPVD1MqKqy48hSAIjOw0EpBSVZxJ6HDcZx1MO+j1esFaeIZaUrwW1xzWzEzSipyVEa8Nw6dePUKfflreiM382PW4c1EUZeL73ib30iK0hUwqKQUlmy5vYmeie6RV2qLF2PPz0bZqRePffsWnUSNsmZmkL13q1ngbL21kb8peNEoNY7pIDum2EW3lBcu8f+e5vAlrK0F0hr/yCqrwcBQ6HVGTpA3+zFWrKlyUVYa3D75NbmEuTUOa8nDzh1EqlDJp8MPZH1x2UZnOnJE3AqMmTEBQKgHpwhI5ZkxxHKqLfYsrD68kz5xH05Cm3N/k/lKPvd7hdYI1wcTnxLP6pHtx5A6SQywsxLd7dwLvHFjmmPBXXkbh54fp+HHyfnWNYNt4sYTbsv0wFIKCid2kzdifz//slnPHmpUlbxZGvP466qgo+fOQu/YHt5xfGcYMVhyWOktf7/g6QZog6gbW5ek2TwOwYO8Ct12hqQvekr5nbdoQM1uKt05/910sKSlVPLN8fH3qa05nnyZIE8Qbnd4A4Kk2T1HHvw5pxjS3ibr8P/9Ev3MnglpNvU8+JmTIEGn+8+YhWl3vhFt2QEq36BrdlX51+5V6rG5gXZ5p+wwgvbfublTbcnNJLXJeh7/8Ej51Sy/cIka8juDri/HwYfJ+cc25Ep8dz/dnJYfHmM5jUAgKXu/4OgE+AZzMOulyP7M1M5OMFdJnLHLkSJT+xTeL/r164d+/P9hsbhH4Sw8sJbswmybBTXii1RPE+seWEkq4+v6aL1+Wo6ejxo+X1eXaEoKf1HnzXSaTSkZPT+w2EZVCxSMtHvE41jln7VqM+/Yj6HTETJ1K1JgxxaION8j71SdXk1iQSKQukqGth5Z6zFftK4u9Pj32KWezXeuXvBp5P/0kCek0GqKnTUXh60vk6NEAZHzwoVvnict5l2VX6JjOY1ApVHSJ7sLtDW7HLtrddtzm/forhn37ELRaosaNJfy111BFRmK5dJnMovoCV7H0wFL0Fj1twtowqMkgeX2jEBT8cekPdie7llAi2u2kzJUSP4Luvw9dm9aA5JCInCD1amd9/AnmS5dcGrfAXCDXmLzQ7gWi/aLpU6cP/er0wyq6l3qR+uZ8sFrx79tXjmYFaSM79MknAEhftMil79mVvOJ6ktFdRpfZ/B3deTQBaukc5kmNSe769dL1wseH6FkzERTFt12Ro94AtRr9jh0upwiYrCY5WejZNs8S4RtBrH+sfL1YuG+hWxvsdpOJ5OnTAQgZMoT6X3yOwt8f07Fj5Hzv2rncgc1XNvP3lb9RCSomd5+MQlAwstNI/NR+HMs85rabynT6DJmrPgWQhSfZX3/ttqv9z0t/sidlT6m1eevw1rJgwp21OUD6suXY8/PRtGpJ8P33E/KE9JnN/uabSgkYBy7nXeavy38ByGut6kC0XzRx/nHYRbvb4txrjexv1mC5fFkS0D1Tfe/NtcLpLCk14uoubGWYw7FW8eZ8TeH5ts/jr/bndPbpcp3P+u3bKTx5EsHXl9DHH/P664c9+ywKf38KT53ir7+l62vnqM7VHknrCRoHN6Z5SHOsopU/L7smeE1/dwVYLPh2745f9+uP+Ff4+RHQ/yYAp9L/Cm2FvH9EEpw/3+55eWPZu6R4sVO8PChDQ6VIf1HEklZxVYYzfeKATPD6mcBeUDmZ2C68HfUC6mG0GuVY2pqEw7XtIKwrg2g0QtF6TuHr67JTPNovmoZBDbGLdv5Ndk9A7CnkPvHI8vvEwbX4dIAWoS1oGdoSi93CLxd+8c5Eq4Dcr+1lUlzliE8vKPAqidM0WOoUzzBmkGl0/brm+H295RIXRVE2OykDpb+xyonrriZReswUpCn7WCOHU7x8MXCBWXqPrwUpXiegjiwUu5YJBs4iqSCJh39+mAHfDyiVEiJarRRs3wHUkuI1DYvNwtcnpT0TR6JpVfDr2RMEgcIzZ9zez72e8XSbp1l7z1oW93POwNYqrBVqhZosUxZX8j2r3qyFd1FLitfimiN17jxsubloWrUkdKi0ka7QauUu1syPPqoywupqbLy0kcPph9GpdLzW4TX5501CmshxvvP+nYfF5ppT2HDwILk/SFEzUVMmo9BqiZ46FYDsr79xucvSYDGwcK/k6nq2zbPE+cfJj73e8XW3I3IzVq7ElpGBT/36smoewP+mm6RFhMVC6uzZLi1oT2SeKCZmuk6UbxY6RHbgrkZ3ISK65PIRRZG0+fPBZiNgwAD8uncr9bi2ZUuCBg0CJILU2bmezz3PmlMS0T62y1iUCmWpx4O1wTIp+u6hd91SSOb/8aekVFSriZ46pVxlvio0lLDnJYItfckS7E5sdoK0IeDY3H66zdNE+Eo37a3DW8su97l75mK1u0a4pi1cJDnyW7YkZIj0HfDt0IGge+8FIGXOHJcd7Uv2L0Fv0dM6rDX3Nb1P/vlzbZ8j2i+aJH0Sq4657trU794tOxajp00j6P770XXogGgwyGSuK0jVp/LOQSn+eETHEYRoJZeNRqlhbBfpPPPZic+4mHvRpXHtRiOp86Wo7NDnnsWnfn0ihr2KMjiYwrPxZH/9jUvjHUk/wq8XfpXSLTqPLvdz5ThPpBpS3e7pTlu6FFtmJj6NGhH6zDNlHldHRhJe9NlNW7TIpTjqhfsWYhft3FLvFjpHdwYgVBvKq+2lGFNX+5nTly7DXlCAtnVrgu4bVObxqHFjERxE0ubNTo9bMkp/SvcpsiO/pFDCsTHnLFLnzZecyz17EjBgQKnHIl4fjiIggMKTJ8n5wfm4MkeSiF20c0eDO+SOO7VCXcrFfDLzZGXDlIE1PZ20t6RrT8Tw4ajj4tA0aULw4AcBKS7ZlfNBtilbFpa81vG1cjeg+9frz011b8IqWpmyY4rL5zB57tnZxaKOV1/Fpyg6PfDOgeg6dUI0GuXfzRUs2b8Eq91Kr7he9IzrKf98VOdR6FQ6DqQdcHlDy67Xy87fsBeeRx0bi9Lfn6giojnzgw9cJpoPph3kp3M/ISAwqfskFIK0dG8e2pyHm0udqvP3zHcpCSVvwwY5RSRyxIhSjwXccgt+vXqV6UF2BisPryTTlEn9wPqlYp7HdhmLj8KH3cm7Xdr4LdiyBf2WraBSETl+XJnHw154AWVQEIVn4yV3s5NYcmAJFruFXrG9uDHuxjKPh+vCGdFpBADLDyznct7lMsdUBWtmJmlF71/4sGFylKIDPnXrEvqYRBKlLVjgEqn/5ckvSdYnE+UbxZOti9/np1s/LV8vPjrqugAjY+V7WC5dRhUVRcTIEajCw4kYLq2p0xcvxpqd7dJ4BotBrlt5ovUTNA2RNkLDdeG8fINU8eNO+oVot5MydSpYrQQMGEDUhAkE3nM3OKqZXBSoGa1GOa3nmTbPUCegjvzY8I7DCfAJ4FTWKZdFXqaTJ8kpcnVGT5yIoFQSeNutqCIjsaVnkLexakf/p8c/xS7a6VOnj/z+VRf+SxHqtvx8WUAXMWyYVx3INYFvT38rb/S3Dm9d6jFVWCggiV2vN4RoQ3i27bMAvHPwHTk62oHM96V1QshDD6EMDvb66yuDgwl9+ikANp+TCObeda7P6PSScESo/3j2R6fvdwvPX5Cvc5EjXq+uqXkMOUL9t9+qXFd+e/pb0gxpRPtFM7jZYDlxKM1YMTntKqpyiguCgDqm6gh1Bynu26Vzpa+XU5gDQIARbLmVO6AFQZBjV385f20I1IpgtBrlWFdX+sQRBKmv3UWneMnXqakIdUd3fUV94lCaFNflO7e3c28Taa9lffx6D2bnHEoSLM5E3jsD2SkeUxRXbbVKIggvwVftKwtgzua4Lpp2OOO91SduLyiQK4Mc1ylVeFGneCWkuPqSRPQZdGUpFZ9GDqd4+aS43npt4tMdcFQXrYtf51EClrdxLOMYQ34Zwsmsk6QZ0vjs+GfyY8ZDh7Dn5aEMDkZ3g3cEH7VwDxsvbSTNmEaELoLbG9zu1HNUISFo20nfUf32a+cWv5ZoFtJMTqitChqlhtZh0vq+NkL9+kItKV6La4qCLVvI++UXUCiImTkLQVWsygy47VZ8O3dGNJlIW+h8nGuhrbA4wrVN6QhXgJfbv0yoNpSLeRf56uRXTo8r2mxSBCoQdP/9+HboAIBf927y5lvK9BkubWR+dPQjUg2pxPnHlXF8hOvC5Yjc5QeWO71JaL54UY6ZjpwwXlI7F0EQBKImTUTw8UG/cyf5G/9waky7aJd6dhEZ2HCgTHY58EanN/BV+XIk4wg/nfvJqTEL/vkH/c5dCGo1kWPHlHtMxOvDpX75/fvJ/+svp8ZdtG8RVtEq94eUh3ub3EunqE4YrUam7ZzmUlynNTOTlCLnVNizz6Bp1KjCY0OHDpUcgYmJZH/xpVPjy25L30iGtirttny9g+TGPpN9RhYoOAPDgQOymCN66pRS37OIUW+g8PXFdPgIueud+9uBRCo6/tYTu02UiRkAnUrHmM7S3/Tjox+7pH4TzWZSZki95yGPPoKubRsEhYLoaVOlCOrff5dVos7irX1vYbAaaBfRrlQsLkhd3b3iemG1W3lz75suCUUyPvhAjsoOf0GqfFAGBRFRtDmV/vbbTpMGJdMt7ml8Dy3DWpZ7nFalZUbPGQCsOb3GZTW94cABcr6RRCPRU6eiKHF+KInQp59GFRNTlNbxsVNjb0/czo6kHagUKll44sDDzR+maUhTcgtznXY2m06cIOd7yXUeNWliKVelAz716xP6lPQ9SZ0/3ynxidVuZdZuKX1gUJNBcnwRSAvU8V0lwvKL487HO+dv3kzBP/+ASkXU5EllBA2q0FDCX5XO5+lLl2ErcK6P7efzP3Mw7SA6lY43Opd+T7tEd+GOBncUn59d+OymzpuHPS8PbatWpYRTEa+9Vpxw8bPznYYrD68k35JPi9AWchRyeZjcfTIBPgEczzzOp8c/dXr8kkh7ayG27Gw0TZsSVrT5DdL1LXrSRClh5JdfMOx3nsjZl7KPvy7/hUJQMLrT6FKPRftF83zb5wFYvG+xSwkCGe9/gDU1FXVcHGElBCgBt9+OX8+e0vlulvMCNZtd6rwHqTf66g2vV9u/SogmhHO55/jmlHOiHLteL6+xwl56sYx7Slo3TAK1moLNm8n/5x+nxo3PjpfXWBO6TsBHWXyucSdRRDSbZTFE6BNPlCGVAZSBgYS9LFUspC9b7lT9zoHUA/x56U8UgoJRnUdVeNyDzR6kW3Q3TDYTU3dOdWndIIoiKbNmSwLQli1LfW5LIvylF1EEBVF49qzT4pkMYwYfHpHiiF/v+Do6VbFjTavSyi7nT49/6hKZbzpzRj73R02eJFfEhAwZgqZZM2y5uaUqTpzBB0c+IEmfRIxfDC+1e6nUY0NaDqFRUCOyTFmsOLTCpXFzvv0W4+HDKPz8iJo8SZrzuHHSe3niJNlfOb/WB2ltnqJPIdYvlmfalBaOuSvyEkWR1DlzQRQJHDgQ387SOlpQqwl5VIqczvr8i0rPBRnGDHlT/enW1e+E7hwlzfG/QIpnfvAhtpwcfBo3JvjBB2p6Oh7hn8v/MGfPHABeueGVMjGMjhhXe26uU+kC1xqPtXyMSF0kiQWJrDldXAtgOHAQw759oFbLxHV1IHToUAojgjgRLgk6+9TpU22v5S3c0/geNEoNRzOOsi91n1PPSX97Odjt+N90U6Xx3TUNv969Ufj7Y01JwXig4toJg8Ugi7debPciPkofeQ/Hq07xjMqd4gCqKnrFRVFE70SfOCBfIwKMkninKjg6qHcl7/Lq7+0q9qfux2K3EOMX41Q0rtwn7uuLIAiyMEm0WJw+Tzl6xXcmuZfm6AksdovsFK+oTxxAm1QsRtIkOydMurPhnagVak5mnXS7islZHMs4Bki1GyUJfHchiiK2or+tKjISiu7Fbd7uFXdEqGe5FqEuimKxU9xLpLitaO9G0OnkFIiqrruixYLPecnVXKAquxfscIpbEhLKrc5ypDldK1K8f73+BGmCSDWk1sj3rTxsuryJp39/mkxTsfBgffx62UXviE73691bThetxbWHKIp8cULiGh5p8YhLseT+N0oiRVcT0f5X0SFS4pNqSfHrC7WkeC2uGex6PckzJHIndOhQOa7TAQeB64jQNuxz7iaxVITrVaQiSArPER1HANJGvrM3HDnffovpxAkUgYFSzGUJRI0diyIgANPx42R/49xG9OW8yzIpMKbLGLQqbZljHmnxCI2DGpNdmO30JmHqmwvAYsGvd+9yo2V86tUj7Hlpgz91/nynFLwbzm3gcPphfFW+5W4aR/hGyC6fJfuXkG+u/KbPbjTKjrPQp54qE9/sgDo6Wt44SVu4sErCa2fSTrYmbEUlqCrd3FYICmb1moVOpePflH9LbdpUBrFI+GDLzkbTvDnhr7xS6fEKnY6I1yWCNMOJ6O8sU5bsthzeYXgZt2WwNpjhHYYDUpR9lqnqmzHRaiVluvQ9Cx78oCzmcEAdGSmTdWmLFjlF1tnsNub9K7m9BjUZVG4814D6A+gW0w2z3SyTvc4g85NVmC9cQBkeTkQJx6K2RQtCiqIWU2fNctp5vzNxJxsvbkQhKJjSfUop8h6k88z4LuNRKVRsT9zOlgTnumjNly6RVU5UNkDw4MFomjfHnpdH+vLlTo3324Xf5N7dkukW5aFbTDceavYQAFN3TnU6FtduMpE8cRKIIkGDBpVJZygJhVZL5GjpO5T5/geYL16sdGyr3SqnXjzW4jHqBpb+TqsUKiZ2LXY2n8g8Uel4os0mXR8cBELHjhUeG/biSygjwqU46qKuysrw1cmvOJN9plSUfkn0rduXPnX6OB3vbDeZSC2Kng4d+mSFQpnQIUPwadAAW2YmGStWVjnPAnMBi/dLEUyO6Omr8UbnN9CpdBxKP8TP550jsfM3b5YqHRQKKcK5hEhGFRZGWJHAI23xEqdIxfjseL47LYl0RnceXeY7VhKRvpGy6GDFoRXEZ7vWBa/f868k8BEEomfOkPukHdC2akXwg5LbPXXOXKdEanbRLvdwP9j0QZqENClzzNDWQ6kbUJd0Y7rTCQLmS5fkfvOoCeNRaIuv8YIgEDVlspRysH270wK17898z6msUwT4BDC84/AyjwdpguSfrzi0ggxjRpVjZnz4Idb0dNR168ppPVdD06ihHE2eOndelZ8LR8JByT7mq/Fs22eJ8YshWZ8sdyRXhsxPPpGuDaGhhL/ycoXHhQwZgjouDmtaGplV9Mvb7DYW7F0ASCKDypy3CkHB9J7T0al07E/d77ToACDv51/I//13UCqJmTWrzOfWAWVwMOEOUn/5cqeux+8eeheD1UCbsDbyZnpJ9K/bn16xvbDYLfLvWhUk5/U0Kab+lpsJLJF8IahURE+ZDBSR0UePOTVmfHa87PiY0HVCmfWNWqFmQjepYuGb09/I0dFVwZKWRtqiomqYESNQR0UB0rnMsVZPX7bc6ai+K3lX+PTYp0DFa/OSIq93Dr3j1Lj5v/8u1yg4rq0OBD/8MIKPD6ajRzFVUhe0+uRqzHYz7SLayS7u6oSDFD+SfoTkguRqfz13YUlOJuvzzwGIHDWq1DXtv4QCcwFfnfyKsVvHYhft3N/0fl664aUyxymDgqBoQ9jVtIZrAZ1KJ4u6PzjygXxPmPmBdH8TPOhe+XtaHVD6+3Ph6ZuwKQVi8pTU08ZU22t5C+G6cLk2zZlUD9PJk+T/9juALMS9XqHQaOT0pMoi1FefWk2WKYu6AXVlZ62DFM80ZbqdLnQ1ZKd4RMXdw3KveHL55z1LQoL0WBV94oBsavA3ithzqzY41AusR7uIdthFO79dcK1+zZsoGZ3uTFe9wynuIMMdTvGSj1UFR3d5QkECV/KubZzssYxj6C16gjXBFQrTAXTZxb9LgME5QWuwNpib6ko1AtUdV304XVpDeIsgFs1m2TWt8A9AESDFiVdVBeAq3O0VT9GnkGHMQCkoK/27uQIHKa4MCZZ/pgwKgqK1e3kpLYXnL+CbLyV06cWyCXvK8HDpvbPbMV8smxDmEFz7qa4NKe6j9OHuRncDUgVmTUIURT4//jkj/xmJyWaiV1wvdj26i0ZBjTBYDaw/J4lBCzbX9olfDziQdoATmSfQKDUMbjbYpef6O3rFd+50q2byfw21pPj1iVpSvBbXDGnLlmFNSkYdF0fEa8PKPUbbsqXcxZoyt+oN7kxjZjGp2LEsqejAvU3upW14WwxWA2/urTqO2ZqdTVqRIyZi+HBURWpBB1Th4USMHAFA+pKl8g1XRRBFkTl75shxnf3r9i/3OLVCzfhuEoHwzelvOJ5ZeWd3wdatxW7FCeMrvIkJe/451HXqYE1JIW1Z5U6f3MJcmZh56YaXyjjvHXis5WM0CGxAlimLdw+9W+mY6cvfxnLlCqqYGMJefLHSY8Oefa6Y8PqwYsLLZDUxZ7fkrHikxSM0CGpQ6bh1A+oystNIQCLynbn5yvv5Z/L//BNUKmLnz6vQZVsSQfcNQtehA3aDocro2ZWHVlJgKaBlaEvubnx3ucc80PQBWoa2JN+c71R8dtYXX1J45gzK4GAi3ihLAILkuvNp0ABbRoZTZN3as2s5kXkCf7U/r3csfzPG0XGrElT8c+Uftlypmmw2JySQsVJ6/ahxY1EGllY4RwwfjioiQiKbPqmaRDFajbLbZkiLIbQIbVHucQ2CGsjRvvP/ne8UyZw6dx6ixVJuVLagVMp97TlrvsV0qnJVeG5hrnweerbNs0T5Vb1Z+EbnN4jxiyGxIFGO268K6cvfxnzxIqqICDm+uTIEDhwoxSabzSRPm14pObz2zFrO5Z4jWBPMCze8UO4xnaM7M7DhQESk819lcV3ZX36J6fARFP7+RI4bW+k8lf5+RE+QyJSM99+n8GzF0Wsp+hT5/PRGpzfkKP2rMa7LONQKNbuTd/Pnpco7HtOXLcdy+TKqyEjCX65YKCP4+MiRz1mffYbpZOWR5ysPryTDmFEmerokov2iebGddA5dtG9RlYkidr2elJlSEkPo0KHoWrcuc0zo0CdRxcZgTUkh67PPyjxeajzRzoxdM7CKVvrX7U+3mIqFFg7c3ehu+tbpi8VuYfKOyU5vdNqNRlKmTQMg+JGHywh8HIgY8bokUjtxwim37S/nf+F45nH81H7yRv7V8FH6FCcInPjCKTI/dd586RzRqxf+N99c5nFNw4bFArV587AVVC5QyzBmsPygJLJ5rcNrhGpDyz3uvib30TqsNQWWAhbvq7zXqjA+nsyPpXNp5NgxlV7Twl9+BVVUFJYrV0hfVrnYZ+PFjfyb8m+pPuaroVPp5PqKT45+UqmLufDcOTLelYSBUePHoQyouMdR4eMjk6GZ73+A+UrF1/Y1p9fIf3uH+7cy1AmoIwtplh5Y6lQKiiU5Wf7Ohb/8chkB6NUIHTIEdf162NIzqnRin8k+I29mjekyplxBiiAIjOs6DpVCxZaELfx9+e8q55yzZg3GQ4dQ+PkRPXlymcd9u3Qh8O6ihKTZs6qMxLWLdmbtniWl+NTtx031bir3uO4x3RlQf4CcfuGMGz9t/nzs+flo27SRq2EcCH7wweL115y5VY5lF+1M2zUNs91Mj5ge3Fyv7PcWJJHXhK7SNceZ+gq7wUDqW0U1Cs89JxMu8nihoQTeKQkaHElPV0Nv0fPNaUmI8UybZ5wiKTxF3cC6dI3uilW0smDvgusqYrMk0pctRywsxLdLF/xv6lfT03EZl/IuMf/f+dzy/S3M/3c+JpuJG+NuZHL3yeX+nQWFAlWodP63XYe94iDdZzcMakhOYQ6rjq3CdPqMVHGjUBD27LPV/voHm0jnwg6nLWQXJQ5d73iq9VMoBSU7k3ZWKRx1XBsCBw5E27z5tZieR5Aj1H/fWO5GeL45X67bevmGl+VKoxBNCEpBiV20OyUEdwbOOMXVMdI52pJUPiku94m3bVuK/C0PueYiUtzknFMckMkqZ8Wu1YFdyUWkuBPR6QC2POl3UxSlyghqNUKRYNzRz1wVfNW+3BBxQ6nXv1ZwiAC6xXSrVNzbpiCYkHyRDvF2br8Y7PT4DtHLL+d/cbm+0RU4osS93SeOIKDw1cmpQc7+TZ2Fu6S4wyXeLKRZqaQkT+AQm6mCi/cIBIVCFtKYL1wo8xzTyRP4FhnA8835ZfZMBEGQRfPmC2VT6BykeEV719UBR3ri5iubnRJRVwesditz9szhrX1vISLyULOHeKf/O/j7+MuVp1+c+AJDwiVpf0ehwP/GskLrWlw7OFzidze+u8J9tIqgbdsWRVAQ9vx8jEeOVMf0/lNoH9kekOpfXa0Oq0X1oZYUr8U1g6ZxExT+/kTPmFHpDYVjg7vwxMkqXdgL9y2sklQEyfEzqfsklIKSjRc38telyqO50xcvlvqYW7Qg5JGHyz0m5OGH0bZpg72goEryc138OnYm7cRHIW20V7a51T2mO7c3uB27aGfKjikVLqRtBXqSi2K9Qx9/vNJYb4VWK8VRA9lffIlh794Kj53/73yyTFk0CmrE4y0fr/A4tbLY5bP65GoOpR0q9zjj0aMy0RIzfRpK/8oVkUp/P6LGF/WvVuJYfe/we1zOv0ykLrJCYuNqPNz8YbpGd8VoNTJl55RKN2AtqamkFMXnR7z6CtqWzqlRBYWC6OnTQKkkf+NGCrZtK/e401mn5Uj0ija3AZQKJRO7TURAYF38OnYkVhwlbr54kfQi0UPEqDdQhZS/cBF8fIiaKP3tsj7/vFISN7kgWRZJDOswjHBdxUr7xsGNeaKV5C6cuWsmeeaKb6BEUSR19hxpY7NbNwLvuqvMMUp/fyLHSaRixnvvY05IrHA8kNz0l/MvE+kbWSXh8WK7F4n2iyaxIFEmnipC/t//yJ3yUZPL37T069qVgNtvB7tdcqxWQigv2b+ELFMWjYMal4lqrQh+aj+m95wOwNenvpYj3yqC4eBBsj79FIDoGTMkxXMVEASB6OnTELRaDHv2kPvDj+Uel2XKkt1yr7R/pdK4NrlqIf0IX5/6utxjzAkJsggpcswYpxxFAXfcgf9NN4HFQtLkyeUKqERRZPrO6RitRjpEdpA3KMpDvcB6crzz3D1zK4zINRwo8b7OnFHl+SygXz8Cbr0VbDaSJ0+pUCV7OP0wX56UKhfGdRlXKnr6ajzZ6kkaBDYg05RZpRM0bdFiSYwWG1uhGE2h1RI5sohU/ODDSkVe35/5nkPph/BV+crn/6ogCAJTe0x1OUY97a23JFFHZCSRFQh8QHKIylH1ixZX2ruaW5jLon1SdPhzbZ8jTBdW4bF96vShX91+WO1Vd6Ln//2PRACoVFL0f0UCtReeR123LtbUVNIXV1wTI4oic3bPIc+cR4vQFpUqs0teIzac38C2hPKvOWLRZxCLBf+bbiLgllsqHBOka3HMTCl1JOuzzzAcKF/VnGXKkpNEnm3zbKk+5qtxc72b6RHTA7PdXGGViWi3S98ViwW/Pr0lMrYKBNxxB749uiMWFpIyu/x4+hR9CssOSOeZkR1HVnotK4mHmj9El+guTtWviHY7SRMnSqRtu3aEv1i+YKgkBB8fYorEH9lffYXx6NFyj7OLdmbvno1dtDOg/oBSNRBXo2FQQzk5afbu2ZXeeFtSU4ud1yNHoo4um1ABEDlmtFS1cPgIOWsr79b+5tQ3HEg7gE6lk8nkijCm8xh0Kh0H0g7ICRQVoWDrVjn1ImbmjDJxitL6azqoVOT/+WeV0f/fnf6OvSl70al0TOkxpdK1eZfoLvLafNrOaVjsFW9ypztEwLGxhD1b/jXeUWOR98cfWFJTyzz+/ZnvyTfn0zCooew4uxZ4rcNrqAQVf13+y+XagGsB06lT5K6XXESRY8dcE7GAN2AX7exI3MErf73CXT/exVcnv0Jv0dMgsAETu01k2U3LZGKwPDiiXCvrN61JqBQqWTT7xYkvOLNKqs0JuO1WfBo0qNbXFkWR7SkSwdXhnEjmyvewm8o696431Amow+0NpW7Oj49WXFuk371bugdRKgmvYB13vcGvezeUISHYsrLQ795T5vEvTnxBnjmPRkGNGNhwoPxzpUIpr8u8FSVeVac4UGWnuKEoOt23a8Xd0w6U6hTPy3OqLue2BrehElSczDrpcqKSN5BhzOBs9lkEBKfErlBCbFDifXX8f2uG8+epmopQl53xVYgA4rIVvP+OjQnf2QlOd84BD9LvFamLJKcwh80Jmz2ZaoWwi3Y5Pt0hLvB4zCJSXOHnh6BQlHCKezc+3WFcOJtzFpPV+fO1t6PTAWzZOQAoQ0uLj/26SbWM5a0lC0+elElxm2grtxbKp2hvtvDcuTKPXev4dICmIU1pF94Oq2jl53PXXoCjt+gZ/vdw1pxeg4DA6M6jmdx9MiqFlPZzT+N7CNOGkViQyLebpX0mXYcOcs97La49ruRfkcXVT7R8wuXnC0ol/r2kc3xFe+L/nxCiDaFBYAOACrmTWlx7/KdI8enTpyMIQqn/WrQodgKaTCZeffVVwsLC8Pf354EHHiC1nI2GWtQMQh5+iCZ/b6pS7aUKDZUjqNMXLa7wBmVn0k5+Pv8zAtKGe2UqT4DWYa1l0mPW7lkVkh763XvI+U5SmV/dx1wSglIpbb4pFOT9+it5FcShpuhT5Djp1zq8VqWjGWB81/GEaEI4m32WD4+W75ZOX1xEdtSpQ8TwyuOXAfx79yZ48IMgiiRNnFRujPo/l//h5/M/y3HjVXWG9Iztyb2N70VEZMqOKWUWtaLFIm3C2+0E3nWX0/E3pRyrM2aUuaE8nXVaJlYmdp9IgE/FLrKSUAgKZvScIcehVkTSiaJI8pQpUgdv27ayu89ZaJs3J/QJaeGQMnNWmc0Zm93GjF0zsIk2BtQfQJfoym+y20e2Z0jLIQBM2zmt3Lh60WYjafwERJMJ3x7dCX6g8o5F/z59CBhwC1itJE+chGgpu8EriiLTd01Hb9HTPqI9jzR/pNIxQSJJGwQ2IM2YVmmMev5vv0kkklpN9LSpFW5sBt45EN9u3RBNJlKmV+xePpR2iC9PSKTitB7T8Pfxr3SevmpfpveYDkiijoo6NG35+bLrL2zok3JHVHmIGjMaQaPBsHcveT+V39e+N2Uva89KhMLUHlNd6uXpGduTB5pKf9dpO6dV6HCXY9PtdoLuvYeA/s5vqPvUrUvEa9L5JHXBAnnToyTe2vsWOYU5NAtpxoPNHqx0vCi/KLnaYNmBZWXcoaIokjJ1GqLRiG+XLtI5ygkIgkD0tKko/P0xHT5Sbofsj/E/siNpBz4KH6b3nF7lNeKFdi/QKKgRmaZM5u8tK3Sym0wkTyqOow/o18+puUZPmYwiMBDT8eNkffZ5mccLbYVM2SGJdO5qdBe96/SudDy1Us2sXrMQEPjp3E9sTdha7nH6nTvJXr1amsPMmZWK0QLvHIi2bVvJ4big/O9tuiGdpfuXAlIyS3nx7hXB1Rj1gi1byF4tnZ9j58+r1CkMEPrYY2iaN8eWk0Pq7DkVHrf0wFIyTZk0DGpYoRu/JKZ0n0KATwDHMo/JUdBXw5aXR4pDoFZJnD5IAoSYGdKx2au/pmBH+SKnjZc28tflv1AJKmb1miVvGFSEdhHteLyVRLJN3zW93GtE9uqvi93AlZxzS8K/b1+C7rsPRJHkiRPLJRrm7plLlimLpiFNea7tc5WO5xBI6FQ69qXuKzeSPHv11xgPHkTh60tM0bq/KgiCQPSUqVI8/ZatUsJLCTjSegxWA+0j2jO4ufPxbyXXDXtT9vLt6W8rPDb7y68w7NqNoNUS++b8CmPTr4Zfz56SMEwUSZ42rVzxzNqzazmYdhCdSseYzuW78UvipRteokFgA9KN6RWKZ0RRJHnSZOwFBWhvaCd3XZcHdWSkTMikLXirwnjyK3lX5DSTkZ1GEusfW+5xDsT4x8hk2qL9i0gsKF/8ZsvLI3mqJB4IffJJtK1alXuctnkzwp6SBAEps2ZVWBmUVJAkC/5e7/h6mR7n8jC2y1gCfQI5mXVSjly/GsZDh2T3d/SMGaWqVkrNs1UrdJ07gdVaRgBcaCvk8xPSteLp1k9Xee3yJtpHtmdB3wUoBSU/nfuJGbtmXFfEeNqCt+SaFV1b722KVxf0Fj2rT67m3nX38tJfL7EtcRsCAn3q9OH9W95n/aD1PNri0UqFcICcWHa9kuIgVTe0j2iPyWbik0KJRAh38f7JHZzKOkW6MR2tUssNlmis6eny+uF6h0MY++elP7mUVzZi115YSMq06YAkxtc0rPge5HqCoFYTcPttQNkI9RxTjnx+e7X9qygVpcVNsX7SNeNyfsVpMs5CtNmwFQklK3WKxxU5xcuJTxdFEf1eySleVZ84FMenBxhFsFoRnaglCtGGcGMdKWa2JtziDoK4RWgLp52AtkwHKV4sLpXPUxnOCxocpPie5D1ei8yvCvnmfJlc7RFbOSluy8kp9/9XBaVCyT1N7gHgx7Pli8w9xYXcCxRYCtCpdDQObuyVMR01Po4EAMf9l93J1ANnEecfR4QuAqvdKhP7zsDhjG8b4U1S3BGfXvqzH3CzlOxZsOnvMvtPpuMn0FhAWUSnlGcE0TQucoqfL+s0L7BI77O/uvL9Km/D4RZfe3atU4IdbyFFn8LQ34ayLXEbWqWWxf0WM7T10FL3eL5qX55vJ60ZPjX8jVlVG51e01h9cjUiIr3ietEouOL9jcrg17sPAPraXnGgOEL9QNqBGp5JLRz4T5HiAK1btyY5OVn+b/v24i/XyJEj2bBhA9999x1btmwhKSmJ+++/vwZnW4urcXU8ckUIGfIouo4dsRsMJJdDhBmtRmbtmgXAkJZDaBPexqlxX7rhJRoFNSLLlFUu6WEr0EukBxD86COV9toC6Nq0lgnTlGnTyjjsRFFk5q6Z5FvyaRfeTnbRVoUwXZjswvvwyIdluhYN+/bJZEfMrMrJjpKIHDcOVUwMlitXZGeQA7mFuczcLZF/Q1sPdTqGaUyXMUToIriYd7FMD3rmx59QePo0yuBg2ZnsDByEl6DRYNi1m7yfi28QbXYb03dOlwnliuIuK0KdgDqM7jwagKX7l5a7CZHz/ffot25D8PEhdv48t/oKw4cNk6NnMz8s3Re35vQajmYcxV/tLxNFVWF4h+HUDahLqiFVdjqWRObHn0iEh78/sXPmICiqPr1HTZmCIigI04kTZH5Stov1x/gf2Zm0E41Sw8xeM8tsXJQHrUrLzF4zZWd7ea5FS2oayTOK4mVffLFSEkn6LExD0GjQb99OzrdlnWQmq4kpO6YgInJP43voU6dPlfME6BXXi/ub3o+IyNQdU8tV+abOn481JQV1/XpVdsqr4+IIf1nqvk2dO68MoWy2mZm5S/q9BzcbXKnTryKM7jyaaL9oruRf4e2Db5d7TMY770hdvBHhRE1w/nvnQOjQJ9G0aok9N1fuznZgZ2KxGGl6j+mVOpscGNxsMN2iu2GymWTy14HcdevR79yJoNEQM2umU59bB9TR0USOkcihtCVLMSckyI9dLUZqFFT1Ql6j1DCr1ywUgoJfzv9SJnbY8b46G0fvgCoigqix0jzT334b8+XSG30rDq3gQu4FwrRhTp8P2ke2l0ndGTtnlHGC2vLySJooXctChjxapRhNUCik3mBBIG/DhnLJ2vn/ziffkk+bsDZOCWSuxt2N7qZPnT5Y7JZKndfWzEx57qFDh+LXs2eVYwtqNTGzZ8sitfy/y0ZGH0g9wPdnJMHb1O5TqyQhQCLzHZHfKw6t4Hxu2Qi81PlvYk1Lw6dBA1lQUhn8evYkZIgkckqeOKlMJGGmMZO5u6Xo5xfavVBhDcTVeK3Da9QLqEeaIU3uTHfAkphI2hKpfiNy9KgK3cDlIWr8OFSRkVISyfLS55w/Lv7BxosbUQpKp4R0UHkkuSUxkfTFRa7lUW+UiZ2uDJpGDQl9TorpTZ07rxQZuunyJjZf2YxKoWJaj2kuk4x1A+oyouMIABbvX1wucVt47hxpi6Rrc+TYMS6TF1Hjx6EIDJRSkq4S+aQb0lmyT/r7De8wnBj/qjtztSptleKZnDVr0G/fjqDREDtvXhnn9dUIffxxtDe0w56fT/KUqWXW5nbRztSd0rW0S3QXHm5eftLS1Xi0xaN0jOwoufF3TCt3oy51XvG1uCohaPgrr0g980nJ5Yp8HCkiBquBjpEd5cjGqhDhGyGfo1ceXllG3GM3m0maPFkSTt17r9ylVxFCn5DO4TlrvsVeWCj//JtT35BmSCPKN6rc3vjqxoD6A5jfez4KQcEPZ39gzu4513TztCIUbN+BfudOUKuJeGNkTU+nUlzOu8yb/77Jzd/dzLx/53Ex7yL+an8eb/k4P9/3M+/e/C4943o6fS5Shl3f8ekgrdcdNVV/t4OsWztXKF7xJrYlSvcZ3WO7E/OyJNzJ/PDDKitKrgc0C2lG3zp9ERHlOPGSyHjvPcyXLqGKiJBr2/4rCCqKUM//80/sZrP881XHV6G36GkR2oJb6pdNrGka0hSgzN6HO7BmZoLdDgpFGRdoSchO8eTkMuc6S2Ii1iTn+sShRKe4Wdo7cDZ2+q5GUmLaLxd+ueZCpN3Ju4GqCeKScNzjKks6xSPCSz3mDFqGtiTQJ5ACS4FL5Kgn+DflX2yijQaBDaoU7tlyc4r/f3ZOhceVh3sb3wvAjqQdpBnSXJ1mlXAQxK3DWlcpnnUW9qLzpqIoCc3hFLfle9cpLgiCy/26VruVk1lSfY13neJlO8VBul8TNBosiYkUnimOeRftdkynTiEAASqJ1C4wl31/ZKf4+dL3jhabRU4bupbx6QC3N7wdnUrHxbyL16zX+FTWKR775TFOZ58mVBvKJ7d9Uu65H6Q9oxjfaDI1Zv7oINSS4jWIfHO+XNn1ZMuqjQQVwa/IKW46dqzahZ2F8fFkvPc+ORWkXV4PcJz3ap3i1w/+c6S4SqUiOjpa/i+8aCGWm5vLxx9/zOLFi+nfvz+dOnVi1apV7Ny5k927d9fwrGvhKgSFgpjZsxF8fNBv3Ubehg2lHn//8PskFCQQ5RvFax2q3oR24GrSY/OVzaUeT1v4FpbERNRxcUSNHu3UmBGvvoKmRQtsOTkkTy29mffTuZ/YlrgNH4UPs3rNcopUdOD2BrfTv25/rKIU3epYPEluRanzMejBB/Dr4fwNjNLfn9g5UiR49urV6Et8N+b/O58MYwYNgxo61bXpQJAmiKk9pGj2z058xuH0wwAUnr9AxoqiTtCJE+Q+PGfhU68e4S+/BEikgy1XuslcfWo1xzKPEaAOqDKWsyIMbjaYbjHFJF3J3kRzQiJpRXH4ESNGoGnsnvJW6e8nE5KZHxTHwJeMcB3RcUSFne1Xw1fty8yeEqG69uxadiYWx4yZTp8h/W2JrIiaONFpIkEdGSmTexnvvFMq3qkkqTis/TAaBjm/wd8hskOFrkVRFEmePBl7bi7a1q2dipfVNGpIZNEGaOqbb5bpjF1xaAUX8y4SoYuQCSxnMbrzaCJ9I7mcf5l3Dr5T6rGCrVvJXfsDCAKxc+c6JT4Je/YZNC1bYsvNleP3Hfjo6EdczLtIuC6cEZ1GuDRPB/x9/GWH+1cnvyoTo248fFgWOMTMmOFW5JSgUhEzc5ZMMBZskfrhjVajLJx5rOVjTqu0BUFgRq8ZckSuI6HBmpFB6nzpuxY+7FW3IjaDBz+Ib5cuiEaj5DgXRZnwKLAU0C7CeTESSI7boa0ll+HMXTPlza2S76uzcfQlEfTAA/h2745oMpW6ThxNPyqnXkztMZUgjfPjDuswrMJUhtQ5c2UCKdLJa5muXTtCHnsMgJTpM7CXcLdsTdjKH5f+QCkomdZzmkvXMgcEQWBaj2my87q8uFAppWMqtsxMNE2bukR86Nq2IewZKQ0mZdr0UhuRFptFFqTc3/R+Okd3dnrcexvfy41xN2K2m8tcLwq2biX3B+kcETN3Dgqt1qkxI0ePQl2/HtbUVFLnlHa2z9kzh+zCbJqHNK/SeV0SOpVOFiT9cPYHuWpDFEWSp89ANBjQde5E8MPOEZUOKIOCiHbEqH/6KYaD0iZKlimLOXukuT/b9llah1XenV0S5UWSO+ZpNxjQdexIyKPOEZUlEf7ii1I8fUoK6e+8C0g39XP3SCKDZ9o8Q5OQJi6PC/BIi0foFNUJo9XI5O2TS30ORLOZpDFjEQsL8bvxRrfmrgoPJ3KUlKqRtmx5KceaQ5DSOqy10wQuSOIZx7V4xq4Zpa7F5kuXSH1TcpBHjnqjUnGaA4JKJZHnPj7ot20j96oY9W9OfcO+1H3oVDpm9JzhNOHnSCfSKrXsSdkjV8s4kP/PP+T++KN0LZ43r8prscLXl5ii71XOmjVloi9/jP+RXcm7ZMGfKyKJuxrdJYt7pu6cWkrck/ne+5jjz6EMCyNy/Lgqxwq4uT+q2BhsWVnk/SK5KfPN+Xx0VBJSvtr+VafEO9WB2xvezuxesxEQ+PbMt8z/d36NEuOizUZaUU976JAh+NSpuKahpuCISH9106vc9eNdfHnyy1IR6X8N/otxXcdRL7Cey2OrworIpvSa6QF1Fu0UdekcD6JC4Jt+12abySH46R3Xm6B778GnQQNs2dlkfV5+usv1Bsd1fv259aTqixMOC+PjyfxIWidFTZ5cZWLO9QZdp06ooqKw5+ejL4pNzTBmsPqkJOof1n5Yuefe5iFSZ7qrXcPlweYgbsNCKxV9qYqEgqLRWMYNbNhT1Cfepk2V1x6L3SI7QIOKHKC2XOdI8X51++Gv9idFn1Jhell1QBRFdidJ+0HdY7o7/TxHRLoqvNiB7yDIbS7EpysVSvl1HY716oZDsO+MCMCx/wRgz8ursAarPDQIakCHyA7YRTsbzm2o+gku4kiG913Tdr30+VX6OZzi0v/a873bKQ7I5gBnHZMnMk9gtBoJ8AlwaV+qKthyijrFr3KKK3x9ZWF2QQmxtSUhAXtBAYKPDwFayfCVbynrpC/uFL+AaC8Wujj6xOHaxqc7Xu/2BlJthyO5sDqxNWErT/72JGnGNBoHNWb1nasr/bz6KH14Sic59NfdqMLa0HlxdC28ix/O/oDBaqBJcBOXBFNXQx0ZiaaohlS/07s1GaIoYjp9mvTlyzl3512cv+tu0pcuJXniREynPV9DVAccpPixjGOYbeYqjq7FtcB/jhQ/e/YssbGxNGrUiMcee4zLRY6r/fv3Y7FYuKVER2KLFi2oV68eu3ZVvsAqLCwkLy+v1H+1qHloGjUk/FWJnE2dM1dWFpWMzp7UbZLLi4l2Ee3krsWSpEfBjh3kfLMGgJi5c1H4OTeu4OND7JtvIqjVFPzzj7RBDqQZ0nhz75sAvNz+ZZcjRwRBYEqPKWWiGjPefVdWjUeNdY0ABEn1GFwUkZk8cRK2goJSsemze81Go9S4NGa/uv24q9Fdcg+6yWIkecoURLPZ6U7Q8hD2zDP4NG6MLTOTtEWSO8vhjh3ZeSQRvhVHoVUGQRCY2XMmvipfDqYd5IMjHwBS3HvSmDHSpnynToQOdV8VB1KXnt+NNyJaLKTMknpO5+2Zh8Fq4IaIG1yKcAXoHN2Zx1pKpNW0XVKMumg2kzRunNQT278/QfcNcmnMoHvvxa9vHynqfuIkRJsNURSZsWuGW6SiAyVdiyWd7TlrvkW/rciFv+BNp+NlQ554QiI/DQaSJ0yUby6OpB/hsxPSxperpCJAgE8A03pIkaxfnPhCVu3Z8vKk6H8g9Mkn8O3UyanxBLVaEp4Udcrn/SHVKpzPOS9XIUzoOqHSHu6q0CuuFw80fQARkfFbx5NlkqIB7YWFksPWbifw7rsJ6N/f7dfQtWlN6JPS5z95xgzsej0rD68ksSCRaL9ohnVwrdcwzj+OUZ2KY9Sv5F0hZc4c7Lm5aFq1JOzpp92ap6BQSA5zjQb9zp3k/riOdfHr5Nh0V8VIIBERDYMakmnKZN6/80q/r/fc7VIcvTxPQZA6cLVaDLt3k7t2LWabWXbOD2w4kP71XPt7lXSCrj+3Xt4YzvvzT6lzVaEgdt58p5NEACJGvC4nXGSsWAlIvWezd0sCjydaPeG0c7k8RPpGymKmFYdXlNn4y/nuOwr+/lv6Hi18C4XGtWtR+LBh+NSvjzU9ndQFxZHRnxz7hHO55wjVhsouZWfhIPP91f4cST8id7/b8vNLxDk/UWWyTEkofH2JnT8fFApy1/8knyc2XtzIn5f+RCWomH3jbJfqFQA6RXWSqzam75pOgbmAvA0b5HNuzMxZLqUxOBDQrx9B994LdjvJEydhN5lKxaa/1O4ll8YrL5K81Dxnz3ZrngqtVko8ALI+/xzT6dMs3b+UdGM69QPr80K7qkVYlc15Vs9Z+Kp82Ze6j/eOvCc/lr5yJaYTJ1AGBREzZ47bPcfBgx9E16EDosFAShGpu+XKlmJBSg/XBSnlJQiINhtJEyZKtRXduhHy+ONOj6dp1EiuOEqdN1+uOLqSXzo23Zk48pKoF1iP4R2HA7Bo3yKSCqRxJcGpJLwMfeopp79nft27EfrUUwAkT56CtShC9+oUkfqB9V2apyAITO0+lQB1AEczjvLFCSkq3XT6NBkfSGvJ6CmTy2yuljuWSkVoUWpE1qpViHY7nx7/lJzCHBoGNeTuxu6tnb2FuxvfzYyekiBm9anVLNi7oMaI8dz1P1F4+jSKwEBZMHu9QG/R8/Wpr+WI9K0JWxER6R3Xm/dueU+OSPdk81t2siaWXy9wvSDrs88Y8rcVhQib8w9UuxMm25QtuyX71OmDoFLJNQ9Zn6xyKe64ptA+sj0dIztitVvl84lot5M8bbp0b9evHwG3DqjZSboBQaEg8I47AGTRz0dHP8JkM9EuvF2FqV7NQpsBcDrbC05xuU+88v0ChUYjE7pX1/YZ/pVIcV8XotMFBPk+z1kyUaPUcGuDW4FrG6F+Pvc8acY0NEqNSwlmcqd4WMn4dNed4nBte8VFUZTTJZxJlrPnlE3icgWDmgwCYF38Oq9fPx3nvhvCvdMnDiU6xYvi0xUB0ufY205xkM59AIfTDpcSmlYER6JB1+iuXq2VsWaVH58O4F90z5//1yb5Z6YTkltd07QpAZoiUryc2ip1nToIajWiyYQlqVjoqrdKpLhGqfGaw98VOCLU/7j4R7nz9ha+OfUNr/39GkarkW4x3fh84OfE+cdV+by+BwqJyRLJ04ryPXctri2sdqssYHu85eNu39c64H+jlJzljV5xURQxnThB2pKlnL9jIBfuHUTGipWYz51DUKtRFiWW5P603uPXqg7UD6xPqDYUs93MicwTNT2dWvAfI8W7devGp59+yu+//87KlSu5cOECvXv3Jj8/n5SUFHx8fAi+yhUXFRVFSgW9dw7MmzePoKAg+b+6dV3byKlF9SHsmadl12XqnDnY7DZm7popR2ffVM91cgKKe48dXYu2/HyZAAt57DH8ulV941MS2ubNiBhRtEk4Zy6FV64wa9cs8s2Ss+ep1k+5Nc9wXXipqMZje38vditOn+Z0HP3ViBo9GnVcHJakJOIXznErNv1qjO86njBtGBdyL7D0m9cx7t+P4OtLzLRpbl9IBR8fYqZLpEP2t98yY+NYjFYjHSM7yt3K7iLWP5bJ3aXN85WHV7IraRdpixZLXaYBAVJsehVRolXOXxCInjJZclXt2MEv6xfy95W/UQnuRbhCcYx6ij6FRfsWkb5yJYWnTqEMDpZINxffa0EQiJkxA4W/P8bDh8n6/At+OvcT2xO3S6RiT9dJRSjtWnQ42yVnmiQUiRz1hksufEGhIGbeXARfXwz79pH1+edlupj71e3n8jxBuim+p/E9iIiSqMNqkqJa09LwqV+fiBEjXBpP26oVYc9Jzo+UmbOwZGcxY9cMrHYrfev0ZUB9zze3xnYZS6OgRqQZ05i4bSJ20U7aokWYz51DGR7uUl1BRYgY/hrq2FisScnsXDGdz49LHYDuiJEABjcfTNforhitRib9Opzc334HpZLY2bPdqihwwKdBAzlO9/g7c1nwr/QZG9ZhmFOx6VdDo9Qwu9dsOVFk/Qdj5fc1euJE9+dZr15xX/ubC3h31yKZqHU39aJ9ZHtZtDJj5wyyUy7K/ZNhzz6Lb8cOLo2n9Pcneqp0LcxctQrT6TO8e+hdkvXJxPrF8vINL7s1z5K4q9Fd3NP4HuyinbFbxsqiDvPFi6Q6UjpGjkTbvLnLYyu0WmKK0lByv1+LftcuLuZelIVPY7uMdVk4AxDtF82YLlIE/tsH3+Zi7kXSFiyQ3Pj16rl8jgDw7dCBsGeluO+UadNJS4xnzm6JCH2u3XNuiw+GdxhOHf86pOhTWLhjrlyBEP7KK2gaue+siJo4AVVEBOYLF1j7wWiXY9OvRqlI8n2LOLJ8tlfm6d+nDwG33go2G5uWjuXbM1IH+LQe01wW/JWZc2BdWUT1/uH32ZW0C/3OnWS+X0SGzpiOOsq59JfyICgURM+YDioVBX9tIvXPX5m9R3pfnmz1JC3DWro85tUJAjsTd5K1ahXGAwdQ+PkRO9e5upWSCH1qKLr27bHr9SRPllzzjgoSV2LTr8aQFkPoENkBg9XA9J1SdVLK7DnY0jPwadSIiNeHuzRexMgRaJo2xZaZSfKUqdjtdmbumikL/h5v6bwYoCSi/KLk88E7B9/hfFa8lOBkteJ/y80E3Hab02MFDx6Mwt+fwrNnufTHOpkUe73D6zWyWXo17mt6n5wG9eXJL5m+a7pTm9fehN1kIn2ZlLAU/uKLbiXgVAccEem3fHcLc/fM5WLeRfzUfnJE+opbVtArrpdXNu/V9aS9iauTkq4n2PLyyP76G+pkwp3+kvNzyf4l1Sqk2JG0AxGRpiFNifaT3L6Bd9yBpnlz7AUFZH78SbW9tjfhcIt/e+ZbcgtzyVm7Vr6Pjp46xeMN6ZpC4J1FEer//ENSxgW+PS1dj4d1GFbh79Q0uCkKQUGaIc3jeF+ZuI0Ir+JI5JQ1a4mUlpJ94r5du1Q5hsOBHK4LRx0grTVdIVEdEep/XPyDQlthFUd7Bw53dsfIji6tkcp7b1Xh7pHiDhfi0Yyj1UrSgZRAkGZIQ6fS0SW66r9pSac4FEdtO4vbGtwmx1U70hS9AYPFQHyOVOHi1X7tMqS4wynu/b9L85Dm6FQ68i358u9SGeSY/xj3XavlwXJZqlJUlVMtFXDTTaBQYDp+XBalmU5KpLi2VUsCfKQEj/I6xQWVCp8GkvDSfKE4Qt3hFL/WLnEHboi4gcZBjTHZTPx24Tevj2+z21iwdwFz9szBLtoZ1GQQK29e6ZQhRBRFjJu38dBWyfzy2fHPylTE1aL68fflv0nSJxGiCfFKjZNfUZ2UfvsOl9I27IWFFJ4/T8HWrWStXk3K3Lmcu+12Ltz/AJnvv4/54kUEHx/8b76Z2AVv0nTnDjnt13jYe+dbb0IQBG6IkIRM16rCoBaV4z9Fit9xxx0MHjyYdu3acdttt/Hrr7+Sk5PDt99+69G4EyZMIDc3V/7vynV8w/n/DVJP6CxQKsn79Tc+3zCLIxlHXOpiLg9Xdy3+/PYbWJOTUdetS+Qo11xkDoQ+9RS6Tp2wGwx8vewlNidsRq1QM6vXLI82tu5qdBe943pLUY1FkZ0Bd9xOwM2udWmXhMLPj5i5UpzoUv1PbsWmX40gTRBTekhkytfWXcTHQOSIEajjqlYEVgbfLl0Iuv9+drQS2FVwBLVCzbSe7hHKV+PuxnfLjttxm94g/vtPAYidPw8fL4ljfOrXJ+yFFzD4wFuJ0mbnU22ekjvTXMXVMep//yG5j6OnT5dvRl2FOjqayKLO49MfLuHNPRKJ8kr7V1xOOCiJkq7FaTunET9prORM69qVkCdcd5/71KlD1DgpkjR98RLe2TyP87nnXepirghju4wlQhfBxbyLLN0wXo5qjZk3F4VO5/J44a+8jE+jRtgyMlj1/jAOpB1Ap9Ixqdskr2xu+ap9Wdh3IVqllh1JO1jx4wSyP5c+XzGzZjrlUqsKCl9fomdMxy7AW7bfsIk2bq1/q9viA9kdqtRysDCePzoKhD3ztFc6J0OHDkXTuhXv3WigwKqnXXg7uXPbHZSMUV+s2ESBFmJmTPd4Mz506JNoW7fmrF8+n8ZLMfJTu08lWOv+uMM6DKN+YH3SjGnM/PI5bFlZaJo3l91SriLg5psJGHALWK1sWzRGVmlP7j7ZK91ngiAwqdukUqIOm7mQxLHjZOdq6FND3R7ft3NnubM7acoUZu6YjtlupldsLwY2HOj2uPc1uY+esT0ptBUy6fcRZH0v9ZPHzpnt1jkCIPy1YWiaN8eWnc20NS+QXZhNs5BmvNDWfUezr9qXmb2KrhGXN3AwOBdN8+aEPfuM22NCUYz6jBnk6WCpajPgemz61ZAjyW0m3u2tR93C83mCROBbA3S801iqBLmvyX1ObX46g4GNBsrrhglbxnFs0kiw2wl64H4Cb7/d4/G1zZoR9vRTACz9czop+hTi/ON46Qb33bGdojrJsetTt07i0kqJYIyaOMGtNZqgVEoiNY0G/c5dfPr1eLdi06+GUqFkZs+ZaJQadiXv4qv1s8j7+Wcp9WL+PKfrCRxQaDTEvrUA1GoKNm3im2+nFlcauSn4c2BQk0H0iu2F2W5m0k+vYjh+DEVAANFTprp0jVcGBRHyhETOr9y1GKPVSLvwdi4nh1QnBjcbLFdP/XD2B0ZuHonBYrhmr5/12edYU1NRx8YS8vhj1+x1y4MoiuxM2smwTcPkiPQCSwENAhswoesENg3exLiu41xOIKgKPvWkyHXL5cvXRb97echevRq7Xo+mWTNeu136Hh9IO8CWhC3V9poOErJPXLHjU1AoZAFN1pdfukzQ1QRujLuR5iHNMVqNfHXgY9LeklI9HOLU/yq0bdqgrlcP0WhkxabZWOwWOkd1rjSm29/HX+5inrV7llwf5w6cdYpDiTSGEk5xuU9cpcK3Q+Ui03xzvlyP9mSrJ2Xjgis91J2iOhHjF0OBpaBMxV91YVeyRIq7Go9bHE1fwinu6BTPdO07F+sfS4PABthEG/8m/+vSc12FI1WrW3S3KkUAot0uixqEotQtV9Mn/NR+shh+Xfw61yZbCY5nHscu2on2i3a6is8ZXN0prnQ4xashSVWlUDlNDhksBjl5pFtMN6/NQSLdLgCgbVFWjKwKD8e3s1S5lbdRSvUynZTcnZqWLWWit7xOcQCfhkUR6iV6xR3rJ1/Vte0Td0AQBNktvub0Gq+uKQwWA29sfkMWeA7vMJyZPWc6LZ42x8djSUqi5zkVzYKaUGAp4JNj/w1x2/8SHH+/h5o/hFbl2r1XefDt0AFlcDC2rCwKtm+Xfy5arZgTEtHv3kPO2rWkLVtG4pixXHx0CGd79+H0De05P/BOrrzwIqkzZ5H9+RdYLl9G0GgIGDCA2IULabpzJ3XffYege+5BGRCApoUkIC88dfq6XS87ItSdrY6oRfXiP0WKX43g4GCaNWtGfHw80dHRmM1mcq5aqKSmphJdjuqrJDQaDYGBgaX+q8X1A13r1oQ98wyZAbAyTeo+caWLuSKU7FpcGLSLHD+B2HnO9QaXB0GpJHbeXNKjdLzXRFIcvnTDS26Tn/K4gsDUHlPxE304G1rIuv46oidP9mhMAL9uXTn6fF+2t1GgsMP0VqM8dlH1j+lDn8RA7ApY+aAf2ofv83ieAPZhT/DJbdLm5eOGdm65PyvC+K7jaebXkGx7AcvuVRL07FMeCQ7KQ9gLz/PdPaFk+YvEmLQeER4gxag/2kSKXn/vdhDuvZXA2513JpWH4MGD0fboxnu3WMm36mkT1lomBT2B7Fo0pPBu9DHJmTZvrlvRuADBDw3G78YbORlp5vPL0vlgSo8pbrk/SyJIEyS7ob7O2cSJuhLR6kokckkoNBpi5swmKUzggzAp3mx4h+HE+Md4NM+SaBrSlIndJOfyh7m/crIOhD7zjKRq9hL8e/dm87MdOB8j4FsoMLq5Z7GlcX6xPHVGeg9W36xC/6R34mEFlYo9I/pzqLECtVVkVFpHjwgPgBfrDyEuR0mOv8BnT9fF34M4+pLzDJk5hRV3qbALIv1Vbbi5vmfnG51KJ4u8/opIZW8LFbFvzkfh434XbdTkyZiDfHmrxTnsop3bG9xO7zq9PZpnSVwt6li28mlMR46gCAyUUjrcPD84EPHGG6hiY/grJIm96fvRKrVM7j7ZI0GKIAhM7zEdX5UvR0zn+KmbQMjjj+PbxX2yVVFUI7G9rYrtYekokSpM3HFel0SX6C7cp5U2nd8bqMBv+ninqyoqg/9N/fj0mTrk+QnUz1LyXN2HPBpPISgYldoRH4vI8QYKdozo65V5qqOjWfdKOxIiBAINMLyud4m08V3H0zSoCZnmbJb0K0DduhXRRRHf3kD4K69wqV0kv7SUNs68IUh5vePr1PGPI9Wcwap+dvz79SPo/vvdHk/TsCGRb4wkKRTeM/wOSGtzV2PTr0aDoAa81kFK1Fie/h2pwRD2/PPo2rmXYqRt0YLI14eTGApL8qUoPXcqja6Go1bBT6njmJDEr50FosaNdSspIPTJJ0mP1rGxfg4AIzqNuO6coYOaDGJR30WoFWr+ufIPj//2OAn5CdX+utasLDKLYukjRo5wuVLDWzBYDHxz6hsGrR/Ei3++yJaELYiI3Bh3IytvWcn6QesZ0nJItTm/1HFxIAjYDQaXnYrXAnajkazPpDShsOefJ8Y/Rk5iWLp/KVa7884gZ2Gz29iRtAOgzPrE/6ab0LZrh2g0yrUG1zMEQeDZtlJyzFcnvsRgzEPbqhWhLlRbXI8QBIHAgXeQEgw/G/YCUm1FVee3kZ1GEqQJ4mz2WTnC1R1Y0x1uZldI8WKnuOFfac66tm2r3B96//D7ZJoyaRDYgMdaPoZPUZ+w8dhRp+erEBSyK+9aRKhbbBb2pki/oyukuGi1ypUkJQX5slM8ufKUzvJwY5zkJPznyj8uP9cVOEhxZ+5p7Pn5UFTX5lNfEjq5U8ngiFD/7cJvXhOUOaLT24W7tzaqCI74dGWRU9zx3bGmpXn1dRzoGCnts1RFiu9O3o3FbiHOP86rorPCs/Fgs6EMDkYVFVXuMQG3SbUG+Rs3AiWc4i1b4q+W3qeKEg58GkvngcJzxaR4gUV6j/19/L3wG7iHe5vci6/KlzPZZ9ieuL3qJziBDGMGz2x8hr+v/I2PwocFfRbwfLvnXVrPFmyRRHT+3brzWicphXX1ydVkGK9/cdv/Co6mH+VQ+iFUChWPtHjEK2MKarVUwwakzprNpaFPEX/LAE6178C5W27h8lNPkTxpMpkr3yNvwwaMBw/KojaFnx+a5s3xv/lmQoc+SdzSJTTbMDttYgAA5tJJREFUuYM6by8n6K47UfqXXndrGjVEUKuxFxRct5VDDqH+/pT91bI+roVr+E+T4gUFBZw7d46YmBg6deqEWq1m06bivo/Tp09z+fJlevTwbsRKLa49Ql95ifce8MPoAy0NwS53MVeEVxo+Sd1sifR478U4NC5GzV4NIS6Gd56NQK8TaJoEjwreUTIGnkzgqV9MAHzX1cpBy/kqnlE1LuddZknMIQDu2W0nbOZHLsWZlIf0Zct48rssgg1wxb+Q+fsWVP2kKmCz25h8ZD4FWmicLDLg7b0YDnhPVeVjhRE/WtEVipysJ/BtP8835K/GkZwT/NpUWiw/+6Oewg2/ezSeKIo89EMGMVkimYECb99kwi7aPRpTEAT+fLYdBxsrUFtERqV08Ep0p6/al6l1X0Cww9a2CvaNvcOj9ABBENBOG83S+9TYFXCLtRk31/OOiKFf3X4MyInDroBl9/ugeMl1N3tJqNq15p2nwin0EWiXpObhuvd6ZZ4lcU+9gdx0RRKiLB+sRfWy50KGkricd5nPoqVIs8f/tmKds8wj1WXWp5/R99uztL4MJpXI2N2TMFlNHs/zdNZpFl2SqiUe3mJHt/gzjEed34i6GqLdTuakabyyvhCFHbYEJ7P27FqP5wmwOGctVyIgSC8y5N1TskLdE7TM8eOu/dJN58pBGjLrBHg0njoqim9ebUlymEBoAYxt5JmQpzyUFHV8GniEE3WllA7HxqQnUPr7oZwyks9ulpa5z4YMpE5AHY/HjfGP4cUESej2TV8lCU96LkBJilbzwZ3Sdef+XSINMzwTc4AUsXv/kgNEZYtkBAnMyV7tFbX016e+ZntgCiobvLy+kPTxkxHt7l97jMePo174EY/9I42x7MoXHM887vE8dybu5FuF1Ff/4q829NPmezTPq6FVaRl3pD4as0Tm//Z6F6+SdWa1wHv36RAVAjcet9PuhNHjMX3VvrxxqTWCXWRzOwV7X+zlMfGqffRBlg7xx+QD7bL8ebipd9bmj7V4jFb5gRh9YOkjvgS+9JxH4+meeKR4npl+PNXC/RSRkojyCWPofulcu7q/isR+rsfbA6hCQvjh0brYlAIdUn3pHNXZK/PzNm6pfwurbl9FuC6cs9lnefSXR2VCpbqQvnQZdr0ebatWBN7peYSjq7iSd4UFexdwy3e3MGfPHM7nnsdP7cdjLR9jw6ANrLxlJTfG3ejVftPyoNBo5A17y+XL1fpa7iDn+7XYsrNR161L4B1SYsYzbZ8h0CeQc7nn2HBug9df82jGUXILcwnwCZDdhg4IgkBkUa1ZztffYCkRiX29YkD9AcSpIshTWfi7vYLomTM9qha6XhA4cCDf36jAphDpFdXdqd7qEG0IozqNAuDdQ++SonedZIWSTnHn49NLflac7RM/n3uer05+BcC4ruNQK9X4dpHO44a9rp0jHRHq2xO2k22qXgHM4fTDGK1GQrWhNAtp5vTzjEeOSERiUFApwYFP4yagVGJNS3OZjHCko2xJ2FJtJEGOKYcjGRKZ3DuualLcEZ0u+PqiipR+T3dESZ2jOlM3oC4Gq4G/Lv/l8vPLg0yKu1l7WBHkTnE/ibBVxxaJRarpHOo4H+xN2VvpfYpDzNC3Tl+vigYLT58CQNOiRYXjBgwYAIKA8fBhjEePYkvPAEFA27y5HJ9eESmuaVTWKe6IT68ppzhIZpDBzaQ1+0dHP/J4vBOZJxjyyxCOZx4nWBPMR7d9xB0N73B5nILNRaR43770rdOXduHtMNlMfHjkQ4/neL1Bb9Ffl9HwDpf4wIYDCde5l0JaHoIfGgwKBZbERAx79mBJSACrFUGtxqd+ffxuvJHgRx4mcvQo4pYuocF339F0106a7dtLo/XrqPvuO0RNmEDg7bej8KtYgCqo1fg0aQJA4alTXpu/N9EyVKpeyLfkczLzZE1P5/89/lOk+OjRo9myZQsXL15k586d3HfffSiVSh599FGCgoJ49tlneeONN/jnn3/Yv38/Tz/9ND169KB794ojmmrx38BX57/jcEwhPhaR57/MIG/dTx6PKdrtZE+ewcjvCtFYBQ5oUvjwqGcX3MX7F3NSTMLfqmLEj1ZSR4zC6qGi35qdTeLoMfQ9amdAdhx2RMZuHeuRYs5kNTFqyygKLHraB7bikQNaDPv2yZ197qBg+w4yP/qYQCPMrPMSCkHBj/E/8tM5z/5WHx79kP2p+/FV+TJJ3w+V1U7S2HFy55GnSJk1i/D9F3h5m7Qw/fjEKjmOzxvIN+czftt4RERutbWg3UWR1DlzMCe4r1zL/uILLL/8ycgNAj6Cmq2pO/n0+KcezfNA6gFWnJdcHs/8acdvyRcYDnjec2LLyyNi8ns8vE3qoFxq+Z0z2WfcHs8u2pl6aglZ/iKxmSJPvn0a/b/eiVrLWbuWJz66RFyGSLavnUn/etad+fbBt4n3ySbAJPDKWiPpc+Z6ZZ4lkb5wEU+vySIuWyBTa2XSnqkeCyQcsNgsjNs6DqPNSMeAVvQ/rqTgr03krFnj1njGo8dIW7IEBTCryeuEakM5lXWKN/e+6dE89RY9o7eMptBWyI1xN/JoyACwWEh8Y5Tb54nMjz9Gv2UrzTI1vNpAcurM2zOPU1meLa5/OvcTP8b/iIDAmDPNCMwykThqFHaz2e0xbfn5JL42nEf/MtM8z48CoZDRm0djtrk/5qZLm/hZPIwgwrCfbJjmLK6WCKrbjE3oexxEhcDbQwIx97ih6ic5AZvdxmzzj+h1Ao2TRfrM3+SVCNXc9evpvmo/Nx6zY1fAuD1TPNq0NFqNjNo8CpNgoV1OIA9ssZAw/DVsHnT3iWYziaNGo8kxMP5UU9nZ6bjJdRfHMo7x1r63AHi90TM0ztGi376dzA/cWzfZCvQkvTEK0WLhwZCbuKnuTVjsFkZtHlVuN5+zyDRmMnG7JLZ4MOYOul7RYNi1m+wvv3R7zKuR/d13BK7eyPMbpe/EhxdWsyd5j9fGX7x/MefMiQTbtQz9y07KlKlYUj1z6eT9vpE6H/zK4O3S9WH+ieWcyznn0Zjz977JxQATQXp49cscMt9d4dF4DuR9+RXDPs/C3wjnQswsPrLco/EW7F/IpQATwXoY9lUu2Svf88o8095aSN/fkuh6XolVITJ6y2i3+lBPZZ1ik1baMH3klzzZKXM94oaIG/j6zq9pFdaKnMIcXvjjBbkn2NvIWLmSnKKKtMixYzxOEHEWoiiyK2kXr216jTt/vJMvTnxBviWf+oH1Gd91PH89+Bfju46nQVCDazIfBxwR6oXxVXevXkuIFguZq6R407Bnn5GJ3ECfQF5oJwnq3jn0jlcEkCXhIEl6xfYqV8Tr26MHvl27IlosZKxY6dXXrg4oCi3cvV3qkf6lnz+qls6TlNczkiJVbG8jfXefynd+jXdvk3vpENkBo9XI9J3T3bq3Ke69dsIpXg75V0yKV5wIZBftzNk9B6topV+dfrLj2ZEiZI4/J7uqnUHj4Ma0DG2JVbSy8eJGp5/nDhzR6d1iurkk7NEXxd/69epZ6rys9PdD16aNdMwe1+7NO0R2IFgTTE5hTrX1rO5I2oFdtNM0pKlTyW0OUlwZFISqqD7LHae4IAhyJYA3ItRFUZTJfa+T4vrSneJygkJKilfFpQ60j2yPRqkh3ZjO+dzyTT+iKMrn+z51+pR7jLswHZei0LXNm1d4jDoyEl0nibxPXyrtlfo0bIjC17eYFLdU4BQvIsULy4lPr6lOcQeeaPUEKoWKA2kH3P7OpepTmbJjCo/8/AjJ+mQaBDbgq4FfyfHQrsCWm4vhoDQP/36S+GF4R6kK5dsz35JUkFTZ0/9TSDekc9ePdzHg+wH8fsEzs5Q3kaJP4Y9LUk3AE608MwddDU3jxjRY/RUx8+cR+9Zb1P/yC5ps/ofmhw/ReOPv1PvoQ2KmTyfsuecIvP12dG3boAoJcUsE46hCMJ067dXfwVtQKpR0jZbEdruTd9fwbGrxnyLFExISePTRR2nevDkPPfQQYWFh7N69m4iihe6SJUu46667eOCBB+jTpw/R0dH88MMPNTzrWniKE5knWHpgKQDDxJuokwkpM2eWWly4g8yPPqZg82bq5vswoenLAKw8vNLtLqO/Lv0ld6/OuXEOscH1sCQmkjRmLKLNPWJNFEWSJ0zEmpKCT4MGzHzqSxoHNSbDmMGEbRPcJuze3Psmp7JOEaIJYeGty6k7aw4AmR9+RP7ff7s8njUjg6TxUqdz8CMPc9Pdr8r9l7N3z3Z74/Vg2kHeOyxtXk7uPpmO4+ajjovDkpBA6uw5bo1ZEjlr15K79gdQKHjo1eVy5+aE7RO8tvCavXs2iQWJxPnHMe3RD9F17Ihdryd5/Hi3PheGAwdIXSARE92eGsuE7tLm//IDy9mfut+tOWaZshizZQw20cZdje5iUJ07wGYj8Y03PBJ1iHZJwGC5fJnBl2LoGdEVk83EqM2jZJWsq/jk2CfsSNqBVqllSnZvtCYbiW+M8pgwMJ06RcrMWWgtMMt/CDqVjl3Ju9xWz+5MKhYqTG06jFCDgtx168j5cZ1H8yyJvD/+IPuLL9BaYH7LcWiUGnYk7uCDI96Jinzn0DscyzxGoE8gb962jOg3JNdG6rz5Lm/K2goKSBw1CiwWAm69lSaPPMu83vMQEPj+zPduxwSKosiMnTO4mHeRKN8o5t44l7hZs1DHxmK5coWUqdNcJnMN+/fLN71RkyfxXL8x9K3TF7PdzKjNo9wiPQDO5Zxj9u7ZALx8w8sMnPAeypAQCk+elPsjXYUoiiRNmID50iW00TEsuf9TgjRBHMs8xqJ9i9waM1WfyrRd0wB4InYQbZNUFGzeTPbXX7s1XkWwZmaSOHw4z/5mpa5BR6bCwLht4zzqj3Rg1fFV7E3Zi06lY/ThugjpmSSNHevRhk5hfDzJ02cgABPqP0+DwAakGdKYsH2C20KUuXvmEp8TT5g2jEUPfYZPTAyWS5dJGj/B7bmmLVsmRdEHBdF36ruM6TIGgCX7l3A4/bBbY+YW5jJq8yisdisD6g9gaN8RRE+RalzSly932QkFkDprFuZLl1DFxBA3ew6zes0izj+OxIJEpu6Y6pYIQxRFpu6cSqYpk8ZBjRnbfwZR48YCkLZwEYVnz7o85tUwHj1K6sxZADxwy2vc1+Q+xCKxojfWDVuubOHrU9J3be5NbxHZoCW2nBySJ050+zNReP48yROltcKzzYfSPaa7LMhwN8pzffx6WeAzM/o5QvSQufI9CrZudWs8BwwHDpD61kLC82CK74OAlFDgLjnw8/mfWXt2LQIC06OfIVgPGStWkl8iWcwd5P/9N9lffIEAzOwzlzj/OBIKEpi20/VrjqOH9iZTAxqmSvO7XnvwAKL9ovns9s+4o+EdWEUrs3bPYvbu2V45d4NEsqa+9RbpyyQxROSYMfhdA4G7wWLg29PfMmj9IF748wU2J2xGRKRXXC9W3LyCnwb9xGMtH6uxyFPHprx+1/W1gZb78y9Yk5JRRoQTdF/p2qxHWjxCjF8MaYY02UnrLWxLLOoTr4AkEQSBCIdb/IcfMF+65NXX9zYyVqzkxq1ZBBsE0lUGfrnwS01PyStYcXgFdgG6nLYT+/shp5+nEBRM7T5VurdJ2sHHRz92+bVlp3hE1W431VWd4uaEROn/V9En/unxT/k35V90Kh1ju4wtHi8kBE1TKVnIsMc10ZzDLb7hvPcTFkpid5J0LukR41qqZsEOqbbAr9eNZR7z7SalJLr6O6sUKvrW6QvApsueXZ8rgkysxjlHrNpyiklxpQekOEgiDwGBvSl7uZJ/xa0xHEjRp5BhzEAlqGgZ6l5CTUVw/M6KgKL49MhIUCjAYvGKuPhqaJQamUCtiBw6mXWSdGM6OpWOztHeTdJxpE/q2lcu2Am8TUpA0Rd99rWtWgFU7RRv0AAAW1aWvKfm2AOraVI8yi9KFmt8fPRjRKuVtCVLSVu8pMoUO71Fz/IDy7nrx7tYF78OEZHbGtzGF3d8Qb3Aem7NJ//vf8Bmw6dJY3zqSOlu3WK60S26G1a7Vd4T/l/AsgPLyDBmYLQaGbN1DJO2T6qwl/5aYvWp1dhEG12iu9AitIXXx9e1b0/woEEE3X0Xvp07o46OrhbBq7aFJHIxnbp+XdjdYqRrpTdF9bVwD/8pUvybb74hKSmJwsJCEhIS+Oabb2jcuLH8uFar5d133yUrKwu9Xs8PP/xQZZ94La5vGCwGxm0dh9Vu5eZ6NzP06SX49uiOaDSSOPIN7Cb3VOf6Pf+SvnQpAFFTJvNAn5cZ1GQQdtHOuG3jXHZhX8m/wtQdUp/kU62fon/zgdRZvgxBKzmpMt591615Zn32GQWbNyP4+BC3ZDH+weEs6rcInUrH7uTdbhF2G85t4Psz3yMgML/3fKL8ogi8/XZCnpTUYEnjJ2BOcL4nULTbSRo3HltGBpqmTYkqIsdfaPuCRxuveeY8xm0dh020cWejO7m78d0o/f2JXfAmKCSCMe9395V1+j3/kjx9BgARw1/Dr3t3RnceTeuw1uQW5vLqplfdJr8c2HBuA79e+BWloGR+7/kE6oKlrl9fXwz79pH16WcujWfNyCDx9RFgtRJ4552EPPE4DzR9gDsb3YlNtDF2y1gyjZkujWkX7UzcNpE0YxoNgxoypfsUYmbMxKd+fawpKSR7QM5kvPee9PnVaKi7fDnz+y8k0jeSi3kXmblrpsubvvtS9vH2wbcBmNhtIj0nLkbTrBm2jAwSR45EtLi3IWvLzyfh9dcRCwvx69uHzs+PZ1K3SYC0meOqUCbLlMXk7RJh9FCzh7i9/wtEvDYMKBL0nPPMnQdSPHLyJOk1Qp99hva3PSbP+d1D73qsOt2dvJtVx6Q48hk9ZxDtF03o0Cfx69ULsbCQxFGjsRcWOjWWKIqkTJ+B5fJl1LGxxMyaiSAI9IztyYs3vAjAzF0zK1SIV4ZvT3/Lbxd/QyWoWNh3ISHaEJSBgcQuWghKJXm//kquC+I4a1YWiW+MApuNwLvvJvjBB1EICubcOIcYvxgu5192i/QwWAyM2jwKo9VI95juvNDuBdRRkcTMk9IDsr/4QroZdBGZH31EwV+bENRq6ixbRt24Fsy9URpz9anV/HHxD5fGs4t2Jm2fRG5hLq3CWjHi5qlEjhkNQNqbCzCdcT/loSREi4XEESOxpqQQGNeAJXd/gE6lY0/yHubsnuMRIXQ0/SjvHpSuuRO6TqDLrLcRdDr0O3e57Wq2GwwkjBiBaDTi17MHdV9+jYV9F8pClE+OfeLymOvi17Eufh0KQcGCPguIjmlCnWXLEdRqCjZtIvMj1zeAC7ZtJ+tjaS4xsyVxyCPNH+HW+rdiFa2M2TLG5ag2URSZvH0ySfok6gbUZUbPGQiCQND99xN07z1gt5P4xiismc5fe3LWrSN3/XpQKIhb+BbK4GCCNEFyZ/Gmy5tkkaEr+PrU12xN2IqPwoc3+7yJTqUj+JFH8OvTW3LQjx2H6EEqgzUzU7pWWCz49+9P2AsvMKHbBJqFNCPLlOXxuiHdkM6UHVMASaHfu0E/4t56C0GjQb9jh1tud7teT8Jrw7EbDPh27UrMG6OY33s+EboIzuWeY84e1wWGZ7PPygKfV9q/Qv/7XydkiCQqTBoz1u0ON2tmJokjRkprnIEDueOJqTzT5hkApu2cxuU812KjL+ReYOaumQC8dMNL3Hz/SEKKOnqTxo5zW1xrSU4meYIkMgh96inibh7Iwr4LUSlU/HnpT1afcr4Dd1fSLrYnbkclqBhx+2wErRbTkSOyE+96hVal5c3eb/J6x9cREFhzeg0v/vmix3G/luRkLg19Sj6PRYwYQdizz3hjyhXiSv4V3tr7Frd8dwuzds/ifO55fFW+DGkxhJ8G/cR7t7xH7zq9qz0ivSr49+oFgH7Xrmpx7LkD0W4n8yPpXjRs6NAyNRIapYZhHaT178dHP/ZaVGiqPpVTWacQkNaSFcG3Y0f8+vQGm430d9y7F78WMJ0+Q+aqVfjY4LGYuwFJBOyt5Keawums02y8uBEBgYe32dHv3u3SWqFJSBP53uadQ++4VNcgiqKLTnEpPt2WkYG9sFB2ievatKmwT/x4xnHePiDdl47vOp66gXVLPe5X9J11kMjO4o6Gd6AQFBxJP+Lydc9Z5BbmcizzGOBan7gtJwfTUel5fjf2KvO4X3dpo1+/a5fLa3lHHdrfl//2ujDMZrexI0n6OzjTJw6lneLKkBAAt80C0X7R8vu8Pn69W2M4cDhDErg2C22GVqX1aKyrUSwkkb4zgkolV3dYk6rHqds9RhK9OUQaV8MhZuge0x2N0ntVRba8PApPS05SXadOlR4bcOuAUv/WtpTECFWR4go/P1lwY74gEc3XCykO8HSbpxEQ2JKwhQPrPiLz/ffJ/OADzg8cSPwtA0iaOImcdetksZDFbmHNqTUM/GEgHx79EJPNRIfIDnw58EsW9l1IsDbY7bnk/iR9L4Kuqsp5reNrAKw/t54LuZ5XztU0tiZsZf056XftEdMDhaDgp3M/MXjDYLfF696AwWLg+zPfA/BES++6xK81NM0lQr/wOnWKQzEpfjDtoNeTlGrhGv5TpHgt/v9hwd4FXMy7SKRvJNN7TEehUhG3YAHKsDAKT58mdf58l8e0pKVJjkW7naD77iP4QcmJMrHbRJoENyHDmMH4beOddmGbbWYpMtGST/uI9nLMi7ZFC2JmSqRrxoqV5P/jGuFhPHqMtEWLAYgcN1ZefDUOblyKsHPlBvFczjlm7ZYcTi/e8CI944o3EqJGj0Z3ww3Y8/JIHP6604RX1qpV6HfsQNBqiVu8CIVWWpwrFUrm9Z5HuC7c5Y1Xh/szWZ9MHf86TO42WX7Mt1Mnwl54HoDkadOxpLjeM1Z49iwJw4ZJrtXbbiPsBSnez0fpw5J+SwjXhROfE8+ozaPcdr5cybsi/84v3fAS7SPbS69Rty6REyThQPrSpZhOO0cyiVarRDykp+PTpDExMyViQhAEpnafSsOghqQZ01z67IK0SeVwXi/quwhftS9Kfz/ili1F8PGhYMsWslZ96tLvDlCwdSsZb78DQPS0aehatyZEG8LCvgtRCkp+vfAr35/93unxMo2ZjN06Frto557G9zCoySAUOh11li9D4e+P8cAB0ha67rYVRZHkSZOxXLqMKjaG2PnzERQK7m1yr1tCGVEUmbZjGunGdBoFNWJ0F4lQDHvhhWJBz4iR2I3ud8Ta8vNJeOVV7Pn56Dp0IHLECADua3ofj7eUNvwnbZ/EobRDbo2fbcr+P/bOOjyK623D96zE3RV3dy1WrECBAqVOqUBLcfciLe5uLbWvrhQKlBanheIOxSUJcfdsduf7Y7JLApHdZEOgv3NfV/8oO3POSbI7O3Oe930eph6aiozM89Wep1P5TgBIKhUBC+aj9vAg88oVopaa14mc+MsWkn77DdRqApYuQe3qanptSL0hNPdrbiqeSc82//dyMfaiyXp9dOPRps8YgEPDhniPUjqEIj6cY1YhgtHZIDsyEptKlfCfNdNk2eRq61ps0UOWZeYencuNxBt423uzoM0C1ColN9q5fXs8Bio58OFTp1p0PUs9coTo5SsA8J0+Hfu6dQGla8ooJM04PMOiDbXPLn7G0Yij2GvsWdBmAVq1FvcBAxRRMTOTe+PGF7sYLTeRixaTdvw4KkdHgtauoXpQAxa3XYxKUvHTtZ/49OKnxRo3VZfKpEOTyJaz6VqhK89VeQ7bKlXwe18RGqNXrSLtxAmLx4344EOyrt9A7e1FwKJFSGo11T2qmzLR15xeY5FTx7X4a8z9R/l+GFp/KM38FQst+7p18DV2YK9YQerhw2aPmR0dfd+x5eWXcOmsbOBIksTsVrMp51yO8NRwpv01zaLNxs8vfs7+0P3YqGxY2m6paQNIkiT8Zs7EpnJlZW4zXXEyb90iIqfT2nvEcBxybUTV9qrN+CbKNXPZiWUWbQ5cjb9qckcY22Qs1T2qm9bpP2cOajc3Mi9fLrYwok9JJWTwO2TfC0dbvhwBC5XvCnuNPWueXmO6b5hwYEKx8jANsoGpf00lPjOe6u7VGd1oNKDYzfnk6nY3954Bcpwkpk8n68YNND4+BC5biqTR4GnvycK2C00bMZbYeabp0hh3YBwZ+gxaBbQy2SP7TJ6MXZ066BMTCR0z1uLiA1mvJ2z8eLKjopTrb07x1IiGI2jk04hUXSrjDowjU2/evakxJig9O51mfs14t55SgOU7aSL2TRorxQLDR1gcsSFnZxM2fgL6xETs6tTBZ+wYAOp41TG9d5ecWMLFmItFjpWlz2LeUaWI6YXqL1CpfH3cX3oJgJg1ax/rbnFQPluD6g5iZYeVOGgcOB5xnJe3v8ypyFPFGi/lwAFu9elL+qlTqJycCFyxAq8h71p51Qomi/S9I+jxcw++uPQFybpkyjmXY3Kzyezpv4cpzadQ0bViqcxfHOzr10fl4IA+Lu6xyUlM3rOHrBs3ULm44Jbz3n2QHhV7UNW9Ksm6ZKtklwL8FaYUjdTxqoOnvWehxxrvBZN++80qbiHWRjYYiJgxA7Kzce7cide6T8HZxplbibfYd9fyYsnHiTWnlefAZyo8QzXfOqDXk/yHZcWafar2oXfl3hhkAxMOTDD7ecyQmoqc86xlTqa42s0Nyd4egOyIiCLzxNN0aab7zc7lO9OnSp+HjjGK4ql//W3R9dzbwdvUvV1cJ62iOB5xHINsoKJrRfwczW8iSv3nHzAYsK1aBW2OWJob+8aNkezsyI6Ksvjz1jKgJfYae8JTw7kUd8mic4viTPQZEjMTcbFxob63eTb+xq5wtZtbrk7x4hf2PFflOUApii1Jbvr56PMA1POyrnU6gC5acd7T+viY/s1koV5KueLGYoHjkcfz3XczxhoanQSsRdqpUyDLaMuXy/Pz5ofW1xf7Ro1M/29XK0cU1xYuisPDueKp2TmZ4tqyyxQ3Ut6lPJ3LK8+Ln+YUeNtUrgxqNbrQUBJ//pnwyVO49nRHvnqzLb02t2XO0TnEZcRR3qU8K9qv4PNnPjf7M1UQuvBw0v5ROmZdevbK81p97/q0D2qPQTaw7ox1YprKitj0WFPx82s1X2NTl0182vVTAhwDCE0JZeDOgaw/u75E14fi8uuNX0nOUu6D2wVb97P2qLGrqYjiutBQiwrxHiUVXSri4+BDliGr1CJDBOYhRHHBY8sft/8wWR7Of2q+qfJM4+1NwEJFCEn49juLuoXl7GzujR2ndDVXq4bfjPdNooe9xp6l7ZaautXMtSBefHwxl2Iv4WbrxuJ2i9GqtKbXXHv1wv3VVwGlK8Vc6zZ9cjJhY8cqom3nzri/8kqe13tX6W16QDQ3Xzx3p2Jzv+YMqTckz+uSjQ2BK5ajdnMj49IlIufOK/IBLu3UaaKMosyUKSabMCNe9l4sarvI4o3XX67/wh93/kAjaVjUdtFDVoXew4ZhV6cOhsRE7k2xrJNZFxnF3XfeVQTFRo0IWLQwj22Lv5M/azuuNdlnz/lnjsUbkzqDjsmHJpOqS6WRTyMG1x2c53W355/HqUMHZJ2OexMnmiWQRi1fTtqxY6gcHAhatQqV4/3qUgetA8vaLcNObcc/4f+w6bx5793jEcdZc0bZsJjafCpV3e///exq1MA3x241atkyi/LFs0JCCJswEWQZtxdfxK3v/Y2Chj4NTYUjC44uMCuj2SAbmHJoiklontZ8mulza1OhAgELleKYuM+/IGnnTrPXCTkdun/8AVotQcuXo8mpBIfiFcp8d+U79ofuR6vSsqjtIuw1ygaLpFYTuHgxai8vMq9dI3Je8fLFZZ2OsFGjybx2DbW3lyJyaO9fc8Y3GU/74PZkGbIYuXekxRZtsiwz4+8ZRKdHU9G1Yh4rQMi5/hq7m7/4vyJ/3xmXLxPxoVEAG/GQBaFapWZB2wV42nlyPeG6SSAoiqSsJFPRSofgDrxe6/WHjvEc9DaOrVoiZ2QQOmIk+qTCc4pj1qwh9a+/lAKfFcvzfMZAyW3LLXoYNyWKYsv1LWy9sdXUEfzgBq73uLHY1aqFPiGBe+MnmCUs6sLDlY52gwHXvn1xe6F/ntcfFJLMqUDN3fEyqekkkxggSRIB8+eb3rtRS4pny24k/ocfiP8/Jd86YOECbHMcf9oFtzO935afXF4su+R5R+cRkhyCv6M/77e4//3u2ue5+13N48Zb1OmR8NPPJG7ZonQ1L12aZ3O1T5U+PFvpWZNTR1xG0bmRD4qKg+s98P3Qvz+u/fqa1mrOJpTi2DIJfWwsttWq4TtpUp7XnWycWNJuCTYqGw6EHuDzi+a5lJyOOm2Kr5ncfDI1PfPaNKocHAhasVxxxTl8mOhVqwsdT5+SQtjIUcg5XcvGgrTcvFzjZYs72zOyM5h0cBJZhizaBLbhlRp575m0Pj745RQpxn78MWknLYsakbOyCBs5goxLl1C7uxO8YQNqZ2fT6/5O/qx5eg12ajv+vvc384/Ot/i+4YuLX/BP+D/Yqe1Y1HYRNmob02vuL7+MU7t2yFlZ3JswweyixfgvviB55++g0RC4YkWe925Tv6YMazAMgLn/zOVafNGb17IsM/vIbG4l3sLHwYf5beabumdVNjYErliBytWVjHPnTDEv5hK9ejVpR/5BcnBQit1yrr8alXIf6G7rzr9x/7Lo2CKz1jnv6DyuxV/Dw84jTyGSpNUStGIFGl9fsm7e5N7kyRbdQ0avXUv6yZOoHB2V71+b+3+nV2q8Qqdyncg2ZDPuwDiSsgr/zvnkwifcTrqNl72XqZvW4603kWxtST971qKimLKkQ7kOfNX9K4KcgghLCWPg7wN55493zC7Mk3U6opYuJeTdIegTErCrVYuKP/+EyzNdrb5Wo0V6n1/7KBbpITkW6QGtWdtxLdv6bCtTi/TCkLRakzXx4/DekGXZ5L7i/srLqJ3y/52pVWrGNFKKR76+/DXhKSUXVozW6eZ0fNrXro1zly4gy0V+R5UFCd99R/rZs6gcHfGdPh0nGydeqq4UGHx8/uPHvjimIM5Fn2N/6H5Ukor3GryHS/fuACRut9wWflqLaVRxq0JshlIgbc7zmLHjVeXoWGCnd24kSbov/t27Z4qFKUgUX3BsAXeS7uDr4MvMljPzzT11aNoEydaW7IgIsix0CetRSemW/O3mb6XyHjB2TRu7dM0lxZQn/rB1OoDK1taUp576l2Ud8nYaO1oHKIUEe+5Y10J92w3Fir59cHs0Ko1Z5+gTEwDr2KcDPF3uaTzsPIhMiyyRRfy56NLJE5ezs9HHKs8xmtyieFAgoDTslAY1PGrgautKqi6VCzF554hNj+V8jPK8bW6Hv7mk5zwLODQxz5LdtbdiNW5bs6aps7yoTnHIlSt+I0cUz1JEcSft43GfMajuIAAOlU8jJtiFit9/R7WjRwn+aBOegwcR0rYqs15Ts6B9PKE2KTinyby1S8+SDVnU/GgfSdu2FVnUL+v1GLKyMKSmok9MJDsuDl1kFLqwMLLu3iVm0yaQZRyaNsUm5/2WG+M98u+3f+dK3OPb/VsYsiwz68gs4jLiqOJWhdGNRwPQyLcRP/b6ke4Vu6OX9aw7s463dr1FWErxXLeKg0E2mCJuXq35apk7I5UUtasrttWqAZB23PJmiEeBJEmm719hoV62PNnvdsF/lojUCGYdmQXA23XfNnVRGXF6qjWeg3O6hae/b7bdd/TKlaSdOKFsZq1cgSqnIthIJbdKvN9Cqd5af3Z9kTfkP1/7mW+vfAsoOeL5Vdr6TpqIfcOGGJKTTRaWhSEbDIRPfx9dSIhiNTx3Tr4PWlObTzXli4/bP44sfcFdOQbZwPt/v8+NxBt42XuxoO39DcLcaP39CVi8GCSJhO+/J+6Tgrv1skJDlW7r7Gycuz3zkChjpKlfU4bWHwooG6+XYguv/L0Ue4n5R+cDyg1QXe+6Dx0jabUELF6EZG9P2pF/zLbE1aekEjJkCNnh4dhUrEjQ2jUPWf0B1PKsxZJ2S1BJKn6+9jMfnbfMcnf9mfWcizmHs9Y5z2asaf2ShP+HH5g6bsOLyD1O3Lr1viXuvHmmitPcVHGvwvQW003zHwg5UOgaw1PCH+q8fhC3F19QNjH0esLGjTNLSDKkpxM6chSGxETs6tfDd9rUh455o/YbtA1qS5Yhi9H7RhdZ1LHy1EqOhB8xFa48WFnr3LEjnoOVG/p706abbU+edvq0aePed+JE7OvnrXJ9sFDGKA4VxOmo06bO5TGNx5g6FY1ovLwIXLxI+Xz98COJ2yyr/pdlmYgPPiD18GEke3uC128wbeAYUavULGyzkJoeNYnPjGfo7qEW2VV+e+Vbk6i/uO1ik6ifG6d27fB4S+lEvjd5SoEPybqoKELeG6pYTrdubfobPUju4pkt17fw/ZXvC12j3qBn2qFphKWEEegUyIetP8z3GimpVAQsXGgSP8JGjy7QYj/hp5+JWbceUJwN7HJupB/klRqv0Ll8Z5PoUdR792LMRZNjxIiGI/LNQlPZ2CguGzmxCkXlixvS0ggdOQp9fDy2tWrmKe4yYhSSPOw8+Dfu3yLjCmLTYxmzfwzZcjadynWib9W+ecfz9LxfDPHllyTv31/oGgsi6Y8/iJg5CwCvoUNx7tQpz+uv1nyVV2sqhWRTD021yO1g562dpuKD+W3m42p735FAkiT8ZszApmJFsiMjzY6FyLh69X5Rx8gROD6wOSpJEu+3eN/k1FHUd7HeoGfaX9PyFRXzrPX997GtVRN9fLxZ9w3RK1eReviIUtCxfJnJsSU3NT1rMqmZIpavOLWiwOw+IxGpEYzfP94UYfJ81efzPc62alX8Z88CIHbjRuJ/+CHf4xS3k7FkXrumFNcsXoykfvg+RJIkZrWaZepsn3RoUqEV87Is8+E/H5qy2Qu6Hrh06YLrc89BjiOEuRubssHAvSlTld+vgwPBmzZiW/Hh7tHaXrVZ0HYBEhLfX/2eLy59Ydb4oLherDyt5EpPbDaRSm55v+MlScJ/7hzlnuHqVSLnzy9yzLQTJ4jMuZb4TpqEQ6OHM1EH1R1Eq4BWZOgzGLN/DAkZCYWO+dXlr0yRMIvbLsbDziPP6zZBgaYitfgvvyRpx44i1wmQvH8/sRs2AuD/wQfYVqmS53VfR1/mt5lv+t0WVWD56cVPTXnnC9oswNshr3WuxsuLoNU5UQW79xC7caNZ60w9fPj+Oj/8AJtyeXMTJUliduvZBDoFEpYSxvt/vV+g/fHdpLt8dE65t5zYdKJpY1Xr44PbCy8AELN23RMjiFVxr8I3Pb6hX9V+aCQNR8KPMGDnAIb8OcS0gZ8fuvBw7rw+kNiPlA5i99deo/y33zz0uy0pocmhLDm+hE4/KhbpNxJv4KBx4OUaLysW6Z030Dao7WO/EejYSnH4stSOuTRIO3KEjPPnkezs8Hj94cLE3DwV+BRN/ZqSZcgyFeMWlyx9FkfuHQHMzwb2HjkCJInkP/8k/ULRLg6PCl1klMkVznvMGFPn7as1X8VWbcuF2Asci7Aswulxwdgl3rNSTyq6VsSlezcA0k+ctNjlzV5jb4qPOx5xnHVni+4WNNlAm9ElbsT4TJV2/IQSA6LR4NCwwUPH7bq9y/Qd8+D9Zm5UdnYmwS3FwkiMjuU6Yq+xJyQ5hHMxBV9Di4NOr+PPO38CikhsLrIsm4RuYxd8fjg9ZeyQtzwGxNi1uuv2Lqt9/2VkZ5gKbY0ZyuaQxz7dTSma1xfTPh2UOIkXqivf719esjwOB5S/nXEvzdqieHZsHBgMoFaj9rh/f+fSVSlQS/z5Z6u4hT2ISlLR3E8p+HrQQv2vsL+QkanpURMfh8K7uS0l7USOKN7YPFHcrf/zlP/mayp88zWqnIJIc0Rx28r5d4o/DvbpADXcqtEw3A6DSmLXS5VROTqidnIkoUFFljSJZFzrW1wOlrCRtLycVpdNh2rwzFkV8p0QEn/8iXsTJ3G9fQeuPtWGq23acLVFS640bca/DRtxuW49Ltesxb+163ClXn2uNG7C1eYtuNaqNdfbteN6x07c6NKVhG+U/XTX3r3yXWN1j+o8U0HJdTdGKT5p/HTtJ/aHKHtsC9osyBMF4GzjzMK2C5nfZj6OWkdOR53m+a3Ps/2m5UVkxeFg6EHuJN3B2cY53z3hJxFjQZuxwO1xxBQdUcSejKB0ebyf/AT/KWRZ5vC9oivbs/RZTDgwgeSsZOp41mFog6H5Huc9cgT2DRpgSEkhdGTRm8ZJu/4wbbr4z52b76YmQM/KPXmx+ovIyEw6NKnAzZy/w/7Ok1XYNij/B3Mpp3tG7eVF5tWrhI0ZW2j2cfTq1STv2qV09ixbitrFJd/jHLQOLGu/DCetE6eiTjH97+kFbr4tO7FM6bxWaVjcdjFe9gU/IDq1eQqfiTlWnYsXk/jbw1/G+uRkQoYMQR8Xh22tmgTMnZvvJrSRwfUG0zqwNRn6DIbtGca9lPwziaLSohixZwQZ+gxaB7bmzTpvFjimbcWK+OUIrtErVxaZx6tk2I4m8/Jl1J6eBH+0KU9X8IO0DWrLlGZTAOXmy1z7suMRx032gDNazcDfyT/f4zReXgSuWA4aDUnbthH3ef6deymH/uLeVMUu33PQ24V2zvSu0pt+VfshIzPh4ISHqm2NJGUlMXTPUGLSY6jiViVP53VuJEnC74PZSr54eLjy3i3EElU2GAifNl35HXt4ELRypemhITcqScXc1nMJdg4mLCWMEXtGFJg5//2V7015vTNazqCKe5V8j/MeNQqH5s2R09KUruDEwoVgXVQUYWPGmoo63F97Nd/jKrlVYnYrpcPws4uf8c2/3+R73L2Ue4zeN5psg2KjZxT2HsSxZUu83nsPgIiZM8m8ZX42UuxHH5Pww4+mjlX7OrXzPc5B68CajmvwdfDldtJtxu4fi05fdAzAv3H/suS4IqKMbTz2IVE/Nz7jxuLYri1yZiahw4ahi4zM87ohPZ3QocPIjojApnJlApcvy1cAM9LMv5mpa3He0Xkmq7QHkWWZBccWmOycl7RbUuBmFCid7cHr15kypSPmPJxXnXr4MOEzZwLg+e67uPV5rsDxjFbU5V3KE54azrA9wwp874YmhzJ0z1Ay9Zm0CWxjsjTPD5sKFfCfq2T0xn32GfHf5V8YIOt0hI4ZQ8b586hdXZXPWD4CKChC0oI2C1BLarbd3MbaM/nbRmfpsxi7fyzhqeGUcy7HrFaz8r0eOLVpc9/qfcpUdFFRBf48+ZF65Aj3xo0HgwG3/s/jNWJ4vsdNaDKB9kGK28GofaPMcju4k3SHD48o4vXguoNp7PtwNpzK0ZHAFctNsRAxawvfVM2OjydsxEjkjAwcn3oq365muO/U4ah15ETkCWYcnlHgRt7Sk0vZfXc3WpWWJe2WPCQqmtZqZ0fQqlWoXV3JuHBByTMvqKDjly0mUc9/9ixT531+9K/Wnx6VeqCX9YzZN6bA7uDkrGSG7hlKVHoUlV0rM6PFjEK/411798bzPcV9JmLWbFIOPbwRGrlgIakHDyHZ2RG0bh1a34I3t5xtnFnafqnSeR32d6GOLZ9e/JStN7ailpTIlsKsdH2nT0MbFIQuLIywseOQswu3p5NlmaiFi0javh00GoJWrjTFFORHx3IdGddkHABLTyw1qxMoMTORiQcmkm1QClIKKj7QeHkRsGC+UlT17XfEf/ttgWNm3rhB6DClaNGlR48Cv9+MBST+jv7cSbrDqH2jCrQn331nN4uOK13aYxqPoZFvo3yPc27f3vRZuTf9fTIuFV4ImXnzJvcmKbb/7q+8guuzPfI9rnVga5NV++zDswvcQNh1exfLTy4HYFKzSQVmpdrXq4ffzBkARK9aXWShT+bNm4SOHqM44PR/3tT1+CAuNi4sbbcUrUrL3pC9rDy18qFjjJ3sWYYsWvq3NG30GfEcNAjJxob0U6dI2WPdbrnSxM3OjVmtZrGtzzb6Vu2LWlLz972/eXXHq7y3+72H7klTDhzg1nN9SD99WrFLX7kSv+nT8r1vLA6yLHM0/Cgj946k+8/d+fzS5yRnJRPsHMykppPY3X83U5tPfaws0ovCKESlnzxVohgeaxCTU5Ds9vzzaDzy/y4zIkkSYxuPBZSOzZJ0ep2KOkVadhqedp4PuZcUhG2VKrj2UrK6o1c+/JksKyLnz8eQkoJdvXq4v3zfft7T3tNUnGgty/lHyfGI4xwJP4JGpWFIfeXeQOvnh30T5d4saaf5Ln9GKrlWYlbLWQBsOrfJZKFfEHoL8sSNGHPFE7duBXLyxB9wjQpPCWf2EeW5cFDdQTT1a1romCYL9b8tc3dw0DqYMraNXc7W4q+wv0jMTMTL3sskRppD1s2bZEdEINnY4NC0YCHR8SmlizztxAmLr1Ptg9tjp7bjbvJdLsddtujcgtgXso8UXQoBjgH5FicXhMEoiru5onZ3A0rWKQ7wYvUX0ag0nIk+Y7bjWG6uxl8ly5CFq60r5ZytWzyWnfNcp/HyyuOi6NS+PZoAf/QJCSTtsMyRz1xaBOQvDh0IVZo8CtpnLS6GjAzSLyj3JIW9l3MjqVQ4NGyY57nbrE7xijmd4kZRXPf42KcDJP3+O732Kmv63eYKV+OvsuT4Enpt6cXO2zuRkOhVuRfb++1g6ntfU+frH6l29B+CN23E4+23sKtbF1Qq9DEx6KNj0CckYEhOVuIrdDooqLhFrUaytUXl4IDaywvX557DpVf+ojjAsAbDUEtqDoQeKNPs7eJwJ+mO6RlqZMORBe6xPVvpWX7s+SMNvBuQokth8qHJTD40udD3lzX4v0uKg9/zVZ9/bN6XJcXoWGKMQnkcMeaKX4q9ZFETk8C6CFFc8MhYeWol7/75LmvPFJyTZ7RmPBN9BmetUjGV2448N5JWq4jGHh5kXrpMWCG2s2knT3JvwgQAPAa+XqQl3+Rmk2kb1JZMfSYj9o4gJCnvpvy/cf8ydv9Y9LKenpV6mjqhC0Lr60PQqlVIdnakHDhA+PT38+1SS/hlC7HrNwDgP3s29g0aFDpuJbdKLGu/DI2kYeetnaaq7Nx8dfkrPr+kCK4ftPrArIcBzzffwGOgUvV/b8oUUv+5b+lhzLbOuq5kVAavX1+kLZlKUrG47WKqulclJj2GobuHPmQrmZ6dzoi9I4hKj6KSayVTvmxhuD3/PG4vvwSyzL3x48m4mn/WpizLhM+apVgj29sTvGEDNkFBRf4eXqrxEm/UfgOAGX/PKDK/PSQ5hLH7xyIj81yV5x7a6HwQx2bNTDa3UYuXKDlduUg/f4HQUaOUje1nn8V77Ngi1zytxTRaBbQiPTudYXuGEZqc10VBp9cxdt9Yridcx8feh/Wd1hd686N2ciJw1Uqli/Wffwh/P3/BR5ZlIhcsUDrDNBoCly1D61dwRpmbnRvrOq7DzdaNC7EXmHRw0kN2eAdDD5q6bIc2GMqzlZ4tcDwpp4jE2BUcMmxYgdXM+pQUQt4dogi2FSvi/2H+bgxGulXsxoiGIwDFLm9/yP48r6fp0hixdwRxGXHU9KjJnNZzCn3veg0bikOzZkrH7/ARRQr4AEk7dhC9TOkm8Z06FeenOxR6vI+DD2s7rsVB48CxiGPMPDyzwKIZgJj0GEbsHWGyHy5I1DciqdUELl2KTZXKZEdFETpsuOn3LRsM3Js8hYwLF1C7uRG8fl2BxT25GVx3ML0q90Iv6xl3YFy+rhKfX/ycb698a+rOqONVp8hx7WrVInDpEkVM+u474r+438GZceUqoSNzfcZGjypyPGcbZ9Z1XIe7rTuXYi8x/sD4hzpZEzMTGbpnKHEZcdTwqMHidkVfz1y6dTMJxUZHgNzIskz4+zNIPXBQERY3rMcmOLjQMVsGtDS5n2w8t5Gfrv700Jhz/pnDqahTOGudWd1xdaFFBt7jxmJbowb6+HjuTZpUpKhoJP38eUKHDUfOiQTxm5W/8A45bgdtFbeDuIw4hu4eWmhHfmJmIsP3DCdZl0wD7wamDdj8sKte3SSExaxdS+Kvv+Z7nCEjg9Chw8i6cwdNgP9DMRsPUsW9CsvaLUMtqdl+c3u+VexfXvrS9NA5p/UcGvo83LmbG5ugIII3blCsyQ8eUu4bHrj2ph0/TvgM5efxfPddk7VfQRiLOhr5NCJFl6II32l5ixt0eh1j94/lWvw1vOy9WNdpnVkPyN4jRyoV/no9YaNGkZEr8zbuq6+I/1LpiglYtBD7ukV/bmt41GBh24VISPx07Sc2X9j80DH77u5jxckVgNJtW5AAakTt5ETQmtU5RTKHi4wCiPvkE1PBWsC8uTi1yd8uNDev13qdF6q9gIzM5IOTC81X1hl0jDswjrvJd/Fz9CuwIMWIU9u2eI9RbIgj5swlNZ8HfV1EBHcHDVYyr+vVM2VzF4SHnQfrOq7DWeusFFj+9XCB5ZmoM0w+NBkZmRerv5hvXEVuvEeOwKFlC+S0NO6++y5ZoflbAOoio7g7aBCGxETs69fHZ/KkfI8zMrTBULpV6Ea2nK3cx8Rff2idUw8pxZK5XScKwu3553F76UWQZcLGjCX9zJl8j8uOiyPknXcxJCVh37AhvtOnFzpuba/apmK6Ty588pD7yR93/uDve39jo7JhWouHixK1vj54vKkUhUYuXGS2Xf7jQpBzELNbzWZbn208V+U51JKav8L+4uXtLzNszzAuRp4laskSxS49MRG72rUVu/SuXawyv9Eive/Wvgz6YxD7QvYhI9MqoBVrO67ltz6/8Vqt10yb2U8SNhUroPH3R87KMnW5lQXp586R9s8/oNHg+VbBBcy5qeNVhy7luyAj51ssYi4HQw8CSve5JZ39XsOGgUZD6qFDFkdolAbJ+/eT/PvvoFbj/8HshwpH36j9BmpJzT/h/3Ax5vHpbi8KWZZN+xH9qvYjyPn+87axmCipGBbqAN0rdefF6i8CMOXQFCJSC+44N3aKq70t6BQPyLFPz3EgfNA6XW/QM+WvKSRnJVPPqx7vNXivyDEdc7qm044ft/ha3rOSUsix6/Yus4qbzcVY6N+9Yvd8nQMLwtj57dCkSYEFuaDYRZuuUxZ26TloHUw22b/ftrx4Ij9+vaHc6z9b+VmLrhnG/PA89umJiRZFrjyIl70X3Ssqn4MN5zZYfL5RDKzrVbfQe7vikJ2TJ655IF9bUqtxf+llAOK//tqqcxpp6a/cv5+LPmcSjXV6nckVxNp54ulnz4FOh8bHB60Ze4IFYbyPyDJkFVhUauwU14WGYsjMNBXTO2rKvlNcNhiIWb+eWndlaul9yTRk0W9rPz6/9Dk6g44W/i34vuf3Dzmiqp2ccGrbFt8JE6j4w/dUO3aUCj/+SMUtv1Bp21Yq7dxB5d1/UmXfXqoeOkjVI4epdvwY1U+fosb5c9S4dJGaFy9Q4+wZqp86SbW/DhGwYH6hRZEVXCvQq7Iimhvj3p4EdAYdUw5NIT07nWZ+zXi9duHPUEHOQXz6zKcMbTDUtK/Qf1v/Usud/jfuX45FHEMtqXml5itFn/CEYCx2ybx2zaLYvEeJj4MPlVwrISNzIvLxtHn/X0CI4oJHhputGwAbzm5g9enV+QpruTt+lrRfQjmXwisgtQEBBK1do3R+7d1L1KKHswYzr18nZOgw5KwsnDp2NHVBF4axo9q0Kb9nqMlWMiI1gmG7h5GWnUYzv2bMbjXbrJtSh0YNCVy+DNRqEn/99aHN2NSjx/Jsbrv165vfMA/RMqAlM1spXY4fnf8oj+ix5+4eFh5T7JxHNhxJz8o9zRoTwGfSJJyfeQZ0OkKHDyfjiiI4Ry5YSOqh3N1evmaNZxSSfBx8uJF4g9H7RptsZg2ygWl/TTNls695eo3Zm1V+U6fi0Ly5IjAOHfbQl55sMBAxazaJP/2sdNguW2rWhryRMY3H0Ll8Z3QGHcP2DCtQGE/JSmHEnhEkZCZQy7MWU5s/bBueH+6vvaoIGXo9YaPHmDaPs+7cIeTdd5HT0nBs1ZKAeXMLFWWMaFValrZbSnX36sRlxPHe7vdM711Zlpl5eCZHI47ioHFgbae1+Vr+P4hd9eoErlxheu/GrHm44zR200fEf5GTEzx/Ho4tiq48r+BagdVPr8ZGZcP+0P3MP3Y/h9UoNhpkA70r92ZIvYKFLiMaT0+CN21E5eRE+omThI0f/5Bop2TDjrrvGLBpI2qnoh9KBtcdTL+q/TDIBiYenGjaoDLIBiYfmszV+Kt42nmy6ulVRQpIklpNwJLFaPz8yLpxg9DhIzAU0oGfduoU9yYrrgUeA1/Ho4Cuvwep7lGdpe2XmjqFZ/w9I98cvozsDEbtHUVEagQVXCooNrVmXNPUTk4Er1+P2s2NjAsXCJ86DVmWiV61SnG70GoJWrPabBtUSZKY1XIWLfxbmIo6crtK/H7rd5aeVK6b45qMo0sF8zfQnZ9+Gp+cwqjIBQtJ3rcPXWQUIe++iyElBYcmTfCfV7jjRW7KuZRjTUclQ/hQ2CHmHr3fgZ6lVzqcbyXews/Rj7Ud15ptkeY1dCguPXuCXk/oqNF5ogCily1Tsq3VagKXL3son70g+lXrZ+qw/PCfD/N04X95+Ut+uf6LknfebhGVXB+OZsiNysaGwKVLTNEVkfOKtnHOvHGDkMHvYEhLw6FlCwKWLinUNQDuux34OfpxO+k2b+96O19hPNuQzfgD47mddBs/Rz+Wd1heZFagW79+9+MWpr9P6tG8wqKSzz1Z6V50dqbcpk1FdsEBtApsxcyWBXwX39ljqhAf3Wg03Svl32X6IPYNGiiOIjnX3uil9+8bsu7cIXT4CNDpcO7aFe9RI80a01Zty6qnV1HBpYJyP7NnmGkDyph39k/4P9hr7FnbcS0BTgFmjavEgnxo+j4OeXcIuogIUg4dInKuYr3vPXYsLl3M/9w+Xe5pJjdTuohXnlqZx7HlStwVk1D7QrUXeLnGy2aNaVejBgE59uNxn31GwpYt+R6X8NNPpigDnwkTcC2keyE3kiQxpfkUngp8igx9BkN2D+Fk5MMCjCzLLDy2kKPhR7HX2LPm6TWFFqQY8Rw8CJcePSA7m7CRo/LEB+kTEwkZPNgUERO8cYNZWapV3KuYPju/3/49j2B1J+kOI/aOIFOfSbugdkxuNrnI66Sk0RC0ahW21aqhj45R1vTAvZk+OZmQd94h+144NuXLE7R+XZEdwipJxYdPfUgjn0Yk6xQ3g+g0Rfi4m3SXkXtHkmXIon1weyY0mVDkzw3KPaRj69bI6encfXfIQ8WVhsxMQocNRxcaijY4uMDYnQfpWbnn/cz2o3NNQl5KVoopF/3tum9T3qV8vud7vTMYjbc3upAQ4j4334r/cSLYOZgPW3/I1ue20qtyL1SSioOhB3np99eYmPApN31z7NK/+doqdulhKWEsPbHUZJF+PeE69hp7Xqr+Er8+9ysbO298IizSC0OSJBxbKxbqZZkrHrNpEwCuzz5r6q41h5GNRqKRNBwKO1RkoXFBGO9hLO0ctClXDre+yrN19PIVZRpNYEhNJeIDxW3O442B2NWo8dAxAU4BJvEsv6Kwx5Uj945wKuoUNiobBtcdnOc1l65dQa0m48IFsu7cKdb4E5pOoKZHTRIyExh/YDw6Q/5icXYxOsU1D0RSPSiKb76wmZORJ3HQOLCgzYICGzdyY1u1KhofH+SMDNJOWLbp3cy/GV72XiRkJhTZGW8uyVnJpuJuS/aF4H5sg7ETvCAkSTJZqFtqGw/QtYLSvPLH7T9K/DmNSosyCatGQc1cjEXrqlyiOHo9huSSdW2+U+8d1JKag6EHLYqJAkz52ta2TodcneL5fGbcnu+HpNWSceEC6eesa+cPihAY5BREtpzNiQjlc3Iq6hQpuhQ87Dyo7ZW/O15xSTuhfP84NGlSouICR60jEsr5BXXzqj09Ubm4gCyTdfu26ZnrcbBPT/7jT7Ku30Dt7MzQp+7fN9f1qsv6TuvZ1HkTNTwe/n56ELWTE/Z1amNXowa2VatiW7EiNkFBaP390Xh7o3F3R+3sjMreHkmrNWtPMz+G1B+CRqXhaMTRJ8Zy+qNzH3E+5jzOWmfmPjXXrHtQjUrDe/Xf47NnPjNFMr3x+xusPbO20Eix4mCMcuhcvrNZ+8JPChoPD2yqKO55ln73PkomN5vMt89+S/ug9mW9lP9ZntynQsETxxt13jBtUn10/iNWnV6V50Y3d8fPpGaTaBXQyqxxHRo2NGUYxn3+BXFffmV6TRcZyd3B7yhdKA0aELgk/wzLfMfVOrC241r8Hf25nXSbUftGEZsem8dSdHmH5WjVRT8QGXHu0AH/OTkWuZ98QmxOTnTmzVuEjhwJOh0u3buZvblt5Lkqz5m64z7850MOhx3mbPRZJh2chIzM89WeZ1Dd/PN8C0LJ412AfZPGGFJSCHnnHaKWLc/b7VWAfXNB+Dn6sa7jOhy1jhyPOG6ymV13Zh1/3vkTjUrDig4rCHYpvPsxzzq1WgJXLEcbHIwuNJSwUfdtZmWDgfAZM0j47juQJPznzcW5Q+Edtg+iklTMe2qeqft66O6hD92E6Q16Jh6cyI3EG3jbe7Oqw6p8s5jzXb8k4Td7Fna1a6NPSCB0xAiyQkOVTq+4OKXLddVqJAvsJJ1snFjXaZ1JUBq5bySZ+kzWnV3HtpvbUEtqlrZfatZNrmnMNm3ydFgm/PyL6bX4H34gerliV+o7dQquPc1/yG7g08CUw/rdle/4/OLnhKcottTp2em08G/BzFYzzX5gsatenaB1a5VCmd17iPjgQ9N1xtRpe/iwkg27YUORnbZGJEliWotptA5orbwP9gwlNDmUNafXsC9kHzYqG1Y+vdLsm0mtjw/BGxUBP+34ccKnTM238jz9wkVChrxnUVFPbp4KfMqUW/zrjV+ZcTivMC7LMjMOz+BczDlcbFxY09E8YcaITXAwgatWKjEAO3Zw85luebJhjVl65qJVa1nWfpnJVeK93e+RmJnIiYgTTP3rfgdgUZ2K+eHx5hu49e8Pskzoe0O53qGDyS0gaM1qiy1b63nXM3Wy/nj1Rz4+/zEG2cD0v6ZzMvIkTlonUyGQuUiShP+cD7Fv2BBDcjIhQ94jOz6euM8/vx//8cEHFl/HhjcYTs9KPfN04f8d9jdLTiii3/gm43kqsOguWADbypUJXLwIJIn4r7/O8537ILp797j79iD0CQnY1a1L0Oo1Zv+efRx82NxlM74OvtxMvMlbu94yCWBGFh1fZBJwVz+9utBokNx4jxmDc7ecoq8RI0yWdgBRixaTvGsXklZL0Jo1D+UbF0afqn3yFCD8Hfa38l18aJJJvC3MRj8/nNu3xz8n1zz2483EfvaZIn4OeU/psqxbl4AF8y3aYHC1dWV9p/WmzPlxB8aRbchm7Zm1psLEZe2XUcuzlkVrlWxsCFq9SnGQiIzkeoenCRn8DhgMuPbpYypGsIRXar7CwFqKbf/7f7/PsfBjxKbHMmLvCNKy02ju15zJzYsWanPj8kxXvIbmxFjMmEn62bw2fAk//0L4dMVhweONN/B827K/mUalYXn75aYCn/d2v2fa5DPyzb/f8N2V70yuF4XFVeTGmC9uumcYOgxDaiqGjAxChg4j89p1ND4+lPv4o0IjYh6kuX/zh7qbY9NjlcK6zARqe9ZmUdtFRRadGFE7OysxNf7+ZN26Reh7Q01uIoasLEKHjyDzyhXU3l4Eb/7YrMITUIo6VnZYSQWXCqYIi3sp9xi6ZyjxmfHU9qzNwjYLze5+M75n7evXx5CYSMjbg0yFBrLBQPiUqaYCmeCNG8xeJ8C79d6ld+XeGGQD4w+M53LsZdaeWUtUehTlnMvxdt23CzxX5eiIz3jFij9mwwaL4yoeJ8q5lGPuU3P5xn8a7a5okAwyJ6uqmPyWhnmtIrmacrPoQQpAlmWOhR9j1N5RdP+5O59d/IzkrGSCnIKY2HQie/rvYVqLaUUWfD1JOOXkiqeWUa545vXrpOzeA5Jk8TW9vEt5+lXrB8Dyk8stFrxCkkK4nXQbjaQp0hkkP7yGvodkY0PaiRNlVlRgyMoidOQosu+Fow0IwHvYsAKPNd4v7L6zm5uJxf+cPCpkWTY55bxY40V8HfMWzms8PXFsodgkJ+0sng2zrdqWpe2X4qx15mz0WVaezN91IDsqJ1PcAlHcvnbefQ2HZvet0c9Gn2XdGSV2Z1qLaWbvVSiFLMWzUNeoNHSrqGSxmxvjVhS77+wmy5BFFbcqVHc3774DlAKxtGOKkFhYnrgRx9bKM4Uxg9wS2ga1xV5jT1hKmEkELi7bb27HIBto4N2gwCK0gjBapWvc3FDl2DxDTvZ2CSjvUt6U22tpPrIx1rGel/VFceOzUH6FThoPD5PTQ/xXBT/3lYQHLdSNxYRtAttYvZgtPcctxBjpUFxUkgonrRNQsCguSRK2le7nij8u9unGLnEAjwEDaFetK192/5Kfev3E1z2+5qnAp6zuRlBSApwCeKHaC4DSLV6WxW3mcDb6LJvOKUWE01tMt1h0buDTgB97/kjPSj0xyAY2nN3AwN8HmhUtZw4x6THsuLUDgAG1BlhlzMcJxycgV7xlQEtqe9a2yLVFYF2EKC54pLxe+3UmNVXsET8+/zHLTykPxFfirpg2jV+s/qLZHT9GXLp1M1lKRs6bR/L+/TldKO+aOmaC1q9DZW+eUGnE28E7j61k++/b57EUdbEp2hL4Qdz6PIfPhPGAktkd+8mnSrdijnDvP29esarnhtYfahI93t39Lq/teI1MfSZtg9oWmBldFCpbW4LXrMGmsrLBHZvTGWBpt1duqntUz2Mz2+3nbmw8p4hoM1vOzDcLtig07u4Er1urWHwfO0bEvHnIej3hU6aS+ONPoFIRsGgRbs89V6w122nsWPX0KtoEtiFDn8HwPcP5O+z+Q96yk8s4FHYIW7Utq59e/dAmQFGo7OwIWrNaiQK4fJkbnTqjCwlBGxxsdifzg/g4+LC+43qctc6cjjpNky+bsOGsYtM1vcV0swWw3Li/8IIpKzR8hiIuJ+/eTcTMWQB4vvMOHq9bLlZ2Lt+Z8U2Uz8TSk0vp8lMXYtJjqOpelWXtl5lViZ8bx2bNCFiyGFQqEr7/npjVio1f9IqVil2yWk3QiuUWOQZAThd++/td+N1+7sZH55VMxVmtZlHfu75F49lVr0aQUVDevp3o5SvyvJ5+/jx333rLZNkauHiR2UU9uelWsZsiEkhqtt7YmkcY33BuAztv7UQjKQUplm4YgPL7NhZMGDs/PN95p9Bs7sLI7SpxM/Emvbf0ZuieoegMOjqW68iEJhOKdT2TJAm/Ge/j0FJ56MZgUNwCPtp0vwPAQnJ3sq46vYrOP3Zm523l97m8w3Kqule1eEyVrS1Ba9co2cchIVxr2YrI+Urhl/fYsWa7iOTGaJvd3L856dnpvPjbiwzZPQSDbKBv1b68VvM1i8Zz7tTJJNhEzptHyqGHM+B1YWHcGfiGKVe+ONeyci7l+LTrp/g5+nEr8RZv7XrLZPf93b/f8c2/3wAwv818i4p8JJWKgPnzsW/QAENSknKvEBtL3Bf/R9xnnwHgP28ejs2bFT5QPuQuQBiye4jpu7hdUDumNJ9SrPeuW98+eI9T4jOiFizkavMWZN26hcbfX+lctfDeBpSujLUd15pyuxv+X0PTd/H7Ld4v1ncEgNrFhXIbNyqWpTmbFQ5NmuA/u3Br8MIY22QsXcp3IduQzdA9Q+m5pSfhqeGUcy7H0vZLLf6OAPAaPhynjh2RcwRaXaTyvor76ivCp04FWcb9lZfxmWRZIZIRO40dq59eTUv/lqZCKqMwfjjsMAuPKy4+oxqNMuWGmovKzo6gtWtQe3uRefUqYeMnKPbfJ08q4u1HH6ENDLR4zb0q92JoAyUO6MN/PqT99+0JSQ4h0CmQNR3XWLyBp/X1pdymjahcXEg/c4awceORdTruTZpE2tGjqBwdKbdxo1lxNrkxxq942HlwOe4yXX/qyp2kOwQ4BhRrnSoHB4I3bsC2ahWyo6O5+/bbZMfEELNmjSkSJmj1KtOmprlIksTMljNN190XfnuBLy8rhaXTWkzDVl14x7lLz57Y1a+HnJb20P3Bk4Ss0xG1ZAnSiJkM+zmDtfsq0c23AxIS+0L20X9bf0bvG21RznR6djo/Xv2Rvlv78vYfb7M3ZC8G2UBL/5aseXoNv/X5jQG1BjyRFulF4dCyJUgSmVevlkmxhLFAz7lTR2wrV7b4/CH1h2Cvsed8zHn+vPOnRecabZAb+TYq1t9W6+dnyu6OXrHykW+oy3o99yZOIvXvv5Hs7QlcvqxQN48q7lVoH9weGZlPL3z6CFdaPPaH7OdC7AXsNfa8XSf/op+SWqjDfRcKgM8vfc7eu3sfOsbUKe5lvihuW7Uq9o2VfQinDh1MRZwpWSlMPjgZvaynW8VuJltzczFaqKcWo2vaONf+kP0PRc8VB6O43qNSD4vuydJPnkTOyEDj44NttaKfbRxbtgCViqybN9Hdu1fk8bmx19ib7ot+uPqDRefmRpZltt5Q8uF7VbGsSxzydooDaHMK6bPuFs/lIDfv1nsXrUrLsYhjZne8xmfEczf5LoBZ8WGWkn5SifspKL7R/VXFWjlpx06yY2OtPn8L//xFcWvnics6HWmnzwBYXLyfH2blile6nytuFMWNYnpZkbJ3L5lXrqBydDTFZtb3rk8192pluq6iGFxvMHZqO87FnDNlzj+OpOnSmHJoCnpZT/eK3c12iXsQJxsn5rWZx8I2C3HWOnMu+hz9t/Vn241tJb6H+e7Kd+gMOup71y8V94my5n6u+OMrigvKHiGKCx45r9V6jSnNFCvgTy98yvS/pzNi7wjSs9Np7t+cSc0KzxQsCM93BuPary8YDISOGMn1Tp3vd6F8ZFnHTG6quFdhRYcVpu4YO7UdazquMdtSNN+1vv22KS8watEikwAatG5toRlNhWEUPZr53d/Er+VZi8VtF5vd2ZMfajc3ym3aaKq0dn3uuWJ1e+Umt81sWIpiF/5m7TdNVbPFwbZqVQKW5OQGf/Mt/9auYxJAA5cuwbVnwXnUZo2vtmVFhxW0D2pvypo/GHqQn6/9zBeXFGvLOU/NKba9k9bfX7HJ1Sh/K7WHh9Lp5WV+FtqDPPjeBcUG/Plqzxd7TO/Ro0zWrXffelux7zUYcH2+H95jRhd73AG1BuTJ//Sx91EKUoq5oenSpQt+M5ROv5h167jZqzexG40dzLNxalu8ByxHrSNrO67F1+F+4cNbdd6y2ILONF6rVve7QD/6iPhvvwWUvMa7b72tCOKNGhH80Udm2eAWxDMVn2Fh2/vC+Pt/v8+OmztMXQ/TW0ynqV/TIkYpGPf+/fF4S+loMTebuzByu0rEZsSSnp1OPe96LGizoESVlJJWS9CKFdjWqql8N6xfZ7Eg8yCv1HyFN2q/AWASbGe3nm16uC8OGg8PgjesR+V0/4HZfcCAEl17tWoty9vnFeob+TRievPpxRIrPd56y/SdGzZ6TB7b4azbt7k9YIDpu83SrtXcBLsE80nXT0yuLW/teoutN7Yy/5higV0cURFyhMV1a00uI9daP0XkvPs238X9zijou9iSLtv88Bw0yLRpATlC3vp1aH3MdyJ4kDpedVjUdlGeDox36r1j6uQrLtrAQILXb0Dt5qa4naxeZZHbyYOoJBXz2syjoU9DMvWZJGcl46x1ZnXH1RY5W+RGccNZaBJCQ0eMIGr5CiI/VNx83AcMwPf990vUJWEsqDM5zewZyvdXvjdFg/Sq3Mti5wAjWj8/glevRtJqSdm3j5R9+5BsbAhetxa76sXf0BpSbwi9K9/Ppje6CpjrwvAgtlWrEmx0b9mzh3/r1iN55+9KvMbqVdjVssyNwEiwSzCrnl5lEpadtE6s7bi22OtUu7kR/PFmtIGB6O7c5dpTbYhZp3TQ+M+eZeputBSj+0kVt/uOE89UeMYsNyxJpcJvquKQkvjLL6SfL1nHXFmgCw/nzusDif1YsX92f+01Wn/8I4ueWcWW57bQrWI3JCT23N3D89ueZ+z+sVyNv1rgeGEpYSw7sYxOP3Ri9pHZJov0F6u/yK+9f2VTl020C273n+640Li7Y5fT0VqcLsySoAsLIzFHzDQWyVqKl70XA2sr7h+rTq8q0P76QVKyUvj6XyXP9qUaLxVrblDWLTk4kHH+PCl7HxZTSwtZlomY/YGSI54TL2Rfv+hiWqPT29YbWy0qHHnUGGQDa84oRciv1nwVT3vPfI9z7twJSasl89r1h+IqLKFj+Y4m16jpf01/qHPOmCluSac4QNCa1XgNfc/0DAkw7+g8QlNCCXAM4P0Wlt8XOLZqdb+QJdKyQpYaHjWo7FqZLEMWu+/stujcB4lIjTDFFvSo2MOic03W6a1bmxex5eqKfT1FZCmOhboxO37nrZ2mGDhLuRR3iesJ17FR2Zgs2c1FNhhMorja1Q0AmwoVAOUZp6T4O/nzQnWl43XVqVVmiVvGrvkKLhWKfe9bEPqUVDIuXwbAoXGjfI+xr1cPu7p1kXU64r/51qrzAzT3a46ExPWE65yIOGFyBTHXPdRcMi5fRk5PR+XqapEbWEEY96pSslIKPMaYK5514+ZjYZ8uyzLR65Q9IPcBr6F2te77qTTxsvcyZV+vPr0ag/yw0+LjwKLjiwhJDsHP0Y9pLaaVeLzulbrzY68faeTTiFRdKlP/msq7f77Ljps7TDn1lpCpz+T7K98Dij7zX8RY9JJ55Yrpei4QPIgQxQVlwis1X2Fac+XLYeuNrYSnhlPBpQJL2xWv4wdyLCVnzVK6AHU6DImJqBwccrpQLO+YyU0z/2YsbruYFv4tWNtxLbU9S55r4zNhPK69lapVlYuLxbaM+WHcfGvu15ymfk1Z23GtVax5tIGBVPjhe4I3bcR/7hyrWOn0qdrHlLfYrUI3RjUqmYgG4Px0B5NjAAAaDYHLluHSrVuJxwawUduwrP0yOpbraMoYn3lYEfeH1h/KMxWeKdH4js2aEbBgAc6dO1Puk83YlLe8a/dBmvk3Y/5T83G2ceal6i8xouGIEo0nqVT4z5+Xp7LWqVNH/GcVvwsQlM/vhCYT6Fe1H3U865js30uC+0sv4TV8OACZORsvXiOG49avZIKPr6Mv6zqto6ZHTV6s/mKJ37tufZ7Da4SyzogPPiR61SpFEE9Oxr5xY4I3bSqWW8CDdK3QlUVtF5kyxicdUgqQXq/1eolFMADfiROo+tchAhYvKnZWVG6qe1RnWftl2GvsqeZejdVPr8ZOU7yiodyoXV2p9PPPVD140LRhU1LGNB5Dz0o9UUkqxjQeY3FuXX7YVqlC0OpVaAMCcH/lFXynWGYRnR/GLvyq7lWp51VPcWKwIAIkN5Ik4T9zJg5Nm2JITSV0yHtkx8aSfu4ct19+RckKrlCB8l/+H9oHchotJdhZEcYDHAO4k3SHaX9NQy/rebbSswV2I5mDxsNDiTHItRng9uKLJS780qq1LO+wnOb+1vsuliQJn0mTcHvhBTT+/gSuXJlvDqmldCjXgektpmOjsuHlGi8zvMHwEo8JYF+nNlWPHKbCTz8WuyAiN7ZqW1Z1WEUVtyo4ah1Z0n5JiS2R1U6OBK1di8rVlYxz50yFU17Dh+M7tXhd/Q9ip7FjZYeVpuiND//5kGRdMg19GjKzpfnRIPlh36ABfjm5tKhUBCxdYqqKLy6SJDGz1Uw6l+9MkFMQq59eTUXXiiUa06FJEwIWLcrzbwHz5ilCQQmo712fdR3X0b1idzZ13kQV95Jtbmp9fSj3yWbUuYoRPQcPLvE9g4uNC2s7riXQKZCKrhUtKv61r18f195KkULknLmPvVVkbpL37+fWc31M9vOBq1biN32aqfuykmslFrVdxC+9f+GZCs8gIfHnnT/pt7Uf4/aP43r8dUDZvD0ecZzR+0bT/efufHrxU5KykghyCmJCkwns7r+b6S2mU8ntv2ORXhRO7doBkLLv0Ym6ALGffArZ2Ti0bIF93brFHueN2m/gYefBnaQ7/Hz1Z7PO+eHqDyRnJVPRtWKxCuGMaDw98Rig2IRGr1yVb3RRaRC9bDkJ338PKhWBixfjZIYFNSjXuc7lO2OQDSw4tuCxvQb8cfsPrsZfxUnrZCoUzQ+1iwuOOYXJSTt2lGjO0Y1HU9+7Psm6ZMbtH0emPtP02v1MccsKpTTu7niPHGm6b91+czvbbm5DJalY0HZBsYq1Ne7u2NVROnstjT2QJIlnKytFmttubLN47tzsuLUDGZkmvk3wd7LsvtxYgGOOdboRY/Z4cYp36nvXp6ZHTTL1mfxy/ZeiT8iHrdeVLvGnyz1tsbujISUFcq4NalflXGuK4qAUvBhdM4w574Vhsk4vhY7O9LNnwGBAGxBQ6DOb55tvABD/5ZcY0tOtugY3OzdqetYEFEERoLFvY5xsrNtRnXZccW1yaNzYKvsVxvUl6Qp2crCpqNyfpN+6SVq2ImCWpX168s6dZF66jOTggMfAgWW2juLyVp23cNI6cTX+Krtu7yrr5TzE3rt7+enaT0hIzG09t1jusvkR4BTAJ10/YUTDEaglNUfCjzDp0CTaf9+eSQcncTD0oNmFhttvbicuIw5/R386letklfU9bmi8vRWXBlkmLScyQSB4EEl+XO+sy5CkpCRcXV1JTEzExcU6FzBB/nx/5Xs+/OdD3Gzd+L9u/0cF1wolHlOflMSdgW+gCw0lcMVysx86ywI5O5vk3buxq1OnxN2KTypJWUk4a52tllkjyzJRCxaSvH8fvpMm4fz001YZNzc6g47JByfzx50/AKXrZ1HbRY9d7k5pok9I4N606ajd3fCbPr3YDgeljSzLRM6dR/yXX+L+2mv4Tpv6WP6dZFkmfNp0En++vzlo36QxwRuKZ59fGH/e+ZOJByaSLWfTLqgdKzusfKy7qjKyM9CqtI/1GkH5G2YZsoq0xP2vkR0fz+2XXkJ3R7H0Q60GvR672rUJ3rQRjWf+3ULFISwljLd3vU1YShj1veuzuetmq/y+006c4N7ESTi0aIH/B7ORNMXv6BaULtmGbPSy3qqfs9TDh7k7+B3Q6/GdOqVYMSBFkanPZNTeUfx9728CnQL5qvtXBXbSWUrqsWOonZ2xq1nTKuOVFvHffU/spk14Dh6M+0svlvVyCiTj33+JmDMHhwYN8B471iqbpoBJzLL0HkQXGcWNbt2Q09IIWLwI157Fc6Z5VMg6HVErVhC3+RMA7OrUIXD5MmyCC8/gvRZ/jQ1nN5jurQGquVfDIBu4nnDd9G8t/Fvwas1XaRPY5rG/LygtMi5d4lbffkj29lQ7cviR3INnx8Zy/emOyJmZlPv0ExxbWp7pnZuvL3/N/GPz8bTzZEffHYUKBJn6TLr+2JXYjFg+bP1hiZzFQLFFvt6pM4bkZAKWLMH1Wcu6Zi0ldvMnRC1eDIDfB7Nxf+EFi86/l3KP3lt6k6HPYHHbxTxTsWRF2NYm25BNn1/7cDvpNkMbDOW9+u8Venzi9u3cGzcebXAwlf/YVaLnsojUCPpv609CZgIvVn+R6S2mI+t0/FtXEQ6rHv672E0Hocmh9N/WnxRdCu/Vf88UL1IcolasIHbDRlx69CBw6RKLzg1PCafLT0ps3R/9/rBY0DbSd2tfrsVfY1bLWRYVQ+uiorjeth1IkvL7NLPQMf3MGW6/9DIqZ2eqHTls8b31L9d+YcbhGQQ6BbK9z3aLrvc6vY6nf3iahMwE1ndab3EkUMbVq9zq1RuViwvVjx0FIOHnXwifOhWHli0o/6l14gxWnFzB5gubqeZejR96/lBodva7f77L4XuHmd58Oi/WsO49VNSSJcR+vBnX3r0JWLigwOPk7GxuPNMNXWgovu9Px+PVVws8tjgsP7mcTy58Yvr/iU0nWj3rOGToMFL27sVnwgQ83y6eW1NuRuwdwf6Q/cxoOYP+1frne0zWnTvc6PoMGc62vD5cibA79uox7DWWx1+VFH1SEjd69EAfHYPX8OF4Dx/2yNdgDTac3cDaM2sp71KeLb23lMiNzZrEpMfQ99e+xGfG82btNxnbZGypzHMn6Q7bbmxjx60deZxS3G3d6VKhCz0q9aC+d/18rykG2UC/rf24nnCdcY3H8UadN0pljY8D4TNnkfDdd3gMHIjvlMllvRzBI8RcXVd0igvKlBeqv8BvfX5j63NbrSKIg1KBXPGnH6l2+O/HWhAHkDQaXJ555n9WEAelg8aaIqUkSfhOmUyVXbtKRRAHJVt6YduFDK47mAG1BvBB6w8eS6G1NFG7uRG8dg0Bc+Y8toI45ORIT59GtRMn8Js+7bH9O0mSpFi05lTVOzRpouTyWlkQByXDfVOXTYxvMp7F7RY/9pvKdhq7x36NoPwN/9cEcVA6YILXb0BlvNnU63Fs3ZryX3xuVUEcINApkC+7f8ncp+ayqfMmq/2+HZo0ocrePQTMmysE8cccjUpj9c+ZY6tWVNr6K5W2bS0VQRyUTvfVT69mRYcVfNPjG6sJ4qC4zDzugjiA+4svUGXP7sdaEAewq1GDCl9+ic/48VYTxEH5jijOPYjW1wevd98FIGrxEgypqVZbk7XRhYdzZ8DrJkHcfcAAyn/9VZGCOEBV96osbb+Un3r9ROfynQG4Gn81j0X6lt5b+KjLR7QPbv9E3BeUFrY1a6Lx90dOTyf1yJFHMmfcF/+HnJmJXd26OBQzTiA3/av1J9g5mNiMWFMMVUFsubaF2IxY/B396VGp5AK22tUVz7eUGLOY1auRs7NLPGZBJPz4o0kQ9xk/zmJBHJTusLfqKuLNkhNLimWVWppsv7md20m3cbV1ZUDNogUs5w4dkOzt0YWEkHHhQonm9nP0Y95TSvTNd1e+Y+etnWTHxSkvajSo3dyKNW62IZsph6aQokuhgXcD3qlXvLgAI07GrunDhy12J/B38jdFXG2/Vbws9itxV7gWfw2tSkvnCp0tOjf18GEA7GrVssj5x65uXVSurhiSk0k7dcqiOQG6VeyGq60rYSlhHAo7ZNG5B0MPkpCZgLe9Ny39LS/g0YWGAuTZozN2imdev46s11s8Zn68WedNszpeDbKB89GKfXppdIqnGJ0Aniq8eEDSaPDI6RaP+/Qzq187H4weM94LWAvZYCA9p2PUoWnJ88QBUxdwYfbp2sBAJK2WNFlxs1BJKuzUZbN/FrV8OfroGGwqVMDzncFlsgZrMKDWANxt3bmTdIetN7aW9XIApfj1/b/fJz4znuru1Rne0Drua/lR3qU8wxsOZ3uf7XzV/SterfkqHnYexGfG892V73h95+t0+6kbK06u4Fr8tTzn7ri1g+sJ13HUOtK3Wt9SW+PjgClX/LjIFRfkjxDFBWVOeZfyuNuV3FozN5JKhaQtniWsQGAOGpWGkY1GMrHpxDKp8hRYRmmIy9ZG0moJ3riBCj/8QLnPPkXlWHprburXlIG1B4r3rsAq2FaqSNCa1dhUqYz7q68SvH5dqb1/vey96FW5V5nazgn+e9hWroxt1aqlOodWraVjuY5Wv+cV/PfxeGMg2qAgsqOiiF61uqyX8xC6sDCiV69R7NLPnLlvlz5tqsku3VyquVdjWftl7H9hP4vbLuaDVh/w5/N/Mr3FdCq7VS6ln+DJQpIknDt0AHgkudj6lBTiv1byvD3fGWyVAlOtWsvIhiMB+PTCp8Smx+Z7XLYhm08vKl2ZA2sPLHbM2oO4D3gdtbs7WXfukLhli1XGfJCkP/4gfIYSs+U56G08BxU/muXN2m8S4BhAZFokmy9sttYSS4xOr2P92fVAjqWtGVbHKgcH0/s3aXvJLNQB2gS1YXBdRdyZeXgm1++cBhSr/OIWNm06t4kz0Wdw0jqxoO2CEnch2tevj8rREX18PBkXL1l8/rOV7luoF8fo87ebvwHQPri9xVa+qX8ronhRgumDSGo1zu3bA5C82/I8dDuNHX2q9AHg238ty7D+9cavgPJ7K04BlVEU1+Yq6LKrUR2Viwv66BiSduy0eMz8cLV1ZWBtxb563Zl1ZBvyF5lvJ94mWZeMndqOqu7WvVfNjo4m899/AXBsVXQBgVvfvqjd3NCFhpL8xx9FHm8JjXzv55nX9apb4ii9B8m8fh19YiKSvb3VikmNkQrJWckFHiNpNNhUKE9Gzu2Qo8axTBo10k6fJuHb7wDwmz3b4vuzxwlHrSNv11Ui1Dac3UCWPquMV6S44P4V9hc2Khvmt5mPjbr0f7+SJFHPux6Tm01mT/89bOy0kV6Ve+GodeRe6j02X9hM36196bu1Lx+f/5jr8ddZeWolAG/Xedtq1u6PK0ZRPOPff9EnF/wZFfzvIkRxgUAgEAgeEyS1Gvu6dUS3quCJw7FZMyr/9ht+709HeoIfsgUCgeBxQ2Vri9+M9wGI++KLYnXdWRtDZiaJ27dz9623uN6pMzFr16JPTMSuTh0q/vwTLl26lGh8T3tPnqn4DH2q9sHV1tVKq/7v4NRRccNK3re/1HOx47/5BkNyMjaVK+Pcsfh53g/SpUIXannWIi07jU3nNuV7zM5bOwlLCcPDzoO+Va3X0aR2csTzHaX7N3rdOgxZ1t1QTz18mHvjxoPBgFv/5/EeN65E49lp7JjQdAIAn134jFuJt6yxzBLzy/VfCEsJw9POk5drvGz2eS49ugOQtHOnVd6/QxsMpalfU9Kz05l8dSmZGqU7szicjjrNxnMbAXi/xfsEOhVvnNxIWq3JYcHSXHFQOmZtVDbcTLzJv3H/WnSu3qBnx02l+MBSpwXZYDCt17F1K4vOBXDuonT6Ju/eXSwx/4XqLyAh8fe9v7mdeNusc+Iy4jgUqnSW96rcy+I5AbJCckTxoPt/e5WDg8lhInr1amQrXTOMHa+3k24XmBt/LkbJE6/lWcvqNtF5nADMcPhS2dvj/tprAMRs+siq3z+2alvWdlxL/2r9md5iutXGNWLqEm/YwGoNVE7anEzxrIIzxQFsKlUmLcfoytHm0TdryDodETNngSzj2qcPjs2bPfI1WJsXq7+Ij70P4anhRTrOlDY3E2+y5IQSjTGm8RirF6+Yg0aloVVgK+Y+NZf9L+xnSbslPB38NFqVlmvx11h5aiV9tvYhIjUCP0c/q0cTPI5ofX2wKV8eDAaRKy7IFyGKCwQCgUAgEAgEAoFA8Jji1LYtrn37giwTPmUqhoyMMllHxqVLRHw4h2tt23Fv3HhSDx8BWcahZQsCFi+mgpl26YKS4di0KSonJ/QxMaSfPVtq8xgyMoj7XNls9hw0yKqRAipJxZjGYwD4/ur3hCSF5HndIBtM+bKv1XzN6u5G7i+/hMbHh+x74SR8/4PVxk0/e5aQ4SOQdTqcu3bFb9Ysq3QFdizXkdYBrckyZDHz8EwMcukWQxRFpj7TJB4PrjfYor+PY5s2qJydyY6MNAlVJUGj0rCwzUI87Ty5qY/gUB0pj6BpLhGpEYzbPw6DbKBX5V50r9S9xGsz4vSUEuuXcsgyK3BQulHbB7cHYNvN/IXTgjgeeZyo9ChcbFxoE9jGonMzLl9GHxeHysEBhwYNLDoXwLF1ayR7e7LvhRerQz7YOZi2QW0BxR7fHHbe2km2nE0tz1pUca9i8ZwAuhDlWvTgd5nHgAGovb3Q3b1LXI57RknJ3fG6/uz6fDtez0Uronh97/pWmTM3KaaiB/NjJ91ffQWVoyOZ//5L8p+WuwAURtugtsxoOYNanrWsOi5A2vETANg3bmy1Mc3pFAewqVSRDBvle8BR8+hF8djPPiPz6lXUbm74TJzwyOcvDew0doxqPAqA9WfWcyqybApGdXodUw5NIUOfQQv/FrxS85UyWUdu7DR2dK3QlZVPr2TfC/uY1XIWTf2aopE0aFQaJjWdhJ3m8Y3AtCYOzXIs1I8JC3XBwwhRXCAQCAQCgUAgEAgEgscY38mT0Pj4kHXnDtErVpb6fLIskx0TQ/Lu3UQuWMjN3s9xq28/4r/6CkNiIhp/f7yGDqXy7j8p/+mnuPZ8VjiFPCIkGxuc2ipiUWlaqCf+8gv6mBg0Af64PlvyPO8HaeHfgtYBrck2ZLP6dN5ogP0h+7mecB0nrRMv1njR6nOr7OzwGvoeADEbN2BITy/xmJnXrhHyzrvIaWk4tmpFwOJFSGrL7ZvzQ5IkZrScgYPGgdNRp/nm32+sMm5x+eHKD0SlReHn6Ef/av0tOldlY4NzZ6WLOHFHyS3UAbwdvOlXrR8A1wKkYnWKf3/le6LTo6ngUoEpzaZYZV1GnHKsxNNPnSI7Otri840W6jtv7SzQZjs/fruhWKd3rdDVYjvflAMHAHBo0aJY13aVnZ3pOpX8558Wnw/wUo2XAPj1+q+k6dKKPP7X64p1enG7xAGyckRxbVBeUVzl6IjPKEWEi1m3nuz4+GLPkZvcHa8/XH24QOd0lBIJUNe7rlXmM6I4AVhuj69xd8dj4OsAxKxZbbWM9dJElmVTp6hDY+vkiUOuTHFdwZniALaVKpNutE/XPlpRPCskhJi16wDwmTQJjft/J8apZ6WedCzXkSxDFiP2juBmws1Hvob1Z9dzKfYSLjYuzGk9B5X0eMlsrrau9KvWj0+6fsKxV49x+OXDdCrfqayX9cgQueKCwni8Pq0CgUAgEAgEAoFAIBAI8qB2ccH/ww8AiPv8c1KPHLHq+NmxscSsX0/Y2HHc6vc8V5s159pTbQgdPoK4zz4j88oVJK0W527PEPzxx1TZ/SfeI0dgExRk1XUIzMPp6RwL9T+LZ01cFHJ2NrGblU5tzzffsprd7IOMbjwagJ23d3Ix9qIytyzz8fmPAUUwKq3cS7e+fdEGBqKPjiH+q69KNFZWaBh33x6kxAjUr0fQ6lVWz2wNcAowddevPLWS0ORQq45vLmm6ND46/xEA79Z7t1jZqS7dlS7s5N93Iet0pn/PjotDFxFRrHXV8lC6S2/5SsW6Ll2OuwzAqzVfNSsf3RK0/v7Y1asHskzynj0Wn/9U4FO42boRkx7D0fCjZp2Tnp3O7rtKJ2/Pyj0tnjN5l5IZXZLYBOdOivhSXFG8VUAryjmXI1mXbMpGL4hr8de4HHcZjUpD94rF6/LXhYeTdeMGSBJ2Nao/9Lprnz7Y1qiBISmJmHXrizXHg9hp7Hi3/rsAfHTuozzi/72Ue1xPuI5KUtHMz7qW15lXrqCPjUVycMC+YQOLzvV44w1ULi5kXrtO0u+/W3VdpYEuNJTsyEjQarGvX89q41rSKW4UxR20DlabvyhkWSZi9gfIGRk4NG+O63O9H9ncjwJJkpjfZj71vOuRlJXEkN1DiEqLemTzn4o8xeYLmwGY2XImvo6+j2zu4qBVa63uuvO4Y8oVv3QJfUrhxSuC/z2EKC4QCAQCgUAgEAgEAsFjjlO7drj1fx5kmbAxY9GFhVllXF14ODd7P0f0ylUk7dhBxsWLGJKVTV6bypVxe+EFAhYtpMrBAwQtX47TU62t1gErKB5O7dsh2dqSdfs2mZcvW338pJ070YWGovbwwO35flYf30gNjxqmrOPlJ5cjyzLHIo5xPuY8tmpbXqv1WqnNLdnY4DV8OACxH32MPrlwYaMgsmNiuPv2W2RHRWFbtQrlNm5E5Vg63YAvVH+Bxr6NSc9OLzMb9a///Zq4jDiCnILoXaV4Iotji+aoPTzQx8eT+o8i8mbHxXGzZy+ut+/AndcGkPDzL2SFhpmdW2y0XA7xBoO/5eLE1birgPKeLA1cjBnbf/xh8blatZauFboCFCkOGzkQcoBUXSqBToE08G5g0XxZd+6QeeUKqNU4Pd3B0uWacGrfDrRasm7eJPPGDYvPV0kqXqyuOEV8e+XbQguAtt7YCkDbwLa42xWvGzZ5t1KwYN+oERovr4del9RqfCdNBCD+m2/IvHmrWPM8SJ8qfQh0CiQ2IzaPC8TB0IMANPBugKutq1XmMpLy119AThyHhQU8ahcXPN98A4CYNWuRs813LygL0k4oXeL2tWujsreeKGgsnilKFLetWJEMoygul06BWX4k79xJ6l9/IWm1+M2aaZUoj8cNe409a55eQwWXCoSnhvPe7veK/HtYg5SsFKb+NdUUt9GlQpdSn1NgOVp/f7TBwaDXk36qbCz2BY8vQhQXCAQCgUAgEAgEAoHgCcB32jTsatdGn5BA6MhRGDIzSzSeIT2dsNFj0MfEYFO+PD4TxhO0ZjWVtm2l+pnTVN7+G/4fzMa1V6//lO3mk47ayQmnDopYlfjbdquOLRsMxG5SOoE9Xh9gVREhP0Y0HIFWpeVo+FGO3DvCR+eUuftU6YOX/cPClDVx7dUTm0qV0CcmEvfZ5xafr09K4u6gweju3EUbGEjwx5tRu7lZf6E5qCQVH7T6ADu1Hccijply1x8VyVnJfHrhUwCGNhiKVlU8gUfSaHB5RhF5k3Is1KNXrkIfGwtA2okThE+dyo1OnbjSsBE3e/YidOQoopavIGHLFtLPnkWflJRnTM8UCec0Gb1a4l6AZeuKz4gnKl3pMKzqXrVYP1NRGC3jU48eK5b1trHbe8/dPWZZiRvF8x6VelgshiXlCPeOzZuX6LqvdnbGsWULoPjd4r2r9MZeY8+1+GucjMw/gz7bkG36eXtVKb51evKuXcD9Dvf8cGzZUrn2ZmcTtXhxsefKjVatZWiDoQB8cuETk6hnFMWN2erWpDjW6blxHzAAtasrWbdukfjrr9ZcmtVJO6FYJzs0tZ51OpjfKa5ycCDTS3E8sct4NIVM+sREIubNB8BzyLvYVqz4SOYtC9zt3FnfaT2edp5cjb/KmH1j0Ol1RZ9YAhYcW0BYShiBToFWj9sQWBdhoS4oCCGKCwQCgUAgEAgEAoFA8ASgsrMjaNVK1G5uZFy8SMQHHxTbPlvW6QgbPYb0s2dROTsT/PFHeL79Ns6dOmFbtSoqOzsrr15gTYw530nbt5vdTWsOKfsPkHntGipHR9xfecVq4xZEoFOgqRv03d3vcjTiKBqVhjfqvFHqc0tqNd4jRwAQ99lnFomVhvR0Qt4bSua//6L28qLcJ5vR+vqU1lJNlHMpx4SmEwDFRn3X7V2lPqeR/7v0fyRlJVHRtWKxLaqNmCzU//wTQ2oqSduV4o6AxYvwHj0a2+rVkbRa5MxMMq9dI/mPP4jduJHwyVO4/eJLXG3WnKutWnP71de4N3060UuW4pfz54uQLOsUvBJ/BYByzuVKLfPXpnx5bKtXB72elGJYqNfzqkc553KkZ6ez527h58dlxPF32N8AJicGc5FlmaScQhvnLiXvfjQWAyTtLJ7Ntqutq+lnyN1FnZsj944Qkx6Dm60bbQOLJyDrIiNNudMuXQv/uX0mTACNhpR9+6wWZdKjYg8quVYiKSuJLy59QXp2OscijgHWF8UNaWmk5/ysjq1bF2sMtZMTnu8MBiBy3nwyb1mna740SDd2ijdubNVxXbSK0G1OZ3KWr1JcYhuTVMSR1iFq2XKl2LFiRTwHD34kc5YlQc5BrOu0DgeNA0cjjjL97+ml5qTy5aUv+fXGr6gkFXOfmmv1uA2BdTGJ4seEKC7IixDFBQKBQCAQCAQCgUAgeELQBgYSsHQJqFQk/vQzCd//YPEYssHAvanTSDlwAMnWluD167AJDi6F1QpKC8e2bVE5O5MdGUnaiRNWGVOWZWI3bQLA/eWXULuUTp73g7xT7x2ctPc3ll+s/iKBToGPZG7nLl2wrVkTQ2oqcZs3m3WOITWV0NGjST95EpWzM+U2f4xN+fKlvNL79K/Wn5drvAzA1ENTORVZ+ragCRkJfHHpCwCGNRiGWlWyCAX7Ro3Q+PlhSEkhctFiDCkpqF1dceneHa8h71Lp1y2KW8UfuwjeuAGfyZNwe+lFHJo3R+OjFB/o4+JIP3mSxB9/Ium33/BKVAqE7qXcs2gtV+IUUby6x8M50tbEpVs3AKJWriQ7pyveXCRJ4tlKzwJFW6hvu7GNbDmbWp61qORayaJ50v75h8wrV5Ds7U3d/CXBpXNn0GrJvHKFzOvXizXGS9VfAmDv3b1EpkY+9LrROr17xe5o1cVzL0jauRNkGfvGjdEGBBR6rG2liri/rHz+IhcsRNbrizVnbtQqNcMaDAPg84uf88u1X8jUZ+Lv6E8VtyolHj83acePI+t0aAMCsKlYodjjeAwciEOTJhhSUwkbNRpDerr1FmklsqOjybpzByQJh0aNrDq2UQxNy04j21C4hby+UhAAqss3rVrElh9pp06T8N13APjNnmWxPf6TSi3PWixvvxyNpGHHrR2sOLnCquMbZAObz29m4fGFAAxvMJzGvtYttBBYH6Monn7xIobU1DJezX3Sz561WgyWoHgIUVwgEAgEAoFAIBAIBIInCKfWrfEeMxqAiDlzSD971uxzZVkmct58krZtA7WawBXLcWhiXVtRQemjsrHBOaejMclKFuppx4+TfuYMko0NHgMHWmVMc3C3c2dUo1EAtApoxXv133tkc0sqFd6jRgIQ9+VX6KKiCj0+5dAhbvTsSeqBg0h2dgRv3IBd9dIVUx9EkiQmNZ1Eh+AOZBmyGLF3BLcSS7dT89OLn5KqS6W6e3U6l+9c4vEklcrkRGAUcBxatURS3xfbJbUam3LlcGrXDs833sB/1izKf/4ZVQ8eoNqJE1T48UcCFi/Ga9gwXLp3J8CtHADhqeEWrcUoildzr1bin6swPAa+jk3lyuijY7g3ZYrF4pixY/qf8H+ITovO9xi9Qc+3/34LKMUTlhL7qWKP79a3r1WiANRubjjlWHQn/GB5ARcoxQqNfBqRLWfz47Uf87yWlJXE3rt7gZJZpyft2AmAS/duZh3vNfQ9VC4uZF65QvzX+XewW0qn8p1o7NuY9Ox05h9TrK/bBrW1ehZ0yl+Ki4Bj69YlGlvSaAhYthS1lxeZV68SMWt2sZ1rSgtj979t9epWL/LK3SGckpVS6LG6QCUKxCY2ibR//rHqOnIj63REzJwJgGu/vjg2a1Zqcz2OtApsxQetPwCU76wvL31plXHvpdzj7V1vs+LUCgAG1hrIoLqDrDK2oHSxCQpUCp2ys0k7faasl2MifNZsrnfsZIorETx6hCguEAgEAoFAIBAIBALBE4bnoEGKNa1OR+jIUWTHxJh1Xsy6dcR/qWwUBsyfh3NONrXgycP1WaVzNGnXLuSsrBKPZ8wSd+3bB423d4nHs4SXarzEqQGn2Nh5I662ro90bqd27bCvXx85I4PYjZvyPSY7Pp6wiRMJGfwO2ffC0QYGUu6jTVbvPjQXtUrNwrYLqedVj6SsJN7b/R4x6eZdAywlJj2Gry9/DSgZ8CrJOluJHgNfR+Pvb/p/56c7mn2u2skR+zq1ce35LN4jhhO4bCnVnhsAFEMUz7FPr+5eusUNKnt7ApctQ7K1JfXgIeI+/8Ki88u5lKO+d30MsoGdt3bme8yeu3sITQnNYztuLpnXrpF68BBIEh4DX7fo3MIwFT/8+BP6ZMus7Y28XFPpzP7hyg958oJ33d5FliGLKm5VqOVRq1hjZ929S8a5c6BS4dLVvO54jbs7PjmFaVHLlpEVElKsuXOjklTMbDkTrep+t3vp5InniOLFzBPPjdbHh8ClSxXnml9/JeHHH4s+6RGSlmOdXhqFf1qVFnuNPQDJusLf12lyJgD2WRBfzOIQc4j99DMyr11D7e6Oz/jxpTbP40zPyj1NRXaLji8qUcSILMtsvbGVflv7cSLyBPYae2a2nMm4JuOsXqwiKD0et1zxjCtXyLx8GbTa/7nClccJIYoLBAKBQCAQCAQCgUDwhCFJEv7z52FTqRLZkZGEDh+BIS2t0HPivvqKmNVrAPCdOhXXXsXvrBOUPQ5Nm6Lx9saQmEjKoUMlGiv94kVS//oLVCo8337bSiu0jNxi0KNEkiST80L899/nsbSUZZnEbb9xs3sPkrZuMwmGlbb+atpoLSvsNfas7riaYOdgwlLCGL5nOGm6wq8BxWHz+c1k6DOo51XPqiKdytYW/w9mo/b0xOPtt3DpUbKccn9HRWC3xD5dp9dxM/EmUPr26QB21avhO2UyoIip6ecvWHR+YRbqsizzyYVPAHi5xssmwc5cYj/7DADnTp2wKVfOonMLw/Gp1thUqYwhNZWEH38q1hgdy3XE296b2IxYdt/dbfr3rdcV6/RelXsVW6RK2rFDWWeLFmi8vMw+z+3FF3Fo1gw5PZ3w6e9bxRa7omtFRjZUnCsa+zamZUDLEo+ZG929e2TdvAkqFY4tmltlTMfmzfAePRqAyA/nkH7xolXGtQbGaBGHJqVjc+1s4wwUnStu7CS3z4Tk3XvIjouz+lqy7t4lZu1aAHwnT0Lj7m71OZ4U3q7zNi9VfwkZmSmHpnA8wnIxND4jnrH7xzLtr2mk6FJo4N2An3r+xPPVnheC+BOGQ7PHSxRP3PIrAM7t21vFkUVQPIQoLhAIBAKBQCAQCAQCwROI2smJoDWrUbm4kH7mjCKMF9AxnPjbdiLnzAXAa+hQPF4f8CiXKigFJLUal549AYotNhmJ/ehjAFy6d/+fzJd3bNEChxYtQKcjet06QBGQQoYM4d6ECejj47GtWpUK336D75QpqBwdy3jFCh52HqzvtB53W3cuxl5k4sGJRebbWkJEagTfXVHszYc3HG51McCpTRuq/nUI3wkTkFQl26IMclZye+8k3UFvMC/n+WbiTbIN2TjbOJtE9dLG7cUXce7SBXQ6wsaNQ59SuPVybrpW6IpG0nA57jLX4/NmdB+POM7F2IvYqe1MmfPmkh0drRR9AB5vvmnRuUUhSZIpjiHu/75Azrb8/alVaU128N/8q9iV30m6w5noM6gklalYwFJkg4GEn38BMF1LzUVSqfCf8yGSnR1pR48S/+23xVrDg7xR5w129N3B5i6brV4olJLTJW5fty5qV+s5cngOehun9u2Rs7IIGzUafWKi1cYuLvqkJDKvKC4QDo1LSRTXFi2Ky7LM3eS7APh4lQedziSKWQs5K4t7U6ciZ2bi0KIFLv/jBY+SJDG52WQ6luuIzqBj1L5RD10vC+Ng6EH6/NqH3Xd3o5E0jGo0is+e+Yxgl/+9e6P/Ag453djp589jSE8v07XI2dkk/qZ817o+17tM1/K/jhDFBQKBQCAQCAQCgUAgeEKxrVSJ4I0bkBwcSD18mLDRYx4SxlMOHuTe5Mkgy7i/8gpeI4aX0WoF1sbt+ecBSDlwAF1ERLHGyLx1i+RdisWo5+DBVlvbk4bPaMVyNfGnnwkbO46bz+Zkh2u1eI0cQcWffsS+fv0yXuXDlHcpz+qOq7FV23Ig9ADzj863WrbvxnMb0Rl0NPFtQgv/FlYZ80GsJbRXdK2IvcaetOw0szPWjdbp1dyrPbLuP0mS8P/wAzQB/uju3rUoi9ndzp2nghTb6we7xY1d4s9VeQ4POw+L1hT39dfIOh329evj0KihReeag2vPnqg9PMi+F07yn38Wa4znqz2PRtJwOuo0/8b9a7L0bxXQCm+H4sU9pB07ju7uXVSOjrh07WLx+Tblypls1CPnzTeJziUl2DkYtUptlbFyk/r3YcA61um5kVQqAhYuQBsYiC40lHtTppZ5vnjaqVMgy9iUL19qcSDGTvHCMsVvJNwgLCUMG5UNzdvnRAn88IPVfj+yLBPx4RzST5xE5eiI/6yZopMZJWJkQZsFNPRpSHJWMkN2DyEitfB7pDRdGh8c+YBhe4YRmxFLZdfKfN3jawbVHVQqn0fBo0EbFITGzw90OtLPnCnTtaQeOYI+Oga1uztObdqU6Vr+1xGiuEAgEAgEAoFAIBAIBE8wDg0bErx2DZKtLSl79xI65D30KamAsjEcOnIUZGfj0qMHvtOniQ3T/xC2lSoqNt4GAwk/Fa9bPHbzZpBlnNq3x656NSuv8MnBvkEDnNq3BxRLZUNaGvaNGlFxyy94Dx2KZGNTtgsshPre9VnYZiESEt9f/Z7NFzaXeMyQpBC2XNsClE6XuLXRqDTU8lRypc/HnDfrnCtxjyZP/EHUrq4ELlkKajVJv/1G4i9bzD7X2BW9/dZ2DLJi2f1v3L/8fe9vVJKK12tblgduSE8n4Ruly9naXeJGVHZ2uL+sdK/HfvpZsQRBbwdvOpXvBMDKUyv58aqSXz2w9sBir8t4zXTp0QOVg0OxxnAfMACXHj0gO5uwkaPIuHy52OspTQyZmUpEBoqlvbVRu7oSuHIlklZLyt69xG0u+TWoJKSfVPLE7ZtaP0/ciFEUT8pKKvAYo91/y4CW+D3bF8nBgaxbt0zrKynx//clCT/8AJJEwNIl2FSoYJVx/wvYaexY/fRqKrpWJDItkvd2v1fg3+ps9Fn6b+vPD1eVzPcBtQbwXc/vqOlZ81EuWVAKSJL02OSKG7/rXXr0eKzvKf8XEKK4QCAQCAQCgUAgEAgETziOLVsSvH4dkr09qYcPc2fAAFIOHiRkyHvIGRk4tmlDwPx5JbYoFjx+uL3wAqBYqFtqTayLiCDxVyWX1/Odd6y+ticN//nzcHvhBZzatydwxXLKf/l/2FauXNbLMouO5TsyqdkkQBENt9/cXqLxNpzbQLacTeuA1jT2LR37YWtTz6seYIEontMp/ijyxB/EoVFDvEeMACDiww/JvHnTrPPaB7fHSetERGoEJyMVYW316dWAYq8e7GyZxW/ili3oExLQBgXh3LmTRedagvvLLyHZ2JBx7hzpp88Ua4xXa74KwF9hf5FlyKKBdwOa+xUvG1uflETyH38A4PZ8v2KNATk26vPn4dC8OYbUVO6+8w5ZoWHFHq+0SP3rLwwpKWh8fUvN8cK+Tm18p00DIGr5ClKPHSuVecwh7XhOnnjj0hfFC7NP33t3LwAdy3VE7eSIS/duAMR//32J50/eu4/IBQsA8JkwAeecoi7BfVxtXdnQaQPe9t5cT7jO6H2jydLfd1PSGXSsPr2a13e+zt3ku/g5+vFxl4+Z2HQitmrbMly5wJo45BTHpB0rO1Fcn5xM8p49ALj2FtbpZY14GhYIBAKBQCAQCAQCgeA/gGOrVpT/4gvUnp5kXr5MyDvvYkhKwr5hQ4JWrRRdCf9RnLt0Ru3uTnZ4uEnkMZe4Tz8DnQ6HJk1KxTb5SUPj7o7/B7MJ3rAel2eeeeKKSF6t+Sqv11I6haf/PZ1DoYeKNc7NhJsme+7hDZ+cuIW63nUB80RxWZa5GncVKBtRHMBz8CAcWrZATk8nbMxYDJmZRZ5jq7alSwXF6vvry19zKvIUB0MPopbUDGswzKL5ZYOBuM8+B8Dj9deR1KVnEazx8sKll5LbHffZZ8Uao4FPA9oE3recfbf+u8V2MEj8dStyZia2VatiV7duscYworKxIWj1KmyrVUMfHUPIO++gT0go0ZjWJmnn7wC4PNO1VK9rbi++gGvvXqDXEzZuHNnR0aU2V0EYMjJIv3gRuC+GlQYmUVyXvygelhLG5bjLqCQV7YPbA+Dev79yzq4/SpS9nvrPUcJGjwaDAdd+ffF4841ij/VfJ8ApgHWd1uGodeR4xHGm/TUNg2zgZsJNXt3+KpvObcIgG3i20rP81OsnmvsXr9BG8PjiaMwVP3sWQ0ZGmawhacdO5MxMbCpXxq5O7TJZg+A+T9bdvUAgEAgEAoFAIBAIBIICsa9bhwrffoNNxYoAOLRsQfCG9ajs7ct4ZYLSQmVri/urSgelJdbE2fHxxP+gWIV6viu6xP8rjGsyji7lu5BtyGb43uF8dsFyu+p1Z9dhkA10CO5AHa86pbRS61PXSxE3r8VfIz07vdBjo9Ojic+MRy2pqeJW5VEs7yEktZqAhQtRe3iQeeUKUYsWm3XeqzVfRSWp2H13NwN/V+zD+1TtQ3mX8hbNn7JvH1l37qByccGtX1+L128pngOVtSbv3k1WSEixxni/xfv0q9qP8U3G0zqgeDbgsl5P3BdfAOD20otWiQZQu7gQvGkjGj8/sm7eJGTosDITXx7EkJFByl6lY9n5mWdKdS5JkvCbORPbqlXQR8cQNnacxQ4mJSX97DnQ6dD4+qINDCy1eYrKFN9zR+kKbezbGHc7dwDs6tXDtlo15MxMErduK9a8htRUQkeORM7KwqljR/xnz37s4y3KmhoeNVjRYQUalYbfb/9Om2/b0G9rPy7HXcbV1pUl7ZYwv818XGxcynqpglJAW748Gm9vZJ1OuT48YmRZJv5bJabE7fnnxef1MUCI4gKBQCAQCAQCgUAgEPyHsAkOptK2rVT+YxflPvkEtatrWS9JUMq4v/Iykq0tGefPk37ihFnnxH/5FXJaGra1auL41FOlvELBo0IlqZjfZj59qvTBIBtYenIpEw9OLDT3Njfno8+z6/YuJCSLO4/LGl8HX7ztvdHLei7FXir02H/j/gWggkuFMrXJ1fr4ELBgPgDxX31F8u7dRZ5Tzb0aL1V/yfT/tmpbhtQbYvHcsZ9+CoD7iy+icnS0+HxLsa1aVbnWGAzE/d//FWsMfyd/ZrWaxcDaA4stLCTv3YsuJASVqytuffoUa4z80Pr5Ue6jTahcXEg/dYp7EyYi6/VWG7+4JPz0E4a0NDQB/tg3aFDq86kcHAhcuQqVgwNpx48TvXJVqc+Zm7QTikWyQ+PGpSo+FZUpvueuIop3LNfR9G+SJOGW0y2e8MMPFhcsAST9vgtDUhLa8uUIXLYUSaOxeIz/RVr4t2BO6zlISCRlJZniQX7u9TNdK3Qt6+UJSpGyzhVPO3aczMuXkWxtcevz3COfX/AwQhQXCAQCgUAgEAgEAoHgP4ak0WBTrpzoRvgfQePhgetzzwFKt3hRGFJTifvySwC8Bg8W75P/GDZqG2a3ms3U5lPRSEpnXJ8tfTgQcqDQ805Hnebd3e8C8EyFZ8rMVry4SJJk6hY/H124hfrVeMU6vZpHtVJfV1E4tW2Lx1tvAXBv2nR04eFFnjOs4TD8HP0AeLPOm/g6+lo0Z/r586SfOAlaLe6vvWb5oouJxxtvAJD440/ok8wr1LA2cTnXSPeXXkLl4GDVsW2rViVozWokrZbkP/8kct78Ygmf1kKflETM6jUAeA4a9Miu9baVKuI/dw4AsR99RPLefY9kXn1SEok//QyAQ7OmpTqXk9YJyD9TPCY9htNRp4G8ojiAa6+eSLa2ZF69SsY5y7tWk/cpXf+uvXujshW515bQo1IPvuj2BZ3KdWJGyxms77QeHwefsl6W4BFgvB6kHTv2yOeOWb8eALd+fVG7uT3y+QUPI0RxgUAgEAgEAoFAIBAIBIInHI+BA0GSSNm7l8zr1ws9Nv77HzAkJmJTvjzOXbo8ohUKHiWSJPFyjZf59JlPKe9Snqj0KIbvHc6gXYPyFYz3h+xn8B+DSc5KpoF3A6a1mPboF20FzM0VvxJ3BYDq7o+H8O8zehR2detiSEwkbPyEIi2nXWxc2NJ7C7/3+52h9YdaPF9cTpe4a/fuaH0fnSjk2LoVtlWrYEhLI+GHHx/ZvEbSz54l/dQpJK0W91dfKZU5HJs1I2DRQkDp/o/bvLlU5jGHmI0b0SckYFO5Mu4vvPBI53bp1g33AQMAuDd5crEt881FlmXCp7+P7t49tIGBuPToUarzGa22U3QP26fvD9mPjExtz9qm4hUjaldXnLsq37vGCBNzkQ0G0o4rbjBOrYsXH/C/TgOfBizvsJz+1fqLgsD/IRxy54pnZT2yedNOnSbtn39Ao8Fz0KBHNq+gcIQoLhAIBAKBQCAQCAQCgUDwhGNbqSLOnToBELlwUYHdiYasLJMg5jHobSS1+pGtUfDoaeDTgB96/sAbtd9Aq9JyNOIor+x4hSG7h/D77d/J1Gfy3b/fMXrfaDL1mbQNasumLptwtX0yYxfqedUDzBDF43NE8cekG16ysSFw6RJUjo6knzxJzLr1RZ7jqHUk0CnQYmEnKzSMpF1/AODx5hvFWW6xkSTJ1C0e9+WXyDrdI50/dvMnALg8+yxan9IrBnDp1g3fKZMBiFqylIQtW0ptroLICg0l/gvFpt5nwvgysdn2nTAeu/r1MCQlETZqNIbMzFKbK/7rr0n+4w/QaglcsRy1s3OpzQX37dPz6xTPzzo9N+45FupJO3aiT0k1e87Mq1cxJCYiOThgV6uWpUsWCP5nsalYEbWXF3JmJumnTj2yeWM2bgDA9bneaAMCHtm8gsIRorhAIBAIBAKBQCAQCAQCwX8A7zFjkLRaUg8dInnnznyPSfz1V7KjotD4+ODau/cjXqGgLLDX2DOuyTi29dlGr8q9UEkq/g77mwkHJtDkyybMOToHvaynV+VerOiwAnuNfVkvudjU9qqNhER4ajgx6TH5HpORncGdpDvA49MpDmBTrhx+s2cDit1q6j9HS2We+P/7AvR6HFu1xK5GjVKZozBcnn0Wtacn2eHhJP3xxyObN+PSJUU0lSQ833qz1OfzGDjQVAAQPnkKUUuXYkhPL/V5jUQtXYqs0+HYqiVO7do9snlzI9nYELR8OWo3NzIuXSJy3vxSmSf94kWiFijd+b7jx2Fft26pzJObgkTx5Kxk/gn/B4CO5fMXxe2bNMGmYkXktDSStm83e87Uvw8D4NCwIZJWW5xlCwT/k0iShFO7tgAkbtv2SOZMv3CR1AMHQaXC6513HsmcAvMQorhAIBAIBAKBQCAQCAQCwX8A20oV8XxXyYSOmDf/ocxeWa8n7mPFytfjzTdR2dg88jUKyo5Ap0DmPjWXbc9tY3DdwSZbX61Ky9jGY5nTeg5a1ZMttDhqHansVhmAc9H55/VeT7iOQTbgYeeBl73Xo1xekbg+2wPXfn1Blrk3cSLZ8fFWHV+flGSyLfd4s/SF4fxQ2dri/srLAMR99vkjy9yOXrkKAJfu3bGtWvWRzOkzcQIeA18HIPajj7nWrj0Rc+aScfVqqc6bduo0yTt/B0nCZ+LEMrWJ1gYEELB4MUgSCd99R0JO5re10KekEDZmLLJOh1PHjri//rpVxy8IJxslUzwpK+/37KHQQ2QbsqnoWpFKrpXyPVeSJNxyusUTvv/erPlkWSYxx3HAuVP+YrtAICgYt+eeAyD5910Y0tJKfb7YnC5xl2d7YFOuXKnPJzAfIYoLBAKBQCAQCAQCgUAgEPxH8HxnMDYVK6KPiSFq2bI8ryX/8QdZd+6gdnXF/YX+ZbRCQVlTzqUcIxuNZFe/Xfza+1cOvniQN+u8+Z/JV63rVXiuuDFPvJp7tcfyZ/abNg2bSpXIjooifPIUq4rGCT/8gCEtDduqVXB86imrjWsp7i+/jGRjQ8b586QdO17q86UeOULKgQOgVuM9Ynipz2dEUqnwnTKFwBUr0AYGYkhKIv7LL7nVqze3X3qZhJ9/sXr3uCzLRC5cAIBrv75l4gbwIE5tnsJr6FAAwqdNI2LOXKv83LIsE/7+++ju3lXE93lzH9ln2pgpnqpLxSAbTP9elHW6EdfneoNWS8bFi2RculTkfBmXLpF57RqSjQ0u3buXYOUCwf8m9o0bow0OxpCaSvLu3aU6V8aVqyT/uRskCa+cYlXB44MQxQUCgUAgEAgEAoFAIBAI/iOobGzwmz0LgIRvvyPt1GlAEQ9iNn0EgPtrr6FydCyrJQoeE1SSikpulUwdj/8V6noXIYob88QfI+v03KgcHAhcthTJxoaUAweI/+ILq4wr63TE/d+XAHi88UaZFgRoPDyUjnggYvbsUs2alnU6IufNAxQx3qZChVKbqyBcnulK5T//IPjjj3Hu0gU0GtLPnCF86lSutW1HxIdzyLhine7xpB07yDh7DsnBAe+RI60ypjXwGvoe7q+9BkD8l19yo0tXYj/9DEOq+ZnaD5Lw3XdKR7xGQ+DyZahdXa213CIx2qcbZANpOqXrNCM7g0NhhwDoVK5ToedrPDxMHd/xP/xQ5HyJv2xR5u3U8ZH+nALBfwVJpVKKUYDYjzdjyMoqtbmiV6wAwLlrV2wrVy61eQTFQ4jiAoFAIBAIBAKBQCAQCAT/IRybNcO1b47gNHMmsk5H6l9/kXn5MpKDA+6vvVrGKxQISo96XvUAuBhzMU8HpxFjp3h1j8dTFAewq1EDn8mTAIhcspT0CxdLPGbSzp1kR0Sg9vLCpWfPEo9XUnxGj0bt7UXWzZvErFlTavPEbt5M5rXrqN3cHmmX+INIKhVOT7UmaNVKqu7bi/eYMWiDgjAkJxP/1Vfc6t2bq81bcL1TZ7NE0vwwZGYSvVRxCPEc9DZaHx9r/gglQlKr8Zs+jeCPPkLj7092dDRRCxdy/emORK9aRdbduxaNl3H5simj3GfcOOzr1y+NZReIrdoWG5USQWLMFf8n/B/Ss9Pxc/SjlmetIsdwz7FQT9r2W6F2zoasLJJycpBd+/Qp6dIFgv9Z3F95BZWrK5lXr3L7pZeIXrOW1H+OYsjIsNocyXv3kbJvH2g0ZfqdIygYIYoLBAKBQCAQCAQCgUAgEPzH8Jk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-      "text/plain": [
-       "<Figure size 2000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "quad_beam = cheetah.ParameterBeam.from_twiss(\n",
-    "    beta_x=54.46506021,\n",
-    "    alpha_x=-1.09035236,\n",
-    "    emittance_x=3.494768647122823e-09,\n",
-    "    beta_y=39.84800591,\n",
-    "    alpha_y=0.78938986,\n",
-    "    emittance_y=3.497810737006068e-09,\n",
-    "    energy=6e6,\n",
-    ")\n",
-    "\n",
-    "quad_segment.plot_twiss_over_lattice(quad_beam, figsize=(20, 5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cheetah.Segment(quad_segment.split(resolution=0.1)).plot_twiss_over_lattice(\n",
-    "    quad_beam, figsize=(20, 5)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "element.name = 'dwallb'\n",
-      "beam_stepped.beta_x = tensor(26.4113)\n",
-      "beam_stepped.beta_y = tensor(59.8389)\n",
-      "beam_stepped.alpha_x = tensor(1.3391)\n",
-      "beam_stepped.alpha_y = tensor(-2.0946)\n"
-     ]
-    }
-   ],
-   "source": [
-    "beam_stepped = quad_beam\n",
-    "for element in quad_segment.elements[:500]:\n",
-    "    beam_stepped = element.track(beam_stepped)\n",
-    "\n",
-    "print(f\"{element.name = }\")\n",
-    "print(f\"{beam_stepped.beta_x = }\")\n",
-    "print(f\"{beam_stepped.beta_y = }\")\n",
-    "print(f\"{beam_stepped.alpha_x = }\")\n",
-    "print(f\"{beam_stepped.alpha_y = }\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quad_segment_replaced = cheetah.Segment(\n",
-    "    [\n",
-    "        (\n",
-    "            deepcopy(element)\n",
-    "            if not isinstance(element, cheetah.Cavity)\n",
-    "            else cheetah.Drift(length=element.length)\n",
-    "        )\n",
-    "        for element in quad_segment.elements\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1007.1619685082806, 1007.1619685082806)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "quad_segment.length, quad_segment_replaced.length"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "quad_segment_replaced.plot_twiss_over_lattice(quad_beam, figsize=(20, 5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Cavity(length=3.10, voltage=58010690.67, phase=1.10, frequency=2856000000.00, name=\"l0a\", device=\"cpu\")"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "segment.l0a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "r12 = tensor(0.6725, dtype=torch.float64)\n",
-      "outgoing_beam.beta_x = tensor(12.8578)\n",
-      "outgoing_beam.beta_y = tensor(12.8521)\n",
-      "outgoing_beam.alpha_x = tensor(-3.5246)\n",
-      "outgoing_beam.alpha_y = tensor(-3.5220)\n",
-      "outgoing_beam.energy = tensor(64000000.3325, dtype=torch.float64)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "ParticleBeam(n=1000000, mu_x=-0.000000, mu_xp=-0.000000, mu_y=-0.000000, mu_yp=-0.000000, sigma_x=0.000065, sigma_xp=0.000018, sigma_y=0.000065, sigma_yp=0.000018, sigma_s=0.000000, sigma_p=0.000001, energy=64000000.333)"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "incoming_beam = cheetah.ParticleBeam.from_twiss(\n",
-    "    num_particles=1_000_000,\n",
-    "    beta_x=0.44865302,\n",
-    "    beta_y=0.44865302,\n",
-    "    alpha_x=0.18863809,\n",
-    "    alpha_y=0.18863809,\n",
-    "    emittance_x=3.494768647122823e-09,\n",
-    "    emittance_y=3.497810737006068e-09,\n",
-    "    energy=6e6,\n",
-    ")\n",
-    "# cavity_element = cheetah.Cavity(\n",
-    "#     length=3.0952440, voltage=5.8010691e7, phase=-3.0555556e-3, frequency=2.8560000e9\n",
-    "# )\n",
-    "cavity_element = cheetah.Cavity(\n",
-    "    length=3.0952440,\n",
-    "    voltage=5.8010691e7,\n",
-    "    phase=np.degrees(-3.0555556e-3 * 2 * np.pi),\n",
-    "    frequency=2.8560000e9,\n",
-    ")\n",
-    "\n",
-    "outgoing_beam = cavity_element.track(incoming_beam)\n",
-    "\n",
-    "print(f\"{outgoing_beam.beta_x = }\")\n",
-    "print(f\"{outgoing_beam.beta_y = }\")\n",
-    "print(f\"{outgoing_beam.alpha_x = }\")\n",
-    "print(f\"{outgoing_beam.alpha_y = }\")\n",
-    "print(f\"{outgoing_beam.energy = }\")\n",
-    "\n",
-    "outgoing_beam"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cavity_element.length = 3.095244 m\n",
-      "cavity_element.voltage = 58010691.0 V\n",
-      "cavity_element.phase = -1.1000000159999999 deg\n",
-      "cavity_element.frequency = 2856000000.0 Hz\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"{cavity_element.length = } m\")\n",
-    "print(f\"{cavity_element.voltage = } V\")\n",
-    "print(f\"{cavity_element.phase = } deg\")\n",
-    "print(f\"{cavity_element.frequency = } Hz\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "math_op.py: module Numba is not installed. Install it if you want speed up correlation calculations\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[INFO    ] : : \u001b[0mbeam.py: module NUMBA is not installed. Install it to speed up calculation\u001b[0m\n",
-      "[INFO    ] : : : : : : : : \u001b[0mhigh_order.py: module NUMBA is not installed. Install it to speed up calculation\u001b[0m\n",
-      "[INFO    ] \u001b[0mradiation_py.py: module NUMBA is not installed. Install it to speed up calculation\u001b[0m\n",
-      "[INFO    ] \u001b[0mradiation_py.py: module NUMBA is not installed. Install it to speed up calculation\u001b[0m\n",
-      "[INFO    ] \u001b[0mcsr.py: module NUMBA is not installed. Install it to speed up calculation\u001b[0m\n",
-      "[INFO    ] \u001b[0mcsr.py: module PYFFTW is not installed. Install it to speed up calculation.\u001b[0m\n",
-      "[INFO    ] \u001b[0mcsr.py: module NUMEXPR is not installed. Install it to speed up calculation\u001b[0m\n",
-      "[INFO    ] \u001b[0mwake3D.py: module NUMBA is not installed. Install it to speed up calculation\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initializing ocelot...\n",
-      "import: module NUMBA is not installed. Install it to speed up calculation\n",
-      "import: module PYFFTW is not installed. Install it to speed up calculation\n",
-      "import: module NUMEXPR is not installed. Install it to speed up calculation\n"
-     ]
-    }
-   ],
-   "source": [
-    "import ocelot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[INFO    ] \u001b[0mTwiss parameters have priority. sigma_{x, px, y, py} will be redefined\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "z = 3.095244 / 3.095244. Applied: emit_x  = 3.2951610962701724e-10\n",
-      "emit_y  = 3.261678925663818e-10\n",
-      "beta_x  = 12.856815106358324\n",
-      "beta_y  = 12.856815106665534\n",
-      "alpha_x = -3.5248686043345985\n",
-      "alpha_y = -3.5248686044202864\n",
-      "gamma_x = 1.04416984819083\n",
-      "gamma_y = 1.0441698482128647\n",
-      "Dx      = 0.0\n",
-      "Dy      = 0.0\n",
-      "Dxp     = 0.0\n",
-      "Dyp     = 0.0\n",
-      "mux     = 0.0\n",
-      "muy     = 0.0\n",
-      "nu_x    = 0.0\n",
-      "nu_y    = 0.0\n",
-      "E       = 0.06400000033252268\n",
-      "s        = 0.0\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "tws = ocelot.Twiss()\n",
-    "tws.beta_x = 0.44865302\n",
-    "tws.beta_y = 0.44865302\n",
-    "tws.alpha_x = 0.18863809\n",
-    "tws.alpha_y = 0.18863809\n",
-    "tws.emit_x = 3.494768647122823e-09\n",
-    "tws.emit_y = 3.497810737006068e-09\n",
-    "tws.gamma_x = (1 + tws.alpha_x**2) / tws.beta_x\n",
-    "tws.gamma_y = (1 + tws.alpha_y**2) / tws.beta_y\n",
-    "tws.E = 6e-3\n",
-    "\n",
-    "p_array = ocelot.generate_parray(tws=tws)\n",
-    "\n",
-    "cell = [\n",
-    "    ocelot.Cavity(\n",
-    "        l=3.0952440,\n",
-    "        v=5.8010691e7 * 1e-9,\n",
-    "        freq=2.8560000e9,\n",
-    "        phi=np.degrees(-3.0555556e-3 * 2 * np.pi),\n",
-    "    )\n",
-    "]\n",
-    "lattice = ocelot.MagneticLattice(cell)\n",
-    "navigator = ocelot.Navigator(lattice=lattice)\n",
-    "\n",
-    "_, outgoing_parray = ocelot.track(lattice, deepcopy(p_array), navigator)\n",
-    "derived_twiss = ocelot.cpbd.beam.get_envelope(outgoing_parray)\n",
-    "\n",
-    "print(derived_twiss)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "r12 = tensor(0.4585)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[INFO    ] \u001b[0mTwiss parameters have priority. sigma_{x, px, y, py} will be redefined\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "z = 1.0377 / 1.0377. Applied: \n",
-      "outgoing_beam.beta_x = tensor(0.2386) / derived_twiss.beta_x = 0.23847352513957534\n",
-      "outgoing_beam.beta_y = tensor(0.2389) / derived_twiss.beta_y = 0.2384735251330723\n",
-      "outgoing_beam.alpha_x = tensor(-1.0163) / derived_twiss.alpha_x = -1.016068759439058\n",
-      "outgoing_beam.alpha_y = tensor(-1.0188) / derived_twiss.alpha_y = -1.0160687594079199\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Cheetah\n",
-    "incoming_beam = cheetah.ParticleBeam.from_twiss(\n",
-    "    beta_x=5.91253677,\n",
-    "    alpha_x=3.55631308,\n",
-    "    beta_y=5.91253677,\n",
-    "    alpha_y=3.55631308,\n",
-    "    emittance_x=3.494768647122823e-09,\n",
-    "    emittance_y=3.497810737006068e-09,\n",
-    "    energy=6e6,\n",
-    ")\n",
-    "cheetah_cavity = cheetah.Cavity(\n",
-    "    length=1.0377, voltage=0.01815975e9, frequency=1.3e9, phase=0.0\n",
-    ")\n",
-    "outgoing_beam = cheetah_cavity.track(incoming_beam)\n",
-    "\n",
-    "# Ocelot\n",
-    "tws = ocelot.Twiss()\n",
-    "tws.beta_x = 5.91253677\n",
-    "tws.beta_y = 5.91253677\n",
-    "tws.alpha_x = 3.55631308\n",
-    "tws.alpha_y = 3.55631308\n",
-    "tws.emit_x = 3.494768647122823e-09\n",
-    "tws.emit_y = 3.497810737006068e-09\n",
-    "tws.gamma_x = (1 + tws.alpha_x**2) / tws.beta_x\n",
-    "tws.gamma_y = (1 + tws.alpha_y**2) / tws.beta_y\n",
-    "tws.E = 6e-3\n",
-    "\n",
-    "p_array = ocelot.generate_parray(tws=tws)\n",
-    "\n",
-    "cell = [ocelot.Cavity(l=1.0377, v=0.01815975, freq=1.3e9, phi=0.0)]\n",
-    "lattice = ocelot.MagneticLattice(cell)\n",
-    "navigator = ocelot.Navigator(lattice=lattice)\n",
-    "\n",
-    "_, outgoing_parray = ocelot.track(lattice, deepcopy(p_array), navigator)\n",
-    "derived_twiss = ocelot.cpbd.beam.get_envelope(outgoing_parray)\n",
-    "\n",
-    "# Print\n",
-    "print(\"\")\n",
-    "print(f\"{outgoing_beam.beta_x = } / {derived_twiss.beta_x = }\")\n",
-    "print(f\"{outgoing_beam.beta_y = } / {derived_twiss.beta_y = }\")\n",
-    "print(f\"{outgoing_beam.alpha_x = } / {derived_twiss.alpha_x = }\")\n",
-    "print(f\"{outgoing_beam.alpha_y = } / {derived_twiss.alpha_y = }\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "r12 = tensor(0.4585, dtype=torch.float64)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[INFO    ] \u001b[0mTwiss parameters have priority. sigma_{x, px, y, py} will be redefined\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "z = 1.0377 / 1.0377. Applied: \n",
-      "outgoing_beam.beta_x = tensor(0.2382) / derived_twiss.beta_x = 0.23837266708240293\n",
-      "outgoing_beam.beta_y = tensor(0.2386) / derived_twiss.beta_y = 0.2383726670721846\n",
-      "outgoing_beam.alpha_x = tensor(-1.0153) / derived_twiss.alpha_x = -1.0155350822276084\n",
-      "outgoing_beam.alpha_y = tensor(-1.0161) / derived_twiss.alpha_y = -1.0155350821972222\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Cheetah\n",
-    "incoming_beam = cheetah.ParticleBeam.from_twiss(\n",
-    "    beta_x=5.91253677,\n",
-    "    alpha_x=3.55631308,\n",
-    "    beta_y=5.91253677,\n",
-    "    alpha_y=3.55631308,\n",
-    "    emittance_x=3.494768647122823e-09,\n",
-    "    emittance_y=3.497810737006068e-09,\n",
-    "    energy=6e6,\n",
-    ")\n",
-    "cheetah_cavity = cheetah.Cavity(\n",
-    "    length=1.0377,\n",
-    "    voltage=0.01815975e9,\n",
-    "    frequency=1.3e9,\n",
-    "    phase=np.degrees(-3.0555556e-3 * 2 * np.pi),\n",
-    ")\n",
-    "outgoing_beam = cheetah_cavity.track(incoming_beam)\n",
-    "\n",
-    "# Ocelot\n",
-    "tws = ocelot.Twiss()\n",
-    "tws.beta_x = 5.91253677\n",
-    "tws.beta_y = 5.91253677\n",
-    "tws.alpha_x = 3.55631308\n",
-    "tws.alpha_y = 3.55631308\n",
-    "tws.emit_x = 3.494768647122823e-09\n",
-    "tws.emit_y = 3.497810737006068e-09\n",
-    "tws.gamma_x = (1 + tws.alpha_x**2) / tws.beta_x\n",
-    "tws.gamma_y = (1 + tws.alpha_y**2) / tws.beta_y\n",
-    "tws.E = 6e-3\n",
-    "\n",
-    "p_array = ocelot.generate_parray(tws=tws)\n",
-    "\n",
-    "cell = [\n",
-    "    ocelot.Cavity(\n",
-    "        l=1.0377, v=0.01815975, freq=1.3e9, phi=np.degrees(-3.0555556e-3 * 2 * np.pi)\n",
-    "    )\n",
-    "]\n",
-    "lattice = ocelot.MagneticLattice(cell)\n",
-    "navigator = ocelot.Navigator(lattice=lattice)\n",
-    "\n",
-    "_, outgoing_parray = ocelot.track(lattice, deepcopy(p_array), navigator)\n",
-    "derived_twiss = ocelot.cpbd.beam.get_envelope(outgoing_parray)\n",
-    "\n",
-    "# Print\n",
-    "print(\"\")\n",
-    "print(f\"{outgoing_beam.beta_x = } / {derived_twiss.beta_x = }\")\n",
-    "print(f\"{outgoing_beam.beta_y = } / {derived_twiss.beta_y = }\")\n",
-    "print(f\"{outgoing_beam.alpha_x = } / {derived_twiss.alpha_x = }\")\n",
-    "print(f\"{outgoing_beam.alpha_y = } / {derived_twiss.alpha_y = }\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "MIN_LENGTH = 0.5\n",
-    "MAX_LENGTH = 3.142\n",
-    "MIN_VOLTAGE = 0.0\n",
-    "MAX_VOLTAGE = 0.01815975e9\n",
-    "MIN_FREQUENCY = 8e8\n",
-    "MAX_FREQUENCY = 4e9\n",
-    "MIN_PHASE = -5.0  # Degrees\n",
-    "MAX_PHASE = 5.0  # Degrees"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "r12 = tensor(0.2209, dtype=torch.float64)\n",
-      "r12 = tensor(0.3506, dtype=torch.float64)\n",
-      "r12 = tensor(0.4803, dtype=torch.float64)\n",
-      "r12 = tensor(0.6100, dtype=torch.float64)\n",
-      "r12 = tensor(0.7397, dtype=torch.float64)\n",
-      "r12 = tensor(0.8695, dtype=torch.float64)\n",
-      "r12 = tensor(0.9992, dtype=torch.float64)\n",
-      "r12 = tensor(1.1289, dtype=torch.float64)\n",
-      "r12 = tensor(1.2586, dtype=torch.float64)\n",
-      "r12 = tensor(1.3883, dtype=torch.float64)\n",
-      "-----------------------------------\n",
-      "r12 = tensor(0.8930, dtype=torch.float64)\n",
-      "r12 = tensor(0.7892, dtype=torch.float64)\n",
-      "r12 = tensor(0.7103, dtype=torch.float64)\n",
-      "r12 = tensor(0.6476, dtype=torch.float64)\n",
-      "r12 = tensor(0.5965, dtype=torch.float64)\n",
-      "r12 = tensor(0.5537, dtype=torch.float64)\n",
-      "r12 = tensor(0.5173, dtype=torch.float64)\n",
-      "r12 = tensor(0.4859, dtype=torch.float64)\n",
-      "r12 = tensor(0.4585, dtype=torch.float64)\n",
-      "-----------------------------------\n",
-      "r12 = tensor(0.4585)\n",
-      "r12 = tensor(0.4585)\n",
-      "r12 = tensor(0.4585)\n",
-      "r12 = tensor(0.4585)\n",
-      "r12 = tensor(0.4585)\n",
-      "r12 = tensor(0.4585)\n",
-      "r12 = tensor(0.4585)\n",
-      "r12 = tensor(0.4585)\n",
-      "r12 = tensor(0.4585)\n",
-      "r12 = tensor(0.4585)\n",
-      "-----------------------------------\n",
-      "r12 = tensor(0.4592, dtype=torch.float64)\n",
-      "r12 = tensor(0.4590, dtype=torch.float64)\n",
-      "r12 = tensor(0.4587, dtype=torch.float64)\n",
-      "r12 = tensor(0.4586, dtype=torch.float64)\n",
-      "r12 = tensor(0.4585, dtype=torch.float64)\n",
-      "r12 = tensor(0.4585, dtype=torch.float64)\n",
-      "r12 = tensor(0.4586, dtype=torch.float64)\n",
-      "r12 = tensor(0.4587, dtype=torch.float64)\n",
-      "r12 = tensor(0.4590, dtype=torch.float64)\n",
-      "r12 = tensor(0.4592, dtype=torch.float64)\n"
-     ]
-    }
-   ],
-   "source": [
-    "incoming_beam = cheetah.ParticleBeam.from_twiss(\n",
-    "    num_particles=10_000,\n",
-    "    beta_x=5.91253677,\n",
-    "    alpha_x=3.55631308,\n",
-    "    beta_y=5.91253677,\n",
-    "    alpha_y=3.55631308,\n",
-    "    emittance_x=3.494768647122823e-09,\n",
-    "    emittance_y=3.497810737006068e-09,\n",
-    "    energy=6e6,\n",
-    ")\n",
-    "DEFAULT_LENGTH = 1.0377\n",
-    "DEFAULT_VOLTAGE = 0.01815975e9\n",
-    "DEFAULT_FREQUENCY = 1.3e9\n",
-    "DEFAULT_PHASE = 0.0\n",
-    "\n",
-    "# Scan length\n",
-    "cheetah_outgoing_beams_length = []\n",
-    "for length in np.linspace(MIN_LENGTH, MAX_LENGTH, 10):\n",
-    "    cheetah_cavity = cheetah.Cavity(\n",
-    "        length=length,\n",
-    "        voltage=DEFAULT_VOLTAGE,\n",
-    "        frequency=DEFAULT_FREQUENCY,\n",
-    "        phase=DEFAULT_PHASE,\n",
-    "    )\n",
-    "    cheetah_outgoing_beams_length.append(cheetah_cavity.track(incoming_beam))\n",
-    "\n",
-    "print(\"-----------------------------------\")\n",
-    "\n",
-    "# Scan voltage\n",
-    "cheetah_outgoing_beams_voltage = []\n",
-    "for voltage in np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10):\n",
-    "    cheetah_cavity = cheetah.Cavity(\n",
-    "        length=DEFAULT_LENGTH,\n",
-    "        voltage=voltage,\n",
-    "        frequency=DEFAULT_FREQUENCY,\n",
-    "        phase=DEFAULT_PHASE,\n",
-    "    )\n",
-    "    cheetah_outgoing_beams_voltage.append(cheetah_cavity.track(incoming_beam))\n",
-    "\n",
-    "print(\"-----------------------------------\")\n",
-    "\n",
-    "# Scan frequency\n",
-    "cheetah_outgoing_beams_frequency = []\n",
-    "for frequency in np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10):\n",
-    "    cheetah_cavity = cheetah.Cavity(\n",
-    "        length=DEFAULT_LENGTH,\n",
-    "        voltage=DEFAULT_VOLTAGE,\n",
-    "        frequency=frequency,\n",
-    "        phase=DEFAULT_PHASE,\n",
-    "    )\n",
-    "    cheetah_outgoing_beams_frequency.append(cheetah_cavity.track(incoming_beam))\n",
-    "\n",
-    "print(\"-----------------------------------\")\n",
-    "\n",
-    "# Scan phase\n",
-    "cheetah_outgoing_beams_phase = []\n",
-    "for phase in np.linspace(MIN_PHASE, MAX_PHASE, 10):\n",
-    "    cheetah_cavity = cheetah.Cavity(\n",
-    "        length=DEFAULT_LENGTH,\n",
-    "        voltage=DEFAULT_VOLTAGE,\n",
-    "        frequency=DEFAULT_FREQUENCY,\n",
-    "        phase=phase,\n",
-    "    )\n",
-    "    cheetah_outgoing_beams_phase.append(cheetah_cavity.track(incoming_beam))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x600 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# r55_cor is always -0.0\n",
-    "\n",
-    "plt.figure(figsize=(20, 6))\n",
-    "plt.subplot(2, 4, 1)\n",
-    "plt.title(\"r11\")\n",
-    "plt.plot([0.8501, 0.7277, 0.6245, 0.5352, 0.4566, 0.3864, 0.3231, 0.2654, 0.2125])\n",
-    "plt.subplot(2, 4, 2)\n",
-    "plt.title(\"r12\")\n",
-    "plt.plot(\n",
-    "    [1.0377, 1.0377, 0.7103, 0.6476, 0.5965, 0.5537, 0.5173, 0.4859, 0.4585],\n",
-    "    label=\"Old\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    [0.8930, 0.7892, 0.7103, 0.6476, 0.5965, 0.5537, 0.5173, 0.4859, 0.4585],\n",
-    "    label=\"Test if true\",\n",
-    ")\n",
-    "plt.legend()\n",
-    "plt.subplot(2, 4, 3)\n",
-    "plt.title(\"r21\")\n",
-    "plt.plot(\n",
-    "    [-0.0263, -0.0743, -0.1253, -0.1740, -0.2190, -0.2601, -0.2977, -0.3319, -0.3633]\n",
-    ")\n",
-    "plt.subplot(2, 4, 4)\n",
-    "plt.title(\"r22\")\n",
-    "plt.plot([0.8527, 0.7409, 0.6546, 0.5862, 0.5307, 0.4848, 0.4462, 0.4133, 0.3849])\n",
-    "plt.subplot(2, 4, 5)\n",
-    "plt.title(\"r55_cor\")\n",
-    "plt.plot([-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0])\n",
-    "plt.subplot(2, 4, 6)\n",
-    "plt.title(\"r56\")\n",
-    "plt.plot(\n",
-    "    [-0.0049, -0.0036, -0.0028, -0.0023, -0.0019, -0.0017, -0.0015, -0.0013, -0.0012]\n",
-    ")\n",
-    "plt.subplot(2, 4, 7)\n",
-    "plt.title(\"r65\")\n",
-    "plt.plot([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n",
-    "plt.subplot(2, 4, 8)\n",
-    "plt.title(\"r66\")\n",
-    "plt.plot([0.7471, 0.5965, 0.4964, 0.4251, 0.3718, 0.3303, 0.2972, 0.2701, 0.2475])\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[INFO    ] \u001b[0mTwiss parameters have priority. sigma_{x, px, y, py} will be redefined\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "z = 1.0377 / 1.0377. Applied: 4444444444446. Applied: "
-     ]
-    }
-   ],
-   "source": [
-    "tws = ocelot.Twiss()\n",
-    "tws.beta_x = 5.91253677\n",
-    "tws.beta_y = 5.91253677\n",
-    "tws.alpha_x = 3.55631308\n",
-    "tws.alpha_y = 3.55631308\n",
-    "tws.emit_x = 3.494768647122823e-09\n",
-    "tws.emit_y = 3.497810737006068e-09\n",
-    "tws.gamma_x = (1 + tws.alpha_x**2) / tws.beta_x\n",
-    "tws.gamma_y = (1 + tws.alpha_y**2) / tws.beta_y\n",
-    "tws.E = 6e-3\n",
-    "\n",
-    "p_array = ocelot.generate_parray(tws=tws)\n",
-    "\n",
-    "# Scan length\n",
-    "ocelot_outgoing_beams_length = []\n",
-    "for length in np.linspace(MIN_LENGTH, MAX_LENGTH, 10):\n",
-    "    cell = [ocelot.Cavity(l=length, v=0.01815975, freq=1.3e9, phi=0.0)]\n",
-    "    lattice = ocelot.MagneticLattice(cell)\n",
-    "    navigator = ocelot.Navigator(lattice=lattice)\n",
-    "\n",
-    "    _, outgoing_parray = ocelot.track(lattice, deepcopy(p_array), navigator)\n",
-    "    derived_twiss = ocelot.cpbd.beam.get_envelope(outgoing_parray)\n",
-    "\n",
-    "    ocelot_outgoing_beams_length.append(derived_twiss)\n",
-    "\n",
-    "\n",
-    "# Scan voltage\n",
-    "ocelot_outgoing_beams_voltage = []\n",
-    "for voltage in np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10):\n",
-    "    cell = [ocelot.Cavity(l=1.0377, v=voltage * 1e-9, freq=1.3e9, phi=0.0)]\n",
-    "    lattice = ocelot.MagneticLattice(cell)\n",
-    "    navigator = ocelot.Navigator(lattice=lattice)\n",
-    "\n",
-    "    _, outgoing_parray = ocelot.track(lattice, deepcopy(p_array), navigator)\n",
-    "    derived_twiss = ocelot.cpbd.beam.get_envelope(outgoing_parray)\n",
-    "\n",
-    "    ocelot_outgoing_beams_voltage.append(derived_twiss)\n",
-    "\n",
-    "# Scan frequency\n",
-    "ocelot_outgoing_beams_frequency = []\n",
-    "for frequency in np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10):\n",
-    "    cell = [ocelot.Cavity(l=1.0377, v=0.01815975, freq=frequency, phi=0.0)]\n",
-    "    lattice = ocelot.MagneticLattice(cell)\n",
-    "    navigator = ocelot.Navigator(lattice=lattice)\n",
-    "\n",
-    "    _, outgoing_parray = ocelot.track(lattice, deepcopy(p_array), navigator)\n",
-    "    derived_twiss = ocelot.cpbd.beam.get_envelope(outgoing_parray)\n",
-    "\n",
-    "    ocelot_outgoing_beams_frequency.append(derived_twiss)\n",
-    "\n",
-    "# Scan phase\n",
-    "ocelot_outgoing_beams_phase = []\n",
-    "for phase in np.linspace(MIN_PHASE, MAX_PHASE, 10):\n",
-    "    cell = [ocelot.Cavity(l=1.0377, v=0.01815975, freq=1.3e9, phi=phase)]\n",
-    "    lattice = ocelot.MagneticLattice(cell)\n",
-    "    navigator = ocelot.Navigator(lattice=lattice)\n",
-    "\n",
-    "    _, outgoing_parray = ocelot.track(lattice, deepcopy(p_array), navigator)\n",
-    "    derived_twiss = ocelot.cpbd.beam.get_envelope(outgoing_parray)\n",
-    "\n",
-    "    ocelot_outgoing_beams_phase.append(derived_twiss)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.5       , 0.79355556, 1.08711111, 1.38066667, 1.67422222,\n",
-       "       1.96777778, 2.26133333, 2.55488889, 2.84844444, 3.142     ])"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bmad_beta_x_length = [\n",
-    "    2.66835501,\n",
-    "    1.32115621,\n",
-    "    0.51830313,\n",
-    "    0.29508156,\n",
-    "    0.65993937,\n",
-    "    1.60716987,\n",
-    "    3.12352021,\n",
-    "    5.19181155,\n",
-    "    7.79299102,\n",
-    "    10.90732124,\n",
-    "]\n",
-    "bmad_beta_y_length = [\n",
-    "    2.66835501,\n",
-    "    1.32115621,\n",
-    "    0.51830313,\n",
-    "    0.29508156,\n",
-    "    0.65993937,\n",
-    "    1.60716987,\n",
-    "    3.12352021,\n",
-    "    5.19181155,\n",
-    "    7.79299102,\n",
-    "    10.90732124,\n",
-    "]\n",
-    "\n",
-    "np.linspace(MIN_LENGTH, MAX_LENGTH, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([       0.,  2017750.,  4035500.,  6053250.,  8071000., 10088750.,\n",
-       "       12106500., 14124250., 16142000., 18159750.])"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bmad_beta_x_voltage = [\n",
-    "    1.01729176,\n",
-    "    0.95397588,\n",
-    "    0.80934467,\n",
-    "    0.63361881,\n",
-    "    0.46047155,\n",
-    "    0.31298502,\n",
-    "    0.20725501,\n",
-    "    0.15462923,\n",
-    "    0.16314367,\n",
-    "    0.23847352,\n",
-    "]\n",
-    "bmad_beta_y_voltage = [\n",
-    "    1.01729176,\n",
-    "    0.95397588,\n",
-    "    0.80934467,\n",
-    "    0.63361881,\n",
-    "    0.46047155,\n",
-    "    0.31298502,\n",
-    "    0.20725501,\n",
-    "    0.15462923,\n",
-    "    0.16314367,\n",
-    "    0.23847352,\n",
-    "]\n",
-    "\n",
-    "np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([8.00000000e+08, 1.15555556e+09, 1.51111111e+09, 1.86666667e+09,\n",
-       "       2.22222222e+09, 2.57777778e+09, 2.93333333e+09, 3.28888889e+09,\n",
-       "       3.64444444e+09, 4.00000000e+09])"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bmad_beta_x_frequency = [\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "]\n",
-    "bmad_beta_y_frequency = [\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "    0.23847352,\n",
-    "]\n",
-    "\n",
-    "np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.01388889,  0.01080247,  0.00771605,  0.00462963,  0.00154321,\n",
-       "       -0.00154321, -0.00462963, -0.00771605, -0.01080247, -0.01388889])"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bmad_beta_x_phase = [\n",
-    "    0.23640452,\n",
-    "    0.23721818,\n",
-    "    0.23783162,\n",
-    "    0.23824209,\n",
-    "    0.23844779,\n",
-    "    0.23844779,\n",
-    "    0.23824209,\n",
-    "    0.23783162,\n",
-    "    0.23721818,\n",
-    "    0.23640452,\n",
-    "]\n",
-    "bmad_beta_y_phase = [\n",
-    "    0.23640452,\n",
-    "    0.23721818,\n",
-    "    0.23783162,\n",
-    "    0.23824209,\n",
-    "    0.23844779,\n",
-    "    0.23844779,\n",
-    "    0.23824209,\n",
-    "    0.23783162,\n",
-    "    0.23721818,\n",
-    "    0.23640452,\n",
-    "]\n",
-    "\n",
-    "-np.deg2rad(np.linspace(MIN_PHASE, MAX_PHASE, 10)) / (2 * np.pi)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.subplot(2, 2, 1)\n",
-    "plt.title(\"Length\")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "    [beam.beta_x for beam in cheetah_outgoing_beams_length],\n",
-    "    label=\"Cheetah beta_x\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "    [beam.beta_y for beam in cheetah_outgoing_beams_length],\n",
-    "    label=\"Cheetah beta_y\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "    [beam.beta_x for beam in ocelot_outgoing_beams_length],\n",
-    "    label=\"Ocelot beta_x\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "    [beam.beta_y for beam in ocelot_outgoing_beams_length],\n",
-    "    label=\"Ocelot beta_y\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "    bmad_beta_x_length,\n",
-    "    label=\"Bmad beta_x\",\n",
-    "    ls=\":\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "    bmad_beta_y_length,\n",
-    "    label=\"Bmad beta_y\",\n",
-    "    ls=\":\",\n",
-    ")\n",
-    "plt.axvline(1.0377, color=\"black\", linestyle=\"--\")\n",
-    "plt.subplot(2, 2, 2)\n",
-    "plt.title(\"Voltage\")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "    [beam.beta_x for beam in cheetah_outgoing_beams_voltage],\n",
-    "    label=\"Cheetah beta_x\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "    [beam.beta_y for beam in cheetah_outgoing_beams_voltage],\n",
-    "    label=\"Cheetah beta_y\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "    [beam.beta_x for beam in ocelot_outgoing_beams_voltage],\n",
-    "    label=\"Ocelot beta_x\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "    [beam.beta_y for beam in ocelot_outgoing_beams_voltage],\n",
-    "    label=\"Ocelot beta_y\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "    bmad_beta_x_voltage,\n",
-    "    label=\"Bmad beta_x\",\n",
-    "    ls=\":\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "    bmad_beta_y_voltage,\n",
-    "    label=\"Bmad beta_y\",\n",
-    "    ls=\":\",\n",
-    ")\n",
-    "plt.axvline(0.01815975e9, color=\"black\", linestyle=\"--\")\n",
-    "plt.subplot(2, 2, 3)\n",
-    "plt.title(\"Frequency\")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "    [beam.beta_x for beam in cheetah_outgoing_beams_frequency],\n",
-    "    label=\"Cheetah beta_x\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "    [beam.beta_y for beam in cheetah_outgoing_beams_frequency],\n",
-    "    label=\"Cheetah beta_y\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "    [beam.beta_x for beam in ocelot_outgoing_beams_frequency],\n",
-    "    label=\"Ocelot beta_x\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "    [beam.beta_y for beam in ocelot_outgoing_beams_frequency],\n",
-    "    label=\"Ocelot beta_y\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "    bmad_beta_x_frequency,\n",
-    "    label=\"Bmad beta_x\",\n",
-    "    ls=\":\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "    bmad_beta_y_frequency,\n",
-    "    label=\"Bmad beta_y\",\n",
-    "    ls=\":\",\n",
-    ")\n",
-    "plt.axvline(1.3e9, color=\"black\", linestyle=\"--\")\n",
-    "plt.subplot(2, 2, 4)\n",
-    "plt.title(\"Phase\")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "    [beam.beta_x for beam in cheetah_outgoing_beams_phase],\n",
-    "    label=\"Cheetah beta_x\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "    [beam.beta_y for beam in cheetah_outgoing_beams_phase],\n",
-    "    label=\"Cheetah beta_y\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "    [beam.beta_x for beam in ocelot_outgoing_beams_phase],\n",
-    "    label=\"Ocelot beta_x\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "    [beam.beta_y for beam in ocelot_outgoing_beams_phase],\n",
-    "    label=\"Ocelot beta_y\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "    bmad_beta_x_phase,\n",
-    "    label=\"Bmad beta_x\",\n",
-    "    ls=\":\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "    bmad_beta_y_phase,\n",
-    "    label=\"Bmad beta_y\",\n",
-    "    ls=\":\",\n",
-    ")\n",
-    "plt.axvline(0.0, color=\"black\", linestyle=\"--\")\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.subplot(2, 2, 1)\n",
-    "plt.title(\"Length\")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "    [beam.alpha_x for beam in cheetah_outgoing_beams_length],\n",
-    "    label=\"Cheetah alpha_x\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "    [beam.alpha_y for beam in cheetah_outgoing_beams_length],\n",
-    "    label=\"Cheetah alpha_y\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "    [beam.alpha_x for beam in ocelot_outgoing_beams_length],\n",
-    "    label=\"Ocelot alpha_x\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "    [beam.alpha_y for beam in ocelot_outgoing_beams_length],\n",
-    "    label=\"Ocelot alpha_y\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "#     bmad_beta_x_length,\n",
-    "#     label=\"Bmad alpha_x\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "#     bmad_beta_y_length,\n",
-    "#     label=\"Bmad alpha_y\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "plt.axvline(1.0377, color=\"black\", linestyle=\"--\")\n",
-    "plt.subplot(2, 2, 2)\n",
-    "plt.title(\"Voltage\")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "    [beam.alpha_x for beam in cheetah_outgoing_beams_voltage],\n",
-    "    label=\"Cheetah alpha_x\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "    [beam.alpha_y for beam in cheetah_outgoing_beams_voltage],\n",
-    "    label=\"Cheetah alpha_y\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "    [beam.alpha_x for beam in ocelot_outgoing_beams_voltage],\n",
-    "    label=\"Ocelot alpha_x\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "    [beam.alpha_y for beam in ocelot_outgoing_beams_voltage],\n",
-    "    label=\"Ocelot alpha_y\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "#     bmad_beta_x_voltage,\n",
-    "#     label=\"Bmad alpha_x\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "#     bmad_beta_y_voltage,\n",
-    "#     label=\"Bmad alpha_y\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "plt.axvline(0.01815975e9, color=\"black\", linestyle=\"--\")\n",
-    "plt.subplot(2, 2, 3)\n",
-    "plt.title(\"Frequency\")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "    [beam.alpha_x for beam in cheetah_outgoing_beams_frequency],\n",
-    "    label=\"Cheetah alpha_x\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "    [beam.alpha_y for beam in cheetah_outgoing_beams_frequency],\n",
-    "    label=\"Cheetah alpha_y\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "    [beam.alpha_x for beam in ocelot_outgoing_beams_frequency],\n",
-    "    label=\"Ocelot alpha_x\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "    [beam.alpha_y for beam in ocelot_outgoing_beams_frequency],\n",
-    "    label=\"Ocelot alpha_y\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "#     bmad_beta_x_frequency,\n",
-    "#     label=\"Bmad alpha_x\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "#     bmad_beta_y_frequency,\n",
-    "#     label=\"Bmad alpha_y\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "plt.axvline(1.3e9, color=\"black\", linestyle=\"--\")\n",
-    "plt.subplot(2, 2, 4)\n",
-    "plt.title(\"Phase\")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "    [beam.alpha_x for beam in cheetah_outgoing_beams_phase],\n",
-    "    label=\"Cheetah alpha_x\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "    [beam.alpha_y for beam in cheetah_outgoing_beams_phase],\n",
-    "    label=\"Cheetah alpha_y\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "    [beam.alpha_x for beam in ocelot_outgoing_beams_phase],\n",
-    "    label=\"Ocelot alpha_x\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "    [beam.alpha_y for beam in ocelot_outgoing_beams_phase],\n",
-    "    label=\"Ocelot alpha_y\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "#     bmad_beta_x_phase,\n",
-    "#     label=\"Bmad alpha_x\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "#     bmad_beta_y_phase,\n",
-    "#     label=\"Bmad alpha_y\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "plt.axvline(0.0, color=\"black\", linestyle=\"--\")\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.subplot(2, 2, 1)\n",
-    "plt.title(\"Length\")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "    [beam.emittance_x for beam in cheetah_outgoing_beams_length],\n",
-    "    label=\"Cheetah emittance_x\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "    [beam.emittance_y for beam in cheetah_outgoing_beams_length],\n",
-    "    label=\"Cheetah emittance_y\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "    [beam.emit_x for beam in ocelot_outgoing_beams_length],\n",
-    "    label=\"Ocelot emittance_x\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "    [beam.emit_y for beam in ocelot_outgoing_beams_length],\n",
-    "    label=\"Ocelot emittance_y\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "#     bmad_beta_x_length,\n",
-    "#     label=\"Bmad emittance_x\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_LENGTH, MAX_LENGTH, 10),\n",
-    "#     bmad_beta_y_length,\n",
-    "#     label=\"Bmad emittance_y\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "plt.axvline(1.0377, color=\"black\", linestyle=\"--\")\n",
-    "plt.subplot(2, 2, 2)\n",
-    "plt.title(\"Voltage\")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "    [beam.emittance_x for beam in cheetah_outgoing_beams_voltage],\n",
-    "    label=\"Cheetah emittance_x\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "    [beam.emittance_y for beam in cheetah_outgoing_beams_voltage],\n",
-    "    label=\"Cheetah emittance_y\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "    [beam.emit_x for beam in ocelot_outgoing_beams_voltage],\n",
-    "    label=\"Ocelot emittance_x\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "    [beam.emit_y for beam in ocelot_outgoing_beams_voltage],\n",
-    "    label=\"Ocelot emittance_y\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "#     bmad_beta_x_voltage,\n",
-    "#     label=\"Bmad emittance_x\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_VOLTAGE, MAX_VOLTAGE, 10),\n",
-    "#     bmad_beta_y_voltage,\n",
-    "#     label=\"Bmad emittance_y\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "plt.axvline(0.01815975e9, color=\"black\", linestyle=\"--\")\n",
-    "plt.subplot(2, 2, 3)\n",
-    "plt.title(\"Frequency\")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "    [beam.emittance_x for beam in cheetah_outgoing_beams_frequency],\n",
-    "    label=\"Cheetah emittance_x\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "    [beam.emittance_y for beam in cheetah_outgoing_beams_frequency],\n",
-    "    label=\"Cheetah emittance_y\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "    [beam.emit_x for beam in ocelot_outgoing_beams_frequency],\n",
-    "    label=\"Ocelot emittance_x\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "    [beam.emit_y for beam in ocelot_outgoing_beams_frequency],\n",
-    "    label=\"Ocelot emittance_y\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "#     bmad_beta_x_frequency,\n",
-    "#     label=\"Bmad emittance_x\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_FREQUENCY, MAX_FREQUENCY, 10),\n",
-    "#     bmad_beta_y_frequency,\n",
-    "#     label=\"Bmad emittance_y\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "plt.axvline(1.3e9, color=\"black\", linestyle=\"--\")\n",
-    "plt.subplot(2, 2, 4)\n",
-    "plt.title(\"Phase\")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "    [beam.emittance_x for beam in cheetah_outgoing_beams_phase],\n",
-    "    label=\"Cheetah emittance_x\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "    [beam.emittance_y for beam in cheetah_outgoing_beams_phase],\n",
-    "    label=\"Cheetah emittance_y\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "    [beam.emit_x for beam in ocelot_outgoing_beams_phase],\n",
-    "    label=\"Ocelot emittance_x\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "plt.plot(\n",
-    "    np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "    [beam.emit_y for beam in ocelot_outgoing_beams_phase],\n",
-    "    label=\"Ocelot emittance_y\",\n",
-    "    ls=\"--\",\n",
-    ")\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "#     bmad_beta_x_phase,\n",
-    "#     label=\"Bmad emittance_x\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "# plt.plot(\n",
-    "#     np.linspace(MIN_PHASE, MAX_PHASE, 10),\n",
-    "#     bmad_beta_y_phase,\n",
-    "#     label=\"Bmad emittance_y\",\n",
-    "#     ls=\":\",\n",
-    "# )\n",
-    "plt.axvline(0.0, color=\"black\", linestyle=\"--\")\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "r12 = tensor(1.3255)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 0.2125,  1.3255,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000],\n",
-       "        [-0.1256,  0.3849,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000],\n",
-       "        [ 0.0000,  0.0000,  0.2125,  1.3255,  0.0000,  0.0000,  0.0000],\n",
-       "        [ 0.0000,  0.0000, -0.1256,  0.3849,  0.0000,  0.0000,  0.0000],\n",
-       "        [ 0.0000,  0.0000,  0.0000,  0.0000,  1.0000, -0.0034,  0.0000],\n",
-       "        [ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.2475,  0.0000],\n",
-       "        [ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  1.0000]])"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cheetah_cavity = cheetah.Cavity(\n",
-    "    length=3.0,\n",
-    "    voltage=DEFAULT_VOLTAGE,\n",
-    "    frequency=DEFAULT_FREQUENCY,\n",
-    "    phase=DEFAULT_PHASE,\n",
-    ")\n",
-    "cheetah_cavity.transfer_map(6e6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "cheetah-dev",
-   "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": "3.9.16"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}