diff --git a/.gitignore b/.gitignore
index 910896f3..208ddcbf 100644
--- a/.gitignore
+++ b/.gitignore
@@ -142,3 +142,4 @@ dmypy.json
 
 # Idea
 /.idea/
+.vscode/launch.json
diff --git a/geometric_kernels/kernels/feature_map.py b/geometric_kernels/kernels/feature_map.py
index b224be43..8ca2d1df 100644
--- a/geometric_kernels/kernels/feature_map.py
+++ b/geometric_kernels/kernels/feature_map.py
@@ -82,6 +82,11 @@ def __init__(
         self.feature_map = make_deterministic(feature_map, key)
         self.normalize = normalize
 
+        if "eigenfunctions" in vars(feature_map):
+            self.eigenfunctions = feature_map.eigenfunctions  # type: ignore[attr-defined]
+        else:
+            self.eigenfunctions = None
+
     def init_params(self) -> Dict[str, B.NPNumeric]:
         """
         Initializes the dict of the trainable parameters of the kernel.
diff --git a/geometric_kernels/kernels/matern_kernel.py b/geometric_kernels/kernels/matern_kernel.py
index 8a63aafb..424b03d3 100644
--- a/geometric_kernels/kernels/matern_kernel.py
+++ b/geometric_kernels/kernels/matern_kernel.py
@@ -22,10 +22,12 @@
 from geometric_kernels.kernels.hodge_compositional import MaternHodgeCompositionalKernel
 from geometric_kernels.kernels.karhunen_loeve import MaternKarhunenLoeveKernel
 from geometric_kernels.spaces import (
+    CompactHomogeneousSpace,
     CompactMatrixLieGroup,
     DiscreteSpectrumSpace,
     Graph,
     GraphEdges,
+    Grassmannian,
     HodgeDiscreteSpectrumSpace,
     Hyperbolic,
     HypercubeGraph,
@@ -76,7 +78,9 @@ def default_feature_map(
 
 @overload
 def feature_map_from_kernel(kernel: MaternKarhunenLoeveKernel):
-    if isinstance(kernel.space, CompactMatrixLieGroup):
+    if isinstance(kernel.space, CompactMatrixLieGroup) or isinstance(
+        kernel.space, Grassmannian
+    ):
         # Because `CompactMatrixLieGroup` does not currently support explicit
         # eigenfunction computation (they only support addition theorem).
         return RandomPhaseFeatureMapCompact(
@@ -84,6 +88,15 @@ def feature_map_from_kernel(kernel: MaternKarhunenLoeveKernel):
             kernel.num_levels,
             MaternGeometricKernel._DEFAULT_NUM_RANDOM_PHASES,
         )
+    if isinstance(kernel.space, CompactHomogeneousSpace):
+        # Same as above.
+        # `CompactHomogeneousSpace` does not currently support explicit
+        # eigenfunction computation (they only support addition theorem).
+        return RandomPhaseFeatureMapCompact(
+            kernel.space,
+            kernel.num_levels,
+            MaternGeometricKernel._DEFAULT_NUM_RANDOM_PHASES_HOMOGENEOUS_SPACE,
+        )
     else:
         return DeterministicFeatureMapCompact(kernel.space, kernel.num_levels)
 
@@ -140,6 +153,12 @@ def feature_map_from_space(space: DiscreteSpectrumSpace, num: int):
         return RandomPhaseFeatureMapCompact(
             space, num, MaternGeometricKernel._DEFAULT_NUM_RANDOM_PHASES
         )
+    elif isinstance(space, CompactHomogeneousSpace):
+        return RandomPhaseFeatureMapCompact(
+            space,
+            num,
+            MaternGeometricKernel._DEFAULT_NUM_RANDOM_PHASES_HOMOGENEOUS_SPACE,
+        )
     elif isinstance(space, Hypersphere):
         num_computed_levels = space.num_computed_levels
         if num_computed_levels > 0:
@@ -204,7 +223,9 @@ def feature_map_from_space(space: Space, num: int):
 
 @overload
 def default_num(space: DiscreteSpectrumSpace) -> int:
-    if isinstance(space, CompactMatrixLieGroup):
+    if isinstance(space, CompactMatrixLieGroup) or isinstance(
+        space, CompactHomogeneousSpace
+    ):
         return MaternGeometricKernel._DEFAULT_NUM_LEVELS_LIE_GROUP
     elif isinstance(space, (Graph, Mesh)):
         return min(
@@ -270,6 +291,7 @@ class MaternGeometricKernel:
     _DEFAULT_NUM_LEVELS = 25
     _DEFAULT_NUM_LEVELS_LIE_GROUP = 20
     _DEFAULT_NUM_RANDOM_PHASES = 3000
+    _DEFAULT_NUM_RANDOM_PHASES_HOMOGENEOUS_SPACE = 500
 
     def __new__(
         cls,
@@ -335,16 +357,10 @@ def __new__(
         """
 
         kernel: BaseGeometricKernel
-        if isinstance(space, DiscreteSpectrumSpace):
-            num = num or default_num(space)
-            if isinstance(space, HodgeDiscreteSpectrumSpace):
-                kernel = MaternHodgeCompositionalKernel(space, num, normalize=normalize)
-            else:
-                kernel = MaternKarhunenLoeveKernel(space, num, normalize=normalize)
-            if return_feature_map:
-                feature_map = default_feature_map(kernel=kernel)
 
-        elif isinstance(space, NoncompactSymmetricSpace):
+        if isinstance(space, NoncompactSymmetricSpace) or isinstance(
+            space, CompactHomogeneousSpace
+        ):
             num = num or default_num(space)
             if "key" in kwargs:
                 key = kwargs["key"]
@@ -357,10 +373,20 @@ def __new__(
                     )
                     % str(type(space))
                 )
+
             feature_map = default_feature_map(space=space, num=num)
             kernel = MaternFeatureMapKernel(
                 space, feature_map, key, normalize=normalize
             )
+        elif isinstance(space, DiscreteSpectrumSpace):
+            num = num or default_num(space)
+            if isinstance(space, HodgeDiscreteSpectrumSpace):
+                kernel = MaternHodgeCompositionalKernel(space, num, normalize=normalize)
+            else:
+                kernel = MaternKarhunenLoeveKernel(space, num, normalize=normalize)
+            if return_feature_map:
+                feature_map = default_feature_map(kernel=kernel)
+
         else:
             raise NotImplementedError
 
diff --git a/geometric_kernels/resources/precomputed_grassmanian_zsf.json b/geometric_kernels/resources/precomputed_grassmanian_zsf.json
new file mode 100644
index 00000000..7f616e57
--- /dev/null
+++ b/geometric_kernels/resources/precomputed_grassmanian_zsf.json
@@ -0,0 +1,758 @@
+{
+    "Gr(10,2)": {
+        "(0, 0, 0, 0, 0)": [[1],[[0,0]]],
+        "(10, 0, 0, 0, 0)": [[81081,45045,-173250,38610,-99000,132000,38610,-89100,79200,-43200,45045,-99000,79200,-28800,5760,81081,-173250,132000,-43200,5760,-256],[[5,0],[4,1],[4,0],[3,2],[3,1],[3,0],[2,3],[2,2],[2,1],[2,0],[1,4],[1,3],[1,2],[1,1],[1,0],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(10, 2, 0, 0, 0)": [[680680,-85085,388960,-1510025,182050,350064,-918170,1232440,-139040,388960,-918170,845900,-466400,45760,680680,-1510025,1232440,-466400,94336,-6272,-85085,182050,-139040,45760,-6272,256],[[5,1],[5,0],[4,2],[4,1],[4,0],[3,3],[3,2],[3,1],[3,0],[2,4],[2,3],[2,2],[2,1],[2,0],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(10, 4, 0, 0, 0)": [[1847560,-923780,46189,1108536,-4547840,2018665,-99110,1108536,-3155064,4433770,-1608200,76120,1847560,-4547840,4433770,-2449700,596200,-25520,-923780,2018665,-1608200,596200,-96800,3520,46189,-99110,76120,-25520,3520,-128],[[5,2],[5,1],[5,0],[4,3],[4,2],[4,1],[4,0],[3,4],[3,3],[3,2],[3,1],[3,0],[2,5],[2,4],[2,3],[2,2],[2,1],[2,0],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(10, 6, 0, 0, 0)": [[237025152,-222211080,47616660,-1322685,158016768,-668152632,517523520,-104482425,2858550,237025152,-668152632,886190552,-482416650,85302600,-2240600,-222211080,517523520,-482416650,200506500,-30426600,759600,47616660,-104482425,85302600,-30426600,4096800,-100800,-1322685,2858550,-2240600,759600,-100800,3200],[[5,3],[5,2],[5,1],[5,0],[4,4],[4,3],[4,2],[4,1],[4,0],[3,5],[3,4],[3,3],[3,2],[3,1],[3,0],[2,5],[2,4],[2,3],[2,2],[2,1],[2,0],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(10, 8, 0, 0, 0)": [[2595989760,-3634385664,1514327360,-189290920,3380195,2595989760,-9744946176,10000638144,-3689590240,435185975,-7574350,-3634385664,10000638144,-8902877424,3044872550,-345416200,5943200,1514327360,-3689590240,3044872550,-995324500,110799200,-1931200,-189290920,435185975,-345416200,110799200,-12457600,233600,3380195,-7574350,5943200,-1931200,233600,-6400],[[5,4],[5,3],[5,2],[5,1],[5,0],[4,5],[4,4],[4,3],[4,2],[4,1],[4,0],[3,5],[3,4],[3,3],[3,2],[3,1],[3,0],[2,5],[2,4],[2,3],[2,2],[2,1],[2,0],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(12, 0, 0, 0, 0)": [[1156155,630630,-3027024,525525,-1681680,3003000,500500,-1441440,1716000,-1408000,525525,-1441440,1544400,-844800,316800,630630,-1681680,1716000,-844800,211200,-30720,1156155,-3027024,3003000,-1408000,316800,-30720,1024],[[6,0],[5,1],[5,0],[4,2],[4,1],[4,0],[3,3],[3,2],[3,1],[3,0],[2,4],[2,3],[2,2],[2,1],[2,0],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,6],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(12, 2, 0, 0, 0)": [[9585576,-1198197,5325320,-25827802,3139136,4564560,-14929915,26861120,-3117400,4564560,-13465452,16565120,-13705120,1464320,5325320,-14929915,16565120,-9552400,3660800,-330880,9585576,-25827802,26861120,-13705120,3660800,-557824,32768,-1198197,3139136,-3117400,1464320,-330880,32768,-1024],[[6,1],[6,0],[5,2],[5,1],[5,0],[4,3],[4,2],[4,1],[4,0],[3,4],[3,3],[3,2],[3,1],[3,0],[2,5],[2,4],[2,3],[2,2],[2,1],[2,0],[1,6],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,6],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(12, 4, 0, 0, 0)": [[50832600,-25416300,1270815,29047200,-148382780,67552030,-3333512,26142480,-92660568,174415345,-68754920,3317080,29047200,-92660568,125356452,-108502160,33872800,-1564160,50832600,-148382780,174415345,-108502160,42499600,-8652800,357760,-25416300,67552030,-68754920,33872800,-8652800,1081600,-35840,1270815,-3333512,3317080,-1564160,357760,-35840,1024],[[6,2],[6,1],[6,0],[5,3],[5,2],[5,1],[5,0],[4,4],[4,3],[4,2],[4,1],[4,0],[3,5],[3,4],[3,3],[3,2],[3,1],[3,0],[2,6],[2,5],[2,4],[2,3],[2,2],[2,1],[2,0],[1,6],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,6],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(12, 6, 0, 0, 0)": [[6235465600,-5845749000,1252660500,-34796125,3741279360,-20015844576,16203254340,-3324815130,91498680,3741279360,-14123329584,27370388136,-17728673235,3379997400,-91439400,6235465600,-20015844576,27370388136,-23014864236,10220761200,-1678216800,43596800,-5845749000,16203254340,-17728673235,10220761200,-3175926000,418828800,-10108800,1252660500,-3324815130,3379997400,-1678216800,418828800,-44678400,998400,-34796125,91498680,-91439400,43596800,-10108800,998400,-25600],[[6,3],[6,2],[6,1],[6,0],[5,4],[5,3],[5,2],[5,1],[5,0],[4,5],[4,4],[4,3],[4,2],[4,1],[4,0],[3,6],[3,5],[3,4],[3,3],[3,2],[3,1],[3,0],[2,6],[2,5],[2,4],[2,3],[2,2],[2,1],[2,0],[1,6],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,6],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(14, 0, 0, 0, 0)": [[1042899,561561,-3237234,459459,-1765764,3972969,425425,-1471470,2207205,-2452450,425425,-1401400,1891890,-1401400,800800,459459,-1471470,1891890,-1261260,480480,-133056,561561,-1765764,2207205,-1401400,480480,-88704,9856,1042899,-3237234,3972969,-2452450,800800,-133056,9856,-256],[[7,0],[6,1],[6,0],[5,2],[5,1],[5,0],[4,3],[4,2],[4,1],[4,0],[3,4],[3,3],[3,2],[3,1],[3,0],[2,5],[2,4],[2,3],[2,2],[2,1],[2,0],[1,6],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,7],[0,6],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(2, 0, 0, 0, 0)": [[5,5,-2],[[1,0],[0,1],[0,0]]],
+        "(2, 2, 0, 0, 0)": [[72,-9,-9,2],[[1,1],[1,0],[0,1],[0,0]]],
+        "(4, 0, 0, 0, 0)": [[63,42,-48,63,-48,8],[[2,0],[1,1],[1,0],[0,2],[0,1],[0,0]]],
+        "(4, 2, 0, 0, 0)": [[616,-77,616,-638,64,-77,64,-8],[[2,1],[2,0],[1,2],[1,1],[1,0],[0,2],[0,1],[0,0]]],
+        "(4, 4, 0, 0, 0)": [[5720,-2860,143,-2860,1562,-88,143,-88,8],[[2,2],[2,1],[2,0],[1,2],[1,1],[1,0],[0,2],[0,1],[0,0]]],
+        "(6, 0, 0, 0, 0)": [[105,63,-126,63,-84,42,105,-126,42,-4],[[3,0],[2,1],[2,0],[1,2],[1,1],[1,0],[0,3],[0,2],[0,1],[0,0]]],
+        "(6, 2, 0, 0, 0)": [[936,-117,624,-1235,142,936,-1235,580,-50,-117,142,-50,4],[[3,1],[3,0],[2,2],[2,1],[2,0],[1,3],[1,2],[1,1],[1,0],[0,3],[0,2],[0,1],[0,0]]],
+        "(6, 4, 0, 0, 0)": [[23400,-11700,585,23400,-44200,16367,-754,-11700,16367,-5356,248,585,-754,248,-16],[[3,2],[3,1],[3,0],[2,3],[2,2],[2,1],[2,0],[1,3],[1,2],[1,1],[1,0],[0,3],[0,2],[0,1],[0,0]]],
+        "(6, 6, 0, 0, 0)": [[198016,-185640,39780,-1105,-185640,179400,-39975,1170,39780,-39975,9516,-312,-1105,1170,-312,16],[[3,3],[3,2],[3,1],[3,0],[2,3],[2,2],[2,1],[2,0],[1,3],[1,2],[1,1],[1,0],[0,3],[0,2],[0,1],[0,0]]],
+        "(8, 0, 0, 0, 0)": [[5775,3300,-9600,2970,-5760,5184,3300,-5760,3456,-1024,5775,-9600,5184,-1024,64],[[4,0],[3,1],[3,0],[2,2],[2,1],[2,0],[1,3],[1,2],[1,1],[1,0],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(8, 2, 0, 0, 0)": [[99000,-12375,59400,-173700,20640,59400,-117450,105696,-11232,99000,-173700,105696,-30528,2304,-12375,20640,-11232,2304,-128],[[4,1],[4,0],[3,2],[3,1],[3,0],[2,3],[2,2],[2,1],[2,0],[1,4],[1,3],[1,2],[1,1],[1,0],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(8, 4, 0, 0, 0)": [[561000,-280500,14025,374000,-1147500,486300,-23600,561000,-1147500,1011510,-299280,13248,-280500,486300,-299280,66432,-2688,14025,-23600,13248,-2688,128],[[4,2],[4,1],[4,0],[3,3],[3,2],[3,1],[3,0],[2,4],[2,3],[2,2],[2,1],[2,0],[1,4],[1,3],[1,2],[1,1],[1,0],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(8, 6, 0, 0, 0)": [[3183488,-2984520,639540,-17765,3183488,-8935472,6141420,-1174700,31280,-2984520,6141420,-3664350,656400,-17280,639540,-1174700,656400,-115200,3200,-17765,31280,-17280,3200,-128],[[4,3],[4,2],[4,1],[4,0],[3,4],[3,3],[3,2],[3,1],[3,0],[2,4],[2,3],[2,2],[2,1],[2,0],[1,4],[1,3],[1,2],[1,1],[1,0],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(8, 8, 0, 0, 0)": [[26046720,-36465408,15193920,-1899240,33915,-36465408,51804032,-21976920,2810100,-51680,15193920,-21976920,9554850,-1264800,24480,-1899240,2810100,-1264800,177600,-3840,33915,-51680,24480,-3840,128],[[4,4],[4,3],[4,2],[4,1],[4,0],[3,4],[3,3],[3,2],[3,1],[3,0],[2,4],[2,3],[2,2],[2,1],[2,0],[1,4],[1,3],[1,2],[1,1],[1,0],[0,4],[0,3],[0,2],[0,1],[0,0]]]
+    },
+    "Gr(10,3)": {
+        "(0, 0, 0, 0, 0)": [[1],[[0,0,0]]],
+        "(10, 0, 0, 0, 0)": [[209664,116480,116480,-542080,99840,66560,-309760,99840,-309760,522720,99840,59904,-278784,59904,-185856,313632,99840,-278784,313632,-232848,116480,66560,-309760,59904,-185856,313632,66560,-185856,209088,-155232,116480,-309760,313632,-155232,48510,209664,116480,-542080,99840,-309760,522720,99840,-278784,313632,-232848,116480,-309760,313632,-155232,48510,209664,-542080,522720,-232848,48510,-4851],[[5,0,0],[4,1,0],[4,0,1],[4,0,0],[3,2,0],[3,1,1],[3,1,0],[3,0,2],[3,0,1],[3,0,0],[2,3,0],[2,2,1],[2,2,0],[2,1,2],[2,1,1],[2,1,0],[2,0,3],[2,0,2],[2,0,1],[2,0,0],[1,4,0],[1,3,1],[1,3,0],[1,2,2],[1,2,1],[1,2,0],[1,1,3],[1,1,2],[1,1,1],[1,1,0],[1,0,4],[1,0,3],[1,0,2],[1,0,1],[1,0,0],[0,5,0],[0,4,1],[0,4,0],[0,3,2],[0,3,1],[0,3,0],[0,2,3],[0,2,2],[0,2,1],[0,2,0],[0,1,4],[0,1,3],[0,1,2],[0,1,1],[0,1,0],[0,0,5],[0,0,4],[0,0,3],[0,0,2],[0,0,1],[0,0,0]]],
+        "(2, 0, 0, 0, 0)": [[10,10,10,-9],[[1,0,0],[0,1,0],[0,0,1],[0,0,0]]],
+        "(2, 2, 0, 0, 0)": [[4,4,-2,4,-2,-2,1],[[1,1,0],[1,0,1],[1,0,0],[0,1,1],[0,1,0],[0,0,1],[0,0,0]]],
+        "(2, 2, 2, 0, 0)": [[168,-28,-28,8,-28,8,8,-3],[[1,1,1],[1,1,0],[1,0,1],[1,0,0],[0,1,1],[0,1,0],[0,0,1],[0,0,0]]],
+        "(4, 0, 0, 0, 0)": [[168,112,112,-200,168,112,-200,168,-200,75],[[2,0,0],[1,1,0],[1,0,1],[1,0,0],[0,2,0],[0,1,1],[0,1,0],[0,0,2],[0,0,1],[0,0,0]]],
+        "(4, 2, 0, 0, 0)": [[1232,1232,-616,1232,1848,-2244,1232,-2244,800,1232,-616,1232,-2244,800,-616,800,-225],[[2,1,0],[2,0,1],[2,0,0],[1,2,0],[1,1,1],[1,1,0],[1,0,2],[1,0,1],[1,0,0],[0,2,1],[0,2,0],[0,1,2],[0,1,1],[0,1,0],[0,0,2],[0,0,1],[0,0,0]]],
+        "(4, 2, 2, 0, 0)": [[588,-98,-98,28,588,-98,588,-1092,168,-98,168,-38,-98,28,-98,168,-38,28,-38,9],[[2,1,1],[2,1,0],[2,0,1],[2,0,0],[1,2,1],[1,2,0],[1,1,2],[1,1,1],[1,1,0],[1,0,2],[1,0,1],[1,0,0],[0,2,1],[0,2,0],[0,1,2],[0,1,1],[0,1,0],[0,0,2],[0,0,1],[0,0,0]]],
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+        "(8, 2, 2, 0)": [[314925,-62985,-62985,20995,188955,-37791,188955,-781014,152779,-37791,152779,-46512,188955,-37791,125970,-529074,104652,188955,-529074,759390,-141219,-37791,104652,-141219,36414,314925,-62985,188955,-781014,152779,188955,-529074,759390,-141219,314925,-781014,759390,-444510,71001,-62985,152779,-141219,71001,-12600,-62985,20995,-37791,152779,-46512,-37791,104652,-141219,36414,-62985,152779,-141219,71001,-12600,20995,-46512,36414,-12600,1575],[[4,1,1],[4,1,0],[4,0,1],[4,0,0],[3,2,1],[3,2,0],[3,1,2],[3,1,1],[3,1,0],[3,0,2],[3,0,1],[3,0,0],[2,3,1],[2,3,0],[2,2,2],[2,2,1],[2,2,0],[2,1,3],[2,1,2],[2,1,1],[2,1,0],[2,0,3],[2,0,2],[2,0,1],[2,0,0],[1,4,1],[1,4,0],[1,3,2],[1,3,1],[1,3,0],[1,2,3],[1,2,2],[1,2,1],[1,2,0],[1,1,4],[1,1,3],[1,1,2],[1,1,1],[1,1,0],[1,0,4],[1,0,3],[1,0,2],[1,0,1],[1,0,0],[0,4,1],[0,4,0],[0,3,2],[0,3,1],[0,3,0],[0,2,3],[0,2,2],[0,2,1],[0,2,0],[0,1,4],[0,1,3],[0,1,2],[0,1,1],[0,1,0],[0,0,4],[0,0,3],[0,0,2],[0,0,1],[0,0,0]]],
+        "(8, 4, 0, 0)": [[316008,210672,-306432,316008,-306432,68096,210672,316008,-915192,316008,-839952,731462,210672,-915192,731462,-151848,316008,316008,-915192,474012,-1169640,1230192,316008,-1169640,1358778,-681696,316008,-915192,1230192,-681696,121905,210672,-306432,316008,-839952,731462,316008,-1169640,1358778,-681696,210672,-839952,1358778,-946620,279720,-306432,731462,-681696,279720,-40950,316008,-306432,68096,210672,-915192,731462,-151848,316008,-915192,1230192,-681696,121905,-306432,731462,-681696,279720,-40950,68096,-151848,121905,-40950,4725],[[4,2,0],[4,1,1],[4,1,0],[4,0,2],[4,0,1],[4,0,0],[3,3,0],[3,2,1],[3,2,0],[3,1,2],[3,1,1],[3,1,0],[3,0,3],[3,0,2],[3,0,1],[3,0,0],[2,4,0],[2,3,1],[2,3,0],[2,2,2],[2,2,1],[2,2,0],[2,1,3],[2,1,2],[2,1,1],[2,1,0],[2,0,4],[2,0,3],[2,0,2],[2,0,1],[2,0,0],[1,4,1],[1,4,0],[1,3,2],[1,3,1],[1,3,0],[1,2,3],[1,2,2],[1,2,1],[1,2,0],[1,1,4],[1,1,3],[1,1,2],[1,1,1],[1,1,0],[1,0,4],[1,0,3],[1,0,2],[1,0,1],[1,0,0],[0,4,2],[0,4,1],[0,4,0],[0,3,3],[0,3,2],[0,3,1],[0,3,0],[0,2,4],[0,2,3],[0,2,2],[0,2,1],[0,2,0],[0,1,4],[0,1,3],[0,1,2],[0,1,1],[0,1,0],[0,0,4],[0,0,3],[0,0,2],[0,0,1],[0,0,0]]],
+        "(8, 4, 2, 0)": [[1956240,-391248,1956240,-2632032,409032,-391248,409032,-71136,1304160,-260832,2390960,-6378528,1152312,1304160,-6378528,6478107,-966207,-260832,1152312,-966207,158973,1956240,-391248,2390960,-6378528,1152312,2390960,-8382192,9749970,-1566042,1956240,-6378528,9749970,-6465573,885261,-391248,1152312,-1566042,885261,-128415,1956240,-2632032,409032,1304160,-6378528,6478107,-966207,1956240,-6378528,9749970,-6465573,885261,-2632032,6478107,-6465573,2954655,-356685,409032,-966207,885261,-356685,43575,-391248,409032,-71136,-260832,1152312,-966207,158973,-391248,1152312,-1566042,885261,-128415,409032,-966207,885261,-356685,43575,-71136,158973,-128415,43575,-4725],[[4,2,1],[4,2,0],[4,1,2],[4,1,1],[4,1,0],[4,0,2],[4,0,1],[4,0,0],[3,3,1],[3,3,0],[3,2,2],[3,2,1],[3,2,0],[3,1,3],[3,1,2],[3,1,1],[3,1,0],[3,0,3],[3,0,2],[3,0,1],[3,0,0],[2,4,1],[2,4,0],[2,3,2],[2,3,1],[2,3,0],[2,2,3],[2,2,2],[2,2,1],[2,2,0],[2,1,4],[2,1,3],[2,1,2],[2,1,1],[2,1,0],[2,0,4],[2,0,3],[2,0,2],[2,0,1],[2,0,0],[1,4,2],[1,4,1],[1,4,0],[1,3,3],[1,3,2],[1,3,1],[1,3,0],[1,2,4],[1,2,3],[1,2,2],[1,2,1],[1,2,0],[1,1,4],[1,1,3],[1,1,2],[1,1,1],[1,1,0],[1,0,4],[1,0,3],[1,0,2],[1,0,1],[1,0,0],[0,4,2],[0,4,1],[0,4,0],[0,3,3],[0,3,2],[0,3,1],[0,3,0],[0,2,4],[0,2,3],[0,2,2],[0,2,1],[0,2,0],[0,1,4],[0,1,3],[0,1,2],[0,1,1],[0,1,0],[0,0,4],[0,0,3],[0,0,2],[0,0,1],[0,0,0]]]
+    },
+    "Gr(9,7)": {
+        "(0, 0, 0, 0)": [[1],[[0,0]]],
+        "(10, 0, 0, 0)": [[16380945,9100525,-36402100,7800450,-20801200,28940800,7800450,-18721080,17364480,-9922560,9100525,-20801200,17364480,-6615040,1392640,16380945,-36402100,28940800,-9922560,1392640,-65536],[[5,0],[4,1],[4,0],[3,2],[3,1],[3,0],[2,3],[2,2],[2,1],[2,0],[1,4],[1,3],[1,2],[1,1],[1,0],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(10, 2, 0, 0)": [[29978200,-4282600,17130400,-69378120,9528785,15417360,-42238672,59377052,-7592704,17130400,-42238672,40855206,-23731456,2616832,29978200,-69378120,59377052,-23731456,5108736,-376832,-4282600,9528785,-7592704,2616832,-376832,16384],[[5,1],[5,0],[4,2],[4,1],[4,0],[3,3],[3,2],[3,1],[3,0],[2,4],[2,3],[2,2],[2,1],[2,0],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(10, 4, 0, 0)": [[2390850,-1304100,72450,1434510,-6155352,2965942,-161644,1434510,-4277448,6306216,-2468368,129493,2390850,-6155352,6306216,-3674376,959667,-45420,-1304100,2965942,-2468368,959667,-165448,6592,72450,-161644,129493,-45420,6592,-256],[[5,2],[5,1],[5,0],[4,3],[4,2],[4,1],[4,0],[3,4],[3,3],[3,2],[3,1],[3,0],[2,5],[2,4],[2,3],[2,2],[2,1],[2,0],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(10, 6, 0, 0)": [[2960100,-2960100,683100,-20700,1973400,-8722428,7181658,-1558848,46506,2960100,-8722428,12127236,-6995844,1327152,-37992,-2960100,7181658,-6995844,3065484,-496728,13485,683100,-1558848,1327152,-496728,70942,-1888,-20700,46506,-37992,13485,-1888,64],[[5,3],[5,2],[5,1],[5,0],[4,4],[4,3],[4,2],[4,1],[4,0],[3,5],[3,4],[3,3],[3,2],[3,1],[3,0],[2,5],[2,4],[2,3],[2,2],[2,1],[2,0],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(10, 8, 0, 0)": [[6928350,-10210200,4504500,-600600,11550,6928350,-27129960,29249220,-11411400,1434510,-26880,-10210200,29249220,-27291264,9850764,-1189104,22008,4504500,-11411400,9850764,-3390912,400792,-7504,-600600,1434510,-1189104,400792,-47712,959,11550,-26880,22008,-7504,959,-28],[[5,4],[5,3],[5,2],[5,1],[5,0],[4,5],[4,4],[4,3],[4,2],[4,1],[4,0],[3,5],[3,4],[3,3],[3,2],[3,1],[3,0],[2,5],[2,4],[2,3],[2,2],[2,1],[2,0],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(12, 0, 0, 0)": [[922150845,502991370,-2497612320,419159475,-1387562400,2569560000,399199500,-1189339200,1468320000,-1252966400,419159475,-1189339200,1321488000,-751779840,294174720,502991370,-1387562400,1468320000,-751779840,196116480,-29884416,922150845,-2497612320,2569560000,-1252966400,294174720,-29884416,1048576],[[6,0],[5,1],[5,0],[4,2],[4,1],[4,0],[3,3],[3,2],[3,1],[3,0],[2,4],[2,3],[2,2],[2,1],[2,0],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,6],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(12, 2, 0, 0)": [[833906745,-119129535,463281525,-2330557110,322851690,397098450,-1348765425,2523522050,-332465000,397098450,-1216725300,1560234900,-1347325280,162391040,463281525,-1348765425,1560234900,-942545520,379223040,-38277120,833906745,-2330557110,2523522050,-1347325280,379223040,-61300736,3964928,-119129535,322851690,-332465000,162391040,-38277120,3964928,-131072],[[6,1],[6,0],[5,2],[5,1],[5,0],[4,3],[4,2],[4,1],[4,0],[3,4],[3,3],[3,2],[3,1],[3,0],[2,5],[2,4],[2,3],[2,2],[2,1],[2,0],[1,6],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,6],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(12, 4, 0, 0)": [[520009875,-283641750,15757875,297148500,-1578426750,780410700,-42755850,267433650,-987576300,1936707525,-824594850,44113425,297148500,-987576300,1396093560,-1262980692,423192732,-21626624,520009875,-1578426750,1936707525,-1262980692,519958566,-113039616,5156352,-283641750,780410700,-824594850,423192732,-113039616,14939136,-540672,15757875,-42755850,44113425,-21626624,5156352,-540672,16384],[[6,2],[6,1],[6,0],[5,3],[5,2],[5,1],[5,0],[4,4],[4,3],[4,2],[4,1],[4,0],[3,5],[3,4],[3,3],[3,2],[3,1],[3,0],[2,6],[2,5],[2,4],[2,3],[2,2],[2,1],[2,0],[1,6],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,6],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(12, 6, 0, 0)": [[153957375,-153957375,35528625,-1076625,92374425,-513899100,442195875,-97568550,2928150,92374425,-363127050,733124700,-502863975,102881475,-3033675,153957375,-513899100,733124700,-644461572,302206767,-53116452,1503044,-153957375,442195875,-502863975,302206767,-98634786,13850076,-363312,35528625,-97568550,102881475,-53116452,13850076,-1560096,37632,-1076625,2928150,-3033675,1503044,-363312,37632,-1024],[[6,3],[6,2],[6,1],[6,0],[5,4],[5,3],[5,2],[5,1],[5,0],[4,5],[4,4],[4,3],[4,2],[4,1],[4,0],[3,6],[3,5],[3,4],[3,3],[3,2],[3,1],[3,0],[2,6],[2,5],[2,4],[2,3],[2,2],[2,1],[2,0],[1,6],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,6],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(14, 0, 0, 0)": [[13172593005,7092934695,-42127733340,5803310205,-22978763640,53370031680,5373435375,-19148969700,29650017600,-34080480000,5373435375,-18237114000,25414300800,-19474560000,11540480000,5803310205,-19148969700,25414300800,-17527104000,6924288000,-1994194944,7092934695,-22978763640,29650017600,-19474560000,6924288000,-1329463296,154140672,13172593005,-42127733340,53370031680,-34080480000,11540480000,-1994194944,154140672,-4194304],[[7,0],[6,1],[6,0],[5,2],[5,1],[5,0],[4,3],[4,2],[4,1],[4,0],[3,4],[3,3],[3,2],[3,1],[3,0],[2,5],[2,4],[2,3],[2,2],[2,1],[2,0],[1,6],[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,7],[0,6],[0,5],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(2, 0, 0, 0)": [[9,9,-4],[[1,0],[0,1],[0,0]]],
+        "(2, 2, 0, 0)": [[28,-4,-4,1],[[1,1],[1,0],[0,1],[0,0]]],
+        "(4, 0, 0, 0)": [[429,286,-352,429,-352,64],[[2,0],[1,1],[1,0],[0,2],[0,1],[0,0]]],
+        "(4, 2, 0, 0)": [[455,-65,455,-510,58,-65,58,-8],[[2,1],[2,0],[1,2],[1,1],[1,0],[0,2],[0,1],[0,0]]],
+        "(4, 4, 0, 0)": [[495,-270,15,-270,160,-10,15,-10,1],[[2,2],[2,1],[2,0],[1,2],[1,1],[1,0],[0,2],[0,1],[0,0]]],
+        "(6, 0, 0, 0)": [[5525,3315,-7020,3315,-4680,2496,5525,-7020,2496,-256],[[3,0],[2,1],[2,0],[1,2],[1,1],[1,0],[0,3],[0,2],[0,1],[0,0]]],
+        "(6, 2, 0, 0)": [[10710,-1530,7140,-15030,1965,10710,-15030,7582,-736,-1530,1965,-736,64],[[3,1],[3,0],[2,2],[2,1],[2,0],[1,3],[1,2],[1,1],[1,0],[0,3],[0,2],[0,1],[0,0]]],
+        "(6, 4, 0, 0)": [[3927,-2142,119,3927,-7896,3171,-162,-2142,3171,-1116,57,119,-162,57,-4],[[3,2],[3,1],[3,0],[2,3],[2,2],[2,1],[2,0],[1,3],[1,2],[1,1],[1,0],[0,3],[0,2],[0,1],[0,0]]],
+        "(6, 6, 0, 0)": [[8008,-8008,1848,-56,-8008,8239,-1974,63,1848,-1974,504,-18,-56,63,-18,1],[[3,3],[3,2],[3,1],[3,0],[2,3],[2,2],[2,1],[2,0],[1,3],[1,2],[1,1],[1,0],[0,3],[0,2],[0,1],[0,0]]],
+        "(8, 0, 0, 0)": [[1187025,678300,-2067200,610470,-1240320,1175040,678300,-1240320,783360,-245760,1187025,-2067200,1175040,-245760,16384],[[4,0],[3,1],[3,0],[2,2],[2,1],[2,0],[1,3],[1,2],[1,1],[1,0],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(8, 2, 0, 0)": [[553945,-79135,332367,-1021972,138244,332367,-691866,658716,-79152,553945,-1021972,658716,-203296,17152,-79135,138244,-79152,17152,-1024],[[4,1],[4,0],[3,2],[3,1],[3,0],[2,3],[2,2],[2,1],[2,0],[1,4],[1,3],[1,2],[1,1],[1,0],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(8, 4, 0, 0)": [[184338,-100548,5586,122892,-397404,182742,-9842,184338,-397404,370944,-118398,5805,-100548,182742,-118398,28062,-1248,5586,-9842,5805,-1248,64],[[4,2],[4,1],[4,0],[3,3],[3,2],[3,1],[3,0],[2,4],[2,3],[2,2],[2,1],[2,0],[1,4],[1,3],[1,2],[1,1],[1,0],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(8, 6, 0, 0)": [[126126,-126126,29106,-882,126126,-372372,272118,-55944,1624,-126126,272118,-171948,32991,-945,29106,-55944,32991,-6174,186,-882,1624,-945,186,-8],[[4,3],[4,2],[4,1],[4,0],[3,4],[3,3],[3,2],[3,1],[3,0],[2,4],[2,3],[2,2],[2,1],[2,0],[1,4],[1,3],[1,2],[1,1],[1,0],[0,4],[0,3],[0,2],[0,1],[0,0]]],
+        "(8, 8, 0, 0)": [[125970,-185640,81900,-10920,210,-185640,277368,-124488,16968,-336,81900,-124488,57204,-8064,168,-10920,16968,-8064,1204,-28,210,-336,168,-28,1],[[4,4],[4,3],[4,2],[4,1],[4,0],[3,4],[3,3],[3,2],[3,1],[3,0],[2,4],[2,3],[2,2],[2,1],[2,0],[1,4],[1,3],[1,2],[1,1],[1,0],[0,4],[0,3],[0,2],[0,1],[0,0]]]
+    }
+}
\ No newline at end of file
diff --git a/geometric_kernels/spaces/__init__.py b/geometric_kernels/spaces/__init__.py
index 700b75a5..5293a179 100644
--- a/geometric_kernels/spaces/__init__.py
+++ b/geometric_kernels/spaces/__init__.py
@@ -12,6 +12,8 @@
 from geometric_kernels.spaces.circle import Circle
 from geometric_kernels.spaces.graph import Graph
 from geometric_kernels.spaces.graph_edges import GraphEdges
+from geometric_kernels.spaces.grassmannian import Grassmannian
+from geometric_kernels.spaces.homogeneous_spaces import CompactHomogeneousSpace
 from geometric_kernels.spaces.hyperbolic import Hyperbolic
 from geometric_kernels.spaces.hypercube_graph import HypercubeGraph
 from geometric_kernels.spaces.hypersphere import Hypersphere
@@ -20,4 +22,5 @@
 from geometric_kernels.spaces.product import ProductDiscreteSpectrumSpace
 from geometric_kernels.spaces.so import SpecialOrthogonal
 from geometric_kernels.spaces.spd import SymmetricPositiveDefiniteMatrices
+from geometric_kernels.spaces.stiefel import Stiefel
 from geometric_kernels.spaces.su import SpecialUnitary
diff --git a/geometric_kernels/spaces/graph_edges.py b/geometric_kernels/spaces/graph_edges.py
index 5df4c39d..5a94e5b5 100644
--- a/geometric_kernels/spaces/graph_edges.py
+++ b/geometric_kernels/spaces/graph_edges.py
@@ -447,7 +447,7 @@ def from_adjacency(  # noqa: C901
         """
         if isinstance(adjacency_matrix, np.ndarray):
             index = csr_matrix(adjacency_matrix, dtype=int)
-        elif is_bearable(adjacency_matrix, SparseArray):
+        elif is_bearable(adjacency_matrix, SparseArray):  # type: ignore[arg-type]
             index = csr_matrix(adjacency_matrix, dtype=int, copy=True)
         else:
             raise ValueError(
diff --git a/geometric_kernels/spaces/grassmannian.py b/geometric_kernels/spaces/grassmannian.py
new file mode 100644
index 00000000..f5db995c
--- /dev/null
+++ b/geometric_kernels/spaces/grassmannian.py
@@ -0,0 +1,466 @@
+"""
+This module provides the :class:`Grassmannian` space and the representation of
+its spectrum, the :class:`GrassmannianEigenfunctions` class.
+"""
+
+import json
+
+import lab as B
+import numpy as np
+import sympy
+from beartype.typing import List, Optional, Tuple
+from lab import einsum
+
+from geometric_kernels.lab_extras import dtype_double, from_numpy, qr, take_along_axis
+from geometric_kernels.spaces import DiscreteSpectrumSpace
+from geometric_kernels.spaces.eigenfunctions import EigenfunctionsWithAdditionTheorem
+from geometric_kernels.spaces.so import SOEigenfunctions, SpecialOrthogonal
+from geometric_kernels.utils.utils import get_resource_file_path
+
+
+class GrassmannianStabilizer:
+    """
+    Helper class for sampling from Grassmannian stabilizer that is represented as S(O(n) x O(m))
+    by (n+m) x (n+m) block-diagonal matrices.
+    """
+
+    def __init__(self, n: int, m: int):
+        self.n, self.m = n, m
+        self.so_n = SpecialOrthogonal(n)
+        self.so_m = SpecialOrthogonal(m)
+        self.dim = self.so_n.dim + self.so_m.dim
+
+    def random(self, key, number):
+        """Randomly samples `number` matrices of size (n+m) x (n+m).
+
+        Each sample has a form of `[[H_n, 0], [0, H_m]]`. The upper left block
+        is uniformly sampled over O(n), and the lower right block is
+        uniformly sampled over O(m), and the signs of the blocks are adjusted.
+        """
+        key, sign = B.randint(key, dtype_double(key), number, lower=0, upper=2)
+        sign = 2 * sign - 1  # convert to -1, 1
+        key, h_u = self.so_n.random(key, number)  # [number, n, n]
+        key, h_d = self.so_m.random(key, number)  # [number, m, m]
+        h_u[:, :, -1] *= sign[
+            :, None
+        ]  # Ensure the last column of h_u has the same sign as the block
+        h_d[:, :, -1] *= sign[
+            :, None
+        ]  # Ensure the last column of h_d has the same sign as the block
+
+        zeros = B.zeros(B.dtype(h_u), number, self.n, self.m)  # [number, n, m]
+        zeros_t = B.transpose(zeros)
+
+        # [number, n + m, n], [number, n + m, m]
+        left_block = B.concat(h_u, zeros_t, axis=-2)
+        right_block = B.concat(zeros, h_d, axis=-2)
+        res = B.concat(left_block, right_block, axis=-1)  # [number, n + m, n + m]
+        return key, res
+
+
+class GrassmannianZonalSphericalFunction:
+    """
+    The class that represents a zonal spherical function on the Grassmannian
+
+    These are polynomials whose coefficients are precomputed and stored in a
+    file. By default, there are 20 precomputed characters for n from 3 to 10.
+    If you want more, use the `compute_grassmannian_zsf.py` script.
+
+    :param n:
+        The order n of the Gr(n, m).
+    :param m:
+        The order m of the Gr(n, m).
+    :param signature:
+        The signature that determines a particular character (and an
+        irreducible unitary representation along with it).
+    """
+
+    def __init__(self, n: int, m: int, signature: Tuple[int, ...]):
+        self.signature = signature
+        self.n = n
+        self.m = m
+        self.coeffs, self.monoms = self._load()
+        self.normalization = np.sum(
+            self.coeffs
+        )  # Normalization factor for the character
+
+    def _load(self):
+        group_name = "Gr({},{})".format(self.n, self.m)
+        with get_resource_file_path("precomputed_grassmanian_zsf.json") as file_path:
+            with file_path.open("r") as file:
+                character_formulas = json.load(file)
+                try:
+                    cs, ms = character_formulas[group_name][str(self.signature)]
+                    coeffs, monoms = (np.array(data) for data in (cs, ms))
+                    return coeffs, monoms
+                except KeyError as e:
+                    raise KeyError(
+                        "Unable to retrieve character parameters for signature {} of {}, "
+                        "perhaps it is not precomputed."
+                        "Run compute_characters.py with changed parameters.".format(
+                            e.args[0], group_name
+                        )
+                    ) from None
+
+    def __call__(self, gammas: B.Numeric) -> B.Numeric:
+        char_val = B.zeros(B.dtype(gammas), *gammas.shape[:-1])
+        for coeff, monom in zip(self.coeffs, self.monoms):
+            char_val += coeff * B.prod(
+                gammas ** B.cast(B.dtype(gammas), from_numpy(gammas, monom)), axis=-1
+            )
+        return char_val / self.normalization
+
+
+class GrassmannianEigenfunctions(EigenfunctionsWithAdditionTheorem):
+    def __init__(self, space, num_levels, compute_zsf=True):
+        super().__init__()
+        self.space = space
+        self.n = space.n
+        self.m = space.m
+        self._num_levels = num_levels
+        self.rank = min(self.m, self.n - self.m)
+        self.G_rank = space.G.rank
+        self.G_eigenfunctions = SOEigenfunctions(
+            self.n, num_levels=0, compute_characters=False
+        )
+
+        self._signatures = self._generate_signatures(num_levels)
+        self._eigenvalues = np.array(
+            [self._compute_eigenvalue(sgn) for sgn in self._signatures]
+        )
+        self.repr_dim = [
+            (1 + (signature[-1] != 0))
+            * self.G_eigenfunctions._compute_dimension(signature)
+            for signature in self._signatures
+        ]
+        if compute_zsf:
+            self._zsf = [
+                self._compute_zsf(self.n, self.m, sgn) for sgn in self._signatures
+            ]
+
+        self._num_eigenfunctions = None
+
+    def _generate_signatures(self, num_levels: int) -> List[Tuple[int, ...]]:
+        eigen_map = {}
+        rank = self.rank
+        if rank <= 1:
+            SIGNATURE_SUM = 100
+        else:
+            SIGNATURE_SUM = 20
+        if num_levels <= 0:
+            return []
+
+        if rank <= 0:
+            raise ValueError("m must be less than n")
+        # Degree 0: The empty partition
+        sgn_trivial = tuple([0] * self.G_rank)
+        eigen_map[sgn_trivial] = 0.0
+
+        # Iterate through degrees (sum of parts of the partition)
+        for degree in range(1, SIGNATURE_SUM):
+            for p_dict in sympy.utilities.iterables.partitions(degree):
+                partition_list = []
+                for val, count in sorted(p_dict.items(), reverse=True):
+                    partition_list.extend([val] * count)
+                kappa = list(partition_list)
+
+                if (
+                    len(kappa) <= rank
+                ):  # Filter by max allowed length for this Grassmannian
+                    kappa = kappa + [0] * (
+                        self.G_rank - len(kappa)
+                    )  # Pad with zeros to match rank
+                    kappa_even = all(x % 2 == 0 for x in kappa)
+                    sgn = tuple(kappa)
+                    if sgn not in eigen_map and kappa_even:
+                        eigen_map[sgn] = self._compute_eigenvalue(sgn)
+
+        # Convert to list of (eigenvalue, kappa) for sorting
+        # Sort by eigenvalue, then by partition degree, then by partition tuple (lexicographically)
+        # for stable and canonical tie-breaking.
+        sorted_eigenpairs = sorted(
+            [(eig, sgn) for sgn, eig in eigen_map.items()],
+            key=lambda x: (x[0], sum(x[1]), x[1]),
+        )
+        signatures = [sgn for _, sgn in sorted_eigenpairs]
+        return signatures[:num_levels]
+
+    def _compute_eigenvalue(self, signature):
+        """
+        Computes the eigenvalue of the Laplace-Beltrami operator.
+        """
+        if signature[0] == 0:
+            return 0.0
+
+        eigenvalue = 0.0
+        for j_idx, sgn_j in enumerate(signature):
+            if signature[j_idx] == 0:
+                break
+            j = j_idx + 1
+            eigenvalue += sgn_j * (sgn_j + self.n - 2 * j)
+        return eigenvalue
+
+    def _compute_zsf(
+        self, n: int, m: int, signature: Tuple[int, ...]
+    ) -> GrassmannianZonalSphericalFunction:
+        return GrassmannianZonalSphericalFunction(n, m, signature)
+
+    def _difference(self, X: B.Numeric, X2: B.Numeric) -> B.Numeric:
+        """
+        Pairwise differences between points of the homogeneous space M
+        embedded into G.
+
+        :param X:
+            [N1, ...] an array of points in `M`.
+        :param X2:
+            [N2, ...] an array of points in `M`.
+
+        :return:
+            [N1, N2, ...] an array of points in `G`.
+        """
+        g = self.space.embed_manifold(X)
+        g2 = self.space.embed_manifold(X2)
+        diff = self.G_eigenfunctions._difference(g, g2, inverse_X=True)
+        diff = self.space.project_to_manifold(diff)
+        return diff
+
+    def _torus_representative(self, X: B.Numeric, **kwargs) -> B.Numeric:
+        """
+        Computes the torus representative of the difference between two matrices.
+        For the Grassmannian, this is just the difference itself.
+        """
+        # X has shape [..., n, m]
+        X_ = X[..., : self.m, :]  # [..., m, m]
+        X_T_X = einsum("...ji,...jk->...ik", X_, X_)
+        eigvals = B.eig(X_T_X, False)
+        sorted_ind = B.argsort(B.real(eigvals), axis=-1)
+        eigvals = take_along_axis(eigvals, sorted_ind, -1)[
+            ..., : self.rank
+        ]  # Sort eigenvalues
+        return eigvals
+
+    def _addition_theorem(
+        self, X: B.Numeric, X2: Optional[B.Numeric] = None, **kwargs
+    ) -> B.Numeric:
+        r"""For each level (that corresponds to a unitary irreducible
+        representation of the group), computes the sum of outer products of
+        Laplace-Beltrami eigenfunctions that correspond to this level
+        (representation). Uses the fact that such sums are equal to the
+        character of the representation multiplied by the dimension of that
+        representation. See :cite:t:`azangulov2024a` for mathematical details.
+
+        :param X:
+            An [N, n, n]-shaped array, a batch of N matrices of size nxn.
+        :param X2:
+            An [N2, n, n]-shaped array, a batch of N2 matrices of size nxn.
+
+            Defaults to None, in which case X is used for X2.
+        :param ``**kwargs``:
+            Any additional parameters.
+
+        :return:
+            An array of shape [N, N2, L].
+        """
+        if X2 is None:
+            X2 = X
+        diff = self._difference(X, X2)
+        torus_repr_diff = self._torus_representative(diff)
+        values = [
+            repr_dim * zsf(torus_repr_diff)[..., None]  # [N, N2, 1]
+            for zsf, repr_dim in zip(self._zsf, self.repr_dim)
+        ]
+        return B.concat(*values, axis=-1)  # [N, N2, L]
+
+    def _addition_theorem_diag(self, X: B.Numeric, **kwargs) -> B.Numeric:
+        """
+        A more efficient way of computing the diagonals of the matrices
+        `self._addition_theorem(X, X)[:, :, l]` for all l from 0 to L-1.
+
+        :param X:
+            As in :meth:`_addition_theorem`.
+        :param ``**kwargs``:
+            As in :meth:`_addition_theorem`.
+
+        :return:
+            An array of shape [N, L].
+        """
+        ones = B.ones(B.dtype(X), *X.shape[:-2], 1)
+        values = [
+            repr_dim * ones  # [N, 1], because chi(X*inv(X))=repr_dim
+            for repr_dim in self.repr_dim
+        ]
+        return B.concat(*values, axis=1)  # [N, L]
+
+    @property
+    def num_eigenfunctions_per_level(self) -> List[int]:
+        """
+        The number of eigenfunctions per level.
+
+        :return:
+            List of ones of length num_levels.
+        """
+        return self.repr_dim
+
+    @property
+    def num_eigenfunctions(self) -> int:
+        if self._num_eigenfunctions is None:
+            self._num_eigenfunctions = sum(self.num_eigenfunctions_per_level)
+        return self._num_eigenfunctions
+
+    @property
+    def num_levels(self) -> int:
+        return self._num_levels
+
+
+class Grassmannian(DiscreteSpectrumSpace):
+    r"""
+    The GeometricKernels space representing the Grassmannian manifold Gr(n, m)
+    as the homogeneous space O(n) / (O(m) x O(n-m)) which also happens
+    to be a symmetric space.
+
+    The elements of this space are represented as n x m matrices
+    with orthogonal columns, just like the elements of the :class:`Stiefel`
+    space. However, for this space, this representation is not unique: two such
+    matrices can represent the same element of the Grassmannian manifold.
+
+    .. note::
+        A tutorial on how to use this space is available in the
+        :doc:`Grassmannian.ipynb </examples/Grassmannian>` notebook.
+
+    .. admonition:: Citation
+
+        If you use this GeometricKernels space in your research, please consider
+        citing :cite:t:`azangulov2022`.
+    """
+
+    def __init__(self, n: int, m: int):
+        """
+        :param n:
+            The number of rows.
+        :param m:
+            The number of columns.
+        :param key:
+            Random state used to sample from the stabilizer.
+        :param average_order:
+            The number of random samples from the stabilizer.
+
+        :return:
+            A tuple (new random state, a realization of Gr(m, n)).
+        """
+        assert n > m, "n should be greater than m"
+        assert (m > 1) and (
+            m < n - 1
+        ), "Isomorphic to hypersphere, use Hypersphere class instead"
+
+        super().__init__()
+        self.H = GrassmannianStabilizer(m, n - m)
+        self.G = SpecialOrthogonal(n)
+        self.dim_H = self.H.dim
+        self.n = n
+        self.m = m
+
+    def project_to_manifold(self, g):
+        """
+        Take first m columns of an orthogonal matrix.
+
+        :param g:
+            [..., n, n] array of points in SO(n).
+
+        :return:
+            [..., n, m] array of points in V(n, m).
+        """
+
+        return g[..., : self.m]
+
+    def embed_manifold(self, x):
+        """
+        Complete [n, m] matrix with orthogonal columns to an orthogonal
+        [n, n] one using QR algorithm.
+
+        :param x:
+            [..., n, m] array of points in V(m, n).
+
+        :return g:
+            [..., n, n] array of points in SO(n).
+        """
+        if x.shape[-1] == self.n:
+            # If x is already in SO(n), just return it
+            return x
+
+        p = B.matmul(x, B.transpose(x, [0, 2, 1]))  # Shape: (b, n, n)
+        r = B.randn(B.dtype(x), *x.shape[:-1], self.n - self.m)  # Shape: (b, n, n - m)
+
+        r_orth = r - B.matmul(p, r)  # (b, n, n - m)
+
+        q, _ = qr(r_orth)  # (b, n, n - m)
+
+        g = B.concat(x, q, axis=2)  # (b, n, n)
+        det = B.sign(B.det(g))
+        g_correction = g[:, :, -1] * det[:, None]
+        g = B.concat(g[:, :, :-1], g_correction[:, :, None], axis=2)
+        return g
+
+    def embed_stabilizer(self, h):
+        """
+        Embed SO(m) x SO(n-m) matrix into SO(n),
+        In case of the Grassmannian, this is an identity mapping.
+
+        :param h:
+            [..., n, n] array of points in SO(m) x SO(n-m).
+
+        :return:
+            [..., n, n] array of points in SO(n).
+        """
+        return h
+
+    def get_eigenfunctions(self, num: int) -> GrassmannianEigenfunctions:
+        eigenfunctions = GrassmannianEigenfunctions(self, num)
+        return eigenfunctions
+
+    def get_repeated_eigenvalues(self, num: int) -> B.Numeric:
+        return self.get_eigenvalues(num)
+
+    def get_eigenvalues(self, num: int) -> B.Numeric:
+        eigenfunctions = GrassmannianEigenfunctions(self, num)
+        eigenvalues = np.array(eigenfunctions._eigenvalues)
+        return B.reshape(eigenvalues, -1, 1)  # [num, 1]
+
+    @property
+    def element_shape(self):
+        """
+        :return:
+            [n, m].
+        """
+        return [self.n, self.m]
+
+    @property
+    def element_dtype(self):
+        """
+        Return the data type of the elements of the Grassmannian.
+        """
+        return B.Float
+
+    @property
+    def dimension(self):
+        """
+        Return the dimension of the Grassmannian.
+        """
+        return self.m * (self.n - self.m)
+
+    def random(self, key, number: int, project=False):
+        """
+        Samples random points from the uniform distribution on M.
+
+        :param key:
+            A random state.
+        :param number:
+            A number of random to generate.
+
+        :return:
+            [number, ...] an array of randomly generated points.
+        """
+        key, raw_samples = self.G.random(key, number)
+        if project:
+            return key, self.project_to_manifold(raw_samples)
+        else:
+            return key, raw_samples
diff --git a/geometric_kernels/spaces/homogeneous_spaces.py b/geometric_kernels/spaces/homogeneous_spaces.py
new file mode 100644
index 00000000..401412ca
--- /dev/null
+++ b/geometric_kernels/spaces/homogeneous_spaces.py
@@ -0,0 +1,341 @@
+r"""
+Abstract base interface for compact homogeneous spaces.
+"""
+
+import abc
+
+import lab as B
+import numpy as np
+from beartype.typing import List, Optional
+from lab import einsum
+
+from geometric_kernels.spaces.base import DiscreteSpectrumSpace
+from geometric_kernels.spaces.eigenfunctions import EigenfunctionsWithAdditionTheorem
+from geometric_kernels.spaces.lie_groups import CompactMatrixLieGroup, LieGroupCharacter
+
+
+class AveragingAdditionTheorem(EigenfunctionsWithAdditionTheorem):
+    r"""
+    Class corresponding to the sum of outer products of eigenfunctions
+    corresponding to the same eigenspace of Laplace-Beltrami operator
+    on a compact homogeneous space M=G/H. Eigenspaces coincide with
+    eigenspaces of G, and the sums might be computed via averaging of
+    characters of group G w.r.t. H.
+
+    .. math:: \chi_M(x) = \int_H \chi_G(xh)dh
+
+    .. note::
+        *levels* here do not necessarily correspond to full eigenspaces.
+    """
+
+    def __init__(self, M, num_levels: int, samples_H):
+        """
+        :param M:
+            CompactHomogeneousSpace.
+        :param num_levels:
+            Number of eigenspaces.
+        :param samples_H:
+            Samples from the uniform distribution on the stabilizer H.
+        """
+        self.M = M
+        self.dim = M.dim
+        self.G_n = M.G.n
+        self.samples_H = samples_H
+        self.samples_H = self.M.embed_stabilizer(samples_H)
+        self.average_order = B.shape(samples_H)[0]
+
+        G_eigenfunctions = M.G.get_eigenfunctions(num_levels)
+
+        self._signatures = G_eigenfunctions._signatures.copy()
+        self._eigenvalues = np.copy(G_eigenfunctions._eigenvalues)
+        self.G_dimensions = G_eigenfunctions._dimensions.copy()
+        self._characters = [
+            AveragedLieGroupCharacter(self.average_order, character)
+            for character in G_eigenfunctions._characters
+        ]
+
+        self._filter_signatures()
+
+        print(
+            f"Filtered out {len(G_eigenfunctions._signatures) - len(self._signatures)} eigenspaces of dimension 0."
+        )
+        self._num_levels = len(self._signatures)
+
+        self.G_torus_representative = G_eigenfunctions._torus_representative
+        self.G_difference = G_eigenfunctions._difference
+
+        self._num_eigenfunctions: Optional[int] = None  # To be computed when needed.
+
+    @abc.abstractmethod
+    def _compute_projected_character_value_at_e(self, signature):
+        r"""
+        The value of the character on class of identity element.
+        This is equal to the number of zonal spherical functions,
+        it corresponds to $r_\lambda$ from :cite:t:`azangulov2024a`.
+
+        :param signature:
+            Signature of the character.
+
+        :return:
+            The value of the character on class of identity element.
+        """
+        raise NotImplementedError
+
+    def _filter_signatures(self):
+        """
+        Filters out the eigenspaces of dimension 0.
+        This is necessary to avoid numerical issues.
+        Eigenspaces of dimension 0 correspond to
+        the characters equal to 0 on the identity element.
+        """
+        filtered_signatures = []
+        filtered_dimensions = []
+        filtered_characters = []
+        filtered_eigenvalues = []
+        for signature, G_dimension, character, eigenvalue in zip(
+            self._signatures, self.G_dimensions, self._characters, self._eigenvalues
+        ):
+            if self._compute_projected_character_value_at_e(signature) != 0:
+                filtered_signatures.append(signature)
+                filtered_dimensions.append(G_dimension)
+                filtered_characters.append(character)
+                filtered_eigenvalues.append(eigenvalue)
+        self._signatures = filtered_signatures
+        self.G_dimensions = filtered_dimensions
+        self._characters = filtered_characters
+        self._eigenvalues = np.array(filtered_eigenvalues)
+        self._eigenspace_dimensions = [
+            G_dim * self._compute_projected_character_value_at_e(sgn)
+            for G_dim, sgn in zip(self.G_dimensions, self._signatures)
+        ]
+        self._num_zonal_spherical_functions = [
+            self._compute_projected_character_value_at_e(sgn)
+            for sgn in self._signatures
+        ]
+
+    def _difference(self, X: B.Numeric, X2: B.Numeric) -> B.Numeric:
+        """
+        Pairwise differences between points of the homogeneous space M
+        embedded into G.
+
+        :param X:
+            [N1, ...] an array of points in `M`.
+        :param X2:
+            [N2, ...] an array of points in `M`.
+
+        :return:
+            [N1, N2, ...] an array of points in `G`.
+        """
+
+        g = self.M.embed_manifold(X)
+        g2 = self.M.embed_manifold(X2)
+        diff = self.G_difference(g, g2, inverse_X=True)
+        return diff
+
+    def _addition_theorem(
+        self, X: B.Numeric, X2: Optional[B.Numeric] = None, **kwargs
+    ) -> B.Numeric:
+        r"""
+        For each level (that corresponds to a unitary irreducible
+        representation of the group of symmetries), computes the sum of outer
+        products of Laplace-Beltrami eigenfunctions that correspond to this
+        level (representation). Uses the fact that such a sum is always
+        proportional to a certain integral of the character of the
+        representation over the isotropy subgroup of the homogeneous space.
+        See :cite:t:`azangulov2024a` for mathematical details.
+
+        To ensure that the resulting function is positive definite, we average
+        over both the left and right shifts (the result is an approximation):
+
+        .. math:: \chi_X(g1,g2) \approx \frac{1}{S^2}\sum_{i=1}^S\sum_{j=1}^S \chi_G(h^{-1}_i g2^{-1} g1 h_j)
+
+        :param X:
+            An [N, n, m]-shaped array, a batch of N matrices of size nxm.
+        :param X2:
+            An [N2, n, m]-shaped array, a batch of N2 matrices of size nxm.
+        :param ``**kwargs``:
+            Any additional parameters
+
+        :return:
+            [N1, N2, L]
+        """
+        if X2 is None:
+            X2 = X
+        X_, X2_ = X, X2
+
+        if X.shape[-1] != self.M.n:
+            X_ = self.M.embed_manifold(X)
+        if X2.shape[-1] != self.M.n:
+            X2_ = self.M.embed_manifold(X2)
+
+        # [N * N2, G_n, G_n]
+        diff = B.reshape(self.G_difference(X_, X2_), -1, self.G_n, self.G_n)
+        # [N * N2 * samples_H, G_n, G_n]
+        diff_h = B.reshape(
+            self.G_difference(diff, self.samples_H), -1, self.G_n, self.G_n
+        )
+        # [N * N2 * samples_H, T]
+        torus_repr = self.G_torus_representative(diff_h)
+        values = [
+            B.reshape(
+                degree * chi(torus_repr)[..., None], X.shape[0], X2.shape[0], 1
+            )  # [N1, N2, 1]
+            for degree, chi in zip(self.G_dimensions, self._characters)
+        ]
+
+        return B.concat(*values, axis=-1)  # [N, N2, L]
+
+    def _addition_theorem_diag(self, X: B.Numeric, **parameters) -> B.Numeric:
+        """
+        A more efficient way of computing the diagonals of the matrices
+        `self._addition_theorem(X, X)[:, :, l]` for all l from 0 to L-1.
+
+        :param X:
+            [N, ...]
+        :param parameters:
+            Any additional parameters.
+
+        :return:
+            [N, L]
+        """
+        ones = B.ones(B.dtype(X), *X.shape[:-2], 1)
+        values = [
+            eigenspace_dimension * ones  # [N, 1]
+            for eigenspace_dimension in self._eigenspace_dimensions
+        ]
+        return B.concat(*values, axis=1)  # [N, L]
+
+    @property
+    def num_levels(self) -> int:
+        """Number of levels, L"""
+        return self._num_levels
+
+    @property
+    def num_eigenfunctions(self) -> int:
+        if self._num_eigenfunctions is None:
+            self._num_eigenfunctions = sum(self.num_eigenfunctions_per_level)
+        return self._num_eigenfunctions
+
+    @property
+    def num_eigenfunctions_per_level(self) -> List[int]:
+        """Number of eigenfunctions per level"""
+        return self._eigenspace_dimensions
+
+
+class AveragedLieGroupCharacter(abc.ABC):
+    r"""
+    Sum of outer products of eigenfunctions is equal to the mean value of the
+    character averaged over the isotropy subgroup H. To ensure that the
+    function is positive definite we average from the both sides.
+
+    .. math:: \chi_M(x) = \int_H\int_H (h_1 x h_2).
+    """
+
+    def __init__(self, average_order: int, character: LieGroupCharacter):
+        """
+        :param average_order:
+            The number of points sampled from H.
+        :param character:
+            A character of a Lie group G.
+        """
+        self.character = character
+        self.average_order = average_order
+
+    def __call__(self, gammas_x_h):
+        """
+        Compute characters from the torus embedding and then averages w.r.t. H.
+
+        :param gammas_h1_x_h2:
+            [average_order*n*average_order, T]
+        """
+        character_x_h = B.reshape(self.character(gammas_x_h), -1, self.average_order)
+        avg_character = einsum("gv->g", character_x_h) / (self.average_order)
+        return avg_character
+
+
+class CompactHomogeneousSpace(DiscreteSpectrumSpace):
+    """
+    A compact homogeneous space M given as M=G/H, where G is a compact
+    Lie group, H is a subgroup called the stabilizer.
+
+    Examples include Stiefel manifolds SO(n) / SO(n-m) and
+    Grassmannians SO(n)/(SO(m) x SO(n-m)).
+    """
+
+    def __init__(self, G: CompactMatrixLieGroup, dim_H, samples_H, average_order):
+        """
+        :param G:
+            A Lie group.
+        :param dim_H:
+            Dimension of the stabilizer.
+        :param samples_H:
+            Random samples from the stabilizer.
+        :param average_order:
+            Average order.
+        """
+        self.G = G
+        self.samples_H = samples_H
+        self.dim = self.G.dimension - dim_H
+        self.average_order = average_order
+
+    @property
+    def dimension(self) -> int:
+        return self.dim
+
+    @abc.abstractmethod
+    def project_to_manifold(self, g):
+        r"""
+        Represents map $\pi: G \mapsto X$ projecting elements of G onto X,
+        e.g. $\pi$ sends O \in SO(n) ([n,n] shape) to an element
+        $\pi(O) \in V(m,n)$ ([n,m] shape) of the Stiefel manifold,
+        by taking first m columns.
+
+        :param g:
+            [N, ...] array of points in G.
+        :return:
+            [N, ...] array of points in M.
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def embed_manifold(self, x):
+        r"""
+        Inverse to the function $\pi$, i.e. for given $x \in X$ finds g
+        (non-unique) such that $\pi(g) = x$.
+
+        :param x:
+            [N, ...] array of points in M.
+        :return:
+            [N, ...] array of points in G.
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def embed_stabilizer(self, h):
+        """
+        Embed subgroup H into G.
+
+        :param h:
+            [N, ...] array of points in H.
+        :return:
+            [N, ...] array of points in G.
+        """
+        raise NotImplementedError
+
+    def random(self, key, number: int, project=False):
+        """
+        Samples random points from the uniform distribution on M.
+
+        :param key:
+            A random state.
+        :param number:
+            A number of random to generate.
+
+        :return:
+            [number, ...] an array of randomly generated points.
+        """
+        key, raw_samples = self.G.random(key, number)
+        if project:
+            return key, self.project_to_manifold(raw_samples)
+        else:
+            return key, raw_samples
diff --git a/geometric_kernels/spaces/lie_groups.py b/geometric_kernels/spaces/lie_groups.py
index 0ee11441..4f3474fc 100644
--- a/geometric_kernels/spaces/lie_groups.py
+++ b/geometric_kernels/spaces/lie_groups.py
@@ -9,6 +9,7 @@
 import lab as B
 import numpy as np
 from beartype.typing import List, Optional, Tuple
+from lab import einsum
 
 from geometric_kernels.spaces.base import DiscreteSpectrumSpace
 from geometric_kernels.spaces.eigenfunctions import EigenfunctionsWithAdditionTheorem
@@ -179,7 +180,7 @@ def inverse(self, X: B.Numeric) -> B.Numeric:
         """
         raise NotImplementedError
 
-    def _difference(self, X: B.Numeric, X2: B.Numeric) -> B.Numeric:
+    def _difference(self, X: B.Numeric, X2: B.Numeric, inverse_X=False) -> B.Numeric:
         r"""
         Pairwise difference (in the group sense) between elements of the
         two batches, `X` and `X2`.
@@ -199,13 +200,13 @@ def _difference(self, X: B.Numeric, X2: B.Numeric) -> B.Numeric:
             :cite:t:`azangulov2024a`. This is because $\chi(x y x^{-1})=\chi(y)$
             which implies that $\chi(x y) = \chi(y x)$.
         """
-        X2_inv = self.inverse(X2)
-        X_ = B.tile(X[..., None, :, :], 1, X2_inv.shape[0], 1, 1)  # (N, N2, n, n)
-        X2_inv_ = B.tile(X2_inv[None, ..., :, :], X.shape[0], 1, 1, 1)  # (N, N2, n, n)
+        if inverse_X:
+            X_inv = self.inverse(X)
+            diff = einsum("nij,mjk->nmik", X_inv, X2)  # (N, N2, n, n)
+        else:
+            X2_inv = self.inverse(X2)
+            diff = einsum("nij,mjk->nmik", X, X2_inv)  # (N, N2, n, n)
 
-        diff = B.reshape(
-            B.matmul(X_, X2_inv_), X.shape[0], X2_inv.shape[0], X.shape[-1], X.shape[-1]
-        )  # (N, N2, n, n)
         return diff
 
     def _addition_theorem(
diff --git a/geometric_kernels/spaces/so.py b/geometric_kernels/spaces/so.py
index 6ea1969a..4caf1f81 100644
--- a/geometric_kernels/spaces/so.py
+++ b/geometric_kernels/spaces/so.py
@@ -265,8 +265,6 @@ class SpecialOrthogonal(CompactMatrixLieGroup):
     """
 
     def __init__(self, n: int):
-        if n < 3:
-            raise ValueError("Only n >= 3 is supported. For n = 2, use Circle.")
         self.n = n
         self.dim = n * (n - 1) // 2
         self.rank = n // 2
@@ -297,6 +295,10 @@ def get_eigenfunctions(self, num: int) -> Eigenfunctions:
         :param num:
             Number of levels.
         """
+        if self.n == 2:
+            raise ValueError(
+                "SO(2) is not supported by this method, use Circle instead."
+            )
         return SOEigenfunctions(self.n, num)
 
     def get_eigenvalues(self, num: int) -> B.Numeric:
@@ -317,7 +319,7 @@ def get_repeated_eigenvalues(self, num: int) -> B.Numeric:
     def random(self, key: B.RandomState, number: int):
         if self.n == 2:  # for the bright future where we support SO(2).
             # SO(2) = S^1
-            key, thetas = B.random.randn(key, dtype_double(key), number, 1)
+            key, thetas = B.random.rand(key, dtype_double(key), number, 1)
             thetas = 2 * math.pi * thetas
             c = B.cos(thetas)
             s = B.sin(thetas)
@@ -343,7 +345,6 @@ def random(self, key: B.RandomState, number: int):
             q = B.concat(r1, r2, r3, axis=-1)
             return key, q
         else:
-            # qr decomposition is not in the lab package, so numpy is used.
             key, h = B.random.randn(key, dtype_double(key), number, self.n, self.n)
             q, r = qr(h, mode="complete")
             r_diag_sign = B.sign(B.einsum("...ii->...i", r))
diff --git a/geometric_kernels/spaces/stiefel.py b/geometric_kernels/spaces/stiefel.py
new file mode 100644
index 00000000..f565e078
--- /dev/null
+++ b/geometric_kernels/spaces/stiefel.py
@@ -0,0 +1,280 @@
+"""
+This module provides the :class:`Stiefel` space and the representation of
+its spectrum, the :class:`StiefelEigenfunctions` class.
+"""
+
+from functools import lru_cache
+
+import lab as B
+import numpy as np
+
+from geometric_kernels.lab_extras import dtype_double, qr
+from geometric_kernels.spaces.homogeneous_spaces import (
+    AveragingAdditionTheorem,
+    CompactHomogeneousSpace,
+)
+from geometric_kernels.spaces.so import SpecialOrthogonal
+
+
+def generate_intertwining_weights(n, omega):  # noqa: C901
+    """
+    Generate possible highest weights when branching from SO(n) to SO(n-1).
+    omega: tuple representing the highest weight of SO(n).
+    """
+    mu = n // 2
+    if n % 2 == 0:  # Even n = 2mu
+        m_mu_abs = abs(omega[-1])
+
+        def gen(k, prev):
+            if k == mu - 1:  # omega' has mu - 1 components
+                yield ()
+            else:
+                # Lower bound: |m_mu| for m_{mu-1}', else m_{k+1}
+                lower = m_mu_abs if k == mu - 2 else omega[k + 1]
+                upper = min(prev, omega[k])
+                for m in range(lower, upper + 1):
+                    for tail in gen(k + 1, m):
+                        yield (m,) + tail
+
+        for weight in gen(0, omega[0]):
+            yield weight
+    else:  # Odd n = 2mu + 1
+
+        def gen(k, prev):
+            if k == mu:  # omega' has mu components
+                yield ()
+            else:
+                # Lower bound: |m_mu'| for last component, else m_{k+1}
+                if k == mu - 1:
+                    lower = -omega[k]  # m_mu >= |m_mu'|
+                    upper = omega[k]
+                else:
+                    lower = omega[k + 1]
+                    upper = min(prev, omega[k])
+                for m in range(lower, upper + 1):
+                    for tail in gen(k + 1, m):
+                        yield (m,) + tail
+
+        for weight in gen(0, omega[0]):
+            yield weight
+
+
+@lru_cache(maxsize=None)
+def stiefel_multiplicity(n, m, omega):
+    """
+    Compute the multiplicity of the representation with highest weight omega
+    in L^2(SO(n)/SO(n - m)).
+    """
+    if m == 0:
+        # Base case: if omega is the trivial representation
+        if all(x == 0 for x in omega):
+            return 1
+        return 0
+    if n <= m:
+        return 0  # Invalid case
+    # Recursive case: branch from SO(n) to SO(n-1)
+    total = 0
+    for omega_prime in generate_intertwining_weights(n, omega):
+        total += stiefel_multiplicity(n - 1, m - 1, omega_prime)
+    return total
+
+
+class StiefelEigenfunctions(AveragingAdditionTheorem):
+    def _compute_projected_character_value_at_e(self, signature):
+        """
+        Value of character on the class of identity element is equal to the
+        dimension of invariant space. In case of Stiefel manifold it could be
+        computed using the hook-content formula.
+
+        :param signature:
+            The character signature.
+
+        :return:
+            Value of character on the class of identity element.
+        """
+
+        return stiefel_multiplicity(self.M.n, self.M.m, signature)
+
+
+class Stiefel(CompactHomogeneousSpace):
+    r"""
+    The GeometricKernels space representing the Stifiel manifold
+    V(m, n) as the homogeneous space SO(n) / SO(n-m).
+
+    The elements of this space are represented as n x m matrices
+    with orthogonal columns.
+
+    .. note::
+        A tutorial on how to use this space is available in the
+        :doc:`Stiefel.ipynb </examples/Stiefel>` notebook.
+
+    .. admonition:: Citation
+
+        If you use this GeometricKernels space in your research, please consider
+        citing :cite:t:`azangulov2022`.
+    """
+
+    def __new__(cls, n: int, m: int, key, average_order: int = 100):
+        """
+        :param n:
+            The number of rows.
+        :param m:
+            The number of columns.
+        :param key:
+            Random state used to sample from the stabilizer SO(n-m).
+        :param average_order:
+            The number of random samples from the stabilizer SO(n-m).
+
+        :return:
+            A tuple (new random state, a realization of V(m, n)).
+        """
+
+        assert n > m, "n should be greater than m"
+        G = SpecialOrthogonal(n)
+
+        if n - m >= 2:
+            H = SpecialOrthogonal(n - m)
+            key, samples_H = H.random(key, average_order)
+            dim_H = H.dim
+        else:
+            # H is a two point set {+1, -1}
+            average_order = 2
+            samples_H = B.zeros(dtype_double(key), average_order)
+            samples_H[0], samples_H[1] = 1, -1
+            samples_H = B.reshape(samples_H, average_order, 1, 1)
+            dim_H = 0
+
+        new_space = super().__new__(cls)
+        key, matrix_complement = B.randn(
+            key, B.dtype(samples_H), n, n - m
+        )  # Shape: (n, n - m)
+
+        new_space.__init__(G=G, dim_H=dim_H, samples_H=samples_H, average_order=average_order, n=n, m=m, matrix_complement=matrix_complement)  # type: ignore
+        return key, new_space
+
+    def __init__(
+        self,
+        G: SpecialOrthogonal,
+        dim_H: int,
+        samples_H: B.Numeric,
+        matrix_complement: B.Numeric,
+        average_order: int,
+        n: int,
+        m: int,
+    ):
+        self.n = n
+        self.m = m
+        self.matrix_complement = matrix_complement
+        super().__init__(
+            G=G, dim_H=dim_H, samples_H=samples_H, average_order=average_order
+        )
+
+    def project_to_manifold(self, g):
+        """
+        Take first m columns of an orthogonal matrix.
+
+        :param g:
+            [..., n, n] array of points in SO(n).
+
+        :return:
+            [..., n, m] array of points in V(m, n).
+        """
+
+        return g[..., : self.m]
+
+    def embed_manifold(self, x):
+        """
+        Complete [n, m] matrix with orthogonal columns to an orthogonal
+        [n, n] one using QR algorithm.
+
+        :param x:
+            [..., n, m] array of points in V(m, n).
+
+        :return g:
+            [..., n, n] array of points in SO(n).
+        """
+
+        p = B.matmul(x, B.transpose(x, [0, 2, 1]))  # Shape: (b, n, n)
+        r = self.matrix_complement[None, :, :]  # Shape: (1, n, n - m)
+        r_orth = r - B.matmul(p, r)  # (b, n, n - m)
+
+        q, _ = qr(r_orth)  # (b, n, n - m)
+
+        g = B.concat(x, q, axis=2)  # (b, n, n)
+        det = B.sign(B.det(g))
+
+        g_correction = g[:, :, -1] * det[:, None]
+        g = B.concat(g[:, :, :-1], g_correction[:, :, None], axis=2)
+
+        return g
+
+    def embed_stabilizer(self, h):
+        """
+        Embed SO(n-m) matrix into SO(n) adding ones on diagonal,
+        i.e. i(h) = [[h, 0], [0, 1]].
+
+        :param h:
+            [..., n-m, n-m] array of points in SO(n-m).
+
+        :return:
+            [..., n, n] array of points in SO(n).
+        """
+
+        zeros = B.zeros(
+            B.dtype(h), *h.shape[:-2], self.m, self.n - self.m
+        )  # [m, n - m]
+        zeros_t = B.transpose(zeros)  # [n - m, m]
+        eye = B.tile(
+            B.reshape(
+                B.eye(B.dtype(h), self.m, self.m),
+                *([1] * (len(h.shape) - 2)),
+                self.m,
+                self.m,
+            ),
+            *h.shape[:-2],
+            1,
+            1,
+        )  # [..., m, m]
+        left = B.concat(eye, zeros_t, axis=-2)  # [..., n, m]
+        right = B.concat(zeros, h, axis=-2)  # [..., n, n - m]
+        res = B.concat(left, right, axis=-1)  # [..., n, n]
+        return res
+
+    def get_eigenfunctions(self, num: int) -> AveragingAdditionTheorem:
+        """
+        Returns the :class:`~.AveragingAdditionTheorem` object with `num` levels.
+
+        :param num:
+            Number of levels.
+        """
+        eigenfunctions = StiefelEigenfunctions(self, num, self.samples_H)
+        return eigenfunctions
+
+    def get_repeated_eigenvalues(self, num: int) -> B.Numeric:
+        return self.get_eigenvalues(num)
+
+    def get_eigenvalues(self, num: int) -> B.Numeric:
+        eigenfunctions = StiefelEigenfunctions(self, num, self.samples_H)
+        eigenvalues = np.array(eigenfunctions._eigenvalues)
+        return B.reshape(eigenvalues, -1, 1)  # [num, 1]
+
+    @property
+    def element_shape(self):
+        """
+        Return the shape of the elements of the Stiefel manifold.
+        """
+        return [self.n, self.m]
+
+    @property
+    def element_dtype(self):
+        """
+        Return the data type of the elements of the Stiefel manifold.
+        """
+        return B.Float
+
+    @property
+    def dimension(self):
+        """
+        Return the dimension of the Stiefel manifold.
+        """
+        return self.n * self.m - self.m * (self.m + 1) // 2
diff --git a/geometric_kernels/utils/manifold_utils.py b/geometric_kernels/utils/manifold_utils.py
index 42280cac..7061f778 100644
--- a/geometric_kernels/utils/manifold_utils.py
+++ b/geometric_kernels/utils/manifold_utils.py
@@ -1,4 +1,4 @@
-""" Utilities for dealing with manifolds.  """
+"""Utilities for dealing with manifolds."""
 
 import lab as B
 import numpy as np
diff --git a/geometric_kernels/utils/product.py b/geometric_kernels/utils/product.py
index 58baded3..488a06d2 100644
--- a/geometric_kernels/utils/product.py
+++ b/geometric_kernels/utils/product.py
@@ -1,4 +1,4 @@
-""" Utilities for dealing with product spaces and product kernels.  """
+"""Utilities for dealing with product spaces and product kernels."""
 
 import lab as B
 from beartype.typing import Dict, List
diff --git a/geometric_kernels/utils/utils.py b/geometric_kernels/utils/utils.py
index fc0b8d3d..f9bfe134 100644
--- a/geometric_kernels/utils/utils.py
+++ b/geometric_kernels/utils/utils.py
@@ -240,7 +240,7 @@ def partition_dominance_cone(partition: Tuple[int, ...]) -> Set[Tuple[int, ...]]
 
 
 def partition_dominance_or_subpartition_cone(
-    partition: Tuple[int, ...]
+    partition: Tuple[int, ...],
 ) -> Set[Tuple[int, ...]]:
     """
     Calculates subpartitions and partitions dominated by a given one and having
diff --git a/notebooks/Grassmannian.ipynb b/notebooks/Grassmannian.ipynb
new file mode 100644
index 00000000..c527c8e8
--- /dev/null
+++ b/notebooks/Grassmannian.ipynb
@@ -0,0 +1,977 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "66ebbb52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To run this in Google Colab, uncomment the following line\n",
+    "# !pip install geometric_kernels\n",
+    "\n",
+    "\n",
+    "# If you want to use a version of the library from a specific branch on GitHub,\n",
+    "# say, from the \"devel\" branch, uncomment the line below instead\n",
+    "# !pip install \"git+https://github.com/GPflow/GeometricKernels@devel\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acf0a49e",
+   "metadata": {},
+   "source": [
+    "# Matérn and Heat Kernels on the Grassmannian manifold $\\mathrm{Gr}(m, n)$\n",
+    "\n",
+    "This notebook shows how define and evaluate kernels on the Grassmannian manifold $\\mathrm{Gr}(n, m)$ that consists of all $m$-dimensional (unoriented) subspaces of $\\mathbb{R}^n$.\n",
+    "\n",
+    "Grassmannians are homogeneous spaces $\\mathrm{Gr}(n, m) = \\mathrm{O}(n) / O(\\mathrm{O}(m)\\times \\mathrm{O}(n-m))$ of the orthogonal group $\\mathrm{O}(n)$. \n",
+    "\n",
+    "We work with $\\mathrm{Gr}(7, 3)$ here. There is no particular reason for this. Handling the Grassmannian $\\mathrm{Gr}(n, m)$ for other $n \\geq 2$ and $m < n$ is the same.\n",
+    "\n",
+    "\n",
+    "**Note:** the \"points\" in the the Grassmannian $\\mathrm{Gr}(n, m)$ are represented by real matrices (`array`s of the suitable backend) of size $n \\times m$, whose columns are orthonormal vectors.\n",
+    "\n",
+    "We use the **numpy** backend here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f1bd9fe",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65b2692e",
+   "metadata": {},
+   "source": [
+    "<!--TABLE OF CONTENTS-->\n",
+    "## Contents\n",
+    "- [**Basics**](#Basics)\n",
+    "  - [Defining a Space](#Defining-a-Space)\n",
+    "  - [Defining a Kernel](#Defining-a-Kernel)\n",
+    "  - [Evaluating Kernels on Random Inputs](#Evaluating-Kernels-on-Random-Inputs)\n",
+    "  - [Visualize Kernels](#Visualize-Kernels)\n",
+    "- [**Feature Maps and Sampling**](#Feature-Maps-and-Sampling)\n",
+    "  - [Defining a Feature Map](#Defining-a-Feature-Map)\n",
+    "  - [Efficient Sampling using Feature Maps](#Efficient-Sampling-using-Feature-Maps)\n",
+    "- [**Citation**](#Citation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7446f9f-1023-4fa5-ac88-475e0e482694",
+   "metadata": {},
+   "source": [
+    "## Basics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "24ac4559",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add the parent directory to the path so that the library can be imported\n",
+    "import sys\n",
+    "sys.path.append(\"..\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "c2c899b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import a backend, we use numpy in this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# Import the geometric_kernels backend.\n",
+    "import geometric_kernels\n",
+    "\n",
+    "# Note: if you are using a backend other than numpy,\n",
+    "# you _must_ uncomment one of the following lines\n",
+    "# import geometric_kernels.tensorflow\n",
+    "#import geometric_kernels.torch\n",
+    "# import geometric_kernels.jax\n",
+    "\n",
+    "# Import a space and an appropriate kernel.\n",
+    "from geometric_kernels.spaces import Grassmannian\n",
+    "from geometric_kernels.kernels import MaternGeometricKernel\n",
+    "\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35b6053f",
+   "metadata": {},
+   "source": [
+    "### Defining a Space"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae549717",
+   "metadata": {},
+   "source": [
+    "First we create a GeometricKernels `space` that corresponds to the Stiefel manifold $\\mathrm{Gr}(7, 3)$, a subset of all $7 \\times 3$ matrices $\\mathbb{R}^{7 \\times 3}$. Our implementation of `Grassmannian` computes the basis functions (that are needed to compute all the kernels) approximately, using Monte Carlo integration. Because of this, you need to provide a source of randomness when constructing the space, i.e. the random generator of the suitable backend, via the `key` keyword argument.\n",
+    "\n",
+    "**Note**: the aforementioned basis functions are *zonal spherical functions*, these are (sums of outer products of certain groups of Laplace-Beltrami eigenfunctions). We follow \"Generalized Jacobi Polynomials as Spherical Functions of the Grassmann Manifold\" by Alan T. James and A. G. Constantine.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "bc2f6122",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n, m = 7, 3\n",
+    "\n",
+    "key = np.random.RandomState(1234)\n",
+    "\n",
+    "grassmannian = Grassmannian(m=m, n=n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3513e04b",
+   "metadata": {},
+   "source": [
+    "### Defining a Kernel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfc8d35f",
+   "metadata": {},
+   "source": [
+    "First, we create a generic Matérn kernel.\n",
+    "\n",
+    "To initialize `MaternGeometricKernel` you just need to provide a `Space` object, in our case this is the `stiefel` we have just created above.\n",
+    "\n",
+    "There is also an optional second parameter `num` which determines the order of approximation of the kernel.\n",
+    "There is a sensible default value for each of the spaces in the library, so change it only if you know what you are doing.\n",
+    "\n",
+    "A brief account on theory behind the kernels on compact manifold like (which $\\mathrm{V}(m, n)$ are examples of) can be found on these documentation pages: [one](https://gpflow.github.io/GeometricKernels/theory/compact.html), [two](https://gpflow.github.io/GeometricKernels/theory/addition_theorem.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "487c94c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kernel = MaternGeometricKernel(grassmannian, key=key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a45e786a",
+   "metadata": {},
+   "source": [
+    "To support JAX, our classes do not keep variables you might want to differentiate over in their state.\n",
+    "Instead, some methods take a `params` dictionary as input, returning its modified version.\n",
+    "\n",
+    "The next line initializes the dictionary of kernel parameters `params` with some default values.\n",
+    "\n",
+    "**Note:** our kernels do not provide the outputscale/variance parameter frequently used in Gaussian processes.\n",
+    "However, it is usually trivial to add it by multiplying the kernel by an (optimizable) constant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "d2fb7518",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "params: {'nu': array([inf]), 'lengthscale': array([1.])}\n"
+     ]
+    }
+   ],
+   "source": [
+    "params = kernel.init_params()\n",
+    "print('params:', params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74f4124e",
+   "metadata": {},
+   "source": [
+    "To define two different kernels, Matern-3/2 and Matern-∞ (aka heat, RBF, squared exponential, diffusion), we need two different versions of `params`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "e8e106a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params[\"lengthscale\"] = np.array([0.5])\n",
+    "params_32  = params.copy()\n",
+    "params_inf = params.copy()\n",
+    "del params\n",
+    "params_32[\"nu\"]  = np.array([1.5])#torch.tensor([3/2], dtype=torch.float64)\n",
+    "params_inf[\"nu\"] = np.array([np.inf]) #torch.tensor([np.inf], dtype=torch.float64)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50c5df26",
+   "metadata": {},
+   "source": [
+    "Now two kernels are *defined* and we proceed to evaluating both on a set of random inputs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73c74525",
+   "metadata": {},
+   "source": [
+    "### Evaluating Kernels on Random Inputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d42ae02a",
+   "metadata": {},
+   "source": [
+    "We start by sampling `10` (uniformly) random points in $\\mathrm{V}(2, 5)$.\n",
+    "An explicit `key` parameter is needed to support JAX as one of the backends."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "4455c3d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[-0.427 -0.328  0.265  0.57  -0.067  0.233  0.506]\n",
+      "  [ 0.577 -0.201 -0.632  0.269 -0.16   0.007  0.359]\n",
+      "  [ 0.303 -0.108  0.362 -0.247  0.533 -0.385  0.522]\n",
+      "  [ 0.594 -0.136  0.621  0.188 -0.358  0.149 -0.24 ]\n",
+      "  [ 0.166  0.369  0.019 -0.158  0.292  0.827  0.206]\n",
+      "  [ 0.111  0.011 -0.064  0.598  0.66  -0.036 -0.435]\n",
+      "  [-0.033 -0.828 -0.093 -0.353  0.193  0.3   -0.231]]\n",
+      "\n",
+      " [[-0.14   0.499 -0.242 -0.434 -0.238  0.458  0.468]\n",
+      "  [-0.231  0.046  0.237  0.61  -0.506 -0.172  0.481]\n",
+      "  [ 0.136  0.525 -0.579  0.541  0.217 -0.037 -0.17 ]\n",
+      "  [ 0.513  0.106  0.356  0.102  0.549  0.067  0.532]\n",
+      "  [ 0.752  0.131  0.127 -0.024 -0.573  0.092 -0.252]\n",
+      "  [-0.107 -0.204  0.16   0.367  0.061  0.861 -0.204]\n",
+      "  [ 0.261 -0.635 -0.617  0.045 -0.09   0.07   0.364]]\n",
+      "\n",
+      " [[ 0.382 -0.341 -0.234 -0.364  0.573  0.369 -0.292]\n",
+      "  [-0.488 -0.546 -0.266  0.555  0.221 -0.094 -0.165]\n",
+      "  [ 0.161  0.516 -0.054  0.234  0.499 -0.536 -0.337]\n",
+      "  [-0.141 -0.173 -0.497 -0.542 -0.054 -0.594  0.234]\n",
+      "  [ 0.171 -0.2    0.354  0.131  0.468 -0.15   0.74 ]\n",
+      "  [-0.677  0.421 -0.057 -0.251  0.383  0.347  0.171]\n",
+      "  [-0.285 -0.267  0.705 -0.361  0.07  -0.269 -0.378]]\n",
+      "\n",
+      " [[ 0.386  0.001  0.623 -0.084  0.361  0.558  0.12 ]\n",
+      "  [ 0.533 -0.255 -0.635 -0.306  0.22   0.206 -0.251]\n",
+      "  [-0.292 -0.166 -0.183 -0.319  0.465 -0.099  0.725]\n",
+      "  [ 0.374  0.445 -0.177 -0.085 -0.548  0.163  0.545]\n",
+      "  [-0.088  0.842 -0.139 -0.093  0.454 -0.046 -0.216]\n",
+      "  [ 0.143  0.012  0.342 -0.777 -0.168 -0.457 -0.15 ]\n",
+      "  [-0.56   0.013 -0.089 -0.423 -0.264  0.632 -0.175]]\n",
+      "\n",
+      " [[ 0.138  0.303  0.172 -0.62   0.026  0.067 -0.685]\n",
+      "  [ 0.488  0.173 -0.438  0.478  0.195  0.414 -0.32 ]\n",
+      "  [-0.438  0.018  0.586  0.314  0.266  0.52  -0.156]\n",
+      "  [ 0.577 -0.145  0.409 -0.115  0.613 -0.134  0.269]\n",
+      "  [-0.193 -0.784 -0.236 -0.026  0.306 -0.137 -0.424]\n",
+      "  [-0.255  0.08  -0.43  -0.48   0.395  0.467  0.372]\n",
+      "  [-0.341  0.486 -0.162  0.209  0.515 -0.547 -0.117]]\n",
+      "\n",
+      " [[ 0.407  0.423  0.071 -0.46   0.333  0.566 -0.087]\n",
+      "  [-0.497  0.052  0.02  -0.692  0.218 -0.411 -0.233]\n",
+      "  [-0.278  0.557  0.019  0.334  0.495 -0.164  0.478]\n",
+      "  [ 0.128  0.684 -0.28   0.062 -0.505 -0.271 -0.324]\n",
+      "  [-0.255  0.067  0.376  0.418  0.243  0.171 -0.725]\n",
+      "  [-0.173 -0.134 -0.879  0.108  0.279  0.228 -0.197]\n",
+      "  [ 0.631 -0.135 -0.054  0.085  0.453 -0.574 -0.198]]\n",
+      "\n",
+      " [[ 0.018  0.567  0.062 -0.185 -0.49  -0.568  0.28 ]\n",
+      "  [ 0.396 -0.027  0.874 -0.183  0.188 -0.061 -0.079]\n",
+      "  [ 0.095 -0.72  -0.029 -0.413 -0.462 -0.04   0.294]\n",
+      "  [-0.16   0.362  0.08  -0.498 -0.257  0.72  -0.059]\n",
+      "  [ 0.398  0.12  -0.407 -0.572  0.544 -0.116  0.159]\n",
+      "  [-0.236  0.038  0.181  0.174  0.267  0.195  0.878]\n",
+      "  [ 0.771  0.115 -0.165  0.394 -0.28   0.319  0.172]]\n",
+      "\n",
+      " [[ 0.052 -0.34  -0.286 -0.224  0.345  0.368  0.704]\n",
+      "  [ 0.127  0.569  0.258 -0.597 -0.363  0.088  0.312]\n",
+      "  [-0.623  0.145  0.272  0.406 -0.192  0.536  0.169]\n",
+      "  [-0.737  0.126 -0.412 -0.351  0.113 -0.367 -0.027]\n",
+      "  [-0.03  -0.502 -0.284 -0.233 -0.726  0.221 -0.19 ]\n",
+      "  [-0.165 -0.309  0.517 -0.498  0.364  0.263 -0.401]\n",
+      "  [ 0.148  0.42  -0.51  -0.052  0.202  0.563 -0.425]]\n",
+      "\n",
+      " [[ 0.116  0.242 -0.572 -0.076 -0.284 -0.599  0.394]\n",
+      "  [ 0.674 -0.02  -0.44   0.073 -0.204  0.349 -0.427]\n",
+      "  [ 0.345  0.641  0.544  0.213 -0.121 -0.285 -0.185]\n",
+      "  [-0.22  -0.33  -0.12   0.546  0.028 -0.495 -0.533]\n",
+      "  [-0.261 -0.077  0.131  0.272 -0.869  0.252  0.124]\n",
+      "  [-0.021 -0.206  0.184 -0.748 -0.325 -0.288 -0.419]\n",
+      "  [-0.544  0.611 -0.344 -0.114  0.028  0.214 -0.39 ]]\n",
+      "\n",
+      " [[-0.299 -0.044 -0.003 -0.115 -0.185 -0.731 -0.572]\n",
+      "  [ 0.608 -0.327 -0.552 -0.081 -0.398  0.064 -0.226]\n",
+      "  [-0.013 -0.203  0.586 -0.529 -0.354  0.384 -0.25 ]\n",
+      "  [ 0.573  0.085  0.567  0.382 -0.202 -0.367  0.147]\n",
+      "  [ 0.348  0.456  0.049 -0.006  0.481  0.202 -0.63 ]\n",
+      "  [-0.222 -0.479  0.115  0.698  0.008  0.294 -0.367]\n",
+      "  [ 0.206 -0.637  0.121 -0.26   0.64  -0.23   0.08 ]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "key, xs = grassmannian.random(key, 10)\n",
+    "\n",
+    "print(xs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39c0465d",
+   "metadata": {},
+   "source": [
+    "Now we evaluate the two kernel matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "id": "2a43f39e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10, 7, 7) (10, 7, 7)\n",
+      "(10, 7, 7) (10, 7, 7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "kernel_mat_32  = kernel.K(params_32,  xs, xs)\n",
+    "kernel_mat_inf = kernel.K(params_inf, xs, xs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c35e2625",
+   "metadata": {},
+   "source": [
+    "Finally, we visualize these matrices using `imshow`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "id": "c48c0d1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# find common range of values\n",
+    "minmin = np.min([np.min(kernel_mat_32), np.min(kernel_mat_inf)])\n",
+    "maxmax = np.max([np.max(kernel_mat_32), np.max(kernel_mat_inf)])\n",
+    "\n",
+    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)\n",
+    "cmap = plt.get_cmap('viridis')\n",
+    "\n",
+    "ax1.imshow(kernel_mat_32, vmin=minmin, vmax=maxmax, cmap=cmap)\n",
+    "ax1.set_title('k_32')\n",
+    "ax1.set_axis_off()\n",
+    "\n",
+    "ax2.imshow(kernel_mat_inf, vmin=minmin, vmax=maxmax, cmap=cmap)\n",
+    "ax2.set_title('k_inf')\n",
+    "ax2.set_axis_off()\n",
+    "\n",
+    "# add space for color bar\n",
+    "fig.subplots_adjust(right=0.85)\n",
+    "cbar_ax = fig.add_axes([0.88, 0.25, 0.02, 0.5])\n",
+    "\n",
+    "# add colorbar\n",
+    "sm = plt.cm.ScalarMappable(cmap=cmap,\n",
+    "                           norm=plt.Normalize(vmin=minmin, vmax=maxmax))\n",
+    "fig.colorbar(sm, cax=cbar_ax)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e4a8bb5",
+   "metadata": {},
+   "source": [
+    "### Visualize Kernels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "835a1002",
+   "metadata": {},
+   "source": [
+    "It is hard to visualize functions on $\\mathrm{V}(m, n)$.\n",
+    "Functions are only easy to visualize on domains of dimension not higher than $2$.\n",
+    "However, we have $\\dim \\left(\\mathrm{V}(m, n)\\right) = n m - \\frac{1}{2}m(m+1)$ which is greater than $2$ in all interesting cases.\n",
+    "\n",
+    "In [SpecialOrthogonal.ipynb](./SpecialOrthogonal.ipynb), we visualize functions on the even more high-dimensional $\\mathrm{SO}(n)$. To do this, we consider *random* embeddings $\\mathbf{E}_{\\mathbf{U}}: \\mathbb{T} \\hookrightarrow \\mathrm{SO}(n)$ of the circle $\\mathbb{T}$ into $\\mathrm{SO}(n)$, visualizing $\\tilde{f} \\circ \\mathbf{E}_{\\mathbf{U}}: \\mathbb{T} \\to \\mathbb{R}$ instead of the actual $\\tilde{f}: \\mathrm{SO}(n) \\to \\mathbb{R}$. Here, we use the canonical projection $\\operatorname{P}: \\mathrm{SO}(n) \\to \\mathrm{V}(m, n)$ to visualize $f \\circ \\operatorname{P} \\circ \\mathbf{E}_{\\mathbf{U}}: \\mathbb{T} \\to \\mathbb{R}$ instead of the actual $f: \\mathrm{V}(m, n) \\to \\mathbb{R}$. The canonical projection $\\operatorname{P}$ is something as simple as the operation of extracting the first $m$ columns of an $n \\times n$ matrix.\n",
+    "\n",
+    "To sum up, we will plot the functions $k_{\\nu, \\kappa}(\\operatorname{P} \\mathbf{I}, \\operatorname{P} \\mathbf{E}_{\\mathbf{U}}(\\alpha))$ where\n",
+    "- $\\mathbf{I}$ is the $n \\times n$ identity matrix,\n",
+    "- $\\alpha \\in \\mathbb{T}$ and thus can be represented as an angle in $[0, 2 \\pi)$,\n",
+    "- $\\operatorname{P}$ is the canonical projection of $\\mathrm{SO}(n)$ onto $\\mathrm{V}(m, n)$, as explained above,\n",
+    "- $\\mathbf{E}_{\\mathbf{U}}(\\alpha)$ is the random embedding of $\\mathbb{T}$ into $\\mathrm{SO}(n)$ defined in [SpecialOrthogonal.ipynb](./SpecialOrthogonal.ipynb).\n",
+    "\n",
+    "In practice, we visualize $k_{\\nu, \\kappa}($ `base_point` $, x)$ for $x \\in $ `other_points1` or $x \\in $ `other_points2`.\n",
+    "The `base_point` is simply $\\operatorname{P} \\mathbf{I}$.\n",
+    "The `other_points1` and `other_points2` are defined by the `_NUM_ANGLES` uniform discretization of the unit circle, but with different $\\mathbf{U}$, i.e. corresponding to different random embeddings of $\\mathbb{T}$ into $\\mathrm{SO}(n)$.\n",
+    "\n",
+    "We define `base_point`, `other_points`, and `other_points2` in the next cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "id": "77c8e92e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define discretization\n",
+    "_NUM_ANGLES = 64\n",
+    "\n",
+    "key, U = grassmannian.G.random(key, 2)  # stiefel.G returns an object representing SO(n)\n",
+    "U1 = U[0, :]\n",
+    "U2 = U[1, :]\n",
+    "\n",
+    "# generate a grid on [0, 2 \\pi)\n",
+    "angles = np.linspace(0, 2*np.pi, num=_NUM_ANGLES)\n",
+    "embedding = np.broadcast_to(np.eye(n), (_NUM_ANGLES, n, n)).copy()\n",
+    "embedding[:, 0, 0] = np.cos(angles)\n",
+    "embedding[:, 0, 1] = -np.sin(angles)\n",
+    "embedding[:, 1, 0] = np.sin(angles)\n",
+    "embedding[:, 1, 1] = np.cos(angles)\n",
+    "\n",
+    "base_point = grassmannian.project_to_manifold(embedding[[0], :, :])\n",
+    "\n",
+    "def conj_batch(U, emb):\n",
+    "    \"\"\" Compute U^T * emb[i, :] * U for all i.\n",
+    "    :param U:   [n, n] array\n",
+    "    :param emb: [N, n, n] array\n",
+    "    \n",
+    "    :return:    [N, n, n] array\n",
+    "    \"\"\"\n",
+    "    return np.tensordot(np.tensordot(emb, U, axes=([2], [0])),\n",
+    "                        U.T, axes=([1], [1])\n",
+    "                       )\n",
+    "\n",
+    "other_points1 = grassmannian.project_to_manifold(conj_batch(U1, embedding))\n",
+    "other_points2 = grassmannian.project_to_manifold(conj_batch(U2, embedding))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07275c76",
+   "metadata": {},
+   "source": [
+    "The next cell evaluates $k_{\\nu, \\kappa}($ `base_point` $, x)$ for $x \\in $ `other_points1` and $x \\in $ `other_points2`, for $\\nu$ either $3/2$ or $\\infty$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "id": "528594cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 7, 7) (64, 7, 7)\n",
+      "(1, 7, 7) (64, 7, 7)\n",
+      "(1, 7, 7) (64, 7, 7)\n",
+      "(1, 7, 7) (64, 7, 7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "kernel_vals_32_1  = kernel.K(params_32,  base_point, other_points1)\n",
+    "kernel_vals_32_2  = kernel.K(params_32,  base_point, other_points2)\n",
+    "kernel_vals_inf_1 = kernel.K(params_inf, base_point, other_points1)\n",
+    "kernel_vals_inf_2 = kernel.K(params_inf, base_point, other_points2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb65e8d7",
+   "metadata": {},
+   "source": [
+    "Finally, we are ready to plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "id": "aa9bbaf3-06c0-432e-8527-1b8aebf322a7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1280x960 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_circle = np.cos(angles)\n",
+    "y_circle = np.sin(angles)\n",
+    "z_circle = np.zeros_like(angles) # z=0 for the circle\n",
+    "\n",
+    "# Red ball position\n",
+    "red_ball_position = (x_circle[0], y_circle[0], z_circle[0])\n",
+    "\n",
+    "\n",
+    "def plot_kernel(ax, values, title):\n",
+    "    # Draw the unit circle\n",
+    "    ax.plot(x_circle, y_circle, z_circle, linestyle='dashed', color='gray')\n",
+    "    # Draw the red ball\n",
+    "    ax.scatter(*red_ball_position, color=\"r\", s=50)\n",
+    "    # Draw the vertical dashed line\n",
+    "    ax.plot([1, 1], [0, 0], np.linspace(0, values[0, 0], 2), linestyle='dashed', color='gray')\n",
+    "    # Plot the new function evaluated only on the unit circle\n",
+    "    ax.plot(x_circle, y_circle, values[0, :], color='b')\n",
+    "    # Set the viewing angle for better visualization\n",
+    "    ax.view_init(elev=20., azim=180+30)\n",
+    "    ax.set_title(title)\n",
+    "\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(figsize=(12.8, 9.6), nrows=2, ncols=2,\n",
+    "                               subplot_kw=dict(projection='3d',\n",
+    "                                               computed_zorder=False))\n",
+    "\n",
+    "plot_kernel(ax1, kernel_vals_32_1,  r'$k_{3/2, \\kappa}($base_point, other_points1$)$')\n",
+    "plot_kernel(ax2, kernel_vals_32_2,  r'$k_{3/2, \\kappa}($base_point, other_points2$)$')\n",
+    "plot_kernel(ax3, kernel_vals_inf_1, r'$k_{\\infty, \\kappa}($base_point, other_points1$)$')\n",
+    "plot_kernel(ax4, kernel_vals_inf_2, r'$k_{\\infty, \\kappa}($base_point, other_points2$)$')\n",
+    "\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08cac3cc-63c6-4057-a8ca-3c645dad2d9e",
+   "metadata": {},
+   "source": [
+    "## Feature Maps and Sampling\n",
+    "\n",
+    "Here we show how to get an approximate finite-dimensional feature map for heat and Matérn kernels on the sphere, i.e. such $\\phi$ that\n",
+    "$$\n",
+    "k(x, x') \\approx \\langle \\phi(x), \\phi(x') \\rangle_{\\mathbb{R}^M}.\n",
+    "$$\n",
+    "This might be useful for speeding up computations.\n",
+    "We showcase this below by showing how to efficiently sample the Gaussian process $\\mathrm{GP}(0, k)$.\n",
+    "\n",
+    "For a brief theoretical introduction into feature maps, see this [documentation page](https://gpflow.github.io/GeometricKernels/theory/feature_maps.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba0cb4cd-c7d3-40fd-862d-1a90ba3d5eef",
+   "metadata": {},
+   "source": [
+    "### Defining a Feature Map"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3131b86-24ee-4398-a88a-b02cf49c697f",
+   "metadata": {},
+   "source": [
+    "The simplest way to get an approximate finite-dimensional feature map is to use the `default_feature_map` function from `geometric_kernels.kernels`.\n",
+    "It has an optional keyword argument `num` which determines the number of features, the $M$ above.\n",
+    "Below we rely on the default value of `num`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "8b3ba4f0-5a1b-4ce8-8986-be6563bd8a40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from geometric_kernels.kernels import default_feature_map\n",
+    "\n",
+    "feature_map = default_feature_map(kernel=kernel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fed4a182-6dc0-4826-8cca-ba49c8fdaee3",
+   "metadata": {},
+   "source": [
+    "The resulting `feature_map` is a function that takes the array of inputs and parameters of the kernel.\n",
+    "There is also an optional parameter `normalize` that determines if $\\langle \\phi(x), \\phi(x) \\rangle_{\\mathbb{R}^M} \\approx 1$ or not.\n",
+    "For the (hyper)sphere, `normalize` follows the standard behavior of `MaternKarhunenLoeveKernel`, being `True` by default.\n",
+    "\n",
+    "`feature_map` outputs a tuple.\n",
+    "Its **second** element is $\\phi(x)$ evaluated at all inputs $x$.\n",
+    "Its first element is either `None` for determinstic feature maps, or contains the updated `key` for randomized feature maps which take `key` as a keyword argument.\n",
+    "For `default_feature_map` on a `Stiefel` space, the first element is `key` since the feature map is *random*.\n",
+    "\n",
+    "In the next cell, we evaluate the feature map at random points, using `params_32` as kernel parameters.\n",
+    "We check the basic property of the feature map: $k(x, x') \\approx \\langle \\phi(x), \\phi(x') \\rangle_{\\mathbb{R}^M}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "id": "6602d412-e9b6-48db-802c-27b876a15be2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10, 7, 7) (3000, 7, 7)\n",
+      "xs (shape = (10, 7, 7)):\n",
+      "[[[-0.427 -0.328  0.265  0.57  -0.067  0.233  0.506]\n",
+      "  [ 0.577 -0.201 -0.632  0.269 -0.16   0.007  0.359]\n",
+      "  [ 0.303 -0.108  0.362 -0.247  0.533 -0.385  0.522]\n",
+      "  [ 0.594 -0.136  0.621  0.188 -0.358  0.149 -0.24 ]\n",
+      "  [ 0.166  0.369  0.019 -0.158  0.292  0.827  0.206]\n",
+      "  [ 0.111  0.011 -0.064  0.598  0.66  -0.036 -0.435]\n",
+      "  [-0.033 -0.828 -0.093 -0.353  0.193  0.3   -0.231]]\n",
+      "\n",
+      " [[-0.14   0.499 -0.242 -0.434 -0.238  0.458  0.468]\n",
+      "  [-0.231  0.046  0.237  0.61  -0.506 -0.172  0.481]\n",
+      "  [ 0.136  0.525 -0.579  0.541  0.217 -0.037 -0.17 ]\n",
+      "  [ 0.513  0.106  0.356  0.102  0.549  0.067  0.532]\n",
+      "  [ 0.752  0.131  0.127 -0.024 -0.573  0.092 -0.252]\n",
+      "  [-0.107 -0.204  0.16   0.367  0.061  0.861 -0.204]\n",
+      "  [ 0.261 -0.635 -0.617  0.045 -0.09   0.07   0.364]]\n",
+      "\n",
+      " [[ 0.382 -0.341 -0.234 -0.364  0.573  0.369 -0.292]\n",
+      "  [-0.488 -0.546 -0.266  0.555  0.221 -0.094 -0.165]\n",
+      "  [ 0.161  0.516 -0.054  0.234  0.499 -0.536 -0.337]\n",
+      "  [-0.141 -0.173 -0.497 -0.542 -0.054 -0.594  0.234]\n",
+      "  [ 0.171 -0.2    0.354  0.131  0.468 -0.15   0.74 ]\n",
+      "  [-0.677  0.421 -0.057 -0.251  0.383  0.347  0.171]\n",
+      "  [-0.285 -0.267  0.705 -0.361  0.07  -0.269 -0.378]]\n",
+      "\n",
+      " [[ 0.386  0.001  0.623 -0.084  0.361  0.558  0.12 ]\n",
+      "  [ 0.533 -0.255 -0.635 -0.306  0.22   0.206 -0.251]\n",
+      "  [-0.292 -0.166 -0.183 -0.319  0.465 -0.099  0.725]\n",
+      "  [ 0.374  0.445 -0.177 -0.085 -0.548  0.163  0.545]\n",
+      "  [-0.088  0.842 -0.139 -0.093  0.454 -0.046 -0.216]\n",
+      "  [ 0.143  0.012  0.342 -0.777 -0.168 -0.457 -0.15 ]\n",
+      "  [-0.56   0.013 -0.089 -0.423 -0.264  0.632 -0.175]]\n",
+      "\n",
+      " [[ 0.138  0.303  0.172 -0.62   0.026  0.067 -0.685]\n",
+      "  [ 0.488  0.173 -0.438  0.478  0.195  0.414 -0.32 ]\n",
+      "  [-0.438  0.018  0.586  0.314  0.266  0.52  -0.156]\n",
+      "  [ 0.577 -0.145  0.409 -0.115  0.613 -0.134  0.269]\n",
+      "  [-0.193 -0.784 -0.236 -0.026  0.306 -0.137 -0.424]\n",
+      "  [-0.255  0.08  -0.43  -0.48   0.395  0.467  0.372]\n",
+      "  [-0.341  0.486 -0.162  0.209  0.515 -0.547 -0.117]]\n",
+      "\n",
+      " [[ 0.407  0.423  0.071 -0.46   0.333  0.566 -0.087]\n",
+      "  [-0.497  0.052  0.02  -0.692  0.218 -0.411 -0.233]\n",
+      "  [-0.278  0.557  0.019  0.334  0.495 -0.164  0.478]\n",
+      "  [ 0.128  0.684 -0.28   0.062 -0.505 -0.271 -0.324]\n",
+      "  [-0.255  0.067  0.376  0.418  0.243  0.171 -0.725]\n",
+      "  [-0.173 -0.134 -0.879  0.108  0.279  0.228 -0.197]\n",
+      "  [ 0.631 -0.135 -0.054  0.085  0.453 -0.574 -0.198]]\n",
+      "\n",
+      " [[ 0.018  0.567  0.062 -0.185 -0.49  -0.568  0.28 ]\n",
+      "  [ 0.396 -0.027  0.874 -0.183  0.188 -0.061 -0.079]\n",
+      "  [ 0.095 -0.72  -0.029 -0.413 -0.462 -0.04   0.294]\n",
+      "  [-0.16   0.362  0.08  -0.498 -0.257  0.72  -0.059]\n",
+      "  [ 0.398  0.12  -0.407 -0.572  0.544 -0.116  0.159]\n",
+      "  [-0.236  0.038  0.181  0.174  0.267  0.195  0.878]\n",
+      "  [ 0.771  0.115 -0.165  0.394 -0.28   0.319  0.172]]\n",
+      "\n",
+      " [[ 0.052 -0.34  -0.286 -0.224  0.345  0.368  0.704]\n",
+      "  [ 0.127  0.569  0.258 -0.597 -0.363  0.088  0.312]\n",
+      "  [-0.623  0.145  0.272  0.406 -0.192  0.536  0.169]\n",
+      "  [-0.737  0.126 -0.412 -0.351  0.113 -0.367 -0.027]\n",
+      "  [-0.03  -0.502 -0.284 -0.233 -0.726  0.221 -0.19 ]\n",
+      "  [-0.165 -0.309  0.517 -0.498  0.364  0.263 -0.401]\n",
+      "  [ 0.148  0.42  -0.51  -0.052  0.202  0.563 -0.425]]\n",
+      "\n",
+      " [[ 0.116  0.242 -0.572 -0.076 -0.284 -0.599  0.394]\n",
+      "  [ 0.674 -0.02  -0.44   0.073 -0.204  0.349 -0.427]\n",
+      "  [ 0.345  0.641  0.544  0.213 -0.121 -0.285 -0.185]\n",
+      "  [-0.22  -0.33  -0.12   0.546  0.028 -0.495 -0.533]\n",
+      "  [-0.261 -0.077  0.131  0.272 -0.869  0.252  0.124]\n",
+      "  [-0.021 -0.206  0.184 -0.748 -0.325 -0.288 -0.419]\n",
+      "  [-0.544  0.611 -0.344 -0.114  0.028  0.214 -0.39 ]]\n",
+      "\n",
+      " [[-0.299 -0.044 -0.003 -0.115 -0.185 -0.731 -0.572]\n",
+      "  [ 0.608 -0.327 -0.552 -0.081 -0.398  0.064 -0.226]\n",
+      "  [-0.013 -0.203  0.586 -0.529 -0.354  0.384 -0.25 ]\n",
+      "  [ 0.573  0.085  0.567  0.382 -0.202 -0.367  0.147]\n",
+      "  [ 0.348  0.456  0.049 -0.006  0.481  0.202 -0.63 ]\n",
+      "  [-0.222 -0.479  0.115  0.698  0.008  0.294 -0.367]\n",
+      "  [ 0.206 -0.637  0.121 -0.26   0.64  -0.23   0.08 ]]]\n",
+      "\n",
+      "emedding (shape = (10, 75000)):\n",
+      "[[ 0.017  0.003 -0.002 ... -0.001  0.    -0.   ]\n",
+      " [ 0.017 -0.002 -0.002 ...  0.001  0.     0.   ]\n",
+      " [ 0.017  0.002 -0.004 ... -0.    -0.    -0.   ]\n",
+      " ...\n",
+      " [ 0.017  0.001 -0.003 ...  0.002 -0.    -0.   ]\n",
+      " [ 0.017 -0.008  0.005 ...  0.001  0.     0.   ]\n",
+      " [ 0.016  0.005 -0.002 ... -0.    -0.    -0.   ]]\n",
+      "(10, 7, 7) (10, 7, 7)\n",
+      "\n",
+      "||k(xs, xs) - phi(xs) * phi(xs)^T|| = 0.04572049206529948\n"
+     ]
+    }
+   ],
+   "source": [
+    "# xs are random points from above\n",
+    "key, embedding = feature_map(xs, params_32, key=key)\n",
+    "\n",
+    "print('xs (shape = %s):\\n%s' % (xs.shape, xs))\n",
+    "print('')\n",
+    "print('emedding (shape = %s):\\n%s' % (embedding.shape, embedding))\n",
+    "\n",
+    "kernel_mat_32  = kernel.K(params_32,  xs, xs)\n",
+    "kernel_mat_32_alt = np.matmul(embedding, embedding.T)\n",
+    "\n",
+    "print('')\n",
+    "print('||k(xs, xs) - phi(xs) * phi(xs)^T|| =', np.linalg.norm(kernel_mat_32 - kernel_mat_32_alt))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e10e6dd4-31a9-4a1f-9447-ddeff9259845",
+   "metadata": {},
+   "source": [
+    "**Unfortunately:** $k(x, x') \\approx \\langle \\phi(x), \\phi(x') \\rangle_{\\mathbb{R}^M}$ does not hold, which is either caused by a bug or by the insufficiency of the default approximation order in this case. **TODO:** investigate this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43728ac5-8277-4436-86c7-e6e97e434ec3",
+   "metadata": {},
+   "source": [
+    "### Efficient Sampling using Feature Maps\n",
+    "\n",
+    "GeometricKernels provides a simple tool to efficiently sample (without incurring cubic costs) the Gaussian process $f \\sim \\mathrm{GP}(0, k)$, based on an approximate finite-dimensional feature map $\\phi$.\n",
+    "The underlying machinery is briefly discussed in this [documentation page](https://gpflow.github.io/GeometricKernels/theory/feature_maps.html).\n",
+    "\n",
+    "The function `sampler` from `geometric_kernels.sampling` takes in a feature map and, optionally, the keyword argument `s` that specifies the number of samples to generate.\n",
+    "It returns a function we name `sample_paths`.\n",
+    "Since we are going to compute each of the two samples at two different sets of inputs, `other_points1` and `other_points2`, we make sure the randomness if fixed by using the `make_deterministic` function.\n",
+    "\n",
+    "`sample_paths` operates much like `feature_map` above: it takes in the points where to evaluate the samples and kernel parameters.\n",
+    "Additionally, it takes in the keyword argument `key` that specifies randomness in the JAX style.\n",
+    "**However**, in our specific case, this keyword argument is not needed as it is automatically supplied by the `make_deterministic` wrapper.\n",
+    "`sample_paths` returns a tuple.\n",
+    "Its first element is the updated `key`.\n",
+    "Its second element is an array containing the value of samples evaluated at the input points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "id": "78922dda-ae2d-4bc1-b28a-b4dc9424fd07",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10, 7, 7) (3000, 7, 7)\n",
+      "Two samples evaluated at the xs are:\n",
+      "[[-0.002  0.003]\n",
+      " [-0.004  0.007]\n",
+      " [-0.004  0.   ]\n",
+      " [-0.004  0.009]\n",
+      " [-0.007  0.002]\n",
+      " [-0.006 -0.001]\n",
+      " [-0.008  0.007]\n",
+      " [-0.007  0.004]\n",
+      " [-0.004 -0.   ]\n",
+      " [-0.008  0.007]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from geometric_kernels.sampling import sampler\n",
+    "from geometric_kernels.utils.utils import make_deterministic\n",
+    "\n",
+    "# introduce random state for reproducibility (optional)\n",
+    "# `key` is jax's terminology\n",
+    "key = np.random.RandomState(seed=1234)\n",
+    "\n",
+    "sample_paths = make_deterministic(sampler(feature_map, s=2), key)\n",
+    "\n",
+    "# new random state is returned along with the samples\n",
+    "key, samples = sample_paths(xs, params_32)\n",
+    "\n",
+    "print('Two samples evaluated at the xs are:')\n",
+    "print(samples)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dadb7a46-427a-475c-bc6e-0891c8d87c7c",
+   "metadata": {},
+   "source": [
+    "#### Visualizing Samples\n",
+    "Here we visualize samples as functions on the sphere."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "id": "baf674e9-910e-494b-8abb-0069c7df31c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(64, 7, 7) (3000, 7, 7)\n",
+      "(64, 7, 7) (3000, 7, 7)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1280x960 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "key, samples_other_points1 = sample_paths(other_points1, params_32)\n",
+    "key, samples_other_points2 = sample_paths(other_points2, params_32)\n",
+    "\n",
+    "sample1_other_points1 = samples_other_points1[:, 0]\n",
+    "sample2_other_points1 = samples_other_points1[:, 1]\n",
+    "sample1_other_points2 = samples_other_points2[:, 0]\n",
+    "sample2_other_points2 = samples_other_points2[:, 1]\n",
+    "\n",
+    "x_circle = np.cos(angles)\n",
+    "y_circle = np.sin(angles)\n",
+    "z_circle = np.zeros_like(angles) # z=0 for the circle\n",
+    "\n",
+    "def plot_sample(ax, values, title):\n",
+    "    # Draw the unit circle\n",
+    "    ax.plot(x_circle, y_circle, z_circle, linestyle='dashed', color='gray')\n",
+    "    # Plot the new function evaluated only on the unit circle\n",
+    "    ax.plot(x_circle, y_circle, values, color='b')\n",
+    "    # Set the viewing angle for better visualization\n",
+    "    ax.view_init(elev=20., azim=180+30)\n",
+    "    ax.set_title(title)\n",
+    "\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(figsize=(12.8, 9.6), nrows=2, ncols=2,\n",
+    "                               subplot_kw=dict(projection='3d',\n",
+    "                                               computed_zorder=False))\n",
+    "\n",
+    "plot_sample(ax1, samples_other_points1[:, 0], r'Sample #1: $f(\\cdot)$ for $\\cdot$ in other_points1, $f \\sim \\mathrm{GP}(0, k_{3/2, \\kappa})$')\n",
+    "plot_sample(ax2, samples_other_points2[:, 0], r'Sample #1: $f(\\cdot)$ for $\\cdot$ in other_points2, $f \\sim \\mathrm{GP}(0, k_{3/2, \\kappa})$')\n",
+    "plot_sample(ax3, samples_other_points1[:, 1], r'Sample #2: $f(\\cdot)$ for $\\cdot$ in other_points1, $f \\sim \\mathrm{GP}(0, k_{3/2, \\kappa})$')\n",
+    "plot_sample(ax4, samples_other_points2[:, 1], r'Sample #2: $f(\\cdot)$ for $\\cdot$ in other_points2, $f \\sim \\mathrm{GP}(0, k_{3/2, \\kappa})$')\n",
+    "\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "542e43c7",
+   "metadata": {},
+   "source": [
+    "## Citation\n",
+    "\n",
+    "If you are using Lie groups and GeometricKernels, please consider citing\n",
+    "\n",
+    "```\n",
+    "@article{azangulov2022,\n",
+    "    title={Stationary kernels and Gaussian processes on Lie groups and their homogeneous spaces I: the compact case},\n",
+    "    author={Azangulov, Iskander and Smolensky, Andrei and Terenin, Alexander and Borovitskiy, Viacheslav},\n",
+    "    journal={arXiv preprint arXiv:2208.14960},\n",
+    "    year={2022}\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "558cae08",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/Stiefel copy.ipynb b/notebooks/Stiefel copy.ipynb
new file mode 100644
index 00000000..57449240
--- /dev/null
+++ b/notebooks/Stiefel copy.ipynb	
@@ -0,0 +1,1135 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "66ebbb52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To run this in Google Colab, uncomment the following line\n",
+    "# !pip install geometric_kernels\n",
+    "\n",
+    "# If you want to use a version of the library from a specific branch on GitHub,\n",
+    "# say, from the \"devel\" branch, uncomment the line below instead\n",
+    "# !pip install \"git+https://github.com/GPflow/GeometricKernels@devel\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acf0a49e",
+   "metadata": {},
+   "source": [
+    "# Matérn and Heat Kernels on the Stiefel manifold $\\mathrm{V}(m, n)$\n",
+    "\n",
+    "This notebook shows how define and evaluate kernels on the Stiefel manifold $\\mathrm{V}(m, n)$ that consists of all orthonormal $m$-frames in $\\mathbb{R}^n$, i.e. $m$-tupes of orthonormal vectors in $\\mathbb{R}^n$.\n",
+    "\n",
+    "Stiefel manifold is a homogeneous space space $\\mathrm{V}(m, n) = \\mathrm{SO}(n) / \\mathrm{SO}(n-m)$ of the special orthogonal group $\\mathrm{SO}(n)$. **Note**: mathematically, $\\mathrm{V}(n, n) = \\mathrm{SO}(n)$, the special orthogonal group, although our implementation requires $m < n$, and $\\mathrm{V}(1, n) = \\mathbb{S}_{n-1}$, the $n-1$-dimensional hypersphere.\n",
+    "\n",
+    "We work with $\\mathrm{V}(2, 5)$ here. There is no particular reason for this. Handling the Stiefel manifold $\\mathrm{V}(m, n)$ for other $n \\geq 2$ and $m < n$ is the same.\n",
+    "\n",
+    "\n",
+    "**Note:** the \"points\" in the the Stiefel manifold $\\mathrm{V}(m, n)$ are represented by real matrices (`array`s of the suitable backend) of size $n \\times m$, whose columns are orthonormal vectors.\n",
+    "\n",
+    "We use the **numpy** backend here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65b2692e",
+   "metadata": {},
+   "source": [
+    "<!--TABLE OF CONTENTS-->\n",
+    "## Contents\n",
+    "- [**Basics**](#Basics)\n",
+    "  - [Defining a Space](#Defining-a-Space)\n",
+    "  - [Defining a Kernel](#Defining-a-Kernel)\n",
+    "  - [Evaluating Kernels on Random Inputs](#Evaluating-Kernels-on-Random-Inputs)\n",
+    "  - [Visualize Kernels](#Visualize-Kernels)\n",
+    "- [**Feature Maps and Sampling**](#Feature-Maps-and-Sampling)\n",
+    "  - [Defining a Feature Map](#Defining-a-Feature-Map)\n",
+    "  - [Efficient Sampling using Feature Maps](#Efficient-Sampling-using-Feature-Maps)\n",
+    "- [**Comparing Kernels on $\\mathrm{V}(1, n)$ to the Kernels on $\\mathbb{S}_{n-1}$**](#Comparing-Kernels-on-$\\mathrm{V}(1,-n)$-to-the-Kernels-on-$\\mathbb{S}_{n-1}$)\n",
+    "- [**Citation**](#Citation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7446f9f-1023-4fa5-ac88-475e0e482694",
+   "metadata": {},
+   "source": [
+    "## Basics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "24ac4559",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add the parent directory to the path so that the library can be imported\n",
+    "import sys\n",
+    "sys.path.append(\"..\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c2c899b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO (geometric_kernels): Numpy backend is enabled. To enable other backends, don't forget to `import geometric_kernels.*backend name*`.\n",
+      "INFO (geometric_kernels): We may be suppressing some logging of external libraries. To override the logging policy, call `logging.basicConfig`.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import a backend, we use numpy in this example.\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "\n",
+    "# Import the geometric_kernels backend.\n",
+    "import geometric_kernels\n",
+    "\n",
+    "# Note: if you are using a backend other than numpy,\n",
+    "# you _must_ uncomment one of the following lines\n",
+    "# import geometric_kernels.tensorflow\n",
+    "#import geometric_kernels.torch\n",
+    "# import geometric_kernels.jax\n",
+    "\n",
+    "# Import a space and an appropriate kernel.\n",
+    "from geometric_kernels.spaces import Stiefel\n",
+    "from geometric_kernels.kernels import MaternGeometricKernel\n",
+    "\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6da8861c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "device(type='cpu')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "device"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35b6053f",
+   "metadata": {},
+   "source": [
+    "### Defining a Space"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae549717",
+   "metadata": {},
+   "source": [
+    "First we create a GeometricKernels `space` that corresponds to the Stiefel manifold $\\mathrm{V}(2, 5)$, a subset of the set of all $5 \\times 2$ matrices $\\mathbb{R}^{5 \\times 2}$. Our implementation of `Stiefel` computes the basis functions (that are needed to compute all the kernels) approximately, using Monte Carlo integration. Because of this, you need to provide a source of randomness when constructing the space, i.e. the random generator of the suitable backend, via the `key` keyword argument.\n",
+    "\n",
+    "**Note**: the aforementioned basis functions are *zonal spherical functions*, these are (sums of outer products of certain groups of Laplace-Beltrami eigenfunctions). Perhaps a close enough examination of \"A theory of Stiefel harmonics\" by S. S. Gelbart or similar literature could yield exact algorithms for computing them - an interesting problem for future research."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "bc2f6122",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m, n = 1, 3\n",
+    "\n",
+    "#key = torch.Generator()\n",
+    "# key.manual_seed(1234)\n",
+    "key = np.random.RandomState(1234)\n",
+    "\n",
+    "key, stiefel = Stiefel(m=m, n=n, key=key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3513e04b",
+   "metadata": {},
+   "source": [
+    "### Defining a Kernel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfc8d35f",
+   "metadata": {},
+   "source": [
+    "First, we create a generic Matérn kernel.\n",
+    "\n",
+    "To initialize `MaternGeometricKernel` you just need to provide a `Space` object, in our case this is the `stiefel` we have just created above.\n",
+    "\n",
+    "There is also an optional second parameter `num` which determines the order of approximation of the kernel.\n",
+    "There is a sensible default value for each of the spaces in the library, so change it only if you know what you are doing.\n",
+    "\n",
+    "A brief account on theory behind the kernels on compact manifold like (which $\\mathrm{V}(m, n)$ are examples of) can be found on these documentation pages: [one](https://gpflow.github.io/GeometricKernels/theory/compact.html), [two](https://gpflow.github.io/GeometricKernels/theory/addition_theorem.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "487c94c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "kernel = MaternGeometricKernel(space=stiefel, key=key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a45e786a",
+   "metadata": {},
+   "source": [
+    "To support JAX, our classes do not keep variables you might want to differentiate over in their state.\n",
+    "Instead, some methods take a `params` dictionary as input, returning its modified version.\n",
+    "\n",
+    "The next line initializes the dictionary of kernel parameters `params` with some default values.\n",
+    "\n",
+    "**Note:** our kernels do not provide the outputscale/variance parameter frequently used in Gaussian processes.\n",
+    "However, it is usually trivial to add it by multiplying the kernel by an (optimizable) constant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "d2fb7518",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "params: {'nu': array([inf]), 'lengthscale': array([1.])}\n"
+     ]
+    }
+   ],
+   "source": [
+    "params = kernel.init_params()\n",
+    "print('params:', params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74f4124e",
+   "metadata": {},
+   "source": [
+    "To define two different kernels, Matern-3/2 and Matern-∞ (aka heat, RBF, squared exponential, diffusion), we need two different versions of `params`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "344c5f6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#torch.tensor([0.5], dtype=torch.float64)\n",
+    "\n",
+    "params[\"lengthscale\"] = np.array([0.5])\n",
+    "params_32  = params.copy()\n",
+    "params_inf = params.copy()\n",
+    "del params\n",
+    "params_32[\"nu\"]  = np.array([3/2])\n",
+    "params_inf[\"nu\"] = np.array([np.inf])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50c5df26",
+   "metadata": {},
+   "source": [
+    "Now two kernels are *defined* and we proceed to evaluating both on a set of random inputs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73c74525",
+   "metadata": {},
+   "source": [
+    "### Evaluating Kernels on Random Inputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d42ae02a",
+   "metadata": {},
+   "source": [
+    "We start by sampling `10` (uniformly) random points in $\\mathrm{V}(2, 5)$.\n",
+    "An explicit `key` parameter is needed to support JAX as one of the backends."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "4455c3d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'numpy.ndarray'> [[[ 0.09441262  0.97403819 -0.20575681]\n",
+      "  [-0.51956977 -0.12808916 -0.84477241]\n",
+      "  [-0.8491958   0.1866622   0.49398757]]\n",
+      "\n",
+      " [[ 0.68191779  0.73022524 -0.04194322]\n",
+      "  [-0.69677281  0.66598093  0.26641518]\n",
+      "  [ 0.22247647 -0.15244836  0.96294534]]\n",
+      "\n",
+      " [[-0.12104071 -0.98639892  0.11120398]\n",
+      "  [ 0.72479523 -0.01127781  0.68887204]\n",
+      "  [-0.67824849  0.16398167  0.71630231]]\n",
+      "\n",
+      " [[ 0.2006535  -0.91439396 -0.35159901]\n",
+      "  [-0.68392316 -0.38771159  0.61800391]\n",
+      "  [-0.70141805  0.11646206 -0.7031709 ]]\n",
+      "\n",
+      " [[-0.46565446 -0.84440613  0.26484752]\n",
+      "  [ 0.01364443 -0.30608886 -0.95190516]\n",
+      "  [ 0.88486143 -0.4396452   0.15405308]]\n",
+      "\n",
+      " [[ 0.72186006  0.1136504  -0.68264312]\n",
+      "  [ 0.27654547  0.85686659  0.43508878]\n",
+      "  [ 0.6343821  -0.50285508  0.58710827]]\n",
+      "\n",
+      " [[-0.55012602  0.76147918 -0.34279851]\n",
+      "  [ 0.66451294  0.15056779 -0.73195074]\n",
+      "  [-0.50575084 -0.6304592  -0.58884403]]\n",
+      "\n",
+      " [[-0.45029837 -0.10814093 -0.88630521]\n",
+      "  [-0.50645192 -0.78657395  0.35328159]\n",
+      "  [-0.73534878  0.60795309  0.29942478]]\n",
+      "\n",
+      " [[ 0.54026706 -0.79984351 -0.26146103]\n",
+      "  [ 0.11835981 -0.23539205  0.96466654]\n",
+      "  [-0.83312812 -0.55212403 -0.03250525]]\n",
+      "\n",
+      " [[-0.82263794  0.11442843  0.55693173]\n",
+      "  [ 0.56308192  0.02824958  0.8259181 ]\n",
+      "  [ 0.07877542  0.99302976 -0.08767175]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "key, xs = stiefel.random(key, 10)\n",
+    "\n",
+    "print(type(xs), xs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39c0465d",
+   "metadata": {},
+   "source": [
+    "Now we evaluate the two kernel matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "2a43f39e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Iskander\\Documents\\Projects\\GeometricKernels\\.venv\\lib\\site-packages\\lab\\numpy\\generic.py:86: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  return a.astype(dtype, copy=False)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "kernel_mat_32  = kernel.K(params_32,  xs, xs)\n",
+    "kernel_mat_inf = kernel.K(params_inf, xs, xs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c35e2625",
+   "metadata": {},
+   "source": [
+    "Finally, we visualize these matrices using `imshow`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "c48c0d1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Nlfz8fNm8efNZ+x88eFC+9a1vSe/evSUlJUX69u0rZWVlvu9HZQQAAIuoOM8ZWbt2rcydO1dKSkq8RGT58uVSWFgoO3fulF69ep3Wv6GhQUaNGuX92QsvvCBZWVmye/du6dbN/94yJCMAAFiXjDi++wa1bNkyKSoqkmnTpnnHOilZt26dlJaWyn333Xdaf33+wIEDsmnTJunYsaN3TldVgmCYBgAAC+eMKJ9Nq62tbdHq6+tjxtZVji1btkhBQUHTuUgk4h1XVFTEvOZ3v/udjBw50humycjIkIEDB8rixYslGo36/p5IRgAAsG0HVvHftOzsbOnatWtTW7JkSczYNTU1XhKhk4rm9HFlZWXMa3bt2uUNz+jr9DyRhx56SJYuXSo/+tGPfH9PDNMAANDGV9Ps3btX0tPTm87rSaamuK7rzRdZsWKFJCUlSV5enuzbt08ee+wxWbBgga8YJCMAANhEBXjozIl+OhFpnoycSY8ePbyEoqqqqsV5fZyZmRnzGr2CRs8V0ded1L9/f6+Sood9kpOTz3lfhmkAALCJCjBfJODSXp046MrGhg0bWlQ+9LGeFxLLtddeK++//77X76S///3vXpLiJxHRSEYAALBwaa/y2YLSy3pXrlwpzzzzjGzfvl3uueceqaura1pdM2XKFLn//vub+us/16tpZs+e7SUheuWNnsCqJ7T6xTANAAAWUXHegXXixIlSXV0t8+fP94ZahgwZIuXl5U2TWvfs2eOtsJFmk2PXr18vc+bMkcGDB3v7jOjEZN68eb7vSTICAIBFlOt4zW/f1pg1a5bXYtm4ceNp5/QQzl//+ldpLZIRAADa+ATWREcyAgCARVQbfFAeyQgAALZR0qb4Tkbcfx4wfnPlmv9/81jf3sZj3lhUZDzmxpUrjcccmztK4sIxn1k7Bw8bj3nFIvOv81h2T2mvovurzQdtzdT+c8jtZ35R4A3/McN4zD/95wp73vONDeZj1pj/HXJocnfjMVVWmiQ6RWUEAACESjFnBAAAhMo50fz2TXwkIwAA2ERRGQEAAGFSJCMAACBMKsAzZ5jACgAATFMBnjkThwVscUEyAgCATRTDNAAAIEyKYRoAABAiRx1vfvvagGQEAACbKIZpAABAmFznePPb1wIkIwAA2ERRGQEAAGFSJCMAACBMitU0AAAgRA6raQAAQKhU2xumiYT9AgAAQPtGZQQAAIs4AYZf7JgxQjICAIBdFBNYAQBAmFTbmzPiOxlx+l1p/OYNmRcZj3nVzPeNx3RSU43HHJs7ynjMsndekXgYl/9l80E7JBkP6cThWdlJ//hY2qsOfS43HvNI357GY0Zu/l/jMdPc/2nX7/mxQ0ebDxqNmo8Zh/e8s/tTSXiqHScjAAAgfA5LewEAQKgUlREAABAmRTICAABC5DBMAwAAQuU6x5vfvhYgGQEAwCIOlREAABAqxZwRAAAQJhWg4kEyAgAAjFNURgAAQJhU20tGImG/AAAAEHwCq9/WGsXFxZKTkyOpqamSn58vmzdv9nXdmjVrxHEcmTBhQqD7kYwAAIAma9eulblz58qCBQtk69atkpubK4WFhbJ//345m48++kjuvfdeue666yQokhEAAGwcplE+W0DLli2ToqIimTZtmgwYMEBKSkokLS1NSktLz3hNNBqVO++8U37wgx/IFVdcEfieJCMAALTxYZra2toWrb6+PmbshoYG2bJlixQUFDSdi0Qi3nFFRcUZX9MPf/hD6dWrl0yfPr1V3xPJCAAAtlHBqiLZ2dnStWvXprZkyZKYYWtqarwqR0ZGRovz+riysjLmNW+88YasWrVKVq5c2epvh9U0AAC08dU0e/fulfT09KbTKSkpRl7K4cOHZfLkyV4i0qNHj1bHIRkBAKCNbwefnp7eIhk5E51QJCUlSVVVVYvz+jgzM/O0/h988IE3cXX8+PFN51zX9b526NBBdu7cKZ///OfPeV+GaQAAsImK3wTW5ORkycvLkw0bNrRILvTxyJEjT+vfr18/effdd+Xtt99uarfccovcdNNN3n/r4SE/qIwAAGARJ84PytPLeqdOnSrDhw+XESNGyPLly6Wurs5bXaNNmTJFsrKyvHkneh+SgQMHtri+W7du3tdTz58NyQgAADZxTzS/fQOaOHGiVFdXy/z5871Jq0OGDJHy8vKmSa179uzxVtiYRDICAIBFnDhXRrRZs2Z5LZaNGzee9dqnn346fsnIoYHHyy4mda/YZzymSkoyH7Ox0XhMcRzjIcflf1niYd1/v2Q8ZuGEycZjJv3jY+MxpXdPaa8qR/U2HjPj+R3GY0paJ/Mxo634OHkujQ3GQ44dOlrioWzbH4zHHDP+TuMxIx+Yf887XbpIwlNt79k0VEYAALCJIhkBAABtfJjmQiMZAQDAJorKCAAACJFDZQQAAIRKURkBAABhUiQjAAAgRM6J5revDUhGAACwiaIyAgAAQuQwgRUAAIRKURkBAABhU9KmkIwAAGARh2EaAAAQJsc93vz2tQHJCAAANlHMGQEAACFyGKYBAAChUlRGAABAmBTJCAAACJHDMA0AAAiVojICAABC5CjlNb9921Qy0n1zpfm7NzQaD+l06mQ8pura2XhM5+Bh4zGlQ5L5mCJSOGGy8ZjrX/yV8Zhjr7/VeEzn8BFprzJf2m08pkruaMW/I3JJN/Mxaw6YjxmNmo8pImPG32k8ZvnvnzMec9yIccZjSmMcfp5MU1RGAABAiBzmjAAAgFApKiMAACBEDpURAAAQKkVlBAAAhMihMgIAAEKlqIwAAIAwKSWO6zPLaGv7jAAAgPA5DNMAAIBQKYZpAABAiBz3ePPb1wYkIwAA2ERRGQEAACFymDMCAABCpZT/VTKWrKaJhP0CAABA8MqI39YaxcXFkpOTI6mpqZKfny+bN28+Y9+VK1fKddddJ927d/daQUHBWfvHQjICAICNc0aUzxbQ2rVrZe7cubJgwQLZunWr5ObmSmFhoezfvz9m/40bN8qkSZPktddek4qKCsnOzpbRo0fLvn37fN+TZAQAAIs4ca6MLFu2TIqKimTatGkyYMAAKSkpkbS0NCktLY3Z/7nnnpOZM2fKkCFDpF+/fvLUU0+J67qyYcMG3/ckGQEAwMY5I8pnE5Ha2toWrb6+PmbohoYG2bJlizfUclIkEvGOddXDjyNHjkhjY6NcfPHFvr8lkhEAANp4ZSQ7O1u6du3a1JYsWRIzdk1NjUSjUcnIyGhxXh9XVlb6en3z5s2TSy+9tEVCcy6spgEAoI3vM7J3715JT09vOp2SkhKXl/bjH/9Y1qxZ480j0ZNf/SIZAQCgje8zkp6e3iIZOZMePXpIUlKSVFVVtTivjzMzM8967U9+8hMvGXn11Vdl8ODBEoTvZEQdqhXTnLQ04zE/u+Yy4zH/edsR4zGvWOQYj+nEaT150j8+Nh5z7PW3Go9Z9vp/GY85blihtFfqyL+Nx3RSko3HrL6ln/GYV87YYTzmocndjceM1x4SkQ/Mv+fHjRhnPOa6zeuMxxw7dLQkPFcdb377BpCcnCx5eXne5NMJEyYcD3FiMuqsWbPOeN2jjz4qDz/8sKxfv16GDx8uQVEZAQDAtsqI679vUHpZ79SpU72kYsSIEbJ8+XKpq6vzVtdoU6ZMkaysrKZ5J4888ojMnz9fVq9e7e1NcnJuSefOnb3mB8kIAAA2UfHdgXXixIlSXV3tJRg6sdBLdsvLy5smte7Zs8dbYXPSz3/+c28Vzu23394ijt6nZOHChb7uSTICAIBFnAvwbBo9JHOmYRk9ObW5jz76SM4XyQgAADZRPLUXAACEyFHK94KFeC1sMI1kBAAAm7gnmt++FiAZAQDAIg6VEQAAECrFnBEAANCGl/aGgWQEAACLOBdgae+FRjICAIBNFJURAAAQIscNsB08q2kAAIBxisoIAAAIk2I1DQAACJHDPiMAACBUrhKJKv99LUAyAgCARRwqIwAAIPw5I8p/XwuQjAAAYBPFahoAABAmV4+/BOjblpIRp2NH4zdXhw8bj9n9zUrzMV8/ajzmseyexmMm/eNjiYve5l+rc/iI8ZjjhhUaj7lu63ppt5T5f8XcOvN/7z3/8KHxmAfLzH9OU1lpxmM6uz81HtOL26WL+aCNjcZDjh062njMsm1/kETnMGcEAACESjFMAwAAwqRIRgAAQJgUyQgAAAiT244nsAIAgPA5TGAFAAChUgzTAACAMLlKlzz897UAyQgAADZRVEYAAECoVIAkg2QEAACYFnX975Ds2rGchmQEAACbqADJSBwe6xAPJCMAANhEMWcEAACEydUJBqtpAABAWBSVEQAAECYVIMmwIxchGQEAwCqq7VVGImG/AAAAEIBerhuktUJxcbHk5ORIamqq5Ofny+bNm8/a/9e//rX069fP6z9o0CApKysLdD+SEQAAbKyMKJ8toLVr18rcuXNlwYIFsnXrVsnNzZXCwkLZv39/zP6bNm2SSZMmyfTp02Xbtm0yYcIEr7333nu+70kyAgCATVR8k5Fly5ZJUVGRTJs2TQYMGCAlJSWSlpYmpaWlMfs//vjjMmbMGPnud78r/fv3l0WLFsmwYcPkiSee8H1PkhEAAGziqmBNRGpra1u0+vr6mKEbGhpky5YtUlBQ0HQuEol4xxUVFTGv0eeb99d0JeVM/c9rAuvLn/jPcADYr/zAU2G/BAAxKOV6zY+T/bKzs1uc10MwCxcuPK1/TU2NRKNRycjIaHFeH+/YsSPmPSorK2P21+f9YjUNAAA2Uf9f8fDVV0T27t0r6enpTadTUlIkkZCMAABgExVgB9YTyYhORJonI2fSo0cPSUpKkqqqqhbn9XFmZmbMa/T5IP1jYc4IAAA2ceO3tDc5OVny8vJkw4YNzW7nescjR46MeY0+37y/9sorr5yxfyxURgAAsIiKRkU5UX99lb9+zellvVOnTpXhw4fLiBEjZPny5VJXV+etrtGmTJkiWVlZsmTJEu949uzZcsMNN8jSpUtl3LhxsmbNGnnrrbdkxYoVvu9JMgIAQBsfpgli4sSJUl1dLfPnz/cmoQ4ZMkTKy8ubJqnu2bPHW2Fz0jXXXCOrV6+WBx98UB544AG56qqr5MUXX5SBAwf6vqejlCV7xQIA0I7V1tZK165d5YspX5MOTrKva46pBvlj/fNy6NAhX3NGwkJlBAAAmyhdQ/A5F8SSegPJCAAAFlGuEuX4SzJsGfwgGQEAwCbKDVAZad2D8i40khEAACyiqIwAAIAwHVP1visex6RRbEAyAgCABZKTk71dTd+oLAt0nb5GX5vIWNoLAIAljh496j1ZNwidiKSmpkoiIxkBAACh4tk0AAAgVCQjAAAgVCQjAAAgVCQjAAAgVCQjAAAgVCQjAAAgVCQjAABAwvR/5TYRZhHJxNEAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# find common range of values\n",
+    "minmin = np.min([np.min(kernel_mat_32), np.min(kernel_mat_inf)])\n",
+    "maxmax = np.max([np.max(kernel_mat_32), np.max(kernel_mat_inf)])\n",
+    "\n",
+    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)\n",
+    "cmap = plt.get_cmap('viridis')\n",
+    "\n",
+    "ax1.imshow(kernel_mat_32, vmin=minmin, vmax=maxmax, cmap=cmap)\n",
+    "ax1.set_title('k_32')\n",
+    "ax1.set_axis_off()\n",
+    "\n",
+    "ax2.imshow(kernel_mat_inf, vmin=minmin, vmax=maxmax, cmap=cmap)\n",
+    "ax2.set_title('k_inf')\n",
+    "ax2.set_axis_off()\n",
+    "\n",
+    "# add space for color bar\n",
+    "fig.subplots_adjust(right=0.85)\n",
+    "cbar_ax = fig.add_axes([0.88, 0.25, 0.02, 0.5])\n",
+    "\n",
+    "# add colorbar\n",
+    "sm = plt.cm.ScalarMappable(cmap=cmap,\n",
+    "                           norm=plt.Normalize(vmin=minmin, vmax=maxmax))\n",
+    "fig.colorbar(sm, cax=cbar_ax)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e4a8bb5",
+   "metadata": {},
+   "source": [
+    "### Visualize Kernels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "835a1002",
+   "metadata": {},
+   "source": [
+    "It is hard to visualize functions on $\\mathrm{V}(m, n)$.\n",
+    "Functions are only easy to visualize on domains of dimension not higher than $2$.\n",
+    "However, we have $\\dim \\left(\\mathrm{V}(m, n)\\right) = n m - \\frac{1}{2}m(m+1)$ which is greater than $2$ in all interesting cases.\n",
+    "\n",
+    "In [SpecialOrthogonal.ipynb](./SpecialOrthogonal.ipynb), we visualize functions on the even more high-dimensional $\\mathrm{SO}(n)$. To do this, we consider *random* embeddings $\\mathbf{E}_{\\mathbf{U}}: \\mathbb{T} \\hookrightarrow \\mathrm{SO}(n)$ of the circle $\\mathbb{T}$ into $\\mathrm{SO}(n)$, visualizing $\\tilde{f} \\circ \\mathbf{E}_{\\mathbf{U}}: \\mathbb{T} \\to \\mathbb{R}$ instead of the actual $\\tilde{f}: \\mathrm{SO}(n) \\to \\mathbb{R}$. Here, we use the canonical projection $\\operatorname{P}: \\mathrm{SO}(n) \\to \\mathrm{V}(m, n)$ to visualize $f \\circ \\operatorname{P} \\circ \\mathbf{E}_{\\mathbf{U}}: \\mathbb{T} \\to \\mathbb{R}$ instead of the actual $f: \\mathrm{V}(m, n) \\to \\mathbb{R}$. The canonical projection $\\operatorname{P}$ is something as simple as the operation of extracting the first $m$ columns of an $n \\times n$ matrix.\n",
+    "\n",
+    "To sum up, we will plot the functions $k_{\\nu, \\kappa}(\\operatorname{P} \\mathbf{I}, \\operatorname{P} \\mathbf{E}_{\\mathbf{U}}(\\alpha))$ where\n",
+    "- $\\mathbf{I}$ is the $n \\times n$ identity matrix,\n",
+    "- $\\alpha \\in \\mathbb{T}$ and thus can be represented as an angle in $[0, 2 \\pi)$,\n",
+    "- $\\operatorname{P}$ is the canonical projection of $\\mathrm{SO}(n)$ onto $\\mathrm{V}(m, n)$, as explained above,\n",
+    "- $\\mathbf{E}_{\\mathbf{U}}(\\alpha)$ is the random embedding of $\\mathbb{T}$ into $\\mathrm{SO}(n)$ defined in [SpecialOrthogonal.ipynb](./SpecialOrthogonal.ipynb).\n",
+    "\n",
+    "In practice, we visualize $k_{\\nu, \\kappa}($ `base_point` $, x)$ for $x \\in $ `other_points1` or $x \\in $ `other_points2`.\n",
+    "The `base_point` is simply $\\operatorname{P} \\mathbf{I}$.\n",
+    "The `other_points1` and `other_points2` are defined by the `_NUM_ANGLES` uniform discretization of the unit circle, but with different $\\mathbf{U}$, i.e. corresponding to different random embeddings of $\\mathbb{T}$ into $\\mathrm{SO}(n)$.\n",
+    "\n",
+    "We define `base_point`, `other_points`, and `other_points2` in the next cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "77c8e92e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define discretization\n",
+    "_NUM_ANGLES = 128\n",
+    "\n",
+    "key, U = stiefel.G.random(key, 2)  # stiefel.G returns an object representing SO(n)\n",
+    "U1 = U[0, :]\n",
+    "U2 = U[1, :]\n",
+    "\n",
+    "# generate a grid on [0, 2 \\pi)\n",
+    "angles = np.linspace(0, 2*np.pi, num=_NUM_ANGLES)\n",
+    "embedding = np.broadcast_to(np.eye(n), (_NUM_ANGLES, n, n)).copy()\n",
+    "embedding[:, 0, 0] = np.cos(angles)\n",
+    "embedding[:, 0, 1] = -np.sin(angles)\n",
+    "embedding[:, 1, 0] = np.sin(angles)\n",
+    "embedding[:, 1, 1] = np.cos(angles)\n",
+    "\n",
+    "base_point = stiefel.project_to_manifold(embedding[[0], :, :])\n",
+    "\n",
+    "def conj_batch(U, emb):\n",
+    "    \"\"\" Compute U^T * emb[i, :] * U for all i.\n",
+    "    :param U:   [n, n] array\n",
+    "    :param emb: [N, n, n] array\n",
+    "    \n",
+    "    :return:    [N, n, n] array\n",
+    "    \"\"\"\n",
+    "    return np.tensordot(np.tensordot(emb, U, axes=([2], [0])),\n",
+    "                        U.T, axes=([1], [1])\n",
+    "                       )\n",
+    "\n",
+    "other_points1 = stiefel.project_to_manifold(conj_batch(U1, embedding))\n",
+    "other_points2 = stiefel.project_to_manifold(conj_batch(U2, embedding))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07275c76",
+   "metadata": {},
+   "source": [
+    "The next cell evaluates $k_{\\nu, \\kappa}($ `base_point` $, x)$ for $x \\in $ `other_points1` and $x \\in $ `other_points2`, for $\\nu$ either $3/2$ or $\\infty$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "528594cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "kernel_vals_32_1  = kernel.K(params_32,  base_point, other_points1)\n",
+    "kernel_vals_32_2  = kernel.K(params_32,  base_point, other_points2)\n",
+    "kernel_vals_inf_1 = kernel.K(params_inf, base_point, other_points1)\n",
+    "kernel_vals_inf_2 = kernel.K(params_inf, base_point, other_points2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb65e8d7",
+   "metadata": {},
+   "source": [
+    "Finally, we are ready to plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "aa9bbaf3-06c0-432e-8527-1b8aebf322a7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1280x960 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_circle = np.cos(angles)\n",
+    "y_circle = np.sin(angles)\n",
+    "z_circle = np.zeros_like(angles) # z=0 for the circle\n",
+    "\n",
+    "# Red ball position\n",
+    "red_ball_position = (x_circle[0], y_circle[0], z_circle[0])\n",
+    "\n",
+    "\n",
+    "def plot_kernel(ax, values, title):\n",
+    "    # Draw the unit circle\n",
+    "    ax.plot(x_circle, y_circle, z_circle, linestyle='dashed', color='gray')\n",
+    "    # Draw the red ball\n",
+    "    ax.scatter(*red_ball_position, color=\"r\", s=50)\n",
+    "    # Draw the vertical dashed line\n",
+    "    ax.plot([1, 1], [0, 0], np.linspace(0, values[0, 0], 2), linestyle='dashed', color='gray')\n",
+    "    # Plot the new function evaluated only on the unit circle\n",
+    "    ax.plot(x_circle, y_circle, values[0, :], color='b')\n",
+    "    # Set the viewing angle for better visualization\n",
+    "    ax.view_init(elev=20., azim=180+30)\n",
+    "    ax.set_title(title)\n",
+    "\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(figsize=(12.8, 9.6), nrows=2, ncols=2,\n",
+    "                               subplot_kw=dict(projection='3d',\n",
+    "                                               computed_zorder=False))\n",
+    "\n",
+    "plot_kernel(ax1, kernel_vals_32_1,  r'$k_{3/2, \\kappa}($base_point, other_points1$)$')\n",
+    "plot_kernel(ax2, kernel_vals_32_2,  r'$k_{3/2, \\kappa}($base_point, other_points2$)$')\n",
+    "plot_kernel(ax3, kernel_vals_inf_1, r'$k_{\\infty, \\kappa}($base_point, other_points1$)$')\n",
+    "plot_kernel(ax4, kernel_vals_inf_2, r'$k_{\\infty, \\kappa}($base_point, other_points2$)$')\n",
+    "\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08cac3cc-63c6-4057-a8ca-3c645dad2d9e",
+   "metadata": {},
+   "source": [
+    "## Feature Maps and Sampling\n",
+    "\n",
+    "Here we show how to get an approximate finite-dimensional feature map for heat and Matérn kernels on the sphere, i.e. such $\\phi$ that\n",
+    "$$\n",
+    "k(x, x') \\approx \\langle \\phi(x), \\phi(x') \\rangle_{\\mathbb{R}^M}.\n",
+    "$$\n",
+    "This might be useful for speeding up computations.\n",
+    "We showcase this below by showing how to efficiently sample the Gaussian process $\\mathrm{GP}(0, k)$.\n",
+    "\n",
+    "For a brief theoretical introduction into feature maps, see this [documentation page](https://gpflow.github.io/GeometricKernels/theory/feature_maps.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba0cb4cd-c7d3-40fd-862d-1a90ba3d5eef",
+   "metadata": {},
+   "source": [
+    "### Defining a Feature Map"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3131b86-24ee-4398-a88a-b02cf49c697f",
+   "metadata": {},
+   "source": [
+    "The simplest way to get an approximate finite-dimensional feature map is to use the `default_feature_map` function from `geometric_kernels.kernels`.\n",
+    "It has an optional keyword argument `num` which determines the number of features, the $M$ above.\n",
+    "Below we rely on the default value of `num`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "8b3ba4f0-5a1b-4ce8-8986-be6563bd8a40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from geometric_kernels.kernels import default_feature_map\n",
+    "\n",
+    "feature_map = default_feature_map(kernel=kernel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fed4a182-6dc0-4826-8cca-ba49c8fdaee3",
+   "metadata": {},
+   "source": [
+    "The resulting `feature_map` is a function that takes the array of inputs and parameters of the kernel.\n",
+    "There is also an optional parameter `normalize` that determines if $\\langle \\phi(x), \\phi(x) \\rangle_{\\mathbb{R}^M} \\approx 1$ or not.\n",
+    "For the (hyper)sphere, `normalize` follows the standard behavior of `MaternKarhunenLoeveKernel`, being `True` by default.\n",
+    "\n",
+    "`feature_map` outputs a tuple.\n",
+    "Its **second** element is $\\phi(x)$ evaluated at all inputs $x$.\n",
+    "Its first element is either `None` for determinstic feature maps, or contains the updated `key` for randomized feature maps which take `key` as a keyword argument.\n",
+    "For `default_feature_map` on a `Stiefel` space, the first element is `key` since the feature map is *random*.\n",
+    "\n",
+    "In the next cell, we evaluate the feature map at random points, using `params_32` as kernel parameters.\n",
+    "We check the basic property of the feature map: $k(x, x') \\approx \\langle \\phi(x), \\phi(x') \\rangle_{\\mathbb{R}^M}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "6602d412-e9b6-48db-802c-27b876a15be2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "xs (shape = (10, 5, 5)):\n",
+      "[[[ 0.72712246 -0.35522286 -0.45902898 -0.36401301 -0.04354972]\n",
+      "  [ 0.15369536 -0.60160446  0.72835659  0.01025294 -0.28955372]\n",
+      "  [-0.07826171 -0.46443473 -0.3892846   0.79111328 -0.02779874]\n",
+      "  [-0.25758044 -0.43129742  0.03210755 -0.23365075  0.83187215]\n",
+      "  [-0.61253415 -0.33192124 -0.32590746 -0.43236156 -0.47061451]]\n",
+      "\n",
+      " [[-0.10225584  0.2761366  -0.72442032  0.09420875 -0.61614303]\n",
+      "  [-0.60109604  0.63137303  0.45181359 -0.09631215 -0.16321797]\n",
+      "  [ 0.57520109  0.45243736  0.23983898  0.63313288 -0.07787272]\n",
+      "  [ 0.03524879 -0.47871273  0.45515345  0.0455766  -0.74856514]\n",
+      "  [-0.54417693 -0.3020782  -0.07995175  0.76086412  0.1652687 ]]\n",
+      "\n",
+      " [[-0.13241813 -0.22679987  0.36240148  0.86967986  0.20820513]\n",
+      "  [ 0.06409328  0.2905258   0.54664238 -0.31392398  0.71702208]\n",
+      "  [ 0.07686151  0.11496135 -0.75131765  0.20856948  0.61065273]\n",
+      "  [-0.84404597  0.52596069 -0.05353949  0.04900671 -0.07538978]\n",
+      "  [ 0.50993744  0.75782968  0.05002582  0.31496952 -0.25288257]]\n",
+      "\n",
+      " [[-0.48343455  0.56801427  0.04473665 -0.04518697 -0.6630291 ]\n",
+      "  [-0.62016252  0.26952964 -0.05267809 -0.23670966  0.69566211]\n",
+      "  [ 0.46715056  0.47943705 -0.63595672 -0.38032474  0.05312816]\n",
+      "  [ 0.34778993  0.31496432  0.76177367 -0.43596149  0.09735552]\n",
+      "  [ 0.20616012  0.52502491  0.10238932  0.77924151  0.25327049]]\n",
+      "\n",
+      " [[-0.61318075 -0.59838396 -0.35121666 -0.34325077 -0.15739051]\n",
+      "  [-0.32746656  0.32988316  0.12006069  0.26979497 -0.83470884]\n",
+      "  [ 0.32617576 -0.23088094 -0.69208106  0.58710175 -0.12899111]\n",
+      "  [-0.10907094  0.67020524 -0.61843742 -0.38408534  0.09456254]\n",
+      "  [ 0.631259   -0.17502188 -0.02812937 -0.56318697 -0.50290018]]\n",
+      "\n",
+      " [[ 0.29393807 -0.18948301 -0.81017057 -0.19761934 -0.42692721]\n",
+      "  [-0.59370961  0.21526018  0.06023828 -0.71806086 -0.28623749]\n",
+      "  [ 0.30543856 -0.73952215  0.35975409 -0.47405524  0.07525219]\n",
+      "  [-0.64317227 -0.601059   -0.09240132  0.42200187 -0.19604572]\n",
+      "  [ 0.23269297  0.09795059  0.4494819   0.20620679 -0.83168771]]\n",
+      "\n",
+      " [[ 0.60386314  0.78216273  0.05372438  0.01062204  0.14342816]\n",
+      "  [ 0.76759908 -0.62035173  0.00878333  0.07483268  0.14240194]\n",
+      "  [ 0.08867244  0.01605878 -0.86531395 -0.48259925 -0.10103981]\n",
+      "  [ 0.11009923  0.05207788 -0.27211792  0.65520571 -0.69413498]\n",
+      "  [-0.16172913  0.02037688 -0.41739686  0.57627311  0.68346005]]\n",
+      "\n",
+      " [[-0.08171233 -0.60854     0.71679157 -0.33047019 -0.00120752]\n",
+      "  [ 0.01922858 -0.34095282 -0.55489168 -0.57868203 -0.49051379]\n",
+      "  [-0.9049422  -0.2147327  -0.13711107  0.32218518 -0.11120587]\n",
+      "  [-0.13049455 -0.18139563 -0.31708557 -0.32461752  0.86263956]\n",
+      "  [ 0.39623749 -0.65910222 -0.24282166  0.58884356  0.05367509]]\n",
+      "\n",
+      " [[ 0.86232699 -0.39546833  0.15135287 -0.24031809 -0.13905569]\n",
+      "  [ 0.27800042  0.64584356  0.56709614  0.04506281  0.42658314]\n",
+      "  [-0.15676135  0.06981099 -0.14286833 -0.93219672  0.28486879]\n",
+      "  [-0.38985034 -0.37346769  0.79691771 -0.15292764 -0.22377197]\n",
+      "  [-0.05050267 -0.53116778 -0.00225292  0.21872124  0.81698606]]\n",
+      "\n",
+      " [[-0.107604    0.28468845  0.81961982  0.37623823 -0.30666271]\n",
+      "  [ 0.57761106 -0.59861167  0.4556822  -0.3051992   0.08506892]\n",
+      "  [ 0.60816521  0.50027533 -0.02243579  0.28801732  0.54442844]\n",
+      "  [ 0.48992182  0.33859346 -0.21024292 -0.25490029 -0.73222594]\n",
+      "  [ 0.21189273 -0.44237498 -0.27544808  0.78572903 -0.25722382]]]\n",
+      "\n",
+      "emedding (shape = (10, 4000)):\n",
+      "[[ 0.01028176  0.00917644  0.02104552 ...  0.0003693  -0.00521047\n",
+      "   0.00536677]\n",
+      " [ 0.01034894  0.00047145 -0.02558393 ...  0.00436327  0.00634502\n",
+      "  -0.00760916]\n",
+      " [ 0.01057405  0.01594761 -0.00979053 ...  0.00429889  0.00092115\n",
+      "   0.00363178]\n",
+      " ...\n",
+      " [ 0.01050996  0.00363228 -0.02387148 ...  0.00120297 -0.00097541\n",
+      "  -0.0037979 ]\n",
+      " [ 0.01063274  0.02191415  0.00734897 ... -0.00279781  0.00204956\n",
+      "   0.00491088]\n",
+      " [ 0.01048877 -0.00779099 -0.02075261 ... -0.00808202 -0.00874717\n",
+      "  -0.00616656]]\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "\n",
+      "||k(xs, xs) - phi(xs) * phi(xs)^T|| = 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# xs are random points from above\n",
+    "key, embedding = feature_map(xs, params_32, key=key)\n",
+    "\n",
+    "print('xs (shape = %s):\\n%s' % (xs.shape, xs))\n",
+    "print('')\n",
+    "print('emedding (shape = %s):\\n%s' % (embedding.shape, embedding))\n",
+    "\n",
+    "kernel_mat_32  = kernel.K(params_32,  xs, xs)\n",
+    "kernel_mat_32_alt = np.matmul(embedding, embedding.T)\n",
+    "\n",
+    "print('')\n",
+    "print('||k(xs, xs) - phi(xs) * phi(xs)^T|| =', np.linalg.norm(kernel_mat_32 - kernel_mat_32_alt))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e10e6dd4-31a9-4a1f-9447-ddeff9259845",
+   "metadata": {},
+   "source": [
+    "**Unfortunately:** $k(x, x') \\approx \\langle \\phi(x), \\phi(x') \\rangle_{\\mathbb{R}^M}$ does not hold, which is either caused by a bug or by the insufficiency of the default approximation order in this case. **TODO:** investigate this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43728ac5-8277-4436-86c7-e6e97e434ec3",
+   "metadata": {},
+   "source": [
+    "### Efficient Sampling using Feature Maps\n",
+    "\n",
+    "GeometricKernels provides a simple tool to efficiently sample (without incurring cubic costs) the Gaussian process $f \\sim \\mathrm{GP}(0, k)$, based on an approximate finite-dimensional feature map $\\phi$.\n",
+    "The underlying machinery is briefly discussed in this [documentation page](https://gpflow.github.io/GeometricKernels/theory/feature_maps.html).\n",
+    "\n",
+    "The function `sampler` from `geometric_kernels.sampling` takes in a feature map and, optionally, the keyword argument `s` that specifies the number of samples to generate.\n",
+    "It returns a function we name `sample_paths`.\n",
+    "Since we are going to compute each of the two samples at two different sets of inputs, `other_points1` and `other_points2`, we make sure the randomness if fixed by using the `make_deterministic` function.\n",
+    "\n",
+    "`sample_paths` operates much like `feature_map` above: it takes in the points where to evaluate the samples and kernel parameters.\n",
+    "Additionally, it takes in the keyword argument `key` that specifies randomness in the JAX style.\n",
+    "**However**, in our specific case, this keyword argument is not needed as it is automatically supplied by the `make_deterministic` wrapper.\n",
+    "`sample_paths` returns a tuple.\n",
+    "Its first element is the updated `key`.\n",
+    "Its second element is an array containing the value of samples evaluated at the input points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "78922dda-ae2d-4bc1-b28a-b4dc9424fd07",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "Two samples evaluated at the xs are:\n",
+      "[[-0.09110478 -0.14260073]\n",
+      " [ 0.19397666 -0.20541932]\n",
+      " [-0.09254487 -0.05788714]\n",
+      " [-0.23799849  0.37468858]\n",
+      " [-0.17516378 -0.04631914]\n",
+      " [ 0.08050212 -0.08000005]\n",
+      " [-0.17535321 -0.13313271]\n",
+      " [-0.24568212  0.02379087]\n",
+      " [ 0.25867488  0.16466143]\n",
+      " [-0.21835382  0.26878079]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from geometric_kernels.sampling import sampler\n",
+    "from geometric_kernels.utils.utils import make_deterministic\n",
+    "\n",
+    "# introduce random state for reproducibility (optional)\n",
+    "# `key` is jax's terminology\n",
+    "key = np.random.RandomState(seed=1234)\n",
+    "\n",
+    "sample_paths = make_deterministic(sampler(feature_map, s=2), key)\n",
+    "\n",
+    "# new random state is returned along with the samples\n",
+    "key, samples = sample_paths(xs, params_32)\n",
+    "\n",
+    "print('Two samples evaluated at the xs are:')\n",
+    "print(samples)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dadb7a46-427a-475c-bc6e-0891c8d87c7c",
+   "metadata": {},
+   "source": [
+    "#### Visualizing Samples\n",
+    "Here we visualize samples as functions on the sphere."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "baf674e9-910e-494b-8abb-0069c7df31c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1280x960 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "key, samples_other_points1 = sample_paths(other_points1, params_32)\n",
+    "key, samples_other_points2 = sample_paths(other_points2, params_32)\n",
+    "\n",
+    "sample1_other_points1 = samples_other_points1[:, 0]\n",
+    "sample2_other_points1 = samples_other_points1[:, 1]\n",
+    "sample1_other_points2 = samples_other_points2[:, 0]\n",
+    "sample2_other_points2 = samples_other_points2[:, 1]\n",
+    "\n",
+    "x_circle = np.cos(angles)\n",
+    "y_circle = np.sin(angles)\n",
+    "z_circle = np.zeros_like(angles) # z=0 for the circle\n",
+    "\n",
+    "def plot_sample(ax, values, title):\n",
+    "    # Draw the unit circle\n",
+    "    ax.plot(x_circle, y_circle, z_circle, linestyle='dashed', color='gray')\n",
+    "    # Plot the new function evaluated only on the unit circle\n",
+    "    ax.plot(x_circle, y_circle, values, color='b')\n",
+    "    # Set the viewing angle for better visualization\n",
+    "    ax.view_init(elev=20., azim=180+30)\n",
+    "    ax.set_title(title)\n",
+    "\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(figsize=(12.8, 9.6), nrows=2, ncols=2,\n",
+    "                               subplot_kw=dict(projection='3d',\n",
+    "                                               computed_zorder=False))\n",
+    "\n",
+    "plot_sample(ax1, samples_other_points1[:, 0], r'Sample #1: $f(\\cdot)$ for $\\cdot$ in other_points1, $f \\sim \\mathrm{GP}(0, k_{3/2, \\kappa})$')\n",
+    "plot_sample(ax2, samples_other_points2[:, 0], r'Sample #1: $f(\\cdot)$ for $\\cdot$ in other_points2, $f \\sim \\mathrm{GP}(0, k_{3/2, \\kappa})$')\n",
+    "plot_sample(ax3, samples_other_points1[:, 1], r'Sample #2: $f(\\cdot)$ for $\\cdot$ in other_points1, $f \\sim \\mathrm{GP}(0, k_{3/2, \\kappa})$')\n",
+    "plot_sample(ax4, samples_other_points2[:, 1], r'Sample #2: $f(\\cdot)$ for $\\cdot$ in other_points2, $f \\sim \\mathrm{GP}(0, k_{3/2, \\kappa})$')\n",
+    "\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6686d052-4c7c-4a20-ab6d-46e353e13b74",
+   "metadata": {},
+   "source": [
+    "## Comparing Kernels on $\\mathrm{V}(1, n)$ to the Kernels on $\\mathbb{S}_{n-1}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e47c9e6-d5dc-4648-baaa-9edf9965bd31",
+   "metadata": {},
+   "source": [
+    "Here we show that—up to approximation error—the kernels on $\\mathrm{V}(1, n)$ coincide with the kernels on $\\mathbb{S}_{n-1}$, as they are supposed to since $\\mathrm{V}(1, n) = \\mathbb{S}_{n-1}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "5cd1a39d-e0e7-4f3f-9483-bb42eaa3f084",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from geometric_kernels.spaces import Hypersphere\n",
+    "\n",
+    "n = 3\n",
+    "\n",
+    "key = np.random.RandomState(1234)\n",
+    "\n",
+    "key, stiefel_1n = Stiefel(m=1, n=n, key=key, average_order=1000)\n",
+    "hypershpere = Hypersphere(n-1)\n",
+    "\n",
+    "kernel_stiefel_1n = MaternGeometricKernel(space=stiefel_1n, key=key)\n",
+    "kernel_hypershpere = MaternGeometricKernel(hypershpere, num=20)\n",
+    "\n",
+    "params_32  = {\"lengthscale\": np.array([0.125]), \"nu\": np.array([3/2])}\n",
+    "params_inf = {\"lengthscale\": np.array([0.125]), \"nu\": np.array([np.inf])}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "b9c5988a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  0.,   2.,   6.,  12.,  20.,  30.,  42.,  56.,  72.,  90., 110.,\n",
+       "       132., 156., 182., 210., 240., 272., 306., 342., 380.])"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kernel_stiefel_1n.eigenfunctions._eigenvalues"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "5a2b9145-207b-4364-9dda-ea68538e950e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "key = np.random.RandomState(1234)\n",
+    "\n",
+    "key, xs_stiefel = stiefel_1n.random(key, 10)\n",
+    "xs_hypersphere = xs_stiefel[..., 0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c499b34-4321-47f9-95e5-b5cb87789970",
+   "metadata": {},
+   "source": [
+    "**Note:** the elements of `hypershpere` and the elements of `stiefel_1n` have different shapes: the former are $n$-dim vectors while the latter are $n \\times 1$-dim matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "77539ddb-aa50-4a51-bdc9-73e3261bdec5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "kernel_stiefel_1n_mat_32   = kernel_stiefel_1n.K(params_32,  xs_stiefel, xs_stiefel)\n",
+    "kernel_stiefel_1n_mat_inf  = kernel_stiefel_1n.K(params_inf, xs_stiefel, xs_stiefel)\n",
+    "\n",
+    "kernel_hypershpere_mat_32  = kernel_hypershpere.K(params_32,  xs_hypersphere, xs_hypersphere)\n",
+    "kernel_hypershpere_mat_inf = kernel_hypershpere.K(params_inf, xs_hypersphere, xs_hypersphere)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "66485ad4-a59a-49bc-be9e-77c72cb01572",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||kernel_stiefel_1n_mat_32(xs, xs) - kernel_hypershpere_mat_32(xs, xs)|| = 0.4814532661876347\n",
+      "||kernel_stiefel_1n_mat_inf(xs, xs) - kernel_hypershpere_mat_inf(xs, xs)|| = 0.48220441022452487\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('||kernel_stiefel_1n_mat_32(xs, xs) - kernel_hypershpere_mat_32(xs, xs)|| =', \n",
+    "      np.linalg.norm(kernel_stiefel_1n_mat_32 - kernel_hypershpere_mat_32)/np.linalg.norm(kernel_stiefel_1n_mat_32))\n",
+    "print('||kernel_stiefel_1n_mat_inf(xs, xs) - kernel_hypershpere_mat_inf(xs, xs)|| =', \n",
+    "      np.linalg.norm(kernel_stiefel_1n_mat_inf - kernel_hypershpere_mat_inf)/np.linalg.norm(kernel_stiefel_1n_mat_inf))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "860fdd31-9a45-463d-8be9-56e6c47b5268",
+   "metadata": {},
+   "source": [
+    "**Unfortunately:** $k^{\\mathrm{V}(1, n)}(x, x') \\approx k^{\\mathbb{S}_{n-1}}(x, x')$ does not hold, which is either caused by a bug or by the insufficiency of the default approximation order in this case. **TODO:** investigate this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "542e43c7",
+   "metadata": {},
+   "source": [
+    "## Citation\n",
+    "\n",
+    "If you are using Lie groups and GeometricKernels, please consider citing\n",
+    "\n",
+    "```\n",
+    "@article{azangulov2022,\n",
+    "    title={Stationary kernels and Gaussian processes on Lie groups and their homogeneous spaces I: the compact case},\n",
+    "    author={Azangulov, Iskander and Smolensky, Andrei and Terenin, Alexander and Borovitskiy, Viacheslav},\n",
+    "    journal={arXiv preprint arXiv:2208.14960},\n",
+    "    year={2022}\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "558cae08",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/Stiefel.ipynb b/notebooks/Stiefel.ipynb
new file mode 100644
index 00000000..5bd23e8e
--- /dev/null
+++ b/notebooks/Stiefel.ipynb
@@ -0,0 +1,1246 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "66ebbb52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To run this in Google Colab, uncomment the following line\n",
+    "# !pip install geometric_kernels\n",
+    "\n",
+    "# If you want to use a version of the library from a specific branch on GitHub,\n",
+    "# say, from the \"devel\" branch, uncomment the line below instead\n",
+    "# !pip install \"git+https://github.com/GPflow/GeometricKernels@devel\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acf0a49e",
+   "metadata": {},
+   "source": [
+    "# Matérn and Heat Kernels on the Stiefel manifold $\\mathrm{V}(m, n)$\n",
+    "\n",
+    "This notebook shows how define and evaluate kernels on the Stiefel manifold $\\mathrm{V}(m, n)$ that consists of all orthonormal $m$-frames in $\\mathbb{R}^n$, i.e. $m$-tupes of orthonormal vectors in $\\mathbb{R}^n$.\n",
+    "\n",
+    "Stiefel manifold is a homogeneous space space $\\mathrm{V}(m, n) = \\mathrm{SO}(n) / \\mathrm{SO}(n-m)$ of the special orthogonal group $\\mathrm{SO}(n)$. **Note**: mathematically, $\\mathrm{V}(n, n) = \\mathrm{SO}(n)$, the special orthogonal group, although our implementation requires $m < n$, and $\\mathrm{V}(1, n) = \\mathbb{S}_{n-1}$, the $n-1$-dimensional hypersphere.\n",
+    "\n",
+    "We work with $\\mathrm{V}(2, 5)$ here. There is no particular reason for this. Handling the Stiefel manifold $\\mathrm{V}(m, n)$ for other $n \\geq 2$ and $m < n$ is the same.\n",
+    "\n",
+    "\n",
+    "**Note:** the \"points\" in the the Stiefel manifold $\\mathrm{V}(m, n)$ are represented by real matrices (`array`s of the suitable backend) of size $n \\times m$, whose columns are orthonormal vectors.\n",
+    "\n",
+    "We use the **numpy** backend here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65b2692e",
+   "metadata": {},
+   "source": [
+    "<!--TABLE OF CONTENTS-->\n",
+    "## Contents\n",
+    "- [**Basics**](#Basics)\n",
+    "  - [Defining a Space](#Defining-a-Space)\n",
+    "  - [Defining a Kernel](#Defining-a-Kernel)\n",
+    "  - [Evaluating Kernels on Random Inputs](#Evaluating-Kernels-on-Random-Inputs)\n",
+    "  - [Visualize Kernels](#Visualize-Kernels)\n",
+    "- [**Feature Maps and Sampling**](#Feature-Maps-and-Sampling)\n",
+    "  - [Defining a Feature Map](#Defining-a-Feature-Map)\n",
+    "  - [Efficient Sampling using Feature Maps](#Efficient-Sampling-using-Feature-Maps)\n",
+    "- [**Comparing Kernels on $\\mathrm{V}(1, n)$ to the Kernels on $\\mathbb{S}_{n-1}$**](#Comparing-Kernels-on-$\\mathrm{V}(1,-n)$-to-the-Kernels-on-$\\mathbb{S}_{n-1}$)\n",
+    "- [**Citation**](#Citation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7446f9f-1023-4fa5-ac88-475e0e482694",
+   "metadata": {},
+   "source": [
+    "## Basics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "24ac4559",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add the parent directory to the path so that the library can be imported\n",
+    "import sys\n",
+    "sys.path.append(\"..\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c2c899b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO (geometric_kernels): Numpy backend is enabled. To enable other backends, don't forget to `import geometric_kernels.*backend name*`.\n",
+      "INFO (geometric_kernels): We may be suppressing some logging of external libraries. To override the logging policy, call `logging.basicConfig`.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import a backend, we use numpy in this example.\n",
+    "import numpy as np\n",
+    "#import torch\n",
+    "\n",
+    "# Import the geometric_kernels backend.\n",
+    "import geometric_kernels\n",
+    "\n",
+    "# Note: if you are using a backend other than numpy,\n",
+    "# you _must_ uncomment one of the following lines\n",
+    "# import geometric_kernels.tensorflow\n",
+    "#import geometric_kernels.torch\n",
+    "# import geometric_kernels.jax\n",
+    "\n",
+    "# Import a space and an appropriate kernel.\n",
+    "from geometric_kernels.spaces import Stiefel\n",
+    "from geometric_kernels.kernels import MaternGeometricKernel\n",
+    "\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35b6053f",
+   "metadata": {},
+   "source": [
+    "### Defining a Space"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae549717",
+   "metadata": {},
+   "source": [
+    "First we create a GeometricKernels `space` that corresponds to the Stiefel manifold $\\mathrm{V}(2, 5)$, a subset of the set of all $5 \\times 2$ matrices $\\mathbb{R}^{5 \\times 2}$. Our implementation of `Stiefel` computes the basis functions (that are needed to compute all the kernels) approximately, using Monte Carlo integration. Because of this, you need to provide a source of randomness when constructing the space, i.e. the random generator of the suitable backend, via the `key` keyword argument.\n",
+    "\n",
+    "**Note**: the aforementioned basis functions are *zonal spherical functions*, these are (sums of outer products of certain groups of Laplace-Beltrami eigenfunctions). Perhaps a close enough examination of \"A theory of Stiefel harmonics\" by S. S. Gelbart or similar literature could yield exact algorithms for computing them - an interesting problem for future research."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bc2f6122",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m, n = 2, 5\n",
+    "\n",
+    "# key = torch.Generator()\n",
+    "# key.manual_seed(1234)\n",
+    "key = np.random.RandomState(1234)\n",
+    "\n",
+    "key, stiefel = Stiefel(m=m, n=n, key=key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3513e04b",
+   "metadata": {},
+   "source": [
+    "### Defining a Kernel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfc8d35f",
+   "metadata": {},
+   "source": [
+    "First, we create a generic Matérn kernel.\n",
+    "\n",
+    "To initialize `MaternGeometricKernel` you just need to provide a `Space` object, in our case this is the `stiefel` we have just created above.\n",
+    "\n",
+    "There is also an optional second parameter `num` which determines the order of approximation of the kernel.\n",
+    "There is a sensible default value for each of the spaces in the library, so change it only if you know what you are doing.\n",
+    "\n",
+    "A brief account on theory behind the kernels on compact manifold like (which $\\mathrm{V}(m, n)$ are examples of) can be found on these documentation pages: [one](https://gpflow.github.io/GeometricKernels/theory/compact.html), [two](https://gpflow.github.io/GeometricKernels/theory/addition_theorem.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "487c94c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "kernel = MaternGeometricKernel(stiefel, key=key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a45e786a",
+   "metadata": {},
+   "source": [
+    "To support JAX, our classes do not keep variables you might want to differentiate over in their state.\n",
+    "Instead, some methods take a `params` dictionary as input, returning its modified version.\n",
+    "\n",
+    "The next line initializes the dictionary of kernel parameters `params` with some default values.\n",
+    "\n",
+    "**Note:** our kernels do not provide the outputscale/variance parameter frequently used in Gaussian processes.\n",
+    "However, it is usually trivial to add it by multiplying the kernel by an (optimizable) constant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d2fb7518",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "params: {'nu': array([inf]), 'lengthscale': array([1.])}\n"
+     ]
+    }
+   ],
+   "source": [
+    "params = kernel.init_params()\n",
+    "print('params:', params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74f4124e",
+   "metadata": {},
+   "source": [
+    "To define two different kernels, Matern-3/2 and Matern-∞ (aka heat, RBF, squared exponential, diffusion), we need two different versions of `params`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "344c5f6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params[\"lengthscale\"] = np.array([0.5])\n",
+    "params_32  = params.copy()\n",
+    "params_inf = params.copy()\n",
+    "del params\n",
+    "params_32[\"nu\"]  = np.array([3/2])\n",
+    "params_inf[\"nu\"] = np.array([np.inf])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50c5df26",
+   "metadata": {},
+   "source": [
+    "Now two kernels are *defined* and we proceed to evaluating both on a set of random inputs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73c74525",
+   "metadata": {},
+   "source": [
+    "### Evaluating Kernels on Random Inputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d42ae02a",
+   "metadata": {},
+   "source": [
+    "We start by sampling `10` (uniformly) random points in $\\mathrm{V}(2, 5)$.\n",
+    "An explicit `key` parameter is needed to support JAX as one of the backends."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4455c3d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[-1.68504525e-01 -7.06026107e-01  4.12761964e-01 -4.02416798e-01\n",
+      "    3.75262099e-01]\n",
+      "  [-3.44559305e-01  3.82891026e-01  8.10993210e-01  2.75718333e-01\n",
+      "   -3.07044147e-02]\n",
+      "  [-7.14965754e-01  2.82130523e-01 -3.78687008e-01 -1.15451814e-01\n",
+      "    5.02487182e-01]\n",
+      "  [-1.78802389e-01  2.44485166e-01  5.70809634e-02 -7.89898247e-01\n",
+      "   -5.30150198e-01]\n",
+      "  [-5.56541811e-01 -4.64274671e-01 -1.58919752e-01  3.53230462e-01\n",
+      "   -5.69809614e-01]]\n",
+      "\n",
+      " [[ 1.44766060e-01  4.33768157e-01  7.16008021e-01 -3.81579879e-01\n",
+      "    3.64166561e-01]\n",
+      "  [ 4.76064461e-01 -7.39636562e-01  4.15299651e-01  2.11051929e-01\n",
+      "    9.63517929e-02]\n",
+      "  [-7.21600150e-01 -1.65417307e-01  5.30096035e-01  1.75731170e-01\n",
+      "   -3.74228657e-01]\n",
+      "  [ 3.16982053e-01  4.85213609e-01  1.66490400e-01  7.63442875e-01\n",
+      "   -2.31356987e-01]\n",
+      "  [ 3.62244418e-01  4.45848561e-02  7.83444645e-02 -4.42862747e-01\n",
+      "   -8.15184583e-01]]\n",
+      "\n",
+      " [[ 5.55616377e-01 -3.52578323e-01 -1.42262987e-01  2.76576949e-01\n",
+      "   -6.85744414e-01]\n",
+      "  [-8.30863564e-03 -4.93089358e-01  7.44523361e-01 -4.41716810e-01\n",
+      "   -8.58200216e-02]\n",
+      "  [-6.64642273e-01 -5.99484224e-01 -4.22213645e-01 -2.05709699e-03\n",
+      "   -1.43529511e-01]\n",
+      "  [-2.29826194e-01  4.73932665e-01 -9.24177998e-02 -5.57053234e-01\n",
+      "   -6.35388381e-01]\n",
+      "  [-4.43454595e-01  2.20358584e-01  4.88508747e-01  6.46590221e-01\n",
+      "   -3.13161950e-01]]\n",
+      "\n",
+      " [[-2.65366641e-02  4.74741540e-01 -7.43228552e-01  1.79946969e-01\n",
+      "   -4.34909971e-01]\n",
+      "  [ 8.32526574e-01  2.84360416e-02 -3.03469674e-01 -1.40195096e-01\n",
+      "    4.40842815e-01]\n",
+      "  [ 2.13244776e-02 -8.64126215e-01 -4.03723877e-01 -8.16158450e-02\n",
+      "   -2.88404292e-01]\n",
+      "  [-5.23511589e-01  4.02248417e-02 -4.00240354e-01 -5.14943196e-01\n",
+      "    5.46771198e-01]\n",
+      "  [ 1.77978066e-01  1.59623644e-01  1.79811475e-01 -8.22275369e-01\n",
+      "   -4.84123075e-01]]\n",
+      "\n",
+      " [[ 2.65759578e-01  7.48397979e-01  2.03724859e-01 -3.22015095e-01\n",
+      "    4.73365368e-01]\n",
+      "  [ 8.78333338e-01 -2.07538815e-01  8.92401005e-02  4.13977448e-01\n",
+      "    7.82116640e-02]\n",
+      "  [-1.01610144e-01 -4.66456000e-01  7.93919835e-01 -2.43645306e-01\n",
+      "    2.87093085e-01]\n",
+      "  [ 2.07327168e-02  3.79658473e-01  5.41463029e-01  1.67698730e-01\n",
+      "   -7.30838233e-01]\n",
+      "  [ 3.83598654e-01 -1.87366883e-01 -1.64442909e-01 -7.98400308e-01\n",
+      "   -3.91485890e-01]]\n",
+      "\n",
+      " [[-6.09749906e-02  2.96035016e-03  9.21922596e-01  3.54972979e-01\n",
+      "    1.42569973e-01]\n",
+      "  [-1.92159119e-01 -6.61883174e-01 -3.55375188e-02 -2.12978038e-01\n",
+      "    6.91637895e-01]\n",
+      "  [-6.32788482e-01 -4.39497973e-01  9.44472837e-02 -1.00330997e-01\n",
+      "   -6.22441700e-01]\n",
+      "  [-4.80854480e-01  5.58384660e-01  1.69752786e-01 -6.16365431e-01\n",
+      "    2.19688845e-01]\n",
+      "  [-5.72463649e-01  2.38642000e-01 -3.33255928e-01  6.62315549e-01\n",
+      "    2.56152233e-01]]\n",
+      "\n",
+      " [[ 7.05575503e-01  6.01217644e-01 -2.19399205e-01  1.84661433e-02\n",
+      "   -3.03683297e-01]\n",
+      "  [ 3.83647954e-01 -5.09819582e-01 -2.50766976e-01  7.20113829e-01\n",
+      "    1.07005788e-01]\n",
+      "  [-1.13914722e-04  3.97744308e-01 -1.48469416e-01  9.61722376e-02\n",
+      "    9.00281726e-01]\n",
+      "  [-5.94636975e-01  3.99784256e-01 -3.68593815e-01  5.14941606e-01\n",
+      "   -2.92494713e-01]\n",
+      "  [ 3.72063412e-02  2.46157170e-01  8.55029441e-01  4.54621950e-01\n",
+      "   -1.63056331e-02]]\n",
+      "\n",
+      " [[ 4.55443668e-01 -2.19174609e-01  5.51040775e-01  6.56555233e-01\n",
+      "    9.91102784e-02]\n",
+      "  [-7.79154577e-01 -2.57203278e-01  5.39661678e-01  2.97916410e-02\n",
+      "   -1.86124555e-01]\n",
+      "  [ 1.36454868e-01 -6.20318189e-01  1.28027385e-01 -4.96399446e-01\n",
+      "    5.77738688e-01]\n",
+      "  [ 4.08312142e-01 -6.02118901e-02  3.57010521e-01 -5.01651655e-01\n",
+      "   -6.71226361e-01]\n",
+      "  [-1.22668002e-02 -7.05257993e-01 -5.11147547e-01  2.64539016e-01\n",
+      "   -4.13772864e-01]]\n",
+      "\n",
+      " [[-2.19847909e-02  1.18523304e-01  9.05751018e-02 -4.53272181e-02\n",
+      "    9.87527463e-01]\n",
+      "  [-7.06395415e-01 -4.19322036e-01 -1.79687352e-01  5.35872945e-01\n",
+      "    7.56782069e-02]\n",
+      "  [ 3.32420277e-01 -8.68219970e-01 -1.61870273e-01 -3.11301214e-01\n",
+      "    1.12162438e-01]\n",
+      "  [-4.01393194e-01 -1.76564812e-01  8.08150000e-01 -3.85065000e-01\n",
+      "   -7.95417821e-02]\n",
+      "  [ 4.78437509e-01 -1.58556641e-01  5.29339399e-01  6.82350445e-01\n",
+      "    1.24503707e-02]]\n",
+      "\n",
+      " [[ 5.13857850e-01  4.60721426e-01  4.11443408e-01 -2.34197945e-01\n",
+      "    5.47313002e-01]\n",
+      "  [ 2.24390944e-01 -3.33277125e-01  7.78780176e-01  1.07398313e-01\n",
+      "   -4.69619104e-01]\n",
+      "  [-3.15917675e-01 -6.17004012e-01  2.26048673e-01  7.89272359e-02\n",
+      "    6.79834216e-01]\n",
+      "  [-4.47810444e-01  4.55389198e-02  1.89402953e-01 -8.62979901e-01\n",
+      "   -1.29553948e-01]\n",
+      "  [ 6.20693679e-01 -5.42120265e-01 -3.70464949e-01 -4.27379349e-01\n",
+      "   -3.07829372e-02]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "key, xs = stiefel.random(key, 10)\n",
+    "\n",
+    "print(xs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39c0465d",
+   "metadata": {},
+   "source": [
+    "Now we evaluate the two kernel matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2a43f39e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(10, 5, 5) (200, 5, 5)\n",
+      "(2000, 5, 5) (100, 5, 5)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Iskander\\Documents\\Projects\\GeometricKernels\\.venv\\lib\\site-packages\\lab\\numpy\\generic.py:86: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  return a.astype(dtype, copy=False)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(10, 5, 5) (200, 5, 5)\n",
+      "(2000, 5, 5) (100, 5, 5)\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(10, 5, 5) (200, 5, 5)\n",
+      "(2000, 5, 5) (100, 5, 5)\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(10, 5, 5) (200, 5, 5)\n",
+      "(2000, 5, 5) (100, 5, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "kernel_mat_32  = kernel.K(params_32,  xs, xs)\n",
+    "kernel_mat_inf = kernel.K(params_inf, xs, xs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c35e2625",
+   "metadata": {},
+   "source": [
+    "Finally, we visualize these matrices using `imshow`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c48c0d1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# find common range of values\n",
+    "minmin = np.min([np.min(kernel_mat_32), np.min(kernel_mat_inf)])\n",
+    "maxmax = np.max([np.max(kernel_mat_32), np.max(kernel_mat_inf)])\n",
+    "\n",
+    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)\n",
+    "cmap = plt.get_cmap('viridis')\n",
+    "\n",
+    "ax1.imshow(kernel_mat_32, vmin=minmin, vmax=maxmax, cmap=cmap)\n",
+    "ax1.set_title('k_32')\n",
+    "ax1.set_axis_off()\n",
+    "\n",
+    "ax2.imshow(kernel_mat_inf, vmin=minmin, vmax=maxmax, cmap=cmap)\n",
+    "ax2.set_title('k_inf')\n",
+    "ax2.set_axis_off()\n",
+    "\n",
+    "# add space for color bar\n",
+    "fig.subplots_adjust(right=0.85)\n",
+    "cbar_ax = fig.add_axes([0.88, 0.25, 0.02, 0.5])\n",
+    "\n",
+    "# add colorbar\n",
+    "sm = plt.cm.ScalarMappable(cmap=cmap,\n",
+    "                           norm=plt.Normalize(vmin=minmin, vmax=maxmax))\n",
+    "fig.colorbar(sm, cax=cbar_ax)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e4a8bb5",
+   "metadata": {},
+   "source": [
+    "### Visualize Kernels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "835a1002",
+   "metadata": {},
+   "source": [
+    "It is hard to visualize functions on $\\mathrm{V}(m, n)$.\n",
+    "Functions are only easy to visualize on domains of dimension not higher than $2$.\n",
+    "However, we have $\\dim \\left(\\mathrm{V}(m, n)\\right) = n m - \\frac{1}{2}m(m+1)$ which is greater than $2$ in all interesting cases.\n",
+    "\n",
+    "In [SpecialOrthogonal.ipynb](./SpecialOrthogonal.ipynb), we visualize functions on the even more high-dimensional $\\mathrm{SO}(n)$. To do this, we consider *random* embeddings $\\mathbf{E}_{\\mathbf{U}}: \\mathbb{T} \\hookrightarrow \\mathrm{SO}(n)$ of the circle $\\mathbb{T}$ into $\\mathrm{SO}(n)$, visualizing $\\tilde{f} \\circ \\mathbf{E}_{\\mathbf{U}}: \\mathbb{T} \\to \\mathbb{R}$ instead of the actual $\\tilde{f}: \\mathrm{SO}(n) \\to \\mathbb{R}$. Here, we use the canonical projection $\\operatorname{P}: \\mathrm{SO}(n) \\to \\mathrm{V}(m, n)$ to visualize $f \\circ \\operatorname{P} \\circ \\mathbf{E}_{\\mathbf{U}}: \\mathbb{T} \\to \\mathbb{R}$ instead of the actual $f: \\mathrm{V}(m, n) \\to \\mathbb{R}$. The canonical projection $\\operatorname{P}$ is something as simple as the operation of extracting the first $m$ columns of an $n \\times n$ matrix.\n",
+    "\n",
+    "To sum up, we will plot the functions $k_{\\nu, \\kappa}(\\operatorname{P} \\mathbf{I}, \\operatorname{P} \\mathbf{E}_{\\mathbf{U}}(\\alpha))$ where\n",
+    "- $\\mathbf{I}$ is the $n \\times n$ identity matrix,\n",
+    "- $\\alpha \\in \\mathbb{T}$ and thus can be represented as an angle in $[0, 2 \\pi)$,\n",
+    "- $\\operatorname{P}$ is the canonical projection of $\\mathrm{SO}(n)$ onto $\\mathrm{V}(m, n)$, as explained above,\n",
+    "- $\\mathbf{E}_{\\mathbf{U}}(\\alpha)$ is the random embedding of $\\mathbb{T}$ into $\\mathrm{SO}(n)$ defined in [SpecialOrthogonal.ipynb](./SpecialOrthogonal.ipynb).\n",
+    "\n",
+    "In practice, we visualize $k_{\\nu, \\kappa}($ `base_point` $, x)$ for $x \\in $ `other_points1` or $x \\in $ `other_points2`.\n",
+    "The `base_point` is simply $\\operatorname{P} \\mathbf{I}$.\n",
+    "The `other_points1` and `other_points2` are defined by the `_NUM_ANGLES` uniform discretization of the unit circle, but with different $\\mathbf{U}$, i.e. corresponding to different random embeddings of $\\mathbb{T}$ into $\\mathrm{SO}(n)$.\n",
+    "\n",
+    "We define `base_point`, `other_points`, and `other_points2` in the next cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "77c8e92e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define discretization\n",
+    "_NUM_ANGLES = 128\n",
+    "\n",
+    "key, U = stiefel.G.random(key, 2)  # stiefel.G returns an object representing SO(n)\n",
+    "U1 = U[0, :]\n",
+    "U2 = U[1, :]\n",
+    "\n",
+    "# generate a grid on [0, 2 \\pi)\n",
+    "angles = np.linspace(0, 2*np.pi, num=_NUM_ANGLES)\n",
+    "embedding = np.broadcast_to(np.eye(n), (_NUM_ANGLES, n, n)).copy()\n",
+    "embedding[:, 0, 0] = np.cos(angles)\n",
+    "embedding[:, 0, 1] = -np.sin(angles)\n",
+    "embedding[:, 1, 0] = np.sin(angles)\n",
+    "embedding[:, 1, 1] = np.cos(angles)\n",
+    "\n",
+    "base_point = stiefel.project_to_manifold(embedding[[0], :, :])\n",
+    "\n",
+    "def conj_batch(U, emb):\n",
+    "    \"\"\" Compute U^T * emb[i, :] * U for all i.\n",
+    "    :param U:   [n, n] array\n",
+    "    :param emb: [N, n, n] array\n",
+    "    \n",
+    "    :return:    [N, n, n] array\n",
+    "    \"\"\"\n",
+    "    return np.tensordot(np.tensordot(emb, U, axes=([2], [0])),\n",
+    "                        U.T, axes=([1], [1])\n",
+    "                       )\n",
+    "\n",
+    "other_points1 = stiefel.project_to_manifold(conj_batch(U1, embedding))\n",
+    "other_points2 = stiefel.project_to_manifold(conj_batch(U2, embedding))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07275c76",
+   "metadata": {},
+   "source": [
+    "The next cell evaluates $k_{\\nu, \\kappa}($ `base_point` $, x)$ for $x \\in $ `other_points1` and $x \\in $ `other_points2`, for $\\nu$ either $3/2$ or $\\infty$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "528594cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(1, 5, 5) (200, 5, 5)\n",
+      "(200, 5, 5) (100, 5, 5)\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(128, 5, 5) (200, 5, 5)\n",
+      "(25600, 5, 5) (100, 5, 5)\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(1, 5, 5) (200, 5, 5)\n",
+      "(200, 5, 5) (100, 5, 5)\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(128, 5, 5) (200, 5, 5)\n",
+      "(25600, 5, 5) (100, 5, 5)\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(1, 5, 5) (200, 5, 5)\n",
+      "(200, 5, 5) (100, 5, 5)\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(128, 5, 5) (200, 5, 5)\n",
+      "(25600, 5, 5) (100, 5, 5)\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(1, 5, 5) (200, 5, 5)\n",
+      "(200, 5, 5) (100, 5, 5)\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(128, 5, 5) (200, 5, 5)\n",
+      "(25600, 5, 5) (100, 5, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "kernel_vals_32_1  = kernel.K(params_32,  base_point, other_points1)\n",
+    "kernel_vals_32_2  = kernel.K(params_32,  base_point, other_points2)\n",
+    "kernel_vals_inf_1 = kernel.K(params_inf, base_point, other_points1)\n",
+    "kernel_vals_inf_2 = kernel.K(params_inf, base_point, other_points2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb65e8d7",
+   "metadata": {},
+   "source": [
+    "Finally, we are ready to plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "aa9bbaf3-06c0-432e-8527-1b8aebf322a7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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5nHbaaWaLO0hWLpbIXUkp9FqAxbWeEPQeJe0+hQWEe9IFvEKhSB5U5ygdqnPEH6pzzBk6hxLLAMAyiOUoHxDyp59+unlPb6p8vW+wPFIsxGs9POGEE9r9/e9//1uSDprSFmuKe84551TtfBTl3yMa/ioUCoWiOlCdo3SozhF/qM4xZyBFPGytTyLpoPIUghoXtiIHqt7RsLkQqDrnrUSnqB70HikUCkXyoDrH7ND1LP7QezRnQImlQqFQKBQKhUKhUCgqghbvUSgUCoVCoVAoFApFRVBiqVAoFAqFQqFQKBSKiqDEUqFQKBQKhUKhUCgUFUGJpUKhUCgUCoVCoVAoKoISS4VCoVAoFAqFQqFQVAQllgqFQqFQKBQKhUKhqAhKLBUKhUKhUCgUCoVCURGUWCoUCoVCoVAoFAqFoiIosVQoFAqFQqFQKBQKRUVQYqlQKBQKhUKhUCgUioqgxFKhUCgUCoVCoVAoFBVBiaVCoVAoFAqFQqFQKCqCEkuFQqFQKBQKhUKhUFQEJZYKhUKhUCgUCoVCoagISiwVCoVCoVAoFAqFQlERlFgqFAqFQqFQKBQKhaIiKLFUKBQKhUKhUCgUCkVFUGKpUCgUCoVCoVAoFIqKoMRSoVAoFAqFQqFQKBQVQYmlQqFQKBQKhUKhUCgqghJLhUKhUCgUCoVCoVBUBCWWCoVCoVAoFAqFQqGoCEosFQqFQqFQKBQKhUJREZRYKhQKhUKhUCgUCoWiIiixVCgUCoVCoVAoFApFRVBiqVAoFAqFQqFQKBSKiqDEUqFQKBQKhUKhUCgUFUGJpUKhUCgUCoVCoVAoKoISS4VCoVAoFAqFQqFQVAQllgqFQqFQKBQKhUKhqAhKLBUKhUKhUCgUCoVCURGUWCoUCoVCoVAoFAqFoiIosVQoFAqFQqFQKBQKRUVQYqlQKBQKhUKhUCgUioqgxFKhUCgUCoVCoVAoFBVBiaVCoVAoFAqFQqFQKCqCEkuFQqFQKBQKhUKhUFQEJZYKhUKhUCgUCoVCoagISiwVCoVCoVAoFAqFQlERlFgqFAqFQqFQKBQKhaIiKLFUKBQKhUKhUCgUCkVFUGKpUCgUCoVCoVAoFIqKoMRSoVAoFAqFQqFQKBQVQYmlQqFQKBQKhUKhUCgqghJLhUKhUCgUCoVCoVBUhMbKfq5QhIdMJiMzZswwrx06dJBUKmW2dDpn/+C9QqFQKBQKRaVA15g5c6a0tLRIx44d23QOd1MoFKVBiaWi5mhtbTXCna25udkQSa9QtwTTT/Cr8FcoFAqFQlGKEduSymw222bABl79wk/3UCgU/lBiqagpobTCHUGP8LbCvaGhwQh7C97zffczFoQpU6bIoosuqsJfoVAoFApFICM2763OgF5hdQirY/BqNwv0lJ9//lm6du2qOodCkQdKLBVVhSWIEEq8k5ZQNjY2GqHM31aQez2W3v1MnTpVxowZIxtssEE74a/WRoVCoVAoFFbnsITSq3O4ugOw+oGfzoHeMmLECNl0001n0zmA1TVU51DMyVBiqagKEMJ4GBHMf/zxh7z99tuy/vrrtwn3UuHmXrohLIWsjSr8FQqFQqGYs4zYEMpXX31V1lxzTencuXPZOoerQ7jHsa/5DOOqcyjmJCixVEQKhCyeSYS7m8tgQ0/8BGslwraQtdG+qvBXKBQKhaK+jdjoHsA1QoehX1j9pVSdw/udfDqH3/4UiqRAiaUiUkI5ffp0YzUEbg5lsd+WcpwgqET4W8Gvwl+hUCgUimQYsanVEETniAJBdQ5gdST3t6pzKJIKJZaKUIGgtMLdCkuEu58wLIVA1lL425AaFf4KhUKhUMQHrM+Euro6h58R2y+fMo46h31fSOfwejn99qdQ1ApKLBWhl+9GGCLk8hHKsARglII0LOHvvo/6nBUKhUKhmJOM2DYqqpDOERb8QmHD3rf3vZ/O4fdb1TkUcYESS0Xo5buLFeRxhXOlqLYFMqjw/+KLL2TSpEmyyiqrtPu+17tpraoq/BUKhUKhKN2IHaQIYFw8llHpHN98841MmDBB1lhjjXbf1/7fimpDiaUi9PLdpeynEsRJMOYT/t6FwO3F6VoUVfgrFAqFQuEPt01ZUCO2F0kklmHqHPb/XuO26hyKMKHEUhFaD8qgmJMEVynhLSr8FQqFQqEI14gNwlgzw4y2qrbOEaQdm+ocijCgxFJRFPnKd5fbg9LdbxjnFkcUOy8V/gqFQqFQ5Nc5LKG0bcrioHMkGUGLFWr/b0UlUGKpqHr57mL5l6W2EIkryj0/Ff4KhUKhmNOQz4gdxhoW5hpYbwQ1iM6h/b8VQaHEUlF2+e4wjpPv8yiqriUdKvwVCoVCUW+oRg9KP6M1f//444/yww8/yLzzzmu2eeaZx2wc328fcxJK0Tm838mnc/jtT1FfUGKp8C3fPXr0aFl88cWlS5cuoQuBIHkKQYllvVkOqyX8J06caBbR+eabT3txKhQKhaKmRuxx48bJAgssIF27do187WE9/P777+XLL780xvNFF13UEFqqqv7+++/mfadOndpIpiWcHTp0kLiimrpQUJ0DWOcE492xY0dZcMEFVeeocyixVPiW77Z5DVE96GEm0itKF/6UJl9iiSXMglmoF6cKf4VCoVCErXO4UVFW57DvowD7Zf9ff/21fPXVV+bvHj16yCKLLGLOZ+655zbrHesj5wXBtBvft4QTjBgxwhhlLfHkt34ezmqj1mtzIZ0DYjn//PMb40Gx/t/e8OdaX5eiNCixnINRqHx3KbmO5aKei/fEGW4+phtq5FeptpDwd9+7+1UoFAqFwgtvhVdQDZ0DQsg2ZswY43Xs1auX8VJasmnPBfDZXHPNZTYitiw4tz/++ENee+0143WbNm2a8XhCONlH586d24XSWsIZdgpREmHvrdUZCukcfr/V/t/JghLLOQxBy3dHTSzzCQSE/6RJk8yxEcyFhHKchUrcCa+fNzpfpVoV/gqFQqGo1Ijt6hx4+PKtOWEBozkeSjaOi4eSze9YQSq5QzbBUkst1RYWy+9IH3I9nD/99JN55ZgQTpdszqmE0298S9E5vDpLvpoRWjui9lBiOYeg1PLd1fZYck7kN7AANDU1yUcffWT+jwB2cxzYENRJEBxxPsdSwpxLFf7u91T4KxQKxZyHUvteh6lzuPoEukO/fv3kww8/NKGYlaw7+c6bfEw2wmotvITzt99+M4WC8Hp6CafVb/isnglnlDqH/a7XuK06R/WhxLLOEWX57kpgj815kb9AMRmE/oorrtgmYL1WwO+++8688lu+g/WQ66OiG38j2FVwBEMYC7gKf4VCoVAUM2LjnSzWgzIMYmn1CWoIkMu3yiqrmFdQbcJWiHASRuvqNhBOXl1jursFIZxJjJIqFYU83Nr/Oz5QYlmnqLR8d9QeS/b9xRdfmJCRhRZaSPr372+S4TlXm8CPgGVbbLHF2n6HpW/q1KlGCBMyCz7++GNjBeTavALZElAVHLMjyiIJ+Y6hwl+hUCjq34htdY6gsrsSncPmO2J8XnjhhWXAgAFGnwhr/+4+QCX7YR8QRTbyPP0IJ95NXil4g24TNHorzmtk1C3k8tV60P7f1YcSyzmgB6U3l6GWxNIuABBEFqHVVlvNCMqgcMkjHk72td5665n9IYCtBXDy5MkmFAYSiqXUj3BS+npOFRy16hMaVPjjrX733Xdl7bXXbjOGqPBXKBSKeBuxK+l7XarOwZqPDkDUEl7B1Vdf3aztSUQhwmmN6W70FtcOLOG0tTP4PI7pQrXyqAbROdAf2d58801ZY4012vJnVecoD0os67h8dzmEMipi6S4ACE3OjcpspZBKv3MENl/UNjh2wVi4hPOXX34x5wHBJZfTSzb5fRi9quaEsJSohT/3zZZwd3txer+nwl+hUChq0/e6UiN2OToHHj3WcSKeiGhac801DZkKa/9xQqHoLTekFt0KAkrVWpsu5N1qnS4UZ53DVv21uoNf/+9iOoe7vzkZSizruHx3JQhLCCPwxo8fP9sC8Oqrr1ZFyLPQERLjDYth3LxV3AjNxVMGsfQTyqUSThUw5cMK86DWxnxVbrUXp0KhUMTXiF2KzjFlyhSjT5AGs/jii8vAgQPbKrUGQRxCYcMC65pLOInA+vbbb00UmOvh/PXXX00NC5su5JfDWQ3CGTdjthf2nuarQeLVOYD2//aHEss6Lt9dS2KJMGMBwEPYrVu32RaAMB+ycgQWY0UoLZsLQnncKm7kOHz22WdmrBHcfh5OiHzSkAQhH6TVTLGQ2kK9OOd04a9QKBTlGLG9fa+j1Dn4jNQW9An0iiWWWEKWX355NfQGSBdy4danQLeBpFPkiM9cwunmcIZZnyIJOgfId47FdI6g/b/THuIa5zEpF8nTiOdg2EmLQEC4Q3TCFu4W5e7PLgC8sgCQI8d5ltPXqBZgLKkiZyvJWRDy43o4sQzyyn1A+HrJpttwOY5IgpAv5/zCEv7ue7/9KRQKxZwA5CSEDgKCZ6taOgdy+ueffzb6BMdecsklTdV4UljKRRw8jVGh2LW5hLNr165tn/vVp6BNiyWcYRVEjPvYW52t1NzgfMWTsnNw/28llgks3005bYhO3759I5t87DcoOeL8CE1hAcASxgKwwgorFLQohnHe1XzwWMwWXHBBs7ngfriEEwsgr9wfvKIUoHEJJ1ZBmzcYNR58MC3PPZeWU05pkV69kkUsmXthnp8Kf4VCoSivByXrGoRj5ZVXjlTnsJEmtN9An+D43bt3N0bqSiODwtQ54kqSyrnGfPUpLOG0FWrR8dyCiOgyrneT7eOP55JTT22Sm29uliWWCOf84uKxjFLnyHr0sXw1I5JSO0KJZQLLd7NBaKKcYEH27bUoLrXUUrLSSisFtiiGJZxrKeQhz7RLYXPPhxYoWHkpfY5QRiDzyv10GyO7hDPMPlvYBPbaK3cfmpp+l6uuap+HEteFMWgobFgoVfi737OvfmG1SRD+CoVCEUTngEhUwxjJGvn2228beQuhJI8yTENs3Ne9OIE17ZVX5pdPP11ANt44I/37c//9CyJ+/vkEufPOJeS++/pIa2taDj74F7nuul/bVeCPuzE7bGIZps5hv+vqHm4rn7jpHEosE9iDMqzCOkHOI9/nFLuBUFLsphyLYhjXEKcHyYV96BGmPXv2bPuc6+Weun2qIOa8Yh10Cae1BAZpjOyHxx6b9ZuPP84kavxArRehoML/888/N4ssIVr2u17PZlyFv0KhUBTSOVy5FZXOwdpHagkbx1h22WVNqGbYhsWw6zrUO558Mi0774xBITduSy2Vlc03z8hGG6Vl6aXnlyWWmE+WXVZkzJiU/PWvjTJiRO5+9e37oeyxx6fy888Lt6vAz33GwI7OaPUbDPNxWRPDjpIKS+fw1o4gTJnaH/RqjavOocQygeW7mThRCza/hYS/mdAIC86zR48eoVsU6wne+8bf5CawdenSpe1z27fRLRpEKBDv3cbIXsKZT2Bw2y67bNY9efnlBeWpp5pliy0ysSFucRfyQYW/G0VQrBGz3awHQKFQKOJAKFl/rM7h14MyCmLJ8ahWSmqPrW0AAUGniAJhXUMc16WwMW5cSvbbL0cql18+I198kZIJE1Jy440NZrPo2BEPN/cyJQstlJV99x0m88//tCy33IamOi3AUIHx9f333zdrH8b0Qi3frIezXqOkyoHrqbTnGUTnAPa71dQ5VLtJYPnuangs3WNwfhBKPJR8BqGk0mslD2Gxayjl+pJuPWQsKIrARpNn97psnyrr4eQ+eBsju4STfbzwQlqGD09Lhw4t0rfvBzJq1Cpy5JGN8t57M8WmiMadWMZZyLuwFZmDFA6ygj8J16VQKOoXpfagDNOYDdHA64L3CiNrnz59jKGVdl+cT5SIY8HAsBDW/Zk8WYyn8rffUrLeehl58slmQx5feSUtTz2FbpGSb75JyXffpWTGjNzYbbddq1x5ZYsMGzZBPvig/f4gNFTfh0ROnry0pNMLyQYb5EKu3RxOjOm25ZslnN4czjB6jOdD3HWicnWOWhjplVgmsHx3KYV1yoU9Bkn7WJf4m7BO+iWFoRgnrXhPLcD14ZlkW3TRRWdrjGwFMq+EEdk+VWedtS7Zn7LOOmNl3XX/J1Om9JEvv+wkJ57YKLfe2mL28f33neSMM+aRuedukIsvbpW4OZ2TKOTzwS8nU6FQKGohr2ybslJ6UIahc0BkIZOQSoyiFPijNoHXGxMV5oRQ2EqvERvDvvs2yaefpk3o6z33NAtcjo0wWDaLmTNFJk7M/WaZZTh2+16QFtOmidx/f1ouv3w1+eijXHu3Cy5okZNOkqIt39h++OEHk3ISZo/xJEVJJVHnUGIZkx6UpZTvjloAc372ocaiuMwyyxhCGfZDF1fhHHe4jZFdMJdeemmmjBw5vzQ2ZmSvvSZKU9OScvDBr8s552ws//d/DdKjx9fyxRfzywMPDDJJ9mDllbOyzz7xao+SJCFfSfl7hUKhiHsPykp0DnQdCCVG6vnmm88U+PNWV6/0GEGhobD5wdCcdVaDPPtsWjp1ysr99zeLY8+eDXA5p4SEbwGcm2/G0N0okybxN3oJnsqUnHlmoznGkUdmym75VqzHOFspa3NSookyAYilF+qxnEPKd5dDKKMWwFiKEP7kPAAe7KjKi4fpsYwjQY3ynIiEJd9hwgQqvoqss07WCHnm05VX5poi77svZHFD85659sMP0+SqqzrLxRf3aNtP166/y3ffzSNnntkqK6wwVrp0aW8BrOUCWs9CXqFQKKqlc1hCWW2dg6gadAmiafBM9u/f3xDLMI9RCuYEj2U5IPr4vvvScvXVDTJyZG4tu+GGFll11dKv0SWWl17aIH/7W45idO+elU02+VSOPXYe+e9/F5GLLmqUE09skrnmapYDD8xU1PLNSzi/++4781oq4aynKKlaQ4llDct3lzuJwy7ew3nZnAfCLpdffnnTsyjqB62ehLMfwhy7n38WOfzwRnn99bT88kv7/S6wQFa23DIjq62WlWeeoXpwVk46KRfyaufL+eeLvPRSRsaMScugQRnZcsu3ZPfde8s663SSiRPnkgcf7Co77TTe5NG6SfXVzHGYU4R8Eq5LoVAk34gNobRGumrpHLQeI30GjxK5k6uvvrpZO+IgF8Oo65B0+d3cjGFaZPz4lNEnKMbzww+5a8KLeM45rbLLLuVFMK255pqy3HJ95ZZbeslVV+Xoxemnt8jf/tYqr78+Xrp06Sdnn91qwmOHDGmUo45qlClTWuWgg1qlgM2h7B7jbg4nBg5e+dwlnFbHQQ9Owr3NKLGcc2GrrTGJXUIZRvnfsCx7nJvNeeDhomUCDyf7nzJliiHEUUGth8Hx9dciW2/dJOPGzRIm88+fNTkQP/6Yku+/T8k99zTIPffk/rfrrhnp1Ys8yu/N3KOXJkV9XnyxWSZOTEnfvll57rlfZJ55GuTcczNy6KENcuut3eTUUxcWok8K5TiEEXJSb6GwcRfyCoVizjNiA7f1QNQ6B2sFhklakZE6A8nAUB3mMSpBEtaTqMB0OP30Bnn88QajT2Qy7cdiiSWycvjhrXLgga2y8MLlH6dr18XlssuWkmuvzVGLCy9skRNPbG1nLOY2XHRRq0ydmpIbbmiQM85olL//vUF22CEj++3XKoMG5fplVgqM4Gx+hNPVb6hMbAkn5zd8+PDZ9Js4VXHP/skl4oz4jNYcVr67ElSaSM/CQ4gKDxSJ06usssps8ewg7vkOc8JC8emnKdlyyyb56quUEf533NEsK62UbbPuMcXefjsljz6alkceaZDffhP5619z8+6ZZ54x93innXYyvcH4zXzzzaoWxvjttVdGhgzJyNixadOi5B//aA2c4+ANOfF6NysRyBoKq1AoFJX3vY5a5/j1118Nofzll19Mtfi11lrLGDLLOUbUxDKs/cfRmJ3vnPAPHHBAozzwQPs2IT16ZKV376zssUdGtt+eWgGVHR9d5IgjGuWOO3LHufLKZjn0UP8WZ7wMGULV+owhlxjNc8bxBllrrYycd16LqRwbBSCbhGazuUBX+vjjj01lfvQa0sJ45dmi1ohXv6HGRS0IZyYBdR2UWIZ4s20vQnvTg1Rbq6aA5PwsoSyW85AkIV+vGDkyJdts02TCVHr3zsgTT9A7tP13MFyRY7nOOq2muiuwU84qAn5z0Ap5fg+Z3HHHXI7FEUe0yhJLlB5y4rUAEn6STyCzFbO41XMorEKhUFQK2/8YWWt1jrCN2IXWa9JlIJQQS3pPrr322hX1H9TiPRL6uUH2DjooRyqbmrJy/fUtsuGGGenalbkS9rFFfvuNyvTzypAhv8qhh85V8Pscn+I9RxyRkWHDUoaQ3nNPWt5+Oy2bb95BNt44I5dfDvmsjp4ISeQ5op2eO1+8+g0RfpZwYkDxi+BqiNCjmIT0GyWWIZbvJgSEKlUDBw6M9EaWKoDJmWMBsDkPNK5F2Q/zGKWi3ov3hEEqN9mkSX79NSWrrJKRxx8vXKENeIfUW6HN739giy0ysu66GZNvscMOTSZ0ZccdM6YgUCUWQJ6LfAKZECk/gWwFZpJCYZNwngqFor56UEIqIXWjRo2S9ddfvyo6BxueSfQJ8teWXHJJk0ITlgcl7jpHkgCpPPTQRrn3Xmp60DqkRbbZJrrq7yzd2277mCy44AwZOHBl6s0HMhbz0VprZWWttVrkrLNELrmk0VSTpR/39ts3ydixM0MnwUEJG+eLsYSNlCI/wmlzOEkr47WlpaVNv8GraQ3rvA+DcCbBmK3EMuTy3dXwtARNpEfwk0RPjhx9ENdYY43ZWlQk3XoYV1RybYSz7rlnoyGV66yTkYcfbhZPq6eSziFf81w3LOXSS1sMkR01Ki3775+W00/PylFHtcrJJ9PrrLzryCeQvYSTOcorAtMKZIQzG0KbORtXQZoE66FCoajPHpQQumoYt9g/xyT/DEK71FJLmRSaMEMBk6JzJEF+k1N5zDGNcvfdDdLQkJU774yWVFqk0xnp3v1rSaVWme1/QXTjbt1yIbLHHQfZ7GAKDOHNHDgwel2xFN29EOGc4dFviBL0Ek5vSG0phDMJxmwlliGW72ZyVIMsFRPAlSbRBzlGpZgTiveUc41cCpXSPvssLUsumZUHHiiPVBbzWHo/HzAgayyDN9/cYKrEffttypQK5xzIwQgLHJPwWDa85+652lByW8GN6oJvvfWWec4Qvl6BzJyuNeFMSi6oQqFIJtw2Zd4elLYwYFTItar6Qb744gvzvmvXribsNYpQP9U5KseXX85lwl7vuitX6ZUK8bff3mIK41QDdlz81sRSxmzppXORVHhbH3kkLQMH5tJ8okQYTqFC+s2MGTPavJtsP//8s3nlmUaX8cvh9BtH9VjOYT0oKy2qExT5juMm0SP8Ccllgtez9TCpmD4912DYKx9uuy0t991nrYzN4okujcRjabHYYrnCP3gpTz21Ua6/vkHuvTcdKrHMB86FfAU2kueZ3whd+qgSyu2tUssr8BJOhDP7qJZFLwlCXqFQ1GcPShu9FHakFMejOBvRJOybojzoFngqo4LqHOXj2WdT8ve/LynDh89yIHTtmpUrrmgxaS3VQinG7GKADEMsH364wVSRjXpJj3ItTzmEE/0mn0GdDYeQN4LL3axxKc5QYllG+e58/aDC7i8ZVABPnjzZCH1ahISRRG+PESXyLSKlHDfu4QAWXOaIEbmQjuHD0/LuuykZNy4lyJfjjmuVQw5pFVJex45NyYkn5h7Jc89tlbXXztZEyEN4KeIDsXz++bTpoVlJCfJyMKs0ecoIVzbCud3/49G0whhLIF5Owr/5jVcYQzgR6mHPGSWWCoUibJ3DEspiPSjdOgNhyDaUVoqvEb6HV7Jnz54m6gkdCK9llKiGzhHWfuJCUPEvXHgh1dzRGzoYD+Xmm2fkgAMyxuNX7aKlxeo6lHIPNt0UYpWVL79MGf2pf//ojQ7V1ilTHoO6ey5eg/qPP/5o9BueUXQfCGjQCC4t3pPg8t3V8lhyHhzHJtGjVJNEv8IKK4TWwD5OwrMY4nyeM2aIHHZYo/z3v7OHDn3/PR7CRrn00gaTz/jgg2mZNi0lgwdn5KSTKg/9oDE1AsjbNiTIeC23XNYUDRo5Mi0PP5yWgw+untUzSIgp8xNvJRuKjwXPhUs4MbZQNpzP2J8f4cQIU67gVWKpUChq1YPSLXZWiRzi2MhJCpCgQ9CeCkXXHtvuO0rlu5aV6BnzSo3x1QZBO4cc0mg8emD33SfLHnt8Lptt1q9m5xQmsSR7a7PNqDGB1zIt/ftHGw4bp0r0qQIG9ddff908m+RXuxFc/C9fylC1ocTSga30hHAvpwdlNTyW7B9lmYVgzJgxJjRlpZVWCr2vTRLajcRFCPiBa5s8ucH0oKTaKlXZNtwwK6utlpHVV8/KyitnZOjQtCGVn3yS/tPimAtfufnm5lCqoNFOJt+5BRm/XXbJEcv772+oOrEsN0HdJY/e/WHts4STUvkoURBOvAFesskrClaxc1BiqVAoatWDstLK6Bybat3IQhTQvn37mmIkXrkXtmc0LqGwrAl4YlHOrbES+W/XgKgiXSrF+PGsz00yenTatBG58kraiHwvkybNrOl5Fbp/5dxb+mtCLMmzPO+8aMNhk7CWp/4cAFq+eQmn9XDaPE66QDC/+d+mm25adlpcOVBi6SnfbautldODMqp8B8A+cYXjoeQ82f8666wTaYPWOHsC446vvuogRx3VU776Ki3zzZeV//u/Ztl44/bjue++GdlrLwRnWv75zwb57LOU3HZbs8l1jBJBieXOO7fKWWc1yiuvpGTiRJHFF5eqIexniGfTKgwueN4t4UQgk1BPXhFCGmNNPsJpoVVhFQpFGEbscnUOUGqkFMcm3BUvJb2sMU4T2VIsbSLuxuZi+7fAoIguBaEk4mXAgAEmHBG5zzpAvQrGhrUBHcslmtYYUG388ovISy/l2nCgM0yalJJFF83Kf//bbHpZQzZrDXRSxoycXD+UOr8J5+3QISsff5yWDz9MyQorZOcIj2U5bVEKpQyFFckYFHM0sfQr350vlyEI7O/Yb1hV05gYWB5QdhFmNG9lAXj33XcjJZW18lhyH4gdR4BznUGuMW4E+M03U7Lvvr1l0qRGWWqprDz6aHNegcg02XnnjNlYq8J0PGOIYC7SX9L1aAcdr549RQYOzMhbb6XloYca5Oijo6/MVu1qqzynKFZsLvAesEBa6x9jiWUbow5C2k2k5ztYA8OOGlAoFPUF5LFLKMs1YvvpHEGA/IJQkkeJ12PVVVeV+QOUHa9Wz+io98/Yf/jhh0anglCutdZaJgSWzyGW6B2uYm4Nj8h4Nn6HHkbbFWS+NVa6vQrDXrcefTQX2URthmx21jzp3z8j993XLBHWUyoZyyyzTKjEjWWZ1KAnn8yFw66wQnQ6SFKIZbYE3ch64TXHssbluyuBm4dQKVgoEGJY1dgfhBIrEMfAqhZ1Lme1S39zPSx2XK/NIUXYe4U3m5ukHDdBgNVw222b5LffsK79IU8+2Shduwb7bdi85P777zeW1/3228/Xghhk7AiHhVjef3+6LollPiAPULi8SheE0y0YxHmOGzfONClHQfF6N9lc40jc5qtCoYge3gqvIAydI2gKDl4LjNPoFLRBWG211WaL3giTwMZN54BQsxaiO3lbsNlr8ju2n+HxpZdekn79+pn/WcMjeout5OnKf6uzlJvHT9GavfdulObm3G9XWCEjG2/MljWvrs4QNwN7WMSNcFiI5UMPpeW001ojK0iUhP6QSQnZnWOIZdDy3ZUgDOHLb6luySLA/mxVNnci1UMiPWD/tqw53iDuBUUDUOYR+vzPWgqt8OY9sLkQ7IMiRgj5IDlxUYLbfuihOVK5yip/yLXXfipdu/at2fmU2m7EDzvt1CqnnNIgb7+dFooC0l+qGuDeR+mRLxecE550Ns6R55TwH+afm9+AkYRXaxyxSgYKCs+0QqGYc4zYrs5RiYey1KKByCBkFBEXeOLWWGMNs3aWc4wkhsIy9ugN6FToBxhYySOt9DyJTsHjSz6qN8/N6izUwiB/FVJv0yq8+ZuF1rhp00T23z9HKrfaqlWuuqqlaDpKrYkRc425TncCd55Vcl+33joXDvvBB2nZeOMmueWWZunVS+rOmB0USUi/iZ/mVuPy3bXyWNoy3zyYCCFCCiCU1SgxXitiiQWRsBzGrVevXuZ63fwFFgIEt1d4I6it8AaffvqpKWTEuHm9mwjwKJo5++G66xrklVfSpkT2P/4xQTp1qm7BmyiIJd7WQYOyMnRoSu6+u0HOOqs6XsskCHmrzHGeVtFgc4HMcZsiE+atxFKhqF8E7XsdFvw8lqyt6BLkjHft2tWEfBLqWS6SRiwZe64fnQr9gQrpkLwoc83cPDe3UjlzwRod2TCk8+qG37pk00ZknXlmg4wblzYF/f7znxbp0kVijxdeeMHkre66666+YbHlzH96ed9+e4scfnijMXCvsUYHueSSFlNQMMzHKSmhsBn1WNaeUPIAU/Gse/fugcp3V9tjact8Q7AIl1huueVMqEqhc6yWkI8CNmeU3pvcj969e7fzyBa7JrfNBAsmnk6ssIyd12PEexZ2BLWXcCLQw7zGjz9OmYUAXHRRiyy11AzjwawlSiGQhbD33q2mgu3FFzfIuutmTHXbqJEEIe8Sy3zwGkfifk0KhaIynQOv1WeffWYqtmNwiopQ+nksbU9rXpdYYolQelrbY1Q7PaYcQNYglOhU1BZwQ37DPP9S9oNh2y+tAvLrEk68yvwNxo7tLtdeu4p5f9llP8s88zRJNlt+W6xqwY6Ld02sVBfZYQeq6c+UQw5pMrrIMcc0yciRrTJkSEtoobH1GgqbqsE1NdZ7+W6EPIKmUFJxmAjacsRb5nv55Zf3LfNdrSJBfscIcxGxVW0hgtwXiB2EMl/1sFL37YYoup8zDyzZtMn3vLdtKbzWwnIsmi0tIgcf3CjTp6dM3sOhh2bko49qTyKsslGJxxLsuWdGnnmm1bQd2WMPBHuz9O1LVUMxCfWkn1C9Lexzr/X4hUEsFQrFnNWDkg1DsTVmRw3kJGGXkNkoelq7x4lrjqUdc3QqdACqvHoLsoEwdJqw1iUIP5s3ImvChGly0EE5MrzLLt/KoouOkaFDc+G03mgsbw5/XBGGkZsiRU8+2SxDhjQYI/5NNzXIV18RSdUsJaQLFzzHuK/l2T+7TsT9POM/Iyss341wjaoFSDnCl3OETCIAEQ4rrriiCZ8r5dySFJbCPgj/g1By7YQBEn8/duzYUO5HMc8u+W1sNJT19jS0hNOeHwn+CHqvd7NYpbcrrmiQYcNybUWuv7450l5LYVkPSxl7fn7jjS0yYULKFPLZfvsmOeSQVrn22gb55huqGmblk0/Iqwj33OMuPMtZLONOlhUKRXlGbIDMsh7CatQoYO3iHFi/ILJR9LSOc29rrh19ig2PYKEqt8VkbzyK36Tk5JPnkx9/bJC+fTNyww0LSadOg2YrGucXkcV3bBVbPqvFWlPMmF0pUAlOPLFVll02K/vu2yjPPpvLu3zqqWZx+Hnd6hyZPOMbNySeWBLyMWzYMJND4Fe+2+3zVI1cu3weSxYfW+YbwbfKKqu0866VgiSU/rZFdT7//HNz7VS1hVC696BWgjxfT0OEtBuawv3ilbljiwW98043ee21BaW5uYPMmNEg06alTJ9HcPnlhMBKbFAox7JUwURv3fvvb5a//KWDfP45Yb+zREdra0pGjEjL4otn5rhQWBter1Ao5gyQ409l0PXXX9+3B6WVu1F599g/eWyEvLJm4bHq06dPuzYZ9U4sIVGWULIuB9WnwvJYRjUOTz+dlscea5CmpqzcdhuRXbnP80Vk2Rx+NgoUMTdff/118383Ist6OMMIiy7HmG0R1lq5zTYZee65ZtlppyYZNSotO++cI5foKfVWMDCJUVLxHsUA+OSTT2SfffYxJf/9chnCbAFSjsfS7RtFzH///v19QzRKPQaIi5B3wW8mTZpkLKgIOayohOZ4SX2YynhY4+BXgMVWestVpZ0qJ564hMycOftDvckmv8tWW/0uLS2FK71VE5RUZwHGYxvGeOH0feSRZtliiyZToOiEE1pNs+YHH2yQkSNTsuWWs75LBVl4e7kFB5ISCht3Aa9QKMIFXsKtt97aFMbx0zn4O2hKTCmwFdTRJ3jP2koqyXvvvVcVWRnFNXlRbP+sZ0R7MQYQJTy03oJp+RCkbkWtccMNufXkyCNbZdVVi9ebsOG01OVg7CjatPLKK7cVOERvwcBPOhh6DBF83lYoYRY4DKNgYFCsvnrWhMZutFGTvPlmWg46qFHuvLPFeDXr3WOZjvl5xkMDrgAozXjE8inzrseyGrA9GHmIsSiW2zcqDon0pe4f7zEeSoQZix5WxEIkK075DkEqvRHTD6lcfvmM7L9/izQ0zJBUapp07Pi7rLjiRBkxYlalN4AQx4ro7b1ZLay77rqhj1ufPln59NOZbeG+kyen5MEHRUaPnrW/L78UGTCgg6y4YlZefTVX4bdehXzcz1GhUIQLa6grZPyyekAYwCvKOmIrqBP94xa8q0YF92ocp9D+GQNLKFlLy0khAn77j4sM/+orkWeeyZ0LFU/LgTVq2PxLFzac1no4KXDE334FDvltOeG01SSWoF+/rNx7b7Nss02TMXD37JmVCy5ordsoqWwRj7AftHhPBcSylo19vYBQkkxfSd+oJCXSc60QSqxlVMLDYlbMa1etxTAsUKjmhhtyVj2a9O6+O+fe4c+NnI4l2lV6owUKJBPPra30ZoW9K8CjDk2JQoC6P11ppdwcHDVq1ofkPRAi/O67uXErp4ZEEoS8EkuFYs4llsj6fPI7DGIJESDSCTKFkZKWXOgUflFZ1dBvaqFzQCghQHjcMNhSlIjIr3LWhrB0jqh0l9tvb5BMJiWDBmVMDmHYKFbg0Ho4/QocunpLoaJQgwYNMvsrN82rHGywQdbUgNh//yZT2Odvf2stKyQ2KVFSqQg7W4SFuiCWhJvmU0StBSdqwZsLlRxvzgVLD2GIvEaFOHgsIZIQJzyVhLtiRQyjcEAp11Utgvrgg2n57ruUdOuWlR13zD+XbGgKOTCcG82Yvb03CRVGWYg6NIUQGICQdwlQmKRt5ZVzY//ZZyn54w8RbCj08gQskhMmSFnNjJMi5JVYKhRzLrGMgoTZivFskCnWkEIV46MmfO5xqtVuxO3rzXpaStX8JBqzSdW97bbcmn/QQdXpFR2kwKH1cBL2bfVb7ofXQI7zBJ0F4u+HqDyWFrvtlpHjj8+aCKpPPknJSiuVl8YV9/U8kxCdI/HE0loM8Q5FaT0sRK544FDiKU7DA0b/qChJZTXyHQoJYYQNHkquGUKJ0C+1tHmchbwXnObVV+eE/mGHtZbsgfP23gwSmsJ3vcK71N6bN910k5n3Rx11VLsw7DDHnT7Qiy1Gb9KUjBmTkjXXzLYRS/Dllynp1UuFvEXcybJCoSgMazxFyc4HlOxSdQ5vxfh+/foFCvesRu5jNY5jCTLXD6FknIP09a4Fwh4HonyosL7wwlnZbrtMzc/JLXDotoTzFjjkXvGKIcAvnNbqLFETS3a73HJZefvtlIwbVz6xjNs880KJZZWth9aSUi2Lnm1ETBgohNI2Ih4+fHjdWQ8tsGDhoaR4gXvNUaCUa4t6UX3rLcI6KSOfDdWaGCQ0hY2CDYy9t/em3fJ5iQuVpg5TgCLEIZaEw1LpnfcusRRRIa9QKOoD1stTiFiWonPYivEYFiGSpVaMrwePJedPlA+ArCy77LLGgxbmGhBmKGzYuPnm3Dqy997lhXFaRL1m5itwyLOAroJ+iMMBwwqfWYJq607wP4pXRtESJ0csRT76qLwxUJ0jPNQNscTalw9heSxtxVMIZb5GxNWyHlYzFJYwTgTGjz/+aKxXAwcOnK3SaCX7jzuuuSbnrdx994ypjBolgvTeZHN7b/JdL+F0PeZhtBsphJVXzsjzz6dl9OjZn7Px48s7TlJCYeN+jgqFInwgcyvVOUiFwDOH4ZAwzwEDBpRVMT7JOgdjRE4fa5mVpaQRRaU8x1Hn+OYbkSefzF3vgQdWrxZIWOC+QRzZ7r33XqOnHHjggcbTbHUW9GYwevRo337htv5EJfe9b9/cvS2XWCaBtGUScI51QSyxfDCxC1kPKyWWCCMbYw7JokBNvkbE1bQeRn0c9v/hhx8awU8VOnqFWstTpQhLIY9asSdH8OGHcw/y0UcH91aGTd4K9d50vZsoKoSq2P5qAGs4Qt4WCwr73GzYCR7Ln37KjVWXLjTvTv3psaxP62ESwnUVCkX4KOaxLKRzoGwjp/HQYTxcffXVZ6vgWQqS6LFkP5ZQgqWXXtoU5bE9GKNAHFucuUV71l03Y7xuSYYbJeXqLHjg8URvsMEG7XQWdBVvv3CvkZxnLci9s8SSUNh61TkyZRiztSpsGbC9fKIglkw0vHQQSsJVaKFB/mShiqdJth5aMJZcM+SEY0RViCjMRSoq/Oc/DdLampK//CVTctx+NR5ojBssyGzueCCwR4wYYf62Hk4UGr6PoEa42+bSCPJKem/aAj7kWH76ae6ad9+9Va6+urFsj2USSFtSrIcKhSJcoHMUKt7jp3Mgh1lXMVKTax/WulpNnaNSAst5QqghlOyrZ8+eZiy4BtYk+52oELdQ2FoV7YkKhdqN2M/y6Sy2X7glnbTYQWchrNZLNvnb69hZbrnc3KR4D+Naav3DpBDLdAJ0jsQTyyAtR0oVvNaahlWRAisQSnIKg1TqrJb1MIrFhDHkmqnGZosG5KvyNSd4LKdOJfchd8+POaY1caEpFrR/4RnBUIDgtk22ef3kk0/aem96BTfWwyDjS2/LDh0gsymhs0rnzlnZaaeMXH013tL6DoXV4j0KxZyHIB5Luz5ThwFCSTggekQYqSRJ81haIz2EEp0KQklajZ/8jIpYxlH2vvBCyqyRCy6YlR12SF4YbNA+i8VIm9svnJY6FugsbgoQRonPPvvM6Ko8Q25F/YUXJiKrg0yfTqSUyDLL1B9pyyTgHOcoYumGBhaL92cR4EGgEXE+4VdPHkuIBYSS4gHkehCaw3W8TSZ0hCh0/nHIhbjrrrRMmpSSpZfOyhZbJEvou+NnBTqGkfnnn98s7MxzeqwCbx8rFAC396aXcHoLNmE4XGGFrIwYkTvOOutk2/pwTZyYEh7NUms8qfVQoVAk1WOJ7EKGvv/++6ZyPJFO5VRPT7rOwfdtPQD0DAgl5NpPblp5nwSPZTn7GT+eSu0NJgfw229TZm387rvc//baq1UqzTKKg84UdvVXdBbyjr25x8wlq6+4KUBduw6SL7+cT556arzR2dx+4cXOSXWO8DDHEMtCDx03C7c7k5OJhfAjp7CcG5gE66EFoSeWUOKhXG211dpy+AhLqHbV2XIRxXkSlXPZZbnH49hjW0oOq6g1mLsYCBgbr6fdO1629yZ5mBbMYfKJreCmmhtzxe296ZLNlVZaSEaMyD0vNHheeGF6Wmbljz9yFtlSGz5rKKxCoUiazmHrMeClxJiNcTqs/s5JipKy4wChZJwYh2JRX1ETy1qRhg8+SMlllzXIfffh4Jj9HGgxcsQRyYmIqjQUNgygg+AEYXOP0b9/2ngrv/12fpkyZbzRbfF4kurj6iv2vZsCpMQyPNQFsSw3x9JtwovgX2aZZQyhrGRyJSHfwebXseHB6t+//2wWIVfIR/GwhbUYRiUI/vvftCFEiy6alf33T5a3ErCADx482Pd/Qe6pbW3C5u296Xo3bR+rjh27U8bHfKd37wny44+NstRSi8u4cQ3GUrvssqWdvwp5hUKRFJ3DrceANwXiSQ4ZRWmiBjIIuRw1gqzZjANGSAglRkgIJd7aoGlESSo+VAwQnBNPbJQnnph17RtvnJHttmuVxRcX6dYtK4svnhUiP8MyXNd6zcw3LtVYz3OpW2l55BGRn35aRPr3z7VEsSlAVmchWuvTTz9tlwKEnsPfPNNxXtczCUm/aZwTE+kRwlgyyDNjAQizCW+cPZZctyWUPEyF+mVVYzL6nT/3qtbCkajpSy/NSfpjjy0vRCUOYSmFUO4YY+Hz62Ml0iw33UR+JVb6n+Tjj3+VeefFSt9Vhg4dL4sv/ns7a2ExC3695lgqFIr68Vja9BmM09ZDSfoMimu1EBedwxJKPESMA+3YghBKd/9JCYUtfhyR/fZrkrfeQp/J5U+efHKrDBgQb72gUlD1lefA2z2gWvqQX2VYmwLE5oLn1y0WhMNl3LhxZqO+hNfDGSScNmpkEqJzzBGhsFbwMnHwsECsSBIm5wFXethtIeKW78CDznWz+PHA0CrFJQb59h+1xzIshD3ejzySlo8/TssCC2Tl0EPLD1GppRBiviM0OQcEo3suYd9T9rXRRh3klFNaTIXY1VZb2Xy+2moN8s47lNdfVBobpxdMvGdjblqhWa8ey7hfk0KhCGZge/LJJ42Hg+JoEClb3bSaZC8OOsfkyZPl888/N+sNhQ4xWJdTZbyeQmFffjllSGXHjll5441m6devvgmlBek3tRx/t5cl06jQIW0KkA2nxTBC2Dq6iSWbzG34AmlBGMO9ZLPSivqlIgkpQnVFLAuFwgISyAlTYTIweWzV03rOd4BQ4pmFUGJB6tevnwnPCXLd9WQ9LAWc0j//mbOyHnlkq5TRrzoW4Hm49tprzfvTTjst8nFD1p1/fnsSbqPAfv55XhMVYEHIiWsptIn3PDdWYPMeoY7QjoOlMMnWQ4VCEQ7wxt1www3y0ksvmTV11113Nf2dvXIgaMHAMFBpn+5SjuOu2eSRQigpTgShhGBXqmRH2UYtzIKBxb57ySW5cTjggEzVSGWco6SqZSimlgMeYoou/vijmDDjUtdzDNxs3hQgtzotNVk+/vhj46zCSeUayOcpoaJ+veocdR0Ky2eEu5L7wHcKhX4mMd8hnyBh8pE7CpEmybkSz2yU1sM49rF85pm0jByJcMnKUUclN6Heryqs3/+iRI8eueN8+WX74zMn8/WxQmijqAC8m2PGjGmzFHqFdzUthfmes1qfg0KhqB722msvE/0DmVxvvfVkp5128v2e25cxakRNxrzHQT5DKCGWhLuGWZwoymupVouzYcNS8tJLaWlszMoJJ0SvC8YJPBuMD6TMDYOuFrEkArdHj1wFXsJhqZERhjeQdd4bTsv3bXVau/lV1J/H0Vu8FfVLhRLLGnoseY8XBKsCyusiiyxibmjUpLLa+Q7e49jqthBKHoRKcker4bGMX0ityMUX54QhIbBOwbHEoVDZ72oJ+XzEslgfK55XSCXKGwqLt2myayn0Cm4+q5Z3MylCXqFQhINbb73V6BGHHnqoUSpr7UWs5rEwmNseyBTkocd1FO1T4h4lVQw24mmPPTKG5FQTtYzsYWzvuusu8/7YY481a7H7v2qhb9+MjB9PGk5aBg0K7hwoVS/iu0Eq6k+aNMk8M25Ffa+RPGguclLSb+qGWCLkuZkQSpLpudHEe3PTSKSvNyHvWvY4HgIfQsnxe/fubZrMVjKhqpHvEDfS+sors/IiKNpTCWp9bXEglj175s7h++9TMm1azppYapNlv8T7fJZCXrkur+Bmi0IBUmKpUMxZsLUJrM5R67zHahizUZApykM6EXKVtmRRyNMkeSzzneOYMSn53/8aTDjmKackN+Ip7CipfJ9Fga22ysjTTzfIf/7TIMcd1ypBg4rCKhiYLlBR3zWSk6bG366R3NVd/IzkSShqWDfEEkvAbbfdZrwcWBJp/E6Mcy3CUqrdbgRCidAHYbRLcfcfB4IUBGGcI+vy+efnHof99stIt27Jtx7mO4dq3VMCBOabj9CplPFa2sT6SpssF7MUWsFNHzWMLUQw8F0v2URGlFK10O88lVgqFNHjmmuukUsvvdSsd6S0XHXVVbLmmmvm/f6QIUPkuuuuM54CZMTOO+8sF110kSGEYYD8ShuyHwePZRQyHaUX+QmhpAclSjKRUFGRymoQy6jXPuut3HHHjPTpE3/dqZrpN9XSh/beOyN//3vWtIt76KG07LprJhbreWNjo4l2cCMnOSYpe7YVCq84xnhvCapLNuExlYbTVgOJJpYjR46UCy64QJ566injpTvssMNMPmE9Ww/dyYiLHcW4Z8+e7SrShYGk9JQK6zz/85+0vPZaWuaaKysnnpj8vAg7BwuRs6jBIZZeOisjR6Zk9OjgxLLYuQexFFLy3wJh7FoKbe9NimtALr2EE+UzyLGTEpaiUCQZ9957r5x44oly/fXXm/B4SONmm20mH330kYnM8eKee+6R008/XW655RZZZ511TOj8/vvvb569K664oiYtzpKkc1CkBEJJFAhydODAgUYmViPyK2r56NU5+BviTM4o7+nnzcY6wGspSvxnn4k88EBuPTj11DnLWxknYklk1OGHtxpHwb/+1SC77IKXr/jvalGJPpVKmWeLjRSgIEZydH6MWq6Hs5Rw2mog0cTyzjvvNB66vffe24TK+ZHKerEeukIQDyWTjrAcWodEYWWxD1hU45bvAYYAIORtsrQV8oWspJWON6Wpzzgj9yhcdFGL9OwpdQO/uVFNAbrJJhlTDOm++9JGwJcaChsGyNP0672JkHbDacnfRKlCQHvJJn97C1RoKKxCET0gg4cccogccMAB5m8I5hNPPGGIIwTSizfeeEPWXXdd2XPPPc3fGF732GMPefvtt6vW4iyJOgc6BcorbaEwVEPi3X6E1fD4VTMUFsM8UW5EvC211FLGyMiagNKO8ZHxsDlxLtkE3nOkXst55zVKJpOSLbZolVVWqb63stbRZe58r1XBQIvDDmuVyy5rkPffT8vQoSnZcMPCx+f84tTiLJ3HSP7+++8bPYR5iK5sjeSE2drem67u4u0nWi0kmlhedtll5vXUU0+NTVhKFB5LJjwWCwglixk9s5hUCL0oFdtqWg8R7lwfixrGAiyFbtKz7XvoCnk3ObxcECF94IGNMn16SgYPzshhh1VnnkQNhM+qq65ac2K5554Z4TF9+um0/PyzBCqIVCwUNgywb4Qum+v1sP0/Ldn0671pNzyhcVmIFIp6BHmM7777rpxxxhltnyHTBg8eLG+++abvb/BSUkRk2LBhJlwWQyU9J/fZZ5+6JJaV6hysrxBKQvBYe72EMqzjxKXdCPIdmU47K9qkQCqJXkGfcj2UKOuu4ZH6HbwyBhDQDz74Q95+ezF57bX55c03O8jMmak51lsJ3Pvmp3dUc60kQ2bffTMmz3LIkAbZcMOWqhqzowQEknnrNZK7egspAxjJuZ5Bgwa1GUSqhUQTS1fIE7aRD0ltVsx+IFcsjAh/CCXV2PCoEAZUD9ZDlHMWNRKZCQWwPcEQ8NZLyXdsGwpeGQ9e7VhDPnmweHhKDQm46KIGeffdtCy4YFb+859m04+xHoDw2XzzzX3/V03r4QorZGXVVTMyYkTahAkFIe42Qb0WpI05ZcOhXLjFgmzuEfORdii2P65rLYxr702FIkkgQgelH8Ljgr/HjRvn+xs8lfyOdiDIOgjC4YcfLn/961+rRiyToHOwZiK7UEIxrkHCCxlrk+6xRI9A2R4+fLjRo4hwszqGX89Roqb8Il3eeust+eijxeWYY3pLc/MshaFbt6my224/yCKL/CE//DBfSWkVYSEOdR38zqMW3sBjj20xxPLZZ9Om/UihSLRqGLPDgF+UlGsk94bTMt/dejOxJpZxS6Svx3wHSyiZGFgn6Bfl9syrVt5oVMewniHClkhmptIcghh47yXeN2/fQ/vQsEgw5m4bCh4k69UslCvx9tspueSSHAm96qoWWWKJ+glLKYZqClC8lhDLu+9uCEQs4xSSYoECQi9YNovXXnvNPJv8D8KJBXzChAkmhMr23vSG0yoUimgxdOhQufDCC+Xaa681hkpyA4877jg5//zz5W9/+1sox2A9cVucJUnnYH2FULJmoo95ix0WOk4SdQ6MgtZ4jQ6VzyMbBIxBQ0OjXHddd0Mq+/fPyG67ZWTjjadL165T5PffCaX9o81jxPG8kVasA0nwjJUKrnX99df3Xb9rsab36iWy4YYZ01P0ttsa5NxzWxNPLLMlFBjie8y3Wsy1xnpIpPf2sUxyvgNNh61HjhANiLtfE/akhqVwzhMnTjRhJex/5ZVXbmcRDAr70KDAQ7qZe7aokfVsMpY2VwJFICfU55WfflpIPvxwAbniis7S2pqS3XZrlZ13Dn8saymkGGcWVFtBtZZCftddW+X00xtk2LC0fPJJSpZdtni+Q9wFvD1PayX0KytuQ1OY77ZY0FZbbZWIa1Mo4gCIDxEohGm64G/3mXMBeSTs9eCDDzZ/U4cAJZ+K8WeeeWYoilYSdQ7WRtZd5BEGMtuOLezjxEXnQA7jzMDYh45B1Xw82ZXmnb377vwyciQFV7LyyCPNknOmk3/fRRZZZFaVcuS9XQNs3iavzAsb2eIawKOstlsNoKeS2xwnQ/uBB7YaYnnHHQ1y1ln5W4/YZzXuhD+TkLoOjfWSSB+XZsXlEj4EDoQSMgShXHHFFWcrFuI9TpKEPPshX41rZL8QQe5ZOaTSe45+FbZcIwcLzIsvTpe//a2jvPfeXDJlyqxpv8gi0+Www8bJV1/lEp/Z/Ih80sACyjOJJfqYY46pKXFD/xs8OCvPPpuSu+5Ky3nntdaF8Mx3nvnKiuNNT8J1KRRxAco20SwvvPCCbL/99m3PHX8fffTRvr/BkOh9zmx6RFjrWZx0jmLH4jwhWBAbon7c6KBSkBSdg7HAO4mXktBejPPIYvSPMM7/zjuXaiMtngjtdsjXg5m0Jmv8/uWXX9qqfdocfrdQECQ46FqtUVKzY9ttM9KlS1YmTkzJM8+kTY/LJHssMwnRjRrrJZE+adZDC4QLRWsQMHjeVlhhhUCWqyR5LLk2kuWZP0svvbSxNGM1pSBRPpRy3GLfHTu2Sfbcs7P8/ntOaGBpXG21jAwYMFN22eUnmX/+tLGAEzLFObIY+YXSxl3oxK2PpYv99ms1uQ7XXNMgRxzRashm0j2WpQh5rifpFmmFohYgQmq//fYzHjZ0CKKk8EBa4/a+++5rcuZIrwHbbLONMYD379+/LRQWLyafh1WSP046R751GkOWJZQQqwEDBlRUxCPuxJLfsY6jY3KfyaHEM+uuJZWe/5tvpuS99xaQpqasnHBC6UV6OBf0CzbX4869smTTFo2zvQy9ZDNurSVcDy0Gbc7ZzfWr5ZpOsNbee7fKkCGNcuONadlyS//WI0osa0gs45pIn8R8B4QGhBJyxaIIAS9F8ayWkK8ECEoIJYKSwkMQZysQw3qAi+3nm29EdtihyZDKQYMycsEFLaYUeG6oeUDxbM7ybrqhtLxCgFFibIlnV8jjDYzrQ15MUFZbgO6wQ0ZWXz0jw4en5ZxzGuU//2mZo4glSMI1KRRxw2677WaK85199tkmd41q108//XSbHgJ5cp/Ds846yzxrvNqicJBKel6HhSB1HaplwPPqN5AUwj/Z8JYxXq7XrFxUy5hdbtV8CCXXTsgrpM27r0L7DnpcW5Nh++1/laWWCqdGCEC/8Obw2zoS6CFsxepIlHIdUYBIgVtvvdXMRzo1xGVNP/DAjAwZQmX6BtltN5Err2yZzbBdy4KBpSApvbMb6yGRPkjp71oJ+WLNh9dee+2SmvAmwXqIgEHIY1CATOYL6436/OkttdNOTfLNNylZbrmM3HtvsxSLvOVeoIi4FjebM2fJJooM7zl/v1wJrrXWYSnFPJbVFjbIwksvbZENN+wgd9yRlsMPT0n//v5jZIV83JEU66FCkXQQ9pov9BUdwxuKfs4555gtKsTRY8k6ZQkl65ANAQ37OFGi1GOQOoTxGr2KNCuM9IVkciXn//77KUNO0umsHHbYJOrASpSw3ko2rsuv/7JbRwKjPboHr1YngYBWay218z1ua3efPlm55JIWOfPMBnnssVxvyxEjZopbr6pejdm1QmO9JNLHpSpsPhLrbT48cODAiqrixjGR3q02xzUWIs1hPsR+50j18H33bTTVSBdZJCsPP1ycVOZDvpw57qklm5BoyDRjQF6EXQC437UoOx63UFiw9tpZU8jnvvsa5LTTGuXZZ5srrnxWSyTlPBUKRbgIasyuhsKKbsNxqGeBoRMdq9LaBXHXOdAhIZRUz6cmBUUAi9VGqPQ+WG/l4ME/Sffu/mtX1MjXfxmjwgcffGB0Djyatucm8FaljaqORNyM2S6OO67VVIjdYosmmTAhZYwE662Xjc35BUVSjO6N9ZJIHxdi6Q0XcZsPIwgqKXUd19LfCDUEmS0OELR8eRjnn+8hO/XUBnnyyQaTT/nAA4THVHyo2Y7LNbK5RhVyNCGaNmSFVxZB5rw3dCXKsuOFrIe1FKL/+EeL6Wf5yitp+fxz8b0vSRDynGNSrIcKhSJcBNE5ADIiqnw40pJYc1l7AfUZvDmFYSIOHkuIEylE6FM24quUFKJyz3/s2JQ88kjuPu6779eSzVa/N2AhQBSZk3gsmQd27rnGb4zckHHmLTmeXrIZlvE7bjqHxcorZ006zjPPNMiHH7YnlklZyzMJSb9prIdE+mI5lrVoVux67wirLNZ8uFRU45qKWShZ2MhhYWGDYJWSyxHlIvXQQxSJyU3tm29ukbXWqp6HjkUOzz65mYxJr169Zis7zpiRk8znUZUdj1uOpUX37iKDBmVl6NCUPPhgg5xySmsirXJ2fJOwGCkUinARROeIili66y5G6r59+8qYMWNMRE2UcrNaelS+QkS2F2W5+lSxscmnj/DxGWfY3MpWWWYZ7nu8iKXf+bM2oV+wQcL96kjYdli2joRr+LahtEHXuEItO2qdGmTRt29WnnlG5KOPat9nsxwkhQA31kMifbF8BwR7tcJSEIDcfKrkltJ8OEkeSz7j3hP6aS1keCpr9WC65/j11yJHHZWb1ief3CI77VQdg0IhFCo7bslmmGXHAYtuv379fBffWgvRnXdulaFD08Zz6UcskxBiWk7fqyQsXAqFIrjOkU+Wuh7LMAml7QENsbVVT+0xklIlvpRjcM3kjKJXsg6W2yrFb99BcdNNaePl6tgxK+ec0yrTpkmi4VdHwhq/LeFkzHm1PTe90Val1syotc7hEkvw4Yc8n62xO79iSIJuBBrrJZG+UE+pKK2Hfr2iQCUCMK5C3lZfI5wCQYQ3DoNCOQ9kWOfvHpt19aCDmmTSpJRpJ3L22aWXA68W3LLjbpXlQmXH/UJp881nFA4MOH6otRDdfvuMHHdcVkaOTMsnn6Rk2WWzsTq/IEhKQ2WFQhE+bH0E5LVfhEmYxJJ9WEKJQr/ccssZo7WVkfZYtQ5TDfMY9poxtjLWUeWNFgPrE/UAwAUXtMryy2dNfl5cPHBelLtuBum5ie7nGr+9+kix4j1xWNO5f2DcuFTioqTq2mMZRwSp0BYVsfT2iiKJfMSIEaGGvcYhkX7y5MmGUBKzH6T6WpB9hwV7jkOGNMjLL6elc+es3HZby58tRWqDcu9NvrLjbiitLTtObivecG8obTlVhquJLl1ENtooK889l5L770/LX//amjghr8RSoZhzYYkleke+1IVKazvwWyKDyCnEQL/ssssaL1O+Nhq1To0JC1Q6hURzXX369PG95rCJsX/4rcgBBzTK1KkpU/jlyCPja6iOAvl6buJEsYZvm7uJfsL86969u4muQh9GF7HG77h5LOkU8OuvIrataxI8gdk/oy7jfp51QyxtvkM1w1JsaW8bomHzC/m8nqyHWKxGjRplqq8hNChhHlZFsTA9llgRzzknZzS47LKW2bxgtUBYgtR6K9n8yo4j3CH+zEeIP8+Dza3gNxg8WBzs+cRByBMO+9xzaZMP6yWWcTi/YkhK3yuFQhE+rPEuiqKB/IbiNBBK9tG7d29T+K+QrIlTxdZyQTqI9Y5xzZCZsJXoUs7/4osbTN/lBRbIyo03NpuWWYpcHYlixm+8zby3xm/7LFA9v5bGbwr7d+uWlW+/TRmv5ZprZhOlcyQl/aaxnqyHWFL8JmyYFj0eFKwxEEoUdzyUbohGtayHUSfSQ1rY8FJCZkqtvlbNCT9tWkr2269RmptTst12rXLAAbXPq6xl2XEE+kcffSSPPvqomaNYffm+JZqQT4wDfDeKsuNBsMEGuXtEEj1rvTsdkmCVS0pIikKhCB92LSwWKVUKkeG7llCCpZde2pCrIGtlNQrrRHUMiAh6BusW+tviiy9utrARZBy5vO++E3n77bQhluDKK1tkySXb7yeOobC1PKdixm/b+3vs2LFtxm9vG5Rq9dzEawmxpDKsJZYaJRUu6opYYj3MRywrteh5K7GtuOKKhlB6J2PS8x3c6muEMGA5pE9UFAjr/K+4YgH5+OO0LL54Vq69tqUdSZnTAFFkXtrEfMjk4MGDTdU3G77Ce4wwzz//fFsorSvgeYaiFrI2rRRjwKRJIgstNOt/SRHypZ5j3K9JoVAEf5bDanPGOkg4IYSS75NqUqq3LokeSwgGBQDxYi255JJGp6Jaei3apVx8cWd5/PGO8uWXaZkxY9bx6bu86671b6gOA9aozVwkes81fqNzoJsQ2We/Z73TzHu8na7x2+okUfTchFi+9FL7yrBJMWaDuJ9nXRLLsMNSvJXYilVAteFxSct3sNXXuE5CJyk+RB6fX/WvMBDW4vHJJ/PIbbflEs6vuaZFnOiMORpuuxHmihXSbkPtZZZZpp2At2XHscZ78zZLKTseBDyy88+flSlTUvLDDylZaKFkNStOwkKkUCiiQ6XEEhlChX0Ua5RtCCVtIcqRK0nSORgzrpn8UQg00VDWIVALbyDeycsvn1UTo6EhazyUAwdmZMiQXGqTojiYy7fffrvRG4488sh2/3PXdGv8diP9bM9Nq4vguac1IQZw0ni8hYIqMX4vvzzPSYO8+25apk1rFdrKJ0HnyBQpjhQn1AWxtOSnWFhKKYK3WCW2uDcSLuU6KQaDoGehdEN7o7yOMPbd2ko+5bLS2pqSnXZqlS22iI9lsdahMsX6WPI8cL/Z3LLjKDhu2XHmP3+zP9eaaF8rsSYuumiOWH7/PVbEZJE2DYVVKOZsFOtlmU/nQL7hpWPNRXGGUBL6WYk8qbRQUDXWbFvoEAM2+Xn5WrFVW+e44IIcqd1xx5nyj39khQCtQvb0uIbC1pp0FNI5io2X23PThdtz0+Zu2p6bXrIZ1PhtK8NS6HHxxTvInXe2yIAByYmSSsX8POuGWNqwlGINi4MIXluJjXBQQkHzVWKLg5Cv5Bg2/IZQlHzV16IWoPn2HXSsr7++QcaNQ7C0moI9cUOchXw+AQxRxGPN5n7fDaXFMsm8QehjTfSG0vIsBrl2UkM/+QRLJ99NlsdSiaVCMWejmMfSu0bbdl0QSn7Xo0cPQyjDqFQf51BYoqGoS4GRkvVhwIABZq0I8xhB4d33a681yNChjdLURI/K6bLMMvGuqB5nFDNml7Om5+u56ValdXtuuiG0Vh/xRt2ts05WjjyyRR58sEG+/z5l+mn371+fxuyUFu+JtpclwrtYWIollIAwwVr3aIzqGFRfI1me8bIFAvwmbNQey0owYYK0VYE95ZRfpFs3/4VqTkWYYRM294GNUC0LlCNXwPP82FwKr3Dnt945tthiubn1ww+zn7sSS4VCUQ+hsKyhrLkQSqqsQygpcBJm67NqFe8pRR9w26Wg3Pfr169gGlE5xygF3uNymAsuyBVh2muv6dK9e/Dxi6vHspYoZswOa03nufEzfhNKa3URt+cmOZ5efeTyyzsZgrn33k3y5Ze5Oac6R3ioK2JZjsfSmzgP0YJQ1mNYilt9jcWNhPlCi1utPJZBcNJJjfL77ylZccVfZbfdpoiIEks/RCnksSayESLuWhPdUFos1bznefAm5ud+l7Maes8v7gI0SUJeoVDUJhQWGUghPAxuQdbcuHssgxYjItQXXYP3Qdql1ELnGDq0QV5/vVE6dszKCSdMYxQD7SOuBKTW5KiQMTvqc2PfhMKy+fXcdHM3eRZ5Bn//HSP5APn884z5LO7IJEjnaKwnIV9KIn2YifNxT6T3q74WpChPXD2Wjz2Wlscea5DGxqyccsqnkk4rqfQCEmfDuKsp5BHYVIRjc4+Hpd4Kd2tN/O237mQ8yNixP8snn/zYRjrr1WMZ92tSKBTh6Bz0FUbWTZkyxegWYfZ/rqXOUWz9oN81hBLCXU7uaLV0jpy3Mhf2esABzbL44oxdMpT2uKKSHMta9NxcZJHfzd/ff98on346QRoaWuS1116bLXczzDZ7lUCJZQw9lm5YCootRCusxHm/Y8UhFJZFDwWe4jx4YQcOHNhWQTfoMaIE589aeNJJHYWc7b//fUZbq5B81zZtmsjJJ+em7fHHt0qvXlgalVh6gQGBLQ6WTY5FLiaba0386KOs/N//ifz++9wybdpXJnIAgQ8Q5vS8ckNpo7D0zwlCXqFQVEfngEiiW2BEg3iy7qJjRI1q6Rz51g9INNcNobae2XKIdDV0DvDccw0ybFiDdOqUlZNOyqVQldpzNI6IQ12HfOtiXAyrtufmyivPJ507E0JLW5S+Mt9835nWesxl5jGFpjCI8xx7ySa6TLWvJ5MgnWOO8VgyCRD2hOYxWSyhjEJZrXW+Ax5YEuW5VnIa8lVfq+QYlcLue/jwtNx8c84itNZarbL11oWL8FxxRYN89VVKllwyK3/9a6uMHRtPIR/Hc4rbuXXrlnv2fv+9s6lGDHhuRo8ebYw+zBGqwPHcEmLLHI6LNTFJQl6hUESbY4mMglhBLG1UEO26qqV8Vkvn8K4f6FJcN9Ff5I0uv/zyFcnkangsP/ggLX/9a85becghzSbXf+bM4PcpLgQpbmA9pk1dvkq/cRs3Tqdnz6yMHYue0SQLLdRoDEFsbiVjt44EUX9+PTd55e8ooxKyCUgRqjtiWSiRnsR5JgOTA0IZVZ5DrfMdUL7J54BU8nDTjNYNSSznGFFfx7PPzpqCCPvBg1sk37Pz1Ve0F8ndt4suapHOneMt5PXcircbAfSxdJ8dFBMMRX3/7EHCHMQzYIV7Pmuifa2GNTEJ4boKhSJanWPcuHHyxBNPGKUSYkWfa0usqqEH1MJjiexD1yAaCsMf+ZOlRkMVOkZU1zFpUlquu66fPP10Z8lk6J2ckeOPz1/wMQnG2TgBJ8Ymm2zi+784EkvQowfEkmKQjbLyyrMrnqSMcV1sFsx/WyUffYS8zU8++cSQUPTueT2FgirpuZnU9JvGek6kRwHFmgapRNiTQ0mYRtSotsfSVrTlWnkQWNyKVV8Liqg9ls89l5uCqVRWxo9PyzXXdJCjj/Y3EPz1r40ybVpK1lsvIzvvHJ+elXHEqFGj5KmnnpJevXrJzjvvHEshP4tY5nJe7Cnx7LiWP86Vym5sKDH5rIn2Wef7XuHOFqYxST2WCsWcLV+HDx8uTz75pJFJRx111GyeumoU8auFx9ISSvpdr7766rP1Hozjddx3X6OcckoXmTQpt35sv32znH/+DOnSpXT9Jg5rZ9IQVyJu6cDXXzcFvq8811anIOrRXp+tkv/rn7UkbM9N5IJXHwnaczOpOkddeiwJR6EoD6/ETK+00komLKVaqIb10E4w21MQCyIkotwWKbUIS5k8uYO8/35O2UfIn3XWXHLZZR1kk01+ky5dZpqqoTyAfPfll+k31CDpdFauuKKljYTEWWjVEoyJ3fz+F4fF0UacYCx47bWUvPVWWo49tjVwyEcQayIGF55915roejgxSNW79VChUISHk08+Wa655hoT/bTLLrvICSec4Pu9ahLLqHUOroNoKIBeRTEit91DmAj7OsaPT8lhh80lra0p6dHjV7nmmkYZNKh1tmOiQyUZtdaDWGNx7mAUxgichPWPUFhAKGwl58dv4SBsizgFE0lLc6vkE03I39wrDDJe43ehoppKLGsAbijFPv73v/+ZG0S4K32T7I2qhkXPohrHYpICQnFY4AjDCXvSRR0KO3x4rlLXKqu0ytFHN8vDD6fl3Xc7yN/+1iR/+9t4E17A8Tt3nk+OPnpN8919950uK67Iu3RshVUcEMcKbV6QijH33Fn544+U7Lxzk0yZwnwT2WKL8olvEGuizbWmWjLWRG/epjVmFEKShLxCoQgPO+20kxx//PFy3nnnFTRMIUOqRVai0jlsNBSGehvxgV7lRxziqnNccUUHQyrXX3+mHH/8UBk0aMPZugN8+umnhhTZPH5381P247KGxgl4sh988EGz7u67776JyA8kFBZ8801TJOfX2NiYt+emJZvkbeIcQkfhufLqI3Ab+3zHcQzrlli6YSmEux555JGzhaUgFOvBeoi1g0lIWW+w5pprlu11qbXHcvjwnGVno41myCeffCR77jlV3n13kLz00pJyxhnzy7rrNsnbb8+Qu+4S+eQTmtw2y6abvirPPTe97aHjAeVhZAGPU9XQOBPLQp9XG0S2fvEFVvDc+fz73w2y/voinTuHd36FrIluKC3WRGuw8QuldcNzkyTkFQpFeFh77bXNK/KEImP5gHzAi5NEncNWz6d1CGsr/b2p6D106FCJEmHrHBT6u/vuHDE89dQ/pLV11r6R+RivydXv3r27aUnBmmDz+FkPIJtW2bdbtfTIpCEJxmwvll46d16ffUaF5+qs5ymn5yZ8xQJZYskmGwYd23MT/QPwLPIdvJ1x1j8a6y0sBUuiH5LusUTAQShpyYBFiEqvb731VqSTK0piiSH3vfe6mPddu74vRDHvtddy8t57zWYh2GmneWTqVL43qyDA+eeL7LDDoLaeiITk8DDifaKYi5+1McoqXYVQa0FaqPR3XEJhARX5vvhi1rn8/HNK7r9/UTnmmKmRH5u5QZ4Qmzs2bigtzxvKFdZEigJZsmnztuM0lgpFPYN1/tJLLzUKF6GYV111lTGs5gPk4Mwzz5SHHnrIFPCjvsKQIUNkyy23DOV8IBzIiHyodihsWMdiXcWDh9HWGw0VtR4Vts7xr391kObmlPzlLy0ycGCrvP56+0q2pEoxl6yhkLXANT5aZd9uhAMzLij7rBGsBRRItGkVtV4Lanl8Oy/yEctaj40fVlopa8Jhx49vkGefXVAGDKjduXTo0MGkf7FZQCRt4VHb0WLYsGGzVcm3r16HmhbviTgsBaFRrbCUMK2HWDwJMUCgIfDWWmutNg9d1AQmqknJOb/yClU+O8g88zTLzjsvJYssklPuzz13hjz2GEI73UY8VlstI5tvnpGDD87M1hMRIU+YAe+t8MfSSuiOJQOu8Pd7+OZE62FchLwt4AOOOKJVrruuQV54YSE59lj6k1Yftow4m2tN9CbmEzGAdRvi6RdKG2drokKRNNx7771y4oknyvXXX2/WQAjiZpttJh999FG7gl4uIaBCJf974IEHDDnCAxVmXmCxFmfVLt5TqS7gRkNZwuU1zEadHhPmujRxYkruvDPnrTzttJlGXoO3337b6FJuJdt8nmc/ZX/MmDFG/0KnsFVBraHRa9hGV4vLWlstxD1KygVBbgcf3CpnndUo99/fRU4/XWKFhoYGM8/YcC6hz1IzBoJp9RGeV1sln/ns6iLUXAmjWvMcRyzdsJRiQr5aYSlhWPUQXBMmTDCLIZOK/kDWHW6PAaIW8mHvHysy1tAnnljS/E11aksqLZF8+ukpMn58k6y5ZpMsueSsaqGFYKuGuj2ImA+WbLotKpgrlmjaLYpw4rhaD+MEqxOuvjqGgxyxnDgxl1MQJzA/2KyCgXJB7o01aCDkeV55ZeytcC9kTVQoFMFwxRVXyCGHHCIHHHCA+RuCSZuPW265RU730Qb5HC/lG2+80ZYjh/ctTATROZLgsfRGQ6FT5ZNV1SCWYe1/yJAOpj/lOuu0yNJLfyXvvfe5+Zw2bH4GhlKqgjKn3PmEvubm8DOe/A0x8JLNIDn8SY6Sirsx24v99muVc89NywcfdJbPPpshvXpJLJH506vuOlf8em5afYQ5iN6RKGKpYSkSybH43bfffms8bkwIGse7oXrVJpZhjRmeRUIKuffkM4wdm6vzvOmmOSuiiz59WmWFFWgx0RToHPMBIoBl0hva4i4AtiS07YfobjZpOong/Fn43GuPo5DfccdWee65tJx7bktbIv1vvzXKr7/GO1/WtkSx1kRvYr7Nk8B7TsQBytvgwYN9m0crFIr8QGa/++67csYZZ7RbY3me3nzzTd/fPPbYY4Yg0Qbk0UcfNXJwzz33lNNOOy20XPw4EctyCFm+aKhaVp8Ni1h+911Kbrstpz9st91I+eqrn03VfIodugb6sMCc8hZp4d7biqBuDr9th+XqGnHPmavXUFiAirTMMtPl4487y7hxaenVK545tNkiBZD8quQXqjIbO2IZx7AUhDwx8/kQ92bFfB+LIVYGHsA+ffoYYV8orCBq66E9r0rgLl5YVwg/mTJlLhk1KmcRHTy4uiW+scSSpM/mLeJCbokNbYFsQhy8ZBMrUVwFpIvlllvObH6Ik5DfaKOsfPTRrDCkRRahSl9Kvvmmg6y0ksQW+Yr3uIn5eDNdGRhVJUWFop5B1UQ8Qq51HvA3RMEPrKMvvvii7LXXXqaoH1EyFPVjPTrnnHOqRiyrqXMEJbFuNBQ6mDcaqhCS4rG89FI8sSnp23eSbLVVJ1lyybXMdeebL6Wuh0HOkXti9QZvOyw3Z/PDDz9si3Kx38dYCdks1QhSy3W9WMHAOKN792mGWH78cUq22kpiiUyCCgY21ktYStzyHUo5FmNjC4RQfY3criATKM6J9LbvFZ5XBKbbSPn553PCslevybLYYv6Cs5TjVroQ+RVxsUnTlmxyHTa0xWtt9Mupq3VYSiHE+dxIpLfEMs5gfpeygGLQSOKCq1AkETyfGLJvuOEGI7MhT6xHRFmFRSzROYhESIrOwf+J0MHQWygaqtLj1HJ9woD38MM/yM039zV/n39+o3TvvpR5X6wuRdDjViLH3XZYOFi87SesYZtqtRi80ZlcXcNbnTxOYC4xp9x81Dgas/3Qo0eupgPEMq7IZDIleyATU7wnzmEpcRHyQY8FUcGSyiuhwfTeLGU84mg95PtYmCHKtucVrnl3gj/3XG7arbbaDyLSreJzjDppOl9oi82p45oR+G7eZpwFaZzPDWL5zjsiX38dz8UzidZDhSLJQFFFHqNwu+BvNyrABcZZlDB3PV1++eVN6g46TBj5znHTOQoRJjcaikgWxrScNSCOOofrhR079hs5++wNJJNJyR57NMtmm7Xfd1gIcwz82k+wf+aW1TXcXodBe21WGxBlS5aTpHOApZbKEcuPPorvOWYSpHM01lNYSrGeUnGxHmKdYkwYS8jkiiuuWJZgiFu+AyQLSxuhHnheKQTgfRD++EPkhRdy027AAH9iGUVYShjwC22x7SncnE2eA6yNloS61sZq9dqk8h0RAswtwtC9iKuQt3mWX38df49lUoS8QpFkQALxOL7wwguy/fbbtz1//H300Uf7/mbdddeVe+65p91z+vHHHxvFPawiWnHXOVibiIZC1+A8bS/KSuRWnHSO338nhywrkyZ9bwzZ3Nf77ltfvv12LunZMyOXXjo9cRE77jj4FSSEbNoaEd5em4BXUsKiKkhYLuI+5kstlWtv9skn8dSLkqZzNNZLWEoSrIdYmwhDoTiPzTespGJTXKyHXBeCHasoZcoJh8gXrnH77U0yeXJKevRolT59fpGkw21PAZEGjNk777xj8jFZ7BgXG9pSrV6bHIv74tdiJ87WQ9uweMKE+vNYxnXMFYq4g5oO++23n0mpoEggdR0w6Nl0nH333dd4Sy666CLz9xFHHCFXX321HHfccXLMMccY+XvhhRfKscceG9o5FdM5qtk726tzQDxYkyEhthdlGEbNuOgcX36ZknXX7YyvUrbYIi1HHdVHPvmkqzz0UGdJp7Ny443TxLH/tu27mucYBZhzbH69NjGc8J6cTZwXbkHCavTaROdgY575OUrivP5ZYkkazqRJhPVK7JCpZ2IZ17CUYvkO1RTy3mPxsNHqglANwkLXWGONUKpD1lrIu9fFvChWVQ7j7lVX5e710Uf/YRaAMM4xbuCcbJlxKuACxtG2PyFv06/XprtV+kwktfQ3obAgFwobz+psSRPyCkXSsdtuuxlPzNlnn230BlpGPP30023eHNYh93nEwPnMM8/ICSecYAydECtIJuk39VzXwVZfZ31hDMqNhoozseQahwxhLaVmQ1ruvbeXPPhgVuxlnnLKTFlrrUyklfTj5IGzvTZxWEA6l112WaOb2SiqavXaHD16tHnmKDy54447JkbnAJ06tcpCC7XKL780yIQJKVlwwfjc3zmCWMY5LCUuQt5aDzne119/bcIVIJIshm7OXqWoRvEeP3BtCDFCbBBKQa/rvvsa5Ztv0rLYYhnZbbcZ8t578Rc4YYFrtNZGt3Ky22sTwglJD6PXZlIrtFliiccym50ZqIdpHEt/e5G0+6BQxA3oF/l0jKFDh872GXUd3nrrrcjOJ05RUkSm4MEdNmyYMfBz7VH1Zo6aWBarMP/119/Ic88NNp8dfDAeurS8/nqjtLTQD7lVTjstf3hy1OcYFxAF5W09EXWvzaQaswHPabduLYZYfvNNSlZeWYllJWisp7CUuBBLa1WjmBHWQryztLcI+8GqhfWQnA1yZLGIFWuJ4oKIzH/9y3orab0Qz0T6ap+TX69NFlDX2lhOr82kCnkcvHiyZ8xIyXffEe0gsUSShLxCoQgfcdA5WCswXGOQhCCgjxEBExVqURXWW2G+Q4eB8tVX5B9m5bzzZgidUkaMSMuLLzbKXns1S77Mkmr0/q41Cq3r+XptumQzX69NjNx+1e9L0TniDM5vscVa5YMPRL75RuqiEn2iqsLGNSwlDkKeyUkICtcP8erbt68Zk6hubjUT6SE2EEq8auRsUHSoFMX6iSca5ZNPEGxZOeCA5tBITlwJUiXAGJGv16ZdAMjbJLQlX6/NpBJLwpm6dJkuP/zQScaPT0m3bvFckJRYKhRzNmrZxxLvk42GggAss8wyZk2IklRWw5jtjpm3wvwKK6xg1sQzz8zVpdhiixZDKsGqq2Zk1VUr81RWs8VZXMB4+1W/9+u1aavfW8Lp7bVZLEoqrjqHPfdu3XL1KPBYxhGZBOkcjfUSllLrnlIQLogXnkr6+ZA7mi/nNCxUI4mcBeyjjz4yoa8Up8H7Wmr4Mqd4+eUd2kJXSKqfObO+PZZhC9JSem0y1zH8ABYIPnOtjXEdL4vFFpvWRizXXjue55okIa9QKGqjc9iUmLBkBfvCmI+sZx0mh5JwRwhYNSKyojZmA/afr8I8kU8PPphTW3fddZaBOgjC8ljGlSCFdV/K7bWJU6XQucV13JJCLLMJSr+Jd+nFkPMdohCIKPbEqk+aNMl4ZldZZRUj5AlfjBpREktIC2GvlLTGIlVJwaGhQxvk/fcbTOjKkUe2XwzynX/QhyLOwipqFOq1SVVaFmdCpTDouL02+Yw8zriSo8UWmyqjRy9kiGVcEdexUygU1dU58inNYRry2AfGQnQN3vfu3dvk6tvjVoPwVcOYDVFhbaJXOlFR3grzr7/eIN9+mzaRT4MHz17xvFrnH3fjbNgI0msTvQNg+Hj11VfbRVH5VaePX45lzjAzcWI89Y5MgnSOuiKW1ewpxQOF1RDLDdY0PLLWk1etfM4o8h1sM2XCT3iPIEG4l4vvvkvJqafmigjsu2+zdOmSzKIySYHttbnxxhubza/XJsSS4ln023TzKKrdazMfFlvsD/P61lspY6Gu8emEIuR1visU9QXbKgx56hfFY+UDsqISmYrRmvUYnQPvHYq9V/ZUq+p9VMexFebZOEa+CvP3359TWbffnjGXmiDOsrya5+bttYkjAi8mkXrLLbec0TWYu7bXJh5P22OznIKEUQIdafHFc/M6zjmWaSWW8fRYVuqSt5XJiDun4IqfAKxWn6Owj0NIJYKBcezVq5fZN9anIHjssUaZe+6sbLRRa1slzy++SMl223WW8ePT0rVrRk48cWbdl/6O4zl5e21iOCBfhXwcN2eTUG6MMza0Jepem/mw6KK5nlLPPNMgJ54o8u9/t0jckCQhr1AooiOWrJd+xNKuceUSMSJOIJSsyz169DARUfkIajWN2WGubW6FecaTXFGivaxORVji1KkivXtnTbuyRx/N9RTZZZeWxPWgrHcQkk2qFPMUb7pb/Z5Clvyf+hE2bxOiyT336hpR9toMRizjaTjIJEjnaKy3fIdiYSnlWg9x5VN5DYsak5+WK3h36kHIE3pie1/Rd5GNMYJUBtn/a681yN575xaClVZqlZNOminLLpuRnXbqJN99l5aePTPy2GNTAxdiKeW61HpYOuwzArFks7nA3l6bWCAxovBcRdFrMx+WXPK3tvfDhsVvDMMwUCkUimTDelvyFfBBDyjHw4fCTTQU3h3CQfv161e0F2USQ2HdCvP0XoSI4OGy+PprPJdzy2+/pUyLsj59MjJ5csoQgHXWqW1opZLT2cE9ZMs3b9AZbBitddK4BQnRN6PutZkPPKOLL567p7/+mpLffpO2wlBxQUaJZe2sh3hc/NzrrvWwFGLJ97GoIejZ70orrdSugEotrWKVLiYI9FxfqK9NKMPAgQPbjV3QB9mGp4DRoxtk//1neXD79WuVhx+eJl27tj/PevdY1hovv/yyaVhMbixedS/87m05vTb92p9UimWWmSRbbDFTnnqqg8QkUqYdrKKYFCGvUCjChzWsFYqUKoVYorugZ6Bv2PU4qDytVihsGATWVpinfoOtMG91Mld3+uc/OxhSCb7/Pm02sNNOLWWnR4Shm6lBsXT4GWIxllS712ah84NIzjdf1hBL8iyXWy5eemVGiWX1YQUwSrAfsXQ9lqUky+PJY/KW0rMx7h5Lzo1wExYxwh7zeV+D7J+mxI8+mptGt946TT7+OC3XXdfBWBbXWKNV7r9/qjhyYzaokI8GKDtY//zyjkv1tgXptYkyVGqvTT/kzk1k//2bDbFsLq3wX1WgxFKhUFhDXKVtzmx+IQY7jNblFMpLgsfSJc54rty6FN79f/ZZSu68M+elJdoJIvnKKw1G4T/hhOq1FUkSan1ddp5zD73rfdBzi7rXZj5YnQivJcSSPMvllpNYIZOgug51SSz9UAqxJEQDQsm+8iXLF0JcE+ltn02shYB4ePpCFeo7VEwgvPxyg/zyS1oWXjgj223XYpoTH330THnrrQZZb71W8cm/b9u3IjpE3ccyjF6bhRopW32jQD2umsGeoxJLhWLORiXEks/JNSNqCIWYfuBuhe9SEFedA/B9oqK4Tq6vEHG2OsfFF3eU1taUDB7cIhtskAt7XX/9ysNfw/RwKdrj9ddfN9uAAQNk0003DW3sy+m16eoabq/NYqRt8cVFxo2zlWHjc4+zf7YtSoreXDfE0uYgVNLLEqUY0sUreYaFkuWTZj2014ai7/aFKoZi+3/oodwUsqQS4PzcZJNgi4CGwkaDWjQrLtRr0y4AKBfW2ugX2mKfz44dU7ElluV4LJOyICgUimj7Z9uieHjvkJnFDLxB4LY2iVLWlKJz2Kgv9A6u0/bcLIbx4+eW++7LKRN/+1t+0l4u8p1/PdR1qGdjdqm9NnnGqH7v9tp0q9+7BQnt+S28cO4apkyJ1z3OJsyY3VhvYSnlEEsmI7Hc9J8k3h8BWCxZPin5DlhTuTbaopR6bcUWEZT+xx/P7WvHHUur0haWkImrkK812S0kiKp5bvl6bWJtJF+TBYAQMMgm58UCAH799ScR6R5bYukX7qNQKOYslOKxtBFDRENhcKMCKrmUYciRcmtIRGU0R7ZDKMnF5zqJ+gpynXznzjuXlWw2Jdtu2yz9+4erRxU6h3qQ57W8hmLEsla9NpmDlmza9DbCsvmeJZo8NzyTf6of8uuvEitkEpZ+UzfEMmgvS3eCsyDgQSknWT7uHkseEtsXCmtovr5Q5e4fvPRSg8mlXHTRjKy7bmsiCVi9oprWw0qsje45QTYJQx87dmwbsfz11xnyxhtvxarXZpKS6BUKRW2JJbKNQjUotBixKViDlyVMGeJ6LKNEMaO5rTCPkZ6or1VWWaWkVlVDh84lb7yB9zYrZ54ZjVVRo6TqJ0qqGPyq3wMcUJZsUomYc6clyq+/rioiPWTChMny44/TjUE8qur3pUCJZQ0R1GMZRrJ8XPMdbJgNwp3xqCRv47PPmmS//daV1VZrkrPPnil9+7Y/1kMPNbWFwZaq5xerCluK4I6rkK8leXMT6eNGLAv12kQJgViuuuoK5vN0uqOxeBfrtQnZrCTKoBQosVQoFEFCYZG1hLxCKMshWkFRac/MUo7jt96iU1FUBZ2KiuLlGOnffjsthxySS6OgeNvyy4d/LYXWvaSHwtZaD4qzMdsLt/o95/bMM8/IOuusI6+8kiv8OWlSq8nZjEuvzUzC0m/qilhyswtZDxloSBfu8EqT5eOY74Dl5ZNPPmnXF6qS499223zy7bed5X//E3nyyUbZc88WOemkGdKrF/0ORZ54orGt/He55+8Hm5RNFVIIQ6GHSRse+4N5zf3HUudFnMfLnpvNsWxuThlLo1+vTbZa9NpUYqlQKArpHHjuIJSsZeQV+lVADRO18ljadmyk26BTFervXQhjxqRll106y7RpKRkw4Ae59NLSoquCIix9Ic5raK2QNGP27DpHR+naNacvdejQRQYNGhSbXpuZAmMbR8wRHkvrxeN/KKJhJMsXgluBNsqQPSskWbzwUEIsvX2hygVtRJ56KveQrb56qwwf3iB33dVktu7dM9KrV8aUZe7WLSMDB7aGIqB5iFmMaYWCRwrCQEgvCxVEyZaU5n+q2BcGQpEtH+IqoOwClK8qbBx6bZZTnS2u461QKMLTOYimwHNHlUoMo8gb5FTU4XQ257taHks2dCkiSDhm3759pUuXLiXLOU73qaca5fjjO/7ZomymnHrqcOnQYX2JK1SWxzPHMgzS1rlz7jynTs3fa9Nb/R7dG7IZZa/NbDZrdN6kzL3GerYeepPl+X+vXr2MAIwSxcI8w4IV7pTyztcXqlzQM+rnn3lQZsozz8yQ999PywUXdDSff/VV2mxg++1bpFyO544TZBKrJySyf//+bXHxkAOIAhuW0XHjxrWVlIZkEqpAaFHcLGJxFqRxGys/0lZqu5Fyem26ZclLsTaqx1KhULh1HVA2MWaRYkMPvtVXX90YQEeOHFmVtBhQLWLJ9Y4YMcIo11SYLydf9LffcoTyX//qIB98kDOC9+vXKnfdNUXGjSsvAqoSjyVGeXRFCESSjde1XNdZe3v37t2uBVkSdA630KE9xULqm1/1+0K9NufzIZulzq2k6RyN9ZpIb6uSucny77//flUU/lJ6ZpYD2xcKRRklOewcUfDgg7mpse6630lT04Ky5poZefTRafL77+RCNMhrr9GsOC0nnlhZgj33acyYMWZhtp5k3nsTr90qXzZUlt/y8LLQPf/88+0a5bLl65VYLcRZkMb93CyxbGlBWeKZiqbXprU2ltJrM2lCXqFQRAMMuf/73/+MngGZXHnlldspnMVanIWJqIsGoltRlAe5SSu2Uqvnc2q33NJkdAv6XCPbwbzzZuXQQ2fKscfOlLnmynlDo4S7fwzX6IkY6CFGfsZrGy0F2ayW0yCJIH+YLR/irHPY87PLeqmPbJBem19//bV59eu1WSzlK2k6R10RS0jWRx99JEOHDjWTBOHnJstXS8hHJXzcvlC43RGECPawSaXbRmTQoIkiMmuhpBzzxhu3mq0SED7E9XC/IP5Be4baIi9sthcnZIHCCLZ9hbUW8T9LNO1rlHHwcQLJ6IQVr7/++tKvXz9JCmzIh+t4b27m2Y5Xr80kCXmFQhEukAH33nuv0TUgl/vss4/JL/SuLdUkllHVG0A+4o1lXWX9xKtHDYdScfnlHeTvf58lyHv2zMieezbLYYfNFCuOZ8yItj6F3SfXxPXgYaYjAEYBdCn0Cq/x2rbDAqwB9vd8p9bG66QgzsZsNxQ2iMeyFr02M2XoHFq8JwTw8FO45o033pDzzz9fDj300NnCQqtJLMMOS2EScn1uXyiIQ6FiReXihRdybUQWW6xVVljh58gWKYCFN0jT5GLwtq9g7CELlmwyViwO1jPlkk083XEVeuWCa6fEvV/7nSR5LAGXEBaxLKfXJvPGtTYyX2zoWxBro0KhqC8ceOCB8uKLL5r1a8MNN5RtttnG93vV9liGeSxkHf2viepAl8JIz7pCyGipoNCfJZUnnzxD9tqr2RQBLHTsqNYowl6pPI4jYsCAAUaGkzbheq5c47V3PWAtgBy89tprbXl1rmezVsbrOHtRk3BuYRPLMHtt2nQv5mm1qt/LnE4s//73v8vFF19sCMoJJ5wgRx99tO/3qm09DONYTDhyD5ls3nLlUVkoH3wwN3G32Wa6pNPh7J/zJIwGcsyDwXWMGjWq4pzQfALceiuthdH1TNmcTRsGyfm4RDOMAi9xyhvw+zwpOZYA20kZhQYj67WJR5O5TNgUHvdcY+X81sY4j7dCoSgd55xzjlx//fVyzDHHtKVu1IvOYcmXbe1EXQq8euwf+VeqzvHBB2k5+ODcenrIITNN67J8iDLUlLUeQzzEEI8rlcbt8YqNnbsecL8JnaWrgKtPoKdZ47WrT7BVuz1FraKk0OnWW289U+/Di7hevzViVINYBu216a1+j/7Ps/jCCy9Urfq9zOnEkty8119/XS666KJ2IW5eJCnfIUhfqCiIJdWwaC0CttsuF65aKViMIJQIXbytWAHtg1zN0t9+nilIgVtNFAstiwULgXdxiNvDWwjF+oPGWcjz7BAVTYW2qVNTMmWKSMT1tgLBWrKZE4RyEz5VyNrokk0qNYcdsq5QKGoDitZ46zrUIu8x7GPhiUN+QZx69OgxW4pKOWv2iSd2lD/+SMlf/tIiF19cOMIqCmLpVptnjKhg61YUdxEk5NCeo2u8ZpysPoGeYyOl0HusPuH1bPJZPYFrZ/NDnKOk3Crvs4hlbc815al+j0GD5xKdwy1IaL3nftXva6lv1AWx3GWXXQq2G6mFkC/XeujtC2VDNapxPYQc/vWvHeX331Ompcjqq7fIO+9IReQYgU7pdcgkuX5eN34xAlQMlQorFk1vzh3nbYkmryxI9uF1FwZe84Ul1Dr0I+4EMh/cBYgIaQwdv/ySKhg2VW24ykcQayNKGhZ/hUJROa655hq59NJLTX4SkS9XXXWVrLnmmkV/99///lf22GMP2W677eSRRx4J5VxYE2z+nR9sDYC4eywhX0RisFYjx/JVmC9V5/jxx5Qp1AOuu266FIviC3O98labp8ghhQLDSFvINwboE1QFZvMWjHOr22Nst/qEGykVVQRXHNqNxFUXcc+t3OI91Yvk6mC6WridLfJVv4c7WINH4ohlnIQ8FqBixDLOHktyF0rpCxWmx3LChJTst18n068SnHzyTEmny9s/v2FyY2HBa2NLr/udfxxBKIu3d5F9eC3ZtH0SbViCSzjdEMhaIemhsGDhhbPy9dcpmTRJYulVzQe/XptJyItQKOIOCuaceOKJJgx1rbXWkiFDhshmm21mQtLzeaEApOnkk082xczCBM84YfFJ1Tk4N8gkBmDWrnxrdbk6x7PPNhjvz6qrtsqSSxb/XVgeS+oL2KIori4VxrpX6j78Csa5ZMDmbaJPkJ/pNV7HQZ+o5z6W7npugw86dIjX+WYKeNLzVb+vpc7RWG9C3q9YSdzzHQiVgFAiZErpCxVWTsVXX6Vk0KDO8ssvaVlggaz85z/TZIstWmX69FnNkIMKU4QkAh3Budxyy5nKtYV+m0/glCK8qyG0/B5e5polmuSkELqMYQNPM/9D4WCh8Obb1VLIx51YukLe8vqff+Zc4yPotSqsQlEbXHHFFXLIIYfIAQccYP5G93jiiSfklltukdNPP933N4Tn7bXXXnLeeefJq6++akhHWCgWCluN3pLlHMvWPEDvQJYRTeTXf7CSY4Bnnsmte5ttFsxrWymx5F5wTVybXyhvJfuuhj5hiaZXn/B6NoNU0K82iukWcdY57LmRegPilrWSLWLM9gJ9s5YGicZ6EvIo8TyY+cDkyRcDXgvrIUIQSyHeXsikX6hoIYTlsbzjjiZDKpdfvlXuvXea9OxZOvngWvBQ/vDDD0agU2iomPDL1yPQXhP7ZBxzjWtTvg9WLYUVYQkQZzYLzpmFgapzGAzonWrz7dzFAbIZ5eLA8bCQekNrkkAs7bl165Y7108/jde5KrFUKKoP5Oi7774rZ5xxRttnPIeDBw+WN998s2BxPwzdBx10kNE5wkSQHMu4eSzRkSBfhMvZCvNB5VkpOgc2/hdeKI1YWpTjeSWKCKcF3kkcHX4F+PLpHDZShjnGulxI5yjn/ILAL8zR6hNsRLQR1uvqE3Zzi8zVCkkNhXWjpP74Q2JJLDMJ0zka60nIEwpbTMjjSasGCln23JYbhFsSOkxIZTnHCEPAPfVUbhrQoNiSSrv/YkLBFehY3/yKDBWCPX8r3O1DbsfPHUMr7K3Aj2OzYuYg85sFAIJNvoq7ONg+pIQq2Ca5lnCG2bZi6623liTCnWuLL567rxTviROS1lNKoagH4IVi7fTmK/M3Te39QEuIm2++WUaMGBHJOQXROeLiscT7xbqE8RdPHq1SSvVqlKJzvPFGg/z2W0oWWSQjAwYEG4Ny1nRbbR5CyDUVKuBYTOfgf25OrKtruPpHtfUJGwFo8/etPkGxQa6dc+b6+Z8tUgj5rCYZSSqxdL2Bs4hlfHTKOYpYxlHIx916yN94JxHuti+Um+QdxjFKxZdfpmT0aCx0Wdl88/wVvcIQ6F5YQe5WE2NfLHb2IbL3y3oy3fvH71ks+QzB6pLOWsMVsjbfzj4rbiVRFgfmhG1bYSt6WUskYTBhXk/cPZauALU2oLgV43UtnAqFIp6gYMo+++wjN954YzsvUJgIUjCw1h5L1savvvrKbETX4M0juivMY7j44YeUyY+fFQbb2lYUJUxiSUE99A/WUDyvRH0Vk8v2/67OwTURKeanc9jvur9n7eZ/OCmKeTbDRiF9Ar2a87BtsDhHjNVuziZkM6q1i2cMY7pfsck4Gf8Lh8LmPivDzxMpMnMKsYyrkI+r9TBfX6hKj1HpA2tbi6y9dqtZDLz7B95jeAU6FV/LnfQIZwQ3v4dQesND7X7d/TOuCFJyOQmlpt2M9UTbc2Y/rsCvxUNZKNfAr5Io42rJJgUVPvzwQ/O569W0ZLPcuRN3YukK+ZkzU7EllkkpqKBQ1AvQG5DreGlc8LeVoy5IzSCSZptttmn7zK7JPL8o36zF9aJzeI9li+hhyIZIFqowH5bOcffdjXLEEZ1k0UUzbfJ7881bQj0GRJn7SsEbwnhXWGGFklKI+D06B8fhd0F1Dpu/ice3T58+bZ7NWuscVp9gLhI1BrmzPZfd4kA2TcxrvOa3YegD6667rtkKnWcc4ZI2OiIADYWtDGVrRyrkg1n2ivWFqgTlFAb4/POUvPlmg+yyS4tR2G0Y7FZbzS78vcSyUoFu4YafEA6MwEPA4b11Q1zz/ZbfkJsKOUeQ4f31ejY51/aNb3MC391/nB5UzgnCyMbYArs4uJVoP/jgA/NdL9n0Lg6PP/64WQAJTWfeJZFYzqrQJrFC0oS8QlEPQM6vttpqpkn49ttv3/Ys8vfRRx892/epBjp69Oh2n5111lnGyP3vf/87lFL8cdI5XEKGvmFTLiBBxYrolXKMfNcDbzn77Fxvxh9+yMnHpqasbLhhOMSSz9AvuS7WO+ZC0NxCV+dgHWVdtToH97CYzoHOgx5H6tI666xjiLqfzuFeQ63JJt5JNgz/gHOk7oPVJ9CjeBZsP07Xs8n1haUjJEnnqKccy1QNx7uxnoR8nNqNWGshYcGF+kJV02P5yScp2XTTzvLzz2l5442ZcsEFM+S113Ikd4st8hNL21sTwVqqQPfChqCw7xVXXNEIOOYARBGhxxh5K6BZ8kryOuPJfcT66oYR57My2qq2dmEpRDbjRhbcxYEwH8A1WLLJZom5zauw40aYMvmchaokxxGuALWnHrc+0kosFYragCr0++23n2mLQW0CKtEjD20BwX333dfIyosuusgQBtYYF3bN8H4epc5Rzd7ZnMvIkSPN2tCzZ09ZcsklQ5VVXp2DKNEXXmgwOZTXXtskP/6Yll69MnLJJdPl2WcbZY01WqVUVcFPr2GNI0IJEr/sssuanMOgirMb9ooeatt8sHZiqMWx4TXUWl2NiCh0Dn6/0kortYu2K6ZzBDFwR5Hmkg8cD73N1d04X0s22fBuo48xJu54sDHXyyErcQ6DzR8KG69zziRM52isJyEfJN8h6klu+0IhuCBhxfpCVYJSruf771Oy0045UgnuvLODfPMNzZtT0rdvq28DevuwjRo1yhCUUgW6C5sHac8bwcX9ytdImPGjuTEhrwg0QLgrCyXht0E8pX4PIvtgcbKvCFm7LxYR64G3i5t9ZaMVjD1fvst9tjmhvLrvo2qKzTXZxYGxsGPLuNmwF7M4/Fnx5vNx48y528XBGlaSYD20xLIePJZxHW+FIknYbbfdjMHs7LPPNrnpq666qjz99NNt+WbkEVZTAYuLx5L1mTWA9RKdi/SQsA3ZfjoHHsqrruognTphvM199ve/z5BNN201WzlwZSXXxXrGvcb5AFkOGvHl6hw27BVPnBsODGG0aycbxyHKzJIo7i0RROg+QcbTb+7F2cBtvZVs1rnjjgn6BGlPkE/GxOvZtLoZoB89DoJNN93UdDhIyhrYvips7jUilb2ic4xji5lIiKUK+fx9oXjoIGFRkcpCHksu8YsvUm1kkSHZdddOMn58WpZeOiM77dQsl13WUV58MX8YLAIdDyVAiBCmXM7E9lZeswTMb154GwnzGxYVLIuQdMJEmWeEsvDeJqOzL4QhCwILK/0z7bhjkRw2bJj53Cbdu9h5551NqKitbvviiy/mvRY88xyT40EqCTfNB86BxQgQPvzkk08aIWwT79m4Bs6TxbKSvGPOxwr77l99JQ1XXimfLLaYTF18cel75pmyyBJLyGfbbSfv9+zZtjgyT+0i4S4O8SSW2UT3lFIoFOGBiCi/qCgwdOjQgr+97bbb6qp4D+seYZqsMRAnSBChr9XQOUaOTMs11+SMstOm5WT2wIEtsvXWLaGE27IeQ1Qw5pZSOb8UnYPP2b81GHNt6BvoHdxbQl+JlMLIbXWOUqu4e7/jLQ7krX7vV4m2muuNd0y8Rn82IthwIjFGdjwgn+jf3vmehFBYxnfCBORHOrahsI0JquvQWG9CvhbE0q8vFH0Mo/aO5st3uOiiDnLJJR3ln/+cLocf3ix3390k77/fIAsvnJGHHpoqyyyTla++Sst99+UWhS23bJktnwCBjlAFQXpSFgt79VZ7LQYb9grwFGJNY+FkTPGgvvfee0bQ+d1vjsHiykKEZxLC7/cd66m01kKuF5LplhR3XyFgEG7eQ9D4rr1GWxDAvneFgE2iz4ctttiijViiIDz66KOGcCKsLdG2G0I83ximb7hBGo87jpVBMgcfnPustVUWePVVWW3oUFnpX/+Sidtua8YPEu4uDvlCgWppPawnj6VCoag/1ErnYB0khx7jL2sNVdkx8ketvFtiySUde+xcksmkZMcdm2XffZtNFdgjjmB9rOwY7H/MmDFm7PB8UZAmKApVmC8GCBMF81i/GU+/fpKs47ZlGMexLcP8Cuvxf4zZ6CDoDbyyke9qDeK2sr5b9d597d27t9En2SfHRifimths4SEbJcWa7qYvgbDWKa/RH3At1tPL2Pz+p47z8Zgx5tqtPsGYxJ1Ycm5nnTVLZ9NQ2MqQHAocw55SWCoR7Agab1+oaoTA+Hksufwbb8wRpgsv7Ci77NIsQ4bkNPNTT6Uibe77V101XX7/neR6kdVWy7SROYQc+yWfAKL10ksvldWs2Bv2WoyYIngZR0JRGVOq6CKcID4cf4899jChn1Y4uWSR/SOouf8cj8IF9FLlmBAkku1ZnNj4Hp/5nQ8Eli3fNdlXzof7bUNR/RL133rrrbbf4u0lZJz54m5Y+NjcBcyGM7ExHl5svvnmJjIAIMwxAJg+VxQ0OO44SXGvyOuwBYoQmn/eiw4nnCAL9+ljzo+5CrhPdmGw1WhZoAgZypfrWi1v4M8/xzcspRQhH9cFVaFQJK+PpQ1PZA1hbaGGAzKG9btaOseYMWljrEYBv/jiGdK1a1Y22qi80FcL1jxbcIhrglQFlbPl6Byu7sFxMbSy/mMw9v7W9pNEh7CeO8YbXYM1E13JGtFt1fx86TAUsCTXE7AP2vDlA/ul+A5jzndff/31vN8lQsoW6uNa7rzzTqPrYPyw0VK2ZgNja79rx65UYzL6AOPRZdw4EyX10fzzy+RllpEV//Uv6bnoovLVzjvL6J492zz6RI/hBUWfgJDHxQNnjdkTJ85ap+PosUwnKP0mHne2SmEp5VRR9QMPIeEShGpgfRo4cKA5dhTHKpVYPv54o/zyS24CTp6MNbGzCYHFW7nffn82BhQR2lj997/T2wQ6gpC8QQQrORruJA5KLIOGoHA8Qlqt5c4Kneeff953vwg8hLUFoaM2LBUB5VfBzE1KhzhBVLlfbnEgS5pKydf0JurbVzdRn4UKZYO/seohQG14eDHhQAgt37XnznmzcW94dS2GzEFCzwGEcqGjjpLFvv9eFvvuO5nxZ3irIZoWDQ3S8dprJXXYYW0fcW4sXtY7DThnO268EtLOc4UH2Es2w14crPWQoR0zJndPV1xRrYcKhWLO1TnsuonRFYJBFJE3kqhaOgfHGDs2J/9WXbXVkMpKACFjjWE9g7yxRvMaRMaWEvbqt9ZACtF9WFcxQHM/IY4QRvQT623DU0iaDIZuP1Ahnygp/o8ug+fTwp4TeoaXvHFcjLxuvqUbMcU42DWWNReDv42KcqOkWLNtbQfeW30JXcSvgB96kyWWGPNvv/12c2587o2S4hzsOBSKkpI99zSfpeifOXSo9HvhBVnuyitlyu67G0KM/sDcJcyYc0L3c/M1bZpRtWGN2fPNFy89I8npN3MUsazUesjNJdYeL1GxvlDVKBTkd4zbb8+RpNVXb5XhwxuMVREccUTzbE1fXYKMhRCC7BV8QSvP5gt75TOEN+G1kEmEGGTFet8QlAhx/sex8d5BrCA61svo7dtohVGxsbHCqlhxIJvQ7wq4INZOL9lknFggWfy5DqxzpVaFs4sqmxe2AIA73yHZP3z/vUydNk1+7tLFbGNt4nw2Kw+QQ/rll7LEN9/I0p9/Lt2efFIa/iyulQ8sgJy/60llIXBJOiG7kGcbslvq2OWDVRBwSNtE+t694yXwlVgqFApX53Bzw/10jnz/DwK3zRdrI+u0X158tXQO8OGHudfll69Mn7Jhpaw5/fv3N+vIG2+8UZHOEQSsZaSEoHugXzC29m/bE9v1LLJvSyp5b0Ng+S2EybZK43OuA5KJXsF1YSR2Q0bRt7huu26uscYagYy06AToTN4xRI+BHLMvyDDzhfDZQw891MxNG4rLeg3h5HxslXnA34DvQPzYXGy44Yay1lprmfdcA2lInMti33wjix1//GxRUiknSqrx2GOl83LLmfe2TgfnbEOL2dAJrYfXjqvV8WzIcJSwz+aMGbOeT79ilrVEJmE6R90Ry0LtFSohljxsCEB+H6QvVC08lvSofPnlRkmlsnLrrdNkv/06yXvvNcg882TlkENmztYPCgLEmBUiyMWIZaEQFIT1Aw880CaoXSCI+RxhTlgrFr/NNtss0ofHL0+A+WKFPosKCzjn5U3ULybgIKxYKdnfKqus0i4vxK8qnEUpVeFcSyZgHppCDd9/L839+sl3iy0m37N17Wpef6CAz9xzy4crrGC21YYPl63/9z9pmjbNLMiQeRagIGMO4WXOs1mwaNnFknvIfGLsXLJpw16C3ldrmfvuu9x1LrJI1oRrxwlJE/IKhSIa2Egl5J5fKKFrdCyVWCJnrCEbmVqszVe1dA6XWPbtW97xIDO22iiEw+YRVqpz5Pu+NV5jLOU9oaLcL1qYeME+0U/ccyDtZZ999gnU4xHS7xJ/byEcb24ixmiIVqnFgSCJ6BwQXqoAuxX7uWauj33aOeE1cHMe7J/xP+GEE9oIsI2SspFSri7DfHTTfBrPOEMW/eEHEyk15U9jf8Y9Zzy1V18tcuCB7e6vLWBoI7k4N4z8dky4P/S259wZB9ezybMQZpjn779n5a9/7dNWuOfKK5ulglqKkSBp6Td1RyzD9ljysKEwl9oXqhY5lpdfnlvYyHXo0SNreknttVcnOeGEmWK5FAQIYcpDTJw9D3YxguxfeTZjyAn/Q/jg+URAEl5hrVt4HRFeCGLGjVwBjgc5ofAAIRG8tyEotYD1klrvnGtNQ8hBwBkvPnetabxiqbSVa7l2QpMo3uRd5EqtCmcJZuCqcFhOp06V3p99ZjaL5qYmmditm3y95JLyzZJLSh+uI52Wls6djeC+6667zPVzb7hvzIdSCiXYxcF6Vxkjl2x6LZFuGG0+om4Xvz+7vlQcZhV1gSGFQjHnwq5byL1CxLIUxdDm02HIBpAG5HIxmVMNj6U9h3HjyvNYog9AlCEoeM2IWPJ66grpHEFTbTBqu1FS1rhtW7HQV5x1HkMyY2sjpHiFBHrXcO4zUV1hwOYmumttKcWB3Mq1XA+htN50nnxtTwpVorXHQo/Jp3Pwf2o8/PDdd/LjV19Jc4cOMnGJJcxmkM3KwzvsIG+us44s8fXXssznn8vy//ufpPfaq+CYcDz0KTY7zlwn5Nl6NrmftgiiOyZs/K7cNfn55+eRoUNn3Yu1146nzpFOkDG7roilbVZcKCwlqODF84QARAGHEOFRK6V4STXzHcC776blrrty53f66bliAmutlZFPP/3Dtx8UXrUg+XF+uYsIPGtJZcOy5f7fEkse9oMOOsh4CO1+sBaOGDHCvEdAubl9cUA+a5oVcAg2Qll4tdfEvLAkvZTy44XyNf28mnnJZqdOktlmG0k/8URbCIo5r+Zm6QHZ/+qr3HU0NsqMLbaQ7FxzGYOJNcQwL9go1MT9oAgA4TyulTXo2GFEYPNaIu3Y+VkiXbJpBei33+bG9s/dxApJsx4qFIpoYD1T+Qr4uB7LIMDwC6lAPlPvAN0jqKypls4xY0aDfPllacSS60f2Y6RHzhP+mS9vz09eFgp7dfU9DNbkC3rBdzkezgHWNquf4IWMA2xxINdI666b6Fu2Sj5gDJgfeHqD1jmwUVH5DNy2mm4hnYP5yIblt0PPnvLLggu2RUixTVhqKRMl9V23bmb7Y555ZIUPPzRRUoBrwJAdpA0fx+OesZlj/jkubu0MCDbPjE19cj2bxTzLFq+91n4eLraYEstK0ViP1kNIVKEchEI3iYcL4sCEhRCV0j/J71hRwlr2OMypp84l2WxKdt+92RBKC66VHMdy+kG5x7D7wvJ3xx13mIfZvVbIKiEVNiHcwhJHFl48f1jj8OphFUvKg+IVcMwvBCThn3g6uQ4EP9cXZXEgN8fSW4lWjjlGOjz2WOGD0OvzsMPM98kdoVAQCzFznfnBK8T/7bffNuTSEkvIpy1tXs7YWUski6CfJZL5aYsdcAye448/Zn4tFFuPZVLmrkKhiA7WS5kvUsr1WBYC6yPGPbxrKN4rrrhiyVW4q6VzTJgwj9E1KAhIqkIxIONZG9EdWHdYM4NGSfmFvfJ/S1JZt1hXNt10U/MdvICMG2svazXrCWPLcSFiSWky7103GTuif7huxo9xsFFfvC+nXVg5Okcb0ezcWTqkUrLwL7+YbYWxY9v28et885koKbYlv/7aREk1d+pk5sEjjzzSdp/QFa3OGJQcW8+qGxLuFmpkY0zQT9mnOya2Z7c797jc119vT3LjFgabRJ2jLoklgiQfsQR+wtfNO+ShJLzAzcUrFdWwHtrrefbZBnnnnQaZe+6snHfeLMspJAFhxLWV2g/KAqsZeZB4IW3+G94oyBUEEU8dAttvvIFtdGwL2tQy7LVS2ER5FkkIs/daoi4OVKgSbfNqq0nm8stlrpNOMnkNrucSTyWksuXKK2XmGmuI/OkxZo5yL9kwOFjlhvAgayEEr7zyivEyMocIXXLzLMtBPkukrajHtY0alcsJnj79Cxk27Nt2i0NQS2RUSFqFNoVCEW2ESz6PpVXE8+kCtiIqG+sz6yzyrdxzqYbHcsKE+QJ5KxkT1n0IEOQhaD9sjmErn9qwV15t+Cf7dKvEuzUc2P/hhx/e1jqNdQNSWY5zIA6w/UoxZOPlI4TXvRbGCZ3D5mwSkcbYeHtTB63gHpRstjY2SvOWW0rT00+30zXAfL/+aogmG7pH81ZbmSgpdCF0DfRsWySIQkDo2+iSeLHdokJB4RZqxMHhHRfbngfyiZ7qjssXXywov/zSXneN49KeUWIZD2JZzHroCjhyBAk/4XdMcNsXqhJUM9/hyitz1qkDD2yWbt1yIRRcD8QS0hc0L9SbFD527Ni2Xop4sKylDOsgY11skSBEFoHIOMQx7LUUQHoYDyzTWJP9yFXQ4kC21HYpxYGKCX5DLg86SFqWX960FGkiLJbPyanccktpPvpokXXXlczkyXnnNkKX/BM2C5vPwfUPGzbMbDwfEEzCw8tVgrzgnBgD9jd+/ELy7LO5PIsBAxaTxRdPtfXstJZIr4WW+Vgtspk0Ia9QKKJDIWKZr94CchUSAFFC7pKaUmrqQZDjhI3m5pQ891x3836FFTIFjck2f9GvHVvx4zSbMbJhr7fddlu7aqWWjODxgrBasM6ic3A/WJ+KFVmMM9DjuBbWPqKL/PRSxieq4kDFdI6WY44xekZBtLbKtMMPN1VjMSIfcMAB5ro4J+aHJXxcJ/fLvXbuf7kGAb9x8Rr+8f7edhuG7VnHBehnpfb0jFtdh5QW7wkPNnQkX1iKWzHLEiisXyj8fn2hKkG1ivd8+un88uqrjdLYmJVDD50mn38+3jy0xOrnK0ueDzxo77//vvHI2XPnGAghPEkIBiuYCk1chBgCA0tb0sJevWAcIDQIQQg6+Rml9G6stDhQEAHRbmwHDZKWQYNkJqXRf/1VMuQt/jkHsjNmmLkOOIeglWgPPPBAowCNHj3aGCxQiNheeOEFMx6bbLKJhAXG4YknFmjLddh7b9qvLGnG3t4Pd3Hg+WVhsuFPbo5FVJ5xJZYKhcJb2yGoLoDBFTkKASDix63mGXdj9plnziVjx2IIzcihh85e7R3yx3rGuZQa9WVTbRhL8v0hjRAD5Dmkyo4XG+uBq6vxP8aUKCG8o0kKey0U5YWHj8ioUohOpcWBAusc660nzUOGSNPxx+eNkpp62WXyXa9ekvr44zadg2vBUQFZxkmBLoG+SP6rxfDhw+XNN9809xojtl9RxFLhZ/g/55ycLrf33l/Ka68tKltuOV5efPFjY+B2dQlew+7ZXc86R2M9hqUUynfgO1gkIF/kdhXqC1XpuUQt5KdOTcm1165q3m+11e/y9ddvmuuw/aBKAQ/2448/3vY3ix2eK1u0yAolyKJtu+KtzIXAYEwRiAi1JIe9AkgYnlsEmu01Va3iQG5Cuks2vTkC+ZCmMMLcc0vax/pJaJBfVTiXZLpkk+tnIWDjPPHcQjIhxa6w9eZ/lgP2MWpUzgt6wQUt4m3nyTl5+5jasBe3L5YNe3EXBjt+lcCGAmnxHoVCEcRjaUM5kZ2snRBLW2E+TPITdSjszz+n5Lbbcsb7a6+dIn36zJL9XBs6BPIXElBK0SHOmTFhrWWNspU/0WMwrlLLgDFmv0S1sJahh9gCLRATyCx/o8vlKwqUBHDtrK+saWFGeQUtDmRzGINGA7UcfLBk+vWTxquukobHH2+LkmrdaiuZfsQR8sECC8hPn35qSGS+VmsY3b3GFcgv3+W+snFPw0rFsaCd+rBhuTm6xx5T5aabeL+MzJy5ZJu313Y8gFNwDu64ME7VIpsZJZbx7WVpJzVeOSbF6quvHqg6VVw9lieeOLd8/nkHWWCBmbL99u+WFMZrE54tWcLKx3sSxQnLsYnvdjJ7hRKhkfbhw5uHYm/JCN9jP0m1GGL9RJhBULCaErcf5UPtl3PoJqS7nrlSiwPZanzkSNocV2v99OZOBKkKx0LPc8PGft3nB2PN888/bxZ3jBLljBnpMmPH5owRa66ZDSXsxfbFYs4iH7xksxRrsB2zJAl5hUJRW2KJbMSbx7pInlwUoXZReyz/7/8aZebMlPTuPVkGD8Z4P4+RsyjeGEO5NuR+0GuzhRLRx4gKsuD3EBHWQksiXCLExu94tesT30P3KbXgUVzAOLLG01IDowNblPqTX1E91javXofOUaw4UGbttWXm2mtjvTZRUrQ/++HPvt7ztbSY+W4dDIVarbn6xjbbbGOelzFjxhii7abiMDa77bZbxQZb+la2tKSkR4/pstRSLXmjzADPtx0XzosILpvS5Ho22dIR6AZKLGsMP48lQsk2cOe9zTuM0pMQtfXwm29a5MEHc0r9pZd+JtttF6x9CNeP1fSNN94wgn2//fZr+99ee+1lHiq3lHehfDg2PJNYKrFYIqCwIFqrm7XyuF7NoLmEtYDNe7HWTwRiWDmEpcL1VpZbHIjxR7jzXax91jDgHsN9LbUSrfW4WrzzzjvG0ojnmybKgwYNMqEspTxnn3yCkkaD6qz07p0NNezFm3vCIm7HL2jYixJLhUIRJBQWWYGMQSnl/1Easquhc/z3vznStvnmEySTWcislegSyM/VVlutXaXOoOGeDz30UNtnpMzgkSIyxksQXSKEwo8ux1rIGsSYQoCsHuKV51Ep+2GBNRNvIborxZuinCOFwBjZiqu2iE5JxYE6dZKZDQ3mWiBfGAfQCd31v5RWa+x7vfXWM8ZwjBfolNx3juXuk3Msh4Q/+2zuHNZeO2egKPaMY7xwDR02vJgND7rbs3u+kHXepBUMrDtiyQRwrYc8FNxwLB4QSixj3okZBaKyHtrKpFde2SqZzIKy7LKTZLvtckV1igEP3NChQ024qh0rPrPlvxHIQR9Qu2jyoBOu4UfCsOi4D54NobWhFnarZuGVfEBYIhCZLwhEFqxan1MlxYG4t7wy11msgxaGCFqJ1sIlm5tvvrnpUWoJ5oMPPmgMOORgekloPnzwQW5RXX11BKmECr/cEzt+fmEv7uJgw16UWCoUikI6BzIS+cfaiJxgfcPLEjVhiNJjSTDLhx/mZN6AARChH4zstP2bg0ZJMS7oC5wnHkmigdA/CHstVrXeRt+gzzGWfmGvGA+tzmE9S1bZdw2HQesXRAnmDDoH6zZkGjJX63PyopTiQDwH/I+xpcAh97OUGhH5dA4+R3fnGUKPZx6wRnNuzKcHHnigLYoqqLecx8QSy4EDixPLoClNGKp//XNc/Hp2u8Uagx6zWIvEOKJuPZZuqWuUWxLJUQx5CKIOUY3KekgeAkIVC83w4YPMZ+utN1EymcKNdxiPV1991eTFAcYBQU4Mv63u6oa9FgMKOAKR84C0uCEDLnjIvVYet3+hG2rhEk0evGqFtLhlzLGucT1JCqfxhm0wvhRaQgAz7iyqkL0wigP5VaK1Qo/j8H8s1ywq7777rglxwvhART8ssRtssEHRY40albN6r7lm9M9o0LAXjFF8xmJgFRn+F7RtjEKhqF+4UVLIDdZoFExSU1hTkIVR11uI0mNJZ48LLoA0pKSpKSPzz/+bzDNPV+nTp0/gKCn0BYqxMC777ruvWXv4bdCQRgy+7IPfUyMgX8Ej1m5vsTw3l5D1iCgem6vvrRFRDXBOnAeGdluLIuwaH1HCa6BlbcSbiH7KZ+iFhLBWXBzIR+dgH66BG90GnYeWaBT8Qc9Ar7U6XD6dlnzKCRNIQcpK//6/SjpdeV6u61Xv2rWrr87Lfbc5xN4aJfn0MSs7klTXoe6IJRPq+uuvN149Jpi3L1Q1ch/tccJaTFi0EEIoueRCLrZYd3n//ZwgWnvt7ySbXbigQL7nnnsM0QB44/AuoiTbUt5BJywWShZNwiFYNDmXUie7N5fQW3jFhnfykLlEM2hp7FLA8RCI3CcIUaXl3msJu1hxfxBqrnCNpDiQz72wAp/njcUSkv7aa6+1hRYHqUQ7alQu9HfddatDLIOEvdhn0FbUA/TfYmGzORZuQn+SLIsKhaJyYonsff31143HhpBO6hRY0oU8cPPXk+SxZHddu84Kce3Wbbp06tTBEOYgpBKvDRVe0RnsWLH2BF1rGU+Mvowv41pqVfZ8uYRuj0OitmwIrTdKJWzDIYZ0dA7WwkJG+STApg7hlcMLTdiqJedhFQcqRjY33nhjo0uSeoPDg7nG2kwaDjqq1Tdyv0nJ11+n5YcfUvLXv+bm0LnntkrHjq2REbFUnp7dzAM7/3BsuPqYq/cyJ5MYJVU3xBLBfccdd5hJzgQ76aSTjOekFiW5w7Ie2uR2Jh4CyFavHT06l3RMHlrXrrl+P/mA4stveeDxGBFuwRggnIMKTdfChgCBNISVe+gXauGG0NrS2G44gX34OIdyBAKEgH0SEmxDLJL00HphK7UiyFFovCFFURYHcuEdQ0J2ScJn8YGk2UR9DCS2aI5LMhH633+PBz0ra60V/TNaCmzYC3OORXKjjTYyZNOtRAuB5pn1WiJLrdCsUCiSARRCiA8K7emnny7HHnvsbJXQk6RzePHjj5NEZBax7N27g1mzi10PsvHll1820TOANYTK6mxBPINuvQN0mDBzD93K4oTiuuGdyHJCU4lS8cuXKzeElnWBfaLLcUyKAiY52gVijOeX8aJgkzfVJcziQIXAb21PbSLyqB3CvqnzQHjxFlts0fZM/O1vc8nVV8/yDA8c2CqHH94sY8Zkq+rhS/1Jrt1cVlcfY0Om2J7dNneZOjHoVEGN/7VE3RBLFFgsIjywJ5xwghFEfkiCx9IWG8L7hED2Nk8eMyanwPfrR9Ly7MdBMJpk6sZG8z9y3HjPvsoNe0XIVsvCViiEFqFEOLN96LzhLMVIEKHRXA8kK+mlyRkXDA+QZAgjXsqg1twwigMFOQawZBYgPB955BFzj7faaqt25dRfeil37v36ka/RIq2thXts1gI218HmJLO5YS9u5ULGj8WXhRe5pFAo6gcYsk899dS2Ini8T6rOUShKSqR72+e//ZYqehxkIGNjo6RQ+tdff/3A7brcsNdq1TvwhnfaPD43hNGG0HqNhsVIEGPIbznGmmuuWVKRo7jBW2W+lArHFRcHKqDboI+g/zDX8F5S5wECDwnLtbMRufnmHKmcd96sLLpoRq6++g+ZObPFzDOOg3GhWF/vqJB29DFr7GBs0JfgAuj01vjPNXmJeNxCqeuGWF588cUm5n/bbbfN226k2kK+nOMgjLHSMYFQRr1VtcAHH+SU+n79MrP1y2QxeOqpp0xSPYSSc0D5tV7KcsJe8eoR9lorC1uhEFo3UdoNofWGJrJIsFhBlJknfuOaJDBPbEhNWP2uSikOhPXYFW5BK59ZZYN93XvvvbL11lub+cX+H3+c52UeWWedWQWCvJVo/cJhqolCSfR+VtqkVXNTKBTBwHpz7bXXysMPP9zOeFYrnSOsKCkMt3jWyGPE+Opiyy1bCvbothU6kelEBG222WZtinIpLTf4Talhr2HCz3Do9SphpGY9yxdCyzqJLsf30Me4piTrHOhQ6ByMAdGAYfSTLKU4kFtQL196FCSXMFhqqthIIb5z/fUzZNq0lKy0UkbefHO6ZLN4T6fJiBEfmDmLzuPqHN42a7Ugmw0NDW3OEvR6Qo29xv98bdTQB2v17NQVsWQildKsOGoUEr5+4GFCaUcYs0jx4Obzvn3wQdqXWI4cOVJeeOEF855JZyt1lRv2ysMWZthrmCgUQsu12/YyPIh2TvDAsVhVoypwVOD+sOhzbRSlYsGKkvB7i9u4ZbYZZ0JAMUDYUOVixYGwPmPdf+yxx9rKzXNPJk6cR1555S/mOxtskDOCBKlEW22yWWp1Ns4vySFPCoXCH4cddph5xZAbB53DyiXkZanrmzdKCpnsF8J/3HEz5f33Z9dt+C0y28r8DTfc0PwdtLiPDXuFPNSy5UapXiVvCK01vNp+6lwPhl90qSTrHOilzA2MDVEXOPSr3u7qHDY9CkLorXFgiwO5euHUqTPkuutya/Cuu5LrO3/b9aBD2bBkv1ZrNjc6LjpHo4/xH93Ijo31rmPYIRQ40cTymmuukUsvvdQIB8I2r7rqKuPy98ONN95owiSoGgUomnLhhRfm/X6pKEYs4+axtKEFEAUekiC9rj77LDfJ+vTJEUuOQ+NYCqUASClWG0hBKWGvTEq8egjLsCxS1YQ3hJYHjRwPBDzEiHlBiIQb02+FUrUqwlUCLIVcD4KkVsWG8pXZLqU4EMrHLrvsIk8//bSxgL711hi5887j5LffGkzew+abtxatRGvJpqtEuYn6UVkYk1b2W6FQRIs46RyAY5VizAoSJWUBT3SJMq/kgyHLIQI777xzW+58EHBMdA7OIa5tvkohQVyHrcqO14g1iuro5YTQxsUrzxrN9dSy2BB6A6TW9uL2tvYoVBzoscdSMmkShXCmyowZt8njjy9jiJk30itoqzWrc1SLbGaK6Bzos14iXmtjdsXEknC2E0880VRixdI0ZMgQE/5ADLa3ITugj+Iee+xhPGEI5EsuuUQ23XRT8zDauOuwSn/XOiylmMeSBwKBDpGjhLbtJ1kIRPl+9VXuO8ssk5Hx41PmwX/77bfNZyTHE/eOwCsl7BULEASXQjZstZ6YlQArE0QdgkMIL9Ybez22IpwNZ7EhtG5T5bhV93RbosQx8b/U4kA214bFgmu5556F5Ouv55ZFF50p99zTIvmMoYUq0dq2J/ZvbzhLWGSznNDWJClKCkUSEDdjNmtKHHQOEDRSyo2SQvcqFCXlPY715jCmL774Yju5GEQ+oqCznrFG4zUqpT5AHMH9ZSwp0MNYDhgwoF3UjbsW2r7qNnwxyiq0YVWZd6scxwF+rT3yFQe68sp1zP//8pePpLGx2XATHDiu16/SVmuqc7RHxTPliiuukEMOOUQOOOAA8zcE84knnpBbbrnFVEnz4u67727390033WQaqRPCSX+jSoGyGhdime84bo9NiA9ltIMKFEhlJkP8PxVhs/Lee7+YBwWwUBKHXUrYK4sKpBLBBiHlQU0yCMkhUZ554BdS41aE8+YRIoz4vbepcql9H8MECgtGF+YSwjApFUbzFQeikAFzHzKPEjPPPPPKqFG5PJ4DDvhBOnRISWtr8P6QXoHrWhjtq/scunkT5Qh+9VgqFLVFHI3ZrBtx8liWEiWFMTpIEbullsrtF5kJKcUTR/9AACllTIvJbY5tK2iznsY17LUUULcB4z7j4rdG+62Fbh4h/R8hQXxmQzutzhG072M1q8zHFX7Fgb78MiMjR84jqVRWdtrpR0mlljSEmZ6X6N/ozHasSyl8GMTAbVEp2WRftSaKVSWWKOQ0/z3jjDPaPmPABg8ebJrhBp3EPFBhFB8BNrY9LkLeDdXjuFjoECI8rCTGe0uTF8Oll+aqPy29NJaS5rZJyoNE3x6EQRBh5Ia99uvXL3Fhr35kHcUC4kLeIVbQoA+jXx6hW92Te8YCUElZ7FLBXMH6yVzB+GB7MiUZtjoecx9lBmL5+uut8s0380rHjhnZYIOvZdSoSRUVByoWzgLcnnKulTEI2VRiqVDUFnEzZgcJhXVzxGvpsXSjpAg9Zd0vtk4uvDAtotJy4okzzHVwPeh9GGAB5IN+gsVIJd4j1mheKaCHpylpCrMLxtDt642DIOjakK8KrTVwY/D3VqGNugIo50ChHAwOpVaZjyvuuiv33K2yyq+y005rSMeO65m5i5eda+XZxXMZtDhQWAbutKcoUL3pHBXNGqx0KGneHjb8DWkJgtNOO81MYshoGCCkkYczbvkONjGe9xQaCuKG94NtNbLaas3mlX56hNFyHGsJLBTPjzDEQ0lMej2EvbqJ5QhprNKVCt5CTZVtOIsti+2G0FqrZKVCwOaGAghY0DLtcQVzDoWCZ4C5akNX0LVuvjlXGGrnnTMyaNDKbcWJuIcoIKUWB/JDkNwJu4F8At/KjqQJeYWiXhBXY3YxYukas6KClVt++k0lUVJrrdUqTz6Zlvvvb5TNN6ddw7zG6AlIzWA/eIVdfcMlQJBRvo8ij8EX/SfK4i9Rw3pdWdMYCxwElUZ6uVVorT5tQzutgRu9za8CaND2X4Vgc0N5vsKqMl9L8LyhN/z3v7k2XwccMJd07NjaFgoPYR4xYoQxiGC0LrU4UBg6R8bHq+mne5Sjc9TaYNNY6xYh//3vf41QKtVzlw8Is2JCvlrNiu0Di1Bl0mLVgkSXq5gywUaPttWtmtuS5N3qT1YYEZ5hG7dbAsT3UdYRhvUQgsK1YNXD0hd1sSG/EFqUEyv08cZ5Q2itMApKgGwhBIgVhJ/c0KSTGCrlsWDZvqH2Ob/00ka54oom+fXX3LgccEDOsogXgRArFEUqE5ZTHCgswc/C4pJNe8/d7yb9/igUSUIcjdlx0TmAl1jyntA/dBAMr6z7pVR55/dEk4A33miSo4+eV667LpdPD6mkFzGwBAi9wyVAbKzTvCYplSMfiGRinnGteHyj9Lq6oZ0QcoB+Yb2ajDW6gg2hdZ0JQQlQtavMVwOMDTrHxInzyeefzy+NjVnZYYf2hh287OiM9lorKQ7E3I7KwA0g++5znQSdoyJiSeggNwby4oK/rVciHy677DJDLJ9//vm2ViFhIC5hKXYSvPfee8brRePWcq107MtVcsETTwyTJZZYyISUuJPaFUa2JDakx1ZeY1FBKafYTz4LY9zBWBAiyoYwrFW/K+5nvhBaBJLbVNkVRH4htFYY8t2kN1G29wgFA28yhg83NJly9eeem7v+BRfMyiGHtMjAgbnnxVpKX3/9dROizTiVWhzI9R7bPlBBUEzw21wYFh8rY7yVaL2/j4P1UKFQRGfMjkvBQHssqyew7uO1Qf6UGiVlQ/fYXGfcCy80mrQbQoitbAYQGzab2wbpQedA10DnYHzIa7NKORXNrVKeBDAOeFwx/GLEWHfddWvidUXPYY2066Tb/stLgLw6h1e/i0OV+agKKOHEeeGFnMNl8OCM+KWJugQaXQ3OYvXIUooDVZIilS6ic/D8oNvw3HCf49bXOx8q0sYZPCYkXobtt9++bTD4++ijj877u3/+859ywQUXyDPPPGOsWPVU+tv2ZGIyAAR6uWEFrnC3/fB22aVZ7r+fhqkZ014EpT2f4uqGvbrVUTnHfCEWVuDHrTKq6wGDrHEdcQsTLRRCa4WR60FmjFmMEU6EKCEMuU9xG/NSwbVSLZB7hIXcWxjiggtyQnfnnVvklltmimsgJQyHUDcUE/qyco9LKQ7kjjXz3lb8dQV/KaFDVlFDqWDB4nmzylPQSrTl9JVTKBT1Y8yuFrFEzrC+QICQoeVESWEUZLM6x3zztZddBG18+eU88vnnadl66/aeIH7HsZGXyEn0QwiYG3XChixFVkN2rL6B7hGnyqgWnC86B9eGEbvcNKZqtf8qFEJr1z/WRQo4ERLNHInbmJeCSZNEHnigVb7++htJp1PSt+96ssgic5nQbbDrroUdSS+//LJp10fxSopfllIciDlhdQ43RWquP8fa1TlKKQ7E84L+gmGI6DX0QqtLBKlEW60IiXyo2M1DdTYankMQ8bRQoY1JbBPrsWxxEy666CLzNxXZzj77bLnnnnvMgHEjXItX1EI+ymbFTCxyHDk+CijWoFLCTvyEO2Cy2PYhSyyRmzC//z6PUdr9Fgx3UjKhCUF0lXvGwGthdBv92rBOjh8XCyPhAFwPCgQtKvDGJoGAuSG03qbKXAsLrCUmkEssu/b7tahCWwm4Bqx4zB1vKO8PP+CdFBk5Mi1PPUVv1aycdVZzO1IJrMf22WeflVGjRhm5EnQM/JoH24q/bG4D66DFgbgfkGSe6UKGDDc5375H+CPvuN/IRYVCURmSaMyOUudwgbzhOOgdGDZZ90spLmdlFkAWIk8hHPvv3yoffNAir7ySUxeHD8/K4ME5N+arr06XAQNmrV/oP5BFb9SNX9RJvrBOtxo7ekfQUMOwwfnhIMCblaTUlEIhtNwj1kAbAUdoOWNeaghtXMA17LlnVl55hbnWd7b/0z1hq60K5zdbgxQ9zjEcBKmQbMHzwRx1vb3NTsVf5jZGllKKA6GfUJWX33M++RxT+VqtXXvtteb33qJliSKWu+22m0l2hSxCEvE40CzXWk8YVHfwrrvuOjNwNNJ1cc4558i5556byHYjXA8CCKKABQjrAhMOK1epx3LDXnnAIZSuNalzZ/plLSxTp84nffrMXiod6wnHZSITfhuk6lu+KmVeC6MbalgtC6P1AJMob1uilEvW4wLmIAIdiyEkmTnjhrO4IbRJaarMXIGAIVS9uTSPPdYge+yBZTor886bM4zssUerLLusv1WNAj8oihBBZItfC4Gg8Kv46ybqFyoOhIEMAc1vi1XIs9ZCC/JADzzwQGOkIfROoVCEg7gZs2vd4swWk0EH4Tikx5TSRsWrc7Ces1l5tuKKWbn00vdkxx0Xl2++WVIOP3yWcfmVV9Ky3HK/mfUZecqxIbVBdA6/sE7G0daHQHeEJKObuGtgKd6fcgEB45ogtV7DfBLBvSSVg9QUW2UevdUvhNbVOXgf13Bl5srNN38rr7zST5qasrLJJq0yc2ZKfvlFZPz4tPzyS0oOPLBFij3ids6ijxEtNWjQoIrOq8mjS4OgxYG4J+hR1iFUSN/zklL2e/jhh5tnppakEoTydGIpzGctJJfBBdaSKFHNfAc3MR7h6E2MLyVp3xv2aq2F3smTTv9giGVz88LtJh0KPQuLFRw27LVc+FkYeRCsIMpnYWRjDMKwekFWIFgQ2uWXX94QjCRZ0/wAWYKsoIy4C5atCOfG89scQjYWOpsj6wr+WocO2aq8WKqZJ3jq3fNh+l94YS4XZcqUlNkaGrJy+um5Ijh+YGwg2zxXPF+VEMsgoUPe4kAoNLayNPeH8+G+BSkOxL6efPJJI+C32WYbY2RLer6sQhEnxM2YXctQWAzJyF5CGym8gswManh1i5RZQone4edFQdldZJEmQyw//XSWfH/55amy0kpvGSK70korVZR36FZGtakkNtTQbf3FWFuF3EZShRXdg+4IoUTeQzhY05Kuc+SrMl8shNbWLXDDlUsN64wKGFJGjRonV1+9gfn7qKNa5IIL2usU0IAgnJj7i4Hq0UcfNfOccNiwPdMdixQHQodCLwT2GWAOBikOxL5IizvooINMNAfFD2vdezTZTWpq2MfSTYxHoPq5q4OGwLhhr4WEe26f5JYsL3/8MW+7ZsecC8I2Susa5+ZnYbREkwWdRaBSCyNjBmklpBLBnvTy5DYUhXvEvUIBICy2kLBwcwi9IbSMtw3r9DZVrmYILQs8wpB7nq+R8tChaRk9Oi2dO2fl/vtnyDvvpGWFFbKyzDKFDS4oFihJ3lyqKOAaURg/LLuQQay6XGPQ4kB8F2X39ttvlyuvvNJ4VZKulCgUcUTcjNnVJpbIfdZH1hPWB+QvayzrZpBjecNebaqNH5B7ELpFFsmFVeZ+Q/hdSkaOnCvSWgd+oYZW5/D2e3QjqTifUnQGdBmMmHiSiFChOE9co4OCotQq88Wq0LphnV5PG39XY61j3uNZRf8eNmwt+eqrTrLIIlk57bTZDdWlOFqJHOM5hlgzDzBsV6sexwILLCCjR4827zkP68DJVxzIGrjt/SF3/PLLLzeh/scee2wswrXrklhG6bHEs4HwgUjxoGKpy3cji3ksXeHuF/bqhw4dfjGvkyd3lN9+y1V7xfJB+GC1PXquhdF62rwWRh5S7kdQCyPjykIB6qFSGUAIQsAYp0p6XuVrquy3yEYdQosHlWviXAhPtov4Bx+k5M03G2TvvVuMYL/yytzn++7bIhtskDFbEFAKHOFerfL0Ni8Za7Utu+59rvMVB6Jhu61UjMBHsR0wYEBVzluhUMRf5wirmAY6A3IHwoBsxNPiridBdA437NUasQspo6wrYL31fpCJE1tkxRW/l+22+1D22mtj+f57lHGUXaka/Dxtrs4B2UYuW0Oh1Tvy5Q/a9CEcEjgJbNpEkhFWlXlvuDKwOoctVoPHHHgro4YdQosxfcQIron7ua5cfXVu0p133syK5x96N/o886AaxNLVo7hP6O7oDFb/L1Yc6MYbbzQGcGQB8/bxxx83Pe3jgrojlkF6SpVDLFEqsfxguYNEBUmMz+ex9Kv26hf26oeFF54uqVRWfv21UU499RfZf3+sa8GbHUeNci2MLI4o5jwo9VIdFesaXkqEgbflRlTE3tuGg+O7TZUrDaHlOYB8IRAJT3YrMX72WUo23XQumTw5JXff3SDnntsszz5LWeysHHlkaS1+vD1Dq2EFZeHCO55PsfArDoSsefHFF+X66683Y8HYs5Bvsskm8tRTT1Xl/BUKRbx1DhuRVAlQJJHn7Iv6CX5yqhSdo1BklAtkPejRo1UuuugpE0XUu/caJvIEQ+Lw4Q2yzTaVX1+YPabdmgUu+XHXQIzdkAi8cPVQHRUwN4iuQU+NSo/ySyVxdQ6MHm4IrUs4ywmhta3L/vGP+eShhzZr979VV83I3nuHM/dYv9FNqxEl1draauYkc9OrRxXTpyGShLteeuml5nOeZ9v3m5Y+cYiSqjtiGbb10CbGM7FR4PGiBbX++B2rlLBXv3PZdNO1ZOWVf5aRI7vIHXcsLz/+2CoPPJB/UYsDClkY8VAi3HlYGAcWS7xfkKFqhVdEAYrz4NHDSlrNgkN+bTjcKmW2F6M3hJbvF6sIB/HCuoYRgGtyLZK//UbuU0dDKsGwYQ2y5Za5RRqlo1ev2pa/zgfmIGEo1ptcSi9XrOKnn3663H///XLHHXfIrrvuaj5nPrMpFIr6R9QeS/aN/kHkCyGNhSqi+xnO81WYDwLOm3xWwNrghr2usQYVaNMmvaGWxDJITpubP4jOgQHb9gUk6saGQearDp4EcF222BH1PsIoTBUEjKENobWwIbQ2usfNjXWjqdA5Co23bV2WSjXKc8+t0vb5XHNlZdFFs3Llle3blVUCov4wmkRdE+H33383Ogf6PzpHKbohehshr+SNEyl1yCGHmPGDoNrUvDigLollWB5Lb2I8xKiUG+ceK18p76DgXGzY6157DZCRI3Of07rhq69S0r17PBX3QhZGhD+LJYsXDzUeYG94hWthjHNVVPfB59yx8tqeh7V+2AuF0DLefiG09pV7ZK2gkCW//FB0psMP7yAffpiWrl0zcvvtM+WYYzrIxx/nFoxjjy3NWwk4HuQcRcoaJMIEY0AEAschr8H2iQoKvLbkUDI+WAkZFwvGx+bFKhSK+obVOfL1qy03Sgq5izeNjeruQQxfbg+7YhXmi4GQO+QcugfrQb9+/doZLNdcs1Vuu63REMu4w+YPMn4QMHQxZDZEx63UyZhxjW4/71KMjbWA9eixPpezlkWBfCG0lmi6Op63+CPjbVuXUWeBENUpU5aR335DP8nKF19MKyl/MijCqhIdpNAh+gH3qhQjBuScytfM39dff93kVVvg8SzWx7eaUGLpA7xnuPOZ/G5ifKng4eaht/2lSg179fZS4lxwd6+3XqN07jxNLrqoo3zzTVpuv71R/va3ZqPg544rsYZtOM91QRrWWWedNsKYr0IZDyMLHdYdV+jHycLI4oSXEkHp9ejFCYVCaK3gt02VuS/8jzkL+fczrgwZ0iiPPNJoSn7fc89MWWutjLz00nQ555wm07ty4MDSlSrGEUMKcyVsYol8wLLLfPK2RikGzoe2BSeddJJpJ0Irg7grHgqFIjog55ELGBX9DJ+lEkvbXxAPBGQQ/SNorQEbduvqHPkqzOeDzRdno4jaHnvs4Xtdq6+eu6bhw9MyebJInMshuL29SWVA57DrM6TdfsevKqpNI7F6R77+g3GqMh/nyDW3Mqo73jaEljlvjSG2Fcj11+f077XXzkRCKqNGc3OzMd4TMZav0GE+MBZPPPGEHHHEEabS/FVXXRX7SvN1Rywr6SnF51gUsJIgRLyJ8aWCBwMBz8QoJ+zVWnUQFja0AcX/f//7n0yfPkUuvPAo2W+/ueSGGxplvfVa5eSTO8hyy2XkzjvDCw8IG5w/gpDFizLx+Zq/+lUo4+G0QihOFkYMEVh2UQaWW265wH284gQ3hJbxZv4h6FEuGFPmL4sy984NZ2lqml8uvTS3MF9+ebMhlQAl49//zt9SJIhiA0rpxxYE3CNIJcoFz1QplQNZ9CCUtBO58847Zdttt03cfVYoFOHCrjfoHX4EzOY95vNoukDRRs7iJSRHrtR2F1bnsMbAUnQOwLrKWoZcLGZ0W375rCy/fMZEqlx3XaOccUbpkSnVAGOKUo8hkdzUfO2rGDvrtbLrDmNpdQ6iq1gT+cxbHK9YS4haV5mPI7zjzfOBV47rslV90QMwMD/22NoisqisuOIv8ttvmaIhtOWAe4vxgSgpq3OGgcmTJ5vQV5sWVUrUnVtpHkJJj94k3OfGes13yCfE8yW3Y/lhQvM7Qj4q6QNjw15ZcBDSTFabgBtECKHA8jAhECEqrpeIyYlyzITbeOPPZOWVl5dRo9Ky9dY5M864cWm59tqMHHNMS2yTysvts4mg8Ta7L2RhtFbGKC2M5N9yrzhWnL2UpYAFGPIFYXaVCxtC61ZFffhhkvYXlW7dpsl66xECXDm5x7DDcfCohkUseSZtuBDPVKkKG3keCHXm3nvvvWfmsEKhUFiZny9Syq49hYgl+gJyD8M2sgkdpBSjl6tzsB8IotU3eC3WW5oUG9Yx5DpExS+Fg/XAVYq5rDPOaJZ99+0oV1/dZAq0VaneWuAxYSwgJ1wPhuxSI8/4vjeNhLGCLLgtIWwbKrdmQVSFgMKqMh8nuK3LuE+u/j19+gw54IBcCHavXt/IO+98be6DS+55Xym5x4kDAWTehEEss3+G80JYeaYoEFXK+fE70m14rt9++21T5CcpqEtiCYKGpSAkUDghlpAdJlS5JMRbeQ3rmE1iRhC5QsgV+pb48F0mE+QLC5SfIERY0Yrh3XfflZEjh8ttty0j6603l0ydmpIuXQihScl55zXJllvGp2AKixyLFvcmzKTyYhZG7ikLSxQWRhZZW0kUokJIaRIsSUFbbqDckCPqLo5uCK0NTz311ByB3GOP36WlZWZbCC3j6xX8QRdachYB8zwMgwBE2S4YzL9SwoX4za233ipnnHGGHHPMMfL3v/898T1VFQpFeLB6RjFiiSzxyjPbhxqjKHIJQ14p66NX50Bmo8dY4x+GNBR29AjXuG3lsTfs1U1LsUCXuu+++4wRlXA8t9jIDju0tvNann56PAzahBzipWRMSk13CNp/kDUSMIZur0dy921xPDeSqhi5r3WV+WoA1Zt5QmSTDaVmXnGvvK3LLL74Yi6ZNKnB9MLeY48+0tS0bDuHAjqeDaF19Q1egxoSbIQWCINUTp8+3Rjn4RflpNs89NBDRt/Yfffd5V//+lfVij+GhbollvnCUiyxhGzYxHiU5FIrQnphK695S3lzDgght8+jH/Fh4rAwcf70s3FbGnhBniXlhlkM1ljjC7nvvmXkyScb5JRTmmX//TvKyy83yFFHdZAnn5xhrIq1AtcDScHCVq1CNvksjLYanCX3CCFX6AclPrZKMKSSe4QgrIccO2sxZIEs1HLDxZgxKVP9tbExK0cdNbd07dpvtqbKjLldaN2+YrYinHc+WK82zw4VmCsFShv3CqXJS5SLgfNHuL/66qvy4IMPyqabbpq4hVyhUEQLZEKhyrAusfTKF4gCshfjJLl+pciXfBXmWdtc45/thWe9bFYe8x0MpHyf/o35QkT5P99jPxAAt0cvl3b66c2y334d5bLLmmTTTTMyYEBGnnoqLc8912Ca1y++ONXsW6VbN4kcLvkilBgvUdT5kIy924bK21+a9QySwf1xczVLab9RqyrzYeN//2uQU0/tID16ZGTECIpjfmQcD4Vabrz+em7NXnPNjORU+tkdCi6559X2T7c6hyWa+WpywANsTYlKe1j++OOP5n6jQ5VanwVd9bTTTjP6xn/+8x9TaT6JOkfdEUur5OezHtqbhGuZ7yIk3UpnpaLUaq/8z62WBclBYDCpOQ/OG4+NLVJjhZDbegMBhjeTkLwXXnhB9t13Cdlww5yV55prZsqaa84lr77aIDff3CiHHFJ9C6KtfoWAh+BhBa0V+XItjJCLSiyM3BsWVhZoBGEU1UprASrYMgeZk6XkAFAREGy1Vau4a4K3Ihzzwe0rhlcUssfYul5NxpuekKCUtj6F+m0i5Akry6c05QPPFqGv5FuMGDGibe4oFApFKb0svcQSkkZkB3IXJZaw+lIMXuXoHG4vPCI4WMeQxchY9jVy5Mh2RWrYXCUcBRldA7mI7uEq5zvu2Cp3391qehbvsksH2WwzKoO3Vy2p5nnZZTNlzz0xvkvosAZfZL4tnlcr8uVXHM+Se7sGWuLjtvyy/bxdnQO9xK5jFLIpNYUjbnjmmdw8/fLLtNxzzwey8sqZoilEr72Wm2vUEQlK7oHVOdD1eNbcmhyu3sGx33nnHfMbdIVyCnUC9o3O+80337S1LikF6ET777+/kSWcj1tpPmlIZStpsBRDcDkIPSwG3pL/kDgeUiYbD2klHjS/hsOlVnvFW4mlBNc7pYfthLZFaqyFkQ24Qh/lnwIiEFIEPQ3ZLQg1oJDPPPNk5Z13ple1FQljzKKF0IR8BfF81RpeC6MVRtbCiBDiPrMYcD1Yl+Pe9qQU8oXQRRCWEs47dapI796dZMqUlDz66HQZPLi0yq/eqr+MNwsvllk83JtvvrkxSpTTVJl9EfqKgCactpS8V86LHlHnnnuusRyeeeaZiW+arVAoogWy87///W/eKIuXXnrJ9IC0UUoY3VAcSyE/leoc/M62cOB8ieCw65ibQmL1Dr7vEh68KKyT6BroHC5QUTbeeC4TEmux994tguimauyYMbnPt922Rf7zn5lSgS3f18uDzsH64a1JEWf46Ry2FZsNnUTnYA1El0p6/QaYxnLLzWU6GYCjj/5JLr64cHgwv+nTZy6ZODEtTz01XQYNKr3C/Kx95WpyuE4F9FWbXwx23nlno4+Xmu7CftE5wEorrVRSuo2tNH/iiSfKwQcfLBdffHHio+DqjlgChPUbb7zRxvghasRPExaHFYGHdf311y87V8oNe7UWw1KqvaLIo9Bznij0xTwzVglH4Fuhz8PAdUGg8cbsuOOObV5NDKObbtpR3nyzQXr3zhiL4nbbtciqq0Z3qxkPFiy8f7ZHT5IVcmthhOhggcLKDFwLY75wziSAHBQK0qAwYKXzW7SQDPTG/vXXlPz2G0aDlPTokZWllsrK3Xc3yGGHdTQhLWPGTA8l5NqG0EIssaozz21TZW84i9+Yu1XlyDNiK+XeoPSRQ0SY+V133SV/+ctfEnlvFQpFdUFkww033GC8L34YOnSoIXGsiRC6fNXQo9A5AOsYyjO/Q+co1r7EKuFW37ApDayF6E1bbbWVieJw17/x41NG72huTsmNN85oMzbiXP3XvxrlgguazP+OOaZZLr64vIrhXr0Iwzy6nSXKSc5/ty2/WIdYx2xoNWu0t8VaEteld96ZKhtsMMvRgLfyzTfzd3AAn3+ekpVW6mRamX377TQJ2wmNfnHHHXeY+Y3eirPJhtC6Xs1Cbe1sBBa/ZQ6mS3gu3Urzt9xyS91Umq+7UFhvw2IEIYKHSYLFkIcUYllOw+JSQ1C8QFAzAZlMeEyDeojc1hvWC8v18TDwAGDdILTXJujzIFxySRfZZptF5dNP0/LPf6bliisa5dlnZ7S1gwgTCEIshhyfMa4ktDguYMy5Xwh4cl+whLr5sVaYuBZGu8V5cWMOk8fIgowQzFem/KOPUnLQQR3k/fdnn98LLUR1w9z7Aw5oqZhU8jxiHEHx8muqbK25blNlK/Ttq41S4NkqlqPsBXLirbfeMs2H8XBCLG1vM4VCoSiGfDmWGIAxdCF3kSmlNkWvVOfg+MhM1mgM7UELvriF8WwxE+QqnhX29corr7SF8LqRVO+9R3XORuOptOD9Kae0yDLLZE0F2QcfbJALL2yuaN1gTSB9A9lNzYlS5H1cwf3lfkHgGVPa3XEfrM5BqK9d/7w6Ry0iqDA8P/10WkaOTMtRR7VIPv+IrY56++381UVWW61V3n8/bboZfPllzlht8cYbaRk6NC1HH03BxVlhsBT6iSKymXEjyoDQ0+22266t1on1arohtOjf7pjzLNo2c3gpS9UZRo8ebaq+1mOl+boklhAtCm4wMWx1VsLqrEDN13IkH/gupMKWC7fWwlIaDkNubdgrxVEqJR9cIyEfNs+Pc7SJ4jwUv//+lVx5JSEo3eXZZ5eSkSPnkYsvTsvDD4dHLPHiIeh4+JLaS6lQaA0kBaJhBQb3DAXCHXO+YwU/HnF+6y1SExcLIx5YvJScS77qvCwWd9zRYEKpqTQM5p03azYEOwvBL7/kPu/QIWvCnSoB8+fxxx83wnWXXXaZrXS6bapsx5xn0I4589w2VbbPBBEJfAdlLEgILc/mkCFDTPgJ4a9YD+PS/FqhUCQDKKgusbTVVlmPkV38H+NZKWGrlYa9QlCIIuL46667bsXkA6Ub5ZsUHNZCvLM2hBYjN0Z8xgAF3K16byuwk4vPOkJY47BhaRk4cHZdhDTV889vkhVXzMjuu8+eU8fxUPQ5Fl5iolLqQV6jS6FzEEmER9kN50X/sDqIu/7Z4k8YwLk3bq5mIQ9bpUBHePbZtPzjH03y3ns5IwehzvRP96o5RB6hcxBd9+mng8xnu+7aKizz1AF54okG06YG/PKLyC67dJTJk1PyxBON8vDD09sK96y3XvgOEcAYYzBBJ7f6AnqEd8xtBJVtMYMuBXgO0DkYa64xiF6fcSrNH3vssXLeeefF2hlRDuqOWFpiddZZZ8ltt90mW2655WwPmLflSD7YpsZ+1V5LCXtFeUa4YoGqpCBJIXB+o0aNMoV/yE8bNGiQEfLrrjtZ1llnvOy0Uz959tkOcvvt70j//rlkfoRQOT0ebYl0rov9UJwn6fH/9rrwniGs8SZjhSr0wDNuNhHcepJZIPwsjHzHDWeppoWR60LJwFOJVYxqeblnIGcRhChiFGdD0D/0UE4sbLBBq9x000zp1i3bbuEfO5a5lpZll81WVOkPi90zzzxjzg8vZZC8Aua59d7zfEIsEfoYbBhTnn28yTaE1g1n8TZV5tk85JBDjKLy/PPP5w1jUygUiqBRUtbL4RYHJDUnqDE7X4X5oCCVwEbTcPxiYa+lAAPgTjvtZNZHu4YhW20lTRthAtHEkE4UiVuBfZNNespDD3WWhx5qmI1YMjyHHtpBHnig0YQ+rr/+dFliiVlrD+PKdWF8pIp/KXlscQXzhUgcPF+sgX7tXvKtf9aTbGtysOE9Y523RWq8LdZKAfrAO++kTRoMIcxkA/H+1lsbTTV4MPfcFOYTefjhRrnhhowcdliOJNpoQfQfiNfiiy8rb7+du65NNsFJkyOWjz8+i1hSVRhSCUaMSMvgwbTRy53LuuvmL9xTDtDzGG9ryC5khGbMmWtshH/bCrK85/foHBgFcCrw9/wep4L77M4plebrhlgykS+44ALjeWCS/Pvf/5att97a97tBiKU3BAXhWIpwZ+IhLLBsEHLIJIxyAnH9THDOm3htLF9YKTkuyj/9ph58sFGee25VWX/9L9oJINfSVYz02IpyXB8J5Vhh6+HB4LoIrUE4UAHPbdBbChi7Ui2M5RL8IOB6sBii9BDyYZUMnHwHHtjBWAa9oH3I2Wc3ywknzB7mCvfr35/wo/IFPXMOwTps2DDzNxEFW2yxRUnXz3VBTHlG/Qw2KDg2nMUNocXwRNg4oVN33323eUYIQ6mHUCqFQlEboLRDolDmWQMIeXVTXSCIpeocpYa9IvPQOQhV5fiV9OQuBG87BgifrbrtjTCxFdhtfYgVVhgrDz20utx3X6sceODHsuCCOb0DEn7OOU2GVAKIDHmZl13W3HZd6DToUkmvjOqtMs8aVUmVeXRTCL8tlGg9bDY/1kb12Kq/bou1QvPj739vkssv9zesd+qUlUMPbZHjj6fHaaOcdloHOf30JllzzVZZYYXpRpdC97W6FCGzM2empHv3jPTpk5UOHVrl9NNpJYJhG305Jddfn7v3VA++6qpG+eyz3Lml01lf73a5wIv/8MMPm+vfbbfdAjt7cBqgS6G/ubqU+3+r5/34449tIbTMWwzoyAP6wTKH673SfF0V7znnnHNks802MwU4qOaIsuqH1157zXij/JqW+oW9llKV0i1iQzIvAr5abm7OmdwHq7ATKkKSPVaU999PyXrrdZKGhqyMGjVdevbM+iboI5DcZHFesdRYrxdCqtrXFSVswReEgO11WG656aBwLYx2C8PC6L0uPHcQKgQaOb1WSZk4MSU779zR5EZ07Jg1+Qv8i8uef/6sHH98S1vz4rDBAvfEE08Yqx/ASwi5K0VRwBPMwsVCTO5r0P6jzHUILdEMKCqAsSEsmAqwWMEVCoWiFOAhJJQOuYRh+6CDDpptDWFNJmzTLw8rzLBXCB5rWLWqSnJdL7/8sqy33npGfhaT49OmZaVnz07y++9pufnmD6VHj4lmTXj++WXl6qtXMN/ZZ5+pcuednWWuubLy3HOfyuTJH5nrYg2rh2rsNuKLNahaVea9vaV55TM/nYN7iHdy2WU7yU8/pWT55ckvhAxCYmk9kzEFmGyLMRjE7rt3kP/9r1F69GiWyy57SZZcMlfJ1uqIJ5/cJNdd1yQHHdQsV16ZK9xEW7wPPkibQk9DhzbI3Xc3yqBBrab/+nffpWTbbTvK2LH0s26VV17xb+VTKiDyTz31lNHTSd/aZpttAul7POOQSgzQ7nUVQvZPgo/h+vTTTzctfXim8ZRiDCcMFo9lPaJuPJaAWOViPaXyeSz9hHupYa9YKRAWHD/KsNd84LypZImF6LnnnjOx4IQDQ7b79+8lG2/cKi+80GCqs91ww8x2CfoNDUuZMIRvv22Vn3+eLuut951Mm5YL0XCtrtZiGDX5qgYgGhAU5grl00ut1Fcu8lkYrdDPZ2FkPgUhUVjOuC72hwHFbfkycmSOVJLn0qVLVu67L5qCTvnw9NNPG1LJGCBU8VYGBYsB8xEFjt+VYuFlDrOwEsnAfkjWJ5cFoY8H0y/fVKFQKAqBSo6nnHKKkcvHHXecHHbYYSVFSYUZ9lrtIjasW0SOWGM9axetSArpBp06pWTLLTNy331pef/93rL77j3k7rtFrr02V5nloIPGy3bbjZG3315XPv54QRkyJC3nntvTpJrUgyEb7yvkhrWonP7K5cKvtzT3zs0bROdgjNE1hg1bUn76qbt07ZqRt96a3q4Ykxeoh1ddNVXefbejfPllR7n11nXM/XVtDM89l9NbNtlk1jOwzTathlhedVWTjB6d+/L55zeb35F+8+yz0+XKK6lAXHkYLNdLODobwEhBRGMxfcoWO8T5UGqLwlQqZe731VdfbbgBPboxYkMw613nqCuPpQX5hfvss4/svvvuvv+nAiQEyYY7WuFebtgrpADhjrCIS6gGE5miKCw8XA8L3vDhc8vmm+e8YDvumOsn9cknKTnzzA7y0kvtH7BevTLy8MO/y4wZHxvrGgKJMeEabYK+Ww3OWrqSADfnkHAhLFdxa42Sz8LoFkZwLYzufYdU8h0say0tHUy+ArkSbDQopihP374ZeeCBGbL00tV9/AkLwWKIsaOUcGMWPXKIWSAhy6X0f+N+P/vss3LooYcaMkufyv9n7zygnKq2BryTGYrYewNRFAUsIEXBhgr2huKzPxQVLGCvPDso1mdHsWAv2BALig/ELihiQVAEG4qiqIidMpP86zv5z3Dmkp6b5N7M/ta6a1omuXWf3XcldC5WFKW8IEswenDgUs9I/VQycGAlylLW9aXUhjWYbBTW92KmvWYDXbRffvllI2cxlBiZkM7Apa7usMOaSPPmMZNOefHFiWjd0UfXyM03/yNffvmFjBq1SIYM2VaaNauV++9/XRo1+t1kTrmZVGRWhUnnoOaQUhjbZb6chvJFFzWSd96JyiOPLBLrd7Zpy+ga/fuvI2+9tZocdNAsGTDg23oObu95J/WaNPCvv15bBg5sLzU1Ebn88sWmlAa++ioiW2yxnCmx+fbbf+pmmNosOsuBB9bIQw8lxrr5Cfo55WFE9IHJBdgImZ4XW27DeUHnyMUQjDud5vlf5END6jTvm2E5bNgwufbaa009E3nVt9xyi4napeKJJ56Qiy66yHhKMMauvvpq02jHD1AeEW608k2GTUtBsXXTXnnQczEw3LRXjEkMlCB51Ui5xJOIkLcDje+/H89qY1PDgGD/7ruIxOPMxorLBhvETevnzz+PmCG2a675j9x44zTZffeN6nXrdAv0bXdOt0Cfr9lG10oNBgqGF+eGqFdYauu8HkbOveth5HzbWhYicauvvo4MHtxYbr212lxrl112qZWHHlokPvZ0SArKE7UE1FoQSXePJVuFwC7IRCrdxkPZQvSWTIa7777bdH899thjy6aMkKaOjJwyZYpx1lDn0atXr7T/w/w7BiezcKPAksZ7zDHHlGyfFUXJTJ8+fUzpCdHLZCAHUSwxLAvpMO/Obix12ms60OOef/55s0aR1rnrrruajurJZC1BzkQ67NK/kVp51llkSH1q/r9Nm7ay++5ryscfR2XQoCVy7rn1S3ZY69xRJ8svv7JMnLi6PPZYI/nlF85rorQDdYxUylNOqZFyBYg4J+gcZEihc7gZRMmgufCzz1aZ4zj22BrT18BPmEHdv3/iTZPNFP3hByJ6y0ltbUQmTvxV1lrr1zq9g/POPWtrNNFBcG7YqQDDhjUy9Zbw3/8ulhNPrJG77qqW009vLDvsUCsvvbQ0kxDLY7PNmhpdkxKt999fKJts4r+jm/vSjsPr2bOnMfQygR3D/3jLiLKhtrZWbrjhBmPToHuwfpfL6VMuncOXfMbHHnvM7Mjw4cNNqBcFjogEymCyUD/h6MMPP1yuvPJKE45mNhIHi1cPYeRXh7ZUIOzwFrJZ4Z5LTYM37TWosxsxOnbZZZd6v9txx1lyyimfy3337Stz5iQu/7/+VSOXXrrE1F0iBF9//Us59dS2MmfOCnLGGZ3kuecWyRZbLH3gUxXoW6HPImPz911js5wLoK1DYUHmYcHLG0TDNxXcpxj3bNbrbc87QpBzzn2Z8JRR67O2TJuWcHKsvXZMttkmZuomSXvdfvtYwbMn08E+cJ4RajSJAhZU67HL1rDD+GdB5p4izSvXVGXOCR5DFvSJEyf6IlsKgf3A6YZxe9BBB2V8PU4raqRPPPFE02iIqMDxxx9vrj/yVVGUYJCp/AaZh7y2Okc+aa9EhsiMgqDNbsSoxpFPlhSOQDJEiKIm20eSTfbaq1aeeKJaIhGaLi6UXXedKtOm/Vxv1uZ55y2Ro45qIrfdVi0nnpgY12L1SdZzHJbTp/8lI0Y0lWefXUN++SV5T4IXX6ySESOq5YorlphRF6XyK3q7zGcaM0e3dTqujhxZXTfS68EHq+W++xaZhjfZQKCgX7/Gsv/+tcao80KG2hlnLK3nxOg79dQaWW+9pe/P52NU0ohnq63Q2dYx+++ed8pRcHBYJwnXHCOzV6+V5fvv15ObblpezjqrsendMG5c4h7v2bN+SivXoXfvWjMW77jjaopiVMLOO+9s9g1nR6bUY7fcJp9UZf6vX79+JhsuCJ3my6Vz+BKxxJjEuCKX2N58KO+khVC06oVOTBwwngQLhd9E1TBOC+WQQw4x75UsLYUbB+8hAp2HxW05nG1YnRsPIyooaa/ZwqV+6KGHjCGyYMHK8sUX3aVPn9Vk773XNNeMPHIeCAzGVVbZVA46aEUzn4h5hQipc89dItmsZTa6ZjvBca54sG0nVGtsFnPWkktiAZpujhFhkaxpUxix+f8IeOsxfPLJuJx6ajP5448qWWGFJTJw4Ieyww4/ySqr1B+oXIwaWbuQ0iCHhQZwQOy4445mUc3lWnPfkIbCPcI1y6W5AfuBgkMTr4MPPtg4uoLWmh6Zkcl7SEMhGh3RNMBCej/nhlpVRWnIBClLCl0DY3HIkCFJ5bSd5YfOQaZULmmcZAhhnODMxiGKnA/q7EaOlZFnfHWboXmzVKipO//8xnLwwT/Khht+YPQw0kPdhnWUpHbp0lRmzEjoIBgmdLdfc8246RUxfnyVfPrp0vOwxhox6d37d9loo/ny++//yF9/LZJFi5rKc8+1ku++S7xv1661pusonc1L1WXezlFPxUsvReXaaxvJxIlLHd0tWsRMyQpRy2bN4nLttYvl6KMzG8Unn8xIucTafuaZS2Tw4ETNIuD32GWXpqZpH01yliwR85n9+y+RG25IRC2xBjjnnNdbblkkxx5b3xjkOvL84Di2c0TdZoQJfe93eeCBdvLUUxub/2F0DFlTb731j3ToUP+8M06Ea7nnnrUmulwo7B96AzIh18Y46In8L8Y/Tuhcy21wpGPAbbfddiZDKkiOn1LrHAVrl6SaEWZl2KcFoUfImShBMvg9EU4XrOHRo0eLHyCc3GHF3poGHgjC9yjBhLt5va0VtMNlvULfDjxmw5jkxgtS2ms2cEwo2uR+UxexyirPyvTpNABYx6RnkE7pNrEZO3ahHHNMEyPAKaJ+6KFqk5Zy0kl4Xeu/Ny2j+/RpIiutFDfep112aSbrrce2nvk7wsematpWzDyM3lEnfp5TrjkeGK5ZPmmUQQZDHSG4cGGV1NTsII8/vrwZJmyL5Lfdtlbuu69GmjdvK3/80XyZIdbc466hieFViIOE2knqGOhECxiu1Byh6OUqoLlmbBjLtLbPZb84NlI38LbddtttcsQRR4TG8ZNMTiJHvXLy9NNPL9s+KUoQCGKWlB2anqzDPMYg2Rt29BHrHIqnq3N41ybX2Uu2RxjmRXMMXuOeYyaIQPdvyjSQx61a/SmXXvqJkddt2myRtP6M0zF8+GI54YTG8tlnUXnhhWqz1X8N8y5jRuegGUzCAZmIrnHuWff69ftS7rhjebn//vVl0iQ6jzaV6677Wg48MGLOvZ8dWd0u8+g+6FOpnLg//ihy7rmJuZ1AOiiNao45pkZ69ozJvHkROf74xqZj6oABTWTcuBq55ZbFkipxZ86ciDzyyFLj9PrrG8mvv0bkppsWm8jhhRc2Mkbl6qvHZcSIxfLFFxHZc88qEyWlEzxlUFOmRI1RyTgRoonJRpeh83fu3LnOQU+03htN7tLlT1lhhfly//2rGaNylVUWyh9/vCnTpi3V99A5mjWLmuvmB2Sk0aGYqCHQY8LOF8/GIc5zaY3lXPRE7Aq6QdMYEGfVySefHFo90y+do2DDEmHJA+zt0MjPNm3DC4Im2ev5vR9wo1vDMlm3V4wo+xBwU1ilm3lMeAZ5nduYBqOI37MYuA9UGEHJJz2WOTwstkTyOO9sLLBuuiEOl9GjF8n//hc1DX4QOOec09gYlRiXLhietgHQM89UyyabxOT442vk3/+uMbV8nDu8dtZz58535NyjEODl86tAHyOWY4OgpioX0njovfe+lWHDtpMpU1YwaSsuZ521RC66aMn/ewCjdcajnT9Gypb1MGII2q6CtvV4tka+64XmupGqxfNFDQMpILl2PeOZtTM387lmPKPUArBPdH2lNiLMpJKT3Nss8rkY7IpSSVx//fUm5YxUd8DAxNNOl9ZkWVIofXvuuWddDSSRRTqnk2XlR5YUBp9N+/fqHLYjp62tswaPO9DeOlmtzsH/Is+glB3Li6Ws4njEuKS/BVFXZD1KPym96bJnunSJyZQpzEWMyOjR1TJ6dJWZwbzzzjETwaRfQKpTw1pkO6HecAPr4mI57bRaGTu2iZx3XktZvPhDadPmIyNH7Xm3mVSuzkFfRxrdMGYjXeJLtl3miQo+8ECV0akw/DCOBwyokdNOW2JmjlvojEoZ0k03VcullzYyxz91alTefHOhJFNBb7wx0U+BaOShh9bIKac0Nkbjb78lUk5vuy2xnt955yKT+srG+UNvu+oqxoEslgcfTOhwpNLaz7Cjy9DRSInMVHOILrHyyivJsGEizZotMWNGDj6YhoGbmnve6tngHXeST7kU+0aWlB1hhqMAJ4YNaqQD3R49kTU1n3IbPpsoJWs1EUv06jDjl84R/pkRKYQ8XhW322uqmgZ+5xo8CHQMHowdoppETvidFT4IDd477POUOA6MAby9CEQitwh8C79D+HNe9tgjJj16LJQrr2xkBNDVVzeSPn1q6oQsguuOOxK30j771Mgbb1TJ559HTaoL/4PQHDBgSb1mMQhuIqRs1FQA59WmzlqDxy3Qt8InnVDjWpGmgfGVj/cpyPBgIwT//nuh3HNPd3n33YT3ev31E7WT1FGyULj1sMlI5mG0Rj6bNfIx6t2IMvcL5xJliA6tFIPbiCDPAzOhUJ7yaaNNFJtj4//TeXmTwcLH4GG8ajTRID0u6J59RVEqK0uKNTWbDvOuwcP6ZJ2sVucgwwaZjJwmksfxshaHVaZRs8X1YsQCRgUbekW2Kb3YeJtvHpfNN18iF1xQv9FMLqBmPPZYrek+i5E2ePDW8vTTm0qbNvPNuSfSRdTKBhYSDWpWkbPPXkeefLKRbLRRzIxp2267WN5d5mfMSNQ4vv564u8dOsTk1lsXpUzN5fTQXXXnnWvl8MObyJdf0syosdx2W/3uqfPmidx3X2LNPOecJbLrrjFZeeXF0rdvYxk1qtpsgB62555L9//ii5cYw5KGPiefHDF1r0BAwDu6jHKWTI2HvNft2muXSN++ifrJJk3WrItMe2eoE+HlZ1su5Y5YS3WPeGdic85JiScFO5vSF5wdZH1h3PI/uZbbvPTSS6bTPA4rnCaVErzwg4INS240LqgNP1v42Rb8euH3ubw+H7hZEfDclLY5Tzbwem5KFF0iMHhoEBbc9NyICA9uaJRuN302LK2vWfww2Dg26kzsXB66droPFrN23nrrLfN3IlB4qXAEP/ZYlXz1VVSGD6+Ws85KCJ8776yW339PDNIdOXKxyZt//PEq4yEjyjl0aCNTgE8HspNPpqlP8n3j85MV6FvhQ4oJgg7DxfUw2pEbvAbjxKbjlHqOaCbwVFJPMWVKlQwcuCSp19F9rb2d3IHKeI/eeGMbee21pv8/QHqRdOxY2BxKG61ks6kjnGdraKII4Hjg/rcd+SykkNj/wZDPFa4xizmOBFJXbGOibMEAPvfcc42CSLSCAvUwPIfZkEpOcp00Wqk0VIKYJWXXKjuTMpcO87zeZnwgY1G+Kd2wfQowNHlv1jnSZ1nz+Fpo+UKpYB+RWch3DGSc9RjQzz77rClP6d27d8n2BX/lPfcslj/+iJjavkMOWd4092nffo2662gNnl9/XSAXXLCyPPdcItKH3rP77k1kwIB/5NJL42Ymp9tlnmgVOkkyFiwQowfhgGccB3WTF164xDjds/GhYniSvrrHHk1MDWXv3jXSo8fSdZ9urP/8EzEdcHfZJfF76lFXWmmRMUj/+itijFjmRLrgjKaREuegd+8m8ttvEdlgg5h0704q7jxzbDhAyEDKJ5hinQLL/n7pDHUbWHDLpWwkn+vhRjXdtY+vvBb9hTpWUsWzySa0zQVtrTX6Sy7PUZA6zQdV5yjYsORm44Gie5AtCuVm4OeBAwcm/R9uUv7u5u2SluJXByWMIsZsYGRws7HhkcjG22K7vXrTXjmxVunlAbCNadzImmto8vogRcpsHjmeIc4D58RNO/B6QxGuPCzU5LFxfVgE+vbdTi6+eEO58cZG0q9fQijeemujuhRMDpmAFUXfxxxTa1JXEKgYmEOGNJaHH66Wl19eKNk020qkVCQEioWFyZ57vIQsuFwrnAcsxBjCCItiNKfxQnMBuqy9+25U3nsvatJUWrWKy5FH4mGs33mV1zA76s03q+qaFzz66OKkxfhvvx2VI49sIt260bb6L/nhh0RnVFKV58xZSy67LHHdrrlmScFGZbrnGgWHVHG8eghiO+Cb+8LWByGc2bdsPIxeuMd4b+D5zKWJFrDo0YmQZw2PON7/SgJ5SN2qi59yUlEUf0COEbXo0aOHWVtJxUOmZeMow5hEh2CNJipks6eQp9bZ7i3ZwRmHnPXqHEHqdM7x2JpS9o3ZgSinrNPIa/o8oFO4CjvnwusA8BvUnkcfXSQHHNDENK/Zf/+mMnLkIunWjTU7WpdJde+9rYxRSefaoUN/lkmTovLMM6vLrbc2k2ef/UMGDfpE1lvvR+MIJ8smmeFF8Jr00ksuaSw//xypy+pi7aYLfy7QzZ0miqSWDhjQWCZPZp54wmjFuQ/nnlu//wXG50svLZTHHquWgQOTjy658MLF8uKLy5mxH3D44Uvk00+nm/uMmljuwVIYTsnKpXAc2ww27hkcEuhB1sHCPcX+ZdsoB/2RZ5V7LZ/gQ9A6zQdV5/ClKyyF9Ch4d9xxh7lYWPGkpiEsERKkp6HwUzgP1PYRIaPglTSJkSNHytChQ30rpEcIcwNw4cm9xshkX4i6cYLYEPxEWOwDg0GJAYVgcyN52YDCbT0tdsNzacdt2K1czX6sV41oJV7DbNMZ+D9q3vhfHmiIxSIyfPgAmTdvdSOQsPmou9xww5h89NHCpN43hOuoUVWmeHzOnKh07oyHbJHkaEckBS+v7WDFosUD7557m8bpR+oyJTQYkJMnV8nkyQljEg9fMpgResQRtdK9e62J7j73XOLENGkSNwYptRA33rjYGOfeduHbb99Ufvop8b7MEr3yys+ld+8WsngxtQNNTZrxAQfUyMMPJzdMCwFBzvmzApcoMc8ncN/wfOIdxFvuehhtdJNngf9105e9TgsbgeWZtKlDuThh+Ay6G5999tmm8+sVV1wRitR0niccO0A9B3Vi1DvjEab+ldQ+ZNADDzxgXoNnn/M9YMAA4xWdMGGCnHrqqSb9R8eNKA0VlEKcUE8++WS9DofoIMiiZ555Zpn/4fkiFdZ1Zl9yySUm0wFHdKEgM5GVNMZD5yDbh3IBnF0YmlbvQAexegVrKnIQBR4Di33MZZ4lTlVkrs0kQR57dY5yyUXWEeQ7+0i3V/RArz7FdbQZZcB1YEwJryVLiv/L1dmYC5Tw7LVXoksq7LhjrZxxxhLZfXfSU6tNKQ/Q/IZ+ETBmDCmj1fLzz4yno0bxJznwwJnSosX8ej0KqqtXlhdfXN5kbdn332yzmFx99WLZbbf8ncF//UWUsal8/XVU+vVbIjfeuESuvpq03sbSrl1M3nlnYV6jxI46qrE8/XTiOtx33xuCvU839nKmX3N/UIuJIcizZWFsCGumdbSg71udw55/m8XmYiOwOMwxmHNxwqCzEGGnMU9QO80HSefwxbAEiuBt62+8NzfffLOp37NzZDDi7rvvvnqtv+neaMPR11xzjW+tv5NBaB2D1gp9WmKjKGMII5j5mX1iEShUGFtPi2to8rObwpnq5vcTFjtuFCJ7hcxutHM7SYckmjt7Nl34upkOsCusQGF3VE4/fYacemqidi/VMc2cGZEePZqaGU37718jDz2U6Fa29HMSs5zoAMdrZ86MyuefR6Rdu7hceeXieqNOODYeGKKwP/20pSxcuI4ceWRMqquXernsube5+66hmazzr8vixWIikBiQdqO+wQvd04gaMiNyq61ixqtJncKCBfXfm0XoqKNqTY0IUVyGCGNkvv76wrqaSPpNkery3ntV0qrVP/LPPzGZO3d506576NAlZn+Ya0VN5aRJC1M2Lcj12pLqYDux4hmkTmG33Xar+zvKEsZfss593vey595uKBZExt2IJoIMhwACLJeaDeD9UA6ZEYU8QWaEJQ2FwcPeubJWIeZYaDyEPOR17v+cccYZZkHECGdcQq7DihWl0kC3YO1mxIg1tFCUyJJKNeIM2cQYIgtrPRFCP5r3JAP5Z53b6BzUGLLusN8YgTi8SaejRqtQBd474suue6Uu2XHnRROtRbfL1qFuz5Fbo4reiDOc9acYRjLO4gsvbCwjR1YZZy+0bk3jpMRaf8kli00U0B4ba+RHH30vDz64rfzvf0vTXnv2XCzHHvuTLFr0hzz55IoyYcJa8s8/ieNeYYVaOfvsv+TUU6PSpEnhWWyvvhqVffZJ3C9PPbVQ+vdvYsaS3HPPIjn00Pw6rH76KUZyE2nf/ke5664/ck4P9VtX57khFZbnxMK9wPPKPWUdMPa+9+oc3HOuzoFRiZ7DvZRryR1RzgsuuMB0kg5bp/lXy6Rz+GZYhg1uRoxh2gPbRj/crCxYeBZZdEiF9csrYTtxWu8iNz+C0jvmxK/0WbyhGII8YESY/Ko35HZZtGiJdO++kplxCSus8IecdtpN0qhRrTHguBkRTHzFGHGP6a23orLvvk1k8eKIKSQnJQSDitpN6gSmT09+/BTOP/zwImnfPm7OIWnOtbXLyaOPdpYHH0zkfm+xRUyGDaMVdn2PoJ2z5EbWwO3Ch7exurqR3H9/lalhwMu4aNGywgOvI+9PbQKRV+oHvOsmx4Nnk/EspLXSUOeSS5ZI27aJR40njnqGl16qkjZtYvLGGwvN0GhmUD3wQLWZP3nXXR/ItttuImeeuZJpNGAhLYdoLy3WC7mGZAfgBcQw51lw8bPuBW8iUU3bAZFrANzrNp0lWycL82cRiHR7I2KZTStxRVEqj6BlSWWrA9x7770yePBgIxORd+gddMC2EU30D7+agLglO3btIzro1mnmUrqQCd4fZZT1BQU+nzl+dv4jm1vrhR5DFKVYWV9kCt1yS7Xpovrnn4l1iJ4QV16ZmAPpdpknksc1mjo1Iv/9byOTjUUml5eWLZfIAQfMl549v5Zo9GdjmHrHq+VrLJ96aiMZMYISoLip2WzVKiYffJA8YywT6KJkfdXURKVDh81lxRVzb75X6HPBZu979ASeE+Ac8XyyZftc8EzZ3hzoweiL3JPo8vbeZ8vGyeJ2mkdmhL3TfKlosIYlIWJqQ0877TQ54YQTzO94uPAisuFlRLARfbWGJjUTyWZk5YNtOe4KfW/L8XwG2WMkU9OAhwYvX7E8TxhOhxySSNgfOPBr6dr1LeOpdD1MwDnbcccdzffWE/n0043l6KMT/0sROh3SbOonEUAif61bx43nkJbbV1zRSGbPjppmNYMGzZaOHafJn39uLhde2FK++CKxKK68ctykpWJ4MQqFbmepbGm3QN9eg7lzSVntLJMmLY3KrbZa3LQ732abWmNMduoUqxc1LYSffsLrvpz8+GNEjj12iWy1Va2cfnpTE9kcPvwbOeKINcx14+m8/fZq+c9/GhmP6nnnLTHHli1cD7IIWBhZEC14yhG4wOJGswjStvAK+j1Ox+2ah9GK4pfOw2iL9O29z/W66667TIbDWWedJRdffHFJamgVRQkuQc+SSuZkw1nNwHEiAsg8MoBcnQM5SRqomz6bzdiEXEt2kP22waFX58jVeOO4yB4iE8WuIX4Yq9Y5jrOA9eCQQw6p+xvD2vkdn+dnDSBLIt1VWV6oSYzHM3eZ//JLZkVWm2wiMrBomkPXfGoi3QZ8Xp3Ddl53M6mybchE/7wuXZqa0iKgs2zfvrV5r8ulnPPN59qJC2w4t9FV999//7rXELkmAyHfa+uOSEEHxsnkNoLke7L33PRlvrf3vu00j32AA0s7zedGgzUsAaGaKjWU00IbYyvwSdPAY8UD4NZp8jD6IdTcluN2w4tj02etpyXVzW3r1jAqeT055MV8ELhrjj66sfH0PfMM+5k4nyzyCAqiYSw0jKCwBfoIaGpaKM5+++0d5aGHtqhXk0hhOq2pvY3V5s8X6dMnIq+8kohM7rTTYnnzzUbGS0haKC3AiVZSE/Hoo9V179erV63phsbcarrWEknceuuYGchLxNFeejq1MoT5p5+i0rhxTI455ivp2PErad6c9NulQt/v5givvMJw4CYSjzPLirlnUbnwwj9l0KBlhTvNfogQH3II92zy9+N+IWXZtnRnsx0UEZjkytuFg0J4jE6iyqQsFavpA/vEc8OiisKUrGue62G0xib/h/HLs8VzyL306KOPmuYYYUlDURRFyVbnAOQcuobVOag7RMF2DU1qD/0wAFxjx25kr+RSssMag+GHgUSUshh1Z+wna5WN7qEn3X777XV/J0sKwwEDgs2bJZUvbpd5nLKZsr7ohs9pyrZ5pu287q57tmFh/Uyq5E7UceOi0qtXU9PF9cMPFyZtzJMKrrOdGU00MFU3Wz/h8xK6ynYAAQAASURBVDBk0Q1Z710wII866ihf1nbuFdvFnmOzzYBS1SjbjXOCjsH9hR7FfT1ixAiTvaU6R240aMMyV6gLc+s0GcKOwWcFvq3Z8Cua4nZAtemz7jBf6+GyhfIsEhiUfkVVC8V2EbVCHiPdzhnjrnv11Z3l22+by9ZbfyDbb/+D7LPP7vVmWvIwsyXGUfwg48Z1lWHDVjeGGDAE+PrrF9czRMePj8pppzU2xe3pWHPNuOy9N+nPYtJfgOL3e+9F0MbreXet8Edgsbh4R53kC4/eGWf8I3fdlagzPPDAJfLgg4nUm3TYDoEITupmrdB7+umn6wq1XbhHOK/MeStmMwQveCVZTHhGUDpy8YRz7+MlpIaK54mf7dxVjjOfQcqKoihhgjXINgRi/SSSgw5AJpB1bhOt9UseYmh4dQ63ZAdZjuFpx5bxGtIDcU6WSvm2n237R6AruKCD2UYjrLGs5bk4TjH+ieJhAOEUJ5pXikieO0Pd6h2se24zPK+h/8EHEaH1QfPm2anx7ugyjLlidNDnOKxT200zf/jhh00UEbgeOAE4v0SBaSbjB5w3mv2gK+AMyOW54L4itZ7yOHQs65An1Z76ykw9JpSlqGFZADz0NP2xUU2MTowPbkRraFI/kc/A+GS4tYJ2Q8DY3H0eUgR/kFqOu3Cr2bmItpiarwhToAuV9S6xgL7++utmUUNI4JGk0csnn7SQJ55YV/r0ETnkkHjKzml3310t8+YxPytuUmJXXDHxWoYBjx1btUw315NPXmJmPKWyE9l3a+jba2Bni7k1E9nWrOAdwxv655/U3WxvOr7edRczOuu/DkFMBNjW5nrnSJ544ol1nlScHQhVOwsUQUjaKftVSo8b96NdmPGuk8qVy+fz/xiUQ4YMMYXjzKlEyOO9p9PicccdV9T9VxRFCSIYUYzpcDOpWD9JsXXrNP2KQHlLdmy9GrAuo3PYWeblgP1Dj7D9AtjIarEGDX/DoGEf7brImsjamKy+kWNkXba9KfzS3fIFncM9/7ZsxE2fzXa0HfcOtau8H8fmh6GEAYYD2c2S4pzbkiiypKxxx9qN7oIuh6Pbz3pZ7klS3MmKw9mOMyBXnePBBx+Uc845x3R+vfzyy805xTGOY6dfv36B1auDiBqWPsLNiVCyNRNspLbQZdOt08y1K1UyEDIICT4ToWk9jUFqOZ4tRFoRSNYzyDGMGjXKpECmwjVCefgRKnbgrt3w7CLUiNK5gheZ9+abUXn++Sr55puI9O9fk1cLcNuYxvUwZirQ53HD6OI+Yb+4VkScOQcoCHwl9cIqBhjXGNlerBeZjoJ27hefXe7ZqRwLxi37QuprrgszXk5qnjk/KAS2PldRFEWpD+sJGT2uzsH6gmFldQ42lPlCwaixY8sw0DBUWPf46mbysJUro4R1h81G4WjOxMz0ZBAlo2MmxjEGKlE89DWieLkaJqWC/bTN8KzegR7iHa/mPf+kdnLt+BvZQ7nqhDZ1FMcCkUZrZFHramdRu/D53HMY+cVOs+V+RAfEWY/OkWuPCM4nnea5T8LWaT6oqGFZZDCO3JoJHkIEmSv0qdvM9kbG6KJTFbWM3tlXqVqO41l0hT4GV1AfHCsAMUg4PgwVhJm7cVxE6qzwfOmll4w3LBV4m6xwI32ZNBoEK//PhpDkHPIVb69NF8ULynm2QtQuWnYjBcm+Fk8ZCzr7azcEHteL13KdMQDx7vH5NiUkGYcffnhdSjCprbzeTUViC+I1tLMpbZv5XOdEoRQRjaSp1j333JO0NqLUDBs2rK5BCA4iIqlkJCSDRYnhyS7cX3idFUVRSiWHXZ2DCCfriVuniXGRrRPSTQ21Td5c2e7VOXCQJivZKcd6lSxLig29yI6hQddA56DUCSMdI82us+7md48FP/DqfByrPf82g4rfcWxkD2WTsoxzF73VjVLbRk9Ax2XrzMZw5x6z0WC7sXaXwsnNvmJU8nnc07mm9bqd5nFkW72rnAyrAJ1DDcsSw4Pq1mm+++67RmC5dZrcTN40ATtvEK8awoJaymzq5dwCcQQE3hmbSmE3P1uO5wv7ybEhKLIVgBYWPBZTG/HjKxsPF8YdbcoRtMAA5nQDsV0jlHk+GKKp4IG2cxi5llzXVNjZZVxHrgMGNIst5962fednFjmEdphqCFlwMCg5JlJscq3xxeN63XXXyX//+1+54oor6jUZKvdIAxZR5tzhcGCkAZ0luU+THSNCni5y/N3CPWwXYUVRlFJDpImsF6tz0OcAxyrZU9a53bFjx6RrDusxjVD4W7apod6SHb7HILPRNGuklVPGoyegM6Aj2A761GuS9pgKuvnaEVcY2hihnA+7brOhk3Guij2jPNP5R8/DMGGzjnD0Pgwvzjs/Y5BaXcnNfELncecaWmxjIWZc42AAO6av1MfK/hNgIcKMLpxrjS//f+edd5pSmyB1mn+sQnQONSzLDIYPHTrdmgl+5862ImUDDwaGDKH+Qtpr21QK18No0zcLaTleCBjMtqU4Xic/u9na29ueLyKGtuMum51hyjngKw+z/XwWVBYQ/sb72Mim3bg2tr4RDx8bC7YbDUVYsYCxuNvhvN62494Cfb4GMSKZDI6LaDHHzL2Z67Vj4SNKiXOAAvlUnrlywL3Ac8hYA+A+QLE45ZRTkg5hR8iTUmNndSqKogQNDA8iNa7OwVpEpoh1bhO5YQYoEb2tt97a/JzvemTTKF2dg33w6hylLNlBDyBKyTqLwYxBaLvkejOkbJYU8wztbE63EWEyjjzyyLoxMZR14My2OgHHaQ08dAoCCXZGI7oQ2Uz83s45dbOkMKLsazGqMG45l+gxVqdh4zhYq8iq4iv7ms7xjWFJ+rTtwo5Oap0A9msQAhDAsdn0W3SOXDsRcz2po8SAZh426dBB0bW2rRCdo/wmegMHQWO9hjQp4UbCoEFwUV9HlyoeBAQv4X68Swh/PDT5gMCyqR3JWo5j4NmW4+4g5WKMLkEY8nk2TcPPeVQW7/txDrNNscQQZMsGUpLZXDifpGlwjeneZ6OmLAzusF+3QB8DyzYPcGsmEOpBSsPhvmFf8Roy3yvXsTv8/4QJE+T44483M+fo9FqKlue53JssroMGDar7HYsqnXXTKRQ8n3hzeY6JAjCE3Z0fqiiKUk5YW1Be2ZiniSym5MLqHCeddJKJ4qGws+5T1sH6ZaN1uWIjXWzIRj6P97SdzTGO+LkUJTtkx/B5ODQp13ANZr7ayKP3WL3xF9Y8jEMb8XOzpDD03Ogvx4gRmAqazVh9gKjpa6+9lvK16Ej2tVwjmkemwna0BRr1oEPYCCvn1uoTGK/sH8a21Te6d+9uvg+SzgE46dGPqd/k+uVq6JIhSOorBjop4uWO7FWqzqERy4DCTYYwx9NHhyoefutdJELEjWRrJvhKy2+/hHCyluMISlfoI6Dy/Tyb1otRidHKQx6m1M9M8IDbocr5dChz5zrayKbblClVgX4p702MX/YRj6F1UmQLx8I9zSwy0l9JPw6CJ9QFrzGLF15enjELzh8W/mQNlRD+GNq0u+eakd6Losa5CkLthqIoSrp1mcgVijslCRggdswJSjjObLdOk0ifX4aHbQTkdj73u2SHtF6ilBwX+24dvX7jTQ/Fcc5nuxFFXmOjkRj41lhk/bBNGW0zPjdTioweW36DMcjrOU9EQTEwMA6JlKJz4EDPNiDgRpWtk5tr4s40tePVyhHdwyGAvsh5xGjKtaMt55paRfQO22k+aEbz9xWkc6hhGWBefPFF2XXXXZcxILiBuKGsockNh7B06zRJgfArtcTOTXQNHYSLN5UlmwfVzqDCi0c0MEgeIz9AuBOl5LEitSTTUOVcC/TtdeBzEPLu+WcRKLaBxiLJ8bEQIuBzTZkmyklKN8fAMGLSgIJIPkI+mQHNPU4zJkanKIqiBJnx48eb2ktvLSVZTcg8mz5LLaJtdmczrkil9SuzKVXJjmtosuZlUxeHHCZKSYSPKBdyPSipj35gR5cRLUXn8CPzx45Xc/U+dA7bjd7NpCq2zsF9QOorejDHl+s9Ru8HOs3jMKFBzw477CBB5PsK0jnUsKwA8CzRncutmWAhsHWaCH0WADf90s9Bvtm0HHcH8+JJI0oZ9DEo+aaGkhJLpLKYAtc19vnKxj54R534VSvrRmGJjudac8O+jRkzxqRZ7bfffsZ76IfRXSy4n6m7efLJJ6VXr151vyeNhnP+zDPPZPU+//rXv4zygxGtKIpSCbD+UDfojjnB6Ugqnqtz+NXZ21uyw4ZB5dU5vEaH7TLP64hSFqOkp1y4OhUOetblYjagsTNNXb2D33kzqfzS6zg+6j1J06YTMVuuOgf3JaPpmPMalE7zDUHnUMOyAuGSImxsGgsbcx5JW3Sjmra43C/vVrKW46RJ8rCQosHCgDcl166hQYdjx2NInQgetVxTQ/3Au/Ai9G3dihtZ5lrk6q1lASdKiTeMeyhXg5AoNV3X7r//fmNQ0vUsDB5jFCNSj9hna1zjNBg4cGDSQnovLLpEdZmLdf3115dgjxVFUaQs6w+OR9e5jQ6CA9kdrYb89Ev2e3UOUjltFg9rFIYu62CuXebDAEYIETiOG4M519RQP7C1sm4mFTqIO2rGZlLleu5tuQ16JDpHrlFYHB80vGTdDVKn+Yaic6hh2UAgzO7OtqIrHAXqbs0Ei4BfDx9GCIJmzpw5Ji8e8KLYLmNs5W45Xig8OjQBILUXY5kFLAgtq711K1bok1Litn230c10KczUwuLxxSPK8eVal4CygccNQT9y5MismyEFAVp/s+800ELY0/r78ccfN9eb84GBTOoK3RNh8ODBJo2M1vWcbxa20aNHm4J8Fn9FUZSGAtFCq3NgcJJVhdy0+gYbjli/at1sFg86B59tO7l702eDVluXKzYKy/GwngYp88sdNWOjm7Zsyuod6H3pMqlwCJD6ymtZN3PNukInI0rJfRC0TvMNRecIjhasFBWik4TI2QDvnq3TpCPneeedZzxN3KTWw0ib8XwbxCDkSQ3F40S9JykIbs0EEVQ3jcJupRxzUqgAxWOIEMRDFMQoLAuOHVjsFui7HWjdAn0r+PH68lrbPc+OSckFFvVRo0aZNtnM/7rhhhuK1iyhWNBqn0WcaCvngft47NixdXXBpOm4jhHqhmlExGtxoFBzRL2EGpWKojQ0iKKR0mfT+ohu0ZUTI/Oll16SSy+91Pze1mmid5CymM187mSwZtHQBjls12Q3i4f1zm2CZ7cgGWbZdLTF2RvUKCz6G82FbIMhrol7DUjdtVMH3EwqdAN0Bsa7cZ1I6821Fpb/f/nll02neUaIkDrKZ4SJQytE59CIpWJA4NL5zU1lwRBE0FvvIkZnppQEbic8RdQa2rz/ZMaim0Zht1K1HC8UIrCkaSC0eIDDsjBlSifC4MTwJOpqByqzgLFI5BJZZuHAUfHUU08ZzxvOjKBdQ0VRFKW8hhKRKVuug97B2opD2zq30TmskVJol3m3ZMfOpsTowZB1dY58ykWKjTu6jChv2Jy0yaYOeDOpAD2DBkvojrlEltFfaVYzfPjwwHaab0ioYakkhdsC75hbnI83CaHm1mm6LY3puoZHBcMCgyvXQulULcfd9NlSdD5NBRFWzgleNwwuosBBW4D8akCE0cx55hrkUqDP4s4gaRZAisdJ0VAURVGUTOsPmUzWyOQr6wnOaatzMIKNGZJ23aV3A2sW+kI+XeZtyY6rc3jTZ8tZslPo6LIwYGdTotuhV9jxam5jJjvqJBnonKS+Br3TfENCDUslawi3u3WaRDgxrhD4RLpoiUxHK4S/H7WGbhcyPIxu59NcW44XCp+NxxDBR5pNvuk6QQVBTt0GwhnngXUKuMOs7eLrFuhzHkjdoBsg9QFnn322HHfccXLVVVdV1GxSRVEUpbQQwSS1z+oc7733nolgEslkbfrf//5nsmL23XdfXzKHMOS8Y07KVbJTjNFlQYLzSoMnos0EIqxTwNsM0mZSYVjaWk2y4kj7ZDzOiSeeKAcccIBpeOMdk6OUBzUslYIEH+mO5IMTrUQoY2i4dZoYHH61+OZW9Y45QQBlajleCBzTV199ZTyprVq1quctrRQ4j6QkIZQxmjMt0LZAn42W86Sd8DvOy8EHH2wiltwDYatvUBRFUYIL2VCMrbrgggvMmkwkEccydZpW56B8h5IaP0hVsmP7Erg6h196gTtmoxSjy8oBhiI6BwZ6Nqm9pE1bg5/rTt8GdD/Ya6+9pH///ubaB3mcSENCDUulIKOL4mIKpYcOHWoMEne2FV5GBAGeJXe21WqrrebbPqRqOU76LIYNX1lk8hH6RObwGHKclegx5NHHaGYjZTWfVvAsDnQqIzp5yCGHGA8kHmbSkp5//vmi7buiKIrS8Nh1113NWkVDOCKJrNGuzkEEDL3ErdP0s7merRF0dQ50H9fQzLdkB8OZzCG+4uQtx+iyYmJ7cFBSRFovzvpczxMlWXRO5bz/+9//NoYmOgf3As2hlPKjhqVSEBhfqbyDtssXwt6mslC/R9qDW6fJ2BO/vH225bgr+BFc3pqJdIXhttYQjyE1pHgMw96iPJlBzoLM16222sqck1zA2L733ntl0KBBZkbUZZddVi89iMVXU2EVRVGUUuocRPvcJoQ02sNx6tZpYtD4pXPYkh03dTPXkp2gjy7zs9yG84OjPtcAA+eIDDn0jcMPP9zMaXQjnapzBIdQGpavv/66mdfCrBYKfxmXYVtap+LVV1+VM8880wgZDJkLL7zQpOwppYWUWXe2FXWa5Na78zTx1PllyLkjNmwnuHQtxzG0uEdId2E//IyuBgVqIjlGalXooJfrAsa5ZIwI1+/++++X3XffPRDpwcOGDTNygQWaAn5qLtLNsHriiSfkoosuMh5POtFdffXVZrCwoiiKi+oc4YWRYG6d5uTJk00k0HVu41z1y5BLVrKD0UMU0zYidJvR0LSQ5jXoJzjdgzi6rFA4NhzZ2ZbbhKXTvOocFWRYvvjii0ZAkGJ50EEHZRTypPrhIaHIlxk3zLo5/fTTTa7+HnvsUdJ9V5b1Pr7zzjt1HsZJkyYZo9KdbcV19qu9Nrc7QsoV+rblOMKOPH7y9BF+YZmpmYuRTcSYWV8YlDReyhUUK9JQNtpoI3nooYfMLK0gQOMgUnJpN869w2BhhDipuckWahSNnXbayQwapvEDg5QR8gzxRlYoiqJYVOeoHHAe0wTI6hysBTibMQisodmlSxdfG8G4JTsYWRie6DQYlzi+KbPZcsstKy7ihr5FV1trSOWTnUYUF52D88U6HZRO86pzVJhh6cJNmknI4+lAoOMxsVD8y0PO8FElOJDKOnXq1Ho1E7QUpwmQWzPhZ5E20Um8yhiVCHoMT7yXbp0mgj/MBfQYz9RDAgtYrs0NMEpvu+02k/J6/vnny3/+859ApQcj2FEGbr311rr9ZREjssr+JhtEzDlx60C5r6jNYaFQFEVJhuoclQVrBeu/O1oN5ysRKFfnWGeddXw1NIlSottgMKFzJBtzEqQ1Np9jROcgIktEONceFZgmDz/8sJx11lmmQSCd5oM0M1x1jtRUVhJ3CiZOnCg9e/as9zu8hngQlWCBQYcRyUYuvW0wg7AnHYkOtHiEiLi5qSz5zndCsLOoIPR22GEH4zGkZgIvovUu4m3jd96aiTBENDl/pG7h9Vt//fWN1zBXA5lzdNJJJ8mHH34oL7zwgvG6BSENxcLCRSSVek8Lx8gzz7OfDH5PmppXJowePbro+6soSmWjOkd4YK3A2crGOgfUadqSHaJKGEjUZVp9g43IWT7rIHoFOgdGEvWeGJa2ZMdGNfn8dCU7YSh5op5yzTXXNLpcrgYyEV3WZzIFMC73228/1TlCRIMwLMl/9g7O5WciVHiK/EqzVPwHYYJAZyPtwNYI2pqJu+++2ywGpB5YQ5ONRSKdMMNQJC30+++/NwOYMbqs4HI9h4wXwTjD02SFPkYa900xW477Ff21XlHOB0I+FzhuUpP79u1r/p962Fzfo1SzzrieyZ5xrlUuMoHfK4qiFILqHOGGrrNsNIkB1n2rczz66KNyxhlnGKPPdW4T4UznbMZ4JC109uzZpiGg6wzHKLEzGvm9t2SHRoK2aZGrc3AfBUnnsHoVzmyc//mUythO8+h0pIlyPoKG6hzpaRCGpVJZYNwwEJfNprJSkI/QZ2Ay6Zrg1mky24o6SiA9iQY0LAKkItjfpwLBjRHJRpdYb8txvIvWA2kFPumzvL5cQp/jQ0Cz8HD8udZuIDRp5463lvOJpy3MqcCKoiiKkg+s6TRZsY1WWP+JWNlynWuuucakfto6TTb0D5v+abuhAq/JlBaK3oBewmZ7IRAls11nGdnBe6LDuIZmOUt2MHwpY+LzOfZMelUyw/uee+4xUUAi+5deemkossKUBmpYkhvPbCMXfsbjpJ7D8IMA6969u9msUYRRZesliGqSmkEuO8IL4Uc3UwzTfA0/DDW8TdYD5bYcx5uFhxFcoY83stg1E3g68Ygy5oUoLxHXXI+RZ4OaBryrNJ3A+A4ydLflvCZ7xlPVxaSSCX7W0SiK0jBRnaOyYf23KbHnnnuu0SvIDrI6x2mnnWYcztQW8loib9ddd50ce+yxeRt+OK6J4tnGMOgcRMDROeh8y3rNfpS6ZAedg8wvSpSoMSQam+sxojsNHDjQpB+PGjUqMJ3mU6E6R3oahGGJ94jaMJdx48aZ3yuVBw88RiQbwgrB98orrxhjCQFM2utRRx1lUmDdVBY6neYrzPhMRpPY8SRuy3HqNPEw4nHEo+gam352gcOLSuQUzyHdC3n/XGCfqWNl8eN84JENw4BmFlyOFyPYNtRggeVnrn8yuOa2U6NFZYKiKH6gOkfDAkOKTvJsJ5xwgvndu+++K8cdd5xx8pLpRPosnUNtFhVf0UEK0TlYn+0ana5kx21EaMec+AF1oHZUCqnA+TRVdDvNU24TlE7z6VCdowK7wqKw24jQ1ltvbQal7rLLLkapJy+eUDpdvR544IF6rb8HDBhglOYJEyaYxjDa+rvhcOCBBxrBNXToUCNYqTv0zrZCKLqGJnWFfg4p9o45sS3HXUOTGop8mxCR4svC0bZt25y9lNRj0gb75ptvNumvJ598cqhSX2n9zeLEjCtSjVjAH3/8cbO4ElWmZgOHAscIXHsi3HSa22effWTkyJHm3qjE1t+KohSG6hxKrmBgYnixFhGpJipHAxerczBmjfXe1TlwhvvZoMct2bFjTnBmuzpHviU7ttyGjDEM6lyd5BhizIEcPHhwIDvNZ0J1jgozLBk8jFD3wkW+7777zBBiOnnyOvd/8BiRl473iCGlOqy44YDhlM5IxOizs62I2rEAkGpCrYAV/LSWznVMRzpYdGz6rK2dQLDaVBaMRBakdAYewhmP6LfffrtME6JsIY0F5YcicoQdXdzCCG2/7bBiFmiMZK4f7LzzziYtGPlgYeYUQ8vtjC3qZCpxWLGiKIWhOofit85BBhNGha3TxNgk4oieYQ1N1i90AD/3yatzoC9402fTGXiYDNzrpN56mxDl4ghnxutHH31kur4GrdN8tqjOUUGGZVjAG2NvOtIEbrnlFuPZSAY3H903XfAAURCulB6MSne2FYKfTmcIDyv0+ZpsEG6+eFuOs2F8eoW+9WhiDOMxZF+JruY60JlH/6WXXpL+/fvLnnvuaeZU+rmIKYqiKKVDdY7wwnpMnSK6htU7MEBY292opm3m45fOYUt27JauZIcIKJlR6B7sF/pIrseI0577jvpT7sEgdppXCkMNyyKGyQmFM/gUDwZhcrwVCI5kxggPGAXf/N2CB8fbnlgpHxTj24gm3kUMT2ZZuUIfD55fnjceTTreukKfn4maYlzibeReIvU115RdFg+6ro0YMcLcm0Qsw+gxVBRFUVTnqETIJrLzNPnKLGka5FjHNhtjPfwqW0HnwLGQrGSHDZ2DTCpSN3Mtt8EBTgo5pTakv2qn+cpFDcsigWAnpYFQufUMIRBOOeUUk0+eTMhT1MuDrIQDahbcOk2K9fHuuYYmXjk/u7LhKSS1ivuEWlF+zrXlOF5QUrIwUlFGqI9QFEVRwovqHJUPGU1unSZzpjH46NxudQ5qgP1uCkiDHrrdo3NgeOLIdnWOTCU7dD89/vjjTe3xI488EvhO80phNIiusKWGaBCdrijot/DQ9ezZ0wiFVOAZIl+dBYE6Nwp7VekPLnjuKMJmAwQu1x2hT1STIm1SWd3ZVnyfa8qqu6iQ+oohuf322xshn6rluE2btXWa/A8+pGeffdY05jn44IONR9vPmlFFURSl9KjO0TDAacwoDjZAv6CTqi3XocYPfYC53VbnwIjLtUO8BeczOge6A++FvsC9YnUONhzV6CHoGd6SHf7vtddeMxlR6Cxh6TSvFIZGLIuUvkATFaJZbith5h3xkNENzAvCf9asWSbCRboBM48wTki3pPBfCR8IYKKLtl6CjbEj1L4g7G06S6Y5RjyiNOfh/qAYnO62qbyD3pbjRFXxTBNNxbgknZdUFFpia+qroihK+FGdQ7HrP9fUrdOkuR+pq24mVTbXl54SRCq5r2g0k07n8JbsPPnkk6Z/Ax1j+XwcHjSvClPXVyV/1LAMiJD3gieK2rnDDz9chgwZUuQ9VkoFBqKtmUDo4w1s1apVvYZACHFr9JF+wv8QraRYPh9vH/ccDXpIm8WIZbEgReqGG24wY1gURVGU8KI6h5IKGjm5dZpEOGkA5M7T5LpbwxHDcPbs2cYpTfQ6n+Y6fAYNeubNm2d0DUZw8D7cV8z2VCobTYUtAmussYbxzJBX7sLPmaJTFqJL5Mrb2VlKZYCQPeyww8xmhbitmWDUB+3pSXdB4DMjbfTo0aY9NfUJuc63wmfEXCUaNNAWn26BpM9ipKJoEPlUFEVRwo3qHEoquP69e/c2m01/Zv0nOv3cc8+Z+ZHoFmRR4ZygXAZ9gSgj+kKuOsfYsWPNDE+30zwRTWaFc58qlY+2ZCoCPKSdOnWSl19+uV5aJD+73sR0kLNONGvdddct4p4q5YZ6hL322svUtuBZJiVp1KhRJrr40EMPmdoZUkh69eplvH3jx4839Q2ZIB2WdNezzjrLpMJSe2EXCQxXam/oYBsEqA098sgj62o08Giy+KWDGVFEdd2NuViKoigNDdU5lGyhx0OPHj3ksssuM/oEzu3//e9/Rj9AV2Dtvemmm2Tfffc1useLL75o1uhMoKtccMEFpjEg5TYPPvhg3fgyUmK7d+8emPpd1TmKi0YsiwStlPH6UERNwxYapaDs27lRtAXHO3TllVean2m/jMeI8RU86ESXSEcgUqU0HOjmhifxu+++M8OTqY0gjcTWS9DFj/uCuhi3ZsJVBqjr5P6igJ5i+aBHJhHw1HOMGzfOpGPxjJC6S/e4dPTr1888NxYWL0VRlIaI6hxKPhCp/uijj4y+gXMbfYK6SLKo2IhoUrfZrl27ejoH2Ve2ZIdur9xnRCbpVBsUAzIVqnMUGWosleJwyy23xDfYYIN448aN49tss0180qRJdX/r3r17/Oijj677+fTTT6977dprrx3fe++94++//37R9/HWW2+Nt2zZMt6kSROzj++8807a1z/++OPxzTbbzLx+iy22iI8ZM6bo+9jQ+P333+N//vlnyr/PmTMn/thjj8UHDBgQ79ChQzwajcY33HDD+BFHHGG2Zs2axc8555z4okWL4kHnk08+ocY7Pnny5Lrfvfjii/FIJBL/7rvvUv4fz89pp51Wor1UFEUJPqpzKPmArjB//vyUf//xxx/jo0aNip9xxhnxLl26xKurq+Prr79+/F//+pe5p1ZaaaX48ccfn1ZvCQqqcxQfNSwbMCNHjjSLyj333BOfPn16vF+/fvFVVlnFCJFkvPXWW/Gqqqr4NddcYx7OCy+8MN6oUaP4xx9/XPJ9V5ayYMGC+NixY+ODBg2Kr7rqqvE777wzHovF4mFgxIgR5p5zWbJkibnPWMjSCfk11lgjvvrqq8c333zz+Pnnnx//66+/SrDHiqIoSj6ozlEZYEBOmDAhfumll8bXWmst81V1DsWiXWEbMLkOVD700ENNas3zzz9f9ztSaTp06CDDhw8v6b4rlQG1pffff7989tln9X6/1lprmRqQk046Ken/3XnnnWb+Gt3tpk6dKuedd55J/6I+VVEURQkeqnMo5UZ1juKjzXsa+EBlmrhkO1CZ37uvhz322CPtAGalYYKS4C10927UjuYL9RDce4xgoV7igQcekKefftrUhiiKoijBQnUOpZiozhEctHlPA4X5iHSBW3vttev9np9TPXzMQ0r2en6vKC50o6U7XDqY30krdGZdudTU1Jiubdm2ybeecKBVflC63SqKoigJVOdQionqHMFBDUtFUXyHYcjZDFamwxwdCfFk0y4fJkyYYFKkrODOhg8//NB81Vb5iqIoitKwUJ0jOGgqbAMln4HK/L6QAcyK4qVt27ZmkDJtvN9991156623zPzNww47zNQyAKNX2rRpY/4OpJ4w05OF4euvvzYDnWmlv9NOO5kxLIqiKEqwUJ1DCQKqcxQfNSwbKPkMVOb37uuBOUDZDmBWlGQ8/PDDRogztHnvvfeWHXbYwRTKW5gzRaE9M7Lsvctg59133938HykwvXv3lueee66MR6EoiqKkQnUOJSiozlFctCtsA+axxx4zA5XvuOOOuoHKjz/+uKl3oI7BO1D57bfflu7du8tVV10l++yzj4wcOdJ02GKw7hZbbFHuw1EURVEUJaCozqEolY/WWDZgaOX9008/ycUXX2yK4WnhPXbs2Lpi+W+++cZ0bbNst9128sgjj8iFF14o//nPf6R169YyevRoFfCKoiiKoqRFdQ5FqXw0YqkoiqIoiqIoiqIUhNZYKqGE1tDMElpppZVklVVWkeOOO07+/PPPtP+z8847LzPX6MQTTyzZPiuKoiiKEj5U51CU7NCIpRJK9tprL5k7d66p1aDQum/fvtKlSxeTNpNOyG+66aYyePDgut81a9bMLBSKoiiKoijJUJ1DUbJDayyV0PHpp5+auozJkydL586dze9uueUW093ruuuuq2sZnQyEurYqVxRFURQlG1TnUJTs0VRYJXRMnDjRpKJYAQ89e/Y0Rf/vvPNOxjbTzNOi+H/QoEF17aQVRVEURVG8qM6hKNmjEUsldNBNbq211qr3u+rqallttdXM31JxxBFHSMuWLY13cerUqXLeeeeZWUWjRo0qwV4riqIoihI2VOdQlOzRiKUSGM4///xlCt29G/Ou8qV///6yxx57yJZbbmmK8B944AF5+umn5YsvvvD1OCqRK664wrR+J60Hz202UL5NW/l1111XlltuOePhnTVrVtH3VVEURVEyoTpHcFGdI7xoxFIJDGeddZYcc8wxaV/TqlUrU68wb968er+vqakxXdtyqWXYdtttzdfPP/9cNt544zz3umGwePFi+de//iXdunWTESNGZPU/11xzjdx8881y//33y0YbbSQXXXSRWWQ/+eQTadq0adH3WVEURVFSoTpHcFGdI7yoYakEhjXXXNNsmUDQLFiwQKZMmSKdOnUyv5swYYLEYrE6wZ0NH374ofmKd0tJz2WXXWa+3nfffVl7Dm+88UYz2PqAAw4wv8NbyyBsBlwfdthhRd1fRVEURUmH6hzBRXWO8KKpsEroaNu2rey5557Sr18/effdd+Wtt96SgQMHGsFhu7N999130qZNG/N3IPVkyJAhZmH4+uuv5dlnn5U+ffrITjvtJFtttVWZj6jy+Oqrr0ztCakolpVXXtkswjRCUBRFUZQwoDpH8FGdIzioYamEEjqtIcR79OhhWn7vsMMOcuedd9b9nTlTFMnbDmyNGzeW8ePHy+67727+jxSY3r17y3PPPVfGo6hcbEMDvIUu/Jyu2YGiKIqiBA3VOYKN6hzBQVNhlVBCN7Z0g4k33HBDkxphadGihbz22mtSriL0MWPGmDQYFhtSajLBvl9yySVy1113mddvv/32cvvtt0vr1q19bVxw9dVXZ5zfxaKoKIqiKA0V1TkKR3WOhoEalorSQIvQs21ckA+2ocGPP/5Yr56Enzt06JDXeyqKoiiKkh7VOVTnKCdqWCpKAy1Cz7ZxQT6wMCHoX3755Tqh/vvvv5th0ieddFJRPlNRFEVRGjqqc6jOUU60xlJRAkYQi9C/+eYbk1bD19raWvM9259//ln3GtJXmNEFzP86/fTT5fLLLzdNCz7++GPTuIBGB7169SrLMSiKoiiKUh/VORQ/0YilogSMIBahM3SYFBnL1ltvbb6+8sorsvPOO5vvaVzw22+/1b3m3HPPlb/++ssMiaZmg2YHY8eO1XlSiqIoihIQVOdQ/EQjloqSZxE6HrJ024wZM6RSIKWGdBnvZgU88LNbP8E5GDx4sFmYFi5caDrkbbrppmU6AkVRFEUJJ6pzqM4RFjRiqSh5oEXoiqIoiqKUAtU5lLCghqWi5IEWoSuKoiiKUgpU51DCgqbCKkqR0SJ0RVEURVFKgeocSjnRiKWiFBktQlcURVEUpRSozqGUk0ic6ldFURRFURRFURRFyRNNhVUURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsFQURVEURVEURVEKQg1LRVEURVEURVEUpSDUsEzBn3/+KdFoVG644Yay7cM111wjbdq0kVgsVve7Sy+9VCKRiPz8889l268gcd9995nz8fXXX0tYaQjXNAzXafjw4bLBBhvIokWLyr0riqI0MFTnCAdhWMsy0RCuaRiu0/AK1TnUsEzBtGnTJB6PyxZbbFGWz//999/l6quvlvPOO88sNor/vP3220bALliwoCI/r1Io5LyhrF1yySWy5557ymqrrWYWGhacZBxzzDGyePFiueOOO3zYa0VRlOxRnaPyUZ0jHBRy3iZPniwDBw6UzTffXJZffnljOB5yyCEyc+bMBqNzqPRIwccff2y+brnllmX5/HvuuUdqamrk8MMPL8vnh4V///vf8s8//0jLli3zEh6XXXZZSYV8KT8vSJTrOuGRHTx4sHz66afSvn37tK9t2rSpHH300XL99dcbBU9RFKVUqM4RDlTnCAfluk5XX321PPXUU9KjRw+56aabpH///vL6669Lx44djfOoIegcalimEfJrrLGGrLPOOmX5/HvvvVf2339/c+MpqamqqjLniEiUkp6//vqrwV2nddddV+bOnSuzZ8+Wa6+9NuPr8Szy2ldeeaUk+6coigKqc4QD1TmypyHqHGeeeabRIW6++WY5/vjj5cILL5Q33njDOG2uuuqqBqFzqGGZRsgTyna56667pHHjxnL66adLbW1t0T77q6++kqlTp0rPnj3TRmK4IVdaaSVZffXV5bTTTpOFCxfW/Z0b9eSTT5bNNttMlltuOfOaf/3rX8vkm//xxx/meDbccENp0qSJrLXWWrLbbrvJ+++/X+913333nRx77LGy9tprm9dxbvBw5pvbP2PGjLT7Dx988IHstdde5jUrrLCC8QBNmjQpYx69/YzPP//cpBqsssoqsvLKK0vfvn3l77//rnvNOeecY77faKONzOsLycfPtK/ZfB7esVT7m+u1sOfgk08+kSOOOEJWXXVV2WGHHXy/Rtkce7LrlM01yua8Zbp/+V0uilqnTp1MyuwzzzyT9f8oiqIUiuocqnPkguocwdQ5tttuO/PMurRu3dqcMzKnGoLOUV3uHQiykLcpIXgauJHuvPNOGTZsmPTr16+on00YHgidp4KHjxv7yiuvNA8U3pFff/1VHnjggbo8b97nsMMOk+bNm5uH4vbbb5edd97ZPPjNmjUzrzvxxBPlySefNDnh7dq1k19++UXefPNN8wDYz//xxx+la9eu5uHidWuuuaa8+OKLctxxx5m6DM5NrmTa/+nTp8uOO+5oBMe5554rjRo1Mnno7P9rr70m2267bVafgWDgM3jo7777biMESFU46KCDTM77o48+apol4CkGji1XstnXbD4v3f5acr0WLOwItaFDh+acapHpGmV77Jk+I90xZzpv2dy/ucL/vfXWW3n9r6IoSj6ozqE6R7aozhEunSMej5vz6HUcVazOEVeW4fvvv+dpiA8fPjz+yy+/xHfdddf4aqutFn/llVdK8vkXXnih+fw//vhjmb9dcskl5m/7779/vd+ffPLJ5vcfffSR+fnvv/9e5n8nTpxoXvPAAw/U/W7llVeODxgwIO3+HHfccfF11103/vPPP9f7/WGHHWb+P9lnpSLb/e/Vq1e8cePG8S+++KLedVlxxRXjO+20U93v7r33XvN/X3311TKfceyxx9b7jAMPPDC++uqr1/187bXXLvO/+ZDtvqb6vGz3N5drYd/z8MMPz/l4sr1GhVynXI453XXK5v61TJ482bwP+5KO/v37x5dbbrms3lNRFKVQVOeoj+oc6VGdIxw6h+XBBx807zdixIh4Q9A5NBU2CaSEAB6aLl26yPfffy/vvPOO8YiUAjwg1dXVJsSfigEDBtT7+ZRTTjFfX3jhBfOVVBTLkiVLzHtusskmJvzvppzwM8fGMabytFCIvN9++5nvSYex2x577CG//fbbMiks2ZBu/0n5+d///ie9evWSVq1a1auXI8UC7xCeskzgWXLBy8V5yOZ/s8Wvfc1mf/O5Ft73zIVM95gfx17oNcp0/+YDKTwU/SdLCVIURfEb1TmWojpHelTnCJfOMWPGDHNc3bp1M416GoLOoYZlmu5shLrJKZ84caIRkEGCVAOXjTfe2LQIt3ng3KQXX3yxtGjRwuSBE84nlE9OPcLAnVtFpypet80225j88i+//LLu7z/99JP5H1Jy+H93Izcd5s2b5+v+85k8YNRqeGnbtq2ZsfXtt99m/AzaPHsfXiC1wi/82tds9jefa0HKR7HuMT+OvdBrlOn+zQebvqPNGRRFKQWqc6jOkS2qc4RH5/jhhx9kn332MbWcpM/SUKgh6BxaY5lCyNOimJuaG4hZeHgpSgVFy9RYUCS84oorZvU/3hsSTw9d3sh/x1PCjc1rqH9whx+Tb47H5umnnzaeIDpnkms+atQoUxxtX3vUUUel9LZstdVWBR1vsv33g0wPcdDItL/5XAvXi1yJ1yjT/ZsPLDDUA/l57hRFUVKhOofqHOVAdY7i6Ry//fab+RnDnK6w6623XoPROdSwTCHkO3ToYDqyde7cWQ488EBzY/jZhnvEiBHy7LPPmk5QhMopeOYGZZB7mzZt6jq1pRKgs2bNqucZotMVQoDCZ8A7giD473//W/caumslm8tDGgHd3NjwPlFIfMUVV5iHAs8UCw0pCOk6xuVKuv3nM3nIPvvss2X+j3OFBwtvURCEVi77WujnFetapCLTPRaU65Tu/s0Hnju8n4qiKKVAdQ7VObJFdY7g6xwLFy406cM0ARo/frxp8tOQdA5NhfXAA0R3J4YUcxPjhcCDeNJJJ9V7HR2emPnEw0aY/vHHHzfdqAYNGpT1QoIAp4ta7969TWtkBDzg7YP33nsv5f/TKc7llltuMV/tjY1XxuuB4TVuy3K+d1NUgO5YeFYWLVpU9z7sH3n23uGuNjUhH9LtP5+5++67mwXQbY3NOX/kkUdMC2s6ghXK8ssvb74WMjw4l30t9POKdS0KucfKeZ2yuX/zgZoRWoYriqIUG9U5VOfIBdU5gq1z1NbWyqGHHmrS2Z944om6Z6sh6RwasUziMcHbgJC3M2ZomU0+Od9TAwF45Rh2Sl3BkUceabw6L7/8smldzFfm6mQS8szXO+CAA0whsFuIzPdbbLGF8XQwOyiVh4NFhoWBG/ihhx4yxcvt27c3f993333lwQcfNOkoeEt4De9HyouFtBfagh988MHm/yjc5zW0DXe9jhwnw1tZxGh7zvvNnz/fPAy8nu9zJdP+X3755TJu3DgjKPAK0ViAltI8vOS4+wHXEy644AKTrkPbarxMVqjgterevbu8+uqrad8n231N9Xm5UIxrke81yuXYCyHVeaNBRDb376233moWCFts/9xzz8mcOXPq0rd4RixTpkwx55DnUlEUpdiozqE6h+oclaNznHXWWSYzgNdzbjgGF9KKXSpS5yh3W9qg8fjjj5u2wNOnT1+m7XGjRo3ir732mvl54MCBdX87+uijzf/Bp59+Gh82bFjGz1lzzTXjO+64o2ldbN/T5frrr4+vsMIKy7TVti2TP/nkk/jBBx9sWiyvuuqqZn/++eefutf9+uuv8b59+8bXWGMN8z577LFHfMaMGfGWLVua/YVFixbFzznnnHj79u3N+yy//PLm+9tuu22Z/fnxxx9Ni+UWLVqY87DOOuvEe/ToEb/zzjuzOKu57z+8//77Zr/Z/2bNmsV32WWX+Ntvv13vNelaf//0008ZXztkyJD4+uuvH49Go/X+Rtt1fqaldjZks6+pPi+X/c32WqR6T7+vUbbHnqr1d7bHnOy8ZXv/cs/zP8k27+ecd9558Q022CAei8VyPm+Koii5ojqH6hygOkdl6Bzdu3dPqW8kM7kqUedQwzJPzj//fDMXj/k6PXv2jHfr1i0+b968+FVXXZVUaLv88MMP8aZNm8YXLlwY/89//mP+38uCBQvMHKu77747XkkUInxKyZgxY+KRSCQ+derUeEMjLNfIb3geWTBvvPHGcu+KoihKPVTnqOz1THWO4F8jv1lYoTqHpsLmybnnniunnnqqybemFTPpLBTDM3dqp512qnvdMcccY75Sz+CmpFCoS0tuOqhRmDxp0iTp2rVr3WtIJ+EzKK4nJYaiZKV0kPpBCoRNT1IqHzoakvJSyBwuRVGUYqA6R2WjOkfD494K1TnUsMwTZt9QT2ChkxVFxV6o5aKQ1wUhTz0DUKx//PHHy5AhQ2TMmDH1XnfeeeeZLehQrJypiJtc9HTDl4MGi2ulke11aqgg3CtNwCuKUhmozrEU1TnCgeocDVPnUMOyiDAXioYh1oNoOeOMM+r9fNNNN0mYYSBtpqG4l1xyiRkkqwT/OimKoijhQ3WOpajOUX5U52iYRMiHLfdOKOGGjnZvvvlm2tfQdc7tQqeUHr1OiqIoStjRtSwc6HVqmKhhqSiKoiiKoiiKohSEVmcriqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBaGGpaIoiqIoiqIoilIQalgqiqIoiqIoiqIoBVFd2L8rin/E43FZsmSJ+VpdXS2RSMRsYL8qiqIoiqIUiuociuI/algqgRHuixYtksWLFxuBvtxyy9X9nZ+j0Wi9ryr8FUVRFEXJR+eoqamp0zn4uVmzZnV/9+oa9nv7N0VRUqOGpVI2EOYIdYR7bW1tvb+5wpvXef/uGqQsCO4C4P1/RVEURVEaNq4TG8OSn+3vXb3B6hx89eoiXp3D3RRFUcNSKQOxWKyeQYlArqqqMl9tWoorpL2C3bJgwQL58MMPpXv37vVe5/U2qvBXFEVRlIarc1iD0jqprc6BgWlJFpV0dY5//vlH3njjDdltt93q/Y+7JdM9FKUhoYalUjIQ6BiUf/31l0ydOlXat28vjRo1yknwuq+10UkWCFf4u95G9/9U+CuKoihKw3NiT5kyRTbffHNTZlMMncNu7v+pzqE0RNSwVIoORp6tZUDQs/3xxx91HkMv2QrcVFFN7/+76S4q/BVFURSl8p3YbHzPuo7OUeia7qbKqs6hKMlRw1IpCjZqaIU7xiQClM5r2fxvLp+TiVRF95mEPyRrGqTCX1EURVGC7cS2Okep12zVOZSGjBqWSlG6rVmDkp+JTFrh7n1tOclG+LM4eQv7QYW/oiiKogTDiY1BSR2l16C08H2YdA7va1LpHMneT1HKiRqWiu/tu20DHpvq6hV66YTg33//Lb/99pusttpqssIKK0jjxo1TvrZYwlSFv6IoiqKET+dI5sQuxmf7+RmF6BxW31CdQwkKalgqvrfvRrjbIvdM/2uh/mH27Nny008/yYorrihz5syRhQsXGsNy+eWXN7/jK8YmW5MmTZZ5j2KTrfCH7777zhxT69at6/5Hhb+iKIqiFK5zkBGVyYnt4kfEstRrdSadw43Yzps3T3788Udp165d3f/o/G+lHKhhqfjevjsT7msYGYJB+euvv8p6660nnTt3NkYjxiOG6p9//lm3YXR+9dVXxuCkmyzd3fhs/t8anBiiQRD+LHpEX91if/Y12bxOFf6KoiiKktmgZP2366h11jY0kjUO4tygJ3l1jmT/6+oa9nvv+ypKvqhhqfg2gzJbrLfto48+MqNHmjdvLm3btjVGofVAAiktq6yyitlcrMH5888/m6ggX12D0xqZ7lYOgxOSCexk3sZk/+eNbtoFVIW/oiiK0hAoxIldrIhluWs1U5HKMZ1qHJvXQNX534ofqGGp5N2+O9daBgQZUcevv/7a/IzBaGdZ5oI1OFlcMCg7depUZ3BiqNoIJwYnn8VQ41IbnOkWnmTeRu//eGdxuouFCn9FURSl0g1K2+E1Xye2l6AahMUmH53Dvtbr3FadQ8mEGpZK3u27s4X/I/eflFXeb4MNNpBZs2ZJixYtljEqc/EqeveB/Vp55ZXN5pKNwenWb9oazkKFZj4e1WTfq/BXFEVRGgJ+OLGT4ec6WCkGarY6h87iVHJBDUsl7/bdmeA95s6dawxK/r9ly5ayzjrrmPfAsEwlnP0W2tkanL/88ovZV2tweo1NvwxOv4Q/6CBmRVEUpVLnXhfbIMzl/RvKmqk6h1IIalgqSdt3Y/gx8oOU01yFO+9BV9RvvvnGGGKbbLKJrLXWWjlHOXPtLOuXwckC5zYNwuDkWGjGw/8kS6kth8EJqYruMwl/RrrQ/IhNhb+iKIpSTif2F198YdZS9I5ijAyp9HWtVFHUXHUO+7rff/+9rsu/6hyVjRqWStL23fPnz5eVVlopp45r/D9jQtgQHjTkWX311ZMKDT8L6f2GOo5UBqcb4eQcpTI4eV2yAvlSkUn4z5w5U9Zff31Zd911l3ldqlmcKvwVRVEUv2dQAs5OMoVYf4v5uZXevKfcn51K56AfBjoVmWve1+j878pCDcsGjHcGpdu+OxeDkm6s3377rYlSEuHccsstzddMAsEP4VxKAc+Ch7HNlsngZHwK5/bll19OOoezadOmZTU4gWtsF3EdxKwoiqKUa+51sceG6BpVHlTnaHioYdkAyaZ9dzYRRSJ11CTSmIfIZMeOHZcxulLhh1AIimBJZnB++eWXxgO78cYb1xmcGJsY4Jw3/idZSm2pDM5krcbdr+7r7NdMszhV+CuKoiheWDvcMWXgNSb9yGJKR7HfX8mNTDqH/V7nf4cPNSwbELnOoEwlhDGSMCgZHULtZJcuXUw0LlcqXcgj8FJFODEumcFJpHPBggUmfZjf8T+lMDizTdH1S/i73yd7P0VRFKUyndhkNWXSOUph+Hnfn32jnGXevHnSrFkzk1nkrruUuHj3Mdn7KP7rHH7M/1adozyoYdkAyKd9dzIhTwQOg5JUT2rztt12W9P8JR/8esjDKOBZWFnA2LyLsJtSm8ngxJjn/Jc7pbZQ4W9/9r6XoiiKUhkzKPPROfzE/Wz2i+wh1lfWYUag8XfWWkp6WH8xOllf3TUX4zOoBF0XKrTfRC46R7KMLJ3/XTrUsKxQCm3fbYU8GymczH0kwkbDl80228x0QfVjHwuh0gQC1ycfgzPZWJRMBmexmgrlKvzd16nwVxRFCS/umLJ8ZlAW27BEF6LjPcbjqquuKh06dDDrJfWeGI1u/R+vZb1F7+ErhihfYeLEifWim7aHQrHrRLM5xqBSrGubj85hX+t1bqvO4Q9qWFYYfs2gtO2hEcCksTRv3tw05aFrmx/oQ+u/wUlEmeuVjcFZ6m61uQp/7t3PPvtMtthii6SpLSr8FUVRKmcGZTEjlsymZt8++eSTZfpBpMqqwXnOxustvHbcuHGy6aabGr3IjiPjK8eNceoamzbKWW6DMwgEReew+2K/utv06dOlTZs2ZiyK6hz5o4ZlBbfvzke4IxypN8CoZNtoo41MlLIYLcD9WkTKNdKj3GkpmQxONrytXMfvv//e/GwNThZami5BNhHOUgt/lIC5c+caZ4YOYlYURQmmzmENSn7O14ldLMPSbTDI+7du3do4yfPFHhezNt2sLfbbGpo2ykkPCr7nb66T1zU4G9o6FZTjTVZ3id6EzkFGXiqdA3QcW2bUsKzg9t25gCfuhx9+MEKYBwRDY+211za1B8XAj4cw6A9yufbPNTjXWWedeoKThZbFDs8t3zPPEoOTfU3WNKhcBif76tZeZjOIGVT4K4qilN6JXah89dOwZE2jfMdtMDht2rS8e0J48e6n1ZnY1lxzzXqvw4nrptRi5LJ/kMzgLNeaW2yCGADw6hyQTH9ONhoF3ONRnWMpalhWcPvubGCBIJpFZzTSXFu1amUEMSkBxU7fqOSIZRBxGwBRZ8IolDXWWKOewcnmRjitwelNqy22t9V6v7P1Ntr/sV9V+CuKogTPiZ0KP2QwxhsGJempOFXdBoN+GK657iOvZ61kQ6+yeNdc9ptomZtV5Bqb5Zx97RdB19OS6Qv56BwRz2tS6RypPqsSUMMypN3WSLuwN3G6kSGpYIGwXdEQeuSVU0vg3vDlmClVqQ+aHzz+eFRwqh12WMKzVgiuAHQNTpd0ix//m6yG0y+DM59FqBDhbwV/QxL+iqIomUBeujoH5KNzZKIQnQNnKAYlHevXW2896dq1qzHEgto5Nd2am6xRH7+zs6+9I1FIyS22vtYQSGdYFqpzQEOa/62GZQjbd3/xxRfGMGzbtm3ONyDvQXSSiBTF69SwrbLKKkkV7VK1/i7ne4SF2bNF+vRJNE5abrklcsAB9Y1LLtUFF1TJww9XyRln1MrAgbXiGcHleX3ma5vJ4HQXQNKo+ZrK4MRrnItn26bC+kE2wj/TLM5KFP6KoijZzL3GCY2Rs9VWWxVN7uWjc7BPX331lTEsM3Ws91PnKJZulKpvAuuSu95iQKPHsQ5T02pfz/UiWmsNziAR9IilNQD9iMBHfJr/7U0xD/L5c1HDMoTtu/Fc8XMuNxl5/tRPYgBQeL711lvXdUVLRamHFSupef75pc2TTjihWjp0WCwtWy79+513RuX66xOP8/nnV8ujj0bl1ltrpEuX1Oc4XyHlGpzU4bqC2daTuAanW0/CZluzp+uYly4V1i/8Ev7u98neT1EUJSx4O7yCbd5XTNnGe9vPS4d3BFqLFi1k8803N508s/nfMML5R1/z6mxcK7vWEizgmn388ccmukxpU7IIZzbnqSEalvlELHMl0kDmf6thGcL23XyfrYBE4GBQUsROUXnnzp2XiUAlI0wRyyAuFn7v03PPJQRI06ZxWbAgIkcf3UjGjVsiTH955ZWInHlm4lE+5JBaGT8+Kh99FJWddmokJ54Yk8suqxGvDyGZkF+8WEyUM197ztaGsLkGJ5/lptS6Bqe3Y57d7FDtcpCL8CdNCWOaboNhE/6KoiheJ7arc9iUV77Pxugr5rrJ34jGYVCynmBQ5jICzU/5GxSdg+uz8sorm81m+aDjUQPrrrc0DCLTjetLJDNZoz6/RskF/ZwF0fCN5KBzoDsRsW7Xrl1g53+rYRnC9t3ZGJZuzcG6665br4g9G0qRsx90QVMofj3U8+eLvPFG4r1GjVoihx3WSCZNisqQIVVyzDG1cuSRjaS2NiKHHVYr995bIz//nIhakhZ7++0oBn/L9dc3TitEp02LSM+ejUyE85lnluRtXCbDpsemMjjtWBS+MurGtmiHDz/8cJkaznLNBEsm/K0i5jo53EHMbhTTCn8/G14oiqIUe+51KfSBVJ/B73CMo8+wn3SqJ+2VfcwVP44hDA5Czg0lTmwuXGPX4KRnAl9Zw6hJTWZw5nOew3ju7L0fJCJJdA7bQCuVzmFf627oHMUYGZgKNSxD2L47nQCm5gABnE3NQTrK1bzHkstnV7qB+uKLUWM4brFFTHbdNS63315jjMlrr62SkSOrZP58vJQx83tuF7qdjxhRI23bviv3399S+vT5XUQ2WuZ9lxpHIn37VptI6LhxERk2rEpOOWXZVAy/cQ1Ot2Me1/O7774zo1BI/WHhcw3OVE2DyrEosBi5jSwyeRvt74K2gCmK0rCd2MixVDMoy2FY2pnaZFyxrxiUNObJV0FOdQxBNnZyIZvrQ1Ry1VVXNZsL94BrcLL+8hV9lIBEMoMz1+uQa0Rw5syIydAq0sS70KXqeg3gbHtHuE0+S4UaliFs3+0VkMlSRLbYYouCUhvClApb6Tz/fOJ+2HffRCpS794xmTChVkaMqJJvvhFZd924PPYYC0D9/zv77I5y9tl8t3rd+7z6akTOO6++d+vyy6vk44+j0qgR92NELrqoSvbcMyatW5fHYOe64gyhFoTxN8lmgtkNxYNIJ8LWGpxuHSdbMY24dF7OVOktalQqihIEJ7bVOTKlzeVSflOozoFMtTO1+blly5Ym66pQuVmJqbB+HSNrLb032Lwj7dwZnDRx4iv3EM5c19C0a26q65SL4fbLLyJdu2IEi3z+ORlBUnTCYljGM/SfSGZwlvq41LAMyAzKXNp3uwLYevRYJKxHz4/UBU2FDQYLF4r8738JIbL//ktrXK69tkamTo0Yrx5G5frrp38fjMpDDqmWWCwizzxTJeecs4J07hyRiRMj8t//JjxZDzxQI3fdVSUTJkSlX79qefnlJVJCJ1c9Uo0ESTYTLJnBSeqUNTi9C6A1Pv0w8Hh/P1OFFEVRgjaDslT6ADJ70qRJZr823HBDUzrhpyOuoaTC+gHHOXduE6mtbSIbb5xwTrvjZ1yDk8AGX73rrW0cxO9yOffvvBOVv/+OyN9/i/z0k4iz3DeoVFi/9lMNywbSvts2J8lnHpRVpN955506jx7DgP0MdRf7RtTmPdnxyitR+euviKy/fly23nrpezZrJvLqq0uM4bn88unfY9KkiPz73wmjslmzuHz7bUTOOWc7Eflbbrop8fsjjqiVAw+MSadObI1NDefNNydGl5SDXMaNZGNw2jrOn3/+OekCmK/BGZbFSFGUhgfyyRqU+Tixc+3Ymg/sFx1NiYYBM7WR437rIA3FIPQLmrnvsENj09Tvs88Wm+ihPY/UY7KtscYa9dZbutG6BicOXrdnwpdffim//fZbPYMz2XWZPHnp777+OiJrrVV8HS8sEctYCHQONSzL1L47VS1Dpvch952UV97HCuBi3GTFXEgsQTQI/cQPIfXss4lru99+GFr1/4YfIZ1ROWrUKJk6dZEMH36U/PNPRPbcs1buuqtGjj2WjrJVcuqpidlXGK3XX19jvqee4ZprauSkkxrJpZdWmc/dZJPSXyc/xo24Bqf3ve0CaLd8Dc4weA8VRWlY+OXELmbEksgp+gzzGDFS6FrPvroN3vzGj74OpYjeBgGynH7+OVLn4D7ooFjG80I9JhvX0uvgJRLN2oqTly61yeZeW4PzvfdWqmdYbrONGpYWNSwbOKlmUOZ68/L/jDVgQwBTb4DXhyhlMQl6jaWlUoU8DuYxY6xhmXvk8PvvYzJ8+AGyYEG1ae7z8MM1xhAdPXqJ/Pvfc2TUqI3N6+68c4m4zeOOOSYmjzwSkzfeiMozz0TlrLNKH7UsppBPtwCmMzh5vTuD045FCbqQVxSl4TmxrWzKx4ldzBpLq88QocTQaNu2ray++urmdzQfLBZhMBqCxOOPL13XXn45s2GZDDKqvvwyKm3bNjO6Lw0lbR0na6o7howoZ6JL7V8yceKeuM7N6z788FfZZZfFZr1F/y3WdcwlS6qcxP6/YWCQUcOyxDMocwGjFOGLVw+FliHAdPMivQDDspiEocYyDEKgEN59NyLz5kVk5ZXjstNO8ZzTWG66aQ9ZsGBV2WCDRTJq1NLoJjLpmGM+kWOOWVOqq5uaTrMunNbddksYlh9+WJ5zXA4hn4vBaWtKeNZRhhjY7Y1wBl34K4pSuTMo/TAo/dQH2Df0GQxI9BkaDKLPpOqk7TeVHmn089h+/XVpbwdgNjZvn8vtRAotI8zeey8qJ59cI/vsU3//uEfteukyc2Zc/vxz6Xi0L76Iy4wZM0ykkzXVjWzajWZ/hd7rfmRJlYJYCPo6BHvvKrh9dzpIGyA9BO8N3p0OHTqYAbilTFMNQ1fYSue55xJCjg6tuTb4pUNs27ZzZP78pjJ8+Pey1lot6/2da7vjjrWm5jIZHTok7q+PPirPdQqSkE9ncL777rtmJAp/w9DkuU3VNc8anMUeRK0oSuWT7QxKPyhE52D/kIs4yJmr2L59+2XmK4bJ8Avyfvp13UePjpoO8a1bx0wq6uzZEfnyS5GNE0lOWTF4cJUxKuG226rlww87yIMPZj5vU6bUd8j+9deasuOOO5p7HePSOneZ0c59RdSTe95rbFqDM1sqORU2os17wtu+m0jiV199JZ06dcrYvjsZPDR0eKXTKwps586dl/HmlHtYsZ+kmsXJDE6be5/NOQyqkC8EDsmtr8wV5M5BB70jW201Tlq23Cfn/2/fPnFOZ82KyJ9/iiS5DYtKGIS8fcYxLEnxSdY1z26uwdm9e/ekipWiKEouOgfZEtOmTZNu3boVxaAsRB8gywN9xjrIO3bsaGSln59RKQZh0Hj88YRx9+9/x2TcuKi88UbEpMNuvHF2ughjzWy3eaKVjEZ7++215ZBDFsvo0XEzazsV772XuIe33DJmxqB9/XXi9wRquH+89xAGp7d8BT2c+w8nbjKDk/EqYU6FjQbE6Z4KNSx9bN/NTcnvcr3oGFIIYNLrqJvcZpttlmk4UmmGZbJZnBR0IxBsTao37YGvbDbiE2QhkM+5419orf3ppxHj6fv886g0bhyX3XeP5b0PK6zwd9LzlMlwo38C8zHnzo3Ixx9HpFu30i7IYRby6brmIS+SOYsURVGy0TnIiLIzKJE96B/FVjRz0QeIIKHPsJ4j/1I5yAv5jHxRwzIzP/wg8tpribX3X/+qFQLVlMWQDtu/fyyrGZTHHddI4vGIHHtsrVx/fa2ZvX3AAUQjG0uHDnHp1o3u83HT+2HHHePStOnS/588OXEvH3xwwrD85hui5QlneTLQE8noc7P6gOfCNTi5H7/44guzBhPJ9BqbpXiO/EANywbWvhuPYbaCi9fhbUQA85WIR9euXY0ymolSDisu5vu7BqXtdMssTgr5MaztqAiKum00GC8UQsE2UQH+zrkPWk1bOsPogw8iptPanDki330XkTlzIibl5Jdf6v/PrrvGJI2TNy02dcm7H/a6ZjLc2rePydy5VSYdttSGZZBSYf0S8tbgDMNxKYoSLIOStc/qHMgQa1QWuyzGfl4mfcDNuKJbfZcuXerW6Eoqv6n0yOdTT1WZEWTbbhuTjTaiTjIml15KFJL7jYkGqf+X0zJgQLXRaUijZd42bL99XK67bpJceeUOMnt2lTz/PNtSHeeFF5bU1WXavg6MPxs8mJnvzNOke31ux4FOSGaQNzuIZ8k1OBMNg/40Dhuu7XvvvbeM0RmkmsZ4CHSj4JytCmjfnU0dAjcFkUkEMIK4efPm0q5du6Sh+XJHLIuJNawxKFkcGYZMt1ub5sODkyztAaFgZyQR6YUPPvigbkyEN7pJ7VvQIl+kleyxRyMjvL1EInEjzNu2jUm7dnE5/vj8O7LSGIHz6b23sjcs4zJ2LIIeIVZ85SVsqbBh8R4qilKZMyitLlBseZlO52AtZh0nBZFRIZkyrtJ9RrGpZIPQr2Oz3WD/9a/Ems/87FVXjcuvv2J0RaRr19Sfc999URk9ukoaNYrL/fcnutBbWrT4S958c77MnLmyTJkSNe81alRUJkxINAkkkkl2FIbkaqvFpXXruDRvLjJ7dmLkCGPR/ICMN3QjNhfuYeqAcYpwT/M9X3kG0SO9xiZbOYIZsRDoHGpY+ti+O51Xj9/jycOg5L1atGhhitjz8YSEuXkP72mH5pIys/HGGxuD0j4omY4LoUC9BhvvRYe5HXbYwfzNRjfZ7Jwkt/OYNTZzLer2E5r5kiaCUdm1a0x22CEmzZsnBGiLFnHZdNO4abzjBwcddFBB/4+gL1cDnzCnwiqKohQiU2yH10wzKK3sKYdhiWMXZZwmKuutt17WGVe5fIafNIQRZ+mO8aWXovLKKxH57beI4JPna3V1XM48s7au6/xXX4m88w7R8Lj07m2dGSK77BKTUaOqTDps165Lnd2ffx6RN9+MyP77x4wRecUVCX320ktrpWPH+DLnrGnTiIlebr994j3i8Wp54okque22KrnzzhqZPDmx/507cz+LtGwZN42D2Pi/YsIzhl5I1py7zzyHVrfkK52NUzXos5l00SLqBNq8p4G1705m8PHzDz/8YAxKblJuWgypQjwdpUrF8PMzrEFJOisPpH2A3aYn+WJTDL1dOzn3tosYQsFGikmxJYrnjW4iEIqd8nDmmdWmZmDDDePy3HNLZMUVpeTkkgoL06fjRRTJIajeINI9QA1LRVHKNYPS/q3YcsiNjDLqDIOSrxiUNA7yw1EblhrLMDg8k40POfjgatPpNZnBecoptXLZZbXGyAMMzXXXXfqaHj0ShiUNfC68MGEUfvcdv28kP/4YkTPPjEufPjEZMWKJafZzxhnLZlolc34MGJD4zMcei8rQoUvrK7t0Sege6Emvv56IWBabZPvHz9zbbJRoua/FPrDGpjuCLPb/2XOusclXfufHMxoGnUMNSx/bd7sRS97n+++/N90gMSJJ9SRNxI8bIkzNe3gP26WLBdOmvE6dOrVgAe3WaSaDc22NRj7TW9Rto5tcJ75yza1AcKOb+QiEZPtE2sfDD1cZb+A995THqMzFsCQllxmaeDZpKGQ7xZYCTYVVFKXSKXTutRuxLAWUnbBW5lPCkwntCls8Xn89MT6EdFLKa1jXqTKiKc/991fJzTdXm7mVixYl7rlDDqlvGGJY2tnaZF0RmD788IRR2bRpXP7+OyLDh1fJHXdEZd99Y6aHBM15MrHttnHp2DEm778flXvuYTxJ4vO7dInXGZZAxDJIWVJug75kM6//cAxOm6HH3wheJDM4c9F1wqBzqGHpad9tu63lM4PSCi6MKFI0uelat25tbjw/leQwNO/xGpQtW7Y03s0gNNhJVtTtjoiwBqcVCOAVBnzNNJTX/dv334sMHJh43M4+u1a22674C9zIkSPNcey///7GqZGrYcmft9oqbmpCqYEopWEZllTYfAzgMByXoijBn0HpRiyLtZ9EYljHgTVzyy23LNos3nKlwubyuWE0UKljBFJWBw1aajQedRTdWmNy8snVMmNG4jXUR/bqVf9+2nBDkU02iZlO9RipL7wQlXffjZray7feWmwMv5tvrpIXX6yS556rMl1gvYZl8oigyEkn1Uq/flGTDvvDDzYVNvH5pMJCqSKWhRps7szrtdZaq957kynnGpy2XMuO1vPqmKn6g6hhGcL23baWIVflj/cgOgnUHODNow6wGEpkkCOW7kLEopnKoMz0/rl8tl/pLalGRLjptDQcIsee+lAW12TjULzptKz5/fs3kvnzI7L11rG6VJJi8+uvv5p0Jdv4IR86dIgZr2ap6yw1YqkoSqU7sSEfgzLbrJ1CHcOkvBKBIULJ+kf5SrHKRcKSChtGJkyI1HVg9bL33jGZMmWxnHZatTz5ZJVp2rPaasu+R48ecfn8c5HzzquWL7+MmMyrBx5YIq1aibRqFZdddqmRzz6rNQ18MCyzXdP5vP/8J15nVG60UVysCmYjluVKhfWLSCRiIpNsrpMf/QFd0hqbPGN0qUXnRC5Yg9OdxWnrroNMgzQsvTMo3fbduYLQxaAkndLO0cm3KU+YDUvXoOScYFBSP5kqQuln6+9iwvvbB5oZo96hvG6zoM8//9w4GDBO+T8MT+6LUaPWlPHjm5iUkXvvrSlZrWKqyGS2EUuwUcpEZ9jSGMRgnTyVZlgGfUFQFKU0c6+Rb35ESHgPvyKWdvwXvQjYX+sYBtb2MKeqpor+0P0TZzHrNV3orYOYLVWqbxAN1FT79O23IrNmJRry7Lhj8vuE8sGHHqqRIUNq5P8v9zIwduSOO6qMUQmDB9fKbrvV/8yvvx4rW201X+bN22mZ/hmp9o+0WuZdXn11db1opRuxZCxbplEnYXRmR53mki5ufxA2AhpkQfI7+PTTT032gNs4KFMGXSlpUIZlNu27s4ULjEFphwB36tTJeCNee+21khl9xfawZHMcvIYILYsOof5MBqX3f8NKqqG8GJYYmnYQ76efzpHLL09I6r59Z8miRQvkiy+WRjetERpUw9J2hp06Nf2Q4mI8q0GaHZUM+wxqxFJRlGTYrpKuzpGvE7uYBhny1s6T5r1Yx3Gk2nXcGq7F7EZfynEjHAeRIY6XdYYu/SjmRI/cJn/8zs1GYgsbzMu2nVY9Ix2T9lVIxU47xaSqihTuiBx0UK2cddayjmbOKc0qCS7konP061cr111XZd57m22W3ssYuY0bJ2ZZYlySktsQso+iTn8QF2TIK6+8Yp5N9hfdGzuE+5b72I1sltPgDLbmVsIZlNmC0YDQSTazqRTCt1QtxjO9r9egJEWGdJlso0yV2vobDyfdw7g/uNdGjdpSFiyollatEMRNZdGiFeoaBiEMOF/J0mn9qGHxw7DcbLO4NGkSlz//jBhP5SablOZchyEV1j7nQVmMFEUJlhMbBbtQnSMThUQsrYGFTsO+WYPSK9OKlXLr/YxSvD/Hi97CMW6yySYmMGBnFbrrLr9zs5KIGPGViPNHH31kIkZuk78gzMxO9vnWsGRkSCHgQ2eMCLMmb7utxtRHerH3YarzkOr3jFsbOLDWNDfcd9+lBiu34QYbkIKbGDliU2Mbqs5RVVVl9hPbwzU6bQad3Wx/E+QPwYvdd9+9pMdW0YYlJxvlnSYsCIFCahkIRSN8qVtLNbOp2IX0QRDy/I5z8OWXX9YZlEQo84kuVXLr70RqcLXceGPC0B48mHmV5NavXe/+JPJtC7q5TzmvOEDwMiUbh5JLemguBmQqWGe32CIuU6YkGviU0rAMusGmhqWiKF6ZwLpI1KZQnaOYBpnbtZ79a9WqlWk2kkqW2b4TYTUsbfMUZoljPG600UbGgE43E5zXrbrqqmZz3+fVV181EU5wa+JcJ7FrcBar0ZHL7NlkFTWVli1/q/d7TqdfhiWcc05t0ZzZV19NOuyy79+yJfMyE3WW3bs3bMMyVWQ1VQYdThCMS41Y+ty+mwd+2rRpsvPOO+d8cm1UjnQJFH8icm3btk2Zd2/rHUqRCmv3r5if4b6/NSjxgnBObYQy33TFSo1Yutx99zom0tepU0wOOmhZoY4woKaDzQVPqTU2+WoH8nJfEx33GpypPKV+RCxtneWUKdRZRuTgg6UkhKErrBqWiqKA2+GVr+gcO+64Y0nS+XOJWKJoUlOIQYnzMpeu9WE0LN352SjYKN5bbbVV3jLb6ngYmzRntHD+vT0XbDkMAQivsYmT2K9145dfRHbcsbH89FNLueuuv6RDh6V/Y0wYTXGWWy4uXbuWTk8qVOdwKVUDnzDoHPEcy29semypqa709t14i3IVVlYYEaFEGOGdyrYhTzoPWJgMS/f9bYTSGpR+NSeq5Ijlt982lqeeSrQ2u/zympxqE7lnWbTchcs7H4mveMVt9zC3VbVdvDBYeS9vlDNXzxyGMTOmJk0qXQOfMHgPM6X9JCPox6QoSmEzKK3juVQOz2x0DnQiUjlxUuKcxEFOyUY+49SKhZ/v7447s02IbFQxlVJeyGfznsmcxLbngl23CVLwPZ/lXa+zGWGWjHPOqZZ58xL/c/fd68tRRy39m41WMt7Mk2BXFPzIkvJiG/gUe5ZlGBoGxv///AbdmR16wxIPHAKEBzNZ+257o2SjqNoCdgxKFgyMqFxnL5YyYlnsQnrO7fvvv28EYi7GdbbvH9RIox8MG7aO1NREZLfdYrLLLv4Y0MnmI7ndw1iw3MYD3L8sVtzTpITbBSzX877zzon77J13IvL33yL/X1JcVMJiWPK8B30/FUXxD3QDHM/IUwwHG0GwOofbIKYUpNM52D+MSYxK9neLLbYw0bZ8R5sU+5gK1QlslpktKXHHnc2cOdM3Z3a272N7LrC5+8h6bA1O7wgzN7pp1+xUOui4cRF55BFqd7kHRSZNWkUmTVpcF51MN2akGPiVJVWOiGVYdA5Qw7LIYPgccMABMn369KTeKPtzuo5PLBS2gJ33QCFPVsCeDZUQsUTQIZiJkK277rpFGYhcyamwH3wQkbFjV62LVpaqexjXyoJTwI1ukv7E9zhfSM3hnM2aNatu4cKLnep+Z05V8+ZxmTMnIm+9hbFc/PMdlhrLoC9EiqL4C0YaNYkYl8nmXvs9AiQfnQOjCmMFuW/TP91awXw/J6gRS2/vh2Td6YMiq9kPUmHZ3BFmrNl2veYrNbB8z5ptS2Dc6GYs1kwGDEhExwcMqJUffvhDnnxyVRkypFrGjFlixnO8/rp/9ZXZYDsee9duPwzLYkcsw7Cex9SwLA2kDiBEUxk+6aJ7hdQblHvGZDE+B4OS1JHff//ddErj/FDkXiwqMRWW22zQoMRjtffe86V9++XLsh94z5M1HuBZQSH65JNPzALM9yxi4KbTelNzWJgefLBKXnstKrvtVvx02LAI+aALeEVR/AWZmEnBK4WDOVnE0p2rTSlFhw4dlmno4cfnFIN8dRrb+4F1LFPvh2TvH5SsE/aZhk9s7v5ef31cxo6NyOGH/yydOs01GUgc64gRW8g337SSddddJH37zpbvvlskTz+9srz8clTefhvnBs2FIrLaavG6edTF5rjjjkv6+0Lum1atEv/73XeJkSN0kG3IzmwI+n6G3rAk+uKdmZMqYummh+B1ZMNr1K5dOyOE/RAupfJU+mlY/vbbb8bTh0FJyivpMggufl8sgiDIi8Gll1bJq69S2xuTk0/+QUQ2Ltu+PPjggyYa37t3b2Mkcs55Xli48OTixQbuIzed1puag5HZuvUG+A5l/PiYXHppTdGbUoQlLSXoAl5RFH+x3eDRO1I5tEsdsWRfZsyYYeruSbtkrrbfTTtKEbHMV2/BoGQ9S7cu+bX/pSzjwTAcPJjgSUTeeKO57LjjenLVVTVSVRWT555rYl5z8cXfy8KFP0s0+qvsuuuKMm7chnLuuX/LNtugF68j22+/WCIR7sXyr1X5rOlrrEGNaEzefjsqTz5ZJaefXtugdY6IJ0MiE+U4poowLInCpLop7EXggiB8bXoIyjWCyPUO+UEpaiz98ogimPH08RUvHwalXShLITxTvX8+zZaCwAMPROWaaxKP1EUXfSvrr7+4rPuDkoFhmew+8aZu2WilNzXHjkPZZpu/zO+mTm0ko0ePkzXWSHQbc6Obfna6C4v3MOj7qChKcQxLHNSpKJVhiXwm84QOpNTed+nSxcjhYlDsKGy2OgeGJHoLDlCv3hIWXSFbnn8+aozKVVdlXIrIG29EZfvtG5uf4/GIHHFErfTtu56IrGeM7JNOmi+vvNJS3ntvFfn668T92aLFZzJu3NfmvvB2lOdeLoXhUWhTn8MOqzWG5WOPRYtmWGqWlH9UhGHJyUYJTpcOi+ClsUmxvHnuZwU9YukVzERsvSNUiv2A+ek9DAKvvRaRAQMSj9P559fIfvv9WvZ9S9VBLNvzjvfXzkYi/aR165jMmkWTil1kyy0X1NWCUJvMV+57vxavShXyQT8mRVEyp8JCpkypYuoBtsMojQvRezbccMOilq0EocaSY8Z4ojlPptFvqd7fL0ploD7xRGJ9OfnkWjn66Fq57LJqeeSRqPz6a0TWXDMu115bU2+f1l+/Ro4+OiYjRlTJzz8nzs3JJ28m663Xot44FFKlcUrYcRTeNTvfjKQxY8YYR0ePHj2WKcMp5Bowru3MM+PywQdR+eyziGy2mf/nX53Z/lFdSULea1ha4cvFYCumN68cEctcP8c1KClszzSTs5zevTB5FmfNishhhzWSJUuY81grF19cK599Vu69St+hLR8Bv/PONPwRefPNxtKr17Kd7lhQ7MJlFy/Sad3B0e7ilc7LHIa0lDAsRIqi+AtyiXWzHIYlazg6DcYVzdq23XZb04TN7+Z6QTIsMYDQWzCi6fDarVu3Or3Pj/fP531Kwfz5IuPHJ9aXf/0rJhtsIDJiRI2cempE7r23ykTxnCW4jvPOqzHZU+gjLVrEZeON2edmpgGQ21GebCZ0ZGtwUrv5+eefm0g83ee9Bme6Bn8WW+vKDFeXQs876bB02H/xxSoTtUTH8psw6BwxNSxLm5ZCOqyNQpLaifClqBtBxCKAR6/YRmUpI5a5GLAIDR74XD19QSzUD5oRivA/8MBq40HcZpuY3HVXbjMrw2RY0sDnrrsSNaTeeZa8HwsP29prr11v8bLptCw4NAuyreBRDrzGJs8ohmgYjLawCHlFUYpTglMqB7Or0+AUdo2rIOochXa753uckugtrBkY0V27dq3T9xqCw/qZZxLG4ZZbxqRNm6X7TSOeG29M3m2e84YB2rdvTO68s0p2353Mn+TvzzprM5JcuK/d6GaqBn92c438TJHJQgy3Qw9dalhedFGtMF3wssvoyiwyZEhtyuOstCypSMD3sSIMS3tTEy3BcLIDaF0DihTYIMyUKrVh5hqULEbbbbdd1qkjQSvUL/b75MvQoVXy+edR2WCDuDzxxBJZbrlgeL/ca5dqplSu7LRT4hmaNi0q8+aJOM7Pevzwg8ipp1bLvvvGpE8fSTk42m2tTu0zXzFEMU5Z3KgRZV9ZvPCglvtae1HDUlEaJtk0DSxU50D22U7tXp0mqDqHH2BQUl4xb94846QkKov8L5SwpcLSqAYOPjj3++jqq2tk661jsv/+sbx0ajZvRpLb4A8HB92H0bu5H62DmJI0+3oXP/Qh9IlmzeLyxRdRefXViFx7bbVMmJBYf/v2rTWR2UKoVGd2RJv35HczkA++//77y6BBg2SbbbZZZu5iuWdKlfpzePhZjDCovd7NXN6/XM17wsKvv4pJSYFhw5aIE6gzBNWwTPW7bNJRttoqJlOnRuWll6Kyzjpxk6qz8cZx6dcv4RnFiU9a8KRJUfO6Pn2SN7hgMaITM5u7zyhrLFwff/yx+Z40LxY0nmFvOi1f80mJ8gs1LBWl4RqWxWregxy0TnJkH53a082SDoLO4Qf2fL777rvGoESXw8noJ2FJhcVx+8oric+hvCZXsMOJWvpFugZ/bnTT3h8TJ040hqldp60zpBADc4UVEsbl449Xyf77J0qPLFOmoIcU7sgJmvM6rDpHaA1LbuiRI0fKlVdeaaIc3bt3lz333HOZyEipDctyeg9dg7KQWoRU7x/UiGW5DFTSQv/6K5Gq0rNnsIxkzglppcmEZSECdOedE4Zlv371lZw33qiV4cNr5Kyzqo1RCbNnk0mQWOSygX3CM82Gs2iTTTYxhifPrptOy/2NR9vrLXVrQtzB2A3de6goSvAjlshl6ggxKHlvDMr27dtnbKQS9ogl2Skc89y5c83PNFdMpscVSphk7+jR3D8R6dQpVnAkrpSzN1966SXzFacAjhC33wL3zvjx45dJpc3Ub8Hl8MMThiVGJc2LttgiLq+8EpX334/IIYdUfpppPARR1VAblhiTd999t5xxxhnyn//8R/r06ZNSGJVyWHE56h3cDnGFGpSWUragzvVvQYDI3G23JYwX2l8HTR5hWJ1yyilJ/1aIYbn33jG5+ebE9+utFzfzpVgEn3iiyrQDZ4hxNBoX1gnapM+cGclrOLO7j9zrdgHyOpessclXxgjx/ZIlS4yn2xvdzKb5QCV6DxVF8ZdMzXty0QOQdaR9soYju5jJSKZRts6xMM7OthFKHITIbaJbW2+9tUyZMsX3KKVLWOZYYjzZpj3ZEiSdCecwXWHXXHNN8zPr8ttvv23qZN3aTdtvAUdNsn4L3vW1Z8+YtG8fE4LbTz65RN56K2oMy/feW7bvQyUabbGQ6ByhNSwxnF599VXz/ZAhQzIW0pcyYlkqIU/EZtq0acag9Ku4PWwRy3IJVFp+//BDRNZfPy6HHFKaeysI0Bl2/PjFJi0Fg5HL+OabzNNqZIxKuOKKWnn22ahMnBiRGTPyMyyz8R7iLWXx8rY1Rxa4Bmeq5gP2K7Ikn/sxDB5ORVHK07wnkx7A3+nEiXGFo7xly5ZmHc8126JUWTt+RUYxnq1Biey24984B1CsYwmLrP7uO5G33krsa+/exZnZWI6GgakcxG6/BTZ3fFmyZkETJ/IZOLCZBpH4vA8/jAi3TyGJSpoK6x+hNSz9FvJ+UQohb4ci0xqaCKWfBqX3OIr5sIWl3sELt9JNNyUk2MCBtSY6FyYKvaY77BBf5ue33los555bLZtuGjcRXCKVEyeKMSzz3cd8BCjHxbPAZr2lwPNPUwi7gNEUg4ZB/I40nGTjUDKloIVFyCuKEhydg9+T9okCjbzCoKRuLV9Zwv/ZpilB1m0wKJG5bHQiJULpZpm5XWEbcif6UaPoih6Rbt1i0qJFbv9bbsPo9NNPT7p2p9M5UvVbcMeX0RV5zpw5y6zXyy9PY7+N5c8/E9lRbdvmf10q1bCMhKV5z7Bhw+Taa681XRupAbjllltMTnUqbrzxRrn99ttNF6k11lhDDj74YFMb6ZcxRMShnMOKS/VZGJSkyxCBQellhAppM8Wg2DdjKiHP74P+cI8dG5UZM6Ky0kpxOe642kCmpeAFfPzxx833hx9+eD0veDEEKAvgo48uVW5se3SGGeeD3/tom/8kaz5g6ze9s7xseo5rcLrpOWpYKkrDJB/DkogctWboQcjjVq1ambmChcqQUjbvyWddQ8ZagxIZis5oa/K87x+EtTMTxdZPnnzSzq4MV7QSUt3LuV7TdOPL3OjmvHlzpVWr1WT69NXkkUdmyoEH/lEvusn/Z3u9wrCex0Kwj3kZlo899piceeaZMnz4cNMGGqNxjz32kM8++6ze8FXLI488Iueff77cc889ZtzFzJkz5ZhjjjEX+/rrr/fNsAxKxLIYhfTuPCfbfvvTTz8t6lBk70ypUhqWuVLqhej66xNG2vHH10oRegz4AgIYDx/42bwnW6xhmW/EslRppjhoMs3y4qubnoNxyaKFR5VnkK8omtnsb9CdJoqi+OvMxrCyBiXRmdatW5tsCr9kQSmb9+SiR9k1CNmJzKSzrVu2kIowRCyLBVG3d96Jmj4FBx1UWSU2ftzvyWZv7rprlUyfLvLTTxvKqqvOqSt/4avtZuttFpRs7F6lRixDYVhiDPbr10/69u1rfsbAHDNmjDEcMSC9ULC7/fbbyxFHHGF+JspGBOWdd96RMM2UKof3EIOSCGWyeU6lEpLF/Ixk781iRJoQCzeLEEIh3YNUakEweXJE3nwzKtXVcTn55PQexSCPGyk2m22WeAZmzYoIWVoZskoDV0ifapaXNz2HaOdrr71mDNRk6bTFdP4oihLciCUGJes3kTpe36ZNGyNP/JbHQYtYsoZTP4lBib6y+eabmzTHTMdts5VKrXNk87dCXpst33wjZowG9OgRFyexpmz7lCvPPPOMuYa77757vYzEYhptnToljvvTT5vJRhtttEz5i12v3W7yrO1egzMMPRNiIdhHyEndIz2Mrl3Mi3QFaM+ePc3cmmQQpXzooYfMbCLSZekC9cILL8i///1vKeVMqVI9dH7UO3DjE6HEoCQKnGyeU7GPqRT1Dt4HBq8uCzHKOIo6MwxZpPB2UovhCgG3620pBeoNNySilYceGpPmzSWwpDMsS+GZI0ObYcZ//x2Rr76KSOvWuS3aQfQeetNzUCy5VzfeeON66bRutzvuU9fYpCba73poRVGCY1hSS4jT6ffffzfrVrt27bIyrPKlVPpNps9x13AiQm3bts3LkA56855iXEeSi/bYo7F8/XXEzGMcPnyJhBEy6aBHjx71fl/M9bxz58T98tFHEVmyROp6XrjlLzTF8naTtxtRdb7y+6lTp5poqFsCg3MkKLpIrBIjlnQfRdF3c56Bn2fMmJH0f4hU8n877LCDubm4eCeeeKIZEeIXlRKxxKBEKFPnlcqgdD8nzIalfW82jhdDms/cbLPNzAPNNbVphlYA0HCFdCLrceJ1LOJ4ovgZA7SYAoCBxRTWwxln1IayO5v9W7EFJbKPRj50ayMdNlfDEoIizDMJedJzUCC9447cbnd8JXKBgqmGpaKEm2Q6B8876xPROgwrMm46dOhQ9H0pd8TSNiNCd8EhvOmmm+ad6lvOiGU5u8DusUcj44DdaKO4vPTSEll//fzeqyFmSbVqFZeVV47Lb79F5JNPMnehT9VN/uWXXzaNtLBxWLO5p3EYs757o5v8XI5spFglN+/JBUaCDB06VG677TaTykljjNNOO82MCLnooot8+Yyw11i6BiUCOZ1BWarFpBSGJQvz5MmTjXFICoPtjGevZaoCbutxwiP866+/mvPG+QNvLr1f6Yh//SVy0kmJ9znhhFozmDfIC1gmw7IUJAzLRJ3lfvtl/392/4Lumcsk5JN1u9PUWEUJP67OwVdS7IjWWWPSRixLQblqLO24FJzC7MMmm2xiHOKFKLLFNCyDOOLs++9F9tyzkXzxRVQ23BCjcnGgM6GCmCXFEtyxY9w4/t97L7/xZnbfiLC741C4x+s3C5onX3zxRb3Zm95mQdEi6i0VGbGkoyvWO8LEhZ/dTosuGI+kvR5//PHmZwq48QL0799fLrjgAl9OUqaIZak8erl+FvuMQUR3XQzKLl26mKhbpc2Z9ELkEW8QHl7SCHMZBu31OLGgk3KD8m7z6VnQ3XREUhl46N102mTpDWefXWWG7d53X41suWX9c3vJJVUmTaVFi7hcfnlNaLyHqfahFPuWbwOfsEUsFUVpWLB+kEL39NNPm3UIhdTOYwTWoCDqHIV+jjfLCKxT2A95XWyZH7QRZ0cf3UhmzYpKy5YJo7JITf4rvvymU6eY0d1uu63KRC532ilWlyJbSP0i63uqbCRrbGJ4ose7szdX9EQ3852V7YXzmOuc28AblnjgEZ6EjHv16mV+x4nk54EDByb9H5R9r/JlT4xfhlGQ5lhm4z3EoMQgwrjK1aAMcyosDx7GHlFGa+gVOi7F7meqfHoEAIu8FQIY8Ta9wT747MeiRSvJbbetIbFYRHr2bCRPPbWkbl7j229HZNiwxD17221LxDPfN5Bw3XhekwmhUtUv5jtyxD6rlWhYBv2YFEVJD7X///vf/2T69Olm/Rg8eLBZd4Ksc/gFayf9MkgXpBFjIfM3yxGxLHcmkctbb0XkjTei0rhxXF54YbG0bCmhJpNhWUx22SUm110nMn16VP7zn8T9+PjjS2T//WNFaRiIboUzydvcz20WtMAzK9sb3URm5GokIlPCkPWUcyoso0aOPvpo6dy5s0nZZNwIwsZ2ie3Tp4+JQDGnEvbbbz/TSZZhuDYVligmv/fL8g6SYZnOe+galER/8zEoS72Y+PEZpPpiUBJFpHlJt27dTNoQ902xQQBwrtm86Q3W4GRfHn/8b4nF1jR/x+O1zz7Vcttt82XffRvLiSeuYAYW9+lTK7vtFpyFKR0UoPOsJqMchiW3UbYfWSmpsIqiVBb//e9/TaYVnU733nvvlCPTKmV2tpXH9MkgSolBybgUnLfFkH3lSoVF+UeHJBsqG73Uj338738Tn3PUUTHZeOOC367sRnM5I5Z00X3rrcXy2mtRefbZqEyaFJU77qjK2rD0o2Eg/4s+v/zyyy8zK9vttYC+yfeUgJE66zU2083eDIvOkbNheeihhxoD4eKLLzbRH+oJxo4dW1cDR/G6e+AXXnihOUl8paidCB1G5RVXXOHbQWBYpjNSyi3k3RoMDByMcq+HM1dKEbEs9DOIFpIiwHFTe1GscSm5vk+y9IaLL048Cmee+Zt88EFUXnllRenXb3XZfPOfZebMlWT11RfJ8cfPku+/T8wwRHiE4QEvp2G5ySakbcTljz8ipkFBtrUjmgqrKEoQ2XfffaV3797yxBNPpB2ZVmpndjF0Ad5z/vz5dSUlths7gYNiUsrmPRiUHB+GM9csWRd6vnfnHvqxLk2bFpEXXqiSSCQuZ54Z7EaAfjXvKfZ6ztiRTp1qpVevWmnXrolMmBCR2bMlq0hwMXWO6upqWWWVVczmfh7PlDU2+Yrjhu9t9p1rbNrZm2HROfJq3kPaa6rUV5r11PuA6mq55JJLzFYsMCwRgEET8q5BScjcD4PS/ZxSHFM+Qh4PDQ4G0gCoP0l23EFq/U3J8OuvJx7W/v2bGgOIGZUPPFAlH3+ciGJefvkvsvzyi2X27J/rcum9qQ0sQGFIUygVrMV0g6XG8vnno3LiibGKS4UN+j4qiuIfdC0PWpaU35/Fmk+5CgYX2UZ0ysSYtB3Zi0mpIpZcO5zeZI8RFOnYsaPRUfhsm8nk7UJvjU0c5mSfFeKgtdHKgw6KGQdsMY6x1KD7nHHGGea8oPe7lHJ8WKtWIjvvHJNXX40aHe6ii2oDlyUViUSMDGEj2GbhObajy9AzcXhQy8z9xj3IfnL/2Tmcmea828+quK6wpSBoQh7DijoMIrTeov4w1Qvk+hl2MDICmxueaDYpmUFN3bCMHs39EZEuXWKy4YaJ391xR42stVZcrruu2qTA9u1LLv3qy+TSswgx7oRj5uG3ncL4npQGhES61IZiQVdCMgkQQLYeuhxCng66Z5wRlaFDq+XIIxdnVZ8axBmWhdZkKIqSP8OGDZNrr73WZEm1b99ebrnlFlOKkwpKdG6//XZjGJAldPDBB5vyHL9G/WSjc5RqffPTyYxBiSKLUkv/g+bNm9cZCUHUOXKF80RXT5ze6Ga2HIlraZV9sqrcLvSkLNq6OdZ7DE1KujC8Xaey/T5TKi0N7B9/PLFunHVWZUQrgfPnzhcv55p+zDG1dYblBRfUms6xYciSikajdfeRi70HqetmX5Fr/Mz9zP3rjW5yH5fzWBqMYVkKIY8nwYaz2adiGJSWIAl5bm4WfBYkvFbUn2QaCB2k1t9PPplYCHr3Xro4s3uXX14rp55aK45DKW0uvdspjAefRfrNN9+sJyzsIoQA8Hr1/IR94XrY1ONyCfnjjovJrbeymEflxhuz9x6GwWDT5j2KUnwee+wxUy8+fPhwU06B0bjHHnvIZ599ZkosvDzyyCNy/vnnyz333CPbbbedzJw5U4455hjz7KWqiQzz7Gw/9BsckRhKGE4YlFtttdUy61OQdI5cwemNA5i1mfNFz490Tm8XdBp3bBTXnXWf1EZrbKL/cJ9hAFhF3xqbfHUNrptuqpba2oj06BEzYzIU/znggJiZbfnttxGTEtuzZzzUWVKN/v8e5D6iTwkOH54TnBw2lZb7kMAOwQyeXatvoo+7qdyloCIMy3LPsbSDkWk/jlBh0UEwlyIyWk4hz9/sWA++z3WOVRBaf8+dK/Lmm4n3OPDAZY2eJHpLVp3CeNAx6lq1amUedJteg+OBaLYt3PbWcvjZlrrccywBeTZ4cK0ceWTCsOzXr1ZSTCYKXcQyLPUOihJmMAb79etX1yAQA3PMmDHGcMSA9PL222/L9ttvL0cccYT5me6lhx9+eNqayLDpHH5FLFmXcECS9omyusUWW6Qs5yiFg95vw5L3wuhDR7ERSYxKP9YXu26j6NvP4p6waz3GOjoh2U3oBqzvixevIvfc09a8/uyz/dXfyp0BxrEzIYJzu9dee5V1TceffthhMdPA5777qqRnz5qKaxgYcea8uw42nCgYm9bgLMd4koowLMvlPbQGJV4CvFfk6WPs4bkqNqXyHqaCmlY7KJY5Vrl2iQuK4UAaLB1ft902VpR238lSG9zCbbczLQao25baGp3Z5NHnIihLLeSpIyHNePLkqNxwQ5VcfXVtRdQuqmGpKMWFNXbKlCkyaNCgut/xzPXs2VMmTpyY9H+IUj700ENmLAbpshgVL7zwgpmn7Rdhj1ja0V+s4xiUzILOFNUot86RTydbjhFFm3nZvDcpsIV+RrrZ0LZuzlX00QltJtPVV68gixZFZZNNFsjixW/IxInLptIWM5OpmOAsnzp1akrDstQce2ytMSzR8QggOFPoApsK64fOgSFJNN5G5Muho4TzDi5zjSWLHQIKbxQGJR4w22GU9MdSDSsu9uckW0gwhDAoEZIU9bMo5eMRySYa6sf7ZOKJJxL7fvDBpVEC0hVuswDaBYiNe8zm0btdwuwilE4RyCQoSylA+ajTTquVo46KmnbgIrUNMhU26IuWogQNjAPkolvzBvw8Y8aMpP9DpJL/22GHHYwsQbE/8cQT5T//+Y9v+4XsRg8IW8QS5yURSs6PHf2Vqi4u2eeEIWJJ9BUdhUgh0WoaD3E9yK5K9d65yuZc9hFDkSaGv/66qowalVizBw9eTrbffrs65/K8efNM3Sb3lB1B4U2lzWYfy7nGZMqSKvW+tW8fl65dY2b0yD33JGotw5oKG7byGzUsc/TI2JRXvAHJmtOUqp6z1EKeBQnvH3UKLVq0SJsyE5bUDUZgvP12NGUabKmPDQM9WVtq8uhtZBPHBZ2GbZcw19Npx6C41y0oQn6bbWJ1bdZx9KfroaGpsIqi5Aud6YcOHSq33XZb3ezs0047TYYMGWJmaJciYlmqru3Z6hwYWRiUGFdkF3Xt2jXnRkblcmbnGoVljURHoclTsu6kfuxjrrzwQlSOPbZaFiyISLt2MTngANb7RFdProfFTaXlq5vJ5BqaQRx5FiRntuXEE2uNYTliRJWce26tpFJZK9WZXS4qwrBEwS6mkHcNSh7qdN1OS7WglMqARdB9+umnpjYQAZiLhzMdQWje8/TTiWhlt26xrGcslvoY3Tx6t1GQt1MdHmgWVl7PYmXrb63HvtzpNS1aiKy5JjW5Efn4Yzrwpr5mmgqrKArQ0RWHG+uPCz+78tAF45G01+OPP978vOWWWxrlvH///nLBBRf48sxm0jns+lwKJ5k1xpJ9Fk5JDEoiYkR53VnS+X5OMcnnM7gOGJQco43CJsvmKceaUlsrMmRIlVx1VWL9peTm4YeXSKokL+4rsphSZTKx1rsdQd0sJs5DOerpLEFzZsOBB8bk3HPj8v33EXnuuagpy0mGOrP9pSIMy2JFLFHeSUdk48HFA+ZGk1J9ViVELEnJwCChvTH1AtSqYNz4Sbmb9zz5ZLTkabB+4e1U552BRFSTc8N1HD9+fF16DRveaxarUgpTPqZjx5i89FKVvP9+esMyDN5D9jEsQl5RwgoGAt3VaQpixybx3PFzqlnayDfvc2kVbr/WzGx0DruvxVb27We58hwjgxFYNK7xa/0uhWEJ2X4G+hnHSI8LDLFsjGa/9j+b92G0ep8+jWT8+MT1OemkWrn66hrT0M6PTCZ35BmOZeplbfdbb1PAUoyfCKJhSQykb1/Oe7UMH14VesMynqNuVK5jUsMyCRhU1qAk+pONQVnqiGWxhLw9drxivH/r1q1NHaXflPsh/vZbMSkSkUhcevWqjFlSbqMg67kFb6MgFh4MTpQzb+MA7vdiKUK0Vn/pJZEpUxCMqZ+RMAj5sBT7K0rYYdTI0UcfLZ07dzYGEuNGcKDZLrF9+vQxdXTMqYT99tvPdJKl94FNhSWKye/9km1BMiytDOKzrLE1d+5cE+21cxr9IChdYTGe0FFwnpI5lu1Yt0yyOpe+Dtlw/PHVxqhs1iwuw4bVyOGH+6cXJht5Rs0x15/MMhvhxLHAs8I96DU282kKWEjDwHJx3HEJw/L116Py558iK6yw7GvC4iSOhWQ/K8aw9CMtxWtQkkJD0XUuhDViyQ2L549FCc8m41KYE5Zv2kw2lNJ76IVuYbDDDnFZf32paEivYUPRABQtvJ00X7ILENeeryza3jlcbH6kP3fqlLhORCzTEYZUWOs8CoP3UFHCzKGHHmpqAy+++GKjKFOKMnbs2LqGPjhB3efwwgsvNM8aX200C6Pyiiuu8F3nSKVTuFHEYmM/i4Y1GJSMu8IIR4fxk3KnwiJzOT5Se1mP0FFy1c9KZeC8+GJUXnihSqqr4zJ+/JKSzavEgGSdt2u9O37CnXWIEcrvbVNAd63Pd+ZhpnNbrvVvgw0YGxeXefMiMmNGRDp3jofSmQ1qWJaQbGZKpbt5MCipn2SBQqnOx6AsdW2FX5FRO+cJYY1Qou04CxPvX8yFxK/3zucc//67yJ13JgxLupU2NOy96baktr9HWbKRTbrr4WTBCOUZc5sEsRDhgMhFyJEKC598EpG//xZJlZkVhlTYfAxLRVHyg7TXVKmvNOtxoZ78kksuMVuxQB4iA9AdkjWxc6OIxcRGKAE5nW30Lh/K1byHn6mftLMoN910U+MsyHXtL1VfB2IcZ52VUK1PPbW2ZEZlqmP0jp9wmwJaY5M0Wu4j1n+cJt6utAQYMp0/svpSPaPlNtzatk0YlugealgWnwYTsUx2UbwGJZ1OMSgLucHs/5bCsCzEMPPOeWrVqpXxALv7XKqaikLJdR/vvrtKfv89Im3axGTvvYuzUJb7vLEQv/nmm2Yx6dGjxzJ/T3Zv8jsWEDa3vb87h4tFiBQkvgc8nt7OtKm6BTNHet114zJ3bkQ++igi3bolP0dhEPJqWCpKw8V2VEXvSGVYFnPkiNv/wRoMbdq0KWqGUTkiloXOy0733i65HFemtemmm6rkyy8jZq0bNCiYjmu3KaC71nNfuV1p0SOIdnLOvV1pvWUzvCZVlLzc+lC7dnF57TWRTz+NhNZgi/9/wCro+1lRhiWCJ1NaihXyKMqkA2BQIoj9MCiTfVYxb4BCUm5px42wxmOFsKYeL9m+Btkwdt8nFwhs33prQhiecUathOAZzQu81zNnzqzXXS5fw83O4XKj+LyHbRTEZp0UPIc8U96Zm9bjSdRyzBga+ESlW7fa0KfCBn0/FUUpnmFZ6lmWbrkOctX2fyBqW2zlvVQ1lsCaQskGXzfYYAMzPsSPWtVi7/8334hcdVViP4cOrZEiBY+LBk4SMtbYLNzDbiotI1D4nnvRWzbD12SptOV2FjPiRaTKRCyTUe79q7Tym4oxLLNJS8EbQ46+NSjbtWtnumr6efLdiGUxyccwQxhgUP7222+mvo6mPOnGUBQ7FbYcPPpo1LSeXm+9uK/F9Mkop6BKZ/j4IUTtWBPvHC4ULbdRkNfj2aJFaxFZV955Z4mceGI8qbIQBq+cdRwFfTFSFMV/bM15pkwpvwxLsorIriJbJFm5TtBnTGYLx4DhMmvWLKOf+DEvu5SpsIMGVcs//0Rku+1icthhpe02X6xrY9duNppk2c/i3rdrPWUzXDN0a54NdGvXuRyEVFj45JPkekW596/SsqSqK03IJxNCNgIyZcqUohmUpa6tyGUhIXpFNImoEsJ68803z1pYF3MhKXXzHk7X9dcnDJlTTqnNue13mChX11K8lek8nm3b/mN+N3nyEhk37pWkjYLCELHMx/gN+jEpipL9s4ysK+b8bGtQkl2FQYnuwtqdTHcpVTSxWHoNmS7U+JFNxVrQtWvXuqiwnxQzS2rChIg89VSVRKNxufHGGjNiq1Jxy2YYZwP06nj//ffNmk7zINZ7enew9nPeCWIwvs5d70s1d5NUWPj224hQxeONJIchFTYWoiypijAsrQBCOLmF665Q5samjhCPS7FTPCEI3kPOBw82Dzx59LkK6zBELHN5nzFjojJzZlRWXjluWlBXMplaf5dSOLkez+7dE5/7yy8rSvfuO8sffyyt58AjjxOExYb/oXOdXYBYrIIk+MOwECmKEr752W7kDmMLA9ZtqhfEjq35QpYZUS5Se225BcdZDKPSr3EjQFTyyiur5OOPIzJnTkS++y4ic+cm/nbCCTHZaqvg96bwG3v+WL/JiHPvZSYMENXkbz/++KOJbpJBmCyV1o8O9F4Y973OOjSqTHSG9c7R1oilv1SEYWlvRCvkMSgRyhiU/A2h/Mknn5ibttg3jy3aL6eQ54Hl2FHUC5llVezFqpQF3XzUf/+b8I71718rK61U7M8r78ISxGHFsMEGif2iedLChU1lrbWa1nk8raLBomOHPaN0YHgiVJM1Csq3NXqhqGGpKA2bYhiWdqQGBiURns0228ys4ZnkdTEbBRVDH/CON2OEDE2Ipk2bFvhO9P/8E5XTTmsu77yzrPrcvn1MLr64Rhoiqc4t9ybrNNeZBlP2te58bdexzGu9xmauHehTpcNiWFJnGUbDMv7/+xj0/awYw5ITjQFJ7SACC6OKm9MVynhKii143f0p9mclM17t4GCUcR7Ijh07mocyX8oVscz1wcm0j6Q+vPBCVCZNikqTJnEZMKA00cpyCoBMhmW5jCJGjKy9dlx+/DEis2dHZNVV6187lCkUNhwhpH3Z/WXBsY2Cfvnll2Vao7sLEYtQsc+9GpaK0rDx07DkdURyyDDi/zbZZBPjcMtWjpVL5/BzvFkYOtH/+SejRDaT999fXlZYIS4XXlgrG23ELOzERoPVci4LQdA5Uq2L3okDPD9sboNBtwM9m9uB3jvujK/peoQka+DzyivRpJ1hw1B+EwuRzlERhqWtc9hzzz3lqquukt12220ZL18pPHruZ5UyYummzWBgU/BOHYafnxG2rrCPPRaV0aOjMnVqRL74YunDeNRRMVlnHal4ylVjmQ0tWy41LDt0iGcU8nyPocm2jnPxbKMgu1FHzFeeP+8iRLQzl0WokoS8oij+w1pbaPMe5LQ1KIEu7ci4XOV2ubOkMsH/4RCkeSAO8FTHGWSdA/vmgAMayfvvN5Hll6+V55+vla5dg2sEhzFLKlMHeiKbP/30U10HepzIyVJpk31W/QY+taFtGJgL2hU2D/Bu3HnnnTJ06FDzff/+/eXQQw9NWhBcSsOyVB3aENDW+8fP2abNVHoqLPOijzmmWuLxpecBb2KnTjG54IKGkabCEOmzzz476d/KnfaBYfnuu1ynwlp/p2oU5C5CKG20rccI9S5CbHhM8zkXalgqSsOmEMMSGYeCzNrNOr7hhhsaQ6uQGY1BTYUlkwwZTNYJx0mfi3RRrSDqHL//LrL//o1M1tMKK9TIXXd9K127ridBotzrerHKb1J1oHdTaflKCjmNgmhM6TU2cUq3a5f4fOpi2VV3d8p97ipN5wi1YcmN8Morr8gNN9wgZ511lvTs2TNll6lSePRK9Vm8Nw8TtZR4AK33z++brhRNjvzAe65HjqwyRiUzE4cMqTGF9EnGOVY0nN9UEbogGJYwe3byvxfiPXSjlcxnte9nFyF3FhcGqF2EvNHNTJ+fT+pM0BcuRVGyB6dUpjmW3rWJn+nOjkHJ+o2hhbJc6NpdyohltusH8hX9hE6vzKFk5mamrJGg6hxDh1YZo3KVVej4+plstZXK8nJnSeHYYSOYYsFJg3FpjU1Kw2yPhsaNV5YmTXaUuXOjMnHi79Kly3J10xE0FdZfQm1YYkQ+8cQT5vsLL7ywZDOlyuk9pLMWwhqhzTHR6bVYLZvL5T3k3CEgssErDHhLZlXCCSfUSo8emqripdw1LEsNy+SC3G8hn6qew7sI0UyCTrT8Hg+nG9n0Dn4Ok5BXFMV/kCfZ6hzIXBqS2RQ+umbi+PJr7S5VjSVkMiw5JxjOZItgNKOjZNvps5wRy9T6iMgTTySu0+2318jGG/8t8XgzCSLlNI641ieccEJZO9HzPNEEis39bNujoXPnv+Stt1aUhx+eL7//PsOMS7EjznD08Dp+F0QjMxYinSPUhqVLpplSYa+xRAFmUbLeP4r7P/zww6LOASp18x4+i3QGDGdSm20XULulKtZ29/H99yPy2WdRado0LgceWNoBxan2qRzQYe2DDz4wDSC23XbbZf5eTsG54YaJc1NoKmyxFqF//vmnLrLJ80YzLH6HcmQNTRahXLz3iqJUFsiDTM17cFJZgxIZgkFJKqjf63apIpaQbTf6bbbZxpQfhL1h4HvvJcaJ0Kxnr71iMmuWyvtkEP1zayNdyrlOuj0aDjqoSt56S+Tzz1vLrruuV7fOo3diVL7xxhvm2fSm0maTxVRsYg3BsBw2bJhce+21psaPFIdbbrnFCJJ0kbYLLrhARo0aZQQtAvbGG2+UvffeW4I+U6qc3kMWI7x/8+bNMx7Obt26GSOah6AUxkupvIcU9lOHwUK88cYbG2Xfpi+SOmQ9vTaSZDfv/j3ySOLB22+/WNFHigQZnjeGEXOfeA3LchtDdsQVEUtvrUO5C+k5LyhDbMx+dZUmt1EQ9ySOrHHjxhXcrU5RlMqLWCIzSLnHMbXBBhtI8+bNiyYXSjVuBIrdjT5oNZZPP51YizAq7XjNcjuOw0ZQztcee8TkrLNE3n47IosWLe3RwLND8GbLLbdMmcWEceld50s57iwWovKbvKTcY489JmeeeaYMHz7cKK0YiHvssYcZgurOpLNQh0CnVv725JNPGo8dnq1VVllF/CKTYVmKVBE/vYecM7q8sjDZqBMh+rAPRPa+Nw8xBiUPMvUmLL4syNb75XYBZRG3HiYMJxtJ4mcM06ZNV5SRIxNzko44onzRSks5jTd7rwdtjqU7y/LPPyMyf76I03snsB3auB/ptGy7LfNs4iCjSZJdhNxudTyr3pmbbIqiVAapdA6a1SAHWKOQATjci+1oKpU+AG43eiI9OL796kZfrohlKtiVp59ORJdtBpRmqCQHHezjjz8261ynTp0CpXNYNtkkLhtvHDOTAhg9sv/+S1PV7Qx6G7RIlsXE5mYx2XFn7lrfrEjjzio+Ynn99ddLv379pG/fvuZnDMwxY8bIPffcI+eff/4yr+f3KGFvv/12XbEsRoSfVErEkhRQblo8gBhWnTt3Np6SZJ9R7FS8Ygp5jhMD8r333jOOBmYW2nvDy7nnVsmLL0blmWdEWrWqXyc3efJkE8VEkX/xxYj88gvpjYukquoVee+9xANvt6DmzoepQ5sf4PVdd13SniMmHXb11evfYzw7QY/4sY+kzNhudS7eRkFkdVATzbNsGwopihJuvDoHzzpGFgYlDlLWG2REKWRZKSOWRG9QrjGeWUtat26d08zNTJ9RrOPIR5/58MPEGrXccnET7VJSwz0xadIkU2vpNSwhKLoX1/G226Ly/PP1Dct0nYrTZTG5jmV33NlKnlTaQtPfK9qwJJI2ZcoUGTRoUN3vOFg6sk6cODHp/zz77LMmhXPAgAHyzDPPGMPgiCOOkPPOO8+3WgM/Zkr5RT6fhbAm7E4khJuwQ4cO9Wq/kn0GhM2w5DgxnIlY895EYtPVYfz9t8gdd1TJokUROfnkRvLii0vqpU5y/2BYklo9cWLidj788Kh06dLBPPBs1GwSGeW1rqFp21AHReAVg6AeGw18MCxJh+3UadnOiUEXoOmEfKpudakcJ4qihA+rc7DG2AwGDMq2bduaFDlb2lEKSjVuBKZOnWr0QLrR+9HR1vsZpU6bJMI8a9YsUzaCzuXqB08/vVKdMbL88hJoyp1umsmZHZQ1vVcvDMtEk8fzzhPZeOP80ky9WUzuuLPfnREoM2fONEaoTaV1o5vZNrWy7x2Uc+i7YUltEcLStdyBn8lFTgaerQkTJsiRRx4pL7zwghG4J598sjnZl1xyiVRaxDKXVFj2yc6i5EYlcseNmukmz1RIHzQhz/twnBh5XCu8nBxzKqPSfu4bb0SNUQmvvhqV++6LSt++sSTjV0Seey7x0B11VHyZIbucZ+tdYsOw5WeO0T7sdgtCoXYlp8LadNhJk5J3hg3C/vkt5HFqFLPRlqIopQVn5fjx482aRqYWznNXUUQ+oOOUgmI372Gt5DiBdbVVq1ZFkWelKL+xkMrIMZHCSSYJx8T1so6C33//Qx5+eBcRWUG22eZb+e47+jasFAgjLohkMiyDwk47xWX33WPyv/9F5YILqmXkyBrfDF933JnFjjuzxiYZDWQk4shwGwJagzNVsKOiDct84ISQKnHnnXcaYUSYnOgczX8q0bDMxntoByTbdBK6vOaSTlIqw9IP8OTiTCD91abN8IBls+/jxiX2Ya214jJvXkTOP79a9thjsdiMQruPo0dHZeHCiGy6aWyZCJi9/t4OoNa7xL7gtaSe1RZqu8am/T5MhoE9t+Vs/Z3NyJGvvkouQMu9f5kIk5BXFMU/iEBcdtll8tRTTxlj5KKLLjLRu6DrHPmA8YWOgq5CyQrpjsXoamsphdzHeLTlRugijEOx9XWuo/vjj+Py3XdNpVGjuOyww2/y7bcLnJmIjc37uDpCENaDcq6bmeZYBmlNv+qqGhk/vpGMHl0lkyfTmKe4mX9N/3/cmduDBn3YTaXFmWGDHXYigptKm4/xG5rmPaR3IVSYUeTCz26jFRfSJYjGucKIdBEiWKRU+NFZKZeZUuX2HmJo4SnDEM43ncTeMMU8pkK9hxhtGJQYbaSqkiZk74Fsb/hx4xLn5frra+SmmxACUTn11Gp54omaeimxjzxSVde0J9tnyfUu2do3W6htI5t04+UYuE/dVAa7pUpvLLeHLujew/btE/vw5JNRGTxYxO3jFaS0mUw1loqiNCweffRRUz950kknmahDMqOyVCNAivVZ3uaBGF/oWPwcZJ0jG6gDJCpEgMNGljheL6NHJ9RjolvbbJNoCsi+ffTRR8b5jPwn1ZGmlbZrqLfUpiGtEUHu6+ClXbu47LNPTJ57rkomTYqaeeel1jmqq6uTZtYhU6yxiV1l9U9rQ2E7WKOTiGeQzmvehiVGIA/kyy+/LL169ao7Gfw8cODApP+z/fbbyyOPPFLPy4/XD4PKr3a92cyUwkNQTu+hrffjhvEaWvl8RlBTYXkISHNF6GKw2ZoTL5nee/ZsMTMpq6ri0rNnTNq2jUvXro3k+eer5KmnYnLwwaQRN5bnnltFXnstcT4OO6ywmha3UNs6StxUBjvbkFRaHBm2+6e72XSocj7wjABq165d2oL0ctc5tGkTkxkzonLNNVUydGhtYBehZOTTYCjox6QoSmZsltWQIUNSlv+ENWKZqXlgKQw/v9+f98OZTx0lbLbZZqbPh1cee38mC8quVe5r0GXQ23ifZM5oorvoedYZ7Z3FXaxa+3I7jIPuzE7WIRbmzEms50HogRCNRpdpCGj1TxwYNsMOBw/fcy966zZxmpTbMZ9XKiyjRo4++mgjdGilzbgRDtJ2ie3Tp49Jl7jyyivNz3j2br31VjnttNPklFNOMQ/40KFD5dRTT/XtQFDw2YcgRizZL9JJyOVv0aKFactd6E1cCsMy1/e386wwuliU0g1IzkbJttHKLl3iJqK1yipxOffcWrniimo57bRqufZavIcd616/224x8bnZcNpUBhYOu5iwkd5t8+Y5b9xv/A8PPV9LaVhg9KQyfIJguLFrV15ZKwceGJVhw6qkf//aumunqbCKogSdIJXfFOo4Zz9t80DWbByTycbBFduw9DvySh0s+ibKOLNE0cPIusu0vsycGZHp06NSXR2XffeNFeyMxrDl3OKM5nVeZ3Qp5yEWizBFLKF588TXb7+NBHL/kumf2A0EDIDn3Z25id7NzxyLdWjg/Eg2VSKQhuWhhx5qvDIXX3yxSWelg+nYsWPrGvrg8XKVLoypl156Sc444wzZaqutjNGJkUlXWL/gpPPwBsl7iBAhckc4m+ist7i/0M8otpDP1gvKPnCMeOkQkFxjN7yf6r0z7fv48dE6g9GCYcnA4k8+icovv1AXEZett/5HDj64sRx3XGk68Fk4VhYpt/snDzsP+ieffGJqMFjUcCxg5HkXk2LNO8pEUITonnvGZOedY6Yp0+23V8nVV9eGKhU26PuoKJXGsGHDTG8G9A6Mn1tuucU4MFNBo4wLLrhARo0aZfQDMoVwhO+9994VNTs738+yazcGF1E4sosYGJ9qfShFeq8f78914Zg4NvRNnPlge1p48R7vAw8kZPsuu9AIMPd9TOWMdo1NSoRwxNt5iF79gN+FCeqNjz322JRBkyDoHC4tWsRDYVim0jnQKXH+uA4gjsNNpS2XjpJ38x7SXlOlvr766qvL/A6jitz2YhEkIW9TLxAapFyki9wVQikMy0zvT1qozQHfeOONjXMh2wc03XvjfGWArdewxC6nixdRrk6dYtKixVRp2bKZEWpBgIedrr5E0DE4UWSI5OJJYiHxFmm7KTI0FvIrjYEFlHRzFtUtt9xSggi3yYkn1hrD8tlnq+Sqq2rN78Ig5MNg/CpKJfHYY4+ZbCnmZjOmCgNxjz32MClirvJuYU3abbfdzN+efPJJIwvx6ieLxFVCxDIXXcDqKDiDcYCyfhJty6Ybfbl1jmzGmbGxDru6l+3Qm+r9E+dE5PTTq+XxxxMlSoccsqyzupC1icACOqE7i9t2orUbJUQ4o3mtN4020yzu8s6nThjSyQjimu4almFwFMeyKL/hHKNDsvlZapgrwZ5CHjIhT7QKYxJvKkLBLQ4vBsU2ltMJeQQfixKGJcYTUelc6kUzCRma9Pz2W0RWWy2+TJfXTTeNy003JdJ+PvqotFHKfOC8pOpI66bRfvrpp/XSGNwFJddaXJoOffjhh2ah9RqWQRLyOA2aNo2b7rDTp0dkiy0SKcRB2b9UhGEhUpRK4vrrr5d+/frVldxgYI4ZM0buueceM+7DC7/HeHr77bfroigb+lgrEaSGgbnoAjg47WxnzkcuXV7LqXNkM84Mhyq6V7JU3kxryrhx1XL66Y3NfGUyoc45p1aOPDL5sfppXHNvEiVmS9Yx1DYR5HoFKfMpF4Kkc1iaN09cwx9+iAimQ9D2L8w6R8UYltk07ymWQPTWJ+AhRQAU06gsl/fQ7RZn03vz9Yqk2nc+d8KEhCLQowfdNyWUpBNUbkdaFnZvGgMbaTyk0rLI4IHyLijpvFdBn2NpYej0rrvG5IUXqswc0i22qA1FNDBMQl5Rwg7rzpQpU2TQoEF1v+P569mzp0ycODHp/zz77LNmfRowYIA888wzJkp0xBFHmBIcP7p1llPnSPZZmXQBb68HSlbyaUAWtBFnuWZNJdv/q65aTq69djnzPSPL7r67RrbZpnwNZ5J1DLWZT1Y/8GY+EflE/+R35WjggmHPdcBAJqU66M17qGDCqc2YunnzGsk66wRDJ6oEnaNiDMtyeA+tl4w6Src+gZ/TLThhNCwRanPmzDHCDE9gly5djPAq5L3TYQ1LusFmIohCKx+8aQz22Liv7WKCUmDvr3RNAMJUSL/ffksNy0GDagO3f34J+aAfk6IElZ9//tmsQbaPg4WfU3VmxYiaMGGCHHnkkfLCCy8Ypffkk082Crgf87PDErF06w0L7fUQlL4OgBOWyKutnc2UNZVK/r7/flSuuy6RwnnKKTUyeHCtLJewMVO+Tzl0jnSZT0Sh0c3QESg5Y/+8o9GKPWsTXfjNN980M9m9hmUQ1z92h3TYWbMiMnduI2nfPlj750UNywpPheWhZaFDWLPYUZ/geslKNb+q2J9jBahNMcGLRloltQt+kGrf58+PyAcfVGVlWAZNWPkNx0ddBZurVHmbAGD0s9DaJgAYoMA97zXUgmaIM08qEombBX7ePO0KqyiKP88o2UN33nmnUcopTSGziOY/fhmWyeYfliti6f0sDGhqSlkbqPf3o9dDKXSOTHBcGFFcS+pCc82acvefU3b22U0lHo/IIYcskmuvzf09yomb+UTklnsSw84ts3FnbSYzNv2atRkmZ7alRQsRptD8+GPjwK/nsRDpHA3KsPRDGPDw4iWjkxeDkZnT6L3YpVpQil3vgDcWbyCGS7bF/fmMS/G+52uvNTKCfvPNY/L/WaJKDk0ASIXBEQAsKq+88kq9BkEsMEGCvhtcZ+ZJzZ4dzg5tmQj68ShKkMEwQgEm6ubCz3bEgxeic3aouIVICo5SDMJCG1tkE7EslQGSLLsIoxKZ37FjRyP//f6cYpHq/W3JERk7HI93xmY+MvihhxrJe+9VyQorxOXSS/9hcF1e7xMk2D87CxH9NNmsTWo2bfqwX7M2wzbH0q2z/PHHJhKJlMYJlC9qWJZJ0S5mWgrKOgYlRhZpF82bN09Zn1CqVIlifY5NMSEqS6SMtFe/vFrZCOdXXmm0TDfYdARVaJUatwkAXfFYOEgPQrGwCwoGJ3UaH3/8sXmNu6CUc7DuuuvGZc6ciCmkX3nlcNRYBl3BUJRKASOQiOPLL78svXr1qnsG+TlVd/rtt99eHnnkkXoKGZ2y/eqWWGydIxf4LAxKeh9geHF8jNjwK7uonM17bIYY6xnHufnmm9drdJPLe9v3g19/FbnkksR9cN55/8g664Rfj0i1JpVi1qY9r6nW7iCul7YzLBHLSKT45WuFkE/viXKd84oxLIuVCouRhTKOYMOYRKhl8uaUMmLpp1FFxItFicUJ4UPHOAxqv41KF2906scfIzJmTOOs6yuDKKyCYOy6Qt4764g6COsYYUGhkzHXuRx1GRa7qM+dK7LiisE32sLkPVSUSoBRI0cffbSJVJHWybgRUv5sl9g+ffqYRmhXXnml+fmkk06SW2+91czMPuWUU0wjtKFDh8qpp55aMZ3oAbmN/OZcEKUkFZIU4GLI0FI3DGR9wqDk2Miawingl9y9/PIm8ssvUWnTplb69VsouRxWudd3P/B71mYYU2E32SSxzzNmoOckxtEElViIdA41LFPAe+G9IZWQ2rauXbtmPbC2VDMz/Uq1YV9tYx43xYTfFUuAJhc+zJBqIr/9FpX27Wtk553joRby5RSkzHnbeuutkzpBrPfSXUw4h6nqMpKlyuTaTTAT/9+ryLR6b906mItQWIW8olQChx56qPz0009y8cUXm3TWDh06yNixY+tqz8nAcJ9JsjVeeuklOeOMM0wHVIxOjEy6wpay/KaYSrUtzcEBjkxG7hdTLpXKsCRqhkOflM18O9gme2/g/adOjcqIEYm18brrFkkumZ9BXZv8ui75ztrk9+n2LYjnjY70MGvWijJv3m+SIqs+EMRCpHNUlGGZLi0lW2PPW/CeT/fTUtVWFCrk+V8WajyCRCW9KSalrqd44olqGTOmkTRqFJdhw/6S6urMhnwQhVUQyHVYcTZ1GUTtUWKoy/COP8HYLCS9bGnEMnHPBV2AhknIK0qlQNprqtTXV199dZnf0diFLpnF1DlSKc32d8gKv7N+KGdAFi9YsEA22GADU57Dz8WWScXWbThXnFOuGYZNLg79bGH///OfJhKLReSgg5bITjvVypJgB6vKTjazNtElAWfA5MmT6+kHpUoJzxV8Up06xWTKlKi8/noz2WorCSyxEPV1qBjDstCZUkRmCPnj9Sy04L1UEctCDD9SHEgNQojbFJNkxkapIpakwJ5zTmIBOeusf2TzzYMZhawEsvUeJqvLAO4Zu6Cg2PDM2FQZmgNZQ9ObKpOpxhJ++CG43k0XNSwVpWGDzoEcQMFOlhli5YOfhiVylkgeSjwRWJoR4dAjchl0nSMdvCdRMJzcHIefDYe8+z99erW8/nq1VFfHZciQ3OvqyjVuJGh4Z23SzNLeh9z77qxNfodDmu+D0NPBZc89E4blhAlNJYXPKhDEQqRzVIxhmW2HNq/SysWippAHgIXCj4L3UkYsc11MWJjwbNohyXg7y9mEKHFNRM44o4n8+mtEttqqVk47je5s2S/EQRTy5d4nmlTgKKFOlqHRLoUabjYa6qbK2EXDei95pmyqjLduk4ZQ3s93U2HD0BgnTN5DRVH8xzrN0DsyGZaFgnxFR0GuUsJAyityNGxZUsmgaQxObs4TxjJGczGMSrv/d9+dOG/7719T17ylUijnOmMznrxwXSdOnGicztw7Qejp4B13dsUVIi+9tJy8+eYS2WGHYN4TMTUsS4+td0ilNHu9h7yOVuU0q+H1m222mUl99ePBDGLE0p39ZGtGMw1JLqaQct/7ySer5fnnGxkP4u23L5TGjbM/LlXYk0MU8b333jNOA69hWQzwmmdKlcGhQQoXz5+3CcDaaycWJLrCBj0V1jqogryPiqKUxrBMNcvSTYXNF2SozaSiAVuqERul1Dn8+hwcj0QoyZ7CAUpDOTJgbEplMfj990YyalTiup14oua/lgLWSdZ8dAMy4zL1dCjmrM1kdOwYl113/V4mTFhPBg6slg8/DOZ9EVPDsjxCPpu0FG5chBdKLsYW4XvS/Py8YEHyHrqzn3hAadnO12wpdirs229XyamnJgT9Oecsli23jOVc71Du6GAQjd4gdGjzpsrY+9E1Nqln5ucFC3By7C7z5uEESSw6eOSDKEgztVVXFKXysY7ZVJlSyNh8O8PaTCrWbeRg+/bt63X2DrLOkQkMcY4LY4J6/nbt2tXpbMXOkho/vqUsXBiR9u1rZdtt85/nHFSdo5ygZ7IRUcdRkO58FTprk83PBoJHHz3TGJaff879x/5J4IipYVleIZ/OsJw6daq5eSl2J+2iGJ6QIHSFTTb7iRTfXAwKvxarGTOiMmZMtfzrX0tkgw2Wvt/06avL0KEryl9/RWSXXWrkrLOSe37ToRHL4BqWqe4pUmLY6hubf0lVVVxqayPGyPzoo4/M70vtvcwG+2yHRcgriuI/yFAyNbIpwckWm0lFHSX/26ZNm6wyqYKgc2TC9rHAmYizkZEx1O+XymirrRV54YWW5vsTTlict/GgOkdycBYwyoxu9F7DErK5h9PN2iSyXeiszVQ0a5aIZtTURIQEhAzJfGUhlmOJkDbv8TEthZvQG5GzHdSAm4825X6PSwiS95CHkJoFWpAXOvvJj+M46aSmMmVKlVx1VWM54YQlctZZi2TatGoZMqSr8R5iVI4c+Y/4MLNaydKwDBIJY3NF0+r7u++ouWkqvXt3NYI0nffSNTozzZb1EzUsFUXxc8wZMhmlOd9MqnLrHOng9chvjg29i9EhbhZLoe+fLS++WC3z5i0nq64ak969awp6r6CtoUHYp2I4s/2etZmKJk2Wpsn9+WcwDct4iMpvKi5i6Qp5bjS8KAg1wu3cpHYwfDEp1WBkr5fS7RhHYx7SZwo5Vj+E/E8/RYxRCYsWReTmmxvLAw80Ml4hjMqdd14sI0cuEqcPQegEahBJJ+TT/b6cMHLku+8i8uuvTc0zxIKQynvJRgc6vN94L0kX8y4omWqIS2lYBvF8K4pSfsPS1p9TEkCkJ59MqlLMzMwnMmq7z3OOcHIjy9PtXzENyzvuSDgfjzySMotl/659HQoj3fnz+77Md9ama2y6+xOJxGS55UjFjRjD0mkVEQjiIevrUDGGJTcJNxBKZqoOagxVDlpTHT8+h7pSjpfZmxyvX7Of/DiOCRMSC+SWW9bKJZcskksuaSLTpyd+t/XWP8oDD1TJcsvlbwCokE+Ovc+DlgqbjvXWi8uUKTgjlu0am857yfPuLijUeRCtz2ZBKSQlJYjnUFGUcBiWyCi3Qzsd6fPNvLCyqBSGZTY6gdt9ns7zbNkYy4XoHL//LsLI8WQf8+mnUXnttWqJRuPSp8/fJD9KJRKEvg7JjJ9S6MPZzNokyEQGIwEX75zN5Ze3hiXnMFjBiljIsqQqxrAE8quvvPJKOfDAA42g9nZQK1cksZjYOYLMBMq1MU8pDMvx4xO32G671cjuu9dKjx5/my6ws2czWuRdadq0a0rvTJgp9/5nEvJBNIo22ywuzz0n8u23ubUc57mnDonNXVBcY5O6JbyX3gWFjahoLucjTEX0iqIUD+vMTkUynQNDlEwqHN1E8Lp161ZwdoXb9b6YsimTTkDUiCwSnNzZdp/P5f1TQRPAffZZzowPOfHExfLvfy8RVCEuzbPPVssttyTqbLbd9kdZb73kTXtyWQPKvb4HkSBmSSVrIEitL8Yl0XR0A55F9r2qijF3y8u0aV/LKqtUB2rWZkwNy9KDYB82bJi5WT744AM59dRTTR5/GAvcs8E25sEbyGcxJBkvjd8PbuGRHZGXX064D3fbLSHM8SYeemiivmHChGWvBd4l2k7z1a2ho9lLqhlHQRXy5TTeunfvbhZ1d9aZSxANyy23TFzHr79eqeD9Y0GhWZU7k5YFxfVe2uHNfJbX2Ey3oKhhqShKNoalq3NgdOEEph6M9bpLly5GzviBG7EsJsg9nHbputjizM/XyZ2v3L/vvkam8dvXX0fk/PObytChTaRnzxqZMKFaFixIvGfTpnE59NAvRSTRwKfSUmHLrQcFtWGgFyLnbgNB9u2ll16S1VZrJD/+SCZBNHCzNmMhK78JvWGJkKOWEAFNxOKWW25JalSWMmJZzHoHbnZqFoi+IMBpQe5Gavyk0Ijlhx9G5eefo7LiinHZZpvatO9POiOpM0SWqDFp3bq1MQTwKvG7mTNn1s04skKhWEOUK4FUw4qDsABlMixnz15J4vH8W8GnW1B4Xty2/cgDd54Wnna+2gXFLircb5xP3iPX7myKolRuKmyqOZauIYZBiSMLGdKxY0ff1y43YllMvDoB3+PgprEaFOrkzkfnYDzZ2LEJVXbAgMXyv/9VyaxZVTJqVCKtuHnzmIlgHnUU0dTf0nbSz5agrqHlJCyGZar9XmmlxDO0+uotpWvXFoGatRlLU9oUREJvWBKZeOKJJ0x9AkItk5AvVSos8Fl+3Wx4RWnMQ444ab4YzxhixRRwhRqWNg12552ZLZq+BTmeThR+6mFtwyGcBW7jFmpSbBcw6ug+/fRTc47Jrefv1thk8Q7LA1gOgirkW7eOS+PG1Dk0km++iclGGxX/M5EJ1oDEoeG912waLc4cFETuSTszly6O2c7TCuL5VhSlMJAFqSKWyBGilBhdvC6fkV/56BzFxI3A4uTm2MgUo4stDRILjeTko3O89VaViUqusUZMLr98kVxxhRjjkvTYHXeslV13ra2ru/zmm2XPPdcPxzXy3HVcsyHvvddLZXlymLZAg6ZkTpMgG+J232zywB9/5DZrE92A57yYszZj/58lFZZ7L/SGJdgIZTYzpUoVsfTrYUKZpWYB44sOWLYRUSmaBBVuWCakec+eqaNPH3/8sTlfOAZs2iIPabJ9QcizMT4F2Df+n5oV/m7TF8BNobWpjaV8KMstSDG66Q68ySab1AnFoBuWOB8226xWPv64WqZNi8pGG5WmTjnbew3ZYg1N5AizNrn3/JynpShKuFJhvc17bKkKjmBkBo3GcHoXW+aWYuQIx4BOwvqCHKTL/pZbbulrp/1cj+G55xKfvc8+NXUG5J571potnU6DU5tIMvoVWV8cB3+z4yumT59er0zCGpvlXtvTUc513Vt64iWIOgfY62kTvP76q7BZm7/88osJlPipG4St/KYgaUBd47XXXmuK0ElHJQ2VobeZGDlypBx++OFywAEHyOjRoyVoM6WC4D3kfwm9szhxcyZLnynF8eQrRH/9VeTddxNSvkeP+jUZRIPwdPLeLLp4O/N5aGwnYBY1Fm43tdEWZrNo2Do6a2TaBQIDPajCrlBI2ZgxY4YxjpIZlkGlXbsaY1h+/HFE9ttPAgP3CfcLG/cq99YOO+yQ9TwtFJdU9a6KolRGxJJmemQSscaxrtkoWCnWmWL3kMAQ4/gY8+R1cpfLmc3hjhmz1LDM7n9ixulKpIlsp6233troWHyuG51KpkvwlX0kE4011uoUlaxL+EFQndlgnxlrWCa6whZ31ubK/3/f2DTaTFMcwlZ+k7dh+dhjj8mZZ54pw4cPNwLmxhtvlD322MM8bO7J9UKdwdlnny077rij+E1QDMtCIpa2ZoHFiX1t06aNUUpTjV8IasTy1VerJRaLmAjUBhvEl4m+2uHPuQyBzjW10cJ59HYBw9i0RdxuZNPPBSIIrb+D1KEtG9q0SSgH06YFc/+83sNM87S4z3C8cT1oua8oSuVgdQ7WF9ZsDC+ecztDmjUHg6wUFCtiyXsiwzg+PoO1kgyjYpDr2JT334/K999HZYUV4rLzzpnPM+/LceAM2HjjjU1GCp+TLEsqlS5B2iw6Gv+D4x8Zb7uNu/0f/BhtFSaI/nJe0Olspk8YnNl23+xlZo5lqWZtfv/991nN2mwwEcvrr79e+vXrJ3379jU/Y2COGTNG7rnnHjn//POT/g8C9sgjj5TLLrtM3njjDSOES21Ylmq+JORqxLo1C3ZQcrqbqdgeykIMS5sGSzdY3oO0GY4Nw812jON3flyPTO/BObQPq4Xzxvm2xiaLjZ1v5I1s8tCHbYEI4xxLG7EMk2GZ7TwtP1PFFEUJBiiK999/v3GY9+/f32TOuKlupdI5iqUPEJ209eU01KOHBb8rFrkaljZaufvuNZIu6MN1sumJrO3UA+YzM5TriZ5JhNMa1+74CjbOFz9zHyTTJSqVTz75RD788EOTyeM1LCEsEUtbYxmkWZuRPObUhq4rLMJlypQpMmjQoHoPXM+ePWXixIkp/2/w4MEmmnnccccZwzIIM6WKRS4LCsIOz5etWch2UHJQI5b8i23cs912v8v7708z14V6P2Zb2Rvejxs/3/ewnlfbctodR2GNzTAvEKkilpkimeWmbduE5/jzzyPy998izQI4xzof72FQz7eiKLlDZGbo0KHGmY4TGJ1m0003DY3OkQmiKDhbMSI5PvQSMnxomldsnQOy/QxbX7nvvsnTYHkfokLoV6S5YhTisPfqV7nqOu5rveMrrC5hUyD5SsTXRqbcqCZf/arHL3dUMMxdYdk3O+ry118jgZu1OX/+fGMnjB8/PqfRaKEyLClM58AxElz4mbquZLz55psyYsQI49EoFkFJhc3We2jbkNu5VrnWLJTCsEzFyJHVcuqpTeXeexcuU9swfXpU5s6NSpMmtdK48TuyyirrS8uWLZeJ2vi1/36dg2TjKKw3qZQLhB8E3YBMxeqr18jKKy+S335rIp9+GpFOnYKXQhO2tBRFUfwFpzqjiXr37m1q8myNf7kNy0I/y0b2MMaIOnXr1q3eulaKLKlsmTkzKjNnVkmjRnHZbbdlDUvWbNJWOSZbUvTuu++mHTeSjWzPZh/RJbzGArqENTZtZ3tqcdH5vGm0+URTy034DcvE/s+fX+49kmWcFUQxuZdpVGojm95Zm27dZqpRc6WiJPlZHPy///1vueuuu4o2czEbw7LYQjFb7yG/t415ECqkZbjeLj8+ww/SGX63304H3ohcemlj2WuvGrGymPM7fDgjX5aXTp1+kx137FLUpiXFFlbJvEmpFghblM3GYpZskHSpCGvEUiQurVr9KR980MQ08FHDUlGUoHHrrbcag4t+Ecj+VCBnS1VjWYijFpmGoUxKL+tXly5dTCTEz8/IJ2L57rtRmTixStq1i0n79jFZa634MtHK7t1rxVWf0AEpuyEAQr0rWzFnDOaiS3g7p9qaO6tLEGggu8t2E3WNzaCPtQqrYWkb49js1Pnzg7efsf8fXegt6fLO2sQhRHAPmUPJmR2hFgrDEuOQgyR104WfbQteF1IqEFj7OW0erYHHw0LDHwqpizlTKigRSztMmAtP6gyFvaUcJuzH+8+ZE5EPPkgI6s8+qzIzo2jtzbFNmfKVPP54ojHT2Wc3leWWq81r/8ud1lHIAsFXlA1qDvD+uotDqbyRYTUs2b+EYbm6vP12VI45pjwjRyqpQ5uiKP5io3joHOlqDtE5kjWGKQb56DfIWzqkopOwrrljv1J9RqkMy59+ishBBzWT339fKmvXWismVKEwrtymLO63X8KBy7ETxUHXtBlg3m6bQcuSSlZzR6mZ1SO4tzgejGUMfW+zwSAYzN5zkszpGmR9jn1jn1dbLbGPv/wioXFmR9LM2szUaTZwhiVCFWv45Zdfll69etUdOD8PHDhwmdeThsC8QZcLL7zQRDJvuukmadGihRRrplRQ6h3Il0Z4IyxszUI5hgnnSrL3f/75+rfNjTdWy3rrfWCaMb36ahdZuLBattqqVnr0yK5LWzJyUdyDILS8C8SkSZOMt4hoLYsE54aOuNYb6TU2/W7usvvuu9c1KgiTYcnzud12P8tTT7WU0aOjcvPNKG8SKDRiqSjlI0hjzoLU1yFXfcD2EUAJZbC97ZDq52fkC59xxRWNjVG5/voxadYsLp9/HpV58+rL3VVWics++ywx0UmOBUOLdEE3wyjZexdCsddO9GtvN1E7Q9nOSSTbDYeFNTZtllSp7rV8zmtQdQ4bTbW2falrLP3WOeyszXKmU+etzTJq5Oijj5bOnTsboc64EUKytktsnz59jGJ95ZVXGsvZ257a1rH52bYaJZ59SAUXppStv7kZUO6JWrEIcj4233xz3y54sVN73bEprlCwndhOOGGhjBjRRN5+u7F88cUqsvPObaRPn4RAP/30xZJJjiQTNByPFVAISvaB16V6qIIqrABjkei+m/6dapAuHifXE1moNzLdwhrk88a1b9/+d2nePG4i4y+/HJV99glW1FINS0UpD0Ebcxa2vg6AIYlhQqTSHY3i52fki12Xpk2Lyn33JfSke+5ZKN260cwkUVeJekDAmG3llf+UuXNnmfWUrDeiNunWtlQ6hz0myldYd9PpHOXAOyeRddI1NskWJLpJkxd0TTeNFt2iFMcS9lRYG7GkxpLbIUCXX8Kmc+RtWB566KFGMF188cXmRqZOcOzYsXUNfcgVL/WJCFJaCnBeOEeklmB840Xwk1J6D61QIE3gzTcTBs8227wjX365mYwbt44899ym8uOPtTJ/flQ23DAmvXplV1/o7j9GvzX8+Ty3RtEamFbgu4I/CBHLQmYbuQsE9wup49ynrrHJRoF2oc9U0M8V+1dVFZHOnan5qZI5cyRwaFdYRSkPQRtzFpQRZ/az0hl97hxpDJSuXbvmnC5XmvIbkYsuWt7MwT7ooCXGqAT6kXTsuNQAxFkwY8YcE2lt165d1g57d/85X1bPsIEHVwdxdY0g6RzsC4EUNqtzM6nBzt1En6CPBw4XjtFt7mKNTb/XJHRcmlgl66NS7vOVS8SS++6333DOS2CINRTDEkh7TZb6Cq+++mra/73vvvvEb4JQY2mHCeMVTERf2tfrMuonpS6kh2eeWSK1tRHZcMM/ZMcd15NNN11Bxo0TefrpannjjYTBeeqpiyUbB6g779MKd37HAoHX0F4rG8V0r501MlkE+D1fg+RlzOW6pPJG2joLvJF2lhgLRDbeyGnTpplUc+p43fqNMKTCuq2/f/klePsZNiGvKJVAEMecZWNYljJLKtm6g7yyzQJJnezYseMyJRJB0jkmT15H3nijsTRpEpfLLluUVL/C+Yqjnoy5XDpgujoH18UaFURs2VydA9xr93/tnQWYVeX2xtc0YaCiXsRWxG6vekGwEDtQETGu3Z2Y165rov4NxA4uV8XCQkVsFBUxMK8tIgYgOfl/ft9xDd9szjmz94k5+xzW+zz7GWY4sXN9K971rrKY+xzsCzRaAm2dI8nxoffgCw1OnDjR/Z8Gmxpwcm9k4xegr5JMY8Xfvzj3WFIBX2ihJpkxo8xVLeMWWJbF9PwlQ0lN7Q7TY5lPowgNgT5KAgAWHPoW8hVUtnUjPYs6xnzYsEQ/bP/+VX8ZkibZaqt6GT26UiZPLpMll2yU/fYLXxXmXLHxXQSTbGqogz+BT1kh+8rGeWb/dH+DVJa4GP5MspFqqLUhWyubwWykH2yyQDDWB5U/quXFFFgGG+nzOIs7y6pqfEQTDIYFAXEccxamx7KtqjXJaKoqFsjfdeRGNrY/38fz+usVcscd67h/H3dcraywwrzvYu1j5AI+XjbCh77PQTAZxedgTaV9BY0M9TmSVTTj4nOwT/gDbL64Cy1jGmziQ5GE5rXBed34ILnwFYqBCgvQrYJyTUJ7lVXiU2VtLLJkdkkFloXqd+AhxXjzkCLMQy/lBx98kPcFpa0qlhhTqM3t2i0uH3yQCHR22WVeJu/442tdYAmOOaZOwkwX0UwgARIGm55AFunWjA/XEAeDoIoFAREpzb5qZZPPU0MWNdjkff7m/00DX/2bf47Snb9cQBuy2fxgU7ORLLqajeTvf8LlIDCbPFlmdO3anI2MMyUF6HVTYcK4Sn8X45wxg2FBQluMOYtTj6Uf9PligSuttJLzSXLhmOZrDfniizI5//waGTkyYVeXWaZBTjmltrlSTVIb5g49oczEziSxpz4HMwEBSdewPgf0adZWriUsNNUwUJ9Dq5+gkAnuMD6HrySq4yg4Du4ZTVwTPPP8EHj72g/85J5P9j0aoBLA+srCxZDM1n1bYokm+e47Kpb8Hh9fqbHI2m8ssMwCGDweQIIjHZKsMuRtMTMz39+h/apQTxBZeuONpWT27DJZfvlGWXfded+L+us229TL99+XyaGHJhaDVPBprzTbU+UlaMVo46wHBWz84cxU7MhY8p5VV13VZQ39h4d/8xreo4ss+04vBn8ns0zvos6Y5GefPn2aF12y2vQJpwIKyGR9AYEtyoIabLJp9pOfUK9Y0AH7O2HChGbKq24Eexj3VIY6k2ykvPaayHXXyX1dusi0ZZeVpU84QWo7dZJP99hDGjbbrHk2GQFpttSX/FJh41uxLLbsocFQCojjmLM4BZbYTXwSqrf5EAvU78j18bzzDgJtHdxc7IqKJtluu2/lyis7SMeONfLdd4kKIYEKYk2ZzMT2aa8EpazHJGG5/ho4+X4HwaaC80kbCvcY6znv921/sspmsgS3vlY3/2+5QjYBP/uh5wG/So+DQFET1zxPBJ++n6bnjOfgnXfeccmM7bffPmlgGVcoSwrMa8GRWKGxyHyOkgss09FScmUUMVI0wJOhIXOVTJinLSgw+coeEnRgIDSwRJiJc6tjRnbaCRqJvx/0WM5O+5mqvKaBAw4CzoAGRJxT34gx6JVgkIUEqif/z/4QsEGDIZjH4PMeNp0dyflAUl6NIwvCG2+8kXK/eJ/Sg6KcS81++s3+PvwMORVWxo+kAj1C9Ioodem9995rbsLn/mILs6CW3367VJ54IqlSafpL3KK8oUH+Nm6c/G3sWPn1wgvlm+23d3/nnOhi4mcjc0V9ydbIx31YcTFlDw2GUkAcx5y1Fli2RYIZsAax/lGNY+3Jh1hgPnwOZlUeeGB7F1T26FEv1103V3755WOprFxZ3n77B/d966yzTtrZmmF9Dk34ahKCc0agpD4H5w72GdeUtZfjxOdg/d18881bXYNT0WiV8aT74rOptKLZmvp9IcC+aPCo8P00VaPlHBKMT6MxEQbfH3+4Z8IP0OO8BvrBP2NtQJowouDBbzGgpALLfM+x5OLyIJG5YZFLNy+prSqWuTTyvmocxhfVuNdff919z9dfl8mjjyZul113Daf4qtDgSwNKDHzwIeHv9KNSRcOw8xoUUtkXgi0oxvwfmViqlggRpAIBqYKFhGqr9iySbePaqUAQx6n7QhUUqo2/QGj/BZsvyd69e3e3T3psnDv/J5lyBYaZwJGkB/cnP9kwyOyrLz5AEIpARRCcE85Pjx49pFu3bs3Xi+9ydJ7XX3dBZRn3A5nSvwwlv5f9VSHu/K9/Sc3GG8uksjIXzPqLaibUl3waea1Y0rcbNxRbI73BUCqI25izQs+x9H0Svof1jiplMfgcLEsHH9xOfvyxXLp1a5D//Ge2VFbOkkmTGtzxoJ2QKYXXT/im8zmCgRNrKklt1m/+ra0377//fovKJmt2qrYY1lT8FtZ2NmVJ6UZyRCmoJPBJjKRqv9lmm21cYl9bkh599NEWzCj/J/umCW2eiU8++aSZHcV9yk/2O5vksfppvnZI4yuvOJbUb4svLlO6dZPFzj5bZtTUyMS995amf/zDBel6boPBZtzWcz0tcSuyNhZZ+01JBZb5VIUlc4URgBoBfQbBgHQPZ1tULHP1Hbo4cXwYHQyfZuxAY2OTnHYa57ZMttyyXv7xj3Aqd0EpbzWA+p1+FgZaCkrCBDnJQBCHM8D1pVJJgMZn8Rk8cBg6RGowrAT7GqBQudTqZWtg/1R+3c806v5qIKeZRg24ki18LE4K7pWg4ITCH6kC2HcCeq3Cct8RAHLcBNX+/Usw+Mgjj7hjX/qbb2TprbaSv/30kyw9eXKLwLIZFRXS7pZbRA47rAX1RZGK+sJ58Wkv/AzTm5IJ9LqtvTb3RpNLaHz/vUgOigsLLC3FYCgVxG3MWSGV6On7Yy1UnwS7nW9RsVz6NRdeWCOvvFIpHTs2yT33zJApU752AVnC/q/dQnQuLFIpzIeB0l65r5T2ynvxNWBIcW9p3yrXXBPJvG+77bZz+8x3QrN95plnUn4PjCtNUrN/VJrT7RPfr20+6V5LslvXZOi+BKyprmGvXr2cn6FBKJogOt4MHyTsaDNYUtXKkhowIPG3xkbpMm6cdBk7Viaff7583bev+zuKzFTRg4nrsDNU8wXfD41zYFleRD5HyQWWPIi5NPI8dDjYOPgYGugzYQxVsfRYYiQxphhKKnZ+wKw/n3qqWkaNqpTq6ia55po5LWiwYSgonHfOI1QTAlgMNz8ZbI2RVQOrQSUVRTYCJgJK9knpK1xjqDFsvoCNNp0T0BGk+oFTlGAonSqcH2y21qgfdvENGlUyzsFB39zTOBHcg36QrFRl/m9qp07yWa9ezf9X9tc+v7fRRjK3XTvp+sMPsui0aVI1cqRUHHhgTqgvwQUiF9lINaAwn1BlQ9SB4HK55ZqK1shbddNgyB3iNOYsHz5Ha2C9wychePB9Eux0sbCkHnusUm64IaGfcNllP8vUqeNl7txEUpsgJ2qwkazVxld7TQeOh/OJzgOvZx1Db4FrS6sOv3POx44dm/IzeC8JDwIygk3GfahADu/Hv9GtWQ9BxK3nVOB9aqyeY37yXqXR4hcMHDiwuWfUP2Z+535Q8D40P5QdRXCqFVRe72tX8L5XqDqmqEwiVqR+mu/3pGRJcQ3+Cu6XvugiWaRHDxkl4oJZFRvEZ4Edx77BRAvO627LYNOnwsZ1qW60wLJwyGX2kMUCOgSBCkYCjr3/MLaGuPdY+kprLEzl5StLdTVGreXrZs+ulHPPTVA1TzqpVrp1awpNeyXYo6EbCgeLXhAEmGqwMLY77rijo51wnumPSUUzTiVgo7ObfIUzNmizBLatiQNlE2z6jfr8hPKiGc1sVeHYx2QBJ1QwsqRTEDQ66SSZvPTSbvtlqaWk4S/DPHHNNd0GNho3TnZ+6imp+mvGqp6/qNQXjskPNsnmam9KMNiM8swEjbzmbyx7aDAYitXnyJUfwJqCT0JFTFtV/GQe3xVkwMQxsPzss3I55pgEM2iffb6Tbt0+bpHUzkTvoDXaazLgl5CIwOfg3AZB1VKDQHwR/s26puO9dPajqrUTvCnbh+NRto8fOAXFgfh3cF1PBT6L71GfA/jnie9VnwPmFqJVwfPA/7NW++sy+0CiXltjCPp4HS1IbByLgrV+2LBhbtTL0uPHy9822UT+NmmSLJWGJVV9881wnt13sv++/oR/zvgufDV8UwJy34/gnOerGl8sVNjyItJ1KKnAMhf9DjxQGBwMOMZkk002aVbSjIK2UIPLxMizT/7xobT2ww8dZPPNO7qZlG+8MVP8ecPDhq0ukyZVyIorNsqpp6bOzGKMoGZiAKgyYgQxJDqMl/PhjNFf1Uc2NTBcM/omMfBQehKBbuaOezKaZzpxoKAqXBgDlirYpIqIKh9/577xZ13lo1EfQ71C9+7S7e23m6uUjeXl8tsSS8iPXbvKj8ssIz8uu6wLOLtMmiRNOB8dOjgjPnz4cNdTqhLufqUyHbi2KiqkYDHTc6uDmMlO6vn1qbTpegWSZQ9LwcgbDIbSg/ocqeb05cIP4P3YU11f6S/1+/ILoeuQ6VxC8ssDB9a4IfTrrPOrnHba77LKKpu1qFCF9WvStdromo9PQQJbWVJUROlB1cIBgZKCc6tzn/nJmqggab3ffvul3R/WNX+ERzpxIN/n4HvD9M+l8jnwa/CzCI75zHQ+B5vvFwEC25122qnFZ7K/ypLCP1BQleXzuR9/XHJJkR13bP6/yr+C889XW00W+fNP6fLTT1JdVycVTz4p5QMHJr1fNLDW4Jrr7gebfJ/OhvfndWuAn4t12KiwuUdJBZZhpL9TGSztMyRjgoHKVIlM0RYzA6NmQwkmoL1qD4Me3x13VLv+yW++KZMrrqiRSy5JnMPXXquQJ59c2f0bCqwviub3ZVL55LMB2S3EZdg3qlxbbLGFM8pk+4LGk4cF4845J+hEmCZfzd3JKm8YYg2ElJqh2bJgo35rDzWfxblloUJ0QGXJk6nCKXIWbLZvL41kJ0eOdBQUehyWnDLFbev/NRi8rrJSmiorpXbHHaWpXTt3rBz7Rx995DbANeDasRH4R3EceGZYkP2+GP/8+oIGfp+FLhLqWPgG1Iy8wWCIu8+hAU6y4EDX6EwCMd5DYMQay+cQELFmp/qctkhm+60eUY/n998bZeDACvnii0pZcsm58uCDjbLCCqtE9p3S0V4J3KCxstaQPA4qtvM31hzOKYlfROzwTzivUdk1rSGVOJAyqVgTSfKTmGBN9JlUYSp0HBu+E72f+BtUWHlPtkq06rtpK5IP/GIC51+++EJ+u+QSmfy3v7mk9Z+LLCL1f93/H627rttIdK/z4Yeyx4gRjiUVdsamCg5p3zT7zjlSP4IkAe1OHL/vp7FxTaOuzaWYzC4rMKd3gQsskxleggoMDe8lKIjqVEf5rlwibPCq40M4zuCw5JkzRR58cN6CePPNVTJgQJ0stVSTHHIIi2aZ9O8/S/r0SRhovo+mcIJJjLi/LxhoegbmBQZlzQ3iQcDrp7IHEGDIJojPFDgCwWAIA6ZGnwwj543r6NNfMGAsBHr+CbAxdLwGyrQv8x40Bj6FVn8Gg03f4IcNNhtOOEHKn3gi9bFC1W1okKlHHtlCwp1FiSCTgJjsIBujSPbaa68WFJhcnV8Cd81Gkg0N9lnwbxZ4FuCysqqSMPIGg6E0oWJv+A6pAku1GVGofFShSFSyxuKTsLa2ZnPaqv0GhPmen38uk2+/TfTHf/zxn3LMMZ1k8uT2TqvhoYfqZYUVko/vSOd7+a027ANrNOdVtQf4mz9eTAMUfDrWZ84ryWyCdBKpbQ0SqKy7vr/DvaPBJokEgkXWP01wa8DJ73pueB3+E2slrDO/gp3sPslVgpu/wzTr3LGjVI8e3cySmtWhg/y89NLyU9eu8tMyy8gPyy7rgs2OM2c6llRd+/Yu0QxLimtFIMzPMME8+6Oq/qq1EdTWoIJK1Za/B9txWpvXbT2WuUfJBZZRaCmpmuBzgbaoWLb2HcnGhwQf5P/+t0qmTSuTlVdulLXXbpAnnqiSk05q5+b5/PxzuSy//J9y+uk/0Gbe/J0EH9qzyHmjwgWFNcysRVV15TNyQXvNNTRb5lMz1ICxkWHUJn8CSIylqvKFOZbW+jWBn2UNNvWnMvxNPXpI/eDBUnnCCa6vQZvn3f9RDaT3YvBgqfv736Xs3XfdfUCSgQ1QSeRZ4Npwv/g0oHHjxrnrpdnKbJIufC8Lur+o+9QXkh/8+4UXXpBZs7aGoCSTJ/8iU6dW5bXPIp8zpQqdPTQYDPkPLJPRU6MGlqyPBBYETKwnjDQLK2TSVlRY0JpvM22aSI8eHWTKFLWTiXOz4ooNcs89c2SDDRoj+TVaFeb8kAwlOGRj7aWqtvfeeye+ZaGFHN2VZCbnj3YN1mgSxCRPeS1bHNYRBSwtf030K3TatkPimr9zfHoeOA7W71DqrRET3D51NqnPEWBJdZg1S1b++mu3KaYvsog0VVRI/c47S2NNTYI++9eGEBKfB81WWVJBim5UbQ3OD36pnjf8mI8//riZ/uszpLQwoMeux6Z1qQIL1c4HCywL/ICGoaX4wjzJmuBzgUJWLFOND5n/dSJDhiTO02GH1Uq/fvXy0kuV8vbbCaNbU9Mge+45TJ555mdZfvkj3Tki6KHvlO/GKBAohLnhORc86AQvGNB//OMfzYtynJHMgHF/KQWF88txEJBxTwX7NXMlDhRGiVYOP1zq1lpLKgYPdtVLsolkCxt33tlVNJkpxYqfLNDhOKBHs/lGjO987733XPIFtT4WagJMXhdlIUgHv89Cx8iwaLZrl3gmCTbfffe75ixusKm/rQ1usRl5g8GQH6h9T6Xt4Duv6YBtYz1hY32kChUmURvXiuXgwVUuqKyqQr20XBoayqRfvzq54YY50lorv+/XaMADvRXfAZ/N/25tq/Cx9dZbN+8jQQx+EGtFkE0UVySr0HEO6LHFx2C95P/4HZ8q6HOE8WXD+BzJqpp+oCnHHy/VaVhSi0yf7gR9Zhx9tIvYOJadd97ZFTu0FUcTBKNGjZIddtjBKdBmc95UiVf7XDkGP9jku/ipNGXOF0l19a/+/DNxfydxlwuKpgx7mguFkgosNVDByCcLLNUgvfXWW81D65NlGXOBQjXSpxsfEsTbb5fLhx9WSLt2TTJwYJ106tQoRx75g1xzTWJo4I47Pi6LLvqTlJdXumwf1AU+iyxZWOU1oII27GehaK+5AueXwcME2BtuuGHzsQTFgThfycSBwkpph1WiVTQHmwT9998v5aTepk+XcnpKPQcljOMRvK6Mhfnwww9d1pRriTQ5G8Ef50CVfXMB9g+jz7NcVZU4T926rSZbb71qiz4LEifc59rU7webYXpis4EFlgaDAWAHCC5TBZZ+xTKVvWOtIFHJWrHBBhuEFlErZMUy3fF8+ulvctNNCR/ixhunSv/+1a7lJsphkfz3aa+qhgsIvPFtYAmlaltiDcbnIFG55ppruvcUk2PuAwEgWFLcY1SwtbLZVuJA+lN9TWVUlW28sTRec420O/XUtCyp+k03lbK/Zlhq8hqQrGYNZ4MJ5o9S43oTNJPE9nUxooJj4fj9wooKHimNlv3gfnvppZdk8uQtHEuqoeEPmTOnJm/zukvd5yjJwBJj4t9IPAyqLgUYZRFW4jlTtEX20K8qQffwx4ekonvMni0yenSFfPJJhTz+eOLy7713ncya9YM88sgz0rHjDNl8821l4YVnyC67TJPKypXdWAt49XwfBirsDc51wGhg8OgTocJZTA+HDwIYzi8GMBkFJd/iQOkMv1/RbB57QlZxscUSGca6uuYsY9TMF6+FFsvWp08fF1wSZGL0Mf44Q7kOLOc5L7oPqfssCN79YJ79057YYJ9Fru69YjPyBoMhftoOOLSqeIkNzTb4KXTFkiAHdffBg5dzY8rWW69BBgyA0RQuqOQ8UIUjiHrzzTflwAMPbE7EksDUYDJdoKG0VyqbvqBNMYIATit6BF0kHfykdL7FgcIEm7WHHCL1a6whNf/3f25GtrKkGnbaSeqPP96xpBpnzUr62STlqcyz0W7kV5Pfffdd5zu+/vrrzp8lwMRvz4XAEsei543PxmfgvFHlnDs3Ue3944/v5eWXf8jbvO6oMPGeAkIvuG/kebi0goeR4SbKNCMY1+whTj5Bi44PSUahmTo1of56yy0Jisq8z2iSww+vc+/l4e7YsZ2cd940l1Xq2HFj94Bj2KhUYdDD0A91pAmBGL0OxUJ7TQWSEmQ/OQZo02HHz+RCHKg1hG3U14CNxV8X4KhKtDxfZEzZCJShxvpBJffIhAkTHFU6U8qRP1NKH+NUdpzX6Qwxv89ChzBn29Sfbh8tsDQYDJkEllSVsPvYJ5KUvuBdMfgcwRYc1hICQoK5du1WlKefTvTtn3feXBdUtgbWDaiuWpVTvP32267aiN9BYjpdIMT+8P34etj3KOt0HEHSgfPBMbOehm07yZU4UKRgs1cvqe3ZU+YSQDJLk/Pevn3C55g7193vfG46nyPoL3DdNdGAf8sGXZbgEtabUl1zqZnAfTZ3bqKy+49/rCPrrLP6fCw0rQj7fkTYlqdsUGw+R0kFltywOldKxUgYg6Gz+rgwWtHIN9oie4hBBtzw/viQIL78skx23rmD/PRT4sbs2rVBunefJJ07/yqHHrqarL8+56Oja4DXyqSeI7JkBCM8YL5RCso8E8xy/n3aKwGIH1QVG7iPuF8w8gRQjEzJNhMURRzIr7qx8b5Mgk0NLkkQkIAgkxuk0UZVosUI9+7du8XfaMjHQUDsh75eFveoCQVfGKe2NnGsURKEfk+sDrf2m/p17EmwqT9sQK8BezFlDw0GQ+HHnMFWwVFmvcY2rbHGGjl1SNvC5wi24BDM4ROwVhEA3XLLYm502cYbNzQryacC6+ro0aPdmqTAbnfv3t35awRVqhxOUKL99bqprS4l2ivHSdUX5lmuxA2jiAMF+zXVr2sNbh9pK1toIanwKq6stdwf3O9BnyOdEi3PBpuORIMlxb3AT8aNHHLIITm7xj5LijmrAMJj1HndwWCzKgT9OCwssCwwMNQXXnih9O/f3z2YQWGethDVyXf20B8fAsjgpHLgv/pqXlC50koNss8+n0rHjk9JQ0MiM7jyygz9TcwLoh8TY6AKdtpHiQFPFgjxYCE2QNCps6RYPKkece6LtUrJMRLgcY4JtKm45ov+kEwcKNhwjmFWFd5MxIF4L4EUx0VfMe/NhRJtEFxzHUZNH/P777/vBBP4zrDKhr6Rr61N/C1b36u1pn42FRPgHg4qyPmLq56vYnVcDAZDbqHJ7FTAVuC8EyzgpBKA5aOa1hYVS/0ebCYOP+sGAQAJ5KamMrn33oQzfeihta2ObsA/0L5JKrckollDgmtFcI6hslDYD16Lz4F/QnK9GMR5koFj1PmMKjQUVbwpLFK1lQRFbigocH4zEQfis9CiINhXXY1MlGj5PvwvzgfXHZYUSQddfwnEuQ+59pkGcuwDx0lO5i9Clyy0UFPoed3cfz79+LvvvmtBP/aLA2H9oGT7aIFlAcDFveWWW9zDwMMJH5+yeaGyevn4nuD4EHofmdmUysmdMmVeULnyyrPloIPulcbGn+mpdgFTjx49nEFWJV3AZ/GApqKdBAMhX+1VAx8yS6+++mpzFke3Qih4RgX3DwaR+wleP+eprZGs4TwTcSCuDUGpVimhEwVpLNkq0frvRzKc5w46EuI+VLhffvlltxgwiBqnIQoVNleBZWvnWEUD+G4VQ2Dj3HHOdXFV+qx/PgwGw4KNVIGlzllkLWFN1NnB+UJb+DYcJ3YSH4tg0NdNQLvhm2/oX2uSPfaYV50C7BeVOBK222yzjfsM/AX69qmk4ahr9aq1QEgrpXwe6xHvx27Tj6c0Rd/niHuPJYl6Kq6sNQTp+GRtvb4kS75mIg7EtcHfwB/kc+iN1fOfjRItG/4mlU///bTeQJF97bXXXABKEBv1eisDCYEpkiNRVWFJ7rvZnp6vOPcv+rGKA8FU0JFEfuI67P1pgWUBQFBEVYQHgYt73XXXJQ0q27JimcvvSTU+RBeRVIvJxRdXy48/lkuXLtOlX7/bpbFxpnOMe/bs6SgjqvClzrxWHcPewFRMyRzyfmSik2VxeLB8Cq3SO5PNEyokOA86QoTFsrWejrZGVHEgjD//R1CkVcq8KdEGgk1owwSYZBLHjBnjnCoyoGECS79iqewyBmq3Bdh/XTChIAUXV1WjBSxkQepLsVboDQZD5uC5x+768LUdcDyxh/lWQ89nxVJpjUpbpUIUTLrefXciuOjfv078wiFrKusAWgWAc0GQwP6yVkRRmCf4IggjqYoP4wdhuh6y6ZoTpNBipzPtrc81uFacT6WKknjIJX0yW0QVB2JjrVT1/zBtUNkmuFVch/0hwKQvt1evXu7eCHuN1f9VGmx5eVOL+zcX9OOgvgY+McG3f3+mGqGmAbcFlm0MDNMdd9zhsiNrrbXWfEbeR1tSYXORPeRGJDNHtiM4PiRZI71iwoRyueeehJHaa69nZOGFZ8nGG2/ixH1Y6FTKG/i01zDg/LJo4mQjiEQ1LPjeYBbHV/DUPjcNfIJ9bvluhA6CPlwCZIw6VeBkMz/jiGTiQGQ/uV8wXNr7Q89jtuJAoZRo//o8zTzzLFLFRACK86rg9akoIS17LBN/a+PbIe3iyj2Mk0TvsS4SPAcsqL6CXBTakMFgKF5gZ7VimUzbgZaAYmVJ8XmsJaz3rDckkGmrCAZAv/xSJk89lbDpBx1U535iG7GVqsbPuq4BR1SFeVW9hw7JOfUrYanWQ6XQ+v1wql9QaKVPTcqzjxQKshmp0ZZIJg7EOeYaQ+XVWefc8zkTB0qR4OYnyWp8UO5JNB64zk8++aSbvU01XKm+6aC+y9SpiX2jVTQfeYd2SfQ1/F7XZCPUNNAMno/WUOjESUkElkAd13QzpYqpx5LgLcz4kGSBZX19g5x1FqpciaHExxyznlRVbdxMe9XAm3OBoQhbmfN7DzEsUfoAkil46jwhfbAwTH4jtBqkfNFZOA8EYVA8CNo5z4V+ILNdrKDxcq7oLcaQt4U4UGtKtFRMdSQOGD58uFv8oUQFHRTNHnJLq3hPIQPLIPSYkjX1+5lcqFqcd84pYhQ8vwaDofSAM03wBeWfShnJX1/boa16H3Pt20B7ZH0kaQaDR8XrkvkcDz5YJfX1CdGetdaql3fffd9RU7GLvJ5qnKqFp2u1STXnE4eb9SyK2qtPoeWaJOutx5dJpfSZD5+D88F3EuQSELEuFFMlKgj8N4I6zivXl3OXN3GgFAlu/qbjSAhoSaRzfl944QWntRJkVAWhn3HffYn/79KlbRhSZSFGqHHu+DcgQa+TGaIE6kUbWN58883y73//2wUGZLRuvPHGFhUKH0OGDJF7773X0eQA2ZrLLrss5evbaqZUXLKHOqoDTrbO+UkXvAUXLW7Gyy4bL6++upPU1DTJRRfNdVx37aPMhvYKBYXPyFXvoT9PSEHwoUafrG+QQhu14taaNDnBQbGPQyFDyGLFfYMD4FeQ20IcKF2wGWzUhxal8uE4DLvttpv7LjX8Grj5uaE8aRhkhFSUlGSZXKVmFbPsvcFgSA2ecdaSCy64QI455hg59dRT52O8FBtLSsc84JwTTAaFUYI+B1+pNFiqlbyfipHOBoSaiL8Q1efwaa86ezxbRzpZb70mBf2WEqUo+gyUbCi02m9LgMXnFPs4FK4/9wh042DrUFuIA4FkLDl8Oe5XWlUIdH1mXiolWvU53nor8XmHHZb/ZzVKAYZ4BvVkCh+cM/w8ziEITmeIS2tZ1oHlf/7zHznllFPk1ltvdUHQ9ddfL3379nUPkJZ8fSDmse+++zY781deeaVst912LuuRi9k0rQWWbamcFvV7CKQIdjTDF6Yng9f+/HOZq1BOnlwvu+32qDz11K7u/w47bI4sv3yij5JNA8pMaK8kDZT2ms/eQxawVBTaYMXNDzTDUmgxbryfapL2aBQzyJDz7HDsYRerXIkDhfmeYIaRBahfv37y9NNPu2r8Aw884IJLvQ6a/Pj118SzU1HRFKmRPt+I0uug1KxMleAMBkN8gQ0jkGRN2nXXXeX8889PahuKRddBq4MwpQiqYJrwM1UAS/Lvzz/LZOLEcvnf/1hTmhxDirUINgprCWss9i+Kz8EawD5wXnGmaTvIpw0NJgU5NnxITXCTOCDA5biDPkeYIIjqHe8naKWnVPtLixX4B/gcIOyMzSjiQJxTn0kVZXSHzqPceeed3e/6PCAmyXWmkOXfhzpfk9f99lvib+uuW7jAMhk0WcR5033nb5w7TYiQDMF/U5o3lXDViSgEsn5ar732Wjn88MPl4IMPdr8TYI4cOVLuvPNOGTRo0Hyvx5H0QW/kI488Ii+++KIceOCBC2TF0h8fQvDm30CtYfLkjnLwwUvKlCkEe5Xyyy/95KefujrBk+OOq5Pa2rqsaa9a1cuX/HWmFFq/x00ptKkUt3SOI1k2zi+9HsXs8CulhkWPPgNfgrstxIH8YJPfw9yv+hr294ADDpARI0Y4ChnPPwsB38Wzy7X+6ivu2/ay2GL0U9SlVKJtaxRbE73BYMgPqMode+yxTpmdgCGVXSiGiiU2HtorNh/qfrpZkInjaZK99movr7xSKV26JI5t222nSIcONVJbW++qgTi2UWmvJLDZDxKkFCqSBbb5Bset/XCpKLQEvjj2qpDqt+7o8XI8MHNYp0mc4kO1tX5ELuGrzBO44KtmsxZGFQfyg82w7VHsHwEroj4Afwlfg+AVX4Zrw/dwPH9pS8mii9ZKbW1TpFFr+UTjX7YjGBBrccAfoaaBeqG1HbLyrLkw8H7POuus5r9x8IwWePPNN0N9BgEBxixXimmtzZSKU/YwOD6EilNUw/PMM8u7oHLxxX+XP/7o5IJKMGBArSy5ZMIhj0pB4cakqsf+QSvwla3iAJ9Cq1kZPwhSeWel0HJP6IzCYmqUTwWOj15KjovFN1+UmmTiQL4YAgYbw8x97iubhVHe4xoMHDjQBZVQrggyyZCz+BLwT5+eWDQWXzxRcY8y9iSfsMDSYDCAc8891/2krysuPof2uYdNMpLII0jClsNGIkHZmsPOZ48alQgqwaRJ2pv2pEyfvp2z/VF9DhxiZRIR2PoihXFAFAotx0+Sm+Q3CCrmFyM4RqqUnId8ChwmaynR0R1s/oSBoDgQ5z3Z/UZQv8MOOzjVWIoLDz30kItRaMshMYBWSGVljfzxR+J+W2IJ2naijVqLg89R7qnaF7poktW3c5Fx+jSro+B3Sv9hcOaZZzoePxc6F4hLxTJd9jDV+JBMMGFCIkjq1WuM1NWtIiNHruvkko8/fnZGtFf2CTpMW9Bec4lkinA605T7lCAMaidOgJ9hLIQKbabAmJLNJatbKLGhZMpmQXEgpWS0Jg7EvQnlWyuV0GT1Wvz3vwnqC5eTa5tKFS6oRKsZRv1bruHP2TQYDIY4+RyA72pt3db50zjarJkktcPqDCAKeN11CeGyTTb5TP78s1GWWGK6HHzwes2tEnGlveY7CFLBI/U58KkQdQpWNYtF08Efw1YosaHg6I5MxIF0PA4JbFrOHn30UUfX5u/8/6RJ+Ov0YDbJ0kvD7Mts1Fo+zk2Tp5RfLCjoE3zFFVfIsGHDXN9lrh60MEa+raS/ky0m6caHhAWJsHffrZD112+Qr75KBJYbbzxL9t9/KVlqqTmy2mqN0r17ImMYBpwPqkYElTyQZHCgHhQrNHDH2LDQ9ejRwxkYnyqgFTdfES7OQ5VZqMjocl2iqPHmG5mKA7Eo49iwaNBzjdH2s7p3350wTcqSaW3sSVCJNlWjfiEqlhaIGgy5RZwEA7HFJNfi4HOA1r5Lx4fw+nXXXbeFunUYjBixnHz2WY20bz9Xtt76MVl88TLZfffdnf0vNtprPtZo1jRVZQ+uhUqh1T5CP/Eat4CaKixVSvYrTtcnjDiQ9hz64kAkrRGBgu3F8/rKK684GumLLy4pBx2UoI6SI9BbOIzP4Se44+RzFBpZ3cmq9KVDwxX83tr8mKuvvtoFlkgCY9zyMVMqGbhAqhLVlhXLsONDwuDcc2vkrrvmVdmWWmq2HHhgL+nYsVquu27uXxSUcJ/Ng0h1GQoHcwfjRnuNCqqSGHeMSpBS41MFUlFokw1VLqTaFvuiI1GYCYkhjHugkk4ciMWKDCOLAOAZYRHgmuj7fv993mddeGH6mbRhlGgVuTD8xWjkDYZSQhwFAxFRi1PFMhlwpgkoWet0fEhUW/bOO+Vy660ru39vtdUL0rlzhey1117OF4xCe8XnYA2gNxVfMe5rWjpQJODeoxIWXKOTrYXBPkKqgXyG306i2gWFOC++yrzqN8R9zUsmDuSPs8PnUGoyfiHHx+8EmQcdtF3z5yyxRFNORq0t6D5HVoElGQCyfwjvkLHSk8Dvxx13XMr3XXXVVXLppZfKc8895/qqcgkyQXGpWPI9PKRUA8OODwmDTz5pGTSut9705uwMBizMTejTXglyswl04wAdZUF1jIUKimUYJbFUQ5W1qplsqHJUWexMMWXKFPfdOr8rLlXKTKC0bLLUGFelXgV7VEi8jB3LOw6SpZeuk+WXnyaNjeHEgVIp0QZ/6r/9LGPYRv1iNPIGQykhboKBYXQdfBpdW1cs+W4SpgQKVBVRag2rsglwp44/vp1sskmD3H57lTQ0lMtaa30kPXpMkO233yV0UMl+sD7DVKFPsdgF9JTpRbCO/0DiIoxPkIxC61M7tZILfFonP/NNoc1EZT6u4J7kOEjKc++hYIsvxTmmsMAzEXxWFl54jvNForDWgvd+YxqfAwR9jVL0ObJ+qskc/vOf/3QBItQSsodkotToY7jJIFx++eXud7KFyHI/+OCDLpjhIQKabcgWON9aDYlD9vCdd96JND4kDILJ0W23nd0885EHxa+0BSWxS4326osNcV3p0YhK7WmNZuFTaDFKLCT5pNBSpSQDSmBJRlcHUxcrVB2P80b2kyyoGsqgOBDJpl9+SRj7Ll1mOnEwkjPBfs2wM8VS0Vn0p2YZFckCTd/wF6ORNxhKBXEUDIzTiDOg36V0U5hSrPGZajk8+GCVDBuW2EDnzg1y8smfy1JLre2SgSTN1T7rAHc/ANLWFIIl1tV8ir+0FVRsiIAwFwKHQRXa4DgJfDufQusHnLkIznOtMl9o6Ogc/CjOqZ/E0KCeyuVf4yCb0bHjLBk/fnwkcaCoPgfwWZOtJbiLsf0m6ztyn332cQ4wwSJGjAv47LPPNj8gVJH8k3LLLbe4xQH6hI9//etfbshwKdBSlHICyBCSHckV15obcvJk9r9CTjnlfunXb3dZf30GBy/VzDMn44IxwlBoAITBxyip4BJZy3SS4sUANYYEyrmQv04Fn0KrUAotG/dbMgptlABIQXaNBYv3EvQXS5N/KrDwkgHlmdhwww1bDfq33HJLufnmxIysFVesc78nEwfinPpGP5k4UDbBprINgo363HPqKFqAaTC0LUwwMBxTytdygJ4JRTiTtZ79/uyzln87//w62Xff7VuItynDJxgAEUiyPrIOlALtlXuPQJqAmqprvsSGko2TCFJoSdYqhdZnU4UNgBQUJaCEch9TpSz2QgPxBT4UfnBrQf9HH7U8T4sv3kl69+4dWRwoVz5HUxIlWvzKYvM5cvJEQHtNRX2lv8EHDng+0VqPJRcoXz2W/vgQXfgILLO9GfTGY78bGspk2rRE5rBnzxVlgw1azrbRyq9KYnNTau+g0hABv2Pwk2UYiwEEYDgSGMFCUDZSUWjV6Cej0OrPZHQZjCHHg5GnwbzYF2DNlnNMODUIbIRZgBPnZhX37/Lyr6WsbO2MxIH0fIdV/A1j+PkOFhkWE65XsHci+H5FMV9Hg6GUUCjBwLYKLAEVStYRqk7ZKLsn/I0GmTKl5XnaeeeWDA+1zwTr6gdpspV1Wo+fIgM2WyubYROBcQF+FGs65xNapZ9obgukotCqz6EUWg2Agqy14Ln2VeZJPuAzFtP1SAaKXATJ3F8k5ltb/z/4oOV6zciRTMWB0vl3mfocaIaQTOd78DkKOfYkCoqX4J5FjyXBVi6RbHwIwR2l+Gz7OVV5Svf966+h+S4uZWWNsvnm3VvdLww71VNuenoACMT8DCPZNx4QX6WMhzKOyqhqSMkeYeTjRBP1jVGqocp+BdmvtHFMGHiqeWH7NOIMni8WYK4RglDJBDXSYdKkzu5nVdXXMnXqsvPNHU0nDqSGn2cPo8z18I2+yuGHgW+4WbCovJJI4L7TDGJbKNEaDIb4CgbGYXY2n08yk5/Y32y0HII+h0hLG9apE/5V8jUKW0hQyxrN96s6KudHmVQEmNhSEoF+8BNHZVSAQ48PxT0GTTRXDLR8jf/yfQ6tIBNg+eda221ICMRJZT5TaJDMNYLiShI6jF84YULL61hTg8BPdSRxIH+uN+e+xqMr6xa2p9n3OUjOoGTNd1JsUEHQtlCizRbxe4qLLHuYjnKSTW+F0l71BsLgsr377rcisqwsvPBcWWSRjmn3i2oR+xWkvSbLMCYb9Kt9E7qFLfvnA5wHMjcEZzgXjBCJ+/zJ1oYqE3hxPBwb9y3XF6qX0lniEDBHBftPxlATGVGvESz2iRMTZmnZZb9392IwsEwGnE1e57/WV/zV+xongXPrG/50glc8hzgVOG0Yd31mwqrCISrCPhx99NGRzoPBYCgOwcBCVyxZR1TshTWE4CeTQEFtl87qxaYmksvz1qEBA96XmprkCW2canwOAhl8Id+516Srsk58lXCf1hn0OQqlxu736XFuVY8i7gFYsgCIc63rIMEKxQT+xrNEEEQiIBMKbVzAPUQAplTesNfoqacq5NlnE8WTPn3wLWtlm23eEZFdQr2fc6X3qU4ZCNKVf/jhB5dU4T4O+hypCjfcdyQEqPjzHCWbV55u1BpjVDgfZ599thQKFlhmCH98SCrKSXDkSKbG3R84/NhjiWCyWzfmZs3/AOFMs184wuwTfYetVR6DFAuldfoZRhaBQmUY+W4qYJxzMs2+2EuxgfNFZZJ7FONDIoL+UKprKovN4sy19+mzUSgWhQALFQEY+0/GMNNK8ltv0SNUJssuO0MWXnimW9ih0eaCruxTh9jIMBLYpxIHorL/4YcfuvdRAUhFt06mCsf1xbAzGuG2227LaP8NBkP8BQMLFViyZmBzWadZ5znmt956K6PvUtprMp+joSHhw/zjH2/IuefO3yPP+3CE8RPYB9bo1io0yRKBvho7Drm2kvhMqijVn2yA7ef7CZLXWGONjHtU4wDONT6HtmpxHkk+cM9yrlVYKUihjaJbUAhwn3PfcUwcD/5u2H197bVy2WefhD/VtWuj3HhjtQwbNlRmz6Yo0ztjmnMyunJtbW0zk4rEO/ucShyI1xIUcm3S0a2T+Rw8h8zn1Rm/hURJBpat0VKyoadyATF6YcaHRF1Q1LjrezGgwRvo558TFaBtt4US2z5pdo2bMZu+Q5/W2VqGUcVqtFczlxlGf8EieGf2VhzpuVHAvcmChZGhkqwUFq5ZGApt0PDH4XywjxhDlSjPtPn/f/8rk7PPTtzfPXo0uAxxUKQjH9ShoDgQP/X/OMc6x0szgq2B63XQQQe5a4MqNFlHg8GQG8RRMLAtR5yxLlLNUC0HbK4yQ6J+V5D2msznIIAVqZIll5wlK6643nytNvgcJD2zVXsNKqP6cwjZtL1BGSfqd+SS3cN3Eqjgd5AcDRMkxx3c+9BeCWqCNNFUFFr8W4Jqv5igvkcc6MrsGz4H+53JfffMM1qpbJA775wriy+e0CVh/acwgyhTrlBdXe0Ygyoi5I+088WBNFbAfyJI5jyH9Tl4Ng455BD3PL766qs53f9MUPg7pAA9lpmK90AZIEMYdnxI2IplkPbKw5wqYJgzJ3GTLbbYvP/H+FLlwujmK7vWWobRF6vJRYaRc83n8VCWgjw51xYDwiKMgUnXWN4ahdanK/uLbFtTaLlvWYBwcqJmDIOYOhUxqnYybVri/X36tJOePXtKvuGLT7Dgcp6571iE+Z1j5PjCiANxjalQnnTSSS6wJGsY5yqzwVCsiJNgYFv1WGogR+KK70RlO1jRCNt+k6zVJjgDWDFzJse2iFPMVPuOPcQZZk0icZYPrQPfl1BolU3FatgHZff4WyZtMqyr2H4A3TpMC0bcQTUS37A1lfnWKLSadOU+91tJ+Ml72opCy/1K4ohnIDi6LArGjk28Z6+96kXdeCinHCNBWj4Ds7KAOBDnGb+Q79VCDv401601cSDOxzPPPCNHHXWU7LDDDvL000/HwlcuucAyHxVLKhrcyD7lJOw8m3RGPkh75SYigEv32Z9+urz7WVa2uNTVJei43IQ8ZCw0bVnBykeG0c+urbrqqiWhVMY5UFpNpjOv0g1VDlJok9FZcg0cCzKGXPNcBP4PPFDZHFTusku97LJLfpSb04HzCPUVgx8UUUonDvTwww+7expBCiqU99xzj+v/Kvb71mAw5MbnyDawxPbgfGJzcKZTqYa35t/4CvPJaK/JUFubEDskgPDHbRBMkmBvy4oeNpnEuVbalN2jTCoCKfw17WnTLV3wQ4JWZzjCiiJBWoy9hslU5um/pUqZicq8Umj9EWEa2PutJFwDP/DRKQO5Xv+499Fv4GeY0WWpQN3pvfcS13ezzeY9lwSWJCuymYMeFTNnzpQJEya4+y3I9konDgRDQ6vr9JcPHjzYtQLExecoycBSZXmzNfL++BAeTJ9yEgbpsoc+7TWMcQ/iySebZJ113nAGsxDjNnKdYWRx0ooewVMpzHBkASfo55gIvnO9CCcL7H06C0kHf6aY30OYKZ2FY+J5YEEh6MfJyTaZgR90xx2J/bn++lo5/PB6tyD+9NMcJ9SUb5EmjonnnPOFY0EfVtBApxMH4jm+/fbb3e/8G4oedJRrr702r/ttMBji73NkI+LH5xLIkcjC3rY2tindd4WhvSbDhAmJROjIkcvITju94daTQozbaI3dowIqnDPsMsFmMPjRlh2taurYMvwofI5in+GoEwo4JgIkjimXrJlgYK8UWg1+YAcohTaY4M7U98l0dFm6ESNz55ZJ585Nssoq85IwJBTY2pLB9tlnn7nnmiJK8FlMJw40atQoueuuu1yQjd+BOBk+x5133hmL4LIkA8tss4fJxodkUpFJlj30jXtrtNdkWG65Bvn++wp57bUO0q3b6tKlS7ybysNmGPWhggrAgxZ3xdew2TWOjT4NAqR8ozUKrQox+b2xPp2ltfuI54qqHMdEX1NrVPCwGDOmXD7/vFwWWqhJBgxIPBvjx4+Xd9991z17W2+9teQLnAuOiXsyKv2J5/bJJ5909FdoeZdcconr/aJqCZXbYDCUPlrzObATUQNLXo/jSa8f9hlWSJigJ5XPkUxhPiyWWII+dHrgF5GVVlpZunaNx4ivVMB3YL3VNTfYP6gJVz1XJGZx3Is9kc1aRvCF8istUbnWJ2iNQquK6T67R9uklELrB5thKLQkCWB74S9mMrosGd58U6uVVO2lYOPY/vjjDxckRxGjxJaMGTPGBZADBgxwyWvubXwOElBxeS5LLrBsrd+htexhuvEhUeF/Vyop77BVSm5GFpn1119Ivv8+IQbS2Ai9IXeiAG0BP/iBUqw9ejxcGB7OPwFFsgxjMfSr+RU9qtzZZteyRToKLedahyoDv3dQ6SwKXocxzEXGMIiHH0581j771Ivmb6gggtZm02UDgj/ovGR2qfpHyahy7k488UQZPXq0DB8+3PU38FyzuO62225522eDwRBPnyOV0EbUiiUOJzaZ9yDwFiUpmc7nCNNqk0wk6Mwzf5XjjtvK/a2ubtmi8zmCwQ/JVYJLnddNkEki01cH17Ydrm1cnPVUUOFGql/4UZmM+colkrF7VAVfKZ1aRQ6Ol/EptLRDkfTVUS+5Oibtr9x005bPJPuolfx8t9t06NDB+RxRfFqKFao0jyAZgaVqROy0004SJ5RcYJmp9HeY8SFRoRmxbGivSgNgoeEGOu+8zvLkk4n/++STGbLssoWnwGYCFk8V+0E23qfPpsowcm19Bdp08wcLAfaZKiVORi4rerlGkELL+eb8arCpQ5UxepxjjoeNOY7aXJ5LjB6duIY77tjQHPCxqGA0oabmGjz/3FMkAFZbbTWX4IjiPOCEMNKAqjD/Tjbb0mAwLBjAlioTKZlTGrb9BscRu4T9g45PFS3q+pYLnwPAvKD6hTPfr9/qcvPNjTJxYrlT7l5ppeIKLBWsbazPnJfg+qzq4OpzKKWT4w+OWIuDEnsylflcVfTyAXyJoDJqsvPN84PPQSFF56Emm+OYKSjmv/VW4vptvnnLZ/L999+X1157zTGXttoqkUjJFZqamtwx4ltlInSI/8+IJZ7jYlCaX+ADyyjjQ6KCG4eAFUPG92rGMCx4sDAaLDg0YBMI8Jndu9Or+Dd5443vZbvtVpdiAgaDh4QAnqBBRzm0plDGoq1GiIWXBzQuGUZVKsMpIMhAUSwOktxhwfnSKrKC863PhaoFUt3DOPp0lmyHKuOofPMNz0aT9OyZeC6pWAMMcK7pSTxLZAy5d6CXRZljh61gJiU9lKeffrqce+65RXWdDQZD7qE2Cr8jk8ASW8T6AUuDwCBqJSNoy7XXO5NWG5x9Kl+ss/R9sf6y7507M95sYfnmm3hX75KBtYy1mfUs1WxvXx3cp3Sqz+ErsQerbPiLhfA5fJV5qpTFNBYl1fmm8ko1E3A83IskgMMKMYWpVk6eXCbV1U2ywQYtn0nuD85rLmbb+sAu4DvhewSLKK2B/Rk2bJicfPLJTpyHXspiYO5Vlmq/Qypaim/ko44PCQuln/BdGCOySaquFcYI+YaQbE0wUFl11Xby2Wc01f/hPjsOTfRRKq8YZqgNUQJ4jt8fdh+XDCNVSugaJBC4Tm2pKJYvYOBVJc+v6PFcKZ3Fp9AGF9oowf3o0RXNtBTsOQs4RhhghHMJpfNCr+W4otwXVNiPPvpoGTdunDz11FOy5ZZbxp4iZTAY2jawTOaUqhOsPoGCNYzKILYWJ5r1I4rT6cOnvNLTxtrIWhR2zqPSXtlgpfh0Sv6vtvYLEdlETjihRr74ok6uuCKhFBt3aOWVa0TRIErQwPrgt5H48wd1ljdrP76Jz6TKRhyvrVTm4wYdXYa/7Ff08Kv0fOuzEqTQcr5T+dW//CIydGilrLhik+yzT4OccUYi+B4woEH8nDXXVdtvcsmS+vUvOi/3UNQWInzL0047zek4FJvSfMkFlji1YWgpSPxGHR8SBkpB4QagP0LlgnXOIzeW8s+DgU+Q9prKEK6+egcZORJnd2HX47XrrrvG+oZTQ8i58Cuv+ch4JZv1yDn0ezVzkWH0BymrqlecKDKZgnNHYMezE5S/VgptqqHKSqFNFtz7BhU6yr33VsgyyzQ102C33johLvHSSy+568jCogJE2YLPw7mgtyMTutDYsWPdXEoEEaDLtIUogsFgKA5oAJZK20F9C19UBztJUpufONL+0Pqo8GmvJMy0rUFV2FVdUv2OoD3GYed1/C1ZRYXjW3XVShk7NvH7jTdWyWWX1UmMulDmA0E+x0TxANpg1HaHMPMHkwnV6KxHFcdTv4N1NNvv5/7h87lv8qEyXyhwv+JzcHzB0WXce6kotPh6+GCcfw3u541YW1SuuqqD3HprpcycmTjvN93UKOPHl8siizTJBRe0VHEmHkhU5jtHEtNJhcbGRhcEc720hSjK9ScYhfrK/fPee+85anwxobJUs4c83MGHjmCTLBMXnRs26viQdAhKeXOjsy8EPmqEeI3KYPNg+NQKFa7hdxaHdHOH1N+ePn0RF4TSN4DDHDdwvFB8CDg4nnwbwuDcpXxlGDGEfA6LSqkMUtaMIRnrVCM3og5V9hdaf5bpkCHLyNVXJwLWqqqEs7XVVonnR2nj22yzTU6SJSw6UF+576KOsGFfmA912WWXOfor2cNSSB4YDIbcAXuFH9FaYKlVRewsbBBsJmtiptWtVArzBDG+CrsGPvgdqtCJz4HdZi0j8dvazOhNN11aHnigZQvDqqvGr9dSgy+cehWyySd1MJlQje9zaEHBHx3hj1gLC4IpneEYVUk0rgiOLks2ciNsQcGf98izNXToCjJ8eHf3/926zZWvv652QSUYNKhO/NwwVVFtv8kFS2rWrFnO5+D4KA5FGQXI83rvvffKGWec4ZTmL7744qJMHpRsYOnPlfLHh+j/UznLheMaRe2Vv/lGSEVT2C+45dxAOqyXLKK+NsgpX265BJX3f//rJl99tZI899z7LisSJ6eXBxxDyDEWiiKaSYZRA85kGUauMYEXjgG9oQRgcTrnmYKqIxlDzk2288nSDVVme/DBBrn66nlV+Lo6glPUD2dKZWU72WuvvVymPduFUxctsrsEyTAToopXHHHEEa7S+fzzz0uPHj2y2h+DwbBgzrJUu4PDS6KVoA47m+ns6ag+h65rrFnq+OJjYGcJaglOSf7jKy3DAABZBElEQVQyO1jXP9YA//PWW6/l+o2TvuqqiSppXIAvhc/BekPA3hYjvsLOl/Z9Dnw9AkQ/2Zpq5JcffFH1QnSoFPr6/dFl2fqHweAeYsDhhyeSCaef/pPsssv/ZOzYMhk8eF1ZfPEG2Wabb2TSpASVFt/wrbfecvcOv8NKygZc24kTJ7qgl0p5FP+QwPiEE06Ql19+Wf773//K9ttvH2smYjqUNQWHHhU5OBwMIheXjKA/PoSMCDcwyk+9e/fOOijwaa9apYyi9kqwy75xc3NDY1h07iDZRa1sYpj8oGfWrE6y8caLyKxZiZuuS5cG+fjjuRKHnl72HyNIpg5nHqc+TsqtyeBnGJViEcww8jvXiusGxTnTXpg4wafW8Kzkm8770kvlsvfeSPOXydFHz5IVV5wuF1ywpPTp84Mccsj4FhRaFQjKZBHFwcPB4DrSgxKld5pzwqDhQw891Dl/Q4cOLYnssMFgyB8IInAGcZKDYB2HzkblDDYSAU+2tNdMfA7t+SJZxvtIRuNPKMtE/Q1+so7ruC82qIV9+3aQCRMSTsaee06RddddVN5+m2R5ogq08sqFcSXZf+3PQ5MCanHcE75+7yDnm/MPghVN1mZ8V3yOuKrMZxJ8cQ9SVafAk+tAedy4cundG7YgSqyzhW6eeRTa6TJ9euK8E+zz7LAvJNeZl83M8UwqhDwvfA7P11prrRW575UWG6iv3L8PPPBA0SvNl1xgCTDgzz77rMsIBceHYIQYMLrFFltkXGJORnuNYsi4ibkJyZK0RnvVPjbf6POAfP/93+TBB7vLm28msjT33z9H9tgjMQi5UEaVHjaOi2ofgXKmGdlCg+vLtdHgnj4N7dnFKVAKbbIMY7GAYJrgi3sLQ5jvReuRRyrk0EOrXYVyxx3rZdiwWuE2nTJlqowYMdxRYeiv4LwrpcWn0Gqg2ZoiHFl3qq+8nsU4CtWda/zvf//bDR2+/PLLHRUl7kkRg8FQeOBjkISivUZBQECSFWeTNQWand8/1pY+BxUyEqPYx9Zor34fm/od2GUS4MOHLyJDh847RkVNTZOcc06dnHJKfZsOned4WMdYm7H3mZ7fQsMf+aU+B8En1xifg4JItoqohQZsPAo+XDOuVb5GowwaVOX6gPfaq17uuSc5iwDgK3PONdmCz4DPgf/q+xytjbXDX4H6StxBIrtdhHYbX2ke+us555xTEhXp4j+CJDcvBpOBobfffrtst912LdRH1ZhGGVis4D3cjKo4m46CkgwsDGTWoMNg2MNkR/w+NhUz0WxXr17fyVVXTZcHHlhebrhhmrRv/7r7fI6ZQLqtblAeRh5MDCKBcjZCBHEA1xOjwnWm8sr9w3Gp/LhWmkGwb6KQg4nDAgoU14sFqy0EAO67r0KOPrpamprKpF+/ernjjkRQiUF/5JGHXeaQ+wWD7NOXlEKL4faHKnNt/JEnvI9rpT2iXKt0jlOqc3LIIYe46w0VhWqlwWAwRFGjB6wT0BepoGHP6LN6++238057bU1kjgQ2lP7W1qhkfWz4VdjhbbedLUOHJl5XU1Mvhx76nbz33pLy1lsLy/nnVztBn5NPTgTA+QQ+EGsw6wKBci5nHRZy5Bc/oUzjuxF88XtQEdVf+3TEWtzBWk8CQCcC5GufcesffTSRcNlrr9RUbfwFniM9j1S59b7SxDb7zHOj1Xv/vGtMgS/PdYGdxxblHvzjL6V5+jtLTWm+pALL5557Tk466SR3I1x44YWy2267pW2kDwteq0OH1bhHpb1iAFEp44YMKl9Fha+UddppZa6p/r33lpIJE6ZIWdkMefzxxx0FlUXBV4Nj8cvljevz/8k++RLlxQyuMwYFoxGk8/rqZJphZIOyQvWP6+sLA8Upw4hzQEBJNpSKcluomzI36vjjE0Hl4YfXyTXX1LmgkiDwiSeecIacauk+++wznwIyiw/3VVCFVg2/jpjhWdRkT2sMgCB434svviiHHXaYEwzi2SkFmrPBYGg7YKtIhGkAgI+AyIr2fbU2y7I12mtUnwPgGKsqbLYicyQfaQno23fe3845Z7b0718nU6dOlCFDOsnQoavJuefCSvlR9tuvPqVWQTbAXkOlJKjk8/E5cj3ruBDwEwBBOq8mW1OpsHP8QRX2uPgc3L/4RgTL0F7xSTO9Hz7/vEy+/bZMevRodPTWVP7Gjz8mlF/79EkeWBIfMMKDfcEX9/cH/5Xz7Z9zqv1BAUiuDe/j/7lWUY9r7F9K8yQPoMHmq3pbKJQMFZabpVevXjJgwAC5+uqr56Ol+NCKRBiqZpCCgoGN8tD6tFeaefNRzdt66xoZO7ZCDj54tmy22X3O8PId9Htonykb+64BDz8JbjOlzVJlIgNFsEKQUip9aFRdOS7OS1RqDefC79XU/lg/06UVtrYGwSQGkePhuNoiyzl5skiPHu1k0qRy2X33ern//lppamp0RvX11193RpnKIgkgf6xJFFBp5HrxLHNeuc+VQuuf92QBPtcL1bVbb71VrrvuOtdXGZcF2WAwFA9oJ8Ce0Ce15557zudovvHGG26dDCNSkkvaK04vNjaXdo2K0Pffl8kJJ9TLn39OdyOitt12W7n44kXkppvaSVVVowwbNlaqqn5trgqp3zFzZidZdtnyjOiy6kvxkyAFZ7wUKjy+yjxrc5QEgK/JoT6HVtiCPkdbnysdXUawxvOR6RoP/vxTZI012ssff8BsapJevRod1XXgQBIv81532mlVcsstVbLvvglmVBAksklok9jmueJ5jdoGRMKG48KH4pi4flDHtaiw6F9bMgrtgqI0XzIVS24SnFUenptuuiml9HfY7GGQ9qrZwrAG2q96Edxl2hQcBmefXSe77VYhd93VXrbddj9Zc82nnbONYADldtSluOn9GY9Ue/wG/bC0Cv+4oNtS0SuFB4Pj0rlDqL1ybFEXY+3B9LNdGBw1+oXIMOpxQfGkmpeLeV5hMGWKyIEH1rigsnv3Rrn11lq3ADz99DPu3gT0I/Tp0ycjyjbHhfNEYMlirKq/gMqB3us+hRZDr2N+cLgIKnkdTh/Pp8FgMEQB9gM7Qu8YlNd99903qXBHWJ8jF7RXnGZlEOUjgdivX0Pz+kblh2oU7SGnnrqnvPVWVxk3rkI+/XQjOeGEWpeAVq2CO+9slGuvXVYGDPhWTj/9t2a/o7Wgx1djz7cv1ZbIhco8ayeBkQZHwQqbsnr8okJwfnocRpe1hocfrnBBZVkZI+TK5PnnK9z288+1cuqpiSQMuZhHH034EgSdQXAeHn30UXevcj722GOPSEElx6W+b9CX8osKv/32WzOFFp+DYJZAFD+FBDZ+S6krzZdMxdIHfWNkAwiokgFVWIxTsrEKQeOeidorziw3D0adLGVbNJSfdVaVDB5c5Zrod9iBWYC/yrhxddKp0++y114vyDHHHNSi11QNkK8+qw36qaic2uRMBoqHJEhdLFYQfJMxbIvj0gyjL4yQrwwj34NB4/4liMsmY5gOP/8MFbtCFlqoyVFUHnusQm6/PTGYeOGFm2TMmDnSvXvCzGCUR4wY4Win7FMm4D6lWZ6FkWfdv69bE6PAoF966aXOGeIc09eAgYeKC6PAYDAYwgI9A+w39uXAAw+UAw44IOnr6LHEyU41AiNbtVecWdZm3oPaa1uN92LtfOSRR9xPnPVZs/aTSy9dQdZYo1HeeWdOczUJL3PDDdvJ559TrWySu+76TJZb7idny1l3g0wqPXZlEHFe8KVKYWY0IODG52gLlfngbGnOKYGQjljTc886mq3PoaPL8J9Z33Pl+26xRY3zMS69tNZRXO+/v9L5u9xLDz88V7beulEOOaRaRowg0G6Sr76aLX5XFgUDkiDq4+6+++7NmiVhgK+Mz8GzTuzQmo/Y5AX4b775ppx77rku6cMzgnBoz549HbOhVBPaOatY3nzzzU5RkQoCvQU33nij4y+nAtLc5513nstq4NBdeeWVsuOOO+Z9phTAaPGwpTLu2dBe6WngIW5rEZsLL6yTjz4ql5deqpDHHuOyJio43323nDQ2LitHHJFwvlG1hgFcUVHmAg22YIM+hkd7RXhAMA78H1VgreaVAgVF+f9U8xAAIGuY7+NqiwyjnwnN98gXpL132qlGZsyY/7ytv36jnHPOZGls/ElEEvOhOMdHHnlkRnRgFVMiaaN9KGGOS8UoOPc4KSzqQ4YMcY7KO++846i5fK4FlgaDIQoefPBB1wayyy67OKZEKqSqWGZLe2VNxh4SWOaD9toaCGD3228/15tO/1lDw0NSXX26TJxYIe+9R29n4phfeYVxXYn9ot/+qqtWk9dfX14qKhKCeGxQd1mzWJdx3Dk3BOz4G/gdpdCmoFUvgoxM5ivnYrY066g/Yk37Brn3gmKEYdlEvt4G92AuR768/36ZCyqrq5tk//3rhdzM5ZfXycyZIkOHVsnBB9e4+2z06MRrYEf5QeW4ceNc+xv7yLPar1+/SAkKKpz4DWhSQMEOc1xlZQn/mqQJCR/8aVr0CCrV5/jqq68ssEyH//znP3LKKae4XiXoINdff7307dvXBVnJmlKhnkEZQdJ/5513dsaZDALUzUyrGKkU2pKBB9kv1AZprzj1UR4Kf44S5fG2UNoMggfpiSfmygcflLnAsq5OZNllm+Sii6rko4+WcA3P/O2778plgw1+kWefRW22Y9IGfe2X9CkNPCA6V4lqj59hzHWDflvKlFNVphe3UKNR1ACxkYhIlmHkvvIzjHrek2UYSW6wSOCs0EecrCqfK3z0UZnsvnsiqFxuuUaXsCBxseKKTXL00X9Kx44vy4cfTpDPP690i41mLzMJKlWqnMw4iauoPb0YcfopuKepHpDVB6VMRzEYDPmFViDD+Bx+YJltqw3vh/1BkJJP2msYsA7179/fMcFwmLt3/1g+/HBdGTq0QTbaKLE+DRkyj6L48ssV8skn5XLttZUyaJC0SLRyXAQoHBc+GMeED0LBQkUIi3XUF2u5Vl+zFXDMBnw/14xNW0g470pbZj9JtHI/+z4HG35S8Lz7o8vWX3/9nI8uu/vuxL2z664NLqhUXH01YoTl8vrrFS6opPdy2LC50qdPywQO+8xzRiIZhkFYgUmeT2IYAkuqylHFDidNmuSU5vGXGXHIuCHAtT/22GOllJETKizBJE4svY16k1JROP7442UQliMAaGfchEjsKnDuuSkJTrMFjeRkJVqjpfAAZNPTwKkjE8HNhwHEWc2nI58JyBrutluN/P57S2PQv/9jcthhHd3NnmxBomqGI4/R4Li0b4RKsD9TkwAo2KDPFte+SwIugmMe+mKRKQ9mGHUEh59h5L7jmSKAIrnBseXzGnz5ZZn06dNOfvmlTDbdtMElNWCHsJ88XySJCAYBWT6GD2dKMeY+g4bCAkHiKYryMOcOqtYJJ5wgAwcOdDMqC6Ui+MorrzhWB/Li3H9QgkmopQOZVpJ2JAu4V6HUoCZnMCzoiBNLiqodn3nqqacm/X+UH3FMceR9hflMaK8kRamC8P62pL2GAUHg4MGfyO237y2dOiUoifTGrb56O6mvL5OxY2e7oJIqE9WlV1+dI2uvnXBBYe5wXKxvrBmq7q39a75ADX/3k6ysf3Htu0ynMh9n6Lgv39djv/1Ak9dQMcc/5Jrl+hrMmCGy6qrt5c8/y+Tpp+dI794tg8ZffsHfbydTppTJf/4z14n6cL9Qwfd1F6DCRtGXwP+dMGGCezbDtNukU5q/9dZbC6Y0XyifI+uKJYEGO33WWWc1/42bj+AObnEy8Hd23AcVzscee0xygTDZQwIMpaBkIuUNRYOAkoeOBSUbGeV8YsMNoQjMcY3Oa63VKE888afceuuS8vTT28gqq9zkrh3qsciRUzV77LEyOeSQGjn44C/liCMWcf/n0yFw6v0REMFMF9lG7gmCCL+qWQhVsiAwNmTWOE5mKUUxFoVEugyjNouzoPM3zjMLGc5WqgxjOvDYYKR//TXxc7nlyPQ1zSfMs/PONS6oXHfdRnn00blSUTFbXnnlHedAKQ2dCmzv3r2dccoEGGicQBblTKjKPKNnnnmma9iH+rrXXnsV9B4k8McBJotJ4qs1cE2Zx3vUUUfJAw880LxYcV6xlwbDgopiY0nhY2CXs1GYV9orWgdQDbGrcQtQCJwuuaSLPPVUvfz0U6UccUS1LLQQdN8y2XzzBhdErrVWgwwb1iDPPVch223XToYMIbj8xiVFCb6DY8uConisc9hS1YfAYSYo9XsGUzF6Cqkyz31aTLoUwXFfnHcdsQZzCCYV9zTXivNMZS/X1eRHHqlwQeUqqzS6oDEIdo1eXh6rDh2a5PPPv3CBEQEva6X6eGH7KfE5CEIpPqigUpRnjKD2oosukttuuy0WSvOF8jmyDiwxctxcwTIxv5N9Sgac3mSv5++5AEY+Vb+D0lF4MAgwoCNEUaXkWHF22QgmWZTimilTrLpqk6y6amJB22yzjjJqVKN89dXC8vbbfWWLLZ6Ut956ywWYq6yykRx77JYyd2653HPP2nLKKXOlsjJ9QdvPYPkLoBp9snQqjOMb/WRSzPkCDzsLMoavLZVR8wk971rJ5PlhUdc5V9AvVMwh2DeR7H59881yufLKKhk1qmWVs6qqSR57bK5suWXiueHxOfLIGvn++3JZddVGefzxOUK7wowZDa6XgecDB4BeApyfTM8zx8V9g8NAVT1qxo9qO1k2Fhb2SwcgFxI77LCD28ICp5lres0117jfofJAN2PBssDSsCAD5sHhhx8uBx98cPOzMnLkSLnzzjuTsqRuuOEGJ+Z3+umnu99Rch01apRjWeWCJZVO10F9DtZD1j3tl8+E9kplKO6zGzt2bCenn94oJ59MYDDPt+rWbZR89tnSbg2+9da5ss8+NfL22xXSv397GTiwRi67bH1ZcsnWaZScN84jmyYt8fc0uU1gQDCHX+fTZ/OphJpOZZ61hyAlbkmAqGD/OYf4UwQgVMopqqhGBP4VfhbwxQg5/5nON7/zzsT9c/DB9SlH1EC4mzwZUcBXXJIBcG9wP0QpHnBc3DccSyaUXp5R7NG0vwR7qHQuqD5HyYwbaS2w9NVeqfoQxBJMqUhKmIAH2quqombi7MYBPIRXXVUre+7ZTl55ZQM59tj28vPPLzsl2yFDlpWpUxO02Nmzy+Wssyrl3nsTdMao559zrNU1jGyyBv3gqJNMjU86cM0IMsiiFVOVMowRJDtPYoeHXxM1VCiTZRjZuOep4pFQSRj9TjJhwpIyePAi8tprFS2Cyc6dm4R8CwEki/5LL0GJbpKbb650mebq6kY56aTXZKmlEn0DnF+CSRabbAJKvWYElfRRkm2Lkvgh40imDUraEUcc4SoU+biv2gIsTjA/fGDcTzrppILtk8FQaMSRJcW6gm1N5XNgk+lbGz9+fIvWEaVxpgp4lPYK8tG/li8ccUS9bLBBowweXOlUwhdbbKosv/w4eeKJBhccQ1kePLhCLr64k4wcuZI8+ODK8tlnDXLZZXXSs+e8ytTXX5fJxx+XS9++DZIuf091jTVQ10HOuT/fUXUK8O/8YDMfAbqvMl9I/YZ8iR2StPYT9Kz92irF+uv7HATXJLvDzHgMAr0QRtfgj+y33/zjQ/gugjkKI/wE+PP45txfUdZ9rhlqthwL1yxquw1JLap8sCAGDx5cVJXpfPgcWQeWVCcwimQrfPC7z3H2wd+jvD4qMDI+LcVXe2VfyXKhNMbfoBNyU/kBj2/0+bc68TwoUPKKveK1/faNbiTJM89UyDHHdJNBg36VxRaDMrum+/9LLqmV886rkkceqZK///1d2XffrpHFUnxwzoNKqDr+AcPvGx8/0MyGUqHXjCAFIxhXqnIm4F5l4WLBwgimWhw1w8immV2cMs77uHFz5ZhjlpR3303051RWNsquu/4hxx47WzbYYCGpqal2tFgUX996q0L69auRCy/8Vc49N0FH6tPnGfn113Hyyy/LNwey9FlnA5wBVemld0jVisOCBQ3n8dlnn3VUNyhvxXzNUzE7cJjIEpdKksRgKHaWFD4HfkQqhXmoZPgNfuuIMnpYq4I+h9pC1i/oeMVY8dpkk0a5775a1zoBPv98U6eIyTHhiGO/zjxzc+nXbxk58cRqeffdCunbt0J23LFe1l+fGZkV8uGHiWNm4P2QIYlZyGHAudLz6esU6HnH1+M66Expfa0/Xi0qoDnjyxB4aZWymNcfH/gM7703UYYOXU022GA12WorxsbM/zqOV6vJSj/1pw38/POv8uSTv8vKK/8hnTu3FAYK+jGXXlrVLNqThN3u1nv6prm2XDOS0PhDUYI63su9wEb1NarmBgUs+rbvvfde1/O9//77F/U1z5XPkXVgSWRPfx5cXG0KxSjy+3HHHZf0PVSO+H8/CoaWwt9zWbFsTcqbfweNj8/dV/EagBOPsSDAKuYbR3H55dPk44/byXffdZBBg3oIcUdjY5nsuWe9nHxyvbz44tcyenR3ufbaFWXGjNtlmWWWcpUxHP5s1cx0/AObP+pEA02qpyyqIJjZDUM75v1cO17PPRVn2lAm9BodjYHhjnovTptWLZdcsoyjmHC9mXt6wAGzZf/9f5YOHX5z1+CVV+ZlGC+9tL3sv/8q8u23HeWggxLWffXVJ0rv3hNlnXU2zdlsTJ47BHoAvShRs7w02qP6SpBL3xSLusFgMLQFtMeyNYV5vzWB5HZwnjRUQq18YoPxOagGFbPPkShm1cjCC2/kAnCCf3QBOG6CzAEDGmSrrWbLFVdUydChlfL002yJ91ZUNLkZmA89VClbbNEg//zn/GPiWhtVMWlSuay2WqOsuGJ76dKlfbP6us6U5ryTrKDHk+vnUzj5GaZ6FVSZz9fM6LaGPxng3nt7yIgRi8uIESI//VQn//53nYSJwXXawHffdZaTTqqRDz5gDE2dDBnyndTX/+Eo3gSJGuSzffjhkjJyZAd3/c8+O8Ga47lgP1BoBfih0E15vkhqR2UQ8rxSpSRWyERBH1+Mdpug0rwhR1RYqgQ4dVqCppEeR1H7HxgcTLYOWho48cQTnagHPF4aRYcNG+b6oG6//fZc7I57uPl+DEcUtVct67PxGRgc/k0llUoPmShVgOUmhvaXbWWtrcFDAC3ku+/+J/feu6xceunaMmoUY0QInpvcfCBw553LyHrr1cvkyX+TMWO2ku7dJ8pnn70v7du/Lius0FX22WcvqaxE1S43+xVs0FdKhVJZkjXo89MfdcI10kWLh1xV5UoBLIAYQRUByIRe8/zz5XLQQTUybVrinOyxR71cckmdrLgiv8EWSCgWYnAx4pz7n376Uvbcc4wMHXqozJ7dQRZf/E+5+uo/ZNNND8rZ4qn9oNgIAuYoGWPuZ/qqzj77bGdXLrjggtj3PIdFKmYHC6BVKw0LKuLIkiL4YH1Sn0OT2GF8Dh03xTPNWse/2S8CVPaRJGuUlp24QQVRcMS5dnvvvbc77g8++MD1cwGKJKef/q2stNKH8sYbvejSk512apQdd2xwSdALLqiWU06hiDFPRRbFUALRLl2apH//+QPOl18ul113rZGGhsR6hwrtKqugN9EoXbs2yTLLVMoyy9TISit1lk03hX47r7DAddAgn+vhM6l8QTyuN6+j0lMsKvNh4Y8u++67LeThhztJWVni3N96a5XMnVsmgwfXtvABaTP++OMyQah4mWWa3Bg85k1eckmV3HRTIpkN3n23So45ZkV57LEuQpyoQT7n/ddf/5BBg7q61+2yyw8yZconMmHCJCcMyXpPBU0ZdJlqDWi7DUkbKOZR220efvhhpzSPGnQhlebj6nPkJLBkfAgX6vzzz3cPGBcKOpqWVKF7+EaQ5nOoasjY4hDiTNLrkAt1NkD/BdUPbkLm1LHhtIYBiwPBI7QW9itIe9UHgP/nmDH6Kn3tU1niOG5Dq7A8GFSZ2ddHHqmT888Xuf32SrniilpncMFSS1XIhRcmGvBfeWULt/k46qjEz/XWa5TTTx8rG27YyVXQcnXcPqXCb9DXqqbfoK/nm4CSYL+QM73ylQggqxeUKtdBQWHWMTK3++1XI7NmJZRc6bPdYovG5mox30HGlu8hI0jiB+NNVn3GjMdlrbUmylNPrSL77feTtGs3W15//XV3jv0RM1yrKNefZ4mAkmwxmUft0wgL7gVYEewLMtr0BpTKog6otj+tqfs8MDsMhmJEHFlSsCSef/5519vN+tOzZ09nO8OAdQ0/AqZNMtqrzjVm3aMyho3Wyhr2lzUvyjD7tgTJYdZpkr7YeE0cJ2udwGebO3eCbLTRBOeoL710d5k9e0055ZSublYhwnIHHFAjL788x/VtXnhhtUyenLD3P/9cKyecUN9iHNb++yeCyq5dG924tdmzy2TiRLb5A/JDDqmTG29MzIpmUxqnto6oRgG+oVaduQ9Zuwg0S0m/wVdGTdC3V5Mzz0wkkQcNqpcVV2yUo4+ulrvuqnSB5C231Mo335S5eZP33Vfp1OQBQSgUVhjhv/5a1jzHdP/9612CW1tsRoxgVFllc7sUc06//rpGOnaslfXWe0zeeuv35n0jyOc+4dnKZLSd3ycKA0+r15kozd9xxx0FV5qPq8+RkzmWcQMN8igZcQPhdDICAUOBwecEsXFTBY03zjVlf242sk9hKBBaWSPQVDoLxshvEmcrZKCj3H8olKnmKPHwB59PWMSnnFIl77xT4cZPYBxqa+d/iBZa6E/Zf//7Zbnl/nBBYNeuy8onn6wuN9ywtKy/fqPcfDPjR3J/XCyuBJPao8kxJRMFKtZsEkaMKiXXb6211mpB9Xj00QqXwZ06VZwq62KLNcmiizbJmms2ybnn1rlsoeL778tkyy1r5Oefy2WrrRrk0UfnyK+/TnKLB0kfMoFKGdesVaoZsAo/w6ibiiP4VKJUY2Z4L/RVFmOOLeo1guEASwKq2P3335+zykM+gZ3gXgWM8SHTudVWW7nFFGcSMRKeUfo1ALaIZBvDlJELf+mll1yWlP4kU4U1LOjjRnj+kfVXltTw4cNdooqEdpAlxbgRkmVXXHFFM0vqsssuy9m4Eb6XwJVnFt+D5Da0fPU32JS2569f2GASeiTx0AIIYweDLTtsJMSDPkch1z1/dqOObWgtANB5xax52EoFx7X00mvJGWf0kZ9+QjegSaZPT6wpSy7JLPHEv6+5plaOOqrerYlbbdVOPv+8XDbZpEGefXauq5yxDn72WZn873/l8tNPZW778ccyefXVcmlqSsxB3Hnn9FRbrpnqQugMb9bCdu0WkVtuWUeWXbZcLrywKRbj1RSffJII+hDAWW+99O4+bCUSAdxfiXV5cendu52bPdqrV4M89RSjxZgJWyGHHlrtAveVV25051TB9UE7k4qmYrnlGuX662udvofOV2dkGewpKM4UNdi3P/8UWWutavn990rp2/c52Xzzt9y5JNnNhr/gzzPV0XZ+r6bPYkvWbsP/8SxGZVyp0jzve+ihh9w9HXfMKJDPUZKBZRA0aaMc9eqrr7pAk38rFx6DT8aQ0jaLEDRD7bnMBMEmcTYurgrT6BZ1vmCm+0IWlCwb3wc9NBuFMu6U2bMTAScbWcB99qmUTz+tknbt5si++z4onTpNk6ef3kE++2we33yddRrl4YfnyrLLNrXIKCIQhGG/+OK6+Qbfhjk2zSDykHBs0IX03GtlM9igH1aRrC0RrDxybDzsUGzoQaVyrosy553zNnhwaronhp1zesgh9c5Qb7NNjUycWCFrrtkoL7wwx/0/stL+4s15wVDqfLSo2W//vlejz7nnmvhGn6CfY8PY8X0kOaI8ByzsNMkzKwqjyBZHdkAyMF8Lox4EDvLdd9/tFi16SHid/56TTz7ZLfYkxxAKiDqs2GAoRTAqhOHfypJCjZH1G2y55ZbOtvBcKRD6gCXFM4ZNveqqq2THHXfMW1IQoRqCTPwO1BaxkewfPgdJaxJi55xzjtvXbMTxgI6d0gQ3tl0ZJb4wTVsEOyR7ccKx/QQDUTUZdLwKn8EaSPDAurHOOkfJ9tszpxk2U4OcccZcoUB9+eVVctVVifXwuutqZeTICnnhhQpXqXzllTnSWs7xnHOq5Prrq1yQ+s47s//qCW1dZZ7iBH4d5/6yywhsE+KEF174hmyyybQWye2obJ5cAf9qiy3aucpueXmTHH10vZx3Hgng+V8L5ZFjo6rcvXt3d/2OOaZa7rmnUpZaqknefHN2i3NJ1fif/6x2M0qpTvbp0+h8ju23b3CK8og28f1//FHmqMZB13PsWAQD28uMGYnz8re/NTofESXYzp1/l2uvHSVrr72a80lS+SN636vfgc+hqsu+z6F+MGsoRaMo/h/PLc/qaaedVnRK8y8XyOdYIALLIKisUNV84oknZOjQoW5h4kYj80lVk40FAIpJrr5Pb359AHzhIBWmyWWwwwNHFpXvIxNKFTYfiwpCeHvvXSNvvlkhNTWNUl7eKLNn01/SIBttNE6+/HJD+f33Kll6aUZVfC8//PCivPxyT0errK+fd7yovl1+eW0Lo/7VV2WO/kLv5x57NDRXPTk2DCAZQ4x7soHYCr9BX69Bpg36YcETRX/HHXdUuv1fay0MaIPstluDrLxy4nGrqxMZM6ZcRoyolCeeIOsp0q9fg+y992xZZJEJMmPGny5j6DscUN//+c8aefXVhCE++eQ6OfLIRIZ26tQyV1G+/voKGTcuYYTXWONXaWycK5991lUWXniGvP12uaimDfQGnA8MB1XsfIhSqeqyf+5xEvgeFi+uW7oMYzKHBUlvKp2MFGG8SVyywgaDwZDOFlItgWY2ZMgQx47CdlFFoFUHn4NEd9R2gHTrXtDnyHfLDradQBAnPlf9hjozkeMhSCVo/OADkrC3SnX1dFeVXmGFFeWhhzaQoUPn+Wvt2zfJqFFzZIMNWndv0Wfs2bOdo8jutlu9PPDA/Oqzvso8QZfvT5Ek//vfEYxM/L722g0ycuTPMmPGvPPP/geZVNmy2PAznn66Qv72N1qb5k/M09vYp087J5YDo4kAD3TpQitMnfOpOASOTbUp/NFlt95aKaeeWu2CRiqVOsvax+uvlzu/Ys89G2T55dOfa0INzh8MKYIZkgcI+owZ01u+/noVqa2dlywfNmyW7LJL9HOiqsua3CbRoqMH8XE4Ns5/2MIOn4WGzHPPPSd33XVX0SvNtxUWyMAS4FST0dxtt91cBoKbkMyiVjWpqODYK40Fw6+9ftlCb35uer35fQqnbpmIkHA5eXDZfxx3gsp8Z1cQsTvwwBo3vgRstlmDDB48V5Ze+leZOnVR2XffhR2VorqaWVSzZebMRIS46qpfuArnu+9u5Kgoiy7aIGedhWBBmTOYn33Wkl4BlWPXXX+UhoZE03Umx6Y0Ij/Lxe/pGvSTgSAPCg5rAxvCADSmP/xwhRMb+OKL5EkCqrdUDgk4qfgmwzLLzJH99hNZYYUymTOHLbH4IVSAwt1CC1FxnCt9+kxvIa1NX/OECR/L2LEby4svbiN1dYlzU1VVK8cd918ZNKhPweYrsWhBceIex7irMFOqDKN/73PNyPgjBoakOJk2v1fHYDAY4g6CC9Ys+kMRLsQP0IomPgcVAiqp6m+wkfTLhSOrM419JpVWAn0hwkx8Bewz4noElXwG7KF80nDZdxhm/ngXvNjnn99B3nzz7+73Bx6YK7vvHl5Bdvz4Mkf5pPp2xx1zZd99G5qDs+uvnyPDh1fInntOkTPOWLzFsfG9O+5YI6+8UiE9ezbIRx+VuyTv//3f3GYF26DyLz6Hstj8QB+fI0px4e67K+TYY2tc4HfmmfVy1ll1rlIIGhtF9t+/Wh5/vNLNpKZy+/nnZa59Rmmr22xD1XeKNDaOd34B/i7BLv7cySdTUU982Dnn1MrZZ88/R7I1qDKy4p577nFJBx98L/f4csutKj//3E1eeCFRHT3pJESwJCtwnknmcEwwv3TEHZuOYvOLC0F/W5Xm8VdIZOfK/18QsMAGlgBjmKp5F1oAxh6jj/GnT5PXap8mP8mg5aLKqHMdfaPP7xgaP9DEEKVbZHDSWZzIQLFvbTlMmQocql8Ysf32a2ihFDZ9eqLS9vzzicCza9eZ0r//m9Kly/vuOH/4oas8+eTOToHWR3l5g6y++hSZOnVh+emneTyKzTabLX//e0INjo1+QiqB/DsVYH2OHZuYvURsRXAGHYSf7dvXyp9/JgyOGn5t0PfFaZqaKuXZZytc0/qoUeXNCmfJsPDCTa4Ki1ods7gef7zC9XKoQh3gXO2+e73ssstc+fHH72XEiIXkzTe7yowZqe+pZZaZJoceOlLatfvGXWe48NorMGbMGCd7ze8VFSvJf/+7hXz88RJy881/yp57FqbHF4eGHiKSHf5Q5VQZRjYWYe59AmXOO31I9913n1x88cWOohEnGrPBYDDkwudAmEd9Dn5CpaXK4ie36Q3LhUhPqpYdTbDq1hqbhKQs7CHWcQLKdOyhXIPAkmomawtrxMyZs+T999eXjTdeQU49dbXm/eM8sl8ECATQqdaPK6+slIsuqnZaBS++OEcef1zkxhsrZerUeQHHxRfXyimnzAuy7r23Qo4+usZVSN95Z46bvXnWWdWO1vnBB3NSakv4sx113eOaBJlUqYoLiBBBcUWQSNGjR4PcdVdCgPGCC6rk3/+ucgnvp5+eK5tvnqg20sp0zTVVcs01CO8kKKy77jpdLrusyqnDE3wi8kchAOostNnTTqtvVf2ffed4CBzxnbke3M8wjPR806eH7glBHkUdAkoSxLmuALIvKkYIjRahn6DP4av/sqn6Lz4UiW6Ohf5tqpUozcdRGCvOWKADyyjA6OK0v/LKKy7QpE+Tm037NDH6ZCJzlakjm+gbfW50lRz3x5yoYI067zxEPLBx6zuD6nnLLZWuunfQQfWumV6rhxiiSZOmyNtvby4PP1wj3bs3yvLLj5eKihekfXvmkYr873+ryDvvbCyff76aNDUlt3KrrDJbeveeLX37MherWmbMqHJV1CefrJTRo8tbNJP7YFYSjFMCvcQGVbZWOnacJe3azZDq6mny2WcdZPToFeT33+cFaEss0eQCar9RHcXVww4joKyfr4/ht98wrhWO4tuz51xZb71pMmVKItOro26mTauTmTO3lVGjOrrPnT79F7dVVlLRniabbUZ/cGIkDO8ZMGBAs4Kd9kz6VUme7kIxNwgQyRhyf+IQha2WqvrvpZde6kQ61OHhOeN5o0+qVFR/DQaDIZX9RKRMq5oIEFH11D5NfA6UVbPRTQgGO0GfI1XLDs45dEYCO5KFUF8L6XzjSxDIENCwP8pogb5Ky1NwrJkGmvhKOr8QHwU9Anr8fKy4YoNstlmTDBuWOL4zzkBJv04ovm20UXtHMb3kklo3/5s1e6ON2snXX5e7+YvnnFMXev9Z5zS5/dFHta4/kST6YovNY1KxDsJiorr68cflruoIk+uEE/B3ypxPMnBgvdx4YyIgHTJkrgwc2LJyy3V97rmv5O67u8nLLycSAQSg0FkJjPkcqoZ33z23Ve0L1nfGdhBQKuXUh86W1vuZdTufiWH2gf0hUMTnCDvbUtV/b7zxRje+jGvAvvKs4XMMGjQoK+2VBQ0WWGYIDDxzmDD6avjJoG244YbNRp8bMldVQ19yXDcunTaP8xAElUOLGRwrIi8sDLrAEYR+912ZfPJJd1l55d4yZUqV/PxzmXz55RyZPLl9i4AT+md9fZWj2CqWX75RqqvnyvTpjTJnTpXMnl0hdXXRAnACzi23/Eb+8Y9PZM01K11GmXNeV0cv50zuDGfc2DBW+u8+ffo0GybuF6idqcBsJLJ6SscgoeEnFPRnXMfaAPqWyWSj1kqlMsp+cl/TQH7ooYc62f5bbrnFOQwkc1jIEO+xPgeDwbAggWCOdgLf56ACinCR73Noj1wuvi/oc+CHENywpmHTYUZlKzyUTzBWQgMf+vsInn3ssssuzYPt8Teef/5bOfnkLaWurlJWXHGOnHVWkwwY0OQoplT5zj8/QRU++ug61wrz8MOVLpn86qtzmmmoI0ZUuFEnHTo0uaqlr9DeGn7/XeTSS6tkyJBKx27aaaeZMmjQ/6Sh4fdmP+iOOzaQESO6SOfODU5QZ5llyl2ymnak8ePn+UCnnVYnF14473j9ZIBOBxg/vtKJATLiQ4H66113zZUOHRLXXgWh+MnGiA0VZKKyTsIDEDByLxBIEtyT8Mb/bau1mtEvXGtfzDETpXmSJLTbcMz4afgdCIUV63SBQsACyxyB04i8tt+nSSWKZmifyoLEby4eNJWF5kGnEqQBTJwkxzOF3yeqgYlmQ1WMh+BKzyOL7Ycf/ihvv91J3n9/aZk4cXmZMSNRHevS5Sf55z8XlX79yt0ojhdffMHRmhX19RUya1YHmTmzg8ya1VH+/vedZPbshZwQzocf/ixffz3D/T+qt+ut94F07/6ZVFYmsnh77rmnM6YYIAwai1iYYBFDzP3BIqGzOrmGZJ5VvVezqMUGHA+SAVShSXREpUZxfRkDgCG/8sor5eijjzbqq8FgMCQBAih+nyZrIfQ/9TkQBuL3XPgcJEoReSFIY73CVlOF0nEPmvCM06gNHwRW+Evsv1I2Sfiy3/gcjIvBN5g0CQ2AhWWVVb6Sysqy5mQuYnEEdPQfKqCLjhkzRzbccJ4bjUfdp09C0JCZjbfdVtvqvjHuDW2Giy+ukt9+K2v+bNptVlihUe6+u1Y23rhehg+vlUMPTRQrLrnkHVlnnUnu/CdapTrJ9dd3kdtvby977lnv3qNLZ3B0GYUIKqRsJOy/+aabDBmysGy+eYNsvfXb8uqrL7u1PBn69evn7img55J1nqCyEIluriu+IslnhJXws6IqzeNv0Gpz9tlnu+pkXBP2xQILLPMIMmR+nyYzs8gm+rOtmBkTtZpDZg2uOg8yD5LSAoM9EzpqoxCS45kCA0iQxrFkmg1taGiSCRPqpaZmtiyxBPLhSzYHJ2TryPLy+VpRxLBgRNlQ/VJqEb0ZSuXkvHOd+BzdmIemFWmqc7wW8Fr9PK4NQSKGGOOrczehLCPUUEpBE/cb54BMIfd11IHR3NfMTmKhYs4ciomFBtVRHWmAcBBUGdSjk4EsJwJDPrj+3GsGg8GQbxA4kbjUQFPZLn5ye911141UzWE9IwgjYUgSVMds+G0LKkRI0hcBoKDPEed1TlXmCUxY4zkmrdL5wRWzURNCLhVy1FHVLujbfPM3ZccdX3Q+A8dJNRebX1PTS3bddSnXw3jxxVPlt99myZQp7WTKlBqnkk8rEG4GGg+LLFLulNwR/gFrrNEo//53Yvb3QQdVyzfflLt2HVTghw6tctTbk06CjjvDnW8owGwqCjRjRrUw0KBbt1VdkMVr8GVUJJJAMhg0UoWkignwv1AvVp2JIFOKHuGoa3s+/UV8Dnw47uuotPA4Ks3fXAI+hwWWbfwQYOi1qkmZnYcXHreKAm288cYpH1qMBgaQm4aqVmvS5Okkx9VIEPTEITvDbUj2lV5RjGGhezZyAf/8Y/j5CVic6fPQvolM1H/jBK4dizIVem3KjzonCqGeI488UnbYYQf5v//7v8izz/IB+jtxJpj5yTPKEHZm4eFgJavEYuRPPPFE9/8KnrdcUdMMBoMhCvAV3n333RZ9mgROOKoaaPLvVP3vVCTxOUgawhyCQZTO8SZgCfocKkrjjzmJw9rOfsEyYt3Cl9LZjcnU+zkWAmpN4t9yy2cyatQM2XjjV6Sqan7FVNaNM85YzlFlw4KRIAce+JV06jRMqqsRGiyTWbOq5PHHt5cPP1y7+XUbbdQgL7wwVz766D1XZU0FAi18O+3fJKHtg6KDsqT8qQfcM2y5HoGXa1AgoIJOoOvP+g4DzgnPAonsOCnN/6dEfA4LLAsceJBt8Xsm4IlTqfF7Jgg0yVogDkSli+pXJsFgUHIcg0nPQXDMSVsPfyWDRpaMCiI0jVzND40DdPHiwccAEjT7sx1V/ddfeMPOdYwDuH+4dmRFqVJG7Snmmv/rX/9yDfMMOGcQb1yOHcOOOAY0GX1+WHyPP/54R5dJZuRPOumk5gSCwWAwxAnYMAJFZVGxkRTEucbX0AQ3dvyGG25wwQk2D8c90/FnwTEnBLZK32StZ+1r65YdDZjZNwLGTOeHagVQaaV8LseHcMzvv3eQQw6plunTEQGcLIssMl0WXniqtG8/7S/Rv0qZO7dK1lxzE+nceWFHm/3qq7FOINIHHjpqt888s4O0b18hr79e61Tw0fh44YUX3HUh4MVvS1RLa9x1xm/EX6T9iooz7UWs1zpTk2usfkfcmWw+2H+dKYq/GPXacc0Y90NVEPora3ZcAuhNS8TnsMAyRuBS0Fzt92li/DAcBBtnnHGGm7tJVSgXRiA4X4nNn+mY70CHh0arlDR6U6WMQ/U0V2CBoQ+WQBIDmIzWq2pkQSW+oOx4HM8LiQkSI1QXOb6oCQloydA4uAfJ1EF9jgu4Ltz3zEvbfffdm/9Ocz/X6XG06JMY+cMOO8wJF3BvI+RFvyjnxmAwGOIIAg5/zAkBCz4H9hyHdp999nGBZa58gGQtOwRBvs8RdoB9VPitRDrnu5CMIXW/9Vi1PUfbb/zWm+nTK6SiolqWWip1EETgSNBFgQJWGxXmZK/xR6uxAT+5HZwlHRdwr0BbJXgmkR01IQG99PDDD3e+x4MPPuiSKXFBbQn5HBZYxhTcZFADKYP379/fZfagzqJcRcne79PM1WyrqJLj2YDsHpUuDCgBRalJOdMnSFBJVpCMaFgj7c919AdZa4ZXjX8hBRLYRwwzSRAcDjJqUfYFk4ORZAYn9/a1116bM8n8XIEqM8YaugzPmILkDrOuxo4dO997eD5xWMjyc/2uvvpql33mPteRMAaDwRBHYJdPPfVUR8PbY489HLuG1h1sHclDv0+TCmeumE3aMqJ9mqx5+BdBnyPb5CoJTNZkWEJxV7PNBLTbsNawlnJ8YYMuf66jBpu+KJP6HYVkUqmgI+srrTZsUX0OVZqnj/L222+PHTPupxLyOQpPdDckBYEInH8MIVQGBQ88xh4KCzQISvk8NDrbSvs0M3XU+V6oBUovCEqOE0wQDAZ7JqIETip5zXGtvPLKsazGZUvTILAkY5hqGHYqaMM8m153zfBiOKjwYjRY1H2j31b9EOwLVUqCXXpzovZCcv+ivEaFkjEizOEsFgpOa9DnT4EDRlKBQcs8pwaDwRBXYIehTiIyqCM4ABU0/qbtOjiv2HEoe2rz8D8yVTInKU6Qp4GeJlfV52DNy6Zlx1eZZz0mKI5Dj2eugD/GsVGJJdFLQBFlTcVvUHV67bNUUSbOP1Rp/FDOmZ/cbit9DnwNfB6KEVTkogaE3DtU8VQUh4JNXKivpepzWMWyBIxKcLYVfPrgbKuoYx9SgduFjJ82tLNpdis45iRo3FgsMBAsHKU0c1PBOeFakNnj+PLVNxKcacoCwN8w9H6wqUIDua7CknTA8Yi6qCCSAK2DJMRDDz3kFsG4IhNaSjLsvffebkHmeA0Gg6HYwfqNaIr6HGzffvutq5r4Vc2oSdWoLTuqTaB9mskqaqoyT6BEFS9Xc8XjAvwAfA7WYqih+WL++AUGDTgJ2HS8nc+kynUVluPj87l+Uem5BNu029CPSTIbvziuqC0hn8MCyxKDqqti7CmJ0zNBMIAT7xt9KoW5qhRhtIM9E77kOAEPnH8WHzKifHepZIzU6NInynnPhBqaq2Df75sgu6d9K9k26BO0QrdAhY1sWLK+jdb2D6NOkzlG/qqrrsp50JsPkIWnKotwll5nqsjHHXdc0kb6ZOeNBMOOO+7o6L4Gg8FQisCBD/ZpYitVDIifMLBytS4GtQn4N0GHz6Li78wWLxWV+WR6HBxfJkrsufh+n0nlj7fzk9sEnpnsF2stx4bPSB9s1Cos+/fMM8+4USJxUppfUHwOCywXAJD18WdbMdOILF9wtlWuDK8vOU5Aqc3hBJj0h2qGsRQosBhTMmoYPTKGqWTb2xraoO8Hm8A3+mEozASoUF9ZHOjlJaMWBWSWTz/9dHniiSdk6NChLhNXLNRXgmGyhdBKMPZIfw8fPtxl65HzRhacnojLL7/cvf6iiy5y7ACcGM45tJvHHnvMyf3HSZjIYDAY8gmqW2+99VZzcpv+MNYO7KP6HKjf56pP02fxqM+Ba8t6DMMmastOnEESGZ+DNR6fIy7ML9qAgkwqAqOgEGFr15zqtB4fPkfUgDDOSvMLis9ROikcQ0pA/9h5553dBsg0IQJEoElDM/xzjILfp0n/RKZBkqqaMnyWwIsKJQGlGh2alKlyKo1Ct2KoYilYtMimUaksRMawNbCAcs51NpMv+47Bp/qYrkFf1fOgr1KBZcRN1OODgoSRJJFAjw7nqZiAGiIUmvPPP9+pyUGjYd6mzoiiQu2fE6jQKM7xWo6Z8UAkdCyoNBgMCxJIIm+33XZuU2f//fffb27XwWFmPUIPwu/TzFTET30OFRxkzYJZo72aOObJWnZSzQyPI/w1mSps1NmN+QaFCXxNpRuzvySWNbkN68mfOqB+h68ATBsXDDvWWCrcUY8P7Q4CSXxckhnFtvbuUyI+h1UsDS6rRBDg90xgwGhy1+wiP8MOXcWIYBx4ACjLJ8s4BXsmfOpmviXHs4Vm1AiOyRgWq6Kt36DPT1UA5nrhCGCcOb6oc6K4n+69916nZgaFgybyUsgUGwwGgyF74HYSIPnzNKE+UqHymVRUZ3KlMp+sZYdktvZpxnmeY5jRZcUApTD7fgd+IokIihsEnug3EDhHvZ+o1OFvoDR/3XXXRWZXGXIHCywNSaF9mkqfJZCi8uj3TFB+940whoHFAQU2Xks/Zdgqlz9bSQ2OZiFzKTmeDXhUqLai+oowAdz/OGUMswUBIcdHZpHrxvFyTaM06BOcnnjiiTJ69Gi55557ZPvtt4/lQm0wGAyG+AAWjd+nSYWT3jr1Odjo8fd9CgJJmEOZqMwrddMfc8JaFVS8L/Qan+nosmLxOTg+fCr8Dc4/wSc+h8+kouiQyo/wleYZlUPVz3yOwsICS0MoYHyZmaNGn5EnBHpq8DH2d911l6PV9ujRI+tew6DkuKqQBY1+rno0WgPGDuPOAkTGUCmmpSgGAO2VpAAINuiTGdYsrwb7GH3+hiMA9RUa0gMPPBA562gwGAwGA2D9p09TfQ7+zTqjfZqsPcwjPPPMM6VPnz5Z9xric2i7iD9DulAtO/7oskxE84qF2ssx+qKO+BxBJpUKM6nfwTXA7ygmpfkFCRZYGjKmZtAg/OKLL7omaYISOPYElZphzGTOYRjJcc0wquR4sGci19kqDPvEiRMdXQaaRlsFs215LalIc36hIqVboP0Gfa7BhAkTnNordFn6I5C6Ro2s1BZBg8FgMBQOJJbHjx/vdCEQgiMgIRDBz1C/gz7NXA2+95VP27plB8FFqL35Hl1WyGtJop5zis+RbgwMVWm/yAAj7oADDnAj9PDNSCowo7LYNBxKGRZYGrLK8CHyg2EdMmSICzr8Pk3otPRpkmHUYDNXs60A2UTf6PuZLd0ylbsGHA8ZMQImAkqCpVKjWKCgR1BJzwZZ0ajKwLwfI8+C37NnT0dJQihhv/32k/vuuy9v+20wGAyGBQ+Mj0CshIQ2zCgqmlrV/PLLL10g5vdpwqDJFVK17Pg+B5XUTH0OfCqO4YcffnCtRm09uqwtwHlDaZ5rx7WKmqgnyESwhqLGFlts4SjUfN62224rzz//fN722xAeFlgasgJZJ4KuZIYU44ixV8lxqluop/o9E/Qp5spw+pLjuqnctW/4wwRPVEXJGJKdLMWMIeeFXkqoKFw/Av6o1wHVNRTYEEugn5IMomZbCTi5tgaDwWAw5ApUKqFNJus1JAmsfZokt2nPYG3zfQ7W81wpuGvLDv6C+hz4IQSXvs8Rpi9SR5exb4jmkbAvJRBqkHhmI2imJzaqz+ErzdNuo1VK/D78zUKroRqKOLAkUGFeC1RMshUjRoxw8/HSAfrEKaec4m5MskDnnnuuc4oNbQeye36fJoEJxtPPLiKvnCuqqcpd+4EmdM9gz4QfNLJQMEKEait8/VLMGHJOyPABaChRFzAWTuZD0U+LLPZpp51WcIEDAB0Gu0A2m0o5Q4ahSaXCf//7XznvvPMcjZtrfeWVV7rBwgaDweDDfI7iBNRV9CC4fgSa9GmSWPbnaTKiIVeJY3wO5kz6Pge/p2vZ8fUN4ji6LBeAUqxK+vgcBN6loDRvPkcJBZbPPPOMC0wwCP369WvVyJMhIQN01FFHyWGHHeZK6PSFjRw5Uvr27dum+25oSWVlvqFPZcEIQ6/VDCM9E1GNUDpoY7hmGMkSsqiosSfjSZDE/ZKtAFEcgVNEvygy7hi2qAsYM5aOOOIIlzUmY0hvSxyAIhzDg1GF455hThpGnP3USqoPZj316tXLDRpmvuuDDz7ojDz3I9feYDAYFOZzlAZob/nggw+a23XwO/AFNtxww+ZAk6AzXc9fLlt2CDhh97BfrekbFCvwGUiuIHgIOypquw3n64QTTnCJmjgpzZvPUWKBpQ9usNaMPKphGHQyJooBAwa4h5zho4Z4gKwUD6Xfp0lmJzjbKpdqoxh07gMky6Fwcj8RbPnZxThIjufiOOl9ZBGDChR1NiVmgkX40EMPdRk5xBNyufhmCww7CYmbbrqp+V6iSnD88cfLoEGD5ns9kuRUbp966qnmv+FQUDFnoTAYDIZkMJ+jdMC6RqXQT26jq4DegO9zZELbTMf4IbmNCA1BF+Czgz5H1AAsbmAN5lwywozzmYm+RpyV5s3nSI3ivnNDAvoljb0+yBqSQTTEBwR0GCA2mrMBRkl7JsgIHXzwwe7hpVKmhr979+4ZU0doxid4pZKJkaA66kuOw9v3Jcd1kHIxKcOS8YP6qlLtUWk/BKXQPVB7JdsGFSVOVB2uDxS1s846q/lv7B/PPM9+MvB3aGpBm8CQZYPBYMgG5nMUBwjoGK/FpjRlgj31ORhnQtV56aWXbvY32KgwZZpsJrCk1YbgkqAC4Tzf58DnwR9J17JTLO02nF98DtRto4Ag7bbbbnOtNtBfzznnnFgF2uZzpEd8rlQeAf8Zw+CD33G46bmDAmmIJ8hQMcKCDXDNdLbVo48+6owO18/vmdhggw1anTVFphJaKBVS7gUMvBougks2spS+5DiUGfovWQQwlL7R5/c40DN8sO8sYKjM0bfBFnUfeXYOOeQQt9iNGTNGNt54Y4kbqMKyWCd7xqnSRrEJ/N1gMBiygfkcxQvYPFSjtSJNew59mvgcVJv/9a9/uSCCipW27LAuhrmmjMdA8BC2D+/TBDVBJJsq2PpjTkh8+y07utGqE0efA7+KdXfZZZd1Ij1Rk9D4WUcffbQL3KjubbnllrE7TvM50mOBCCwNpQMCvu22285tWnGELgFtlgb9G264wQV+GHrNLrIAYIgV/D8BIkabzGM6WigGjQWDTakcfKefXcSQ5FJyPFcZNfoaOFb6gvzjD7tA0BdEf9A222wjjz/+eEn2fxgMBoPBkAokjQlu2JTBQzVO23WoahJokND2+zTpKVQQnNJ3S2AZZnQZQSSv0XnQfKeOOeEzUHQP0mfxOQrZssM+ot/w22+/ybrrrtvi+MOCogGsNNRd8euS9Soa4o8FIrDk4USUxQe/8yBa5rC4QRM8PX9s0AwIiOD1q9GnokkQSQCJ0ccY02ANp53BuplQWvlOglENSKFt+GNOyDBmKjmeC9ArysLHd7LARf1eAmdU1zhH1113neurjBP1NQgWMBbUZM+4LsxhbUKq1xsMBkNYmM9RuoDZRBDJRj+dqrpqn+YFF1zgks2M2yLIpLI4bNgwueiii6R///4ZUVr5TiizbOpzKH2WCh/MJNbtoM/RVi07BL34HNzb+FmtMcaKRWk+FcznSI8FIrDkRn/66adb/G3UqFHu74bSAoEjPZdsBEQAqsELL7wgV1xxhcuosRAQhPbs2bO5qklfZ6bBky/2k0xynECX31lgqPppnyYLTC4pHjoqBVEAFjWUX6N+PgsUGUMdDYNwUtzB4klVlgqr0pc4F/xOP2gycM1VqVFhNsFgMOQC5nMsOGCN1VYTVEIBFUxaR1D9hNIJCDj1HmBjbc20bxCfI9iyA8VafQ7aX+hzzHfLDt+L8CF+B7NFGZcS9fN9pfnnn38+Nkrz6WA+RwkGlmRqeHAUUAzGjx/veOs8ZDTUMviduTeA5muUm6he0S/20ksvyfDhw51qm6H0QUYIaWgqjFxz7hNmaJJdfOKJJ9z9gqHw+zSRH8+0WR7Diow4G8EdYH6Tjjkh8IOmyncGeyYyDW5ZVMgYQkeheht1VAoLBOeGZ2WPPfZwlOJiGrdCogD1OCjQHD9CTyysBMmABZ9rgfgQOPHEE6V3795yzTXXyE477eQyyuPGjXO0JoPBYPBhPochakWLETUEGygDE3DRpwmLiiQ3jCDWXNp0NNBk7Yo6U9r3OQga2VQ5tbWWHRLc9HVm6nPg0+DHsM6y71FbZVRpnueD88DYjTgpzbcG8zlKbNwI82y22mqr+f7ORb777ruduhfUBF7nv+fkk092jdM0FTOk1IYVLzigoZxG6WRGFAMcnG2FMdY+TQJNDB+GOFdQyXE1/PybRxHjHFVyHDoF9zUBNJXKqBQSFgieB5wiBv7uv//+sWuWDwMcOR1WjBgT1BquG6A/hsUd+6CAEs3Qch1WfNVVV5XksGKDwZAdzOcwRAW9kPgMyVpRWP8JOH2fg3Wcdcvv08xlj2GwZYeN/Qj6HGFaZ+ijZP85PtheUdttSICz3tJqE0el+bAwn6OEAstiAU663nTrrbee3HjjjS6zkQzcfJrpUMBTRx3M0LbgkSA7rQYf6XGa5WkoD862yuV3+pLjbAR8VA01u4jR96uoLArQR1iQ2Leg4lgYQGHBOWLRIYOGsIDBYDAYig/mcxQnVMFdRQjxOUhIEHz4Pgd001wlfflOKmy+zwHzSX0Of8yJfqffbkO7EdXRqPtDkp8qJT8ffPDBWCrNG7KDBZZ5AtRLSuEIoJDBoExOtoJAIFkWCiNPqZz/V/DAZhIsGPKTfdTZVmwolqESq3LjbAR3uWw49yXH2ZAcpzlejT3OA3RaejWiCkLw2D/88MPunhs4cKCbUVlMc7IMBoPBMA/mc5QWEOF74403mpPb77zzjksw+4Em6qu5nO9IMjvoc2jLDjRb/CDuEXyOTNpt6CmknxKlee5TU5ovTVhgmSdg2DfZZBNXKtdMDzOKUBEbNGhQUiNPUy8PsyH+INNHn6ZWNZHJJqj0Z1vR3J1LBUDoI9wfZDahogC+U8WAwkqOIyR05plnujmgQ4YMkT333LMoqa8Gg8FgSMB8jtIGiWZ68jS5TdCJT+D3aXL9c6mNoC07VCgR2QH4Csqg0p+t+Ry0G6GKe9tttxWF0rxhARTviTuYIYgSGA39Ch6ibbfd1iltpgJUyBVWWMEtCIjHIL281lprtdFeG6KAJvutt97abQADP2HChOZAk2wcwR/XkUBT+zRVLjwTkANCIMKfTUlGUbOLYSTHUcWlz4fsI/co1BqDwWAwFC/M5yh9wChCyZ6N68w1QzxH+zTvu+8+5x9AgdaKJj+zrUBDWUV0kOooAoj+mBO+j3sPESDf5/DHjajSPP2dxaI0b8gOVrHMA1DgQg2KjJIvJYxCHBLUVLqC4IGjj4+HlwzR1Vdf7bj2GA4a/w3FBR6r//3vfy36NKEc0cPoU1lY1MNUCzHiNMuTjWThTzafKig5zkZllX1g5AnV0yeffFKOPPJIN3qlrWZcGQwGgyF/MJ/DALRPU30OfAaSxxpk8nPVVVcN5XOQtCZZru02qVplgj4HgSetQkrdxeeAFYWwTTEpzRsyhwWWMTHyQVB5Qm1r3333ddLUhuIHVBLtmcD4k2Gm98Xv01x77bVb0ErISiJtrypiUJui0FbJJiJ7jiIhn8F7kfQm6wkNaosttsjT0RoMBoOhLWA+hyFVQpoEggaajDyB0eQnt6lw+qqu+Bw//PCDSzqgahpVMIj7iO+jqooAET2gMLyYT8kYHkZtGEobRoXN0wwjggPUOn3wOyMhwoAHfYMNNmgxO8tQ3IBGsttuu7lNex11ttVzzz3nBigD7dOkmkmWD0GG/fbbz9FNooLZVXwu6m2jR492+0BAy3daxdJgMBiKH+ZzGJKBiiHjLHSkBeI8uv5TnWbcBX9DOZhAE6VX2nigUBMEZjJi7dtvv5Xzzz/f3Y9Uv1dZZRVXweQ7fYqsoXRh3bN5AA47PXAvvvhiiywQv/vZxNaaphl4j/KooTRBnyOzjphrRGBJdhFjT+DJQO3DDjvMUVEeeOABN+vpqaeekl9//TXUZ3O/3XHHHW6B2Hvvvd3nUe2EzkLmEPEenbcUB/U7AmftDaWxHzpNOnDeyKL6GwuhwWAwLGgwn8MQBgR2JK2pZKs/QXJ7wIABjrrK2osQ4YgRI+TSSy91An/0WIYB5EdUiGFBMYOTz6H1h4QFgespp5zi/JE4wHyO/MIqlnkCDxHzAZnRw0OF9Df9bjo3iioU1BUCBoBiFg8j/Hd46syiIvNDcGFYMECGjyG70EgIKO+//37nFGjPxIUXXujEd1ZbbbUWVJaVVlqpBVWF+4eBw1BgHnvsMSftHWfVVww8ixdS5NBoeEaQJGfGVTocfvjh7rnxA3WDwWBYEGE+hyEqEHhCs4GEAjRqGFK77LKL+zdJbvpu8UXwMfyWHXwQ36eAfUWwSkA6dOjQ2CvNm8+RX1hgmSfss88+rqcOSgDzBgkYnn322WaFLpqsfbllqlXctLwW+gHZRx5uZiMaFixQsYS6AoUEYNQPOOAA92+UZrVPE7l4AkiUZtXgc+9ccsklrh8T+klYGlShQKDMc0G2VAclM9Sb42dRg8KbChj1uB+fwWAwtAXM5zBkClUPRshJ7yU2gLCT9mkOHz5cTj31VNczqT4HazSUWlp1ikFp3nyONgDiPYYFFzfddFPTCius0FRTU9P097//vWns2LFpXz98+PCm7t27u9evvfbaTSNHjmyzfTXMj1mzZjWNGTOm6dJLL23q27dvU3l5edMxxxzTVF9f31QMGDp0aFOnTp1a/K2urq6poqKi6dFHH035vt69ezd17ty5aYkllmhaa621mgYNGtQ0c+bMNthjg8FgMGQK8zmKG3Pnzm168803m6666qqmXXbZxa3Ve+yxh/t7McB8jvzDKpYLMP7zn/84+gzN2vTbQZ3p27evG4uBWmkQZDNRjINKs/POOzvawO677y7vvfeeUzM1tD0YIdKrVy+3nX322U4inGxisQwfJlsevNdQkUO5lv9LhYEDBzpxI7KLUHXoGeW+pSfEYDAYDPGD+Ryl0c8LhZrt9NNPd72J+CG+mn2cYT5HG6ANgldDTEG28Nhjj23+vaGhoWmZZZZpuvzyy5O+vn///k077bRTi79tuummTUceeWTe99VQXDjzzDMZY5R2mzhxoqu0rrbaavO9f8kll2z6v//7v9Df9+KLL7rP/PLLL3N8JAaDwWDIBcznMOQL5nPEB1axXEDBfEP48MwaUlDlUq59MvB3so0+yDYiEGMw+KAP46CDDkr7Gnox6Ff45ZdfWvy9vr7eqbZF6WVQhVuk8rU31WAwGAzxgPkchnzCfI74wALLBRTITCMvro39Cn5n9mEyQBNI9vp09AHDggnmZbK1Bpr/USTE4UA8AjAaBan8KONQxo8f736aVL7BYDDED+ZzGPIJ8znig+JoxDIYDCWJNdZYQ7bffnunTsg8rddff90p3TJXS9XZfvzxRzcPi/8HX331lVx88cVuYfjmm2/kiSeecFL69Jmqqp3BYDAYDAaDD/M58g8LLBdQdO7c2TVbT548ucXf+T0VHYC/R3m9wRAGDzzwgDPizNtE8rtnz55y++23N/8/c6ZokmdWlooHvPDCC7Lddtu590GBYW7Wk08+WcCjMBgMBkMqmM9hiAvM58gvymi0zPN3GGIKyv4MUmaGD4AKsPzyy7vszaBBg+Z7PXONeND8h4mhuWRsUHkzGAwGg8FgSAbzOQyG0of1WC7AoCn+n//8pxsSi7FH+nvmzJly8MEHu/+n1N+1a1cn9Q1OPPFE6d27t1xzzTWy0047ybBhw2TcuHEtMj0Gg8FgMBgMQZjPYTCUPiywXIBBNnDKlCly/vnnu2b49ddfX5599tnmZvnvvvuuxTxEMoXMkTr33HPdzMRu3bo5dTabJ2UwGAwGgyEdzOcwGEofRoU1FCWQhj7++OMdRYaFCL77DTfcIAsttFDK92y55ZYyZsyYFn878sgjjVJjMBgMBoMhJcznMBjCwQJLQ1Fihx12kEmTJsltt93mGq2h0myyySYuu5nOyK+22mpy0UUXNf+tQ4cOssgii7TRXhsMBoPBYCg2mM9hMISDUWENRYeJEyc6+sw777zjejUAYgCoe1199dXNktHJgFE3RTmDwWAwGAxhYD6HwRAeNm7EUHR48803pVOnTs0GHmy77baOnjJ27NhWZaaRPadH46yzzmqWkzYYDAaDwWAIwnwOgyE8rGJpKDrQ9L/UUku1+FtlZaUsvvji7v9SYeDAgbLCCiu47OKECRPkzDPPdLOKHn300TbYa4PBYDAYDMUG8zkMhvCwiqUhNmCOVVlZWdrt008/zfjzjzjiCOnbt6+ss846st9++8m9994rI0aMkK+++iqnx1GKuPTSS51CH7QeMrdhQPs26n9dunSR9u3buwzvF198kfd9NRgMBoOhNZjPEV+Yz1G8sIqlITY49dRT5aCDDkr7mpVXXtn1K/zyyy8t/l5fX+9U26L0MjCsGXz55ZeyyiqrZLjXCwZqa2tl7733ls0331yGDh0a6j1XXXWVDB48WO655x5ZaaWV5LzzznOL7CeffCLt2rXL+z4bDAaDwZAK5nPEF+ZzFC8ssDTEBksuuaTbWgOGZurUqfLuu+/KRhtt5P720ksvSWNjY7PhDoPx48e7n2S3DOlx4YUXup9333136Mwhw6+ZP7bbbru5v5GtZV4Zc8gGDBiQ1/01GAwGgyEdzOeIL8znKF4YFdZQdFhjjTVk++23l8MPP1zefvttef311+W4445zhkPV2X788UdZffXV3f8DqCcXX3yxWxi++eYbeeKJJ+TAAw+UXr16ybrrrlvgIyo9fP311673BCqKYtFFF3WLMEIIBoPBYDAUA8zniD/M54gPLLA0FCVQWsOIb7PNNk7yu2fPnnL77bc3/z9zpmiSVwW26upqeeGFF2S77bZz74MCw4Bjhh0bcg8VNCBb6IPf04kdGAwGg8EQN5jPEW+YzxEfGBXWUJRAjS3dYOIVV1zRUSMUyy23nIwZM0YK1YQ+cuRIR4NhsYFS0xrY93/9618yZMgQ9/oePXrILbfcIt26dcupcMGVV17Z6vwuFkWDwWAwGBZUmM+RPcznWDBggaXBsIA2oYcVLsgEKmgwefLkFv0k/L7++utn9JkGg8FgMBjSw3wO8zkKCQssDYYFtAk9rHBBJmBhwtC/+OKLzUZ9+vTpbpj00UcfnZfvNBgMBoNhQYf5HOZzFBLWY2kwxAxxbEL/7rvvHK2Gnw0NDe7fbDNmzGh+DfQVZnQB5n+ddNJJcskllzjRgg8//NAJFyB0sPvuuxfkGAwGg8FgMLSE+RyGXMIqlgZDzBDHJnSGDkORUWywwQbu5+jRo2XLLbd0/0a4YNq0ac2vOeOMM2TmzJluSDQ9G4gdPPvsszZPymAwGAyGmMB8DkMuYRVLgyHDJnQyZOm2Tz/9VEoFUGqgywQ3NfCA3/3+Cc7BRRdd5BamOXPmOIW81VZbrUBHYDAYDAZDccJ8DvM5igVWsTQYMoA1oRsMBoPBYGgLmM9hKBZYYGkwZABrQjcYDAaDwdAWMJ/DUCwwKqzBkGdYE7rBYDAYDIa2gPkchkLCKpYGQ55hTegGg8FgMBjaAuZzGAqJsia6Xw0Gg8FgMBgMBoPBYMgQRoU1GAwGg8FgMBgMBkNWsMDSYDAYDAaDwWAwGAxZwQJLg8FgMBgMBoPBYDBkBQssDQaDwWAwGAwGg8GQFSywNBgMBoPBYDAYDAZDVrDA0mAwGAwGg8FgMBgMWcECS4PBYDAYDAaDwWAwZAULLA0Gg8FgMBgMBoPBkBUssDQYDAaDwWAwGAwGQ1awwNJgMBgMBoPBYDAYDFnBAkuDwWAwGAwGg8FgMGQFCywNBoPBYDAYDAaDwZAVLLA0GAwGg8FgMBgMBkNWsMDSYDAYDAaDwWAwGAxZwQJLg8FgMBgMBoPBYDBkBQssDQaDwWAwGAwGg8GQFSywNBgMBoPBYDAYDAaDZIP/B7OxDLA0NcUGAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1280x960 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_circle = np.cos(angles)\n",
+    "y_circle = np.sin(angles)\n",
+    "z_circle = np.zeros_like(angles) # z=0 for the circle\n",
+    "\n",
+    "# Red ball position\n",
+    "red_ball_position = (x_circle[0], y_circle[0], z_circle[0])\n",
+    "\n",
+    "\n",
+    "def plot_kernel(ax, values, title):\n",
+    "    # Draw the unit circle\n",
+    "    ax.plot(x_circle, y_circle, z_circle, linestyle='dashed', color='gray')\n",
+    "    # Draw the red ball\n",
+    "    ax.scatter(*red_ball_position, color=\"r\", s=50)\n",
+    "    # Draw the vertical dashed line\n",
+    "    ax.plot([1, 1], [0, 0], np.linspace(0, values[0, 0], 2), linestyle='dashed', color='gray')\n",
+    "    # Plot the new function evaluated only on the unit circle\n",
+    "    ax.plot(x_circle, y_circle, values[0, :], color='b')\n",
+    "    # Set the viewing angle for better visualization\n",
+    "    ax.view_init(elev=20., azim=180+30)\n",
+    "    ax.set_title(title)\n",
+    "\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(figsize=(12.8, 9.6), nrows=2, ncols=2,\n",
+    "                               subplot_kw=dict(projection='3d',\n",
+    "                                               computed_zorder=False))\n",
+    "\n",
+    "plot_kernel(ax1, kernel_vals_32_1,  r'$k_{3/2, \\kappa}($base_point, other_points1$)$')\n",
+    "plot_kernel(ax2, kernel_vals_32_2,  r'$k_{3/2, \\kappa}($base_point, other_points2$)$')\n",
+    "plot_kernel(ax3, kernel_vals_inf_1, r'$k_{\\infty, \\kappa}($base_point, other_points1$)$')\n",
+    "plot_kernel(ax4, kernel_vals_inf_2, r'$k_{\\infty, \\kappa}($base_point, other_points2$)$')\n",
+    "\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08cac3cc-63c6-4057-a8ca-3c645dad2d9e",
+   "metadata": {},
+   "source": [
+    "## Feature Maps and Sampling\n",
+    "\n",
+    "Here we show how to get an approximate finite-dimensional feature map for heat and Matérn kernels on the sphere, i.e. such $\\phi$ that\n",
+    "$$\n",
+    "k(x, x') \\approx \\langle \\phi(x), \\phi(x') \\rangle_{\\mathbb{R}^M}.\n",
+    "$$\n",
+    "This might be useful for speeding up computations.\n",
+    "We showcase this below by showing how to efficiently sample the Gaussian process $\\mathrm{GP}(0, k)$.\n",
+    "\n",
+    "For a brief theoretical introduction into feature maps, see this [documentation page](https://gpflow.github.io/GeometricKernels/theory/feature_maps.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba0cb4cd-c7d3-40fd-862d-1a90ba3d5eef",
+   "metadata": {},
+   "source": [
+    "### Defining a Feature Map"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3131b86-24ee-4398-a88a-b02cf49c697f",
+   "metadata": {},
+   "source": [
+    "The simplest way to get an approximate finite-dimensional feature map is to use the `default_feature_map` function from `geometric_kernels.kernels`.\n",
+    "It has an optional keyword argument `num` which determines the number of features, the $M$ above.\n",
+    "Below we rely on the default value of `num`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "8b3ba4f0-5a1b-4ce8-8986-be6563bd8a40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from geometric_kernels.kernels import default_feature_map\n",
+    "\n",
+    "feature_map = default_feature_map(kernel=kernel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fed4a182-6dc0-4826-8cca-ba49c8fdaee3",
+   "metadata": {},
+   "source": [
+    "The resulting `feature_map` is a function that takes the array of inputs and parameters of the kernel.\n",
+    "There is also an optional parameter `normalize` that determines if $\\langle \\phi(x), \\phi(x) \\rangle_{\\mathbb{R}^M} \\approx 1$ or not.\n",
+    "For the (hyper)sphere, `normalize` follows the standard behavior of `MaternKarhunenLoeveKernel`, being `True` by default.\n",
+    "\n",
+    "`feature_map` outputs a tuple.\n",
+    "Its **second** element is $\\phi(x)$ evaluated at all inputs $x$.\n",
+    "Its first element is either `None` for determinstic feature maps, or contains the updated `key` for randomized feature maps which take `key` as a keyword argument.\n",
+    "For `default_feature_map` on a `Stiefel` space, the first element is `key` since the feature map is *random*.\n",
+    "\n",
+    "In the next cell, we evaluate the feature map at random points, using `params_32` as kernel parameters.\n",
+    "We check the basic property of the feature map: $k(x, x') \\approx \\langle \\phi(x), \\phi(x') \\rangle_{\\mathbb{R}^M}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "6602d412-e9b6-48db-802c-27b876a15be2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(10, 5, 5) (200, 5, 5)\n",
+      "(2000, 5, 5) (100, 5, 5)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Iskander\\Documents\\Projects\\GeometricKernels\\.venv\\lib\\site-packages\\lab\\numpy\\generic.py:86: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  return a.astype(dtype, copy=False)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xs (shape = (10, 5, 5)):\n",
+      "[[[-1.68504525e-01 -7.06026107e-01  4.12761964e-01 -4.02416798e-01\n",
+      "    3.75262099e-01]\n",
+      "  [-3.44559305e-01  3.82891026e-01  8.10993210e-01  2.75718333e-01\n",
+      "   -3.07044147e-02]\n",
+      "  [-7.14965754e-01  2.82130523e-01 -3.78687008e-01 -1.15451814e-01\n",
+      "    5.02487182e-01]\n",
+      "  [-1.78802389e-01  2.44485166e-01  5.70809634e-02 -7.89898247e-01\n",
+      "   -5.30150198e-01]\n",
+      "  [-5.56541811e-01 -4.64274671e-01 -1.58919752e-01  3.53230462e-01\n",
+      "   -5.69809614e-01]]\n",
+      "\n",
+      " [[ 1.44766060e-01  4.33768157e-01  7.16008021e-01 -3.81579879e-01\n",
+      "    3.64166561e-01]\n",
+      "  [ 4.76064461e-01 -7.39636562e-01  4.15299651e-01  2.11051929e-01\n",
+      "    9.63517929e-02]\n",
+      "  [-7.21600150e-01 -1.65417307e-01  5.30096035e-01  1.75731170e-01\n",
+      "   -3.74228657e-01]\n",
+      "  [ 3.16982053e-01  4.85213609e-01  1.66490400e-01  7.63442875e-01\n",
+      "   -2.31356987e-01]\n",
+      "  [ 3.62244418e-01  4.45848561e-02  7.83444645e-02 -4.42862747e-01\n",
+      "   -8.15184583e-01]]\n",
+      "\n",
+      " [[ 5.55616377e-01 -3.52578323e-01 -1.42262987e-01  2.76576949e-01\n",
+      "   -6.85744414e-01]\n",
+      "  [-8.30863564e-03 -4.93089358e-01  7.44523361e-01 -4.41716810e-01\n",
+      "   -8.58200216e-02]\n",
+      "  [-6.64642273e-01 -5.99484224e-01 -4.22213645e-01 -2.05709699e-03\n",
+      "   -1.43529511e-01]\n",
+      "  [-2.29826194e-01  4.73932665e-01 -9.24177998e-02 -5.57053234e-01\n",
+      "   -6.35388381e-01]\n",
+      "  [-4.43454595e-01  2.20358584e-01  4.88508747e-01  6.46590221e-01\n",
+      "   -3.13161950e-01]]\n",
+      "\n",
+      " [[-2.65366641e-02  4.74741540e-01 -7.43228552e-01  1.79946969e-01\n",
+      "   -4.34909971e-01]\n",
+      "  [ 8.32526574e-01  2.84360416e-02 -3.03469674e-01 -1.40195096e-01\n",
+      "    4.40842815e-01]\n",
+      "  [ 2.13244776e-02 -8.64126215e-01 -4.03723877e-01 -8.16158450e-02\n",
+      "   -2.88404292e-01]\n",
+      "  [-5.23511589e-01  4.02248417e-02 -4.00240354e-01 -5.14943196e-01\n",
+      "    5.46771198e-01]\n",
+      "  [ 1.77978066e-01  1.59623644e-01  1.79811475e-01 -8.22275369e-01\n",
+      "   -4.84123075e-01]]\n",
+      "\n",
+      " [[ 2.65759578e-01  7.48397979e-01  2.03724859e-01 -3.22015095e-01\n",
+      "    4.73365368e-01]\n",
+      "  [ 8.78333338e-01 -2.07538815e-01  8.92401005e-02  4.13977448e-01\n",
+      "    7.82116640e-02]\n",
+      "  [-1.01610144e-01 -4.66456000e-01  7.93919835e-01 -2.43645306e-01\n",
+      "    2.87093085e-01]\n",
+      "  [ 2.07327168e-02  3.79658473e-01  5.41463029e-01  1.67698730e-01\n",
+      "   -7.30838233e-01]\n",
+      "  [ 3.83598654e-01 -1.87366883e-01 -1.64442909e-01 -7.98400308e-01\n",
+      "   -3.91485890e-01]]\n",
+      "\n",
+      " [[-6.09749906e-02  2.96035016e-03  9.21922596e-01  3.54972979e-01\n",
+      "    1.42569973e-01]\n",
+      "  [-1.92159119e-01 -6.61883174e-01 -3.55375188e-02 -2.12978038e-01\n",
+      "    6.91637895e-01]\n",
+      "  [-6.32788482e-01 -4.39497973e-01  9.44472837e-02 -1.00330997e-01\n",
+      "   -6.22441700e-01]\n",
+      "  [-4.80854480e-01  5.58384660e-01  1.69752786e-01 -6.16365431e-01\n",
+      "    2.19688845e-01]\n",
+      "  [-5.72463649e-01  2.38642000e-01 -3.33255928e-01  6.62315549e-01\n",
+      "    2.56152233e-01]]\n",
+      "\n",
+      " [[ 7.05575503e-01  6.01217644e-01 -2.19399205e-01  1.84661433e-02\n",
+      "   -3.03683297e-01]\n",
+      "  [ 3.83647954e-01 -5.09819582e-01 -2.50766976e-01  7.20113829e-01\n",
+      "    1.07005788e-01]\n",
+      "  [-1.13914722e-04  3.97744308e-01 -1.48469416e-01  9.61722376e-02\n",
+      "    9.00281726e-01]\n",
+      "  [-5.94636975e-01  3.99784256e-01 -3.68593815e-01  5.14941606e-01\n",
+      "   -2.92494713e-01]\n",
+      "  [ 3.72063412e-02  2.46157170e-01  8.55029441e-01  4.54621950e-01\n",
+      "   -1.63056331e-02]]\n",
+      "\n",
+      " [[ 4.55443668e-01 -2.19174609e-01  5.51040775e-01  6.56555233e-01\n",
+      "    9.91102784e-02]\n",
+      "  [-7.79154577e-01 -2.57203278e-01  5.39661678e-01  2.97916410e-02\n",
+      "   -1.86124555e-01]\n",
+      "  [ 1.36454868e-01 -6.20318189e-01  1.28027385e-01 -4.96399446e-01\n",
+      "    5.77738688e-01]\n",
+      "  [ 4.08312142e-01 -6.02118901e-02  3.57010521e-01 -5.01651655e-01\n",
+      "   -6.71226361e-01]\n",
+      "  [-1.22668002e-02 -7.05257993e-01 -5.11147547e-01  2.64539016e-01\n",
+      "   -4.13772864e-01]]\n",
+      "\n",
+      " [[-2.19847909e-02  1.18523304e-01  9.05751018e-02 -4.53272181e-02\n",
+      "    9.87527463e-01]\n",
+      "  [-7.06395415e-01 -4.19322036e-01 -1.79687352e-01  5.35872945e-01\n",
+      "    7.56782069e-02]\n",
+      "  [ 3.32420277e-01 -8.68219970e-01 -1.61870273e-01 -3.11301214e-01\n",
+      "    1.12162438e-01]\n",
+      "  [-4.01393194e-01 -1.76564812e-01  8.08150000e-01 -3.85065000e-01\n",
+      "   -7.95417821e-02]\n",
+      "  [ 4.78437509e-01 -1.58556641e-01  5.29339399e-01  6.82350445e-01\n",
+      "    1.24503707e-02]]\n",
+      "\n",
+      " [[ 5.13857850e-01  4.60721426e-01  4.11443408e-01 -2.34197945e-01\n",
+      "    5.47313002e-01]\n",
+      "  [ 2.24390944e-01 -3.33277125e-01  7.78780176e-01  1.07398313e-01\n",
+      "   -4.69619104e-01]\n",
+      "  [-3.15917675e-01 -6.17004012e-01  2.26048673e-01  7.89272359e-02\n",
+      "    6.79834216e-01]\n",
+      "  [-4.47810444e-01  4.55389198e-02  1.89402953e-01 -8.62979901e-01\n",
+      "   -1.29553948e-01]\n",
+      "  [ 6.20693679e-01 -5.42120265e-01 -3.70464949e-01 -4.27379349e-01\n",
+      "   -3.07829372e-02]]]\n",
+      "\n",
+      "emedding (shape = (10, 4000)):\n",
+      "[[ 0.01012604  0.04832249  0.03225227 ...  0.00097565  0.00939747\n",
+      "   0.00236858]\n",
+      " [ 0.01030492  0.00298575 -0.01061714 ...  0.00304948 -0.00614895\n",
+      "   0.007134  ]\n",
+      " [ 0.0104957   0.00262354  0.00435389 ... -0.00494006 -0.01634618\n",
+      "   0.00388014]\n",
+      " ...\n",
+      " [ 0.01001997  0.01508339 -0.00997905 ...  0.00358436  0.00184751\n",
+      "  -0.01379119]\n",
+      " [ 0.01032481  0.01244233  0.00572501 ...  0.00356898 -0.0096881\n",
+      "   0.00485596]\n",
+      " [ 0.01049497  0.01700098 -0.00029208 ...  0.01397857 -0.00642329\n",
+      "   0.01760543]]\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(10, 5, 5) (200, 5, 5)\n",
+      "(2000, 5, 5) (100, 5, 5)\n",
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(10, 5, 5) (200, 5, 5)\n",
+      "(2000, 5, 5) (100, 5, 5)\n",
+      "\n",
+      "||k(xs, xs) - phi(xs) * phi(xs)^T|| = 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# xs are random points from above\n",
+    "key, embedding = feature_map(xs, params_32, key=key)\n",
+    "\n",
+    "print('xs (shape = %s):\\n%s' % (xs.shape, xs))\n",
+    "print('')\n",
+    "print('emedding (shape = %s):\\n%s' % (embedding.shape, embedding))\n",
+    "\n",
+    "kernel_mat_32  = kernel.K(params_32,  xs, xs)\n",
+    "kernel_mat_32_alt = np.matmul(embedding, embedding.T)\n",
+    "\n",
+    "print('')\n",
+    "print('||k(xs, xs) - phi(xs) * phi(xs)^T|| =', np.linalg.norm(kernel_mat_32 - kernel_mat_32_alt))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e10e6dd4-31a9-4a1f-9447-ddeff9259845",
+   "metadata": {},
+   "source": [
+    "**Unfortunately:** $k(x, x') \\approx \\langle \\phi(x), \\phi(x') \\rangle_{\\mathbb{R}^M}$ does not hold, which is either caused by a bug or by the insufficiency of the default approximation order in this case. **TODO:** investigate this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43728ac5-8277-4436-86c7-e6e97e434ec3",
+   "metadata": {},
+   "source": [
+    "### Efficient Sampling using Feature Maps\n",
+    "\n",
+    "GeometricKernels provides a simple tool to efficiently sample (without incurring cubic costs) the Gaussian process $f \\sim \\mathrm{GP}(0, k)$, based on an approximate finite-dimensional feature map $\\phi$.\n",
+    "The underlying machinery is briefly discussed in this [documentation page](https://gpflow.github.io/GeometricKernels/theory/feature_maps.html).\n",
+    "\n",
+    "The function `sampler` from `geometric_kernels.sampling` takes in a feature map and, optionally, the keyword argument `s` that specifies the number of samples to generate.\n",
+    "It returns a function we name `sample_paths`.\n",
+    "Since we are going to compute each of the two samples at two different sets of inputs, `other_points1` and `other_points2`, we make sure the randomness if fixed by using the `make_deterministic` function.\n",
+    "\n",
+    "`sample_paths` operates much like `feature_map` above: it takes in the points where to evaluate the samples and kernel parameters.\n",
+    "Additionally, it takes in the keyword argument `key` that specifies randomness in the JAX style.\n",
+    "**However**, in our specific case, this keyword argument is not needed as it is automatically supplied by the `make_deterministic` wrapper.\n",
+    "`sample_paths` returns a tuple.\n",
+    "Its first element is the updated `key`.\n",
+    "Its second element is an array containing the value of samples evaluated at the input points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "78922dda-ae2d-4bc1-b28a-b4dc9424fd07",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(10, 5, 5) (200, 5, 5)\n",
+      "(2000, 5, 5) (100, 5, 5)\n",
+      "Two samples evaluated at the xs are:\n",
+      "[[ 1.34908228e-01  1.54170272e-01]\n",
+      " [-1.17429825e-01  1.87486894e-01]\n",
+      " [-9.38074768e-02  4.45006898e-01]\n",
+      " [-3.51751700e-02  2.80236131e-01]\n",
+      " [ 1.60102498e-01 -2.90260934e-01]\n",
+      " [-1.09198069e-01  1.38012727e-01]\n",
+      " [-8.07935483e-02 -1.87024650e-01]\n",
+      " [-2.24546440e-02  2.97882519e-01]\n",
+      " [-2.85165356e-01  1.76208205e-01]\n",
+      " [ 2.94772105e-01  1.16215544e-04]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from geometric_kernels.sampling import sampler\n",
+    "from geometric_kernels.utils.utils import make_deterministic\n",
+    "\n",
+    "# introduce random state for reproducibility (optional)\n",
+    "# `key` is jax's terminology\n",
+    "key = np.random.RandomState(seed=1234)\n",
+    "\n",
+    "sample_paths = make_deterministic(sampler(feature_map, s=2), key)\n",
+    "\n",
+    "# new random state is returned along with the samples\n",
+    "key, samples = sample_paths(xs, params_32)\n",
+    "\n",
+    "print('Two samples evaluated at the xs are:')\n",
+    "print(samples)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dadb7a46-427a-475c-bc6e-0891c8d87c7c",
+   "metadata": {},
+   "source": [
+    "#### Visualizing Samples\n",
+    "Here we visualize samples as functions on the sphere."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "baf674e9-910e-494b-8abb-0069c7df31c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered out 0 eigenspaces of dimension 0.\n",
+      "(128, 5, 5) (200, 5, 5)\n",
+      "(25600, 5, 5) (100, 5, 5)\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[21], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m key, samples_other_points1 \u001b[38;5;241m=\u001b[39m \u001b[43msample_paths\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother_points1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams_32\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m key, samples_other_points2 \u001b[38;5;241m=\u001b[39m sample_paths(other_points2, params_32)\n\u001b[0;32m      4\u001b[0m sample1_other_points1 \u001b[38;5;241m=\u001b[39m samples_other_points1[:, \u001b[38;5;241m0\u001b[39m]\n",
+      "File \u001b[1;32mc:\\Users\\Iskander\\Documents\\Projects\\GeometricKernels\\notebooks\\..\\geometric_kernels\\utils\\utils.py:107\u001b[0m, in \u001b[0;36mmake_deterministic.<locals>.deterministic_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    105\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    106\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown key_argtype \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m key_argtype)\n\u001b[1;32m--> 107\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m f(\u001b[38;5;241m*\u001b[39mnew_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\Iskander\\Documents\\Projects\\GeometricKernels\\notebooks\\..\\geometric_kernels\\sampling\\samplers.py:80\u001b[0m, in \u001b[0;36msample_at\u001b[1;34m(feature_map, s, X, params, key, normalize, hodge_type)\u001b[0m\n\u001b[0;32m     77\u001b[0m     key \u001b[38;5;241m=\u001b[39m B\u001b[38;5;241m.\u001b[39mglobal_random_state(B\u001b[38;5;241m.\u001b[39mdtype(X))\n\u001b[0;32m     79\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hodge_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 80\u001b[0m     _context, features \u001b[38;5;241m=\u001b[39m \u001b[43mfeature_map\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     81\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnormalize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnormalize\u001b[49m\n\u001b[0;32m     82\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# [N, M]\u001b[39;00m\n\u001b[0;32m     83\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     84\u001b[0m     _context, features \u001b[38;5;241m=\u001b[39m feature_map(\n\u001b[0;32m     85\u001b[0m         X, params, key\u001b[38;5;241m=\u001b[39mkey, normalize\u001b[38;5;241m=\u001b[39mnormalize, hodge_type\u001b[38;5;241m=\u001b[39mhodge_type\n\u001b[0;32m     86\u001b[0m     )\n",
+      "File \u001b[1;32mc:\\Users\\Iskander\\Documents\\Projects\\GeometricKernels\\notebooks\\..\\geometric_kernels\\utils\\utils.py:107\u001b[0m, in \u001b[0;36mmake_deterministic.<locals>.deterministic_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    105\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    106\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown key_argtype \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m key_argtype)\n\u001b[1;32m--> 107\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m f(\u001b[38;5;241m*\u001b[39mnew_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\Iskander\\Documents\\Projects\\GeometricKernels\\notebooks\\..\\geometric_kernels\\feature_maps\\random_phase.py:114\u001b[0m, in \u001b[0;36mRandomPhaseFeatureMapCompact.__call__\u001b[1;34m(self, X, params, key, normalize, **kwargs)\u001b[0m\n\u001b[0;32m    110\u001b[0m weights \u001b[38;5;241m=\u001b[39m B\u001b[38;5;241m.\u001b[39mpower(spectrum, \u001b[38;5;241m0.5\u001b[39m)  \u001b[38;5;66;03m# [L, 1]\u001b[39;00m\n\u001b[0;32m    112\u001b[0m random_phases_b \u001b[38;5;241m=\u001b[39m B\u001b[38;5;241m.\u001b[39mcast(dtype, from_numpy(X, random_phases))\n\u001b[1;32m--> 114\u001b[0m phi_product \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meigenfunctions\u001b[38;5;241m.\u001b[39mphi_product(\n\u001b[0;32m    115\u001b[0m     X, random_phases_b, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams\n\u001b[0;32m    116\u001b[0m )  \u001b[38;5;66;03m# [N, O, L]\u001b[39;00m\n\u001b[0;32m    118\u001b[0m embedding \u001b[38;5;241m=\u001b[39m B\u001b[38;5;241m.\u001b[39mcast(dtype, phi_product)  \u001b[38;5;66;03m# [N, O, L]\u001b[39;00m\n\u001b[0;32m    119\u001b[0m weights_t \u001b[38;5;241m=\u001b[39m B\u001b[38;5;241m.\u001b[39mcast(dtype, B\u001b[38;5;241m.\u001b[39mtranspose(weights))\n",
+      "File \u001b[1;32mc:\\Users\\Iskander\\Documents\\Projects\\GeometricKernels\\notebooks\\..\\geometric_kernels\\spaces\\eigenfunctions.py:236\u001b[0m, in \u001b[0;36mEigenfunctionsWithAdditionTheorem.phi_product\u001b[1;34m(self, X, X2, **kwargs)\u001b[0m\n\u001b[0;32m    233\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mphi_product\u001b[39m(\n\u001b[0;32m    234\u001b[0m     \u001b[38;5;28mself\u001b[39m, X: B\u001b[38;5;241m.\u001b[39mNumeric, X2: Optional[B\u001b[38;5;241m.\u001b[39mNumeric] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m    235\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m B\u001b[38;5;241m.\u001b[39mNumeric:\n\u001b[1;32m--> 236\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_addition_theorem(X, X2, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\Iskander\\Documents\\Projects\\GeometricKernels\\notebooks\\..\\geometric_kernels\\spaces\\homogeneous_spaces.py:172\u001b[0m, in \u001b[0;36mAveragingAdditionTheorem._addition_theorem\u001b[1;34m(self, X, X2, **kwargs)\u001b[0m\n\u001b[0;32m    170\u001b[0m \u001b[38;5;66;03m# [N * N2 * samples_H, T]\u001b[39;00m\n\u001b[0;32m    171\u001b[0m torus_repr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mG_torus_representative(diff_h)\n\u001b[1;32m--> 172\u001b[0m values \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m    173\u001b[0m     (degree \u001b[38;5;241m*\u001b[39m chi(torus_repr)[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m])\u001b[38;5;241m.\u001b[39mreshape(\n\u001b[0;32m    174\u001b[0m         X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], X2\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    175\u001b[0m     )  \u001b[38;5;66;03m# [N1, N2, 1]\u001b[39;00m\n\u001b[0;32m    176\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m degree, chi \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mG_dimensions, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_characters)\n\u001b[0;32m    177\u001b[0m ]\n\u001b[0;32m    179\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m B\u001b[38;5;241m.\u001b[39mconcat(\u001b[38;5;241m*\u001b[39mvalues, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\Iskander\\Documents\\Projects\\GeometricKernels\\notebooks\\..\\geometric_kernels\\spaces\\homogeneous_spaces.py:173\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    170\u001b[0m \u001b[38;5;66;03m# [N * N2 * samples_H, T]\u001b[39;00m\n\u001b[0;32m    171\u001b[0m torus_repr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mG_torus_representative(diff_h)\n\u001b[0;32m    172\u001b[0m values \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m--> 173\u001b[0m     (degree \u001b[38;5;241m*\u001b[39m \u001b[43mchi\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtorus_repr\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m])\u001b[38;5;241m.\u001b[39mreshape(\n\u001b[0;32m    174\u001b[0m         X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], X2\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    175\u001b[0m     )  \u001b[38;5;66;03m# [N1, N2, 1]\u001b[39;00m\n\u001b[0;32m    176\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m degree, chi \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mG_dimensions, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_characters)\n\u001b[0;32m    177\u001b[0m ]\n\u001b[0;32m    179\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m B\u001b[38;5;241m.\u001b[39mconcat(\u001b[38;5;241m*\u001b[39mvalues, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\Iskander\\Documents\\Projects\\GeometricKernels\\notebooks\\..\\geometric_kernels\\spaces\\homogeneous_spaces.py:248\u001b[0m, in \u001b[0;36mAveragedLieGroupCharacter.__call__\u001b[1;34m(self, gammas_x_h)\u001b[0m\n\u001b[0;32m    240\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, gammas_x_h):\n\u001b[0;32m    241\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    242\u001b[0m \u001b[38;5;124;03m    Compute characters from the torus embedding and then averages w.r.t. H.\u001b[39;00m\n\u001b[0;32m    243\u001b[0m \n\u001b[0;32m    244\u001b[0m \u001b[38;5;124;03m    :param gammas_h1_x_h2:\u001b[39;00m\n\u001b[0;32m    245\u001b[0m \u001b[38;5;124;03m        [average_order*n*average_order, T]\u001b[39;00m\n\u001b[0;32m    246\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m    247\u001b[0m     character_x_h \u001b[38;5;241m=\u001b[39m B\u001b[38;5;241m.\u001b[39mreshape(\n\u001b[1;32m--> 248\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcharacter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgammas_x_h\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maverage_order\n\u001b[0;32m    249\u001b[0m     )\n\u001b[0;32m    250\u001b[0m     avg_character \u001b[38;5;241m=\u001b[39m einsum(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgv->g\u001b[39m\u001b[38;5;124m\"\u001b[39m, character_x_h) \u001b[38;5;241m/\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maverage_order)\n\u001b[0;32m    251\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m avg_character\n",
+      "File \u001b[1;32mc:\\Users\\Iskander\\Documents\\Projects\\GeometricKernels\\notebooks\\..\\geometric_kernels\\spaces\\so.py:231\u001b[0m, in \u001b[0;36mSOCharacter.__call__\u001b[1;34m(self, gammas)\u001b[0m\n\u001b[0;32m    229\u001b[0m char_val \u001b[38;5;241m=\u001b[39m B\u001b[38;5;241m.\u001b[39mzeros(B\u001b[38;5;241m.\u001b[39mdtype(gammas), \u001b[38;5;241m*\u001b[39mgammas\u001b[38;5;241m.\u001b[39mshape[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m    230\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m coeff, monom \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoeffs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmonoms):\n\u001b[1;32m--> 231\u001b[0m     char_val \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m coeff \u001b[38;5;241m*\u001b[39m \u001b[43mB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprod\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    232\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgammas\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgammas\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgammas\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmonom\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\n\u001b[0;32m    233\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    234\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m char_val\n",
+      "File \u001b[1;32mc:\\Users\\Iskander\\Documents\\Projects\\GeometricKernels\\.venv\\lib\\site-packages\\plum\\function.py:380\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kw_args)\u001b[0m\n\u001b[0;32m    377\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ex \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    378\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m method, return_type\n\u001b[1;32m--> 380\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw_args):\n\u001b[0;32m    381\u001b[0m     __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    382\u001b[0m     method, return_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_resolve_method_with_cache(args\u001b[38;5;241m=\u001b[39margs)\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "key, samples_other_points1 = sample_paths(other_points1, params_32)\n",
+    "key, samples_other_points2 = sample_paths(other_points2, params_32)\n",
+    "\n",
+    "sample1_other_points1 = samples_other_points1[:, 0]\n",
+    "sample2_other_points1 = samples_other_points1[:, 1]\n",
+    "sample1_other_points2 = samples_other_points2[:, 0]\n",
+    "sample2_other_points2 = samples_other_points2[:, 1]\n",
+    "\n",
+    "x_circle = np.cos(angles)\n",
+    "y_circle = np.sin(angles)\n",
+    "z_circle = np.zeros_like(angles) # z=0 for the circle\n",
+    "\n",
+    "def plot_sample(ax, values, title):\n",
+    "    # Draw the unit circle\n",
+    "    ax.plot(x_circle, y_circle, z_circle, linestyle='dashed', color='gray')\n",
+    "    # Plot the new function evaluated only on the unit circle\n",
+    "    ax.plot(x_circle, y_circle, values, color='b')\n",
+    "    # Set the viewing angle for better visualization\n",
+    "    ax.view_init(elev=20., azim=180+30)\n",
+    "    ax.set_title(title)\n",
+    "\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(figsize=(12.8, 9.6), nrows=2, ncols=2,\n",
+    "                               subplot_kw=dict(projection='3d',\n",
+    "                                               computed_zorder=False))\n",
+    "\n",
+    "plot_sample(ax1, samples_other_points1[:, 0], r'Sample #1: $f(\\cdot)$ for $\\cdot$ in other_points1, $f \\sim \\mathrm{GP}(0, k_{3/2, \\kappa})$')\n",
+    "plot_sample(ax2, samples_other_points2[:, 0], r'Sample #1: $f(\\cdot)$ for $\\cdot$ in other_points2, $f \\sim \\mathrm{GP}(0, k_{3/2, \\kappa})$')\n",
+    "plot_sample(ax3, samples_other_points1[:, 1], r'Sample #2: $f(\\cdot)$ for $\\cdot$ in other_points1, $f \\sim \\mathrm{GP}(0, k_{3/2, \\kappa})$')\n",
+    "plot_sample(ax4, samples_other_points2[:, 1], r'Sample #2: $f(\\cdot)$ for $\\cdot$ in other_points2, $f \\sim \\mathrm{GP}(0, k_{3/2, \\kappa})$')\n",
+    "\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6686d052-4c7c-4a20-ab6d-46e353e13b74",
+   "metadata": {},
+   "source": [
+    "## Comparing Kernels on $\\mathrm{V}(1, n)$ to the Kernels on $\\mathbb{S}_{n-1}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e47c9e6-d5dc-4648-baaa-9edf9965bd31",
+   "metadata": {},
+   "source": [
+    "Here we show that—up to approximation error—the kernels on $\\mathrm{V}(1, n)$ coincide with the kernels on $\\mathbb{S}_{n-1}$, as they are supposed to since $\\mathrm{V}(1, n) = \\mathbb{S}_{n-1}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5cd1a39d-e0e7-4f3f-9483-bb42eaa3f084",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from geometric_kernels.spaces import Hypersphere\n",
+    "\n",
+    "n = 5\n",
+    "\n",
+    "key = np.random.RandomState(1234)\n",
+    "\n",
+    "key, stiefel_1n = Stiefel(m=1, n=n, key=key)\n",
+    "hypershpere = Hypersphere(n-1)\n",
+    "\n",
+    "kernel_stiefel_1n = MaternGeometricKernel(stiefel_1n)\n",
+    "kernel_hypershpere = MaternGeometricKernel(hypershpere, num=8)\n",
+    "\n",
+    "params_32  = {\"lengthscale\": np.array([0.125]), \"nu\": np.array([3/2])}\n",
+    "params_inf = {\"lengthscale\": np.array([0.125]), \"nu\": np.array([np.inf])}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a2b9145-207b-4364-9dda-ea68538e950e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "key = np.random.RandomState(1234)\n",
+    "\n",
+    "key, xs_hypersphere = hypershpere.random(key, 10)\n",
+    "xs_stiefel = xs_hypersphere[..., np.newaxis]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c499b34-4321-47f9-95e5-b5cb87789970",
+   "metadata": {},
+   "source": [
+    "**Note:** the elements of `hypershpere` and the elements of `stiefel_1n` have different shapes: the former are $n$-dim vectors while the latter are $n \\times 1$-dim matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77539ddb-aa50-4a51-bdc9-73e3261bdec5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "kernel_stiefel_1n_mat_32   = kernel_stiefel_1n.K(params_32,  xs_stiefel, xs_stiefel)\n",
+    "kernel_stiefel_1n_mat_inf  = kernel_stiefel_1n.K(params_inf, xs_stiefel, xs_stiefel)\n",
+    "\n",
+    "kernel_hypershpere_mat_32  = kernel_hypershpere.K(params_32,  xs_hypersphere, xs_hypersphere)\n",
+    "kernel_hypershpere_mat_inf = kernel_hypershpere.K(params_inf, xs_hypersphere, xs_hypersphere)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66485ad4-a59a-49bc-be9e-77c72cb01572",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||kernel_stiefel_1n_mat_inf(xs, xs) - kernel_hypershpere_mat_inf(xs, xs)|| = 109.61672377495772\n",
+      "||kernel_stiefel_1n_mat_inf(xs, xs) - kernel_hypershpere_mat_inf(xs, xs)|| = 124.4607617407797\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('||kernel_stiefel_1n_mat_inf(xs, xs) - kernel_hypershpere_mat_inf(xs, xs)|| =', np.linalg.norm(kernel_stiefel_1n_mat_32 - kernel_hypershpere_mat_32))\n",
+    "print('||kernel_stiefel_1n_mat_inf(xs, xs) - kernel_hypershpere_mat_inf(xs, xs)|| =', np.linalg.norm(kernel_stiefel_1n_mat_inf - kernel_hypershpere_mat_inf))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "860fdd31-9a45-463d-8be9-56e6c47b5268",
+   "metadata": {},
+   "source": [
+    "**Unfortunately:** $k^{\\mathrm{V}(1, n)}(x, x') \\approx k^{\\mathbb{S}_{n-1}}(x, x')$ does not hold, which is either caused by a bug or by the insufficiency of the default approximation order in this case. **TODO:** investigate this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "542e43c7",
+   "metadata": {},
+   "source": [
+    "## Citation\n",
+    "\n",
+    "If you are using Lie groups and GeometricKernels, please consider citing\n",
+    "\n",
+    "```\n",
+    "@article{azangulov2022,\n",
+    "    title={Stationary kernels and Gaussian processes on Lie groups and their homogeneous spaces I: the compact case},\n",
+    "    author={Azangulov, Iskander and Smolensky, Andrei and Terenin, Alexander and Borovitskiy, Viacheslav},\n",
+    "    journal={arXiv preprint arXiv:2208.14960},\n",
+    "    year={2022}\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "558cae08",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/compute_grassmannian_zsf.py b/scripts/compute_grassmannian_zsf.py
new file mode 100644
index 00000000..0d010db0
--- /dev/null
+++ b/scripts/compute_grassmannian_zsf.py
@@ -0,0 +1,533 @@
+"""
+Standalone script to precompute Zonal Spherical Functions (ZSFs)
+for :class:`~.spaces.Grassmannian`.
+
+This script is based on
+"GENERALIZED JACOBI POLYNOMIALS AS SPHERICAL FUNCTIONS OF THE GRASSMANN MANIFOLD"
+by James and Constantine, 1972.
+Edit `recalculate`, `storage_file_name`, `order`, and `groups` variables below
+in the code and run the script as.
+
+.. code-block:: bash
+
+    python compute_grassmannian_zsf.py
+"""
+
+import json
+import sys
+from collections import defaultdict
+from functools import reduce
+
+import sympy
+from beartype.typing import Union
+from jackpy.jack import ZonalPol
+from sympy import Poly, Rational
+from sympy.functions.special.gamma_functions import RisingFactorial
+from tqdm import tqdm
+
+from geometric_kernels.spaces.grassmannian import (
+    Grassmannian,
+    GrassmannianEigenfunctions,
+)
+from geometric_kernels.utils.utils import get_resource_file_path
+
+# set to True to recalculate all characters, set to False to add to the already existing without recalculating
+recalculate = True
+
+storage_file_name = "../spaces/precomputed_grassmanian_zsf.json"
+
+# the number of representations to be calculated for each group
+order = 25
+
+# We compute ZSFs for all pairs (n, m) with m < n and n < max_n.
+max_n = 10
+
+
+def is_sub_partition(sigma, kappa):
+    """
+    Check if sigma is a sub-partition of kappa.
+    param sigma: tuple of integers representing the partition sigma
+    param kappa: tuple of integers representing the partition kappa
+    return: True if sigma is a sub-partition of kappa, False otherwise
+    """
+    len_sigma = len(sigma)
+    len_kappa = len(kappa)
+    max_len = max(len_sigma, len_kappa)
+
+    for i in range(max_len):
+        s_i = sigma[i] if i < len_sigma else 0
+        k_i = kappa[i] if i < len_kappa else 0
+        if s_i > k_i:
+            return False
+
+    return True
+
+
+def hypergeometric_coeff(a, sigma, rank):
+    """
+    Calculate the hypergeometric coefficient
+    Eq. (10.7) from James and Constantine, 1972.
+
+    param a: sympy.Rational
+    param sigma: tuple of integers representing the partition sigma
+    param rank: integer representing the rank of the partition
+    """
+    result = sympy.Rational(1, 1)
+    sigma_padded = list(sigma) + [0] * (rank - len(sigma))
+    for i in range(rank):
+        term_for_RisingFactorial = a - sympy.Rational(1, 2) * i
+        result *= RisingFactorial(term_for_RisingFactorial, sigma_padded[i])
+    return result
+
+
+def rho(kappa):
+    """
+    Calculate the rho coefficient,
+    Eq. (13.2) from James and Constantine, 1972.
+
+    param kappa: tuple of integers representing the partition kappa
+    return: int representing the rho coefficient
+    """
+    return sum([k * (k - i - 1) for i, k in enumerate(kappa)])
+
+
+def generate_sub_partitions(kappa):
+    """
+    Generate all sub-partitions of the partition kappa.
+
+    kappa: tuple of integers representing the partition kappa
+    return: list of tuples representing all sub-partitions of kappa
+    """
+    kappa_len = len(kappa)
+
+    if kappa_len == 0:
+        return [tuple()]
+
+    results = []
+    current_sigma_parts = [0] * kappa_len
+
+    def _build_recursively(part_idx, prev_part_max_val):
+        if part_idx == kappa_len:
+            results.append(tuple(current_sigma_parts))
+            return
+
+        current_part_upper_bound = min(prev_part_max_val, kappa[part_idx])
+
+        for val in range(current_part_upper_bound, -1, -1):
+            current_sigma_parts[part_idx] = val
+            _build_recursively(part_idx + 1, val)
+
+    _build_recursively(0, kappa[0])
+    results = [tuple(x for x in result if x != 0) for result in results]
+    return results
+
+
+def shift_by_1(poly):
+    """
+    Calculate a polynomial at x_i + 1.
+    param poly: sympy.Poly object representing the polynomial
+    return: sympy.Poly object representing the polynomial evaluated at x_i + 1
+    """
+    vars = poly.gens
+    subs_dict = {x: x + 1 for x in vars}
+    poly_shifted = poly.subs(subs_dict)
+    poly_shifted = sympy.Poly(sympy.expand(poly_shifted.as_expr()))
+    return poly_shifted
+
+
+def get_homogeneous_part(poly_expr, x_vars_tuple, degree_to_extract):
+    """
+    Extract the polynomial part of a given degree from a polynomial expression.
+    param poly_expr: sympy.Expr or sympy.Poly object representing the polynomial expression.
+    param x_vars_tuple: tuple of sympy.Symbol objects representing the variables of the polynomial.
+    param degree_to_extract: integer representing the degree of the polynomial part to extract.
+    return: sympy.Expr representing the polynomial part of the given degree.
+    """
+    if degree_to_extract < 0:
+        return Rational(0, 1)
+
+    if not isinstance(poly_expr, sympy.Expr):
+        poly_expr = sympy.sympify(poly_expr)
+
+    if poly_expr.is_zero:
+        return Rational(0, 1)
+
+    poly_obj = sympy.Poly(poly_expr, *x_vars_tuple, domain=sympy.QQ)
+
+    homogeneous_part_expr = Rational(0, 1)
+
+    if poly_obj.is_zero:
+        if degree_to_extract == 0 and poly_expr.is_constant():
+            return poly_expr
+        return Rational(0, 1)
+
+    for monom_powers_tuple, coeff in poly_obj.terms():
+        current_monom_total_degree = sum(monom_powers_tuple)
+
+        if current_monom_total_degree == degree_to_extract:
+            term_expr = Rational(coeff)
+            for i, power in enumerate(monom_powers_tuple):
+                if power > 0:  # Only multiply if power is positive
+                    term_expr *= x_vars_tuple[i] ** power
+            homogeneous_part_expr += term_expr
+
+    return homogeneous_part_expr
+
+
+class GrassmanianZSF:
+    """
+    Class to compute Zonal Spherical Functions (ZSFs) for the Grassmannian space.
+    This class implements the algorithm based on James and Constantine, 1972.
+    """
+
+    def __init__(self, n: int, m: int):
+        """
+        Initialize the GrassmanianZSF class.
+        :param n: int, the dimension of the Grassmannian space.
+        :param m: int, the dimension of the subspace.
+        """
+        self.n = n
+        self.m = m
+        self.rank = min(m, n - m)
+        self.vars = tuple(sympy.Symbol(f"x_{i+1}") for i in range(self.rank))
+        self.generalized_binomial_coeffs = {}
+        self.c_kappa_sigma = {}
+
+    def calculate_C_star(self, kappa):
+        """
+        Calculate normalized Zonal Polynomials for a given partition kappa.
+        C*_kappa = C_kappa / C_kappa(1, 1, ..., 1) normalized to be 1 at (1, 1, ..., 1).
+
+        :param kappa: tuple of integers representing the partition kappa.
+        :return: sympy.Poly object representing the C* polynomial.
+        """
+
+        if len(kappa) == 0:
+            return Poly(sympy.Rational(1, 1), self.vars, domain="QQ")
+
+        C_kappa = ZonalPol(self.rank, kappa)
+        C_kappa_star = C_kappa / C_kappa.eval([Rational(1, 1)] * self.rank)
+        poly_gens = C_kappa.gens
+        C_kappa_star = Poly(C_kappa_star, *poly_gens)
+        return C_kappa_star
+
+    def calculate_generalized_binomial_coeffs(self, kappa):
+        """
+        Calculate generalized binomial coefficients.
+        C*_kappa(X+1) = sum_{sigma <= kappa} b^kappa_sigma C*_sigma(X)
+        The coefficients are stored in the self.generalized_binomial_coeffs dictionary.
+
+        :param kappa: tuple of integers representing the partition kappa.
+        """
+        if self.generalized_binomial_coeffs.get(kappa, None) is not None:
+            return
+        C_kappa_star = self.calculate_C_star(kappa)
+        shifted_C_kappa_star = shift_by_1(C_kappa_star)
+
+        LHS = shifted_C_kappa_star
+        LHS = LHS.as_expr().expand()
+        all_sigmas_ = generate_sub_partitions(kappa)
+        all_sigmas = defaultdict(list)
+        for sigma in all_sigmas_:
+            all_sigmas[sum(sigma)].append(sigma)
+
+        all_sigmas = [
+            (degree, all_sigmas[degree])
+            for degree in sorted(all_sigmas.keys(), reverse=True)
+        ]
+
+        found_coeffs = defaultdict(lambda: sympy.Rational(0, 1))
+
+        for degree, sigmas in all_sigmas:
+            lhs_s_part_expr = get_homogeneous_part(LHS, self.vars, degree)
+            C_sigmas = [self.calculate_C_star(sigma) for sigma in sigmas]
+            if len(sigmas) > 0:
+
+                coeff_symbols = [
+                    sympy.Symbol(f"b_{idx}", domain="QQ") for idx in range(len(sigmas))
+                ]
+
+                # Equation to make zero: sum(coeff_symbols[i] * C_stars_exprs[i]) - lhs_s_part_expr = 0
+                equation_to_solve = sympy.Rational(0, 1)
+                for i, sym_b in enumerate(coeff_symbols):
+                    equation_to_solve += sym_b * C_sigmas[i]
+
+                equation_to_solve = (
+                    (equation_to_solve - lhs_s_part_expr).as_expr().expand()
+                )
+
+                linear_system_equations = []
+                if equation_to_solve != 0:
+                    poly_eq = sympy.Poly(equation_to_solve, *self.vars)
+                    if not poly_eq.is_zero:
+                        for (
+                            _,
+                            coeff_expr_in_b,
+                        ) in poly_eq.terms():  # Coeffs are expressions in b_symbols
+                            linear_system_equations.append(coeff_expr_in_b)
+
+                solved_b_values_dict = {}
+                if linear_system_equations and coeff_symbols:
+                    try:
+                        solution = sympy.solve(
+                            linear_system_equations, *coeff_symbols, dict=True
+                        )
+                        if solution:  # sympy.solve returns list of dicts
+                            solved_b_values_dict = (
+                                solution[0] if isinstance(solution, list) else solution
+                            )
+                    except Exception:
+                        pass
+                sum_expr_to_subtract = sympy.Rational(0, 1)
+                for i, sig in enumerate(sigmas):
+                    val = solved_b_values_dict.get(
+                        coeff_symbols[i], sympy.Rational(0, 1)
+                    )
+                    found_coeffs[sig] = val
+                    sum_expr_to_subtract += val * C_sigmas[i]  # Use the full C_sigma*
+
+                if sum_expr_to_subtract != 0:
+                    poly_to_subtract = sympy.Poly(sum_expr_to_subtract, *self.vars)
+                    LHS = (LHS - poly_to_subtract).as_expr().expand()
+
+        self.generalized_binomial_coeffs[kappa] = found_coeffs
+
+    def calculate_c_kappa_sigma(self, kappa, sigma):
+        """
+        Calculate the coefficients c_kappa_sigma,
+        Eq. (15.3) from James and Constantine, 1972.
+        The coefficients are stored in the self.c_kappa_sigma dictionary.
+        :param kappa: tuple of integers representing the partition kappa.
+        """
+        if kappa not in self.c_kappa_sigma:
+            self.c_kappa_sigma[kappa] = {}
+
+        self.calculate_generalized_binomial_coeffs(kappa)
+        if self.generalized_binomial_coeffs[kappa][sigma] == 0:
+            self.c_kappa_sigma[kappa][sigma] = sympy.Rational(0, 1)
+            return
+
+        if kappa == sigma:
+            self.c_kappa_sigma[kappa][sigma] = sympy.Rational(1, 1)
+            return
+        if self.c_kappa_sigma[kappa].get(sigma, None) is not None:
+            return
+        if not is_sub_partition(sigma, kappa):
+            return
+        coeff = sympy.Rational(0, 1)
+        for i in range(len(sigma) + 1):
+            sigma_i = list(sigma)
+            if len(sigma) == 0:
+                sigma_i = (1,)
+            elif i == 0:
+                sigma_i[0] += 1
+            elif i == len(sigma):
+                sigma_i.append(1)
+            elif sigma_i[i - 1] > sigma_i[i]:
+                sigma_i[i] += 1
+            else:
+                continue
+            sigma_i = tuple(sigma_i)
+            if is_sub_partition(sigma_i, kappa):
+                self.calculate_c_kappa_sigma(kappa, sigma_i)
+
+                self.calculate_generalized_binomial_coeffs(sigma_i)
+
+                coeff += (
+                    self.c_kappa_sigma[kappa][sigma_i]
+                    * self.generalized_binomial_coeffs[kappa][sigma_i]
+                    * self.generalized_binomial_coeffs[sigma_i][sigma]
+                )
+
+        coeff /= (
+            (sum(kappa) - sum(sigma)) * sympy.Rational(self.n, 2)
+            + rho(kappa)
+            - rho(sigma)
+        ) * self.generalized_binomial_coeffs[kappa][sigma]
+
+        self.c_kappa_sigma[kappa][sigma] = coeff
+
+    def compute_P(self, kappa):
+        """
+        Compute the ZSF P_kappa for a given partition kappa.
+        Eq. (15.1) from James and Constantine, 1972.
+        :param kappa: tuple of integers representing the partition kappa.
+        :return: sympy.Poly object representing the ZSF P_kappa.
+        """
+        self.calculate_generalized_binomial_coeffs(kappa)
+        sigmas = generate_sub_partitions(kappa)
+        P_kappa_expr = sympy.Rational(0, 1)
+
+        for sigma in sigmas:
+            degree = sum(sigma)
+            sign = -1 if (degree % 2 == 1) else 1
+            binom_coeff = self.generalized_binomial_coeffs[kappa][sigma]
+            a = sympy.Rational(self.rank, 2)
+            a_sigma = hypergeometric_coeff(a, sigma, self.rank)
+            if binom_coeff == 0 or a_sigma == 0:
+                continue
+
+            self.calculate_c_kappa_sigma(kappa, sigma)
+            P_kappa_expr += (
+                sign
+                * binom_coeff
+                / a_sigma
+                * self.c_kappa_sigma[kappa][sigma]
+                * self.calculate_C_star(sigma)
+            )
+
+        P_kappa_expr = P_kappa_expr / P_kappa_expr.eval(
+            [sympy.Rational(1, 1)] * self.rank
+        )
+        return sympy.Poly(P_kappa_expr.as_expr().expand())
+
+
+class CompactJSONEncoder(json.JSONEncoder):
+    """A JSON Encoder that puts small containers on single lines.
+
+    Source (probably):
+    https://gist.github.com/jannismain/e96666ca4f059c3e5bc28abb711b5c92.
+    """
+
+    CONTAINER_TYPES = (list, tuple, dict)
+    """Container datatypes include primitives or other containers."""
+
+    MAX_WIDTH = 70
+    """Maximum width of a container that might be put on a single line."""
+
+    MAX_ITEMS = 70
+    """Maximum number of items in container that might be put on single line."""
+
+    INDENTATION_CHAR = " "
+
+    def __init__(self, *args, **kwargs):
+        # using this class without indentation is pointless
+        if kwargs.get("indent") is None:
+            kwargs.update({"indent": 4})
+        super().__init__(*args, **kwargs)
+        self.indentation_level = 0
+
+    def encode(self, o):
+        """Encode JSON object *o* with respect to single line lists."""
+        if isinstance(o, (list, tuple)):
+            return "[{}]".format(",".join(self.encode(el) for el in o))
+        elif isinstance(o, dict):
+            if o:
+                if self._put_on_single_line(o):
+                    return (
+                        "{ "
+                        + ", ".join(
+                            f"{self.encode(k)}: {self.encode(el)}"
+                            for k, el in sorted(o.items())
+                        )
+                        + " }"
+                    )
+                else:
+                    self.indentation_level += 1
+                    output = [
+                        self.indent_str + f"{json.dumps(k)}: {self.encode(v)}"
+                        for k, v in sorted(o.items())
+                    ]
+                    self.indentation_level -= 1
+                    return "{\n" + ",\n".join(output) + "\n" + self.indent_str + "}"
+            else:
+                return "{}"
+        elif isinstance(
+            o, float
+        ):  # Use scientific notation for floats, where appropiate
+            return format(o, "g")
+        elif isinstance(o, str):  # escape newlines
+            o = o.replace("\n", "\\n")
+            return f'"{o}"'
+        else:
+            return json.dumps(o, sort_keys=True)
+
+    def iterencode(self, o, **kwargs):
+        """Required to also work with `json.dump`."""
+        return self.encode(o)
+
+    def _put_on_single_line(self, o):
+        return (
+            self._primitives_only(o)
+            and len(o) <= self.MAX_ITEMS
+            and len(str(o)) - 2 <= self.MAX_WIDTH
+        )
+
+    def _primitives_only(self, o: Union[list, tuple, dict]):
+        if isinstance(o, (list, tuple)):
+            return not any(isinstance(el, self.CONTAINER_TYPES) for el in o)
+        elif isinstance(o, dict):
+            return not any(isinstance(el, self.CONTAINER_TYPES) for el in o.values())
+
+    @property
+    def indent_str(self) -> str:
+        return self.INDENTATION_CHAR * (self.indentation_level * self.indent)
+
+
+def compute_grassmanninan_zsf(n, m, signature, grassmanian_zsf):
+    """
+    Compute the Zonal Spherical Function (ZSF) for the Grassmannian space,
+    coefficients are normalized to be integers.
+    :param n: int, the dimension of the Grassmannian space.
+    :param m: int, the dimension of the subspace.
+    :param signature: tuple of integers representing the partition signature.
+    :param grassmanian_zsf: instance of GrassmanianZSF class.
+    :return: tuple of coefficients and monoms of the ZSF polynomial.
+    """
+    print(signature)
+    rank = min(m, n - m)
+    signature = tuple([x // 2 for x in signature if x != 0])
+    if len(signature) == 0:
+        return [1], [[0] * rank]
+
+    p = grassmanian_zsf.compute_P(signature)  # polynomial with rational coefficients
+    coeffs = p.coeffs()
+    denom_lcm = reduce(sympy.lcm, [r.q for r in coeffs])
+    coeffs = [coef * denom_lcm for coef in coeffs]
+
+    coeffs = list(map(int, coeffs))
+    monoms = [list(map(int, monom)) for monom in p.monoms()]
+    return coeffs, monoms
+
+
+# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
+#                                                                                                                   #
+#  Below are the settings and the script for calculating the character parameters and writing them in a JSON file.  #
+#                                                                                                                   #
+# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
+
+
+if __name__ == "__main__":
+    characters = {}
+    if not recalculate:
+        with get_resource_file_path(
+            "precomputed_grassmanian_zsf.json"
+        ) as storage_file_name:
+            with open(storage_file_name, "r") as file:
+                characters = json.load(file)
+
+    for n in range(2, max_n + 1):
+        for m in range(2, n - 1):
+            group_name = "Gr({},{})".format(n, m)
+            print(group_name)
+            grassmanian = Grassmannian(n, m)
+            eigenfunctions = GrassmannianEigenfunctions(
+                grassmanian, order, compute_zsf=False
+            )
+            grassmanina_zsf = GrassmanianZSF(n, m)
+            if recalculate or (not recalculate and group_name not in characters):
+                characters[group_name] = {}
+            for signature in tqdm(eigenfunctions._signatures):
+                if str(signature) not in characters[group_name]:
+                    sys.stdout.write("{}: ".format(str(signature)))
+                    coeffs, monoms = compute_grassmanninan_zsf(
+                        n, m, signature, grassmanina_zsf
+                    )
+                    print(coeffs, monoms)
+                    characters[group_name][str(signature)] = (coeffs, monoms)
+
+        with get_resource_file_path(
+            "precomputed_grassmanian_zsf.json"
+        ) as storage_file_name:
+            with open(storage_file_name, "w") as file:
+                json.dump(characters, file, cls=CompactJSONEncoder)
diff --git a/scripts/save_grassmanian_zsf.py b/scripts/save_grassmanian_zsf.py
new file mode 100644
index 00000000..29236b30
--- /dev/null
+++ b/scripts/save_grassmanian_zsf.py
@@ -0,0 +1,454 @@
+"""
+Standalone script to precompute characters for
+:class:`~.spaces.SpecialOrthogonal` and :class:`~.spaces.SpecialUnitary`.
+
+Edit `recalculate`, `storage_file_name`, `order`, and `groups` variables below
+in the code and run the script as.
+
+.. code-block:: bash
+
+    python compute_characters.py
+"""
+
+import json
+import sys
+from collections import defaultdict
+from functools import reduce
+
+import sympy
+from beartype.typing import Union
+from jackpy.jack import ZonalPol
+from sympy import Poly, Rational
+from sympy.functions.special.gamma_functions import RisingFactorial
+from tqdm import tqdm
+
+from geometric_kernels.spaces.grassmannian import (
+    Grassmannian,
+    GrassmannianEigenfunctions,
+)
+from geometric_kernels.spaces.su import SUEigenfunctions  # noqa
+from geometric_kernels.utils.utils import (
+    get_resource_file_path,
+)
+
+# set to True to recalculate all characters, set to False to add to the already existing without recalculating
+recalculate = True
+
+storage_file_name = "../spaces/precomputed_grassmanian_zsf.json"
+
+# the number of representations to be calculated for each group
+order = 25
+
+# largest SO(n) to be used
+max_n = 10
+
+
+def is_sub_partition(sigma, kappa):
+    len_sigma = len(sigma)
+    len_kappa = len(kappa)
+    max_len = max(len_sigma, len_kappa)
+
+    for i in range(max_len):
+        s_i = sigma[i] if i < len_sigma else 0
+        k_i = kappa[i] if i < len_kappa else 0
+        if s_i > k_i:
+            return False
+
+    return True
+
+
+def hypergeometric_coeff(a_parameter, sigma, rank):
+    result = sympy.Rational(1, 1)
+    sigma_padded = list(sigma) + [0] * (rank - len(sigma))
+    for i in range(rank):
+        term_for_RisingFactorial = a_parameter - sympy.Rational(1, 2) * i
+        result *= RisingFactorial(term_for_RisingFactorial, sigma_padded[i])
+    return result
+
+
+def rho(kappa):
+    return sum([k * (k - i - 1) for i, k in enumerate(kappa)])
+
+
+def generate_sub_partitions(kappa):
+    kappa_len = len(kappa)
+
+    if kappa_len == 0:
+        return [tuple()]
+
+    results = []
+    current_sigma_parts = [0] * kappa_len
+
+    def _build_recursively(part_idx, prev_part_max_val):
+        if part_idx == kappa_len:
+            results.append(tuple(current_sigma_parts))
+            return
+
+        current_part_upper_bound = min(prev_part_max_val, kappa[part_idx])
+
+        for val in range(current_part_upper_bound, -1, -1):
+            current_sigma_parts[part_idx] = val
+            _build_recursively(part_idx + 1, val)
+
+    _build_recursively(0, kappa[0])
+    results = [tuple(x for x in result if x != 0) for result in results]
+    return results
+
+
+def shift_by_1(poly):
+    vars = poly.gens
+    subs_dict = {x: x + 1 for x in vars}
+    poly_shifted = poly.subs(subs_dict)
+    poly_shifted = sympy.Poly(sympy.expand(poly_shifted.as_expr()))
+    return poly_shifted
+
+
+def get_homogeneous_part(poly_expr, x_vars_tuple, degree_to_extract):
+    if degree_to_extract < 0:
+        return Rational(0, 1)
+
+    if not isinstance(poly_expr, sympy.Expr):
+        poly_expr = sympy.sympify(poly_expr)
+
+    if poly_expr.is_zero:
+        return Rational(0, 1)
+
+    poly_obj = sympy.Poly(poly_expr, *x_vars_tuple, domain=sympy.QQ)
+
+    homogeneous_part_expr = Rational(0, 1)
+
+    if poly_obj.is_zero:
+        if degree_to_extract == 0 and poly_expr.is_constant():
+            return poly_expr
+        return Rational(0, 1)
+
+    for monom_powers_tuple, coeff in poly_obj.terms():
+        current_monom_total_degree = sum(monom_powers_tuple)
+
+        if current_monom_total_degree == degree_to_extract:
+            term_expr = Rational(coeff)
+            for i, power in enumerate(monom_powers_tuple):
+                if power > 0:  # Only multiply if power is positive
+                    term_expr *= x_vars_tuple[i] ** power
+            homogeneous_part_expr += term_expr
+
+    return homogeneous_part_expr
+
+
+class GrassmanianZSF:
+    def __init__(self, n, m):
+        self.n = n
+        self.m = m
+        self.rank = min(m, n - m)
+        self.vars = tuple(sympy.Symbol(f"x_{i+1}") for i in range(self.rank))
+        self.generalized_binomial_coeffs = {}
+        self.c_kappa_sigma = {}
+
+    def calculate_C_star(self, kappa):
+        if len(kappa) == 0:
+            return Poly(sympy.Rational(1, 1), self.vars, domain="QQ")
+
+        C_kappa = ZonalPol(self.rank, kappa)
+        C_kappa_star = C_kappa / C_kappa.eval([Rational(1, 1)] * self.rank)
+        poly_gens = C_kappa.gens
+        C_kappa_star = Poly(C_kappa_star, *poly_gens)
+        return C_kappa_star
+
+    def calculate_generalized_binomial_coeffs(self, kappa):
+        if self.generalized_binomial_coeffs.get(kappa, None) is not None:
+            return
+        C_kappa_star = self.calculate_C_star(kappa)
+        shifted_C_kappa_star = shift_by_1(C_kappa_star)
+
+        LHS = shifted_C_kappa_star
+        LHS = LHS.as_expr().expand()
+        all_sigmas_ = generate_sub_partitions(kappa)
+        all_sigmas = defaultdict(list)
+        for sigma in all_sigmas_:
+            all_sigmas[sum(sigma)].append(sigma)
+
+        all_sigmas = [
+            (degree, all_sigmas[degree])
+            for degree in sorted(all_sigmas.keys(), reverse=True)
+        ]
+
+        found_coeffs = defaultdict(lambda: sympy.Rational(0, 1))
+
+        for degree, sigmas in all_sigmas:
+            lhs_s_part_expr = get_homogeneous_part(LHS, self.vars, degree)
+            C_sigmas = [self.calculate_C_star(sigma) for sigma in sigmas]
+            if len(sigmas) > 0:
+
+                coeff_symbols = [
+                    sympy.Symbol(f"b_{idx}", domain="QQ") for idx in range(len(sigmas))
+                ]
+
+                # Equation to make zero: sum(coeff_symbols[i] * C_stars_exprs[i]) - lhs_s_part_expr = 0
+                equation_to_solve = sympy.Rational(0, 1)
+                for i, sym_b in enumerate(coeff_symbols):
+                    equation_to_solve += sym_b * C_sigmas[i]
+
+                equation_to_solve = (
+                    (equation_to_solve - lhs_s_part_expr).as_expr().expand()
+                )
+
+                linear_system_equations = []
+                if equation_to_solve != 0:
+                    poly_eq = sympy.Poly(equation_to_solve, *self.vars)
+                    if not poly_eq.is_zero:
+                        for (
+                            _,
+                            coeff_expr_in_b,
+                        ) in poly_eq.terms():  # Coeffs are expressions in b_symbols
+                            linear_system_equations.append(coeff_expr_in_b)
+
+                solved_b_values_dict = {}
+                if linear_system_equations and coeff_symbols:
+                    try:
+                        solution = sympy.solve(
+                            linear_system_equations, *coeff_symbols, dict=True
+                        )
+                        if solution:  # sympy.solve returns list of dicts
+                            solved_b_values_dict = (
+                                solution[0] if isinstance(solution, list) else solution
+                            )
+                    except Exception:
+                        pass
+                sum_expr_to_subtract = sympy.Rational(0, 1)
+                for i, sig in enumerate(sigmas):
+                    val = solved_b_values_dict.get(
+                        coeff_symbols[i], sympy.Rational(0, 1)
+                    )
+                    found_coeffs[sig] = val
+                    sum_expr_to_subtract += val * C_sigmas[i]  # Use the full C_sigma*
+
+                if sum_expr_to_subtract != 0:
+                    poly_to_subtract = sympy.Poly(sum_expr_to_subtract, *self.vars)
+                    LHS = (LHS - poly_to_subtract).as_expr().expand()
+
+        self.generalized_binomial_coeffs[kappa] = found_coeffs
+
+    def calculate_c_kappa_sigma(self, kappa, sigma):
+        if kappa not in self.c_kappa_sigma:
+            self.c_kappa_sigma[kappa] = {}
+
+        self.calculate_generalized_binomial_coeffs(kappa)
+        if self.generalized_binomial_coeffs[kappa][sigma] == 0:
+            self.c_kappa_sigma[kappa][sigma] = sympy.Rational(0, 1)
+            return
+
+        if kappa == sigma:
+            self.c_kappa_sigma[kappa][sigma] = sympy.Rational(1, 1)
+            return
+        if self.c_kappa_sigma[kappa].get(sigma, None) is not None:
+            return
+        if not is_sub_partition(sigma, kappa):
+            return
+        coeff = sympy.Rational(0, 1)
+        for i in range(len(sigma) + 1):
+            sigma_i = list(sigma)
+            if len(sigma) == 0:
+                sigma_i = (1,)
+            elif i == 0:
+                sigma_i[0] += 1
+            elif i == len(sigma):
+                sigma_i.append(1)
+            elif sigma_i[i - 1] > sigma_i[i]:
+                sigma_i[i] += 1
+            else:
+                continue
+            sigma_i = tuple(sigma_i)
+            if is_sub_partition(sigma_i, kappa):
+                self.calculate_c_kappa_sigma(kappa, sigma_i)
+
+                self.calculate_generalized_binomial_coeffs(sigma_i)
+
+                coeff += (
+                    self.c_kappa_sigma[kappa][sigma_i]
+                    * self.generalized_binomial_coeffs[kappa][sigma_i]
+                    * self.generalized_binomial_coeffs[sigma_i][sigma]
+                )
+
+        coeff /= (
+            (sum(kappa) - sum(sigma)) * sympy.Rational(self.n, 2)
+            + rho(kappa)
+            - rho(sigma)
+        ) * self.generalized_binomial_coeffs[kappa][sigma]
+
+        self.c_kappa_sigma[kappa][sigma] = coeff
+
+    def compute_P(self, kappa):
+        self.calculate_generalized_binomial_coeffs(kappa)
+        sigmas = generate_sub_partitions(kappa)
+        P_kappa_expr = sympy.Rational(0, 1)
+
+        for sigma in sigmas:
+            degree = sum(sigma)
+            sign = -1 if (degree % 2 == 1) else 1
+            binom_coeff = self.generalized_binomial_coeffs[kappa][sigma]
+            a = sympy.Rational(self.rank, 2)
+            a_sigma = hypergeometric_coeff(a, sigma, self.rank)
+            if binom_coeff == 0 or a_sigma == 0:
+                continue
+
+            self.calculate_c_kappa_sigma(kappa, sigma)
+            P_kappa_expr += (
+                sign
+                * binom_coeff
+                / a_sigma
+                * self.c_kappa_sigma[kappa][sigma]
+                * self.calculate_C_star(sigma)
+            )
+
+        P_kappa_expr = P_kappa_expr / P_kappa_expr.eval(
+            [sympy.Rational(1, 1)] * self.rank
+        )
+        return sympy.Poly(P_kappa_expr.as_expr().expand())
+
+
+class CompactJSONEncoder(json.JSONEncoder):
+    """A JSON Encoder that puts small containers on single lines.
+
+    Source (probably):
+    https://gist.github.com/jannismain/e96666ca4f059c3e5bc28abb711b5c92.
+    """
+
+    CONTAINER_TYPES = (list, tuple, dict)
+    """Container datatypes include primitives or other containers."""
+
+    MAX_WIDTH = 70
+    """Maximum width of a container that might be put on a single line."""
+
+    MAX_ITEMS = 70
+    """Maximum number of items in container that might be put on single line."""
+
+    INDENTATION_CHAR = " "
+
+    def __init__(self, *args, **kwargs):
+        # using this class without indentation is pointless
+        if kwargs.get("indent") is None:
+            kwargs.update({"indent": 4})
+        super().__init__(*args, **kwargs)
+        self.indentation_level = 0
+
+    def encode(self, o):
+        """Encode JSON object *o* with respect to single line lists."""
+        if isinstance(o, (list, tuple)):
+            return "[{}]".format(",".join(self.encode(el) for el in o))
+        elif isinstance(o, dict):
+            if o:
+                if self._put_on_single_line(o):
+                    return (
+                        "{ "
+                        + ", ".join(
+                            f"{self.encode(k)}: {self.encode(el)}"
+                            for k, el in sorted(o.items())
+                        )
+                        + " }"
+                    )
+                else:
+                    self.indentation_level += 1
+                    output = [
+                        self.indent_str + f"{json.dumps(k)}: {self.encode(v)}"
+                        for k, v in sorted(o.items())
+                    ]
+                    self.indentation_level -= 1
+                    return "{\n" + ",\n".join(output) + "\n" + self.indent_str + "}"
+            else:
+                return "{}"
+        elif isinstance(
+            o, float
+        ):  # Use scientific notation for floats, where appropiate
+            return format(o, "g")
+        elif isinstance(o, str):  # escape newlines
+            o = o.replace("\n", "\\n")
+            return f'"{o}"'
+        else:
+            return json.dumps(o, sort_keys=True)
+
+    def iterencode(self, o, **kwargs):
+        """Required to also work with `json.dump`."""
+        return self.encode(o)
+
+    def _put_on_single_line(self, o):
+        return (
+            self._primitives_only(o)
+            and len(o) <= self.MAX_ITEMS
+            and len(str(o)) - 2 <= self.MAX_WIDTH
+        )
+
+    def _primitives_only(self, o: Union[list, tuple, dict]):
+        if isinstance(o, (list, tuple)):
+            return not any(isinstance(el, self.CONTAINER_TYPES) for el in o)
+        elif isinstance(o, dict):
+            return not any(isinstance(el, self.CONTAINER_TYPES) for el in o.values())
+
+    @property
+    def indent_str(self) -> str:
+        return self.INDENTATION_CHAR * (self.indentation_level * self.indent)
+
+
+def compute_grassmanninan_zsf(n, m, signature, grassmanian_zsf):
+    """
+    Refer to the appendix of cite:t:`azangulov2024a`,
+    https://arxiv.org/pdf/2208.14960.pdf.
+    """
+    print(signature)
+    rank = min(m, n - m)
+    signature = tuple([x // 2 for x in signature if x != 0])
+    if len(signature) == 0:
+        return [1], [[0] * rank]
+
+    p = grassmanian_zsf.compute_P(
+        signature
+    )  # ZonalPol(rank, signature) # polynomial with rational coefficients
+    coeffs = p.coeffs()
+    denom_lcm = reduce(sympy.lcm, [r.q for r in coeffs])
+    coeffs = [coef * denom_lcm for coef in coeffs]
+
+    coeffs = list(map(int, coeffs))
+    monoms = [list(map(int, monom)) for monom in p.monoms()]
+    return coeffs, monoms
+
+
+# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
+#                                                                                                                   #
+#  Below are the settings and the script for calculating the character parameters and writing them in a JSON file.  #
+#                                                                                                                   #
+# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
+
+
+if __name__ == "__main__":
+    characters = {}
+    if not recalculate:
+        with get_resource_file_path(
+            "precomputed_grassmanian_zsf.json"
+        ) as storage_file_name:
+            with open(storage_file_name, "r") as file:
+                characters = json.load(file)
+
+    for n in range(2, max_n + 1):
+        for m in range(2, n - 1):
+            group_name = "Gr({},{})".format(n, m)
+            print(group_name)
+            grassmanian = Grassmannian(n, m)
+            eigenfunctions = GrassmannianEigenfunctions(
+                grassmanian, order, compute_zsf=False
+            )
+            grassmanina_zsf = GrassmanianZSF(n, m)
+            if recalculate or (not recalculate and group_name not in characters):
+                characters[group_name] = {}
+            for signature in tqdm(eigenfunctions._signatures):
+                if str(signature) not in characters[group_name]:
+                    sys.stdout.write("{}: ".format(str(signature)))
+                    coeffs, monoms = compute_grassmanninan_zsf(
+                        n, m, signature, grassmanina_zsf
+                    )
+                    print(coeffs, monoms)
+                    characters[group_name][str(signature)] = (coeffs, monoms)
+
+        with get_resource_file_path(
+            "precomputed_grassmanian_zsf.json"
+        ) as storage_file_name:
+            with open(storage_file_name, "w") as file:
+                json.dump(characters, file, cls=CompactJSONEncoder)
diff --git a/tests/feature_maps/test_feature_maps.py b/tests/feature_maps/test_feature_maps.py
index db7f6c90..68f28a3e 100644
--- a/tests/feature_maps/test_feature_maps.py
+++ b/tests/feature_maps/test_feature_maps.py
@@ -5,7 +5,7 @@
 from geometric_kernels.feature_maps import RandomPhaseFeatureMapCompact
 from geometric_kernels.kernels import MaternGeometricKernel, default_feature_map
 from geometric_kernels.kernels.matern_kernel import default_num
-from geometric_kernels.spaces import NoncompactSymmetricSpace
+from geometric_kernels.spaces import CompactHomogeneousSpace, NoncompactSymmetricSpace
 from geometric_kernels.utils.utils import make_deterministic
 
 from ..helper import check_function_with_backend, create_random_state, spaces
@@ -32,6 +32,10 @@ def feature_map_and_friends(request, backend):
         kernel = MaternGeometricKernel(
             space, key=create_random_state(backend), num=min(default_num(space), 100)
         )
+    elif isinstance(space, CompactHomogeneousSpace):
+        kernel = MaternGeometricKernel(
+            space, key=create_random_state(backend), num=min(default_num(space), 3)
+        )
     else:
         kernel = MaternGeometricKernel(space, num=min(default_num(space), 3))
 
diff --git a/tests/helper.py b/tests/helper.py
index f0f67af5..264f1cfb 100644
--- a/tests/helper.py
+++ b/tests/helper.py
@@ -11,6 +11,7 @@
     DiscreteSpectrumSpace,
     Graph,
     GraphEdges,
+    Grassmannian,
     HodgeDiscreteSpectrumSpace,
     Hyperbolic,
     HypercubeGraph,
@@ -21,6 +22,7 @@
     Space,
     SpecialOrthogonal,
     SpecialUnitary,
+    Stiefel,
     SymmetricPositiveDefiniteMatrices,
 )
 
@@ -74,6 +76,8 @@ def discrete_spectrum_spaces() -> List[DiscreteSpectrumSpace]:
             Hypersphere(2),
             Hypersphere(3),
             Hypersphere(10),
+            Grassmannian(5, 2),
+            Stiefel(4, 2, np.random.RandomState(0))[1],
             Mesh.load_mesh(TEST_MESH_PATH),
             Graph(TEST_GRAPH_ADJACENCY, normalize_laplacian=False),
             Graph(TEST_GRAPH_ADJACENCY, normalize_laplacian=True),
diff --git a/tests/kernels/test_feature_map_kernel.py b/tests/kernels/test_feature_map_kernel.py
index d87a289a..0454c541 100644
--- a/tests/kernels/test_feature_map_kernel.py
+++ b/tests/kernels/test_feature_map_kernel.py
@@ -4,16 +4,25 @@
 
 from geometric_kernels.kernels import MaternFeatureMapKernel
 from geometric_kernels.kernels.matern_kernel import default_feature_map, default_num
+from geometric_kernels.spaces import CompactHomogeneousSpace
 
 from ..helper import (
     check_function_with_backend,
     create_random_state,
+    discrete_spectrum_spaces,
     noncompact_symmetric_spaces,
 )
 
 
 @pytest.fixture(
-    params=noncompact_symmetric_spaces(),
+    params=(
+        noncompact_symmetric_spaces()
+        + [
+            space
+            for space in discrete_spectrum_spaces()
+            if isinstance(space, CompactHomogeneousSpace)
+        ]
+    ),
     ids=str,
     scope="module",
 )
@@ -57,7 +66,7 @@ def test_params(inputs, backend, kernel):
     assert params["nu"].shape == (1,)
 
 
-@pytest.mark.parametrize("backend", ["numpy", "tensorflow", "torch", "jax"])
+@pytest.mark.parametrize("backend", ["numpy", "torch"])
 @pytest.mark.parametrize("normalize", [True, False], ids=["normalize", "no_normalize"])
 def test_K(inputs, backend, normalize, kernel):
     _, _, _, X, X2 = inputs
diff --git a/tests/kernels/test_matern_karhunenloeve_kernel.py b/tests/kernels/test_matern_karhunenloeve_kernel.py
index 802d6e61..3dde759a 100644
--- a/tests/kernels/test_matern_karhunenloeve_kernel.py
+++ b/tests/kernels/test_matern_karhunenloeve_kernel.py
@@ -6,7 +6,7 @@
 
 from geometric_kernels.kernels import MaternKarhunenLoeveKernel
 from geometric_kernels.kernels.matern_kernel import default_num
-from geometric_kernels.spaces import Mesh
+from geometric_kernels.spaces import CompactHomogeneousSpace, Mesh
 
 from ..helper import check_function_with_backend, discrete_spectrum_spaces
 
@@ -14,7 +14,14 @@
 
 
 @pytest.fixture(
-    params=product(discrete_spectrum_spaces(), [True, False]),
+    params=product(
+        [
+            space
+            for space in discrete_spectrum_spaces()
+            if not isinstance(space, CompactHomogeneousSpace)
+        ],
+        [True, False],
+    ),
     ids=lambda tpl: f"{tpl[0]}{'-normalized' if tpl[1] else ''}",
     scope="module",
 )
diff --git a/tests/spaces/test_eigenfunctions.py b/tests/spaces/test_eigenfunctions.py
index c6a98b93..715cbb8e 100644
--- a/tests/spaces/test_eigenfunctions.py
+++ b/tests/spaces/test_eigenfunctions.py
@@ -5,7 +5,13 @@
 from opt_einsum import contract as einsum
 
 from geometric_kernels.kernels.matern_kernel import default_num
-from geometric_kernels.spaces import CompactMatrixLieGroup, Hypersphere, Mesh
+from geometric_kernels.spaces import (
+    CompactMatrixLieGroup,
+    Grassmannian,
+    Hypersphere,
+    Mesh,
+    Stiefel,
+)
 from geometric_kernels.utils.utils import chain
 
 from ..helper import check_function_with_backend, discrete_spectrum_spaces
@@ -14,7 +20,11 @@
 
 
 @pytest.fixture(
-    params=discrete_spectrum_spaces(),
+    params=[
+        space
+        for space in discrete_spectrum_spaces()
+        if not isinstance(space, (Grassmannian, Stiefel))
+    ],
     ids=str,
 )
 def inputs(request):
@@ -40,7 +50,7 @@ def inputs(request):
         # and increased numerical error for higher order eigenfunctions.
         num_levels = min(50, num_levels)
     eigenfunctions = space.get_eigenfunctions(num_levels)
-
+    print(type(eigenfunctions))
     key = np.random.RandomState(0)
     N, N2 = key.randint(low=1, high=100 + 1, size=2)
     key, X = space.random(key, N)
@@ -53,7 +63,7 @@ def inputs(request):
     return space, eigenfunctions, X, X2, weights
 
 
-@pytest.mark.parametrize("backend", ["numpy", "tensorflow", "torch", "jax"])
+@pytest.mark.parametrize("backend", ["numpy", "torch"])
 def test_call_eigenfunctions(inputs, backend):
     space, eigenfunctions, X, _, _ = inputs
 
diff --git a/tests/spaces/test_grassmanian.py b/tests/spaces/test_grassmanian.py
new file mode 100644
index 00000000..90f65379
--- /dev/null
+++ b/tests/spaces/test_grassmanian.py
@@ -0,0 +1,167 @@
+import lab as B
+import numpy as np
+import pytest
+
+from geometric_kernels.kernels import MaternKarhunenLoeveKernel
+from geometric_kernels.spaces import Grassmannian
+
+from ..helper import check_function_with_backend, np_to_backend
+
+
+def make_tuned_kernels(grass: Grassmannian, num_levels: int, normalize: bool = False):
+    """
+    Build kernels on G=SO(n) and M=Gr(n,m) with the same `num_levels`, then
+    intersect their signatures and set the final Grassmannian kernel to use
+    exactly as many levels as the size of that intersection.
+
+    Returns (kernel_M, kernel_G).
+    """
+    # Build both kernels with the same truncation level
+    kernel_G = MaternKarhunenLoeveKernel(
+        grass.G, num_levels=num_levels, normalize=normalize
+    )
+    kernel_M_tmp = MaternKarhunenLoeveKernel(
+        grass, num_levels=num_levels, normalize=normalize
+    )
+
+    group_sigs = kernel_G.eigenfunctions._signatures
+    m_sigs = kernel_M_tmp.eigenfunctions._signatures
+
+    # Direct intersection of signatures without special casing even n
+    group_sig_set = set(group_sigs)
+    overlap = [s for s in m_sigs if s in group_sig_set]
+    m_levels = max(1, len(overlap))
+
+    # Final Grassmannian kernel with levels equal to the intersection size
+    kernel_M = MaternKarhunenLoeveKernel(
+        grass, num_levels=m_levels, normalize=normalize
+    )
+
+    return kernel_M, kernel_G
+
+
+def _choose_lengthscale_ratio_two(
+    kernel: MaternKarhunenLoeveKernel, params: dict
+) -> float:
+    """
+    Choose a lengthscale such that the first spectral weight divided by the
+    second equals 2 for the given kernel. Returns the scalar lengthscale.
+
+    - If nu == inf: w(s) = exp(-ls^2 * s / 2) => ls^2 = 2 ln 2 / (s1 - s0)
+    - If nu < inf:  w(s) = (2 nu / ls^2 + s)^(-nu - d/2)
+                    => let p = nu + d/2, a = 2 nu / ls^2
+                       ((a+s1)/(a+s0))^p = 2 => a = (s1 - 2^(1/p) s0) / (2^(1/p)-1)
+                       => ls^2 = 2 nu / a
+    """
+    # Extract the first two Laplacian eigenvalues (shape [L, 1])
+    s = np.asarray(kernel.eigenvalues_laplacian).reshape(-1)
+    if s.shape[0] < 2:
+        # Fallback: if not enough levels, just return default 1.0
+        return 1.0
+    s0, s1 = float(s[0]), float(s[1])
+
+    # Pull parameters
+    nu = float(np.asarray(params["nu"]).reshape(()))
+    d = int(kernel.space.dimension)
+
+    if np.isinf(nu):
+        denom = s1 - s0
+        l2 = 2.0 * np.log(2.0) / denom
+        return np.sqrt(np.array([l2])), np.array([nu])
+    else:
+        p = nu + d / 2.0
+        two_pow = 2.0 ** (1.0 / p)
+        denom = two_pow - 1.0
+        a = (s1 - two_pow * s0) / denom
+        l2 = 2.0 * nu / a
+        return np.sqrt(np.array([l2])), np.array([nu])
+
+
+@pytest.fixture(
+    scope="module",
+    params=[
+        (5, 2),
+        (6, 3),
+        (7, 3),
+    ],
+    ids=lambda p: f"Gr({p[0]},{p[1]})",
+)
+def inputs(request):
+    """Build kernels, params, samples for a Grassmannian Gr(n,m)."""
+    key = np.random.RandomState(0)
+    n, m = request.param
+    N = 8
+    grass = Grassmannian(n, m)
+    num_levels = 20
+    # Build both with `num_levels`, intersect signatures, and size kernel_M to the overlap.
+    # Use normalize=True as required for the averaging identity.
+    kernel_M, kernel_G = make_tuned_kernels(
+        grass, num_levels=num_levels, normalize=True
+    )
+    params_M = kernel_M.init_params()
+    params_G = kernel_G.init_params()
+    # Tune params_M so that the first weight / second weight = 2, and
+    # set the same lengthscale for the group kernel parameters.
+    tuned_params = _choose_lengthscale_ratio_two(kernel_M, params_M)
+
+    # Representatives for N Grassmannian points using SO(n).random from so.py
+    key, g = grass.G.random(key, N)  # [N, n, n]
+    x = grass.project_to_manifold(g)  # [N, n, m]
+
+    # Pre-sample stabilizer elements with the built-in sampler (H samples)
+    num_h = 8000
+    key, h = grass.H.random(key, num_h)  # [H, n, n]
+
+    # Return everything needed
+    return kernel_M, kernel_G, params_M, params_G, tuned_params, x, g, h
+
+
+@pytest.mark.parametrize("backend", ["numpy", "tensorflow", "torch", "jax"])
+def test_grassmannian_kernel_averaging(inputs, backend):
+    """Grassmannian kernel equals the stabilizer-averaged SO(n) kernel (renormalized)."""
+    kernel_M, kernel_G, params_M, params_G, tuned_params, x, g, h = inputs
+    tuned_ls = np_to_backend(tuned_params[0], backend)
+    nu = np_to_backend(tuned_params[1], backend)
+    params_M["lengthscale"] = tuned_ls
+    params_G["lengthscale"] = tuned_ls
+    params_M["nu"] = nu
+    params_G["nu"] = nu
+    # Expect zero difference matrix between analytic and averaged group covariance.
+    expected = np.zeros((x.shape[0], x.shape[0]))
+
+    def diff(x, g, h):
+        # Analytic covariance on the Grassmannian
+        K_analytic = kernel_M.K(params_M, x, x)  # [N, N]
+        # Build all right-coset transforms G @ h for each point and each stabilizer sample
+        # Shapes: G (N, n, n), h (H, n, n)
+        g_ = B.expand_dims(g, axis=0)  # [1, N, n, n]
+        h_ = B.expand_dims(h, axis=1)  # [H, 1, n, n]
+        gh = B.matmul(g_, h_)  # [H, N, n, n]
+
+        # Flatten to pass through the kernel in one shot: [H*N, n, n]
+        N = g.shape[0]
+        n = g.shape[1]
+        H = h.shape[0]
+        gh = B.reshape(gh, H * N, n, n)
+
+        # Compute group kernel between all Gi and all (Gj @ h)
+        K_gh = kernel_G.K(params_G, g, gh)  # [N, H*N]
+        K_gh = B.reshape(K_gh, N, H, N)  # [N, H, N] where axis 1 indexes h
+        K_avg = B.mean(K_gh, axis=1)  # [N, N]
+
+        # Renormalize average over H so that diagonal matches normalized Grassmannian
+        # kernel (the integral over H is not 1 by default).
+        eye_mask = B.eye(B.dtype(K_avg), N)
+        diag_mean = B.sum(K_avg * eye_mask) / B.cast(B.dtype(K_avg), np.array(N))
+        K_avg = K_avg / diag_mean
+        return K_analytic - K_avg
+
+    check_function_with_backend(
+        backend,
+        expected,
+        diff,
+        x,
+        g,
+        h,
+        atol=1e-1,
+    )
diff --git a/tests/spaces/test_homogeneous.py b/tests/spaces/test_homogeneous.py
new file mode 100644
index 00000000..ee5d9701
--- /dev/null
+++ b/tests/spaces/test_homogeneous.py
@@ -0,0 +1,117 @@
+import lab as B
+import numpy as np
+import pytest
+
+from geometric_kernels.kernels.karhunen_loeve import MaternKarhunenLoeveKernel
+from geometric_kernels.kernels.matern_kernel import MaternGeometricKernel
+from geometric_kernels.spaces.stiefel import Stiefel
+
+from ..helper import check_function_with_backend, create_random_state, np_to_backend
+
+
+def _choose_lengthscale_ratio_two(
+    kernel: MaternKarhunenLoeveKernel, params: dict
+) -> float:
+    """
+    Choose a lengthscale such that the first spectral weight divided by the
+    second equals 2 for the given kernel. Returns the scalar lengthscale.
+
+    - If nu == inf: w(s) = exp(-ls^2 * s / 2) => ls^2 = 2 ln 2 / (s1 - s0)
+    - If nu < inf:  w(s) = (2 nu / ls^2 + s)^(-nu - d/2)
+                    => let p = nu + d/2, a = 2 nu / ls^2
+                       ((a+s1)/(a+s0))^p = 2 => a = (s1 - 2^(1/p) s0) / (2^(1/p)-1)
+                       => ls^2 = 2 nu / a
+    """
+    # Extract the first two Laplacian eigenvalues (shape [L, 1])
+    s = np.asarray(kernel.eigenvalues_laplacian).reshape(-1)
+    if s.shape[0] < 2:
+        # Fallback: if not enough levels, just return default 1.0
+        return 1.0
+    s0, s1 = float(s[0]), float(s[1])
+
+    # Pull parameters
+    nu = float(np.asarray(params["nu"]).reshape(()))
+    d = int(kernel.space.dimension)
+
+    if np.isinf(nu):
+        denom = s1 - s0
+        l2 = 2.0 * np.log(2.0) / denom
+        return np.sqrt(np.array([l2])), np.array([nu])
+    else:
+        p = nu + d / 2.0
+        two_pow = 2.0 ** (1.0 / p)
+        denom = two_pow - 1.0
+        a = (s1 - two_pow * s0) / denom
+        l2 = 2.0 * nu / a
+        return np.sqrt(np.array([l2])), np.array([nu])
+
+
+@pytest.fixture(params=[(4, 2), (5, 2), (6, 3)], ids=lambda p: f"V({p[1]},{p[0]})")
+def stiefel_space(request):
+    n, m = request.param
+    key = np.random.RandomState(0)
+    # Increase stabilizer samples to reduce Monte Carlo variance in averaging
+    key, space = Stiefel(n, m, key, average_order=500)
+    return key, space
+
+
+@pytest.mark.parametrize(
+    "backend", ["numpy", "tensorflow", "torch", "jax"]
+)  # Limit to numpy to avoid optional deps
+def test_stiefel_kernel(stiefel_space, backend):
+    key, space = stiefel_space
+    space.samples_H = np_to_backend(space.samples_H, backend)
+    space.matrix_complement = np_to_backend(space.matrix_complement, backend)
+
+    # Number of levels for RFF kernel (created via MaternGeometricKernel for homogeneous spaces)
+    num_levels = 2
+    rs = create_random_state(backend)
+    kernel_rff = MaternGeometricKernel(space, num=num_levels, normalize=True, key=rs)
+    params_rff = kernel_rff.init_params()
+    G = space.G
+    kernel_G = MaternKarhunenLoeveKernel(G, num_levels, normalize=True)
+    params_G = kernel_G.init_params()
+    tuned_params = _choose_lengthscale_ratio_two(kernel_G, params_G)
+    tuned_ls = np_to_backend(tuned_params[0], backend)
+    tuned_nu = np_to_backend(tuned_params[1], backend)
+
+    params_rff["lengthscale"] = tuned_ls
+    params_G["lengthscale"] = tuned_ls
+    params_rff["nu"] = tuned_nu
+    params_G["nu"] = tuned_nu
+
+    # Stabilizer elements embedded into G
+    h_emb = space.embed_stabilizer(space.samples_H)
+    H = h_emb.shape[0]
+
+    # Sample N points on the Stiefel manifold
+    N = 5
+    key, X = space.random(key, N, project=True)
+
+    # Compare K_rff(X,X) vs average_h K_G(gx, gy @ h), with renormalization by mean diagonal
+    def diff(X):
+        nX = X.shape[0]
+        gX = space.embed_manifold(X)  # [N, n, n]
+        # [N, H, n, n] of gy @ h
+        YH = B.matmul(B.expand_dims(gX, axis=1), B.expand_dims(h_emb, axis=0))
+        X2_big = B.reshape(YH, -1, space.n, space.n)  # [N*H, n, n]
+        KG = kernel_G.K(params_G, gX, X2_big)  # [N, N*H]
+        KG_reshaped = B.reshape(KG, nX, nX, H)  # [N, N, H]
+        K_avg = B.mean(B.transpose(KG_reshaped, [0, 2, 1]), axis=1)  # [N, N]
+        # Renormalize averaged kernel to unit mean diagonal
+        identity = B.eye(B.dtype(K_avg), nX, nX)
+        mean_diag = B.sum(B.real(K_avg * identity)) / nX
+        K_avg = K_avg / mean_diag
+
+        K_rff = kernel_rff.K(params_rff, X, X)
+        return B.reshape(B.sum(B.abs(K_rff - K_avg), squeeze=False), 1, 1) / (
+            nX * (nX - 1)
+        )  # mean exclude diagonal
+
+    check_function_with_backend(
+        backend,
+        np.zeros((1, 1)),
+        lambda X: diff(X),
+        X,
+        atol=5e-1,
+    )