Repository: HenriquesLab/NanoPyx (language: python)

Issue:
Add rotate and shift_rotate methods to nearest_neighbor.pyx
Create def and cdef methods in class Interpolator. 
![image](https://user-images.githubusercontent.com/763757/216613154-98d14919-4a1d-4d8f-b18f-5668503edef7.png)

Make sure you run pyx2pxd to update the associated pxd file.

Then generate an equivalent file to image_shift.py

![image](https://user-images.githubusercontent.com/763757/216613732-9453e3d3-2348-4418-935f-ef75ea8699f1.png)

Then the equivalent to image_shift.ipynb

![image](https://user-images.githubusercontent.com/763757/216613945-0ac31d03-59f4-4bd9-90d6-248f8c3db84b.png)

Then a test similar to test_image_shift.py

![image](https://user-images.githubusercontent.com/763757/216614148-788b7f55-b7c1-4583-9299-497f9b68e838.png)



Discussion / hints:

Fix patch:
diff --git a/src/nanopyx/core/analysis/ccm/ccm.pxd b/src/nanopyx/core/analysis/ccm/ccm.pxd
index 45b0347c..375c5993 100644
--- a/src/nanopyx/core/analysis/ccm/ccm.pxd
+++ b/src/nanopyx/core/analysis/ccm/ccm.pxd
@@ -1,5 +1,6 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
+
 cdef float[:, :, :] _calculate_ccm(float[:, :, :] img_stack, int ref)
 cdef float[:, :, :] _calculate_ccm_from_ref(float[:, :, :] img_stack, float[:, :] img_ref)
 cdef float[:, :] _calculate_slice_ccm(float[:, :] img_ref, float[:, :] img_slice)
diff --git a/src/nanopyx/core/analysis/ccm/helper_functions.pxd b/src/nanopyx/core/analysis/ccm/helper_functions.pxd
index 5629f9fd..80705f37 100644
--- a/src/nanopyx/core/analysis/ccm/helper_functions.pxd
+++ b/src/nanopyx/core/analysis/ccm/helper_functions.pxd
@@ -1,5 +1,6 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
+
 cdef bint _check_even_square(float[:, :, :] image_arr) nogil
 cdef int _get_closest_even_square_size(float[:, :, :] image_arr) nogil
 cdef float[:, :, :] _make_even_square(float[:, :, :] image_arr) nogil
diff --git a/src/nanopyx/core/analysis/pearson_correlation.pxd b/src/nanopyx/core/analysis/pearson_correlation.pxd
index 853dff31..9311d397 100644
--- a/src/nanopyx/core/analysis/pearson_correlation.pxd
+++ b/src/nanopyx/core/analysis/pearson_correlation.pxd
@@ -1,4 +1,5 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
+
 cdef float _calculate_ppmcc(float[:, :] im1, float[:, :] im2, int shift_x, int shift_y) nogil
 cdef float _pearson_correlation(float[:,:] im1, float[:,:] im2) nogil
diff --git a/src/nanopyx/core/image/add_gaussians.pxd b/src/nanopyx/core/image/add_gaussians.pxd
index 5cbb7689..813fc071 100644
--- a/src/nanopyx/core/image/add_gaussians.pxd
+++ b/src/nanopyx/core/image/add_gaussians.pxd
@@ -1,3 +1,4 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
+
 cdef void _render_erf_gaussian(float[:,:] image, double xp, double yp, double amplitude, double sigma_x, double sigma_y) nogil
diff --git a/src/nanopyx/core/image/add_ramp.pxd b/src/nanopyx/core/image/add_ramp.pxd
index 73f17a7f..570c2a4b 100644
--- a/src/nanopyx/core/image/add_ramp.pxd
+++ b/src/nanopyx/core/image/add_ramp.pxd
@@ -1,2 +1,3 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
+
diff --git a/src/nanopyx/core/image/add_random_noise.pxd b/src/nanopyx/core/image/add_random_noise.pxd
index 1d2860ea..97fbff1e 100644
--- a/src/nanopyx/core/image/add_random_noise.pxd
+++ b/src/nanopyx/core/image/add_random_noise.pxd
@@ -1,5 +1,6 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
+
 cdef double _log_factorial(int x) nogil
 cdef int _poisson_small(double mean) nogil
 cdef int _poisson_large(double mean) nogil
diff --git a/src/nanopyx/core/image/add_squares.pxd b/src/nanopyx/core/image/add_squares.pxd
index 73f17a7f..570c2a4b 100644
--- a/src/nanopyx/core/image/add_squares.pxd
+++ b/src/nanopyx/core/image/add_squares.pxd
@@ -1,2 +1,3 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
+
diff --git a/src/nanopyx/core/particles/simulate_particle_field.pxd b/src/nanopyx/core/particles/simulate_particle_field.pxd
index 69b78a60..be382698 100644
--- a/src/nanopyx/core/particles/simulate_particle_field.pxd
+++ b/src/nanopyx/core/particles/simulate_particle_field.pxd
@@ -1,4 +1,5 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
+
 cdef bint _get_particle_candidate(float[:, :] _image_pdf, int particle_index, float[:] xp, float[:] yp, float min_distance) nogil
 cdef double _get_closest_distance(float x, float y, float[:] xp, float[:] yp) nogil
diff --git a/src/nanopyx/core/particles/simulate_photoswitching_time_tracks.pxd b/src/nanopyx/core/particles/simulate_photoswitching_time_tracks.pxd
index 16aac6fb..3fdeaa8a 100644
--- a/src/nanopyx/core/particles/simulate_photoswitching_time_tracks.pxd
+++ b/src/nanopyx/core/particles/simulate_photoswitching_time_tracks.pxd
@@ -1,3 +1,4 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
+
 cdef void _simple_state_transition_model(int[:] states, double p_on, double p_transient_off, double p_permanent_off, int initial_state) nogil
diff --git a/src/nanopyx/core/transform/image_rotate.py b/src/nanopyx/core/transform/image_rotate.py
new file mode 100644
index 00000000..fd80b033
--- /dev/null
+++ b/src/nanopyx/core/transform/image_rotate.py
@@ -0,0 +1,66 @@
+"""
+Combination of functions for rotating an image, using several interpolation methods.
+"""
+
+from scipy.ndimage import rotate
+import numpy as np
+
+from ..utils.time.timeit import timeit2
+from .interpolation import (
+    bicubic,
+    bilinear,
+    nearest_neighbor,
+    catmull_rom,
+    lanczos,
+)
+
+from skimage.transform import AffineTransform, warp
+import cv2
+
+@timeit2
+def catmull_rom_rotate(image: np.ndarray, angle:float):
+    interpolator = catmull_rom.Interpolator(image)
+    return interpolator.rotate(angle)
+
+
+@timeit2
+def lanczos_rotate(image: np.ndarray, angle:float, taps: int = 3):
+    interpolator = lanczos.Interpolator(image, taps)
+    return interpolator.rotate(angle)
+
+
+@timeit2
+def bicubic_rotate(image: np.ndarray, angle:float):
+    interpolator = bicubic.Interpolator(image)
+    return interpolator.rotate(angle)
+
+
+@timeit2
+def bilinear_rotate(image: np.ndarray, angle:float):
+    interpolator = bilinear.Interpolator(image)
+    return interpolator.rotate(angle)
+
+
+@timeit2
+def nearest_neighbor_rotate(image: np.ndarray, angle:float):
+    interpolator = nearest_neighbor.Interpolator(image)
+    return interpolator.rotate(angle)
+
+
+@timeit2
+def scipy_rotate(image: np.ndarray, angle:float):
+    return rotate(image, np.rad2deg(angle), reshape=False)
+
+
+@timeit2
+def skimage_rotate(image: np.ndarray, angle:float):
+    rotation = AffineTransform(rotation=angle)
+    shift2center = AffineTransform(translation=-np.array(image.shape[:2][::-1]) / 2)
+    shift2og = AffineTransform(translation=np.array(image.shape[:2][::-1]) / 2)
+    return warp(image, shift2center + rotation + shift2og)
+
+
+@timeit2
+def cv2_rotate(image: np.ndarray, angle:float):
+    rotmat = cv2.getRotationMatrix2D(np.array(image.shape[:2][::-1]) / 2, np.rad2deg(angle), 1)
+    return cv2.warpAffine(image, rotmat, image.shape[:2][::-1])
diff --git a/src/nanopyx/core/transform/image_shift_rotate.py b/src/nanopyx/core/transform/image_shift_rotate.py
new file mode 100644
index 00000000..2b9502b8
--- /dev/null
+++ b/src/nanopyx/core/transform/image_shift_rotate.py
@@ -0,0 +1,65 @@
+"""
+Combination of functions for rotating an image over an arbitrary center, using several interpolation methods.
+"""
+
+from scipy.ndimage import rotate
+import numpy as np
+
+from ..utils.time.timeit import timeit2
+from .interpolation import (
+    bicubic,
+    bilinear,
+    nearest_neighbor,
+    catmull_rom,
+    lanczos,
+)
+
+from skimage.transform import AffineTransform, warp
+import cv2
+
+@timeit2
+def catmull_rom_rotate_shift(image: np.ndarray, angle:float, cx:float, cy:float):
+    interpolator = catmull_rom.Interpolator(image)
+    return interpolator.rotate(angle, cx, cy)
+
+
+@timeit2
+def lanczos_rotate_shift(image: np.ndarray, angle:float, cx:float, cy:float, taps: int = 3):
+    interpolator = lanczos.Interpolator(image, taps)
+    return interpolator.rotate(angle, cx, cy)
+
+
+@timeit2
+def bicubic_rotate_shift(image: np.ndarray, angle:float, cx:float, cy:float):
+    interpolator = bicubic.Interpolator(image)
+    return interpolator.rotate(angle, cx, cy)
+
+
+@timeit2
+def bilinear_rotate_shift(image: np.ndarray, angle:float, cx:float, cy:float):
+    interpolator = bilinear.Interpolator(image)
+    return interpolator.rotate(angle, cx, cy)
+
+
+@timeit2
+def nearest_neighbor_rotate_shift(image: np.ndarray, angle:float, cx:float, cy:float):
+    interpolator = nearest_neighbor.Interpolator(image)
+    return interpolator.rotate(angle, cx, cy)
+
+
+#def scipy_rotate_shift(image: np.ndarray, angle:float, cx:float, cy:float):
+#scipy does not have a simple rotation around an arbitrary point which preserves image size
+#shift + rotation + shift would work but we lose information
+
+@timeit2
+def skimage_rotate_shift(image: np.ndarray, angle:float, cx:float, cy:float):
+    rotation = AffineTransform(rotation=angle)
+    shift2center = AffineTransform(translation=-np.array([cx, cy]))
+    shift2og = AffineTransform(translation=np.array([cx, cy]))
+    return warp(image, shift2center + rotation + shift2og)
+
+
+@timeit2
+def cv2_rotate_shift(image: np.ndarray, angle:float, cx:float, cy:float):
+    rotmat = cv2.getRotationMatrix2D(np.array([cx,cy]), np.rad2deg(angle), 1)
+    return cv2.warpAffine(image, rotmat, image.shape[:2][::-1])
diff --git a/src/nanopyx/core/transform/interpolation/bicubic.pxd b/src/nanopyx/core/transform/interpolation/bicubic.pxd
index bb97dd6f..dbd2b2ef 100644
--- a/src/nanopyx/core/transform/interpolation/bicubic.pxd
+++ b/src/nanopyx/core/transform/interpolation/bicubic.pxd
@@ -1,6 +1,7 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
 from .nearest_neighbor cimport Interpolator as InterpolatorNearestNeighbor
+
 cdef double _interpolate(float[:,:] image, double x, double y) nogil
 
 cdef class Interpolator(InterpolatorNearestNeighbor):
diff --git a/src/nanopyx/core/transform/interpolation/bicubic.pyx b/src/nanopyx/core/transform/interpolation/bicubic.pyx
index d2d1d50f..4ba22ce8 100644
--- a/src/nanopyx/core/transform/interpolation/bicubic.pyx
+++ b/src/nanopyx/core/transform/interpolation/bicubic.pyx
@@ -22,8 +22,8 @@ cdef double _interpolate(float[:,:] image, double x, double y) nogil:
     cdef int w = image.shape[1]
     cdef int h = image.shape[0]
 
-    # return 0 if x and y positions do not exist in image
-    if not 0 <= x < w and not 0 <= y < h:
+    # return 0 if x OR y positions do not exist in image
+    if not 0 <= x < w or not 0 <= y < h:
         return 0
 
     cdef int x0 = int(x)
diff --git a/src/nanopyx/core/transform/interpolation/bilinear.pxd b/src/nanopyx/core/transform/interpolation/bilinear.pxd
index bb97dd6f..dbd2b2ef 100644
--- a/src/nanopyx/core/transform/interpolation/bilinear.pxd
+++ b/src/nanopyx/core/transform/interpolation/bilinear.pxd
@@ -1,6 +1,7 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
 from .nearest_neighbor cimport Interpolator as InterpolatorNearestNeighbor
+
 cdef double _interpolate(float[:,:] image, double x, double y) nogil
 
 cdef class Interpolator(InterpolatorNearestNeighbor):
diff --git a/src/nanopyx/core/transform/interpolation/bilinear.pyx b/src/nanopyx/core/transform/interpolation/bilinear.pyx
index 4b86fb99..6d991de1 100644
--- a/src/nanopyx/core/transform/interpolation/bilinear.pyx
+++ b/src/nanopyx/core/transform/interpolation/bilinear.pyx
@@ -20,8 +20,8 @@ cdef double _interpolate(float[:,:] image, double x, double y) nogil:
     cdef int w = image.shape[1]
     cdef int h = image.shape[0]
 
-    # return 0 if x and y positions do not exist in image
-    if not 0 <= x < w and not 0 <= y < h:
+    # return 0 if x OR y positions do not exist in image
+    if not 0 <= x < w or not 0 <= y < h:
         return 0
 
     cdef int x0 = int(x)
diff --git a/src/nanopyx/core/transform/interpolation/catmull_rom.pxd b/src/nanopyx/core/transform/interpolation/catmull_rom.pxd
index 2ba04d56..34ce95fd 100644
--- a/src/nanopyx/core/transform/interpolation/catmull_rom.pxd
+++ b/src/nanopyx/core/transform/interpolation/catmull_rom.pxd
@@ -1,6 +1,7 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
 from .nearest_neighbor cimport Interpolator as InterpolatorNearestNeighbor
+
 cdef double _interpolate(float[:,:] image, double x, double y) nogil
 cdef double _cubic(double x) nogil
 
diff --git a/src/nanopyx/core/transform/interpolation/catmull_rom.pyx b/src/nanopyx/core/transform/interpolation/catmull_rom.pyx
index 53ff87b2..914ac939 100644
--- a/src/nanopyx/core/transform/interpolation/catmull_rom.pyx
+++ b/src/nanopyx/core/transform/interpolation/catmull_rom.pyx
@@ -28,8 +28,8 @@ cdef double _interpolate(float[:,:] image, double x, double y) nogil:
     cdef int w = image.shape[1]
     cdef int h = image.shape[0]
 
-    # return 0 if x and y positions do not exist in image
-    if not 0 <= x < w and not 0 <= y < h:
+    # return 0 if x OR y positions do not exist in image
+    if not 0 <= x < w or not 0 <= y < h:
         return 0
 
     cdef int x0 = int(x)
diff --git a/src/nanopyx/core/transform/interpolation/lanczos.pxd b/src/nanopyx/core/transform/interpolation/lanczos.pxd
index 964ee53f..731b46f2 100644
--- a/src/nanopyx/core/transform/interpolation/lanczos.pxd
+++ b/src/nanopyx/core/transform/interpolation/lanczos.pxd
@@ -1,6 +1,7 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
 from .nearest_neighbor cimport Interpolator as InterpolatorNearestNeighbor
+
 cdef double _interpolate(float[:,:] image, double x, double y, int taps) nogil
 cdef double _lanczos_kernel(double x, int taps) nogil
 
diff --git a/src/nanopyx/core/transform/interpolation/lanczos.pyx b/src/nanopyx/core/transform/interpolation/lanczos.pyx
index 254035e8..b95091cd 100644
--- a/src/nanopyx/core/transform/interpolation/lanczos.pyx
+++ b/src/nanopyx/core/transform/interpolation/lanczos.pyx
@@ -22,8 +22,8 @@ cdef double _interpolate(float[:,:] image, double x, double y, int taps) nogil:
     cdef int w = image.shape[1]
     cdef int h = image.shape[0]
 
-    # return 0 if x and y positions do not exist in image
-    if not 0 <= x < w and not 0 <= y < h:
+    # return 0 if x OR y positions do not exist in image
+    if not 0 <= x < w or not 0 <= y < h:
         return 0
 
     cdef int x0 = int(x)
diff --git a/src/nanopyx/core/transform/interpolation/nearest_neighbor.pxd b/src/nanopyx/core/transform/interpolation/nearest_neighbor.pxd
index 9971c94c..25956494 100644
--- a/src/nanopyx/core/transform/interpolation/nearest_neighbor.pxd
+++ b/src/nanopyx/core/transform/interpolation/nearest_neighbor.pxd
@@ -1,5 +1,6 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
+
 cdef double _interpolate(float[:,:] image, double x, double y) nogil
 
 cdef class Interpolator:
@@ -10,3 +11,4 @@ cdef class Interpolator:
     cdef float[:,:] _magnify(self, float magnification)
     cdef float[:,:] _magnify_xy(self, float magnification_y, float magnification_x)
     cdef float[:,:] _shift(self, float dx, float dy)
+    cdef float[:,:] _rotate(self, float angle, float cx, float cy)
diff --git a/src/nanopyx/core/transform/interpolation/nearest_neighbor.pyx b/src/nanopyx/core/transform/interpolation/nearest_neighbor.pyx
index 3416a58b..94099832 100644
--- a/src/nanopyx/core/transform/interpolation/nearest_neighbor.pyx
+++ b/src/nanopyx/core/transform/interpolation/nearest_neighbor.pyx
@@ -18,8 +18,8 @@ cdef double _interpolate(float[:,:] image, double x, double y) nogil:
     cdef int w = image.shape[1]
     cdef int h = image.shape[0]
 
-    # return 0 if x and y positions do not exist in image
-    if not 0 <= x < w and not 0 <= y < h:
+    # return 0 if x OR y positions do not exist in image
+    if not 0 <= x < w or not 0 <= y < h:
         return 0
 
     cdef int x0 = int(x)
@@ -143,4 +143,39 @@ cdef class Interpolator:
                 for j in range(y_start, y_end):
                     imShifted[j,i] = self._interpolate(i - dx, j - dy)
 
-        return imShifted 
\ No newline at end of file
+        return imShifted 
+
+
+    def rotate(self, float angle, float cx=-1, float cy=-1) -> np.ndarray:
+        """
+        Rotate an image by angle radians around (cx,cy) using interpolation
+        :param angle: rotation angle in radians, positive angles are counter clockwise
+        :param cx: x coordinate of the center of rotation, defaults to image center if negative
+        :param cy: y coordinate of the center of rotation, defaults to image center if negative
+        """
+        if cx<0 or cy<0:
+            cx = self.w / 2
+            cy = self.h / 2
+
+        imRotated = self._rotate(angle, cx, cy)
+        return np.asarray(imRotated).astype(self.original_dtype)
+
+
+    cdef float[:,:] _rotate(self, float angle, float cx, float cy):
+
+        cdef float[:,:] imRotated = np.zeros((self.h, self.w), dtype=np.float32)
+        
+        cdef int i, j
+        cdef float rotx, roty
+
+        cdef float cosine = np.cos(angle)
+        cdef float sine = np.sin(angle)
+
+        with nogil:
+            for i in prange(0, self.w):
+                for j in range(0, self.h):
+                    rotx = cosine*(i-cx) - sine*(j-cy) + cx
+                    roty = sine*(i-cx) + cosine*(j-cy) + cy
+                    imRotated[j,i] = self._interpolate(rotx,roty)
+
+        return imRotated
diff --git a/src/nanopyx/core/transform/radiality.pxd b/src/nanopyx/core/transform/radiality.pxd
index 698f578e..dfcb7e98 100644
--- a/src/nanopyx/core/transform/radiality.pxd
+++ b/src/nanopyx/core/transform/radiality.pxd
@@ -1,6 +1,7 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
 
+
 cdef class Radiality:
     cdef int magnification, symmetryAxis, border, nRingCoordinates
     cdef float ringRadius, psfWidth, gradRadius
diff --git a/src/nanopyx/core/utils/random.pxd b/src/nanopyx/core/utils/random.pxd
index 7d019db1..37265be3 100644
--- a/src/nanopyx/core/utils/random.pxd
+++ b/src/nanopyx/core/utils/random.pxd
@@ -1,3 +1,4 @@
 # autogenerated by pyx2pxd - https://github.com/HenriquesLab/pyx2pxd
 
+
 cdef double _random() nogil


Test patch:
diff --git a/tests/notebooks/image_rotate.ipynb b/tests/notebooks/image_rotate.ipynb
new file mode 100644
index 00000000..1af1ce07
--- /dev/null
+++ b/tests/notebooks/image_rotate.ipynb
@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from nanopyx.core.transform.image_rotate import *\n",
+    "from skimage.draw import rectangle\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "angle = -np.pi/2\n",
+    "\n",
+    "image_GT = np.zeros((256, 512), dtype=np.float32) \n",
+    "rr, cc = rectangle((256//2-13, 512//2-25), extent=(26,50), shape=image_GT.shape)\n",
+    "image_GT[rr,cc] = 1\n",
+    "\n",
+    "image = np.zeros((256, 512), dtype=np.float32) \n",
+    "rr, cc = rectangle((256//2-25, 512//2-13), extent=(50,26), shape=image.shape)\n",
+    "image[rr,cc] = 1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nearest_neighbor_rotate took 0.482082 mseconds\n",
+      "bilinear_rotate took 0.698090 mseconds\n",
+      "bicubic_rotate took 0.607252 mseconds\n",
+      "catmull_rom_rotate took 0.894070 mseconds\n",
+      "lanczos_rotate took 6.312132 mseconds\n",
+      "scipy_rotate took 8.933067 mseconds\n",
+      "skimage_rotate took 1.049280 mseconds\n",
+      "cv2_rotate took 0.257015 mseconds\n",
+      " \n",
+      "*** Profile printout saved to text file 'prun_Rotation.profile'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%prun -s cumulative -q -l 100 -T prun_Rotation.profile\n",
+    "# Profile the run\n",
+    "\n",
+    "imageNNRotation = nearest_neighbor_rotate(image, angle)\n",
+    "imageBLRotation = bilinear_rotate(image, angle)\n",
+    "imageBCRotation = bicubic_rotate(image, angle)\n",
+    "imageCRRotation = catmull_rom_rotate(image, angle)\n",
+    "imageLRotation = lanczos_rotate(image, angle)\n",
+    "imageScipyRotation = scipy_rotate(image, angle)\n",
+    "imageSkiRotation = skimage_rotate(image, angle)\n",
+    "imageCVRotation = cv2_rotate(image, angle)\n",
+    "\n",
+    "\n",
+    "rotated_images = {\n",
+    "    \"nearest-neighbor-rotation\": imageNNRotation,\n",
+    "    \"bilinear-rotation\": imageBLRotation,\n",
+    "    \"bilinear-rotation\": imageBLRotation,\n",
+    "    \"bicubic-rotation\": imageBCRotation,\n",
+    "    \"catmull-rom-rotation\": imageCRRotation,\n",
+    "    \"lanczos-rotation\": imageLRotation,\n",
+    "    \"scipy-rotation\": imageScipyRotation,\n",
+    "    \"skimage-rotation\": imageSkiRotation,\n",
+    "    \"cv2-rotation\": imageCVRotation\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UU0/FueeeG5deemmUlpbGDjvsELfeemvssMMOP2ifl112WdSuXTtuvPHGeOyxx2KnnXaKv/3tb9G5c+f8p1Wtrv79+8dHH30Uo0ePjkceeSQ222yzuPXWW2PChAkxadKkH+UxAABYsZtuuinatGkTY8eOjXvuuSeaNWsW5557brlLkwwZMiRmzZoVI0aMiK+//jpKSkpi9913j/POOy/mzZsX48ePjzvuuCO23XbbeOCBB+Kcc84puH/16tVj3Lhxce6558Zxxx0XS5YsiTFjxkTr1q2jS5cu8dxzz8VFF10U1157bXzzzTfRrFmz2GGHHeLYY4/N72PLLbeMRx55JE4//fQYMmRIrL/++jF8+PD48MMPVyvWbbPNNnHVVVfFbbfdFlWqVIntt98+/v73vxfEyjK/+c1vokGDBjF69OgYO3ZstGvXLm699dbo27fvSh+natWq8eCDD8ZZZ50V11xzTSxYsCA6deoUY8eOrfAD44D05bLvn1sM8BOaO3duNGjQIC6++OI4//zz1/Z0AAAAYK1yzTrgJ7NgwYJyY2XXMunSpctPOxkAAABIkLfBAj+ZO+64I8aOHRu9evWK4uLiePrpp+NPf/pT7Lnnniu8GC8AAAD8XIh1wE9myy23jGrVqsWIESPiq6++yn/oxMUXX7y2pwYAAABJcM06AAAAAEiEa9YBAAAAQCLEOgAAAABIRKWvWZfLVV+T8wAAWOOy7Nu1PQVWg/UoAPDfrjLrUWfWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCKqre0JAP97lmRjf9LHq5br/5M+HgAAAKwpzqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCKqre0JAAAAwPIsycau7SmsMdVy/df2FIAEObMOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiai2ticAAAAAy1Mt139tTwHgJ+XMOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkIpdlWVapDXPV1/RcAADWqCz7dm1PgdVgPQoA/LerzHrUmXUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgETksizL1vYkAAAAAABn1gEAAABAMsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYB/xoWrVqFf379//B991nn31Wut3YsWMjl8vFyy+//IMe53/de++9F7lcLsaOHbu2pwIAUCFrxrXPmhHSJtbB/4D58+fHsGHDYtKkSWt7KlTCtGnTYtiwYfHee+/94H2MHz8+rrrqqh9tTgDA/z5rxv8u1ozw81VtbU8AWH3z58+P4cOHR0REly5d1to8pk+fHlWqeA1gZaZNmxbDhw+PLl26RKtWrX7QPsaPHx//+Mc/4tRTTy0Yb9myZSxYsCCqV6+++hMFAP6nWDP+d7FmhJ8v/x8S1oJ58+at7SmsEUVFRf8zf/Dnz59f6W1LS0tj4cKFa3A2lZfL5aJmzZpRtWrVtT0VAGA1WTOmz5rxp5VlWSxYsKDC2xYuXBilpaWrtf//1d85/vuIdfxsDBs2LHK5XLz99tvRv3//qF+/fqyzzjoxYMCAcn9kb7311ujYsWPUqlUrGjZsGIccckj861//KtjmqaeeioMPPjg23HDDKCoqig022CBOO+20cn88+vfvH8XFxfHOO+9Er169om7dunHYYYdFxHd/sK+66qro0KFD1KxZM9Zdd9049thj44svvijYx8svvxw9evSIxo0bR61ataJ169Zx5JFHRsR315to0qRJREQMHz48crlc5HK5GDZs2AqPR5cuXWLzzTePadOmRdeuXaN27drRokWLGDFiRLltFy1aFEOHDo2NNtoo/70OHjw4Fi1aVLBdRdcfef3116OkpCRq1aoV66+/flx88cUxZsyYyOVyFZ7S//TTT8f2228fNWvWjDZt2sQtt9xS4fznz58fxx57bDRq1Cjq1asXRxxxRLnjFhExatSo6NChQxQVFUXz5s3jhBNOiLlz51Z4LF555ZXYbbfdonbt2nHeeect99jlcrk48cQT47bbbsvv++GHH46IiMmTJ0fPnj2jXr16UVxcHN26dYvnn38+f9+xY8fGwQcfHBERXbt2zT9fZW9Hue+++2LvvfeO5s2bR1FRUbRt2zYuuuiiWLp0acF8H3jggZg1a1b+/mWvti7v+iOPP/547LrrrlGnTp2oX79+/OIXv4g333yzYJtV+R0BgP9V1oyFrBnLHwtrxtVfM1b2Z7rsGoWPPPJIbLfddlGrVq0YPXp0TJo0KXK5XNx+++3xq1/9Klq0aBG1a9eOr776KiIiJkyYkP/dbNy4cRx++OExe/bsgn2v6HcO1jZvg+Vnp0+fPtG6deu49NJL49VXX42bbropmjZtGpdddllERPz617+OCy64IPr06RNHH310fPrppzFy5MjYbbfdYvLkyVG/fv2I+O4PwPz58+P444+PRo0axYsvvhgjR46Mf//73zFhwoSCx1yyZEn06NEjOnfuHFdccUXUrl07IiKOPfbYGDt2bAwYMCBOPvnkmDlzZlx77bUxefLkeOaZZ6J69erxySefxJ577hlNmjSJc845J+rXrx/vvfde3H333RER0aRJk7juuuvi+OOPj/333z8OOOCAiIjYcsstV3osvvjii9hrr73igAMOiD59+sSdd94ZZ599dmyxxRbRs2fPiPjuD+l+++0XTz/9dAwcODDat28fb7zxRlx55ZXxz3/+M+69997l7n/27Nn5xcW5554bderUiZtuuimKiooq3P7tt9+Ogw46KI466qjo169f/OEPf4j+/ftHx44do0OHDgXbnnjiiVG/fv0YNmxYTJ8+Pa677rqYNWtW/g93xHcLieHDh0f37t3j+OOPz2/30ksv5Y9vmTlz5kTPnj3jkEMOicMPPzzWXXfdFR67xx9/PP785z/HiSeeGI0bN45WrVrF1KlTY9ddd4169erF4MGDo3r16jF69Ojo0qVLPPHEE7HDDjvEbrvtFieffHJcc801cd5550X79u0jIvL/d+zYsVFcXBynn356FBcXx+OPPx5DhgyJr776Ki6//PKIiDj//PPjyy+/jH//+99x5ZVXRkREcXHxcuf62GOPRc+ePaNNmzYxbNiwWLBgQYwcOTJ22WWXePXVV8u9rWJlvyMA8HNgzfgf1ozWjD/2mrEyP9Nlpk+fHoceemgce+yxccwxx8Qmm2ySv+2iiy6KGjVqxJlnnhmLFi2KGjVq5PfbqVOnuPTSS+Pjjz+Oq6++Op555pmC382I5f/OwVqXwc/E0KFDs4jIjjzyyILx/fffP2vUqFGWZVn23nvvZVWrVs1+/etfF2zzxhtvZNWqVSsYnz9/frnHuPTSS7NcLpfNmjUrP9avX78sIrJzzjmnYNunnnoqi4jstttuKxh/+OGHC8bvueeeLCKyl156abnf26effppFRDZ06NAVHIFCJSUlWURkt9xyS35s0aJFWbNmzbIDDzwwP/bHP/4xq1KlSvbUU08V3P/666/PIiJ75pln8mMtW7bM+vXrl//6pJNOynK5XDZ58uT82Jw5c7KGDRtmEZHNnDmz4L4RkT355JP5sU8++SQrKirKzjjjjPzYmDFjsojIOnbsmC1evDg/PmLEiCwisvvuuy9/3xo1amR77rlntnTp0vx21157bRYR2R/+8Idyx+L666+vzKHLIiKrUqVKNnXq1ILx3r17ZzVq1Mjeeeed/NgHH3yQ1a1bN9ttt93yYxMmTMgiIps4cWK5fVf0c3XsscdmtWvXzhYuXJgf23vvvbOWLVuW23bmzJlZRGRjxozJj2299dZZ06ZNszlz5uTHXnvttaxKlSrZEUcckR+rzO8IAPyvs2YsZM1ozfhjrxkr+zOdZf95vh9++OGCbSdOnJhFRNamTZuCY7F48eKsadOm2eabb54tWLAgP/7Xv/41i4hsyJAh+bHl/c5BCrwNlp+d4447ruDrXXfdNebMmRNfffVV3H333VFaWhp9+vSJzz77LP+vWbNm0a5du5g4cWL+frVq1cr/97x58+Kzzz6LnXfeObIsi8mTJ5d73OOPP77g6wkTJsQ666wTe+yxR8FjdezYMYqLi/OPVfbKz1//+tf49ttvf6zDEBHfvbJ2+OGH57+uUaNGbL/99vHuu+8WzLN9+/ax6aabFsxz9913j4goOCbf9/DDD8dOO+0UW2+9dX6sYcOGyz29fLPNNotdd901/3WTJk1ik002KZhPmYEDBxa84nb88cdHtWrV4sEHH4yI714ZXLx4cZx66qkFFzA+5phjol69evHAAw8U7K+oqCgGDBiw3O/l+0pKSmKzzTbLf7106dL429/+Fr179442bdrkx9dbb73o27dvPP300/nT8ldk2Z+rr7/+Oj777LPYddddY/78+fHWW29Ven5lPvzww5gyZUr0798/GjZsmB/fcsstY4899sgfr2Wt6HcEAH4urBn/w5rxP6wZ/+OHrhkr+zNdpnXr1tGjR48K99WvX7+CY/Hyyy/HJ598EoMGDYqaNWvmx/fee+/YdNNNyz2fEeV/5yAF3gbLz86GG25Y8HWDBg0i4rvT+2fMmBFZlkW7du0qvO+yf+jff//9GDJkSPzlL38pd22FL7/8suDratWqxfrrr18wNmPGjPjyyy+jadOmFT7WJ598EhHf/YE/8MADY/jw4XHllVdGly5donfv3tG3b9/lvjWgzIIFC8rNpVmzZvn/Xn/99fOn/5dp0KBBvP766wXzfPPNN/PXOFnePCsya9as2GmnncqNb7TRRhVu//3npmw+FV1X5PvPUXFxcay33nr5a5rMmjUrIqLgNPmI7xaXbdq0yd9epkWLFlGjRo38119++WXBtWRq1KhRsHBp3bp1wf0//fTTmD9/frnHi/ju7QqlpaXxr3/9q9xbM75v6tSp8atf/Soef/zxcgud7z+XlbG841A2r0ceeSTmzZsXderUyY+v6HekXr16qzwHAPhvZM1ozWjN+J95/ZA14+effx6LFy/O316rVq1YZ511Kv0zXeb7x3BFt63o+9h0003j6aefLhir6HcOUiDW8bOzvE88yrIsSktLI5fLxUMPPVThdmXXeFi6dGnsscce8fnnn8fZZ58dm266adSpUydmz54d/fv3L/cpREVFReU+nr60tDSaNm0at912W4XzKVvo5HK5uPPOO+P555+P+++/Px555JE48sgj47e//W08//zzK7zuxB133FHulb8syyp1LJad5xZbbBG/+93vKtx2gw02WO7jr6rKzGdNWfYVuYiIU045JcaNG5f/uqSkJH9B34q2/zHMnTs3SkpKol69enHhhRdG27Zto2bNmvHqq6/G2WefvdqfblVZa/N5AIBUWDNaM1bEmvE/VvY8HHDAAfHEE0/kx/v16xdjx46t9M90mRUdw9U9vhX9zkEKxDpYRtu2bSPLsmjdunVsvPHGy93ujTfeiH/+858xbty4OOKII/Ljjz766Co91mOPPRa77LJLpf7I7LjjjrHjjjvGr3/96xg/fnwcdthhcfvtt8fRRx9d7pXOMj169FilOS1vnq+99lp069ZtuY+zPC1btoy333673HhFY6tqxowZ0bVr1/zX33zzTXz44YfRq1ev/GNHfHdB2mXfYrB48eKYOXNmdO/efYX7Hzx4cMHbPcpeKVyeJk2aRO3atWP69OnlbnvrrbeiSpUq+UXq8o7jpEmTYs6cOXH33XfHbrvtlh+fOXNmuW0r+1wsexwqmlfjxo0LXiEFAFbOmrHieVozWjMu67e//W3B2Y7NmzePiFX/mV4Vy34fZW/BLjN9+vT87ZA6CRmWccABB0TVqlVj+PDh5V6Zy7Is5syZExH/eRVp2W2yLIurr7660o/Vp0+fWLp0aVx00UXlbluyZEn+o+K/+OKLcnMpu57HokWLIiLyn1r0/Y+XX2+99aJ79+4F/1ZVnz59Yvbs2XHjjTeWu23BggUxb9685d63R48e8dxzz8WUKVPyY59//vlyX0VbFTfccEPB9Viuu+66WLJkSf4Tybp37x41atSIa665puD43XzzzfHll1/G3nvvvcL9b7bZZgXHrWPHjivcvmrVqrHnnnvGfffdl39bRUTExx9/HOPHj4/OnTvn30JattD5/vNV0c/V4sWLY9SoUeUer06dOpV6i8N6660XW2+9dYwbN67g8f7xj3/E3/72t/xCFQCoPGvGiudpzWjNuKyOHTsWHJuya/dV9mf6h9huu+2iadOmcf311+d/7iMiHnrooXjzzTdX+nxCKpxZB8to27ZtXHzxxXHuuefGe++9F7179466devGzJkz45577omBAwfGmWeeGZtuumm0bds2zjzzzJg9e3bUq1cv7rrrrgqvk7E8JSUlceyxx8all14aU6ZMiT333DOqV68eM2bMiAkTJsTVV18dBx10UIwbNy5GjRoV+++/f7Rt2za+/vrruPHGG6NevXr5P5q1atWKzTbbLO64447YeOONo2HDhrH55pvH5ptvvtrH5Je//GX8+c9/juOOOy4mTpwYu+yySyxdujTeeuut+POf/xyPPPJIbLfddhXed/DgwXHrrbfGHnvsESeddFLUqVMnbrrppthwww3j888/X+VXXZe1ePHi6NatW/Tp0yemT58eo0aNis6dO8d+++0XEd+9annuuefG8OHDY6+99or99tsvv12nTp0KXgH9sVx88cXx6KOPRufOnWPQoEFRrVq1GD16dCxatChGjBiR327rrbeOqlWrxmWXXRZffvllFBUVxe677x4777xzNGjQIPr16xcnn3xy5HK5+OMf/1jhWzo6duwYd9xxR5x++unRqVOnKC4ujn333bfCeV1++eXRs2fP2GmnneKoo46KBQsWxMiRI2OdddaJYcOG/ejHAQD+11kzlmfNWHk/9zVjZX+mf4jq1avHZZddFgMGDIiSkpI49NBD4+OPP46rr746WrVqFaeddtqP9n3AGrXmP3AW0lD2EeOffvppwXjZx7ov+5Hwd911V9a5c+esTp06WZ06dbJNN900O+GEE7Lp06fnt5k2bVrWvXv3rLi4OGvcuHF2zDHHZK+99lq5j0Dv169fVqdOneXO64Ybbsg6duyY1apVK6tbt262xRZbZIMHD84++OCDLMuy7NVXX80OPfTQbMMNN8yKioqypk2bZvvss0/28ssvF+zn2WefzTp27JjVqFEji4hs6NChKzweJSUlWYcOHcqN9+vXr9zHuy9evDi77LLLsg4dOmRFRUVZgwYNso4dO2bDhw/Pvvzyy/x2LVu2zPr161dw38mTJ2e77rprVlRUlK2//vrZpZdeml1zzTVZRGQfffRRwX333nvvCudZUlKS/7rs+XriiSeygQMHZg0aNMiKi4uzww47rOBj5stce+212aabbppVr149W3fddbPjjz8+++KLLyp1LJYnIrITTjihwtteffXVrEePHllxcXFWu3btrGvXrtmzzz5bbrsbb7wxa9OmTVa1atUsIrKJEydmWZZlzzzzTLbjjjtmtWrVypo3b54NHjw4e+SRRwq2ybIs++abb7K+fftm9evXzyIi/5zNnDmz3M9glmXZY489lu2yyy5ZrVq1snr16mX77rtvNm3atIJtVuV3BAD+V1kzFrJmXPmxWB5rxhVb2c90li3/+Z44cWIWEdmECRMq3Pcdd9yRbbPNNllRUVHWsGHD7LDDDsv+/e9/F2yzst85WJtyWeaK4cBP69RTT43Ro0fHN998s9wL0wIA8PNmzQj8XLlmHbBGLftR9hERc+bMiT/+8Y/RuXNniy4AACLCmhFgWa5ZB6xRO+20U3Tp0iXat28fH3/8cdx8883x1VdfxQUXXLC2pwYAQCKsGQH+Q6wD1qhevXrFnXfeGTfccEPkcrnYdttt4+abby74mHkAAH7erBkB/sM16wAAAAAgEa5ZBwAAAACJEOsAAAAAIBFiHQAAAAAkotIfMJHLVV+T8wAAWOOy7Nu1PQVWw993PHhtTwEAYLV0e37CSrdxZh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCKqre0JAGlbko1d21NYY6rl+q/tKQAAsBJdnu8dk3a8d21PY43o/sK98dgOvdf2NIDEOLMOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAichlWZZVasNc9TU9FwCANSrLvl3bU2A1/H3Hg9f2FAAAVku35yesdBtn1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBE5LIsy9b2JAAAAAAAZ9YBAAAAQDLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAB+Av37949cLhe5XC6qV68erVu3jsGDB8fChQsLtivb5vnnny8YX7RoUTRq1ChyuVxMmjQpP/7EE0/E7rvvHg0bNozatWtHu3btol+/frF48eKIiJg0aVJ+n9//99FHH/2g72XhwoVxwgknRKNGjaK4uDgOPPDA+Pjjj1d4ny5duuQft2bNmrHZZpvFqFGjCrZZvHhxjBgxIrbaaquoXbt2NG7cOHbZZZcYM2ZMfPvttz9orp9//nkcdthhUa9evahfv34cddRR8c0331R6rmX/jjvuuPztr732Whx66KGxwQYbRK1ataJ9+/Zx9dVXr3Quq/rcrq733ntvuc/99+eQkqlTp8aBBx4YrVq1ilwuF1ddddXanhL8pMQ6AACAn8hee+0VH374Ybz77rtx5ZVXxujRo2Po0KHltttggw1izJgxBWP33HNPFBcXF4xNmzYt9tprr9huu+3iySefjDfeeCNGjhwZNWrUiKVLlxZsO3369Pjwww8L/jVt2vQHfR+nnXZa3H///TFhwoR44okn4oMPPogDDjhgpfc75phj4sMPP4xp06ZFnz594oQTTog//elPEfFdqOvRo0f85je/iYEDB8azzz4bL774YpxwwgkxcuTImDp16g+a62GHHRZTp06NRx99NP7617/Gk08+GQMHDqz0XMv+jRgxIn/bK6+8Ek2bNo1bb701pk6dGueff36ce+65ce211650v5V9bn9Mjz32WLnnvmPHjhVuWxZ5v++HxtIfcr/58+dHmzZt4je/+U00a9bsBz0u/FfLAAAAWOP69euX/eIXvygYO+CAA7JtttmmYCwisl/96ldZvXr1svnz5+fH99hjj+yCCy7IIiKbOHFilmVZduWVV2atWrVa4eNOnDgxi4jsiy+++DG+jWzu3LlZ9erVswkTJuTH3nzzzSwisueee2659yspKclOOeWUgrF27dplhxxySJZlWXbZZZdlVapUyV599dVy9128eHH2zTffrPJcp02blkVE9tJLL+XHHnrooSyXy2WzZ89epbmuzKBBg7KuXbuucJtVeW6zLMsGDx6ctWvXLqtVq1bWunXr7Fe/+lW2ePHiLMuyrLS0NOvWrVu25557ZqWlpVmWZdmcOXOyFi1aZBdccEGWZVk2c+bMLCKyyZMnL3dOQ4cOzbbaaqvsxhtvzFq1apXlcrn8XEeNGpXtu+++We3atbOhQ4dmWZZlo0aNytq0aZNVr14923jjjbNbbrml3PdY0f1+qJYtW2ZXXnnlau0D/ts4sw4AAGAt+Mc//hHPPvts1KhRo9xtHTt2jFatWsVdd90VERHvv/9+PPnkk/HLX/6yYLtmzZrFhx9+GE8++eRqzeW2226L4uLiFf576qmnIuK7s8q+/fbb6N69e/7+m266aWy44Ybx3HPPrdLj1qpVK38m12233Rbdu3ePbbbZptx21atXjzp16kRExCWXXLLSub7//vsREfHcc89F/fr1Y7vttsvvq3v37lGlSpV44YUXVnpMGjduHJtvvnmce+65MX/+/BVu/+WXX0bDhg1X+j1X9rmNiKhbt26MHTs2pk2bFldffXXceOONceWVV0bEd2+pHTduXLz00ktxzTXXRETEcccdFy1atIghQ4asdB7Levvtt+Ouu+6Ku+++O6ZMmZIfHzZsWOy///7xxhtvxJFHHhn33HNPnHLKKXHGGWfEP/7xjzj22GNjwIABMXHixIL9ff9+EbHS52zZtxnDz121tT0BAACAn4u//vWvUVxcHEuWLIlFixZFlSpVlvvWySOPPDL+8Ic/xOGHHx5jx46NXr16RZMmTQq2Ofjgg+ORRx6JkpKSaNasWey4447RrVu3OOKII6JevXoF266//voFX7ds2TL/1tL99tsvdthhhxXOvUWLFhER8dFHH0WNGjWifv36Bbevu+66lb4G3tKlS+NPf/pTvP766/m3pM6YMSO6dOmy0vsed9xx0adPnxVu07x58/xcv/9W32rVqkXDhg1XONe+fftGy5Yto3nz5vH666/H2WefHdOnT4+77767wu2fffbZuOOOO+KBBx5Y6fwjKvfcRkT86le/yv93q1at4swzz4zbb789Bg8eHBHfPSejR4+OI444Ij766KN48MEHY/LkyVGtWuH/1N95552jSpXCc3WWvW7f4sWL45Zbbik3h759+8aAAQPyXx966KHRv3//GDRoUEREnH766fH888/HFVdcEV27dl3u/SKiIAJW5Ps/r/BzJtYBAAD8RLp27RrXXXddzJs3L6688sqoVq1aHHjggRVue/jhh8c555wT7777bowdOzZ/9tSyqlatGmPGjImLL744Hn/88XjhhRfikksuicsuuyxefPHFWG+99fLbPvXUU1G3bt3819WrV8//d926dQtuW1NGjRoVN910UyxevDiqVq0ap512Whx//PEREZFlWaX20bBhw0qdwbY6lr2m3RZbbBHrrbdedOvWLd55551o27Ztwbb/+Mc/4he/+EUMHTo09txzz0rtvzLPbUTEHXfcEddcc02888478c0338SSJUvKRa2DDz447rnnnvjNb34T1113XbRr167C/bRv336582nZsmWFsXDZMxIjIt58881y1/vbZZddyn24xvfvFxGx0UYbLffxgULeBgsAAPATqVOnTmy00Uax1VZbxR/+8Id44YUX4uabb65w20aNGsU+++wTRx11VCxcuDB69uy53P22aNEifvnLX8a1114bU6dOjYULF8b1119fsE3r1q1jo402yv9r2bJl/rZVeRtss2bNYvHixTF37tyC/X/88ccr/TCAww47LKZMmRIzZ86MefPmxe9+97v8GV8bb7xxvPXWWyu8f8SqvQ22WbNm8cknnxTcf8mSJfH555+v0gcXlJ11+PbbbxeMT5s2Lbp16xYDBw4sOAtuZSrz3D733HNx2GGHRa9eveKvf/1rTJ48Oc4///xyHwAxf/78eOWVV6Jq1aoxY8aMCh9vgw02KHjuvx/Oyt5i/H3LG1+Ziu7nbbBQec6sAwAAWAuqVKkS5513Xpx++unRt2/fqFWrVrltjjzyyOjVq1ecffbZUbVq1Urtt0GDBrHeeuvFvHnzKj2XVXkbbMeOHaN69erx97//PX9W4PTp0+P999+PnXbaaYX7WGeddZZ7hlXfvn3jvPPOi8mTJ5e7bt23334bixcvjjp16qzS22B32mmnmDt3brzyyiv5Tz99/PHHo7S0dKXf77LK3sK57JmKU6dOjd133z369esXv/71ryu9rzIre26fffbZaNmyZZx//vn5sVmzZpXb7owzzogqVarEQw89FL169Yq99947dt9991WeT2W0b98+nnnmmejXr19+7JlnnonNNttspff1NlioPLEOAABgLTn44IPjrLPOit///vdx5plnlrt9r732ik8//XS5IWP06NExZcqU2H///aNt27axcOHCuOWWW2Lq1KkxcuTIgm0/+eSTWLhwYcFYo0aNonr16qv0Nth11lknjjrqqDj99NOjYcOGUa9evTjppJNip512ih133LGS33l5p556ajzwwAPRrVu3uOiii6Jz585Rt27dePnll+Oyyy6Lm2++ObbeeutVehts+/btY6+99opjjjkmrr/++vj222/jxBNPjEMOOSQf9GbPnh3dunWLW265Jbbffvt45513Yvz48dGrV69o1KhRvP7663HaaafFbrvtFltuuWVEfPfW19133z169OgRp59+ev76d1WrVq3w7aQVWdlz265du3j//ffj9ttvj06dOsUDDzwQ99xzT8E2DzzwQPzhD3+I5557Lrbddts466yzol+/fvH6669HgwYN8tvNmTOn3DX66tevHzVr1qzUXMucddZZ0adPn9hmm22ie/fucf/998fdd98djz322Ervuypvg128eHFMmzYt/9+zZ8+OKVOmRHFxsbfT8rPgbbAAAABrSbVq1eLEE0+MESNGVHgmXC6Xi8aNG1f4ibEREdtvv3188803cdxxx0WHDh2ipKQknn/++bj33nujpKSkYNtNNtkk1ltvvYJ/r7zyyg+a95VXXhn77LNPHHjggbHbbrtFs2bNlvvhC5VVVFQUjz76aAwePDhGjx4dO+64Y3Tq1CmuueaaOPnkk2PzzTf/Qfu97bbbYtNNN41u3bpFr169onPnznHDDTfkb//2229j+vTp+U97rVGjRjz22GOx5557xqabbhpnnHFGHHjggXH//ffn73PnnXfGp59+GrfeemvB8ezUqVOl57Wy53a//faL0047LU488cTYeuut49lnn40LLrggf/unn34aRx11VAwbNiy23XbbiIgYPnx4rLvuuuXeUtq9e/dyz/29995b6bmW6d27d1x99dVxxRVXRIcOHWL06NExZsyYSn0wyKr44IMPYptttoltttkmPvzww7jiiitim222iaOPPvpHfRxIVS6r7FU8AQAAAIA1ypl1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHiRg2bFjkcrn47LPPVrhdq1aton///vmvJ02aFLlcLiZNmpQf69+/f7Rq1WrNTPRnzrEFANY268b/Do4t8EOJdcD/nFGjRsXYsWN/8P0/+OCDGDZsWEyZMuVHmxMAAOmxbgRSVG1tTwBYNdOnT48qVVbc2W+88cYoLS39iWaUnlGjRkXjxo0LXkleFR988EEMHz48WrVqFVtvvXXBbT/3YwsA/Pewblw560YgRc6sg/8yRUVFUb169RVuU7169SgqKvqJZrTqFi5cuEoLl3nz5q3B2aya1I8tAEAZ68a1K/VjuzwrOobz589frX0vWbIkFi9evFr7gJ8DsQ4S89lnn0WfPn2iXr160ahRozjllFNi4cKF+du/f+2Rinz/+hjvvfde5HK5uOKKK+KGG26Itm3bRlFRUXTq1Cleeumlcvd/66234qCDDoqGDRtGzZo1Y7vttou//OUvBdt8/vnnceaZZ8YWW2wRxcXFUa9evejZs2e89tprBduVXRvl9ttvj1/96lfRokWLqF27dnz11VcVzr3sGizTpk2Lvn37RoMGDaJz584R8d0f94suuig//1atWsV5550XixYtKjg+U6dOjSeeeCJyuVzkcrno0qVLpec8adKk6NSpU0REDBgwIL+PsrdHVHTtkXnz5sUZZ5wRG2ywQRQVFcUmm2wSV1xxRWRZVrBdLpeLE088Me69997YfPPNo6ioKDp06BAPP/xwhccCAGBFrButG1ekMs/N2LFjI5fLxRNPPBGDBg2Kpk2bxvrrrx8REV26dInNN988Xnnlldhtt92idu3acd5550VExCeffBJHHXVUrLvuulGzZs3YaqutYty4cQX7XvZn6aqrrso/F9OmTav09wA/V94GC4np06dPtGrVKi699NJ4/vnn45prrokvvvgibrnlltXe9/jx4+Prr7+OY489NnK5XIwYMSIOOOCAePfdd/Ovuk6dOjV22WWXaNGiRZxzzjlRp06d+POf/xy9e/eOu+66K/bff/+IiHj33Xfj3nvvjYMPPjhat24dH3/8cYwePTpKSkpi2rRp0bx584LHvuiii6JGjRpx5plnxqJFi6JGjRornOvBBx8c7dq1i0suuSS/eDn66KNj3LhxcdBBB8UZZ5wRL7zwQlx66aXx5ptvxj333BMREVdddVWcdNJJUVxcHOeff35ERKy77rqVnnP79u3jwgsvjCFDhsTAgQNj1113jYiInXfeucJ5ZlkW++23X0ycODGOOuqo2HrrreORRx6Js846K2bPnh1XXnllwfZPP/103H333TFo0KCoW7duXHPNNXHggQfG+++/H40aNar0cwkAYN34HevG8ir73JQZNGhQNGnSJIYMGVJwZt2cOXOiZ8+eccghh8Thhx8e6667bixYsCC6dOkSb7/9dpx44onRunXrmDBhQvTv3z/mzp0bp5xySsG+x4wZEwsXLoyBAwdGUVFRNGzYcIVzByIiA5IwdOjQLCKy/fbbr2B80KBBWURkr732WpZlWdayZcusX79++dsnTpyYRUQ2ceLE/Fi/fv2yli1b5r+eOXNmFhFZo0aNss8//zw/ft9992URkd1///35sW7dumVbbLFFtnDhwvxYaWlptvPOO2ft2rXLjy1cuDBbunRpwVxnzpyZFRUVZRdeeGG5+bVp0yabP39+pY/DoYceWjA+ZcqULCKyo48+umD8zDPPzCIie/zxx/NjHTp0yEpKSsrtu7Jzfumll7KIyMaMGVNuH98/tvfee28WEdnFF19csN1BBx2U5XK57O23386PRURWo0aNgrHXXnsti4hs5MiR5Q8GAEAFrBsLj4N1Y3mVfW7GjBmTRUTWuXPnbMmSJQX7KCkpySIiu/766wvGr7rqqiwisltvvTU/tnjx4mynnXbKiouLs6+++irLsv/8LNWrVy/75JNPVjpn4D+8DRYSc8IJJxR8fdJJJ0VExIMPPrja+/6///u/aNCgQf7rslf/3n333Yj47nT/xx9/PPr06RNff/11fPbZZ/HZZ5/FnDlzokePHjFjxoyYPXt2RHx3DZSyCxYvXbo05syZE8XFxbHJJpvEq6++Wu6x+/XrF7Vq1ar0XI877riCr8u+/9NPP71g/IwzzoiIiAceeGCl+1zVOVfGgw8+GFWrVo2TTz653LyyLIuHHnqoYLx79+7Rtm3b/Ndbbrll1KtXL/8cAABUlnXjd6wbC63Kc1PmmGOOiapVq5bbV1FRUQwYMKDc99GsWbM49NBD82PVq1ePk08+Ob755pt44oknCrY/8MADo0mTJiucM1DI22AhMe3atSv4um3btlGlSpV47733VnvfG264YcHXZQuwL774IiIi3n777ciyLC644IK44IILKtzHJ598Ei1atIjS0tK4+uqrY9SoUTFz5sxYunRpfpuKTstv3bp1/r+XLl0an376acHtDRs2LHiLw7LbR0TMmjUrqlSpEhtttFHBeLNmzaJ+/foxa9as5X7fZVZ1zpUxa9asaN68edStW7dgvH379vnbl/X95yDiu+eh7DkAAKgs68by20f8fNaNixcvjs8//7zg9iZNmqzSc1Pm+8ewTIsWLcq9DXnWrFnRrl27cp80vLzvY3n7BpZPrIPE5XK5H21fFb1aFhH5a3uUfdLWmWeeGT169Khw27JFzyWXXBIXXHBBHHnkkXHRRRdFw4YNo0qVKnHqqadW+Ildy746+q9//avcH+2JEyfmL+j7/e2XtTrHY1XnvCas7DkAAPihrBsL/a+vG5999tno2rVrwW0zZ85cpeemzPKO4aqc4bg8P8Y+4OdGrIPEzJgxo2BB8vbbb0dpaWm5T5JaE9q0aRMR353G3r179xVue+edd0bXrl3j5ptvLhifO3duNG7ceIX3bdasWTz66KMFY1tttdUK79OyZcsoLS2NGTNm5F+1i4j4+OOPY+7cudGyZcv82PIWZpWd86os7Fq2bBmPPfZYfP311wWvkr711lv52wEA1gTrxor9XNaNW221Vblj06xZs6hdu3ZEVO65+SFatmwZr7/+epSWlhacXWf9Cz8e16yDxPz+978v+HrkyJEREdGzZ881/thNmzaNLl26xOjRo+PDDz8sd/uyb0GoWrVqubPBJkyYUO76FxWpWbNmdO/eveDfstdEqUivXr0i4rtP7VrW7373u4iI2HvvvfNjderUiblz55bbR2XnXKdOnYiICvdR0byWLl0a1157bcH4lVdeGblc7id53gCAnyfrxor9XNaNDRo0KHdsatasuUrPzQ/Rq1ev+Oijj+KOO+7Ijy1ZsiRGjhwZxcXFUVJSslr7B5xZB8mZOXNm7LfffrHXXnvFc889F7feemv07dt3pa8g/lh+//vfR+fOnWOLLbaIY445Jtq0aRMff/xxPPfcc/Hvf/87XnvttYiI2GeffeLCCy+MAQMGxM477xxvvPFG3HbbbflXWX9sW221VfTr1y9uuOGGmDt3bpSUlMSLL74Y48aNi969exe8BaBjx45x3XXXxcUXXxwbbbRRNG3aNHbfffdKz7lt27ZRv379uP7666Nu3bpRp06d2GGHHSq83sa+++4bXbt2jfPPPz/ee++92GqrreJvf/tb3HfffXHqqacWXBQYAODHZN1YMevGyj83P8TAgQNj9OjR0b9//3jllVeiVatWceedd8YzzzwTV111Vblr8gGrTqyDxNxxxx0xZMiQOOecc6JatWpx4oknxuWXX/6TPf5mm20WL7/8cgwfPjzGjh0bc+bMiaZNm8Y222wTQ4YMyW933nnnxbx582L8+PFxxx13xLbbbhsPPPBAnHPOOWtsbjfddFO0adMmxo4dG/fcc080a9Yszj333Bg6dGjBdkOGDIlZs2bFiBEj4uuvv46SkpLYfffdKz3n6tWrx7hx4+Lcc8+N4447LpYsWRJjxoypcNFVpUqV+Mtf/hJDhgyJO+64I8aMGROtWrWKyy+/PP+JYwAAa4J14/L93NeNlX1ufohatWrFpEmT4pxzzolx48bFV199FZtsskmMGTMm+vfv/+N8A/Azl8tc1RwAAAAAkuCadQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACAR1Sq7YS5XfU3OAwBgjcuyb9f2FFgNf9/x4LU9BQCA1dLt+Qkr3caZdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACAR1db2BIC0LcnGru0prDHVcv3X9hQAAFiJLs/3jkk73ru2p7FGdH/h3nhsh95rexpAYpxZBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYt3/Y+/Oo6Qo78Zvf3tgWAdENhE0M4CoqAki7hoHgcgSo1ECP1AjIBFxiYoL7oJZjAsJKgZFE0GjMQYVnyAaIxHFCC4h4gJKXECfF3EXIuuw1PuHh35sZ4ABF27jdZ0zJzPVd1XfXTXR8jNV3QAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJCIXJZlWbUG5oq/7LkAAHypsmz11p4Cn8Pf9++ztacAAPC5dH1y4ibHuLIOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJCKXZVm2tScBAAAAALiyDgAAAACSIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAA8BUYOHBg5HK5yOVyUVxcHK1bt47hw4fHypUrC8atH/Pkk08WLF+1alU0adIkcrlcPProo/nljz32WHTp0iUaN24c9erVi3bt2sWAAQOioqIiIiIeffTR/DY/+/X2229v0WtZuXJlnHrqqdGkSZMoKSmJ3r17xzvvvLPRdTp37px/3jp16sRuu+0WY8eOLRhTUVERV111VXTo0CHq1asXTZs2jYMOOijGjx8fq1ev3qK5fvjhh3HsscdGw4YNo1GjRjF48OBYunRptee6/mvo0KH5x5977rno379/7LjjjlG3bt1o3759XHvttZucy+Ye289rwYIFGzz2n51DSubMmRO9e/eOsrKyyOVycc0112ztKcFXSqwDAAD4ivTo0SMWLVoUr7/+eowePTrGjRsXI0aMqDRuxx13jPHjxxcsmzRpUpSUlBQsmzt3bvTo0SP23nvvmD59erzwwgsxZsyYqFWrVqxdu7Zg7Lx582LRokUFX82bN9+i1zFs2LCYPHlyTJw4MR577LF466234uijj97keieeeGIsWrQo5s6dG3379o1TTz017rzzzoj4JNR17949rrjiihgyZEjMmDEjnn766Tj11FNjzJgxMWfOnC2a67HHHhtz5syJhx9+OO6///6YPn16DBkypNpzXf911VVX5R+bNWtWNG/ePG6//faYM2dOXHTRRXHBBRfE9ddfv8ntVvfYfpGmTp1a6dh36tSpyrHrI+9nbWks3ZL1li9fHm3atIkrrrgiWrRosUXPC19rGQAAAF+6AQMGZEceeWTBsqOPPjrr2LFjwbKIyC6++OKsYcOG2fLly/PLv/e972WXXHJJFhHZtGnTsizLstGjR2dlZWUbfd5p06ZlEZF99NFHX8TLyBYvXpwVFxdnEydOzC976aWXsojIZs6cucH1ysvLszPOOKNgWbt27bJ+/fplWZZlV155ZVZUVJT961//qrRuRUVFtnTp0s2e69y5c7OIyJ555pn8sgcffDDL5XLZwoULN2uum3LKKadkhx566EbHbM6xzbIsGz58eNauXbusbt26WevWrbOLL744q6ioyLIsy9atW5d17do1O+yww7J169ZlWZZlH3zwQdaqVavskksuybIsy+bPn59FRPbss89ucE4jRozIOnTokN18881ZWVlZlsvl8nMdO3Zs9oMf/CCrV69eNmLEiCzLsmzs2LFZmzZtsuLi4mznnXfObrvttkqvsar1tlRpaWk2evToz7UN+LpxZR0AAMBW8OKLL8aMGTOiVq1alR7r1KlTlJWVxT333BMREW+++WZMnz49fvzjHxeMa9GiRSxatCimT5/+ueZyxx13RElJyUa/Hn/88Yj45Kqy1atXR7du3fLr77rrrvGtb30rZs6cuVnPW7du3fyVXHfccUd069YtOnbsWGlccXFx1K9fPyIiLr/88k3O9c0334yIiJkzZ0ajRo1i7733zm+rW7duUVRUFE899dQm90nTpk1jjz32iAsuuCCWL1++0fFLliyJxo0bb/I1V/fYRkQ0aNAgJkyYEHPnzo1rr702br755hg9enREfHJL7a233hrPPPNMXHfddRERMXTo0GjVqlVceumlm5zHp7366qtxzz33xL333huzZ8/OLx85cmQcddRR8cILL8QJJ5wQkyZNijPOOCPOPvvsePHFF+Okk06KQYMGxbRp0wq299n1ImKTx+zTtxnDN13NrT0BAACAb4r7778/SkpKYs2aNbFq1aooKira4K2TJ5xwQtxyyy1x3HHHxYQJE6JXr17RrFmzgjF9+vSJhx56KMrLy6NFixax//77R9euXeP444+Phg0bFozdYYcdCn4uLS3N31p6xBFHxH777bfRubdq1SoiIt5+++2oVatWNGrUqODx7bbbrtrvgbd27dq488474/nnn8/fkvrKK69E586dN7nu0KFDo2/fvhsd07Jly/xcP3urb82aNaNx48YbnesxxxwTpaWl0bJly3j++efjvPPOi3nz5sW9995b5fgZM2bEXXfdFVOmTNnk/COqd2wjIi6++OL892VlZXHOOefEn/70pxg+fHhEfHJMxo0bF8cff3y8/fbb8cADD8Szzz4bNWsW/qf+gQceGEVFhdfqfPp9+yoqKuK2226rNIdjjjkmBg0alP+5f//+MXDgwDjllFMiIuKss86KJ598MkaNGhWHHnroBteLiIIIWJXP/r7CN5lYBwAA8BU59NBD44Ybbohly5bF6NGjo2bNmtG7d+8qxx533HFx/vnnx+uvvx4TJkzIXz31aTVq1Ijx48fHL37xi3jkkUfiqaeeissvvzyuvPLKePrpp2P77bfPj3388cejQYMG+Z+Li4vz3zdo0KDgsS/L2LFj43e/+11UVFREjRo1YtiwYXHyySdHRESWZdXaRuPGjat1Bdvn8en3tPv2t78d22+/fXTt2jVee+21aNu2bcHYF198MY488sgYMWJEHHbYYdXafnWObUTEXXfdFdddd1289tprsXTp0lizZk2lqNWnT5+YNGlSXHHFFXHDDTdEu3btqtxO+/btNzif0tLSKmPhp69IjIh46aWXKr3f30EHHVTpwzU+u15ExE477bTB5wcKuQ0WAADgK1K/fv3YaaedokOHDnHLLbfEU089Fb///e+rHNukSZM4/PDDY/DgwbFy5cro2bPnBrfbqlWr+PGPfxzXX399zJkzJ1auXBk33nhjwZjWrVvHTjvtlP8qLS3NP7Y5t8G2aNEiKioqYvHixQXbf+eddzb5YQDHHntszJ49O+bPnx/Lli2L3/zmN/krvnbeeed4+eWXN7p+xObdBtuiRYt49913C9Zfs2ZNfPjhh5v1wQXrrzp89dVXC5bPnTs3unbtGkOGDCm4Cm5TqnNsZ86cGccee2z06tUr7r///nj22WfjoosuqvQBEMuXL49Zs2ZFjRo14pVXXqny+XbccceCY//ZcLb+FuPP2tDyTalqPbfBQvW5sg4AAGArKCoqigsvvDDOOuusOOaYY6Ju3bqVxpxwwgnRq1evOO+886JGjRrV2u62224b22+/fSxbtqzac9mc22A7deoUxcXF8fe//z1/VeC8efPizTffjAMOOGCj29hmm202eIXVMcccExdeeGE8++yzld63bvXq1VFRURH169ffrNtgDzjggFi8eHHMmjUr/+mnjzzySKxbt26Tr/fT1t/C+ekrFefMmRNdunSJAQMGxC9/+ctqb2u9TR3bGTNmRGlpaVx00UX5ZW+88UalcWeffXYUFRXFgw8+GL169Yrvf//70aVLl82eT3W0b98+nnjiiRgwYEB+2RNPPBG77bbbJtd1GyxUn1gHAACwlfTp0yfOPffc+O1vfxvnnHNOpcd79OgR77333gZDxrhx42L27Nlx1FFHRdu2bWPlypVx2223xZw5c2LMmDEFY999991YuXJlwbImTZpEcXHxZt0Gu80228TgwYPjrLPOisaNG0fDhg3jpz/9aRxwwAGx//77V/OVV3bmmWfGlClTomvXrvHzn/88Dj744GjQoEH885//jCuvvDJ+//vfx5577rlZt8G2b98+evToESeeeGLceOONsXr16jjttNOiX79++aC3cOHC6Nq1a9x2222x7777xmuvvRZ//OMfo1evXtGkSZN4/vnnY9iwYXHIIYfEd77znYj45NbXLl26RPfu3eOss87Kv/9djRo1qrydtCqbOrbt2rWLN998M/70pz/FPvvsE1OmTIlJkyYVjJkyZUrccsstMXPmzNhrr73i3HPPjQEDBsTzzz8f2267bX7cBx98UOk9+ho1ahR16tSp1lzXO/fcc6Nv377RsWPH6NatW0yePDnuvffemDp16ibX3ZzbYCsqKmLu3Ln57xcuXBizZ8+OkpISt9PyjeA2WAAAgK2kZs2acdppp8VVV11V5ZVwuVwumjZtWuUnxkZE7LvvvrF06dIYOnRo7L777lFeXh5PPvlk3HfffVFeXl4wdpdddontt9++4GvWrFlbNO/Ro0fH4YcfHr17945DDjkkWrRoscEPX6iu2rVrx8MPPxzDhw+PcePGxf777x/77LNPXHfddXH66afHHnvssUXbveOOO2LXXXeNrl27Rq9eveLggw+Om266Kf/46tWrY968eflPe61Vq1ZMnTo1DjvssNh1113j7LPPjt69e8fkyZPz69x9993x3nvvxe23316wP/fZZ59qz2tTx/aII46IYcOGxWmnnRZ77rlnzJgxIy655JL84++9914MHjw4Ro4cGXvttVdERFx22WWx3XbbVbqltFu3bpWO/X333Vftua73wx/+MK699toYNWpU7L777jFu3LgYP358tT4YZHO89dZb0bFjx+jYsWMsWrQoRo0aFR07doyf/OQnX+jzQKpyWXXfxRMAAAAA+FK5sg4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6yAhI0eOjFwuF++///5Gx5WVlcXAgQO/tHl07tw59thjj02OW7BgQeRyuZgwYcKXNpeUdO7cOTp37ry1pwEA4Lwxcc4bgc9DrAP+q1x++eVx3333bfH6c+fOjZEjR8aCBQu+sDkBAJAe541Aqmpu7QkAm2/evHlRVLT1W3tpaWmsWLEiiouLt/ZU8i6//PL40Y9+FD/84Q+3aP25c+fGZZddFp07d46ysrKCx/72t799/gkCAHyFnDdumPNGIFVb/5/awGarXbt2Eic6uVwu6tSpEzVq1PjSnmPZsmVf2rY3V61ataJWrVpbexoAANXmvHHr+LqeN25oH2ZZFitWrPhc2165cmWsW7fuc20DvinEOkjQ+++/H3379o2GDRtGkyZN4owzzoiVK1fmH6/qvUcWL14cw4YNi7Kysqhdu3bssMMOcfzxx+ffx2TChAmRy+UqXab/6KOPRi6Xi0cffbTSPGbNmhUHHnhg1K1bN1q3bh033nhjweMbeu+Rl19+Ofr27RvNmjWLunXrxi677BIXXXTRJl/3wIEDo6SkJF577bXo1atXNGjQII499tiI+OTE4eyzz44dd9wxateuHbvsskuMGjUqsizLr5/L5WLZsmVx6623Ri6Xi1wul99Pb7zxRpxyyimxyy67RN26daNJkybRp0+fgv0xYcKE6NOnT0REHHroofltrN83Vb33yLvvvhuDBw+O7bbbLurUqRMdOnSIW2+9tcr9NGrUqLjpppuibdu2Ubt27dhnn33imWee2eR+AQDYEOeNzhur8tRTT0WPHj1im222iXr16kV5eXk88cQTBWPWv+/h3Llz45hjjoltt902Dj744Ij45Pfm8MMPj4ceeij23nvvqFu3bowbNy4iIl5//fXo06dPNG7cOOrVqxf7779/TJkypWDb639X/vSnP8XFF18crVq1inr16sV//vOfar8G+CZzGywkqG/fvlFWVha/+tWv4sknn4zrrrsuPvroo7jtttuqHL906dL47ne/Gy+99FKccMIJsddee8X7778ff/nLX+L/+//+v2jatOlmz+Gjjz6KXr16Rd++faN///7x5z//OU4++eSoVatWnHDCCRtc7/nnn4/vfve7UVxcHEOGDImysrJ47bXXYvLkyfHLX/5yk8+7Zs2a6N69exx88MExatSoqFevXmRZFkcccURMmzYtBg8eHHvuuWc89NBDce6558bChQtj9OjRERHxhz/8IX7yk5/EvvvuG0OGDImIiLZt20ZExDPPPBMzZsyIfv36xQ477BALFiyIG264ITp37hxz586NevXqxSGHHBKnn356XHfddXHhhRdG+/btIyLy//tZK1asiM6dO8err74ap512WrRu3TomTpwYAwcOjMWLF8cZZ5xRMP6Pf/xjfPzxx3HSSSdFLpeLq666Ko4++uh4/fXXk/iLNwDw9eO80XnjZz3yyCPRs2fP6NSpU4wYMSKKiopi/Pjx0aVLl3j88cdj3333LRjfp0+faNeuXVx++eUFQXPevHnRv3//OOmkk+LEE0+MXXbZJd5555048MADY/ny5XH66adHkyZN4tZbb40jjjgi7r777jjqqKMKtv3zn/88atWqFeecc06sWrXqa3m1IWwVGZCMESNGZBGRHXHEEQXLTznllCwisueeey7LsiwrLS3NBgwYkH/80ksvzSIiu/feeyttc926dVmWZdn48eOziMjmz59f8Pi0adOyiMimTZuWX1ZeXp5FRPbrX/86v2zVqlXZnnvumTVv3jyrqKjIsizL5s+fn0VENn78+Py4Qw45JGvQoEH2xhtvVDmPjRkwYEAWEdn5559fsPy+++7LIiL7xS9+UbD8Rz/6UZbL5bJXX301v6x+/foF+2a95cuXV1o2c+bMLCKy2267Lb9s4sSJlfbHeuXl5Vl5eXn+52uuuSaLiOz222/PL6uoqMgOOOCArKSkJPvPf/6TZdn/7acmTZpkH374YX7s//zP/2QRkU2ePLnqHQIAsAHOG503VmXdunVZu3btsu7duxfsx+XLl2etW7fOvve97+WXrf8d6t+/f6XtlJaWZhGR/fWvfy1YfuaZZ2YRkT3++OP5ZR9//HHWunXrrKysLFu7dm2WZf/3u9KmTZsq9yewcW6DhQSdeuqpBT//9Kc/jYiIBx54oMrx99xzT3To0KHSX7IiPrnEf0vUrFkzTjrppPzPtWrVipNOOinefffdmDVrVpXrvPfeezF9+vQ44YQT4lvf+tYWz+Pkk08u+PmBBx6IGjVqxOmnn16w/Oyzz44sy+LBBx/c5Dbr1q2b/3716tXxwQcfxE477RSNGjWKf/3rX9We22fn1aJFi+jfv39+WXFxcZx++umxdOnSeOyxxwrG/7//9/9i2223zf/83e9+NyI+uZUAAGBLOG903vhps2fPjldeeSWOOeaY+OCDD+L999+P999/P5YtWxZdu3aN6dOnV3rfuKFDh1a5rdatW0f37t0rvY599903f7tsRERJSUkMGTIkFixYEHPnzi0YP2DAgIL9CVSP22AhQe3atSv4uW3btlFUVLTBj4V/7bXXonfv3l/oHFq2bBn169cvWLbzzjtHxCfvpbH//vtXWmf9ycMee+yxwe1WVFTEhx9+WLCsWbNm+TcbrlmzZuywww4Fj7/xxhvRsmXLaNCgQcHy9bcZvPHGG5t8PStWrIhf/epXMX78+Fi4cGHBJf5LlizZ5PpVeeONN6Jdu3aVPmFtQ/P67Ino+hOwjz76aIueHwDAeeM387xxxYoVlebSokWLeOWVVyLik0i2IUuWLCkIga1bt65yXFXL33jjjdhvv/0qLf/06/j0Md3QtoGNE+vga2BL/8pZnW2sXbv2c297c8yYMSMOPfTQgmXz58/Pf9x97dq1K53EfBF++tOfxvjx4+PMM8+MAw44ILbZZpvI5XLRr1+/r+xTqTb06WefPgEEAPg8nDd+fl+H88a77rorBg0aVOmx9fO7+uqrY88996xyGyUlJQU/b+jKty/iijhX1cGWEesgQa+88krBX6FeffXVWLduXf7E5LPatm0bL7744ka3uf6vZ4sXLy5YvqG/Lr711luxbNmygr+S/vvf/46I2OA82rRpExGx0bl06NAhHn744YJlLVq02OjcS0tLY+rUqfHxxx8X/JX05Zdfzj++3oZOLu++++4YMGBA/PrXv84vW7lyZaX9sTknuKWlpfH888/HunXrCk4Uq5oXAMCXwXljoW/KeWP37t0r7ZuI//uQjIYNG0a3bt02a5vVUVpaGvPmzau03PkvfLG8Zx0k6Le//W3Bz2PGjImIiJ49e1Y5vnfv3vHcc8/FpEmTKj22/q9v6//FPX369Pxja9eujZtuuqnKba5Zsyb/8ewRn9yGMG7cuGjWrFl06tSpynWaNWsWhxxySNxyyy3x5ptvVjmPbbfdNrp161bwVadOnSq3t16vXr1i7dq1cf311xcsHz16dORyuYL9Ur9+/UonUhGf/HXys1ewjRkzptJfiNefZFa1jarm9fbbb8ddd92VX7ZmzZoYM2ZMlJSURHl5+Sa3AQDweThvLPRNOW/cfvvtK+2biIhOnTpF27ZtY9SoUbF06dJK67333nub9TxVvY6nn346Zs6cmV+2bNmyuOmmm6KsrCx22223z7V94BOurIMEzZ8/P4444ojo0aNHzJw5M26//fY45phjokOHDlWOP/fcc+Puu++OPn36xAknnBCdOnWKDz/8MP7yl7/EjTfeGB06dIjdd9899t9//7jgggviww8/jMaNG8ef/vSnWLNmTZXbbNmyZVx55ZWxYMGC2HnnneOuu+6K2bNnx0033bTRj4u/7rrr4uCDD4699torhgwZEq1bt44FCxbElClTYvbs2Vu0P37wgx/EoYceGhdddFEsWLAgOnToEH/729/if/7nf+LMM8/Mn1BGfHKCMnXq1PjNb34TLVu2jNatW8d+++0Xhx9+ePzhD3+IbbbZJnbbbbeYOXNmTJ06NZo0aVLwXHvuuWfUqFEjrrzyyliyZEnUrl07unTpEs2bN680ryFDhsS4ceNi4MCBMWvWrCgrK4u77747nnjiibjmmmsqvVcKAMAXzXljoW/6eWNRUVH87ne/i549e8buu+8egwYNilatWsXChQtj2rRp0bBhw5g8efIWb//888+PO++8M3r27Bmnn356NG7cOG699daYP39+3HPPPV/KbcnwjbRVPoMWqNL6j0+fO3du9qMf/Shr0KBBtu2222annXZatmLFivy40tLSSh8z/8EHH2SnnXZa1qpVq6xWrVrZDjvskA0YMCB7//3382Nee+21rFu3blnt2rWz7bbbLrvwwguzhx9+uNJHzpeXl2e777579s9//jM74IADsjp16mSlpaXZ9ddfX/Cc6z9afvz48QXLX3zxxeyoo47KGjVqlNWpUyfbZZddsksuuWSTr3/AgAFZ/fr1q3zs448/zoYNG5a1bNkyKy4uztq1a5ddffXVBR9Jn2VZ9vLLL2eHHHJIVrdu3Swi8vvpo48+ygYNGpQ1bdo0Kykpybp37569/PLLVe7Lm2++OWvTpk1Wo0aNgn1TXl6elZeXF4x955138tutVatW9u1vf7vS/li/n66++upKrysishEjRmxy3wAAfJrzRueNG/Pss89mRx99dNakSZOsdu3aWWlpada3b9/s73//e37M+t+h9957r9L6paWl2fe///0qt/3aa69lP/rRj/LHbN99983uv//+gjHTpk3LIiKbOHFiteYLFMplmXc2BwAAAIAUuEYVAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBE1KzuwFyu+MucBwDAly7LVm/tKfA5/H3/Plt7CgAAn0vXJyducowr6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAElFza08ASNuabMLWnsKXpmZu4NaeAgAAm9D5yR/Go/vft7Wn8aXo9tR9MXW/H27taQCJcWUdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEpHLsiyr1sBc8Zc9FwCAL1WWrd7aU+Bz+Pv+fbb2FAAAPpeuT07c5BhX1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBE5LIsy7b2JAAAAAAAV9YBAAAAQDLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAC+AgMHDoxcLhe5XC6Ki4ujdevWMXz48Fi5cmXBuPVjnnzyyYLlq1atiiZNmkQul4tHH300v/yxxx6LLl26ROPGjaNevXrRrl27GDBgQFRUVERExKOPPprf5me/3n777S16LStXroxTTz01mjRpEiUlJdG7d+945513NrpO586d889bp06d2G233WLs2LEFYyoqKuKqq66KDh06RL169aJp06Zx0EEHxfjx42P16tVbNNcPP/wwjj322GjYsGE0atQoBg8eHEuXLq32XNd/DR06NP/4c889F/37948dd9wx6tatG+3bt49rr712k3PZ3GP7eS1YsGCDx/6zc0jJnDlzonfv3lFWVha5XC6uueaarT0l+EqJdQAAAF+RHj16xKJFi+L111+P0aNHx7hx42LEiBGVxu24444xfvz4gmWTJk2KkpKSgmVz586NHj16xN577x3Tp0+PF154IcaMGRO1atWKtWvXFoydN29eLFq0qOCrefPmW/Q6hg0bFpMnT46JEyfGY489Fm+99VYcffTRm1zvxBNPjEWLFsXcuXOjb9++ceqpp8add94ZEZ+Euu7du8cVV1wRQ4YMiRkzZsTTTz8dp556aowZMybmzJmzRXM99thjY86cOfHwww/H/fffH9OnT48hQ4ZUe67rv6666qr8Y7NmzYrmzZvH7bffHnPmzImLLrooLrjggrj++us3ud3qHtsv0tSpUysd+06dOlU5dn3k/awtjaVbst7y5cujTZs2ccUVV0SLFi226Hnhay0DAADgSzdgwIDsyCOPLFh29NFHZx07dixYFhHZxRdfnDVs2DBbvnx5fvn3vve97JJLLskiIps2bVqWZVk2evTorKysbKPPO23atCwiso8++uiLeBnZ4sWLs+Li4mzixIn5ZS+99FIWEdnMmTM3uF55eXl2xhlnFCxr165d1q9fvyzLsuzKK6/MioqKsn/961+V1q2oqMiWLl262XOdO3duFhHZM888k1/24IMPZrlcLlu4cOFmzXVTTjnllOzQQw/d6JjNObZZlmXDhw/P2rVrl9WtWzdr3bp1dvHFF2cVFRVZlmXZunXrsq5du2aHHXZYtm7duizLsuyDDz7IWrVqlV1yySVZlmXZ/Pnzs4jInn322Q3OacSIEVmHDh2ym2++OSsrK8tyuVx+rmPHjs1+8IMfZPXq1ctGjBiRZVmWjR07NmvTpk1WXFyc7bzzztltt91W6TVWtd6WKi0tzUaPHv25tgFfN66sAwAA2ApefPHFmDFjRtSqVavSY506dYqysrK45557IiLizTffjOnTp8ePf/zjgnEtWrSIRYsWxfTp0z/XXO64444oKSnZ6Nfjjz8eEZ9cVbZ69ero1q1bfv1dd901vvWtb8XMmTM363nr1q2bv5LrjjvuiG7dukXHjh0rjSsuLo769etHRMTll1++ybm++eabERExc+bMaNSoUey99975bXXr1i2Kioriqaee2uQ+adq0aeyxxx5xwQUXxPLlyzc6fsmSJdG4ceNNvubqHtuIiAYNGsSECRNi7ty5ce2118bNN98co0ePjohPbqm99dZb45lnnonrrrsuIiKGDh0arVq1iksvvXST8/i0V199Ne6555649957Y/bs2fnlI0eOjKOOOipeeOGFOOGEE2LSpElxxhlnxNlnnx0vvvhinHTSSTFo0KCYNm1awfY+u15EbPKYffo2Y/imq7m1JwAAAPBNcf/990dJSUmsWbMmVq1aFUVFRRu8dfKEE06IW265JY477riYMGFC9OrVK5o1a1Ywpk+fPvHQQw9FeXl5tGjRIvbff//o2rVrHH/88dGwYcOCsTvssEPBz6WlpflbS4844ojYb7/9Njr3Vq1aRUTE22+/HbVq1YpGjRoVPL7ddttV+z3w1q5dG3feeWc8//zz+VtSX3nllejcufMm1x06dGj07dt3o2NatmyZn+tnb/WtWbNmNG7ceKNzPeaYY6K0tDRatmwZzz//fJx33nkxb968uPfee6scP2PGjLjrrrtiypQpm5x/RPWObUTExRdfnP++rKwszjnnnPjTn/4Uw4cPj4hPjsm4cePi+OOPj7fffjseeOCBePbZZ6NmzcL/1D/wwAOjqKjwWp1Pv29fRUVF3HbbbZXmcMwxx8SgQYPyP/fv3z8GDhwYp5xySkREnHXWWfHkk0/GqFGj4tBDD93gehFREAGr8tnfV/gmE+sAAAC+IoceemjccMMNsWzZshg9enTUrFkzevfuXeXY4447Ls4///x4/fXXY8KECfmrpz6tRo0aMX78+PjFL34RjzzySDz11FNx+eWXx5VXXhlPP/10bL/99vmxjz/+eDRo0CD/c3Fxcf77Bg0aFDz2ZRk7dmz87ne/i4qKiqhRo0YMGzYsTj755IiIyLKsWtto3Lhxta5g+zw+/Z523/72t2P77bePrl27xmuvvRZt27YtGPviiy/GkUceGSNGjIjDDjusWtuvzrGNiLjrrrviuuuui9deey2WLl0aa9asqRS1+vTpE5MmTYorrrgibrjhhmjXrl2V22nfvv0G51NaWlplLPz0FYkRES+99FKl9/s76KCDKn24xmfXi4jYaaedNvj8QCG3wQIAAHxF6tevHzvttFN06NAhbrnllnjqqafi97//fZVjmzRpEocffngMHjw4Vq5cGT179tzgdlu1ahU//vGP4/rrr485c+bEypUr48YbbywY07p169hpp53yX6WlpfnHNuc22BYtWkRFRUUsXry4YPvvvPPOJj8M4Nhjj43Zs2fH/PnzY9myZfGb3/wmf8XXzjvvHC+//PJG14/YvNtgW7RoEe+++27B+mvWrIkPP/xwsz64YP1Vh6+++mrB8rlz50bXrl1jyJAhBVfBbUp1ju3MmTPj2GOPjV69esX9998fzz77bFx00UWVPgBi+fLlMWvWrKhRo0a88sorVT7fjjvuWHDsPxvO1t9i/FkbWr4pVa3nNlioPlfWAQAAbAVFRUVx4YUXxllnnRXHHHNM1K1bt9KYE044IXr16hXnnXde1KhRo1rb3XbbbWP77bePZcuWVXsum3MbbKdOnaK4uDj+/ve/568KnDdvXrz55ptxwAEHbHQb22yzzQavsDrmmGPiwgsvjGeffbbS+9atXr06Kioqon79+pt1G+wBBxwQixcvjlmzZuU//fSRRx6JdevWbfL1ftr6Wzg/faXinDlzokuXLjFgwID45S9/We1trbepYztjxowoLS2Niy66KL/sjTfeqDTu7LPPjqKionjwwQejV69e8f3vfz+6dOmy2fOpjvbt28cTTzwRAwYMyC974oknYrfddtvkum6DheoT6wAAALaSPn36xLnnnhu//e1v45xzzqn0eI8ePeK9997bYMgYN25czJ49O4466qho27ZtrFy5Mm677baYM2dOjBkzpmDsu+++GytXrixY1qRJkyguLt6s22C32WabGDx4cJx11lnRuHHjaNiwYfz0pz+NAw44IPbff/9qvvLKzjzzzJgyZUp07do1fv7zn8fBBx8cDRo0iH/+859x5ZVXxu9///vYc889N+s22Pbt20ePHj3ixBNPjBtvvDFWr14dp512WvTr1y8f9BYuXBhdu3aN2267Lfbdd9947bXX4o9//GP06tUrmjRpEs8//3wMGzYsDjnkkPjOd74TEZ/c+tqlS5fo3r17nHXWWfn3v6tRo0aVt5NWZVPHtl27dvHmm2/Gn/70p9hnn31iypQpMWnSpIIxU6ZMiVtuuSVmzpwZe+21V5x77rkxYMCAeP7552PbbbfNj/vggw8qvUdfo0aNok6dOtWa63rnnntu9O3bNzp27BjdunWLyZMnx7333htTp07d5LqbcxtsRUVFzJ07N//9woULY/bs2VFSUuJ2Wr4R3AYLAACwldSsWTNOO+20uOqqq6q8Ei6Xy0XTpk2r/MTYiIh99903li5dGkOHDo3dd989ysvL48knn4z77rsvysvLC8busssusf322xd8zZo1a4vmPXr06Dj88MOjd+/eccghh0SLFi02+OEL1VW7du14+OGHY/jw4TFu3LjYf//9Y5999onrrrsuTj/99Nhjjz22aLt33HFH7LrrrtG1a9fo1atXHHzwwXHTTTflH1+9enXMmzcv/2mvtWrViqlTp8Zhhx0Wu+66a5x99tnRu3fvmDx5cn6du+++O9577724/fbbC/bnPvvsU+15berYHnHEETFs2LA47bTTYs8994wZM2bEJZdckn/8vffei8GDB8fIkSNjr732ioiIyy67LLbbbrtKt5R269at0rG/7777qj3X9X74wx/GtddeG6NGjYrdd989xo0bF+PHj6/WB4Nsjrfeeis6duwYHTt2jEWLFsWoUaOiY8eO8ZOf/OQLfR5IVS6r7rt4AgAAAABfKlfWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdkFdWVhYDBw7M//zoo49GLpeLRx99dKvN6b+dfQwAfN04Z/zq2cfwzSLWQQLeeuutGDlyZMyePXtrT4UqzJgxI0aOHBmLFy/e4m2MHTs2JkyY8IXNCQD45nHOmDbnjMAXRayDBLz11ltx2WWXOfFK1IwZM+Kyyy77Uk68DjnkkFixYkUccsghWz5BAOAbwTlj2pwzAl8UsQ74UmRZFitWrNja06jSmjVroqKiYmtPIyIiioqKok6dOlFU5B/HAMA3j3PG6vm6njOuW7cuVq5cWeVjy5Yt+9zbX758+efeBqTo6/X/dEjQwoULY/DgwdGyZcuoXbt2tG7dOk4++eSoqKiIDz/8MM4555z49re/HSUlJdGwYcPo2bNnPPfcc/n1H3300dhnn30iImLQoEGRy+Uil8vl/6LWuXPn2GOPPeL555+P8vLyqFevXuy0005x9913R0TEY489Fvvtt1/UrVs3dtlll5g6dWrB/AYOHBhlZWWV5j1y5MjI5XJf2H4oKyuLww8/PB566KHYe++9o27dujFu3LiIiHj99dejT58+0bhx46hXr17sv//+MWXKlIL1178Px5///Oe47LLLolWrVtGgQYP40Y9+FEuWLIlVq1bFmWeeGc2bN4+SkpIYNGhQrFq1apPzWrBgQeRyuRg1alRcc8010bZt26hdu3bMnTs3IiIeeeSR+O53vxv169ePRo0axZFHHhkvvfRSwX4699xzIyKidevW+eOzYMGCiIgYP358dOnSJZo3bx61a9eO3XbbLW644YZK+2bOnDnx2GOP5dfv3Llzwev+7PuPTJw4MTp16hR169aNpk2bxnHHHRcLFy4sGDNw4MAoKSmJhQsXxg9/+MMoKSmJZs2axTnnnBNr167d5L4BAL46zhk/4Zzx63nOuGrVqhgxYkTstNNOUbt27dhxxx1j+PDhlfZtLpeL0047Le64447Yfffdo3bt2vHXv/41JkyYELlcLh577LE45ZRTonnz5rHDDjvk1xs7dmx+fMuWLePUU0+tdIXi+t/xWbNmxSGHHBL16tWLCy+8sFrzh6+bmlt7AvB19tZbb8W+++4bixcvjiFDhsSuu+4aCxcujLvvvjuWL18er7/+etx3333Rp0+faN26dbzzzjsxbty4KC8vj7lz50bLli2jffv28bOf/SwuvfTSGDJkSHz3u9+NiIgDDzww/zwfffRRHH744dGvX7/o06dP3HDDDdGvX7+444474swzz4yhQ4fGMcccE1dffXX86Ec/iv/93/+NBg0afOX7Y968edG/f/846aST4sQTT4xddtkl3nnnnTjwwANj+fLlcfrpp0eTJk3i1ltvjSOOOCLuvvvuOOqoowq28atf/Srq1q0b559/frz66qsxZsyYKC4ujqKiovjoo49i5MiR8eSTT8aECROidevWcemll1ZrbuPHj4+VK1fGkCFDonbt2tG4ceOYOnVq9OzZM9q0aRMjR46MFStWxJgxY+Kggw6Kf/3rX1FWVhZHH310/Pvf/44777wzRo8eHU2bNo2IiGbNmkVExA033BC77757HHHEEVGzZs2YPHlynHLKKbFu3bo49dRTIyLimmuuiZ/+9KdRUlISF110UUREbLfddhuc64QJE2LQoEGxzz77xK9+9at455134tprr40nnnginn322WjUqFF+7Nq1a6N79+6x3377xahRo2Lq1Knx61//Otq2bRsnn3xytY8dAPDlcc5YyDnj1+uccd26dXHEEUfEP/7xjxgyZEi0b98+XnjhhRg9enT8+9//jvvuu69g/COPPBJ//vOf47TTToumTZtGWVlZ/tbtU045JZo1axaXXnpp/sq6kSNHxmWXXRbdunWLk08+OebNmxc33HBDPPPMM/HEE09EcXFxftsffPBB9OzZM/r16xfHHXfcRvcPfK1lwBY7/vjjs6KiouyZZ56p9Ni6deuylStXZmvXri1YPn/+/Kx27drZz372s/yyZ555JouIbPz48ZW2U15enkVE9sc//jG/7OWXX84iIisqKsqefPLJ/PKHHnqo0nYGDBiQlZaWVtruiBEjss/+I6C0tDQbMGBA/udp06ZlEZFNmzZtA3ugcN2IyP76178WLD/zzDOziMgef/zx/LKPP/44a926dVZWVpbfP+ufa4899sgqKiryY/v375/lcrmsZ8+eBds94IADqnxdnzV//vwsIrKGDRtm7777bsFje+65Z9a8efPsgw8+yC977rnnsqKiouz444/PL7v66quziMjmz59fafvLly+vtKx79+5ZmzZtCpbtvvvuWXl5eaWxn93HFRUVWfPmzbM99tgjW7FiRX7c/fffn0VEdumll+aXDRgwIIuIgt+lLMuyjh07Zp06daq8MwCArcI5Y+G6zhk/8XU5Z/zDH/6QFRUVFRybLMuyG2+8MYuI7IknnsgvW//7NmfOnIKx48ePzyIiO/jgg7M1a9bkl7/77rtZrVq1ssMOO6zg/wPXX399FhHZLbfckl+2/nf8xhtv3OSc4evObbCwhdatWxf33Xdf/OAHP4i999670uO5XC5q166df1+JtWvXxgcffBAlJSWxyy67xL/+9a9qP1dJSUn069cv//Muu+wSjRo1ivbt28d+++2XX77++9dff31LX9bn0rp16+jevXvBsgceeCD23XffOPjgg/PLSkpKYsiQIbFgwYL8rQXrHX/88QV/Pdtvv/0iy7I44YQTCsbtt99+8b//+7+xZs2aas2td+/e+b9sRkQsWrQoZs+eHQMHDozGjRvnl3/nO9+J733ve/HAAw9Ua7t169bNf79kyZJ4//33o7y8PF5//fVYsmRJtbbxaf/85z/j3XffjVNOOSXq1KmTX/79738/dt1110q3gkREDB06tODn7373u1vtdwAAKOScsTLnjF+vc8aJEydG+/btY9ddd433338//9WlS5eIiJg2bVrB+PLy8thtt92q3NaJJ54YNWrUyP88derUqKioiDPPPLPg/fhOPPHEaNiwYaXXUbt27Rg0aNAm5wxfd26DhS303nvvxX/+85/YY489Njhm3bp1ce2118bYsWNj/vz5Be8J0aRJk2o/1w477FDpvUK22Wab2HHHHSsti/jkFogvw9KlS2Pp0qX5n2vUqFFwMtO6detK67zxxhsFJ4frtW/fPv/4p/fht771rYJx619TVa913bp1sWTJkmjSpEl8+OGHBW8AXLdu3fy6Vc3tjTfeiIhPTmKrmttDDz0Uy5Yti/r161d6/NOeeOKJGDFiRMycObPSG9wuWbKkYA7VsbF57brrrvGPf/yjYFmdOnUKjkFExLbbbvul/Q4AAJvHOaNzxoivxznje++9V/C7V1JSEiUlJfHKK6/ESy+9VGn99d59992Cn6s6vht6bEOvo1atWtGmTZv84+u1atUqatWqtcHtw38LV9bBl+jyyy+Ps846Kw455JC4/fbb46GHHoqHH344dt9991i3bl21t/Ppvz5VZ3mWZfnvN/SGwFvyAQSjRo2K7bffPv+1/k2O1/v0Xwy31Ja+1qOPPrpgbmecccYXPrfPeu2116Jr167x/vvvx29+85uYMmVKPPzwwzFs2LCIiM06xltqQ/sFAPj6cM64+Zwzbp7qnDPus88+Bftm1KhREfHJ/L797W/Hww8/XOXXKaecUrCdje3Dz7t/v4zjAylyZR1soWbNmkXDhg3jxRdf3OCYu+++Ow499ND4/e9/X7B88eLF+TedjdjwydEXYdttt630SUoRUemvVNVx/PHHF9yaUJ1/WZaWlsa8efMqLX/55Zfzj38Rfv3rXxf8ZbBly5abnFdEbHBuTZs2zf+FdEPHZ/LkybFq1ar4y1/+UvDX3c/eCrCxbWxsXutvLVhv3rx5X9j+AgC+Gs4ZnTN+Xc4Z77jjjlixYkX+5zZt2kRERNu2beO5556Lrl27fuG/g59+HeufLyKioqIi5s+fH926dftCnw++LlxZB1uoqKgofvjDH8bkyZPjn//8Z6XHsyyLGjVqFPzFMuKT93z47Mepr/8XfFUnSJ9X27ZtY8mSJfH888/nly1atCgmTZq02dtq06ZNdOvWLf910EEHbXKdXr16xdNPPx0zZ87ML1u2bFncdNNNUVZWtsH3s9hcnTp1Kpjbpra7/fbbx5577hm33nprwX5/8cUX429/+1v06tUrv2xDx2f9Xyg/fYyXLFkS48ePr/R89evXr9bx3XvvvaN58+Zx4403xqpVq/LLH3zwwXjppZfi+9///ia3AQCkwzmjc8avyznjQQcdVLBv1sezvn37xsKFC+Pmm2+utM6KFSvyn+q6Jbp16xa1atWK6667rmD//P73v48lS5Y49+Uby5V18Dlcfvnl8be//S3Ky8vzH2O+aNGimDhxYvzjH/+Iww8/PH72s5/FoEGD4sADD4wXXngh7rjjjoK/GkV8cnLUqFGjuPHGG6NBgwZRv3792G+//Tb6fg/V1a9fvzjvvPPiqKOOitNPPz2WL18eN9xwQ+y8886b9YbFW+r888+PO++8M3r27Bmnn356NG7cOG699daYP39+3HPPPQVvJPtVu/rqq6Nnz55xwAEHxODBg2PFihUxZsyY2GabbWLkyJH5cZ06dYqIiIsuuij69esXxcXF8YMf/CAOO+ywqFWrVvzgBz+Ik046KZYuXRo333xzNG/ePBYtWlTwXJ06dYobbrghfvGLX8ROO+0UzZs3r/RX0IiI4uLiuPLKK2PQoEFRXl4e/fv3j3feeSeuvfbaKCsry98uAQB8fThn3DTnjP+3jdTOGX/84x/Hn//85xg6dGhMmzYtDjrooFi7dm28/PLL8ec//zkeeuihKj88pTqaNWsWF1xwQVx22WXRo0ePOOKII2LevHkxduzY2GeffeK44477wl4HfJ2IdfA5tGrVKp566qm45JJL4o477oj//Oc/0apVq+jZs2fUq1cvLrzwwli2bFn88Y9/jLvuuiv22muvmDJlSpx//vkF2ykuLo5bb701Lrjgghg6dGisWbMmxo8f/4WceDVp0iQmTZoUZ511VgwfPjxat24dv/rVr+KVV175Sk68tttuu5gxY0acd955MWbMmFi5cmV85zvficmTJ2/1v5R169Yt/vrXv8aIESPi0ksvjeLi4igvL48rr7yyYN/vs88+8fOf/zxuvPHG+Otf/xrr1q2L+fPnxy677BJ33313XHzxxXHOOedEixYt4uSTT45mzZpV+iSySy+9NN5444246qqr4uOPP47y8vIqT7wiIgYOHBj16tWLK664Is4777yoX79+HHXUUXHllVdGo0aNvsxdAgB8CZwzbppzxk+keM5YVFQU9913X4wePTpuu+22mDRpUtSrVy/atGkTZ5xxRuy8886fa/sjR46MZs2axfXXXx/Dhg2Lxo0bx5AhQ+Lyyy8v+MRf+CbJZZ+93hoAAAAA2Cq8Zx0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIRM3qDqxXu+xLnAYAwJdv+aoFW3sKfA43tx+ytacAAPC5nPjSTZsc48o6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBE1NzaEwDSlMvViFWrP4y165Zu7al8KWoUlUTt4sYREZFla7fybAAAqMq6LBf1i1dH/ZqrI4vc1p7OFyoXWSxbUxzLVhdHUS7b2tMBEiLWAVVatfrD6N/o+BjY7q1YtrrW1p7OF6p+cUVMeKVl3Ln4toiIqFVzm608IwAAqlK/eHX0f6FrZL87IXINam/t6Xyhso9XxZHDToz+ZctjxRr/aQ78H/9EAKq0dt3SGNjurej85A9j2QdPbe3pfKHqN9kvYv/74van1l81KNYBAKSofs3Vkf3uhBh+6dBoWue/626I91fWiAeWjo2f1Own1gEF/BMB2KBlq2vFsg+eilqLXtjaU/lCLYv4r7taEADgv1EWucg1qB1N66yNZrUrtvZ0vmC1oqio3n/d7b3A5+cDJgAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAImoubUnAKSrfnFF1G+yXyzb2hP5gtVvsl/UL75va08DAIBNyEUW2cer4v2VNSKi1taezhfq/ZU1Yt265ZGLbGtPBUiMWAdUqUZRSUx4pWXE/vfFstX/XSdG9YvviwmvtIwaRSVbeyoAAGzEsjXFceSwE+OBpWOjqKje1p7OF2rduuXRq+SkWLZm+daeCpCYXJZl1cr49WqXfclTAVKSy9WIVas/jLXrlm7tqXwpahSVRO3ixhERkWVrt/JsgK/K8lULtvYU+Bxubj9ka08B+Iqty3JRv3h11K+5OrLIbe3pfKFykcWyNcWxbHVxFOVcXQffFCe+dNMmx3jPOgAAAABIhNtggSpl2dqoVXObiNhma0/lS+OKOgCAtBXlslixpmasWPPf+5+urqoDPsuVdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAInJZlmVbexIAAAAAgCvrAAAAACAZYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAX4GBAwdGLpeLXC4XxcXF0bp16xg+fHisXLmyYNz6MU8++WTB8lWrVkWTJk0il8vFo48+ml/+2GOPRZcuXaJx48ZRr169aNeuXQwYMCAqKioiIuLRRx/Nb/OzX2+//fYWvZaVK1fGqaeeGk2aNImSkpLo3bt3vPPOOxtdp3PnzvnnrVOnTuy2224xduzYgjEVFRVx1VVXRYcOHaJevXrRtGnTOOigg2L8+PGxevXqLZrrhx9+GMcee2w0bNgwGjVqFIMHD46lS5dWe67rv4YOHZp//Lnnnov+/fvHjjvuGHXr1o327dvHtddeu8m5bO6x/bwWLFiwwWP/2TmkZM6cOdG7d+8oKyuLXC4X11xzzdaeEnylxDoAAICvSI8ePWLRokXx+uuvx+jRo2PcuHExYsSISuN23HHHGD9+fMGySZMmRUlJScGyuXPnRo8ePWLvvfeO6dOnxwsvvBBjxoyJWrVqxdq1awvGzps3LxYtWlTw1bx58y16HcOGDYvJkyfHxIkT47HHHou33norjj766E2ud+KJJ8aiRYti7ty50bdv3zj11FPjzjvvjIhPQl337t3jiiuuiCFDhsSMGTPi6aefjlNPPTXGjBkTc+bM2aK5HnvssTFnzpx4+OGH4/7774/p06fHkCFDqj3X9V9XXXVV/rFZs2ZF8+bN4/bbb485c+bERRddFBdccEFcf/31m9xudY/tF2nq1KmVjn2nTp2qHLs+8n7WlsbSLVlv+fLl0aZNm7jiiiuiRYsWW/S88LWWAQAA8KUbMGBAduSRRxYsO/roo7OOHTsWLIuI7OKLL84aNmyYLV++PL/8e9/7XnbJJZdkEZFNmzYty7IsGz16dFZWVrbR5502bVoWEdlHH330RbyMbPHixVlxcXE2ceLE/LKXXnopi4hs5syZG1yvvLw8O+OMMwqWtWvXLuvXr1+WZVl25ZVXZkVFRdm//vWvSutWVFRkS5cu3ey5zp07N4uI7Jlnnskve/DBB7NcLpctXLhws+a6Kaecckp26KGHbnTM5hzbLMuy4cOHZ+3atcvq1q2btW7dOrv44ouzioqKLMuy7P9n787jraoL/f+/z8R4mFFBNEDEuRRR0zRRIQe0TE2voolDqak5YGqlhV3Lm2bhdDWyBE1Nf4491Mo0cbiCQ4rXAOWriHpzRBQSEA5w1u8PYsfxgBw0YpnP5+OxH52z9met/dl7c72LF2tobGwsBg8eXOy+++5FY2NjURRFMXPmzKJXr17F9773vaIoimL69OlFkmLixIkrnNPIkSOLLbfcsrjyyiuLPn36FFVVVZW5Xn755cUXv/jFol27dsXIkSOLoiiKyy+/vNhggw2Kurq6YqONNiquueaaZu9xeet9WL179y5GjRr1kbYBHzeOrAMAAFgDJk2alPHjx6dVq1bNnhs4cGD69OmTW265JUny8ssv58EHH8xXv/rVJuN69OiR1157LQ8++OBHmst1112X+vr6D3w89NBDSZYcVbZw4cIMGTKksv4mm2yST33qU5kwYcIqvW7btm0rR3Jdd911GTJkSAYMGNBsXF1dXdq3b58kOe+881Y615dffjlJMmHChHTu3DnbbLNNZVtDhgxJdXV1Hn300ZV+Jt27d88WW2yR73znO5k3b94Hjp89e3a6du260vfc0u82STp06JCxY8dmypQpufjii3PllVdm1KhRSZacUnv11Vfn8ccfzyWXXJIkOe6449KrV698//vfX+k8lvX888/nlltuya233pqnnnqqsvycc87Jfvvtl7/85S856qijctttt+Xkk0/OaaedlkmTJuXYY4/NkUcemXHjxjXZ3vvXS7LS72zZ04zhk652TU8AAADgk+LOO+9MfX19Fi1alAULFqS6unqFp04eddRRueqqq3LYYYdl7NixGTp0aNZaa60mYw488MDcfffdGTRoUHr06JHtt98+gwcPzuGHH56OHTs2Gbveeus1+b13796VU0u/9KUv5bOf/ewHzr1Xr15Jktdffz2tWrVK586dmzy/zjrrtPgaeIsXL85vfvObPP3005VTUp977rnssssuK133uOOOy0EHHfSBY9Zdd93KXN9/qm9tbW26du36gXMdNmxYevfunXXXXTdPP/10zjzzzEydOjW33nrrcsePHz8+N954Y+66666Vzj9p2XebJGeffXbl5z59+uRb3/pWbrjhhpxxxhlJlnwno0ePzuGHH57XX389v/vd7zJx4sTU1jb9q/7nPve5VFc3PVZn2ev2NTQ05Jprrmk2h2HDhuXII4+s/H7IIYfkiCOOyPHHH58kGTFiRB555JFceOGF2XXXXVe4XpImEXB53v/nFT7JxDoAAIB/kV133TVXXHFF5s6dm1GjRqW2tjYHHHDAcscedthh+fa3v50XXnghY8eOrRw9tayampqMGTMmP/zhD3Pffffl0UcfzXnnnZfzzz8/jz32WHr27FkZ+9BDD6VDhw6V3+vq6io/d+jQoclzq8vll1+eX/7yl2loaEhNTU1OPfXUfOMb30iSFEXRom107dq1RUewfRTLXtPu05/+dHr27JnBgwdn2rRp6devX5OxkyZNyr777puRI0dm9913b9H2W/LdJsmNN96YSy65JNOmTcucOXOyaNGiZlHrwAMPzG233ZYf//jHueKKK9K/f//lbmfTTTdd4Xx69+693Fi47BGJSfLMM880u97fjjvu2OzmGu9fL0k23HDDFb4+0JTTYAEAAP5F2rdvnw033DBbbrllrrrqqjz66KP51a9+tdyx3bp1yz777JOjjz468+fPz1577bXC7fbq1Stf/epXc9lll2Xy5MmZP39+fv7znzcZ07dv32y44YaVR+/evSvPrcppsD169EhDQ0NmzZrVZPtvvPHGSm8GcOihh+app57K9OnTM3fu3PzsZz+rHPG10UYb5dlnn/3A9ZNVOw22R48eefPNN5usv2jRorz99turdOOCpUcdPv/8802WT5kyJYMHD84xxxzT5Ci4lWnJdzthwoQceuihGTp0aO68885MnDgxZ511VrMbQMybNy9PPPFEampq8txzzy339dZff/0m3/37w9nSU4zfb0XLV2Z56zkNFlrOkXUAAABrQHV1db773e9mxIgRGTZsWNq2bdtszFFHHZWhQ4fmzDPPTE1NTYu226VLl/Ts2TNz585t8VxW5TTYgQMHpq6uLn/6058qRwVOnTo1L7/8cnbYYYcP3EanTp1WeITVsGHD8t3vfjcTJ05sdt26hQsXpqGhIe3bt1+l02B32GGHzJo1K0888UTl7qf33XdfGhsbV/p+l7X0FM5lj1ScPHlydttttwwfPjw/+tGPWrytpVb23Y4fPz69e/fOWWedVVn20ksvNRt32mmnpbq6Or///e8zdOjQ7L333tltt91WeT4tsemmm+bhhx/O8OHDK8sefvjhbLbZZitd12mw0HJiHQAAwBpy4IEH5vTTT89///d/51vf+laz5/fcc8/MmDFjhSFj9OjReeqpp7LffvulX79+mT9/fq655ppMnjw5l156aZOxb775ZubPn99kWbdu3VJXV7dKp8F26tQpRx99dEaMGJGuXbumY8eO+eY3v5kddtgh22+/fQvfeXOnnHJK7rrrrgwePDjnnntudtppp3To0CF//vOfc/755+dXv/pVttpqq1U6DXbTTTfNnnvuma9//ev5+c9/noULF+bEE0/MwQcfXAl6r7zySgYPHpxrrrkm2223XaZNm5brr78+Q4cOTbdu3fL000/n1FNPzc4775zPfOYzSZac+rrbbrtljz32yIgRIyrXv6upqVnu6aTLs7Lvtn///nn55Zdzww03ZNttt81dd92V2267rcmYu+66K1dddVUmTJiQrbfeOqeffnqGDx+ep59+Ol26dKmMmzlzZrNr9HXu3Dlt2rRp0VyXOv3003PQQQdlwIABGTJkSO64447ceuutuffee1e67qqcBtvQ0JApU6ZUfn7llVfy1FNPpb6+3um0fCI4DRYAAGANqa2tzYknnpgLLrhguUfCVVVVpXv37su9Y2ySbLfddpkzZ06OO+64bL755hk0aFAeeeSR3H777Rk0aFCTsRtvvHF69uzZ5PHEE098qHmPGjUq++yzTw444IDsvPPO6dGjxwpvvtBSrVu3zj333JMzzjgjo0ePzvbbb59tt902l1xySU466aRsscUWH2q71113XTbZZJMMHjw4Q4cOzU477ZRf/OIXlecXLlyYqVOnVu722qpVq9x7773Zfffds8kmm+S0007LAQcckDvuuKOyzs0335wZM2bk2muvbfJ5brvtti2e18q+2y996Us59dRTc+KJJ2arrbbK+PHj873vfa/y/IwZM3L00UfnnHPOydZbb50k+cEPfpB11lmn2SmlQ4YMafbd33777S2e61Jf/vKXc/HFF+fCCy/M5ptvntGjR2fMmDEtujHIqnj11VczYMCADBgwIK+99louvPDCDBgwIF/72tf+qa8DZVVVtPQqngAAAADAauXIOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6KImxY8emqqoqL7744pqeCiuxyy67ZJdddlnT0wAAPoHsM3582GcEPiyxDvi3dN555+X222//0OtPmTIl55xzjh1hAIB/Y/YZgTIS64B/S/+MHa8f/OAHy93x+uMf/5g//vGPH35yAACUgn1GoIzEOuBjYe7cuWt6ChWtWrVKq1at1vQ0AAB4H/uMH92KPsOiKPLee+99pG3Pnz8/jY2NH2kb8Ekg1kFJ/fa3v83ee++dddddN61bt06/fv1y7rnnZvHixU3G7bLLLtliiy0yZcqU7LrrrmnXrl169eqVCy64oNk258+fn3POOScbbbRR2rRpk549e2b//ffPtGnTKtuqqqpa7mPs2LGV7bzwwgs58MAD07Vr17Rr1y7bb7997rrrrmavd+mll2bzzTdPu3bt0qVLl2yzzTa5/vrrV/rejzjiiNTX12fatGkZOnRoOnTokEMPPTTJkp2H0047Leuvv35at26djTfeOBdeeGGKoqisX1VVlblz5+bqq6+uzP+II45Ikrz00ks5/vjjs/HGG6dt27bp1q1bDjzwwCb/Gjp27NgceOCBSZJdd921so3777+/8jm9//ojb775Zo4++uiss846adOmTbbccstcffXVTca8+OKLqaqqyoUXXphf/OIX6devX1q3bp1tt902jz/++Eo/FwCA97PPaJ9xRR599NHsueee6dSpU9q1a5dBgwbl4YcfbjLmnHPOSVVVVaZMmZJhw4alS5cu2WmnnZIkffr0yT777JO7774722yzTdq2bZvRo0cnadl3e//996eqqio33HBDzj777PTq1Svt2rXL3/72txa/B/ikql3TEwCWb+zYsamvr8+IESNSX1+f++67L9///vfzt7/9LT/5yU+ajH3nnXey5557Zv/9989BBx2Um2++OWeeeWY+/elPZ6+99kqSLF68OPvss0/+9Kc/5eCDD87JJ5+cd999N/fcc08mTZqUfv365ayzzsrXvva1Jtu+9tprc/fdd2fttddOkrzxxhv53Oc+l3nz5uWkk05Kt27dcvXVV+dLX/pSbr755uy3335JkiuvvDInnXRSvvKVr+Tkk0/O/Pnz8/TTT+fRRx/NsGHDVvr+Fy1alD322CM77bRTLrzwwrRr1y5FUeRLX/pSxo0bl6OPPjpbbbVV7r777px++ul55ZVXMmrUqCTJr3/963zta1/Ldtttl2OOOSZJ0q9fvyTJ448/nvHjx+fggw/OeuutlxdffDFXXHFFdtlll0yZMiXt2rXLzjvvnJNOOimXXHJJvvvd72bTTTdNksr/vt97772XXXbZJc8//3xOPPHE9O3bNzfddFOOOOKIzJo1KyeffHKT8ddff33efffdHHvssamqqsoFF1yQ/fffPy+88ELq6upW+tkAACxln9E+4/Lcd9992WuvvTJw4MCMHDky1dXVGTNmTHbbbbc89NBD2W677ZqMP/DAA9O/f/+cd955TYLm1KlTc8ghh+TYY4/N17/+9Wy88cYt/m6XOvfcc9OqVat861vfyoIFCz6WRxvCv1wBlMKYMWOKJMX06dOLoiiKefPmNRtz7LHHFu3atSvmz59fWTZo0KAiSXHNNddUli1YsKDo0aNHccABB1SWXXXVVUWS4mc/+1mz7TY2Ni53Tg8//HBRV1dXHHXUUZVlp5xySpGkeOihhyrL3n333aJv375Fnz59isWLFxdFURT77rtvsfnmm7fw3Tc1fPjwIknx7W9/u8ny22+/vUhS/PCHP2yy/Ctf+UpRVVVVPP/885Vl7du3L4YPH95s28v7XCdMmNDsM7zpppuKJMW4ceOajR80aFAxaNCgyu8XXXRRkaS49tprK8saGhqKHXbYoaivry/+9re/FUVRFNOnTy+SFN26dSvefvvtytjf/va3RZLijjvuWP4HAgDwd/YZ/8E+4/I1NjYW/fv3L/bYY48m39m8efOKvn37Fl/4whcqy0aOHFkkKQ455JBm2+ndu3eRpPjDH/7QZHlLv9tx48YVSYoNNthguZ8nsGJOg4WSatu2beXnd999N2+99VY+//nPZ968eXn22WebjK2vr89hhx1W+b1Vq1bZbrvt8sILL1SW3XLLLenevXu++c1vNnutqqqqZstef/31fOUrX8lWW22Vyy+/vLL8d7/7XbbbbrvK4fFLX/+YY47Jiy++mClTpiRJOnfunL/+9a8f6fTOb3zjG01+/93vfpeampqcdNJJTZafdtppKYoiv//971e6zWU/14ULF2bmzJnZcMMN07lz5zz55JMfap6/+93v0qNHjxxyyCGVZXV1dTnppJMyZ86cPPDAA03G/8d//Ee6dOlS+f3zn/98kjT5vgAAWsI+o33G93vqqafy3HPPZdiwYZk5c2beeuutvPXWW5k7d24GDx6cBx98sNl144477rjlbqtv377ZY489mr2Plny3Sw0fPrzJ5wmsnFgHJTV58uTst99+6dSpUzp27Ji11lqrsnM1e/bsJmPXW2+9ZjtPXbp0yTvvvFP5fdq0adl4441TW7vys98XLVqUgw46KIsXL86tt96a1q1bV5576aWXsvHGGzdbZ+nh/i+99FKS5Mwzz0x9fX2222679O/fPyeccEKTa2Q0NDTk9ddfb/JY9toqtbW1WW+99Zq8xksvvZR11103HTp0+MDX/iDvvfdevv/971euX9K9e/estdZamTVrVrPPtaVeeuml9O/fP9XVTf+TuqJ5fepTn2ry+9KdsGW/LwCAlrDP+MndZ3zvvfeafTZJ8txzzyVZEsnWWmutJo9f/vKXWbBgQbP30Ldv3+XOeXnLW/rdrmzbwIq5Zh2U0KxZszJo0KB07Ngx//mf/5l+/fqlTZs2efLJJ3PmmWc2+5ewmpqa5W6nWOZ6E6vi9NNPz4QJE3Lvvfc22/lpqU033TRTp07NnXfemT/84Q+55ZZbcvnll+f73/9+fvCDH2T8+PHZddddm6wzffr09OnTJ0nSunXrZjsy/wzf/OY3M2bMmJxyyinZYYcd0qlTp1RVVeXggw/+l92Z6p/9fQEAn0z2GT/Z+4w33nhjjjzyyGbPLZ3fT37yk2y11VbL3UZ9fX2T31d05Ns/44g4R9XBqhProITuv//+zJw5M7feemt23nnnyvLp06d/6G3269cvjz76aBYuXPiBF6S94YYbctFFF+Wiiy7KoEGDmj3fu3fvTJ06tdnypadZ9O7du7Ksffv2+Y//+I/8x3/8RxoaGrL//vvnRz/6Ub7zne9kyy23zD333NNkGz169PjA99C7d+/ce++9effdd5v8S+nyXnt5p2kkyc0335zhw4fnpz/9aWXZ/PnzM2vWrCbjVrT+iub19NNPp7GxscnO4vLmBQDwz2Kfcfk+KfuMe+yxR7PPJvnHTTI6duyYIUOGrNI2W2JVvlvgw3EaLJTQ0n9FW/ZfORsaGppcB2RVHXDAAXnrrbdy2WWXNXtu6etMmjQpX/va13LYYYc1uxvVUkOHDs1jjz2WCRMmVJbNnTs3v/jFL9KnT59sttlmSZKZM2c2Wa9Vq1bZbLPNUhRFFi5cmC5dumTIkCFNHm3atPnA9zB06NAsXry42XsYNWpUqqqqKncxS5bs9L1/ZypZ8tm+/1+PL7300ianUyxdP8lyt7G8eb3++uu58cYbK8sWLVqUSy+9NPX19cvdgQUA+KjsMy7fJ2WfsWfPns0+myQZOHBg+vXrlwsvvDBz5sxptt6MGTNW6XWW9z5a8t0CH54j66CEPve5z6VLly4ZPnx4TjrppFRVVeXXv/71RzpN8vDDD88111yTESNG5LHHHsvnP//5zJ07N/fee2+OP/747LvvvpXD6Hfeeedce+21zea0wQYb5Nvf/nZ+85vfZK+99spJJ52Url275uqrr8706dNzyy23VP6VcPfdd0+PHj2y4447Zp111skzzzyTyy67LHvvvXez64e01Be/+MXsuuuuOeuss/Liiy9myy23zB//+Mf89re/zSmnnFL5V8RkyU7Kvffem5/97GdZd91107dv33z2s5/NPvvsk1//+tfp1KlTNttss8qpG926dWvyWltttVVqampy/vnnZ/bs2WndunV22223rL322s3mdcwxx2T06NE54ogj8sQTT6RPnz65+eab8/DDD+eiiy760O8XAOCD2Gdcvk/6PmN1dXV++ctfZq+99srmm2+eI488Mr169corr7yScePGpWPHjrnjjjs+9PZb+t0CH8G//P6zwHKNGTOmSFJMnz69KIqiePjhh4vtt9++aNu2bbHuuusWZ5xxRnH33Xc3uzX8oEGDlnu7++HDhxe9e/dusmzevHnFWWedVfTt27eoq6srevToUXzlK18ppk2bVhTFP27PvrzHmDFjKtuZNm1a8ZWvfKXo3Llz0aZNm2K77bYr7rzzziavNXr06GLnnXcuunXrVrRu3bro169fcfrppxezZ89e6WcxfPjwon379st97t133y1OPfXUYt111y3q6uqK/v37Fz/5yU+a3Ja+KIri2WefLXbeeeeibdu2RZJi+PDhRVEUxTvvvFMceeSRRffu3Yv6+vpijz32KJ599tmid+/elTFLXXnllcUGG2xQ1NTUNPncBw0aVAwaNKjJ2DfeeKOy3VatWhWf/vSnm3xmRVEU06dPL5IUP/nJT5q9ryTFyJEjV/rZAACfbPYZm87dPuOKTZw4sdh///0rn23v3r2Lgw46qPjTn/5UGTNy5MgiSTFjxoxm6/fu3bvYe++9l7vtlny348aNK5IUN910U4vmC/xDVVG4ojkAAAAAlIHjUwEAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEqitqUDq6rqVuc8AABWu6JYuKanwEfwp+0PXNNTAAD4SAY/ctNKxziyDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASqJ2TU8AKLdFxdg1PYXVprbqiDU9BQAAVmKXR76c+7e/fU1PY7UY8ujtufezX17T0wBKxpF1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQElUFUVRtGhgVd3qngsAwGpVFAvX9BT4CP60/YFregoAAB/J4EduWukYR9YBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJVBVFUazpSQAAAAAAjqwDAAAAgNIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAD4FzjiiCNSVVWVqqqq1NXVpW/fvjnjjDMyf/78JuOWjnnkkUeaLF+wYEG6deuWqqqq3H///ZXlDzzwQHbbbbd07do17dq1S//+/TN8+PA0NDQkSe6///7KNt//eP311z/Ue5k/f35OOOGEdOvWLfX19TnggAPyxhtvfOA6u+yyS+V127Rpk8022yyXX355kzENDQ254IILsuWWW6Zdu3bp3r17dtxxx4wZMyYLFy78UHN9++23c+ihh6Zjx47p3Llzjj766MyZM6fFc136OO644yrP/+///m8OOeSQrL/++mnbtm023XTTXHzxxSudy6p+tx/Viy++uMLv/v1zKJPJkyfngAMOSJ8+fVJVVZWLLrpoTU8J/qXEOgAAgH+RPffcM6+99lpeeOGFjBo1KqNHj87IkSObjVt//fUzZsyYJstuu+221NfXN1k2ZcqU7Lnnntlmm23y4IMP5i9/+UsuvfTStGrVKosXL24ydurUqXnttdeaPNZee+0P9T5OPfXU3HHHHbnpppvywAMP5NVXX83++++/0vW+/vWv57XXXsuUKVNy0EEH5YQTTshvfvObJEtC3R577JEf//jHOeaYYzJ+/Pg89thjOeGEE3LppZdm8uTJH2quhx56aCZPnpx77rknd955Zx588MEcc8wxLZ7r0scFF1xQee6JJ57I2muvnWuvvTaTJ0/OWWedle985zu57LLLVrrdln63/0z33ntvs+9+4MCByx27NPK+34eNpR9mvXnz5mWDDTbIj3/84/To0eNDvS58rBUAAACsdsOHDy/23XffJsv233//YsCAAU2WJSnOPvvsomPHjsW8efMqy7/whS8U3/ve94okxbhx44qiKIpRo0YVffr0+cDXHTduXJGkeOedd/4Zb6OYNWtWUVdXV9x0002VZc8880yRpJgwYcIK1xs0aFBx8sknN1nWv3//4uCDDy6KoijOP//8orq6unjyySebrdvQ0FDMmTNnlec6ZcqUIknx+OOPV5b9/ve/L6qqqopXXnlllea6Mscff3yx6667fuCYVflui6IozjjjjKJ///5F27Zti759+xZnn3120dDQUBRFUTQ2NhaDBw8udt9996KxsbEoiqKYOXNm0atXr+J73/teURRFMX369CJJMXHixBXOaeTIkcWWW25ZXHnllUWfPn2Kqqqqylwvv/zy4otf/GLRrl27YuTIkUVRFMXll19ebLDBBkVdXV2x0UYbFddcc02z97i89T6s3r17F6NGjfpI24CPG0fWAQAArAGTJk3K+PHj06pVq2bPDRw4MH369Mktt9ySJHn55Zfz4IMP5qtf/WqTcT169Mhrr72WBx988CPN5brrrkt9ff0HPh566KEkS44qW7hwYYYMGVJZf5NNNsmnPvWpTJgwYZVet23btpUjua677roMGTIkAwYMaDaurq4u7du3T5Kcd955K53ryy+/nCSZMGFCOnfunG222aayrSFDhqS6ujqPPvroSj+T7t27Z4sttsh3vvOdzJs37wPHz549O127dl3pe27pd5skHTp0yNixYzNlypRcfPHFufLKKzNq1KgkS06pvfrqq/P444/nkksuSZIcd9xx6dWrV77//e+vdB7Lev7553PLLbfk1ltvzVNPPVVZfs4552S//fbLX/7ylxx11FG57bbbcvLJJ+e0007LpEmTcuyxx+bII4/MuHHjmmzv/eslWel3tuxpxvBJV7umJwAAAPBJceedd6a+vj6LFi3KggULUl1dvcJTJ4866qhcddVVOeywwzJ27NgMHTo0a621VpMxBx54YO6+++4MGjQoPXr0yPbbb5/Bgwfn8MMPT8eOHZuMXW+99Zr83rt378qppV/60pfy2c9+9gPn3qtXryTJ66+/nlatWqVz585Nnl9nnXVafA28xYsX5ze/+U2efvrpyimpzz33XHbZZZeVrnvcccfloIMO+sAx6667bmWu7z/Vt7a2Nl27dv3AuQ4bNiy9e/fOuuuum6effjpnnnlmpk6dmltvvXW548ePH58bb7wxd91110rnn7Tsu02Ss88+u/Jznz598q1vfSs33HBDzjjjjCRLvpPRo0fn8MMPz+uvv57f/e53mThxYmprm/5V/3Of+1yqq5seq7PsdfsaGhpyzTXXNJvDsGHDcuSRR1Z+P+SQQ3LEEUfk+OOPT5KMGDEijzzySC688MLsuuuuK1wvSZMIuDzv//MKn2RiHQAAwL/IrrvumiuuuCJz587NqFGjUltbmwMOOGC5Yw877LB8+9vfzgsvvJCxY8dWjp5aVk1NTcaMGZMf/vCHue+++/Loo4/mvPPOy/nnn5/HHnssPXv2rIx96KGH0qFDh8rvdXV1lZ87dOjQ5LnV5fLLL88vf/nLNDQ0pKamJqeeemq+8Y1vJEmKomjRNrp27dqiI9g+imWvaffpT386PXv2zODBgzNt2rT069evydhJkyZl3333zciRI7P77ru3aPst+W6T5MYbb8wll1ySadOmZc6cOVm0aFGzqHXggQfmtttuy49//ONcccUV6d+//3K3s+mmm65wPr17915uLFz2iMQkeeaZZ5pd72/HHXdsdnON96+XJBtuuOEKXx9oymmwAAAA/yLt27fPhhtumC233DJXXXVVHn300fzqV79a7thu3bpln332ydFHH5358+dnr732WuF2e/Xqla9+9au57LLLMnny5MyfPz8///nPm4zp27dvNtxww8qjd+/eledW5TTYHj16pKGhIbNmzWqy/TfeeGOlNwM49NBD89RTT2X69OmZO3dufvazn1WO+Npoo43y7LPPfuD6yaqdBtujR4+8+eabTdZftGhR3n777VW6ccHSow6ff/75JsunTJmSwYMH55hjjmlyFNzKtOS7nTBhQg499NAMHTo0d955ZyZOnJizzjqr2Q0g5s2blyeeeCI1NTV57rnnlvt666+/fpPv/v3hbOkpxu+3ouUrs7z1nAYLLefIOgAAgDWguro63/3udzNixIgMGzYsbdu2bTbmqKOOytChQ3PmmWempqamRdvt0qVLevbsmblz57Z4LqtyGuzAgQNTV1eXP/3pT5WjAqdOnZqXX345O+ywwwduo1OnTis8wmrYsGH57ne/m4kTJza7bt3ChQvT0NCQ9u3br9JpsDvssENmzZqVJ554onL30/vuuy+NjY0rfb/LWnoK57JHKk6ePDm77bZbhg8fnh/96Ect3tZSK/tux48fn969e+ess86qLHvppZeajTvttNNSXV2d3//+9xk6dGj23nvv7Lbbbqs8n5bYdNNN8/DDD2f48OGVZQ8//HA222yzla7rNFhoObEOAABgDTnwwANz+umn57//+7/zrW99q9nze+65Z2bMmLHCkDF69Og89dRT2W+//dKvX7/Mnz8/11xzTSZPnpxLL720ydg333wz8+fPb7KsW7duqaurW6XTYDt16pSjjz46I0aMSNeuXdOxY8d885vfzA477JDtt9++he+8uVNOOSV33XVXBg8enHPPPTc77bRTOnTokD//+c85//zz86tf/SpbbbXVKp0Gu+mmm2bPPffM17/+9fz85z/PwoULc+KJJ+bggw+uBL1XXnklgwcPzjXXXJPtttsu06ZNy/XXX5+hQ4emW7duefrpp3Pqqadm5513zmc+85kkS0593W233bLHHntkxIgRlevf1dTULPd00uVZ2Xfbv3//vPzyy7nhhhuy7bbb5q677sptt93WZMxdd92Vq666KhMmTMjWW2+d008/PcOHD8/TTz+dLl26VMbNnDmz2TX6OnfunDZt2rRorkudfvrpOeiggzJgwIAMGTIkd9xxR2699dbce++9K113VU6DbWhoyJQpUyo/v/LKK3nqqadSX1/vdFo+EZwGCwAAsIbU1tbmxBNPzAUXXLDcI+GqqqrSvXv35d4xNkm22267zJkzJ8cdd1w233zzDBo0KI888khuv/32DBo0qMnYjTfeOD179mzyeOKJJz7UvEeNGpV99tknBxxwQHbeeef06NFjhTdfaKnWrVvnnnvuyRlnnJHRo0dn++23z7bbbptLLrkkJ510UrbYYosPtd3rrrsum2yySQYPHpyhQ4dmp512yi9+8YvK8wsXLszUqVMrd3tt1apV7r333uy+++7ZZJNNctppp+WAAw7IHXfcUVnn5ptvzowZM3Lttdc2+Ty33XbbFs9rZd/tl770pZx66qk58cQTs9VWW2X8+PH53ve+V3l+xowZOfroo3POOedk6623TpL84Ac/yDrrrNPslNIhQ4Y0++5vv/32Fs91qS9/+cu5+OKLc+GFF2bzzTfP6NGjM2bMmBbdGGRVvPrqqxkwYEAGDBiQ1157LRdeeGEGDBiQr33ta//U14GyqipaehVPAAAAAGC1cmQdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh18Al0zjnnpKqqak1Po3RefPHFVFVVZezYsWt6KgAAa5x9xuWzzwisbmId8LEyZcqUnHPOOXnxxRc/9Dauv/76XHTRRf+0OQEAUC72GYGPs6qiKIo1PQngX2vRokVZtGhR2rRps6ansspuvvnmHHjggRk3blx22WWXD7WNffbZJ5MmTWq281YURRYsWJC6urrU1NR89MkCAHyM2We0zwisGY6sg0+g2tra0ux0NTY2Zv78+Wt6GkmSqqqqtGnTxk4XAEDsM67Ix3WfsSiKvPfee8t9bv78+WlsbPxI2587d+5HWh/4B7EOPmbefffdnHLKKenTp09at26dtddeO1/4whfy5JNPVsY8+uijGTp0aLp06ZL27dvnM5/5TC6++OLK88u7/khVVVVOPPHEXHfdddl4443Tpk2bDBw4MA8++GBlzLhx41JVVZXbbrut2byuv/76VFVVZcKECR84/2VfZ/PNN0/r1q3zhz/8IUkyceLE7LXXXunYsWPq6+szePDgPPLII5V1x44dmwMPPDBJsuuuu6aqqipVVVW5//77kyS//e1vs/fee2fddddN69at069fv5x77rlZvHhxZRu77LJL7rrrrrz00kuV9fv06ZNkxdcfue+++/L5z38+7du3T+fOnbPvvvvmmWeeaTJm6Wf6/PPP54gjjkjnzp3TqVOnHHnkkZk3b94HfiYAAP9s9hn/ffcZGxsbc9FFF2XzzTdPmzZtss466+TYY4/NO++802Rcnz59ss8+++Tuu+/ONttsk7Zt22b06NG5//77U1VVlRtuuCFnn312evXqlXbt2uVvf/tbkuSmm27KwIED07Zt23Tv3j2HHXZYXnnllSbbPuKII1JfX59p06Zl6NCh6dChQw499NAWzR9Yudo1PQFg1Rx33HG5+eabc+KJJ2azzTbLzJkz8z//8z955plnsvXWW+eee+7JPvvsk549e+bkk09Ojx498swzz+TOO+/MySef/IHbfuCBB3LjjTfmpJNOSuvWrXP55Zdnzz33zGOPPZYtttgiu+yyS9Zff/1cd9112W+//Zqse91116Vfv37ZYYcdVvoe7rvvvvx//9//lxNPPDHdu3dPnz59Mnny5Hz+859Px44dc8YZZ6Suri6jR4/OLrvskgceeCCf/exns/POO+ekk07KJZdcku9+97vZdNNNk6Tyv2PHjk19fX1GjBiR+vr63Hffffn+97+fv/3tb/nJT36SJDnrrLMye/bs/PWvf82oUaOSJPX19Suc67333pu99torG2ywQc4555y89957ufTSS7PjjjvmySefrOy0LXXQQQelb9+++a//+q88+eST+eUvf5m11147559//ko/FwCAfxb7jP+++4zHHntsxo4dmyOPPDInnXRSpk+fnssuuywTJ07Mww8/nLq6usrYqVOn5pBDDsmxxx6br3/969l4440rz5177rlp1apVvvWtb2XBggVp1apVZbvbbrtt/uu//itvvPFGLr744jz88MOZOHFiOnfuXFl/0aJF2WOPPbLTTjvlwgsvTLt27VY6d6CFCuBjpVOnTsUJJ5yw3OcWLVpU9O3bt+jdu3fxzjvvNHmusbGx8vPIkSOL9/+ff5IiSfHnP/+5suyll14q2rRpU+y3336VZd/5zneK1q1bF7Nmzaose/PNN4va2tpi5MiRK51/kqK6urqYPHlyk+Vf/vKXi1atWhXTpk2rLHv11VeLDh06FDvvvHNl2U033VQkKcaNG9ds2/PmzWu27Nhjjy3atWtXzJ8/v7Js7733Lnr37t1s7PTp04skxZgxYyrLttpqq2LttdcuZs6cWVn2v//7v0V1dXVx+OGHV5Yt/UyPOuqoJtvcb7/9im7dujX/IAAAViP7jP+e+4wPPfRQkaS47rrrmiz/wx/+0Gx57969iyTFH/7whyZjx40bVyQpNthggyafRUNDQ7H22msXW2yxRfHee+9Vlt95551FkuL73/9+Zdnw4cOLJMW3v/3tlc4ZWHVOg4WPmc6dO+fRRx/Nq6++2uy5iRMnZvr06TnllFOa/KtXkmanMCzPDjvskIEDB1Z+/9SnPpV99903d999d+W0gMMPPzwLFizIzTffXBl34403ZtGiRTnssMNa9B4GDRqUzTbbrPL74sWL88c//jFf/vKXs8EGG1SW9+zZM8OGDcv//M//VA7L/yBt27at/Pzuu+/mrbfeyuc///nMmzcvzz77bIvmtqzXXnstTz31VI444oh07dq1svwzn/lMvvCFL+R3v/tds3WOO+64Jr9//vOfz8yZM1s0fwCAfxb7jCv2cd5nvOmmm9KpU6d84QtfyFtvvVV5DBw4MPX19Rk3blyT8X379s0ee+yx3G0NHz68yWfx5z//OW+++WaOP/74Jtcq3HvvvbPJJpvkrrvuaraNb3zjGx84X+DDEevgY+aCCy7IpEmTsv7662e77bbLOeeckxdeeCFJMm3atCTJFlts8aG23b9//2bLNtpoo8ybNy8zZsxIkmyyySbZdtttc91111XGXHfdddl+++2z4YYbJklmz56d119/vfJ4++23m2yzb9++TX6fMWNG5s2b1+Sw/KU23XTTNDY25v/+7/9WOv/Jkydnv/32S6dOndKxY8estdZalZ3B2bNnr3T993vppZeSZIXzeuutt5pdSPdTn/pUk9+7dOmSJM2uIQIAsDrZZ1yxj8M+49tvv93ks1k6r+eeey6zZ8/O2muvnbXWWqvJY86cOXnzzTebbPf9n+EHPfdB72OTTTapPL9UbW1t1ltvvRVuH/jwXLMOPmYOOuigfP7zn89tt92WP/7xj/nJT36S888/P7feeuu/bA6HH354Tj755Pz1r3/NggUL8sgjj+Syyy6rPH/yySfn6quvrvw+aNCgygV9k6b/mvnPMmvWrAwaNCgdO3bMf/7nf6Zfv35p06ZNnnzyyZx55pkf+e5WLbWiu4IVRfEveX0AgMQ+44p8XPYZ999//zzwwAOV5cOHD8/YsWPT2NiYtddeu0kEXdZaa63V5PcP+gw/6ufbunXrVFc7/gdWB7EOPoZ69uyZ448/Pscff3zefPPNbL311vnRj36Uiy66KEkyadKkDBkyZJW3+9xzzzVb9v/+3/9Lu3btmvw//oMPPjgjRozIb37zm7z33nupq6vLf/zHf1SeP+OMM5qc3rD0XwpXZK211kq7du0yderUZs89++yzqa6uzvrrr59kxadm3H///Zk5c2ZuvfXW7LzzzpXl06dPbza2Jad3JEnv3r2TZIXz6t69e9q3b9+ibQEA/KvZZ2zu47LP+NOf/rTJmRnrrrtukqRfv3659957s+OOO/7TY+ay72O33XZr8tzUqVMrzwOrnwwOHyOLFy9udmj+2muvnXXXXTcLFizI1ltvnb59++aiiy7KrFmzmoxryZFdEyZMyJNPPln5/f/+7//y29/+NrvvvnuTf/3r3r179tprr1x77bW57rrrsueee6Z79+6V5zfbbLMMGTKk8lj2mibLU1NTk9133z2//e1v8+KLL1aWv/HGG7n++uuz0047pWPHjklS2dF5//tbOr9l32dDQ0Muv/zyZq/Xvn37Fp3i0LNnz2y11Va5+uqrm7zepEmT8sc//jFDhw5d6TYAAP7V7DN+/PcZBw4c2OSzWXrtvoMOOiiLFy/Oueee22ydRYsWNXu/q2KbbbbJ2muvnZ///OdZsGBBZfnvf//7PPPMM9l7770/9LaBVePIOvgYeffdd7PeeuvlK1/5SrbccsvU19fn3nvvzeOPP56f/vSnqa6uzhVXXJEvfvGL2WqrrXLkkUemZ8+eefbZZzN58uTcfffdH7j9LbbYInvssUdOOumktG7durLT8oMf/KDZ2MMPPzxf+cpXkmS5Owur6oc//GHuueee7LTTTjn++ONTW1ub0aNHZ8GCBbngggsq47baaqvU1NTk/PPPz+zZs9O6devstttu+dznPpcuXbpk+PDhOemkk1JVVZVf//rXy93hHDhwYG688caMGDEi2267berr6/PFL35xufP6yU9+kr322is77LBDjj766Lz33nu59NJL06lTp5xzzjkf+X0DAPyz2Wf8991nHDRoUI499tj813/9V5566qnsvvvuqaury3PPPZebbropF198ceXzXlV1dXU5//zzc+SRR2bQoEE55JBD8sYbb+Tiiy9Onz59cuqpp/7T3gewEmvuRrTAqlqwYEFx+umnF1tuuWXRoUOHon379sWWW25ZXH755U3G/c///E/xhS98oTLmM5/5THHppZdWnl96y/hlJSlOOOGE4tprry369+9ftG7duhgwYMByb3e/dC5dunQpOnXq1OTW7iuz9HWW58knnyz22GOPor6+vmjXrl2x6667FuPHj2827sorryw22GCDoqampkhSmePDDz9cbL/99kXbtm2LddddtzjjjDOKu+++u8mYoiiKOXPmFMOGDSs6d+5cJCl69+5dFEVRTJ8+vUhSjBkzpsnr3XvvvcWOO+5YtG3btujYsWPxxS9+sZgyZUqTMUs/0xkzZjRZPmbMmCJJMX369BZ/RgAAH4V9xiX+nfcZf/GLXxQDBw4s2rZtW3To0KH49Kc/XZxxxhnFq6++WhnTu3fvYu+992627rhx44okxU033bTcbd94443FgAEDitatWxddu3YtDj300OKvf/1rkzHDhw8v2rdv36K5Aquuqihc9RxYck2OE044oclFfz/IokWLsu666+aLX/xifvWrX63m2QEAUAb2GQFWP9esAz6U22+/PTNmzMjhhx++pqcCAEBJ2WcEWHWuWQeskkcffTRPP/10zj333AwYMCCDBg1a01MCAKBk7DMCfHiOrANWyRVXXJFvfOMbWXvttXPNNdes6ekAAFBC9hkBPjzXrAMAAACAknBkHQAAAACUhFgHAAAAACUh1gEAAABASbT4brBVVXWrcx4AAKtdUSxc01PgI5j1zQ3X9BQAAD6Szpc+v9IxjqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAAAKAkatf0BAAAAGCFqhvX9AxWn0bHzwDNiXUAAACUl6AFfMKIdQAAAJRWdd2iNT2F1aZxob+SA835LwMAAADlVN2YeTM7JUmKoipVVcUqb6Ioqio/r2j9Zcd80LjlrVdVVVTWX3a95c23qnrJ70XjkvFtOr/ryEGgGbEOAACAcll6nbrG6qw1dHpS/feg1ab1kp8XLUrmzU/atEoa/x7EGhuXPN/4vmvcLVq8ZFltzT+2s1Rt7ZJt1dYm8xck1VVLfm5YuMxcqv6x3tJtVVf/43Vqa/4xh1Z1S9Zd+tyixUvmWF3d9LX/PubdB7svea+CHbAMsQ4AAIDyWCZeLWqoS5+vbZe6tE6SrLe4VxZkUd6ueTuLszAdG7tkbvWcJMniLAlsHRu7pCY1+Vv17CzOwnRp7J6/Vc9O+8b6dEuHysvMyrzKz53TLnOKBVlUtTid0y6vVs3Igqr3mmxvWX+rnp0kad9Y3+S5pa/5fh0bu6RbOlRec3b126lKTR47fHKqUi3YAU2IdQAAAJTD36PV4oV/D2CNVTlhrU0yZXZNFhfJRt0aM3V22/RLh8xetDjbrVWVqbO7JUlmL1qcx4pHc0DnDdOz7cL8eWbnyvIe6ZAs08K261aVDrXt8+eZddm4U2M61C7On2f+fZvdqjJ1dq/MXrR4ydi1qjJ1dnXluVfmVadXu46ZOru6su5SY19fmM6N9elQ06rJ29puraq/v8aSOY1f9GZ2rNsg82a9nNbt5qe23XzBDqjwXwIAAAAAKImqoihadOXMqqq61T0XoGQWFWPX9BRWm9qqI9b0FIA1oCian5rEx8esb264pqcArE5/P7JswZx2adPp3STJvLc7pW2nOWt4YqvPeldWpXPd+nnpF4/nrT/2Tdsuf1tyh1hH2cG/rc6XPr/SMf6vHwAAgHKobkybTu+mYU67NMxpl24n1qx8nY+xIa32TNt0XHKTCoC/E+sAAABY8xqrk8bqFEVV2nabnbbdZqf6yUlrelar1fGbvJnaonbJXWQB/k6sAwAAoByqG7NofuvM+r8emfV/PXLDfx+6pme0Wv16Wo/MzF+TefNTNFat6ekAJeFusAAAAKx51Y1JktpWC9N5/deTJAd+5rm898paa3JWq9VX+72eVycPSNpNS1V1iy4nD3wCiHUAAACseX+/oUJVzeJKuFv0t3Zrckar3d8a2mR69ctrehpAyTgNFgAAgDWruvEfjyR1HealrsO8VB/+mTU8sdVvncU9ksRpsECFWAcAAMCa1fiPv5oWi2uy4O2OWfB2x9Tc+fAanNTqd99rnbOoanFSXe00WKDCabAAAACUQ2N1Fs1vlcxvlSSprlu0hie0ev31vcbMrJ6RzJnnyDqgQqwDAACgNGrbNKRx8d+vX1f173202ZTixcwpZibVVSkKsQ5YwmmwAAAAAFASjqwDAACgNBY11KVxYU2SpNW/+dFm7xYzM3fRjKSx+Lc/ihBoObEOAACAcqhuTF3b+anqsDhJMu+NrmnVfv4antTq06GqW7rWrpvUP+QGE0CF02ABAABY8xqrk8bqFItrKou6fP3f+6+sO7TaIPVFfTJn3pqeClAijqwDAABgzaturPy4aF6bJEnDHTOStF9DE1r9Zi/8+91g27VZ01MBSkSsAwAAoDQaF9ZmzszOSZJXX/hU1uv/4hqdz+r0aOMTmfneM8m8RSka/72vzwe03L/3McUAAAB8rFTXLUqnnjPSqeeMbLDNpDU9ndXqzbmP51PtPpfU1qx8MPCJIdYBAABQGovmt87ihrosbqhL9cGbrunprFZbtxuWhryXLFy4pqcClIhYBwAAQClUVRWpbbUwjQtrWMSXWgAAZXhJREFU07iwNrXjx6/pKa1WHdM2HYquSV3dmp4KUCJiHQAAAKVQFEuu21ZdtyjVdYuS9buv4RmtXkPWrk3bom3S2Jiq6mJNTwcoCbEOAACA0qiqWZxWneakVac5qdrrgjU9ndXqoM/8b96tfjdJ3GACqBDrAAAAAKAkxDoAAABKoaqqyOKGuhSLalIsqsl73zl9TU9ptfrP/9k6i7MwaXQKLPAPYh0AAAClUBRVqa5blNY93k7rHm+n+sit1vSUVqtD+72RdRb3SGprKtfrA6hd0xMAAACAisbqNLzZJUlSe9OEJN3W7HxWo6OeeS3ta7ok7dqmuqZxTU8HKAlH1gEAAFAKVVVFGua2ycI5bbNwTtvMe2WtNT2l1eq8T/VPQ+YlcYMJ4B/EOgAAAEphyWmwi/Pe3+rz3t/q0/5Tb6zpKa1WN73cJg15L5kzd01PBSgRsQ4AAIDSWDCnbWa8sVZmvLFWanv/e58a+sDCu7OomJ/inQVreipAiVQVRdGi285UVdWt7rkAAKxWRbFwTU+Bj2DWNzdc01MAVrfqxjQurE3j4pokSYd+f01124XJVn3zzpiazJ7ZJev0fymvTe2bqqoin/rsXzLn5R5Jks5bvZA/37B7evf+v3TdYloaZnTO3De7ZsLTn8mQIffn9f/XJ42NS45X6b3906mpn5/ZU/qkumZx2vd5LQ/cPDSvzu2QA4benWlPbZa/vt09SbLL7uMy/k+fzzqd38kW3/zfvHrdpzL5hX5Zr+tbeXdeu2y582Np/dklf62eOGpgPj1kfOb+de20X+/NzJi0YWa+1TWNjdVp1/a9VFcviY9z57VLu7bvpcdGL2bhnCXXq6uqdkdY+CTofOnzKx0j1gEAnxhi3cebWAefENWNyd+jWqob06rTnDQurM2Cdzqktk1DWnV5N/Pf7JLGxdVp0/VvWfhu+yRJm7Xfyfy3OqV153eTJO/N6JLqmsZU1y1KXac5WTy/Vea90TVJUtd2QWrbzU/rdd7JvJd6pHX3WZn3WrcsbqhLh96vZ95r3dK6y7uVKS1+r3Xmz+6QDr3ezOIFdSkW16Th3XZ579326dL7tdS0WXJk3IJZHdKm++zMf6tT0lidxsXVaVxck3brzMzi91qnqnZxkmT+251S22ZBikU1Ih18woh1AADLEOs+3sQ6+AT5+xFoaaxO0ViVomh684XqukWV595vaRBbNL91qqqWHLFW225+ikU1aVxYm2TJtfEaF9ekts2CVFUVqaou0riwNkVjVWpaL0zj4uolr5Fk4Zy2qalbnKKxasnymsZU1S6ubKu6pnG581g6trpuURY3LPn7dG27+UueW9AqjYurK68NfHK0JNbV/gvmAQAAAC23zJF1ValOVZYJWtWNqaoqUlQ3puoDrsJe2+of/0DTuLA2aayuhLGq6sWprlkSBIvFNSmKxiXbrV4S2ZIlQS1JauoWV56rqS4qR/5V1zRWomJV/jHfpSpjk8prLd3msssA3s8NJgAAAACgJBxZBwAAQDk1Vv/jGnZL/7exOsX7rmvXbJ2lljmddrnbXdE6yxv/QXP8gOeK98/vg+YFELEOAACAMltefGvp8yuLYe+Pdsvz/nj3vnC4wvm8f8yK4iDA+4h1AAAAlF9LAteqRLClY1sS9Fr6Ou+Peh92bsAnmv9aAAAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJSEWAcAAAAAJSHWAQAAAEBJiHUAAAAAUBJiHQAAAACUhFgHAAAAACUh1gEAAABASYh1AAAAAFASYh0AAAAAlIRYBwAAAAAlIdYBAAAAQEmIdQAAAABQEmIdAAAAAJREVVEUxZqeBAAAAADgyDoAAAAAKA2xDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAAAA/gWOOOKIVFVVpaqqKnV1denbt2/OOOOMzJ8/v8m4pWMeeeSRJssXLFiQbt26paqqKvfff39l+QMPPJDddtstXbt2Tbt27dK/f/8MHz48DQ0NSZL777+/ss33P15//fUP9V7mz5+fE044Id26dUt9fX0OOOCAvPHGGx+4zi677FJ53TZt2mSzzTbL5Zdf3mRMQ0NDLrjggmy55ZZp165dunfvnh133DFjxozJwoULP9Rc33777Rx66KHp2LFjOnfunKOPPjpz5sxp8VyXPo477rjK8//7v/+bQw45JOuvv37atm2bTTfdNBdffPFK57Kq3+1H9eKLL67wu3//HMrmpptuyiabbJI2bdrk05/+dH73u9+t6SnBv4xYBwAA8C+y55575rXXXssLL7yQUaNGZfTo0Rk5cmSzceuvv37GjBnTZNltt92W+vr6JsumTJmSPffcM9tss00efPDB/OUvf8mll16aVq1aZfHixU3GTp06Na+99lqTx9prr/2h3sepp56aO+64IzfddFMeeOCBvPrqq9l///1Xut7Xv/71vPbaa5kyZUoOOuignHDCCfnNb36TZEmo22OPPfLjH/84xxxzTMaPH5/HHnssJ5xwQi699NJMnjz5Q8310EMPzeTJk3PPPffkzjvvzIMPPphjjjmmxXNd+rjgggsqzz3xxBNZe+21c+2112by5Mk566yz8p3vfCeXXXbZSrfb0u/2n+nee+9t9t0PHDhwuWOXRt73+7Cx9MOsN378+BxyyCE5+uijM3HixHz5y1/Ol7/85UyaNOlDzQE+dgoAAABWu+HDhxf77rtvk2X7779/MWDAgCbLkhRnn3120bFjx2LevHmV5V/4wheK733ve0WSYty4cUVRFMWoUaOKPn36fODrjhs3rkhSvPPOO/+Mt1HMmjWrqKurK2666abKsmeeeaZIUkyYMGGF6w0aNKg4+eSTmyzr379/cfDBBxdFURTnn39+UV1dXTz55JPN1m1oaCjmzJmzynOdMmVKkaR4/PHHK8t+//vfF1VVVcUrr7yySnNdmeOPP77YddddP3DMqny3RVEUZ5xxRtG/f/+ibdu2Rd++fYuzzz67aGhoKIqiKBobG4vBgwcXu+++e9HY2FgURVHMnDmz6NWrV/G9732vKIqimD59epGkmDhx4grnNHLkyGLLLbcsrrzyyqJPnz5FVVVVZa6XX3558cUvfrFo165dMXLkyKIoiuLyyy8vNthgg6Kurq7YaKONimuuuabZe1zeeqvioIMOKvbee+8myz772c8Wxx577CpvCz6OHFkHAACwBkyaNCnjx49Pq1atmj03cODA9OnTJ7fcckuS5OWXX86DDz6Yr371q03G9ejRI6+99loefPDBjzSX6667LvX19R/4eOihh5IsOaps4cKFGTJkSGX9TTbZJJ/61KcyYcKEVXrdtm3bVo7kuu666zJkyJAMGDCg2bi6urq0b98+SXLeeeetdK4vv/xykmTChAnp3Llzttlmm8q2hgwZkurq6jz66KMr/Uy6d++eLbbYIt/5zncyb968Dxw/e/bsdO3adaXvuaXfbZJ06NAhY8eOzZQpU3LxxRfnyiuvzKhRo5IsOaX26quvzuOPP55LLrkkSXLcccelV69e+f73v7/SeSzr+eefzy233JJbb701Tz31VGX5Oeeck/322y9/+ctfctRRR+W2227LySefnNNOOy2TJk3KsccemyOPPDLjxo1rsr33r5dkpd/ZsqcZT5gwocmfryTZY489VvnPF3xc1a7pCQAAAHxS3Hnnnamvr8+iRYuyYMGCVFdXr/DUyaOOOipXXXVVDjvssIwdOzZDhw7NWmut1WTMgQcemLvvvjuDBg1Kjx49sv3222fw4ME5/PDD07FjxyZj11tvvSa/9+7du3Jq6Ze+9KV89rOf/cC59+rVK0ny+uuvp1WrVuncuXOT59dZZ50WXwNv8eLF+c1vfpOnn366ckrqc889l1122WWl6x533HE56KCDPnDMuuuuW5nr+0/1ra2tTdeuXT9wrsOGDUvv3r2z7rrr5umnn86ZZ56ZqVOn5tZbb13u+PHjx+fGG2/MXXfdtdL5Jy37bpPk7LPPrvzcp0+ffOtb38oNN9yQM844I8mS72T06NE5/PDD8/rrr+d3v/tdJk6cmNrapn/V/9znPpfq6qbH6ix73b6GhoZcc801zeYwbNiwHHnkkZXfDznkkBxxxBE5/vjjkyQjRozII488kgsvvDC77rrrCtdL0iQCLs+yf15ff/31rLPOOk2eX5U/X/BxJ9YBAAD8i+y666654oorMnfu3IwaNSq1tbU54IADljv2sMMOy7e//e288MILGTt2bOXoqWXV1NRkzJgx+eEPf5j77rsvjz76aM4777ycf/75eeyxx9KzZ8/K2IceeigdOnSo/F5XV1f5uUOHDk2eW10uv/zy/PKXv0xDQ0Nqampy6qmn5hvf+EaSpCiKFm2ja9euLTqC7aNY9pp2n/70p9OzZ88MHjw406ZNS79+/ZqMnTRpUvbdd9+MHDkyu+++e4u235LvNkluvPHGXHLJJZk2bVrmzJmTRYsWNYuwBx54YG677bb8+Mc/zhVXXJH+/fsvdzubbrrpCufTu3fv5cbCZY9ITJJnnnmm2fX+dtxxx2Y313j/ekmy4YYbrvD1gaacBgsAAPAv0r59+2y44YbZcsstc9VVV+XRRx/Nr371q+WO7datW/bZZ58cffTRmT9/fvbaa68VbrdXr1756le/mssuuyyTJ0/O/Pnz8/Of/7zJmL59+2bDDTesPHr37l15blVOg+3Ro0caGhoya9asJtt/44030qNHjw98/4ceemieeuqpTJ8+PXPnzs3PfvazyhFfG220UZ599tkPXD9ZtdNge/TokTfffLPJ+osWLcrbb7+90rkua+lRh88//3yT5VOmTMngwYNzzDHHNDkKbmVa8t1OmDAhhx56aIYOHZo777wzEydOzFlnndXsBhDz5s3LE088kZqamjz33HPLfb3111+/yXf//nC29BTj91vR8pVZ3nqrchpsjx49mt1duCV/vuDfhSPrAAAA1oDq6up897vfzYgRIzJs2LC0bdu22ZijjjoqQ4cOzZlnnpmampoWbbdLly7p2bNn5s6d2+K5rMppsAMHDkxdXV3+9Kc/VY4KnDp1al5++eXssMMOH7iNTp06rfAIq2HDhuW73/1uJk6c2Oy6dQsXLkxDQ0Pat2+/SqfB7rDDDpk1a1aeeOKJyt1P77vvvjQ2Nq70/S5r6Smcyx6pOHny5Oy2224ZPnx4fvSjH7V4W0ut7LsdP358evfunbPOOquy7KWXXmo27rTTTkt1dXV+//vfZ+jQodl7772z2267rfJ8WmLTTTfNww8/nOHDh1eWPfzww9lss81Wuu6qnAa7ww475E9/+lNOOeWUyrJ77rlnpX++4N+FWAcAALCGHHjggTn99NPz3//93/nWt77V7Pk999wzM2bMaHbq41KjR4/OU089lf322y/9+vXL/Pnzc80112Ty5Mm59NJLm4x98803M3/+/CbLunXrlrq6ulU6DbZTp045+uijM2LEiHTt2jUdO3bMN7/5zeywww7ZfvvtW/jOmzvllFNy1113ZfDgwTn33HOz0047pUOHDvnzn/+c888/P7/61a+y1VZbrdJpsJtuumn23HPPfP3rX8/Pf/7zLFy4MCeeeGIOPvjgStB75ZVXMnjw4FxzzTXZbrvtMm3atFx//fUZOnRounXrlqeffjqnnnpqdt5553zmM59JsuTU19122y177LFHRowYUbmWWk1NzXJPJ12elX23/fv3z8svv5wbbrgh2267be66667cdtttTcbcddddueqqqzJhwoRsvfXWOf300zN8+PA8/fTT6dKlS2XczJkzm13vrXPnzmnTpk2L5rrU6aefnoMOOigDBgzIkCFDcscdd+TWW2/Nvffeu9J1V+U02JNPPjmDBg3KT3/60+y999654YYb8uc//zm/+MUvVmm+8HHlNFgAAIA1pLa2NieeeGIuuOCC5R4JV1VVle7duy/3jrFJst1222XOnDk57rjjsvnmm2fQoEF55JFHcvvtt2fQoEFNxm688cbp2bNnk8cTTzzxoeY9atSo7LPPPjnggAOy8847p0ePHiu8+UJLtW7dOvfcc0/OOOOMjB49Ottvv3223XbbXHLJJTnppJOyxRZbfKjtXnfdddlkk00yePDgDB06NDvttFOT6LNw4cJMnTq1crfXVq1a5d57783uu++eTTbZJKeddloOOOCA3HHHHZV1br755syYMSPXXnttk89z2223bfG8VvbdfulLX8qpp56aE088MVtttVXGjx+f733ve5XnZ8yYkaOPPjrnnHNOtt566yTJD37wg6yzzjpNTilNltwB9/3f/e23397iuS715S9/ORdffHEuvPDCbL755hk9enTGjBnTohuDrIrPfe5zuf766/OLX/wiW265ZW6++ebcfvvtH/rPAHzcVBUtvYonAAAAALBaObIOAAAAAEpCrAMAAACAkhDrAAAAAKAkxDoAAAAAKAmxDgAAAABKQqwDAAAAgJIQ6wAAAACgJMQ6AAAAACgJsQ4AAAAASkKsAwAAAICSEOsAAAAAoCTEOgAAAAAoCbEOAAAAAEpCrAMAAACAkhDrAACA/7+9e4/7er4fP/68qqvj1fmgg1wVTactCcsc0oFSGJJD4YqGVDOKNsfysznNltMiG8UX0yL2VQxNjhVGzSoaFr7fhEiRVleH9+8Ptz5fn64OV2G9Nvf77Xbdvq7X5/V+f16f98f2fe9xvd+fDwCQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0k6pBDDokOHTpsc16LFi1i0KBB3/yC2KIxY8ZEQUHBzl4GAPAt5Jzx34dzRqC8xDrgW+Hee++N66+/foe3X7VqVYwZMyaeeuqpr21NAACkxTkjkIKCLMuynb0IoKxDDjkkPvroo5g3b95W561ZsyYqVKgQhYWF/6KV/Xs64ogjYt68efH222/v0PYfffRRNGzYMEaPHh1jxozJe2zdunWxbt26qFq16ldfKADAdnDO+PVyzgikwJV18G+uSpUq38qTrs8//3xnLyGnUqVKTroAgKQ5Z9z5/l3PGVevXh0bNmzY7GNf9fhu2LAhVq9e/ZX2Af+JxDrYST777LM499xzo0WLFlGlSpVo1KhRHHroofHKK69scZvHH388qlevHieddFKsW7cuIsp+/sjEiROjoKAgnnvuuTjnnHOiYcOGUadOnTjrrLOitLQ0li9fHqeeemrUrVs36tatG6NGjYpNL7C97rrr4gc/+EHUr18/qlWrFp07d47777+/zHr++c9/xjnnnBMNGjSImjVrxlFHHRWLFy+OgoKCMn9JXLx4cZx++umxyy67RJUqVaJ9+/Zxxx13lOtYbXxNTz/9dAwdOjQaNWoUu+66a+7xcePGRfv27aNKlSrRtGnTGDZsWCxfvjz3+CGHHBLTpk2Ld955JwoKCqKgoCBatGgRERGlpaVx2WWXRefOnaN27dpRo0aNOOigg2LGjBm57d9+++1o2LBhRERcfvnluX1sfI2b+/yRdevWxRVXXBG77757VKlSJVq0aBEXXXRRrFmzJm9eixYt4ogjjojnnnsu9ttvv6hatWq0atUq7rrrrnIdGwDgP5tzRueMEV/POWN5ju1TTz0VBQUFcd9998Ull1wSzZo1i+rVq8enn34agwYNiqKionjrrbeiT58+UbNmzRg4cGBEfBHtRo4cGc2bN48qVarEnnvuGdddd12Zf2cKCgpi+PDhcc899+Teiz/96U/lfg3wbVFpZy8Avq2GDBkS999/fwwfPjzatWsXH3/8cTz33HPx2muvxd57711m/tSpU+O4446LE044Ie64446oWLHiVvf/4x//OBo3bhyXX355zJ49O2677baoU6dOzJw5M3bbbbe48sor45FHHolf/vKX0aFDhzj11FNz295www1x1FFHxcCBA6O0tDTuu+++6N+/f0ydOjX69u2bmzdo0KD4wx/+EKecckp06dIlnn766bzHN/rggw+iS5cuuf/n3LBhw3j00Udj8ODB8emnn8a5555brmM2dOjQaNiwYVx22WW5v+KNGTMmLr/88ujZs2ecffbZsXDhwrjlllvipZdeiueffz4KCwvj4osvjhUrVsT//u//xtixYyMioqioKCIiPv300/jd734XJ510Upxxxhnx2Wefxe233x69evWKF198Mfbaa69o2LBh3HLLLXH22WfHMcccE8cee2xERHzve9/b4lp/9KMfxZ133hnHHXdcjBw5Ml544YW46qqr4rXXXosHH3wwb+6bb74Zxx13XAwePDhKSkrijjvuiEGDBkXnzp2jffv25To2AMB/JueMzhk3+irnjNt7bK+44oqoXLlynH/++bFmzZqoXLlyRHwRF3v16hUHHnhgXHfddVG9evXIsiyOOuqomDFjRgwePDj22muveOyxx+KCCy6IxYsX547lRk8++WT84Q9/iOHDh0eDBg1yQRT4kgzYKWrXrp0NGzZsi4937do1a9++fZZlWfbAAw9khYWF2RlnnJGtX78+b15xcXFWUlKS+33ChAlZRGS9evXKNmzYkBvff//9s4KCgmzIkCG5sXXr1mW77rpr1rVr17x9rlq1Ku/30tLSrEOHDln37t1zYy+//HIWEdm5556bN3fQoEFZRGSjR4/OjQ0ePDhr0qRJ9tFHH+XNPfHEE7PatWuXeb5NbXxNBx54YLZu3brc+IcffphVrlw5O+yww/KOy80335xFRHbHHXfkxvr27ZsVFxeX2fe6deuyNWvW5I198skn2S677JKdfvrpubGlS5eWeV0bjR49Ovvyf53OnTs3i4jsRz/6Ud68888/P4uI7Mknn8yNFRcXZxGRPfPMM3mvq0qVKtnIkSO3clQAgG8D54zOGbPsq58zlvfYzpgxI4uIrFWrVmWOd0lJSRYR2c9+9rO88YceeiiLiOznP/953vhxxx2XFRQUZG+++WZuLCKyChUqZPPnz9/mmuHbzG2wsJPUqVMnXnjhhXjvvfe2Ou/3v/99nHDCCXHWWWfF+PHjo0KF8v3HdvDgwXmX2X//+9+PLMti8ODBubGKFSvGPvvsE//4xz/ytq1WrVrunz/55JNYsWJFHHTQQXm3W2y8XH3o0KF52/74xz/O+z3LsnjggQfiyCOPjCzL4qOPPsr99OrVK1asWLHV2zi+7Iwzzsj76/D06dOjtLQ0zj333LzjcsYZZ0StWrVi2rRp29xnxYoVc38p3LBhQyxbtizWrVsX++yzT7nXtalHHnkkIiJGjBiRNz5y5MiIiDLrateuXRx00EG53xs2bBh77rlnmfcFAPj2cc7onHGjHT1n3JFjW1JSkvf+ftnZZ59d5nVUrFgxzjnnnDKvI8uyePTRR/PGu3btGu3atdvqmuHbzm2wsJNce+21UVJSEs2bN4/OnTtHnz594tRTT41WrVrl5ixatChOPvnk6N+/f9x0003btf/ddtst7/fatWtHRETz5s3LjH/yySd5Y1OnTo2f//znMXfu3LzPy/jyidw777wTFSpUiJYtW+Ztu8cee+T9vnTp0li+fHncdtttcdttt212rR9++GFERLz//vtl1vblk4RNn+udd96JiIg999wzb7xy5crRqlWr3OPbcuedd8avfvWreP3112Pt2rVbfL7y2nhsNj0WjRs3jjp16pRZ16bvVURE3bp1y7wvAMC3j3PG/+OccevnjOvXr4+lS5fmPV6vXr1Yvnx5uY/tRlt6TZUqVcr7HMCNr6Np06ZRs2bNvPG2bdvmHi/PvoH/I9bBTnL88cfHQQcdFA8++GA8/vjj8ctf/jKuueaamDJlShx++OEREdGkSZNo0qRJPPLII/GXv/wl9tlnn3Lvf0ufT7K58exLH/z67LPPxlFHHRUHH3xwjBs3Lpo0aRKFhYUxYcKEuPfee7fzVUbum6NOPvnkKCkp2eycjZ/j0aRJk7zxCRMm5H0Q8pb+uvdV3H333TFo0KA4+uij44ILLohGjRpFxYoV46qrroq33nrrK+170w8Q3pItvVfZJh/ICwB8+zhn/D/OGbd+zvg///M/ZULYjBkzok2bNhFRvmO70ZaOYZUqVcp91eaWfBPvD/ynEetgJ2rSpEkMHTo0hg4dGh9++GHsvffe8Ytf/CJ34lW1atWYOnVqdO/ePXr37h1PP/30N/6FAw888EBUrVo1HnvssahSpUpufMKECXnziouLY8OGDbFo0aJo3bp1bvzNN9/Mm9ewYcOoWbNmrF+/Pnr27LnV537iiSfyft/Way0uLo6IiIULF+b9dbm0tDQWLVqU93xbOgm6//77o1WrVjFlypS8OaNHj86bV96TqI3r2rBhQ7zxxhu5vyhGfPHBvsuXL8+tGwCgPJwz5nPOuHmNGzcuc2w6duwYtWrVKvex3RHFxcUxffr0+Oyzz/Kurnv99ddzjwPbx2fWwU6wfv36WLFiRd5Yo0aNomnTpmW+pr127drx2GOPRaNGjeLQQw/9yn+525aKFStGQUFBrF+/Pjf29ttvx0MPPZQ3r1evXhERMW7cuLzxTW+9qFixYvTr1y8eeOCBmDdvXpnn+/Kl+j179sz72fSvppvq2bNnVK5cOW688ca8v/TefvvtsWLFirxvGatRo0aZY75xfRH5fyl+4YUXYtasWXnzqlevHhERy5cv3+qaIiL69OkTERHXX3993vivf/3riIjNfvsZAMCmnDP+H+eM21a1atUyx6Zu3brbdWx3RJ8+fWL9+vVx8803542PHTs2CgoKclEZKD9X1sFO8Nlnn8Wuu+4axx13XHTs2DGKiopi+vTp8dJLL8WvfvWrMvMbNGgQTzzxRBx44IHRs2fPeO6556JZs2bfyNr69u0bv/71r6N3794xYMCA+PDDD+M3v/lN7LHHHvHqq6/m5nXu3Dn69esX119/fXz88cfRpUuXePrpp+Pvf/97ROT/VfHqq6+OGTNmxPe///0444wzol27drFs2bJ45ZVXYvr06bFs2bIdWmvDhg3jwgsvjMsvvzx69+4dRx11VCxcuDDGjRsX++67b5x88sl56500aVKMGDEi9t133ygqKoojjzwyjjjiiJgyZUocc8wx0bdv31i0aFHceuut0a5du1i5cmVu+2rVqkW7du1i0qRJ8Z3vfCfq1asXHTp0iA4dOpRZV8eOHaOkpCRuu+22WL58eXTt2jVefPHFuPPOO+Poo4+Obt267dDrBQC+XZwzOmf8unxTxzYi4sgjj4xu3brFxRdfHG+//XZ07NgxHn/88fjjH/8Y5557buy+++5f2+uAb41/+ffPAtmaNWuyCy64IOvYsWNWs2bNrEaNGlnHjh2zcePG5eZ07do1a9++fd52b775ZtakSZOsbdu22dKlS7Ms++Jr3EtKSnJzNn5l/UsvvZS37cavit+43UYlJSVZjRo18sZuv/32rHXr1lmVKlWyNm3aZBMmTCjzVfNZlmWff/55NmzYsKxevXpZUVFRdvTRR2cLFy7MIiK7+uqr8+Z+8MEH2bBhw7LmzZtnhYWFWePGjbMePXpkt9122zaP15Ze00Y333xz1qZNm6ywsDDbZZddsrPPPjv75JNP8uasXLkyGzBgQFanTp0sIrLi4uIsy7Jsw4YN2ZVXXpkVFxdnVapUyTp16pRNnTo1Kykpyc3ZaObMmVnnzp2zypUrZxGRjR49Ou/YftnatWuzyy+/PGvZsmVWWFiYNW/ePLvwwguz1atX580rLi7O+vbtW+Y1de3aNevates2jw0A8J/LOaNzxo2+jnPG8hzbGTNmZBGRTZ48ucz2m/t3YKPPPvssO++887KmTZtmhYWFWevWrbNf/vKX2YYNG/LmRUQ2bNiwcq0Xvs0KsswnmANfn7lz50anTp3i7rvvjoEDB+7s5QAAkCDnjABb5jPrgB32z3/+s8zY9ddfHxUqVIiDDz54J6wIAIDUOGcE2D4+sw7YYddee228/PLL0a1bt6hUqVI8+uij8eijj8aZZ54ZzZs339nLAwAgAc4ZAbaP22CBHfbEE0/E5ZdfHgsWLIiVK1fGbrvtFqecckpcfPHFUamSvwUAAOCcEWB7iXUAAAAAkAifWQcAAAAAiRDrAAAAACAR5f6AgIKCwm9yHQAA37gsW7uzl8BX8Ocu/Xf2EgAAvpIesydvc44r6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAiKu3sBQBpW5dN3NlL+MZUKhi0s5cAAMA2HDL76Hiqy0M7exnfiJ4vPBTTv3/0zl4GkBhX1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARBVmWZeWaWFD4Ta8FAOAblWVrd/YS+Ar+3KX/zl4CAMBX0mP25G3OcWUdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAiCrIsy3b2IgAAAAAAV9YBAAAAQDLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAD+BQYNGhQFBQVRUFAQhYWF0bJlyxg1alSsXr06b97GObNnz84bX7NmTdSvXz8KCgriqaeeyo0//fTT0b1796hXr15Ur149WrduHSUlJVFaWhoREU899VRun5v+vP/++zv0WlavXh3Dhg2L+vXrR1FRUfTr1y8++OCDrW5zyCGH5J63atWq0a5duxg3blzenNLS0rj22mujY8eOUb169WjQoEEccMABMWHChFi7du0OrXXZsmUxcODAqFWrVtSpUycGDx4cK1euLPdaN/4MGTIk9/hf//rXOOmkk6J58+ZRrVq1aNu2bdxwww3bXMv2vrdf1dtvv73F937TNaRk/vz50a9fv2jRokUUFBTE9ddfv7OXBP9SYh0AAMC/SO/evWPJkiXxj3/8I8aOHRvjx4+P0aNHl5nXvHnzmDBhQt7Ygw8+GEVFRXljCxYsiN69e8c+++wTzzzzTPztb3+Lm266KSpXrhzr16/Pm7tw4cJYsmRJ3k+jRo126HWcd9558fDDD8fkyZPj6aefjvfeey+OPfbYbW53xhlnxJIlS2LBggVx/PHHx7Bhw+L3v/99RHwR6nr16hVXX311nHnmmTFz5sx48cUXY9iwYXHTTTfF/Pnzd2itAwcOjPnz58cTTzwRU6dOjWeeeSbOPPPMcq9148+1116be+zll1+ORo0axd133x3z58+Piy++OC688MK4+eabt7nf8r63X6fp06eXee87d+682bkbI++mdjSW7sh2q1atilatWsXVV18djRs33qHnhX9rGQAAAN+4kpKS7Ic//GHe2LHHHpt16tQpbywisksuuSSrVatWtmrVqtz4oYceml166aVZRGQzZszIsizLxo4dm7Vo0WKrzztjxowsIrJPPvnk63gZ2fLly7PCwsJs8uTJubHXXnsti4hs1qxZW9yua9eu2U9+8pO8sdatW2cnnnhilmVZds0112QVKlTIXnnllTLblpaWZitXrtzutS5YsCCLiOyll17KjT366KNZQUFBtnjx4u1a67YMHTo069at21bnbM97m2VZNmrUqKx169ZZtWrVspYtW2aXXHJJVlpammVZlm3YsCHr0aNHdthhh2UbNmzIsizLPv7446xZs2bZpZdemmVZli1atCiLiGzOnDlbXNPo0aOzjh07Zr/97W+zFi1aZAUFBbm1jhs3LjvyyCOz6tWrZ6NHj86yLMvGjRuXtWrVKissLMy+853vZHfddVeZ17i57XZUcXFxNnbs2K+0D/h348o6AACAnWDevHkxc+bMqFy5cpnHOnfuHC1atIgHHnggIiLefffdeOaZZ+KUU07Jm9e4ceNYsmRJPPPMM19pLffcc08UFRVt9efZZ5+NiC+uKlu7dm307Nkzt32bNm1it912i1mzZm3X81arVi13Jdc999wTPXv2jE6dOpWZV1hYGDVq1IiIiCuvvHKba3333XcjImLWrFlRp06d2GeffXL76tmzZ1SoUCFeeOGFbR6TBg0aRIcOHeLCCy+MVatWbXX+ihUrol69ett8zeV9byMiatasGRMnTowFCxbEDTfcEL/97W9j7NixEfHFLbV33nlnvPTSS3HjjTdGRMSQIUOiWbNmcdlll21zHV/25ptvxgMPPBBTpkyJuXPn5sbHjBkTxxxzTPztb3+L008/PR588MH4yU9+EiNHjox58+bFWWedFaeddlrMmDEjb3+bbhcR23zPvnybMXzbVdrZCwAAAPi2mDp1ahQVFcW6detizZo1UaFChS3eOnn66afHHXfcESeffHJMnDgx+vTpEw0bNsyb079//3jssceia9eu0bhx4+jSpUv06NEjTj311KhVq1be3F133TXv9+Li4tytpUcddVR8//vf3+ramzVrFhER77//flSuXDnq1KmT9/guu+xS7s/AW79+ffz+97+PV199NXdL6htvvBGHHHLINrcdMmRIHH/88Vud07Rp09xaN73Vt1KlSlGvXr2trnXAgAFRXFwcTZs2jVdffTV++tOfxsKFC2PKlCmbnT9z5syYNGlSTJs2bZvrjyjfexsRcckll+T+uUWLFnH++efHfffdF6NGjYqIL96T8ePHx6mnnhrvv/9+PPLIIzFnzpyoVCn/f+r/4Ac/iAoV8q/V+fLn9pWWlsZdd91VZg0DBgyI0047Lff7SSedFIMGDYqhQ4dGRMSIESNi9uzZcd1110W3bt22uF1E5EXAzdn031f4NhPrAAAA/kW6desWt9xyS3z++ecxduzYqFSpUvTr12+zc08++eT42c9+Fv/4xz9i4sSJuaunvqxixYoxYcKE+PnPfx5PPvlkvPDCC3HllVfGNddcEy+++GI0adIkN/fZZ5+NmjVr5n4vLCzM/XPNmjXzHvumjBs3Ln73u99FaWlpVKxYMc4777w4++yzIyIiy7Jy7aNevXrluoLtq/jyZ9p997vfjSZNmkSPHj3irbfeit133z1v7rx58+KHP/xhjB49Og477LBy7b88721ExKRJk+LGG2+Mt956K1auXBnr1q0rE7X69+8fDz74YFx99dVxyy23ROvWrTe7n7Zt225xPcXFxZuNhV++IjEi4rXXXivzeX8HHHBAmS/X2HS7iIg99thji88P5HMbLAAAwL9IjRo1Yo899oiOHTvGHXfcES+88ELcfvvtm51bv379OOKII2Lw4MGxevXqOPzww7e432bNmsUpp5wSN998c8yfPz9Wr14dt956a96cli1bxh577JH7KS4uzj22PbfBNm7cOEpLS2P58uV5+//ggw+2+WUAAwcOjLlz58aiRYvi888/j1//+te5K76+853vxOuvv77V7SO27zbYxo0bx4cffpi3/bp162LZsmXb9cUFG686fPPNN/PGFyxYED169Igzzzwz7yq4bSnPeztr1qwYOHBg9OnTJ6ZOnRpz5syJiy++uMwXQKxatSpefvnlqFixYrzxxhubfb7mzZvnvfebhrONtxhvakvj27K57dwGC+XnyjoAAICdoEKFCnHRRRfFiBEjYsCAAVGtWrUyc04//fTo06dP/PSnP42KFSuWa79169aNJk2axOeff17utWzPbbCdO3eOwsLC+POf/5y7KnDhwoXx7rvvxv7777/VfdSuXXuLV1gNGDAgLrroopgzZ06Zz61bu3ZtlJaWRo0aNbbrNtj9998/li9fHi+//HLu20+ffPLJ2LBhwzZf75dtvIXzy1cqzp8/P7p37x4lJSXxi1/8otz72mhb7+3MmTOjuLg4Lr744tzYO++8U2beyJEjo0KFCvHoo49Gnz59om/fvtG9e/ftXk95tG3bNp5//vkoKSnJjT3//PPRrl27bW7rNlgoP7EOAABgJ+nfv39ccMEF8Zvf/CbOP//8Mo/37t07li5dusWQMX78+Jg7d24cc8wxsfvuu8fq1avjrrvuivnz58dNN92UN/fDDz+M1atX543Vr18/CgsLt+s22Nq1a8fgwYNjxIgRUa9evahVq1b8+Mc/jv333z+6dOlSzlde1rnnnhvTpk2LHj16xBVXXBEHHnhg1KxZM/7yl7/ENddcE7fffnvstdde23UbbNu2baN3795xxhlnxK233hpr166N4cOHx4knnpgLeosXL44ePXrEXXfdFfvtt1+89dZbce+990afPn2ifv368eqrr8Z5550XBx98cHzve9+LiC9ufe3evXv06tUrRowYkfv8u4oVK272dtLN2dZ727p163j33Xfjvvvui3333TemTZsWDz74YN6cadOmxR133BGzZs2KvffeOy644IIoKSmJV199NerWrZub9/HHH5f5jL46depE1apVy7XWjS644II4/vjjo1OnTtGzZ894+OGHY8qUKTF9+vRtbrs9t8GWlpbGggULcv+8ePHimDt3bhQVFbmdlm8Ft8ECAADsJJUqVYrhw4fHtddeu9kr4QoKCqJBgwab/cbYiIj99tsvVq5cGUOGDIn27dtH165dY/bs2fHQQw9F165d8+buueee0aRJk7yfl19+eYfWPXbs2DjiiCOiX79+cfDBB0fjxo23+OUL5VWlSpV44oknYtSoUTF+/Pjo0qVL7LvvvnHjjTfGOeecEx06dNih/d5zzz3Rpk2b6NGjR/Tp0ycOPPDAuO2223KPr127NhYuXJj7ttfKlSvH9OnT47DDDos2bdrEyJEjo1+/fvHwww/ntrn//vtj6dKlcffdd+cdz3333bfc69rWe3vUUUfFeeedF8OHD4+99torZs6cGZdeemnu8aVLl8bgwYNjzJgxsffee0dExOWXXx677LJLmVtKe/bsWea9f+ihh8q91o2OPvrouOGGG+K6666L9u3bx/jx42PChAnl+mKQ7fHee+9Fp06dolOnTrFkyZK47rrrolOnTvGjH/3oa30eSFVBVt5P8QQAAAAAvlGurAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOuBb5e23346CgoKYOHHizl4KAAAJc94I7CxiHZDnpZdeiuHDh0f79u2jRo0asdtuu8Xxxx8ff//73/9la1iwYEGMGTMm3n777R3ex7333hvXX3/917YmAADyOW8E+GYUZFmW7exFAOk47rjj4vnnn4/+/fvH9773vXj//ffj5ptvjpUrV8bs2bOjQ4cO3/ga7r///ujfv3/MmDEjDjnkkB3axxFHHBHz5s0rc+KWZVmsWbMmCgsLo2LFil99sQAA31LOGwG+GZV29gKAtIwYMSLuvffeqFy5cm7shBNOiO9+97tx9dVXx913373d+9ywYUOUlpZG1apVv86l7pCCgoIk1gEA8O/OeWOasiyL1atXR7Vq1co8tnr16qhcuXJUqLDjN9l9/vnnUaNGja+yRGAb3AYL/0EWL14cgwcPjqZNm0aVKlWiZcuWcfbZZ8eLL74YBQUFceedd5bZ5rHHHouCgoKYOnVqRET84Ac/yDvhioho3bp1tG/fPl577bVyraOgoCCGDx8e99xzT7Rv3z6qVKkSf/rTnyIiYs6cOXH44YdHrVq1oqioKHr06BGzZ8/ObTtx4sTo379/RER069YtCgoKoqCgIJ566qmIiPjjH/8Yffv2zb3G3XffPa644opYv359bh+HHHJITJs2Ld55553c9i1atIiILX/2yJNPPhkHHXRQ1KhRI+rUqRM//OEPy7zeMWPGREFBQbz55psxaNCgqFOnTtSuXTtOO+20WLVqVbmODQBACpw3fiHl88YNGzbE9ddfH+3bt4+qVavGLrvsEmeddVZ88sknefNatGgRRxxxRDz22GOxzz77RLVq1WL8+PHx1FNPRUFBQdx3331xySWXRLNmzaJ69erx6aefRkTE5MmTo3PnzlGtWrVo0KBBnHzyybF48eK8fQ8aNCiKiorirbfeij59+kTNmjVj4MCB5Vo/sONcWQf/Id57773Yb7/9Yvny5XHmmWdGmzZtYvHixXH//ffHVVddFa1atYo//OEPUVJSkrfdpEmTom7dutGrV68t7jvLsvjggw+iffv25V7Pk08+GX/4wx9i+PDh0aBBg2jRokXMnz8/DjrooKhVq1aMGjUqCgsLY/z48XHIIYfE008/Hd///vfj4IMPjnPOOSduvPHGuOiii6Jt27YREbn/O3HixCgqKooRI0ZEUVFRPPnkk3HZZZfFp59+Gr/85S8jIuLiiy+OFStWxP/+7//G2LFjIyKiqKhoi2udPn16HH744dGqVasYM2ZM/POf/4ybbropDjjggHjllVdyJ2wbHX/88dGyZcu46qqr4pVXXonf/e530ahRo7jmmmvKfXwAAHYW543/HueNZ511VkycODFOO+20OOecc2LRokVx8803x5w5c+L555+PwsLC3NyFCxfGSSedFGeddVacccYZseeee+Yeu+KKK6Jy5cpx/vnnx5o1a6Jy5cq5/e67775x1VVXxQcffBA33HBDPP/88zFnzpyoU6dObvt169ZFr1694sADD4zrrrsuqlevvs21A19RBvxHOPXUU7MKFSpkL730UpnHNmzYkF144YVZYWFhtmzZstz4mjVrsjp16mSnn376Vvf9X//1X1lEZLfffnu51hIRWYUKFbL58+fnjR999NFZ5cqVs7feeis39t5772U1a9bMDj744NzY5MmTs4jIZsyYUWbfq1atKjN21llnZdWrV89Wr16dG+vbt29WXFxcZu6iRYuyiMgmTJiQG9trr72yRo0aZR9//HFu7K9//WtWoUKF7NRTT82NjR49OouIMsfrmGOOyerXr1/2QAAAJMh5Y/rnjc8++2wWEdk999yTN/6nP/2pzHhxcXEWEdmf/vSnvLkzZszIIiJr1apV3rEoLS3NGjVqlHXo0CH75z//mRufOnVqFhHZZZddlhsrKSnJIiL72c9+ts01A18ft8HCf4ANGzbEQw89FEceeWTss88+ZR4vKCiIE044IdauXRtTpkzJjT/++OOxfPnyOOGEE7a479dffz2GDRsW+++/f5m/rm5N165do127drnf169fH48//ngcffTR0apVq9x4kyZNYsCAAfHcc8/lLsnfmi9/9sZnn30WH330URx00EGxatWqeP3118u9vo2WLFkSc+fOjUGDBkW9evVy49/73vfi0EMPjUceeaTMNkOGDMn7/aCDDoqPP/64XOsHANiZnDf+e5w3Tp48OWrXrh2HHnpofPTRR7mfzp07R1FRUcyYMSNvfsuWLbd4xWNJSUnesfjLX/4SH374YQwdOjTvM/n69u0bbdq0iWnTppXZx9lnn73V9QJfL7EO/gMsXbo0Pv30061+41bHjh2jTZs2MWnSpNzYpEmTokGDBtG9e/fNbvP+++9H3759o3bt2nH//ffnfQvWihUr4v3338/9LFu2LG/bli1bllnjqlWr8i7J36ht27axYcOG+J//+Z9tvtb58+fHMcccE7Vr145atWpFw4YN4+STT86taXu98847ERFbXNdHH30Un3/+ed74brvtlvd73bp1IyLKfH4IAEBqnDemdd64bNmyvGOzcV1vvPFGrFixIho1ahQNGzbM+1m5cmV8+OGHefvd9Bhu7bGtvY42bdrkHt+oUqVKseuuu25x/8DXz2fWwbfICSecEL/4xS/io48+ipo1a8Z///d/x0knnRSVKpX9r4IVK1bE4YcfHsuXL49nn302mjZtmvf4T37yk7wPHu7atWvuw3wjYrPfPvVVLV++PLp27Rq1atWK//f//l/svvvuUbVq1XjllVfipz/9aWzYsOFrf87N+fLJ55dlWfYveX4AgG+a88avx7bOG4899th4+umnc+MlJSUxceLE2LBhQzRq1CjuueeezW7fsGHDvN+3dgy/6vGtUqXKV/r2WGD7iXXwH6Bhw4ZRq1atmDdv3lbnnXDCCXH55ZfHAw88ELvsskt8+umnceKJJ5aZt3r16jjyyCPj73//e0yfPj3vtoSNRo0alfvLZMT//ZVwa2usXr16LFy4sMxjr7/+elSoUCGaN28eEV/cfrE5Tz31VHz88ccxZcqUOPjgg3PjixYtKjN3S/vYVHFxcUTEFtfVoEEDX00PAPzHcN6Y1nnjr371q7y7MzaGzt133z2mT58eBxxwwNceM7/8Oja9UnLhwoW5x4GdRx6H/wAVKlSIo48+Oh5++OH4y1/+UubxjX+5a9u2bXz3u9+NSZMmxaRJk6JJkyZ5Jy8RX3xGyAknnBCzZs2KyZMnx/7777/Z52zXrl307Nkz99O5c+etrrFixYpx2GGHxR//+Md4++23c+MffPBB3HvvvXHggQdGrVq1IiJyJznLly8vs48vv56IiNLS0hg3blyZ56tRo0a5bm9o0qRJ7LXXXnHnnXfmPd+8efPi8ccfjz59+mxzHwAA/y6cN6Z13ti5c+e8Y7Mxdh5//PGxfv36uOKKK8pss27dujKvd3vss88+0ahRo7j11ltjzZo1ufFHH300Xnvttejbt+8O7xv4eriyDv5DXHnllfH4449H165d48wzz4y2bdvGkiVLYvLkyfHcc8/lvn79hBNOiMsuuyyqVq0agwcPLnNJ+8iRI+O///u/48gjj4xly5bF3Xffnff4l/8qur1+/vOfxxNPPBEHHnhgDB06NCpVqhTjx4+PNWvWxLXXXpubt9dee0XFihXjmmuuiRUrVkSVKlWie/fu8YMf/CDq1q0bJSUlcc4550RBQUH813/912ZvP+3cuXNMmjQpRowYEfvuu28UFRXFkUceudl1/fKXv4zDDz889t9//xg8eHD885//jJtuuilq164dY8aM2eHXCwCQIueN+VI8b+zatWucddZZcdVVV8XcuXPjsMMOi8LCwnjjjTdi8uTJccMNN8Rxxx23Q/suLCyMa665Jk477bTo2rVrnHTSSfHBBx/EDTfcEC1atIjzzjvva3sdwA7aeV9EC3zd3nnnnezUU0/NGjZsmFWpUiVr1apVNmzYsGzNmjW5OW+88UYWEVlEZM8991yZfXTt2jX3+OZ+yiMismHDhm32sVdeeSXr1atXVlRUlFWvXj3r1q1bNnPmzDLzfvvb32atWrXKKlasmEVENmPGjCzLsuz555/PunTpklWrVi1r2rRpNmrUqOyxxx7Lm5NlWbZy5cpswIABWZ06dbKIyIqLi7Msy7JFixZlEZFNmDAh7/mmT5+eHXDAAVm1atWyWrVqZUceeWS2YMGCvDmjR4/OIiJbunRp3viECROyiMgWLVpUruMDALCzOW+ckds+5fPG2267LevcuXNWrVq1rGbNmtl3v/vdbNSoUdl7772Xm1NcXJz17du3zLYzZszIIiKbPHnyZvc9adKkrFOnTlmVKlWyevXqZQMHDsz+93//N29OSUlJVqNGjXKtFfj6FGSZT0QHAAAAgBT4zDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABJRqbwTCwoKv8l1AAB847Js7c5eAl/Bn7v039lLAAD4SnrMnrzNOa6sAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIRKWdvQAgbeuyiTt7Cd+YSgWDdvYSAADYhkNmHx1PdXloZy/jG9HzhYdi+veP3tnLABLjyjoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkoiDLsqxcEwsKv+m1AAB8o7Js7c5eAl/Bn7v039lLAAD4SnrMnrzNOa6sAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASIRYBwAAAACJEOsAAAAAIBFiHQAAAAAkQqwDAAAAgESIdQAAAACQCLEOAAAAABIh1gEAAABAIsQ6AAAAAEiEWAcAAAAAiRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkoyLIs29mLAAAAAABcWQcAAAAAyRDrAAAAACARYh0AAAAAJEKsAwAAAIBEiHUAAAAAkAixDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAPgXGDRoUBQUFERBQUEUFhZGy5YtY9SoUbF69eq8eRvnzJ49O298zZo1Ub9+/SgoKIinnnoqN/70009H9+7do169elG9evVo3bp1lJSURGlpaUREPPXUU7l9bvrz/vvv79BrWb16dQwbNizq168fRUVF0a9fv/jggw+2us0hhxySe96qVatGu3btYty4cXlzSktL49prr42OHTtG9erVo0GDBnHAAQfEhAkTYu3atTu01mXLlsXAgQOjVq1aUadOnRg8eHCsXLmy3Gvd+DNkyJDc43/961/jpJNOiubNm0e1atWibdu2ccMNN2xzLdv73n5Vb7/99hbf+03XkJL58+dHv379okWLFlFQUBDXX3/9zl4S/EuJdQAAAP8ivXv3jiVLlsQ//vGPGDt2bIwfPz5Gjx5dZl7z5s1jwoQJeWMPPvhgFBUV5Y0tWLAgevfuHfvss08888wz8be//S1uuummqFy5cqxfvz5v7sKFC2PJkiV5P40aNdqh13HeeefFww8/HJMnT46nn3463nvvvTj22GO3ud0ZZ5wRS5YsiQULFsTxxx8fw4YNi9///vcR8UWo69WrV1x99dVx5plnxsyZM+PFF1+MYcOGxU033RTz58/fobUOHDgw5s+fH0888URMnTo1nnnmmTjzzDPLvdaNP9dee23usZdffjkaNWoUd999d8yfPz8uvvjiuPDCC+Pmm2/e5n7L+95+naZPn17mve/cufNm526MvJva0Vi6I9utWrUqWrVqFVdffXU0btx4h54X/q1lAAAAfONKSkqyH/7wh3ljxx57bNapU6e8sYjILrnkkqxWrVrZqlWrcuOHHnpodumll2YRkc2YMSPLsiwbO3Zs1qJFi60+74wZM7KIyD755JOv42Vky5cvzwoLC7PJkyfnxl577bUsIrJZs2ZtcbuuXbtmP/nJT/LGWrdunZ144olZlmXZNddck1WoUCF75ZVXymxbWlqarVy5crvXumDBgiwispdeeik39uijj2YFBQXZ4sWLt2ut2zJ06NCsW7duW52zPe9tlmXZqFGjstatW2fVqlXLWrZsmV1yySVZaWlplmVZtmHDhqxHjx7ZYYcdlm3YsCHLsiz7+OOPs2bNmmWXXnpplmVZtmjRoiwisjlz5mxxTaNHj846duyY/fa3v81atGiRFRQU5NY6bty47Mgjj8yqV6+ejR49OsuyLBs3blzWqlWrrLCwMPvOd76T3XXXXWVe4+a221HFxcXZ2LFjv9I+4N+NK+sAAAB2gnnz5sXMmTOjcuXKZR7r3LlztGjRIh544IGIiHj33XfjmWeeiVNOOSVvXuPGjWPJkiXxzDPPfKW13HPPPVFUVLTVn2effTYivriqbO3atdGzZ8/c9m3atInddtstZs2atV3PW61atdyVXPfcc0/07NkzOnXqVGZeYWFh1KhRIyIirrzyym2u9d13342IiFmzZkWdOnVin332ye2rZ8+eUaFChXjhhRe2eUwaNGgQHTp0iAsvvDBWrVq11fkrVqyIevXqbfM1l/e9jYioWbNmTJw4MRYsWBA33HBD/Pa3v42xY8dGxBe31N55553x0ksvxY033hgREUOGDIlmzZrFZZddts11fNmbb74ZDzzwQEyZMiXmzp2bGx8zZkwcc8wx8be//S1OP/30ePDBB+MnP/lJjBw5MubNmxdnnXVWnHbaaTFjxoy8/W26XURs8z378m3G8G1XaWcvAAAA4Nti6tSpUVRUFOvWrYs1a9ZEhQoVtnjr5Omnnx533HFHnHzyyTFx4sTo06dPNGzYMG9O//7947HHHouuXbtG48aNo0uXLtGjR4849dRTo1atWnlzd91117zfi4uLc7eWHnXUUfH9739/q2tv1qxZRES8//77Ubly5ahTp07e47vssku5PwNv/fr18fvf/z5effXV3C2pb7zxRhxyyCHb3HbIkCFx/PHHb3VO06ZNc2vd9FbfSpUqRb169ba61gEDBkRxcXE0bdo0Xn311fjpT38aCxcujClTpmx2/syZM2PSpEkxbdq0ba4/onzvbUTEJZdckvvnFi1axPnnnx/33XdfjBo1KiK+eE/Gjx8fp556arz//vvxyCOPxJw5c6JSpfz/qf+DH/wgKlTIv1bny5/bV1paGnfddVeZNQwYMCBOO+203O8nnXRSDBo0KIYOHRoRESNGjIjZs2fHddddF926ddvidhGRFwE3Z9N/X+HbTKwDAAD4F+nWrVvccsst8fnnn8fYsWOjUqVK0a9fv83OPfnkk+NnP/tZ/OMf/4iJEyfmrp76sooVK8aECRPi5z//eTz55JPxwgsvxJVXXhnXXHNNvPjii9GkSZPc3GeffTZq1qyZ+72wsDD3zzVr1sx77Jsybty4+N3vfhelpaVRsWLFOO+88+Lss8+OiIgsy8q1j3r16pXrCrav4sufaffd7343mjRpEj169Ii33nordt9997y58+bNix/+8IcxevToOOyww8q1//K8txERkyZNihtvvDHeeuutWLlyZaxbt65M1Orfv388+OCDcfXVV8ctt9wSrVu33ux+2rZtu8X1FBcXbzYWfvmKxIiI1157rczn/R1wwAFlvlxj0+0iIvbYY48tPj+Qz22wAAAA/yI1atSIPfbYIzp27Bh33HFHvPDCC3H77bdvdm79+vXjiCOOiMGDB8fq1avj8MMP3+J+mzVrFqecckrcfPPNMX/+/Fi9enXceuuteXNatmwZe+yxR+6nuLg499j23AbbuHHjKC0tjeXLl+ft/4MPPtjmlwEMHDgw5s6dG4sWLYrPP/88fv3rX+eu+PrOd74Tr7/++la3j9i+22AbN24cH374Yd7269ati2XLlm3XFxdsvOrwzTffzBtfsGBB9OjRI84888y8q+C2pTzv7axZs2LgwIHRp0+fmDp1asyZMycuvvjiMl8AsWrVqnj55ZejYsWK8cYbb2z2+Zo3b5733m8azjbeYrypLY1vy+a2cxsslJ8r6wAAAHaCChUqxEUXXRQjRoyIAQMGRLVq1crMOf3006NPnz7x05/+NCpWrFiu/datWzeaNGkSn3/+ebnXsj23wXbu3DkKCwvjz3/+c+6qwIULF8a7774b+++//1b3Ubt27S1eYTVgwIC46KKLYs6cOWU+t27t2rVRWloaNWrU2K7bYPfff/9Yvnx5vPzyy7lvP33yySdjw4YN23y9X7bxFs4vX6k4f/786N69e5SUlMQvfvGLcu9ro229tzNnzozi4uK4+OKLc2PvvPNOmXkjR46MChUqxKOPPhp9+vSJvn37Rvfu3bd7PeXRtm3beP7556OkpCQ39vzzz0e7du22ua3bYKH8xDoAAICdpH///nHBBRfEb37zmzj//PPLPN67d+9YunTpFkPG+PHjY+7cuXHMMcfE7rvvHqtXr4677ror5s+fHzfddFPe3A8//DBWr16dN1a/fv0oLCzcrttga9euHYMHD44RI0ZEvXr1olatWvHjH/849t9//+jSpUs5X3lZ5557bkybNi169OgRV1xxRRx44IFRs2bN+Mtf/hLXXHNN3H777bHXXntt122wbdu2jd69e8cZZ5wRt956a6xduzaGDx8eJ554Yi7oLV68OHr06BF33XVX7LfffvHWW2/FvffeG3369In69evHq6++Guedd14cfPDB8b3vfS8ivrj1tXv37tGrV68YMWJE7vPvKlasuNnbSTdnW+9t69at491334377rsv9t1335g2bVo8+OCDeXOmTZsWd9xxR8yaNSv23nvvuOCCC6KkpCReffXVqFu3bm7exx9/XOYz+urUqRNVq1Yt11o3uuCCC+L444+PTp06Rc+ePePhhx+OKVOmxPTp07e57fbcBltaWhoLFizI/fPixYtj7ty5UVRU5HZavhXcBgsAALCTVKpUKYYPHx7XXnvtZq+EKygoiAYNGmz2G2MjIvbbb79YuXJlDBkyJNq3bx9du3aN2bNnx0MPPRRdu3bNm7vnnntGkyZN8n5efvnlHVr32LFj44gjjoh+/frFwQcfHI0bN97ily+UV5UqVeKJJ56IUaNGxfjx46NLly6x7777xo033hjnnHNOdOjQYYf2e88990SbNm2iR48e0adPnzjwwAPjtttuyz2+du3aWLhwYe7bXitXrhzTp0+Pww47LNq0aRMjR46Mfv36xcMPP5zb5v7774+lS5fG3XffnXc8991333Kva1vv7VFHHRXnnXdeDB8+PPbaa6+YOXNmXHrppbnHly5dGoMHD44xY8bE3nvvHRERl19+eeyyyy5lbint2bNnmff+oYceKvdaNzr66KPjhhtuiOuuuy7at28f48ePjwkTJpTri0G2x3vvvRedOnWKTp06xZIlS+K6666LTp06xY9+9KOv9XkgVQVZeT/FEwAAAAD4RrmyDgAAAAASIdYBAAAAQCLEOgAAAABIhFgHAAAAAIkQ6wAAAAAgEWIdAAAAACRCrAMAAACARIh1AAAAAJAIsQ4AAAAAEiHWAQAAAEAixDoAAAAASMT/B1dAMDB+uEhtAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1600x4800 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from nanopyx.core.analysis.pearson_correlation import pearson_correlation\n",
+    "import numpy as np\n",
+    "\n",
+    "plt.close('all')\n",
+    "# Create a figure with 3 subplots arranged in a row\n",
+    "fig, axs = plt.subplots(len(rotated_images)+1, 2, figsize=(16, 3*16))\n",
+    "sns.heatmap(image, ax=axs[0,0], cmap='inferno', cbar=False, square=True, xticklabels=False, yticklabels=False)\n",
+    "sns.heatmap(image_GT, ax=axs[0,1], cmap='inferno', cbar=False, square=True, xticklabels=False, yticklabels=False)\n",
+    "axs[0,0].set_title(\"original\")\n",
+    "axs[0,1].set_title(f\"rotated {np.rad2deg(angle):.1f}\")\n",
+    "\n",
+    "for i, id in enumerate(rotated_images):\n",
+    "    rotated_image = rotated_images[id]\n",
+    "    error = image_GT-rotated_image\n",
+    "    rmse = round((error**2).mean()**0.5)\n",
+    "    pc = pearson_correlation(image, rotated_image)\n",
+    "    sns.heatmap(rotated_image, ax=axs[i+1,0], cmap='inferno', cbar=False, square=True, xticklabels=False, yticklabels=False)\n",
+    "    sns.heatmap(error, ax=axs[i+1,1], cmap='inferno', cbar=False, square=True, xticklabels=False, yticklabels=False)\n",
+    "    axs[i+1,0].set_title(f\"{id}\")\n",
+    "    axs[i+1,1].set_title(f\"{id}-error\")\n",
+    "    axs[i+1,1].set_xlabel(f\"RMSE={rmse} PC={round(pc, 2)} MaxError={round(np.max(error))}\")\n",
+    "\n",
+    "# Show the figure\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nanopyx",
+   "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": "3.10.9"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "7603a37bd9dfed82c1c43d2e0a103f66c5b5a86e3543acd1be82d9c441c0be6e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tests/notebooks/image_rotate_shift.ipynb b/tests/notebooks/image_rotate_shift.ipynb
new file mode 100644
index 00000000..4c7979a5
--- /dev/null
+++ b/tests/notebooks/image_rotate_shift.ipynb
@@ -0,0 +1,157 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from nanopyx.core.transform.image_shift_rotate import *\n",
+    "from skimage.draw import rectangle\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "angle = np.pi\n",
+    "cx = 300.0\n",
+    "cy = 100.0\n",
+    "\n",
+    "image_GT = np.zeros((256, 512), dtype=np.float32) \n",
+    "rr, cc = rectangle((int(cy-20), int(cx-40)), extent=(40,-40), shape=image_GT.shape)\n",
+    "image_GT[rr,cc] = 1\n",
+    "\n",
+    "image = np.zeros((256, 512), dtype=np.float32) \n",
+    "rr, cc = rectangle((int(cy-20), int(cx+40)), extent=(40,40), shape=image.shape)\n",
+    "image[rr,cc] = 1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nearest_neighbor_rotate_shift took 0.517130 mseconds\n",
+      "bilinear_rotate_shift took 0.811100 mseconds\n",
+      "bicubic_rotate_shift took 0.565052 mseconds\n",
+      "catmull_rom_rotate_shift took 0.887632 mseconds\n",
+      "lanczos_rotate_shift took 7.258892 mseconds\n",
+      "skimage_rotate_shift took 0.960827 mseconds\n",
+      "cv2_rotate_shift took 0.270128 mseconds\n",
+      " \n",
+      "*** Profile printout saved to text file 'prun_Rotation.profile'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%prun -s cumulative -q -l 100 -T prun_Rotation.profile\n",
+    "# Profile the run\n",
+    "\n",
+    "imageNNRotation = nearest_neighbor_rotate_shift(image, angle, cx, cy)\n",
+    "imageBLRotation = bilinear_rotate_shift(image, angle, cx, cy)\n",
+    "imageBCRotation = bicubic_rotate_shift(image, angle, cx, cy)\n",
+    "imageCRRotation = catmull_rom_rotate_shift(image, angle, cx, cy)\n",
+    "imageLRotation = lanczos_rotate_shift(image, angle, cx, cy)\n",
+    "#imageScipyRotation = scipy_rotate_shift(image, angle, cx, cy)\n",
+    "imageSkiRotation = skimage_rotate_shift(image, angle, cx, cy)\n",
+    "imageCVRotation = cv2_rotate_shift(image, angle, cx, cy)\n",
+    "\n",
+    "\n",
+    "rotated_images = {\n",
+    "    \"nearest-neighbor-rotation\": imageNNRotation,\n",
+    "    \"bilinear-rotation\": imageBLRotation,\n",
+    "    \"bilinear-rotation\": imageBLRotation,\n",
+    "    \"bicubic-rotation\": imageBCRotation,\n",
+    "    \"catmull-rom-rotation\": imageCRRotation,\n",
+    "    \"lanczos-rotation\": imageLRotation,\n",
+    "    #\"scipy-rotation\": imageScipyRotation,\n",
+    "    \"skimage-rotation\": imageSkiRotation,\n",
+    "    \"cv2-rotation\": imageCVRotation\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x4800 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from nanopyx.core.analysis.pearson_correlation import pearson_correlation\n",
+    "import numpy as np\n",
+    "\n",
+    "plt.close('all')\n",
+    "# Create a figure with 3 subplots arranged in a row\n",
+    "fig, axs = plt.subplots(len(rotated_images)+1, 2, figsize=(16, 3*16))\n",
+    "sns.heatmap(image, ax=axs[0,0], cmap='inferno', cbar=False, square=True, xticklabels=False, yticklabels=False)\n",
+    "sns.heatmap(image_GT, ax=axs[0,1], cmap='inferno', cbar=False, square=True, xticklabels=False, yticklabels=False)\n",
+    "axs[0,0].set_title(\"original\")\n",
+    "axs[0,1].set_title(f\"rotated {np.rad2deg(angle):.1f}\")\n",
+    "\n",
+    "for i, id in enumerate(rotated_images):\n",
+    "    rotated_image = rotated_images[id]\n",
+    "    error = image_GT-rotated_image\n",
+    "    rmse = round((error**2).mean()**0.5)\n",
+    "    pc = pearson_correlation(image, rotated_image)\n",
+    "    sns.heatmap(rotated_image, ax=axs[i+1,0], cmap='inferno', cbar=False, square=True, xticklabels=False, yticklabels=False)\n",
+    "    sns.heatmap(error, ax=axs[i+1,1], cmap='inferno', cbar=False, square=True, xticklabels=False, yticklabels=False)\n",
+    "    axs[i+1,0].set_title(f\"{id}\")\n",
+    "    axs[i+1,1].set_title(f\"{id}-error\")\n",
+    "    axs[i+1,1].set_xlabel(f\"RMSE={rmse} PC={round(pc, 2)} MaxError={round(np.max(error))}\")\n",
+    "\n",
+    "# Show the figure\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nanopyx",
+   "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": "3.10.9"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "7603a37bd9dfed82c1c43d2e0a103f66c5b5a86e3543acd1be82d9c441c0be6e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tests/test_image_rotation.py b/tests/test_image_rotation.py
new file mode 100644
index 00000000..c1ca5e1d
--- /dev/null
+++ b/tests/test_image_rotation.py
@@ -0,0 +1,111 @@
+import numpy as np
+
+from nanopyx.core.transform.image_rotate import (
+    bicubic_rotate,
+    catmull_rom_rotate,
+    cv2_rotate,
+    lanczos_rotate,
+    scipy_rotate,
+    skimage_rotate,
+)
+
+def test_bicubic_rotate(random_image_with_squares, plt):
+    image = random_image_with_squares
+    theta = np.random.rand() * np.pi/10
+
+    shifted1 = bicubic_rotate(image, theta)
+    shifted2 = bicubic_rotate(shifted1, -theta)
+    delta = image - shifted2
+    assert shifted1.shape == shifted2.shape == image.shape
+
+    plt.figure()
+    f, axarr = plt.subplots(1, 4)
+    axarr[0].imshow(image)
+    axarr[1].imshow(shifted1)
+    axarr[2].imshow(shifted2)
+    axarr[3].imshow(delta)
+
+
+def test_catmull_rom_rotate(random_image_with_squares, plt):
+    image = random_image_with_squares
+    theta = np.random.rand() * np.pi/10
+
+    shifted1 = catmull_rom_rotate(image, theta)
+    shifted2 = catmull_rom_rotate(shifted1, -theta)
+    delta = image - shifted2
+    assert shifted1.shape == shifted2.shape == image.shape
+
+    plt.figure()
+    f, axarr = plt.subplots(1, 4)
+    axarr[0].imshow(image)
+    axarr[1].imshow(shifted1)
+    axarr[2].imshow(shifted2)
+    axarr[3].imshow(delta)
+
+
+def test_cv2_rotate(random_image_with_squares, plt):
+    image = random_image_with_squares
+    theta = np.random.rand() * np.pi/10
+
+    shifted1 = cv2_rotate(image, theta)
+    shifted2 = cv2_rotate(shifted1, -theta)
+    delta = image - shifted2
+    assert shifted1.shape == shifted2.shape == image.shape
+
+    plt.figure()
+    f, axarr = plt.subplots(1, 4)
+    axarr[0].imshow(image)
+    axarr[1].imshow(shifted1)
+    axarr[2].imshow(shifted2)
+    axarr[3].imshow(delta)
+
+
+def test_lanczos_rotate(random_image_with_squares, plt):
+    image = random_image_with_squares
+    theta = np.random.rand() * np.pi/10 
+
+    shifted1 = lanczos_rotate(image, theta)
+    shifted2 = lanczos_rotate(shifted1, -theta)
+    delta = image - shifted2
+    assert shifted1.shape == shifted2.shape == image.shape
+
+    plt.figure()
+    f, axarr = plt.subplots(1, 4)
+    axarr[0].imshow(image)
+    axarr[1].imshow(shifted1)
+    axarr[2].imshow(shifted2)
+    axarr[3].imshow(delta)
+
+
+def test_scipy_rotate(random_image_with_squares, plt):
+    image = random_image_with_squares
+    theta = np.random.rand() * np.pi/10
+
+    shifted1 = scipy_rotate(image, theta)
+    shifted2 = scipy_rotate(shifted1, -theta)
+    delta = image - shifted2
+    assert shifted1.shape == shifted2.shape == image.shape
+
+    plt.figure()
+    f, axarr = plt.subplots(1, 4)
+    axarr[0].imshow(image)
+    axarr[1].imshow(shifted1)
+    axarr[2].imshow(shifted2)
+    axarr[3].imshow(delta)
+
+
+def test_skimage_rotate(random_image_with_squares, plt):
+    image = random_image_with_squares
+    theta = np.random.rand() * np.pi/10
+
+    shifted1 = skimage_rotate(image, theta)
+    shifted2 = skimage_rotate(shifted1, -theta)
+    delta = image - shifted2
+    assert shifted1.shape == shifted2.shape == image.shape
+
+    plt.figure()
+    f, axarr = plt.subplots(1, 4)
+    axarr[0].imshow(image)
+    axarr[1].imshow(shifted1)
+    axarr[2].imshow(shifted2)
+    axarr[3].imshow(delta)
diff --git a/tests/test_image_shift_rotation.py b/tests/test_image_shift_rotation.py
new file mode 100644
index 00000000..299278be
--- /dev/null
+++ b/tests/test_image_shift_rotation.py
@@ -0,0 +1,122 @@
+import numpy as np
+
+from nanopyx.core.transform.image_shift_rotate import (
+    bicubic_rotate_shift,
+    catmull_rom_rotate_shift,
+    cv2_rotate_shift,
+    lanczos_rotate_shift,
+    #scipy_rotate_shift,
+    skimage_rotate_shift,
+)
+
+def test_bicubic_rotate_shift(random_image_with_squares, plt):
+    image = random_image_with_squares
+    theta = np.random.rand() * np.pi/10
+    
+    w = image.shape[1]
+    h = image.shape[0]
+    cx = np.random.randint(0, w) / 10
+    cy = np.random.randint(0, h) / 10
+    
+    shifted1 = bicubic_rotate_shift(image, theta, cx, cy)
+    shifted2 = bicubic_rotate_shift(shifted1, -theta, cx, cy)
+    delta = image - shifted2
+    assert shifted1.shape == shifted2.shape == image.shape
+
+    plt.figure()
+    f, axarr = plt.subplots(1, 4)
+    axarr[0].imshow(image)
+    axarr[1].imshow(shifted1)
+    axarr[2].imshow(shifted2)
+    axarr[3].imshow(delta)
+
+
+def test_catmull_rom_rotate_shift(random_image_with_squares, plt):
+    image = random_image_with_squares
+    theta = np.random.rand() * np.pi/10
+    
+    w = image.shape[1]
+    h = image.shape[0]
+    cx = np.random.randint(0, w) / 10
+    cy = np.random.randint(0, h) / 10
+
+    shifted1 = catmull_rom_rotate_shift(image, theta, cx, cy)
+    shifted2 = catmull_rom_rotate_shift(shifted1, -theta, cx, cy)
+    delta = image - shifted2
+    assert shifted1.shape == shifted2.shape == image.shape
+
+    plt.figure()
+    f, axarr = plt.subplots(1, 4)
+    axarr[0].imshow(image)
+    axarr[1].imshow(shifted1)
+    axarr[2].imshow(shifted2)
+    axarr[3].imshow(delta)
+
+
+def test_cv2_rotate_shift(random_image_with_squares, plt):
+    image = random_image_with_squares
+    theta = np.random.rand() * np.pi/10
+
+    w = image.shape[1]
+    h = image.shape[0]
+    cx = np.random.randint(0, w) / 10
+    cy = np.random.randint(0, h) / 10
+
+    shifted1 = cv2_rotate_shift(image, theta, cx, cy)
+    shifted2 = cv2_rotate_shift(shifted1, -theta, cx, cy)
+    delta = image - shifted2
+    assert shifted1.shape == shifted2.shape == image.shape
+
+    plt.figure()
+    f, axarr = plt.subplots(1, 4)
+    axarr[0].imshow(image)
+    axarr[1].imshow(shifted1)
+    axarr[2].imshow(shifted2)
+    axarr[3].imshow(delta)
+
+
+def test_lanczos_rotate_shift(random_image_with_squares, plt):
+    image = random_image_with_squares
+    theta = np.random.rand() * np.pi/10
+
+    w = image.shape[1]
+    h = image.shape[0]
+    cx = np.random.randint(0, w) / 10
+    cy = np.random.randint(0, h) / 10
+
+    shifted1 = lanczos_rotate_shift(image, theta, cx, cy)
+    shifted2 = lanczos_rotate_shift(shifted1, -theta, cx, cy)
+    delta = image - shifted2
+    assert shifted1.shape == shifted2.shape == image.shape
+
+    plt.figure()
+    f, axarr = plt.subplots(1, 4)
+    axarr[0].imshow(image)
+    axarr[1].imshow(shifted1)
+    axarr[2].imshow(shifted2)
+    axarr[3].imshow(delta)
+
+
+# def test_scipy_rotate_shift(random_image_with_squares, plt):
+
+
+def test_skimage_rotate_shift(random_image_with_squares, plt):
+    image = random_image_with_squares
+    theta = np.random.rand() * np.pi/10
+
+    w = image.shape[1]
+    h = image.shape[0]
+    cx = np.random.randint(0, w) / 10
+    cy = np.random.randint(0, h) / 10
+
+    shifted1 = skimage_rotate_shift(image, theta, cx, cy)
+    shifted2 = skimage_rotate_shift(shifted1, -theta, cx, cy)
+    delta = image - shifted2
+    assert shifted1.shape == shifted2.shape == image.shape
+
+    plt.figure()
+    f, axarr = plt.subplots(1, 4)
+    axarr[0].imshow(image)
+    axarr[1].imshow(shifted1)
+    axarr[2].imshow(shifted2)
+    axarr[3].imshow(delta)