Repository: teamtomo/torch-cubic-spline-grids (language: python)

Issue:
add support for interpolating on grids with Catmull-Rom splines
The existing B-spline interpolation guarantees C2 continuity but does not guarantee that the curve will actually touch all data points. 
Catmull-Rom spline interpolation instead guarantees C1 continuity and that the curve will touch all data points.


Discussion / hints:

Fix patch:
diff --git a/.gitignore b/.gitignore
index d38d9cc..b6503a9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -104,5 +104,5 @@ ENV/
 # IDE settings
 .vscode/
 
-torch_cubic_b_spline_grid/_version.py
-src/torch_cubic_b_spline_grid/_version.py
+src/torch_cubic_spline_grids/_version.py
+src/torch_cubic_spline_grids/_version.py
diff --git a/README.md b/README.md
index 4f902d4..26e8f5b 100644
--- a/README.md
+++ b/README.md
@@ -1,21 +1,118 @@
-# torch-cubic-b-spline-grid
+# torch-cubic-spline-grids
 
-[![License](https://img.shields.io/pypi/l/torch-cubic-b-spline-grid.svg?color=green)](https://github.com/alisterburt/torch-cubic-b-spline-grid/raw/main/LICENSE)
-[![PyPI](https://img.shields.io/pypi/v/torch-cubic-b-spline-grid.svg?color=green)](https://pypi.org/project/torch-cubic-b-spline-grid)
-[![Python Version](https://img.shields.io/pypi/pyversions/torch-cubic-b-spline-grid.svg?color=green)](https://python.org)
-[![CI](https://github.com/alisterburt/torch-cubic-b-spline-grid/actions/workflows/ci.yml/badge.svg)](https://github.com/alisterburt/torch-cubic-b-spline-grid/actions/workflows/ci.yml)
-[![codecov](https://codecov.io/gh/alisterburt/torch-cubic-b-spline-grid/branch/main/graph/badge.svg)](https://codecov.io/gh/alisterburt/torch-cubic-b-spline-grid)
+[![License](https://img.shields.io/pypi/l/torch-cubic-spline-grids.svg?color=green)](https://github.com/alisterburt/torch-cubic-spline-grids/raw/main/LICENSE)
+[![PyPI](https://img.shields.io/pypi/v/torch-cubic-spline-grids.svg?color=green)](https://pypi.org/project/torch-cubic-spline-grids)
+[![Python Version](https://img.shields.io/pypi/pyversions/torch-cubic-spline-grids.svg?color=green)](https://python.org)
+[![CI](https://github.com/alisterburt/torch-cubic-spline-grids/actions/workflows/ci.yml/badge.svg)](https://github.com/alisterburt/torch-cubic-spline-grids/actions/workflows/ci.yml)
+[![codecov](https://codecov.io/gh/alisterburt/torch-cubic-spline-grids/branch/main/graph/badge.svg)](https://codecov.io/gh/alisterburt/torch-cubic-spline-grids)
 
-_Cubic B-spline interpolation on multidimensional grids in PyTorch._
+*Cubic spline interpolation on multidimensional grids in PyTorch.*
 
-The primary goal of this package is to provide a learnable, continuous
-parametrization of 1-4D spaces.
+The primary goal of this package is to provide learnable, continuous
+parametrisations of 1-4D spaces.
 
 --- 
 
-This is a PyTorch implementation of the model used in
-[Warp](http://warpem.com/warp/#) for continuous deformation
-fields and locally variable optical parameters in cryo-EM images. The approach is described in
+## Overview
+
+`torch_cubic_spline_grids` provides a set of PyTorch components called grids.
+
+Grids are defined by
+- their dimensionality (1D, 2D, 3D, 4D...)
+- the number of points covering each dimension (`resolution`)
+- the number of values stored on each grid point (`n_channels`)
+- how we interpolate between values on grid points
+
+All grids in this package consist of uniformly spaced points covering the full 
+extend of each dimension.
+
+### First steps
+Let's make a simple 2D grid with one value on each grid point.
+
+```python
+import torch
+from torch_cubic_spline_grids import CubicBSplineGrid2d
+
+grid = CubicBSplineGrid2d(resolution=(5, 3), n_channels=1)
+```
+
+- `grid.ndim` is `2`
+- `grid.resolution` is `(5, 3)` (or `(h, w)`) 
+- `grid.n_channels` is `1`
+- `grid.data.shape` is `(1, 5, 3)` (or `(c, h, w)`)
+
+In words, the grid extends over two dimensions `(h, w)` with 5 points 
+in `h` and `3` points in `w`. 
+There is one value stored at each point on the 2D grid. 
+The grid data is stored in a tensor of shape `(c, *grid_resolution)`.
+
+We can obtain the value (interpolant) at any continuous point on the grid.
+The grid coordinate system extends from `[0, 1]` along each grid dimension. 
+The interpolant is obtained by sequential application of
+cubic spline interpolation along each dimension of the grid.
+
+```python
+coords = torch.rand(size=(10, 2))  # values in [0, 1]
+interpolants = grid(coords)
+```
+
+- `interpolants.shape` is `(10, 1)`
+
+### Types of grids
+
+`torch_cubic_spline_grids` provides grids which can be interpolated with **cubic 
+B-spline** interpolation or **cubic Catmull-Rom spline** interpolation. 
+
+| interpolation      | continuity | interpolating? |
+|--------------------|------------|----------------|
+| cubic B-spline     | C2         | No             |
+| Catmull-Rom spline | C1         | Yes            |
+
+If your application requires that the result intersects the data on the grid you should
+use the Catmull-Rom spline grids `CubicCatmullRomGrid1d/2d/3d/4d`. 
+If you require continuous second derivatives `CubicBSplineGrid1d/2d/3d/4d` is more 
+suitable.
+
+### Optimisation
+
+By minimising a loss function associated with grid interpolants the values at each 
+grid point can be optimised to more accurately describe the 1-4D space.
+
+<p align="center" width="100%">
+    <img width="60%" src="https://user-images.githubusercontent.com/7307488/226992179-049a63a0-a2f3-4432-b38e-6e8bcaa6a4a8.png
+">
+</p>
+
+The image above shows the values of 6 control points on a 1D grid being optimised such 
+that interpolating between them approximates a single oscillation of a sine wave. 
+
+Notebooks are available for this 
+[1D example](./examples/optimise_1d_grid.ipynb) 
+and a similar 
+[2D example](./examples/optimise_2d_grid.ipynb).
+
+### Regularisation
+
+The number of points in each dimension should be chosen such that interpolating on the 
+grid can approximate the underlying phenomenon without overfitting. 
+A low resolution grid provides a regularising effect by smoothing the model.
+
+
+## Installation
+
+`torch_cubic_spline_grids` is available on PyPI
+
+```shell
+pip install torch-cubic-spline-grids
+```
+
+
+## Related work
+
+This is a PyTorch implementation of the way
+[Warp](http://warpem.com/warp/#) models continuous deformation
+fields and locally variable optical parameters in cryo-EM images. 
+The approach is described in
 [Dimitry Tegunov's paper](https://doi.org/10.1038/s41592-019-0580-y):
 
 > Many methods in Warp are based on a continuous parametrization of 1- to
@@ -33,3 +130,10 @@ fields and locally variable optical parameters in cryo-EM images. The approach i
 > on the grid.
 > To fit a grid’s parameters, in general, a cost function associated with the
 > interpolants at specific positions on the grid is optimized. 
+
+---
+
+For a fantastic introduction to splines I recommend 
+[Freya Holmer](https://www.youtube.com/watch?v=jvPPXbo87ds)'s YouTube video.
+
+[The Continuity of Splines - YouTube](https://youtu.be/jvPPXbo87ds)
diff --git a/examples/optimise_1d_grid_model.ipynb b/examples/optimise_1d_grid_model.ipynb
index 2073aa1..8317c46 100644
--- a/examples/optimise_1d_grid_model.ipynb
+++ b/examples/optimise_1d_grid_model.ipynb
@@ -2,13 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "outputs": [],
    "source": [
     "import torch\n",
     "from matplotlib import pyplot as plt\n",
     "\n",
-    "from torch_cubic_b_spline_grid import CubicBSplineGrid1d"
+    "from torch_cubic_spline_grids import CubicBSplineGrid1d"
    ],
    "metadata": {
     "collapsed": false,
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "outputs": [],
    "source": [
     "N_CONTROL_POINTS = 6\n",
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "outputs": [],
    "source": [
     "grid_1d = CubicBSplineGrid1d(resolution=N_CONTROL_POINTS)"
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "outputs": [],
    "source": [
     "def make_observations(n, add_noise: bool = False):\n",
@@ -117,10 +117,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "outputs": [],
    "source": [
-    "optimiser = torch.optim.SGD(grid_1d.parameters(), lr=0.02)"
+    "optimiser = torch.optim.Adam(grid_1d.parameters(), lr=0.02)"
    ],
    "metadata": {
     "collapsed": false,
@@ -144,20 +144,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "outputs": [
     {
      "data": {
-      "text/plain": "[<matplotlib.lines.Line2D at 0x143f0dde0>]"
+      "text/plain": "[<matplotlib.lines.Line2D at 0x11f503880>]"
      },
-     "execution_count": 13,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
       "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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\n"
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OuvDq3IvxzHfXY2toeCNrf8BETqyixO02NTNWh12AC3coxJ/W30Mh7lNyKknuZ7dzwZeoRGYmcFD/j3H3lOOwvnIEHv36Trw08zRUV9sQCnG7jAymi7KzKRx69QL23Re49FJ62nQk4TAdg4PB1Cms8nJGrg48UCMviqJ0PK1Zv9s18nLEEUegOW309NNPp7zPvHnz2nGvlI4kHmfh6sqVvKxZw3oQEQSbN3PRDAQoFNLSAI/Hhh83HIAjh3+IF769Eo9/9RvUxvs1iBGXiykZSQX5fKaNOjeXNSfiuBuNcrGuraV4kMhILMboSCRiRgQEg0bQ1Ndzn+Q6iUZUVRmvmZoaIFqwDdXBPAzIXYp7TzodPx13CG5/7x/4ZtnoBj8YSdfk55uUWHk5cNZZwOTJHTfpWiJNEl0qK+Pr8/k4hkEiU+13OqMoitI2tGvkpTPQyEvXIRIBvv6a6aGFCylUiospPLZt4+Lp9wORSAKXHPYU1m7bB9+vOwjxOJDmCaJndgmqIoMaZhLl5zOKAjCSUVBgohpS5CtFuJFIY7EihbuycMvvIojk9liMosTvp1CJRPh3RQV/hkLmeRIJipiCrGpcceh9uOzgB5HmDiAad+CVH6/Fne/8AcXlmaiv5/NkZzMS43LxdQwaBEyZAvz0pxQQ7U0wyAjY2rXA3Ll8TTJDKj8fGDeO+zRyZMfsj6IoipXWrN8qXpR2IRgEvvySni0LFlC4iFPupk2muHVIj5V45MKf4ZBh07GseCSOfeAHROIe+HxGYEiqJjeXc4UKC01hrogOMaqrrWW0Ixg0t8mARp+P4sHjMU68UicTjZqIg/yMx00HTiDAhb66mqmvbdsYnZFiYJ8PGNRjA2454VcNXjILtkzAOU/OxrZtNgSDfLzMTF5kf/v25YykK69s/zlJoRAwYwbHGlRUGF8X8X3JzweOPx447DAeI0VRlI6ky6SNlD2Tujrg888pXBYtonDx+7lYr14tniIJXHX0P/HnM26Az1WPQNiHF7+7FHanA2lOipaMDEYoXC5g8GBeJO0h9STp6RQSmzebLpm0NC7EWVkUPD4fr3M6TVRGRgiIQBIBIymjYJA/09IoMIJBRoq8Xi76dXVs5d62jbcFg8DKLf3xs+dex9EjP8QfTrwa9314GwIBG7Ky+DySvopEuF82G6MggQDTSD//efuOF5BJ2VVVdAIOhUy30eDBrElasgQ45pj22wdFUZS2QMWL0qbU1FC4rF0LLF3KKEt9PS8bN3LRT3NU4JlfXoYT9mVB7syVR+PG157Ahm2D4XAA6V4utNnZxjTN4eCiX1jICEU0ysdbv57iJi/PCJcePShe0tL4vHV1vG8wyJ/WOUbW4l+5v9NpioBlYGRVlZmpVFFBsZGbSzGycSPbpaXw99PFx+OHTUtRE3A3CKHJ+3yE4qremL9+X4TDJvJhs/EYBYM8dj//ObDffu3z3mzezP3s2ZNdXZJOk/RZz55mVlSfPu2zD4qiKG2BihelzfD7KVxWrwaWLeOiHo8zjSO+Ir2z12P6zQejd85mhKMu3PPxPXjy62sRj9uRkWHSOz17UiwkEhQyPXuyHiMYZA1NOPy/epMC3t67Nxdcp5MRkk2bGte6uFyNL263ibrIBeD2En2R+hmXC+jfn2ImHKaQ2baNr6+01Bi8rVrF68NhoKzCjdxciipvZDX+OfVcuJ0h/ObVR/HMzEsRi/G4ZGVxX0pL6TYcCgGXXw4cckjbvz/BIB/f6+XzVVaajq/cXB5jEVuKoihdGRUvSptQXQ189hmFy4oVXNgTCdaHlJUx+gEAVZF+WFEyEsFIOq55+SUs3DS2YWqzz0eb/dxc8XphpEMiFBs2UEzI9tnZwLBhFDHFxSwKBkwRrgwc9Hr52CJcxPdFaluSi3jtdtNeLXUxkQhFCcBUVVoaF/tt29hBBbDFuLSUws3v522BALDP0BwsLJ6EQ4Z8iL9PvQwHDf4K1z73KELRNPj9rIFxuykmvvnGRGGmTGlbQzufj8dVImAyvdvpNBGmHj20WFdRlK6Pihdll5FU0cqVjD5s2EBhsGYNF+R4pB4elw12pwc2mx3Xv/48gmEfauszkJ7O6ENODqMUHg8X1L59uZDGYkxBSfTFZuPP/v0pDFat4vbSKWS3G2GTmWm6k6QdOhBoXJBr9XQBGrcSS2rJ+780ljWtJEZ0TicX+759+Xrjcaa2Vq/mJRgEflicj2sr38MvjvwLLhp7K6ZOfBr79JmPM//+LrZU9kEiwX31eBidmjOHQikYBE47re0ETGGhGc0gxnxCWRmf56ijuJ2iKEpXRsWLsksEAsD06cbqf/NmCoCVK3lmn+vdgldvOA2Lt4zBb19/Aj6fDVv9hbDZKDByc2nWZrNx+4wMFo9KO3VVFQWCx8M6lt69GeVZtIjPH4nwZ34+62PS0ihSpP5EinvtdkZdACNY5Hdx2RXPFxEmUtBqjcaIx0xGBkVXr158Xr+f123axGjTkCEUX4sXs45kw0Y77nr7ZqzxT8L1B56N/frPw1e/PwBnPvwOflg/HrEYH0+iI3Pncj/icQ53bAsB4/ez1kYiLtJ1FY9TNDmdvN3v7xpzmBRFUZpCxYuy09TXU7gsX06xIqmi5cu5AA7KW4J3rj8O/fI2YlDhavzzqz9gXVkf+HxMvfTpw7SPiIUxYyhoKisZCZAUUVERrwf4PJLCycnhDCFJM4lAyc42BnXSCi3dRYBZsIHGhbvW0QCJBO9XV2d8X+SnteDX5+N+ZGcDI0ZwHMDy5eza8XqZSlq1ilGZ6mrgyfeOwMbK7/CHI07CoLzFuOiwp/HDs+Mb2rBjMYqg+nrgxx+BJ5/kvpx++q6b2ZWXs17I4UCDgZ5EmqSLav16bqfiRVGUroyKF2WnCIcpXJYt42K9eTMX3hUruDCOHzADr159KnLTq7CqbG9c9uwHWFvap6G9ea+9GEWpq+NC2bs3IwHbtlGc+HxMpeTlMX2ycaOxrO/Vi2kat5vRgowMCpxo1EROJIUEmBEBMjJAIijWuhaJriQjhnQSzQkE2CJdWsooRXU1L3a7ETKDB1OUff01U2ijRvF1LFzIY/PhjIFYXzwLVx99H+6ffivS03l9OMyfEoURAfP44xRbZ565awKmpMR0HImvjYgXaydWSQkwfPjOP4+iKEp7o+JFaTXxOPDVVxQty5axWLa+nsKlthY4ad9X8dTlP4XHFcacdZNwxbPvoKQyH5mZXJT32YeCIxCgiBkyhOmWDRtMy3JuLhfx0lKKA5+PkZpevYywyc423UG1tY1t7W02iiEp1vV4TAu0tEHvjBCIxylOYjHTJr1mDQVBXR0v4gczciS3/fpr7ndWFutNSkqAxSuycEvFHRh/IAWK0xHDscNfxYuzzgFgQzzOYxSJMEX2+OMUXLtSA+NwUHBJrYv19cv07YqKjhtXoCiKsrOoeFFazTffMC2yZAkX4kCAQqamBjj/oKfwz4suh92ewEeLTsMvX3oBNQFfQ33LyJFGPBx4IEXIli2mkFYKYKuq/jc7KErRMmQIIzS5uY39W2Ixs18ycygjw7jYSoSlrZAOJoD7WVgIjB3LiNHq1UwRlZdz3+vrKQTGjeP1paXAAQeYNFtFBYXNhAnA2YOuxhn7PoZD9/4CVz/9KIIJKoi0ND7X4sXAo49ShJ1wQuMUV0vZuJHRI8F67ATpRlIURenKqHhRWsWCBUx/LFrExbi2lgtxbS2FRmVdPmIJB56ffRlueftRhMIOZGdzkR8xggIlJ4cLfn09xU8waNqiq6uZ2ojHKVYGDmRnUa9eXLjr6igUrF1Bkq7JymLEwypWpOYlubPIegEadxhJCkl+pqqJsWKzsWA4P58TozdtMhEpEVg9evAxtmwB9t6bqbMlS3jcvv4aOKTXWMQTNlxxxOPITa/EBf98DsGgq8FAzmbjMX/oIb7eo45q/Xu3bt2Ohy4mEtxOURSlK6PiRWkxq1ezjXfBAqZJqqp4HYcrcnH+dNmpOOnv32FJ8X6IRGzIzubCPXw4RcSgQRQx4oFSX0/BEY/zuro6ipC8PEZcBg5kFEUGJFrrWGQ4Y3o6r5d6l2CQgkGGJ7YV0nGUnH6y4vUCQ4dy34uL2ea9YQP3vWdPbr92LWt0XC5GYSoqgLteuRLl/jzcctT5OPOAV2BDAj/95/MIBFwNry8e57G/914KmIkTW7//LREvbektoyiK0h6oeFFaREkJIwQLF1K4VFSw1sPvBy479BF8tPBElNYOgs8HLCkei2iU4qJXL0YaIhF2ExUWsiYkEOBi7PNxYZfBhQUF7NgZNIiLqJi95eQYwVBYaGYDRSLcJlUKRJCoidVJN9XfVqR9Oh43Iki8Uaz+KFb3Xre7cTRo8GAKtz59GHHZsIH77vEwDZSdzWMjqabHPjgTgZAXdx1/Bs444FUAaCRgAArAefOAu+4C/vxnHtOWIimottpOURSls1DxouyQmhpOI5ZUUXk5owfV1cD/HflX/OWcG3Fd+f045sH5CESyEI0aD5Rhw7j4H3QQF/eKCqZKPB5eX1HB50hLY7RlxAjeNxDg7U4nF+xYjB1JWVkUEeLYK4gfS6q0j7RFW9NEO8Lq7SKixGpuJ4McJcJTX8/bZIK1x2OGS+69N1NKeXk8flu28PoffqDIGTqUz1VRATw97STEYq/jnpNOxxkHvIpw1I0LH3sOQOMI03ffUcDcey/FXksQn5u22k5RFKWzUPGiNEskAnz5pZkOvXUrayKqq4ELJz2Gv5xzIwDg+W+vQCCShViMaZ6ePbkoAxQuAO8TDHJhr67m4u/zMYrSowdHA0gXj5X8fGPvHw4bwWAVKVIXkowIF8EabUnGKm6kRTpVRMdmM8Z5VoM76diR7ifpdnK5GHGRupycHAoYj4fdR8XFZmJ2dTXw7OcnIR5/A3eddB4+WX42nE5juCcRmFCIgvLOOyliWuLLIqm95GNiRcSRoihKV0bFi9IkiQSHBS5aRMFSWkoTs4oK4PSxz+Hhn/4fAOBv027CP768GdEohUuPHhQuTic7isJhU7hqt1MAORxcyIuKGJ0JBLidmMMBXKhzc/l7TY2x6Xe5KGasIiS5uNZ6acrDpbnXbS3wldSRDGpMJEx9jSARGtlWfGFCITNiwOulSMnJMaZwXi9TSMuXM4Kybh3TYM9/cRJ+3LQW8BQgL4+pMzHiS0/nc9TVAe+9x8e57TZGc5pjwICWiZeWRnIURVE6CxUvSpMsWkQPkhUrzKTm8nJgyj5v4l+XXQy7PYF/zfgl7pt2Z4NwKSigcPH52FEkA/8cDooTv58poqwsLuQDBphuILGo93i4EEukxeEw97F6tIjJnAiWtkLcc1P5nYiwkbSR1cVXELFijcjU1VGgiWjJzKQQTE/na/V6ebz79+f1NhuwaGUB+vblceqTsw6HDX4Lf/vougZDPDlmL75IwXjNNaaNOxW5uc0LF4C3i2BUFEXpqqh4UVKycSP9XGQ2z6ZNjLyM7/clnrvqHDgdMbzw7SW444OHEInYkJHBRXnYMC7I0got6ZPyci7kmZlcHIcPZ8GqDCAMhUwaKTubP91uCoFQyJjSperw6Uiswsbj4XXJc5Gs6SaXy6Sg4nG+VumwGjyYwsXp5Ovzes1k7PXr+To3bwb2HlSN9649BD0yNsOOOB786PoGT5u6OqaaHn2UNUZTpzadFlu+vGXiZfly4LDD2uZ4KYqitAcqXpTt8PtZT7F4MUXHpk3GRXdV2TAs2zISayuG4bev/wvhiL1hMvTee3MxHj2aoiUS4cK8ZYtJE0lKye02XUIul5mqnJ7O30W4xGJcpHNyum4hqUyylhoYmb0kQka2sdtN55KImOxsvr716ykYHA5GYOJx48uyYl02Xvz+F7j2iJtx73k3oKIuH899fVFDCqmmhqm8u+9m3dDhh6fez9WrW/Z6WrqdoihKZ6HiRWlENGo6i7Zs4Zn/5s2mlbmkujdOeWQGbHY3whFHw5DF4cMpTkaPNm650SijNh4PIym9erFjKBBgNMXl4n2zszmryOHggi+pGOnWSUvbdeGSXIybTHIL9c4iU6yl/iUc5muViId4rcilro7XDR5MkXj44Txe8+YZw7hEArjr7d8hx1eOiyY8gMcvuQwlVUX4dPGxyMnhMaqpYbTst78Fnn2WEbBkpCNqR7R0O0VRlM5CxYvSiO+/ZwfM+vWMtqxfDzji1Thu1Ax8sOBk+HxAMJLVMDvI5zPtzSNHcjF2Ohl5qajgNnY7u4/y8/kcHg8FiUyWzs9vnAqSoYHS1dMcqYpr5bpkF93WkKrlWi4OR8sEjt1uCnUjEYoYGX5os5niXoDisGdPvt6DD6ZYk+La9esBwIbfvnwf8tLLcPKo5/DCL87CEX/+GkuLRyE31wiYZcuA668Hnn7aHG+hqKhlr72l2ymKonQW6qWpNLB6Nf1DVq6kKd369UB9IIznf3YG3rjuFPzfMf9sKI6Vwtrhw5nmGTWKC7DXS+fdrVu5MEciNJzr2bOxc27//hzQWFi4fQ2LdeqzIHUjUiNTV8e0k99PoSRuvdZ0jQgYIdkHxtqNlBxxsRbmivCwPm9NjXlOEVvN4XJRYGRlmaJaOY7W58rJ4TGaMIGXgQOZCuJgSTuuee7f+H7DYcj2+fH29SeiMLME1dXcd5+P+/r11+w+CgYb70OPHjseuuhwcDtFUZSujEZeFAAUHF99BSxdys6itWuBysoEHjr/ahy1z2eoqc/A3PUHwe3mYutycSK0CJdwmJGUTZsoXOJxLsL77ssOpKIi3u52M+pSUNB89EKKXq2Xpkhuibb+nkqY7AiJ1lijOE1dADPsUDqg5JIKh4PHweejuAiHua0IPSnetduB8eONXX80KgMsPbj4qTfw3i8noqY+Ax53HJEqiqrMTD5OKAS8+irfn2uuMa999Gg+t9/f9GuXmiVFUZSujIoXpZERXXExbf+3bQMuO/RRXHb4vxCP23DZUy9hZflYxOOMAgwebGpcAC66K1eywNdmY3Rl//2NRb4s9Lm5vF8y4lYrP1NFMqxpm5YMTNxZrNGZVFi7h2SfrV4woZBJeUkUKXkfpdjW6zUREumsSiR4WyLBri1JoYlIqqjJx9QnpyEYy4c/kgGXi48hLeXSmv3AA4xuTZ7M+xUVse4oEOA+i2uw/JThmJo2UhSlq6PiRcH339PPZeNGFohu3QpMGvwZ/nredQCAP7x9D75afSLicS7EvXszqjJmjFnIly1j9Mbt5hn/pElMKUnhrt3OFJHXy+eURV8uViTKIsZv7eHlsiukqsWxCplIpLGRXTBo5h8lFx47HIx2yHYyNiEUohDp2ZPRKxFGc+fyfmtKBzS0lcdiwODClVhZMgx2Ox/P76eQvOEG4K23mLqLxZiCktlSctzFoTgtjYXTzUW5FEVRugJdZDlQOot16+jnsno1Bwdu2QL0TF+N539+FpyOGF785gI8PuPXiMdNPUSPHizOlZTK0qWM1KSlsU7jlFPMghuNmjN6h4MLtNSpSL0IYKIdVidbETatdcjtDOx24/yblUUB4fGYGpNIhILB7+frTvZbcbl4P5+PxystzRy7/v0Z4Ro9mpEYn4+3BwIAkMDvT/0T5t85HKfs/1aDk3FGBt+fNWuAX/yCkZi6Oh7LvDzjVCyRIY/HDLtMnhulKIrS1dDIyx5MbS3TRcuWsVZlwwYuXJdOfBN5GZWYs2YCbnz1CdhsNthsPMuX4YkuFxfgVau4iObmskvm4INZdFpXhwbBI4MWrYjZmyyg4hYbjzPaIp02InDEzK27YHXolXoWGX8gYwNkErX1dXm9vE6OVyTCYzBoEI+DHI/vv+fttbU2FGRWwGGP46krLsAhd3yLVWUjkZlp6mpmzQJuv50iprqaj53ciQRw2+pqnSqtKErXpxstB0pbkkgAM2eyzmXTJkZeZFjiQx//Gltre+HrVUcjbvMiHjNzhkaNMsMGly3jYpqfDxx5JKMuHg+jMNEot5Mp0ICJTqQqaJU0hqSVACMA6ut52dHsnq6KvA6v18w8kvRSJGLceiWlJKkf6XByOCjsBg0yIigYZEu71wvc+sZfMbznfBw48Eu8ds1pmPTH7xAI5CAjw3RoPfssI2byeOJFI0aA0qUVDG4/GFNRFKWr0cWD8Up7sXgx6yc2bGD0pKoKiEYTSCS4oL32/U9RHS5CKMRFLjubwqWggF0ty5ZxwSssBKZMYXFuNMp6ilCIYic/nyLF6+V9xAI/WbjI4tmUEZ11mnR3x+ls7CIsqbJAwLgSCx4Pt5OUktPJAuixY1lPNGTI/9JpdheufPZVbK7qj2E9V+LZq6YiHIohGGQUxW5na/cjj/C9ycszwx2DQf5MT+f1ktpTFEXpyqh42QMpL6eL7ooVvJSXs0D33eunoCi3DDYbRUY4zAU2PZ3FuUOHUsQsW8ZFNjcXOOIIjgUATK1EYSE7Vqy1H83VrEjtTFPbSAppZ8zmuioyXFHGIiSLGKkFkrSb18tjmZ7OgujRo/meFBVR1FTUFuLK595EfcSL4/b9ALf95I8NrdiZmXz88nLOp/L5+JwFBaxFKigwQik7u+vXFymKoujX1B5GJAJ88QWwZAm9XLZsAXLcm/Hfn52HY0ZNww3H3wOPx3QIeb2MqowYwcVz0SIurpmZTBWNHMnFMxzmtoMGcUGUqEJLkILcpoYGirBp65boroAIxWQRI1Oo5Zj4fGZUQk4Oo2B77w0ccAD/9niA+Rv2x+/f+RcA4Hcn/Rkj+yxsiKzIFOpgkO95ZqZJE0Uipli4f3+2uSuKonRltOZlD2PuXLZFr1vHTpT6YARv3nA2emRtxfwNY3Dne39u8P1wuylaRo3iAjl3LutiCgqAo45iR5HM8HG7ef3OFHtKDUZ9fWoH2EiEC/yO3GG7MyJiPJ7GTsGRiBn6KOmj2lrWr4wbx/fjgANYv5RIAC9/+1OM6v0D5q7eF8uKRzfU2MhjBwKMvvTowflHUvNSV8fnnTRJHXYVRen6qHjZg9iyhYvcqlVM/fj9wJ9P/w0mDZuFqkA2LnziNcRtPkT/JxYGDODCWFAA/PAD62Ly84FjjgEOPNCYqkkLtbXYtrVIMWt9feO5PuI6m+qxrbOLki9yu/VnU6QayrgrDr27gtj8ezyMioj4iERMG3VmJo95nz6cRB0IAPvtB3z7LY/drW8+gIwMI/akSFfGB0QiTBdK7QvA+40aRVG6O0a4FEXZvVDxsocQDgPTpwPLl9OXpaICOG7Um7jm2IcAAFc+9V9s9g9FNMqFLj+f9Sz9+zPi4vezluXYY4GJE/mY9fVc6Hr25GK7KzidTItYow5inibRnfr6xtb9HU3yPCRx+W2v50pPN63S8TijI243hVx6OrcZMIDvUzAIVFZSlEqHVmEhEKktx7GjPsALsy9sqLMRUbhuHd9PGao5bhzvE4vt3lEuRVG6Pype9hC+/x5YsIDCZfNmoMC3EY9dfBkA4MGPfo1pS09FPI4GP5ejj2ZNxcKFFC45Obxu0iSKh7YULtZZQTKoMBYzXjDNzTayRkaSoyTJP5vCGqGR31NNpbbOM7KSamRBWyGt5SLqwmFGY3w+XhwOdh5VV3ObbdvY8RWJAPmZlXj7hv3RN3cjKmry8enSE+H1Gv+X8nKKoCuvZHQNMOMJFEVRujIqXvYANm1ike6iRaxzCQSAvpl+lNcWYnXZUNzxzp2I/W9RzswEDjmE0ZXly1kf4fUChx7Ky64IF+tMoOThhlas9vtNDVrs6ALepoY0Wq+z7r94u7SFmJFUkswwkiiMx2O8dA46iJGXcBj4/HPer6wqF9NXnIoLJjyCJ6+4EONvnY+KYF94vSai8+qrwHHHmYjL7loYrSjK7oUtkdi9zrP8fj+ys7NRXV2NrFQTAPcwAgHg+ecpXr75hmfl4vSa6atFXkYVymr7Ih5nse2BBwJTp9L/ZeFCiohDDwWOP54LW0uFiyzq1gGGTX3SrNOgk8VKVyd5SGOqCJHdzuNodd3dleeTKAxghjHG46xJevFFRtlmzWKExoEQ3vi/Q7Bvn+/xxdIjMeXeT5GWZofTacYFHHAA8NJLpraou5oBKorSvWnN+t0NlgdlZ0gkeJY+fTqdWJcsYUohEeOqZ7cDgXAGSvx9EYuxlmLIEOCcc7jdokVcGA84gCZ0UkfRlHCROor6egqmujo0+IxEo0a4yELudjOakJ7OixSpulxdawjjjpAokbyejAz+FPdagMdGXHHr6pia2dmaHYnCpKebturaWh7f3FzgzDPZRTR8OI9hJO7BtS+/gEA4DUeMmI4bjv8rQiE0vOfxOD8f991nXIAVRVG6Ot1kiVBaQyjEGohVq9iBInUuHlslfrxzH1xz7INIJBINjrYOBwXJ1Kn8+9tvuRiOGgWcfDIXuWCQi2WPHmbysYgV8SQR23vrOACnk9vLgpuWZub3OBy7Z4pCWr/lNYursLSgy5DGQMBEwVqLFDg7HLx/IMDH7dkTOOMMYJ99+LvdDqwuG4Y7PnwYAPCH02/B6D5zG4pyxVH3tdeAOXO61/woRVH2XFS87EZEo7SBDwRM1GXZMtau1NYm8I+LrsDQnqvws6P+gaw0Tv6z2XjGftppbL39/HM+ztChwOmnm+nF8TgLeRMJE1URsSKLb/KiLUJFoim7o1DZERKZkQ4h63gEiciIx0prxx/IDCSJgskspL32Yppv/Hi+Bw4H8NysS/HRkjPgdkZw59k3NYwAEPfj8nLgrrsYdVMURenq6HnWboCkiKRTJBzmWfSyZewwqq4GfjrpPzj9gNcRjrpw0eMvIhBOh81mCnQPPxx46y0+Tv/+wFlncWEsL+ciK3NvZIG1FqW2Z8vw7oYMpUwkKPyiUR5T+d1u337S9I4QAz8ZbhkIcLr3li0s4p01C7Dbbfjt60+gtLoIv3nuDthsfK8lxRUKMX10zz0UMfp+KorSldHISzcnHKY4CQZ59h4KsXBz7lxg3jx2C/XOWof7z78WAPCHN+/AjxvHA+Cit88+wLnnAh99RO+XnBzglFPYwVJezoU1J4dCRlJAaWkmiiBRFaV1iIeNz8fjKSk88bORNFBLcbn4Hom5XyAAnHoqvVuGDuU222rzcMeHf0fcmQPACCaHg+9tKMTC3WnT2va1KoqitDUqXropsRhTRJImCgR4Bu1200V30SJg9WogFIrjX5ddjExvLWatPBh/+/jXSCS42PXvzwLdWbOA9espSE4+GRg4kN4u4pzbo0djsdJdimm7C3Y7RWF6upkJFY9TTNTVtVzEOBx8DKmDiUSAs88GJkxgK7TdzsfMzQWczgQuPfxJZLmKEY+b1FNFBaMvFRXt93oVRVF2FV2GuhmSIqqqonCprW08eXjZMkZc5s3jwveLo/+Gw4Z/idr6dFz6xH8Riztgs3ExO+YYYOtW3ic9nX4fBx7IBS4tjaKloEAjKx2FzUbxkpZmhjQmEnw/JCW0I8SZVyI56enASSdRwEgnUX098Nef3ozHLrkcj116JUKhRIOgjUaZanzwwc5xMVYURWkJKl66EZEIaxi2bTPtsVlZTBekp/O2adPo81FRIS61CYSjLtz44gNYVzGkoUB3//2N9b/Px5qX4483c3AyMrhdZyB+Ka0tYN1dkJRSeroRMZJOkinRO7p/WhqFEECn5MMP5yBNp5P3f2fBVISibpy433s476D/NsyQcjoplF56CZgxo/1fq6Ioys6g4qUbEI+zrqWsjNGWWIwLW24uxYvPx+s/+YRFl2vXGhOzv318PcbcvBhPzbiioUB3yBAO4Js5kwvc2LH0B/H7+VxeL2cbdTSxGBfO2lruS20t/95TRQxAEWMVIrFY4+Ls5hDvHAA48ki6JvfpQ3G6cOMoPDz9TwCAB86/Fvm+jQ3jGWw2oKQE+Otf+blTFEXpaqh46eIEAlxIqqoY0vd4KCxyc7moxWKMuCxcyIjLjz9y0QfiDXN5VpUOg81mg88H9O7N9NCcORQqgwcDF1xgRILLxZRSR7c1x2LGwM3h4OuUqdV1dXu2gJF0Unq66UISr5gd1cN4vcZX5/jjOZsqI4Ofi0c//zV+2HAQstP8eOKyy1Bfn2ho7Y5G+Rl54glNHymK0vVQ8dJFiUTYKbR1K393ONiuLMWzDgdFSnk5t/v8c+CHH/j32P5z8M3t+2O/AT80nE27XLz/QQfRsK66miLlkkvM80mBbmcU5Irrq/iOAKaQNRbj7Xs6NhsaBiva7aYeRuYdNYWYBPbuzTqn/fajmAlHHPj1a08jGPbimFHTcOmhjzcyr/P7gWeeYWpRURSlK6HipYsRj7OmZcsW1jgATA0VFTHlA/D64mIKlXCYomXBAjrq2hHCvy67GGP6z8d1x94Pp5MLXXY2MGIEhU9xMf/+6U/ZBl1fz2169Ogch1Vx65Ui02RcLt6+J0dfrMg8I6mHkXRbc1EYMQ8cP54ppAED+F4v27I3/vrpXwAAd5/zG7htVQDM52DdOuChhySapyiK0jVQk7ougnhzVFaas2ifj+khh4Nh/FCIYkVqU2w2/r5gAbuLAgHg1lP/jJF9lqC0ugeufe7hhuLNvn2ZIlq7ln+feCLn30hNQ2GhqavorNfflHiRglWlMeKxI1ErcTy2Rq+syPt77LEcvFlezs/bY9N/ib17/IjHpl0GfzCnwc/H4aAg+uIL4OWXgcsu69CXpyiK0iQaeelkRLSUlbFDSNI8BQWMikhtQzjMn1VVph22sBCYPRv4+msuRPv0mY9fH8+z6F8+8yj89flwu7ndyJHAxo1clCZOBCZPNsIlL6/zB/KJuVoqEgn1lmkKu71xYa4U9DbVVu12M8J2xBHsPnK5gFjMjl+/9h/8uPkQ2Gz8zMnn0G6nwHnqKfoGKYqidAV0SegkxK+lspLCIxRihCE9naJFbP7jcbNtNMrbs7IobhYuZDvrsmVAPBbFYxdfBpczijfmnI63fzgTDgcfa/hwih6Xi3Nvpk7l8wJMRUk6qrNwOExqKBWSUtoZvxkpWk512Z2QriQxqBO35WQkRTdyJNunBw407rqZmbz/sF6rkOvZ3DD2IR7ncM9//rN1rr+KoijtRYeIl0cffRQDBw6E1+vFhAkT8N133zW57dNPPw2bzdbo4u3ssEAbIguL32+M5uQsNzOTERDroEOnkwuGdIFkZFC8VFWxNXr2bHbjXDvlfowbNBeVdTm49tlHGob29e/PBUtqWq680njE+HyMunQFrN1FItjE28TaAROJ8BIOc9tQyPifyEUch2VAZVOXVNvIoh8O8xKJmPlDXT11JVEYSQ9J1M6635Jiys5m19nBB/N3u52v94LDXsbcO0bj0Yt/hnA40RB9CQSAd9/lsE9FUZTOpt3Fy8svv4zrr78et99+O3744QeMGTMGU6ZMQVlZWZP3ycrKQnFxccNl/fr17b2bHUIoZCz9xWgO4BlzVpaJLohVvMNBYRKPm6Jbn48L+2ef0ZCupARIJBI4fO8vAAA3PP8gyuuK4PVSrOTk8P6ZmewssttNS3RBQacdCgB8HTKU0FrzUlfHlJYIO6n5ETFhFRQiKnYlomK9bzzeeFCiVSjJzCERObI/sVjXiuRIca7UCknULrkwurCQtU/SfRSPA8tK94XdFseJ+72Pn4x9scF5F2CX2uOPm6idoihKZ9Hu4uWBBx7AFVdcgUsuuQQjR47EY489hrS0NDz11FNN3sdms6FXr14Nl549e7b3brYr4bCx8q+oMHNj0tLM0EOvl4LF5+NiEQwyOiOLR06OWURWrwY+/hhYvJiLUiJhw6kPvY/TH3oLz826CHY7C32Livg42dmcWdSvH/dFojAdUUciYkBEgERJRARIlCMS4WuVwY+ZmTwuaWmm9kIiUdL67XZze4/H+Jn4fI0vaWlNX2Qbua/Xy8dyu/n4LhefSyZnCyJyRNxIu7K8nq4gaKQjyZpGkjSk9bWMGQNMmcLuI4cDWLp5BB7+/BYAwANTr0Wao7xhgng0ykjfM890/SiUoii7N+26fIXDYcydOxeTJ082T2i3Y/LkyZg9e3aT96utrcWAAQPQr18/nHrqqVi8eHGT24ZCIfj9/kaXrkIkwghCVRUjLeXlXEjS07k4FxQw4iJThWWWjQxbBLigSlgf4OL47rv0damr43U8w7bjnR9OhdNpQ3Y2hUtdHQ3txo1jgWYggIa5Rm3ZEm2NoEiUQgSKpGEkWiJREkEWRqsgkanVIl5EYIiwEHEhwkLEhd3Ox7NemkO2kftaBZKIFxFIXu/2+yKRMnlv5DgkC5pwWERm2x3zlmCzGTEMmFSYteXcZjPpo5wcXvfo9N9iyZbRKMwqx1/Pu64hUmez8bP84ouss1IUReks2lW8lJeXIxaLbRc56dmzJ0pKSlLeZ++998ZTTz2Ft99+G8899xzi8TgmTZqETZs2pdz+7rvvRnZ2dsOlX79+bf46BFmgd+Q3Eg4bsSJpIim2zcgwAw+TW1pjMS4OUmiZkcGLla+/Bt5+m+miPjkbcO+51yPNXdtQN+P1UpzEYhQuAwawQLemhvff1c4iiThYRYo1giLHRxZqEQdWcSIREhED1miHNdLR0S6/LUFejxQZW4WNONlKpAjgcZDjZU03daRnjUSnHA7TXm8VUhkZHA8xciTfj0C9G795/UnE4nacP+l5HLn3+w11R/E4sGIF8NhjahyoKErn0eW6jSZOnIgLL7wQ++23Hw4//HC88cYbKCwsxOOPP55y+5tuugnV1dUNl40bN7b5PkWjTPlIFKW6mn9b21ElNF9eTldcOeOORLggZGVROBQWmrZWK+EwH1uKa3NythcZxcX021iwgI/70AW/xLVTHsQ/L7kCABen/HyzWPXqBVx+uVlkJBXTEpKjCFLvIYtvKpEiC7pVoEikwipOuqow2VVEpFlfv0RoRMyI+BPh11FCxuUy74XNxroViQbF48CgQRwf0K8ft/1+7QH414xfAQAeuejncNv8DaIyEGDa8rPP2n+/FUVRUtGuJnUFBQVwOBwoLS1tdH1paSl69erVosdwuVwYO3YsVq1alfJ2j8cDTyo10EaIcIlGzVm3CJVolItBPM5tpI00kTAhe4kkWOfSJBMMmhSQ00mhk1yPEg4Dr7/Obo+6OuDU/d/CyWPfQTjqwp1v3dpQF5ORwf3p2xc46yz+HYlwEW1qSrQsYNZLUykOOQbJ6RZle0TQiaizFgSL8JPCYKBxGqw9kGnhNhs/c7W1RlR6vcDpp7Oeats21mXd+9GfcOTw9/HyN+fCX+eF28t9DIdZvPvkk8CECZ0zxFNRlD2bdl123G43xo0bh88sp2jxeByfffYZJk6c2KLHiMViWLhwIYqKitprN5tFCkxDIUZctm3jxe9nhKW4mGexkYhpVZWCUFkUsrJSCxepbxHhklzfIkQiHJL37rvApk1AhrcGD/70agDAXz/4DZaXjGy4bzzOepcjj2Qxpswssg5bjMdbVmhqjabIfBxrmseaHlF2jBxP6Qbyek0tCdA4IiMFzG2NtNBnZlJQy2c0LY3i9pxz6Avk9QI1wTRMeWg+7n73dkTj7oa2dXHe/fZb4KWXtHhXUZSOp93HA1x//fW46KKLMH78eBx44IF46KGHUFdXh0v+NxHwwgsvRJ8+fXD33XcDAP70pz/hoIMOwtChQ1FVVYX77rsP69evx+WXX97eu7odMunY6pUhZ8siWDIygJ49uRCIOylgXHCbirbEYqYWBjAdR8mEw3TffestzjCKxYDbz74NffM2Y1XpENz19u/h8Ri/luxsYP/9gVNO4SII8DZJATW10CRHUnbX1E5XQo6zy2XSSSIepSVcCojbMhojUUGbzXRMyfOMHg2ceiojK+vXA/VhN9LS/tfWn4gCCRscDgdiMYr4l17iuIFhw9pu/xRFUXZEu4uXc845B1u3bsVtt92GkpIS7Lfffvjoo48aing3bNgAu+X0vbKyEldccQVKSkqQm5uLcePGYdasWRg5cmR77+p2JBLGZ8XrNTb9Mj9GzkIzM00nDWCiFE0t/pGI8TCx23n/VHN9wmFGe777Dnj/fd5nvwE/4BeTHwYA/PK//0AMPmT/TyR5PHTQPecc1s8kEqaDxFqfo0Kl62G3G3M5a2G4XGw2UzPUVni9JuJWX2/qc04+mZOkpUvO4wFGFc3BQ+f/DM/MvBj/mXVNQ+v00qXAf/4D/OEPnTsbS1GUPQtbItGV7LV2Hb/fj+zsbFRXVyMrK2uXHisU4iBD6QoSfxZJowAUN0VFZpvmoi0AFwkxp2uqvgUwwmXbNuDmm+mmGwoBH/1mMo4a+RlenH0eLnr8BaSlsebA7ebgxZ//nAImHue+5Odv3wqsQqV7IBGY5FSeeN201fsoAgYwAubrr4E//YleQuEwcNGkx3Df2T+HP5iJsbcsxda6Pg2prb33Bh5+mOMGFEVRdpbWrN9asdAM0j1idcYVD5ZgkOJCoiceT9O1LUJdnREuHk/q+hbACJd4nHUu337LfbDbgcv+/Sz+M+MS/PqFB+B2m+6hnByajQ0fzr/T04Hevbdv4VXh0n2w2Ux9jNttfIAiEdOp1BanHlLDBJiW9wkT+HkqLOT1z393Jb5bcxCyfDX4y9m/aog6JhLAunXAf/9rBn0qiqK0NypemkHOcqureUlPZ1FjLAZs2WKGHWZmUiQ0JQwSCRb4Sg2KOMim2l6ESyIBLF/ODqPycmOotqmiCJf/6ylU1PVq8O7IyAAOPBA46SQzoK937/brWlE6HqfTFEuL4I1G2664V8z/AOPEe+65wD778LMaidjxuzceQzTmwFkTXsVhQz9s6Iyqrwe++AL44IOuNSZBUZTdFxUvzWCNVOTkGIdRGWzndjPa4vM1/RhiPBcO8/4idFIRCjE1FQqxIPiFF4AlS/gY+w+c2zB/RyI96emMqgwcyLlFIlYKClS47K44HMbhV0SM1KxY65p2hmQBU1gInH02hbDTCSzaNAaPfXEdAOBvF/wCHkeg4XNWVsbP65Ytu7YPiqIoLUHFSzPIYpCVZeoO0tMbO+VKx0YqIpHGxnPZ2Y0N6sQILhxmZKakxITt580DPvqIi9JJY9/D7NvH46nLpwJINMz/sdu5wJx3nuk2SmVup+x+JIuYRMK4+O6K6Z1VwIRCHCtx0EGMOCYSwIOf/gEbt/XD4B5rcf2UPwMwk8/nzmX30a6KKEVRlB2h4qUZZKJxXh4FjKRu0tIoRPLyeHuq9uP6epP+cTopKpxO07Js9Y8JBBhpSST4eOEw8PTT9JHxOIN4YOo1AIDN2/rC6bQ1dJ3k5HAmzcSJvK/Px/3q7oiRm/qH7BgRMdaamFCIl51N4VgFjN0OnH8+o3v0fsnATa/9HQBw0JDZSMRjDd1qlZVMc+rcI0VR2hsVL81g7dLJzmb4vKiIokG6hFI5zFoLc6UGJRymSJHODlmYIxFuL8W3bjfwzjs8i41EgBtPuheDCtdiY0Vf/PntWxtMxbxeYMgQ4MIL+TgyGqA7E4uZMQRyqa/v2DlA3RWpiZGW+1jM1MPsDOK8CwBDh3J0QEEB//502ak4/eEPcey9n6E+5GgobI/FWKf17LOmvktRFKU9UPHSDLIgiFFdOGymJAcCvF5GAAA8062qYgpI5gm5XI09YKQI2O2m6AmFjMtpIkFjsGefZVfToMK1+PXxfwEA3PjC/YgkMhraZHv3ZkGldBsVFnbvOhcRLuGwMW6z280xVwHTMmSGkXwWpDNpZ6JY0jZts9H0cMQI1mzF48DMNcfB4bDDZuP7I234dXXAhx+yQ05RFKW9UPGyA3w+46gbjxs3VBls5/HwZyDAUQF+P/+WEQHJlvDS9hqLcVuAf4tl/yOPUMDE48Bfz78OPnc9Pl98FF7//qwGMVVQAEyaxA4jgPUI7TjeqUMQPxMRdYAxbpNUm9IybDYznFNSSSIMW5tK8ngolvPzaX7Yo4eZ+u12A5m+Gtx00p+Q5q5taJ3esAF4/nkKeUVRlPZAxcsOiMeZIsrN5e8SgZHZRRKBqaigaJEamczMxpOFrZ1L0g4N8Dbx7pgxA/j8cy40x4/5ACePfQeRqBPXPvt3uN22hmnFQ4dy6CLA/dhFL75OR6zxm/LIcTobR6+UliH1MHJco9HGUayW1hZ5vXysww4Dxo9nCjUW42f7netPxB/OuB3XH3tng3NzMMjW6c8+09ZpRVHaBxUvzSBn/FKgm5HBS3a2aZGuqaEQcTp5W1GRcdltzoAOMGfGMvTxscfoqGuzAQk4sL68P/7+ybVYWTayYSEaMID1B/n55oy4u2NtAU+FdNPoQth6xOjOGoUJBIzZYktri6Sz6ZJLmLKUgt5/TP81AOC64+7HoIIVDe9hcTE7j0pK2vkFKoqyR9Lus426O5IqSiT4eyJh5sxIW6qkiDIymnewjURMB5JEY2pq+Bivvw4sWMBtnE7go/lTMHrxkoazWZ+PQmW//XgGbLOxzqU7THUW0WEVH9brrBEAeT3WbeX25qZYpxI2Tb0X1uvl9+TrrNfvDq7EDgc/Q8GgESoSyQMoqmMxE2VJRoY5Dh8OHHccfV1KS4HpK07GRwtOwHH7foD7zrkGZz76Iex2G8JhzuR6913gssu6dz2Woihdj26w9HUudju/7CsqmMOvrma9y6ZNFB52e/OOuYJ4viQSphW1poZ/b97MGoFAwCzO8TgQCKcjFEuHy0VhtNdewIknUjjl5nb8IDwRGiImZICgTCYOh3mRVt36elPgLP41cpGpydZ0UChkHleeJx7n9jKTSSIwyZem9jfVxfrYya8j1Wuxvg7ZdxmcKIK2uyAF41IjVV/P19CS2iKbjeJm6lR2uqWlAbGYDbe9/RBCETem7Psxjh/9dkPr9NatFOVr1nTMa1MUZc9BxUszSAFicTGFhsfDL+9gkAKmrIwposzM5h8nWbikpZli3XAY+Pe/WeQYiwH/uPhKXHTIv2CzxRsWmowMoF8/+rkMHcoz4B09Z0uxRj5kEd/R4p0sPqwLuSzmTUVCrBOtxV5eogIej/HWcbnM8ff5eJxF9EkapKUX6/3k4nKZKc1ykX2RxTdZjFqPlfU4yTFKFjZdTdRIbZF8jiUaIu9pS2qL7HamRs87j5HARALYUDkMD3/K9NG951yHNE+gYer0/PkUMOFwB7xARVH2GFS87ACJBng8pkMokTDFuDsiGm0sXDIyzN+RCLBoEWfChMPAlH0/xmWH/wuPXPhzDOu5sqGluqAAGDWKU3udTuO30RypFtrkxTY5mpAcUUgVVbAKEFnsZfEXQWAVF+JJIzUTVjEh28uU7owM06EVj5tUhRRHW311ZD9acrHeL1k4WcVLU/tv3W8RPFaRIyQf72RR09nGe9baIulIEi+XaNTMNNqR6LLbgVNPZQozK4v3/ccXN2NjRT8MLFyPX06+r+E5qqqA994DFi5s71enKMqehNa8NIMsQPn5jadIu93sKHK5jOmcLAJWrMLF5eIX/bZtjSMdDz3E61yOEB6c+ksAwCPTrsHqrXvD5eLzDB7M1uiCAu6LLO5NpU9ae8ZvXeitf6e6rT0RQWGtLeoKNT2p6mKSsaakrL8D5qe1IDaVEGtv5HmstUVWP51o1ES6dlSj4vMB//d/wNKlNGQMhNPx+zcewImjX8eT0y9riKBFo9zmtddYL5Oe3v6vU1GU3R8VL80g0QfxZolEzBRnt5tf9HV1qc+mZSCjFPzm5PBvWSASCeDttznDKBoFbjj+YQzrtRIlVT1xx1u3w26n2OnZExg9Ghg5klEA6U5qCamiEMnXd0W6gmBpLdYojxWroEkWNtbPjfgBpXJsbivECTccblwvJZ1sNTVmTpGkLJtj3Djg6KMpvsvLgWnLzsTr352JUMj4wNjtFDcffQRMnsztFUVRdhUVL80gC4mYe4l4kPC6NSVhJRYzpnYyg6i21sybicVYzPjvf1P8FGaW4qZT7gAA3PzKX1AbyobXy0jL8OHA2LEs0JW5RalESCqBonQ+IkqsJBcNi6CxDjS0prfa8r10ucwwUOneklqY9PTGQx4lytjca7vmGmD2bAofq2ljLAakuWpQl8hENMqi3ddfB/bfn59lRVGUXaEbnuN2HFKLUVlpoi7yhR+J8Hpr3YDUsVRUmK6N9HTWyWzbZuof6uuBxx9nkW40Ctxx5u+R5avBnDXj8dysC2GzMeLSpw8wZgzrXXr3bnkNhgqXro0IGvl8yWfIKlREUEinlkTsdhXrIEdxdRaRInVc1jqYHQ147NGDYypycihYXC6gT14pXvzF2fjy5glwOyOw2Zh2/fxzmtd1tUJmRVG6HypedoDMLhJvDGknra01U6ClMDMQYAeStJ9mZPA2mRhtt3NBWLKE81/q64F+eRtw4SFPAwB+9dzDAOwNE6vHj2dRZN++ZsaMsvuRSsxYPW0kKtNWQkYEjPgTpaU17j6S/QDMzKnmnu+CCzj3KC3tf5EkhwdHjJyOEX2W4tJD/9nwuJs3M/pSWrrz+64oigKoeGkWKWzs1YsRlLo61q0EgxQ1hYXcTrpIrDUuBQVmUJ3MNZIz63//mzUC8Tiwuao/Dv3zbPz+lTvx7eqJcLkYZdl7b2DQIEZdWtLVpOw+WId3WqMyQGMhYy2y3RkkLZWqxkYEjnU2UlOdUl4vcMUV/MwnEkAwmoM73r4TAHDLqbejILO8YQjprFnAtGk66kFRlF1DxUszSC2C10uh0rMnPS6Kivh7RoZJ2QQCXGTS0hhKd7lYByBuvLLAfPIJJ+5KUWQiAcxZfQDuee9m2Gx87NxcYJ99OHhR6wP2bKxRGa+3sZCxppakRqUtsdv5nFIH09wIgSlTGCnMyuJn+5W5l2H+hjHITa/CTSfe3iDcS0uBt97i8FFFUZSdRcVLM1hbS202GsNlZTEKY/Udqakx5mo5OfwpnUWy0MRijLY89RRrYDzOAAYXrm4o3pTHz81lUeOgQYy+aKpIsWJNL8lnEDBOxG1ZHwMYPxj5HMvjp9rummvMyIoEHPjdqw8BAK448jGM7L2ooVbsu++YNm1ulpKiKEpzqHhpBmktTfVlDZjaF0kviXDx+7mQSPhfupVefBFYsYKP9+sT/or5d43EtVPubygE7tMH6N+fPw89NLV3jKIAJiIjqSWZWm5NK0kxbls8lzwHYEz3khk9mu3Qubm8fd7mI/DW3NPhsMdx91m/gsNBRVVeTuO65ct3fd8URdkzUfGyAyRML11CVmdaSQvZ7fzClvRRIMD7+nzm97VradQVDAJ9cjfixhP/Ao8rjE0V/QDwjDUri91Fo0ezzkZRWoJ1XpE1GiMt0W2VUpLuNsC4NSdz9dVMfYoj9R/evg/1EQ+G916KHpnFDcZ1P/wAvP++jg1QFGXnUPGyAyRXX1XFYYzr1wMbN5o2ZzGgk64jmVmUlsZiXYD3f+wxoKSEX+h3n/M7pHmCmLHsULw25yz4fKyTGTmSDrqHHNJZr1bp7kg0xu1uXBsjKaVdTdXI+ATAjBSwUlQE/OQn9CSKx4HimsE4/W/vYcRvlmNTRe9GYwPef5+T1BVFUVqLipcdEAoxzB0O8wtZ5goFAmyBllZqGQUAsMgxFDKdR19/DUyfzusmDp2F8ya+gHjchuufewh2uw29ezPyMmgQcPDBvL+i7Ap2u6mNsXYqyciLXREx0gkFmBSVlZ/9jCMtvF7+D3yzbjJC0fQGUz67nc+/cCEFjEQnFUVRWoqKlx1QXU3hkpXFL+xAgF++mZn8cpbxAOLlIl/qUvNSX8+oC2+P48ELrgUA/GfGpfhxw/7IzaVwGTmStS777tuJL7aLYLXSV3YN6XZLrovZVREjaSqAj2EVMD4fvV9yc83/BNNNcZx/0NPonVfSaGzA3Lm7+ioVRdnTUPHSDOEwxYnPxy9hayFudjYFTW0tjeliMZ7hejwmXZSdDbz8MvDjj1wsLjzkGYwf9D38wUzc8uqdcLsZZu/Thz8PP7x7zvVpK8TxVVp/27LodE/HWhcjhbciYna2JkY+78D2Aubss41xXTTK/6EnLr8ST15xCW495ZaGsQTLlgHvvMP6MUVRlJayBy+VO0bC3NIxJFbpWVnGlt/vZ3RFWp2rq3nfjAx+Mb/wgnHjzfTVoS6Uhj+/dSvKa3s2+MYMHsy26AEDOvf1diYiXGKxxuZp4misAqbtSBYxUhMTibQ+2pUsYMSN1+EArrrK+BQ5HMDzsy8DAFx0yFMYO+AH2GwU+p99xvlIiqIoLUXFSzNYBzPKlFzJ40cirHGR27KzKWQSCWPx/vTTwOrVZkH+52e/wPAbV+CRadcgI4NOur16Af36ARMndvar7VxiMbPoWYdLOhxmmKXSdljN76zdSRLtao2IsbrxxuNG5B9xBI0WMzL42Au2TMSLs8+H3Z7AfedeC4cjgUQCWLWK0Zdt29rlpSqKshui4qUZxNbf7+eXr9QMRKM8YywpQcP052CQX/rSNv3hh7wEg2auUTwObN7WBwmbB716MeoyZAjddKUQeE9ExElTKTOJwGgNTNtgTc9JVMt67KXFujWC0W6nYLcKGLsd+PnP+dmWaNqf3vkLAiEfDt5rJs444DXY7YzWTJ8OfPll279WRVF2T1S87ICMDC6a69axPXrTJmDNGg5XTCTMPBfpmMjLo6h54QVgyxZ+kd9++h9w6N5fNEQWcnKAgQNZqNunDzB2rBlFYL1I2qolFxka2dJLNNo+l0ik9RdJWTR1f7letrNe39pLqmOR6ngmvxe7C02l58TlObmotzX1MKkEzJgxwFFHMaUaDgOVoX544KPfAADuOP238LpDSCRoQfDuu/zfURRF2REqXppB3EoBs6hFIozESJQlkQC2buXfaWnc9pVXgK++4pf1hCHf4raf/BGf/OYoDCpcA7ebaaLMTGDYMBY1er0tX2iburRG6KRanNvqsjNImqip+8v1yaMSdmb/Wir8mhNlTb1Hyce2K7Kj9JykPVPVw7SEZAETiQBXXsn0qMvF6x757EZsqSzCoB5r8X9H/71haONXX7H+paseO0VRug7Ozt6BrkwiwbqWRIJFteLt4vXyImeMvXpRuGRksO3zzTelNTqBv069HgDwzMyLsGHb4Ibuon79GHkZPbp184vaa9v2fIyWIh1bqa53uVo3LqElC2CqbZKv29E2LXkeq0hI/tmRx7el6Tmn0wwctQo0KV5P9R4lP47HY7yOevcGTjiBNWAVFYDLlY4/vHknLjv8McxcfnBD5GfLFuCDDzgao3//Nn/5iqLsRqh4aQaZXZSWxkUmEjGeGbm5NK8LBPhlXljI3P1bb9F8KxYDzjjwdUwaNgt19Wm49bU/w+cD9tqLgx2HDwfGjePvChdFiYyIC6v1b0lntJT2EgWphMvOXJeMCJn2FDZNRbCs+yDbyfPLeAwp4pUamR29H1YBk0gA55/PupbaWj7G6/Muwn+/ugixmL2haDgcZtfRtGnApZfqUFJFUZpG00bNIF/2UlRo9Xipr+dZqd3OOheHg1+677/PL2iXI4S7z/ktAOD+D3+N8ro+6NOH2w4fbn4qRBxhHQ6TypFJ3daOmM7GKjKkG83hMBeJWkikyBrFkPoSEWdWRKhZ01bWFJUcj11Jqexsek6EiKSSWlrQa00h9egBnHoqbQYSCcDlssPhsDdEg2w2PnlxMQvdV6/eyRepKMoeQRdZErom0h4taQuZGxMM0s8lFuMsIo+Hhbxvvsli3ngc+MUxj2JIjzUoruqFBz68ERkZjLpkZjKMPm5c11mQuwpWS3uZltyVhMvO0JTQSRY4VnFjFQ/WWp1kUdNaQSO1LU0V4IpYbCriIWMBpNZLCnqbe34RMHY7cPLJ7K7zenlfrxfITq/BH35yC5752fmw2/m6vvkG+Phj9fZRFKVpuvGy0P643TxT9PtZlFteTpGyeDFz97EY0LMnv9Tfegv44guGyXPSKvH7U+8AANz22p8RRQaGDuWX9fDhvM/AgZ35yro2stjvKWmDZHGTStikEjU7I2hEnFhbz030o2X1LG5344LeHY0ZEAFTWAicdRb/pyQlNaBwM35z0l9w9oSXcMSI6QD4f/bxx8DSpS08gIqi7HGoeNkBHg+jLMXFFB/iKCrixeMBvv+e6aLSUn6Z14Sycc2z/8A7P5yM52ZdjLw8FiAWFjJSc8ABnf2qlO6CCBurqJFU1I4ETSox01bpOXHple13FIWx2/n/c8wxnOPl83H/1lcOxxPTrwIA/OWcG2C3xxGLAXPmcO6RdPspiqJYUfGyAwIBpnr69eP8FZnBMnIkzyDLyxl1+f57foEzTWDHi7POw08efAdujwMjRnBhGDaMIwB69OjUl6R0cyQVlSxokiM0qaIzEiER8bEr6TmbbfsoTHO+MA4HO/POPpt1Y1KIfe8Ht6OqLhtjB8zDBYc8C4Buu598wuJ3RVG6DokEu2lLSsxA4s5AxUszhEIUK/n5FCo5OfzyHT6c7rj5+Tw7/OQT01Kd5q1vqFNwuYC+fVmcW1TEL+xx4zr7VSm7I01FaJJrWKxipq1ci6UWRjrEmvOFcTqBo48G9tuP0ZdYDKiJFOKe934PAPjj6Tcjw1uHeBz44QcW74bDu76PiqLsOmVl7Bp8911zmT6d13c0Kl6aIR7nl7wUkWZlUbBkZvL20lKaaq1YwW0PHzEDK+4bhAsmPdUwqHHYMN530CD+np3dua9J2XMQQdOUmJHIjDUqs7NFsqk6ksTnJZn8fOC888zYAJsN+NeMX2JN2SD0yd2CXx13PwDWmn3yCb2TFEXpXES4rFrFdax/f/5ctapzBIyKl2aQL35JB+XkGOESi9FQa948GQ0Qx33n34CinBIcMGQOPB6KlcxMmtJlZvJsU1E6i2Qxk5xmkqiMFP/uTGt2qihMct2KzQYccggwYQI9lOJxIGH34pbX/gIA+L/JDyPNE0QsxrTRBx+Y8RuKonQ8iQSwaBHrPwcO5PeEdNwOHMjfFy3q2BSSmtQ1g8dD0VFZyS9kK/PmsbtIinSnHvwixg/6Hv5gJv705h9RWMiUkdvNOpeRI834AEXpClhrXKwt2fK71MdYu79a0gEmURiJ5ogQcrnM/bOy2Hk0b54ZXvru/LPwpzeX4JmZlyAc88FmY9r200+ByZOBww9v+2OgKMqOqapip63Hw0jovHk8UcnLYz1or168vaqKBq4dgYqXHZCXxy/Xykq64bpc/P3NN9nKGYkAPncQd519EwDgnndvQl2sB/YZxu0HDODYgNGjO/mFKEozWP1oWiJkWlLcK0XA4sobDpvrbDZg//2Bgw9muLmqCnC5bLjr3T8gEjG+N9Eo/8/ef5/bS+RTUZSOIxSiXcj69cDKlTRidTho1lpczOzCgAHcrqPQtNEO8Pn4xmRnA8uX0778ueeABQuYkweAXx3/IPrlb8T68v545NPrMGAAlajTyfvuu+/2kRtF6aqkSi9Z62SSU0vNIdYCIoqsxbzZ2cAppzB3LtuIuEkkgFF9F8JmS6Cuju7V33zTvq9bUZTUuN3AunXAkiVAXR3/T/PyaH8AAMuW8faOXOc08tICVqwA3nuPgqWsjNblpaX8Eu6ZXYrfnnw3AODW1+5CWqYP/fvzi3/oUIbHR4zo5BegKLuARFqaisjIsMemjAWlpTrZe8btZh3YoYcCmzeb9Gw0msDjF1+MCw99Bj/527t4d+5JWL0aePttYPz4jgtLK4pC4nHaglRWcm1bs4ZlENnZNF0NBHh7R7pia+RlB8yfDzz4ILseNmxgyKy01BhynTT2fWR6azFnzXi8Pvc8DB5MMzqvlz/322/HrqWK0h2wRmSSO5eka6m5FuzkYt5QiF+Axx7Lqe0eD7dzuWwo9fcCANx91o1wOSMIBoHPPwe++qqDXqyiKA1UVLB8oqKCRfRlZUwhLV3KbERlJdfEioqO2ycVL80QjwOvvgp8+y3DYj/8wDNEq+/Ef2Zcigm3fYur//sY+vSxo2dPXj9sGMNqQ4d2zr4rSnuSLGSkBsbafp0qrSTjBWT7WIy+SUccwXoWSR399cObsdVfgOG9l+HKo55AIsGTh7fe6hxPCUXZk0kkmBbautVYIEjUNRAAtmzhpSO7jVS8NMOqVWzTXLeOX5ipzLJiMeD7tQdiWdk4DBjAduqMDAqXceP2nPk8yp6LuP02Vx9jjcYkO/OmpwMTJ1LwSw49GM3Gn976IwDgllP/gJz0aoRCwIwZjMAoitJxRKPsJpLOQBEuMl2+rg7YuLFja15UvDTD5s0s0q2v3/62/QbMw8DCtQB4trjXXhQsiQR/79GDLWRK65F6is6ynd5TaI/j3JK0kkRjJI3kcDD6csghzKHL/Z+eeSWWbhmOwsxy3HTKXUgk2Nnw9tsMWSuK0jHMnMkOo+TvC+v/dXU160E7ChUvzbB0aWpzLLsthv9edRGW3TccJ419Fz4fxUp6OgVMZiYLC5XWIYpe/hna0sJeMXTEcW4urWSNxkgUJieH0Ze99mLti8MBOJxO3PTyfQCAqyc/hME91iEcBmbNYvdRRxYHKsqeSlUVRUmqzIO1SL++nif8HYWKl2ZYty719Rcf9jT27b8QdaF0fL3iYOTksKsokWDou18/Hb7YWmRRi8cbm6JJblUFTNvQGce5qbSSnLXFYhQwI0Yw+pKXZ7qXPll8Ij5ffBQqavMxoGAdEgmmcN9/n3VoiqK0LytX8n8u1XdDImH+p+Pxji3Y1VbpZti4cfvr0j21+PNZtwAA7njzVlTW5cGXza6Jnj3pC6PDF1uPtN9azc/EOC0e50W7tnadzj7O1rZreT4RVFlZjL58/z3P9ihqbLj8yadR7s9DKJbeYFw3Zw47AAcN4v+coihtz9atTAdVVqa+PZHg/2MiwQhrVlbH7VuHRF4effRRDBw4EF6vFxMmTMB3333X7Pavvvoqhg8fDq/Xi9GjR+ODDz7oiN3cjoyM7a+78aT7UJRbglUlQ/DotF8AYJFhPE6HwUGDmDaSgia97PgigwGtFezWSzxuTNGaegxZCPeEi7VWpbUXibikQgRMR0S5UkVj7HZgyBBg0iTaDIhhXYm/HwLh9Eb3r6gAPv6Yqd3k2UmKouw6iQSbVsrKmi/Ele8Lp5MjcTqKdhcvL7/8Mq6//nrcfvvt+OGHHzBmzBhMmTIFZU30O86aNQvnnXceLrvsMsybNw+nnXYaTjvtNCxatKi9d3U78vMb/907dzNuPJE5+N++dA8iMb6jPh9dQr1eHb64KzS3qO6IXVnQu9tlZ0WPVeg1JQBbKwhbsr87wlobk5PDerFRo/h/Ja3VDgcQj8dx/sRnccJ+HyAaBX78kdGXYLBr179oAXrHoce67diyhUW6S5a0LBrr9fIEvqNod/HywAMP4IorrsAll1yCkSNH4rHHHkNaWhqeeuqplNv/7W9/w3HHHYcbb7wRI0aMwB133IH9998fjzzySHvv6nYUFjb++89n3YI0TxBfLTsEb8w5veH6tDSKl5EjjVeFXlp/sc7Nacn1u/NFUjm7cmmKpr7Yk69vKyG1o8ibVQiJM/WBBwK9extPGIcDuOqox/CfKy/Eg1N/CbczhMpKDm1cuDB1R2BnowXoHYce67YlFmOR7saN9HVZsKD57xSbjYX2tbUdt4/29nzwcDiMuXPnYvLkyeYJ7XZMnjwZs2fPTnmf2bNnN9oeAKZMmdLk9qFQCH6/v9GlrWh8NpfA2q2DUFufjhuevx+AeScLCniWuO++bbPo7GkXWZyAphdhh6PtFvXucGkLASTDDeUis4pS3SbHV1qc20JctZRkQZSVxbqxUaPYwSf7/tysi7ClsgiDe6zBLyY/iliMX6offMCuwEBg51OKuxo9SvWatAC9Y9Bj3fZs2MCI5ooVtCeQNmkAOHLk57jhhL/C7TRTGBkZ3Y3ES3l5OWKxGHqK7ez/6NmzJ0pKSlLep6SkpFXb33333cjOzm649GtDc5UtW6x/2XDHm7eh7y83Yc6aAxttF41yarTYmyutx/qFI/8k1hoNe7t+UvccdnScpf6kLcRVKoGULJZSXVwujgsYNw4YOJDhaKcTCMfTcfvrfwYA3HTKHchLr0B1NTB9Ot2vpS4qmbaIILWkdsu6vZz5W4+vHG+JEOxq+lAhcmzlsw2Yz6gce6XlhMPstF21ip/TBQtMm7TdFsNDF1yHv069ETedcnfDfaJRGtXpVOlWcNNNN6G6urrhsjFVi9BOUlOz/XXVgZztrotGdfjiriILp/ULR76QrO21yq7RlY5zc+IoN5fiZcQIpmIlavT87Iswf8O+yE2vwq0/+RNiMRbtfvIJIy+RSOPX2FHRI6CxELIKl1Sioy0KzTuzyL6zi9atx0CEYFMRNeswUL3s+LJ6NSMoa9fyfysYNGlZsQmprMvBwx9f0+jzHwzu4hdCK2lX8VJQUACHw4HS0tJG15eWlqJXr14p79OrV69Wbe/xeJCVldXo0la0tHJ6n32M1bmy88iiI8WbyS6tStvQHY6zw8E6srFjTfTFbgfsDgd+8+L9AICfHfkP7NVrBWprgS+/5IC4SMSEuDsietScSEp1mzWt0dJLV6Szi9ZTCcLWXK+X1JdAgHUuy5ZRsKxezZ92e2qbkOTPxNKlHfcZbFfx4na7MW7cOHz22WcN18XjcXz22WeYOHFiyvtMnDix0fYAMG3atCa3b08OP3zHosRuB047rUN2Z4+hq39x7y509eOck8PuveHDGYlxuXj5cvlkfPDjCXA5o7j7nN8iHmdufvp0tlDHYgxhpxoM2R40JzpSCabW1jS1REDtyqWji9Hb49LUMW2r+rE95bJmDf93SkpYCB+JNE4FPTPzQizeNLLBJkQ+40JHju1o93jB9ddfj4suugjjx4/HgQceiIceegh1dXW45JJLAAAXXngh+vTpg7vvZv7s2muvxeGHH477778fJ554Il566SV8//33eOKJJ9p7V7ejXz+2SycFghqRl9ex7WGKsqcgvhFjxjCE7ffzy9TpBH738n3ITa/E3z6+HjYbQ9ZffAEcfDBw7LHcTqJLsVjjxay9kQUzHk/9fJKm6yqisavsx84gUcTmjrUINKV5/H56uixeTGO64mL+X2Vk8GdVVQZ+99I9uOXVPyMacwFofMxtNqZ4O4p2Fy/nnHMOtm7dittuuw0lJSXYb7/98NFHHzUU5W7YsAF2yydr0qRJeOGFF3DLLbfg5ptvxrBhw/DWW29h1KhR7b2r2+HzsZNo2zZ+GSbjcBhXXUVR2p7cXHYdLV7McHZ9PReqFaUjcegdsxrmJsXj7JCYNo2WBYMGMfJitS6XBU6ETHtit5s6DIm6SGheC9DbFj3WbcOqVUB5OaOXP/xgIpdeLwemSs+MCBfApOYA2hocd1zH7W+HVGpcffXVuPrqq1Pe9sUXX2x33VlnnYWzzjqrnfdqx2zbxp99+jB0Vl5uzuIyM9nGabNxuz59OndfFWV3xOlkBHTffdkBUVlpoi/xuBEoTkcUwaATM2cy3Zubyy/caJQnF9YCTik6bc9ojDUiIM8LdGwEaE9Bj/WuU1HBy+LFwKZNjLwEg1zjxg+Zj18ddg1+5r9vu05bK4MG0R27o1BN2gzV1XzzZOii281LWhpz73l5/L26urP3VFF2X3JzWRQ/ZAgjnW638atJ8wRw+09uxY93joLHGcSWLew82rrVpJnC4eYHQ7ZXbUx3KIzeXdBjvfMkEhy+uGULTw4WL6a4t9kAny+Baw79Ncb2mYHrT3igycew22kuqa3SXYScHHq3yJleRga/OJ1O/oxEeHtOTmfvqaLsvrhcdNsdPZqdR5mZZqFKwI6pk57B3kXLce2UBxEKAd9+C8ydS1FSW8szSPF/sdvN4ibpBInGRCLtY2rW1Qujdyf0WLee4mKegC9dShETCjE9m5YGHD/mQ4zv+ylCETduevnuJh/DZgMWLWLUpqNQ8dIMI0Yw3+f386wvP58jA3r35t9+P29XjxdFaV/y8vh/Nngw//88HoqaSMyLW17ll+pvT7obhZmlKC5m55Gkeevq6Nkk3iuAOVPfUTSmrYWMonQlZAzA2rX8f1mzhuLF5QJysqL4v4k3AgD+/sm1WLd1UMrHkJqz4mK2WHcUKl6aYetWek3k5rIYsL6eZ2xeL0Nsubm8fevWzt5TRdm9cbmAoiIW7/bvz3oWSQ28OudczFl9ADJ9tbj99NsRDgOzZgHz5vH2RILRF78/dXqouWhMe6aVFKWzWb+ewn7lSg5glOhjWhpwzgFPYlDeEpTX5OMv792c8v7WAulwmCmnjkLFSzMEAuw2OvlkFiMB/BIMBtkeffLJjMQEAp27n4qyJ5CXR8+XAQPMFHevFwDsuPFF5uMvO/xfGNF7MbZuBT7+mEWIHg/FRzDIL+pUIwSApqMxyWklFTLK7kA4TPGybBnTPaWlvM7jAYoK/Lh0/G0AgDvfuR2heE7Kx7Cm6BKJpv+32gMVL82QlsYvx169gKlTgUsu4c9zzwXOO4/XezzcTlGU9sXtZsHuqFHs7svLM2Jj9mpOenfY47jn3F8jEmHty1df8cs1Lc04iNbX80u6uZSQNRqTnFZSIaPsDqxezVqXtWspYGTMQloacPqYJ5GfXoaVJcPw7OyfoXfv1I9hdTK225mN6ChUvDRDz56soN68mV9effsCe+3FnzYbrx86lNspitL+5OUBw4bxf7B/f7ZBezz8f7z5lXsQiTpxxIjpGFS4GhUVwIcfMuXLzgk+Rm0tv6hDocZ1MKkQnxBrWkmFjNLdqatj6cOiRcYAMhzmyXpWFvDU19fgF8/8Cze8+DBy8tw49tjURdBWM0afj5YGHYVO5GkGmw049FCKlGXLeLaXlsazt82bWcB76KFa2a4oHYXHw5OF0aNZIFhczEiKywWsLR+Ky/79H3y7+hCs3ToQiQS7jr74gmlfl8uEtsXDIhLhl6/LtcOnbkgrAcYUTc48RbiIf4yaoyldmZUrWaAr06OlNVoccrcUO/DvZZcjKws48kjT3Wc1a5V1z27n7UOG8ISio9B/rx0wYADTRKNH04xu9Wr+HD0aOOccHQ2gKB1Nfj6/KPv25f9fWpqJvrw4+6dYUzawYduqKuCDDzj7KB7n2aHdzi9r8aSQ31sTOUn2FWkqIqNdS0pXo7KSTSYLFlDE1Nfzs5qWBgzssQn+qnrU1PBz3acPxct33zWezyWffYeDFiJ9+wJHHNGxWQiNvLQAKRAsLWXUJS2Nb5JGXBSl4/F4gB49OPNoyxbWnkknoBTXOhzA+EHfYtHGfTB/fgamT2e6yeFgxKW21rSESkdSOGzESGtIjsgkR2WsqSkROepFonQGVkO6tWtZqCs+ZpmZCdw2+afomb4WFz3xPFZWHYJJk4A5c1jYa7dz7UtPZ4RG0qleLyObhx3WsSJdIy8txGbjl+TgwfypXzyK0nnk5/OEol8//szIMFEVux244/TfYuatB+HXJ9yL2lrg/fdpwiXCRorsa2sbi49odMfFvM1hrZERYWRNH0lURlqwpVZGIzNKR1BaysjLjz8yGmltjZ48/F2M7/8lCjLLUFrTH4MGcb2bO9d4mg0bxghLURFPIPr25UiAKVP4d0emSjXyoihKt8PrpY3B6NE8eywqYlTU5aIY+O5/M1iuP/6v+PcXV2LJkr745BMW3NtsFDuSLvL76ZLtcpkaGGtUZleQL3OJ7lgjMtZODev2GplR2oN4nPUta9fyZ0UFBbTbDeRkRYwh3bRfIebtjyOPBD77jFEam40nDAcdBIwdy/+1eJwF9Lm5tCCIRDr2M6uRF0VRuiUFBabrSKIvXi8FwNs/nI6Zyw9BmieIP51xC+rq2Hm0ZIkxrcvK4hd3PG7mk3k8RnDIXKS2iopY62Rcru1rZYDGkRmpmdHojNIWbNxIof7DD3TSjUT4mcrIAE7f93EMKliBMn8hHv/qdxg2jAJl1SqmZDMzeaIwbBjvU1DA33v14ufT5TLT2zsKFS+KonRLfD6aRO67Lx13+/Wj+GDxrg2/eel+AMBPD34Go/vMw4oVwEcfmVlH0SgFjMPBL+CaGn75yvwygH+HwztuqW4Oa8TFSioxk5xmkvs2JWhU1CgtIRJhxGXJEqaLamv5GfL5gMLsalxx0B8AAHe9+0dkF2Rh4kR6JG3dyv+HIUPYWdu7N//X6uoo+EMh/l1UxOdRkzpFUZQWUFDAs7+BAxmFycw00Zfv1x6IF2efB7s9gXvPuwHBYALvv09vC4Aixmbjl6/DQZFSV8cvdafTRGESCX75y5lqS4nHeZ9QiI8dCpm0VCqsnRzJYsYanbEKmlSixhqp2dOETVNCcU9n7Vp2yc6dS3sBSfGkpQEXjb8TeekVWFY8HO8tuwL77MP/kY0beSwLCoCTTuL/l9vN/6/evZkyKijgT/F5aW2x+66g4kVRlG6Lz8cv0H32Yfh7wACKDq+XX6i3vHo36iMeHDlyOo7f9z2sX8/i3UDApI9kYrzDQYERDBrfC2sURmpkWhKFEeEivi8iQqSFuiXh9eS2VJerZaImuShYhE2qiM3ussi3VijuSQSDNGr84QemgYJB46TrdieQ49kIALj9rb+iV5ETAwYA8+czxeTzAfvvz06iggL+34RC/L8oLORnsbKSxzk7u2MLdlW8KIrSrSksZKfDwIEMX+fkmOjLpsoBePjj67ClsghORxT19ax9mT+f95WFXcZ82O3M8dfXG5FijcLIfXZUCxOL8XbraAFJEyW3T7eWVKLGOsrAWvQrWFu4rREbETfJAscawUkWO11N8LSFUNydWbkSKCvjoNJt20xrtM8HVFbacNHjL2LSn+Zi0bYTsO++wOzZQEkJj2PfvsBPfsL/MY+Hkc28PN6/tpaPlZtL8S81ZB2FdhspitKtSUtjJ8SoUfSjGDCA9SuhENNAd71zC+5+9xYEwhmIxeiO/cYbLEDMyOCZqBT7xuMULqEQF3+PxwgQt7vxghgKpfaFEXHS1FmoLKxOZ9t1Z1gFUjIiNqw/k69Ltd3OPH9Tf7f0tp3ZXowArV47gDnO0WhjB+X2Fl+7+vituf+Otq2sZPrnq68YfQmFeB8ONKWJYywGrK3eH6NGUZSvWcPt8vJoULfXXtzW6WT0xeWi14tVJLrd/B9KJDqu40jFi6Io3Z6ePWl3PnQoxUhuLr+A6+uBQDijURQkFAI+/ZTeFMceawSLz8cv9UTCpB8AI2AA84UtAkYiFS6XESuyoDT1JW6dwtsRX/TNCRuhOTHT1O2p7t/U3+2FvFdSm5Tqdiki3dNaz8WQbv161rD4/fysejxAmi+Gi/b/I/6x7SqUxXqjTx9g772BWbMYnfF4gBEjgMmTmQ6qqWH0xdqdJxOos7N5EZNIFS+KoigtJC2NZ4UjRvDMccAAflmLjwsAJBJxTD34BfTILMGjn/8ar70GjB/PNFM4bNIvHg+3D4fNwmcVMNYojEziDYe5gErLKJ+v+UhIV1pMWyJwUtES0bIjIbMrQkeOcVNRLputbY/3rj7Gzt6/NfeTbbdsociYOZPCPhzmZ9jrBU4c/h9cf+wdOHv8f3DM39egb18XNm1iMW8iwTTsCSfw/ykQMBPcMzKYLpLPuZhC7urr2xlUvCiKslvQo4eJvtTX8wu3vp6RmEgEOGSvGXj6ygsQirjx1twzMGPGIHz1FXDGGfxiDwSY0xejO8CIEilStKaIHA5epFbEmkqS9utUJneS4uhK4mVnaU26qD2QOp7k4mXr7TZbY1G5JxCLcQ7fggWmNTqR4Gc4N8OPa4+6BQDw2BfXI7/QhV69OMC0poa1KwceCBx+uIlI+ny879at/KxL2kgsBoLBjh+ZowW7iqLsFqSnU7CMGMEv2/79KUZ8Pn6pzlh2OD5bfDQ8rjDuOPMmVFYCL73EM1RJOwSDfCyJtLjdpkU6HG48VVdILui1ihkp3AVMLYx1HIGya8ixbKood3cSiq1h3To66H75JeteIhEKOLcbmDr2bvTMKsWq0mH4cPUv0KcPsHAhi3qdTv7fnHyymV+Uns60UDDI/wEZrSHHPi3N2Ax0JCpeFEXZbejZk2eOw4eziDcvj6Fuutna8JsX70c8bsM5B72Mcf1n47vvgGnTTJeEdN4ApmPJ5TLdQdbbrUgqSc7wZbFMblEWD5eObCnd3RFxokKR1NezzuXLL1mcXl/Pz5vbDfTLW4srD30AAHDnh/cjK8eNRIIjNsJh/r8ccQSNH0Mh/i9lZfF41tfzd4CFvtXV5jOdlWWmU3cU+i+kKMpug0Rfhg3jGWH//jxrlLPFBRvH4L9fXQIAeGDqtaipiePFF+l/IbUu4oNhsxm/GLvdnN1Lq3QqHA4+jqSOrB1F4tOiwqVtEYEpEZg9XSiuWkXRMmcOBYYUlHs8wNUH/wYeVxjTl07GkuqTUFQELFvGIl2fj6L/lFP4Gc/OpvD3eEy7vMPB/w853iLYJdWq4wEURVF2ApuN0ZfMTH4R5+RQzGRlmajIra/fCX8wEwcMmYPzD3oGCxYAb7/N+4sPi6SP7HbTVmolGuWZaVM4naZGRsSLiJ493XekPRAB4/HwuHs8e6Zwqa5mGvTjj1n/JTVYLhcwuucMnDr2NcTidjzwxQPIzbWhpISTpgE6VZ9yCkVLejqFS3o6b5PjKIZ0TmdjUzqxBlCTOkVRlJ0kI4Ot0sOG8WyyXz/+LSZ0pdW9cOfbtwIA/nzWTYhH6vHqqyxu9Pn4GNboikRTgMZ+IuK421S3jM1m6mHkPjIrSUVM+yBRsj2txkVYsQJYupSXmhrzGXS5gFlLxuChj2/EUzOvRhVGIz2d3i+1tfz/2H9/To12u026SHA6+Xmvq+PxzcpqLFSCQf5/Wf102hvtNlIUZbfCZuNZ5LZtLN4NBhl9qa019v+PTLsGE4Z+i0c+uRaBkBerV9O4bsQIRlrEZVcmP0tYXGpepFVaBAyHQTa9P9KGba1/kU4m6VpSlF2hpIRFtx9+yJoUcY52uRgxCQSzcftb92LIEM4mWreO0RmPh066Z53F33NyGFWxtvzLqACZBxaPmzqjYNAIno5EIy+Koux2ZGRQsAwZwtB3nz78Uk5Lo1CIxDw4++HXMGvVoQAoQN58E/juOxMpsaaPAFPLApjuDfkir6/fcSRFRExyJEZm8ohnjKK0lnictS4zZrDeJRAwdSl2RFBbm4DNxghLVhY/12Vl/Ozl5wNHH01vJDGcs4rpmhpu5/EAgwcbX6S6Ov7MzDQGdh2JihdFUXY7pPYlLY1jA7xefnHn5xsXXREeDgeQm7YVmzcDr7zCs9G0NN6eXNvi9RphIw6j0mZtnYe0o30TESM1MeIEqwMFlZ1h/XozBsDvN943TidwxcQ/4JMbD8fYgT+id29+tjdvZjQmK4vp1eOP5++5uY1FSG2tSZ9mZRkzyKIicyko6HjhAqh4URRlNyUzk2eJAwfybLJ3b1P74nYb8fKbE+/CmgcG4Ii9P8aHH3IBkE4jYHtRIi3UYkpnjaRIBKUlWGtirMWlsZiZjGxt/1WUVIRCwNq1nJZeUWHSOE4nkO9Zg6uPvh+Thn2F0YPXweNhxKWigp/ZwkLgzDNp8Jiba2q+AEZW6uv5uxS8CyK+O7LGJRkVL4qi7JZI7YvHA+yzD7/Q8/IYkRFhYrcD+RlbkeYJ4t5zf4Xqqgief56+F9IGCjAMb7WZlxZqa82LpJSkILc1SNup2914wKCklGRqsqIks2oV8OOPnF9UU9NYUPz++BvgdYUwc+VRmF9xKuJxRhbF8v/AA4EDDuDvmZnmfoGASZlmZGwfWbG2pHcWKl4URdltycpi9KV/f35BFxaaL2r5Qr7jrduxtaYAI/ssxWWH/gMzZwKffMIvZ5/PRFnkLBRo3EIdjfI2ER/W61obNbG2/CZHYyIRYwSmaSUFYGHumjXARx8ZTxdJS47t9QlOHvsWojEHHpn9MGw2G8rKWMielsY6sLPP5v9ETo4p0K2vp3gBWC9mtQqIxcztcmlpurStUfGiKMpui83GvLzbbaIvmZn84pboiT+Yg9teuxMAcNtpt8MV24oXXmDbqc1mQunJ4wFkyB1g6lUkDdSaQt6m9tsajbH6xVjTShKR0dTSnkciASxfTifdjRsbF+nGo2HcfeY1AIBnv/slioP7oLaWwiWRoIA/5RR21xUUGJEcCrHOBaDAsaaRRLhIm7/MlQqHO0fAqHhRFGW3JjOTNS99+7Jgt6CAEZmCAgqNRAJ46svL8OP6/ZCTXo1bTr0NP/4IvPuuaZe2uu9axYjTaQSMeMOIqLEW8u6KbboUXooBm9jhiwW+pJZkCrYKmT2DLVsosGfMYLpIBIjdDpw//u/Yq9dylNcW4qVFt6O+nsKlro7/C6NGASeeyKiLNd1ZU8PffT7jSi1I5K+ujjUzW7fyp9TGdORoAEDFi6Iouzl2O6MvLhcwejR/ZmSY6IvdDiTgwK+e+xsA4PIjnsCQvPl48UVg/nw+hrXLyNo+DTQWNxKdkboYsayX6da7KiySnWStEZl43ESANCqzexOJMOry3nsUJZEIPwtOJ1Bbm8CJY2gZ/fev/oKaUA78fqaYXC7WfE2dykJ2q/D2+/m7203hInUtEumrqeFzWV12nU6KF5ksreMBFEVR2hDxrygqYmeFWKBL62giAXy98jC8+t3ZiMRcGN13HlavBl5+mV/6QOP2aWv9C2Am9gKm40iEQzTKkH5NDR+rrc5QrRGZ5BqZ5KiMNTIjIX+l+7JmDT2JlixhmkeiJ9EoEAza8JNHPse1r7yIz9deDL+f9TCRCDuKjjwSOPhgfl7DYX42t27lZyQe5/XyeRFTxkiEoiUcZlRGBo46nfw/ikb5+e7Iz5WKF0VRdnvsdgoVp5MTcz0eU7SYns4v7EQCuOH5BzD2liV4ZubFCIU482j2bN5mt5sagFQt0dbupLo645EhdTZOJ+9XVbW9+NlVrDUyXq+JyljFjERmpF5GojNWoaV0TRIJ8x76/cDChSzSraoy75vdboSM3eHE7C3noqbWjro63ic9HRg0CDjvPKZNRdxaIynScSST0a3zimIxfrZEvNfVGSHucpnW/o5CxwMoirJHkJ3NropolEJGCg379+fZZzgMlFT3QaKKQiAaBYqLgeeeY7qpb18TYZEz1oyMxjNeJH0khY3WGTBpaVwgpEsD4GO1xzC75CF51gJL+V2iM6nuK4tX8qWrI69NFt6ugggMq0BMvk7ek6a2FxYuBKZNYzu/dLnZ7UCoPoaLDv4XXv3+YuTme2G3MxpSXc3bCwuZLho1ip/vWIyfQ5l9JB1HTb3PLpcZsREOG6EuPkcdfby70NurKIrSfthsjLQ4nRQjHg/PRnv04Be3FO/KgnHQ0G9w6aH/xGefAZ99ZhZ6n6/p+hfAjA1wOrm4WCM0bjefMxYzZ7AdUegoM5SkXsZaM5O88EitQzRqipBDIe6rNZ0gERvx+7Aeu46mrVt45bVYBZ+1BkSOjxwjuYjHj0S2ZEaWNXUnF7mPPI4cx6aOpc1Gg7kff2TKSKIsIjbO2v8p/G3qz/HJjZOQnRVHZaV53vx8YOJE4PzzjSt0IMD7ejxMJ+1ooGUgwChNbS0/wzJV2u/npaOnSmvkRVGUPYbMTLaJxmKc5SJf7gMH8ks5EuGisU/v+Zh520REok7MXHkEnnlmBCZMAIYP5+OkpXF7ESBWL4xEgsJA5hbJ7VKX4HKZNBRgCmvbKwqTCmtawIos1tbFOzki0FKBIgth8s/k31Pdp6VI9EiEiqTK4nGmNaTzS8Rmqvun+r29aOqYNPfT2iK/fDk9iLZt42uU1mhbpBK3n3YzAODj5RcgWG9HfT2jLpIe/dWvzGgM8YRxOBgdbO64RyImUhiPs+A3FDKt2VlZJrrj7EBFoeJFUZQ9BpuNKaPKSobPN21i9ERaqOXsff6GMXj/x5Nw4n7v4f7zfokz/zENb79ta+jQkPqXQMD4u8gXtyw4ElYXASNFtTJ3xus1Akc6kpzOzpkTI0jtTCpaImiaS4u0F5KiE8EoHS8Oh3E6torL1tCU4Er1+86Itdawbh09XZYt42dWombBIPCnU25HQWY5Vm0diTeXXM0p0v8THAUFTBeNHNlYuNjtjdOaqRCREo0ah+qKCh5Xm82kQmXgqRTxdgQqXhRF2aPIyGCYPBoFhg7ll384zIm50lERiwHXPfs3HL3PNBy9z2c4YdSrePHFs3HYYQy/A03Xv8gZqAxuBEzXD2AiM7KtLLKSipAoTFMiorNoad1Lc3UbrYl0pLo9+fklSiSpkOTtJAqTqiajJcKkqxAIAF9/DXzxReMUTTwODMlfgMsP/wcA4KGvHkZpmauhw62gANhvP+CSS3ic/H5+xmy27adHp3pO+cxKRIvdTEawS9opK6vj04Za86Ioyh6FRF+cTp6NZmQwipKZyeulo2hd+WDc+95NAIB7z/0VSjfV4MUXKXAEa/2Ldf6RhPOlsFEiLjU1FCfW+TOyAIgzbyJh0lndcQxAcqeKw2EuEqGSItHmLuIubL0kbyOPJVGt5OtFBCbvh1wnl65elDx/PtNFGzeadI/dDgSDcTx4/s/hdMTw6YozMHPV0QgGKVJ8PloD/PrXjDzV1BgPouaEi3xORbjIZ7W2lqm43FygXz+monr1Yr2YGNV15PFT8aIoyh5HRgZTRTK0UYp1Bw/mF7ukbu597zdYUzYYfXK34NfH34F33+XZr/UMU/xfYjFTwCu1FpLKkGiMRFxSTZ52OLjgyGJhtWPXNubUiOBoSuTF411blLSE0lJg+nRg3jyTLrLbKS7OPeBpHDRkFgLhdDw04yGUl/P6RIJRl9NPB8aNoxiRVE9WVtOpnViM24pTs91u2uglOmiNcrnd/FuKj1W8KIqitCMy88jlomDJzaXY8Hh4VimCJBzz4brnHgYAXHPMg0iPr8Tzz7P+QLDb2X0BNE4PiYBJS+MlJ8ec8UodTCpR4nKZiA5gQvVSTKwYRBSmEoOAqcHoSm3TrSEaZZ3L55+z1kSEmETnvl59JD5bdhKemP1HLF3ft0FA5+RQlF9xReMoSmZm46ifFXHRFT8geZ5wmI9pt1OsVFczyijdV8Fg45qXjkJrXhRF2SPJyGABYiTC1mlpLR0wwDiOBgLABz+eiKe/uhQzlx+ClSVDUPIF8MEHwOWXm5oWiZoEgxQlkiIBGi+ckqKQFup4nPdLPmOVVJIMvpPCXlmMrS2yezoul7Gwt3YbRaOmPby7snQphcuqVaa7yOUyVv6ltYPw63ffQTyWaPBg8XiY0vn5z7mtGCJmZTVdDC6fW4mgOBxmqKgYOEq6ST6Tsq1EC2WidUeh4kVRlD0W6Tzq35/5e2lbHjyYC4TUnVz+ryfhdvO22lrg2WeBAw8EDjjAPJbc3pSBneB08gtfFodAgH+n2la6ksQNVUXM9kiEy+o9Y7OZGpmuVvjcUmpqgI8/Br75hp8RqdUJBgGXPYi4w4e8PACwoWyrrSHVU1gITJ4MTJhghEtm5vbCRdrM/X7TZSft5oCJREp9TV4eP38ZGSZNJ35GgYDZtqPopsE0RVGUXSc9nV/Kdju7MuQMsm9f+ln4fCZVI4WS6W4/1q2qxauvmrlHgrWAt66u6TSPRGpk8nQw2HzIXRZoqTdIJLhYS6fUnp5OSk7RpaUZf5fuSCLBdNGXXwIlJbxOPlfRSBSf/mYiHr3gUvTMqUBlpYmCZGYCe+0FXHSRSRVlZBhhLWnNYJAiXCZDl5ebiejitpudzXRqXh5/ygTqSMSk6ySKmEjwuVW8KIqidBC9e/PLvbCQKSOvl1/IQ4fyS9vtNgMZjxvzMebfNRK3nnwzXnsNmDFje+GQnt7YKK0pxCtGFiUpzm2OZBEDNG5f7cjZMl0R6SrqrjUuwtq1wKefAosWNY6y1dUBVx35MEb3nY9j93kb9fWJhg42t5ui+8wzKSSk003EsXXSeDBI4VJWZqKE2dkU7L17s5g9I8O4RQPGVddmMzVb1u4lqfvqKLr5W6woirJrpKVxRIDNBowdazqEevTgF3lGBrez24H6kAN98zbj50c/gj7eOXj22cbFuwAfJz3ddCCJWVgqbLbGHUZSHLmjSIpVxMjZbixmzqq1uLf7EokA778PzJxpRgDIqIleWZtw04m3AwAe+uIerNpYAIDvdX4+h44efLDxGBLxAhhBV1ND4SLpysJCftaLiswA0VQ4HBQpImzS0kzX3o48Y9oDFS+Kouzx9OplzLaGD+fvfj8wYgS/nMVW/bPFk/HC7J/Cbk/g7xdcia9mRPHee9tHTOx2frkDZgJvc1ERj8e4wMqZcUs8XhwOc19ZdKwpJY3GdD++/JLt+Bs38m+n00Tm7jrzV8jw1mL+5ol46stLG9JFGRmMupx/PsV3Roapc5G6n2iU6aGKCv6enk7BUlTUdH1WMg4HP9fZ2UwnZWebTqOORsWLoih7PF4vv8QBjg2Q1I/Xy9Zp6+C6G567HxW1edhvwI+46KC/4Zln6MGRjLSa1tZy0di2zRRFpkJs1iXlFAi0fGijtLGmpTWekWSNxohPh9J12biRUZfvvzfFsw4Hxe/kfT7Cafu/hmjcgZvf+idqavgme71M95xyCtujpUYFMEMia2s5Ib22lp+Pnj1ZpJ6ZuXMF31azv85CxYuiKAoYOvf5GMkYM4YLR2UlXXglVA4AW2t64KZX7gMA3HbabagrW48XXqA4EWIxU7ArgiIcNi6lTYkIqYORKIoIj9akgJxOLmher6lZSCR4ti2PFwoZIzKl4xGPFCmgra9nOufVV1lHVVvLz4K0Onscdbj/nJ8DAJ6f80t8t2JMwxyq3FwK7jPPNI7CdXX8vEWj9GWpqOD1mZlsoy4o6L7FzEK7ipdt27Zh6tSpyMrKQk5ODi677DLU1tY2e58jjjgCNput0eWqq65qz91UFEWB280aFwDYe2+ewcriMWwYBYyIgae+uAQzVxyGdE8Afz33F3jrrQQ+/dSkeiRd4/E0joZIJEQ6QVJhs5l6FsDUzbTWAEwWP5/PpJVEyEhLt9XfozuOIujKyHGORk0EJBjke1lf37iANh6naJk5E9iwwfj8iBndsF4rkO4NYEt1f/z5nTsa2sF9PqY8p041URr5nESjTH1GInysjIzG4y+6O+0qXqZOnYrFixdj2rRpeO+99zBjxgxceeWVO7zfFVdcgeLi4obLvffe2567qSiKAoDFixJhOeAALgg1NcCQIbxNwvGADVc99ThCETeqA1nwV4bw9NPA8uXmjNpqjmYtypVOjR2lcFwuk0aSmoemXHl3hKSVRMi4XCa1JN4x4vVhXVQ1MtM0iYSJoFgFihxHiXBJBMR6PGX2k8x4qqigGd3cubxdIiiBALddsXUsfvLkUlzxzFuoqOYH1OlkS/NRR7FQV4rEZfyERPgcDgrxHj26f7TFSruZ1C1duhQfffQR5syZg/HjxwMA/v73v+OEE07AX//6V/SWU5wUpKWloVevXu21a4qiKClxOnl2umIFa1369mUdQlkZU0k1NSb1s3TzcIz5/TKsLh0Eh4NmYq+8Alx3nXFDtZKWxvtFInycjIwdLyZS+BsON14EPZ6mu0J2hAwjlE6UWMyc/Vv/FmRBdDjM73uCMZ5MSbZe5Bi1dIKyHKvkn9bjF48Db7/NIt26OiM0JXrndDLNU7ItD0u25SGR4HXp6SwuP/98ihi73YjQ2lqTsrTO6tqdaLfIy+zZs5GTk9MgXABg8uTJsNvt+Pbbb5u97/PPP4+CggKMGjUKN910EwLN9BqGQiH4/f5GF0VRlJ1FWj9jMbqUynTo/HwKm549jZfIyuJBDQZggQDFy1dfNT0sUCIpNtuOO5CsSDFuchRmV1M94pDq8ZiojJz1ywJrTTNJ6kPSTWITL6MOunqkRgSINWISiTSOmkhqR16jRE+sKZ7kCIqYu8mgQp/PGOVJy7KMLkgWftOnM+qyYQP/Tk/ncwSDwGWH/RNTD34BkUgCfr+JpIiny+WXMyoImP2uqeHzSUfQ7ihcgHaMvJSUlKBHjx6Nn8zpRF5eHkrEMjAF559/PgYMGIDevXtjwYIF+O1vf4vly5fjjTfeSLn93XffjT/+8Y9tuu+Kouy5OBwUKTU1PKMdNgxYtowRmAMOYIg/N9cMyotEgD55xXho6i/w6Oe/wgsvHIphwximl7oVQWzrJfpRV8fFqiXh/FRRmGiUj9dWC5Tsl7XtWhZ7uSRHIlIh0QVrlCH5d+vP5N+TSRZF1r/l91Q/U/3eGpJfhzVy0hbTqrdsAd57D5gzh/sn7fJ1dcCQHivx5zOuh89Vj6n/zsf7pVMa3puCAuD444EjjzRRFulky8jg507SSLsrrRYvv/vd73DPPfc0u83SpUt3eoesNTGjR49GUVERjj76aKxevRpDRGJauOmmm3D99dc3/O33+9GvX7+dfn5FUZTcXAqXbduAgw6i42ksxjPx4cNNaF5mH/32pLtw2vg3sU/fRTjmwfl4910fLrqIj2Xt+IlEuPhkZZm6l9YIGIBCxek0aQURMnJ9WyIdLcn7JiLGKmasEYmdFQu7ijWS1dTxTBZTzV3ak2gUeO01Rl1kdpHbLaMiEnjo/J/B56rHzFWT8eGPxzYM+0xPZ0v0BRcYP6Bg0HSZSQRtd6fVH/UbbrgBF198cbPbDB48GL169UJZWVmj66PRKLZt29aqepYJEyYAAFatWpVSvHg8HniST28URVF2AbudYfnqai4O++8PzJ4NlJaydXrDBi44mzdzwfz9K3fg1P3fwLBeK3HNEbfjxZfvxZgxdDuVTh67nWfE4oqbnm5SR60VMNJSLe3P8TjFkDxHexdmSrFvqudJVStivV5+t26f6jGspJq6bf1dRJw4C0saR+YbdZQgaQ2ffgp89BGwaRP3OSPDDPW88JD/4NC9piMY9uH6lx4HwB13uViLdcEFbHuW1JdMJk9P795TtFtDq8VLYWEhCgsLd7jdxIkTUVVVhblz52LcuHEAgM8//xzxeLxBkLSEH3/8EQBQJA5SiqIoHUBmJjuMSkrYzbFkCX1fios5Udrvp7jx+4HqQA5+8d/H8eZ1J+PaKffj7Xln4ZVXDsDIkXyMRMKkjARZbKwCJi2tddETaY8Vp1U5Cxfn3c6Y8dPRIkHazxMJM+zS2kElhoNdhWiUBeFvvAH88IMRLmJMWJRTgjt+cgMA4P5pf8Lq0sFIJPh+5uUBxxwDTJpkBJ446LbUJXd3od1e6ogRI3DcccfhiiuuwHfffYevv/4aV199Nc4999yGTqPNmzdj+PDh+O677wAAq1evxh133IG5c+di3bp1eOedd3DhhRfisMMOw7777tteu6ooirIdNhsNvcRg7tBDuThUVjKlNHAga2OkEPOduSfhpW+mwmGP4x8XXoLPPgnh+eeZepIupdraxn4tImDEAl66kVq7n1LQK2fdUkAcDO7+rrpWTx1ZvCUCJcKmM5F0oaQat20DXn+dhboyPNHjkTRiAvef93PkpFVh4eb98fAn1yEeN23uY8YAF19s0pDy3mdm7lnCBWhnn5fnn38ew4cPx9FHH40TTjgBhxxyCJ544omG2yORCJYvX97QTeR2u/Hpp5/i2GOPxfDhw3HDDTfgjDPOwLvvvtueu6koipISMQEDmEYaMIALzbp1wMSJxvhLog3XPPM3lPp7YJ++i3HZxLvwxhtsoZZUjixgqQSMCI9AYMfTpVMhxmbW6I3MSWIdxS4dii5JKk8dKy6X6RLqSCTyEwxSkFodjT/5BJg2jSLGbmc0Rd6j8YPm4sR930I46sIvn3sK0Zizoa19yBDgoov4mROTuvT03b8wtylsiURXb25rHX6/H9nZ2aiurkZWVlZn746iKN2ccBhYuNAURz73HK/r04cL0HffsaC3poa3n3XQa3jpF2dhRfFeOPiuBTjlNA9uuIGzZAAKGK/XmOFZkRlEQGOX3Z1BFlBrJEcWQnHb7e7EYkzbNXWcEgkez6ys9q0DsvrjpBq7IF1CixYBDz5IAROJsP1eLPwlNXT0qOnok7kc//7yqgavlqIi4JxzgMsuM0XUramR6i60Zv1ut1ZpRVGU3QG3m0Jl1SoKin335SDGzZuB0aNNJ9Ly5VyQXv3mTOSkPYkXZ52DcNyDTz9lu/VllxmnXRmSmLz4SOGleI7EYua61iKpE0l7iReLjCcQEdPdF0CpcUmVNkkk2i+dImIl2dRPkO4gEYoVFcBbb9EHKBLh++pwoMG/RVreZyw/EnV1RyIep3DJyODn7IwzGqcIdwfxuSvsYVkyRVGU1lNYyLP3cJjFkhKq37wZOOQQLihFRcYH5N/TL0Uolo5YDNi6FXj3XS5assg2Z+gm7a4AFzkZ8LizWNNJ1roQSWtImqo7zjaSrqKm6oQkpdQWAs06D6qubvtJ3dZZUhkZRqjabNzu3XeBd96hWHG5mC6qqaFIPWbUxxhUuAZ2O98PwIiZwYOBs87i9ntymigZFS+Koig7wOFgzYvdzqjFYYfx961bedt++3FxkTNiaQu22eK46siH4fTPxbvvMnoTi+3YYt/tNotULMY6mV2t27DZzLwk68Iaj5sW3e4oZKSeSFrGxXMmFDJdV60leXhlbW1jsSLFsuJOnJ5uxGGyUEokKFxfeolt0QDFcE0Nj3ef3M148pJzMP3GfTEsf26DuPR6aXR4xBFsuc/M3H3dcncGFS+KoigtIDeXAiUWA4YONZ1IGzbQNCw/n2fJ4isSjQI3nXI3Hph6LR676ALM/DKI99+nV4xY8DeH02naX+NxLqA7U8ibClnU09ONFwqwvZCxFpp2VaT+Q7qLRGDI62tJ1EUKf2WoYqrIilWspKWZYycisCmWL+fYiAULeHxzcnhcKUgT+NvUK5CdVo2VZSOxaPMYRCJ8/LQ0iuILL2z/mp3uiIoXRVGUFmCz0SDM5eLCc8wxFBb19axnOPhg3tanj1nM/vHJz1BS3RMjei/FLw/7Pd5/n91HLY2i2O0UMNJNI2methQTTicjMSIApFPJ6pVSV9e1xYzDYepDsrL4My0tdRREJkCLUJGoirw2iTpJkW0qsdLSOprycvq5fPopn8/n48Xv5z5cdMjTOGafDxGKePDL559GKOxsELbDh7NOqqhI00SpUPGiKIrSQtLTTeu0z8czY7ud6YDMTGDUKEZgxHejorYAVz75JADgmmMfREFsOj77DFi5kotZS7DZzJA/gIKiLdJIqZ5HXGlTLdTJYsYanegqgxllYCawvUiRfZaBklahIlEV8VORNFBrxYqVUIhdRa+8Qm8gh4Ofjaoq7sOgHmtx15nXAgDu+egOLCseCYBiacAA4NRTORhUSY2KF0VRlFZQVMQFrr6exbpZWVz8NmxgV0h+Pj05JJ3w/rwT8eSXnNn2zwsvwtzZ1XjnHaCsrHVpIOuwPUkjtZcBW3MpElnIrXUhyQLBOpFZpk7LRObWihzr7CRpRZZp0MmToJOfP5WwkoiK222Emry+5InaO0siAcyYATz1FIu6EwnWuci+2W1R/OvinyLTW4Nv1xyCf3x+fUO6KCeHn6uzz961fdjdUfGiKIrSCtxupo9sNi5ERx/N68VJd+JELvJ9+5qF8FfP3o81ZYPRL38jfnfstfj4YxqV1dS0zlHX6WRUR1I7EgVp7wLbZDEj3TQej+nmkQVfRE1TAkNEhjgOy0WER/Jtcp04BtfXNy2MRKCIF4rLZUSK7LdEVGSQZVunZMJh4PvvgWeeoa9LNEpB63Ix6hKJAJcd/m8cOHgW/PVZuPr5ZxEKOxr2a//9ObsoPb1t92t3Q8WLoihKKyko4BlyNErzuSFDuBBu3gxkZ5sITFYWz/TrQhm45IlnEIvbce6E5xGpWIGPPqJfTF1d69xvxVlV/F+i0bYt5m0pIg48HpNqkf0Sgz2rl8yOOqySBzkmY7PxMex246Eizy/t5SJQZD/E50ZGOLQXiQTFVE0NfX/eeIORl3CYYjMvjzOyolHuyytzL8U/vvwdfvfaP7CmbGBDJGjYMOCUU4ARI9pvX3cX1KROURSlldjtjL7U1PBy3HHAE09wsSor4+JTUsJowKJFXNxmLj8YN7zwN8xeORFLN++FLbV8jKIiFvlmZrauo0QWZbH+l/oTMT/rDCTi0RKamyzd3BTprkTyNOuaGta5vPMORanbzRqpkhIzPNLjAZxuN/709t0N0SKppTrsMODEEzv7VXUPNPKiKIqyE2Rl0YdDogWHHspFtqSEC9qECRQS/fubtMojn1yN+RvHIRrlQvfxxzQvq63l360twrXbG0dhxBOmvr7zi2d3hMyDskZU5GK9rTXCRdJV7T3LSMwDJeIlNTmzZwOvvkr/H5uNwrS6mu9tNAqcMeFNpHkjCIV4eyRiioPHjwemTjWF2UrzqHhRFEXZSfr25WITDLJWQTqRNm3iorTffkwf5eaaiEQ0ygV6ZNGPOKr/E/j8c+Czz3bNjE4mC0tLtaQwOjqV1FlI6qy6mnUl1dXbD8DcVcT4Tszl5LHF+G/hQhrRrVxJMSPCdutWipQzJ7yB/1xyOl676jA4bBQwXi+jZ/vtB5x5ppl/pewYFS+Koig7idvNtlabjYvmSSdxMaqtZXvsoEEUOP37c6Gy27mgDSlcgZm3TsBDU38OZ+VX+OorYO5c00W0MwJGWqrT083zBIOtLwrubohwqa835ntNTfDeGSTKIlb+8bgZuZCZyWO+ciXw4ovAnDncPieHtTebN1NADu6xDg+ddxkA4OuVRyBQ74LDwc/P0KHAkUcycqe0HBUviqIou0BBgXHeTU8HDjyQi9umTVzwhg3j9f36mcLV5cV74ZXvzoXDHsfjF07FtzO24YsvuNjF4zuXQhKkI0lSSfG4MWPbHUVMfT0FijgFS92N18vrW+qnYyUW4/38/sZRFqeTYiUz04jR4mLghRdoRBcM8r0uKOD7X18PuBxhPH3FuchJq8Lc9RNw38d/RCLB+xcWclbWmWeqg25rUfGiKIqyC9hsjL643VzsDj2UYiYYpPeL1DPk5nKxkpqOq59+FCtLhqFf/kbcdepl+PDDBD780DjoSp3EziKpJI/H1MMEArtXOkkKZiVdlox1gveOsKaFxENHZhhJlCU9vfE4gOpqRlzee4+RN4+H73FJCT8L8Thw97k3Y/8B36IqkINfvPASaurcDa3bBx5IP5fc3DY7JHsMKl4URVF2kbQ0dgwBXMQmTzY+MLW1PBPfe29uI/OK6kIZmPqPlxCOunDy2LdwVN9H8emnLOKVIuBdjZbYbDzDt4qYeNykk2SB7q5IoWxTbdA7muAtgkUKpiUtBJhalqwsE2WxUl/P4tw33gC2bGHkJDeXKaaKCj7nmRPfxVWH3w8A+NUrT2PlloENHjP77ccutX32aZtjsaeh4kVRFKUN6NWLC10gwJTN+PG8vriYEZQhQ1j/MmAAhYTdDsxduz9+99J9AIC7zrwB0dI5+PJLYOZMLp4iYHbVSdcqYrxeI2JSpUa6E9Kp1JRJnwgba8eSpISsgkUiMzLnKSuLwqWpiE4sxnbo118H1qzhc2RnU8AUF/N9y0yP4M6f/BIA8PiX1+G9eac2+NMMGcK26GOPbcODsYeh4kVRFKUNcDgoTBwOFuseeig7jSIRnpnb7TzLLioCevc2xml/+/gavPn9T+BxhTF1wr8wbRrw5ZfA4sU8QweMu+yuIikQWZzFqVeKUv1+Pk97txq3FVL02lR0KhKhAJFok99vxGAqwZKezsfbkZnep59yZpE46EodzMaNfC6HA8jIcuGSZ6fh+W8uxV0f3IN4nPvSqxfrXE47TduidwU1qVMURWkjsrMpTrZto4A58UQWc/r9rI/IyWH6SKYYl5YC0agNlz7xH3y17HA8/Mkv4fEAH3zA9FJ+PqM1Yosfj3OhbAvTNpeLl2SjtXCYF5kBJJeuahRnLcyVehSZtwSYKJMgow7E/be1r+u774DnnmN3WH09BU9uLgVqJMLH69mTz7lw3TD8dtOTCAS4n1lZFC6nnGLSjMrOoZEXRVGUNmTAAIqO2louVgceSFFQUsLFLTcX2GsvYOBA3u5wADX12fj7tGsB2BEOs1Pl88+ZlqiuNnNuwmGmOtpylpHD0TjyIAIgHufzBQIUXzKRORrtOnUysh9uN/e3qgooL+ext9n4umQ8gQy2tKaEWitcFi8GHnsM+PZbPofHw/e6spLHx2YDLj7yJUwYMK2hdTsYNLOUDjiAs7DGjWvzQ7HHoZEXRVGUNsTlAoYP5xl6SQnFy5o1rIXYuJEt00OHMuIRDAIrVhiLf5sNcDuC+PvUX+CjxT/BN9+cjPR0DurLzDQeMH6/ERptiURZZLiiDD6Mx800Z6m/kRlD0v4tP9sDmUYtF+uEasHjMfsus4LaMmq0ciXw4IMculhZSUHSowePx7Zt3GbS8Lm45yeXwOUI49RHZuDb1Qc3iKTRo5lKPOaYrhvF6k6oeFEURWljCgoYXVm5kg6rU6YAzz/Ps/NIhGf/w4YxkhEIAOvXm8X4mikP4+LD/oPTxr+BY/76Pb7PHor0dOCcc3g/GeRYW2uGILY1klqRmhireLEKh3h8+3oTq91/Uzb/1gnUyT/lIl1CO4oyyXNJhMU64botiMUoPu+7D5g/n6k+ibgAjJIlEkCf/K148uLT4XXV4+NFJ+GHDRMbjsWgQcAhh9DE0Odru33bk1HxoiiK0g4MHMgUzw8/cDEdNQpYsoQRGGm/tQoY6Up68MNf4ZT938bEYbPx7OUn47R/foOsrGxkZrJWIiPD1MwEg2Y+TntOTbbbGcmQAmKJzMhFhIwID7m+rffBOlVafrZXFEO6sbZsAR54gPb/GzZQuOTkGGfdRALweqL4zxXnonf2BqwuG4brXn4W4bAdbjeF7OGHU8AWFbXPvu6JqHhRFEVpB+bMAf79b6aPpLDT5+Ni5vGwXTYnhxEaESMVFUAs5sZZD7+O2X84EMN7L8PDZ5+La954Fx6PE+np9JCRTiFpcfb7eZ2Ii/YmOTIjSKTEKmSsF9km+bGsP5MjNiJYOgrxfpF00IMPstZl7Vq+bxkZrFtasYI1QQ4HcP9Pf4MJAz5HXSgdlz/zJsqqchqM6A4/nFGXMWM67jXsCah4URRFaWNmzwZ+9zumG8ThtbKSi/GGDdwmM5Nn4oWFbKEOBNh66/cDZTVFOONvb+OL3x+CY0d/hF+U/Ab/+eyBBuO0SZNMTYekkSQl5fN17GJvRaz5u6PVfSJBkSnGfdXVwMMPA0uXMsIiBb9FRcCyZRScDgdwzQn/wk/HPwgAuPbFp7Fwwz4NPj6TJnG6+FFHaZ1LW6PdRoqiKG1IPM5Fb9Eidr+I3XxNDQVGVRXTR6Wl/NvhYGvtvvuyNiI9nQvd/I3745InngEA/PKYBzGpx78xaxY9Rr77jkLFbjfGcwCFkt+/66Z2exLiAVNdTfGSSFBI/v3vTBUtX06hKC7Kq1fzfbPb+b4NK5gHAPjLB3/Eu/PPbCjQ3X9/4KCDgBNOoPBR2haNvCiKorQhCxcCX31FwSLFppI2kY6iigpenE5g5Ej+7N2brrwyEykYBN764Uz84fU/4pfH/g3LNg/BvA0UK1KDst9+7Djy+fh3XZ2ZYRQOc8HtjlGQjiAWM+khweHgcbv/fuDHH4FVq3hcfT52ia1aRfFps7Fg1+EAbnzlUbw/7wR8sfLEhlTX0KF00J0yhXOulLZHxYuiKEobsmQJO4zED0XSBdaumZoaCotIBFi3jvUvXi8XyAkTuKBu2cKF9O73bsVTMy7HxvLecDg4OiAjgzOQHA4WArvd/D0ri9ED8WPx+3nWn2o2z+6I9Zgn1+NYt6mvb9wl5XTyGNXUALffbmpc5Pq+ffk+SUt0v15VcHkzUFLmRChkw5erTkI0yu179qTt/6GHAoMHt/tL3mNR8aIoitKGiNNqUwWqAIVM3778vbycfjC9elHQ9O/PdMOMGUBZGR14y2p6w+lktGBA9kLM/6YAXm8RPvyQjzFypEkdiSFaMEjxEwrxp4iY3bH2IhJhtElciO12NBTMyowoORbWLiiXi9s5nXzfbruN0ZX163m83G5GxDZv5vuUSABFPQL47yUnoaouE5f8+2UkPFkIh80U7+OPZ8rogAM673jsCah4URRFaWNa4kAbjQLnngs88wzbpDMyeMnJ4Rl7IEAn1/JyMy/n4L2+wtu/Oglrtg7GGf/4Em53Fj76iM83YgQXa4CLd3o6F+BAwAwjDIV2PxETiTCVE42agZfxOF93IMBjYu12stkoNDwek1JbsgS4+26Klk2bmCZyOFicW1IiIhLIz4visZ+eg7F9v0Z1IBv9C7Zg6ZashsebMoVmdFqg2/7sAYFERVGUjqOlJmSJBGchTZ7MhXLtWhMVyMlhOmjffdmWKymfUn8fBMM+jB3wI/514en44L0wtmxhCmnJEqaJrDidTCVlZPA5pKOmutqImu6OtItLfU8iYZyBq6p4SSR4W1oaj7lsG4sxDXfbbUwLFRdT9DkcjISVlVG8hMNAdnYCD5xzJQ4f+h6CYS9++u/3sLxkONxuvj9HHcUapOOP1wLdjkDFi6IoShvSUq8Vj4e1MaNHs1A3GqV3iNjZZ2cDY8cyJZSVxevWVwzG6Q9/gJpgBo7e5zPce/rFeOONOLZuBT75hJ0x5eXbP5fLZWYXyQIfCpkpy01NZe7qWAcyhkKsWfH7TfrI66VAkdlNHo+JiNTWAu+8A9x7r4muZGbytqIiHkepO8rOBm4/9Sactu9/EI058LNnXsacdYc0mORNnMgxEFOmUHgq7Y+KF0VRlDaktrZl20ktRlkZCzzFbXfVKuPVkp/PAt4RI7iwxuPAjxv2x/n/fB2RqBPnTXwRfzzxKrz4YgJVVYzArFrFGo1UqSu3m4t4ZqaZixSJcJ+rqkwUozsgs6FEgAWDJpIkUZacHJMCEiIRtqk//zzwxBPs+qqs5DGJRlNFXICrJz+IyybeAwC44eUnMG3pKQ1meqNGAUccwUvv3h19FPZcVLwoiqK0IS0dllhYSHESjXKx/MlPuHBWVRkberudjrwHHURxIwLm86XH4vKnnkMsbscVR/4Lvz/uWvz3vwnU1gIffcQU1Pr1TUdUnE6mkrKzTSeSRGNqakxayVp43NnIlOu6Ou6fNcIiNUFerxFnbjf3XURGPE6Rs2ED8PjjZmJ3IMCIVDhMH5fycoqb+noen349yvGzg/8EALjj3bvxytxLAfBxBw8GTjyRxbl77dWZR2fPQwt2FUVR2pC6upZtFwxy8QuHKRiCQQqYF19kuiItjeKmvp7Tiw85hPdbvpyL8GtzzoHXVY/HL7kYQ3uuRHVlBP/+txsXX8wU0uGH87GLikw6JBm7nZEJn49CJRzmT6tFPmBGAcjww/Zuu5b5SMnDIK3YbNzv3Fzuc0bG9o8TCpnXFggwKvXsszyG4kzscvF19+7NmpetW3k/SbNV1BbgjEem4Zh93sej038LgMegd2/g9NOZ1hs/vn2Ph7I9Kl4URVHakM2bW76dx0NDsyVLKGDy81nw+fbbHC3g8ZgC4N69WVcRj7M2JhAAnpt1Ecpre+CTBUchFHEjUgn861/AhRcCX37JlFM8TqO0wsLmO2BcrsapJLlYJ0oL1jEA1oGJ1rlETdHU5Oh43Ax0bCraIwLK5TI+Lh6PSXlZu42kLVoGWH7zDaMt4mzsdJrRDb16MU20dSu3zcoCeuZWoTqYg61bga228VhUTIXicFBMnn02o2FHHKGdRZ2BihdFUZQ2pKU1I7JdejoFzIoVrL/o1w84+mhg2jRGCMaONZ0xAwYYl16Zr/PRguMb/Erq6xM4cti7eOqpk3HqqTbY7UyvjBnDxb2oqGWdMFYhYxUvEgURt+DmXmuqBb01KSi73UR65Geqx3S5WNti9XkREeR2Mw00bRpdj/1+HjOfzzjl9ujBSNe2bbwtJwc4+4Bn8Ltjr8NZj36EctuBDc9ts1EInnUWo2bHHqsOxp2FihdFUZQ2ZN99W79dbi6FyZo1PPsfNYqRmNmzaVN/4IFcnAG68YqL7LJlpjbF6wXuPPO3uP74+/DQh9fiptcexCGH2BpqPQ46iFGIggJGeFqKjCKQLiqJlIhAkKiJdZK0bNcc1onR8rs1mtOaaIbLxfoUGZEgkaGFC4Hp0zlnKhDgPno8LNB1uXjcN2+mkKmvpzD56YTHcNsJ/we7LYFTx76KBZsPbNi/3FzgjDMoXI47ruOmeCvbo+JFURSlDRk0yERKmsLh4HZWevakuNi0iamNiRO5EC9YAMydS/FRVcVFdNgwPr7dTgFTV8cUSEntQADAdcf/DeneOlzz7GMoKXHguOO4OB90EEVFTQ1TJeLK2xpaMjnaKmLkb7nvjtJKO0M4zKiJFBiXlQE//ADMmsXC5Xic0ZtQiMJFWqc3bDADGfPygCsP+Qt+PfkmAMCTM36BP79/T4PAys0FTjuNUbITTjCGgErnoOJFURSlDRF322TDOCvp6dsXvdpsTBnFYkxjlJXRwK6+nimlOXMoPsrLue3ee5tFedEiCphHP/k/1IXS8fD5l+KKI/+NdE8dLv/303ix0o3DD2cE5oADzKyenBzWwrR16qM9BEoy0h0lc5wAvr6lSxmt+v57/m23M8ri9/MYZWZSvKxbx+vCYaCwMIHrjrgJPzuU7dAPfPJ73PPhHbDbbQ3C5dRTKRqPP57CR+lcVLwoiqK0ITKFWGYKWSMQYk0vU6CTsduZPorFGH0pL+dZfjjMxfabb4CDD+Ztdjuwzz6mHXjRIkZUnv7yIgTDPjx+0VScP+lF9MwqxVl/fx0ffZSDjRspig47jK29VVW8T0EBhUx3KDyNx83wSelACoUYRVm+nCJv7Vpe7/Vym23buE1ODo/X2rUUNtEoUNQzglun/AxnjfsPAOAPb9+Hf0z/dUN0KS+P78Fe/9/enUdHXWX7Av/WkKrKnEBmCEGGEKYWBcllFBUXCo2o917oq03j7bZpFfs+h1ZR1LTtACrP63pK6xVtRZdLlt2t2E94aAuigLTYSBRIBCGTDAlkqFQlFWo874/tyS8oU2JS4Vd+P2vVQiq/qjp1RH+bffY5u1B+7cySG/UcBi9ERN0oJUWCk0DA6PLcsVlgMGgsW5yMXlIKhyV4aWyUv/X/9a9Sn6EDmCNH5EY8erSxjFNWJssib/5jLrxtKXj5xn/HZaM24oPFl2L8g59hxw4bGhvl5j15shQGx8dLMNTYKEGMPs33XBMIyFwGAsZz+sC5/fsleNuzR7Iruij5+HGjE3RGhgRqR48adTEZGUCrz4o+iUcRjlhx15v/g9e23dg+n5mZkv0aPlwCl8zM6H9vOjkGL0RE3chqlfqVQECKRHWNCCA324QE+fnpzkqx26UoVCnjBNg5c4A1ayRo2bpVgo/Dh+WaESOMote9e6Xo9/99cQWu/N+b8eain+KZv98JBRuOHwe+/loyLkePyvLKNdfI64NBee+GBsk2pKT0/HkuZxIIGN2gO2aw9MF+1dXyfcvKJIix2yUYC4eNk3d1t+f6evne+rnERN3fyYZfvrga4wZ9ho++ugQ2mywzZWfLNuiRI2WpiIHLuYXBCxFRN0pNlRtdXZ3cePUuHB1cOBzy89TU07+PwyE7iywWufF6vcBVV0k/nro6aSg4daoEMMGgBCA6Y7B/vzy/s2oMxpZ8BUtcEpKS5Mad4mrA0aN94fXKzfzgQdn5NHeuBCyhkIz72DFZZklLO/tTg3+oSMQ4LO9kS25tbRK0fPON7Mzat0/OZ1FKslr6wL/GRqPxpdUq1+ht0omJwMTBGzHpvHV4+N0n4fdbYLcnYVP5JYiLk6xNv34SHI4YIYFLVlZ0vj+dPQYvRETdKD1d/vYfFyc3Qd1zx2aT530+42TYM3E4JAMDSADT2irH0a9dK8HFRx9JAFNfL4FJUZF8rl6iqq4GmluT2jMS+Rl1WH/7WLyz42rcvfopVFc70NAgO2727JGC4CuvNA7G8/mMhoWpqUZ36u6iT9HVXaC/e26M1SrXeL0SjNXWSu1PVZWMKxiUYEPXwLS2yjzY7fK8zyfPeb3yPikpCv9Z/AR+d/l9sFkj+OrIcLzx6a/g98v1LpfUHP3LvxgZl4yM7vu+1H0YvBARdSOvV/52n54uQUFrq5F5sVjk+cREue5sdq10zMAcOybZiVmzpIfR0aMSwEyeLNc1Nkq9jNMpQYbTKTd6n08e08a+h/59DmHR5SswpqAUP3/uDRxtyUdlpZHd0buaJkyQDJHPJ8/rIuHkZKN/UHz86XcWdTw9t+Mpuh0Pu/sum81oFtnUZDRJPHRIApimJqNuyOEwApTmZnl9YqJ8js8nz7W1SUDXN6UZj81egBkj3wEArN5+A97acR38fglakpJkni+4QOqIZs5kh+hzGYMXIqJupPsBRSLGVlwtEjH6DOnrzkZcnNxYbTbjePsZM4CNG+WmvnUrMHastBA4dEhO0nW5jAzEgQMSDKz6+BdobO2D5+dfj0mFW/H5Iz/BzS//D97ZORdWqyw3JSVJIPPPf8p27LFjZdlELyk1NMgDMDI8OmvhcMhYz7bgVy+phcOSOfH7JeBobpYxHDki39fjkUc4bGS19GuOHJFxuVwyP7qwV8+93Q5cNHAznrz2FxjQpwr+kANL3n4Wq7bciFDIgoQEqfEZOlSyLaNGSeByqn5QdG6wKHWu9AztHh6PB6mpqWhubkYKN+MTUZRVVQELF0ogAMjNU/fb0csiQ4YAL7wADBzYufcOh6Xe4/BhIxDavNnYGlxYKIfPVVYaW4r37JGMzb59kkEJBIDC3P1Y+cvrceGA7QCA17b8Are//gxClhS4XPI5TqcELLm58r4FBbIMlppqZF1OdffQp+V2/FUX/+oO1vpAOb3cowMWnQFqaZGHzyfvkZAgrwmH5Ve9jKbbCOjTfnWTy0hEgqgFxf+NB2bdCatVoaZxIH7z2pvYvv+i9vnLyZFs1YgRwJgxcuS/Xjaj6OrM/ZuZFyKibmSzyY01GJSb4ncLdvUSTVdqR2w2YMAAuVkfPCg36okTJetQXm40bBw2TD5HKSnG3btXrvn6a1laOnB0CGY8uQX3XfUH/Ndlj2H+5Fdx2N0fD/71UYTDxumxTU0SVFRWStAyYIAEXrrjclqaBABJSUYLAZ3x0Tr2RepYl6IDFt04UR8419ZmBB+JifK5+vTctjaj67PLJeNsa5P39vuNTtEWi1H8u/3ARVCw4M1/3oAlbz2NY+4UWK0y9kGDJCArLJRlsosvPnHsdO5i5oWIqBt98glw661GliUhwWgXoPsT2e3As89K4NEVSkkAVF0tN3yHQzI9X3whn5ORIVkEvfVZKaPItbpalpZ0gDBl2BbcP/te/Nuza9HgSYFSRnFxYqJ8ng4IdEfnxERZSsrJMbIwDseJPYn0FnHdKkBnRvQ/611NOnAJBGSuHA5jZ5G+1uORoMtikYyQrqUB5PvrA+fCYSDe1oxRuduwoewKhEIytmG55dhVPby9MLdvX8m0pKdL4HLJJcC4cefm+TY/Jsy8EBH1In3j93ole6F3G6Wny82+tfWHvb/FIsW0TqdkRbxeyfLEx0utyrFjwKefSr1KUpIEKwUFcr3TKVmHAwfkdVv2TcaspzfD6ZQsh9er8MbN12B7RTFe+Og22J3x7We+tLVJsKB3ANXUSMDhcsn3Skgwloo6FvJaLCc2bNTFxHFxRp1MMGgc9R8IyBy53UYLBB20aOGwEfwEAkA4FMZVo1/F4ivvQ2p8E6Yu24OjbYMRDAI7Dwxvz+To7FFSktS3zJgh9S5kLgxeiIi6UV6eFIAePSrBREaGccKu3S5LJVlZct0PlZIiS0Q1NZKJycyUnUeffSafs22bLBuNHCmZGZ1Rsdkk2Ni/XzIaPp8EDnFxwLxJazD7gncw+4J3sHDac/j924/g/+66HolJNiQmSgCgD+ALBCTY8PkkO2K3SyATHy8PXchrtxvBjK7F0ZkVQJ7Ty0Z+vxGw6EyPzuroQKXjMpTfrzBp4Lu4Z8a9KMrdAwA4cKwQWelNqG6Ua2w2ybaMHm0sdY0fL7u2+vT54f8eKPq4bERE1I0iEeB3v5OtzElJcrO32+Vmq+s9rrgCWL68+06wDYdlO/Hhw8YSTGmpLBtZrVIYPGqUZGBqa2UMBw7ImA4dkiJgr1deGxcXwbziN3DPFfehf3oNAGB/3RA8tf4evLXzF0hIcrTXvMTFyXu0tRlbwjsW6HZ8AEadjw5i9NKPfp3dbmRlEhPln71eCUD03OoTdwP+MKYOWoObpj6BCwuk8LjJl44/broPL378W7i9TiglQdSAAUYfKH1y7qWXnry/FPWezty/GbwQEXUjvx947TXpReR2G4W6ut4jLQ34138F5s83evB0F7dbApHmZgkq9u+XRzAon1tcLDfsvXvlmkOHZCtyKCS1MG638dpEVxtuuuT/4OZpT6JPouyNPtTUD1Mf3Y7GtjwkJkpwlpZm9HNSysiO6AxJMHhi0bLFgvaTbDtusdYZmkBAxnD8uPGa9qWhsFEjE29z4/MH85HkakFb0IWXNt+GZzfeg6PuNASD357t0ldqf5KT5X0KC4HZs6Xehc4950Tw8uijj2Lt2rUoLS2Fw+GA2+0+42uUUigpKcHKlSvhdrsxadIkPPfccxjaiQVJBi9E1JsaG4H335dllO3bJcOhC0UHD5blipQU2ZLbE0sWfr9kV+rqjHNQdu+WpR2HQ27g558vWZrKSqmPqaqSIKOhQX7f3CwZD6WABEcr/nPKC7hp2nK4W9Mw/qHdAKSYZcLQbfj62GiEkITERKl50VupnU6jkFfXvnQstNVLP8GgsWSkm1jqQ+aCwW+X26wBjMrchEmD3sODa5YjHLbAYgHuuvIROOx+vPjxb1HrzmovLE5Kkhqf/Hyjt9HEicBPf3rmtgzUe86J4KWkpARpaWk4ePAgXnrppbMKXh5//HEsXboUq1atwnnnnYcHHngAu3btQllZGVwu11l9LoMXIupN9fVyfL/ONuibcFyc3Nz1LpyZM3vu6HmlJIty+LBx6FtZmdGJOj1dgqjsbAmuqqqkV1B9vWQ3jh41Trj1+b5d1rH4UZBRjaqGQllesvhQsTwTVksEH5RdgY3lV2B71cWobBgGq9XSXqtitxu/6toXHaDo4txQyJgv4NvWCuk1GJW1GVOHvIvLitYhJd4DAJj19BbsqJnUHgjpHUlKSdDSr58ELXpJa+BA4D/+QzIwvd1okk7vnNht9NBDDwEAXnnllbO6XimFp59+Gvfffz/mzJkDAHj11VeRnZ2NNWvW4Gc/+1lPDZWIqNskJhrBQ0HB94+Yr642WgT0lI5tCOrrJZsSHy9Byt69EpR88IHc5IuLJSNUUCDnwOzbJ8FVS4sEA8ePSzaptdWJqobC9sPlhuRVoc6Ti0GZBzB7zBrMHrMGAFDnycaX34zD6s9+iXW7rpUt1tYgXHFtaPEnw2q1fDtGhWRXC/okN8IbSUVrSCbq0sI1KJn1W/RLP3jCd6rzZGPdl9eg1p0Fv984j0UpyWTl5cl31sW9Lpe0OZg6VXYXMXCJLefMbqPKykrU1tZi+vTp7c+lpqaiuLgY27ZtO2Xw4vf74e9wzrbH4+nxsRIRnYo+AK2lxWgIqGs3bDbJCKSlRedm6nDITT0tTZaRUlIk2/Lll5KFqaiQ7ExhoWQmhg6VepDSUqNVgNttBASNjfK9IhGgomEExj30NUbkfolZ56/B5MJNuOi8bchOqcPlI9figz2Xo61NgowLBn+Gtf9rEiIRC8LKhoiywmYJw24LAwDue/s5rPrkJkQiwDFPH/RLP4hQ2IYvvrkAH311GdZ9MQefVRXDYrEiLs5oEZCSItkrl0uyWzqr06+f7CQqKjKyOhRbzpngpba2FgCQnZ19wvPZ2dntPzuZpUuXtmd5iIh6Wzgsh7d5PLJU09xsnPOSmirBQU6OPBctCQmyfNK3r2QnsrIky7J7twQnOljRjQkLCyVLs3271MXonkOABD86GxMKWXAsdD6e33o+/vuDEqiwH2PyP8PIfruwee+09rqW5LhjAACrVcGKE1tH+4MOhALH4XZLkLexdDxmPPkhdlZfhLagpKfsdglUdEPI9HQJrNraZFyRiAQw/frJuS2DBxu1P6mpzLrEok4FL4sXL8bjjz9+2mvKy8tRVFT0gwbVGffeey/uuOOO9t97PB7k5+dH7fOJiDrSXZEbG+Wm2bevsVVaKXk+GOxae4AfwmIxbv59+8qZMEOHSu+j3btlXM3NsqxUUAAMHw7MmyfBzSefSPuBjtmk/Hz5XrqXkBTXOnEsMhnvV07G8Yh83vHjwPrdc5DzXy1IdHpgQQQWRBCJWNHkS4fPHw/A0n46bxgufFo5TbY450rmKDNTghPd7LKpSd43I0PGkJYmmZYRI4xAxeGQ4EVv6abY0qng5c4778QNN9xw2msGDRrUpYHk5OQAAOrq6pCbm9v+fF1dHcaMGXPK1zmdTji7e78hEVEXOZ2yRNPSIifc6kPXrFb5WXm5/Ly3/relgxi95NKvnxzeVl4uy0kNDRLE7NtnBDjFxVI78tVXRqDT1CS/KiW7ppxO48A6v1+yPfrIfvk1EUolSoHtt4W2KU4g3SaBRny8vE9+PtC/v2RXfD4pHj5yxGhpoA/+S02Vs1uGDZNMlm5DoAt529okUNOdpnmmS2zpVPCSmZmJzMzMHhnIeeedh5ycHGzYsKE9WPF4PPj0009x880398hnEhF1N7dbsgLZ2ZIl0P16dI8evezidn+/mDfaEhLkkZEhTQqLi2WpS9fEVFbK8lFysgQyBQXAZZdJMHDwoBQfezxSENzQIAfV2e1GtsNul2Ci47KNw4H2k3p1FigtTYKfSEQ+r6ZGlqyam+Wz4uKM7c+FhcBFFxmHzB06JL+2tBj9mqxWuT4pSV7PmpfY02M1LzU1NWhsbERNTQ3C4TBKS0sBAEOGDEFSUhIAoKioCEuXLsU111wDi8WC2267DY888giGDh3avlU6Ly8PV199dU8Nk4ioW+n9AwUFsqTS2mrcUFNS5IZaX29cdy6w2yWToTtHT5woAcQXX0gw0dgov6+uNrZ89+kj14ZC8qvbLQFaa6sENK2tEqTpZooul7xWn8AbDErQU1NjXK+7QusxuVySiSkslJ1Denu3FggYJ/j27WsciGe1Gv2SOp7wS7Gjx4KXBx98EKtWrWr//QUXXAAA+PDDDzFt2jQAwN69e9Gsq8AA3H333WhtbcXChQvhdrsxefJkrF+//qzPeCEi6m1Op5Fpyc6WIEXvNnI6JaDRJ8yei/TJtH37SvGuDlzKyiTL0dQk3+nIEakpiYsz+g/pwMHlMupgwmHjALqWFuP03WDQWFbSzRzT0iSAKiiQ+pXiYmk4eapt5TqL4/HIGDouHQEyhpQULhnFIrYHICLqZps3y5LLyUoAKyrkhjxlSvTH9UO1tkrwcviwBDSNjRI46IPm9A4qXXzbsReR/rkO5BwO4/Tb7GzJ3gwcaHS/Plt+v2SE9LKVfv/ERAnAOvt+1HvOiUPqiIh+rIqK5GZaUSG1IvHxkgU4dkwyC1HckNmtdK1K//5Sd+L3G9uVOy796F5E+gRcnZ2JjzeaVSYnG8toP2Q3kFKS0amulkyQLs7NyZGgJbb+ek4agxciom6WmQlMmiS7c44ckQxFXJxkXIqK5OdmZ7HI8pDLJTuDesuhQ8DOnTLPuh+TxWIEUqmpcu4LxRYGL0REPSAzUx5ut9GYsbd3F8WaQADYsUOCRF0IrIOXlhZ5PjnZaNBIsYPBCxFRD2LA0nNaWuTcmWPHJOty5IjRBDM3V5aldu8Gpk/vmQ7e1HsYvBARkSk1NUlbg6oqCWT0Vmy/X9ofJCXJPzc1MXiJNQxeiIjIlKxW2fVUVyfLQj7fiee81NXxnJdYxeCFiIhMqblZiqF9PlkustuNLdqtrUaPqQ7HiVGMYPBCRESmFAwaZ8zYbBLE6IJd3QxTH4hHsYXBCxERmZJeJrJYZFeXbi0ASPCSkCA/9/l6bYjUQ7gSSEREpqSLcFtajL5Guqt0MCjPd7yOYgczL0REZEpxcdIzSTdg7HhSbygkzx8//sNO8KVzE4MXIiIypfp6qWmx2STbcvy4UfNis8kjGJTrKLYweCEiIlOqrTWaQQJS56LpnkahkFxHsYU1L0REZEoJCUbwYrNJwKIfNps8Hw7LdRRbmHkhIiJTio+XJSIdwOjAxWIxMi8Wi1xHsYXBCxERmZLDIQ+P58zXUGzhshEREZmS1Xri2S4nEwqxPUAs4r9SIiIypUAAaGs7/TVtbXIdxRYGL0REZEoHDpw5MAkE5DqKLQxeiIjIlOrqjMLcU1FKrqPYwuCFiIhM6UxLRp29jsyDwQsREZmSy9W915F5MHghIiJTSk7u3uvIPBi8EBGRKemu0d11HZkHgxciIjKls+1ZxN5GsYfBCxERmVJqavdeR+bB4IWIiExp0iSjAeOp2GxyHcUWBi9ERGRKI0ac2DHaYjEeWkKCXEexhcELERGZ0qFDQG6ubIX+bv8iq1Wez82V6yi2sKs0ERGZVp8+8qiqAtxuIBKRwCUtDRg48MQsDMUOBi9ERGRKBQVAVhbQ3Axcfjlw9KicphsfL8/X1EixbkFBb4+UuhuXjYiIyJRycoBp04BwWLZD62xLWpr8PhyWn+fk9OowqQcw80JERKZksQDXXiuBypdfAg0Nxs/sdtlldO21XDqKRQxeiIjItAoKgFtuAT7+GCgtBVpbgcREYMwYYOpULhnFKgYvRERkagUFwM9/LnUvPp9sj87OZsYlljF4ISIi07NYWNvyY8KCXSIiIjIVBi9ERERkKgxeiIiIyFQYvBAREZGpMHghIiIiU2HwQkRERKbC4IWIiIhMhcELERERmQqDFyIiIjKVmDthVykFAPB4PL08EiIiIjpb+r6t7+OnE3PBi9frBQDk5+f38kiIiIios7xeL1JTU097jUWdTYhjIpFIBIcPH0ZycjIs3dyVy+PxID8/H9988w1SUlK69b3JwHmODs5zdHCeo4dzHR09Nc9KKXi9XuTl5cFqPX1VS8xlXqxWK/r379+jn5GSksL/MKKA8xwdnOfo4DxHD+c6Onpins+UcdFYsEtERESmwuCFiIiITIXBSyc4nU6UlJTA6XT29lBiGuc5OjjP0cF5jh7OdXScC/MccwW7REREFNuYeSEiIiJTYfBCREREpsLghYiIiEyFwQsRERGZCoOX71ixYgUGDhwIl8uF4uJibN++/bTX//nPf0ZRURFcLhdGjx6NdevWRWmk5taZeV65ciWmTJmC9PR0pKenY/r06Wf890Kis3+etdWrV8NiseDqq6/u2QHGiM7Os9vtxqJFi5Cbmwun04nCwkL+v+MsdHaen376aQwbNgzx8fHIz8/H7bffjuPHj0dptOb08ccfY/bs2cjLy4PFYsGaNWvO+JpNmzbhwgsvhNPpxJAhQ/DKK6/0+DihqN3q1auVw+FQf/rTn9SePXvUr3/9a5WWlqbq6upOev3WrVuVzWZTTzzxhCorK1P333+/iouLU7t27YryyM2ls/N83XXXqRUrVqidO3eq8vJydcMNN6jU1FR18ODBKI/cXDo7z1plZaXq16+fmjJlipozZ050BmtinZ1nv9+vxo0bp2bOnKm2bNmiKisr1aZNm1RpaWmUR24unZ3n119/XTmdTvX666+ryspK9d5776nc3Fx1++23R3nk5rJu3Tq1ZMkS9dZbbykA6u233z7t9RUVFSohIUHdcccdqqysTD3zzDPKZrOp9evX9+g4Gbx0MH78eLVo0aL234fDYZWXl6eWLl160uvnzp2rZs2adcJzxcXF6je/+U2PjtPsOjvP3xUKhVRycrJatWpVTw0xJnRlnkOhkJo4caJ68cUX1YIFCxi8nIXOzvNzzz2nBg0apAKBQLSGGBM6O8+LFi1Sl1566QnP3XHHHWrSpEk9Os5YcjbBy913361Gjhx5wnPz5s1TM2bM6MGRKcVlo28FAgHs2LED06dPb3/OarVi+vTp2LZt20lfs23bthOuB4AZM2ac8nrq2jx/l8/nQzAYRJ8+fXpqmKbX1Xn+wx/+gKysLPzqV7+KxjBNryvz/Le//Q0TJkzAokWLkJ2djVGjRuGxxx5DOByO1rBNpyvzPHHiROzYsaN9aamiogLr1q3DzJkzozLmH4veug/GXGPGrqqvr0c4HEZ2dvYJz2dnZ+Orr7466Wtqa2tPen1tbW2PjdPsujLP33XPPfcgLy/ve//BkKEr87xlyxa89NJLKC0tjcIIY0NX5rmiogIbN27E9ddfj3Xr1mH//v245ZZbEAwGUVJSEo1hm05X5vm6665DfX09Jk+eDKUUQqEQbrrpJtx3333RGPKPxqnugx6PB21tbYiPj++Rz2XmhUxl2bJlWL16Nd5++224XK7eHk7M8Hq9mD9/PlauXImMjIzeHk5Mi0QiyMrKwgsvvICxY8di3rx5WLJkCZ5//vneHlpM2bRpEx577DH88Y9/xOeff4633noLa9euxcMPP9zbQ6NuwMzLtzIyMmCz2VBXV3fC83V1dcjJyTnpa3Jycjp1PXVtnrXly5dj2bJl+OCDD/CTn/ykJ4dpep2d5wMHDqCqqgqzZ89ufy4SiQAA7HY79u7di8GDB/fsoE2oK3+ec3NzERcXB5vN1v7c8OHDUVtbi0AgAIfD0aNjNqOuzPMDDzyA+fPn48YbbwQAjB49Gq2trVi4cCGWLFkCq5V/d+8Op7oPpqSk9FjWBWDmpZ3D4cDYsWOxYcOG9ucikQg2bNiACRMmnPQ1EyZMOOF6APj73/9+yuupa/MMAE888QQefvhhrF+/HuPGjYvGUE2ts/NcVFSEXbt2obS0tP1x1VVX4ZJLLkFpaSny8/OjOXzT6Mqf50mTJmH//v3twSEA7Nu3D7m5uQxcTqEr8+zz+b4XoOiAUbGlX7fptftgj5YDm8zq1auV0+lUr7zyiiorK1MLFy5UaWlpqra2Viml1Pz589XixYvbr9+6dauy2+1q+fLlqry8XJWUlHCr9Fno7DwvW7ZMORwO9Ze//EUdOXKk/eH1envrK5hCZ+f5u7jb6Ox0dp5rampUcnKyuvXWW9XevXvVu+++q7KystQjjzzSW1/BFDo7zyUlJSo5OVm98cYbqqKiQr3//vtq8ODBau7cub31FUzB6/WqnTt3qp07dyoA6qmnnlI7d+5U1dXVSimlFi9erObPn99+vd4qfdddd6ny8nK1YsUKbpXuDc8884waMGCAcjgcavz48eof//hH+88uvvhitWDBghOuf/PNN1VhYaFyOBxq5MiRau3atVEesTl1Zp4LCgoUgO89SkpKoj9wk+nsn+eOGLycvc7O8yeffKKKi4uV0+lUgwYNUo8++qgKhUJRHrX5dGaeg8Gg+v3vf68GDx6sXC6Xys/PV7fccotqamqK/sBN5MMPPzzp/2/13C5YsEBdfPHF33vNmDFjlMPhUIMGDVIvv/xyj4/TohTzZ0RERGQerHkhIiIiU2HwQkRERKbC4IWIiIhMhcELERERmQqDFyIiIjIVBi9ERERkKgxeiIiIyFQYvBAREZGpMHghIiIiU2HwQkRERKbC4IWIiIhMhcELERERmcr/B6/99z2kWPeiAAAAAElFTkSuQmCC\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -210,7 +210,7 @@
    "cell_type": "markdown",
    "source": [
     "this model has very little capacity to overfit to noisy data because of the small\n",
-    "number of points on our grid (parameters)"
+    "number of control points on our grid (parameters)"
    ],
    "metadata": {
     "collapsed": false,
diff --git a/examples/optimise_2d_grid_model.ipynb b/examples/optimise_2d_grid_model.ipynb
index e20eb6e..9cbeecb 100644
--- a/examples/optimise_2d_grid_model.ipynb
+++ b/examples/optimise_2d_grid_model.ipynb
@@ -2,12 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "outputs": [],
    "source": [
     "import torch\n",
     "\n",
-    "from torch_cubic_b_spline_grid import CubicBSplineGrid2d"
+    "from torch_cubic_spline_grids import CubicBSplineGrid2d"
    ],
    "metadata": {
     "collapsed": false,
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "outputs": [],
    "source": [
     "N_CONTROL_POINTS = (8, 12)\n",
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "outputs": [],
    "source": [
     "grid_2d = CubicBSplineGrid2d(resolution=N_CONTROL_POINTS)"
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "outputs": [],
    "source": [
     "def make_observations(n, add_noise: bool = False):\n",
@@ -104,13 +104,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "outputs": [
     {
      "data": {
-      "text/plain": "<matplotlib.image.AxesImage at 0x12348ea70>"
+      "text/plain": "<matplotlib.image.AxesImage at 0x1180e93f0>"
      },
-     "execution_count": 13,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -153,10 +153,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "outputs": [],
    "source": [
-    "optimiser = torch.optim.SGD(grid_2d.parameters(), lr=0.01)"
+    "optimiser = torch.optim.Adam(grid_2d.parameters(), lr=0.01)"
    ],
    "metadata": {
     "collapsed": false,
@@ -180,519 +180,519 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 7,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "9.349543571472168\n",
-      "10.343859672546387\n",
-      "9.537872314453125\n",
-      "9.990056991577148\n",
-      "10.107559204101562\n",
-      "10.601482391357422\n",
-      "10.78204345703125\n",
-      "10.006673812866211\n",
-      "10.184402465820312\n",
-      "10.157295227050781\n",
-      "9.274003982543945\n",
-      "9.979869842529297\n",
-      "10.365550994873047\n",
-      "11.231354713439941\n",
-      "9.706147193908691\n",
-      "9.5869140625\n",
-      "9.591609001159668\n",
-      "9.26749324798584\n",
-      "11.015094757080078\n",
-      "10.057036399841309\n",
-      "9.619118690490723\n",
-      "9.78116226196289\n",
-      "10.368104934692383\n",
-      "9.604239463806152\n",
-      "9.739543914794922\n",
-      "10.02828311920166\n",
-      "9.68470573425293\n",
-      "9.83878231048584\n",
-      "8.354658126831055\n",
-      "9.754671096801758\n",
-      "10.145146369934082\n",
-      "8.97883415222168\n",
-      "8.939720153808594\n",
-      "10.756671905517578\n",
-      "9.921228408813477\n",
-      "9.366230010986328\n",
-      "10.171582221984863\n",
-      "10.813288688659668\n",
-      "9.142390251159668\n",
-      "9.24456787109375\n",
-      "9.670952796936035\n",
-      "10.182373046875\n",
-      "10.087995529174805\n",
-      "9.661208152770996\n",
-      "9.557595252990723\n",
-      "9.49161148071289\n",
-      "10.016218185424805\n",
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      ]
     },
     {
      "data": {
-      "text/plain": "<matplotlib.image.AxesImage at 0x1232de1a0>"
+      "text/plain": "<matplotlib.image.AxesImage at 0x1281a4550>"
      },
-     "execution_count": 15,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
diff --git a/pyproject.toml b/pyproject.toml
index b8b4f16..635c529 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,8 +5,8 @@ build-backend = "hatchling.build"
 
 # https://peps.python.org/pep-0621/
 [project]
-name = "torch-cubic-b-spline-grid"
-description = "Cubic B-spline interpolation on multidimensional grids in PyTorch"
+name = "torch-cubic-spline-grids"
+description = "Cubic spline interpolation on multidimensional grids in PyTorch"
 readme = "README.md"
 requires-python = ">=3.8"
 license = {text = "BSD 3-Clause License"}
@@ -47,8 +47,8 @@ dev = [
 ]
 
 [project.urls]
-homepage = "https://github.com/alisterburt/torch-cubic-b-spline-grid"
-repository = "https://github.com/alisterburt/torch-cubic-b-spline-grid"
+homepage = "https://github.com/alisterburt/torch-cubic-spline-grids"
+repository = "https://github.com/alisterburt/torch-cubic-spline-grids"
 
 # same as console_scripts entry point
 # [project.scripts]
diff --git a/src/torch_cubic_b_spline_grid/__init__.py b/src/torch_cubic_spline_grids/__init__.py
similarity index 100%
rename from src/torch_cubic_b_spline_grid/__init__.py
rename to src/torch_cubic_spline_grids/__init__.py
diff --git a/src/torch_cubic_b_spline_grid/_base_cubic_grid.py b/src/torch_cubic_spline_grids/_base_cubic_grid.py
similarity index 96%
rename from src/torch_cubic_b_spline_grid/_base_cubic_grid.py
rename to src/torch_cubic_spline_grids/_base_cubic_grid.py
index 5590573..d947854 100644
--- a/src/torch_cubic_b_spline_grid/_base_cubic_grid.py
+++ b/src/torch_cubic_spline_grids/_base_cubic_grid.py
@@ -3,13 +3,14 @@
 import einops
 import torch
 
-from torch_cubic_b_spline_grid.utils import coerce_to_multichannel_grid
+from torch_cubic_spline_grids.utils import coerce_to_multichannel_grid
 
 
 class CubicSplineGrid(torch.nn.Module):
     """Base class for continuous parametrisations of multidimensional spaces."""
     resolution: Tuple[int, ...]
     ndim: int
+    n_channels: int
     _data: torch.nn.Parameter
     _interpolation_function: Callable
 
diff --git a/src/torch_cubic_b_spline_grid/_constants.py b/src/torch_cubic_spline_grids/_constants.py
similarity index 100%
rename from src/torch_cubic_b_spline_grid/_constants.py
rename to src/torch_cubic_spline_grids/_constants.py
diff --git a/src/torch_cubic_b_spline_grid/b_spline_grids.py b/src/torch_cubic_spline_grids/b_spline_grids.py
similarity index 89%
rename from src/torch_cubic_b_spline_grid/b_spline_grids.py
rename to src/torch_cubic_spline_grids/b_spline_grids.py
index 4c6dd8a..fdff52e 100644
--- a/src/torch_cubic_b_spline_grid/b_spline_grids.py
+++ b/src/torch_cubic_spline_grids/b_spline_grids.py
@@ -3,14 +3,14 @@
 
 import torch
 
-from torch_cubic_b_spline_grid._base_cubic_grid import CubicSplineGrid
-from torch_cubic_b_spline_grid.interpolate_grids import (
+from torch_cubic_spline_grids._base_cubic_grid import CubicSplineGrid
+from torch_cubic_spline_grids.interpolate_grids import (
     interpolate_grid_1d as _interpolate_grid_1d,
     interpolate_grid_2d as _interpolate_grid_2d,
     interpolate_grid_3d as _interpolate_grid_3d,
     interpolate_grid_4d as _interpolate_grid_4d,
 )
-from torch_cubic_b_spline_grid._constants import CUBIC_B_SPLINE_MATRIX
+from torch_cubic_spline_grids._constants import CUBIC_B_SPLINE_MATRIX
 
 CoordinateLike = Union[float, Sequence[float], torch.Tensor]
 
diff --git a/src/torch_cubic_b_spline_grid/catmull_rom_grids.py b/src/torch_cubic_spline_grids/catmull_rom_grids.py
similarity index 89%
rename from src/torch_cubic_b_spline_grid/catmull_rom_grids.py
rename to src/torch_cubic_spline_grids/catmull_rom_grids.py
index 52b80d7..bd62afe 100644
--- a/src/torch_cubic_b_spline_grid/catmull_rom_grids.py
+++ b/src/torch_cubic_spline_grids/catmull_rom_grids.py
@@ -3,14 +3,14 @@
 
 import torch
 
-from torch_cubic_b_spline_grid._base_cubic_grid import CubicSplineGrid
-from torch_cubic_b_spline_grid.interpolate_grids import (
+from torch_cubic_spline_grids._base_cubic_grid import CubicSplineGrid
+from torch_cubic_spline_grids.interpolate_grids import (
     interpolate_grid_1d as _interpolate_grid_1d,
     interpolate_grid_2d as _interpolate_grid_2d,
     interpolate_grid_3d as _interpolate_grid_3d,
     interpolate_grid_4d as _interpolate_grid_4d,
 )
-from torch_cubic_b_spline_grid._constants import CUBIC_CATMULL_ROM_MATRIX
+from torch_cubic_spline_grids._constants import CUBIC_CATMULL_ROM_MATRIX
 
 CoordinateLike = Union[float, Sequence[float], torch.Tensor]
 
diff --git a/src/torch_cubic_b_spline_grid/interpolate_grids.py b/src/torch_cubic_spline_grids/interpolate_grids.py
similarity index 97%
rename from src/torch_cubic_b_spline_grid/interpolate_grids.py
rename to src/torch_cubic_spline_grids/interpolate_grids.py
index 1ab8e62..96059cd 100644
--- a/src/torch_cubic_b_spline_grid/interpolate_grids.py
+++ b/src/torch_cubic_spline_grids/interpolate_grids.py
@@ -1,19 +1,19 @@
 import einops
 import torch
 
-from torch_cubic_b_spline_grid.pad_grids import (
+from torch_cubic_spline_grids.pad_grids import (
     pad_grid_1d,
     pad_grid_2d,
     pad_grid_3d,
     pad_grid_4d,
 )
-from torch_cubic_b_spline_grid.interpolate_pieces import (
+from torch_cubic_spline_grids.interpolate_pieces import (
     interpolate_pieces_1d,
     interpolate_pieces_2d,
     interpolate_pieces_3d,
     interpolate_pieces_4d,
 )
-from torch_cubic_b_spline_grid.utils import interpolants_to_interpolation_data_1d
+from torch_cubic_spline_grids.utils import interpolants_to_interpolation_data_1d
 
 
 def interpolate_grid_1d(grid: torch.Tensor, u: torch.Tensor, matrix: torch.Tensor):
diff --git a/src/torch_cubic_b_spline_grid/interpolate_pieces.py b/src/torch_cubic_spline_grids/interpolate_pieces.py
similarity index 100%
rename from src/torch_cubic_b_spline_grid/interpolate_pieces.py
rename to src/torch_cubic_spline_grids/interpolate_pieces.py
diff --git a/src/torch_cubic_b_spline_grid/pad_grids.py b/src/torch_cubic_spline_grids/pad_grids.py
similarity index 100%
rename from src/torch_cubic_b_spline_grid/pad_grids.py
rename to src/torch_cubic_spline_grids/pad_grids.py
diff --git a/src/torch_cubic_b_spline_grid/utils.py b/src/torch_cubic_spline_grids/utils.py
similarity index 98%
rename from src/torch_cubic_b_spline_grid/utils.py
rename to src/torch_cubic_spline_grids/utils.py
index cb123c0..4365f8a 100644
--- a/src/torch_cubic_b_spline_grid/utils.py
+++ b/src/torch_cubic_spline_grids/utils.py
@@ -3,7 +3,7 @@
 import einops
 import torch
 
-from torch_cubic_b_spline_grid.pad_grids import pad_grid_1d
+from torch_cubic_spline_grids.pad_grids import pad_grid_1d
 
 
 def generate_sample_positions_for_padded_grid_1d(n_samples: int) -> torch.Tensor:


Test patch:
diff --git a/tests/test_grid_optimisation.py b/tests/test_grid_optimisation.py
index 0181034..508dd4d 100644
--- a/tests/test_grid_optimisation.py
+++ b/tests/test_grid_optimisation.py
@@ -1,7 +1,7 @@
 import einops
 import torch
 
-from torch_cubic_b_spline_grid import (
+from torch_cubic_spline_grids import (
     CubicBSplineGrid1d,
     CubicBSplineGrid2d,
     CubicBSplineGrid3d,
diff --git a/tests/test_grids.py b/tests/test_grids.py
index 1b1abb8..77acf47 100644
--- a/tests/test_grids.py
+++ b/tests/test_grids.py
@@ -1,7 +1,7 @@
 import pytest
 import torch
 
-from torch_cubic_b_spline_grid import (
+from torch_cubic_spline_grids import (
     CubicBSplineGrid1d,
     CubicBSplineGrid2d,
     CubicBSplineGrid3d,
diff --git a/tests/test_interpolate_grid.py b/tests/test_interpolate_grid.py
index a74373d..0fddefc 100644
--- a/tests/test_interpolate_grid.py
+++ b/tests/test_interpolate_grid.py
@@ -1,7 +1,7 @@
 import torch
 
-from torch_cubic_b_spline_grid import interpolate_grids
-from torch_cubic_b_spline_grid._constants import CUBIC_B_SPLINE_MATRIX
+from torch_cubic_spline_grids import interpolate_grids
+from torch_cubic_spline_grids._constants import CUBIC_B_SPLINE_MATRIX
 
 
 def test_interpolate_grid_1d():
diff --git a/tests/test_interpolate_pieces.py b/tests/test_interpolate_pieces.py
index d7faeb0..19ea21e 100644
--- a/tests/test_interpolate_pieces.py
+++ b/tests/test_interpolate_pieces.py
@@ -1,7 +1,7 @@
 import torch
 
-from torch_cubic_b_spline_grid import interpolate_pieces
-from torch_cubic_b_spline_grid._constants import CUBIC_B_SPLINE_MATRIX
+from torch_cubic_spline_grids import interpolate_pieces
+from torch_cubic_spline_grids._constants import CUBIC_B_SPLINE_MATRIX
 
 
 def test_interpolate_pieces_1d():
diff --git a/tests/test_modules.py b/tests/test_modules.py
index 67d0a3b..6907448 100644
--- a/tests/test_modules.py
+++ b/tests/test_modules.py
@@ -1,4 +1,4 @@
-from torch_cubic_b_spline_grid import (
+from torch_cubic_spline_grids import (
     CubicBSplineGrid1d,
     CubicBSplineGrid2d,
     CubicBSplineGrid3d,
diff --git a/tests/test_pad_grid.py b/tests/test_pad_grid.py
index 0df2655..2d8597b 100644
--- a/tests/test_pad_grid.py
+++ b/tests/test_pad_grid.py
@@ -1,6 +1,6 @@
 import torch
 
-from torch_cubic_b_spline_grid import pad_grids
+from torch_cubic_spline_grids import pad_grids
 
 
 def test_pad_1d():
diff --git a/tests/test_utils.py b/tests/test_utils.py
index e9f4b74..24dd787 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,6 +1,6 @@
 import torch
 
-from torch_cubic_b_spline_grid.utils import find_control_point_idx_1d
+from torch_cubic_spline_grids.utils import find_control_point_idx_1d
 
 
 def test_find_control_points():