Repository: plenoptic-org/plenoptic
Pull request #325: Refactors initialization to use `setup()`
Author: login: billbrod
Created: 2025-03-18T14:25:50Z | Merged: 2025-03-20T14:04:16Z

This PR adds a `setup` method to the OptimizedSynthesized classes: Metamer, MADCompetition, and Geodesic. setup handles the initialization of the synthesis image(s), optimizer, and scheduler. It does not need to be called by the user, in which case we call it the first time `synthesize` is called. All arguments are optional, in which case we do the behavior, which is unchanged from before and depends on the synthesis class. Splitting off setup like this makes the interaction with load easier: if load is called, setup must be called after it and can only be used to setup the optimizer/scheduler, if necessary. This also simplifies the logic of our optimizer and scheduler, and allows the user to pass an un-initialized version of the objects.

Also adds a warning when load is called if `setup()` will need to be called to initialize optimizer or scheduler.

This follows up from #313, because I realized that calling load with different values for initial_image or initial_noise (for Metamer / MADCompetition) wouldn't result in an error, which felt unlike the behavior of the rest of the initializing args, and unlike Geodesic, where a different value for initial_sequence would result in an error. 

Still to do:
- [x] Make docstrings more informative
- [x] Update docs

Example usages with metamer:

All code blocks start with this:
```python
img = po.data.einstein()
model = po.simul.Gaussian(30)
po.tools.remove_grad(model)
```

- Default case:
  ```python
  met = po.synth.Metamer(img, model)
  met.synthesize(10)
  ```
- Set initial image:
  ```python
  met = po.synth.Metamer(img, model)
  met.setup(po.data.curie())
  met.synthesize(10)
  ```
- Set optimizer (kwargs are optional):
  ```python
  met = po.synth.Metamer(img, model)
  met.setup(optimizer=torch.optim.SGD, optimizer_kwargs={"lr": 10})
  met.synthesize(10)
  ```
- Set scheduler (kwargs are optional):
  ```python
  met = po.synth.Metamer(img, model)
  met.setup(scheduler=torch.optim.lr_scheduler.ReduceLROnPlateau, 
            scheduler_kwargs={"factor": 1e-3})
  met.synthesize(10)
  ```
- with save/load (after load, when calling load, only optimizer and scheduler args can be set, initial_image or the kwargs will raise a ValueError; their values are set during load):
  ```python
  met = po.synth.Metamer(img, model)
  met.setup(po.data.curie(), optimizer=torch.optim.SGD, optimizer_kwargs={"lr": 10})
  met.synthesize(10)
  met.save("test.pt")
  met = po.synth.Metamer(img, model)
  met.load("test.pt")
  # this is necessary because we don't know how to initialize the optimizer object
  met.setup(optimizer=torch.optim.SGD)
  met.synthesize(10)
  ```


Diff:
diff --git a/examples/00_quickstart.ipynb b/examples/00_quickstart.ipynb
index 0317c2d1d..74777d9eb 100644
--- a/examples/00_quickstart.ipynb
+++ b/examples/00_quickstart.ipynb
@@ -4314,8 +4314,8 @@
     "metamer = po.synthesize.Metamer(\n",
     "    im,\n",
     "    model,\n",
-    "    initial_image=curie,\n",
     ")\n",
+    "metamer.setup(initial_image=curie)\n",
     "\n",
     "# we increase the length of time we run synthesis and decrease the\n",
     "# stop_criterion, which determines when we think loss has converged\n",
diff --git a/examples/06_Metamer.ipynb b/examples/06_Metamer.ipynb
index 32e6dd226..a0a4cc999 100644
--- a/examples/06_Metamer.ipynb
+++ b/examples/06_Metamer.ipynb
@@ -275,9 +275,9 @@
    "source": [
     "The loss is decreasing, but clearly there's much more to go. So let's continue.\n",
     "\n",
-    "You can resume synthesis as long as you pass the same argument to ``store_progress`` on each run (several other arguments, such as `optimizer` and `scheduler`, must be None on any run except the first).\n",
+    "You can resume synthesis as long as you pass the same argument to ``store_progress`` on each run.\n",
     "\n",
-    "Everything that stores the progress of the optimization (``loss``, ``saved_model_response``, ``saved_signal``) will persist between calls and so potentially get very large. "
+    "Everything that stores the progress of the optimization (``losses``, ``saved_metamer``) will persist between calls and so potentially get very large. "
    ]
   },
   {
@@ -10386,15 +10386,15 @@
    "source": [
     "## More Advanced Options\n",
     "\n",
-    "The solution found by the end of the [Basic usage section](#Basic-usage) is only one possible metamer. **UNTRUE**: For this model, it was relatively easy to find a decent metamer (as seen by the relatively low loss and representation error), but that's not always the case. Optimization in a high-dimensional space with non-linear models is inherently challenging and so we can't guarantee you'll find a model metamer, but we do provide some tools / extra functionality to help.\n",
+    "The solution found by the end of the [Basic usage section](#Basic-usage) is only one possible metamer. In general, optimization in a high-dimensional space with non-linear models is inherently challenging and so we can't guarantee you'll find a model metamer, but we do provide some tools / extra functionality to help.\n",
     "\n",
     "### Initialization\n",
     "\n",
-    "By default, the `initial_image` arg when initializing `Metamer` is `None`, in which case we initialize with uniformly-distributed random noise between 0 and 1. If you wish to use some other image for initialization, you can initialize it yourself (it must be the same shape as `target_signal`) and pass it as the `initial_image` arg.\n",
+    "By default, we initialize the `metamer` attribute with with uniformly-distributed random noise between 0 and 1. If you wish to use some other image for initialization, you can initialize it yourself (it must be the same shape as `target_signal`) and pass to the optional `setup()` method before calling `synthesize()`.\n",
     " \n",
     "### Optimization basics\n",
     "\n",
-    "You can set all the various optimization parameters you'd expect. `synthesize()` has an `optimizer` arg, which accepts a pytorch optimizer. You can therefore initialize your own optimizer after initializing `Metamer` like so"
+    "You can set all the various optimization parameters you'd expect. `setup()` has an `optimizer` arg, which accepts an uninitialized pytorch optimizer, and an `optimizer_kwargs` arg, which accepts a dictionary. You can therefore change the optimizer from the default `Adam` and/or specify any of the arguments you would like:"
    ]
   },
   {
@@ -10427,8 +10427,8 @@
    ],
    "source": [
     "met = po.synth.Metamer(img, model)\n",
-    "opt = torch.optim.Adam([met.metamer], lr=0.001, amsgrad=True)\n",
-    "met.synthesize(optimizer=opt)"
+    "met.setup(optimizer=torch.optim.SGD, optimizer_kwargs={\"lr\": 0.001})\n",
+    "met.synthesize()"
    ]
   },
   {
@@ -10563,9 +10563,9 @@
     "    img,\n",
     "    ps,\n",
     "    loss_function=po.tools.optim.l2_norm,\n",
-    "    initial_image=im_init,\n",
     "    coarse_to_fine=\"together\",\n",
     ")\n",
+    "met.setup(im_init)\n",
     "met.synthesize(\n",
     "    store_progress=10,\n",
     "    max_iter=500,\n",
@@ -14847,9 +14847,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "plenoptic",
+   "display_name": "Python [conda env:plenoptic]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-plenoptic-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -14861,7 +14861,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,
diff --git a/examples/07_Simple_MAD.ipynb b/examples/07_Simple_MAD.ipynb
index 367fe2d68..c8305b006 100644
--- a/examples/07_Simple_MAD.ipynb
+++ b/examples/07_Simple_MAD.ipynb
@@ -127,15 +127,14 @@
     "for t, (m1, m2) in itertools.product([\"min\", \"max\"], zip(metrics, metrics[::-1])):\n",
     "    name = f\"{m1.__name__}_{t}\"\n",
     "    # we set the seed like this to ensure that all four MADCompetition instances have\n",
-    "    # the same initial_signal. Try different seed values!\n",
+    "    # the same initial_image. Try different seed values!\n",
     "    po.tools.set_seed(10)\n",
     "    all_mad[name] = po.synth.MADCompetition(img, m1, m2, t, metric_tradeoff_lambda=1e4)\n",
-    "    optim = torch.optim.Adam([all_mad[name].mad_image], lr=0.0001)\n",
+    "    all_mad[name].setup(optimizer_kwargs={\"lr\": 0.0001})\n",
     "    print(f\"Synthesizing {name}\")\n",
     "    all_mad[name].synthesize(\n",
     "        store_progress=True,\n",
     "        max_iter=2000,\n",
-    "        optimizer=optim,\n",
     "        stop_criterion=1e-10,\n",
     "    )\n",
     "\n",
@@ -474,7 +473,7 @@
     "for t, (m1, m2) in itertools.product([\"min\", \"max\"], zip(metrics, metrics[::-1])):\n",
     "    name = f\"{m1.__name__}_{t}\"\n",
     "    # we set the seed like this to ensure that all four MADCompetition instances have\n",
-    "    # the same initial_signal. Try different seed values!\n",
+    "    # the same initial_image. Try different seed values!\n",
     "    po.tools.set_seed(0)\n",
     "    all_mad[name] = po.synth.MADCompetition(\n",
     "        img,\n",
@@ -482,16 +481,14 @@
     "        m2,\n",
     "        t,\n",
     "        metric_tradeoff_lambda=tradeoffs[name],\n",
-    "        initial_noise=20,\n",
     "        allowed_range=(0, 255),\n",
     "        range_penalty_lambda=1,\n",
     "    )\n",
-    "    optim = torch.optim.Adam([all_mad[name].mad_image], lr=0.1)\n",
+    "    all_mad[name].setup(initial_noise=20, optimizer_kwargs={\"lr\": 0.1})\n",
     "    print(f\"Synthesizing {name}\")\n",
     "    all_mad[name].synthesize(\n",
     "        store_progress=True,\n",
     "        max_iter=30000,\n",
-    "        optimizer=optim,\n",
     "        stop_criterion=1e-10,\n",
     "    )\n",
     "\n",
@@ -621,7 +618,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,
diff --git a/examples/08_MAD_Competition.ipynb b/examples/08_MAD_Competition.ipynb
index 470929cfa..b3bc26473 100644
--- a/examples/08_MAD_Competition.ipynb
+++ b/examples/08_MAD_Competition.ipynb
@@ -117,15 +117,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/billbrod/miniconda3/envs/plenoptic/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)\n",
-      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
+     "ename": "NameError",
+     "evalue": "name 'po' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m mad \u001b[38;5;241m=\u001b[39m \u001b[43mpo\u001b[49m\u001b[38;5;241m.\u001b[39msynth\u001b[38;5;241m.\u001b[39mMADCompetition(\n\u001b[1;32m      2\u001b[0m     img,\n\u001b[1;32m      3\u001b[0m     optimized_metric\u001b[38;5;241m=\u001b[39mmodel1,\n\u001b[1;32m      4\u001b[0m     reference_metric\u001b[38;5;241m=\u001b[39mmodel2,\n\u001b[1;32m      5\u001b[0m     minmax\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      6\u001b[0m     metric_tradeoff_lambda\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10000\u001b[39m,\n\u001b[1;32m      7\u001b[0m )\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# optionally, set a different initial noise value:\u001b[39;00m\n\u001b[1;32m      9\u001b[0m mad\u001b[38;5;241m.\u001b[39msetup(\u001b[38;5;241m.04\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'po' is not defined"
      ]
     }
    ],
@@ -135,9 +138,10 @@
     "    optimized_metric=model1,\n",
     "    reference_metric=model2,\n",
     "    minmax=\"min\",\n",
-    "    initial_noise=0.04,\n",
     "    metric_tradeoff_lambda=10000,\n",
-    ")"
+    ")\n",
+    "# optionally, set a different initial noise value:\n",
+    "mad.setup(0.04)"
    ]
   },
   {
@@ -217,9 +221,9 @@
     "    optimized_metric=model1,\n",
     "    reference_metric=model2,\n",
     "    minmax=\"max\",\n",
-    "    initial_noise=0.04,\n",
     "    metric_tradeoff_lambda=1e6,\n",
     ")\n",
+    "mad_ssim_max.setup(0.04)\n",
     "with warnings.catch_warnings():\n",
     "    # we suppress the warning telling us that our image falls outside of the\n",
     "    # (0, 1) range, which will happen briefly during synthesis.\n",
@@ -234,7 +238,7 @@
    "source": [
     "We're making progress, but it doesn't look like `SSIM` has quite saturated. Let's see if we can make more progress!\n",
     "\n",
-    "To continue synthesis, we can simply call `mad.synthesize()` again (`optimizer` and `scheduler` will both need to be `None`, the default, so we reuse the ones from the initial call), and we then pick up right where we left off."
+    "To continue synthesis, we can simply call `mad.synthesize()` again, and we then pick up right where we left off."
    ]
   },
   {
@@ -305,9 +309,9 @@
     "    optimized_metric=model2,\n",
     "    reference_metric=model1,\n",
     "    minmax=\"min\",\n",
-    "    initial_noise=0.04,\n",
     "    metric_tradeoff_lambda=1,\n",
     ")\n",
+    "mad_mse_min.setup(0.04)\n",
     "with warnings.catch_warnings():\n",
     "    # we suppress the warning telling us that our image falls outside of the (0, 1)\n",
     "    # range, which will happen briefly during synthesis.\n",
@@ -354,9 +358,9 @@
     "    optimized_metric=model2,\n",
     "    reference_metric=model1,\n",
     "    minmax=\"max\",\n",
-    "    initial_noise=0.04,\n",
     "    metric_tradeoff_lambda=10,\n",
     ")\n",
+    "mad_mse_max.setup(0.04)\n",
     "with warnings.catch_warnings():\n",
     "    # we suppress the warning telling us that our image falls outside of the (0, 1)\n",
     "    # range, which will happen briefly during synthesis.\n",
@@ -485,7 +489,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,
diff --git a/examples/Metamer-Portilla-Simoncelli.ipynb b/examples/Metamer-Portilla-Simoncelli.ipynb
index 924c52551..f4e7b9089 100644
--- a/examples/Metamer-Portilla-Simoncelli.ipynb
+++ b/examples/Metamer-Portilla-Simoncelli.ipynb
@@ -391,10 +391,9 @@
     "    img,\n",
     "    model,\n",
     "    loss_function=po.tools.optim.l2_norm,\n",
-    "    initial_image=im_init,\n",
     "    coarse_to_fine=\"together\",\n",
     ")\n",
-    "\n",
+    "met.setup(im_init)\n",
     "o = met.synthesize(\n",
     "    max_iter=short_synth_max_iter,\n",
     "    store_progress=True,\n",
@@ -503,9 +502,9 @@
     "        img,\n",
     "        model,\n",
     "        loss_function=po.tools.optim.l2_norm,\n",
-    "        initial_image=im_init,\n",
     "        coarse_to_fine=\"together\",\n",
     "    )\n",
+    "    met.setup(im_init)\n",
     "    met.synthesize(\n",
     "        max_iter=long_synth_max_iter,\n",
     "        store_progress=True,\n",
@@ -1567,14 +1566,11 @@
     "    img,\n",
     "    model,\n",
     "    loss_function=po.tools.optim.l2_norm,\n",
-    "    initial_image=im_init,\n",
     "    coarse_to_fine=\"together\",\n",
     ")\n",
-    "\n",
-    "optimizer = torch.optim.Adam([met.metamer], lr=0.02, amsgrad=True)\n",
+    "met.setup(im_init, optimizer_kwargs={\"lr\": 0.02, \"amsgrad\": True})\n",
     "\n",
     "met.synthesize(\n",
-    "    optimizer=optimizer,\n",
     "    max_iter=short_synth_max_iter,\n",
     "    store_progress=True,\n",
     "    change_scale_criterion=None,\n",
@@ -1679,7 +1675,14 @@
     "    differentiable models, for mixing two images.\n",
     "    \"\"\"\n",
     "\n",
-    "    def _initialize(self, initial_image):\n",
+    "    def setup(\n",
+    "        self,\n",
+    "        initial_image=None,\n",
+    "        optimizer=None,\n",
+    "        optimizer_kwargs=None,\n",
+    "        scheduler=None,\n",
+    "        scheduler_kwargs=None,\n",
+    "    ):\n",
     "        \"\"\"Initialize the metamer.\n",
     "\n",
     "        Set the ``self.metamer`` attribute to be a parameter with\n",
@@ -1706,7 +1709,11 @@
     "        metamer = initial_image.clone().detach()\n",
     "        metamer = metamer.to(dtype=self.image.dtype, device=self.image.device)\n",
     "        metamer.requires_grad_()\n",
-    "        self._metamer = metamer"
+    "        self._metamer = metamer\n",
+    "        # initialize the optimizer\n",
+    "        self._initialize_optimizer(optimizer, self.metamer, optimizer_kwargs)\n",
+    "        # and scheduler\n",
+    "        self._initialize_scheduler(scheduler, self.optimizer, scheduler_kwargs)"
    ]
   },
   {
@@ -1737,14 +1744,11 @@
     "    imgs,\n",
     "    model,\n",
     "    loss_function=po.tools.optim.l2_norm,\n",
-    "    initial_image=im_init,\n",
     "    coarse_to_fine=\"together\",\n",
     ")\n",
-    "\n",
-    "optimizer = torch.optim.Adam([met.metamer], lr=0.02, amsgrad=True)\n",
+    "met.setup(im_init, optimizer_kwargs={\"lr\": 0.02, \"amsgrad\": True})\n",
     "\n",
     "met.synthesize(\n",
-    "    optimizer=optimizer,\n",
     "    max_iter=longest_synth_max_iter,\n",
     "    store_progress=True,\n",
     "    change_scale_criterion=None,\n",
@@ -2276,8 +2280,11 @@
     "# Set the RNG seed to make the two synthesis procedures as similar as possible.\n",
     "po.tools.set_seed(100)\n",
     "met = po.synth.MetamerCTF(\n",
-    "    img, model, loss_function=po.tools.optim.l2_norm, initial_image=im_init\n",
+    "    img,\n",
+    "    model,\n",
+    "    loss_function=po.tools.optim.l2_norm,\n",
     ")\n",
+    "met.setup(im_init)\n",
     "met.synthesize(\n",
     "    store_progress=10,\n",
     "    max_iter=short_synth_max_iter,\n",
@@ -2290,8 +2297,8 @@
     "    img,\n",
     "    model_mag_means,\n",
     "    loss_function=po.tools.optim.l2_norm,\n",
-    "    initial_image=im_init,\n",
     ")\n",
+    "met_mag_means.setup(im_init)\n",
     "met_mag_means.synthesize(\n",
     "    store_progress=10,\n",
     "    max_iter=short_synth_max_iter,\n",
diff --git a/examples/Synthesis_extensions.ipynb b/examples/Synthesis_extensions.ipynb
index 69341447d..c305e3fd1 100644
--- a/examples/Synthesis_extensions.ipynb
+++ b/examples/Synthesis_extensions.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "fa6acf5c-5dc9-493d-8eba-2c5d5beb6362",
    "metadata": {
     "tags": []
@@ -22,8 +22,6 @@
    "outputs": [],
    "source": [
     "import warnings\n",
-    "from collections.abc import Callable\n",
-    "from typing import Literal\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import torch\n",
@@ -40,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "dee9c4a2-8233-4326-82f3-49362f9be356",
    "metadata": {},
    "outputs": [],
@@ -48,31 +46,14 @@
     "class MADCompetitionVariant(po.synth.MADCompetition):\n",
     "    \"\"\"Initialize MADCompetition with an image instead!\"\"\"\n",
     "\n",
-    "    def __init__(\n",
+    "    def setup(\n",
     "        self,\n",
-    "        image: Tensor,\n",
-    "        optimized_metric: torch.nn.Module | Callable[[Tensor, Tensor], Tensor],\n",
-    "        reference_metric: torch.nn.Module | Callable[[Tensor, Tensor], Tensor],\n",
-    "        minmax: Literal[\"min\", \"max\"],\n",
-    "        initial_image: Tensor = None,\n",
-    "        metric_tradeoff_lambda: float | None = None,\n",
-    "        range_penalty_lambda: float = 0.1,\n",
-    "        allowed_range: tuple[float, float] = (0, 1),\n",
+    "        initial_image: Tensor | None = None,\n",
+    "        optimizer: torch.optim.Optimizer | None = None,\n",
+    "        optimizer_kwargs: dict | None = None,\n",
+    "        scheduler: torch.optim.lr_scheduler.LRScheduler | None = None,\n",
+    "        scheduler_kwargs: dict | None = None,\n",
     "    ):\n",
-    "        if initial_image is None:\n",
-    "            initial_image = torch.rand_like(image)\n",
-    "        super().__init__(\n",
-    "            image,\n",
-    "            optimized_metric,\n",
-    "            reference_metric,\n",
-    "            minmax,\n",
-    "            initial_image,\n",
-    "            metric_tradeoff_lambda,\n",
-    "            range_penalty_lambda,\n",
-    "            allowed_range,\n",
-    "        )\n",
-    "\n",
-    "    def _initialize(self, initial_image: Tensor):\n",
     "        mad_image = initial_image.clamp(*self.allowed_range)\n",
     "        self._initial_image = mad_image.clone()\n",
     "        mad_image.requires_grad_()\n",
@@ -83,7 +64,13 @@
     "        self._reference_metric_loss.append(self._reference_metric_target)\n",
     "        self._optimized_metric_loss.append(\n",
     "            self.optimized_metric(self.image, self.mad_image).item()\n",
-    "        )"
+    "        )\n",
+    "        # initialize the optimizer\n",
+    "        self._initialize_optimizer(optimizer, self.mad_image, optimizer_kwargs)\n",
+    "        # and scheduler\n",
+    "        self._initialize_scheduler(scheduler, self.optimizer, scheduler_kwargs)\n",
+    "        # reset _loaded, if everything ran successfully\n",
+    "        self._loaded = False"
    ]
   },
   {
@@ -91,12 +78,12 @@
    "id": "7a28e199-3a47-47f9-aae6-92c551c63820",
    "metadata": {},
    "source": [
-    "We can then interact with this new object in the same way as the original `MADCompetition` object, the only difference being how it's initialized:"
+    "We can then interact with this new object in the same way as the original `MADCompetition` object, the only difference being how `setup()` is called:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "b6e7ac33-87a3-4f54-a601-6e4c448eae8e",
    "metadata": {},
    "outputs": [
@@ -104,16 +91,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/billbrod/Documents/plenoptic/plenoptic/tools/data.py:126: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)\n",
-      "  images = torch.tensor(images, dtype=torch.float32)\n",
-      "/home/billbrod/miniconda3/envs/plenoptic/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)\n",
+      "/home/billbrod/miniforge3/envs/plenoptic/lib/python3.11/site-packages/torch/functional.py:539: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:3637.)\n",
       "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
-      "/home/billbrod/Documents/plenoptic/plenoptic/synthesize/mad_competition.py:130: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
-      "  loss_ratio = torch.tensor(self.optimized_metric_loss[-1] / self.reference_metric_loss[-1],\n",
-      "/home/billbrod/Documents/plenoptic/plenoptic/synthesize/mad_competition.py:134: UserWarning: Since metric_tradeoff_lamda was None, automatically set to 0.10000000149011612 to roughly balance metrics.\n",
-      "  warnings.warn(\"Since metric_tradeoff_lamda was None, automatically set\"\n",
-      "/home/billbrod/Documents/plenoptic/plenoptic/synthesize/mad_competition.py:134: UserWarning: Since metric_tradeoff_lamda was None, automatically set to 0.009999999776482582 to roughly balance metrics.\n",
-      "  warnings.warn(\"Since metric_tradeoff_lamda was None, automatically set\"\n"
+      "/home/billbrod/Documents/plenoptic-org/plenoptic/src/plenoptic/synthesize/mad_competition.py:151: UserWarning: Since metric_tradeoff_lamda was None, automatically set to 0.10000000149011612 to roughly balance metrics.\n",
+      "  warnings.warn(\n",
+      "/home/billbrod/Documents/plenoptic-org/plenoptic/src/plenoptic/synthesize/mad_competition.py:151: UserWarning: Since metric_tradeoff_lamda was None, automatically set to 0.10000000149011612 to roughly balance metrics.\n",
+      "  warnings.warn(\n"
      ]
     }
    ],
@@ -122,11 +105,13 @@
     "curie = po.data.curie()\n",
     "\n",
     "new_mad = MADCompetitionVariant(\n",
-    "    image, po.metric.mse, lambda *args: 1 - po.metric.ssim(*args), \"min\", curie\n",
+    "    image, po.metric.mse, lambda *args: 1 - po.metric.ssim(*args), \"min\"\n",
     ")\n",
+    "new_mad.setup(curie)\n",
     "old_mad = po.synth.MADCompetition(\n",
-    "    image, po.metric.mse, lambda *args: 1 - po.metric.ssim(*args), \"min\", 0.1\n",
-    ")"
+    "    image, po.metric.mse, lambda *args: 1 - po.metric.ssim(*args), \"min\"\n",
+    ")\n",
+    "old_mad.setup(0.1)"
    ]
   },
   {
@@ -139,13 +124,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "705a020c-93b1-483e-95f5-ba780ad74b00",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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GW8UNr1q1SjCZTMIzzzwjHDhwQLjnnnuE7Ozs6+KuN27cKKxdu/a689PT06NiyV9//XUBgLBr1y7hwIEDwjPPPCNkZmYKFotF2Lx5M2vndDqFgoICIS8vT/j5z38unDhxQnjttdeEH/zgB8LOnTuj7iGRSIRPf/rTbztOvEk8/tq1a4Uf/vCHwv79+4Vjx44JP/rRj4SUlBQhNTVVmJqaYu2eeeYZQSKRCKdOnWLHrly5Inzve98TDh06JBw6dEj4xje+Icjl8uuS7wwMDAhJSUlCdXW18PTTTwunTp0S9u7dK/zTP/2T8Nhjj71t3+vr6wWFQiHs3r1bOHbsmPDrX/9aSEhIEO699953NJaTJ08KAITf/va3b3vvgwcPCnv27BG+9a1vCQCEb3/728KePXuEgwcPRrXLzs4WtmzZEnXs5z//uSASiYRvfOMbwsmTJ4V/+Id/EEQikfBf//VfUe2+9rWvCUqlUvjbv/1b1vff/va3wic+8Ymo+PfFWEq8vdvtFrKzs4WSkhLhlVdeEV599VWhrKxMyMzMFFwuF2sXDoeF1atXC6mpqcLzzz8vHD58WNi4caOQkJAgDA4Ovum9/1zzdHzU6cLw8LCQmJgobNiwQTh06JDwwgsvCDabTVi5cmVUcrBTp04JEolEeOaZZ9ixpa6lmxnPYiwlT8eBAwcEkUgk/MVf/IVw8uRJ4Uc/+pGgUCiEr3zlK1HtlkpfYnTjT59ufOiFjr6+PuHWW28VtFqtYDKZhCeeeELYv3//OyYugiAIzz33nJCXlyfI5XKhqKhIePnll4WKigrhYx/7WFS7mZkZ4Utf+pKQkZEhyGQywWQyCevWrRP+/d//PaodgLd9+ajdjYjLl7/8ZaGkpESIi4sTNBqNUFhYKHzlK18RJiYmotrRPPHjbm5uFtauXSvEx8cLSqVSqKysFP77v//7hvcfGhoSHn/8ccFqtQoymUywWCzCtm3bhN/97ndv23dBWEhMtGrVKkGhUAhms1n44he/KHg8nnc0FnqGhw4detv7pqenCwCu+6Snp1/XbuPGjded/9RTTwm5ubmCXC4XcnJy3jRp2rPPPiusXLlSUKvVgkajEQoKCoQnnnjiLTM4LoV4CMKC0Hf33XcLcXFxglarFXbt2iX09fVd185utwuPPfaYkJCQIKhUKmHLli1CQ0PDW977oyh0fBTogiAIQmNjo7Bt2zZBrVYLer1eeOSRR4Tp6emoNm/GiJe6lpY6nsVYitAhCILw0ksvCWVlZYJcLhfS0tKE73znO0IoFLqu3VLoS4xu/OnTjQ+F0PHHxtDQkKBQKIR//Md/fF/vA0D4h3/4ByEYDEZlzPso4mtf+5pQXFz8kZ+Hd4pgMCj85je/+bMWOv7YiNGFDx9idOPd4cNAN0SCwBkxPwKYn5/Hl7/8ZWzbtg1GoxG9vb3413/9V0xMTFznOPReg3eI++1vf/ueJBT7U8XatWvxxBNP4BOf+MQfuyt/cjh37hzWr1/P/u/q6npTD/oYloYYXfjTQIxuvHN8WOjGhyp65YOARCLB+Pg4/uqv/gp2ux0ajQbr16/Hnj173lfCAiAqMuTdOhn9qeP8+fN/7C78yaKioiJqLaWlpf0Re/PngRhd+NNAjG68c3xY6MZHTtMRQwwxxBBDDDH8cfChDpmNIYYYYoghhhj+fBATOmKIIYYYYoghhg8EMaGDw6OPPhpVOKe/vx8ikegdJx16P0Flv+lDGBsbw9e+9jVUV1dDr9fDZDJh69atUXUACI8++mjUNejzpS996bq24XAYP/7xj1FSUgKlUonExERs27YNY2NjN93348eP45Of/CSys7OhUqmQnZ2Nz3/+8zfMCHmj/olEIjQ0NFzXdmRkBJ/+9KdhsVigUCiQmZmJr33ta1FtpFIpu8avf/3rJfX30UcfxalTp5bUtrm5GZ/97GexbNkyyOXyd1ViO4YPJ2J0IkYnboQYnVgaPnKOpDeD5ORkXLhw4UNdxIhKdRPq6urw+9//Ho899hhWrVqFQCCAn/3sZ9i0aRP27duHO++8M+p8k8l0XWn7GznOPfzwwzhy5Ai+/vWvo7q6GrOzszh9+vR1dS6Wgqeeegputxvf/OY3kZWVha6uLjz55JM4cuQIGhsbr0vb++ijj+Kzn/1s1DG+8iewQPjXrl2LzMxM/Md//AeSkpLQ399/Xer18+fPY3R0FHffffdb9nHfvn3QaDTYunUrO+bz+fCTn/wEn/nMZ6JSHPOoq6vDwYMHWZbXCxcuvO18xPCnjRiduIYYnYjRibfFHyVQ90OKRx555LrEMR9WUIKXxYliHA6HEAwGo44Fg0EhLy9PWL9+fdTxRx55REhJSXnbez3//POCRCIRamtr33W/BUEQJicnrzt2+vRpAYDwm9/8Juo4AOEb3/jG215zx44dwvLly4VAIPC2bfv6+gQAwq9+9as3bdPa2irce++9wj333CNs375d+MpXviIsW7ZM+Od//mfB6/W+6Xl8pshvfOMbS0oAFMOfFmJ04saI0YkYnVgKPrLmlePHj6OqqgpKpRLZ2dn4xS9+cV2bG6lNH330UaSmpqK2thZr1qyBSqVCfn4+Dhw4AAD40Y9+hIyMDOh0OuzatQtTU1NR1/zJT36CwsJCqFQqJCQkoLq6Gnv37n3PxqXX6yGVRiuwpFIpKioqMDIy8o6u+bOf/QwbN27EsmXL3osuwmQyXXds+fLlAPCO+tjT04MjR47gC1/4wptWy7xZFBYWYs+ePbjjjjtw7Ngx/P73v8fLL7+Mr371q1CpVG96nlj8kX2l/iwRoxNLR4xOxOjEUvCRHHlbWxtuv/12qFQqvPDCC/i///f/4sc//jGOHz++pPPn5ubwqU99Cp/5zGewd+9emM1m3HPPPfibv/kbnDx5Ej/96U/x4x//GCdPnsQTTzzBznvuuefwN3/zN/j4xz+OgwcP4rnnnsO9997LShAD12ywS7UNLgWBQAAXLlxAYWHhdb9NTk7CaDRCKpUiLy8P//Iv/4JwOMx+DwaDuHTpEoqLi/F3f/d3rCLnypUrceLEifesj1Ss6EZ9/PnPfw6FQgG1Wo0tW7bg7NmzUb9T7L5KpcL27duhUCiQkJCAT33qU++41HhnZycefPBB7N+/H1u3bsUDDzyAu+++G//yL/+C+fn5d3TNGP60EKMT1xCjEzdGjE68A/yxVS1/DHziE58QEhMTBbfbzY4NDg4KMpksSm1K6jW+psEjjzwiABBOnz7Njl29elUAIOTl5UXVFPj//r//T5BKpezYE088IVRWVr5l325U1O1GeDO16Y3wta99TRCJRMKZM2eijv/7v/+78B//8R/C8ePHhQMHDgif+cxnBJFIJDz++OOszdjYmABAiIuLE8rLy4WXX35ZOHDggLBp0yZBJpMJly9fftv7vx3m5uaE/Px8obCw8DqV7yc/+UnhhRdeEM6cOSP87ne/E8rKygSpVBpVX+Of//mfWR//8i//Ujh+/Ljwi1/8QjAYDMKyZcuiVJmCsDS16SuvvCIcPXpUEISFZ37y5Elhfn5e+P73v39d7Ys3w0dNbfrnhhidWECMTsToxHuJj85IOWRmZgoPP/zwdcc3bdq0JGKi0WiizvP7/QIA4S//8i+jjv/iF78QALDCP08//bQgEomEv/qrvxKOHj16XTGjm8FSiclzzz0niEQi4Vvf+taSrvulL31JACB0dnYKgiAIIyMjAgBBqVQKIyMjrJ3L5RJMJpNw//33v+MxCMKCHfmOO+4QtFqtcPXq1bdtPzc3J9hstqgiXt/73vcEAMJdd90V1faFF14QAFxXVXIpxIQHEZObxUeNmPy5IUYn3hwxOnE9YnRiafhImlfGxsaQlJR03fEbHbsR9Hp91P9yuRwAkJCQcMPj5Ln9qU99Cj//+c9x6dIl7NixAwaDAXfffTf6+/tvcgRLw2uvvYZHH30Ujz/+OL7zne8s6ZyPf/zjAIDa2loAC2MSiUQoKiqC1Wpl7bRaLVavXo36+vp33L9IJIJHHnkEx44dwyuvvIKysrK3PScuLg533HFHVDpf8hDfvn17VNtbbrkFAN5VH4EFVfamTZve1TVi+NNDjE68OWJ04nrE6MTS8JEUOpKTkzExMXHd8Rsdey8hEonw2c9+FjU1NZiensYzzzyDmpoaPPDAA+/5vY4fP4777rsPu3fvvqHz21L6CizYP7Oyst603btxiPrc5z6H3//+93jhhReiQs5upn8AUFxc/JZtP8pOWzG8c8ToxNL6CsToRAxLx0dyllevXo2DBw/C4/GwY0NDQx9oMaGEhAQ88MADuP/++9Hc3PyeXvvChQvYtWsXtm7div/5n/+5qZfpueeeg0gkYl7iALB79260tLREeYy7XC688cYbUe1uBn/zN3+DX//61/jtb3+Lj33sY0s+b25uDvv378eKFSvYsVWrVsFiseDIkSNRbQ8fPgwA77iPMXy0EaMTb44YnYjhneIjmRzsm9/8Jvbs2YNbbrkFf/u3f4tAIIBvf/vbS1abvlP8xV/8BeLi4rB69WqYzWZ0dnbid7/7HVPvAcCzzz6LT3/60zh+/Dg2btx40/dob2/HHXfcAaPRiL/9279FXV1d1O+rVq0CAAwMDODhhx/Ggw8+iJycHPj9fuzduxdPP/00PvvZz0YlOvrKV76C3/3ud7jtttvwrW99C3K5HD/84Q/h9Xrx1a9+lbU7deoUNm/e/Lbluf/lX/4FP/rRj/DpT38aubm5uHjxIvvNZDKxe//whz9ER0cHNm/eDKvVioGBAfzwhz/E+Pg4nnvuOXaOVCrF97//fTz66KP43Oc+h7vvvhvd3d34xje+gU2bNmHLli03PY/vFF6vFwcPHgSw8CwA4MUXXwQAZGRkoLq6+gPrSwzvDjE6EaMT7xc+0nTij+1U8sfC0aNHhYqKCkEulwuZmZnCU089dV3SnzdzELtRohzcIDkNOXF1dXUJgrDgILZx40bBZDIJcrlcyMjIEL70pS8Js7Oz153zdg5Jb+YgRsff7EOw2+3Crl27BJvNJigUCkGlUgmVlZXCf/7nf17nxS0IgtDR0SHceeedglarFdRqtbB169brPNL3798vABAOHTr0ln3fuHHjm/bvkUceYe327dsnrFmzRkhMTBSkUqlgMBiEu+66S7h06dINr/vss88KxcXFglwuFywWi/BXf/VXgsvluq7dzTqI3Qzo2m83thj+NBCjEzE6EaMT7y1ipe3/RPH000/jscceQ3d3N9LT069L9PPHwNe//nXs27cPTU1NH9paAuFwGP39/cjJycGvfvUrfOYzn/ljdymGGN43xOjEO0OMTrx/+OOvwBjeFXJycgAAHwbZ8fTp0/j617/+oSUkAKBQKKKSGsUQw0cBMTpxc4jRifcPMU3Hnyjsdjv6+vrY/3/WNsD3EHV1dYzwZmRkwGg0/pF7FEMM7x9idOKdIUYn3j/EhI4YYoghhhhiiOEDwUcyZDaGGGKIIYYYYvjgERM6YoghhhhiiCGGDwZLDXPBW4RXxT6xT+zz5/25GTohlUrZeQqFQlAqlQIAQSKRCBqNhv3GH4+Li7vungUFBUJycrIAQFCpVOy4yWQSAAhWq1UwGAzsO39PnU4niMViAYAgk8mirrt582b2PSEhQQAg7Ny5UwAglJSUCADYdTUajZCens7a07WoHd0jIyND0Ol0UfeRSCQCAMFisQgmk0kwGAzsujRPKSkpgtFojDqP5oVvq1arhcTERHYefy+LxSIAEJKSktixoqKi6+ZTJBIJZrNZUCgUQkZGhgBAMJvN7Hcai81mY78vHpNSqRRUKhUbG/UJgJCfny8AEOLj49n4ZTIZu+6NxqjX66PGqFar2XcAN1wXN/rw/aDvSqWS9V8mk7ExrVq1irW12WzsO7XNysoSZDIZG8/iD61vmUzG1qVOpxO0Wi1bH2KxWJBIJOyaSqWSPU+5XH7dM6f50mq1Ucf4+QHAQpH5NZydnS3Ex8cLWVlZgtVqFeRyedQ5GzZsYN+zsrJuOKdyuVyorq6OWrf8eGm9q1SqqHeYX1vA0ujEkn06RCIRKioqrjtOWezEYjGEhQJyEIlEUX8jkQgikQhrJxaL2W8A2G9SqRRqtRpWqxXx8fGYn5/H3NwcZmdn2X0ikQiMRiNsNhvUajWCwSBaWloQCoWgVCqh1Wqh1+uhVCqh1+vh9XoRCoXg8XhgMplgNBqhVCoRiUQgFosRCARYXwEgFAohFArB6/VCJBJBoVBAr9dHtReLxYhEIggGgwgEAgAWkr0AC17PNBYadzgchlqthlqthkKhgFgsjvKMFolE7HqLEQqF2NglEgmbA6lUilAoBJlMxvpPz0AqlUIqlSIYDGJ2dhZut5u1E4vFkEgkUKvVEIlEcDgc7Byfz4dQKAS1Wg2xWAyNRgORSAS5XA65XA61Wg2fz8dqRUQiEUgkEvh8PgQCAQQCAahUKqjVajYuYCH8LBQKRa0ZQRAgl8uj5l8qlbK5mJ+fh0wmY+fI5XJEIhE2HzRGQRAgkUgQDochkUjYXM3MzMDhcCAuLg7x8fGQyWTsWfj9fvj9fvh8PrjdbgSDQczNzbE5orHKZDIoFAp2b5FIxPpJ46A5kEgkcDqdmJ6ehlQqhU6ng1wuh0gkQjgcRiQSQSAQgMfjQTAYhM/nQyQSYfdTKBSQSCTsHjKZjD1vel70vrhcLkxOTiISiUCv10On07H5pncwHA5DKpXC6/VCIpEgFApF9YfWAf2VyWQIBAJQKBQQiUTsuUgkEjz++ONLjnoQiUTMWTEUCkEqlSIQCMDr9SItLQ1TU1OQSqWYmprCyMgIdu/ejb6+PgiCgKGhIVitVphMJnR1dcFiscDlckEikSA9PR1nzpxBSUkJxsbGYDAYIJVKYbVa0dnZifz8fFy5cgU5OTnwer3sfX/00UfZ83355ZexfPlyXLp0Cbm5uRCLxUhLS4NYLIbJZEI4HMZrr70Gm82GYDCIwsJC2Gw2tLW1YevWrWhubkZKSgouXLgAhUIBq9WKy5cvQ6vVIi8vDzabDbOzs/B4PJBKpawU/dTUFJu/srIyOBwO2O12xMfHw+12Q6fToaioCL29vejq6oLZbIbf72dJv+rr6yGTyZCdnY2+vj72/oRCIeTk5KCiogI//elPMT09je3bt2NsbAzAQlZOn8/HSszX1tZi+fLl6Ovrw/T0NKthEggEMDQ0hEAgAL1eD71ej/b2dhQXF2NqagoOhwOFhYU4efIk0tPT4fP5oFKpYDQaoVarUVNTg3Xr1mFqagrj4+PsffP7/SgqKsLExATS09Nx9epVeL1eVFZWoqmpCUlJSZDL5RAEAcuWLUNTUxOkUin6+/tRXV2Nffv2IT8/HykpKejo6IBYLIbFYsHs7CwcDgfEYjEyMjKQnp6O48ePQ6/XIz09HZ2dnVAqlbjnnnvwq1/9CoWFhbh69Spuv/12TE9Pw+l0IiMjAz6fD4Ig4MqVKygtLUUoFMLs7Czi4uIwNjaGsbExZGVlQaPRoLa2FqOjo0hKSoJMJoPRaIRWq0VOTg4OHz4Mn8+HkpISHDt2DB//+McxNjaG7OxsnD9/HpWVlZifn4fRaITH42HXP3LkCFJSUrBs2TJcvnwZwWAQK1asgNPpxKVLl5CSkgK1Wo1jx47BZDIhJSUFNpsNV65cQV5eHsLhMEZHR1FWVobExEQ0NjbCbDbDYDDA5/PBaDTiyJEjGBoaYjVxOjo6sGHDBrjdbnR3dyM1NRUulwujo6OwWq2IRCKw2+1ITEzEzMwMfD4fbrvtNvT29sJisaChoQFGoxEJCQno7++H3+9HSkoKlEolHnvssSXRiZsyrxChJ2JIC5YYNQkYRGSJsRJxI0ZJTCgcDrM29CIR4SJmrlQqowg/MTcizsSIgGsMn78WCTh0n0gkAr/fD0EQEAgEGAMhgs0zvmAwCJlMxhi61+uFQqFgggd/TeoPL0zwaYWJSPCMn65NH4lEwhgGzSVdn5hJMBhkAg2Nk/ru9/vZNWje5XI5ZDIZpFIplEolExhkMhncbjeUSiUkEgm8Xi98Ph9EIhFmZ2cBIIo5SSSSqHHSdSQSCWOY/DiJgfr9fjZ/YrEYcrkcUqmUCQGLBVKaQ5obek78OOk5kaBB/aC5IkGAni2tJbo+fYLBIGPIMpkM4XCYCRXEtGnu6Xo0Fho/XZOfB37OaNyL1wIvdAeDQdZvEtR4AYf6wR/n51gsFiMUCkWdKxaL2fiCwSCbV1r7fD+lUin8fj/kcjmbR5q7mw0bzMrKQlxcHLKzsyEWi1FbWwubzcYYlFgshsvlYkx17969CIVCSE1NxerVq6HT6RAIBFBQUICZmRmMj4/D5XKhr68PK1asQG1tLaxWK1wuF3Jzc2EymXD//fdjbm4OVVVVSElJQTAYhNFohFQqxcTEBGZmZjAzM4PS0lKcPHkSa9aswezsLKanp2EymZCcnAyFQoHm5mbMzc1BJBLh9ttvh1wuR319PUpKStDc3IzCwkLMzs7C7/czQcXtdmPDhg1MeBkZGYFYLIZMJsOVK1egVCqxfPlyjI+Pw+12w+l0or+/HwaDARKJBC6XC/Pz85iYmIBSqURFRQXm5uZQWloKt9uNgYEB6HQ6qFQqjI+PQy6Xw2q1MsH18uXLOHToEGMq9E7X1NQgKSkJW7ZsQXFxMcbHx9mc+v1+5OXlobm5Gf39/fB4PJDJZCgpKWFjiouLg16vh9FohN/vRygUQmZmJoLBIDo6OiCVSvH000+jtbUVu3fvhtfrRW5uLoLBIEwmE7xeL/Ly8jA1NQWTyYSJiQno9Xq43W4cPXoUeXl5UCqVSE5OhkgkQltbG2QyGWZnZ7Fp0yYMDw/j4x//OBoaGmCxWGA2m5GYmAiNRsOeJQni58+fR1JSEtLS0jA0NIT09HSEQiFcuHAB6enpCAaDKCoqQkdHB3v/JicnEQwGMTw8DLPZjIMHD0IkEqGkpIS9m8XFxRCLxWhra8OaNWtgs9mQnJwMQRDQ3NyMuro6vPzyyygpKYHX64XZbMZtt92Gl156CWazGc8//zzKy8sRiUSQlJQEj8cDjUaD6elpXL16FUqlEtu3b4fT6YREIkFGRgZbIzKZDElJSdBoNKiurkZubi5SU1PR2dnJBALaTDqdTgwNDcFkMkGj0eCVV17Btm3b0NLSAo1Gw+att7cXBoMBdXV16Onpwbp165jwW1JSgkgkgrS0NKxevRoGgwE5OTlYs2YNAMDv9yMxMRHJyclsY5+amoqxsTEMDw9jamqKhWW/HW5K6CDhgidEtHuiXR8JFwAYY6b2RCgJRKyJINP/Pp+PnUefYDDIdtwKhYIxLn7XSwyCFhb1TyKRQKlUsnNI+AHAmDiAKIGKGAVdk3bdgiBAqVSy+y4WrmjBEtPhtSik5SCmSOeGQqHrdvF0jOaFF1SoH8RYqA0xI5prmjsag8fjYQKc1+uFWq1GXFwc5ufn2e6M5oL66HQ62XioCmYwGGTtQ6EQE95IqKLzaSdBzJM0CSQ8kTaD12IRs6ddMjFzXoKWyWRMIOTXnEwmg0qlAnCNWdKzI+0QLxzRfUh4o34IggCVSsXmY/H65LVYJOguXk+8MMuvJ/458X2mvtHv1A/qJ78eScNDcxsIBNi6IMGI1iMxQBL26C/fLhwOQ6VSMY0RPxe8tmkp6Ovrw9zcHFpaWqBUKrFr1y4MDw8jOTkZgUAAra2tSE5OhtVqhdPphNlsxujoKIaGhlBbW4tAIIDx8XFkZGTA4XBg165dSExMRE5ODubm5pCdnY3c3FysWLECc3NzUKlUCAQCyMnJwfj4OEKhEPLz85mWUiKRQKPRQKvVorGxke3uExMTYbPZ8IMf/AANDQ3o7OxEdXU1PvWpTyE7OxsjIyOYnJxEdnY2BgcHkZaWhrq6OuTk5CAzMxMFBQWYmprCt7/9bbS1teHKlSsYHBxESUkJVCoVXC4X7rnnHib4rVq1CjabDY2NjYz5BwIBaLVaeL1euN1uXL16FYODg8jNzcXAwABefPFF5Ofnw+l0IisrC1qtFj6fD729vTAajQiHw0wAiUQimJ6ehkajgUwmwxe+8AVcvHgRZ86cgcfjYYLD1atXkZCQAIfDgfLycuzYsQMejwelpaUIBAJoampCQ0MDnE4nzp49i+HhYej1eraGZmZmcOedd2JiYgIVFRWQSCQ4cOAAGhsbcfbsWWRmZkKpVCI3N5etHaVSCZVKBZlMhrvvvhtKpRLhcBherxeXLl2CSCSC1+uFXq+H1WpFfX0927lv3boV586dg9PphEKhQE1NDaqqqtDd3Q2j0YiUlBSkp6cjOTkZSUlJWLt2LfLy8pCfnw+lUonW1lZMTk5Co9Fg+/btcDgcqKqqgkajgd1uZ0JwXl4eHA4HXnnlFfZeGo1GhEIhTE1N4cSJE/D5fKipqYHX60VKSgqysrJgNpvR3t7OmLPX68WmTZtw4cIFPPjggzAYDAiHw3C73Zifn0dvby90Oh30ej1uvfVWvPbaa9Dr9TAYDBCLxRgeHobD4QBwTVtrNpsRDoeRlpbG5lan08FoNKKwsBAAkJqaCpPJhNnZWVRVVaGurg7Jyclob29HIBBAY2MjiouLUVBQALVajeLiYly4cAGlpaUQBAHx8fHIzMzEnj170NnZCZFIhNbWVlgsFrS1tUEkEuH06dNobm5Gbm4uozW33HILCgoKEIlE0N3dvSQacVNCB79Dov+JofGMdPHOldryWEy8F7eja9IuFbhmhiGVHADG9HjzBM/4gsEg/H4/2tra4Pf72fV4As/v3ImQEzEngScSiUCr1bIdYzAYZDtjXjNC80EaGdr58/0ErgkSJATRtag9sMDA+PkgDcBijQMJGCToiEQixkSIyRIzpF1aKBTC5OQk3G43Y+oKhQLBYBAajQbz8/Pw+/1QKpVsx0kmCLqX3+9nwtLMzAzC4XCU2YAXomh+aNwkiNAYaV6oL6TpUCgUUKlUbMwqlYoxbGKONP90DxLuFms4iPASg+fNXzTnxJBpTvm+8cIR3Zu0QbReaG5JsKJr0vomUwN9p3XBr+fFAkQwGIwyvdF3ErjonSRBhO9nMBhkY6H1TM+M1gfdhz50Dd7Es1QYjUakpaXB4XBgbm4O4+PjUCqV6OrqwtjYGO68807o9XqMjo5iZGQENpsNO3bsgFwux/Lly2EymZhZNTExEYODg5iZmUF3dzecTidEIhH279+PrKwsTE1NQavVoqGhgTF3o9EIn8+Hvr4+FBUVoaurC0lJSdDpdBAEAVNTU/B4PMxEkJeXh4qKCpSXl0MsFqO5uZkJ9/n5+bDZbBgbG8Mbb7yBkZEReDweFBUVwePxIBAI4OjRo8xsqtFomEnD6/UiNTUVANDU1ASHwwGpVIrCwkK0trZiZmYGer0eKSkpCIVCqK2txZYtW9Db24umpiYUFhbi3nvvhcvlQlJSEqampjA8PIy8vDzY7XacPXsWwWAQtbW1SE9Ph9FoxIoVK5CVlQWXy4W9e/fi8ccfZ+s6KSkJCoUCWq2WaYrGxsZQX18PrVaL1tZW1s+0tDTYbDZs3LgRTU1NMJlMbH5JcNXpdDCZTBCJREhJSUFlZSVCoRD0ej0SExOh0+lgt9sxMDCA3Nxctv4vX76M7OxsxryVSiVbO36/H8nJyWhtbUUwGITVakVPTw8yMzMxMzODkZERJjBVVVVBKpWiq6sLqampiEQi6Ovrw/PPP4+Ghga0tbVhfHwcu3btgsFgYCaEgoIC9PX1YWRkBPPz88jMzMSWLVsQDAaRlpaGu+++mwl1fr8fwWAQjz/+OOMj8fHxCIVCSE9Px8zMDORyOebm5tDd3Y2RkRHEx8fD7/ejvLwctbW1GB4ejnrnpqenMTw8jLa2NvT29mL58uWIj4/Hzp07ERcXB6/XC6vVioqKCvT09ODQoUPo6OhAQUEB9u3bxzZTUqkULpcLTqeTren29nb4fD5oNBosX74cLpcLFosFTqcTW7ZswcDAAFQqFTNPeTweOBwOGI1GjIyMYGJiAp/+9KcxPj6OrKwsWCwWaDQaRCIR9Pf3QyQS4dZbb0V3dzcGBweZwDY9PQ2VShVVh+etcFNCBzFcXt3NazZ4UwkxBeDaDpa3V/PmDF5tzZtYiMHSi7OY0PO7PXoQvCqadoYAmJ2WmA2dS4ue+k4MSyQSQalURvlU8GPlbfW0+yT1NZ1Du0o6jzQfxCSIWQLXhCC6j0wmY+pouieZUHhNCj/nNDb6nxg7tSWNBO8zEQ6H4XK52E6a1PTE2GgnTwKF1+tlghKZTmg+xGIxVCpVlEmE70sgEGCCCrAgPNKzJI0ZCSa0fniBikxji018i7VCdA5vjqE55rVvJBzQ8+KvQc+Wnh+Nj0BrmDe/yGQyJjDwmhje5MhrTfjr8VopEsR48xYJAKQtIu0dr13j+019IybBvys0f7ywxZukxGIx5ufn2ThuBoFAAPX19VAoFPB6vejs7GTPiISQ0dFRbN++HSUlJcjKymL+Q7W1taipqYHVasUf/vAH7NixAwqFAgUFBQAWSs2bTCasXLkSv/zlL7F9+3bMzMygrKwMfr8fAPDKK6+gqakJxcXFmJmZYXbsxsZGJCUlISsrC4IgwOfzwefzobq6GhMTExgaGkJTUxPS09OZPd9kMsHn80GhUODBBx/ErbfeCqfTiaSkJKZxkclkGBsbg0KhQEtLC2ZmZtDa2oqqqiocP34cZWVlSEhIwOHDh+H1emEymeByubBu3Tq0trZibm6O2cTb29tx3333YceOHZiamoJYLMbly5cRCATgdDpRWFiIcDiMDRs2oLy8HEVFRTCbzUhPT8fp06fR19eH06dPQ6/XY/fu3bh06RJUKhXi4+MxPDyMo0ePIiMjA1NTU4iPj8fQ0BA6OzuRlpYGp9MJuVwOu93OGOnY2BhsNhveeOMNOJ1OnDt3DtPT03jjjTdgs9ngdDrZOzMyMoLKyko4nU74fD50dnYiEAggIyMDXV1daG9vR29vL4aGhrBz507o9XqIRCJotVpMTU1BrVZjdHQUly5dQlVVFdra2jA3N4f4+HhMTU1Bp9NhamoKcrkcra2tGBkZYRqHuro6OBwO9PX1oaKiAhs2bIDJZEJeXh5jtDMzM/jDH/7A/IlSUlJQX1+Puro6/OAHP0AoFEJ/fz+cTicqKythtVpx8uRJCIKA7373u3C5XIx/TE9PQ6vVIj4+HoFAABqNBlarFXq9ns1dZ2cnsrKyMDY2huPHj0Mmk2FqagqrVq3CpUuXsGzZMmaO6+zsRH19PWZnZ7FmzRpYLBZ0d3djamoK27dvR0JCAuRyOSwWC+Li4jA6OholBJEvpE6ng81mYxrF4uJipKWl4dFHH4Ver0dmZiY8Hg/S09PR0NCADRs2oKGhgW087XY7AoEAlEolJiYmMDg4iNbWVmRlZeGWW27Bxo0bMTk5Cb1eD7FYjKNHjzITkNvtZj5Mb4ebEjp4EwB/jBYegChmwDNR+n+xYAFEayZ4GzgRSmLWJBDwu1HeKY8ILW+yIFMMT6B5G3sgEGDXl8lk8Hg8N+wfb7oh/xJee8Or72kHTwIIz8z4XTYvfPAmHp5JkyMezTP/G/WdGAjfB7/fz5wCxWIxc4wUi8Vwu93M1icIAjQaDZRKJebn5xnjJzMFACY9A4DT6WSOecScaD5JGCEHVNrtk6aHnhHtEkkzIBKJmGaF35nTd7/fHyXh81oDXvDhtUK8mYIEWF4LQQwdAFMdU19IGAbAhDy6FvWBN+uQgANcM0Hymg96Rrxgwt+LNzPypkn+XeF9Vvi5oOvzfSQNDYHXXABga5PWFb3TJBAunrubgdfrRUJCAnp7eyGXy/HQQw8xXwR6Zmq1GsePH0cgEEBtbS3Onj2L06dPY/Xq1Wwnu3v3bjQ1NTEzx+TkJHNsy8vLwxe+8AWcPn0a6enp8Hg8EIvFSE5OZvbz6elpJCYmwuPxwOl0wuPxoK+vD263G8899xwqKiowMDDAxjcwMACpVIrW1lam6m9vb0dCQgJSUlLQ3d2NcDgMhUKBjo4ODA8P49SpU5iYmGDvWFFRESwWCz7xiU8wlf7Ro0chkUhwxx13QBAEVgOlt7cXWVlZmJ+fh1wuZ74Nly9fxuXLl3HlyhWMjIyweVOr1ZiZmWF+IOTrtmrVKnR0dCAuLo69M/39/WhoaIDf70dJSQl6e3ths9lQVlaG3t5eRi9tNhtsNhu8Xi+mp6fh9/tRWVmJ1NRUWK1WNDQ0ID09HTk5OaitrcXmzZuZADg2Noa4uDg0NjZCEASsXLkSjY2NmJ2dxYkTJ9Da2gqtVguz2Yza2lqIRCKkpaVBr9fj9ddfZ+YWWg9Xr15FTk4O9Ho9fD4f0tLSEB8fD6VSienpaRgMBqxevRozMzP42Mc+hr6+PjQ3NyMtLQ2RSAQJCQkoLS3FpUuXcOrUKWba1ev1KCsrw86dO1FRUQGXy4VAIACTyYQtW7ZgdnYWt912G5xOJ3p6ejA1NYXGxkYcO3YM5eXlaGxsxLZt25hzbGFhITZt2oT9+/fD7XbD5/NhYmKCaTR6enogk8lQVFQEl8sFnU6HgoIC5jtz6dIl3H///RgfH2drSq/X4+TJk0zb0NDQgNzcXGYemZ6exsmTJ5lme+XKlUhJSYFMJoPX68XWrVvR0NCApKQkNDY2wuVyobW1FSdPnsSpU6fgcrmYky/5eWzduhUTExO46667cPjwYeazQT5D7e3tzGzS2dmJ6elpFigQiURw+vRpbN68Gf39/ZDJZEhOTmZmobfDTWs6eJs4r8olIkkEj3eYJLs8EE006Tr0l4gs7c54RqZUKqPU46Qe5pkOqaGJCfCqe953gHaExHzoWsS0+Z02qenpunzECmkyeOZBzIQiH+ha/M6a7s07si7W5pCgsljI400K9NftdkdFb9AumBg72eyBa0yUhBh6OWl+SLADALfbjUgkwqKEyC+GngFv7iGhhDQi9J1APiQymYw5tFJEBe+3QAJbOByOilghgYT67/P5IJVKoVAo2FyRIMGb/3gNjs/nY46VNFZeWFgstJBgSMIP7f59Ph/rB78eeUdhXgigZ03j4jVStE55R2w6hzdd0vrhTW70P2mHeM0GPw7+faW1yt+HmBCZM/n3gF97S0EwGERBQQF27twJADh37hwbJ+3KfT4fMjIy0NTUhBUrVkCn02Ht2rXMpKLRaDA1NYXS0lImbJSXl2N8fBw5OTkIh8N49tlnsWzZMjQ0NDA19sTEBPLz85lDolqtRjgcRllZGdRqNYxGIzIzM/Hxj38czz//PFJTUxEOh+FwODA+Po7U1FTo9XrEx8fDYDAgNTUV8/PzGBoawtDQEEZHR9kmKCcnhzlQVlZWIisrC36/HzKZDBcuXIBMJoPFYmE769bWVgwODmLt2rUQi8XIz8+HQqGA2+2Gy+ViWkyDwYC5uTlkZGRgaGgIAJCSkoLJyUlIJBImSNHOvKOjA7///e8xOzuLnJwcdHZ2wmazMRNIS0sL1Go17HY7i74Qi8WYnZ2FIAjMn8tqtaK0tBSJiYlISEiAx+OB3W5Hc3Mz4uPjYbFY0NjYCLfbjZycHEgkEkxNTSE1NRWCIKCmpgYVFRUwmUywWCzsfZ+YmIBYLIZOp0NWVhby8/OZJkmhUKCqqgpDQ0O49dZb0djYCIfDwd7j+fl56HQ6TE9Pw2az4fTp08jLy0NXVxduv/125OXl4ezZs8ws0dfXh82bN6OpqQkejwfnzp2D2+3G8ePH8atf/QoZGRmYnJxkppPZ2VmsXbsW7e3tMJvN2Lp1K+rq6pCdnY3k5GT4fD7s2rULo6OjKC0txdTUFNMokSatuLgYeXl5WL16NZxOJ+Li4mCz2ZCamsrWYVxcHCseFw6HUVNTg8nJSaSkpECj0WBwcBBqtZo5lQqCgJaWFhQUFDA/pezsbEYH29vb8cILLzBz4dDQELZs2YKZmRmsXbsW4XCYmXwqKysZfV+7di1kMhn0ej1mZ2cxPDyMCxcuwGw2QywWw+PxoKGhAcPDw5icnERtbS08Hg9ycnLg8Xhw/PhxZGRkwO/349Zbb2VmUHJoXSrecXIwXrNARAVAlFCy2HGOiCWv8ud3VXQ+MUD6S8SawAsE8/PzUWpr6gsRB+CaMxzv40D/0/Vp50fqayLCarWaRYDwO1bqAzFdOg8AE3roL68N4lX+FDnB94XOp3PomnwoLo2JmAIJaHxoptvtZoRMpVLB4/EwFT7ZoN1uN+Lj4yGXy6OEEl7L4HQ62cJyu92MeZJq1ev1MqcwUnEDYEIBmW/4kFXSzCiVSuYHw0c1kZBJ4+T9Dmjt8eYqXujjTW0kFBLInERaL1KX0m/8OuHXKl13cWQK/zx5J9jFUTD8WgSumSzoOJmWFjuB0ph44ZVfh7wphRe4aE7pGvy7R4Idb7aRy+VMEKN+0dh4LeZSUF1djddeew2jo6MsLHXdunXw+/0wmUwYGhrC/Pw8Vq9ejerqalgsFuh0Oubs2N/fj0gkgri4OJw9exZarRa7d+9Ga2srdu3aBZfLhf7+flgsFrz88svQaDR48MEHUVRUhDVr1iAjIwP9/f3M8ZzMBXFxcQgGg7h48SJaW1tx1113ITExEePj41i3bh2qqqqQl5eHhIQEzM/PY3x8HDMzM8jOzkZJSQkSEhKg1WrZ/WtrazE4OIgdO3bg4MGDmJ2dZRoDpVKJsbExFlJZWFiIBx54AKtWrcKVK1dgs9kwMDDAHCrj4uJw8eJF6PV6dHV1wWg0Ij8/HwUFBTAajRgcHERFRQXm5+dx6tQpzM7OoqCggD3bJ554AhKJhIXbzs/PQ6/XAwDS09NRUFAAkUjETBS5ubmorq5GX18fpFIpUlJS0NbWhr6+PqSkpODixYuYn59Hfn4+1Go1RkZGkJycDIvFApvNhuHhYSQmJrJ3n3zBrly5gvb2dqSmpuKuu+7C8PAwTp48yUwpbrcbjY2N8Hg8GB8fR2JiIoaGhqDRaCAWizE2NhZFM+vr6zE1NYWsrCzU19dDEARMTExgdHQUe/fuZc6zmZmZ0Ol0SE1NRXt7OwRBgE6nQ0lJCYAF0zppXPLy8piJb2JiAlNTU0hOToZSqYTT6cT69etht9tht9sRFxcHnU4HhUKB9vZ2pKWlISsrCwqFAg0NDTAYDBgeHobb7cb4+Dg6OzvR0dEBmUyG8fFxeDweZGZmwmw2Y8WKFYxfkGOoXq/HwMAAkpOTUVlZienpaeYfI5fL0dnZiYcffhjz8/MsBJZ41ObNm+F0OjE7OwuZTAaXywWRSASTyQSdTgeJRIKVK1ciPT0diYmJ6OrqYv4fg4ODGB4eRlZWFhITE2E0GmE0GjEwMICSkhJoNBp4PB5UVlaitbUVNpsNKpUKlZWVkEgkGBsbw8TEBJxOJwoKClBSUhLle/l2uOmQWWJ0RND4XSaAKOLJm0uIcQDXtB28IMG3JzUS/SXbPx9ZwtvTifjSX7oer/am8/hIE7KfkyaGFwJIqCFivPhcUp3fKNyRNBX8eKnPdH1ewKC/vDaE95Ohc3lmyDuz8uf7fD6IxWJotVrmhBkIBFgUj0gkgs/nw+zsbJS/jNvtZvM7Pz+PUCgErVbLBC/a2QNgBN3hcMDlcsHj8bAx0m8KhYIxNGJugUCA5fSgtcM/SxLuSEDh1f2kOSK1Mj17XiggYYKeH/8saey8SYv6Rn4oPHOmdU47f2LWtK6A6MgO0uAIgsAEON6swwvgJAyRwLG4f7SeeKGY13zQc+f9oRb7uNBa4vu92CeF7kUCPr/meTPRzaC2thZJSUno7+9HKBRiBI6YYm5uLhISEvCf//mf6O7uxsWLF+F2u5ktOjk5mWnqIpEIysvLcfnyZXg8HgBAf38/uru7EYlEcPvtt6OiogKtra1ob2+HXC7Hb3/7WzzyyCMAFvJbiMVi7NmzB3a7HQ6HA9PT01i3bh1GR0eRmpqKbdu2oba2FhMTEzh8+DASEhJgMBggCAthlVeuXEF3dzcTJmw2G6xWK8sdND4+juzsbMZQJicnWeirVqtlAv/MzAxGR0dhNpvR09MDr9eL8fFxJkj+5V/+JcuVkJ+fj5mZGWa6LC8vR1dXFwwGA/R6PcbHx9He3o7S0lLIZDK0trayPqxfvx7AghmLNDskcGzatAk2mw2tra3o6urCypUrYbPZ0NHRgbKyMqhUKtTU1CA7Oxt2ux1zc3OQSqWw2Wyw2+3o7u6GWCxGfHw8vF4vVq5cidLSUnR3d+PMmTPQ6XQIh8OYmJhAS0sL9Ho91q1bB7FYjMnJScYgs7KyUFJSgvHxcVRWVsJgMGB+fh4VFRWIj4/H6Ogo1Go1cyz2+XwIBoNYs2YNFAoFsrOzERcXh6GhIeZESs/WZDJhxYoVbA4pvDMhIQHr1q3D/Pw8xsbGoNPpoFQqMTo6Cr1ejwceeIBFqpC2x2634/LlyzAajUhNTUV6ejoGBwcxOTnJmLtGo8E999wDqVSKuLg4FBQUoLW1FePj4wgGgxgbG0NiYiImJydRVFSEvLw89Pf3w2q1oqSkBGvXrkVqaio0Gg0uXboEQRDg9XohCAJ2796Nw4cPw2QyMcfpzs5ODA4OAgDzAenq6sLBgwdRUVGBwcFBrF+/HsXFxVAoFDh9+jQuXryIvLw8eDweZGdnIz09HdPT0xgdHUVPTw9uueUWTE1NYc2aNXA4HCgoKMCuXbtQVlaGdevWob29HXFxcXC5XFFO+ikpKWhqaoLFYmH5R5aCmw6ZBa45LS4mZIsFEN63gnaEdD6vTeA1AbxqGwBT1RMTJmbGez0T8ee1CDxRpb7zToiLNSy0I+XNK7SjJDMEMRw+zJbmgu7DJ+sitTdFZBCjUCgU7EPn0j1Jc0FtqW+Um4ReQJoz0vTQ3JG/AXnRUzuK6lCpVEyQo2vLZDKmoiSGqdFo2A6RhDJ6rhRLbjabodPpmIBFDIxyPtA9aMdPTFSlUkUJheSvwz8/6i8RZV6AJW0NMWEyM3i9XhYRAlzTbNF96RpkKuHNRLwGhNYyXV8mk7Fr0nVFIlFUbgsSTnihgdYHCcS8MEZzPz8/z/pAwgRptWiOFvsF0bvDm1T4SJfF7ybv30Hg3z9+zmlOaJ5uVugwGAzMX2Fubg7FxcUYHR1FcnIysrKy0NjYCAAoKChgxN3v96OnpwdDQ0NM6Onr60MkEsH58+ehVquRmJiI/fv3o6WlBeXl5cjPz4cgCGhvb2eELzExERs2bEBjYyOefPJJ7N69G1arFZs3b2a+DCaTCb29vfD7/Thy5AiOHTuG0tJSAEBVVRUOHTrEmERKSgoaGhqQkpICiUSCZcuWQSqVwm63Iz09HVKpFHq9HhMTEyzMNzExEX19fbDb7WyH393dHRWqqdVqo3y1xsfHmQZIKpVifn4eEokEFRUVyMvLQ2dnJ3Jycli0RHl5ORISEhAKhRjhz8jIwIoVK9DT04OOjg40NTWhrq4Ok5OTGBgYwLFjx3DlyhU4nU6Ew2FkZ2djfHwc09PTbM59Ph+Kiopw+fJlbNu2jSVpu3jxIiQSCfLz85GUlIRQKASdTodz587hwoULSE1NRUlJCaTShWR0vb29TBNDfjfl5eXM/4bWdHp6Os6dO4dAIIATJ06wTYdCoYDH40FXVxdMJhMyMjJQXFwMk8kEpVKJ4eFhhEIhJpT09vZCoVAgNTUVFouF5eno6OiAVqvFr371K4yMjKC3txednZ0QBAENDQ1sQzA9PY1Dhw4xDczMzAxSU1Mhl8thNBrR1dWFzMxMnD17FpWVlUxQcjqd6Ovrw+HDh3HhwgWUlZWx3X93dzf6+vqgUqmYievSpUvw+XzMd+LkyZNobW1FW1sbACAzMxMdHR1wuVwwGAzo6OjArl27YLFYsGfPHpZDo7q6GjqdDnfeeSfGx8dRVVWFTZs2weVy4fLly2hvb2ffc3JymLYsOTkZoVAI9fX12LJlC9OUtLW1YWBggOVqUqlUqK2txcjICEZGRpCTk4MLFy7AZDJhcHAQTqcTWq0W6enpWLduHQYGBqBQKOByuZZEI27apwO43nmUQDtxXtVNRJIYDO+vQNfiNRRE9PldPxFN3txAGgDaNfPOjLzGgWz3xLwItLvk1fQECmMk5k4Om+QXQIIBtaUx8eYD/n58TgZqs9iRkhfKyA+DZ56kJeHDGHk/EvJ9AcCYGzHjUCjEMju63W6W34CcHT0eD0SiBU9yInykBZmfn2eRGFqtlt2LfAl8Ph90Oh3blQHRzrDz8/MsqRrva0L9p7VB46Y5J4ZK+Tx4kwYxeZoTynNBZibyCyGzGfmDkMDCR4GQMypvrlhsPqHnR995Ew7llCHBhDQ09Mx4xk6+IDR+euYkxPL+KLxWjMbMCx/8O0hC02ITEz1f/j7UjuaWf69IY0L9lslkUSazpWBmZgbp6enMwx5YyKCamJiI2dlZhEIhxMXFobW1lc2hSCRCcXExXC4X25lOTEywhE9dXV3IyspCKBSCxWLB2bNn0dPTw5wqaa3X19fDbDbD5/Ph9OnTCAQCyMvLg1gshlKpREJCAiKRCFJSUuDxeFBRUYGKigpMT08jKysLzc3NqKyshMfjwbZt21iUx8mTJ+FwOFBXV4fh4WFUVFRAEBacQo8fP474+HhoNBocPHiQaeGmp6dZpmQKVa2trcWxY8egVCoZ8ykvL4dGo2EEv7a2FmazGW1tbcyUo9frWR4Qp9MJi8WC1tZWHDx4EHa7HatXr0ZtbS3LPpqVlcXCgPft2wcAyM/Ph8ViYc6zxFw9Hg/m5uagUCjY/W02Gw4cOICUlBRUVVUhHA6jtLQUkUgEr7/+Oqanp6FQKFBSUoKioiKsXLkSa9asQVNTE9RqNUpLSzE2Noa6ujosX74cdrsdp06dwuuvv468vDy2aw6Hw3A6nSgtLYXdbofZbMbw8DDbcASDQTgcDnR1daGlpYWlPbDZbLjzzjuRl5eHAwcOQCaTwWazsayhTqcTDQ0NePDBB9Hb24vU1FSW60Sj0SAvL48lHCPzMWl29Ho9y3ficrkwMDCAtLQ0vPrqq6x/NTU1UKvV0Gq12LZtGzIyMlBZWYlIJMI0yyaTCenp6dBoNMjMzEROTg5GR0dRWFiIY8eOQSqVYm5ujoUyz8zMoK2tDenp6ejp6YFUKoXH40Fvby88Hg9WrlzJnGCHhoaYU7XX60VbWxu6urpQV1eHtWvXMmfRDRs2MOG3vb0ddrudRclQKPSOHTvg9/uRm5uL4eFhzM/P49VXX2UZvePj4/Hb3/4Wu3btQn9/P4CFDUN+fj6OHDmCQCCAzs5OjI6OLplG3HSeDiA64ycR58W2bwLPPIm5EHFd7NnPO6LSzo+YH79zpB09MQ0SOoBranGeWPImFLoeMRle2KAdJzEAuh6dRw6ZxPRoTnhTEe9IywtJi/tPfac55AUHfs7oXF4wob7w55DwIxaLWR4IPtyUF4zIhs77S9B9yNzgcrmYI2kwGITb7WbXDAQCSExMZCnRyWxCAgqZYsibmZhfJBJhvia8nwMvqPJzxZsj+HwkNA4+HwbtknhzDG9aoLnnGTKvbSG/hsUCMr+eeYdN0kaRYEnPmgTUxZo0wmJHV7oPmTP4dU+gftKa5U00vJ8H/37ywj0vYPNCPKlKSVvDO3vTtUm4XirkcjlzJqTcCpFIBEVFRWytqtVq3HnnnUhISMDVq1dhNBohCAISExORmJjIxjQ2NgaVSoXk5GSo1Wrce++98Pv9LDxxeHgY+fn5SEtLQ29vL1paWhAOhzE4OIiRkRE0Nzfj6tWraGxshMlkAgCWNpt2zS0tLejq6mIpwCcmJliiJrlcjoKCAmzatAlbt26F3W6H1+vFxYsXmSd/MBhk4ZGUwn3jxo2Qy+UYGxtDb28vtFotZmZm0N/fj4yMDKbFiUQijFHTs127di1qamqQl5eHuro6pKamoqGhAVlZWSw3xc9//nMsW7aMpbum6JRwOMyyl/b09KC3txf33nsvdDodEhMT0d7ejuXLl0MkEqGhoQE5OTmQy+VYu3YtSkpKYDQamXnL6/WiqakJMzMzKCkpwYEDB+Dz+bBx40aYTCbs3bsXzc3N6OrqQkNDAyKRCHbt2sV2ynq9HnFxcUzot9lsMJlMePbZZ/Hyyy9DEARcvHgRmZmZGB0dRXZ2Ntrb2yESiZCZmQm73Y6qqipMTU1BJpOhoKAAPp8PVqsVSqUSly9fRk9PD3uep06dQmlpKYqKijAwMACLxYKenh60traiqKgICoUCnZ2d0Gg0yMrKgtFohMlkwu7du9Hf348dO3ZgbGwMkUiEpbOn90ipVGLr1q0wm83IzMxkQppSqcTQ0BBqampYFlfKcdTc3AydTge/34+jR48yDd3p06dx6623wuPxwOVyISMjAzMzM8jNzYXZbMbs7CwKCwvh8XjYGhKJRLDb7ejp6WE5U8LhMAYGBrBlyxYkJSXhzjvvhEajQWlpKbKystj7k5aWhsbGRnziE5/AhQsX8IUvfAFr165FSkoKy3ujVCpRVVUFs9kMtVrNNFMXL15k+Tpef/119PT0YGBgAN3d3ZicnER8fDzm5uZQXV0NQRBgNBqXRCNuWtNBxIwIKs+0ifnzjIHOo995Rs+baYhRUDt+R0dMnpiMRCJh6nGeIQNg2ULpXNqxAWCqa2JgPEMhRs/vGnkmRUSeTzhF6nre/s4LBrzJhXcWJNDOnO8rnU9zQ/NBWg4aK92LNz8RQyEGSrU0yDHT4/HA6/UyTQeZB6RSaZQ2gGqPeL3eqH6QaUehUMDpdEKtVsPpdGJ+fh4qlYppcEjgo0RkvL8K7yfD77ZvlIuF10Lx6fZJOKC1wPs+8A659Fz5KKPFQgkJgfxa4nNT8IIQ/533m6D70NwTE6dx0z34sZAmhtYIHwXFzwuvBeTXG61fakt9452AAUQJnLzphA/b5dcub156M8HprSCRSNgObNWqVSgtLUV6ejpeeukluFwupu0IBAIYHR1FWloaAoEALl++jMnJSbS1tbF7KxQKjI2NITk5GWNjY+jr60N6ejpUKhW8Xi/kcjmOHTsGi8WC6upqlJWVYX5+Hnl5eSzro81mw/3334+xsTGWknr//v1s10jRFX19fSw0vLa2ltVg6erqYir0hIQESKVSmM1mzMzMYHBwECKRCKWlpSy0lpjD+Pg4kpKSkJSUhNHRUbS1tWHZsmU4ceIENm7cCK1WixdeeIFtYiKRCDZv3oxwOMz8FFavXg2Hw4EdO3bA6XRidHQUUqkUf/3Xf40rV65AIpGgqKgI+fn5LDfK5cuX2e6a8qT4fD6Mj49jw4YN6O7uRnx8PJKSktDa2orMzExcunQJjY2N6OjoQH9/P4s0oQgYpVKJgoIC5ObmsrDdpKQkVFRUsLo/g4ODaGxsRF5eHsvxQeGsxED7+/uxdu1a5hC5bNky9Pf3syi+1NRUbNiwga1dCg0mPwyv1wuNRsPMLgcPHoTNZsP+/fuRlJTEIlISExMxOjrKoirUajWWLVvGTKIUOm232zE5OQmLxYIjR45gw4YNGBwcRDC4UM9rbm4Ot912GxQKBU6ePAm32800kF1dXcwXzGAwsFBvjUbDav6QFik/Px+Tk5MIh8NITEyEQqFAX18fbrnlFjQ0NLB8GDMzM+jo6MD4+DjMZjN6e3uhUqnQ3NzM6vuMjIwgKSkJDQ0NsFqtOHDgAIaHh9Hc3Ayj0Yj6+nokJSWhrq4OIpGI5U45c+YMioqK8Prrr2NwcJBFMl24cAEzMzN4/vnnWRiuXC7Hf/3XfyEuLg5paWmwWq1ISUnBbbfdhoGBAdxxxx2Mx1BK9Q0bNiyZVty0IymAKCLIay944WKxJgOI3l3yKnRiorSLI0lucb4DugYRN9opkqqc+sfvNonZ07nEXGg3xfsi8P2nfi92niTGT3+JMS6OkKE+ka8CCSa8AyWBGCnNDXBtF8prUoiZ03XJP4D6QIIHmYSAa4LC/Pw8C2slXxCVSsXMHiSM0e+k8aCdilgsZurbYDAIrVbLmD8JY+T4SQ519JwpIoaEGNJA8MIfaSxIACBCTAyKnhuvdQgEApibm2PhfXxeFF57wZsheJMXmUYEQWCh1yTM8EydtAy8VoiEIt7fhPf9IOGN1i89v1AoxHxHiPGTwMn7+1BYMWkcSHihtnRNWudkolnsvMxrPPiwcT70lhdU+HebQrFvBlRIz+Px4IUXXsClS5fgdDrh9/tx9epVDA8PsxBsvV7PHPcAoLi4GEqlkqXynpqaQlJSEjo6OiCXy9Hd3Y2BgQFmujAYDNi1axc6OzshFovR1dWFuLg4jIyMIBgM4tixY5icnMS5c+dgMBjQ3t4OqVTK/BUSExNRUVGBZcuWITs7GzMzM1AqlVCr1SgqKoJIJILRaERVVRV0Oh3eeOMNxMXF4eTJk4iLi8PU1BRUKhX6+/vx7LPPorCwkIUkrlq1ivnsKJVKFBYWwul0YufOnejt7WX+H9XV1azQolwuR19fHzOpTE5OYmxsjGkKbDYbFAoFent7UVxczBJ8hUIhWK1WDA8PY/369Swxm9lsRnFxMSv6ODk5icLCQgwPD+Pq1avweDxwu90wGo3Iy8tDUVERkpKSYDKZ0NjYyDQklJiqra0NBoOBRUsEAgFs2rQJYrEYo6OjsFgsOHHiBM6cOcPya+j1emRkZMBisbDCd5mZmdi5cyccDgdUKhVUKhWmp6exf/9+vP7660xI6ejoQEJCAqxWKzIzM2Gz2eByuTAxMYGBgQHm5Eg+N8Tkh4aGWGKubdu24Q9/+AMmJycRCARw8uRJJCYmskzLjY2NuPXWWyGRSFBSUoKMjAycPXsWVVVVSE1NRXNzM/x+P7xeL4LBIBoaGlBVVYVgMIjDhw+z0OPOzk4WySIIAurq6lBWVoY1a9aweerv72c0Oz09nYVEV1dXIycnBxs3bsT27dvZZo5yId17770Ih8OYmZlh/j0f+9jHsHfvXhb+2tnZibNnz6KxsRHT09OYnp7G3NwcXnvtNVgsFuTk5KC1tRW5ublQq9WIj4+HyWRCcXExAKCkpIQJXFNTU/jrv/5rFpKckpKC3NxcnD59GmazGefPn0dDQwMSExOxevVqtLa2QqfTMQ372+GmHUl5tS/PqHntBjE/Os6rd4nh8CYaINpBjog+cC28lhdi6J48oSSbNxFnnpFTP4jJAIi6z2KBhnf+oz7yO0Te3s47fNI1eBs5nw6dxriYuRHITEEaDH53zavQZTIZM2cQwxQEgZk3aCfNq+vJgTQ+Pp7ZS0kzQeYCPoGWQqFAfHz8dQyY7uF0OplgMzY2xqqner1elr1PoVBgbm6OzRMxJJfLxaIJaO55Z1XSAszOzrI8IjROp9MZlQmRz/3BEwc6h3I10DOg50++FaSR4NcT73tDgiX1idYLLygvdiam58+/E4tNbbywTX+pD7TuqM+LNYO8aYo/lwRM+p8EE/5d5dcbvTu0Tvj+0ft2s5qOSCTCVLxbt25lTGJmZgYKhQIrV65ER0cHpqammPq9ubkZZrMZUqkU6enpMBgMWLlyJStOVV1djZSUFBQXFyM5ORkajQbp6elwOBy4evUqMjIy0NraytS9JpMJTqcTeXl5AID4+HgcOHAAZWVlaGpqws9+9jMWRuhwODA0NMR8O4xGI8xmM/R6PWZmZjA2Nga1Wo2EhAQkJSVhYmIC2dnZ8Pl8LGJk8+bNuOuuuyCXy9nOXKvVYmJigvkP6HQ6zM7Ooq6ujgm4y5cvR3NzMxITE1k69+LiYlitVvac5HI5nE4nEhMTmQmpsLAQzz33HPr6+nD16lXU1dXBYrHAarXiypUrOHjwIJxOJ1auXInW1lasX78e7e3tUKlU0Gq1GB4exu23347Z2Vm4XC74fD7mfGgymTA5OYnk5GQ4nU7mlElJqo4cOcKEAIokIpMI5V2pqKjAxMQEUlJS0Nvbi/b2dkQiEaSmpkIikaC3t5dtFoh+kDaCdvgikYjl5iDhzW63IykpCZs3b0ZCQgLbmMlkC8UrLRYLurq6sGzZMpZJs66uDvfeey/Lc1JZWckql2dnZ7MiafHx8XjjjTdgtVpxzz334NChQ6ioqEB3dzfcbjfuu+8+9s729vZi06ZNLJEZCWdKpRLx8fFobm7GHXfcwRyEDQYD0zIS3SQhSa/XIxAI4NChQ2htbUV3dzesViu0Wi2USiWam5sxOjqKvr4+GAwGZGRk4Pjx48jNzUVZWRlsNhsKCgpQWlrKNE9arRbZ2dmsQFt/fz8zXRoMBmYmevXVV5lTqN1uZ/laQqGFartUMZieM2nekpKS8MQTT7BNlEQiwYkTJ9jzfDu8Y5+OxV7+RNx4fw5qS0z2RgSK2tD5vPMjEVzebk67aWIIi8/nTQwUpsknZeJNPLzmgN/V0jFeA8LnVqA2dA4xU2LQvNMePy+83wKdT/3l55aYPmkh+N05APZwqS0A5tRJ88VraOg7qQfJR4W0GKFQCG63mzmOkoOk2+1mmU3JIZeEIgCYnp5mDnCBQIBFskxNTTHNhsPhYIII1bzgNV70XPk5Ja0CX9yPBB+5XM5s7uS/wpuQyGRF62jxtUmAuJFfBjHrxaGzdG/eEZl3MubXFDFxEgjpujfyy+HXMa8dBBD1PtB1aF2RAE8RXLzwwJsU+fXDC8O8rwsJ//Qu8nPGC9M3g7a2NgSDQWYKUSgUKC4uRnZ2Nnp7e2EymVBRUYGWlhakpqYybWAwuFDBdGZmBs899xyrmHrq1CkMDAzg4sWLLLuoTqfDsmXLkJGRgfb2dpSXl2PZsmVISEiAyWRiGSRJe7BixQq8+OKLiIuLQ3l5Oex2O44ePQqdTofc3Fzm9Dg7O4v29naWpEmr1bKS9sCCIBcXF8fWQkpKCtrb29Hc3Izz58+jt7cXgiBgbm4Oqamp8Pl8TMAuLS1ldU8EQUBhYSFyc3PR0NCAEydOIC4uDh6PByMjI6wqbSSyEIlGDoUGgwGnTp1iFWFXrFgBvV6PwcFBJCQkoKioCNnZ2cjPz8fx48cxNDSEp556Ctu3b2dmisLCQkgkEuzevRsikQiDg4Nwu904d+4c5HI5fD4fkpOTkZGRgdOnT8Pr9aKoqAglJSXYsGEDxsfHmRZCJpNhdHQUs7OziI+PZ/4nV65cQW9vL6t8W1NTA4fDgRMnTuC2225ja4QK3k1NTcFut2N+fh5Wq5VpP6xWK+bm5iAIAoaHh3Hs2DGWPZQPnScz48aNGzE/P4+MjAzMz88jLS0NUqmU1aSZn59nwuW5c+cQDi8k65qdnUUgEEBvby/6+vqgVqtRV1cHhUIBh8OBlpYWCIKAoqIiKJVK7Nu3D6WlpRgYGIDL5UJFRQW6urqYhmnv3r0ArtXdoQijyclJVFZWssyuFosFbrcbwWAQOTk5yMrKYhq5lStXIjMzE4ODgyguLkZubi6rjtza2srqotx5553M2b6wsBBGoxGzs7NoaWnByMgIc6SdnZ3F1NQU3G43CwX2+/3o7u5m+Ud6enqQnp4Ov9+PjIwMtgGm+jVr1qyB3W7HoUOHMDk5ifz8fOj1epSXly+ZPtyU0EG7bd7hk9dA8P4ci3dYRKB5B0Ag2tmR2gPXhBfauRNB5X0gADAmAlyreEvhlLy6mZgxH1VCfSMHOhojLxgQESefAX5nyRNr3lSzmKAT4+KFI5onEhZ4HwEyhzidTja/4XCY+atEIhGmBSDtiMfjYUlkyMGT1P3z8/MseoVydpDQRcyFHIrUanVUqC2F99HLrVarmVmDtB5URZLmgQSOubk56HQ65gil0WiYaYicrmiO6TxeaCUnLvpIJBKWrI3Gp9Fo2A6f1gP1Syy+VvSNfud3/7wphLQH/FqgNrwWiHfC5PvJaxGIqdMzp+sSc+V9JUj44n2JSKvCrzde2CVBksKleZ8fwo2EG+obr91YLAjxQi6t8ZtBWloaysrKMDk5iatXr2L9+vVsB5iTk4Ps7GwcOXIEzc3NUKvVmJ2dRX5+Pnw+H+rq6lghst27d6O8vBxVVVXMWS4jI4Mls/J4PGhpacHly5fZ/X7/+99DLpejoaGBOR0KwkLU0M6dO5Gfn4+mpiZYrVYUFxfjG9/4BsbGxtDV1cUKddH7m5qaitTUVIRCCyXd3W438vPz4fF4MDs7C4vFwoq8icVi2O12dHZ2ssqrZF4zmUyw2WxMG0Mmytdff535Jni9XmRnZ0OtVmN8fBxWqxVFRUVYv349xOKFkE6pVIr169ejtLSUFY8j35YNGzYgLi4OfX19rCjc3r17mdaHypR3dXXh9ddfR2NjI+rr65lfQnJyMvLz85Gamorjx4+jr68PbW1tzIzR2NgIr9fLwkBTUlKQnJwMhUKB2tpaZio1GAyIi4vDxMQESkpKUFtbiwsXLqCzsxMSiQR9fX1Yv349Ll26BJfLBZfLxSImrFYrxsbGoko+rFixgr13tbW1qKysxObNm5GYmIg1a9agsbERy5YtY9oTl8uFixcvwuFwQKlUwmg0oqenhzl5UgSI0WiE2+1mNWfE4oXCZZQUS6vVQiKRICsrC8PDwxCJRKirq2PaOGLQlD01Pz8fQ0NDyMzMRHZ2NkvZ3tDQgKKiIlitVojFC6kCli1bhosXL6KlpYVFE4ZCIZZ0jUxp3d3dqK2tRXJyMrq7u+FyuXDs2DF4PB6Wt0UsFrOcKBMTEyzsnPJ4kMNsWVkZFAoFrFYrBgYGIJFIsGbNGhYGTvxZJpMxbcfhw4fh8XhQX1+PmZkZ5lwLgBUgzMzMRE9PD+6++26cPHlyyTTipoQOPpwOiDZH8Iya35nx5/J+D0RMSVAgokg7OEoStdgUwzsAAteqp/KEmdIR066ZF3BI4KFdM68WpzHxzprks0CEZHHuAmIqpNngtQ+8mYUXvBZHKRCzJ00AMSDadRAxpDwjlGWU5k+n07HseUSIeD8X0lCQqYNXwZMfB6VDJoZJc0ZhiSS8AGAaIhJq+LCz8fFx5vdBqjdi/A6HA/Pz84zx834xpAmg4xqNhgkQNH5aQ3ykzmIfG5pzKhdOwiL5hpCfBJ+tlI9W4QXbxeY8kUjEQo1pPfDri65B1yUzDQmXtPb4onf0l7ROvGmD1iavqQHAtCu8vxL1g39+JEjxydjoveS1lYujcHgN1M1iaGgI7e3tLDnRxMQEEhIScOjQIeb8/fnPfx7FxcUIh8MoLy9HT08PI97kpEw5DkjlTP0yGAwYGRlhu/HZ2VmIRCKMjo5CJBJhamoKCQkJGBsbw+DgIPr7+7F8+XI8++yzcDqdWLVqFSoqKvDyyy9jbm4Ofr8f4+PjWLFiBbKzs6FUKrF+/Xo4HA72fnq9Xuh0OlagLTU1FVNTU7DZbKipqYHBYEBVVRUbS2trKy5evIhwOIy+vj4cO3YMc3NzSE9PZwJKdnY2UlNTcfLkSVitVlitVszPz6OoqAgOhwPDw8P43e9+x0Irg8EgG9fatWsRHx+P+Ph45ObmorGxET09PcwkEQwGsWHDBmi1WqZyv3z5MkpKSlBZWYn8/Hykp6czwdZut0OlUjHfC4PBgNzcXFaY7pZbbsHMzAzTHJ89e5Y5O1J+H0rnbrVa8frrr7PCbXK5HCUlJdDr9YhEIpiYmIAgCOjt7UVHRwcqKytRU1ODy5cvY2hoCA0NDWhvb0dNTQ2am5vZ+s7MzEQwGERPTw/27t3LssUODw+zir6vvvoqjh8/jvHxcUxOTqKzsxNVVVUssqexsRGFhYVMa1tYWIjp6WmMjIwgPz8fAKDVatHZ2YnVq1djaGgIa9asQWJiIlJTU5lmdWBgACkpKUw4bmhoQHd3N6NZHR0diI+PR1FRERITE1mW0sHBQYRCIdTV1QEACw8uLy+HRCJBamoqUlJSWIE60nLI5XIMDg6yUOuDBw/CarUiLi4Og4OD6Orqgs/ng8VigUQiQWlpKZKSkjA3N4fm5mamHXI6nTCZTBAEAU6nEwaDgTl0u91uZuI6c+YMHnnkEfT19SEtLQ3Lli3DK6+8ApVKhYaGBkxNTaGsrAzT09OoqanByy+/DKvVirKysiXRiJsSOkgVD9zY7strKXhiTSBiTX+JIPNRGsC1sEyyxfOqan5HS/2gnR7v6c/3jWcaJBSQoMDv6skHg77TGHkzC2lWeFMJaWH47JV8vgnaoROz5Mu/0/e5uTkmbZJ/CplLSBNCZgq/3880GwAYUaL+0eKkHTrNF/1Ou3TKD8Cr0inBCwk4ItG1/B0+n49Fq5CGgXIrkGAzPj6OiYkJ2O12ljRJIpGwui28WSwYDEZpaGht0XOgZF/0P/XvRs+IGDytGxonmVAoBJg0Z5SmnUBrhxdmFoPXXvDCK2koeOGS1jSff4PGzl+bzEF83he+zWKtG+93wd+XBDe+DbXjhXx+nZBgQv0jMxVpZUgjcrOgTI8XL16EWCxGQkICcnNzmWOc3W5nURKXLl2CXC6HVqtl5gWr1QqpVIre3l6WuOvixYuYnJxk/RaJFpJurVmzBjMzM4hEFur/TE5Oorm5GbfffjuMRiNkMhn6+/vZ/Gq1Wjz//PNIS0vDqVOnoNfroVQq8dJLL+HZZ5/FlStXkJSUhObmZng8HmRlZWHPnj04ceIEfD4fK3Jlt9sRDAaxbt06xuyl0oWCcaTtIKfLL3/5yyy8lJgv5eAIhxdqv5BNnbR7c3NzjLAXFBSgra0NR44cYbkrZmdnMTs7y64hk8lYRVYq1qbX62G325GTk4NAIIDBwUHYbDYkJCSgv7+fbVwKCgrQ3NyMkZERvPHGGxCLF6r+rlu3jpU/UKvVSE1NZQzYZrOx/A5utxupqalwu93weDxYsWIF7r//fkxMTECtVuPkyZPo7+/H9u3bsXr1apYriIrDzc3NITs7GzabDbt370Z2djYUCgUCgQDz46AqqIODg1i5ciXzQfF6vRgdHUVOTg5KS0tRVVUFg8EAnU4Hi8UCl8vFzFPbtm1jm6+4uDjU1NRAqVSyzMoSyUI9mUgkgqGhIYTDYZw6dYo5OsvlctTX17NntmzZMgwNDaG4uBherxcrVqxAX18fi6ghflhYWIiWlhaYzWbk5+czvySj0cjeRdq4NTQ0oKurC6tWrYLdbofRaERaWhoyMzMhFouRlpYGi8WCV155BaFQCOXl5cyBlpxaa2troVKp4HQ6ceuttyInJ4dFgZWXl8NsNkOpVKKkpASXL1/G8uXLmUl8bm4OFosFdXV1GBgYwOTkJNLT01k0mFwux6ZNm1jtnOrqatjtdmi1Wpb47+1w07VXeCLFq255AYI3L/BEkGfUvFc8CRK0+yKzCP8/347f0fIMiA8zpHZ80iy6B79DpPLxfBZIAr/75v0feAdDCsmibJ/ANU0A9Z8P8yTticPhYA6RDocjitFQTQZyFiXBZHZ2Fk6nk42RfEn8fj+zG9vtdlYHgnZr9DwomoOIGwDmlEoMQavVQhAElsSLcvpTDQLKaEjRPAAQFxfH6r0kJiYiLS2NCYxzc3PMZkzhkiR4kS3Y7/fD5XJhZGQEQ0NDGB4ehsPhYA53tK5I8FCpVOw50DObnJxkc0X+IMTgyO9DJFpIVkZCyeLoKN6vh9dEkPmB11YQ8yMfC35t8uue15zwWkIAbP3wGh9iBLygzPv8kNCwWKinhGS8wM9fh57ZYu0O7wjLa0Bu5MeyVJA2rqioCHq9HkajEeFwGB6PB7m5uZBIJDCbzRgcHIRUKmWZPqVSKcrKyiAWi9maqqqqwr333otHH32U9bGkpASlpaWQSqWYmJiAVqtluzyLxYLZ2Vn2nCm1t9PpxL333ou2tjYkJSWxeaqsrITNZsPmzZtRWVkJjUaD0dFRbNu2DS6XC1euXMHHP/5xrFu3Dnl5eZBKpTCZTCys9ezZs9BoNMyDPykpCXa7HZFIBBcvXkRfXx/q6+tZUiqqekop0ClVuclkYo6rJSUlyM3NxalTpzA9PY2zZ88yR3BBEDA+Po677rqLpbEmGtTZ2YnU1FTs2bMHmzZtwsTEBItQEQQB/f39aGxsxNmzZxEOL4RvkmPtY489hvj4eCQnJ2N8fBwAcOXKFVawbGpqCkqlElevXkVPTw8ikYUszUNDQyznQ3l5Oebm5jAxMYHz589DqVTC6/Vi+fLlcDqdMBqNOHv2LDPRlJaWor+/HwkJCSwK57vf/S5mZ2cxOTkJu92O1NRU1NbW4syZM4zB0s7e4XCwomtNTU2oqqqCUqlEcnIyampqUFhYiMnJSZw4cQJ5eXmYmZlhfmoOhwPLly+HTqdj9WrOnTvHcndQXqKHHnoIDQ0N6OzshN1ux/Lly6HRaKBWq9HS0oLp6WnMzMzgzjvvxC9+8Qt0dXUhJSUFWVlZAMAcbbds2YLLly+jpaWFCRBTU1NoaWlBTU0N7rzzTiiVSsTFxSESWUguF4lEsHz5cgALG3nyoSksLGThvVQMkLK4kpANLOSkIVNTUVERBEHAsWPHWNROMBiExWKB0+lEUlIS4xcmkwnDw8PMbHnhwgWmOVGr1WhtbYXdbmd9SEhIYHxgKbhpoYP3eSBBA4iO+OCjNfgwU+CaiSOqE5yfBqn+tVptVL0QIsq82nnxzp1s+NQn2uXy6ng+qZdYLGaCBAkFi233VGCHF2D4ssy06+X9LkjbQLtF6jcxArLx07WUSmWUT4fT6URCQgIsFguAhQqts7OzUUIDb0Yg5zbyowDA6qvQvJOWgU/nTt9pvni1vVarZeYEAKzom0KhYLH5TqcTSqUSk5OTAMCKQvn9fuZFbbVambMZaTy6u7uZSpeenV6vZ3kNEhIS2H1pvZD/SiQSwczMDNNSUSgg7zxJWWPJdCUWi6FWqxEXF8fWC++cy5ua+GfPa7p4XyTeTEZrlKJgqC2vNSEBjTQofN0e8mQn8JpEPsMq74dEgjitQ2rDCxgkEC72LeIFFd5hl86juab732zILD3z+fl5lh00Li6OafqGhoZQX1+Pixcvwmw2Y2pqCh0dHUhKSkJhYSGzk09OTiIUCqGvrw9PPvkk+vr6MDQ0BJ/Ph5aWFpYHQ61WM4//zs5OXL16lTHsiooKZGdnIyEhAQUFBdizZw9uu+02hEIhGAwGTExMsLoavb29qKysRHJyMtMcajQabNy4ESKRiNUSobmmHTYlTqI5nJubw8jICDO52Gw29Pf3M8ZMhcGysrIwOTkJm82GqakpFBcXIxgMYsWKFTh37hxGR0eRmZkJg8HAfAioSBoA/PrXv8Ytt9wCnU6HnJwcVhemtraWFT6TSqU4dOgQQqEQC1eVSqUoKSnB8PAwo7e7d+/G73//e/h8Pixbtgx6vR4NDQ2Ynp6GTqdDd3c38vLyUF9fzyoC0+ajoKAACQkJ6O3txYEDB6DVahEKhVBZWYmEhATYbDbIZDIsX74cIyMjCIVCKC4uhl6vRzgchlarhclkgtVqZaHigUAASUlJzLxmNBpZuKjFYmERPoFAAFu2bEFycjJMJhNj2Pv374fZbGYhnrt27cKpU6dw5MgRiEQixMXFIS4ujmkSOjs7MTIywiI+ampqMDY2BmBBs1pSUoL169czrQkJzlVVVRgbG0NOTg56e3shkUig1Wrx3HPPMVpDAtDExASLklGpVOjr64NOp0NFRQXEYjHLsAuAabPC4TCef/55jIyMMKF9fn6eORjT+2UwGJCfnw+z2cwikEhbQSH6er2eZQY+fPgwbDYbmpubmUMzpf53u93QaDRITU3F5OQkS0jX1NTEfJPOnTuHqqoq1NfXw+/3Izk5mc3VUnDTeToW7wSJYAPXp24Grnnck88DncPvJBdrTN7MDs3fk3aqvIBDjohEgGlHy6ue+aRIPPgdLdnCeRMOXZMPm5RKpSxUlJg4pRSnBUptyUxE4atxcXGMiVGOC9JsEGOanZ2FQqFg2f2I+eh0OjZ+AOw8cuSiuZTL5azv5B9BBDMUCrF7LjZJAWDCC7WlyJDFa4ESjFHIGPlLkI+Jz+djEvzc3ByT4ClRlEgkYs55PJOmsdFaoJ0rH+bKh8tqNBomyFG/vV4v0+rQy0rCFO+zQuuHN93RPFAkD6+h4NcPb9Lg1zL51PAOw7Qmee0fOQ2TsE1aDF7IWOzTwecx4d8FGgfvk0L94hPU0ftETIhMUjT/5MNE590M5HI5zp07h/j4eFZO/dKlSwgEAujp6UF/fz/UajXrJ0W49PX1obOzE4FAAH19fZicnER2djba2tpQWFiIhoYGJCQkoKWlhYW+ulwullBpZGQE5eXl2LFjB0QiEfLy8tDT08Ns7b29vSgsLGSF6ILBhQJip0+fht1uR2JiIkt419XVxbRyY2NjkMlk2LBhA3Q6HcbGxphpjjJlUmprh8OB3bt3IyMjgwmUHo8H9913HwRBwNDQEKu8a7Va4fP5WL6DvXv3wul0YmhoCHq9HkNDQ8jOzsb27dtZFVW32426ujr09/ejqKgIIyMjcLvdzPchGAyyWkmNjY1ISEjAihUr0NraipKSEpZAi7QKw8PDABY0GrR5ovWbn5+PSGQhezFpOVevXo3c3FxMTEygrKwMlZWVUKlUGBgYYD45ycnJkMlkTMszMzOD/fv3M58vv98Ps9kMiUSC3NxcyGQyVghNEASUlpbi9OnTUKvVqKmpwcDAAABgzZo1OHv2LHp7e1mxPLvdjhdffBGTk5MwGAw4fvw4ZLKFcg3AghZbEASWCCwjIwOlpaXo6upCcnIyRkdH4XA4kJubi/j4eEilUjQ3NyMtLQ3bt2/HpUuX8L//+7+YnJzE0aNH4XQ62XMlM3JGRgaOHj0KqVSK+++/nwklw8PDmJiYQH9/P1tXarWalYyPj49nWuWZmRn09vbiypUr6O/vh0wmg8lkQllZGauxIxKJ2Do1mUxYv34968fAwABOnDiB1NRUlnOlvr4eVVVVLC/H+Pg48znRaDRoamrCnXfeiaKiIhw/fhxer5clrSNTEwnZs7Oz2LhxI9tsrlixAufPn4dKpcLmzZsRDAaRmJjINopvh5sSOojILU5sRCDNBn0HrqlxeUK+WEOyWO1MxJ/XitBLzid8onBY2gHz5hbeb4MIK92bdsK82YX8Qog50D2oX3z4K7DAlL1eL2MStDDIXKNWq1lbSjdLGhMqLERmGPKAF4sXKjiSeo36TsV0SJUfDoeZmpCIN12TGDZpQChkley+xEAp1JXmNhKJsIqY5Mg5Pz/PGHsgEGAvDjFJqsVCmR2JGJOQQEKoWq1mtmylUgmtVssEJ95HY3p6GmNjY0ySl8vlLFOqWq1mQg3PLMn+zBfJ0+l0LLU05SZQqVSs7+TMuVioJYGAhBK6Jm+24B2Z+RBsWk+0TnnzBzF4EpBJICHNB2kDeYGE+kXvDp/nhv+dzGy87wj1id4BXsjiBenFpkReGOITid0MvF4vCgsLEQgEmLaOqlrm5+cjISEB5eXlSE1NRXl5OTMNRiIRLFu2jGXgnJqawqVLlzA6Oso89Ulr0dPTA7PZjNTUVJjNZtx11124cOECxsbG0N7eDp1Oh5aWFrS0tEChUCAvLw+hUAjd3d1ob29HbW0tXC4XJicnmbmSaqGEwwt1RmZnZ6FWq+FwODA3N4fGxkb09vaisbERWq0WqampmJubY9oOk8mEzs5OHDlyBBKJBJmZmWhra4NYLEZTUxP279/Pas6Ulpay4mpJSUkYGhpCfn4+KisrmaAWCoXQ0tKCZ555Bn19fWyHnpGRgby8PBZm2tnZibi4OBiNRnR0dERpcEQiEctcmZ6eHpUosLS0FMnJydDr9di1a1dUZBilRE9JScHPfvYzjI+P48UXX0R/fz8zAYlEIly8eBGRSAQDAwPQ6/Xo7OyEw+GAVqvF7t27sWzZMrS3t+Oxxx5Dd3c3xsfHmX8JZWqdm5tjeR5eeukl1NTUoKioCIODgzAYDDCZTKxMPPGS0dFRlpSNNEVNTU0IhxfSwJM/D22IxsfH8dWvfpWZwHNycjA1NYXs7GzGWJOTkzE/P4/77rsP/f39EIlEqK6uRklJCeLj4/EXf/EXMBgMaG1txZUrV9DV1QWj0Yju7m6sWLECJ06cQE9PDyorK5nvCq3VjIwMxMXFYWBgAFevXkV5eTkqKyuZJjwvLw/FxcXYunUrkpKSmLM8ObJSKvuioiKW/Gz//v3Mx+6BBx5gphCFQsFqFQWDQdTW1sLv96OhoQHr169HKBRCcnIycnJy0NnZib1792LFihU4fPgwJBIJW0/T09PIyclhNIkKCSYmJkImW6jv4/F4UFNTE5X+fSm4KapChIyIGpkRyJOfGDdwLacGn3eDj1DhQ1l5r3se9BvPGGgnTcyWdrq8ipqEFjIfeDwexqT4CAg+Fwi/8+WZOa9iJgGD+kkMgdeA0LnAgm8GqdYpNGpqagpOp5PtIHiGr1ar4XK5GCMlR7HZ2Vnm5ON2uyGVSmGxWBhhAcDCSMkWSWpK2qlTP2nnqtfrWR4HYlZ2u53tQiORCFOJ0/gokQzNP4XKkmaIBELSKpDQQclneKZNqkKKVCIhg+yWFFJH/inT09OYnZ1lZhW/34/5+XkWWmy321nmS16jQtoefm3QGlocmcJrL3jNGZlo6Hdek8BHwvCh4LymjuaBd6LmhQPSBvJmHF7AJ4GXngv/LvGCBnup/58gyDuO0j1pTsg/gNbEYrMpmVnoXVoqyEdpenoaQ0ND6OjowNGjR2E2m5lX/cjICGZmZvD73/+eqdglEgm6urowNDSEy5cvs1oXpaWliI+PZ9VF8/PzIRItJKO6evUqY9Lr1q1jdSKGhoZQWFiIkpISiMViOBwOZGdno7S0FLm5uSgoKIDJZILRaIROp2MhlHv37mUlxskE6vF4WHiy0WhkETkXL15kgi1VOM3IyEBGRgZLEBYfH8/m8O6770Z2djbbTSsUCly5coVVDE1OTobdbsfZs2exe/dujI+Po7GxER//+MeZE2dxcTEuX74MkUiE+vp6hEIhbNmyhWmMyBxbVVWFpKQkuN1ulJSUwO/348qVK9BoNJiamkJeXh5EIhHOnDmDqakpjIyMwOFw4NSpU7h69SqOHTuGkZERTE1NYd26dUyLqNPp2LsrCALy8/NZqYVAIIDq6mr09fVhbm4OTU1NaGtrY5qncDgMvV6PtrY2jI+Pw+l0IhRaSJhFGqBvfvObLCqjuroaSqUSfX19zMGztLQUd911FxISEpCdnY2kpCSIRAspDyh7LRWIm5ubY+nWs7Oz0dLSgrGxMfz85z+HSCRCQUEBxsfH0drayrSwpaWlmJiYYPQnHA7DYrFAqVTi7Nmz8Pl82LVrF+bn51FeXs60NPX19Swcd82aNaww3erVq1lodXx8PDIzM7F7927me0e0qq+vD1KpFGfOnEF/fz+CwSBOnz4NjUaDgYEB1NTUMBMGvauU6DE5ORlTU1MoKCiA0+nE2NgYsrKykJycDKVSidnZWVy+fBnFxcV4/fXXWZK9goICdHR0sE3gbbfdBofDwTZ6OTk5CIUW8kWVlZWxNfP6668jOzubbRaSk5PR0tKC1157bck04qbzdPC+CcFgkAkWRGSJ8ADXcm3wxJ00FETYeHUwn8NicUZOXjNCxJLfmXm9XkbkeUEGuMb8iQHTPcmBlN8lElHm1d9EsKkfZG7gHWa1Wi3TbvDXJ6aqVquh0WgYcyMTCIWbERMWi8Wsaqper2fJYwKBAPOG5zUZ1JZ2sxQ2GwwGo+z9VHxNLF4I+6I54Uu8U6grMXQybfDMlM4FolOsy+VyFiFCmh7a1bvdbrab7+3txeTkJEZHR+H1euF0OqHRaGA2m6HRaKLyYjidTpbOlxgw7XjIlEU7JVIrk+aEqkCS4Eg1LkiA4qOnaF7oQ/NGjJjMZ7Re6dktjqji822Q8ErrkPwvqB2/dundAqJrsNA96R6LtRC8toM3jdG8Uz94YYl8e+gcfmx0bRI4SOC5GVBYMj2fpKQk7NixA7Ozs9i3bx/OnDmDSCTCMipKpVL09PSguLiYFfmiQllWqxU1NTWIRCK45ZZbkJqaivn5eVgsFlY2Pi0tDYIgwOVyoa+vj3n0JyUlYWBggKWCTkhIwMsvv8xyXgwMDLC0z1RynGrGPPTQQ9Dr9ejv72dF3JYtW4bJyUmmvs7NzUVKSgpCoYUS6sPDw/B4PJicnGTVTCUSCUtORYya3n+qHfPiiy/C6/WioaEBqampzAE0NTUVO3bswKFDh2CxWHD48GH09PTgtttug8vlQlpaGpRKJbq6ujAyMoKTJ08yE9Xg4CCmp6dZaGN7ezuCwSCGh4dZSXaJRILk5GSWZn5mZgb33XcfLBYLCgoKcPHiRbhcLqxatQotLS3Y9P9KpwuCgJGREYyNjWF8fJxpTYaHh9Hd3Y3U1FR4PB5YLBZotVpcunQJFy9eRGFhIUZHR5GYmMjOj0QiMJlMMJvN6OvrQ0dHB/Ly8hCJRDA4OIjx8XGsXr2aVeul+k7hcBitra0Ihxeq1HZ2dqKjowPAQgLEsbExdHd3sw0eJRObnZ3F8uXLkZGRgWeeeYaValAoFFi2bBkuXbqElpYW5ObmIhAIMCfLnp4eWCwWZGRksGyf5Hjqcrmwfft2dHV1ISEhAb/73e/Q3d2NI0eOsNo8ItFCgq309HR0d3eztPQUPpyZmQmRSASr1cp8Pu69915MT0/DbDYjJyeHRQ2Oj48jLS0NbW1t0Gq1OH36NHp7e1kyPvI3GRsbQzi8UMcnPj6e5Yw5ceIE+vr64HA4UFxcjMrKSoRCIUxMTODChQvweDw4ffo0Nm3axMKWDx8+jLm5Ofh8PmzYsIGlcx8eHsbg4CA2btyIjRs3LplG3Lz+FNH+FbTb5x1GeTUw7wew2KOeN7nwmoXFKmT6f7FQwfuFkIBDTJ7uQQIDaSeo3gevXeGjSug69BtvpyImQoyI+k5CFO3CaSHzzIsYuEgkgtlsZrZ68uEgkwkxcb7/ZrMZSUlJkEqlSEhIYNIpnafT6VgIFj0fqplAO3WDwcDGSfc3GAwsvJZ2I6QeJcZBAgxFtJDAQfchEwbNCTmDkSczCVaUgjczMxMWi4V57Pt8PrS1taG5uZnZy8meHokslCEn5zQyU5FzotVqRWpqatTzoh0ACYDEgOn5EBMm0xytV56p8z5LxJTJPMebO3gfJj4RGD1v+s4LEbx/EJln6NpkdlkcgQIgqoYP3YuOKZXKKO0jr/2iuSEhhTQm1IZ2Tvza57Ph8hrEpaCgoAByuRwZGRmsdgXZqSsrK3HHHXcwYXDt2rXIzs6Gw+FAY2MjZmdnMTo6yhij2+2GzWZjSYrIae7KlSusGubw8DArNUD1PE6ePIlLly4hOTkZVqsVd911F7q6urBx40Y4HA4MDAzA5/OhpKQEKpWK+WVQ8bmzZ8+yMRgMBgSDQczNzUGr1TI7OtnYx8fHWTl3Cr88e/YsbDYb0wL29fWhrq4OpaWlCIfDzIE2Ozsb8fHxKCgoYCYHt9uN3t5eTE9PMxrhcDhwyy23IC4uDh0dHfD7/cwRMzs7mwnq9N6tXLkSIyMjmJ+fR01NDYxGIyvYptVqmZaTtDF5eXkYHx9n4bcmk4kV/woGg9iyZQv8fj86OjqYSbS1tZXlw3C5XMjPz0d8fDxmZ2eZSfPixYvMv+Ly5cu48847WTuPx4O+vj4MDg5icHAwKr9QRUUFbDYbi0has2YN/H4/RkZG8Mtf/hJdXV0sWZxer0dVVRU+9rGPQa/XIyEhgZlvSXCWSq+l16fiapFIhDl2ZmZm4ty5c6ioqIBKpcKhQ4fgdrvh9XpRW1vLMiD39/fD7/ejtbWVZf5MSkrC+fPnUVxcjD179iASiaCkpAQ7d+7E9PQ0srKyWH2dmpoanDlzBunp6axuSU5ODurr6zE/P4/6+nosX76c8awVK1agubkZ586dw8TEBFuTPp8PK1asgMvlQmFhIcrKylhko9/vR2NjI6amplj5gWXLlrFAhBUrVkAkEiE+Ph6hUAj19fWMh1VWVuLRRx/Fli1b0NXVhXXr1qGyshJGo5G9l2KxGK2trSgrK0NFRQXL38Rv8t8ON117BbhWT4WIL/3Gh+FRJ3g1MRBdzpsIK5lb6Fxi3GQWIEGDJ/i8gxuZeUhgIUZLRJdPvU394U0+fCgkAJapk/I5UN/5vBd8GXA+ZJJ8OvgoGV64UigUGBgYYJnwKAKFsnRSmCqlOictEvl2UFQMERgyo5AQRJEwJOAQU6G+LfbnoPAvYsxKpZJVZ6QkZORIBiwwMMqpIZPJEBcXB4PBwGLfialS+nSyo/J+OxR9Q/chz3W/34/R0VFG2MViMQtVdjgc8Hg8TPMxPT2NyclJTE1NMYdR0naR5ob6S7/T+uRTuZPmjRgv78jMR4HQGqW5BcBMSov9JGgdE9EmjQEJCrx/EQkNJIDwQgjvK0Sg/vECBS8Q3UioII0OHad3ib8n9ZH+fyehsgDQ0dGBjo4O1NbWwu12w2QyMRV5c3Mz+vr6cPHiRYyMjKC7uxtjY2NYsWIF86mYnZ3F4cOHWWGzYHChABUJMGNjY0zzpVAo0NjYiNbWVpaALD8/H+Xl5di1axcaGxuRnp4OtVqNsrIyaDQa5OTkIDMzE8PDwzh48CASEhIQiUSYmcHtdrPdMdnch4aGMD4+zvKOlJaWwmw2Y+PGjSgoKIDVasW5c+cALGRknZmZwalTp2C32wGA9YeqhbpcLmRmZgIAy4FB9vfc3FxkZWXhscceAwBWdbelpQUXLlzA0NAQ0tLSWFZPPusoqdXr6+tZKO727dvx4IMPsqq0RqORhXXm5uaiv78fBoMB69evR3x8PHQ6HU6fPs2yWU5MTCAcDqOzsxMPPPAAVCoV7rnnHpSXlzPnbsrLQ+bVrKwsCIKAqqoqGI1Gthm5cuUKPB4PNm/eDKvViq1bt7LCcitXrsTQ0BCSk5OZqYG0Ax0dHTCbzXA4HExz09HRgXA4zOq4zMzMYHR0FC0tLaxiLiWyeumll5hmiWpPUSipTqeDIAgYGxtjOUEoadbY2BjKyspgNBoxODiIuLg4BINBVFRUYHZ2Fnq9Hj09PTAajfD5fNixYwcrqkfZmGtra9Hb2wuz2czMcHV1dQiHwxgdHUVHRwdKS0vR2dmJLVu2oLGxEe3t7bBYLPjRj37EctYIggC73Y6SkhIolUosX76cPXfy83A4HFCr1di5cydEIhHzp6KIForImZ6eZrVdZDIZRkZGIBKJ0NHRgT179kCtVqOxsREtLS0IBoOIj49HYmIi5ubm0NbWBo/HgwsXLsBsNqO8vJxlbeXN2G+Fm9Z0EFPhneiIoN4Ii23N1DlS41PhLbrOYoLMJwYjPwwSWmg3TMeJ0FN6bboOX82UvzYJEyKRKKqiJyWVIimZt7ETs+XNNFKpFFqtlhVIAxYEM5fLBYfDwcwn1E8q8kMmEtLwAIDRaGTalWAwiJmZGYhEIkxPT7NSzMQkSW1PphJKlkOM1+v1sggV0qBQfymhktfrZc+TiB9lAdVqtSwBmFqtZlVneUdeEo6CwSBjCORnQ/MwMzPDih9RNsmZmRkm3FEStGAwGKXRIcai1WpZnQESOFpaWjA4OMiiWIgRU84Pvs4LvVwAonxNaD3w9V2Aa3lWaI2TPwaBhBpeyCZhgK5Bx6k9MXP6nRg/zR2ZQuhcXhgiokPvEGGxPwppJngt5GKnV2pLwiitI15jRUI5nXMzEASBZf5Uq9Ww2WxYv3490tPTmfq4oKCA/d7V1YXx8XEYDAaoVCqUlZWhqqoKZrMZNpsNAJCTk4OWlhasW7cO69evh9lsZv49ZWVlkMvluHDhAtLS0jA0NASxWIyOjg5mt25ubsbJkycxNjbG1vaKFSvw7W9/m+WW8Hq9TENwyy23RDnWUkE2g8GAdevWobOzE2lpafjhD3/I1tyaNWugUCjQ2dmJjRs3Mmf1nJwcjIyMoKWlBSKRCElJSfD5fKivr4dIJEJKSgoTtsm5UqVSYc+ePZicnGS1S06dOsUK5l26dAkbNmyA3W7H3NwcpFIphoeH0dLSgvPnz6OmpgZi8UKEjMPhwPnz53H//fdDLBazRFw0h5FIBHv27MHY2BgkEgmGh4eZ5oz8VSKRCOLi4jA5OQmfz4fu7m4oFApkZWUxbaXFYoEgCLjjjjtw+PBhDA0NYWZmBgkJCZicnERFRQWSkpJQU1ODAwcOYHR0FOfPn8cnP/lJ5Ofn4/z588zR9oc//CHTKjU1NSEnJwc6nQ4pKSnM92xsbIyl16dw4VBoIWU9JdMiDVowuJDNVSKRsPT4u3btYoz0jTfegMFggF6vZ+/6wMAAS6M/MjLCMnk2NTXh7NmzcLvdTMu1du1aDAwMIBJZKBvx9NNPMyGWNiktLS1RSbrIYV+pVGJ8fJzxpMTEROTl5bE8GCaTiYXDJicn48KFC0ygn5qaQm1tLcuUq9VqMTQ0xDZiKpUKGo2GOXo6HA4YDAaUlZVhaGgITqcTycnJSEtLQ2VlJcrKyrB8+XIMDQ2xsgDkj2IwGDA8PAyXy4Xk5GTYbDa0tbVheHgYw8PDuHTpUpQW/K1wU0IHb6bg1bw3EjiIEPN/b+RISlgcBcP7NvDmFnJiJIJIfSKiTwyFFxKoHf3O28J5DQdPlEkwIgFEoVAwpsH7fUQiEZbgiswvwLXMkmR7BhBVY4WYFjkpkrMraVYo2VNcXBwzUxFzmp2dZY6TNA/UTwqt5csMk/lHr9dDIpGwWGx6JsRkeedRekHJeZXm0uv1sj6RdmpqaorNLxFhqghL2iJylk1ISGCLWavVMmKu0+mY+o4c90KhEKanp1n8t16vZxELlJCmt7cXExMTTBvEjzEYDLL0zCTkLI7soPsRkwWi67RQSDQv5JLamzev8FoDXsvHm/5ovsmsw78rdF9eqOYdlmlN8u/JjSJOaB3TGPlQXV4DSRsAer/IxMS/p7RObwZ0HVL9nj9/njFHsVjMfA5efPFFlv7fbrezctwA0NDQwJxNt27dii1btjDN3vDwMMRiMbq7uzExMcGeY3V1NUwmEyQSCc6dO8dMH6tWrcLy5ctZ0jCZTIbMzEy0trbi+9//Pvbv349AIID+/n7mqNrQ0ACPx4Pe3l5Wx0Kj0bDdbzAYZNk2g8EgjEYj6uvrodfrUVFRAZfLBYvFwurAjIyMsFLvlMsmIyODRYKMjY0xE8LJkydx/vx5Rv8ob8jmzZuxdu1adHV1wWKxsPDY9PR0zM3NMT8WkUiE0tJSFBYW4uDBg7hw4QIikQirYrphwwYUFRWhqakJPp+PVRwdHx9HV1cXcnNzYTAYGP07f/48Ll26BJFIhIGBgag8OCdOnIDf70daWhqmpqZQX18Pj8eDVatWIT4+HmazGS+99BIyMzOZgLB7924kJiaivLyc1W85ceIEampqoNFoYLfbYbFYkJqaCq/XC4PBgIGBAXR1daGiogJGoxHf+973mN+ZTCbDpUuXcOr/FQXs7+9HWloa9Ho9AKC+vp75Yly4cAHr1q2D3W7HU089xTQrycnJ6OvrQ1NTE9OGFBUVYWZmhiXOEovFMJlMTEtFaf7n5+fR0NCAsrIyxg/J5yMSiSAxMRF+vx8lJSVQKBS488474fF4EAgEMD09DYvFwjQSLS0t6O7uRmlpKdxuN5KSktDT04P5+XmW3dnlciElJYWF+JIQn5+fz7TWly5dgtFohESykFBsbm4OIpEIubm5LLOvy+XC+Pg4TCYTjh07xqK1urq6AIAlbaupqUFOTg78fj+SkpKwcuVKRCIRVneI1vqGDRuY9vzt8I40HURYeULI7+RISiZixhO5xdoR3mEPANvd0u6TdnqU+IlnSnQ/frC8vwfVsuBDcOkeRCDpOx+lQdegEvB8NVre1ML7OpAam4iuSCRiXt+hUAgul4uluiVtBB/mSSp9QRCYWppebmI6ZHoBwDQR5AVOLyBJ25SkSS6Xw+PxMAdLPrkXCUvEEKitSCRiZeL5Hb4gLBR841O2U5gU5QAhocrv97OwXspqaDab2XUolTtlTqW07eSgS8+dhBjyM5HJZCxWnbQYs7OzLNR2aGiICTUkWFHUDZ/fgjcNAtcEDQBM+0NrTKX6/2n7r+A40/NKHD9opEYDnRsdkBs558g0jENyZjijmZFGGkmWZHkll8t27cXe7N5s7YV94V3btcEueVeyZa3GI2mkyYFxGEGQAEnknIEGGkAD3Q00QiM2/hfY8/Al1r/10FX/r4pFEujwhfd9n+c9zznnSZDkge8h+nC4bKEm5RxfKuLApCUpKUkSCNVgjLCnyn3iXFHLJryPh+cVExfOH7Xcqc4xIikbGxsyR5i8qDLb51WvFBYWiqEQ5YIejwdpaWkwmUw4ceKE1J89Hg/y8/OFKK3X6+F2u/HCCy9gaGhI1GCdnZ3weDzPKJrYojwSiaCwsBCZmZno6upCJBLBD37wAzF7mp2dxdLSknCM6HhrNptFtcKOn+SRhMNhWK1WOJ1OcXfU6XTSFyMnJwfXrl3DH/zBH2Bubg7Dw8PSmTMQCIgLL8dJamqqGPwNDAxge3sbN27cgMFgwMzMjNiCL/+fdvQtLS2IRCLo7OyEz+dDXl4eAoEAkpKSUFRUhLW1NdkUpKWlYX5+Hjdu3IDFYsHIyAju3buHe/fuYWdnB9XV1djd3cXIyAiKiorw85//HL/97W8BAP39/dja2sJrr70mHUU9Hg8yMjIwMTGBpKQkZGdni/mX3+/H4OAg7HY7rly5ghdeeAGXL1+W5pMXLlzA0NAQQqEQCgoK0N3dje985ztIS0vDF198gdnZWbS2tqK3txczMzOYn59HV1cXEhMT8corr2BkZAQFBQX45je/iVAoJC3hOab7+vqg1Wpx584dHD9+HPn5+airq4PNZsO5c+dgNpuxurqKpaUlZGZmIjc3F+fPn3/G1v5//s//KbbpZrMZgUAAGxsbKCsrQ25uLqKioqQnSmVlJXJzcxEIBKDT6TA3N4eamhrZKPn9filLPHz4EB6PRxIFo9EovVSysrIwNTWFu3fvYmxsDNXV1ZidnUVCQgIGBgag1WqRlpaGM2fOCCK+sLCAcDgMn8+H/v5+9Pb2YnV1FXl5eUhISMDw8DAyMjIE8VhZWUFqaioCgQBSU1MRFxeHmZkZPHnyBDMzM1haWsLExIQk5xaLRWLE+fPnBaE2GAxYXFwUn5bS0lK0tbVhe3sbxcXF6OzsBABRC6Wnp2N9fV1KiV/leK6kg0oGtTyhkt64cyKRDnjqfwE829WV5QzV7VMtv6iJjLpgqjs7Jhvc4anE0sNqAw5eFXrmeen1eilp8Nq4EDO4MoHhZ9P4i/eBE4+/Z72VBB6tVotI5MBsh8GaNdFAIIDFxUUEAgHpcrixsSGlDjLrHQ6HkKRIPKUxGHfi0dHR2NjYkN04O0iazWYEg0Hs7+/DbDYjJubAgpmBnH4nvIbt7W2BWelvwfIGSzFqiYnPgPJYXkdMTIz4BJhMJkGv9vYOXCdDoZAkUnNzc5idnRVzGgZu8lTU5oAxMQd21AkJCbKrWVxclEZYVAyZTCZRxXD8qugbgGfGIbuA8nnTA0TlJZHXwuRSlbBybKn8CgZ0jjcAIm0GniJuTJQPI29qaYQJG8+Dr42KipIEQuUsqYkJr5Wv4XXxPIGn5FN+71clh/Ho7e1FWlqacHPYuKqlpQXZ2dno6OgQp1Cr1YqHDx8K94Dfq9frUVVVJWqJpaUlHD9+HG63G0VFRdBqteKmmJSUhIKCAiwuLuJb3/oWVlZWsLq6ipqaGkm0+/r6sL6+jsLCQpSUlCAxMRElJSWyU1xcXITdbscrr7wiQXx9fR1paWl44YUXkJKSgtHRUUxNTeHJkyfSoK23txf5+fnY2toS8iFVYuFwGA0NDVhbW5MunZyz4XAYb775JoaHh6HRHJgvjY2NCaclIyNDztFisWB+fl66kI6OjmJsbExkp0Rvjh8/jpGREbz88stoamrCsWPH8MILL2BhYQEJCQl49dVXsb6+jq2tLZw9exbx8fEoLS2VNuexsbH4N//m3yA5ORlffvklSkpKkJWVBZfLhd3dXXGjNBqNokIJhUI4c+YMamtrYbfb8fnnnyMuLg7p6em4efMm3G63SEPz8/OlpYLD4UBjYyNmZmawu7srMluTyYRAIICYmBjxLurr64NOp8PCwgK8Xi9u374Nh8OBBw8eiOpHJY3SYv769evQ6XSorKxET0+PBOlLly4hKSkJdrsdnZ2diIuLQ0FBAerq6vD5558jJydHlD/vv/8+RkZGUFpaiqioKIyMjGBychKDg4OYmZlBdnY20tLSsLOzg7q6OlRXV2N9fR2vvvoqWltbhTsxMTEharpQKITf/OY3cLlcEjdph97c3IxI5MATY3x8XOS+5eXlKCsrw8bGhnz39PQ02traYDKZxJXW4/FgY2ND1F0ajQZWqxVGoxHz8/M4fvw4xsbGsLS0JOtCb28vfD6fcANJLM7OzkZcXBxWVlawvr4Ok8kkJSeTyYT09HSEw2Hcu3cPNptNkKuvcjy3I+nhRYi7K7X2ywWUv+dizYSB3AYu/Ax4TAzUZIOoiCrpA54uyCzVcIFUgyB3aqqBGIMEF1wGax6sy5Ogp/p5cBdL3gDPnUgBJwplg6ypkaQXExMjUDIREt4XJjpECViaIepDua3FYgFwkAAuLy+L5wL7v5DcSQkrDcOINDCRYTMsmnwxoFutVgk4NANjcKPzJ+8/iatEHOx2u0CHdCKNj4+X5GZ5eVkMbLiDXl9flwSGz5sacBJkiR5FRx+45PF19AlR+Tfksezs7IhfBMeV6mvCe66WctQxRa4MESaOZbU0o9YwmcioyTjwrP8HEwa1RELUQS0XMtnlWOVc4Xg+XOZUEzH1u9XEjUk+7xOA/wvFUOcd3/u8nA4AuH37NqKiohAIBFBcXAyz2Yzs7Gx8+umnyMnJwcLCAqKiotDV1YWSkhKUlJSImyRVU0tLS9BqtRgfH0dsbCzu3r2Lnp4e4ULExcXh/PnzMBgMMgdbW1thNBrx4MEDuN1uuFwuZGZmys59cXFRaukjIyOSTDx69Ajt7e147733kJCQIA2xxsbGkJeXB6/XK6WgmpoacY9cXFzEyMiIqEsyMzORmZmJoqIiOBwO/PSnP8XIyAhqa2uxvLyMyclJWK1WBAIBxMfHIzU1FQ0NDUhKShJvhLm5OZSUlKC3t/eZ8uTy8jI2NzcxNzeH6upqXLhwARUVFRgaGkJUVJS4++r1elRUVOD48eOYmZlBbGysJBVLS0t44403RO1G9de9e/fQ09ODiYkJnD17Fnl5eRgZGUFzczPy8vKkwWNGRgYKCgqQkZEh0ubNzU1MTExAo9Hg/PnzmJ2dxf7+Pr72ta9JOdXv98t6zPWJ1vBJSUk4cuQIdnZ24HK5ZP7S7M1oNKKnpwdLS0vIysqCwWDA1NQU4uLi8OjRI9jtdvh8PoyPj6O4uBivvfYa7HY73nzzTaSmpsLv96OyshKZmZmora3FRx99JBbu3/jGN+D3+3Hr1i3xTPnzP/9z6eJLN2Ov14v5+Xnk5eVJ64fh4WF4PB709fWJYosxaGpqCjk5OVhZWZENFl1cExIS8IMf/ABerxdmsxlWqxXFxcVISkpCfn6+8HAMBgNCoRC6urpgt9sFjSPSsr6+jtTUVKSkpDxjc85WGF1dXcjIyBC0u7a2Fg8fPpT7zA0oEwuiiBQ4zM/PS9M4t9stCTztDlZXV1FfX4/Gxkb09/ejv79f2mH8S8e/SjKrJg/AU6tmtbxCVICJgMqfUCWJKjzNZIELJfAUxlZr5cBTop5K9Dtc01bRFvINiE6ottpqMFHPXXVlVLtu7u/vQ6/XP8MfId+BlrfkcnCnQ9dIi8UCnU4niAe5DVarVdzeNjY2RDZLkqqaDO3s7EgZQiXQJicnw2AwCEphs9lEsra7uyvJBcs7RF9UeScDOUtGsbGxIrflfQmHw1hZWUFSUpIkdPyOzc1N6Riqoii0LU5LSxO0htI8NSnT6/VC3EtISJASB8tZ3DmpZlx6vV7KEjxHFb3g82FnUJ6z3+/H5uamyEN5MPlgCUjlVnC883rVkg3nBcmxaomRY1clrXLMqQkLk1smxKo8V+Ut8fp4D5iUqvOLCQP/TTXQYRI1P5vzmMkvn/nzHuXl5Th+/DiysrKwtLSEq1evAjhoT56QkCA7QMLnbCA2PDyM1tZWcf+sra3F7OysSPZY087JyREy5s7ODn77299ib29P4O2EhAT09PTA4/Hg9u3b+PDDD1FaWoq4uDgEAgEsLS2htLQUu7u7+P3f/32Ul5cLT8hgMKCqqgpRUQedY9vb2wWVbGxsRHd3N7RaLX74wx9KMnzhwgUAB+jV0tISnjx5gunpaXz9619HTMyBGZjFYkFtbS3a29sxNDSEyclJhMNhSZqamprw6NEjuFwu9PT0yGaI7emBAxXMuXPnYLPZ8M4778i6ZjQa0dHRIWvEjRs3MDY2htOnT2N/fx8vvvginjx5gvHxcayurorHw+DgIBYXF/H666+L5HdlZQUpKSmwWCwi5c3OzkZPT4+oeugXkZeXh5MnT4p/kNlshsvlgk6nw40bNxAXF4eHDx+irq4OFotFSh9sRVBSUoKRkRFp/Z6bmwuNRoOuri5JFEwmEwoLC0XunJOTA7fbjfPnzyMjIwMLCwt4+eWXERcXh6mpKbz33nsoKirCl19+iUAggNjYWOl3Mz4+jjNnzoga7uOPPxbZbCgUwuTkJP7oj/4Io6Oj0Gg00tPk+vXrgtYwIcjKyoLX64XBYEA4HEYgEMDY2BgqKyuxt7cHh8OB7OxsjI+PIxgMSmdg9jRJS0sTPg7Xl+Hh4WfI736/H1NTUxgcHJT7fv/+fRiNRikdEyHv6elBYWEhfD4fmpqaEBUVJYkrFWQulwv5+fkYHx8XdJ99sujDQRSIfBT6Mi0vL8NkMmFoaAgbGxuYmZnB1NQUSktLBeFR0d7/1/HcktnDSYKafKhfykVYVZyopDV1YQWebUylsuZV1r5az1Z3fwAkQ+ZCzj/cQfOcmZEyiVCJc/xenhuDPK9RfT8DGs+b50gOx+joqBgRUVLLdu/Ly8sCNYZCIfh8PoyMjGB5eVnKBdwV6HQ6IfkwcbJardJQjZbfy8vLCAQCojRh0kVjmP39/WcaeXH3TzksA8xhG3eWHEggo0cIr5lIiV6vl10MSzc7OzvCvfD5fFKqGB8flzbMhJwBiBspSXQajQYmkwk7OztirgQcoF1MxFi+iIl52kOEjqVUwfDc+PyYZLAMw2fHhGRnZ0d6wXAsMylieeIwqqHKqdUSh0q4VhMIjn2iRXzP4XnBe6SeB8c/E0cSjw+/7zCCov5fRffUeaeOZ/X7nudITExEV1cXWltbkZiYiMLCQjQ3N2N8fFwCHRUHMTExqKioENL08vIyUlNTEYlE0N/fj7S0NExNTeH8+fN4++23Re44NTUlPT8qKytl5zY/P/8M0udwOFBfXw+n04nU1FSEw2HpLsoyZltbG3Q6HaqqqvDOO+9I4CWamZ6ejvHxceFpBYNBkR9WVlZicnJSUDX6xly6dAnz8/N488030dLSgvn5efT09Ah6Nz8/jxMnTuA3v/kNTp48Ca/Xi5mZGbHETkxMxPr6Oj755BN88MEHWF1dxdjYGL744gt0d3fD6XSK8yW9NKj4sNvtuHnzJh4/fozExETcu3cPXq8XL7/8MiKRiDj8vvnmmzCbzVhaWsLk5CTW19eFP5CcnCzN66anp6HRHPhUGI1GTE5OIjo6GktLS7h16xbW1tZQUlKC2dlZxMXFYWRkBLGxsRgdHUVsbCxMJhMePHiAcDiMtLQ0REVFISkpCZ2dnWhqasKJEyeekee63W7Mzc3hzp07CAaDSElJQSgUwvr6uhh4hcNhzMzMwGAwwOPxwOfzobu7G1lZWfjyyy8FFaFE2mQyYXl5GT09PYKOWiwWREdHSxJQUVEh8t/o6GicOHECLS0t2N09aFLn8XiQnZ2NjIwMREdH40/+5E9EMrq2tgan04nY2FjpDbS3t4eGhgYsLy+LQoRqwN3dXYRCIYyNjeHOnTtiTVBWVoaSkhLo9XqUl5ejvLwcq6urePDgAerq6mC32zE2NgatVovLly8jNzdXeEKLi4uoq6vDL3/5S3EE9ng8AACDwYBIJIJbt26hpqYGer0e4XAYGRkZQozu7+9HR0eHGJiFQiFUV1fDbDajpKQEN2/exIkTJ5CUlASXywWbzSa8K4vFgtTU1K+0PjxX0nGYp6EmE8zW1Dq0Wo5Rd01q8sBFmf8+/Bnccal8DHXxBw6SHe4gGZgoFWUtnr9TyaUqo59BhDCgSnDl+1hOYYBQTacAyINiksByCjkKGo1GGqjNz88Lf0Gr1cLhcACAcDlYKgiHw9JBlsgKr4u+GzTFUXfZ5AaEw2Ep90RFHbSoZ1mAcl1m1nzG5AfwGknM5L3n/V5dXZVEhjwdGnuRZMXeFiRHkndAEiy7FKakpMjzDgQC4tFiMBiQnp4ukkp+DoM9SbHkCvH5cldP6SyRKwbdw8ZZDK70LOF4jIk5cJNU5bCHSaREB9TGaRzjRDJUXxiVeMqf8X0q4qImN2oyro57dd5w3KvolVqiUflR6h9+Bt2FVWXPV23ixMNut2Nqagrr6+s4efIksrOzZbdstVqRmJiIt99+G9HR0fD7/UhMTMT169eRm5sLh8Mhc6OyslIWzrq6Oty8eROffvopUlNT4XA48Prrr0sDM4fDAbvdLjXvb37zm1hfX0d+fj729g4MtNLS0hAMBjE8PAyv14uuri7cu3cPjx49Qm5uLqqrq9Hb24vy8nLs7e1hamoKnZ2d0Ol0+OKLLyQYGwwGuN1u6br55MkTjI6OIhKJCBG1oKAAc3NzSEpKwpdffomqqipYLBa88MILqKiowAsvvIDR0VFMTExIkHS5XHC73bh7964kTvPz8zh16pT0Trp58ybS09NRU1MjxmncsXo8Hqyvr6O4uBgnTpxASUkJ6urqMD4+DrfbjRMnTkhZMyUlBevr6/i7v/s7KaOeO3cOS0tLUtOPiYkRp0yn04lz584hOTkZgUAABQUFsFgsGBgYEDXd4OAgJiYmZB4nJiZKstfd3Y3s7GzxyEhJScHIyAiOHDmCSCQiTes8Hg9WVlakCVtycjKCwSBGR0dht9uxvLyM7OxsDAwMYGVlRSScAwMD4jvi9XrFk2VtbQ2jo6PCkamsrJTuumtraygqKhKLhdu3b8Nms0Gv18NkMmFtbQ1XrlzB6dOnBQVimXtgYACjo6NC0jx+/Lj0jfroo4/w7rvvorGxETabDe3t7fi93/s9REcfWLmTME902mq1IjU1FS6XC2tra9Dr9c+Ul9PT01FZWSlzf3NzEyUlJdje3sabb76JsbEx5OTkQKvVIjs7Gx6PB6dOnUJpaSkqKiqkxEP0LzMzE21tbaIKnJqagk6nQ2FhIS5cuCAqL4vFApfLJU62w8PDePHFFxETc9CCIzs7G1NTU1hcXMTu7q705fkqx3OXV7hIq7Vj4KkyRC17HCbE8d9c2GhgdZgQpyY3h4mmamKinpNazybxksGbqIW6S+Rizh2qyjvhgkskhYkRg6sKjwOQgE1yXmJiIvb398WOm/eG2WxSUhLi4+OxtrYmpFMqS9QyDRMQKkFoM06LaUpwqe7gzozGaBsbG0hMTJRSBUstGo1GyhaqkRevh3wX1ciKCAqluEz8WNYADgIek4LV1VXxfVA9UEKhENLS0oRkmJqaKvAsG3LxGqlI2ds76NtAvw6Wtohqqbweq9UqHAaWz+guSK8T9rLhc1HHAxMAolk7O8/ayZNDc1iVRU8NJiwqsfowl4JJBP/NcpmKavA1aqKuym0Py86ZKJADxHnE+8NEmgkWr5mJkkoGJ1GWv3+ew+fzwWAwiAtjV1cXNJqnknBaShsMBtldO51Oac5lMBgwOzsrhOTMzExRH8XExGBubg5DQ0O4du2ayMrv378vkuna2lpcuXIFvb29opyamppCc3Mz7t69i/LycuEG0eE2OTkZbW1tOHHiBNbW1rC2tobp6WnhTZ08eVJQAZvNhrm5OZSWlqK3txc7Ozv43ve+h+zsbOnmefnyZUEzQ6EQzGYzrl69it7eXgSDQSwsLODb3/42NBqNeB6Ul5cL4hgdHY1QKCSN8mjAV1FRgYGBAdy6dQtTU1PY2dnB9evX4fP5UFZWBpvNhmvXruE//af/BL/fj1/84hfIycmR0uTGxob0I2G/DpZpWRYm+hoMBkWuyVp+UVGR+DRcunQJbrdbSipMXtra2lBdXY3m5mbMzMxIYzOSImnbvr29DZfLJUkxk0l2yu7t7UVqaiqOHj0Kr9eLmJgYvPjii4KE+f1+QbRYFnI4HLDZbFhfX5f1r7a2FpOTk8jPz8fHH3+MY8eOYXR0VMoxbE1vNpvx6aefCmeE439wcBCJiYmyQdRoNMjKyoJOp8P4+DhGRkbQ0tKC48ePo6SkBAaDAS+//LI8e4fDgcHBQczNzUmQpjPz4OCgvKetrQ1paWl49OiRcJ7otzEzM4Pa2lrcvXsX29vb+PLLL8XYjOM+OvqgBYTRaBTTu93dXdTW1qKlpUW4PlStREdHIxAIiEyZlIPl5WXpIxQMBtHS0oKpqSnxkFlaWoLJZEJrayuam5uxv7+P3t5e9Pb2fmVDwedKOrgIHiaXMdirOzW1Lqwy51UpJHffXEBVpEJVCqify/r54X/zu6jf5iJMaJuWxCp3hERBAJLYqFwSSnQ1Go1wMFS2P8+dTcUIETOYJyUlYW1tTXbjKrGP/AsAUrogcgAcQNSETimjWl9fl8WMnV5JIltaWvq/XFqTkpJEyqqqW0hyVVuMLy0tySAmjAw89Y4AIFwRoiUs71AazGvZ39+Xej3Jgfx+fkckEoHNZoPT6URiYqKogRISEhAMBiXYkzhJRIlI1Pr6uiRKHDd8duzRwBINUQ2OU61WK0x+Pg8mMup4IrpFEiwlzf9cII+Li3smgKtyWSY0KgGU/2fSS3mrirJEIpFnElp13qkcDCp8iN7xvNVz4Wfw2jhfeT85N9Q5zu94niMtLQ0LCwuYnp5GMBhEVVUViouLYTKZpE/HP/7jP+Lx48fw+/2IiYnB5OQkYmJiYLVaodFoUFtbi8HBQZw8eRJWqxVDQ0NC0MzPz0djYyOKi4uRnp6OpqYmVFZWYmFhQeybJycncezYMUxMTIhZkl6vx9mzZ5GbmysBVDUoC4VCCIfDEkCPHTuGY8eOYX9/H9PT02JQpdFo4PP5cPv2benCfOvWLczMzCAhIUF6suh0OiQnJyM7OxvDw8OoqakBcABzB4NBzM7OCrKUnJyMR48eYX19XVxTV1dXkZmZCQDiZUK3zqioKBQVFcHv96OhoQGZmZnSTTQxMRGnT59GZ2cn7HY7/uIv/gKzs7NYXl5GJBIRnkNeXh729/fFKjsUCsnmY3f3wGSL60J+fr74StDw8ObNmygqKsLOzg6ysrIwPj4Ok8kEl8uF9vZ2VFdXi9wyEomgsbERFosFL774omwMp6en8fjxY+zvHyjq6NpJ/yGtVouBgQEUFhaKU2xJSQliY2PR3d0tpMvk5GR4PB65D+Qw2O12XLt2DQsLC2hpaUFGRgZKSkrw7W9/GzabDUajEQ6HAxUVFaJY+vnPfw69Xi+djY1GIzQaDfR6PSorK5GYmCgdVauqqpCTkwOdTofJyUksLi5ienpanFB9Pp+gt0R/aGU/Pz8vpcJTp06hvb0dMzMzOHPmDKqqqpCfny+b1Nu3b2N+fh4/+tGPZLynp6ejr68PFotFeEZUKmZlZWFsbAwLCwswGo04evQoOjs7YTKZMDs7i7S0NOzt7eHs2bPCBwsGg5ifn4fdbpeEPC4uDidPnsSxY8ewvb2Nuro6+P1+UQtWVlaivb0dJSUlOHbsGEKh0FdaI567y+xhqaFKIgWe7U0BPGXEA89KWlWkgJ9JtYgK83JhV79ThY2fuRjNUzMwniuDHv/Ng7vTwztLliZCodAzVuYMOKyds/bP6+COn9JSBl/uIra3t0X1QuSCSQADE3c5e3t7kgQAkPO3WCywWq2C4rC9NQMz+xdQXhsfHw+TyST8DDVJY8kgISFBkgPyJhiQWY9l/XNzcxNGo1F2RXFxcSLX3dvbg9FolHvGf+/vH/iOUJZFIur29jZSU1OfQRzIvQgGg9JtcXV1FZOTk88E8N3dXSG3quhMJBKB3++Hx+ORZ6sSS2mexuSD/BN6kjBYMwnQaDTieaLX6yWB5WeS38MklAGBY573msktkSl+Np+9+lyIQPEzVVWNOp+IVvGe8FrVEgzfp/KwVHTyMIeKpT+eGzcFz3PMzs4KROvxeHDjxg10dHTgyJEjguhR3//d735X/Gja2towODiIqakpKattbGxgYWEBx44dQ2ZmJgYGBtDd3Y2PPvoIPp8Pu7sH3g0Gg0FanCcmJqK2thaBQAAlJSUi4dRoNJiensavf/1rhMNh/MM//AM+//xzaYGel5eH7u5udHR0CGzPBmmpqakIhUJi5GWxWDA4OChmZGyFnpycjJ2dHbF4ZzO6nJwc9Pf3yxhjU67NzU0sLCyIyVZGRoYQpc+ePYvu7m6kpaVJ2/Kuri7YbDaYzWbxuhgfH8fi4qK0mV9ZWcHQ0BCampowPj6Ob37zm6ivr0d/f7/A6ux7Mj8/j/PnzyMlJQUlJSWoqalBb28vqqur4ff7UVRUBLvdjv39AwvumzdvQq/XC2fs3XffRXx8PG7duoWqqipkZWXh3LlzaGlpwf7+PhYXFxEOh+HxeHD37l08fvwYS0tLOHr0KNLT0zE5OYnGxka0t7fDaDQKIdzpdKKqqkr+39TUBJvNho2NDVy9elXWt8nJSXR2dqK/vx8XLlzA+Pg4Xn/9dcTGxqK+vh5+v1+61rLfVCQSwcDAAHw+H371q1/B7XbjH/7hH1BRUYFjx46JIVtRURGampqg1+uxtraGkZERLC0t4c6dOzAYDEhLS5M5u7q6iuzsbCQkJODIkSM4efIk0tPTMTMz84yqzOl0IhKJiFVAJHJgsf+Hf/iHUhJ58uQJ3n33Xfj9frS3t2N5eRl1dXWIRCJ4+PAh2trasLCwAKvVirq6OgSDQRQWFkpvHIPBgJWVFVitVszMzCAUCollOS3zGY8WFxdFmVVeXo65uTn4fD5Z92ZnZ+HxeISz1Nvbi5iYGNy/fx/p6emiZMrKysLa2pokI//S8dycjsO8DjWRUP9wMWOwOAznqgslX8+k4DBszN8zcAAHZELWwYGnSQTfT/IkSZxqcAOedu1kqQR4asKkQtQMWOQycGfIn6kW3iqh0efzYW5uTgLT9vY2gsEgrFar1BTpeElDLlVKyoSDvBGeD10b+R2sgbJfDYPjysqKvI7QNs+RKA/5H7xnJNcCB7sr2rfzfpFnosop2RCOGnQmefSSoAcHmeacgOSicAELBoOSvNL8KxQKidx3YWFBkhDC6gaDQcigLGu53W5kZmbKc2VSQCRlbm5OZJN8LoT2WX7i+CZ6QT5NTMyBYRifBROAwwidmhDz+8l/4lhRSxwssajEZX6emnwwaVBRErWUyblwuMyj8pb4+Uzw+V6WZlRUhKTH5zlcLhdaW1tht9uRlpYGl8uFmZkZQTPW19dRWVkpsszOzk5UVVXh6NGjWF1dxYULF7C8vIyMjAzMzs7KbnZsbAxerxebm5uorq4Wztbs7KwkBjT7mp2dFVi9srIS8/PzePz4MeLi4sQp1GAw4N/9u3+HqKgo1NbWor+/XyyhbTYb+vr6MDU1hWAwCJfLJeXP7e1teL1efPe735UafX5+PvLz89HX1ydogslkkuSlp6cHNTU1UmaiWV5GRoaUsiYnJ9He3o6kpCTs7e3BZrMhPT0dT548wcTEBGw2m7hHTkxM4NixYzhz5owgh7m5ucJHePvtt/Hll1+ioaEBOzs7+OSTT1BSUiKNxjIyMvDqq6/C4XDgww8/xMzMjJiwpaenY35+XsYfIfu6ujocP35cGsGRCO5wOLC+vi73/Wc/+xmOHz+OxMRE9PX1ISsrCydPnkRUVBSqqqoAAC0tLdJHZmNjA01NTfjd736HmzdvIisrS3hhs7Oz4lTKNaWiogJGoxGPHj3C8ePH4XK5MDk5ifv374tvRDgcxtWrV9HR0SEyXEpguf6+9dZbqK6uxtjYGDQajShcJicnRXL605/+VDaidXV1WFpawsWLFzE+Po719XU8evQI3d3dqKiokEaVExMTQqRNSkrCtWvXRBDQ2dkJt9stqJff70ckEsG3vvUt4b81NDSgtLQUU1NT0qTvypUr2NraQmZmJo4dO4aoqCh88MEHGBgYEDlzamoqJicn4fP5hPRaWVkJq9WK9PR0lJWVITExET6fD6Wlpdjb28Pg4KCoE/v6+pCQkIDs7Gy0t7ejq6sLHR0dmJycFEfYjY0N2O12hMNhTE1N4datW9je3sbMzAyio6NRXFz8ldaI50o6Dqs11F0QExImESpJ8zDScbiEopLq1M9mgOKOjYvW4XPiZzKwAhD1As+FPg+Ew9VdqUoMVZUoKrS8u7srbojcsRACZAmEC8jGxoYgH3xNYmIicnJyhF+RnZ0tr1ETEyIksbGxUl9m2YXyssHBQQnwvC9qQhcXFydwIV1MSUZjkGPpiAGXQYzMct5TJj0sjURHR0sTInIbVOUKURIGV6IsJIESBqRJ2Pb2trR4ZiJpNpulLkvC6vr6OoaHh+H3+7G3t4f5+Xn5HiYdqjWw0+mE3W6X7rvp6emIjo5+RvVC+ShlbzRiYwBWk06VJKpaphNdUNE7dZ7w9SqXgskQ8LQsyLGs8kQ47lRliequy/NUlSqqHweTFJWTwu/i2ObPDivPeF8Pl1L/pcPr9aK2thbb29sIh8MoLy8X99WYmBhkZWXhn/7pnzAzMyOJPLt/ms1msfm+f/8+iouL0dbWhv7+fiQnJyMjIwNHjx6VbsokUu7tHXRrHhwcxPnz55GQkCAKhuXlZej1euTk5CA/Px9vv/229DCam5tDW1sbvF6vlBO6urrkOR09elRKB1tbW1haWkJ6erq0Tdfr9SgoKJDut7xXly5dAgB0dXXhk08+QXJyMmJiYqDT6eReq4lUTEwMioqKxAvD6/WKC2ZTU5OgriMjIygsLERtba3U2U0mk7QR2N/fR39/Pz744AMUFhais7NTmo319/eLS6lWq8WHH34o15SRkYH79+8LqZLNvqampsRkanBwEPPz8+jv78fKyoqowtra2lBYWIjc3FxYrVbY7Xb4/X4kJCTA7XaLuoRW6PX19VhcXMTa2hpycnKQkJCATz75BH/5l3+J5eVlzMzMiGLtpZdegtPplB4+3KxFRUWhpqYGzc3NePz4MS5evCiJOd0619fXUV9fj9bWVuzs7ODhw4fIzc3FyMgIwuEw2tvb0dDQAKPRKByN7Oxs/PCHP5SOqxcuXIDP5xNkiaTKxMREBAIBNDU14fjx42htbcW9e/cwPT2NkydPoqSkBBsbGygvL0d2djY6OzuFwzI2Noa5uTnk5+fLxqSzsxMJCQnY3NzE3/3d38FgMKCyshJTU1Noa2vDD3/4Q4kRTE7YOVir1Yq6JTk5GXt7B03wOCc6OjrQ0NAgPDuW7ugB5Xa70dXVhbW1NeTl5UGr1eLUqVPSbZyeICUlJTAajVhdXcXp06cRExODjIwMbG9vIyMjQ+zgv8rx3EmH+reKQnAycdE8vPNT2fSHa8WHkxf1/az/A085JerOkQs1kx0usvSk4Gep566eGxMd1bRJlQzy36y901uDO1gSsWJiYrC2tob9/X1Rk9DrgrtHGlXt7+8L9EaiKSV37COi9nNZXV3F3NycMNVNJhM2NzdhMBiQmZmJ5ORkxMfHY2Nj45mOtYTxwuGwQOfkJ/De0ZuASRjvBYl7DNQkhe7v78v1q1wJBmsiLdx5877t7x+4R05NTcFutyM5ORnAUwUGESaWDex2O3Q6nUwqr9crHBcSddkwjzyXlZUVrKyswOfzSb8Yck6io6PFjIdBgLXaqKgDUzSbzfaM9bLKX1KNwtikUJVmH05SOebVDrBMoFWUkOOdBxNIzicmgnyNyp3imOZ95nvVciS/h+oUypPJkWHixuSI30uV1POWVwDA4XCI/G9wcBDr6+tiQ723t4fc3Fw8evRImk/dvHkTOzs7GBgYkHtjs9nQ3NyMtLQ0JCcno7GxEQ6HAwMDA7hy5Qqio6Ph9XqRmJiITz/9VDw7PB6PlCLZn2V5eRl2ux137tzB+++/j/r6eqSmpuLWrVuIj49Hf3+//P39738fdrsdp0+fhtfrxejoKFJSUuDxeJCVlYXu7m689NJLyMnJEakhlVjJycnCeaL66nvf+57Ir7Ozs9Hf34+FhQVxLv3Wt76F8+fPS7Kt1WpRUFAAn88nsuDExETxhsjNzcW9e/ewsrKC//W//pcQpwOBAGZmZoQMCEA4DkxKjh49CoPBgJ/97GcoLS1FTk4Oamtr4fV6UVVVBaPRiO7ubuj1euTm5oo1emJiIhISEjA7O4v19XWMjY1JqayiogJjY2Nob2+H3+8XKfTw8LD4QmRkZMDn8yEmJgZXrlxBaWkpampqsLi4CKvVitLSUrz77ruoqakRf6PW1lY8fvxYvF4mJyfhdDqh0RxIzGn2VlhYKCUpNoKjp0ZmZibMZjM2NjZw4cIF8RdhH5zPP/8cDocDfr8fubm5mJmZgc/nw9GjR8WWgIZd1dXVYipG36XBwUFBtWZnZ2E0GjEyMoJAICDPs7CwEG63G2lpaXjllVcwNzcHg8EgHDxuOokGvvzyywCAJ0+eoK6uDllZWVLS2t7exueffy4kV5JSc3JyYDAYMDAwIITjzc1NsSB477335NkFg0HExcWht7dXZMpEuYeHh9HS0oLbt28LCj02NoZvfetb2N4+6PwdExMjiM3g4CCam5uF+P9V+V/PXV7hLkxdZNX6NfCs54YqBfznyKCHD9WNEXjal4KLJF0/Gci5i2NNnz9nkOB3qyUUJgw8F14XJaRERtTvYDt2niMDCPkQfHCEAsnmZsBnOWBxcVFksuQFOJ1OFBQUoLi4WIyK9Hq97OKYaOj1ehQXFyMvLw9msxkWi0WkqkyI1tfXBdYlx4O78Pj4eKkl0gmUSAZLMGazWTq66vV6QWtoYQ4coEgJCQmiFmFysLy8LHAdExpyOigbZn8Xg8Eg9459Mfj8WN8lb0UlPe7t7T1TVlpdXRVyFxMMGmVRtcLSCscr/02/Eo1GI11LWVYhvyUqKuoZjgtRFZbaWKZR1SxqsqLynqjkURE+ok+cRypxVk3YOe84n5gUMKFgKYRjl7Vkvuew4oWyZ84PlYirJjhqqfOrHiwfNDc3Izs7WyzOc3Nz8fjxY1nMfT4fpqenUVRUhNTUVJSUlCApKQkzMzMoLCyE0WiUEuDHH38MjUYj8k/Cwzs7O6itrcXFixdRUVGBx48fY319HUeOHEFpaSn29w9k42z7npOTI03bSEwlSZwlBp/Ph9HRUSG3+nw+zMzMyAZoeHhYeCcWiwWzs7NCqrTb7bh16xZ2d3fh9XrR2Ngongjt7e3Iz89HVlYWEhMTMT4+jrm5OTx58kS4bHNzc+jt7cXY2JiY4+n1eszOzookMi4uDqdOncIf/uEfYnp6WhIcv9+PmpoacXVluSojIwNVVVWIRCIwGo342te+JsoFt9uNvLw8LC0t4Ve/+hUaGxthtVphMpkwMTGBtbU1Mf5KSEhAcXEx8vPz4XK5hDdWWlqK3NxclJSUwGq1wuVywWq14vTp0xgeHsbAwABqa2uxtbWF48ePo6OjQ/p/jIyMiHNmTk4Obt68KVLqtLQ0aDQatLe3IzExER0dHbh27Rry8vKQlpaGsrIyFBQUSBLGucx14tq1a1hZWYHL5UJ/fz+Ki4vhdrtx5MgRJCcno6qqCuPj43jttdfEPbqurg47OzswmUzo7u7G7du3ARy03KioqJD5SKdWukuXlJRgeHgYe3t7ePDgAWw2G4aHh9Hb24tAIID8/Hz87Gc/E1fS3NxcaLVa1NbWilz8jTfewNTUlKiHgsEgwuEwmpubMTw8jLa2NsTGxuLChQtwOBzy7Mkf2tjYQF9fn6AWsbGxKC4uRlZWFra2tkQCHQgE8Nprr2F8fBwZGRlIS0sT1KmyslLs97OysvCnf/qn6OrqQlJSEqanp4XPQmLpiy++iN7eXrH8/yrHv8ocjAmFWqYAnk1KgGfdDoGnQUUtiajlDC7GnNzkIAAQRIGfq+4YD5dE+F7W9VUS6WEiKstAvC4GDNa3eT0MGFz0ycUAID9XW6jz+5kUkCFO2J5JAJMe7i7JMieB02w2IykpCTabTc7FZrM9s1jyOhiA6JDHcwgGg3LPef5EH8jAZ6BWA05CQsIzPWnYLI5yV/5cJbrSE4S27MDTDp2rq6uIjY1FIBCQZISoC2VvLDkxIaLEj+jN7u4u1tbWEIlEpJS1sLAgkO3u7q6QRTlW+DfvM2XLfA277hJe5r0kEkBkg8H8n+M1UbrGMUYUSJ0XaklGReDUEgp/z0RARQc5xlkeU5MItWzCsgvfwzmnJvuqKRqRHfVcVU7I8x4JCQkYHBxETEwM2tvbpV18b28vHA4HAoEASktLxRGWyiyqTb7//e9jb+/AbbGrqwsmk0nuP3f0NKzb2NjA6uoqmpubMT09jcrKSjQ0NMBisQhfIC4uDr/+9a+xv78Po9EoZEXaoHPhdjqdMJvNGB0dlZ4aCQkJmJ6exu7urgRpvV4Pv9+PnJwcaDQH3Ud/8pOfYG9vD0NDQzhx4gQ0Gg0uXryI9957D1qtFmazGQ6HAzMzM3C5XDAYDHjhhRewtraG4uJiLCwswOfzCdK2tbUltu7ciY+Pj6OiokJIf19++SX+4A/+AFNTU8JVWF5exokTJ2STUFlZifj4eNy/fx/x8fG4evUqtre3MTU1hYcPH+Kv/uqv0NnZKRvHsbExpKSkPOPZsbe3h4GBAWRnZyM/Px8nTpxAW1sbSktL0dXVJeWg8fFxUa+srq7i448/lgC6uroKg8EAr9crNu9RUVFobGyURnmxsbHC/drZ2cGdO3cQFxcnDqn5+fk4deoU7ty5I5s7o9GIkpIS5OTkYGJiAoWFhdjY2MDt/2PFT/uBnZ0d3Lp1C62trfjrv/5rhMNhOJ1OHD9+HO+8847Mk88++wxtbW2YmprCj3/8Y9TV1WF2dhYajQYzMzNiHU/31PT0dOzt7SE7OxunTp1CVFSUJGwApMSyuLiIjIwMJCQkYHt7W1rWT05OQq/Xw2g04tatW3j48CG8Xi/Onj2LpaUlFBUV4Zvf/CZ0Oh30ej2ysrIwPT0tPB+TyQSbzYa9vT3paLu8vIxbt26htLQU169fh91ux8LCAnp7e9HZ2YlHjx5hZGQEubm5opgi2kvrBUra29vbMTw8jN3dXeluvL6+jkAggImJCZSVlSEcDuPJkydfeX34V3WZ5SLJ/6vMfR6HJbBEFrhoswzC4/Bix0WfB1EIljc4MdWdHhfUf+58DstjAUgCADwlpqrXqbaZZzmH0luSTUOhEObn5yVg+Hw+BINB+P1+WazY94MDh1JOBk21u+Xu7q6475EDodPpBM4kRB4VFSXWuvQ8oSpE3SnTxlaVrRLVYHDd3NwU+Szfz+sJh8Oy6NMinXDf7u6uSHf53JxOp+zY+Vm0ZGdSw6QoOvrAj4AeIrSEZzmECgaj0YjMzMxndv6UDVPpoqJUq6uriEQiz3Ry5dhjnw66t1J9w/tBREt1XSWawXvn8/kkWaUHApNgtfTIxIOBVS0l8TuJJnDM8vtUbpSaMPB6yBXi/GOSQmSA56EStVXjMNVCXUUuOSaIQqlql696fPnllwgGg5J8aTQajIyMoLi4GLm5uWIYRfOnpaUltLW14ezZsygpKcFPf/pT9PT04Pjx46ivr8fw8LCoEMhhoKsiG3QxwW1tbYVOp0N7ezuCwSBGRkZw7tw5JCQkYH5+XmSN09PTGBoagsPhQG1tLTSaA/UXCdhzc3M4deoUent7UVJSghMnTmB1dRXDw8NYXV1FTEwMmpubBXa22WwIBoNITU3Fhx9+iJSUFFy9ehUbGxuiSoiJicHS0hK2trYwMjKCa9euYX19HTdu3EBycjIikYgEYaI/DFJ6vR4Oh0NKiqmpqaitrcX169fFxyM6Ohrf+MY30NvbKyhJV1cXmpubkZOTg/HxcZw6dUpawAcCARw9ehRutxszMzN4+eWXERsbi56eHlgsFiwtLWFjYwPDw8OIi4tDXl6eJHAlJSXY2trCN7/5TTQ2NsJgMGBtbQ2Dg4N49dVXERNz0PKB8k7yMvR6PQoLC1FZWSkbD4PBAKfTKclVcnIyXC4Xamtrcfv2bZGZHjt2DMFgEKWlpfj6178uSFhsbCx8Pp9wulZXV/HSSy9henoazc3NuHz5Mh49eiTo5iuvvIL4+Hjcu3cPDx48kE3d0aNHUV5eLn4tjx49EtOvoaEhRCIR6Vl148YNHDlyRJQdtDjf2dnBqVOn8PnnnyMUCmF4eBjvvPMOHjx4IHNsfHwcf/3Xfw0A0n2YDQYrKiokRmxsbODmzZu4f/8+dDod0tLSsLi4iNOnTyMvLw8ejwdOpxPz8/PY29vDxYsXAUAS4o8++khcePf3D9yLKyoqkJubiytXrsDn86GlpUXGc25urrjC0guKCOHu7q50iqbx2smTJ9HV1QWLxYKysjLpIv4vHc/N6eBOS4WQVQ4GF1/uAolKqCQ6/uHP1H4YXBgpC1U/k4siFSNqkkKiFgcW8DTIqOUUfi8DsLrYEhnh71RiKrNB7tKZRWs0GrEfpq04gzz9IGiqxcSAizoXZAYXJhsM5n6/H7Ozs+LwSB09IXPuxPf29sS0a3l5GQsLC3Ju/I6dnR1RqpCFT/MyDko+W9ZO6ZNBJIKBjCQiJiEkrJJwqQZEnh/LTSSuMlGMjo4WJrzRaJTnTx+X1dVV4Zcw6eP5EAkpKCgQM6BQKCSscHqOMEHY2NgQci69PJggcYElqkH+AYlUVBsx+JPcFw6HpbxGOTUPNeDze9RkguOA9VAmASyx8DM4dtXEhmUfJjx8LRcXXhvHABM2JhJUFzHR3Nvbk+tTyazPSyRNTU1Ffn4+YmNj0dTUhM3NTUEQxsbGxA7fYrHg1q1bsFgsKCkpQWFhIR49eoTbt2+jvLwcUVFR0tqb1v/sSGw0GtHX14eYmBjcvn0b6+vr0n6djotM1DUaDZqbm6VkYLfbUVlZiYKCAiQnJyM9PR2RSAQnTpyA1+tFamoq6urq8ODBA1y9elWUK6mpqTCZTIhEIigqKsLQ0JDsMgsKClBUVAS3243l5WWcPn0a3d3d0q1zaGhIPB5IyNNqtTh69CiioqLw5MkTmEwmZGdn44svvsCRI0eg1Wpx+vRpjI6OShM7Ihokv+7s7MDr9SIrKwsdHR1ISkpCc3OzNJVMTk6G0+lEcnKySGFZAg2FQtDr9ZiamsLly5dFDhyJRJCWlobu7m4xaHM6nbh+/Tp6enowPz+P73//+0hNTUV8fDxGR0fFDyg7O1s8NfR6PRobG7G4uCiKtIWFBWkQt7u7K/4j9CSam5tDTk4OHA6HWA0UFhbC4/EgOjoanZ2dyMzMxO7uLn79619jc3MTMzMzSEtLQ1VVFdLS0tDV1YX4+HjMzs4iLy8PNpsNp0+fxre//W3s7u7CZDJhbm4OS0tLSEhIQGZmJl555RVRpGxvb2NiYgIWi0U2Sunp6eIdwnJXVFQU3n33XTidTrhcLvj9fnzyySfY2NjA9evX8fu///uYnp6Gw+HAqVOnYLfbsbS0hI6ODsTExCAvLw8rKyvSeVntnwMc9CmKRCKoqqoSci/H/29/+1ukpaXhyJEjGB4eRl1dHXw+H65cuSI9rra2tlBYWIj+/n4Eg0EUFRUhHA7j7NmzcDqdaGxsRGNjo2zyenp6MDIygtbWVgwMDMBkMiE/Px+/+MUvkJeXh/T0dCkXGY1GuFwuPHr0CKFQSPw7/v/S8E3lZQDPtuRWoXn+nGiH+jtVzQI821NFXSBVgp1KPuXuUA0M5Guo7pF8H79fRWTU6+AOkImIKiHkeZLMt7W1JfwFksNozEQTJ04mVZVCmDYpKUkClHrvqJogj8JisYgDp9PpFF8Dlh6ioqIkgWAA57UwqHAHTgMvmnkZjUZJ8ChJZQmFKhvu5HkNTEpU23QmZyyjUGnC5nVckEmq1Wg04jzKhCcuLk54Fzs7OzCbzRIMSU5iCeowt8Nut0tStLq6isXFRdGVz8/Po7e3F6Ojo/B6vQgGg1hbWxM2NuV3MTExYthD51bySxiMWVdViaWEIfkMVKRBnQtqUnt4fvCzOHZ5j9U5wnHC+61KXJm8qVJXVfKqJuosCzH5p3+KWg5issL/q+PzeY709HRkZGQIMbOsrAxXrlxBJBJBTk4Ouru7MTY2hs3NTRQWFsLlcmFxcRGDg4NIT0/H2bNnBfolR6KsrAxOp1OsyaempqQsyF3Y0tISiouLEYlEcPLkSSFUZmdnS1KbmpqKra0tCZ7l5eUYHBzE+Pg4hoaGcOrUKURHR0uSOTMzIwZ/LFcWFxdjampKPCxoCmUwGGC1WgEAIyMjqK6uRn19PdbX1zE5OQkAKCwsRH19PYaGhpCXlyfl1kuXLiESiWBqakqMqVZWVvDZZ5+htLQUCwsLKCgoQF9fH9rb21FaWorR0VHZhNhsNtjtdrjdbtntp6eno7CwECkpKbLJSElJwcDAAAKBALKysuDxeBAOh3HkyBHY7XYYjUbs7u5KgsJ1b3t7G6+++ir0er0gNfQxee+997C9/bR5ItUdpaWl+Oijj5CZmYlgMCicr7GxMVnjuFZQKpyWloaBgQEUFBSI1TcANDQ0ICUlBQBkznPTRQLo9evXpQS9traG8vJyIfWbTCb09PQgKytLZNobGxtITk7GnTt38M477wiZc3Z2VjbLo6OjYtzITWdnZye2trbEqp9qIK/Xi8LCQmlq19/fj+zsbKyursJqtQo9gM1AtVotQqEQbty4AavVijt37qChoQHT09OiQnrjjTfEROyVV15Ba2ur2CsMDw9jenoabrcbN2/exObmpvAB6f66tLSEnJwcIaDW1dVhYGAAc3NzGBkZEUv8zc1NZGRkIDU1FW63GxcvXsTY2Bji4g6604bDYXR3d0sfJa49ZWVlyMrKQn9/v6ybX+V4bnMw7rpUgyPu2LiAqmiI+jOVcMf3HS55cDHm7/h/NTnhDl81a+IOlYs7JwwXdgYIvp5BjztH1VWUCy3RDwDPOFiSeU5ERfU8SExMhFarhcViEVImd5b8fLYiZqBISkoS2Ji7cRIvTSYTsrKy4HK5pN7GIMRdLoOzKtckk16VxtJshwebwamcEJXsyVoouzLSeGxzcxM+n0+eE+8/ESUu2rznwFOyMQM87wlrhHREJHzJZI0+HCqiYzabBR0LhULiquj3+xEIBOD1eiUR2NnZkYUjFAo9oyohKsIxQzSKnhnkkHBno9PpxEyNCRgDNktu/BmT38MJrIo+MYHiOFDlrRz7/L2aTPNnvPf8w8/kmFYTaNUIT30dz4H/V0srarnxqx4PHz5EVFQUKisrERcXh8XFRZw8eRJzc3PY3d1Fbm4udnd3UVVVBafTiZmZGVRUVKCxsRGzs7NoaWkRUp9GoxG+zvDwMILBoKBwLC/SP2BychLj4+NwOBz4p3/6J0kinzx5ImN2c3NTFvy0tDRxzqRsOxQKYWZmBgsLC7h06RLy8/Ph9XqFu+DxeGCz2Z7haEQiEemBRFfSrKwsPHz4EJcvX8bW1hbOnTuHiYkJ3LlzB3fv3hVLbVr0h8Nh3L17F7GxscjOzhbb6qqqKni9XuTm5krppby8HFVVVaiursb4+Di2t7dx+fJluN1uOBwO+Hw+JCcnY3d3Fy6XC1qtFo8fP0Z0dDQ+/vhj+P1+DAwMSF8nkkuPHz+O+Ph4uFwu8eEYHR1FOBzG9PQ0qqqqoNVqUVRUhAcPHogs9o//+I9x+/ZttLW1SSfUtbU1dHZ2wu/3Y2RkBNHR0fIc3G437ty5I46sq6urUp4hovvll18iISEBjY2N8Pv96OzshMfjERRnamoKJ0+ehN1ux/T0NGw2G0pLS4Xfk5WVhZ6eHkGgHzx4gOjoaFy7dk26yz58+BCffPKJKAW1Wi08Hg86Ojqwvr4ucmmOZ7aeLy0txfr6Oi5dugSNRoNHjx7B6/UKeTYmJgbHjh2TEiLXT51Oh76+PmRnZ8s6trW1hTNnzqCmpgZarRY9PT1ivjg9PY1PP/0Ura2t6O/vRyAQEL5SdHQ0XC6X9PmKRCLo7OzEZ599Jl265+fnpWS9sbGBmJgY3Lx5E8vLy0LWn5qaQnFxMSYnJ7G6uorR0VHk5+cjFAohKSlJGhiWlpaivLwcR44ckWcRE3PQ0iAQCGB+fv6Ztf5fOp6fmo7/G+ZV5Xv8mQoP82+V8KkSEHmoSQgDGLkaKkGOiyiRDVVFoC6sXLTJwFYJf9wtckfK71F5IcCzDbSICNAinGUeojoq34WSTe7m6anBniu8R8vLy0hMTMTGxsYzxloWi0XuCYk9Go1GeCQkSxISX1pakr4n7GdC8ifZ7MFgEHq9HsnJyeLbofqebG1tYXFxUc6VwZdETiZg5Nsw6yacT1kcu9HyUAdkOByWpEZNEOPi4sQWOyEhQUirvJ9Mqsxms6hR+B3JycmyKyIXg9cLHLSM5v1aXV1FfHy87LRYltra2oLVapWkjooDoitarRZbW1sihyQpmlwPjhcmsqoniors8d9qqYNzQU04OAdI9lU/h3I4AM+UXgA8g05wbKvfTZ4Fz5mlL1Vuq5Z0/jWcjqtXr+K73/0upqampA13VVUVnjx5IknH3NwcrFYrlpaWMDExIb4B6pjX6/XCDXny5Am0Wq300QCAUCgEt9stnTd3d3eFmU//AUrFTSYTNjY2MDIygpycHFGadXZ24q233sLPf/5zZGZmCnfJ5/OhsrISfX19eP/99/H666+jv78fkUgEiYmJ6O3txdraGnJzc3Hnzh00NzcjKysLDodDvCWcTif29vbQ3d2NoqIiTE5O4vjx42JEde7cOfT390sHXJPJhMHBQbzxxhvY3t7Go0ePUFdXh+7ubgBAaWkptra2cP/+fWlL/uqrr8Jms6G7u1uIupubm1haWsKvf/1r2Y1mZGTg5s2bMBgM4vHAsTwxMSFzyWazISsrC+np6dLy3O12w+PxYGpqCllZWaipqcEvf/lLOJ1OxMQcGAa+8cYb0nRucnISdXV1CAQCqK+vx+bmJm7/Hxlmf38/Ll26JF17I5EIPvnkE6SmpqKwsFD8eSYmJmRNY1khOjpa+tG0tLQIUra/vy+IysmTJ9He3g6dToeioiJ4PB4UFxdDo9GgpqZGHGGtVitaWlrgcrng8/mEN0G7/snJSaysrODf/tt/i2vXriEtLU18Tshn29nZwblz5ySpmJ6exuzsLC5fvoz6+npER0cjPT0ddrtd1C8sLblcLuGxsBGdyWRCW1ub9JFhI7lgMAibzYaWlhasr6/D7XZjdnYWwIEZ38LCAt544w189tlnKCsrw/j4uKDQ7IdDJSKddV955RXcuXMHfX19yMzMhM/nw+bmppxLQkICHjx4gJSUFLzzzjti+15eXo5AICDIs8fjQUJCAkZGRr4y2vFcSQc5D6p0j4uEWtJQpbJcxDjAGMD5GSqkzJ+pO0FaixP+5YK6ubkpBCu1CRYDmSpF5Hnxew6fJwMHd5RMdojKUOlBz33yPNSyhooAsf8LF3gaYRGq5HXy/Inm0FXUbDZLgsL7Mj8//4zKgPdcNT1j+cBgMEhgYdDgorKysiK9Uph06PV6aRZHKHZ3dxcOh0N29AymKysrMBgMsuPnDpINywA8I62l/DYQCCA9PV2eFZUxRKuYvLAL4+bmJpaXl5GSkoJwOCzPU5WEqkZZTNhYr6aElvLelZUVrK2tCUrC2raKQpBgS4UOCbZcCImcMMlSXW857jguqWjimOc1qeOeyJSKKnB+8JmqHClVeUInWJ4/iaFMHtSD40Cda+zIq5ZS+Dn8jn9NwgEAjY2NwsVhUmG1WjExMYHc3FwsLi4K0uVyudDT04PNzU2UlpZKKVCr1WJ4eBh2ux3BYBDLy8t49dVXsbCwgNHRUZw6dQpHjhzBl19+KZyPwcFBHDt2DH19fTI2uNtjj4yJiQl8+OGH+MM//EPcvHkTZWVl6Ovrw+uvv47f/OY3sNvtSElJQVRUlPT3iI+Px6NHj7C/v4+bN29Kq3QiFmfOnEFWVpbIbZeWlvDWW2+ht7cXxcXFQhhfWlrCRx99hIyMDFy4cEEIkPPz80hJScGnn36K119/HR0dHbBYLDhy5IgQwb1eL2ZmZpCVlQWbzYakpCRkZWXh8uXLMJvNqK+vl7VqcXERS0tLKCwslHs3OjoKj8eDoqIi5OTkICUlBWNjY4iKisLQ0JB417D1wfDwMGpra2G1WnH79m3Y7XakpqZiaGgIW1tbaGxshNlsRn9/P958801MT08jPz8f0dEHvj6fffYZjh49imvXriE1NRUGgwEtLS0wm824du2aJKOE7JeWlsTUqqCgQMy0jhw5go6ODjgcDmRmZmJtbQ1NTU3So+bq1auSCLCMVlZWJkkDVS5tbW0IhUIYGRnBiy++KGsdvXnox5Kbm4u5uTm0t7dje3sbo6OjKCwsFMQ0JiYG5eXliEQi6OnpEVWi3W7HzMwMnE4nKioqEBMTA4fDISqd+vp61NbWYmZmBt3d3YKYWywWdHR0YGJiAgkJCSgqKhI1UDgcFrdPihLYC8dgMGBiYgJXr14VVKWxsREff/wxAKCkpATd3d2orq7Gq6++iubmZtmwEmFOTk5GT08PGhsbxQ2VTd3Gx8dRV1cnnBm9Xo/y8nK8//77sj6ypMiy4ld1L36upEMtoaiSRCYWKrTLRY6v5+/Vfg5qKUNFSLiL5QLKBEFdOAkNcuFVVS/crajICYO8uiNlMON5s3yk7vJ4LXt7e1LL4s1leUXdidJbQqvVQq/XIxQKPdPdke9nFsoOmiTPMrDGxMRgYWFBWOn8TJ1OJ0gNnTT39/cFGbFarXKvGMyY/MTEHHR5VY1caPVNRrJ6f1ZWVuR9NDYzGAxCsGRXWXbzJNJDlQ4DdTgcRnJyMmJjY5Geng6/3w+z2SzBWEXO9vf3pTwTFRUlhmtErIhUMAHm+dNuPTU1VZ4t/U2Y2MzOzkrSs7GxIYGcAZ6ySC5GJDZz3AWDQUELOL6IeHCcM3lSCaVEjTiOWNbg/VK9VjgeuSgCT8uVRJ6YwKjzQS0FqkkHr11NYlieO4xm8P7zmv415RV+58LCAgYGBmA2m7G4uIiFhQXZ9ZL38fDhQwBAUVGRdE4tKCiQEtq5c+fQ0dGBjIwMZGRkSIkzLS0N09PTsFgssNvtWF1dlXWltbVV/DOoRFlYWEB2dja8Xi+io6NRU1ODkpISIW4PDAxgenoaGRkZWF5exuzsLKqrq+V9DIaZmZnyTB48eIDc3FwEg0EYDAZ8/PHHqK6uRk9PD1566SV89tlnUgJmHwzKYf1+P375y1/i/PnzcLlcYuH/6quvCseKJlc6nQ4pKSnY29uDy+WCx+NBamoqHj16hJiYGGRmZorNul6vx8DAAIqLi4UTQV7J7du3xeSKqN7MzAyys7PR1NQEjUaDjo4OmZenT59Gc3OzlPIuX76Ms2fPYmdnR3rEsOPr4uKimE4NDw9LQrS8vAyLxSJ9nrgeVFRUSPdWt9st58yNJTdM7PfU1dUlKNLa2hq8Xi/8fr8oSqKjo9He3o6amhqMjY2ho6NDuHJxcXFYWFiQ8hNjztDQEDIyMrC1tYVjx45henoaer0eXV1d2N3dxczMDHJycoQD5HQ6peGcx+PB6uoqkpOT8eKLL+LWrVuyRjY0NODjjz/Ga6+9hunpaTQ0NGB5eRlTU1Pw+XzCs/P5fMjMzBTprsvlwpUrV8RKfHp6Gr/73e9w8eJF7O8f9LHJysqCVqtFXl4e5ubmsLi4iK997Wvo7OxESkoKlpeXZaPEdXh+fh7d3d0oLy/H9evX0dDQAJPJhJaWFiF1Dw4OCqJIorXf78f+/j6ysrJw9+5dWXsjkQhyc3MxPz+P9vZ2mEwmrKysCEn4qxzPTSQ9bLOsLlLqAg4821WWqAGDqqpmUeFcHqpSQi3fqEoMdcHlYn5YHcNdOwOtWof/57gl/DyVj0JyIz0bCIPzOxmEeG/4naxjc0EkkZLJSCgUgsFgkH4iJKByN81ugABkd72+vo6EhAQpnZhMJlFxOJ1O6PV6GAwGqUM6nU5BHki+o2KESiCWJnh99Nngs4mPjxdzII1GI+dLVCc6+sAa3Wg0yjhhNs3gmJiYKOxw8llUiahGo0FiYqIgC/v7++KlQXt1vkYtpezs7GBqakpkuXSR9Pv90u10cnJStPTJycnPmK1FR0cjGAxKcA6FQlhaWhLXQY5Nm80mz4OqCI5Ponj8/+HyCg/KvFW0g0kXx6iK4BAJYTLLe0r+kjpPmLSzHMYkhAgXf6YqaYioEFVTbfV5P/41B9VB3/zmN1FWVob4+Hjk5+eL3DQUCmFoaAjV1dWoqqrCysoKLl26JEREvV6Pnp4evP/++1hYWBCL876+Pgna8/Pzknisrq5idnYWL7zwAqKjD1p2JyQkYHl5GVqtFtXV1RgeHpadZHx8PH72s5/JGpScnCznvLi4KLtk1vk5hjn+Q6EQzp07B4fDgZKSEkl4FxcX8eqrr4q1OcnTd+/exdbWFurr65GXlyd8r4SEBEmm/+Ef/gGzs7O4c+cO9vf3UVBQIO3JQ6GQJOtELUwmE6qrqxEIBHDp0iWMjY3h/v370Ov1mJycxOXLlzE+Po4rV67gwYMHYl29vr4Oj8cjbpUpKSl4/PixtCfgOGpvb8f6+jr6+vqQkZGBF198ETdv3sTo6KioSoLBIEKhEN577z1MTk5ibW0NGRkZSE5ORmZmJtrb24WEmZiYKN2B2ZeHDp5VVVVIT08X59Pl5WXs7u4iPT1dvEtOnDiBTz/9FAsLC9Dr9dI/hLbn586dw9DQENLS0vD9739fyjDd3d1C/P6P//E/wuVyiXtoZ2cn/vEf/1FQIAbeb3zjG/jhD38o6wRtxi0WC7xer0hMt7e38ed//ufo7+/H1tYWsrKyxHyNslWTyYRAIICCggLExsaira0N169fx9DQkHAtVlZWJOn1er2iHKJpZEdHB+Lj4+F0OpGRkYH29nbExsYiKysLjx8/htlshsvlwvLyMqqqqjA2NoaWlhZ4PB50dXUJWZRGlXFxcXj11Vfx1ltvYW/voA0BkTWangEHPXJCoRBefPFFnDlzBtvb21KWY4kuPz8f9fX1eP3115GTk/OV1ofnbm3P4/CiqgZwlZl/mOOhyvsOJyn8OfDUt4MLJT9DLXmo7erVUoLq4QHgmV2hWhvnOZMTcpj4x2vkYqzubsPhMEKhkOjECVttbm4KgYnnT+if5CHKr3Z3dzE/Pw/goMEbWd1Go1EePP30qfKw2WxCXiORlDsonp8qEd3c3JTrIwrDcgQ5C5QnEkUBIMRJrVaL7e3tZ4zCNJoDB09mttyhm0wmaQWtcg2IRDDxo58IkQQG0UAgIMgSF1v2uuFzjY6OluSG9zEvLw8AMDExgbm5OUl22UwuOjoaCwsLWFpaks/ndUQiEeHL6HQ6Yf/TUl6j0cBsNkvQJly/uroqBEImDxwn6jzhPeBYV4nU6rjnmOMY5Bzja5jwc9yzfKPOR7WkyP9vb2/LeakS9sMcDlUxo96bf81RU1MDj8eD1tZWhEIhOJ1O5OXlISMjA5mZmTh16hRSUlIwMzODR48ewWaz4dNPP0VycjLOnTuH7e1t5OXl4eTJk9JFuaKiQrqhmkwmnDhxAjabTRp0ud1udHZ2oq6uTgjUJpNJEpz4+HgEAgF8/PHHGB4eBgDxYoiOjkZqaqrwsDY2NpCXl4fExETMz8+juLgYDodDSnaEv4PBIObm5kSdkZGRgStXrohy5tSpU7h9+zYqKiqwtLSEmzdvCvs/KysLf/u3fytGS6dOncLExAQyMjJgtVrR2dkp/WncbjcWFhaQkJCAUCiEM2fOwGw24969e2hsbMTm5iaOHTuGoqIi9PT0oK2tDd/61reQkZGB4uJicWA9efIk5ufnkZWVJS3Kp6am8Cd/8ieiPuN6Nzk5KY6W8fHx4uBJCXFHRwdqa2uRmpqKP/qjP5IEqaysDFqtFm1tbdI/hGVLyi1HR0clUYyNjcXjx4/R1dUlJbn09HScOXNGxs7W1hauXbuGF198ETabDT6fT7qfrq6uSvPA+vp6PHz4EH19fc/Io8fGxtDT04Ovf/3rAIC0tDT09PTA7XbjBz/4Afb29lBYWIi7d+9Ku/b19XVMTU3h/v37ePHFF6VMs7q6ioyMDCQlJWFiYgL19fXQarUYGRmR/lharRYdHR2CfB07dgwejwdRUVF46aWXUFBQAKfTienpaeTl5UmJ1el0Ym5uDjU1NZifn0dVVRXeeecd5Obmwmg0ore3F4uLi6Io8fv9OHv2LBYWFhAXF4empqZnPFBOnz6NF154AeFwGFtbW3jjjTfEUPKTTz7BjRs3kJOTg0AggLNnzwo69/DhQxQXF+PrX/861tbW0NbWht3dXYyNjSE2NlZabRB9Ag76DI2NjX2l9eG51SvcxXMXBjztYXKYR6GWLSjTO5w8cEFW69cARNZJWJmfp5I3VcIcX8cyCPkPDHSE6NU6u8rJUJn+allIDQyqBJg7/XA4LPJZLvQsrbBERL4DX7+3d+DfPzs7K7W7UCgkngQkU87OzmJiYuIZHkE4HBZSFXdqUVEH/RF2dw/Mq7ibVD1J9vb2xMckEAg8g8pw904yq8rPodqGz5q6biJObOi2ubmJtbU1CVoMqvwe4Gk56rBsmc+fnI7o6GjpQ8FnyYSISQSVJvv7B31saA9dWloKt9styAqfLwBpHMdxQdQlKSlJ7o1Go5EEkDb0/B5eIw1yVBty8n6YiKuJDZMMHkwe6AOj3iuOExWRULlTHPeHxz75P5ynnENqCYifSTRF/TeN6fiZVAt91TotD7vdjl/96ldYXV2Fz+fD8vIyiouLodVqxevkN7/5jXhqREcf+LewrNLa2orY2Fh0d3dLy/L9/X20trY+I83s7OwUeWNGRoaUOa5cuYLq6upn5sD+/kF7+djYWJSXlyM2NhYOhwMdHR14+eWXpY8Rd9gXL15ER0cHurq6kJOTg5GREURFReH+/fsIBAI4efIk/H4/xsbGMDIygsXFRZjNZrS2tsLpdIpvBZtwLS0tyTx5//334XQ6MTk5iTNnzgi3ikS8uLg4eL1exMXF4ciRI7DZbLh9+zb6+vrQ19eHnJwccbcsLCzE8PAwbt68ibGxMemXQb8O4ID0npycjM8++wwzMzPIy8tDYWEhHj9+DLvdDqfTiZ/+9KeIi4sT9M9ut+P48eOIiYkRfgS5OdHR0RgYGIBOp5OeKJcvX0ZtbS3W1tZw584deL1e1NXVYW5uDmazWTxTKisr8eWXX8JoNKK8vFwMxWJjY1FVVYWsrCz89re/xeTkJEZHR/HgwQOsrq6itrZWNl43btxAZWUljEYjkpOTYTQa0dzcjN3dXbS0tOD3fu/3RDKflZUla0daWhqSkpJw+fJlfP7559LGnugIOxMPDAxgeXlZ+BZWqxU3b95EQ0ODxBYiF1VVVeKvQmTZZDKho6MDaWlpuH//PvLz8zE1NSXlcJPJhJycHJGMt7e3S+mMyUdKSgpeeuklpKenw+FwCMcoKysL4XAYHo9HTBX/x//4H4iJiZFrDoVCGB8fR319PTo7O7G+vo7FxUUpN5vNZrjdbrhcLmRlZSE+Pl6a0m1sbGBgYEAMLpksNjQ0IDY2FkVFRVhZWUFbW5tsIqOiovDf//t/lx4/X+V4bvxULVFwIWUCospngaccEP5fJY4y+VCREuDpzlhNKrgoq3wDVXJINIWJB4MsEw8qJbjAEwlhUD4sP1RLMFy0KAvl91BxwWBOQiUNo5h8kInPgBeJRJ5p2c4SAfsnrK2tCSeAvhFECqKiDjoMUuHCZIaLOa+PrdjpXjo3N4eVlRWpYZOvQaSCJSDeL6I/KvxOaR25BlQbcPDxfpOAy94sfF5EpRgACfurAV8tN3CR0ev1Ir/lPWLdcnV1FQsLCzK+dDodFhcX4fF4RCLMBlxWqxWxsQe9aQg1kuzLEg6/Q+U70LeECW1c3EHDPMptiaiR16MqqA6TQIkocOfBZIX/JmeDiSSTBqJmhx161bnGuaAm8Jw7/F4ayzHhByAcF7VbrYqAqK7BX+Xw+Xw4cuQIHA6HEHjZKCs6Ohr9/f3SxGtkZARGoxErKyvwer1ISUmB1WoVtZFOp8Po6KjIZYmEra+vIzk5GRUVFWhqahKuzdzcHMrKymA2mxEIBAT1y8vLw/DwMLRaLfLz86VJFv03qN5Q52FhYSFSU1ORmZmJ6elpOJ1OkV5TPZKeno6amhokJyejrq5OGgoaDAacOHECZ8+elaSfpmZswDg+Po6qqirYbDYkJiaitrZWvF/S09MRGxuLP/uzP8OtW7eQkZGBc+fOoaCgAGlpafj000/x5Zdf4tq1a9InY35+Hq2trcjNzUVaWhq8Xi/u3LmD6elpxMTEoKCgAOfPn8fm5iZu3LiBsbExBINB/P3f/z1yc3MxPT2N5ORkKd+Ew2GUlZVhenoa6enpcDqdKC8vR1FRkSAhJNWzXwmb0lVWVmJtbQ2FhYWyDpBMa7FYsLi4KKRLt9sNq9WKxMRE+P1+NDU1YXFxEbu7u3C73dDpdHC73SgpKUE4HMZ3v/tdjI2NwWq14vz589J/h6Ze7777Lnw+H9LT0wWl4rqckpICm82GxsZG9PT0oLu7G6mpqejo6JCSsd/vR1lZGZaWluD3+2G1WmVN39zcxNGjR8UbiB2/ubHt7OzEyy+/jPX1dVitVuTk5ODevXsYHR1Fa2srpqam4PF4sL+/j9deew0ajQZpaWmIiopCV1eXkNv/7M/+DPfu3RPVT2trKwoKCiShXFhYQGpqKu7cuYPy8nLMzs7Kfezo6IBWq0VKSgqys7PR1dWF6elpsQigV8qHH36IwcFB9Pb2yoYnNTUVubm5aGpqQjgcRmZmJtLS0uDxeDA3NyeuuUR9fD4fmpubcfToUVy6dElaY/xLR9T+YSz4/+uFUVEC26hvoYxTrUNzZ8v/H040+Ad46svBxToxMVE6n7JpEYMDBz8XL6/XKxwFlTtCPwUGEraBd7vdsFgsInllIGEJRE0SCK+zB4rqLbG/vy+ETO6mAAgyQxfLSCSCjY0N2O12ud5QKCSv5ftUkyYSLsn7IPlUPbhrUu8xAzxlxAzgSUlJIpVl4CJHhXVqJhp8HkysaDnPwMfzUHfP/H5VnaSSJoksES0gv4UBk8+AgY5Ng2JjY7G5uSm8C3psMLBSiryzswOLxQK9Xi81UiZAVK6wPEPHUyJe5LbQJExNNEiu1Wq1UpKiXJvXzOdHci2/h31cGLjpx6Ba1QNPVTfsM6PODyYCRLgAiBeJTqeTkhATQ14TEz+OAXIzmECoCYmK7JHDw4Pntr+/jx/+8If/V8no/+uIiorCxYsX4XA40NbWBr/fj9TUVAQCARw7dkwSkOTkZLS2tgIAhoaGcPHiRfFbCQQCyMjIEK8NNRFMT0/Hhx9+KH4FsbGxOPl/7JjZZfjcuXOSUIdCIbFUHx8fh9PpRF9fH9xuN0KhkHQ1ZSClJ05ZWRksFgs6OztlEV9aWsL8/LxI0j0eDxYWFsRfZm5uDsePH8fExAQaGhrw5MkTBAIBVFdXY2hoCIuLi3C73Whra5POzqFQCFlZWfj7v/97vP7669jfP3DmNZvN8Hq92NzcxMLCgvyM6AEJjUTgmGhoNBoUFRVhZmYG/f39iIuLwze+8Q18/vnnSE5Oxqeffor/8B/+Az788EO43W6srKwgNTUVBQUF6O3tlUZ8LC9kZGTA7XZjenoaLpdLSj4ajQb5+fkYGBhAKBSCy+XCyZMn8bvf/Q52ux1HjhzBb3/7WxlzcXFxqK6uxuPHj6WU6XA4kJubi3fffRe5ublYW1sTcqNWq0UwGBRzK6fTid/+9reIjo7GhQsXpCxG+bvX60V5eblIkIPBoMxVyo+TkpLw9a9/XfxZHj58iLy8PExNTSE+Ph4TExOorq6GzWYTR2mv1ysIWDgcRlNTk5gOWq1WZGRkCFLIJJubURJraWk/OjqKqKgoMZWz2Wxi1MbNVjAYhMfjwcbGhji1ejweaZhXWVmJrq4uuFwuKdf5fD44nU6EQiHodDrYbDbh4tDBeWtrCxsbG6ivr0cwGBS1j9vtFi4QeUzJycnig7S+vo6cnBxMTU2JMnJ/fx9+vx/AAbLJe/6nf/qnQt7+fx3PjXQcTjgIETNQsfzABUiFdLlwHCZ5cqHkACHaATyV4aj8EFpgc8Hlbo6IiEpsVdUgwFP/D7U0RL4Dv5PBmUGACzOTCu6UuaMn2ZLlFKIJZrMZKSkpiEQi0n6d7HAu/kQGzGaz9HOJjX3aFI2eFTTzMZvN2Nvbw/z8vChj9vf3hS+iBnhyOCiPpDcHSx5Ec3hPSSDk7paokEqiUwmUfC58PQeqSrRVeQdqiYTPnDwHPhcqdCi9I/xM1Q93VyRzMimYnZ3F8vIy/H6/8EB4L5OTk6WrI8cFJ5aaoBgMBqSnp4ubKtEfjqONjQ34fD74/X6Bhff29sRYSiWCEjXgOFd5LrwvKklUnVcqx4JJO+8Pxw6fAdELoiJMWA4Ts9WSmfodLPWoSSCfEdHA5znoC3Dv3j2YzWYUFBRI4jQ8PCwtvT/55BOUlZUhOTkZr7zyipTLiBjk5eVhb28Po6Ojwq0wmUxYXFwUFQmN7tra2rC4uAin04nNzU0MDw9jbm5OauplZWVobW0VdYLL5RL1G3fDdXV10jV1Z2cHXV1daGlpQXZ2tjSZm5+fF6OupaUl8WUg56eoqEis+L1eL+x2O2JiYjA9PY3BwUHMzs4iEokgPz8fHo9HEuXt7W2cOHECGxsbGBwcxOTkpHT2nJubQ11dHfb29rCysoKMjAwZB3SrpLqB8HZbW5s4bs7Pz+PnP/85YmJikJ2djTNnzuD27duorKyEwWDAzMwMzGazqDnsdju2traQm5uLiooKrK+vCycmFArhyJEjMo/v378vZO+kpCR88MEHUir2+/1ISkpCVVUVGhoaZH20Wq3Y2NgQ47be3l5kZ2fLZrGqqkrKm01NTcKrunXrFpqammCxWITIa7fbhWs2MTEBn8+HvLw8RCIRVFZWCv9Dp9PhT//0T5GdnY3BwUEAwL179ySYzs7OIjU1FeXl5bLGbW5u4rPPPsPc3BwyMzOFQNzc3AydTofq6mo0NTXJPHc4HBgdHcXMzIyUxGn2RR+mubk56YnT3t4Ot9stmw6a4t2+fVs2ChqNBlarFRcvXkRmZiZiY2MxNzcHh8OBo0ePoqmpCXNzc8jNzUVOTg6ioqIQCoXQ3t4uvWC6u7sRDAaRlpaGV155BY8ePcLs7Ky0irh8+bJYpJeWliIuLg4PHjxAT0+PEG65ScjJyYHP58Pi4iJMJhPS09MxOjoKn88nSMpXOZ4r6VB3wVzEVPWJurNlEFcXPy6I/JuZKDNnQpH8DuDZUopKQj2sXOECzkRIXYDpYcDP5e5dJfjxNVxo1XPjLlQtvdBOnLA1eRe0KmfDnJWVlWd6WiwvL0s5Znl5Way2D9uA05JYTSJMJpPsBqKjo4WISVdDIjYMGGovESIc3OHrdDpJOBhcCdHTK4M7dxWdUnfOLJGo44L3lIFW/TeflSqVZlCOjj5o8MZ7riIRNA3jro5KFrKxl5eXhZBKdCM6+sAwjZA5zYbW1tYkaSOT2+FwIDs7W7L/lZUVWcRo+0xZ2NLSknzfzMwMwuEw1tfXMTc3J8gUnwPwtAwCPJWmquOZiaxKoFUTd3Uscu6o3A61HHY42ed45fvVJII/J6GXiR0JyiyBqajlVzk2NjYEWmYDKNqABwIBDAwM4Pr168jPz0dvb6+cL2XSCwsL2N/fx8jICCYnJ/HSSy9Bo9GI9NBqtSI/P1/8EaqqqsSQi2VFcm4GBwdx/PhxMc4i/4Jy9aqqKrz00ktoa2vDP/3TP0kyu7a2BofDgZP/x/WyuLgYNptN5gOVUfX19UhNTQVwQPg2m83o6+uTNY+lErPZjDfeeEOCTSgUEnSBHiMss7322mtCALfZbMjIyMDIyIg0E8vJyUFMTIzwJsrKyqS0wZ42NTU1SE1NRWJiIsrKypCTkyMBwmQyoba2FlFRB1ba+fn58Pv90i5gYmICOTk5mJ6elsZzJpMJlZWVskMn4TY2NhZXr15FTk4OnE4n8vPzkZycLAH/6NGj8Pl8uHfvHmJjY8VkUKPRQK/XIy0tDbu7u1hcXBQvlJmZGfT19UnAZONLh8OBUCiEjIwMmM1mQW5efvll6PV6/PjHP0ZnZydiYmIwMjKC3/3ud9jY2EBGRgY8Hg8+/vhjkbrqdDoMDQ1he3tb+u/wuymP5bqYnp4u/JfvfOc7eO2113DkyBFsb2/j8ePHaGtrQ0zMQZ+Yuro6ABAJcUFBgTi6rq6u4siRIxgYGEBXVxeOHj2KDz74AIFAAA8ePMDS0hIePXqES5cuiXdIXV0dLBYLurq6RJE3NzcHAOjs7JQWDuwym5WVJUZkubm5WFlZQVZWFkKhEAYHB3HlyhVUVVXBaDSKCZpGcyCVzs3NxcTEBGpqamC32+FwOLC/f2DH/ujRIwwNDeHOnTvQarWw2+2w2+2Ijo7G8vIySktL0dDQ8JXXiOdaUVSyJxME/owLnqoMAZ4aigFPCYMMUurOT4WV1T9cXFUS6OG6u1oi4YJJ6E3lkwBPkyTVM4GlA9VgickFpayEzyjnZFkJOFjMSVpkAqaqb3g/IpGIWH6TjLm/vw+73Y64uDgEg0FpQMXsGDhY5PR6PWJiDtrUq5+vqh5UoiYZ/ExYVLIm7z3hO+62GSiJKiQkJMBoNAonZn9/X1w8mZzwO9SeMIe5Bio5UiW/RkdHP2NwRaXN/v7TpmUsdezvH/Q88Xq9wg/hdxiNRkRFRcl5+f1+ufcLCwtCsCP5k/fVbDYjOTlZyjcsgdAJkeN0eXkZBoMBZrMZfr9fAjUdU+lZwkRGPX8GbZZVyNtgAkeUgfeG38sxSCRQbVXPUiLwlO9ElI5jg0gd7y3HPH/GhINzlom76tjL63/eg94IPPeMjAw4nU6sr68jNzcXUVFRWFxclDbltPO2WCxob2+H3W5HQ0ODWEx3dXWJaoNeAyxDskxBNcPRo0dhtVqFKzIzM4OMjAxxMV1bW5MkiMnYd77zHfzjP/4jxsbGcOnSJeFxeTweMbSLRCLSz4LtzYlWdHZ2oqCgQJp4/eAHP8Dw8DC6u7ul6d27774rZZi4uDiMjo6ioqJCvDeYNHV3d+Po0aMYGBgQflJDQwPm5+dRW1uL4eFhPH78GC+++KK4UhJaHxgYwNGjR7G6uorBwUEp+YXDYRgMBkxPT2NtbQ2Li4uw2+04f/48xsfHUVxcjPr6eoyMjEgCqtfrUVJSgocPH4rhGNdpOndubm7i1KlTcDgcCAQC0Gg0ws24ffs2GhoaoNPpkJOTA71ej8HBQWxsbCASiWBlZQV6vR79/f1ITU3FyMgI8vLy0Nraiu9///tiwc2yJ+ezXq/H/fv3UVFRIQ6oCQkJWF1dRWFhIWZmZqDT6WT8sLeP0+mU9dlisaCpqQljY2Po6urCmTNnJFnb2NjA/fv3kZ6ejpKSEqytreGFF16QTrtMYIPBIPLz82X3HwqFhMRcXFyM6elpUQptb2/jxo0b+Na3viWk/+bmZnzta1+Te5aUlASv1yst67OzszE3N4fW1lZERUXh+PHj+OCDD2AymdDQ0ICZmRnMzMwgEonA7/dLicvn8+H06dPo6+tDcnKyKOyMRqOUCOnqzPu7sbGB4uJirKys4NatW8jLy8Pq6irsdjsSExMxNjYGr9cr/YIAyIaLUu6urq6vvD48tw26KglUod7DMlMmEdzJMejy4EDiz9RdmZrYMIgzUDEB4cLL+j7fy98BEPiYSYPaBVSF1lUpLH/Pa2JpQ6vVimyRQUe9HzREIjmUSQm5KexGqiZd3NkTlo+NjX3GZIXObwkJCbBarYIK8Ht5jRsbG7L4squtmiAy0HC3oSJCvBd0RmWpiAcTC7XUQJMfPiNCpgyUfD0buZH8qZZn+Gz5b5YQOFZIlI1EIkhISJBkwW63S4LCoLG2tiaITkJCgnS/Vf1HdnZ2BHUiuhEfHy/jYm5uTnabRBqY9ALA0tKSfD53E5RIm0wmFBYWwuv1io6fvAs16eQ9ZzmECZbqY6Pyc4gm8TPU+aPOQZKniaqoyQXLk2wOxvquek68To4LXjOf3fMe5NiwPNLX1yfjn8ZICQkJ6Onpgd/vR0NDA9rb2/Hqq6/is88+QyAQQGtrqyg6WJsmx2B9fR0WiwVFRUW4du2aEP/GxsbEHG5sbAzV1dXQarXo6+uDRnMgfT5x4gQ8Hg9MJhMMBgM8Hg+Gh4elBTtLKpx/W1sHbehZ0mGJxOVyYWpqChUVFVhdXUVsbCyGh4eRmZkJj8cj43ZsbAyJiYmSKL377ruw2+2oqKjA+Pg4tra24HA44PV6kZaWBq1Wi4GBAZHjNjQ0oK2tDYWFhcIzo5pgYGBAkmrOT6fTiYmJCWkKt7i4iJSUFKysrODMmTPw+/2YmZl5BqrXarW4du0aKioqRHFjMBhw69YtQVdWV1fh9/tht9sRHx+PgoICLC0t4ciRI7h37x5eeOEF3L17Fw6HA5OTk8jJyRFCamVlJSYmJlBYWAibzYb19XUMDg4iFArh+PHj6OjoEG7Wv//3/x77+/vw+XyIjY1FQUEBbt26JUjo1atX8cMf/hC9vb1SfgKAEydOSBmouroajx49kvLRysqKWKBrNBoMDAwgJycHXq9X3EP39/fx3/7bf0N2djaOHDkinJi3334bq6uryMnJwfj4OAYHB3H06FFJBjweDywWC9xutwR/lsYpc01OTkZ9fT2ioqJw5MgRtLW1ISMjA4FAADExMWJgODk5KbwdIqh03H748CHS0tJgtVrx6NEj4RseO3YMHR0dACDow9LSEgYHB0Vu7HA4hANDUu/8/Dy2t7cF/aFCqbGxEf39/aiurobBYMDi4iKMRqPwI3NycsSgjkTapKQkeDyer0w6/1e5/3BBUz0dgKccj8OkRO6agKeLpQqx8//caTEAq79XpbZ8HRdbBkvgKfeDCzkXc7UkpJZyDh9qoOY1kETK3eBh3ghlpJmZmbBarVLjZFBiiYbXvLe3J2gNf8fgEBUVJZwGGm8xiSBXQYXFKZ8lD4WIEPBUMql6QqilFF4PyUGclHxu5KswUKmkT7XMQqJVUlKSeHkAEBibiQoluiRpsZ7J4L+3tyc1WjUQEo3h+wHIe/f398VUzGg0iqxVLe/ExcWJGsJms0k3Sip9gsGgmDSpHA6/3y/PKCYmBoFAAD6fD7OzswgGg3IfOjs70dHRAZvNhoSEBEkO19bWnlGPqHwLoku8v/w38BSV45xQCbvkXajjkyUklYOkqmiYCKrlMSahTBCZUDNZ5fx+Xk5HSUkJhoaGcOvWLSnvFRcXy3zb3t7Ga6+9ht3dXeEnDQ0NwWq1orm5GSUlJcjNzZX5Mzw8DJfLhf7+foyPj8u9iEQiePLkiSSXVVVVOHPmDBwOB0wmE0pKSoRNTzOktLQ0XL9+HRqNBn6/H/X19VhbW0N+fj4yMzNht9uh1Wrx2muvCWpIeSjLnYWFhbh48aLItFdWVpCSkoLCwkLs7h64j46Pj6OzsxM6ne6Zcunjx4/xwx/+EIFAACsrKzLmbTYbdDqdqBhiY2NhtVqRlZWFqKgo1NTUIBQKYXFxEaWlpVhZWcH6+rr8nk28bDabGEL19/fD7/dDq9WitLQUkUgEXV1dsFgsSE1Nhc1mk9r+/Pw8Ll68iO3tbfzoRz8Sg0Gr1SoKn/j4eKysrCAQCCAzMxMDAwMoKysTC/UHDx7g6NGjcDqdghpTzRKJRFBcXAyv14u///u/l2T6tddeQ2VlJUpKSqTE2dvbiydPnsjc9Hq90Ov1GBsbw+rqKhoaGiRxIoH4+PHj2N3dRUFBgTxzk8mE3NxcmM1mnD17Fuvr69jd3cXAwADcbjdycnJgtVqlC/H6+jpqa2vhdDoxMjKCUCiEV155BdPT0+L3Mj8/j/X1dXzxxRcoLi7G0tKSEEl3dnaQmJiIQCAgXCMGZCJDf/M3f4MnT57AbDZDr9eLf8qNGzeQmpoqvZ92dnbQ29uLra0tlJeXyxxqa2tDV1cXoqIOnJrj4+Ph9Xqxvr6OtLQ0cZrd2dlBdXW18JVYnmTXW0poo6Oj0dDQAK1Wi5KSEjQ0NEjvrUjkwLWV42x//8D3rWS15QABAABJREFUg7bwjx8/lnFNbxC139b/63hu9Yoa5NWkgMFdLV3w/4ffo3I61N9HRx84VxqNRpnkfr9fjLKKi4slqHd3dwuTnws3AxknDeWolKwWFRVJYFD5Eqo6hIQh8iLYlRKABAoGFVV6ynola4GURpIfQVTD6/VKIOSDp5spBytLOHQHjY+Pl8VMTTCoOoiJiZGAQXIrraH5ewY2JoUMNEyeWNpQu50y4PI+qcZR/DxyZNSgyfukJiqEVfl7liDW1tZkXKjkXgYWjiU1gWNiQ+nxzs6O2BTTpwSA6Ob5XAhtkgtjNBqxtLQkxLf5+XlkZGTAZDJheXkZRqNRFjGbzSbIF3fNTALIM9FqtUhMTJRnD0AgTI5BlbCsJoQ0/OIzVOcTJdTT09PY2NiA0WiU8aRypJikAs8iiWoizeSHCU1c3NOmiyrayIQvKioKP/7xj59LvVJeXo7R0VHk5ubCbrejra1N1B5utxvZ2dkYHh5GSkoKWlpakJWVBbfbjcXFRQQCAWi1WpFxsxOtw+GQkh9RLYPBIL0yTCaTIBo6nQ5f+9rXYLPZ8ODBAwwNDSE1NRUbGxvIz8/H48ePxcuDXA2NRoOhoSEAQHZ2tkDwOzs7olCJRCKyriQmJmJubg5arVYajO3v7+NrX/sahoaGsLq6itzcXNy+fRt+vx/FxcXo6+tDdHQ0srOzkZKSgsnJSUxPT0Or1aK2tlZsvYnWPX78WEjNw8PDqKurw9raGqxWq5huLS8v4/Tp02hpacGLL74ofhWRSAQ2mw2ff/45HA4HGhsbZZ0mYfXChQsIBAKwWCzY39/HpUuX8JOf/AT7+/tIS0tDMBgUv4eGhgb84he/wNtvv43PPvsMr7/+OhISEoTw6nK5sLa2hoKCAkxMTKCyshItLS3Y3d1FMBhEdnY28vLysLm5iVu3bglZlWUWAJL006/j4sWLmJ+fx8OHD0XuyiZ0NCdLSkrC4OCg+Fuw6+no6KgkNOPj4/B4PACAlJQU6PV6fPbZZ3A4HFK6ZPJN87OsrCxJfDo7O1FUVCTk0unpaVRWViItLQ0tLS34/d//ffzmN7+By+VCUVERrFYrQqGQzOX5+XlBGEgO3d3dRXZ2NjY2NnDv3j184xvfkP49MTExGBwcRCQSkZ44kUjkmXJdJBKRDsjkMVFNRcnu2NgYCgoKsLNz0GuqpqZGWglkZ2djamoKXq8XRUVFiIuLw8DAgCiFlpaWhKg+OTmJqqoq7O0d2CU8efIEpaWlsFgsUlJ3u9146aWXRNXy/zqeG+ngTpcLlhrwOKj5t0rq5ILERVb9PfBsoOGOjEFLhXtVeF4lk3JBpZqG6AoRBvI7GChUlINBmQGZcBvwrJskZbUM2gykVCyo50rJLCHlSCQiclDggHDHxYX3cH//oKGaihqw5kdSJAM9r3F3d1f4Idx5r66uigqEWevOzo40hKP9MncjfC0TLMqNjUajdOOkDFktZfFeMEEiGZD3RIXxVbdOBg0u5OTpUJoKHCQMZLSrCiNVGUP5s1arlSZXtL5mm+m1tTVBLAAIJ0Oj0WByclJ2iVqtFoWFhVJiMZvNIqelAonv5xgll8VoNIrRFZ/x3t6e8F8OE0lVrhJ5HUQnDqOGHKdEH3hQ+cXfM0hzLjBpVOerWlbjPCRywteoPJt/7eH3+0UpwPvndDpx9uxZ5OXlYW1tDUVFRUhKShJHTZYIwuEwLl26BJ1OJzv8EydOICsrC1arFXa7HcvLyygrK0NBQQF2d3eh1+vx4MEDNDY2ivGR2WzG8PAwnE4nWlpahEz85ZdfYmZmBoODg5iZmYHRaERWVpbYiRcWFiItLQ0WiwXLy8sYHh7G7u6uJCdmsxk+nw97e3swGAzSk8hoNOLkyZPQ6/WIiorCwsICPvvsM9lw7OzsYHx8HIFAAP39/bhx44b0KIqNjUVLS4vsVl0uFx4+fCg+M+Xl5TCZTNKIa2FhAcFgUPrS7O7u4uWXX5ZOpZmZmXC73ejt7cWbb76JtLQ0PHnyBDabTfyCmpqaEIlE4Ha7haT4wQcf4MqVK9JGwWKxYGxsDA6HA1evXsVrr72GqKgo5Ofny/1ll9/c3Fzk5+ejvb0dGo0Gg4ODCIfDSE1NRUpKCoLBICYnJ3Hjxg3MzMwIAtPf34+2tjb4fD7MzMxgYGAA165dEwfmd955R5ryra6u4tNPP8XAwICsL1tbW1LSmJmZwe7urnBzoqOjcfXqVfT29sJsNsPhcGB1dRX9/f1YWVnByZMnMTAwgKioKMzMzGBnZwfd3d3Y2NjA5cuX0dnZic7OTjFboz/Ij3/8Y/me119/HY8fP8aRI0dw9uxZMXyk663D4YBGo8G5c+dQX18v48Hn8+Hu3btobW2Fw+FAS0sLFhYWEIlEMD09LehaT0+P+Cz9wR/8gdgIZGZmis/JrVu3sLi4iNHRUZGCE3ULh8MoLi5GXl6eNOujTNzn86GqqgqLi4vo6+sTYnRaWhpiY2PR0NAg5x4TEyPE1IyMDHGwXlhYgM/nQ29v71fuvfJcBVvufFRolwsn0QK11nx4sWUAJxfgmRNRkpPt7YPuq6zrq34WDK4qzK/W3hkIeZ5qwsNzUb+LZFEGMzWYqws+r4nJlto0jT/j53Ng8fdELmiRHhUVhdXVVQk8zGbD4bDsqBmoSZJkBsmgTTmoKn3l+4j6sHRDZCc6OlpMxhjklpeX5TzVkgSJj+wSy50zgxnRAyYfOp1OSjdUcZCbQWJqbGysoBPqbpaDleOCiSGD0P7+/jP1TRVVog3vysoK4uPjpYcGx4DqScLxR2Rqb29PkiR6dajGblarVUo/y8vL0i14fX0dBoMBDodDTMGioqJgNBrh8/kQFXVgK89k7fDBxICJgEraVJNujk/1fSx/8JqImqnEbJVT9c99n4o4cU6pSAZrszTDet4jNzcXfr8fZ86ckc6mT548EXUJ1V1UXplMJnR1dUGj0aC2tha/+MUvxKpbp9OJI2QgEIBOp0MgEMDQ0NAz5MJXX30Vc3NzGBoaQlFREex2O6anp7GwsICLFy9iZGQEx44dQ1tbGy5evIi9vQPr683NTQSDQSwuLmJqakrmRUxMjBjzpaeni3eIz+dDWloa4uLiMDg4iJKSEszPz2N2dlb4Puvr60hJSUFBQQGGh4elpPHHf/zH+PLLL2E2m+X5VFRUYGRkBC6XC6Ojo0JkZCfT6elpfPHFF8jOzkYoFEJKSgrMZjOysrJQUFCA8vJyKT2wvXpzczOcTid0Op3wjAoLC2X8WCwWJCQkQKfTYXBwEAUFBZicnITFYsGPfvQjxMbGIjU1Fc3NzUKK/Pa3v429vT2RppOU3tnZCbvdLs7M7e3t4sgcFxeHUCiE/v5+JCUl4eLFi3C5XDAajaivr8ft27dhMBiQmpqKjIwMPHnyRLpBHz9+HJ2dnaipqcH+/j6OHDmC9PR0TE1NweVySbNMu90uzqE0EmxqasLCwoK429bU1CAm5qA53vb2tnDNiO4wVplMJhQUFGBmZgYXL15Ea2ur+GDcu3cP3//+92XMnThxAg8ePMDGxoZwBn/5y19if39fumNfvXoVhYWFSExMxNTUFKanp3H69Glp/nf69Gm8/fbb+M//+T8LMZ/r9Pb2NoaGhpCXl4f4+HgMDg6iv78f8fHx6OrqgtlsRnl5OSYmJvDWW2+hu7sbGRkZgujPzc1Jkt7Z2SklS5ZwLBaL8GaIrvX396O2thZ3796FxWLB559/LqgM+83cvHkTOzs7aGhogMfjwfb2NtLS0rC0tCQE/X/peC6k4zCT/bBSBXiKQKgLmyqn5e5LXeTURIIQ+uFdMYOGWs9WkwLu4vh6qkn4WrW+re7ymLQAeKZ+zfPhezlhuTNUr4fBnfwEXnswGEQwGJTFm9Iq7uwSExOluyIDMQBpg80W34uLi8JrACBBlx4W9Ihg4kREgkkZZaU0ZlLlpwAERWBpgqUCs9mMuLg42Gw22O12uN1u2Gw2kQGSMEiJKz1FbDYbXC6X2CpTqknSJicYSZMq0ZU7bdqVazRPre95D9hois/MarXC5XIhLS0NbrdbIPjY2Fi4XC7Y7XbExj7bawY4gHPZvCglJUV4GPv7+0Kc6ujowPDwMAKBADweD3p6ejA2Nob29nYJ2io6xjFMOJUI0OFE+59LmIlIEXlQVSUsV6nKHyJwKjmYn6MiI2qZRZ077NbLJIWJHO+rSmp9nuPOnTtISUmR1vbsLZKRkYHFxUWcOHECY2NjcLlcGBkZkfNhKTUjIwPd3d3wer0wmUzQaA5kfVtbW3jy5AkuXbokO+i8vDy4XC48evRIYOWYmBi0traKSd/Ozg6Wlpbw+PFjpKamYnt7Gz09Pejv78ft27eFoMxSJpVqDFI6nQ4Wi0VUORrNgfvk5OQkOjs7sby8LN2J//qv/xparRZVVVVoaWlBVFSU7MIfPnyIcDgMu92OiYkJBINBdHV1YWpqCgMDA6iurpaGhCMjIxgfH8fJkyel0/RLL72Eb3zjG8IH8fl8+NWvfiVE6AcPHkg58PHjxygoKEBPTw9OnjyJtbU16bJ87tw5ke7W1NRgenoaBQUF6O7uxtramnQRZWkrOjoa8/Pz+OijjzA0NIQLFy5IfxL2uqF7aEJCAk6fPo3S0lLU1NTglVdewauvvoqKigpoNAfOrPX19bh165aogFhqIi/kxIkTuHPnDp48efLM5ool4/n5eTHhGh4eRk5ODoaHh2EwGMSBU6vVorKyEmVlZRgfH4dWq0Vubi46Oztlnnz00UcIhUKYmpqSUgITgsePH0uX1q6uLnznO98Rr5HW1laMjY1JV+L4+Hh88cUXWFpaksZ1v/nNbxAdfWBiGQwGsbCwAL/fj4cPH2Jvbw+5ubno7+/Hj370I+zs7Ejgpm15YWGhjOWenh7s7e0JctjQ0CCqKa/Xi8nJSeHPJScnIxwOY2lpCX19fdja2sKJEycwODgIl8uF7OxsRCIRjIyM4MKFCzCZTEhLS8Pi4iI0Gg3+7u/+DklJSQgEAsjOzsbs7KysYbxmkrknJydx6tQpREdHo6+vT0w3/6XjuRu+qWgFFwsVUVBJi+r7+HuVDAo89WTgz1lmONxmnYuo6rCmHurnEXlQEZnDaMvh8gD5DAwevD6V2MlFm8kGkQo2FlO9N2jDzcyXygIaBqmOpUajUaD4QCAgQZ+9CSizJWzOUgV9N1jC4bURcuT102WOjnI6nQ5WqxUWiwUGgwEJCQni8Z+Wliae/1arVRZ9BlF15866P4Mk5XlkXtMALCYmRsolRCEY7Hw+n+jFiYion0vUi6W8pKQkWYTpfjc/Py+EWLPZjLS0NBQUFMBms0n9U+1FQwMjcjDYRA+ASMu442Iisrm5CaPRKAqD9fV1gVD39vak7TNJnbyvLF8RaVCVOmpJkomFOndUdAKA7E45z6juUQmq3KkTkWLiwu9UNwFqsqHODyaERKue53A4HEKKGxkZQVFREXZ2DjoBc+H7L//lv8BisYgfxokTJzA+Po7k5GT09fWJHwN7mGRlZSEvLw/b29tSq/Z4PMjPz8fW1hZWV1dRUFCAqKgocQWtq6tDbGwsKioqMDw8jOPHj0Oj0WB2dhZtbW146aWXsL+/j8rKSjQ0NGBtbQ3Nzc2yY+zr6xO10+DgoKhdzpw5IxJJSgirqqqQmZmJlJQUfPe73xUkITMz85kgNjc3J14w2dnZWFpaQnNzs6w/N27ckOT89OnT0Gq1GB4eRk9PD9bX13Hr1i2MjY3B6XTC4/EI+pmSkoLKykoUFBQID6SsrAxRUVE4f/48gsEgysrKpJbv8/kQHx+Ps2fPory8XOZJU1MTUlNT8e6770qfFtqqBwIB1NbWIjY2Fr29vYiOjhajMqvVira2Nuh0OuTl5QkSODs7i66uLuh0OtjtdhiNRng8HjEy6+npQWFhIfr7+xEOh3Hx4kU8fvwYOzs7SE9PR11dHXw+n1i59/f3Q6fToba2FnFxcRgfH4fD4RDbfI/HA5fLhbGxMUnK7969i46ODkRFReHBgwf4/ve/j/j4eNTW1uJrX/sasrOzYTQa4XQ65Xy50WL5yOFwYHZ2FgUFBbBYLCgpKUEwGMTm5iZKS0sBAG+99RaKiorETv8v/uIvUF9fj5mZGdTV1SE+Ph6ZmZnY2trC9PQ0vF4vpqenZbPT0tIi3Y6TkpLw8OFD8f85ffo0LBYLqqurEQ6H8ejRI8TGHjSIO3HihKgb8/LyUF5ejt7eXsTExMDpdMLr9SIqKgrp6ekYGxtDd3e3zKmNjQ08ePAAfr8fhYWFKCoqQnFxsSh6mLgtLi7C4XDg4sWLsNlsSE5ORjAYRG1tLa5evYqkpCQxZfsqx3MjHaoKRE1ADpPWCB/yPQyA/xzEqypZyI9QlSYq1MyFlLCzmggRhQAgSQeTGRW2Ap52omVA5Weo6ggVelbNlwiFqyRLXgPrZSTv8FwMBoPIWvV6vQSk5eVlQW9Yi2ZphDtj2n1z50yCE0sQ29vbgqAweKvSXQYklbvCn5OzwcCTkJAgZEgVolcVECrRkG2rmVQkJSXJeTPAEW2hfJrnEhsbK8kEn5sa5Nhbhs+Jf5g00iyHhGPa16soChMnp9MJp9MpiY/NZkNubi4cDgf8fr+81m63i/NodHQ0ioqKkJ2dLS2uSQw2Go2STNLxlXVOAFJeIRFWTaz5bNU5wWtXE15e52H1CiFe9eBr+Yf/V4mmTD7Iy2ESRrSF847PmD1+nufY3NzEkSNHABwYZtFLgbLz/f19YcMvLy8jKSkJ/f39OHXqFAwGAyoqKhAXFyeQ+d7eHnw+H6amppCamoqVlRXhJXV0dEjSMD09Lbvi0dFRqT//+te/ht1uR3JyMhITE2G325GRkYF33nlHnCBnZ2elTXdKSgoWFhawvLyMhYUFeZ46nQ49PT24c+cOIpEICgoKxMfgwYMHwu/Z2tpCUlISvvjii2fKZ3TQLCoqko3CuXPn8NZbb6GiogKLi4ti7V5fXw+9Xi8tyWtqahAIBOByuXDt2jUUFxcjPz8fTqcTZ86cwaNHj7C6uoonT55Ao9HAZrNha2sLPp8PBoMBk5OT8Hq9MJvN8qwvXLiAGzduoK+vTzgDUVEHirUf//jH2Nvbw/j4OI4dOyadpsPhMN588015tmlpaWhqagIAFBYWYnBwEAMDA5Jov/7665idnUVrayv6+/tx7949IW9vbW2hsLAQDx48QFpamsyDhoYGRCIRQT9SUlJk7eM96+/vF48SEtJVZRflsU1NTbDb7cjKysLq6iri4+PR0tKCtrY2rKysSOm5qqoKJSUlOH/+PI4dO4bu7m4ZK+3t7bh06RLq6+vx/vvvo7+/H6FQCAUFBUhJScFvfvMbudexsbFYWlqC1+vF+++/L92Pb926hcLCQgAHhHSn04n9/X1pY6/T6YS0bLFYRAlz/PhxKenu7u4K90av1yMYDGJsbAxPnjyBx+NBTk4Orl27hvb2dunPUlhYKA0HWZqkHDwUCqGzsxNWqxU9PT0YHx/HxMQEoqKihJA6OjqKnZ0deL1esXEPh8NobW3F/v4+lpaWpItxJBIRo7x/6XiupIO7IXWxUhML4FnXUZW0BjwtcTDwqfXqw8GdUKeKkDCxYILA7+I58XtZluB3MJjSZ0FVCajBjLvPw9emOpryNURfmNmqclKeE4mVdLDk9xEJiI6OFitf1kvZHI3XR9MqKlto+81zpnqC58ceDeRxMFEgSqMmDmzjzj8krpJHQTiRiQiTBDZ8o3slE0UmdgaDAUajUV7LpEIlGXMMkTWu3mP12ngvucsnwU2v18Plcsk5m0wmUT2xFKGSJ5nMcFdgNpsFyuRz3tjYwPz8vJS27Ha7nKPT6YTJZJKxpvKCyCMBDsjOcXFx4sZJpIWlP76f84AlMY4tPguicHwtkQcmHfx+PleVl0GSK9+rbgDU8wbwjPU/FTcquvJVtfc8jEYj7t+/j46ODty+fRvR0Qfy8VAoJO3MVWJ2MBgUv4qdnR309fWhrKxM5JwjIyNIT09HdHQ0WlpaYDKZpEsylRGdnZ2IiopCR0cHXC4XVldX0dXVhbt37+LSpUtCCmaZZHZ2FlVVVQgGgygsLMSXX36JnJwcVFZWwul0IjExEefOncOJEyfQ2Ngo/jZFRUUoLy+H2+3G2toaurq6EB0djTfeeAM7OzuYm5vD+Pg4pqamJPltamrC0tISgsGgECFDoRBCoRCmp6dx9epVjI6OYnp6Gh988IGotG7cuIHJyUncuXMHoVAIRqMRi4uLOHbsGP7rf/2vWFlZkZYK586dQ19fH+x2O/Ly8rCwsCCNCh8+fCjrC5MCs9mM+fl5MexiiYoy/O7ubuE3DAwMoLu7W/hoHGfr6+ti3uZwODA2NobS0lL80R/9kchLBwYGkJCQgMLCQtlgkVtAbwer1YrJyUnExsZK8zZyIChxHxsbEwOqR48eAQDy8vKQlZUlLqqZmZnCiaOhWE9PDzQaDU6fPg2dTof79+9Dp9MJt2R8fBybm5vikzI1NYXS0lIUFRWJVDcxMRF/+Zd/iYWFBdTV1QlfJxwO45NPPhHlD60Ljh49KhwPk8mEkydPimkcXzsxMYGOjg5861vfwokTJ+Dz+fDee+/JmrWwsCD3w+1249q1aygsLITb7RbkbGdnB9/+9rextLQEk8mEa9euPZNkud1u3LhxQ0rr6enpSElJEb8U9jja3d0Va/9QKISVlRX09vZif39fSqRMcmhnkJqairm5OYyOjkrjQPrCfJXjucsrh5MF1RNAZdvzd3wfEQ01mPN3wFOnUBWJ4M6dixQXbxKBgGfr19xps2EcnQmNRiMsFouoSIgsqLs/ckGYpKg27iQacUFQ6/PsAUKmudpDhp9DbgGJjmwjnZqaKru2hIQE6QdCK3VyQlhKIRdif39fjIKYSNF/gq6pvMdsUUyuB+8369zkv6jkUQY8Jkdra2tiH06baN67nZ2D/i5Uj0xOTmJ8fBxjY2OYmpoS+XFSUpIkLfybC1gkcmBIxtIZ7/dhKSelt2tra88ojNg9cXNzE1qtFqmpqdjfP2hKxMDFhZ/nyZ0TE1SSZNlgiyWyyspKMT/Lzs5GeXk5XC4X0tPTkZSUhJmZGbnPOzs70Ol0woXhmACeluk47jiemawRHeP/VQmySlZVEwqVTK3OI5LkeH85DzlXmXhqNBpBY4iA7OzsiDncP4eo/EvH7u6ukEPLy8vF34HlppycHLjdbuTm5mJmZkZg/ubmZjEh6uzslN4bbrcbu7u7KCoqgsFgwNzcHE6dOoX9/X1kZmbKrtdqtWJrawtVVVXSCp0da8PhMG7evInZ2VlMTk7i3LlziI6ORmtrK0KhEF566SVRkMTHx6O3txePHj0S2D8YDGJqagqBQECk0kePHsWpU6dQU1OD/v5+eV5U4dDzo6WlBQ6HA2azGcFgUAycwuEwent7UVtb+0yrejZVMxqNyM/PR2NjIxobG3H06FFpYlZYWIj19XVUVFSgv78fv/71r8WAa2pqCnFxccjKykJ5eTnC4TDcbjc+/fRT5OfnIz8/H9PT0xKIGKzZPVmj0cBut0vpc3l5GWlpaQiFQtja2sLt27dhNpuxsLCArKwsfPbZZ1heXobD4cDy8jJaWlpgt9vFajwSOfDxYVI3OjqKX/ziF/irv/orjIyMCE9rd3cXDocDxcXFiI+Ply68lMYuLS0hOzsbHo9HkMzJyUkkJCTg+PHjWFxcxO3btxEMBrG9vS3Iw8WLF4Voe+LECezu7iIQCMDr9eKVV17B2toadDodZmdnMTAwgLt372J1dRXT09Pi46HValFfXy/dgOPj4+HxeBAOh5GTk4OdnR1JLubn55GXl4c33ngD4+PjmJmZwePHj3H9+nXp0JuYmIgjR46gvb0dn3zyCVwuF3784x9jcnJSSmDhcBjnzp3DysoKvv71ryMcDuOLL75AcnKylBP/5m/+Rs7BZrPB6/UK6T0pKQkvvPACMjIy8PnnnyMUCiEYDMLn82FiYkISqp2dHTgcDuTl5aG2thYXL15ETU0NcnJyABw0Y0xJSYFOp0NdXR38fj9mZ2eFmFtSUiLloa96PJd6Rd3FqwmGigAwSQCeJhsAxH1RDSaE+4lsUEVACJCKEJY62ABM3QFrNE9NsagosNvtstvnZ9JZk8FFldUCzyZPvB5VlaJyDlRCHhMNLv6UFjFrZd8ATmL2+8jIyIDBYJDa4MbGhvhLcId7WEbMVvL02AAgja9YZqFclqRJOmOSg8LPZsDlveP5Un3CJI6N7fb3D+S8vHaSF4meEOJkUPT7/ZLQra+vY29vT85RlU0zoPK+qIoKJlqUghIV4fez2RXHDIMnEw8mTLx+VabLBEYtq3Fi2mw2AMDExARCoRCKi4uRlJQkduiJiYmyWHFcmM1mua+quZfK/eHPEhIS4Pf7JTFgp1+WnDgOSRRWme38PnW8qvOQh1piYdlE5SkRbeI85TNm8kNV1/Meu7u70Ol0cDqd6O3tFR+G5ORk6S1Bjx16pfzv//2/cerUKVG1HDlyBPfv38ff/u3foqKiAgkJCWJvnZ6ejhs3biAcDkvHU51Oh8XFRdTX12NsbEzg+HA4jLW1NRkPWq0Wy8vLGB8fh9lsxvnz51FVVSWl0JycHKSlpeHo0aMYGRmREgNlo/Pz87KjtVgsmJmZwdraGvR6PbKyspCeno6PPvoIDQ0NQsre3t7G1NQUfve738FiscDlcqG8vFzIhZSvW61WjI6Oyue0tLSgsrISb7/9NoLBIPb391FbW4vNzU3pnJyUlITs7Gykp6ejtbUVjY2N6O3tlTnDDqfkSxQVFcFisaCtrQ1OpxMLCwuYmJiQpCE3NxcfffQRvve978FgMGBsbAzb29v4kz/5Ezx48EB4Ku+99574cURFRWF8fFwQZLfbjbm5OYyMjIi7JcuqY2NjSEpKwltvvSXlzIGBAczNzYmF+d/8zd+goqJCSLDR0dHIz89HQkKCkMf7+/tF7nvv3j2UlJQgKSlJHGjJ1aIVPwCkp6djYGAAdrsdBoMBr732Gvr6+mAymfD48WNsbGygsbFRNmOpqanY2trC48ePcezYMQwMDODnP/85CgoK4HQ6sbe3h1deeQUrKyuCxJw8eRI6nQ5TU1MiESc/ZXV1FcvLy9JfKCsrCzs7OygvLxe+2MbGBk6cOIGf/OQnyMnJwf3799Hf349bt27BaDSiqalJmh/yvhQXFyMmJgYLCwt44YUXcOXKFbHCZ0n4D//wD/HkyROZW+Pj43JeiYmJkuT++te/hlarRV1dnfBVuKZ1dnZiYGAAQ0NDKCwsFI7JT37yEzidTpjN5n9WqffPHc/de4WBREUvGNjVhIKLIxddPnwGZhXyVmFwws2UVDKYqwmPSoTjDiM+Ph5GoxHJycniPsnPo5ESEwm+nwGH6A2/Sy1HqEkWXR+5U2EitLCwIDVr9Xopm6VLJu2fOUHX19exsLCA1dVVCerp6eki6SRqwSSLUD0DK+/1wsKC+AawnspuhvQBoLpEhdY1Go0obsjPoKSUiA9byxPpISlSfY7BYFCs2HntRDY4RlTeBD1H4uPjpfkdn6uagPIaSDCOiYkRzwOabcXGxmJ1dVVey7HAz6cMlqRbtR8Nx9/W1pb4fRDViI2NlUnJXh1UEul0OkHNNBqNMNjV8c1W8SrixWBPqJ+BX01y+TpVZqyW/vhvAM8kv3wmHN+8NhWp4M+YOEdHRz8jKSZfSFWtqCqzr3JERUWJmoomWl1dXdjb20NBQYEoC3Z3d1FaWoqRkRGUlpaKY2dWVpY8q/Lycvj9flitVhQUFCA3NxcPHz6EVqtFRUUFQqGQuE4y2Ho8HhQWFqKiogKbm5tobm7G6uoqGhsbMTMzI0hbbm6uSEa//vWv4+7du2IKR2Sxr68PZ86cgclkkkaCbrcb8/PzuH79OrRaLVZWVlBSUiJy25WVFdy4cQMvvfQSent7xZHzG9/4hhD5UlNT4ff7YTabZXEfGxvD9773PdnNbm1t4dGjRygpKYFGo8FHH32ECxcuYGRkBBMTE6ivr8fNmzcBAAUFBeKOurOzg7KyMty9exevvPIKqqqqcP36dfT29qK4uBirq6sYGRlBRkYG5ubmpLnX4uIidnZ2UFRUJPNKo9Gguroa/z/a3ju4sTO7Ej9gBBMIgAAJgDnn0GST7KjOre6WuiW1gmVN3PF47PWM7bVd66rdrdryVm3Ztbve2lmnssf2BM2MNNJopB61UqsjOzPnnEESAEECBEkwE8DvD/rc/sid347aVfuqujoR4b33ve/ee+4553Z0dMDv94ut9re//W3cu3dPCOoOhwNzc3OC0tBHIioqStxPiRpzIm9fXx/Ky8tRUFAgxFONRiND8I4cOYL3338f1dXVeOedd/Dqq68iOjoa9+7dw/r6OtbX11FbWyt8LxYei4uLguIw5lRXV+POnTtimx4M7rjZkhCZmJiI3Nxc9Pb2oqCgQLgmMzMzsNlsyM7ORm9vL772ta+JUVwoFEJ3d7dcv+eeew5tbW2YnJyEx+ORhI9oMAO32WxGZmYmnE4noqOjMT8/j7i4ONy5cweh0I6p4KlTp7C2toampiaxm09KShKPIcqiv/SlL2Fubg4Gg0Ek17GxsRgdHZWigUT9kpISjI+PY2xsTNrEattvdnYWNptNuIIZGRlCYI6Pj4fFYkFjYyMuXryInp4eLC8v486dO7Bardi3bx8eP378/0Yyq5I9GZAZtOUNlU1xfX19l2GWSgDdC6GrMla1n6/2uLnJqrJVvid5BKwAQ6GQBAdu6pubm7Jg1d440RK11cINnsmNSpol54HJErXOJDORkMXvvLCwAJfLhbW1NanW19fXJfslj0Wv1wvEDuxwQljVMolioOZnzs3NIRQKwWw2Cz+EiAIJobwmrAD5Pjx3Bj/aVqvSQcqlaHWuJpsk46qqI7U1wHYUgziTNUL6dGwlIgRArg3bW0RkgCduiqzcIyIipPphMOeYeVaaLpdLCJ9s9VAR5HQ6MTk5CZ/Ph5mZGayurmJ2dhaJiYmoqKiAzWbD9va2mKRtbW0JzMw2GLN73ncqjbhBMJmNi4uTZCo6OloSYzUpYLLA9agqg7iG9xKamXirCUhcXJw8N+SNqGoZlaiqthuJtKizd55WMru+vo7q6mr4/X44nU6sr68jIyMDzzzzjAwJW11dxYkTJ7C9vY35+XkZ3ub3+8Xu+8yZMzAajSgqKsLQ0BAmJyel4geArq4uaDQazM/P4/r168IXKS0tRXR0NPr6+vDgwQOsra2Jodfs7CwuXbqEmpoaaX+dPn0aTqcTR44cEYtvQuArKyv4xS9+gWAwKJbec3NzSE1NhUajEd5Ec3MzsrOzsb6+jtLSUrzyyisyCp4yQo/HA51Oh8jISPzjP/4jTCYT7HY7AoEAfD4fSktL0d/fj6SkJLhcLnz9619HWVkZ9Ho9nE4nSktLcf36dQwPDyM5OVnasiMjI7h79y5iYmIwNTWFw4cPY25uDiUlJVhbW0NXVxe8Xi+SkpLw4YcfYm1tDUePHsXExIQYuNXV1aGiogKZmZkoLi4W6ef58+dRXl4uxWN1dTXa2towMDAAn88nZNfs7GxkZWXBYrEIJ4qGhSSodnR04OjRo/IMFBQUwGQyobOzEz6fD16vF3V1dbBYLNjc3ITX60VxcTEGBgawubkp/iDl5eV46aWXUFdXh7GxMYH9Hz58iJqaGkFtFxYWsL29jfT0dPT09IiHBz1L3nrrLXH/XFhYwPz8PDQaDSYnJ9HS0oLa2lrx6Ojr68N7772H3t5ekdLr9XpkZGSguroa586dQ3NzMz788ENcvnwZmZmZqK6uFhlrePiOC214eDgcDocgP5yy/PDhQ3z66acIBALiKNrf3y98Cu71FotFfn/llVdk0izbyrRmoJEdOTMcVGc0GpGTk4PBwUHY7XZpt+fl5SEsLExI89euXUN7e7sMJIyPj8e9e/eQk5MjppF6vV7mKA0NDSEY3Bmk90WOp0461B4xNy1yAVQIW52oyYCtSk0ZoNibZoBi4Fd5HypczY2T34HJD6tqwua0AOeG6/V6pYJiMGPCQzhZRV9UfsleaJp8BI1GI20M8h1o3EVjqLCwHW16IBBASkqKyGVDoR1ZMCed0kuCFT2dLnmuJPRFRUWJ/XpYWJhAW3w/1TOD98Pj8cDv98umrSZRTOJWVlbEOEd1FmXlzM9XCbGcoUIkil4l6oRYyoZpGMRrRg6BGkRZXdGDhJA/0Q4iFLwOGo1GtOz8OUqX6arJSoRTZqkeYmLCw2QyQavVori4GFlZWTLob3NzUyb7ajQapKamilsg1w05ROQt0ExHTSjIj2HSplqnM2lgG5HrWSVLq8kHEy7+PJEsJvwqksH1wGeDhQKRFf7i+6vJONf10xwJCQlwuVyorKxEWloaMjIyEBsbi7t378Lr9eLgwYOor6/H+++/j87OTiG1tbe3Izc3V6TjPT096OvrE2QkGAxieXkZ586dE8JbX18fXC4X9Ho9rFYrtFotWltbUVFRgZSUFISHh6OsrEw27vn5eXz/+9/H4OAgmpubhecRCu3YftfX1+PBgwfY2tpCZ2cndDodampqZBZIXFwcDAYDPv30U5SVlQnpNDU1Fd3d3YiI2DGYm5qawqNHj3Dp0iUEg0EcOXJEUJ/6+nqcO3cO7e3tCAsLE7i9r68Pfr8f4+PjiI2NxZUrV6DVavHee+/B5/NheXkZFy9ehNVqhc1mw507d+Dz+XDs2DGRqYZCO4PSent74ff7kZmZiZ///OcwmUxISUnBv/k3/wZWq1Wk706nE9/+9rfR29uLsbExdHV1wW63o6ysDFNTU3j8+DEmJyfFpKu7u1ue77q6OgwMDCAsLAx9fX1YXl7G0NAQDhw4ICTyrq4u2Gw2uN1uUStxFEBkZCQ+//xzGI1G5OXlYWBgALOzs1Jg8LoEAgGkp6dLcZaUlCTupU1NTSLNLS8vR19fHzIzM6VoyM/Px8LCAs6dOyfkz8uXL6OtrQ3PP/88WltbsbCwgJmZGZw4cQLl5eXIyMiAVqtFd3c3NBoNiouL8fHHH+O1115DWlqaPN8RERE4ePAgEhISYLfbEQqF8Nprr+Gdd96RUe96vR56vR5VVVVITEzE0tKS7G0rKytITEyUmGg2m8WYzOPxyPiGtLQ0kRt/8sknCAQCsNvtmJ+fR0pKCgYGBrCysiLGaaOjo/D5fHC73aJkorqlpKQEXq8X5eXlQlUgwllaWiqqlLq6Ouzbtw9LS0tITk7G9PQ0SktLYbVaZX0mJSVhcXERZ8+ehc1mkxk9X+R46qRDbU3w3/aS/7ixMmCpPW0VCgMgyQc302BwZ/w70QgmIGp7hkgAAyE3WxUSZuZHgiJ5DvSEUIMvz4PtGH4/EgzVoKi2gdQ2E9EB4Anxj+iJxWIRFIasdr4HVSmsnlkJq3bfJI4SxaD7IPkmlLNRuglAIFIadhkMBvHcYADkeTNZALDLIIrqIVbNlPqurKzIe/h8PvHAmJubE+kqk8hgMCizFchnoFKGXBSuDQYXBmB6FqgBkcRVJoVMIClXZbISGRmJlZUVuN1umbxrtVolacvKykJJSQnKy8uRlZUFk8mEzMxMkRXOzs5KMs31EB0dLYPeeK25Afp8PhkUBewkytPT05iYmBDJM5MjokJcI0y8+HxxfTIB3cud4hrnGlaTD7Udsra2tkv9AuzmYqkEVFU9wwJCbUF90WNxcREjIyO4evUq1tfX0d/fj/X1dZl586Mf/QjR0dEoKSkRy3i6fLa2tgIAMjMzkZCQgMrKSuTm5grBdHZ2FteuXZP1yKmnycnJ6OnpgdFoFPk4XSFpbjQ2NoaKigqcOXMGaWlp0Ov1OHbsmBBOqdyhUVxpaSmeeeYZrK6u4tlnn8XExARWV1dx584d7N+/H7GxsRgfH8fGxgb6+vqg1WoFunc4HDAajbBYLNL+Iwdoc3NTDJk4gKukpERmOZ08eRJxcXHQ6/UYGRkRnlpaWhp6e3thsVgwNDSE6upqeRY5wnxkZAQDAwPIyspCXFwcmpubUVZWJsqzqakpvP/++8LzotLl1q1byMvLQ1VVFVZXVzE5OSlJ8/LyMo4cOYLW1laMjY1hbGwMDodDgp3D4RB+QUREBK5cuYK4uDiYzWYcP34ci4uLCAQCOHfuHDo7OxEK7UgtdTodjh07JvtRXl4eAoEA2traoNfrEQqFpLVUU1Ozy00UgARmh8OBjo4OLCwsyM8nJCRgcHAQW1tbmJ6exueff47ExEScO3cOfr8fVqsVAHDhwgUkJiYiJSUFdrsdCQkJiI6OxquvvoqwsJ1ptBkZGfjmN7+J0tJSaf0cO3YMtbW1MisoMzNTUGmTyYTh4WHcuXMHkZGRGB0dxejoKFpaWjA+Po7t7W18//vfRyAQQG1tLfLz82E0GoVvRLt0Pt99fX0i8y4qKkJnZycuXbokBWVBQQH6+vpEnfLCCy/A4XBgdXUVHo9HXFB1Oh2Ghobg8/lgsVhw//59BAI7rryPHz/G+Pg4Tp06JfdmfHwcbrcb169fR2FhobSmu7q6JFEn1y4qKgp/9md/JrLgX3c8FaeDGV4oFJLNWFWXcBNkIFE3NJUjwapLhW65yVGetrS0tMssSq38iFbExcUJskDIWk1wSPJkG4BkSY6sBiDfQ4Wo+YtVH6tmtl3UdhFJfmwV0JVPVR+w/UCUhFJT8guICjH4sBJWeRwqjAY8cbSMiIiQRIHXCYAkM6q8ksjL/Py8IANEd/gzqrySLRQSPYlQqB4dm5ubkrGHhYVhdnZWErBQKCSmWQwUdHskoZMoDtGxUCgk14WcE5X8yfdmpu7z+WQeCp1ACf1SNk3CKHkWAITrw0SISA8RHgDSu01NTRXokv4VVK2oiScTJX5PchsAICkpCSsrK3Kf+HO8X2oSzn9XE2I+d6q/x95namVlRb4D7yOfT3VdqfNzmLQRReR5MGlTOUBf5DCbzThy5Aj0ej0ePXqElJQUIXO63W4cOXIE6enpaGlpwb59+8RNk9As7ZZjY2PR3t4uCV5/f794o/zJn/wJ/uzP/gxf+9rXEBsbC4/HI3tBVlYW7ty5g4SEBDGPI3Ln9XqxvLyM06dPIyUlRdRMhPHdbjesVivef/99pKam4p//+Z9RUFCA9fV1dHZ2SkGUnJyMhYUFNDQ0iOU2uTssEsbGxrC8vIzExER0dHRgaWlJ1qHRaMTk5CRmZmawvb2NpqYmad1kZmbC4/Hg5Zdfxj/8wz+gtLQUbW1t6OrqwpEjR1BYWCiS9fLycnznO9/BX/7lX2JsbAx//Md/jKGhIaSkpOD27dtISUnB9evX8corryAvL09cYPv7+8XWfXFxERcvXoTdbsfdu3fxG7/xG1heXsbZs2fFgdRkMiEiIgKFhYXweDw4cuSIqEloGhYVFSWTdtkyYnJy6NAhjI6O4uTJk9ja2kJ2djY6OzvhdruxuLiIZ599FlNTU7h//z42NjZQWVkpnK2DBw/KFGiNRoOZmRnExsaivr4eS0tLGBwclOecVuGHDx+WgaFUmbndbpHMEkFmq4QeMDSBu3fvHiwWCw4dOoSRkREYjUZ0dHTAZrPhueeew3/5L/8Fx44dg8vlQnx8PNrb2/Hyyy/D5XIhOzsbGo1GiMl8Lsnxm5mZwdmzZ8Uynuu2t7cXhw4dwrVr12R/mpubg9lsRlNTE0KhEHJycqTVtLCwgPz8fIyMjCA9PR3t7e0yDDA7O1v8SsbHxxEXFweHw4GYmBhMTk6ivb0dFy9eRGZmJiYnJ5Geno6ysjKMj4/LpF7apb/66qvChXr8+DGqq6sxODiIzc1NZGRkCJfvk08+wcDAwBfaI556yiw3beDJXAhCuaoPh3owYLBvzNdyE937b7zo3JDpRFhSUoKEhARMT0+jra1NNuaEhAQxywF2KmqLxYKsrCxsb2/D4XBgaGho18RXIgUqb4AojtfrlSSABEPVo2Rvxa32+Zl0qD8/Pz8vC3x5eRkVFRWSiHi9XiFqzs/PSzDguPmpqald0lEmGVTi8OcCgYDIoBhUEhISdhlC8ZyYOPD8mWSQH8Pkg4kRA1BcXJzcfxKV+J1Unw1m/UwY2daiXJhID4M1kSFW2STfMnllokJC8NramvAplpeXRa6qrk3Ck8zGVUUIUZilpSXpTXJ2C6dDMhEjL4cmbnQzZWXM5G51dVWqZp4XK3mLxSKSThKOyd3hOtHr9fIdVeSCbQ6O+eamqbY42XNX259c1+pnELnYSxblM0szMFXFtbm5iW9/+9tfmFDKZ99sNsvEzampKWxvb8NqtQoKsLa2hn379kk1/+mnn+LixYvC3TAYDAgPD8fRo0cxODgoPfpgMCiIFVspgcCOy6zD4UBWVhZ8Ph9eeOEFJCYm4u2334Zer5ekubS0VBx3f/GLX+DChQsoKCiQWUjDw8Nwu92ihpmZmRGiI9cTC4fe3l74fD5cunQJzc3NAuuPjIygtrZWLK/9fr84iQaDQezbtw9dXV0yyTM+Ph7p6elobm5GdXU1pqenMTg4iGPHjqGqqko8MSIiImRAWUlJiexjU1NT8txubW3h4cOHOHToEFJSUtDa2oozZ85gZGREnkODwSCusURn8vPzpWXK67i5uYmJiQnExcXB4/HAYrGgrKwMjx8/RlVVlfDdvF6vWBP4/X60tbXJvvbVr34VIyMjaGhoQE5OjvCUqqqqsLCwIEiH1+tFXFycPEdOpxMTExM4e/YsPB4PoqOj8f777+Mb3/gGVlZWMD09jfj4eOHBFBQUYGxsDHfv3sWJEyfw6NEjpKamYmlpCWVlZaiqqsK1a9dkuq3D4cD6+jrsdjsuXLiAQCAAl8slLQvKQwGgra0N2dnZOHjwIDo7O2VSbX9/P7RaLc6fP49Hjx5hcXERg4ODqK+vx927d5GYmIjR0VGYzWaZhuzxeGA0GoV3Ex8fj5aWFtkj+Cy73W6kp6djenoay8vLSE9Ph9/vx+XLl2UNTExMiDKLRZHT6ZTig46ibrcb7e3tyMrKEi4H0W2j0Siuvl6vF3l5ebDb7cI/4p4TCARkxlZ2djYsFgtu3ryJwsJCDA4OYmxsDD/4wQ9kX/y/HU+FnarVEzc8tb2i+nAQquXmyQ2Umx8DFFsETDj4HoTWWdEz0+UGo/aa1WmtrOaYBJDESFRAVX2opFcGUGC3v8jev6sySH5vleDJDVsdasWx16FQCCkpKdLm4EZHRQvPg8GC7SAGYvaMubnzffnw07uDSQPRHAZvJhmqKojXTZXoMgHhz1DKq5JM+bMqWYqJBAmsvN80ztLpdILq8F7wvFX+AtEBbkgMkKojKLlDvCa8FrxeXq9XkkEGba5VSnzNZrO4ZdKUzWQyCQGZiSgRBVblzO55LmwvMTlTlU1RUVGYn5+Hx+ORtaLeI/J4iIzxs3gt1OSM/8+1r65f/lltiXH9/qoWifp+/DO/t4r+7S0gft2RkpKCQ4cOYXV1VabzJiYmYn19XaB2jlbv7e0FAHi9XtTW1u6aIaTT6ZCRkYH79+/D5/PBYDBgamoKgcCOid7w8DAOHz6Mjo4ONDU1iXmcVqvFgQMHMDExgZs3b+LmzZuihiPXICwsDKOjozhz5ozIAv1+P5qbmzE8PIyOjg4xs0pMTMQLL7wAm82GsrIypKamIjs7G3FxcaioqMCzzz6LmJgYaccwEA4NDWF1dRWZmZnIyclBbm6uzH2Zn5+XFg9ljVSUUFaanp6OtLQ0OBwOdHZ2CnKXlJSE1157DXq9HpGRkSgsLMTCwgI2NjbgcDgQFrYzRM7tdmN0dFTmvPCZLigoALCDAjscDpSXl6O4uBjp6ekyGZdj1VlFkzSp0Whw8+ZNMa5aW1vD8PCwDAvr7u7G4uIi4uLi8NJLLyExMRHvvvsuurq6UFxcjIiICHGGHRoaEikoxwqo0sx9+/YhNTUV7e3tmJ2dxeTkJL761a+ir68Pc3NzWF5extzcHF588UVkZmbin/7pnxATE4PLly+js7MTBw4cwJe//GUhQ3Z2dmJjYwMzMzPo6+uDw+FAdXU1ysrK4HA4MDMzg9zcXMzMzCA9PV2SAwB45ZVXMDMzI1b2Pp8Pjx8/RmlpKdbW1vC///f/Rm5uLjY3N1FYWIiRkREYDAbY7XYJ9FSH0COGe9nw8LDswbOzsxgbG8PMzIwkE11dXdKSjo2NxdWrV8UnhOKJ1tZWDA8PIxgMwul0iv+SRqPBJ598gqKiIok3UVFR2Ldvn/AIJyYmxJelqKgIHR0dkgy53W6UlZWhrKwMGRkZklQSIQyFQoiJicHMzIwg/l/keGobdHIN1H9TkwUeKtdBDWBq24WvJwQM7J6QyWqbyQbfU+1FE+5mpcsKnuhBZGSk9AEZfNh3B544naoEPX5/EiXVRIuBgkHW7/fLxs/vEQgEMDc3BwAiOaVsNjs7G/39/ZienhaZGjkJDPzUmVNJwMRM7ctvb28LcqLVasWlk7wGclpYKbONwX+jgRWTQ15T3kcmDXuvC6fEsv1CSRg9Nfi9VD4KKyjyZFg18rVqkqquL1a3DP7ks/B6svXECg/ALp8LGpoxq6faZWlpSZJYzl1RCZtEt3hNaCjGFgwlu0wqAUiSSDIvK7eFhQVMT0/vMnJT1x6TVybtXHtMNmgGx3vD/+O6ZZLCP3MonNpm5Jok6kT0Yy//is6VrEb5fD7NQXOs4uJi+Hw+/PKXvxSHzI2NDSwuLmJ4eBhZWVlwOBwiAeW6yczMlAq+tbUVLpcLWq0WU1NTMlWTBLvW1laEQiEcOHBAnCq1Wi30ej3KysoQGRkpRmLs2U9MTGB5eRllZWUICwvD4OCgeC68+uqriIuLw7//9/8esbGxOHbsGBISEkTN4fF44HA48OjRI/HsuHfvnqgEQqEQ7t+/D6PRKFCzx+PB6uoq9Ho92trakJOTg4iICNy+fVvUC2azGTqdTgixcXFxqKqqwtDQkARsJuiZmZkS/O7fv4/h4WHEx8djc3NT5mSwlRoXFweNRoO0tDSp4vl8r62tyeRfzoYpLi7Gn/7pn+K3f/u3JfFgsk3lUUdHB6KiooSHotFo8Pnnn0tBEgqFUFNTg5WVFeTl5SErKwu/8zu/s4uXUV5ejs3NTZw8eRK9vb14/PgxgsEg+vv7hevz6NEjIXdzX7xx4wb8fr8kbIODg7h27Rree+89ZGRkYGpqCsPDw9i/fz8A4Je//KXsG+Hh4Th//jzCwsJQWFgorrIkPnNEQmVlpXDBaLUOALm5uTAYDEhKSkJVVRXi4uLQ0dEBi8WCnJwcPHjwAPPz89JGGR8fFzQuOzsbWq0WUVFReOutt+BwOOB2u7G9vY2Wlhbk5+fj4cOHGB4exsrKCoxGo7Tbnn/+eSl0p6amkJOTI/va0NCQFO4kvpNsGxkZCYPBgG9961vS5szMzERJSQnGxsYQGRmJxcVFrKysiHV7MBjEM888g1u3bol0dnh4GC6XC83NzZidnUVBQYHYnS8tLeHx48e7ugxf5HhqR9K9hDQGf25eKvGSXABW4mr1BjxpVVDDzb4+f6mtF3W4Fb+DyoHgxhkIBHZVqZysSNYxqzl+NgMe34uJFSFwlSBLuFntmdNlcm8rhN/X4/FIpVdaWioufKxEOa49KSkJGRkZMJvNUskTKZKb9S/nzkAXHR2NlJQUIWqy7cJWBRcrz58IjIoU8NqRSKqShFWpsTrbRb1WTMqYoFCtwnvOB55JBglz/BzyOMiZUbknJLxx3URHR8NsNkvLhwcTGbPZDIPBsKvt5vF4MDk5KWuWklsiIVwP7MNyaJ/H48HS0pJ4imxubmJxcVFcX0lQprEUkyaTyYRAIIDk5GTZsJnoqGZbKsGavCQmulxbqgmYmlipCQrvA0mnJDPzeeBzovJ0uI65RvhaNfFnQqKuvy9yDA4OYmJiAk6nExsbGwKPs7qvrq5GQUGByJapWDEYDPB6vZiZmcHDhw/FpO1b3/qWeClQDdPR0YH79+8jPT0dCQkJ8Hq9KCkpgdlsFiSK3hGZmZkyPIyeA5R+RkVFiczyL/7iL9DX1we32w2Px4O0tDQZJd7c3CzmgurcDZKhXS6XkC9ZLHBzppX+4uKitFQ//PBDVFVVITMzEwsLCwJl09kxMjISw8PD4rng8XiQm5uL1dVVWK1WnD59Gt/5zneQkZGBqKgoIdpGRETIs05J8ejoKO7cuYPKykqcOHFCnisAePHFF4VUnZ2djfv376OxsRHDw8Oora2FxWKR0fFE+U6ePIm8vDw0NjbKaPeysjIUFRWJIoYSZ7PZjNzcXDQ2NmJychIul0vcP202GxwOB6Kjo1FbW4vw8J05R0Q9yb85fvw4tre34fP5UFFRgeXlZRmWdubMGdTX1+P5558Xszleb7ooJyYmSlvtwYMHOHnypLSD7Ha78Kzo5vnee+9JwA0PD5f2GlusWq0WTU1NWF5exoULF7C2tibKPE76bWpqwtbWFvx+Py5duoTbt2/Dbrfjzp07woX40pe+JLyLoaEhaQsScZyZmcHdu3dFdu33+1FQUICBgQEYDAa57qFQCElJSVKABQIBmM3mXVw5AOI4GhERgZaWFuTl5cFqtaKyslLmU62urqKxsVFQeu6lnKel1+sxPT0thnmlpaXw+/2IjIxETU2NEHR/3fF01HQ8qcJ4qNWwSkIEnvh6qNXSrzIr4nuoED2Dm8oT2SsBZF9cnYrJYMQMOTExUWB6QrcMunvJrtyMVehZ/Wz1/Nnz5uYeGRkpVQorZpIio6OjUV5ejlAoJMZQWu3OOGgmDIuLi5IYMEtlMCfXhOdGzgjbHCSx8f/JjaBUjQGLlTXRItUbQz0vtr40Go1wRIggqO0vfo+9Mk2dTidTb4lAsUVE/wNW+Gpg5N+ZbKp+KfzePp9PuC88t2AwCIPBAJvNJnbwlLQajUaYTCZZO1xzPJ/5+Xkx0AEgGv/Z2VnMz8+L7wfnQAA7yZ3X68Xs7Cy2trZ2VWNEUaKjo+FwOOByucQoi2666hEM7lhnEwnci2QweVUTd9Whlf4h6i9+Ps+Va5QJBteO+gxyfTPhZpvvaSWzAKSyo3IkJSUF0dHRGB8fx/T0tEy5XF9fl34wE1VWomxP/emf/ikKCgrgdDrR3NwMvV4vk08//vhjlJeXi836wsICnE4nKioqpJ/d0dGBH//4xxgcHERiYiIOHTqER48eITw8XGZHjI+P48iRIwgGg8jMzMTf/u3f4s6dO0KeO3HiBDY2NvDo0SOcPXt218ZdVlYmJlNLS0t47rnnUFdXJ6jfz372s10DBsfGxvDiiy8iOjoax44dg06nE1Ts/PnzKCgoQGZmpiS/Z8+eBQAZQz8xMYG1tTVcu3YN+/btQ3FxsayV5uZmGZT3/PPPi4nawYMHhZg4NzeHw4cPIxQKYXZ2FgDw3/7bf8Pm5iaysrLg8XjQ39+PlpYWGI1GLCwsiHLn8OHDWFlZweTkpEiM9Xq9eHGQcKvVauV9hoeHZXDd/Pw8VldXZd9bXV2F2WzG3NwcsrOzsba2htOnT+P06dMoLS2F0WhEc3MzzGYz8vPzcf/+ffzhH/4hNBoN6urqEBERgRs3buDGjRs4d+4cDhw4gMHBQUnSy8vLxefk9u3bkmwdPXoUsbGxGBsbQ35+Pl566SXhM5WVleHQoUMy8ZWE5O7ubvHyiIzcMZf7X//rf6G7uxtXr17FzMyMDI8rKSlBUVERDAYDfv7zn4vD59zcHA4dOoSGhgY8fPgQi4uL8Hq9GB8fx71794RQPTg4iLy8POj1evT09CAlJUWI60VFRVhfXxe1Cs3QkpOT4fP5UFJSIu2doaEhsb9fX1/Hd7/7XQSDQfzmb/4m3n//fTx+/FjaRTk5OZiYmEBVVZUM+dNqtTCZTNi3bx/KysoQHx+PrKwsaLVa4eeUlJRgYmJCzOa+yPHUkllWRGrLhIcaBIgMANgVrAgRA7udSNVkhYuGqIIK/autFeCJDbqKwpAoyEokPDxc2hdqQqQmFur3ZZDmxsvvx6RIheKZEISFhYkLJ+WoGo0G8fHx0s8n8Sc8fMe5kHNRWKFSi86ATatvXj8iEqz62d5R4fP19XUsLCzA6/WK2RXbApxtogYSIiNMcNRgzoSG/hbkyHCWy8LCggTWxcVFgcsJmfP+eTwekUCTY8H7yvdWVU1qe4DXOzIyUrJqkq3UZIweAJTYMgkgC35paQnBYFB4BgCESBUfHy/j66mMYZuAbSoGY66lqKid+Rrb29swGo0ICwvD+Pi4XNvFxUWpmBISEpCcnCwIF5E24AnfhcoqtkpUTxngyWA2lYNBBIZreW/Lhc+H+r5qq5CtGCbQ6i++x9O2V2ikxqp+fn4eVqtVBkaFh4fDarXC7XajoKAAycnJMomT96m6ulqUBJxISwjXZDJBo9FIC0Ud2071yaeffipk48TERNhsNmRmZiIYDOLhw4ewWCzYt2+fKIzoYrx//35oNBqcPn0aBw4cwPb2Nk6ePIkHDx4gKioKhYWFuH//vlTen332GcbGxmR9zM/P42c/+5k42a6treHSpUuCiIVCO0qGgYEBxMfH49atW9Lrd7vduHnzJoaGhvDLX/5ShgiyjUcfjIKCAly7dg2BQAAZGRnCk2BvPSUlBcATawGTyYSSkhIkJSWhpaUFsbGx+PnPf46xsTFsbm7i8ePHiIyMxCeffCLOwhznzinBaWlp0vbyeDwymI1D04jMPH78WKzoDQYDiouLsbW1hYGBAWg0Gvj9fiwuLuLevXsYGBjA4OAgXC6XtCTn5+fxySefoL29HTMzM5idnYXL5cLGxgbGx8dx9OhR/O3f/i3a29slqfV6vSgqKsLU1BQGBgZgs9kQFRWFZ599FgsLC2IOp9PpZH4Khzzu27cP4+Pj+Pu//3t0dHTA6/VCp9MhEAjgyJEjmJqaQkFBAWJjY3H8+HExnfP5fGhvb8eFCxdgNpvx27/924iLi4NOp5PZJ52dnUhLS5O9fmhoCOXl5VheXkZWVhacTiciIiKwvLyM1NRUZGRkoLa2FuXl5Th8+DA8Hg8qKipQU1ODO3fuoKKiAvfv38e9e/dw79494ftRrTQxMYGIiAiMjo7uUndSJXP//n2UlJSgr68P6+vrOHnyJA4ePAi9Xg+32y0u1JWVlWhtbcXm5qaQZa9cuQKv1yto8NLSkpyfVqtFfHw8BgYGpCj7dcdTYaeEWhk09labezcuFSFQNzK1JcMgyKDKv6uvZ2BRK0D1F3vZhMop9WOCxJHyhKGpyuD3YEAl2ZXnqcpoiQ4w6KhtJiZJ3NSTkpLE3pva78XFRRmmxvHci4uLSExMFKIQAwuDPY3HYmJiZNgUAxL1/+zP01KXfBWSGAnzM9Cy/UG0B4BwHlT0hteZFuG857w2i4uLmJ6ehtPplPdRJcb0GkhJSZE2BqshoifcmKkQIYdBRSNIPKRiQOX58CC5ODIyEgkJCVhZWZHrwjkV4eHhMu+Ebqu0m2fCQcSG6pBAICC9cSZC7PMmJiZKhef1ehEMBuWzeR29Xi+0Wi2sVqtAnUxYSDBm4GdlzGeFVZnawlSJzipxS+VFkYTMZEIlfVN2zqSNbTKuXyJKahv0aZGOxcVFUWv5/X5UVFRgbGwMNpsNPp8P9fX1wogfHR1FVFQUqqqqBPELhXZUP6dPn8aPfvQjFBUV4cc//rEMNPvGN74BjUaDDz74AFqtFunp6WhoaEBxcTEuXbqEkZERZGRkwO/3S4uioKAAKSkpePfdd3HmzBkYDAYAQHJyMlZXV6VN6HQ6sby8jPn5eURHRyMzMxNTU1OwWCzo7OxEfn6+JJavvPIKpqenERMTg87OThQWFiIY3JG2X7t2TaS82dnZsNvtgtxxb5idnUV5eTmmp6fR2tqKZ599FltbW3j++edx9+5d8Seh2ic6Olomr7L1sbKyIm6sJpMJzz//vFTkGRkZIo0PCwuTmUF6vR6BwM5MGXISIiMjsX//fvj9ftTW1uLu3bv48pe/jK6uLvT29kordnBwENXV1RgfH0dBQQF6enpQUFCAsrIyNDQ0IC8vD1tbWzh48CA8Hg+6urqQl5eHpqYmdHR04MCBA3jrrbewf/9+tLW1SUFy8uRJLC8vi8LI7XZj//796Ovrw/79+xETE4O5uTnExcWJK+Zbb72FqqoqXLx4UdpVgUAARqMRdrtdgm54eLiQRh8/foycnBxMTk5KbAgEAkIEfuedd/A7v/M7cLvduHXrFkpLSzEwMIC5uTnYbDZppVLV+Pbbb+OrX/0qPvjgA+Tn56OkpARbW1toaGhAWloaGhsbUV9fL6Td5uZmafOVlpaiubkZBw4cQExMDCYmJpCYmCgtRibmo6OjOHjwoCQ/S0tLYjVPn426ujoMDQ0hNjYWN27cEB5gUlKSJK7V1dUyWHBpaQkOhwNarRY1NTUYHh6G0WhEWlqaKK5GRkYQCoVgsVig0+mQmZmJ5ORkPHjwQJx4Od/GYDAIJ+SLHE+FdBC+VWV9rI5VQinwpGetShm5wamvJeytHioxjifCTZTBnahDVFSU2CdzIiRJm2ynbG9vi0SWMkZC/XvbCQwCantHhZ4J+XMz5vVQrwETG9WRlb1DjqonCXFhYQFTU1Pwer27AhOHrtFdk8GDsHpsbCxWVlZgt9sxNzeHlpYWGUHNQBcTEyPe+vQI8Pv90vYg4ZQJGa+7Kqmi8oioC61zmR1TGUBkgD3j9fV16f+RI0FlC0mLTAyJfpAQxXPmfSeRdmtrS+YFkGCpcjuYlBqNRpHMcj2pFu/q+6WmpsJqtYr0mH4uTFwDgQAcDgfm5+eFyMcZLEzKDAYD4uPjJcHz+/3o6+vD5OQkiouLUVNTI+vZ6/XuQs1USS/X2V6+FP8vIiJCkBF1TahJP9tmfBbVBILJmqrUUonT/F3lXT0t0pGeno6ZmRmsra0hLS0NW1tbGB0dFUjd4/HA5/MhIiIC2dnZSEtLQ3d3N6anp6HVajE4OCgql5ycHADAc889JwTpW7duYXBwEAcPHoTFYsHw8DDy8vLQ09ODv/zLv4ROp8OLL76I9PR0BAIBtLS0YHp6Gnfu3JFzHRoawsOHD9HR0YHR0VFJJnp7e2EymWQNG41GmSwaHh6OzMxMHDx4EOnp6bL2wsN3fGFI5KZFOT1w3n77bQwNDUGv14tvxMrKCqqrqwEAAwMDyMzMhM/nk7keVE/Nzc2hqqpKqv4zZ84gGAwiIyMDjY2NmJ+fR1ZWFmw2GwKBAK5fv47m5maYTCZkZ2eL06RGo8H4+DgGBwfx7rvv4t1330Vrayt8Ph8mJibQ3NyMn/70p9ja2pLA+PnnnyM5ORnnz58XnsQf/MEfwOl0IiUlBWNjYzCbzUhMTMT9+/cRFxeH7OxsBINBNDY2wuVywel0oqmpCZGRkTh79iza2tpQXFws49ep/vjwww/x5ptv7ppt1dLSgpKSElH0zczMwO12Q6/Xw+v1oqCgAD6fDzdu3IBerxdjPu5//f394o0SDAaxsLCAqKgo9Pb2YmRkBJWVlUhJSRGTtJiYGPy7f/fvZIptMBgUH5H8/HwYDAa5V59++imysrKwf/9+tLe3iwTcbrfj5z//OV566SV4vV7k5OTg9u3bGBoaQn5+vnjUJCcni6FcX18f7ty5g+PHj4tXSF1dHXp7ezEzM4OioiLodDo8fvwYzc3NEsvS09PR19cHr9eLrq4u+P5lGvB/+A//AbOzsygpKUFcXJx4yuTm5iIpKUmcccvLy2E2m9HS0oLTp0+joKAA29vbYl6ZnJwszwLHRIyMjGB2dhZ+vx/19fXi5FpYWAiz2fz/xpFURQHYp2f1zwSBfAtukqpTJ1+nOkwSJlYJjNwsmRD8qnYKD26ehH04+4IzHBYXF2UTYyBnz5voxt5qkJ/HipWIB/CEc8D+PBMWJhkM4sBOksI5HKy+yT9h4CTKQo7A/Py8cBYYkKi+IRpAlGRsbAzz8/NwuVwiYWIyFRERIdp5fp7qlsnqReVLqERVckkowQwGg0K05GAplXfg9/tlwXLjJBmXslqV48HWQGxsrKwBJqPkHajXncFTNbbi/BQO4+L14eA/kocJAapzaXhtR0ZGhOyblJSE/Px8sdTmZ5CsRcSFiqjV1VXhjrAdEh8fD7PZDKvVipqaGlRXV0sSER4eLtNoVXUVlT3qwdYRrxnbKFxDKvGTr9+rRlGfUyIcahuLaA5fyz9zPajJxxc9pqamsLKygt7eXmxvb2N0dBSZmZnCiOczvbCwgAMHDmB9fR2VlZUwmUxwu93Yt28fNjc38emnn6K+vh52u11s/LOzs0UZNjw8LNMuacP91a9+Fbm5ufjkk0/EQCs1NRU6nQ7T09MoLi5GfHw8rl27JnL15ORkdHd3w2g0orKyEsPDw0hMTMTGxgamp6dRVlYmKCMnrY6MjMDpdGJwcBARERF49tlnER4eLiPJi4qKEBcXh+rqaiFixsTECLclNzcXCQkJyMnJwalTpxAWFibrRKfTITU1Fd/97nfR1dWFjz/+WNRSV69exdjYmLSiZmdnZVpsY2MjNBoNTp06hdLSUkkYHjx4gHfeeQd2ux1+v19mwFCWSw+JM2fOCLfml7/8JWw2m3DLVlZWUFhYiMePHyM3N1eG5w0NDYm1u16vl1lFi4uLYq7FJN/pdOLrX/+6BGeXyyXS6KioKBw+fBgWiwU3btzA5OQk8vLyZKDaJ598IigAp+ZmZ2ejqKgIaWlpMkqC8442NjZw6tQpxMbGYmFhAREREWhvb0dpaSmqqqpw8OBB+Hw+2Utzc3NRVVUFg8GApaUltLS0IC0tDTabDdHR0fD7/dJCzsnJQU1NjUxxVYvqBw8e4NixY7jzL4PQUlJSUFBQIG1ag8GA4eFhGWB4/PhxaRsXFxfDbDajvLxcWpEsHu7fv4+wsDAkJibK97179y6MRiMuXboEp9OJvLw83LhxA1evXkVqair6+/vR0dEhyWJPTw9WV1elre92u0UB09TUhNbWVuj1ehiNRpw/fx6BQACjo6PiLRQTE4ODBw+itrYW0dHRyMrKQkdHBwYHBwHsqNYqKyu/0B7x1ERSYLdfB4MmFRNEANTevDozQ5XWqhua2j9WfTC4WXOT5s+p8kpCzQ6HQwI31QkctMaKmQGNf+YGzM8mirM3CKjBj8GWyQUD0V4YPBjccTDkL8om2R+joycTtK2tLfl+dFNlQkapI7NRykk1Go1A+mpbiNwGnidluZRJkTNDNQdRJHVeDRO81dVVkZHyHHjd+B6zs7MCO6rXjoOfAIiMmEY0DIZqy4zJHKtNfneuLT6MbH2QWa0mUbyvTISIRKmqD7ZTOIeFxj3T09O7CKCUKk5MTGBmZgaLi4tITU1FSkoKYmJi4Pf7Bc6k70JiYiIMBgMOHjy4C8UjD4fPCK+luq64pin14zpSlWFMOMgl2EtO5fVTnzHeT1VRxOuh8jiYBPFZ4/X6okdkZKRYibtcLiFZBgIB8b7Q6/VISEhAZ2cnLBYLpqamRM2SlZWFx48fIyIiAi6XC/n5+ZJcNzU1CdegsLAQa2trcDqdcDqdyMrKQmtrK+7cuYP19XWMjIxgeHgYq6urePToEfbt2weLxYK///u/h9VqxczMDNLS0uDxeGQIYXx8PGpqagQ9YGLC/eD8+fPo6+vD1tYWkpOTkZubK8S/lZUVfPrpp9BoNKioqEB4eDgGBgbgdDp3JY2dnZ2IjIzEw4cPYbfbsbGxgYyMDHz00UfSGx8aGsLZs2eFCD43NycTcPv7+xEREYGKigqZ+dLd3Q2bzSYcDxYuXV1dAHam0DqdTthsNpHJZmVlSevQbDbDZrNhcHAQs7OzMoOmqalJzp/cr9XVVYyPjyMxMRFnz57FysqKTAeOitqZKsspplNTUxgfH4fvXyb3/tf/+l/FGfbIkSPY2tpCZWWlIKtGo1HeIz4+HiaTSbgIW1tbyMnJQUtLC0ZGRmRirN1uR3JyMiIiIvDMM89I4p+SkoLV1VXs378fer0e9fX1+OCDD+Dz+YSTVlBQgNXVVUREROCDDz7A+++/L+aRRKX5LHH/+eUvf4mPPvoIDx8+REREBFpbW7G4uIjk5GQYDAY0NzeLopDWBG63GxEREejp6YFer5filzHkwoUL4ljMsQmZmZl49tlnxSfj1KlTCIV2pN+3b98WHprD4YBGo0F/fz/S0tJgtVpRUVGBnp4e2Gw2pKam4sGDB2K25/f7MTU1JcUbkz2fzwen0ym+TLm5ufD7/UhOTkZZWRncbrdcP5vNhoaGBmRmZiItLU3sHygB/3XHUyUdrL74Z8LLwO5EQeVKqB4RAHaRQ1WkQCWasqLdu+FRhUI5rQoxE9aem5uTwEz0gd+REkCj0bhLNssqj0GW1aT6ffh6lcDIXrC66ZMnQISCHhoAZIPj+83NzSElJUUWH+WzgUBAlBM8NyZfWq0WY2Nj4uXB5KW2thaFhYVITk4WG/mlpSV4vV7hoLB1RAc6depuMBgUpIIVtXo/VW8OBkFyVNbW1uQhUyXS6+vr8Hq9mJiYQDAYRFpaGsLCwoTjsLy8DKfTKUkCibBEIzY3N6UtRX4GvwvvL6t1lasxNTUFl8uFuLg4ITfx9ayG+JqVlRXYbDYkJyfLhhkdHY309HRJWhk0VTdRTtZkn5eIh8/nw+LiIsrLy8WWnygIfxHtUv/M9U3EkIGO911Vn6iqFCYHv6o9wkRD5W6EhYVJq0xNPtTXE4lR18AXPerr67F//35kZGQgPj4evb29iI+Px6lTp6QNGhUVhaGhIWxtbYkFdXl5OXJycvCDH/wAkZGROHfunEhca2trodPpcPjwYZFFdnZ2IixsZ4ppSUkJVldXRXLe1dUl+w3Hp4+NjSEYDKKsrAwxMTFSabtcLgA7XJRPPvkEGo0G7e3tgsAAwMTEhMx94VyMpaUlLCwsQK/XyzN3/vx5xMbG4p133sH29jbOnj2LQ4cOCSFvYWEBCQkJWF5eRl9fH8LDw2UYXlxcnCjtNBoNurq6kJaWJhNbyX/Izs6G1+vF8PCwFDC1tbUoLS0Vvhb5S8ATZZrRaMTY2Bj+5//8n6ivr5e9g3OHZmZmcPLkSRnlkJeXh5mZGczMzCAqKgr37t2Dx+PBwsICPv/8c1gsFkxOTsqQuNnZWZnCqtPpMDk5CafTiaNHj6K1tVVm0Vy+fBk6nU6GvTU2NgrJ+ubNmygtLYXP58Po6Chyc3Px05/+FN3d3VhaWsLs7CyCwSDOnz8Pp9MJk8mEZ555Bm63Gx0dHXj06BG2t7dRV1eHrq4ubG9vw+l0CopcWFgoSODMzAweP36M1NRUmWlD9DMnJwcdHR2oqqpCXl4eNjc34XQ68ZWvfAVnzpzB7/3e72Hfvn0YGhrC1772NcTExGBoaAjp6elipsb1V1VVhd/6rd/C97//fURHR2N0dBRxcXHo7OzEzMwMvvWtb8FgMODu3buYmprCvn37sLCwgGvXrqG5uRk+nw8DAwN49913kZ2djVAoJHNsXnvtNYSHhyMrK0uk50wcAoEdd2S73Y61tTV0d3dja2sLOp0OycnJMkeLZpMs3kwmE3Q6HVwuFw4ePIju7m7hJsbFxaG/vx/5+fli8EjExel0ijvzrzue2qdDVYkwA2RlzPYLKyhuZmpCQvicCQkfMgYq/iw9O1RCHJMRBm1WkURVSE7UarVITk4W/TTh7cTERIHOmWywymObgQ+pqtLhoSoFgJ0KdK87KhMDnieHAjFo0xmRkL3D4cD29s6U19nZWeGjbG9vi4kW5amBQEBY7WazGVtbW8jKykJdXR2ys7ORkZEhXgYkzzLBYCXJ70NWPAMlUQImHGwH8Z7wOqmtGFVaSUY/zzEyMlKsfdPS0mRRq+0NMsUZeHneXGvAk5k8DNJM0OLj44WrwutPRImbLcmvKysrMpWX58u2kepQS6IdCaFUEoWH70wYJSmVqERcXBzS09OFoEmkIy8vT9z7mHDMz8/LrA1+NyYGPDcV3VOfMz5DTIZV7hT/n8/QXqL13mSCyJKaiBPd4/PNhJJr/GkOl8slk1H5HTY2NtDR0QG/34+RkRFYLJZd8zG4qY+Pj+Nb3/oWCgoKJKlgsjc5OYn8/HwsLS3BbrcjLy8P29vbMBgMcLlcoijIzc3F5cuXRZY7MzODjo4OHD9+HJ9++qmcZ35+Ptra2nDgwAFUV1cjKysL9fX1GB0dRXR0NOrq6iRwT0xMoKioCDExMTKcTqPZmXDc1dWF+Ph4JCUl4cGDB0hMTJTpsw8fPkRCQgLcbrcoVBwOB1ZWVnD69GkEAjsTVOvq6mTOCafsnjt3Dna7Hbm5uVIx833pMtrb24vMzEwsLy8jMzMTw8PDSE1NxcrKCsbHx2GxWKSQ0ev1OHLkCDIzM1FbWyvtz83NTVFzrK6uorq6Gv39/RgcHITVahWuxOzsLNxuN1588UW8+uqrAICsrCxkZmaiqqoKpaWl0o6YnJxEKBQSA6/nnnsODx8+lMRHo9EgMzNT1msoFEJTUxOeffZZBIM7BlXJycn4+c9/jj/4gz/AqVOnhEc1OzuLlJQUfPTRR5ifn8f777+Pqakp1NfXY3NzEw6HAzdu3MDnn3+O7e1tvPnmmwgL2xne1t7ejvn5eZSVlcFut6OwsHAXJ8hgMKCsrGzXWuMY+6mpKSwsLKCyshIlJSWorq6W2TIA0NvbK1Nxm5qaAAANDQ3Y3NzEvXv38Ed/9Eew2WxISUmR9m9aWprseWazGTU1NTKQraioCMvLyzIvh8Td2dlZtLe34zd+4zdw69YtTE1NiYCgpaVFijwqYzi9V6/Xw2QywePxICEhAY2NjRgbGxMTs9XVVcTHx2NlZQX3799HWloaIiIiUFJSIm3I7e1tXLx4EQ0NDbDZbNDpdDJqJDc3V2Yd/brjX4V07N0kuZnxd9W7gxun2m/euyGqCYAqr917MACxElOrN8LClOWpbQ1W5DqdDltbW1hcXNxVbfK9GWQBCLStqmm42bPfTyifAYBBJCIiQhQaweDOTBW2A2j5q9FoxHiGUs+EhARJ5NjrpCSTydfW1pZsTGazGUajUXgCRAF4PdQ5MORiEIkBnqiQ2NrhtQWekBmJFGk0GmkFMfhxmicA6RPHxcWJMQ4lcj6fT64VOSbkmVA9QoKr2sZSry3/j0qf6OhomEwmSR6Wl5eldRQIBMRuXq/XQ6fTyVA+JhK8v4RXeU5sMWVnZyM3N1cks0xeaAnP62YwGGA0GoVfwiSFiE1ERISQJ5lEUubMZ4B+KXtRCq4/lVuh8p0I+fJZUdeh+vwQyVPbaHxe+TzyfPg8sK31tJyOkZERlJWViVuiTqcTB8mJiQmcOHEC165dw/j4uLQCmJBkZGTge9/7niTk29vbwrOpq6tDc3OzIFO08z5x4gSKi4vhdrthMpnEWnpxcRGPHj2C1WoVW3SSfem7kJubC4/Hg46ODmxvb6O3txdlZWX46le/ioWFBXg8HvT29qK4uFgqYIPBgEAggImJCYyMjMg+QhdJrVYr8t2SkhLMzc0JIU+j0aCyshLR0dFwu91yv0ZGRlBRUYHbt29jaWlJpO30ZAgPD0dDQ4PwNhwOB/Lz84WUGgqFMDo6CpvNhqmpKZkAzbXp9/slQbDb7VheXkZGRoacl9lsFh7W0tISMjIyZBhcd3e3FG5ZWVn4+OOP4XQ6MTk5KUULzaRaW1vh9/uRk5Mj7pgRERHo7OxEVVUVdDoduru7kZaWJutwZWUFer0epaWl+Oyzz4Tnde7cOdTV1SEpKQlXrlwR80SNRoNr167h8uXLSE9Ph8FgwMmTJ4WcrNFoUFpaiueeew6BQAC5ublYWVlBfX09vvKVr8Dr9aKxsRFxcXH47LPPYDAYZKJxbW0tBgcHkZWVJY6h5D6VlJSIRPXtt9/Gu+++C5/Ph5qaGtTU1OxqK1ksFty7dw9VVVUiTU5PT8fU1JQkB1FRUcjOzhZekNPpxK1bt9DW1gafzydeIVqtVlA5cpSofGH7Y9++fWJoR9fd+vp6+P1++Hw+SUQ3NzeRn58Pp9OJ3NxcnDx5EhUVFaKiNBgMGBwcxPPPPy/FCxGgvr4+WK1WuFwuSaKdTucu8vsXPZ5q4BuZ0MATbga/3N6DG6OqQFFfx59R/48VpFqRMQjTBTAsLAyPHz/GysqKbNZ8LQNEdHQ0kpKSBLIkesEAQ8SEQYGfQUIdh3sxiJKQGBYWJn4TlLQSmQkEAlL5R0REYH19HSsrK5iZmZHgMjY2JhwHvV4vyYUacIPBoLQfZmZmBIKlRwUrA3rh8zrQl2Rrawsej0eqc543sOPLQJc+BmzKysLCwkQZoRqSxcTEwOfzwe/3y/kzgDHh4NRcIhfz8/NYXFyUlgcA+P1+aDQaFBQUwGw2S7vH5XIJERl4MhmYScHGxoY4NqpJITdmriUG+ZWVFcTExCAxMVHUR5x3MTExISqD+Ph48fYgRyQhIWFX+yshIUFaMZxU6vF4ZOqmVqsVMh/Nl3Q6HXQ6nUja/H6/kM54L0hI5bqlXFQllRI95L0lCkb4k6gTkwUmLCoapT4baiuKKCOTOCJZRCgpyaZk+3d/93fxBbcJJCQkSFW1vLyM+Ph4WTNUfdy5cwfnz5/H2toalpeXERMTg8bGRuTk5GB7exvFxcW4f/++bGTV1dXY2trCzMwMiouLMTc3h83NTWRnZ2N+fh63b9+Wvamqqgq5ublYWlrC1atXpa/f1dUlw/6ioqKQm5uL2dlZ5OXlSTurvr5eGPjBYFA4JdHR0dIn52esrq7igw8+gMvlwuuvv47p6Wnk5OSI5JoqDo/HI4hMbGwsfD4fqqqqcPPmTRw/fhyjo6NITEzEe++9h0uXLmF5eRkTExOIiopCfX09Hj9+jPDwnQGWCwsLu5L/8PBwIVzS7djlcqGyshI9PT0yv4Xre2JiAna7HZcvX0ZLS4sUZVqtFhaLRRDeuLg4XLlyBZcvX8bY2Bhee+01hIWFoa+vDxsbGzh+/LgQ13U6Hba3t9HZ2YmSkhLcvXsXoVAIZrNZeAdzc3OCNkZHR6OmpgYul0smB3NNTkxMQKfTSeF6/vx5kd0+ePAAJSUlGBoaQl1dHebm5jAwMICcnByZxFpcXIzExEThnK2urmJqagp6vR5JSUnw+/0oLCzE7OysXL+Ojg4J5KFQCJOTk7h+/Tq+9a1vobOzUyYAp6amIjY2Fm63G16vF8nJyVheXoZOp8PKygoqKysFASBvamRkRFrfH3/8sZiHzc7OYmRkBCUlJcjPz5dZK729vdjY2MDp06fhcrmg0Wjg8XhQUlICj8cDg8GAjz/+GHV1dbBYLHjw4AGqqqqQnJyM8vJyrK+v4+OPP8bo6Chefvll9PT0oKamBrOzs9DpdBgbG8PQ0BAOHTqEvr4+cVF97rnnhNeWkZGBvr4+xMbGor+/Hzk5OUhKSkJERIS0+1dWVpCfn4/m5mZUVFRIW+ob3/jGF9onntqnQ2W/c0NTCWkqRKuSZVSWPOF5vk6tttREgwtDtQRnQGC1xqqegZ8bldvtFriISQcPQn8JCQm7JIt7N2ciKnQ6paqDlSN/ht8DgHxXGg+x6qfjJc+PB4flUDbKtglVGEQZVD5LfHy8tE1I8NLpdOjp6ZEBPvHx8UJYMhgMoniIj4+H1+uVIVokPpKrwKBD23K1hcF2F3v9tCknOjI+Pi6M95SUFJlKyLWyvLyMyclJuN1uFBYWyojpubk5SWSYZPEz1OvFJIbfdXl5WRIjACIl5qas0+lEuRQTEwOj0SiyxPj4eEFAaBymGoCxzUSjOdXEjOtsdXUV0dHRGBgYEDkrN9rl5WVhxxsMBnnd3hYK8AQ14TpmQsJzJrrEhItVopr0M6CSPMyEn9eP/0bbeiYnbPupLUN+N17npzn8fj+OHz+OxsZGnD17Fjdv3kR5eTn6+vpgt9sRCATwzDPPoK+vD6mpqUKwKywsxNLSkvA1srKyMDU1hYSEBExOTmJrawsZGRkICwtDZmYmHj58KL3xM2fOyPwT8kZ4vzgTory8XPgPTU1NWFpaQmJionhcMMGanZ3FgwcPcPjwYVitVszPz8Nut2NiYgK5ubkYGRmROSSUSmdkZGB6elr4D5zOmpGRgdnZWSFK0jF3enp61+TW69evC7nVYDCIydft27fR39+P06dPC1cIAO7evYuLFy9Cq9VieHgYZrNZ+A7BYFC4HRsbG+jr60NGRgYKCwuh1+tRUFCATz75BIODg4iJicHRo0cRERGBmZkZvPbaa2hubsbq6ioOHToEm82GtLQ03L17V1orTU1NaGtrQ3l5OdbW1pCamgqfz4eEhAQEg0HU1NQgLi4OMzMz8Hg8KC8vF2SPs2VWVlaQkJCAsrIyGRPAEfbj4+OC5Lz99tvIzc3F9vY2XnjhBdjtdsTHx4tLKYuMyMhISSLX19eh0+kwMTEhrqk/+clPcOzYMYlRw8PD0vI5ePAg+vv70dTUJEl+Tk4Obt26hcrKSmndtLe3IyUlBSsrKzh58iTGx8eRm5uL9vZ2VFVV4e2338bZs2cldtGt2GazYW1tDeXl5dja2kJXVxfS09NRUVGBqqoqOBwOsVHguU5OTiIyMhKTk5MoKSmRNRgIBHDhwgWRsKalpWFgYAALCwuy7kdHR1FSUoLu7m4AwOzsLIaGhmA2mwEAJpMJCwsLiI2NRXl5OWpqanD9+vVdhXVFRYXsbaurq9JStNlsWF1dRXJyMvLy8lBUVITp6WlMTEzIPJYvcjw1p0OFnIDdky5VeFjdyNQAzf/nL5V0qsLpamBXoeb/v0yKSQk3f7Lfh4eHZS4CM1AmDvx+qvGYuuEzwWH1yeRJbQVxY1crV1UJw/6xqpwg0Wh7e2e6JRUgqmcCURxVlkzWNSsXnsfa2hoePXoEj8eDEydOYN++fWLRbLVahQcQFxeHhIQEWK1WhIWFyaAfmluxumYCCEDuGQBBb5ho0ayLMjgORCIDmhwPel/Q64BSSnqH8BfbHPxsbtbk+DCBoc+EVquV9cTKnKZAbO3wGvr9fphMJhiNRqmCaFJFojPlZHQTpdmYSvDl9SenhOhDfHy8GGKRuMakjNUpq2yia1y39EzhfeZzprZIVP6TimgwKeDv6vrluTNhVJN7XmOeOwmqakKsrtmnOZjg3bx5U4ZnHTlyBAkJCThx4oR4yExNTUGr1cLn88Hr9WJ7e2cAVmVlJcbGxgQNnJ6eRiAQgNfrxdjYGBwOBwwGAyYnJ1FUVITFxUVkZWXBYrGgoKBAlBBUGQE7wwMjIyMxODiI8fFxxMfHS0KyuLgIq9WK69evIy0tDa+++ioyMjKwvLwMm82GvLw81NXVISoqCv/pP/0nDA8P48yZM1hYWEB4eDh+/OMfIycnR6rhtbU1gbetVqvYn2s0Gnz44YewWCzCverp6ZEJuTExMeJTwuLi/PnzMkG2rq4OwA45kYgKg8ng4CCio6PFLl6j0Yg6hfNd/H4//vEf/xFra2v4jd/4DfzH//gfsbGxIUPoZmZmMDo6ivn5eaSmpmJtbQ2fffYZ0tPTkZiYiN7eXhw6dEg4Hu+//z4+/vhjvP/++zLJtLGxEXa7Hffu3UNycjLu37+PxMREvPLKKyLVZNHq9/sRFRUlE01DoRDS09NRUlKCQCAgk2nZfuD0Warwzpw5I8gY+S00rmLy5nA4cPLkSTEaa29vlxkmGRkZWF9fx+HDh5Geng4AsmYSEhLEBXVwcBBJSUkYGRmByWTCT3/6U/E3MRqNuHr1Kv7wD/9Qnq/e3l5kZ2fDYDCIr8fk5KS4xjqdTiQkJODu3bu4c+cOwsPDcfjwYayuruLu3bsoLCyUpLavrw/b29vIzs6GXq8Xh2vOf6qurpYWfjAYRH19vUz+PXfuHJxOJ0pKSmSgX25uLiwWC2w2Gz7//HP09vaisrISPp8PJpMJm5ubaGhogNvtRlRUFKxWq8w7cjgccDgc0Ol0+MUvfoHGxkZsbGzgwIED0Gg0u6wx/m/HU7VXKioqfmU7hNXW3spURQzU16i/81ADOBclkxi6FiYlJUGr1eL27dvw+/3i8UBUhMFAhdXZGoiJiUFqaqr4N7DSVfknrEZJolTHzfP/SGyjiZeq7ODmTcdJuhuGhYVhYmJi1zh2JjAke7L/aTabERkZiZycHBnFzJs5OzsrzpxpaWkS0FpbW5GQkCAa9vDwcIH5GKA4XVOt5ikBJdmTCAmDHRGajY0NaaVQomswGBATEwOn0yl9aLrfUZY2NDQklu4kQwEQmHl7exs2m01Mo0hmDYVCu9ApJoVsvdB8jBwMknM5nC05ORlms1kY/1TxbG/vWJJvbGxgdnYWycnJAHbPJqEsmO0m3nsmnnNzc5LgLCwsYGFhAUajURCalZUVxMXFISUlRQimS0tLkjwyWWCiRDkiky4mt3vXkt1uF9iapGI+d6q0lagSE3S11UJGvcqbIemXsk7Vu4Ntmy8Km/JzS0pKBLEgmjU/P4+CggLk5eXBZrPhe9/7HrRaLaqrqzE9PS3zQW7evAkA4mnBYFRfXw+PxyO98GvXriE1NRUdHR3QaHasyx88eICvfe1rgg5ev35duDaFhYUoLCyE3W6XhPnAgQOYnZ3FxMQEXC4XpqenxcitqqoKZrNZ7pPVakVPTw/y8/ORk5ODu3fvyvwYj8eDsLAwtLS04I033kBzczOKi4vR39+PyspK3L59G5ubm8jJyRFSH6XEERERGBoagtfrFWIq55lYLBZYLBaZeFpQUCC+P36/H5cvX8bo6CgKCwvR39+PrKwsDA8PY25uTtoxNptN9oP+/n4AOwojl8uFmpoaLCwswG63i3S1qakJERERmJyclLH3b775JuLi4pCUlCROrZOTk7h06ZLsryRKU+a/tLSE7Oxs4ZhERETg3r17+MpXvoKpqSlkZmZibm4OP/zhDxEVFYWUlBRBonJzc4WD5vP5sLa2hj/5kz/BW2+9BaPRiH379mF9fR1vvfUWjh49Ki1xti7Ky8vFMZP8DcalyMhIjI+Pw2QyyZ43NTWF6elpVFVV4c0334TVakVqaqo8txsbG4KU0UWZfLpHjx4JJ2V8fFzQEJ/PB6vVisePH2NmZgbp6emSKBqNRgSDQbz99tv4nd/5HSQlJaGnp0f2mvj4eEREREhxeuPGDVEn0W8oPT0dP/rRj2CxWHD8+HFcv34dhYWF0vbmtYyKisL777+PQ4cOieEc34NJf2ZmJrxeLz7++GNJSEian5+fR25uLgKBHdt9jigIBAL4/PPPkZmZib6+PthsNnz3u9/F/Pz8r90jntqnQ219cAMjJEvyGf+dqADJlaq8VX0/8j+YoKhqFeDJcC5u9nT7A/B/JA1c9FQXWK1WkUSy+uFio2yMr1VJrmrrRP2uPFe+TlUH8Nw0Go0ErujoaHEFJe/CarUKSkAZ69ramkg6Aezyy+B7UipKuD0sLAxOpxNJSUmwWCxYWlrCxMQEOjo6cOXKFfzkJz/B1atX0dTUhGAwKAY7Y2NjQhgzGo3Sp+R1JeGVvhKqeylbP0xEKCNNSEjA0NAQ/H4/zp8/L9NWL126BGBHAXDt2jU8fvwYo6OjEli5afHaRUZGiokXgz2vI68LX8tEjb4klOUtLy9jbm5OJgwz0QqFQlhYWJAkw+VyidqFduz0JCEawvkyZLLzYWQSkZ2dLYqjUCiEwsJCGfZE0i6fFSZSRDuIaIVCIZG1bm9vyzOkJvQ0GFKvExMMzjpRkwmOHed6Vlub5HIQpeSa5v3mvQfwr0I6xsbG4Ha7UVFRISx6Wnq3trbiu9/9Ls6ePYvKykpBHX76059ifX0do6Oj0m4cGhqCxWJBMBhEQ0MDJiYm0NTUhOvXr4vbJMekNzU1iTkXZc5WqxWJiYmilGhoaJCEdGNjA21tbRgdHYVWq0V5eTleeeUVZGVliXy3vr4eKSkpgii+/PLL2N7eRl9fH6anp9HT04N79+4hMTEROp0OL7zwAqampmAymRAZGYkDBw7g9u3bMJlMKC0tFa4HkThC0jExMSJrZMuILYvm5mYYDAapyhsaGhAKhcS8Kzk5GWNjY9i3bx9GR0extbWFvLw8jI6OiitrTU0N3nnnHVitVnzjG99AU1MTXC6XPI/t7e34z//5P2N8fFxGyxcVFaGhoQF//dd/jX379iEyMhIdHR1YXV2FyWQSO/WFhQUZ82C1WtHS0gKfzyfKPbaXzWYzzp07J4ZvdNB8/fXXYTAY8OUvf1muwZ07d3D//n1UVFQgJSVF2gX0JikqKsJPfvITHD16FBsbG0hMTERZWRna2trgcDiQmpoqE3AZyJOSkkTdUllZiaNHj8Jut8NutyMjI0P4Rq+88oqs/aamJqSmpmJ6ehrDw8OClK2treHBgwdwOBwoLCxEWloabt68iVAohOXlZZm2SyUl9xf6pXR0dCAUCuGP//iPZX+pq6tDfX29tGcZX6hmSUlJkcGUIyMj+Ju/+RuYTCZ85StfwejoKJKSkpCamoqwsJ0xHElJSRgaGsLg4CAyMjKwvb2Njo4OiXlc9z6fDy6XCykpKXjuueeQmpqKw4cP4969e+ju7pYZNrT/v379Ora3dwapnjx5EoFAQH7/f+JIqrLoeahseAZr9VBbBmrLRCVO8ucIITNBIaeD76nyLNTERUVIVGia0B0JoTqdDrGxscLG5vdgNciKkaoTBmDK7NRNfC95Vm2psB/GBcc2AlnuRqNRMmhVFuxwOCRgqmPkiYgwsaPHiN/vh9vtBrDDp7h165b0qOnGGhsbi9u3b6O1tVVmURiNRiwuLso8A35HJhRhYWFCeNze3hYrYQZN/j9RnMjISAwMDAiMmpSUhJmZGYSFheHevXsiG01KSkJ1dTVcLhcGBwd3ubMyi+d9p9qIrRk1eSDqxFYM1+DGxobMtWHCGBkZKWS+8PCd4UtkpnNaLFtvnIvi9/tFgbK9/WQaL91VTSaTKKVWV1eRkpKC9PR0REVFyTwcfh5hZNWSnesoENhxySVaRgREVaZwjfI6qIiDSizmGiYXRyWNEnFRCwASb3lO/E4qd4Tr+WmPoqIi4VPo9XpUVVUhOzsbFosFZrMZFotFkrRnnnkG+/btQ11dHdra2uS6Ly8vy7wV2oovLS0hJydHYHkS9U6cOIH19XXMzMyIv47D4UBWVpZY+JM4zGKjpaUF4+PjWFpagsFgkJ+1WCzynI2NjYmrZVhYGB48eACXywWj0YjTp08jPDwcX/rSl2T9bm5uYmFhARaLBXfv3hUeVyAQQHt7O+x2O8xmM9LT0wUtLS0tleBqtVqxsbGBkpISHDt2DLGxsRgeHhbr/MnJScTFxQm6QpfJl156SSbnJicnY3p6WtoEp06dgkajQW1tLeLj4/GDH/wAd+/eRUdHBwwGg9htm81mfO9738O1a9ewsrIinI+UlBSYTCbExcUJR4SDK6kYa2xsRCgU2tUCYjvY5XIhLCxMxt3/+Z//OYaHh2G1WnH69Gl0dXUhPDwc/f39Mh04LCwMr732Gt555x2RiT58+FBknV/72tdw6NAhWCwWFBYWSiJYU1OD/Px8NDY24ty5c9je3kZhYSECgQCam5sRHx+PCxcuwOPxwOFwICcnR/g8er0e3d3dmJqagsFgwOLiImpqajA4OIjDhw/D5XKhra1NZpckJCSgpaUFPT09wh/c3NxET08PFhcX8fWvf1329KKiIvGPqaqqwqFDh4RfVlJSguHhYWg0GpljZTabhZ/R2dkJp9OJzs5OiXsjIyOoqalBVVUVPv74YywtLSElJQVJSUkIBncs32mfvrW1hfT0dOh0OuTk5MjclaysLOzbtw+xsbEYHx/HyMgIxsfHERsbi6GhIRw4cADZ2dnY3t7GwYMH8dprr2Fubg4ajQZra2t48803RfUyPj6+a5bZrzueKunYy7dgYP5V6hUmDGqioSYUlD3u9fmg10Z8fLy0AfbKcRjo+P575bXBYFB62Xzw1Y2XvAAGOJ4LkxomCQzuJDgyKDDwMyFgcON5q8iN6kLJ82AAoQEWA5EKb09PT+9CkdhGILJDrsPW1hYWFhbQ398Pk8mEc+fOAYBsVAcOHJBWwdGjR6UtQTMrBnJKQtVpvKy8VZkwZYyxsbHQ6/VIS0vD2toaTCYTTpw4IXbnnLyqjrG3WCwoLS1FRkYG5ubmRHIbCoXEop3KoLCwJ8PHqEYhqkH4n5UE7zdbYrw3RJN4H1npLy4uyoZJCStha/ZGuQ7Dw3cG/HHtkB9D7grRk9nZWWi1Wni9XkkC+D0iInaGEDKR5HplAgc8SUaYnJBAyvXMNbYX2SNCohK8KSPma/k9ePDZ4zPJc+J704/mX3vQDbK3t1dY8C6XC6FQCI8fP0ZpaakkB5999plYVNNwjVbPqvNiTU0NCgsLxamyuroabrcbra2tmJycFN4CLbGfe+45TE5Oorm5GeXl5XA6nThz5gxu3rwpTqj19fUYGRlBYmIi1tbW4PV60dbWhomJCXg8HjidTtmgTSYTvF4v8vLyEBcXh1u3bsFqtcLhcACAKG9aW1ul137r1i0cO3YMMTExMt+FslSbzYbx8XF8+9vfloFoVIgx4dfpdCgqKsLk5CTefvttTE9PIzExERrNjtze7/fjwYMH+OCDD6TdwinTKSkp6OzsxOjoKOx2O0ZGRuD1etHf34+ysjK8+eabePz4MaamptDV1YWTJ09K6/CNN97Ayy+/LPM62PKijN/j8aCyslISx/T0dPF/WFhYwLFjx7C8vIz09HRRdh06dAg1NTV4+eWXcfHiRVRUVGB5eVkkq11dXXj8+LGYUNHrhvyoAwcOwG63y/cjctHb24va2lpERkaira0NqampsoZramqwuroKg8EgBN7bt28L3yMrKwterxfZ2dlYWFhATk4OfD6fSJ/ZriUHiqM2iCIxSeLsm/7+frEwuHXrFrKysmA2m7G8vIxHjx4hOTkZ169fF9SytrYWMTExOHnyJK5evSoot9rG5f7D2VYLCwswGAyIjo4WZQ4lyG+//TYSExMF6bFYLDKlfGZmBnNzcxgaGpKRDxxsePLkSeHbLC8vo7+/H7m5uXjmmWcwPDyM8PBwvPvuu4JAulwu1NXVSfcgLy8PCwsLX3h/eGqfjr0tB1XaygRBRSqAJ0ZNDL7cKFXVBzdStlHIEVB7cXurOtXrQA0qvNDcbPmzVDkEg0EJTpQ+8vPCw8MF3mdQ58RPBgKVCAVAAh8TAaIirEwp401ISBBi2/LysrQO+F6E51glABACJhOfqKgomS5Koubk5CS2t7fFkVFt2fzzP/+zBGWz2Yxnn31Wxj+zZ0xJFK8F/8wgx54/75XVasWpU6ckiTl06BDq6urEKE2r1eLw4cMydCo8PBxpaWmorq4WB8cDBw5Aq9XusqanbwlbJ3yd0WiURIOJTygUEpSI95jXiwgKbaxZ0dNbhKZdtDrnZ9FoR6PRiD8ClUFERrh+Njc3kZWVBY1GIzJcj8ezi2PBuQXctMLDwyXRBp54kbCVxCRaJY2qa5kIHL+DSgpVW5OqmozqK7Zw+Hom5Tz4PPJzVOTjaY+enh5ERETAZrNJ64n96ZKSkl1JEJPYwcFB+P1+lJaWwu12w263y0yPqqoqDA0NiQlSUVERHA4HXnjhBZw8eVIqSQ4DtFgsAnFfvnxZBu9tbm6iuroaR44cwTPPPAMAuHDhAkwmk5h2VVRUyPtTzru1tTO07vDhw5iYmMDq6ipOnz4t0zUHBgZw//596PV6ZGVlCWr4+uuvo6urCwMDAwgLC0NpaSlGRkbE5VGr1eIP//APUVFRgdLSUlgsFimSOASup6cHMzMz2LdvH5aWlsRg7PTp02hsbMQ3vvENmYfi8/mQnp4ussfZ2VlJBGJiYkQ6npeXh08++UQcf202G7q7u3H06FGp5n0+n5CjU1NTUVJSgqysLOGSra+v486dO/IMnjp1CjMzM7BYLBKwbTYb/u2//bcoKSlBQ0ODSE2PHDkiRF0S3zUajShiDh48KKqQF154AdPT05iensa+ffvgdDoxPz8vw+P0ej3a29vx+uuv4/jx47Db7VhYWIBWq0V3dzfa2tp2Ke+io6Ph8XhEYlxQUID19XVJcgHAbrfjwYMH4ilCxRvndeXn56OlpUVMF6enp2UwXFpaGmZmZtDV1YX//t//O44dOwaHw4Hjx48jPj4e1dXVePPNNzE7OysCAgCora1FT08PBgYG4PP5UFlZifX1dRw9ehTl5eU4dOgQUlJSYLFYUFFRIUkwjfUKCgowPz+PH//4x7h16xZOnTqFlpYWmEwmbG1tYW5uDqWlpbh06ZIkWy0tLdjc3MSdO3dQXFyMgoICBINBaeVMTU0J0f6NN94QrgfbhFNTU8jJyUEwGERubi6Kioq+0P7w1EkHgF1JhkpgU5MSKg64sagyWpVcqr6Ow8IYuBis1YOJBTdUFSUh9A5AAhqDaHx8vGygv6q65IbNhCIQCAhpk9W/iorw3NWWBCt0cjWioqKEK8CJmSToESZVDyZPDJwjIyO7JI401yKawACiJj9bW1viehcevjNbhMGbh06nE5ks+Ru8Tio6pZ4bEyHKWdmyoFxwYmICXq8X4+Pj8Hg8yMzMxJkzZ3Dy5EkcPXoUx44dg9VqxdLSEtLT00VlFBcXJyhAbm4urFarBFomWOp9i4x8Mt+F1T09QTgddnV1FdPT07s4MGtra2htbRUEgr1oIj+BQEAkyn6/X0y+SDre3NzE+Pg45ufnRcVDTxCiNEx+iJQlJCSIAsZgMAjUT/kyz4kya7aNVGWU+uyoyT6fAd4zoh5MTpnM8nVEiJikqK9VW4Zq+0dFZZ7m4OwMcodefPFFpKSkwOl0ShuspaUFbrcb+fn52NjYQGpqKpxOp0x9DQ8Ph9vtxurqKn72s59J4hkVFYU7d+5ge3sbw8PDWFtbw/r6uhDNOzo6MDIygnfffRdra2uYnJyUdkZsbCwcDoeMAbfb7ZienkZ3dzcqKyvlmsTGxsJsNiMrK0sUUCkpKYiNjUVGRgYmJiZw+/ZtdHZ2wuFwwGazSUX78ssvC4H9+vXrKCsrQ1ZWFvR6PSYnJwXloUPv3bt3RfG0sLCA5ORkrK+vY25uTtQnWVlZYtNPQu7k5CSOHTsGu92OO3fuQKfT4eHDh/j888+h0WhgMBiQl5cnBc2JEydw69YtMb5LTU3FsWPH8Kd/+qdIT08XVc/zzz8v9v+5ubnSWgUgFgCUDH/9618X/hIAmUI9MDCA1157DVNTU2hpaUFCQgJqamrQ19cn9t+fffaZQP9WqxVf/epXUVxcjK9//euor68X1Y/f78fFixdhNpvx4YcfYmtrC7W1tdBoNBgYGBDy9o9+9CNMTExIxd3R0YHY2Fjk5ORIoUsHzaysLDx8+BB+vx9XrlxBR0cHPv30Uxw4cAB37tyRQXOHDx/Gj370I3z00UdIT09HcXGxmDIyGc3KykJiYiLy8/Ph9/tx8+ZNkeRXV1fj8ePHyMrKgk6nQ0pKCgYGBvClL30JS0tLMrfF5XJhaWkJZ86cQW5uLiIjI1FcXCzttenpafzVX/0VXC4XVldXMTg4KElXd3c3Njc3cfXqVfz+7/8+rFYrLly4gMXFRZSWlooq7/Tp0/B6vWhoaMAPfvADbGxsQK/XC3+E7RuLxSIkWrPZjLm5OWxsbODtt9/GgwcPYLfbodVqkZKSgkOHDuE73/kOOjs7xQ7hixxPLZkF/k9n0v9b9aUOO1J/Xt1QVYkmAIG0+BpWvurBhIObNatTlXzK15MIxiAF7K70+DomIyrxjtC6en78DEL3fF/+O/vxhPxJYOXo7o2NDaSkpIhXPYMqEws6a1LKyuo4KipKhnz5/X6RwMbGxgqMOzs7i7KyMjz//PPiqHn27FmcP38e09PTu2YyqEOteF1UF1dVzkp0iBsikSCdTofMzEyxxSXBdHZ2FomJiTh48CD2798Pm80mjp7t7e0yVyMpKQlms1l68rwW9MYgd4LrgfAxUSgy44koaDQamQrpdruxsrICj8cj7Y+UlBQYjUYZ+cxpv3Nzc4iOjhbej9frxerqqsybIPeCAT0YDIpSRafTYXV1FUtLS7sk3kScuHapSCHpMxgMwu/3/x+Ea75WbcNwfajIH9tGTFSItrFdwmdARRHZ7uMzEwgE5Of5uUyeVVXMFz34vcLDw6UiovOj1WpFeno6vF4vYmJixHJ8e3sb9+7dE0IypwZnZWUhLy8Phw4dwvLyMpaWlqDVarF//36ZfBkMBuHxeGC329HR0SHOjHa7HVVVVUhNTRVpLVUVVqtVmPsWiwVVVVWYnJxEYmIi5ubmJOHifImJiQkhaZNUTX5WfHw8ZmZm5Nq99957CAvbMdJKTEzE3bt3ZZ4PFWnAE2JhdnY2Pv/8cykEaCdut9sRFRWFixcvwmKxyFCx7e1tDA4OoqWlBR6PB48ePYLL5ZIWyNGjR7G5uYn9+/eLuofIZU5ODg4fPoy5uTnMz8/DYDCIcd2BAwdQVVWFixcv4kc/+hHCw8MxPT2NhIQEnDt3TnxV3G43xsbGxONkYGAAdrsdXV1dwhGYnZ3FlStXkJmZCYvFgpycHFRXV+M3f/M3kZmZKTNSCgsLsbm5KWPm6QpKnlpnZyfS0tIwNTWF1dVVaausrKzgzJkzCAQCMjXXaDSKmpHFyvT0NA4cOIC1tTVYrVYsLy+js7MTH330kdh3x8bG4uHDhzAYDOLt0djYiIyMDCwsLOD06dNITk7GjRs3cPPmTQwPD8PpdCIiIgKZmZnY2NhAU1OTEGlZoKSmpoqtP4nnTBSuXr0qBOn5+XkpwCjTD4VC6O7uFqTKYDCI8SKfU7bPOJ22trZWeETkv0RERMDn86G4uFhauykpKSgrKxNPmPn5eRw6dAj79++H1+tFd3e3jMxgITk4OIiDBw+KFJ5jPfr7+/HGG29IyzMjI+ML7RFPTSQFdqMTTB72EkpVNGGv2ZHKCVE9A1hVk5TCDZYbKgCpfFVZrVqVcwOmSoBVLA2u9vbE1YCvSjaZxNB8i4kSuQasLJnQqNeILQBC9AaDQRQzGo1GNNCUbPIamc1mREdHS+Cn3wU9KmjwxOtH8lpycjISExORlpYmfcfS0lKcOHECzz77rLjGcSBRWVkZMjMzYbVaRXartpPm5uawtLQk501YXx3wxraEz+fD/Pw89Ho9wsPDsbS0JHMK+ODPzs6ir68PTU1NGBkZgcFgkF4uN1uj0Siw7NLSkgQ/BlaS2XgNGNxVBQjX0fr6uiAclMu6XC5R+eTk5CAnJ0dkrmtra1hdXcXy8jIsFousEZ/PJ1Xr6uoqVlZW4HA4diUBHo9Hvg/PmeiZVquFXq8XdIOvod8IeSdsHQJPfG9UlEFFJtTWEyXD6rNDnobKBwIgbRbeYyJq0dHRIpNmKwrYzdt6moObeCgUQmtrK8xmM0pKSvD48WMEg0GxKi8qKpKBeW63W0jbDGZMLicmJpCUlCScAafTibGxMQA7yXpOTo4k6C+99BIOHTqEgYEB7N+/H48ePUJYWBhqa2vFbj03NxeDg4PIzs6W6z08PCxKhvHxcSwsLCAYDGJubg6vvPIKkpOTBUEbHh7G/Pw86urq4HK5EAwG8dxzzyE5OVm8YYLBoPg+1NfXC3GZKItOp8Pa2hq6urqQkJAg1e3FixcRGRmJgoICeDwedHd3Y3JyEpcvX0ZKSoo8OwcPHsSLL76ItbU1zM7OIi0tTVRbXV1d2NjYkKQ5IiICVVVVMhU0OTlZ/BuOHj2K06dP48KFC9BoNPiDP/gDzM7OSmunoKBA9uKMjAxkZmbiwIEDQuZ1uVwYHx9HamqqPHNsMZSWloqb8fj4OBYXFzE9PQ2NRoOTJ09ibW0NN27cEMkylXi3b99GY2MjWlpasLi4iBs3biA1NRWFhYXwer0yysFiscDhcEigfv7557GysoLXX38dSUlJ0Ol0mJmZwY0bNzA/P4+oqCiUlZXB4XDg1KlTUnz19vZi//794kIdDAZx5swZZGZm4jd/8zdlOBrlqNHR0Whvbxd+RWpqKjo7O6HT6TA/P4/R0VEh8q+uruLv/u7vpEUzPz+PyMhIVFVVYXR0FEePHkV6erokcVR/Li0tITIyEqWlpdJ+zMjIQG5uLhwOB06cOIHOzk7ExcVhfn4ebrdb5hhRrRUdHY3p6WkUFBTg3r17Imuenp4WRGpxcVGm4PJ+Z2VlYW5uDvv27UNlZSXKy8vR09ODqakpcZlNTk7G7du3sbq6Cr/fj3PnziE+Pl7G3P+641+FdDBzZouBlTj/zk2bQYEBe9cHh/2f8yEY5FQERP0ZNcDzdxXZ4HehGyE3dv6/2n5RWxZ8P1VhoHIF+L57/6y2m1S5otpGUpUUZMmzOiPpkMEvISFBvuPCwoIsUgDSF6dW3mazYWlpCUlJScjLy8Pk5CS6u7uxuLgIp9MpfeStrS24XC4MDQ1henpaNOnATmVG1IDQNcmsGo1G5pgw+BBapQEPk8JQKCSzDzIzM1FQUCDwp9vthsfjgVarRXFxsSAilC4SUSGMnJ2djbS0NHE0JcKzvb0tBFDeNwZVVr2UHtKFNi4uDsPDw3A4HIiIiJDqjxbqer1ekgyiKbyHNApjQsMHLBjc8d3gvByXy4W1tTUxAoqLixMYGsCuZ4Kb2uLioqAe/Dyu9b3qLiYSe58Lrk8SfIlskC/F91VboOowQ74WeIK08bX8eXJOnubIzc3Fa6+9Jhuf0WjE0tIScnNzsbGxIVUrr0NPT4+gjiR0ajQauZ/b29tCEtVoNBgcHITX6xU3UPoxXLhwQXgSJ0+exNjYmNjRU/6Xnp4uBlfT09NIS0sTqeShQ4dw+vRp1NXVISwsDPn5+cjMzMT3v/99kc2aTCbk5eVJRb+4uCheDZRUHjlyBNvb2zh37pygnWNjY2hoaBDfEQ5kPHfuHKampnDgwAFERkais7NTPkuj0YiL6Pe+9z3cvHkThYWFOHXqFKKjo9HX14epqSkJLjzy8vKEL2U0GkUVwRbPnTt38P3vfx8RERF4/PgxRkZGxGXyn/7pn9DV1SUeHCMjI6isrJS9aHV1VYbDAcD9+/eFbDo5OYmHDx+iqalJXEnX1tYwOjoqvCWj0YjR0VE4nU7U1tYiISEBFRUVIntVZdynTp1CVFQUnn/+eSQlJeGXv/wlDAYD8vPzxTTOYrGgvLxchivSvt3v9yMpKQlWq1X8MiYnJ7G5uYmysjJERkaKtJbrqL29XYoqGns9evRIkt+TJ0/ipZdeQmRkJKqrq5GSkiIqNxbgWq1WHE55X3U6HQYGBhAVFYWkpCRJcNfW1uQeHjx4EDabTZIYo9Eo68FoNOL69euIj49HY2MjJicn8emnn8qwuFOnTsHtdkOr1SI9PR25ubmYnp6Gw+FATU0NOjo6sLKygpaWFpSXl6OsrAzZ2dmilEpOTsbq6ir0ej0+/fRTdHV1yWDDd955B8FgEKdOnUJ1dTViYmLQ19cHl8slxOO0tDT88pe/hNfrFQrBrzv+VUiHimLwYNDdW6X9qt/VnyHpdK8Ul+8FQFoKhIRVGa7aGgAgFRwrNZJIaWnN/1eREgZ2/l1VtagESrUnr5Lw+P35vcjZ2OvBwGQtNjZWzom8EJIc1c2CVt+UOBKCJ4Kwvr4Oo9Eo5B62A5xOJ+x2u5DJfP8yRZbSUyaBLpdLVC4qnKy2owAIikQiLomza2trwh2hiRX9RnJzc3Ho0CGUlJSgrKwMGRkZooLh+zPZYNCPjIwU0za2z3gf2QJgckjlj6qSIprAIMqplIQpabpjNpuRnJwsdu28N1T0cFCS2r6hFFYdBBgMBgUpITJFZIY/Q0SG64HJneqrQXRQTTK4lphwcO3z5/l8cK3zvNWWDNevyn9Rnx1V2svvyueSidCvei7/b0d3dzfsdrskeuTCrKys4JVXXsHw8DDKy8sxOjqK48ePIyMjA6dPn0ZKSgru3r0LnU6HsLAwLCwsyFRQzvWJiIhAfX09oqKipEXX3t6O+Ph4jI6OCrF3fn4e2dnZSE9Px/DwMCIiIiTZDAsLw+DgIDQajYwCDw8Px/3799Hc3AyHw4Hw8HAMDAygoaEBPp8P3d3dQkyll86VK1fw8ssvY3FxETabDSUlJSgtLcXq6ipmZ2dx48YNpKSkYG5uDkVFRbBaraivrxdTvbCwMHg8HiwuLuKzzz6DVrszGTsiIgJWqxXV1dVYXl5GRUUF5ufncezYMWlLbW1tobq6Grm5uejs7ITdbkdycrLMBqH81ufzYWJiAr29vTLAjkm/y+XC6OgoPvnkE8zPz8PlcqGsrEzQ0Pfeew9TU1OIj49HX18fTCYTfvzjHyMiIkL2F1pyb2xsSMGg1WrFZr6lpQWZmZkoKipCZGQk0tLSUFFRIQHLbDZjZGQEBQUFMpeKXIGRkREcOXIEi4uLKCgowJe//GUkJiZibGwM4eHh4oKakpIiBVhycrKY7cXGxkrbJhDYGQDJ/cHj8cBoNOLdd99FSkoKHA6HOK6urq5iZGQEkZGReOGFF3D8+HEkJCRgenpalG2RkZGora2FyWQSB9HPP/8cra2tGB0dxcLCAuLi4tDQ0CDJFhGvnp4elJWVYXBwEKFQCOfPn5c2CFFZr9eLyclJ7N+/Hw8fPsT58+dlFs/FixdRU1MjpPWOjg5ERUWhtbUVH374oaAecXFxuHnzJoxGI06dOoWkpCRMT0+jra1NEjD6EK2vr+Phw4fIz8/H4cOHEQwGMT4+jhMnTiA/Px9DQ0Po6+sT6/MDBw5gZmYGFRUVgiAXFhYKAvnrjqeWzKqbETcrtiPUTVFFN9TEgq9R/50Hf17lc7BNwvdnf0x9HdsZKgrCSao8VAmr2lZhINvLGeH34+ZMXor6c/yOapDZW5nqdDqplAEgPj5etPqE+cPCwpCTkyO+Brm5udDpdCJl3drakqFgTAwSEhKQnJws/e+4uDhER0eLUx5d9ahFZ+CiFwbRFpJkeS5U7jAJYWBTybsqsQzY6S9SgbK9vQ2n0ymD7rhmQqEQYmNjER8fLy0FOgUyWFMqrXpTqOuOQZTfla0KEnd5j9mW4cwOrVaLwsJCad3RVIleICpKoNVq4XA45LqQq7G9vS3nu7i4KIkDUZhQKCTKA060JeeAhGIAu2TPTIbVJIPPlsrV4OcQeeO/AxBl1d5nT+Vk7JXe8r15L9V2DFtDfF5VrtUXOZi0bW1toby8XLgpCwsLePDggQSBzMxMNDQ0oKurS1ww09PT8dFHH8lcpKKiIsTHx8PlcomfgsfjEZLn/Pw8dDodNjc3RUGQnZ2NUCgEv9+P/v5+jI6O7prTsb6+juzsbLjdbnR0dIgjpdFoFKkqg4/JZILBYEB5ebmgeyRDxsbG4vr164iLi8Pi4iLa2tokiUhPT0cwGMS9e/fgcrlgsVikP85BgAxOdXV1iImJQW1tLVpbW1FSUgKfz4fc3FwAOwhndXU1VldXYbfbMTU1heXlZdy9exc5OTl49dVXBUXa3NxESUkJ3n33XbjdbszPz+M73/kOTpw4ga2tLezbtw9Hjx6FXq9HRUWFtP6SkpKg0ex4mwQCAVG4nDx5Eu3t7SgpKcGNGzdQWFiI+fl5WK1WcUlNSkrCO++8A71ej4GBAeGyUEUzNjaGGzduyOC5zs5OtLe3Y3p6WpJKYIfcXltbC5fLhZiYGBgMBpmO++DBA2lJR0VFwefz4ZVXXpH2M3k1RFC3t3fGLPh8PkEWiJLyeV5ZWcHt27fR1NSEjz76CIuLi5idnYXRaMTrr7+OvLw89PX1IRAIYG5uDsnJyWJvwGs0Pj6Oe/fuwWaz4ciRIzIArqmpCUNDQzhy5AimpqZw6dIlDA8PAwBefvll2Gw2JCUlIRQK4fbt21KshYWFoaOjAxMTE5iYmBCJdFdXF7RaLRobG2Wa74EDBxARESGtkGAwKGpBojPh4eHyXvv27YNOp0NlZaWs4+LiYvEd0Wq1SEpKksKvuroaDQ0N+OlPfyoo8dzcHFwuF65cuYJnnnkGTqdTeCyM01/k+FeZgzFo7SWG8lBbDdzwVD6IuvkxgKnoCDd0VVLJIETkga/bS2RlpQ7sdlOk3wN/hlU78GSsvfrZavuFAUlFN+ghwX48N24mKlS+kBi3ubm5y+WRFT2dCgGINW8gEJAWhSo/NhqNSElJQV5eHpKSkuS7MZmwWCxihsZqirwEvV6/awgb54vwPNkbZeXLxAOAtHwYTGn/zv9nG8lsNotKY3t7G16vFxMTE+JMyHtEiSkTSdUYbHNzUwzSOJdGRTr4HZhckKvD+7C6uiq8H3JmiouLkZOTI8ofTqJUh9XRs4MTermZ8d4ZjUYkJibKcCW24GZnZ6VfTTKsz+eD2+0WFELlnRA54TUm6sNEee96Vten2s5jIqZeV15PPi8qmqeiVkw4+DyrbSWVoMuW2tMexcXFIufj9NLl5WXcuHFD2kEmkwl+v18mmBJNoGJEr9cL2Zhjxe12O2ZmZvDgwQPhMNGWnIhbamoqIiMjJclzu93o6uqC0+lEKBTCvn37xOQoKysLfr9fJKLJycni1MtrmJGRAYfDscva3Gq1oqSkRKyxY2JiRLJLAyatVoszZ87AZDJhYmICiYmJaGtrE7vvgwcPorW1Fe3t7YiNjcXg4CCOHTuG//E//ocgKnFxcdjc3ERGRgasVitOnjyJ5ORkZGRkoKSkBA8ePJCE9N69e+KCe/LkSZSVlUk7ym634/Dhw5idnYXT6UR6erqgb8FgEF6vF/X19Th8+DBOnTqFwsJCIXBzHkxycrIgCMPDw9Dr9ZLwX7hwAZubm0hISEBaWhpu3bqF5uZm+Hw+1NXVycDMuLg4VFRU4Jvf/Ca8Xq8kZ48ePcIHH3yABw8ewGKxyBDMtLQ0WK1WmcESCASQlpaGI0eOoLe3F0lJSYiJiUFDQwMMBgMaGxtlbMTm5iYGBweRkJAgCRARS6pPampqAABHjhzB3Nwcjh49iosXLyI6Ohqzs7Oora3F9PQ0XnzxRczMzGBtbU0ImB6PB5OTkzI0c2BgQBxsDx8+jJiYGPT09MBgMOCtt95CX18fKioq0NTUhKtXr2JhYQFdXV1i9c4JwkajEUlJSbDZbEhNTYXFYkFubi42NzeF7F5VVYX5+XkUFhbijTfekNlEPT090Ov1sNlsOHr0KGpqamC1WhEVFSUut1SKbW3tTG1eXV0VknUoFEJubq4UYadPn0ZBQQECgQB6e3uh0WhQXFyMtLQ0/PCHP4TL5ZI4QhXNFzmeKukg6ZObIRe8il6obHluenurNpUTwoMV196KlkFVbcGoic/eFgwAgd0JHzNp4IVVoWXgCYcDgCQ2qn01WzJ7JbIMFvz+qocHM2Ju8sFgEHq9HgDEBZQPrcVikYeciQKvDyFqlUNCbgXlqyTB0nabvIOtrS3pbfK8mLyps2OYJPE9SDrktWRyyXYHkycaezFhoLqGxmEJCQnIyMgQbT/vO5MFNalgACXHhNNh916HvaReXl9W2ERmONguLS0NJSUliI+P38U9IkK3sLAgsmafzycEM641yrjZCtNoNNBqtfD7/aL7Z0JKFIrzZ8gxAHYbganyVeCJsyhROH6+ahQGPJlPxDWrKrF4H7leVcI2n6W9rU117aoJDJGnX8XF+iLH6OgoAGBqago9PT2IiorC4cOHkZaWBrPZLDJUTg4FdipAp9OJ9fV14fpMTk7CYrHgxo0b0Gh2fBy0Wi3KysoQFRUlMHRjY6NMGSX/4ODBg9Dr9Thz5gxqamoQHR2NjIwMmc9kNpsxPDyMsLAwGXkOQBwtY2NjERsbC5vNhqysLGRmZmJoaAi1tbXIzs5Gfn6+DILTaDRoa2vDkSNH4Pf7YTAYZDAZVWKVlZUim01NTUVLSwuSk5NRU1MDm82GQ4cOobW1FQcPHpT5FlQKxMXFyetXVlZQUlKC8+fPo7CwEOHh4RgaGhIeQGJiIkpKSrC0tITa2lrodDqxb2dbd2hoSDgI5eXlgioEg0Fcv35d/Cm4z+Tn50vSw8nWTL4jIyNx69Yt2UNovJaRkYGYmBh0dHQgOzsbb731FoqKiuD1enH37l2kpqYiGAwiKSkJr732Gr7zne/gyJEj4nHi8XgwMjKC/v5+DA0NIS0tTdra3d3d2NrawsOHD3H9+nXk5eXBbrdL6zssLAxlZWWw2+3Iz88XzhcTs/fee08UR/RNysnJEUvz3t5eGAwGVFRUwOl0YmtrC52dnbLGlpeX0dHRIXHis88+w8rKCmZnZzE2Nob19XXs378fdrsdbrdbyOVNTU24efOmJBIDAwPy7NL2gMnb9vaOSaLb7cb09DS8Xi8iIyPhcrnQ0dGBu3fvCmfDaDQC2BkE2NbWhkePHmF8fBzATot5eHgY6enpMgnZYDCgqKhIirikpCQxTKNrLg3baAuQmZkJp9OJkZERNDY2wuFwiK0An5P/J5wOVmbAk+qLF0091LaKaoakBnRuaHvVLupnMbCrss1fxaYn/4IbuZoEAU9MzdRNl4iHisowuWEVqiZQrPZJIlUtwdmGUKF9fr46mI6VBb8zeRE0r2IbhWZdnCzIAMcgQg8QWpozmWDrgtJQGvVQhUM1B9sErMSZuPA8aZHN66MGQiZKrMzJJmeCGBcXB7PZLDNSmETSPlyv14s0kteZbqMcVhcfHy9GNGrixqDI+0lEgwkJAFlbWq0WcXFxqK+vR1lZGZKSkgTpIK+Gve21tTXEx8djcXFRrgW9S4i0ER2jyRWrd74PvQ8yMzPFrZSJn8pPoeKJHCBuLry/aiKuJhZ8ntiW4XmqiTkTaZX3xGeVz6VK+Gayw+9GpIMto38NkbSqqgphYWHIy8vD8vIyxsbGsLKygtbWVgSDQZlGSni6rKxMOBlhYWEoLCzE+Pg4kpKSUFNTg4aGBlRXV0syYTabkZubi6mpKQlEXEv79+/H1atX0dnZiba2NszPz2NychLR0dFwuVzo6upCQ0ODwPBWqxWDg4MoKSnB9va23Lv6+npxdQ0LCxMjLQA4ffo0Ojs7cePGDfzTP/0TvF4vSkpKUFlZicbGRpw6dQoDAwOYnZ1FT08PUlNTYbVa0dTUJJ4wfX196OvrQ319PSYnJ+H3+3Hjxg0cP34ckZGRyMjIwMzMDGJjY1FUVCQ268vLyyL/5mwRjUYjs1GGh4fx6aefoqmpCdvb2xgZGZGppC+88II832NjY9jc3MTQ0BCio6ORmpqK/Px8rKys4KOPPpKKuLGxEW63G729vVhbW8OPf/xjcaKkVJVD67hfHz9+HOXl5QiFQigvL0dGRgZCoRBeffVV/OIXv5AkXqPZsdPOyMiQz6F899KlS6iqqpLAnZmZCb1eL5yU1dVVMQ00Go3weDzC1aqrq4Pf7xeFiV6vx/z8PG7duiVeGoFAQIbqmUwmREdH48tf/jKeffZZzM/Po7i4GEtLS/joo49w+PBhLC8vo7S0FO+++y5qamrw7rvvCmn0yJEjeP3116HValFZWSmEY86vofMy1VLf+c53YLfbYbPZJAlzOByIjNyZjPzaa68hKipKZp3k5OQI1+fy5cvSTnzhhRcQEbEzFG52dhYAMDQ0hOPHjyMiIkLOMz8/H9XV1ZJkJSYmIisrS2JMd3c3WlpaUFJSghMnTqCnpwc//OEPMTg4iPj4eLz++usAIInLpUuX8MYbb+CVV17B4uKiKG22tra+MKcj4tf/yJNDRTlU5j2TApWABmBXNcdNVoV11c1RRQ6IKABPEhcAwo3Y27Lhn6kIICLAxGJzc3NXK2IvKsH35obNQKy2fhgQGUT4Pbjpq5u9Sh6knTHbEpGRkVheXpaqfX5+XsijNECKi4uT3znCmw82Kw2SGDnDgZwOcjwI5/O6k2BGMqTH4xHZK89ZlaLuhesZ5FS0QaPRCNGVo7mZeHFcNxMiogJEUWj+RkQoJiYGCQkJ0Ov1EhxVS3YeJNKqaBI5P1xz1OzTs4JtNRqthUIhTE5OiqcITcFofkZzMABSGTPxUc3amEDExcXJdWRSSnt1Ji58DVE1ohHq2ibCwGdpL/GT58x1riIb6jXa2xZhAs3EWG0Fqn/nuuLaV5GVL3p0dHSIY+Vv/dZv4cqVKzLDISoqCtevX0dJSQnu3r2L4uJiPH78GNvb20hLSxOb5ZycHDidTvh8PmRmZkKj0Yh1Podr/f7v/z46OztlMnJzczO2t7dx4sQJpKSk4M0338TIyAiKiopQWFgo3gOHDx9Gc3OzyEFZRebn54u5UXl5uRCHSaZkgPqHf/gHFBQUQK/XY3l5GQ6HAwsLC8jOzkZ8fDx+9rOfoaysTJAGuuweOHAAjx49QkTEjlOrwWCA3W5HcXExEhMT0dDQgHv37olTZzAYFPiavAKPx4P8/HzcuXMH5eXlKC8vx9zcHOrr63Hw4EF89NFHQsx2u92oqqrC8vIyQqEQBgYGEAwGUV1dLdw2q9WKUCiElZUVmM1mrKys4Otf/zpWVlaEH0MPjuLiYrz66qu4desWsrOzZfLo6dOnodPpcOvWLZw4cQIzMzMYHx/HgQMHMDIyIs/wz372M9TX18u9JFpisVjES4ey48nJSUxPTyMrKwu1tbXY2tpCW1sbvvnNb+LevXvCeVlbW0NBQYFMNI6MjMTMzAw6OjqQlZWF3t5eGSxH/lpCQoLw4yjv1Wq1mJ6eRjAYFE+U7e1t5OTk4NNPP0VFRQVaWlpQUFAg15uTYXt7ezE3NwedTofZ2Vl5XgwGA7a3t1FXVwedTof29nbodDrZh7OzsxEMBsXPiFOpGxsbZVLt1tYWrFYrsrOz0dzcjJmZGTQ0NMBms8HpdOKnP/2pTCbOzMzEqVOnhDZgs9lk2GBWVhaionYmtW9sbIh8fXFxESUlJZicnIRGoxHu1Ne//nV4vV4cOXIEH3/8sQyTu3Pnjihe1tfXRYLMxONX8SJ/1fFUSIeq0GDg2QvbqoGasDgTFG56KmKhVvBEKQgtM0FRERbVOIyHmuyQfKgqVcgfoTyVAYKQOWWwKuFO5TWEQiExZ1E5FtygWW0zILHlFB4eLiQ2v98Ph8Mh/g1MRFTmsurgCgDZ2dki/ST8yUCr1Wrl3KxWq9i6JyYmChlTJexSmaG2OFR1iJo08FB5ASRr8p7w2tGojOZNhHE5hp7VRkREhEgl+R0Y5DUajfgbMFFSk1R6ZBBdUxECDs4jVMiFz3szNjYmUy/pL0JviLW1NWxubiItLQ3h4eFISkrC1tYWVlZW4Pf7JdHkGuM15rqmaojoiE6nQ2RkJAwGg3Bn9qqpWD0TMeJ35XVRyZzqs8WElr9z/ZNzwmunonf83iTc8n6pSSXlw1y7AHYlcl+0T6sexcXFMgo+JSUFQ0NDsgfY7XZ0d3ejvr5eph9bLBY8evQIsbGxaG5uxvLyMkZGRsSVMiIiQgyO3G43AoEAbty4IT4V7PUHg0EUFhbik08+wcrKCqqrq5GdnY3GxkYAQEFBAcLDw1FTU4P+/n4kJCTg+PHj8Pv9+PnPfy5BqrOzU0Z+BwIBHD16FMnJyejo6MD+/fulhdLb2wuTyYSlpSUhstKBNjMzE8FgUCrMUCgEk8mEyspKGQ5oNpuRlJSEhoYGHDp0CJWVlRgbG8Nf//VfS0K+tLSE2NhYmbcRDAbx0ksvwWazAdhJKCcnJ3Hz5k3xlEhLS4PD4cD4+Li4hw4MDMDpdKKvr0+Kh6NHj6KgoED8HkhSpS/Q2NgYZmdn8dxzz6Gqqgp/8zd/IzNm0tPTUVhYiNu3byM5ORnnz59HV1eXWLV/9NFHKCoqQnh4ODIzM2UuztbWlsikSbqkh5BGs2PsNzw8LAkIi5fy8nJ0dHRgZmYGVqtVfEEGBgZw5coVxMfHo6GhAd3d3SgrK8Pq6ioyMjJEYRcRESFmdBUVFcIrGhsbQ0xMjCS79EgqLS1FR0cHcnJy0NHRgffffx/JyckoKioSF9TV1VVUVVUhKioKs7Oz6OrqArDTdianLTw8HGNjY0hISMDp06dx+PBhbG5uYmBgAIcPH4bRaITD4RCScSgUklEMTATa29sRCoXQ39+P8+fPo66uDiaTCVarFRaLBZcvX8alS5dkErNWq8WdO3dQX1+PnJwctLe3w+/3o7e3F06nE5WVlSgtLUVycrKMpYiOjsYzzzwjBQzl1dHR0eL0m5OTI3wZq9WK1tZWzM3NISUlZVd79tcdTz3ansfe3jKTBrX6IoltrxqFGyQDnIqI7PWFYIXJJII3g4GJJ0vuhsql4HfY666pIi4AdiVLJKrys7gRs+/Pn2dmtxeJYRXKBIqthNTUVCGdLS0t7RrBTSieI9UZ/NijpV8BAwwRlJWVFZhMpl3D8RjgGGDY+mCbg/wPIhpM8qjcUGF24AlXhqoL/huTQSZnlP2yVUCkhzJVmodRxULkg+oStiwYUHnPgsGgvD9VESoPRDWSU9UYTIZmZmbEZpibAWFqyhb7+vrkGlKuzLkUAKQlw/MBdhJoynz5DKhtsvDwcDHdUdcZAJleqwZ/VVbNZ4bXhGuBzxefHSYqvH68l2riwfdaWVmR/1NbO0yY1OeZiTbwBNF8msPlcmFyclK4HWfPnsXi4iJSUlLEBpwKodTUVMzNzYlD43PPPSfQdGtrK9577z2MjY3h7bffFjdMm80Gm80Gq9UqI99ZHf7FX/wFUlNThUTIGRJzc3O4c+cOOjs7MTU1JRXi6OgoRkZGRKo7OTkpFf7W1hYWFxcF7aiqqsLY2BguXLiA/v5+VFVVISIiQkyU8vLyYDabkZaWJnM5aHzndrths9mg1+vh9XolsPb39yMvLw+9vb0wm804duyYOIc6HA5pD9y8eVNs+z/44APk5eWJs25ZWRnKy8vR3NwMvV6Pjz/+GFVVVaiqqsLU1BQAYN++faiqqpLvt7GxgV/84hdwOBzCbzGbzbBarRL0OCm6q6sLc3Nzsq7Iw7l16xY0Gg0++eQTdHV1CXExJycHJ06cwIMHD3DkyBE4nU48//zzmJiYwOjoKO7duyd8FSLFc3NzAIDf/u3fRmJioth4m0wmhIeHY3h4WIYFpqSkyITr/Px8nD59GoFAAK+//jpMJhNcLhcAiLcLnUDPnz+P+Ph4dHR0YHp6Gm63G2fPnkV7eztu3ryJa9euSUt0ZmYGiYmJ+OEPfygoaWNjI4xGI7KysrCwsICBgQGZOWKxWKTNdeHCBUmYvV4vtre3kZeXh9bWVoyNjSErK0t8OYhODw8P4+bNm0hLS0NlZSUGBwdx5coVtLa24tGjR7DZbDh58qTMtNnY2MDY2JiQS51OJwoLC4UAbzQa0d3djYmJCSkqCwoKsLq6io8//hj9/f0i98/Pzxf0l1btLS0tsge1tLSIzcDk5CSWl5cRCARQXV0t7fG4uDikpqZ+of3hqc3BuPGpbQk1EeCmyA2WgQN4wptQq0UAgjSwAmbQYaDdS2xTSaUqE5+fxcqPmyovHjdaNbEh3M9/53uqGzfPTXWVBJ74IqjBgEEdeNIaIteB/gQ6nQ7ADomJpNKwsDBh/KtJDSsCZva8pgAk41bnkzAh4JRX9ttYMav3jlU8VTa8B1Sj8BcDOhMxnidRBt4jJorUm7NvS05KTEyMDL6jikQl6TIJIclN5fJQysWqhN+H11m1wOcxPT0tU0EdDoesm66uLoSFhWFpaUlex89kNWM0GqWdx0miRAz4HWJjY+Hz+aT9trW1tauVw/YNE2MGfI4J532IiIiQZFdtA6ryZD4v5G6oiB3XBBNLoli8prx2THD4PiqipLY8VVWWyjH5IodGo8HRo0eF5Dg8PCzcAvKaRkdHodfrpefNluPs7Cw+/PBDLC4uSnL+zDPPoKCgABsbG8jPzxcXUwaN1dVV9PX1YXh4GDqdDsePH4fD4cCrr74qfBKS9MrLyyVJ1Wq1OH78OLKyssSllsnP1tYWXn75ZRk2d+zYMdy/fx+pqanIyMhAR0cHbDYbtFot3G63rIeFhQX4fD58/PHH6OnpQXh4uHi42Gw26Xn39/djc3MT77zzDgYHB1FQUIC8vDxsbW2hu7sbhw4dQmNjoyADb7zxBnJycmA2mxEREYFnn31W0AK28JxOJ4qLi/Hw4UPU1NTgo48+EodQtnRCoZCoSo4ePYpnn30WycnJ0trkiILMzEy0tbXhxIkTotry+/0oLy/H4OAg/uiP/ghbW1tITEyUIZYJCQni0BoVFSW8rO7ubkRHR+Pq1auy5507dw5utxtHjhxBVlaWEBXPnTuHX/ziF7Ln/eIXv0BraytWVlawsLAAq9UqDqZskQCQOVY+nw9OpxNRUVFwOp0wmUwYHh5GdnY23n33XSlYqqqqEAqFUFBQgI6ODgAQ9U5hYaE8101NTSgqKkJeXh4iI3cm5S4uLsLtdiMnJwcnT57E1NQU7t69K14Z1dXV6Ovrw9DQkOxLmZmZsg8ykTObzYIwcl+Zm5uT+U4HDx7ECy+8gLq6OmRlZYnE+qtf/SomJiZgNpuxf/9+xMfH73JcXlxcxPDwMCoqKoQ4feHCBdkPo6OjkZmZiZWVFQwMDGBoaAhra2swm83wer3w+Xwwm8345je/ifPnz0Oj0cg8Fq/XK8jR2NgYHA4HcnJycOvWLQwMDAiq9+uOf5Vkln/mwYDzq0ieKiIB7JaxqgRSVrYqKqIiFns/i0kC++P8s8rMV9n3RADUZIiwPBMSqhBYMe5VtxC6Z/XJnyNSom7cfA1JU1xoHNe8vr6OpaUlBINBkYf6fD6srKxAo9FIX9vlcsnC3NramXjLYA/sBCw6mXIWC3kP6ndTEwr1O7EdsNfAjHNjWMEDEHhX3VhUySYAmYESCATg9Xql2mMfWb3Gv8pLhb1MrhWeC1shRAh4H6gyYvIQEREBnU4nCcXFixdl8Nrk5CTa29vhdrtRXV0tJDUmMJwyazKZYDQaUVhYiEAggPb2djidTkGCaMS0srIiFTKJpURgWF0QxVLbQXuJsUyq1amTanKsJvEqb0htt6l/38sL4WuJTO1t6+xt7agk1L0E7193hEIhQZTIHxofH4dGoxE0oqqqCv39/WKf7fF4kJOTg/379+PIkSOSfFJuqXJ9SEJllUw0imPjw8LCZOaIw+FAWloajh49iqSkJEk0IyMjMTg4iGvXrmFlZQVlZWXo6+tDeno63G439Ho9fvKTnyAnJwctLS147733YDab0dfXJ8jSzMwMJicnkZWVhdu3b2NmZgZ9fX1CJD9x4gSio6MxPj6OsLAw/NVf/RXKy8vx4MED1NbWYnV1FTabDcePHxfSo9/vF4LrwMCAFBp/93d/h7KyMty7dw+zs7MYHBxEeno6lpaWcPnyZfT19WFtbQ0+nw+HDh1CeHg4Ll++jN7eXvT09IgHEI3KUlNT0dzcjNHRUXHWJWGRrpNnz57F/fv34fP55NmJj4/Hb/3Wb2FgYADh4eHIzc0V/phOp5NWk1arFRUE28lzc3OoqqoSR2OdTofvfve7iImJES7BlStXZBy7RrNjl15dXY2amhpp1aytrcHtdqO/v19aEjSF6+npwdramrRqGhoakJeXJ0MEW1pa0N3dLetOp9NhcnISWq0Wv/u7vyuk2ampKQwPD2N5eRm/93u/h/b2dmi1Wly7dg1er1ds2qurq5GUlISjR49ibm4O3d3d6OvrE16N0+mUhGhhYQFZWVlCJh4aGoLZbIZOp0N+fj4ePnyIM2fOyPolEfjx48eora2F0WiEXq/HlStXYLfb0dPTA5PJJMqX7e1tmWW1vLyMoaEhmVg+Pj4uRRWTBoPBgLCwMFitViGeHj9+HBaLBXl5eejp6cHw8DA8Hg8sFgu8Xi/cbrdwDA0Gg9gspKamPhX366mRDgZVAIIwqMFXhXAZHNQeNDflvQkK+R4MJKxw+VncmIEnfBEmEXvJqHw/tYLbWzmr31/1GuEwJ36u+h3UdhGDBue18JyZtfJ6EXnhBNh9+/bJoCUyiElQnZ2dhcvlkiBJ7gYJSiqywGvKCY9UxjBw00SNyRJbM3wfBhPyEhISEkSdwbYBeS683pTZstXh9/uRkJCwq31DNInXPyxsx12S/hu0uWb1z0pQdXllcqLygvhvTGqY+PB9EhMTdyUnrMx0Oh2ionamDS8uLmJtbQ2HDx+WaZQ2mw0JCQm7vGEWFhZgMpmECGu1WkU9wWSTs2L8fr8kOuSMbG9vY21tDV6vF7GxsZIAMrjTc4Rrnut3bxuK64+/9ipT1ESdAZ68JjUJ53rXaDSCiqnIIPk6KtLB7/e0RFKj0SiwMyWlZWVlcLlcYi5kMplkWmZBQQHOnTuHx48f4/r161JZJyYmwmw2Y2trC+Pj41hbW5NrWVdXh2AwiOHhYQQCAYyMjCA7Oxtms1mqrbW1NVRWVqKzsxNXr17F5uYmWltbMTAwgPHxcbnvGo0G09PTwjvS6/W4ffs2KisrRcJOUnNlZaWoRJKSksTT45lnnoHNZoPb7UZlZSVMJhNu376NwcFBHDp0CBkZGaiursbQ0BCysrLgdrtRUVGBAwcOiC8DA7TJZMLg4CDeeOMNrK+vIzk5GXV1dZibm8PAwABMJhNmZ2fx53/+53j22WextLSExcVFnDx5ElFRUejv78fnn3+OxcVFkcROTU1hbGwMKSkp4mhKUygaQq2treHzzz/H1NQU0tPTMTExgZqaGiQmJiIsLAzJycnYv3+/KExCoRASExOh0WiQn58vM208Hg96e3vhcDhw9+5dSUqioqLQ1taG6elpjI6OIiIiAs888wyWlpbk2Y2KisKtW7dkT4qMjMTExARGRkbEbDA/Px9VVVXiOKvVatHS0iJmfF/72tcwODgo3B+Px4Oiov+Ps/cObizNrsMPATAikgRAkARzzpnswM7N7p7Qk3ak3dldbbSStZalsi1XWZZKll2WXeU/XCVZZVnaXa12NTuzszOjmenp6ZzIbuacMwIJgMhEIkGCwO8P+t75yF3/dlqvqqu7GYCH97733XvPPefcavb62N/fx4cffsj7W2dnJ86ePQuVSgWTyYTi4mJ8/PHHqKysxPnz5/Hee+/B6XTi6tWrqKurg0KhQEtLC/R6PZ49ewaDwYDW1lakpaXh1KlTbOpVVlaGpqYm9i5aXFzEzs4OvvGNb8BgMKCiogImkwmxWAy3bt1CbW0tent7kZ6eDpvNBpPJhObmZhgMBlitVhwcHPDUZoPBgJdeeondRDc3Nxn5SUlJYZSjtrYWOp2OVXUbGxtISUnB5uYmfD4fmpqaYDQaYTabcffuXdy+fRtnzpzhfYu4cKQcKioqQlFREbRaLaMi4+PjuHbtGtrb27/wHvHcnA5RYkqHiHpQ8KEACHxOzBSDN32d/tDXqL9N3wc+n/VCG6G4ORKqISIootyVkgBxExdbQIR0iDA1bba0uRPiQZU3fQZCSOg9KXCJyhexZQQc9vONRiNkMhlycnJ4CBwxyen8dnd3mZhGFTQlOqTQofMX+/8UaOi1KDEip1H6XNTWSU1N5fOmCpqq5OOeJKTICAaDR3w0aBCbqEwS35+SNEoCKTBSy4U8NCgA7u3tMYlTvCbEByGURCROhsNhJvEpFAqEw2GGbimxU6vVaGxshFKpxMLCApOcSUpL48sTiQS7jubn57M8maYrUgKs1WpZCicmxcnJh+OkaaAabdyUINGzQ+tHfDZIUii2wUQUkZKP40m1mGzQGqWvExooes+I65tQqONoJT0Lz3NEIhEUFBTgr/7qr1BQUIDNzU1WBywuLuLEiRNMmtZqtfj444/x93//91CpVDysS61WY3x8HAsLC2wuR1OUnU4n+2j4/X4olUqcOnUKEokECwsL8Hq9AAC9Xo/V1VXuuVdXV8NoNPKaSyQSKC4uhkKhwCeffIKlpSW2Sj937hy2t7fx/e9/H1lZWWhra0N7ezsmJyfZJIyk1h999BFMJhMcDgdOnjyJhYUFGAwGnDt3DlKpFIuLi1haWsLZs2e55ZmVlYX29nZMTU1hYWEBGxsbGBgYQFZWFiwWC6MONDStqKgIkUgEra2tyMrKwvLyMrq6ujA7O8voGUlCiTvj8XgYjVQqlaziCoVCsFgsiEQiOHfuHEpKSpgEe/78eXR0dAAAq25Iti+TyXi4oVarxdOnT3kKrclkQigUwunTp6HX63HixAk0NDQgEAjgo48+YlQgkUigtbUVMpkMT58+xebmJrRaLSwWC54+fcp72urqKs9ooc9XXl4Ot9vNxmQOhwN5eXlYWFhguWp2djbef/99AIe+G+S4ubKyApvNhuzsbNTW1uLXfu3XOHmzWq0YGhrC/Pw82traOHmorq5m4z9KTGOxGPu57O7u4uWXX0YkEmEyrd/vh1R66Jvy/vvvY3x8nCd5t7a2wu12Y2pqCg6HAyqVCh6PB0tLS3A6nejq6mKL/Zs3b3JB0t7eztYIKpUKpaWlWFxcxPj4OEZHR1FYWIi9vT1uf2xvb2NtbQ2Tk5M8IZb26OLiYmRlZeHUqVPQ6/UYGxvjBOLkyZPIyMjAyMgInE4nent7Gc0lAQQ5u05OTqK4uJhRObvdzjPBvsjxXEkHBT7gcwRAhGKpihYPqsLE3z1+UEIgVlaUuNBBmx9VjZRQ0GZNiAVVcKIXAXECKBmgzVk0TRIrPxHpENEbCsbUez+u9BCNwKh1QdeHYHMxYaIhbMTwJs4CoRbFxcXweDzQarVMUAwEAjwOnFozRColIiNdN5Ipkq01kREpSaPPQYRXQgQUCgVLcCkJpNem1yTUIRKJIDU1lc3AfpnEk67pzs4OIzWEXFBbhFQqdO+pDUHBlVoR4XCYzbpoZLi4TmKxGGfoOp2O+R8kJQuFQlhbW8PY2BhkMhmrW4gHpFAoeDrn4uIiE2WlUimMRiMikQhfn52dHaSmprJlvU6nYw5IMBhEVlYWK1ko0RBbcWJrkD4DJVOE4tD9IrSOCL3iz9D9E9VXdIi/Iz4vYuJNSR4ddJ9FVPCLHoQKvPnmm1hZWUEgEIBKpcLrr7+O/f19jI+PcxCbmprCN7/5Ta6619fX8U//9E98jciN8uLFizxEy2AwsIFTSkoKV/X7+/soKipCa2srhoaGoNFooNPpUFlZiVdeeYXl4r/2a7/GHAby+jhx4gSMRiPW1taQnJyMpaUluFwuNte6ffs2Hj58CKlUiq9//esIBoPo6upiMzkiUSclfe4ASs62LS0tCAQC6O/vZ1IjcOiAm5GRgampKVy6dAn19fWcSBUWFrLse3x8HLdv32biZFpaGk6cOAGFQsFtmJaWFkYeyG1ycnISarWa+S5bW1tM1q2urkZSUhLW19fx05/+lL1zzGYzlEolfD4fCgoKWDZMBRAhk16vF5WVldwOzM7OxsLCAsLhMFpaWniq9JUrV3Dp0iUolUp4PB7s7e3h0aNH7HVTVFSE1dVV1NXV8f78O7/zO7hw4QKamprg9/t5zzAajcwJo5kiZIO/tLTELbeqqiosLCwwKruxsYGuri7Y7Xa8/vrr0Ov1uHv3LrRaLaampuB0OlFRUYGJiQlEIhF+rb6+Pg7Mubm5mJ+f5zjg8XigUCjwd3/3d2yQSF5MGo0G3/nOd/DSSy+hqqoKVqsVUqmUPZQImbt//z5SUlJQVFSE4uJiHBwculB/5zvfYeRrYWEBFouFTd2IiFtTUwOpVIrq6mpkZ2fj3LlzWFlZgd/vR0VFBbq7u3HixAlMTEygoqICbrcbm5ubWFlZgcPhQDgchlQqRVNTE9bX15GcnMyuumazGXa7He3t7dBoNCgqKoLX60VKSgq3hgiRJ+R6YWEBxcXFX3iP+GdNmRVbGuL3kpI+n9tAh1jFUSIgckPEr9NrUCUnSm3pb6rI6TzonChoAJ8PlaNNVkQoSHVBFSb9nLi5i+dGHhXiJkyvSRs5cNSCnb5PXyciHxHYtFotjy1eXV1FfX091Go1ysvLudqmz65SqRCJRJjoJdqTUwJACYHonEnXj4ISIVApKSnQarUAwIuNApZIGFWpVKzooPsqDmUj6JmUJGL1TWoK0U+Ckpf09HTerAjipPsmmlJRskL3Wbyv1HoSK3tqx5DEmAyg6PpQO44c+hKJBG/c1IoS213xeJynZNbU1CA/Px8ej4dZ3mTZLRp7kWSXEriCggJGNwg1Or6GCaESlSd0b+kaikmD2MojRJASF7puYrtTTMDF74sqFvq3+DyJ6+x5jlAoBKvVirq6OgwODsJoNGJ6ehoOhwMlJSU8/v38+fO4cOECzGYzGxatrq7y/aitrYVMJkNfXx96e3shlUpZKpmSkoKzZ89ytZ6UlITJyUlIJBKUlZXhtddeg8FgQEFBAe7cuYO+vj6MjY2xK+XS0hIWFxf5GlZWViItLY3t0Gtra2E0GlmqnpOTg+vXr+P8+fP47//9v8NgMCAWi+HKlSswGAyMIPT396OqqordcDs7O3kQmUKhQGlpKQfogYEBZGdn4/XXX8e7777LzzDxiGgK7ve+9z3k5eXxZNH6+nq0tLSgpKSEp92SjHV2dhYjIyOQSCRQKpVoaGhgIjgl+FtbW5BKpaitreXhac+ePYPL5cKpU6fgcrlQXl6OzMxMbG1tIRwOY2NjA1euXGEJKBEx8/PzuZWck5PDs04o2aNheidPnkQ0GkVhYSEaGxuRnZ0NjUbDbq0OhwOVlZU804WSlpycHJ48PDo6CoVCgd3dXTQ1NSESiSA3NxfxeBwtLS2w2WzQarVQq9Xo6OhAQ0MDD+NTKpXIzMzEzMwMvF4vOjs7GbWwWCzQaDS4ePEisrOz0dHRgby8PMhkMoyMjGB//3Do5vXr17G0tASv14v6+nq0tbUx92tsbAxTU1P42te+Bp1Oh48//pjH3VPbb3BwEC0tLVhZWWHuBI2yeOONNxAMBuHz+XgI3f7+PrKysrC5uYmNjQ1uZ2u1WpjNZqysrGBlZQUejwcWi4X3RIlEgr6+PszOzqKurg7z8/Nwu908jddoNLITdXV1NXQ6HUpKSnjUQ2FhIS5cuICpqSnMzc2x42lRURHa2trYej0nJwcVFRX47LPPEAqF0NfXxzzDX3X8s4ikx9ENsRqiqk1UdIjBWEw4RN6HWIHR64vBiBCK4z4dx4mt9D16L9pQqR1CiAa1Veh3KGgBRwfUkdUvcRXEQ1TS0KYutiSoAqXFIKp2jEYjzp49ixdeeAG1tbWMHJAlMc1myc7OZsdM4gtEo9Ejbpl0HYirIfqaUCJC90OlUh0ZEkZ2zzKZjP+mXjf5bQCfW3VTq4iQCrofsdihQ6hMJuNWjthWEpEkQolITkoBm95HbKtQi4Z4KaKqiN6XeCHEWyH7cZFHEolEuKUUDAZ5UJFEImF/Da/Xy/M+iMNByZ5EImECViKRwMrKCiNDNpuNfREoCYrH4/x74po/Li2ndU5oECWptN5E5I2+RggQzaA5jgjSOiPkkf6I65/WiZhoiM8jHceLi191UHtqYmICFy5cQCAQ4A08GAyipKQE29vbGB8fR19fH6xWKzY3N1FUVITy8nIcHBzw5E+73Y6zZ8+ipqYG29vbGBoa4rk5kUgEL7zwAqxWK9LT09Hc3Iy7d+/iww8/hNlsxuPHj1FUVMR23gQR0yyTtrY2SKVSWCwWyGQyBAIBOBwONDQ04P79+wDAfKns7Gw8e/YM/f39OH/+PNLT0zEwMMDy3cLCQoavCwoKMDQ0hN7eXpYlLi0tweFw4N69e0wmLyws5PYKtUOI87G9vY1QKISioiJ2H/7d3/1dNDY2oqOjg2Hsv/7rv0ZBQQEyMjJgtVoxNTWF73znO/B6vejo6MCTJ0+g1WrR2dmJmpoaTE9P49KlSwiFQhgdHUVubi56enrYLZj2N0JHXnjhBfaimJmZQXt7O0pLSzE5OYlz584dQQf1ej0qKip42BvZawOHtvhELC0rK4PH42Fui16vRzAYRFtbG8tVNzY2GDHMyspicqnZbEZ6ejqk0sMZOzRDJSsrC3l5ecjIyMBPfvITWCwWvPPOOwCAtbU1LC8vQ6FQwGKxYGBgAA6HA52dnSgsLGQPFpfLhaamJv6b/DqofeF2u1FbWwu73Y4bN25gYWEBPT09CIfDsFqtiEajeO+99ziZIG8V4qD92Z/9GdbX1znxICfb3t5e2Gw2nD17lu3PCdkOBoOoqqrCzs4OTCYTf56CggKcOXMGL7/8MrfO19bW4Ha70d/fj56eHpjNZvh8PqSnp0Ov12N5eRn7+/u4ffs20tLSEIlE8ODBAzgcDjQ1NWFgYADLy8sYHx9HLBaDWq2GyWRCVVUV5HI5O5TevHkTS0tLUCqV2N7exm/+5m+ipKSEvUi+yPFcSQcFY7HHTiRJsR0hcjgoMIqMeaqOxd8BwBIiUpKIfAhKOsQ2xXGiG1W89FCKJl3A0YqQNn/ahAnOPx4kiUl//HOIxFkighLaAXyOClGApt8RFQl6vR4lJSXMUxCJoIRqEKGOEiQKqsfVO3TOIiqTnJzMEyRlMhkjFHT/qPVESVdqaiqPYCcINCUl5YhpmMhhoYTK6XT+AuGXWOvxeJwREroehChRokJtHLofIuIk8hAomRTXHJ0P3aekpCT2PqHXo0SAXFCTk5NRUVEBp9OJlZUVKJVK9kkRIeXMzEyeBkmyyvT0dGxvb8Pr9bL6iPwXRC8Tuj7kVkqJAF1DUV1C5y0+I/TZaE2IqAUl8NTOo9chgiqhSpQkEvpIyYbIYaJ1KaJ2IhpyPNH+VUcikcDly5eRlHQol87MzOSJx2Sklp+fD5lMxpOWMzMz4XK5uG2XnZ2N1tZWhEIhLC4uQqvVwufzsfxTr9djcXGRJebnzp1DY2MjCgoKkJWVhZqaGqSnp/Mo8rS0NHbOvXr1KgwGA3skFBcXo7m5me+dxWLByZMnWRlQWFjISpnMzExWxpSXlwMAVlZWYDabkZycjI2NDTgcDqjVasTjcSiVSthsNlRXV6O+vh6lpaXM16F7SQ6kFy9exPb2NiwWC9LT09HV1YW3334bmZmZ+Bf/4l9wsfDTn/6UHTmTkpLw6NEjmEwmXL58GS0tLcjOzua+v0ajOUJsLCsrQ2pqKre8DAYDVCoVXn75Zezu7sLlciEcDvPelJ6eDrvdjgcPHvBadLvdWFlZwfb2NlwuF9xuN9/X/v5+9tCw2WxISUnhdlFBQQFCoRA++OADmEwmLC0tITs7GwcHBzCbzbh58yZyc3ORlZXF9gKRSAQTExN4++234ff7eVYPuYqGQiHmB9E+W1FRgezsbHz7299mPxHihxmNRlRWVqKkpASBQADDw8OsUnM4HLhx4wbC4TAWFxe50ElOTobBYGB+RjQaxb/6V/+KHZ0HBgaQm5uL5ORkXLhwAcnJySgvL0dTUxPm5+fx7NkzAEBvby/vKaWlpXA6nbh37x5UKhVsNhujFsXFxTztltxkDQYDtra2WPJLfjUmkwlWqxVOp5Nb0Hq9HqOjo2hqakJLSwuSkpIwOzvLs7oIddLr9Yx+DAwM4Ld/+7e50LRYLFAoFPje9753pJB78uQJysvLkZKSgpmZmSNJcVZWFivJftXx3APfCCqm45cFWdpgKWiIg8gkks/dRkU1Cf1b9HygxQR8LlulTRX4HFWhapICo9j/p9+hIEABVAySRHIUA7tIoqOKXqz6CEoSFQD0GUS/D/qbXk9Uw+zt7TE/Qy6XQ6PRHHkt2lSJ0KhUKjmpCIVCiEajTHSizyxC4qRKoCCTkZGBaDQKj8fDFTV9ZlElIgZw+jyEQFFyQgkJJQmEsuzs7PDDkpqayi0aSmzkcjmjUFqtlpMICrJEhhUrf5EoTFV+JBJhtQgRSwnJINO0QCBwBL0icpbI8cjOzub39/v9UKlUCAaDSEpKQkFBAQwGA3Jzc/m9STqWlJQEp9PJ14MIe2QiR79HiZU4cVZMdonYKD5PIvpHyYBIfKbElZJGEUmUSCT8vImtRUri6HqKz51IRqX3EdfQ8xw7Ozt49uwZO7rScDetVgun04lnz57B7XbzBGAaqV1aWoonT56wdfbS0hKjB/39/azsWFpagsViYR5OSUkJ3n//fdy9exeVlZXQarW4ceMGlEolPvvsM3boJCXT9vY2FAoFysrKsL29jampKTx79gxFRUVwOp0c+A8ODqd2DgwMIC8vD6+88sqRSbh0vTIzM9lQ7rXXXoNOp4NGo8Hrr78Oq9XKz7XJZMLOzg70ej1mZmagUCiws7MDrVaLCxcu4Mc//jFbVodCIQwPD6OsrIzVGY2NjfB4PNwGoNEI1GIYHh4GcGjMtr9/OMNpbGyMA9ri4iLzSsg7huYwDQ0NobS0FEqlEiqVCu3t7UhNTcXt27fR2NjIpEGTyQSJRILOzk7k5eUhFotxv58GJT58+BA6nQ719fX8rL377rvIysrCf/tv/w0SiQQajQYrKyvY29uDyWTiybYulwterxeXL1/mSdSNjY24evUqt446OjpgNpuxtLSEx48fIzc3FzabDaurq/B6vSgvL0c8HofJZOLpryRxJh7X2NgYPvjgAwwPD2Nqago3b948wj+LRCKYn59HSkoKzxepr6/HxsYG/H4//uIv/oIt0ml2y+XLl+F0Orl4ffDgAXp7e9n1lDxfJiYmkEgczqUBgGvXruHSpUvY2dlBXV0dPB4Pm8W1tLTgZz/7GZukZWVlsSIlFovBYrGgoaEBzc3N6OzshMFgQHV1NWKxGCKRCFZWVlBUVMSxhhLC/v5+hMNh9Pf3Mz9nbGyM41wikeCEhhD3M2fOMLq6tbWF9PR0PHnyBPX19dyS/KL8r+fmdIgbGf0h3wfg8ySEqnCxDSK2UETlihhgKPGgCpcCI22wx+FfqtzE74keE8fbKCK7XyRJir1vQmlEl0d6XfEaiCiL+Lnoe4SCEJpAiAm1O0SFCPXUqKVjt9uxurqKg4MDBINBJpju7u7ybAYKjvQa9D6UHIjtHZEIRFWo3+/nZIXIhJRUigmmKM2kRINkpiQx3t3dhVwuR3p6OiKRCCMGFBTJFpzWg1QqRSgU4vMmgjAdhPhQcAXAsme6viIXiD4nzbcpKSlBIpHg96B7HI8fTuBNSkriwWzhcBgul4uvDxkpeb1eWK1WNvZxu93IyMhgXTqNUxeVOKROoXkutC7oelLyRokccTpowxPVR/R5yFWWElJ6TYJuRVRHdNQV0RSRI0P/F9ErkRMinrOIiHyRg9RD5C9D1vepqakoKyvDW2+9hc7OTthsNpSVleGTTz7B06dPWfXR1NSE5eVlrK2t4erVq2hoaEBNTQ3q6urgcrmYr/DCCy9ApVKx1XdnZyckEgnS09NRVVUFt9uNgoIC7Ozs4OHDh5ienkZWVhbzBhYXF5Gamoqamhqsra1he3sbJ06cgMPhQFlZGWw2G5P19vb2MD8/j/r6euh0OnaaNRgMsNlscDqdkMvlmJ2dxdLSEk6ePIne3l4mq66trSE3NxcFBQXQaDRob29HLBZjN1aC1xUKBcbHx3Hnzh1UVFRAp9Phxo0byMzMxPDwMNtnh0IhOBwOGI1G2O12RCIR9PT0IBAIcLshGAxywgsAt2/f5sBJSoPS0lLcunULr7/+OtuXk1un2WzmuRyUhBmNRqSmprKXBXlkqNVq9PT0oLOzE/v7h2MEPvnkE0xNTaG0tBR5eXlISkrCqVOnkJWVhcrKSrS1tfF+Mzo6iqmpKUilUtTU1GBxcRG9vb0oKiriqrqqqgoFBQUskya1xZMnT/Duu+/yOi0oKOC9vbu7m/16CD2jPaO5uRltbW146aWXkJKSghMnTnCCRehbb28vLl68iPT0dPh8PpbSnzhxAlNTU/y9c+fOscLQarVCrVbzkEmz2QyVSgW/34+BgQG88MILzHkbGxvD9PQ089t8Ph9fl6qqKmRnZ+P3fu/3sLq6itOnT3PS/dlnn0EqlXLiZ7fbYbfb0d3dzcjLxsYGSkpKkJR0qIxMSkpiTg4hh8nJhxOCyQq+uroaLS0tCIVCKC0t5bkw0WgUd+/eRVdXFzo6OpCbm4vV1VWEQiHcvn0b/f39cLlcX3jK7HM1bI8TQOkQEwExEIvBWUQkKKEQ2yvi5ii2LogDIPanSfJGFTNBPCIJjjZtaokQwiD2r+l8xUSBAqs4s0VEWyiZ+mXQM30OCjBiIkZBn85LKpWyZ8LxJIUWOEG2SUlJsNvtfO6UyND50zUjQyqC00UeAb0/AFYYhUIheDwedmmkhE+08wYOkzWfz8dMdXESLQU6snBXqVSIx+PMhVGpVJyUirwNkvARjCleG9os6Q/1dKl6p3uyt7fHU3hFnolSqeThecFgkK9TLBbj8c6lpaXw+Xy8VmjUNk39pYeN+qxkrkPeKTQnQSr93EmVvk4cJLqvx5E1QlzoXtA50Nqj+0Xrj9YQ/U2/T8mrKMOlpIYSGmqJiq0mIgbTz4hmbGS1Tef5vEkHTd6l829pacHq6irm5+dx9uxZyGQyXkN9fX148803YTKZ4PV60dzczFV4bW0tfvjDHyInJwc+n48HUNGeEolEIJFIOMGgoWQZGRno7+9HZmYmcnJy+O94PI6xsTGYzWa0tLSgq6sLW1tb8Pv90Ov17G1jMBhgt9uRlZWF7e1tLC4uslGfTCZjO/DCwkL09fUhOzsblZWVKCgowPb2No8eJ4Z/Xl4eiouLsbCwwO1mr9cLs9kMqVSKnJwcNk/r6upCIpHgoWw+nw9dXV1HevvT09NISUnhFgVV4SqVCpubmxgdHeXprh6Ph+H1kpISbG1twWKx4PTp03C73djY2MD169fx6P8O8iJuSnl5OSYmJrC2tsbJ6Pr6OoqLi7m1s729jcrKSmxsbOD27du8Lr1eL6amplj5s7u7i5qaGjQ2NkKhUMDlckEqlaKrqwt7e3vY2NjgdWo0GrG5uQmv14u0tDS43W4mfC4sLCAzM5NHrdNz2tXVBZ1OB61Wyw6udA+tViv0ej02NjZw8eJFDAwM4MyZM5icnMTAwADa2trY6G95eZmN6ra3t1nhEQwGodPpUFRUhJ///Oc4c+YMG7ltbm4ikUjg6dOnOHfuHLxeL0pLS1kRFwqFkJ+fj83NTSgUClRUVGB8fBx1dXXw+/1obGzk1uzY2BgODg4wOjqK0dFRZGZm4smTJ+jq6sLm5iYWFxdx6dIlBAIBXLt2jf2cXC4XxsbG0N7ejqGhIVy6dAnBYBAdHR348Y9/jC996Uvwer2IRqMoLy/H+Pg4j6WnGUSxWAyFhYWIx+PIzs5m/yX6/DSRl5Co3d1dVFRUcCvm3LlzyMnJ+cJTZp974JtYkYlyv+OIBSUEoqxUrMR/2WZGgVQkddImLL4Ovfbx36Vzo+BMJEj6Waoq6bUo0FJlSZs+cLT6I6WGCHNTEiUacFHSQP8mtIHIkETgBMBKB4lEwr0wCiZEQKQqPDU1lf0HKPmiRI+ge6qQxaSIfp6uw3FUI5FIYH19HZubm1wZRSIRHsfs8/mwsbHBkDbJIenB9Pv9jEgQX4SCvlQqRSAQ4FYLXUfgc98TkrPSuZBsk1omOp0OOp2OkzNxnZG/yN7eHkPt+/v78Hg8nMzQWiRlj8Ph4JHg4XAYRUVFzJchDkpubi4b4qhUKl4HJNGVy+VQq9VQqVQcuGn9iagMXXvih5D5Gf0c/R7dT0KgRDUMVWWi5JnkxCT5pmdQJGmLLTxKHmltUNAmboF43qQeovtFa+l5j9TUVGRmZsLr9eLdd9+FTqfDlStXsL6+juHhYfzoRz+CVCpFXV0d3n//fcRih2PlV1ZWkJmZifr6eoRCIR7BnZmZiVdffRWRSAQ5OTlob2/HwcEBioqKuI3T2NiISCSCjY0NDsD7+/tM+uzt7UVxcTHa29tx7949DA8PY3JykmXrGo0G9+/fR2VlJerr6zlpzczMhEqlQm5uLubm5vDRRx9xQMvJyYHRaERKSgoGBweRnJzMvgrd3d3w+/2Ix+OYnp5GZmYm24QHg0H2Aunv72cSp9/v5+mvT58+RX19PQBwC6eurg7l5eUoLS1FY2MjdDodqqqqYDQa8fOf/xy5ubloamo6MlkZ+Lz6T01NZRSPHDnpeUkkDr1pqFix2+3M/Th//jyAw+JlYmICWq0W0WgUjx8/RigUYgR2YGCAx7W7XC5sbW2xF4bFYsGDBw94yCMhReRAHI1GMT8/z+THWCyG4uJilJWVYW9vD9euXePJw8RbuHHjBhKJBGpra+F2u7G9vY1IJAKdToeDgwMsLy+jurqaB5plZGRwIlNcXAyz2cwkUJqGS7b8VqsVSUlJLFdeWFjA5cuXEQgEMDs7i3g8ju7ubsjlcnR2dsLn87GxWmtrK86dO4fk5GT84R/+Ibq7u1FWVga1Wo3c3FzcuXMHcrkcRqMRicShio5au8+ePWNuXUlJCU9SlkqlMJvNaG1thd1uR3FxMUwmE0ZHR1mEYLPZ2LV2dXUVDQ0NGBkZQXd3NyQSCX784x8jIyMDzc3NKCsrg0wmwze+8Q2kp6djbGwMPp8PfX19UCqVsNvtWFhYwPz8PIaHh/Ebv/EbjFqGw2F2gy0sLMTi4iI2NzePxLf/v+O5kQ4R3hZJlf+vfg5VXWKgFvkSx2FjWtwi1EwtDnEDFJEL2mBJJir+PG3U5AhK701sfhEpEFEZ0d57Z2fnCAmUzlmUdwLgDZvQC/oavQ/xWpKSkvhBB8BEUwo2kUiEg2YkEoFGowEAaDQaduYjzgQlf2RAJcohRfieHCdp5gnJvxQKBRPh6D5R8AuHw+zsl5SUhJGREXi9XshkMhQVFTEhDwD7VtA9p0FV29vbTGalqlwksorvSUkjzS0hZITWhliRkz+IRCI5Mqhud3eXnSvJRyQpKQlWq5U3SXqo9/b2MDs7i9TUVB45TRU+XSs6H1K6EB+Jzn1/f5/bHJS4iute9LURnwORl0QEV0oI6BC5LeLzR7+bkpLCVseUsIrtRBExpPcWESNKqun6ioRkkdz9vAdZn4dCIeh0OoyOjrLR3cmTJ5GTk8PV38mTJ7l1QZV8LBbD6dOn8Y//+I888n52dhbt7e1YW1vDwsICnE4n5ubmcPnyZdjtdiwuLiIlJQW5ublMhm5qamJXzGAwCIvFgrq6OlRUVMBgMPB058bGRqysrODixYtYW1uDXC7nZFgqlWJ4eBhf+cpX2M47OTkZQ0NDaGxsxOrqKgoLCyGTyeB2u5n9DwA6nY5JiOnp6djY2MDJkyfZ/bOtrY0dJ8vLy9lFdHt7G5cuXUJfXx/S09ORn5+PjY0NzMzMQKfToaCgAHNzc5BKpZienmaEwe/3w+12o76+HsPDw5BIJMjJyUFaWhoaGxuh0WjY3TU9PR2nT59Gf38/5HI59vf30dDQgPX1dfz4xz9GRUUFSktL8cMf/pDX/O7uLqxWK370ox8xMdTr9eLMmTP427/9W5SWliIUCsHlcqG3txexWAxnzpyBzWZj10yaMlxaWoqPP/4YVVVVmJyc5CF+drsdHo8HbW1tGBoagtfrRVlZGXJzc7G3tweLxcJqlDNnziAQCMDj8bAcXyqVYnJykiXXKSkpCIfDMJvNiMfjLJeura3lvdDj8cDpdCIl5XBi78OHDxEIBJCbm4uGhgbI5XIsLCxgYWEBer0eHR0d8Pl8WFtbQ2NjI1JTU+FwOJiv5nK5IJfL8aUvfQl/93d/xxb0+/v78Pl8KCoqgslkwuLiIurq6rC+vs57Cg1729nZgdvtRmlpKSNV4+PjUKvVHEvI9XRiYgKxWAy1tbVYXFyEXC5nr4/d3V1uy7z44ouIRCJwOBwsae/q6kJtbS28Xi/UajXPniEzsP39fVy7dg0zMzNsr2A0GvHZZ59Br9dzskoo+xc5nptIKrYaABzZWOkgBEFEP2iDo+/T94BfTFpEOZ+YtND7i4iKyN4XyXHA58nLcQ8P+h0x0RB77fRzVEUS/Ec8D3p9CpS0QdG5UutFrLSpahT77ZS4UPuBKjNy0ZRIJIhEIkz4ooSJNjUKrIQ2iJ+Drgl9bmpFAIcBcWlpifu1u7u7sFgssNvt2NrawubmJmw2G8PPVqsVz549w+TkJHw+H5O8COItKCjgQE3Xn9oNtD7EQEvnJlbghG4AYGksJSN0PUVuifg6tGbo9RYXFzE8PIzZ2VlMTU3h0aNH+Oijj+D3+xkGXltbQ2pqKvR6PYqLi9kGnvgT1Hqh5INQK2o9kST4OJpFHBhCN+icCW36ZRwmUXFEn41acCI3g9YRHZQQ0Wci51Qx+RQn0Io/J6JyhLiJPI5f9lx/kUOn02FiYgInT55EOBxGRUUFKioqcOLECWg0Gp510dHRgZmZGUgkEpw9exZTU1MYGBiAXC6H3W7H06dPeZDZ/v4+ysvLsbCwgMLCQh5bTtLCRCKBt956Cy6XCxsbG4hGo6ivr8f777+PqqoqJCUl8XwQUsHs7Oygq6uLv19cXIx3332XP79Wq0V9fT0KCgpQVlaGu3fv8nOyvb2Nnp4e9vZ48OAB/H4/+vv7cfnyZeaBlJeXQyKRoKCgAFtbWyyXJHJpdXU1VlZW4PP5YDKZmGvx0UcfwePxoKysDJWVlZibm+MJppmZmYhGo+js7ERbWxu7vsrlcmRlZWFra4sJlWlpadxGNJlM+MEPfsBcquHhYfzt3/4t0tLSuA21tbXFZloEl4+NjWF/fx8DAwO4d+8eGhsbuf2sVCoxMTGB5eVl3h8+/fRTfPrpp4jFYviTP/kT+Hw+rKyswGKx4LXXXsM3v/lNnuxMhR/JZ3NyctjgS6FQ4NSpUzxkzmq1IiUlBfX19Thx4gTS0tLgcDiwubnJLZzU1FRMTEwgKSkJg4ODKCgoYPt3clMtKyvj+zA8PIw7d+4gEAhgamoKP/zhD9lsjVR1TqeTUagLFy7wwELaL6xWK/b395njMjc3xy3f4eFh9PT0ICsrCzKZDJmZmSgpKeHntKurCw8ePOAZQYFAADabDSUlJUxM/9nPfoZTp07xSABKJCWSw/ESU1NTbNTm8/lw5swZ1NbWYm9vD8+ePYPX62WvDbPZzLNWaKbK8PAw3G43F+szMzPsIqzX69Hc3IyHDx8yurq3tweDwYCvf/3rPHWW1Es0gftXHc+FdIjBDAAHUeDzJIQCBylCRPhZDNbA5+0aMRgBRw2TaDOnDVFMVkiBQQkNBTq6gLSBU8vkOGdETFJEwif9n3rfosSRgqdMJuOKkX5ORF+IMEqBmBQk9Ls+n499MYioSFC6Wq2GQqGAzWbjaruwsJDvAbUiqGURjUZ5jDMFJzKVAsC+HuRzkkgkeDPzer0YGxtDcXExotEoFAoFKw729/cZZpPL5Th16hRqa2tZ5pWcnMxTQEV+AH1+SoICgQAjBTKZjJEBANx2ENcK3WuqruLxOJMy6TPQNY1EIoyIAJ/zVG7dusXIh2jGRcRW6s8XFRWxKVMoFOJ1RIiA2M6iNaJQKPh86DMfR+dETgfdL0IiCJ0SWyhia1BER+h+0joWUQ1RmULXUPT0oPen54qSnePPh4hgiq0XugbPcxAv4uHDh3jxxRexurqK2tpaWCwWFBcXY2ZmBhUVFZienua2ViAQwN7eHtrb29k8LxgMIicnh4MNzW6hpPrx48c4efIk0tLSODFOS0tDS0sL+vv7YbPZWKmWn5/P1djq6iqys7OhUCh4lo7f70dBQQG6u7sBgGeH1NbW4t1330VHRweCwSDUajUMBgMnifPz80hLS8OFCxcQCoXQ0dEBi8WCxcVFHBwc4Nq1a+jr6+NqndoWNpsNZ86cwebmJntbfPrpp2w0VlBQgFgshmAwiMHBQVZANDU14eDgcAYHTbVVKBRHlHe/+Zu/Cb/fj4WFBVbPEBcjOflwhDw9w1lZWcjPz8d7772HpqYmWK1W9qKIx+PIysrCr//6r2NwcJA5YYSM0Ch6uVyO4eFhfOc738Hf/M3fICkpCV/+8pdRW1uLra0tdHR0IC0tjUfcm0wmFBQUIB6P4+LFi+jv72fO2Pe//31cvHgRW1tbXP3H43EUFBSwLXdJSQkikQicTidqa2sRCoWwt7eHH/3oRzw7an5+HhqNBp988gkqKytZLh2LxbC+vg4AmJyc5MFxDocD8/PziMfjsFgs3B4hNG5nZwevv/46JiYmeKBaKBSCwWBgiXdGRgZyc3P5OXS5XLh06RLee+89tLa2ssvr8vIy6uvrsbe3h/HxcSYA7+7uMm+vpaUFz549g06nQ09PDz744ANup21tbaGzsxODg4OorKzkhNDhcCA/P595OADYh+SP/uiP8KUvfQlWqxVbW1uMQBcWFrLR2tbWFurq6jA9Pc0Eeb1ej9nZWbhcLmRlZTEvZGhoiEnJJSUl7GLrdDq/0B7xXEmHyHA/znYX0QoROv5lXxe/dryiEjfOg4MDbj3Q5kfBSYSqaXHS5kvJBD2MVDmL0kI6H9p8aaOlAEDnLrZbxOr8eICkcxNbLaSqoQAskUg4WKWmpsLv93MlStwVl8vFfd/q6moOcgSfUpJFqAu9HrUuKCBRJUsHBUYi8r300ks4f/48ZmZmMDIyArPZjM3NTW77JCcnsx9FZ2cnTp48CYvFAolEwiSk7OxsSKVStmCnhI1+nxIvIh+J6BJZjVMSGQqFoFKpEA6Hj5AmKSmjoEz3OyUlhYe7UYClni79Hp0DrTWqMGjMeiKR4HkJNL1XqVQiHA5zy4Ze/7gJm9jOICRNTELpehD6QUiXuIZoDUokEh5oJnKH6L6KhGlxvgGtAUoURLI2yZ+PP3OU0IuIBr0WrfPj7dPnOcLhMDQaDVwuFxYXFxGPH8oXnzx5gsrKShiNRszPz6OlpQXb29twu91sgBcOh3Hx4kXcuHEDOTk50Gg0sNvtaG5uRmFhIUZHR7G5uYmDgwM0Njay+VJ9fT2mp6eRnp6OkZER9Pb2cguNEmQy11Or1ejs7MS7776L119/nU3j7t27x73//f19lJaWwu/3M+/HZDIhKSkJDQ0N7Ch66dIlPHnyBLFYjDkBm5ubaGtrg9Vqhc1m4z2C3CXPnz+PJ0+e4MGDB9jf32cHYq1WC6vVyrwPuVwOuVyO5uZmDA4Osrx+cXERFy5cwJMnT7C8vIyKigqeakrS1WAwiIqKChiNRmRkZGB1dZWNtLxeL+7du4fMzEwYDAY8ffoUZ86cwfj4OAcuWhfl5eXM6XjnnXcwNzeHhw8fsoSfih632w2Hw8HSTZfLhc3NTZ7CSp4fXV1dzCX58MMPYTKZWLEyNzeHkpIShMNhTE1N4cKFC7h//z5qa2vZNHFsbAxlZWV8vXZ3d1FWVgav14vTp08zv4CeI7PZjMLCQqytrSEePxwQ+OKLL2JoaAiVlZU4efIkZmdnMTY2hkgkwoMgaQ2QPXswGMTc3NwRX5wzZ87gk08+4WTms88+w5e//GVUVVVhfn4eSqUSN2/exKVLl1hFYjab4fV68fTpU6yvr+PMmTNwOBxQKBRYW1vD/Pw8ampqMDIygoODA1gsFjb/OnXqFHZ2drC8vIyVlRVkZ2fDZrNhb28PHR0dWF1dxa1bt9DQ0ICxsTFUVlZidnYWmZmZqKysRCgUwquvvorp6Wlew9FoFGNjY3j55ZfhcrkQi8XQ2NiIoqIi/O3f/i2uXr2KpKQkRtSWl5chkUhw7do1lrHTc0mcsy9yPFfSIVZYIvwtwvhisAZwZKIpbZ60KYqoBR1UBVKQp5438TBEmF5UuNB7ULVJZl70/nTu1C4RyXxULZLCQtyURX4GXVhS0VAApNekAECeHhQUxHOjpColJQWZmZlM+qRgTXMpKBkiKIxmiXi9Xk4eqOonQiMFefpcdK7U5iDU5ODg0EKcgmtnZydPcyRyIkl4a2tr0dzczPI+iUQCk8kEg8HARDtaC2SWRddtb2+PHU/9fj/7kJAShFAOkT9Da0CcLyNyDOheUfAXq3yacUKtMVqPovcF2bnTGqPkhFAySogSiQQ7UorrilAVSmxoHZA3AiFalIBQIkstNHHdUCIdi8V4LoP4LNHzQWgFJaaESND1oHtNPyeiJiInQ1Q9UTJG7T7xXEUo9XkTD3KzpQGGdrsdBoOBgw3NbJienobBYEBOTg63BYxGIyYmJpCZmckmbKTaeO+991BYWIi33noLd+7cwcLCAhobG2EymXjjv379OkKhEG7duoWMjAyUlZXxOc3Pz0MmOxw3HovFGHqne0nSz7KyMgSDQWxtbUGv1+Ps2bOw2+08vpw4C5cvX8YHH3yA06dPIxaL8Qh5lUqF9PR0nlDs8Xi4GkwkEvj4448RiUTYgfXevXs4ceIE8vPzsbS0hFdffZWDSSgUQmtrK7dqs7KyUFJSgoODA1RVVaG2thbLy8uc8I6NjWFvbw+NjY1wuVzQ6/X8vWg0iu7ubp40SnbYhK7u7e2hpaUF+/v7PK13ZGQE8/PzqK2tZXVOTU0NRkdHoVQqsb+/D6PRiO3tbXz7299m7lZnZycUCgX+4R/+AV//+tchk8kQDodx69YtdHV1cfuHxr3fuHED0WgUNTU1PGMlKysLLS0tLIEndIbku4SSUQua5sYAhwoqKt5ouF0wGMSbb77JnhLRaBTr6+tceCiVShwcHOD8+fNQqVQYHBxEWVkZf87d3V0olUpotVoMDAzAbDZDr9ejqKgI4+PjaGlpgdvtRl1dHXp6enDnzh243W6Ws+bn5+Pu3bvIyMhAZWUlfu3Xfg3vvvsu9vf3eWhkdXU1j43f2Njg57+npwc//elPUVhYyHtbXl4etre3+VztdjsuXLjA635mZgZlZWXY399HU1MTysrK4HA44PP5OIGh5+Px48coLi5GUVERkpIOZ/KUlpZieHgYFy9e5FhMHJ6/+Zu/gc/nw4ULF3i6dnd39/+T13n8eO6kQ6zwj/MwgM83SgoU9PP0O6KiRdzQRFIqvZcoITz+M5RAiLI+kh8CnwdcAGyLLf6uWMmJGzMFUDGZIY4JESWPcxPEZIraAtT+oOtEqAq9L0G04mtQkE0kEsjJyYHL5eJNu6Kigm3DHQ4Ht3QoaJOnhVar5XOVy+Vwu91crZMMUCKRsBqG5Ko6nQ55eXnMpJdKpWxjPDk5CZfLxQjB8vIyVCoVQ74KhQLJyclcrScSCayurrI6pby8HAqFAh6Ph8dhi5NUiagmojHA5+gN9X4pISAUQUS4iIG+vb19ZI3S3xScw+EwsrKyjnBtSIYtk8m4P04JDK0pSoZpfdH6pgSLgreIcAHgBI/WAjkHUpIoolFiu5E+H60NMYGgZFRMVimRoURIdAkW2zx0z4+TVsWCQmxzPi+vIxgMcmujsLCQ++LV1dU8O0Kj0XCympWVBafTibS0NB7THgqFUF5ejqqqKnz44Ydobm5Ga2srmpubMT4+zsovGghWUVEBu92OR48eoaCgAIWFhdja2mKb7fHxcRQUFDAy6HK5mExK98jpdEKr1WJ6ehoKhQLnzp2D0+lEIBDAzMwMfD4fKisrEYvF0NDQgEQigatXr2JychIymQxVVVXIycmBSqXCysoKVCoV5HI5MjIyeE2urKxAJpPh1Vdf5dYhoQEymQwqlQp2ux3r6+vIzc1FY2MjhoeHmYPT19fHEHtdXR12dnbw2muv4R/+4R8Qj8dRX1+P4uJipKWl4d69e6isrITJZMLLL78Mq9WKlZUVVFZWYmRkhJ9vhULBCUc0GsWDBw+g1+vx2muvoa+vj59FIkfSlNKpqSn8y3/5L7G4uIhvfOMb2N3dxTvvvHOkAKI2yNjYGE6cOIGcnBxsb28jKysLPp8PEokEQ0NDMBgMiEajmJqawuLiIgKBAC5duoTd3V2EQiGo1WoMDQ0hLy8PZ8+exdraGgYGBtiYKjMzEx0dHUgkEkhPT8fy8jIuXrwIp9OJoaEhfOMb30Bvby+Gh4dRXl6OWCyGGzdu4OrVq3C5XOzVkpGRgd7eXjQ1NWFvb4/XsUqlQkVFBbxeL7RaLQ+Lo2e2q6sLNpsNBoMBMtmh5b9cLkdlZSXUajVycnLwySefIDc3l9U59+7dw6VLlzA6Osoza0i2Gosdzr6SSCTIy8uD2+2G0WhEfn4+jEYjotEonj17huTkZGi12iNt5pSUFJ7Xkp2djcePH3PievPmTZw6dQrDw8M8KTwUCvEcMJqAW1VVherqaly8eBFWqxVutxtbW1t8fq2trSgoKEBfXx8yMjJY0kvJ0686nquMoeB5HN0Q/02LlAKCGJCpAjtyAkKAp4N+lwIKbfTHK2KCc2gzpU04Ho8fkVnSA04bDG2stPGK7Z/jrSF6PxEap++L14SCPyVLVGmLkBydIwUsgspJ8krvQZs9WdUWFRXB5/MhkUhwJUUICiVUotSWhgeNjIzAarVy5e5yubC9vQ2Px4OVlRVsbGxwMkFGPyqVClVVVTAYDAybyuVyRKNRzM3NwWazQSKRwGq1oqioCPF4nNsnMpkMGxsbWF1dBQAeuOb1euHz+bCzs8NqF6q0d3d32TmW7v3u7i4CgQAneeQoSH11mu1Csl3iXpDJjkjaJbIlSXnT09Oxvr4Or9eLUCh0xFeC5Md034jPQ+dGiYpImKWEiM6XUCJav3T/SRYoImmEKIhtGHGNEiolvr+oNKEKWCRE0/uJBGtRUkuoDv2ueC/ENUokU3qen+coLi5Gbm4uk1yXlpbYtXVvbw+1tbWoq6tDf38/3G43FAoFWlpaEI/HodVqUVtbCwAsQ93b20Nvby/sdjtfe+IKUPJeUFCAmpoayOVyaLVaeDwe6HQ63Lp1i9ely+XC0tISe1ikpqZifn4ee3t7OHnyJFeqZF9vt9sRjUZhtVrx+7//+zxSPSUlBdPT01haWsLe3h5eeeUVVFZWYnNzE3NzcxgeHmafBqPRyMjF3t4ec7OmpqYwMTHBnKmhoSFuiVECvLy8zH43RUVF6OrqQiAQwNbWFm7fvg2r1YoPPvgAOp2ObdITiQQ2Nzdx9epVrK+vY319HS6XCx9++CHu3bvHbq7E81pfX8fg4CDW1tbw6NEj1NfXo7W1FaOjozAYDNjfP5xY29TUhNbWVrZ5/73f+z1sbGygqakJIyMj+Kd/+idotVo0Njbis88+g0ajQUNDA37wgx8gJSUFPp8PW1tb8Pl8WF5exvLyMgYHB7mVQAMVk5KSsLCwgA8++ICNsqgoKC4uxsTEBK/Lg4MDzM7Owm63Y2Jigomc1dXVkEgkWFlZQXt7O/r6+phEv7S0hNHRUZw+fRpPnjyBRCKBw+FAcnIyFhcX8eqrr0Kr1XKCSRyJ0dFRFBUVseNwLBaDUqnEwsIC0tPTEQgE2DArEAhwm1YqlaKvrw8KhYKTy/v376OoqAiLi4uIxWJwOp2sHsnOzsbo6Chzf2ZmZngdqFQqzM3NYWFhAUajkV1io9EoLBYLt31Jjnz58mVUVFQgOTkZq6urKCkpwdLSElZWVmC323nuCg3eC4fDyMvL48mz77//PkwmE/x+P3vBRCIRVFdXs6uu3W7Hl7/8ZdTV1SEUCn2h/eG51SuiEkFEJYCjwZnQBNoAxXaGSC4V+/T0PZEnQRsv8IuBXtzUCUEQUQgx2NPri6gGnZ/4e+JmTBm7CFVTFfr/Qkno9ch2naBwSoYIfRETLYIJKZgSyUsul/PPUdAi5GdnZ4cDKlXOGRkZ7I64t7fHsxQcDge2trbYKpw2jv39fSax0pyEvb09rK2tweFwYG1tjdnKxIonmJfkqIFAgIM3VfJS6aHrXW5uLiorKzlw0bAuUnfQz8fjh9pvCpJ0PyhAEsGTqtvt7W3+/NRWIQiXnE0pKSNOCAB2J9XpdLwG1Wo1AoEANjc34fF4EAqF2LjJ6XQyi11cr/R6IoFZXO/i2qV1Ss8AtWiIg0Fr63jySq0NWl/iWiVEQ2wbUauI/Eto3YjJNa1vSqboa5Q4iSikSIx93oPIjf39/ejo6ODrvbKyAoVCgXA4jKGhIZw4cQKbm5vo7+/nHv/W1hbS0tJw69YtTExMID09HXNzcyznJNVEOBzmSZcEn9Pa2N/fR0FBAebn53mi6fz8PKRSKerr63nDpnu2vb2NH/3oRwCAn/zkJygsLGQ+RjgcRm1tLVZWVpCamoqsrCyoVCrk5OSwW+xPf/pTLC0tYWFhASkpKXjxxRfx9OlTLC8vAwC8Xi/y8/PR3NzM81N6enpQVVXFRUheXh5SU1PZ/2NtbQ0bGxvweDxQqVS4ffs2HA4HcnNz2ZuDxhrs7++jvr4eU1NTPL+D5taQwaBWq8X58+chlUrR2toKj8fDXjVGo5GLE5/Ph6GhIaSkpODRo0dszre1tcUI1P7+Pqanp6HRaGC1WhGPx1FeXo7i4mIOftPT09DpdOjo6OApvpWVlTy6wWg0orq6mlsxycmH85D29vZw/fp1OBwOeL1eTE5OYmxsDNFoFLdu3cL6+jpmZmbQ3d3NAwI3NjaQmZmJqakppKWlob6+Hl6vFxLJoeX6nTt3uJVMfidPnz5FYWEhcnNzuc0UCATgcrlgsVjgcDiYNyKTyVi18uGHH2JnZwe3b9/GxMQEcnJycOfOHVy7do2TONq3a2pqMDU1hc7OTvZaUiqV2N3d5Tbi2bNnkZ+fj7KyMuzu7mJ8fBz5+flceOzt7bHbKe3hdC07OzuxsrKCtrY2VFZWshoxNzcXpaWlPNvFbDZDp9OhtbUVhYWFnJCQKV5WVhakUimbhRmNRpSWlkKhUPDAxtHRUY4XH3300ZE2+oMHD474wvyq47nNwX5ZBUYHJQwiYZA2N9oExSRCbD+IgZ7QD/pDLQ+RLCryQQi5IMieoG4RsqcqEjhqk36cMErncTzBEP8tKi2OIzgin0CE9cXNnBAYClhUoVK7hQJzUtLhvBTiSpCdrkwmQzAYZJMvABzEw+HwEffSQCCAg4MDlq5NTU0xcrG9vY309HQolUreuCORCDweD+x2O0vC6D5mZ2djY2ODe6qUDHi9XgSDQUSjUSZqBoNBrK+v86aVmZnJFYvT6eT7QQs3KSmJZ6UQf4HuMT30lADQQxKPx9noiyzh6X6KiAMANjADwGRTh8PBLSBiwcdiMX6APB4PbDYbO2LSujneChPVVSLKIa47sb0BfM7BILSDDkqiRN6U+FyIibv4LFCiISYY4vuJz8rxdqXIM6JnSeQGPe8hlR7OtlGr1RgeHkZDQwMMBgOam5uxubmJ1dVVuN1uhEIh5lOQxbbD4WBTtvz8fMzMzDAiRt4sc3NziEajrECQSCSMlpHdfk5ODg4ODjA+Po6srCx89atf5T3CarUiOzubgwC1QmdmZnD58mV2CC0sLEQgEGBnS3oeKamivjohOzU1NSgtLeXpxJSgp6am4p133oHb7UZOTg46OzvhcDg4ca6pqcHBweF03fT0dFbtZGZmIi8vj43SyOZcp9MhJSUFGxsbUCgUKCwsxNDQEObm5iCRSGCz2eB2u+F0OuHxeJCTk4NAIIATJ05gZGQE0WgU0WgUBoMBg4ODTD598cUX2Zfn2bNnaGxsRHl5OUwmE+x2O1QqFU/V1Wq1GB4e5gF0LpcLOp2O7eTNZjPu3buHrKwszMzMIBgMYnZ2lvdGSoQ8Hg9kMhn8fj/7hdjtdpSUlOD+/fvIz8/HpUuXkEgk0NnZCa/Xy/bdJC9NT09HUlISNjY2sLOzg/7+fvj9fuTl5cHlcnF7RaPRIBwO4wc/+AG6u7tRWlrKBORYLMaD34xGI6qqqrCysoK8vDwMDw8jNTUVJSUlqKiowPz8PNra2lBaWoqkpCRkZmbi/fffh9vtxuTkJBfe6+vryM7OxqNHj5CRkYELFy5gZWUFRqMRTqcTd+7cwaeffsoxyuv1QqVSYWpqiouniooKnD59Gmq1GpFIBHq9HtPT0zCZTJibm8PXvvY1zM7OMlKbnp4Oi8WCrKwsngtTU1ODePxwPo/FYkFbWxuj2DabDXK5HOvr68jIyMCVK1ewsrKCmZkZZGRkQKPRwGazQaPR8CC6U6dOobCwEK+++ipeffVVnD59Gg8ePOBJuL/qeH7sFJ+3RIBf7jJKG7C4YQLgqpZ+TqzE6P9iiyQpKYkXFPXGKamhoE0/T+9FLReqYsS+v/g3fQ4AzHmgYEc9eGqBiJu0yFkRiaaULFBgIG4CwfP0MyK5lQi19LP0uURVCrHWCXIdGxtjfwCaAEuIDDlpkmGO0+nk6Z5kPEStAPLXJ/iaEpaUlBRkZ2fD6XTCarUiKysL4+PjCIfD2NnZQXFxMeRyOQ4ODtiSOD09HR6Ph89DHHtPqpvS0lImrNHgOgoUhPIQekKkS/LNICJcRkYGYrFDl1ZKzABwwkbSOHJa1Gg0TEgkrX1WVhZfM6ry5ufnEQ6HuXVD0DoNbiNeDPFJsrKyGLUhNI2If4QYiM8HrUdqo9AzQckC8TzE1hAlNrT2RMSMngdaN7SuRYUX/Q6tY7rOhC6JyYrYihKREaq2nucgp81oNAqtVstzWJKTkzE1NQWdTscJHr02kYybmpoQCATw9OlTnDhxAqmpqSwvHBwcRFNTE5KTk9HV1YWGhgY4nU6WNcZiMW7j7e/vs3NsW1sbXC4XzGYzOjs7sbm5yT4zxJEgA7Py8nJsbW3hW9/6Fj7++GNUV1fz/AkamlZSUsKqBYPBAIlEAqfTCbVajdXVVVy/fp1nfZSVleHRo0coLy+HUqmEx+PBs2fPEAgEGPUoLS3FwMAALl++DACw2+1QKpX4jd/4DV73AwMD2N3dxZkzZ7hFSWPXDw4OTZmys7Nx9epVtLW1YWlpCT//+c/Z/OrGjRtob2/H48eP0dPTg3g8joaGBiwvL0OtVuP+/fu4fv06zGYztra2cPbsWbS1tWFhYYFn6XR1dWFpaQmbm5vIy8vjYDgzM4OVlRXk5OTAbDbD7XZjfn4e586dY04CAFy8eBFvv/02AMBgMMDhcCAQCLCMd3FxEUqlEnl5eYhEItBqtext8vjxY8zMzLBE3+VyweVyIRAIcGvJ5/MhHo9z8qJQKOB0Ovl5v379OkwmE9LT09He3o7Ozk5MTU0hEokgLy8PXq8X2dnZsFgsrEai6chra2s8XXhiYgJXr15FPB7nwioeP5T1KhQKbo+7XC6WbGs0Guj1ekxOTqKkpAQmkwnd3d3smjsxMcGxr6WlhduBAwMDCAaDUKlU/IwWFhairq4OkUiE1XoejwctLS28j5I9eTAY5Nk3U1NTMBqNvG+2tLRgeXmZ0XPgsOW3s7OD3/7t38bNmzdhs9mQSCR4ejPxrtbX11kufOvWLbhcLpw9e/YL7xH/LKSDNjHyUKBDbGHQZidWbGIFKFZ+YrtFZOqL6hKRayGiISLCIDp00gYbiUSOtDRIHknvRxsrJQoEbdMhyiDpd8RkQkyARL8F2vjpvEReCwUmsWoWX1sqlUKpVDJcrlarWYFy5coV5OTkcCtBRFxIlmowGHhiJKlS3C015zcAAQAASURBVG43IwmhUAixWAyjo6OYn59HUtKhYydV8vQz1G5Qq9U8b8BqtTLn4uDggAMKAEZKwuEw90o3Nzexvr6OBw8ewOVyMVRI029lMhkUCsUvtMLERI6SGUIUUlNT+TNQAhEKhTiQ0jh5uVyO/Px8FBUVobi4mImyKpUKGo2GoVVKXun6AeDrJ94fut7z8/M89IrOgw5KiOj52N/f5/HblFDQWqN2G60D+hvAES4JJSW0LinRpz/0syIyQuua3oe+R+uLnmMRjROTcXE9P8+xvr6OZ8+ecZK6sLCAjY0N2Gw2eDwe1vuXlpZiY2MDExMTAACr1YqFhQV2WqyoqIBSqWS0jAa6BQIBRCIRPH36FJFIBG63G5988gnKy8vhcrm4bUhoHc2IWF1dxcTEBCMa9+7dw+XLl5njc/78eVy5cgUqlQqffPIJdDodEx4TicOpm6dOnYJWq0VaWhoyMzOxs7PD64vGxJPktaurC8vLy9Dr9fw6KSkpaG9vxxtvvMGoDblcGgwGmM1mOJ1OVvjQ4LNgMIi0tDTcv3+fnSLpfpPDK63nu3fvIj8/H1/72te4Gm9ra0NWVhYA8MwQatNSW2dycpLt2n0+H09XXVxcxOjoKP76r/8aZrOZW7cmkwk3btzAj370I+afjI6OQiaToaSkBCsrKwgEAnjy5Ak//8TvWl1dZbWOWq3G+Pg4Owffvn0bTqcT9fX1nBw3NDTgxIkTsFgsWF1dhVwuh8FgwNLSEpRKJaLRKHQ6HQ8GJM6KWq1GKBTCyMgIc90kEgm2trbw0UcfIRQKIS8vj027dDodXnnlFUilUvybf/Nv+Fq9+eabzLVoaGhgO3ySU1NLi/wtaN339fVhdnYWfX19zH2zWCxQqVSwWq1Mui4tLeVCjtquZOz2u7/7uzg4OEBLSwscDgd6e3uRm5uLiooKtLa2IjMzE1euXMHw8DDm5ubw+PFjrK+vY2VlBZFIBAMDAxgZGUFKSgrm5uag0+lQWFiIp0+f8p5OCG1paSnq6uowOzsLnU6HxsZGGAwGSKVSPHz4EDabjZPivr4+vPfee7h8+TJaW1uPSPl/1fHcU2bFoE79YwqwvwyOpY2WNlARJTlOLKWePm3awOdVGx0kcSTFAwVyseVCr0PBCvjcIIw2bPEcaMPniyIkSPTzlHzQ71EPnf4vnitt6HRN6PfEBEFMqETvDQoghHCQ2oMqXmJSE4+CiFYAGMWIRCJc9bndbq6I9/f3mUhGJjk7OzuIRCKsTKG5ErQxAIDZbOaKyuv1svIFACwWC3Z2dtiiPRKJwGKxYGVlBVtbW2xRTO2YtbU12Gw2HBwcMMGVkCVCawhtIg4GzUWh+01jtMUgRIZjBGnrdDpOZoBD7gZJdul6U5An5IJ6scR9oTYSjXemRFilUjG73WKxsEEQSZbFhIIQK3oWaM3T9+jeiuoUes5oXYryX1LH0BoipI8kt2JLk15D5LiIfCsxeRFbpvT8iWq1L3p4PB52nVQqlbyGiouLUVxczKx6qVSKlpYWdHd3IycnB1KpFFlZWThz5gxGR0cxPDyM3t5eZGdnY39/H/Pz8zg4OGC1iEajQX19Pbq6ulBeXo7p6Wn89m//NvepZ2ZmcPXqVfT392N4eBhvvPEGmzqdPHkSarUaVquVHRhpwJZEIsHm5iZyc3MxODjIypSKigpEo1GYTCbk5+ezhHR5eRkpKSls+kRrk+YXkXV4YWEhMjIy2JF0YWGB0bV4PI6f/vSnaGlpwfnz57G9vY2pqSmeautyuZCWlob09HRMTExALpfj0qVLuHLlCvr6+tgl+PTp04jHD910VSoVrly5gkePHkEmOzTkI4M6hUKBu3fvQq1Ww+/3c4U/PT2NnZ0drK2tYXx8HFarlb16KBmkVubg4CCePn3K1TNV1gaDgd0vaS/u7u6G1+tFQUEBTp06xc8KtbsoAVteXmbDxH/4h39ATk4ONjY2sL+/j7a2NnR3dyMtLQ1WqxVlZWXo6OjAp59+isHBQcRiMWRmZsJoNKK2thZlZWXsmKxUKvHZZ59hb28P/f39mJmZwdzcHKsC1Wo1nE4nbDYbVlZW0NPTg6dPn7LlfGVlJaRSKTo6OhCLxbgVMTY2xkhAX18fNjc3UVVVxWgLSYhbW1tx7949BAIBbnU9ePAAr732GkpKSnju1c9+9jP09vbyqIuCggL8+Z//OUu9qb1HhnYrKytYWlrC1NQU3nzzTU5G5HI5rFYrWltbUVlZydPLz507h/39fZYu+3w+RnKrq6uZA/js2TOeZk5+LJcuXUJHRwfy8/OhVCpx7tw5foZsNhtu3LjxhfeI50o6jlf8omqFvnb8/+JBkDC9FnBUNksbpNifFlEAyu5Flj59jRxBRTSGpIskCwXAfArxnCiBoJ+hz0VQmdjjJuMYsToUOSEETYuESApu4iZ+/GfotYgoRP16SnaSk5O5RUBtFXpvkWNAn9toNDIJivrHTqcT8XgcGo2GEQeNRsOBVfQj0ev1HHjEQUnxeBxut5vRKLJMp4yZVBg7Ozs8+GloaIiDcU5ODvx+PxwOB98/SqDoc1ISAnzuwklseyIdix4ddE/oIAWDRqOBTqeDRqNhRQJZmqvV6iNogVKpZJ0/yf3I7ZXkuPS3UqlETk4OXzO675RcATgStGndi2tPROioxSE+Q7ReqL0ktlloXdLapBaLiLTROqa1EQ6HjyTkhBqJ7RqxZUnPjsg3+SJHOBzG+Pg4ysrKkJaWxi6ePp8PgUAARqMRCoWCe99arZb72TLZoVNvbW0t1tfXmcRstVqRSCQwNDSEsrIyxONxZGZmYm5uDoFAABcvXoRWq8Xk5CQePHiA4uJi6PV6/PznP8f58+fR0NDAiY1CocDDhw+Rn5+PWCwGo9GIjo4OfPzxx1zBFxUVQaFQ4MKFC7h+/TqWl5exsLCAtbW1I+Ps1Wo1zxvx+Xzo7+9HIpHA2toaT/bUarXo7u7G1tYW0tPTkZqayqoYi8XCA+UuXbqE+fl5drakKbalpaXQ6/VQqVQwGAwoLS1FMBhEPB7H22+/jby8PPbXGR4eZg+GlZUVfPjhhzx8z+PxIDs7G/Pz88jNzcVrr72G/Px8VqjQtFWa1jo9PY0zZ85AKpXi5MmT6OnpYX8PjUaDtrY2nDlzBnK5HGtra+jp6UFZWRkWFhYwOTkJAHA6ndjY2MDCwgI+/PBD5mF8+umn8Pv9ePjwIbcuHQ4HPvnkE1y7do0Rhc3NTU4M79y5g3feeQdra2s8eIx4JqdOncLi4iJcLheePHkCu93O1XplZSXPh7l48SLUajXOnz+P8vJyZGVlMeKj1+tRWloKg8GAUCjEs13C4TBGR0fxk5/8hJ+r3/u930NRUREXXGlpacjLy4NEIoHH44Hf74dCoYBOp0NtbS0/QyTVbm5uxsmTJ/Hpp58iGo1iaWmJZbFtbW2cCM3OzuLq1asIhUKw2WxoaGjA6dOnsb6+zgh+fn4+SktL8eDBA2xubmJ7extKpRKdnZ1ISUlBYWEhYrHDAXfEaysoKOA4UlBQwG1KnU7HtvEHBweYmppCMBhkWe/s7CyqqqrYuZoM+Kqrq5Gbm/uF94jn4nSILZHjfA369/EKXwzwv8yxTPx9QhgoEEilUkYoRPWLyPs4zrYXN1JxQ6bN9Hi/nRZdIpHgQC72zvf399n3QuRn0KZ9PLESkxd6HZFXIlafxO8Q2zEEv4tkQHpPatNIJBI2CpPL5XwdIpEIZ8Lr6+vcktjf38fKygpyc3NhMBiYVb+9vY29vT0sLy/DYDDAaDTC5/MhKSmJ+5B+vx8mk4nhT0oSrFYrE8iCwSBL+ciSuqCggCukyspK5kMQ+ZTIazk5OWwtTFpzQtGIM0IJCSWUBweHs1q2t7ehVqt544rH4yyt29nZ4cydep2U0NFaIQttcZ0olUrI5XKWstF9ItSDCK7JycnIzc1FUlISc0ZErgrdL2of0XFwcMBKH6r0iDwq+ntQknmcUErnTi0SWrtUOYpka9Gan1qClHTQ18SEQ0zoKbF5Xk5HVlYWVlZWoNfrARz27/V6PT7++GNUVlbC7XYjHj9UAFksFqjVauzu7qKjo4N9VoxGIztPFhcXY3l5GU6nE2fPnmXEg54dvV4PvV4Pm82GiYkJNl2Kx+N44403GOl7+PAhP/PhcBhLS0uoq6tjX4uenh5WkVVVVSE/Px9PnjyBz+eDWq3mwDQ2Nobk5GR24qTR9jQa3WQy4eLFizCbzWzU9N5776GmpgZPnjzBSy+9xO1Oo9GIgoICmM1mbr/dv3+fp9rK5XKkpKTgzJkzsFqtbASl0Wh4TyHyoc/n44FqwKHR1O7uLiYnJxkZ2NnZQU1NDWZmZhCJRFBaWoqMjAxsbW0hOTkZk5OT2NvbQ3NzM37rt34LH3/8MbRaLUwmE3NhiE8QCoXw8OFDdHR04LXXXmNpciKRQF5eHlJSUnD37l1UVFTg8ePHSElJwcrKCtbW1hCLxTA7O4vh4WHec3d3d3Hq1CncuHEDBQUFSEpKgt/v5+fZ6XQiNTUV+fn5nIxLpVI0NTXxZ+/t7UVpaSlSUlJgMBhw584d9iXa3t5mq3xKZn0+H4xGI3N57HY7E97j8TimpqZ4Lzx37hxGRkaQnZ3NviOffPIJCgoKMDExgeLiYmi1Wn7e9Xo91tbWYDQa4fF4mNsyNjYGlUrF6zoYDKKwsJDNvRQKBUpKSjA3N4eKigrYbDb4/X5+Pr7//e+jpKQEBQUFiEajyM/PRygUQlFREScmxcXFGB4ehlwuRzAYhFwuh8lkYmUiWdTPzMzAYrHwnhYMBpGSkoKqqiqsr6+z/83Jkyd5b3W73YhGo7hz5w7q6upgsVjYJv2LHs+FdIgbHoAjVTpwdDolcNS/g9CA40mG2Go43lOm6pFemw6xv00JAKEVoiKF/qaEg9ji9F7iz9BBPX4RRQDAVTYlNpQkEJRNiQJV3PSaVH0S3E3XRWwBiQmG+J4iKkQ/I/bjCQmh89rZ2WEESCKRQKvVMq8FOOybr62t8bmR577H48Hi4iJmZmaws7PDPcG+vj4sLCywCZMoSyRnRxpsZjabud1RUFBwxJCNRiE7HA5YrVaGaIm4SfeHECFCLggBIBItBVWC/ihRoBZTNBpFXl4eKisrkZeXd2ROCyE7+/v7TFYVfS7Iap34PrFYDHK5nBOheDzOPVt6DmhmAl1PaqVQFSDK3ggdI/WNVqtlQqO4jkUuhfiZxWRETBZEFZnISaLnjX6e1quIaIjPAaEbdM6ELj3vkUgk0N3dDa1Wi52dHTgcDp4aarPZsLGxgbS0NKSlpcFkMmFnZwd+vx+PHj3CwMAAioqKIJfLsby8DJfLhbfffpuVIlarFVqtloefXbp0CWazGYuLizAYDDh58iRXv+fOncPi4iKbu+Xk5GBlZQXLy8v4+te/jrfeeov5F/n5+exuOjExAYvFgsePH0OpVOLatWuor6/HpUuXYLFYcP78eeh0OgwODkIqlaKwsBASiQTNzc0wm80oLi6G1+tlfwWSp+/s7LBdNXnjtLS0oKqqiq2/6TMQeVar1WJ0dJTHz8/Pz+Nb3/oWy1pra2tZWVBVVYXl5WWkpaXB6XRyy628vJyJhdFoFB6PB83NzYwGDQ8Po62tDZFIBDU1NczBmpmZQTgcxvT0NBMme3t7MT4+jvfffx/z8/MIhUJYWVnBrVu34PP5sLq6ys8mOcBSYhaPx7mFe+7cObhcLrzwwgvY2tpCfn4+t2ZDoRC2t7fx4x//GGazGSMjI0gkEqioqMBbb72FpaUlWCwWDA8P814dDAah1Wrx4osvQiKR8OTVy5cvIxgMIhwOM7GePGNef/11fOtb34LBYEBvby9LTwl1czqdOH/+PJaWlhCJRFjKfP78eTx+/BiBQAAtLS1QKBTsDOtyuTAwMIBIJILNzU0sLCywi/PW1hZycnLgcDiwt7eHpaUllJeXY3l5Gfv7+8jNzUVtbS2cTifu3r2L/v5+eDwe7OzsoLW1lXlR165dw+XLlxGNRnlK8ujoKI+9p9EC3/3ud6FSqaBWqzE5OQmHwwEAvIenpKTg/PnziEajjOhEIhFWK9G9UKlUAIDFxUXo9Xrs7x8OJX3ttde41UvziL7o8dzDFWhDpGAuIgtigBWRBqoWaTOjoEk/KwZ94BdlqMDh5kiboJiAiCx7kYFP1agYrGnjFd8b+LztQ5sv9bRFuPw4YZaSLwoS5ABJkDehHSKkLaIt9J4id0T0SiA1g/gZReItjd+ORCIcrGUyGfMOlEolE96Ijb23t8deB+JrUcAh87DOzk7k5ORAq9UCOKxWa2trYTAYUFRUxK6PRqOR2xL0fikpKTzzQq1Wo7m5mWcLuFwuOBwOmM1mqFQqNjqj60ScB3GOCCVrZCJGSQ6hAMFg8AgBtKurCxqNBiqVii256fPR/aLkgRIJtVoNnU6HzMxMJoIRryQtLY3lyUQMlUgkrJAAwHCjaP5F6IjI2yBCKZ0/JTqkjCFZqM/nw8bGBlZWVjhxstvtWF1dxczMDA9hIik18V2oUhd9OOhvkTRN6iJ6julvSqiPm449zxGPx7mfn5+fD5vNhlOnTqGtrQ319fU4ffo0ysrKcP78eUgkEmxsbCAvLw+NjY04deoUNBoNPvroI+aCpKenY2BgAFKpFHl5eTxWfHBwEJ9++iknDmSVvbm5ibGxMYagSd1wcHCACxcu4PXXX8fS0hL+6q/+iqvC6elpfPDBByguLkZPTw/cbjcqKiqQnZ2Nn/zkJ1AoFFhaWuIhV/X19XjjjTdgsVhw8+ZNbGxswOv14tq1a9x+7OjowJtvvolQKASlUomsrCyWJzY1NbGq6j/+x/+IWCzGxEpylSTzuubmZmxvb7Pl9crKCra3t5GcnIyRkRFcvXqVnVSNRiNzR3Z3d+H3+9He3g6LxQK3242FhQXMzMxga2sLkUgEfX19KCsrg16vR3t7O8viXS4XfvKTn2B/fx9utxuVlZUoLS2FRHJouEXKi//yX/4LXnjhBUYIFAoFjEYjdDodK0T8fj+TDA8ODlBQUMAcDofDgaKiIiQnJ3O1/7//9//mUe1OpxN+vx+NjY3Y3t7G06dP0dnZCZ/Ph56eHiQnJzNXY2lpCXfv3kV6ejqmp6dRVFSE/v5+1NbWIpE4tOMPBAJoamrC+vo6NjY2sLe3B4/Hg7NnzzICTG2La9euYXp6mk3jqDiz2+24evUqdnZ2oNVqsbm5ibW1NUZm8vLy2N+COBRdXV3IyspCX18fe8xkZ2ejv78f3d3dWF5eRnp6OsxmM+x2O4LBIFpbW+FyuWA0GrG1tYXt7W1uZ29ubuInP/kJT1k+e/Ys7xtmsxn379/HH/3RH/EwO5FrePHiRbhcLt4LNRoNfvCDH6Cnpwft7e2w2WxHHHtffvlllJWVIZFI4P79+7w/KhQKtLa2IpFI4L333uPC9oscz22DDuAXSKEiJwH4vGVyHJ043h8WEQXxIG6ACIUT6ZBeS2zF0L9F+FxUP9DcB+BopSe2PkTkQuSBJCUlcWCkQEGbsihRpE2dzpEeNPH1KQGhFgslOLTpE2pByUk8HudKXeSC7O/vQ61Wc1JEBmFk8kXER6pyg8EgrFYrotEo5HI5vF4vz3IBwBMjiWxI1tLUDiLoLBKJ8PeJ5Nna2sqGTnK5nKcp7u/vw+fzsWcDKUWUSiXDouRQ6XA4+POFw2G+ppFIBGlpaYzgEOJCJmYiV4dg0FOnTgEAy9UoeaDWhZgQEwpBf5PckiTURJQuKio6MqqeCKnkZkqeDJmZmZxQxGIxaDQa7O7uchJDqiBqt9D1JbSGiH6EENH6ENEwkga73e4jUmnisWRmZh6ZQSMmc5T8iOgitWGIR0NFAkHYz8vp0Ol0uHjxItbW1jA4OIhf//Vfx8zMDJ49e4bs7GxEIhFkZ2djaGiInR8fP36Mr371q0hLS8Onn36KtrY2mM1mVFdXo6+vDz09PYwarK+vo7W1FdXV1aiurkZJSQk2NzehUql4auvo6CiAQ+XZ06dP8Vu/9Vv42c9+hlgsxh4d1L54+PAhioqKYLfbodFoUFRUxP4RPp8Pb7zxBmQyGUpLSxEOh3mgFkmq8/LysLa2BrPZjNraWn6GFhYWoFAocPr0aXzyySdIT0+Hw+Hg+69QKGAwGPDCCy9AIpFw0i6VSrG+vo6qqipsbW3h4cOHePXVV3H79m20tLTAarXC4/GgsLAQjY2NWF1d5RZkVVUVBgcHUVNTg+XlZRQUFCA7Oxv37t1Dc3MznE4nq1KSkpLw4osvore3F5cuXUJLSwtsNhv29/fx2Wef4Td/8zfx4x//GEVFRTxYjgoxiUQCo9EIk8nEw9+IEEwIqM/ng0wmw7/9t/+W243UelAqlVhdXcWZM2fg9XpRV1fHCPHZs2e5MDAYDDz9NjU1Fd///vdx8uRJvP766xgfH8fIyAiTO8vKynDz5k0mcs7NzfHeR+TknJwcPHnyBOfOncOLL76Ig4MDnDp1Crdu3eL4RC2LkZERLrAcDgc6OjowOzsLk8kEhUKB1dVVpKWlQaPRYH19Hd/97nfhcDiwurqKvLw8fPTRR9BqtTzhWCKRoKioiNspXq8XbW1tPNOlrq4OIyMjaG5uZnSpuLgYbrcbycnJaGhogNfrxcLCAhKJBBNAqZ0mkUgwMjKCePzQpnx9fR1DQ0Os7tvc3EQicTjt+pvf/CYjYnq9Hm1tbdw2zM3NhUx2aLRos9mwsLCA3t5eVFVV8VTjaDSK8fFxlJSUMIpoNBq/8B7x3FNm6RBVKHSI5Djxa8d5D2JbRZTlHW+9AOBetTgxUxxuJfa7KTmgrxEK4/V62dxHRA1oIxZna4iyRXpQCLmgZEtUk9B5i54Gx+FrOg/RXIyCDf2bepTieYvySjrEtoxKpUIgEGBeB1mDk/MenVtrayvkcjkTNonkRIGVNNcU3ElySCxz4DCJoiw9NTUVer0ee3t7cLvdbNADAPn5+dBqtbDb7ewfQMgFcSxI8pqWlsYZcjQaRSAQQFpaGvuFUNJIyZpSqWQ5HLVj6PpGo1G0tLRAo9Fge3v7CF+CEDJab+QPILYuaOMHwMGWWi0kYU5KSjqSoJEXCZkaUUuJEg0yM1MoFEy0zczM5M8JgNebSPCkiag7OzucPFFiQteFnhFqE5FiCTiU+5JaJzs7mxUSYktRbOeIpFfiMYlS3uc5NjY28Pbbb0Oj0fAo+8XFRZZ0rq+vo7i4mD0qFhYWcPr0aQwMDDA/hwav2Ww29PT0sDeFwWBg47n8/Hz4/X4m7ZGJERGoOzo6WOlhNptx8uRJPH36lPvpdrsdH330Ebq6urCwsIDu7m5MTExgYWEBL774IiwWC1v3j4+PY2trC6dOnUI0GkVJSQn29/dZQtjd3Y1IJAKXy8WBNRgM4tq1a3j27BkODg5w+/ZtVkU0NzfD7XZjbGwMXV1dcLvdCIfD3F+nz1JeXo7Kykp88sknOHnyJLcTLl26hOzsbPj9flaFhMNhzM3NITMzk1svLpcLt2/f5sTb4XCgtbWVFVE/+9nPcPHiRYyMjLCctLGxERqNBmtra6iursYbb7yBzz77DC+88AI+/PBDdHd3Y2FhAW1tbUwuLCwshEKhQFVVFbxeLzY2NuBwOHDx4kWoVCpG8LKystgI7Hvf+x7m5+dRX1+Phw8fMqLwrW99C0+ePOEBbZ9++ilSU1Oxvb2Nr3zlK5icnMTbb7+N/f199lEhp9uKigq+ljk5OQgGg6ipqeH5NbOzswCAhoYG5OfnM9/LYDDAZDJBr9ejtrYWY2NjUCgUGB8fx8LCAiSSQ/M5mqNDg890Oh3S0tJ4Bo9Wq0VJSQlkMhnOnDmDnJwcmEwm9p2Ry+VIJBK8tsLhMEwmE8rLyzEzM4OsrCxuq5w+fRomkwm7u7vIyMiAyWRCbm4ux4SUlBT28CA0RORhFRcXM8eI2ldKpRJGoxEffvghfD4fpqam8PWvfx0zMzOor6/H2NgY8vPzeSwEAAwMDODatWsYGhqCVHpookjtss3NTWi1WtTV1cFqtf6C0vT/dTy3OZiYKBxPMigYi5W/2Pagv4FfnEbLJyQ7Onr+OO9D/L4YvKkyExMYktYSiUcMUtRjp82WXuOXoTXUzhCTASL6Ab/YuqFWCm30dM7HuRqUUNDmT0FSTGKO811EhIWkbmQDToFPLpdDqVQeSdgokJnN5iNE1kQigezsbB7URA6dycnJPHRIKpWy2oPmmxDiQ1U+vR/xMYjXQORLMjij84/FYjw9V+T6UGVPxmCUWJLlOiUEdA3pusRiMTQ1NR0hAmdkZByR2xIBlYiiBJFSC4Puf3Z29pF2CrUwKLjRGlAqlazKIPt6EVWhIXwA+DrFYjG2ApfJDp1Wt7e3OamWy+XcCqPEhPhEoVCIk1Ma303XgFpBVFE7HA5G20ilQEoJMdGg51BUzyQnJ7Pa5Xhh8auO3d1dvPzyy5iZmWG4maZenjlzBltbW7h58yauXbuGtbU11NfX87NCVSbJkyWSwxk/JSUlqKmpgcViwf7+PpaXl5Gdnc1zfMi4SCaTYW1tDUVFRfjggw/Q2NiIubk5OJ1OFBUVobCwEAsLCzwbo6mpCeFwGAaDgS3JacZQa2srP7f/6T/9J7z33nuYnJxEY2MjvF4v0tPTmYORmpqK6elpvs4Ejy8uLsLv9+Pg4ABvvPEGzyvKyMiAy+VCRkYG3n//feYsqdVq1NTUYGJighG74eFh6PV6zM/Pc3tmZ2eHydmZmZlITk7m60YOwzTno6uri83VLly4gOHhYTgcDly7do2JijTbZWNjAyqVCvn5+UhOTkZOTg6ePXuGV199lRE/QlZzc3MZZayursbBwQHefvttnDhxAikpKTwnhQasAYDP50Nubi66u7sBAFqtFg6HA7//+7+Pzc1NNjMrKytDXV0dxsbGmBBLarcLFy5gdXUVKSkpmJ+fx5kzZ5CXl4ecnBwMDQ0x/4U8QioqKhAOh5lnlpGRgfr6em6ZxOOHk2jJb8NkMiEWOxzsSDbm+fn5XNjp9XrMzc3hP/yH/4Dx8XGMj4+jqqoKKpWKlTrNzc3w+/3weDzIyMjAt7/9baysrLCtejwex/Xr1+F0OhGJRFhdmEgkUFhYiJ2dHYyNjcHn86G8vBwdHR2w2WyQSqWorq7mvYnaLYSKlZaWora2FqOjoyzP7uzsZOQ2Eong8ePHKCgogMFgwJUrV2C323ki78zMDNrb23nPJeddq9XKyTC5vG5ubuLkyZP42c9+htLSUk5Wv8jxz+J0UOAW2xQiZA18Tn6knxPNicR+syj7o0RGfA1Rtgr84vA1UTEjShRpIyUfETFpEWF2+l3xcxDP4TjDX6wGjyco9Fr0OSkxET+L2I6ipOx4NUkBmForotqFKl6SfdJmQ8oSGp9NLqDUBqLPrVAouOeoUCggl8v52lA1Qu9LZKjNzU0eqLS6usq+GHT/SI8ukiLj8Th0Oh0HPbomarUaSqWSWxKUJFFVTWZctNjp/oocFGq/iBwEACgoKGBCnEKhgEQiYbdVSiioBSKTyZCZmclmcrQOMzIyGN0gBEOlUkGn0yEpKYkVOHQ/yYGUIEwKnHTexMynJIW+TgkFWRdTkCXkiVxRSdFA0C+dX2pqKvx+P6NEEomEhzH5/X5OGOlZ83g8GBkZwb179/DgwQMsLi5y0iOigwSdS6VS7tH/MsXZ/99B3AQanf3s2TPIZDKcPn2akYYvfelLWFhY4Emp09PTsFgs0Gg0jPoplUqEQiE0NDRgfn4ei4uLKCgoQG1tLbKysrC4uIisrCxkZWUdcd+lZyw7O5tnaVitVty8eRMrKyuoqqriau3evXuM7ExPT/P8IbPZjJs3bzJXa3BwkNUVKpUKRUVF6OjowObmJh4/fgwAOH/+PBcBNGuGpKVVVVW4efMmO3WSa6TT6URxcTFKSkpw/vx5nD17FqWlpSgpKWHeBlXEer0e3d3d2Nvbg9/vR39/P8/JePjwIdra2lBeXo709HQoFArMz8/D7/djdHQUKSkp+OY3v4mpqSm0trbi3LlzSE5Oxj/+4z8iHo9je3sbi4uL+OpXv8rtT9obyHr9Rz/6EfLz89Hf34/W1lZ8/PHHcDqdaG1txerqKra3tyGXy3Hjxg3Mz8/zc0jzkl588UWcO3cO5eXljILp9Xq88sorGB0dxc7ODsLhMFZXVznRT05Oxvr6OpRKJUs/+/v7WQmTkZHBKr27d+/C5/MhNTUVFRUVbOe+urrKCQe1NYqLi5krQnEjKSkJg4ODCAaDyM7OhtVqZXQAOCzw1tfXMTU1hZqaGmxsbGBxcRG7u7t4/PgxK2jS0tKwurqKV155BdnZ2XA4HBgeHmaX5+rqakZRlpeXkUgczv6RSqXQ6/VoampCfn4+srOzmWtz7949dmNuaGhAJBKBwWBAXV0dcnNzcefOHR7YSaZnycnJ0Ov1SE5OxtbWFqLRKMLhMA8NpGmyxGmTyWSora3FX/7lX2JiYoLJxKmpqRgaGkJ1dTXbvdfX16OlpQWPHj3C7/zO78BgMGBoaAjNzc1faI94bqRDDP5iQD3+ffHfFKBFtENEPYCjrRuxVQHgCPlOTE4oYaC2BQX0jIyMI5wOCqyEjKSlpXEPm1QGIjIhojiUBFB/XeSSiEgLfU38HVJgEApCCQ29vki6pcrzOBlWTIbo8+7v73PLoqysjB9EmjlBxmAajYbZ+2ILgTwoJBIJKzTi8ThvNCKaQRk1JVwpKSlQq9WQSCRcWdN5yWQyRj0o0dzZ2eHkixIa8g+hloGoMgE+TyzoQaONlioTao/RmklNTcUrr7wClUrFrRUAnBzRdaUeL11PAHxPKeAQ4kN8kUQigUAgAAA8yZPuOxmSiS0zMQGmNUKJBxmOxeNxvq4AOMAQMZjug5hsEzmUngONRgO/38/cJ6ri6Ln0+/18j0QOERnGke10aWkpdDodS60pwSbk7XmRDjLaUiqVGBsbQ1lZGWKxGAoLCzE/Pw+1Wo1nz56hvr6e2xYnTpxAXl4eHj16xFJbska32+2Qy+VoaWnB4OAgtra2IJFIkJ+fD5lMBpvNxmS8rq4u6HQ6nvEzPT3N48NbWlpQW1uLkZERlJWVwev1IjU1FU6nE6WlpZibm4NcLsfly5d5fkhfXx8MBgMnROQREQqFUFlZySMBFhYWeL4F8UT0ej08Hg8UCgVcLhfy8vJgNBpx9+5d5ObmIjs7G4nEodPpm2++yUMZ09LSUFZWhlu3bqG4uBgnT57ke2I2m7G5uQm/34+vfOUrcLlcMJlMuHbtGu7fv4+mpia4XC4kEgnU1tZyokZ8r9XVVeYMpaeno7GxEUajkQ0IY7EYsrOzYTQamcz5p3/6p/jggw9QVlaGra0tOBwObiOREsPhcKC2tpZJwSSHPn/+PDvEUtKdmpqKtbU15OXlwel0AgBKSkowNTWF9vZ2LnII1fL7/RgbG+ORCvn5+XC73cjLy2NL9qmpKZSWlmJoaAitra0s762srGTn5MzMTJjNZrz99tvY29tDbm4uP7/kbdHV1cXPUigUQm5uLgfnv//7v8fVq1cRDodRWlqKx48fo7i4mLkVZKQWi8XQ0dHBDqBarRYrKyuYnZ1FWVkZm5I9efIEV65cYTXhiRMn8JOf/ATt7e0oLCyExWLhvSY/Px/RaJSVSlQQkdlYbm4uS8L39vagUCjgdrtRUFCAubk5pKWlsTLs5MmTbLh38+ZNlJeXY3Z2FjabDR0dHSgpKcHa2hq2trawtrbGdgqrq6s8+2V0dBThcBhnz57lFqfP58O9e/e+0B7x3EgH8LlMVDzENomIIhACIkoBKUCLfA8xGaENVmTP06ZJ70WBWQzG9N60sVPAopaPSJ4TWf18MSRHvUdE3wQK8tQKodeh60HnK5qJ0dfpgT5+EKkvFotxG0Ds8YstGfH/EokECoWCH5CysjJe8ACYM0FzI0TOwMHBAbcu6NoS+ZSuiVwuh1wuR15eHsrLy1ndQUiFVCrlKbp0DYjfQMkPJYHUhiJIEDgM3olEgqt6QjPC4TDC4TAjFCTv2t3d5YqPWktiC4omKFKLQVRRUaK0u7t7BHmiFo3I7aCqRpx1QyRNGhtO6Bn9TEZGxhHCMhGP6XrS9ZVIJHC5XLw2UlNT2UeDZjZQ64eSVvoZqgKJlKtSqbCzswOj0cj3ku4BJTJ0HY4f1G4j3s7k5CT6+vrYaIt+l97/n3uEQiGcPn2aeRjz8/NoaWlBMBjEhQsXkJycjEgkgoaGBpjNZlZanD59GgqFAklJSaivr+d22tbWFhobG9kxMzc3FyUlJWhsbMQ777yDkpISqNVqruqMRiO++c1vQiqVIjc3F3a7HU+ePEE8Hmdfiu7ubnbCPHv2LJaXl/HZZ5/x+V65coUVHaFQiKWera2tMJlMKCgowMmTJ7G3t4ehoSFIJBLmWJFbME35dLlcWFtbYx+IlpYWvvfr6+uora1l35oPPviAA6DD4YDdbsfMzAyMRiOKiopw+vRpfPrpp1heXobP50Nvby/PfrHZbCxHJpJxdXU1nE4nzpw5g93dXbjdbpZpTk1NYXl5Gc+ePcPExARMJhN6e3s5kZidneVJt3l5eaitrUVqairKyspw5coVZGRkoLq6msmNRUVFkMlkaG5uxtTUFNbX11FeXg6v18tyzvr6epZlktS0vb2dp0cDwNraGlQqFV555RXIZDIUFBQwb40+4+nTpxEIBFBbW8tDLslN2e12IyMjg5OTu3fv4qWXXoJSqURycjLMZjO2t7c58SHVhsvlwuzsLNra2uDz+RCNRlktQzNSRkdHYTAYkJ2djeLiYuTk5ECtVqOpqQktLS1IJBIYHx9HR0cHenp6cO3atSPS+bm5OdTU1MBsNuPUqVNMkKdEzu124/Tp00hKSsLHH3/MA/eogM7Ly2Pvjh//+MdISkpCTU0N5ufn0dDQAL/fj7S0NCwtLWF/fx9GoxENDQ38PbVajZmZGXZtvnDhAoqLi9Hb2wutVosXXngBbrcbAKDX6+FyufDxxx9jeXkZm5ubiMViOHv2LMbHx7l9WFRUhPLy8i+0Nzx30iHCvQCOJBbAL46ip+BPUL9YGYokUEoiqI1A70GBmX73l7U9gF+0IKf3JFWIyL+gZIOSGnpPei36WWqREOwt8kXocxFhUawKRaKfyJ+gIEhyWFHKSZ+TeBiErtAhIi8iL2Jvbw85OTmQy+X8AFF163K5mFxI8k6xDQSAkQtqlSQSh6PfCXaXSA6Ntej8aYonBWsi4dIDAXw+QE+UcZLahP5PSefBwQF2dnaYUU/8BUryDg4OWK1C14GCP3BIHu7q6mINOQVdej06b0oMRETs+AwcWgdkVEbrhNZRMBjExsYG3y9qY4ioB60hMlkTLfuBQ5twqsTJsTMtLQ1KpRKpqakIBoN8jUSCtERyaHBEn50CG90TACwfpsSY7iEF7lgsxmuVzo2kvGtra3j48CEePHiAqakprKysYH19HcFg8ItvDgDy8vK4Onrw4AGWl5eRk5OD3d1dLC0tcXW/traGrKws3tzINGpychI9PT2orKxEb28v9vb2OFm7desWTpw4gZdeegk7OztYXV1FcnIyXnnlFfh8PoyPj8NkMiE1NRVbW1v44Q9/yAoFajPp9XpUV1czWTMnJweJRAIejwdutxtlZWXo7OxEX18fhoeHeeQ7QfuVlZXY3t5mpIKqz7a2NszMzKC6uvoIF4cSV4lEgp6eHqSnp2N1dZXnrBAaeP/+fajVakSjUVRUVCAQCMD/fwcUymQy1NfXY3V1FVlZWSgsLERrayujQpWVlQiHw8jNzUVRURHvW5mZmSwBjUajSE9PR319PVJTU1mdk0gksLS0hHj80OGTOE1kP/7JJ5+gqakJWq0Wer0earUaCwsLPMOovLwcBoMBPT09KC0txdTUFOrq6jAzM8Nre21tDTqdjou2iYkJ5OXlQa1W48tf/jLi8TiGhoZ4UOPBwQF6enqQmZmJGzdu4Ny5c9Bqtejq6sLU1BQSiQSmpqaYt7S8vIyWlhaMjIxgfX0dm5ubCIVCqKmpYUfWP/zDP8Tu7i50Oh2TK2kvbm1thdPpxLVr15CTk4OXXnoJMtmh/T2ZX4VCIaSmprKZIbm+kjLE6XTC5/Ph/v37mJ6eRkFBAVJTU/HDH/4Qjx49Qnt7O1JTU2E2m6HVajE/P49gMIiVlRVcvHgRc3NzeOutt5CRkQGfzwefzweXy4Vz586xs2hzczPi8TgMBgO2trYwPDyMP//zP+c5M0ajEbOzs8y3qaurY+l5fX09IpEIlpaWMDIyckSaPz09jd3dXbz66qtYWFjAxMQEcnJyeM309vZCp9PxOU9NTeEv//Ivsb29jZWVFQSDQWg0GqysrHyhPeK5kg4K2LRBU39atFSmgzZgqjhFToeIQhwPpqIcl2yDKTDQOYjJCgU6SnBEjwdKSsRkh5IE8fv0bwomokKAkh8iOIoJlpgAURABjlq2i0mP2JKidgJdJzpHSriAzxMEgiePK31kMhkyMjIwMTGB4eFhbGxswOfzMdkrEonA6XQiFAodMXoi9Q/B72lpaYwukU+/RqM50q4SiZk0eZKIa1SRU+VO95buKV0j+j9VLBSMiZxM95c+HxkN7e/vIxwO8zqj60Wqktdffx0+n49npwCHzn8ajYYJr7RGjnOBCAkLBAJMnqS2Dp0jrTFSBQUCAd7YyfGP1ECUuNC9S09PZz8R8fpTK4rMi2h9kEKH7rnIPyIVDK1JIuFSYpNIHEriaOAdkdMoaSGptLje6Pna2dnh+7ewsICVlRVYrVZMT08/zxYBm82GK1euwOv1orOzE06nEx999BEcDge3WgjVLC4uRigUQmZmJnQ6Hfx+P0pKShAIBJgsSqTN5ubmIxNnDw4OHXhXV1dhsVhQUVGB/Px8BAIB5OfnIx6P4+rVq7yxFxcX49KlS0y6o2tKaiqTyYSysjJ89tlnmJ2dhcFgQEpKCqOFVHjk5uZyUJDL5RgcHIRer8fAwADy8/N5rADdx3A4jObmZshkMkYVmpubWS5MUPjFixfZvCoUCkGj0SApKQklJSU8NK+jowM6nQ7BYJBN+wKBAHw+H/N7FhcXUV1dDZlMhunpaWRkZKC3txfl5eX49NNP8T//5/+E2WzGzMwMcxwUCgVSUlLYrMvr9eL69esIBAJob2/nEfeRSARGo5HbeMR3KikpQW9vL3MRKioqcPXqVZ7gSxV8WloadDod22zfvXsXP//5z5GdnY2amhpWKL388sts8pZIJJhATdLooqIiNDQ0oLq6Gg8ePEBBQQFmZmbYh4LGHiwuLvJeYLFY8Ad/8AcYHR3FyZMnUVZWxlLjyclJ/MEf/AH8fj9OnjyJiYkJzMzM8GTfvb09FBYWcvJO/jlKpZKdPxUKBZqamtDW1oYTJ05gYmICNpsNjY2N3Bqz2WxQq9XIzMzEV7/6VajVagSDQYyNjaGyshJjY2Mwm81ITk7GwsICLl26xO2ziYkJHBwcIDs7m9fO8vIyRkZGoFKpeBbQlStXcOvWLej1eoyPjyMrKwt7e3uwWq3Y2dnB1atXIZFIUFpaivb2dtTU1ODq1avIyMhgNeLg4CCSkpJw4cIFrKys4OrVq7BYLNyaI3R+YmICd+7cgc1mw5MnT77wHvHcSIdIdPxlBkJiT5tO8Dg/gZIPquiAz2dQiBC4yJEQ1RuEBIgkUjpEdIACAAVsyrRFiF1EJYDPpZAUKEUTJUo66HPT+YscEEJOKHGhf1PlShUyIT30O6L6RWwFUWVPv0uHVCplx0C73Y7U1FR4vV7YbDYEAgEeFud2u+H1ejkJovciFQadC9lwazQaZGRksNEPEU9zcnK40iGCGAUsmgVD95TUM5TUEBmRPElEkixZnIty4Xj80JeFgrLf7+f1QPed7sMrr7zCiQG1Qqanp7G+vs6JAcHMRN4UOSek1CGkgRAhEQ0jvw7iCtE1JOSBUBFKaAhxoSSHOCs0VI6UKPTZqSKmdgbZ7tN1E9s91BohtILWCs35IGJlPB5n0izxijIzMwHgSJJJBNW0tDSEQiEkJSUhJyeHuT/P60qq0WjYlCsUCuHUqVPo7OxEXl4eTCYT96NTUlJw48YNFBcXw+FwMAlvaWkJ09PTjEpVVFRgd3cXExMTKCsrw/z8PA8D9Hq9yMzMRFtbG+LxOO7cuYPa2lo8e/YMTqcTSUlJWF5e5uGJNJgtIyMDmZmZyM3N5YFqVP2VlpbC5/OhoqICm5ubPMuEWq3/9E//xJNyTSYTNjY22E2T9oqkpCTU1dWhpqYGSUlJcLvd0Gq1iMfjaGxshFwux9jYGBuXUeunvLwcxcXFeO2113ia6p07d2A0GhGLxfD06VN+bgwGA7c/qcVKbca5uTl2+qyurkZzczM++ugjeL1e2O12lqrSoC+tVsscGEq6p6amkJaWhidPnmB0dBQLCwuw2WyoqKhAQUEBqqur8fHHHyMWi2FjYwNFRUVYWlrCN77xDYyOjnL1TFJKuVzOCaXP58Pu7i7+5E/+BN/5zneg0WhgNpvhdrvR1dWFDz74AH6/H8+ePUNHRwfW19dhs9kYcSKEeXR0FNXV1ejv78fq6io6OzsxODiIaDSKsrIybGxsQK/X4+DgAK2trVhbW0NJSQmWl5eRmpqKubk5mEwmVFZWYmNjA2tra7h58yb7ddy5cwfDw8OsjCSy7OXLl6HVanmMRE5ODoqKiuDxeDgRpuGYSqUS58+f52Kro6MD77zzDt577z0Eg0Fu6ebm5rLEPCnpcFI48b5MJhOjM/Pz8/jHf/xHFgUQCXh8fBz5+flQKBR46aWXMDg4iMrKSiwsLLD0ViaTYXBwEK+++irzQA4ODvDo0SMWEjQ0NOC73/0uEokES6/J3fnixYuQSqWora1FLBZDUVERq61o0OEXOZ57yqxIgKTKTuRrAJ+3FX7ZfBIRNj5+UCJDMLAY6KlyPl7xE8uZ/g2AF6b4flTdUbAXq2tCGOizHW9tiHwSEd4XlSUUjMTPL1b8xz+72GISX0NsN1GSQRWt+HnJYXBvb4+DYSJxqAG3WCzsVBoOh+H3+xntIF8HUfablJTESg0im4qBWqPRcMAjhQkFbuIZ0GehgEbnSQFPJBOLUlLqdYokU5LtEumTCK10TQkNqqqqwrVr1+Dz+ZhfkpaWhrt37zI0KBJG6XXFdpaYGNM92N/fZ3UNyVdpDVKS4vV6GSVSKBRMUFYqlUeQFZogqdfrkZ6ezugFrWNS0FBiKSad1N8GPp83JCZNlMzQfaEWFLVe6D0AcEIpkUg4oaP7IrbDADDfh/xHnucg7waCXkk2TZVufn4+Pv30U1RXV+PKlSt49uwZSkpKGNXw+/3Iz89HS0sL6//J92JwcJA/X0pKCtra2mA0GrGxsYGUlBS8+uqriMViqK+vRywWw9bWFveZe3t78dlnn6Gmpoblljs7O9jc3ERBQQFOnDiB/Px8lJeXY2hoCHV1dUzSpmmeiUQCer0ePp8P3/72t3FwcIDa2lquHL1eL5aXl5GVlYUHDx6wqqG4uBjV1dXsJkoybUI1WlpacHBwAKPRCLfbjXfffRdGoxEVFRVISUmB2WxGV1cX83GkUil8Ph/vk7m5udzGGBsbY5UToaT/63/9LwBATU0Noy4ffPABtyiGh4exvr6OeDzOgauwsBBOpxNvvPEGJ+USiYTnr8zOzuKP//iPkUgkkJubi0AgwMPJ7HY7ysvLcerUKdTU1CArKwtOp5OlyHq9HsXFxZibm8Pf/M3fYGtrCxUVFaiqqsLt27exu7uL8vJyXLx4kX2EaL+mYBwKhWA2mxm9o5bU/fv3IZPJMDw8jM7OTjx48ABXrlzBa6+9BplMhu7ubiiVSoyMjODWrVvMb+nr64PH48HMzAzkcjmqqqrQ1dWFpqYmHBwcwOFwsCJqfX2dB/4RWkReIiR5LSgo4KTNYrEgkUigs7MT8Xgc3/3ud6HT6dgfaX9/H1arFYFAAC+99BKuXbsGvV6PyclJltT+2Z/9GWKxGLxeL/NmlpeXuZiQSg+HGd67dw8lJSU8aZesBoiETzLb9PR0RgJpGi15tZjNZi4EaM8DwOZkd+7cQUpKCtu0E2JJ7b5fdTz3lFmqnAjmFoMhgCMBlgI4Vbciv4MeGLEtI36fDjGREDkEtAgpoaBNW3w9CiTUTqDzo6TpuF+8+P6EiBD8TudBskKxHUSJCP1fJNFSoBDVO2IiRckZBWG6ViLyAXwuaUxJScHMzAw741FwFxVBVBkkEocGYl6vl4MnBd1QKMS/Q5wGSqb29va4LUEQtGiGRa9BMH4sFmNjMdG5k7ga9DqkGiLbb7IVFltaBPXT+RGaQCgCXaednR3863/9r3mAHCVIwWAQa2tr0Gg03LOmcyWUgKSvIjeE2nKxWIwZ9yLHhBQ1dJA8mZxZaY1Q4kUJC7VXdnZ2WCVEPBNa1ykpKXxehEjRUC9Ksui60joSCb10D+gzUCIUCoUYySBUg0ix1OsXEw25XH6EdJyens5W+F/00Gg0LH0lr4W1tTVcuXIF9fX1iEajSE1NxTvvvINAIIDm5mYMDAxgcHCQiYlra2swmUwoLCxEXl4ekxLJwbampgaRSATr6+uYmJhAZWUlnE4nKyfIiE2tVmNubg7hcBhlZWWcxFIrheSkTU1NSCQSCAaDmJmZ4Z54PB7HlStXMDg4iJaWFtTU1KC9vR0SiQR37txhtYdEIoHf78eVK1cwMzODWCzGZlklJSXo6+vDhx9+iHg8jrq6Oty5cwfNzc3sfbG4uMjKpK2tLd4TRkdHUV5eDplMBovFAr/fj6GhIeTk5KClpQUOhwNjY2Ow2WwsRScp6t7eHh48eIA//dM/xfXr11mZtb6+DplMhosXL2J6ehrLy8tob29Hb28vlEolbt26hdTUVDQ0NCAQCPBwuEePHqG4uJjl0ADwwx/+EGlpaZiYmEBhYSE++ugj/I//8T9QWVmJiooKJmGSXDU3NxeTk5PY2trC5OQkTCYTKioqeNDf97//fWi1Wuh0OkgkEnzwwQcIh8NYX1+HVqtFfX091tbWcHBwgLKyMiQnJ2N6ehp+vx/BYBDLy8vsqJmVlYX09HRsbW3h13/919kTZmNjg9VHgUAAUqmUrcLPnTuHF154gQuIcDiM8vJy9puh+AMAtbW1OHPmDA4ODpjLQfddKpVicXGR27NFRUW8P7ndbqhUKmi1WqytrSE5ORl2ux1msxlra2uIRqP46KOP4Pf7UVdXh8HBQSgUClitVjbSS09PZ3UX2dPTwE5RwutwONDc3Iy2tjZ885vf5CRid3cX7777LhcC1H4yGo2Ynp5GLBaDw+HAiy++CIPBwHLy+/fv4+TJkzAajdwClkgkqKysRG5uLnMKf9Xx3EkHgCNBWORx0M+I3xerSLEFQRumuMlRlUctFtpQKTgcf036tyiLFEmbJMkkPoDI+6BKn+BjsSKn5IoCLP1bbOmIBFoRGREREqryqKVCXAtCN6gPf/y6ko01Vd6EKLhcLgwODiISibCtO/0tHolEAuvr6zCZTMzDIHkb/SHSqCh7pUVEiAElPFlZWdx2IGUBfY9aK4QkETE0FotxwKVEjgiaALgNARxFmsggTWyxiCRk4JCk+cd//Mc8lptsxeVyOT7++GNsbW0hNTWVe8/A5z4s5OpI3yNCJa014HM0Kx6Pw+/387qgzxAOh3lTIvUKtZFoqKBUKuWeNCV3lISQfBYAVyIA+F4QN4iUKSLxk5AW+v7BwQHC4TCjVYR40XMm8pkAMMyamZmJcDjMZmaEgFDiRLM7nmeQEwAYjUZGQNfW1qBWq5Gfn89eA9QDb21t5fupUqnQ1taG+fl5FBcXw2q1QqlUYnFxEUtLSygpKcHly5dhMBig0+lgsVgQCARgMplQUlLCQ7zm5+dhNpsxMDCA4uJirKys4OzZszCZTDh9+jSuXbvGgwZJLUajAHZ2djAyMsItCbvdDp/PxxA/jRePx+NMxiRb/0QigaqqKszOzmJhYQEqlQpNTU0oLi6GzWaD2+2GRCJBeXk5+vr6cP78eX7OHz58iOzsbBQWFuL27dsAgO7ubiQSCWRmZmJ/fx///t//e+zs7PDk542NDQwMDGB7exvNzc38HJnNZrZxf/bsGerq6vDWW29hbW0NxcXFqK2tRWtrKzIyMvDOO+9AoVDAbrfjr/7qr/Dtb38bAwMDeOutt3iCrEqlQmVlJeLxOKqrq3k/Ojg4QGFhIZNLRVLrN77xDTidTpjNZlbt6PV6vp6RSARqtRo5OTkoKSnh1wwEArh06RIXec+ePUNlZSUnHqmpqWxeVVVVxXLsoqIiZGZmwm63IyUlBZWVldjb24PJZMLt27fx7rvvcttUo9Ew0fK//tf/yoTWL33pSwgEAnC73XC5XKisrMTOzg70ej0cDgcyMzOxuroK4LDaX1pagsvlgsfjQU1NDU6cOMHD95aXlxGNRtne/9SpU8z7odY37SNDQ0N49uwZW8wXFxdzTKEC4fr16ygpKcHs7CzUajU+/fRTBAIBFBYWsuIpMzMTt27dQn9/PwoLC5GVlcUDCOPxOHJycrC0tMTTb7e3t/Enf/InCIVCiEQi0Gq1cLlcWFpaQlNTE0+8paGFBoMBdrsd165dg9frxdjYGCsNrVYrtznJ9+RXHc/dXuFfFFokx9EJkeMgoh20edPriBwFOmjDFwM5JR0UnEX4moIV/aHXJqKmmJzQH1GqSQnL/v4+V4MiqiLKL0UImgIJBbBfhqRQi0RsSVE1Sn8osRETGbq+9DNSqRTLy8tYXFxEeno6BxuRU0EtEPqsAGC322G1Wo+MoqdrTsGEgj+ZTdE4ehGZoetMXALqeVKlTIGLrp14D2goGck6gUMfjlAoxOTNYDDIXizUW6RFLc7/SEo6tKN/6623GBqkyoM8G0jySIRhOh9qN4n8GTp3SiT29/exvb0Nj8eDQCCAROJwcJ7YNsrKyuKNVyaTIRQKAQCrRuhvItZSq4IQQloHAJiDQp9NJvvc94USLXJPFRNv4hQREVTkURF6Rrwckh8TgiSXy5nbIrZkkpOT2R9EbDuJ9+2LHKurqwz3nzt3jn0iOjo6MDIywvA2zQGiSbAej4c5LpQwut1upKWl4f/8n//D7qPp6eloaGhAYWEh2tvbWanlcDig1+thMBhw6tQpGI1Gvh8qlQrr6+sYHx/nYB0MBvHiiy/C4/Fga2sL+/v7ePPNN+F2u9nSvKenh4MVSbrtdjsn1f7/O3E5JycHIyMjMJlM+Pa3vw23280SR1Ig/MZv/Aby8vLgcDhgsViwtLSEcDiMy5cvIz8/H1arFV1dXSgoKIBUeujXYrPZGAkiBUhbWxs2NzeRm5sLtVrNagez2Qy9Xg+j0Yg//dM/xfnz5/l91tbWsLCwgMHBQU6IysrKOEnNyMhAX18f0tPT8Z//83/G7u4u5ubmUFRUhHA4zN4QNpuN16hUKmUpKBU+ZWVlkEql0Gg0qK6u5qJhcnISp06dQmtrK6RSKScomZmZGBwcZAVNeno68yAKCgq4dZOdnY1Hjx5hY2ODn+vt7W2srq6yXw2ZEfb393Pi/pWvfAVtbW0oKSnB48ePWUZLDqckBx0eHoZOp0M0GsXs7CzsdjvW19d5jyJ+XH5+Pvb393HmzBlsbm7i5s2bePr0KUwmEwwGA+rr66FUKpGens6zSdLT03mQ39raGgwGAx4+fMg28devX0dXVxfLiYnX09fXB7vdzjyc9PR0bqWR3F2v1/OAPuLbeTweLpL1ej1UKhU/V+T3UVVVhXv37jFiVFhYiNOnT0OlUqG2thY6nY4VOvn5+djb20NzczMbhvX09KCkpATXr1/nYovMI7/I8dxIh0icFPkaIvnxOGdD3OhFEiqfhBBsxeRA7L3Txnu83QIcRV7oNQhSpp+lSpXOUaz+KPmgZIY2chHOF1s5YisjHo8f8aCgcxRllaJXhaiGoJ+n1xSJrfT5k5OTYbVaufqj8yPnTOrHEyGUAhq9zsrKCjweDxKJBFZXVzm7JZMpCuyitJU+A1W8Ho+HVRPRaBRbW1ucsW9vb3NSIapD6JoQcZJaVh6Ph69FNBplkisd9HuRSIQHoNFG5/F40NjYiH/37/4dD0Ki+yaXy3Hv3j3YbDZeE9FolM2cgM+RA+K5UNCggEuQvEqlQnZ29hG0h8iaogqFklKyQU9KSmJPE7quCoWCz4HQBEIvwuEw98rpvOi8SaJLJORQKMRqFBEFFIm59HURxSFWejQa5SSDkmvy4xAJ24T+iV97noPaSJubm9BoNNjY2IBSqYTBYOB1QLLSaDSKtrY29PT04PLly4wGvfjiiwAO5z5otVpcuHABzc3NSE1N5UBOwVShUKC+vh65ublMlnQ6nVhYWOCx87W1tSxxNRgMnEAHg0FuqaSlpeHx48eoqanB4uIiioqKMD8/z5D07OwsJJLDoV00LTg/Px9ms5mH2XV0dCApKQnV1dXIysqCxWKBwWDgytjpdOLSpUsoLS1FVVUVAPB1CofD0Gq1TLitq6tDe3s75ufn4fP50NbWBgA81DE7Oxu1tbXIzMxkBCQajeIv/uIvkJaWhtnZWVgsFlRVVeF73/serl27xkMBqfW6s7ODxcVFpKWlwWazYWlpCbW1tRgYGIBEIsH8/DwsFgt7iSgUCphMJuh0OqSkpPDgO0r66Llqbm7G0NAQuru7eejdw4cP2WDKZrPh0aNHMJvNKC0tRWZmJi5evMhtA2p97u3tsQU4AGxtbfEAOplMhqGhIfj9frjdbty+fRvJycl44403IJFIoNVq8d3vfhepqakwGo34rd/6Ldjtdpw5cwY3b97E2NgY4vE4mpqasLy8zIVzXl4ewuEwXC4Xc4mGh4fZ12Jzc5O5N0ajET09PSx7JgkvuQInEgncvn0bOTk5SEtLY6lsdXU1KioqoNFo8OjRIx4OGAwGcePGDYRCIXR3dzNClpGRwclPfn4+dnd3eSAnueJSYnzp0iXMzMxAo9HA4XBgenqa99nR0VHodDr09/dDrVaju7sbk5OTsFqtXMCsr6+jtLQUubm5yMzMxJ07d5hD5PF4UFBQwCqi8fFx5OTkwO12s1LwixzP5UgqbkQiaZISELG6p8pdbIvQzx9PSo4rUsTE5DhJFPg8waH+t5gIiUiFSKIj7gMFePKNoHOk7JASHJHkebzKFM+T3peUByTlJMhTbClR8kE8EwqWRJQUzcji8cMppjabDVtbWwwD0+/SexCxjAIgJUkiuuT1evnzSKVSGI1GltTG43FoNBr2fFCpVOxgSPecpGWUXGRnZ3MlThsEnQ8lapR8AIf+EWlpaXC5XMz/IMKZeE+Id0L8CUo4yBzsa1/7Gr72ta/xw00olEqlwsjICG7fvs1cCILQyUSNEheavktEVeII0f2ga09rSiaTseSZEgsKnoReuFwu+Hw+bqeI0lSyPCfuB6FUlBDQs0L3lr6WSCR4mi0hEyIxm5IHQmHo2SPejfh9uVzO0na6zikpKczGpwSFeCEZGRl8/5/XIIwmiGZmZjKcbLVa2aiIzomeYfJiIKItVZtmsxlf+tKXsLm5ieHhYWi1WvZJWF1d5apva2uL3RcpgJhMJraqJot0GlJFKqKioiIMDg6yA2ggEGAn3+bm5iNDAvV6PVvM0/osKCjAysoK8vLyUFFRgdzcXIyNjaGqqgpLS0s89pxQwEAggIqKCgDA/0fbe0c3dp3X4psk2MAGAgRAsPfe+/Q+Kp6RRmMVy4oll+Q5y4lXbL8ky35JvF5Zz35+jp/tdFcllixn1KWRNEUznBmKU9h77wUkGkGABNgB/P5g9qfLsfOeJmv97lpamuGAwMW9556zz/723t/NmzdhNBqxtbUldfC1tTVcu3YNJSUliI+Px5tvvimlkPHxcWRlZYkF99ixYwJ8GBo3OjqKq1evSgkpMTERZrMZvb292NzcxBtvvIGsrCzYbDZkZmZiaGgINpsN5eXlwl76fD50dHRInxs6eCjUZJLnL37xCxw7dgxzc3OSLZKYmCgbo7W1NQwNDUGlUgn45lx7/vx5dHR04Pjx45L3MTExIQ6llJQU2Gw2WK1WeDwe9PX1iVXZZDKhqalpT0I1n0PmqSwuLuIb3/gGysrKoNfrhZ1JTU1FWFgY3nnnHVy7dg3Hjx/H7Owsbt26hYaGBgwPDwuAYxfk2dlZxMTE4OTJk5iZmcHc3BweffRRXL16Fc8//zzeffddua/BwcGSMRISEiL9V8bHx6HVahEI7Madx8fHIz09HTabTbRmHo9HGu8VFxdjdHR0zzMyODiIQ4cOSahZcXGxxCOoVCp85StfQX9/P0JDQwW8vvLKKygtLYVWq8XU1BSWlpbw9NNPi91Xo9Hgo48+EnDB7sPh4eHCbiUlJaGzs1OaHTKRlkmwTz75JG7evCmdc+Pi4j7RHPFATIfScsmDGgcuAEoXxv3MCIA9u1oe3JUrmQylGFTpHFG+B4GCsm4PfJz1cP/iwZINhUL/nguGr1MKU3k+DFxS6kqUDIMyNIvnzcWPdP7CwgKmp6cxMTGBwcFBjI6OYnp6GqOjoxgbG5P/pqenJc5c+b0pUgwEArL40lVyP9PB60J2YW5uTt6XORgUHHLhIhXP92C5wev1Ym1tDVarFW63W5gEggVOBqurq1KbJUVvsVikORpjg/mZ7JZLwMJ6OUGU1+vFZz7zGXz5y1+W8abU4KhUKrz00ksiFExISEB0dDS2t7eh1WpFcMsmdNSdBAIBmby4SNO9w3FAsfP95Q9qTjwej7hSWBbi2OD7qVQqYR+U70fmg2zGzs6O2FYJimlNJgBWltzuB/cej0euOx1FfAbCwsL2OHr8fr+I7ZQ6JZaivF7vnufxkx7MLTly5Aiam5tRXFyMoqIiiY/e2dnttMscg+LiYgQFBYlint2NTSaThCzV1dUJwzQ3N4fg4GDU1dVhfHxc2ATm07hcLjzyyCOYnZ2VvkR5eXlYXFzE9vY2jh07hi984QuYnJzEoUOHZL7gdSftHhsbK9oDJs9Spc+fxcbGii6mtbUVWq1WMkG6u7thMBhk9zc7O4uenp493ZhzcnIQCARQXl6OoKAgiSRnWqrH48Hs7CxWV1cxNDQEp9OJwcFBrK2t4eWXX8aVK1cEgH700UcoLi7G5uamaD6ysrKwurqK6upqHD16FIcOHcIjjzyCq1evoqCgAJ/+9KexuroKi8WCoaEheDwe2O12jI+PIycnB7du3ZIIb61WC5/Ph8bGRpw9exavvvqq9ApJTU2VXk7h4eEYGRlBTk4O7t27h4mJCRFKzszM4OLFi9je3sbIyIh8dnJyMhYWFuB2uxEbG4t79+6J3mJ1dVWYVqPRKDouljk4/6rVaphMJnz1q1/FZz7zGZSVlYllv7S0VKzT/+t//S8JxXI4HLKBOnXqFMLCwrCysoLGxkacP38e4eHhqKysxPT0NCIjI2E2m9Ha2oqZmRm0t7ejrq4O+fn5mJubk/KWy+WC0WiUoDUy0Ha7HWtra1Cr1VhYWMD8/DzW1tYwMzODzc1NvP7661haWsLFixeRlJQk4IKA6cqVKxgdHZVwL3bpJlPHzXFbWxtqa2sRHR2N9PR0uN1uYejn5uYQGhoKj8eDzs5OmEwmrK+vY2hoCK2trfD7/Th+/DgmJyfx5ptvwmq14vDhw7JxGh0dhc1mw9jYGBISEjAwMIDm5mZERERIds0nOR4IdHCnr9QpKJkApb6CNDF95fx9LmzA3jKMUguhFJXyginZB4IFJVBQgoD7tSf3Ax0yChy8SgqdC6XSXaIU5rGMQHbnfncLrwMnegCw2WwYGhpCY2Mjrl27hpaWFklPXFxchMPhkJ0o7WDMpqBlle3KadPk4sySCG2ev3WDFaUrujBmZmYwNTUlkdGsXVLoxZ0PnSNBQUFwOp1iuV1ZWYHH48Hy8rJ0SuSCyh0hBbycTAgqlpaWMDs7i62tLSQmJkKr1e7JTgkKCpKFXKXabYNdWFiIF154AS6Xa48Amdfgvffew+TkpDhN4uLiZAwpyyssgXFsEnjSqXD/GOBCz3HC/8h8EFSy6R138hyfrD/z+ig1HtSxqFQqud46nW6PjoK2V4IzAPL5fP+wsLA9QWwUSLIJIMEuAQhzMvjdlWyhz+cTYSRrtcoNxic9IiMj8eqrr8Ltdguo4nnxHGpqamAwGDAyMoKTJ0/i05/+NFwuF4qKijA9PS3X3+/3o729HZGRkejp6YHdbhdwZLVaYTKZkJycjKCgIDQ0NCAkJASvvPKKNP0ioI6OjkZGRgYuXLiA119/HaGhoWhra0Nubi4Cgd1eJSUlJSgoKMDMzAy6u7vh9Xpx7do1dHV1CXNgsVgQHBwsGwOySMzzUAqJZ2ZmpLzFcWG1WlFdXY2lpSXs27cPBoNB8iny8vLw9ttvo6OjAy+99BJu3bqF7373uwgLC8Mbb7yB1dVVpKam4mc/+xk0Gg2uXLmCq1evYnp6GgcPHkRWVpYwIUajEYODgwIIqqqq0N3dDZfLhfPnz8NisWBubg45OTmIiYlBeno6PB4PsrKypNV6UVERKioqMDExgZGREbGXXrp0CY8//jjW19cxNjYGvV6Pzs5OrK+vi4h7cXFRykWLi4vIzMzEvn37kJ6ejtXVVWEx4+LiJOVXq9XC6/ViaWkJCQkJePvttxEREYH/8l/+CwwGAzY2NtDc3IyEhASsrKzgxo0byM7ORiAQQEVFBRITE3Hy5EkpuczMzAjAzc3Nxa9//Wusrq5KgitBMAOxqGWrqqoS8Wdvby9aW1ths9lE8P2Zz3wGqampAtRjYmIQHx8Po9EIi8UCtVqNgYEBTE5OwmazSV+kra0tabgWHByMtrY2nDx5UhoZpqam4syZMxgdHUV6ejouX74syaDM5MjPz8e+ffukqzBbIsTFxSErKwvHjx/HlStXEBYWhrfeeku6i+fn54vIVafT4ciRI/B4PBgZGcEXv/hFNDQ0ID4+Hv/jf/wPrKysQK/XIz09Hbm5uTh06BDsdjsGBwdRU1ODRx55BFtbWzCbzXjiiSeEZfqkxwPNKEqdBSc54LfzJ5STNg+l0PL+97v/Z0p2gu/LHSInduBjLQdLPMrJmdZE7jQ4MROEcHep3MlxsmDOhNKZwv4dPB8CGdphubDz7z6fD4ODg9JXIihotycHmRmNRiPUPnfJ3EUToZNaZxMwXndas7io3k/5K/UyXJx53pz0FxcXheVITk6W/AdlKSo0NFQ6ICYnJ0svAmojqCsJBAICKtiFVtkh1ev1Cn1NL3hqaqosuCzRkD0gI7S4uIjExER87WtfEw0CBY50gLjdbrz22muSXcEShbJUpWTAOAaio6Mlt4Pfg9+LPSB4H5QLPlkNvi91BhTTkT1hUzyOBe5wCcTZjI9OJI5LAmzuGvlzijzJQOzs7Ij1lQCEr2WJhGOHoE7ZnI8lFpVqN02XomCOIY/Hg8TExN/JTP7fDrJJMTExqK6uhs/nkxqwVqvF2NiYCD7b2trEMu3z+fDss8/i0qVL2NzclKhzp9OJ/fv3Iz4+XgAw46mLi4sREhIiXUybmprg8/mQlJSE7u5upKamIj09HRaLBYWFhZiYmBChHZ0CYWFhcDqdSExMREdHh3SBpcV2fn4emZmZCAoKwvz8vDS3stvtouWx2WxYX1/Hww8/jJaWFjQ3N+NTn/oU/vqv/xptbW14/PHHMT09jaioKCwvL2NqagpbW1v44IMPkJycjLfffhtOp1OcP9euXUNYWBhWV1fx+c9/HoHAbsYDI753dnYQExODb3/725LLQQtlaGio2GpTU1MRFxeHe/fuYXFxEUeOHMHU1BRcLhecTqc8H3l5eSKo5bxVWlqK7u5uYR5OnDghzOrhw4dlk6FS7Saf7t+/Hz09PVIeuXHjBvbv34/8/Hy8+uqreOyxxzAwMIDOzk5UVFQgKSkJc3Nz6O/vR3p6OjY2NqRpW0xMDK5du4aqqiqxHE9MTCAvL0+i3XmP+vv7sbi4iPz8fBw7dgw+nw8JCQlYWlpCTk4OJicnUVVVhTt37uDChQuSoEwXTWJioti4U1NTBbAkJyfjxo0bKC4uxu/93u9hZWUF+/fvh9/vR2RkJNra2pCZmSklIaa11tTUSD8WamScTifKysqQlJSEN954A8HBwThz5oywFAz9IoA6ffo0VldXhYV0OByoqKjA6OiozENjY2NYWloSsD00NITe3l5oNBokJCQgJCQEubm5cDqdaGhowMLCgpS1Dxw4gJs3byIkJAQnTpyAy+WS9by2thYWiwXR0dEYHBzE1tYW8vLyJLHWYDDAarXKxojCXYqHP8nxwEJSpZNAWVYB9u4Sgd9mMu4XfCrtkEoGheUM1pPJKAB7bYB0nvC8CBqU50VwwXMmGCEjw/fnv3Mh5O/yd/h/LlxK4Sk1A7S1jo+P49atW9IdkbHhvC5Evdw5c/fOBZOLQ2RkpCz+LDeQTuV1omWTDwOvEa8h79f91z4QCIirZXBwUESVTNXjwkZRIfUbDLMiiCCTwORQukAo1KRan4Me2KWWmfGhTO7k+anVasn4eP7555GVlbUnH4Zjz+l04p/+6Z8EyChdTtST8DN5PZgnogwcIxAg80OdSHR0tLiFOFltbGwIC0P7KWupcXFxMibI9nDskLkjeOB94jiiSJcsBIEoQSoZMAB7GJdAICDgQafTQaX6OOmX5S6WZ2jR5pjj/zm2qO+IiIiATqeTrJUHOTweDzo6OpCamoq5uTl0dnbKjhYADh06hObmZgwNDaGsrAxbW1tITU1FSUkJbt++jcjISOh0OkxOTgooWV1dxY0bNxAUFITFxUW89dZbsNlsApLr6uokHdTlciEtLQ0ajQZDQ0MiHg8K2g20MxgMMlbLysowOjqKra0ttLe3Izc3F01NTUhNTcXNmzfh9/vx5S9/WdJ5Dx48iNLSUtE1BQIBlJaWwmg0wu/3480335Rd6djYGGpra5Gamiq9TUJCQnD69GmUlpZCr9djZ2cHL774ovQvYQmS0dUHDhxAY2Mj9Ho9hoeHUVBQgO3tbej1ekxNTWFsbAyhoaHCWjK8qbq6GlNTUwJsV1dXcfjwYbz66quw2WzIzs5Gfn4+FhcXZfe+srKC8vJy6HQ6HD58WCh76ml4DQmW+vr64PP54PF4cObMGelg29zcLDHo6enpeO2111BfXy9JqFlZWRgZGUFbWxuCg4Ph9Xrxzjvv4Pbt23jppZeQkpKCrq4uZGdny/PCsqvNZhNxZUxMDI4fP467d+8iJiYGX/3qV/H5z38eNTU1mJqakmZ7ADA/P4+PPvpIBJO0eHZ2dorWw+12C3DLy8sTB8fU1BQuX74sJe4XX3wRV69exebmJsbGxhAbGwuPxwOLxSLPaFBQkDiMFhYWEB4ejvb2dty9e1daKbz33ntITU1FYmIiIiMjkZGRgd7eXklXvXz5Mra3tzExMYGysjLcvHkThYWFaG1tFUdWaGgoampqcP36dQQCARiNRilBUZgeFBSEoaEhjI6Oora2VswEZDGWlpZw69YtWK1WmeOSkpIQGRmJ6upqJCcnw+fbjX7X6XTo6ekRcwMAEd2bTCYMDw9/ojnigWPQuesDPra3Kh0XwN6odL5OPvA+EamyfMGDwIXNxQDsodQJLPhZwMd9UPh5SguqEmAof5f/fr/2RLkIKr8vSzn8XQoSvV4vxsbG0NbWhp6eHrFbajQayergedGmphSbktEheOFr6Z5QlnLo7iAg2N7eljCo2NhYxMTE7CkNKAGR8przWrFcwiZUZrMZ09PTmJqawvLysoRW2e12+VzS5mRKzGaz5BE4nU7p1jg3Nwez2bynnbxer4dWq8XOzs6e7qi8t3FxcbDZbJifn8djjz2GT3/608K8KO9dVFQUfvnLX6Krq0tEuhSr8XpzUVWWgyiMpaByZWVFxJcEeUoNB8+R45QsGACxLPPz+RqWSIKCggRkcGfIg5kdBNYbGxsSKqZ8ZggUlOOVn0WHFtktikGpy+Br+SywXwRBBrUMZJDIoii//3+kvFJdXQ2LxSL2VroDOI4aGhowNTWFoaEh1NfXS6M1hmXFxcVBp9OJlZBJloxpZjAaBXDT09Nwu92Ii4vDwsICNjc3pZur1+tFZ2cnPvzwQ2n+ZTKZEBoaihs3bkCj0SAnJwfb29siALbb7Th79qz8bHV1FXfv3sXs7Czi4uLQ19cHlUqFyspKfPTRR/IMHjhwAPHx8VhdXcW1a9fg9/slg4E22+985ztoamrCxYsX4fF4kJSUJIseWciEhASUlpaisbERmZmZ+OlPf4pnn31WAryoQQB2d5pKN8vJkyfxgx/8AJWVlVhYWBBtU2NjI1544QWEhYXh2rVrMJvNiI6Ohs1mQ2RkJLxeL4aHhxEIBNDc3IyQkBDZgISFhaG3txdWqxVbW1uYnZ2Fx+PB+Pg4IiIi0NXVhRs3bsDhcKCwsBADAwOIjIxEb28vwsLCcOHCBSkzzM/PIyoqCnNzcxgZGcHKygp0Oh0aGhqQkZEhosTl5WVkZWVhYWEBHo8HJ06ckAZzLF03NzdjbW0N586dQ3BwMOLj4+HxeFBdXY3Q0FC0tLSgqqoKf/7nf46f//znIvoNDQ1FQkIC0tPTYbfbMTU1hZ6eHly9ehU/+clPpLR29uxZJCUl4dSpU7h586aIjJmcy3tbVFSEgoICmM1maLVaYZKmpqYE1NlsNtTX1yM6Ohp5eXkoKSmRUlNnZyfCwsJQW1uLyspKOJ1OFBQUoLKyEvn5+TCZTMjJyRFApNFoxGXHpnNBQR+3L0hKSkJBQQGysrJgsVhQXFwMo9GIpaUl5OXlISgoSBjWoKDdVgG5ubnQ6/Wor68XC3JjY6Poecie5uXlITY2Fp/61KekZUZVVRWmpqY+8fzwH9J0yC8rFn7uNPl3pZCRv6v8PSX4uP+1dGVw4SQYuB+cKN+TZQpqSJS1d4IKAgllR1j2ZKD+go4ZJfvBiVtZNgoEAiLwYq+D9fV1sTex7wUXIpZtlAyE0m2gtNcScJBtCQ0NFTEksNtumCBiY2NDrKXBwcEwGAzyuyEhH3eFvR/s8ZoBHwOP+fl5jI+Pi8i1p6dnjwiMHRddLpdoUSYnJzE7OysPIq2B3EVxbHCRpCODoIwlJY1Gg7S0NPn83Nxc/Lf/9t/2jDkuttHR0XjxxRdx584duS+8bgRmvDbUJ7C8QLcMd/PMs6AeJigoSMCEUqcTCAQk3E2ZDMoyEncVjETnokdrMQEM34u9cpQLPOuiZL6UDKDX692Tokq1PPUdHP8sHfJ9QkNDpXRHgW5YWJh0waW7hwCW50GG5UHdKwDQ0dGBoKAgdHd3S8YBx9nW1hbu3buHc+fOITs7W0TJrNWzjNjX14fu7m5kZmYiIyMDNpsNPp9PJjydTgcA0Ol0sFgswuqUlJRgaWkJk5OTWFtbg06nw4kTJ3Do0CGMj49jZmYGISG7/SPKy8sRFxeHsLAw0ZLk5OTgypUrEnVtMpkwOTkJh8MBk8mEmJgYnD9/Hn6/HxMTEwgEAsjMzITPtxuVzQZ1hw8fFqdASUkJ7t69i+bmZnGQPPzwwxgbG4PT6URLSwsOHz6MM2fOQK1Wo6enBxEREaiurpbo+ObmZoSFhSE6OhqFhYW4ceMGcnJykJycjIqKCuTn52N4eBi9vb146qmn4PF4cPr0aezs7HabJmOg1Wrx0EMPITc3F7m5uSgsLERMTAz27duHQCCA5eVlVFVVoaKiAuPj4zh27JgkcrIsm5CQgHv37glL8cEHHyAjIwN6vR4dHR0YHx+XnfnFixfFJutwOMQts729jZmZGQQFBaGlpQVXr16VsZeWloa//Mu/hMFgQFRUFAoLCzE+Po719XXk5OQgIiICS0tLmJqaQnV1Nb7yla9IMJXD4ZDMj/z8fHznO9/B5OQkHn30UTgcDvj9fty9exeTk5PIzs7G1atXUV1djZycHDz66KM4fvw4UlJSkJycjPb2dhQUFMBut0ujQKPRiIaGBpSXl8Pv321aGBcXJ/NvREQEoqKiJJqcOh5qlux2u2xC5ufnER0djYMHD0qK9PDwMOLi4rC1tQWr1YrZ2Vncvn0bHo8HExMT2N7exuDgILRaLcrLyxEcHIySkhJsbGzAbrcjLi4ObrdbetCQdWaTPXYdHxkZEV1PdnY2/H4/rFYrfvOb32Dfvn0IDg5GYWEhIiMjRcjrcDgQFRUFu92Ora0tDA8PY2RkBBcuXEBWVtYnnh+CAvev5P/eC4OCUFJSsqcuzklZyXTcDzx4M5SliftBBv/OkkloaCjy8/ORkZGB6OhouFwuufBKoR7wMUXNRZXUtzIKmja5rKwsGAwGocSVpRIuhE6nU/IldDqdLNwUjM7NzclkwnKGcuFwOp2IjY3dA2KoUeDCRlpRmTDJhZXnQZBCCp+L1vb2x+3byX4EBwcLrRcZGYnx8XFYLBYEBQWhtLQUfr8fKysrWFpakoXu/msPQCh4LuBxcXHSdIilHz4wLLMoY81JxfP68vtwcdNqtdIKXil8ZPmor69P7ME/+clPkJSUBL/fL04ZLpDvvPMOXn/9dbGkso67vLyM1dVVHD9+XPrTGI1G+Y5kFwDssYQqxx3BBM+fgIHswNra2p4YdaaHkmHhM0Cr5NramvRxYRmEoIgCU3a15TVmpoeSCaK1mgDufjaRpZ2NjQ3ZbfP8+OzRubK2tobg4OA9wXQ8H35vtvIOCgrC97///d8C/P/eERS02+xsdXUVWq0WJpMJly5dwv79+xEZGSlsU1JSEkJCQnDv3j0cPXoUV69exbPPPotbt27JbnBychI9PT2oqamRpMihoSHZtQUCARw7dgwxMTGoqalBd3c3PvzwQwBATk6OlAZHRkYQFBSE5eVlNDQ0wGQyoaOjQ5rTlZaWSpPAsrIyfPazn0V6eroI5KhdUKvVUsYpKyvD/Pw86urqMDs7C4vFgvz8fCwsLODo0aNiuVxZWZHMDkZh05rIernD4RAGKjk5GfPz89jc3ERKSgrcbjfm5+dx6tQpdHV1weFwwGAwICEhAU6nU5w2Go0GH374IWZnZ3Hy5EkMDQ1JCBfbEuh0OtGEsPFYREQE7ty5g0ceeQRzc3Oora3F2NgYLBaLxIZzoQoODsbs7Czq6upE/FlVVQWr1SpzUE5ODpaWlqBWq7Fv3z6Ehobigw8+QGhoqGxYQkND8cwzz+DXv/41Dhw4IBbZJ554QoLV2NdkfHxcHEghISFoa2vD+vo6FhYWEBERgV//+tcoKSmB2+0WASedatevX8fFixdRXl4Oh8MBm82GQCCA7u5uREVF4dixY7/VITs/Px8XLlxAZmam5MyEhoZieXkZmZmZAt5sNpuIdZOSknDixAmYzWaJaWdn4u7ubrkmdIvwGVtcXMTm5iaysrJgt9tFtzQzM4N9+/ahr68PPT09OHjwIObn5+V5P3z4MHp7e5GYmAi73Q6fz4f9+/ejvb0dJSUl6OnpQVZWFrq6ugQ8ajQaseNSKxcbGysbDNqfS0tLsbq6CrPZDKfTiYMHD2JtbQ3j4+MoLy/H+vo6Njd3m0tarVZ5b6PRiNdffx0dHR3/zznigZgOJVugzN/goqtkB5SWWeBjLQaZCy6kypIFAQXRokajkX4L/BzWxu9fOJWiQYIS1tSVuhFOslzklW4aLpBkEZTsQ2hoKEZGRqQXAc9XmYvBMCi+F3/P5/PJYOVul7/D3SnrpdROMDeCDwOdI8wyUAIlAGJr2traEisku1gajUbk5+cjKytLxGVKsMHryu8cHBwsNla6W5iYZzabYTabJbDMarXKjpt5EkotDq2FSoDG+8TSA5tfud1uRERE4C/+4i+Qnp6+R8dDMeW9e/dw4cIFiXHnZyhFqaGhoQKWlEJI5mhQL8PFmC4N2kp57ckQ0BZJDYXRaJQxRtGkWq3eI4il7ojsAstcSptrbGws1Gq1LP5BQUFwuVx7SoPKsUVWbG1tTd6L45vnTaqcTOHq6qqUSNxut5SRfD6f6D2UbMjy8jI8Hg8SEhL2uHo+6WE0GhEVFYWCggJxQ9G6urm5iYMHD2JlZQULCwswm80oKyvDe++9JxM4y4NtbW0YHh6WOHOfz4exsTHk5OSIYp8gSa1WY2lpCe3t7eIguHPnjuzKDAYDTCaTNDGbnJzEiRMnsLa2hpMnTwrIYd7GxMQE+vr60N7eLvcgNzcXBoMBhYWF2LdvHxwOBzIzM9HX1yfiP61Wi3379uGNN96A2+3G3bt3sbi4iJSUFBQXFyMjIwOTk5PY2dnB/v37UVFRgRs3bsBoNMJqtUKn08Hr9UKr1QLYZYb4b7Ozs2hra0NKSgpOnjwpO3Uu4iyx0FKan5+PmpoarK+vIzw8HHV1dQgEAtIJdmhoSEKuTpw4gfLycjz++OPIyspCbm4unnzySVy9ehVGoxE5OTnC7FRWVko0fWhoKDo7O7GysiK77+3tbcTHx8Nut+Ptt9/G3/7t3+7pnOr1emXjV1hYCJfLhZmZGZSXl6OxsRGzs7PIysqCw+GQOba6uhobGxvo6uoSLUFCQgK+/e1vIz09He3t7YiLi0N/fz+SkpJQVVWFubk5XLp0CTU1NRgfH8fbb7+NkZERpKSkYG5uTlyBSUlJiI+Px8rKimTCfPnLX4bVakV8fLxsSOj+YD6G0+lEXFwc6urqsLCwgB//+Mfo7+/H6OgolpaWcPfuXfh8u91tV1ZWUF1dLf1UAoEAqqurMTY2hoGBAdnMrK+vw263o6ioSBiykpISfPWrX5WfVVZWYmtrC5mZmRgeHoZWq0VnZyfeeecd+P1+vPjii9jc3MTIyIgwTHl5eRgdHcWFCxewsrICl8uFyclJlJeXo7KyEouLi7JB4cY4LS0Nn/3sZ+FwONDX14eioiK0trZiZGQEU1NT+PDDD+F2u3Hs2DGkpqbKeXyS44HLK8rFncBBCSz4b1xc+Dvcvd/vWFECF+X7x8bGiqbh/uwJUtr3symkmZW1dS4qtA1SeHe/DkW5INBGS5eD1WrF+++/j4GBgT3fg4FPyno/d7MREREiVlTaD7nTZUS1TqeT37m/jMD3pPCU6nQu4Fxo19fXER8fL46F2NhYZGdni6CIZSY+aNnZ2VLP48FFkiUK1qkpFFVeZ2V5jN+L154LLhd29mZR9mHh/WD8NbMIVldX8d3vfhdHjx7dkxPCxf369ev4wQ9+IJHkShcHALmuHJMEQAQXBH8svymDupg2SpBIsS53Aow3jo+PF+DMa0jBJZke0uAEntSPMNyL4k1eJwJiCpxpcyV4YwmFryMLQqDE54daG2W5heA0PDwcGo1GgArHJHU5fD4oZCWIVGqBPslhtVrR2tqK8fFxcUXEx8dLguzVq1eRm5uL3t5eJCQkYHZ2FsePH4fRaMTy8jJSU1MF8MTFxcnu2WAwwGg04vLly7BarQAgEfVarRaf//znUVhYiL6+PqSnp0uGBturU+QWExMDm82G119/Hc8//zz6+vpw/fp1+fn29jaGhoawvb2Nxx9/HKmpqYiKisLw8DDsdruUK6OjozE7Owu1Wo3ExESsr6/j1q1b+NM//VNsbGygqakJ6enpiImJQUtLC2ZmZhAaGoqUlBTY7Xax254/fx7Xr19HZ2cnoqOjRbSXl5cnmReHDh1CSUkJCgsLUVBQIG6drq4uAMDw8DAaGxtRV1cnmUDLy8tYWFiQIMDbt2/Dbrfj2WefxfXr1yUPJTc3VzIl2PyrtLQUzc3N+PrXv465uTncvn0bpaWliIuLk9LfysoKCgsLYTKZUFNTg+rqagwODmJubg4fffQRQkNDodfrsW/fPhQWFqK2tnZPDHl3dzesVisGBgZQV1eHkZER6HQ6FBQUSA6E3++X4K+enh5haEZHR/Hss8+irKwMPp8P+fn5AsJUKhVu3ryJP/qjP5JGbBqNBvn5+Th48CDeffddALsMZ0JCAubn5/HBBx8I46BWq6XvC0voLF08/PDDGB0dRWhoKCorK7G+vo5f/OIXeOqppyS+/rHHHkNlZSXOnj2LkJAQTE1NISUlBb29vTAYDBgYGJANMSPQZ2ZmAAADAwPw+XzQ6/VyTyoqKvAP//APSExMRGpqqnT0XVxcRF5eHgwGA44fP47V1VVER0cjMzNTyqenT5+GSqWSHlxk57RaLRITE9HT04OxsTEcOXIElZWVKCoqwvDwsKyPnZ2dcDgcyM/PR2NjI5aXl6HX61FaWoqKigosLy9jbm4O7e3taG9v/8RzxAMLSQH8FqDgzziBKx0oyn+730mh/Nn976cUe7I8w/9zsv1dIWXKDqnAXnGrMneD4IJ+cb6vEkBFR0ejpaUF77zzjsRlK8WfdAmQIqdwk+4Tnh8dHcpQM76/0kFBip0ggfQ6cytYt1YySgy54uJGxwXjsPlZXHR5TSkIUl4nhmSRWfH7d5sFMXTJ5XKJXS86Olp0B7xe1DyQyaJAUdlVlQpvBgOx7bLdbse3vvUtHD16VIKgCO4iIyNx9+5d/PVf/zX8fj8SExP36C42NjYk64SiwK2tLQEVBCd0fZAJIXhjMmkgEBDLLy2ksbGxEihGMBIIBESnwPfV6XR7GBfqcRiWRYaLzATpaGXJheUmt9stCybHJB1FBFDK3AueFwEqrz9DrQg8+f39fr98Z5at1Gq1OFg49pQs5IMcFHQyVdLlciElJQUTExPiHtHr9YiJiZGAI4vFgszMTInV5q6YGqCuri5YLBYcOXIEa2trOHz4MI4cOSLn+sMf/hD79+9HQ0ODuBYWFhag0+lQVVWFyspKDA4OYnFxEUVFRUhISMDrr7+OjY0NVFZWYm1tDenp6YiIiMC+ffsQEhKCvr4+ZGZmYnl5Gfv27RNHzPLysmh3ysrKkJOTg1/96lcYGRlBZWUlXC6XdKNlp1meq1arRUNDAzQaDebm5jA1NYVjx47hwIEDmJmZQU1NDfx+v7Saf/LJJ6UfUlJSEvr7+9HT04N79+6hrKxMSpUHDx7E+vo6tFqt5L0EAgGxsJaWlsLn82F+fh4AcP78edjtdrjdbuzbt0+0WxRasux46tQpJCcno6OjA+np6QgKCkJhYSGsVismJibgcrlgNptFzwDs9mAhy1xRUYGNjQ309vYC2HWm6XQ66QRcXV0t+guybyqVCgkJCeju7hbg7/F4sLGxgd/85jf45je/iaKiImRmZsLtdsNutyMtLQ2Dg4N47bXX8MorrwjLnJaWhunpacTExMDn8yE+Ph4VFRWora3FyMgIkpOT8ad/+qfSFJOx9xSI6nQ6LC4uAgA+/PBDlJeXS7lheHgY2dnZmJqaEkfH4OAgBgcHodFosLCwgOzsbCwsLMBoNOLatWvQ6XR7mPPCwkIsLCwgOTkZn/3sZ2E2mzE8PAy/34/MzEzcuHFDIhciIiJgs9mQmJgIYFfPRNdQZmYmxsbGYLVakZqaitjYWCwsLEjUPEPjysvLsbW1hZSUFCwtLSEkJAQOhwMXLlwAsNsPh60uxsbGpPlhTEwMMjMz5d6pVCosLS1hfn4eubm5SEtL+8TzwwODDjIPBAjcUXFSVi6I3NVxECt/j4fSfcJyC/MsuIDHxMTIoOE5cOdHEKB0vHD3pzyUNXB+pnKXzp0ewc3y8jIuX76M8fFxxMbGyi5Zo9HsATLcUfMho/WRtS8uBFwEyPJwBw1AwIRGoxEaXqVSSR4/z4mCVgKC7e1tqedS8MoSkXIXDEDuFXfTUVFRKCsrk+/Ge8BJnOAjPDwcer0eer1eNCtmsxlLS0uiGDcajUhKSpJymF6vh8FgEABE5ovXnwswa6lxcXH4xje+gc9//vOS06FkrqKioiSFj/ZEgkva57gzZjBYeHi4BHApyyaMjlapVOJqASClHb1eD7VaLTHqBKMsgSgBMxmYQCAg0e0sSdH5Q288SwIEHGRNWAohexEVFSVggSmPLE9RV8KSjNvtljHl8/n22IOVfYNYKlpZWZHfJWCMjIyUvi4A9jyjLpfrgRu+AcCpU6eQk5MjsdRkLLKzs7G2tibhduwR4nQ6ERISgoWFBQEsnNQCgd2mey+88AJUKhWWl5dRWFiI9fV1vPvuu3jiiSfg9Xrx8MMPS+M1MiEExmyWZjAYoNVq4XA4ZGzl5eVhYWEBGo1G3BoEmltbW+jo6EBxcTEuXboEm80Gg8GAnZ0dyXiYnp7Gj370IwFPjLynxZPjZXh4WMTDExMT6OzsRGhoKJaWlvakRv7mN79BWloaUlNTRW9x4sQJTE9PixNifX0ds7Oz8vydPHkSFosFd+/elQwKxlx7PB7o9XpcvnwZiYmJUKlUos07dOgQenp6cOLECRQUFKCoqAiTk5PieqEY99lnn0VRURESExOlY2x0dLTYSMlm5ubmCh2flJSE5eVl/PKXv0RpaSm2t7eRlJSEz33uc7LzJ9M5NTUFrVaLubk50S5QR0U2ed++fVCr1fjzP/9zfPWrXxVwx7bxg4ODqK6uxvz8PH72s5+hpKQEaWlpCAsLg8vlgk6ng9VqRVZWlizaDCn7yU9+gtXVVcTGxkrK6/Hjx/Hiiy8iPT0dlZWVCAR2m89tbW3h4MGDYrmura3F1NQUMjIykJSUJI0F2dtlZWVFAsNOnjwpuS+3bt1CUVER2trakJGRgdHRUYyMjKC2tlae0du3b+PJJ59EWFgYWlpaxH69traGwsJCBAUFYW5uToS/FB+PjY1JmS4nJwfT09PQ6XTSB6qsrAzT09Pw+/14+umnMTs7i/Pnz+PevXuiJ4uNjUVNTY1sjHNzc1FdXY3Z2Vnp53LixAnJlpmfn8fBgwc/0fzwwJoOpYBUeXCRU1Li3O3zd7gzVToplGmIylo4aTwla0CwQFUuQQ1/rswQIYDgeXBHz9IFD6U2hBoOi8WCzs5O0Qv4/X4JauEOkOUBfgfu8smokEEh28FJnRM97ZEUKnKS465VydooS1RKlicoKEicPgD2gAp2vlWKI5Vs0draGqKjo6URl/Ja0AHCXRQAcXswy8HtdsNqtWJubk7yBVgT5MLF70DAw/ehbTYzMxNpaWnYt28fnn76aemGCOxlvVQqFdLS0mSiI8OjzHXZ2dmByWQSgADs2mq5mLNsosydoNiYoJaCWTJzBEccFwSnSm0S2R+yI3xPsjtarVb6GQQHB8uuheNE+QyQgSDgoh17fn4eNptNxjVf4/P5JLcgODhYdqfULvF5IBBnt1BuENRqtTi5yPRQgMoxrmQiP+kxPT2N1dVV2O12fP3rX4dOp0N/f79YTp1OJ44cOYLGxkY899xzcl3W1tbQ2tqKtbU1zM3NoaGhAU6nEwMDA2htbZUFdWlpCZcuXYLRaERjYyOOHz+O69ev4+2338bTTz8tbKDVaoVGo8Hs7Cz6+/uhVqvR0dEhbdaDgnaTdvPy8jA4OCiJv8BulozBYIDBYJBSQlJSEoxGI4aGhrC1tYUbN26gpaVFgLNWq4XH40Fqaio2NjZw/PhxDA0NoaSkRFJ/AUjqaVFRETIyMjA2NgaNRoO2tjYcOnRISgoUBTN62+VyCZiPioqC0+lEdHQ0WltbcfPmTeTm5iI1NRWzs7PY3t5GVlYWNjY2MD4+jpCQEJhMJiwvL+PGjRuYnZ1FUlISXnjhBczNzeHNN99ETEwMsrKyEBkZicrKSiQnJ2NxcRFWqxVRUVFobW3F1NQUTp06hbS0NIyOjmJ2dhZ6vR6RkZHi8jh+/Dg8Hg9KS0vh8Xjw8ssvo6+vD1FRUZicnERWVhbOnz+PnZ0dPPzww7KJSEhIkHltZWUFn/vc5zAxMYHs7GxZUB999FHY7XaYzWYkJCQAgIABMr0NDQ1ISEgQZtpms4kehqLfvLw8PPnkk3j33Xdx/PhxALsbhrNnzyIuLg4ffPAB4uLiYLVaJbU1NzcXQUFBsNlsqK2tRXd3N5aWliTUKz09HY8++iieeuopREREICUlBXV1dVCpVBgfH8e1a9egUqkwOzsLs9mM8PBw5ObmIj4+XnoDcQPLdeLatWswGo1YXV2F1WrFwsIC2tra0NbWJjkjLS0tWF1dRUlJCYDd9fGxxx6T7tncPHLjevXqVURGRiIqKgqXL19GUVERxsbGJPxyZWVFeuh0dHQgNzcXs7OzuHz5Mo4fP47y8nJsb2+jv78fIyMjCAsLQ2pqKpqbmz/R/PDATIeSKQA+1nYAHy/6yteQ6lbSyvy338V88H3IBrD8oHwNWQkCCy7G/GxSyZzIlX01lGmSwMepqLTq3blzB3Nzc9I1j1HUXEBpjaSFkRM+9QoUklJPQGChdPBQ3MfFkvoFfh8ukAQU1Cz4fD6h7j0ej4iyuFvm7p7XLCQkRNp4r6ysSC4E0zu9Xq+UQ/j+LIMoswWoW2GnRI1GI9d7Z2cHq6urItBjLDodLUpxLpspxcbGIjk5WcoKn/3sZ/e4Xfg7BIybm5vIyMgQUEMbGA/eX04+SkDKc6WDiOM1Li5OmBKCON5rjilleYr3j/ZVgg3eax68h2QSaFcmmA4LCxN7dVxcHCIjI6UnQ2hoqJRJ1tfXsbi4KNkZrPcTEBBIGY1GREdHS8Iq2bX7nSnALptD5kKtVgu7s729LUAG+BiYK/safdJDq9VicnISfr8fJpMJV69eRVtbG1544QVMTU0Jzcto7K2tLcTFxSEpKQkGgwF6vR7T09PSlMxqteIb3/gGLBYLPB6PLBzZ2dnY2dnB3NwchoaGkJ+fj9DQULS3t2NwcBB5eXmS1NnQ0CDR15/73OdQU1MDn2838t3tdkOv1yMuLg55eXlYWVnB+Pg4PvjgAwQCAcndoYVxbW0NBw8elE6sTqdTVP1arRZVVVUIDw9HX18f3njjDVRXV0s3VpPJhJSUFKHDW1paMD8/j4qKCiwsLODYsWNob2/H6OgoLBYLRkdH4XA4YLFYcPTo0T1l7aKiIikhut1unDp1SjJpjEYjZmdn4Xa74fF4UFZWhoKCAni9XtTU1Iidt7e3Fzs7O+js7ERGRgZu376NwcFBNDc3C1BJT0/H9vY2MjIysLy8jMXFRfz4xz+G1WpFSkoKTpw4gStXrqCnp0eANLVVs7OzyMvLQ35+voTvdXR0yOfU1tbi3XffRXV1Nex2uwg7c3Jy4Ha78eKLL+KLX/yi2OlPnDiBhIQEyfnh0draKswpQRtLwlNTU6isrERVVRWcTqdoFKxWK374wx/iwIEDArZYkujq6oLH40Fubi4iIyPx+uuvS+8dsifR0dEi8u3u7sbMzAySkpJw4cIFWK1WEZa/+uqrOHr0KIKDgyVr5sqVK3j22WcxMDCA0NBQDA4OQq1WiwiaQVzT09PY2tqCWq0WAWpmZiZqampgsViEabJYLOjv74fT6ZSS3vDwsPRC4YZ4eXkZfr8f1dXVmJmZgdfrxaVLl6TMS0EtRcJOp1PKkiyHTU1NwW63w+PxoKCgAKGhoRKAxs7J/6/jPyQkJTV5v6gQ+LjkcL9Qk5OXcjHZcyL3/Q5BCgV6zKtXghxgb9gX6/zKBZGAhAiaoIcMA0V7nZ2deO+997C9vb2HgmbEK2ltjUYjAtOgoCB5LbUHGxsbkpQYGhoKrVa7h8ngeXIxY5mFwIwMyv0lFeoveP2VQkJadLngcnEiMKIIkqUAvje1CLR3KYW+yuunBAShoaEStctdodJBsbS0BIfDIQJFxtcTSBFMLS8vSx8IJjAqBcEcS8AucC0uLpbvqASr8fHxAHZrkVyMKfbjvYmKikJcXJyACYoo+V09Hs8exoTaDCXNy5IT7bIUZRIo8FqQoWNJkbsWrVYrOh7ef1KZvO7se2Kz2WTXFh4eLtqTpaUlYf7o8uB5kVmjbkMJqpVaJKPRKM3nCD6p+6CWh/kiBLsPcvD7q9VqzM7OYmZmBp/61KcwNjaGvLw8eDweyQFwuVySE6DRaBAbGwuVSoUnn3xS7JhHjhzB22+/jejoaKSlpYkQbv/+/fD5fCgoKIDD4RCHS0jIbodPakF2dnakDTdLV3NzcwgLC5P2Am+88QY0Gg2ampqQm5srO2iWhchg6vV6DA4O4le/+hU8Ho8IK2/+W3ppUFAQJicnhTE5deoUsrKysL6+jsceewxzc3MYGBgQgEU3g9frRUVFBSwWCwoKCqDVapGRkYHHHnsM5eXlEg/O6HKWBnU6HV599VXEx8djZmZGxvba2ppofrxeL2ZnZwVsXL16Fd3d3WhraxMdT0FBAeLj4+H3+3Hs2DFkZ2eL6LOlpUXcTI888gisViv27duHpKQk0enodDqsra3hwIED6O3tFXa4oqIC6+vrMBgMeOyxx7C8vCwW9qmpKdy5cwdBQUF47bXXBKzb7XaEh4eLQHJhYQE9PT1ITEzE4cOH5TlaWloS1rK4uFiiFeLj46XB5Pj4uIyx9fV1vPPOOzh+/DgMBgPUajUyMzOFNYmIiJAU4YmJCWxtbUl3Ypadfv7zn2N8fBwdHR0YGhpCamoqnnrqKaSlpaG8vBx/8zd/g4qKCiQkJODmzZvw+XxYW1vDO++8I8aBqKgoHDp0CC0tLfB6vUhJSUFKSopoRYxGIzQaDaqqqiT51u/3iz6Ez+iBAwfQ0NAAm80mCarXr1/HxsaGgDDOzXFxcTAYDDh79iy0Wi2ys7ORnZ0NtVqN8+fPy2fU1NQgLCwMra2tUsJU6u6SkpKkjGyz2dDY2IjU1FQkJycjOjoaIyMjn2iO+A9pOgDsWfiVu6H7AQV3nPzvfkuqErRwsVF2EGWuAClTPtwAxKnCgywAkS4XME4+ynNmD42uri40NjaKaCkuLm4P7czzpkWT587eGDwHlWq3LwYXX9YLCWLozSeNT9sjQQGdL0pbMq8LbZL8OR0KykVGadXk+7OFMXd1Sp0Gz5fWVZPJJGUapUtI+Tm8jqT+GK5FFoDnQJstX88medwJsQzHWGllWex+7Q1ZgtjYWBFkcrfOhXl9fV2Cpfj7Xq9XBLwARN3NPiNkhujqoVWb45JjTmkb5WJPHQU1QRznvA/04qtUKrHTut1u9PX1yedRAExARsBG7Q9j2AkalNeN943aCKXWhIwc9SQUKFOvQ4EqgTJBPMEutSzMlPhd+qj/28F7TwHt2bNnhc6PiIiAyWTC0tKSMBcTExO4efMmbt68CYvFIgyc2+3GxMQEUlJS0NTUhPz8fFgsFoSEhCAuLg6tra0wGAwICwuDyWTC+Pg4amtr0draCgB45ZVXkJqaiqmpKVgsFtEc3blzR9JOp6enYbFYJAH14MGDeO+995Cfn4+EhASEh4fjj/7oj3Dnzh309PTg7/7u7/DWW2/JeJmdncXOzo7oBJxOJ3Z2djAxMSGUNJsUXr58WfqLjIyMCPhraWlBRkYGtFqt7MjpOGBIGVmvgwcP4tFHHxVx5Icffoj9+/eLOJRalvX1dWmmODg4iPT0dIyMjODll1/GZz/7WajVahQUFEiZlD079Ho9ent7sbCwgMLCQjidTuTk5MBkMonu5lvf+pZ8xu3bt+X1Wq0WZrNZgqpY7qOzZHR0VHbi7AxcUVEBs9kMg8EgVtjV1VW43W7p1Lu0tITKykqcO3cOAKREdeLECUxMTODevXtQq9VISUmBWq1GTEwMHA4HNjY2UFpaCqfTKR1oDx06JOLkqakpGI1GlJSUSDS4yWQCADz99NPiBmTn26SkJCQmJkoTOybHdnd3Izh4N6iPLMvw8DASExMxOTmJnJwc6PV6WUt4berq6pCbm4vbt29LUm9XVxe0Wi0uXbqEpaUllJeXIykpCWazGeXl5VCpVNLfJDg4GIuLizAYDMjKyoLP58PnP/95aDQaVFZWwmg0IiwsTAB+eHg4uru7YTabYbPZJPCro6NDwt1ycnLQ29uLlJQU/OIXvxBBt0ajQUNDA7RarWQ9VVRUoKqqSoA8+8J8kuOBQAfdDVwMlFoApXVUeXAhVApNlRkf3LGSflZa/QgYlJZB5fvxs5Xvw9/njo3ahODgYCkXEMjcvn1bgoO402KXVE7C3EUqSw/K8gI/m5M7002V4Vl0YxAw8Lw5ALlDUVKG7E5K4LS1tSX5EUqnAs+Pu3ElM6HRaMReTGaEwIJZH7ymGo1GaEslwxAbG4t9+/YhJSUF8fHxokfh52dkZCAjI0NCrVhaWV9fl14stMlSJOz3+5GdnY1jx46JZgX4WDPE/5Q2ampB+P0pqqRA02g0yn3jvWcJYmNjAysrK/J37opYEqNdmQu1y+WS68IcCwaT8WfK8tHGxoaUe1ZWVvaUWDY2NtDT04Pr169Dr9fLjofZGCx5kAolu8LxBHwMxggoCCoJIpUpq7wedDwpLeRkPChc5iKl1LDwfXj8rs7F/7eDCaxarRb5+fkYHx/Ho48+Kn1ICDxPnTqFxMREGI1GuN3uPZ1JvV4voqOjUVBQALfbjSeeeAI9PT3Q6/UICtrtJREXFydWyu3tbZhMJty9exdxcXHSFpz9VJikaTQaUVRUhDfeeAPvvfcejh49KumR7e3t+Oijj5Cfn4833ngDubm52NjYwPDwMGJjY2G1WnHkyBGUl5dLQ63S0lJxI1ADVlNTI8FmkZGRmJ6eRmhoKB555BEJKuSYf+KJJ1BWVoaenh5YLBapt1PT9fu///tYWFiAzWaDXq9HaGgoXn75ZSwvL+Phhx/G4cOHERwcDLvdLq6Vnp4eGAwGmM1m9PT0IDg4WBY2v9+Pn/zkJ9KAbHp6GikpKThz5ox0UQ0LC8OZM2fg9XpFCBwdHY2rV69KG/Senh7cvHkTNTU1+PSnPw2bzYbq6mocOnQIOTk5EpgWExODgYEBLCwsoKCgAOXl5ejo6JDOsrdv3xamLiMjA0VFRbLBampqQlBQECoqKvDZz34Wi4uLGB4elo1Ye3u7LIYOhwM+nw+Li4vybFZVVcHhcCA6Ohq5ubno7u4WIXBXV5cEYA0MDAjIMxqN0Ol06O3thcvlkswRjUaD+Ph4pKSkYGNjQ1rPb29vSx+TjIwMmM1m+T5ZWVmor68X9shisaCtrQ1LS0vIz8/HP/zDP4gQNjMzUzY9Fy5cQHl5OUJCQjA+Po6rV6+is7MTLpcLw8PDGB0dxdzcHF599VWJUHjjjTeQlJSElpYWiesPCwtDYmIiZmZmxB22f/9+bG9vIz09HY888ojMQ3S0XLp0Cc8//zzu3LmDkZERsbz7/X4UFxeLKJ/2f79/12U1ODgo0e2f5HigRNLy8nIprSh1GcDHDAgBAydJHkqxJwEF/52LDsGBXq9HRUUFcnNz4fP50N/fj9u3b8uujwu+UifBf1NaN0nH7+zsxlKnpaUhLS0N4eHhuHv3rugUCGyWlpYkRpYBZVwEuOizVh8WFia7RQCSlUFAE/i3PBCCNLIYISEh8mDRqcLf5bXz+XxwuVyy0LlcLmEUlpeX4fP5YDKZZBevXPB5P3h+BGIEKl6vV85/a2sLUVFRmJ6elk6n7GUC7AKT8vJy/MVf/AVmZmawvLyM9fV10cewXEPWYHFxETdu3EBXV5c4ceLi4pCSkiIlJ2A3xv2ZZ57Zs5jyft4v7CXTER4eju9973uYmZmB2+0We2FMTAzOnj2L0dFRDA4Oik0WgARH+f1+cUMpczJYvuIizLowyxkA5Bor2TiOOQYzMdciNHRv35b19XVpbFVQUAC9Xi+KdqWAms8RRa/KwDAGYNEjT7DCz2BJkaUWgiCOf94fjgOOXwJYMj/KXBR+Dz7nP/jBD35rM/HvHYWFhdjY2EBxcTEiIiLwxhtvIDk5GZmZmRgZGUF6ero8bzabDTk5OVhYWEBNTQ2mp6fhcDgQGxsrwDQvLw/d3d3SIyc7Oxvt7e3CIBYWFopz4NatW3j99ddRU1MDAHjooYfw/vvvS9w6y1bz8/N46qmnsLCwILvBa9euobS0FACQn58v4XqXL18WHdHMzIwIYdmZeWtrS1Ip6RQICwtDRUUFvF4vpqenUVVVJcLanJwcdHR0iJWVehy2hGdmA3vMXLp0CUeOHIHP50NTUxOqqqqgVqvh8/nEcdPf3w+Xy4W6ujqo1WrMz89jaGgIDz30EGJjY/Hhhx8iNTUVoaGhyM3NxcLCguzEyUgCQFVVFUZGRuD1etHX1ycOkeTkZNHP8Ln/4IMPUF1dLeMyPz8f3/jGN7CxsYF/+Id/gFqtRk1NjYCW+fl5ZGVlobi4GE1NTejs7MSvfvUr7N+/H3fu3EFqaiq++c1vYmdnB6Ojo8Ji/eEf/iG6u7tx9OhRbG1tYXx8HFlZWejp6UFGRga6urokSnxmZgbp6el45ZVXcO3aNWg0GrS0tCA5ORl2ux3f+973MD4+DofDgfb2dqyvr6O8vBwajQaNjY3Izs5GSkoKdDod7HY7lpaWEBMTg4mJCZw6dQqzs7OYnZ0VrVhUVBQmJiak3Mqcm97eXiwuLiI9PV02u7TYcz0qLCxET0+P9HXp6+vDysoKzp07Jyyb0+mU+Tk/Px8ulwv9/f2or6+XGHytVguv14vk5OQ9PVxu376NM2fO4Nq1a8jOzkZ0dDTcbrcAvLm5OVy9elVa2MfExGB2dhZlZWVYWVlBXFwchoaGxPFCE8Hc3JzM43R6MVH129/+9ieaJx6I6fhdb6h0Vtyv5wB+u9Ms8LFQlKyFUkxKRoMTsHLnCUDoeiUrQrACQNBbIBCQ+GIuvgQp7e3t0nyLbAY793Hx4CLMCZ2v9Xg8e2roZBJomWP+AfMSAMjkpNQjKHerLOfwGm1tfdwqnd+PO9qgoCD5N57DxsYGNjc3ZYfKnTvLEKTWg4KCoNPphElgmYCAJjo6Gjqdbo/92O/3IyUlBaWlpThy5AhOnTqFY8eO4ejRozh06BCOHDmCffv2oa6uDk899RS+853v4E/+5E+g1+sRHx+PzMxMGI1GqFQqoTkff/zx3wqIU5ZUOC54PVhSY0Oo5eVlsQqfOnUK5eXlmJqa2hOmRXEpy0FkiyjGJWNB3QnfT+nYILjg9QwEAnvKRlFRUTImVlZWhEVYWlrC8PAwhoeHYTQaUVhYKJH8GxsbwsaQ+aGynOmRwMe5J6y7cyzQQkoGhoCJJR0GxLE0xuvHZ5HnCEDKLPeDLE6MvI4PcnDHyDLDI488IoA6Pj5enB+Dg4PIzc2FVqtFXl4e5ufnERERIVqCnZ0dDAwM4OLFiwB2BarsTjo1NSW9Lebm5qBWqzE3NweVSoWzZ88iIiIC7e3teP/99+F0OjEyMoLMzExER0fDYDDg5MmTUv75T//pP+Hy5csICQmRfAW73S4ZPTk5OVCpVBIMxk2J1+vF6OgoEhMTJcSQUdkLCwsICQmB0+mEXq/H2toaJicnBSyQsdnZ2cHg4CDcbjd0Oh1GR0dRWFiIlJQUZGdnw2w2IxDYjcXu6uqSxmI7Ozvo7u6GWq2W0LOnn34aH374IWw2GzIzM1FfX4+RkRE0NTWhtLQUERERKCkpwbvvvisR6klJSQgKCpJFvqWlBdvb26ivr8cLL7wgWRV6vV6a03m9Xin9LS4uYmNjQxjLe/fuYX5+Hl/60pfwla98BRkZGfjsZz8r9/ncuXOIi4vDn/zJn+D73/8+Lly4gNjYWDz33HNITU2F3++HzWbDo48+iqysLHz+85+X8i/nbJPJJJoNMooEHNQgzczMYGJiAq+99hq8Xi/Kysrwta99TVJFNzc3cfjwYej1ejlnYLcD8vz8PFpaWqSHFIW+V65cEXbE4/HA6XTC7XajrKwMRUVFWFxcRFhYGJKTk6Urb05ODpKSkqTpXkxMDAoKCmCz2TA4OIigoN3kXeqR9u3bB5VKhYmJCdhsNgl1Y3Ce0+lEVlYWAoGAsElvvfUWCgsL4fF4EBMTI2Usm82Gq1evysZwbGxMrNI0S6SlpaG4uBhWq1U0ZSxl/+IXv4DD4cDa2hr0ej3GxsawtraG1NRU2Gw2LCwsoKGhAVFRUZifn8fg4KBoof5fxwNrOrjAU5uh1Goo7XVKq6qy/KJ0nihfp3SBkLGgKE3pZiA7oNSWcFIGPl60lQsad3ek0Wg1U6J8BhVtbW2JoE/pgOH7cZFQumUYOKW0zCo1Icr8j6CgINmBxsXFifaEpRaWYJTaFYpRt7e3xY7JzwQgYIbnw/fyer17ygzK13Bx4W6fDABzCMLDwyW6nSxSWFgYoqKiJEyKu2mlfTcmJgaf//zn8d3vfhcPP/wwKisrkZmZifz8fBQXF+PMmTPiFAJ+W0DMPyuFvxR15uXlAdgFcRkZGXjmmWdw5swZWcioQ+A9ul88y909yxQsSdG5QXsxAZESCPGgjoLXlWWLhIQErKyswGazYWpqCtvb2ygpKUFRUdGeEkfg32zYvPdkKAgQeR+UYJ1jhmOUz5Fy7KlUKmg0GgH+FJyyQZ0yS4fPpFK0zO/Mchh7ryhLfp/k2NjYwNLSEq5cuQK1Wi29anp6evDMM8+Ig+Pw4cPS1pt2Tjas4gYAABoaGvDhhx9Co9Ggvr4etbW1yMrKkuZXbHoVHh6OgoICjI6Owmaz4Utf+hLOnTsn0eZ8bzYvy8zMlPjoffv2ISIiAhUVFbLZ4EL08ssvo7OzE1/4whckT6GyshLd3d2oqKjA1NQU0tPTkZaWhvj4eLhcLuTl5Ym7o7e3V+4vHTAMkoqMjERiYiJ8Ph/a2trwqU99SjQtV65cQXp6OkJCQiR4yWw2Y3x8XPRUd+7cQU5ODmpra9HU1ISTJ08iISEBNpsNarUaDz30EIqLi1FaWoqjR4/izp07qKmpwcjICNxut/SfOX78OK5cuSJza0ZGBhwOB/R6PaxWK5qbm2E0GuH1eqHX61FQUIC8vDzRgb3yyivY2dlNCz116hQ2NjaQnp4uCZyLi4s4c+YMzGazsAN+vx9Hjx7FD37wAzz88MP4gz/4AxQWFuLcuXNwu92SqBwaGoq8vDwkJCSgtbUVw8PDcLvdOHToEHQ6nXzf6Ohoub+HDh1CeHg4qqqqEBMTgy9/+ct4/PHH0dfXt8fen52dLfq0hx9+GP/6r/8qLpJ79+5JXkxQUJBEt29ubqKhoQGJiYkIBAJobW3Fq6++ivLycoSGhko2CwH2zMwMdnZ20NjYiP7+fmRnZ6OyshJdXV24fv06hoeHUV9fj7y8PPj9fiwtLQEAJicn4XK5kJWVhbm5OWFY2buHcQDJycno7++HXq8X9w/nZ1rU2ejv5z//OXp7eyV3KS4uDunp6eKCycvLQ2trKyIiIvD1r38d4eHhqKysxMDAANLT08XZZTKZkJ6ejtnZWUxPT2NzcxOzs7MSXf//Oh6obzUnO05YygmJIIET9O/K5ODPAeyZiJUHa+xRUVGy+JtMJkH0SjChrMnzvbjj5wTPckZwcDDcbrcIgxjkw0WICwAndu5ISe0rWRnmBVCfQMBECypFmgQJPCeGVFGrwWtGe5XP5xMaOTo6WnbPWq0Wbrcb6+vrYslV7uJDQkKk6RL918vLyzAYDCJYpPiND5myvTmB3ebmpgxm7vyXl5eF/VHeL2W5QekKCgR2sx6SkpLw3HPP/RaDBUDqrkp90P3sGNku/n1nZ0f8+isrK/jqV7+K4uJirKysoLu7W0At9Rz8Tqy7KsssLDnQ2RIUFCT5Gfw5vzNLMQQsStcWQ7wsFos0mkpNTUVDQ4Owd2tra9BqtXvKin6/XwLwCAiCg4NFeU+QwcWKscZGo1GsawRuPC9lMi2bh7EdNcswFKCSaWGJhqU3apvoXuFrH/SgYJe0tslkgtvtxve+9z2kpKSgv78f1dXVsFqtmJ+fl5/V1dVheHgYLpcLpaWlCAsLg8PhQGRkJDo6OmAwGDA1NYWqqir09fWhvLxcxKmBQACTk5MyIXu9Xrz++utwu90ihAwLC8P+/fvR1NQEg8GAp556Ch999BF2dnZgNBrx2muvoaqqCsnJydJV0+/3o7a2Fr//+7+PZ599FidOnEB0dDQWFhYQGxsrFt6+vj7pQREREYHJyUkZC1NTU9jZ2cHS0pLUxLVarTzXSUlJ0lcpKioK9fX1GBwcxM2bN1FWVob19XUUFBRIiq7FYkFdXR1u374tdl6DwYAvfOEL+PnPf478/HwsLi7C5XKhsrISV65ckYaAb731Fo4dOwar1QqLxQKDwYClpSUUFxdL+amxsVGAdFhYGLq6usRymZCQgPfffx+pqaloa2tDYmIigoKC0NHRgZiYGGmZAADp6elIT08X+21ycjIsFgtiY2MxMTGBmJgYpKenY2lpSfrEaLVaKY8BkFRjMp0ApCzT3t4Og8GA8fFxVFRUiJ6O5YSFhQV8+ctfhkajwdWrV3Hz5k0YjUaxQrN3TXZ2tjRQY1+WiIgIuN1upKSkYHJyEgaDASMjI6ioqMBHH30Eq9WKQ4cOyX1pb2/fwzTu7OwgOTkZarUaCQkJKCwsRHh4OP7lX/4FiYmJ0Ol00jE3IiIClZWVcDgcCAkJgcVikTTjmJgYCSebnJxEWFgYtFotrl+/jsLCQhG8jo6Oyv2iazElJQVlZWX43ve+h9/7vd/D8vIyzp49C4fDgZKSEty5cwdNTU0ibmfvmps3b0Kj0cDj8WB2dhZBQUFIT08X6+zQ0BA+97nPYXR0FFVVVVhcXER9fT1sNtsnmh8eCHTc7xZROlGUP+OhBBS/qzTDnb2yvBIcHLyH3WDpgAsmRYlc4AgwOJkTIFCLQXQIQPIxGH7C8gEAcSsod5LcTdKeRpDF/AVO1pyced5UsZM1IDiiWJGfQa2HUjSoVqtlh0lGg+I86kyUr+d5097L0CAuSOwnwh2UEjyQQtdqtaJPCAQCiI2NxebmJpKSkqSefT+oVN7r38VU8NyU7g6fz4ePPvoIm5ubYtsiNc08E953pb6DgI/hYM899xxKSkrEvrqysiLfV3l/goKCRO3Oe0ABLxdt5lrwmrMJGn9f2cuEDBsnlqWlJQwODgrYoDuJY5T2ZbJdKysr0nQuLCxsj8CWjhNeR54rFykCCk5EyhIlKWh+Fq21FC3zu5N5IMPBexMaGipWOJYiOUYeFHSYTCb4/X6UlZXBbDZL3Ven06G6uhrj4+M4e/YsXn/9dfnOfO7IFJlMJly8eBENDQ1obGzE0aNHERERgY6ODjgcDmkgt7GxgezsbISHh6OlpUU0Z3a7HX19fTh9+jSamprg9Xpx4MAB9PX14d69ewgEAqLbKi4uluwHAmPqTcrLyyUV9YknnsD8/DxMJhMGBgawvLyMlZUVNDU1Yf/+/SgtLcU777yD8+fPw2KxICMjAwsLC+js7MRjjz2Gzc1NdHR0YGlpCZOTkygtLUVLSwuqqqpw7do1JCcnY3JyEk6nE11dXbKL7evrk5jplJQUvPTSS6ioqIDD4UBBQQF0Op2k8b744ouwWq3y2ubmZpjNZlRVVeGnP/0pcnJycO7cOURFRYkAkp1/IyMjUVFRIWUuMhtmsxkHDx7E7OwsUlJSRGDLBnqzs7N4+OGHJSGTiay9vb3Y3t6WuO34+HhJ4GXUPI/IyEgpNbIE+atf/UrSLn0+H86dOydBYc8//zz8fj90Op1Y5hcWFkSXZDKZUFdXh6ysLDz99NMYGRlBVVUVbt68ieDgYOTm5uL9999HaGgovvrVr2J2dhbh4eHY3NxETk4O2trakJycjK2tLRF7Xrx4EbW1tfB6vaiursbCwgIMBgPa29tx8uRJuFwuDA0Noa6uDn19fThw4ICEvv3mN78BAGRkZAgIPX36NLq7u1FeXo7m5masra1h//79suFLSkrC7OwsNjY2sLi4iK2tLXEcmUwmPPXUUzCbzRLV39HRIQGWxcXFGBgYwPPPP4+FhQUcPnwY29vb0menpKQE7733HhYWFlBeXo7c3FzcuXNHNsS1tbWYnZ1FZmamhI5dvXpVRNK9vb0IBAJITEzE0tKSJJ5+Ulb0gZkOJZOh3Amxbq48CBTuTzVUlmOUO2cCAB5cBCh+VC6WPJQ0uJJdUU6aXLQowlNqNgKBAObn5wUlUn9BMR8XdE7UFIRycmJfDuoF+PlsWMTFip55+ujv33mz/KNkUgiYqHrnQqZ0/gQFBclulmUdv3/X+strxtdwV83SD0s7dD8QVOzbtw89PT0itmJ/DiUIYMlDOSaU/w8JCUFMTAycTqdEC0dERGBhYQGtra0i1EtNTRW7Fm1vBAPKvAkCkxdeeEF2fyEhIaKyp+hXCYRZbuHERTDKe0lrKFkmjufw8HDp9qq8J+x47PPtNuVaXFxEUlISkpOTRRy5vb0tJT+CuMjISNEQ8DUsH3Cc8zryeeEY3t7ehtfrFfsegSifLWWJRKnNIWtH5oyTOtkbZjkQ1HAcMGeGzM+DllcWFxeh0+mwtLQksdhsfkfNxOjoKE6cOIF79+5hdXUVpaWlmJmZQXR0NJKTkzEwMID4+Hh0dXXh0UcfRVNTk2gnnnzySdjtdgCQnadKpcLhw4fx1ltvYXt7G4WFhbh37x5iYmIwNTWFkydPwmq1wu/f7WcRGxuLvr4+jI2NISkpCe+//z4SEhKwuroKrVaLlZUVZGdnS1+LyclJrK+vQ6PRIDExEY2NjSgvL4fJZBJWsqWlBY899pjMbePj48jOzsazzz6L9vZ2rKysyKJ48uRJbGxsoKioCCqVSgS1HPOhoaE4cuQILl++jOLiYkxPT6OgoEBEgQS/DBhj86/U1FQ4nU588MEHOHfuHLRaLdbW1iRjIRAIQKfTYXBwUJhSg8GA7OxsDA0NYXx8HCkpKYiKikJfXx9mZmaQlpYmMfFerxednZ3ST+fUqVOSUso49fX1dVRVVWF+fh4qlUpYlOnpaUxMTKC8vFwa8NFtVVBQgK6uLrS1tcHr9cJkMqG5uRmBQAD5+fmw2WySGXH06FHEx8eLIyUzM1OExG+++SbOnz+PlZUVnDlzRiL1s7KyJGlXq9VibGxM5t233noLf/RHf4Rf/epXOHjwIIaHh5GZmYmIiAgMDw9jdXUVBoMB6enpcDgcsjlhgBhzWYaHh1FUVIQ333wTxcXFWFxcRHZ2Nl577TXk5+ejtrYWy8vL8hw6HA4YDAbk5eWJE4Y9bgoLCxEVFSUMwq1bt6Rxn8Viwe3bt0XHAQB2ux1PPvkkWltb4ff7kZCQgM997nOiPyHzZbfbER8fL71iKisr0dnZCbvdLu6X4OBg1NbWikamuLgYN27cwIEDB2TtSUpKknWONuLu7m5MT09/ojnigUAH8LFbRWlRZamDB/+NEyMn0PvBh3LBup/p4ISvXNC5QAD/vhtGCTi4w1eeN1kPLlCcyNVqtViBeM5Kl4aS5eACxo6c4eHhonTe3t4W0RltmVyECF6oNeCiwoXS6/UiKioKa2trWF5eRnx8vHwebzj1GgRF1BbwOlAYyJ8xpIqiWqYFkgalDYrnQIDxh3/4h/jv//2/IyRkNzyLeR+8zkqAqASb1Ousrq7i2rVrQjuSnXE4HEhPTxexVV9fHy5evIj09HQEAgGhBEtLS4Wq5vfa2dlBZmam9E4JDw/H2NiYgEeWP5S9TAgglTZtXgd+l/X1dSmVKV0/8fHxktbH1zkcDgwMDMBoNKK2tlbSPLmYc9FQluKsVuse7QupS47L1dVVCS9THryX1B4pGY1AICAiU7pQ2OKd4IYaHOpZ+B5KQMRxwNcQpFP8qQRHn/TQ6/W4c+cOHA4H3G63jK2EhARxQkxPT8tOmf1/BgcHERUVhbq6OszMzODo0aMixkxJScGVK1fgdrsRFRUljpSZmRn4/X6Jg3/88cdx8+ZNPPTQQ5ibm8PJkyexsLAgup8DBw7gxRdfxJkzZ6RhWXV1NdxuN0pLS+FwOGTxycvLQ3BwsOgTLl68iJGREXzxi1/Ea6+9hri4OPT19eHw4cPCILpcLuj1eqSlpaGzsxMajQYajUbsrR0dHcJ6Mn+BFu6JiQlx+iwsLKCkpAROpxNPP/00+vr6UFhYCK1Wi56eHmg0GnzwwQfIzc2VGHKLxYIbN27gxIkTwnaSkQsKCsLMzIwAn6SkJCwtLaG5uRmZmZkijGQYImv+aWlp6O/vR25uLgBI/DyZofr6erz33ntShjh+/Li478LDwxEfHy95HeyCzBh3bvBu376N999/H2lpaRgYGJCmZswXiYiIgN1ux4svvoihoSFERkYiNTVVqP2MjAxYLBacOXMG09PTcLlcqKioQGdnJ3Q6HYaHhzE/P4/NzU1MTk4iKipKWjHk5+ejp6cHcXFx6OzsFD1PWloaioqKBPBUVlZibGxMyq/h4eFITEzEzs4O0tLSRO+htOi/9NJLqK+vlw3n6Ogo4uLiUFxcjLa2NhGKMtzxN7/5DVJSUrC8vAy32420tDS8/PLLKCkpQVdXFxISErC5uSm5H4FAQADCwsICrl+/joceegjvvPMOjhw5goWFBQQCATQ2NuLxxx+XEnp6ejqA3VyXhoYG+c779+/H2NgYbt++jQMHDiA2Nhbvv/8+MjIypKWAzWYT597KygqcTid+//d/H2azGRUVFZ9ofvgPxaBzp8sFnjt/JeMA7E0jZe3+d7lZuMDztVxYuUPnbpUgR5lfoHwv/qdMJOV5keWgVoMKZGDXwkmVPiluagNYFyctTW0Gd4ZE/VwgCUaox6Bm4/46OnMjmJ/B8gWdBKzvk23RarV7FkSv1yuiIzbT485UKd7lz1l24qHUVSitxeHh4RgeHoZGo8H3v/99seLeDxqVmhQAsuOz2+345S9/ie9///tSCyYI5INEJoG+etLF/M4XLlzAF77wBTzzzDP44z/+Y/zjP/4jBgcHRQCqdBVNTk5KeizBKQ8u2lxstVqtuB6YaMiyFJkgJahW1qc9Hg/m5uawtbWF2tpaVFRUCFvAceHz7YawcULg+RJ8ENwq9UjAXnsznUx0+3AcUxDLZoIcpwSWzIPhe5EZ4vO0trYmpT61Wo2oqCgRfjFBlempLpdLRKXMD3mQY3FxUdqw19fXo7S0FElJSYiNjUV/fz82NzfFksea9+nTpyUUjfbsoaEh+Hw+GI1GLC0tSaLp5OQk8vPzoVar0dDQgJSUFBnHMzMzqKqqwszMDCwWC+x2O1JTUxETEyPKe/ZaKSwsxNDQEG7evClujM3NTXE9TE1NQaVSwWw2Izk5GSdOnEBYWBj6+/uxurqK9vZ2yfDQ6/XY3t5GUVGRWLejo6Nx8+ZN2Gw23Lx5E3/zN38jCxZLTrdv30ZzczPUarUsYm+++aYALZPJhAsXLmB8fBzb29u4dOkS9Hq9hIuNjo7Cbrejt7cX/f39OHnypCSwFhUVSdmDgWfHjh2TOG7uVOPj4zE6OorDhw9Dp9NJCJtKpcK7774rqZ0Uv25vb0Oj0cBisaCmpgY/+tGPcOvWLcTExKCvr090FVqtFpp/66brcDgwNTUlScG9vb34zne+g3/5l39Bb2+vMBmpqamYnp4WbVFSUhKSkpLQ3NyMZ555Buvr60LlX7lyBU888QSeeOIJPPTQQ/irv/or9Pb2IikpCe3t7SgqKoLJZEJpaSn6+voQERGB7u5u3L17F1lZWZidnUVycrLMd7x/VVVVeOihh7CwsACHw4H19XX09PQI0Jqbm0NmZqbME2wHzxJlc3Mz1tfX8bWvfQ2f/vSnER8fL6zcwsICQkNDpS8OmwvabDakpKTA7/djenpaNlgq1W7fFnZdrq+vR1xcHA4cOIC8vDxZO9LT05GYmCjlkJ6eHhw8eBBpaWkiBXC73UhNTcXi4qJ8L+pwyLZlZGQgOzsb4+PjGB8fR1FREZaXl1FSUoLw8HDRMO3s7Eio3r179zA0NCQbqf/X8UCgQ7nIcOfIHR1ZDeXCphRZKrUb1GZw4SCYAD4GEtw5cHcI7LXfEkzwZwQ7ynKOkramAFO5C+ZO0Ov1Srtefi4nb1LvtDISxVI/wDbyrIcx1ZFsBjMW3G43VCqV1Nl1Oh1UKhViY2MFfJBR4fXktVNqVghseE3IgrD9Nc9TGV1OoSHBHK81xZD8nhsbG3Kd+vv7cfToUXzta1+T4CuyCbymPI/IyEjMz8/j5Zdfxk9+8hMsLCyIfYrBWVarVRYVn88nO7HNzU0MDAwgJiZGPOHBwcESB+xyufD666/jv/7X/4pvfOMb+Lu/+7s9TovZ2VkAkCRP5VgjY8XmdysrKwLMoqKiZKENCQmRXR53LKyP0n5Koacybp33gP1LlPZsPi/h4eHQarXw+/1YX18XHQo1O7ymyiaA1Fiwzw/Pl4Cazxl/n/eZ8e687ny+GL/MTA673S7uKWUZh/oWPlNKAP8gB4Gvw+GAw+HA3bt3pdlXaWkpRkdHMTQ0hJMnT0KlUuHKlSt4/fXXYTKZMDo6Co1Gg4yMDGkfPz8/j+DgYJSXl4s+g3HbFHBrNBoYDAY4nU68+uqrUKlUqK2tFa2Iz+dDamoqBgcHpdvq0NAQ/vN//s84fPgwNjc3YbPZcPLkSaSmpop4l89oU1MTlpeXZYIuKyuDVquF0WiUtM7U1FT5nM7OTvh8Ppw4cQJ5eXlSyomLi0NYWJiEnaWlpcFkMkmKq9VqxYkTJ6TRn06nQ25uLkpKStDX1we9Xo+BgQG5f4cPH4bRaERDQwMyMjIQFxcn7oKmpiYJHpuZmUFoaKiE1J0+fRrJyclwu91obW3FE088AaPRiPT0dHR2dmJmZgaDg4MAdrUIdKQwrIx0/c2bN5GdnY3z588jISEBpaWl6OrqQlJSEjY2NtDV1SVAo7a2Fm1tbfjqV7+Kn/70p5LA++qrr6KkpARGoxExMTEoLi5GfX29JNK6XC74/X5MTExApVJJMBzzMvLz86HX63Hr1i28/PLL+OY3v4kf/ehHAIChoSG0t7fDZrNJeclgMODevXvw+XwyL7EHD3V/ZIl+7/d+T8wH7JVTW1uLpaUlEUgXFxdjY2MDer0eOTk52L9/P5KSkuBwODA0NISFhQXs378fS0tLKCkpwd/93d9Bq9WKc27fvn2w2+1QqVRis11YWJAGmG63W5gIs9mMuLg46QlEdqynpwelpaXybxUVFWhtbcXi4qLEuBcUFMDn88HtdsPhcCAhIQEOhwM5OTnIz89HY2OjtBpQqVSYnJzE1tYWdDqddDW2WCzSsZrr1u3bt8VZ9UmOBwIdv0ufAXzsNrj/Z1wolbQud9/3xytzcuNiq+wpobQt3m8l5MKlPAflwkhnAFmBnZ3dBmXsTUAHA8sVSoDh8/kkfZCWUTId/C5bW7tNl5xOp9DgXIyVwkaKAnldlEFetOdyEeT7ArviV7VaLXV9LiQMguLiz0WFO1vmh5CVAj62BZNq58LMSXtzc1Niu/v6+jA/P48XXngBpaWlok3hA0mNg8vlwt///d/jf//v/y07PJaYAoEAXC4XbDab7HpmZ2cxNja2x64cGhoKq9UKh8MhfVkSEhLwrW99Cz//+c/x85//HN/+9rdx7tw5FBUVybW1Wq1wuVzynZQBbsDHmg6v1ys7fF4fgjeONS7MvB8EQSEhIRLBTraBYzQyMlLGIJkF6msIThk7HhoaKj1uuAjy+99vY6V7i0DA6/WK9W1lZUWeH35uRESEsExkazjWCCK9Xq+AKX72/Z2TKVLmPWai6YMeCQkJKC8vx/j4uIhafT4f5ufnxTEBAE1NTZJv8/jjj8NsNiMrKwuDg4OIjo7GxMQE9Ho9JiYmEBsbK4LAO3fuQK/XIyYmBrdv35b3YE6Bkj5mnHZycjLGx8ehUqmEVTt69Cjeeecdodjr6+uxuLgoc0ViYqKEKWk0GqytrYlobnt7G2VlZSgvL5cmi3a7HRMTE5ifn0ddXR3m5+cxNjYGp9OJjIwMZGZmwmaz4d69ezh8+DAAYHx8XMrG1HJ4vV709/dLu3M+u8eOHcP29rY0A1Or1bhz5w7S09PFYkmNmlqtxhe/+EXk5OQIi5KdnY3jx4/DarWir69P9Ag5OTno7+/H+vo6pqamUFRUhLi4OKysrCArK0tK0iMjI0hOTpaePSzPMe9kZmYGo6OjyM3Nxfz8PJKTkxEUFCT38pvf/CZ++ctforCwUPqIsCnewsICxsfHZXyMjIxIeih1Orzfo6OjSE5Oxr/+67+ioaEBDQ0N+Od//mf8+Mc/xvPPP49Dhw7hqaeeks+uqakRYXxdXR2Sk5NFM9Hb2yv6mNTUVMkqunPnjvRoMZlMcDgcMBqNKC0thcViQXR0NN566y0cPnwYTqcTsbGxGB4ehsViQWZmJtra2uDz7bZTOHz4sDTczMnJQVFREcbHxyWYjGN4c3MT+fn5Mpe88847KCgogFqtRnV1NaKiomTtstlsWF5eRnBwsDjkzGYzCgoKkJqaKmO8oaFBUmoHBgbEVk1X5crKCjY3N2G1WqWvjFarRXBwMB566CEYDAZp2zA0NCSiax7Ly8t73Eaf5PgP9V5R2iN53J+/oSy9cPfJHbvyd36XU4ELNGvkPJQuF6VWgqIo5ecorZhqtRqxsbFS5qCwUxkpTbqbC01ISIikyAGQiYGiOzI7ZC8ITjjJs6QDQBYzomd+F5Zv2CNla2trT2omWRE2LFOWgMhqUJOhXNCU90EZu0xwxp0thYfAXgeKXq/H5uYmWlpa5DrymtJBMjk5iR//+Mf41re+hampKWi1WtGSALu7f6vVCo/Hg8TERISEhGBychLz8/Nixw0ODhbPPNs32+12fOlLX8L/+T//R1pJM5zs2LFjOH/+vIwPpjcqWa/7rbfKMUqQRSZLrVaLuJILMZkPpWslKChoT0iWUs+kLMfx33jPCFaUpT0lGOX9J5tHETDLMizrUSvE8DKCRjJYZGh4X+8ve9K+rcwV4TihfZgBYwT7tP89KNORmpqKuLg4tLS04IknnpDcjYGBATzzzDNYWVmRiHMyFRqNBi+//LIIx9nivKamBmazGU8//bS4Azo7O1FbWyv08OnTpzE5OSlam9XVVczMzGB6ehrBwcEYHh5GSkoKDh06hIqKCrGAr6+v48qVK9jc3MTMzAyOHTsGr9cr+RQsnSQlJUmjRwASgX3x4kVoNBp0d3cjOjoalZWVYlU8fvw4bDYbDh06hOTkZPT29srEvbW1hSNHjqCzs1N0NMeOHZPf6+jokBLe4cOHkZWVheDgYIyPj+PFF1+UpmZsFJmYmIje3l6J8+7r6xOd2Pvvv4++vj7ZpLhcLvT19cFms8k9pxPFbrfjvffekyj0xcVFnDx5ErOzs5ifnxdh+7lz5xASEiJlmZ2dHVy7dk3yLUJCQoShYAr0+fPn8Td/8zcIBAJIS0vDysoK0tPToVKpxCHH0uXy8jJeeukl3Lx5E1NTUxgZGUFBQQEGBgbQ09ODhx56SFjV5557Dv/8z/+MlJQUSYKNiopCQ0MD6urqpLX7W2+9haWlJcnPCQ0NRUpKCioqKhAaGorU1FRsbm5ifHxcytZk6i5fvixgxGw2Y3NzE/39/dJplSXkgYEBcZfMzMwgPj5egKnNZkN3dzeioqJw584dhISE4Pjx4wLa/P7drsGJiYkYHR0VpxBj9pk/Mzk5Cbfbjfj4eBlLsbGxohUJCgqSxOqwsDDk5+djenpacldKS0vxj//4j1hcXERsbCy8Xi8KCwuxtLSErKwsYUrYP8fj8cDn8wnQOnXqFPr6+qRZ59TUFI7+WysB9uT5JMcDzSgECTxYBlACEGUpQKnh4A5dWQ7h65VghGp/lWq3GZoyiEoZKEUWg9oSokOCFaW1Vsm2KF0uwMdNzYhKlTtgaiFY6yb9z10JJ3i1Wg273b4nXEpZimIeBd+bgr2oqChYLBYBGqTceS24wBAgAR+HU/G6MSGRu1M27OK15LlzR+zxeMSDz0WO4E2j0UjMOdXbjz76qGgWtre38c///M9oamqCx+MR4acyDp6lJkYmJyQkYGNjA7Ozs9D8W4vqlZUVrKysyCTEkobT6cRf/dVfoaioSPrVENzdX9KhaI+LOEsWDEojuGSJjoFXSt0PAQMBK1E/9RcAZOEne8H3YymO+orw8HCsrq5KhDd1HNTl8NmhNon3hxkuSp0Ux2lkZCT6+vqQlZW153sxT4XPAr8Dha/3h9opGRSWBhmkpGRl2B2VY5BK+wc52MArPz8f6+vrKCsrQ2RkJAwGAyYnJ6FSqbC4uIi0tDS4XC44HA488sgjGBsbg8PhgNPpxKc+9Sm8+eabaGhowMrKikReb29vS5dSNkhbXV1FQkKClKDq6ur2OIRqa2vxxhtvSKO24uJirK+vY3V1FTU1NVhdXYVGo8HFixdhMBiQlJSE8vJy+P27zrLZ2VlxosTExEi4VUlJCSwWi3T7XVxcFFsy+64we0Sr1aK0tBQejwcejwfj4+MoKSmRXkLt7e3Y2NjAlStXkJycLOzT0NCQuBGYB+F0OmEwGLC2toapqSmUlpbC6/UK81hSUiKLEhmC/v5+eYYKCwsRGRmJq1evIjExEXFxcVhbW8PDDz8Mj8cjfXCOHz+OxsZGAVAzMzMICQmRZmYtLS1ITEzE5uYmrly5AofDgcLCQoSGhorF8vLly7hw4QKysrIQHh4uugiGdmVkZGBkZARTU1NYW1tDQUEBJiYmYDKZkJqaio2NDRw4cAAffvghMjIyBJSyKVpOTg5cLhcGBgZw7tw5mWP0ej2WlpaQlJSEwsJCvPHGGygoKEBYWJi4jC5evIisrCzRL6SmpmJiYkIchtSFhIWF4fLlyzAajXjooYcQHLxrZWbJlP1rcnJycPXqVfh8uz1gAoEAkpOT4fP5JFCxv78fs7Oz2NzcxNDQEK5fv47U1FRoNBr09vaiqqoKTqcTcXFx8Hq9WFxclNJbaWmpbBq5YUxNTRXm4Yc//KEIawsLC+H1evGzn/0M//N//k90dnYiOTlZ9FlWqxWVlZUIDw/HxMQERkdHUVpaio2NDbS1tclzb7Va0dPTg+PHjyM/Px8//elPsbOzg9LSUlitVmHuqI3S6XSfaI54IKZDWbJQUsPKhUAJMpS7TKV2Q6nv4L/xUIIQWjqVGR3K8gp3eizD8PO5IDA5lDVmZb4FFwFOqgzy4q6AgIGTcUhIiNSxOFHTRkoBHwAJYSKDwQmOJRO6cbjgsZZPwKEsDTH4iVoMACIu5PUkvUktAUWF1DzwenAi4vUkA0I9C1EyfzcoKEgoN2buf+ELX8BPfvITzMzMSFARd4d2ux07OztYW1uTjpEGgwFutxtjY2PIyclBXl6eiBr9fj8MBoPQ+H6/Xyhy5rLcr+VR2mF3dnYkqY/lDuWCrvw/7z13FnT6cHzRWaL8PwC5lkqQyvIRAQMBCBc5Tvb8j+23aRsli0GGgeOdAJDnTNo0KioKUVFRwqoQ8HJsckxSYKtSqaREx0VqZ2dH6uL8zgSxPH+KaXnODAh70MPv98tnLS4uYmJiAisrK2htbUVvby/cbjdWV1exubmJhIQEPPXUU9K7JDs7Gzk5OSJK9Hq90g/F5XJh//79InxrampCe3s72trasL29jeeee052dqmpqVL6YuOw2tpaHDx4EHfv3pW8iMjISBFr5ubmilOGgNFsNkOn0+HQoUOyiF2+fFlApkqlQmpqKubm5pCfn4/k5GS0tLRIWiM1PZmZmejt7RVXG1uY/+M//iMcDgdiYmKQlpaG/fv3w+FwCAhVq9XIyclBZmYmxsfHBXi89957Etp07do1zM/Po7i4GDk5OQgPD0dTUxPS09ORn58vrJXRaJT7o9FoUFJSgv379yM2Nha3bt2CxWKRtFRGmptMJrS2tmJoaAgGg0E6VRsMBnHZsZRTW1srtt0vfelLeOGFF/DDH/4QeXl5kh2kUqkwMzODtbU1XLlyBS0tLRgbGxPH1MTEhNhhl5eXsby8jJaWFikvnz17FiUlJTCbzeKE0Wg0yM7ORlNTk4whOvrYxbe7uxsqlQomkwnx8fEYGxuTHA61Wo3jx48jISEBBw4cwP79+7G+vo6LFy/iscceQ0lJCdRqtVj9mTgaFhaGzs5OALtlp1u3bkGtVqOiokLer6urS55HCj2zsrJw+vRpNDc3Y3V1VQTROTk5cDqdqKqqwvLyMpKTk5Geng673Y7NzU0BZsnJybh79y4sFgucTieuXr2Kf/3Xf8VnPvOZPa6z4eFhpKWl4e7duxgfH8fKygoGBgag1+tRXFyM8fFxZGRkoKSkBN/4xjeg1+uRkZGB5ORkWY+8Xi+KiorQ3d2NyMhIHDp0CPv370dWVhbi4uLE4dTV1fVAc8QDMR0EF6Rt718UlIwHFwMu8kqBqdLaylo2/51IjgLJtbU1qYszE4PvTZaFMdB0lPBh52czDIqDG4DUwba3t2E2m0UkSK0Hz2N1dVVcLmQMyLAwHZJiNtqpaH/kuUZHR/+WbTUyMlK6EiYlJckO1u/3SxdG7l4IogKBgMRK8xrz2rJ8omSfeL1ok+NCQ4EewROvHVkiMik7Ozvo6OhAeXk5bDYbxsbGRABLEdjq6qpQ/A6HQ5wWSUlJ4oevr6+H0+kUB4perxfGJjQ0FENDQ3A4HEhNTd3zvQi+CGj5Z5VKBZfLJQFXHC/UufB8lE4XLuy0KlPHwB0tAZ4yqIg1U4IaLvxKAElmgHonXnsAsmskcCEjQxqekfscSyxP8dpz4lSyW7zfZDYIbJX6C45x3luNRiPJlTxXHgTYcXFxEuJEUTEB+4MeZDR43VmzLi8vh9lsRl5engj7urq60NPTA2DXamsymeByuYRtiI2NFS0SmQS73S5NBLOyspCeno6bN2/i1q1bog9gHZudRa9du4bPfOYzeOqpp9DS0oJ9+/ahpqZGRKchIbtR7GwlT1tnR0cHXC6XUOPZ2dno7+9HUVERysrKJHNkZGQEWVlZUKlU6OnpQXFxMbxeL+7duydiQD7LdXV1MJvNeOqpp6DT6XDr1i1kZGRgYmJCdvIWi0XmosbGRgFKn/nMZzA4OAiHwyEAVKvVwmq1Ij4+HgkJCcJO0BVDenxjYwP9/f3Y3t6GwWDA4OCgUP0LCwvY3NyEy+WC2+3Gzs4OTp8+jaCg3bA19pUCIA4gp9OJuro6XLp0CVFRUfjiF7+IK1eu4O7duwgODsbRo0fR3t6Offv2YWNjA263G2q1GhcvXkRVVRUyMjJgNBoxPDyMnZ0difHmPMMjISEB6enpeP3115GUlITjx49ja2tLbLBZWVlISkqS13d2duLo0aOwWCzweDwi4hwbG0NlZaX0kens7JT4gP7+fsTExKC9vR1utxtnz54VpjsrK0uapY2NjWFrawt9fX1iES0rK8Pi4iJWV1cxPDwsrSqqqqqkSVtFRQVOnTqFnp4eAbC0zj788MO4fv06YmJipLTjdruh1WqxtbUl/YCYZ0PHV39/vzS97O3tRV5enjA15eXlwlyOjo7C5/NheXlZkmmNRiOam5tRVFSEtbU1LC0twWg0ynw3MzMjqbkulwt37txBcHAwVldXcfz4cTzxxBO4ceOG5LNQcPpJjgcWknLiU+ZyKP8d+FjgyUVOCSj4e3wv/l1ZplEq8FmL5PsqyzAEGxSTkkFgqSQQCMh5UEDIxZufSRUyU/oYRMadKickJv9xh8xDo9HIZKic0LlgUPzHUgD1BBQzmkwmOXcuJmx6xbwFAPI+1EworwED1CiopECIC6QyMpsaFI/Hg4iIiD39MXw+n5RfWDYaGBiA3W5HWFgYqqur8fd///d44oknZCIlO6Ls78EulZGRkSguLhbrWUREBAwGwx6mZXh4GIuLi3JdlNZeZcmOpQICObbiJmPEcgTZA353gj/g47RXAlCOv/uFtX6/X8o/XHgpRg0KCpLfJ0Dz+Xyieuf1Zk8RlmaoqWB5gxZlZRkPgPwbWQrS9xwbFKECEJBEV5BSeK3cMQMQMScZMaV1loCMwlKCKZarHuQIDQ1FXV2dxDPn5uYiJCQEmZmZyM7OhlqtRlpaGpxOJyYnJ5GSkgKNRiPP5gcffICUlBRcunRJAuosFgvUajXq6urgdDolUE4JomhdbmxslFbrWq0WDz30EHw+H7RaLe7evSuOkPn5eYyPj0Ov16OlpUUElQUFBcISLi0tIS0tDbm5uYiOjkZ+fj7i4uJQXV0Nm80Gh8OB3t5eTE1NYXl5Ga2trfj0pz8NnU4nLi2DwSAuJGaV0NnS39+P5uZmpKamSvnzzJkz2NjYgE6nQ2JiIgYHB/HUU09Jsy7mmszPz2NqagrHjh1DdHQ0MjIy0N7ejvb2duzs7MBkMuErX/kK/H6/RMsHAgFoNBrJ6zCbzVCr1bh586ZobdjDpK6uDn//938vllaXyyUWZJvNht7eXgwNDeHatWuiyWDYn8FgwO3bt6V9ek5OjpQDOzs7RYNx6dIlvPTSS6isrERtbS22trZw9+5dOBwOzM/PC1Pg8XjQ09MjHW2Zr+L376aShoSEYH5+HsPDwxKE9u6772J+fh5BQUGSs1FZWQmz2QyHwwGbzYazZ8+KsSAxMRFOpxPl5eV4+umnMTc3h8XFRQwODiIzMxPLy8vIzs5GaWkpDhw4IKxTIBCQ7I+RkRGsrq5K2uvU1BSCg4NhMplgsVjw05/+FENDQ7DZbNjc3IRWq0VqaiqamppEJzMyMoKYmBiMjIyIs5FsWEdHhwhe+/v7xeY/OzsLo9EIk8mEhoYGWRNHR0fR1NQkz4rVahVAS9txS0sL3n77bZSXl2N0dFRMF7m5uZidnUVERASqq6sRHR0tfV06OzsxNjaG06dP48iRI/jNb34jY+OTHA9cXlFOzMDeJNH7KXEyC9xJK39X6WxR7mLpFqDdlGhfyVKQKeEky89Sviewl2JfXl6WXZtOpxPxIMVyXPBIVQMf94HhokItCcs0KpVKvNkUDPL3mJ2/s7Ozp+8KY365cJBKZwY/tRPUSXDRo0iKn09xILCrbI+JiZFrpNRp8HNWV1clEIuix/sdGPw+XDyJjgcGBlBSUoK//du/RXp6Op577jns379fxgSvBRfnnZ0d5OfnIyUlBXNzc7Jz1Ov1QjfSukWRK/BxkqnyHioBB/+uUqmk0RC/I9XfISEhsuCRIeDizGtBfQMXWeW5K11SShBJR4lyvPO9mPdBgMtxTMaEtk3asZUsH8EES2vKbJfFxUVhFVl2o9OKDA2vJUESI90ZCkYQRq0H2Sxg19HCc6c7x+/3i8ZIaVf/pAcTVCMjI9Hb2wun04mIiAhMT0+jt7cXw8PDuHPnDhISElBcXIzGxkbExMQgKSkJo6OjomOhRiM1NRWJiYlYXl7G0tKSsHS8XgsLC0hLS8PQ0BCCg4NRUFAg6YojIyO4c+eOuKz4HAK7ZdCf/exnGB8fR05OjgiHCXIqKiqEcmYmRVhYmCwEY2Nj6O7ulgXA5/Ph8OHDmJqagtPpFEGnx+NBeXk50tPTkZKSgoSEBOj1egwODqK8vBwpKSno6OiQWv61a9fQ19eH3t5eYbsaGxvh9/sxPz+PoqIibG9vo6KiAvX19XjppZekjPDoo4+ivr5edETt7e0IBAKYm5tDfX09Hn30UdGItbW1CWuZnZ2N+vp6uN1uxMTESNgYm7utrKxIgJ3P5xMHy2OPPYaysjJxd/zsZz9DcXEx3n77baytreHpp5/GyZMn0dTUhKtXr8LhcEgJKTQ0FLm5ufizP/szcbatr68jJydHAPrRo0cRGhqK6elpjI2NISMjA4ODg3A6ncjMzERkZCRmZ2elHEdB+tbWFh577DFUVFQgJCREgrMY5MjGiK+88oowK9PT0/I96EABgPz8fEmKZdDazs4O+vv7hclvbm6W7uPx8fGIjo7G7du3YTKZoNfrMTQ0BJPJhOLiYgQH74YMZmRkCGMWHR2N3NxcREZGSh4Lu9FGROx2Xt7Y2MDMzAz6+/vR2NiIyMhIFBQUSBk0Li4OycnJePPNN0Xwr9VqUVZWhqWlJXR1de1x9NCanZaWhtTUVHR1dSExMRFJSUkiuD179qxomJjIe+DAAZw+fRrvvvsumpub4XA4UFZWhoSEhE+c6fNAoON+pwP/rGQplNqK++l/ggWlLuN+EMP35IJIUaPSXsqDgEApvOR5skTCfAUCCeZycOKmjoNJnawfrq6uys6Q50uRJcsoSuYGgOgTiICVQs1AYDdBUqfTiYNFuaBxULN/i7JjqPK7c5GgZZDXQ9lIjroJgiCyCEqBo3JnzbhsimZZT2Zq4Pj4uCxyBCdf+9rXoNfrxUkC7Nqn/H4/MjIyEAgEMD4+Ljt/Zm4wDpxlAZZA+AATRBKAKP8jOOVEyoWbII0lMeoqqPFQij8JGvh9lDojAlyCGAIaAjEAko1BUamSYWGyIMckd+90UBGs8vuSmeJ787oDEKaO55uQkCCgkyJpPndKEe/6+rpQqBQrKxsMkskjcKUNm+UeuiKUkfoPeqSkpKCpqQl6vR5zc3MSbU3QVlxcjJ2d3bTTAwcOICcnBz09PSgqKkJoaCiys7NRUFAgYUMEqzdu3MDw8DCWlpYQEhKCsrIybG9v486dO9K23Gg0wu1247nnnkNdXR0aGhqwurqKjIwMEZ8yaZSR4k6nUzItjEYjLBYLJicnodfrZe7Yv38/XC4XJicnYTKZUF9fLyAlKysLWVlZ0lvFYrEgOzsbQUFB0Ol0WF1dRUhIiGTpEBwNDAzA4/EgOjoaJpMJ8/PzKCgoQE5ODqKjo8WumJ+fj6WlJczPz+PDDz+EWq1GdHQ0Ll68iJ2dHZjNZqSnp4ulsaWlBdPT0+ju7oZOp8O5c+ewvr6OhYUF2O12zM/P4/Tp08IycvdfXl4OlUolOq6ioiIBy0lJSQgNDUVaWpq4drq6ujAwMIDR0VFER0djbGxMQBebu/3pn/4pjh07huLiYgkqY28css937tzB9evXUVBQgMTERJhMJhw9ehQzMzNibV1cXMT09LSUKMmsBgIByT+h9sloNOLGjRswm81ob2+XyPGuri6UlZVJCenYsWMYGBhAUVERAGBwcBAmkwkrKyt499130d/fj+DgYNFa3Lt3D263G2azGdXV1UhLS0NISAhyc3PR29uLAwcOICMjA1VVVfjCF76AoaEhNDU1ITc3F+Hh4RJQxvualpaG1tZWFBQUYGFhAW63W5o60s3IUqLP58ORI0cwMzOD7OxsSVqNjo7G3NwcBgYGMDc3h3PnzsHj8eDatWsSxri+vo6ioiKMjo5ifX0dFRUV8Pv9qKurQ1BQEPbt2yfMkUqlQlZWFlwuF9588005jwMHDmB5eRn9/f2YmppCamoq0tPTsbGxAYvFgunpaej1+k80PzwQ6PhdugwlyKCQDfh4h6r8vftpc+D/3jSOfT+YpkggoxSxEnjw7wBEOAhA7KRsvub1euF2u2X3x5IJwQbfg5ZC0nMAhMqmKJFhYCxpUGtAVoOTFr8fwYDT6cTKyooIQSk0ZZmFrAW7o/L9ScnTXUCwxkUiJCRExFFkgrgjJMrnrpmsAK8RF0wuZNzpBwcHY3BwEDMzM0LXkzb+xje+ISJQAgiVSiVhRBEREXLNLBaLsAv8fIIGn88nuRgej0c0FMpxoyyJMcuDQmB+NwIIWkdp/6M7hg8VqXju5sl0MX9AaXFVOkA4SQIQDQj/TJDB8UPdDbM2goODpcZKNoDlQ1pVueBTAa/VakWszN0WAGEieC95HkwYZFIqwQXHMe15ZMGMRqN8nsfjEf0EQ7F4vx7kiI+Pl7Ch48ePCwBKSUmBVqtFdXU1bt26hdXVVdhsNukDYTAY0NzcjLi4OKGWTSYTFhcXERUVhaGhIdTU1CAjI0P6Pezs7KCgoABFRUUIDg4Wa/bGxobEYR88eBDHjx+X/jMffPABfv3rX2NiYgKpqakCzD73uc+hoKAAi4uLOHr0KNbX1zEzMyPPHXfMZPV4nbOzsxEIBGA2m5GdnS3N38xmM+7duyfJxzs7O7DZbPJ88vq7XC6J2zaZTOLAOH78OMbHxzE4OIihoSGkpaXhzJkzWF5eFodfRUUFvvCFL8But6OsrAwOhwMejwdnzpzB2toajh07Js/du+++C7PZLGwSd9kEsF6vF+Pj46I/cLlc0nk1JSUFgUAAWVlZGB8fx/LyMqqrq9HQ0ICIiAhkZWUhNjZWsinYDTgjIwNpaWlIS0sTay8b2J0+fRo+nw93794VMej09DQGBgag0WjgdDphMplw+/ZtPP7445L0a7VakZiYiJiYGGRnZyMvL09EwFwP3n77bZSWliI1NRVBQUESIhgUFIRf/vKXKC4uRl5enqTc8nrTTTQ/P4/ExERkZGSgpaUFSUlJcDqdqKmpkc2g1WoFAKSlpWFychIajQZdXV1ob29HX18f/vIv/1K6y+p0OvT19aGyshJGoxEZGRlYXV3F+Pg48vPzkZubi+TkZLS2tiI9PR1Hjx5FX18fLBYLjEYjUlJSkJ2djXfffVfWBK1Wi4qKCkxPTyM9PR379u2TVN2pqSnU19dLnxbOiQy9m5mZwdDQEH72s59hcnJSvuM777yDt99+G+vr61Cr1Th48CDm5uZQVVUFt9uNqqoq1NfXQ6fTSeqvVquVuPz/X8orwMdsh7KkopyYOQFSv8AFRMluAHtjy3+Xy4WTPCen31XnVwpR7xexsozAHWl0dLToQ9gXBfjY4kngwffWarUCUgwGg2hBuLNn+YO7Q6VuggwKwRJ375y8SH1xweTCzB0+RYd8D7odlKwJX8PdfEJCAoKDgxETEyML7f33hvdLKX5U7pRpxSI9y13Y2toaWltb9wS6ra2toa6uDg899JDs/v1+P9xut+SOaDQaYQ40Go1MHAR6gcCuZZZJiryX948RpY6HIJBlLWVZCIDQxwAEbCjBGtkrgmVqYniPWF7gjorXi4sw2RCOMaUOBvjYXcQcEI4XClEJIEJCPu5KTADBRn/cySUkJGB7e1todi4QZGj4TPE+KnNXuLDxniu1HMHBwSKoZXmG+g3lZkEJ6D/p4fV6cffuXRiNRty8eRN+vx+5ubno7+/HzZs3kZGRgZqaGpSUlGBnZ0dyWjQaDbKysjAzM4P9+/dLWaWkpARbW1vIzMxEY2Mj1Go1xsbG8PLLL0OlUuHevXuYnp7G+vo6DAaDZLdotVo4nU7RAnR0dGB+fh5RUVFyXe12OzIyMnDhwgXRVkRFRcFms2Fubk529263G0NDQ1L+tFqtcDqd8swHBQXJOQwMDEgsOgPAgN0SaENDA4xGI8rKyrC6uooTJ04gPT0dZrMZQ0NDmJqaQlpaGq5du4a2tjbEx8eLCNPr9WJ4eFhs6GzkRuBsNptx4MABSZvMy8vD+Pg4tFotbt++jcrKSkxNTaGgoADl5eU4ffq0WK+LioowPz8Pg8GA06dPY319HZOTk2I/tlgs8Pt38ydWV1elAZ7ZbBZ6f2NjA5WVlXjllVekfDA+Po6ZmRn82Z/9mbgzUlJS4HA4cPPmTYyOjiIzMxOVlZUIDQ1FVVWVdFeNj49HXFwcjh49io8++gg1NTX44Q9/CKPRiLm5OUxNTaG5uRnBwcGYnZ2VOXBkZAR/8Ad/IAFXtHzGxsZCq9XCYDDAarUiKioK8fHxaG9vh9lsRn9/vwDewsJCeZYKCwvR3t4uTAuD71iOslqtiI2NlbRUCn5/8IMfiIB7ZGQEW1tb6OrqEgY3KysLRUVFYlO12Wyoq6tDaGgo+vv7AQBLS0syFgcGBlBWVgar1Yrt7W2Mjo5CrVZjenoapaWlAnoWFxfxx3/8x1CpVKioqBBmo7+/X4T+Ozs7OHLkCKxWq7BPnEeLioowMzMjybmZmZni+Ovr6xOJABn3vLw8nDx5EltbWzCZTJ9ojnjgGHTlZMddrzI3godydwp8XDLh73CiVdbvSQlz0ucirMwiUC5AnNB/l+U2ODhYLIzr6+tYWVmR5EJO4HzP6OhoWYj4nqz7x8bGykJAWyG/FwNZdnZ2sLS0JPS+EmxERUXJYkuLqVJTQO0Kd9X8/tQGEHyQXYmJiRGRGhsMkTkgi0BGiCp0gj6WEZQhVLw3LD1QOU3RJctUQ0NDcDqdcg+pZH7mmWdQVlYm90pZviF44eS8vLy85/qbTCZkZ2dLqcBut8vCd7+Wg+MsODgYNpttT12ffyYwYNM6ijmpuvf5fBL+RqEkGROWlpQBZ1yUOcY4dskYEPxQH8TvC+wuvnRfcDzyu5GZYpAcxxXvA4OHtFqtxJKzhMTJUOnwIdjlnwmuuOvnPSGAoUiYQWhKizKBEUtUZGw+6cGAu9XVVcTHx4unPzk5GRsbGxgZGUFjYyOmp6fhdDolFK63txexsbE4ceIEVldX0dbWBrvdjqWlJbFuA5AcCDYYe/zxx2EymaQjaFBQELKzs9HX14fw8HDo9XosLi6KpoLWbt7HQCAgaagtLS0wGAwYGxuT8wCAnp4eeL1eFBQUoK6uDi6XC+vr69ISfXR0FFVVVdDpdMjPz8fGxgaWlpYwMjKC+fl5Ecqy3Pj+++8jMjIS3/3ud+W6k8Xp7e3F6dOnJSSMMdmzs7NYXV3FY489hsHBQaytrSExMRGXL19GaWkp4uPjhXHp7e3F5cuXJRcnMTFRxnFycjK2t7fx61//WoKhJicnsby8jO7ubnGOlJSUiACe4tDx8XFUVlYiKytLhIq1tbV45JFHJIekoKBAND0mk0kix//sz/4MJ0+ehFarlQXaYDCgpKQEi4uLmJycxJ07d+B0OlFYWIiBgQFsb29jZGQEDQ0NWF5exo9+9CNhj7Ozs5Geno74+HgUFhZKzotKpcLY2Bg2NjZw6dIlBAIB/PznPxeWlpqP7e1tTExMIC8vD/39/VCpVBIMNzU1Bb/fL92WAYjDjU6ga9euweVyYWZmRuYxAuXNzU188MEHwtzm5OTA7XYjODgYdrtd4sg5n3u9XuTk5OzZPGk0Gommj4qKQklJCdbW1vCZz3wG9fX1qK+vR3d3t5RiVCoVFhYWUFpailu3bsHn8+HOnTvSPJHaxfn5eQmXKykpETZ1bW0N58+fh9vtljKvTqeTUotGo0F0dDTm5+fhcrlEu0f9kd+/a5H/JMcDu1eUpRAl0FCWXjjJclG7nx3hwUWCr1GWZkh5q9VqxMTEyGv43kotBxco1vz52XwfOkdYYx8dHZVMCeXCxhoyd5qs6bMWz0mcmQwApD0zBzXz+wlkmHaqFNgSaNHhwmtjsVgEWDHCm4s1F11qU7gwcgHlbpX2WJZoqN0AIMFVLG3QBqZ0jFB7QFEixbZutxu9vb2ywHKnHx8fj69//eswmUyiv+HDExUVJXZgfk+WvRISEkRLEhsbK02hlMmeymvFRV+lUsmOjws4ryUXX34fThRMk1SpVPLwE6hQgMoSD3f+ZHrIZikZgPsBInUfBJg8Z+pUOB7JFEVEREhZhWxVIBCQa8vQKYJUipt5TrweHKPKeHTmLBAscGdFUOb1euFyueByuaS8w+Z/QUFBewTNHPMPchiNRtE0qVQqNDc3w2KxiK4nJSUF5eXl0jwqNjYW4+PjqK+vB7BbV7dYLDCZTDCbzQgKCkJLSwsSEhKEOeNzm5CQIAA0KytLAP3m5iYMBgO0Wi1GR0eRmJgoQUjZ2dkwGo3i+Hn//fexvr6O6upqKdd4PB689957uHr1KlwuFwwGAxITE7G2tobY2FgcPnxY+qaEhYWJtuTVV1+Fx+NBXV2duHSKi4tRV1eHiIgI6RDLQKj6+nphO8kKxMbGor29XWyYGRkZ6OjogEajkZ4uNptN5kUKs9977z288sor4rB47rnnUFtbi8HBQWlzsLq6iu7ubukVExoaKgvmwYMHcfToUWxtbaG/vx9jY2NYXl7G3NwcXC4XjEYjKisrpedKSEgIUlJS8Oqrr8Jut2NmZkZ6gJjNZvT19clCTJvq9773Payvrwugs1gs+Kd/+icsLCzg8ccfR1xcHDweDzo6OjAyMoJ79+6hoaFByuPh4eGYnJxEIBDAxMQEwsLCcPfuXYSHh2Pfvn2i0wF27eoDAwMiaidLceTIESkRTU9PY3l5GXV1dRJOFhYWBqPRiJWVFSQnJ8Nut8u/DQ0N4eLFixKu5XQ6kZWVJVH1drsdLpcLSUlJMBgMks2zubmJoqIisYpfvXpVmDG6WYaGhrCzsyMgIiEhAe+//z7sdjuCg3cze6iZSUpKEma8srIS9fX1uHnzJsrLy6Wc6vV6kZ2djampKWxsbEgJimVBj8cDi8WClZUVEb92dXXh2WefRVFREQYGBtDV1YXW1laJgGdZOCkpSYT1DocDly5dwsLCwieeIx6Y6VBOvsoSCnfKygWWAkD5MIXuQ8lOKF/DCdvv9wstfn9WAOvurOErz42TLAChnMkYJCQkwGAwiEqZu0OWPJQWR7/fL4mNISEhosGg2NTn84mqmGUl6hoiIiIwPz8PrVYregylfkL53cmwREdHIz4+XkoUFP+RdeH5EXwQULBkw4WIAlllDw3eh/uZEQpFuThzIlJ2C2T9mt1neb15rWnv++IXvyhOCX4vuosIRiIiIhAVFYXExEQBMgCkFkghkvL6KIErwcLy8rKUP1hyiYmJEbcIxw+/E3f/HBsEkdxtkGkgQOD3JiihAJK/r9Pp5GcUALNLJcdJcHCwlOQIkpl1sLOzI86Bra0t2TW4XC6EhITs8dNT6MvnRulkIZPDcUP3zf2/w/HBe6dSqSQCXmkDDgQCUjrijudB3Ss879nZWWk+ptPp4PF4cOzYMczMzGB7extutxvj4+NwOp2ieh8YGIDBYBCmJSkpCXa7HceOHUNGRoZosYBd7cjc3Bza29ulDMh5yOPxYHFxUdjNjz76CHa7Hfn5+TCbzeLEiIiIwNNPP42ysjKoVCoMDAxgcnISOp0OJ06cQH19PZqbm2Uj4XK5MDo6KuUOuuDoYKmtrcXc3By6urpQUFCAiooKqFS7Dc2cTic2NjZQXl4u9+/evXtob29HfX29gEWlSJIN8BjDvbOzA7vdDoPBgMXFRSwvL0va54EDByTFMxAIoLu7G6+88opYbfPy8rB//36EhYWhtLQUVVVV6O3thc/nwzvvvCPno9frkZiYiEuXLiE0NFQ0CWazGWFhYXIvMzMz0draikOHDiE4OFiyiOLj49HW1ibMx/DwMMxmM/R6PUJDQ/G9730P7e3tSEhIwJNPPomjR4/C6XSira1NmL7Z2VmUl5fjkUceQWRkpIQN+v27FtnJyUlsbOw27Ny3b58EFAYFBaG/vx9RUVEwmUySFEoWKjIyEteuXUN9fT1iYmKwuLiIsrIyvPrqqzCbzXC5XGLJpbiZwYxkIGJjY/HYY4/B4/EgNzcXCQkJ0i5jfn4eMTExSElJQWZmpizy6+vrkneh1+sxMzMDv9+PoaEhzM/Po76+Hmq1Wjp6d3Z2or29HSUlJTAYDFhYWIDL5UJ1dTUMBgOuXLkCs9mM7u5uqNVq/PjHP4ZOp0NeXh7W1tbQ19cnG9TV1VUkJydjdnYWFotFzBDsMF5UVAS3242JiQkRKzPCgHPhvXv30NjYCJVqt9leYmKiaJBmZmZw9OjRByrDPhDoUJZL7gcKSjDCPyvr6cpSivL1tDMqXQT8M3c0XLgIcpQshnKRYMmC4jsiTQII/j07O1u6ePIc2TlPpVJJPT0hIWGP5TAoKEgoV/amIBOivA5kZpipQCCitP0qk03ZlIyDmq4d7la5Y2Y8NxdLpaCWu29ef4Iwaiw4IQMQOyV36H6/X3bCLG0pM1AIjGZmZkS/oAR46+vrOHLkCEpLS7G9vQ2XyyViWWpqWLuPjo5GREQE5ubm4Ha74fF4xJJHBK4UCyvHGEVh9N9TR0FRr7I8xnHC68IFHPgYwAAfl+w4DpTOKzItZMgIeihADgQCopjn5/P1ZCbormFwECcyZWgYv6vSZp2QkCDjaW1tTXY2BIbK89va2hIASSDE+6ZSqeQ+sM8Qr3VERITYIJUgkn/e3t5+YCHp6OioxGkTDFGQd+fOHcTHxyMmJgYajUa+76lTp7CysoK0tDQEBwdLjkBsbCwKCwsxNzeHtbU17Nu3TzYhfX19SE1NRV1dnZS6yP5xfHBiLSwsxL59+xAIBJCeng69Xg+r1Qq/348LFy5gdnYWkZGRyM3NRWtrK55//nmsrKyIfTAtLQ0+n08Yk+vXr2NoaAhdXV3i2jh06BBmZ2cFPDQ1NeGf/umfMDg4iNOnT8NiseD/o+2/gyPLr+tw/DRiN0LnjNCNnIFBHGAwOe7szAYul9wluRQpimKJVskul0sql10ulUtVLtllW5ZMy8zR3EByc5hdTs4YpEGOjZwaHdAJOf7+gM7dh/35a+2oSq9qamewje7X733e59577jnnqlQqUSKMjo7CbDYjNzcXy8vLyMrKwsrKCvx+PwoLCzE+Pi4k+KmpKQQCAXR1dWFtbQ3l5eVYWlpCUlKS+G6whUK58cbGBmw2G7q6uuR3V1ZWkJ6ejtnZWSwuLqKiogKpqal4+umn8f7776OsrExmK5WUlCAajcLn8yEYDIp30PT0NOrq6oQ7RUOxgoIC9Pf3o7+/H++//z4mJycxOTkpyTvdX6urq/G1r30NkUgE77//vkiWZ2ZmsLGxIX4acXFxmJycxNtvvy1y72g0ilu3biEuLk7IxZOTk5iYmMDKygq6urpkWu34+Djm5uZkf9br9TIa/vHjx2JRv7m5ifLycphMJkxNTclE2OTkZExOTsLv96OmpgbAvu1ATU0N/uZv/kY8TiYnJ8XUzGq1YnJyEo8ePUJnZ6cQd9vb29HV1YVYLAaj0Qin04nNzU184QtfQFpamqAqp0+fRkJCAhYXF6WlyNaIWq2WCb+RSAR3795FT0+PzKMpLi5GT08PVCoVHA4Htre3kZGRgfHxcZkBtLW1JYML9/b2UFBQgOHhYXg8HmlBTk5OoqWlReYfFRUV4fLly9J1CIVC+MUvfoHi4mLxaAmHw2LW93mOJ1avEBlgoAVwgJ/x2cSEycBnkxQl6sHeKoeqMZngRq+E/4FPR2ez/8zgubOzIzp6VgYc1kTtNyF2t9uN+fl5sQFnUkIIl/JPnjeHdymDPomFer1ezpVSRLVaLQoOBmJW5UpS7dbWlsCd5IKwt87sly0Swmyrq6viekcUg9m20g5dWcEzoBOBYMLD+8P7xp/TYpv3ZHNzUyo9Ji9KRGttbQ1/8Ad/AIvFIkF5fX1/aNDc3JygPuvr6xgaGpJqjkY/lDLz+/N6svrhWmJySKSL58tWg7IFxj+8FnyNknNCTg3REK5JBu74+HiBsYkGUB3DPimTM0qYiQ7xXiwtLWFpaQl2ux3Z2dkyvI/f0Wg0yroPBoPSimC7iK0p3icm58prw7YQeSlKlJFmZlTlEMnb2tpCMBgUmTETFrZwiBQ96bGxsQGdToeFhQXU19dDpVLh+PHjSEpKwo0bNzAzMyMjwsvLywHggOtwc3MzhoaGMDg4iF/96leSrLe2too/TUVFhXxvSjn5bGs0Gpw5cwa7u7uYmZlBYWEhhoaGcOvWLXR0dMBqtaKkpAQtLS3IycnBxsYGent7hctw79494YGdOXMG9+/fRygUwr1793D//n0UFBSgqalJ1o3RaERqaqpYhZeVleH8+fOw2WzIz8/HysoKGhoaoNPp5DNzcnIwPT0tg+xYqbOdk52dfQB1iouLQ2NjI5qbm/H73/8eZrMZBoMB0WhU1CKvvPIKSktL5frv7e2hvLwcTU1Nsg+6XC6Rd5JjEhcXh9zcXDEcGx8fx87OjkyoLSkpgdfrxa1btyQZJqFwbm4Oq6uruHXrFmpqarCzs4MzZ87g1Vdfxd7e/vyRhYUF/OY3v8HVq1cxNTWFb3/72zLLhrwdosgqlQput1sSZafTicXFRQwODqK1tRWnT5/GxsYGenp6UFRUBLfbjerqalF9bGxsIBAIYHFxET//+c+xvLwsxeMHH3yA/v5+UdWcOHECN27cgFarFe7c7Owsrl+/DqfTiVOnTuGVV16RRLigoAA3b97EkSNHUFtbi46ODhw/fhyZmZlYWloSFOz06dPY3d3F6uoqDAaDTHzNyspCeno6jh8/joSEBIyNjUGn04kPzO3bt6FWq7GwsACz2YxwOIzi4mL4/X55llZXV6UldOnSJbz88su4e/cuZmdnMTAwgLm5OcTFxaGwsBBerxenT5/G+++/j/n5eTQ1NWF+fh4ul0v4F8vLyygtLcVzzz2HrKwsFBQU4F//63+N5eVl+P1+jI6Oori4GGazWYpLojhUmCUmJqK3t1emR/9jxxP7dBDeV7ZTWBWTGa+sFok0kATIzVCpQvnsawmdazQa2UyVBzd6JiLMfDlyWqvVShDe3d33o2fAYwDR6/Vwu93SB6YMEYBsZExU1tbWJBgyySFpkzeHCgP21jkZluxmmnAx0CuljUajERsbG9BqtQKBc3NNS0sTAmdCQoKwssnHIL+CgYjwOn/G60XFSkLCp5bqvBdsKyjRDpKJeA05Gba9vR3AwQF+bGGUlpbi6aeflvtCrkRGRgbC4TD8fr/MBSHc7HK5kJ2dLY56RLGUSA6RCl4/ZauAgVev18v/J1pDfwSlUoRtDiWqxqDGZGV9fR2xWEwQH6URG89LSX4GcOB+JiQkIBQKYWFhQdw0s7OzYTabYbPZ5FkhgZXIw8rKCqamppCWliaoy2efPyVXgygH229KdIN+K0yM+ezGx8cfaJ0wKWNLg+uHM0iUDrif50hM3J/zUVtbi4WFBXz00UcCUSclJeHixYuYmJiASqXC1atXMTY2hv7+fkQiETGdoolUU1MT8vLysLi4iHA4jKKiItTU1IhHTUlJCSwWC/R6PSYmJrCzs4Pc3FxoNBoMDAzAZDLBaDRicnJSnof6+noYjUZkZmbimWeewalTp1BYWIjnn38eGxsbUp0TBaUccHp6GmfPnsXx48fFRpyb78jICHp7e7G2tibEw8HBQfyLf/EvkJKSgvv37x9Aed98802EQiE89dRTKC4uFsSpubkZdrsdN2/eRHJysrh8pqamYmhoCCrVvhOv0WhEKBRCWloa+vv7EQwGhXyrVqthsViQkpIiM0OGhoZQWVmJ+fl5BINBZGVl4Te/+Y0M1JuZmZEWskajwcjICL7yla/gzTfflH0tFovh0KFDMuBxbm4OTqdTECausyNHjkgiUVFRgfT0dNTU1OD8+fMwGo3Izs5GZmYmnn76aZmgzH0gLi4Or732GsbHxyUJGR0dRVzcvq9JQUGBtN/42fPz87InMRHi81dQUIDy8nJkZGQgEAjA5XIhEonA6/Vic3MTr7/+Op5//nnEYjG0trYiFovh8uXLOHz4MHp7e9He3o6EhAThg9AAkhJctVqNN954A//zf/5PGQBXUVGBcDiMuLg4Od/m5mYYDAaoVCq8+eaboiJKSUmBw+HAO++8I0MCzWYzvv71r4tXjdFoxOXLl0UOa7PZMDIygrfeeguzs7Po7u6GXq+HRqNBVlYWSkpKkJGRgd///vewWq147bXXEIlEsLKygrKyMrz44osYHBxEQUGBDJWrrq6W+V1zc3MYGBjAlStXYDKZ0NPTg7a2NgQCAdy9exc/+9nP4Ha7kZubK1yQjY0N2O12jI2Nfa494omTDmVLgwmIMgB8VnXwWaiav6M8lBs4/85+1N7enigM+Fq+vxIx0Ov10iYg2XJzcxM6nU4kXPw9ujfyd/b29g4wclnh0WOBM1aYfdM7hNU2kQieGzd4Et0AiL7eZDLJZs/roEwE+NmpqamIRqOiiuF5RKNRUUwoq1kiUHTe5EaglGWylUWkQimrVCIMTN5YiTOx4ATNhYWFA/weJj5ra2s4fPiwwOoWiwUmkwmRSEQ8CsiFyMjIgMlkQkpKCpxOpzCwlS0iHkxwVCqVoEJsBfAeULlAkzBl64UscqUkmSgEr4tOp5PvSjLsZ5McJbpAUijPmSjC7Oys2EybTCZh2BNRIUmYzxGTcaJnW1tbMJvNouThM8QkRfkdlTwfKgZ4HlwbRCCBT6cWs+3A9gmTFa55Th5VrtHPe1RUVGB+fh4jIyMoKSmBzWaDxWIREnN2djZKSkpQUlIi01IzMjIQjUbR1taGO3fuIBgM4urVq1heXsbs7CyOHTuG5uZmdHd349q1a2INfePGDdy4cQN+vx9zc3PSpsvOzobRaERhYaFMf11bW0NhYaGoFEKhENbW1lBRUYHh4WFsbW0hNzcXwWAQMzMzYovtdDrx8OFDpKenY2JiQj5HyZFiP53KsmAwiPn5efzv//2/0d3djcrKSgwMDGBvbw92ux35+fl49OgRlpaWMDU1Ba/Xi/n5eTgcDkG3gE9dhF0ulzhnUv6fl5cn50/JbEJCgjgAE4ovKSkBsD/9F9gPzFSoULXhcrlQXl6OnJwc6PV6lJaWoq2tDX/+538OrVaL3t5e6HQ6TExMYHt7G0ajESsrK/D5fOJhtLW1P5G3tbVVWhV9fX3wer0AgEAgIIZ+Pp8Phw4dkueMraDl5WVUV1dje3sbdrtdqmyXy4WUlBR861vfglqtRkNDg/hXLCwsoKamBoODg9jY2EAwGJQ94cSJE7h69aoYql26dAnJycno7++HWq1GRUWFJG/Hjx8Xl9C0tDR89atfRUVFBUZGRuBwODA/P4+MjAzk5uZKwZSZmYnExET8yZ/8Cebn5/GVr3wFKysrCIVCMpGYnjOcl0I05s0335QpyzabDcXFxcIJTExMhFarRXx8PAYHB2VeC5HaL37xi8jNzRU+2pEjR5CSkoKamho8fvxY/E6uX7+OiooKWK1W6PV63LhxA3fu3EFVVRX29vbg9XpRXl6OH/3oR5iYmEBubi4cDgeOHj2KixcviuSXsfb48eOCOP/oRz/C4uKiqNQmJyfR0NDwufaIJ26vKKs8pXpE2e5g8qFMQJSohFJeqdzUlPA4Ie5oNCrERn75z1ab7C8S5iQkabfbpZoEIEiExWKRCo4OhuR70H9DpVJJINrd3Z/Fwaxf2SIhEZMHrwN7/Dk5OQgEAtBqtQeQASYt4XAY8/PzYsPMeSIkBTJYkZDKibarq6tSmTIQ8t/K32OyoPR/UMqFCa2zGmUwpCOlEnpnu4S9688SfHd2dlBYWChmPgaDAV6vVyZsTk1Nob29XSourin6ALBvyOvPe6689ySVsbrnBs31wkSBr6GsWSkjpY8HiW8ApEIib4RrUzmrhSiDMoizndbX1ydcApfLhby8PGnxxWIxRCIRsSFfXl6W9iSfleTkZCGQ0tOEqBz5CbxH5CAxKSeSw2SCpnBMXnmuyhYSNzC2rEiaVXJYlM/O5z0eP36Mra0tFBUVIT8/H3V1dfD7/WhtbcXMzAx+97vfIT09HS0tLXC73aioqEAsFkN+fj4uX74MrVYLp9Mp04oTExOxsLCAra0tVFdX4+jRo+ju7hZrfRqQkdHf29uL+fl5zMzMoLu7W4JbZWUllpeXYbVaJdAmJibi3r17cLvdIoutq6sDsE9UvXz5Mu7fv48/+qM/khkyxcXFGB8fx927d6FSqXD37l2MjY1hbGwMi4uL+M1vfoOsrCwxzSI/wefzia8BEZehoSEkJiaisrISer0eV65ckWmwnOUzNjYmzzKfBRJsP/nkE+h0OgwNDeHEiRMwGAzweDzIzc1FZmYm3G43vF6v7M8kF3PQpcfjkTHtdrsdgUBAZLDr6+uiVvtX/+pfobS0VMjkNpsNX/jCFw4kDxxkmJ+fj66uLqSnp+Pq1auIxWJ4+PAhVlZWcOnSJdm3Gxoa8NJLL6GxsRHr6+tob29HOBzG9evXEYlEkJSUhL6+PuHuRCIRQRxmZ2cxPT0NADKUMTk5WYoYrVaLqqoqDA4O4tKlS0hNTUVNTQ1u3ryJ7e1tMT/b3d33HgmFQmhubsb29r6t/u7uLt566y2sr69jfn4ea2trsNlskvCXl5djbGwMnZ2dsFqtMo3Y7/eLydq7776LmpoakaOGw2HcvXsXOzs7CIfDYriVnp6O0tJS6HQ65OXlScxhQseWanl5Obq6ujA9PY3r169jb28PR44cgd1uFz6M1+uFy+WSliYt1rmXud1uqFSqA4NK2VXIzs4WV9RXX30VS0tL8Hg8ggKT15SXl4dAIIA//dM/xYkTJ2SfSElJQWtr6+faI/5JScdnZbOssolqKP8oGfRKZEIZgJXVemJi4oGBWpw1oQw+3KiBT82hCN+Gw2FBK5gocCSzRqORoE7zE/ZNOdWVMlG2MIBPDZ9isZjA56wamQQQRVAOoOK50WWTASgcDh8gjFI1QMKpkowaCASErMgx5+RzKDcjVttra2sSKJhwMFgxMyaZk9eRUkm2Fmw22wFuBNGSuLg4mZHB78b7xnuzsbGBuro6RCIRdHV1YW5uDomJiTLFlyOlzWazjN4OBoMIBoPo6urCzMyMJHpMoJTv7/V6Dzhy8nvHYjHs7u6KioVJxO7uvqpDGYgDgQCAfaUFicFcS0SyOAyP94hqIJJEAUgisL6+DrvdjpycHFitVlmHXPv0OqE/Crkh5F4w4VpaWkJZWZkkUsrhbnRCVPJR0tLSpCLiGuBzx7H0lERTYaNMSpTPB78jvy+vpVLJ9HkOkjUB4MMPP0Rvb69I+dgzBvaVSk6nE9evX5dkkVXh8PAw7HY7DAYDNBoNFhYWMDIyIpNSc3JyDkiRu7u70dbWho6ODpjN5gNjAj788EMZRrW9vY3XXntNbNBVKpU4eZaUlEjP/MSJEwiFQmhra0NNTQ2Wl5cxPz+Pjo4OQT2eeeYZdHV14ciRI2hoaBDzqdzcXLS3tyM+Pl7Q2uXlZdTV1YlxmslkEmvq7e1tQUPj4uKg1+sRCATELK2srAwzMzOor68X8uKDBw+kRWQymXDq1CmMjo5iZWUFJSUlGBgYkGtptVqh0WiQn5+Pubk5nDt3DjqdDqWlpThy5Ai8Xi8mJibw8OFD8S7p7+9HUVER1Go11tbW8Itf/AJjY2NYXl7GhQsX8NZbb+GTTz7Bl7/8ZYyPj+PmzZs4ffo0jh8/Dq/Xi5KSEhkomZ2dDZfLJW3i+fl5geQbGhrw2muvyQh3j8cDtVotyUNaWpp4T9AhlYRsFh8ZGRmyB3m9XoTDYWxtbeHKlStQqVQiwx8YGBD7cSIkbOVZLBa89tpr4qsUDAbhdrvx3nvv4fLly/D7/UhNTYXX6xV30oaGBlgsFrGrj4+Pl5bbxsYGXn75ZfzqV7+CwWDAwsICVldXcfbsWdjtdmi1Wmi1Wpw+fVr29o2NDeFbWCwW5OXlHTAi9Hq9+PKXv4yKigqJE5FIBI8fP0ZWVhZ6e3tFrfPxxx/jL//yL5GZmYmSkhJRP6nVatjtduHiEF3Jzc1FZ2enIFmcGLu4uIji4mJRIhkMBjQ2NmJychIPHjzAj3/8Y6ysrMDlcolA4/Mc/6TZK9wIuakqlSU8lIGCLRX+jK9lUGGQVyIiJO7xBn1W0aD8LLJ7SUhlBQ9A9Ok8ZwZQoiiE1gHI65hkUH7Km2+xWETeygeULHyiDxz8w42dKgGv1wuNRiNV5e7u/hC6pKQkeT+VSiXV+O7u/kwP/T+MNVbOylCSBZXIC6FypYyWc0PIaVldXRW5LH0YlHNBeG/oM8IkimjS7u4uBgYGxCqbyR/PYXt7G3l5eXjmmWdks7NYLMjPz8epU6eQm5uLCxcuyOh4VtPLy8vi4KgMnuzFk88QCASwtbUljpr8vhqNBunp6YLOEHUjOZITf8mTIR+HryPUy/VDxIMKKq4pJhFsZWi1Wnmod3Z25BqxjbS9/enQQJ1OB41GI2tTaZUeCoWkLcLkhDyL9fV1Uf+QbGyxWGQSKgnUycnJwh1iIq6UqFMqqkyKiJwokwsG5CdNOHikp6fj/v37CAQCuH//Prq7u/H1r38dBQUFiEaj6O3tRW5uLtbW1lBSUoLMzEykpaWJ4yWTgfT0dBw9ehQ1NTVYWlqCyWTClStX5O8jIyOYmprChQsX0NzcDKfTiT/6oz86gFJduHAB29v7BoBNTU34xje+gbi4ONy5cwfT09Pw+/0wGAzw+/1ob2/HF77wBWxvb2NychJJSUnSurHb7bh06RJqamoQHx+PmZkZNDU1wev1ykC5srIy6PV6HDp0SCpaOoQmJCQgKysLk5OTYrjU29uLkZERMdTKzMzEzZs34XQ6EY1Gheuys7ODiYkJ4XOVl5ejt7cXZ8+exe3bt2X8OdUO4XAY2dnZ8Hq9iMViqKiogMfjESMqTnAl2rWwsICCggLodDox/vJ6vSKPdDgcKC0txaFDh3Djxg1UVlZCrVYLQbykpAQPHz7E3/3d38FoNKKiogLV1dUYGxtDT08PsrKysLm5CZvNhqSkJFy/fh1xcXGoqqrCX/3VXyEcDuP5559HRkYG/u2//bdoaGhATk4OnnnmGXzzm9/E6OgonE4nMjMzMTc3h6amJuj1enR3d8NqtcoYeBKBiY4lJSXB5/Ph+PHjsNvtIn11uVwoLCwUzk9aWhoSEhIwNTWFp59+GgUFBRgYGEBjY6MUixMTE1hbWxNicmdnJ7xeL+Li4tDV1SUqo2g0KveRZHDGjbW1NUxMTMgk3Nu3b2N+fh4Wi0WUJYFAAK+//joyMjIwNDSE8vJyaUHfu3cPbW1tYkNPpGFmZgbNzc3w+/1oaWlBZmYmHjx4gHA4jO9973swm81iKmgymTAxMYFYLIbBwUHs7u7i1KlT4ikzNzeHQ4cOwWg0wuVySWFHR9nl5WV84xvfgE6nQ319PTIzM9Hf34+6urp/Hk4H8CncrUxAlCoSvkZZqfLnSoULoXklWVD5GlbWynYHN0N+PhUvTBqoNKASAvh0hsrKyorA8cFgUGaNBINBqNVqmM1m2WTZUmCgZr+bRDHaqaelpcmmzWRFq9UemJNBWWUsFsPY2BhMJpOQ1Mia/ux3JSr02bYUAy2DJ0lrwKeGVWypKImSnDVCXgADNe8hX8OKmwmQsk3BwEt4fmxs7AC6pVwfiYn7480ZAM1mMwYGBtDS0iL+HOTYEC0JhUIYGRkRsinPjUgHqxZazvP/kcfCylLZDuDf2Xphgkz5KM+d95nXjEGaxk2ch8NkUcnvINeGPIvP3g8mHJSOE1khUsbf83q9MvuEKBkNupQmYGlpaUhPT5e+Mj9DOeCPa4dtEuUQP65TrnGSffl3SruZsPH+fN7DYDBgfn4e+fn5KCsrg9lsht1uR1tbG3Q6nbRO7927J9bbN2/ePDBTZWlpCe+//z7ef/99jI+PY2BgQLgyFy5cgNfrhcFggNFoRE5ODm7fvo2VlRXU19fj2rVrIu02m80YGxuTcd7Ly8u4evUqRkdHYbVaJWlISkpCfn4+dDodfvnLX0oxodVqRR3yzjvvyPnn5eUhFovJveczMzo6KjyBWCyG2dlZBAIBmEwmmEwmPHjwAFlZWSJt3NvbQ1FRER4/foyUlBTcvn0bOTk5SExMhNFohNFoxMLCAjQaDTIyMqDRaDA4OAi9Xo8jR46gra1NeFeRSERagdvb2xgeHsbq6ir6+/sxNTWFnZ0dVFRUiCMmybasUCmtJdeKyNfHH38s+8js7Czy8/OxuLgoyVcsFhN/na9//euYnZ2Fz+fD+Pg4AGB8fBy9vb0i46RsNCUlRYiIRFiLiopw7do1XLt2DSMjI6itrRWEAQDu37+P+fl53L59Gz6fDxqNBsFgEENDQxgfH0diYiLeeustPHr06IA9wa9+9SvY7XYsLi7iG9/4hhhnkeSdnJyM4uJiVFdX4/r16xgeHobZbEZ1dTWuXr2Kzc1NIV8ODw/j0aNHOH78uHiBHD9+HOPj41hdXRXOntPpxNbWFsrKynDmzBkpCiORCKxWq6Dm2dnZePDggQyNm5+fR11dHRYXF+FwOHDjxg0xyiNin5OTA7VajZ/+9KdwuVyiCrt9+7bwlTIzM6HX63Hs2DG0trZicnISU1NTGB8fRyAQwMrKCl544QVotVr87ne/Q3FxMTo7O1FbWwutVovu7m6YzWasr6+jt7dXFDC3bt0SVebY2JgUSmtra/88A9+4Qf9/JRz/N+UBAIG1lYGUG/5nJXlK2JxVHTdq/j4/n5AWWfys0PV6PUwmk5A/w+EwTCaTtCNWVlZkEJLZbBZL6vj4eHGgoxyO/UqlU+fGxgYcDod8D0ppqeLhd+O12dvbg9vtPiBhZEuDDqIk7dGtkg6brEb5Oxz4w+tBUivbIwxO5AEoibW8B+ScUCHEil2v12NjY0OQEI5+J1LFBGljYwMjIyNyPz570IdfrVYLNEllwtDQEAwGA9RqtQQOJpTBYBA3btyQRJHfnedOC3WiDyRFEu1JSEiQhICJCRMprleiC/TlYFClSiouLk5cRclh4fpSSm739vYkceOaIPGYSZySYMtrrHx+OGmXUk9apjMRYXuPyAoTnmg0KigcrzcAQdqUU4xpZ070KxKJIBaLCVrEniyVU/TUYZ/2s7ydf+wIhUJCpKOclEMPJycnodVqUV9fL5W5yWRCdXW1EBI9Hg/Kysrw5S9/Gbm5uSgqKkIoFEJ2djaKi4vx4MEDAPuOkxwVnpmZic7OTjidTlgsFqhUKhQVFYnXhtlsRkVFhbR9/H6/IKDf+MY34PV6Bb3Y3d339BkdHZXW4OTkJL7zne/gnXfeEXlne3s7MjIy4HK5pOXHCnJxcVHmn9BIamZmRpRuHPCWn58v6yUQCOD555+H1WqVgsblcuHEiRPIyMhAf38/LBYL5ubmxODM7XbLOHSVSgWbzSbD1/h8EaEcGRnBwsICuru7AUAcYWlYRtk/lR1zc3MYHx8XsjnXSGpqKmpra+X+EqlsbW3FRx99JJOnyavr6+uToWoJCftW3Uxqqao5fvw4rl27Jkk1z6OzsxMqlQoZGRl48OCBEGYjkYhY1lutVtTV1SEUCiEYDMJoNKKlpQUDAwNISNh3oz516hRaW1uRkJCA999/H7OzswD2UXCDwYDR0VFpH9ER1eFwYHh4WFxKrVarPJMWiwXt7e3QarWYnZ1FMBjEyZMnBY1l4edwOESKXVpaimvXriEhYX8+y9raGlwul7Q9R0dH4Xa7hRowNTWFkpISZGdni7S2srISS0tLyMrKwtbWFk6dOoXk5GTcvXsXe3t7qKioQHZ2NmZmZnD9+nV4PB5UVlZibm4Ou7v7Dr5vvvkmEhMTcfr0afh8PgwMDCAajeLu3bvik7O+vo7i4mKMjIxgenoabrcbOzs7CAaDyMzMRFtbG1ZXV3Hs2DHY7XbYbDZsbGz88wx8IwQPfAqBsyoEPoXY+W8mD0ob6c++RplEKE3AuCH7fL4DRFK2WZRwtVarld+jEkDpMcBWBoOyMgiRwJecnIyCggJRmQCfBiJWwsD+nIxYLIZoNIpAICBkHCWyw/NjoGBSkpSUhMnJSaSlpckAOioZOIeDlatSNUISqXISKgMTOTbKZI6VPQMfr8Nn/82EheoHSkMTExPFiIw/U/JHNBoNRkdH5b4q0Snen7y8PLzyyitITU3FzMwMdnb2J6dys5mensbe3r5jbEVFhQTkg/xVAAEAAElEQVTc8fFx/PCHPzygvGAStbCwIGgPkwJ+L05y/SzfiEkK+RF8jU6nQ3JysiAJbOExkSbkCkASWq4vImzp6enSiuHaIGmV58bnhS0kvpaJOF9jNBrhcDhkiBd9WgAIaZjJBTkirJ4AYGlpCRqNBjqdTpQ5RBiZKDEZ4v+nAoYJMsmyvJcbGxtyDk9ylJaWwuPxICUlRczCxsfH8fjxY/h8PiwsLECv1yMuLk5UKmTonz17VqadVlVViVnW9vY2srKykJ2djfz8fJG8M2mncqOjowNf+tKXxKWSfzjc69KlSzCbzcjMzIRKpcJf/MVfYHJyEh9++CHKy8tx6NAhDA8PS/LCiaAjIyNIT0+XYoYW0Lx/3d3dqK2tRXd3N06cOAGr1QqVSiXDy9RqNV588UWUlZWhp6cHDocDWq0Wubm5Mohsfn5eOCB3795Fd3c3PvroIwQCAVHVPP/881hZWRFHUL/fj8nJSSwuLuLIkSMYGRkRQ7OxsTFotVoMDAygrq4OTqcTVVVVMrmalup0UF5eXhYzqrm5ORQUFCAzMxMDAwNob2+Xv4+MjKC1tVX8chISEnDy5Ek0NDSgsbFRhpElJibi0aNHQkBdX18XkzCPx4NAIICioiI4HA4UFBTI806yP63S19bW8NWvfhXl5eWorq5GXl4e+vv78Z3vfEdQ3IaGBpn9wiJza2sLTqdT+B80rMvJyUFvb68gIiqVSgquaDSKoqIizMzMIDU1FRMTE4iL259gbDQaZdIsY9Tt27fhcDjwd3/3d6isrITT6UQoFILD4cDq6ip8Ph88Hg+Gh4cFURsfH8fMzAySkpLk+UhNTUV9fT2mp6cxNzeH8vJyIRrrdDpMT0+jt7cXAGSNrqysQKvVymBEFmLFxcXQ6/WwWCxobW0VKXlRUZG0v9RqtayfjY0NzMzMYGpqCtPT04iPjxfTMY1GI94xzc3NYpoZi8WQmpqK3t5eQe3cbvfn2h+eKOkgifKzmzvwKQqhrHwJ6fN3lCjAZ9s0PGiGpAwun5Xk7u3tCXGLm7uyZ8/3TUtLE7tjng8TDSILycnJAr2p1Wrk5eVhenr6AG+FSgMlx4ObdCwWk1YOgxnbA/w5q0273Q6v1ytjmDk+nUoV9gAJQfN6EDGhXp5EViZPSrIuz4PnwADC5IDOlnw9+Sc00OJBCSeJvAzOTKQo31WShHkviWBpNBoYjUaUlpYKF2J9fV2kWD6fTxKK8vJy0Zu3tbXhN7/5jSAKXDNTU1PynXkwSWMrJRwOC8LDtcBgzPYIrwG5IkozMKIKRLXIuWHy81lnUq4TBnEebN8wGWRyQh7O7u7ugTk1THK4vtgyVBrSMdkg94T3jigJeSh8PrmGlOgJK0klgZnXl79LK3MmI0967O3tDx7zeDz44he/KA6xJEEHAgFMTEygqqoKX/ziF5GWliZTkj/++GMcO3YMgUAAAwMD6OnpwcLCAg4dOiSmSWxRzMzM4K233kIwGERZWZncC4/HI22VnJwcGI1G8adgm2J7exuFhYUoKSnBhQsX0NTUhNTUVMzPz+PSpUvY2tpCIBBAOBxGbm4u7HY7pqenhVtRWlqK73//+9jZ2cHt27eRm5uL/v5+PPPMM5iYmIBer8fY2Bjm5+eRnZ2N2dlZ3Lt3T+Z1OBwOmTFCvldOTg7m5uZw/PhxzM7OYmNjA5WVleLRwBbf5uYmPB4P7HY7jhw5gvr6ehw7dgy/+93v5PlYWFhAbm4uzGYzCgsL0d7ejrt372J8fFzI9tvb2ygtLcWbb74pLphjY2NIT0/HK6+8Ar1eD5/PB7vdDovFgmvXrkkicvbsWTQ3N6OmpgbBYBCTk5Pwer149913pRXj8Xhw9OhRbG9vS+Kl1WqRnZ0NvV4v9t5f+tKXsLu773HBSaoOh0Oqdjqqut1upKenw+fzITc3F21tbfgv/+W/YHNzE9PT0+KOyVk/XV1dUnCq1WohwY+OjspeXlBQIN4nW1tbgoaRxFpfXy++PtPT0zK3he27Y8eOiTS5vb0dAwMDolhxOp2oq6vD3/7t3+L+/fs4fvw4wuEwNBqNzEUhj2NhYQGvvfYaVlZWUFFRgbi4OCl66eCck5OD4uJi/PrXv4bNZoPRaMT4+Dji4vYtCAoKCjA7OytkWYoY8vLy0NbWhv/8n/8z3G439Ho9QqEQCgsLZVRATk4OsrOzkZycjEAggKqqKvz0pz8VJLy6ulq8iBISEvDcc8+JPH9tbQ12ux2Tk5Ofa3/4J3E6CMECn0L2SiKpEgFRIhpKmSt/V1mhM+skMZQtCyViQZhPr9fLzIXl5WXEYrEDcD973nR3pMJErVZLFU/SJ5UgS0tLEnQ4odVsNiMtLQ0mk0mCOCF6El1pu07IntbS5ECwfw4A2dnZWFpaEtgzMTFRNhNeJ7ZRVKr9mSIpKSlSPfN6KfkGtLVloOO1JJLC78REgtedlT9tupmUsJ1CqG1lZeXAXBVW3CSTKr0g+BqiI+vr+0PtKM3T6XSiZAmHw8KHSEhIQENDgzywr776Km7dunXAoIyzN5gccZ4L1xnXIn0nAIgpD30pGAAZTHnPmEgQQeEgNyYZbGERAaBXB5VWPEcie6yGiObQu4TtHvIB1Go1fD4f4uLixNCJUtidnR2pfJl48vrv7OyI0y6TaiU6wu/MxJftPCJdcXH7U4IDgYAkwElJSTI5l39/Up8OALhz5w4cDgccDoeMlq+trRXiZlFRkXCqRkdH5RrodDqZRnvq1CmYTCZkZGQcIJiSSJ2Xl4c7d+7gueeek+AYCoWwvb0Nj8cjREGNRoPU1FSB+Kenp1FQUIDbt2+jt7dX5Lh3797F8PAwMjMz5VkrKCiAx+ORQNLQ0IDx8XHhFlitVmlP0JSMvffe3l4xyvP5fDKI7datW2LIlZeXh7i4OBQVFWFrawttbW2or6/Ho0ePhOy7sbGBqqoqUbNtbGwgIyMDiYmJqKurw507dxCLxeDxePD000/DYDDA7XbDZDLJMMXJyUnh2gD7yqG4uDhkZWUhHA6jsbERFosFX/nKV3DhwgV0d3fj0aNHwqugNbbRaMTe3v5ohfb2dgwODmJtbQ35+fk4c+YM5ufnUV5ejsrKStTU1GB7e1tMt7j/WSwWDA0NyQwTv9+P69evIysrS67fkSNH8OqrryISiaC0tFRM0vr7+2EymXD58mVRevT39+M3v/mN+KM4nU4xdDQYDEhISEBVVRXi4uIQCoXEjK2jowNGoxHz8/M4ffq0PCtOp1OeFZfLhe7ubhiNRuj1eqytreHIkSMyNXloaEisz202G7KzszE3NyeeJktLS1hdXcWf/dmf4bnnnhP0kf4WNpsNo6OjUnjY7XY8++yzWFpawtDQEFwuF27evIm+vj688MILGB0dxeLiIs6cOYNoNCptE7aX6fXCfZXtjoyMDFgsFpw/fx7p6ekyDHFsbAy1tbXIysqS0QM2mw0vvfQSZmZmkJKSgkAggM3NTXR3d4uDt8vlwg9+8ANx0p2ZmcHg4ODn3h+emNMBHEQnlAEH+HQQmLLCUyYpfA2hbB5K1QrhbAZIJapCWRn7bBqNRoiE3GCUxkt8b7PZjEAggFgsBq1WC4PBgPX1dczMzGB+fl5QhlAoBKPRKIO7aC7EDY3VKAmkKysr4nxJjwlCi0rCIQmsHFzF/jErXG7wTFTYbiFZVDlbhO0cOqwyUVCiGnS1pAKCCRcrYPIPGISVbqW89kSO+P8Z7CjZHRgYOED4JCrBBMjr9cLr9YpFPe/b4uKimELFxcWJMVt8fLwMP1pbW8P3v/99dHZ2yr9p8KX0G1Eia2yfkX/DRIUtLiZ5yiCrbF/RD4USbSJGRD3Y/mMCq0RTyC3iNaCRHHlJyhYhCZ/kiZB7sLa2Ju0zcnc2NjZgMpkQH7/vGEkeCtc3URhyQ5hMKRVV/N68l0Q6SG5liyIuLk6GpSmt5J/0sNvt2NzcxPDwMEwmEwKBAEZHR8UEbH19HT6fDx0dHYjFYgiFQtBqtWhubhYb6Q8//FDmP1y8eBGLi4sYGBgQguLm5ia+9KUvwefzSdK/vr6OkpIS7OzswGazwe/3Q6VSSV++r68Pi4uLmJubw3e+8x2prKenp1FUVISsrCzU1dXh3r17sNlsgvT5fD5MTk6KYyeNzTiiPCsrC8A+Ekw+R1FREV577TUcOnQIiYmJmJiYQHx8PP70T/9UWhe0vGeilJ6ejvn5eVRWVuJP//RP4fP5MDMzgw8//FAcjldXV5GXl4fBwUF4vV643W6o1Wrk5+fjrbfewurqquxZx44dw9LSEoB9PyKz2Yz8/HzhjEQiEXR2dmJ9fR3nzp2TFkhubi5yc3MRi8XQ0tICm82GL33pS6ipqcHs7CwyMjIwOjqKM2fOYGlpCY8ePcLdu3elzW2xWFBaWori4mLk5eXhb/7mb6DX67GwsACtVismXF1dXZK43bx5E5FIRCy3T506hcTERExPT+PMmTPS8pyZmcHk5CROnz6NlZUVDAwM4Oc//7mgFdFoFG63GxMTE3A4HMKT2N7eRlNTk6A2mZmZ0Gg0GBoaEu8es9ksbrNpaWkYHx/H5OQk1tfXMTs7C7/fj1AoBJfLhdTUVFRUVAhniIUcRwtQcswZVGq1GuFwWPiBe3v7ppdHjhyBTqfD4cOHUVhYiP7+fnl+d3Z2cOTIEWRkZGB5eRkej0cQjurqaiEqZ2RkYHBwEFqtFi6XSwqRiYkJWbNarRZra2syk6a9vR06nU7M+1QqlVAQfvGLX4hPUDQaRXt7O7q7u6UFQ9WScmSC0Wj83PvDEyUdSvMvJcFP2VpR8jWUCAZRDGUCwU2a78Ngx2pRyTngJqscbEUdPCtYpb8Hg4Pf7xdCoNFohEajEVtomoetrKzIVMv4+HhkZGSgpKQE8/PzssHTH4FICav8hIQE4SPwO6Smph6QPRLFoBeDwWCAz+eTIMHAwGocwAETL7Y1mGywxUF1AgeHKVsG7O9tbGxIW4xtos8iMLyXvOZMRFQqlTjS8Rrz7yqVCnNzc9Jm+r9xezhVta+vT9AWk8kkA+7I/OeMmXA4DJVKhbKyMklY3377bQCQ4M81wtYBkyHybpSv4/VXOnBS4USvAKUiiPdXadTGa0oTHV4DolFKTguTOqVKi9ee56xEKwhPcgjYxsaGzB8h+kTbZyJWTGSBT9U7ycnJkpgRpQI+RYJIgOXzyZYh7zcTEaVvzdramkCpT3rk5uaivr4eVVVVYive19eH+Ph4VFVVSeKek5ODaDSKqakpBINBDAwMIDs7G6+99pq0EBYWFjA6OorU1FQYDAZYLBYEAgGkp6cjFAqhsrISXV1diIuLQ0tLC6LRKObn53H37l1cvnxZkrGHDx9iY2MDFosFkUgEn3zyiUwTnZiYQEJCAkZHR/Haa68JiTMvL09IdYuLi3j8+DH8fj9yc3MRCATQ0NCA4uJiUThQucDKvqSkBL/61a9QV1eHhIT9wXsjIyMIh8MYHh4Wgue1a9fwwgsvwOv1YmxsDKOjoxgaGsJTTz0lXBFOM33w4AG8Xi/y8/Nx//59RCIRZGZmIhwOi9eQwWCQ5Gp9fR1Wq1WShOXlZUnwzWYzamtrxRU1ISFBEpaFhQW43W44HA74/X7cvXsX7777LrKystDR0SEGWRqNBlarFQaDAYODgxgfH5f5Og6HA0NDQ4LSOBwO3Lt3D+FwGEeOHEFeXh7u3r2Lmpoa5ObmiuooEolgaGgIDx8+hE6nk2qbKA8JoM899xyOHj0Kk8mE69evi7fE4OAgTp8+jfPnz2N2dhYWiwVJSUl4+PChuNgS1V5fX8fg4CDW19dF4RQOh2VYnM1mE9T9xIkT+OEPf4iNjQ00NTVhamoKLS0t0hbxeDxobGzE0tKSDL1kAejxeKTIKCoqEiUM0ewrV66IBTx9SIqKihCLxTA9PS3EWK5TGoEVFhbKeW5vbx/Yz9hxSElJgclkQm5urswcq6ysxNbWFhYXF4Xsz8Lm0qVLSEpKgtFoREZGBhISEpCfn4/6+noUFRXJvjk4OIiqqio0NTWJ18znOZ54yiwhIiYQ/zc5Kzc4JhjcCJU8Cf6eMhHZ3t4WbwT6E9DchYgB++Dc3BkAOOadPg6sJslHINqi1WrFqpZ9vvX1dSwtLUnFxxkaKSkpor1eW1tDfHy8tGQY1ChX5PdkJUvORXp6upwfuQd2ux2pqakyY4CblVK6yvcjEsLgzmqULQFyEZSBhUGEPBdWwrzeDHxMRIhyMOgR0WEywgSPbYXExE+HI4VCoQPmbry3GxsbGB4eFs4BpX/keTCAhkIhcev0+/0Ih8MyV0OtVsPj8aCrq0t63GznKBMyXgf6dVDpA0C4DDT1YvW+u7sLvV4vMwe4TpjU7e3tCa+CDzNba0zY9vb25F4oZ5Tw+iqRFCJfycnJkhhRLWUwGOQcqKfnPQVwgHezu7sr9ti8L/xuRC74DDIBZWLEtgnXBmFd+sXs7e1J22txcVHW6ZMcHOvNthkt0A8fPgyz2SwEbM6gyc3NFa4FAFGb1dfXiwsoN3RWjQxoN2/exNTUFHJycuDxeFBaWoqFhQX84R/+IXJycvDBBx+grq5OCJPNzc0y4XZvbw+BQADj4+MoLS3F48ePxQuC80Xu3buHYDCI4uJinDx5El/+8pdx9OhRxMfHCwEX2Cf2PXz4EMB+W7eoqAgmk0n2EUpMyXU5fPgwAoEAvvrVr0Kj0eD8+fO4c+eOkCRJmo3FYvijP/ojfO9738Pq6iqCwSAsFgsePHggz3YkEoFer8e5c+dw4sQJNDQ0CA/jt7/9LeLj42Ui7f/4H/8DAFBTU4PV1VUsLS2hubkZc3NzuHfvHioqKnD48GFsbm6iqqoKaWlpMtK+qakJubm5kpRMT08L9+LixYsip93e3rcwDwaDch40Ktve3saJEyeQnJyM27dvIzU1FU6nEz/5yU/EAO79998HsO/18qUvfQler1dQMqvViqGhIWxubqKlpQUzMzPIzs7G7u4ubt68iYGBASwtLcFoNOLatWsYGxvDwsKCzFCxWq3Iz88XszadToeNjQ1Rs8TFxYmb6+bmJnZ3d+FwOIQzFA6H8e/+3b+TWFJbW4vExEQJ0sFgECkpKWhsbERGRobMU5qcnEQgEEBycjJcLheGh4cxPDwMv9+PmzdvQqfTIT4+Hq+88gqam5tx6tQp/Mt/+S/xwQcfQK/Xw263w+/3C4nZYDAgPz8fOTk52NnZkUQpOzsbt/5hKB89i5xOJ4aGhgQpTElJEV4hlUrr6+uYm5vD0tISVlZW0NLSAovFAo/Hg6SkJJw8eRJJSUkYGxsTg7S9vT04HA6Ztsz9+vMcT5R0EJYFPlWyKFsn7JUrTaaUqAhw0IeD6AgTEmVVxc2HEisAQoLjxknUgolQamoqMjMzsbu7K8oWBlkiE8wslQZJnMgYDofFZIzQJec5sA+v5E3woCSS14DvwY2cFbKyz261WmW8eyAQkImuRHkYrOgsqeQqUE5LwypeHyY/PBjYyMlgS4sVODN4tmDIX2DSlpqaiuTkZEkK+B0py11dXRW5oRKliouLw9LSErq6umSGx8rKimT+bPUwgSAhNzExEaFQCEtLS3C73XC73SgsLERrayump6cl2WSlzMBPZIfXZm1tDWq1Wmyf2RZi0sdzV071VbZtyM/h69lyUiZXPHdyE+jFwSSH11q5pknsIroQjUaxtLQkbbm9vT05J6PRKG0gJpu7u7sSbMgvIbGU8mFg39I9FAodQHKUCSfba0yiiMz5/X7Mzs4KsVXZovy8B4dQUcHQ19eHoaEh4Uix/8x5OwsLCyJP1Gg06OnpEVnezMwMysrKhAy+tbWFsbExVFdXY2JiAgUFBZiZmcHDhw+FmJqZmYmf/exnMrPl5s2bOHz4MGw2G2ZnZ9HT0wOLxYJLly5hcXER29vbCIfD+MM//EPMzc1JH5yOkwUFBfjoo48k6b9//764aUYiEfT09KCsrAwmkwkzMzOorKxEX1+fPPdutxs2m01MsqqrqxEKhXDq1CncunVLpovW19dL0piXlwe/3w+dToeOjg5kZmaiq6tLEsuJiQmkpqaisbERZrMZnZ2dePPNN7G+vo4333xTHIXdbjdGR0exvr6OY8eOoaysDGtra+ju7sb4+DgOHTqEW7duoaSkBA6HA9evX8eDBw+g0Wjg8/lw584dWQfT09MIBoOyjmg7vry8jLffflsGt2m1WoTDYTidTuTn5+PixYsIBoPo7OyUZyE5ORk1NTUIBAIwGo1ob29HS0sL2tvbodfrxSvkwYMHwufp6OhANBqFzWZDYWGhSDXpbXP69Gn8n//zf5CVlQW73Y5IJCJtO6qw6IbMfWJmZgY6nU7Mxog0PXr0CCaTCUVFRVJA0GJ/dnYWq6uraG9vl5H02dnZAID8/Hy8//77woNjQTs+Pg6n0ynxZmZmRuzUT548idXVVRQVFQnSkpCQgN/+9rcoLCwUqXl8fDxKS0tx8uRJJCYmCrrCuBWJRNDf3y/cHcbUkpISRCIRmbRLsnhlZaUMpLx37x6Ki4tFfUPb9pMnTyI+Ph6PHj3C5OSkdATGx8el0OF8pUgkcqDw+n8d/ySkA/i0HcK/8/99dmP+7MHfY+BWEh/5PvSp8Hq9iEQi8nNauQKQNkRiYqJowNmq4LkQMiJRlEGbUsOkpCSEQiFYrVZxKI1EIgfIkYWFhZiZmYHX6z2g/lA6WgKQNgUrVL1eL+gLrcn5PQh7V1RUYHp6+kAVSytqch14Pmxv8Bp+lufC4MtrzvYKKzJ+BmE3VvpUPfD+Krko8fHxIj1jsOK14ecGAgEJykpTrd/97nfw+/0SyIlCMOlJT0+HWq0W7orT6ZRWWTAYRCgUQmpqqvRpu7q6JOlUmoCRm0BjK7YH6EpKwzZulLxWlJYuLy+LukhpIKZSqUQtxLVHLgdJjbwGdEMl05x8GqVdOvk1tGlPTk7G6uoq9Hq9PC8ksNIxlckAkT66npJsy/tCHhM3GhKlSfpTqq6YDLGAWFtbg9/vl+R2dXUVZrNZnqnPqoU+z0E+yvb2NsrLy5GZmSktDBK4JycnYbfbkZ6ejpmZGdhsNoRCIVy8eBF9fX0oKCiQ0edMqvLy8lBfXw+TyQSv1ytJ9927dzE0NIQ7d+7A5/Phr/7qr3DixAloNBr4/X4xdGKCGwqF8POf/xwNDQ3wer0yZj4vLw9jY2O4e/cuXn31VXEA/oM/+AMZQaDX68WTYmFhAU8//bQkVLFYDL/85S8RCASkoozFYhgdHUV6ejrm5uawsbGB69ev4+rVqygoKEBFRQVsNhtaWlrQ1dWF3t5eNDQ0iBqhoaEBZWVlSE1NRXZ2NhobG1FbW4tr166JZTnlydnZ2WhqasL6+jra2tqQnp6OiooKLC0t4b//9/+Oc+fO4c6dOzh06BCKioowMDAAlUoFi8WCkydPoqCgAEajEYuLi3jvvfeg1+tRWFiIF198UdRner0eX/nKVxCNRqXqra+vR2Njo4w7AACj0YiBgQGZCzI5OYmdnR3h1aSnpyMrKwu//OUv0d7ejnPnzqGkpAQGg0E4UUVFRWhsbMS5c+dw+vRpcXz96KOPkJ2djZaWFvT09CA1NRVutxsGgwF/8zd/g6SkJOTm5gLYV/Hk5eVhampK9vhLly7BZrMhIyMDJ06cQGpqKnJycnDixAlMTU2hvr4eXV1dGBwcxMjICBoaGuQ+LiwsYG1tTdxab9y4IXtKa2uroCck3dbX12NnZwfRaBQul0t8Q2hFQDlvb28vOjs74fF4xBLearWiv79f7sni4iJ6enrEoHJ9fR0GgwHl5eXiF0PX5r29PZw+fRqzs7PC3WDr3263491338WXv/xlAEBOTg42NzfR1dUlvLuGhgYEAgHMzs5iYmJCEmGz2YyGhga0t7djaWkJLS0tMJvNMJlMn3t/+CclHUry3mclr8rXfpbox6D8WbWD8u+s7rioiaRwzgTRAAZIwsTAPopCtIK+CITy+f8Jm1E+uLGxITbHGo0GXq9X/ChY3VgsFszMzMjMEZK/+D6Ez5npKbkAhNg5mIgs7p2dHTEH4iatbB0xmPEhprMkP4fBhd+fLQ0mY+z1UxXBypbtF3ISlEiAEqHiteK1Y1K0trYmSRxHbjOgsrqbnJzEb37zG5Hk7u3tiQsriZIAxICM1QjtgMn4J+F2cXFRfoetoL29PfFLYfBUqnJ4LxISEgSpYtuLia2y96ls8fG6EIEgksbEhudAZCkcDgsvh20MqmwY/Hn+29vbck18Ph8yMjKk9cUkQ5mw8LwSEhKkdchgC3w6iZRJL9EEkodTU1MRDoclYSYnh2gOJYVsZ6anp8PpdEr760mP5ORk5OTkQKfToaWlRZCrgoICWf/b29uorKyUWRmcBWG1WvHgwQOkpqair68PycnJ4spptVqF8DY3Nyfzi2KxGF566SVYLBYYDAbo9XpMTk4iFArJ2G4AaGpqEo7G8ePHceLECZSXlwvxlEZ2ly9fxtNPPy3PpEq17w/T19eHH/zgB8jOzkZCQgKmp6fR2NiIt99+WwbJ0ZCLXhYsZrq7u7G9vW+tPj09jVOnTsFut6O9vR0LCwsoLCxEcXExbDYbTp48iVu3buGHP/yhcCM2NzexuLiIYDAIv98Pp9OJU6dOYW9vf2r1hQsXMDQ0JJNsvV4vTp06JTylwsJCSWyMRiP6+/vx7rvvoqSkREaZLywsAIAQKF988UUUFhZCpVJJoLPb7VhZWUFPTw+sViuOHj2Kvr4+3L9/H5988glcLpe0XH7yk5+gqakJCQkJePnll6X9u7m5iczMTExPT+OTTz7Ba6+9hubmZgwMDCArKwuPHj1CZ2en+HTQHp/D6MbHx1FVVYWuri4p6sbGxvDJJ59gYmICJpNJWhLRaBT5+fmw2+1SYHC9tbW1iYcF1TgajUb2qUuXLiEajaK6uhq9vb3Ca0hNTYXNZhP09ujRo9jZ2cH8/LyY0D1+/Bg9PT2YnJyE3+9Heno6+vv7RbW4sLCA+/fvi5x5a2sLBQUFyM/PR21tLZKSktDY2Iif/OQncLvdSExMxOHDhwXlLygowKFDh+T7BINBeDwecco+fPgwrFYrhoeHsbi4iOTkZHR0dIhUf2VlBampqXj48CHcbjdWV1fhcDiwtLSE+/fvw+Fw4O2334bJZEJ2djZ0Oh0mJycxMzODaDSKH/zgB2hqakJpaSmeffZZxGIxSY4+z/FPGm2vnGAKHCSYKv/LjVZZgfMgUZAkUSU0D3zKj2DwNJvNMrdC6YFAaJsBllwPwuOsXpOSkkQrzo03HA4LK5wth5SUFDG+2tnZd3R0OBw4dOgQNjc30dbWdkAuTE8RABKMtVqtBHFKRgEcsIpNSkpCIBCA2+3G3NycWP1yPgLloVwoDEzx8fEHpJnk1zBR4nVkVq90amWg5XkxWFPRwYBG7wZeX71eL7CcUtWRnJyMmZkZ+Hw+ue7b29t4/fXXD7TdUlNThadDpjhVG+SOkAjFyn9zc1M090zEiGIAkIF5XIO8B2q1WuaekAzJVhXfV1npc82QS6HkIa2urkqriwkoj62tLVk3rOqJMHBt8J4TheL7ajQaIUHTCZKfwaRc+b2YZDK5JdoTiUTke5LozBYZn1N+x93dfadNJamY14xBV6/Xi9U3k1zlQLzPc9hsNjx69AhGo1G4CdPT00hLS0NXV5cgYuFwGGazGW63G+Xl5UhPT0d8fDwKCgpw4cIFvPzyyxgbG8O1a9fgdruFfDkwMIBAICCw/d7eHt59910hrebl5claLSkpkXtfUVGBBw8e4KWXXsLExAR6enpw8+ZNbG1tobCwEADQ3NyMlZUVpKenS/UYDAaxuLgoY9d5PcrLy9HS0oLk5GRYrVZB/Orr6xEfHy8ka4vFgoyMDEHXWKwYjUYxWvrtb3+L+fl5MWJKTU3Fd77znQOux7m5uRgaGsK9e/fQ3d2N1tZWkWFPTU3BaDTKwLQLFy7IlO3d3V3Y7Xbk5eXBYDAgOztb5MCVlZWorq6GXq8XwjLRz6mpKbFcJ+Kck5OD1NRUvPPOO9jc3MTQ0BCysrKwuLiIxMRE2Gy2A9U87d5bW1tx9epVfPzxx8jIyJBqPhAIoLq6Gh988AHy8vJkMFpWVpa0FtiWZZAtKirCw4cP4XK5RPXFtgeN3G7evCkFXG5uLh4+fIj19XV4PB6srq4iFAphd3cXjY2NGBsbQ29vryT7Tz/9NMbGxnD//n3k5+cjEAhgeXlZ/s2Wy8bGhsy5SU1NFURFpdqf7lpVVSVxsKGhAZcvXxYif1ZWlgxba2lpgdfrhUqlgt1uFwm5Wq3GF7/4RVGUEO2gqiwhIQFutxs6nQ6pqakwmUzo6ekRG34ic1qtFr/+9a+xvb0tz0tKSgpsNpsM4QsGg1hfX4fJZEJOTg6SkpKQkZGByclJaQPpdDoYjUZotVocPXoUDodDuCa8flRK/WPHE0+ZpdSSiQYrfWXioGTJc8NTJhjc3JUbIn+udMNkQC0rK4NGo8Hy8rJAxERQ6DS5srIClUoFo9EoBDn27yKRiLQsCKMTbeFmzkSGQ4nm5+fle5LVn5+fj8LCQnR1dSE1NRU7OztyoelzwfclGVOtVgtEzu/FALO0tISMjAxJQEiSY2AnMhQOh6UtQyIjfT6USYdGo5GWAJMQyiXZ3iDMzjaCkqdBiB/4lBTKpEl5zdmyYrIyOzsrHJCOjg653gCE+ESpMMl1PGf2MNn/VipvyAJna4TVPeFKJceDiQPbazqd7gDPhkiQ0mWT31Wr1QrioUTm6PinROiY9JCXQaIwVQtcWzxnJhRUOBEFmZqags1mE2WUMslmYOazlJ6eLkRTohicVEt+BxMV3metVovNzU2ZcMzWDRU3/B0+c2tra4JCpKWlwe/3ixT8SQ6v1wur1YrBwUGcPHkSWq0WGRkZovaIRqMyaNHr9aKjowN9fX2Yn58XG+rFxUWMjo4iFouhsLAQXq8XFosF0WgUR44cwdraGhYWFrCzsz9q2+VyYWhoCC+//LI8KyUlJZidncXY2JjwspaXl3Hr1i3x3SBn6/r16wiHw3j8+LHIVtfX1zE9PS3PPBN/uqJyRkpeXt6BIXRLS0t46aWXkJeXJxbYXq8Xu7u7cLlcYpPtcDhw4sQJdHV14bnnnoNarcaDBw+kFdPb24vHjx+LMqepqUm8H5qbm5GbmyucIN7r5uZmmV+0s7OD/v5+zM3NyRo2Go0Ih8MIBAJISkrCj3/8Yyk+dDqdQOwej0fmvYRCIVRXV8t9SEtLw7e+9S2RkZtMJpjNZjgcDuh0OiwtLcFsNkOtViMUCqG/vx8ZGRkoKipCZmYmbDYbSkpKEA6HcefOHdy4cQNf+MIXMDY2hueeew5GoxGRSATBYBAlJSVigvbaa6+JRPe73/0uFhYWxKBwb28PH3/8MX72s5/h+PHjaGhokEm6s7OzSEpKgs1mQ2Zmpvi3eL1eaZsfOnQIwWBQzMVsNpugpWtrayguLsbly5fR2tqK3//+95iamkJxcTE2NzdRX18v9g3JyclobGxEe3s79vb2cP78eezu7oqbayAQEI+OvLw8+Hw+NDY2CpE0FArh5MmTmJ2dRVdXl1xHxiC2SfmcFhcX49q1awgGg8KfmZ6ehlarxUcffSTxkf5HWVlZ0Gq1MJvN2NjYQCwWQ3Z2Ns6dO4eenh4EAgEMDw8jPT0ddrsdOzs7MplYo9Ggu7sbk5OTQtAdGhpCLBZDdXW1FM2f51DtKXfZ/9cLVSpUVVXJRqXkb3wW1WACwoN/V/p5KA8l8U6v16Oqqkrse5XKFPYMCT0r5wIwi2T2z0qaG67NZpOKlJkuiXnkJFBKRu+A5ORk2Gw2rK+vC7eASAmNhHQ6nQQd4NPKlAlLVlaWeH7wGjAY8ntzzkFpaalA9wwSVKvExcVJpU+VCc+F/yVawSo+MTFRAieDJCs1GpgBnw4nI0GUDwGRi7i4OIHkVldX5boTCThy5AhefPFFTE1N4Sc/+QlCoRAeP34sE26NRiNOnjwpLSZyI4gCUDHCRCsYDIpp1dbW/tAkShLZXiKqQ2SN7SiuE34GB5p9NmGjbwrvP5NW4FOUjUkjrw+9DZgU8D6yFbe1tSV+MIS2le1HkkdTU1Nx/fp1VFZWin8IAEksmJjt7e3JmgsEAgeMyCjbVhKheS93dnZksCATw0gkIp9DNRYltqFQSIatEYmLRqNYXFyEVqvFr371K3zObQIqlQrPPvssHj16hIaGBty9exdWq1Vkhu+99x6OHTsmhHOeVyAQQFZWllR/fL45l0WlUqGwsFAqQXpj6P9hCjOfabVaLQqOUCgk30ej0ciGDECCOAnrZrMZ/f39uHz5Mjo7O+U+0s12fn5ekplr167h0KFDSEhIwI0bN1BaWirJr9lsxsjICJqbmxGNRvHJJ5/g3LlzmJubkz2GvCmHwyGmhMePH0dnZ6e06XJzc/HgwQM4HA54vV5cvHgRXq8X/+t//S/U1dUhLS0NVVVV2NzcxMjIiIwcp+Ta7/cjLS1N5vSYzWZMTEzAYrFgfX0dnZ2deOmll4SPsrm5KW6WTAg5W8Pj8SA1NVUm31JmazQaxdxwdnYW09PTOHToENRqtRRhTL7cbje0Wi0uXryIpKQk/Pmf/7lYbg8NDSEvLw82mw0GgwEOhwPLy8tSOExPT8v68vl80Gq1KCgoQFtbG1JSUpCTk4P79+/D7XYjISEBR44cke/FgXDnz5/H0NAQ4uLiRAmSk5MDvV6P3d1dtLW1SaKfnp6OjIwMIeTevHkTa2trKC0tRTgcRkNDA9ra2mRwKOMGlT1f/vKX0dvbC4PBgM7OThQVFYmZ3ObmJs6ePYvvfe97OHfuHFpaWpCfnw+/3y8qo4qKCuzt7cFut+PHP/4x0tLScP78eWkxra+vCxcuIyND1Gb0n/J6vbDb7dBqtfD5fCgqKsLc3BwAyGTvhoYGScjtdjuWlpaQn5+Pjo4OsTO/ceMGdnZ2YDAYMDs7C71eD5fLhenpaXz1q18V4z/6kdy+fRvBYPAf3SOe2KcD+DR54KYPQNALZbWoDMbAp54efED5h6+Vk1K8F9+PrQuiE8DBQXI0gmKfXaVSSfZJeR75E4TKd3Z2MDAwcKA6BiC/w+FVVJHQC4RQNF1Qw+GwfBfaJwP7/VEmMLFYTCA/ZbKws7ODzMxMpKSkYGlpSVo87I8x4JHLwhYIP4v3hRVzamqqtGa2trYEkeH1JRlX2brhtdzc3BRDKraxmORRqsk5J2xpJSYmymb1s5/9DAMDA5iamkJlZaUY2VA/r9VqBWJmC4wcF1Zn29vbyM7OFmfKzMxMUaKwWmXSwGqLHAklMVOJxOl0OnGxpVyVFRITRBJbmXyRmMkHXMnh4LUngVW5bjmPx2w2w2AwyBqlciUlJUVcQjMyMoR4xvddWloS9Q/X5ebmpqwHutMyeU1NTZXkmcgh14OydckEiedJ7gx77Wq1Gjqd7oCTIc2enuTgenjllVcwPz+P4uJi6Yffv38f1dXV0qZcXFyUAY1qtVpQxOLiYjz77LP48pe/LB4LVqsV4+PjWFtbw/3793Hz5k3s7OzA6XTCYDDAbDYjIyMDTqcTOzs7QvzmGnC73XC5XFIUbW1twefz4ejRo/D7/TIfiK2CDz74AFarFbm5uRgZGYHFYkF1dTWCwSAOHz4sY803Njbg8/lQV1eHiYkJ9Pb2ori4GGNjY9jc3B/nPjQ0hL29PRiNRhQWFiI3NxdpaWkYHR1FJBKRtR4IBIRcOz09jeTkZKSlpSE/Px89PT3o6OiAXq9HWVkZKisr8dFHH+Hq1atyPmtra/K91Wo15ufnsb29jUePHsmMjsTERGRnZ0Oj0eDtt9/G7u4uhoaGsLS0hOLiYlitVuTl5cHhcGBnZwdvv/02SkpK4HQ6D9jY05rdZDIhNTUVhw8fFq+hxcVFcT8dGBhAVVUVbt68icHBQWRmZuKv//qvcfv2bWRnZ+Pll19GTk6OFA4Wi0W8Jbq7u7G3t4fc3FzMzs6Ksdjk5CTGx8fR1NSE+vp6/PSnP0VRURH0ej3y8/Nx7do1dHV1we/3C8r06NEjAPvGdb29vSgrK4PBYMCjR4/w+uuvIysrC/n5+XC73TCbzVhcXMSjR4/g8XiwsbGBiooKXLlyBVarVUbZ0zaAiXB1dTVcLhfa29vR19eHX//61+K2a7VaEYlEYDQa8cYbb0jyy4m+s7OzOHHiBI4fP47FxUXMzs7i5z//uQxEpGU8k5Ld3V0cO3YMsVhMEqykpCR0d3dDp9NhcHAQDocDTU1NaG1tFYt9h8MBjUaDBw8ewOfzweVyYWxsTLg8wD6pVafTif3/wsIC7HY7TCYT+vr68M1vfhOvv/46gsEgHA4HampqoFarZS//x44nRjq4OAj9MgFQVpw8lBwL/lxpbsT/B0DUJxaLBRUVFdDpdPB4PNJ6YfDkZxH9YO8qHA5jd3dXlAparVYGDaWmpopBze7uriAgrPpZbXMDJKE0Li4O4+PjQrbZ2NiAzWaTaoRkRioIaKhCGWl8fLyMsGdyxEE5tEuPRqPCZB4aGkJNTY3ApX6/X1AKpVSUBMitra0Dg+J4TkRCDAaDXG+lrTe9OJS8DSYSPE9yAkikZOUCQGR0RA92d3dRXl6On/zkJ9jd3UVtba0gF8FgENvb27LA6R4ZiUQOtMB2dnZk/HV8/P4MGwZncnlSU1OxsrICo9EoiAZNfugnwUqNAZwoAPug5LUoPT/YeiP/JTExUVoLXOt8byYiKtWnLq4qleoAkZMETmXQZwBMSkrC3bt3odPpUFxcfEBlQ+QH+NQQjmtbpVLJ3AbKsvm55OywrZeamioohpJYSykxk2+63a6vryM/Px+ZmZnyTPAarK+v44033vjcSIdGo8GZM2cwNjaGWCwm9ucFBQVCNiNRNjk5GbOzszh79izef/99qFQqVFZWoqenR7gW7ONHIhEkJibi/v37MBqNYpOu1+vxwQcfwG63w263w+fzYXV1FeXl5eLrER8fj/z8fPh8PhQXFyMuLg5zc3NiXe71elFVVYWf/exnaG5ullYdRylcuXIFGRkZyMrKwv379/H8888jHA6jqalJeAtDQ0My2XZjYwMFBQVoaWmBXq9HSkoKJiYmpHA6dOgQ4uPjcefOHRQXF4vrZ0JCAmpqag4EyJ2dHXHuvXPnDurr6/HLX/4S58+fh9PpxMDAAIB9MrPJZMLg4CCOHTsmQ/HeeOMNXLhwAYFAQJJM+kXcuHED6+vrgkDp9XrU19cjJSUFra2tOH/+PMbGxpCSkoLFxUUUFBQgEonA4/Fgb29PFDAXL17E0tISJicnEYvFxPPB7XajpqYGxcXFWF9fF+TmjTfegMFggMvlgs1mw8rKiqAGY2NjMl2VU1BpZ76wsCB7TmlpKUKhEBobG8ULIxgM4tKlS+jo6EBSUhJcLhd++9vf4uWXX0YsFsO7776LCxcu4N69e6ivr4fRaEQoFILZbMbY2Bhyc3MRDAaRmJiIhoYGhMNhjI2NwW63Q61WY3x8HCaTCe+//z4sFgtqa2vFcn91dRWPHz+GWq1GaWkpdnZ2MDQ0BJVKhUAgIMmtSqWCwWCQeSvnz59HR0eHJF5E8Xd39+fX9Pb24qWXXsLy8jI6Oztx6NAhTExMYGhoCBkZGfjjP/5jmRA7NDQkluyxWAyrq6vQarXIysrC8vIy1tfXBUUaGBg4MDeH6MW1a9eQlZWFmZkZcUK9fv06zGYzkpKSUFdXJ/F1dXUVGo0G9+/fx8WLF/Ff/+t//VyJxz/ZHIyQMg+lAkC5USt1/mxpKN/v/yb/pFMj/63T6STIEXamHJSSSVb6ZCmvrq5ibGxMIHYloZAjxhMTE2G1WqXi9Pv9B6B9Gt3Q2IpkI36OTqcTfoCSjLm7uytzYTi9EMABsh8AIXRywzQYDJibm5NAzCDH68YKLT4+Xgaxkc/A5ICcDSIKvC5czExgOLROKcfl9eX7MUlRqnB478kTITT7+9//XvqiAITMSeOzSCQi9ucajUY+l0gDeTkMiNFoVLxEyLfY2dmRmSBUhuzs7Mg9UCp3+HOl0RnvOb9nXFycOFsyWGxubiIajQoyRGIoScWUqvL6KQf+MeizhUPPDaIwVNv4/X5kZ2cLFyouLk5eS/SJXBHeG8pyiZQpET2eM0nAKysrYpqmlBArn1nlc8k2x9TUlNjBx8XFSdX9JAfnaQQCARw7dgx37tzB9va2DHCjsstsNiMUCsFkMqGrqwtLS0tITU3F3NwcMjIy0NjYiPn5edl8Var92RJf+9rXYDKZEAqFpD9++vRphEIhZGVliXfE5OQkRkZG5PkfHBzEkSNHpB2xs7MjFalarZb5HWtrawiFQmKsNTg4CIPBgNzcXDidTpSUlOCTTz7B4uIiHj58KEluUVERxsfH0d3dDY/HI3LZiooK6PV6kYQ/++yz4rjJVm9DQwOeeuopQQ+bm5tlXgjbX9evX8czzzyDpaUlNDQ0YGFhAePj47BarfB4PDh16hRKS0vxla98BWVlZZiamhIJZDQaRXZ2NlpbW8Xw66233kJ1dbW0ti5cuIBjx44Jh6KhoQHBYFBGFBw7dkxknSw4x8fHcfjwYfEaYuFltVpRVFSES5cuIRwO4969e+jv75ekR6/Xw2g0yhyqiooKuW91dXXo6+vDjRs3kJOTg5dfflkIjbFYDPn5+YKw6PV6LC4uYnp6Gmq1WpJOg8GAxMREtLS04OLFi1CpVJiZmcGZM2cwOjqK3NxcRCIRPH78WDh1Tz/9tDxfkUgEt27dwieffIKNjQ24XC6Mj48jLS0NDx8+xPHjxwVxM5vNeO+998SsKzc3F/Pz84hGo8jLy0NWVhbKysqkeKRvB8+3s7NTVEEejwfz8/Po7u7G1tYWfvWrX6GpqQmbm5sYHByEy+WCxWJBTk4O8vLykJ6ejjt37mB4eBiTk5OytyYmJqK9vR0AhKNDBJIeHLm5uRKvHA4Hvv71r2NiYkLayg6HA+vr61hYWEB2drYU8XNzc5idnT2w9z/33HPo6+v75/HpUPa8CdkzaVD6Q/DE+W8qLeiRwUpayehXwuEqlQrBYFA24o2NDaSmpsoGr5yGmpqaKpskzaCUVSErjWAwKOYmJEjSHZLJj91ux/r6Ovx+P7a2tg64W4ZCIaSkpAjMRUkpsO9CyP56JBLB8vKybNZEcCjF5LUh1M8qlAodusLt7OxIJc4R6spgx0AdDAYl0SGyxOtKNISmZgyKbEtsb28L74IVOQM3jaP4vcmNYUBnUkQJqsVigUajkSm4nItDFIUVNyfLbm1tyZAnBvSdnR0YjUapHKmiUE715Rqk9I2kYbbNlpaW5L4QtWCAZfLHCp6QIKW7RA6UjrMk2nFdUWZK9IgoHB9A3mOiYUzAU1JSRJnE9cYpw0qJNMnGTECIJNE5kH4aJAMTcQEg91bJ+2FLiuohmpPxPvN8WRlRYhwXFycEzCc5PvjgAywsLKCiokJIeFNTU7h79y6eeeYZjI2NScXvcrmgVqtRUFCAxsZGLC4uIhqNoqenR5ImBl5WubQiT05OFo+FyclJqFQqjI6O4vTp07h06RJ0Oh2OHj0q66qqqgq7u7u4d++esP6dTiemp6fx5ptvwu12SzuhtbUVf/Inf4KNjQ04HA6xmmYVfObMGWRlZUmSkZeXh0gkgjt37uD8+fPSVk1JScHw8LAUarRnpzEasF+ssKJOSEjA8PAwHj58iLa2NpSUlOC73/0u7HY7Kioq4PF4UFtbi7q6OqytrYl/wte+9jWZ8jo7OysSU6ogBgYG8N577+Hy5cvIz8/H7u4uampqMDMzA7fbLa0mn8+HQCCAlZUVzM/PY2BgAElJSYJKrq6u4vDhw6iurobdbhd0bWxsDO+8846cA4s4q9WK1NRUnD9/Hj6fD9FoFENDQyIxpv327u6uqDgGBweRnZ0tbV5C/mlpaSIDfemll/Do0SO5t1arFVlZWbBYLGhra4Ner4fZbEZOTg4CgYBIt+fn52E0GtHd3Y3l5WW4XC78+Mc/xvLyMlpbW+FyuZCVlYX4+HhUV1fD7XZjYGAAY2NjyMjIEH7Z+Pg4nnnmGUmgn3nmGbS3twuhlkjt48ePxdjO5/NheXkZmZmZcLvdqK6uFjm9y+XC9va2EJH39vaEB8OiiXNrWltbEQgE4HA4JB5xACDHKQQCAdTV1WF1dRXnzp1DWlqaxM3Ozk4sLS1hYmJCpLY0UyN/g+aZdrtd+GXLy8vIyMiA3W5HKBQSUjHN+Z6EcP5ESQerbqWKgcGEScNneR3AwWFu/198EMrHkpOTEYvFJECwKla6hyo3XI4XV5L7qNggpEy9fzAYPOCKl5SUBJ1OJ2oPficS/ngoYVvOc6HcFIBs0kx6GOBjsRjC4bCoF7iJciYJOQiEsq1WK5xOpxCKGHyYVRIVIEyv1WpleBCrX2XLiuehbHkR5QAgCAE5G8rkg8Ge78uguL29Ldk0vzer8YKCAlgsFoyPj6Ozs1MCFlszJI+yh0hlhtVqFZWHSqUS3sPS0pJ8HxpmKdEeriPKWplI8WfKNcrvzEREaRpHxCgxMRFms1nel8jI7u6ubI70i2GipGzTkQjNxEiJsBCtmZ2dhd1ul3XIdczklF4dKpVK7LqJKpEfw4STn0ukj6RI/n+fzydcp3A4LM8CP5f3jd+FLSvecwb+JznsdrtA31NTU3A4HEhPT0dpaSnGxsZQXl4ujo5sIwSDQczMzODpp5+WdmE0GkVaWhomJiZkw6Yio7m5GVqtFt///vdx/fp1zM7OwmQywWg0Cj/h+vXr2NnZweXLl2G327G9vY0HDx4IcZD8i6ysLBlvnpOTgwsXLiAzMxP379/H+Pi4SGJPnDgBu92OgoICjIyMwOVyyb4yMjICm80mRlyjo6MyfdVsNosXR0VFBXp7e+H3+6HRaOB2u4VDwqBBvofdbgcAvPHGG+jo6EBqaqqgYPStSU5Oxvj4uHiAJCUlYXp6WqzYY7GYzIcpKCjA9evXMTw8DKfTiTNnzmB2dlYGX3K/s1qtuHfvHqqqqmAymUTKfOPGDajVapkzwxbU6OgoVCoVjh07Bq1WiyNHjohnxd///d+jo6MDHo8HOp0OfX190jKbnZ0VpV57ezsuX74Mh8MhTp91dXXo6enBwsICbDYbrFarOCv39PSgvLwc0WgUjx49wr1797C4uIi+vj7U1tYiNTVVLLt9Ph9qamrgcrlQWlqKrq4uVFdXw+l0Ijs7G6dOnUJCQgKKi4tx584dmX3T1tYGjUaD0tJSmStz7NgxpKamorS0FB0dHaioqJBxDyxuqcJjUr+5uSlk6OzsbCwuLootuc1mw8DAAILBIHQ6nQyc+9a3viW+Gqurq5ifn8e5c+ewsLAgxZDNZhM1Hp15zWYzmpubsba2ho6ODqysrAjZMxgMYmFhAQkJCQiFQoLO6nQ6jI+PS5twZGQEv/zlL5GWliYt60gkglgsBp/Ph8ePH4tx3/T0tBjXNTY2fu494ol2FKXMkpu5UrWiTCj4X26oNHQCIJk//zidTrGT5ewHatn1er1s3EoCKn0RWCmTJ0EuAp3tlJU/R2CHw+ED8ksmHAkJCSIr5c0hz8FutyMcDiMzM1NGIlOVwqqF1STfh5wIWokzaGq1WqlCotHoAeibCgy2EyivJapAhQmrVbZ9lFbsvAZMcjY3NwWOV84JIXrAip6tF3IcKOXkPaMMlyRQXj+lJFOn04lBEwOFWq2WyoVeKXNzc+JTodFoBGIkgkQTHuX7M/jy3q2vrwuvgigO2wlKHgMAsT4nGZdoF68NE7tYLAaz2SzcICZ4/C78bGVrkG6lTIY5Y0ZJ1uT5KudFcANRqVSyXugjomyxMYFSzlwhY55IRzQaFVO9cDgsXB6Ov+b1UKrLiBjyWY6Pj8fo6Cjm5+dFVZKZmfkkW4RIXrOzs5GZmYnh4WHMz88Lcknel91uR0lJCTIyMhCJRJCfnw+1Wo3y8nJYLBYMDg6isrISZrMZly5dEsLov/k3/wYulwtGoxEOhwMNDQ2orq5GTk4O2tvbBf799re/DbVajZs3byIjI0OGy9ntdoyPjyMzMxMXLlyQRH16ehpra2tYWVmB3+/H6OiozEsxmUxob28XlKWpqQnt7e0YGBjA6OgoPB4PHj16JGPpS0tLkZCQgMHBQQwPD4uKgpy10dFRsTLX6XQiTR0aGsKNGzfE3ZHPbFZWlhiFvfvuu1CpVHC73TK7hIoyr9crah7Ka4eHhzEzM4OEhAQx2vJ6vWhpaYFOp0N2djbq6uqQn5+P9fV1HD16FAaDAW1tbYiPjxdJbn19PYLBINRqtYx2p9kavR329vYEYdna2kJlZSWOHz8OjUaDhYUF5OfnY21tDWfPnkVCQgLy8vLQ3d2NwsJCIU9+61vfkrlU29vbKCsrQ2lpKVQqFTIzM6FSqZCTk4NIJAKn04mFhQWcO3dOkNlwOIzBwUGUlZUhPT1d1glJwufOnROUtKenB319fdL6SExMRGVlJS5fvoysrCxcuXIFFotFSMEPHz7EwMAAYrEYDAYD8vLyRMqakpKCrq4ujI+P4/3334fX6xX578jIiLTKT/7DHBP6RFGu2tbWhs7OTmi1Wty8eROTk5NwuVwwm82Ii4vD3bt3UVRUhN3dXTQ0NGB4eFh8TYaHh1FYWCh7LocNFhQUoKioCFVVVUJ0TU5OxpkzZ1BcXIxPPvlEYlMsFhPLebrLLiwsoL6+XlCfpKQkDA0Nwe/3w263IyUlBe+88w62t7dleOLnOZ7Yp4N/PptwcDNhogEcVK98ls/Bg0GHgYUEP/4eSXurq6uwWq0yw4SBhsFW+VkctEMpJs2C2BdnNch+LIMDq1omBExatre3xTabTogMRnxfyhCJBhCO5Pdh4OB3YrCj3wKwnwSYzWbs7u6KgoDXjgRRJmA8N0rWlJA9X0cuAFEFVvwktlIuykSH14KoBK8JgyFbAdvb2/JzjUYj7Rcyz2kDHgqFRK5stVolySS6s7i4iEgkApPJJJMQycWw2WySUJI4yTWnTAYYOBnsuQbi4+NhMBjktSRmMolSIiNMGLiWWUEQNeH78/vzPKjC4f1kG03ZogEgyiiSkzMzM7G9vX0ATaPUlWuUbQWek1LlxSSV/CXKQ+lMSn4HN2Kl0og8KHqyKJFLzityOp3Izc0VhOZJDiIGAwMDuHnzJpKSknDkyBHMzc2hpKQEcXFxAmEDEEi5vb0d9+7dE3fE06dPw+v1YnFxER9//DFGR0eFj+D1evH6668LyXZubg7JycnIzMzE8vIyTp06hZKSEhw5cgQqlQpTU1N4+PAhGhoaUFtbKxLG1tZWhMNhHDp0CLW1teKfYDQakZmZidbWViQnJ2NsbEyM1V544QVMTEygpqYGFRUVqKmpgcPhgMPhQHd3t0zvbGhowAsvvID19f0pr48fP0Z/fz8SEhJQX18va2N2dla8NcrLy5GSkoL29naUl5djfHwcLS0t8rwePnwYFRUVmJqagkajwejoKJaXlyUQNzc3IykpSfgkCwsLkpCS/EmeGZP5V199FV1dXYiPj0dtba2gY+vr65icnBRHTxKWd3d34XQ64ff74XA4UFBQIPc9HA6LadTQ0BA6OzvR0dGB7e1tFBYWoqCgALdu3UJaWhqKi4uRkZGBr33ta2hpacHExASamprQ29uL0dFRTE9PIyUlBR9//DHa29vxySefiNNoLBZDZWUlLBYLqqqq5Np98YtfFFI5OSaXL18WQi/N9Hh/STLf3NwU19zp6WkMDg4iGo3iu9/9rqDAdrtd5glx//L7/eju7sb09DQ2NjZQXV0NjUaDF198Ebu7u6Ke7OjokLb+9PT0AU4LbdCPHz8Ok8mE48eP45133gEA1NbWIi0tTVAsIsS3b99GWVkZ6uvr5V7dv39filYm6LOzsyguLsbdu3exurqKoaEhZGZmYnBwEH19fTh8+DDW19cxNjYms1MsFou4j3Kys9frlWK8qKgIWVlZmJycxObmJr7//e+LMmp5eflz7RFPhp3yl+IODjxTbsD8Nzdm5R/lQUIbA+L6+roYGdH+m8FO6V3BCp99fiYhhKIot2J/nsGKvgY0TmJSQu8CpWoD2N+A2QMnryIvLw8jIyMyqCgYDIrfPROZYDAorRF+FwASoNm6oQqDFYJSmUJ5KPBp+4DVMNsrTNJYpSvVF6xQWPGT/KpEJ6hgYQuGvAClpTnfj/eAFTat0Il6KFUXRKEoEWO1TsvfmZkZmR2gVqsRiUREw2632+F0OqW1wLXGJIj9TT4ARH2YNCqlxcp2GdcjrxURGxphMallwsyAriTh0uRM2d4hqVUpOybaQbIx+Rirq6sYHx8XRRFJxUxymEQp7zs/h0km22FM3uh6yZ9FIhEhBHPdMnHY3NyE0+lEYmKiEGzJoeJ1MxqN8nxQ2aIknH6eg7NWgsEgjh8/jmg0iuXlZTQ3N+PKlSvweDzo6OjAxMQE7HY7JiYmUFxcLP13Traky+fZs2fR1NSEo0ePSk/b6/UKz0mr1SISiUi1tra2htHRUbz55pv40Y9+hNLSUlFDbG9vy8C2uLg4fOlLX0JTUxO+//3vw+l0wufzob+/HzU1NeLAOD09jfA/TPDc2trCj3/8Yxw9ehSDg4MyYdjn86Grqwu7u7siu7969Sr++q//GiaTCY8fPxZ0My0tDa+++qq4fFZVVcHtdqOgoAA9PT0oKSnB6dOnsbi4iMrKSmlxFBQUoLa2Fna7HeXl5dBoNDhx4oSYX/n9fjx8+BDz8/M4cuQIAKCkpETWOls9rIY7OzvhcDjkeYuPj8ebb76JnZ0dWCwWhMNhlJSU4KOPPhJOAXlnwWBQjL12dnYwODiI8+fP48SJE9ja2hKL+JmZGZw6dQrDw8PiWHnx4kVoNBrMz89jeHgYv/jFL5CZmYkjR47g5s2b0Ov1yM3NxejoqAwrY6FIPlJ5eTkePnwIv98PvV4Pm82Grq4u/Pa3v0VdXR1aW1sxPz+PUCiEBw8eID4+HjMzM+Lb0tDQgIKCAiwvL+OZZ55BT08PJiYmMDg4iFgshqKiIrjdbly9elXavZOTk3A6nQAgoxkmJyeRnZ0tjs05OTno7+8Xy4WUlBRpm3MybFJSEnJycnD9+nVB3sxmM3w+H7KysjA1NYXd3V18/etflwLPbrfjyJEjMsXXYDBgeHhYkFfOD1teXhbvE8p/SViOxWLQ6XS4ceMGWlpasLCwgEAggIcPHwr3kR2GaDQq3kVXrlyROUyrq6vi+7G5uYmcnBz8/d//Pfb29pCdnf25DcKeOOlQ+ml81meDaAd72f+vP8C+ZbKybeByuaDT6eBwOGCxWKSvRK8MPkC0rKZKhNwDVk9MIHQ6nVR6DKYMEk6nU6yoKQGlbbjFYsHa2hr0er20MmiqkpOTI1wMVvSsBsPhsAQIShMBiFwzGo1ifX1dprdSkkelB5U5DEZEaoBPZ59QcstePwMcExryLTi4R5m0AQd5OYTuCbUTRaHUk/wTJiQpKSkHUBHg0+SRv0+uC6sjWmmvrq4iLS0Nhw4dkgqf6M/e3h6GhoaEOU50g8kokzKiXkrTNCVRlMZsDNZEbZTnyTYdW2hMXsglIEJgMBjEDI1tMQD/f8m2UjLLe01UiNwPymGXl5dRXl4u6BzvLa30ef2Y4DGhY/LI1hARjfj4eCwvL0uiwkDM78p2Fcl2BoMBPp9PyLFMYph88l77/X5JapUKtc9z9PX14cqVK0hJSUE0GoVWq5V1XFVVJRyeyspK4a7Mzs5ie3sbX//612G1WoXzERe371fClhehZYvFgm984xs4ceIEtre38ed//udSMUYiEXR1deHFF1/Es88+i/X1deh0OphMJmRlZSEnJwf37t1Dbm4uPB4PZmdnxYqbbQFOo01PT4fD4UBGRgb6+vqQm5uLvr6+A74dKpUK5eXlOHXqFJxOJ7q6uiSxYwU4PT2Nuro6/MVf/AXee+89sWendHdoaAjp6emwWCxob29HZ2cnJiYmpP25srICk8mEf//v/704ddIvaGdnRyYVU/mytraGqampA3slsJ8UE6U4efIkMjIy4Ha75bmprq4WQ7S8vDxEo1FUVlYiGAwiNTUVNTU1whUhf0Wr1aKwsBD9/f2ybrkPEy2jQSHXbEpKijwjhw4dQl9fH1ZWVmT+h9vtxl/+5V+KoVxcXBxOnz4Ng8GAyclJtLa24siRI1hdXRU3U6fTiWPHjqG7uxs2mw3PPfcc4uPjhdi/traGsrIyBINBLC8v491330V5eTkmJiYwNjYGm80mjsg+nw/37t1DcnKyXMempiZpY4X/YdYS/ZTy8vKQm5uLWCyGp556Cl1dXUK8pH15OBxGb28vfvCDH+DRo0eora1FIBBAMBhEZWWlDFTz+/0Syzjnhzyb8fFxoSk4nU5xzSZ6RadVg8GA119/HeFwGENDQ9DpdMjKykJXVxcqKytx4sQJuFwuaDQa5ObmCl+wqakJ6enpeOqpp0T5tbOzg8ePHyMcDovl+tjYGA4dOoSVlRWRPs/Ozsq03X/seOLZKwwG3NyUGzLw6dC2z/I7+BpWfjabTRILJheUXFG+yNYEZaE0r6LOPy4uToh7RC6WlpaEbEVpKf+waidRleoQspCZ/NA3g/1ctmiYbNB8BfiUjMmEQQnJU17KIK3RaGA0GuW9GTA5bAuAkAl5bnTXJOmPlt1M7NjXJ79DKb2MRqPQaDSSaNEEiu0mfiardw4E47lz4i2TA7qLMrteW1uTe0euT0pKCtLS0rC2tiYVikqlgsfjkbZDZmamtH+IMiQlJWFkZEQSDBId6cJJQiyVSUywqPJhQsrkLT4+XiDNz3KClP4ZSp4RN261Wo3+/n6srKzIxs7z4vVWq9UijWU/mOgIZcZM6HZ3d+Xhp4sj0YXPSsqViQqfB5JfeQ+U/Cm2s5TPIAuD5OT9SbZ2ux16vV6qdn6mUmJM+3QiLwzAT8JK58GNkMEqGAyKGmN6eho7OzviF9DR0SHtlomJCbS0tMi90Wq10gNPTk5GUVGR8Kp8Pp8oo/7Tf/pPyMrKEjL3yZMn8eGHH6KlpQWBQAAff/wxTCYTpqensby8jCNHjqCzsxPhcBjT09PQ6XTQ6XSYmpqCXq9HX18fKisrMTY2BrVajXA4DKvViqmpKbz00kvo7OzEwsKCTOBcXl4WS/WSkhKRNWq1WmknBAIB/Mf/+B9x8uRJcSClaqqurg7Ly8tYWVmB3W5HXV2dKNlOnTqFqakpSbQ0Gg1u3ryJoaEhuN1utLe3IycnB6WlpfD7/ejv70dubi6qqqpkMJndbsfMzAwCgQBWV1dx7NgxrK6u4vr165JkUZZaV1cnrpI+n0/UdCaTCUlJSRgcHBRn3pqaGgwMDKCzsxMjIyOi2MjNzcXY2BiWlpbQ1tYmaLbJZEI4HMbs7CwqKiqQk5ODoaEhFBQUYGlpCd3d3bBYLPB6vbh69SoGBgYk0D548ABOp1O4YZFIBFarVSrxtrY2IU7u7e0JSXZubg75+fl4+eWXZb/gM2GxWFBfX4/Tp08jLS0NnZ2dcDqdsNvt0oYJBALw+/2CEBERLCwsFFtznU6Ha9eu4fr16xgfH4fdbkdVVRXef/99uZ9xcXEoLCzE4cOHcfr0aWxtbQny2N/fj6mpKZn26vP5UFtbK0qWQCCAiooKrKysYGRkBJubm5ienoZKpUJ1dbXYltOETqvV4pVXXkFWVhY2NjbEffro0aNYWVlBNBrF2NgYHjx4gOTkZLjdbiwvL8NiseD48eO4evWqjK4oKChAWloacnJyJEHa3d2f90P+ls/nE2+Uz3M8Mafjs0kEf84NmVUqAyJ/TvibZD9CuUqZLIlmdHpkwKOMkhA4ORz8XCYjdAqlR0Z8fLygF0pSJgCB6shNYEbMipfzKBjgGagjkYi4dM7OzkqPj+9FSWUwGBQEgteKbqFso6SkpEgAJFeA35sBm4gEr3N6erp8H6IwvCbAp74NysSJGmu2fNjyUKqQGAgZqJS9fHJomEzSXpeVtpLfo0w2tVottFotQqGQtKDY4igsLBRuDNdNQkKCZNgkZ3Ld8P4vLy+LOikuLk5aLkpZNr9PQkKC3A9yT5iUsLonUsHrReKv0+mUfun29ragGazWlJ4aJDIziSQ6w4R8dXUVHo8HmZmZco2ZDNHwjK0o/px8m/X1dUmkV1dX4ff75f2ZLJKnw9YRFV9ra2twOp1wu92iBOIzzBaVMtlmQk5nS97HJznYrx8eHkZxcTGWl5dlzXg8HiEHLy0tiYSYviVEK/Ly8hAXF4cHDx7g9u3bQjTnXBIqKfjMVVdXY3V1VeBvZeuCUsu/+qu/Ep5RJBLBN7/5TYyPj6OxsRHp6ekoKyuDy+XCwsICysrKsLS0hKWlJczPz8NgMAgxmAoQPoONjY2SVPG52NnZwczMDGKxmMwboYR0d3cXpaWl2NzcH9jn9/vl/sdiMZSWlqKiogJarRZtbW24c+eOGKTNz8/LxNLU1FR4PB5897vfxa9//WuRRmdkZOCNN96A0WiE3+/HrVu3BOU6evQoiouLkZKSgg8++AChUAjj4+Oora2F2WxGQkKCyDHT09Nx+fJluN1uLCwsiMIoIyMDCQkJ4rRKdUZjYyO6u7vFC4UD5LhXr62tyRrPyspCb28vJicnUV1djcXFRQDAuXPnhH9A0rTT6URPTw8uX74sbar09HS0tLTA5/NBr9eL/JpBz+/3Y2NjA4FAAAUFBYiPj8cHH3yA/v5+KXBSUlIwODgodgWct0PSb25uLk6dOoVz586JVDY5OVm4PS6XC6dOnUJVVRUWFxdhNpsFHZ+ZmUEwGERFRQXq6uqg1Wrx+PFjjI2N4fDhw4iPj0dbWxvu3r2L+fl5pKamQq/X4969e7h16xbOnj2L/v5+RKNRjI+Pi+JqbW1N1Dm7u7s4cuSITDLu7e2F0+nExMSEtLju3LmD1dVVdHd3ix3BwsICxsbG0NzcjJdeeglms1nQPg5vO3nyJNxuN/r7+yVej46OYnJyEvHx8aLC4vO6urqKpqYmeDyez7VHPDHSAXw6DEyZeBCKUXoGkJjITRvYh/joPMnNj5WhXq+X39vY2BCJFB0X09LSBCpjlU3eAd0tk5KShOvAtgVh2vT0dAlmfCg4PCc+Pl7MoQAIrLW8vIyZmZkDioitrS3k5+dLO4CKFZ7L3t6eTA5ktUpzG8LwStIjKzt+Lrkg5DKwumcgpfEU4XhW+ZzroJSIKmXN5CdotVqpYBjAGXQY/LlBMEgp0QbCilSDEMFg0KcbaDgclqRPr9cLT4MyTrPZLKQ2Jp6cdcJrQut4Sn0J1wMQ4hJVNrx3RHmYkJDPQRkokYPl5WW5f4SGAUjiB0CuKd9baWvO8wb2W0VKN1S2J/b29hAKhZCQkCCVB9c2IVpKv6lAYV+VCBURFeV783op57ukpKQcIJmWlJQgOTkZnZ2dcm/oKaK0wAf2k4tIJCLIUiQSkeT/SQ6n04kHDx6grq4OKysrQrScmZmR6zMxMQG1Wo0rV66gvLxc/Dc8Hg/KysrQ398PvV6PmpoaGT5369YtQUTYOuzo6BA0ye/3w2azob6+HgUFBSgrK4P+H0YVZGdn4w/+4A+kDbW9vS1zImKxGDIyMrC5uQm73Y7s7GyxwuYzOTY2JgHD6XTiT/7kT6Ttw/VHpIJSTQAoLCxEZmYmxsbGhPNhs9nw+9//Hm+++abINsfHx5GdnY1vfvOb2NraEpmjTqdDUVERdDod3nrrLUSjUQwPD+MLX/iC2PP/6Ec/EgifvBgadlEpNTExIestKysL4XAYTz/9tMwyamtrQ25uLiorKzE/Pw+9Xo+srCwMDQ3JyHkA8Hg8QjLU/8M02gsXLmBoaEiIvtPT07I3NTY2IhqNYnBwEE6nU9oJHH6Zn5+PK1euCL9Jo9HA5XIhPT0dOzs7OHToEIqLi3HhwgXs7u6iv78fHR0dMJlMInk2mUxoaGiAw+GAz+dDfX29cEuYePt8PvFG8vv9GB8fx+rqKmw2GzweDyorK6HX6wV9ys3NlemrAwMDmJubw89//nNkZGRApVLhxIkTaG1txczMDD788ENEo1FcunQJX/ziF+HxeHDo0CHMz89jamoK/+2//Te8/fbb4irLZMJoNIoT8OrqKqanpwWRT09PF3M4s9mM7u5uZGVlISsrC4ODg1hcXITFYhEuysrKCqanp8VGf3FxEXl5eTh79izUarXMeKHU22QyIRAI4ObNm3Kf1tfX0dvbi5qaGqyuruLHP/6xjBbg3lRcXCwSW7VajaNHjyI9PR0lJSXo7u6W/f0fO/5JRFJWcQxCSh4Hkw5WkNwcCVFnZ2cjPT1dnCrpC8ANk/C8kthJAx2+Vtm+YXbKn9G+2m63y8LjxsDqgr73ZH6TnGg2mwV+5tAis9kMq9Uq/iBUpiwvLyM/P182ZfIjCM9TUUJkg8GKgYLkQp1OJ99lc3NToEh6VYRCIdlo19fXxW1SqbQhKkGWuxKKJweAhlG8rrxPRHOU7qZEf3gwyFKpw7YVHURZbbO1oNFoYLFYJKmjsRMhS6Xpm8ViEWUL0Qtm8vSbUPalWeHzGjHZ4fdNSPh0tg2TNCJCvA9MdtkLJUOd11GphKKUVemiqkxweN5M2pS8IipGZmZmZFIp1wARMJKf2aOnyRlfk5SUJAhPLBaTVhqRPd4bqm2A/bYJlRw0BWLrgXya+Ph4pKenH+CVMLFRq9UwGAzi3PskB6tXQsXDw8NSkRoMBpl5MTc3h5qaGikW3G632E1TWZCWliZzeCoqKsQUrKKiAtFoFLW1tSgrKxMfDGDf38Pr9aKzsxMfffSRJB4+nw86nU5aBCTnDQ4OIiUlBZmZmSgqKhKZ6+7uLrKyspCWliZD4/b29pCZmSmDxrKysuS51uv1WF1dhdFolL1jdnYWW1tbeO655zA2Nia8jaNHj6KhoUHGpuv1evT09ODhw4fY2NiQnnlOTg56enpQVFQke2Fvb6+Q2oeHh/HVr35VZJkc8jcxMQGz2SzDKk0mE4qLizEyMoIPP/wQIyMj6OnpwQsvvIBgMIiXXnoJgUAAb7/9NvLz8/Hee+/JYLXi4mJRyWRkZKC8vBx2u11GOCwvL8twTK7tuLg4WK1WAPsFVVVVFW7fvg2tVguLxYKZmRmYTCZotVrU1tbC5XJhYmICV65cgclkQkVFBTQaDd566y2sr6/DbDbj1KlTOHr0KIxGI6xWK06ePCnP5tWrV+H1epGVlYWxsTHcvXsXe3t7KCsrk1a73++XJJ+zru7fv4/i4mL8/ve/R3Z2Nubn59HX14dPPvlEJKN6vR7nzp1DZWWlyNR/9rOfIRgMIhqN4qmnnoLRaMTf/u3fYmhoCMFgUAZJUqllNBolHgBAT08PzGYzamtrRb03OzuLX/ziF7BarQiHw/jggw8E4WSStbu7i4KCAhgMBgwNDeH555/HwMAAcnNzkZubi/j4eDQ2NmJgYED8TliEHjt2DPHx8bh9+zbW1tYwOTmJqqoq6PV6BINBTE1NYXt7Gx0dHRgcHMQXvvAFPHjwQCS0lBWzhbW6uor+/n48/fTTCAQC0Ol0Qj7/x45/0sA3ZYXOgMn/p+Qm8EFloMjOzobRaJRKnsGFfAVmwSsrK7IhcjOnvIkIAYMjx/3SRS0SiRxoEbD3zhYFK72dnR3odDrJholysGfIhATYn1xrs9mQnp4uJj3b29sIh8NCEqT/BL8P4UpWnkQDGDAZ2Bn02QJgYKHfBJ1MmWSwr8gx55xAS6RFWRkz0aO6hwGTyQdHpu/s7AgnhmoQIgwMsDST4X0nQsJACkDu2erqqqiPotGobPa8lwkJCQI/U2nCh41BVHm+SgSJSQgTBKWBGs+N64PnzzaNRqORdcDX0pFVpVIdOE/eI7ZjmPQordnJIVLKaHm9ucY4AdXlcgmXghsBW3h8htjeYfKkbFnxOtGXgz4ivAdMZsnbobKC60h5EPXa2tqSz+dred/pH/N5rY15VFRUIBwO4/e//z2Gh4dlUNfhw4eleuSwvbS0NDQ0NAih8tixY0hISIDX64XZbMb09DSOHj0qbchgMIj4+Hg4nU6pFHd3d2Uo3NTUFNLS0nD27FkUFRWJSmd7e9+m3OFwYG1tDS+++CL8fj/W1tbQ1NSExcVFrK+vo62tDcePH0cwGITBYIDVahXiIJOIBw8eyPkZDAb09vYiJSVFeBcMMGfPnhUIv7W1FfX19bDb7ZicnITP58P4+Lh4HvT29qK2tvbAnhUOh+F2u9Hc3CwzNLKzs/HHf/zH6OjoEEImn3+r1Sp25/TxYFI7PT0t00BdLhfy8/MRiUTEafPdd9+VAqm7uxvBYBAajUbszS0Wi6iNtra24HK50NfXB5fLBY/Hg+TkZASDwQNKDwZejkWn8eHy8jLq6+uFkJiSkoKHDx8iLy8PBQUF6OrqEpXM6dOn0dLSgp6eHvz2t79FWloaSktL8eDBA5FWE/WKRCIyRyUpKUnOy2w2Izs7G01NTRKTkpKSkJaWBpfLhWvXrsFqtWJhYUHab1QI0ZmaUvatrS10dHSgqKgIgUBAkMnt7W04nU54PB40NDSIl0lBQQFSUlLwxS9+UVrwPT09gsjPz8/LrB0mEy+99BKqqqpgtVpRUlIicvyNjQ2xFaDa78aNG3jmmWdEMpyUlIT+/n4cO3YMPp8PNpsN5eXluHbtGsLhMPx+P9xuN4qLi1FWVgan04mtrS2ZXbO7u4uFhQX4fD60tLQIB5HOwR6PBwaDAYcOHRLEbGlpSQqUz8v/emKkQ1lRMdgxu+XP2Ccmh4PZtsViQXx8vMjKiEQQ2rZarQKt0+J2bW1NBjdtbW2J2ZHBYMDS0tIB3wX258hzWFlZQTgchs/nw+bmppiC0UuDJE0mBayI6Uq5uroqBCI++EoUgRJfg8Eg1bxyhDzHwdNPgsPhaEKkvFZK91RWn0RPyPRWIhtsH1GdQ0UCgAMJDgDhK7DiVc4eUbaFCO0pK3FyR3iu3BiJBgGQhIDXkOdL5ZHVahVpKoNcXFwcRkZGhHmfmJiIQ4cOyabAAMjPICpEAiwDEZ1KmYzwuvFg4NTpdJL0KFtCSoULExBlwsx7vbW1JUGfSJNSzsrATjSO15smQVxT9ONgm40JgnLWCpM/ms0xuVfyXKhKWVlZwfb2NoxGo9xTJhRECNmiJB+FiAbveVxcnJCdlckOWzxPcnR1dcHtdiMjIwNnz56FVqsVLlN/fz/S0tKQm5uL9fV1TE9P49GjR2hpaUF5eTnu3bsnioWUlBSUlJTg8ePH0hKbmJiAzWYTFKyrqwtzc3PS3tTpdNjc3MTt27eRkLDvMhkfHy+IREZGBgoKChCLxaDVauH1emV+CfvVHo9HzMnW1tZw+PBhpKSkwGKxYHFxESdPnsRzzz2H6elpFBUVYWlpCffu3UNaWtoBA7uHDx9KcsQka25uTrwvDAYDamtrAQAWi0WGmPn9fng8HkxMTMDj8aCzs1PW0OjoKD7++GOZ0HrhwgXk5uZKO2h9fR1utxvZ2dl4+PChILdsj3i9XkkUysvLxbuDAzZNJhMcDgfOnTsnATcpKQmlpaVITU0VT4rOzk5sbW1Br9cjLy9P1h9Ru1AohPr6emi1WgwPD4tKKDU1FXa7XTgXROzi4uLw6quvQqPR4Fvf+hbi4uKEQ7C7u4v79+/LfstEjG6ijx8/FvRDp9PBZrPhqaeeEnSAKqDJyUlkZmaKffejR4+ws7ODZ599FoFAADdu3EBKSgrq6+vR09OD7u5uOJ1OHDp0CB0dHRgbGwMAnDhxAgDEPXd4eBgJCQmoqqqCz+fD4uIiVCoVLl68iLGxMcTFxeHRo0eYmprCc889h6NHj+LcuXNQqVSw2Wy4cuUKjh8/jqWlJZSUlGByclJmEs3Pz2NxcRFnz54V91raMVitVoRCIXR3d0v7cW1tDYFAAK2trcjOzpYBb9XV1RgaGsL09DSysrKQkJAAv9+P+fl5QW/MZjOampoQjUYRjUbR0NCA8+fPY3JyEh9++CEKCgpw9uxZDA8Po7u7G0ajEXa7HW+88YYgVzk5OZ9rj3iipINVFCF6tlm4AXIzZAsE2K+qcnJy4HK5pIomO55MYnIGyDHgBswLbDabBUoH9mVx4+PjMo+Fh5JUp/x9tlP4Myo4GEyYoSUnJwtvJC4uTnzok5OTsbu77+6YkJAArVYrcy0IYSUnJ4uhChUoJB6Rv8LrxWFXDCxsobC1wsFiKpVKRpwT8uf1ZpBj24T3hRUviaPKFhKTCBJWea92d3cRCoUQi8UkESSawyBE0zEmPkxoaJjFKp1oAn0nHA6H8HGCwaAklPy+ExMTmJqaEpKky+WSYM8ET6mqINrBa0oiq7KlwsSGDxSAA+Rb/oy9Z64LKlS4TtlCJH+I6AuTleXlZWi1WiFE87wJ5W5ubiIQCMjsDq697e1taZ+pVCqZvMr1S3M75XuSY0EeCbk3fBZ5HTIyMg7MGQIgn0PEhOtbyadhkh8KheRZf1JjMAAwGo24f/++IAT8jJmZGWxvbwsXim6LAJCfn4/r169jY2MDJ06cQEJCAux2OzIzM8UlkUPgtre3MT4+jrt376K+vh4Gg0ECHq3Tr1+/Do1Ggx/84AcIBoOYnp5GX18fIpEI9Ho9Hj9+jGg0ikOHDqGzsxM9PT1ITU0V3wQilXa7HR9++CFMJpOQlsfHxxEKhVBYWIiOjg7U1dXh1KlT8Pl8cDqduHPnjqAKTGKCwSDW1tbgdrsRi8Vw9epVbG9vw+Vywe12C0eNry0vL0dCQgKys7Ol/z4yMiIzTV555RVcu3YNb731Fnp7e+H1etHd3Y3e3l689tprSEhIkHEEL730kiRO+fn5mJqaQk1NjfB79vb2kJeXJ/5Ai4uLKC4uFn+HvLw8TE5O4ujRo2hsbBTDMbVaLaZt4XAYwWAQ8/PzePTokfT479+/LwUGeT1sqWVkZMBms6GoqAg5OTmigrt58ybu37+Pb3zjGzCZTIJccSyC0WjE8vIyent7hR/FGS+RSAQPHjwQXxWfz4eGhgZ0d3djYWEBXq8XNpsNra2tgox/8MEHMBgM+NrXvob33nsP77zzDmpqagR1/eijj6Rocrvd0Gg04pPCtlpRURGuXbuGb3/727JPXrt2TfxVaHX/6quvYmpqCn6/X2a4nDhxAiMjI7h37x6am5uRlpaGnp4e+P1+ZGRkIDMzEzdv3kRLSwuOHDmCyclJrK2t4fXXX0dmZiaqqqrw9NNPIxqNCm+ssLAQg4ODcLvdCAaDmJiYQFpaGp577jmMjo4KBYFoC5Omzc1N6T7Mzs7i6tWrknB7vV6R2ZtMJnR0dKCpqQlOpxMff/wxJicn/3mIpAxYygSDSAUZ8EwkWJ2lpKTA4XAIr4OBhGxrJhmRSOQAMZSjwfm+tNg2m83i2RGNRsXvXjkBlEPHgE+HYBEmZkBlhalU2bD6Y+JCO2tlZUson5s2rcwphWQgJORN6J2Et62tLemXsgXCDYqKCiIK6enpkjBx6iqrWV5PtnWAT51OmRASVWKSwlbP3t6eyFxJOmXwYkBjcsFWGH9OVIYwMP/N6jMcDgv6tbW1JUQkKosYXFmhE5qlTTSH01FtwiSBdvi8rkx2mEwpW348P6JsROKUvBUqb7hGyHnY2toSwyk+hImJiYhEIvL7yvdTJnapqalIS0sTvsXi4qI4ZVJpRbifiSrXFp8Dkpr5LDG5UqvVktCR56GU7SYnJ4vrKb8TgAM2zUSUUlJSBFXks8q2G6XdfJaeNPEwmUwoKyvD6OgoZmdnERcXh5ycHGRmZkrQCwQCmJ6eFuk0B5r9h//wH8TA7+HDh5JIMJFkL7uoqAgFBQUy0yMnJ0cIc5ubm/jmN7+J3NxcfPvb3xZTLo/HI5bloVAI9+/fx8LCAp566qkDclWioyTUulwuGZ9QXFyMtLQ0jI2NITExEXNzc4hGo3A4HLh48SIikQi+853vIDExEYcPHxZ0KTMzU/ags2fPYmBgAMXFxXj33Xdht9uFG8Z9Mj5+35b93XffFeI3CanBYBB9fX34sz/7Mxw7dgwPHz6UZ4JF0uPHj3Hz5k1otVpMTEwgFArh4cOHIhkeGRmB2+2W1sLs7Cz6+/ul1fXrX/9aUAmz2YxYLIaZmRlEo1E4nU6MjIzg1KlTyMvLQygUwqlTp1BTU4PMzEwpGvb29lBVVYVAICBcjs3NTXR1dWFrawtvv/02/H4/3n77bTgcDpw4cQLBYBB2ux21tbV47733JHm+e/euPJOvvvoq9vb24HA4MDg4iJycHHg8HvEvKi0thdfrlXbkj370I+HA9Pf3w2KxoLKyEk6nU+Z9sd2blZUlLrNnzpyRa6rT6aTQ1Wq1uHbtGmZnZ2Gz2RAMBsXGv729HSsrK5ibm8NTTz0Fl8uF9vZ2JCYmivVCdXU1srKyhLxL/w632y1tfLVajeLiYszNzcHpdKKxsRFqtRqdnZ1Qq9XQarW4ePGiUAF+8IMfyOeNjIwgLi4OR44cwb1799Db2wuDwYBTp06Jb87CwoIUURaLBXFxcQiHw+ju7kZpaSnq6+tx8uRJNDc3S+HJIXGcFVNWVoa2tjY8evQI6enpMuH28xxPjHTwoeDBIEyInZUxIdq8vDyprpQcDj5M7CXGxcXJhMPPchC2t7fFnCs+Pl76pgxCfA29OJS9Tm7KNKhh24fIgdKDgX07EqQ4+yASiUhW7fV6EQ6HD/TV2aLQarWScSoDCZMkkun487W1NUkK+F0SExMRDAYFgszOzpbgSB4K2zdKySNbSwzswKfmW+xtA5DXK/k5ycnJSE9PF/SGPU++juRCchaY0AAQTw4mMISXmSAB+8nQ/Pw8VldXZSQ1EyAmN2SZ8zooVSMckAd8OjRQqWphYCR6RWSK8mEmylQqMfHR6XSSSDBBWVlZEfidlSATEgBiuMY2GtfN3t6e/D6wX/F7PB44nU6pZHl9GOyJRlFtw0RKaQjG9UOyKBN3vV4vZFUmogsLCweeU95r9p75/JAnRMdetojY0x4dHRVy9+fdSHgMDQ0BgIxHn5qaQktLi9hhT09Po76+Hn6/HwaDQZCF8+fP491330UsFkNJSQncbjempqbEYIvTgJ9//nksLCyIeycHjm1ubsLr9UpgvXbtGiYnJ1FUVASPx4OnnnoKBQUFiIuLw/Hjx9HQ0ICZmRlsbGzg7NmzaGtrw8zMjATU5ORktLa2wmAw4PHjxwJdc5LoysoK8vPz4fF4MD4+LpL7QCCApKQkUcc0NDSgsbFRiiyv14vc3Fx0dXWhrKxMEML79+9ja2sLubm5wrs4c+aMoDiBQAAGgwGNjY3Y3NzEyMgIXn/9dXzhC1/AxsYG8vLypB1ZUVEh16mvrw8+nw9qtVqmhB4/fhyDg4MyK2NgYAAZGRliAHXmzBno9Xrx2SgqKhKnUla+n3zyiawPv9+PWCyGrq4uSbjfe+89JCUlwWQyIRgMoru7W5L6xMREXL58WWbPcN3S3IryY4vFIios2iysr69jYGBAnvXOzk6kp6fD5XIhLS0N1dXVmJycxMLCAnZ3d3Hs2DHxx2lqahL0LSUlBUtLS7K3Dw8P4/DhwygvL0dHRwdu3ryJ/v5+ZGRkYGpqCjabTfbxl19+WVw5a2trpXVEDxmDwYDf/va3+Oijj2Tfq6+vR3p6Oh49eoQ333xTVG2JiYkyYZhBvLi4GNFoFKOjo5iZmcHPf/5zFBYWYm5uTpAGm82G3t5e+Hw+8buampoCsN8WpsEcbf27u7vFAOyFF14QAURWVpaM73A4HLJmY7EYlpaWoNfrceXKFayvr6Ozs1OEAgMDA1hfX0dDQwOKiorg8/kOiA/+X8cTJR0MEkpDI6VhkhLy39vbH3fONoOSkEfUgMFO6RZpNpsFRmflTpc8Qv8k9hgMBqyurmJxcRErKyvShgFwoMdNpQKTGUpwdTqdEEKV5k/kYbBqVp4/DaS4aROO9/v9Ig0lagBAZMPszTNBIdzOypaBhOfHzHh3d1ceNgAyDprXTYlscKHw95RoFLCPlhBZYTJB7gv7t0z0mAApIXyr1Srny+qbn726uio+JQAEXaE8jv4FKysrSEpKQklJifR1qfog0sI21PLyMgwGgyBpSi8S5XXltN3k5GQJ+uSuWCwWGAwGSWS4zljZAxDDNb43URomsyQZM1Hiw0VOzWf5NElJSWLsk5GRId4AcXFxEmSopOJ14kwUSozZDuF9p7SW6185q4XoXnp6ulRCNAxjksHrzGvp9XqFM8T7nZCQIC6KoVBIOAhPejBJ+9GPfiSf29zcjKKiIqSnp+P69euorKzE3bt34fP5UFpaivb2drzwwgsI/8OYd5oH5ubmoqOjQ5Anr9crSfq9e/cQDofxq1/9SsaQB4NBTE5OIiMjA8PDw+jq6oLRaMQHH3yA733ve9jd3YXZbJZAmpCQgDt37iArKwvl5eVITk6W8euEmflc2u12/OIXv5Ckl+t2cXERXV1dcDgcePjwIcLhMMxmMywWC+7cuSO8goWFBbS3t0tBptVqhXB96NAhUb/09fVJW6OtrQ2NjY3Izs6G0+nE6OgoXnzxRaSnp+Ppp5+Gw+HAU089JSRBIlTj4+OorKwU3gmD7+TkJN566y0sLi6ivr5eUIKJiQmx6Od1DYfDYt3u9XoRCoUETSktLZX5JhqNBhcuXEB1dTUKCwvx8OFD8ZNYWVnBoUOHhEdAhDE9PR0bGxuorKwUV0+iGcphlnV1dSgpKZEBe5cuXZJ99/Tp0zLb6f79++L8eenSJSkYFhYWsLOzg66uLuHNeDweIbhbrVbZJ6ampjA4OIikpCS4XC48++yz6OzsxM7OjiARMzMz+OlPf4p79+5hb28PHo8H7e3tQuydn5/H2toajh49iuzsbDQ0NKCpqQm/+c1v4HQ6oVarZWR9dnY24uPjkZGRIW6rXq8XJpMJ+fn5KCoqwtzcnJCwi4qKhGf49ttvIzU1VRBEpeJqamoKeXl5OHz4MAoLC6Vwm5+fF/kvC32l6pPcSa/Xi0AggO3tbfT396OqqkoG55E/k5ubK9PXuXf+s6hXCC0T/gU+9edQ/pcBicxi9vdZKdO/gOoLtjxWVlYkAJIkquzvK4PWzs4OAoEA4uLipAUxMzMjMyeYFPz/2HvvMLnq63z8nT7bZ3vvvWiLei9IQhJNIAQYbAwYfrHj8thxcOIWl5RvnLjETmIDbhgMAdsUIYEkVFAvK2kl7Urbe++7s3223t8fm/foMyMBK4qMwz3Ps4+0s3fu/bR7zns6hTLN2L6+vnJfxlfwfir4YUQ0USCFWGRkpFtAo5o9Qi2VqVzUmFV3D83iwJUuvBaLRbrk0srCeAJWd2RQIAUWzf6MOVDdLnQTEQSyaiUboJGhAlfH6TCwEIDMjZr5wMCAPJM/BGlq8zAeZB8fH3FBpKWlueXoT01NIT8/X+IpGCjpmebLLAx1vdTUarrJ6IYhAGIAKWMbKLQZ5EmBzTOsFuzi+JldxPRcgjC6R9S0ZTUl2mKxoKKiQrIZGI/E7CsfHx95UQmc1MZs/J2WEJ4dWpkYXGyxWDA0NITOzk65P+dHAEnXCy0nZPq0ABLkEfAQ5CQmJiIzM1M6ns6VNm7cKH1JWHp8/vz5qKmpwcsvv4zGxkbExcXh9OnTyMzMlOqOa9euxc6dO7FixQpUVVXhzJkzki7Jd+r48ePo7u5GV1cXtm/fLkXZNm3ahN7eXnR1dWHjxo0YHBzE0NAQVq9eLTwhJiYGixYtgtPpxI9+9CNcuHBBmGZCQoIw9KioKGRlZYlmW1ZWhtTUVNx6662oqqpCbGystGuni3TNmjVYt26d1MVYv3497PbZvkLLli2TQmMhISFYu3Yt7rvvPgGUJ06cQHV1NYKCguB0OlFVVSWm9aSkJKSnpyMqKkqCIa1WKy5fvow//OEPEqQ5OTkpFV6XLVuG8vJy9Pb2wmw2S6XRjo4OXLhwAbfffru4hWkhyMnJwZIlSzA9PY1FixYhPDxcXGIREREYHR3F6tWrRagODQ1JTBoz+J555hm0t7fj6NGjWLBgAVasWIH29naYTCYRgvPnzxfezHb0tK4+//zzOHnyJG677TaYTCacO3cOpaWlAkwYr1NXV4fMzEyJqYmMjERLSws2bdoEADh06JBYDvr6+iTtOT4+HiMjI2hvb5fYsfLycgQEBOD06dOShUYFj/VmHA6HWFxo7YmNjUV+fj5aWlowMjIiacVtbW2Ynp5GdnY2Tp06BbPZLBYZZklGRkbCYrEgKCgIfX192LVrFzZu3IjVq1cjLCwMiYmJMJvN0vyQQaaMNezq6kJERAQiIiJQVFSEy5cvSwG3trY2eHl5ITo6GiaTCXv27EFPTw/WrFmDtWvX4t5770VgYKBka7a2tsq8mpubpTKp0+lEf38/BgcH0d/fj7i4OERERIhSc+TIEfT19eG///u/MX/+fCQnJ0s6+FzoPcV0qAFuZFr8PwDRJqhBqoKbZmqawdXfw8LCBAH7+vpK9ktwcLAIR2pmQ0ND4m5gqivNd6yNwYDJ1tZWYdYkMvnu7m45FNSUqd2y5gOFBINeVSuFy+WSZkTUTgnO1AwbNbuH46RPn9ksNNvT5E4hZrVahSmqAalTU1NScIwWEgpaBr6xJgi1epr3KWAZQEgAQbBDwcp4B1qQPK04qqDjtWoQMUEqX9iGhgYMDg6Km23evHki/Ejsa8MS4LSOEazRFcCgZJYO59h5njgfWpe8vb3lTBCg8Lk8W8weoaAOCAiQmAICAn6XQpwAif+6XC50d3cjLS0NIyMjbo3aCJzoJqKFiqnBTO1l7IX6rvHM8V2htYPdl3lu6DIBILEJ6r5yv1TLEveTgawdHR0S53A9tH//fhQUFKCmpgbe3t4oLCxEX1+fMKTg4GA0NjZicnISFy5cEK2KGivrVtxyyy2IiYnBrl27sHnzZmlTfv78ecyfPx/nzp2TtvAdHR3SqC04OFjm3N/fj56eHvT39yM7Oxv9/f3o7u7Gtm3b4OPjI301Ll26JNH8rMZaVlYm/WLKy8ul38ry5cuxfPlyyVhLT09HVVWVmJdjYmJw+vRpNwBnMpkQHByM5uZm7N27F7t27YK3tzeampqQk5Mj8RWXLl2S1vMsd81sBAJyl8uF3NxcNDU1ISsrSwI5L1++LKnPK1euhN1uR39/P3JyclBVVSW89sKFCyKIy8vLMW/ePHR2dqK6uhoXLlzA4cOH8dZbbyE8PBxtbW04deqUBC62t7cjNDQUqamp0DQN8+fPl3gXFlKk1bi4uBjl5eWYnp5t5z4wMIAdO3agoaEBGRkZsNlsCAwMxODgIPLy8lBQUICFCxdKrZGoqCi3tWcsxOXLl2V9GNjZ1taGS5cuwWAwYNWqVfD29pYuuF5eXjKX6OhocYvTFVFTU4ONGzfC29sbOTk5Eu9WWVkp1abb29vR3NyM+vp6hIWFIT4+HmfPnkVkZCScTidCQ0ORmZkpFq7W1lZpndHa2oqTJ0/i/vvvl/M/OjqK0NBQ6YMDAPX19ViyZAlOnDiBw4cPY9WqVejt7UVpaSnsdjtKSkowOTkp7SSGhoYQERGBqakpNDc3w2q1yvvDWI3Vq1djcnIShYWFKCkpwblz59DQ0IDU1FRkZWUhLy9PAppXrFghWX7d3d3S8Zup101NTTAajThx4gTy8/OxfPlyfP3rX4fL5ZLMHrUz+juRQSPHebcLDQbk5uaKBq1mQjC2AICY71nmmpoyYxempqak+ZfBYBBN1NfXF35+fqJdkyEzhsHb21viOsLDw9HS0iI+TDJfl2u2jXRnZ6eY8Bjw6HK5xKIREhKCgYEBBAUFSVMsMmxaC1wuF3p7exEWFiZChoF2FGjT07OdPllExmAwCHBiRUOa8VmXgFozNXGmfdJvS5BAsGEwGKT6X3V1Nfr7+xEZGSkgiUBpZmZGCrRwP1h0SnWHARB/Il1GQUFBIkwBiDuBwpduAeBKfAiBFU3MFJysMUBLjAp4KFhra2sRHBwsgWjR0dHo6+uDxWJBYGCgWLQASGVWFlEjSGIsBeuasDy8WjeG2UgEEoy1YXwOLR0M8OX3CFDVeCDWgiEw4f6QaD4ODw+XwjyLFi0S4U5rEuelZs6o7g1aqThHAq6ZmRkBa7QU0a3j7e2Njo4OscDxXRgaGpIzMjQ05AYO+V3VSjQxMds5kmvMmKhdu3ZhjmxCgjorKiqwZcsWOBwOtLa2IiAgAP39/Th9+jQWLFgggDo1NRWxsbFi4o+KioLD4cClS5dgsVhEYFVWVqK1tRX5+fnCOxg/kZGRIeXiExMTcebMGRw4cAB/8zd/g5aWFqmlUFxcjJtuuknm2tjYKKmiTIk/efKkBAXfd999OHz4sJwvTdPQ1tYmwZL+/v44fvy4+MoZe8VW8jMzM8jMzERfXx9OnjyJtrY2KbhFQLJ69Wps3LhRSrtT4Tl58iQWLVoEk2m2OmxLSwtuvvlmnDlzBvX19cjPz0dZWRnGx8exZMkSDA4OYvXq1RI3UFtbK63qKehHRkaQnp6O9vZ20eZpXRseHkZjY6P0/CgrK8OGDRtw4cIFnDt3DuvXr5eqzmazGcnJybh48aK0fM/JycHw8DAaGhqQkJAgvIDvDgNuOzo6YDKZEBoaipGREWkWFhoaitdee01qW1y4cAH9/f1Yvnw5XC4XLly4gAcffBDAbAG6tLQ0ieNrbW1FX18fKisrsWXLFon36e/vR0JCgmT+REZGoqmpCStWrMCuXbukJw6zd4xGo1TBfvjhh/Hzn/9clOZ58+bJu3Xu3DksXLgQnZ2diIyMRG1tLUZHR5GUlISqqiqxAlMWWSwWKYRmMplw8uRJ5OfnSw2SsbExyRjy8/PDHXfcgenpaZSVlQlPMxqNEtA6Pj4uFZ57enoko7Knp0e6GicnJyM6Ohqtra2IjY3FzMwMzp8/D7PZLEG3zc3NSExMRGdnJ6Kjo+WziYkJ+Pn5oaysDFVVVbj//vsxPT2NkpISqUmTnp6Ozs5OSWHv7u5GbW3tnPjEdbtXPN0qKpHhBwYGilDky0VXB10GLEbEqo0UKmS0gLtLxel0iv+6paUFAKRoFYU36xOwWRa1aQCSouvr64uRkREEBASIlUENTmSQ5NDQEIKDg2X8FKoGgwHd3d1wOp0SRxIZGSl+daNxtuomx88eJdScqTXT1M8ATcaOMDCSWUEUiOzjEBAQIG4Ki8Ui2j7TJpnlwD3iWtJET2JUv91ul8p3BAVWq1WYixqASu2ZWTYERbQ+UZhRsDPYdWJiwm3/w8PDpSgSzf0MXPSsScLKhbTkEEiS1PoWtDQxHYzrQ6sRrUM8qwS9vEbV+q1WqwT/segZBQKDmMn0eF9m0/T390sXVYJyptBSwKvxLwQ6BEe0VthsNgkgpY+bJm3uE601YWFhEidCSxiBMd8JgkXel+eMABKYDfpV68R4npt3I5bRXrFiBZqbm9HR0YHjx4+jvb0deXl5WLhwIe644w4EBgZi1apVUpVz+fLlEng4NjYmtTx+//vfo6ysDDMzM0hJSZE5NTQ0CJBiVL2maVIngT1ATp06hfT0dGm85eXlhd27dyMrKwuxsbEC9EdGRnD27Fnk5+cjOjoafn5+OHHiBJYuXYqGhgbRgNlKwel0or29XcDzqVOn0NPTA8f/tlrv7+9HUFAQent7kZycLNUfN2/ejJCQEKxatQp33XUXpqenUVpaihMnTqCvr08KXM2bNw/z5s3DrbfeiuzsbCxatEhqc4SFhWHBggUoKChASkoKVq1ahfDwcFRWVkr2UlNTkxQ5S05ORlxcHJKTk1FXV4eoqCgsX75c3oG2tjapxcHYgKSkJOzduxc5OTn40pe+JGCVQZmFhYUSN0N3+6lTp5CTk4PBwUFRvm666SZs3rxZ4iYASDPOjo4OvPXWW+jq6kJ5eTnWrFkj73x+fr7EprS3t+PRRx+F0+mURmPd3d2S2cTiXz/72c8QGBiItLQ0sY53dHQgJSVFEhMCAwNFYWWVzVOnTiE0NBRnz55FSkoKvL298ctf/hIdHR2w2WySaVRfX4/jx49j69ataG1tRXNzM8rLy1FaWorW1lbJNiGYT05ORm5uLgYGBpCeno7Dhw/jxz/+sbjQ6+vr8bnPfQ7r1q2DwWDApz/9aaSkpIisCQ8Pl3eKympgYKC4eKqrq7Fs2TK4XC5UVVVJI1N27n3++ecRGhqKvr4+PPfcc/D398e8efOQnJyMsLAwCSxmmfi6ujq3EhPz5s3DI488gtraWpw+fRqRkZEwGo3IzMyE1WrFN77xDRiNRmRnZyM3N3fOPOK6G76pP/xMdR2YzWapOgrArYEa/cv0U1MYUPixfgHTY8mYySBZLImlnJlJwGwLlo2mhYFFmQhM1PoXFIYUNBRkJpNJ/Olq/QaOm+CH/n3GIrAyKv1lBFcEEqo5n5o0wRejqJnlw0ZpvD/XDoCkaTE9laiXY6cLiUKEvTxokaClib9TSHGeJNWVRsFMhk+gQEZMoctsH5vNJuCNwI9WAZdrtmdEZGQkgFkGRPROiwB9h9SQCNLo+gKuFO6i9YlgZXp6WixXPJNMZWalWd6D1g5qImrBIuBKy3nVCqKCOE8XjZ+fHyoqKsQ3TIsdzznXh9k91Jy4tky5ZYwMc/5Vyx9jRxiYyswZuhVZNE21cvHsqGeO+6taLAFIATu6N6+3ImlISAjOnj2LmpoadHd34/Tp01i8eDFKSkpw9uxZ9Pf34/vf/z6ioqKwe/du6UjKYlY1NTWYmpqSZmIbN24Uyxn955cuXZJUVgo9FfRNT88Wijt8+DA+//nP49lnn5W+NxUVFQgKChKTMAuBLVu2DMePH8fly5elQFJDQ4OUHY+OjpaUUVZ3rKioQEREhFhkg4KCUFVVJansTIe9ePEiHA4HEhMTMT09jerqalRXV4sbweVy4d5775WmgMzC6OzsFOFnMMx2aq6srERBQYEIis2bN2Pfvn0oLS2VaqppaWnYvn271M9wOp04ceIEAODWW2+FyWTCa6+9BqfTiT179kjF1ejoaOTl5clZSUpKwokTJ/Dzn/8c8fHxAGYtj8x4MBhmu5xGRkaisrISq1atQkNDA5KTk3HhwgWMjIzgzTffRFNTEyYnJ3Hy5EkkJiZKtdmIiAikp6dLWn1gYCDOnTsn9TnuuOMOaaewY8cOaJomwcgM4OZ5qa2tlfosdXV1uHDhgtRbmZiYkAqrJSUlaG1tlQB1h8OBBx54AM3NzUhNTUVhYSGamprgcrmwatUq1NTU4NKlS8jKyoKvry+WLl2KvXv3SiPAnp4ebNmyRaq00lXM91itIrpx40ax1pSVlaGtrU0qofr7+0uVZsarsWnh0qVLkZubKzVZGHy6fPlyHDp0CNPT0+jq6sKaNWvQ0NCA5557Dj09Pbj33nvR3d2NpqYmfO5zn8Py5ctRW1uLiooK1NTUiOK/detWiUGj7JuZmZGmby6XS0BOU1MTOjo6cPnyZfz6179GSkoKysvLUVdXJ7L+3ei6K5KqPmn+rmZKEDDQDcOgNYKKvr4+8SXbbLPdXRldD0AYHYNN6cem+Z9Bh7SKUOjQpNze3i7FTyhsgoKCxEXR3d2N5uZmacxEITU0NCQ+KWraZNY8nIODg+Iu6u/vFz85zY2xsbEyDwoFZsioWmdAQIAESDJymIyTAoQCW62PwfiJ2NhYqaAIQGJPGA9CrV+tA0GBS0FFoEfgxv1TAQhdGLQe0FqkxnXQfcV6ELQAEAgQsBEIECBxDN3d3SgvL5c97e3tdRs/rQNcUzXNk24HWhwokOkO4vWMJ2LWyeTkJHx9fa8qaU4hrKbn0uKjupKAKwCEZ5c9fRgUyFgWZmp5uiPV1PPAwEBZO7oBCTYIHgmi1H4sPj4+4iqieZhrTlDPXH41w4Z/p+tFBVMsrEbQyOrBc6XBwUHccccdki0RHx+PoqIi5Ofno6enBwaDAbfffjuOHTuGBx54AO3t7dJBlg3XNE3DkiVLsHjxYgm+27NnDzZu3IiysjJRXlJTU/GJT3wC8fHxUmypqKgI27ZtE8sPS0wfP34cJpMJCQkJCAoKwsmTJ1FSUiKWjieffBJf/vKXJa29s7MT4+PjGBwchNPpxLFjx9DQ0IDx8XGcOXNGCpsNDg6K1Y7uhubmZixevFiAU1JSkjTP0jQNiYmJaG9vR3l5Obq7u5GRkYGwsDAsWrQIo6OjEhSYnp6OtLQ0nD59GjMzM1K4bXBwUCyUjY2NaGtrw+rVq/GrX/0KoaGhsNlsIuhPnz4No9GIVatWISEhAfX19QK4/P39ER4ejry8PClY9tZbb6GnpwdBQUEICgpCbm4uHnvsMbS3t0vKr9PpRF5eHvr6+mT9IiIihLezwVt3d7dkGdbV1SEyMlLKn09NTUn57bNnzyI1NRXnz5/HokWLcPDgQXR1dcFgMEhRuYGBAVy4cAH79++Hw+FAdXU1gCsl/ScmJtDZ2Ym2tjaxEvb394v1tLW1FaWlpXjggQewadMmnD9/XqxQjBMifwwPD0ddXR1cLhe2bduGjRs3Ii0tDWazWVz7vb29wkP37t2Lzs5OPPbYY8jNzZUiixUVFTh+/LjUDjl+/DiOHDmCnJwcNDc3w9vbW97j4eFh2Gw2LFmyBN3d3eI6bGxsRHR0NCoqKoSXT05OYt++fdi/fz/8/f2l6d3JkyexefNm3H333VKtlTVeLly4gAMHDsiaxcfHw2KxIDo6Gs8//7x0Jfby8hLgTAttcHAwWltbER8fL3GAq1atki7RCxculNjHudB1915RMz8A98BCZgRQKFCjY0wFA6polqepenR01I1BM3+bcQF89uDgIEZGRhAWFiYmLMf/tv1VA+78/f0RGRkpaXZBQUFiLXE4HOJaIdDo6ekRi8ng4KDUuTebzdKdlCYvariMMSF4GBoaQkBAgFv1U/rv+RmZPut8mM2zraQpQKmhMXaAFhv64dTAyYiICLS0tMBisUhQLAEHtXsKI/pVKXwBiMWIRbj6+vpkjGqBKpPJJILX6XRKATf6P/mCc0+YgaQCJTWmgueE2nRMTAyAWauCyWSSOCC6N1h7hS4Bz/gaBpjy/DBuRrX6sLoqszl8fX1FWLObLquAAlfcehMTE2LRoSuCrgdaSHj2WPJ9dHRU6koQOBKcMsBRzRhRwQ4Dg+luNBgMbpYqEq1gXGem8rJuDINiWdhOjQvh3vIc0IfN9FqCK4JYWjTnSsPDw3j11Vexf/9+cUktW7YM1dXV0vsEALKzs+FyueDv74+ioiIYDAbU1dVJAB9jqVwuF9LS0pCQkICGhgYpFU5z+eHDh1FdXY3i4mJ4e3sjPz8fTz31FIaGhuBwONDS0oLs7Gz4+fmhp6dHUnKZ0hoeHi69I5jCPDU1hczMTGRlZSEgIABNTU0YGxvDhQsX8OSTTyIsLAwVFRWwWCzIzs5GXl6edPQ0m2ergbLfSG9vr2TtREdHo62tTYpS3XrrrfD19UVRUZE03IqMjERbWxsiIyOxe/duHDhwAHl5eairq0NAQADy8vKk2ywDjbdv3476+nps3boVYWFhUs8hNjZW2p93dnaitLQUTqdT+MXIyAhSU1PR09ODrKwsqYLr7e0t1uTi4mIcOXJEGoPV19fj5MmTaG9vF74dExODqqoqLF26VIppUYmy2WxobW3FZz7zGURERODEiRPIzs5GeHg4TCYT2traJA4nPj4eAwMDkilTV1eHhx56SOqONDY2YvXq1TAYDEhPT0dmZiZiYmIEKFGZYDon66jEx8cjNjYWixcvxtmzZ/HEE09g2bJl6O3tFb509uxZOJ1OrF271s1F39HRgddeew2Dg4NobW0V99rWrVsRGBiI/v5+bNu2DUuWLMGlS5dw9uxZaNpsY9PExERYLBacOHECgYGBGBgYQFZWFi5fvozLly/jO9/5jmQC+fn5Sfdbuhzr6+sRGRkpJdx9fHyQnp4uAcTp6ekoKyuT93jp0qX44x//iPr6ehw9ehRBQUEoLy9HR0cHBgYGJLMxMjISVVVVUk/m7rvvlviMsbExJCcno7OzE7W1tejp6YHJNFsmvaioCPHx8Vi3bh36+/sxPj6OlStX4uTJk5g/fz46OzvnxCPeU5dZ4EqRKeBKLIfZPFu+mFqrWnCJ2jtjF6ilAZAiYWSuFPDAFU2aVhJ+j9YSCjI+Uy0lTobMADxmYURGRiIxMRFeXl5S9Ic5yvT3kakx35sCjimg9H1TWKmmaH9/f0HNRKYULgAkpZNZJhTutA7RogFccZPwu7xPdnY2pqenJVCO68R7UZhQSFKAUOAxdZPaLGMlKFwp+OlmopDs7u4W1xLjUzgXxtGoMQwcN6P5+S+1fbt9tqsoLWiMm6EFium9PEdq2rXqfqB1gumxLGKjpviqWURcWwrciYkJScHluWRcjZoVQzDDeA2667y9vVFSUoLIyEhERUWJYGf6NWNSeF+CXKbSEQSxpghjLQiuyMjVLBcVTPFs8KyxhgqLtqnng2umuobo2gLgVqmVYPR6aMuWLZg3bx7Gx8eRl5cnJcwbGxsRGBiIs2fPSlpxV1cX5s2bJynjRqMR8+fPR2lpKRobG5GdnY0zZ84gNzdXanPceeedkp7OVgi+vr44duwY6urqJJXW4XDAbDZj7969kr3CQNmQkBAkJSVJNdLk5GSpGNrX14epqSlxyTzwwAPIzs7GunXrkJSUhLKyMoSGhsJut6OyslLqQNjtdrS2tkpNiPHxcdTV1WHp0qWoqalBaWkpQkJCcOnSJcTExCAoKAiZmZkwGAxS74AC68yZM4iMjMTixYsxPT3bSsJqtaKzsxP+/v5ISUlBUlKSWFrIxxoaGqTwWkdHB6Kjo/HSSy/h2LFjKC0txdjYGHJzczF//nz09/dL5tz58+cxb9485OXlwel0YvXq1VKvYtmyZRKPkpWVJRkSvr6+aG1txeuvv45PfOIT6OrqQnZ2NoKCgqBpGsLDwxEeHg4vLy+UlpbCarUiISFBrF/19fW4+eabERMTg9jYWCkfX1paKu6rp556CsPDw+jt7UVISAja29uRm5uL6upqHD16FJ2dneIaSk1NFaWLcXs9PT04evQo2tvbcejQIeFfAwMDcDqdqK6uRnZ2Nmw2G+bPn4/nnnsOdXV1UgNkeHgYixcvxrFjx7B27Vq89NJL0rX2yJEjSE5OxuXLl1FWVoaenh54e3sjPT0dU1NT2LFjBwC4uVlCQ0PhcrmQnZ2N1tZWKchGy0N/fz9iYmIk+21iYgKhoaGIjo6W9/78+fNITk6Gl5cXPv3pT8PhcODcuXM4ceIEbrnlFoSGhmL+/PlIS0sTy3BSUpLwmOnpaeTl5YkSWl1djXnz5iEsLAz79+9Hc3MznE4nSkpKsHr1aiQkJKCyslLKvwNAdXU1wsPDcfHiRWzcuBH79++fM3+4btChpsWqgaSMxyBDHRwcdKtboKbnkYGTofMzf39/NyuIWjiKh4ngRdUkAciGMBWVxbioAVMwUwhNT08jOjoaycnJcn1TUxOmpqakD0l3d7cIiq6uLgBXGnbR2uHv7w8/Pz+5nn03yPh4HTNGmFpLdxAZP3/UQlgUzCy8xb8Ds4IhLS0N9fX10miJ1gzVMqAG5bJpHveNJn9mUhBcMWKbcTBq1g4FIc3wtHgxINjf3x+9vb0YHR2VfG9aaQYGBgSAMqq+ubkZgYGBbmXvAbgFx1L7ZsAnASGZLcehZlURMHCe3DeeT55ZNdhYLR5Hc6HqLlLdHQAkNofnsKamRgQZLW8UJHxnCAI0TZP9UANMOT7+TmsBwS0tPQBkPtx7zpPrQBcXzz3nTeshP1etiRwfz5BnsPhcqKmpCSaTSYpKvfDCC5iYmBDt2GKxoLy8HFNTU/D19UVLSwtSU1OxbNkytLW1SSVS1tSJj49HQ0ODdPZkK/W2tjZYrVbcc889WLduHVasWIGOjg74+/vj4sWLor3n5uZKPMD58+dFoGqaJr1dQkJCpLDY9PQ0qqqqcODAAVy+fFnAWEtLCwoKCuByudDZ2YnW1lacOnUKFy5ckIq+fX19CA8PR3l5OZxOJwIDA5GdnY3MzEypAeJwONDZ2SlVSwcGBhAZGSmZAQyOJB9g6/e6ujq0traiu7sbVVVVssZDQ0NSw4M9VoaGhqS65x133IGysjL4+vqK355uPL6PS5cuxYULF8TNdezYMSkE9vLLL6OiogInT57E5cuXpcbO6Ogo0tLScNttt6GhoQElJSU4ePAgOjo6kJmZidHRUZSUlKC+vl7Wxt/fHyUlJQgKChJetX//ftnrS5cuob+/HyMjI4iJiZGGZk888YS4jvbt24eMjAzceeed4p6i1auoqEiKmrE8wblz52C32xESEiKFJmmR3bBhg1jkOzo6sGXLFrFGMCV7YGAAK1euxLFjxxAQEIBPfvKTKC8vx5YtWySYNSMjQ6wSIyMjCAwMxPbt29HX14eIiAjpenvp0iU899xzwsPZkp61TOjyYZnyyspKFBYW4tSpU1J0cP/+/Vi0aBGGh4dRVlaGuro63HrrrRJQOj4+jvnz5+O3v/0tnP/bCbawsBALFy5EYGAghoaGcOHCBcTHxyM6OhrBwcFSSfvmm29GQUEBli9fjtTUVAEqK1aswPz58xEXFycxMXyfm5qakJmZ+eHEdKhaIomMjJ0laVZjFVBmOdBvxXgQpqgxKp9CkfENFBrUgKips/GMmgqqFnOi4KDbgOiRabsMnCSznZqaErTJxk4DAwMyFhaToWWAZcZpRWG6mdPpdBMitCaMj4+LGVyNYRkaGpIxBgYGikBjWi9TGY1Go9ShYOofg1RNJhOWLVuGiooK9Pb2umnPBC90FTGGgfdkPQaO02AwwN/fX/yjvIYmcAo/uqkohBm4x9gQVlMdGxvDwMAAOjs7pThSW1ubmHdpFYuNjYXJZEJvb6/sE4M9ua5dXV0YGxuD0+lET0+PBEf19vaK9YKl1AGIL54BqtT81VbnagaMGjRLS5Zar4VWFIJeugFpefH29kZ1dTW8vLzEXci1UIM+WbWWQJuxKCwyxSZpnJPanp6mY4IMZloR1HNPHA6HADHGJXHcHI9q4aDliKCNc2bAtJoWPBdibNPChQthNptRXl6OrVu3wmw2IzU1FVarFYsWLUJOTg7mzZuH8PBwREZG4uWXX0Z1dTXuv/9+7NmzRwpY0YJAQJWQkACDwSD9NVwuF3bv3o1jx46JlbO+vl7K2X/iE59AeXm5pCOuXbsW8fHxCA8Px9TUFNatW4fg4GAcOXIEPj4+aGpqEssH1561J0pKSuByubBu3TrEx8e71ZGpq6tDUlKS1HNZsGCBFOtqbW2VcdfU1KCxsREtLS0oKirC2NgYcnJy0NHRgfXr18Nqtco5aWhoQFlZGYqLiyWV9ejRo+jq6oLD4UBdXR3OnTuHqakpREREoKenB5qmibskJycHn//851FZWYnvf//70DQN3d3dmJmZQV5eHuLj45GcnIz8/HwJZl+1ahVaWlrQ2toqqZcbNmyAn58fFixYAJvNhsWLF6OzsxPJyclSf6OsrAwJCQnIy8tDZGQkjh07JoXDmBnBpmtJSUkICwvD6OiogAQG7xYUFODkyZMYHh7GwMAA7r77blRWVmLjxo1Sp4IBnJcvX0Z0dLTEtzB1nLEizAy688470dvbK/Upenp6EBMTg+DgYBQWFqK4uBgDAwNSWry0tBSTk5Oorq7G8PAwzp49C7PZjK6uLuTk5ODIkSMIDw9HfHw8JiYmkJubi7GxMVE4x8bGxIrh5+cnRcSYWLBkyRLMmzcPMzMzOHfunNQSGRoawtTUlBQtO3PmDFpbW5GSkiKxdZmZmdi2bRvOnz8vmT8dHR2orq4Wy3ZmZiYqKiqQn58v7rM777wTb7zxBgCIQtXR0YFLly4JT+rp6UF4eDhOnjyJrq4uaJqG5uZmHD16FJOTkzhx4gRqamqkZkdoaKhbHGF2dvbccMT1MBRV06O2FRISguTkZKk+Sm2cgIJ+Ti4o4z2onTG4j8FRdrtdmLZaLIvCg9koExMTgtCYkknAw0UYHh6W2AYGuVI4kQFTIBsMBjF5UnPu7e1FS0uLpJQykFQNrmT5cmr6IyMj6O7uFkHhcDjcfP6sUOrn5ydjIligZYNzVStg0kdPgcPrmULV0tKCyclJKftL64AaNwBAhC7vTy2a1zGzh+lXdDcwMJPWI9W8z7GoBd8Yj8IYAmalsKkR++dwf+jLbGlpQUdHB7q6usSMy9iLwcFBt/HTNM71ocsHgLh6iL6533R5cL/U9FxaLljxlSifdVXo8uFaqlasy5cvIzMzU4Ao14TuFcZpcA/Uui1M+aUplHtCMO1yuSQ9nHMmcKAViuZuVo5l6XXuFddGdcGogbAEk7yegu96e6+Mjo4iNjYWb7zxBoaHh5GVlYXy8nKpAXLXXXfh8uXL0rOCAGDr1q1SApxFjfz8/OBwOBAbG4u4uDg89thj6OjoQG9vrygzjDdqbm6W85iWloasrCxcunQJBw8exOOPPy5u04MHD6K4uBivvfaauCTGx8dRUFAAb29vhIeHIysrC0ajEenp6VizZg327duHiYkJFBQUICkpSfhYY2OjZKPQBB8REYHw8HB0dHRg5cqVqKiowNDQEKqrq5GcnIzx8XGsW7cOkZGRyMnJwYULF1BUVISIiAjU19cjLi4Ofn5+mJmZkYqu4+PjaG9vx6pVq/DFL34R2dnZKC8vlxo1ly9fRn5+Ps6ePYuuri5UVFRgenq2yuSePXuwaNEiNDQ0wGw2o7W1FTabDS+99JJk0UxOTiIoKAhf+tKX4Ovri4ULF2Jqagrh4eGSicQg0YKCAukx1dDQIEGiixcvlmyMkpISLFq0CLW1tUhISEBkZCTy8/MRERGBqKgoaYOwadMmFBYWSq2Z8vJyVFdXi0tu3rx5yMjIQGpqKgIDAyVmpbGxEe3t7fDx8UFLSwuqqqoEWHp5eaGjowM+Pj74/e9/j+joaJw9e1bcWlQO2traxBoTERGBpqYmKQOvNmmsq6vD2rVrcfjwYSxfvhydnZ3w8/NDe3s7Dh48iBUrVqCurg7l5eVoaWlBcnKyyCGj0SgWCFamPXTokHQuP3/+PAICAsSdX1BQgOzsbClal5mZiezsbJnXTTfdhOLiYsTGxsJut+OWW26Bn58f7r33XnFDa5qGsrIymM2zFVmDg4MxPj4uAbnl5eXIysoCMOsiSUpKQmpqqsy3o6MD8+fPlwJjAQEB2LRpk1T4LioqQkJCAiIiIvDmm2+irq4O58+fh9VqRWFh4dxwxHVxlP8l1ruIjY1FTEyMaPS0IFDoqSXQp6enERwcLEGATANkACGFHsEHGR41PrPZLGl8Y2Nj4jJhuWW114QazU/0z2hmAGIpoOk5IiICdrtdkF5wcDCGhoZEIBEFMj6E2ijb0lNLoDBiQA21dWq/LBjGICVqpwxYZGQwAQ+DWhlgypgIggMA0tuAxY7o/qAViJYPWp+oyff29gK4EgjMeA5aM2hhYlYF94fC3cvLS0AhXUV0EVksFkRFRUmQJ9uPq7VB6EZgqqtaRXV6elq0H2oOBK2swDo6Ooq+vj4RoABEIyY4ontJTalllDhTmQk+aC0gAGP8D9eITaIowOny8/X1RU1NDYaHh5Gamir7QsBFi4pq8fLz85N0XFqZaFEgs2JpfFbs5VrSmsLv8oyFhYXJ2eB8qKmTGanpwLTW0UqlWjq4z06n87pTZtvb2yVToaOjQ0ztAQEB8PX1RXFxMebPn4+ioiJkZWXBbrejvb0dvb296OvrE9M2fdmrV6+W7Igf/OAH2LRpk5R+n5qakk6+mzZtkrMdERGB3t5eiR97/fXX4e3tjXPnzklZ+g0bNsBgMEim0eTkJEpLS9Hb24vz588Ln4qPj0deXh4iIiJEWDG7Zf78+cjJyUFGRgZaWlpw+fJlFBYWor29HQMDA9IUiz1sLl68iNLSUgwNDeH8+fN46aWXkJCQgPXr16Ourg5OpxNnz56FzWaTWK2JiQksXLhQ9vv06dPYv38/FixYgLy8PCQkJMi5Wbx4MZKTk7F8+XIUFhZKzQqHw4GoqCjcdNNNGBoawn/+53/CZDJJS3hgFiyePn0a8fHxMBgMSElJkbTOzZs3w2g0Ijg4GCUlJQJo/fz8EBkZiVWrVsFoNEoqbVhYGAoLC6WQ229/+1ucO3cOPT098q5UVlYiMTERt99+O5KTk9HW1oaVK1ciOjoa69evR2NjI1wuF6qrq6FpGjo6OvDEE0/g5MmTAhYPHz6MwsJCOQtpaWmIiIgQC8zU1BRKSkoQFhaG9vZ2GI1GqZuRmJgosRYXL17E2rVrUVtbi8WLF4v7i0K7srJS1rq6ulrqODHNlTwxKSlJ5NXAwACKi4tx/vx5Wefq6mq4XC4sWbJE+uywMF5jY6NYfdiu4q677kJaWhqSk5ORmJiIl156CWFhYaiurhZreGlpKd58802pQmsymeB0OpGYmIjo6GhRKJqbm7F27Vqkp6fj5MmTsFgsEiPz1ltvobu7G6WlpYiJiUFdXZ0E+QYEBGBoaAjl5eXIy8vDfffdh4aGBpw4cQKLFi0SXjg6OooNGzbMiUdcV0XSvLw88bGSCVPLovuCsQRkfMwyYREu+iDJ+KgFT01Nib9JjQGx22fbUlOIMqiRLgfW6GCcBnubqIW5aOJmgCdwJdXX6XQiODhYAAUbJzF1kxkNDCCMjo6WAzc4OCjxC56FtG655Rb4+PgI6Onu7kZISAicTiciIiJgNBrR2dkp/sWZmRlpoBQVFQWj0Si9VtgCnJo4XR60IrBb4ZkzZ5CWlibVXem6op+fa8EUYWZyMCuG60i/uxpnwEBaonhq6zQZUuDTrTAwMICenh4AkCZ4AwMD4ncGIHEvavElViBVYx0843b4LBbQYZYTuwirmSu0UDA+g3vFM6aW02fcDy16asYLwYGaisyg1VdffRW+vr5YtWqVgGa1qyuBCMHy9PQ0HA4H+vv7JViXoBOAgHGeJ1r2WOiH1sH29nZ4e3sjJiYGHR0dblk9fF8IzgGIe216+kqzO8acMMWWrhZm2xgMBmluNVc+sWHDBmiahnnz5knBK4KioaEh1NTU4J577gEwWxckMDAQ0dHRMBqNKCwslNRqtrYfHR1FYGAgLl++DJfLhQULFmB0dBS5ubno7OyEj4+P+OvZi0TTNOnoTKDKQnKBgYFSjbK3t1dqdVDw0L0UHR0tDdGio6MRGhqKFStW4OTJkyguLkZOTg4uX76MhoYGrFmzRlymtbW16O7uxgMPPID6+nqEh4dj165dCAgIQHh4OPr6+tDd3Y2Wlhb89re/RVlZGYKDg2Gz2bB7926kp6fL8/Py8lBcXCyB8WfOnEFDQwNiY2Nx2223weFw4PTp08jOzkZtbS3mzZuHQ4cOYcGCBXC5XOJ7X7hwIXbs2AFfX19cvHhRGuqlpaUJsCwvL0dubi6mp6cl9uPYsWOYmZlBWlqauDFyc3Nx8eJFdHR04JFHHsGpU6eQkJCA48ePw8vLC9nZ2QKOGQPC/hxVVVUCuK1WK4qLi6U/Tm5uLgICAtDS0oLh4WHceeed0vV6165dYt2Ki4vDzMxsA0cWzsrIyMDZs2cRFRWFzs5O2O2z3XZLSkrgdDrFTeTv748dO3bgoYcewqFDh+Dv7y98pb+/HwUFBVKnoqioCEuXLkVHRwcqKiqQk5ODpqYm6ZsyMTGBuro6Ed5LlixBT0+PFItk6XsqyqdPn5bkBVZanZycRHl5OVwuF7Zv347q6mpRkujuycvLg91ux9KlS/H0009jYmIC8fHxGB0dxdKlS/GnP/0JUVFR6OrqQkFBAebNm4f6+nocOnQIeXl5aGlpQV5eHi5fvozy8nJkZ2dj//79yMjIQEhIiFiDq6urERISgqysLJjNZlRUVKC6uhqpqakShJyRkSH8uKamRhTe3t5enDlzZk584rpAR05ODkJDQ6UJE+sXEAQwOwWAuCzU9Dvgig+ZxBRZmpdpEmaMgclkQkNDgwATujioAUZFRYm2bbVaRZOlKZ7aD4MfWZeAmqjRaJQUSi8vL1RXV6Ojo0PSrCggenp6xLICzNZWAID+/n6x0AwMDIilZd68eYiJiYHVapX2w/S3BgUFYWJitq8Cg1BV1wprnTCIMyYmBhMTEwLkWI2UAILCc2pqCqdOnUJBQQH8/PykcibBHeNpeKgZD0BByEBUAOJSYXE21nugVk7gwfvRLMkxjYyMSJCVy+WS4kpqgStmv/Cl9/b2dktnbm5uRlRUlJw/PpfxQLRGkNkzNgKAxNUwSIzz55gBCNgjgBsdHRVrhsFgEIanphASDFMwd3Z24uTJk2IyZ1o3C8YR5NjtdqlmS9eGWmadQp/XUptkjQ1N08RVR3dTaGioRMzTnErL3OTkbPXF4eFhybIiQGTMBwCpY0JBzfsDV6wrhw8fvi7QsXLlSqxZswYnTpzA0NAQ5s2bh7GxMeTn50s659jYGHbv3o24uDhs374dNTU1EkMAzFqtBgcHcerUKYn9YSt1VrT18vLCmTNnEBUVhdjYWPHr04pJCgsLk7RDrk1LSwtWrVqFc+fOSWYBK8HW1dWhv78fycnJiIyMxIkTJ+B0OqWUeFtbG4DZfhmZmZm4//77xWxOl255eTkyMzMlO6K0tBR9fX2wWq2IjY2Vdu//9E//JG4h9iX67Gc/iz/+8Y9i/WJgJXuHWCwW6TVCS0d6ejqsVivq6urkzOTk5ODMmTNITEyUIoIzMzOIiorC888/jwcffBAnT56UOiElJSUICAiApmmIiIiA2WzGW2+9JetbWlqK5ORk1NbWSmO/wMBAnDx5UrKukpOTUVNTg5tuugmVlZXo6uqSomcxMTE4ePAg7r77bgwPD0vGoNPpRFRUFHbt2oW77roLJtNsqfC0tDTpbxIeHo69e/ciICAA9957r8S+lJWVobu7G3l5eZiYmJCKruRx1PojIyNx9OhRKb7W19cn3ZQDAgLg5eWFpqYmSbXOzMyUMg89PT04ffo0kpOTER4ejmXLlmHXrl0CIFkIkK5gPz8/9PX1SRVp8ucf//jH+PrXvy6pxtwTAvLW1lYYDLP9nlpbW5GZmYnLly8jJycH7e3tqKmpEUugt7c3MjIy0NPTg8WLF0vFUcb2rFq1Cnv27MH8+fNFqaFydeHCBTidTillzswUlhpgNp7RaMTg4CDCw8Nx4MABrFq1CgcPHkRqair6+/vhdDoFeMfExODcuXPiTXgnui73CtvBk7kAs+l/1ChYhY0xDlarVeIQGGnv6+sLh8MhZk4GINJvzWwNMmn653gwyJSpkVKLIZpkMS4W75qenkZnZ6cE0k1PT2NgYEB8YGrVxqmpKSQkJMDhcKCjo0M6KtKFExQUJIXO6BYKDAxEQkICMjMzkZOTI2CksbFRNDWW/2WhNArz6elptLe3S88Mmuzpl2eHU8C9wyrdPnRjcR0DAgKQkZEhxX+Gh4flgLNQG4EWXRCs1spS3yMjI+jv7xfXByu90iVGzUC1mtAaYrFYRLtn3Al7u/j4+IhFQrWGESwAs0Kuvb0dra2tspdNTU0SbMvgWub483u0+NBSQW2dsTJcCzVrh0CPJkn2A6JbhnE/atddWnS47qGhoaLpsAEbA6gZaMrxjY2NScwGwR5wJSWccSMcJ92PTGm22WwiTCcmJhAQEIDBwUEBRQTYBOoMAqWAJ6BgXAddS6qbi/PidRSG10t9fX3Ys2cPYmNjsXr1anHH9vT0IDc3F21tbRgcHER+fj62bduGwsJCcTFSi6+srMSiRYvwwAMPYOHChbBYLNISvLi4GIODg1JafcGCBRJYyn483d3d4n4ZHh7GqlWrJNp+cnISq1evxsmTJ3Hq1CkkJyejo6MDDQ0N4p5avHgxuru70dHRgb6+PqSmpiI9PR02m03Ki99yyy0IDAzE008/7WY9iomJkRTEyMhIzMzMoKCgAJ/5zGewYsUKqTYZHByMX/7yl2hra5NAx9DQULS1tcHb21uyXVwul/CeoaEhcT3HxMRIk7jS0lIJDM/JyZF+Mn5+fhgYGEBXVxdqa2slndNoNOKrX/0qwsLCZL/YUbahoQEtLS1SvmDZsmXYvXu3NPG85ZZb8MQTT+DUqVNob2/HsmXLEBUVhQ0bNqCxsRHBwcFioWZZcX6WmJiIvr4+5Ofni9uBGUQFBQVSEXfLli3i3luwYAFmZmawbds2zMzM4OzZs5LVEhkZiaKiIhQXF6O6uhpWqxXNzc1obGxEVlYWKisr0dvbi0uXLiEhIUGszWpxLIfDgTfeeENSbgcHB1FeXo6BgQFcvHgRVVVVuOWWW+Dl5QUvLy+Ul5fDaDQiJiYGdrsdDQ0NEjSclZWFqKgorF69WlJMvb29sWPHDqxevVqa7XGsxcXF2LFjh1QzJR+64447pGEeXdOLFy9Ge3u7BGBfunQJVVVVePrpp92qyebm5qKwsBDp6ekYGRlBbW0tOjs70dXVhUOHDiEkJARr1qzBsWPHsHHjRqmPQtBUVlaGmpoaHDt2DLGxsdi5cydcLhdOnTolFle+B3TNE8jMha7L0nHXXXfBz89PLBrR0dGiPbPAFN0dakElFtiiuZauDcZ6sAASg/lUpksNn9H4fBGcTiccDoegWgo/+rSCgoIkNQy4kqFADZ59U+gWYpZLREQE2traJKgtODgYAQEBomU7HA4JbiVaZ8Gs5uZmjIyMCICJiYmBn5+fFHpiMCBdHpqmSYEtxrEQADHugVo9YzO4XtHR0WJyJXBigFtDQwPq6+uxYsUKTE5OStEoNZ0SuFLhUwUKXC9q6QQoDNRlZgvHQkHOOAiuM+dqMpnEnUQQxoJgdM0x3sHHxwcVFRUSf8IAL54NavEsMOfn5ycR8sywoctD0zSJpyHA4zoAQG9vr4AMWkXocqEZeWpqSrQQBpfSGsW4mB07dmD58uWYN2+eCB7WESE45HNoVRgcHJT6L2rZYbUUPNeVAMrpdEpwH/eBQJrWPTVYm5lOvr6+EvfB4FZalki0cgFXXFjMcjGbzTh58uR1WTpuv/12KWbEFtmss0HXXktLC8LDw6Fpmlgra2trkZubK1aJoaEhFBUVwW63Y9u2bairqwMA5OTk4OjRo+jo6EBQUJCUrzabzZIFwloGJ06ccAsYBIC6ujqJPwgJCUFDQwNaW1vFdNzb24u4uDjU1NSIokRgOjIyguLiYilfvmHDBpSWliI8PBzLly+HxWLByy+/DD8/P6SlpaGrq0tanvv6+uLy5ctITU0VIZ+SkiLB+CzUZLfb8cILLyA1NRUVFRXIzMxES0sLEhMTpdLl1NSUCMnm5mZkZWVB0zRJZW1ubkZfXx+2bt2KY8eOoaKiQsz0PGdFRUXo6+vD9u3bUVxcjMzMTMTHx8PPzw979uxBVFSUgHzy7YiICPj7+0sn3bS0NDQ3N4twZDnz6elpEfxTU1N48803sXHjRtTU1GBgYAAOhwPx8fEoLCxEb28v7rzzTpSUlGDLli148cUX3eqDVFRUSIErWkRHRkbQ19cHu92O7OxsKfJIvu/v74/Ozk709vZi1apVqK+vx+TkJNLT0xEYGIi9e/di8eLF4m5UgcBtt92GwcFBNDc345VXXsHWrVtFdsTFxeHChQtYtGgRrFar9I8KCQmB1WrFgQMHkJGRgYaGBqxatQpxcXE4fPgwXnjhBcTExODee+8VBb2rq0sEdWFhITZt2oTjx4+L+3B4eFgCx1NTUzEyMoKBgQHExsaitrYWBQUFApJaW1tht892o7VarViyZAmmpqZw6dIlBAUFSawOS8fTgmO323Hp0iX86Ec/AgCUlpbi6NGj2LZtG1599VVER0ejqalJCukxZbuxsRF/8zd/gyeeeAJeXl5ISEjAyZMnJXTiHXnE9YCOu+++WywNfIFV0zg1S2p09OHTNM04A0b9kwGSqPlSCFDrYybC9PRsjfmoqCi0trYKU/Xx8RHzKCP8Q0NDJS6DrhemP1LwMraAJlFaS1jBlFrgwMAAAEjw1fT0NPr6+kToM8uCGjcFr81mk/x7AAKovL295QCohbWYm62mKqoBuAQrjOmgSY8CeWxsTBodlZSUoK+vD4sXL5YUWpoLaUajoOWakcFyzaiV06oEXKk1Qa1OTZGmAKcgBWZ99uzboGmaZB4ww4VxMcBs7Mfly5cxPj6OqKgoSWEcGxuTQCmulbe3N1paWqRYVkhIiPR84ZhZ80OtVcKmfC6XS9aVqbUMKrXb7RgeHha3hdPpdMt2IYDZvXs3BgcHcfvttwuoZPYHi6bRXE0XFoEW4zto6SO4Y2YTrVBMbZ6YmJA1s1gskr3idDqlNwnjbrjOBOEMoiaoZ7wMn8F9Vt0rdDFNTU2hqKhozqAjLCwM69atQ1FREVatWiXMrre3FwaDAbW1tbh48SJuvvlmWCwWxMbGSoXNpUuXiqLS0tKChIQE4SUXL15ESkoKmpqaMD09LX59tlUHZtP2W1pa4HQ6pSFZSEgITpw4IS3keQ6YHbR69Wr84Q9/QE5ODkpKSuDn54fOzk4JjDxx4gRuu+02dHV1wW63i+W1oaEBmZmZGB4eRkREBMrKytDe3o6IiAgEBAQgLCwMXV1dcLlciIuLkxiAoqIiDA8PIz8/H9PT05LmTW2ehZlCQkLQ0dGBTZs2Sb+Ls2fPIi8vD/X19VizZg3Ky8ulCSRBcGRkJMLCwnDu3DnMzMxg7dq1AhwXL16MhIQEHDp0CPHx8Whvb0dtbS3MZrMUvlq+fDkiIiLQ0NCAyMhIlJaWIjQ0FMHBwTh16pTE37S3t0v6KWPD3njjDdx1111yJqmYhIWFoaqqCpmZmRgcHISfnx9KS0tx0003SfD75OQkCgoKcOjQIcTFxaGtrQ2tra347ne/i6eeegr+/v44c+YMMjIyMDEx26+quLgYixcvhpeXF7q6uvDyyy/jV7/6lbgZq6qqsHr1avT19cHpdErzv9zcXBw+fFjiwdgiIyYmBsePH0dMTAz27dsn7vX8/HyEh4fj1VdflRiP+Ph40fBDQkJEEQgICMD4+Dh8fHxw7Ngx3H777fjhD3+IhQsXIjc3V2IB1WKApaWlSExMlL46Y2NjqK6uxsaNG6WRG2OR2tvbceLECSks53Q6xbVIxTs8PBw1NTXIzs4WRWVoaAiVlZVIS0vDvn37RPFjlh575rAZX2xsLI4cOSIKIwPiGYzPz8vLy2EymZCfny/Wpnej63KveHl5SWMjAJLR4O3tLabvkZERGAyzbaenp6elo97ExAS6u7vR3t4uCK+jo0Pa1QOQrA1OTk05ZGAekbrZPNsXY2pqtoZ/T08PhoaGJEBSNfVQaE5MTCAsLExM/+r4WRacVf9YNIfmdwKbpqYmaZjD9E5aLcjkvb29ERoaKqWqWayGcSt9fX1i3eG/zPRhCiDdN3THcP0YE0NLAv9PIeb83+632dnZGB8fR3l5OUZHR9Hd3e2WUeF0Ot2Ej6+vrzBjumOoTauBwQRW/J2xC2ohNrUJnPa/RdGYeUKrBSuK0lrCvhxhYWFISUlBZGSkFIGLjIxEQEAAJicnJdqezc1YT4SWFI5btVoAEMFKnziLYDF2goyDgc/cG7qYGKdiMBjgcDjQ2NiIpqYm5OXlifWDwb4UbIxZYWCs83+7wxJ8EqCqabWezd6YWhsUFCQvPuNlWF2RYJ4ps1NTVyqYcj1oWSFIA9yLlTFXX632S3fk9VB3dzeysrLw5JNPShaByWRCTk4OLBYLkpKScNNNN6GjowPz5s3DmTNncO7cOYyOjqK6uhpvvvmmNLUqKirC1NSUNMLimc/NzYXdbseZM2cQFhYmVTTr6+tlzxMSEhAaGgqn04nMzExxLxw/fhxOpxOtra2Ynp7Gzp07ERsbi/Lycnnve3p6MDMzgzNnzqC/vx+tra2orKxEVVUVXC4XnnvuOUnx7OnpEQsa3QUJCQnYuXOnpLCfPn0ax48fl3o6GRkZOH36NMbGxiS+4M0330RFRQXi4+OxZs0aFBYWIi4uTgItm5qakJycjOnpaWzfvh2VlZW47777pLLk4OAgFi5ciJqaGrS1tSE2NlYsDNXV1UhISIDZbMaLL74Ig8GAAwcOoLOzEzExMThy5Aj279+PpKQk9PX1SdxVb28vkpKSJEj9rrvukqJidKu3t7ejr68PFy5cwJYtW1BfX4/GxkaxMiQkJGBgYADz5s3DwMAAAgIC4O3tjaysLLHY0MK5e/duaXnf3NyMO++8E8899xysViuOHz+O8+fPo66uThTeoKAghIeHi+J02223CR8NCgrC4sWL0dzcLHFnwcHBSExMlHbvAwMDwjOrqqrQ2NiIixcvIiEhAXfeeacb0C4uLoafn5+89+fPn0dLSwvmz5+P8fFxJCQkoK+vDw0NDcjJyUFPTw+2bt2Kffv2SWZeb28v2tracPr0aemqzuqmDNKkNbK3txdHjhyB0WiUOh/t7e2Ij4/Hli1bcPfdd6O9vR0pKSnyzi1cuBA+Pj7o6+uDw+HApUuXUFtbi5qaGnEBHT16FPn5+XA6nWhubkZ0dLTwtszMTACzXW2npqYQFRWFjIwM8WAEBQVJ76OLFy9icHAQDocD8+fPR01NjVgi342uC3QEBwfDz89P0symp6elyyWFKuMuaPKmqZa+e5vNJgyAFgK6QWh2Yp0I3kcNWKTVg4yamuLAwIBEwgOQbptMUaXAYO0M5sIzq4LFtiiwBgYG0NzcjLa2NgEd4+PjCAsLQ3x8PObNmydFboArJbWppdI9xBgNMlICj/HxcQFYDFSke2ViYrZ5ESOqaUZnACrXnlo15zI5OSmNqFyu2S6JtbW1aG9vl2BBBqLSVcAYCIIGxiFQ6PDlUJ9H8GCxWCRdjcCTJnnGjDDIls+gdUstbEV3Rl1dHQwGA8bGxtDY2CjoPSEhQdaBWR7UjnhuODcAAvbUeickMigCKH5XzUpRTa4MKOV9aSlgpUo2FiOA5Xqy3glBHi1fPF+0vBFEs/onwRpLfNN6QzcNU1sJ3hgfRJDKrCMG6DLNVtM08ZcTiHnGC6nVTgk2aS2aKzEY+PDhw+js7BQt/+LFi2JRy83NRX5+Pjo6OqQltsViQXh4OJYsWQKj0YgFCxZgYmICzzzzDGZmZrB06VKpQMpS0owLuHjxIqKjo6UZmdPpxJo1azA5OYnExETk5uYiLi4OAJCYmIja2lpxAXd0dEiAbnR0NBISElBSUgKDwYAtW7bgjjvuEGtWWVkZ+vr6cP/996OpqUn6Yxw8eFDAXVpaGlpaWvDQQw/B4XBg5cqViI2NRWJiIqamprBhwwbExsYiJSVF+sG4XC4kJyejuLhYUmWZAcZaEWlpaQCAgIAAlJeX46abbkJnZyeKiooECJSXlyMpKQkFBQUYGBiAj48P0tLSpEhiVVUVkpKSAMy6VsPCwuDl5YW7774bJ0+elFiPEydOID8/HyMjI9L1e8mSJWhsbMSiRYvQ0tKCmpoalJWVYf78+UhOTpYKmeHh4di+fbvELrhcLsyfPx8jIyNobGxEfHw8TCYTamtr8corr8But4trh27GmZkZZGdn449//CN6enoQGBiI1atX49vf/rYoUTabDSEhIRgcHMTJkycBAPHx8aioqEB5eTl6e3vxxhtvSH2fqqoqpKamore3FykpKUhPT0dAQAA6OjowPDyM7u5uHDp0CNHR0QJmGxsbpYw7rQVMlc7MzJRU7/T0dCnfzvRU8g2Ck/z8fAQHB2N4eBhxcXHYv38/6uvrRSaYTCYsWLAAly9fxsjICFpbW5GWlibl8JcuXSpKeVNTE86ePYv169fj5MmTWL58OY4dOybxefHx8WL9ZtBzd3e3FNwbGRlBUFAQ1q5di4sXL6KoqEjOT0pKCkZHR3H27FlMT09LYTsGx7LI5JYtW6TJJrMnVZftO9F1uVcefvhhCRKln5MZCnxRmClAMzQFBH3TAGRx+IJRWyZIiYqKkpeOgYzU4DRNk/gCTrS3t1eqfTJok2bl6elpsabQ108NnvEfFKr0q9MnTxcCrR2c38zMDCIiIkSA9/b2SrdFCghqygRSLLaimvcZQArMWnlCQ0PdqhyyMia1WRUUcEw029OvT5DAeIbu7m4UFhZK8SNaNSh4aN5lbA6/y2vVg8S58UVRC4pRS5+ZmRErFQOEe3p6kJqaCgBSz4PAhM9vbm7GuXPn5HzRrTF//nzExMSIJYeAi+vE4mB0N/G5alotXT0ENACkDgmLeTGtlAyc1hNalvg9s9mMpqYmHDt2DJmZmeJDVyvv8ryNj4+L24oCXM2aAiCBfrRw8HwEBQWJwCfgoyWRmUus3gtAsq8GBwcls0zNWuJ36bZjVs/w8LDUauHn6rkfHR2VuhVz5RObN29Geno6cnJyJGi7s7MTVVVVuPPOO9HX14fe3l4BCky3nD9/Pnp6elBYWIjHH38cBw4cgJ+fH/bv34/o6GjU19djwYIFiIiIkPT1s2fPiqKybNkyqQPBWiB0WQ0ODiI4OFhM88uXL0dLSwvKy8vhcDgwNDSEiIgIiRXz8/OTGhGpqamor6+X7tDNzc0YHR1FYmKiuOK8vLxw+fJlxMXFwWq14vTp07jzzjvR0NAAi8WC1NRUqdsRFBQkNYEqKyuxZs0a/OY3v8GaNWsQEBCAyspK+Pn5Yf78+di9ezdiYmIQEhICh8OBkZERXLp0Cffffz8qKyslLTM5ORnBwcESNM+O12wzPzw8jJ6eHoyMjCAyMhJBQUHIyckRl4mvry9+//vfw2Qy4eGHH8bFixeRk5Mj7SQYl/PWW28hNTVV1nR0dBS33HKLaNJqPN7w8LBknzidTixatAjj4+Noa2uTTCL21Tlz5gxuvvlm9PX1CRCIj4+Hy+VCamoqmpubpcjXiy++iMWLF4u7bXx8XOI/XK7ZNuxFRUVITExESkoK2trapHT9smXLUFlZibKyMtjtdixfvhyapmHfvn0wm81YvHgx/Pz84O/vj9bWVpFXo6OjSElJwaFDh/CpT30K3d3dcCrddsvKypCWloYjR47I+93b24vTp08jNTUVDocDGRkZGB4eRltbG5YtWyYgpbi4GNnZ2dIrh9krjDWsrq5GVFQUqqqqsGnTJnR0dEh5+u3bt4uLmHzh+PHjyM3NlRpUJSUlWLx4sYBs9sIhD1GLdU5OTiIyMlKsMy0tLZKJFh0dLYkEVVVVYlE8fPgwMjIycPDgwQ8+Zfbhhx+WRkcqMd2QpiHGHgQEBKC5udmt8qTNZhPNngKUEeJGo1F8tfShs7Q5U4yYVkgGwNRFjpHxI6zVYbFY0NHRIe4DNWugq6sLXl5eUtiLwooaId0PgLtWyKBJrgOLMqlCguuSnZ2Nzs5OiSdgJVIKRy8vLwQFBUm8AN0HrEPCNWLJWQoSzsPX11cKaTHgiy++v7+/FDI7duwYsrKyxMfOmg305asggG4PZmtQq1ZdQQSQdAvQ0sCAO4PBAC8vL/T394sPNCgoCC0tLWJVoeWEc+nu7kZXV5e4ApjlRPeaWguFqb0GgwFOpxMmk0nSqDkWNS6G8Qs0DVMzZXo1S5ADV+qKDA8Pw8fHR4p6MWZm165dGB0dxa233irBo4xp4bwIsmnVorsHgPi6h4eH3cARgTH7FqlgieeALiEfHx8JTKU1iRY2WsPofiPYYRE3VuXle0Ogxf/TysW9vHjx4pxBh7+/P7Zt2yZdTmkRiI6OFlN0b28vhoaGcOzYMdxzzz3SLXloaAhVVVVITEzExMQE4uLiEBMTg8LCQsTHx6OpqQkhISH4xS9+IabopKQkSXUmY2ZKf1RUFOrr66VIUmBgIHbs2IFbbrkFu3fvRkBAgHRuHRsbQ319PTo7O8U1Sq2vqqpKQG1ZWRmCgoIQFRUl87BarSgpKcHIyAiioqIQHx+PEydOSAaL8397EDErhynbFKrd3d3YtGkTGhoa4O3tjfb2dknTNxgMKC0thaZpCAgIwLx589Da2opbb70VZ86cwebNm/HWW2+JctbW1iapu0FBQTh8+DBuvvlmUQTZdZXfj42NRUtLi3QXPXnyJEJCQsRlu3XrVnR3d6Ovrw/Jycno6upCREQEOjo6pIgUa5js3LlTasmw4jCtoLyf0WiUdNuoqChcvHgR9fX1iI2NlTL4TBTYt28fbrnlFoyOjiIyMhKVlZViUYiOjhZrxIoVK6BpmlS+pULh6+srDQZZOyMhIQG7d+/GP//zP+PChQuor69HXl6etJFIS0uTrJz09HTk5+ejtLQUmZmZuHDhAi5evAij0Yi1a9eira0NCxcuxM6dO6XyJ985Ly8vtLa2orOzEw8++CA6Ojpgt9tRW1srli8Goff19eHYsWPYtm2buDiZehwbG4s333wTcXFx6O3tFQtUZWUlAgMDERgYiMHBQdTX12P9+vW4dOkSjEYjGhsbkZiYiKNHj6KgoEBcr11dXeK6rq6ulqKTRqNRSqi/+eab6OnpQVxcnOx5fX09gNmYLWbO+fv748KFC8jOzsbAwABqa2vn1CDyutwrkZGRwvTop2cHQApauiu8vLzQ29vrJogo1FRtzWq1IjU1FUlJSVLfnoicQYqM5qfWycAWamRq8JsazMqAOgp2BsbxHqwMybQmtr0PCQkRNxLTgQmCAIgLhC8H3Tg8MDSph4WFYe3atZIOzGwUaq5qyWs1jZIuIwbtUnNgFTkCLwoltfAVU5PVwEX2gigpKRHNlmZ6Wh0Y3MgAW6ZokoaHh8W0NjMzIz1oGAsAQGqhMHiSWj59oWq5bcaqEOzQhJmQkID09HRkZ2dLpVgicRYtYzYQcKVjLwCJ7laDlFnQi+vCokJq+qifn5/4grm/BMjMjCKYZs8XNmXi2Oi+o4CntUeNkSBgU10wZFBq6XTuJ61GLAlOYcU4FrqD+D6pYB24UvpdrczK/aN1g/+qgEiNG5qryZRks9mQkpICf39/VFdXw+l0StbGk08+ieLiYhw9ehTNzc3SHM/lcknPGIvFIkDz/Pnz6O3tRXZ2tsTDNDQ0YNGiRYiNjZWy1WFhYWhqaoKXlxeioqKkQitB5tmzZ9HT04O9e/cCAA4dOoSIiAiMj48jOjoaVVVVOHXqlLiGAKCnp0eexxozDQ0NkobJeg6hoaFITk7GunXrsHr1akRHRwvAsFqtUgiM1rLe3l6xorBgWUREBFatWoXx8XFUVlaioaFBms/xPGdnZ8PhcCAlJQUulwv79u3DmjVrMDExgdbWVil/HhkZiYiICKxZswarV6/Ggw8+iHnz5qGxsREJCQkIDAxEa2srdu3ahfj4eFRWVqKpqQnh4eGS3t7Y2IiIiAgEBQWhvb0dBw4cQGtrKyoqKlBRUYGzZ88iNTVVzlpLSwu6u7vFVXb//fcjISEBvr6+YoGx22cbrhmNRrzxxhvibqqvrxeXZ3R0NF555RU0NTVJlVm6Nvv6+hATE4NVq1ZJTZykpCR86lOfkmaAERERyMrKkhTltLQ0jI+P47bbbkN+fj5MJhMqKirw+OOPY/fu3TCZTAgNDUV/f78AFdb8uPXWW9HW1oY9e/agpaUFr732GrKyspCVlYXly5ejvb0dycnJaGxsRFxcHBwOB4BZhWXp0qXw9/dHbW0tUlNTMTg4iJSUFMTGxqKjowOtra2SxXjq1CkBMZQTvr6+aGtrQ3JyMg4dOiQuDLqZ4+LikJSUhO7ubuzdu1cy2FRetGTJEpSUlGDdunXiSma5h5mZGWmMyOxCgtT/+Z//Ed47PDyMN998E/39/dJvKDw8XCxrU1NTWLJkCWpraxEQECCK0bvRdYEOaoyMbqVAYA0Gmt9ZqIgmZdWfTabHGAZq0Uwbo6bX398vucM0bauFn9R4ELpWxsfH4e/v71bqmQKaPn5qcSwoRUDhcDhEcHt5eUndeWbHBAQEIDQ0VKw23DAG56kBdxTqDOak/5hryHVT14ZCkCCDmgmzICgIKbBYtpwVNenWIlij339kZARjY2PCHJnWxJLnBDNqXQaiXtbtoMVJLa/OrAICDwo2asxquXOCRoINNeWVqbkMRAauVBGlBqv2fqGVAIBbLQ7ugepioZAmuOLaM3BYLUbHLovsGaTWoaH1AABKSkpgsViQkpLi5p5R42r4PcYTcawEpwRCdJkxZomuPc6F510FLwQF6u9MmaY1h/vE86BWlmWFV+45LUT8neuhBpNeD9Hat2DBAmRlZcFiseATn/gE7rvvPtx+++3w9fXF5s2bERcXJynUZWVlePLJJ2E2m6UTbHZ2tlR33bNnDxobG+Hr64uEhARZU4PBgDfffBPl5eWIjIzEpUuXpNFWZWWlmKEXLVqEwsJC3HfffcjIyEBaWpqc73379om1sLOzUwTF8uXLUVdXJ8UAlyxZgri4OBQUFCAoKAhpaWm4ePEiVqxYgfr6epSUlGBgYACTk5PIz8+XMxsWFia8xs/PT5Qg7sPBgweRlJSEoaEh9PT0wOFwICYmBu3t7Vi7dq0AUVqLmHV11113SaO12NhY+Pn5ITU1FUajEYmJiTh48CB27doFg8GA+vp6aVA3OjqKvLw8nD9/Xrq+RkZGYteuXZIJVFVVheeffx4ulwuHDx8Wi01YWJhkelCT5rvOjDu6t48ePYru7m5JsWSGRFhYGIKCgrB37150dHRg6dKlSEpKQltbm1SGZvwczz+L91VVVYkmb7VaUVFRgddffx3AbOZSRkYGSktLERERgV27dsHpdGJ8fBzV1dXo7OyUqp1//OMf0dvbKwDP399fLFBsFldbW4uUlBSEhYUhNDQUW7ZsQWdnJ1JSUlBZWYnOzk64XC4UFhZK/Fd4eDjWrVuHU6dOwW63IyYmRhqy7d69G7W1tVizZg1iYmJQXV2N5cuXS4Gx+Ph4XLhwAa2trcILqdQ7HA6pZ8JYOVbb3r59O5qamrB582YcOHAA3t7eaG5uRlFREbZv3w4/Pz8Jti4vL0d6ejoaGxsxb948BAUFob6+HlarFXl5eWhubpamfvQO3H777UhJSUFLSwuCg4MlfZcdwktLSyXVnJbmd6PrAh3UHAFIG3dGp1NQUqOksKEgowXAaDRKgy/6wAkq/P39ERoaKml0LS0tGBwclHgJms3Z9I2BVjRDMt2VAjokJESieWmupnCl0KPZlYKD/S6MRiMcDod0MWQL+6SkJMTGxorPnNokcMWtMj4+jqSkJOk66XA4xMLC7Ai1WioFmlrDhG4lz9RPImTGtdBKwWqtFED8YY2PgIAASYc7cOCA237RukGQQEuOmjZMIED3AYUrARWj3hnLQOBDQR8QEIC2tjYR4LS0qC4TAi7eOzAwUMArAQfXh3E31JKYMUPrBC0XHBNROJ9JodPf3y+BzAQYtBSpgaC0tPT09CA2NlZANserxo4wRoixJxT23EtmYdHSRQbD7Beur9PpdIsr4fhpIeSZACAxJdR2VDcUgQ1dgjwzBL0UkBwH140g6XqI6a0s3pSWloaXX34ZhYWFaG5uFqBHZePUqVPYsGEDHnzwQcTHx6O3txdr1qzB4OAgfH19cerUKaSmpsr+dnV1ITc3V6xww8PD6OjoQHt7Ozo7O5GamoqIiAiEhoZKhdrq6mosXLgQL7zwAtra2nDixAnYbDasXbsWmqZh8+bNWLt2rbT5rqysFEsEqwa3t7djZmZGXAHDw8O45557MDQ0hIyMDBQUFEi1SrZMDw0NlawJmrZXrlwp3W5bWlqwceNGpKWl4ejRo1i3bh1sNhsOHz6MkJAQHD58GMHBwYiJiZGaSO3t7YiLi8O+ffuwfv16JCcnIzAwEOPj47j55psRFBSE3/72t4iNjRU+Sqvn/v37UVBQgMbGRuTl5aGkpASZmZkSK+Dj4wOn04kvf/nLmDdvHlpaWrBp0ybYbDYcOXIEhYWFYoVjMgABR09Pj1RlPnXqFLKzsxEdHY3e3l6kpaUJYMrMzBTFo6WlRTrGMn2dwY4szLZq1SqprtrS0iLWWqfTKdl8BQUFor3n5eVh8eLF+NSnPoXR0VEsWrQInZ2dKCkpQXV1NW6++WZMTk5i06ZNiIiIwOLFi8XSZrVaZR4EiKwaWllZiejoaBw6dAgxMTGIi4tDU1MTbr/9dgksZ0kGLy8vHD58GACQmpqKrq4u3HHHHairq0NdXZ1YrMrLyxEdHQ0fHx/U1dWJ0jI2Nobz58+jvr4eixcvlmDs8PBwDA4OoqSkRPhWc3OzuGtuueUWvPnmm9K4tKqqSnjpU089hU2bNqGrqwvJycnYs2cP+vv7cfvtt6OnpwfNzc1YvXo1EhMT0dLSgiVLloir7NixY4iJiRHPAd3uk5OTYg0jPpgLXRfoUIM3ybzoqyaTU5kqtWEKUAIOaoeM3GfKKQtZBQQEIC0tDSaTSdqh04Wjanomkwnt7e1ob28Xc3ZPTw/GxsYQHR0t2iVfJqbVAlcKU6maPefg7e0tEd80wdENMzk5KSVgmWngSWazGatWrRJLCwPd1EwAPtfhcIgwZ9wLa11QEFLQU1gTVFFIUVNWrSdMbaU1iQGd+fn5SExMxL59+wBA4jFoaWGAJi0njFtR+3ewgBiFMjALPsbGxqQKKoUX6/ozapq9VThWtT6Ij4+P2/4yDZRpp0yP5vox6JEVHjkfpgHzzFGAMn6D6ZdGo1GCU5lOOzMzI8CIoItZJvRrFhQUyD6y6qd6tnkPxgQRqBMAEdwwdZp7yjGzvgffM75TBCgMMCbgIGinq4Wgi5Yz1cLCfeR7qVZYVV0qPEPX617p7OwUkM1nBQYGoqurC9nZ2QgLC0NLS4sA8JiYGHh7e+Ps2bMCUl588UWMjIxg5cqVUrTJx8cH7e3tKCgoQGFhIRYsWICkpCRs3boVsbGxyMnJQWZmJsrKytDS0gJfX18sX74cp0+fFtcD/e4rVqxAQkICampqcMcdd6CoqEgKJcXFxYlyMjw8LBH++/fvR1hYGGpra9HV1YXi4mKkpqZKgzO6SSoqKqQfT3t7u5QO5zkwm80YGBhAfX09+vv7pVhVWFgYDh06hMjISAkSjY+Pl7Lx4eHhKC8vR1BQkFRfbWtrk7oe7e3t2L17Ny5evIiYmBhkZWUhKCgIR44cQW9vr9yjqqpKzu3dd9+NU6dOCdDt6urC5s2bUVNTI5kdjz/+OFwuF7Zs2YLs7GxkZmYiIiICXV1douQxtba5uRkBAQGIjY2FwTBb8j8pKQlnz56F0+nEkiVLUFRUBH9/f8THx4uLmLET09PTWLFiBTo6OhAREYGZmRn86U9/QnBwME6fPo0HH3wQ09PTaGpqErf8zMwMLl68iF27dqG5uRmVlZU4dOgQjhw5IkHZXV1dSElJkfcmJSUFJpMJwcHBEtAbGhqKsbExKdfO2MPAwECJ5zl79izy8/PR3NyMgYEBzJ8/H9XV1bBYLFi0aBG8vLwk9banpwfz589HdHQ0zGYzzp8/jzVr1iA/P18avPX392N4eBjt7e2wWCyihDKg1OVyobi4WOrvMO2fFvqQkBDk5+dL6fH9+/dj48aNyM3NFff20NAQOjo6sHnzZjQ3N2NwcFDqmMTGxkpMzsTEBA4ePCidjcvLy4UPbdy4EY2NjQK8jhw5IgHLjHliVuNc6Lq7zDKWQzUjs/ANg/2Yi83sDDJtMj36lug6UNNbiRQDAgIQExOD4OBgOJ1OtLS0oLe3Vxh0RESEaHdRUVGioTMbZWRkBB0dHQJOyBioAVOoU+Om7xxwzxZgPQu6Wlh3QzVZA3ADFCtXrhQ/G4MFWTqWYI2V5AhKzGazZB0AEEHLYk+sgqmmZVLA0Q8NXBG6qqBVU0npPwwODsahQ4ckk4j+dAJACmweeNaqYCAi42AAyAtKNwe/R3BE8GK1WqW/gGrBoUAlUiaAIgBTXUssiEVLD4ErU1EJdgCI0KRWTEsUgRUBL834BAcEJGq6b3d3N2pra6WjMd1iDBbls/nDGBDOh8Qzz6Bilp4meOOeMTuM/YJovqZ7iOed9yYQVMdO4KCeF4I6AlI1BVu1tNEyqY59LhQTEyPxLp2dnVLZMTg4GBUVFbh48aI0ZnP+byOu+vp6BAcHIyoqCt7e3li5ciXq6urQ0tIiMV4sPf7Tn/5U4g4qKioQEREBh8OBPXv2YP/+/QgPD0d0dDSKi4tRXl6OnJwcbNiwAfPmzYOmaVIf4sKFC7Db7SgrK0NjY6N0iGUQcXl5OZYtW4aKigokJSXBarWiqqoKa9aswdq1a5GVlYWmpiZkZmZKimZ/fz8yMjKQkZGBz372s+jo6JC28DMzMwJYTp8+jampKXz1q19FX18fWltbpZxAfX29BL8uWbJE4huY5UOwyM7LTU1NiIyMREhIiChFwcHBOH/+PEpLS5GTk4Pa2lqJ08jOzpa+TCwSFh8fL/WGdu7ciaGhIcTGxuJrX/saCgoK8IMf/EAyFEtLS1FZWYnu7m6JA2HBsUWLFsFisaCyslJ6zzAoOjw8XIDy+Pg4iouLcfnyZUxOTqKmpkbqpNTV1aGzs1OsBhbLbAE5i8WCAwcOICkpSfrMcO8tFgu2bNkisVEELN3d3fj1r3+NmJgYDAwMIDMzExMTE3A4HNIXqL29HdXV1ZiYmEBMTAxaWlokNZ+xaZcuXYLT6ZSCZAymvHDhgsgPyhqe1ZqaGnEDpaWlISMjQ9LGz5w5I6naJpMJaWlpqK2tRUREhJSIsFqtuHjxogDWjIwM1NfXS8sKBjuzaeiOHTtgt9vhcDjEChQfH4+JidkuxSMjI4iLi4PdbkdaWhqSkpJQWFgoQfJUgIuLi/HSSy/Bx8dHvAMEJ+TdQUFBCAsLE8tpXl6e1O6aE2lzJAD6j/6j/3xMf+ZKsbGxWlRUlGa327X4+HhtwYIFcg8/Pz8NgBYVFSWfJSQkaAA0q9V6zefGx8e7/e7j43PN63x9fTUAms1m0ywWixYcHOz294SEBG3lypXyu8Fg0AIDAzUAmtFofNt5m83mq8bq+XtCQoIWEhKieXl5uX1uMBiuul9QUJCWlpameXl5aQ6Hw+1v2dnZWkxMjPyenp4uz09JSXG71tvb+5pjNBqNWlxcnBYVFaVlZ2df9XyuH/dAfd61fkJCQmQ+ycnJV/2d+xYeHq4FBwdrycnJWnR0tGa1WjWHw6FZrVYtMjJSCwoK0hYvXqwB0BwOhxYaGqoZDAbNbDZrkZGRGgAtKytL8/X11Ww2m6xdbGysrFNMTIxmsVjk2StXrtQ2bNigmUwmLSUlRcvJyblqfPPnz9dCQkLkjOTm5mo2m00DoNntdrdr8/PzNbPZrIWFhWlGo/GqNed3cnNzNQCy32/3k5OTI2vm4+Mj5+2dfnx8fLT4+HgtJCREs1qtWnR0tOw/n5eRkaFZrVZZC97XYDBoSUlJGgAtLCxMzkZSUpLb+xUcHCzvIs8Rf+e7EBISIutksVjk70lJSVp2draWnp4u91P/Pheac8qsTjrppJNOOumk0/uh63av6KSTTjrppJNOOr0X0kGHTjrppJNOOul0Q0gHHTrppJNOOumk0w0hHXTopJNOOumkk043hHTQoZNOOumkk0463RDSQYdOOumkk0466XRDSAcdOumkk0466aTTDSEddOikk0466aSTTjeEdNChk0466aSTTjrdENJBh0466aSTTjrpdENIBx066aSTTjrppNMNIR106KSTTjrppJNON4R00KGTTjrppJNOOt0Q0kGHTjrppJNOOul0Q0gHHTrppJNOOumk0w0hHXTopJNOOumkk043hHTQoZNOOumkk0463RDSQYdOOumkk0466XRDSAcdOumkk0466aTTDSEddOikk0466aSTTjeEdNChk0466aSTTjrdENJBh0466aSTTjrpdENIBx066aSTTjrppNMNIR106KSTTjrppJNON4R00KGTTjrppJNOOt0Q0kGHTjrppJNOOul0Q0gHHTrppJNOOumk0w0hHXTopJNOOumkk043hHTQoZNOOumkk0463RDSQYdOOumkk0466XRDSAcdOumkk0466aTTDSEddOikk0466aSTTjeEdNChk0466aSTTjrdENJBh0466aSTTjrpdENIBx066aSTTjrppNMNIR106KSTTjrppJNON4R00KGTTjrppJNOOt0Q0kHHDSSDwSA/v/vd7+Tz5557DitWrEBoaChsNhsSEhLw6KOPoqmpaU73PXToEFauXAkvLy8EBQXhwQcfRGdn5wc+/uPHj2P58uXw8vJCREQEvvrVr2JsbMztmvc7F08aGhrC448/jrVr18Lf3x8GgwGHDx++rnv86le/QkZGBmw2G9LT0/Hkk0++p7G8H2pubsb27dsREBAAf39/bNu27Zpr0t/fj8ceewwhISHw8fHBhg0bcOnSJbdrjh8/7naWampqbtQ0dPoQ6O34AgCUlpbi5ptvhq+vL4KDg/HII4+gr69vTvedy1m6EbRjxw4UFBTAbrcjPj4e//zP/4zp6emrrpsLf5kr6XzjI8w3NJ1uGAHQHn30Ue3UqVNaV1eXfP7Tn/5U+4d/+Adtx44d2uHDh7WnnnpKi4mJ0WJiYrTBwcF3vOfRo0c1s9ms3Xrrrdobb7yhPfvss1pcXJyWnZ2tuVyuD2zsxcXFmt1u17Zu3aodOHBA+9WvfqU5HA7t3nvvdbvu/czlWlRfX68FBgZq69ev17Zt26YB0A4dOjTn7//yl7/UDAaD9s1vflN76623tG9961uawWDQfvGLX1z3WN4rjYyMaCkpKVp2drb26quvajt27NBycnK0pKQkbXh4WK6bmZnRVqxYoUVHR2v/8z//o+3Zs0dbvXq1FhwcrDU3N8t1g4OD2qlTp7Rvf/vbGgCturr6hs1Fpw+e3o4vtLa2aiEhIdrKlSu1PXv2aC+88IIWExOjLVu2TJuenn7He871LH3YtHfvXs1oNGr/3//3/2lvvfWW9uMf/1iz2Wza3/3d37ldN1f+MlfS+cZHl298ZEHHBykwPyoEQPvud787p2v37t2rAdBeeumld7xu/fr1WnJysjY5OSmfnT17VgOg/fznP38/w3WjO++8U0tJSdEmJibks2eeeUYDoBUVFb3jd+c6l2vRzMyM/H///v3XxTwmJye10NBQ7dOf/rTb54888ogWHBzsNpf3Sk8//bT2btj9pz/9qWY0Gt1e8rq6Os1kMmk//vGP5bMdO3ZoALS33npLPnM6nVpgYKD2pS996W2f/XECHR8nvvCVr3xFCwgI0Pr7++WzI0eOaAC0l19++R3veb1n6b3QmjVrtIceeugdr8nPz9dWr17t9tn3v/99zWKxaO3t7fLZ++Ev1yKdb3x0+cZHwr3yu9/9DgaDAUePHsU999wDh8OBJUuWAADOnj2L7du3IyYmBl5eXkhPT8c3v/nNq8xua9euxcqVK3HgwAHMnz8f3t7eyMnJwauvvnrV81544QVkZGTAbrdj3rx52LlzJ9auXYu1a9e6Xdfd3Y3Pfe5ziI6Ohs1mQ0ZGBn75y19+aOugUnBwMADAbDa/43WnT5/Gxo0b3a5buHAhgoODr5p7fX09PvnJT4rrIz8//5rr40mTk5PYu3cv7r33XlgsFvn83nvvhdVqxWuvvfaBzOVaZDAYrvs7pFOnTqG7uxuf+tSn3D5/8MEH0dvbi+PHj7t9/stf/hJ5eXmw2+0ICQnBo48+OmdT9jvRzp07sXTpUqSkpMhniYmJWLFihdva7dy5E1FRUVi3bp18FhAQgNtvv/1d1/j/In3c+cLOnTtx6623wuFwyGerV69GXFzcu56H6zlLH9Z8mpubcfHixWu+f5OTk9izZw+A989frkU63/jo8o2PBOggffKTn0RiYiJeeukl/OAHPwAANDU1IT8/H08++ST27t2LL3/5y/jtb3+LRx555Krv19bW4stf/jK++tWv4pVXXkFkZCTuueceN9/V/v378clPfhIZGRl45ZVX8Pjjj+MrX/kKqqqq3O41ODiIlStXYvfu3fje976HN954A7fffjv++q//Gv/1X//ldq3BYMDDDz/8vuc/PT2N8fFxlJSU4Ktf/SqysrKwadOmd/yOyWSC1Wq96nObzYbLly/L783NzViyZAmKi4vxH//xH9i5cyfmz5+Pu+++Gzt37nzHZ9TW1sLlciEnJ8ftc7vdjuTkZJSVlX0gc/mgqbS0FACuGnd2djYAuI3761//Or7whS9gw4YN2LlzJ374wx9i79692LJlyzX9z9c7Ds8xcBzqGN7puqamJgwPD7+vcfyl0seRL4yNjaG+vn5O5+ZaNNezdD3zuV56u/cvMTER3t7eMof3wl8+TNL5xodL1696foi0fft2/Pu//7vbZ3fffbf8X9M0rFixAv7+/vj0pz+Nn//856JFA0BPTw+OHj2K1NRUAMD8+fMRGRmJP/7xj/jmN78JAPjud7+LrKwsvPrqq4KGc3JysHDhQqSlpcm9fvazn6GxsRGXLl2S+23YsAFOpxPf//738dd//deiuZtMJphMpvc9//DwcPT29gKYtVYcOHAAdrv9Hb+Tnp6O06dPu33W2NiI9vZ2N63he9/7HjRNw5EjR2TNNm3ahObmZnznO9/BHXfc8bbPIGoPDAy86m9BQUHXRPXvZS4fNL3duIOCgtz+3tDQgB/+8If47ne/i+985ztyXVpaGlauXIldu3bhzjvvBDALpjRNk2tmZmYAAFNTU27PMJlMcr76+vredu36+/vdxpuQkHDN64DZYDFfX993n/j/Mfo48oX+/n5omva256aysvIdvz/Xs3Q98/E849qse97tc4PBIHN+J74RGBgof38v/OXDJJ1vfLj0kbJ03HXXXVd9Njg4iL//+79HcnIybDYbLBYLHnzwQWiahurqardrU1NT5cUBgLCwMISFhUm07/T0NM6dO4e7777bzfy2YMECJCYmut1r7969WLJkCRITEzE1NSU/mzZtQm9vrxvSnJqawm9+85v3Pf+DBw/i5MmT+M1vfgOn04mNGzfC6XS+43e+/OUv48yZM/j2t7+Nrq4uVFRU4MEHH4TRaITReGV79+7di1tuuQUBAQFXzae4uBiDg4PCQNSfGzmXPxft378fMzMz+OQnP+k29yVLlsDPzw9Hjx6Va9evXw+LxSI/jz76KAC4fWaxWHDkyJE/13T+z9HHnS98mDTX+TQ0NFx1xo8ePYpnn33W7bPk5OQ/84xuHOl8473RR8rSERkZedVnjzzyCA4cOIB//Md/RH5+Pnx8fHDmzBl84QtfgMvlcruWyE4lm80m1/X09GBychJhYWFXXRceHu72e1dXF2pqatysBSpRi/8gKS8vDwCwbNkyrF27FikpKXjyySfx9a9//W2/88lPfhIVFRX40Y9+hH/5l3+BwWDAfffdh1tuucXNvdLV1YVnn30Wzz777DXv09vbi/Pnz7v5BQG4aVsquib19fWJ2fH9zuWDJnXc6tmipsLz0tXVBQBuvlOV1L1+6qmnMDQ0JL+//vrr+P73v4+zZ8+6fSc9Pd1tHG+3dqom807XqfP5uNHHkS84HA4YDIa3PQ/XmpNKcz1Lc51PVFTUVWf8s5/9LKKiovDd735XPrPZbG5jAK7NN/r7+2UO74W/fJik840Plz5SoMMz+MflcuG1117D9773PXz5y1+Wz99rrnlISAgsFoscFpU6OzsRFxcnvwcHByMsLAw/+9nPrnkv9XB8GJSUlISgoKA55VL/0z/9E77+9a+jrq4OYWFhCA8PR2ZmJlauXCnXBAcHY9WqVfj7v//7a94jKioKISEhV70EAESbpK+T5HK5UFdXh3vuuecDm8sHSWRWpaWlbsyD2ltWVhaAK4Gu+/btu+YLqprqPfedwG7hwoXvOA7PteM4OAZet2/fvmteFxcX95Eykd5I+jjyBW9vbyQkJLztuVmzZs07fn+uZ2mu87FarVedcT8/PwQHB7/t2Vffv2XLlsnnDQ0NGB0dlbP/fvnLB0063/hw6SPlXvGk8fFxTE9PX4XCPQvozJVMJhMWLlyIl19+2c2/VlRUhPr6erdrN2/ejIqKCsTFxWHhwoVX/fj5+b2nMcyVSktL0dvbO2dzpY+PD+bNm4fw8HDs3bsXFRUV+NznPid/37x5M0pKSpCdnX3N+dhsNvj5+V31OTDLcDZv3ow//vGPbi6Xl156CePj4+8YD/Je5vJB0bJlyxASEoLnn3/e7fPnnnsOQUFBWLFiBQBg48aNMBqNaGpquubaeJrYr5fuuOMOnD59GnV1dfJZQ0MDTpw44bZ2d9xxB1pbW91MrIODg9i1a9e7rvHHiT4ufOGOO+7AG2+8gYGBAfns+PHjaGxsfNfzMNez9GHOJy4uDnl5edd8/ywWC7Zs2QLg/fOXD5p0vvEh058jT9eT3ilveOnSpVpoaKj2zDPPaG+88YZ29913a8nJyVflXa9Zs0ZbsWLFVd+Pj493yyXft2+fBkDbunWr9sYbb2jPPPOMlpiYqEVERGjr1q2T65xOp5aRkaGlpaVpTzzxhPbWW29pu3bt0n74wx9qd9xxh9szTCaT9pnPfOZd54m3ycdfsWKF9qMf/Uh7/fXXtQMHDmg/+clPtOjoaC0mJkbr7u6W65555hnNZDJphw8fls/Onz+v/cu//Iu2Z88ebc+ePdq3vvUtzWq1XlV8p7GxUQsPD9cWLlyo/e53v9MOHz6svfrqq9o//dM/aY888si7jv3ChQuazWbT7rrrLu3AgQPar3/9ay0wMFDbvn37e5rLoUOHNADa008//a7P3r17t/anP/1J+853vqMB0L73ve9pf/rTn7Tdu3e7XZecnKzddNNNbp898cQTmsFg0L71rW9phw4d0v7hH/5BMxgM2n//93+7XfeNb3xDs9vt2te+9jUZ+9NPP6098MADbvnvnjSXfPvh4WEtOTlZy8nJ0Xbs2KG99tprWm5urpaYmKgNDQ3JddPT09qyZcu0mJgY7YUXXtD27t2rrVmzRgsMDNSampre9tn/V+t0fNz5QktLixYcHKytXr1a27Nnj/biiy9qcXFx2pIlS9yKgx0+fFgzmUzaM888I5/N9Sxdz3w8aS51Ot544w3NYDBof/VXf6UdOnRI+8lPfqLZbDbt8ccfd7turvxF5xt/+XzjIw866uvrtc2bN2u+vr5aaGio9oUvfEF7/fXX3zNz0TRNe/7557W0tDTNarVqWVlZ2iuvvKLl5+drd955p9t1fX192le+8hUtISFBs1gsWmhoqLZy5UrtP/7jP9yuA/CuLx+vuxZz+epXv6rl5ORofn5+mo+Pj5aZmak9/vjjWmdnp9t1XCd13pcvX9ZWrFihBQQEaHa7XSsoKNB++9vfXvP5zc3N2qOPPqpFRUVpFotFi4iI0DZs2KD9/ve/f9exa9psYaKlS5dqNptNCwsL07785S9rIyMj72ku3MM9e/a863Pj4+M1AFf9xMfHX3XdmjVrrvr+k08+qaWmpmpWq1VLSUl526Jpzz77rLZkyRLN29tb8/Hx0TIyMrQvfOEL71jBcS7MQ9NmQd+2bds0Pz8/zdfXV9u6datWX19/1XW9vb3aI488ogUGBmpeXl7aTTfdpF28ePEdn/1xBB0fB76gaZpWUlKibdiwQfP29tYcDof20EMPaT09PW7XvJ0gnutZmut8PGkuoEPTNO3ll1/WcnNzNavVqsXGxmrf//73tampqauumwt/0fnGXz7f+EiAjj83NTc3azabTfvHf/zHD/U5ALR/+Id/0CYnJ90q5n0c6Rvf+IaWnZ39sV+H90qTk5Pab37zm//ToOPPTTpf+OiRzjfeH30U+IZB0xQn5seAxsbG8NWvfhUbNmxASEgI6urq8O///u/o7Oy8KnDogyY1IO7pp5/+QAqK/aXSihUr8IUvfAEPPPDAn3sof3F0/PhxrFq1Sn6vrq5+2wh6neZGOl/4yyCdb7x3+qjwjY9U9sqNIJPJhI6ODnzxi19Eb28vfHx8sGrVKvzpT3/6UBkLALfMkPcbZPSXTidOnPhzD+EvlvLz893OUmxs7J9xNP83SOcLfxmk8433Th8VvvGxs3TopJNOOumkk05/HvpIp8zqpJNOOumkk07/d0gHHTrppJNOOumk0w0hHXQo9PDDD7s1zmloaIDBYHjPRYc+TGLbb/6Q2tvb8Y1vfAMLFy6Ew+FAaGgo1q9f79YHgPTwww+73YM/X/nKV666dnp6Gj/96U+Rk5MDu92O4OBgbNiwAe3t7dc99oMHD+JTn/oUkpOT4eXlheTkZPz1X//1NStCXmt8BoMBFy9evOra1tZWfOYzn0FERARsNhsSExPxjW98w+0as9ks9/j1r389p/E+/PDDOHz48JyuvXz5Mj772c9iwYIFsFqt76vFtk4fTdL5hM4nrkU6n5gbfewCSa+HIiMjcerUqY90EyO26iYVFRXhD3/4Ax555BEsXboUExMT+MUvfoG1a9di586duO2229y+HxoaelVr+2sFzj344IN488038c1vfhMLFy7EwMAAjhw5clWfi7nQk08+ieHhYXz7299GUlISqqur8d3vfhdvvvkmSkpKrirb+/DDD+Ozn/2s22dq509glvGvWLECiYmJ+M///E+Eh4ejoaHhqtLrJ06cQFtbG7Zt2/aOY9y5cyd8fHywfv16+czlcuFnP/sZHnvsMbcSxyoVFRVh9+7dUuX11KlT77oeOv1lk84nrpDOJ3Q+8a70Z0nU/YjSQw89dFXhmI8qscCLZ6GY/v5+bXJy0u2zyclJLS0tTVu1apXb5w899JAWHR39rs964YUXNJPJpJ07d+59j1vTNK2rq+uqz44cOaIB0H7zm9+4fQ5A+9a3vvWu99y0aZO2aNEibWJi4l2vra+v1wBov/rVr972mrKyMm379u3a3XffrW3cuFF7/PHHtQULFmj/+q//qo2Ojr7t99RKkd/61rfmVABIp78s0vnEtUnnEzqfmAt9bN0rBw8exPz582G325GcnIynnnrqqmuuZTZ9+OGHERMTg3PnzmH58uXw8vJCeno63njjDQDAT37yEyQkJMDf3x9bt25Fd3e32z1/9rOfITMzE15eXggMDMTChQvx6quvfmDzcjgcMJvdDVhmsxn5+flobW19T/f8xS9+gTVr1mDBggUfxBARGhp61WeLFi0CgPc0xtraWrz55pv40pe+9LbdMq+XMjMz8ac//Qm33norDhw4gD/84Q945ZVX8PWvfx1eXl5v+z2j8WP7Sv2fJJ1PzJ10PqHzibnQx3Lm5eXluOWWW+Dl5YUXX3wR/+///T/89Kc/xcGDB+f0/cHBQXz605/GY489hldffRVhYWG4++678bd/+7c4dOgQfv7zn+OnP/0pDh06hC984Qvyveeffx5/+7d/i/vvvx+7d+/G888/j+3bt0sLYuCKD3auvsG50MTEBE6dOoXMzMyr/tbV1YWQkBCYzWakpaXh3/7t3zA9PS1/n5ycRGFhIbKzs/F3f/d30pFzyZIleOuttz6wMbJZ0bXG+MQTT8Bms8Hb2xs33XQTjh075vZ35u57eXlh48aNsNlsCAwMxKc//en33Gq8qqoKn/jEJ/D6669j/fr1uO+++7Bt2zb827/9G8bGxt7TPXX6yyKdT1whnU9cm3Q+8R7oz21q+XPQAw88oAUHB2vDw8PyWVNTk2axWNzMpjSvqT0NHnroIQ2AduTIEfmsuLhYA6ClpaW59RT4m7/5G81sNstnX/jCF7SCgoJ3HNu1mrpdi97ObHot+sY3vqEZDAbt6NGjbp//x3/8h/af//mf2sGDB7U33nhDe+yxxzSDwaA9+uijck17e7sGQPPz89Py8vK0V155RXvjjTe0tWvXahaLRTt79uy7Pv/daHBwUEtPT9cyMzOvMvl+6lOf0l588UXt6NGj2u9//3stNzdXM5vNbv01/vVf/1XG+PnPf147ePCg9tRTT2lBQUHaggUL3EyZmjY3s+mOHTu0/fv3a5o2u+eHDh3SxsbGtB/84AdX9b54O/q4mU3/r5HOJ2ZJ5xM6n/gg6eMzU4USExO1Bx988KrP165dOydm4uPj4/a98fFxDYD2+c9/3u3zp556SgMgjX9+97vfaQaDQfviF7+o7d+//6pmRtdDc2Umzz//vGYwGLTvfOc7c7rvV77yFQ2AVlVVpWmaprW2tmoANLvdrrW2tsp1Q0NDWmhoqHbvvfe+5zlo2qwf+dZbb9V8fX214uLid71+cHBQi4uLc2vi9S//8i8aAO322293u/bFF1/UAFzVVXIuzEQlMpPrpY8bM/m/RjqfeHvS+cTVpPOJudHH0r3S3t6O8PDwqz6/1mfXIofD4fa71WoFAAQGBl7zc0Zuf/rTn8YTTzyBwsJCbNq0CUFBQdi2bRsaGhqucwZzo127duHhhx/Go48+iu9///tz+s79998PADh37hyA2TkZDAZkZWUhKipKrvP19cWyZctw4cKF9zy+mZkZPPTQQzhw4AB27NiB3Nzcd/2On58fbr31VrdyvowQ37hxo9u1N998MwC8rzECs6bstWvXvq976PSXRzqfeHvS+cTVpPOJudHHEnRERkais7Pzqs+v9dkHSQaDAZ/97Gdx5swZ9PT04JlnnsGZM2dw3333feDPOnjwIO655x7cdddd1wx+m8tYgVn/Z1JS0tte934Coj73uc/hD3/4A1588UW3lLPrGR8AZGdnv+O1H+egLZ3eO+l8Ym5jBXQ+odPc6WO5ysuWLcPu3bsxMjIinzU3N9/QZkKBgYG47777cO+99+Ly5csf6L1PnTqFrVu3Yv369Xjuueeu62V6/vnnYTAYJEocAO666y6Ulpa6RYwPDQ3h5MmTbtddD/3t3/4tfv3rX+Ppp5/GnXfeOefvDQ4O4vXXX8fixYvls6VLlyIiIgJvvvmm27V79+4FgPc8Rp0+3qTzibcnnU/o9F7pY1kc7Nvf/jb+9Kc/4eabb8bXvvY1TExM4Hvf+96czabvlf7qr/4Kfn5+WLZsGcLCwlBVVYXf//73Yt4DgGeffRaf+cxncPDgQaxZs+a6n1FRUYFbb70VISEh+NrXvoaioiK3vy9duhQA0NjYiAcffBCf+MQnkJKSgvHxcbz66qv43e9+h89+9rNuhY4ef/xx/P73v8eWLVvwne98B1arFT/60Y8wOjqKr3/963Ld4cOHsW7dundtz/1v//Zv+MlPfoLPfOYzSE1NxenTp+VvoaGh8uwf/ehHqKysxLp16xAVFYXGxkb86Ec/QkdHB55//nn5jtlsxg9+8AM8/PDD+NznPodt27ahpqYG3/rWt7B27VrcdNNN172O75VGR0exe/duALN7AQAvvfQSACAhIQELFy68YWPR6f2Rzid0PvFh0ceaT/y5g0r+XLR//34tPz9fs1qtWmJiovbkk09eVfTn7QLErlUoB9coTsMgrurqak3TZgPE1qxZo4WGhmpWq1VLSEjQvvKVr2gDAwNXfefdApLeLkCMn7/dD6m3t1fbunWrFhcXp9lsNs3Ly0srKCjQ/uu//uuqKG5N07TKykrttttu03x9fTVvb29t/fr1V0Wkv/766xoAbc+ePe849jVr1rzt+B566CG5bufOndry5cu14OBgzWw2a0FBQdrtt9+uFRYWXvO+zz77rJadna1ZrVYtIiJC++IXv6gNDQ1ddd31BohdD/He7zY3nf4ySOcTOp/Q+cQHS3pr+79Q+t3vfodHHnkENTU1iI+Pv6rQz5+DvvnNb2Lnzp24dOnSR7aXwPT0NBoaGpCSkoJf/epXeOyxx/7cQ9JJpw+NdD7x3kjnEx8e/flPoE7vi1JSUgAAHwXseOTIEXzzm9/8yDISALDZbG5FjXTS6eNAOp+4PtL5xIdHuqXjL5R6e3tRX18vv/+f9gF+gFRUVCSMNyEhASEhIX/mEemk04dHOp94b6TziQ+PdNChk0466aSTTjrdEPpYpszqpJNOOumkk043nnTQoZNOOumkk0463RCacyDpRznoRyeddPpwaa5e2KCgIBgMBphMpqu+OzMzA4PBALPZDE3TMDk5CWCWt5C/GI1GaJqGmZkZAIDJZILRaMT09DSmp6dhNpvl3pqmwWKxYGpqChMTE3Itn8Xn8XeTyQSDwSABggaDAZqmwWAwyPO8vb1hMpmkIJhaMIvj5DgmJydhs9lkLCqP5P81TYPRaHT7m6ZpMJlMmJ6ehqZpsh5vx2ONRqOsC5/tOW7+y+unp6dhMpnkc3UevJ/RaMTk5KTbehuNRlgsFvnM19cXISEhaGpqgtFohM1mg9lshtlsxvj4OJxOJ4xGIwIDA+U7nK+XlxeMRiO8vLwQFBQEu92OwcFBGI1GzMzMwOVyYWhoCC6XC+Pj4/Dz84OXlxdmZmYwNTWFzs5OmEwm+Pj4YHBwUD73nC+Ja8q/TU9PY2pqStZJ3W/1zPGs8Jx67gP3kOvKayYnJyUbiPf23G+OhX/z3IuZmRlYrVZMTEy4fYf7Zzab5W9cN46D/+fZUN9RjoOfXet88X5cG4PB4PYuea6H5zM8yWAwzKmz7nVlr+Tn51/1GRdQnbi6uTyI6qHmpqjMCJgt3uLt7Y2oqCgEBARgbGwMg4ODGBgYcGMmISEhiIuLg7e3NyYnJ1FaWoqpqSnY7Xb4+vrC4XDAbrfD4XBgdHQUU1NTGBkZQWhoKEJCQmC322XDJiYm3DZkamoKU1NTGB0dhcFggM1mg8PhcLuemzU5OYmJiQkAs8VeAAgTMpvNMu/p6Wl4e3vD29sbNpvN7fBys3g/T5qampK5k9nOzMzAbDZjampKGAQPJZkYmeLAwACGh4flOqPRCJPJBG9vbxgMBvT398t3XC4Xpqam4O3tDaPRCB8fHxgMBlitVlitVnh7e8PlckmvCDJyl8uFiYkJTExMwMvLC97e3jIv4MrLr54ZTdPkZeP4zWazrMXY2BgsFot8x2q1CtMxGo0yR5WBq0Knr68P/f398PPzQ0BAACwWi+zF+Pg4xsfH4XK5MDw8jMnJSQwODsoaca4WiwU2m02ezReTjIbnwGQywWQywel0oqenB2azGf7+/rBarSKkZmZmMDExgZGREUxOTsLlcgmjs1qtsNlsIhQNBgMsFovsN/eL78vQ0BC6urowMzMDh8MBf39/WW++gxROo6OjMJlMmJqachsPzwH/tVgsmJiYgM1mg8FgkH0xmUx49NFHr8kPrkUq2Hg7Zs+z78nYPIU256Iya54dCtTR0VEYjUa3d0HdK5VvcB08mbPdbpc1m5ycvOZ7yHFwrbi+KlgiqfyOe8o5eRLn48nYPe+n8kt1f1X+QJ6jPktdY1UI8Xyp75TBYIDdbgcwy3e4l+QHVqsVmqZhfHxc3jd/f38YjUb4+/vLvc1mM4xGI7KzszEyMoK2tjZ0dHRgenoao6Oj8PHxgbe3NyIiIpCcnIzW1lZ5BwwGA8LDw+FwONDZ2YmWlhb09fXB5XJhdHQU4+PjAjxV4EEewPVR11yVRaqM8hTK6l57Evk/zwDn6fk8kvq7ChjV/b3WMz2Bm+ccOE/+/53Oluc8+A6oz1DXQ13Pd1oP9Sy+05q9HV0X6FAXmRtMIcwfTwRKpsb/E0V5TkLdBDI9ALDb7XC5XDAYDG7CjUxFRb8U+Oq9uHkc78zMjBzciYkJEc68l9lsFiFBpM/x8YUZGRmB1Wp1u6cqYNX5qkiXjF8FakTiPERTU1NuB0K9P+9hs9ncruHfxsfH4eXlJd8zmUywWq2wWCxuGuL09DQsFgucTifsdjsmJiYwOjqKiYkJWCwWDAwMIDAwENPT07BarSJUVWZusVjgcrlgMpnc0svUMWqaBpfLBYvF4sYYSaqGoQIGjk8FY/ydLwn3icBEBWT8nc/ivLgfnMPk5CRMJpMAHFW7mJ6eFq1LHR/XXWX43DsCB555VSNXSd0zAG7zUzUn3tdz7amR8Dt8rygU+BnnNzk56WYt4D05TmqtHD/fg8nJyfeUNsgxqsLzWlq5yhu4pyaTCePj425anyrYVabJ9SLz9dT81PXm91RhrDJtXsd1UvdBPVOq0CEY5Rnx1Cq5Bp5nUwUw6vnwFF7q+0CBBQDj4+MwGo0YHx93O9sqsFetF+rzZ2Zm4OPjAy8vL7k3hRFBNoXa5OQkrFYroqOj4XQ63dbe4XDAx8cHAODj44OAgAAZA/cpMDAQ/f398PHxgcvlwvT0NMbGxgTo9/X1weFwwGq1oqenR/hKfX09wsLC5Ex4e3sjOTkZQ0NDcDqdGBsbcytNz3kQUHI/OF8qJ55nVAVob2e5UMGg+r6rgloFCKogV/m35+/qvnvum+f3VNDCM6E+X+UlnvJOBSWe1hzPsfM7KuBVr387oKGC+7nQdYEOdXFIFL4qQ1YPHw8wv++pCXmaL6k5UwioLxw1bJr4qJ3xoKiLyn/JGOx2u3xHFfY8aDycJFVD0jRNhBs1IwBXMWYyMZWhENBw3Kp2zLmp68v1U81tXCeVSfIZZAyqMKJA5EHlHEZGRhAUFCSWHG9vb1itVrS0tGBkZAReXl4CxjhGp9OJsLAw2Rc+k1oxzXHqM7lu4+Pj17RicK6Tk5Ow2+1uJlMymrGxMXh5ecnZUhmAxWIR0zb3jS8Pv6uCIFo5KJT5Oa07vL/6QnMt1DPK86lasby8vDA5OSlz4LhU5sXzxDGq+0RGq86BpAIslZmQiXJNKHh4JtXzyPVSrSZcJ64FAZbKrDhn1do0F1IFpToXghnPd3R8fFz2hVYZ1bKn8pRraX+qdY98whMkqOtJjZ33vZYlgM8iqfyF68Yzpe4xn3EtK4vKFwnmVQGjfsdT2HCunJ8qUPhOcb09Lc88Yyr/5HkjDzSbzfDx8RFLEc8yvzs5OYng4GDMzMzA29sbAQEBYq2wWq0ICQkRkDA6OorR0VFMTk4iIiICmqbBZrNheHgYo6OjGBwcRE9PD4aHhzE0NISWlhZ4e3vLu869IW8B4GbpttlssNlssNvtAl5omZqcnHQTpJ7WA/IAupRU+WQymUTRVffRU9Dy3qpbQuVN/Ew9/9cCpOoaq/dR35NryUf1XFwLeHiOyRP4e95LBccqMFHvq96ba6CuK98F9f7vRNcFOniIPdEk/1VNXBycJ6ojkblw8a+F+mmSJvFeqvBQhR4FsCr4JicnMT4+jvLycixevBgTExOw2+1XmXatVqsITS70xMSEmFxNJhN8fX3dBDkPlnoIKSA4br7Eqq+MjN9ovOI/pXanMhUCIvVzvjR8LsetasUGw6w/dXx83A38UKO1WCwYHx8XTYNMwWazYXx8HD4+PhgbG4PVaoXdbkdfXx8GBwdhNpsRFhbmdg9aZ+hzpfuF8yaDI5DhfMjsyDC4FxwDBw5gOAABAABJREFUtXiaXF0uF8xmM7y8vATw8IVSXxAKfe6B3W6Xa8bHx2UM6stNLYkuGzJk9TrVkqcKBVX7oNWP54jnUQUCtCrx/zyHFGxTU1OihfIzWiooNLhmquD0FFY8o2SwAGSsHCOtbioY4lpS8/W0Sr4beWqPJE/Ni++ZzWZDYGCguLg8zeaqpUkV+qqw4LrZbDZpD6+uL5/raUkg6PcEMypQVD+jFZLjV3ka95HPUIUFx6EKAU+Fgv9XLRqqEsK9Jr9Uv6PyLHW8BIy8B/mzGpehaZoIfVXZCQoKAjDbIn5iYgJpaWkwGAzCOy0WC3x8fMQVQteJwWCAj48PzGazKAUjIyMYGxsTARUUFITExETY7Xb09PRA0zR0dnZibGwMDocDABAWFibWDI67v78fAMQy6efnJ65KKqrcA/Wsq8JSXV8AbvLsWnukWue4/uTx/L8K1DwFuyrE1edyjzwtiZ6xOJ5KtDof1aWsXq++y+pZvdYYPBVmdbye68fvq+ujym1PGf92dF2gg4OkxcJzYKqW5OkeUDdV1dQ8rQsqECCC4vM8Gb26mADkRVIPGf8fFxcn4EHdIGraHLvNZpMXj39TtQeCEa6FCjbo67RYLCJsib4pwMhAOX4KS75YnCNdIzzY/BvBCe9HcMPvqAJSvQ6AAIqZmRk3l8LQ0JCbMLLZbCLYqHFw30dHR8VqRHcT98toNIp7hzEBnutG4ATMgsfR0VFZK4IG9Yyo5loKUQpDlTlzDSkcKJTUl0EFYKo5ls9QXy7uLQGMqoUCV6wtqpmf+85nMHYIuGLGVoWFKphUywwBIs8SrSvquTGZTCIAVMHjCYS5rjw/6pngmKhdEoRMTU3J+bhWfMM7kaodqcR98GROmqahv79f5jg6OnpVcKaqNXpqZiSVd7zduHhPVRO+FqBU109l3Op76BlXoL7Lnpom32PyH08LDOdIsO5pHfYEGJ5ap6qU8GwwrooAmtZZ3pt8hRY98mGLxQKHw4HAwECYzWYEBwcjISEBKSkpaG9vx+joKMxmM0JDQ+Hv74+xsTG0tbUJqKfC4OfnJ7xqamoKPj4+cr6431RqRkZGEBISAh8fH7ezNzAwIOPr7e0VEE/LIr/PNWGclCoIVXDqqSjyGvVceFpJPM/atfb57c6fp1XP8778V+U7KsD0fFc8v6/KUU/y5FfqXD1/9wQS17rG83d1jNdj5QCuE3TwpVSZpTop4OpAMuCK8OdCeC48N8TTlKq+RAQItGrwZSLSpXlY1VxVM53KoFUTME3T1MpHRkauOT7VbGe1WjE2Nua2IaoWprpduGa8n8qsVMsQGZqnVqxGNvP7/JvKwPgyUUjRR0/3gRo8OzAwAB8fHzFR+vj4YHx8HGNjYyL4qW0DgMvlQkhICIaHh+F0OhEcHCwBfGNjY257RlMt14cMgvthNpsxMjLi9nIZDLNRz6ppXWX6KkNQg/cIuFQhpZ4n9cUmgKXLR7VQcM0IXjTtijttfHxcrCUkAluCIdW9ocboqIGZPMcEJqrbi0xRfek9gbvql1fPjCfY5LlWSV0/dU+4LgSajHXyXLvrIXXtVcb5TsxTfc9VbZXzU90MHI/q7vQEn/yde66eI3UMqplYVaQ8AYcqeFRFi/ukWnHU8aiC6VoaocozgCuWMk9ApBJ5AMfxdhotx8a9BWbPi91uF5egy+VCcHCwgE4AiI6ORmBgIOx2O6anp5GQkIDc3FzU1NRgZGQEcXFxEutE/tnT04Px8XFERUWJUmO320VBYazIyMiIuEd4/mlloQWzsbERvb29mJ6eRmtrq1hsg4KCxG1jMpmE/2iaBn9/fzkrjB1RlRC+6wS26jng/vGd9uT16l6q1g7e71qgQwWBnuBGfa6n7OSZUP/mGc/Fa9SxvR3YfjuARFJlh+fz1Ht4nj/Pfz3l5bvRdVs6VDOiOkiV4an+Y/X/npNTJ6WCC7PZDKvVKqiZDIGWBBW9q0FnPJBqIBQPkxrMSPR9LSSsxneQCVC4ERBQ+yPqVoWHCg4YM8Dvqc8E4BYLQfMcBQR/5wH3XCf1xRgaGpLob9VKwsBb+uyHh4dF6JIZ0xytRuST6HJhlpAa2U5gZbfbMTk5KUBjenpa7qkK9tHRUfj5+bkxe7o/+FyuGwEIBaGqXRJ80pyqrgUtA9fSXlT3FoGSzWYTV4caj6KCYGr7FEwMoCUwUWNoeA/VKsf7TUxMCBBlXAzPIq03aoaRyjBUoKGOU9WePeOSaGny1Jh4P3WcXMuZmRlZI0/wMVfiHnkKQ3Ue6njVMfJvqvBWXQOq1YPfVd0tb2cB5bxVQKG69jhG9XPV7K4qTNxTT4GhMuFrgQaVd6qAhetAfsfraPlQASRBojqvqakpcfsSdHEfVF5oMBjg6+srWSYulwsBAQEIDAyEl5cXfHx8EBsbC19fX4yOjkoMB+89OTmJoKAgWbOBgQGEhITAz88PTqdTLNI9PT3w9fVFUFAQAgMD0dTU5Gax7u/vl6D1sbEx2O12iQ0xGAwCQiIjI9HT0yNAgryOc2RGIV03drtdlFKm4XJdKXdUSzstqiqP4nvM/9MKyzVVZRn3l5+plkoV8KiWML5z6llVz7t6Dkk8557yhb+rLhb1OlW2qXxIPZeqMqw+j59dC8yoQOta7/lc6D03fFMFtDpIFUyoE1OZrGdgnDpgajtGo1H+9TTxkiFQ0/bUCDzHomYmqJYYLqCq+VHQkdl4e3uL6VxlOHwBKIQonDgnPpO/X2szqV2rYwGumOJVlEq3jxrIBswyLpoteZCZ4UOg5uXlhZGRETnsTAcdHh6W1GSj0SigRNUGnE6nxEkYDFfSZ4eGhuDl5SUaByPpaSFRwdbQ0JBb4B2tAwQsnLP68jNmQw1kVJm56q7y1ER5D8ZTkBh4bLfbMTY2JoGL/Ju6L/w/56AKHU90z5dZtVTxnKvMhaTGsajvgmrlUudGTZUMiueY54RnjPfi+SUgU6PuVVeCGrehAnRPK8T1kKfW7fnjqWx4MmYVUKiaHfdRtXSoQF79Ue+pMl2V93gCAr5vXBdVeFzLUqLO0RPcqGfyWudTHSvvw2eTD1GIqDyH58VzjfmuqRYOjsXb2xt2ux0+Pj5iwZicnERMTAxiYmIk/sJiscDf31/OVHBwMPz8/ADMpmjzDHV3d2NmZgaBgYHw8/NDW1sbfHx84Ovri+npaYSHh8PPzw9jY2NoaWnB0NAQAIi1liA5LCxMXCjkgwMDA7DZbAgODkZsbCysViucTic6Ojpk/uPj4xgdHcXIyIiM28vLS3gx60RYLBbhTWq6/czMldIE5C2qgsp1V91anmdFPQfq3qrnxHMfVfCnysVrnV3PM6KCDc/zx+eqFlaeG9Vlr37fk4ep76PnfD3nrAIWT7DkaZV7O7rulFl18VRtRA3I4iFSB69qKp5MhRPzdGEQ7dMnykXkNbwP7+kp3Cn8Seo4eS8eVpWJqYtIPyK/S4ZP9M0AST6TzyHKVteBQIX3U7U7AgJV2KmR7qqmBMBtrvydY2RNkJmZGQEcvr6+sgfMeVctKsPDwyJ8xsbGYDab4XA4MD4+Dm9vbzdfO9ekv79fLDoUZpOTk/D19b0mwudaqfUUVK2ebgz+rgbgqSBDtT6ogpouNDVWg2PgGqtChK4rrhHPAveS+8v/q/ERBMV8hsVigd1uh9PpFODFcfMs8b1RLWT8vwooKIC4rp6AgBYTgipVsAKQM8f1UcdAUlOIORf+n+BZ1ezfC3mal3lPvgcqEFPJU5NSzxHX1dO66OniuNb/+UPwyXurf/O0Mqnj9gRknkz7Wq4QT9Chzk8VZADEukcFQw0CVi1IKnBTQTbvoVptWTcnMDAQPj4+8Pf3R0REBGJiYuDt7S3BmoGBgQgKCkJvby8sFgu8vb3h7+8vgIP1alwuF+x2O0JDQwFA6nQQ9HZ2dqK5uRkOhwN9fX0CbMfGxhAcHAxfX19ERkYiOjoaPT098PPzE4uFj48PNE1DSEgIoqOjERoaipGREXR2dkoNHADCo1SFRY0BY8wY10e1RJKvqIKT75vK8/nu8Cyo7s1rgU/1/yqP8JRZniBCPU+ee+r5mXpuOHZ1z9X3xvNe6vjIa9Tg12sBY/W7qjzyPOvXes470XWnzKoMTp04f+cBBOA2wGstmufm8YVXNRWa6sl0bTabMHiSKijInFTTO8euFpZRGaI6Vs96CbwHszIITLgJ6sHiWLguZN7U2vly0A2iAiZV0zWbzTJWdaMJdlTTHZE9587nM9uELxStFbRYEB0TmPn5+aGnpwc+Pj7iLiHgm56edqvFMTw8DE3TEBERgampKQwPDwtDoqbNF1+dB19cLy8vccVwfdXYBzIPNVZDdZ0QWNFVRJDB+6jWI/6QmRBI8Pl0o01PT7udKa4bNSXuOe/LuTHVT00z5nd5plWQwL3kZzQN021nMFwp0KSCdE8ri8owVe2dAOZaQk4l9R68J9dHZeDcg/dCKvNUQde7CWjPcXKsvE613nBPPN0Q19JI1fupQIK8SLWsqrxIPZeeipL6+zsxbfX/qnuF/3J+PJcMYudaeVrD1DgMAlgA4q7w8/OTAnVWqxUOhwOhoaFISEiQoomapiE4OBiBgYHCQ0dHR8VVSzcmebrdbofBYMDw8LCAt/DwcHR2diImJgZVVVWor69HaGioxHeMjY3JOAikuru7MTAwgPHxcQEOjDFzOp0YHBzEhQsX4HQ6MT4+js7OTrS2tkq9JvJyTZsNQubej42NXRVUDlwpikUATkswY9g4F09Qy2ep7gnPs+TpZuPnnt/xdA+q7hD1PKoWBVWJ5b1Ud7Iq2zwBrqfMVUEVx63+7vneqWugPlud07XO9lzoumM6OIFrkYq2PF9G1fd0LZ8VF131kammUy4CN4BCSPVdU2Pz3CwGVKpWDy6iupEk+v/J8GmSpw+dGibRs+oCoiCjBjo+Pi5pkAQE6tyuBbTUZ3NdOS61aJbK5AimgCspxXypp6amMDQ0BF9fX8mXDw0NxfDwsAhLb29v8ecSrHC+fCmpwTPglunI/v7+YiYFrgQ4apomrhs/Pz+3PeMe8Gx4WhU4RwpjLy8vsWLQakKmwCA0Cg36bDk+AiDuE7Uv7jNTf9X4Hs6FzyAo4h5QEyJTpruKgp/xK6pQY7lnzl8V9j4+PjJv9bkq0FF99iqwUS1kqiA0GAz/P21/+tx4dqV34g8A7gQIECS4M5O5VlZWlZQqSVXd6k1q2+1uz4TD4QhPxET4H5t3fjlLxHSMx+5pt9ualiWruyVVqUq15Fq5cl9BANyx/F5wPocPrpClZP3C3wgGSSzf5d5zz3nOc5YbITkHJG5sMV7uRaMcKYv+JsfrDK6HMHqFblxh8nledw/SHQBfu6m310upIkN8z3OuWLseGvVxfR3T8XWKF2DoQDbVY8hRNpuNKg4vjUYeKXdlncBWDg8Pa2BgQMPDwyqVSpqentbMzIz6+/uVz+e1sLCg+fn5CJHMzc2p1WqpWCxKOg+h0BsH/XZ8fKyzs7PIsygUCpqamtLo6KgmJiYi3yqbzerhw4c6OTnR9PS07t27p93dXZ2cnMTn2u226vV6JJYeHh7G9QjHrKysSJIODg60ubmpkZERbW9v69WrV139OHCScrnzjqgHBwcRxsHhQIZgCdH95HrgnLh9QdZZB+gfr0JzAOry9Tr5fR0Dx7wxtw4YUqYilbkU7KZ5UL3WAX+nQMff4xyeKI/8cc+e9+Ky38uxed1x6T4dXNwVNIaWw4GFx5QQ0F7xIj8vhggE714yk4w35qCD8+Ftc6BsqRvnfE5fSt0VJP6MfI9whaQ4P/fqYRKEyIXAPV9exyD4s+H9pjHFVLDSXAEHJIeHhxHn9PvhGVionhfi1+nv71e9Xg+FCNNTKpUCaE1MTOj4+DioYJQmCXHDw8NRV4/x83tLkbMvAvfAM5mLPBKex7195tGBICDJQyI8pxtk9zxQhJ5Qy+e5TzxglBZAx0OGVBylisHXhnsuroA8RJUmPjOPgECe1xWqK1xnHBlrZxiRa549zRNBLi/rxaSenBtuFLkrUehw1iXjmcoEryEbzhC6J+uynIJ6H3cH+ow913YZSwFTOq8oZz+/A1tkxMM5XvbtoUKq8MhbcCaH8WPckDESSWGkpqamdOfOHV29elXz8/MR0iDJ8uzsTOPj4yGnVJQgb5S60oac0HOpVFKtVlOzeb4nyvj4uMbGxtRoNEKXdDodTU9P6+DgIBiP3d1d7e/vq7+/PyrgXrx4oeHh4Xje1dVVPXv2TJIi9Dw0NKQXL15EFUyj0VCpVNLq6qoODw9VLpd1enqqfD7fNTZuj9geoFqtxrxT7o+M0+OD8cARYT1lMhkdHBwol8tFd2rAiK81ZNJ1NzLhMu+A3+8b2YGJ4TX0PDLr7Jg7Smm4hWu6TUpBReq4wvbxeb8HbCOfIY/In+lNj0szHWk8KUVSnrzi3+P9NKFGUpfi53M8BAqFiUA5UsudKgIvb+QceGyECUC1blBcQadhIUldAsnCcJoLpZ7eP9dHSbgS8T1MUiTtQtXpXFDdLuyZTCZirJ78SZ4AJZuM28HBQew5MjExoUajEdQ+rAjAgTyO8fHxWFBUbUCDTk9PRwyYpFLyMFCehB/8vj1HgdfxMpEhqpN4D3DpoSZ+e/jEx4F5xUC70UImWNB4HXhHjJsDIY/rOgBifDqdTgAkVwhcg3tG+XO/0kXMnnvkHpwNcHlDIfJZZImxdpqZ7zAWyCTyymd87FHKlwUdriNYC7zuMsDrLuOu2HwtONjm75RS9mfza/i4p/PpOqiX89Pr2Rhj12H+025f5Cxxj57X5cYRveZha9Y0Y+f3BMhEnjHOgJipqSl95zvf0bvvvhuJmC7rR0dHEXqBTaUyDSONDuA4OztTqVSKUtS9vT2dnp7q/v37evvttzU2NhZ6eGpqKpjNycnJrvU6PDyssbExbW9v69mzZ9rf39e1a9e0vr4eid3NZjPK88lFKZVKKpVKevDggRqNhra2tjQ5OSlJodOlC/2B4zAyMqKrV6+qr69Pm5ubEYZeW1tTLpeL/WS8t5IzUg48mQuML+sFdjYFBQ5omXN0PDbA88343YvV8Fwef9+BDeDBHVxfM87qpefx9e36IF0nvO9ghsPBy5scl04kdYowDY24ovHFLHUnlbmX7oqZv1lELMY08YeB4dpOd0sKBMx7njADy4FCRTkwgT5RPjkYAfekPUfEEaV7OXzHabRUEDxsw706K8PC8soSz4HgNYSZkJCk2MTt9PQ0+nG02+0uKhXj63S1Awfu9/j4WPl8PpJFPeHq6Ogokk77+/u1ubkZ3ijhC953BqJer8f94yE6/e2eR8o6AJYYN0/gxBv0eDfKhZATihQg4PMPyHPgirJhTmAZkE9kDVDK/yg2Qm0pXcuacsYBGXIg78+D0UBOMRzOAgDi/RlYR8gbn3camO+Qq3OZI13LfrjydE/PjW6ahc/3ep0nBSTp4ffRS6H7/SAfrrvS5+rlSKFznHljLJ2idvCJXPOZlFFyj5O/aYJFmLa//7yhHtUp8/PzunXrlm7fvh2VIYQXnVlJHSnAOrkgrEeYXJp3tdttra2tKZ/PK5/P6/j4WIVCQU+ePFGr1QqQsL+/Hzkhe3t7UZGyvb2tra0tbWxsqNPpaHZ2NmS7WCzq5cuXGhoa0p/8yZ8om81qdXVVpVJJT58+VbVa1dLSkiYnJ/Xtb39b9Xo9eoCcnp5qZ2dHu7u7Ghoa0uDgoIaHh6PF+uzsrBYWFpTNZvXq1SvNzs5qbW0t9BW6D1vmbCEg2L1+l11PIvfvObvvjo6z0qnMInfIDN9LP+Py5/KFLLmD32sd8X4qz/5+ygjyTO6cuOOQArTfdfz/tfeKP7B7KGlMFICRDlQ6iD7QXCulef1vjAcL16s93JBzfxgZT8YBcLiSdM/QnwlDgtLmvI5cHXkCmEDhzmIwoe5R+WZ2aRkcoROUkLd7pp8JaJ77YzzIMRgYGFCxWIxYLZ4W4QIPZw0ODmp0dFT7+/tx/4CVbDYb4Rbp3HtAyVGGCqtUq9XCY3dww1wyPwBBL1f2RC+AAcoQWpXdcvku88/4joyMqNVqdXWRZcxgZXgNL8+bH3FfjI0rbqc/HUQy/xh/pzJ53z1tX+jIFV6Uezpcxz0NB/zIBOfy6h+u42E7wF3qabM23EC96cEa5m/ukf9Tg+6K0p+Hz6fK7HWK0s+fel3OoLhCT8fP77UX8HAHyb/nMuqMKWuG8/n8k1zOvXiCO6ATufadjtELfX3n1WXsuH3z5k3Nzc2F9+5tB5BZupTSm8a9VZyKRqMR90pY4bPPPovE0qmpKf3qV7/S2NiYVldXA4hMTU1pZ2cnrl2tVvXkyRPNzs6GgzYyMqJ33nlHP/vZzyInrNPp6MWLF8rlcpqdnY0GYMViUQMDA3rvvfc0NDSkyclJZTIZra2taX19PVibO3fuBNNKee3+/r4qlUqU/S4vLwdgQx8BOHzOHQg7U52CXHcU0vWZhlMcXDCu6Aw/XDdxjV6GnHtzsJyyLL1YEz88NywF3j4Ofm+uC3qdPz3H1x3fOKfD478crhT9NZ8wP3xBstgAC84KgCTxmp3+8u87RT0wMBBeaJrbgALCA+fenO5HGXD/eAuuWPgO3gTgw42UG9h0DNzj8rGFVcGQupHo6+uLGnUMEwYfZsWTNT1vpdFohBID6afMAdnmGDBnJ2imRetzdoYk7FIsFtVsNrW1taVisajDw0Pt7e1pbGysK1vcgSBygJLGI3AK08MJAwMDyufz4S0Q3/WcCMCkz4ErcTw3Km481MI4e0UU4w7gZLz8Pc85aDbPWzkzTr2MkxtmZCMF8awHX0PeDMzDT/zmHA5Snd7lOqnHwt/IC/Nx2cO9OuYoDXnx3B5Oc4+wl5fmayY9UqWevu7Gn9d7gZlenp47Cg7O0vwU1xewXalXzJpFT/BdkmJdDpFV5M5DLwCOq1ev6ubNm7px40bsleJsMeuIeeGeYSxTw3dwcKBqtRq5W+iUvb09DQ4O6s6dO1EVt7i4qM8//1yFQkHlcllDQ0N6/PhxrKmdnR2dnp5qbGxML168iAaGV65c0dzcnNrttra2tjQ4OKjZ2Vm99dZbunbtmhqNho6Pj7W3t6dKpaLt7W3VarUuh48EWXJSnj17FuxrLneeu0dzsrOz886ry8vLGh8f16tXr0K3eP4Ec+OVS4yPh2bdyXAdlbL3LhfOIjOvvcJ3yKDb0NQxT+1c6ij7ukmBCc+HnvU1kNopX8d819eeM5IesXiT41JaBaPrhjuNA3HzDA7vM+gMDp/3cAeDx/nw2PHGnSXh776+vshN8K52oFAGCPCSUuKZTKZroyhoTQ6/rjeQcSPD93K5XNekuqJyRsWFjti+Kyw3llQ1QIFyP+xLAKBifBE2TzQjf2VkZESnp6eRr0GrdG8Vj1JCgFBOCBXn8LI5ugEyDjTeajabGhsbUyaT0c7OjiqVijqdTnhuzgSQW+LMQzab7XpmxsBzaijrY7ECKrw8lrg3yZ8eB+Y6fD8tEW02mxoZGQkFw3fxVF1mU2oWQ8N5mcdUMTB/KeMCMHIPifc7nU5sysd7LrsObriGe+cuty7nvi6lN+8y6Id7d+7NYzQ8H4Nrc8/85l5SXePfcTDGtXheB3XOmHm4JQWA7vGif1yZeskzY+e6gnP6hoeMt4dhcIp8DjCAXlUBuwBDkcudJzNeuXJFb7/9tu7cuaOZmZnQAwByN4i7u7va3d1VoVDQ+Pi4Go1GyCjhF+5teHg4mnwNDJzvHru3t6eJiQnt7OzELtWbm5s6PDzUixcvNDc3p5mZGVWrVX311Vf69re/refPn8c+T3t7e1pZWdF3v/td3b9/X6urq1Gt8j//z/+zJGlyclJHR0f68ssvQ/+g105OTjQzM6OTkxO9evVKx8fHsVFlu93W7u6ums2mFhYWNDg4qOXlZW1uboZxr9frWl1djfArY8U6Jz+G6zqQdPYV/cB8e0hbunBUYJiZX7pmp0AjBQmAQ3dC+Vwv4OHrgdccADkg8TWY5jc58PTrslZTp9i/kzosb3pcCnQw6Gn8N0VBrgR4Pc174OaZHKeyKauS1GXkGQwGV1IXEEBBnZycdLXc5n6ovAAUoBzcC3KFxHdTEOEDDLp1atuFzlFiLyYFwZSknZ2dLqHHa0AgSczkN+NHiR1j6NndLAoWMvsuMKawDoRNmANPRKzVahGyoj8Gc9FsnvfpIMzRbDZVLBaDOfGx2dvbC4DieS8sesYOuhR2xOWHa7pH7wmkjB9bbJMoxxzwHOSyeEJoaiAYDzeGjB9GyRkszsH3vbIFZQTD5sYRShxgjMFww+drjKRagCXj4ywGwIQQmgMrfvNd5MDBiPTbnQnf5OD6KH3XAe45OUDwdcezcH3mx1mmlO5lHFNF7PqJI/VA0/vyMUnPw2f9XMhML1rdwafrTGfR3JBxkKtB+JH8LYDD3bt3dePGDb311ltRqu3jxv1TlZHNZsNZkBSMJSFVxsx3cj47O4vKFMKxa2trGh0d1dTUlO7fv69CoaCtrS01m009fPhQExMTWl9flyStrKxobm4u+nF89dVXGh4e1vPnz/Xhhx8GKNna2lK1Wo1wCXPZbDb1xRdfxC62rPuFhQVNTEzo1q1bWltb09raWozv48ePlc1mo9vqycmJqtWqstms7t69q1u3bqnTOQ/n8B3GhGR8klFdPlgb6BrmF9vAOZBZ5J5wr+dCpDLksp+GNXrJnB8OBlLmzWU+tYN81wF7eo1eIdJv4oD0Oi4FOrwM1ZVzSvF4vNkPBiddbCklRfJgu90Ow+NhAOmiQxoD7XkH3AuKyZUThg1Wwj18z3Fwg4DAoUgxEJzXkzedpvNnHhkZCYPq2zADKujxgKHinH19F/u3YCT5jYKo1WphrABtNDNjsQKcPLwyOjratWtsu31eS0/CKcm4+Xxe+/v7sUiJuVLbXygU1N/fr1qtpu3t7a4mYdTqp7tcMpfEjT2/I5M5z4Vh8cNQ0H6dChGfI8YVUOyx8FarpYODAw0MDETmvpd8cV8uNxiGrsXy/4XNnMHCy0Cu3UvnO9wjz+7hL68mQZZg41zGWHPu6bthzOVyvxWj5nO+HtPyYgcqXi6HbL5OEb7u4FwOAtL16mvRmQdnFJ2yRXZTRsL/dmfBFaTrgNRZ8rHkfvkuBsWBR0ppM18OoNLPpEaglyPEwXpFFliXw8PDsavrnTt3dPv2bU1PT8f6Reewhwl6jfyq6elptdvtMOxDQ0Pa399XJnPRm6LT6Wh/fz82T9vf349OxtVqVSMjI2o0Gnrw4IG+973vaX19PfZo2d3d1fPnz3X16lV9+eWXkYcBS12tVvXuu++qVCrFthLT09Pa29vT2dmZfv7zn+vevXu6evWqOp2OCoWC/vIv/1I3b97UvXv39OMf/1hTU1PqdDp6+fKl9vf31Wg0Qj6azfM9XwAnJJfSXOzWrVsaHBzUw4cP9fLly9jU0ze5hJElrN1oNLqcm17GXLpoXumJwXyOBos4wi6n7gC4bKTy5Uev9/3+XhdicQDhr7uspmxK6hD4b67rzMjr7rnXcemgLYMldSeceFgkTdT0BeieBAeK3D1tlBT/dzqdoNfds3GQQBOp1Ktw4+LsiqSgLr0yhcOpaf6GJsOLAYyg+L0hFQqPkIkzMvSwyGbPt3pG4UgKpeCJjmSGcz2MK2MB8MDI8Tyea4JC8kQmjHWj0dDY2Fi0MCczHHAyNjamw8NDbW1thUIZGBgI0FGr1dRoNDQxMaG5uTm9fPlSp6enqtVqOjg4iOZghGugSTudjkZHR8Mz497Z94EKEgChpGB7nInY3NzsYsd41p2dncholy46Pvb19YXC8VAbxhz5cGOCMnf5ZBwcZLrRTZWKrxnkh/cAXqnS8MVNeMaZCkld+TlusPkuAIXr8T5j4QoQdgp5v8zBtTz84c/uygyZdIYK5c3z+TxwPmd+UkWbKj8/t4M2/2zK6KQsibNNfvgc9wI8XN/nn9f4XLPZVLlcDllstVqxARvJ3NPT07p27VrkPszNzQVA9MRSxsNBJuXbtVot9Eir1YqEcp6RfK5Op6NaraZqtarNzU3dvn07wD/div/P//P/1N27d/XDH/5Qjx490vj4uN5++23VajXl8/lob04zwqWlJX388cf6oz/6o9C3rVZL1WpVy8vLymQyKhaLmp2d1RdffKGVlRX9xV/8hQYHB/UP//APyufzGh8f19HRkWq1mh4/fqytrS1du3ZNGxsbsTcL/UpqtZqKxaKq1aoODw/j2ekPhK3BQcvlchHeRX5Z6x4iY6xcfplzwjYevmy327GLN/LC511u/Uh1BK+5Dkj1AjaZ8zmr6fqrF4hIAXzqLPh7PIMD9MuADY5Lgw48MG5A6vZupG5qEk8Oj8CrUjh4QGjobDYb3TFduWK0fTC9wiObvYjhSxe9NTzJEI+ZgUVZe3zVWRdyIKB4HVQRkvB8DYQXcOSeiCsFKjqgOAFPnU4nYqd9fX3a2tqKjG5vJTw8PBznz2bPm/rs7u5KUpS0+X4GgKVmsxlG3sNAPCsJWsRDMda1Wi2agbFTJfuMbG5uamhoSLOzsxF+YU+Gqakp7e7uanV1VXNzcxoaGtKTJ09ULBZjXCWpVCp1eYzkhiAvzElfX592d3djDmFECL1ks1kdHByEPELZQld7qMEb+RBicC8Uponxca+W+UKpO1jhB7kGhDK+zBkAgmoZrkEogWcEFCDjAHHKJz2kg9LkesicK0gO1pYn9rJGuT5g6rIH8u6K0cFYykagJAHaHuP2+wU4pZ6ZGwOOr1OeHK9T/P63A4te1/FndXbU9Rr3LynWPSBgdHS0i/mlP0WhUFCpVNLU1JRmZ2d169YtlcvlGKeDg4PfCg/AhHqDKQeTzeZ5qXi6nxJ6k5yp//Jf/otyuZzK5bLm5uYiJIjT9uTJEw0NDemdd97RkydP9PHHH6vRaOidd96JcvyTkxNtbW3p5cuXoQd2dnb0/e9/XxsbG7FD7K1bt/TixYtI/jw4ONCtW7f0k5/8RO+//76++uor7e3tBYtK3srQ0JCePn2q4+PjCCfXajUdHR2FrmJNw5SwQV2n04kKOKoGCUFjF1IZYb0yr54Y6usy7b/jTqkDf+bH2bhUj6RhkdexIb0YizRE4qDdv5+e36/D+27n0jXwdexMr+Mb9+lwwOE3jCfoN4ThT70HByp81r1a/vf3+D5hB+8A6Xkh7sX5fXuvAj+4riNHngPjg+AxFn19fdGlD7oOQEBbXklhAPge/TG2t7fjPeJ/ePoolcHBwYgJch0QO4CJEA1bT3Mej0VK58CBJl5uXFLwyGcBNSijdvuiLJMxJb9kYmIiwku+EGleBhuCwNfr9SjlpZqEElzv3ogsYLBhYWBNAHjEvlko5HR0Oh2NjY1Fbki5XFa9Xo/PeSzfq4Y8LECoyg2/AxM3OC5n6WvOmjlzwTlhsQBQvO7hG6m7jTdrwQG6rzOu583AWE8oQA8LomD8e5c5UgaB17imM6Lcv+d4MZbci993umZT0OLXdYXJPTjj4B5nqmd8rNELsK2cA+YDkJc+d+oZMqb+vJ7U19d33pyrUChoZGREk5OTmp6eVj6f1/T0dOyZAgPJGqGDKMyUGy5khbVMrtXAwIBqtZo2Nzej+RbPd3x8rM8//1yt1vmOsc+ePdPU1FQAbarjhoeHtba2Fka+WCwqn8+rv79fKysrunLlitbX19XpdHTjxo247tLSkg4ODvTxxx9raWlJCwsLqlQq+vLLLwNwf//739fDhw+Vz+ejgmVlZUUzMzP66KOPlMlkotvqnTt3VK/Xw17QPp38i1KpFJV0q6urOjo60szMjLLZrGZnZ7W7u6ujo6Nw5jyxFtny9YATIF3kk2GMkVd0JXOQJp/6vOPkIfeeRI7OdwbDZd/l3xkKl+kU7Lvz60y567SUbUnBjH/Gr5d+5nXHpUCHU5wgc0f0eE2+6Dwu60qYwfKF63kbhAecGQFlp14/72cymfBAPW/Dk30wnHjzntjp1R9UkKTsDQeeP2PB3+5ZUw9frVZVqVTiHilNc0+HxL9CoaDDw0Ntb2/HbrG5XE67u7vh8bZaLR0eHqrdbkd1y8HBQcRkeUaMPrQjoZRc7ryjK0YFADE8PKz9/X1NTExodHRUOzs7Ghsbi+RUSmePjo40ODiofD4fCnBjYyMaBLEDJOOIZ7KzsxNJwsgJAIAGQlTUlEqlSPZknDz3B2PZ39+v0dHRMOgDAwMaGxuLBFcy8lH0UMjk5bix8TBap9OJeCyetxtHN2oebiNk4oaLBYliZB74joNmVxhunACe6QLnnt2jQm5xAPzznlvhyjX1+NPKjDc9WD+9KOJeXhvg2DPy/bo8Uy+FltLEKchJvbXX3a/fi48B5/N15zIDW8hnenmAPJMzZmwnD6tTKBR0fHysmzdvamBgQDMzMwE4SqWS5ufnVSwWAwCyh08aFkhzYTKZTDAUrDvu5/T0NFqRkyg+NDSk1dVV/fznP9fNmze1v7+vmZkZdTodlUol/epXv9L8/LwkaXNzU1evXtXu7q4+++yzaE7WbDZVrVZ17969YGJevHihg4MDDQ0NaWJiQr/+9a8jBPvee+9pZWVFCwsLIecfffSRms2mrl69qoODAx0cHGhpaUn1el3j4+O6ceOGjo6O9Nlnn+nw8FCbm5s6Pj7WzMyMrl69qlwup3q9HuPe6XT06NEjdTrnTcmQsXq9Hg4PFY+EtOr1elTFMdfIBEwsdo11hq1hvMkNSUNzzA9r2l934MD1XIYd3KbsmjtCvg58TaagRvrtPdMcwPvaSm2+X+dNAYd0SdCRXtArS/jtca1eZXdMFEe6MP3gPR9UV9J4h8TlGASACPFO+lqwyL3vB9dgoDHInN8NT0qv8iyUvWLMCEkMDQ1F4mOj0VA+n9fW1pYymfPEJZgY2BGMMIaUuGqpVAqg0d/fH9tTQwlKF90FqSQZGhqKvhzHx8cBbGALSqVSJHHyXIAMhIzkV+7x8PBQk5OTOjw8VCaTiXEn0RdACL1JP418Pq/V1dXfCoFIUqFQ6KLzqTap1+txbj5Px0EAoXTRzRUZKRaLajQa2t7e7mJ7HNAiQ86+pcYGEABF659xmXePFdAqKbxCzgUITrPZPaThyaTINt/BQ2Zd+b2kBhNgCOhGibEueR5knjlxowqLxbhd5uD+0tCGr2vu9+u8KFd+vT7zuvdcSbui/rrX02ul73memMtKqrD9Hvgshqivr0/5fD6MPTkFhUJBxWJR3/rWt9RqtTQzM6OhoSGVSqVwGAAVlL9T0eI5OZICZKAj+/r6wjFBr6GrmHNCn5VKRf/7//6/S1L00CiVSspmz/PORkdHoy/GzMyMxsbG9NFHH2lrayt00dDQkD744APNzs7q448/DkAwMHC+0y1l3qurq3Efz549082bNyPp/Gc/+5lu374dclSv11UsFmOjukwmo8nJSeVyuegyOjk5qcHBQW1ubmpra0snJye6fv26qtWq6vV6tGgvFova39+PJHZJUdnDRnawp3t7e2o0Gl32oJf98lAguWaMBWkC7ty6Uyx151dJ3exa6uy+bk2kAMBzutIjdYbcIeh1/hTouAyl53yT49J9Opzq8XivozkGwJUsSgSazhdiSgl6UqHTlFI3leookPikAwnuZ2hoSDs7O11UOe8R03SqycMhZ2dn4eFzH51OJzwMnhuq3yeRa2ez5/0mqArx56Vfhvd4IB5aKpWUyWS0vb0t6Tw0QNwRpgOwgoCxFwKJpYwD5aoAExKtpItadYSJrG7yKvAWGGcoXuaIhmKETLLZ80x6vHraaWezWT19+jQ2nANMTU9PRyMfqkxarZZ2d3djZ8pisahcLhfgg0oVxn50dFTHx8dxD3guAEeSX3lGZNLlit/ezZHP8z5rwEEHQAZ5QOZRQMwp4UBPVPW1JV0wdg5IuAaf4R7c43Ygw7jznBxOFXMtHwsACDLmTOBlDg9vfh0wSJkNz+nw77CefL374WDLHRXOmSpHBxGMiTMlaRjmdffvQC4FL73CSCMjIxofH4/eDez8PDk5qbGxMX3rW99SrVaLXKdKpRL5X9yzz10asoFhlBQsKM2xSKSsVquR11StVkNHvHz5Uk+fPlWr1QodAnPHtdbX1/Xxxx/Hpm4PHjyINuejo6Oq1Wr68ssv9Wd/9meqVquRDN5unzf0GhkZUblc1qeffqqbN29qZGRE//W//lddv35dfX19qlarev78udrt8/4buVxO+/v7wUgMDg7q6dOnyufz+rf/9t/q6dOnOjw81JUrVwJIHx4ehsGn6mZ9fT3uBRZ3b28v9pEZGxuLXiE4kAB/wAHMJ2MPwEA+aEcAg0pejW/NwJrF7pHXw/p1uWTc3DlxWXdw4LoodaJSdrHX/35OdxScSfHX0u+4A/Emx+VbDtqFuAmnnj3ejAHHoKYeB8LsQMaT+VKgwWupYnFPFhqRAUAZg07dw2WxOuVEshUDi9BIFwmBboharVYXQKHxjG9PjcdKHBbUzXdpy4sX4CXCuVxOU1NTIbTj4+NR5ss5CKmQu9DpdDQxMREtzGkyRILl1NSUstls5IB46/J6va5bt26p1WpFQhio1puAIQe5XC6SWimlJWwDtUhpXSaT0fj4eBjNvr7zzZju37+vsbExjY2NaWBgIIBNu93W/Px8gDKUyPHxsaampqL8jlp8jB0gsdFohAGGWmRxE5rDc/ESWgfQXiLLczoIc+WEPDHfyDhrIQ2FINck8WEAeoVy8Gw9bMJrgEDCS6w3wFsKmF05pE3tJEUe0Tc9WM+cw9kep28BJqxRmCWntDOZi+oiByM8K++7pyj9tq7pxXI4A+QynX7H2SzO66CP8fS5TscZVoOyTpIZadf91ltvxVYFmcxF1Uk2m435cEfMWZRcLtflkWcymdiynrBvPp9XvV6P/hj8PTk5qa2tLW1vb+uv/uqv9OGHH2p0dFSffvqpvve976larWp8fFztdltPnz5VpVKJRHUqM3Z2doLJHB4e1tTUlP7u7/5OrVZLd+/e1dbWVnQonZubU7FYjHDq5OSkCoWCNjY2lMlkQh8MDw9HTgng6+TkRH/6p3+qvr4+ra+vB4Nx5coVffrpp5qeno4cl08++USnp6eamZlRvV5XuVzW9va2jo6OND09HUzJzs6O5ubmIkzS6XRULpe1uroq6TzBnWaIuVwu2FtY4hQQwKwizzC1Dm7dYUamvWrL80f4v7+/P5Jb0Q8eRnO5drl1OXR59c+9jnXk+u5ApH+zTj3c8ruOS++9knpK6Xu++D12xOu+4F0xu6Jhy2MMgQ8Wi8wT3ByNc16SCzudzm8lWWWzF7sDShctwBlET+rkXkC6hGbSjdwIc2DwvErCExMHBwf14sULDQwMxHlhaagYAahgQFqtVuQtEMIgZgjjgHGFBWC8vO6fyhLPWvdyLs7baDTU398fCieTuYgNZ7PZYFn6+/sjJ4V25xhVcjMkdfXUyGQy4bVwnbm5uWBYtre3VS6XA2DQ64NFTogJ9giPHvrZQ0LIILk1yIp3XIV5c/YAgOjdWpExT+xi8SP/vnhRFHjugJfBwcFgmBhPwlSsAZhAAHCae0Eoi+cD9PZSCN7QCAWCR8M1Xbnxmn/vsoc7Bb62HdSxvrwhFN/lcEXIOPPZlA3hPb9+6nl5HDs9PJ+Lc31d2CQ9v583vQb5SIxBPp8Ppm9yclJTU1PRy4Ykcpcv5tTDbq7kvZ8KCalsuEa5OnNJyLfRaOjly5eR//Dpp5/qhz/8oaampvTTn/5U3/ve98KBXF1d1U9/+lONjY1pdna2i5Eol8va3d2NNfvee+9pfX1d/f39unv3rlZWVvTRRx+pVCrp1q1bevDgger1ujKZjFZWVnTnzh1tbGxEuLfVamlxcVGlUil6gbx8+VL1el1XrlzRJ598orm5OU1OTmp5eVnXr1/Xs2fPVK1WNTMzo0qlEuxGqVTSt7/9bf30pz/V9vZ26FUANqGnoaGhyA0BoLHdA9toABal7n5V6FdnDJFvN9rIvctMGkZxB5x8RmdKkAWXSXeukVf/XC8AwnWdbUR+vKGhh2n4n5w1dC7f78VAvu64NNPhisTZhjQfg8MXJw+HgfOQAgvVEz4zmYvEP58IQAuK3lEiXoTfbxq/Tz0xjDlCgrH0HhcAHmLvZITjYdN/AgPXarUiuRMDTS13qVRStVoNQ0mzq6Ojo8iZgCWp1+sqlUra3t4OQ0HfDMIr0nliKxUzjBUMBoDJe4XQSpiSXpI19/f3I6SCR9NsNoPypUKHa3Q6nWBD1tbWVCqVND09HcaQMAmggsS5g4ODrgZpzOf09LQ6nU6EW0iAlc6pUxr7rKysqFKpaGlpKYAe4AgQeXx8rGKxGKGws7OzaN/u8sB8A8a4T8/VwMhLF1QqgMDBp7N6LHzGiXOxLhz0ch++yAE+rlBS+tN/4xmnPUMYG78myjJlPdIQFPd7mcPXmceknYlw9vF3eWvOujiwS5mGXmEU/7uXJ+dy7A6Sn9fP7WOdvsf3U4/Pt2U4OjrS0NBQ5E7Nzc1pfn4+doblODg40Pj4eCh0KlSYY79WJpOJXCfG9ODgIOQYxwS90mg0Ivw6MjKix48fh8MxMDCgGzduSFLkWH311Ve6fv26bt26pb29PRUKBc3Pz6ter+vVq1fh/c/NzelP//RP9emnnyqbzeof//EftbOzo2azqe985zva3NzUgwcP9N3vflf1ej1K6DnXixcv9MUXX+jmzZv6/PPP9dlnn6nRaOiDDz7Q48ePNTMzo08++USlUkkvX77U0dFR6Ly5uTk9efJEv//7v68vv/xSExMTmpqa0scffxwJ3LC9169f18bGhnK5nCqVihqNhtbX13V6ehrVPPRCWV9fjx11YTz6+voiD8RBtTsXLivMUxoG5TOAu2w2G9UzJNGnhRkp6HVAw/k8RwSn3eXew6acI2VD/f4zmYxGR0fDbrtNd2bwdYA+PS4FOlBgLFAWUq/DkZvHYzHerpR5WJ8sj4N7aSdeM4oY5eXv8Xkm0T07V1YpGkzpU4weIR9AD54mFSD7+/uRL+G0eyaTiTJQSV0MBBNPuIJcBhQLPTYKhUKABso+6SbYbDY1OTnZRb+zj4yX7LI4SOTa39+PXh1O1+N5wuTAaLTb54mnsCR0I2Uet7a2Yny94mR/fz+eO5PJRNMjno28DxqSMQfNZjOedXt7O4xjqVTS+Pi4Tk5OovXz06dPNTExEZ4i/UpIFgNkkMuD7PpC93mVFAbXe6fwOdaBe5+SujwHPs/4oozwgDDsvlYw+KypXkbZKX3WjK8r7zHjSWnOMqZUL/MOWPE1idK6zOGGnftAMfv6a7VaXdU1/izuhLCmAECpN5cqwBRgvM4D8/F3BjQ1FK7YGU8fQw8n+/vMO+ceGhoKZlOSKpWKBgcHNT8/r7m5OeXz+ZgLd6w4F2xGqnfxPpEdGMlOpxObsw0ODqper0e34ePjY62trWl8fFz/7b/9Nx0cHGhyclIzMzPKZM7Dk3fv3tUvf/nLSBSdn5/Xf/7P/znyJ9bX1/Xw4UNduXJFExMTKpVK+lf/6l/pq6++iqqUp0+fqlAo6F/+y3+ply9f6vDwUP/m3/wbPXv2LHaKHRgY0Le+9S199NFH+uyzzzQ+Pq6lpSX95V/+pdrttorFoj7++GOVy2X9t//23/T+++/Hunzy5Ilu376tQqGgx48fq7+/Xw8fPtTf//3f68/+7M+0tramgYEBTU1NaXt7W9vb2xocHNTdu3c1NzenV69e6f79+1pZWVFfX58mJiYib47GYoAMlwF09vDwcLDcyAC5HOgF7BLrOQUkUndYDhkk7OnMgzsvvWTe/3cZdvvHPTmD5joFYJQCdGxdCvBZnx7G/V3HN2I6/OFSb8MTmxxB8V2nbNzrcoUOrY0yZrAdJcKANJvNMCjcF0qMBYjnlxoNJhsFiHHgHHgPGEyneal1B0E7bQeNR14HTXAQKtgJYu7EsWF5Dg4OVKlUtLu7G2Aik8mEx5LP53VychLJoIwH+RT5fF6dzvm27iRMcR2ae52enmp/fz+AzuTkZPQOIeEVitHnfGRkpOu5Mf54cISDAAb9/f3a39/X4uJiMBx4bggu4A7vD3DGYuH+OKgAqFarXd1bqelH0bJgq9VqMEjIU2pMPeny8PBQ+Xw+lCLJuniiqaJ3qp/zOiOYGpBs9jypkAodj9+Tp+IG0PMXUu/bFQuABtDoAKSXMqLcGTnkAEA5i/imh3tJzgqkybO8795UL5DA2Dnw8u+lIZzXMRrpdV2BelJm+vleCt7Hyc/jh4f3RkZGNDw8rGKxqOHh4ai2IJdjfHxckiJPiyROfyZkC0PkskE+B7oXbxtjubKyoq2trWBoP/nkk3AMhoaG9N577ymbPU/I/uKLL3R8fKz/4//4P3RwcKC5ubnQwx988IGOjo704MEDbW9v69q1a/qDP/gDFQoFffHFF/rNb36j//pf/6va7bb+43/8jxoeHtaNGzf0s5/9TPV6XVevXtXGxoaq1ar+9E//VLu7u2q1WvrFL36hv//7v9d3vvMddTod/dVf/ZUODw81NzenxcVFPX36VHt7e/r+97+vK1euxMZwP/zhD9VoNPT06VNJ50miNCjL5/MaGRnR7OyspqamtLq6qkwmoytXruiv//qv9fbbb+uTTz5Ro9HQwsKChoaGdPXqVTUaDW1ubgbDODU1pWq1qlwuF7vvwm6SoM6a9nAoybusI4CEs/1+OEMGM0PoBZl9k5BnyoIgOx52Rf7dSXLd4DKHjABG3J46BnhTwCFdEnSQp4AS6EVFsjAYbEdBDKxT1T6gTI57PemgOP3jO8PyHr8BEpzPSyu5LjFSYp7pvisoSk/ikS5CJ548uL+/H/H5TqcTtfTkGZDsBfNBU7Fc7rwHB89CC3C6kDKhrdZ5lUe9Xo+t59kfgM84Q8F3WHS1Wk17e3uanJzU+Pi4dnZ2NDk5GYuDa5HDgrABNABNVI+Q8IkcMAe0Qc5kMlHRMzc3p6tXr8a8ck06mdLYa21tLVgdeoCQgEeIB2+8r69PlUolKGS6Hy4sLKiv77xpG7k9GGJPOGR+kVkP9TDf2Ww2eiowz8gynqeH31KZZqEDZN2AA9aQJ1gHL1NF7p3VA7Cxa60becJpGB73ypF7rxrj3j2XAwDCdVPl+CaHKzd/fs7n3peH/LgHp3/5TaVAylaiWJ35kHp3X+SafI81gjwBJJ1FSFkQ1z8wZNw738NQMN8kb46NjalcLqtcLsd2ARiy4eHhAO6UvaO3PF+ur+9iQ7ZcLqf19XVtbW2FASQ0SmOs1dXVYANHRkb0D//wD+Gw3L17V3t7ezo+PtarV6/0t3/7t+G4PH78WL/3e78XFR0rKyv65S9/qUajocnJSRWLRb3//vva3t7WT37yk+jJMTExoU8++USTk5O6ceNGlMzeuXNHs7OzWltb040bN/TixQv93d/9nd555x0tLy/r9u3bGhoaipDKvXv31N/fr83NTV25ckW5XE7Ly8uanZ3V8fGxbt26pStXrug//af/FPlta2tr6uvr040bN3R8fKwPPvhAJycnwdbcuXNH8/Pz0YzxD/7gD7SxsaH+/n7dvn1bm5ub2tzcDIe1UChobGxMkiL3JZPJhLPjfY8ABOgq5MPlSbpIH3BmwO2c6xIS6N2Guq1zYOAy62kD6Vp2kM56SIG/gwgccdYU33XWxc/xJselO5KmhxtmLuwD5wqNgXLGgYFyr9cnAaXAoLi3ySJ0r8KNINdKAQXVH155wAFdls1mo0027+MlU+7G82HgyEineRadR8lv6Ovr087Ojjqdi6RPjAMCAkvQ398fmdPu6ZXL5TCg7CaLIJHoCntBngctlSnVI3ek0WhE2R4Gb2JiIgSJkBHKzzdt4zP9/f1RbUILY5D6xMRE5LBAyW5ubiqfz6tWq0lSJNSRDMvzY1gZK5QspXmt1sU+Li5zvnsmcWtyfHhmqbuTqHuJrtidYfISaWTBK0VYByxwvB73ygEMbsidindDzHlY3Bhm92J5DmSYzzCOGGJAPtdhzNIwj687Z/0uc/jnfQ26DKcAzYGTe2XOALqhT1kUgFWaqOdMAednjtLvpCyGh634vnuLKcPL+uY15AJZZI2wQ2qxWIxKrlTfkVfG/DkDw/0gj5JUr9c1MTGh/v7+aBWOfnv+/Lk6nfPmXj/96U81NDSkf/Ev/oW2trbi3L/4xS/02Wef6Q//8A91cHCgR48e6Y//+I9VLpf1i1/8QmdnZ1pdXVWpVNI/+2f/TGtra3ry5In+5m/+JuR5ZmZGr169UqVS0Z//+Z9rd3dXv/nNbzQ/P68PPvggdNPk5KR+/OMfa3h4WPPz83rx4oXeeecdPX/+XKurqwFQ7t69q//4H/+j7ty5o1u3buk//af/pCtXrqjRaOgP//APNTQ0pP/lf/lfdPv2bR0fH2tsbEzz8/Ox5T2bUO7t7WlpaUmfffaZyuWyfvzjH+v4+FjvvfeeVldXVSgUNDs7q0ePHqler2txcTFK90lclxS5FoR9qZgrFouanJzU2dlZgJFqtRp2x2ULgO9AGyYbxh7AwBrleqmT7/IAEPCcEa8UQ3Y5PCTsQNrBhq9jf431Q4I8n3PG93cd36hk1o23L173BFOPgwH3he50MwPr5ZRpoo4PAEYapeYJh6nSRYliJNhllclx5eiIEDpMutgQDiNQKBTUaDQCNHQ6nei5MT4+3pV4xDgNDJzvckopKuf3MSThC2+fpCVyW7x6hdc6nU7sRNlut6PzJ+BDOhc0wi6Hh4caHh7u2l0XtO7AkvsaHR3V2tpajMvR0VE0CqvVamGUWTjsaeDlxevr69rZ2dHCwkIIfaFQ6DLQPCvgiGoeABXKALaMZ2efFww/8gMQBjTt7OyE4Sa8xNx6uIfFjHfN86QUJPKQNptjHjGaeJ4oGRgVzpkCD6/EcmXjeQI8H9cnVs/1fV25oQXMpaDIGTUPp30TpoPn8vFKWQKnf7mWMwuSunQB69KZGQcpadg3ZWBTTwymi2ukB2s2ZUfS/K52+6L7JIqdbr2sKc7B/I+NjcUOpOg3EgbRjWyQiIwDAOlFRMl3sViMHhSHh4eqVquq1Wqam5vT1taW1tfXo9Lj7bff1ubmpv7Df/gPWlpaUrFY1M9+9jNtbGzoe9/7XrCy//yf/3NtbGxod3dXQ0NDevnypX7v935Pp6en+uijj7Szs6Ojo6NgPaenp/XBBx+EfI6MjGh7e1v/+l//a/X39+vJkyfq7++Pz9PTZ25uLpocbm9va2hoSG+//bauXr2qv/qrv9LY2JgajUYkj377299Wq9XSw4cP9eLFi2j0hX5cXl7WgwcPVC6XdefOHf3jP/6jVldX9e1vf1u3bt2K9f/Hf/zHOjw81I9+9CO9ePFC9+/flyRdvXo1wH5/f3+0J/DkdCpdyJtzB218fLyrPwmJ9NggZ7OR3zSvTlKXHvPwqctpqqucEfXcJ2cvUsCfnsPl3G2u6ybWc8qQpID/645vVDLrHovTO46mPNThHqR0gbA8DsT/AITUS0ljRq7kJQUixfjxgyLmfomfQ6E7Be1K3xvEOO2FsfNxcOUEOHny5EkYULw1uo0iZBMTE8FGUEs+Pj6ukZGRqO4YHBzU/v5+7LIIjQogy+fzgbJRzpSa9vX1xX4LZL+7wW42m9Ebg/HEeAFqMplMGFC8tWz2oqPo2dlZ5Fj09/dHh9FSqaR6va5KpRL7PHCtR48eRRip1WoFrTw+Ph7MDiEeWBCqfKRzY8RzE2PEC+F+eY2tuj1/QTpf2OzJgHx5KS3KDBlG4SB7zDugCU/OZcp7fMBOpLKPwgLkOuBA/gHrHhJCsXBPhIN4NhQNMg0Ad8XE0ctpQOn5mn6TI6008bBKGldOmQRnIphXzudZ/Zybce4FjlLw4ffH+JBb5XFy7sfngPP5tRwMpnPBGoNhc9aOnZydyZAuQjKUjfLcnuCcAsNW67yUnjJWQqD8bG5uRuiGeV9fX9e7776rQqEQJbU/+MEPtLu7q9u3b+v09FR/8zd/o83NzWA/YRieP3+uZrPZ1d2zUqlE7sWrV690dnamiYkJTU9Pa3BwUA8ePFBf33kvnZ2dndB98/Pzymazun//vqampjQ8PKw/+ZM/0fHxsT777DMNDg5qaWlJZ2dnevDggb71rW9pfHxcP/7xj1WtVjUxMaGdnR3NzMyo2Wzq1atXUXmC8W80GhFKfvHihaampvR7v/d70Wn0+PhYjx8/1uLioiqVSshMuVxWJnPe/2ZjY0O/+c1vtL6+HrIAa478oO9I4ocF5bNHR0fhfOIYM/eeIEyiu68NlxGX0VTWXT65DmuC1/kedhi57cV2pqFbZ4L9b7fPb+qgXAp0eIa+LxaPD/tgOc3oD5PSmHgzqbfiCsIVEIqZz5Mk6awIRtbjrdDbHsv1awOM+B6eMt/z6gAHLhi4/f39CAeQELizs6NM5qJhFSWqlGIRtiBfAZajr+9iEy9yPDBg3DNx31KpFHODdwT9hSJCoMkHkdQVugHIMG/cJ+ETQAfGnYx4eqkw/rRPZkt5aEjAChUx+Xw+enHMzc0FyGi1zhuUkXw3OjqqSqUSXpIbTxY+uRowFuQ70N+DklgywvEsYbuQIUkBNJDNvr6+KF9Elj3b3L0VPBtfkE6p8rezC7zGwk2Ntq87D1Nyz6mn7g2EuI4b1dcxAcSx+Qyy7s3x3uRwdsPzjRwEObuTAg+u78aVcAP3yPse0iW+nt7H6/7HMGBAPCbO+PG91Ovk3hg3HyN0HnkcrA0af1UqlShFBbByvZTa9hAODg/yBZDudDrhpPAc5DTRZ4d9n/b39/Xee+/pP//n/6zj42P94Ac/0B//8R9rd3dX3/ve9/Tv/t2/U7vd1t7entbX1/WjH/1If/RHf6T/7X/737S6uqrp6WnNz89rbGxML1++DCA/OzurcrksSZGweXBwoN/85jcql8va3NzUw4cP9dZbb+n999/X1taWVldX9fHHH+vWrVt666239Pz5c21ubmpgYEAffvihHjx4oCdPnqhcLuvDDz/U+Pi4/st/+S9aW1vTtWvXtLu7q3fffVeffPKJ8vm8pqamwknZ2dkJtvnatWv6yU9+oj//8z/Xr371K3366af64IMPApD9xV/8hQYHB3X9+vW4h5WVFa2trWlyclI/+tGPdPfuXf37f//vtby8rC+++CIcslqtpuvXr2tkZCRyZnASO51OgE7ybmDEpPN8RACHO7rIdi6X6+pezPrvJdeAQw/vutywHpGp1Ln3qIHrA//fbTd6kHXhbOCbHJcOrzgF4wc34kiIwxVL6mV5RYrTRHwPxZImi6YhF7xIcjKYRPopuHJ3WhkPlfvyuLfTWngcMBleXuj5ITANLEiqFKTzEitPAKS6gy3knaU5Pj4OpkQ6z33wkBb3j8Dl83ltbGxEKap0zv6Mj493eW4ADXZg5fvj4+PxPJS9YsQlBRCAGcDw5vP5Lm97aGhImUxGtVotkuQAUSjDhYUF1et17e7u6tatW2HQHz58qGq1qnK5rGw2Gw1/+vr6YidMjA5yBi3J/E1MTARj4btHZrPZYIpoT+wJXw4gpO6mP4TBALt8xxclDcZ8HTBOyK3H/N2LODs7i9LoNHTgygbwwrx7KAzwCsBCXj1EiVw7yMErcnYRtkjqjgW/yQGocqfEr836Zk0hL2nYxStKPEHUz5fm0aTsK3oDVsDZpXa7HdVDrCtnMl1nOevhQJD7Rfk6iKDUHv0yODio2dnZACg8E7lJyCTzBdvomwSSZ+PAG/Z0bGwsqP1WqxVVHKVSSZubm5HLtba2pkKhoB/84Aean5/XkydP1Gw29emnn2p/f1+Hh4ean5/Xv/gX/0Kjo6P68ssv9e1vf1vf+c53osR9e3tbMzMzajQamp+fV7Va1YMHDyLp8uc//7ny+bzm5ub09OlTzczM6Pr162q32/rJT34S91cqlXTjxo1o7kW4lv1U2K/l29/+tn7xi1/o0aNHGh0djfeXl5f17rvvRkL58vKystms5ubmdHp6qunpaX322We6fv26Pv74Y21tbemdd97R7u5u7Og7Ozur9fV1/fVf/7Wq1WowybA5/+v/+r+GrLCb9sHBgcbGxkJ/whjT5G19fV27u7tqNptaXFyMMDmt3XEgnall7j0NwOdb+m0w6g6TOyRuT92u8jdOmrMUKfB3RtLXXepUsZZ7sYqvOy4FOlCCvShL3nfPz1/jxn3hM4CpwuCcnjOSergscPf6YCSgTv0clLziZaUZ5g5suFcAQjabjWZcKEJXjpK6dozEEA4ODmpjY6MrAxojXKlUdHh4GKwBIQFYB8IcGA7YCxgX/6FvR3r/ePPkJCDIsB4wSNlsVtvb21FKS8ImCwIDQu6G0/AYLapZACokvPIenx0bG1OtVlO73Y7eAH19fXr16lVQwXt7exGu4f64F9ilg4ODyOIHSDAXw8PD8b50kQCMzNAkiMXX19enQqEQ98Uc05yHzfegUAkn+j4h3AMy6uFGp+MBww56eZ175TvIsY81h98nYZleVTLcizctIrkUY52GN5CNy3owyJ2v/VT5+bPBVnBPztjwfJ7s5gegKAVf/iycl2s7c4Di7OWteXa/6yTk0QGMK26AC6weY08yKc9KLxxnTjlGRkZUrVYjpInsOwhFrhxscR3yeyYnJ9VsNvXy5UttbGyoUqlofn4+Eh37+vr0f//f/7cmJib07rvv6uDgQP/0n/5T3bhxI8IpzWZTd+/ejbX3+PHjyK2iTcH29rZqtZoODg60t7en/v5+3bt3L3K4bty4oZs3b+qzzz7TV199FbpscnJSf/iHf6iPPvooQsn0Amo2zxsbYrD/+q//Wo8ePdL09HSU8BYKhXA0nz17plqtpkqlorfeekuZTEYPHjzQz3/+c42MjMQGcO+//75GR0d1eHioW7duaXR0VNvb23EPpVJJV69e1dLSkra3t7W8vKyjoyMtLy93OYa+z0ur1Yo271NTU7GP1NbWlp4/f65MJqN33nmnK+QPC4Wu4Hy+t4s3+PPf6d/IuDMazk44q+pyJnWHQjlvyjj6umDd/K6y3d91XHqXWR7IQxa+CFkAvJ4qNI8X+4BIF94gHjMeh3tnHhd35SpdLHynZomlEjPnAPE5mwJ9S48KSVGRgoHxXQSdMifHgH1TyOUgnkl4hbIrqDVXqhifVqulvb09zc3NBUg5PT1VuVyOMjJJkVgJIzI8PBxbOs/Pz6tWqymfzwcgAPRIivbIhEtI9BocHIzfIyMjkZtRKBS0urqqSqWi5eVlTU9PhweAIS4Wi7FnQbFYjOZE1L4vLS2p2WwGA7GwsNDlSdPHY21tTaenp1pcXFS9XtfR0ZFu374dMtJsNlUul7t2bxwYGNDBwUEkuXnyp7MMXAOAeHx8HCEo38ETgJzP55XP5wO4AHQIWbnn4IbfvQ5PPgOQOlsFeIaNAWj4fLkiYU3wWWfkUprWjRbXcqYDGSS3hpAazES6xt5UR/QylhweIvJ7wxFwkOCf9XHgWTys+Lr7SRUx3+e+PMfJ58Xv19kQd3acFYW18Xg4lDvzz3YBzDEgDRbLQ2wOkpgj7s3DS+gB2LZSqaSxsbFgJNg5FXZxcHBQtVpNg4ODkfN1/fp1ffDBB5FTkslkAtxzXzAy/lyrq6tdYUrGY2hoSNvb27py5Up0BR4fH1dfX1/sJfU3f/M34fVfuXJFlUpFm5ubMa+NRiOS88kBWV9f15UrVyQp9MvU1FSwKp9//nm8t7S0FOHRoaGhaFPw/e9/XycnJ6rVanr+/HnkxZRKJZXL5cj5mJubCz2xtrYWOpQeJ/RXYS3t7OxEhQ7gY29vT7lcLvqGbG1taWtrK3QucttoNALUeBrD68CGy3O6xlOmw0Gqn7fXmnHZS8GN67U00nCZ49I5HdwsHpV7L697AOkiCRUF7chM6mYaUtqYh/QEHSbMv4+3i5cP/UNJpCtQFDP0mJ8PI+rxXgxAf39/13Worjg7O+vyxBFwmA8Siqanp2MxA7BYZJ6TADsCPUxpFTvOnp6eBh1IeITqmLOzs2A/CC+Qm4HhpOTO8xQcAI6MjHT1kiBvhYXnnvjZ2VnswcAcpmGZK1euaGNjQ9PT0/H9UqkUIRsamHU656W2MA+UFm5sbES3QAwyzIwbFfI/9vf3g6KGzh0YGNDa2lp4F8gj8XcHshgjSpORgdTQM3ZpLgXngSJlrcAuueHHcJAn5Asaw+QGD8XiPTXcI/EyTFcuAG9AtOcsIX++LmFuLnNw734/KCrXFVzXk2ydifHPu9PB4WxE6sFx+PdRxE5R+/igC1Dg6XlStiYNH7vRJaeLdcaO0RgewnWAO+7Htx/gvJw7VfZOfzuDC9vh5dyAZE8wZu0uLi5qdnZW09PTsaPr+vq61tfX41z1el3Xrl3T8PCwRkZGogoOxg4jPDc3Fx1BT09P9Sd/8icaGhrSL37xCy0vL+vevXt69uyZPvnkEzWb5xs43r59W++9955arZZ+/etfa3R0VIuLi9rY2NDh4aHW19c1NzcXlSIjIyOxqVuhUFCxWFSr1YpqloODg9jN+eXLlxodHdXc3Fz0IpGkH/7wh3ry5Ek4KqOjo5Fcu7GxIenc2ZyZmdHS0pKy2aweP36sL7/8MjqbSucOTCZzvoklDRsJrw8MDOg73/mO1tbWdHZ2FnkyABBYMyoJvfM08pUC2NfJuNvZFFg4CeBy7XKVEgO87+dzkOOOTcqWvMlxKdDhSIr/Obg4yiGlJqXuJBandl0BEZNmsPwzntTn95QOBoY49SJA4fy4V4oS8EYorliazWbEgD0EREMe2Bk8ZxI2+UxfX5/m5ubi3NPT09rd3e0CYOy1gfDRwhsw9OrVq+jit7CwEEaD56Q6g54YXrmB8UWZA6wIIeBhkR/CmHq797GxMeVyuWBgMJajo6NRubK7uxugj8oWSaGAh4aGNDs7G62HT09PtbGxEaCSjHNatZOw2tfXF91SC4WC1tfXI3kNJU4Ijc6PMCHknGxvb4e3hrLMZDJRmgcYKBQKXbu6cp+e54HB530YGPdGnY7EcOJR47k6/c/nU7nzUCLyyesAEu6D+0PuPY/DE609lADz6OuUcb0s0+Fr3pUY500VICAoDXOg0Jz1cObHz9mLmUh/p+GdFAQxFsyLPwNzwjgzfs4+SN1t431NFotFLSwsqFKpaHR0VPv7+5qdnY3zwH5hxJ3BQGdyL860IG8eLkMeGo1G5PiwzQBVFsjju+++G4wCe48QPrh3755qtZpevnwZieHc79raWlce0P7+vl6+fKmPP/5YV65c0b/9t/9WGxsbevnypZaXl1UqlTQ1NaXPP/9cExMT+mf/7J9pY2NDp6en0WmYcGsmk9Hy8nLsjTI6Oqrl5WXNzc0FABgYGNDTp091cHCgZ8+eRcfj09NT7ezsBJtcKBSiE/GvfvUrDQ4O6p//83+uL774IjZ3Gx0d1fe//31VKhXt7OzEvljHx8d6/vx5AAKYi83NTX3++eexaR6OKOXFdGJGF09MTOjk5ESzs7O6f/++crlcsMeHh4cqFApduhcb4PLGGnaZdrbNnf80pOKgIAUjviZ83fpnnOHjgMnyddeLGXndcWnQ4TfodKTTyT4QnoPB4YszfWAGmP9hGzxRBuXtitqTufr7+7sac71ucPFCYSe4R8834W8SwtyTpV8GxgpvulQqqVgsRoyT621vb6tUKqnT6Wh5eTlCGJTMcv/skcK16ItB2SyJZrOzs7FlfKvVCiajUqmoUChEIy7GDW+WJjagdB/nbDYbzAn3QGdR8mW8/wShCgBWoVDoql/3/JF8Pq8XL17o3r17XZ35GHM8zaOjI01NTanTOS9DJGSCBzE6OhpjwliklUYwUQCi4+NjjY+Ph+Gq1+vBRB0dHUUjMy+V9sVOWSuARfrtzY4cLCD7vgOs06aEDpHRWJB9F5VUbkw8FOK5DunYMYcOTlBAeKQONAAChAw9zyBVNm9y+Hh4fsjrPCJfY6xJd0rSBmYY1fRwRif1/l039GIq3KPkXtOYenqvrqMADZy/0+kEs8HmYePj45qYmIiEQTx1wratVisS0AGUvcYRsOuAg+dm7GBVGSd/1mKxGJ9Dz7RardCXzWZTe3t74TyQy0bp+urqajCytVqtKz+Lbe/39vb05ZdfSpLu3bsXa5pk0ocPH4aj8OWXX6pcLuvg4CAqQMrlcjT3g0Wdn5/XxsaGtre3Q7ccHh5GAufp6Wns3k1i7czMjIaGhvTrX/9aU1NTWlpa0u7urqrVavThgL0h1LS/v6/V1dXIuZicnNSXX36ps7MzTU1NqVgs6tq1a9rY2AhgR4i6XC7HzrTkgNGYcWZmJhqW0ceDdYfeIZ+FUDn3yXrHZvr6QF7SECR2GfubrjPXu75uHWzzvwMcd9rRU6ytXtGOXsc3KplNUU1K3fgixtvyh+0FRDi8Q6d0UUmBlzc6OhpKVrrwcmmuAz3l8VX3kFqtVtdunh4XxpuFGfFrUJKGQvKKCb4DiOCc7FOCghkeHtbW1pYymUwwD0NDQ5qZmdHc3FwwELQ4z+XO26ujEEDunU4nqjy8WqHdbsfuknREJbmz2WzG/WSz53026GZKuKdQKIRBb7fbsTioruBZTk5OIsRAJ8RWqxUthcvlsjqd851i2+22Go1GUL9QmmNjYzF2hUIhduPt6+sLCnVnZ6ertTfXoVUx4RN26MQgwUw0Go0u4+OAGJmhPJp9aFDs5LVkMpnYjI8SZZgDwJcbfTcS0oWRBKwgpxh02BLWy9nZWcinJ3uiRDyplJb1nM/Dn9wfIB0v2L1ynof1iLzzfG6kv8mRMgy85jqC19JwBffYi/L1z7m3lTKraTjFw3B+OIDgc34/jKEfjBs6DAfEQ3fsuEz5JsmesHczMzORMMpOppIC3DvgRNc4ewvwZ41ICkCTyWQibwRjBnNAvhlySmiYxFDAyOHhoWZnZyM8gBfOlg/Xr19XPp/X2NhYbG8vScvLyxoaGopwEv0wtra29J3vfEc/+MEPoknYp59+Gs7K+vq6nj59qnK5rIWFBUnnYQ76GRGS4X5PT08j56S/v19Xr16NXke0lW+323r//ff15Zdf6ic/+Yn+p//pf4rEUEABez5hwAcHBzU5ORk6fHFxUS9evNCDBw+ihH9hYUHDw8Pa2dmJZG5ywUgGJvSLI3rjxg2trq5qZ2dH5XJZR0dHkVsjKXb1Rq9RiYQ8AjiRRWdJkX8O1ju6gvO4w80acUDSa/2kYMfZRc87+u8COlCUKCUGw5F2+uBS986LntXvysYXOl43wiVd7OzHed1j5D1X+gADjLHnizg9md4DCxGFjaLCYGCwDg4OwjBhpOjH79fHMO3u7gaixptGSTkooqyTTG4MN1R3JpOJbZsRIrwVEC3dAgmv7O3taXp6WtIFy+TfZWtqNwS5XK6reVK73Q6FeXBwoMPDw2g5zPOXy2Wtr693dWM8OjoKShlQR8kawAoQtrW1FYmvAKTJyUk9f/48eh00Go3wMEjG3djYiPepRKlWq10y6KGOsbGxrm2xG41GhIFgRZhDr6pAhphjNwTk+XgTK77n7ISvB4wq4MvfBwgzPu5pAE5hc7xNeCrLyLwDZgAcn3OlxNrIZrNdrM5lDmcTAEG+DtMQhh84KtyXs0NfpxzT+3RPzT0y3utFM3uolXl43flZP+gcnBDkjPWUz+e7WmrDfsAowG6QbEwID1aNOXYmBrnjHmGp2A+KsWWu8ca94k1SAAi2Ezg5OdHCwkLIHfNwfHzctZ8SORN8h0qVL7/8MrYPoEy0Wq1qfX1d4+PjGh0d1S9/+UtVq1UtLi5qcXEx1i8MSiZzXm5PuIcqRLoX3759Oyr7stls5JTVarVgZWq1mp48eRLNAtEP/8P/8D9ob28v9qIqlUqR68Z30QnoenK1aLG+urqq5eVlFYvFABoONGnJDsBHP/T19Wl6elozMzOh7ycnJ1Wv17W8vBzhaMKvkrrmypkt1kin0wm219dCymh4qBKZ6RVdcGffQYWvNf+u/7h++13HN9plNkU3oKquE/9//0OPomCd+XAAgnL1kkEvzWFhMtEgeu7Hu02mXp10wXzwvnTR/lpSGEjuG0+apCUMAwja9/egp4akyL4mP4HscGKXXAdPFeDDGEgKDxYwIClqvDudTpx3b2+vy5hSFeIlor5BG8qw2WxGXw6e03uLIEBUNJRKJdVqtVAoKBuegXlptVqamZlRvV7vqqBg8dCGmOdnD5aZmZn4mySrXO48UfTw8FDFYlETExOhRHK5i/4EBwcH2tjY0LVr18KQE6oils0ihME6OzuLxmZUIwEYMLS+rwv3gxJCwcFG4Jm6cQJUnp6exjw7pekUKbKK4fLcItYLgMGBDCEfD93g2Xp3TA87YfhdRpwJ8mRDr/Z608ONfQqWejFBHD52znS4XumVhe8gJAUive4tBQ8O8DwxPWVu03vkmfjNvI2MjETeUbFYlHRR6QHjRViPzRvRYcy5G4g0RITOdIeIEK8zVg5avReRpAAhzeZ5Xxvf4mFlZSVYzu3tbRUKBY2MjOjq1ashG8ViUZ1OR3t7eyqXy1Flwu+trS3lcjktLi5qd3c3jPGrV6909epV/cVf/IXy+bwePXqkFy9eKJfL6erVq6rVatre3tazZ88CpE5MTGhsbExbW1v61re+pfv378eOuVToUCY8Pj6uTCajmZkZjY+Pq9FohE66ceOGPv30U52cnOjWrVvK5/PBsC4uLkbocXZ2Vnt7e12ggDL+9fV1tdvteD7mlz5E6Mt6vR7sMbtVS+eJp9/+9rcj3wUdho5oNBoxfzi3rBW3uw4isBkpm+gODeskXSO8BqMGQ4b+T0MunMcBC+dIwzdfd3yjRFJ/KKf/UnrTM+udnvQFxaC4csIQsgj9AVutVjR/co8NWku6qHf2BFKu714dE8t5fbI9uWtwcFCjo6M6OzuLhKFMJhPlXqB/YqAYCvpB5PP5QKQsJmdoMNB4RLlcLpJTfe8WksIAb3yPkthM5nwHRFgJuh9yDQdb3s2QTd8QmpmZmTDGtVotFBvfB7Xj/VCJ41QvHriDmJGRkWgv3mq1uuhalBm5NSTEQkVimGnqRS+EQqHQtfcEoALA5mVpJPlKF7088Ai3t7e74uieHU8iK6Ez96ygYzHqKS3v8XhAg9P1yCP3C4B04+fMoHvo9DdIGUVCdw76UqaBcYZWZi0CZFifb0qZ+tp2RcR5OJeHezxclDKe0LqAbg8L8dn0/pwVcWWcKm7PG3HGyVlQdIZTyYA7ztlsNmNbBYAqu8cuLi7GZmCDg4PRNRj5HRsbC3aDNc94sEYZT6fR6U8Ck8Z4sz5pbpV6quhJp9Vh9VjjfX19WlhYCKeKHWd3d3ejpxDh0MePH2ttbS16dNCZc3JyUr/+9a/13nvvqdPpqFKpBGPx3nvvSVJsPPfHf/zH6uvr0+eff67NzU21Wi2trKx0hRemp6d19+5dDQwMaGtrSyMjI7p3716wmk+ePIm12dd33un5k08+UV9fn773ve9pampKKysrevXqVQCXVqsVbEk+n9fKykqwn59++qnefvttPX78WPV6Xd/97nfV398f6+Lo6Eh/+qd/qkePHoX+JVRDXt3MzEzMW61WC5A3OTmpyclJ/eY3v9H9+/cDxABGcQaoiqTyECaCCIADUWconGF02XB2LNVP0sUOzsg9eikF217hmAKSy+iJS+d0cEPShefhFKBTQE4N+UC4x+A0MHErFqErF5QNigE6EeoWgEA/DL7H687IuGeDsfDXUVo8Jwv85OQkkhk3Nze7Ym08Z7PZ7Cq/xNjQjRTKHm8fz5+JK5VKXbu70v8DBQVQSZUPAoCB8eoRlBKgDCYAFoMwCuEeElDZlAqGAbaJhZLNZoMh8TlzxXx4eBjlt/TWoBSTEIsntb58+TI+T7wbatpzO0qlkvb397tYC+aS8RkYGFCxWNTg4KCGhoZC8cOSEQYBmLCo6P56enqqvb29GH9kaGJiIhokjYyMdDENKcWOjHjoyo0ZwMh7i3Ae7sv7SPAeoIjxcIDv6yxlSFxheKWFg2AS4y4LOFhzTtOmSjFlC1yx9fLGeoU2/Ht+8F2/nrMjKZPijhLXdyPtzhRj1IvtQP/l83mVy+VIOMSLxVkgz4q59Fi5e5GesO5hKL//FMBJF/ltHhJ28NLLc4Y55h4AwJIib4Jdouv1ulZWVqKp1uDgoN56660wkAcHB3r48KGuX7+u+fl5ra+v6+TkRHt7exE6IbH65OREf/u3f6tWq6VKpaKxsTGNjIxobGxM/f39evHihRqNhiYnJ3X9+nX98pe/1KtXr1StVpXL5TQ/Px/sUKdzvqcMZcqEktlxd3l5WWdnZ6pUKvrqq6/0ne98R9lsNqpMMLaNRkM3b95UvV7X22+/rfn5+UjO39/f187Ojl69eqV/8k/+iW7fvq21tbVodU/y/OLiYoCITuc8fI1zx2ae7LAtKeQGXUkSqjMbHITdXA4Byb6+3A74fKchFtdZyHKv9eVsouuPFJT0WpO9jks3B/PB8lipV7I4HZjGornZXjEkBsMVhyM2PuOZvpwHtEaiKZQRCA8jzedJnMKgYqQxSjwvisETNPf29iRddKGEMu10OtEWnKoJqiToJEroBWOQ9iZByDOZTLTqBcRsbGwEJUvGM9UbNCBi8iuVSuRgQLmzGIlbFovFGANJXQwDCVqwTR56Yjy98RqUMYlQjDUHnyM5ivH0vIn+/vPGOijOs7OzaH/ONvWHh4fR5pimSLAFhNmOj481MTERY0z5cq1WC1B7cHCgWq0W4CebzUZMHVZoYmIiGqORiAuYgMEhJNLpdIJBIn4OAPfFjmwir7BC3BeGA9nH86HCSlK85kCCzzoLydoBpAPaXIkAFh2ocC6v6HrTwxkO94zSNe/Awr+bHulnU33hn/Fre+Jcysj2AjWpwkwZhvSABmfehoeHY5+TcrmsycnJANeekD46OhpsbTabjc6/fA55cIYuzX/hftK8Dge1XtLKc2OEkFt3ElmzHlJzZ4b1ShiS8tapqSmVSqXoN1GtVpXP56P0nXXKeA0PD3ftYIuBpOLm7OxM8/PzMYbPnz/XJ598olevXun4+FjT09OqVCoBZE5OTvT06VNls1ktLS3prbfe0s7OTuy2m8lkdOfOHb18+VLPnj3T4OCgrly5Enu2oLvJPYNVzWazWllZUbN53h316dOnyufzWlpaimsTaiekQn4fdoJz4RjhnI6NjcVGdPQdIp+DbrXoeBL/JXVtJsmBHnK5Ttk96be7hjO/Lvu+jhxQODjx933tsF7e5PhGW9unNK8nkPoCd8H1wUL5uJfug8AgQcHz3VwuF56vN73ix8MoXIPcijQ5C2/RjYiDJO6DifbESjbywbv2rHLABxUb9NuABh8cHIz+G9ns+f4itObt7+8PoSUTvN1uB9AgxFKr1SKxCpaBraGz2WwsWsIsVGns7e2pUChEC3aMP8fJyUnESzOZTCwEvDXPd8HgZzIXlTjsTAk9yuEbcZEoy3cZI0IpKCYapDGegKfx8fHIZOcaULiSgtnheSXp0aNHMV4oh3K5HGEvnh2QkclkopQWQ00NPgAGJQojhrxQAeNsRmrkkC9klrXggIM1gLfk56H8V+ruiyNdVDGxBjBMfAZD6dUzGDqnaz3X6jKHhyL8Na6fKqvU++r1+V7X8N8crnP8PlLF6uPhDJef36sEuOdUpzE2lHGTAO1trVHa6J5O52J3W6nbeUMmPBTC6+gnv3fvceJxeJgZPsfaQH8iFySgZzKZSNhEbhkXZ10ymUwwDWysBtv35MkTTUxM6J/8k3+i9fV1lUolPX/+PHKgqGTpdDoBDDqdjnZ2duLz9Mj45JNPJJ0b1KWlJf3oRz/SwMBAhEjI+ZiYmNC9e/f03e9+V8+fP1dfX59++tOfxjYKQ0NDKhaLUSZcLBb1e7/3e3r8+LGazaZ2d3d1cHCgmZmZcE4ePnyoK1euRHXN5uamJiYmdPXq1ciZKxaL2tzc1OrqajT4arfbcS0ShtFFrCnYw4mJCU1MTOjZs2ehh91mejEBu+k2m81wwNgdmEIF5Cplwlxmpe6QiTseLlO9/uZwliP9/puyHNIlQQcUI4vSF6GHNFjMPuCgLwx4Sh0yaNDJfJfmW51OpyveTAY1HThZaEyeVx34oPggcZ8YDvcwACfQ151OJ8pJ8f7dE/BQE5sroeCHh4fj3oifc41CoRCTCeolIQomIZPJaH19PdgNH3OP+SHovl09ihED7U23AB0ktbVarRDiZrOp6enprrKvTue8ERBtnGE8ABmMLUqVTp/EhRcXF2OuhoeH4xwYWipSAEnValVzc3Nd1Tl4BfyWFCEKDHetVtPo6KjGxsaivHd/fz+8r7Ozs6iBd6PHQqbTKQqVklmYE0CWGxXkDrn05mGACnKRPAMdQ4P8sD6YU/cukDcSmpl7p1OdAeHwKhDkFS+RBDjAnVfoXBZwcG7Gw9dX6kEhv6kecSPqazdlzfjtypHrupFMrympa1ydoubwPA9kE4Ps88DcET7BS2VNwHZKihJST3b3/ZYY+/S5ua7rNf6GsYA1k/RbTe34HM4JSaewZuSAuPPE92Fo8dwJxY6OjkbJL+e5d+9eNCU8PT3Vxx9/rOvXr+vmzZu6cuVKJKXCslLVc/Xq1XACt7a2oi8Hjp0k/eQnP1GhUNDVq1dVLBb11ltvaWxsTPV6Xb/+9a9jG3vWTF9fX4SpcZDq9bo+/PDDAOXXr18PZ4Sw96tXr6J5GqGchYUFTU9PR6fT/f191Wo11et1HRwcaHJyMqp76JnEVhToHuaGtVkqlTQ/P69Hjx5pZWUlmGZJmp6e1tDQkDY2NrraNMAGZTKZSFyXuvsruWOC/PP8zoQ5+5iCC3cM/LPOGvq6TlmSNzkuXTKL5+SJWp7U4gsZQZYulAxhD87hC5hz4cW6F+A0j6RAjSher3rx3AOuhZF3jxRjxn3j3biX5wuf7p0YOxSIe6IgXxKharWaDg8Pg27l+yMjI1HPTUwPDxovg9bf5DSQl8EiJfmp0+kEMzIxMRFj5bkKZFhTbYMAEiIaHR0NBobX6V3R13feDRTaj7gvVRInJyfRZwOKkK28SYqtVCrq7+/X4uJibD2NMU4pu4ODg7gXem3AWMFUAIC5f5TZ/Px8zO3w8HCUJ0KXouTZZZdFjdwByJgvQgydznm2PmwB8oVy94RHGCcOWCM3XKwZElE9twTjQjwehQLzBIDx9eA0vhtQnt1BTCaT6aqm8bXMmHhl2WUOVz7MaxqmcGPOmvSQrOdhOfDnSBkRruWeGP9jUB2EOFhBXwD0HHz5HCIn3COsI4mVrMWnT59qfX1dCwsLsQcHIUWcF6/s4rxUmMEyuj7hs7CAyAHPi071cUSOMBrOqmUymQDW7vk6GHNAzPfw/jnH6elp5KrU6/WocJuYmNDk5KRevHgRbcZpuvXuu+/qZz/7mfb396NzMwCBRNVcLqfV1dVgM6ampnR8fKy/+7u/k3TucJZKJV27dk23bt3S+vq6Dg8P9eLFC7VaLZXLZS0uLgaIKBQK+u53v6tqtRr7uDx69ChCqsvLy7py5YpGR0e1s7Ojx48fa2lpSd///vc1Pj6ug4MDPXr0SPV6PZwV9mvBeZqdnVU2e7631uzsbMgdfZIAHzjP4+PjoXO8Iml/f19/8id/or/8y7/sCj87m8E8A1aYY//bWyxw/tTxRvZ8TfXKEUmBR8pyvCnY4Lh0IqkDDPdM3AhIF0rQFZk/HDfr/3t5nKNDZ0Tw7gltcC0WGcDI4/x4H+4dOCXp4ITzeajFY5koD0+YJZEM9AdLsra2FgYlk8moXC5HAuDe3p6y2fPda/f397tyBvCm2QIepUTslW2lydlgm2iMIWObz+c1PDwcDApdPQ8PDwPsYLx4PpQYYSEUHSVoLCxAId9nf4SdnR3lchebImFsqRYhMY2wFQqejHJJATQIidCEhxg4lUB4SxsbG5qbm1M+n9fq6mqwNyyo09NTXb16VdevX4+kN5igRqOhvb09FYtFZbMX1TpkkyMD09PTymazoSxJynXPkP97GVpJ4Tli9DOZTIAul1GUAWsI44MSgjJ3RYaSIPnPqXTyjjBonA9DC6vGfKQle5c53Nil7IR7SbyWvu9MUC9nJqWRXV94bop7etJvZ9c7S8l5PbTF+HBeDw2jQzxXg+/s7OxECBMGhOZ+nsDuspOCmzRpFV0EmHAD43Pk4+PGxEECc86z8AyMl3vOPrapwcOpYb7Y8whnpNFoaGFhQWtra2q1WlESW6vVdO3aNVWr1Qjt0gzsxYsXevXqlc7OziJUcXR0pMePH0fSdKdzXuVWKBT05MmTmKNr165pbGwsANnW1lY0U6SnEgb93//7f6+rV6/qxYsXGhoa0vXr1yPUnc1mI5wzNDSkTz75RJ999lnsycK6JYQzOjqqt99+W1evXo2mY+zJQjt1qghxljqdThQl0JUW5ou+JpK6ekXBdDir5akEDqiZ914gP3Xi0znuBSDS6MDrgIfruq87Lr21PYcrBh7cmQsUaZrH4QtJ6u5yKnUjLjfsDJwvdPbacHZEulDuHorxeKcDGP/NogY4sMA5L30XMplMVze5/v7z7eXxyjDmeKxQ/ySJUhp7dnam9fV1DQ8PRwkdxoxqhkqlot3d3TDEs7OzarfbGhkZiURS2gjDmCAYJJbitVFS6hUTLFoMKXRpJpOJ+DQGjvHA+AISAA2wCsgHckC4BlRP5U6qSCkvY/xrtVqXN0+YgN4ntHO/detW7MNAkzLK3ABtGxsbKhQKcX6MY7vdjnwZxv7w8DBybEZGRiIhrr+/P4BOvV6PnXZh8AApvShLgISDUz886Q+5JYTDd7kGXrGfx8Mz/A/o86x3QA6GLa184f6Q3cserrhSRebr2OXDAYIbf/9+GipNaWT/8c+kytfzNPyeU2/RQ0TItNPThFUA6HijvMZaZoM0xl3q3qzOS565B+/7kiaR8jzO/vhY+PM5cPA5diaU91xfu652wIVOZH6890e1WtX169e1tramgYEB7e7uqtls6s6dO2q1WlpfX1elUgk909/fr7fffjvWOAzh3bt3ozXB5uamBgYGNDc3F83+KEUul8vh/Jyenmp9fV2jo6O6cuWKlpaWND8/r83NzQihMM4//vGPdfXqVb18+VJnZ2daXFzUF198od3dXd2+fVt/9md/pna7rb/5m7+J3WXZd4bxHxkZ0c2bN1WpVHR2dqYnT57o/v37+uCDD7S4uKhcLqe9vT0tLy9reHg4Gq5hl5ABqnZoGYB+ePz4sRYXF/XVV18FEEx7SPlaSI19ylA4SOdv1z8pIE/DnazJVA5T2/6mx6WrV1wRcePQktyI3wwLB4PBYnCk5N4MD4bH5tQ0MXOa2XiMNqWwvTyVc6NgnaHhHvidhoV84cKQoBgwoGztzOIlluugBYVO7oRnV5fL5chUBlQMDQ1pZWUljOHQ0JAajUaElUqlUiSIUS3jyWF8xvM0KNva3d3V+Ph4LIK+vov+GjAq0oXydQEl45vKFmr5yWXAmDGOgCtCKXhyqReXzWYjpyOXy0XuBX1KRkZGAqwA8ghNLC8vq9FoaGxsTNeuXYu20pSmMb+MPXIkKWK4rqTHxsZCXpwloMkXoSF6s0jqquRh3FyJ+xh6ZRV9YDAiPj6876FDB2tuIDzsBxjxviCELFiPDk6cHQE4k39yWabD10ivwx2PXoe/7uDCv5MyHqmn5z+92BbXG6xbD+UyLj53GHTG20ERyciEHwCreLKERNNcNk9g5X3mg7niAHB6l1IHBi4T6C7O72E96HYHZQB/1pbrZmQpzQlyZ7Hdbmt8fFzVajW2L1haWoq1Ozg4GHkRhGnx2qWLcMl7772n1dVVDQ0NaWJiIspUm81m9OpwO3Lr1q0AetPT01pYWIiGYP/4j/8Y4dX9/X09fvxY3//+91UqlfTgwQNlMudNxB48eKClpSXdvHlTb7/9tn7xi1+oUCjo9u3bkeN148aNuF8ckrW1teiUPDo6qrt374YuHRkZ0fr6erDZjGej0ehKAGXfFyox+/r6VCwWlclkYs+ex48fd/Va8pwtbIyDznSt8L+HgHnNmUQPv6Rg1c/5urV1mePS1SspzejsgGc/I/xOJ7pn5ouW8/EwCJKzHk59eqIpCtnpZZIk8a6plACU8DoDCVp2LwIlzeegxLgPvBuQe7VajclFUQEUoPRRDr7tvaQAR5SalcvlYEzIiQDYVKtVTU5ORpLS6elpVMdwXbrosajZKwEjRr4GixcPGPqeDHoWOHNcLBYD9NEsDSaBeSd0wfmYL+hDDCB5KvQEwIuCWWm1zjfQo/U5YSKAycjIiFZWVrp6l4yMjGhra0udTifKjSlVHh0djUZGsDx8p91uRzyX+5UuShw3Nzc1NTUV7+F1dTrnm2YNDg52dUv1heqeOkYCYOilsIAGPo/8wfoQy+fwEJGvBZLGWFfOPpIjggJqt9sBuLi+sw2wdJc5UuaFcXCFmAKB9LeHB1JK15Ulxo/DQwkpk+rX43voDBS3j5VX1qXX8lAa83JwcKBsNhtJ4/wNy8Fr0PPoyNRQcH98DpCAoeKAkfI5S8dLutDTrmv9eRyIObPGPXlI3dkwZNplBedgcHBQn332WeS09PX1aWtrS1euXNG1a9eiXJ18t729va4iBcZwcHBQ169fjz2mNjY2Yr2hW/f29lStVoMZQTdub29Hoj6h1p///Of64Q9/GPvPzMzM6Ac/+IGazaZevHih5eVlTU5Oam9vT//u3/07SedhVUC5b8jX19enDz74QNI5K12tVjUxMRF6xhuswTLTcdT3voGxZ7NFmNcvvvhCH374oTY3NyOPzR1oZBv93UuOUhvrhwPsFES7HPF3un78Plze3uT4RrvMuhLNZrPBKvAe3h8KOE06dfQHdeweh1eRpEwIg4/CJNGPCcPwokDS0tpWqxX5CBh7DBEGCAaBBU0VBrkKjAOgBI8XgZSkWq2marWqw8NDTU1NxXhRCSGpyyPhPmiMVSgUwovCy/EDo8zfjDHPCkUL1UdM1JOBJXW1UHaFg3fPPLlCcAUF+HIWg3Hh3hjTTuci6a3dbnexUcgBz8T9UP1Tq9W6PLeBgYEodSuXyyoUCpHrgQIErBDTpW0xyWq5XC520wQkSoqdImGYAKpUBvDMzeb5BlQk12LAkT1k0nteuBEB0NJgjOdG1oeGhoJ54twcAG8We5rX5EmLTn9ms9kAg254UuqWtXYZZeIy7QbVjbtfA7lNnY+UFnadwbi7/nDjl143ZVzc8PIZ7tudDr+GPwNj5WwB36eKgg3QYNaQL6l7l1zGA31AxRTzg3HlOeiA7EwV48P6YV2l8+bgwYEda5VwiYdh3KgBKNrti9JPzgtIopJwdnZWc3NzevLkSWxtPzg4GJ0/yTean5+PNUKC5vz8fDTHolqQsQGsoPvZvRVHlZ21nz9/Hm3OaTh27969yIl7//33ww4QQr5y5Uo4jrdu3dLi4qIkaXV1VZ9//rmOj49Vr9dDN8zOzmplZUUbGxvqdM7LgCuVSuwbs729rYWFhXhGdNDY2JgGB883BgWkIBv05JiamtLTp0/1y1/+MtYlzqIDDCoLfc04AEBm3blIUwl8Dfi65Ltp+oOHc1zGeoV5XndcmunwCzkj4DShIy339KQLzyv1CD2XAiGS1FUWyYOx1weGw8/JwnKat9VqRRmnJ+jxfzZ70Xoaj8QniYH3fA4yrSnHHB4eDoUAsIDe8/wEmsI480ClCwwPxjCXywXqZ5+Vubm52N8A2pZ8EYQXCpZKGsojUcyeJ+BsT7PZjA6isBwYMPJnpItkN+aFxQBIAyDBWCAb5EzwOveJQSIvhzkkMx4mgJwYDCdAgS5/yAc/xI4rlUq0M0dhVyoVbWxshAzSG4VwkVPO/E8raMIQNEPb2NiIMYBtQP54Ts9j8MWODDjV7iDbqXhnEVlrACDWCQoIAOJr1a+ZevSEbjqdTrBkfq03PdwQuwFzBZga/NcpL1eejEdK8fJszqCgfJHF1GPzsUhj1Q5Y0rHza/jaQf943H17ezvKOD0PwpP/Uk8RWeG+PQ/EwRQ6ykNszkC7YeL86AaXw5TddcDrz+v6knOkcgEL7O9du3ZNe3t7IVvNZrOrZF9SNOyTFHlwJycnwSbCAuTzeS0uLmp/fz8aNI6MjAQ7ubS0pNXVVfX19enu3buanJzU2dn57riVSqXLEaGlAGuG8aTjKKHX4eFhXb9+Xbdu3dL+/r6q1aqeP38ejtznn3+umZmZ6AdClRL9PQYHBzU3Nxcl1DBfuVxO+Xxes7OzunLlSlTfSIpcslwuF3t/+dpJ/0befa6RT//M65gQ/547J/66fzZlTtLzvMlxKdDR6yHca+bwuKIrLhYOvx0AsOjwJjl/SvdxTlds0kVyFsDB6UsvH8pkMuFhO5XNs3j+CTQaSUgYW2JvJABxPrwSYn8gTIAR4ZGRkRFtb29rf39f/f392tvbizIqspqLxWJkUwPEqCDZ3t4OZgVFBbCihLVXrA9GgmRE4vZO05O7QJWLt5UHeCGcsDIpPYeSAbyliYl4WNwTKJ69WciVYV8WkkM7nU6Ap9QL8lJD8kG2t7eDZdrd3Y3rDQ0NaX19PZrwTE5OamJiQqenp1H6S7M1SVGuS3MgdrRFgR4fH2tnZydeZ4yJXTvgZh1wL5zPPU/veeIVYawdxhdgybN7yNAZQtYZ4M29I+4PVgngj9ynSuh3Ha6QPPThyqtX3keqAFOWxNc+r/EMKRBxBduLqXkdlex6J1XMqSfnYQkHHzgJDlCYX+7H+wqlzw+gJS/MGRhy2dK8NHcg0pCQGxIHE9wPIMMBjtQNGB3gSAog5+dxuWLjREqJ0e30uUBPSRfJ+YVCQRMTEwEw9vf3Y+3DatC/SDoHOTgS2ex5S3NYi5OTk9jV+5133oldt+mj0Wg0dOXKlUgQz+VyGh0d1Xe/+93oOPof/sN/0EcffaT5+XlVKhWNj4/r3r170dhrZWVF77zzjkqlkt55553oTkqPJhhMdD8l+uTSsXcMO3ZvbGwEcF1fX9fQ0FA4qoyrz63UnfDJ/66rewH+9PA5d2cnfS8F/Ok6cVD8u45LgQ5uCCHiIdOF7gPidCIPxSJyz88Vof+w6DwxM921E2rZvUwovJTK5HyeXEV83BssAV4AAfR9YJMwwkqSYkdBnhHWxg0tzw1FSLv2TqejqakpDQwMaG9vL7Z7pjRUUpThwXj4+VE2MBiM+dDQUFe3OsYeYNRqnZeoYsQ9YREvYHh4WMViMXJiOp1OdPEEnHANzkvehtPsADvkBgCIkeUZiGt2Op0IK0BndjodTUxMaHV1NVgXrkHyFfcFk9RqtSIOzO6f7H8gKdpW53I51Wq12IsBwIGcEkKBpSK5lI6plAnisfj9Y1gALNDZHgbBQACIALceDvMcp/7+/t9i6mAGPYk0pUehy1GEfIdrA0I8Zn9ZL6YXQOB11x/ce3q4IfTXXF+4AnXWwlk13usFFFL2INVhvOeOiztJ6AzAMu87/U2lE6Ff5sapaz9I6gaMMDb+fcaOcfX7cz3DZ9NxSL+PvkidipSpSscnZaoymUx06nXAKinYHhwdZ+wYv06no1qtFvsctdvt2KJBOme7MeilUinu+ejoSFNTU8GyAjhYn4VCQZ988knkbzQajegtRA8imgZOTExoc3Mz2IU/+IM/kKTIxcCpGB4e1pMnTwIIlUql2L12ampKmcx5Euj09LSKxaJqtVro73q9HswL4PTatWva39/X2tpajM/q6mrMF4wuz+d5XSlwdDCNfLiMpHPGb7dTjG3KdvnRix28jK64dHglVSop3egUMMrQPTQOgIajNzeifi43VAAQFC9eGt/lPQYMDxqh5X7xwKH0fYI4AC4kK5JLgFD7eDSbzSjLpP0trbzZ3ZQcCSZobGxMkqKPRn9/vzY2NoLeo4EPDXfwjLkuz3h4eKixsbFAzrATjkoZG5QKoIqxoDMqMWEOgAUKlfH2+Z6YmIh8Cp6bcAgg0PukMP8eQ4QCRlZQDu12OyhW8mMAKChmlAljNTQ0FEm2kiJBdn9/PwDm9PR0JICenJxobW0tci8IdQB6JUVi2vDwcDQrOjk5icz7O3fuaHV1Vaurq6rX6xFLdmPAmGcymQB5ADLGzvNz2u12VzVRLxoV5c1aS8EFSoqukjBxfk/OkjhLydxd5kgNe68DkJUyAqnu8LBJ6lm/DtikgMTPhy5x5ez/O7Bwg+vsCvdMpRjVCoAPwrhjY2OxJrk3Z2Gd2oftg2GFGncGg7F1ZrmXkncZSY2Dj4ezr4yLAzh+cw9+TTdyzAXMJMmWjB/hZSpy0G2c03PynGFmE0e6H7NW0Q25XE7T09NRTca2BXQ03t7e1rNnz7S4uKh8Pq/x8XG1Wq3YOfr09DQ25WP9AFrW1tY0MTGh999/X9VqNTaWpP349va2JicnJUkrKyt6/vx5OItDQ0OanZ1VuVzW0NCQdnZ2tLW1pYmJiSjhB5zkcjlNTExoYWEh9napVqtqtc6T9ufn57uAKLrVGxa6w53Om6+JlPVLWb7XhVZ83tHRDlD8vTc9vtHeKwxYipocUTmidtrPjaDfvOcjMIgoR67hSts9DR8swAmK3O/LwzxpzJsjpY/a7XZUmaSKCGNKGen09HQocDp8cs8IBaEPaHNoRqpuMplMlFF5CVY2m40tpmEXQPuALgwZ4+qlqgjFwMD5vicYM8BPu90Ow4Si8RCM9z3BaLBgCQNBpaIY6SXCPUrq2pcFluPs7Cy6GcIAkZTLtZlTQikk/tLxEGqbyiAUKuNMnxHiuShesugJobjh2tnZCZqbEE1fX18kfJEH88knn2hiYkLvvPNOMFFUFSEbTkECsmA8HITwPvfHczv4hoViLtxAOXB3wON7bnBN1gj3wlqC7WM9fdPDPXoPibqSQ+a5T1+zKTPi67XX2oW9ceCVsh98LnWcUoYgBX+MhYeEfR49XDA2NqZKpRIN+wBYkroMMM/PXOHkcH/ugKXMDuObvuZssn8mHTen5tN5SVkUHxOfO58b16/OxLlc+vkcuLiOxckYHx+P8v7d3d0AHwMDAxE+zWTOW44/e/YsQt7sjj0+Pq6FhYVY45ubmxobG1O5XNbu7m6saXohoaMIYb969SpC1oRMNjc3g9WtVqsqFosaHR3V0tKS7t27Fzlh9Bl5+fJlJAevrq5qcnIymBo2yOt0OrGTLlU4AJt8Pq8rV67o/v37oZvQ77AeHmblNWc7HHQ4gHZA6uvF5T0FE37eFMD0Ai2vO75RySxKLkU8vAdSR8gcuaNQWVR8jkXnPQxQlBwYLhahsx0sfo83oqQxAq7UubYPMgML8uW3J7zCkCAA2Ww20DUTPDo6GhPBewgbYRjYF9AxSUZ8BtQNwGq3LzK8JcWzkUzqxoJ4ICyPe9fZbLarRTnjjxeP0sPzYO8UN0RuKOm0RyURYSr+Z97IccFTIQRBTgiLyeOYzBPhC6qESLRigdVqNc3MzKharWpgYCD2Rcjn8yqVSuGJQSkfHh5GTHh7ezso0PX1dV25ckWlUklbW1sqFotaWVmRJE1OTkZDqGw2G55TNnu+uyXNyYrFojqdjra3tyVJCwsLUV7seTbORpBkxnN7oiAgjbHn+b2aifnFA0oTTt2o+PV9HSAD2Ww25j4NObzJkbIOrC/uidCBK0APmzg748rtdQyKe3Gvu183lqnD4SyLK2LGNDX2AGHAHe+fnp7GDtOLi4uxP4jn2/hcOOBiXPz+0XHuMDkg7XWkrMTrDIHr6V7z5+/hSXsY13O0HKS7sUp1agpyed2fB5lzmZQUyeXkTXizrmKxqKmpKe3u7qparXbpHdYY1SmE5WFLyXPCwSLva2JiIvppLC8va3x8PEI06+vr2t3d1crKir71rW9pampK169fj/y2jY0NNRqNrj4dVCHRZRqdTs5HoVDQrVu31NfXpxcvXujFixeqVqsRWqV3E7oK3YvTC2uFfncZcgDp9jrVA9jQ38WM+Jz6cRldcWnQwQJmMtOQRIrsna51hsSpdBdyzkeMM0VzriQcgTtVTZIoxszzOzDWviDxWtyzdCXBedxIuCGFHkWpwSZIithuu92OXAJJXUmaVCF0Op1oaEV9d6dznjS5vb0d3pGXuTabTRWLxRAcqEmE3HNh8vl817wwLow5YKmvry9KYxkrjIUbNK7f6XQivNHpdIJV8Ix2QjswKsgDyoq29oAhckeGh4cjV4P+IwDCcrmsRqOh4+NjbW1taX9/P8qb2UK80WhEm3jyPQAOz58/72Kc7ty5E0prfHw8+gcQTsnlctrZ2dHExESwTPQlmJycjIoFrs2Ok145BVDyEnOYmtRgpHLo7wMQCIXxHB5T942hPFQBqOF7Lvvco1/7mx7upQM0mHde7wUmXHml3tXrFBtj6uEJZzlSJeqetnvujAHjAdvI4WPl50VvDA0NqVwua2ZmJoAhz572QWE9OavzujyXlJF5XQjJjzTU0osFSWl2Pitd7NvjxsqdSu6fsXAdCKBMQY/rbdhSxsdZWu6D5GzOR/dlWow3Go2oONvf35d0kdOGLs7lcsEe3L59W/V6Xbu7uxoeHo4E9ePjY83NzQXjmMvltLKyonq9ruXlZd25c0flclnlclmrq6uxAd7Z2Zk2Njb00UcfxfgAPkZHR6NBY6VSif1lYJndeR0fH9fDhw9Dv4+Pj2t/fz/AFO3am81mV1t3t4vocxwvl88UcCCDqW32UCKf5X3+7+UMpPL2dcel26CnHgyGyVkIf0+6CJ24oklZErx1SZEERFyfgXEP0I2sexLkXjgC90HL5XJd1yJZFNYCwOJ1+v5MgC3fNI3XOP/AwEC01pYUyaeEYqAFyZGoVCpRsTE5Odm1CEmSBN1j4Gm/7t4D38tkMuEdU+4KNczW7ShWQiOMO+NL4iNMB8oRL9grLrzevdk876zKubzxVX//+Q6wVGggN8R5kQsPP5EJzn4qNOrh+tPT05IUpXT05nCjwbwgfyS8AUrwNKmIIaFzYmIiYsnVajWSv6i3n56eDgYlk8nElteZzHlZ9ejoaJQJ+5HmFXBf7pW4zPv33BtHScCyee6HG6H0esg+ANPPKV3sJUJn18scr1NS/iwAVb+3lMr3c/U6P4dfh+dw5eesGGsaOXNd5gbU2Qd0l+sD9/5brVYkQJMsyVpAF/BsPv58vt1uBwubghkOd+w4B5/191IH0Mc/ZXV9zh2Q+TOmYZ5UTvx+AF0Obvw7/HYjl94jzpfv0O2sM4AdEH98fBxJ7iRu+jOQn0HZ6eTkpFZWVjQ3NxfOFuc7PT1VuVzWV199Fc7S2NiY7t69q/v37+vx48cRQsP27O7uhg6BlYZhJl+D0l2cE9hvOkgjD2ytkM1mY9POZrMZe9c0Gg198cUXKpfL0Zl5cnJSv//7v6+nT5/q008/jfVK3ynmvRew5H+XjTcF9+n3U3n9Xcc3ag7GkQqq9NsJog4w/G9fYA4kjo+PozTKPWjiX57cloICjBzIdnR0NK7h8W1XJCgX/ufwyULJgCrxDjGOLDYUDM++t7cnSSoUCqpWq+rr6wvjBUpeXFzU5uZmGGLpnAU5Pj6ObY6Xl5d/aztjPHd6gGQymcipYG8O6LnT09NgCwAkGF9CKfTA4KDaQzpvfd7pdKIvhSdfeWiLxTs5ORlzd3JyEnkWsAgsDnoROAhCwRBiymazURGCMqBGH+AyPT0diZvseMvGS+VyOZQE3VMZR2r2yZuBFaFvysrKir788ssu743vrK2t6Z/+03/aJYskBDOuJOECOlOPEaPv+RRp1QnKn7Hzyh+MlzcJ4tx8xtejKwkYj0wmE2Er2DbmM2Vf3uRIE2fT9S11Kyuu4Qykv8/9e0UNABZ59VBTqlQB+w6wHJy5Qk7f97WdHil1LSn2UCJ8iXH0xFxCwO7tMlbptdxx68Vs+NymDIiD7F5HCmRS7zVNcMVIO/jivlO74DLmoFrqvakn84yM+hxQPk4iPk6hMwQ8D8wxz0PSOLkhIyMj6nQ6Wltb069+9SvlcjktLCzEjrUnJydaXFwMpymTycTGmMgozGq1Wu0CnUtLS6FDAZ1seX/79u3Im+NeYYWPjo66QkLLy8taWFgIJgXGgpLgRqOha9eu6auvvtKrV6/09OlTzczM6ObNm3r16lWXg+8gl6aaLu8+3ilzkRIMLvcpwABU/ncJr/SiVNy7wKCntJpTkCkNiCHHIKL40m3WQdx47Z5kKl0IMIrUc0M8fOP/09MCmg8P0wc2k8mEsPIMKEhAC3XleNBULbC1fbVajQUwNTUVdeN9fed7nhAeIYlIOu+KOTY2Fv36SbT0fhQkd6LEeDaMPaDB92aB6fFeJdlsNkpyER4vcWUu6d+RNmzjPFSruIGF7s/n81F27POyubkZwAe2x+VCUldlDd07qUapVCpaX19XqVSKElY+T98Nksm4J3bBZFxotS6dh7XY94ESWkAByYF0R3z06JGuX7+uVqsVwIIMfcAMcX0Scln4ng+Q5ty4TLsxYP6li1JKDA9sH3Le33/R6h0PHkPi7AJ5Kr5W/V5h8N70cJCSxvVThQa44X58jXJggJBTwL2zEjgEqYH17zjocqOW5oTxWurY+POwW6h0ASjY0TSfz0ceFdfhfF41xPPytztD6fOnz+VGv9frvRijXgYhZaX8s4ydl9W6oXJmLZ1T/zu9vufK+G8+5ywOc+IgiN8wRaw38vYAb+SWoY9zuZz29/e1sbGh2dlZvf3221pZWdFXX32lTqejX//615qcnFQ2m9Vbb72lXC6nkZER3b59O8rhx8fHw4lwx/bKlStaXl6O7e4HBgY0Ojqqvr7zTd0WFhZ0eHiozc1NVatVTU9Pa3FxMeyUpGgyubOzE/aI9gMffvihTk9P9ctf/lLNZrNrT5dMJqPl5WWNjY2pWCxG8QKFCblcLvRyCiJ6gQv/ncpN+lmfuzcNrUjfkOlw4UKw0uSfFHmnD+qUm8eSQNeea4DSxytECH03RgSU73piJ8Y9l7vYxwLg4rQuhkq6aIDDPbpnxb2isGgw02w2I3ERb52FC/sgKRiD4eHh6AGRy+U0Ozsb3U5ZXNI5ykVopIvETIATGdKUi5LcSRWMK2ZJXeCCplAchKcIHzmghB7M5XIRVsjn8wEO8Ox9bxIAGvk56b0wTzBYrmSJgXqpM++TXwHNuLOz0wW88KAnJiYknTM3rVYrwkmET3K5nF69ehVtiQlBAYbefvttHRwcxB46PpeASZqatVqtuM7R0ZFmZmbCG3KPHZlxI8Gz+ZoCUCL3jCU5M+7hO7D2/31NYrw9DwQ5IN8A8M08p+D+soevodQRYf4BqrBZzrgwLv49N2A8a6/DQUZqEBmrTCbTVUHCed348Rr5HW4UCfXBcNCZEvnDyHKtlB1Aj6RAIdWl6ft+wGq48fZxeZ0H+jqwkQIPdCH6AkfEwy8pwPT75W/Gg/PyHs5L+l1kBjYaR8TnD8Dhr0mKcIzvvM1xdHSksbExvfPOO9rc3NTjx4/19OnTaBBIXh15gPl8PhhTdC49Owhhs5vu8+fPAxDNzc2pv79fy8vLsb7cgUXe0Ofsjk3rBZ7t/v37mp2dVaVS0crKSoSMGBuqDNGlXmzAuGC/Uhl5nQz0Yjj8M73e++/CdDgNyv/pTb5OkfZKWHNaB2OSMh4prehJoL4YoS0xKJ7xD0MC4PCEO+4VZYB3gvAziU5rAyTIOfESOFcWxBslRXyf+B2bnVGzfXR0pJWVFc3MzEi6MAg0raL0kooPSkthWxiLYrHY1TzLDQzjikGhXp1xYOzxkAF+gAS+j/LguT2/hnyWTCYTdCPMFcbTlQf358qHefBFwjNlMufJZIBLFFixWIz/Nzc3o4wRZce8AsxgJxx4Hh4e6uDgIJK96Do7ODgYNfQ8EyEqSZFHAjgkmVBShJEI/TEXPDPvudIkEc7lGk8uBS9ObTpdipeUMospEMFoZrPZoLDdYDiwedPDjYorK1dO3CtzzPOTwOyHr8v0fKlBTvWRg2YPV/iYuDPhn2e8AMoebnLgQCiSUk/oeBhFnBJPpvX7dz3qz8zv1wGKXqyYU/gOIPgM53ydM+hzyAFjIHVvc5GyR72+7/cFwGIeuQfvq8Rne4HU9PzoFtaiv8Z4k99Asvb+/r7y+bzW1tZ0eHiopaUl3bhxQ0+fPo2y1oODAzWbzahiyWaz0WagVCrp6OhIW1tbKpVKsWtuu93Ws2fPdHJyotnZ2eiumslkwk60Wi2Vy+V4j7XPb0pw2SAUR+/09FSffvppgJJGo9G1xmg2yT1yXc6Nbk7lwMcyZUH8c68Du6mNf9Pj0uGVlFb02HPqqQJSUtrMFyw3y/kQblgL92xZaNBFUveOidD44+Pj4Xlw3pGRkUC9TjVBSZMLgidDAiMGlYlLFwyNoEh8BRigaOhoOjs7q06no62trWBi5ufnA6xIUqVSCXR+fHysvb292HMFD5D4NEzHwMBA7KRILTvlou12W3t7exFfhuGRFL0xAE2Uo2Hw6K9BGIm8h3q9HogdAMpmSGl1iu8lk8/nw2AisDAAeN/MrY85c4ziQz7opiqdL7pOp6NisRhNdVZWVoKqpF05gI+xwMBi1KA0NzY2NDU1pZOTE927d08vX77U+vq6rl+/rmz2PKckn89HZvv8/HyU29HCnjFHhjKZTJRJY2CRRebRuw16JYAnq2az2a5x9bXFunEvlTWL0eQ5GXcHqNwDDYigaS9zpGyLG0LWsis5z+lwpyQFVCmrCgjBaHvemBtQxsOBhzNrXh6cgjOeAQDMuCG7nsg9MjKi6elpTU9Pq1AodDkqrutSvYNe8VwHDu7bk1w5/F7TpHyu5WOehonS2L9fM70HBwOc09dyer/+zOm88DnWuM8xz8gad1YIw+46AaeFMfRcNdoRkNdHuITwbKVS0dramlZWVrS/v6/bt29rfX099D+yz/iNjY0FwLh27Vp8Dufv6OhIS0tL0QsE1oTO0iMjI5qamgq9yXMRCvK1ipMmnbO9BwcHsSUEdoriB29m2Ol05zwyZikoTMGbM6zONLkeScNgznT1kpuvO77x3iue9OQMgIMOVxRQdC44DBYeNDQaQgbtz+JmAzBHbQhHf39/VBRMTU1pfHw8vKa+vr6g/lG6XlYrdSe08TzcF9dxKpjrAzBceSAcmcx50ub09LTa7fP8BXaNvXLlisbGxrS3txfdNkkaIlSTKr7BwcGg+HkPLxz2hnJZNiYjwRLwwLnJA2HsuF+qTwBgABGMN89OIhQLHySPUdzZ2QnDdnBwEPkBgDG8dQwq48LCgxkg2bTVanWFzTC8fJ45w/OZn5+PsAjPf3p6GnkwrVYrElQJd9CKmW6Dz549U61W0927d4PpGBsbi8ZvJK7RVMgNk88ji5/XhoeHAxBls9nYHh0jiByS/AUYgv1i/vlcyi5yHRQDytMVhJfEurJzg/xNQiusYwyQgx3Wh78GcHVlx0/q4KQKEbYH4AHA8+TMlPpP2U0cCcaQ8yGvKePGuuAgQXBkZEQzMzPROdg3gPQ58UounsfnzQEC8+WGm/vhSHUur7GWUuPvib6pd+r3y31wDg8VeW7F14V2UoPmzCyy4Y0LHcz5OTxnx4G6y4bPE+vF7we5uXLlSuhrKtfYNoHeO6urq1paWtLu7q4mJyfDGZyZmdHBwYE2NjaiKu3GjRvxfJTkw1q32+1olY7sbW1tRR5VoVCIKjzWBj8UHpCbQq4f6xbnIM29c/aQyju+4zKcso6cw5nNXnPq76VA5E3ZjkvvveLMBR4Whj2lL72CAuHwjHBXlgilh0gQUEds0kU1CQLYbp9n5xaLRVUqldjSmPPRbtypUmcsmGgP36SUE8CFBchk5vN5bWxsRKt0f17KZqkn39zcDC+bXh4bGxuq1+th1BcXFyMhFdYCgYEyd0pRUuyWSutlmo0dHR2F14UiTJUTpVydTifYAKoZcrlcxKdhMNhjxedxb28vWrHz7Pl8PoAm+RfkWNBzhLwTFASAD8Pl7As5PWNjYzo7O1O9Xle9Xu/q34F3Q5Ls4OBglMHSCdb3o0H+SLolz4NKn8XFRY2Ojur58+eSzgEmu/vCJGWz55v4eRgLzxegg+HHUO7v70eyoYftMPiMAcYJ4+mGlIXu3iPnciPqRo/XAM6es+FsH8aEa1z2wKC74fIQIL95bgy/dOHYuHfllSu+Zl0Z+lr1325c03AAh7MzntfB86fGGyBM/hPVTp6Qy736ufwecQC80ZtXhrzu6AUUnMXh2jxrymQ4c5SGL9I58jFxMOShJ2TFQ1/ONHFuvxdnvAAUsE3pc3riM4bOmRpnDR14YpDdceM3OoB8NHKyqG7DIYLBODo60sTEhJrNpra2tkJHttttffzxx6EvcVbQhVevXlWpVAo9ToI/8gtLTNEA1U6wZHwOuUI3MH7pVhzoENcn/r/Lgo+Xf85BiIPFdF7978sADukbMB0IgtOgjvxdqAkFOFL35K+UZZAuFh4KFEBAjBvl6l0LMUbFYjHO1el0QrGj+Jkk7sWFy71Bb2rlCJLfhF54TpgT/6zv+ErP/qOjI5VKpQiTkNgI40AZLeNF2SiKDfTKlu3EFgFb9PSAUaDZF2NCDgbJo67Uj46OdHh4GN/HmPOMsA5OcXtSLx6Lzw0LDO+DsSNuyV4KMELcG+BOUjAyGHMarDF3tVotZAQmplQqxaIi2Zj4bi6XC+pyd3c3wmHkYlSrVS0sLOjmzZtqtVra2dlRsVjU8fFxhKTw+hhvnjubzUYeT6vVigz2ZrMZXUmhSplPyqpZVy6P3qcg9TZZQxgLr9biWil1715nup7dy3IWxA3ym+oI96RcGTkY4CdlFln77nU7q+m0fKpHUi8tpX5TAOH3nDIZHipm3bFWPGeMtud0E/YN8/xaGEaeEV0EmE5BgM+PszBpGKbX9zzslj6//3ZA6mPicwaYcGPUbre7HAo3UOn4upHkGdI5d0eWo5dn7s4i53CwBHhlLSCLXJtrEgbv6+uLPCw2pmPNoCcqlYqePXumTCaj69evq9FoRMUhOo0qw+3tbbXbbS0uLgZ7TLJ7uVwOENNoNLoaawI8p6amYmM6B2Kw3Mi6g7b0cFlhnBxAogNSdszn0h2XXiyWAxUHgP9dmA4WTYoeofJTlJ4iV39IjBYHA4zx6hWz9Gt4KASjxiAfHh7GzqIMJv32AR9sTCRd7Hvi94zwSd1UpHvinU4nnr3Vaqler4d3jbeLV97pdDQ9Pa2+vr7IuWg0GpF41mw2I/8C+pCkIf4m5DQwcL47Iq15AWScD1YDjxbw1263g81wj7HZbMZGcW6A/H0U+snJSXj5KA06s3pogHIySRHyIUubsEba1AqvHgWPkaHU1lvZcy6SvZgLL0+lRFlSgJVmsxk9OEgyk87bnDebTb399tvRFIxN3piXTCaj+fl5SYrxhfVATqBteWaMBGwLyhVPyUNc3iCK8ed5ARGMC+DDlQvr0pkMnpmxZH4cQPr5mWtn/i5zODhP2Ydef/s9OPvpusYNDGvdgQKgjCM1fikjlB4p6HhdaAYG1qsE8vl8sIH0dnBgxH0zH2lCnwM0XuP/NOTjOjFlYr/u6HQ64QikRikdI2d00vvx8XZ97gxIapj426/n3/cxcic0NazcD/KYGlMfQ2f50E2sKeaV7yPjAwPnW0YcHh5GewPeZw3SHwgWndwu+i1VKpVgWZaXl2PPlLGxMc3Pz0efDRo75nIXe+3w98zMjHZ2drS5uRn7wbAmm83fbhXhjJb/IDPOvnvYKmWjUllyAJICQj7rc50C3687vhHTIem3lIO/54sKMOECmwoL3hjnwSPFA/A4pnvSDCKC5IiLsqZOpxOsA70/eD31mBBKqitopeuMBgocJMuEAESki5IsWIuZmZlI/CR3gXNAqVFSOjg4+FtGt9PpBLrOZDKRvIpBI0Y5OTkZCac0x0LRUWaJEffEq/7+/ohR+hbqhCmYG+6V+CJzx4ZpW1tbXc+C4af8i3wGb9WN9y8pMq8ZT/ZbYS5dBsilYV5oSe7sFVVCtEUvFova2NiQJC0tLcUcA/JQAJlMRhsbG5G4yvMPDAxob28vKmVoJkYDIW+x3mq1tLy8rFwup1KpFPKMQsNAAVx8DQEIYLmc3ky9ClgOwCLnRQ5dAQMm+a7nDXFuZwSRwcsczl64p9Qrh4Bnxft1Kp31yN8+BtKF08HfvJca4TQfwsELh7O2zEEv9oG9MgBvyIxvYOghCq7vLBQH1L8nlHKf6dHLuKYMgv/toMtZBTdGadgJmU094DSHJQV03JvLVTr2fn+9QJU/lxtO7i0dF59T/00YmU0x0Q28x3xtb29HKIUNH3d3d6MapdPpaH19Pdb02dmZPv74Y01MTOhHP/qRTk9P9etf/1qFQkHb29uhowYGBmKH8ampKf3Zn/1ZtG1/+fKldnd3NTs7q6tXr4aeIoySy11UE+JEsecKZf7OBDJXPhYeFnGdxfrykGF6pEDC12zKOrlT4ezLmx6XAh2Ag06nE8oYYXKhdiaAB3AknXpmnBshIakPrx+U5ouD8AEdLknM8aQ9DBMIkWRJOldKF6EiTxDlB4CBUSLs4gl4eNZsqzwyMhJesCswFA+IGoCBYfPkMjzhTOYij2N0dLRrh1anzj0ZiUXm3jdeK0aZhYVR8s/wP4oKgTs8PIywEnM1OjoaW0SzCDY2NqJPSKfTiWoXDB6sBaEXWBzfu8V30aXXCAuHc5NUXK1WtbOzo3b7osNqtVpVoVAIpgFmhTwLSZHrAxA6ODiInWGZN0Dl/Px8dFHN5c6TAZeXl3/LwwCUcp+1Wi3yVWBkmCc+x3y5V5gyEYwf7/XyTjudTjA3yC0/ruyp5weoewtulAysy8HBwaU8GNZxel3WmXta7oyg4PisU8h+Dtcl/J0aUMBA6iABOnys/ZwcnryKwUVGUn2GLMHOHh0dqVarBdhH0bPWPWTLeWD3eG5//l6HP0evw9kC/+3gz8ec++CalHJyeD8ZzuHnZt33yslwJi5la3gOf4915GEUPsvn3JakQAs9zVzxPnN6cHCg3d3dcEqZ59HRUe3t7UXfDUK1OAyDg4OanZ3V0NCQPv74Yy0tLWlubk71el03btxQoVCIluiEUXz35OHhYS0tLenq1atdOr7VakVeEOvD2RX0ITqSsJznSnI+2Bp0FCkHlNe6M5SCc9djPk8OKPiOz0vKBvYCM72OS4EOqBqoKk+Qcg/AY2gYszQ+S0jAERiKNa1OYREActLrwRigcHK58wZPZJLDjnA9Kg+8MsXbElMpAKDwfAqUESwGypo8C0AHCxMjj7dfr9cDwQ4MDER4pr+/P7qa5nK5aGmOQUAASOZEiPgctB4Lp90+z5z25EwUCgmfeNyMd+r54DEw7nQ1ZS68WRKysbi4GLkoePEkcRLL9MZjDlTdU+fZkSU6C0oXoQr6IwDaPBxGpYt3r2RzKBiUtbW1yCwnNOZGs1gshpyyg2Wr1dLGxkb0MvHQWKvVin0TaG/sdD0ACIXHejg5OQmZ8QRn9+B5Bk+Ok7qTuDwkBpjwTH9X9OQ5EVZElnndw4aXPRxAuKH2ZFHec5Dv+iEFBrzmCpLncr3TC6SgHD1G7joKJe9GCtCejq0zRs5gsKHY+Ph4F8MjKeh0nATYNambbeLoFVtHHlw3+uEgCwPs85B+x1kd/neDhIFCn/ZiGpBF7tU/0+u6PjeeNJvOn3vx/vk02div6+PNuLHesBdcD2YVxtQ3YUP2d3Z2ND4+HkwIjsw777yjtbU1Xb9+XZIiR+Nb3/pWFBXgnDLn6+vrarfbXYZ/enpa5XI5HCzud3p6WpOTkwFifHsGZMOBNsnqsNIwzLRdwBHxsXenP503xi/9cdaPueoVgnuT49LVK47KU2qPm3FkxEO+LqTipbIoEjyhZrMZhgBER3jAPSfCJ3gVx8fHQfET5/cF7/fNgveul8588D/3ncvlApRIF0DI43QkEVar1RAcFsb09LRKpVKUyFLp0W63o0W4dNFRj5JSQj0Y/kzmoj07MWQWGEoRUMAYE7JA0XqIhbCMCxGfASRgHF2REOZBHmBlYMII7RDbZMM3mvVwHB4ehtzADhAiYYFQreKlt319fXE9B227u7vR4nxwcLBr7xkMLUxJNpuNvhqAIoCwK2I2kMpkzpuwUYUDAKEiiUVPQze2uS+VSgHmUB6es5F6cU5xOn3qYNfBOcrVkxl9ffrhniP/ewwZZq+Xgfu6w8F7erzOI0qNVmq0HSD4550RSY0Ua5n3X3fulG1gXY2Pj4f8MjfutXMtQPnZ2VnonNcxKuhPSqHTkId7/DxnKgcpC+IevwM8fvOdNEQiqSeAcZmSLkJPKXjw+/Rrp4cDAmTX54/DQ3rO+KSMSToG6fn5jR0BeKMvyFvb29vrarJFb52Tk5MItZTLZe3t7QXz12q1tLKyomKxGAUABwcHmp+f18bGhra3t6M/FDqh3W5HA0icUxxFmsgR1t3f39fc3Jzu3r0bjtXExEQXmw4D6g5WsVgMxoZeQOjrdOx8zDxMkrJ4KWvFe71Anp/nTY5Lt0Fn8F0RdDqdLqH2G4WWTmM/vjCcik0XlbfO5mGdyoPuxtPFg3ehI4mQnApJv5X85ywK909ZnCNyDAX36VnPGCxJUY9dKBSiUqPRaOjdd9/V/fv34zuAlWq1GiCAOm4qHTjcwwAMtNttlcvl8J5hQTw/gd9ObxOawniBiPv6+mJ+iQvipUvnjbh8F03mBYAISEARs6hhHLg/QBHjCIBoty/KoKnsIB9lcHBQtVpNlUoljLt/R1IXOIXRgoHqdDrRSIwEsMnJya5OnAAimBFJEQPGq8hms6rX6xGqQE6Zn/Hx8YjfUhJNAtrw8HD0Y4GWRp6QCWck6C+DIuW9Xt66pGCVOD/3xrzDIDHPbpw9T8arwy5z+P078EgVmd+fH+gTN2oeHmEcHIwhh6l3xvimijJ1gtzZGB4ejuRgAPnBwUFXdRtz5pVkfNcTvlPw7rkuziJxbw480vFgbJlrfw7Xpe6N+ji8Lkzm5+IcnnjsxsXnw7+Dk8ZnXsdS+XOiY9L5dpnk8/6a0/+vAxo+Rmyaxk6wzhRy3lSGAM2jo6Pa2dkJ/cNOtjdu3IguxLVaTSsrK5qYmNDw8LBWV1d1fHysmZmZaAdASMTDVt5cEX1OXsf8/LzW19fDUZqentbp6ak2NzfV6Vx0d6Ylw/b2djiW6LfDw8OoOOxVDeZz9Drgls4dOsrnh/l/3bz3Oi7dkRThxQilQpsKgzdgShkQwAOoHyFwJQD9hALwwUNZ+6C1Wq1QGK3WeaMXvG12byX3g4nGIHMO/vbW5gw+4Qjuk4znTqejV69exV4fUKg7OzuqVCra29vTO++8o8ePH+vs7CzyStiufWJiQsViMQQGb9XLF3l2DDx7PnAtT0IFRGFE8vl8hGYwnjAXnU6nizVxBQTdyAZpoH7m0ctZM5mMCoVChBoAYnh3dC2lpLPTuaD58ay5F7r4YRzxKiuVSsgEB/cFkDo4OIh53tnZUbVa1dzcXNCtfX19XbFd7r1arYahh/1hvmm85iCF3J16vR75IWwIh3fDLrvE+knYdWBwcnKi8fHxALrIlodHAFYk+qYJXc4YIo+sMxwFN1JQsswxa5nfrClXlG9yuPFn7lx/8Mz8dv2Bnkh1CfeVgin/OzWMvMb30S9p7hTPS64Q/VpczzA+6AXmiZ2OCWsyZxzOkHrVBUCANc49+TO7V+rnS1ma9HD6ne+nwCNljFIw6PPvLIqzFenr/n963vR19L57265n/fVeXrYzQ2l1lc8brDe6A/vAOmJ9+blKpVJssun7cA0MDHSVuv7+7/++fv7zn6tcLiuTyWh7e1u5XE6VSkX5fD7kCEcXhoNQuj8DTAw6+fDwMPLB2EhwcnJS9XpdH3/8cZdzy55Tk5OTevTokfr6+lQul4NNdQbanXqfG5e5FFCk64nvpMDlMqHYS4EObsLzNjwBMf1cKuTSRRa91J35zI07akVZcg68YRQooMB3GWTxYgyLxaL29vYi0dPpeRS83yNGOqUrHfmjMBxc9ff3a3Z2Nu4ZowoQeO+992Lb9cnJSQ0NDWlubi68a14nhEAHTBgBziUpQhywGV4NwniSjFssFkOw8fxQgrAj3g/CWQ5JEbpgTpgjgByK0NvIk3uxt7cXRhOGgD0C0nEncQrFQA299//odDqR4AVYYkGNj49H1Q5swtnZWexXwL1xXeZpe3s7KEpAAuC02WwGwKCJGonL29vb2t3dDZYJ7xZwMTg4qFevXml9fT1CaF6BwwFT5SyEMxkermCdeYdW7plz8WxuIJBRxsDXnAN8mEHPrfomOR2pt808p8Yu/Zsj9WxdYfIdDmchkX/3kB10pB4uB2vJ8zSQH1iJTCYT4bdc7rxp3vDwsGZmZmKfHd86gbFODarrERLPHYh83fE6bzKdo5Q1c3Dgn/VwVAruPHzn1/V5cRCYUvg+9v78DsTcsKWAg2u6cfbv+Hj5fHpOlCdpO9BljRGG73Q6Xflk7DqNbjg+Ptbs7GzM+yeffKK9vT19+OGH8Tl0A/fn2x0ANpAPKp6c/eIeYTW57traWhQLnJycRFNEWF/aAnz55Zdqt9uqVCpqt9td9slBRspm9BpT11EpKE0ZMwf2v0t+OS5dMutG31Gn1C1ICL7U3XbV0boDDH8gFowbIT6XZklns9muJFE8x+Pj42jgUqvVokwUOpyFkAo3752dnUVXTfccmEDAAgYhm81Gbgbb0GcymSi7Wlxc1MOHDwP97u/vh7LD+yVOiAHGG/XESyowSErCMAMEQPewCGyzDnXn+SiSuoTcKyRYtDATAClo1JGREe3t7UW5bqfTiWZbgCB/Lo9Fes4MrIcri9TIcn+NRiOMoieiDQyc7/hKV1JCbZ4HQvO1ubk5ra6uxjMgU5ybcR0aGgqqEkAHVUmi7NTUlI6PjzU1NaVaraZnz55pcXFRkrS/vx+bwxUKhdjLBfaGvBEvmUZmYFkYM0CbrzEPKblBI5zg44bBSKlr8niYewcGvbyZNzl8XTNHvsZTQOA/frjD4ayO6xG/116elhtKnjtVqIAAQAtjSiiQNcW6o+tvsVjU+Pi4rl69Gu2mvSIHOU7BHX97WNE9TJ9L/k/DLim74M6dG3LG+XXzyfV5Xs7Ha260XFf7uLqz1useXCbS930+UievFyBxWeC+/H7QGc5gt1qtrlwbGDicIDfIyOTc3Jx2dnbiM2wXXy6XtbCwoEqlor//+79Xu93WD37wA2WzWX366adqtVqRF0LOmjvKMMKAoXQrCe55dHRU4+PjOjs7i9yTVqul3d1dDQ0NaWpqKroaDwwMaHV1VdlsVleuXFE+n9f9+/cjjORRBMbW11KvOXZAwjz3YsVSxu1NdcWlQIczEq5Q3BtJf1IBTgUKwXAw4gqJAUs9QP9BgdIym1I/WJixsbGuumXoNe7DKU6vyvEyWjeYJLLyXaexstmsJiYmIp6H17u/vx9hInY63N/fj11hWQiM7+npaVQQDA8Pa3d3V+Pj410GCW+MMNT29nZsy8x+LND8LNxCoaBOpxNsj6Tw5Bl3hJBrsYBYNNK5UV1eXtba2lrXJn2AwNHRUZVKJU1PTwfoy2Qy0fZ3YGAgWpRTIYL3l7IR9Dah5Jc8H45WqxWMRKFQiBg8YwfVyH4nGAnirY1GIxJQz87OorMtScAk7zabTa2srEhS7ECbyWS0u7urdrsd12YcURIwYMgoionKHnIEUMCZTCYAW0qFIh8kl/F55gvDiRflxgta1xsOscaQXebQDfI3PTivgwdXZO6N+lpOWTDpt3NC3AijM9BBqQJ1ZvZ1itH36gDUMj5cr9M5D1MuLS2pUqlE7xcv//a15oY0BQ9usF/3TD4WaUiKz/vhY+u/mXP/XGpA0NMe6vHPMLYcDi79tXSefDz8Hv19fruX7WPH99DBfN4TYT23zFkR1hevsa4bjUZX2JoqFthZbMHx8bEmJyc1OjqqWq2mTz/9VLOzs7p9+7Y2Njb02WefaWlpSb//+78v6Zzd5TqE3gCy0kWpvus9wBN6w/ODKpVK9D/CJsB2SucO3dHRkfL5vPr6+sKxdRbDAQfP68ySgwqfo17zzeFzelnn5FKgw5PLnMp0Y5WWyKZgJEXKnK+XoPrgoERRTtB/0PC1Wi3oaxqBkYTXbDZDwVNFAdVPzI8f31gLowfF3d/fH6jX44aAJp4DYIPy73Q6kfhYq9W6WIW9vT3V6/XobAhFR7ttT2KTFLT6yMiIdnZ2oonN+vq6arWaZmZmVCqVNDs7q8PDw0jGzGTOKy5IQgJ4eb06484eLRgwGA/KUFutljY3NwMg0Ba6Vqup3W4H4FpeXo5GN41GQxMTE5Gk6Zvx+cZu7XY7GvdwX56s6bvcsjDdO8QjwcCxmEjKgr2CEZmfn1etVtPOzk4kmzlNSl5QNpuNxj4bGxva2toKoDg/Px9JatVqVY1GQ69evdLBwYF++MMf6s6dO+HV7O3taW5uLuQXAAIDJl1URLlXj8cEM+KxaDeoXgaYJhMy516VAohMHQFk/LJMh9834+/r2D/nzIwrPcadOfT7cIPphtD1CzqI66QKthd9zGdYp6x/PFNAAhQ51We09YcJ8/P6uHoYq5eXzjU5uCd/LR1Dv1bKYOBde+Ifn2Xc0vCIfw4jyBpN780Ntp/jdY5lCjZ8PlyO0TV+fS919rny8eMz2J70f5hfWC1YWZcBwrC0RifHh3y0fD6vw8ND7e/v6+7du7p37144HtVqVblcTgsLC8Gcco84a1Qtsf7S4gs+226fh4tPT0+1sLCgp0+fduWb0V7g8PAwWNfh4WE9fPgwdK+3s3CA24t98vl4nXz5d1IA+jq5fN3xjZgOJpWLY0BTr8bBhXvDvO/AIvVMuE6KeFGe6QOz6RYZ+LVaTVtbWyoUCtrf31er1YrKC2j0NKkMoafCBWWHZ80Phr9Wq4XShBUpFArxP6Ed9u0g9DE4OKi9vb3YmO3s7Ey7u7tRNgrlTY4A94MS55mePn2qw8NDNRoNbW1txfdow04pVat1XiIG++CLknbwLGoWpVeMoBDYZfXw8LDL6FFLzn0SWqF/BjtxdjoX/QMYr/Hxce3v78d4Ochg8fjC8XLZg4MDVavV2FSJ8aH8rV6vq1arRWKgG3mSsZ48eRKhp8HBwQAEu7u70S210+nERnBkoZOQOjExEeWSmUwmPA7yid5///1IRs3lcrEbredQpAbXZQ+ASBgLGSIxWFIAQffwXBmzbtKKCkC1G2mAi7MdlzkcIDgA8cMZD1eCzlD4D+fx++4FQlyGstmLEm/WKIbMPV4fZ5wmHBaStLPZbKwHwOTAwIDGxsY0MzOj27dvx26krD33zN0wc99Q9+RCEUpOWQr/++tAR6+x5XroZdexPCtjCBiD7eO+kQ03PO5gpV6037OHD/iMywaf8Vw5lz1+AFCsAQcxnkPH+QDxOHs+p7AfVHkB/tCbyNDh4WHsuzQ6Oqrl5WVJijV0dHSkyclJXb9+Xfl8XrVarYuJwBH0SibmwHN+sKMw5pTgn5ycRPh2eHhYr1690ujoqCYmJmL366OjI01PTyubzerly5fhHHuIzAEb8oMt9s7czAmtIzwnidxJT01wFs7DiG9yXDqRlJvjYKA81t4rvtqL6kwROAeG0lG6x3URRAQulzvvnbG6uhpZw/RJoJKByScRDOPBgDrQAW33un8WCwN9dnYWdHVKe7bb7bj+2dlZVKUcHByEgiL/QTr3XulmCDCBSQG05HK5YDBQpl6mynkINyBYnU4nQhz9/f0Bzqh0YEHQLdXp1larFfuo8Ax8h3Ha2NiIzex87EZHR2MOt7a2VKlUIizEPjC+IAhFEU5xqhcwxPgTEvIcI+KcgDGvgvF5JpxSKBSiPM29rWq1GrLU6XT0/PnzGJ/5+fkYO5izVqulUqkUBu34+FgffvhhV9gMloG14Y3CkCu8Sp4POWKRI/vtdjsAYdpvAcXtY8ta8vCKG3D39qQLo8h4fdPDAUMKrjB8KD6nzjlSJoDPpmHbTqcTjdr4ju+9xMFYeniUA10iKWQIphOD5pVVxWIxuvHu7+/Hho0+7synsy3+bKnDxZjx2dexBP6+H653GRcfRzcWaYgakOQ62sPo3IuHK9L1yzP5/37/KQvmbJYbx1737UDEjSpyjLy6/YGVwslBJzPOrrc9Hwrd+ezZMzWbTS0sLGh/fz8S4be2tjQ8PKydnR1du3YtmNv9/X1tbm6qXC5rYmJCQ0NDUV4PIBkaGorQsutZZ6CWl5c1MDCgcrmsYrEY+0Ftbm5qYmIinL92u63l5eVgU9CBqZy5XAEmeM/H2MNwDih97aVzyj2/qYNyKdDhdJzH0Jwm5eZ5OBceSg85hwOKNOnFH54jmz3fpQ90fHZ21kUjYWQILTCReIQouXK5HADJ7xsQ4YyEo3qe1z0k9gvh8B1SMSojIyPa2NjQzs5OVyLR1taW3n333WApvFPh9va2tre3uzx94oRPnz7V9PS0RkdHYyHcuXMnhOb4+Dj2Q+E75BQQwjg7O+va6ySXy0WIwNEtwgbb44lc5KgcHByoUCgE5YfioaqI3XUXFhZ0dnam2dnZ2BSt2WxGGSrz4cDl9PQ0mujwugMwgGMmk4lcDXb1BTSQ0zE4OKidnZ0uj/fg4EBzc3Mhh9zT4uKinj9/HrIIwCqXyzo7O9O1a9d0cHCghw8fqlqtqlKp6OzsLMJX7733XoTLABr8cO+tVqur7bQvbN9/B/lmDNwopUogVc6ea4CCOz097WoVzVi49+rJq5c5HMD4D+9x+PVSb7hXDoCDJuQyNdZ4dYA71rZfxxkcN5hO2SPjGCrWBIAcpokNHO/fv6/Dw0O99dZbeuedd2JtwGKl8W/mOh03n1PWe8rypuzw6w6XAz+nAx0HG+j2VBZSj9bv0UEI95Iy0ykwwkC5XKSy0yus53Pm8g1IIkyLHBDGZt0iD9lsNvS1O7XMEWsxlzvfToEGgPPz83HemZkZNZtNVatVLS0thQ7NZDLBjh0dHenJkyexQSTOKVVwgBDy2wi7jI2N6fbt25E3SBdlrrOwsKD19XV9/vnn2t/f7wJKzvilW5EwF2l+lc81Do0DMQd5Li8pMHYZ/l3Hpft0SN3gwxcHN5KiZRcijw/6wzo97ILvSiKTyQRVxbk4Px4cIZapqanINcBrKxaL0UOfuD2DCnDyjpTppEHvOZNxdHTU5XlhGDjXxsZGl2GEhel0zin71dXVMPJkJJOYhgdNNQxCODw8rEqlopWVFS0tLWlhYaGrX4bv5wLLQukpRo7yW+rP2aiI5wOAQFf7Imeu8dJZ1OyrAsiZmZnpkh8MOOXKLD7ACSELZM2v5zlCxEfZyRWGgHg8cwCQIi8GYFCv11UsFgMUopBJmKUrIb04uG9YJlpdj46OanFxMWSgVCqpr69PMzMzkT8Dtb29vR27HwOoAXuAEZIXAR94Dyj2FAy798xYpV516qEydwBQrkO4ke+klTFveqQhldcBDYweuiFlVXoZpPR3L+DCmnQWBXrevfOUlfTxdsDlYWLkExqbZ61Wq9GMiXXnz/a6cfKx6hWKQi/yPvf5dXPi7EFqwNOxT4FqGoLhcLDxOtDC0eu8vO4A1716rpEyKn54ON7nBJCADPM9WC8a8QE+HKBQBUjIFRvivYyq1WrswJ3NnjdRHB4ejlJ8X7schM1xjrlP8s9IYu90Ol2bhrZa57tz7+/va2VlJcbj0aNHyuVyunLliiYmJvTy5ctwJmk86M/mwN3Dde5ke+Kq22N0BN9JZQj9wVxyvC6U2uv4RkxH1wn+P2TEg2IAeAhXXi6APiAex/MwR3pgyL0kKmVL6B3hW7XjkeOp7O/vx71jxFJq11uC4/mxoB3IkDTK8zDxJLC22+2o46aZ0MHBgUqlUrTU5bzlcrnrGiSrlctl1ev1mFgSmiqVisrlssbHx1Wv18NYQA9CnTebzQgxQb1huAAqABMWP+CQfAXADOG04+NjVavVSCal2RnsCWwESqZcLmtgYCA2R5IUCaNkkruy4RlcgXU6nfDQnRmifl1SNAmbnp4OkNpqtSLLmzAg9DkggjLrdrsd1HmtVtPGxkaM38HBgZrNZuRlDAwMaHx8XMPDw9rf39fR0VHU89ODodlsRoOyycnJyAXysBfgzYG2hw9SOWfBEwb091AKbgwczDudytwT2sA4I/8OPi57uJJK2Qz/2/Mf3MC6Z8v7Hv7iGs6iYDSQZeS1l1fNge5xcOfPwPUHBs53EPVx397eDkbDe62k+TXOavh98Ow8r48bB8ZC6gZAXzfuKRhlLH0e0rFzxyt1CgndOlhwg5TOcy8POp0HruXfd6CXGjWXW+miTBoGF/2BzuX7hCnZJoGEbfSCdLGXE/LTbp9vKTE8PBxtBp49e6Yf/ehH+uKLL/Ty5UstLy/r5s2bXffkLfFxeDOZTHRGpn8R1W6ZTCaACc4065EtHd55551wYH7yk5+oUqnoj/7oj3R8fBylsYS/YZcBQ86qAUbQ774Wfa59fp0AcFn1CEYvmf1dx6WZDi7gRt+Rt9Ofjmo50hixexyci0WbKtNOpxO0EwPiioqBPT4+DuqJpEI8Yy+dJQsY4OHeF9nLzWYzktEwFL3Kuoj98nwHBwdBq5MXsLOzEzkOeKwwDaenp1peXla73Y4N4VZWVpTL5QIoMFYnJyeanp7W7OxsXKfdbkdSKiEEDy9AOa+urqpYLMaix7t3pMrCIYeCjqTct4cKKLc9OztvKY5Xv7+/r0ajEYzKo0ePlMlkdPv2bVUqlVgo6+vr4ZlKF11fPbwGAAHs8D/zSBUSe5/wbDA6k5OTkqQnT56oXC6rUCjE/MPQ8DzkvUjn5cWzs7PhgdAWGY83kznvRDgzM6Px8XFtbW2F/JBFTpv8W7duqdPpRPiJZ3WWjNfd43ZFSrIZrwOSmDuUiXsrjJ+HdLzMG8XtVDXVRB5zftPDqXMHD84kMEYAWA+d4DV6uNW9a09sTBWnK0Y3nmkIypN33Xvn/v27tEQvFApRYUW1AJQ4jeXYrRigBphMQ0M8hxt3jvR+3IlL4+ap88a5uC+u7Z9HZ6Xf4X2vSuQ9ZyB83B3c8Nv1lAM4f9/nwjsSs/Z5Vp975DwNu3vo1MEeXr107tyQEE5Yw5OM+YGlgk1mDunD8w//8A/6H//H/1EfffRRhHwpq/UGhCTGw64DXEjS9LF1poa5Rt/X63UtLy9rbGxMx8fHOjw81FdffaWtrS398Ic/1NnZmR48eKBs9jzRmUR/bxaY5oxwj+h3d2hc3twGM54eZnNZ7sVMfd1x6eoVR7/cpMeNHemi2HgI9xz8ewiqex2uYL0luAs8g0IIAIUqSZubm5FIitfG0el0VCqVwvgg4KlyRvnhzeDxoxz5jAMk7pXKE4SYDYB4Po6VlZU4dy6XixBAp9OJHgAIBvksVC7QmS6TOY8lfv7553r+/LmmpqYiozqXO6+YIGSUz+e1u7urarWq8fFxlUql2E2VZ6frZqvVUr1ej4Q6qjDIbge9s7ifPXum/v5+jY+Pa3p6OtglZKVer+vFixfa3NzUW2+9pWKxKOk8t8VzHQAxLg/MG+EzwiQAI0nBJqHIxsbGonIJSpRdIPP5fCxmSn1JsPWYPgwIVQkodfJXBgcH9eDBgyhnzefzka8CKzU+Ph7f82oSZBKWIa3e4Zkxzm4Y3SNpt9sBKp2xciqU1whHATII+6XgHwbmmzAdqRftxh7ZppV9Cmo8j6GXp8zYpV5vGoZIDTlr3EMP/r47Rm6U+Y2RQg9Rsu0sBPPiIUE3nM5k9WKAerEcvUIp7rH6GPAdQFfKRPAbBqvTudjJOQ2Jp3PZa8yk364wdCCCrkyd0tTB8ftirrkX1/e9wBnjjpMLeMhkMrH5GUaYsCKJ8F6Bh5NC3x5kH2BQLpe1vr6uv//7v9e1a9ci5LG9vR1b1jebzdAJVL4VCoUYg729vQAUPDfyAzuBUzU5OamRkRHNzc1pbW0tHOnBwUEtLy/r//l//h8tLi6qWCxqZ2cnmF9yChk/nxvkkLnuBRS8UhNg7JGIXkxWOje/67g00+GJKL2oml5I2BmJNCzyOuHrxY58HaJCGRBzPT4+1vr6ulqtVih+PH9HapK6Go95X3ueAyMAQmSQASQAH6lbIaL4b926pU8++SSeo1QqhSFpNBoaHR0Nmp8xIITjVQRsAMczsKiPjo70q1/9Sqenp/rRj34UIGtkZETVajVqvKHgRkZGtLW1FXkGrVZLMzMz4fECLqTunSgdrbfb7WBtjo6OtLW1pfHx8TAmZGyzHwrzePXqVT179kxfffWVrl+/3iXEjC9jmXoinIvzweIwPjTeGh0dVaPRiPtGkUxOTgbCPzw81O7ubgCPXO68SyxhqVwuFx1tAYXIOyGclZUVdTodTU5OBggjj+P09FRLS0vRCIgxhb5FSfLMNCDDW+dvXwMeHnEGxDtppp4grJHne7iSQMH7XLNW3VO6zJEaKF7zqqhOpxNjwbWcseEzrlO4Hw85oEvcAEvqAl3Sb+8u6+PqgMDP6fqLxHT2O4LhcwCBzHto0c/v98Z48+xetszhzgzvuQ7091OAkupXPp9+35kpr1ThWlwjlR1npD0XpBdI4j58rtP3OJyF4z33pv03OtjnwB0DCg1gWzHqPDMOIqEX2txXKpVY91TatdvnOXZ7e3u6d++e7ty5o7/927+N5oOU1A8NDek73/lOjH+1Wg1Dv7q6qunpaVUqFe3u7kZ5Pc4CRt7ZGOm8AvDmzZt6/PhxfO/o6EgHBweR99dut8NmABwYa5dtd/pT5orxc/kAoPhcSL/tHKQy/nXHNyqZZQI9/JEyFbzugIMb73VzCJNTOCBXyls9VCFdLDRnKvze2L9kbW1N1WpV8/PzqlQqsZMkRrPXhEA7I6Au5CT/EF5AyXgSIP/j+aJUaCgE+9PX1xfbJxPrJ+kxk7nY6dRZHlgL7v3Xv/61CoWCvvvd70aoYm5uLkI1c3Nzev78ebQEHxoa0uLiolZWVrS7uxthCpgQaGESnk5OTlStVmPMDw4OYq+TtbW1rrbv5C8cHBzo0aNHkTRLFz9Jmp6eVrVa1cOHDzU3N6f5+Xnt7OwEoMFQYkidGcM7IwGVeSsUCtGRNZvNanp6OvIp2EBpd3c3KkoePnyoqamprpCb59Lw/MRmx8bGlMlkYvfgo6OjKGseHh4OlgXvZnp6OrxIwKMDODwrlCIK0FkPX2vOOLjMuxHxOLgbcM85cuXgBssrgjAQaez2TQ9nTlxRpevbQVW6/l2h+ff9+XjeVJHyWRS555iloZmUCUh1AcAO9sLBH7kBsJH+WX6QgRQQuJHuxTC87uA7KRjhcMYDB8LnwEPeHvZyoMn3kTHGIZ1bp+jRZw4Ue4ERN3R+7w6oXeZSoME9M6bcn7MWLhfoJWQZHegheoAGO0+vr69HF9KXL19Gk67d3V2Njo7qo48+0o0bN3Tnzh3t7Ozo1atX2tjY0OzsrMbHx/Xq1St1OueFAp1OR5ubmyGHAArGgiR8n7+9vT1tbGxEZ22aIM7OzkZ/DulcP8FyoDdhXFJ55n9n5Hx8feyZd+kCGPv4p+fkOv/dQAeL3T1Uj7chbNy8h15c8Px8Uvdi4HVnIgABR0dHUe8sXRhjX2S8Pjo6GnFWKLL5+fmurdlhEnxxoNS9dIh7Qri5pk8Kz5bJZMIDp7Yaun1kZESTk5M6ODiIfAwU1NTUlHZ2dmJbZB87SRFyYGfabDar1dVVTUxMqFwuxzbL9Xpdv/jFL1Sr1VQqlTQzM6PJyUkVi0UtLy/r+PhY09PTunLlilZWVrS5uamRkREVCgWNjY1FzTnhBTw8QFCnc9FgB3RfKBT05ZdfqlAo6C/+4i/0d3/3d7p//77+5b/8l/q//q//S8+fP9ezZ89ULBZ19erVSPSsVqtdHiZz7WGtTCYT3go9NzxznL4k7PhIojDxXoBWvV4PkJDJZLS+vh47v9Ls5/DwMJinw8PDAF7lcjl2EyVs1mw2de3aNW1sbKjTOU9yfeutt5TP5yWdlz1LF0ydV+4gM1TPADLIIfK11m63u6q2GCdoZMbGwYWXR2Ms3dP1fiysX/p+eJz5skxH6on6/7A5jAV5OO6U+Oelbk8tNVweqspkMpEo7Gyqr1kfT85DMrUffA7ngnGDYTw8PIztxPFUSWCXuqvcenn/AC5nVHycezEy3FcaquB1P5hPr0bysfRQN/fDdZ2Wd6aDc3A+N1wALf52z9cBF8DQgYPnDyDX/kycy8GbnwMjKilCwuRosfaliw1AMdAYZsLybFNBF1Kepa/vfEfqk5MT7ezsaGRkJBjk6enpyH/CGd7Y2Ih9qb7//e9ramoq2OzJyUnVajWtr69raWlJrVYrQARJyuhU9C45ZltbW5qcnNT9+/eDpcER3Nzc1PXr16NfE3OfgjjkzfOW0uRkTxdw1tXPlRIHKWPyu45LgY5eJ05ji791AUPArkBYZP6A6SJKvZE0yYwDYXaakKQvFndf3/nupxhXzsXn3dtFIWaz2WhQ5bHv1HNJ75W4nnSx8RcLtVwuq1wua29vr2tfgEwmo9XV1QBDnjDZarViIzJvwtZoNLS5ualisahnz57p5cuXmpub0+TkpD799FMdHx9rbm5O/+//+//q+vXr0bBmZGQkEm1HR0e1tbXV1UAnm81GzxPilOQs8D5GmYX94MEDFYtF/et//a/V19cXJV8//elPIzRRqVT0rW99S1988YWq1aoWFhaUzWa1t7cXC5j5hWEAlGSz2WhJ7GDDFR9AiHI5KMeNjQ1J58Z/d3c3ckGGhoa0u7sbCorwDJu/AVZhfgiDDAwMaGdnJ6hVZzU8zwbgRPa6KwLWRK1Wi06IrhT8OQF5mUwmAI2kuCay1el0ogzZk0YldRl7SdHp8OzsLMqHGVPuw9femx6sQfeI+I2z4M5BavA9fpx6X+7dOzOCAnQ2EOCKwvTwrd+nswbOqGHoGBNKIKH/a7Va7BxMEzf0Rr1ej7wAejEwP667eCavFPJx41l7GYdezhnnBJDxrMiRn5vPOXjw5F28bzf2DlL4PuPGuXxdEpYEWDO3MMLo/Gw229VR2I0a+TNsY+CyyRpHX5Aj5M3cGAeMOztQ810Ph1Fef3JyonK5HA4A833r1i2tr6/Hzq8UKbRaLV2/fj2Y79PTU924cUMDAwNaWVmJ/L7BwUEdHBxENWKpVAqngbGHxeC6tHeoVqvRH+jZs2caGBiIBobZbFZbW1vK5/MaHh6OLRo8lyWdZw4fTz5LsimfdbDhn0/Xzpsel+px7ECBh0iF3j8LanahJywArYzXyoMSk4fm9wHwgeE7KCA/oLR9En3xgCgZYI9NYmQ7nYuNn/AIWTSAGZSSe4eEeXhmwIIbAIwjtDvK1fccWF5e7lKKGFAUKzTa2dn5ToT379/X5OSk/vzP/1zSeVlW9v9H23vFSJpe5/1PhY7VFTqHyTObuMvlMi9FSoJoW5Ih2QJsC5YF2pAAAw6ADcuCdSEYtmDf2jBgONwYDoB8I0iALxxEyQq2aJISuSSXXO5yd9JOT09Px8qdu6vqf9H+nXrq3Zrd6f3DHzCY7uqvvvC+JzznOec9bzarz33uc6HULLViB15SQEQB+/v70aTLo0mEnzEAuNDwC5bgi1/8YhRKzczMRCoGx7K0tKSXXnpJV69e1c7OzkC9CRvVUQhGNMKzl8vl91RdU6PDfOMcmBty7swjDrrZbKrdbkdhFnv3VKtV7e7uBrPAyh76cGCkoE4xaNChgBgHsoBEOtOm9KQDbGctcFDIMzKWMntpR1FkzsFYmq5B99BJ3olrE6l/mCMFKx71urFLA4v08DQQ74tueZT+JPDgkbTriqdXnhSdITNeSyb1ARt2hTQiUSkgHQfrLb69FiVlc9whpOPD4cyPvyvvm5437J0cLPgSWO7vrC3nw+gxds7gnJ2dRboAvfTxTZ9zGJh01snBFc/oTJCDWP4xrimYwW/4M6W+huAAYMOqJBgt32+qVqtFE0fYdnaAZcXS5uamRkZGwjYcHBzozp07evz4sZrNZmw4CagjmKN8QOo3l6RIH8ByfHysZrOp2dnZ6KnkQfP+/n4EOXSN5hyCcAfoqWx5IIQd4fppaoX5Z26cUHia48J9OtLIx+muVIkRXNAgTozJdqF0IaKIB6FxgWKQKcTkPl4ExfpoFAs0C21Nnszvi0EBDfOsp6ensQzJI5VOpxP1Fjg+lthyTYz52NhYRESLi4vRf4PvdTqdiE7b7XbsViopdnkF+JAXlBQswOrqqs7OznT16lXNz88HY3B4eKh//+//fTjl+fl5/eRP/qTeeOONWOpHlEv+G2DjDhOmhzFaXl7W9evXdXBwoG984xv6/Oc/H/sOwCB84QtfULPZ1NbWlnK5nFZWVvTJT35SlUpFCwsLsX+Mt6ZfX1/X+Pj4QOOdy5cvx/NjTBhXDJ338/B5arfbwUy02+2IGKiJqdVqWllZiShrbW1NxWJRU1NTsQyu2+3GCh2vRO90Orp+/brW19c1MzOjk5MTVavVATlmvwdXfF92DYD1FVQAHvQJ5gLj5GAipcG9ZgSHmRY78/20zTnXAHhghFIm4oOOlNLlM7cHHGlKwW2Kfxfj5hGuf2cYC+AOPQ2MMKIO+vxwkOfOHv32gj/mjNbWvtqMdyXVxRh4bxXu4xGlg6p0fJ4ERjyFwuc4Ak9z4MDdIXmxILLrssQ1nZXlc5x62o/Jg0wYHICtP1/qM3hmXybsgZfPdZoy9LQNBwyjp1uc3WAu+DmTyURBabPZHOjS3O12dePGDeVyOT148EDNZlMvvfSSrl+/rmKxGOzu8fFx7LtC6hoWjKWvuVwu9ozCN8HOF4tFXbp0SZ3O+WaT2LhOp6ONjQ0tLi5G7yfpfGECbCoMLAESbBKyysHPHiQwZ9gnly/XG853eX0S2B12fOg26P5Anifk755KccPl9KIvpZX6SwcZeM9LcqQpGgAPDpNzx8fHIw9+dnYWRULp82PYoZV5F8AS+X4EnigQJseLOsnXZzKZMDSNRkPj4+O6efOmNjc3AwzRw98PJptlmnfv3o1oHWDE1vSuXA5+Tk9Ptbi4GEwG9R+XL1+O+5RKpVA2GpYBaJyd8nSLpGi4Ri+QkZERXb16VY8ePYpmOpI0MzOja9eu6cd//Mf1+PFjtdttXb58OQqhrly5ovv370dBL2vob926NUABoqguV4x7q9WK+WLfC6+PobsrCn54eKjvfOc7mp2djeLdYrEY14cupScJu0UiV9VqVe+++64KhYKKxWKwUtR40IWViHdiYiLSeIBf2C0+A3gQUaFjyDbykM43ssJ5/O7gkPlDF9E5DDjv7Dn8YVHqRYwJhxuyNBhJHdqweo4UTDi1Pyza5R29kN0dI58zTunY+eFgUOqzQgQ96DeOldos+nkAkIk4sXGSwj6xmgi76LUpTwJB/rn/7s6CvwGWsFfMNSDSgzZqGrgG/9zZp3bcQUVar+eMLnMIKIGF5PvIOr/zPET96EgaWDBWaeo+Tb9xkN4iKGLPq0wmE20TYKuk/vJV7DBj0W63defOHV2+fFmVSiVsxd7enr7xjW+oUqlodXVV3W5Xs7OzeueddwYAZ6/Xi3QujSAZV0Bgu93Wu+++q9dffz3+ztjdunVLDx8+VKvV0o0bN7SxsRH+i9q6lZUVra2tBVvvtpNxwtYzbz7XaWDwJHCITUvTlU9zfKjmYE6dcngFthsJnLgLIkbB2Qk3qAgfA4LS+wFqdbR1dHQU+XeP6kB7HoH5RmsOHHh+BptVCv5ePpFMnk+wK8Pp6almZmZUKBRixcby8rIWFxe1vb0d2ygzniMjI8EAIEjQ5TQ5g2WBAZmcnNRnPvMZTUxMaGtrSx/96Ec1Ojqq733ve9rb29NP/MRP6Itf/KIePHgQwGB0dFQLCwva3t4eyP9SjORFaPyj8+bOzo6WlpaCBeJzFLXRaESr8WvXrkWqZ39/XwcHB3rzzTeVz+dVLBY1PT2tbrerarWq9fV1zc3NRTQCw5DP59VqtSJqAqRQaJXP5wcadu3t7WlzczOWSu/v70f6Y3FxMdJKjx8/DuNzeHiohYWFSMvUarVglSYnJ9VqtQZocqIHGq2xNA7WRlKAIPLNGE7GttvtRoSCs+AgF44OIR/uGLiWG27GDlnyvw/TGeaae5N2pM7gooWkHO70U1rXAcCTQI0HAK636XdSEMKBnjJXXAN5Sh1Vem0PQqDfCXg6nU7IIzViNKbL5/Nqt9tqNBrhmKempsKRuRP3eXEQgaw4YONZPdDy8cVxpClnD9w8mAD4wMwAAkhFMR4OJhg/bDLPxnh6US56AMOWpg157xRQ4mQzmX63Tv7mASfvn6bb0vkHjBeLRUmKtgN0C6a4m3HE8bv+cJRKJe3t7YU9e/z4cQQki4uL6na7+trXvqarV6/q6OhIq6ur0d+jUChoZmYmUtVjY2PRcI50L1tgUC/He9Ck7/Lly9rf39edO3d0/fp1tVqtaMBIWvvmzZu6c+fOe/TOGafUX7m+eQqQ8U2DnTQIuijbceFCUmkwOkmLOjlc2Z0W9of1F2KAstlsUNAetXAe1BjOl/sjXG6kyIdJin0vnrTaBXbCjQKOD6XkmXBUXifixg/lwwhMT09rYWFBOzs7evjwoba3t+Mz+kmMjp7vKEiuGAaCwkSK1bz+pFgsant7WwsLCyqXy7p8+bJ2d3eVyWT00ksvaXZ2VicnJ5qdndXo6KiuXLkSBUoUMrE7L0aIpls4HhwpvSYojgPgbW1t6fDwUPPz82o0GqrX69rd3R3YEppVI7u7uxofH9f09HQUppZKpRDiw8ND7e7uqlAoRDTCc83NzUWqzAEA6TQcPE734OAgWpk3m01tbm5qeXk5+pG02+2oND88PIyU161bt0KmGo1GzDc7Ou7v7+vZZ5+Nua5WqyGLjUYjulNiZGFlWKmRyWQCxGCMcTDoAe/lDCDGndSjpJAdzsf5YMzTzxxoS+cGrVwux5Jdd0bOflzkcIPk4CKNmt3wpRES752CLWdA/LlSHUxTKsNoZX+u1PhCkbv9YQk343lychKpO6h5Dk8r+JJVbBt2IQVVadGmy4HbTWdPeGZ/d9gdB2qeSvEgx98fJ+823VNBDlycrZMUhbaMF4AZBgG55jvoMr9D/2P7qVHg3nTfRUZT8OGpF09NkhZj3lkSz7zROZnlqWlKam9vL5olbmxsxD1I2bDr9fz8vN58880YX9iGxcVFVatVnZ2d6dGjR+p0Opqeno6aON+rZ319XYeHh9GskIaEpNMpyC8UClpbWwvG/OjoSFeuXNHOzk4AHQIzaotgZpFp5iBlAB3EekYgBbL+v+vq0xwfiunwSnCnatxIOPJ1hMrhiAuhcZp0mEHwFS5pfo+8KEueUEzQKgWTPnAYE5AuXR1hYcj1U+MA4qQK2dNNHlnx7qenpwMrKdgo7MGDB6pWqxGBQNOTOslkMtG7Y2FhQdK5g5mZmdH4+LharZbm5+fVarU0Ozurubk5ra6uBlo+OjrSzs5O7Ii6ubkZ6J2aDbqd1uv1gejEu0TyjszF6elpKMr29rauX78e79toNJTP53Xt2jVlMhltb2/r7bffji2YJycn9ZGPfCRatlMoDFPBfinQkZ4DR0loFw/gozDz4OBAU1NTymQy0fKYQtDXX389ottPf/rTKpVKsbqA5cRsU+00d61WU7PZ1KVLlwKUnJ2dBWCgrmR3d1cLCwuanp6OlBaV6lK/oBonlMvlgslCj5AfZNYdkUexrhcwI+hE2ieG6/pB2gW2A/DitDLPzPfTIu4POrxwkvdwGh5DyGee8uTvwwxYGkWlDIyD/tQOue1xp+TfS1lOZAGAQ8TO+X7/09PTcJrscAyIZrklIAZWAMaBZ/DAivx+ygDzfG6j3Ob0er1YseEpbWSIeqd8Ph96xLNwH4ApQMHTM9zfnx8HTCrCGa5erxdFjf4uvl0EheQOthxcU2TJOyMzw8Awz8l51JcxX9g3ghmaGGaz2SgeZY47nU6wDegFS/IBSlNTU7ETOMzkM888o2q1qo2NDU1NTUW9F3tDlUoldbvdWEFIHyE2icPOwAwvLS3p0qVL2t/f19raWmxqSeDHEt61tbVIsyOrpNOGlT44g+VynwYbro8+5sPYkqc9PhTT4U6Ww6MQHjBVdv4fVgw07OBaRINO53FPR/SSgjL0XDfKTTTM2momgwjD2QyEFlaFSAQl8kgDg8Vz4UR5Hp4d8OKtwakLyWQywXDQcwPWYXZ2Njpdcn6r1dLo6KiWlpaC5qevBAKxubkZKRlXRozU5uZmUIXQsDhvjI2kAEewLRTAHh4eRh0EbAj5zFu3bkURKMWU1D4A8nq9XuRbMTKAQ4weEYVTxGxhD0BCSZhn/t/Z2YkN25aXl6MDII3XvCgLIIYBbTQaUYwLZU69EXLc6XSCKUE+PPrjnFwuF8wNjAfOx6MIBxkYSxyCR7MewbusA2owFE65owuuO5yPrCOrTrNeFHTw3v5zymikjsmB0vuBhicxIhzvx6T45ykLgz1DN9wQc06r1VKpVBqoJYDdoMGTM1awX1Dpo6OjwZqmYNBtKXU2MAk8m8sBc+1saq/XrxkCgPJuXjTJOKBPgBevK8AuAoClft0JDp97M14UXbpd8xomtzHDgB7XBRz46jHGwFNQ2Op0/gHjjBX6AXObyWQ0MzMzsLkj52N/WCnCs8Ks+MpBluaPjo7q8uXLAWRqtZpyuZympqaiYH5nZyfkBnkgHU1vGbZKqFarARrq9bo2NjbiOvV6PZ6LerdarRY1dMw96e3j42OVSiWNj49re3s72FqCApdx5MWJBffhwwCG+/SLMKIXsigINwLgNKgrUkrLOkPhxtPRM9eR+lXSfObFWzh9jDLPlVZbp+cgqN5IyQc6zd/xfBhnnxyMDcZAGtwZ0yNT0geg5qmpqWizC7399ttv6+bNm6EcCwsLunfvXoAkCkJ5borbYCs8Si+Xy5ESoWkX7EI+n48mNN3ueR0FQIHnpvLZKU+iNI8gSBuUy2UtLi7q6OgooqyNjQ1ls9nIYeIMAC2ALBwx71AoFMJQYEQdAHp0hWFk3b13iSVaW11djSKs559/PuhtxphOo8w/gOfu3bsRWTFvLIGbn59Xs9mMHSMpQiOyZeXL4eFh7HlDmo98LrIKHU2khUw5cAHIEPHxjsgmeWCn4D2V4aDCdcrpZ+6HofbltBddveL1EtgL1230lc/SgjS3G/6d1PjhJLELaRDjwCMFOx51O1BxSpmD+fH0FOf4ig3+wSahDwQY1AJgz7iOs8KezvH5cieLPUBHeQbaZgMgMpnzNB765CtUsAc4eMAB0b/bAK4FMCCAkPp7HdEPgtUZzoB4yobnZLUc6UVPhxQKhWBs6HkCUwio85ShzyuOnXQKz4ATTeeRJoAzMzOS+lsG4CPGxsaCua3VaqrX69GCgUUAn/vc53R0dKQHDx5EKndmZkZLS0s6ODjQO++8o5GREV26dElzc3PR4ZnluQSnPt8w39gIluryDix8IIA7ODjQ3bt3gxmqVCoBNJrNprLZ813A5+bmom8Qc+k65nrkvw8rn/DD7c3THB+6OViKbPxvTu+mtKcrtleVew6WAYbmA2l6ftAdpdQ3NnyPz5wyR5m4flo9zr25DygXgEBuzZ8d6o1BB2Cx8oX23CgYqylo8rS0tKSNjQ1Jik1+NjY29Nxzz+n73//+gLKwQyqd7Tznxk6YjANK12g0lMvloq0vY0nTMp774OBACwsLEe2CpKX+qpi06ZRHHjSmOTk5iXXltVotdn0l99ntdqPxDjJTLBajoQ8b2XljLtidXq83sMstkVun04lahIODg3D0CwsL2tzc1Ec+8hHdvHlzYMfhzc3Ngc3qms2mlpaWYofedrutlZWVcOjU29DRtFwu6+zsTFtbW8pkztNh09PTsZImm81G8WEmkwkDQVMuZA1dAsC6bKMPOBcHuL4qgGt4ytC7abpzQ64pBHRgwc9Q+E+q1/qgAyfOOzhg4B25Nj87EPM0Rsp0DGNB/DPeD+fk7ADPwrl8jkPGngxjZ9KUlz8jh0eIPo7oDTKcvgt2C+fK35hf5p7n5/r0miHlwBjCWsHaeTDl9UE4YNI/2C/0BNbD7Tm/Y38qlUroKzaVFIakeAYAjDe/Oj4+jkgfNhlnChNdrVZjrxGYSsahVCoNyChBS1qI636GscPucW3sE0XprJ7sdrsR6HoX31u3bmlsbEzvvPOOarWayuWy5ubmND09HSvYmEfqCumQ7Qwi9Wi53PkSe1ZZAl4pim+1WgMtF2q1WhQzs8S+2+0G8OE+p6enqtVq79nJG5lOwTzz7mwpuuRy6d9xP/80x4VARxr14Hg8Z5RGGWnRqTsrV2qMI9cDXKDc/E0abA/tKRiegSIvX4IFgGHgUGQoMwQBJT05OQl2gclzwwFV6A7CI1GU3w1GpVKJNtuSYjksxY0IfqPR0MrKSowXrA1jSW3F2dlZOGfvIEg+cHJyUrOzs5qYmNDe3t5AfhSFZzxwVBSIOnWMk0eQiQQajYampqaCPQHosANnJpPR7OxsPDOUJs6X+UJpTk/PG50Biig4xRiQOnJa3tMvFJ8dHx9rdnZWxWJRtVpNL774YnSPJPJH9ohe6H6a0uTemZaU0/j4uPb29oI5A7TBQpEnZiwx6B4he/rCmxo5APCKf/RnWBrC8/PIMsYhZeI8OgTUIMOkEnmuYRT2Bx1pdE4QgHy6cUoZEXTI39nPTYMaBwGpQfVzfax8HtyQ+v2wFWnkx3WZK7cbuVwu7IyDGeSSMdjb29PZ2Vk4tfTePCNpYE8dek8JWIB6vR71B4ANnKrbWebSQRMMH/eAFZE0YCud4cnn81HvRP+hg4MDNRqNaLRH/yDso7Mw2EZof1aQEHRIimdYWFiItC2bVHa7/QJL5JU5cwYDPffiaGSfFTqMRaVSUaFQUKvVivFDfmlJzlxRk0FqZH9/f6Av1NjYWKyKm56ejp5D+A/YlGazqampKU1OTgZrDaC4fft27LxN/Re1elevXg1WxVOlFMZiQ/b29pTNZmPvq7Suxn924ABAdUCBPjsrOEyXnxZ4XAh0sJqBiXkS0uFzDA7KAKJzuguBT3PHjt4cYQ3LHXnRGk7RnwfDi9F1g5+yMkRevCufEe3TrAkmACPgXexAqZIGlkSyyyvPTCQErQmYoJfEjRs3AjCA8CkIY5keLXV7vV4wHdJ5wRLKd3R0FA4XFgIDg0FD6dmhN11SxxjxLoCHZrMZ40pvFHZb9boM+hgQBY2MjERhLstOidoodmXuPLXCZ6R/Mpn+PiRSf98LmjW9+uqr+uhHPxq9MBh/Ov7dv38/9m3h/hhBjCLGlm6FGGwKd3O5nBYXF6OpELuNtlqtmBMvaGaO0voPZI/DnSXnu2GQ+n0ZGB+AKkZYUhh2T2d4AIBxRi8o8OWcixzuxFMDBdDhZ08pMK/oGd8Zdu3U1vi5bis8VeL3duDBszhoc1Cbplu4B4DK07Wwsb7SbGxsLLpIMi9udzjPgxcHGoBqzmHDyF6vF30nWq1WBB8EInQJTYE2ciQpdI1nQCeRJYAX70XaiOCEJfCkTMbGxtRut7W5uRkBn9fHYDMkhU3LZvsN8qR+ISvyi9OkfgpGgLliawTfNNG7WUvnvgQm1uURJw3Ah/Vh51a3L8fHx5Hm4N2lfoppd3dXW1tbwepeuXJloLCbJf3YcAIz7o+9zOfzWlpa0p07d2LzTDp0n52daWdnJ7bQgNVhDn1+YWhg9ak9c3mT9B7fnZZBpHrj5/k52I+nOS7MdHgKhId2Y+C0MM6B84gIHKikFfxEPhgeV2wmL83l8TNpARd0hJcon0EkuuJcogv+7nUc2Ww2jDCGnefA6Dtt6cWDBwcHsb05VGK73Y7olGWk7MnS6XSiuLJQKGh6elqrq6tBo7GEttvtRvMrupay+gbniOHM5/Pa3t6OHhWjo+d7h9B1k3fGuKV5cV/hw3PzdwosUSKAF42/MMaAQdA+BVVEYazeqVQq4Ry9JTsHIMrZJGp+kLmZmZkoECOdARBBXldXV6NglNoY2CFoTumc/oTyxCkiu0S2hUIhxhHmg6gPIMZ3AMaAdpdtB8IouUczvDNyjhFIx8jTLugqMu8RC7LK78iVF/VdlOngWdFxr1tJHb5HXbwPQO9JDIBf31kOgJSf4wFLynz4z87Seq3Ik46UxZH67Gs2m43l13t7e9G+n4JrT69J/VV3njrx1OHp6WlsRoYtazQawSi0Wq2BOibpvD1As9kM20QwAfiV+qs5fJwJCHCozkJjZ/b399Vut3VwcBA9L6DwR0bO9yva3d0daMUNoELuvbAee0qAJJ3XvZXL5Vjdgc2ADUafvJDamRiCLEAKsuzz6mwUesx3mQP8FWPAvTkH++NpkZGRES0uLsaeO9ja3d1dNRoNlUqlaGUA8IZBhgWjzwu9gpwlWV9fH1gqzzsydzwzjDxzl4IJZM/tJkcaHKSAI72O6/TTHBcCHSiV5w79cyaHzzxCht7ymg5yuRgOIgOQJy/vyws9ZeLgA+HG6XpOGmPiER5CFQOR73e/Q0gRbmg1b1DFRGPciYBB7rwvRaM7OzvhhIhA2PKYlRLFYnGglfqNGzciHcPqFcYd2r7bPd++HpSMk/NVDRg9zsF4+aqdcrkcoMEL/ZiXbDYbURy0LNERqQZJA8oOQIKtAe3zr91ua2FhQfv7+2o2m2E4+IcDZqksoBeq2ivjJQWIcgbn/v37+uQnP6lCoRDAMZvNant7O1bA3Lx5U7u7u5qdnY3lsMiARxHeUZGIBkDJ6gSMCH04XO48ovXIA7DthZ0Uc2JcMNL873S41xt4cyeeO5PJRDMk11PeCRAI2ADISRroP/G0h+sqOpyCGE/toCvoNrLnYCBlJP1zj+LT73I8yYjys6cdYOdSkOMgQ1I4UcAiuo/hR16dHUsZYp8j5pdn4b2Ojo5i9UGz2Yy9PgA3MBG+esWjWQo0YRTQ3Ww2G2ke7CLXAcjTqAqGA+dLbwvvB0Eg47szOzMu9VOEnrJxlhMmo16vDxSqYn/Y+C2Xy0Wqg2uwqo7UT6FQiDQ0tSUjIyOxU7UHmMwFoIx0KozqyMiI5ufng2ne398P3WClCvP+5ptvxk60Z2dnWlxcjKJe+vh0u9332BnqxhYWFnT9+nXt7OwM1H9ks9kIZGFvkBdWtZCuQfYI/FNZ43dk3FPODuqfxBi6XjiJ8FT24anOGnKkSutFKURzOGxnSHgRBy2e/nAqEYFgohkYXpJ/CI3Tmn4vmA4GlEl0UMTzOc3sk0Pe33d/dYfC/bwIE2GanJzUpUuX9MYbb+jWrVt6/PixpqamtLm5Gc/RarX03HPP6fT0VLOzs9ra2tLIyIjK5bLq9Xqs0JAUW7CfnJxobm5uoECLvQKIalCYbDYbIAFHxXjyOUrL+0sKR+iGmmieroM4DG8pzpI3nFmlUtHu7q56vV4otFdh+wobitGYP7o9svsrc+zAhOfgc8DQ+vq6Njc3NT8/r5OTk2A1WJqGobx582Yo+O7ubkR8zB/08cTERNSO0E+BqIJ553m2t7c1NTWlubm5ATmj1sMZJY/+0BlklgjKP2OeqAlxsO81CVzr7OwsQFM6x6kT9twt4ONpD3eqHh1hI5xJSaMl5NeDG54JgMFY+aoE7AtgNwUS6fN4KgO75XKMHjt7gvPBdrhOuCH2ICmb7a8agIlMe6F4OojURjabjfTc4eFhFIxub2+rVqtJUgQhNLc6PDyMn7GZPH+pVIruvKy6gpWDDRkZGVGlUokll7Ozs6GPzoSSumEDQ1hH3oWULEXqbkdw/BSou911p0Vh6cTERAShMI4wSYBy2gHwPZys71JdKpVUqVTU7XajF5KnrryQmGCTFBO2CQdPatv7NO3v7+sHP/iB8vm8qtVqgFPv/VEoFGIPFmR3Z2dHCwsLMbawJe12W88//7xOT0919+7dkAFs9MTERHTKbjabqtfrkhTBAz4K2QWUesradYTxd7vK3LhdYI5TwII+OVvyfseFm4MhzP7gjqJciPiOf4Ygu2EBZfM3z/1RgOSFbWkeODU0ODSYC6fQvEjIKTqME9f0vCuGGGVhAhCelBYHHBCxInQvv/yy3njjjUDoi4uLwQBks+cNqVAyDMH+/r5KpVJsPw/TIGlg3xJfgpbJ9Neiu4FOU0o4PVbZ+P4f5BCh/bw2hPf05XAeNcAWwALgZD3NAetAftrrHmByeDcMI0aHFTne84IiUz8ePXqkZ555RoVCQY8fP9bCwoKy2ay+973vKZvNql6v6+joKN4VgDE3N6fd3d3IVR8fH8c21NS07O/vq1gsqtFoRC789PQ0WDbvkrqwsBAsU7fbjWjFI0zYL5wS8+Vz5TUdvLcXfqH4yIIDbN9fCFl14I4+YEAcEF3URkjvXTaf/j1lJnzVCd/3c9xBERAAmN0g+vf8fwdAbj8ASfzvzpoone+jT27Me71e2BpWRsEgwS7gQEkpwoLwLE7jY79gNaDmWXINi0Z6ZWdnR5ubmwOBBHqF0wCst9ttLS0taX5+PlJuAGhWY9F4cHR0NFgOHDQsCjrqqQcKWinER9bTVDiBmIM0wIjUTzfBfvp3Ll++HAFNujQ5pfexLyzxBQhRZ+UBKjoyOzurkZGR2D0YHXOgyzN6igcWtVqtDsgq7AjLgEkPkUIBiGYymdiqwZnQV155JdJV7PGC3mJ/mROAEelhxg2ZcgbD58TZbD5z8OA65froOuW6+TTHhZfMYqD8AZy+SY/0cy+Uc+qGRk8OMKR+OsXv5blXFAsAg9F2ZkXqr013MATd5rkt3znSIy2uTSREisWNrKd5+A7pnmz2vFVuuVxWtVqNzqLSeRMrHBhLX2/evKmNjQ0dHBxobm4unA0sAcYKRE1U7qyDI1ZQN4aBZyXCYQwwWt4AjQpyIiMo1rGxsTCGjDdRSC6Xi5zk7OxsfA7o4x5+EEnxDPyjeA5j5kYfgOkAhFTY0dGRfv7nfz6M6+rqqjqdjra3t/Xqq6/qP/7H/6hr166FYVpbW9Ps7Kyee+45dbvnXVMfPHig733ve3rhhRf0kY98RN1uVwsLC9ra2gpAiMGntTw6Aigk1eK5d6+fAfBgQEiFcE4K7L0WAznz8/g7hsEL6vjZ2URpcMWGA+6L5Gr98CiK3z2aTVkCp3n99xQspAyrp0w84n6/w98JwOz0OkaY50WfYZmQNb8OPyN/CwsLWl5e1qVLlzQ1NTUQSfs8DUsDwQY0m81IAVK0zWqrx48fxxYE9GVAP09OTqIeilVqnU4nnOLo6KiuXbsWkTVgGnsAcM1kMrEyw2snkBn08+zsLByvF54zj9hYghpJA1vCSwoWjiJyasTQcWpZsMMUcfN8gBUCS/xGrVaLdATg79q1a8GQ+H1JZQFqWHnkAB47zOpI0ixXr17V5uZm9MWQFMtc+T5yAkPT6513NN7Z2Qnb5kEEHZOlc3ab1UrOVHpKkLkAAHuaLWUoUt/pKVs+R7+G2QDXPfTxaY8LMx2OajBgaU4VxcIQUEPAgIOyU+V3Z0QhJNd1QMEAO8XO5x6p4YCh55zK9Of24jEcmCuO01JERSim16FwDfKQnt4hzfCJT3xC3/rWt7S9va3Z2dmBdfZbW1va29vTyspKKD/7m6B8KD/brM/NzUXREgKLohANeJ1DNju4tTY9J8gF4vCkwToXog9JAWAymUw0H2M++A6RBIwC4yz1lwqD0JEJj8pB8hgSDBa0Jk6AeSANRYRxenqql19+Odqwj4+Pa21tTblcTl/4whe0s7MjSVpZWRlovjM+Pq56va65ubl4xuXl5dg7ZnFxUbncec+UWq2mvb09TUxMBOVJoenJyYlqtVo0CINpoFgNI8YcM97sTJsyBh51Y5wcqDPmyIhT1cg7RhXdQD7J5bv+wEB+kAN/2sNBkwMST4GgYw7e/TwctwMsv1YakaUMB5/5ePg88BnnuJH2Z4Kp9fcaGxvT1NRUAI6lpSVdvXpV8/PzUZQs9Xe/Tp+LufDAAFsDG1Cv11Wr1WJb9XK5rJdffjmWxaOjrVZLjx8/VjZ7viqCvT96vfMVVzs7O7G/0czMzECDrlKpFDLjNU0EDDTLY6wcSBH1YxcBOwRHvIsvHXYGiOcol8uamZmJzsGwpe12W9VqVbVaTY8ePVKpVNKNGzciRdFutwfmUdJALx90ZmNjI+bJrz8+Pq5yuRxBHHPNChqKZb3WhDH1bswU0qdyQqALSAIU8azYIGx9q9XS9PR0yOjMzIy2tra0uroaqTQHhh5gug/zuULWCJ597tJAwfUSHRgGRlKG8YOOC9d0DIuAUho0ze04zSb1DcswSoYJ4XuSQmjd8PDSXIfvSP1qfZCppyQcxTGobpi5Bw5C6vcnSd/BGRIKtHAITALPzvWPjo50+fJl1Wo1LS4uamxsTGtra1peXo6dSommms2m5ufnQ5lQHAo+QcsoL4VPvJfnz4lqeK+RkZFgH7wZm9fmwEQgzGNjY7FnjCQVi8WB9/OaG6ftmCuuj+HhuZl3ACIrZQCgPmfUV5BfRy4oMONdHj16pOvXr8e4QCHfvHlTY2Nj+spXvhJULaCLCKvXOy/MWlxc1KVLl3T//v1If9HYLJ/Pa25uLhqQecEXhcPHx8exBh9QinN3+XHQTnEZoBOZdToaQOA6x5gybmndAufzjMg9c0Rr+dTZ+n2e5vBAYRgd60aNoASd5lmcORt2LYxwqsvONqbnexDhz+MpG38up6G9zgId576eQp2YmFClUtH8/LwqlUqAjdPT02DqKDgm1eg1IqQYXEeoZeLf+vq69vf39YlPfEKf+MQnVCwWwyGz+spTj+gVgBUWZWdnJ2oE6CVBkSVAAPDg6StJMV/8nMvlIshAPnF6pDn4Pj096KVDQy9qwLBTbI4JC1kul6NNwPz8vJaXl7W9va1vfetbWlpa0jPPPKOJiYmoMSFgYMk6IIiOqRRi5vN5NZvNSKu67Sftgd56mQBjJymWLHtNFytdSMdyDfZOIVjwuj+Cl0wmEyuE6vV6BBOHh4d6/vnnlclktLq6Gl1NsZEASqnfOgA747aXuUjTq6R48QFu09E1nh2/gwz4/x90XAh0OJhAYTzvDEJLK7xd6Ycd6ctwLwcl3Ie0gdOqUt+AeQGYGw2iX5TZDZ4XSzFRXJdJcco6m81GysHfwXPpnv6Q+mkHB0y3b98Oh3jt2jWdnZ1F1XWz2dT169f18OFDzc3N6d69e1EvMT8/H0qws7MTAjI6OhoFZkSrRFUoCo4FehjKnQp7qDqem1UuLNdllQ0GZHz8fNdYUioYidSBIBvML5E/Swhzuf5GaCgAUVQ+n488JXQmRbZemIa80QiNcSIXLikM97e//W3NzMzo/v37AVQAZ+yS+84776hSqcQYXr58OTaX6/V62t7e1szMjEqlkk5Pz/etqdVqqlararfbmpmZ0fT0dCwDRZ68FghgyoE8MD8YD9g6UmgABTdc6KXrDawU33Hnj1z4KixJ4WwcLD/tkTIJ2IlheWEHC4COlMp1EMvPXqfkDtGDB6mfqklTren1JA0ELXyOHPNMXowOK+o9ICqVikqlUkTH3lkTho6ImgZX6CVziP4yl9iVTKa/BcQnPvEJPfvss6rX61pdXY1mXaQRsRNE29RCwSryc6PRGCh25j18+SrMnINXqHtSzNhtHHvKXmGraAoIo0jqgV2wm82m3n33XTUajThvdnZW1WpVW1tbwYhQK3H16lVNT09rd3dX3//+9zU/P6+5uTmNjIxEAEbBODaG96C7MOzi3NxcMCFeiD89PS2pzx4z77AJjO/e3l7MLXJI0zEC0p2dnYFmgYBMtk6glo5UOzuOIxveHqFSqSiXy8XcIjfHx+f7xhBUMgdukx1oeJDp7Sac8XMQTuoKu+w69bS24kPtMstDD/sb0benMLwYjgdM0zQYApwlCuNIOX1Rj25YocL1Pep2AMLgYjw8xcI1na2hoIqD+3n+kM/JE3okStRKiml8fFxzc3N66aWX9Md//Me6d++evvCFL6hcLuuZZ56JdrtERtQnFIvF2PyNsacWwOkyB13uzHB2GEnqDIhqPOfMfd3Q4eAo6GTFDLUfAByAloOXw8PDAeOKgjmjhTHjHi7wTs1jDMhXYwh9dQdKurCwMABoAWmrq6vq9XphZNfW1nRycqLFxcUwON1uN4rDXn755Sjqq1QqWl1d1QsvvKDr169rd3c3aGMiYQzKlStXYtx9pZNH+z5fAFJnr0i/+FigA7wz93fmxA2/OwbXX7+Wg33k/sMcDgJSdtEjJO7/JPrWDz/HIzQ/14ME19XUiPq102BoGDPj/0t9G8S/qampWKWFfMMusFM026MXCgUVi0VlMudNrbLZ7EBqVuqzuqOj53to4EBWV1e1vb2tj33sY7p06ZI2NzdjSeXh4eGAHGazWV27dk1vvPGG2u22isWiFhYWohcOToOVEru7uxobG1OlUolzvH8Mz0TvGXTb65So5/AmYsg9/0jtsYoDXe12u3r33XcjYOD9u92ulpaWoo6l3W6HTrAB39zcXNhGLxolxQH4gInwAv319XXdunVLi4uLA4Fm6i+wb6TGYCFgXSnEbzQaajQaAagoUkcGPbjF7tKvg9qwvb09TU9Px5YULItm99mpqSmtrKxoZWUl7NPt27fV6503h2TlX6PRGGAsARuuT85g8LkDyFQvXY/Szz6IWPDjQxWSegSU5nN4WM8Ju/OT+obBjawbHKccpX66BHRNhOjKyv3daRFFguj4nPuASPkO53l1/97eXgApWBYON4A4DSJGPifSRHER5suXL+tHf/RH9cwzz+jy5cuxUmJ+fj6azeBQt7a2YoktSoUC0JODiNWbUUmKqmacHjtltlqtSD1RY5HuIgs1CLPgcw8IYT6cfk7TTMyPMzInJ+fbPfOesBW+OgC5gkGiERljTL1JynZAqxJZwcoUCoWgotl1ljEZGRlRrVYLWpxVA4uLi9HWvFwux94Kd+/e1Ysvvqjx8XH94Ac/iIhheXk5DClshwMnDJinVJxhg33yHLjXLjmlSc48je79mt6ECDlGbgEWDuI9SJD0Ht39oMPtAnOOQUdv3bAxNsPABnLj75cyKRzUBaX3TXPNDnT8OYcBEh8XxgK9RvcoRqQmAVD9+PFjFYtFTUxMRAQNu4czIf2BnXJWkRohQMvm5qYqlYqmp6d1+/Zt7e7uKps937jwhRde0De/+c1gINg3aHV1NXRkd3c3gAw1CeVyOdIbyBopB2fGWBpeKpXUbrejv1Cz2Yy0IuyYz1Gvd76MnJoDnBp2mMBqbW1NU1NTunnzptbW1jQyMqL19XVtbGzou9/9bug/Wy74PiyPHz8OELOxsaFCoaClpaVgCAuFQtSUsSustwifmZmJAIR0LvPvgYAHBzAd+BT8hO9Vlcmc108RNHvanFYHU1NT0esEm/vw4cNoaf/48eOooyP1dXh4qLfffjvY5vv376vb7cbcnJ6eanp6OgqJYU5TgO6y7SRBqicpQ5oyj+j8Req/LmRRHAxwAyJzz/+7UWVSEGrPSXsBKINB9TKAwKvsWfbky5UwZJJCoEHpThdJfSeFg3YDiJCmThJD4e2/eWafGPKAOGOP8siTOaWVyWS0sLAQdKHn4FjBUalUlM/nI03AePs6bKIIxpt5wHjRehxKk9x9CrpYvgUah+7EAJLeYM4Y2/39fW1vb+vKlSsxxhjhXC4XhZY8u48f0c/U1FTUeKRFxt7kzOs82L+GIkjvAMqmbMiGpyB472effVY/+MEPIo3VarW0vLwcVfFHR0eanp7W7OxsgAmeE4PfarXU7Xa1srISxWQAA4wthXU8B0baUwpEUbwzjhJq24Gw6w3sFkYeupco1GlQdBa5TR2xO2NnCr3W4WmONAJinp3Z9P+duRx2Hbc1PHOaomG8PBXin3ugwTnOyAy7t9PLfj1kjvnF+fF+6B9BhncHJm2RyfSburlz8yCNtAPdPalJOjk5iQ0kX375Zb388svK5/O6e/duLLPd3t6O/T9gWFi9QQM7gLt3Bma+kBkcDs9EUAXVjy66bWdsYTRwloAqGBGc7dnZmW7evKlPfepTWlxc1PHxsba3tyN14K27AZIUVVL0T1ojn8+rXq8rl8tpeXk5bBh9UiSpWq1GUMQ9YHpTxg//BZhlx1uKcEmTUESMjYSB4f0JqgiO6DvEM6Vp693d3Rgvqb+jbalUiu0bLl26FEDs9u3bOjs7i7RUsVjU9PS0MplMtGZ3pjMF064brlep3rkvdV1Dl/+fpFfcmWL4vDANJU1z1DwUxg3UOIxaxbkCMhwEpPUhABKQs9duMLHuJOkV4VQaQATjBzWH4kiKa7og8JwYI4/WmBz/38EJ9yb6wYnSwIbxqdVqgeLn5ubUaDRCyRFwaiKogXDjTu6WtAV0YLVajfuQ4sDQ+MoRjxjdGAKUOp1OtNjlXUhFYeQ4oGVJV2UyGc3NzUWNBk7WI1QHnMwFjpRrME++kd7+/n7sxMumfW5EvcbDWxI3Gg0tLCxEZHTlypVo7MW9a7Watre3o5Btfn4+GARSSfQBWFxcHGighAN1FtCLaf05kXlAIEru8jUyMhJAAyYQxgkn4VRxKhvulHEoqTFJ06gXOdBVfx8HP0SA/I1jGO077Lp+vtSvP0Je/V143zT1xPXSKNBrRvw9SKfAEOJYiKLL5XL8jrOV+gERFD/v6Ewrz4SeUQcyNTWlZ599NlZMlMtl/dAP/ZB+9md/Vg8ePIil2hSiw7ZI0vT0tCqVSsjLtWvXlM1mtbu7G8CH8eG5eE7GDNliPLCj9O+BBYR5w77SLZWIe39/X7VaLYKPyclJXb9+Xa+++qquX7+uYrGoT37yk/r93//9YCph+KjLIO1Ol0+KN+knkslkYgdcGJjp6Wm1222Njo5Grx6ed2dnR/Pz86pWq1GEy1hJ/ZQINg8fwecAavSMZbTUXEh930cKZmtrK65HYObF7plMJmpIkAlAC3YWPT89PdWNGzfU7Z6vWqHNwtjYWKTiCVSplUPmPbhDHpljgg3kEX3kPK/t4DtPe3zomg43JL5Ejwf0Ctg0uhpG53BwPe92iXN058ORRnWS3sNiQCMDMLgWRt2dkee/U6qfQkiu7fL/g3wAAQAASURBVMjP3wsGgmsxHjAIMCukDvh9ZmYmGv9Qjf7iiy+q3W5rcXFxID1AHpMcbUpp8uxQriinV5UfHBzELrBEIcwTQgU1jPGAEmbJ7tjYWHQnXFpaUq/XG2jTSzqFnDegDWWTFGPrUS8sCvNL6gi6EqDIs5IfpffKjRs31Ov1BnLEzE+r1dLi4mIUqZ2dncVKoaOjI62srOjevXuxJfTc3Jw6nU6wIJcuXVKnc946PpfLDazEYZ0+Ra7IBSwHSox8eErAHRDggop75MijE19t4qko2AVPJ6ZROyCdiBzj6Trqq14+zJEyDQ44PHBwataf06PnYflofxd+Rq/8GRyoAba5Rhr4SP0VeARQ6DtOj0iXPUIoRiwUCgE6SK3SwZb6DVY9UNgoKfQTsCEpnMbIyEj08ymXy1paWgpZlRSpwU9/+tMR8UuKHhfsZ8Tyy+XlZWWz5z2Dms1mpGNJn7B6wmXH54COptRy4AsoRnY/QOvvXC4XdQno8ezsrF555ZWwydJ5L4rPf/7zevTokd5+++2wH6SUea4bN27o5s2b0e48nz/f+fbu3bthXwiyKPTGDtA7g3mu1+sBCNgvKgW5nmYiEMaudbvdaEzockgRqLOSbBp3cHAQG0zScoCasampKc3Pz0cNC6kgfCBBIbUeR0dHqtVqUayOrcznz/eEmpiYiECT1C7gEH1wYJ4yHry/6wtzhl65nDzN8aFqOtLDDZM/sDt+p95Ada7kbhwRcIypO2oKhDjXQQr35mfyvG6Uh1FLHgHhWBEuqEPeh4nz/DpH6rBdAX05FYYWpOrRVy6XU71ejyK0R48eKZPJaGNjI57dI0Qfs06nE/023FAzDkQ/KCZr+D3Xz1gCOKRzBa7X6wFSqNz2JXl03aPbKswGBVKZTGagbsMZGn5nbEi78G9paSkYIcYXoFKpVKJyG6NeLBZj87x2ux3jdHZ2pkePHml9fV03b95UvV4PWbl06VIYECJWWh4DGMjjjo+PBxVKjhfamWV9LlMps+aAlQJolz3mC/lDhvif71NICtDgPb3QlCiRVBPPwpwD2JErKOu07uRpj5RJSVkE10E31P45v1OPNQx0pOAj/ZkjBSXcJ2VL0uuhU9gwACLgo1gshpP2YlJWpXiUCotIHQhROHPpY+U1IxMTE6pWq8FufOYzn9H169d1enqq1dVVra6uanJyUrOzs/rCF76gXC6n//N//k8AJrdPKysrwZZ0u13dunVL6+vrA2mXqampcG7YGN6fcaF2hXoEgL4vFsAxwp6xCoSeE9K53K+ururSpUvBStM1+K/+1b+q//7f/7veeOMN7ezsxNyPj4/r05/+dAQ38/Pz6vV6wagsLCwE09put6M54czMjAqFgubn57WysqKlpSVdunRJly5dGuhLghy4P3EmAEfLe8IMU2JArRqMR6PRGAhmT09PB1Yxbm9vx+af2PtqtRoMrRem0qLeWSeALF1LqZGRFEERz828wJYAHNAzqZ/JcJ8yDEgg1+kKsqcNUC684Zs7Xj4bdriR9Vx1mn7xA2CCwkr92gFnFYbROSiI00C+WZc02G1QGqyGd6MPPY3BY7LcMHjxZDabjWJMBp6I25kOdl2V+puTQTu6M6FwkarrYrEYTWQAX+xE630d+D4on3dkHLwPBnUd7777bigw6SPaD1MF/ejRIx0cHGhpaSlWrNBnAEaClSBEV61WK4pVmU+K45Adxsi36mb8c7lcMA+AM+QHgOHPSA0JXV1hkHjGTqejzc1Nff3rX49xvXbtWig8Tnh5eVlnZ2dR+wK4HBkZifRXr3deeMZ3kD/mmAPFZI44j7/xPyCQansH2xgL9MJ1geI9ZyT4LvrjS4+9kJOx57l9hRUynjJ5T3PgZF2vPEDwzz1Kcj113XaKmiMFGikQ8c8dcPC52w+eJQUgTq/zzyNO+kbQFZM+C4w1DpfCUWoOACw4e99oj+fiHoDPF154QfPz88F8TExMaGVlJdqUowuf+MQndHJyojfeeENbW1shj3Nzc1pZWdHk5KRqtVroCaxFpVKJAmqCFpchZASwz/sBrpyxgylxPYZJYWwAX5OTk7GTdqVSCVt4dnamz3/+87p27Zru3LkTDOOVK1e0srISvYp6vZ4eP34cdSKkQn0l49HRUaSur1+/ruXlZV27di3mFNDt4J6gytPmyB8gX1LM09HRUaTaKALl+7CdznrDwBLMSBoIbnZ3dyP9BGhDZ5FPgj7Xe0+N9Ho9VatVzczMhK5jLzzAdd1wRsP1KAXq7v+eFDS833FhpiM1Krzwk6gVXwnik+jGxAWclwepuxI7cnfmwpEWE+EUM9Gpd2Ej/+sD6awMgkX0ixH2OoN04JlsT/U4XU4uE6qOScJooNTkJ6mZICdbqVS0vr4egkvR6tnZWTSgAoEzzp6HBByVy2Xdvn1btVpNU1NTWl1djfOcjaJIFOfw2muvRfRw7dq1WM4mKdogM+ds8NZsNjU7OzvAAPH86T0Bjaxl942hJA1E5B4x+EZ1GADyvNR+rK2t6X//7/8dq5EKhYJOTk705ptvamzsfMffxcXFiPAZK56H9fCAG56d6IW5d+OV0vquB55e8GI+jxZ89YrrH99l+R4GDYOCw3TGkHs7Y5SyCDy7M5oXZTpSZsODAP/Mz3fD9kHGywMFfufgfRyEDIu+nMVALocdMHk4IneWgAeaQHGupFiZhQ7jnLgW//hOmoJ1hnJhYUErKyvBvlI4TVEhkWun01GlUtEXv/hFXblyRY8fP9bo6KhmZ2dDT6rVahSzYicmJiaioykACrYMRpcDAAx9j1PElmI/Aa8uW9Iguzw6OhrvgL6Njo4OPN+tW7d06dKlYBGl83QSzcWoheh0OlG4iT4SFLi+VyoVlcvlCBRJ38Be+X41PhfO2OEfAIfIGHU8bL/AO8ICedoTZmR5eVmSIh3te2XhN2gwl9oObAOBA4Xz1B6STmIO8IOMNZ95MJQGAOhjCkBTAO9g6mmOCxeSpnRbSh/zmf/PAzmycsOTghYEEFbE2QWPdvmeF6b6yzPIROYMlAsUv0v9RjicBwWFcUAQ/BzGgwJClM7z8N54ieIm/oYQAorIG/M+bFLUarWiOAkjyfIrKLZyuRz3R1gQJlIRGIzbt28Hi/LMM89EEzIYHiJ0co3UOExNTen5559XsVjUgwcPtLW1pT/9p//0QHoKZ5x2NUxRsacTPB+NcU1rILy2xK+DzKDY77zzTjT1Yb+C73//+zH+uVxO9+/f161bt7SwsKDp6Wk9evQoOhpSmzE3NxcGwiM6oidk0OcZYIuu8MzUozAn7mx9STKGvtPpN//iXOTIV5HhJDDUgGMcnKcapT4wR/4xPJzDc/P7k1jJJx1pXtgNF+/rIMgP5MODEwcRrnMpg8q7+zlPc/Bczr44q4F++/JYlkGTUux2u6HvyL4HLRQ/shlaahPcofi4YP/cziEbjHGxWAzgT2Bx48YNXb16NWoF6vV6FCYC6AEZc3NzsRKEGgzuwThTiM5YAfKxE5lMJpw7+kGDMoIibLSkqIMZGxuLQm+KYNlAEVvG6jWanpH6q1arajabsbLj0qVLsfxXUpxHrx6WC7N6hxUryA1AHH/g78hYIBukLer1eqxgyeVy0VeFOQXM0LOF1uqk1umLIp0HVNhx0tsUtBeLxeisCshkvyzGCJaY6xwdHUWb9Lm5OZXL5dhmA3COrntwApvi+uFA33XbMwSufx90XHjJrBt6V/w0r8MEpcDDz/Vowz93BImC8nIYba4JgsPpEJG4c/Z+BD7IDlIcBPB7JpOJQqXUeILmeX4oScbHKUWek3NZ2kV1++HhYVSC0zZ7amoq1qB3u11dvXo15qDX6wX7QnETzbxwThgYSQO7njKWzz33nN566y3VajV9+9vf1vXr16OnB4pzenqqjY0Nvf322yoUCvr85z+vF198UaVSSY8fP9bIyIh+9Ed/VM8991xE2FwfZ9fr9dRqtYIpyOfzwQxIes+SUOYJQU5pSjfcIyMj0RKdgzqVL3/5y8F8EFkiM/y+s7Oja9eu6fT0VM8//3wUbaG4AAbytMgIS2eRVYCVs19e08F84SwwxJyDrLnz51kdbHhxteeKARruLABXzjJgRFP9cAYTwwaTRurnaY+U5XBq1g+3I/6M6Tn+f8qYuO3wwIX7vd81GQ9n2tLrOnAjsi8Wi3EvdkFGrv37btucwWJ+nSX2I2Vw0po4/7vLJu0EKFj0DdWo2cCpYGeKxWIwAtSi+L3d2aIXzuqNjIxEOkbqM2V8v1QqDRTfc9Abg8+oS5EUXTfX19dj/AlCsPXNZlONRiOAX7lc1uLiYtgE9J5nACiRziGQ8TlxkOs+h7ljjGFXvAZN6u+Gji9AvllYwf/olqSw78ViUePj47GfitfSUcd4dnYW4MGXW+/s7KjVag2saqERHGA0lzvfq6tarYbt82COe7k/TYkDZz6wg+jbRWu/LtwG3dkBV1ZXej7jnPTz1EB47tgNJ9Ge1C9+ZMBcQBgEjC9KiqAitPxzA4PxdcTq78e1EEKi6dRB8mxcG3RPbp7nxVmNjY1FkRBL1DqdjnZ2dtTtnjeWeuGFF8LJ1Wq1iAIYH96N+8Gg8OxOLeMY8/nz1sI//dM/rR/7sR/T97//fb322mtaXV3V+vp6sCwjIyNqNpuq1Wr67Gc/qx/6oR/Sw4cPg5b81Kc+pdnZ2YhCiIaYG18eWKvVYtUMz3VwcBCK2O2eF56WSqWBZc7MF9XhnEvkSX0JBqTZbMZ1mXsYM0lBjUOr9nq9aDNMr5lisTjQrIfrA1yJijydQdrMQSjjQdQLi+AyhAxms1kdHh5GXVAK7Lkm/Ug4kAEMIjqAscb4uc4B6J3R4FrIeZo+vcjhBjxlP3kPByFpWtb1yu+fApR0/IYZR/89/dnv68/pgY+kYDcA+ETmkmL1APKAE4dux1YBWrAtyJQfPjYOpvg9HRfslke6rVYrnDbggq3VaYrFqoaxsTFNT08PPDMsrI8pNg3Wgv4UpE/YIBKHyrMyXjAOjCX3p4EWekqdC4XhjN3BwYFqtdqAgyOFAHPBsnXqHGDKGSdqZ+iXRLCH7/GgMwWEHqCmLAqfebBF8OkOvdfrDbRr6HQ6oe+lUikK0QGvPA/MEbrbarUihX16ehr22W2qpPApFL5jF7k+TDzv4ynv9/PVqZ6mQCMNLJ50XAh0eIQF7YeQ8WBpXhXh5EU8gnC0xEEUyCRCdZEm8Xw4ApHmnKAEqSZmUpjwFM0RLWYymUCwGGWvz4ByBX3iAD1v60qBQKZ0KdHm9PR00OkYYhqCYVTy+byq1apGR8/3EqnVagEeiPopaMTJ816+1LTX6wVr0ul0okHP+Pi4PvvZz+rBgwe6e/du5BNZwvviiy/q4x//uC5duhSV5A8ePNDS0pI6nY6mp6dDFmiWxbiRmpicnFSj0YhCTArr0rXjyAKFnE7pMoaek2RuGQ8abEELI48e1YyNjb0n/4nRQM7Ys4AUjcsVERTKixycnp53QYXR8j4ngAI+R27S5X0OmLmnp2IApkRejAdz7Q7Lv+f66wAMvXEAwnN1Op0B3b3I4Y7SWYdhhiplM3h2v86TgANj5fbA54vP/Fk4x+tZHKxhmH15KxF9qVSKglBsC44eQEH0DUhluTj1H8irPz8/p7UlsGfOVPEOvnIMB5/Nni9pxcnRch0ggi7OzMxEyodl365rHiyim+VyOc5xYElhuDfY8mgYPSUQ8Z427KicyWTCmU5MTARQarVa8YydTmdg00pSJKQdDg4Oor4GucUmek0Wh6eznJlyO0FA4ToLGPDFFC73yCjsijOFgCuKgZkHOkbjpzz1howyngRl2GiYbp799PQ0grRUvijSdVlKmT6/FuOCL071mrG66Eq3C4OOVOF9cHiZNE/qk4JQ+3f8Z3cunvNPz/GqfV7Wq5a9J4TXCzgbkqaGpMFWx05pEln7+WlkgrOjxsOZEiaS+0KZ+TVcqRcXF7Wzs6ONjQ0tLS3p2WefDUpxc3MzaDycNo2E5ubm4lkLhYJ2d3cjsmJteDZ7vmMq43R0dBTLyebm5uIdoDy/+93vamdnJxiCO3fuqFQq6f79+3rllVdiqR3ovdfr6d69exFtPfPMM9FanHwozBJsFgrjdQQAAy9Uo+iKZ2SOiPaazeZ7qEFXkP39fc3MzIRBxblihNfX1yMSY564H/LI3KOsXiTmDJekAHgeOXoKytkop+R5v2EOGzDqYBWjCBAiknS9dTDqBpZzUn0CnF/kQL7RsxQw+P/DdHBYtJQaOw4MNeONzrrBZdy5F++I7DmrkM6rd+iFvaBgGgofPeZ3dME3UMQZeudPP54E7FL76eDYneXy8vLA+8CcwT7QE8SX/BKU+eErnbg/uuhpTi9s5hyAijOL6TW8LgUQD+vi8kgUvrCwEO8MqCay9zEdFhgyd7AcPANRPbbJdc0ZDYAGfga5QZ+xHdgdgAJBG8CUOaEub2pqSqVSSUtLS1pZWYnveCrXAxvqY1hpJClW+01OTsbePClT4mnWk5OTSEN7+YD7VeYjZeCG6aP7eu7h1/ug40Kgg5t4JOWOlZdmEtMCU64xDGz4554zQpAlvScihgJFUBjwbrcbEwc65XdYBp4NZ+fAgfP82QAqvg7anxvnTyEVDoprO12LkSJnjgJ4rvTo6EgLCwtaWlrS5ORk5OdKpVIIGu9ZqVTU6ZwvtV1YWIgltw8ePFCn04lCpJ2dnehpce/evWgoQwHp0tKSstls7P+ysbExQKk+fPgwutytra3pmWeeUbfbVa1Wi+KwR48eBZrmWWBnSAGUy+VYm+/0NPPlTcyy2fPVKXfv3lWnc772XDp3OHQ3dIYCsMk48vzOhLz77ruxqVUmk9HS0lLUh9A2GgaJ+cJQ8jnyDyCi/bLLIDJLxEp0BECRNBCxAkQwcP6dVEf4DJ3D0foyWl8Si1wDejC4OCMAMOcwfk8bvaQ67sbKbYTUB+uckzIUKUjiep6+ZIx8LDiQG49eGQdp+IoZD5RgJAEeUNy+DTv6in7zHjCbOB2W1RLMYG+4Bw7Vx8oBlAM2vsOzOoUOeGbc2KeDFKw3IyP6dzbL6558/NMUgjtvng3WlfFDpngOn0OCoRQcwCAA7mA7YSJhVNjRmkCByB5mCrBFUawz68wTLAbvz+Fygq7znjhvnsMZBD6j+RnpNMCRpNhSYn9/P3al5lyeq16vR6EnY+YroGg/3+l01Gg0YmUeY07QA5Ai2GZBAMEn53EP7C9z7LrhBfHIKXKIfiGTHsS833Hh1SsohjtoBwkIF07a0Q/GxRXdJ9wF3QfFUyJ8hlK6IXOaiIF3ReJzDp7PjR4rVvg7yN0NFY4jBVUp4vcKaYyu07q8I04MJaKy3B01hgpjgUFhhUgmc7552/b2dnQt3NnZiYrog4OD2ODpzp07KpfLsUnR3t5epECmpqZiE6H79+9raWlJS0tL+va3vx0bos3MzMRyVJ8P2AJSE3Nzc9EuHMBA0VO6MoUo0YEs4MuX38H2uHMg8gPEcC7GlJVFe3t7KhaL0fFPUjwTkRwdX5977rl4bhgf5O3w8DDmAfrR5T11ZB4xpSkjZMupbam/p5EzAABmFN3l2dNDztQhs87EeF0I90IGuT9OPXXoH3QMS6E44+R67yzI+10DvU4DHT/XDWV6TU+1pNd0Ctl11b/rLCWrVAg+nIrm7+iuO1Gu5asDhoGu9Pd0LHCezJWnoXGWyAb35R25NqDJ383tuad0fEy5PjbSz8cpc30ifmTVHTXPAlChyNPHHXsNk4CO8N6wAGmhuQeFXovlY4lf8GBQ6u8N5ayzsyHoFcEGy/o9APFgl/mC4SEgYkfgXC6nubk5jY+PR4EqoIIUHc9/dnYWQGNvby8AB/pJ11lqOJwF9cDFywqYD+TF/V6qQ84C+Tj49542QPnQzcHiAkbHOCp32tQpYP+epzr8xfylcBweAaQ0DoJBExQMrEcVnidDuAAyXuPhA+gCChDiedzopIbUC4gQPpQA48D7Qov50lKccj6fj4Y+0jnLwQ6P7NDKjoug2v39fW1sbATCbrVasdPizs6OTk9PY1+SZrOpSqUSLdUZbzYOYtt3GofNzs7q0aNHunLlikZGRmI33N3d3ZhzDG673Y7dXJeWluJ+ExMT2t7ejnwzUT4UJI6WOcGgFQqF6J46NTWldrsdIAGZ5H8v4mKc9/f3ByrkR0ZGtLm5GTtoUrx2dnYWaaRqtRoFWNK5YmNUPTXmq6s8evF6lVSRvXeLOxkoYa+b4vuu+MgWMkk07jS1Oyzk09MoDtwANNRzeG3QhzlcJzyY8CNNuziQSJmS97uPv+uT7sHPzoB46sXP49lxsL7E3QMep5UdlACgfWUAzGcaGKVHygSnQZLbUmQOx+mAyalvBxYwGikdznWflO7ingQLKfCTNACE/fsuYxR3AyboNeIBJQ6a9/OaAWw5DAPnUR+GvLhzdB/lqQRPRzmLzvv4u2KXceDOevR6vYGUC8fZ2VkwTDw/dXcpA+n+qdvtRn0cdUR8tre3F+kc/FCxWNTCwkIAF8aI8QK0pT4YfWFMfO4cIGJrGBcHrB4IPO1xsUX4NnGeenDkKPWLotxgSn3UlyLqFDF75EeHO6J+j5Y8h8W9UEBAhOf9/X/eQ1LQTggFOXhSIGnuy5/bqWovYML5ebqJ63MeRoBzeS9flUIUCovw7W9/W+Pj47p69Wq0SoaRofq71WqpWq1qe3tbly5dUrlcjn0WRkZGQnhv3rw5kI5gFcrs7Ky2t7e1trammZkZfec739H+/r4ODw91/fr16Ia6v78ffUWq1Wo8B1Tn6elprLq5efNm0KJs5kRhEywJ7AkRENXvRBVUytN+GWqUqKrdbiufz6tUKqlarcY6eLbgbjQaUbzV6/V07do1Xb58WT/4wQ+izwl7a2xsbGh8fFxLS0tRF0M9yczMTOz46UCR1u8OupE1jJWDkG63G1QsNDvnAV6RPY/C0QfkBrn2FV58x1k1ZBOZ4/78zeXanelFDmcc0s+dXUgdVgoO/HtPAifp4YaT8zygSe/t7JJHtKQLiNiZS2o5CHwoFsbG4Hx8aboXHzsI8LHiWdyu8vwpMEmBnLMZ2GIHDm6zfJMxaXCpYzr+KRvFs2LPHPh43YM/d5pqpNaNe3nanMZYjCGpXwd07ld6vV7s4OubWgLYub93DU4BFPfy+XB2j78DyLFFzC0OnbSKAyf+h51Ol12Xy2XNzMwEAOFdAQ0EMwRI+Xx+YNXd2dnZQA+T9fX1GBsCcIAZvtBTR67XaY2Os7UOzB0kDdPLpz0uzHQ4UkwLT7yOw42MC74LoD+8oyZ3/n4uAwSYSA0lKx74Tq/XixSAtyknN8b9PP1Bro3DAYnUB0YeZWMwHI076ge48JwIGmjUiyK5No4Z5wsa/4mf+Am988470XrcJ56mVTjKUqmkBw8eRC6aJX+04P3Wt76lcrmsZ555Rmtra1Edns1mo98FIOT09FQ7OztaW1vTjRs3Ije7ubkZLcPb7XasDJmcnNTm5mYYqQcPHujq1avKZrNhKHCyVHkzxk73Aojovimdp6OgKAuFgs7OziIX2u12o/K9UChoeXk5al5OT0+jij2TyahSqYQjga6lAyCrDVBwqd+6/gc/+EEsNSyVSgOsyv7+fuTwMSTIoDR87xVSRNQESf3aH372glrXI0lhcL2ID0Pp13A55UjlHz33v1/kSJ2XG3q3Fcw1DuxJQIXn4rk9aPDvpYbP/+bsgj+XR3XOTvl5/nzMI7pILtydlKfQ3Jkypl5/8yQ2x9mONDADtHrBOylYekXkcrlYqYYM4kycBRvG9Ph4pcGWg5SUik8Bh7+TgxwAMOODbaSfDvVRMLXdbr8vjusi4+g9Kqi/wa54GskdpgMlHx++51s1wJY3Gg1J/ZSQyy/MgwMZalkAetTYTE1NRYo69QvYBWcpWE3Jqpf5+fkIXPkbY4XtdVvg8uzpOAfLyIWzGe5XfE6RD89QXBR8XHiXWQQGFoAH95v7gSMYhuQZ2BQ9IUTSYPqG36HfpMFoxVkIlIEJwpgPiwZwbl4BzXs6xeoACnrLHUBaiMWYOGJMo620gCef7zfPcsYA4SmVSnr22Wf1zW9+M1IRzi60Wi31eudV3vV6Xbu7uxofHw+n9vDhQ52dncVqFJA6KJqtke/duxeboK2urkbelaJRBP3hw4e6fPmyKpWKbt++rZWVlQAb7H44NzenTOa8a+Hu7m6kdAAaIHbvlEmTnF6vF7IAUKxWqzFP7MDJmHU6Hc3MzESahHFjyR+yRTSazWajwyQKdXJyonK5HEYRIMJ8sond48ePYynw7OzsQKqO6zggwcB0u92BPgceWbieMfeej81kMtHC3ZlBaGqnzmFMMKie/0+djesQY8Z3LnKkcu6fp3bCgYEzEsOuyXWd/k6vwbijxx6JMw5+LWyXMxUYVEA843t0dKRGoxGsG4YatklS9FHBfrBawRkJbBJRLWxo2oQNO+Z2jmf1sfR0IsWb3W53AOhKitVMjAeMqoMPB3nSYI8YB1OSQradVfA5wI5L/WCUucV38B6kIOg9gcMGNDBOAFfmH31zFtlXbfk7M54OMlLZPz09jR4knOspdcAF/xwQYrepIzk8PIz9qbDtLJNlHxx22aZlfqPRiPo6/AvB0OHhYey/QzOxzc3NgUCd92KlJuMFMONdkSefD8Yl9Vdex4lfc1/I+KYp7fc7Lrz3CkeaquCzFKX74VSxo2GffM9P4/C5ltdX4DA6nU6kHtJIDsNPQaKkgWpzngnjna5vxlh4yoOoHkXinnwHxMjzZTKZECAfr/Qc3pHVGBQUYRRGRkYixUFahedCQXiPkZERXb58Wbdv31Y+n9fm5qYWFxfVbrdjx8L79+9rYmIi2umyayTPtLCwEI6PAlWef3d3N9I79+/fVzabjY3SXClrtZpGRkZ0//59/fAP/7A6nY4WFxe1tbUV6QxJUTfBckO2Apf6hufg4CAKrjBEzL9vpCed7wMzNzenUqmkcrkcaReUw5kFFI4W9qRQDg4OVCqVYjMp5rnTOS+IrVQqqlarA8AZMIyBZo6R+xS4MvcpU8H1WMnj1C2Kj1xi8DwSdefO+2G4kdV0maHLsjMAaf3U09oIBxY4ymHRUfrdJ0VNT/pbyloM+96wc9NnSAMgnAapKO96mzb3Auihi5yP7nsKhHF33U8ZBY7Ujqasgv/v1Lmzr8hlKhdpBJsCDgc5w1gZ5M+ZMgfbHlRxH64N88P1sffeH8bBgbMdmUxGU1NTMS/oE0y2NGjjAU7OLsJewXY4iCBtwfMzju6HYLCOjo4ieMGp8/zIAABmZGRES0tLWl5e1tzcXPwdG8410VXG0Oez0WgEszE+Pq5Lly6FfSKwJrgmVe2ggfly/8Y8+/ymMogNQ+bd36Uy/DTHhZmONCJJow7/3AVN6tdbpNfkSFM3CKQ0WHXrucJU8VIj5GAESs6VmaiDyXRFw0iAXL0+w/N6AwOazw9UMHtEhfA6uPKIVeorhNOg3NO3jKZRGHuAQOPjgN99993ISZ+enuru3btR1Hnnzh1JimVUd+7c0dLSki5fvhz9/iuVihYWFtRoNPTgwYOBaum9vT2tra2p0WhoZ2dH7XZbrVZLW1tbajQaWltb05UrV2JJ63PPPRfOnFbj1WpV7XZbi4uLmp2dVbvdjrXkRELUjLTb7XhvjAfLwMrlctRcEDVdv35dh4eH2tnZCQDDDq6sOul2z3OtrIbxtBYpG4AWMkIRHIZieXlZmUwmaka8VoX5IhfL0el0omgXlgcQ5P09WDaYFpR6TpaxIHp2Z9PpdAZa8wO23NnwuwNvAAnA5qI1HdJ7tzlwNjA9h5/fDxDwXY/AUgCS/v6kz93ADmMePWp0oCEp6oaQD38ntjb3AmFqmJy65zOocf/nAZu/e2pn3K6ik1LfRuJkh7EDPo4e8Pn3kB+OYXPDNZxRw156sOTzx0FghGyylN2ZKhx5oVCIQu+JiYlgPZ3hcTvL8+DIncEm/cQ7p8vZYRVxtJJiGW673R64Jm3nAQpcF/CQBsUrKyuanZ0NVhUm18EeQMOfw/X2+PhYu7u7YYfwJ8MKXlutVhTbO4DzuU31CJ33+XN9c5lwgI++DAskhh0XboMu9Z1pCjw8iuNzT6ukBoBzU3TOP6cX3Uj5OncENJPJDKy/xlHwjDAZjuSd9uNzcvyO4iQNbHeMIsNgMGE8C+/GWPG7537d0Lrz8HtC4WIMUDAXHl/9QDU37zQ3NxfLqyRpbW1tILXEZnLdbjf2MxgZGdHW1pbGxsaCKYHe63Q6UeSFEcAwr66uanJyUq1WS1euXInVNDhhVuAcHR0F+3Dz5s2oV8nn89rf3495hNGhAtyjEZa1AhRoh358fKzr16/rueee087Ojh49ehSO09kOolCnJFkGS9qIepFqtRrX8OLRbve8mhyaWNJAJIYhg5XBuGJwnU1AFn3Zq4MCd9oUlfGM6BjyTvTHu6I/Dlr8dwfkyBD6nW7+9LSHBxxO/3KkrEJq+Py8NCpPdcfPHfZz6lR5b7c3w76H3rK02gMe/54XoHNNGD+ABeezCkF6b7dZB0V+OLhwu8pnbnPTIMvtrtsVqZ+qGzYezt76fRhzj3SROa5JpA5Id8bAx8nTqSx5LxQKkhTsNIwIbQSQfQ9AuKaDMwAXTIODa1JmOGscMvaFcUHH+YxzvCEc38UOEyDCxvL8hUIhAMfk5GS0z+d5WervbCU66T4XoCH1N/uUBuui+J3v+HylP0uDDIgDEmevXD5dr1KdTAHzk44Lr17xIj+nTB0oOJWFMUMZpPduHpMebpA5ut3+WuzUOBBBuPIRjfJdBFHSe54TAaPCmFUVae2I39OX0WFQAEm+XtuZEwcLfl/+ef0G9QQOjtyAsgU0W7yThiCvS7pgeno69j7p9Xp69913o4Mh18IR7uzsqFAo6NVXX9Xm5mbkGZeWlqINuqQYp8uXL0fNCGPV7XZjjXmv19PNmzd1eHio+/fvx74mjUZDly5dUqlUCnYJA5bN9gvjPFcOnck/wAgKjEy8+uqrqlQqOj4+VrFYDCYBec1kMiqXy7HjY7lcHgCSgKSxsbFI6TQaDU1OTg5sTnd6ehoRLxvlUehKtToFuxSMtVqtYFpgMryIlnE4ODhQo9EYMHYbGxva3t6O6y8sLCiXy6lQKAxsB87+E2m0i/wQOSGbHgA47ex6cpHDgQTvM8wYpQCDe6VBSxo9+ffSa6TfGcaGOBvgY+Dn4Oi4Pob66Ogo9viQ+qsvCAgAG3zmoI3VD7wjK+QchPLePl6eJ/fAxd/LAzB+dgCKzUlZoDRy9SAylQ0HMM7COujwtIinI7gGjB1OHZvhAJvr4LSZQ2whY4mt4ZlJz/g4Ic8EZr7k1cECvwMwnU1BXmA8PF3qttzviQywGR6rVZxF8GfGpjkjNYz1800ofR49+HY/loIxn+tUj5hbD8JdDlJgkaZkLmInLtwGXRpsEuaGaRgS50jXMPP9YUaJiXMqnP0wuJanYvjZ6XPAEZNKG1nPXaNA7uAAR9SBZDKZgeiXymYE1r/jAoXgpqtSMplM0JE4OSaTFAEKCtL2CUa4EGJvELa3t6fR0dFYVUFk0G63tba2Ft02a7WaRkfP93KRFGACZH54eKhnn302FGlmZkZSv/aCXXF7vZ4++clPamtrS+Pj49EP5MqVKzo9PY0unRMTE1peXo4mXblcLtqnn5ycaHNzM96PraopzGJLeADQ4eFhFPR5rU4mc54S+vznPy9JsbkSq6AwUg6aYSH4v16vB32az+cDzF27di0Mw8nJSewh0263Ywlcp3O+D43TrZVKJVgiohR6myD/0PSHh4fBJlEIhny4oYfi393dVa/XXypNHcv09PTAHjTIpTM7bnAA7O4EiSK998FFjjSS4jM3oh4B+3nM5bDrOUDw6/l1nUVJz3GWIf0OzwAgSB0r53nwRCqQnWeZH1/6SWqWKBjbQb6dCJ45HhaxOhDDtjkT67aLsUqLj/1nZz7cfjv4GsaKcF93SIBinLaPMbUGACu/B+d50IkTp1CagwAGm036AVn19AH35pxMpt/t14uyGSdPsThD4O9O4bDXWXgahrlwlqdQKETN3PT0dNhqX3GCrTk7OwvbygpMGBzXC96D5x42PryDN0XzkgDGjXdm3FP9YozcZnpA4vOBXXna40OlV3jQFCw4k+GfpZQhgo6ipYiOc6R+m2jfMRMlReE4F3DgBXO93vlKjoWFhQF2w2lNaHxfpSIpIsVMJjOwW2I6yEyGV82niBBDgGH3ws+zs7PodOjPnUae3Ju/l0qlWBoKwqUfB0ABYFAoFLS3txdNxWj4NTY2Fm3AvTq7Wq1qdnY2hBfEDguwsLAQ3fVu3LgRY3/p0iXNzc1pY2MjtqyGuaDGYmxsTHt7e1EwK52zGTAB9AsBNKIMxWJRjUYj2It0mdonPvEJVSoVNZtNZTL9egkiTuSt1WoNRJfMFzuI4mypos/lctHMzAEazBRt073t8OHhYUTGU1NTwXpMT0/He0r9ojfmlWW9XAPwBDDxtsk4SIwWKSwiK1bVwObwnsiqF9KmdUz0I7go05E6+pStSBlMZzsdiDibKvXrFWBr3OClAMfHZxjrMYwh4Rx3mn6+AwFnG7APXljqTh9nApBjLukGPDk5OcCE8mweaKS2xp+HZ8SuMH8wr1yTOfZr+Hv4dXF6fI4NcieF43UwTmDIPXGMsAF+f4/q0/oT6mEc7DgoSx0vYMDlzZnklFn2olE+czsLcABw4BdIvbPKhvP5PinefP58k7rl5WUtLCyoVCqF/hLkYDNo6AXDynP4ePh8OzvuwVP63DxTCp6Zu2EAw+Xe5WoYQGecU11/muPC6RUHCinIwBl75I8j9Xwhg+dGIR4oP9g8xxUx/bs7byIzH5SRkfPe/r6xDpNBegRB5hrD2Jpe7zyd4WAAelzqK4002FWVyR0mONyDSAEH4nQ4zyYNNpkiCpidndXDhw+jVgDHVygUYqtoABuObHV1NcYVhDw7Oxv1MER5IyMjKpVKYSxZckqzGhgfonzuxwZMU1NTsYyXz3GgoHv6gLhjwoBQL4JBp/4EQOAKL507g1deeWUgnQW9SZ0EOV3SIoeHh5qbm4sKdOZ/dnY2GDJkhp0qK5VKyECxWFS9Xler1dLo6GgspYVVYRM+STFOZ2dnsVIonz/vtNpsNgNUs6EYrJ2kqAXAUeVyuYFqfaJKalI2Nzejf4p0DgSXlpY0PT0dc4oTcmqYa7GC6EmpkQ86UpDhzsB1w/XFdW3YPf27/nv6vTTd65F5mi5KAYtfL10W6A7Pxw7Z8xQh5zB+zJ8/S7PZDJBPEOUMAk7RxzIFUP6eztwyDsxn6pBT1scDP5eDYc6baN6fF71iHEkt+X3d0cHE8Q4pIKbA3usbpH7xqNc0sZcUPSocWBCwOIDg3T3Fgj/IZrMDHU4BG17DQSDEO8MecA2KX33bemwXfoK9p05Pz5t/tdvtYIl4b57RV7+4b2OcOQBoXgfirI7PY6pfnO/X97F3uUl1+8McH6qmwwFEWljFQLiievoCBfEUCD8zKH4NwAMvmFJbfk8vfoGmombAQYsrWgqIuKdHMB4pMPg+Dnzm9DTAxN/FAZTn633ymGBniLygKpPJxAqVbrerS5cuqd1uq1wuq1gs6vDwULVaTRMTE5qeng6BYhXFyspKbAjndB/OHKU8PT0dOG9tbW1gbxhSBACLqampAWZifn4+DA9jQu8LInaqq4mqS6VSND0DRLFUmfkj/cIY8/mVK1f0qU99KnqOUACKwcCQTU9PR5qn1WoF3Sj1nTvzAoNE9TwrcDBedCCFKcP5M4+ALOQHI0Akms1mI/WXKvbIyEjU63D4XiyNRiP6r2Sz51uawwKNjIzEvSWpWq3q0aNHymTOU1CAEIyf170wntPT0wHGLnI4OEgdXRpgpJHWsCheGmwIKA3mk9NzU/DgcuIpJ+7pz+1plzS6owbBAwe+j3Ny4EFKgcDIQRL1Pr4snnsjR6kjcBYnBUqAHIqJsbfD3skBmqdofOzTQMdBlAMi7GAulwuGj98zmfNmaqSWqDmTFMABR+1staeMeA5f1SX1G6ThkN3ueLoHlsLTLbynF6Y7CAYI8M5+jUwmE8wO48Oz8C5+HfZCIYApFouanp4OmTw6OlK9Xo8+HQAJB0boJbLnrBAgh3/4VmftHQS77rjPZs499Zwybql8pD7rIseFQYc7f3eo6d/9Z88FDmM9pEEK0ak4qV9I6JPggwYIQWChLBFGp6oo3iOHDY2X0k6ucHyXiXWBcGUcli+kqIfx8EgDIXJQ4+BnGKiRzgWalMWtW7f09a9/XaOjo5qcnNTi4mI0BqtUKrF+m2sVi0UdHBzEqpFCoRBNt9iN1dkMChMBXKOjoyqXyxHBcR2pv+FdsVgMw8eKGmQANsZXnuDcGHOABW3PT05O1Gg0tLW1NRBNIjNjY2P6mZ/5mdiBF7k7ODgIIwgg9OWqkmJOR0dH1Wg0gvFxQEYDslKpNNBtlLy8p8wcfCMjKC7t47vdboyrpNg1mMJg5sGdHMtv0YNKpaJGoxEABqoevWw0GjFHXkNEw7h33nlHxWJRN2/e1Pz8fABZHCzM20WZDpdTfne9cuOVnsuR6qD03m3ekSeMZQpEMLTuwDCi6Ch/Sxlb11EHv27bnOlArpk7X6bJfOAMcRpnZ2eq1+u6cePGwNj4ezhocBuAnLmjcHtJLYA7ROwXz4Kj95oP5p/5wml5zh+9oyiS56NmgxozHJ8vD4bVRFZ5Np4BO4Xsc798Ph+MBu/mARLz4Lu88rOz7tieTCYThakelLizB6TAiDCXXivD82GPOe/o6Ei1Wk25XE7NZlPdbjfsSqFQiPGgqB7QwTO4bet2u1FwD0AjPe1yx3jwXjw/cw3QcvlGzvxe2ELmnqDP/ZHrk/972uND7b3iKJTDDbnTtq7QHiU4aHCExqB6QRvfx3nzksNAD/d2o4Ui+CB7tTeHU34oEs/pu7063cd48Lxejc0/QMWwCI6/8c7Q7m6Y0/HLZDJRhHl0dKRbt27p3r17AXAQyq2tLV25cmXAcHU6nUhdUMPgwIh848jISKDytLKa9Isvz6W+gVQK0QJpKO/OWSqVgo3i+rAZp6enmpycDPQO4KzX67HnCnMEFTs/P69PfvKTkWJwQ4yxw8mn1KpT3/TZINXhjimNKJ1VA9wyNqS7qOPAUO7s7Gh2djYoXIoMSX2R+uFedPyFkaE4LZM57w1y+fLlKK5lPngmCqrTA8NE3Q4N4y5fvqzFxUVNTk5GYTLnXfRw5+/6xWd+njvYYZ/zu+vmk67nxzDDyPddl4axHjy3R9q+kiJ9BuyCL6/0AAkZcWaz1WrFqjJnR7l/+gxuD9LAztNGDiD8edOIF8edRrsOOvw7XC9NJfEzBc38gxHFRqF3BFd8zso3T/Ggc9hM/j45ORlAH1vBvFBvwbv7En/GlvqxXq8X9gUQ6A7Z09/pczvLlMoLsgLAI+2KvQAIYhdqtVqsmPG6Qmywr0KDiQbgYBthO/CHfh7PRjrIU4QeCCCfPvcpw+Gy7t9x3XraAOXCoANBZDBSxiJlLzy6kAY7FPp3eRmUwYuQeElYCkf4KLc7TVdgaHVfTeJFdO48eBYcBYbeIwzewdkOBJscPJOEMQAdunHyRmkp2nd2xcfH7814dDrnXT43Nze1vb2tlZWVKMZkCWylUlGhUIg+GDwP41MoFAa6rZIawSjh6EgZEaXwvBhS3sk7NuK4mAPYEEBcp3Ne9d5qtQZ2VMTIdTqdWK3COLjxnJiY0KuvvqqFhYVoliad767rBgua0xkxAKJTvJ1OJ3p6wFbw3u12Wzs7O7p161asnmHFDikjjnq9Hkv7/HN2ymXVAn8jndJutzU7OzugzKQIm81mtHYvFApqt9sBcvy5uR4yzP4fkqJmB53pdM6X8t6/f19vvfVWrDQCcNM19sMcHkWj38PYhBSIeBSWOtlh57lz9flFz7lOei9nETnH7+sOedi74bzQJ8bbc/K+eSTyDJOHXDuLkBr5FGg4k+bAOmV53J5hP9Eh5N57uHCuR71phI+9IYBARz3Awn7l8+cN71hGjpMk7e01S7BBnlrhHaV+OhI95RlgDB3o8TOyx15IFLtOTExEPyK+3+v1gulj/Emh8NzO/rlfY9wdLABiPEXm7A3Nxmq12kCqlfGRFGDs7Ox8tRpjjl3CBpKaBlR42wVkaxhITgGts0feiygFEi5/rndeS/JBx4daMuuUGy/m1dtMAt/xPBUKgJCnCumRIR3onBpK2Q3uy6APU3Kui1B7DtsZGZyTrxCAEnWB9HsjULSmdfSPI3MnzLXSHDAT5j8jDO7UHYXDarz++ut67bXXYpOy2dnZUPjt7e33zA2UGUAJ45PP5zUzM6NsNqtKpRLdSdkBluJFeoB4XQvRIDncFBF7gSyRkUdTXpfDZzQBOz09jRbtPqf5fF7FYlF/4S/8BdXrdRUKhVCKb3/726pUKtrZ2Rlgphj/tPUyUUm3242Iij0hONh/BkBDn5BCoRDFoa68pO8kxYZRjD9KSodWACYrdHzlDArOKhhqfHw5MKksimN7vV7sBQFoqdVqmp6ejlx4r3ded8L3AHxvv/12gKKHDx8+jWl4z4Gx85oBP57k8P3/9Hrp716j4QAkBRccfm1nWZHhlJXl2kTmAESu4TrrOs3vOGuiXb8mkffh4aGmp6clDXYj9pR0On78Dbvqdsmjbmd23YkSefuyVO7Fc3j3TndwOHvegecgIGRcsKsOHNBrTwGw7Jhn4Fy3szAevnUCzAb2xllX7IQXdTOv2GXvNkuaxR2ts7M8D2loxtFtfbfbjVWDBLrYCPQbP1av11Wv17WzsxOdnrGhzBe9orw2A3mVNJACIZXivhD5whemQbOzG9giZImAPJWlFLCnnw/TuWHHh1q94ijPjbI/DKjLC1hcwRFId7BEhlwzZRakfmoEhUpTJO5gcQAefTjLwLU4t9vthvDyN+g7N5IIt6+p5hm8fgBh8kiX+yOwXqXtwCiN7H2suE+73dbq6mp0EK3Vanr8+HGs2slmswMbvnl9QaVSiWchKslkMtGDgi3eeRZWp7AyheiZtAVRCykBSdFnw/O7PAMGjRbnjBtzTXqm3W6Hw3YjyDz8zM/8zEAH07GxMf3Jn/yJ3n33XX3kIx+JiB9jA3g7PDx8T8MhlxkHVAAH3p9nbTabymazsU8LCossezR8enoaK4C63W7siusMGfVFpF1YakexLudQrJoyR3Nzczo5OVG1Wo3+AHQYPTs7C+dGCgrjQ+3M3t6eisWiFhcX1eudLzW/aE0HB7LP/w4GfA5Tmhq9TEFGypD43z1i43ful9aC+Dn+t9RRey0UwQM6CqPBgb2A5of9kPpNwfid7x0dHUUbbe+CjG0bBtbcDmI/3TGjW36ON7/iPB8XB08uqzgyt01Ez/yOrBMAeb2Fs7QcgAFSyPgBVp5Q+O1Fk9xrbGwsln6T+sNeMoaMt9sTmGgc9MHBQfQzIp3KxmupLUefeXdYFd7R0x3YeWwv7AW2kfk/Pj7Wzs6ONjY2VK1WQ54c8Pkc+kEgQdDmsuuAi7F2Hfd6xFT/XEedLfTfnank55R9T3X2SceF917hhinj4JECP3uejOP9jJinDaC8GBwYFQTQKS4Msk8cyJEBdCBBvtDBjU9Eytw45ef0vqeEiE6YfJQlXfqVAi/GaViqB2OAA2OiR0bOW5W/++67kevkuw8fPlQmk9Hy8rIkRbESxUje0Q5QSJoAQwBjANiZnJyM/hr046AgNZfLDQAN3ssLSJEBoiQ3OkRcaUoll8upXq+HIccgEEGenp7qox/9qP7sn/2z2tnZidUa4+Pj+p//83/q+PhYExMTQWV70x5H+vQKQaHIi/J+nIezB4ju7OyoUqkMRBE4c/qMkPLI5XJaWFhQNpsN9ggn4ZQ8Y05OGGAk9be3J2puNpuRooIRBKiSevHxZ4VRq9UK1sp7ofjqAgr9CoVCFNE+7cG7SYPFnsgxRhzwl7ISaSohBSkebLgTRlZTgMGcOmB3cINuo4PIHmPjtDGGnHcCkLq9QP8x8swJqU0vMD49PdXW1pbm5+dVKpUGnDg/p3rlUSV2wt/dAZs7GO4J8CdVgU3BeQImeBfsBf9Tf0Ugga0dHx9XvV4f6FUh9fc7ARiTZmW8ATjHx8c6PDwMho/35Tzv5okO0nOIIIodtmGzeXfmNJPJRIvyTCajer0+ACIYWwcRvlLFg1XenfN5Z3ptsALPi4h7vZ62t7djiwaugzz7nKQBJnPIsyAfvnLF2fj0vZAdt8cpS8a4cy/XBwCc1yam333a48K7zHrRk1N8aaSAQ3WanQHjcMfvtCMO3M/nZT3KSNM9w6IajBufM1A4OwcXnrv0c5zNIWeGMPOu/M533WBCg3MugkBVMALk4+AMjDTY8Ox73/teLC3d398fKHg9OzvTu+++q16vp+XlZZVKJdVqNc3Pzw9Q9c1mM3o2EDk4jYpyo0jeUQ/nSk8JajbceDsViyJRa+JForT29pSW7+zabDYHDCdjd3h4qL/39/5epCdwmu12W/fv39fKykqAFp7VC6+IFNxw8Fm5XI4dh+mIClDiIMUEO4ADIMrBiLJsmesBEpBHwArGHIdHK3d3Fhglvz8G2Hs+eCErTd2I9Kh5oaiOXDZFrMhgr3eeenGW7mkODzzQN66ZptoccDhN67o67HPXFTd8zDU/O2WcsgbpM6bXckqec9Avj8JxhhQI4jTTHLfn4rFH7XZ7oAuus7b+vD5Wzg6lESZj7IELNQhp/UU2m42In/NgRVj5QTGmpJBnelNwLgDJI2QCEUkhl+mSc3ZBLZVKA/NFMbgDO+yeM8iZTCZSg4BxT20BnGBAkH++B2uRBsS8n4MV5px39cUEXjxMcSzPwrhK0t7enlqtlnZ3d1WtVgfYF94HPXeZJwgaJpsOSF0eOIYxWfyMLKaMmt+X/z3gxX+lOuPf/aDjQ21tj0D7i/k5jhpdUdLBciVzoSJCg65yZiF9OWcQ+OfMB0rH4PEZwueGnkjGox+WSJI7dEVwg4QzcMcEA9DpdAZYF4SD6DkdV5wQY4RSbW9v6969e+r1erEChK6kfvR653usHB8f65lnnlG329Xm5qauXr0awkbED4AAfFCXwvJLSZqZmQklopCR+WTlC3UOGA6cq3dyZWMjSQOGwalqwMvh4WEwDYANzqvX6/rH//gfa2lpKaId6TzC/8//+T9ra2tLN27c0OTkZKRmGHcYGJ6NNKDUZ34weN3u+XbSLHU9OzsL9oECUvqTUOiGkcvlcpFyAXhRJ8L4Y3gBQb4hHEVvMCOAPo8AYXLY1ZbndDaA52a+WC3BnjxefEY6jP1F6Kh6kSO1B3zmAILn8gh0GAPqtgGgwrVT+jm1CX5PD4T8GT3ly3ecguY+/I1x5HMidCh8Uok4DwIkWDd0JS0qPDo60tTUVMh3CpA8HeLP48/lrAXO11eN8KySwolJfT2ETdzf348U0d7eXuiy/88z84+umjwny/e9dw+pJHTBV2sAgKU+i+CRd8oye+rT2UkHgThHD+BguplH72sCgwoj6jqDTfNrOuPNHHvgzPcZl1arpWazGQ3+AGwu9x6oA944L2XnPBhN2a1UXobpooPzFHAMO5+f/XOXVcbmaY4Lp1dcGJyFkPoO1xXaFQTD4QgsfVGnBLkek8u5KFn6fTcWTlXxTPwDDDD4Tm0hcNQCMDm5XO49q0mYeNJAPkbDak1QDhdaz72lBtKjqjt37mhzczMi3E6nM1ADwD4fjso3NjaiHmBnZ0elUilSAlSPo2zQv0QfRBuuaM40oIzUH+CwURLmoNVqRVqHA7YDBuHk5CSKVImgcKa+/wcMx8///M/rJ3/yJ6OjKeP7+PFj/fZv/3YYOy+GOzg4iLH2ol6W5JJWarVa8c7j4+OxrwbvVywWBzZ+29vbi7HHqZOSymazarfbUXsBm4PcIF8YLE/78T99H/ib5/xJNXqRNaAImYRVgrFhB9xMJqNSqRTXBtSgI7A1XnD3NEfKPDjzMSxA8d9TI4yepeekP6fXSQ0z5wz7nuub56qdNXUwgk1z25cacHSQZ3BgANAi0Nnb21Oj0VC5XB4IWFLQ40xH6gjcDmJbON+LIanDqlarkVKkQVwmk4lzGo2G9vb2gp0AiMNYtFqtABpsX3BwcBAAbHJycqAjp4+Rg+ezs7MAQOgbMgLYxSZ4/YqnEmBpKbCW+nV1Ur9OCnANGEOf0R0Haz6PMCfuUB0cANgodPfaEUnvWfrPtd2mOVhxAIHdTWXS2QauMSwgRz7c5/r5Hji7zPn5KTjncHlMGY8POi7MdDj6c6PikUj6gAhM+h2nh3zQ+OfUDs7Dld0RbPqcROo4QkeNfB8hBXwAnijC9KWUCDKCwN+lfutu2APSTgADQA3sBt/h3jx/yhChKPfv39fa2lrsp+IFhO40PIfIde7evauFhQVNTEzo3r17ev755wM8kToZHR2NYi4Ug78TzXh9QLPZjBbjpA2INDAEzBMtx/lXrVbDGB4fH6tWq0UhJeNCZAS9y3NUq1V97nOf06/8yq9obW0tnPbZ2XmzrN/6rd/S48ePI90BjYtBmpiYiGVqkqK+YGpqKqLUcrkcMpwyeuRukRvSMa1WS+VyWcfHxyqXy5qYmND29nbUUTSbzXAy3rDs6OhI8/PzYewBHkRV7XZblUolijx7vcFcMXqBAWRtP31OstmsJicno2FaqVSK9BhAxFM4vd5gTxPe+6KHO8YU9Lu+OlPIkRq+J90//Zs73dRgvh/gwPmlNWrDrs14S/1CTt4H8DjsGZ1u53N+Zs+clZWVCJLSe8OUplGpj5Xn4FMGh3HBKfZ650XCv/d7v6ePfvSjeuWVV1QsFsMJHxwcqFqtqlarRaTObs7tdludTieaWrXbbVWr1Uj/TU5Oanp6OnZdRtZ9E0pkC2YNG0h6Aj3AVnqtAvPFGHjtGH9zwIDOYZcAYd1uV9VqVZLCrlIHAjBhbN2nOXju9XoDjA/3BHA4y8tcYTuYL6+x8PlyOfTidZ93L1kYBlJdDlP/muonf/Ogn+ulJRXSezfq+38GOnwAU8DgtJfXWPiDcn4azXjulX9cJy0SlfoAB2Pp0YfTc15E1+32V6ZAozniJp2CYYEG5bl4D0fCDLSDCQyQI2hpcDWPR87UReDYnUlpt9t6/PhxFJsxfigZKB+H5gVzzi7VarV4n1wup8uXL0cBZbfbVaVSCQNBQZtXdLM0lEh+dnZ2QKmZe0f6KIukaEC1s7MTKYq9vT1tbGwMzAm0LsWfDjjq9bq+9KUv6Utf+lLsK4LjLZVKeu211/Q7v/M7qlQqyuXO94phzxnSFmdnZ2H4qP+A5h220orxw1ClXQthL3Z2dlSv1yOdwi6+GNLx8fEADd4afXJyMnSFueWzXq8XK4nGx8cj/YduADZZeYTukSrxv8OWAAZZ6dNsNqPVO3J/fHysycnJmP+LplcwQClYcIcrvbcFuv+fXis1oun3kRVARKqf/r8bbAcRbsDTyM2/n74PY0p6DLDrPSlIEZBSgkWkr8rm5qZeeOGFCFIA6B7w8M4eZfMMyLDbJvpSIKupzV5aWtLnP/95ffWrX9XJyYm+8IUvKJc772T76NEj7ezsaHt7O3Qe3eRnNo9M03K9Xi9qRZBrWJxSqTRQyO3bOQBYAGjNZjPSe24vCeh6vZ4ajcYAOCaQYcdrlp0ydl4vCEvIWLnv8HSvpEjRM/7YA1JSDvy99gud83ICB60p04AdQoYdbLl+eIrPwTX/GB+/D9d3vXFf48y9g3bsyjD2g7+nuvVBx4VAB87eBwBFZwBTSpMBxLg6Q+Hnca1hxslzegweNDnFcz64XoSKkvLsOB8MNQcGm0jfCzd9QKkD8cnDmUv9QlPv8skW6aQNdnZ2oliSQihPuUj91u/7+/uqVCrx3jgf2BUiBQqyUqZDGtxCfW1tTWdnZ1pcXIwCQxwXCg9NT1FctVoNISMydnaE+XIa0ZcbQtk2Go0wKDs7O8pkzml86lKI1MbGxmKDpOPjY+3v7+uv/JW/or/5N/9m7Afjhaf5fF6//uu/Hn+bm5vT1NSUDg4ONDMzEwCLpb4+ft6/gDmfnJyMGh4YKWch+J2IhvoQfgesetEb7AM1RMgrxXUYMooRj4+Po508rJobN+YZowtgAGhQO8L3KeCjoLbb7UaERzEu8n5wcBCrlVJn/0HHMAbCdTk9L4200nNTcPFB0ZRf06O7NOfsBtuNtDMlT3qu9GecFctJvcso9sAdD2OLPG1vb0dggJFHf7Cv6XP7+6Y5/TQdyrP1er1YqTY6OqrPfvazunbtmra2tnT79m0tLCxEA7z19fWwTfv7+5FmodYDvUG2eaZsNhtMWrfbDSAxMzOjmZmZSH26DPCenkKniJ9rsAEhth8Qxedch8AN28NGap5KQZd8zGAbSaekNgZd47vpdRxA8JkHnoAeZDH1gfgyP1xmhzEg3NNBEnLnftTTMei4p6tclhxEeMA8DPSn8vj/hOlwdM3DOBPgypEqA+jIi4DSwXEDwMQwYM4+ePQDEzIscpL6DtcPBJNJdoTn1cTUGnA9hBylRrDcOKPoCG2329X29rba7bbW1tbUaDRCuL1Yk6VfIOf9/f3or1GpVAaor0zmfOkXjWcwDsViMToA+vu7gOzv72t1dVUHBwdaXFzU7Oxs5GfZfAxH1mw2oxtprVbT1NRUgAqKXbvdbuwoSzqj1WoFXUttBtXajUZDjx8/Vi6X07Vr1waMMu/WbrfDaW5tbekjH/mIfuEXfiEiG4wBY/jf/tt/0/379+OZyuVyyJDvj4KhJBoFCBCd8AzIAAAV4yf1qV5AL2xVoVBQo9F4T0MxHHw2m43cc6lUCvaHos1u93x3W+RDUtSDEJ1xf9iPTOZ8NZcDNlrYt9vtKOajSRBywioVns/1jKWHsEMpK/k0B7LKd1M6NzWIT2I6Un1Or/MkEOP3cDpZ6uf7AZNeN+b3TK+dGlS/Zq/XC/n21Cy6DPhwFhW5ks7ndHd3V4uLi8GQuW105o374cDc4WJvYVT8Z+47NjYWS99pAHfp0qWwC8vLy9re3o59fXDeuVwuip5hVt3m+ZhR1zQ7O6v5+XnduHFDS0tLqlQqEezx7jwn70K6hL/xvjCppICRdWd+vIcIQQOMHelGnzNnE/At/k4OKp1h5PsECh4Ycw3m0FmyFGx4kOlz7KmcVI5dPgk8/LyUzXAwyDunPsH/7hkA/7uPG9d3v3mR48I1Hbwok8EDO0XsRptjmGFJoww+c2qb6/reHF7L4ZNDakNSRAusJgDpA0Kg/xxEAYoAEQgLNPqwJj8oH5PoS7feeustra6uqtFoKJPJxKY9pDTSCm4iWaIFnAsbSTHu1CHgVFFYL1bz8XQKH7C1sbGhdrutvb09Xbp0KZrleCpqZGQk6hEuXbqker0elfjppk2tVisKRykqI2rY39+PvSZgI65cuRKRHimabrcbNQ/Hx8fa2NjQ0tKSfumXfiloaZxtr9eLbeF/8zd/c4AVmJycHEhVOQOGDACgKPqU+owF244zD+7wnd0BSJycnKhUKsVYAHxcLulvgnNiMz4Kd5FLHEShUAgHxu/U4CADdE51I+ApEmQHUOeb85FigRXBmSBDe3t7Wlpaeg9g/6DDqddU3/ndgcAwEDHMyTO3w1gT/93BQHpPruOOYBiT4/dJ75U6LU8Be/BFcIVe+soRxggbdXZ2Fg4fQOgsVcoE+7P7/1Lfibt9huGg4Z/Ud47IEFslnJ6eamVlRVtbW1pfX4/2+KRGfZyx9WmB7NTUlAqFgm7cuKHr169rfn4+uiSTggIA85x0z2VDNMZwdHQ00qAwhch5vV4f6N/B/ZF/wI/PnafTXA5JhSIPFKD6UmLAJN8noMCXoO8wng4InNXJZDJhN1K/OAx4pI7e7dqT5NflhMPHwtlwbGLKsDHP7mPdXzuASesq3++4MOhwVJvmpDxK9Jfnb6kTcCbEDyaIfDIITBrc3dEpIgCIU9dEgk5RdbvdaAudGjmADn0pPIfvTA0CTlEhUWEul9P29ra2tra0ubkZzAEbq/H+RNge+WAEcKperyEpViVQaMmzkQrgvRiXNFedplyIAtbW1tRsNnX9+vUAJJVKRaVSKYwlY0jHz7OzsygsI73j6SD+3ul0ohcBxVySdOnSpYFoOy0wZqlrq9XSP/gH/0A3b96MPLELeq1W07/7d/8uls16PY+v04dF6vX6jcYYR9IN+XxeW1tbA1vFS31WjGvTOIyqdVYT0aLZ99VwOUMmAY/ME04JWeB5ACCed8ZhYayRIZip2dnZAeYCg+m9IzzPzBJpAAhFv+Pj45qdnQ0jf5Hj/ViLlK3zVOD7AQX/bsqmpuc7e+IGO63Fwk5ApTsr80HvlgIqp6EBIZzjBZ6k0nq9XjBinLe+vq6dnZ1oo+22z4GZgwwHGjwHdtABB8/m9V84dfQaJpB0J053a2tLk5OTEZCQpgBsOLhh1+lKpaLFxUVduXJFMzMzKhQKKpfLAQq8To9icVLQ9JbhmZFxlpHzM4EaBeDMd7vdHli+6sBf0sDf0roelzlfIgtr7UDFV7Sg49hwT+nzHm7fPDBMHTjylTISXCdlOgjEnV1xoOMAxIEwz8dzMTewsJ5WHgbo/XNA4NMyHhdug+5O39Mk/lCpMjoaS+naNDKS+vkz9rrwvLcLuzRYMOnRTFpg6oZhGIrkZ6cpQX7cC8HnPShI3N/f1+bmpo6Pj2PfjF7vvBAQBUMw3NlSuMr74PAwHEQFTCiOEuNAfnN8fDx6VbRaLTUajRBszvMx8LFmydu9e/c0MzOjUqmkRqOhYrGomZmZiA7Y7t4BB87Rq9ypVQGAkI5iLubn5zUzMxPRA0aIuS2Xy3rw4IEePXqkn/mZn9Ff+kt/KTZocqReKBT0L//lv9R3vvOdAUPGvDC+uVwueohIish+b29vwODRFwPqGyWlPgXZ9lQa+wJ5ZIuiUxPjNRSesz08PAw2RFL0aiBKctYNto5zSUlRiAdjhAGFCofxcXajVCppd3c3WtzT3IyCWOTdQcFFDnQ5BRJuVPnd5XEYaPBrOFuXGj4/N2X20ntJfSfAvZDNYQzLMECUXot78rO3/idXjy2R+nIJCGSfpGazGQWO2Cl/Xw+OPEfPfaH/YYWlflttUikTExNhn9BtbAVACIavXC5rZWVFzWZTJycnsU27j6fb1UKhoMnJSc3Ozmp2dlaVSiWWz7qdw8EBNJyRcBCD7ZMUS1K9+F9SbPFOQOFMNLU1nm7ylgXOlDNfzCMyCGh03SY9h971er2BehxACnZ7GOvn5QgOKt2H4cewBQ7WXL4ZC/SVVJIzI2mahrlgLrEjzDXA2X2hsxrMobMiT3v8/6rp8EFzA5UaGL7r30vzVX4QbXsh3rCX88H34h5XQi/eYfB82aOvB8cJeQdTJgjB5feTkxO1221tbGyEQaE/QqvV0tzc3EDelUlKARm9MohYib48Zw+6Jz+5sLAQz4PCYmgXFhYiKgaQQPmlVBwGh1QI4K5cLmtyclJra2uxpFPSQERUr9cH+nxgUFutVgAOn2tYGVZkAMrq9bry+bwqlYrGxsb01a9+VY8ePdKzzz6rf/JP/slAlALomZqa0n/4D/9BX/va1+IZRkZGIrKHwfB9YUipSQowRvRC2ot5QCYYL+YR4EMrZZcl0k20GmdjNZ8Hmnghd7VaLfp55PP5ABzICkCs0znfAMpZkGw2q2q1OrAbJf94H67DvPoeNfV6XZVKJVb3QHcD8gFvF129Ig0uM0wZC7cZLo9udNPoHjniPObGgx0cgdPKbleGgRO/t5/Dz/43/8xlA5uIPSNQcFvozoTPvMaAaHVzczNqtd4vrcX1UmDlARHPgIMoFouxzD11FthvnxeCoKmpKc3MzKjRaAwUgVIEz/1I1cBqlEqluJ8HDJ7ixKZhp0nNkELkPTKZzED6DyCFQ+Z5+RywgkwAJvgZMICzTZmLFOD6OPI71+Sd+F63241gxMfW7zVMNvnfA3ru5QyXd1J29s5lwu/J8zE2nMe9nCnt9XoDYIOxTcG9z0uqt09zXBh0uNC40Ugfgof1CfQII607kPrAYWRkRLOzs1HhnaKsdKI8KuA6rMjw9IWzFhzk9XkXnA/nsgQLI16tVnX37t2obYC6B4zUarXYqIvJIRVDmiWbzUYKB4fEe52dncXnrPyAtfGI21kc3n18fFzT09PBvGQyGT377LPqds874rH23ilXHzM+I+oql8va3NwMsEF3VhQLtsP7akjnbIKj8kwmo0KhEFQrBoYdWmnj/Y1vfENbW1taWlrSP//n/3ygsIt00+TkpP7Lf/kv+v3f//3YsIkxweiyLJXaChTr8PAwUlC+JJRN0UiHdLv9AmWiGM6hZgX5oNcFMpTJnC/hJS1FPQ5pHgAr4+S1Mfl8PparMr/soVIqleIZ0ihlb28vNpND7qnGB1zxfplMJlgQwCRUMuxHoVDQ3t7ewPg/7fEk44NBc8YnZYeGMRh85g6aw+lud/7+DClYGfaMwwDGsIP7wS74Z1yn2+0OdBMGlGKnCIi8Ay26R4tsUhHvd6TOwJ0V/zMehUJhQA4Ycy+KZqx8vqnzYh8n3puGZqRQsXGwGgAcgAWg2uvtGEdYECh9CkD9XbCvnkZxQOHMhafWPGhw+WGsAH60K3A5on8R8gPoIYiAvRymH8yJs6a8k893CqidoXDGw9kKgIuXJvA7suhpGNcDvsPY8w+/DJDhnZg3UpB+LcabebnIcSHQ4WyBP9iTjIYPKNE4A4IA+kPjXFlnTbUz+3NwH59MrsHkON3oy6VcORwMOOBAGdygEEn2ej299dZbun//foAvWALeHYqca3klO0VapI54H2dioMhJs0C1np6extp/2mdTF8KYn5ycqFwu6+TkRDMzM2o2myoWi5GfXV5e1ubmZhRfsjkSz+HvTH8LzuE+tCXHKXvE4KwKzqXX6+/f4Tlq5IKmYMfHx7p9+7aazabGx8f1D//hP9S1a9cGVpVQ7/DVr35Vv/Ebv6FSqRT7spAWwdHDPHgPDknRgRMHDPPA+NO7gl1XMQIUf+LsFxcX1Wq1BnLRKDPRNgoJqKHw10E4z+PF0azQceeJbPFeZ2f9zqjO3sGOsSSTVBireejLMTExEW2pWTYLtU8jtbm5Oe3s7HwopgOddPvgYDktduZInUUKDtAJp4f5ngNxzn1SJObP48/rvw+LRv13zgHocfDsODF3MgQN6IO3vScw2NjY0JUrVwIgPOlwJ4qNG+YEWSLrTsdX7KRjgL1yNqJQKGhxcTGCQNKd1H0cHBzE+/C5b6/AvJEa592wzz5mT1rdgf1BN2BneW+3Q+6jAN+jo6NRb+agh++4POIXGBN/Dr7r15EGu3Lzuad3PLWKTKTyzs8OpFM/BejgmfmXfs+BiesE1/T3SOtLHLy7f3uSLjjwfprjwkwHhyMlp3EwqjyID6gDDmdEeGF/MVpxe1Th1B73Y3BJxUCjc31yYR4hst7ar0kEitKB1KemprS+vq6vfe1r4RS5L9Eg7+G0mjffYuUBk3VychKFj3SJlBQV3O60oSehwyVFtM4qg0wmo+np6Yi6s9msbt26FSge48fy27m5Od27d29gzxbPb5LTGx8fV7VaVa/XG9hzhfmUFNXjUl/xUBQAALugAh6YH1JU9Xo9uhv+63/9r/VjP/Zj8V7M0djYmH7/939f/+bf/BsVi8WBDerYsh3A5cwQzpfnJIpnJQrvBGPDeYAHKulhqdI24syn14GwKoRCN4pOcTpEi17Dk8n0N7BiZQ1skj8Xz4tBw7gBKgCt0MgYK+qjeB+eq91uDwBtHBS1Bmma9GkOd/buNNIID8eAPrkupwCBwyNBdwhcxw+uT0W+s4PDAAdz4X013MD6c2CvmAPeD93lZwA7ztSdoQNe5LTZbMYePf59dBgbB3hBjxg7ACg1Suhe6hQYF09J+TjgaFhRQv2R78HCuFPnROABg1csFuOZcbQ4W+wUy9xhiVn6T+qvVqtpd3c3gjXvqgoYx15Lit+dCQD0SP1eR1K/ToT3TeUMNsjt3sjIyEDPEN7FZYJghDlljnkeAho/HDSnugJQ4P787s0CKQsggHGGxWWdVLT03hU8nrKS+g0t+ZvrrT/3RWu/LlxIyo2G3dyBRho1eD2EP6AblRTU8D/Ahv+9SNSvQx4cA+7Fpa6cTDDAg4H2JbrkF//kT/5E3/rWt8KBQvMTgTMhRMdEm7wrfSDoNMnnRNuAAiJYDIF0rkA4SZqSeeQGm4OB6nbPe2bQPRDlAikz7tlsVs8995xu374dm36ljBO08OLioqrVaiz7LZVK0WEQ5809AIg4DiIfj9q8UyJswdbWlnZ2dvSrv/qr+rEf+7EBR8hc/uEf/qH+7b/9txobG4ulnMgay1xPTk70zDPPRPMv2qGjxBjGkZGRqLs5PDxUu92OVE2r1Ypaj7GxsejQ6vneXO6846k37OJaGGT6ibTb7YHmZ56e3Nvbi9oP5mtsbEzNZjMcCAYER4SzAIxSh3RychJyiS5gMAAaUKW5XC7o43a7rbm5OXW73YhY6T7rK78uYhtSald6b18LByZ+pOcNu46kcBbI3jC7gqynz5ayGp4aRv6dtX3S4ZEpY56CG+wMtoAxpTOngyfAaL1e18zMzABtjfPgfJwYoCSNUmEb6KFD7dWwgBDd9JU22GwHJACnYrEYNobr8nev4XA22gNBDl8yyxxKGoiuJyYmNDs7q+3tbbVarbDpAHFnJSgSlTRQlwcQAXyzkg57D4DwuhZnQfiZPkS+H5QX7bosMJ7ObPPZsNUyzlD483s7dpdpZ3NcpvnnwTY2yoGI62gaJAzzx3647vDsFwlOLgw6uLinJPzwqIWX56HSfBEv4CgeZ0wx5cjIiIrFoorFovb39wdQKcpBFEh0DpLzw6lIBg06zyM9FLNer+u73/1u9FGgMyh5Pa6JMSff7iicaBGF8o529FeQ+t1H2YyNz9m/gGdCgGgPfHZ2NtBaGODBeDqAQeiI+kqlkj72sY/p29/+digzDs5z0mNjY5qfn9fh4aEajYZqtZr29vY0NTWliYkJTU9Ph4Ml4vZ0is8zzwGjBD1bLpf11/7aX9Mv/uIvRl8QqZ96m5qa0muvvaa9vT1dvXo1rkG1erFYjBoUUgk4T2+JTKdPKs9brVaktJrNpqanpyMNhmJDnzOXDlxhYOjgmc1mgz0ixcEzpg3B6JngqRCWWGMkmANkCRmDsTg4OIiUWqfTGVjCC7ggyqNItVKpDCyDZiUPMufpRmj0ixxudIc5+NTYcaR2IT0/BTLosp/v0ST/M37pffxIbdGwd/Lj/ajmlBnBwbsj93nBIcEY7O3t6dGjR7p27Vo8lwMTZ7twOk71E9AgJwQBbvucrnd7447MHRjgghqPXC4XcoqtSIvv3d77797OHDDGM8IE7u/va3d3N2QcvfPCTRgP7Lf/PswB8g6kjGBsnHVg/l0emC9J73He2PU0RUE9mTtvntlTQpzPfd3OA6xSefMxTf0Zn/sc+jOkYIG5Sj9Pr+O+Bx0bNsbvB9D9uHBNh1NVAxfKD7ZfRVGcDUD5PG/Hzw5IQJ0UW+HYGRwK9/z6XNtzqS7wXqQ6LOriGUZGRrS5uan19XXNzs6GUaZDJrl1qEboRKm/BwJOFUfe650v56JHA+kRZ1pYtglwgHY8PDwcKKYFTHiuEBr+7OwsHBLMijfJ4ho4odnZWS0tLcX274wJz++GhN4NjUZDx8fHURcC/VosFjU9PR1KSJSOgyeyYC1/r3eesjk5OdHzzz+vv/yX/3IwHDwrP+fzeV29elVTU1OqVCoxnq7oZ2dnunz5cgDAbDYbYIw5wzFjRACoyC3RGIYjXR6NvDGu0rljzmb77cyRB5qcUTMBKGk0GlpaWgpjnuoOxbUo9v7+vmq1mqTzlGM+n49OpZ1OR7u7u1E75Mv0eF8o7Gw2G8WjjNvk5GRsuEe9SLvdju/7uU97eDTtej6MARkWcaWHR2AcmUxmoFCZ+Xdb4AfjkAZMznp6dCq9F6w86fmGAZaUgU0dobMJzkLB2m1sbGhzc1OXL18eiLT5Lo620+kEaE6Bni+VBhhwDbdZHj37XPu7MLbYnm63q3K5LElhA7yQlfnwmjb+BuPg70Ugl8n0t1mo1+tqtVpRv4Q/ODo6irojGGJkwZsMpo4dFpwUtrMNPIeDDk+fwHKk9YjUV0j9Og33Nw4ukS2u62PP/Ln9ccYiBdouWw6M/H9P0ZOyH9aMbJh+MScO7Lm2A540qBgGaoYdF2Y6UgTlyuy0HQ/ktB7/8/0n5YJQSKIzpwKlPp2K4uEUeD5PseA8qNEg1+pRBLnIw8NDfe1rX9P+/r5mZmaikRQonGiTFSJc05EgeUhf/so4MZntdlvT09NRDIWBZlJxGAAKcqYe5dC6fGFhIZ4lk8lEDw3W3Hv6BLBDBE6rdQddzMnZWX9DI+ZrfHxcc3Nz2t/fj8p1ihUPDw+j7oRK+XS9OH/L5/OxNG5vb09//a//9VCSFLQC7q5fv67x8fOt5kn3cABslpeXB6IyUmT7+/thoDDabGPPs/IeFMNmMv39UtJIE7mk54q3hMZ5wGpR8IlMj46OamtrS8ViUfPz87GvBSwMxZ3VajX2n+n1egOpIgDCyMhI7EKbzWaj22uxWIz0mDu7ZrMZssrus77SBTnDoHsK4yJHmq5IwYb/7g4YPUmNcXqkn3nkNYw+Zj6dxeFwWhr5dxuWgh2PTHleZ1zS9/DImPQWskH0Kymiea7x6NEjLS0tDegP80Hqz50Qn2OPCGQYG+7D83MeDAFUvD8n10Fn6ZwLC+1F0IeHh7EKD7CKr3BGRupvIsmKMp6BlADggHHGFhHkAbg9ZUrqCpaP+UL3PJDFLnkDrFTO0wJQrkHqB5sAeIMRBZAwF17L4myDgzoHPi5rvLvPm8+fX495IVjwOUa2n8TiuYw708LfGeNUb13eL3J8qEJSBiNt/5o+EOem3x02yamxQUgPDw91eHgYOXEmksMHA0rfBYHfASk+CSx5PT091be//W1973vf0+joaESE1HFQ44DAZ7PZWCqLswC4UEDFO5HPJ+onZYEw4iigV50dAExxP4o6iQxIa3AuhVEYcfL2Xk9CCgQhnZ2dDYrewSDjh5NDaXB01B6QR+Xv1WpVY2NjKhaLymQysboDOp9no0vpX/yLfzGahfn8IEvSeUTx0ksvhcHAeXS75/uVSNL09HQ4Y9ot41BZlgtYwIjyrt7tFIPIu1O7guxREAqjhJGkwNTrCzi/2+1GrwNWjnBdqO5MJhO58u3tbWUymYGiVel84z1qVjiX8Xc5dbBA1Eu0u7i4GAXOxWIxAM/Ozk7QzpLic773tIfT+amRw/m6/nudkfReFiI1zunPfvjnnkpII9lhjAvf5xou8zyfpwCceua9cUauDwDNTCYzkFKFdfNiUIKCg4MDra+v69atW5qdnQ1H4Pf0RokwCBwUbxOUUePEewCKsYEEKci8pNhB1pdRE8TBPOzt7WlmZka1Wm1gHyiuj41DH1yXCJp4TpaTe4Qv9fuBeO0G4wBoo64NvWI8iPixKb6ChnEAHGE3PPDBfu7t7QUgZcUdYMvrugheKIylTw+fuz8iGM/n85qZmdHu7m7ID/KRrhzBZqe6RcALM83cpoE+75DqAQdylQLtNOh3UmEYAPqg48JhDErmjj9FaA4oECA3rikadAMpKQSW8yne8ahF6hsxDqhG6KTUCPgzo2Tf+c539Ad/8Ad6/fXXgzYESKDsuVwuKG+enb0xPIphSdbx8XEwAVRWt1qtcMb0b4CuZDUGIM5pLelcwEH+3W43jBfCm1Yxc/3j42NVKpUwFBRwAm6g/AuFgpaXlwO0MaY+nigJikuBF2yPRzXHx8cDTX880iciyWQy+uEf/mG9/PLL4Xg8kuR6GHtqV2h8htxRODs3NxdzBOAChEmKVunsM+LV5dTdYFBgM7iXO55isRgski/xI8Lq9XqxaVU+n4+eBc1mU2+88Ubcj34EOLjj42NVq1Xl8+cdU0ulUjRjAth4zpu0ibNT7pAwxtDVOID9/f0orENX0TWWXSOLsEAXOVLQwNgwfm5Y3fk/LUXr5/o1cLAeWafU8TBDyzN7JOgRJmOELTo7OxtgbwkMvAjRr8N88D1kCtvAZzgvGN7d3d0oKOa6vtdRejCH7mRdL3G62F+i4hSAeXDBO1Go7iAG3WbZOewDthLnPzIyEiw0Gz82m80AH4Dcbve8sJztHrxegjHzvkCMu3c85Xs4e6nfCdplEIDI79QC8hwOEPjM5QbQ4rLsaSVYEc71QlnAI76k0+mE30jlOQ3Mh7Ek/k7OhLivS4OAYTqQsh3oyjDGchho9zH9oONCTIfnO70Q6f0ikJTOBJWlDAhIjPs4zelLwjyS4ntOhyIwjuqgn0kXUPz3zW9+U+12WzMzM9EpE2eNoee9nJ6lrsBzlRh3+nS44fLz6PbY6/Xzub1eL+hKr+A+PT2N56LeA0dKoyzGhqgXJ8Z3SeGAvj2CcGdVqVR0enqqR48exdifnp6qVCrpU5/6lN56661QeqmvrOVyWUdHR9rc3FS1Wo3VKowPrbj5B4V/69YtffGLX4yGYi60Djz4Nz4+Ht1RvaiyVCrp8ePHWlxcVKPRUC6XG6gor1QqOjo6ilUpbLXNuaQViBwZL56FvWVIpRAVHh4eRmt4X7bXarVUKBQi8jk6OtL3v/993bt3Ty+++GIwa8gChtnb58NYDKNgAR8eNbOKBfBUq9XU7Z6vqOF87kPPF0mq1+sqlUqhe7A8MESSgrW7yJEGEe4E/X1c/5l3//swUOIGE713Y+dONgUo6TW5jwMUZB8Q4obXnZcHUbyfv5MDHHc4Duj9uUkveECwu7sbhdM8s9tF7uN6A4j0pbu+aVmn0wk5S9MV29vbkXoFbPv7EQWfnp5GarTRaAwEYFI/xQjYTZ0X4JxzAfmkbTzV4gwMwIJrMLYwADAT3ufI0zoODD31gU9ypoe5BuCgP8gdPsXlzoM/WIyUEeP+Xrg/LOWP3qT+049hQH0YSEjnMb1Gep1U34aBjyfp59McFwIdrPf2al2PAqXB7Yg9FcJDeTWwU6o+0AgRyk9xpjsmBtELXKCXiCY9f+eMS6/X0ze/+U1lMhktLCyEU6MboPcyQEC4Du2svcFXLpeLWg+YGWguHCCKjuOgKhsD4w2hABc0lGq1WoHmoQ1ZspnJZKKLJYADZsOVG6aFvVAYG77f7XY1Pz+vR48eDczf2dmZ/syf+TN69tlnVa/Xo7A1m81GzhjWYGNjQ3/4h3+o73znO7HvzOTkpBYWFmLDKEmam5vTT//0T8fcpQrJ/A4Iaj6vubk5ra6uRj3Kpz/9aU1OTurXfu3XdPv2bb311lsDyzwdVLGE0KlXWB5qKQCk7mhJs3FN5hRZ6/V64dw9quv1znfe/eY3v6lsNqsf/dEf1fz8vLrdbjQ9w+B2u11NT08rm80O7A2BrI6MnLeLXlxcHMjZ4qR6vV7Q5AAKlnC7U3JjyLPC/Lh8SYoVZGk/gac53FD54c45/VsaiQ1jNJ7EcAyL4hzk+Of8zf/n76kt83dwEOL2CiczLHp0WtudxzD7BwDGBhwcHOjRo0d64YUXwjYwdzyHR6SMATVagApkG6c8Pj6umZmZsJknJ+d7tezu7urtt99Ws9lUPp/X9PS0SqVS1EVlMplYTbKxsaGtrS3t7u5qb29Pc3NzkRryQn1+7nbPu+ZSC8bYktIg8ED+YAEZM4IVr1cDHMFQSIplxr6zss8z4+xzm57jbI7XjTiQI+ACSObz+bAf7v/wWbyLsxwpsPCULHOLfxsmwy77KXDw+7i8pWCf9x+mj8PkOT343tMyHBwXAh3DHs7pVGmwEyC/O1KXNAAUhhkXBp+JbDQakYsDGXpeDscnKYwpSkcUiLB0u1299tprgfK9CRQdIkdGRiICdINCPwXAC6sUKLZi9QX0uzsqQAuNn4hWadoE84Cw0krdi5AAfeyASkEZzoYoAQEmn8hzsgKF/CHP6Y3QZmdng+bHSV++fDkaYXkqxA0gqaIf//Ef15e//GX9j//xP5TP53Xt2rWo2ZicnFS5XNaf+3N/bmB8GGOXE1ci/n79+nV9//vfjwi9UqnoT/2pP6VXXnlFv/M7vxMygKNMoyTmgPGcmJhQq9VSt9uN8YbxQD6d9clms2G4YT/29vaCAi6VSkGXAtAWFxcj9QOlDBtTKpWCheh2uwNGlEgPQCj1i9UAlE5PI2fOZMEquTP1wkHYL5gbqR+FMl4XiWBch3G4KdvhUbrPvdsHZyE4+Izv++/Ii1PMT4rMhrEdHgEStGBfPPDxlXHu+AEkHnjxLCnLw9x44XTKhKL3jx8/1t7eXgB4Ar4nOQSvK3GaHwaTjSBdzpvNph4/fqwHDx7ozTff1OjoqGZmZqIDaafT0aVLl2IH5fv37+uNN97Q48ePg21jczcKxTmWlpbiPgcHB5GOoW/I4eGhFhYWgh3tdruxFxOrVLDPMIekIrG7vpAAFtlr5pxdcT1wGXBfQlDAwTU8xeaABVbS2SbqgXxlkds37IkzIA6ah8nuMF3wd3PWLf2+6x7y7M+UMjZPAtd+fz/Qjac9Lrx6BVqJh/XBcsqJl0VJeEmv6/DogQNji2PnXpxHt0Xuky5Hw2njoBFWQMuDBw9Uq9UGNjGSzmlkBI5eDxhnR40UPjl1dnp6GoVQNL/KZrPhBPgcY0XVdrlcjnE8ODiIZzg4OBjYXGlmZuY99+HZpP6+AJICQNHmm888R+uGjmY+GO2FhQVtbW1FXhY2BfrPhTqNXClw/MVf/EX9yI/8iL761a8OdLUsl8v6+Mc/PrC9e5qeYw7doFBg9txzz0k6d64rKyv6uZ/7Ob366qv6oz/6owA1MGuACkkBOunYSuoMYHt8fBz7XQDUPIJw+aKOotvt70Y8MjKiqakp7e7uan9/X9vb2yoUCvroRz+q8fFxPX78eMB40CnSwWFKofuBzFDA52lG0jq93vkGehRcU9iHMfYiPQwp+W/kFMAE+1MsFmOPmqc93HgPS3tw+Dv6WKf2wG2HR5H+fQcAzigMYzpSg+lyPIx9wfZgT1IA45FoCmJ4Rn9W0hvIn6SBzrQwqGdnZ9rd3dX6+rqWlpbCVjpLQtrAo25nCFhy//jxY929e1f379/X/v6+/viP/1jz8/PKZrN6/Pixtra2tLe3N7Ab8tjYmK5evar5+Xm9/fbbWlpa0vr6ut59991omc8ct1qtAEXsWszWCwB62BtsFSvYYDVY5k6DPgA3aU2KTvf29gK0UIflckzxs4MM5gZfgf4SPAC4GXeu5+PLxojHx8fBUgIOfUWbA0NnSD1ATmUcOUrTdC5fKbvlLIgvkmCs/FrDMgl85vKS6goyltojns3lO003vd9xIdDhdAw34vDUBeemhsdfyMGIH0QZ9H7o9XpaXl7W22+/HS8r9akyzxvCbiBMPCdRYbPZVLPZHGjUxGChNMViMVahYIh9ySqMg3eIZCKJgkmZABJ4Jqh5EDljRp1Bp9MJhWPvk9PT09hLhRbZUn9dOA5oa2srnE2hUFC9XtfCwkIoNcpJEahXhBNlsLdLNpsNJqFerwf7kzJYzKcb2l6vp3a7rZWVFX3pS18aQPcoCTUURNIOXrm211Ug/Ldu3dL4+LharZb+7t/9u3rppZfUarX0+uuvhzKTv+adTk9Ptbe3F4wBkTyGi+p8Oq7yuXc8pDAU1on5Zpvwzc1Nvfnmm+p2u7py5Yo+97nPheM9ODjQzMxMzBN6QfoCNi+bzWp6ejr6FVAPAgiq1+taXFwMZgXgxnMxzyMjI7Gy5fj4OIAYqTVnWgDPDtIrlUoYfs69qI0YFpU96WeXGze2biuedG0/UiP6JJCRfubXHAZ++dkDH/+bB13peTg67CTGnjl1446zwsj3ej3t7OxobW1NL774YqwASwO0VH9gN2AVHj16pDt37ujtt9/W7u6uFhYWdP36dc3Pz2tsbEyXL19WvV7Xo0ePtLu7q83NTUnnRZ93797VgwcPInBjeSt7Mvk4Otis1WoaGRnRu+++G0ya17NQ0A2TOjU1FUXggGipvxqRoMALMcfGxqL+iufwuhKCA8CAp0NhhJ2lYl4cRPKzL0pwJge9Ojw8DPuGTJPm5joAAWxwCpA93YF8kXbh7x+kS1zPUyb+Oc+C/XIw/0HAwu/neutBo4/d+x0XLiRNo5BhUZk/9LCfOVxQnfrCYWQymYhEmSxfGoQA+aADEFB4EK10voIhnz/vRAm44L6sViAn6sWyrVZLs7OzIdAerbCE1p13rVbT2dmZFhYWBgqViEw97woQ4dpsB312dhbr+WmSQ3dNP5/nHh0dDcBRq9XCIbFLKSyOgwdqZWB9GFd2SV1ZWQkqPwWVPtcpNchBhM38dzodfeUrX9Hx8bFu3boVIALan+jJhVrqry6gOdiXvvQlffSjH42GXLRTh/lhfjKZTHQIZQ56vV60l6dw1FeyUFjK96GQnXKfnJyMGqC33norwAb1IMgoeWEiu1arFWk7ltGRDmTFCePIs2LsARS0bPeVGtlsNt4nlzvfCZklhP7ubFtPJMfcUEMk9WloZOSioCPV+SdFPykF7EyQMwkfdJ8UmEjv3fZ+2HX8fn6dlN3y5xl2X39W5BgHy7m+wo3rOuPrzsGXvB8dHenBgweq1+tRZ+Tg1UEhz0Oh8OHhoXZ3d3Xv3j298847qtVqevHFF/WzP/uzunbt2sCGkYD1er2u1dVVfe9731Or1dLJyYmazaZWV1d1dHSk/f39gS0WYCGxzRSN+2o16bxg2RkEmAJYiVKppJWVlWASCLZGR0c1NTUVtUu5XC5WsPnhoCsFGZIGWD7SPa47LqO+8gRbSbEt1yJIINjlSBmtbLa/t4wzfZ4lcDDhwMMZDo5hssl9U9Du1/BxSksakMsU+L/f4QGDP8fTHhdmOvxhXeh9yRkHL56+PErjgpkiP0nhBCh+dGfJkVJIPqEMPk4LqpmaDRzbo0ePNDc3F9EEVD2RKPSzKxvolbwnwsz92c8CZ0UBKr3/08ib9I8zKQAm9jjBkaX5cKJZisK63fOlv56+QsHJNYLGHZUjvD/0Qz+k7373u3rttdciYqYvCfdFUIdFiox7sVhUrVbT7/zO78S7PH78WN/4xjc0MTGh+fl5XblyRdevX9czzzyjy5cvR0+RtN8EBv0XfuEX9LGPfSyYGppr+V4WyKR3CGVueDbmznuIIM9jY2PRadTnhA6qnU5Hd+/e1cbGhlZWVnTp0qXIk1NTQ5qw1ztPp9BplHOoy0HOna73gjIKAFl2642lmDPy3l6bA2sHc0bRKOwN+6wAapAD+szA/LiuXdRW+OGykv6fyk+athsGHIYFNMPsAM/yfiBl2DO+37tIg8yKOw2P6HFK2CD0Fnn0FS1+b19J9fDhQ929e1eLi4sDKRSvMfBngfVaX1/X+vp6LBV/+eWX9YUvfEE3b94MB4quYZ+o/1peXlYul1Or1dLOzk70qaAupF6vRy0R6WaWuuJo6/V6jAf2kqXgpPG2t7e1ubmpd999V6urq1pZWVGhUAidxS4hvw62OIfgjCJcUo2whM44EIim8+d1I85m4pA9TY4u+/NI743yHQwCNgmqeBb+YZuwNSkA4DNn8IbJIv/8+XmG1L+67Hp95ZPAtd/XA0KX//8n6RWpP5g+SQiXDwKfuQFNJ8YVPGU6MPju0HEQPigp8nfAQYTvzw3rwUBhyCcnJ4M+9FqCbDY7QNvhFLrdbijI2NiYqtVqsAI7OzsaHx+PZZkYC4TXd36kZgIHytb19Xpd09PTcT/AhisXaQAiC1JJrGRAYaEiASZTU1Ox3wZFrTwDAONv/a2/pX/6T/+pcrlcFLM5fZgKcAjU/41i2u22fu/3fk+vvfZaFH+Rp7527Zrm5ubU6/X0xhtv6L/+1/+qa9euqdfr6fLly/rYxz6ml19+WTMzM5EWggm5ceNGrP4YGxvTnTt3QgkwGBg/6mtYyeFG35uR0VwIGpmxnp6ejsZhnLe7u6s333xTi4uL+sxnPqPT09MBIwQ49FTc1tZWGPRM5jyFQa1ELpeL7edT0M1c0vPEGY1erxd7a7DUdXJyUtVqNcDN6OhoLN/1ynsHRMgB52CApqendXZ2NgCOnubwqH0YIE1BBA5pGOvg4MNz2Bx+/jDwMgxEpExd+rt/PszAp5+nqRxfvcRYApjdsfCz58LTouKzszNtbW3pwYMH+sxnPhOsLvLoRdLcs9PpxD5JtMt/7rnn9Morr2hlZWWAsnfnhkOenp4Ou1MsFnX58uVgEpeWltRqtWKprPfLIF0CWwyjhlxht9EhAp6pqSk1m00dHx9rf39fvd55j52FhYWQd2wmTCC2WdJAUODpKcbTnX3KZgFOsKPYKAetXleI/fb0j6e8sOUpEJX623zA9GDXARPcD+fvNjaV31QG3ek7sHKZ9jQ392Qs+XwY4BimP9wjfb73A/R+fKg26FJ/snlZj5RTdDSMxvKH5W8Ihy8dg8r2wh1psBOpD4YPulcvw3JIipUEPNfCwkKseEBwfAvn6elpNRqN6HKJ8LCU8fDwUDMzM/E9WmxDEeL0cPBQ7dQPUECKcrH8lrqSo6Mjzc7ORkQBHe4biDHeHjUzFplMJuooODwXCbuDw3777bf1Iz/yI/pn/+yf6e/8nb8TKQaiIubMHQyGZW1tTb/927+tBw8eDCxT7fXOt16/evWqMpnzGgqi7Y9//OO6c+eOnnnmGR0dHek3fuM39C/+xb/Q+Pi4rly5oo997GP6/Oc/rxdeeGFgeWcmk9H9+/djnL0/B3PpdTf0Ijk4OFC1Wo2KewzJ7u5uRH/dbndgIzQMbS6X02c+85noxEjfDe9GizHJZrMDDdk8ZQfAY9x8+TNFxbBhgIperxcMENcCWCKvXMsdHQwX8ss7bm5uBsOHs2JuRkdHwwn8/zncSHqAgdy4E+ZvyLJfw+lmr/kZZvBS5oBrpOxn+ox+nWGAJP3OsPu67vlKIGTCV8FgR5w55N1gefP5vO7du6dqtToAAL2Y2dPNyDe6PDMzoxs3bmhhYSGCH0C2gw/ejbSfr/5gi3pYik6no0ajoV6vF0tg6/V6FH1iBynk7nQ6sXUBmyO6bXSQ7jYGvYWdBhRTryH1GdBMpr9BGv97sIKPYU54N4pbvfkkwI6mYdhQZDXt5jpspZOzDx6wSv1+Vy6TPBMyPaw+IgUiXJ/vOLBKfS/y5vqAP05JAv/+k1i/VE9T4uH9jguBDk+lpLSrP7CzIf5i77m5CVf6EunyNGmwGh2Q4/fydA5Kg6CwBwdRMH0wKLDD+XPt0dHzbb8RMBw7tB3nolgYfbo6ehESVCNLV+nm6Y2mHK3jxNOCH1IUUPg8a6fTiVy/bxzHuHg9CeCO8eR6UJQUsH7/+9/X3/7bf1u/9Eu/NLAhHEYTAYZyXF1d1de+9jW9/fbbyufPe2qwrv7g4ECNRkOlUkmlUil2q8VZ3r9/X7Ozs9HqPZvNanFxMRzgb/3Wb+l3f/d3tbKyorm5Of3qr/5qREQPHz4MWYLdQJ6QkcnJSe3v70e/EwoxWU49MTGhZrMZhpg56vV6sQoomz0v9IS5ouBT0sCceDRGlERajMiwVCpFC2zmB3lKm8D58zLe3HtiYmKgo6PvfeH1SawE8ELmsbExlcvlgTROKrtp1PQ0hwcdaXTp/zvwQO8xhA4CpP7Ot64Hw9gI/zzNN6fnpcHRk5gNf68n1Yn4z2nqVxq+0/KwoIm5JGLnWnfv3tXt27d15cqVSHMSXDj4pJ4jlzvfaLBcLmtqakoLCwtRG8Gz4CxxVnxOxC/1Nyn0HkIAFwAIIAkbOTY2pt3dXU1MTKhWq8VWCXTCReaxU3Nzc5IUPUXQEZob4tw9ePFCb6lfqOmN79B/T3njpPFfLNvlex6kYjdgWp2NIljkd2QCucDeDktBp/KWyuUwRsZTaamMun9wGUvBQup/3S978OjHMJ0YpiPDGJH3Oy5kUZ5UnTqM+kxTKvwOEEijDUACEww17rl4ruOGCsEieoNu9o58+Xxe1Wo1VqtQPEifDs+1oxC8Lw2dHARgNFCAcrmsjY2NASXG2Pu4IayZTCbQu6S4DikeR+3tdjucJkAlk8kMFCeR6sEJYVBwGNSdEIXjlFDM/f19VSoV7ezsaGlpSZOTk3rjjTf06NEj/cIv/EIUsgIAO51O7Ey6ubmp3/zN39Rbb72lmZmZSA/t7OxEbQQ7q05NTenhw4fa2toKpyedA7OtrS2Vy2WdnZ3vMHnt2jX9jb/xN3T9+nVtb2+r0Whod3dXtVotjN/Dhw+DgfKiWQwb0QqtmjFcvs8CThzHTHEdfTsymUwYWHpcIKO+vwWsBr1eoG2Re4qAfR8hFBgjiAPh/J2dnaCdy+WydnZ2okbD61AAFzs7OwEucBRTU1NqNBrBWkxMTKjRaKjb7cZeGrw/IBD61/t3PO3hBj5lOVJWILUp2JHUsH1QWtaDkWHBTWqQ0+9wzjDD+SQHkX6HiNPPSWtTHGygu+70cViwjjicer2u73znO3rppZd069atgbEEBHM+6VRSAez+7E4L9hQnj0zTqRenSWoRmaJw2yPzbrcbxeyPHj3S/v5+MGXogM8frJ0X8Xt9i2+4CDsKMEnBJmAhje55pxTA8jn6xk7V2EwfT56HZ6K2gznGHxCYesDLnDrDP4yBQM/8b0+SwfRwH+j+N2UeHGCnwIfPsCPOHD7pvn48KR30QceF0ysoa/qSjtw9KnBElSolE5UaEegtCt44nBFBOBBECopcmbn35ORk9LcgQgBwoEwgb5xNNpsNpyj184Ns0AY9TmRMZO81I9CehUIhimG9xz91BaRwWFoLaIC6pGeGp4AohIQtgXHZ2dmJFA7ncnj9hvd38HnMZDKan5/X48eP9Sd/8if683/+zw8IY7FYVDab1VtvvaUvf/nLev311zU9Pa2ZmZmgG6Xz6H9zc1PHx8daWlpSLpfT/fv3dXBwoNnZ2XDY9Xpd4+PjsYyZXWd/6qd+KozD5cuXdeXKlRhXmKjV1dWQRac4XVZdiQBZmUwmxshXmXS73ShQhe0AwDJXLodeJ4Lx43dqKZzydjBKdAabgBxQb0Eaib4HRH6sQOIZjo+PY2UV8woIwRjxHcDWyMiIWq2W5ufng4YHlGCsSNu5/DzNMYxpSGlkN1ApLZsCAnf2aRQ3LDrjYFw9YOE7w85Nf/bnTwGSO740yiQY8WvgiHiXlP0lxQJwJTDJ5/vt8N9++23duXNHS0tLUSBNgIWcAxyRa4qficbdUQE+WN6/ubmpZrOp3d3dYFmoy0iXt2KTqe/Y3d2VpKjzYBk6QDuTyQzskQJLwbgA4pFXUt+5XC6WynqthaQIFtBnls96fw2vFeK7pCdbrVYEXhTLE2x62scPr7nhmUlXEuCQvklTcT7vHDwnMooepKCSz5wdTOXOfWjKlPg9UhvpTIp//0ng28fTj6dlOaQPkV5J6SAe1g0CA+N1HE9SuPQliMZAllNTU0FzOWsC0sbZQMkBVnxpraPOlNZFqTudTnTtdIBE7UOn0wmFcwHLZrMDu3QyJp76wblwbXKThUJBm5ubUSAGcHKBI68I4GAcGLdyuRytizudTmzYxVjy7CjI3t6eRkZGNDc3FykhUH2lUok25+Pj43r77bf1Uz/1U+GETk9P9Z/+03/SH/3RH0Xb9cuXL4eRlPptwtfW1pTNnq/iOTo60sOHD1WpVHT16lW1Wq1Y63/16tUwrrVaTf/oH/0jvfjiiwG4UmDqdCArLIj8iRIZe8/Fnp2dxTPjkHCoGO5WqxXGB0NNjYWnNYiESM/QTKndbgdohTEj2kN30gIsgCbz4GzaxMSE3njjDd28eXPgvagbQhd4BwpfkUsH4IwHTEm5XI4OlexNUy6XYxUD4wqYfdrD9Z95GmYc+SxlB/muV/y7vqaAJk3lOjD0NBHf8/+xS5zjgGeYYeVwp51GsSkjwns5sEprJviM+8LYehp7a2tLb7zxhj72sY9pbm4u7A/sLc48n89HKiVdmcWYck9SiIxvu91Wu91Ws9lUrVYb6CeUzfZ3WGZeqBWieBr59vGmGNl1EWfuoIp+M7lcLurZmBPsOu8BKOr1egObbgIeONA/rg0DCZPjzAfzji1ED33lCkt6CZhYjUj7hZOTk+ie7bJEMOCBgMuM13Gk6cxhLEjK4KQgxHXJ6yz5fBijmKZ2/H8PEPx5+TxNH37Q8aGWzDpd5nUcPjCpceHh3y+S4cH5HorlCogzkBQCj3NmQBAcr2gmrQIYgXqi9z+NvDKZTNRsdLvnPTpgMUDHLD+lTgSKDSVgRQz3oBCTaNkFhmWMvJsXgvI5zEe3241UC0Bpb29PhUIhHCgUOYWAGJpyuTzQ8Il8JopOIzCnYre2tnT37l29+OKLevDggX71V39Va2tr0SmQyBtHtry8rLOzM62trWlkZEQLCwvReOill17SzMxMtE8eHR3V4uJi1DMcHh5GXQoRvws1MsHn3MeNPw5U0oCiMPe9Xn+1Bk4Aw8LPsCg+low184XMYeQxjNRqeM0My1GnpqaiQJr/AbSADww6OsJS4EKhEKwKMiIpWrgTIUMDHx4eRmt9drBsNBrRcprzpX5dFatfiBi5zpMc75MOQLcXlqdAIbUNjBVzhlH3upknpV7SYMejsfQc6b31G/63NBKV3rtBZXrvNGgaRnOn9QRePMt7E0h4wakzVvv7+/rud7+rH//xHx9Y0UExsttfbKGnA1IbKw1u6zA5OalisRjtx4ne3enA2HmXYWwe8uzBFqyL22fYj263326cMfb3J4Dje8iCB5w4PopBfTwlxQox9IUOrXyHgIz5Alz4qkc+Zyy9LxOF3uVyOfrzUINFoOmAAHZnGOgdxoSkAGAY++DfddCQ+lhn3zxN5fP4pJ+dqUn164NYxGHHhUDHk5BS+oA8AMLEdzk8z+XCw99Anwg5RUjUJnBtjBrCnc32t5n2Za40g3JHRqfI09NTra+vx1Ixaj14jna7rVqtJkkDSDufz0d3SIwCdDTLH3lWClZ92erExITW19d1dHSklZWViGAR4kePHgU9iiL0eufV1t5yl7GFOnT2ifFK60SmpqbiHbz62nOTKPa3vvUtvfLKK9re3tadO3c0OzsbNTKkgCjGhJodGxvTysqK7ty5o9PTU7366quq1Wp66623lM1mNT8/H4zNyMiIfvCDH2h3dzdSKK5MTkvzcz6fDyoXxoLaBOhSNjJDznDsLFUmAgIwAPAAgRMTE6rX6wORHiC31+v3UoAZIJ/rNGWz2Yx5d0aGDf5OTk5iZcDe3l6kpxh7AK6zW8w3zAaNoEgFua4xt7RHJ53oQQMrpMrlsqrVqqanpyMfTzR+kQPH6oYO25CeN2xuhxm0YYY0ZSWGGd0UXKT3SEGPP6vrEXPvspQCHP+bX8eDtJR5cWDM3z3lgCzADDx+/Fhf/epXdfny5dB5Z5Q9jeL2MLXXvBtzC0AhTQvYABQAAj0Vyf2QH5gMUjz8jD2FfcE2A3aw5cgNNhqww3h6B138BqCBMWCseU/v83NychK1J9S0ub74eMFke9ohBacAK0DGzMyMpPMlwfQ1IRAkWHBGL5VfqV8w7fdM5XYYA+H64DLtepiCfu7jOuPvmKbihumyP9dFjgsXkjLYaSEof+fFUSBHnj4oXAvhd7SLkrFShNUTKVonx+YGH4GgULPb7S9LI6ftld+bm5vRDAcQ4asAPPXw8OHDUCoGulKpqFarRc8EHOnR0ZHK5bLq9fpAoSIKzrPgOBDIs7OzWLoJZYdjhSHwMeBe9N8gQuXcTCYTkbIDFyLjbrcb+xnAnLAvw9jYmN58882oE/nUpz6lX/mVX9H/+l//S7/+67+uZrMZgo0hAOS88cYbmp6e1nPPPRcbV01OTqpSqUjqd05cX18PZojKcKfSU0dE9LO9vR3b1TPPAA2AhKcYJEUNCwCj1xtsFoQs5XK5SP8w9hSjZjKZ6JGCHENNAzaJ5MiLA0bJJ1OzAUDLZPpbdpOyId1UqVTCSHh9BoCAVGSpVNL29nY8z+7ubjAvkqJo1PvCIB/cD7odMIVcXeRIjaJH2SkISI0d50Lj847DAERqMPnc7c2TDperlCpOQYs7hJS14fA0sr9H6jjREa818b8DSpxiz2T6vXTa7ba+8pWv6BOf+IQ+/vGPR9rTnQCMnzMrqZPwMcRm+r082ndmlGf1fkPos7+3swF094WFQ5c82ERXHMj43wio3K/wfS9U9VYLPDssN77EZZrrMebDamLQee7jgIax86ZnACGvLfHxRgZSgOA/uyymzp65crYiPdfBzbC/8Zlfw8HIMB12YO32+f0CiycdF06vpPkjlNxpNT5nUp1O5G9uHBzF5XLny7HYiAykjJHhu1DEjpR5vpRm63bPdy9EQWZnZwON0s3TaTyEHwPR6XSigJSIGeUmGqBpE8KGkQDEwGQQGfh4grBRJt+0C8PrRVhpmqhYLEZUz/sDllCOdrsdUQwKzD0ocuV9lpeXw/m02229+eabevXVV/Wv/tW/UiaT0Ze+9CXdu3dP3/rWtwYMKU42k8no+eefj74dvV4vdrDt9XqRjmI/GRdi5nmYYCMn+fz5xn04yk6nE71X8vn8QKMtCjhpC+5RCmwDxcD7+/uxdBUZcKNEzw93arlcLpgLnDpGmfkZHT1v1c5yR6eIAYLIGwB8cnJSGxsbsW8Lhso34nM2DZDk4KXb7W8LwEoEgLqkAFJ8DmBLwe5FDubQjaxH2M5ucH7qnD1iT2s2+I7fL73Ok9gMBxDpdfwcgCnjTg2PMxXDmA10YRiDwzGMFUbXSKEBGviZeZbO2Y4/+IM/0PLysq5fvz5QzMy90pVmfk2ONOLG3pJeZf5J86E3BEgAU2yPpygpVqYuL2VUPF2FvEr9zryeaoFZpB7Lg13AAL97a3+KYEmt+ooyenf43GLPvdWAzxngzgGV13ZRD8Wceu8Qxjqfz0cAcnBw8B75S4Mkd/4un6nuACxTcO4ynl7L7+PnOVPrn6fPOuwcv+/7HRcCHSmFyY38QZ8k4G40Upo0jUzcIbLd9zBDQaoCQfM8vReeTk9Pq9lsSlKsJCEfR2trqG6ciG9L7LuVUqjJ6gY/Op1OtMmFVnfDAeIHcbtDYzUJQoqzcSfZ6/UiCqRrIKgdY8DeBJlMRnNzcyH8XnlNdC2dOyfaZRcKBVWr1XhWAMrdu3ej+ybK9ku/9Ev65V/+ZW1ubobBqdfrGhkZiWV9d+/e1djYmAqFQvTnwHHSVM03QoIpgQ5Oi6qcbqSeA6PrDbGoowAkoPApq4HB8nGBCYPN8Dy21K9g954DyEGxWFS73Y5nJ60HO+Lt5yWFcXZdoGYHo4TOUfhLzYivYEGfWIk1PT0dXXHHx8djM0AHSGdn5xuCsYPp6OhobCUOY+QO7WkPX1Hm8+W66+CYz1Na2A/sAT97wOM1ETik1CD69VN2xal5DiJkqb+HB3VGfk1ncpDj9DqAZD+fCN17uvAsDjy5Vzabjfufnp7q9ddf1/PPPz/Q8MsDMy+GBDx57ZtH2Z6ihgnMZrOxKo7zsH08J8/tq5y4L3pG6sHnju+h79Q+7O3thc3GiRKkObPM99AvgADBjtd0EUA2Go0oMD0+Po5ieuSC1SqsWAGE49soInX5492Rn5WVFdXr9WCAJGl3dzf6j+Tz+bD/zWYzglWXF6+PTOXYz+G5sGHIl8tfygQOY1UcpDwJOABqCIQY51TWU519v+PCNR0cCBIG1l9Eeu9yHf5P0RgD6A/tgk4zIy8i5Trc26u9MRhM9OjoaAAFDC1VxxRaskLEOzCyyRuOvlgsRs4TB08EQMHhyMiIGo1GnDM/Px+sBErQ7XZjLTxLIjOZTFRG06Qsl8up2WxqcXEx6EUQOgpBFMMSWiJUHBwRCGyLNLhMi118R0dHVSqVBupkiPSz2fPlsaurq7p582Yo78LCgn75l39Zv/ZrvxYFWgC0zc3NAFHj4+Pxmc8DoI7oplgsRnt2AJUzVm4kT09PVa/XlclkgukA5HE9jDq5VO/Yybk4BEkDaZqRkfPutTyDOxnAhhf/esEvskjdjTcRKpfL8VxpB0SeGTmG5ZiamhqYf6nfjMwN9OjoaMgpKZ1OpzPQdwNji5FaXFwciCTZ6K9er0f3y3TZ4AcdKdBw8IEhZI68ZuRJ1OyTIixkwtN7wwBSakQdhDg78qTz+U6aPvH0i4MnP4dnc6aCc5AdB73YEq7p8u/R/Nramr7yla/oxRdf1JUrVyIFxzy7wxoGyNz2Mr9jY2MqlUqamprS0tLSwBzCAsJKEOhR40Fa2mvPJAWD6L0xkDfkloDPtztgl2zGgyCCVCH1HNiqarUauiAp7nF2dhYNCpEzGG7mFJCRMgAeIPOZF+xyb1hNgByB8PLysvL5fKQ8JycnIwWD8waQMY7IFfcbBj54LnyKs+apfvD7MB0Y9n/6M9elBse31kiLqz0Y+KDjQ/XpcIVIEVUKQlIW5Ekv7g/PQHe757ny7e3t9wyiU4kpJcQA4GgqlYqmpqa0ubmpRqMRG1qBaKmvIMrs9XqamZkJRwKNiSJi4KGkO51ObCOP42J5LQrm0cPU1NQAPevMDO81MjIS1/BICIfBOYzT3NxctM/GYXpRKONCVASr4MICUICG3Nvbiw6l3/jGN/TCCy8E6Dg4ONBnP/tZ/eRP/qR++7d/O5Sg2WyqXC5rZmYm9ijodDoxB6RgmLOrV68OPNP7Ran8fHh4GJGCp4UkBYAk1cLzEtEQ9TE+IyPnTcWImDB6yC7LEmFKUEKUrtPpxLj3er2I7gDILKcFDHvUnKYAidhY6re8vBwG1IsKYWgA3swxG75JikZz/h4YKeh0xtJrn9yJYkA/zDFMv7EbOEdfas0cPQl8DKOHub5H3qmRHQZaUtvkNsV/Tms3PH3j1/bn9ry/p1gkDTAYgEzXP2dOMPTuWHjX09NTvfnmm/rd3/1d/ezP/mxE3NRQICNPen8+8wCRVC6y6YFgqVQKu+iRNHIJ6+DME/LpQArbhf6l9VeecuBz3oMAwlfpeU8Tzuf6MB7Uj7j99jEh5QEYd9nyeQcsAAzwfTBHHoTPz89H4OFdg0nhNJvNAb8AMOTvqS9Lf0/Bo8uhMxCpr0yvx+dPAtopmHeQPow5+X8COpxNSOlRr+eQ3tub3VGc9ORdZYmOmUxXftA095T6dDcHQpXNnrebrtVqUTcwPj4+sDkXLAD7ZzAhoNdM5rwb5cHBgaampmJZIcaciv+zszNVq9W4jjMwOKP9/f2on6BAyWsGqPWAzqNehZUU1WpVUr85FywQqyGob/AipWazGQ5TGtxXxgXUo86zszM1m01NT0/HnO/t7ekHP/iBarVa1Ahks+eN0n7u535O6+vrev311yPKoh0y78TqiXq9LkmhhMvLy5qZmdH6+rpyuZx2dnbC8WFgEXyP6Le3t8OxE8G4QWLTOkApLdg7nU4An1wuF+CFaAemAIOFU6YFOXJG86K0SJOmR5lMJuolSqVSGGQq9pmHubk5tVqtoGORb3b2nJmZCVnAoJbL5WCVHHj7ygLkCsPHnDSbzZAPdgIlT08aCONMvxZvzvc0x5PYBo8g0wJz113/TgpYnPVKKWFPGbyfjeFzr+x/PwPq9xgGOFIbNYye5nwHNjwDtjNNE/lzOlPBNVutlr7+9a9rfn5eP/3TPx21SL5CK6XA3cHwHGNjYyHbvnIFWw8g8OdjHAA67DXF/ZyF455pfZC/O0EDQMaBG8/qwJj7OLghQJQ00G0VlgM7gZzA5Hr9EfPCtan14H293w7XcmDFgS3FP/DujJGn1t2Pon/D5D+VHZdzBwgODNLvutwOkweXV2fyAFfcO3229LsfdDzdXrT/93CF4GZPYjEc/aWDxOF5dlAe3yc1wvpxznGF9QHzfCn35jo0kSI/ePv27Yi4nb5iXbcvBYOixlHBAkA/002TaGN+fj62k6YeIBVUBI0lkYzN5uZmRNB0Hex0OqrX66HERJ7etpfzoB0zmUykaLxQi8ZVgI5msxmdTZkbUjMYJBB+s9nU9773vcgRI7DT09P6+3//7wedCMhhx1yWA/OeAI65ubmoJSmVSlpbW4scKOPhY4Ui5fN5vf322wMGjrGE+uN9WClCATFRBkaKMaY3Bf1CMLakvlIGgDSZjz3vS38X/o6Tx8ghM6SkPN3B2G5ubkY6j+XiAA8AD/IMiGm1WmEcWL2EbgBwmRda01NbRFSdyWSiDgTmKQ0mPuhwg4SB8uiQcQR4PClKSgHHMGc/zEByrRSUpM/H/6nRH/Y+/gzDgI4zIu6IUgOdAiNPR3B9bCDMll8Tx3d4eKijoyPduXNHX/7yl/X1r39d9Xo9nCzy6mxc+m6ePnVARQqX5wOU8GwERm7TkR1/N3yFFwKny1axZ6Qo2bWb7RCw/ch+rVYLhgGGRTpfmk7fjXq9rlarFf2TvK1Br9d7Txdg918OUl02eHdSnOi2s1YTExOh/3TALpVKoXcEYXt7e/E9QI3LxrBAy2XRUy8pqOa7KQAeBi44UuZn2HnMu2cg0iMFIu93XAh0gAZdSUHFDKA7WJ8Yqa88aaGpn8NEI6SOUv0ckC9/c6rU6UgosZGR8y6cCwsLWl5eju2VeX7WkdN9stvtRsfGXC4XjZjokNrpdLS7uzvQV4HCyPHxcT169EgzMzNR78Ezef5QUijf1NSUpqenI0VBWgU0zvN55OPr3RE4CmQ9l888UHdCTUWhUAiGAPZnZGQkwNf/196f9Uh6Xdnd+IqInCqnmHKqzKyBVawSKZICKTUpqNVyC7DRgG3YvrAvDV/4c/gr+N4wYBv2hWEIbfeNDcNCW4YhwzRFSqIkslSsgTVkZuUQc+Q8RMR7Ef/fzhWHUWSlDLf/L948QCIzY3ie85yzz95rr73PPlJf4DhI7v79+wMxeOjVcrmsf/yP/3EoKJ6L0BNghMJAnO9Ccm+lUpEkzc/Ph1y5zLjsnJ2dRcIq+RqALFgh5IdnwvtHNgCRLGoYLE96A/TSZ5RWLpdTuVyO1wi/cIKtF+xy76DT6UTFx7Ozs6h3cnJyMlDAK5fLqdFohOIl0Zd1A7hE0eVyuZAbKPD0O55LwbNNTk7G9zOZTLA5FKRqtVpfoVFftQ1TXK4rXGG+jOp1A82ad/CZZuynHp5f1/WVfx59xfVTOtqv6wocb5z3WZNpeND74X1gzOmbg5bUW+VZHejzMzo6qufPn+u//Jf/ogcPHmh3dzdAMt8BRCPv6FTGH9kDgMA093q9AOM8I+EQD8369lKec2xsLELIvM/3fDyxGZ7keXJyEuxAu93W1tZWzDv6eW9vT/v7+5H4TBE8wjHk7aF3nT1BvlkHMOopoHJWhnnynSsOEMhHIbRLXSZYGcafMWYcU/lCFhzkuWPu8gEwdHlg/FMdPQxYe4jIX/O1yk+6dX5Y+OkieuJCoMMXVAoUfHHzN0KEkDqg4DNnZ2dhKBhI/oaJ8DwRVxyOFJkcciZ6vV4k4QEg+P/27dsDggkCb7fbEZ+cm5vT3NxcGFK8Xspzk+0NE+LjADPTbrdjsXp2uXReuIl8EnISZmdnB047ZazPzs6iPDeKgGt1OueFsRh/Flur1YrFh/BSRwIPHUUBOPD96p4D8ezZs4HMb+5zeHioP/3TP9U777yj09PTOEWWCq6ZzPkZK9PT05qYmNDa2pparZb29vbUbre1u7sbCiWlBxnbTKYftlhfXw+ly/OSx9Lr9QLYQWUiT3zeWTgMOHKQerCwKCins7N+OXXAJjVQPPHUmQmS53Z3d3X//n395je/CXYhPa/Ft1nPzc2FPBEb9jiy94/zLlBqzBeZ7cwDu1sYa7xKN6A8FyzSH1oG3ZWQG96UkfC5cKDgf3uYIAUH/r+zEcO8vNSTTEGPg4kU9Hj/vL9f5925UfFn4PV0LBxQuuHhuZlr+k3tl08++UR/8Rd/od/+9rfBFlIOH4Pv4+DOIH+7h8zzO7Ps4N/DtQ4k0jARbJ4zOvygW7xmBlvokdejo6OoBF2tVtVoNLS7u6tarRYlx2u1WuiQVqul3d3dcHgcbPCchDBfxj4zDl5R2oERzdks/kdnOivA+GCfnIHyufcf+un5Wj7u/J0676ksDmO3nH1MnTuXT/++A2U+62vIbfqrtAvvXnGv2cMhxKh8sXpH/TNQd3yePdgkdgImUPTQ5SyUdGFiPAl1UKIcii6TyQSdDMV+8+ZNra2tDYCn6enpACaNRiO2jRJOIcHQBYniN3gXoG6Oe3/nnXeCzsZYpEzHwcGBJicn48C4TCYTfSf8APPBWEHjg8jz+Xwoom63G9fhWgA45gLAMz4+PpC8xOvtdjueTTo/ofHBgwd6//33Bw5AA3j8o3/0j/T06VNtb29LUtCJ9XpdS0tLkvqn5j579iwE9vHjx8H04HUBKPz69K/ZbKrdbod370lsFGgjjpvJZELxsv0ahqjT6UTOSy6XG6g1wDVgiWABYIHwJqnxwrP6OTjsJMpmsyF7S0tLEesl/+P0tH+QG+GyWq0WJeYJv5C35EoLBY/yRrlTnZTn6HQ6cfKvpIGxOz09jf5SOp0QTjabHdiq/r/TXqYMX2aUU2U4jL1w4JHeKwUeNGduUmXt30nvOQx8uGfJ9dxT9H7h7frn/f2U7eD+LwMq7u12Ov2dW7/61a9Ur9f14x//WN/73veCNURu/ZlfBtjc8KbMjTuKnqTK51gH3IP1R3IorIn/sD4AL7DC5Iig63D0cP6uX78egINcDq5Bf2j0y8eAxNFh4T8azCg5La5P3QjjzLE2yd84OTnR7u5uMNGE37FjDnBcntJ5TgEz8gOY5BnTHKUUEA9bY+n7zG/6fe+PywzPf9F24TodLB6AgQ9M6mFIX90um34WFOdCj6LFUHDP6PTIeeEblC/V8QAI5At0u/2dHcTGybmAWsaoe8Y3yt7/93MIUOTEDInnO7KfmppSPp/X9va25ufnw/skWXFmZibqhczMzKjVaml2djYWDAmO09PTUfF0ZGREpVJJjUYjSmxjjFjkgDXABuNFjN7ZEUf/XjiKBeL5Jmyp/eSTT/T+++9/xZPsdDr69re/rb/1t/6W/sW/+BfhHXQ6Ha2srKhWq0UyZK/XC+bjxo0bA4mQTn+7bJCv4cwF8jQy0q/ISdY6eRq7u7vxjCTcSoriZMib194gR4IKo9lsdiDZlH4xjoyxb1U9OTmJ2HIul1OpVNLCwsLANl1kn4Pi8FqfPXumxcXFAVDn6w/Wgz4wd4C2TqcTCdPuKcO+5HL9rdjUYIDN6PV6AcpGR0dVKpXUarUCDL1qS70h1rozVyjdYYY1fWauCYDi2m6oU5CR/u33TRWpGxtk+WVAxv+G9Uq/5wrbnyVlYtLm+nHYd51NSPuFk8Qc7+zs6IMPPtBrr70WeVxuJHC+3Ighl+gIZ6YxaM5CAzjYIUgumYeZu93zY+9xWli/aTVcnpF1AGtBWXGMOTlZMLgwAQ403J54eQHfIMDnXDYcOLru5LPpnPB5nCRJATa2t7cjZ8538TGOqZ5LwWgaSXBQw7UAWfTHf6fNAYXLIt9JcyTTa6ZAyNdRmlvyTe3CoMMn1DvqitQ75xRdqnhorsD5G+YAA5WCmBSZFgoFdbvnpbC5RrFY1OHhoW7evKl6va5er6d2u61SqaRCoaBms6lerxel1jl7I5PJxAKhhgHhF3YfgDbdOPOsnU5Hc3Nzevz4sebn59VoNDQzM6NyuRyxRwdwXpmy2+0G2geEQf93u/04LDkeLhggavaAe8yUhcnhcCSI0m/fZ08fSWxkviYnJ/Xll19qc3MzDmtzAT08PNT3v/99/Yf/8B/UaDQ0Pz+vcrmsVqulnZ2d8OB3d3e1srIiSRFyofbGMA/TPYx6vR7Gg0RcaFgOY/NjqXu9XtT/cK8CRohxyefzUXWWnSzIAMoUGXfZJS9jdHRUrVZLzWYzQGu5XI6juZF/rx2C4kBeULpzc3OqVCqRS5MaI4ACwBcwy+4lT17GeDCHbDUEgLDzBjk+Pe2fHUR9BV9jF2msfc9FcMXHGgYYoU/4rjc39MgrMsBnATIewmTMhwEQBy7DHKPUU2Rd8/ew3S/ugL1sPJwJSNcvv70/vDbM83UDCWB4+PChWq2WarWafvSjH+nOnTuxCwr595w316vIwTBmykEJjdAhbAOyCejgf5gN/nYQgp5JmQ3CgPv7+8GIoG/Yvcb6SQEpz+V5hr4lVlIw6q4nvHlI3MMsL8tRBNwcHByoVqupXq/H7kI2JpBLlu6C8es5AGH8mDN+3H64vKbXcnvr88z1/HvOvjAevOfXSuU1dShepf1BxcHSDjjSTAfB6Tne94dKgYgj8ZOTkxBGvpNOjCfwcFDb0dGRWq2WlpaWYneGpAgbzM/Ph+FeXFyMLaLtdjvi4JlMJpRvt9s/bbZQKAwYc+l8qyYCTeIrXu9rr70W52Dw/HibY2NjEXohPEBYA6Pn9f9ha4jNI9AIPvfEg8ZYUn/EDxSjr71eLwwQR1Bns9lI0OJ9xnVvb0/Pnz/X6urqQAa51Effd+/e1d/7e39P//7f/3sVi0VtbW3p6dOn6vV6qlarevTokbLZrAqFgsrlsrrd/q4dDhojHPYyRmxtbS3mnbGhHygw/icBN5PJRMVNGAtCSxwHjzclKfJcoK5hYQhRuOxRvO7p06c6O+ufmHvjxo1g6fD8WC/0w2OyzNfOzo7y+bwKhYJ6vfMQmqTw6pgb1gDGgJoxAHfChBMTE8G4IDvscuCsHkA1DBGfQ/4u2ljbGLGUQh5mTHl9mEF0hoI1NIwdQTmmOod78dvvz5hyn1SBujeaMiXc0/9PQZW39NlSMOYgTdIA0OV7ntuW3gPDSwVaitzdvXtXN27ciAKBODEAaDxpAATXoi9eG4Z7st0cgM2aAXRgFDHEgFh+AA3oIxjjTqcTOh/Ww8sZAM7dofXXGH/f+ushEMYZ/eBAwu0W18IR5FpXrlwJ3cq40Y+joyNVKhW9ePFCOzs7EbKFkaaiMOUN0vsxfynTAKhyEAeoStmXFAS7DDuj56yHy7KvD//usGu5HKfRiK9rfxDocGTptN8wNO4ejysOX3SSBgScE2GJp1O50+/Hb+hkPNRqtRrhBJiJcrkcnjCv5fP56DPJifPz80GPczYICaN403Nzc/G82Wy/VgWFxBAkjBV929vbUz6fD8r/+PhYs7OzAQrYAeGHsM3MzGhvb0/ValW3bt2KMuWECgAYxPNgRA4PD8NQ8Fm2mvV6vUhI9OTXTCYTVS7Pzs60uLgYtCjjiiFfXl7WgwcP9P3vf/8rcdxerx+7/aM/+iP9y3/5L/Xpp59GUip1IEZHR/Xs2TPNzc3p7t27+uKLL6L0+oMHD7S2tqb333//K/Qf19/a2orsepeZ3d3dgS2ru7u7oWQ8WTabzaparWp6ejrYDZdBlCLF4lionF0CYwIoJuFtaWkp4sRemRZZ4bOE3igUh1fIPL311lvBRlDDI5fr1zqpVqsh6wcHByGLXvWWNcVZL74lenR0VFtbW3E/1gIn3fqhby6DF2kpYBy25h0Y8Kwo0ZQV5XOeEOhhCp+7YazMMNYkVaJ8zpkPp78dGPjnUw/TGRJX7Cl7wLOm/fJrSF/dsePPN2yMfeyr1aru3bunnZ0dffbZZ/qzP/szvfXWW2GokF+AHkAWneQ5HYeHhwGg+R5ggFCihwh5Xg7dROfBXrMmATaSVK/XY51yRAIOXDoPLkeMQWpIpXM7xZrp9c7zWwAisBXMOwCE+UGXuoHHpvl7nU4nCllWKpUI4aITfMyY2zQEmbJpLocAOt5zo+/y/LLm7w+TtbQPLntpS0E/r31TH2h/0NkradLosJv5YCJcKTBxtgNggiBBfc3OzkZFPBcKvyfbvKDnMOTUaICCls6LibG1KpfrH+u9t7en3d1dzc7OxvZJalYgXDAdhUIhPNxCoRB0eaPRiIXGGRbsEtja2tLy8nIYI9iTycnJr1S4xFgCENhSenx8HOd7YNDwSgEHnCDLPZyN8FoWU1NTkUTIGStOExLymJmZiZyL8fFx7e/v6969e7GtEy+En7OzM92+fVt/5+/8Hf385z/XzZs31Wq1tLy8rOvXr+vFixd64403Ihsdb3pvb09/7a/9NUmDlRm5Joa2Wq3q9PQ0EoVJSLty5YpmZmYCTNHnbDYbz0E8mO8iL57zAlvAjiJCVswPY0ToaWxsTOVyOXZAEaIg6fj09DSYq3w+H7IhKc4xQBYJi8AwSRpIECV0lslkgq0DKLBG8CzdA2McyVMhDFQqlQYUYloP56KAI133/J86Jf6694+5Sl/3n1TBpq+5IuZZkCkP9fpnXeZ430MRw/ROSjmn3jfP+zKaO9WBKWPin03/5n83XDh16M1crr/1+vDwMIruHR4e6oc//GEAOOSQvC48d8JvDsIA191udwBs470704GuZTu4pAjBACJg7vb394M5ISzM59zO+Hzh8MHKAC685gnP6Mypf5ekceaa8YQ5B6QzTjiRGGPGhbE6ODjQ+vq6vvjiC7148SL0t7M/LoveXOb8eV4GtoalLHgo8euMvwM0l7dhMpiO+zCA7fd6VbbjwmXQfXH5jRgoFlvqIfhnARhOZ/pnmFhoPR9sX/QwChxElMvlBiqAUgkT1iOfz+vo6Ei1Wk3f+ta31G63tbOzo5WVFc3NzWlra0uSIuEJQwUVx+Li0J75+fkQOsIjIyP9YmSeoDc1NaW1tTU9fvxYd+/ejcO4YDv4PgCHMfQcFzxojFQud15mV1LkN3gSKcIFqPE4P/dk7CkBzpbcTCYTSa/EEUlaPDk50ePHj/X+++8P0MHIx+joqD744AN98sknymT6lTfv3bsXgKNWq+nmzZva2dkJQNRoNPTgwQPdunUrFqjLUSaTUbVa1cHBQYSqULIs7pOTkwHg4Z4IIbBstr8DyPNZCMnwP14NoI/r4qH5WHKgk68J5oO4MePtYR2UG8+6tbUVCdCAEA4nBAADtrLZrOr1eoBdQmvUYGFtEELZ3d1Vt9uNarqE2zxBmvgzwJp8jmHswdc1N9woQUB0SiW73Azz3Pw3a8INfXodD+06mEido2EKNo2xuyFyhev3GgY8/N4OPNKWeojD4uIp4EqNDffEQMEO+zNwr6dPn+pnP/uZ5ubmdOvWrYE8DIBwr9cPpe7t7QUokDQAMrywFyERAGq3288NY01g/NEdgBx0PEXOJMWGAQA3z8Fac2Yb+5AaQWfTCaegX1hvOAo+R4yv53fASPq65jswNr7W2MrLWoM99DLsMBUpw8Fv5MCT5FPZ43swew5Q/Dpf11Lw/zIwwRi/TH5Tvf+q7cJH2/tWHWcwGASnBhEcry3hcV6QpyMk/wzIly1T7kVIfYFZWFgIg4MXi3dKfJoCVtxrf38/lPzc3JxOTk7ie57A59QcNCEo/erVq3FPkvFS78OpwZs3b2pzc1PSeZ2MiYmJKECGIOPNpp4TCaAYTO4DtU54hHgt4RpAAgKKNwlgYSFOTU1FQiL38/kg8RTvhhDLMCE/OekfeDQxMaFKpaLXX39dKysrymazun//vu7evauJiYk4RrtarSqbzapWq+lnP/uZ/u7f/bsql8sDIbxcLheHN6FMeFbYBM++p/+EVVCAKDmAAnFuDHA22z8FF0ULCPGkNfJmMMyACmRDUgA94uqwdiSreoz3ypUrAWL9ur79ltAgxoJt2g5aYbl8Jwvx9ZOTk9guztr18Bw7p/Aw8RIvynbwXUJQzAFyxJpPjTTOBXojZTb47LD8Dn8/pX39cw5aUvYhdZSk8x169Inffg8Pb/Iaz+s7/LhPCpoc2KTsB7qW99FDnnCL/LG+GV/AuHRekHBjY0P/+T//Z/3Zn/2ZXnvttegLDCj1fNrtdoT9PFGUpE6eEd3M9zliot1uSzo/XI17oKtwiFhPrDWexcefMXVb4+PHunY7BBNJc2fLw1oAEZghSfG3O8aUTvD5ZLy8fAK5XG+99VbYrKOjI7148UKNRmMgPOvrgmdhXFxO0nALf/vce+jNbRD/+xi6854CaV83L5NPl7t0LX0T2KFdCHRAwadeDC1FZo6U/Tsp0vOwDV4mxmRnZ+cr+RxMVqlUiix+snyZCLw+jBDhAPIc6N/4+Li2t7e1srKiO3fu6IsvvtD09LSkc/RNCVtJcXR9LpeL45hhZFAWPAuJm9R5GBsb09OnT3Xr1q2gLD00wu4N33fNuAIkCEVgmGAfuC/KHkPsnoKfh4KBhAHwGCsevoegSKjESD58+PArBXjoa7fbL8D2D//hP9RPfvITra2taWxsTNvb25qamtL9+/e1urqqQqGgyclJvfPOO/r1r3+tSqWiL7/8Uv/8n/9z/ZN/8k/CqGK8Nzc3B5K4eJ9CW+S8eOwTjwsZRPbIsUFGOGEVcEF4CSbLE/iYS0AjC5yQFwDVs/2ZN8bJz405OztTqVTS1atXtb29HSEtgAU1WtglAxDOZrMBmur1uubn50Pm3BNk/pAJ1hMgHTDjjAngB0boVVuqeOhHCk6HKVeUN2szBR7+XY83p4DjZS3Ni/Drum5Jr+vfG3afYYAh/Zz/7w5ZCnRc6Tug4DdeMDrTQRpr3/vijOHZ2ZkePXqkUqmksbHzU48pjAgrATjHa9/f31e73Q5GBHaDzwFU9/b2YgcZnj2ePn0lVwPbwJEArFn09rDcHWQDR8rHk+/yGf9NmBXdOoxpYEydYXBmnvkHmAGOyPVi/XDY5fHxsbLZfuI7Cewp2+e/U1lMm4MIl5kURKTMnF8XIAZQSSMNzijxv+t2D1E6sP66fg9rFwIdKHwe3hkKZzloL4uDpgPmXgeJmxhMELVn+kv9yp2lUimM+uTkZIQ1QKpUv8QTxSChgPHwl5aWtL29rdXVVd2+fVuPHj3S7du3o894r3j6bsxJRMTDyOfzUQKdeCb1D5aWlvTLX/5ShUIh6jowWdPT0yoUCrGQYU+g/rPZbCxwBNiVkHtg9IukVBqAAmMNmOl2u3FOAAaJRUW5b1gjkh+pAjg7O/sV5cpYXLlyRaVSSXNzc3r69GkomMXFRbXbbZ2cnET9j7ffflu//vWv1Wq19PHHH+snP/mJ/sE/+AexeDOZjJ49exaAkQaTQQMEUAdF6oMBTnuEJWDsnP2BaeKzmUwmPByy7xkP6GKMgS9ISdEnlO3Z2Vlsf6UPsFrspCIMMT4+Hkzd1NRUgCEHGx4iOzw8jFAdCs5pdmdPpqeng8Lu9XqRCyUpQpL5fF7VajUA30VbChRSxZp6UOl8OsOXyhV/py1lTP176X2G9c8NQspguOFODUfKrqDIHagMC9G4sXOP15+NceBvPG/3iFPPX9IAcPNDzQAC1WpV6+vrwURQwRSZPzs7i/ygTCajSqWiZrMZz4XBB7AAhk9OTlSv15XJZAKkAE48T8lDJH54IddM590BA+FQdvfx7F4xOAVgjBHnQvG+M0MuD6yxdA1Jg9uEDw4OIt+O3V+A/oODA62urqperw+c6u2y5MAqldevM+bD1sUw1iKVUQesfu9UPv111+dpRCJlQf6PgA5u5jdPJ4wHHZadzYB6eARg4UKAQiZcwOf53MzMjAqFQpzEyRZEV1T1ej0KdGWz/aqQhCEoJw5Twbamer0eZchbrZYmJye1uLgY8W7ilgcHB8rn81F6mjoQLCxKS8NC4DHPzMzo+vXrqtfrKpVKAYhgETyDnDAKZ2h0u91gWLrdbnjV1BeZnp4O1oSxdSYlkzk/+RQBYjwphjU1NRVgh8Xu5b4ZW2KzjUYjgJ8bikwmE+AOA0t45ejoKCpibm5uKps9z4X54IMP9OGHH+rg4ED/9t/+W83Nzemv//W/HklqHAmN980uFlfAGGQ8J+q0wOpANWNMvUQ8i5I+E4LKZPrJmYTFPFEU44R89nr9uHihUBjor4cqSNxlt9LTp09VKpXic7u7u5qZmQkASplz1gHjT5I0bIfnfgAonaXx5FiXJ4zCxMRE5C2xeysFed/UMAxpzB0dkLIBjJvrDg9R8lqaVMj10rCJN67jcpkafr+HGzh+aB6/5/PcYxgV7p4nr/l4pBS7gyP3Pv0ZU4bDWedhzUOCzM2tW7e0vLwcuW3I0sjISJyHtLOzoxcvXkTogCJXJDV72IyttNvb2xEqAaTD1KXsjCe2syYJBwOsfJ75Pgwy4MiNHcDer+86wefFwQYNHcCY8j9rBcAA4KjX66rX69rZ2YmTqCWFbaDoYb1ejzO60Dkud27b/LVUXlIDnwIFby4z3nz8PR2Az3MfX6OpA+DsRgpaXrVdOKfDF6Y0mEhKp/zhfUGkk+/ghc+R38C9MJK+sNkxUigUglIDAZOb4Aqi2+2Gtz01NRU7VJ4/f661tTVduXJFi4uLajabYUhJ9tzc3BzYGUDoArqb/AsWLbkPbsT4HgmUH330kdbW1lQsFuOcGGo5AFRgKSifztke2Ww2mA52ZfizMr4AIbwCrokQEYdl+yYePULE9WFWOBckk8nEVsx79+7pzp07cU+XjbGxMW1tbWlra0srKyvqdDoqFAqq1+sRZllZWdHx8bGazWacA/L9739fH3/8sQ4PD/XP/tk/U7FY1HvvvRe1NAhFARjdOMFyEW5BYZIUhvyhuMjNgE0ivguYIXkYJQDbAFhmTLLZrBYWFiKWTVwcwJBu+SbHCYXfbrf19ttvR+Jus9nU3t6eZmZmtLOzo7m5OZ2engaz5Dkq09PTccJmsViMaxJyazabWlhYiPHBK+T+xOtRzlSBpMLrRZQJzb19X/e8ltK1KdOQKtPUiHufvk75urJMDfgwL9IdqtRBgm3lOrwOuPV++v2+zgN0RZ46bekY+fOkOS+sWw8v8DnWRK/X09zcnObn50M3dTqdqOFy9epVvfbaa8pm+0nKm5ubevr0qba2tqJ+Eeye63Gv3+GGHCeFZG5nOWCd03FHh7vDyji6sWTt+ZygY3FSGT/XawAJH1/P/fA5IC+E92FlSJKt1WpaW1uLcBG6aX9/Xy9evJCkOEqjUqkMOHwux8MYBgeeKUs4TH7S114mc1zbnUo+67vzUvlzMMmc+7rlc6/aLgQ63IhiXFJU5gKZeizSYKEf76jHyaDw2JbqSUL5fH7gtD4UJieFOqofGxuLxNCTk5PYIgjNvbS0pK2trcjkX15e1vb2tpaWljQ3N6ff/e53un79enyHSel2+zkinBQKKOE9qol6LgVUZ71eV7FY1M7Ojubn5yPbm8JgeCaEVPjb6yeQV+IeOQYYkEBoBg82l8vF51i0JIylHjS7LKBJS6XSQCVNjNHGxsbATg43Ghiw3d1dffbZZ3rnnXeUy+WiQimhD8BAu90OsPTWW2/p448/1tnZmf7iL/5C7733Xix4AIHHd6lZgjeBfIDmeS6vv0FSrieFkkCMl9Zutwe2DHt1wWz2/BwcFCbsnBsuT9YkXs1Ywyasrq4OeHBssd3d3dXCwkLIDnOXGhZkE+DK6xTM8/LueJxsveWePB/3YAvjRXM63JtzPUCfXHmmAACw4hR0qle8udL+OkWLvDBX6fc8QTENW6ZOVfpdN57OoKVeuHuS0nnOio+Dv8+zo1NSWp55JIzgP66Dkb2RkZFwULLZbLBwjx8/jvDd9evXVSqVtLKyojfffDPy1nZ2drS+vq5qtaoXL15oa2sr8jXIe+OgQu5H7hdbvX3rPs4PgI3fsIo+VwARPgOr559D1zMWOGbItRdYJAyD/RodHY1KvsgBzCZnIk1PT0ciLEdfPH/+PJ7ZiwceHBzo+PhYn376qd58800dHh7qyZMnoWM8CZ7ncxaLxny7nKSy4vKCLXVdnK4Zv8+wUIkTAQ48CHF76kIK/ocxKy9rFwIdzlyA2lJ6kvccYafhmHQw3JugXgHbqTg1EUH1uJwvcIp6cZosCNWPLO/1euFt93o9FYtFLS8v6+HDh3G+CQIH1f3s2TNdu3ZNuVwuttA63QeVKJ3nQYC2x8bGYgukVwqlUuqXX36p27dvq1wuR/IfhZy4Xj6fH9imKSmOdEYQoOG9bgYovdlsRuJViqxHRvoFoQqFgkZGRiKU4osXgYL6nJqaioW8vr6uRqMR4RJH6cfHx/riiy90cnKiVqulzc1NXbt2LRR1vV7X2dlZJM+enZ2pUqlEUuWbb74ZFUw//fRT9Xr9pNDp6enwdugz40AYAiUnKcCSnz3C3BUKBWWz2fBIUCAAvHw+H31jXgBuXknRi6exTjKZTIA0jzcDKKivUalUdP369UjaBDyQ1AtgRpESCvODBgEVHqbEO3Hltb+/H/lIAC92JFy/fj3WxMOHD9XpdHTr1q0LVyR174f/h/0e9j0HKikD8U1sgY+7Xy8NY6RGHYDMd9BZjOcwD/PrvE+npmkOrl7GaPC3h4vovwMb153oPffi0cmsjdSjhtHr9fp1hZ48eaJ2u61sNqs7d+5oZmYmwr6ZTCZCzPl8Xs1mM2rN1Gq1AfZiZGQkDjjDsLI2XZeka8QBmSd/+q4QZz4ABnzf2VnXVzAmjAnG1pNu3dC7LUMPe00nAMfOzo5qtdpXkv6Pjo5iXeNArK2thVPJPDC/HiJKQ4YpMB8GqF3OHEy8LNzn4P1l68nlxMcyBRnO9L0q0PB2IdCBIWcgfaIcCKQLLA3HpKDDQyu0drutZrOpFy9exAMCJogzE2YApU5NTalUKqler0deBRPeaDSiguPMzIyOjo4CoHBCa7PZ1NTUVCRJ3rx5Ux9++GF4nOR2uIBLClociorkvKWlJfV6vUjmIsZ+dHSkhYUFffzxx7Fll5AE+QggdRgTDD+JVh7DZHzY5UBjjvb39zU7OxvAAfADo9Tr9SIEQ6LU5ORkMDYcRueVTkH2W1tbmpub+4qirNfr+vTTT6Nc+/7+vur1engVsALEiTmbptFoSJJu3rwZeSq/+MUvtLi4GAoE7w/PgXAH9VV6vX7lVQ4+4xlHRkYiidi3GrPwYZV8u7EbUdg2lB2JsIQ6mJM0kS8W20i/5Dhy0m63Va/X9cYbb8S6AXAQ4qPQF57h5ORkyDdyiHIlv4NdMjxv6t3Tj263G2CV05GlfkLp3NzcVzyiV2muWFOF5KDCja9Tye7xp97/1ylf/xz3SBV4ysr6dV1Ru3Plyj1V4imI/zrWhc+64h72Ps0ZYjdYvt6lvi6+cuXKV4xzCji5DgmQ9Xpda2trevLkSQCRhw8fKp/PD5w5Aks4Pj6u2dlZra6uKpfL6cWLF1pbWwtmrNFoRE6Yb9H27zsLBlCRFICdccORwqCzzjGoPI+HTZB3WA9nFFjH/ln6MQx0MFaUbGAH29HRker1uprNZjCXPCOlGXi2s7MztdvtSF5PGQiXk2HsQmpDXwZ0XT6xrf5aKlcpw+ZhzWEAwokEbMcw+b4I+LhQ5Z+U6UgXsy8IhCBtw5Cme2eg8aOjI21tbanVasXr7EaRzik5qo6CjsmxIMwB44DyhWK+cuVKbFNdWFiImDk5ABiRu3fvam1tTVtbW7H7A68VAyIpijyBeguFQrAvlCbnOaC933nnHT1//jyyv2EIuEez2Yz+4LEzhqn34wyFdF70irNHuIfXQygUCrHrgfklBMBccvibex2e1FetVgMgoQSuXLmiP//zP1elUhlgGTxJFY+K7cnLy8tRShlPAhDZbrf16aefBuikQqB0ngMEG+bULR49z0hYDRaDEAu1LJz+zGQysW0Q2QOUeel0ZInET54JRSUp/ua5YYqgcFkvAB4qprJzBeAMy0MeB/MyNTUV80JiMiwLCdF4b5lMZkBhUrGSfBTKq7OmLppImgIEN/IOTF3ZOTX7qqxG+tlhDEpqRPzHr5ca9WEUtvc57auDj2GMzjCP1l9LjWDKxrix9f7ncrkBj96T6f05xsbGdOPGDa2urqpcLiuTyWh7e1sbGxsBPqvVqp48eaK1tbVgBNEHMHXj4+NaXFzU9evXo8Lw1atXYzceLPT4+HjUCHGd4PkTbhuc/fCdLugZ/50y7YzlyMhIrH/6TII+4IOx5X4w0g4wYUkkhZPXarVUr9e1vr4eDC19wA7gxEjnhSvRS/4MKZPAM3hINpUd2jCg6yAmlcd0TaTrJV07LvOpvKfAKAUqw8KfL2t/EOjwzr3sZr1eL7w3/yyK0Re+/93pdGLf+NbWVrAY09PTA0l0LAiSNqX+xJGUSD0Cdonwvpf3ZfvgxsaGlpaWdOXKFW1tbQX9SEhjfn5ea2trevDgQTAqvusGIYfSh64kvyCX65ckzmazEXfvdDp6/fXXY7saO3acDrxy5YqKxaIkRUIo98G48PyZzHltE+m8NkM2mw0q3eniTCYTyU9eYY/3GCs/JIpkKkAcNTcwqCi4p0+f6ic/+Ul4Sr1eL0p2+yFmeEbUyJifn49+7uzsqNVqaW1tTdvb2/EdvJher38aJeeHpAmfzAUMAIlhDmwBJ3hAkgaSwgh3oPABNr6NVupv0yXsQhgDtsVrqHB9xoRquGz/A2Q4YPEcJUrAw+pICiYH0EtiKAluU1NT4ZkRNgKsOAtIOHNmZkbLy8vhwf4hLTW8XwcIvsnjH/YdWurV0VLA4PrlZcrYlWmq+IeBgfT/lz1nquTTPvC/P4v3xfPc+PEwwbA5ymTOt3pL/e2cN2/e1PLysubn50PX4YiwzRMvHseM7ztg6nb7lW2LxWJshwfMwhqjMwCtzpBL5wYWXZfOlbM2MJvpc/oYAog87wod6mCM3ynY88bnANuEVXZ3d9VoNCIXBnYJx8RZHGwHx294nRDm0xl/xsjnnf75z9fJCADUx3mYXKRsnzMZL3MUXIZTQPOy8M/XtT/oaHunZdw4+4ClnoyHWKSvbjVzdC6d50dgPOfm5mJxUPYaI4eHSB4JBgKGACEqFouhmKH2ybLu9XphxB8+fBg1Mw4PD3X16lVdvXpV9+7d08cff6z33nsvJgrDBJUOCEEQz87OItH08PAwzswYG+sfTnfz5k09e/ZM4+PjKpVKmpmZCVoOoc7lcmGYQObck0n3LbcIt1Pz7GZhARNaAMh4+WFPSmWX0P7+fhxsR3/Gx8e1tramnZ2dYIsk6d/9u38XoEzqgwvydObm5gYKAjH+i4uL2tzcVKFQiL3/HBMPpQuLIekruytgPvgM441MuPcCYHMETw0WxhVWDIaG50amT09PIyznZ/UA4lCmgEyX6ytXrsRZP4VCIfoIwGYe6CcnDwNuAX+EWyhtnhpXlB0gn3Ai22IZM2doGAdCa74741Ubz+B9QQd4+NWZBn57LoWPm/RVFsU9VD7L2kiBSMpe+HX8eynrgoJ1j9if0RNQfU26zksdM/+ev+b9QxZcyfM9DKPv3PM8M+n8aPZMJqNr167pzp07unbtmqTz07bX1tbUbDbDsK+trUXCqFf0ZawctJNr1Ov1YtcUR9LTP+bamWrWg+cg+QGOMK0py+Hf4W8ABSCH1wFBDrxcdtKxd0DCeHotkr29PW1vb2t3dzfYdsLWhEN5Vp9vz3twkOmywjMCuFIg7VEEBx7+v8sMwAvHdBggcADp16SPHib3PqXywPNchOWQLsh0eFINg4yRdeDA677wvDgKSJ2J5sG4FvQwg/bWW2+FokagGEgq6bGltVQqaWRkJBLkoMe8yiYxQ4wzE5XNZrW4uKixsTG9ePEinpOs/tdff113797Vp59+Gltz6/W6JAWqTWP7FHliiyYonD3cKysrAUBQBK5cYG96vV4UkwIZe8KXeyyUI+Y7ABLAGjsqUFzSOdKFIUAY2SHjY07Iigzs9fX12K78y1/+MsZb6i+EYrEYB7+x04Q+A4TGxsZ08+bNkIfR0dHwvDKZTORiSP1FCsPhCXTs5On1epHwxrPABLFTSFI8qwMYX6DkwjhDx2IjEWxmZiauT3iIzwEUcrlcFFGDBXn27JkWFxfDS3MjlskMnldCMjKAwMEyO3Lwdphncnj29vYGclXYccN3WHN+kuj09HSclJkq6FfREal35rkFqYfvhtzBRGpsPYfJ9UWqzP211DPz+U09uZcxEX59B0mpl8z308Jsaeggndv0nr4t1WP/3MNBG3qUz8HiOqs2MTGha9eu6fr165qbm9Pc3JxKpZJKpZJu376tfD6vycnJ2BH3/PlzPX/+POTEmQHX29RA4kDOUqkUpQjYIebhDd9VgiPkNgNZ5D0aTi3PD6hIr4tuRRdgkzyJlGdxBj41tOhvnIdWqxVbXpnPbLa/NXZvby92q/g1mX/u6S39nzUwjClI10oKnLy/fAY95ePnLQW36ec8bcJDKi5XaV98zb5KuxDocNSf3tDDDcNoGkdoCJl/xzuOwExOTur111+P2Fgul4tEJd/iB4UNKzA2NhYgpdlshhHGyHKOwOlp/1A2PD76Q3imWq2qUCio0WjEs0xOTuo73/mOHj16NGAIoK6JrY+OjkY8HRak1+upVqvF2M3Nzen4+FilUimOHJ+dnY3wwcjISBhI7o+RA2G714TSg/WZmpoKw8FiYYcQiV0Y1JShQohTpoDs7LGxsQhpEJLa2NjQf/2v/zW2rgK8Go2Gbt68qbm5uYGdN3gT7XZbu7u72t/f19LSksrl8kBtEbaWUrsEGfQEWy8UBsD1iogAZmSNMcYbhAFBNr0gF/IN6IPJgCFC0XW73WA/ALf0yU+ZzOVy2tnZ0ezsrFqtVoQ5vOz42NhYhJ0ymUzIMUaLZ+M+Hj7j3IeRkfNtktJ59VaejyReZ/rITQEQAqpftaXspstmChj4HOtBGvRsXem6/vFrpcYj/d/7lRpwfrtxT8GHg6MUIDglzrOitB0Mo6tYQwBEb6kH72sbY821cBocYLmBcKbh6tWr+ta3vqVyuRyFDMmVmpqa0o0bNyIBO5PpH6q4s7MTThSyRr8cgBBidbCB8waD4fUunJkglMzYOGPDOBDGJYRMvojrfpwq/qZgI+PG71Q++dvXPOyms+OuX3CkSIpvtVpxJAU/DqRTmXR2zvvga9BBD3KZAux0naWy7uykP/sweWGsfV153iV983sMy4tyIPJN7UKgA4HwG/rDe+eHdcQFwZOf+Gx0yq7F9RBABETSgHB1u93wpLnewsJCJD+NjIxE/gRUeafT0b179wa8Y0nxnUajoUajodHR/iFGY2NjsSWTapDZbDYObSOcU61WJSlO+Gw0GrGzgEXn3ubq6mrsSmCRlUolSYptb759C2OOcAEeUALEHQEe7mFCDwLSPFeCQlZevRPqtNvtxlZNgAcKYH19XRMTE/pX/+pf6d69e3r27Jm+853vaHFxUaVSSeVyWdvb25qdnVU+nw9QwIFnklSpVAKIXr9+XSsrKzo6OtLq6qomJiY0MzMTe+RRYoSEAC+emOlMHB5ZoVCIbW3UBWH/PYlwLDoUP/v0PYeDsfdEMeSWw+vm5uZULBZDRkdGRiJ3B69pZWUldo5w3Xq9rmy2v/sHuaTGjKQ4fAo2iCqpKGaAJWASxeVJfDB+3W43gDrF2fb29gZKOw9LBv+6hgfmLGeqL1wvDKNu/XvOevBZv0fKTqEfHCRwb/QCLWUaaA5E0n77NVIDhsHxJEcMiXRehtufw7319Fp8xj9Pnlbafwc5jOHISL+G0NLS0sCZReyyoOQ9icjtdlu1Wk3VajWMaToeKQAiUTufzyufz2t6ejrCdB4S9GRznsMrKEvn56SgY93Y+pjyrPwNsHFWxp0S5sl3X6QhHEADY0e9EnQz10MPEr7wPDjWnocLHbzSF/5mbJ1BS0F7ytq5LPp8D3P0U1vsa4u/kU/6n6Y9uHPg4WW/TwqkvqldSKM40nU6ik7wHs1ZEV73ycbw+/vuPRAOyGazYTxRqGNjY7HFlZwBjEOn099yOD4+rp2dHWUymUgMIv6OAK2srGh/fz/idYVCIRJKv/Wtb+nLL7/U7OysDg8Pg+o/ODiIE0F3dnbU6/WiHgaH0GEsdnd346A58ixgEAAGzWZTKysrun//foR3OMeAnRdsQcPYAD6IZ3rhLAy0AwYADgaLbWrkGOCxe00TPodhcyOMJ5PJZFSv1/Vv/s2/0Ycffqhut6vvfe97unLlipaXl1Wr1XR2dqbNzc3wtMbHx9VqtcLj5rA5nonaIW+99VYI9enpaZyeSjy5WCzG2QbpUfUYcLaWEo6hABFjh1ePl4WihCHKZrNRTRSvCkPtVDHxVBgevEcUBwnIY2Nj+uSTT3Tt2rX4nIc4HPiwM4vwmp8+jFdHrg/PMDIyoqWlJR0cHERYhXUD8EXJAPgYM8qz85wU57tIc4YsVbQYR090G6aInapNafhUSbuecXZgGFuRtpTq9v4zJk5ZuyFIQRDX8xyPXq8XcgYIZA3TrzSc4P1w5U/eDXVyCPtifD2ngJbL5XTt2rUBUEMxvV6vF3lFJB+zDb7RaMS2a65LCNABFLqEJOrx8fEAEx4SAeT6YXA8OzoIufedMz4ujBMMhDPavlvLd2gxbj5X6EK2CUvntXykvn0AjC0sLGh2dlbPnz/X5uZmpBawuxLAwRyR+8W6SpNDPcl/GBh2ViHNxxgG2FOn3l/36wy7H7LgYMsdfZdfB0iprPrnX7VduDiYIxwf9NQDcW+L5iiK6w1L6hkdHQ1lh6eKEuj1epH8iPClxZMw0Ovr6wM1RWZmZiK+TuZ2sVgMlM+2WBRzt9vV0tKSNjc3tbS0FIeUkV/A8ev7+/uR34AHeXh4qJWVlajKB/L3w5FY7EtLSzo9PY1tbIRHvB8sHLbUAkycmkNQWPzMAffL5XIxFp4MyqJj7rgfgITEV9/66c9wdnamn/70p6rVarp+/bokxfHw5XI5dqJsb2+HF0F4yxcvDAD3J5cE2tLDOgCfTqcTgIMxwwABMFGYzJHH2SlTjILIZrMDoRBPyvITMsmzIMTEuGEMAC4YHPb8j46OqlKp6J133on+8D7yQ1E5/x/lLCnWx9TUVADSdrsdFDP5TAAo316b7iBgHQMCp6amYmyXlpYiHHgRHcG6dY/Mw6lO33oIiu+7DNJXfx9dkOZFcF/0UdqvYUzFMCWaXtNZFfrN/6kj5sAdcOgeNPfwZ2Q9MTfOLqEvPT8JFoM1wesOvrrdrmZmZsIRgx1tt9t68eKFnj17FrsxkA/keXt7W9VqNUp3M39uYOkb28XRs6wV5slD7qxz5o5+YgNYv3wu9bRd11G8kbXhn2W8CA0zxowVY4r+cb2wvLyscrmspaUlFYtFHRwcqFQqaWdnR41GI86BcjkBAPkYuSFODbLrIG/OQqQAwgE0n3Wdn8q1y7cDaQdB7vw745GCe66bhhh5FmfeXqVdOKeDG9FBDBiDSMf8fxAsXh0CRGdRSL64a7VaKGpO2yTuT2xNUiD/bDYb8etMph8Dv3LlShyfXqvVIts4m83GabGccpjL9U+BPTo6UqVSCWSOYqdq5YsXL2ILItsa6/V6bIklzjczMyPpvLoeWzEZG6h+2AN26NTr9fD8Wews6tPT8+PqCXHUarUAOnhajCuKxIua8V0MEOXbEWIUJB4Mz43XjFJzA9/r9eJYdY50X1hYCHDk++53dnaiX9euXYvaHV4UK5PJ6P79+6EQ/FRfZJDzSUgaJmxWr9djXlBwLC7CGNCj7N5h6y7eP9VbnRFCrsi/wGOChSMBkDn20vSEPzj5Fnk7ODgYOPfEvTYA6cHBQawfdh1Iiri+s3jMLf0kqZiEN04H9q3V9JedByMjIxE2JNfmojoidSaQEbYZek6XKyqnwVNvLgUC7sAMU7YOEoZ5aN5Xb2l/uJb/MF/Q295XBwG9Xi9kbpghSO+XssIegiY0w7XTsEHa/16vF/kbgNLj42NVKhV9+eWX+vzzz/XkyRPVarWBSsQwrJy54uyGG61MJhP1OGZmZmIN8ePhFfIvPCcDQ+3Pzd+pjeBvrkXyqm9f530HX2ljrtAbkuKE54WFBX3wwQcql8u6du1aMDfLy8v64Q9/qO9+97sqFAqhM93YpzkRqcw5OBjGcPBdT5QGuKWy6oyNXy9dI8MAg18rk8kE2ETWfMz88/6sfi9/tou0CzEdaSgFZeI0oE9A+sAYUV8wfK9UKoWXvru7G4sVRcWR5RyFzsAjQHgdCB5KG0NWKpVUq9V09epVSQoabnJyMmp7QEWdnfWryZHjsbS0pPv378dBc5VKRbdu3QoabXd3V+VyWTMzM1EWm9gfSJ5ts3yeSefQOqqesuhXVlbU6/WisFZKSWKcmA+SHPHAMUYwKzRAIt4CiZEsXu5DvBXjiFEkaTabzYZxOj3tn1ty584dHR0d6csvvxyokUJSGizB48eP4/lmZmY0NjamnZ2dmFfyHjinhtAKHiSJXjw7OzZYuLxG/wFrfs6IpIHqsnhd09PTqlar4X3yHpVq3XMECGGYO53+oXbIvcedDw4OIv9laWkpjK/L7tnZWYBrQiscEc6WZUBkJpOJ8Wf+qF4LkOOwuFarFeEirkFeCnQ7Z2VgEFBIw0IQX9dQuq58Wf8wZnisTsGnSi1Vou4FporOGYiU0Ug9Mzf6KQDgO/47jb37dVI6GrDnYAQjkRqHlJHx/2HEPMyA4WXsXKd67J9rdbvdOMyxWq1qYWFBzWZTn376qX7729/q2bNnAdwJAzInVCylinMa3nK9TxiQPA5K80vnBwzCOCAfOD+pPeFv38rN2vBQEgAGptuZRq6DMU3vzb1waNE3y8vLevvtt1WtVgd2rPF3r9fTxsaGWq1WhGqnp6ej3gnPQ58ddKR985bKhYMXlws38qntdPA97HrpffhN2QB3NofliLh8uz4YBvJfpV04p8NvmA4OQpiiemc9/DeNSnk8EMe1U7fi+Pg4DAULmjADHjiInvdnZmYiIY7rFgoF7ezshAcKJebXJhwyMjIy4OVzONy7776rra0tvXjxQvl8PtgUSaG8PXcAFqPVasU9pqamYmslJ4fCBFy5ckWNRiOUDyDIY/2e4HXlypVIivTnGBkZGdglgiLDi3f2AI8e74NQC8m3CB15BzAd0nkmPswBYO7p06cDW3tJUAX0bWxsKJ/Ph2ElfFUoFGLnyOLi4sD1yWGAKuZ0XOSQ0Bhj5+XPiVdDd8N2kfPi30HR4gGOjfVPtUWpoPgxLJRNxzCgNMmrYKyOjo60tram999//yvGmWt4qXoSPFHigIxsNhsASFIADvrLAU3dblcbGxsD7BTfh3mgj9zv4cOH4alS/OmizZUP3n8mk4mDs2CPhuVe8B1X0s4AuE7BiLjXhzzzvTSkm/bR7+2/3esbRh9jVJAt/mcrcxrv99h4ei1JAUDpr4MP343leXDSOZPKtQFyhFWPjo7UaDSUy+VUq9X0+eefa2trK1g0npPcIKkvp7VaTbVaLbx+z42gD86CTE5ORh0OPktIlzwO+seaRKcQYut2uwO6PmUGxsbGoh6NAxGAF+Eg7ufVdJ1dy2T6eWijo6N67bXX9MYbb+jtt99WsVhUsVjU3t5e5It1Oh3t7e0FIGd7MOw+R2pUq1XlcrlgIl2OXd5cBlwGPWSXPnsKjNNQ3zDZTJsDED7joM/1ggMOd/C5d/pM6f2/qV0IdKSL1zvinkbqcfDZYYufxE1yDPBEGRBQ+P7+fnjHvkD9jAwaXhoxR4QRY1CpVGKrl291YpGSnMkCOzs7Uz6fj2I6sBawARh+qMV2u639/f3wykmAZBHAwMBMMGFnZ2eam5tTrVZTpVIJMONelGdqg/ARfGclvDKfpKD1x8fHI27Kdl+298ISoaxhjvDmvYgV4IKf09NTzc/Px/kqJycnajQaunLlStSj2NnZUafTUblcVq1W0/b2tnq9/pHbzWYz5p1DpvDYs9nzAkIeb2QsCAu4koFdInwGQ+Nb8jAOXgwtm82G0QaUSYowDkYFQ4dHlGatO+3e6/V3Rt27d0/ZbFarq6txwi+hQj8zhrXgyYseV/ckZHZkkVtCiGV6ejpCZFDdZ2dnsaMKQOzUaT6f18HBgZaXlwfybi7aUpYTGQHAputVGtxG6oaG96RzJYd8OgvibAgKNc13oD+p15n+jQPgbJoDH/dGnYlADrgO3/G4tyd+0jxHAj3lISa+40wyDJ3LHXLijBD9rFQq2tnZidAAxhc96SAHYLi3t6f9/f04xyoFCRheHBsK3TGX6A2uhz5kfTE/OEiSIrSYGlN29fE6Do6DTNY/YNANoeeW7O3t6dq1a5qZmdF3vvMdTU9Pq16vR1idsCRhdHJcHj16FLYgl+vXBfKkdXYN0m/GDPlM++NMmM/XqxhwBwIpu+dyDEBkDaSEAJ9Nr+fXcD3kNpxr+OdepV1sP9z/r/ki4TcddSFIB9kb3ur09HQYhb29vQAhJI0eHR3F1j2KOUmKDGQUGAqUYjdkEYNcx8fH40TU2dlZtdttlcvliOt7nFbqK2B2pkB13759Ww8ePNDS0lKEa6S+UqG65N7eXuxSANFLGjD2oGHyF6C72SvP7pNCoaBerzewTxxPAO+fsWXho5gITaB8yV/BawFswPbQXxIQ/W+AHcwMTBIsAeMnnXteCwsLarVamp+f18TERJznce/evQFmpNVq6eTkRDdv3ox+UpkUWUOYnWJGQQIa8Y5ghPifRdfr9SI5mWfFCAMA3YDBCoyPj0eya7FYjNwaFC7biLkuckttEdilg4MDffnll3ENtn8DcgCJXI8+oGh7vV6wRczpxsZGjBMeGYnR7LjxMVpdXY1tjpwSiiJh51Wv19Ps7OwACLxISxmLlLV42ee5V6o0+dvbME8rDdmm9+B6qeL06/hnXvb+sL47+EhbquQdCDiY4TVnxmA5kHnvt/fRvWQfA5LcMeiwhA46HLzRf/QDeT7Iq++88QZDiiMDqAAgsWuFRHzAMXKMMySd25L0WQE3znAwr87+OLMJ803SNYndnKME+9NqtfTFF19od3c3CoE1m80YO3KicH44yJPG9vZh8p7+n8pJyvINa8NYjGEOfCrb/O/3/CY5dYCUsofptfyaF8nruDDo8A47anf6aNjCHtYWFxcjGTGXy+nGjRuSpPn5eWUymaD/Go3GQGVNL9G7t7cXdDh5IAwSnhtJn71ef+dLt9vV8vJyJOcBfEicmp+fV6vViloc3W43Qh6vvfZa0I6dTr8iKc/abDbjuPSJiYnIFcEociYLiYf1ej0YFyqWsgDpp5+RwDkA165di9fc0yLkJJ1vadvd3R1IvMUL6fV6UaHS8zUw8nyGLb9U8uRsEKdLJQ0UR+OE3c8//zwSM6k18e677+r58+fK5XIDz3D//n29+eabsYuChQgVD6MBeDs4ONDe3p5mZ2dD4QAQMpnzXQO8Rz8BbAAqFP309LTa7XYoecAB84+Sd9n2nAxOGsZz7Ha7mp2djdocJHH+0R/9USSvSgpggYJGqbKFETYEbwNlCtCD5QCsugcNwydJCwsLKhaLsXV5ZGQkcmegojmjplKpRChkWELeN7VhRjxlL1wePZ/Bc76GGXn/HNdPFbw3V57cx/POmNMUKPl8DwNA6X2HGRPew8iytjC0rtA9sd7vmz4PAILPDANbXA+QAGNHTpl/zhkhn5P9/X2122212+3QEQBtD++mIMqBAfqLflIPhPHAO4YRAIg49e8ME3YCdsHvLSnWM4nr5LS5I0zIdnNzU/v7+3r48GE4Hd1uN8KU9MdBkG+V5zwrgIkzMy4jqTykAC8dx7SlayCV0xSQoJ/Qa6n8pCGUtDGH7gD4GnLGNX2+V9UVF8oS8wVIIt2wWCVCxP98V1KERBYXFzU/Px/FZKi3QXyaktUsHgwFiUpMNIl70IaAgO3tbeVyubiuby8kUZUBBAwg1NDKvV5P5XI5QjQkfubzef3+97+XpNiuCWDwjHyUC0b6ypUrkcjn3h2HbUmKZEL6BhtB0h8hIZQUCpTdNngm7HQgRwUhZEslOQvMDd/3vgNiUHQjIyPRfw4JY+4ABh7bnZmZidomjx49CgCyuroaISDA5djYmB48eBCCjXLzqqDIH9fEGHNvFgy0KzuViG+jNN2w8bzdbjfCEBMTE/r888+1v78fzBD98jwOmA3YJ6+54Tkl3W5X9+/fVz6fV6lUii2DsEc0gAohJtZDJnOe/Mq8s75gC4fFijnJdmlpSYVCQc+fPw/KWDpPtmNtYSCo1eFJeK/afDzdq05DscP0An132jmlb1MAMczwD1Po3reUVkZGWP+uvIexJKwl/0zKjvh4cF30Zdp8DLgWz8m8Mz7c18cYfctvQAeJnYQvCNkxjqmuwsCS7wHgIFxNXlBqcJy1YU14SIjiYBR4HB0djbwh3+lC3Q1+T01NxesUCMRJ5XqAOnSGj6nPczab1e3bt7W8vKxut6utra3Y0YgDQyXWdrsdCfboZnQUDoakYDMB7ox9GoLgd/rjnxnGPrjcDQuP+HXQM8PYiGHXTwHHMPCS3tOJBV9r/vlXaRc+eyVVFryOQk4TjnidASGG7kW06DAUFZUeMXiEIaDAyT3gvoARKoWSZEdiD8l5LHxJsXWQLZlsdSU5j6RJDDyGutVqRdGt9fX1KCbDtdhSWavVYksZY0W1UJQLJdIZq16vF8+NwYaRYJwJa6AAQPW+j5+CW4Cb2dnZgXNf2O7KXLKIu91uGCqP5eOBo5hmZ2djtwlbLt37lxT3nZ2dVaPR0MHBQZzGenJyort370YCLXIzMjKiBw8eRNgDb4nxQjnAehHT9vweDCWKj/lAuQFK8O5hKhivTCYTW+XwbmDyOJ8EOcIjYncLIJL8DAD5wcGBHj16pNXV1Rhj9/729/fVarUGEj2JF3P6Z7fbz8+oVCoDWxzZmeO1Z9jxdXh4qOXlZd28eVP1en0gF8IL72EUAeSHh4fa2tqKefzfba74MHipF8XfTgmninVYOMMpbeTPDWjaf8bX5d5/80MyuO+QYB14LgPXYSwdPNFXDFL6zKlnmnrXnufiobhhoWoALsAGB4dj51kffJ8wHToCNti39xNeYV3xPO7V+/MyLoRiACHIe8pUkFRPgqiDfnbDsG75cfDBZ93geYictVEqlbS0tKQbN25E6BkGBLYbcIEegrF0uWQ+KIfQbDbDceNZHcAyLvxOgZD/Tg37sLXj/7vhd/lx2XAQQnuV9Yys+npAxpEDby/r+8vahU+ZTb0JHhJj7vUgfHHx+bGxMV29ejXe94PY5ubmguKWzoUmk+lnvp+eng6clIqizWazUR6b5MizszOVy+V4n+qUsCQLCwthRHxHjHv1+Xxe29vbarVaoTDJO3j99df19OnTgV0pfgbI1taWbt68GYmHrtBB8p5pzsTRFxJSMXjNZjPKuRNqwMPFs8YzJ1QjnSdMYuxOTvrnu1Buu1AoxHiSkDkyMhLnxvjWWYwoOxsODg4izwTFAkjb3NwM5dfp9LeSeriq1Wppbm5Omcx5JjkMgod5POxDmXdkDgBC4h00slfhhIYFsErntV2o7UE+BQsI4OensWLQ3QhRaE1ShP2oUwJIGRsbU6PR0MjIiK5duxbJlIwTIUBP1JUUcw8j4vF996wZd5gaAOXp6anefPNN7e7u6le/+lXksRDa8xL4PHer1QqWqtVqxfb0i+oIaXjtDKd13Ui4Fz9Mobr+cJYjVaZu2FM2gx/Aght9NyhcyxkHmCb/DH115iIN+fj48l30iLMV9N0BBgYc5c/1er3z7Y0uB84uI2tvvPGGVldXlc/nNT4+rlqtpo2NjQASAH6/Hs/Arhd2bcE6YngA+k7hDwuJwNI6G8l9/HM4TsiJj4fPM8DDQSY2xx0xxvjGjRsqFosRRsdhYQ3iOMFWuBw5KwSoYH5PTk5UqVRinSKXpAD4eKZhM5fxYYAgZThSMMFzD2PXXO6d0fWW2mRfU3zH3/d16Gs4veerOih/UCIpyFg6VzIoD+LRoE06xMRcv349UCuLmR/i2wgqSmJ0dDQOX0PZMwkkkyLE1COYm5sLYe/1emE4UKbSudHiHuyioNzw7u6u5ubm4rvdblf7+/sRGwV4gLhPTk6Uz+cDjaPIM5lMGCs8aRA21VYlRQY4BmR/fz/2yhcKhTCm5IjwOQwZ1LxX2POdOYSUmCeSbXle0D99pHksF/ZpampKtVotgIbfe3R0VPPz83rx4oUODw9Vq9ViSzTV/WAh5ufnNTIyEnk7eP94Y3hBLGRfsABMGC8AGwm7eK+ctzIycn7ujee5eNKkJ6hJiqRmGv1A/nlmvCY3Snh8a2trunv3bow79yeWjMdHPgzbfmECyTWifgfrBCMEE+dgfXV1NZQp15mcnNTGxobK5XJ4bIBNmBWYSEKSF20OOjx05MoPXeEMgYMSBxoouJQd4R589lWUsM8bhseBwzBFTD8xPO5s8Z5vmXVDIZ3vxuAaPkZcZ5iH6CxPquyRd2cauDbJnDdu3NA777wTRekII6+trUXZAM9X8vXFvBwcHIS+Q585kwDT6GPGGLkDyHijt1wW3FD52VrOFODsdLvnVakZV9Y0hxNWKhVNTEyoXC7HeUu3b9+OnXSw1zCS5FrB8DgQcPYKMIaO2tnZiWu5c5OmFryMZUhl2kG4gz8c+BQIpKyTy2zq7DubloKQtI/uZPh9+Yy/7/33Z/im9gcd+DbMS+E9z03wh8pms7p+/XrscSZmB3IeGxtTrVbT8fFxCDnbBzHmKGvQP4JN2IEYm4cIiL0TosDT63Q6yufzQUPDcmDEOXtAkorFohYXF6PyHkxKs9mMJMFisRgIHNYGQ93r9SKfwVE9CaO+vZftjF5vAhan2+1G8TEWNEleMC3uGTuVTWIiuRoYHV4jJ4adDoAxaFdPQstms+ExuEfCnB0cHKhYLGp8fDyKrHnJegwxSm90dDSKFwFwvL/UFmFR49FL50odpcki8fwNT2pDDvgsFVkzmcxAP5kjGBZnFmASut1uhC1QAow3MlapVNRoNHTjxo0Bg8f1ybeRNAC0fbcCCgNDSW4Pyh3DRS4LShjmLVUGsHUkqkK9U0Z9ZKRfLpvtgxdpX+ftoARdZtz4p4ox3c0y7F4vYzT8J813wTA600E/kIuUqeC7GCBCDg54uKaDk5cZGjdsacjC2Rc3eCkz5QDIPdUrV65E4jBMMWzetWvXtLy8HM4B10KOPLHz9PQ0Qixef4M2LMwkDVZS5fPO0Lnh4n30OX11gEYYlIPgqNxL/lgm02dLnzx5oidPniiXy+mNN97QzZs3tbq6Gnly09PTOj4+Vq1Wi628Pq/cTxp+lg1ta2tLOzs7EVoGUBH2dnlO5cf/TvMmHCCk4DeVf8bzZYA8ZQSHMRTDGI+0Pz4uKfvh17lIu1i5QfVLSXtYIKV7PaYFSjs6OlK5XNb8/LxyuVxU+oSJgMJfWFgIKh0hPzw8VL1eDyW4sbGhbrc7ULxFUtDqVJ7s9Xqx9WlnZ0cnJydqNpvhreMJI+AY/+Pj4yiIdHBwoKdPn0Yy1ezs7EBC397ennK5XOwKILSRyWSiZDsxSwAF9yJHhLECdZMHImmgYBQePQaPbWDsznF2yQGOpMhXIJRFSMLBSibTL2w0MzMTXjaKz3MNPKkMA46QMob0d2pqSo1GQwsLC6EcMHLZbFYPHjxQvV4PL/vdd9+VpACJ/DAX+Xxe2Ww/AZZ8GCqVAkYYNxqGk731vs0Xw4EMAUAcMDPXeGkAOM8FcKUwOjoac8TBgV5ky89xoa+Uzc9kzusmMMckw3a73YE8F4wDDAcHHAKS6G+z2QzDQz4KjAZzjqfr+TGSopbIRZrnBgDCXAGnICCNQbvy5bd7/e6Z+e+UHXEFSUPeHSRKg6W3kU1nrFx5u8PjCcEOOGDMYAK4jo8HY5GOzzBj5Tlxw9icbrcbciWdV3H2MCM5C+gQnDT0N3oYdo4QAkmU6FTG2UGa94NxTEGHpNCD5G4A9jz3j2elLw4yrly5otnZ2XD+0LfPnj3Tl19+qY2NDfV6PS0uLgYoZ/7a7bYajUaEVNgSzIF3Lr+MY8rgIBOcV+NHIrg80lx+0zZMntP3ATB+b57HZSkFE+l1vI8pyEjvnwIg1sowwJHO/6uCjwuBDrwoHhxq0tExxsjp59dee003btwIL5rseOhlJpWcBBQw6Hxubm6AYm+1Wvryyy8jBELzpDr/PuEUXmMHB8aECR0fH9f09HSwB8ViUTMzM0H7bWxsaGSkf1w0QkuZ6vHxca2trQWTQuZ1s9kcoO3OzvoFwFgAnG+Sy+Vi6+7h4WEk1aZ7wH1bGc/EeHpohMRRGBuUIOwHcU/YmUajEXkFKCw3Qr7IoBglxTYyvD7YBCjQq1evxg4lwjEYv+npaT158kTPnj2LJMkbN26E8ALwfFcFi5Exxdvgns4e+JZRT77lNfbeu3eFTECbAlDYQgqoIr9odnY2whf0u1KpRNy8Wq3q7t27wbh4Ybb9/X1lMhmVSqW4Jiwdxdy4JvVMiLli2FiLjMPKysrAOUOS4j4oH+S7UCjE9QD5jUYj1vr/bmEwN/6u/Fw5edzcvXU+g6wP+/GcAJ5vmGforIL/nyp0DCpUO/Liu2b8u25Uvb/DDA0hFkAJ4+TXcPCDzvHdKfQR54IxxWi7M8hujG63v1Pj3r17UeSKMC45DjCCyL973BT28nOA6EeaqwBY8blx4+U5Ea6fPXeM1wDv/l0SS5njZrOp9fV1PX78WI8fP1az2VSxWNTCwoKOj4+1ubkZrDr6+/Hjx3r48GGAe5c1not59t1A6ALkwcN6XkbAgaTvphkGkP1vl32ukwJnZ7+4FmPhNpm+D5NJl+VhIMITkn09p2ORrqEUgHxdu/DZKyyauIAhejqHMu92+zkHlL5275iDqDDs1WpVxWJxIKfCcwwAPH42RrvdDoHFUI+NjUWM2rf1Os1HYSqYChYIYR6MNoiYvBGSKX27rtT3CKngiCEk/wSkSJjl9PRUOzs7wZpQmhtlhwdAtVKAFsXMJA2MNQXApHMvGsMGq0T9BfrQ6/UGjqv3QlUu2Cx68jjIX/D7cLAYORqET87O+kW0VlZWopYKuRvQ+gCkWq2mTqejubm5SErFO2dOCoVC1PBgXhhfNzIwIYwTn89ms3HWDN9rNBoDZ7oQbpqdndX6+rpGR0eVz+eVyWTUarUGdg5xPYAdWxQdtGxvb2t8fFyrq6sxLigtwoIwQih7xjIFV3h8GEJkXDqvPQKzwrMDPOkXa3NyclILCwt6/vx5rFV2Nkh9kEdZ52Ge9UUays5DsamxcmDxMs/NlaQ0PFGU6/l1WAcpw8J3XZcxloxHCl4c5NAnT6QERDB3bshT2tv7yHXJO0pBiz9Dyo64ceMzOHb7+/uqVqtxbMOXX34ZuQicfO0GBAPobBBywQ8srOtTz53zeWDtMR68Tr6TpGBZuT9rE6DhOVOAkW63q52dHT148EDPnj1TtVqV1A+Dl8tlraysaHV1VVevXlWn04lEda926jksDix5fgC3PxMHJWKQGX838GkIzK/hDAKyMwwYpOvAQYLL3rDm4RkHP+n6SRufd/lDxjwEmjbv36vqiguBDgcACJHTh3Ta6Zzbt2+Hp0xpZhYkWyZbrZay2ayq1aqOj4+Vz+fDsLEgd3d3Ix8Ez5D7oJypxTE5ORlFmUDXrVYrvu+JX64YEDY//EpSJJ8eHx/HbhU/qA3ankqn09PTIeSehEhNkV6vn9i6t7enfD4fJdKZ4J2dHdVqNY2Ojur69et69OhRJGqWSiXV63Xt7u7GYUSSIuRAnRNXnORADNuJguFnxwoxYYwUi4+CXAgeMgAIJAGWhFUoUcJrL1680PT0tMrlsjKZTDBEgJvDw0Pt7Ozoxo0bwVIUi8XINUHekC+YjWw2G33kUDTp/CwJchUIaQDYTk9PI6uf2iYOKP2gJ/JfJEURLShmtlZCRbOwS6WSPv74Y924cSNCOSQsw7IBGo6OjoLFIfEVhTszMxPP4gfW8UzUPcCzQ0G7h+nH3jP3FBUDOPLsFIVCvr20/Ks05MMVkCtR/vf3WIfOOKSGN1WYL4t7OwBxjzCNn3v/hnnnyJr01VNvvXFdjAYOGPdwjzBledyYOFhhPmnMpydUpkCEeaW/e3t7Uc+GLdmUQieHB3CPHKBPfSyQC+QbIO+GyY0l44qcuvefjmW32w2Hh/Hh+RlLGiD98PBQm5ub+uKLLwJEjY6OBnBnTW1ubqper2thYUH379/Xs2fPdP369WBspHPAM4x9S+eZuSHxm8+ncz0MHKdGGZYH5himnLFKc3y8D8PyelK2zQENsuU7aryljAuvMf48t7+XMn4vWxsvaxcCHY5+4gJDUBQdW1hYiJwGz7dgQInZuRLngDe82dHR0VCCJDhyjgbVE8kDIWmI2h6gWDxE9yQ5LbRarardbqtYLEaCX+p5eqycXALf+XJ8fKxms6mxsbEokcszIpRUoSSMRAwV5cHCIZRSLpeDAiyVSlHWulKpBEvCsxIaYBzdUAGoTk/7ZYjZlQAwQSnhSXA94q2+YBYWFtRoNGKLKmCh1+sFM0M/GW+Yi42NjSiyNjY2pjfffFOff/65JA2wKh62gwmDQUGxooBRkrOzswMVAj1eTLIwIQeAoJd35iwdED1gxQ2BL2B2HzFfvV5vIDfoypUrqtfr6vX64Q5nJVB0hFO4p29VZW3AhEgKlsZZJFiow8ND7e7uBjDimsgguTbQ17lcTltbW1E1F2ZrZGREKysrOjk5Ua1WC3B+keYGxhWo5ze4vkgVsuc+pNf7unu68U2Bjxt/V9QYcKfB3fNP9ZrT3s6C+FhjlL1fblRTIOX5Iw4+6DfAwo0245SyNJ6bANvR7fYTqP10ZMA3n0c/OCvl+WH7+/sDid2AHhwAN058FxDpeV8+tzgKvo2W9wDzTtuTn0So6OnTp2EH2AnJOgRIVSqV0GXtdlv1el07OztxSqyDHO7Paykb4WCVcffXHQh6+IvfqQzyWZcbH3/GLG2+Llw/IS8OrPl+mqPi1xoGHlzG6OswwO45WN63b2oXPmXWPRIYhrRls9ko1IUyxpPv9XphiDxvYH9/PxiQ0dHzA8iYQK9FgRdYrVbDK93b29Pa2ppmZmaUy+Uibk2Ix/dpEwYhvwJWxQWcbbqHh4ex3ZN8Ew4Dov9eDIedLORzrK6uan19fSAhkr+ZMADHyclJHBbnJX3JuuawOQweuQskoOIduZAwbxzDTC6CJ8y5l0NioXS+pZhEVN+qy08ul4sdPX4YGtdizu/evasXL15EsaKTkxO9++67un//fsgGihFmAMYqNQLEpKW+Ea/X6xEOYwso8dezs7MIMfgpszA6jBd0r4eX2H2DYkNRkITMWMAMMe6jo6O6f/++5ufnVSqVIh+Dwl0APUJerCnuyS4eQkyu5Hq9XrB8yCK5MiTbEQ7Eg/XrsDZIIkTRsWOF11577TXlcrk4qfkiDblj/XH9VFe4Z4gjkxrSlwGA1MtLgQDNWR+XWf5/mRHxvjuoeJXmLIH323NOGBM+6331cA3Nx8VBMM3vxfdhDJx5RoemjARyhTGmwjJ60ndzMG4pwPS5ApynBf5orgP9p9vtDtTC4TP1el3r6+u6d++enjx5MuBcOHBk88Ds7KzK5XLk1N26dSt2+7mR9hAmP2nYg+fzLfGsW2TQ7aIb7zSUlsqCG3H6xDWGsRLOHHF9H39klZyvTCYTtahSeUrZPq7j4zLsuVIwlbIz39QuFLAdllDlCM4R0NzcXJTsxvsjbi4phNL/X1hYCKaDUMzZ2VnUFWA3R6fTr1nQ6XSi4Bf70U9PT6PqJwCHrGYoJkmx66NSqURoZXx8PHYDeF0HjqLHqyUpkNBBs9mMhE0WQCaTGdhh414X/QR44U0QP4UZQgmNjY3pW9/6VtzfFQEFx6D2j46OQqlgTD1eT04G3icJhChjGAQMKwgcTz1F571eLww5n2Whsijxyubn5/X06VO12+0ABe+8804oRxpJboTGYC1QVISO8OYIPdF35InngV6cnJwMmUCxcF9kC3YJQ53P5yM5FQ/K49SE1lBKzEGlUtHdu3e1v78fY5cyJsgosgyYILRHf1lfXpuAnV6dTkcLCwuRgEe+EmE92DWfV+bLmSXmk3DT1taW9vf3tbS0dBEVIemcBvYcIqd3GTsMkdO5GBGae1v+ussL3iXjBgDF6LEWnBFxkOMKVxo8EdsBB6/7HKIjvBqlPw/XcAPFmpAUYJ5xcwYvbf6aGwBnvs7OzuK0a4A0zw4LgZPnOxFJckbn+EnLfuy9MzdpLo4/v/eLHCYKBHo1UVhK/i8UCpqdnY2KpNlsVpubm3r48KF++9vfRhKoNLiTktw71urOzk4k6a+trcU443CRi+YOGGPmzI0XGyPMBIPkcgRbDfvgusW3t/J5fvsaZH0ytg6G+Z2GEdO15LLpIC5lDNPwpQN2+sb69Tl1feS5Hu4cfVO7cHjFlYQrUY9BUebcdzkQlsDzZEFUKpXYHUDYgtAK38PwssuDapgkYHLto6MjLSwsaHt7W/V6PYw7R79jkOfm5iIxkNg4niFbOyk/jXLHq2WHzdTUlPL5vDY2NuJUVD/cC6+fBD9AhteM8FDPwcGBqtVqgARXWCgRCmtdvXo1jAs5ITAFvuMC9J/GR9nKindMOMYT2jCA9Jk4KEAEgSNnYmpqKpgRPxgpl8sF4Ol0OlpeXtb9+/dVLpd1+/ZtbW5u6tatW5HsJWmgKBGhMYqouYEkkx2l5qCSmDXAF8+ee6RJcYQukGGuRQ4GoMyVBeyFdH744OLioj755JMA0zBuKHTqnXhODaEh2ADiu/5dZM8ZGL5DmERSfJc5hAXCS0Ox4fGi5MbGxsIQMab1ej3CZa/a3Bi6YnMlyfgNa+7BpyyPV/z1+2HE04RxZxQcDKQsQdpS4INRgull/qgjhHwj++5VslvKlbOPTer9DhtP7s3nAfHDWAcMDgYwBVA+NxhsQqrIH2vct8Dj7B0fH0exsRQk0g/0CLqO9zBYHsbk+QDHvV4/3w02+enTp3rw4IEePXoUxfE4toL174nje3t7qlQqqtfrKpVKAbJarVbIOAbatwEzVzgdPAd9xlEZNp6pLKUhhxTUpp91w+6fc5Dr8jBMPtI1g+2Rvhqy5NopYMhk+lvoHcS4XPJ5dwTc7r9qu3B4hU5g3Fx50DmO10Z4JA3s98ebz+fzoQD52+9BgSsUKLUu1tfXJQ1ug8SAnp6ehoeIt0US3enpqaanp7W/vx/JqmRxd7vdSKobGekfWV4ulyNsQMJoJpOJqnfZbDbqL3BIHcfeS32Dtbm5GQmsKITDw8OBZMButxu5IyS6khSK9727uxtnd9RqtWADQOIkpqKU8QKcanN6e3Z2NgwchklSADBnfTwBVTqvepjJZAaK4sD8kAjH37zPvC4uLmpzc1MHBwe6cePGQDlzZxsmJydjDhifmZmZYKekfkEyPzcFuYEdoU4HtVl8IWUymSi8RiKuswrs+iGkhTGndDlySx4GHnej0dB7770XiZ/dbjdOyEUefeeSJ7f2er0Ayixu31XSbDYj0ZQwYzbbz7fhIEO2bWOckCfocwy07zqgMVbMyUXCCmlzxcXv1MNytoPm90wVH991RhGlm9LN3obtPOG3Gwinil1Ro8i9b55E74wJfyPHw8CBAxAPJfAMPJ/fz508N5be91wuF6XCCW2zRqXzwl2sZ/qEN+zMEN9n5wrfH9boJ86GpHDU/H3YQcAgOo7+kE+0tram58+f6/Hjx9ra2hrYdZbNZsM5BIgSNsEYFgqFYEpI7OfEbT8RG4YYQOkONf1OwXPK7jOfw0IMDrR5P2UY0uZznF7rZZ/1v9NrDgtvpeuSefUSFswV65P/Xd5T4Pwq7UKgw6kh/9tDB5lMJoyspKh7gSBms+cV5vCIAQVU+MSrhMoi0QkPnm2NsBNQc71eL16Dzsfb43ruOXqFUAxwLpcLA8EiBH1ns/3yt8TwUVAwDGxFAwTwm4lyD8d333D2BomZUI6AEfIHer1+4Ztf/vKXYYDYTgpTgfeN0LJYeR0FkslkIncGA+Z1GVyoPNHU8wI4YbZWq0UuBYfL0R8HfmTAT09P6+rVq2q329re3g6mAnqTnTLdbjdKgMMSEKpgQQKunOpkjvmbsePZU7bDPX12tZD8CQiUNIDsh9GoMzMzun//frBxJGmenp5G0ql0zsJ4WXuvluo7YVDGzkgg55IGDAs7tACZKFqYQGfQ3Ii6sd7d3Y16NSQqX7SlStPH6euMffo7VZTSYKzbQzKAqTRez/UdoPD8w+457Fmkr+q5NNTBvXkPZ8Kv8TLD5VS9swVuyDzpM6Wx/fseWvEQHt+BGQN00kccPpwHQDbrllLh6Vil44AB5z03zk77OyMD0Nnd3dXW1pa++OIL/e53v1O9Xh/YxcM5T8gTIQjGaWJiQtevXx/YecaWcO6ztLSkarWqer0eNsirJbseSeU2DS/wzPx2cJl+JjXOw1gvlxG/9qs0rs39PUSUMlzevg7geCg97U8qoxfp74XPXnGB8eQi3pueng7WIpPJhLcIGqWmAczE9vZ2eKTSeXU9z2c4OekfMUyZamJq7BxhwQMwMFIYWU6pPTo6ijoGTAx1Lij0xeFA0IBQ85OTk1pfX1c2m41MboqOjYyM6Pbt218pz40B4xosdMIyjAMsCuMJJetMBcZvYmJC165d05MnT/TOO+/E/Un4REkwdkdHRxH6AQQRVvDyvShK+k54xqlJ5hBlJin6hFJEQZKEBB0Kg8J8ss20UqmoUqnovffeCxbHi7vRN0APyZEAMTLxOekXxY+hp6Q+oJK99n6aMM/EomQ+CH35llKMvdPQsCmrq6t6+PChrl69OrBoUQRulJyGLhaLsbhhPwALLGTkCS/t4OBA8/PzarfbA8mShFGI21PW33cbYIQxDmyplTRQMZYk4Yu01KDyGvd15eugwpXuy0Igbujc4wZ0wHKRs/R1O1HcWKTvpYrWwUDKgPgOAnekuKcbdq7trICHazyBEc/S54zXfLzoA/eBQcWQu3PDnJLQDCONUZbOcyPoE2G4ZrOpdruthYWFgTFinF2uh3nbPC99R0Z5rv39ff3mN7/RRx99FCXGvW+jo6MqFovKZDKxPvi9vb2tUqmkO3fuRD0fNg/UajVlMv0t+levXtXS0lIUy8tm+7WWPFEWdoZNAl6XJw0jpcDa5SkFE95SeXPw6a852PHxTVsKWpAl7+uwUA39TfvB3KN30XPuDLjD4q+/SrsQ6HDDgvDzcBgtPDZCIuwFZzGWy+WBugMzMzPBRJADQDXPFHVzTP3CwkLE7zgIzRGoH6ne6XR09erVqLZZKBTU6/VUqVTi+ihctpIeHx/H6atQ564EpL6xajQagaB3d3cDYPB5QAChDL4LQwFz4rkPk5OTsZPFlRmGkOdbWlrS+vq6Xn/99UDzGCaUC4wGcUwMCIwRoaWRkRHV6/WBbXUowFwuF4c9NZtNFQoFtdvtYAYonjU5ORnJhwglGfCEPsg3QFCnp6c1Pj6u9fX1YBXwUhjTcrmsvb292M1DaI8QHUrWlSmACS8dJcruG9/pA6NCjQqSEVHiKD9CEQA2GBKelTyZg4MDLS0tDTBPLGDmVTr3vplnDARbXwHuXugOBYFss+MLI0xCLcqYMAnjBPiAHel2uwO7AAAv7A4CbF6kpWEI1mUa3nPvnPXiinZYLNuNlqToGzIBg8hvruXP5oDZvbzUEHgSrO8w4bsYTZdJ6dwI831vHqJ0w+VjB3CQFGyrz73rBO8Pz4aMsq65BuNdKpUiSRsnCSDLOSvIFfVjONIh3UnGcxAGp7ljCsvrzpikCK2QR8Hzwci+/fbbwYDPz89rb29Pd+7cUbvd1srKih4/fhxMXqVSCf1KnRlykjy83mg0VCwWNT8/r/n5+djFuLa2FmsNlnYYWHUgNUxmXEZcjtPG/Po1fDy5BrLnsu9j73KDnKdMiifzp/Pj1+E1Z6dhiJHtYSyXg8z/Y0wHzeOTDOzIyIiWlpYGUBW7GsgmJzY/OTkZFBdFwlD+hFSkc0+aMt2Li4sDFLkvPkIdGFWo63a7HZNCcuH09LQ2NjbilEGAVKPR0MrKSoAkr9wJwCDrmePnYTJgKNjCy4JEATFB0Ox4CCxMR8+EGFJPkHF966239PHHH2t9fT226LJ1FaDB9lfyPrzoF7uJstlsGHwUNswFxgqjOTIyokqlEuAkmz3f/ULuC6ADYImSabfb8eyMJV7L6urqwCKB5SGxGM+D8e31ejE/LHAPTxGCcaMknZ8ISwMIHR8fRxIxuRZ4z862SOdn/HS73djpAej6X//rf+nq1ataXl6OHCYUMuODzMKaMFbIfrFYHPDkAVcUSnPPlX5wTZQFVDPXYR0yPqwfPDh+4ywA5s/OzlSv1y+kF/AU3St3j9bHnt9uFFPPLlWM6XeYp9QTRMmiOP0991p9XabUeRq7x/NzoEEYQ1KMvSt114Wey8DYuEw7CIEFcUaKnSDSYKjP/4bJpF+c3TMzM6Pr168HaGZ80LfT09NhvJvNpq5cuaKdnR0dHR2p1WqpXq+r2WwGcwig8B0OPKOzab7VHsCPTUA2cI7eeust5fP5qGDsTPWLFy+0vLys8fFxFYvFqMWUy+X0+eefDxxKx7EQ7ApDj2Gzjo+P4xRawEqz2Qzgz/lSbuNcfn1+U5l2Q+wMgBtl5MbbMOYNg+/5dsMYpPS79NHDH4BVBzy+HtJn8mRoB7a+9nxNDQv7vaxdGHQ4EvP/z87ONDs7GwqV0uDQ0P5wfBeFzmucacJCQAjYsooBZYcIeQ+SQhnzPoYDD5jD3vhOp9PRysqKer2enj17pna7HRnRjUZDIyMjOjg4iNLjOzs7sQsGw4hxg4kg92F5eVmdTicysimVDcOB9wJD47Fp4ouuvA8ODsKzhsLd29vT3bt39T/+x/8IwEFMlzCKU9AUWSPzHEXd6ZyXrUfICRfQT09kREkwrwgpLMbs7KweP34s6ZxZQPFQryKX65cgJ6H27t27kTWNMmT+9/b2giHwLXinp6eqVCqRC8JY8UzsukHpkiDlDIgncEr9RcVzE8JhTABhKR1JIlu329WjR4/0N/7G3wjAibF3wOi5IAAxZ6foH58lARYPND3XyHfPEPqDLWLXAeDXd+T4/NBQwD6nfrrwqzRPrnRD5KEOH79hoZRUoafKjLXCfHvIgrHwnAo+5zLP51yJuuJNDY4b9mGK3xsKmTlxeprnwcnwfkjniZ7p+71eb4BRcP3b7XYjfMuhaGxN7Xb7idvs/gJUdzqdcMZ2dnZCp3ItADufpTBXq9XS/Px8AHI8aQ+xMD4+rrBqJGB7CLzdbg8Lk118AAAnw0lEQVSwKYuLi7pz504kuMPgURH66tWroT8rlYo+/fRT7ezshPzRF99V5GFeqa/Py+WyisVifM+BHjoR5soBYRrOcFlNDTVz6mPhn3Mg49dymSAB/OuYBAcENGdAfL14nscw8ER/XFbdEfBxcHDtOYFf1y4EOoYtNgbk5s2bsYUM4ecAM5CmH11O0uHm5qZmZmZC+GA5+IHuJXdja2tLd+/eDbrWF6KX3cZIHx4eqtFoxK4DFg0hjYODgzgoCO/RC5ORH3HlypUAEZubm2H42L5FvYzp6elQ7uy2wVBS+IndMIQtQPcwA3j4GCMHYygSgMUPfvADffjhh8pmsyqVSsrn8wOUGvQ83jZhAnYWQbNirNxQEUJh2ylGCbAEdUsNDK+mur6+HgpLOvfSMKSwYrAstVpNvV4vZIYxYJszVUNRKoR4CA0AMDxZ1hNH/XybdOeVJ816ZVOSKUmuBPRiRGB1Jicn9cUXX+jKlSuamZmJM4AwHnyParLuxVA+nvGsVqsxJhxPz/iThwIo8sP8AIIkFQNaAU5Q2J6VLg2eH+Hj02w2Y3fURZobcbxer9EhDS/fPEz5uYKmn+lOKvQJ+skNBJ4hz01/0j4AhD0kxD29Hz5e3NN3TDgVzedQyM5YpHo0VeJ8n+cDRLkT4SCb5xsbG9Prr7+u733ve1HcDUNBvhPnsUgKvezMFLINmN/e3o5wRavV0ubmphYWFgbyjdC7KcPjCd737t1Tu92WpAAZnU4nKg7DpBwcHOj999/XwsJCHBtBpWXyl371q19pcnJS9+/f1y9/+Uttb29HqISQOaCTgxqxIYSTKpVKOJqEWGFVfU58raRy6fPA/KXgIWVKmE9/z9eNM10egkwTn72luSN8DyYqkznflo+8AmpdRrlvGopJQbivW36n8vl17cKgIx3Uubk5lcvlgdP/fDcEpcI5QnhqaiqAAhS1pKj8iHBBF5MXAmU2NzcXJavn5+e1ubkZihHjCTOCkSZGCX2M9wtbwfar1dVV1et1bW9v6+zsLIqMTU1NaWFhIQ5dI6yCwisUCuGJ7+/vq1Kp6Nq1a5FD0mq1NDU1pSdPniifz6vZbAbjwFZG2BDoPxSCdH7+AaCOrYzdbjdilOvr65qZmQkQhzL1RDSuhWCiYDCSrnwwuowZoAk6GIBGyAnFmslkAkyhANjBA4UPMMKAkJFOwprTggAElAMeHUqCZEk8NEAH7AiJoCxCSQPevR/3DXNBEiqGHLlCbt3gsc32s88+05tvvhlA1OuZ0PL5fBimTCYTAIu5oh+wS6wRclKcFcR7wZsjYbher8f2X9+lhZcP8MRg8byePAxAGhsbCzr+VRvXxdNPdYcr3GHNFZx7asiKzx9z6h6pe2OpJ4Y8pd4pAMmVakqJpywNDVl09imlrumLdE5DM+cpwOG56Y+HG1l73Cv1lq9cuaL33ntPq6ur4UCwVgmfokclxXqen59XpVJRt9tP5Kb6se9YQcZqtZq2trZCx3h+FP1lXmAWms2mPvvsM/33//7fA6TPzMxodnZW29vbks5zaDqdTgCJv/k3/6auXr0a9qNWq+n111/X2dmZtra29J/+03/S/fv3BxLhPZTl+XeAcwA4ss/5NOhDNim4PuR3Cqx87FMZcRDhwATZQ1ZSRs3Dis5SDJPRYfdD5nwtdbvdYLVwtlNQlLIYvO7MrN/PGUOuk4Kzl7U/KKej0+kf9IVnLSk8ShQzXjsGtNPpxHkivIYA+sIjk/7k5CR2HZDM5/kaeNZ7e3taXl4OOp4ESpQsp2WOj49HQSyoIJTR0tJSFOeCultbWwtlfXZ2pmq1qlKppHa7rWazqV6vF548oQMGfXt7W9euXYu+z8zMqNVqaW5uLlgB32qKsSNHAu+aHJTJycmgP0GUCM7+/r7effdd/fznP49DjZgL5oDwAmEISarValELAk8cxTwyMhJGe3Z2NhgMN2CwVQ5UMCyjo6NaXl7W1tZWgDJX5J5ozHxgMABFeEXIG8DEE0D39va0sLAwcF1J8czIgCuLs7OzAUqVBFWKgHm+y+TkZIBLEosBwfShWCzqiy++iEQ38nUAXJlMZqBuCbVGGGt2W7BGYJN4HsJTKAsWPOPUarV0enqqxcVFVavVuCbhFApWoWwBuWTlA/7oK0bUY/sXaSjMNJSKIXBllypdV5ZpqMNDNXwHcMd9XDG6ocD4pMrV+5oacIw7suUUsxtXBwvpThXmKtWdaXgHWWTeuT4KH5l3pQ+Y4HuENldXV6PfzqjhbMEwYDTQZw8ePNCLFy8GKjLDbna7/bBtrVbTs2fP4kiF5eVlFYvFAZbIw32MD/rq6dOn2t3d1dTUlK5fvx4hYQdiOHR8dn5+Xq1WK46sf/78eTw/LLIDbxxZ9B/yy7wjL4wBzh6lGDwxm2dCFpxJY/yQC+bHwUYKVB1wp6yBzxfX9jCVs1bObKSgJ/0/dVL8ew4afL05eEvviUwypv73q7YLVyTtdDq6efNmFF8hnCGdVw+FipYUlSolhfe8u7sbig/PHWMAE4IXNzs7q0ePHgUzwGBQ24Jtq06rQ6XjNSMkJHiiGChhXi6XNTLSP3vi888/182bNwNMeEx7a2tLKysrkvpea7vdVq1Wi2qW7pHiBVer1WA/5ubm1G63tbS0FBMGq9DtdlWtVjU1NaXl5WVNTk5GHoobUWo4QGmSH/Hee+/pF7/4hYrFYpQQBtH7Nj731qHsWbiAuoODg5iDVqs1EMoi5ECOA2DP2RdKHmMYC4WCcrlc7IaglgB5L0+fPlWvN3h8e1ooDGAGGwDgJXTk3gDflRTzTVLtwcFBsBaEnySFVwYYheXguRk/FBpUdLfb1WeffRZ5PJ7EhrLAMCCLrVZrYBcQhd1QgowhYUrYjvn5+YhzZzIZNRoNTU5OanV1VVtbWwGWfE3SYGC8eBveYK93vtPIDTeg+aI6wn9Lg8bVmzOn7hGiKN2YpaDAjQKgAJaBz/k9U3Dj93JAnPaPZ+F9p6EdXA2j1f0Z/XrS+U4VB5LDmuezuKFwkIZcsTOPk1ir1WowF6enp7HTg+3YtVpNjUZDm5ubocucksfIjIyMDBS0gyH49re/rVu3bgXw8MRuHCaqM2MzOFuKA9tee+212EiA3vT8CwDJ7OxsHNo4MjISO+UYbwCHj7+DXJ93z92gkB5ssud8+bjzO2UZUmCRsmjDPucsxjADPqz5M6TXchDj69dlymUyfR6/TnoNbIWvD7+ug/z/I0xHp9MvuDU5OalWqxX7nXu9XsSq8VDTinEgfN+O5K3X62ljYyPCMplMJrKLJYVw+LHtkiImiMeG4JKNzGm0x8fH2traGti2iUHBYyiXy6rX63Ek+Y0bN9TpdFQoFFStVsO7zmb7yWEcBAdDA9txfHysx48fa3V1VRMTE9rZ2dG1a9diEWNEYHwwrNSaqFarwSCxb56tZA7IoEw5+Oudd97Rhx9+qPfee0+SwqN2QUHgfDseyasYOadMYRio9wD1yHdho9huhpIYGekfM00tjZWVlZAVjDjvwVZMTk5Gpdjd3V2tra1peXk5+o1HTj4QbATlkXlOXzgAGAwHSrTX68UZMORMeKJxJpMJAAHjwfXZw9/tdvXw4UO1221973vfC8VLPoSfDMvWaWjsZrMZAAYmjxLa5Gsgt74NlLBPq9XS6uqqqtWqqtWqJiYmVK/XQ5ZPT09VLBaDaSMRFUqdxpyT68OYoZhhjy7SkA1pMEY8jOUYpvTSeeSaqeJEprkXc+IKf5gidDaCa3o82oEIfaE/XNtZGv53I8f3vK8OWqTBUu3uHHljHjyRNaW56f/ExIS2trY0Njam+/fv6+OPP9aTJ08iSRMZI0yDAwMzzDgC4rw/JFZS72dra0tbW1va2NjQG2+8oZWVlYG6H1xnbGxMU1NTevvtt/XOO+/EThjOR+n1erGNl3IL5HE9fPhQy8vLGhvrH4Gxvr6u6enpqEoMyBgWxnNWKgW8Ht4kDIqTkQJdl8FURtPmsuqfSUMszqY5UEqv4/f0loKF9DPp5x1wv6wv6drz+/tYDvt7WB+/rl0IdFD4yztL2AN0ijHxuDceJTsKmFhoX2KNUNfsGslkMtra2tLU1FScT0JcH88NupDtYSRlNhqN8Jq3t7c1NTUVyo7aG2NjY8GgIHw3b96MImKwFSMjIyqXyxH2wdgdHx+rWCzGGS97e3uxGJ89e6ZSqRQeqqQwJDALnU5Hm5ubWllZUavVGghzSOdVE5l8KFxnLjKZTPRlampKb7zxhh4/fqx33303jBkAgyqiKANYFpgDalWA+GFCMMDZbH/rXbvdHqApnUVBgaI8SFRldwoJsHxeUlSjHR0djfLoo6P9Y+mfP3+uN954QxMTE9re3lYul9Pi4uJA+A1lQdIsChQgytiz2wgGh3vncjm12+1I1ES5ZjKZkBtJId9sB56fn9eHH36oubm5OKOHvKRcLheg/Ozs/NAstq36DhKKOe3u7gYT4V5ZPp+Pra5Sn0mDaWN8eXZnp/w9lAdhJFgOcp98B02v14ut4a+aHEbz8IcbfQ/luDzT3DikRp/x9FwN6XzHjq8FZBJnwq/tVDHf5z4p1e33ehkL4UwDITzvC4abZ8KoMx7uoabNQQDPQT99zfmz1Ot1ffrpp/roo4/0m9/8Jk5jJRneDYyPL/dKKXZnU5xtPDg4UL1eV6PR0KNHj/Tw4UO9+eabeuONN3Tjxo1I3KZcfzbbP7X47//9v6+RkRH97Gc/0+7ubuRkVatVzczMRFE9+sm6+M1vfiNJmpub087Ojmq1WhwSx7imzpU7lamceckFnEW2uLt8OIjgt8uD5+8MYwpS4EpLgQxywxh7G3Z9fxaXj5Tt8++7rPnvtKUM4DAA58xyOmav0i6kUa5evRp5ACMjI+G94tXW63WVy+VIkMN7IicABQ465ZA3AAnJfBhG8h9QIGNjYyqXy3EYEMekk2uAom+1Wrp+/XpQ6SjQXq8XVCMHv21ubsZJoo1GI85OKZVK+uyzz1Qul9XtdiMRljohGBNK9Z6cnGhtbS2SXNvtth4/fhyHqcH6UICK/hwdHWljYyMAjXQuoACwWq0WtDvjxS4FlBcG5Pbt28rlcvrkk0/0wx/+MOYAg+dC6YdntVqtCJHg3aeHvDmD5IsG0Ef/UAQsuoWFBWUymWA2OJsGpep1WTY3N8P7XlpaUrPZ1BdffBFJkdCgsC2Uzs9k+vFbBx4ciufnRrDQzs7OQs68IizPz7Mxl/59DPrGxobW1tb0x3/8xwOgisRTACT34bRlZLLX6w0UJPIiXTTCJcTaFxcXo5orYJKQC9sRqfGys7MTNVUosEa1TkCA52BJGiiqhcxdpPnuERQTCgzFylgOS1xzJekhDM+DYFww6qmny9zxfT43zKujeRgj/V5qRFz+kWE3ACmr6InRXnTM5RGwxjrwnWI0TwjmGbn/yMiINjc3tbGxoe3tbTUajQHglDI+rCX0mDM2Pn/Mg3+H/KTDw0PV6/VgPJ4/f6633npLN2/eVKlUCtaUpNO7d+/qb//tv61KpaJPPvkkHIPT09MIU1cqlcifevjwoW7cuKH9/X0tLi6GPmo0Gvr9738frA761Q2j51o4yGJ+0BHNZjNyPVw3Ym9gmPku4+6G2OfaGQDk0efLx9b/Hwa0+U2ffY5cViWF05ACckCCAxvun7J16HL0YMoU8vkUICG3r6orLrx7Bcr5+vXrkhS5GT7AtVpNExMTEcNHaeOpwyAQNvDBRaGikPGOGJhms6nl5eWIW7MFdX5+Pmg5JiNNliOZCnZA6hveWq0WgvXw4UOVSiUdHx/rzTff1MnJycCBXrAnFBbrdDqqVCpBubvXsLOzMzBpTCrJThif3d3dME7kazgLwG4QDBbKCzDlXsjMzIzeeOMNnZyc6Fe/+pU++OCDMJ6egMpvDkxrt9s6ODiIHI9msxm5MVCrCDJbNBkTpyUJoY2Pjyufz8e2XKkf7qGGy8zMTBhUTkiVFEqtXC4PhOS++93vxk6kqampKEvPNTEQJI6xU4qxRsZI0JUUnhKnqzLuvE94zw08dVomJyf1y1/+Uvl8Xrdu3YpYNwsSwMGOFa6LjMDseF4MoSLi54RDCIGVy+VQTpSlbzabA6f6kpjNnHjiq1PxgAuYLpcLwmh4hRdprFUUs1PgrtyGUb0OZPntytn/d2WZhktoKbNB8yQ96bx4mYdpXNlz3dRj9hosqbFxJsIB0zAvl/c8BAP4wJmQzvOxuC4/Hi4k1y1lLoZ50MOofX8fQ4ZugbX2JFb08Nramvb29rS5uanFxUUVi0UVi0UtLy8HCBkbG9Pc3JxWVlb0+9//PnQDwGd/f1/1el0rKysRBiyXy8rl+gX5nj17FvN369Yt3bt3T2trazFeOC/IFH11gOjsFjrb58hlyAFm6mj5uLkcuywP+9/H1+UmZTKcnfim7yEb6bwhJ8yX9zFdazQPT7mTgEyneTMpk/Iq7UKgg/K6XlmR7XnQ0YCKarWqbDardrsdYQWOsR8b6x9SBgNSKpVi0bOrAmOKYq7X61HOFuEiFk1CEQZkbGxswJBBYZ+cnMRpnPQfNoUaIVNTU9re3o5EWeLrExP949ufP38e57Qw4Cx6hJwdJ8TRT05OtLS0FPeAhpfOy3Dj+XIkOiETEru63W4UykIJoDyhx6mFMjExobfeekt/+Zd/qd///ve6fft2nEeAsiGZjAXlrFN6IB5gRVIYQoSc3RAs8tHR0aiMilA3Go3IK+h2uxF6AVR0Op0oUbywsBCAZX9/X4eHh7p69WqEzZA9nsfLeMNywIRI5wXoeCZXcgBbwmzkX5B8ylj4Xn924zx58kTPnz/Xn/7pn4ZxdgXH+HgohXAI4BPmyXNcfOzI+SHsQ/9gPchBmZmZiSMFKM1PSAaGjZyoTqcTQB9ljaJiXNwguzJ+lcZ1APHIl3tPX8dw8HfKjnAdZwOGNVfuqXJ82bN4f6RzcOY7GYaxI8O8XP8+14YBTO+fUu8pw8U80XeSKwHYviuHMBm7MNJ5TO+ZGkQfP38+ZwcAY9D4MJw4DpwOS0hjcnJSi4uLevfdd/WDH/xAc3Nzsd7YyYc+8BAJxRl/97vf6c///M/jIMYPPvhAOzs7+tWvfhVVf/0QRemrdTV4BvQl88i9yVnj2VOWLjXubvSH/U/z/B4cgJeNrzMYLr8ePmTd+DX4jTw4Y8w1UibOv0f4VhpkCF32HWwxJsy7t4voiQuBDnZ5FAqFge2FvV4vjPPs7Ky63W4czV6pVIIhQIFsb28HuCAjOpPJqFgsBqXL1iqy+BFuvFfACN4nyq5cLkftBra2YpwARYVCIajwXC6ner0eSB46rdVqReyQwmDHx8daWFiI8AAxyXq9Ht4nFUGZIBIU2dvOtizyBoi74/2zoLe3tzU7OxtbbUdGRlQqlWJXhCf7kejK1kmKav3oRz/Sf/yP/1GTk5NRN2R6elrtdjuMt9dskM4P/+J/2ApJA3PDrovt7e04K6RWq0UyqdfTIF+CMQHMYJxIgvzyyy81PT2tw8PDCOOwW6rRaEhS7GgC8B4dHcWZNxh3DDNJo14rg7mFQXBQSZgDz5JKiCSquoH+7LPPNDExoYWFhQAjKGKn/X3Ber0ZGA7Gt1gshrEAUHA2DR41JzZz7YmJicgPoqAYW2xhaDgbBw/Yk/VSZQvARS5Ysxdpns+RekPSV7dS+v9O+0qD1HLqOaLIUX4wbsO8zlRJOjikT9yPPnjIIb0ez0lL6Wm/bspuuBfu90yBH8/j/fPtv/wGVANC2M3l9/L+DmNeHTSlnjDGh+b6C51M6JA+4kD2er04nv7TTz/VnTt3onQBawfGr1AoSOoz2TzPw4cP41TvGzdu6NmzZ7p3755arZZu3boVuYTIh1cWZswB2r7rB9DB86ey6jKJjk29/nTuh72WMnrMg/fFx97vwXspw+LXT3NPfMu1fy/9bgr40+ukY8Xn0Odeu8M/96q64kKgg61+JCSSgIeim5qaGtga2Ol0tLCwMEBfzszMRIIlVSvb7fbA7ojl5WUtLS1FWINcEASZbVh4U1DIlNnm9fn5+fCiqXXhLAEVQqGzHfmS1Q39zGmF1HVYWlrS/Py8ZmdnNTY2pmq1GuMgKQy/pNgmjGebzWbVaDSUz+cHhIN+kPFNsid0fkp1kbAJg+A1Hdi18Cd/8if66KOPgqViZ4WfKkobGxuLw5VISgRwdLvd8NoxTrBEsDClUiloSwzKxMSEqtWqFhYWggVC0RGPLRQKun79uj755BMVCoUALs1mU9/97nfjEDc8KLwlklypUeCgxs+dYc4AnX6o3unp+VH2LDgACAsTICT1F/ajR4+0ubmpN998c+CMHMrR46kxDxgfjKQnaHotEc49yWazQSv7XAM+AGrkosB0eDE53yXFWnTqFHkDxDhgI2dJenlRrJc1N/QpiHAF7PSv/07pWmca3DAgI8y/J2n69VKjwL0d2Hi4wKl0mBX38P3azKvHs5lnPEI3Ng52HEg5IPLnSEMtgBLGycEFc05CP9+nDz4Hfm/PW+F/+gigADh5bgfPnIIp5M3B4ebmpvb29vTixQvNz89HeP3srF8mYW5uLhi+7e1tbWxsRF85mbzX6+np06c6OTnR4uKiarVahPbTkIIDB3KYeD70s7PTXgfFx8Hz0xzcOgPEfHvSsLNAPo/MASws8ufsKIad/jtD6HOfMjDZbFZTU1PBbrqO5d6pAzCMkfP14uvOcz1etrZeVVdcuDgYRhE6i4Ei7kzooNvtbwFdW1sLRgNDf/Xq1YHjtLe2tqIM7vXr1/XixYug8PHaEAIUNnQ7QEZSVA2VNEBdU56c/ynDDYputVoBRihdjlCyrYwBpVoqIILXfAG6t3P79u1gAyqVSuyWGRsbi7NeSqXSAOMjnXvZ/ED/E76CQRkZ6dcXabfbWlxcjJLFCMbo6Kh+8IMf6Oc//7m+/e1vxzZgvGkEES8Z40rpb8ISGFeUFsrJ58mrlWYy/boBjUYj5g6mxhchYOnmzZuamprSzs5OhJDeeOMNlUqlgcXh3ozUB3R+EBV9k87PsUiZJTw0mA5ALyE5PHzCOeQJHR4eqlwu6/e//70mJyf19ttvD5xDQy0EckUYWzfiDrxgylg3mUxmIAyX5qJICiDIGPmWb5KYuTdVchkPGA4SiMnVIeQpnZ9544r1Io3PD9vRgcFHGbtHyXOiSDGY7L7iNUCaywQG0Fm79F40dlMATt1DZq7Qab42CMW6QaUPGLZhAMgBCveQBrfcsgb8b8bDqXU3bHjwnofC+sPBQFd63p2DD/+e1A+VFovFWCs4geR2DQsvpZ4wY+VgC9BP/tHMzExUD/7Wt76lt956SxMTE3r69KkymUzc20HVxsZGGN9KpaJf//rXIV/0n7FmDrLZbPTd5RjZ4qwZdzbRH+gmz40hrwu5dFllPLg+8+my6IwrzdkCnoPPu54clgjs8iNp4LR1XgeMpUwO/XVbwc5S1gVj6ax0yo7RsP+v0i68e+X09FQHBwcRQyZ5kxvmcrlgREjQzOVyURGTOh28nsvldOfOHVUqFa2vr8fBPYVCQeVyOUCFJ0ziwXocnTg+pa8p6Vyr1cKwA1JIekT4WZBswyLJU1IknyKcsCQsVs94RhFIfSFaXFzUj3/8Y/3rf/2v1ev1YicPxpBYJrtREEr6hNEn/jg6Ohrn1Tj1TQE2chBgm+jnxMSEvve97+mjjz5SuVyOwl0sChQnAkhehJdMJnSGMigUCjFXCCDGlTNu2LI7MzOjZ8+exemUx8fHkRTr26gXFxc1OTkZp0d6tjRjR8gNr9+BGLkOc3NzA1uwXVF0u91IRsVgE9ZisTJuXtOl2+0nOT9//ly1Wk1vv/12bOPudDpxPgz9JoSEEUeBkJPkSoDcJKmvTGBhkDfGkNCPK3JCi4Aewp0+vyhIgIzHcvmMe9UoGvrwh7Rh3lj6Pms5NWjoBWnwdFf+TxWeJ8Y6dSyd78YBvODEeB/cC3XD4M+AouV3CmbQTSnjgrfJ9dLfjK/LJ543zz7su8PGGr3GmsEhYP7po4d8HESRiAxjzZlVDn7cY3bPmPA012UcAUruMROaXVpa0g9/+MOopDo3N6fHjx/HjivGAvkGRDuD5OG31FPHYfN5S3M1fPxcJtIcBa9j43PL/35f1yX89nDcyxgB74dfm+t9HcPgMozspOtv2H39fn6Nl4UPhz2DM0Ov0i5cHIzOXLt2LQyVhwIwrvV6PRgPPGKMAHQ81DdnqoyPj6tSqWhrayuu4zQqlRQRPrxDii7Nzs5qdnZ2oMw2Z1KwQ4E6CniL0JGU38WwsSvCTxuEVcAY+4mJ0nm8lGzrt956Kyp9uqdBWMCNGwsVI881AFt81vMKMDpeI2V/f1+lUimEiCTb+fl53b59W//zf/5P/ehHPwoPAPADiwQTQHVLN6bufRweHobnnMlk4jRU33UD0maXBTtueH4UInkqNAzwwcFBsFd8F+ZCOj+TxmXPvVg/IA3vhjmH0eH0TRgsDL6HG7LZbDAEv/3tbzU6OqrXX389wjMAVfcs6CceJx6TK3qvM0IeihfSc8AJeHFlzP+Ucma+MHy5XC4Sht1bx+tCWXtZ9pTKTRXcN7WUbncl7wYuvbYrMQ998L/vmmBe0pb2FSPM9b1uhPdT0kDRwmGJfz42Po/+Pp5x2g/3VNMxcOPp4RMfh/Rabvxc7tMx8DkETDrY9edwwOaGFADjXjwG3D+Pns5ms8FiwJ5ybcIqsJp37tzR9evXtbi4GCfMFotFvXjxIhwyGiFkSQMMuIOyFCBjCB14+dj4cw6bU2/YAHcyh13Hx88Zh69bRyn4S6+dygTzQp89JObz5KGxYSArvS9g7uvyn7zPaf9f1UF59YoeUtCLklSpVFSpVKIKp9NS7smjcKGh8f6IyxOHkvohhfn5eV27dk3tdlvr6+tqt9uqVqvhpYyOjsahbxzIVq1WlclkogYESaLECuv1+gDyx7iS2FksFmM/OYAjm82qUChoZWUlqknOzMzo1q1bunbt2gDdikJBSRwfH+vWrVu6detW5CxgdNgdcXZ2Fsm2vm2YxYOHjhcvKYwKjJHnCJydnUWCqIdlyDnJ5/P64z/+Yy0tLekv//IvB+YLdgOWBE+decS4uvIHGFCTpV6vh/DBQAGsOOTtxYsX4UGjAFGGkiKBlWsXi0XV6/UwrKnHBuCCuXB2gl0i9IkQCfekFHuj0Yh6LihNYuMYEa83U61Wde3atQhv0F9Ch9L50ep4dSMjIwPhp0wmEzuxuL7Hbhlfkmk9dIe84T07NYqy9zH1k5+hm5EZFAreJP1wb+9lXtnLmisx/nYjP+zz/rcbfjcoyLqzILznytMNJe9zbe8Ha9EpZn9mV9a0NOmQ5swBzgHzSu4TY+xrCaeCHW3oyFSGUg+WOfL5cc/Ux97nc9h48ZqHwjyHw9kL7pPeywEMTiRHGkxPT2t2dlalUknlclnXrl3T1atXdefOHb322muRw0WCcyaTCfbTj0zI5/MD68tlJM1HcLArKbaXu0y44X0VJoD7DmPv3ICn10g/N+yeLwMb6Zyl10v7gR3meg72PaSSrhWu5fLkcpf2z9clP8MYyJe1C4EOkCs0McqZhcBnUKp4wxhQAIczHCw4vPfZ2Vnl83ndvXtXuVxOL168iO1YKQWZy/WPmd/c3AxGoFqtRtltWIipqSk1m01Vq9WIjcOUoIzx1CVF0ilondLv0OCNRkPNZnNoApvU98J+9KMfBdPCmSauLLkvO2kYWxgaN4QsfqcX8XRRGMwFSgJvDU/56OhI1WpV7777rl577TX99Kc/lXTuNcC0sCMI5gTmxc8oIfSEUZYUIS3CPCgx2AZqbFDnhL76YX7Quk6rcl2UOeABlgMmiaRjaFgWgRuT8fHxOFbbPX4WIElXACNAF8zWkydPJCnKzEuK3Ucu21wDI+PKHdCTzWZVr9cH2DL6THIr64w1hSEbGxvT/v5+KAxAO6FOQBfMmRtL5pF1yfsoOuQFGfrfCa/QUk/PFa2HTKXzQkPSOUAEcDBPafZ8CgY8hu3j7YyhfycNI3k1U/rhhs2dg5eBKe+Pgwf6QIj69PRUh4eHcdAj4Ws/gIx54fsODLwPDgZ4Zp+DNBafeuYUL+TeXq/FjVNqAH2cp6en4/wnmMN8Pq9CoaClpSXdvHlTi4uLun37tq5duxbrdXt7O5L6ccYAHOgFWCpnlBlnZIT17DLFtRyop595WUufledPx//rgId/32XI26swIWlDt7nT6k6My1sKmHwefa14ErRHNl7WDwc13zSWtAsnklIHwLOA6/V6bGWFKcCTpWInAIO8A5iJTCYTlHav1xvY1sf9ABzT09Oam5tTp9PR0tKSdnZ2NDY2poWFBe3t7anT6cTujP39/SjYNDU1pXK5rEajEWXI/Zj0TOZ82y0UGt4mB14Vi8Xw6AkVeOa3x7T+5E/+RJlMZiDPguJlgKvT01O12+3wBkZGRgKknZ2dRR4JHjs1GwhpcZ1ut1+AiiRIL3ONoU13Svz4xz/WT3/6U/23//bf9IMf/CByKrxkNsJEqIFEXMIM9Iu8DA8vuKfNWPKdjY2NOOuEAm0sFgQa5YHS63Q6A/IGA4DHzz5+5M69ZcAAfZE0MKbMO1VNJUXIhjBgr9dPXnv8+HFU3AU4Y+QB2cwNCX2e+S6dAwRyogqFQpT6h2ViF0y73Y4qpoS3mBs/cVjSwGGDvtMJJYRjAKiDwWLcHXggWx4ue9XmyW2plyfpK4o5Vdi85gmfnpPiYcE0/MC903BMmnXvzKTnwzig4LNuqP19/vecDNgTl0Fk1r1rb260Dg8PBwyigyhe57nd2A4DPwBG1pbnZKQsEM0NDiDLnxt9Sb89FOe5WdybHY5cDwANKDk6OtLc3JxarVYkjEt9xptk67Ozs9iiD7PJfbiXzwt2g80CyI8/j4MFN5Suz2luoF0/MS6M6bB8olTOh+WfuMz7nLtsOdtJf/l+mlyMrPjcpLlByCnXoa/OlDC22GiXta97xldpFwId//Sf/tOLfPz/s+3+/fv/t7vwyu2jjz76v92F/9e1X/ziF/+3u/D/ty0FD7RhbIcrYV5z9s6rLMKAYNT5W9IAYOW60iDNnjIX0iBI8X6lfUSxuyL3Z/Hn8ERTZ3f5HvdKGaSUok7zYDBs3gd/jQYQdQY2ffZhz8B76dylRtBZHmeUYDlgJhg32ErKFRAigSF0Bo7rwXBg9GCMYYPJN/NdRvSVrfILCwvBFlOhNU2S9DFKx50xcDAwDER/nXf/MsYjZZteNtZ+n5cxI85yOQB+Wb8coKUgCFAxDJynO86GOQ+v2jK9i3z6sl22y3bZLttlu2yX7Q9sF8rpuGyX7bJdtst22S7bZftD2yXouGyX7bJdtst22S7bX0m7BB2X7bJdtst22S7bZfsraZeg47Jdtst22S7bZbtsfyXtEnRctst22S7bZbtsl+2vpF2Cjst22S7bZbtsl+2y/ZW0S9Bx2S7bZbtsl+2yXba/knYJOi7bZbtsl+2yXbbL9lfSLkHHZbtsl+2yXbbLdtn+Str/A70T6ZUOh2OyAAAAAElFTkSuQmCC",
       "text/plain": [
        "<PyrFigure size 522x650 with 4 Axes>"
       ]
@@ -163,7 +148,7 @@
     "        new_mad.initial_image,\n",
     "    ],\n",
     "    col_wrap=2,\n",
-    ")"
+    ");"
    ]
   },
   {
@@ -312,7 +297,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,
diff --git a/src/plenoptic/synthesize/eigendistortion.py b/src/plenoptic/synthesize/eigendistortion.py
index f88c2fb73..7a374823f 100755
--- a/src/plenoptic/synthesize/eigendistortion.py
+++ b/src/plenoptic/synthesize/eigendistortion.py
@@ -129,6 +129,7 @@ class Eigendistortion(Synthesis):
     """
 
     def __init__(self, image: Tensor, model: torch.nn.Module):
+        super().__init__()
         validate_input(image, no_batch=True)
         validate_model(
             model,
diff --git a/src/plenoptic/synthesize/geodesic.py b/src/plenoptic/synthesize/geodesic.py
index f6246d907..b4a67def2 100644
--- a/src/plenoptic/synthesize/geodesic.py
+++ b/src/plenoptic/synthesize/geodesic.py
@@ -45,10 +45,6 @@ class Geodesic(OptimizedSynthesis):
     n_steps
         the number of steps (i.e., transitions) in the trajectory between the
         two anchor points.
-    initial_sequence
-        initialize the geodesic with pixel linear interpolation
-        (``'straight'``), or with a brownian bridge between the two anchors
-        (``'bridge'``).
     range_penalty_lambda
         strength of the regularizer that enforces the allowed_range. Must be
         non-negative.
@@ -111,7 +107,6 @@ def __init__(
         image_b: Tensor,
         model: torch.nn.Module,
         n_steps: int = 10,
-        initial_sequence: Literal["straight", "bridge"] = "straight",
         range_penalty_lambda: float = 0.1,
         allowed_range: tuple[float, float] = (0, 1),
     ):
@@ -130,35 +125,106 @@ def __init__(
         self._image_a = image_a
         self._image_b = image_b
         self.pixelfade = make_straight_line(image_a, image_b, n_steps)
-        self._initialize(initial_sequence, image_a, image_b, n_steps)
+        self._geodesic = None
         self._dev_from_line = []
         self._step_energy = []
         self._step_energy_dims = None
-        # call this to cache some initial calculations
-        self.objective_function()
+        self._geodesic_representation = None
+        self._most_recent_step_energy = None
 
-    def _initialize(self, initial_sequence, start, stop, n_steps):
-        """initialize the geodesic
+    def setup(
+        self,
+        initial_sequence: Literal["straight", "bridge"] | None = None,
+        n_steps: int = 10,
+        optimizer: torch.optim.Optimizer | None = None,
+        optimizer_kwargs: dict | None = None,
+    ):
+        """Initialize the geodesic and optimizer.
+
+        Can only be called once. If ``load()`` has been called, ``initial_sequence``
+        must be None.
 
         Parameters
         ----------
         initial_sequence
             initialize the geodesic with pixel linear interpolation
             (``'straight'``), or with a brownian bridge between the two anchors
-            (``'bridge'``).
+            (``'bridge'``). If None, use ``'straight'``.
+        n_steps
+            the number of steps (i.e., transitions) in the trajectory between the
+            two anchor points.
+        optimizer :
+            The un-initialized optimizer object to use. If None, we use Adam(lr=.001,
+            amsgrad=True).
+        optimizer_kwargs :
+            The keyword arguments to pass to the optimizer on initialization.
+
+        Examples
+        --------
+        Set initial sequence:
+
+        >>> img = po.data.einstein()
+        >>> model = po.simul.Gaussian(30)
+        >>> po.tools.remove_grad(model)
+        >>> geod = po.synth.Geodesic(img, img/2, model)
+        >>> geod.setup("bridge")
+        >>> geod.synthesize(10)
+
+        Set optimizer:
+
+        >>> img = po.data.einstein()
+        >>> model = po.simul.Gaussian(30)
+        >>> po.tools.remove_grad(model)
+        >>> geod = po.synth.Geodesic(img, img/2, model)
+        >>> geod.setup(optimizer=torch.optim.SGD, optimizer_kwargs={"lr": 0.01})
+        >>> geod.synthesize(10)
+
+        Use with save/load. Only the optimizer object is necessary, its kwargs and the
+        initial sequence are handled by load.
+
+        >>> img = po.data.einstein()
+        >>> model = po.simul.Gaussian(30)
+        >>> po.tools.remove_grad(model)
+        >>> geod = po.synth.Geodesic(img, img/2, model)
+        >>> geod.setup("bridge", optimizer=torch.optim.SGD,
+        ...            optimizer_kwargs={"lr": 0.01})
+        >>> geod.synthesize(10)
+        >>> geod.save("test.pt")
+        >>> geod = po.synth.Geodesic(img, img/2, model)
+        >>> geod.load("test.pt")
+        >>> geod.setup(optimizer=torch.optim.SGD)
+        >>> geod.synthesize(10)
+
         """
-        if initial_sequence == "bridge":
-            geodesic = sample_brownian_bridge(start, stop, n_steps)
-        elif initial_sequence == "straight":
-            geodesic = make_straight_line(start, stop, n_steps)
+        if self._geodesic is None:
+            if initial_sequence is None:
+                initial_sequence = "straight"
+            if initial_sequence == "bridge":
+                geodesic = sample_brownian_bridge(self._image_a, self._image_b, n_steps)
+            elif initial_sequence == "straight":
+                geodesic = make_straight_line(self._image_a, self._image_b, n_steps)
+            else:
+                raise ValueError(
+                    f"Don't know how to handle initial_sequence={initial_sequence}"
+                )
+            _, geodesic, _ = torch.split(geodesic, [1, n_steps - 1, 1])
+            geodesic.requires_grad_()
+            self._geodesic = geodesic
         else:
-            raise ValueError(
-                f"Don't know how to handle initial_sequence={initial_sequence}"
-            )
-        _, geodesic, _ = torch.split(geodesic, [1, n_steps - 1, 1])
-        self._initial_sequence = initial_sequence
-        geodesic.requires_grad_()
-        self._geodesic = geodesic
+            if self._loaded:
+                if initial_sequence is not None:
+                    raise ValueError(
+                        "Cannot set initial_sequence after calling load()!"
+                    )
+            else:
+                raise ValueError(
+                    "setup() can only be called once and must be called"
+                    " before synthesize()!"
+                )
+        # initialize the optimizer
+        self._initialize_optimizer(optimizer, self._geodesic, optimizer_kwargs, 0.001)
+        # reset _loaded, if everything ran successfully
+        self._loaded = False
 
     def synthesize(
         self,
@@ -204,7 +270,9 @@ def synthesize(
             )
         print(f"\n Stop criterion for pixel_change_norm = {stop_criterion:.5e}")
 
-        self._initialize_optimizer(optimizer, "_geodesic", 0.001)
+        # if setup hasn't been called manually, call it now.
+        if self._geodesic is None or isinstance(self._optimizer, tuple):
+            self.setup()
 
         # get ready to store progress
         self.store_progress = store_progress
@@ -542,7 +610,6 @@ def load(
             "_image_a",
             "_image_b",
             "n_steps",
-            "_initial_sequence",
             "_range_penalty_lambda",
             "_allowed_range",
             "pixelfade",
@@ -550,7 +617,7 @@ def load(
         check_io_attrs = [("_model", ("_geodesic",))]
         super().load(
             file_path,
-            "losses",
+            "_geodesic",
             map_location=map_location,
             check_attributes=check_attributes,
             check_io_attributes=check_io_attrs,
@@ -567,10 +634,6 @@ def load(
         if len(self._step_energy) and self._step_energy[0].device.type != "cpu":
             self._step_energy = [step.to("cpu") for step in self._step_energy]
 
-    @property
-    def initial_sequence(self):
-        return self._initial_sequence
-
     @property
     def model(self):
         return self._model
@@ -587,6 +650,10 @@ def image_b(self):
     # combines this with the end points
     @property
     def geodesic(self):
+        if self._geodesic is None:
+            # in this case, setup() hasn't been called yet. returning None matches the
+            # behavior of other synthesis objects
+            return None
         return torch.cat([self.image_a, self._geodesic, self.image_b])
 
     @property
diff --git a/src/plenoptic/synthesize/mad_competition.py b/src/plenoptic/synthesize/mad_competition.py
index 2f5535adb..041c7cc0e 100644
--- a/src/plenoptic/synthesize/mad_competition.py
+++ b/src/plenoptic/synthesize/mad_competition.py
@@ -57,9 +57,6 @@ class MADCompetition(OptimizedSynthesis):
         `def` statement)
     minmax :
         Whether you wish to minimize or maximize ``optimized_metric``.
-    initial_noise :
-        Standard deviation of the Gaussian noise used to initialize
-        ``mad_image`` from ``image``.
     metric_tradeoff_lambda :
         Lambda to multiply by ``reference_metric`` loss and add to
         ``optimized_metric`` loss. If ``None``, we pick a value so the two
@@ -108,7 +105,6 @@ def __init__(
         optimized_metric: torch.nn.Module | Callable[[Tensor, Tensor], Tensor],
         reference_metric: torch.nn.Module | Callable[[Tensor, Tensor], Tensor],
         minmax: Literal["min", "max"],
-        initial_noise: float = 0.1,
         metric_tradeoff_lambda: float | None = None,
         range_penalty_lambda: float = 0.1,
         allowed_range: tuple[float, float] = (0, 1),
@@ -140,15 +136,15 @@ def __init__(
                 "synthesis_target must be one of {'min', 'max'}, but got "
                 f"value {minmax} instead!"
             )
-        self._minmax = minmax
-        self._initialize(initial_noise)
+        self._mad_image = None
+        self._initial_image = None
+        self._reference_metric_target = None
         # If no metric_tradeoff_lambda is specified, pick one that gets them to
         # approximately the same magnitude
         if metric_tradeoff_lambda is None:
-            loss_ratio = torch.as_tensor(
-                self.optimized_metric_loss[-1] / self.reference_metric_loss[-1],
-                dtype=image.dtype,
-            )
+            other_image = torch.rand_like(image)
+            optim_loss = optimized_metric(image, other_image)
+            loss_ratio = optim_loss / reference_metric(image, other_image)
             metric_tradeoff_lambda = torch.pow(
                 torch.as_tensor(10), torch.round(torch.log10(loss_ratio))
             ).item()
@@ -157,40 +153,103 @@ def __init__(
                 f" to {metric_tradeoff_lambda} to roughly balance metrics."
             )
         self._metric_tradeoff_lambda = metric_tradeoff_lambda
+        self._minmax = minmax
         self._store_progress = None
         self._saved_mad_image = []
 
-    def _initialize(self, initial_noise: float = 0.1):
-        """Initialize the synthesized image.
+    def setup(
+        self,
+        initial_noise: float | None = None,
+        optimizer: torch.optim.Optimizer | None = None,
+        optimizer_kwargs: dict | None = None,
+        scheduler: torch.optim.lr_scheduler.LRScheduler | None = None,
+        scheduler_kwargs: dict | None = None,
+    ):
+        """Initialize the MAD image, optimizer, and scheduler.
 
-        Initialize ``self.mad_image`` attribute to be ``image`` plus
-        Gaussian noise with user-specified standard deviation.
+        Can only be called once. If ``load()`` has been called, ``initial_noise`` must
+        be None.
 
         Parameters
         ----------
         initial_noise :
-            Standard deviation of the Gaussian noise used to initialize
-            ``mad_image`` from ``image``.
+            ``mad_image`` is initialized to ``self.image + initial_noise *
+            torch.randn_like(self.image)``, so this gives the standard deviation of the
+            Gaussian noise. If None, we use a value of 0.1.
+        optimizer :
+            The un-initialized optimizer object to use. If None, we use Adam(lr=.01,
+            amsgrad=True).
+        optimizer_kwargs :
+            The keyword arguments to pass to the optimizer on initialization.
+        scheduler :
+            The learning rate scheduler to use. If None, we don't use one.
+        scheduler_kwargs :
+            The keyword arguments to pass to the scheduler on initialization.
 
+        Examples
+        --------
+        Set initial noise:
+
+        >>> img = po.data.einstein()
+        >>> mad = po.synth.MADCompetition(img, po.metric.l2_norm, po.metric.mse, "min")
+        >>> mad.setup(1)
+        >>> mad.synthesize(10)
+
+        Set optimizer:
+
+        >>> img = po.data.einstein()
+        >>> mad = po.synth.MADCompetition(img, po.metric.l2_norm, po.metric.mse, "min")
+        >>> mad.setup(optimizer=torch.optim.SGD, optimizer_kwargs={"lr": 0.01})
+        >>> mad.synthesize(10)
+
+        Use with save/load. Only the optimizer object is necessary, its kwargs and the
+        initial noise are handled by load.
+
+        >>> img = po.data.einstein()
+        >>> mad = po.synth.MADCompetition(img, po.metric.l2_norm, po.metric.mse, "min")
+        >>> mad.setup(1, optimizer=torch.optim.SGD, optimizer_kwargs={"lr": 0.01})
+        >>> mad.synthesize(10)
+        >>> mad.save("test.pt")
+        >>> mad = po.synth.MADCompetition(img, po.metric.l2_norm, po.metric.mse, "min")
+        >>> mad.load("test.pt")
+        >>> mad.setup(optimizer=torch.optim.SGD)
+        >>> mad.synthesize(10)
         """
-        mad_image = self.image + initial_noise * torch.randn_like(self.image)
-        mad_image = mad_image.clamp(*self.allowed_range)
-        self._initial_image = mad_image.clone()
-        mad_image.requires_grad_()
-        self._mad_image = mad_image
-        self._reference_metric_target = self.reference_metric(
-            self.image, self.mad_image
-        ).item()
-        self._reference_metric_loss.append(self._reference_metric_target)
-        self._optimized_metric_loss.append(
-            self.optimized_metric(self.image, self.mad_image).item()
-        )
+        if self._mad_image is None:
+            if initial_noise is None:
+                initial_noise = 0.1
+            mad_image = self.image + initial_noise * torch.randn_like(self.image)
+            mad_image = mad_image.clamp(*self.allowed_range)
+            self._initial_image = mad_image.clone()
+            mad_image.requires_grad_()
+            self._mad_image = mad_image
+            self._reference_metric_target = self.reference_metric(
+                self.image, self.mad_image
+            ).item()
+            self._reference_metric_loss.append(self._reference_metric_target)
+            self._optimized_metric_loss.append(
+                self.optimized_metric(self.image, self.mad_image).item()
+            )
+        else:
+            if self._loaded:
+                if initial_noise is not None:
+                    raise ValueError("Cannot set initial_noise after calling load()!")
+            else:
+                raise ValueError(
+                    "setup() can only be called once and must be called"
+                    " before synthesize()!"
+                )
+
+        # initialize the optimizer
+        self._initialize_optimizer(optimizer, self.mad_image, optimizer_kwargs)
+        # and scheduler
+        self._initialize_scheduler(scheduler, self.optimizer, scheduler_kwargs)
+        # reset _loaded, if everything ran successfully
+        self._loaded = False
 
     def synthesize(
         self,
         max_iter: int = 100,
-        optimizer: torch.optim.Optimizer | None = None,
-        scheduler: torch.optim.lr_scheduler.LRScheduler | None = None,
         store_progress: bool | int = False,
         stop_criterion: float = 1e-4,
         stop_iters_to_check: int = 50,
@@ -211,13 +270,6 @@ def synthesize(
         max_iter :
             The maximum number of iterations to run before we end synthesis
             (unless we hit the stop criterion).
-        optimizer :
-            The optimizer to use. If None and this is the first time calling
-            synthesize, we use Adam(lr=.01, amsgrad=True); if synthesize has
-            been called before, this must be None and we reuse the previous
-            optimizer.
-        scheduler :
-            The learning rate scheduler to use. If None, we don't use one.
         store_progress :
             Whether we should store the representation of the MAD image in
             progress on every iteration. If False, we don't save anything. If
@@ -232,8 +284,9 @@ def synthesize(
             loss has stopped decreasing (for ``stop_criterion``).
 
         """
-        # initialize the optimizer and scheduler
-        self._initialize_optimizer(optimizer, scheduler)
+        # if setup hasn't been called manually, call it now.
+        if self._mad_image is None or isinstance(self._scheduler, tuple):
+            self.setup()
 
         # get ready to store progress
         self.store_progress = store_progress
@@ -395,11 +448,6 @@ def _check_convergence(self, stop_criterion, stop_iters_to_check):
         """  # noqa: E501
         return loss_convergence(self, stop_criterion, stop_iters_to_check)
 
-    def _initialize_optimizer(self, optimizer, scheduler):
-        """Initialize optimizer and scheduler."""
-        super()._initialize_optimizer(optimizer, "mad_image")
-        super()._initialize_scheduler(scheduler)
-
     def _store(self, i: int) -> bool:
         """Store mad_image anbd model response, if appropriate.
 
diff --git a/src/plenoptic/synthesize/metamer.py b/src/plenoptic/synthesize/metamer.py
index a169b9e67..70fa47e4f 100644
--- a/src/plenoptic/synthesize/metamer.py
+++ b/src/plenoptic/synthesize/metamer.py
@@ -23,10 +23,9 @@
 class Metamer(OptimizedSynthesis):
     r"""Synthesize metamers for image-computable differentiable models.
 
-    Following the basic idea in [1]_, this class creates a metamer for a given
-    model on a given image. We start with ``initial_image`` and iteratively
-    adjust the pixel values so as to match the representation of the
-    ``metamer`` and ``image``.
+    Following the basic idea in [1]_, this class creates a metamer for a given model on
+    a given image. We iteratively adjust the pixel values so as to match the
+    representation of the ``metamer`` and ``image``.
 
     All ``saved_`` attributes are initialized as empty lists and will be
     non-empty if the ``store_progress`` arg to ``synthesize()`` is not
@@ -53,9 +52,6 @@ class Metamer(OptimizedSynthesis):
     allowed_range :
         Range (inclusive) of allowed pixel values. Any values outside this
         range will be penalized.
-    initial_image :
-        4d Tensor to initialize our metamer with. If None, will draw a sample
-        of uniform noise within ``allowed_range``.
 
     Attributes
     ----------
@@ -93,7 +89,6 @@ def __init__(
         loss_function: Callable[[Tensor, Tensor], Tensor] = optim.mse,
         range_penalty_lambda: float = 0.1,
         allowed_range: tuple[float, float] = (0, 1),
-        initial_image: Tensor | None = None,
     ):
         super().__init__(range_penalty_lambda, allowed_range)
         validate_input(image, allowed_range=allowed_range)
@@ -110,57 +105,127 @@ def __init__(
         self._scheduler = None
         self._scheduler_step_arg = False
         self.loss_function = loss_function
-        self._initialize(initial_image)
         self._saved_metamer = []
         self._store_progress = None
+        self._metamer = None
 
-    def _initialize(self, initial_image: Tensor | None = None):
-        """Initialize the metamer.
+    def setup(
+        self,
+        initial_image: Tensor | None = None,
+        optimizer: torch.optim.Optimizer | None = None,
+        optimizer_kwargs: dict | None = None,
+        scheduler: torch.optim.lr_scheduler.LRScheduler | None = None,
+        scheduler_kwargs: dict | None = None,
+    ):
+        """Initialize the metamer, optimizer, and scheduler.
 
-        Set the ``self.metamer`` attribute to be an attribute with the
-        user-supplied data, making sure it's the right shape.
+        Can only be called once. If ``load()`` has been called, ``initial_image`` must
+        be None.
 
         Parameters
         ----------
         initial_image :
-            The tensor we use to initialize the metamer. If None (the default),
-            we initialize with uniformly-distributed random noise lying between
-            0 and 1.
+            The tensor we use to initialize the metamer. If None, we initialize with
+            uniformly-distributed random noise lying within ``self.allowed_range``.
+        optimizer :
+            The un-initialized optimizer object to use. If None, we use Adam(lr=.01,
+            amsgrad=True).
+        optimizer_kwargs :
+            The keyword arguments to pass to the optimizer on initialization.
+        scheduler :
+            The learning rate scheduler to use. If None, we don't use one.
+        scheduler_kwargs :
+            The keyword arguments to pass to the scheduler on initialization.
+
+        Raises
+        ------
+        ValueError :
+            If ``initial_image`` is the wrong shape.
+
+        Examples
+        --------
+        Set initial image:
+
+        >>> img = po.data.einstein()
+        >>> model = po.simul.Gaussian(30)
+        >>> po.tools.remove_grad(model)
+        >>> met = po.synth.Metamer(img, model)
+        >>> met.setup(po.data.curie())
+        >>> met.synthesize(10)
+
+        Set optimizer:
+
+        >>> img = po.data.einstein()
+        >>> model = po.simul.Gaussian(30)
+        >>> po.tools.remove_grad(model)
+        >>> met = po.synth.Metamer(img, model)
+        >>> met.setup(optimizer=torch.optim.SGD, optimizer_kwargs={"lr": 0.01})
+        >>> met.synthesize(10)
+
+        Use with save/load. Only the optimizer object is necessary, its kwargs and the
+        initial image are handled by load.
+
+        >>> img = po.data.einstein()
+        >>> model = po.simul.Gaussian(30)
+        >>> po.tools.remove_grad(model)
+        >>> met = po.synth.Metamer(img, model)
+        >>> met.setup(po.data.curie(), optimizer=torch.optim.SGD,
+        ...           optimizer_kwargs={"lr": 0.01})
+        >>> met.synthesize(10)
+        >>> met.save("test.pt")
+        >>> met = po.synth.Metamer(img, model)
+        >>> met.load("test.pt")
+        >>> met.setup(optimizer=torch.optim.SGD)
+        >>> met.synthesize(10)
 
         """
-        if initial_image is None:
-            metamer = torch.rand_like(self.image)
-            # rescale metamer to lie within the interval
-            # self.allowed_range
-            metamer = signal.rescale(metamer, *self.allowed_range)
+        if self._metamer is None:
+            if initial_image is None:
+                metamer = torch.rand_like(self.image)
+                # rescale metamer to lie within the interval
+                # self.allowed_range
+                metamer = signal.rescale(metamer, *self.allowed_range)
+            else:
+                if initial_image.ndimension() < 4:
+                    raise ValueError(
+                        "initial_image must be torch.Size([n_batch"
+                        ", n_channels, im_height, im_width]) but got "
+                        f"{initial_image.size()}"
+                    )
+                if initial_image.size() != self.image.size():
+                    raise ValueError("initial_image and image must be same size!")
+                metamer = initial_image.clone().detach()
+                metamer = metamer.to(dtype=self.image.dtype, device=self.image.device)
             metamer.requires_grad_()
+            self._metamer = metamer
         else:
-            if initial_image.ndimension() < 4:
+            if self._loaded:
+                if initial_image is not None:
+                    raise ValueError("Cannot set initial_image after calling load()!")
+            else:
                 raise ValueError(
-                    "initial_image must be torch.Size([n_batch"
-                    ", n_channels, im_height, im_width]) but got "
-                    f"{initial_image.size()}"
+                    "setup() can only be called once and must be called"
+                    " before synthesize()!"
                 )
-            if initial_image.size() != self.image.size():
-                raise ValueError("initial_image and image must be same size!")
-            metamer = initial_image.clone().detach()
-            metamer = metamer.to(dtype=self.image.dtype, device=self.image.device)
-            metamer.requires_grad_()
-        self._metamer = metamer
+
+        # initialize the optimizer
+        self._initialize_optimizer(optimizer, self.metamer, optimizer_kwargs)
+        # and scheduler
+        self._initialize_scheduler(scheduler, self.optimizer, scheduler_kwargs)
+        # reset _loaded, if everything ran successfully
+        self._loaded = False
 
     def synthesize(
         self,
         max_iter: int = 100,
-        optimizer: torch.optim.Optimizer | None = None,
-        scheduler: torch.optim.lr_scheduler.LRScheduler | None = None,
         store_progress: bool | int = False,
         stop_criterion: float = 1e-4,
         stop_iters_to_check: int = 50,
     ):
         r"""Synthesize a metamer.
 
-        Update the pixels of ``initial_image`` until its representation matches
-        that of ``image``.
+        Update the pixels of ``metamer`` until its representation matches that of
+        ``image``.
 
         We run this until either we reach ``max_iter`` or the change over the
         past ``stop_iters_to_check`` iterations is less than
@@ -171,13 +236,6 @@ def synthesize(
         max_iter :
             The maximum number of iterations to run before we end synthesis
             (unless we hit the stop criterion).
-        optimizer :
-            The optimizer to use. If None and this is the first time calling
-            synthesize, we use Adam(lr=.01, amsgrad=True); if synthesize has
-            been called before, this must be None and we reuse the previous
-            optimizer.
-        scheduler :
-            The learning rate scheduler to use. If None, we don't use one.
         store_progress :
             Whether we should store the metamer image in progress on every
             iteration. If False, we don't save anything. If True, we save every
@@ -191,8 +249,9 @@ def synthesize(
             loss has stopped decreasing (for ``stop_criterion``).
 
         """
-        # initialize the optimizer and scheduler
-        self._initialize_optimizer(optimizer, scheduler)
+        # if setup hasn't been called manually, call it now.
+        if self._metamer is None or isinstance(self._scheduler, tuple):
+            self.setup()
 
         # get ready to store progress
         self.store_progress = store_progress
@@ -324,22 +383,6 @@ def _check_convergence(self, stop_criterion, stop_iters_to_check):
         """  # noqa: E501
         return loss_convergence(self, stop_criterion, stop_iters_to_check)
 
-    def _initialize_optimizer(
-        self,
-        optimizer: torch.optim.Optimizer | None,
-        scheduler: torch.optim.lr_scheduler.LRScheduler | None,
-    ):
-        """Initialize optimizer and scheduler."""
-        # this uses the OptimizedSynthesis setter
-        super()._initialize_optimizer(optimizer, "metamer")
-        super()._initialize_scheduler(scheduler)
-        for pg in self.optimizer.param_groups:
-            # initialize initial_lr if it's not here. Scheduler should add it if it's
-            # not None. This is used by coarse-to-fine optimization so we know how to
-            # reset across scales.
-            if "initial_lr" not in pg:
-                pg["initial_lr"] = pg["lr"]
-
     def _store(self, i: int) -> bool:
         """Store metamer, if appropriate.
 
@@ -506,7 +549,7 @@ def _load(
         check_io_attrs += additional_check_io_attributes
         super().load(
             file_path,
-            "losses",
+            "_metamer",
             map_location=map_location,
             check_attributes=check_attributes,
             check_io_attributes=check_io_attrs,
@@ -574,9 +617,6 @@ class MetamerCTF(Metamer):
     allowed_range :
         Range (inclusive) of allowed pixel values. Any values outside this
         range will be penalized.
-    initial_image :
-        4d Tensor to initialize our metamer with. If None, will draw a sample
-        of uniform noise within ``allowed_range``.
     coarse_to_fine :
         - 'together': start with the coarsest scale, then gradually
           add each finer scale.
@@ -622,7 +662,6 @@ def __init__(
         loss_function: Callable[[Tensor, Tensor], Tensor] = optim.mse,
         range_penalty_lambda: float = 0.1,
         allowed_range: tuple[float, float] = (0, 1),
-        initial_image: Tensor | None = None,
         coarse_to_fine: Literal["together", "separate"] = "together",
     ):
         super().__init__(
@@ -631,7 +670,6 @@ def __init__(
             loss_function,
             range_penalty_lambda,
             allowed_range,
-            initial_image,
         )
         self._init_ctf(coarse_to_fine)
 
@@ -659,12 +697,21 @@ def _init_ctf(self, coarse_to_fine: Literal["together", "separate"]):
         self._scales_loss = []
         self._scales_finished = []
         self._coarse_to_fine = coarse_to_fine
+        self._initial_lr = None
+
+    def _initialize_optimizer(
+        self,
+        optimizer: torch.optim.Optimizer | None,
+        synth_attr: torch.Tensor,
+        optimizer_kwargs: dict | None = None,
+    ):
+        super()._initialize_optimizer(optimizer, synth_attr, optimizer_kwargs)
+        # save the initial learning rate so we can reset it when we change scales
+        self._initial_lr = [pg["lr"] for pg in self.optimizer.param_groups]
 
     def synthesize(
         self,
         max_iter: int = 100,
-        optimizer: torch.optim.Optimizer | None = None,
-        scheduler: torch.optim.lr_scheduler.LRScheduler | None = None,
         store_progress: bool | int = False,
         stop_criterion: float = 1e-4,
         stop_iters_to_check: int = 50,
@@ -673,7 +720,7 @@ def synthesize(
     ):
         r"""Synthesize a metamer.
 
-        Update the pixels of ``initial_image`` until its representation matches
+        Update the pixels of ``metamer`` until its representation matches
         that of ``image``.
 
         We run this until either we reach ``max_iter`` or the change over the
@@ -685,13 +732,6 @@ def synthesize(
         max_iter :
             The maximum number of iterations to run before we end synthesis
             (unless we hit the stop criterion).
-        optimizer :
-            The optimizer to use. If None and this is the first time calling
-            synthesize, we use Adam(lr=.01, amsgrad=True); if synthesize has
-            been called before, this must be None and we reuse the previous
-            optimizer.
-        scheduler :
-            The learning rate scheduler to use. If None, we don't use one.
         store_progress :
             Whether we should store the metamer image in progress on every
             iteration. If False, we don't save anything. If True, we save every
@@ -723,8 +763,9 @@ def synthesize(
                 "change_scale_criterion, or things get weird!"
             )
 
-        # initialize the optimizer and scheduler
-        self._initialize_optimizer(optimizer, scheduler)
+        # if setup hasn't been called manually, call it now.
+        if self._metamer is None or isinstance(self._scheduler, tuple):
+            self.setup()
 
         # get ready to store progress
         self.store_progress = store_progress
@@ -808,8 +849,8 @@ def _optimizer_step(
                 self._scales_timing[self.scales[0]].append(len(self.losses))
 
             # Reset optimizer's learning rate
-            for pg in self.optimizer.param_groups:
-                pg["lr"] = pg["initial_lr"]
+            for pg, lr in zip(self.optimizer.param_groups, self._initial_lr):
+                pg["lr"] = lr
 
             # Reset ctf target representation for the next update
             self._ctf_target_representation = None
diff --git a/src/plenoptic/synthesize/synthesis.py b/src/plenoptic/synthesize/synthesis.py
index 64cbe9b72..1262e8299 100644
--- a/src/plenoptic/synthesize/synthesis.py
+++ b/src/plenoptic/synthesize/synthesis.py
@@ -3,6 +3,7 @@
 import abc
 import importlib
 import inspect
+import warnings
 
 import numpy as np
 import torch
@@ -35,6 +36,10 @@ class Synthesis(abc.ABC):
 
     """
 
+    def __init__(self):
+        # flag to raise more informative error message if setup and load are both called
+        self._loaded = False
+
     @abc.abstractmethod
     def synthesize(self):
         r"""Synthesize something."""
@@ -159,12 +164,12 @@ def load(
             ``torch.load``, see that function's docstring for details.
 
         """
-        empty_on_init_attr = getattr(self, empty_on_init_attr)
         check_str = (
             "\n\nIf this is confusing, try calling "
             f"{_get_name(examine_saved_synthesis)}('{file_path}'),"
             " to examine saved object"
         )
+        empty_on_init_attr = getattr(self, empty_on_init_attr)
         if empty_on_init_attr is not None and len(empty_on_init_attr) > 0:
             raise ValueError(
                 "load can only be called with a just-initialized"
@@ -281,6 +286,19 @@ def load(
                 # if init_attr is None, then we haven't set it yet, so we set the saved
                 # tuple as the attribute, and handle this later
             setattr(self, k, v)
+        setup_attrs = []
+        optim = tmp_dict.get("_optimizer", None)
+        if isinstance(optim, tuple) and optim[0] != _get_name(torch.optim.Adam):
+            setup_attrs.append("optimizer")
+        sched = tmp_dict.get("_scheduler", None)
+        if isinstance(sched, tuple) and sched[0] is not None:
+            setup_attrs.append("scheduler")
+        if setup_attrs:
+            warnings.warn(
+                f"You will need to call setup() to instantiate {', '.join(setup_attrs)}"
+            )
+        # Make sure we specify that we have loaded and setup has not been called
+        self._loaded = True
 
     @abc.abstractmethod
     def to(self, *args, attrs: list[str] = [], **kwargs):
@@ -360,6 +378,7 @@ def __init__(
         allowed_range: tuple[float, float] = (0, 1),
     ):
         """Initialize the properties of OptimizedSynthesis."""
+        super().__init__()
         self._losses = []
         self._gradient_norm = []
         self._pixel_change_norm = []
@@ -371,7 +390,7 @@ def __init__(
         self._allowed_range = allowed_range
 
     @abc.abstractmethod
-    def _initialize(self):
+    def setup(self):
         r"""What to start synthesis with."""
         pass
 
@@ -415,23 +434,22 @@ def _closure(self) -> torch.Tensor:
     def _initialize_optimizer(
         self,
         optimizer: torch.optim.Optimizer | None,
-        synth_name: str,
+        synth_attr: torch.Tensor,
+        optimizer_kwargs: dict | None = None,
         learning_rate: float = 0.01,
     ):
         """Initialize optimizer.
 
-        First time this is called, optimizer can be:
+        optimizer can be:
 
         - None, in which case we create an Adam optimizer with amsgrad=True and
-          ``lr=learning_rate`` with a single parameter, the synthesis attribute
+          ``lr=learning_rate``
 
-        - torch.optim.Optimizer, in which case it must already have the
+        - uninitialized torch.optim.Optimizer, in which case it must already have the
           synthesis attribute (e.g., metamer) as its only parameter.
 
-        The synthesis attribute is the one with the name ``synth_name``
-
-        Every subsequent time (so, when resuming synthesis), optimizer must be
-        None (and we use the original optimizer object).
+        In either case, we then initialize the optimizer with a single parameter, the
+        synthesis attribute, ``synth_attr``
 
         If we have loaded from a save state, self.optimizer will be a tuple with the
         name of the optimizer class (e.g., torch.optim.adam.Adam) and the
@@ -441,76 +459,62 @@ def _initialize_optimizer(
           state_dict.
 
         - else, the saved and user-specified optimizers must have the same class name
-          and we load the state_dict.
+          and we load the state_dict. in this case, ``optimizer_kwargs`` must be None
 
         """
-        synth_attr = getattr(self, synth_name)
-        if optimizer is None:
-            if self.optimizer is None:
-                self._optimizer = torch.optim.Adam(
-                    [synth_attr], lr=learning_rate, amsgrad=True
+        if isinstance(self.optimizer, tuple):
+            # then we're calling this after load()
+            if optimizer_kwargs is not None:
+                raise ValueError(
+                    "When initializing optimizer after load, optimizer_kwargs"
+                    " must be None!"
                 )
-            elif isinstance(self.optimizer, tuple):
-                # then this comes from loading
-                if self._optimizer[0] != _get_name(torch.optim.Adam):
-                    raise TypeError(
-                        "Don't know how to initialize saved optimizer "
-                        f"'{self._optimizer[0]}'! Pass an initialized version of "
-                        "this optimizer to `synthesize`, and we will update its "
-                        "state_dict."
-                    )
-                state_dict = self.optimizer[1]
-                self._optimizer = torch.optim.Adam([synth_attr])
-                self._optimizer.load_state_dict(state_dict)
+            if optimizer is None:
+                optimizer = torch.optim.Adam
+                err_str = (
+                    "Don't know how to initialize saved optimizer "
+                    f"'{self._optimizer[0]}'! Pass an un-initialized version of "
+                    "this optimizer to `setup`, and we will update its "
+                    "state_dict."
+                )
+            else:
+                err_str = (
+                    "User-specified optimizer must have same type as saved "
+                    "optimizer, but got:"
+                    f"\nSaved: {self.optimizer[0]}, "
+                    f"\nUser-specified: {_get_name(optimizer)}."
+                )
+
+            if self._optimizer[0] != _get_name(optimizer):
+                raise ValueError(err_str)
+            state_dict = self.optimizer[1]
+            self._optimizer = optimizer([synth_attr])
+            self._optimizer.load_state_dict(state_dict)
         else:
-            if self.optimizer is not None and not isinstance(self.optimizer, tuple):
-                raise TypeError("When resuming synthesis, optimizer arg must be None!")
-            params = optimizer.param_groups[0]["params"]
-            if len(params) != 1 or not torch.equal(params[0], synth_attr):
-                # then the optimizer is not updating the right target, which means they
-                # initialized it wrong.
-                if not isinstance(self.optimizer, tuple):
-                    raise ValueError(
-                        f"For {synth_name} synthesis, optimizer must have one "
-                        f"parameter, the {synth_name} we're synthesizing."
-                    )
-                else:
-                    # a totally possible way this could happen is they initialized the
-                    # synthesis object, initialized the optimizer, then called load. if
-                    # you do it in that order, the optimizer target is incorrect
-                    raise ValueError(
-                        f"Optimizer parameter does not match self.{synth_name}. Did "
-                        "you initialize this optimizer object before calling load? "
-                        "Optimizer objects must be initialized after calling load"
-                    )
-            if isinstance(self.optimizer, tuple):
-                if self._optimizer[0] != _get_name(optimizer):
-                    raise ValueError(
-                        "User-specified optimizer must have same type as saved "
-                        "optimizer, but got:"
-                        f"\nSaved: {self.optimizer[0]}, "
-                        f"\nUser-specified: {_get_name(optimizer)}."
-                    )
-                state_dict = self.optimizer[1]
-                self._optimizer = optimizer
-                self._optimizer.load_state_dict(state_dict)
-            self._optimizer = optimizer
+            if optimizer_kwargs is None:
+                optimizer_kwargs = {"lr": learning_rate}
+            else:
+                optimizer_kwargs.setdefault("lr", learning_rate)
+            if optimizer is None:
+                optimizer_kwargs.setdefault("amsgrad", True)
+                self._optimizer = torch.optim.Adam([synth_attr], **optimizer_kwargs)
+            else:
+                self._optimizer = optimizer([synth_attr], **optimizer_kwargs)
 
     def _initialize_scheduler(
         self,
-        scheduler: torch.optim.lr_scheduler.LRScheduler,
+        scheduler: torch.optim.lr_scheduler.LRScheduler | None,
+        optimizer: torch.optim.Optimizer,
+        scheduler_kwargs: dict | None = None,
     ):
         """Initialize scheduler.
 
-        First time this is called, scheduler can be:
+        scheduler can be:
 
         - None, in which case we do nothing.
 
-        - torch.optim.lr_scheduler.LRScheduler in which case its optimizer must match
-          self.optimizer.
-
-        Every subsequent time (so, when resuming synthesis), scheduler must be None (and
-        we use the original scheduler object).
+        - uninitialized torch.optim.lr_scheduler.LRScheduler in which case we initialize
+          it using self.optimizer.
 
         If we have loaded from a save state, self.scheduler will be a tuple with the
         name of the scheduler class (e.g., torch.optim.lr_scheduler.ConstantLR) and the
@@ -519,15 +523,22 @@ def _initialize_scheduler(
         - If scheduler is None, we raise a ValueError.
 
         - else, the saved and user-specified schedulers must have the same class name
-          and we load the state_dict.
+          and we load the state_dict. In this case, ``scheduler_kwargs`` must be None
 
         We also check if scheduler.step takes any arguments beyond self and epoch. If
-        so, we set the `_scheduler_step_arg` flag to True (it was set to False at object
-        initialization). Then, we will pass the loss to scheduler.step when we call it.
+        so, we set the `self._scheduler_step_arg` flag to True (it was set to False at
+        object initialization). Then, we will pass the loss to scheduler.step when we
+        call it.
 
         """
-        if scheduler is None:
-            if isinstance(self.scheduler, tuple):
+        if isinstance(self.scheduler, tuple):
+            # then we're calling this after load()
+            if scheduler_kwargs is not None:
+                raise ValueError(
+                    "When initializing scheduler after load, scheduler_kwargs"
+                    " must be None!"
+                )
+            if scheduler is None:
                 if self.scheduler[0] is not None:
                     # then this comes from loading, and we don't know how to initialize
                     # the scheduler
@@ -536,10 +547,8 @@ def _initialize_scheduler(
                         f"'{self.scheduler[0]}'! Pass an initialized version of this "
                         "scheduler to `synthesize`, and we will update its state_dict."
                     )
-                else:
-                    self._scheduler = None
-        else:
-            if isinstance(self.scheduler, tuple):
+                self._scheduler = None
+            else:
                 if self.scheduler[0] != _get_name(scheduler):
                     raise ValueError(
                         "User-specified scheduler must have same type as saved "
@@ -547,22 +556,17 @@ def _initialize_scheduler(
                         f"\nUser-specified: {_get_name(scheduler)}."
                     )
                 state_dict = self.scheduler[1]
-                self._scheduler = scheduler
+                self._scheduler = scheduler(optimizer)
                 self._scheduler.load_state_dict(state_dict)
-            elif self.scheduler is not None:
-                raise TypeError("When resuming synthesis, scheduler arg must be None!")
-            if self.optimizer is not scheduler.optimizer:
-                raise ValueError(
-                    "Scheduler's optimizer must match that of this "
-                    f"{self.__class__.__name__} but got two different optimizers! Did "
-                    "you initialize this scheduler object before calling load? "
-                    "Schedulers and optimizers must be optimized after calling load"
-                )
-            self._scheduler = scheduler
-            step_args = set(inspect.getfullargspec(self.scheduler.step).args)
-            if len(step_args - {"self", "epoch"}):
-                # then we do want to pass the loss to scheduler.step
-                self._scheduler_step_arg = True
+        else:
+            if scheduler_kwargs is None:
+                scheduler_kwargs = {}
+            if scheduler is not None:
+                self._scheduler = scheduler(optimizer, **scheduler_kwargs)
+                step_args = set(inspect.getfullargspec(self.scheduler.step).args)
+                if len(step_args - {"self", "epoch"}):
+                    # then we do want to pass the loss to scheduler.step
+                    self._scheduler_step_arg = True
 
     @property
     def range_penalty_lambda(self):
diff --git a/tests/test_geodesic.py b/tests/test_geodesic.py
index ee2d231dc..f2752003a 100644
--- a/tests/test_geodesic.py
+++ b/tests/test_geodesic.py
@@ -160,10 +160,12 @@ class TestGeodesic:
     @pytest.mark.parametrize("n_steps", [5, 10])
     def test_texture(self, einstein_img_small, model, init, optimizer, n_steps):
         sequence = po.tools.translation_sequence(einstein_img_small, n_steps)
-        moog = po.synth.Geodesic(sequence[:1], sequence[-1:], model, n_steps, init)
+        moog = po.synth.Geodesic(sequence[:1], sequence[-1:], model, n_steps)
+        optimizer = None
         if optimizer == "SGD":
-            optimizer = torch.optim.SGD([moog._geodesic], lr=0.01)
-        moog.synthesize(max_iter=5, optimizer=optimizer)
+            optimizer = torch.optim.SGD
+        moog.setup(init, optimizer=optimizer)
+        moog.synthesize(max_iter=5)
         po.synth.geodesic.plot_loss(moog)
         po.synth.geodesic.plot_deviation_from_line(moog, natural_video=sequence)
         moog.calculate_jerkiness()
@@ -175,7 +177,6 @@ def test_endpoints_dont_change(self, einstein_small_seq, model):
             einstein_small_seq[-1:],
             model,
             5,
-            "straight",
         )
         moog.synthesize(max_iter=5)
         assert torch.equal(moog.geodesic[0], einstein_small_seq[0]), (
@@ -199,7 +200,6 @@ def test_endpoints_dont_change(self, einstein_small_seq, model):
             "img_b",
             "model",
             "n_steps",
-            "init",
             "range_penalty",
             "allowed_range",
         ],
@@ -212,13 +212,11 @@ def test_save_load(
         img_a = einstein_small_seq[:1]
         img_b = einstein_small_seq[-1:]
         n_steps = 3
-        init = "straight"
         moog = po.synth.Geodesic(
             img_a,
             img_b,
             model,
             n_steps,
-            init,
             range_penalty_lambda=range_penalty,
             allowed_range=allowed_range,
         )
@@ -254,12 +252,6 @@ def test_save_load(
                     ValueError,
                     match="Saved and initialized n_steps are different",
                 )
-            elif fail == "init":
-                init = "bridge"
-                expectation = pytest.raises(
-                    ValueError,
-                    match=("Saved and initialized initial_sequence are different"),
-                )
             elif fail == "allowed_range":
                 allowed_range = (0, 5)
                 expectation = pytest.raises(
@@ -277,7 +269,6 @@ def test_save_load(
                 img_b,
                 model,
                 n_steps,
-                init,
                 range_penalty_lambda=range_penalty,
                 allowed_range=allowed_range,
             )
@@ -292,7 +283,6 @@ def test_save_load(
                 img_b,
                 model,
                 n_steps,
-                init,
                 range_penalty_lambda=range_penalty,
                 allowed_range=allowed_range,
             )
@@ -309,6 +299,60 @@ def test_save_load(
             # check that can resume
             moog_copy.synthesize(max_iter=4)
 
+    @pytest.mark.parametrize(
+        "model", ["frontend.LinearNonlinear.nograd"], indirect=True
+    )
+    @pytest.mark.parametrize("seq", ["straight", "bridge", "FAIL"])
+    def test_setup_initial_seq(self, einstein_img, model, seq):
+        geod = po.synth.Geodesic(einstein_img, einstein_img / 2, model)
+        if seq == "FAIL":
+            expectation = pytest.raises(
+                ValueError, match="Don't know how to handle initial_"
+            )
+        else:
+            expectation = does_not_raise()
+        with expectation:
+            geod.setup(seq)
+            geod.synthesize(5)
+
+    @pytest.mark.parametrize(
+        "model", ["frontend.LinearNonlinear.nograd"], indirect=True
+    )
+    def test_setup_fail(self, einstein_img, model):
+        geod = po.synth.Geodesic(einstein_img, einstein_img / 2, model)
+        geod.setup()
+        with pytest.raises(ValueError, match="setup\(\) can only be called once"):
+            geod.setup()
+
+    @pytest.mark.parametrize(
+        "model", ["frontend.LinearNonlinear.nograd"], indirect=True
+    )
+    def test_synth_then_setup(self, einstein_img, model, tmp_path):
+        geod = po.synth.Geodesic(einstein_img, einstein_img / 2, model)
+        geod.setup(optimizer=torch.optim.SGD)
+        geod.synthesize(max_iter=4)
+        geod.save(op.join(tmp_path, "test_geodesic_synth_then_setup.pt"))
+        geod = po.synth.Geodesic(einstein_img, einstein_img / 2, model)
+        geod.load(op.join(tmp_path, "test_geodesic_synth_then_setup.pt"))
+        with pytest.raises(ValueError, match="Don't know how to initialize"):
+            geod.synthesize(5)
+        geod.setup(optimizer=torch.optim.SGD)
+        geod.synthesize(5)
+
+    @pytest.mark.parametrize(
+        "model", ["frontend.LinearNonlinear.nograd"], indirect=True
+    )
+    def test_setup_load_fail(self, einstein_img, model, tmp_path):
+        geod = po.synth.Geodesic(einstein_img, einstein_img / 2, model)
+        geod.synthesize(max_iter=4)
+        geod.save(op.join(tmp_path, "test_geodesic_setup_load_fail.pt"))
+        geod = po.synth.Geodesic(einstein_img, einstein_img / 2, model)
+        geod.load(op.join(tmp_path, "test_geodesic_setup_load_fail.pt"))
+        with pytest.raises(
+            ValueError, match="Cannot set initial_sequence after calling load"
+        ):
+            geod.setup(po.data.curie())
+
     @pytest.mark.parametrize(
         "model", ["frontend.LinearNonlinear.nograd"], indirect=True
     )
@@ -428,58 +472,73 @@ def test_load_attributes(self, einstein_img, model, attribute, tmp_path):
     @pytest.mark.parametrize(
         "model", ["frontend.LinearNonlinear.nograd"], indirect=True
     )
-    @pytest.mark.parametrize("optim_opts", [None, "SGD", "Adam"])
+    @pytest.mark.parametrize(
+        "optim_opts", [None, "SGD", "SGD-args", "Adam", "Adam-args"]
+    )
     @pytest.mark.parametrize("fail", [True, False])
     def test_load_optimizer(self, curie_img, model, optim_opts, fail, tmp_path):
         geod = po.synth.Geodesic(curie_img, curie_img / 2, model)
         optimizer = None
+        optimizer_kwargs = None
+        check_optimizer = [torch.optim.Adam, {"eps": 1e-8, "lr": 0.001}]
         if optim_opts is not None:
-            if optim_opts == "Adam":
-                optimizer = torch.optim.Adam([geod._geodesic])
-            elif optim_opts == "SGD":
-                optimizer = torch.optim.SGD([geod._geodesic])
-        geod.synthesize(max_iter=5, optimizer=optimizer)
+            if "Adam" in optim_opts:
+                optimizer = torch.optim.Adam
+            elif "SGD" in optim_opts:
+                optimizer = torch.optim.SGD
+                check_optimizer[0] = torch.optim.SGD
+                check_optimizer[1] = {"lr": 0.001}
+            if "args" in optim_opts:
+                optimizer_kwargs = {"lr": 1}
+                check_optimizer[1] = {"lr": 1}
+        geod.setup(optimizer=optimizer, optimizer_kwargs=optimizer_kwargs)
+        geod.synthesize(max_iter=5)
         geod.save(op.join(tmp_path, "test_geodesic_optimizer.pt"))
         geod = po.synth.Geodesic(curie_img, curie_img / 2, model)
         geod.load(op.join(tmp_path, "test_geodesic_optimizer.pt"))
+        optimizer_kwargs = None
         if not fail:
             if optim_opts is not None:
-                if optim_opts == "Adam":
-                    optimizer = torch.optim.Adam([geod._geodesic])
-                elif optim_opts == "SGD":
-                    optimizer = torch.optim.SGD([geod._geodesic])
+                if "Adam" in optim_opts:
+                    optimizer = torch.optim.Adam
+                elif "SGD" in optim_opts:
+                    optimizer = torch.optim.SGD
             expectation = does_not_raise()
         else:
             expect_str = "User-specified optimizer must have same type"
-            err = ValueError
             if optim_opts is None:
-                optimizer = torch.optim.SGD([geod._geodesic])
+                optimizer = torch.optim.SGD
             else:
                 if optim_opts == "Adam":
-                    optimizer = torch.optim.SGD([geod._geodesic])
+                    optimizer = torch.optim.SGD
+                elif optim_opts == "Adam-args":
+                    optimizer = torch.optim.Adam
+                    optimizer_kwargs = {"lr": 1}
+                    expect_str = (
+                        "When initializing optimizer after load, optimizer_kwargs"
+                    )
                 elif optim_opts == "SGD":
                     optimizer = None
-                    err = TypeError
                     expect_str = "Don't know how to initialize saved optimizer"
-            expectation = pytest.raises(err, match=expect_str)
-        # these don't fail until we call synthesize
+                elif optim_opts == "SGD-args":
+                    optimizer = torch.optim.SGD
+                    optimizer_kwargs = {"lr": 1}
+                    expect_str = (
+                        "When initializing optimizer after load, optimizer_kwargs"
+                    )
+            expectation = pytest.raises(ValueError, match=expect_str)
         with expectation:
-            geod.synthesize(max_iter=5, optimizer=optimizer)
-
-    @pytest.mark.parametrize(
-        "model", ["frontend.LinearNonlinear.nograd"], indirect=True
-    )
-    def test_load_optim_wrong_time(self, curie_img, model, tmp_path):
-        geod = po.synth.Geodesic(curie_img, curie_img / 2, model)
-        optimizer = torch.optim.Adam([geod._geodesic])
-        geod.synthesize(max_iter=5, optimizer=optimizer)
-        geod.save(op.join(tmp_path, "test_geodesic_optim_wrong_time.pt"))
-        geod = po.synth.Geodesic(curie_img, curie_img / 2, model)
-        optimizer = torch.optim.Adam([geod._geodesic])
-        expect_str = "Did you initialize this optimizer object before calling load"
-        geod.load(op.join(tmp_path, "test_geodesic_optim_wrong_time.pt"))
-        with pytest.raises(ValueError, match=expect_str):
-            geod.synthesize(max_iter=5, optimizer=optimizer)
+            geod.setup(optimizer=optimizer, optimizer_kwargs=optimizer_kwargs)
+            geod.synthesize(max_iter=5)
+            if not isinstance(geod.optimizer, check_optimizer[0]):
+                raise ValueError("Didn't properly set optimizer!")
+            state_dict = geod.optimizer.state_dict()["param_groups"][0]
+            for k, v in check_optimizer[1].items():
+                if state_dict[k] != v:
+                    raise ValueError(
+                        "Didn't properly set optimizer kwargs! "
+                        f"Expected {v} but got {state_dict[k]}!"
+                    )
 
     @pytest.mark.parametrize(
         "model", ["frontend.LinearNonlinear.nograd"], indirect=True
@@ -490,17 +549,17 @@ def test_resume_synthesis(self, einstein_img, model, load, tmp_path):
         # Adam has some stochasticity in its initialization(?), so this test doesn't
         # quite work with it (it does if you do po.tools.set_seed(2) at the top of the
         # function)
-        optim = torch.optim.SGD([geod._geodesic])
-        geod.synthesize(10, optimizer=optim)
+        geod.setup(optimizer=torch.optim.SGD)
+        geod.synthesize(10)
         geod_copy = po.synth.Geodesic(einstein_img, einstein_img / 2, model)
-        optim = torch.optim.SGD([geod_copy._geodesic])
-        geod_copy.synthesize(5, optimizer=optim)
+        geod_copy.setup(optimizer=torch.optim.SGD)
+        geod_copy.synthesize(5)
         if load:
             geod_copy.save(op.join(tmp_path, "test_geodesic_resume_synthesis.pt"))
             geod_copy = po.synth.Geodesic(einstein_img, einstein_img / 2, model)
             geod_copy.load(op.join(tmp_path, "test_geodesic_resume_synthesis.pt"))
-            optim = torch.optim.SGD([geod_copy._geodesic])
-            geod_copy.synthesize(5, optimizer=optim)
+            geod_copy.setup(optimizer=torch.optim.SGD)
+            geod_copy.synthesize(5)
         else:
             geod_copy.synthesize(5)
         if not torch.allclose(geod.geodesic, geod_copy.geodesic):
@@ -516,6 +575,45 @@ def test_model_dimensionality(self, einstein_img, model):
         geod = po.synth.Geodesic(einstein_img, einstein_img / 2, model)
         geod.synthesize(5)
 
+    @pytest.mark.parametrize(
+        "model", ["frontend.LinearNonlinear.nograd"], indirect=True
+    )
+    @pytest.mark.parametrize(
+        "optimizer", ["SGD", "SGD-args", "Adam", "Adam-args", None]
+    )
+    def test_optimizer(self, einstein_img, model, optimizer):
+        geod = po.synth.Geodesic(einstein_img, einstein_img / 2, model)
+        optimizer = None
+        optimizer_kwargs = None
+        check_optimizer = [torch.optim.Adam, {"eps": 1e-8, "lr": 0.001}]
+        if optimizer == "Adam":
+            optimizer = torch.optim.Adam
+        elif optimizer == "Adam-args":
+            optimizer = torch.optim.Adam
+            optimizer_kwargs = {"eps": 1e-5}
+            check_optimizer[1]["eps"] = 1e-5
+        elif optimizer == "SGD":
+            optimizer = torch.optim.SGD
+            check_optimizer = [torch.optim.SGD, {"lr": 0.001}]
+        elif optimizer == "SGD-args":
+            optimizer = torch.optim.SGD
+            optimizer_kwargs = {"lr": 1}
+            check_optimizer = [torch.optim.SGD, {"lr": 1}]
+        geod.setup(
+            optimizer=optimizer,
+            optimizer_kwargs=optimizer_kwargs,
+        )
+        geod.synthesize(max_iter=5)
+        if not isinstance(geod.optimizer, check_optimizer[0]):
+            raise ValueError("Didn't properly set optimizer!")
+        state_dict = geod.optimizer.state_dict()["param_groups"][0]
+        for k, v in check_optimizer[1].items():
+            if state_dict[k] != v:
+                raise ValueError(
+                    "Didn't properly set optimizer kwargs! "
+                    f"Expected {v} but got {state_dict[k]}!"
+                )
+
     @pytest.mark.skipif(DEVICE.type == "cpu", reason="Only makes sense to test on cuda")
     @pytest.mark.parametrize("model", ["Identity"], indirect=True)
     def test_map_location(self, einstein_small_seq, model, tmp_path):
@@ -586,6 +684,7 @@ def test_funcs_external_tensor(self, einstein_small_seq, model, func):
         moog = po.synth.Geodesic(
             einstein_small_seq[:1], einstein_small_seq[-1:], model, 5
         )
+        moog.setup()
         no_arg = getattr(moog, func)()
         arg_tensor = torch.rand_like(moog.geodesic)
         # calculate jerkiness requires tensor to have gradient attached
diff --git a/tests/test_mad.py b/tests/test_mad.py
index 0fe51ab23..4aeb8fc2e 100644
--- a/tests/test_mad.py
+++ b/tests/test_mad.py
@@ -192,6 +192,52 @@ def test_save_load(
             # since this is a fixture, get this back to a grayscale image
             curie_img = curie_img.mean(1, True)
 
+    def test_setup_initial_noise(self, einstein_img):
+        mad = po.synth.MADCompetition(
+            einstein_img, po.metric.mse, po.tools.optim.l2_norm, "min"
+        )
+        mad.setup(0.5)
+        mad.synthesize(5)
+
+    def test_setup_fail(self, einstein_img):
+        mad = po.synth.MADCompetition(
+            einstein_img, po.metric.mse, po.tools.optim.l2_norm, "min"
+        )
+        mad.setup()
+        with pytest.raises(ValueError, match="setup\(\) can only be called once"):
+            mad.setup()
+
+    def test_setup_load_fail(self, einstein_img, tmp_path):
+        mad = po.synth.MADCompetition(
+            einstein_img, po.metric.mse, po.tools.optim.l2_norm, "min"
+        )
+        mad.synthesize(max_iter=4)
+        mad.save(op.join(tmp_path, "test_mad_setup_load_fail.pt"))
+        mad = po.synth.MADCompetition(
+            einstein_img, po.metric.mse, po.tools.optim.l2_norm, "min"
+        )
+        mad.load(op.join(tmp_path, "test_mad_setup_load_fail.pt"))
+        with pytest.raises(
+            ValueError, match="Cannot set initial_noise after calling load"
+        ):
+            mad.setup(0.5)
+
+    def test_synth_then_setup(self, einstein_img, tmp_path):
+        mad = po.synth.MADCompetition(
+            einstein_img, po.metric.mse, po.tools.optim.l2_norm, "min"
+        )
+        mad.setup(optimizer=torch.optim.SGD)
+        mad.synthesize(max_iter=4)
+        mad.save(op.join(tmp_path, "test_mad_synth_then_setup.pt"))
+        mad = po.synth.MADCompetition(
+            einstein_img, po.metric.mse, po.tools.optim.l2_norm, "min"
+        )
+        mad.load(op.join(tmp_path, "test_mad_synth_then_setup.pt"))
+        with pytest.raises(ValueError, match="Don't know how to initialize"):
+            mad.synthesize(5)
+        mad.setup(optimizer=torch.optim.SGD)
+        mad.synthesize(5)
+
     def test_load_init_fail(self, einstein_img, tmp_path):
         mad = po.synth.MADCompetition(
             einstein_img, po.metric.mse, po.tools.optim.l2_norm, "min"
@@ -214,7 +260,7 @@ def test_examine_saved_object(self, einstein_img, tmp_path):
     @pytest.mark.parametrize(
         "model", ["frontend.LinearNonlinear.nograd"], indirect=True
     )
-    @pytest.mark.parametrize("synth_type", ["eig", "mad"])
+    @pytest.mark.parametrize("synth_type", ["eig", "met"])
     def test_load_object_type(self, einstein_img, model, synth_type, tmp_path):
         mad = po.synth.MADCompetition(
             einstein_img, po.metric.mse, po.tools.optim.l2_norm, "min"
@@ -223,7 +269,7 @@ def test_load_object_type(self, einstein_img, model, synth_type, tmp_path):
         mad.save(op.join(tmp_path, "test_mad_load_object_type.pt"))
         if synth_type == "eig":
             mad = po.synth.Eigendistortion(einstein_img, model)
-        elif synth_type == "mad":
+        elif synth_type == "met":
             mad = po.synth.Metamer(einstein_img, model)
         with pytest.raises(
             ValueError, match="Saved object was a.* but initialized object is"
@@ -314,7 +360,10 @@ def test_load_attributes(self, einstein_img, model, attribute, tmp_path):
     @pytest.mark.parametrize(
         "model", ["frontend.LinearNonlinear.nograd"], indirect=True
     )
-    @pytest.mark.parametrize("optim_opts", [None, "SGD", "Adam", "Scheduler"])
+    @pytest.mark.parametrize(
+        "optim_opts",
+        [None, "SGD", "SGD-args", "Adam", "Adam-args", "Scheduler", "Scheduler-args"],
+    )
     @pytest.mark.parametrize("fail", [True, False])
     def test_load_optimizer(self, curie_img, model, optim_opts, fail, tmp_path):
         mad = po.synth.MADCompetition(
@@ -322,91 +371,181 @@ def test_load_optimizer(self, curie_img, model, optim_opts, fail, tmp_path):
         )
         scheduler = None
         optimizer = None
+        optimizer_kwargs = None
+        scheduler_kwargs = None
+        check_optimizer = [torch.optim.Adam, {"eps": 1e-8, "lr": 0.01}]
+        check_scheduler = None
         if optim_opts is not None:
-            if optim_opts in ["Adam", "Scheduler"]:
-                optimizer = torch.optim.Adam([mad.mad_image])
-            elif optim_opts == "SGD":
-                optimizer = torch.optim.SGD([mad.mad_image])
-            if optim_opts == "Scheduler":
-                scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
-        mad.synthesize(max_iter=5, optimizer=optimizer, scheduler=scheduler)
+            if "Scheduler" in optim_opts:
+                scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau
+                check_scheduler = [
+                    torch.optim.lr_scheduler.ReduceLROnPlateau,
+                    {"factor": 0.1},
+                ]
+                if "args" in optim_opts:
+                    scheduler_kwargs = {"factor": 1e-3}
+                    check_scheduler[1] = {"factor": 1e-3}
+            else:
+                if "Adam" in optim_opts:
+                    optimizer = torch.optim.Adam
+                elif "SGD" in optim_opts:
+                    optimizer = torch.optim.SGD
+                    check_optimizer[0] = torch.optim.SGD
+                    check_optimizer[1] = {"lr": 0.01}
+                if "args" in optim_opts:
+                    optimizer_kwargs = {"lr": 1}
+                    check_optimizer[1] = {"lr": 1}
+        mad.setup(
+            optimizer=optimizer,
+            scheduler=scheduler,
+            optimizer_kwargs=optimizer_kwargs,
+            scheduler_kwargs=scheduler_kwargs,
+        )
+        mad.synthesize(max_iter=5)
         mad.save(op.join(tmp_path, "test_mad_optimizer.pt"))
         mad = po.synth.MADCompetition(
             curie_img, po.metric.mse, po.tools.optim.l2_norm, "min"
         )
         mad.load(op.join(tmp_path, "test_mad_optimizer.pt"))
+        optimizer_kwargs = None
+        scheduler_kwargs = None
         if not fail:
             if optim_opts is not None:
-                if optim_opts in ["Adam", "Scheduler"]:
-                    optimizer = torch.optim.Adam([mad.mad_image])
-                elif optim_opts == "SGD":
-                    optimizer = torch.optim.SGD([mad.mad_image])
-                if optim_opts == "Scheduler":
-                    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
+                if "Adam" in optim_opts:
+                    optimizer = torch.optim.Adam
+                elif "SGD" in optim_opts:
+                    optimizer = torch.optim.SGD
+                if "Scheduler" in optim_opts:
+                    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau
             expectation = does_not_raise()
         else:
             expect_str = "User-specified optimizer must have same type"
-            err = ValueError
             if optim_opts is None:
-                optimizer = torch.optim.SGD([mad.mad_image])
+                optimizer = torch.optim.SGD
             else:
                 if optim_opts == "Adam":
-                    optimizer = torch.optim.SGD([mad.mad_image])
+                    optimizer = torch.optim.SGD
+                elif optim_opts == "Adam-args":
+                    optimizer = torch.optim.Adam
+                    optimizer_kwargs = {"lr": 1}
+                    expect_str = (
+                        "When initializing optimizer after load, optimizer_kwargs"
+                    )
                 elif optim_opts == "SGD":
                     optimizer = None
-                    err = TypeError
                     expect_str = "Don't know how to initialize saved optimizer"
+                elif optim_opts == "SGD-args":
+                    optimizer = torch.optim.SGD
+                    optimizer_kwargs = {"lr": 1}
+                    expect_str = (
+                        "When initializing optimizer after load, optimizer_kwargs"
+                    )
                 elif optim_opts == "Scheduler":
-                    optimizer = torch.optim.Adam([mad.mad_image])
-                    scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
+                    scheduler = torch.optim.lr_scheduler.ConstantLR
                     expect_str = "User-specified scheduler must have same type"
-            expectation = pytest.raises(err, match=expect_str)
-        # these don't fail until we call synthesize
+                elif optim_opts == "Scheduler-args":
+                    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau
+                    scheduler_kwargs = {"factor": 1e-3}
+                    expect_str = (
+                        "When initializing scheduler after load, scheduler_kwargs"
+                    )
+            expectation = pytest.raises(ValueError, match=expect_str)
+        # these fail during setup
         with expectation:
-            mad.synthesize(max_iter=5, optimizer=optimizer, scheduler=scheduler)
+            mad.setup(
+                optimizer=optimizer,
+                scheduler=scheduler,
+                optimizer_kwargs=optimizer_kwargs,
+                scheduler_kwargs=scheduler_kwargs,
+            )
+            mad.synthesize(max_iter=5)
+            if not isinstance(mad.optimizer, check_optimizer[0]):
+                raise ValueError("Didn't properly set optimizer!")
+            state_dict = mad.optimizer.state_dict()["param_groups"][0]
+            for k, v in check_optimizer[1].items():
+                if state_dict[k] != v:
+                    raise ValueError(
+                        "Didn't properly set optimizer kwargs! "
+                        f"Expected {v} but got {state_dict[k]}!"
+                    )
+            if check_scheduler is not None:
+                if not isinstance(mad.scheduler, check_scheduler[0]):
+                    raise ValueError("Didn't properly set scheduler!")
+                state_dict = mad.scheduler.state_dict()
+                for k, v in check_scheduler[1].items():
+                    if mad.scheduler.state_dict()[k] != v:
+                        raise ValueError("Didn't properly set scheduler kwargs!")
+            elif mad.scheduler is not None:
+                raise ValueError("Didn't set scheduler to None!")
 
     @pytest.mark.parametrize(
-        "model", ["frontend.LinearNonlinear.nograd"], indirect=True
+        "optimizer",
+        ["SGD", "SGD-args", "Adam", "Adam-args", None, "Scheduler-args", "Scheduler"],
     )
-    @pytest.mark.parametrize("fail", ["optim", "sched"])
-    def test_load_optim_wrong_time(self, curie_img, model, fail, tmp_path):
-        mad = po.synth.MADCompetition(
-            curie_img, po.metric.mse, po.tools.optim.l2_norm, "min"
-        )
-        optimizer = torch.optim.Adam([mad.mad_image])
-        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
-        mad.synthesize(max_iter=5, optimizer=optimizer, scheduler=scheduler)
-        mad.save(op.join(tmp_path, "test_mad_optim_wrong_time.pt"))
-        mad = po.synth.MADCompetition(
-            curie_img, po.metric.mse, po.tools.optim.l2_norm, "min"
-        )
-        if fail == "optim":
-            optimizer = torch.optim.Adam([mad.mad_image])
-            expect_str = "Did you initialize this optimizer object before calling load"
-        elif fail == "sched":
-            # use old optimizer object
-            scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
-            expect_str = "Did you initialize this scheduler object before calling load"
-        mad.load(op.join(tmp_path, "test_mad_optim_wrong_time.pt"))
-        if fail != "optim":
-            optimizer = torch.optim.Adam([mad.mad_image])
-        with pytest.raises(ValueError, match=expect_str):
-            mad.synthesize(max_iter=5, optimizer=optimizer, scheduler=scheduler)
-
-    @pytest.mark.parametrize("optimizer", ["Adam", None, "Scheduler"])
-    def test_optimizer_opts(self, curie_img, optimizer):
+    def test_optimizer(self, curie_img, optimizer):
         mad = po.synth.MADCompetition(
             curie_img,
             po.metric.mse,
             lambda *args: 1 - po.metric.ssim(*args),
             "min",
         )
+        optimizer = None
         scheduler = None
-        if optimizer == "Adam" or optimizer == "Scheduler":
-            optimizer = torch.optim.Adam([mad.mad_image])
-            if optimizer == "Scheduler":
-                scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
-        mad.synthesize(max_iter=5, optimizer=optimizer, scheduler=scheduler)
+        optimizer_kwargs = None
+        scheduler_kwargs = None
+        check_optimizer = [torch.optim.Adam, {"eps": 1e-8, "lr": 0.01}]
+        check_scheduler = None
+        if optimizer == "Adam":
+            optimizer = torch.optim.Adam
+        elif optimizer == "Adam-args":
+            optimizer = torch.optim.Adam
+            optimizer_kwargs = {"eps": 1e-5}
+            check_optimizer[1] = {"eps": 1e-5}
+        elif optimizer == "SGD":
+            optimizer = torch.optim.SGD
+            check_optimizer = [torch.optim.SGD, {"lr": 0.01}]
+        elif optimizer == "SGD-args":
+            optimizer = torch.optim.SGD
+            optimizer_kwargs = {"lr": 1}
+            check_optimizer = [torch.optim.SGD, {"lr": 1}]
+        elif optimizer == "Scheduler":
+            scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau
+            check_scheduler = [
+                torch.optim.lr_scheduler.ReduceLROnPlateau,
+                {"factor": 0.1},
+            ]
+        elif optimizer == "Scheduler-args":
+            scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau
+            scheduler_kwargs = {"factor": 1e-3}
+            check_scheduler = [
+                torch.optim.lr_scheduler.ReduceLROnPlateau,
+                {"factor": 1e-3},
+            ]
+        mad.setup(
+            optimizer=optimizer,
+            scheduler=scheduler,
+            optimizer_kwargs=optimizer_kwargs,
+            scheduler_kwargs=scheduler_kwargs,
+        )
+        mad.synthesize(max_iter=5)
+        if not isinstance(mad.optimizer, check_optimizer[0]):
+            raise ValueError("Didn't properly set optimizer!")
+        state_dict = mad.optimizer.state_dict()["param_groups"][0]
+        for k, v in check_optimizer[1].items():
+            if state_dict[k] != v:
+                raise ValueError(
+                    "Didn't properly set optimizer kwargs! "
+                    f"Expected {v} but got {state_dict[k]}!"
+                )
+        if check_scheduler is not None:
+            if not isinstance(mad.scheduler, check_scheduler[0]):
+                raise ValueError("Didn't properly set scheduler!")
+            state_dict = mad.scheduler.state_dict()
+            for k, v in check_scheduler[1].items():
+                if mad.scheduler.state_dict()[k] != v:
+                    raise ValueError("Didn't properly set scheduler kwargs!")
+        elif mad.scheduler is not None:
+            raise ValueError("Didn't set scheduler to None!")
 
     @pytest.mark.parametrize(
         "metric", [po.metric.mse, ModuleMetric(), NonModuleMetric()]
diff --git a/tests/test_metamers.py b/tests/test_metamers.py
index c278bf5d9..2e5a929bc 100644
--- a/tests/test_metamers.py
+++ b/tests/test_metamers.py
@@ -159,10 +159,82 @@ def test_save_load(
     @pytest.mark.parametrize(
         "model", ["frontend.LinearNonlinear.nograd"], indirect=True
     )
-    def test_load_init_fail(self, einstein_img, model, tmp_path):
+    def test_setup_initial_image(self, einstein_img, curie_img, model):
+        met = po.synth.Metamer(einstein_img, model)
+        met.setup(curie_img)
+        met.synthesize(5)
+
+    @pytest.mark.parametrize(
+        "model", ["frontend.LinearNonlinear.nograd"], indirect=True
+    )
+    @pytest.mark.parametrize("fail", ["shape", "ndim"])
+    def test_setup_initial_image_fail(self, einstein_img, curie_img, model, fail):
+        met = po.synth.Metamer(einstein_img, model)
+        if fail == "shape":
+            img = curie_img[..., :255, :255]
+            expectation = pytest.raises(
+                ValueError, match="initial_image and image must be same size"
+            )
+        else:
+            img = curie_img[0]
+            expectation = pytest.raises(
+                ValueError, match="initial_image must be torch.Size"
+            )
+        with expectation:
+            met.setup(img)
+
+    @pytest.mark.parametrize(
+        "model", ["frontend.LinearNonlinear.nograd"], indirect=True
+    )
+    def test_setup_fail(self, einstein_img, model):
+        met = po.synth.Metamer(einstein_img, model)
+        met.setup()
+        with pytest.raises(ValueError, match="setup\(\) can only be called once"):
+            met.setup()
+
+    @pytest.mark.parametrize(
+        "model", ["frontend.LinearNonlinear.nograd"], indirect=True
+    )
+    def test_synth_then_setup(self, einstein_img, model, tmp_path):
+        met = po.synth.Metamer(einstein_img, model)
+        met.setup(optimizer=torch.optim.SGD)
+        met.synthesize(max_iter=4)
+        met.save(op.join(tmp_path, "test_metamer_synth_then_setup.pt"))
+        met = po.synth.Metamer(einstein_img, model)
+        met.load(op.join(tmp_path, "test_metamer_synth_then_setup.pt"))
+        with pytest.raises(ValueError, match="Don't know how to initialize"):
+            met.synthesize(5)
+        met.setup(optimizer=torch.optim.SGD)
+        met.synthesize(5)
+
+    @pytest.mark.parametrize(
+        "model", ["frontend.LinearNonlinear.nograd"], indirect=True
+    )
+    def test_setup_load_fail(self, einstein_img, model, tmp_path):
+        met = po.synth.Metamer(einstein_img, model)
+        met.synthesize(max_iter=4)
+        met.save(op.join(tmp_path, "test_metamer_setup_load_fail.pt"))
+        met = po.synth.Metamer(einstein_img, model)
+        met.load(op.join(tmp_path, "test_metamer_setup_load_fail.pt"))
+        with pytest.raises(
+            ValueError, match="Cannot set initial_image after calling load"
+        ):
+            met.setup(po.data.curie())
+
+    @pytest.mark.parametrize(
+        "model", ["frontend.LinearNonlinear.nograd"], indirect=True
+    )
+    @pytest.mark.parametrize("fail", ["synth", "setup", "continue"])
+    def test_load_init_fail(self, einstein_img, model, fail, tmp_path):
         met = po.synth.Metamer(einstein_img, model)
         met.synthesize(max_iter=4, store_progress=True)
         met.save(op.join(tmp_path, "test_metamer_load_init_fail.pt"))
+        if fail != "continue":
+            met = po.synth.Metamer(einstein_img, model)
+            if fail == "setup":
+                met.setup()
+            elif fail == "synth":
+                met.synthesize(max_iter=4, store_progress=True)
         with pytest.raises(
             ValueError, match="load can only be called with a just-initialized"
         ):
@@ -288,97 +360,140 @@ def test_load_attributes(self, einstein_img, model, attribute, tmp_path):
     @pytest.mark.parametrize(
         "model", ["frontend.LinearNonlinear.nograd"], indirect=True
     )
-    @pytest.mark.parametrize("optim_opts", [None, "SGD", "Adam", "Scheduler"])
+    @pytest.mark.parametrize(
+        "optim_opts",
+        [None, "SGD", "SGD-args", "Adam", "Adam-args", "Scheduler", "Scheduler-args"],
+    )
     @pytest.mark.parametrize("fail", [True, False])
     def test_load_optimizer(self, curie_img, model, optim_opts, fail, tmp_path):
         met = po.synth.Metamer(curie_img, model)
         scheduler = None
         optimizer = None
+        optimizer_kwargs = None
+        scheduler_kwargs = None
+        check_optimizer = [torch.optim.Adam, {"eps": 1e-8, "lr": 0.01}]
+        check_scheduler = None
         if optim_opts is not None:
-            if optim_opts in ["Adam", "Scheduler"]:
-                optimizer = torch.optim.Adam([met.metamer])
-            elif optim_opts == "SGD":
-                optimizer = torch.optim.SGD([met.metamer])
-            if optim_opts == "Scheduler":
-                scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
-        met.synthesize(max_iter=5, optimizer=optimizer, scheduler=scheduler)
+            if "Scheduler" in optim_opts:
+                scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau
+                check_scheduler = [
+                    torch.optim.lr_scheduler.ReduceLROnPlateau,
+                    {"factor": 0.1},
+                ]
+                if "args" in optim_opts:
+                    scheduler_kwargs = {"factor": 1e-3}
+                    check_scheduler[1] = {"factor": 1e-3}
+            else:
+                if "Adam" in optim_opts:
+                    optimizer = torch.optim.Adam
+                elif "SGD" in optim_opts:
+                    optimizer = torch.optim.SGD
+                    check_optimizer[0] = torch.optim.SGD
+                    check_optimizer[1] = {"lr": 0.01}
+                if "args" in optim_opts:
+                    optimizer_kwargs = {"lr": 1}
+                    check_optimizer[1] = {"lr": 1}
+        met.setup(
+            optimizer=optimizer,
+            scheduler=scheduler,
+            optimizer_kwargs=optimizer_kwargs,
+            scheduler_kwargs=scheduler_kwargs,
+        )
+        met.synthesize(max_iter=5)
         met.save(op.join(tmp_path, "test_metamer_optimizer.pt"))
         met = po.synth.Metamer(curie_img, model)
         met.load(op.join(tmp_path, "test_metamer_optimizer.pt"))
+        optimizer_kwargs = None
+        scheduler_kwargs = None
         if not fail:
             if optim_opts is not None:
-                if optim_opts in ["Adam", "Scheduler"]:
-                    optimizer = torch.optim.Adam([met.metamer])
-                elif optim_opts == "SGD":
-                    optimizer = torch.optim.SGD([met.metamer])
-                if optim_opts == "Scheduler":
-                    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
+                if "Adam" in optim_opts:
+                    optimizer = torch.optim.Adam
+                elif "SGD" in optim_opts:
+                    optimizer = torch.optim.SGD
+                if "Scheduler" in optim_opts:
+                    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau
             expectation = does_not_raise()
         else:
             expect_str = "User-specified optimizer must have same type"
-            err = ValueError
             if optim_opts is None:
-                optimizer = torch.optim.SGD([met.metamer])
+                optimizer = torch.optim.SGD
             else:
                 if optim_opts == "Adam":
-                    optimizer = torch.optim.SGD([met.metamer])
+                    optimizer = torch.optim.SGD
+                elif optim_opts == "Adam-args":
+                    optimizer = torch.optim.Adam
+                    optimizer_kwargs = {"lr": 1}
+                    expect_str = (
+                        "When initializing optimizer after load, optimizer_kwargs"
+                    )
                 elif optim_opts == "SGD":
                     optimizer = None
-                    err = TypeError
                     expect_str = "Don't know how to initialize saved optimizer"
+                elif optim_opts == "SGD-args":
+                    optimizer = torch.optim.SGD
+                    optimizer_kwargs = {"lr": 1}
+                    expect_str = (
+                        "When initializing optimizer after load, optimizer_kwargs"
+                    )
                 elif optim_opts == "Scheduler":
-                    optimizer = torch.optim.Adam([met.metamer])
-                    scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
+                    scheduler = torch.optim.lr_scheduler.ConstantLR
                     expect_str = "User-specified scheduler must have same type"
-            expectation = pytest.raises(err, match=expect_str)
-        # these don't fail until we call synthesize
+                elif optim_opts == "Scheduler-args":
+                    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau
+                    scheduler_kwargs = {"factor": 1e-3}
+                    expect_str = (
+                        "When initializing scheduler after load, scheduler_kwargs"
+                    )
+            expectation = pytest.raises(ValueError, match=expect_str)
+        # these fail during setup
         with expectation:
-            met.synthesize(max_iter=5, optimizer=optimizer, scheduler=scheduler)
-
-    @pytest.mark.parametrize(
-        "model", ["frontend.LinearNonlinear.nograd"], indirect=True
-    )
-    @pytest.mark.parametrize("fail", ["optim", "sched"])
-    def test_load_optim_wrong_time(self, curie_img, model, fail, tmp_path):
-        met = po.synth.Metamer(curie_img, model)
-        optimizer = torch.optim.Adam([met.metamer])
-        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
-        met.synthesize(max_iter=5, optimizer=optimizer, scheduler=scheduler)
-        met.save(op.join(tmp_path, "test_metamer_optim_wrong_time.pt"))
-        met = po.synth.Metamer(curie_img, model)
-        if fail == "optim":
-            optimizer = torch.optim.Adam([met.metamer])
-            expect_str = "Did you initialize this optimizer object before calling load"
-        elif fail == "sched":
-            # use old optimizer object
-            scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
-            expect_str = "Did you initialize this scheduler object before calling load"
-        met.load(op.join(tmp_path, "test_metamer_optim_wrong_time.pt"))
-        if fail != "optim":
-            optimizer = torch.optim.Adam([met.metamer])
-        with pytest.raises(ValueError, match=expect_str):
-            met.synthesize(max_iter=5, optimizer=optimizer, scheduler=scheduler)
+            met.setup(
+                optimizer=optimizer,
+                scheduler=scheduler,
+                optimizer_kwargs=optimizer_kwargs,
+                scheduler_kwargs=scheduler_kwargs,
+            )
+            met.synthesize(max_iter=5)
+            if not isinstance(met.optimizer, check_optimizer[0]):
+                raise ValueError("Didn't properly set optimizer!")
+            state_dict = met.optimizer.state_dict()["param_groups"][0]
+            for k, v in check_optimizer[1].items():
+                if state_dict[k] != v:
+                    raise ValueError(
+                        "Didn't properly set optimizer kwargs! "
+                        f"Expected {v} but got {state_dict[k]}!"
+                    )
+            if check_scheduler is not None:
+                if not isinstance(met.scheduler, check_scheduler[0]):
+                    raise ValueError("Didn't properly set scheduler!")
+                state_dict = met.scheduler.state_dict()
+                for k, v in check_scheduler[1].items():
+                    if met.scheduler.state_dict()[k] != v:
+                        raise ValueError("Didn't properly set scheduler kwargs!")
+            elif met.scheduler is not None:
+                raise ValueError("Didn't set scheduler to None!")
 
     @pytest.mark.parametrize(
         "model", ["frontend.LinearNonlinear.nograd"], indirect=True
     )
     @pytest.mark.parametrize("load", [True, False])
     def test_resume_synthesis(self, einstein_img, curie_img, model, load, tmp_path):
-        met = po.synth.Metamer(einstein_img, model, initial_image=curie_img)
+        met = po.synth.Metamer(einstein_img, model)
         # Adam has some stochasticity in its initialization(?), so this test doesn't
         # quite work with it (it does if you do po.tools.set_seed(2) at the top of the
         # function)
-        optim = torch.optim.SGD([met.metamer])
-        met.synthesize(10, optimizer=optim)
-        met_copy = po.synth.Metamer(einstein_img, model, initial_image=curie_img)
-        optim = torch.optim.SGD([met_copy.metamer])
-        met_copy.synthesize(5, optimizer=optim)
+        met.setup(curie_img, optimizer=torch.optim.SGD)
+        met.synthesize(10)
+        met_copy = po.synth.Metamer(einstein_img, model)
+        met_copy.setup(curie_img, optimizer=torch.optim.SGD)
+        met_copy.synthesize(5)
         if load:
             met_copy.save(op.join(tmp_path, "test_metamer_resume_synthesis.pt"))
-            met_copy = po.synth.Metamer(einstein_img, model, initial_image=curie_img)
+            met_copy = po.synth.Metamer(einstein_img, model)
             met_copy.load(op.join(tmp_path, "test_metamer_resume_synthesis.pt"))
-            optim = torch.optim.SGD([met_copy.metamer])
-            met_copy.synthesize(5, optimizer=optim)
+            met_copy.setup(optimizer=torch.optim.SGD)
+            met_copy.synthesize(5)
         else:
             met_copy.synthesize(5)
         if not torch.allclose(met.metamer, met_copy.metamer):
@@ -466,15 +581,69 @@ def test_coarse_to_fine(self, einstein_img, model, coarse_to_fine, tmp_path):
         )
 
     @pytest.mark.parametrize("model", ["NLP"], indirect=True)
-    @pytest.mark.parametrize("optimizer", ["Adam", None, "Scheduler"])
+    @pytest.mark.parametrize(
+        "optimizer",
+        ["SGD", "SGD-args", "Adam", "Adam-args", None, "Scheduler-args", "Scheduler"],
+    )
     def test_optimizer(self, curie_img, model, optimizer):
         met = po.synth.Metamer(curie_img, model)
+        optimizer = None
         scheduler = None
-        if optimizer == "Adam" or optimizer == "Scheduler":
-            optimizer = torch.optim.Adam([met.metamer])
-            if optimizer == "Scheduler":
-                scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
-        met.synthesize(max_iter=5, optimizer=optimizer, scheduler=scheduler)
+        optimizer_kwargs = None
+        scheduler_kwargs = None
+        check_optimizer = [torch.optim.Adam, {"eps": 1e-8, "lr": 0.01}]
+        check_scheduler = None
+        if optimizer == "Adam":
+            optimizer = torch.optim.Adam
+        elif optimizer == "Adam-args":
+            optimizer = torch.optim.Adam
+            optimizer_kwargs = {"eps": 1e-5}
+            check_optimizer[1]["eps"] = 1e-5
+        elif optimizer == "SGD":
+            optimizer = torch.optim.SGD
+            check_optimizer = [torch.optim.SGD, {"lr": 0.01}]
+        elif optimizer == "SGD-args":
+            optimizer = torch.optim.SGD
+            optimizer_kwargs = {"lr": 1}
+            check_optimizer = [torch.optim.SGD, {"lr": 1}]
+        elif optimizer == "Scheduler":
+            scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau
+            check_scheduler = [
+                torch.optim.lr_scheduler.ReduceLROnPlateau,
+                {"factor": 0.1},
+            ]
+        elif optimizer == "Scheduler-args":
+            scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau
+            scheduler_kwargs = {"factor": 1e-3}
+            check_scheduler = [
+                torch.optim.lr_scheduler.ReduceLROnPlateau,
+                {"factor": 1e-3},
+            ]
+        met.setup(
+            optimizer=optimizer,
+            scheduler=scheduler,
+            optimizer_kwargs=optimizer_kwargs,
+            scheduler_kwargs=scheduler_kwargs,
+        )
+        met.synthesize(max_iter=5)
+        if not isinstance(met.optimizer, check_optimizer[0]):
+            raise ValueError("Didn't properly set optimizer!")
+        state_dict = met.optimizer.state_dict()["param_groups"][0]
+        for k, v in check_optimizer[1].items():
+            if state_dict[k] != v:
+                raise ValueError(
+                    "Didn't properly set optimizer kwargs! "
+                    f"Expected {v} but got {state_dict[k]}!"
+                )
+        if check_scheduler is not None:
+            if not isinstance(met.scheduler, check_scheduler[0]):
+                raise ValueError("Didn't properly set scheduler!")
+            state_dict = met.scheduler.state_dict()
+            for k, v in check_scheduler[1].items():
+                if met.scheduler.state_dict()[k] != v:
+                    raise ValueError("Didn't properly set scheduler kwargs!")
+        elif met.scheduler is not None:
+            raise ValueError("Didn't set scheduler to None!")
 
     @pytest.mark.skipif(DEVICE.type == "cpu", reason="Only makes sense to test on cuda")
     @pytest.mark.parametrize("model", ["Identity"], indirect=True)
diff --git a/tests/test_models.py b/tests/test_models.py
index 87cc6ce40..98d6b3576 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -782,18 +782,20 @@ def test_ps_synthesis(self, portilla_simoncelli_synthesize, run_test=True):
         met = po.synth.MetamerCTF(
             im0,
             model,
-            initial_image=im_init,
             loss_function=po.tools.optim.l2_norm,
             range_penalty_lambda=0,
             coarse_to_fine="together",
         )
-
         # this is the same as the default optimizer, but we explicitly
         # instantiate it anyway, in case we change the defaults at some point
-        optim = torch.optim.Adam([met.metamer], lr=0.01, amsgrad=True)
+        met.setup(
+            initial_image=im_init,
+            optimizer=torch.optim.Adam,
+            optimizer_kwargs={"lr": 0.01, "amsgrad": True},
+        )
+
         met.synthesize(
             max_iter=200,
-            optimizer=optim,
             change_scale_criterion=None,
             ctf_iters_to_check=15,
         )


Reviews:
user: login: BalzaniEdoardo
body: This looks good, I have just a question about the flag _loaded. 

Unrelated:
Is there a reason why you are using the notebooks directly instead of myst-nb? It is hard to read what's changed in the notebooks, it would be nice to be able to use the notebook changes as an example to better understand the before and after of the change
state: APPROVED
author_association: CONTRIBUTOR
submitted_at: 2025-03-18T20:14:07Z
user: login: billbrod
state: COMMENTED
author_association: MEMBER
submitted_at: 2025-03-19T14:19:54Z
user: login: billbrod
state: COMMENTED
author_association: MEMBER
submitted_at: 2025-03-19T14:22:14Z
user: login: billbrod
state: COMMENTED
author_association: MEMBER
submitted_at: 2025-03-19T14:24:19Z
user: login: BalzaniEdoardo
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-03-19T14:28:23Z
user: login: billbrod
state: COMMENTED
author_association: MEMBER
submitted_at: 2025-03-19T14:38:23Z
user: login: sjvenditto
body: Looking good! My only comments are about adding additional tests for setup errors that aren't being covered
state: CHANGES_REQUESTED
author_association: CONTRIBUTOR
submitted_at: 2025-03-19T15:37:35Z
user: login: sjvenditto
body: Looks good!
state: APPROVED
author_association: CONTRIBUTOR
submitted_at: 2025-03-19T20:18:56Z

Review comments:
diff_hunk: @@ -130,14 +125,20 @@ def __init__(
         self._image_a = image_a
         self._image_b = image_b
         self.pixelfade = make_straight_line(image_a, image_b, n_steps)
-        self._initialize(initial_sequence, image_a, image_b, n_steps)
+        self._geodesic = None
         self._dev_from_line = []
         self._step_energy = []
         self._step_energy_dims = None
-        # call this to cache some initial calculations
-        self.objective_function()
+        self._geodesic_representation = None
+        self._most_recent_step_energy = None
 
-    def _initialize(self, initial_sequence, start, stop, n_steps):
+    def setup(
+        self,
+        initial_sequence: Literal["straight", "bridge"] | None = None,
path: src/plenoptic/synthesize/geodesic.py
user: login: BalzaniEdoardo
body: ```suggestion
        initial_sequence: Optional[Literal["straight", "bridge"]] = None,
```

Why not using the optional type?
created_at: 2025-03-18T19:57:53Z
author_association: CONTRIBUTOR
diff_hunk: @@ -130,14 +125,20 @@ def __init__(
         self._image_a = image_a
         self._image_b = image_b
         self.pixelfade = make_straight_line(image_a, image_b, n_steps)
-        self._initialize(initial_sequence, image_a, image_b, n_steps)
+        self._geodesic = None
         self._dev_from_line = []
         self._step_energy = []
         self._step_energy_dims = None
-        # call this to cache some initial calculations
-        self.objective_function()
+        self._geodesic_representation = None
+        self._most_recent_step_energy = None
 
-    def _initialize(self, initial_sequence, start, stop, n_steps):
+    def setup(
+        self,
+        initial_sequence: Literal["straight", "bridge"] | None = None,
+        n_steps: int = 10,
+        optimizer: torch.optim.Optimizer | None = None,
path: src/plenoptic/synthesize/geodesic.py
user: login: BalzaniEdoardo
body: same as above
created_at: 2025-03-18T19:58:03Z
author_association: CONTRIBUTOR
diff_hunk: @@ -130,14 +125,20 @@ def __init__(
         self._image_a = image_a
         self._image_b = image_b
         self.pixelfade = make_straight_line(image_a, image_b, n_steps)
-        self._initialize(initial_sequence, image_a, image_b, n_steps)
+        self._geodesic = None
         self._dev_from_line = []
         self._step_energy = []
         self._step_energy_dims = None
-        # call this to cache some initial calculations
-        self.objective_function()
+        self._geodesic_representation = None
+        self._most_recent_step_energy = None
 
-    def _initialize(self, initial_sequence, start, stop, n_steps):
+    def setup(
+        self,
+        initial_sequence: Literal["straight", "bridge"] | None = None,
+        n_steps: int = 10,
+        optimizer: torch.optim.Optimizer | None = None,
+        optimizer_kwargs: dict | None = None,
path: src/plenoptic/synthesize/geodesic.py
user: login: BalzaniEdoardo
body: here too
created_at: 2025-03-18T19:58:13Z
author_association: CONTRIBUTOR
diff_hunk: @@ -204,7 +230,9 @@ def synthesize(
             )
         print(f"\n Stop criterion for pixel_change_norm = {stop_criterion:.5e}")
 
-        self._initialize_optimizer(optimizer, "_geodesic", 0.001)
+        # if setup hasn't been called manually, call it now.
+        if self._geodesic is None or isinstance(self._optimizer, tuple):
+            self.setup()
path: src/plenoptic/synthesize/geodesic.py
user: login: BalzaniEdoardo
body: After this, you'll set loaded to `False`, is that what you want at the end of synthesis or do you consider the model as "loaded" once synthesis has been performed?
created_at: 2025-03-18T20:10:52Z
author_association: CONTRIBUTOR
diff_hunk: @@ -130,14 +125,20 @@ def __init__(
         self._image_a = image_a
         self._image_b = image_b
         self.pixelfade = make_straight_line(image_a, image_b, n_steps)
-        self._initialize(initial_sequence, image_a, image_b, n_steps)
+        self._geodesic = None
         self._dev_from_line = []
         self._step_energy = []
         self._step_energy_dims = None
-        # call this to cache some initial calculations
-        self.objective_function()
+        self._geodesic_representation = None
+        self._most_recent_step_energy = None
 
-    def _initialize(self, initial_sequence, start, stop, n_steps):
+    def setup(
+        self,
+        initial_sequence: Literal["straight", "bridge"] | None = None,
path: src/plenoptic/synthesize/geodesic.py
user: login: billbrod
body: Ruff doesn't like it. From rule [UP007](https://docs.astral.sh/ruff/rules/non-pep604-annotation-union/), which prefers it as more "concise and readable" (given that our minimum python version is 3.10).
created_at: 2025-03-19T14:19:53Z
author_association: MEMBER
diff_hunk: @@ -204,7 +230,9 @@ def synthesize(
             )
         print(f"\n Stop criterion for pixel_change_norm = {stop_criterion:.5e}")
 
-        self._initialize_optimizer(optimizer, "_geodesic", 0.001)
+        # if setup hasn't been called manually, call it now.
+        if self._geodesic is None or isinstance(self._optimizer, tuple):
+            self.setup()
path: src/plenoptic/synthesize/geodesic.py
user: login: billbrod
body: the `_loaded` flag is only used to raise a more informative error message if someone calls `load()` and then `setup()` while trying to set `initial_image` / `initial_noise` / `initial_sequence`, e.g.,:

```
            if self._loaded:
                if initial_sequence is not None:
                    raise ValueError(
                        "Cannot set initial_sequence after calling load()!"
                    )
```

if they call `setup()` again after calling synthesize, then they'll get this error, which I think works:
```
                raise ValueError(
                    "setup() can only be called once and must be called"
                    " before synthesize()!"
                )
```
created_at: 2025-03-19T14:22:14Z
author_association: MEMBER
diff_hunk: @@ -130,14 +125,20 @@ def __init__(
         self._image_a = image_a
         self._image_b = image_b
         self.pixelfade = make_straight_line(image_a, image_b, n_steps)
-        self._initialize(initial_sequence, image_a, image_b, n_steps)
+        self._geodesic = None
         self._dev_from_line = []
         self._step_energy = []
         self._step_energy_dims = None
-        # call this to cache some initial calculations
-        self.objective_function()
+        self._geodesic_representation = None
+        self._most_recent_step_energy = None
 
-    def _initialize(self, initial_sequence, start, stop, n_steps):
+    def setup(
+        self,
+        initial_sequence: Literal["straight", "bridge"] | None = None,
path: src/plenoptic/synthesize/geodesic.py
user: login: billbrod
body: Which I agree with: `Optional` (meaning a value of None is allowed) is confusable with "optional" (meaning there are default values), so typing it with `| None` is cleaner. See [typing docs](https://docs.python.org/3/library/typing.html#typing.Optional) which point this out.
created_at: 2025-03-19T14:24:19Z
author_association: MEMBER
diff_hunk: @@ -130,14 +125,20 @@ def __init__(
         self._image_a = image_a
         self._image_b = image_b
         self.pixelfade = make_straight_line(image_a, image_b, n_steps)
-        self._initialize(initial_sequence, image_a, image_b, n_steps)
+        self._geodesic = None
         self._dev_from_line = []
         self._step_energy = []
         self._step_energy_dims = None
-        # call this to cache some initial calculations
-        self.objective_function()
+        self._geodesic_representation = None
+        self._most_recent_step_energy = None
 
-    def _initialize(self, initial_sequence, start, stop, n_steps):
+    def setup(
+        self,
+        initial_sequence: Literal["straight", "bridge"] | None = None,
path: src/plenoptic/synthesize/geodesic.py
user: login: BalzaniEdoardo
body: sounds good, I have a ton of Optional in nemos
created_at: 2025-03-19T14:28:23Z
author_association: CONTRIBUTOR
diff_hunk: @@ -130,14 +125,20 @@ def __init__(
         self._image_a = image_a
         self._image_b = image_b
         self.pixelfade = make_straight_line(image_a, image_b, n_steps)
-        self._initialize(initial_sequence, image_a, image_b, n_steps)
+        self._geodesic = None
         self._dev_from_line = []
         self._step_energy = []
         self._step_energy_dims = None
-        # call this to cache some initial calculations
-        self.objective_function()
+        self._geodesic_representation = None
+        self._most_recent_step_energy = None
 
-    def _initialize(self, initial_sequence, start, stop, n_steps):
+    def setup(
+        self,
+        initial_sequence: Literal["straight", "bridge"] | None = None,
path: src/plenoptic/synthesize/geodesic.py
user: login: billbrod
body: ruff can fix it all for you
created_at: 2025-03-19T14:38:23Z
author_association: MEMBER
diff_hunk: @@ -130,35 +125,106 @@ def __init__(
         self._image_a = image_a
         self._image_b = image_b
         self.pixelfade = make_straight_line(image_a, image_b, n_steps)
-        self._initialize(initial_sequence, image_a, image_b, n_steps)
+        self._geodesic = None
         self._dev_from_line = []
         self._step_energy = []
         self._step_energy_dims = None
-        # call this to cache some initial calculations
-        self.objective_function()
+        self._geodesic_representation = None
+        self._most_recent_step_energy = None
 
-    def _initialize(self, initial_sequence, start, stop, n_steps):
-        """initialize the geodesic
+    def setup(
+        self,
+        initial_sequence: Literal["straight", "bridge"] | None = None,
+        n_steps: int = 10,
+        optimizer: torch.optim.Optimizer | None = None,
+        optimizer_kwargs: dict | None = None,
+    ):
+        """Initialize the geodesic and optimizer.
+
+        Can only be called once. If ``load()`` has been called, ``initial_sequence``
+        must be None.
 
         Parameters
         ----------
         initial_sequence
             initialize the geodesic with pixel linear interpolation
             (``'straight'``), or with a brownian bridge between the two anchors
-            (``'bridge'``).
+            (``'bridge'``). If None, use ``'straight'``.
+        n_steps
+            the number of steps (i.e., transitions) in the trajectory between the
+            two anchor points.
+        optimizer :
+            The un-initialized optimizer object to use. If None, we use Adam(lr=.001,
+            amsgrad=True).
+        optimizer_kwargs :
+            The keyword arguments to pass to the optimizer on initialization.
+
+        Examples
+        --------
+        Set initial sequence:
+
+        >>> img = po.data.einstein()
+        >>> model = po.simul.Gaussian(30)
+        >>> po.tools.remove_grad(model)
+        >>> geod = po.synth.Geodesic(img, img/2, model)
+        >>> geod.setup("bridge")
+        >>> geod.synthesize(10)
+
+        Set optimizer:
+
+        >>> img = po.data.einstein()
+        >>> model = po.simul.Gaussian(30)
+        >>> po.tools.remove_grad(model)
+        >>> geod = po.synth.Geodesic(img, img/2, model)
+        >>> geod.setup(optimizer=torch.optim.SGD, optimizer_kwargs={"lr": 0.01})
+        >>> geod.synthesize(10)
+
+        Use with save/load. Only the optimizer object is necessary, its kwargs and the
+        initial sequence are handled by load.
+
+        >>> img = po.data.einstein()
+        >>> model = po.simul.Gaussian(30)
+        >>> po.tools.remove_grad(model)
+        >>> geod = po.synth.Geodesic(img, img/2, model)
+        >>> geod.setup("bridge", optimizer=torch.optim.SGD,
+        ...            optimizer_kwargs={"lr": 0.01})
+        >>> geod.synthesize(10)
+        >>> geod.save("test.pt")
+        >>> geod = po.synth.Geodesic(img, img/2, model)
+        >>> geod.load("test.pt")
+        >>> geod.setup(optimizer=torch.optim.SGD)
+        >>> geod.synthesize(10)
+
         """
-        if initial_sequence == "bridge":
-            geodesic = sample_brownian_bridge(start, stop, n_steps)
-        elif initial_sequence == "straight":
-            geodesic = make_straight_line(start, stop, n_steps)
+        if self._geodesic is None:
+            if initial_sequence is None:
+                initial_sequence = "straight"
+            if initial_sequence == "bridge":
+                geodesic = sample_brownian_bridge(self._image_a, self._image_b, n_steps)
+            elif initial_sequence == "straight":
+                geodesic = make_straight_line(self._image_a, self._image_b, n_steps)
+            else:
+                raise ValueError(
path: src/plenoptic/synthesize/geodesic.py
user: login: sjvenditto
body: Is it worth adding a test for an unknown initial sequence?
created_at: 2025-03-19T15:31:56Z
author_association: CONTRIBUTOR
diff_hunk: @@ -110,57 +105,127 @@ def __init__(
         self._scheduler = None
         self._scheduler_step_arg = False
         self.loss_function = loss_function
-        self._initialize(initial_image)
         self._saved_metamer = []
         self._store_progress = None
+        self._metamer = None
 
-    def _initialize(self, initial_image: Tensor | None = None):
-        """Initialize the metamer.
+    def setup(
+        self,
+        initial_image: Tensor | None = None,
+        optimizer: torch.optim.Optimizer | None = None,
+        optimizer_kwargs: dict | None = None,
+        scheduler: torch.optim.lr_scheduler.LRScheduler | None = None,
+        scheduler_kwargs: dict | None = None,
+    ):
+        """Initialize the metamer, optimizer, and scheduler.
 
-        Set the ``self.metamer`` attribute to be an attribute with the
-        user-supplied data, making sure it's the right shape.
+        Can only be called once. If ``load()`` has been called, ``initial_image`` must
+        be None.
 
         Parameters
         ----------
         initial_image :
-            The tensor we use to initialize the metamer. If None (the default),
-            we initialize with uniformly-distributed random noise lying between
-            0 and 1.
+            The tensor we use to initialize the metamer. If None, we initialize with
+            uniformly-distributed random noise lying within ``self.allowed_range``.
+        optimizer :
+            The un-initialized optimizer object to use. If None, we use Adam(lr=.01,
+            amsgrad=True).
+        optimizer_kwargs :
+            The keyword arguments to pass to the optimizer on initialization.
+        scheduler :
+            The learning rate scheduler to use. If None, we don't use one.
+        scheduler_kwargs :
+            The keyword arguments to pass to the scheduler on initialization.
+
+        Raises
+        ------
+        ValueError :
+            If ``initial_image`` is the wrong shape.
+
+        Examples
+        --------
+        Set initial image:
+
+        >>> img = po.data.einstein()
+        >>> model = po.simul.Gaussian(30)
+        >>> po.tools.remove_grad(model)
+        >>> met = po.synth.Metamer(img, model)
+        >>> met.setup(po.data.curie())
+        >>> met.synthesize(10)
+
+        Set optimizer:
+
+        >>> img = po.data.einstein()
+        >>> model = po.simul.Gaussian(30)
+        >>> po.tools.remove_grad(model)
+        >>> met = po.synth.Metamer(img, model)
+        >>> met.setup(optimizer=torch.optim.SGD, optimizer_kwargs={"lr": 0.01})
+        >>> met.synthesize(10)
+
+        Use with save/load. Only the optimizer object is necessary, its kwargs and the
+        initial image are handled by load.
+
+        >>> img = po.data.einstein()
+        >>> model = po.simul.Gaussian(30)
+        >>> po.tools.remove_grad(model)
+        >>> met = po.synth.Metamer(img, model)
+        >>> met.setup(po.data.curie(), optimizer=torch.optim.SGD,
+        ...           optimizer_kwargs={"lr": 0.01})
+        >>> met.synthesize(10)
+        >>> met.save("test.pt")
+        >>> met = po.synth.Metamer(img, model)
+        >>> met.load("test.pt")
+        >>> met.setup(optimizer=torch.optim.SGD)
+        >>> met.synthesize(10)
 
         """
-        if initial_image is None:
-            metamer = torch.rand_like(self.image)
-            # rescale metamer to lie within the interval
-            # self.allowed_range
-            metamer = signal.rescale(metamer, *self.allowed_range)
+        if self._metamer is None:
+            if initial_image is None:
+                metamer = torch.rand_like(self.image)
+                # rescale metamer to lie within the interval
+                # self.allowed_range
+                metamer = signal.rescale(metamer, *self.allowed_range)
+            else:
+                if initial_image.ndimension() < 4:
+                    raise ValueError(
path: src/plenoptic/synthesize/metamer.py
user: login: sjvenditto
body: Test for this error?
created_at: 2025-03-19T15:34:30Z
author_association: CONTRIBUTOR
diff_hunk: @@ -110,57 +105,127 @@ def __init__(
         self._scheduler = None
         self._scheduler_step_arg = False
         self.loss_function = loss_function
-        self._initialize(initial_image)
         self._saved_metamer = []
         self._store_progress = None
+        self._metamer = None
 
-    def _initialize(self, initial_image: Tensor | None = None):
-        """Initialize the metamer.
+    def setup(
+        self,
+        initial_image: Tensor | None = None,
+        optimizer: torch.optim.Optimizer | None = None,
+        optimizer_kwargs: dict | None = None,
+        scheduler: torch.optim.lr_scheduler.LRScheduler | None = None,
+        scheduler_kwargs: dict | None = None,
+    ):
+        """Initialize the metamer, optimizer, and scheduler.
 
-        Set the ``self.metamer`` attribute to be an attribute with the
-        user-supplied data, making sure it's the right shape.
+        Can only be called once. If ``load()`` has been called, ``initial_image`` must
+        be None.
 
         Parameters
         ----------
         initial_image :
-            The tensor we use to initialize the metamer. If None (the default),
-            we initialize with uniformly-distributed random noise lying between
-            0 and 1.
+            The tensor we use to initialize the metamer. If None, we initialize with
+            uniformly-distributed random noise lying within ``self.allowed_range``.
+        optimizer :
+            The un-initialized optimizer object to use. If None, we use Adam(lr=.01,
+            amsgrad=True).
+        optimizer_kwargs :
+            The keyword arguments to pass to the optimizer on initialization.
+        scheduler :
+            The learning rate scheduler to use. If None, we don't use one.
+        scheduler_kwargs :
+            The keyword arguments to pass to the scheduler on initialization.
+
+        Raises
+        ------
+        ValueError :
+            If ``initial_image`` is the wrong shape.
+
+        Examples
+        --------
+        Set initial image:
+
+        >>> img = po.data.einstein()
+        >>> model = po.simul.Gaussian(30)
+        >>> po.tools.remove_grad(model)
+        >>> met = po.synth.Metamer(img, model)
+        >>> met.setup(po.data.curie())
+        >>> met.synthesize(10)
+
+        Set optimizer:
+
+        >>> img = po.data.einstein()
+        >>> model = po.simul.Gaussian(30)
+        >>> po.tools.remove_grad(model)
+        >>> met = po.synth.Metamer(img, model)
+        >>> met.setup(optimizer=torch.optim.SGD, optimizer_kwargs={"lr": 0.01})
+        >>> met.synthesize(10)
+
+        Use with save/load. Only the optimizer object is necessary, its kwargs and the
+        initial image are handled by load.
+
+        >>> img = po.data.einstein()
+        >>> model = po.simul.Gaussian(30)
+        >>> po.tools.remove_grad(model)
+        >>> met = po.synth.Metamer(img, model)
+        >>> met.setup(po.data.curie(), optimizer=torch.optim.SGD,
+        ...           optimizer_kwargs={"lr": 0.01})
+        >>> met.synthesize(10)
+        >>> met.save("test.pt")
+        >>> met = po.synth.Metamer(img, model)
+        >>> met.load("test.pt")
+        >>> met.setup(optimizer=torch.optim.SGD)
+        >>> met.synthesize(10)
 
         """
-        if initial_image is None:
-            metamer = torch.rand_like(self.image)
-            # rescale metamer to lie within the interval
-            # self.allowed_range
-            metamer = signal.rescale(metamer, *self.allowed_range)
+        if self._metamer is None:
+            if initial_image is None:
+                metamer = torch.rand_like(self.image)
+                # rescale metamer to lie within the interval
+                # self.allowed_range
+                metamer = signal.rescale(metamer, *self.allowed_range)
+            else:
+                if initial_image.ndimension() < 4:
+                    raise ValueError(
+                        "initial_image must be torch.Size([n_batch"
+                        ", n_channels, im_height, im_width]) but got "
+                        f"{initial_image.size()}"
+                    )
+                if initial_image.size() != self.image.size():
+                    raise ValueError("initial_image and image must be same size!")
path: src/plenoptic/synthesize/metamer.py
user: login: sjvenditto
body: Test for this error?
created_at: 2025-03-19T15:34:47Z
author_association: CONTRIBUTOR
diff_hunk: @@ -444,61 +464,50 @@ def _initialize_optimizer(
           and we load the state_dict.
 
         """
-        synth_attr = getattr(self, synth_name)
-        if optimizer is None:
-            if self.optimizer is None:
-                self._optimizer = torch.optim.Adam(
-                    [synth_attr], lr=learning_rate, amsgrad=True
+        if isinstance(self.optimizer, tuple):
+            # then we're calling this after load()
+            if optimizer_kwargs is not None:
+                raise ValueError(
path: src/plenoptic/synthesize/synthesis.py
user: login: sjvenditto
body: Test for this error?
created_at: 2025-03-19T15:35:09Z
author_association: CONTRIBUTOR
diff_hunk: @@ -526,8 +535,14 @@ def _initialize_scheduler(
         initialization). Then, we will pass the loss to scheduler.step when we call it.
 
         """
-        if scheduler is None:
-            if isinstance(self.scheduler, tuple):
+        if isinstance(self.scheduler, tuple):
+            # then we're calling this after load()
+            if scheduler_kwargs is not None:
+                raise ValueError(
path: src/plenoptic/synthesize/synthesis.py
user: login: sjvenditto
body: Test for this error?
created_at: 2025-03-19T15:35:26Z
author_association: CONTRIBUTOR

PR comments:
user: login: billbrod
created_at: 2025-03-18T14:33:38Z
body: Documentation built by flatiron-jenkins at http://docs.plenoptic.org/docs//pulls/325
author_association: MEMBER
user: login: codecov[bot]
created_at: 2025-03-18T16:11:40Z
body: ## [Codecov](https://app.codecov.io/gh/plenoptic-org/plenoptic/pull/325?dropdown=coverage&src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=plenoptic-org) Report
Attention: Patch coverage is `99.23664%` with `1 line` in your changes missing coverage. Please review.

| [Files with missing lines](https://app.codecov.io/gh/plenoptic-org/plenoptic/pull/325?dropdown=coverage&src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=plenoptic-org) | Patch % | Lines |
|---|---|---|
| [src/plenoptic/synthesize/geodesic.py](https://app.codecov.io/gh/plenoptic-org/plenoptic/pull/325?src=pr&el=tree&filepath=src%2Fplenoptic%2Fsynthesize%2Fgeodesic.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=plenoptic-org#diff-c3JjL3BsZW5vcHRpYy9zeW50aGVzaXplL2dlb2Rlc2ljLnB5) | 96.00% | [1 Missing :warning: ](https://app.codecov.io/gh/plenoptic-org/plenoptic/pull/325?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=plenoptic-org) |

| [Files with missing lines](https://app.codecov.io/gh/plenoptic-org/plenoptic/pull/325?dropdown=coverage&src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=plenoptic-org) | Coverage Δ | |
|---|---|---|
| [src/plenoptic/synthesize/eigendistortion.py](https://app.codecov.io/gh/plenoptic-org/plenoptic/pull/325?src=pr&el=tree&filepath=src%2Fplenoptic%2Fsynthesize%2Feigendistortion.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=plenoptic-org#diff-c3JjL3BsZW5vcHRpYy9zeW50aGVzaXplL2VpZ2VuZGlzdG9ydGlvbi5weQ==) | `98.91% <100.00%> (ø)` | |
| [src/plenoptic/synthesize/mad\_competition.py](https://app.codecov.io/gh/plenoptic-org/plenoptic/pull/325?src=pr&el=tree&filepath=src%2Fplenoptic%2Fsynthesize%2Fmad_competition.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=plenoptic-org#diff-c3JjL3BsZW5vcHRpYy9zeW50aGVzaXplL21hZF9jb21wZXRpdGlvbi5weQ==) | `93.68% <100.00%> (ø)` | |
| [src/plenoptic/synthesize/metamer.py](https://app.codecov.io/gh/plenoptic-org/plenoptic/pull/325?src=pr&el=tree&filepath=src%2Fplenoptic%2Fsynthesize%2Fmetamer.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=plenoptic-org#diff-c3JjL3BsZW5vcHRpYy9zeW50aGVzaXplL21ldGFtZXIucHk=) | `92.67% <100.00%> (+0.45%)` | :arrow_up: |
| [src/plenoptic/synthesize/synthesis.py](https://app.codecov.io/gh/plenoptic-org/plenoptic/pull/325?src=pr&el=tree&filepath=src%2Fplenoptic%2Fsynthesize%2Fsynthesis.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=plenoptic-org#diff-c3JjL3BsZW5vcHRpYy9zeW50aGVzaXplL3N5bnRoZXNpcy5weQ==) | `95.06% <100.00%> (+0.89%)` | :arrow_up: |
| [src/plenoptic/synthesize/geodesic.py](https://app.codecov.io/gh/plenoptic-org/plenoptic/pull/325?src=pr&el=tree&filepath=src%2Fplenoptic%2Fsynthesize%2Fgeodesic.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=plenoptic-org#diff-c3JjL3BsZW5vcHRpYy9zeW50aGVzaXplL2dlb2Rlc2ljLnB5) | `98.31% <96.00%> (+0.56%)` | :arrow_up: |

... and [4 files with indirect coverage changes](https://app.codecov.io/gh/plenoptic-org/plenoptic/pull/325/indirect-changes?src=pr&el=tree-more&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=plenoptic-org)

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author_association: NONE
user: login: billbrod
created_at: 2025-03-19T14:20:30Z
body: > Unrelated: Is there a reason why you are using the notebooks directly instead of myst-nb? It is hard to read what's changed in the notebooks, it would be nice to be able to use the notebook changes as an example to better understand the before and after of the change

Agreed. I am switching over to myst-nb soon, it's my next major PR after this one.
author_association: MEMBER
user: login: billbrod
created_at: 2025-03-19T16:42:17Z
body: Added the tests you pointed out, plus modified my existing tests for the optimizer/scheduler to check that their class and parameters were set correctly.
author_association: MEMBER