diff --git a/.gitignore b/.gitignore
index 2baecac0f9..7106fe50ab 100644
--- a/.gitignore
+++ b/.gitignore
@@ -150,3 +150,4 @@ deployment/bentoml/mednist_classifier_bentoml.py
 deployment/ray/mednist_classifier_start.py
 *.nii.gz
 3d_segmentation/out
+*.nsys-rep
diff --git a/acceleration/fast_training_tutorial.ipynb b/acceleration/fast_training_tutorial.ipynb
index 30c405581c..dcfe2d295b 100644
--- a/acceleration/fast_training_tutorial.ipynb
+++ b/acceleration/fast_training_tutorial.ipynb
@@ -35,21 +35,30 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 2,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
+   "source": [
+    "This cell verifies that you have key packages installed and sets up the matplotlib environment.\n",
+    "\n",
+    "Important: if you are interested in profiling, comment out this cell after you finish the checks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
-    "!python -c \"import monai\" || pip install -q \"monai-weekly[nibabel, tqdm]\"\n",
-    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
+    "# if profiling, comment out this cell to ensure the converted python script runs smoothly\n",
+    "\n",
+    "!python3 -c \"import monai\" || pip install -q \"monai-weekly[nibabel, tqdm]\"\n",
+    "!python3 -c \"import matplotlib\" || pip install -q matplotlib\n",
+    "!python3 -c \"import nvtx\" || pip install -q nvtx\n",
+    "\n",
     "%matplotlib inline"
    ]
   },
@@ -63,7 +72,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "# Copyright 2020 MONAI Consortium\n",
@@ -118,6 +131,11 @@
     ")\n",
     "from monai.utils import set_determinism\n",
     "\n",
+    "# for profiling\n",
+    "import nvtx\n",
+    "from monai.utils.nvtx import Range\n",
+    "import contextlib  # to improve code readability (combining training/validation loop with and without profiling)\n",
+    "\n",
     "print_config()"
    ]
   },
@@ -125,7 +143,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup data directory\n",
+    "## Setup data & output directories\n",
     "\n",
     "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
     "This allows you to save results and reuse downloads.  \n",
@@ -134,23 +152,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "root dir is: /workspace/data/medical\n"
-     ]
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
     "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
     "print(f\"root dir is: {root_dir}\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, outputs will go to `outputs/`. \n",
+    "\n",
+    "You can run this tutorial twice, once with profiling and once without, and the outputs will not conflict with each other. \n",
+    "- When profiling, the output is `outputs/output_base.nsys-rep`, which you can then visualize using the GUI of Nsight systems (a brief guide is provided in the \"Profiling visualization\" section below).\n",
+    "- When not profiling, the outputs are `outputs/loss_dice_comparison.png`, `outputs/metric_time_epochs.png`, and `outputs/total_epoch_time_comparison.png`.\n",
+    "\n",
+    "We set up the tutorial such that figures are only generated when not profiling, but that does not have to be the case. In general, the figures make more sense when training is run for a higher number of epochs (e.g., hundreds), which is usually not the case when profiling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# outputs\n",
+    "\n",
+    "out_dir = \"outputs/\"\n",
+    "\n",
+    "if not os.path.exists(out_dir):\n",
+    "    os.makedirs(out_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Profiling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This section sets up profiling for this tutorial.\n",
+    "\n",
+    "The number of epochs is automatically set based on whether profiling is being performed, but you can modify as needed.\n",
+    "\n",
+    "- If you are not interested in profiling, please set `profiling = False` and move on.\n",
+    "\n",
+    "- If you are profiling:\n",
+    "\n",
+    "  - Because of the currently supported functionality of Nsight systems (`nsys`), profiling can only be performed from the terminal, and not from within this tutorial. For more information, including installation, refer to the [NVIDIA Nsight Systems page](https://developer.nvidia.com/nsight-systems).\n",
+    "  - Perform the following steps:\n",
+    "  \n",
+    "    1) Make sure `nsys` is installed;\n",
+    "    \n",
+    "    2) Set `profiling = True`;\n",
+    "    \n",
+    "    3) Make sure all lines in \"Setup environment\" (first code cell in this tutorial, above) are commented out;\n",
+    "    \n",
+    "    4) Save this notebook;\n",
+    "    \n",
+    "    5) Open the terminal and ensure that you are in the directory of this notebook, then run this command:\n",
+    "    `jupyter nbconvert fast_training_tutorial.ipynb --to python && nsys profile --output ./outputs/output_base --force-overwrite true --trace-fork-before-exec true python3 fast_training_tutorial.py ; rm fast_training_tutorial.py`\n",
+    "    \n",
+    "    This command converts the notebook to a Python script locally and runs `nsys`. The output file is `outputs/output_base.nsys-rep`, but you can modify `--output` to specify the desired location."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "profiling = False\n",
+    "\n",
+    "# if profiling = True, it is recommended to set max_epochs = 6 for faster prototyping\n",
+    "# to see the trend in training curve and dice results, set max_epochs to be larger (600)\n",
+    "# note that before optimization, training can be quite a bit slower\n",
+    "if profiling:\n",
+    "    max_epochs = 6\n",
+    "else:\n",
+    "    max_epochs = 600\n",
+    "\n",
+    "# to improve readability\n",
+    "\n",
+    "\n",
+    "def range_func(x, y): return Range(x)(y) if profiling else y\n",
+    "\n",
+    "\n",
+    "no_profiling = contextlib.nullcontext()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -162,8 +272,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "resource = \"https://msd-for-monai.s3-us-west-2.amazonaws.com/Task09_Spleen.tar\"\n",
@@ -184,8 +298,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 19,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "train_images = sorted(\n",
@@ -210,51 +328,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
-    "def transformations(fast=False):\n",
+    "def transformations(fast=False, device='cuda:0'):\n",
     "    train_transforms = [\n",
-    "        LoadImaged(keys=[\"image\", \"label\"]),\n",
-    "        AddChanneld(keys=[\"image\", \"label\"]),\n",
-    "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
-    "        Spacingd(\n",
+    "        range_func(\"LoadImage\", LoadImaged(keys=[\"image\", \"label\"])),\n",
+    "        range_func(\"AddChannel\", AddChanneld(keys=[\"image\", \"label\"])),\n",
+    "        range_func(\"Orientation\", Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\")),\n",
+    "        range_func(\"Spacing\", Spacingd(\n",
     "            keys=[\"image\", \"label\"],\n",
     "            pixdim=(1.5, 1.5, 2.0),\n",
     "            mode=(\"bilinear\", \"nearest\"),\n",
-    "        ),\n",
-    "        ScaleIntensityRanged(\n",
-    "            keys=[\"image\"],\n",
-    "            a_min=-57,\n",
-    "            a_max=164,\n",
-    "            b_min=0.0,\n",
-    "            b_max=1.0,\n",
-    "            clip=True,\n",
-    "        ),\n",
-    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
+    "        )),\n",
+    "        range_func(\"ScaleIntensityRange\",\n",
+    "                   ScaleIntensityRanged(\n",
+    "                       keys=[\"image\"],\n",
+    "                       a_min=-57,\n",
+    "                       a_max=164,\n",
+    "                       b_min=0.0,\n",
+    "                       b_max=1.0,\n",
+    "                       clip=True,\n",
+    "                   )),\n",
+    "        range_func(\"CropForeground\", CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\")),\n",
     "        # pre-compute foreground and background indexes\n",
     "        # and cache them to accelerate training\n",
-    "        FgBgToIndicesd(\n",
+    "        range_func(\"Indexing\", FgBgToIndicesd(\n",
     "            keys=\"label\",\n",
     "            fg_postfix=\"_fg\",\n",
     "            bg_postfix=\"_bg\",\n",
     "            image_key=\"image\",\n",
-    "        ),\n",
+    "        )),\n",
     "        # change to execute transforms with Tensor data\n",
-    "        EnsureTyped(keys=[\"image\", \"label\"]),\n",
+    "        range_func(\"EnsureType\", EnsureTyped(keys=[\"image\", \"label\"])),\n",
     "    ]\n",
+    "\n",
     "    if fast:\n",
     "        # move the data to GPU and cache to avoid CPU -> GPU sync in every epoch\n",
-    "        train_transforms.append(\n",
-    "            ToDeviced(keys=[\"image\", \"label\"], device=\"cuda:0\")\n",
-    "        )\n",
+    "        train_transforms.append(range_func(\"ToDevice\", ToDeviced(keys=[\"image\", \"label\"], device=device)))\n",
+    "\n",
     "    train_transforms.append(\n",
     "        # randomly crop out patch samples from big\n",
     "        # image based on pos / neg ratio\n",
     "        # the image centers of negative samples\n",
     "        # must be in valid image area\n",
-    "        RandCropByPosNegLabeld(\n",
+    "        range_func(\"RandCrop\", RandCropByPosNegLabeld(\n",
     "            keys=[\"image\", \"label\"],\n",
     "            label_key=\"label\",\n",
     "            spatial_size=(96, 96, 96),\n",
@@ -263,7 +386,7 @@
     "            num_samples=4,\n",
     "            fg_indices_key=\"label_fg\",\n",
     "            bg_indices_key=\"label_bg\",\n",
-    "        ),\n",
+    "        )),\n",
     "    )\n",
     "\n",
     "    val_transforms = [\n",
@@ -289,7 +412,7 @@
     "    if fast:\n",
     "        # move the data to GPU and cache to avoid CPU -> GPU sync in every epoch\n",
     "        val_transforms.append(\n",
-    "            ToDeviced(keys=[\"image\", \"label\"], device=\"cuda:0\")\n",
+    "            ToDeviced(keys=[\"image\", \"label\"], device=device)\n",
     "        )\n",
     "\n",
     "    return Compose(train_transforms), Compose(val_transforms)"
@@ -308,24 +431,31 @@
     "3. `ToDeviced` transform: to move data to GPU and cache with `CacheDataset`, then execute random transforms on GPU directly, avoid CPU -> GPU sync in every epoch. Please note that not all the MONAI transforms support GPU operation so far, still working in progress.\n",
     "4. `ThreadDataLoader`: uses multi-threads instead of multi-processing, faster than `DataLoader` in light-weight task as we already cached the results of most computation.\n",
     "5. `DiceCE` loss function: computes Dice loss and Cross Entropy Loss, returns the weighted sum of these two losses.\n",
-    "6. Analyzed the training curve and tuned algorithm: Use `SGD` optimizer, different network parameters, etc."
+    "6. Analyzed the training curve and tuned algorithm: Use `SGD` optimizer, different network parameters, etc.\n",
+    "\n",
+    "(A note on code: to improve readability and support the profiling flag, we used the `with nvtx(...) if profiling else no_profiling` context pattern, where `no_profiling` is a null context from Python's native `contextlib` with no effect on the code. An acknowledgement is provided here[<sup id=\"fn1-back\">1</sup>](#fn1).)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "metadata": {
-    "scrolled": true
+    "vscode": {
+     "languageId": "python"
+    }
    },
    "outputs": [],
    "source": [
     "def train_process(fast=False):\n",
-    "    max_epochs = 600\n",
     "    learning_rate = 2e-4\n",
     "    val_interval = 5  # do validation for every epoch\n",
-    "    device = torch.device(\"cuda:0\")\n",
     "\n",
-    "    train_trans, val_trans = transformations(fast=fast)\n",
+    "    if torch.cuda.is_available():\n",
+    "        device = torch.device(\"cuda:0\")\n",
+    "    else:\n",
+    "        raise RuntimeError('this tutorial is intended for GPU, but no CUDA device is available')\n",
+    "\n",
+    "    train_trans, val_trans = transformations(fast=fast, device=device)\n",
     "    # set CacheDataset, ThreadDataLoader and DiceCE loss for MONAI fast training\n",
     "    if fast:\n",
     "        # as `RandCropByPosNegLabeld` crops from the cached content and `deepcopy`\n",
@@ -344,6 +474,7 @@
     "        # disable multi-workers because `ThreadDataLoader` works with multi-threads\n",
     "        train_loader = ThreadDataLoader(train_ds, num_workers=0, batch_size=4, shuffle=True)\n",
     "        val_loader = ThreadDataLoader(val_ds, num_workers=0, batch_size=1)\n",
+    "\n",
     "        loss_function = DiceCELoss(\n",
     "            include_background=False,\n",
     "            to_onehot_y=True,\n",
@@ -411,87 +542,132 @@
     "    metric_values = []\n",
     "    epoch_times = []\n",
     "    total_start = time.time()\n",
+    "\n",
     "    for epoch in range(max_epochs):\n",
     "        epoch_start = time.time()\n",
     "        print(\"-\" * 10)\n",
     "        print(f\"epoch {epoch + 1}/{max_epochs}\")\n",
-    "        model.train()\n",
-    "        epoch_loss = 0\n",
-    "        step = 0\n",
-    "        for batch_data in train_loader:\n",
-    "            step_start = time.time()\n",
-    "            step += 1\n",
-    "            inputs, labels = (\n",
-    "                batch_data[\"image\"].to(device),\n",
-    "                batch_data[\"label\"].to(device),\n",
-    "            )\n",
-    "            optimizer.zero_grad()\n",
-    "            # set AMP for MONAI training\n",
-    "            if fast:\n",
-    "                with torch.cuda.amp.autocast():\n",
-    "                    outputs = model(inputs)\n",
-    "                    loss = loss_function(outputs, labels)\n",
-    "                scaler.scale(loss).backward()\n",
-    "                scaler.step(optimizer)\n",
-    "                scaler.update()\n",
-    "            else:\n",
-    "                outputs = model(inputs)\n",
-    "                loss = loss_function(outputs, labels)\n",
-    "                loss.backward()\n",
-    "                optimizer.step()\n",
-    "            epoch_loss += loss.item()\n",
-    "            epoch_len = math.ceil(len(train_ds) / train_loader.batch_size)\n",
-    "            print(\n",
-    "                f\"{step}/{epoch_len}, train_loss: {loss.item():.4f}\"\n",
-    "                f\" step time: {(time.time() - step_start):.4f}\"\n",
-    "            )\n",
-    "        epoch_loss /= step\n",
-    "        epoch_loss_values.append(epoch_loss)\n",
-    "        print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
-    "\n",
-    "        if (epoch + 1) % val_interval == 0:\n",
-    "            model.eval()\n",
-    "            with torch.no_grad():\n",
-    "                for val_data in val_loader:\n",
-    "                    val_inputs, val_labels = (\n",
-    "                        val_data[\"image\"].to(device),\n",
-    "                        val_data[\"label\"].to(device),\n",
+    "\n",
+    "        # profiling: full epoch\n",
+    "        with nvtx.annotate(\"epoch\", color=\"red\") if profiling else no_profiling:\n",
+    "            model.train()\n",
+    "            epoch_loss = 0\n",
+    "            train_loader_iterator = iter(train_loader)\n",
+    "\n",
+    "            # using step instead of iterate through train_loader directly to track data loading time\n",
+    "            # steps are 1-indexed for printing and calculation purposes\n",
+    "            for step in range(1, len(train_loader) + 1):\n",
+    "                step_start = time.time()\n",
+    "\n",
+    "                # profiling: train dataload\n",
+    "                with nvtx.annotate(\"dataload\", color=\"red\") if profiling else no_profiling:\n",
+    "                    # rng_train_dataload = nvtx.start_range(message=\"dataload\", color=\"red\")\n",
+    "                    batch_data = next(train_loader_iterator)\n",
+    "                    inputs, labels = (\n",
+    "                        batch_data[\"image\"].to(device),\n",
+    "                        batch_data[\"label\"].to(device),\n",
     "                    )\n",
-    "                    roi_size = (160, 160, 160)\n",
-    "                    sw_batch_size = 4\n",
-    "                    # set AMP for MONAI validation\n",
-    "                    if fast:\n",
+    "\n",
+    "                optimizer.zero_grad()\n",
+    "                # set AMP for MONAI training\n",
+    "                if fast:\n",
+    "                    # profiling: forward\n",
+    "                    with nvtx.annotate(\"forward\", color=\"green\") if profiling else no_profiling:\n",
     "                        with torch.cuda.amp.autocast():\n",
-    "                            val_outputs = sliding_window_inference(\n",
-    "                                val_inputs, roi_size, sw_batch_size, model\n",
+    "                            outputs = model(inputs)\n",
+    "                            loss = loss_function(outputs, labels)\n",
+    "\n",
+    "                    # profiling: backward\n",
+    "                    with nvtx.annotate(\"backward\", color=\"blue\") if profiling else no_profiling:\n",
+    "                        scaler.scale(loss).backward()\n",
+    "\n",
+    "                    # profiling: update\n",
+    "                    with nvtx.annotate(\"update\", color=\"yellow\") if profiling else no_profiling:\n",
+    "                        scaler.step(optimizer)\n",
+    "                        scaler.update()\n",
+    "                else:\n",
+    "                    # profiling: forward\n",
+    "                    with nvtx.annotate(\"forward\", color=\"green\") if profiling else no_profiling:\n",
+    "                        outputs = model(inputs)\n",
+    "                        loss = loss_function(outputs, labels)\n",
+    "\n",
+    "                    # profiling: backward\n",
+    "                    with nvtx.annotate(\"backward\", color=\"blue\") if profiling else no_profiling:\n",
+    "                        loss.backward()\n",
+    "\n",
+    "                    # profiling: update\n",
+    "                    with nvtx.annotate(\"update\", color=\"yellow\") if profiling else no_profiling:\n",
+    "                        optimizer.step()\n",
+    "\n",
+    "                epoch_loss += loss.item()\n",
+    "                epoch_len = math.ceil(len(train_ds) / train_loader.batch_size)\n",
+    "                print(\n",
+    "                    f\"{step}/{epoch_len}, train_loss: {loss.item():.4f}\"\n",
+    "                    f\" step time: {(time.time() - step_start):.4f}\"\n",
+    "                )\n",
+    "            epoch_loss /= step\n",
+    "            epoch_loss_values.append(epoch_loss)\n",
+    "            print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
+    "\n",
+    "            if (epoch + 1) % val_interval == 0:\n",
+    "                model.eval()\n",
+    "                with torch.no_grad():\n",
+    "                    val_loader_iterator = iter(val_loader)\n",
+    "\n",
+    "                    for val_step in range(len(val_loader)):\n",
+    "                        # profiling: val dataload\n",
+    "                        with nvtx.annotate(\"dataload\", color=\"red\") if profiling else no_profiling:\n",
+    "                            val_data = next(val_loader_iterator)\n",
+    "                            val_inputs, val_labels = (\n",
+    "                                val_data[\"image\"].to(device),\n",
+    "                                val_data[\"label\"].to(device),\n",
     "                            )\n",
-    "                    else:\n",
-    "                        val_outputs = sliding_window_inference(\n",
-    "                            val_inputs, roi_size, sw_batch_size, model\n",
+    "\n",
+    "                        roi_size = (160, 160, 160)\n",
+    "                        sw_batch_size = 4\n",
+    "\n",
+    "                        # profiling: sliding window\n",
+    "                        with nvtx.annotate(\"sliding window\", color=\"green\") if profiling else no_profiling:\n",
+    "                            # set AMP for MONAI validation\n",
+    "                            if fast:\n",
+    "                                with torch.cuda.amp.autocast():\n",
+    "                                    val_outputs = sliding_window_inference(\n",
+    "                                        val_inputs, roi_size, sw_batch_size, model\n",
+    "                                    )\n",
+    "                            else:\n",
+    "                                val_outputs = sliding_window_inference(\n",
+    "                                    val_inputs, roi_size, sw_batch_size, model\n",
+    "                                )\n",
+    "\n",
+    "                        # profiling: decollate batch\n",
+    "                        with nvtx.annotate(\"decollate batch\", color=\"blue\") if profiling else no_profiling:\n",
+    "                            val_outputs = [post_pred(i) for i in decollate_batch(val_outputs)]\n",
+    "                            val_labels = [post_label(i) for i in decollate_batch(val_labels)]\n",
+    "\n",
+    "                        # profiling: compute metric\n",
+    "                        with nvtx.annotate(\"compute metric\", color=\"yellow\") if profiling else no_profiling:\n",
+    "                            dice_metric(y_pred=val_outputs, y=val_labels)\n",
+    "\n",
+    "                    metric = dice_metric.aggregate().item()\n",
+    "                    dice_metric.reset()\n",
+    "                    metric_values.append(metric)\n",
+    "                    if metric > best_metric:\n",
+    "                        best_metric = metric\n",
+    "                        best_metric_epoch = epoch + 1\n",
+    "                        best_metrics_epochs_and_time[0].append(best_metric)\n",
+    "                        best_metrics_epochs_and_time[1].append(best_metric_epoch)\n",
+    "                        best_metrics_epochs_and_time[2].append(\n",
+    "                            time.time() - total_start\n",
     "                        )\n",
-    "                    val_outputs = [post_pred(i) for i in decollate_batch(val_outputs)]\n",
-    "                    val_labels = [post_label(i) for i in decollate_batch(val_labels)]\n",
-    "                    dice_metric(y_pred=val_outputs, y=val_labels)\n",
-    "\n",
-    "                metric = dice_metric.aggregate().item()\n",
-    "                dice_metric.reset()\n",
-    "                metric_values.append(metric)\n",
-    "                if metric > best_metric:\n",
-    "                    best_metric = metric\n",
-    "                    best_metric_epoch = epoch + 1\n",
-    "                    best_metrics_epochs_and_time[0].append(best_metric)\n",
-    "                    best_metrics_epochs_and_time[1].append(best_metric_epoch)\n",
-    "                    best_metrics_epochs_and_time[2].append(\n",
-    "                        time.time() - total_start\n",
+    "                        torch.save(model.state_dict(), os.path.join(root_dir, \"best_metric_model.pt\"))\n",
+    "                        print(\"saved new best metric model\")\n",
+    "                    print(\n",
+    "                        f\"current epoch: {epoch + 1} current\"\n",
+    "                        f\" mean dice: {metric:.4f}\"\n",
+    "                        f\" best mean dice: {best_metric:.4f}\"\n",
+    "                        f\" at epoch: {best_metric_epoch}\"\n",
     "                    )\n",
-    "                    torch.save(model.state_dict(), os.path.join(root_dir, \"best_metric_model.pt\"))\n",
-    "                    print(\"saved new best metric model\")\n",
-    "                print(\n",
-    "                    f\"current epoch: {epoch + 1} current\"\n",
-    "                    f\" mean dice: {metric:.4f}\"\n",
-    "                    f\" best mean dice: {best_metric:.4f}\"\n",
-    "                    f\" at epoch: {best_metric_epoch}\"\n",
-    "                )\n",
+    "\n",
     "        print(\n",
     "            f\"time consuming of epoch {epoch + 1} is:\"\n",
     "            f\" {(time.time() - epoch_start):.4f}\"\n",
@@ -524,7 +700,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true,
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "set_determinism(seed=0)\n",
@@ -553,7 +734,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "set_determinism(seed=0)\n",
@@ -573,6 +758,43 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Profiling visualization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we give a brief overview of key observations from the `nsys-rep` output file when opened in Nsight systems (2022.2.1).\n",
+    "\n",
+    "In the GUI, select File -> Open -> output_base.nsys-rep. Here is a sample of the display:\n",
+    "\n",
+    "![png](figures/nsys-all-annotated.png)\n",
+    "- To get a better view of details, you can left-click and select a horizontal section, then right-click and \"Zoom into Selection.\" To return, right-click and select \"Reset Zoom.\"\n",
+    "- Sections B and C show training before and after acceleration (when fast=False and fast=True, accordingly). Clearly, MONAI optimized training is much faster than regular PyTorch training. B and C both contain two rows; the upper row shows per-epoch time, and the lower row shows per-action time (user-defined, like dataloading, forward, backward, etc.).\n",
+    "- Section A shows GPU utilization, where the height of the blue bars represents utilization rate. Regular PyTorch training shows sporadic and varying levels of GPU utilization, while MONAI optimized training shows consistent and high levels of GPU utilization.\n",
+    "\n",
+    "Expanding one more thread in the lower left corner and several more threads below \\[4648\\], we see the following:\n",
+    "\n",
+    "![png](figures/nsys-transforms-annotated.png)\n",
+    "\n",
+    "Sections D and E both include information on the transform chain.\n",
+    "- Section E: In MONAI optimized training, results of all transforms in the chain until the first randomized transform is stored to prevent repeated operations. This explains why E is chronologically before any of the training epochs in the figure.\n",
+    "- Section D: In regular PyTorch training, CacheDataset is not in use, and the transform chain is performed every epoch on all data used.\n",
+    "\n",
+    "Here is the display of the transform chain when zoomed in:\n",
+    "![png](figures/nsys-fast-transform.png)\n",
+    "\n",
+    "And a display of one training epoch of MONAI optimized training when zoomed in:\n",
+    "![png](figures/nsys-epoch-short.png)\n",
+    "\n",
+    "Notice that the per-epoch time is >20 times faster than the regular PyTorch training."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -582,12 +804,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
+   "execution_count": 23,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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36HXX6g3xu6sKhLPZbL0Vqkaru/NOFuedPJrd04zWdtXqDXrdtXqDXnet3hC/u6pAuOXEE+Gzn623RlXUa7zmmeK8k8V5J49m9zSjtV21eoNed63eoNddqzfE764qEG74z3/g8cfrrVEVTU1N9VaoCuedLM47eTS7pxmt7arVG/S6q/OemJi6W9B9YAAmJ2vz+t3d8NxzM9pEyTbPZOCaayA8QkMKMslxHy+qAmHT0gJKC7zHxsbqrVAVzjtZnHfyaHZPM1rbVas36HWvu/c//gHvfz/ssw/sthv88IcwMhK97rXXwuLF8Mc/AkXcX/EKeOUrYf36eF3//GfYcUfYZhv4/OdhaMgG5hs2wPBw2Zsp6G0M/OlP1v/Nb4Y994Q774THHoM3vAE222x6cBw3jz4KTz+de3z33XDyyfCpT8Gvf834Y4/F+nKqAuGGOXOg3h+YKpkzZ069FarCeSeL804eze5pRmu7avWGFLobU1YGMXbv666DT37Svn6Y+++Hb30rf9lJJ8Gll8LChTBnjg2Kt94awlOK9/bC+94Hg4Nw2mmwcWO0uzGwbp0NIPfeG26+ufp9+cc/4Kij4J3vhGOPhSOPhOXL4W1vg3PPhU02gVmzYMUKaGuzgeo555Tc7DTvv/4VjjkG2tvt62Wz8L3v2duDDrKB8b//DRs3wl/+Up57Zye88EJl+zs0BAceCNtvb9v6k5+E/fe3Fx7f/jaceCJt3/9+ZdssgapAONPcrDYjPDAwUG+FqnDeyeK8k0eze5rR2q5avSGF7qeeCu94R8nVYvX++99tIPeNb8A990x//qtfhY99zJYugA28nnvO9j+64Qa44w646SZobbWBZ2dn7n8/9jF48UW45BLo64Mzz2Tojjvs6wX3c3zclkWcfDIsWmQDzEIZ5jDDw7b0YWICvvIVOPRQmxG9/3649Vb48Ifh9tvhssvgX/+C97wHPvc5+MEP7Ppz58IVV5R8mWlt/vnPwy232H35xS/gwQfhrLPg3nvhXe+y+/Lkk7DppvC735W3L0ceaQPzffe1fuVw+eW2bY85Bn75S/s+vuc98Mwz9gLk/vsZOO208rZVLsaYuvzts88+plKyb3qTMVX8XxrIZrP1VqgK550szjt5qnEHVps6nTvr9VfpOVvrMaHV25iE3c8/35j3v9/+/e530evstZcxW2xRclOxef/rX8bMmWPMrrsa09hozGc/m//8xIQxCxcaA8bcc49d9sAD9vHll+eve9ddxrS2GvOGNxjz9NPGnHeeXe/Tn7bPf/3r9rH/N39+7n+7u+2yb3/bmOuvt/d/9avS/tmsMZttlr/dE080ZmCg/DZ43/uMWbasjJcKtfmyZcacdlrp7b///cbMnm3M4GDpdbfZxpgddzRm553tvpSzH/vua8wuu9i22LDBmIceKu1eJoXO26oywuMiajPCXV1d9VaoCuedLM47eTS7pxmt7arVGxJ0HxuzmclLLoGLL7b3o9i40dauZjJFN1fSe906+9P///xP4czqU0/ZbOaKFTaze/DBcNVV+evccYctbwD4z3/s7ZNP2tttt81fd9Uq+P73bYZ5q61sfeorXwn/+7/2+Y9+FE49leGzzoIzz7SZZb8UY2jI3ra1wWtfa8ssfvrT4vsINr55/nlbm/vFL8Kvf22zonPnlv5fnyVLbEa5xMgKeW0+MWHfq803L739Y4+178Ff/1p63fFxW1bhl2r4bV6Ie++Fu+6CM84AEeuz667F3WNAVSDcumCB2kB46dKl9VaoCuedLM47eTS7pxmt7arVGxJ0Hx+3t+ecY//WrbPBVxBjbHCVzZYc4aCk97e+ZQPE73/fBqiPPJL//PAwvPWt9rWuuQaWLbM1tI8+av98rr0WGrywZ80ae1soEAZbo3r++fC1r8HDD9tShFmz7HONjXDRRcz53vdgiy1sKYTfhykYCDc0wHvfCzfeCGvX2udOPTU6kPTLFY44wgbcxx9vA8JKWLLEBraDg0VXy2vz55+379fy5aW3/5rXwNKlcOWVpdcdG4OWFtu5D0qP+vXjH9v2ffe7i64W93GuKhAeAbWd5To6OuqtUBXOO1mcd/Jodk8zWttVqzck6O4PH9bcDHvtZe/ff3/+Oj09ufWefbbo5op6d3fDT35i61Svu87W6H7wg7nnjbEd1x54wGZQ/YD2mGPsbTArfO21tiPW5pvnspNr1tg63kWLol//Qx+y2eBddokMSjs6OmzAC7kA2L/1O6SdcooNiH/0IxugX3yxdQ5nt/3gtZIMcJglS+xtiaxpXptv2GBvywmEm5ps2/75z6UTk+PjNhDefnvbdk88UXjdwUH41a9sp8BC70WUewyoCoRnK84It7e311uhKpx3sjjv5NHsnma0tqtWb0jQ3Q9wW1pgjz3s/XvvzV9n48bc/ahAuLNzqiNaUe8LLrCB5Sc/CW98o+2UdtddudEoHnzQdhw7+2w4/PDc/y1fDgccAL//vX38wgu289wRR8B22+WXRkRlg8ukvb19eiDsD2PmL99iCzjsMNvx629/s6NSrF9v9y2InxGeN69qn6KB8MUX26HhCLV5JYEw2PKIwcHppSdhxsdtp8PZs2HlyuIZ4euvt9t8z3tKvnzcx7mqQHjEGLWBcGew56kinHeyOO/k0eyeZrS2q1ZvSNDdL41obrbDd22+Odx3X/46L76Yux8VCJ96KvzXfwFFvEdG4LvftTWzu+1ml+2zjx3H1i9puPVWexsVQL31rXbEhXvvtdlksIHw9tvHFgh3dnYWzgj7ywE+8AGbFT3/fBsAH364HcHCr1mG2gfCP/uZvWDIZvPb3C9dKTcQPvRQewH00Y/azH8h/NIIsOURxQLhW26x6x54YMmXj/s4VxUIz1q4UG0gvMQ/OJXhvJPFeSePZvc0o7VdtXpDgu7B0giwky5UGgivXz+VEV6yZIkNmg46yA7ZtXGjnVDh+OPtOp/6VO7/9tnH3t59t7294w4bjG+11fTXOO44W2aw99526LNly6zr9tvb1+jutsNyzSAQXrJkSXmB8JvfbIPTD33IPv6//7NB5Ne/nlun1qURQ0O2Pe+/P/9Y2bDBZm7LPX6ammx2uaPDBsNRTE7arL0fCO+wgw2EC3Xiu+UWO16wX4NdhLiPc1WB8CjY3qe1mrawhvQGr/oU4byTxXknj2b3NKO1XbV6Q4LuUYHwo4/mJ6r8QHj5chtshhkcnMos9/b22sD01ltttnTbbWGnnezP5V/7GrzqVbn/23VXG1z5gfDtt9sSiKhOZVttZTO/X/yiDbBOOsnW6m6/vX3+xhttPDGDQLi3tzcXuPqBbLhG2CdY+7rnnvCWt8BvfpNbFkdGePFiexsVCPslG3//e/6xsmGDzepX0jFv773tBcoll+Sy7UH8Xw1aW+3tjjva9vEn2Pj3v3NZ+cFBW7by6leX9dJxH+eqAuGWBQvsHYUd5ubN5MCuI847WZx38mh2TzNa21WrNyToHqwRBhvUZTL5ozls3GiDzr32is4IDwxMBUvz5s3LbfPzn7clDSedZDOIn/pUfoDW0mJnObv7bptRfewxm0ksxKab2hEY1q+H//f/7DI/EPZHbphBIDxv3rzSNcKF2Gqr/NKCODLCZQbCecfKhg3ll0UE+cIX7P9deOH05/w4LVgaAfY9HR+3GfKTTrLLbr/dXpCUGQjHfZyrCoQn/A+DwvKI4QrmAE8TzjtZnHfyaHZPM1rbVas3xOT+wAOwzTb5pQ1hgjXCkBs5Ilge8eKLdrrerbcuHAh7we/w8HBum7vsYqc7vugiOyZwFPvsYzOId9xhHx9wQHn75uMHvn4gvN12lf1/gOHh4fJKI6KYP9/WO/vlAnFkhJub7XYLlUYA3HILw8Hnqw2EZ82y/+dvN4j/fkYFwjfeaGePu/NOe0Fzyy32oumVryzrZeP+jKoKhBv9g0phINzq/zygDOedLM47eTS7pxmt7arVG2Jyv+8+OzmFX3oQRbg0YpttbBYzHAgvW2ZHC+jvt4GPTzabVxrR2to6PctcjH32sdv71a9stnjffcvePcCWLCxfbgPAWbPsNMBV0traWnr4tEIsWGAzoX5gF0cgDLlJNcIMD9tOh2NjzFq92i4zpvpAGGzpQ9Sv9OHSiBUr7OgRTzxhR/KYO9e2zw9+YAPhPfe0AXxZLxnvZ1RVIJz1i6gVXrFnSsysk1acd7I47+TR7J5mtLarVm+Iyb2/3976ozJEEQ6EGxrsKALBQHjjRtuJbeVK+ziYFfYDRW87mUxmegaxGH6HuSuusIFdNYGjXx6xzTa5STaqIJPJ5EoZgoFwc3OufQrhl3v6FwmDg/Z/ymmDYixZMj0jnM3aJOIRR9jXuP763GsPD5c3q1wUhQLhcGlEQ4PtMPfII/CHP9j66JNOskPf3XZb2WUREP9nVFUgPHWw+1dNipBKZ4dJCc47WZx38mh2TzNa21WrN5Th/qUvwetfX3ydSgLhYMDmjxzhj+/rZ4S33NI+DgbC/ne4F/yKyPRyi2Lstpt97fHxyssifPxAeAb1weC5R9UIlyqLgFwG1A+EBwZmng2G6EDYTyC2t8MrX0nTP/5hH1c6hnCYWbOKZ4SDx8gOO9gAvKPDTizy3/9tg/PR0YoC4bg/o6oCYfGvnvwPqiIaZnDFWU+cd7I47+TR7J5mtLarVm8ow/1Pf7L1mcV63ftBmT8FcRThjDDYQHhgwE4jDPmlERAdCHvbaWhoqKw0wu8wB8U7yhUjpkC4oaHB/uQvkj9qRDmBcDimGRycWUc5n2KB8Jw58IY30HD//fY9mmkgXG5pBNg64UzGLjviCHvM+HXBwZFBShD3Z1TVJ37CL41QmBGe8D/kynDeyeK8k0eze5rR2q5avcFzHx2NnuRgYsJ2hAPw60OjKCcjHJW93XNPe3v//TYQHB62pRHLltn1gkOo+QGjt52JiYnKMsKQK4+oNiPsd5CbYSA8MTFhg+A5c/JLIyoJhJPICAc78L35zfb+NdfULhAOl0ZArsPcYYflAv5vfxvOO88eJ2US92dUVSDc4g+irDAQnlXGINFpxHkni/NOHs3uaUZru2r1Bs/9y1+G17xm+pOPPJILNu+6q/BG/EB47drCY/ZHZYR32w0aG+0sbv6IE8uW2drQLbYomhGeNWtWZRlhgJNPtrPJ7bxzeeuH2XdfWyZw0EHV/b/H1PHS1lZ5IFzL0oi+Ppt99QlmhPfYA7PFFvYXAj8QjrtGOKo0Yvfd7e3b355btt9+dvrsCoj7M6oqEB5uarJ3FJZGDEUNL6IA550szjt5NLunGa3tqtUbPPfnn7cd1cLcc4+9bWuzw1YVwg/KxsdzQVKYqEB41iwblN53X34gDLY8okiN8NDQUGWd5cD+pH7JJdV3dFuxwraTP/RblUwdL3Pn5tcIlxoxAmpXGuGPJRwcOSIYCIsw/oY32Ikw1q61gXO1wWWpjHCwNGKPPeyQd+96V3Wv5RH3Z1RVIDzPv2JRmBGeX+awIGnDeSeL804eze5pRmu7avUGz73Q7Kv33GODrCOPLJ0R9pNOhcojCmVv99prekYYCgfCxsDkpPWutDQiJUwdL9VkhGtZGgH55RGhsY2bjz3WBse//331ZRFQWUYYbAZ4hjW+cX9GVQXCPePjthZHYUa4J6pmSwHOO1mcd/Jodk8zWttVqzd47plMbuSGIPfcY+t499/fZnqfey56I/39dlILKBwIFwpa99zTbvehh+zjTTaxt1tuaV/T/6k+mMwaH7felZZGpISp46WtrfLOcn72Nzh8WpyBcKGMMNCzxx7Wsa8v2UA4BuL+jKoKhJe0t9sDR2FGeIl/YCrDeSeL804eze5pRmu7avUGzz0qIzw5aUsW9tnHZuSgcFa4vx923dUGuaUywlGBMMDf/mZv/UB4xQobnL/wgn0cCoSXLFmiNiM8dbxUkxFubLSBr5/cGxiIb9QIyM8IhwLhJcuX205rEE8g7M+O5xNVGhETcX9GVQXCHR0dsGhRdI/YlNPR0VFvhapw3snivJNHs3ua0dquWr3Bc48KhJ94wgZCe+9tg9XGxsKBcF+frTHdeuvCQ6iVCoRvu81+V/vZwKVL7a0fmPmZU29bHR0dajPCU8dLMBAut0YYbHlEHUojOjo6bJkMVN9RDmyga0zu/fOpYUY47s+oqkC4vb3dfqA6O+utUjHt7e31VqgK550szjt5NLunGa3tqtUbPPeo0gh/uuS997YB2u67R3eYM8ZmJ+fPt8OKVVojvHixrQfOZHLZYH855H6qD2WE29vbaxo41ZKp4yXYWa7cjDDkAuHxcfuXUEa4vb3dzu62aJE9LqrFz/iGyyNq+H7G/RlVFQh3dHSoDYS1Zhmcd7I47+TR7J5mtLarVm8okhG+5x476cNOO9nH++5rM8Jf+AIsXAg/+pFdPjpq/3/+fDvO7pNPTv/JG4qXMfhZ4eC4sOGa1WAg7GeElZZGTMsIG1NZIDx/vg2E/Sx5HBnhefNsh8dSGWE/njr66Opfq1AgXMPSCJcRXrJEZSCsNcvgvJPFeSePZvc0o7VdtXpDkYzwPffYoav80SD23dfOLveVr9ig9N577XK/VtXPCPf3R3/fFiqNgNxwZMFA2M8I+4FZVEZYaWnE1PHid5YbHbXBcCUZ4f7+XJvEEQiLTJ9Uw88Iz56d7z3TWdpcRjhZuru71WaEu4O9NxXhvJPFeSePZvc0o7VdtXqD5z4xMT0j/NBDNhD2Oe44+MxnbAe6nXYCP8Pm16ouWJCbcS2qPKJYIOxnhMstjZiYsN5KM8JTx4ufEfYzr5XWCPsZ4ThKIyA6EG5pmboYiu04r0MgHPdnVFUgvGDBAhsI+/U0iljgjxeoDOedLM47eTS7pxmt7arVGzz3TMZmJIMlDSMj+ZnGBQvgq1+1wXF7ey65FMwI+1MQFwqEm5ps5jGMnxHedNPcstmz7V+BGuEFCxbkgms/a62EqeOlrc22eTAwLge/NCLOjDBMD4RD5RqxHed1KI2I+zOqKhAeHBy0UzVC/uDcChgM9pJVhPNOFuedPJrd04zWdtXqDZ67P1ZvsDxictKOFBFFe3suIxwMhLfe2t5fu3b6/4yPF87crlwJP/yhnf44yOLF+aUR/k/yExPWe3zcZg+jgusUMxjO5Pqz+lVaGhF3Rnjx4unjCAey1LEd56UywjW4sIn7M6oqEJ49ezbsuKN98Nhj9ZWpkNleXY42nHeyOO/k0eyeZrS2q1Zv8Nz9QDhYHlEsEA6WG/qlEfPn2wBn9uzocfsnJgoHwiJw5pm5pJVPMDAbHLSjFQCMj1vvYsF1ipk6XvzA159Vr5JAeGQk1za1ygiHAuHYjvNigXCNLmzi/oyWFQiLyOEi8riIrBGRTxdY5zgReUREHhaRy2K19BgfH88Fwo8/XouXqBnjyko5fJx3sjjv5NHsnma0tqtWb/DcozLC2WzhTlHt7TZgmpzMZYT9n57nzMnVvAYpFggXYsmS/NIIv254YsJ6T0yo6ygHgePFD3z9jHC5NcL+dMEbNtjbuANhv0QmVBoR23FerDSiBmUREP9ntGQgLCKNwAXAEcAuwAkisktone2BzwAHGWN2BT4cq6VHY2OjfXPnzoV162rxEjWjsdDVeMpx3snivJNHs3ua0dquWr3Bcw9nhI2xgXCx0gi/tjVYGgE2cPJHGwhSTdAaLo3wh1QbH7feSjPCU8dLOBCuJCMMuUA4zs5yY2O59y+UEY7tOC+VEa4BcX9Gy8kI7wesMcasNcaMA5cD4UHnTgMuMMb0ABhjNsZqGWbTTXM/PzgcDofD4bCEM8L+bbHSCLDlEeFAOM6MsF8akc3abQYywlO3CjPCU/gBbHBc4XIIB8JxZoQhd/FRyWx3lVAsEK5RRjhuygmElwPB9Ot6b1mQHYAdROTfInK7iBwel2CQSf8Kd9NNc3OWK2EyPJyNEpx3sjjv5NHsnma0tqtWb/DcwxlhPxAuVhoBNoDr64NZs3IBaaGMcDXZW780wu/o5AfC4+PWu4YZxFoydbxUmxH2LzrWr7fvUVz1r+FJTEKlEbEd58VKI2r0fsb9GY2rs1wTsD1wCHAC8BMRWRheSUROF5HVIrJ648aNjI6OMjw8zNDQEGNjY/T395PJZOjp6cEYQ6dXwO/PIjIwMIAxhvFFizAvvEB/fz9jY2MMDQ0xPDzM6OgoAwMDTExM0NvbSzabpcu7GvK34d92d3czOTlJX18f4+PjDA4OMjIywsjICIODg4yPj9PX18fk5OTUmHXhbXR1dZHNZunt7WViYoKBgYGC+zQ8PBy5T52dnRhj6OnpIZPJpG6fxsfHK36f0rBPLS0tVb1P9d4nv83jPPaS2KehoaFEP09x7pPfA7mS98lRmhaFQQ3o9QbPPRwI+7fFSiPABsL+9Mo+cWeEx8Zyv+YGAuGWlha1pREtwYsGqLxGOJgRnjs3vs5l4UlMQhnh2I7zOpRGxP0ZLWdciw1AsPvnCm9ZkPXAHcaYCeApEXkCGxjfFVzJGHMhcCHAqlWrzKxZs/I20uo16CKvN+lS7ycbfxaROXPmICK0rFwJt9zCfO8D2xpIv/vbXLhwIQBLvKsifxv+7WLvIPHHo4tqWH+Zv254G/62/ddqjvgQ+26tra2IyLR98h/7+5y2fWpsbKTS9ykN+9TX11fV+1TvfRr3vhTiPPaS2KdZs2bR2NiY2Ocpzn2aHZppqZJjz1GYkZERlW2m1Rs8d7/UwM8ElwqEw6URwUC4rS1/CC6fagNhgGeesbd+xnJiIuetsN2njpc4aoTjnDEtXBoRygjHdpzXoTQi7s9oORnhu4DtRWRrEWkBjgeuDq3zB2w2GBFZii2ViBh8cGbM9WtwNt3UTg8ZbvgUMzeuAviEcd7J4ryTR7N7mtHarlq9wXMvVBpRKhCuNCNcaSDiB2bhQHh83HorzQhPHS8zzQiPj8fXUQ6ml0aEMsKxHed1KI2I+zNaMhA2xmSAs4DrgEeBK4wxD4vIl0TkKG+164AuEXkE+AfwCWNMV/QWq6fPH+PQn7FGUYe5KXdlOO9kcd7Jo9k9zWhtV63e4LmHO8v5AXGhGuHWVhv8dnbaGuHgrF1x1giHM8KBznJ9fX1qO8tNHS9+cNbVZeusyx3ZIHjhEVdHOShZGhHbcV6H0oi4P6NlTflhjLkGuCa07OzAfQN81PurGf5PlSxbZm9feMHOYqOAKXdlOO9kcd7Jo9k9zWhtV63e4LlXWiMMNivsZ4T9GeUg/hphgKefzn88Pm69lXaWmzpeWlvtxUY2W9noDK2t9m9sLN5AuLXVXsh0ddn3a2IirzQituO8DqURcX9GVc0s53dimcoIKxo5YspdGc47WZx38mh2TzNa21WrN3juhTLCxQJhf5rlqBrhQuMIVzNqBERmhDs6OtSWRkwdLyK5QLPc+mAfPwsfd1mOP2Sd/x4GAvTYjvM6lEbE/RlVFQj7nVg0lka0x1kEnyDOO1mcd/Jodk8zWttVqzd47pUOn2b/MVcaEVUj7M9O5lNNGYM/pXJEjXB7e7va0oi846XaQNhv8zgzwpCbXS4iEI7tOG9qssdWgqURcX9GVQXCU1cBm2xib11GuOY472Rx3smj2b0QInK4iDwuImtE5NMRz68UkX+IyL0i8oCIvCluB63tqtUbQhnhaksjwjXCxkQHOZVmb2fPtn/r19vHL7WMMMw8I1yLQLi7O1feEvCK9Tj3SzuC1LA0wmWEwTbuokWqAmGtWQbnnSzOO3k0u0chIo3ABcARwC7ACSKyS2i1z2M7Pu+FHQnoB3F7aG1Xrd4A7UuXTs8El1sa8dxzdt1wRhim1wlXUxoBNjDzfebPt+UEL6WMsF/aUOkMbrUsjah1RhiiA+Ealka8rDPC/mD+gLrZ5fLcFeG8k8V5J49m9wLsB6wxxqw1xowDlwNHh9YxgB/xLACei1tCa7tq9QboCpYLVloa4a8XrhGG6XXC1QbCfhZ47lzr09ICExO2zZV2lss7XtJaGuFfyAQC4ViP80IZ4Rq9n3F/RlUFwv4g+YD9IP3+9/Dss/UTqoA8d0U472Rx3smj2b0Ay4F1gcfrvWVBvgicJCLrsSMC/U/UhmYyG+iiRYtSMctk8Lac2QsbGxtTMctkNfs0NzD50cjQkN0nL4idhMKztgZ+wp6cO3dqn0a94Hmsuztvn8zEBGNe3XAl+5TxMp/ZtjYmJycxTU15UyyPesF72mbOLLZPzc3NU++T8QPNtraKjr0JL3AebWqKdZ/M4sWY4LTWAS9jTGzHXra5mezISN4+ZcfGmGxqqsn7lPUu2ir9PBWirOHT0kJ/f//UbFA8/LC9/dWv4DOfqZtTueS5K8J5J4vzTh7N7jPgBOASY8w3ReRA4BcispsxJhtcaSazgfb29qZilsngbTmzF46MjNDU1FT3WSar2afedevwLWb7s5012a/5xpaWgvs0b5ttpl6vcdGiqX3yO7S1Tk7S6mUrFy5cCOPjtHo/41e0T17/nob5822pRmsrTEzYdSYmmOW9btpmziy2T2NjY7nZQAOTa1R07HnPzWpvhziPvSVLbKbf//V8zpypbTU1NcU2GyizZ8PEBPOCGe3xcZg9e8oxzvfJb7u4ZgNVlRFuC/7ccI03rLGS2eXaKv2pJCU472Rx3smj2b0AG4AtAo9XeMuCnApcAWCMuQ2YBSyNU0Jru2r1BmgLdk7ySx3KLY3wqWWNsF8a4QdMLS0wPm7bXGlnubzjxb9fbY1wLUojANZ5PxAFXGM9zhMujYj7M6oqEB4dHc09OPhge3X5/PP1E6qAPHdFOO9kcd7Jo9m9AHcB24vI1iLSgu0Md3VonWeB1wOIyM7YQDjWrtha21WrN8BYMGCtdNQInyRqhP2Ar7nZlkSMjqqtEc47XsLTLZeL3+a16CwHuUA4EKDHepwnPGpE3J9RVYHwtJ9HNtvM9nRVQNRPOxpw3snivJNHs3sUxpgMcBZwHfAodnSIh0XkSyJylLfax4DTROR+4NfAKd4MobGhtV21ekOo1rGSQDiYEQ4On1YsI1xN0OpnKIMZ4YkJ2+ZKR43IO17SOHwaRAbCsR7n4UA4m63p+xn3Z1RVjbBfID2FokB4mrsSnHeyOO/k0exeCGPMNdhOcMFlZwfuPwIcVEsHre2q1RvATEzkHlRSGjFv3lSZQj0ywtlsVm1pRN7xUm0g7PdRSLA0ItbjPBwI+8dhjQLhuD+jqjLC0xIW228Pjz+e+6CnmJiTLYnhvJPFeSePZvc0o7VdtXpDKBCuJCMskiuPCAZjhTLC1QatweHTYCojbDIZ+z2uMCOcd7xUWyN8xBFw3nmw556xeQHTSyMCnV1jPc5nzcoPhMfH7W2NSiPi/oyqCoSbmkIJ7N12sx9Qf8rGFDPNXQnOO1mcd/Jodk8zWttVqzeEfuKtZEINsOURs2blB6NRGeHJSTvbXLUTasC0jHCTH9gozAjnHS/V1gjPnQuf/GTp96hS/KEhe3rsyA6BXwViPc7DGWH/fo0ubOL+jKoKhMfCxdg772xvn3gieZkKmeauBOedLM47eTS7pxmt7arVG2A8qrNcOaURYAPhYH0wRGeEZ/Kzd9SoERMTjA0MVL/NOpN3vFRbGlErmppyZRchp1iP83Ag7GeEa/R+xv0ZVRUIzwn/3OBfXfb0JC9TIdPcleC8k8V5J49m9zSjtV21egPMCmbKKs0Ib7cdrFyZv6y52f4FM8J+IBxjRniOvy2FgXDe8ZK2QBhyFx+h4zrW47xQRrhGpRFxf0ZVBcID/lWjj3/12teXvEyFTHNXgvNOFuedPJrd04zWdtXqDTDc3597UEmNMMD/+39w7bXTl8+Zk58R9rN91QTCm24KX/86HHecfexlhAe9mcs0lkbkHS9+aUSaLqb8i4+QU6zHecIZ4bg/o6qKoabN/qQoENY6c5XzThbnnTya3dOM1nbV6g0wb/bs3INwIFyqNKKtLTqT2dYWX0ZYBD7xidxjLyO8wA/SFGaE846X17zG1voeeGDdfKbhZ4RD722sx3nCgXDcn1FVGWF/HvAp2trsVa6CQHiauxKcd7I47+TR7J5mtLarVm+AvqB7ePi0ajtihTPCMwmEw3gZ4Z4XX4xvmwmTd7y0tdnRH0JTkdeVAhnhWI/zhEsj4v6MqgqEly4NzQAqYsc8VBAIT3NXgvNOFuedPJrd04zWdtXqDbAwODNZpaURhSiUEY4j2+dlhBcHh1NTRuqPFz8QDmWEY/VubYXgbG81zgjH3eaqAuGOjogZQBcuVBEIR7orwHkni/NOHs3uaUZru2r1Bujt7Mw9qLSzXCHirBEO403i0e1nhBUGwqk/Xgp0lovVu7XVHmf+sVbjQDjuNlcVCLcHp4H0WbAAensTd6mUSHcFOO9kcd7Jo9k9zWhtV63eUCAjXO7waYWIs0Y4jFcaMZURVlgakfrjpUBpRKzefgmEXxJR49KIuNtcVSDcGbza9Vm2DNavT16mQiLdFeC8k8V5J49m9zSjtV21egP0Bd3jKo2oZY2wVxrR62f4FGaEU3+8FCiNiNU7HAjXOCMcd5urCoSX+G9okL33hoceyq9PSSGR7gpw3snivJNHs3ua0dquWr0B5gezfnGVRtSyRtjLCC/wR7tQmBFO/fFSoDQiVu+EA+G421xVINwbVQKxzz6QycAjjyTuUwmR7gpw3snivJNHs3ua0dquWr0BhoL9ZeIqjahljbCXEZ4aR1hhRjj1x0uB0ohYvRMujYi7zVUFwvP82WiCrFhhb59/PlmZCol0V4DzThbnnTya3dOM1nbV6g0wOxic1jojHGONsOaZ5VJ/vBQYRzhW74QzwnG3uapAeDj4YfRZtszebtyYrEyFRLorwHkni/NOHs3uaUZru2r1BhgLuiuqER7zZwpTWBqR+uNl003toAJbb523OFbvhAPhuNtc1cxyrVFp9k02sbf+8CspJdJdAc47WZx38mh2TzNa21WrN0BeGBmeUGMmo0ZMTNi/5ub4a4SzWZr9YF1hRjj1x0tbGzz3HARnHSRm74RLI+Juc1UZ4UwmM33hnDl2fu+UB8KR7gpw3snivJNHs3ua0dquWr0Bsn4mDuLNCEOuPCLujDCQVZwRVnG8zJljJyALEKt3whnhuNtcVSAsoTdyik02gRdeSFamQgq6pxznnSzOO3k0u6cZre2q1RtA/KAX4p1ZDnKBcNwTagDib1thRljr8RKrd6FAuEYZ4bjbXFUg3FDop50tt4Rnn01WpkIKuqcc550szjt5NLunGa3tqtUbQoFwXKURfkbYrxOuQUZYRkbsY4WBsNbjJVbvQqURNcrwx93mqt7BCf8DGGabbWDt2mRlKqSge8px3snivJNHs3ua0dquWr0BJoPj6dcqIxx3jTC6SyO0Hi+xekdlhJuaqr/4KkHcba4qEJ41a1b0E9tsY0sjUtx7s6B7ynHeyeK8k0eze5rR2q5avQGagz8ZxzV8WgIZ4SY/gFKYEdZ6vMTqHRUI17ATYdxtrioQHgoO4RJk5Up7m+Kplgu6pxznnSzOO3k0u6cZre2q1RtgvNjwaTMZNQJqWiOc8SdIUJgR1nq8xOodVRpRw4uauNtcVSA8f/786CeWLrW3XV3JyVRIQfeU47yTxXknj2b3NKO1XbV6A7Q2BUZEDdcIp3HUCC9YaqnxKAO1ROvxEqt3VEa4hu9l3G2uKhDu6emJfsKfQrCzMzmZCinonnKcd7I47+TR7J5mtLarVm+AUb/WFuKvEa5hacREb6/NWFfrWEe0Hi+xeidcGhF3m6sKhJf4Ae/0J+xtijPCBd1TjvNOFuedPJrd04zWdtXqDTAnmIWLqzSiUEY4xs5yLf5kHQrRerzE6p1waUTcba4qEO7o6Ih+QkFpREH3lOO8k8V5J49m9zSjtV21egMM9/fnsqpxlUaEM8J+GUNTDBPT+hnhvj6VZRGg93iJ1Tvh0oi421xVINze3h79xLx59kOZ4kC4oHvKcd7J4ryTR7N7mtHarlq9wcsI+wFILWeWa2qaNlNZVXiuzWNjajPCWo+XWL39Yy6h0oi421xVIFzwKkAE2ttTPc2yu2pMFuedLFq9Qbd7mtHarlq9wcsI+0FJXMOnzZplv2ODNcJxBa3ediaD3srQerzE6i1i37+ESiNcRrgQK1akevg0d9WYLM47WbR6g273NKO1XbV6A8xpbrbZ2sbGXAA805nlRGxWOJgRjivI8bbTODqqNhDWerzE7t3amlhpxMs6I9zd3V34yZUrUz3NclH3FOO8k8V5J49m9zSjtV21egOMDg3lZvQKZ4RnMsvXnDk1zQiboSG1pRFaj5fYvcOBcA1LI+J2L+uTISKHi8jjIrJGRD4d8fwpItIhIvd5f++L1dJjwYIFhZ/cYgt48kkwphYvPWOKuqcY550szjt5NLunGa3tqtUboKWhwQaUwYzw5OTMhyVra8ufUCOuoNXLGsrQkNqMsNbjJXbvYCBc49KIuN1LBsIi0ghcABwB7AKcICK7RKz6G2PMnt7fRbFaegwODhZ+co897JXqhRfW4qVnTFH3FOO8k8V5J49m9zSjtV21egNkRkZyGeFgacRMssFQ84zwtPuK0Hq8xO6dYGlE3O7lfDr2A9YYY9YaY8aBy4GjY7Uok9mzZxd+8uSTYfZseOCB5IQqoKh7inHeyeK8k0eze5rR2q5avQEaIVcjHCyNiDMjXIMa4Wn3FaH1eIndO8HSiLjdywmElwPrAo/Xe8vCHCsiD4jIlSKyRdSGROR0EVktIqs3btzI6Ogow8PDDA0NMTY2Rn9/P5lMhp6eHowxdHozxfk9BF988UWMMfT09JDJZOjv72dsbIyhoSGGR0fJbrklExs2MDExQW9vL9lsli5vSDV/G/5td3c3k5OT9PX1MT4+zuDgICMjI4yMjDA4OMj4+Dh9fX1MTk5O1aOEt9HV1UU2m6W3t5eJiQkGBgYK7lNHR0fkPnV2dhbep+FhRkdHGRgYqNs+9fT0VPw+pWGfxsfHq3qf6r1Pvb29sR97SexTR0dHop+nOPdp48aNFb9PjtKM++PNKkOrN0B2fHx6RjiOQDiJjLDSQFjr8RK7d4KlEXG7iylRUysibwcON8a8z3v8bmB/Y8xZgXWWAIPGmDEROQN4pzHmdcW2u2rVKrN69eqKZEdGRopfCRxyiL0KvvnmirabBCXdU4rzThbnnTzVuIvI3caYVTVSSiWVnrO1HhNavQEmjzmGxiefhOeegxNPhO99Dz76UbjoIujvr37Db34zbNwId90FxxwDTz0F998/c+GODthkE3v/4IPhpptmvs2E0Xq8xO59wAGwYAFcdx0sXw5HHGGPuxpQrXuh83Y5GeENQDDDu8JbNoUxpssY410KcBGwT8WGcbBsmf2wOhwOh8PxciOTmT58WhwZ4blzYWDA3o8zI/wSKI1weCRYGhE35QTCdwHbi8jWItICHA9cHVxBRDYLPDwKeDQ+xRyT/ge7EJtsktpJNUq6pxTnnSzOO3k0u6cZre2q1RvA+LO+xV0asXgx+ENWxVkj/BLoLKf1eIndO8HSiLjdS04WbozJiMhZwHXYWvyLjTEPi8iXgNXGmKuBD4rIUUAG6AZOidXSo6VUwy5bBr29Ne+xWA0l3VOK804W5508mt3TjNZ21eoN0DA5Ob2zXByjRixZYgNhY1xGOITW4yV271mzwOuzUesYLG73koEwgDHmGuCa0LKzA/c/A3wmVrMIRkZGijeAX2u0caOdaS5FlHRPKc47WZx38mh2TzNa21WrN9jOcg3NzfFnhJcssdvp67NBTly1pUEvpW2u9XiJ3dvPCBtT89KIuN1VzSw3d+7c4isEA+GUUdI9pTjvZHHeyaPZPc1obVet3gCNxtRm+LTFi+1tV1e8GWGRXACstDRC6/ESu7cfCGcyNhiu4cVB3O6qAuG+vr7iKyxbZm9TWCdc0j2lOO9kcd7Jo9k9zWhtV63eAJmxsega4ThKIyAXCMcZ5PgBsMKsKug9XmL39gNhf2izGr6fcburCoQX+1elhUhxRrike0px3snivJNHs3ua0dquWr0BmiG6RjiO0giIPyMM6jPCWo+X2L39QNjvMFfD0oi43VUFwv5A9wXZbDP7U8vTTyfiUwkl3VOK804W5508mt3TjNZ21eoNMDE6Wpvh0/xAuLvbZvziDFqVZ4S1Hi+xe7e2wuhoIhnhuN1VBcLt7e3FV5gzB7bdFh58MBmhCijpnlKcd7I47+TR7J5mtLarVm8IZIRrXRpRi4yw0kBY6/ESu3eCpRFxu6sKhMu6CnjFK+CBB2ovUyHuqjFZnHeyaPUG3e5pRmu7avWGQI1w3KURCxfaX1trEQj721JaGqH1eKlJRnh8HM49N/e4RriMcCm23BL+8x+49dbaC1WAu2pMFuedLFq9Qbd7mtHarlq9AZqy2dpMqNHYaIPhWnSWcxnhuhC796tfDVtvDRdfbB9vsUXx9WfAyzoj3NXVVXolv4EOOqi2MhVSlnsKcd7J4ryTR7N7mtHarlq9ASbHx6OHT5tpaQTY8giXEZ6G1uMldu9DD4W1a22dcF8fHHxwvNsPELe7qkB40aJFpVdK6dVZWe4pxHkni/NOHs3uaUZru2r1BmjIZm1AGcwIx1EaAblAOO7OcsozwlqPl5p5NzbC/Pm12bZH3O6qAuH+/v7SK6U0EC7LPYU472Rx3smj2T3NaG1Xrd4AZmKiNhNqQG6aZddZLg+tx4tWb4jfXVUg3NbWVs5KtRepgrLcU4jzThbnnTya3dOM1nbV6g0gk5O1GT4N8ksjajGhhtLSCK3Hi1ZviN9dVSA8OjpaeqWVK3P3/SviFFCWewpx3snivJNHs3shRORwEXlcRNaIyKcLrHOciDwiIg+LyGVxO2htV63egJ3eNtxZLpuNp0Z48WI7WZUxLiMcQOvxotUb4ndXFQg3l/Ph22EH+MAH7P2RkdoKVUBZ7inEeSeL804eze5RiEgjcAFwBLALcIKI7BJaZ3vgM8BBxphdgQ/H7aG1XbV6A7lAuFalEcPD9r7rLDeF1uNFqzfE764qEM6Wm+HdeWd7OzRUO5kKKds9ZTjvZHHeyaPZvQD7AWuMMWuNMePA5cDRoXVOAy4wxvQAGGNin5dea7tq9QaiM8JxBsI+LiM8hdbjRas3xO+uKhA2xpS3ol8/4l+9poCy3VOG804W5508mt0LsBxYF3i83lsWZAdgBxH5t4jcLiKHR21IRE4XkdUisnrjxo2Mjo4yPDzM0NAQY2Nj9Pf3k8lk6OnpwRhDZ2cnYAe89x8bY+jp6SGTydDf38/Y2BhDQ0MMDw8zOjrKwMAAExMT9Pb2ks1mp4ZG8gfN92+7u7uZnJykr6+P8fFxBgcHGRkZYWRkhMHBQcbHx+nr62NycpLu7u7IbXR1dZHNZunt7WViYoKBgYFp+zQwMFBwn4BU7xMTE4xls0yKMJnJMDQ0RDaTIWPMjPdpZM6cqeMi29wc3z4Fpliu5H0qduyl/X1Kwz719PSo3aeenp6q3qdCSL2+BFatWmVWr15d0f+MjY3RWs5sJVdcAe98Jzz0EOy6a5WG8VK2e8pw3snivJOnGncRudsYs6pGSjNCRN4OHG6MeZ/3+N3A/saYswLr/BmYAI4DVgA3A7sbY3oLbbfSc7bWY0KrN8bYTPDZZ8Ntt8HAgL191atg1iy4/vqZbf/66+ENb7D3f/hDOPPMmTsDnHgi/PrXcNVVcMwx8WwzQbQeL1q9oXr3QudtVRnhsbGx8lb0M8K/+Y09OaSAst1ThvNOFuedPJrdC7ABCE7rtMJbFmQ9cLUxZsIY8xTwBLB9nBJa21Wr91QpRHNz7WqEfWpRI6y0NELr8aLVG+J3VxUIzwn8NFMUPxD+8pfhL3+pnVAFlO2eMpx3sjjv5NHsXoC7gO1FZGsRaQGOB64OrfMH4BAAEVmKLZVYG6eE1nbV6k0mY2+jhk+La9QIn1rUCCvtvKX1eNHqDfG7qwqEBwYGyltx4cLc/ZQMGl22e8pw3snivJNHs3sUxpgMcBZwHfAocIUx5mER+ZKIHOWtdh3QJSKPAP8APmGMiXXeUq3tqtU7LxCu1cxyPrUYR1hpRljr8aLVG+J3b4p1azVmYTDALUZwLOG5c2viUillu6cM550szjt5NLsXwhhzDXBNaNnZgfsG+Kj3VxO0tqtW72kZ4bhLI9rabLDqpljOQ+vxotUb4ndXlRH2ez+WJDgPdUrqYMp2TxnOO1mcd/Jodk8zWttVq3fBjHBcpREiuaywG0d4Cq3Hi1ZviN9dVSC8dOnS8lYUyd1PyVjCZbunDOedLM47eTS7pxmt7arVu2BGOK7SCKhNIKw8I6z1eNHqDfG7qwqE/bHiysKfXS4lYwlX5J4inHeyOO/k0eyeZrS2q1bvop3l4gqE/Q5zrrPcFFqPF63eEL+7qkC4vb29/JX/7//sbUoC4YrcU4TzThbnnTya3dOM1nbV6s3EhL2tVWkE5DLCrrPcFFqPF63eEL+7qkDYn5GkLPzhNVISCFfkniKcd7I47+TR7J5mtLarVu+pjHB4HGFXGlFTtB4vWr0hfndVgfCS4PAtpWhshNbW1ATCFbmnCOedLM47eTS7pxmt7arVu2hnuTQHwso7y2k9XrR6Q/zuqgLh3t7eyv5hzpzUdJar2D0lOO9kcd7Jo9k9zWhtV63eRYdPi7s0Is6g9fWvZ+zEE0HpT/Vajxet3hC/u6pAeN68eZX9Q1tbajLCFbunBOedLM47eTS7pxmt7arVu+YTakBtAuHddqPx5z+PzzFhtB4vWr0hfndVgfBwpUFtijLCFbunBOedLM47eTS7pxmt7arVu+YTagC86U3wwQ/CDjvEsz0PtW2OXnet3hC/u6qZ5VpbWyv7h/nzUzPFcsXuKcF5J4vzTh7N7mlGa7tq9S46fFpcpRGbbgrf+U482wqgts3R667VG+J3V5URzvgf9HJZvBi6u2sjUyEVu6cE550szjt5NLunGa3tqtU7kdKIGqG2zdHrrtUb4ndXFQhLcMa4ckhRIFyxe0pw3snivJNHs3ua0dquWr3zxhGuVWlEjVDb5uh11+oN8burCoQbKv15Z9Ei6OmpjUyFVOyeEpx3sjjv5NHsnma0tqtW70SGT6sRatscve5avSF+d1UtMeFf8ZaLnxH2r4zrSMXuKcF5J4vzTh7N7mlGa7tq9S44oUacNcI1Qm2bo9ddqzfE757uT0eIWbNmVfYPc+fak8GCBXD33bWRKpOK3VOC804W5508mt3TjNZ21eqtuUZYbZuj112rN8TvrioQHqp0KLRNNrG3g4OwahU89hjceWf8YmVQsXtKcN7J4ryTR7N7mtHarlq9Exk+rUaobXP0umv1hvjdVQXC8+fPr+wf3v3u/Mc77wz77x+fUAVU7J4SnHeyOO/k0eyeZrS2q1bvRIZPqxFq2xy97lq9IX73dH86QvRU2vGtuRlWrKiNTIVU7J4SnHeyOO/k0eyeZrS2q1bvaaUR2SwYo6I0Qm2bo9ddqzfE764qEF7iT++oEK3uzjtZnHfyaHZPM1rbVav3tIww5LLCKQ+E1bY5et21ekP87qoC4Y6Ojsr/yZj4RaqgKvcU4LyTxXknj2b3NKO1XbV6T8sIQ25s4ZSXRqhtc/S6a/WG+N3L+nSIyOEi8riIrBGRTxdZ71gRMSKyKj7FHO3t7bXYbCJodXfeyeK8k0eze5rR2q5avadNqBFclvKMsNo2R6+7Vm+I371kICwijcAFwBHALsAJIrJLxHrzgA8Bd8RqGKCqq4A5c+IXqQKtV1/OO1mcd/Jodk8zWttVq3feOMLhjHDKA2G1bY5ed63eUJ+M8H7AGmPMWmPMOHA5cHTEel8GzgNGY/TLo6qrgKuvhu23j1+mQrRefTnvZHHeyaPZPc1obVet3pE1wuPj9jblpRFq2xy97lq9oQ4ZYWA5sC7weL23bAoR2RvYwhjzl2IbEpHTRWS1iKzeuHEjo6OjDA8PMzQ0xNjYGP39/WQyGXp6ejDG0NnZCeSi/7Vr12KMoaenh0wmQ39/P2NjYwwNDTE8PMzo6CgDAwNMTEzQ29tLNpulq70dvvOdaS7d3d1MTk7S19fH+Pg4g4ODjIyMMDIywuDgIOPj4/T19TE5OUl3d3eeh3/b1dVFNpult7eXiYkJBgYGCu7TM888E7lPnZ2dle9TV1ekTy32af369RW/T2nYp+7u7qrep3rv0/r162M/9pLYp6effjrRz1Oc+/TUU09V/D45SuO/z9rQ6h0ZCCvJCKttc/S6a/WG+N3FlOhMJiJvBw43xrzPe/xuYH9jzFne4wbgRuAUY8zTInIT8HFjzOpi2121apVZvbroKtOYnJyksZoP9M03w8EH5x5nsyBS+XZmQNXudcZ5J4vzTp5q3EXkbmNMTfpCpJVKz9lajwmt3px3Hnz60zA8DBdeCB/+MKxZA9ttZ5NBH/xgvQ0LorbN0euu1Ruqdy903i4nI7wB2CLweIW3zGcesBtwk4g8DRwAXF2LDnODg4PV/WNbW/5j/8o5Qap2rzPOO1mcd/Jodk8zWttVq7fm0gi1bY5ed63eEL97OZ+Ou4DtRWRrEWkBjgeu9p80xvQZY5YaY7YyxmwF3A4cVSojXA2zZ8+u7h/DHeb8n4sSpGr3OuO8k8V5J49m9zSjtV21ek8Fwo2N6jrLqW1z9Lpr9Yb43UsGwsaYDHAWcB3wKHCFMeZhEfmSiBwVq00Jxv2r20oJZ4Sr3c4MqNq9zjjvZHHeyaPZPc1obVet3mQymIYGGwQrqxFW2+boddfqDfG7N5WzkjHmGuCa0LKzC6x7yMy1oqm6niU8L3UdMsJaa3Gcd7I47+TR7J5mtLarVm8mJmxZBKibUENtm6PXXas3xO+e7k9HXCxcCOefD1t4pc51CIQdDofD4agZmUwuEA7XCCsOehyOWlNWRjgtTPrzplfDhz5kM8P/9V91KY2YkXsdcd7J4ryTR7N7mtHarlq9yWTsZBqgrjRCbZuj112rN8Tvrioj3NLSMrMN+CeJOmSEZ+xeJ5x3sjjv5NHsnma0tqtW77yMsLLOcmrbHL3uWr0hfndVgfCMB7KvYyCsdRB+550szjt5NLunGa3tqtWbTAbjB7zKhk9T2+boddfqDfG7p/vTEWLu3Lkz24B/FVGH0ogZu9cJ550szjt5NLunGa3tqtWbTAbxkz3KMsJq2xy97lq9IX53VYFwX1/fzDZQx4zwjN3rhPNOFuedPJrd04zWdtXqTSZD1g+AldUIq21z9Lpr9Yb43VUFwosXL57ZBuoYCM/YvU4472Rx3smj2T3NaG1Xrd5kMjT6v3oqGz5NbZuj112rN8Tvnu5PR4iOjo6ZbcA/SRx0EGzYUHzdmJmxe51w3snivJNHs3ua0dquWr3JZMiI2PvKhk9T2+boddfqDfG7qwqE29vbZ7YBPyMM8ItfzGxbFTJj9zrhvJPFeSePZvc0o7VdtXozMUFTa6u9r6w0Qm2bo9ddqzfE764qEJ7xVcCsWbn7AwMz21aFaL36ct7J4ryTR7N7mtHaruq8fd9Mhgk/I6ysNEJdmwfQ6q7VG1xGeGYb2Gyz3P2vfhV6e2e2vQrQevXlvJPFeSePZvc0o7VdVXk/9BAsWwZ33QWZDM1+ssdlhBNDq7tWb3iZZ4S7urpmtoFly/IfP/LIzLZXATN2rxPOO1mcd/Jodk8zWttVlffzz4MxcPvtNiPsL1c2fJqqNg+h1V2rN8TvrioQXrRo0cw20BSaUXrdupltrwJm7F4nnHeyOO/k0eyeZrS2qyrvsTF7+9BDkMnQFM4IK5lQQ1Wbh9DqrtUb4ndP96cjRH9/f7wbfPbZeLdXhNjdE8J5J4vzTh7N7mlGa7uq8vYDXS8QzvjLlZVGqGrzEFrdtXpD/O6qAuG2traZb+Svf7V/8+cnmhGOxb0OOO9kcd7Jo9k9zWhtV1XewUB4YoJGf9QIZaURqto8hFZ3rd4Qv7uqQHh0dHTmGznsMPu3aBEkOLNKLO51wHkni/NOHs3uaUZru6ry9gPh/n546imyhcYRTnlphKo2D6HVXas3xO+e7k9HiObgOMAzpa0Nhofj214JYnVPEOedLM47eTS7pxmt7arK2w90AV54ASk0s1zKM8Kq2jyEVnet3hC/u6pAOJvNxrexOXNgaCi+7ZUgVvcEcd7J4ryTR7N7mtHarqq8g4EwYPyAV1mNsKo2D6HVXas3xO+uKhA2xsS3sba2RAPhWN0TxHkni/NOHs3uaUZru6ry9gPhuXPtrT8ykrIJNVS1eQit7lq9IX73dH86QjSFhz+bCVGlEevWwZln5k4eMRKre4I472Rx3smj2T3NaG1XVd5+ILzXXgCI/5NxuEY45RlhVW0eQqu7Vm+I311VIDzmj5kYB1GlEaefDj/+MfzjH/G9jkes7gnivJPFeSePZvc0o7VdVXn7ge4++wAw6Wd+lZVGqGrzEFrdtXpD/O6qAuE5c+bEt7GojHAmE71uDMTqniDOO1mcd/Jodi+EiBwuIo+LyBoR+XSR9Y4VESMiq+J20NquqrzHx0EE9tgDgKZCw6elvDRCVZuH0Oqu1Rvid0/3pyPEwMBAfBuLygj7dSf+EDQxEqt7gjjvZHHeyaPZPQoRaQQuAI4AdgFOEJFdItabB3wIuKMWHlrbVZX3+Di0tMDuuwMw4X+HKSuNUNXmIbS6a/WG+N1VBcILFy6Mb2NtbdDZCZ/8ZG5ZDQPhWN0TxHkni/NOHs3uBdgPWGOMWWuMGQcuB46OWO/LwHlATQYU1dquqrzHxmwgvPPOIEKLnylTNnyaqjYPodVdqzfE764qEO7q6opvY36w+41vwPPP2/s1DIRjdU8Q550szjt5NLsXYDkQnDZzvbdsChHZG9jCGPOXYhsSkdNFZLWIrN64cSOjo6MMDw8zNDTE2NgY/f39ZDIZenp6MMbQ2dkJQEdHB11dXXR2dmKMoaenh0wmQ39/P2NjYwwNDTE8PMzo6CgDAwNMTEzQ29tLNpudej86Ojrybru7u5mcnKSvr4/x8XEGBwcZGRlhZGSEwcFBxsfH6evrY3Jyku7u7shtdHV1kc1m6e3tZWJigoGBgWn79OyzzxbcJyBd+zQ+TralheysWYx++MP0vOY1DAwMMOaV+U16Ew8MDA+nep+efPLJit+nYsdekvu0fv362I69JPfpqaeeSuTzVIt9euqpp6o69goh9RpCY9WqVWb16tV1eW0A3vEOuPJKe//qq+HII+G1r4WbbrJTMB92WP3cHA5HqhGRu40xsdfVxoGIvB043BjzPu/xu4H9jTFneY8bgBuBU4wxT4vITcDHjTFFT8h1P2c7pnPaaXDNNbBhQ/7ytWth223hNa+Bm2+GZ56BlSvr4+hwpIRC521VGWE/6o+FL30JTj3V3n/sMXvrD9Jcg96UsboniPNOFuedPJrdC7AB2CLweIW3zGcesBtwk4g8DRwAXB13hzmt7arK268R9phyV1YjrKrNQ2h11+oN8burCoTb29vj29jOO8OFF9r7fsrcz46HZuuJg1jdE8R5J4vzTh7N7gW4C9heRLYWkRbgeOBq/0ljTJ8xZqkxZitjzFbA7cBRpTLClaK1XVV5hwLhKXdlw6epavMQWt21ekP87qoCYb+2JDYaGuyMPAMD8Lvf5QLgGmSEY3dPCOedLM47eTS7R2GMyQBnAdcBjwJXGGMeFpEvichRSXlobVdV3qFAeMpd2fBpqto8hFZ3rd4Qv7uqqUWWLFkS/0bnzoXvfAe+/e3cshpkhGvingDOO1mcd/Jodi+EMeYa4JrQsrMLrHtILRy0tqsq71AgPOWuLCOsqs1DaHXX6g3xu6f7MjFEb29v/BttacnVBvt4PSTjpCbuCeC8k8V5J49m9zSjtV1VeYcC4Sl3ZcOnqWrzEFrdtXpD/O6qAuF58+bFv9Fwb1uAj38c+vtjfZmauCeA804W5508mt3TjNZ2VeUdCoSn3MOd5VJeGqGqzUNoddfqDfG7p/vTEWI4PCVyHExORi+PeeaSmrgngPNOFuedPJrd04zWdlXlHQqEp9yVZYRVtXkIre5avSF+d1WBcKs/j3oSrFgBP/lJbJtL1D1GnHeyOO/k0eyeZrS2qypvf2Y5jyl3ZcOnqWrzEFrdtXpD/O6qAuGMN1tOYlx6aWybStw9Jpx3sjjv5NHsnma0tqsq7/FxCAQFU+7hznIpL41Q1eYhtLpr9Yb43dP96QghNZj6eAp/jvYge+wR2+Zr6l5DnHeyOO/k0eyeZrS2qyrvUGnElLuy0ghVbR5Cq7tWb4jfXVUg3FCLq9qbb4bzz48OhKOWVUlN3BPAeSeL804eze5pRmu7qvIOBcJT7soywqraPIRWd63eEL+7qpaY8D/UcfLqV8OHPpSbXS7I6CjceSeIwH/+M6OXqYl7AjjvZHHeyaPZPc1obVdV3qFAeCIc+GYy9vsr5dk/VW0eQqu7Vm+I311VIDxr1qzabXx0NHrZRRfZ+zfcMKPN19S9hjjvZHHeyaPZPc1obVdV3qFAeMo9mDFLeVkEKGvzEFrdtXpD/O6qAuGhoaHav8gvf5m7PzqaG094/vwZbTYR9xrgvJPFeSePZvc0o7VdVXmHAuEp92AWWEEgrKrNQ2h11+oN8buXFQiLyOEi8riIrBGRT0c8f6aIPCgi94nIv0Rkl1gtPebPMBgti733zt0fHc2NJ9w0s9moE3GvAc47WZx38mh2TzNa21WVdygQznP3A2AFtaCq2jyEVnet3hC/e8lPiIg0AhcARwC7ACdEBLqXGWN2N8bsCXwd+Faslh49PT212KzlgQfg/vshmHJ/9FG45hp7f4YDONfUvYY472Rx3smj2T3NaG1XVd6hQDjP3Q+EFWSEVbV5CK3uWr0hfvdy0pz7AWuMMWsBRORy4GjgEX8FY0xwPuI2wMQp6bNkyZJabNay++729vnnc8seeih3f4ap+Jq61xDnnSzOO3k0u6cZre2qxnty0v4FAuE89/AIEilGTZtHoNVdqzfE717ObybLgXWBx+u9ZXmIyAdE5ElsRviDURsSkdNFZLWIrN64cSOjo6MMDw8zNDTE2NgY/f39ZDIZenp6MMbQ2dkJQEdHBwBr1qzBGENPTw+ZTIb+/n7GxsYYGhpieHiY0dFRBgYGmJiYoLe3l2w2S1dXV942/Nvu7m4mJyfp6+tjfHycwcFBRkZGGDEFYvjh4dw2NmwAY+jq6iKbzdLb28vExAQDAwMF92nt2rWR+9TZ2Vn7fRoZYXBwkPHxcfr6+picnKS7uztyG+F9euaZZyp+n9KwT/5f1D4Ve5/qvU/PPvtsVe9Tvfdp7dq1sR97Se3Tk08+WfH75CiN347aUOPt95wPBMJ57opKI9S0eQRa3bV6Q/zuYgoFfv4KIm8HDjfGvM97/G5gf2PMWQXWPxE4zBjznmLbXbVqlVm9enV11rVkeBja2nKP994b7rkH/vd/4YtfhHXrYOVKO/3y+95XN02Hw1E/RORuY8yqenskSWrP2S9X+vpg4UL41rfgIx+Z/vyCBbaz99KloDjocTjiotB5u5xLxQ3AFoHHK7xlhbgcOKYiuzJJ5AomPIf1woW2btivEb7/fnt72ml2nvcy0Xr15byTxXknj2b3NKO1XdV4j4/b21IZYQWlEWraPAKt7lq9IX73cgLhu4DtRWRrEWkBjgeuDq4gItsHHr4ZmNnsEwVob2+vxWbzCZ805s+3GWK/RtgfRQJshrhM2mfNgv/5nxl3ukuaRNq8BjjvZNHqDbrd04zWdlXjHREI57krqhFW0+YRaHXX6g3xu5cMhI0xGeAs4DrgUeAKY8zDIvIlETnKW+0sEXlYRO4DPgoULYuoFr+2sOY89xyceKK9P2+enWrZD2D7A/0Cgx3rSjByzjnw/e/bP0Uk1uYx47yTRas36HZPM1rbVY13RCCc566oRlhNm0eg1V2rN8TvXtbguMaYa4BrQsvODtz/UKxWBViwYEESLwObbQb+FUcwI9zTA15nIQCy2bI32eoPbl7B/6SBxNo8Zpx3smj1Bt3uaUZru6rxjgiE89wVlUaoafMItLpr9Yb43dN/qRhgcHAwuRdbscLeGgOzZ9tAePFi+NzncutMTtrbZ5+Fyy8vurkJ/6SV8jnfwyTa5jHivJNFqzfodk8zWttVjXdEIJznrqg0Qk2bR6DVXas3xO+uKhCePXt2ci/mZ4R7e20g/MQT09eZnLSB8oEHwgkn5LK9V18N//xn3qpN/slIWSCcaJvHiPNOFq3eoNs9zWhtVzXeEYFwnrui0gg1bR6BVnet3hC/e/o/IQHG/Q9+Evip974+O2rEmjXT15mctEPXPPecfeyP63j00XDIIfmrZjL2jrJAONE2jxHnnSxavUG3e5rR2q5qvCMC4Tx3RRlhNW0egVZ3rd4Qv7uqQLgxyQ/0Km+ouRNOsBnhKCYn4YILco/9QNgnm7VB8V//ivgBsLJAONE2jxHnnSxavUG3e5rR2q5qvCMC4Tx3RTXCato8Aq3uWr0hfndVgXCirFhhA9l3vctmhKPIZOyfTzgQHhqyZRJHHIH4E5coC4QdDofDkUIiAuE8FJVGOBz1RNUnZNLvnJYUftBaLCMcDH7DgXDgcdavH1YWCCfe5jHhvJNFqzfodk8zWttVjbc/oVMgEM5zV1QaoabNI9DqrtUb4ndXFQi3FLryrTWFMsLhQPjQQ+10zD6B5xqVBcA+dWvzGeK8k0WrN+h2TzNa21WNt58RDsyGmueuqDRCTZtHoNVdqzfE764qEB4ZGanPC5ebEX7wQTj88NzjwHMZ/76yn6nq1uYzxHkni1Zv0O2eZrS2qxrviNKIPHf/u0bBd46aNo9Aq7tWb4jfPf2fkABz586tzws3N9vbz31u+oQawRphgOAc2IFAuNk/WSnLDNetzWeI804Wrd6g2z3NaG1XNd4RgXCeu6KMsJo2j0Cru1ZviN9dVSDc19dXnxf2TzhLl9pJNXzCGeEwgefG/SmalQXCdWvzGeK8k0WrN+h2TzNa21WNd0QgnOeuqEZYTZtHoNVdqzfE764qEF4cDEKTZHTU3s6Zk7+8s7PsQHiWf1JSFgjXrc1niPNOFq3eoNs9zWhtVzXeEYFwnruiUSPUtHkEWt21ekP87un/hAToCJYdJInfOzfcae7hh4v/X2AawFGlV191a/MZ4ryTRas36HZPM1rbVY13RCCc566oNEJNm0eg1V2rN8TvrioQbvenPU4aPyNcaPSIQvT3T92d5WeClQ1ZUrc2nyHOO1m0eoNu9zSjtV3VeEcEwnnuikoj1LR5BFrdtXpD/O6qAuG6XcFEDFNTkGDKPhAIjw0M2DvhznUpR+tVo/NOFq3eoNs9zWhtVzXe5WaEFZRGqGnzCLS6a/WG+N2bYt1ajanbFcx3vgPz5+cPjVaIefOgu9veDwTCUyF0JgMDA/DUU/CKV8SuGjdarxqdd7Jo9Qbd7mlGa7uq8R4ft31OAhlflxFOHq3uWr3hZZ4R7goOXZYkW24Jl15aXkZ43rzcfT8LDIwPDdk7k5N22uY99siVXKSYurX5DHHeyaLVG3S7pxmt7arGe2zMZoMDHbDz3BXVCKtp8wi0umv1hvjdVQXCixYtqrdCNF/7Wu5+cHw7P/gFmo2xd/r6YPVqe3/9+gTkZkZq27wEzjtZtHqDbvc0o7Vd1XiPj09LzuS5K8oIq2nzCLS6a/WG+N1VBcL9gVKDVLHPPrn7wUD4s5+dujvpjyDxjW/kSieefTYBuZmR2jYvgfNOFq3eoNs9zWhtVzXe4+N59cEQcldUI6ymzSPQ6q7VG+J3T/8nJEBbW1u9FaIJji9cYMaTxmAZhD8c26OPgp8p9lm3LtcJIgWkts1L4LyTRas36HZPM1rbVY13RCCc566oNEJNm0eg1V2rN8TvrioQHk1rTW3wTSkwxJrxZ5YLctZZ8O1v5x6PjcHKlfDe9+avNz4OGzbEIFo5qW3zEjjvZNHqDbrd04zWdlXjHREI57krKo1Q0+YRaHXX6g3xu6sKhJubm+utYAmPHhF1FR5CRkait3XFFfZ2chLWrrX3r7wyf53TT4cVK+rSuS41bV4hzjtZtHqDbvc0o7Vd6+796KPw4oul14sIhPPcFZVG1L3NZ4BWd63eEL97+j8hAbLZbL0VLNdeCxs35h4HA+GmAiPSRWWEAe64Az71KfjmN2GXXeyy8LTN11xjb3t67O1jj+WC5hqTmjavEOedLFq9Qbd7mtHarnX3Pvpo+MIXSq8XEQjnuSvKCNe9zWeAVnet3hC/u6pA2ITraetJ8Iok2HO30kAY4Otfh5tuyj0O76c/JJvfyW7nnWHbbctWnQmpavMKcN7JotUbdLunGa3tWnfvjRshOGHAxAR89asQ/lUxIhDOc1dUI1z3Np8BWt21ekP87qoC4aZCQWY9CAbC/v0FCwoGwlJqauViA0T7gXAS4/719eUF4qlq8wpw3slSE++nn4Y774x/uyG0tnna0dqudfXOZu1ETIEx6LntNvjc5+DGG/PXjQiE89z9jLCC0gitxwroddfqDfG7p/8TEmDMH20hDQQD4Xnz4Lzz7PjA1b5Bjz+e/3hiws5oNzGRC4T/9a/pZRNx8vjjsHAhXHwxfOQj8MMfpqvNKyAR71tugV13LZ7trxDX3gG23hr23z/+7YbQ2uZpR2u71tV7aMgmIoKBcF+fve3tzV83IhDOc1eUEdZ6rIBed63eEL+7qkuCOcFhyupNuFj7k5+0t9UGwuHM1w9/CB/+sO1E5wfCn/scvPBCddsvh4cesrfXXgu/+x0Ac047rXavV0MSOVY+9CF45BHbuSU4lvQMSNUxXgFavUG3e5rR2q519faD3uA4qX5QHBUIh1zz3BUFwlqPFdDrrtUb4ndXlREeCF4l15vAtJZ5VHvSCde8+FnGF1+E+fNzy8M/j8WJP35xIMuQqjavgES8/XKXGL9oXHsnj2b3NKO1Xevq7QfAQQd/mR8k+4yNTZtZLs9dUWmE1mMF9Lpr9Yb43dP/CQmwcOHCeiuU5ogj4tmOPx7x6Gh+9jmTiWf7UUQEwjVv85ERe1HxrW/FutlEjpUaBMIqjvEItHqDbvc0o7Vd6+pdSSAcURqR564oI6z1WAG97lq9IX53VYFwVxKdxWbKoYfmRncAG8iefHLl2/Gv9MfG8uuCg53u4g6KIwLhmre5/3PfN75RfL3OTvj85/P3vwiJHCt++8f4RaPiGI9Aqzfodk8zWtu1rt5+sDs4mPuVsFhpRCgQznNXNHya1mMF9Lpr9Yb43VUFwkuXLq23Qnn4Nb1gA9pvfAPe8IbKtvGb39jb0dH8QHjNmtz9wcHqHaPwC9ADJ9eat7l/si41LuBZZ8G558J115W12Snvnp7aTVntB8Ixjmmo5hgPodUbdLunGa3tWldvP/ubzebK4yrICOe5K5pQQ+uxAnrdtXpD/O7p/4QE6AiOrZhmwlfgm2xCx69+Vdk2/vlPezs6WjiQi7vGJ+J1at7mftajVDDpj6FZZlA75b14MbztbVXKlcAPhGPMzJfV3gccAGecEdtrxoGaz2YEmt3TjNZ2rat3VCe5CgLhPHdFGWGtxwroddfqDfG7qwqE24uNtZsmIjrStbe3w9e+Vvz/rr02VxvsEy6NCBJ3Rth/nUBQWvM290sdSgXC5WaOPfK8//KXKsTKwHePMRAuq73vuAMuvDC214wDNZ/NCDS7F0JEDheRx0VkjYh8OuL5j4rIIyLygIjcICJbxu2gtV3r6h0Mdv0A2L8tozQiz11RjbDWYwX0umv1hvjdVQXCnZ2d9Vaoms7OTjuV8tNPwxe/GL3SYYfBj3+cv+wPfyhcDhAMhDduhA0bZibpZ1sDdbiRbS4Cp546s9fy8YPIUjPF+BcXZc4ok8ixUoOMsNZjPJXeRx8NP/lJydVS6T4DRKQRuAA4AtgFOEFEdgmtdi+wyhjzCuBK4Otxe2ht17p6R2WE/dsyMsJ57opKI7QeK6DXXas3xO+e/k9IgCVLltRboWqm3LfcEv73f6NXEoHZs8vfaFcX/Oc/9v6yZbBihT05ltmhjN5euPXW3GO/RjhQfrBkyRI7drEIXH99bt2LLy7fM8xNN8E//mHv1ygjvGTJklhrd6f429/ge9+z92uQEVZ1jN94I1xyCVBj72rfx6uvhtNPn758333zOmeqavPy2A9YY4xZa4wZBy4Hjg6uYIz5hzHGnwnmdmBF3BJa27Wu3sFgN1waUUZGOM9dUWmE1mMF9Lpr9Yb43VUFwr3hE4EiprkXGhC6koGijzgCdtgB3vrW3LLWVjjqqPL+/8AD4aCD7IQQkKvDHRqaWqW3txduvtk++OEPp2/DGBsgZ7O2tOLZZ0u/7mtfC697nb1fbocz/6ReKMh/4AHbmS7oXe4FQSUcdhh88IP2fg0ywqqO8de/Ht77XqDG3nGPjvLII/Dkk1MPVbV5eSwH1gUer/eWFeJU4Nq4JbS2a129Z1gjnOeuqDRC67ECet21ekP87qoC4XnB0RjSwCmn2CG9onjjG+GrX516OM39kUdy97/2NfjlL+39SjLCPn/4Q/7ja64pvO5tt9mRFAAee8ze+j8z+L2UA53w5s2blyvBmDdvemnCZZfZETEuucSO7LDlltNP2MXwg5yBAbjqqunPP/SQ7Tjol0aMjkZvZ//984ZXmzdvXu3HXK5BRrgmx3gmA3fdFf92A5TtfeeduQurcjnlFPjEJyp2isQYe5wH6u5Td15JEBE5CVgFRI5fKCKni8hqEVm9ceNGRkdHGR4eZmhoiLGxMfr7+8lkMvT09GCMmfrJsqOjg3nz5tHZ2Ykxhp6eHjKZDP39/YyNjTE0NMTw8DCjo6MMDAwwMTFBb28v2Wx2amgkv0OMf9vd3c3k5CR9fX2Mj48zODjIyMgIIyMjDA4OMj4+Tl9fH5OTk3R7Q1iGt9HV1UU2m6W3t5eJiQkGBgam7RNQcJ+Amu5Tprsb481OOtrRwfj4OFk/EPaO246ODnvumZyElpa8fZo1a9bUPk1456dMNlv0far1PpXzPvntXsn7lJZ9EpHYjr0k9ymTySTyearFPk145+9Kj72CGGPq8rfPPvuYSunr66v4f9JCpLv9WjZmfDy37NZbc8tn8heFv+0PfSj/9a+91j4+7jj7+KCDpp7r6+sz5vzz7eMPfMCY0dHc/z36qDH//d/2/re/bczmm9v7DzxgXyubjfYIOj7wQHFvf/nxx9vbH/yg+DZHRnLt3d9fvD0q4aGHjDnnnOi29tsvBkoe49ls5fv0qU/l3pdqOeMMY045JX9ZwKPsz2a57ldcUd4xXYhC7TQ8bJedfPLUomrOK8BqU6dzZ6k/4EDgusDjzwCfiVjvUOBRYJNytlvpOVvr+bqu3m94gzErV9pj9Ec/ssvmzzemqcku6+iwy/zj+Gtfy/v3PPdPf9qu86UvJSRfPVqPFWP0umv1NqZ690LnbVUZ4dbQdJKaKOoenDmumoxwmHDniIMOgiOPhBtusI/9EggfvxTi+eftbeDKqbW1Nfd47txcHTHY0oqnn7b3g1m1T34SXvlK+OMfS7uWm0319ynsXmB7ra2t8WaEDz64cG13oRKMbLbiMYzzjpPnn4fPfS6/bKSaffr3v+1tcKKXX/8aLr20/G38+MdT9cBRxPbZfOYZ+Mxn4LjjZradQqU2/q8egXbUfF4pwF3A9iKytYi0AMcDVwdXEJG9gB8DRxljNtZCQmu71tW7v9/29QD7K1k2a2+Xe5Ut/q9tEZMfQchdUY2w1mMF9Lpr9Yb43VUFwpla/tRdYxJ3DwZnt94Kf/5zLuC97z743e9yJ8oigXAmk8mVSrS05Jcm9PbaId+C2wB4/HF7W85Yf+XW8fqu3/xm8fW8n0wymUzhYeeqIXgBEKbQe/ue9+RmCCyTvOPkPe+x5TW3355bVs3kIP57FrzIOvFEu/0gHR2w7ba5mnGA970vcjjAot5RPP108Tb0Oemk0sMMlkOh994/TgO+ms8rURhjMsBZwHXYjO8VxpiHReRLIuJ3IPgGMBf4rYjcJyJXF9hc1Wht17p69/XBZpvZz1x/vz1ejYEttsg9DwUD4Tx3RTXCWo8V0Ouu1Rvid2+KdWs1Rsr4Qk4rZbvHcaWTzcK6dbDVVvk1vX427M474e1vzwU4fnDwwgv2NlAjLCK5TOIll+RnfoMMDua25x+k4extNgvnnJN7vPPO8N3vlr9PAM89Z4eJ8zMkYbwASETizQgXe/8KvY5f913RywRex//SC2b4ywkmw/jvQ/CXhyj++EdYu9ZebFx0kV3205+W9RJFj++xMdh66/KyvHGNjV0oEPY/A4HnNZ9XCmGMuQa4JrTs7MD9Q2vtoLVd6+rd3w8LF9pf3wYGcudiPxD2OwkVCITz3BUNn6b1WAG97lq9IX73sj4haRicHaBBwQe6EJHuUct23tn+DL10KVQzRMirXmW36/+MHexdGczaQi5IHhqyAYgfhAQC4YaGhtzjdets2UMUwQDGP0mHO7bdcw986Uu5x489BrfcUnKXgPwAsFiw5AWlDQ0N8QXCIyPFZ/GLMeDOO078YC0YwM4kEI7KvgfdS43MUYSin01/P/70p9yyakb06OvLDfd3+unFR0cpFQgH9lvzeSXNaG3Xunr398P8+TbhMDCQGzGizIxwnrui0gitxwroddfqDfG7l9xaWgZnB6Z6Cmok0n39+vyfoX1OP93+TN3ZaX+aBlvj65chgD25HXWUPWkGueIK2GUXuPtu+zg4yYbXe3Qag4P5AXMgkzsxMTE9gC60DZ8XX5y2HWD6rHlghz0rRDCbHQyqi41K4bXzxMREfAHqf/938edLvc6LL9qxa8sYWm7ihRegqcmOtex/2QWvfqsJhP22i/L03yvIfWEWG8pur72mj2dKic9m1BB51WR+jznG1ruPjNiJMoKB9XSh6OURgbDm80qa0dqudfOenLSfiwULCgfCJTLCee6KSiO0Hiug112rN8TvXk5YnYrB2QFmRQVSSoh032wz2Gmn4v/ol0rssgscfnhueV+fHTbtwQdhjz3sstNPt9vceedcgB0MfsPDrPn4GeEo7+bmygNhny9+MT+DHBWIhTvUBTOFftAC+eNrlhEIz5o1q3iA+qUv2QCznMzk/fcXf/7BB4u30aWXwurV8J3vlHyp2Y88Yp3OPTf3ZRf80M8kI5zJ2NKS4FBq3/qWdYPyMsL33Zd/QeZR9LPpvw9B90IZ9mI/efm10uW8Z6VqhAPPaz6vpBmt7Vo3b/8zEcwIh0sjSmSE89z9z7OCzJ/WYwX0umv1hvjdy/mExDY4+0zGpATYsGEDxugak9Lfp+effz5yn0qNoZfxgpjJxYvzB5GePZuOzk5YuZKh178egNHJSSYnJxndaivMU08x2N3NWDlj+g4N0VMgW7lxwwayxcoCfAYHmQxmcH2+8Y2pfRoJBrMF6Fi/HoD+Cy/MZbWBye5ujHfwT9x2G2O//a19n558kvETT8xtIJOho6ODoaEhujfmOsOH3yfj1SoP9fZOvU/Zs85i6Lvfnf4+lQq8zjuPzIc/nH/s+TPnAYNeRnbEe/+KHXu9/jimg4MYP1h7//unAsRuv0NjxD4VOvaM9/p9XV2w/faw33459299C/bdl0wmw7D/2uPjU58nE/qitRvqmzp+fZ577rmpfZq4805Gfve7qX0aihr8fGAg8vMUPILMwoV5/2K8gDoTCKjz3qfvf5+JVaswk5OMfP/7U+sEP0/jnks2cI547rnn8jzKOUc4SjNUzgV0Cqmbt39+jCqN8EeSKBEI57kryghrPVZAr7tWb4jfPdbOcoHB2Q+Oet4YcyFwIcCqVatMOKr3h8RYtGgRAEuXLgWgvb0dgJUrVyIiU8/P98oCgkNp+Ntc6H2J+lPx+dvwbxcvXgzAggULAGiJ+ML3l/nrhrfhb9t/reaIzki+24oVKxCRafvkPy64T96XbuOyZVOvA0BDw9Q22jyPWbNnQ2MjjVtvDdksc//yF/szWxRNTblM3dAQiwp0pNp8992Rcr74BwdpLJB5mH/vvbByZVkn5PaWFrjySuafcUbe8sbBQdv577HHaPaGMWs1Bt71LjuVrs/EBO3t7WSzWRoCZSMNDQ3575P3M33brFnQ2mrb+4ILaAP44Afz36cypvhtevHFqYkZFi5cmFe/Otd7D2Y3NMDVV7N4p51g8eLIY6/Ve0+bJiZyWct77rG3mQyL29oK71OI8DG0oK0tP8se9G9qosnbdpMITf4Mh7NnTx+poq9v6tgD4Kqr2OLoo2loaLD7tP/+NEOutCWqA+iVV9L+hS8Aoc9ToK1l8eK8MgzxjtfgSSvvffrUp2geHoZ//IPZ552Xa4eWllxb+DXkk5NTbbeFl22r5hzhKMz8cNmWEurm7Qe5CxbYYPjpp3OB8KJFtgNdidKIPHdFNcJajxXQ667VG+J3LycjvAHYIvB4hbcsDxE5FPgcdlzKKn6/LU2PPyOaQqp290sOQtmxPPwLCj9z6QUHnHwy3HFH9P/svXfufpHSiLKCYLAn7EI/ax9yCGyzTW5Gu2J88IPwjndMX75xIyxblr/MmOnZWi947Onpyf95vFAtcpF9n6KMQJitt7Y+f/rT9PGDvZmiGBqCo4+2YyyHGRyE229nwJ/lb2RkegA6MFC6NMIY+z588YvTnytVyxyuEf7LX6LLUMLL3va24sd3VJnCb38b3e7BZV4gOo1CGfrdd7e3Tz2Vv9yvkf7+93OjlATaQvN5Jc1obde6eYczwv39uWXz5tnvgBIZ4Tx3RaNGaD1WQK+7Vm+I372cT0gqBmeHXAZWI1W7ByezKISf7fIDmOBrXXfd9PV33BE23TT3eGgovz6tGnp7S483e+SRpbfjj0EcZmQEDjssf9n4+PQpnz/4QXjmGdvewcDPKx8B8oOtZctsXXUxoko+woyOwi9+YTPBF12UH8T69x96aPrr+xx/PBx4IAv8oHFkZHoA2d+fn9G94orchCY+/vPBYep8SnUw8L8w777b1p2/5S3R60WUOhQ9vqMC8AcftB3vwgQ7Ra4IdDVoCuSBg9tbsCDX4c+/IAwH6n77/8//2KEDIa8tNJ9X0ozWdq2bdzAjHK4Rnj/fLg8HwqFfW/LcFZVGaD1WQK+7Vm+I371kIJyWwdmBaXWJmqja3Q8MigXC/s/ifiDsZ4TBjjwR5rHH8jMJwaxoscxzMeK6QisWrB18MARKAxgcnB6k/vvf8N732vYOBkzB4DNUdpE3BjLYGfjWr89tu5zOWSMjuaDUq3Oe9toPPmhvx8Zyo4H43HgjAANPPpnbXrgt3vve/IuBd74T9t8/fx2/c2Rrq33v/+u/cs+Veo/8NnjqqeKjeURkiTs6OmyQGXWcF3pP16yxt8bYIQOHh/MvILYI/BAVLKMKvq/9/bnOf36wHA7UoyYhCWxD83klzWht17p5F6oRbmmxn+cFC3LHtv/LUCgjnOeuqDRC67ECet21ekP87mX9ZmKMucYYs4MxZltjzLnesrONMVd79w81xiwzxuzp/RUZ4LN68uoSlVG1+89+BqedBvvsU3idcG//4NXSunX56775zfY2WFMaDIS9WuWKieoQVQ3FZk6bPTt/drTBweiyhWzWtncwYAoGY36wWYhDD7VB2De+Yb+M/vOf0t4jI7nXC3/xRBX2hyeq8EpQ5gUnNwn/X6AD3hQbN9o/Y2zw6gfCbW122Lef/Sx/3WIUC/gPOCB3P2J/2p97zgblwY54PrvtVvx1//QnOPNM6xw8wQUD4eD7F84w+xeJ/rETPhYvu2z6awa2ofm8kma0tmvdvMOB8NiY/Tz7kxiVURqR566oNELrsQJ63bV6Q/zu6f+EBHhZXsHsuCNceGEu2/Xgg/nDX0HuhBeuEYbpgeLvf29vg4HwI4+A37mo2ozw8HA8UxoX20ZLy/RAOKpsobl5ekbYb5s1awrXTYf59a/B69BVkmAgfHXoB5FCUwaHO8MBI888U97rBVm50gbWe+xhncG+7/7kEz7FjsG5c22GOYqddrKlGz7+tK9B9tzT3oZLNT760VL2uZkLwxQKhMMBu1/H7v96Et7ehz88vbY6sD3N55U0o7Vd6+YdLo0AO9yhX64WzAgXCIRdRjh5tLpr9YY6ZYTTgruCwWbXVq3KX+af8Pygt7UVbr45+v/9E2cwEB4dzWULC0268a53lXYLDO1VNXEEwqtX0/7ww9G1qR//eGU+BUZZmEawlCEQ2Ja17UDGcnY1mfWxMTu+L9jSELBfmN6wYFMUO3mUGo4mXEpT7sxw3/526XXC79N228GWW+bXbgcv6MIdOP3HfrAblfleu3b6a77wAkxOqj6vpBmt7VrXjLCI/WXED343bMgPhKvJCCsIhLUeK6DXXas3vMwzwt2FMkcKqKl7OCMM8OpXF/8f/wQa/tnM79AV5k1vyp/UIkgwczdTKgmEzz03OhDu7YXXvnZ6gHXlldODqGKIFK/NDjIwAP/v/5W/bchlkQMTdmSCMwG+9a3lb8t/H6Nqwn2qvYo2ZnogfMMNpf+vnNE2YPr7dMopNrMcNewa2JKVIOGMcHC2PJ/vfS//cWenDbQ//nHV55U0o7Vd6+bd12eDXpFcRnj9+lwg7JdGGFMwEM5zV1QaofVYAb3uWr0hfvf0f0ICLCg0Jq4CaupeztS4YfyMcHhs1HC22WfRIvDHlg3yl7/YURKi1v/e92CTTcp3gsoC4T/9qXgHsPC2Hnyw8kA42DmvGOFylXL42Mfsz/bXXz+1qCmYTa7kmPHf+yeeyF9+9tm5ETP+/OfKHWF6INzdnT/LYSHKLZUJr1dqvN5f/jL/sdfRcCojHBUI//CH+Y/9i7qf/Uz1eSXNaG3Xunn39+c+834gHKwRXrDAflaCQyuGPit57opKI7QeK6DXXas3xO+uKhAeLDXea4qpqXs5U+OG8QPh8EQMhUoqFi+OPqHusQe84Q12rGDIdcZbsgTOOis/cC2HYp3lWlunB/uB2eemEdWpKjg8VynuuQe+8pXy16+Uyy6DCy4o/HxwyLBSRAX4K1fa8YTDgWOlRAXC5VDuRUQ4EPYzweXMaAhw8cW2Q6P/3laS+e7rU31eSTNa2zV2787O6I6uYfr7c9lfP/iF/NIIsFnhAoFwnrui0gitxwroddfqDfG7qwqEZ1caVKWImrr7WdxCnZ2i8E+g4ezb7Nk2oxYeWaHQaBKtrTZz6tfsbLutvfUD1kqCOSgeqLa0VBbIhgPh0dHK/j+KN72p8HOzZuVPVDJTCsz2F0lULfPmm9v3ptL3IEw4EC63brrcC7NCgXBUPXsh+vtLTzZSAM3nlTSjtV1j9/7KV+xINKWmu+/rm54RhvzSCH+9AoFwnrufIFFQGqH1WAG97lq9IX739H9CAowXyxamnJq6b7utDVaOPTZ/ebE64aiM8L772ttNNrGdlYIUCoT9E7GfkfQnQfCDoDizES0tudf56ldLr+8HWN/4hg0G//jH3Fi+hShUGuJTbBSEHXcsXNdaDTPNCPtj7wa309ubP7ZwORgT75fp+efb2002sV/ohUojXvMaO2lIOR3uJiervsjRfF5JM1rbNXbvf/7TJgYCfQEiKZQRDpZGgP0MFwiE89wVZYS1Hiug112rN8TvrioQblTwgS5EXdzDHZqWL8/d9wPgbbbJLbv22tz9sG+hYdX8E7GfAfa3d9pp0duZCc3NuYAvPN1yFCefbG/f/nYbFBYro/CJmokvSPgD2NcHH/iAvb/DDoWzlz//ue2IWGja4Cgq6YQYlaWNCoQXLChdgxtF1P+87nWVbwdsycQHPmDbrrV1+kWNfzEhYqfbjiqxOPfc/Mf+9NNV1I5pPq+kGa3tGqt3X18uAC41mozfWQ7yZ/gsVBohMu38mueuqEZY67ECet21ekP87qoCYUeFBIOyjRvh0Udzj9/4Rnt79tn2duXK/Ik4AJYswSxdaidKKBTg+QHS975ne/u/5S02w/fZz9rlcR6wIpUFwj5NTfkzkxWj1IQi4UB4/nw46CB7v7Exup3mz7dB+a672nKFcvjFL+AjHylvXYC//336sqhAGCoruQCbET78cLjtNjj11OnbL5dvfcveLltm/9cvZQhns8NBd5RveJ+6u61ncOpwh6Pe3HprbmSbUoFwVGc5mF4a0dtrPzt+WVohFI0a4XDUE1WfkMlKOoOljLq5v/71NlPZ3p5/ct1vP5vFPewwmwn2e94H6exk6Kmn4Pbb85e3tuaydv5Jdptt7Cxmra02SPFP0P7J+Le/jWd/qgmEofwsaLEvlmAtdJC3v92O/nDOOdFBW3Cb5Y7scdJJ+cFecGa3cokzEBaxDt/85vTtl8tb3mLHmn7LW4r/b/g9KCcQPu44exs8LsIdET//+ciXm4wab9oxY7Ser2P1vvlme6weckh5gbAf9DY3586xhTLCEee0PHdFpRFajxXQ667VG+J3VxUIt1Tzk25KqJv79dfD449HP+cHHIcfnuvkFmKa97p1dpD3++6bPk1wFP5JOCrTesQRpf8/jF8HWm4gvOuudrzYmU4B/bvfwWOPwStfCTfdlP9cc7OtYy1UGhEM7Mr5AEfVMd9wQ+XjAPtfpOEvwlLHYrjMIjhWc7D0oNJAuLnZZmxFitdSh8eGjhq2r9CXezDjHvx5+ec/L7jfes8q6Ubr+TpW71tugX32sf01Hn20cEdTf1i04DHrJy7CNcL3329nkNx66+LuikojtB4roNddqzfE764qEB6pZAzYlKHVfZr3ihW2hGKnncrrdOWfhKMO3ODMYWecMf35Bx6Ahx/OX/a3v9nRMYIB2QUX2CxjFNdeax3KGengYx8r/Nzb3mYDXYCDD7b3Dzxw+npxZITDNbHLl9tgMDh1djn4QXc5GdYg4R65UZOWQOWjUQTXLxZEh18vqpzEP67Crq96Ve5+sL1OPrmg74jigeXTzEvmnFf9huz44q95jR1NJpst3FnXH9c6eF7zA2A/OJ471wa3P/iBLY3wp1Mv5K4oI6z1WAG97lq9IX53VYHw3HJn+UohWt1n7B2sU1uzxpZi+Gy1Ve7+j340/X+XL4dddslf9vrXw+WX5wdzr361nVxjeNiOXRwkXPdciP32KzwzXFSntccft/V/YaKCzGCNnh+cvuIVhV2CgfCjj5bube531gtTaCbASq+m4wqEg21TLCMcvliICoT94P4978lfvueeufu7757/nN/2odeeqyBQ0MjL9pznc+edtoTh1a/ODatYqDzC/6xGZYT9ZSK5zq5/+APsvHNxd/9XMwVT6Wo9VkCvu1ZviN9dVSDcV2ocxhSj1X3G3sHpn7fd1k6+ATaTd/rp0f/z05/aQLPYCAvBoMoP7GbPtqULQcodb7BYcBjsZFiKHXecviwqI/yTnxTeRjAQ3mmn0sF8oXYqNCFFXD8rVRpABgPnYu9LOBCO+iL3g/Oww2ab2RFLzjhjetbZH6YtdBId2LixiLSjWtSf87q6Cl8ElsMtt9jP/qteZS+mFy8uHAj7U6MHM8JRI0h84Qu2TOvgg4u7gw2+n3su+pyUMrQeK6DXXas3xO8+w5H2k2VxJUNPpQyt7jP2DgbCkAsKzzijcFZw881zZQiltgv52zn+eJuFOeWU/NcrRbHgsJKxgb/wBRu4rl0LRx9thxiLyggXKw2odLDwQu9RoYxwOZ3lbr8dnn3WdkIrFAxUGggHX7fQcHyQX95Q6HX8YFkEjjoKrr7aPt50U7jwQns/fCEQDIS7unIqcY797JhC9Tmvq8uWgV18MZxwQnUbuuUW2G23XP+IvfeGe++NXvcPf7Dnif33zy0L1whDyZFkprV5sPwsxWg9VkCvu1ZviN9dVUa4o9LOQilCq/uMvf0gww8A/aAwmy0ckJXzU14wwA0GMiLTfy4PEu7o5lMsEK4k4Gtqgg9+0E4a4W8zKiMcFXx98pP2ttLhjgr9TFRuIBxVprH//rkxoQvVNc+kRrhQIHzRRdGlKOHX8oNzEfjc53LLgx3rwu+pHwgHAwugZ8OGws6OqlF9znviCdsxt9CU8+XwyCP5M03uvbetEQ4PwZjN2uES3/jG/DKgcGlEue4K0eoNet21ekP87qoC4XYFtU6F0Oo+Y++f/hTOPDP3U54fFBpTOJCq9DUryehtv33l2yg3qxwmahzPYhnh884r/VPsVVfZGfJ8nn02esKJhQsLz7534ok263rVVXYmvahaZyidOZ5JaUShQLjUeNU+fjs1NETvf3Bb/sQfBQLhRZWOfuEoC9XnvKeftg8KZXBLMTFhyxJWrswt23tvGwQfe6y9/+Uv2+X/+IcdjSd8AR+VES7HXSFavUGvu1ZviN9dVWlER0eH2jdPq/uMvbfYAn74w9xjPyg0pvyM8Oc+Nz2LEiQiiB17wxtoDQ4vdO+9dgzbQsF3LYaS8QPFcjPClNHexxxjbw86CP79b/slGRUI9vTkP/7Zz3IdDxcvtj/bBrd37rn5mVXItdVMSyO++U3rGGzjUjMVRi0Pjvzht2NDQ+GMeEODnc3Pny68QI1w7/PPU8DGMQNUn/P8QPiBB+zFa6UXfc89Z4/RYCD8qlfZ7O4DD9jbs8+2AfFvfmNrg48+On8by5bZMqsKXlt1myv0Br3uWr0hfndVgbDWNw30usfu7f/0t8UWhUsAwjWyX/lK8W1GBJWtf/tb/oI997R/UUNlbb45fOYzxV+jGvxAMmoc4QL7XnZ7/+lPNpO7cOH0QNivkQ3i10wX4rOfrTwQDl5UbLONrYtetMgG4Ucfnctcb701vPWt+f8704xwsEa4UEYY7DjSPgUCYVcjXBtUn/OeecY+GBmxHXfDo9eUYt06exsMhJcvt+OZi9iyiwMOsFngkRE7gU74vPfxj1dcn6y6zZWi1V2rN8Tvrqo0oivQwUUbWt1j937b22yAFByz9/3vn9k2I7K8Bb3D686ebScI2WuvmTlEEZURft/77G2BnzvLbu9Fi+DNb7b3g4HgPffYURPioNyM8Mkn0/3Xv9o6x2eftUFEsKd61P8He8cHqbQ0QqRwRjiMH1SEJnIZePHF8v7fUREzOnd0dubKExKmq6vLvrZfm1tNecSzz9rbYCAMuXPBrFk2Ezw6an/piOrXsGiR7WxXAe57Jnm0umv1hvjdVQXCi6JmJ1OCVvfYvf1e/n4QZQx8//vxvgZFvMOBcLX1v+UQVSP81a/aDNDs2bZz3PHH5/1LVe0d/J84A3o/KC0UCPs93N/8ZhZuu63Nas2da7/8/SmPIbqzXbESiGIu4W02NNhfBM48s3THpte+1u5LsHPglVfSdvjhxf/PURUzOne8//22lKAO08AuWrTIBsKve509tmYSCEd1/PTZcUe47DI7DnjU5DxV4L5nkkeru1ZviN9dVSDcX6gXvAK0uifi7Qejt9wC//xnLJss6F2PQDj4Gg0NuY5y5503bXaoqtq7Vie04MVKFPvsA4ODcNxx07332SdXhxweDs3nrrumT2JSTUZYxNahv/rV0f8bJlgKceyx9BfKTjtmRMFjuVSHUGPseWDDhpmN2lAl/X199leN7bazk7JUGwgvXly8bAdsUuD734/tPOS+Z5JHq7tWb4jfXVUg3FbqpJJitLon6v2qV9npSGOgoHelQ37NhKiMcAmqau+4AuGbb7Y/1/qU8m5unvqij/R+1atsULPpptH/v2rV9PGiC43qEQyERfID4UoJjRKh9bOZdiLbtaPD/tx/3nmF/3HtWvAnObn88tIvND5uM6pXXlmdaIi2wUFbsrDllvYXlnvvrXxijXXrppdFJIDWY1mrN+h11+oN8burCoRHR0frrVA1Wt1fct7h4C5YqxzFTEaT8IO0CoK1qtq7nAkyyuHVr84vaQgOdVfidas+TsLZ2ELBQzDbO39+7v/KnUI7SKhznNZjPO1Ma9ds1nbafOQR+N//LVwDfNtt9naPPWxw63dyLMRtt9kJYP7v/2aqDMD4f/5j72y1lQ2Ee3pypQ5hBgZgaGj68mefrUsgrPVY1uoNet21ekP87qoC4ea4vvDrgFb3l7S3MXDOOdOX339/7v7gYPUSwbFuyyRV7e17F/IPuFbtvfvu9vbcc+Hhhwuv9+1vw913wze+YQOf00+HCy6AD3+48tcMZYRT1eYvIaa16/nnwzXX2BFaGhoKj9Ry6622M+k559hRXv7+9+IvdN119vaee+wxMlNvf4IVPxCG6PKIbDY3ffI3v2mzyD7PPlu8PrhGaD2WtXqDXnet3hC/u6pAOFtohisFaHV/WXoHO1PN5ANXxc/3qWrvpUvhE5+A66+Pfj7QNlV7L1pk2+mzny0+RFVLix1z9eMfh513tiUu739/de9PKCOcqjZ/CZHXrv/6F3z603YYvXPPtcfV5Zfnsr9Bbr3VDi12xBF2mL1S5RF/+5sdGnH27OihA8MMDsIVVxQem9zPVG+5pT0XNDTA735nA/dTT83939/+ZscE3mwze1zuu6/NXg8M2GHS6pAR1nosa/UGve5avSF+d1WBsKm0TitFaHV/2XoHp+qtXsLeVhAIV+299daw007V/W8hRODrX58+hJM/z3sgCFV1nIQywqrcFTHVro88Akceacea/ulP7XH1iU/YAPKEE2w29fnn7boDA3Ya4gMPtBc/xx4Lf/hD/mQqQTo6bCb42GPhne+0ozAMDBQX++pX7br7728D2RDyzDP2GJ83z54Hdt4ZfvlL+2vExRfDt75lVzz/fLsP995rO7w99JDNSEeNIZwQWo9lrd6g112rN8TvrioQbkqyo1PMaHV/2Xo//TT4tYLV4o9DWqoD4MknT9U3Vu29di08+mh1/1spt91mv/gDM16pOk5CGWFV7opoamqC9evh8MPtxcdf/5rr2Dl3rg1aN9nEZlO32gpuugnuvNOWHLzylXa9977XBraFpgu//np7wfnGN9pymcHB4hnkbNaOd73bbnb2t1Wr7GudcsrUiDWN69ZZH5+LL7bb7OiwGe0vf9mWY1x3nR36rKUF3vEOu+5NNxUeQzgBtB7LWr1Br7tWb4jfXVUgPDY2Vm+FqtHqnmrvF16wX2YRzNi7vd0OnzQTNtvMZokuuKD4ej//uf3ZmJS3t88OO9gAIIAKb5/GRluKceedgDJ3RYyNjcFHPmLLBK69Nj+4BDjkEPsePPaY/UXjhBPgqqvsc/4Y1QcdZCebOO+8/Np9n+uus9nbffax5RS77WYD10LcdJMNzj//eVuTftZZ9sLo6qttVnl4GPP00/mu++1nM8iLFtladWPssGezZtngG2xAv+uu8I9/lDeGcI3Qeixr9Qa97lq9IX53VYHwnDh+rq4TWt1T7b1smQ02I0iN9667VjTyRGq8K0Sd97nn2ppOFLorYc6cOfDjH9tgdc89C6+4447w299CX5+9aNx11/wpuL/1LRvsnnoqZDK55cbYOt1DD7UXNyI2YL39dnuRHMWll9pfao46ytbAf+tbNnj94x+hqwt+9jMawhnhIFtuaYPo8XE7gUxwqtfXvtbWQj/5pPUpcG6qJVqPZa3eoNddqzfE764qEB4oVfuVYrS6O+9kcd7Jo9k9zQwMDNgAtpxZ03bfPTfDZHj9xYvtc3ffbTOyPg89ZGuLDzsst+zII+3tX/4y/TWGhuxwbMcdZzvWBXnVq2wW+stfRkZGCgfCYIdc/Pzn7RBwQV77WlvL/Pvfw/LlyY5Z7qH1WNbqDXrdtXpD/O6qAuGFwSyBMrS6v2S9t9wyEY9Kecm2d4rR7J5mKm7X974XLrlkqkwoj7e/HY4+Gs4+G9assbW+n/qULWsITpH9ilfYkoQ//Wn6Nq66ygbD73739Of8DnwvvmgfFzs/tLbaOuEVK/KX+30B1qypS30w6D2WtXqDXnet3hC/u6pAuKurq94KVaPV/SXp/cILtmd6CnlJtnfK0eyeZipuVxFbD7ztttHPXXCBLTM64ww70sS119rShs03z1/vqKPs2MP+uL5dXTZw/chHbKa30JTfxxyTe+1iGeFCLF2aG3qxDvXBoPdY1uoNet21ekP87qoC4aVLl9ZboWq0ur8kvZcts0MjpZCXZHunHM3uaSb2dl2+3A7nd+ON8MlP2s5t//3f09c78khbonDjjXZ4tO22s5nk/fazWeFCE8Q0Ntpyh2XLooPxcnjta+1tnTLCWo9lrd6g112rN8TvrioQ7ujoqLdC1Wh1d97J4ryTR7N7mqlJu552Grz+9Ta4veii6DG6DznEDs/2k5/Am98MbW12xIm//KV4pz2Ad7+bjgcesP9TDYccYm/rFAhrPZa1eoNed63eEL+71GtQ5VWrVpnVq1fX5bUdDodjJojI3caYVfX2SJLUnLMzGfsXmhglj2OPtZ3W5s2DW26BPfZIxm1w0I5u8dWvVp9VdjgcNaHQeVtVRrizs7PeClWj1d15J4vzTh7N7mmmZu3a1FQ8CAbbIW72bDssW4VB8Iy8586F3/ymbkGw1mNZqzfoddfqDfG7q8oIG2OQCqarTRNa3Z13sjjv5KnG3WWES1P3Y2J8vKIxvH3q7j0DtLpr9Qa97lq9oXr3l0RGuLe3t94KVaPV3Xkni/NOHs3uaabu7VpFEAwp8J4BWt21eoNed63eEL+7qkB4Xkp7+peDVnfnnSzOO3k0u6cZre2q1Rv0umv1Br3uWr0hfveyAmEROVxEHheRNSIybbRzEXmNiNwjIhkReXushgGGh4drtemao9XdeSeL804eze5pRmu7avUGve5avUGvu1ZviN+9ZCAsIo3ABcARwC7ACSKyS2i1Z4FTgMtitQvR2tpay83XFK3uzjtZnHfyaHZPM1rbVas36HXX6g163bV6Q/zu5WSE9wPWGGPWGmPGgcuBo4MrGGOeNsY8AGRjtQuRyWRqufmaotXdeSeL804eze6FKONXvFYR+Y33/B0islXcDlrbVas36HXX6g163bV6Q/zu5QTCy4F1gcfrvWWJo7WHI+h1d97J4ryTR7N7FGX+incq0GOM2Q74NnBeDTzi3mQiaPUGve5avUGvu1ZviN890c5yInK6iKwWkdUbN25kdHSU4eFhhoaGGBsbo7+/n0wmQ09PD8aYqbHi/FlEent7McbQ09NDJpOhv7+fsbExhoaGGB4eZnR0lIGBASYmJujt7SWbzU7NSe1vw7/t7u5mcnKSvr4+xsfHGRwcZGRkhJGREQYHBxkfH6evr4/JyUm6u7sjt9HV1UU2m6W3t5eJiQkGBgYK7lN/f3/kPnV2dqZ6n3yHSt6nNOxTQ0NDVe9TvfdpZGQk9mMviX3q7+9P9PMU5z719fVV/D6lnJK/4nmPf+7dvxJ4vcT87dJQaCrjlKPVG/S6a/UGve5avSF+96Yy1tkAbBF4vMJbVjHGmAuBCwFEpGP27NnPVLiJpYDWUaC1ujvvZHHeyVON+5a1EImJqF/x9i+0jjEmIyJ9wBJC7SAipwOnew8HReTxCjy0HhNavUGvu1Zv0Ouu1Ruqd488b5cTCN8FbC8iW2MD4OOBE6sQyMMY017p/4jIaq2D2Gt1d97J4ryTR7N7rQkmLypFa7tq9Qa97lq9Qa+7Vm+I371kftkYkwHOAq4DHgWuMMY8LCJfEpGjPKl9RWQ98A7gxyLycFyCDofD4aiIcn7Fm1pHRJqABUBXInYOh8ORIsrJCGOMuQa4JrTs7MD9u7AnW4fD4XDUl3J+xbsaeA9wG/B24EZjjEnU0uFwOFJAWYFwiqjqJ7qUoNXdeSeL804eze7T8Gp+/V/xGoGL/V/xgNXGmKuBnwK/EJE1QDc2WI4bre2q1Rv0umv1Br3uWr0hZndxSQCHw+FwOBwOx8sRveNnOBwOh8PhcDgcM8AFwg6Hw+FwOByOlyVqAuFSU4bWExG5WEQ2ishDgWWLReTvIvIf73aRt1xE5LvefjwgInvX0XsLEfmHiDwiIg+LyIc0uIvILBG5U0Tu97zP8ZZv7U0Xu8abPrbFW17z6WQr9G8UkXtF5M/KvJ8WkQdF5D4RWe0tS/Wx4rksFJErReQxEXlURA7U4K2VNJ+rw1R6Dkwb5Z5L0kYln8k0ISIf8Y6Th0Tk1953USrbXPTGJVHe3/COlQdE5CoRWRh47jOe9+Miclg1r6kiEJbypgytJ5cAh4eWfRq4wRizPXCD9xjsPmzv/Z0O/DAhxygywMeMMbsABwAf8No17e5jwOuMMXsAewKHi8gB2Gliv+1NG9uDnUYWEphOtkI+hB2K0EeLN8BrjTF7BsZwTPuxAvAd4K/GmJ2APbBtr8FbHQrO1WEqPQemjXLPJWmjks9kKhCR5cAHgVXGmN2wHVGPJ71tfgk645JLmO79d2A3Y8wrgCeAzwB4n9XjgV29//mBdw6qDGNM6v+AA4HrAo8/A3ym3l4hx62AhwKPHwc28+5vBjzu3f8xcELUevX+A/4IvEGTOzAHuAc7c1Yn0BQ+ZrC95w/07jd560mdfFdgT0CvA/4MiAZvz+FpYGloWaqPFez4uE+F2y3t3lr/NJyrS/gXPQem6a+Sc0ma/ir9TKblj9xsjIu98/GfgcPS3OYojUvC3qHn3gr8yrufd34JfmdW8qciI0z0lKHL6+RSLsuMMc97918Alnn3U7kv3s/uewF3oMDd+0nwPmAj9mrxSaDX2Algwm5508kC/nSy9eB84JNA1nu8BB3eAAb4m4jcLXbqXUj/sbI10AH8zPsJ+SIRaSP93lpR235lngPTxPmUfy5JE5V+JlOBMWYD8P+AZ4Hnsefju9HR5j4vhfPefwHXevdj8dYSCKvG2EuV1I5TJyJzgd8BHzbG9AefS6u7MWbSGLMnNiuyH7BTfY1KIyJvATYaY+6ut0uVvMoYszf2Z7QPiMhrgk+m9FhpAvYGfmiM2QsYIvSTa0q9HQmi7Ryo/Fyi8jPp1dMejQ3kNwfamP4TvhrS2MalEJHPYcuZfhXndrUEwuVMGZo2XhSRzQC8243e8lTti4g0Y78AfmWM+b23WIU7gDGmF/gH9iephWKni4V8t7RMJ3sQcJSIPA1cjv1J8zuk3xuYyohgjNkIXIW9AEn7sbIeWG+MucN7fCX2Szjt3lpR134VngPTQqXnkjRR6WcyLRwKPGWM6TDGTAC/x74PGtrcR+15T0ROAd4CvMsL4iEmby2B8NSUoV6PzOOxU4SmGX8KU7zbPwaWn+z10jwA6Av8VJEoIiLYGaYeNcZ8K/BUqt1FpN3vNSois7E1fY9iA+K3e6uFvf39qdt0ssaYzxhjVhhjtsIewzcaY95Fyr0BRKRNROb594E3Ag+R8mPFGPMCsE5EdvQWvR54hJR7K0bVubqKc2AqqOJckhqq+EymhWeBA0Rkjnfc+N6pb/MAKs97InI4tgzoKGPMcOCpq4HjxY6wtDW2s9+dFb9AvYqhK/0D3oTtLfgk8Ll6+4Tcfo2tGZrAXu2eiq3XugH4D3A9sNhbV7C9qp8EHsT2QK2X96uwP408ANzn/b0p7e7AK4B7Pe+HgLO95dt4H4I1wG+BVm/5LO/xGu/5bVJwzBwC/FmLt+d4v/f3sP8ZTPux4rnsCaz2jpc/AIs0eGv9S/O5OsK1onNgGv/KOZek7a+Sz2Sa/oBzgMe8751fAK1pbXP0xiVR3muwtcD+Z/RHgfU/53k/DhxRzWu6KZYdDofD4XA4HC9LtJRGOBwOh8PhcDgcseICYYfD4XA4HA7HyxIXCDscDofD4XA4Xpa4QNjhcDgcDofD8bLEBcIOh8PhcDgcjpclLhB2vGwRkUNE5M/19nA4HA5Hadw521ELXCDscDgcDofD4XhZ4gJhR+oRkZNE5E4RuU9EfiwijSIyKCLfFpGHReQGEWn31t1TRG4XkQdE5CpvfnhEZDsRuV5E7heRe0RkW2/zc0XkShF5TER+5c0Y5HA4HI4qcedshyZcIOxINSKyM/BO4CBjzJ7AJPAuoA1YbYzZFfgn8L/ev1wKfMoY8wrsDDn+8l8BFxhj9gBeiZ25BmAv4MPALtgZgg6q8S45HA7HSxZ3znZoo6neAg5HCV4P7APc5V34zwY2AlngN946vwR+LyILgIXGmH96y38O/FZE5gHLjTFXARhjRgG87d1pjFnvPb4P2Ar4V833yuFwOF6auHO2QxUuEHakHQF+boz5TN5CkS+E1qt2rvCxwP1J3GfC4XA4ZoI7ZztU4UojHGnnBuDtIrIJgIgsFpEtscfu2711TgT+ZYzpA3pE5NXe8ncD/zTGDADrReQYbxutIjInyZ1wOByOlwnunO1QhbuScqQaY8wjIvJ54G8i0gBMAB8AhoD9vOc2YmvSAN4D/Mg7aa4F3ustfzfwYxH5kreNdyS4Gw6Hw/GywJ2zHdoQY6r9dcLhqB8iMmiMmVtvD4fD4XCUxp2zHWnFlUY4HA6Hw+FwOF6WuIyww+FwOBwOh+NlicsIOxwOh8PhcDhelrhA2OFwOBwOh8PxssQFwg6Hw+FwOByOlyUuEHY4HA6Hw+FwvCxxgbDD4XA4HA6H42WJC4QdDofD4XA4HC9LXCDscDgcDofD4XhZ4gJhh8PhcDgcDsfLEhcIOxwOh8PhcDhelrhA2OFwOBwOh8PxssQFwg6Hw+FwOByOlyUuEHakEhF5WkQOrbeHw+FwOCrnpXgOF5FrReQ99fZwxIsLhBXinWBGRGQw8Ld5lds6RUT+VWKdm0RkNPR6f6rOvnaIyCEiYkTkU/V2iQNvX7art4fD4UgHSZ77ReRHInJpxPI9RGRMRBZX87reNi7xzm9Hh5Z/21t+SrXbnoGTEZEhr027ROQGEXlncB1jzBHGmJ8n7eaoLS4Q1suRxpi5gb/navx6Z4Ve78gav141vAfoBk6uxcZFpKkW23U4HI4KSOrc/3PgbSLSFlr+buDPxpjuGW7/CQLnau/8ehzw5Ay3OxP2MMbMBXYELgG+LyL/W0cfRwK4QPglgogsEpE/i0iHiPR491cEnj9FRNaKyICIPCUi7xKRnYEfAQd6V8G9VbzuISKyXkQ+KyKdXsbiXYHnF4jIpZ7XMyLyeRFpCDx/mog86nk9IiJ7Bza/p4g8ICJ9IvIbEZlVxKMNeDvwAWB7EVnlLf+UiFwZWvc7IvLdgN9PReR5EdkgIl8RkcZAm/3by1J0AV8UkW1F5EYvY9ApIr8SkYWBbe8tIvd6+/Nbz/srgeffIiL3iUiviNwqIq+oos0LtqmIbCci//TarFNEfuMtF28/NopIv4g8KCK7VfraDocjXdTq3G+MuQ3YABwb2FYjcCJwaalzYRn8CXiViCzyHh8OPAC8ENq///K+I3pE5DoR2TLw3HdEZJ13TrtbRF4deO6LInKFd64cEJGH/e+FUhhjOo0xvwD+G/iMiCzxtnmTiLwv8BqR318isrmI/M57T54SkQ9W0C6OhHGB8EuHBuBnwJbASmAE+D5MBYnfBY4wxswDXgncZ4x5FDgTuM3LLCys8rU3BZYCy7FZ2QtFZEfvue8BC4BtgIOxGYD3el7vAL7oLZsPHAV0BbZ7HPbkuDXwCuCUIg5vAwaB3wLXeR4AlwNvEpF53ms2etu9zHv+EiADbAfsBbwRmDrRAfsDa4FlwLmAAP8HbA7sDGzh7QMi0gJc5W1zMfBr4K3+hkRkL+Bi4AxgCfBj4GoRaS2yX1EUbFPgy8DfgEXACm9dvP16DbCD97/Hkd/WDodDJ7U8919K/i9shwLNwDUUOReWySjwR+B47/HJ3utNIbZ04rPY83s7cAv2vOpzF7An9nx7GfDbUMLkKOx3wELgarx2qYA/Ak3AfuEnCn1/eUmJPwH3Y78TXw98WEQOq/C1HUlhjHF/yv6Ap7FBX6/394eIdfYEerz7bd56xwKzQ+udAvyrxOvdBAwHXq8X+LL33CHYQLItsP4VwBeARmAc2CXw3BnATd7964APFdnHkwKPvw78qIjj9cD53v0TgA6g2Xv8L+Bk7/4bgCe9+8uAsWCbeP/7j0DbPFuibY4B7vXuvwabQZHA8/8CvuLd/6HfboHnHwcOLrBtA2wXWlaqTS8FLgRWhP7vddifIg8AGup9DLs/9+f+Kv+rw7l/JTDhn0+AXwHfKbDu1Lkw4HpogXUvAb4CvAq4DRuovgjM9s6Zp3jrXQucGvi/Bux30ZYFttuDLW8AG6ReH3huF2CkyL5OO996y18A3uXdvwl4n3c/8vsLmzx5NrTsM8DP6n38uL/oP5cR1ssxxpiF3t8xIjJHRH7s/VTeD9wMLBSRRmPMEPBObAbgeRH5i4jsVOHrfTDweguNMV8IPNfjvYbPM9gswVJs9uCZ0HPLvftbULweLPgT2TAwN2olEdkCeC32JA32Kn4W8Gbv8WXYABfsz3p+NnhLz+95r1ShF5ul3SSw+XWh11omIpd7ZRT9wC+9/QS7zxuMd+aL+P8tgY/5r+W93hbe/5VLqTb9JDZTc6f3U+B/ARhjbsRmQy4ANorIhSIyv4LXdTgc6SCxc78x5llveyeJyFxssHsplDwXlrv9f2EzvZ/D1h2PhFbZEvhO4HzZjT2/LfccPu6VJvR5zy8IOYS/Q2ZJBX09RKTZ84uqhy70/bUlsHnoPP9ZbOLFkUJcIPzS4WPYAv/9jTHzsdlJsCcNjDHXGWPeAGwGPAb8xHvehDdUBYskv0PFSuA5oBObTdgy9NwG7/46YNsYXv/d2GP5TyLyAraUYRa58ojfAod4dXNvJRcIr8NmhJcGvljmG2N2DWw73D5f9Zbt7rXzSXhtDDwPLBcRCay/ReD+OuDc0AXFHGNM8Ke+UhRtU2PMC8aY04wxm2MzxT8Qb+QJY8x3jTH7YDMjOwCfqOB1HQ5HOqn1uf/n2HPsscBTxpi7veXFzoWV8EtvH6aNUIE9Z54ROmfONsbc6tUDfxJb5rXI2PKOviodCnE09hfPOwu4RX1/rcO2U9B5njHmTTF6OWLEBcIvHeZha8N6xQ5rM9XT1btyP9oLVsewP61lvadfBFZ49a0z4RwRafFOTm8BfmuMmcSWSZwrIvO8Tg4fxZ74AC4CPi4i+4hlu2BHiAp4D3AO9idB/+9YbG3wEmNMB/YnrZ9hT1CPAhhjnsfW035TROaLSIPXAeTgIq81D9t+fSKynPxg8jZgEjhLRJq8+rZgbdlPgDNFZH9vf9tE5M1+/XIBWkRklv/nLSvYpiLyDsl1lOnBflFlRWRf73WbgSFsfV4Wh8OhnVqf+3+Hvdg+BxsUB1+30LmwEr6LLVm7OeK5H2E7q+3q7c8CrzbXf/0MtgyuSUTOxtbqzhgRWSy20/cFwHnGmKj+FIW+v+4EBsR21J4tIo0ispuI7BuHmyN+XCD80uF8bH1VJ3A78NfAcw3YYOk57E88B2N7wwLcCDwMvCAinUW2/33JH7vy7sBzL2CDruew5QlnGmMe8577H2zgtRZb+3UZtsMYxpjfYjugXQYMAH/AdnooGxE5AJsdvcDLhvp/VwNryJVEXIbt6HFZaBMnAy3AI94+XInNnBTiHGBvbObhL8Dv/SeMMePYTh2nYuvyTgL+jP0CwhizGjgNW6LQ4/mdUmIXH8Z+yfl/76VImwL7AneIyCC2c8iHjDFrsV8QP/Fe9xlsR7lvlHhth8ORfs6nhud+r7zid9jOt78KPFXwXFgJxphuY8wNoZIy/7mrgPOAy73yi4eAI7ynr8Pu6xPYc9oooVK2KrjfO3euwXaa/ogx5uwC3pHfX14C6C3YhMxT2PflImzZhiOFSMSx53CUjYgcAvzSGLOixKovS0TkDmwnv5/V28XhcDgcDkc+LiPscMSIiBwsIpt6pRHvwQ779tdS/+dwOBwOhyN5XCDscMTLjtjxI3uxHUDe7tUiOxyJISIXi5085aECz4uIfFdE1oidtGbvqPUcDofjpY4rjXA4HI6XGCLyGmxHpkuNMdNmEBSRN2Frzd+EHff0O8aY/ZO1dDgcjvrjMsIOh8PxEsMYczPRY5/6HI0Nko0x5nbsuLPFOok6HA7HS5KyB5aOm6VLl5qtttqqov8xxpA/RKsetLo772Rx3slTjfvdd9/daYxpr5FSEiwnv4f9em9ZXhmPiJwOnA7Q1ta2z4477oj/K6KIkM1maWhoIJvN0tjYSCaToampiUwmQ2NjI5OTkzQ1NTE5OTm1XkNDQ942/PaP2kbUrb+t8K+Z1W7Dd/C3MTk5SWNjY8ltpXGf/OM4vE/F3qc07NPExATNzc0VvU9p2SdjTFkemvYpzs9TLfap0PFSap/uvffeyPN23QLhrbbaitWrV9fr5R0Oh6NqROSZ0mvpxxhzIXbKblatWmXcOdvhcGil0HlbVWlER0dHvRWqRqu7804W5508mt1nwAbyZz1cQW7Gx1jQ2q5avUGvu1Zv0Ouu1Rvid1cVCLe36/0lUqu7804W5508mt1nwNXAyd7oEQcAfXGPbqK1XbV6g153rd6g112rN8TvrioQdlcwyeO8k8V5J49m90KIyK+xU37vKCLrReRUETlTRM70VrkGOzPhGuyMg++P20Fru2r1Br3uWr1Br7tWb4jfvW7Dp7l6M4fDoRURudsYs6reHkniztkOh0Mzhc7bqjLCXV1d9VaoGq3uzjtZnHfyaHZPM1rbVas36HXX6g163bV6Q/zuqgLhRYsW1VuharS6O+9kcd7Jo9k9zWhtV63eoNddqzfoddfqDfG7qwqE+/v7661QNVrdnXeyOO/k0eyeZrS2q1Zv0Ouu1Rv0umv1hvjdVQXCbW1t9VaoGq3uzjtZnHfyaHZPM1rbVas36HXX6g163bV6Q/zuqgLh0dHReitUjVZ3550szjt5NLunGa3tqtUb9Lpr9Qa97lq9IX53VYFwc3NzvRWqRqu7804W5508mt3TjNZ21eoNet21eoNed63eEL973aZYroZsNltvharR6u68k8V5J49m9zSjtV21eoNed63ekIx71mRpkMJ5S2MMo5lRMtkMIkJLYwvNDTZY7B/rZ+PQRua2zKW9rZ2mhqY87/90/YcGaWDTuZvS0tjC0MQQE5MTzG2ZS2tTK+v61vFY52MMTQwxq2kWLY0tCEJzYzMHrjiQ5kb7On2jfdyx4Q4Wz17MktlLGJ4Ypme0h4GxAUYzo4xPjtMgDRgMPSM9dA53AjCvdR4tjS2MTIyQyWbYbZPd2H/F/gjCmu41bBjYwND4EKOZUWY3z2Zeyzw2n705+6zcJ7b2VRUIr7p4FXtutieXHXtZvVUqpl7jNc8U550szjt5NLunGa3tqtUbprsbYxCRov+zcWgjfaN9ADQ1NDGvdR7zW+fT0thS8euPZcZobWrNWzY0PsT6/vVsHNrI+OQ4WZNl60Vbs82ibRgcH+TvT/6d25+9nUVti5jXMo+mhiYMNrDrG+3DYNhr071Ytfkq2lramMxO0tzYzNyWuYxMjHDP8/dwz/P3sGFgAxuHNjKSGQFAEJoammhqaKKtuY15rfPYpG0Ttl64Nbtusis7Ld0JsEHmTU/fxA1rb+D2Dbfz3MBzrJi/gs3mbsbg+CCdw52MTY4BsHzecj7xyk9w4BYH8kzvM1z24GVsMmsTTt77ZJobm6f256anb+Lf6/7N8vnLOXKHI9m1fVfW96/nmb5neLL7SZ7seZLukW6GJoYQhKVzlrJg1gJGJkYYyYyw6dxN2bV9V4Ynhvnrmr/yRNcTvHXnt3L63qfzTN8zXPHwFTze9ThZk2VicoLuke4pR59GaaSpoSlvuSDsuHRHjtrhKDafszm/fOSXrH6u+rHBd99kdy466iI2Dm3kjD+fwXMDz1W9rUo4dY9TuWjlRbFtT1UgbDBMZCfqrVEVTU2qmnoK550szjt5NLunGa3tWon3yMQI45PjzG+dz3MDz/Hju3/MVY9dxcVHXcy+y/eNxWcyO8l9L9zHglkLWD5vOev613HrultZ072G8clxAPZbvh+v2/p1PNPzDH+/7+/cuu5WHtr4EM/0PcMmbZuw1cKt2G7xduy0ZCeWzllK31gf6/vXc8NTN/DQxoemvWZLYwvH73Y8HzngI+y56Z4YY/j3un/zw9U/5G9P/o1FsxaxfP5yPnXQpzh8u8MB+Nq/vsbnbvwci2cvZvvF2zM0McS6vnX0jPZE7tfclrmMZcaYyE4gCIbpFyCCICJkTems66ymWSxrW0Zbi+1IlTVZJrOTjE+OMzwxzMC4zUz67L7J7hy27WFc/cTVPNH1BE0NTeyxbA92ad+FDf0b+E/Xf5jXOo8ls5ewePZiAG559haueuwqdm3flUc6HplyPudf57Dnpnvy97V/t5nLptnst3w/7nvhPq5+/Oo8zyWzl7Dt4m1ZNncZbc1tGAxdw11s6N/A7ObZzGqaxUMbH+IPj/2BpoYmXr3y1Ry0xUFc8cgVXPHwFf+/vTePj6wq8//fJ/ue7qTD1gvd7LTsNC6AoiiCG8uIIipuwzgzL52v851xQ/3pDI7Od3BGnXFwGZfRwQVcZxhFQQHFDaSRtWkbeqe7gU4qqaSSSu3n90ctVNKppFK5dep+7PN+vfLqqls3N+976sntp5489xwAjlp+FOeuObeU7A50DjDQOZD/EGEtqWyKqfQUmVyGQ7oP4ZDuQ5hMTfL05NP8Zs9v+OTdnySTy7B+aD2fvvDTLOtYxpOTT5LKpuht6y0l9VOpKVb1reLEoRNZ1rGMRCZBMpNPrHeP7+Z9P3sfz/3Sc7FYTj7kZL7wyi+Qszki8Qjdbd0s61hGb1svna2dtDW3lT6oLe9czmDnIMYYYskYqWyKztZOAO5/8n7u2XsPLU0tHDtwLKv7V9PT1kNHSwfxdJzJ1CRdTV0LxsNikFpZ7qTrT+K4Fcfx/Su+Xyer+jExMUFfX1+jNRaN93aL93ZPLe5+ZbmFCWtM3LXrLr6/+fv844v/sfSfbzkLeU+lprjm9mv4xa5fsGn/JrI2S0tTCzmbw1pLS1MLV558JV+79GsAxJIxHos8xplHLPyn3EeHH+W6X19HIpPg9MNOJ5FJ8OX7v8wTE08csG+TaaK9uZ2czR1QDTx+8HhOOfQU1i1bx/74fnZGd/J45HH2xvaW9mlvbuf5Rz6fC466gJW9KwFIZVNMpibZPLKZ/3rwv5hKT804bn97P5eccAmJTIL79t3HrvFdfOvV3yKWjPG2m9/Gy455GSt7V/L4aD6JXNO3htX9q1ndt5pDew6lvbkdi+XxyOM8+PSDdLZ08orjXsFJ/SfR3dPNRHKilPS2t7TT195HKpvi/ifv5/6n7iedTdPc1EwmlyGWjNFkmjj98Hy1eKhraMHqdzQRZWd0J7/e/Wu+/vDXuXvP3Txv1fN4x1nv4LITL6Ordf4Eayo1xec3fp7vPPodXnLUS/izM/6Me3fdy2cf+Czbx7bzquNexavXv5qzV59dSvwe3v8wu8d3s6Z/DWv617CsY9m8P6NIIpPAWluK0Xg6zi2P38LaZWs58/AzFzzX+RhPjLNp7yaed9TzlnScieQEH/3FR1neuZx3n/3umv6KUNPPrfHaUum6LZUIn/b501jTv4abr7x54Z1DRiaTkayQeG+3eG/31OLuE+GFcRkTT8aenNH/OBfWWj5196d470/fS9Zm+atn/xX/9rJ/m7HPU5NP8fl7P4/FkrM5do7v5LHIY5x+2On8y0v/hbbmNi696VJ+svUnXHDUBWw4YgPLO5YzOj1Ka3Mrbzr1TXz8lx/npk038fS7n6artYu3/s9b+dbD3yL6/igdLR1zusXTcf7ih3/B1x/6Ot1t3azoWsHO6E4AXnr0S7nqlKvI5rLsmdjDId2HcM6aczhhxQk0mSbS2TS/2/s7fr7z5wx0DPCqE17Fqr5Vc/6cWDJGNBFleedyulu7502CxqbH+ObD32Q4PgzA2mVrec3615QqruOJcV7+zZdz9567MRjOX3c+P3z9D2tKhhp1/ZhKTZXOp1ZUr32q3lC7e6XrttQoGGvI2myjNWoiFotJruTivd3ivd2j7B5mXI3r2PQYx37mWM5dcy43X3nznIlYLBnj6v+9mm9v+jaXnXAZQ11DfOZ3n+GVx72Slx790tJ+n7nnM3z8Vx8vPV/dt5q1y9byH/f9B79+4tesH1rPLY/fwudf8Xn+fMOfz+nz+pNfz5fv/zL/u+V/OXv12Xz9oa+TyWXYGd1Z6kudzXW/vo4bHrqB95z9Ht57zntZ0bUi3/eZSXJ47+Hznn9rcyvnrDmHc9acw9jYGMv7Ko95b3svve298x6vyPLO5bzj2e+o+Hp/Rz+3vvFWXvOd1zCRnOB7r/1ezRXBRv0OLjUJBt3rh6o3BO8ulQi3t7aTzWkmwsuWLWu0Qk14b7d4b/cou4cZV+N6x447mEpPceu2W3nLf7+Fr//J12fcYf+HkT/wJzf9CVsiW/h/L/5/vPec95LIJPjVE7/iLf/9Fh7+y4cZ7BrMH2vnHZy96mx+/ae/nnGj2W3bbuPK713JI/sf4SPnfaRiEgxw3pHncUTvEXzj4W9w9567yeQyAGwf2z5nIvzE+BNc9+vruOJZV3DdBdeVthf7UheD61juaevhx2/4cVU35c2H8u+gqruqNwTvLjWPcC6bK11U1IhEIo1WqAnv7Rbv7R5l9zBTz3Etb+m7bdtt9LX3ce0Lr+Vbj3yLv/7JX5def3T4UZ7zpecwEh/hp1f9lPed+z6MMXS2dvL1y77O01NP82/35NsjJpIT3Lv3Xp572HMBZiR2Lz36pTzw5w9w0+U38ZHzPjKvW3NTM1eedCU/3vpjvvj7L5YqzttGt825/zW3X0PO5vinl/xT7QNSoFGxvJQkGLR/B1XdVb0heHepRLijrUO2NWLFihWNVqgJ7+0W7+0eZfcwU69xff/P3s8FN1yAtRZrLbdtv40XrX0RH3rBh/i/z/2/fOZ3n+Hjv/w40USUS2+8lI6WDn73Z7/j/HXnzzjO6YefznlHnsdNm27CWstdu+4ia7O8cv0r5/y5q/tX89pnvbaqpO8NJ7+BTC7DVHqKf77gn+lu7Wbb2IGJ8D177uEbD3+Dd5/9bo5cdmRtA1KGaiyreoOuu6o3BO8ulQjbrJWtCA8PDzdaoSa8t1u8t3uU3cNMvcZ1S2QLt++4nd888Ru2jW1jZ3QnFxx1AcYY/vml/8wbT3kjH7rzQzzvy89jR3QH33vt91i7bO2cx7riWVewJbKFh55+iDt23EFHSwfHdByzZMfTDjuNMw4/g8tOuIyTDz2ZoweOPiARttby3p+9l0O7D+X9575/yT8TdGNZ1Rt03VW9IXh3qR7hjvYOplJTC+8YQoaGhhqtUBPe2y3e2z3K7mGmXuNanF7rX+/5V1609kUApfaDJtPEVy7+CpF4hB9v/TGfe8XnOHfNuRWP9er1r+Ydt7yDGx+5kTt23ME5q89h9eGrl+xojOGXb/0lzaYZyM/7+ljksRn73LrtVu7adRfXv/x6etp6lvwzQTeWVb1B113VG4J3l6oI5zI52daIkZGRRivUhPd2i/d2j7J7mKnXuBZvmP7+5u/z1Qe/ypH9R3LMwDNV3NbmVr5/xff57Z/+lj8/s/JNbQArulbw4qNezNce/BoPPv0g5687PzDvrtau0iprRy8/mu1j20tJfM7m+MDtH2DdsnVcfcbVgfw80I1lVW/QdVf1huDdpRLhzvZO2daIwcHBRivUhPd2i/d2j7J7mKnXuOZsjsN7Dsdi+d3e35XaIsrpaOnguaueW1U/7xXPuoInJ58E4Px159fF++jlR5PIJHgylv853330u9z/1P1c+6JrA12EQDWWVb1B113VG4J3l0qEc5mc7PRp0Wi00Qo14b3d4r3do+weZuo1rjmbY03/Gi494VKAGfMA18JlJ1xGa1MrvW29bDhiQ128jx44GqDUJ/wPd/0DJx1yEleedGWgP0c1llW9Qddd1RuCd9fqEW7rIDulmQj39lY3iXnY8N5u8d7uUXYPM/Ua15zN0WSa+PALPsxkapILj7lwScdb3rmct53+NppNMy1NLXXxPmr5UUB+LuGVvSt5eP/DfPrCT9Pc1Bzoz1GNZVVv0HVX9Ybg3aUqwjanO2tEPB5vtEJNeG+3eG/3KLuHmXqNazERPvWwU7n1jbfS19635GN+/pWf5/pXXA/Ux/vI/iNpNs1sG93Gjx7/EQCvPG7uadqWgmosq3qDrruqNwTvLlURbmttk22NaG9vb7RCTXhvt3hv9yi7h5l6jWvO5gKvpJZTD+/W5lbW9K9h29g2frfvdxw/eHypXSJIVGNZ1Rt03VW9IXh3qYpwE02yFeFMxnu7xHu7RdUbtN3DTL3GNWuzM5ZQDpp6eR89cDQPPv0gP9/587pUg0E3llW9Qddd1RuCd5dKhJtNs+z0aUtdgrJReG+3eG/3KLuHmXqNa7E1ol7Uy/uoZUfx6PCjpLIpXnHsK+ryM1RjWdUbdN1VvSF4d6lEuLW5VbY1oqlJaqhLeG+3eG/3KLuHmXqNa70T4Xp5F1sh+tr75l3kYymoxrKqN+i6q3pD8O5SI2GskW2NSKfTjVaoCe/tFu/tHmX3MLOUcb1r113c9MhNc75W70S4XvFw9PJ8Inzh0RfS2txal5+hGsuq3qDrruoNwbvr3Swn2hrR0dHRaIWa8N5u8d7uUXYPM0sZ1/f+9L3sn9rPFSddccBr9U6E6xUP64fWA5TmP64HqrGs6g267qreELy7VEXYZnWnT5uammq0Qk14b7d4b/cou4eZWsd1dHqUe/fdy0RyYs7XczZHs6nfrBH1iocTh07k4b98OPBFNMpRjWVVb9B1V/WG4N2lKsKdHZ2yPcJ9fUuf67IReG+3eG/3KLuHmVrH9Y4dd5CzOWKp2Jyv17siXM94OOmQk+p2bNCNZVVv0HVX9Ybg3aUqwplURrY1YmxsrNEKNeG93eK93aPsHmZqHdfbtt0GQCqbIplJHvB6Nlff6dOU40HVXdUbdN1VvSF4d6lEuKerR7Y1YnBwsNEKNeG93eK93aPsHmZqGVdrbSkRBuZsj6h3RVg5HlTdVb1B113VG4J3l0qEk9NJ2daI4eHhRivUhPd2i/d2j7J7mKllXB8ffZxd47t43qrnAczZHlHvRFg5HlTdVb1B113VG4J3l0qE+3r6yNos1tpGqyyaoaGhRivUhPd2i/d2j7J7mKllXIvV4Fef+GqgMRVh5XhQdVf1Bl13VW8I3r2qq4kx5iJjzBZjzFZjzPvneP0txphhY8wDha+rA7UskJhOAPkLoRqqn768t1u8t3uU3cNMLeN627bbOGbgGE497FQAYklfEV4Mqu6q3qDrruoNwbsvOGuEMaYZuB64ANgD3GuMudla++isXW+y1r4zULtZ9PXk7xTM2izN1G/6nHqg+unLe7vFe7tH2T3MLHZcE5kEd+68k6tOuYretl6gckW4ual+13/leFB1V/UGXXdVb2hMRfjZwFZr7XZrbQq4EbgkUIsqSSVTAJI3zI2OjjZaoSa8t1u8t3uU3cPMYsf11q23Mpma5NITLqWvPV/0mCsRztr6zhqhHA+q7qreoOuu6g3Bu1dzNVkJPFH2fE9h22xebYx5yBjzXWPM6rkOZIx5uzFmozFm4/79+0kkEsTjcaampkgmk0xMTJDJZBgbG8Nay8jICFBWBi90RIyMjpDJZJiYmCCZTDI1NUU8HieRSBCLxUin00SjUXK5HJFIZMYxiv+Ojo6SzWYZHx8nlUoxOTnJ9PQ009PTTE5OkkqlGB8fJ5vNlgZ99jEikQi5XI5oNEo6nSYWi1U8p1wuN+c5jYyMYK1lbGwslOfU1NS06PcpDOfU399f0/vU6HNqbm4OPPZcnFMul3P6+xTkOWWz2UW/T56F6e/vX9T+33n0Owx0DvCitS8qJcKNuFlusd5hQtVd1Rt03VW9IXh3s9CNZ8aYy4GLrLVXF55fBTynvA3CGDMITFprk8aYPweusNaeP99xN2zYYDdu3Lgo2X+88x/5wF0fYOx9YyzrWLao72004+PjkoHnvd3ivd1Ti7sx5j5r7YY6KYWSxV6zFzOuiUyCQz5xCK991mv50sVfYiI5Qf//6+cTF3yCd5/97hn7HvnpIzl/3fn85yX/uSj/ajnYYjkMqHqDrruqN9TuXum6Xc3H6r1AeYV3VWFbCWttxFpbnPn8S8CZizasgo72/PrSiq0RnZ2djVaoCe/tFu/tHmX3MLOYcb1t223EUjFes/41APS09QDz3CxXxwmPlONB1V3VG3TdVb0hePdqrib3AscaY9YZY9qA1wE3l+9gjDm87OnFwObgFMsotEYoziWcSqUarVAT3tst3ts9yu5hZjHjWmyLOH9d/g+JTaaJnraehkyfphwPqu6q3qDrruoNwbsvOGuEtTZjjHkncCvQDHzFWrvJGHMtsNFaezPwf4wxFwMZYBR4S6CWBVqbWwEkl1lubtaa5aKI93aL93aPsnuYqXZck5kkN2+5mctPvLx0jQfoa++r2CNcz1kjlONB1V3VG3TdVb0hePcFE2EAa+0twC2ztn247PE1wDWBms1B8eKn2Brh8Xg8ngP51e5fMZGc4E9O/JMZ2/va++aeNSJX31kjPB7PwYXU1aQ4d3A6m26wyeIp3pmuhvd2i/d2j7J7mKl2XIvJ7sq+mZMR9bb1NqQ1QjkeVN1VvUHXXdUbgneXSoQ72/IN0umcXiLc1tbWaIWa8N5u8d7uUXYPM9WOa7HVrdnM/HPnfK0R9UyEleNB1V3VG3TdVb0heHepRNhm81O9pbJ6Td6qc496b7d4b/cou4eZase1ePPz7L7f3vbGVISV40HVXdUbdN1VvSF4d6lEuK87P8m6YmtET09PoxVqwnu7xXu7R9k9zFQ7rjmbnw5odnLb195Xefq0OibCyvGg6q7qDbruqt4QvLtUIpxO5BNgxdaI8fHxRivUhPd2i/d2j7J7mKl2XCu2RrTNfbNcvRNh5XhQdVf1Bl13VW8I3l0qER7oHwA0WyMGBgYarVAT3tst3ts9yu5hptpxXag1YvbqpzmbOyBpDhLleFB1V/UGXXdVbwjeXSoRjsfigGZrxPDwcKMVasJ7u8V7u0fZPcxUO67ztUZkbZZEJjFje9bWd/o05XhQdVf1Bl13VW8I3l0qER4aHAI0K8JDQ0ONVqgJ7+0W7+0eZfdKGGMuMsZsMcZsNca8f47X1xhj7jTG3G+MecgY8/KgHaod10qtEb1tvQAHtEfUuzVCOR5U3VW9Qddd1RuCd5dKhCfHJwHNHmHVT1/e2y3e2z3K7nNhjGkGrgdeBqwHrjTGrJ+124eAb1trTwdeB3w2aI9qx7VSa0Rfe/7m6NlTqNU7EVaOB1V3VW/QdVf1hoO8InzoikMBzdYI1U9f3tst3ts9yu4VeDaw1Vq73VqbAm4ELpm1jwX6Co/7gX1BS1Q7rvO1RoCvCC8GVXdVb9B1V/WGg7wiPBWbAjRbIyKRSKMVasJ7u8V7u0fZvQIrgSfKnu8pbCvn74A3GmP2ALcAfxW0RLXjWrE1ov3A1ojijXP1TISV40HVXdUbdN1VvSF4d6lEeMXyFYBma8Ty5csbrVAT3tst3ts9yu5L4Ergq9baVcDLgRuMOTC7NMa83Riz0Rizcf/+/SQSCeLxOFNTUySTSSYmJshkMoyNjWGtZWRkBMj/6XL58uWMjIxgrWVsbIxMJsPExATJZJKpqSni8TiJRIKp6XyBYzI2SS6XK/0nl43nE+Q9w3sAGB0dJZ0pXPstTE5OMj09zfT0NJOTk6RSKcbHx8lms4yOjpY8yv+NRCLkcjmi0SjpdJpYLHbAOTU3N1c8J6Cqc4rFYqTTaaLR6Ixzmu0zOjpKNptlfHycVCq15HPq6emZ85zme5/CcE7FJXMX8z6F5ZxaW1sDiz2X52StDTT2XJ5TLperKfYqYWZPTeOKDRs22I0bNy7qex554hFO/srJfPFVX+TqM66uk1l9iEajLFu2rNEai8Z7u8V7u6cWd2PMfdbaDfUxWhrGmOcBf2etvbDw/BoAa+0/lu2zCbjIWvtE4fl24LnW2v2VjrvYa3a14/rJ336Sv73tb4m+L0p/R39p+5aRLZxw/Ql8/bKv84ZT3gDk/xrY/g/tfOz8j/GB53+gapfFcLDFchhQ9QZdd1VvqN290nVbqiLc35O/SCr2CHd3dzdaoSa8t1u8t3uU3StwL3CsMWadMaaN/M1wN8/aZzfwYgBjzIlABxDoHSjVjutCPcLlN8tV2jdIlONB1V3VG3TdVb0heHepRDiXzl8EFXuEE4nEwjuFEO/tFu/tHmX3ubDWZoB3ArcCm8nPDrHJGHOtMebiwm5/C/yZMeZB4FvAW2zAfx6sdlwXmjWivEfYRSKsHA+q7qreoOuu6g3Bu7cEerQ609XeBWj2CLe2tjZaoSa8t1u8t3uU3Sthrb2F/E1w5ds+XPb4UeCcejpUO66Vbpbrau2iyTQRS7qtCCvHg6q7qjfouqt6Q/DuUhXhFpPP2xVbI4rN3Wp4b7d4b/cou4eZase1UnJrjKG3rdd5RVg5HlTdVb1B113VG4J310qEm/KJsGJrRKNuSlwq3tst3ts9yu5hptpxrdQaAfkp1CZSBybCs6vHQaIcD6ruqt6g667qDcG7SyXCba1tNJtmydaIlhapLpQS3tst3ts9yu5hptpxLbZGzFXl7Wvvc94aoRwPqu6q3qDrruoNwbtLJcLJZJLW5lbJinAymWy0Qk14b7d4b/cou4eZasd1vpXi+tr7ZrRGFKvH9UyEleNB1V3VG3TdVb0heHepRLirq4vWplbJHuGurq5GK9SE93aL93aPsnuYqXZcs7lsxVaH3rZe59OnKceDqruqN+i6q3pD8O5SiXAsFpOtCMdisYV3CiHe2y3e2z3K7mGm2nHN2uyc/cFwYEXYRSKsHA+q7qreoOuu6g3Bu0slwsuWLaPJNGHRa/JWXcHFe7vFe7tH2T3MVDuu81aE293PGqEcD6ruqt6g667qDcG7SyXCkUgknwgL3u1YXO9bDe/tFu/tHmX3MFPtuM7bI9zm/mY55XhQdVf1Bl13VW8I3l0qEV6xYgUGU7oYKrFixYpGK9SE93aL93aPsnuYqXZcF2qNiKVipeJHafq0CvsHgXI8qLqreoOuu6o3BO8ulQgPDw9jjJFsjRgeHm60Qk14b7d4b/cou4eZasd1odaInM0RT8fz+84z1VpQKMeDqruqN+i6q3pD8O5SifDQ0JBsa8TQ0FCjFWrCe7vFe7tH2T3MVDuuC02fBpT6hF20RijHg6q7qjfouqt6Q/DuUonwyMiIbGvEyMhIoxVqwnu7xXu7R9k9zFQ7rvO1RvS29QJuE2HleFB1V/UGXXdVbwjeXSoRHhwclG2NGBwcbLRCTXhvt3hv9yi7h5lqx3W+1oiOlg4Aktn8BPouEmHleFB1V/UGXXdVbwjeXSoRjkajstOnRaPRRivUhPd2i/d2j7J7mKl2XHNUbo0obi8mwC4SYeV4UHVX9QZdd1VvCN5dKhHu7e2VbY3o7e1ttEJNeG+3eG/3KLuHmWrHNZur3BpR3F5cWrk0a0SFCnIQKMeDqruqN+i6q3pD8O5SiXA8Hs+3RgjeLBePxxutUBPe2y3e2z3K7mGm2nHN2sqtEcXtxdkiXFSEleNB1V3VG3TdVb0heHepRLi9vV22NaK9vb3RCjXhvd3ivd2j7B5mqh3XairCxQS4WBmuZyKsHA+q7qreoOuu6g3Bu0slwplMRrY1IpPJNFqhJry3W7y3e5Tdw0y14zrf9GnF7bNbI+qZCCvHg6q7qjfouqt6Q/DuUomwMUa2NcIY02iFmvDebvHe7lF2DzPVjmvYWiOU40HVXdUbdN1VvSF4d6lEuKmpSbY1oqlJaqhLeG+3eG/3KLuHmWrHdTGtES4SYeV4UHVX9QZdd1VvCN5daiTS6bRsa0Q6nW60Qk14b7d4b/cou4eZase1ltaISolzECjHg6q7qjfouqt6Q/DuUolwR0eHbGtER0dHoxVqwnu7xXu7R9k9zFQ7rmFrjVCOB1V3VW/QdVf1huDdpRLhqakp2daIqampRivUhPd2i/d2j7J7mKl2XBczj3AxIa5nIqwcD6ruqt6g667qDcG7SyXCfX19sq0RfX19jVaoCe/tFu/tHmX3MFPtuFbTGuGyR1g5HlTdVb1B113VG4J3r+pqYoy5yBizxRiz1Rjz/nn2e7UxxhpjNgSn+AxjY2OyrRFjY2ONVqgJ7+0W7+0eZfcwU+24hq01QjkeVN1VvUHXXdUbgndf8GpijGkGrgdeBqwHrjTGrJ9jv17gXcA9gRqWMTg4KNsaMTg42GiFmvDebvHe7lF2DzPVjmstSyzXMxFWjgdVd1Vv0HVX9Ybg3au5mjwb2Gqt3W6tTQE3ApfMsd9HgX8CEgH6zWB4eFi2NWJ4eLjRCjXhvd3ivd2j7B5mqh3XairCLlsjlONB1V3VG3TdVb0hePdqriYrgSfKnu8pbCthjDkDWG2t/VGAbgcwNDQk2xoxNDTUaIWa8N5u8d7uUXYPM9WOa1XTp81qjaiUOAeBcjyouqt6g667qjcE777kj9XGmCbgk8DfVrHv240xG40xG/fv308ikSAejzM1NUUymWRiYoJMJsPY2BjWWkZGRoBnsv+tW7fSZJpIpVJkMhkmJiZIJpNMTU0Rj8dJJBLEYjHS6TTRaJRcLkckEplxjOK/o6OjZLNZxsfHSaVSTE5OMj09zfT0NJOTk6RSKcbHx8lms4yOjs55jEgkQi6XIxqNkk6nicViFc9p+/btc57TyMgI1lrGxsZCeU67du1a9PsUhnMqfi32fWr0Oe3evTvw2HNxTtu3b3f6+xTkOW3btm3R75NnYaquCC9m1ohc/WeN8JUy96h6g667qjcE724Wqq4aY54H/J219sLC82sArLX/WHjeD2wDJgvfchgwClxsrd1Y6bgbNmywGzdWfLkiG/5jA4f2HMqPXl/X4rPH4/FUxBhzn7W2LjcFh5Var9kLcdYXz2Koa4hb3nDLAa89Hnmc4/79OG647AbeeMob+d8t/8vFN17Mxj/byJlHnBm4i8fj+eOl0nW7mo/V9wLHGmPWGWPagNcBNxdftNaOW2tXWGvXWmvXAnezQBJcK6Ojo7KtEcUqmBre2y3e2z3K7mGm2nEN2/RpyvGg6q7qDbruqt4QvPuCVxNrbQZ4J3ArsBn4trV2kzHmWmPMxYHaLEB/f7/srBH9/f2NVqgJ7+0W7+0eZfcwU+24hm3WCOV4UHVX9QZdd1VvCN69qquJtfYWa+1x1tqjrbUfK2z7sLX25jn2fWE9qsEAk5OTsrNGTE5OLrxTCPHebvHe7lF2DzPVjmvY5hFWjgdVd1Vv0HVX9Ybg3aVWluvs7JRtjejs7Gy0Qk14b7d4b/cou4eZasd1vtaIYkV4dmtEpQpyECjHg6q7qjfouqt6Q/DuUolwKpWSbY1IpVKNVqgJ7+0W7+0eZfcwU+24ztcaUZo+zWFrhHI8qLqreoOuu6o3BO8ulQg3NzfLtkY0N9evglFPvLdbvLd7lN3DTLXjupjWiOK/9UyEleNB1V3VG3TdVb0heHepRBiQbY3weDwez4GE7WY5j8dzcCF1Nclms7KtEdlsttEKNeG93eK93aPsHmaqHdewTZ+mHA+q7qreoOuu6g3Bu0slwm1tbbKtEW1tbY1WqAnv7Rbv7R5l9zBT7biGbdYI5XhQdVf1Bl13VW8I3l0qEZ6enpZtjVBdltV7u8V7u0fZPcxUO67Z3DyJcIXWiEr7B4FyPKi6q3qDrruqNwTvLpUI9/T0yLZG9PT0NFqhJry3W7y3e5Tdw0y14xq21gjleFB1V/UGXXdVbwjeXSoRHh8fl22NGB8fb7RCTXhvt3hv9yi7h5lqxzVr57lZbvasEbn6zxqhHA+q7qreoOuu6g3Bu0slwgMDA7KtEQMDA41WqAnv7Rbv7R5l9zBT7bjW0hpRz0RYOR5U3VW9Qddd1RuCd5dKhIeHh2VbI4aHhxutUBPe2y3e2z3K7mGm2nGtpjXC5c1yyvGg6q7qDbruqt4QvLtUIjw0NCTbGjE0NNRohZrw3m7x3u5Rdg8z1Y7rfK0RkE96XfYIK8eDqruqN+i6q3pD8O5SifDw8LBsa4Tqpy/v7Rbv7R5l9zBT7bjO1xoB+T5hl60RyvGg6q7qDbruqt7gK8KyrRGqn768t1u8t3uU3cNMUBXh5qbmA1oj5tt/qSjHg6q7qjfouqt6w0FeEY5EIrKtEZFIpNEKNeG93eK93aPsHmaqHdf5eoTBfWuEcjyouqt6g667qjcE7y6VCC9fvly2NWL58uWNVqgJ7+0W7+0eZfcwU+24LqY1olgZrmcirBwPqu6q3qDrruoNwbtLJcITExOyrRETExONVqgJ7+0W7+0eZfcwU824Wmux2EW3RtQzEVaOB1V3VW/QdVf1huDdpRLh7u5u2daI7u7uRivUhPd2i/d2j7J7mKlmXKtJbF3fLKccD6ruqt6g667qDcG7SyXCiURCtjUikUg0WqEmvLdbvLd7lN0rYYy5yBizxRiz1Rjz/gr7vNYY86gxZpMx5ptBO1QzrsVK73ytEa57hJXjQdVd1Rt03VW9IXj3lkCPVmdaW1tlWyNaW1sbrVAT3tst3ts9yu5zYYxpBq4HLgD2APcaY2621j5ats+xwDXAOdbaMWPMIUF7VDOuxUrvomeNmCdxXirK8aDqruoNuu6q3hC8u1RFOJfLybZG5HJ6zuC9XeO93aPsXoFnA1uttduttSngRuCSWfv8GXC9tXYMwFq7P2iJasa11tYIY0wAhhWchONB1V3VG3TdVb0heHepRNhaK9saoegM3ts13ts9yu4VWAk8UfZ8T2FbOccBxxljfm2MudsYc9FcBzLGvN0Ys9EYs3H//v0kEgni8ThTU1Mkk0kmJibIZDKMjY1hrWVkZATIT3hffG6tZWxsjEwmw8TEBMlkkqmpqfxxpqfyP8hCNBoll8uVpkYqTZpvIUeO0dFR0tk0TaaJVCrF5OQk09PTTE9PMzk5SSqVYnx8nGw2y+jo6IxjFP+NRCLkcjmi0SjpdJpYLHbAOcVisYrnBCx4TolEglgsRjqdrnhOxX9HR0fJZrOMj4/X9Zzme5/CcE6zj/XHcE5hf5/GxsZkz2lsbKym96kSplH/CWzYsMFu3LhxUd+TTCZ50/++iYeefojN79hcJ7P6kEwmaW9vb7TGovHebvHe7qnF3Rhzn7V2Q52UloQx5nLgImvt1YXnVwHPsda+s2yfHwJp4LXAKuAu4GRrbbTScRd7za5mXMemxxi4boBPXfgp/vq5fz3nPuv+dR3PX/N8/uuy/+KDt3+Q635zHen/L121x2I52GI5DKh6g667qjfU7l7pui1VEU4mk7KtEclkstEKNeG93eK93aPsXoG9wOqy56sK28rZA9xsrU1ba3cAjwHHBilRzbhW0/PbbGb2CNfzRjnQjgdVd1Vv0HVX9Ybg3aUS4a6uLtnWiK6urkYr1IT3dov3do+yewXuBY41xqwzxrQBrwNunrXPfwMvBDDGrCDfKrE9SIlqxrWaBTJmzxpR70RYOR5U3VW9Qddd1RuCd5dKhGOxmOysEbFYrNEKNeG93eK93aPsPhfW2gzwTuBWYDPwbWvtJmPMtcaYiwu73QpEjDGPAncC77HWBrpuaTXjWvWsEWU3y9VzxgjQjgdVd1Vv0HVX9Ybg3aWmT1u2bJlsa8SyZcsarVAT3tst3ts9yu6VsNbeAtwya9uHyx5b4G8KX3WhmnGtZh5h160RyvGg6q7qDbruqt4QvLtURTgSici2RhTv3FTDe7vFe7tH2T3MVDOuVU2fVlYRztps3RNh5XhQdVf1Bl13VW8I3l0qEV6xYoVsa8SKFSsarVAT3tst3ts9yu5hpppxraY1wnWPsHI8qLqreoOuu6o3BO8ulQgPDw/LtkaU5sQUw3u7xXu7R9k9zFQzrmFsjVCOB1V3VW/QdVf1huDdpRLhoaEh2daIoaGhRivUhPd2i/d2j7J7mKlmXBfbGuEiEVaOB1V3VW/QdVf1huDdpRLhkZERmtBsjSiupqKG93aL93aPsnuYqWZcw9gaoRwPqu6q3qDrruoNwbtLJcKDg4MYo9kaMTg42GiFmvDebvHe7lF2DzPVjGstrRHzJc1BoBwPqu6q3qDrruoNwbtLJcLRaBSDZmtENBpttEJNeG+3eG/3KLuHmWrGtbSy3CLmEa53RVg5HlTdVb1B113VG4J3l0qEe3t7ZWeN6O3tbbRCTXhvt3hv9yi7h5lqxrWY4M7bI1xWEXYxfZpyPKi6q3qDrruqNwTvLpUIx+Nx2daIeDzeaIWa8N5u8d7uUXYPM9WMazWtEa57hJXjQdVd1Rt03VW9IXh3qUS4vb1dtjWivb290Qo14b3d4r3do+weZqoZ11qWWK53IqwcD6ruqt6g667qDcG7SyXCmUxGtjUik8k0WqEmvLdbvLd7lN3DTDXjWtX0aY7nEVaOB1V3VW/QdVf1huDdpRJhY4xsa4QxptEKNeG93eK93aPsHmaqGddaWiPm2zcIlONB1V3VG3TdVb0heHepRLipqUm2NaKpSWqoS3hvt3hv9yi7h5lqxjWMrRHK8aDqruoNuu6q3hC8u9RIpNNp2daIdDrdaIWa8N5u8d7uUXYPM9WM62JbI7K5+s8aoRwPqu6q3qDrruoNwbtXdUUxxlxkjNlijNlqjHn/HK//hTHmYWPMA8aYXxlj1gdqWaCjo0O2NaKjo6PRCjXhvd3ivd2j7B5mqhnXalsjXFaEleNB1V3VG3TdVb0hePcFryjGmGbgeuBlwHrgyjkS3W9aa0+21p4GXAd8MlDLAlNTU7KtEVNTU41WqAnv7Rbv7R5l9zBTzbhW2xrhcvo05XhQdVf1Bl13VW8I3r2aK8qzga3W2u3W2hRwI3BJ+Q7W2omyp91Qn96Fvr4+2daIvr6+RivUhPd2i/d2j7J7mKlmXEsryy1iieV6J8LK8aDqruoNuu6q3hC8ezVXlJXAE2XP9xS2zcAY8w5jzDbyFeH/M9eBjDFvN8ZsNMZs3L9/P4lEgng8ztTUFMlkkomJCTKZDGNjY1hrGRkZAWB4eBiAHTt2AJDL5chkMkxMTJBMJpmamiIej5NIJIjFYqTTaaLRKLlcjkgkMuMYxX9HR0fJZrOMj4+TSqWYnJxkenqa6elpJicnSaVSjI+Pk81mGR0dnfMYkUiEXC5HNBolnU4Ti8UqntPu3bvnPKeRkRGstYyNjYXynPbu3bvo9ykM5zQ2NlbT+9Toc9q3b1/gsefinHbt2uX09ynIc9q5c+ei3yfPwoyNjS24TzHBnbdHeNbNcvNVj4OgGu+wouqu6g267qreELy7WajNwBhzOXCRtfbqwvOrgOdYa99ZYf/XAxdaa98833E3bNhgN27cuGjh99z2Hq6/93riH9RdFcXj8WhjjLnPWruh0R4uqfWaPR//84f/4dKbLuW+t9/HGYefMec+b/7vN/OLnb9g51/v5OXfeDmR6Qj3XH1PoB4ej+ePn0rX7WoqwnuB1WXPVxW2VeJG4NJF2VXJ8PCwbGtEscqkhvd2i/d2j7J7mKlmXKu5Wc51a4RyPKi6q3qDrruqNwTvXs0V5V7gWGPMOmNMG/A64ObyHYwxx5Y9fQXweHCKzzA0NCQ7a8TQ0FCjFWrCe7vFe7tH2T3MVDOuVU+fVmiNyNr6T5+mHA+q7qreoOuu6g3Buy94RbHWZoB3ArcCm4FvW2s3GWOuNcZcXNjtncaYTcaYB4C/AeZti6iV4eFh2VkjVD99eW+3eG/3KLuHmaoqwlXMGtFkmnxFuEpU3VW9Qddd1RuCd2+pZidr7S3ALbO2fbjs8bsCtarA0NCQbGuE6qcv7+0W7+0eZfcwU824VtUa4Xj6NOV4UHVX9QZdd1VvaEBFOEyMjo7KtkYU75RXw3u7xXu7R9k9zFQzrottjXCRCCvHg6q7qjfouqt6Q/DuUolwf3+/bGtEf39/oxVqwnu7xXu7R9k9zFQzrtUuqFHeGjFf9TgIlONB1V3VG3TdVb0heHepRHhyclK2NWJycrLRCjXhvd3ivd2j7B5mqhnXapdYdtkaoRwPqu6q3qDrruoNwbtLJcKdnZ0YYwDkqsKdnZ2NVqgJ7+0W7+0eZfcwU824llaWm68iXD5rRK7+s0Yox4Oqu6o36LqrekPw7lKJcCqVwlBIhMWqwqlUqtEKNeG93eK93aPsHmaqGddigrvgynIOZ41QjgdVd1Vv0HVX9Ybg3aUS4ebm5tJFUK0i3Nxc3762euG93eK93aPsHmaqGddqWyNc3iynHA+q7qreoOuu6g3Bu0slwkCpNUJx5giPx+PxPENVN8sZt9OneTyegwupK0o2m5Vtjchms41WqAnv7Rbv7R5l9zBTzbhWNX1aUzMWi7U2P2vEPElzECjHg6q7qjfouqt6Q/DuUolwW1ubbGtEW1tboxVqwnu7xXu7R9k9zFQzrtW2RhT3dVERVo4HVXdVb9B1V/WG4N2lEuHp6WnZ1ojp6elGK9SE93aL93aPsnuYqWZcq22NgPw1P2vrP2uEcjyouqt6g667qjcE7y6VCPf09Mi2RvT09DRaoSa8t1u8t3uU3cNMNeNabWsE5JNmFxVh5XhQdVf1Bl13VW8I3l0qER4fHy9dBNUqwuPj441WqAnv7Rbv7R5l9zBTzbhW0xpRfM1Va4RyPKi6q3qDrruqNwTvLpUIDwwMyCbCAwMDjVaoCe/tFu/tHmX3MFPNuFbTGlF+zXeRCCvHg6q7qjfouqt6Q/DuUonw8PAwLU0twDMXUBWGh4cbrVAT3tst3ts9yu5hpnxcn4w9yaU3Xsreib0z9imtLDdfRXhWa8R8+waBcjyouqt6g667qjcE7y6VCA8NDZUuiplcpsE2i2NoaKjRCjXhvd3ivd2j7B5mysf190/+nv/Z8j985OcfmbFPsTWieBP0XLhujVCOB1V3VW/QdVf1huDdpRLhGRVh6yvCLvDebvHe7lF2DzPl45rOpQH46gNfZcvIltL2bC67YIW3NH2ao5vllONB1V3VG3TdVb3BV4RLibCvCLvBe7vFe7tH2T3MlI9rOptPhLM2O6MqnLXZBRfIKL6eszmyufpPn6YcD6ruqt6g667qDQd5RTgSiZSqB2qJcCQSabRCTXhvt3hv9yi7h5nycS1WhF+z/jXctOkm7n/yfqC6JZNdt0Yox4Oqu6o36LqrekPw7lKJ8PLly2Vvllu+fHmjFWrCe7vFe7tH2T3MlI9rsSJ8zbnX0NrUyo2P3AhU1xrheh5h5XhQdVf1Bl13VW8I3l0qEZ6YmJC9WW5iYqLRCjXhvd3ivd2j7B5myse1WBFe0bWCnrYepjP5laGqaY1wPX2acjyouqt6g667qjcE7y6VCHd3d8veLNfd3d1ohZrw3m7x3u5Rdg8z5eNarAi3NrfS1txGKpsCamuNqPf0acrxoOqu6g267qreELy7VCKcSCRke4QTiUSjFWrCe7vFe7tH2T3MlI9rsSLc2tRKa3NrKREOY2uEcjyouqt6g667qjcE7y6VCLe2tsr2CLe2tjZaoSa8t1u8t3uU3cNM+bhWqggvpjUia7Nkbf1njVCOB1V3VW/QdVf1huDdpRLhXC4n2yOcy2ktCV3Ee7vFe7tH2T3MlI9reUW4rbmt9LyaVofi6656hJXjQdVd1Rt03VW9IXh3qUTYWivbI2ytbbRCTXhvt3hv9yi7h5nyca1YEa5iXmDXrRHK8aDqruoNuu6q3hC8u1Qi3NLSItsj3NLS0miFmvDebvHe7lF2DzPl41q8Xjeb5iW1RrhIhJXjQdVd1Rt03VW9IXh3qUQ4mUzKriyXTCYbrVAT3tst3ts9yu5hpnxc07k0rU2tGGNobWqdmQgvsjViocR5qSjHg6q7qjfouqt6Q/DuUolwV1eX7M1yXV1djVaoCe/tFu/tHmX3ShhjLjLGbDHGbDXGvH+e/V5tjLHGmA1BO5SPazqbprU5f4PLoqdPc9waoRwPqu6q3qDrruoNwbtLJcKxWEz2ZrlYLNZohZrw3m7x3u5Rdp8LY0wzcD3wMmA9cKUxZv0c+/UC7wLuqYdH+bgWK8KQT4SLPcPZ3MKtEa6XWFaOB1V3VW/QdVf1huDdpRLhZcuWyd4st2zZskYr1IT3dov3do+yewWeDWy11m631qaAG4FL5tjvo8A/AXWZULR8XCtVhKtpjSj1CDuqCCvHg6q7qjfouqt6Q/DuUolwJBKRvVkuEok0WqEmvLdbvLd7lN0rsBJ4ouz5nsK2EsaYM4DV1tofzXcgY8zbjTEbjTEb9+/fTyKRIB6PMzU1RTKZZGJigkwmw9jYGNZaRkZGABgeHiYSiTAyMoK1lsnpSVqbWpmYmKCZZhLp/HHSmTRYSKfTRKNRcrlc6f0YHh4GIDaRr/6Mjo8CkEqmSKVSTE5OMj09zfT0NJOTk6RSKcbHx8lms4yOjs44RvHfSCRCLpcjGo2STqeJxWIHnNPu3bsrnhNQOqexsTEymQwTExMkk0mmpqaIx+MkEglisdi851T8d3R0lGw2y/j4eCDn9PTTT895TvO9T2E4p23bti36fQrLOe3Zsyew2HN5Tjt27Ag09lye044dO2qKvUqYRk2hsWHDBrtx48ZFf9+DTz3IaV84je+/9vtcduJldTDzeDye+THG3GetDbyvNgiMMZcDF1lrry48vwp4jrX2nYXnTcAdwFustTuNMT8H3m2tnfeCXOs1G+Ct//NW7thxB7v+ehdXfPcKHnzqQf7wzj9w6Y2XsiO6gwf/4sGK3/uLnb/ghV97Ibe+8VYu/PqFfPRFH+VDL/hQTR4ej+fgpdJ1W6oiPDw8LNsjXPzEoob3dov3do+yewX2AqvLnq8qbCvSC5wE/NwYsxN4LnBz0DfMlY9rOjurR7iwoMZiWiOKfcX1bo1QjgdVd1Vv0HVX9Ybg3aUS4aGhIdke4aGhoUYr1IT3dov3do+yewXuBY41xqwzxrQBrwNuLr5orR231q6w1q611q4F7gYuXqgivFjKxzWdK+sRbpo5a8SCN8vNKn4slDgvFeV4UHVX9QZdd1VvCN5dKhEeGRmR7REu9sWo4b3d4r3do+w+F9baDPBO4FZgM/Bta+0mY8y1xpiLXXmUj+vsivCiVpYrXPOLVeR6V4SV40HVXdUbdN1VvSF4d6mlRQYHB4lF8zdOqCXCg4ODjVaoCe/tFu/tHmX3SlhrbwFumbXtwxX2fWE9HMrHtbwi3Nq8yAU1ChVhV60RyvGg6q7qDbruqt4QvLtURTgajc6YXF2JaDTaaIWa8N5u8d7uUXYPM+Xjms6mS21tsyvC1S6xXPyeeifCyvGg6q7qDbruqt4QvLtUItzb2yu7xHJvb2+jFWrCe7vFe7tH2T3MlI9rpQU1qlpZblY7XL0TYeV4UHVX9QZdd1VvCN5dKhGOx+OyN8vF4/FGK9SE93aL93aPsnuYKR/X2QtqpHNprLWLa41w1COsHA+q7qreoOuu6g3Bu0slwu3t7bI3y7W3tzdaoSa8t1u8t3uU3cNM+bjOrggXty2mNaJYRV5o/6WiHA+q7qreoOuu6g3Bu1eVCBtjLjLGbDHGbDXGvH+O1//GGPOoMeYhY8ztxpgjA7UskMlknqkIi/UIZzJaiXsR7+0W7+0eZfcwUz6u5RXhYkKcyqZC2RqhHA+q7qreoOuu6g3Buy94RTHGNAPXAy8D1gNXGmPWz9rtfmCDtfYU4LvAdYFaPuMiu6CGMabRCjXhvd3ivd2j7B5mysd1ropwKpsKZWuEcjyouqt6g667qjcE717NFeXZwFZr7XZrbQq4EbikfAdr7Z3W2mLTxt3kVzIKnKamJtke4aYmqS6UEt7bLd7bPcruYaZ8XGf3CBe31dIaUe9EWDkeVN1VvUHXXdUbgnev5mgrgSfKnu8pbKvEnwI/nusFY8zbjTEbjTEb9+/fTyKRIB6PMzU1RTKZZGJigkwmw9jYGNba0qTJxeX09u/fT1NBOZVJMTExQTKZZGpqing8TiKRIBaLkU6niUaj5HI5IpHIjGMU/x0dHSWbzTI+Pk4qlWJycpLp6Wmmp6eZnJwklUoxPj5ONptldHR0zmNEIhFyuRzRaJR0Ok0sFqt4TpFIZM5zGhkZwVrL2NgYmUwmdOcUjUYX/T6F4ZzS6XRN71Ojz6m4LcjYc3FOIyMjTn+fgjyn2R7VvE+ehUmn0888rlARztncwhVhxwtqlHuroequ6g267qreELy7sdbOv4MxlwMXWWuvLjy/CniOtfadc+z7RvIrGp1nrU3Od9wNGzbYjRsXt6JnOp2muaWZ5mub+bvz/o6PvPAji/r+RpJOp2ltbW20xqLx3m7x3u6pxd0Yc5+1dkOdlELJYq/Z5eO69tNrOW/teXzt0q9xw4M38Kb/fhNb/2orl950KccOHMv3r/h+xePsmdjD6k+t5gPnfoCP/+rj/Ocl/8lbTnvLUk+nKm81VN1VvUHXXdUbanevdN2u5qP1XmB12fNVhW2zf8BLgA+SX7N+3iS4VqampmgyTbQ1txFPa039MTU11WiFmvDebvHe7lF2DzPl41peES62SKSyqapaI2ZXhBeqIC8V5XhQdVf1Bl13VW8I3r2aRPhe4FhjzDpjTBvwOuDm8h2MMacDXyCfBO8P1LCMvr4+AJZ3LCeaiNbrx9SForsa3tst3ts9yu5hpnxc09kK06dVcbOc6x5h5XhQdVf1Bl13VW8I3n3BK4q1NkO+3eFWYDPwbWvtJmPMtcaYiwu7fQLoAb5jjHnAGHNzhcMtibGxMQCWdy5nLDFWjx9RN4ruanhvt3hv9yi7h5nycU3nDrxZrurp05rcTp+mHA+q7qreoOuu6g3Bu7dUs5O19hbgllnbPlz2+CWBWlVgcHAQyFeER6dHXfzIwCi6q+G93eK93aPsHmbKx3WuinCtrRH1ToSV40HVXdUbdN1VvSF4d6n5M4p3cytWhIvuanhvt3hv9yi7h5nyca1UEV5Ma0Qqm5rxvF4ox4Oqu6o36LqrekPw7lKJ8NDQEJCvCI9NayXCRXc1vLdbvLd7lN3DTPm4lleEF72ynOPWCOV4UHVX9QZdd1VvCN5dKhEuVYQ7fEXYFd7bLd7bPcruYaY4rtlcFoutvKBGyOYRVo4HVXdVb9B1V/UGXxEG4PDew4kmokyldKb/UP305b3d4r3do+weZorjWkxgKy6xvFCPcHGJ5cKsEQvtv1SU40HVXdUbdN1VveEgrwgXV6Q6ZuAYALaNbWukzqIouqvhvd3ivd2j7B5miuNaTGArzRpR9fRpjirCyvGg6q7qDbruqt4QvLtUItzf3w88kwg/Hnm8kTqLouiuhvd2i/d2j7J7mCmO6+yK8OwFNRbsETYzK8L1ToSV40HVXdUbdN1VvSF4d6lEeHJyEoAj+48E8ktvqlB0V8N7u8V7u0fZPcwUx3W+inA1rRHGGAzGWUVYOR5U3VW9Qddd1RuCd5dKhDs7OwHoaOkAnplOR4Giuxre2y3e2z3K7mGmOK6VeoTTuXRVrRGQT35dVYSV40HVXdUbdN1VvSF4d6lEOJXKJ77lFQUViu5qeG+3eG/3KLuHmeK4zlsRrqI1AvI3yLmaPk05HlTdVb1B113VG4J3l0qEm5vzlYOWpvyCeEqJcNFdDe/tFu/tHmX3MFMc16XOGgH5PuHicaqpIC8F5XhQdVf1Bl13VW8I3l0qES5ijKG9uV0qEfZ4PJ6DkdkV4fIFNaqZRxjctkZ4PJ6DC6krSjabLT1ua24jmU020GZxlLsr4b3d4r3do+weZorjWmnWiHQ2XdXKcpBvjXB1s5xyPKi6q3qDrruqNwTvLpUIt7W1PfO4uU2qIlzuroT3dov3do+ye5gpjuvsinCTaaKlqYVkNonFVt8a4agirBwPqu6q3qDrruoNwbtLJcLT09Olx2qJcLm7Et7bLd7bPcruYaY4rrMrwpC/fk+n869X0xrhsiKsHA+q7qreoOuu6g3Bu0slwj09PaXHaolwubsS3tst3ts9yu5hpjiuxdkeihVhyF+/E5kEUN2SyS57hJXjQdVd1Rt03VW9IXh3qUR4fHy89FgtES53V8J7u8V7u0fZPcwUx7XUGlFWEW5taiWRzSfCVfUIm2emT6smcV4KyvGg6q7qDbruqt4QvLtUIjwwMFB6rJYIl7sr4b3d4r3do+weZorjWmqNaNZojVCOB1V3VW/QdVf1huDdpRLh4eHh0mO1RLjcXQnv7Rbv7R5l9zBTHNe5KsJhbo1QjgdVd1Vv0HVX9Ybg3aUS4aGhodJjtenTyt2V8N5u8d7uUXYPM8VxrVgRzuQrwtW2RriqCCvHg6q7qjfouqt6Q/DuUolw+aeA9hatBTVUP315b7d4b/cou4eZhSrCi26N8BXhBVF1V/UGXXdVb/AV4dJjtdYI1U9f3tst3ts9yu5hZnZFuKWppfRaa3ProlojfEW4OlTdVb1B113VGw7yinAkEik9VkuEy92V8N5u8d7uUXYPM8Vxnb2gBszsEa4msS3fp96JsHI8qLqreoOuu6o3BO8ulQgvX7689FgtES53V8J7u8V7u0fZPcwUx7XighqZxbVGlB5Xsf9SUI4HVXdVb9B1V/WG4N2lEuGJiYnSY7VEuNxdCe/tFu/tHmX3MFMc10oV4VKPcJWtEUXqXRFWjgdVd1Vv0HVX9Ybg3aUS4e7u7tJjtUS43F0J7+0W7+0eZfcwUxzXShXhUo9wFRVel60RyvGg6q7qDbruqt4QvLtUIpxIJEqP25raSGaSjMRHiMTD3+tS7q6E93aL93aPsnuYKY7rXBXh1qbWRfUIl1eN650IK8eDqruqN+i6q3pD8O4tC+8SHlpbn7mQdrV2EU/HGfpE/u5B+xHbKK2qKHdXwnu7xXu7R9k9zBTHdcEe4ZC1RijHg6q7qjfouqt6Q/DuUhXhXC5Xetzd1s1karKBNouj3F0J7+0W7+0eZfcwUxzXSj3COZt/PWytEcrxoOqu6g267qreELy7VCJs7TNV3+7W7lKlQYFydyW8t1u8t3uU3cNMcVzTuTRNpmlGAtvW3FZ6vNjWiGoqyEtBOR5U3VW9Qddd1RuCd5dKhFtanunk6GnraaDJ4il3V8J7u8V7u0fZPcwUxzWdTc9oi4CZbRJha41QjgdVd1Vv0HVX9Ybg3aUS4WQyWXrc3aZ1x2O5uxLe2y3e2z3K7mGmOK7pXHpGWwTMrAgvdh7heifCyvGg6q7qDbruqt4QvLtUItzV1VV63N2qlQiXuyvhvd3ivd2j7B5miuM6V0V4sa0RLnuEleNB1V3VG3TdVb0heHepRDgWi5Ueq1WEy92V8N5u8d7uUXYPM8VxXbAiHLLWCOV4UHVX9QZdd1VvCN5dKhFetmxZ6fHsHuHinclhpdxdCe/tFu/tHmX3MFMc14UqwmFrjVCOB1V3VW/QdVf1huDdpRLhSOSZhTNmt0YU56QMK+XuSnhvt3hv9yi7h5niuM5VES5/Xk1FuDz5rSZxXgrK8aDqruoNuu6q3hC8u1QivGLFitLj2a0R8XTctc6iKHdXwnu7xXu7R9k9zBTHNZ1beo+wy9YI5XhQdVf1Bl13VW8I3l0qER4eHi49nl0RnkpNudZZFOXuSnhvt3hv9yi7h5niuKazWrNGKMeDqruqN+i6q3pD8O5SifDQ0FDp8ewe4bBXhMvdlfDebvHe7lF2r4Qx5iJjzBZjzFZjzPvneP1vjDGPGmMeMsbcbow5MmiH4rguVBEO281yyvGg6q7qDbruqt4QvLtUIjwyMlJ6PLs1Ymd0p2ObxVHuroT3dov3do+y+1wYY5qB64GXAeuBK40x62ftdj+wwVp7CvBd4LqgPYrjulBFOGzTpynHg6q7qjfouqt6Q/DuUonw4OBg6XFX68x55H6+8+eObRZHubsS3tst3ts9yu4VeDaw1Vq73VqbAm4ELinfwVp7p7W2+Ge0u4FVQUsUx3WuivCMleVC1hqhHA+q7qreoOuu6g3Bu1d1Raniz2wvMMb83hiTMcZcHqhhGdFotPR49sVwe3Q7v9j5C97143fV68cviXJ3Jby3W7y3e5TdK7ASeKLs+Z7Ctkr8KfDjuV4wxrzdGLPRGLNx//79JBIJ4vE4U1NTJJNJJiYmyGQyjI2NYa0tVWqGh4eJRqOMjIyQzqYxOUMmk2FiYoJkMkkukyv9jMR0gnQ6TTQaJZfLle4IL/YBDg8Pz0iWJyYmSKVSTE5OMj09zfT0NJOTk6RSKcbHx8lms4yOjh5wDMjfbZ7L5YhGo6TTaWKx2AHntHfv3ornBPlqlLWWsbGxGec0NTVFPB4nkUgQi8UWPCeA0dFRstks4+PjgZzTyMjInOc03/sUhnPasWPHot+nsJzTvn37Aos9l+e0e/fuQGPP5Tnt2rWrptirhLHWVnwRSn9mewy4gPwF9V7gSmvto2X7rAX6gHcDN1trvzvvQYENGzbYjRs3LrTbDDKZzIw1ps3fm9LjC4++kFu33QpA9sPZulcNFstsdxW8t1u8t3tqcTfG3Get3VAnpSVRKEZcZK29uvD8KuA51tp3zrHvG4F3AudZa+ddt3Sx1+ziuJ7zlXPobOnkZ2/6Wem17z36PS7/Tr5mct/b7+OMw8+Y91hv+e+38LUHv0azaSbz4UzVDrVwsMVyGFD1Bl13VW+o3b3SdbuabLGaP7PttNY+BOTmOkBQxOOVb4ibSj8za8RkqnLm3yjmcw8z3tst3ts9yu4V2AusLnu+qrBtBsaYlwAfBC5eKAmuheK4BjJrRGEfFwUO5XhQdVf1Bl13VW8I3r2aq8pi/8xWN9rb2+fc3tHSQTwdL10kJ5ITLrWqopJ72PHebvHe7lF2r8C9wLHGmHXGmDbgdcDN5TsYY04HvkA+Cd5fD4niuAYya0STu0RYOR5U3VW9Qddd1RuCd3faP7CUfjOA/fv3z+glKbJ+cD1TyanSxTUyGXHem7VQf0wkEgllb9ZC5zQ+Ph7a3qz5zimTyTS8j6mWcypuC2tvVqVzGhkZke03m32Mat6nMGOtzZBvd7gV2Ax821q7yRhzrTHm4sJunwB6gO8YYx4wxtxc4XA1U7xGz1URnrGyXBUV4WIC7CIRLv+/RQ1Vd1Vv0HVX9Ybg3avpEX4e8HfW2gsLz68BsNb+4xz7fhX4Yb16hOPxOF1dz8wWUewRfuMpb+SuXXcxnhhnPDnO3X96N89Z9ZxFHbvezHZXwXu7xXu7pxb3MPcI14vFXrOL43r8vx/P6Yedzo2X31h67Ve7f8Xz//P5APzhHX/g+BXHz3usd/zoHXx242fpbu1m8gP1bX072GI5DKh6g667qjfU7r6UHuEF/8zmiqamuXX72vqIp+O0t+TL5WFsjajkHna8t1u8t3uU3cNMcVwX7BEOWWuEcjyouqt6g667qjcE777g0ar5M5sx5ixjzB7gNcAXjDGbArUskE6nZzx//K8e5/4/v5+u1i5G4iPsn8q3uoUxEZ7troL3dov3do+ye5gpjuuCPcKLuFmumqR5qSjHg6q7qjfouqt6Q/DuVc0/Ya29Bbhl1rYPlz2+lzpMyD6bjo6OGc+PGTgGgB9s/sGM7WFMhGe7q+C93eK93aPsHmaK45rOzr+gxmJWlnNREVaOB1V3VW/QdVf1huDdpWrjU1NTc26fvcrceHLchc6iqOQedry3W7y3e5Tdw0xxXNM5rdYI5XhQdVf1Bl13VW8I3l0qEe7r65tze3db94znw1PDpcfT6Wnuf/L+unpVQyX3sOO93eK93aPsHmaK4zpXRTjM8wgrx4Oqu6o36LqrekPw7lKJ8NjY2JzbZ1eEn556uvT4qh9cxRn/cQbRRLSeagtSyT3seG+3eG/3KLuHmeK4ZnKZeSvCYWuNUI4HVXdVb9B1V/WG4N2lEuHBwcE5tw90Dsx4Xp4I/3T7T4H8anMLTRVXTyq5hx3v7Rbv7R5l9zBTHNd0Lk1L08zbUcLcGqEcD6ruqt6g667qDcG7SyXCxYnuZ3NI9yEznj89+UwiPJXK95Ks/tRq/uGuf6if3AJUcg873tst3ts9yu5hZnh4GGttviI8+2a5RS6oUZo1oop9l4pyPKi6q3qDrruqNwTvLpUIDw0Nzbn9gES4rCKctdnS4+vvvb4+YlVQyT3seG+3eG/3KLuHmaGhITK5/ApQSjfLKceDqruqN+i6q3pD8O5SifBiKsJztUGkc42bN0/105f3dov3do+ye5gZHh4uXXOVpk9TjgdVd1Vv0HVX9QZfEZ5ze29b74znyWxyzrmEU9lUXbyqQfXTl/d2i/d2j7J7mBkaGiKdLSTCsyrCzU3Ni2p3cDlrhHI8qLqreoOuu6o3HOQV4dHR0Tm3G2N47bNeO2NbeXtEkeJFuRFUcg873tst3ts9yu5hZnR0tGJFGJ5pjwhba4RyPKi6q3qDrruqNwTvXtXKcmGhv7+/4ms3XX4Txw8eT0dLBx+844M8Pfk0xw0eN2OfRlaE53MPM97bLd7bPcruYaa/v5/98fyy97MrwsVt05np0LVGKMeDqruqN+i6q3pD8O5SFeHJycl5X7/2RdfyyuNeCcBTk08d0Cdsadz0aQu5hxXv7Rbv7R5l9zAzOTlZXUU4ZK0RyvGg6q7qDbruqt4QvLtUItzZ2bngPkcvP5pm08zD+x8mno7Puc/u8d38xQ//gmQmGbRiRapxDyPe2y3e2z3K7mGms7OzYo8w5BNhg8EYs+Cxiq0R1bRRLBXleFB1V/UGXXdVbwjeXSoRTqUWbm3obuvmlENP4bd7fkssFTvgdWst7/vZ+/jCfV/g25u+XQ/NOanGPYx4b7d4b/cou4eZVCq1YEW42gqvy9YI5XhQdVf1Bl13VW8I3l0qEW5urq4acPyK49kV3UUseWAiHE/H6WvLr1P9pv9+U2nBjXpTrXvY8N5u8d7uUXYPM83NzQtWhKut8LpsjVCOB1V3VW/QdVf1huDdpRLhaulv72c8Oc7HfvmxA14bnR5lRdeK0vPd47tdqnk8Hs9Bw3wV4dam1qpXinM5a4TH4zm4kLqqZLPZhXeikAgnxvnag1874LWxxNiMhTX2TOwJzG8+qnUPG97bLd7bPcruYSabzUpWhJXjQdVd1Rt03VW9IXh3qUS4ra1t4Z2A/o5+ktm5b4Qbmx6b0TLhKhGu1j1seG+3eG/3KLuHmba2NskeYeV4UHVX9QZdd1VvCN5dKhGenp6uar/+9spzzI1OjxJLxVjZuxKA7WPbA3FbiGrdw4b3dov3do+ye5iZnp5euCK8yNaIavdfCsrxoOqu6g267qreELy7VCLc09NT1X79HQcmwmcefiaQb42IpWIMdg1y7ppz+dYj3zpgvuF6UK172PDebvHe7lF2DzM9PT0LVoTD2BqhHA+q7qreoOuu6g3Bu0slwuPj41Xt19vWW3r86Qs/zch7Rrj1jbcC+daIydQkvW29XH7i5Wwb28b+qf118S2nWvew4b3d4r3do+weZsbHx+etCLc2t4ayNUI5HlTdVb1B113VG4J3l0qEBwYGqtqvfCnl3vZeBrsGGegcoNk0MxIf4cGnHqSnrYdnHfIsADYNb6qLbznVui+WTC7DZKp+K8TUy7veeG+3qHqDtnuYGRgYWLgiHMJZI5TjQdVd1Rt03VW9IXh3qUR4eHi4qv0uPv7i0uOu1i4AjDEs61jGTZtuIjId4dCeQ3nWUD4Rfvjph4OXnUW17ovlTT94E73/2Fu39o56edcb7+0WVW/Qdg8zw8PDkrNGKMeDqruqN+i6q3pD8O5SifDQ0FBV+7W3tPOGk98AzKwOD3QOsCO6A4DrXnIdh/Ucxsreldy99+7gZWdRrfti+dYj3wLyNwHWg3p51xvv7RZVb9B2DzNDQ0OBzRrhsiKsHA+q7qreoOuu6g3Bu0slwov5FLCqbxUw8wK8vHM5AH3tfRzSfQjGGM5dcy537bqLnM0FKzuLen/6qtfsF6qfGr23W1S9Qds9zCxUEb7w6At59YmvrupYxQS42gryUlCOB1V3VW/QdVf1Bl8Rrnrfj5z3ET594ad57bNeW9o20JnvKzl6+dEYYwB45XGvZF9sH/fsuYdMLkMmlwlWukA9Pn09Mf5E6fG2sW2BHx90PzV6b7eoeoO2e5hZqCL8plPfxCcv/GRVx3LZGqEcD6ruqt6g667qDQd5RTgSiVS9b2drJ+967rtmVBAO6zkMgKMHji5tu/j4i2lvbuemTTdx+L8czgu/+sLAfMtZjHs13LbtNtZ8ek3p+ZXfu5Iz/+PMQH8GBO/tCu/tFlVv0HYPM5FIZN6K8GJw2RqhHA+q7qreoOuu6g3Bu0slwsuXL1/S9x+9PJ8AH9p9aGlbX3sfFxx9Af96z78yEh/h10/8ekk/oxJLdZ/Nx3/58dLjy9dfDsDvn/x9oD8Dgvd2hfd2i6o3aLuHmeXLl89bEV4MLqdPU44HVXdVb9B1V/WG4N2lEuGJiYklfX9nSyfAATMsnHroqTOeRxPRA7738cjjjCdqn7tuqe4HHC+ZP95Pr/opZ686u7Q96NkjgvZ2hfd2i6o3aLuHmYmJieAqwg5bI5TjQdVd1Rt03VW9IXh3qUS4u7t7Sd9/yQmXAPC2vCjavwAAGWxJREFU0982Y/vqvtUznv/zb/6ZSPyZ0vtPtv6E4/79OF75rVfW/LOX6j6bXeO7+MsNf8lLjnoJxw0eV9r+vc3f4+GnHw5sFomgvV3hvd2i6g3a7mGmu7s7sIqwy9YI5XhQdVf1Bl13VW8I3l0qEU4kEkv6/mMGjsF+xHLmETN7aYsLaxT52C8/xhXfvQLIV4IvuTGfQP9q96845BOHMJ2e5tatt5LNZZ25Azyy/xE27ttIJpdhdHqUoa58w/jLj305Fxx1AQCv+c5rOOXzp3DWF89a8s+DYLwbgfd2i6o3aLuHmUQiEVhF2GVrhHI8qLqreoOuu6o3BO8ulQi3ti7tYlqJc9ecy+N/9Ti5D+c48/B8knz7jttZ+cmVHPfvx5HKpvjVW38FwHB8mFM/fyoXfeMiPnjHB3ks8hgj8ZEZx5urPWGp7mPTY5z8uZM564tn8dsnfgvAUHc+ETbG8LHzPzZj/+1j29k9vntJPxPqN+b1xnu7RdUbtN3DTGtra6kiXO0KcpUofv9Sj1MNyvGg6q7qDbruqt4QvLtUIpzL1W+u32MGjsEYwz1X38OTf/skAPti+0qvn7PmHCLvjfDW097K46OPA/BPv/4njv/34znlc6fwph+8iQ/d8SE+c89naLq2CfP3hlsev4V4Oh6I++aRzaXHL/jqCwA4pPuQ0razVp5F5L0RfvnWX/JvF/0bAN/Z9B32TuwlZ3Pct+++GYuLVEs9x7yeeG+3qHqDtnuYyeVypLNpWptaS9NV1orL1gjleFB1V/UGXXdVbwjevSXQo9WZei0jXE5zUzOH9RzG5nds5sTrT2Soa4gPveBDQH4e4q9c8hV+8IcfEE1E6WvvYyI5wZOTT3LDQzcccKxXfPMVrFu2jhetfRH7J/dz2uGnceXJV7J+aH1pn6cnn+YPI3/g+Uc+H4Mhno7T3XZg/8u20QPnCS62RhQZ6Bzg3DXn8txVz+VffvsvvPun7+bdP303J644sZRI/+j1P+JZQ89iTX9+6rW5/oPaF9vHEb1HAHOP+T177mF1/+rSPmHERazUA+/tHmX3MGOtJZ1LL7ktAtzeLKccD6ruqt6g667qDcG7SyXCLS3udE9YcQJb/2or65avO+Diu+NdO4in4xzafSi377idy266jLbmNjpbOvnKJV/hoacf4v6n7qerpYsbHrqBrzzwFQB+uPWH/MMv/4HjBo/jschjM4752me9lhWdK/jsxs9y3OBxbDhiA998+JucdcRZnHXEWTw++jgGw9QHpjj2M8eyN7aXtcvWzune0tTC3VffzeH/cjgws5r8im++Ysa+fe19rOpbxQVHXcC/vPRf+Ld7/o2/ue1v+OiLPsrrTnodT4w9wYqeFeyM7uQlR72En23/GRffeDEAn3vF51i7bC2Hdh+KMYYHn3qQ15/8+gP+47vhwRs4bvA4JpITPHvls+lt750xptbaUkJ+7957aW9pJ5VNseGIDfO+R8lMEoulo6WDn2z9CRPJCV52zMvobe91GitB4r3do+weZlpaWkoV4aXiskdYOR5U3VW9Qddd1RuCdzeN+lSwYcMGu3HjxkV9z8TEBH19fXUyqp2czWEwc1ZXI/EIsVSMX2//NUeuOJK7dt3Fx375sVLLxCXHX0Iqm+LOnXeSyOQbwDtaOkqPy7ngqAu47arbeGryKeLpOEctP2perz0Te3h68mlu2nQTPW09tDe3k86lufGRG9kZ3clUeoom0xTo8tI9bT10tXZx+mGn09rcyvKO5QdUy1uaWljRtYJDug9hRdcK7thxBwCnHXYaDzz1wIx91y1bx67xXVx0zEWcs/ocptPTvPioF/ObJ37DJ37zCSZTk5x22Gls3PdMLL3n7PfwwL4HWNGzgueteh6nHXYamVyGPRN7uODoC+hv7+cnW39CJpdhMjXJm097M5F4hKHuIXI2RyQeYfPIZtb0r2FfbB8bjthAW3MbkL9hsb+9n1V9q0rv9/DUMMs6lpU+AORsblH/YSczSVLZFFtHt7Kuax2plhQ7xnZwSPchHNF7BO0t7TP2z+Qy/P7J33PWEWct+U/OQRHW381qqMXdGHOftXb+T2p/ZCz2mj0xMcH7f/l+vvPodxh+z9KWRH3o6Yc49fOnctUpV/Ffl/3Xko61EAdbLIcBVW/QdVf1htrdK123pRLhTCYj+ymm3D2RSbBnYg9DXUP0d/QDMJ4Y5ydbf8LZq89mVd8q7n/qflqaWtgysoW25jayNst5R57HYNdgoF7WWkanR/nkbz/JHyJ/4AVrXsAFR1/Ag089yOj0KP3t/fzv4//LU5NPsWVkC2cecSbvPfu9/Gr3r2hvaefGR25kRdcKXn3iq0lmk9yx4w4i0xE2D2+mtbmVpyefJmuzDHQO8I6z3sGn7/40PW09HNF7BDuiOxhPjJO1z8y+UaxQPzr8KK849hWkc2nuf/J+ptJTpQ8Pc7Gmfw0re1fy2z2/XfQYFD8MNJkm2prbDvgQ0tbcxsrelTQ3NbN1dCuQ7yk/pPsQmkwTv33it2RtllV9q+hv72fzyGbOO/I8utu66W3r5bjB44glY2we2cyeiT3sn9rPmUecSXtzOz/4ww8O8GlrbpvRz/2clc+hv6Of27bdxkmHnMQj+x8B4PUnvx6D4YKjLiAyHaGrtYvOlk52RHewvGM5T00+xfbodp59xLPZEd1BzubI5rK0NbexrGMZh/UcRiqbosk0ccbhZ9Dd1k00EWV0epT25nb6O/ppaWrhyP4jeWLiCVqbWjmi9wgeH3289BeQdC5NV2sXXc1dDHYP0tnayWfv/SwvPfqljMRHOOPwM9gzsYeOlg762/tJZBKMJ8c5duBYAOLpOFPpKTK5DEf0HsHO6E427d/EeWvPo6etB5j5waJ4DrX+uT2VTfGbJ37D2mVrS39RqeW64hPhhclkMvzlLX/Jjx7/Efv+dt/C3zAPm/Zv4qTPncSbT30zX730q0s61kL8sfw/o4SqN+i6q3pD7e5/FInw2NiY7Gooqu5L9Y4monS0dNDe3I4xhmwuO2PZ63Q2TTQRJZaKsXl4M+evO5/O1s4DjpOzObaMbKG1uZU7d9zJMQPHcN7a89g9vptsLsua/jVYLOOJcbrburlzy5287FkvY8/EHr7+0NeB/BLbO8Z2EEvFOOXQU4gmojwZe5L7n7qf23fcziuOfQV97X1MpadY2bsSay1rl63lhodu4KRDTiKdSzPQMUAsFePh/Q+TzqZJZVNMpiZ55XGvZM/EHqKJKKceeio/2fYTcjZ/s1BkOkJHSwcre1eSzqXZPb6b1qZn7qg/ZuAYBjoHOHvV2fx61695zurncPTA0Tz49IPc+MiN9Lb1Ek1ES/vXQktTC5lcpubvrzetTa2csOIEHt7/MACDnYMMdQ9hMDw++jhDXUM0NzUTiUeYzkzT3drNaYedRiKTYFnHMg7vPZyfbf8Z2VwWi+XMw89kdHqUydQk+2L7WLd8HWuXreV3e3834ybYlxz1Er50wZc48rAjF+XrE+GFGRsb45GJR9g9vps3nPKGJf3szcObWf/Z9bzttLfx5Uu+vKRjLYTqtRp03VW9Qddd1Rtqd/+jSITLe0nVUHX33ksjmUkynhxnoHOAlqZnPsEWE+ietp5S2wVU9rbWcufOOzl79dm0N+dbJSLTER586kE6Wjrobc8ny12tXfS29TKRnGCwa5Aj+4/kgace4IQVJzCdmaanrYeczTE2PUYsFePJ2JMMdg1y7957aTJNrOxbyaHdh5LKpngs8hhZm2XvxF6aTBNPTDzBzuhO3n7m24nEI2RyGfo7+plITpQq5fF0nJ3RnaRzaTK5DMcOHMvJh5xMKptiX2wfo9OjfOn+L3HVKVexdtlaYskYXa1d7I3tZUd0B6867lUcP3g8X77/y+yL7WOgM//B4/Cew8naLGv61hBPx9kR3UFkOsKTsSdpb2lnJD7CWUecxZHLjuTOHXdijKGvvY9UNkUsGeOYgWPYF9vHiq4VnHzIyWwe2Uwik+C+J+/ji6/6IlefcfWi3lefCC9MkL+Dj0Ue4/h/P56rT7+aL178xUCOWYmwXDtqQdVd1Rt03VW9oXb3Stdtqbp4JBJhxYoVjdaoCVV377002lvaOaTlkAO2tzW3MdA5cMD2St7GGM5fd/6MbSu6VvDio168oENxAZny2Ui6WruA/E2hAKccesoB33fWyuoXZRkZGal6vKtJZF51/Kuq/tkw/4VxvtceizzGgD3wffAsnSB/B13OGhGWa0ctqLqreoOuu6o3BO8ulQirvmmg6+693eK9a2O+6sB8r5UvT+4JliBjwuU8wo2O5aWg6q7qDbruqt4QvLvUghrDw0u787iRqLp7b7d4b/cou4eZIMfV5fRpyvGg6q7qDbruqt4QvHtVVxVjzEXGmC3GmK3GmPfP8Xq7Meamwuv3GGPWBmpZYGhoaOGdQoqqu/d2i/d2j7J7mAlyXF22RijHg6q7qjfouqt6Q/DuC15VjDHNwPXAy4D1wJXGmPWzdvtTYMxaewzwKeCfArUsMDIyUo/DOkHV3Xu7xXu7R9k9zAQ5rsXWiPIZZ+qFcjyouqt6g667qjcE717Nx+tnA1uttduttSngRuCSWftcAnyt8Pi7wItNHW5HHBwMdg5dl6i6e2+3eG/3KLuHmSDH1WVrhHI8qLqreoOuu6o3BO9ezVVlJfBE2fM9hW1z7mOtzQDjwAGmxpi3G2M2GmM27t+/n0QiQTweZ2pqimQyycTEBJlMhrGxMay1pay/2A+yc+dOrLWMjY2RyWSYmJggmUwyNTVFPB4nkUgQi8VIp9NEo1FyuRyRSGTGMYr/jo6Oks1mGR8fJ5VKMTk5yfT0NNPT00xOTpJKpRgfHyebzTI6OjrnMSKRCLlcjmg0SjqdJhaLVTyn3bt3z3lOIyMjoT6nffv2Lfp9CsM5RaPRmt6nRp/Tvn37Ao89F+e0e/dup79PQZ7Trl27Fv0+eRYmGo0GdiyXrRFBertG1V3VG3TdVb0hePcF5xE2xlwOXGStvbrw/CrgOdbad5bt80hhnz2F59sK+1SsXx/MK8sp4b3d4r3d41eWq45aVpYLKiaiiSjL/2k57zn7PVx3wXWBHLMSB1sshwFVb9B1V/WG4FeWq+bj9V5gddnzVYVtc+5jjGkB+oHIoi0XIB6vvMRu2FF1995u8d7uUXYPM0GOq8vWCOV4UHVX9QZdd1VvCN69mqvKvcCxxph1xpg24HXAzbP2uRl4c+Hx5cAdtg5L1rW3twd9SGeountvt3hv9yi7h5kgx9Vla4RyPKi6q3qDrruqNwTvvuBVpdDz+07gVmAz8G1r7SZjzLXGmIsLu30ZGDTGbAX+BjhgirUgyGQy9TisE1TdvbdbvLd7lN0rEYYpL4McV5cLaijHg6q7qjfouqt6Q/DuVTVZWGtvAW6Zte3DZY8TwGsCNZsD1XWxQdfde7vFe7tH2X0uyqa8vID8zc33GmNuttY+WrZbacpLY8zryE95eUXAHoEdq1gRLv5bT5TjQdVd1Rt03VW9IXh3qZXlmpqkdGeg6u693eK93aPsXoFQTHkZ5Lg2mSYMhpam+t/coxwPqu6q3qDrruoNwbsvOGtEvTDGDAO7FvltKwDVWaBV3b23W7y3e2pxP9JaG8qlmYKc6ccY83bg7YWnxwNbFqGiGhOq3qDrruoNuu6q3lC7+5zX7YbNnVHLfyLGmI2qUxapuntvt3hv9yi71xtr7X8A/1HL96qOq6o36LqreoOuu6o3BO+uWxv3eDwez1yEZspLj8fjCTs+EfZ4PJ4/LkIz5aXH4/GEHbVlRWr6E11IUHX33m7x3u5Rdj8Aa23GGFOc8rIZ+Epxyktgo7X2ZvJTXt5QmPJylHyyHDSq46rqDbruqt6g667qDQG7N+xmOY/H4/F4PB6Pp5H41giPx+PxeDwez0GJT4Q9Ho/H4/F4PAclMonwQkuGNhJjzFeMMfsLc3MWtw0YY35qjHm88O/ywnZjjPm3wnk8ZIw5o4Heq40xdxpjHjXGbDLGvEvB3RjTYYz5nTHmwYL33xe2ryssF7u1sHxsW2F73ZeTXaR/szHmfmPMD8W8dxpjHjbGPGCM2VjYFupYKbgsM8Z81xjzB2PMZmPM8xS8VQnztXo2i70Gho1qryVhYzG/k2HCGPN/C3HyiDHmW4X/i0I55kY3L5nL+xOFWHnIGPMDY8yysteuKXhvMcZcWMvPlEiEzTNLhr4MWA9caYxZ31irGXwVuGjWtvcDt1trjwVuLzyH/DkcW/h6O/A5R45zkQH+1lq7Hngu8I7CuIbdPQmcb609FTgNuMgY81zyy8R+ylp7DDBGfhlZKFtOFvhUYb9G8i5gc9lzFW+AF1lrTyubwzHssQLwr8BPrLUnAKeSH3sFbzkErtWzWew1MGxUey0JG4v5nQwFxpiVwP8BNlhrTyJ/I2pxefIwjvlX0cxLvsqB3j8FTrLWngI8BlwDUPhdfR3wrML3fLZwDVoc1trQfwHPA24te34NcE2jvWY5rgUeKXu+BTi88PhwYEvh8ReAK+far9FfwP8AFyi5A13A74HnkF9ppmV2zJC/e/55hccthf1Mg3xXkb8AnQ/8EDAK3gWHncCKWdtCHSvk58fdMXvcwu6t+qVwrV7Af95rYJi+FnMtCdPXYn8nw/IFrASeAAYK1+MfAheGecwRzUtme8967TLgG4XHM64v5f9nLuZLoiLMMwFYZE9hW5g51Fr7ZOHxU8ChhcehPJfCn91PB+5BwL3wJ8EHgP3kPy1uA6LW2swcbiXvwuvjwKBT4Wf4NPBeIFd4PoiGN4AFbjPG3GfyS+9C+GNlHTAM/GfhT8hfMsZ0E35vVWTHr8prYJj4NNVfS8LEYn8nQ4G1di/wz8Bu4Eny1+P70BjzIn8M1723AT8uPA7EWyURlsbmP6qEdp46Y0wP8D3gr621E+WvhdXdWpu11p5GvirybOCExhotjDHmlcB+a+19jXapkXOttWeQ/zPaO4wxLyh/MaSx0gKcAXzOWns6MMWsP7mG1NvjELVroPi1RPJ3stBPewn5RP4IoJsD/4QvQxjHeCGMMR8k3870jSCPq5IIV7NkaNh42hhzOEDh3/2F7aE6F2NMK/n/AL5hrf1+YbOEO4C1NgrcSf5PUstMfrlYmOkWluVkzwEuNsbsBG4k/yfNfyX83kCpIoK1dj/wA/IfQMIeK3uAPdbaewrPv0v+P+Gwe6siN36LvAaGhcVeS8LEYn8nw8JLgB3W2mFrbRr4Pvn3QWHMi8he94wxbwFeCbyhkMRDQN4qiXA1S4aGjfIlTN9MvvesuP1Nhbs0nwuMl/2pwinGGEN+hanN1tpPlr0UandjzFDxrlFjTCf5nr7N5BPiywu7zfZu+HKy1tprrLWrrLVrycfwHdbaNxBybwBjTLcxprf4GHgp8AghjxVr7VPAE8aY4wubXgw8Ssi9hZG6VtdwDQwFNVxLQkMNv5NhYTfwXGNMVyFuit6hH/MyJK97xpiLyLcBXWytjZe9dDPwOpOfYWkd+Zv9frfoH9CoZujFfgEvJ3+34Dbgg432meX2LfI9Q2nyn3b/lHy/1u3A48DPgIHCvob8XdXbgIfJ34HaKO9zyf9p5CHggcLXy8PuDpwC3F/wfgT4cGH7UYVfgq3Ad4D2wvaOwvOthdePCkHMvBD4oYp3wfHBwtem4u9g2GOl4HIasLEQL/8NLFfwVv0K87V6DtdFXQPD+FXNtSRsX4v5nQzTF/D3wB8K/+/cALSHdczRzUvm8t5Kvhe4+Dv6+bL9P1jw3gK8rJaf6ZdY9ng8Ho/H4/EclKi0Rng8Ho/H4/F4PIHiE2GPx+PxeDwez0GJT4Q9Ho/H4/F4PAclPhH2eDwej8fj8RyU+ETY4/F4PB6Px3NQ4hNhz0GLMeaFxpgfNtrD4/F4PAvjr9meeuATYY/H4/F4PB7PQYlPhD2hxxjzRmPM74wxDxhjvmCMaTbGTBpjPmWM2WSMud0YM1TY9zRjzN3GmIeMMT8orA+PMeYYY8zPjDEPGmN+b4w5unD4HmPMd40xfzDGfKOwYpDH4/F4asRfsz1K+ETYE2qMMScCVwDnWGtPA7LAG4BuYKO19lnAL4CPFL7lv4D3WWtPIb9CTnH7N4DrrbWnAmeTX7kG4HTgr4H15FcIOqfOp+TxeDx/tPhrtkeNlkYLeDwL8GLgTODewgf/TmA/kANuKuzzdeD7xph+YJm19heF7V8DvmOM6QVWWmt/AGCtTQAUjvc7a+2ewvMHgLXAr+p+Vh6Px/PHib9me6TwibAn7Bjga9baa2ZsNOb/m7VfrWuFJ8seZ/G/Ex6Px7MU/DXbI4VvjfCEnduBy40xhwAYYwaMMUeSj93LC/u8HviVtXYcGDPGPL+w/SrgF9baGLDHGHNp4Rjtxpgulyfh8Xg8Bwn+mu2Rwn+S8oQaa+2jxpgPAbcZY5qANPAOYAp4duG1/eR70gDeDHy+cNHcDry1sP0q4AvGmGsLx3iNw9PweDyegwJ/zfaoYayt9a8THk/jMMZMWmt7Gu3h8Xg8noXx12xPWPGtER6Px+PxeDyegxJfEfZ4PB6Px+PxHJT4irDH4/F4PB6P56DEJ8Iej8fj8Xg8noMSnwh7PB6Px+PxeA5KfCLs8Xg8Ho/H4zko8Ymwx+PxeDwej+eg5P8HIfwxNNmiutgAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
        "<Figure size 864x864 with 4 Axes>"
       ]
@@ -599,41 +825,42 @@
     }
    ],
    "source": [
-    "plt.figure(\"train\", (12, 12))\n",
-    "plt.subplot(2, 2, 1)\n",
-    "plt.title(\"Regular Epoch Average Loss\")\n",
-    "x = [i + 1 for i in range(len(epoch_loss_values))]\n",
-    "y = epoch_loss_values\n",
-    "plt.xlabel(\"epoch\")\n",
-    "plt.grid(alpha=0.4, linestyle=\":\")\n",
-    "plt.plot(x, y, color=\"red\")\n",
-    "\n",
-    "plt.subplot(2, 2, 2)\n",
-    "plt.title(\"Regular Val Mean Dice\")\n",
-    "x = [i + 1 for i in range(len(metric_values))]\n",
-    "y = metric_values\n",
-    "plt.xlabel(\"epoch\")\n",
-    "plt.ylim(0, 1)\n",
-    "plt.grid(alpha=0.4, linestyle=\":\")\n",
-    "plt.plot(x, y, color=\"red\")\n",
-    "\n",
-    "plt.subplot(2, 2, 3)\n",
-    "plt.title(\"Fast Epoch Average Loss\")\n",
-    "x = [i + 1 for i in range(len(m_epoch_loss_values))]\n",
-    "y = m_epoch_loss_values\n",
-    "plt.xlabel(\"epoch\")\n",
-    "plt.grid(alpha=0.4, linestyle=\":\")\n",
-    "plt.plot(x, y, color=\"green\")\n",
-    "\n",
-    "plt.subplot(2, 2, 4)\n",
-    "plt.title(\"Fast Val Mean Dice\")\n",
-    "x = [i + 1 for i in range(len(m_metric_values))]\n",
-    "y = m_metric_values\n",
-    "plt.xlabel(\"epoch\")\n",
-    "plt.ylim(0, 1)\n",
-    "plt.grid(alpha=0.4, linestyle=\":\")\n",
-    "plt.plot(x, y, color=\"green\")\n",
-    "plt.show()"
+    "if not profiling:\n",
+    "    plt.figure(\"train\", (12, 12))\n",
+    "    plt.subplot(2, 2, 1)\n",
+    "    plt.title(\"Regular Epoch Average Loss\")\n",
+    "    x = [i + 1 for i in range(len(epoch_loss_values))]\n",
+    "    y = epoch_loss_values\n",
+    "    plt.xlabel(\"epoch\")\n",
+    "    plt.grid(alpha=0.4, linestyle=\":\")\n",
+    "    plt.plot(x, y, color=\"red\")\n",
+    "\n",
+    "    plt.subplot(2, 2, 2)\n",
+    "    plt.title(\"Regular Val Mean Dice\")\n",
+    "    x = [(i + 1) * 5 for i in range(len(metric_values))]\n",
+    "    y = metric_values\n",
+    "    plt.xlabel(\"epoch\")\n",
+    "    plt.ylim(0, 1)\n",
+    "    plt.grid(alpha=0.4, linestyle=\":\")\n",
+    "    plt.plot(x, y, color=\"red\")\n",
+    "\n",
+    "    plt.subplot(2, 2, 3)\n",
+    "    plt.title(\"Fast Epoch Average Loss\")\n",
+    "    x = [i + 1 for i in range(len(m_epoch_loss_values))]\n",
+    "    y = m_epoch_loss_values\n",
+    "    plt.xlabel(\"epoch\")\n",
+    "    plt.grid(alpha=0.4, linestyle=\":\")\n",
+    "    plt.plot(x, y, color=\"green\")\n",
+    "\n",
+    "    plt.subplot(2, 2, 4)\n",
+    "    plt.title(\"Fast Val Mean Dice\")\n",
+    "    x = [(i + 1) * 5 for i in range(len(m_metric_values))]\n",
+    "    y = m_metric_values\n",
+    "    plt.xlabel(\"epoch\")\n",
+    "    plt.ylim(0, 1)\n",
+    "    plt.grid(alpha=0.4, linestyle=\":\")\n",
+    "    plt.plot(x, y, color=\"green\")\n",
+    "    plt.savefig(\"outputs/loss_dice_comparison.png\")"
    ]
   },
   {
@@ -645,12 +872,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
+   "execution_count": 24,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 864x432 with 2 Axes>"
       ]
@@ -662,27 +893,28 @@
     }
    ],
    "source": [
-    "plt.figure(\"train\", (12, 6))\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.title(\"Total Train Time(600 epochs)\")\n",
-    "plt.bar(\n",
-    "    \"regular PyTorch\", total_time, 1, label=\"Regular training\", color=\"red\"\n",
-    ")\n",
-    "plt.bar(\"Fast\", m_total_time, 1, label=\"Fast training\", color=\"green\")\n",
-    "plt.ylabel(\"secs\")\n",
-    "plt.grid(alpha=0.4, linestyle=\":\")\n",
-    "plt.legend(loc=\"best\")\n",
+    "if not profiling:\n",
+    "    plt.figure(\"train\", (12, 6))\n",
+    "    plt.subplot(1, 2, 1)\n",
+    "    plt.title(\"Total Train Time(600 epochs)\")\n",
+    "    plt.bar(\n",
+    "        \"regular PyTorch\", total_time, 1, label=\"Regular training\", color=\"red\"\n",
+    "    )\n",
+    "    plt.bar(\"Fast\", m_total_time, 1, label=\"Fast training\", color=\"green\")\n",
+    "    plt.ylabel(\"secs\")\n",
+    "    plt.grid(alpha=0.4, linestyle=\":\")\n",
+    "    plt.legend(loc=\"best\")\n",
     "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plt.title(\"Epoch Time\")\n",
-    "x = [i + 1 for i in range(len(epoch_times))]\n",
-    "plt.xlabel(\"epoch\")\n",
-    "plt.ylabel(\"secs\")\n",
-    "plt.plot(x, epoch_times, label=\"Regular training\", color=\"red\")\n",
-    "plt.plot(x, m_epoch_times, label=\"Fast training\", color=\"green\")\n",
-    "plt.grid(alpha=0.4, linestyle=\":\")\n",
-    "plt.legend(loc=\"best\")\n",
-    "plt.show()"
+    "    plt.subplot(1, 2, 2)\n",
+    "    plt.title(\"Epoch Time\")\n",
+    "    x = [i + 1 for i in range(len(epoch_times))]\n",
+    "    plt.xlabel(\"epoch\")\n",
+    "    plt.ylabel(\"secs\")\n",
+    "    plt.plot(x, epoch_times, label=\"Regular training\", color=\"red\")\n",
+    "    plt.plot(x, m_epoch_times, label=\"Fast training\", color=\"green\")\n",
+    "    plt.grid(alpha=0.4, linestyle=\":\")\n",
+    "    plt.legend(loc=\"best\")\n",
+    "    plt.savefig(\"outputs/total_epoch_time_comparison.png\")"
    ]
   },
   {
@@ -694,12 +926,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
+   "execution_count": 25,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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0EatWreKrX/0qTU1NDB8+nN/+9rfsu+++GGO46KKLePjhh8nLy+PWW29lwYIFadMbRlm505WYYlzqNU0H6nfo4pNX8M9vJtPS0kJRUdGg/5i59MUF3NKTLi0iQlFRUZ+/oLp2fvqg56yzzuLRRx+N23bIIYewZs0aXn/9dfbYYw+uvvpqIPbr6Y9+9COuvfbanfR8+9vf5q233uLVV1/l+eef55FHHgHg5ptvprCwkPXr1/PNb36TSy+9NOkeOnGtvIYKyWiLXWr3wE89ibbD4N655JKedGvJzs7mF7/4BWvXruWll17ixhtvZO3atXznO9/h8ssv5+WXX+bKK6/kO9/5DgCPPPII69atY926dfzhD3/gvPPOS6veMMrKndqRYtrb28OWkFbU79DFJ6/gn99MJxlfymKz89zBJT3p1JJIWbp2fvqg5zOf+Qzjxo2L23booYeSnR0b1LrffvuxadMmAEaNGsUBBxxAbm5unJ68vDwOOuggAIYPH86CBQu63vPAAw9w5plnAnDCCSewfPnylNU718prKDHYttildg/81ZNoObp2LrmkJ91aJk2a1DWCYfTo0ZSWlrJ582ZEhLq6Otrb26mtrWXy5MlArM0944wzEBH2228/ampq2LIlJddk6JEwysqbDojOP76+oH6HLj55Bf/8KoP74pyVlcX8+fOZO3cuRx11FDU1NUnXc+CBB1JWVjbg491///2sXbu23+978MEHu4bQ98aHH37ICSecMFBp/ca181P1wC233MLhhx/e42s96ampqeEf//gHixcvBmDz5s3sumvsCunZ2dkUFBRQWVmZEq2ulZeyncF2YCS7Le5Jj7bF23HtXHJJT5haNmzYwKuvvsqiRYu4/vrrueSSS5g9ezbf/va3u0aqdW9zAUpKSti8eXPaNIaRjzcdEI2NjWFLSCvqd+jik1fwz++QQmRAt2FZWT2/lgAjR45k1apVrFmzhnHjxnHjjTcO2kY0Gh3U+zs6OuKe2770RiKRXo+zdOlSLrnkEutnTZ48mXvvvbf/IgeIa+en73quuuoqsrOzOe200xLSE4lEOOWUU7jwwgvZbbfd0iHRqkdJEclsh0NqiwfbDoO2xenEJT1haWloaOD444/n+uuvZ8yYMdx0001cd911rF69muuuu46zzz47FF07EkY+3nRAjBkzJmwJaUX9Dl188gr++VWSx/7779/1K8K7777LkiVL2Gefffj0pz/NW2+91bV9v/32Y968eXzve98jPz8fgKeffprPf/7zQOyXvPPPP59bb711p88477zzWLhwIXPmzOHyyy/v2j5t2jQuvfRSFixYwF//+teu7S+88AIPPvggl1xyCfPnz+fdd9/lwAMP5Bvf+AYLFy7kV7/6Ff/4xz9YtGgRe++9NwcffDBbt24F4NZbb+Wiiy4CYusAXHjhhXzyk59kt9126/qiu2HDBubOndu1/3HHHceSJUuYOXNm13xTiM3z32OPPdh3330555xzOP/88weUsWvnp896br31Vh566CHuuOOOXn+93lHPueeey8yZM/nGN77RtW3KlCls3LgRiP0TVltbS1FRUUo0u1ZeSmpIRlvcueCjtsU949q55JKeMLS0t7dz/PHHc9ppp3HccccBsGzZMo477jjGjBnDiSeeyMsvvwzEt7kQW0R2ypQpadMaRj7edEBUV1eHLSGtqN+hi09ewT+/SnLo6Ohg+fLlLF26FIj9o/XrX/+aFStWcO211/K1r30NgIsuuoiLLrqI1atXU1JS0uuxeuOqq66irKyM119/nWeeeYbXX3+967WioiJWrlzJySef3LXtk5/8JEuXLuXnP/85q1atYsaMGQC0tbVRVlbGt771LQ444ABeeuklXn31VU4++eS4ob7d5x1v2bKF5557joceeojLLrusR32rVq3i7rvvZvXq1dx9991s3LiRDz/8kB/96Ee89NJLPP/8813/AAwE185PX/U8+uij/OxnP+PBBx8kLy8vIT3f+973qK2t5frrr4/bZ+nSpSxbtgyAe++9l8997nMpW3DPtfJSkk+y2mJbOwzaFrt2LrmkJ91ajDGcffbZlJaWcvHFF3dtnzx5Ms888wzV1dU8+eSTzJw5E4i1ubfddhvGGF566SUKCgqYNGlS2vSGUVbeXIZzoL33xhhWb1vN5NGTGZ83PsmqUkeqfq1wFZ/8+uQV/POrDI7m5mbmz5/P5s2bKS0t5ZBDDqGhoYEXXniBE088sWu/1tZWAF588UXuv/9+AE499VS+/e1v73TMzsX9euKee+7hD3/4A5FIhC1btrB27Vo+/vGPA/CFL3whYd3d9920aRNf+MIX2LJlC21tbXGXX+u+WvUxxxzDsGHDmD17dtcvczuyePFiCgoKAJg9ezYffPABFRUVfPazn+1ayPDEE0/knXfeSVhrd1w7P33Qc8opp/D0009TUVFBSUkJP/zhD7n66qtpbW3lkEMOAWILUf7ud78DYr8A19XV0dbWxv3338/jjz/OmDFjuOqqq9hzzz27Fks7//zz+cpXvsLZZ5/NF7/4RXbffXfGjRvHXXfdlXQPnbhWXkrySHZbbGuHQdti184ll/SkW8vzzz/P7bffzrx585g/fz4AP/nJT/jjH//IRRddRCQSITc3lz/84Q8AHHHEETz88MPsvvvu5OXl8ec//zmtesMoK286IMrLyykuLu7xtUg0wtsVb7N622pe3/o62xq38VHDR/y39r+sLV9Lh+ngq/t8lZs+f1OaVQ8cm9+hiE9+ffIK/vlVBkfnvOOmpiYOO+wwbrzxRs466yzGjh3LqlWrEj5OdnZ215zj9vb2Hi+D9v7773PttdfyyiuvUFhYyFlnnRW336hRoxL+vO77XnDBBVx88cUsXbqUp59+miuuuKLrte7zoEeMGNH1uLcV2bvvk5WVZZ3XPBBcOz990HPnnXfutM02l3jDhg096umtzuTm5sYNVU8lrpWXkjyS3Ra3t7eTk5OjbXEvuHYuuaQn3VoOOOCAXuvBihUrdtIjIklZr2qghFFW3kzB6Ay2oa2B377yW370zI/42j+/xmF/OYxp109j7k1zOeVvp/Cz53/Gw+seZlPdJqaNncbF+1/Mzw7+GT/63I9CdtA/XDnp04VPfn3yCin0awxEoxCJQHs7tLVBSws0N0NTU+yxI4jILSKyTUTWdNv2cxF5S0ReF5H7RGRst9e+KyLrReRtETms2/Ylwbb1InJZt+3TReQ/wfa7RWR42syliLy8PG644QZ+8YtfkJeXx/Tp07v+qTLG8NprrwGxX4r/9re/AcT90vuxj32MtWvX0traSmNjI8uXL9/pM+rq6hg1ahQFBQVs3bqVRx55JCFto0ePpr6+vtfXa2tru+Z/dg6F7yQZ1+v+xCc+0TUMNBKJdPkfCK61R17rSWBhweIJE/reL424Vl5K8klWWxyNRqmpqdG2uBdcO5dc0qPtsJ0wysqrERDjx4/nmueu4apnrwJg9PDR7Dl+Tz6+y8e59FOX8sldP8leE/cie1jmx+JSz2M68Mlvv702NMArr8B//gNbtmz/h7un++6PO/9B77zv/rin++432HnbAG8dHR1kiWzf1v3zenvc136JsGQJJPglJg3cCvwGuK3btieA7xpjIiLyU+C7wKUiMhs4GZgDTAb+JSJ7BO+5ETgE2AS8IiIPGmPWAj8FrjPG3CUivwPOBjJnyFcv7L333nz84x/nzjvv5I477uC8887jxz/+Me3t7Zx88snstddeXH/99Zx++ulcddVVLFmypGuI7K677spJJ53E3LlzmTZtGnvvvfdOx99rr73Ye++92XPPPdl111351Kc+lZCuk08+mXPOOYcbbrihx1XSr7jiCk488UQKCwv53Oc+x/vvv9/1WjJWgp8yZQr/+7//y7777su4cePYc889u3z3F9faXtVjp3zePIpXrw5bRheu5aOkhmS1xdOnT9e2uBdcO5dc0uOSFtB2GEB6GyLiMgsXLjQDue7vj//9Y77/1Pc5aNpBPP7Fx4dER4MyBIhGY//0d97a2uLvuz9ubYXa2p1vNTW9b+t+eZ2xYyEnB7KzY7feHmdnw7BhsV7YYcPiH3e/3/Hxjr23A7wMY6832+f29tpA37PbbtDPa3iLyApjzMJkVY0djj0NeMgYM7eH144FTjDGnCYi3wUwxlwdvPYYcEWw6xXGmMOC7d8Ntl0DlAMTg86M/bvv1xs9tcNvvvkmpaWlA3QYDk1NTYwcORIR4a677uLOO+/kgQceCFtWymloaCA/P59IJMKxxx7Ll7/8ZY499tid9svEMvWWZP1qloHfC3cklW2xa2hbnNkk0hZnYnl6i7bDXdjaYW/+A6+orOCPK//I4umLefT0R4d850NVVVXXojY+kDK/LS2walVs9MCmTT13CvS0rbfHPW3rY2XnPhk+PNaxUFCw/TZp0vZtRUWwYAHsu2/scYbhW10eIF8G7g4eTwFe6vbapmAbwMYdti8CioAaY0ykh/3jEJFzgXMh9qtUS0sL0WgUY0zXPF3TOWolmOOak5PTNXe3vb2d7Ozsrtej0Sgi0jVXsvNx5/asrCw6OjrIzs6OO0ZOTg6RSKTr9WHDhmGM6Vqpv/NxNBrt8xhlZWVdlz0rKCjg5ptv7lptvSc9A/WUqJ6+PLW3tzN8+PB+H2NHPZdffjn/+te/aG1t5eCDD+aYY47p0ZMxhrq6OkaMGEEkEkFEGDZsGO3t7eTm5vLhhx+y6667Ul1dTVFRUdcvKZ33VVVVFBQU0NDQwMiRI2lra+u6nF5HRwfDhw+nubmZ/Px8amtrGTdu3E7HqKyspLCwsGuYdUtLCzk5OXF1r7W1lby8PDZv3szUqVOprKxk/PjxXceoqKigqKiImpoaRo8eTVNTU6+eGhsbGTNmTFI8bdy4kWnTpg3KU319PWPHju3b09SpjKitJTJyJNLRwbBIhPZRo8itqqJx0iTGbNjABwcfzPTHHuv6Ba7zvmrWLAree4+GKVMY2daW8nLq9PT++++z2267Jb2clMETiUT6XPgxmaxYsYLzzz8fYwxjx47llltuCVVPXyRLzxVXXMG//vUvWlpaOPTQQznmmGMGdBzXviu5pMclLQBVs2Yx7u23w5bRRRj5eDMC4r3K95jxmxncdORNfHXhV1OkzB06vwz7QlL8GgPr1sU6Gzpvr70W6yQAyM2FESNiIwVycmL/+O/4uKdtA923l20d+flkjRu3vbMhN3fwATpMJtXlMEZAiMj/AxYCxxljjIj8BnjJGPOX4PWbgc65JEuMMV8Jtn+RWAfEFcH+uwfbdwUe6WmkRXdS+atb93+8XcAlPenW0leZunZ+eq0ngXrRkZNDVufftN5I4/fCVOWjIyAG3xa71O6B33oSKU+v274+0HbYThjtsDtdiSlmc9VmAHYZtUvIStJDQ0PDgOeRZSID8vvWW3D22bBtW+x5ZSV0Xgt31Cj4xCfg4oth0aLYbfLk5IoeIA21tVq2CgAichbweWCx2d6bvBnYtdtuJcE2etleCYwVkexgFET3/UOh89d9V3BJj0tawL3zU/XYaZgyhYJuV8UIG9fyUbbjWlujeuy4di65pMclLaDtMHjUAdFCbEX7sbljwxWSJkaOHBm2hLTSb78vvwxHHAFZWXDwwbFto0fDwoWxzobZs2OvOYiWrQKxK1oA3wE+a4xp6vbSg8D/icgviS1CORN4GRBgpohMJ9bBcDJwajBq4ingBOAu4Ewg1Im3yVhdPJm4pMclLeDe+al67IysqAhbQhyu5aNsx7W2RvXYce1cckmPS1pA22HwqAOioiFW2L50QLS1tTF8eMZfSS9h+uX3iSfg2GNhwgR4/HHYfffUiksyWrb+ISJ3AgcC40VkE3A5satejACeCIaBvmSM+aox5g0RuQdYC0SArxtjOoLjnA88BmQBtxhj3gg+4lLgLhH5MfAqcHPazPWAa1MDXdLjkhZw7/xUPXbaxoxheEND2DK6cC0fZTuutTWqx45r55JLelzSAtoOg0cdEG3RNgDycvJCVpIeXJl3lS4S9nvPPXD66bDnnvDYY7HFGjMMLVv/MMac0sPmXjsJjDFXAVf1sP1h4OEetr8H7DsYjcnEpXm+4JYel7SAe+en6rGT1doatoQ4XMtH2Y5rbY3qsePaueSSHpe0gLbDAG6NH0ohnR0QI7JHhKxECY2bboKTT45dDeKZZzKy80FRFDtZWVnMnz+/67ahn/Msr7/+epqamvr9mo0f/OAH/Otf/7Lu8+CDD3LNNdf0+9iKoiguom2xoii94c0IiOb2ZgCGZ7kzBCeVdF5Gzhesfo2BH/0ILr8cjjwyNgoiL3NHwmjZKpmC/DC5vxCZy/se8jpy5EhWrVo14M+4/vrrOf3008nr1kZ0DrXt6bVObKtIX3nllX1+7tKlS1m6dGmf+7k27Ne181P12OkY4daPMK7lM1QZCm1x97ZP2+Kdce1cckmPS1pA22HwaARElCjgTweES3Od0kGvfqNRuPDCWOfDGWfAffdldOcDaNkqSn9oaGhg8eLFLFiwgHnz5vHAA7H1NRsbGznyyCPZa6+9mDt3LnfffTc33HADH374IQcddBAHHXRQ1zFEpMfX8vPz+da3vsVee+3Fiy++yJVXXsknPvEJ5s6dy7nnntv1BfWss87i3nvvBWDatGlcfvnlXXreeustAG699VbOP//8rv0vvPBCPvnJT7Lbbrt1vTcajXL++eez5557csghh3DEEUd0vRYWrp2fqsfO8Lq6sCXE4Vo+SuoYbFvcOeVB2+Kece1cckmPS1pA22HwqAOisbUR8KcDorm5OWwJaaVHv21tsfUefvOb2OU0//xnyMlJv7gko2WrKL3T3NzcNeT32GOPJTc3l/vuu4+VK1fy1FNP8a1vfQtjDI8++iiTJ0/mtddeY82aNSxZsoQLL7yQyZMn89RTT/HUU091HTMajfb4WmNjI4sWLeK1117jgAMO4Pzzz+eVV15hzZo1NDc389BDD/Wocfz48axcuZLzzjuPa6+9tsd9tmzZwnPPPcdDDz3EZZddBsDf//53NmzYwNq1a7n99tt58cUXk5xe/3Ht/FQ9dprHjw9bQhyu5aMkj2S3xdFo7IdEbYt7xrVzySU9LmkBbYfBow4IyYr1nI7IcmvYS6rIz88PW0Ja2clvYyMsXQp33glXXw3XXguOXTJpoHhftopioXPY76pVq7jvvvswxvC///u/fPzjH+fggw9m8+bNbN26lXnz5vHEE09w6aWX8uyzz1qvgd3bcN6srCyOP/74rudPPfUUixYtYt68eTz55JO88cYbPb7vuOOOA2CfffbpdV70Mcccw7Bhw5g9ezZbt24F4LnnnuPEE09k2LBhTJw4MW6URlik4vz88pe/zIQJE5g7d27Xtr/+9a/MmTOHYcOGUVZW1rX9iSeeYJ999mHevHnss88+vPzyy12vHXjggcyaNavrn6Bt27YB0Nrayhe+8AV23313Fi1a1O+56f3BtfYrf/PmsCXE4Vo+SvJIdltsWyhP2+L0nksbN27koIMOYvbs2cyZM4df/epXXa/9+te/Zs899+STn/wk3/nOd7q2X3311ey+++7MmjWLxx57LG1awb12Rtthjzog6hpjw11ysjL/F/BEqK2tDVtCWonz+/bbcPDBsctt/vGPcNll4NhqxYPB67JVlH5yxx13UF5ezooVK1i1ahW77LILLS0t7LHHHqxcuZJ58+bxve99zzo3uLf5kbm5uV1filtaWvja177Gvffey+rVqznnnHNoaWnp8X0jgvmfWVlZRCIR6z4QP9e481dAV0jF+XnWWWfx6KOPxm2bO3cuf//73/nMZz4Tt338+PH84x//YPXq1Sxbtowzzjgj7vU77rij65+gCRMmAHDzzTdTWFjI+vXr+eY3v8mll16adA+duNZ+1e62W9gS4nAtHyV1DLYtts1T17Y4vedSdnY2v/jFL1i7di0vvfQSN954I2vXruWpp57igQce4LXXXuPZZ5/l29/+NgBr167lrrvu4o033uDRRx/la1/7WlrXHXCtndF22KMOiKzhWWQPy2aY+GF53LhxYUtIH8Yw7oMP4PvfhzlzYpfYfPVV+Otf4StfCVtd0vGqbPHPr5JcamtrmTBhAjk5OTz11FN88MEHAHz44Yfk5eVx+umnc8kll7By5UoARo8eTX19fdwxsrOze32tk84vuOPHj6ehoSEl84E/9alPcf/99xONRtm6dStPP/100j+jv6Ti/PzMZz6z03FLS0uZNWvWTvvuvffeTJ48GYA5c+bQ2tpKax+XOHvggQc488wzATjhhBNYvnx5yhaUc639Gvf222FLiMO1fJTUMdi2uLMd7um17mhbnHomTZrEggULgFhZlJaWsnnzZm666SYuu+wyRowYwbhx47o6fR944AFOPvlkRowYwfTp09l9993jRqulGtfaGW2HPeqAqGmo8Wb9B4Dy8vKwJaSWjg547rnY2g677QYLFsBPfgITJsANN8C770IwtG6oMeTLdgd886skl9NOO42ysjLmzZvHbbfdxp577gnA6tWr2XfffZk/fz4//OEP+d73vgfAueeey5IlS+KG1La3t/f6Widjx47lnHPOYe7cuRx22GF84hOfSLqX448/nsmTJzN79mxOP/10FixYYJ06kg5cOj//9re/MXfu3LhfLL/0pS8xf/58fvSjH3V1MmzevJldd90ViP1TU1BQQGVlZUo0uZQPQPm8eWFLiMO1fJTUMdi2uLMd7um17mhbnF42bNjAq6++yqJFi3jnnXd49tlnWbRoEZ/85Cd55ZVXgPg2F6CkpITNaZyG4Fo7o+0wseFEmXbbZ599TH+58OELzdhrxvb7fYpDtLUZ89hjxvzP/xizyy7GgDHDhxtz5JHG3HyzMdu2ha1Q8RygzDjQRqbj1lM7vHbt2gFnpyRGfX29McaYiooKs9tuu5ktW7ak9PPCKtP333/fzJkzZ6ftn/3sZ80rr7yy0/Y1a9aY3Xbbzaxfv75r26ZNm4wxxtTV1ZlDDjnELFu2zBhjzJw5c8zGjRu79tttt91MeXl5si2kn9hFpwd/GwJoW6xtcapJZ1vsannW19ebBQsWmL/97W/GmFjbev7555toNGr+85//mGnTpploNGq+/vWvm9tvv73rfV/+8pfNX//617BkpxZth7uwtcNejYDwZQFKcK+3b1C88w585ztQUgKHHQZ/+Qt85jOxBSbLy+Ghhyg/6igoLg5baVoYUmWbAL75VeJ/6XIBl/QceeSRzJ8/n09/+tN8//vfZ+LEiaHqceH83LRpE8ceeyy33XYbY8aM6do+ZcoUIDZE+NRTT+0a8jtlyhQ2btwIQCQSoba2lqKiopRocyGf7ugvb0qiuNTugXt6fG+L29vbOf744znttNO6FvMsKSnhuOOOQ0SYPn06w4YNo6KiIq7NhVib3dk+pwPX2hlthyG7712GBlnDs7yaglGc6f+Mt7TA3/8eW0Ty6achKwuOOgrOOgsOPRRGjozbPeP99gOfvIJ/fhXIcexyuS7peeaZZ8KWEEfY52dNTQ1HHnkk11xzDZ/61Ke6tkciEWpqahg/fjzt7e089NBDHHzwwQAsXbqUZcuWsf/++3Pvvffyuc99DknRQsVh57MjxatXhy0hDtfyUbbjUrsH7unxuS02xnD22WdTWlrKxRdf3LX9mGOO4amnnuKggw6iurqatrY2xo8fz9KlSzn11FO5+OKL+fDDD1m3bh377rtv2vS61s5oO+zRGhD1TfVedUCkaj5rylm7Fr75TZgyBU47DT74ILa2w8aNcN99cPTRO3U+QAb7HQA+eQX//Cr0uhp5WLikxyUtkJrz85RTTmH//ffn7bffpqSkhJtvvpn77ruPkpISXnzxRY488kgOO+wwAH7zm9+wfv16rrzySubPn8+8efPYtm0bra2tHHbYYXz84x9n/vz5TJkyhXPOOQeAs88+m8rKSnbffXd++ctfcs011yTdQyeutV+VpaVhS4jDtXyU7bjW1qgeO+k8l55//nluv/12nnzyya7LHD/88MN8+ctf5r333mPu3LmccMIJLFu2DBFhzpw5nHTSScyePZslS5Zw4403Wi+rmmxca2e0HQaJTdHILBYuXGi6Xwc8EU6850TWlK/hza+/mSJVbhGNRhk2LMP6l266Cb72NcjJgWOPhXPOgc99DhLwkZF+B4hPXiGz/IrICmPMwrB1pIOe2uE333yTPffcc9C/JhtjUvaL9EBwSU86tRhjeOuttyi1fFly7fz0Wk8C9SKalcWwvi5/l8bvhanKR9viwbfFLrV74K+eRNph8Lzt6wNth+2E0Q67UTPSQEtbC9nDvJlxQl1dXdgS+s/q1VBQAJs3w913w8EHJ9T5ABnqd4D45BX885vJ5ObmUllZyWA7ttN5ffBEcElPurQYY6isrCQ3N9e6n2vnp+qxUzdtWtgS4nAtn6FCMtpil9o98FNPou0wpPlcEunzVrfHHn3vlyZca2e0HfZoDQiGQZakb7hP2IwaNSpsCf2nuRnGjBnQYpIZ6XeA+OQV/PObyZSUlLBp06ZBL2jk6y9diZBOLbm5uZSUlFj3Sev5mYDvUXl50NRk3ymNvyy51n6N2rIlbAlxuJbPUCEZbbFL7R74qyeRdhjcO5dcams0Gzth5ONNB0R7pJ2sYf50QLS0tDi3YE+fNDf3uL5DImSk3wHik1fwz28mk5OTw/Tp0wd9nPr6ekaPHp0ERcnBJT0uaQH3zs+WcePI6asDIo1oPnZcy2eokIy22LW2RvXYce1ccqmt0WzshJGPN1MwDMarERAunWgJM4gOiIz0O0B88gr++VXcK3OX9LikBRzU09gYtoQ4NB87ruWjbMe1slE9dpzT41Bbo9nYCSMfbzogItGIVyMgotFo2BL6zyA6IDLS7wDxySv451dxr8xd0uOSFnBQT7ZbAzs1Hzuu5aNsx7WyUT12nNPjUFuj2dgJIx9vOiA6TIdXIyAy8eomg+mAyEi/A8Qnr+CfX8W9MndJj0tawEE9aby0WyJoPnZcy0fZjmtlo3rsOKfHobZGs7ETRj5edUD4dBWMbMd61xJiEB0QGel3gPjkFfzzq7hX5i7pcUkLOKinuTlsCXFoPnZcy0fZjmtlo3rsOKfHobZGs7ETRj7edEBEOvyagtHa2hq2hP4ziA6IjPQ7QHzyCv75Vdwrc5f0uKQFHNRTUBC2hDg0Hzuu5aNsx7WyUT12nNPjUFuj2dgJIx9vOiCM+LUIZV5eXtgS+s8gOiAy0u8A8ckr+OdXca/MXdLjkhZwUM+2bWFLiEPzseNaPslGRHYVkadEZK2IvCEiFwXbrxCRzSKyKrgd0e093xWR9SLytogcFpZ218pG9dhxTo9DbY1mYyeMfLzpgPDtMpz19fVhS+g/g+iAyEi/A8Qnr+CfX8W9MndJj0tawEE9u+4atoQ4NB87ruWTAiLAt4wxs4H9gK+LyOzgteuMMfOD28MAwWsnA3OAJcBvRcL59cy1slE9dpzT41Bbo9nYCSMfbzogjBiv1oAYO3Zs2BL6zyA6IDLS7wDxySv451dxr8xd0uOSFnBQz/r1YUuIQ/Ox41o+ycYYs8UYszJ4XA+8CUyxvOVo4C5jTKsx5n1gPbBv6pXujGtlo3rsOKfHobZGs7ETRj7edEC0tbd51QFRWVkZtoT+M4gOiIz0O0B88gr++VXcK3OX9LikBRzUM2dO2BLi0HzsuJZPKhGRacDewH+CTeeLyOsicouIFAbbpgAbu71tE/YOi5ThWtmoHjvO6XGordFs7ISRjzcdEGaYXyMgxo8fH7aE/tHRAW1tA+6AyDi/g8Anr+CfX8W9MndJj0tawEE9a9aELSEOzceOa/mkChHJB/4GfMMYUwfcBMwA5gNbgF/083jnikiZiJRt27aNlpYWmpqaaGxspLW1lbq6OiKRCNXV1RhjqKioAKC8vByAiooKjDFUV1cTiUSoq6ujtbWVxsZGmpqaaGlpYcSIEbS3t1NTU0M0Gu36J6XzGJ33VVVVdHR0UFtbS1tbGw0NDTQ3N9Pc3ExDQwNtbW3U1tbS0dFBVVVVj8eorKwkGo1SU1NDe3s79fX1O3kaPnz4oD3V19cnzVNOTs6gPSWjnDo9ZWdnp6+cZsygPS+P+pISWgoLaSoupnHiRFoLCqibOpVIbi5Zra0YESrmzo0dI7ivmDsXI0L1zJlpKafa2loKCwvTV065udRNnUprQQGNEyfSVFxMS2Eh9SUltOflUTNjBuPefJPK0tLYMebNi7uvmjWLjpyclJ9P3T0VFRWl5HyyYozJuNs+++xj+suM62eYL/z1C/1+X6aybdu2sCX0j4YGY8CYn/50QG/POL+DwCevxmSWX6DMONBGpuM2kHY4UVwr81To+dKXvmSKi4vNnDlzurZVVlaagw8+2Oy+++7m4IMPNlVVVcYYY+6//34zb948s9dee5m99trLPPvss13vueSSS8zs2bPNnnvuaS644AITjUbjPueoo46K+4xkk9aygj5v2+bO7Xu/NKL52ElVPi61xUAO8BhwcS+vTwPWBI+/C3y322uPAfvbjp+qttiHdrg3/vvf/5oDDzzQlJaWmtmzZ5vrr78+7vVrr73WAKa8vNwYY0w0GjUXXHCBmTFjhpk3b55ZsWJF2rR2om1N72g2dsJoh/0ZAeHZGhDFxcVhS+gfLS2x+wGOgMg4v4PAJ6/gn1/FvTJPhZ6zzjqLRx99NG7bNddcw+LFi1m3bh2LFy/mmmuuAWDx4sW89tprrFq1ittuu42vfOUrALzwwgs8//zzvP7666xZs4ZXXnmFZ555put4f//738nPz0+69u44V1aO/cKv+dhxLZ9kIyIC3Ay8aYz5Zbftk7rtdizQWTAPAieLyAgRmQ7MBF5Ol97uuFY26dSTnZ3NL37xC9auXctLL73EjTfeyNq1awHYuHEjjz/+OFOnTu3a/5FHHmHdunWsW7eOP/zhD5x33nlp09qJc+XlUFuj2dgJIx9vOiDaI+1edUB0DqXJGJqbY/cD7IDIOL+DwCev4J9fxb0yT4Wez3zmM4wbNy5u2wMPPMCZZ54JwJlnnsn9998PQH5+PrH/Y2Dz5s1dj0WElpYW2traaG1tpb29nV122QWAhoYGfvnLX/K9730v6dq741xZBcN8XUHzseNaPingU8AXgc/tcMnNn4nIahF5HTgI+CaAMeYN4B5gLfAo8HVjTEcYwl0rm3TqmTRpEgsWLABg9OjRlJaWsnnzZgC++c1v8rOf/axzhAoQa7vPOOMMRIT99tuPmpoatmzZkja94GB5OdTWaDZ2wsjHm//Io0TJCudKRqFQVFQUtoT+McgOiIzzOwh88gr++VXcK/N06dm6dSuTJsV+GJ04cSJbt27teu2+++7ju9/9Ltu2beOf//wnAPvvvz8HHXQQkyZNwhjD+eefT2kwr/T73/8+3/rWt1J+fW/nyuqNN8KWEIfmY8e1fJKNMeY5QHp46WHLe64CrkqZqARxrWzC0rNhwwZeffVVFi1axAMPPMCUKVPYa6+9GDZs+2+4mzdvZtdul1YsKSlh8+bNXe15OnCuvBxqazQbO2Hk480IiGg0yjDxxi41NTVhS+gfg+yAyDi/g8Anr+CfX8W9Mg9Dj4h0jXQAOPbYY3nrrbe4/fbb+f73vw/A+vXrefPNN9m0aRObN2/mySef5Nlnn2XVqlW8++67HHvssSnX6VxZ7b572BLi0HzsuJaPsh3XyiYMPQ0NDRx//PFcf/31ZGdn85Of/IQrr7wSiP1f4RLOlZdDbY1mYyeMfPz5j7yn/uchzOjRo8OW0D8G2QGRcX4HgU9ewT+/intlni49u+yyS9ew3S1btjBhwoSd9jnssMN47733qKio4L777mO//fYjPz+f/Px8Dj/8cF588UVefPFFysrKmDZtGgcccADvvPMOBx54YEo0O1dWGzf2vVMa0XzsuJaPsh3Xyibdetrb2zn++OM57bTTOO6443j33Xd5//332WuvvZg2bRoffvghCxYs4KOPPmLKlCls7HZubdq0iSlT0nv1VOfKy6G2RrOxE0Y+3nRARKPRuF+ThjpNTU1hS+gfg+yAyDi/g8Anr+CfX8W9Mk+XnqVLl7Js2TIAli1bxtFHHw3ERjp0zjd+4YUXaG1tpaioiKlTp/LMM88QiURob2/nmWeeobS0lPPOO48PP/yQDRs28Nxzz7HHHnvw9NNPp0Szc2XVQ6dNmGg+dlzLR9mOa2WTTj3GGM4++2xKS0u5+OKLAZg3bx7btm1jw4YNbNiwgSlTprBy5UomTpzI0qVLue222zDG8NJLL1FQUJDW6RfgYHk51NZoNnbCyMebNSBEBPFoGMSIESPCltA/OjsgcnMH9PaM8zsIfPIK/vlV3CvzVOg55ZRTePrpp6moqKCkpIQf/vCHXHbZZZx00kncfPPNfOxjH+Oee+4B4G9/+xu33XYbOTk55ObmcvfddyMinHDCCTz55JPMmzcPEWHJkiUcddRRSddqw7myqq0NW0Icmo8d1/JRtuNa2aRTz/PPP8/tt9/OvHnzmD9/PgA/+clPOOKII3rc/4gjjuDhhx9m9913Jy8vjz//+c9p09qJc+XlUFuj2dgJIx9vOiCixq25WqkmEok4d8JZGeQIiIzzOwh88gr++VXcK/NU6Lnzzjt73L58+fKdtl166aVceumlADQ2NjJq1CgAsrKy+P3vf2/9nGnTprEmhZf8cq6sRo506sud5mPHtXyU7bhWNunUc8ABB8Rd5aIn1q5d29UWiwg33nhjOqT1inPl5VBbo9nYCSMfb6ZgGIxXUzAyzusgOyAyzu8g8Mkr+OdXca/MXdLjkhZwUE9HKFcs7BXNx45r+Sjbca1sVI8d5/Q41NZoNnbCyMebERCAV1Mwul8eKCMYZAdExvkdBD55Bf/8Ku6VeVr19PFFYFhhIVRX24/Rxy93ycS5sopEwpYQh+Zjx7V8lO24VjYutcOgbXFfuNTWaDZ2wsjHrRJJIcb4NQKivb09bAn9Y5AdEBnndxD45BX886u4V+Yu6WkPhvy6gkvZgObTF5qPkiiulY1zevRcsuJSPpqNnTDySXkHhIgsEZG3RWS9iFzWw+tTReQpEXlVRF4XkZ5XeFH6Re4AF3MMjUF2QGSc30Hgk1fwz6/iXpm7pCe3qipsCXG4lA1oPn2h+SiJ4lrZOKdHzyUrLuWj2dgJI5+UdkCISBZwI3A4MBs4RURm77Db94B7jDF7AycDv02FlqiJejUFo7GxMWwJ/WOQHRAZ53cQ+OQV/POruFfmLulpTPOl3frCpWxA8+kLzUdJFNfKxjk9ei5ZcSkfzcZOGPmkegTEvsB6Y8x7xpg24C7g6B32McCY4HEB8GEqhIiIV1MwxowZ0/dOLtHcDMOHwwDnIWWc30Hgk1fwz6/iXpm7pGfMhg1hS4jDpWxA8+kLzUdJFNfKxjk9ei5ZcSkfzcZOGPmkugNiCrCx2/NNwbbuXAGcLiKbgIeBC1IhJBr16zKc1X0tjOMazc0DHv0AGeh3EPjkFfzzq7hX5i7pqd5jj7AlxOFSNqD59IXmoySKa2XjnB49l6y4lI9mYyeMfFxYhPIU4FZjTAlwBHC7iOykS0TOFZEyESnbtm0bLS0tNDU10djYSGtrK3V1dUQiEaqrqzHGUFFRAUB5eTkQuwwnxEKORCLU1dXR2tpKY2MjTU1NtLS0UF9fT3t7OzU1NUSjUSorK+OO0XlfVVVFR0cHtbW1tLW10dDQQHNzM83NzTQ0NNDW1kZtbS0dHR1UBfN8djxGZWUl0WiUmpoa2tvbqa+v77eniooKjDE9eho5cmRGeWquroaRI62ebOVUVFTknKdEymkgdW/s2LFDzpOtnEaPHp0xnlKFiNwiIttEZE23beNE5AkRWRfcFwbbRURuCNbdeV1EFnR7z5nB/utE5Mxu2/cRkdXBe26QkIeLFRUVhfnxO+GSnqI33wxbQhwuZQOaT19oPkqiuFY2zunRc8mKS/loNnbCyEdMCi8RIyL7A1cYYw4Lnn8XwBhzdbd93gCWGGM2Bs/fA/Yzxmzr7bgLFy40ZWVl/dJSeE0hX9zri9xw+A39N5KBlJeXU1xcHLaMxPniF+H55+G99wb09ozzOwh88gqZ5VdEVhhjFqbguJ8BGoDbjDFzg20/A6qMMdcEC/wWGmMuDRbyvYBYh+4i4FfGmEUiMg4oAxYSm/q2AtjHGFMtIi8DFwL/ITYS7QZjzCM2TQNphxPFtTJPq54++n7K582jePVq+zHSeOk3l7IBzacvfMknVW2xi6SqLdZ22I4v51KPZFg+mo2dMNrhVI+AeAWYKSLTRWQ4sUUmH9xhn/8CiwFEpBTIBZL/c6I/yz8AOPVHIyEGOQUj4/wOAp+8gn9+e8IY829gx2WTjwaWBY+XAcd0236bifESMFZEJgGHAU8YY6qMMdXAE8CS4LUxxpiXTKxH+rZuxwoF18rcJT19fmlJMy5lA5pPX2g+SqK4VjbO6dFzyYpL+Wg2dsLIJ6UdEMaYCHA+8BjwJrGrXbwhIleKyNJgt28B54jIa8CdwFkmBcMyolG/roKR6iHhSWeQHRAZ53cQ+OQV/PPbD3YxxmwJHn8E7BI87m3tHdv2TT1sDw3XytwlPeXz5oUtIQ6XsgHNpy80HyVRXCsb5/TouWTFpXw0Gzth5JPyNSCMMQ8bY/YwxswwxlwVbPuBMebB4PFaY8ynjDF7GWPmG2MeT4UO366C4VpvX5/oCIiE8ckr+Od3IASdtikfrzfYtXgSXWdj7NixTq1bU1hYmL71UHJyqJ02jbb8fBomT6a5qIjmoiIaJk+mLT+f4fX1dOTkUDVrVuy9wReZzvvK0tK0roeSk5OTvnKaMYP2vDzqS0poKSykqbiYxokTaS0ooG7qVCK5uWS3tGBEqJg7N3aM4L5i7lyMCNUzZ6Z1HaisrKxB1b1+ldPUqbQWFNA4cSJNxcW0FBZSX1JCe14eNTNmEM3KYlgk0mO9qZo1a3vdS+P6Qp3fy1JxPimDw7W/vc7p0V+xrbiUj2ZjJ4x8UroGRKoYyHy3MVeP4ct7f5nrl1yfGlGOUVVVxbhx48KWkTj77w+jR8PjA+t/yji/g8Anr5BZflM571hEpgEPdVsD4m3gQGPMlmAaxdPGmFki8vvg8Z3d9+u8GWP+J9j+e+Dp4PaUMWbPYPsp3ffrjVSuAeFamadVTx8d5VWzZjHu7bftx0jj33WXsgHNpy98yUfXgBg82g7b8eVc6pEMy0ezsRNGO+zCVTDShk9TMAoKCsKW0D8GOQIi4/wOAp+8gn9++8GDQOeVLM4EHui2/Yzgahj7AbXBVI3HgENFpDC4YsahwGPBa3Uisl9w9Yszuh0rFFwrc5f0FAxwod5U4VI2oPn0heajJIprZeOcHj2XrLiUj2ZjJ4x8/OqA8GgKRkNDQ9gS+scgOyAyzu8g8Mkr+Oe3J0TkTuBFYJaIbBKRs4FrgENEZB1wcPAcYlexeA9YD/wR+BqAMaYK+BGxxYFfAa4MthHs86fgPe8C1itgpBrXytwlPQ1TQl2eYydcygY0n77QfJREca1snNOj55IVl/LRbOyEkU922j8xJEzqp0c7xchB/DMfCoPsgMg4v4PAJ6/gn9+eMMac0stLi3vY1wBf7+U4twC39LC9DJg7GI3JxLUyd0nPyGAdAFdwKRvQfPpC81ESxbWycU6PnktWXMpHs7ETRj7ejIAwxng1BaOtrS1sCf1jkB0QGed3EPjkFfzzq7hX5i7paRszJmwJcbiUDWg+faH5KIniWtk4p0fPJSsu5aPZ2AkjH386IDBeTcHoXHk7YxhkB0TG+R0EPnkF//wq7pW5S3qyWlvDlhCHS9mA5tMXmo+SKK6VjXN69Fyy4lI+mo2dMPLxpgMC/FqEMqMwZtAdEIqiKIqiKIqiKIrbeNMBkYmXGx0MHR0dYUtInPZ2iEYH1QGRUX4HiU9ewT+/intl7pKejhEjwpYQh0vZgObTF5qPkiiulY1zevRcsuJSPpqNnTDy8acDwrMpGMOHDw9bQuI0N8fuB9EBkVF+B4lPXsE/v4p7Ze6SnuF1dWFLiMOlbEDz6QvNR0kU18rGOT16LllxKR/Nxk4Y+XjTAYHxawpGc+c/9ZlAEjogMsrvIPHJK/jnV3GvzF3S0zx+fNgS4nApG9B8+kLzURLFtbJxTo+eS1ZcykezsRNGPt50QPh2Gc78/PywJSROEjogMsrvIPHJK/jnV3GvzF3Sk795c9gS4nApG9B8+kLzURLFtbJxTo+eS1ZcykezsRNGPv50QBi/pmDU1taGLSFxktABkVF+B4lPXsE/v4p7Ze6SntrddgtbQhwuZQOaT19oPkqiuFY2zunRc8mKS/loNnbCyMebDgjErykY48aNC1tC4iShAyKj/A4Sn7yCf34V98rcJT3j3n47bAlxuJQNaD59ofkoieJa2TinR88lKy7lo9nYCSMfbzogfLsKRnl5edgSEicJHRAZ5XeQ+OQV/POruFfmLukpnzcvbAlxuJQNaD59ofkoieJa2TinR88lKy7lo9nYCSMffzogPLsKRnFxcdgSEicJHRAZ5XeQ+OQV/POruFfmLukpXr06bAlxuJQNaD59ofkoieJa2TinR88lKy7lo9nYCSMfbzogwK8pGK719lnRERD9wiev4J9fxb0yd0mP/nJiR/Oxo/koieJa2TinR88lKy7lo9nY0REQKcS3KRiu9fZZ0REQ/cInr+CfX8W9MndJj/5yYkfzsaP5KIniWtk4p0fPJSsu5aPZ2NERECnEtykYlZWVYUtInCR0QGSU30Hik1fwz6/iXpm7pKeytDRsCXG4lA1oPn2h+SiJ4lrZOKdHzyUrLuWj2dgJIx9vOiDArykYhYWFYUtInCR0QGSU30Hik1fwz+9Q5brrrmPOnDnMnTuXU045hZaWFpYvX86CBQuYP38+BxxwAOvXrwdiZX7PPfcwe/Zs5syZw6mnntp1nO985zvMmTOH0tJSLrzwwrSMbnOpDha+807YEuJwKRvQfPpC81E62bhxIwcddFBXO/urX/0KgKqqKg455BD2228/DjnkEKqrqwF4+umnKSgoYP78+cyfP58rr7wyrXpdqyt6LtlxKR/Nxk4Y+XjTAeHbFIy6urqwJSROEjogMsrvIPHJK/jndyiyefNmbrjhBsrKylizZg0dHR3cddddnHfeedxxxx2sWrWKU089lR//+McAvPrqq1x99dU8//zzvPHGG1x//fUAvPDCCzz//PO8/vrrrFmzhldeeYVnnnkm5fpdqoN106aFLSEOl7IBzacvNB+lk+zsbH7xi1+wdu1aXnrpJW688UbWrl3LNddcw+LFi3nllVdYvHgx11xzTdd7Pv3pT7Nq1SpWrVrFD37wg7Tqda2u6Llkx6V8NBs7YeTjTweEZ1MwRo0aFbaExElCB0RG+R0kPnkF//wOVSKRCM3NzUQiEZqampg8eTIi0vWHr7a2lsmTJwPwf//3f3z961/v6pWfMGECACJCS0sLbW1ttLa20t7ezi677JJy7S7VwVFbtoQtIQ6XsgHNpy80H6WTSZMmsWDBAgBGjx5NaWkpmzdv5oEHHuDMM89k1KhRnHnmmdx///3hCg1wra7ouWTHpXw0Gzth5ONNBwT4NQWjpaUlbAmJ09wMw4ZBTs6AD5FRfgeJT17BP79DkSlTpvDtb3+bqVOnMmnSJAoKCjj00EP505/+xBFHHEFJSQm33347l112GQBvv/0277zzDp/61KfYb7/9ePTRRwHYf//9Oeigg5g0aRKTJk3isMMOozQNcyldqoMt48aFLSEOl7IBzacvNB+lJzZs2MCrr77KokWL2Lp1K5MmTaKlpYWJEyeydevWrv1efPFF9tprLw4//HDeeOONtGp0ra7ouWTHpXw0Gzth5ONXB4RHIyByBvHPfNppbo6NfhhE+WSU30Hik1fwz+9QpLq6mgceeID333+fDz/8kMbGRv7yl79w3XXX8fDDD7Np0ya+9KUvcfHFFwMQjUZZt24dTz/9NHfeeSfnnHMONTU1rF+/njfffJNNmzaxefNmnnzySZ599tmU63epDuY0NoYtIQ6XsgHNpy80H2VHGhoaOP7447n++usZM2ZM1/acnBxEpOu784IFC/jggw947bXXuOCCCzjmmGPSqtO1uqLnkh2X8tFs7ISRjxcdEL6t/wCxL/AZQ2cHxCDIKL+DxCev4J/foci//vUvpk+fTnFxMTk5ORx33HE8//zzvPbaayxatAiAL3zhC7zwwgsATJ48maVLl5KTk8P06dPZY489WLduHffddx/77bcf+fn55Ofnc/jhh/Piiy+mXL9LdTCanR22hDhcygY0n77QfJTutLe3c/zxx3Paaadx3HHHAbDLLruwZcsWotEoW7Zs6ZoCN2bMGPLz8wE44ogjaG9vp6KiIm1aXasrei7ZcSkfzcZOGPn40QFBrAPCpykYGdXpkoQOiIzyO0h88gr++R2KTJ06lZdeeommpiaMMSxfvpzZs2dTW1vLO8Fq0E888UTXdIrPf/7zPP300wBUVFTwzjvvsNtuuzF16lSeeeYZIpEI7e3tPPPMM2mZguFSHTRZWWFLiMOlbEDz6QvNR+nEGMPZZ59NaWlp1+gzgKVLl7Js2TKMMSxbtoyjjz4agI8++qirvF5++WWi0ShFRUVp1esSei7ZcSkfzcZOGPm41QWTYnyagpHtWO+alSR0QGSU30Hik1fwz+9QZNGiRZxwwgksWLCA7Oxs9t57b84991xKSko4/vjjGTZsGIWFhdxyyy0ALFmyhH//+9/Mnj2brKwsfv7zn1NUVMQJJ5zAk08+ybx58xARlixZwlFHHZVy/S7VwezORXsdwaVsQPPpC81H6eT555/n9ttvZ968ecyfPx+An/zkJ1x22WWcdNJJ/OlPf2LatGncc889ANx7773cdNNNZGdnM3LkSO666660fq92ra7ouWTHpXw0Gzth5ONWiaQI13q+0kFraysjRowIW0ZitLQMugMio/wOEp+8gn9+hyo//OEP+eEPfxi37dhjj+XYY4/dad+2tjZ++ctf8stf/jJue1ZWFr///e9TqrMnXKqDrQUFjKitDVtGFy5lA5pPX2g+SicHHHBAr9+Ply9fTl1dXdyaEOeffz7nn39+uuTthGt1Rc8lOy7lo9nYCSMfPzogPJyCkZeXF7aExEnCCIiM8jtIfPIK/vlV3Ctzl/TkbdsWtoQ4XMoGNJ++0HwUIKFFv/Nyc2M/ENlI4w98rtUVPZfsuJSPZmMnjHy8WAOiE5+mYNTX14ctIXGS0AGRUX4HiU9ewT+/Qx6RPm/1s2f3vV8acakO1u+6a9gS4nApG9B8+kLzURJF64odzceOS/loNnbCyMeLDggfp2CMHTs2bAmJk4QOiIzyO0h88gr++VVg7Pr1YUuIw6U6qNnY0XzsaD7pRUR2FZGnRGStiLwhIhcF28eJyBMisi64Lwy2i4jcICLrReR1EVkQlnatK3Y0Hzsu5aPZ2AkjHz86IDycglFZWRm2hMRpbobc3EEdIqP8DhKfvIJ/fhWonDMnbAlxuFQHNRs7mo8dzSftRIBvGWNmA/sBXxeR2cBlwHJjzExgefAc4HBgZnA7F7gp/ZJjaF2xo/nYcSkfzcZOGPl40QHRiU9TMMaPHx+2hMRJwgiIjPI7SHzyCv75VWD8mjVhS4jDpTqo2djRfOxoPunFGLPFGLMyeFwPvAlMAY4GlgW7LQOOCR4fDdxmYrwEjBWRSelVHUPrih3Nx45L+Wg2dsLIx4sOCB+nYJSXl4ctIXGS0AGRUX4HiU9ewT+/CpTPnRu2hDhcqoOajR3Nx47mEx4iMg3YG/gPsIsxZkvw0kfALsHjKcDGbm/bFGxLO1pX7Gg+dlzKR7OxE0Y+fnRAeDgFo7i4OGwJiZOEDoiM8jtIfPIK/vlVoNixXwdcqoOajR3Nx47mEw4ikg/8DfiGMaau+2sm9itZv34pE5FzRaRMRMq2bdtGS0sLTU1NNDY20traSl1dHZFIhOrqaowxVFRUANv/0aiYOxcjQvXMmURyc6mbOpXWggIaJ06kqbiYlsJCcmtqaM/Lo2bGDKJZWVSWlsaOMW9e3H1VVRUdHR3U1tbS1tZGQ0MDzc3NNDc309DQQFtbG7W1tXR0dFBVVRWno/O+srKSaDRKTU0N7e3t1NfX7+RpxIgRdk8VFRhjqK6uJhKJUFdXR2trK42NjTQ1NdHS0kJ9fT3t7e3U1NQQjUa7hp7vqKdq1iw6cnKonTaNtvx8GiZPprmoiOaiIhomT6YtP5/hDQ105ORQNWtWj7lUlpb26anPcuqHp5ycHLunZJbTjBm05+VRX1JCS2EhTcXFNE6cSGtBAXVTpxLJzSW7tRUjQkXwz3bnP91xdW+w5ZSgp3Hjxg2q7vWrnHo5n+pLSrrOp6I33+z9fOqseyk+n7p7Gj9+fErOJxuSiaMDFi5caMrKyhLevyXSwsirRnL14qu57IDL+n7DEKCiosK5IUe9kpsLF10EP/3pgA+RUX4HiU9eIbP8isgKY8zCsHWkg/62w10kMBWuYu7cvocopvFvV1rrYB/5aDZ2NB87vuTjUlssIjnAQ8BjxphfBtveBg40xmwJplg8bYyZJSK/Dx7fueN+vR1/QG2x1hU7mo+dDMtHs7ETRjvsxwiIDOxkGSxFRUVhS0iMaBRaWwc9AiJj/CYBn7yCf34VKHrjjbAlxOFSHdRs7Gg+djSf9CKxxcduBt7s7HwIeBA4M3h8JvBAt+1nBFfD2A+otXU+pBKtK3Y0Hzsu5aPZ2AkjHz86IDycglFTUxO2hMRoaYndD7IDImP8JgGfvIJ/fhWo2X33sCXE4VId1GzsaD52NJ+08yngi8DnRGRVcDsCuAY4RETWAQcHzwEeBt4D1gN/BL4WgmZA60pfaD52XMpHs7ETRj7Zaf/EEOgcAeHTVTBGjx4dtoTEaG6O3Q+yAyJj/CYBn7yCf34VGL1xY987pRGX6qBmY0fzsaP5pBdjzHPQ669fi3vY3wBfT6moBNG6YkfzseNSPpqNnTDy8WIERCc+jYBoamoKW0JiJKkDImP8JgGfvIJ/fhVomjAhbAlxuFQHNRs7mo8dzUdJFK0rdjQfOy7lo9nYCSMfLzogTP8WFx4SjBgxImwJiZGkDoiM8ZsEfPIK/vlVYERtbdgS4nCpDmo2djQfO5qPkihaV+xoPnZcykezsRNGPn50QHg4BSMSiYQtITGS1AGRMX6TgE9ewT+/CkQG2R4kG5fqoGZjR/Oxo/koiaJ1xY7mY8elfDQbO2Hk40UHRCc+TcHImM6WJHVAZIzfJOCTV/DPrwLS0RG2hDhcqoOajR3Nx47moySK1hU7mo8dl/LRbOyEkY8XHRA+TsEYNixDijZJHRAZ4zcJ+OQV/POrwDDHfq1wqQ5qNnY0Hzuaj5IoWlfsaD52XMpHs7ETRj5ulUiK8HEKRnt7e9gSEiNJHRAZ4zcJ+OQV/POrQPuoUWFLiMOlOqjZ2NF87Gg+SqJoXbGj+dhxKR/Nxk4Y+XjRAdGJT1MwcnNzw5aQGEnqgMgYv0nAJ6/gn18FcquqwpYQh0t1ULOxo/nY0XyURNG6YkfzseNSPpqNnTDy8aIDwscpGI2NjWFLSIwkdUBkjN8k4JNX8M+vAo2TJoUtIQ6X6qBmY0fzsaP5KImidcWO5mPHpXw0Gzth5ONHB4SHUzDGjBkTtoTESFIHRMb4TQI+eQX//PYXEfmmiLwhImtE5E4RyRWR6SLyHxFZLyJ3i8jwYN8RwfP1wevTuh3nu8H2t0XksNAMAWM2bAjz43fCpTqo2djRfOxoPkqiaF2xo/nYcSkfzcZOGPl40QHRiU9TMKqrq8OWkBhJ6oDIGL9JwCev4J/f/iAiU4ALgYXGmLlAFnAy8FPgOmPM7kA1cHbwlrOB6mD7dcF+iMjs4H1zgCXAb0UkK51eulO9xx5hfXSPuFQHNRs7mo8dzUdJFK0rdjQfOy7lo9nYCSMfLzogfJyCUVRUFLaExEhSB0TG+E0CPnkF//wOgGxgpIhkA3nAFuBzwL3B68uAY4LHRwfPCV5fLLGhYUcDdxljWo0x7wPrgX3TI39nit58M6yP7hGX6qBmY0fzsaP5KImidcWO5mPHpXw0Gzth5ONHB4SHUzDKy8vDlpAYSeqAyBi/ScAnr+Cf3/5gjNkMXAv8l1jHQy2wAqgxxnRe52kTMCV4PAXYGLw3Euxf1H17D+9JO+Xz5oX10T3iUh3UbOxoPnY0HyVRtK7Y0XzsuJSPZmMnjHy86IDoxKcpGMXFxWFLSIzmZhg+HAZ5DdqM8ZsEfPIK/vntDyJSSGz0wnRgMjCK2BSKVH3euSJSJiJl27Zto6WlhaamJhobG2ltbaWuro5IJEJ1dTXGGCoqKoDtf9wqKiowIlTPnEkkN5e6qVNpLSigceJEmoqLaSkspL6khLHvvkvNjBlEs7KoLC2NHSP4g915X1VVRUdHB7W1tbS1tdHQ0EBzczPNzc00NDTQ1tZGbW0tHR0dVAUrTnfq6LyvrKwkGo1SU1NDe3s79fX1PXoqLCy0ezKG6upqIpEIdXV1tLa20tjYSFNTEy0tLdTX19Pe3k5NTQ3RaJTKysoe9VRVVdGRk0PttGm05efTMHkyzUVFNBcV0TB5Mm35+Qyvr6cjJ4eqWbN6zKWytDQhT32WU4KecnJy+vaUrHKaMYP2vDzqS0poKSykqbiYxokTaS0ooG7qVCK5uWS3tGBEqJg7N3aM4L5i7tztdS8Z5ZSgp6ysrEHVvX6VUy/nU3teXtf51Hn9+Z3Op1mztte9FJ9P3T11/jCUivNJGRzFq1eHLSEO174LaD52XMpHs7ETRj7SOTogk1i4cKEpKytLeP+KpgqKf17MDUtu4IJFF6RQmTuUl5c7d8L1yEUXwbJlUFMzqMNkjN8k4JNXyCy/IrLCGLMwjZ93IrDEGHN28PwMYH/gRGCiMSYiIvsDVxhjDhORx4LHLwZTNj4CioHLAIwxVwfH6dqvt8/ubzvcTXSfu5TPm9f3H+g0/u1Kax3sIx/Nxo7mY8eXfNLdFofJgNpirSt2NB87GZaPZmMnjHbYixEQPk7ByJR/2GhuHvT0C8ggv0nAJ6/gn99+8l9gPxHJC9ZyWAysBZ4CTgj2ORN4IHj8YPCc4PUnTayBfBA4ObhKxnRgJvBymjzshP460DuajR3Nx47moySK1hU7mo8dl/LRbOyEkY8fHRDBIpQ+TcHoHCLpPEnqgMgYv0nAJ6/gn9/+YIz5D7HFJFcCq4m16X8ALgUuFpH1xNZ4uDl4y81AUbD9YraPfHgDuIdY58WjwNeNMR1ptBJH5/QCV3CpDmo2djQfO5qPkihaV+xoPnZcykezsRNGPtmJ7igiI4Gpxpi3U6gnpfg0AqKgoCBsCYmRpA6IjPGbBHzyCv757S/GmMuBy3fY/B49XMXCGNNCbHpGT8e5Crgq6QIHQMF774UtIQ6X6qBmY0fzsaP5KImidcWO5mPHpXw0Gzth5JPQCAgROQpYReyXMURkvog8mEJdSSUT17kYLA0NDWFLSIwkdUBkjN8k4JNX8M+vAg1TQrsAR4+4VAc1Gzuajx3NR0kUrSt2NB87LuWj2dgJI59Ep2BcQezXtBoAY8wqYquuZwQ+TsEYmYR/6tNCkjogMsZvEvDJK/jnV4GRwWr/ruBSHdRs7Gg+djQfJVG0rtjRfOy4lI9mYyeMfBLtgGg3xtTusC2hYQUiskRE3haR9SJyWS/7nCQia0XkDRH5vwQ19RufpmC0tbWFLSExktQBkTF+k4BPXsE/vwq0jRkTtoQ4XKqDmo0dzceO5qMkitYVO5qPHZfy0WzshJFPomtAvCEipwJZIjITuBB4oa83iUgWcCNwCLAJeEVEHjTGrO22z0zgu8CnjDHVIjKhvyb6wscpGJ3XHnee5maYMPgizxi/ScAnr+CfXwWyWlvDlhCHS3VQs7Gj+djRfJRE0bpiR/Ox41I+mo2dMPJJdATEBcAcoBX4P6AW+EYC79sXWG+Mec8Y0wbcBRy9wz7nADcaY6oBjDHbEtSUMD5OwcgYkjQCQlEURVEURVEURXGbhDogjDFNxpj/Z4z5RHD7XrCael9MATZ2e74p2NadPYA9ROR5EXlJRJb0dCAROVdEykSkbNu2bbS0tNDU1ERjYyOtra3U1dURiUSorq7GGENFML+mvLw87jjV1dVEIhHq6upobW2lsbGRpqYmWlpaqK+vp729nZqaGqLRKJWVlXHH6Lyvqqqio6OD2tpa2traaGhooLm5mebmZhoaGmhra6O2tpaOjo6uS5vseIzKykqi0Sg1NTW0t7dTX1/fb08VFRUYY3r01NDQkBGeOhobYeTIhDzZyqmjo8MZT/0pp4HUvU7dQ8mTrZxaWloyxpOSHDpGjAhbQhwdHaFdkXQnNBs7mo8dzUdJFK0rdjQfOy7lo9nYCSMfSWR6gog8AZxojKkJnhcCdxljDuvjfScAS4wxXwmefxFYZIw5v9s+DwHtwElACfBvYF7nZ/XEwoULTVlZWZ+6O9lct5mS60r4/ed/z7n7nJvw+zKZtrY2hg8fHraMvikuhhNPhN/+dlCHyRi/ScAnr5BZfkVkhTFmYdg60kF/2+EuEliLpy0/n+F9rcqcxql1aa2DfeSj2djRfOz4ko+2xX2gdcWO5mMnw/LRbOyE0Q4nOgVjfPcOgWC6RCIT9zcDu3Z7XhJs684m4EFjTLsx5n3gHWBmgroSwscpGM3NzWFLSIwkTcHIGL9JwCev4J9fBZrHjw9bQhwu1UHNxo7mY0fzURJF64odzceOS/loNnbCyCfRDoioiEztfCIiHyOxq2C8AswUkekiMhw4GXhwh33uBw4Mjjue2JSM9xLU1S98ugpGfn5+2BL6xpikdUBkhN8k4ZNX8M+vAvmbd+ynDheX6qBmY0fzsaP5KImidcWO5mPHpXw0Gzth5JNoB8T/A54TkdtF5C/Epkl8t683GWMiwPnAY8CbwD3GmDdE5EoRWRrs9hhQKSJrgaeAS4wxlf010oeOZB4uI6it3fGqqQ7S3g7RaFI6IDLCb5LwySv451eB2t12C1tCHC7VQc3GjuZjR/NREkXrih3Nx45L+Wg2dsLIJ6HLcBpjHhWRBcB+waZvGGMqEnzvw8DDO2z7QbfHBrg4uKUEH6dgjBs3LmwJfdM55CcJHRAZ4TdJ+OQV/POrwLi33w5bQhwu1UHNxo7mY0fzURJF64odzceOS/loNnbCyMc6AkJE9gzuFwBTgQ+D29RgW0bh0xSMjFiVP4kdEBnhN0n45BX886tA+bx5YUuIw6U6qNnY0XzsaD5KomhdsaP52HEpH83GThj59DUC4lvAOcAvenjNAJ9LuqIU4OMUjOLi4rAl9E0SOyAywm+S8Mkr+OdXgeLVq8OWEIdLdVCzsaP52NF8lETRumJH87HjUj6ajZ0w8rGOgDDGnBPcH9TDLSM6H8DPKRiu9fb1iI6AGBA+eQX//Cr664ANzcaO5mNH81ESReuKHc3Hjkv5aDZ2nBsBISLH2V43xvw9uXJSQ+cICJ+mYLjW29cjOgJiQPjkFfzzq+ivAzY0Gzuajx3NR0kUrSt2NB87LuWj2dhxbgQEcFRwOxu4GTgtuP0J+HJqpSmDobIyqRcSSQ1J7IDICL9Jwiev4J9fBSpLS8OWEIdLdVCzsaP52NF8lETRumJH87HjUj6ajZ0w8rGOgDDGfAlARB4HZhtjtgTPJwG3plxdkvBxCkZhYWHYEvomiR0QGeE3SfjkFfzzq0DhO++ELSEOl+qgZmNH87Gj+SiJonXFjuZjx6V8NBs7YeTT1wiITnbt7HwI2ErsqhgZgY9TMOrq6sKW0DctLbH7JHRAZITfJOGTV/DPrwJ106aFLSEOl+qgZmNH87Gj+SiJonXFjuZjx6V8NBs7YeTT11UwOlkuIo8BdwbPvwD8KzWSUodPIyBGjRoVtoS+SeIIiIzwmyR88gr++VVg1JYtfe+URlyqg5qNHc3HjuajJIrWFTuajx2X8tFs7ISRT0IjIIwx5wO/A/YKbn8wxlyQSmHJpHMKhk+0dI4ucJnODojc3EEfKiP8JgmfvIJ/fhVoGTcubAlxuFQHNRs7mo8dzUdJFK0rdjQfOy7lo9nYCSOfREdAYIy5D7ivp9dE5EVjzP5JU5VkfJyCkZOTE7aEvkniCIiM8JskfPIK/vlVIKexMWwJcbhUBzUbO5qPHc1HSRStK3Y0Hzsu5aPZ2Akjn0TXgOiLwf+EnQZ8moIRjUbDltA3SeyAyAi/ScInr+CfXwWi2Qn3jacFl+qgZmNH87Gj+SiJonXFjuZjx6V8NBs7YeSTrA4Ip+c4+DgFo3PUh9MksQMiI/wmCZ+8gn9+FTBZWWFLiMOlOqjZ2NF87Gg+A0NEfiYiY0QkR0SWi0i5iJwetq5UonXFjuZjx6V8NBs7YeSTrA4Ip/FxCka2Y71rPdLcDCIwfPigD5URfpOET17BP78KZHd2TjqCS3VQs7Gj+djRfAbMocaYOuDzwAZgd+CSUBWlGK0rdjQfOy7lo9nYCSOfZHVAZMR/9j5NwWhtbQ1bQt80N8dGPyShYygj/CYJn7yCf34VaC0oCFtCHC7VQc3GjuZjR/MZMJ3f0I8E/mqMqQ1TTDrQumJH87HjUj6ajZ0w8klWl8cXk3SclODjFIy8vLywJfRNZwdEEsgIv0nCJ6/gn18F8rZtC1tCHC7VQc3GjuZjR/MZMA+JyFtAM3CeiBQDbi2tn2S0rtjRfOy4lI9mYyeMfBIaASEix4nIOhGpFZE6EakXkbrO140xa1IncfD4OAWjvr4+bAl9k8QOiIzwmyR88gr++VWgftddw5YQh0t1ULOxo/nY0XwGhjHmMuCTwEJjTDvQCBwdrqrUonXFjuZjx6V8NBs7YeST6AiInwFHGWPeTKWYVOPTFIyxY8eGLaFvktgBkRF+k4RPXsE/vwqMXb8+bAlxuFQHNRs7mo8dzWdQ7AlME5Hu351vC0tMqtG6YkfzseNSPpqNnTDySXQNiK2Z3Png4xSMysrKsCX0TRI7IDLCb5LwySsMPb8i8ikRGRU8Pl1EfikiHwtbl0tUzpkTtoQ4XKqDmo0dzceO5jMwROR24FrgAOATwW1hqKJSjNYVO5qPHZfy0WzshJFPoiMgykTkbuB+oGulCmPM31MhKtn4OAVj/PjxYUvomyR2QGSE3yThk1cYkn5vAvYSkb2AbwF/IvYr2mdDVeUQ49e4NavPpTqo2djRfOxoPgNmITDbuHY9vxSidcWO5mPHpXw0Gzth5JPoCIgxQBNwKHBUcPt8qkSlCp+mYJSXl4ctoW+S2AGREX6ThE9eYUj6jQRfYo8GfmOMuREYHbImpyifOzdsCXG4VAc1Gzuajx3NZ8CsASb2900icouIbBORNd22XSEim0VkVXA7ottr3xWR9SLytogcliTtA0Lrih3Nx45L+Wg2dsLIJ6EREMaYL6VaSCrxcQpGcXFx2BL6prkZkqQzI/wmCZ+8wpD0Wy8i3wVOBz4jIsOAnJA1OUWxY78OuFQHNRs7mo8dzad/iMg/AEOsk3itiLxM/EjgpX0c4lbgN+y8VsR1xphrd/is2cDJwBxgMvAvEdnDGNMxKBMDROuKHc3Hjkv5aDZ2wsgn0atg5IrI10Xkt0Fv7i0ickuqxSULH6dgVFRUhC2hb5I4AiIj/CYJn7zCkPT7BWJfYM82xnwElAA/D1eSW1Q49uuAS3VQs7Gj+djRfPrNtcAvgCuAY4CfBM87b1aMMf8GqhL8rKOBu4wxrcaY94H1wL79l5wctK7Y0XzsuJSPZmMnjHwSnYJxO7GhZ4cBzxD7wuzWNU0sdI6A8GkKRlFRUdgS+iaJHRAZ4TdJ+OQVhqTfkcBNxphng+flwL9D1OMcRW+8EbaEOFyqg5qNHc3HjubTP4wxzxhjngH+C/yn2/OXgQ8GcejzReT14Ae9wmDbFGBjt302Bdt2QkTOFZEyESnbtm0bLS0tNDU10djYSGtrK3V1dUQiEaqrqzHGdP2D0TnUumLuXIwI1TNnEsnNpW7qVFoLCmicOJGm4mJaCgsZXltLe14eNTNmEM3KorK0NHaMefPi7quqqujo6KC2tpa2tjYaGhpobm6mubmZhoYG2traqK2tpaOjg6qqqjgdnfeVlZVEo1Fqampob2+nvr5+J085OTl2TxUVGGOorq4mEolQV1dHa2srjY2NNDU10dLSQn19Pe3t7dTU1BCNRrsW39tRT9WsWXTk5FA7bRpt+fk0TJ5Mc1ERzUVFNEyeTFt+PtmNjXTk5FA1a1aPuVSWlvbpqc9y6oenrKwsu6dkltOMGbTn5VFfUkJLYSFNxcU0TpxIa0EBdVOnEsnNZVhbG0ak65/tzmkHcXVvsOWUoKexY8cOqu71q5x6OZ/qS0q6zqfCt97q/XzqrHspPp+6exo3blxKzicbksh6OiLyqjFmbxF53RjzcRHJAZ41xuzX55tTwMKFC01ZWVnC+7++9XX2+t1e/O2kv3Fc6XEpVOYO1dXVFBYW9r1jmEyeDEceCX/846APlRF+k4RPXiGz/IrICmOMdWV0ESkDPmmMaQueDweeN8Z8Ih0ak0V/2+EuEhiJVj1zJoXr1tl3SuNacGmtg33ko9nY0Xzs+JJPIm1xP4834HZbRKYBDxlj5gbPdwEqiE3t+BEwyRjzZRH5DfCSMeYvwX43A48YY+61HX9AbbHWFTuaj50My0ezsRNGO5zoCIj24L5GROYCBcCEZIhLBx4tWtzF6NEZsKZdEkdAZITfJOGTVxiSfrM7v8QCBI+Hh6jHOUZv3Nj3TmnEpTqo2djRfOxoPgMmae22MWarMabDGBMF/sj2aRabgV277VoSbAsFrSt2NB87LuWj2dgJI59EOyD+EAwR+z7wILAW+FnKVCUZH6dgNDU1hS2hb5LYAZERfpOET15hSPotF5GuhctE5Ghiv4YpAU0T3OrfdqkOajZ2NB87ms+ASVq7LSKTuj09ltgVNiD2/fpkERkhItOBmcSmeoSC1hU7mo8dl/LRbOyEkU+iV8H4U/DwGWC31MlJLT4tQjlixIiwJdiJRqG1NWkdEM77TSI+eYUh6ferwB0iciOxIbibgDPCleQWI2prw5YQh0t1ULOxo/nY0XwGTPd2G2JrNXyxrzeJyJ3AgcB4EdkEXA4cKCLzibX/G4D/ATDGvCEi9xD7kS8CfD2sK2CA1pW+0HzsuJSPZmMnjHwS6oAI5qv9BJhsjDk8uFTQ/saYm1OqLkn4OAUjEok4d8LF0dISu09SB4TzfpOIT15h6Pk1xrwL7Cci+cHzhpAlOUdk5Ein/kC7VAc1Gzuajx3NZ2AMtN02xpzSw+ZevzsbY64CrhqQyCSjdcWO5mPHpXw0Gzth5JPoFIxbgceIXZcY4B3gGynQkxJ8nILh/GiP5ubYfZI6IJz3m0R88gpDz6+I7BIsLvZXY0yDiMwWkbPD1uUS0hHaj3494lId1GzsaD52NJ+BISIFIvJL4GngaRH5hYgUhCwrpWhdsaP52HEpH83GThj5JNoBMd4Ycw8QBTDGRAC30ksA1ypgKhk2LNGiDYkkd0A47zeJ+OQVhqTfW8ngDt10MCwSCVtCHC7VQc3GjuZjR/MZMLcQu/z8ScGtDvhzqIpSjNYVO5qPHZfy0WzshJFPop/YKCJFxOarISL7Ae6MHekDH6dgtLe3971TmCS5A8J5v0nEJ68wJP0OiQ7dVNI+alTYEuJwqQ5qNnY0Hzuaz4CZYYy53BjzXnD7IRm8JloiaF2xo/nYcSkfzcZOGPkktAYEcDGx1XlniMjzQDFwQspUJRkfp2Dk5uaGLcFOkjsgnPebRHzyCkPSb0Z36KaD3KqqsCXE4VId1GzsaD52NJ8B0ywiBxhjngMQkU8BzSFrSilaV+xoPnZcykezsRNGPgmNgDDGrAQ+C3yS2Gq9c4wxr6dSWCrwaQpGY2Nj2BLsJLkDwnm/ScQnrzAk/e7YoXsbcMFADyYiY0XkXhF5S0TeFJH9RWSciDwhIuuC+8JgXxGRG0RkvYi8LiILuh3nzGD/dSJy5mBNDobGSZP63imNuFQHNRs7mo8dzWfAnAfcKCIbROQD4DcEV68YqmhdsaP52HEpH83GThj5JHoVjCzgCGBa8J5DRQRjzC9TqC1p+DgFY8yYMWFLsJPkDgjn/SYRn7zCkPQ7Azgc2BU4HlhE4qPReuJXwKPGmBNEZDiQB/wvsNwYc42IXAZcBlwafO7M4LYIuAlYJCLjiF0ebiGxkRkrRORBY0z1IHQNmDEbNoTxsb3iUh3UbOxoPnY0n4FhjFkF7CUiY4LndeEqSj1aV+xoPnZcykezsRNGPomuAfEP4CygCBjd7ZYR+DgFo7o6lP8bEifJHRDO+00iPnmFIen3+8GX10LgIOC3xDoC+k2wCvtnCC7rZoxpM8bUAEcDy4LdlgHHBI+PBm4zMV4CxorIJOAw4AljTFXQ6fAEsGQgmpJB9R57hPXRPeJSHdRs7Gg+djSfgSEiRSJyA7GrYDwlIr8KptINWbSu2NF87LiUj2ZjJ4x8Eu2AKDHGHBcswPPDzltKlaUAn6ZgFBU5/ncxyR0QzvtNIj55hSHpt3PBySOBPxpj/gkMH+CxpgPlwJ9F5FUR+ZOIjAJ2McZsCfb5CNgleDwF2Njt/ZuCbb1tj0NEzhWRMhEp27ZtGy0tLTQ1NdHY2Ehrayt1dXVEIhGqq6sxxlBRUQFAeXk5ABUVFRgRqmfOJJKbS93UqbQWFNA4cSJNxcW0FBZSX1LCmA8+oGbGDKJZWVSWlsaOMW9e3H1VVRUdHR3U1tbS1tZGQ0MDzc3NNDc309DQQFtbG7W1tXR0dFAVzLfs1NF5X1lZSTQapaamhvb2durr63v0VFBQYPdkDNXV1UQiEerq6mhtbaWxsZGmpiZaWlqor6+nvb2dmpoaotEolZWVPeqpqqqiIyeH2mnTaMvPp2HyZJqLimguKqJh8mTa8vPJbm6mIyeHqlmzesylsrQ0IU99llOCnrKysvr2lKxymjGD9rw86ktKaCkspKm4mMaJE2ktKKBu6lQiubkMi0QwIlTMnRs7RnBfMXfu9rqXjHJK0FPn946B1r1+lVMv51N7Xl7X+dTJTufTrFnb616Kz6funjpHqKbifEoydxFra48ntgZaOXB3Kj7IFYrefDNsCXG49l1A87HjUj6ajZ0w8pFEpieIyE+JDed9PPWS+mbhwoWmrKws4f3/s+k/7Hfzfvzz1H9yxMwjUqjMHcrLyykuLg5bRu/ccQecfjq8/TYkoSfQeb9JxCevkFl+RWSFMWZhH/s8BGwGDgEWEFvI7GVjzF4D+LyFwEvAp4wx/xGRXxG7PNwFxpix3farNsYUBp99TbeF1JYTm5pxIJBrjPlxsP37QLMx5trePru/7XA30X3uUj5vHsWrV9t3SuPUurTWwT7y0WzsaD52fMknkba4n8dbY4yZu8O21caYecn6jIEyoLZY64odzcdOhuWj2dgJox1OdATES8B9ItIsInUiUi8iGTP/zccpGM7/w5bkERDO+00iPnmFIen3JOAx4LBgusQ44JIBHmsTsMkY85/g+b3EOjW2BlMrCO63Ba9vJrb2RCclwbbetodCn3+Y04xLdVCzsaP52NF8BszjInKyiAwLbp3t+JBF64odzceOS/loNnbCyCfRDohfAvsDecaYMcaY0cYYt1b0sNA5ysOnKRgpGoKYPJLcAeG83yTik1cYen6NMU3GmL8bY9YFz7cMdHSZMeYjYKOIzAo2LQbWErvKRueVLM4EHggePwicEVwNYz+gNpiq8RixxYULgytmHEqIX647h4W7gkt1ULOxo/nY0XwGzDnAHUBrcLsL+J9M+0GuP2hdsaP52HEpH83GThj5JLry+kZgjcnwy0noCAiH0BEQA8Ynr+Cf3wFwAXBHcAWM94AvEetcvkdEzgY+IDbqAuBhYlc0Wg80BftijKkSkR8BrwT7XWmMCe1C1frrQO9oNnY0Hzuaz4ApAE4DphtjrhSRqcCkbqPPhhxaV+xoPnZcykezsePyCIj3gKdF5LsicnHnLZXCkknnFAyf6FwkylmS3AHhvN8k4pNX8M9vfzHGrDLGLDTGfNwYc4wxptoYU2mMWWyMmWmMObizMyG4+sXXjTEzjDHzjDFl3Y5zizFm9+D25/Ac0bXAoiu4VAc1Gzuajx3NZ8DcCOwHnBI8rwd+E56c1KN1xY7mY8elfDQbO2Hkk+gIiPeD23AGvlp7aPg4BaOgoCBsCXaam2H4cBiWaB+YHef9JhGfvIJ/fhUoeO+9sCXE4VId1GzsaD52NJ8Bs8gYs0BEXgUwxlQHo86GLFpX7Gg+dlzKR7OxE0Y+Cf331/3Smxl9GU6PpmA0NDSELcFOc3PSRj9ABvhNIj55Bf/8KtAwZacrgIaKS3VQs7Gj+djRfAZMu4hkQWxIrYgUA9FwJaUWrSt2NB87LuWj2dgJI5/k/PysOMfIJP5znxKS3AHhvN8k4pNX8M+vAiMrKsKWEIdLdVCzsaP52NF8BswNwH3ABBG5CngO+Em4klKL1hU7mo8dl/LRbOyEkY92QAxR2trawpZgp7YW8vOTdjjn/SYRn7yCf34VaBvj1kWWXKqDmo0dzceO5jMwjDF3AN8Brga2AMcYY/4arqrUonXFjuZjx6V8NBs7YeST0BoQIvIpY8zzfW1T3CErKytsCXZefx3mzk3a4Zz3m0R88gr++VUgq7U1bAlxuFQHNRs7mo8dzWfgGGPeAt4KW0e60LpiR/Ox41I+mo2dMPJJdATErxPc5iQ+XgXDaerr4e23YcGCsJUoiqIoiqIoiqIoacI6AkJE9gc+CRTvcNnNMYBb3UkJ4NNVMDo6OsKW0DuvvQbGJLUDwmm/ScYnr+CfXwU6RowIW0IcLtVBzcaO5mNH81ESReuKHc3Hjkv5aDZ2wsinrykYw4H8YL/R3bbXASekSpQyeIYPd/jqUCtXxu6T2AHhtN8k45NX8M+vAsPr6sKWEIdLdVCzsaP52NF8lETRumJH87HjUj6ajZ0w8rF2QBhjngGeEZFbjTEfAIjIMCDfGONWekoczc3Nzp1wXaxcCRMnwqRJSTuk036TjE9ewT+/CjSPH89why6b5VId1GzsaD52NB8lUbSu2NF87LiUj2ZjJ4x8El0D4moRGSMio4A1wFoRuSSFupRBkp/EK0wknZUrk77+g9N+k4xPXsE/vwrkb94ctoQ4XKqDmo0dzceO5qMkitYVO5qPHZfy0WzshJFPoh0Qs4MRD8cAjwDTgS+mSlSyMca/RShra2vDltAzzc2wdi3ss09SD+us3xTgk1fwz68CtbvtFraEOFyqg5qNHc3HjuajJIrWFTuajx2X8tFs7ISRT6IdEDkikkOsA+JBY0w7ZN6lJQR/FqEcN25c2BJ65vXXoaMj6SMgnPWbAnzyCv75VWDc22+HLSEOl+qgZmNH87Gj+SiJonXFjuZjx6V8NBs7YeSTaAfE74ENwCjg3yLyMWILUSqOUl5eHraEnknBApTgsN8U4JNX8M+vAuXz5oUtIQ6X6qBmY0fzsaP5KImidcWO5mPHpXw0Gzth5NPXVTAAMMbcANzQbdMHInJQaiQpyaC4uDhsCT2zciUUFcGuuyb1sM76TQE+eQX//CpQvHp12BLicKkOajZ2NB87mo+SKFpX7Gg+dlzKR7OxE0Y+CY2AEJFdRORmEXkkeD4bODOlypKIybzZIoPGtd6+LjoXoJTkTodx1m8K8Mkr+OdX0V8HbGg2djQfO5qPkihaV+xoPnZcykezsRNGPolOwbgVeAyYHDx/B/hGIm8UkSUi8raIrBeRyyz7HS8iRkQWJqip30iS/+l1Gdd6+wBoa4PVq5M+/QIc9ZsifPIK/vlV9NcBG5qNHc3HjuajJIrWFTuajx2X8tFs7Dg7AgIYb4y5B4gCGGMiQEdfbxKRLOBG4HBgNnBKMHpix/1GAxcB/0lQj9IHlZWVYUvYmTVroL09JR0QTvpNET55Bf/8KlBZWhq2hDhcqoOajR3Nx47moySK1hU7mo8dl/LRbOyEkU+iHRCNIlJEcOULEdkPSOSaHfsC640x7xlj2oC7gKN72O9HwE+BlgT1KH1QWFgYtoSd6VyAMsmX4ARH/aYIn7yCf34VKHznnbAlxOFSHdRs7Gg+djQfJVG0rtjRfOy4lI9mYyeMfBLtgLgYeBCYISLPA7cBFyTwvinAxm7PNwXbuhCRBcCuxph/JqhFSYC6OgcvUrJyJRQUQAquf+uk3xThk1fwz68CddOmhS0hDpfqoGZjR/Oxo/koiaJ1xY7mY8elfDQbO2Hkk1AHhDFmJfBZ4JPA/wBzjDGvD/bDRWQY8EvgWwnse66IlIlI2bZt22hpaaGpqYnGxkZaW1upq6sjEolQXV2NMYaKigogtrCGMabTB9XV1UQiEerq6mhtbaWxsZGmpiZaWlqor6+nvb2dmpoaotFo15CUzsU5Ou+rqqro6OigtraWtrY2GhoaaG5uprm5mYaGBtra2qitraWjo4Oqqqoej1FZWUk0GqWmpob29nbq6+v75QmgoqKiV0/Dhg1zzlN0xQoiH/84kY6OAXmyldOoUaMyspwGUvdyc3OHnCdbOeXk5GSMJyU5jNqyJWwJcYwaNSpsCV1oNnY0Hzuaj5IoWlfsaD52XMpHs7ETRj7S+c+5dSeRXOBrwAHEpmE8C/zOGGOdMiEi+wNXGGMOC55/F8AYc3XwvAB4F2gI3jIRqAKWGmPKejvuwoULTVlZry/vxDMbnuHAZQfy5BlPctB0P64eWl9fz+jRo8OWsZ1IBEaPhq99DX7xi6Qf3jm/KcQnr5BZfkVkhTEmZQvpukR/2+EuElgMuL6khNGbNtl3SuBvV7JIax3sIx/Nxo7mY8eXfLQt7gOtK3Y0HzsZlo9mYyeMdjjRKRi3AXOAXwO/CR7fnsD7XgFmish0ERkOnExsKgcAxphaY8x4Y8w0Y8w04CX66HxQEiMnJydsCfG89Ra0tKRkAUpw0G8K8ckr+OdXgZzGxrAlxOFSHdRs7Gg+djQfJVG0rtjRfOy4lI9mYyeMfLIT3G+uMab71SueEpG1fb3JGBMRkfOJXcIzC7jFGPOGiFwJlBljHrQfQRko0Wg0bAnxrFgRu09RB4RzflOIT17BP78KRLMT/dOUHlyqg5qNHc3HjuajJIrWFTuajx2X8tFs7ISRT6IJrBSR/YwxLwGIyCIgoVEKxpiHgYd32PaDXvY9MEE9/cKQvmEsrpDI1Jq0snIljBoFe+yRksM75zeF+OQV/POrgMnKCltCHC7VQc3GjuZjR/NREkXrih3Nx45L+Wg2dsLIx9oBISKria35kAO8ICL/DZ5/DHgr9fKSiyQwL2eokO1Y7xorV8L8+ZCik845vynEJ6/gn18Fspubw5YQh0t1ULOxo/nY0XyURNG6YkfzseNSPpqNnTDy6WsNiM8DRwFLgOnEroRxYPD48JQqUwZFa2tr2BK2E43Cq6+mbPoFOOY3xfjkFfzzq0BrQUHYEuJwqQ5qNnY0Hzuaj5IoWlfsaD52XMpHs7ETRj7WLg9jzAfpEqIkl7y8vLAlbGfdOmhsTGkHhFN+U4xPXsE/vwrkbdsWtoQ4XKqDmo0dzceO5qMkitYVO5qPHZfy0WzshJFPolfBUDKM+vr6sCVsZ+XK2H0KOyCc8ptifPIK/vlVoH7XXcOWEIdLdVCzsaP52NF8lETRumJH87HjUj6ajZ0w8vGiA8K1xUfSwdixY8OWsJ0VK2DECCgtTdlHOOU3xfjkFfzzq8DY9evDlhCHS3VQs7Gj+djRfJRE0bpiR/Ox41I+mo2dMPLxogOiE8GfRSgrKyvDlrCdlSvh4x+HFF5n1im/KcYnr+CfXwUq58wJW0IcLtVBzcaO5mNH81ESReuKHc3Hjkv5aDZ2wsjHqw4Inxg/fnzYEmIYE+uASOH0C3DIbxrwySv451eB8WvWhC0hDpfqoGZjR/Oxo/koiaJ1xY7mY8elfDQbO2Hkox0QQ5Ty8vKwJcR4/32orYV99knpxzjjNw345BX886tA+dy5YUuIw6U6qNnY0XzsaD5KomhdsaP52HEpH83GThj5eNEBYfBvDYji4uKwJcRIwwKU4JDfNOCTV/DPrwLFjv064FId1GzsaD52NB8lUbSu2NF87LiUj2ZjJ4x8vOiA6ETEnzUgKioqwpYQY+VKyM6GFPf2OeM3DfjkFfzzq0CFY78OuFQHNRs7mo8dzSe9iMgtIrJNRNZ02zZORJ4QkXXBfWGwXUTkBhFZLyKvi0hqf7npA60rdjQfOy7lo9nYCSMfrzogfKKoqChsCTFWrIh1PowYkdKPccZvGvDJK/jnV4GiN94IW0IcLtVBzcaO5mNH80k7twJLdth2GbDcGDMTWB48BzgcmBnczgVuSpPGHtG6YkfzseNSPpqNnTDy0Q6IIUpNTU3YEtK2ACU44jdN+OQV/POrQM3uu4ctIQ6X6qBmY0fzsaP5pBdjzL+Bqh02Hw0sCx4vA47ptv02E+MlYKyITEqL0B7QumJH87HjUj6ajZ0w8tEOiCHK6NGjw5YAmzZBRUVaOiCc8JsmfPIK/vlVYPTGjWFLiMOlOqjZ2NF87Gg+TrCLMWZL8PgjYJfg8RSgewFtCraFgtYVO5qPHZfy0WzshJGPFx0Qxvi3CGVTU1PYEtK2ACU44jdN+OQV/POrQNOECWFLiMOlOqjZ2NF87Gg+bmFiX1D7/SVVRM4VkTIRKdu2bRstLS00NTXR2NhIa2srdXV1RCIRqqurMcZ0zfHuXO2+Yu5cjAjVM2cSyc2lbupUWgsKaJw4kabiYloKCymfN4/2vDxqZswgmpVFZWlp7Bjz5sXdV1VV0dHRQW1tLW1tbTQ0NNDc3ExzczMNDQ20tbVRW1tLR0cHVVVVcTo67ysrK4lGo9TU1NDe3k59ff1OnrZt22b3VFGBMYbq6moikQh1dXW0trbS2NhIU1MTLS0t1NfX097eTk1NDdFolMrKyh71VM2aRUdODrXTptGWn0/D5Mk0FxXRXFREw+TJtOXns23+fDpycqiaNavHXCpLS/v01Gc59cPT1q1b7Z6SWU4zZtCel0d9SQkthYU0FRfTOHEirQUF1E2dSiQ3l48WLsSIdK130Hnlh7i6N9hyStBTfX39oOpev8qpl/OpvqSk63xqmDix9/Ops+6l+Hzq7qmxsTEl55MNycR/zhcuXGjKysoS3n/5e8s5+PaD+fdZ/+bTH/t0CpW5Q2trKyNSvO5Cn1x+Ofz4x1BfD3l5Kf0oJ/ymCZ+8Qmb5FZEVxpiFYetIB/1th7tIYDHg1oICRtTW2ndK49+utNbBPvLRbOxoPnZ8yceltlhEpgEPGWPmBs/fBg40xmwJplg8bYyZJSK/Dx7fueN+tuMPqC3WumJH87GTYfloNnbCaIe9GAHhI5FIJGwJsREQpaUp73wAR/ymCZ+8gn9+FYiMHBm2hDhcqoOajR3Nx47m4wQPAmcGj88EHui2/Yzgahj7AbV9dT6kEq0rdjQfOy7lo9nYCSOf7LR/opIWnLjk6IoVcPDBafkoJ/ymCZ+8gn9+FZCOjrAlxOFSHdRs7Gg+djSf9CIidwIHAuNFZBNwOXANcI+InA18AJwU7P4wcASwHmgCvpR2wd3QumJH87HjUj6ajZ0w8tEOiCHKsGEhD27ZsiV2S8P6D+CA3zTik1fwz68Cwxz7tcKlOqjZ2NF87Gg+6cUYc0ovLy3uYV8DfD21ihJH64odzceOS/loNnbCyMetEkkRpv/r+2Q87e3t4Qp49dXYfZo6IEL3m0Z88gr++R0IIpIlIq+KyEPB8+ki8h8RWS8id4vI8GD7iOD5+uD1ad2O8d1g+9siclhIVgBoHzUqzI/fCZfqoGZjR/Oxo/koiaJ1xY7mY8elfDQbO2Hk40UHRCeuDcFJJbm5ueEK6LwCxvz5afm40P2mEZ+8gn9+B8hFwJvdnv8UuM4YsztQDZwdbD8bqA62Xxfsh4jMBk4G5gBLgN+KSFaatO9EbrCysyu4VAc1Gzuajx3NR0kUrSt2NB87LuWj2dgJIx+vOiB8orGxMVwBK1fCzJkwZkxaPi50v2nEJ6/gn9/+IiIlwJHAn4LnAnwOuDfYZRlwTPD46OA5weuLg/2PBu4yxrQaY94nNgd537QY6IHGSZPC+ugecakOajZ2NB87mo+SKFpX7Gg+dlzKR7OxE0Y+2gExRBmTpn/8e2XlSthnn7R9XOh+04hPXsE/vwPgeuA7QDR4XgTUGGM6JxluAqYEj6cAGwGC12uD/bu29/CeLgZ97fmKij6vPV9fUsLIbducufZ8XV0deXl56bn2fFVVn9eeN8Y4de35aDTq1LXnO7Kznbn2fEdHR9fq4im/9nwk0ue156NZWbQHv3T1eu35adPSdu15YwxtbW0pO5+UwTFmw4awJcTh2ncBzceOS/loNnbCyEdMGq8zmiz6e83jJ959gkP/cijPfulZDph6QAqVuUNlZSVFRUVhfTiMHw8/+xlcckmaPjJEv2nGJ6+QWX7Tfe15Efk8cIQx5msiciDwbeAs4KVgmgUisivwiDFmroisAZYYYzYFr70LLAKuCN7zl2D7zcF77qUXBnTt+djB+9ylsrSUojfftO+Uxr9daa2DfeSj2djRfOz4kk+62+IwGVBbrHXFjuZjJ8Py0WzshNEOe3UVDMGfNSBC/Yetc/2HNC1ACSH7TTM+eQX//PaTTwFLReQIIBcYA/wKGCsi2cEohxJgc7D/ZmBXYJOIZAMFQGW37Z10f0/a6fMPc5pxqQ5qNnY0Hzuaj5IoWlfsaD52XMpHs7ETRj46BWOIEuoQxM4OiL33TttH+jTk0iev4J/f/mCM+a4xpsQYM43YIpJPGmNOA54CTgh2OxN4IHj8YPCc4PUng0u/PQicHFwlYzowE3g5TTZ2onNYuCu4VAc1Gzuajx3NR0kUrSt2NB87LuWj2dgJIx+vRkD4RHFxcXgfvnIlTJsG48al7SND9ZtmfPIK/vlNEpcCd4nIj4FXgZuD7TcDt4vIeqCKWKcFxpg3ROQeYC0QAb5ujOlIv+wYxatXh/XRPeJSHdRs7Gg+djQfJVG0rtjRfOy4lI9mYyeMfHQExBAl9BEQaZx+Ae71bqYSn7yCf34HijHmaWPM54PH7xlj9jXG7G6MOdEY0xpsbwme7x68/l63919ljJlhjJlljHkkLB+gvw7Y0GzsaD52NB8lUbSu2NF87LiUj2ZjJ4x8vOiAMGTeQpuDJbTevtpaWL8+7R0QrvVuphKfvIJ/fhX9dcCGZmNH87Gj+SiJonXFjuZjx6V8NBs7OgIixUgCK5MOFTovk5V2Xn01dp/GS3BCiH5DwCev4J9fha5LTLqCS3VQs7Gj+djRfJRE0bpiR/Ox41I+mo2dMPLxqgPCJwoKCsL54BAWoIQQ/YaAT17BP78KFLz3Xt87pRGX6qBmY0fzsaP5KImidcWO5mPHpXw0Gzth5KMdEEOUhoaGcD545UqYMgV22SWtHxua3xDwySv451eBhilTwpYQh0t1ULOxo/nY0XyURNG6YkfzseNSPpqNnTDy8aIDInaVOb8YOXJkOB8cwgKUEKLfEPDJK/jnV4GRFRVhS4jDpTqo2djRfOxoPkqiaF2xo/nYcSkfzcZOGPl40QHRieDPGhBtbW3p/9DGRnjrrVA6IELxGxI+eQX//CrQNmZM2BLicKkOajZ2NB87mo+SKFpX7Gg+dlzKR7OxE0Y+XnVA+ERWVlb6P/S118CYUDogQvEbEj55Bf/8KpDV2hq2hDhcqoOajR3Nx47moySK1hU7mo8dl/LRbOyEkY92QCjJY8WK2H2ar4ChKIqiKIqiKIqiuI92QAxROjo60v+hK1fChAkweXLaPzoUvyHhk1fwz68CHSNGhC0hDpfqoGZjR/Oxo/koiaJ1xY7mY8elfDQbO2Hk40UHhMG/RSiHDx+e/g/tXIBS0r/WRih+Q8Inr+CfXwWG19WFLSEOl+qgZmNH87Gj+SiJonXFjuZjx6V8NBs7YeTjRQdEJxLCP8Zh0dzcnN4PbGmBN94IZf0HCMFviPjkFfzzq0Dz+PFhS4jDpTqo2djRfOxoPkqiaF2xo/nYcSkfzcZOGPl41QHhE/n5+en9wNWroaMjtA6ItPsNEZ+8gn9+FcjfvDlsCXG4VAc1Gzuajx3NR0kUrSt2NB87LuWj2dgJIx/tgBii1NbWpvcDV66M3YfUAZF2vyHik1fwz68CtbvtFraEOFyqg5qNHc3HjuajJIrWFTuajx2X8tFs7ISRj3ZADFHGjRuX3g9csQIKC2HatPR+bkDa/YaIT17BP78KjHv77bAlxOFSHdRs7Gg+djQfJVG0rtjRfOy4lI9mYyeMfLzogDDGv0Uoy8vL0/uBIS5ACSH4DRGfvIJ/fhUonzcvbAlxuFQHNRs7mo8dzUdJFK0rdjQfOy7lo9nYCSMfLzogOhH8WYSyuLg4fR/W1hZbAyKk6ReQZr8h45NX8M+vAsWrV4ctIQ6X6qBmY0fzsaP5KImidcWO5mPHpXw0Gzth5ONVB4RPpLU3a+3aWCdEiB0QrvVuphKfvIJ/fhX9dcCGZmNH87Gj+SiJonXFjuZjx6V8NBs7OgJCSRpp7c0KeQFKcK93M5X45BX886vorwM2NBs7mo8dzUdJFK0rdjQfOy7lo9nY0REQKcLg3xoQlZWV6fuwF16A0aNh993T95k7kFa/IeOTV/DPrwKVpaVhS4jDpTqo2djRfOxoPkqiaF2xo/nYcSkfzcZOGPl40QHRiYS0QGIYFBYWpueD2trg73+Ho46CYeFVp7T5dQCfvIJ/fhUofOedsCXE4VId1GzsaD52NB8lUbSu2NF87LiUj2ZjJ4x8vOqA8Im6urr0fNCjj0J1NZx2Wno+rxfS5tcBfPIK/vlVoC6ky/n2hkt1ULOxo/nY0XyURNG6YkfzseNSPpqNnTDy0Q6IIcqoUaPS80F33AHjx8Mhh6Tn83ohbX4dwCev4J9fBUZt2RK2hDhcqoOajR3Nx47moySK1hU7mo8dl/LRbLZTU1PDCSecwJ577klpaSkvvvgira2tHHLIIcycOZNDDjmE6urqlOvQDoghSktLS+o/pK4OHnwQTjoJcnJS/3kW0uLXEXzyCv75VaBl3LiwJcThUh3UbOxoPnY0HyVRtK7Y0XzsuJSPZrOdiy66iCVLlvDWW2/x2muvUVpaytVXX83ixYtZt24dixcv5pprrkm5Di86IIzxbxHKnHR0CNx3H7S0hD79AtLk1xF88gr++VUgp7ExbAlxuFQHNRs7mo8dzUdJFK0rdjQfOy7lo9nEqK2t5d///jdnn302AMOHD2fs2LE8/PDDnHnmmQCceeaZ3H///SnX4kUHRCeCP4tQRqPR1H/IHXfA9Omw//6p/6w+SItfR/DJK/jnV4FodnbYEuJwqQ5qNnY0Hzuaj5IoWlfsaD52XMpHs4nx/vvvU1xczJe+9CX23ntvvvKVr9DY2Mi2bduYNGkSABMnTmTr1q0p1+JVB4RPpHzUx0cfwfLlcOqp4MDVRXwa5eKTV/DPrwImKytsCXG4VAc1Gzuajx3NR0kUrSt2NB87LuWj2cSIRCKsXLmS8847j1dffZVRo0btNN1CRNJy1UjtgBiiZKe6d+3uuyEadWL6BaTBr0P45BX886tAdnNz2BLicKkOajZ2NB87mo+SKFpX7Gg+dlzKR7OJUVJSQklJCYsWLQLghBNOYOXKlUyYMIEtwcKYW7ZsYcKECSnX4kUHhMGtnq900NramtoPuOMO2HtvKC1N7eckSMr9OoRPXsE/vwq0FhSELSEOl+qgZmNH87Gj+SiJonXFjuZjx6V8NJsYEydOZNddd+Xtt98GYPny5cyePZslS5awbNkyAJYtW8bRRx+dci1udQmlmHQMKXGFvLy81B183Tp45RW49trUfUY/Salfx/DJK/jnV4G8bdvClhCHS3VQs7Gj+djRfJRE0bpiR/Ox41I+ms12fv3rX3PaaafR1tbGbrvtxp///Gfa2to49dRTufnmm/nYxz7GPffck3IdXoyA8JH6+vrUHfyOO2LrPpx8cuo+o5+k1K9j+OQV/POrQP2uu4YtIQ6X6qBmY0fzsaP5KImidcWO5mPHpXw0m+3Mnz+fsrIyXn/9de6//34KCwsZPnw4y5cvZ926dfzrX/9iXBouE5ryERAisgT4FZAF/MkYc80Or18MfAWIAOXAl40xH6Ra11Bn7NixqTmwMbEOiAMPhClTUvMZAyBlfh3EJ6/gn18Fxq5fH7aEOFyqg5qNHc3HjuajJIrWFTuajx2X8vE5G/lh36P/BelzuQJzeXKXM0jpCAgRyQJuBA4HZgOniMjsHXZ7FVhojPk4cC/ws1Rq8oXKysrUHPiVV2D9emcWn+wkZX4dxCev4J9fBSrnzAlbQhwu1UHNxo7mY0fzURJF64odzceOS/loNnbm5KdfT6pHQOwLrDfGvAcgIncBRwNrO3cwxjzVbf+XgNOTLcK1y6+kg/Hjx6fmwP/3fzB8OBx/fGqOP0BS5tdBfPIK/vlVYPyaNWFLiMOlOqjZ2NF87Gg+SqJoXbETZj7Tpk1j9OjRZGVlkZ2dTVlZGcOGDeOQQw5hw4YNTJs2jXvuuYfCwsLQNLqWT1VVFV/4whecyMe1c2tNQ/r1pHoNiCnAxm7PNwXbeuNs4JGeXhCRc0WkTETKtm3bRktLC01NTTQ2NtLa2kpdXR2RSITq6mqMMVRUVABQXl6+/SAGqquriUQi1NXV0draSmNjI01NTbS0tFBfX097ezs1NTVEo9GuHrPOY3TeV1VV0dHRQW1tLW1tbTQ0NNDc3ExzczMNDQ20tbVRW1tLR0cHVVVVPR6jsrKSaDRKTU0N7e3t1NfX99tTRUUFxpgePW3cuDH5niorMXfdRduhh8LYsWn3ZCun8vLyjCyngdS9rVu3DjlPtnL68MMPM8aTkhzK584NW0IcLpWvZmNH87Gj+SiJonXFTtj5PPXUU6xatYqysjIAfvCDH7B48WLWrVvH4sWLueaaa/o4QmpxKZ/y8nKuueYaZ/IJO5sdmZuffj2SytEBInICsMQY85Xg+ReBRcaY83vY93TgfOCzxhjr9VIWLlxoOk+4RPjH2/9g6V1LKTunjH0m79MvD0o3Hn8cDjsM/vY3OO64sNUoinOIyApjzMKwdaSD/rbDXSTrakRDdWRbMvLRbOxoPnaGQD7aFveB1hU7juczbdo0ysrK4kaFzJo1i6effppJkyaxZcsWDjzwwK7LLSYdzad3HMsmkTUgEmEga0DY2uFUj4DYDHRf6rMk2BaHiBwM/D9gaV+dD0pidP66m1TuuAMKCuCII5J/7EGSEr+O4pNX8M+vAhWO/TrgUh3UbOxoPnY0HyVRtK7YCTMfEeHQQw9ln3324Q9/+AMAH330EZMmTQJg4sSJbN26NTR94FY+FRUVbN261Zl8XDu3whgBkeo1IF4BZorIdGIdDycDp3bfQUT2Bn5PbKRESi6M2tfKnkORoqKi5B6wqQn+/nc46STIzU3usZNA0v06jE9ewT+/ChS98UbYEuJwqQ5qNnY0Hzuaj5IoWlfshJnPc889x5QpU9i2bRuHHHIIe+65J9Ltl3cRiXseBi7lM2vWrLjXw87HtXPrjYb060npCAhjTITYtIrHgDeBe4wxb4jIlSKyNNjt50A+8FcRWSUiD6ZKT9gnYzqpqalJ7gH/8Q9oaHDu6hedJN2vw/jkFfzzq0DN7ruHLSEOl+qgZmNH87Gj+SiJonXFTpj5TJkSW05vwoQJHHvssbz88ssUFxezZcsWALZs2cKECRNC0wdu5fPMM8+wyy67OJOPa+fW7nnp15PqKRgYYx42xuxhjJlhjLkq2PYDY8yDweODjTG7GGPmB7el9iMqiTB69OjkHvD//g8mT4bPfja5x00SSffrMD55Bf/8KjB648a+d0ojLtVBzcaO5mNH83EHEdkgIquDH9/Kgm3jROQJEVkX3Id2GQOtK3bCyqexsZH6+vqux48//jhz585l6dKlLFu2DIBly5Zx9NFHh6KvE5fy2WeffZzKx7Vza2NL+vWkvANCCYempqbkHayqCh55BE45BbKyknfcJJJUv47jk1fwz68CTSH/crMjLtVBzcaO5mNH83GOg4If3zoXarsMWG6MmQksD56HgtYVO2Hls3XrVg444AD22msv9t13X4488kiWLFnCBRdcwBNPPMHMmTP517/+xWWXhVZ1ALfy+fSnP81ll13mTD6unVsThqdfT6rXgFBCYsSIEck72F//Cu3tzk6/gCT7dRyfvIJ/fhUYUVsbtoQ4XKqDmo0dzceO5uM8RwMHBo+XAU8Dl4YhROuKnbDy2W233Xjttdd22j5p0iSWL18egqKecSmf1tZWxowZ40w+rp1btZH06/GiAyKVlxp1lUgkkrzG+o47YM89Yf785BwvBSTVr+P45BX886tAZORIp/5Au1QHNRs7mo8dzccpDPC4iBjg9/+/vTOPj6q89//7mwVCSAgkgCyhBkQwkEBYBNxRq1KxCCKCFZWrwv1J1drt6rVUqa2t14u9yNXWpbWuFy2yiNbaqkjdF8DIDlWMZYlAQkjISpbn98echASSk0kyM+dJzvf9es1rzpw5c87n+znf+c7MM8/zHGPM48BJxphc5/lvgJMae6GIzAPmAQwYMIDy8nJqamowxhATE0NFRQXx8fEcOXKE7t27k5+fT8+ePTl48CC9evUiLyODlC1bODx4MIm7d1PauzedCwup6tIFqa4mqqqKopNPpsfOnZT07Uu3nBwKhgwhZds2DmZm0mvTpsA9cOjQIZKSkiguLqZLly4cPXqUaKe3bHV1NZ06daKsrIyEhAQKCwtJTk6u01F7n5+fT48ePSgqKqJr166Ul5cTGxvbIKbDhw+TkpLSdEx5eaSkpHD48GESExMpLS2lc+fOVFVVISJERUVRWVlJXFwcJSUldOvWjYKCAlJSUk7Qc2joUJJ27aK4f3+65OVxtFs3oisCF+mr7tyZTkVFFAweTK+NGykcNIjkHTsa+rJpE/np6fSoqXGNqdnz5MQ0dNFQdpfvpnen3hRWFdIlqgvVppoqU0XX6K4cqjxERkIG64vWM6TrELaVbCMzIZNNxZvq7ofGD2XLj7aE5jydcgpdc3MpT04mtqSEmpgYTHQ0MWVlVCQlEX/gAPnp6fT9+GPyhw+n5+bNHMzIoNfmzQ1zr6qqbefp0CH6PNyH/p37k1eZR7eYblTUBM5T56jOFFUV0TO2Z51PO0p3nOBLetd0dpbsJO8HeW0+T4cPHyYxLq7R91Nl167EHTpESd++RJeVUdyv34nvp02bjuVeYWFI3k/xUfEkxyZTUl1CjMQQLdGU1ZSRFJPEgaMHGBA3gOKqYgYkDGBz8WYyEjLq7rcUb2Fw/GB2l++mqKioxefJDWmPP85bes3jl7e/zNQXp7Jh3gZG9R0VRmX2UFpaSnx8fNt39PXXkJYGv/wlLFjQ9v2FiZDF2w7wU6zQvuKN9LXnRWQA8AyBL6kGeNwY85CIJAMvAmlADnCVMaZAAjPxPgRcCpQCc4wxG5x9XQ/Uvsl/ZYx52u3Yrbr2fOBAzW5S2qsX8QcPum8Uwc+uiOZgM/6oN+6oP+74xZ9I1+LWICL9jTF7RaQ38AZwK7DaGNO93jYFxhjXeSBaVYs1V9yxzB/5RfN6esX24mClux5zT4jOl0X+qDfNyPHQH7c6rHNAdFCiokJ0apcuDdx/73vu23lMyOJtB/gpVvBfvC2kCvixMWYYMAH4vogMo+lxxN8BTnVu84DfQ2DiM+AeYDwwDrjHy8nPoqqqvDp0o9iUg+qNO+qPO+qPPRhj9jr3B4CVBGrvfhHpC+Dch+Xy9MGgueKObf5UGbv02OSPeuOOF/7Y9W5WQkZlZWVodvR//wdnnAGDBoVmf2EiZPG2A/wUK/gv3pZgjMmt7cFgjDlC4HLH/QmMI67twfA0MNVZvhx4xgT4COjufMm9BHjDGHPIGFNA4N+4SZGLpCGVXbt6dehGsSkH1Rt31B931B87EJGuIpJYuwxcDGwGVgPXO5tdD7zsjULNleawzZ+u0Xbpsckf9cYdL/zxxRwQfiQuLq7tO9m0KXB7+OG27yvMhCTedoKfYgX/xdtaRCQNGAV8TNPjiPsD9a+3tMdZ19R6T4g7dMirQzeKTTmo3rij/rij/ljDScDKwKg4YoD/M8a8LiKfAn8WkRuBr4GrvBKoueKObf4cqrRLj03+qDfueOGPL3pAGNrfPBdtpaSkpO07ef75wGU3r/Ls8y9oQhJvO8FPsYL/4m0NIpIALAduN8YU1X/OBCb6CUkRFJF5IrJORNYdOHCA8vJySktLKSkpoaKigqKiIqqqqigoKMAYQ15eHgAHnbGOeXl5GBEKTj2Vqrg4ir71LSqSkijp04fSXr0o79GDI6mpFA4cyOFTTqEmOpr89PTAPjIzG9wfOnSI6upqCgsLOXr0KMXFxZSVlVFWVkZxcTFHjx6lsLCQ6upqDjkf9rU6au/z8/Opqanh8OHDVFZWcuTIkUZjKioqco/JGAoKCqiqqqKoqIiKigpKSkooLS2lvLycI0eOUFlZyeHDh6mpqSE/P79RPYcOHaI6NpbCtDSOJiRQ3K8fZSkplKWkUNyvH0cTEtg/ejTVsbEcGjq0UV/y09ODiqnZ8xRkTN98803zMYXqPJ1yCpXx8RxJTaW8Rw9Ke/WipE8fKpKSKPrWt6iKiyN33DiMCHkZGYF9OPd5GRnHci8U5ynImPbt29em3GvReWri/VQZH1/3ftp35pmNv5+GDj2We2F+P9WPae/evWF7P9mMMWaXMWakcxtujLnPWZ9vjLnQGHOqMebbxhjPfqmU9O3r1aEbxbbvArb507ezXXps8ke9cccLf3wxCeWq7auY9uI0Pvv3z8jqkxU+YRZRU1PTtvFyNTWBySczMuC110KmK1y0Od52hJ9ihfYVrxcTn4lILPAq8DdjzG+ddTuAicaYXGeIxVpjzFARecxZXlp/u9qbMebfnfUNtmuMcE5CWRMdTVR1tftGEfzsimgONuOPeuOO+uOOX/xpD5NQhopwTULpl1xpFMv8CWYiwWiiqcZdTyQnWoyUP+pNM3I89EcnofQhBQUFbdvBe+/B7t1wzTWhERRm2hxvO8JPsYL/4m0JzlUt/ghsq218cGhqHPFq4DoJMAEodIZq/A24WER6OJNPXuys84SCIUO8OnSj2JSD6o076o876o8SLJor7tjmz5CudumxyR/1xh0v/NE5IDooKSkpbdvB889DfDxcfnloBIWZNsfbjvBTrOC/eFvIWcC1wCYRyXbW3QXcT+PjiF8jcAnOLwhchvPfAIwxh0Tkl8Cnznb3etn1N2XbNq8O3Sg25aB644764476owSL5oo7tvmzrcQuPTb5o96444U/vugB0R6HmbSVNo2BPHoUli2DqVMhISFkmsJJexjzGSr8FCv4L96WYIx5zxgjxpgRxpgs5/ZaU+OInatffN8Yc4oxJtMYs67evp40xgx2bn/yLqpj49JtwaYcVG/cUX/cUX+UYNFcccc2fzIT7NJjkz/qjTte+OOLBohahObHwXQUevXq1foXv/46FBS0m+EX0MZ42xl+ihX8F68CvTZt8lpCA2zKQfXGHfXHHfVHCRbNFXds82dTsV16bPJHvXHHC3981QDhJ9rUUvz889CzJ1x0UegEhRnbWsbDiZ9iBf/Fq9j374BNOajeuKP+uKP+KMGiueKObf7ov/xNo964oz0glJDR6pbioiJYvTpw6c3Y2NCKCiO2tYyHEz/FCv6LV7Hv3wGbclC9cUf9cUf9UYJFc8Ud2/zRf/mbRr1xR3tAKCGj9jrdLaKoCH7+cygvb1fDL6CV8bZT/BQr+C9eBQ4NHeq1hAbYlIPqjTvqjzvqjxIsmivu2ObP0Hi79Njkj3rjjhf++OIqGAb/TUKZlJQU/MaHD8P//i/8z/8E5n6YORPOOCNs2sJBi+Jt5/gpVvBfvAok7drltYQG2JSD6o076o876o8SLJor7tjmz64yu/TY5I96444X/viqB4SIfyahLC4ubn6jggK45x5IS4O774ZzzoFPP4UXXoB25lVQ8XYQ/BQr+C9eBYr79/daQgNsykH1xh31xx31RwkWzRV3bPOnf2e79Njkj3rjjhf++KIHhB/p0qVL00/m5wd6OyxZAkeOwBVXwIIFMGpU5ASGGNd4Oxh+ihX8F68CXfLyvJbQAJtyUL1xR/1xR/1RgkVzxR3b/MmrtEuPTf6oN+544Y+vekD4iaNHj5648sABuPNOOPlk+PWv4TvfgY0bYfnydt34AE3E20HxU6zgv3gVONqtm9cSGmBTDqo37qg/7qg/SrBorrhjmz/dYuzSY5M/6o07Xvjjix4QxvhvDojo6OhjD775BhYtgt//PjDB5KxZ8LOfwbBh3gkMMQ3itZTKykr27NlDeXl5m/ZjjPHVcCIb442LiyM1NZXYdnSlmPZEdEWF1xIaYFN9UW/cUX/cUX+UYNFcccc2fypq7NJjkz/qjTte+OOLBohaBLt+xISdffvggQfgscfg6FGYPRvuugssm33VL+zZs4fExETS0tLa9IO6pqaGqCj/dF6yLV5jDPn5+ezZs4eBAwd6LUdRFEVROhS7d+/muuuuY//+/YgI8+bNY968eSxcuJAnnnii7pKcv/71r7n00ks9VqsoSkvxVQOEb9i9m+hf/QqefhqqquC66wIND4MHe60sbFRXV3stoVnKy8vb3PgA/uvRY1u8IkJKSgoHDx70WkqHpbpzZ68lNMCm+qLeuKP+uKP+KMHiZa7ExMTw4IMPMnr0aI4cOcKYMWM466yzAPjhD3/IT37yE8+01WLbe6lzlF16bPJHvXHHC3+0AaIjkZMD998PTz5JLMCcOfCf/wk++Je2U6dOXksIilAMJbBtOEK4sTFeGzV1JDoVFXktoQE21Rf1xh31xx31RwkWL3Olb9++9O3bF4DExETS09M5cOCAZ3oaw7b3UlGVXXps8ke9cccLf+zp16y0nl274Kab4NRT4U9/gptu4sj69fD4475ofAAoKyvzWkLEqKmpafVro6OjycrKIiMjg+9+97scPnw4dMIcJk6cyLp161r9+lWrVrF169a6x8HGu3r1au6//37Xbfbt28eVV17Zam1KZCjr2dNrCQ2wqb6oN+6oP+6oP0qw2JIrOTk5fPbZZ2RmZgLw8MMPM2LECG644QYKCgo802WLP7X0jLVLj03+qDfueOGPLxogDHZ14W4z5eWwYUNgiMV118GQIfDcc3DzzfDll/C739G1A00wGQwJCQleS4gYbZmIqUuXLmRnZ7N582aSk5N55JFHQqisdRzfBff4Boj68VZVVTW5nylTpnDnnXe6Hqtfv3689NJLrVSqRIqEvXu9ltAAm+qLeuOO+uOO+qMEiw25UlxczPTp01m8eDH9+vXj5ptv5ssvvyQ7O5u+ffvy4x//2DNtNvhTn70VdumxyR/1xh0v/PFFA0Qt7a7btDHw9dfwyitw332Bq1cMGwYJCTBmTGCIxYoVcNtt8NVXsGQJpKYCUFhY6K32COOneEM1ZvaMM85gr1MEv/zySyZNmsSYMWM455xz2L59e936CRMmkJmZyYIFC+q+LK5du5bLLrusbl+33HILTz311AnHuPnmmxk7dizDhw/nnnvuqVuflpbGHXfcwejRo1m2bFnd+g8++IDVq1fz05/+lKysLL788ksmTpzI7bffztixY3nooYd45ZVXGD9+PKNGjeLb3/42+/fvB+Cpp57illtuAWDOnDncdtttnHnmmQwaNKiu0SEnJ4eMjIy67a+44gomTZrEqaeeyn/8x3/U6fjjH//IkCFDGDduHHPnzq3brxIZCgcN8lpCA2yqL+qNO+qPO+qPEixe50plZSXTp0/nmmuu4YorrqCwsJCTTjqJ6OhooqKimDt3Lp988oln+rz253gGdbFLj03+qDfueOGPzgFhC0VFsHkzbNwYuG3aFLivP05o4EAYMQKuvBIyMwPLgwdDI/+IJycnR1C897S7eG+/HbKzW/XSJt+0WVmweHFQ+6iuruatt97ixhtvBGDevHk8+uijnHrqqXz88cfMnz+fNWvW8IMf/IAf/OAHXH311Tz66KMt1nrfffeRnJxMdXU1F154IRs3bmTEiBEApKSksGHDhgbbn3nmmUyZMoXLLrusbqiEiHD06NG6YR0FBQV89NFHiAh/+MMfeOCBB3jwwQdPOHZubi7vvfce27dvZ8qUKY0OvcjOzuazzz6jc+fODB06lFtvvZXo6Gh++ctfsmHDBhITE7ngggsYOXJki2NXWk/yjh1eS2iATfVFvXFH/XFH/VGCxctcMcZw4403kp6ezo9+9KOAnuRkcnNz6+aGWLlyZd0fCl5g23tpR6ldemzyR71xxwt/tAEi0lRVwRdfHGtgqL3l5BzbJikp0MAwe3agkWHECMjIgMTEoA9z8ODBussU+QE/xVtjDFGt7M1TVlZGVlYWe/fuJT09nYsuuoji4mI++OADZsyYUbddhXON4g8//JBVq1YB8L3vfa/FM0//+c9/5vHHH6eqqorc3Fy2bt1a1wAxc+bMoPZhjGmw7Z49e5g5cya5ubkcPXq0yUthTp06laioKIYNG1bXS+J4LrzwQpKSkgAYNmwYX3/9NXl5eZx33nl1X4xnzJjBzp07g45ZaTsHMzPptWmT1zLqsKm+qDfuqD/uqD9KsHiZK++//z7PPvssmZmZZGVlAXDHHXfw2muvkZ2djYiQlpbGY4895ok+sO+9lJmQyaZie/TY5I96444X/mgDRDg5ePDEHg1btgTmcIBAz4WhQ2HCBJg791hjw4AB0MbhIn77QG938QbZU6Ex2jJuqnYOiNLSUi655BIeeeQR5syZQ/fu3cluQY+MmJiYBpNDltfmdD2++uorFi1axKeffkqPHj2YM2dOg+26du0a1LFEpMG2t956Kz/60Y+YMmUKa9euZeHChY2+rnO9yxw1dSnP+ttER0e7zjGhRA6bPpjBrvqi3rij/rij/ijB4mWunH322Y1+bl999dUeqGkc295LNv3ABrv8UW/c8cIfXzRANPXjI2RUVMC2bQ17NGzaBN98c2ybk04KNC58//vHhk+kp0NcXFgk+e1fBT/FW1lZSWxsbJv2ER8fz5IlS5g6dSrz589n4MCBLFu2jBkzZmCMYePGjYwcOZIJEyawfPlyZs6cyQsvvFD3+pNPPpmtW7dSUVFBWVkZb731FmeffXaDYxQVFdG1a1eSkpLYv38/f/3rX5k4cWKz2hITEzly5Ejd4+Pfv4WFhfTv3x+Ap59+ug0uNM7pp5/O7bffTkFBAYmJiSxfvrxu9m0lMtj274BN9UW9cUf9cUf9UYIlkrkiv2j+T7dg/qU190Ru0nnb3kv6L3/TqDfuaA+IMCO0cRJKY2D37oY9GjZuhB07oHZiwM6dYfhwmDTpWI+GzEzo3bvtAbQAv32g+ynetjY+1DJq1ChGjBjB0qVLef7557n55pv51a9+RWVlJbNmzWLkyJEsXryY2bNnc9999zFp0qS64QoDBgzgqquuIiMjg4EDBzJq1KgT9j9y5EhGjRrFaaedxoABAzjrrLOC0jVr1izmzp3LkiVLeOmll06YPHbhwoXMmDGDHj16cMEFF/DVV1+13Yx69O/fn7vuuotx48aRnJzMaaedVhe3Ehls+mAGu+qLeuOO+uOO+qMEi225YtMPSFB/msMmf9Qbd7zwR8LeOyAMjB071tROSBcMy7Ys46qXrmLzzZsZ3nt48AcyJtC4sHYt/OMfgVtu7rHn09KO9WaovQ0eDDHet+vk5+eTkpLitYyI0R7i3bZtG+np6W3eT1VVFTERyrHS0lK6dOmCiPDCCy+wdOlSXn755Ygcu5ZIxltLcXExCQkJVFVVMW3aNG644QamTZvWYJvGzqeIrDfGjI2kVq9oaR2uI4jhZfnp6aRs2+a+UQQ/uyJaX5rxR71xR/1xxy/+aC1uBstyJZgeEOld09lW4q4nZD0g1J9mBNnjj3rTjBwP/XGrw97/UrYJY2Dr1mMNDu+8A7WT1/XtCxMnwllnBa42kJERmCzSUnr06OG1hIjip3ijG7nqSbhYv349t9xyC8YYunfvzpNPPhmxY9cSyXhrWbhwIW+++Sbl5eVcfPHFTJ06NeIa/EwPyyb9tKm+qDfuqD/uqD9KsNiWKztL7NKj/rhjkz/qjTte+OOLBghDM602b7wBjz4aaHDIywusGzAALroIzjsvcBs8uM0TQ0aSoqIiunfv7rWMiOGneKurqyPWI+Ccc87h888/j8ixmiKS8dayaNGiiB5PaUhRWhrdv/zSaxl12FRf1Bt31B931B8lWGzLlbQuaXxZZo8e9ccdm/xRb9zxwh9fNEDUcvxYciAwl8OUKZCcDJMnBxobJk4MDK9oRw0OxxPsFQY6Cn6KNyqqLdfBaH/4LV4FutYf6mYBNtUX9cYd9ccd9UcJFttyJbfCLj3qjzs2+aPeuOOFP75qgDiBkhK46iro3h0++yziE0WGk/Ly8pBNVtge8FO87XHelrbgt3gVKE9OJra01GsZddhUX9Qbd9Qfd9QfJVhsy5Xk2GRKK+zRo/64Y5M/6o07Xvjj7waIW28NTDL55psdqvEBQnelhPaCn+JttCdPB8Zv8SoQW1LitYQG2FRf1Bt31B931B8lWGzLlZJqu/SoP+7Y5I96444X/vi3b/Njj8Gf/gQLFsAFF3itJuTU1NR4LSGi+Clev/UI8Fu8CtRYcCWh+thUX9Qbd9Qfd9QfJVhsy5UYsUuP+uOOTf6oN+544Y8vGiBO+AHz8sswfz5ceincfbc3osKM3360+S3e1hIdHU1WVlbdLScnp0WvX7x4MaVNdBtze86Nu+++mzfffNN1m9WrV3P//fe3eN9K+8R4cOUTN2yqL+qNO+qPO+qPEiy25Uq02KVH/XHHJn/UG3e88MeuJpgwIwh8+CHMmgVjx8Kf/wyWtUKFikhfNcBr/BRvW4YkdOnShezs7Fa/fvHixcyePZv4+PgWPVddXd3k5TTvvfde12OKCFOmTGHKlCmtE620O2LKyryW0ACb6ot644764476owSLbblSVmOXHvXHHZv8UW/c8cIfX/SAqKO8DKZNg9RUePVV6MCzL1dUVHgtIaL4Kd5Q/mNUXFzMhRdeyOjRo8nMzOTll18GoKSkhMmTJzNy5EgyMjJ48cUXWbJkCfv27eP888/n/PPPb7Cfxp5LSEjgxz/+MSNHjuTDDz/k3nvv5fTTTycjI4N58+bVxTFnzhxeeuklANLS0rjnnnvq9Gzfvh1jDE899RS33HJL3fa33XYbZ555JoMGDap7bU1NDfPnz+e0007joosu4tJLL617TmlfVCQleS2hATbVF/XGHfXHHfVHCRbbciUpxi496o87Nvmj3rjjhT/+anqO6wLPPAOnnAK9enmtJqw09i90R6a9xXv767eT/U12SPeZ1SeLxZMWu25TVlZGVlYWAAMHDmTZsmWsXLmSbt26kZeXx4QJE5gyZQqvv/46/fr14y9/+QsAhYWFJCUl8dvf/pa3336bnj17NtjvbbfddsJzJSUljB8/ngcffBCAYcOGcbcz5Onaa6/l1Vdf5bvf/e4JGnv27MmGDRv43e9+x6JFi3jiiSdO2CY3N5f33nuP7du3M2XKFK688kpWrFhBTk4OW7du5cCBA6Snp3PDDTe0yEPFDuIPHPBaQgNsqi/qjTvqjzvqjxIstuXKgaN26VF/3LHJH/XGHS/88UUPiKS4JIb3HE7nmM5w8cWBBogOzpEjR7yWEFH8FG9bekDUDsHIzs5m5cqVGGO46667GDFiBN/+9rfZu3cv+/fvJzMzkzfeeIM77riDd999l6RWtNZGR0czffr0usdvv/0248ePJzMzkzVr1rBly5ZGX3fFFVcAMGbMGHJycqiurj5hm6lTpxIVFcWwYcPYv38/AO+99x4zZswgKiqKPn36nNBLQ2k/HBkwwGsJDbCpvqg37qg/7qg/SrDYlisD4uzSo/64Y5M/6o07Xvjjix4QkwZP4pJTLvHV5fy6d+/utYSI0t7iba6nghvGmJDl8vPPP8/BgwdZv349sbGxpKWlUV5ezpAhQ9iwYQOvvfYaCxYs4MILL6zrvRAscXFxdfM+lJeXM3/+fNatW8eAAQNYuHAh5eXljb6uc+fOQKABo6qqqtG5I2q3AZ3ErCPS/YsvvJbQAJvqi3rjjvrjjvqjBIttufJFqV161B93bPJHvXHHC3980QMCID8/32sJEUXj7bhUVVWFbF+FhYX07t2b2NhY3n77bb7++msA9u3bR3x8PLNnz+anP/0pGzZsACAxMbHJf6zcnqttbOjZsyfFxcUtmpsh2HjPOussli9fTk1NDfv372ft2rVBH0Oxi/zhw72W0ACb6ot64476404k/bnhhhvo3bs3GRkZdeuWLVvG8OHDiYqKYt26dXX+vPHGG4wZM4bMzEzGjBnDmjVrIqZTaRzb3kvDE+zSo/64Y5M/6o07XvjjmwaI48esd3Q03o5LbGxsyPZ1zTXXsG7dOjIzM3nmmWc47bTTANi0aRPjxo0jKyuLX/ziFyxYsACAefPmMWnSpEaHN7g91717d+bOnUtGRgaXXHIJp59+etAag413+vTppKamMmzYMGbPns3o0aNbNXRE8Z6emzd7LaEBNtUXr7156KGHyMjIYPjw4SxevLiBNw8++CAiQl5enmf6vPTneG/gWO7Y4A1E1p85c+bw+uuvN1iXkZHBihUrOPfccwN6HH969uzJK6+8wqZNm3j66ae59tprI6ZTaRyva83xbC62S4/6445N/qg37njhj7TH7stjx44169ata9FrDh48SK8OPvFkfTRe+9i2bRvp6elt3k9lZWVIGyFspyXxFhcXk5CQQH5+PuPGjeP999+nT58+YdHV2PkUkfXGmLFhOaBltKYOAxDE8KGDGRn0au4DOoyfXdXV1YwdO5b+/fvz6quvMmHChLpePAcOHGDcuHGsWrUqPAdvxh8vvdm8eTOzZs3ik08+oVOnTkyaNIn77ruP8ePHs3v3bm666Sa2b9/O+vXrw9NoY3HuNObNo48+SlJSEuXl5eH3Bqz0Jycnh8suu4zNxx1z4sSJLFq0iJNPPvmEz25jDCkpKeTm5jYYbtcStBY3g2W5Ir9oXk9GQkazP5TMPSHKXfWnGUH2+KPeNCPHQ3/c6rBvekDY/uM01Gi8HRc/NT5Ay+K97LLLyMrK4pxzzuHnP/952BoflPDS7AdzmHnooYcaNC599NFHdZO3nnHGGXUTpXqBl95s27aN8ePHEx8fT0xMDOeddx7/+Mc/APjhD3/IAw884PlcS17505g3K1asoFevXtZ4A96/t46nsc/u5cuXM3r06FY3PiihwbZcse1fbPXHHZv8UW/c8cIf3zRAeN3tMdJovB2XyspKryVElJbEu3btWrKzs9m6dStz5swJnyglrOTVGzMeafbs2cNf/vIXbrrppmN6nPpSVFTEmjVrmDp1qkfqvPUmIyODd999l/z8fEpLS3nttdfYuXMnL7/8Mv3792fkyJGeaavFK38a82b37t08++yz1ngD3uZPYxz/2b1lyxbuuOMOHnvsMY8UKbXYlisZCXbpUX/csckf9cYdL/zxxVUwAFJSUryWEFE03o5LTIxv3raA/+JVIKWJS7RGgttvv50HHnigwYSqtfVl1apVXHjhhXTr1s0reZ56k56ezh133MHFF19M165dycrKoqqqil//+tf8/e9/90xXfbzypzFvKioqePjhh63xBrzNn8ao/9m9Z88epk2bxjPPPMMpPrhcuu3Ylitbiu3So/64Y5M/6o07Xvjjmx4Qhw8f9lpCRNF47SQUc65UV1eHQEn7wcZ42+PcOe2Jw4MHe3LcV199ld69ezNmzJiGepz6snTpUq6++moPlNXT4pE3tdx4442sX7+ed955hx49ejBo0CC++uorRo4cSVpaGnv27GH06NF88803nujz0p/jvRk+fDi7du2yxhvwPn+Op/a9dfjwYSZPnsz999/PWWed5a0oBbAvVwbH26VH/XHHJn/UG3e88Mc3DRCJiYleS4goGq99xMXFkZ+f3+Yfr9HR0SFS1D6wLV5jDPn5+cTFxXktpcOSuHu3J8d9//33Wb16NWlpacyaNYs1a9Ywe/ZsEhMTycvL45NPPmHy5MmeaKvFK29qOXDgAAD/+te/WLFiBXPnzuXAgQPk5OSQk5NDamoqGzZs8Gz+FS/9Od6b66+/ntzcXGu8gcj6c/XVV3PGGWewY8cOUlNT+eMf/8jKlStJTU3lww8/ZPLkycycOROAhx9+mC+++IJ7772XrKwssrKy6vxUvMHrWnM8u8vt0qP+uGOTP+qNO17445u+zaWlpZ52m400Gq99pKamsmfPHg4ePNim/dTU1BAV5Zu2QyvjjYuLIzU11WsZHZbS3r3p9q9/Rfy4v/nNb/jNb34DBOYTWbRoEc899xxFRUW89NJLXHbZZZ43PHnlTS3Tp08nPz+f2NhYHnnkETp16uSZlsbw0p/jvenevTtFRUVWfTZF0p+lS5c2un7atGl1y0VFRQAsWLCg7nLPih14XWuOp3en3vyr3B496o87Nvmj3rjjhT++aYDw22zKGq99xMbGMnDgwDbvp6Kiol3EGyr8Fq+XiMgk4CEgGviDMeZ+L3R0Liz04rBN0rlzZ1544QXuvPNOr6V47s27777b4HFFRUWDxzk5ORFUcyJe+nO8N9Dws8lrb8D7/Dkere0nonW4cQqr7NKj/rhjkz/qjTte+OObBoiqqipffdBpvB0XP8UK/ovXK0QkGngEuAjYA3wqIquNMVsjraWqSxfPP6AnTpzIxIkTA3qqqli7dq2nemqJpDfBXD+8T6c+fHPUfU6DkF1fPQjUH3famz+R9MYGtA43TZeoLhRijx71xx2b/FFv3PHCH7v6NYcRG66/HUk03o6Ln2IF/8XrIeOAL4wxu4wxR4EXgMu9ECKWTTxqUw7a5k21sUuP+uOO+mM9WoebwLZcUX/csckf9cYdL/zxTQ8I28aQhxuNt+Pip1jBf/F6SH+g/kxEe4DxXgiJqqqK2LGC+Ze2R0wPCqoKXLeJ1D+1kfQmGKqMXXrUH3fUH+vxZR0OBttyRf1xxyZ/1Bt3vPBH2uPl5ETkIPB1C1/WE8gLgxxb0Xg7Ln6KFdpXvCcbY3p5LaI1iMiVwCRjzE3O42uB8caYW+ptMw+Y5zwcCuwIkxzbzrlNemzSAqqnOVSPO+HS0y5rcTB12FkfiVrsl1xpLarHHZv02KQF/KOnyTrcLntAtOZDRUTWGWPGhkOPjWi8HRc/xQr+i9dD9gID6j1OddbVYYx5HHg83EJsO+c26bFJC6ie5lA97timxwKarcMQmVps27lRPe6onqaxSQuoHvDRHBCKoiiKK58Cp4rIQBHpBMwCVnusSVEUxU9oHVYUpcPTLntAKIqiKKHFGFMlIrcAfyNw+bcnjTFbPJalKIriG7QOK4riB/zUABH2bsOWofF2XPwUK/gvXs8wxrwGvOa1Duw75zbpsUkLqJ7mUD3u2KbHc7QON4nqcUf1NI1NWkD1tM9JKBVFURRFURRFURRFaV/oHBCKoiiKoiiKoiiKooQdXzRAiMgkEdkhIl+IyJ1e62ktIpIjIptEJFtE1jnrkkXkDRH5p3Pfw1kvIrLEiXmjiIyut5/rne3/KSLXexXP8YjIkyJyQEQ211sXsvhEZIzj3xfOayWyETakiXgXishe5xxni8il9Z77T0f7DhG5pN76RvPbmcTqY2f9i86EVp4gIgNE5G0R2SoiW0TkB876Dnt+lRNprhaLyLecPPnMOe/N5n84dERSS0t0icjJIvKWo2etiKTWey5kdT1IfyKipSW6mtIkIlki8qFTezaKyMxwaYiUjpbqcjtfzvPdRGSPiDwcTh2R1KI0jdbitumKRP3TOtw2HZHU0hJdkah/7aIOG2M69I3AJD5fAoOATsDnwDCvdbUylhyg53HrHgDudJbvBP7LWb4U+CsgwATgY2d9MrDLue/hLPfwOjZH27nAaGBzOOIDPnG2Fee137Ew3oXATxrZdpiTu52BgU5OR7vlN/BnYJaz/Chws4ex9gVGO8uJwE4npg57fvV2Qg40W4sJjEO82VkeBuTUWz4h/8OlI1JaWuHPMuB6Z/kC4FlnOWR1vQX+hF1LCP0ZApzqLPcDcoHuHngTEh2h9Kbe8w8B/wc8HE4dkdKitzbni9ZiD2txCGpNh63DIfKnw9bitnoTKh3N3fzQA2Ic8IUxZpcx5ijwAnC5x5pCyeXA087y08DUeuufMQE+ArqLSF/gEuANY8whY0wB8AYwKcKaG8UY8w5w6LjVIYnPea6bMeYjE3hnPVNvX57QRLxNcTnwgjGmwhjzFfAFgdxuNL9FRAgUlJec19f3LuIYY3KNMRuc5SPANqA/Hfj8KicQTC02QDdnOQnY5yw3lf/h0hEpLS3VNQxY4yy/Xe/5UNb1YP2JhJaW6mpUkzFmpzHmn87yPuAA0CtMGiKhozW6mjpfiMgY4CTg7xHQESktStNoLW67rnDXP63DbdcRKS0t1RXu+tcu6rAfGiD6A7vrPd7jrGuPGODvIrJeROY5604yxuQ6y98QSBhoOu725keo4uvvLB+/3kZucbpEPSnOkARaHm8KcNgYU3Xces8RkTRgFPAx/jy/fiWY2rMQmC0iewjMAn9rC14bSh2R0tJSXZ8DVzjL04BEEUkJsaZg9xUJLS3V1ZSmOkRkHIF/hb4Mk4ZI6GiNrkY1iUgU8CDwkwjpiJQWpWm0FrddV7jrn9bhtuuIlJaW6gp3/WsXddgPDRAdibONMaOB7wDfF5Fz6z/p/PNrPFEWATp6fA6/B04Bsgh0CXvQUzUhRkQSgOXA7caYovrP+eT8Ku5cDTxljEklMAznWefD0O9aavkJcJ6IfAacB+wFqlVLHa6anJ5SzwL/Zoyp8YGOYDTNB14zxuxxe3EH1qI0jk31zyYttdhS/2zRUR+b6p9NWprTFOn656mOmHDu3BL2AgPqPU511rU7jDF7nfsDIrKSQDeb/SLS1xiT67yRDjibNxX3XmDicevXhll6WwhVfHud5eO3twpjzP7aZRF5AnjVeeiWx42tzycwbCHG6QXhebwiEkug8eF5Y8wKZ7Wvzq/PCaYW34jTTdQY86GIxAE9g3xtKHVESkuLdDldRa+Ausa86caYwyISyroeVHwR0tIiXU1pch53A/4C/MwZ1hUWDRHS0WJdLufrDOAcEZkPJACdRKTYGNOaCbvbmjuh1KI0jdbiNuqKQP3TOtxGHRHU0iJdEah/7aMOmzBMLGHTjUAjyy4CE9TUTsYx3GtdrYijK5BYb/kDAgX5v2k4id8DzvJkGk7i94mzPhn4isCkND2c5WSv46sXZxoNJ2UMWXycOEnhpRbG27fe8g8JjG8EGE7DyZZ2EZhopsn8JjDBTP1JKOd7GKcQmJdh8XHrO/T51VuDc91sLXbO2xxnOZ3AWF9pKv/DpSNSWlrhT08gylm+D7jXWQ5ZXW+BP2HXEkJ/OgFvEeh5FVYNkdARSm+O22YObZuEsk3+hFKL3tqcL1qLPazFIag1HbYOh8ifDluL2+pNqHQ0qzMcO7XtRqDL1k4C43t+5rWeVsYwyEmiz4EttXEQGOv/FvBP4E2O/RgT4BEn5k3A2Hr7uoHAZD1fEOh25Hl8jq6lBIYdVBIYs3RjKOMDxgKbndc8DIiF8T7rxLMRWE3DBomfOdp3UO8KD03lt5Mznzg+LAM6exjr2QSGV2wEsp3bpR35/Oqt0Tw4IVeBe4EpzvIw4H2nzmUDF9d7baP5HyodXmlpoT9XOu+VncAf6r+nm3pfhNGfiGgJhT/AbAJ1NrveLSvS3oRSRyhzp94+5tDGL5ttzZ1QatFbm/JFa7HHtbit76VQ6QiVN4S4/rXFn1BrCVXu1NvHHNrWGGx9HRbnAIqiKIqiKIqiKIqiKGHD60lcFEVRFEVRFEVRFEXxAdoAoSiKoiiKoiiKoihK2NEGCEVRFEVRFEVRFEVRwo42QCiKoiiKoiiKoiiKEna0AUJRFEVRFEVRFEVRlLCjDRCKoiiKoiiKoiiKooQdbYBQFEVRFB8gImkisjkE+5koImeGQlMkEJGnRORKZ/kPIjLMa02KovgTrcNahxWI8VqAooQDEekK/BlIBaKBXwJfAL8FEoA8YI4xJldEBgOPAr2AamAGUAq8CHQj8D652RjzbqTjUBRFsZCJQDHwgcc6Wowx5iavNSiKooSAiWgdVtop2gNC6ahMAvYZY0YaYzKA14H/Ba40xowBngTuc7Z9HnjEGDMSOBPIBb4H/M0YkwWMBLIjK19RFCUsxIjI8yKyTUReEpF4ABEZIyL/EJH1IvI3EenrrL9NRLaKyEYReUFE0oD/B/xQRLJF5Jz6OxeRhSLytIi8KyJfi8gVIvKAiGwSkddFJLaZ480VkU9F5HMRWV5P31MiskREPhCRXbX/pDWGBHhYRHaIyJtA73rPrRWRsc7yJBHZ4BzrLWddVxF5UkQ+EZHPROTy0FmvKIoCaB3WOuxztAFC6ahsAi4Skf9yCvMAIAN4Q0SygQVAqogkAv2NMSsBjDHlxphS4FPg30RkIZBpjDniRRCKoighZijwO2NMOlAEzHe+jDbVQHsnMMoYMwL4f8aYHAI9xv7HGJPVRM+wU4ALgCnAc8DbxphMoAyY3MzxVhhjTncahLcBN9bbb1/gbOAy4H6XGKc5cQ4DriPQsNwAEekFPAFMd441w3nqZ8AaY8w44HzgvyXQo05RFCVUaB1G67Cf0SEYSofEGLNTREYDlwK/AtYAW4wxZ9TfzmmAaOz174jIucBk4CkR+a0x5plw61YURQkzu40x7zvLzwG3EeghVttAC4Fha7nONhuB50VkFbAqyGP81RhTKSKbnH297qzfBKQR+FLa1PEyRORXQHcCw+X+Vm+/q4wxNcBWETnJ5fjnAkuNMdXAPhFZ08g2E4B3jDFfARhjDjnrLwamiMhPnMdxwLcIfAlXFEUJBVqHA2gd9inaAKF0SESkH3DIGPOciBwG5gO9ROQMY8yHTsvvEGPMFhHZIyJTjTGrRKQzgSLcC9hjjHnCWTca0AYIRVHaO6aRx0IjDbQOkwl8kfwu8DMRyQziGBUAxpgaEak0xtQes4bA9w634z0FTDXGfC4icwiMc26wXwcJQkdrEAL/xu0I0/4VRVG0DrujdbiDo0MwlI5KJvCJM9ziHuBu4Ergv0TkcwJzOtR2B7sWuE1ENhKYzKcPgWL7uYh8BswEHoqkeEVRlDDxLRGp/cL5PeA9YAdOAy2AiMSKyHARiQIGGGPeBu4Akgj8G3YEaLT3WJA0ejznuUQg12kkvqaV+38HmCki0c6Y5vMb2eYj4FwRGehoSHbW/w24VZy/BEVkVCs1KIqiNIXW4QBah32K9oBQOiTGmL/RsMtYLec2su0/CYyTq88u4OkwSFMURfGSHcD3ReRJYCvwe2PMUWcysSUikkTgu8FiYCfwnLNOgCXGmMMi8grwkjMx2K0tvUKQy/G2AD8HPgYOOvet+YK9kkBN3wr8C/iwEQ0HRWQesML5gn8AuIjAFZMWAxud9V8RGOusKIoSKrQOo3XYz8ixHjmKoiiKoiiKoiiKoijhQYdgKIqiKIqiKIqiKIoSdnQIhqIoiqIo7Q5nIrZnj1tdYYwZ74UeRVEUv6F1WGkNOgRDURRFURRFURRFUZSwo0MwFEVRFEVRFEVRFEUJO9oAoSiKoiiKoiiKoihK2NEGCEVRFEVRFEVRFEVRwo42QCiKoiiKoiiKoiiKEna0AUJRFEVRFEVRFEVRlLDz/wHIOhRa3a5zXAAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
        "<Figure size 1296x432 with 3 Axes>"
       ]
@@ -734,42 +970,43 @@
     "        return None\n",
     "\n",
     "\n",
-    "plt.figure(\"train\", (18, 6))\n",
-    "plt.subplot(1, 3, 1)\n",
-    "plt.title(\"Metrics Time\")\n",
-    "plt.xlabel(\"secs\")\n",
-    "plt.ylabel(\"best mean_dice\")\n",
-    "plt.plot(best[2], best[0], label=\"Regular training\", color=\"red\")\n",
-    "plt.plot(m_best[2], m_best[0], label=\"Fast training\", color=\"green\")\n",
-    "plt.grid(alpha=0.4, linestyle=\":\")\n",
-    "plt.legend(loc=\"best\")\n",
-    "\n",
-    "plt.subplot(1, 3, 2)\n",
-    "plt.title(\"Typical Metrics Time\")\n",
-    "plt.xlabel(\"best mean_dice\")\n",
-    "plt.ylabel(\"secs\")\n",
-    "labels = [\"0.80\", \"0.80 \", \"0.90\", \"0.90 \", \"0.92\", \"0.92 \", \"0.94\", \"0.94 \"]\n",
-    "x_values = [0.8, 0.8, 0.9, 0.9, 0.92, 0.92, 0.94, 0.94]\n",
-    "for i, (l, x) in enumerate(zip(labels, x_values)):\n",
-    "    value = int(get_best_metric_time(x, best if i % 2 == 0 else m_best))\n",
-    "    color = \"red\" if i % 2 == 0 else \"green\"\n",
-    "    plt.bar(l, value, 0.5, label=get_label(i), color=color)\n",
-    "    plt.text(l, value, \"%s\" % value, ha=\"center\", va=\"bottom\")\n",
-    "plt.grid(alpha=0.4, linestyle=\":\")\n",
-    "plt.legend(loc=\"best\")\n",
-    "\n",
-    "plt.subplot(1, 3, 3)\n",
-    "plt.title(\"Typical Metrics Epochs\")\n",
-    "plt.xlabel(\"best mean_dice\")\n",
-    "plt.ylabel(\"epochs\")\n",
-    "for i, (l, x) in enumerate(zip(labels, x_values)):\n",
-    "    value = int(get_best_metric_epochs(x, best if i % 2 == 0 else m_best))\n",
-    "    color = \"red\" if i % 2 == 0 else \"green\"\n",
-    "    plt.bar(l, value, 0.5, label=get_label(i), color=color)\n",
-    "    plt.text(l, value, \"%s\" % value, ha=\"center\", va=\"bottom\")\n",
-    "plt.grid(alpha=0.4, linestyle=\":\")\n",
-    "plt.legend(loc=\"best\")\n",
-    "plt.show()"
+    "if not profiling:\n",
+    "    plt.figure(\"train\", (18, 6))\n",
+    "    plt.subplot(1, 3, 1)\n",
+    "    plt.title(\"Metrics Time\")\n",
+    "    plt.xlabel(\"secs\")\n",
+    "    plt.ylabel(\"best mean_dice\")\n",
+    "    plt.plot(best[2], best[0], label=\"Regular training\", color=\"red\")\n",
+    "    plt.plot(m_best[2], m_best[0], label=\"Fast training\", color=\"green\")\n",
+    "    plt.grid(alpha=0.4, linestyle=\":\")\n",
+    "    plt.legend(loc=\"best\")\n",
+    "\n",
+    "    plt.subplot(1, 3, 2)\n",
+    "    plt.title(\"Typical Metrics Time\")\n",
+    "    plt.xlabel(\"best mean_dice\")\n",
+    "    plt.ylabel(\"secs\")\n",
+    "    labels = [\"0.80\", \"0.80 \", \"0.90\", \"0.90 \", \"0.92\", \"0.92 \", \"0.94\", \"0.94 \"]\n",
+    "    x_values = [0.8, 0.8, 0.9, 0.9, 0.92, 0.92, 0.94, 0.94]\n",
+    "    for i, (l, x) in enumerate(zip(labels, x_values)):\n",
+    "        value = int(get_best_metric_time(x, best if i % 2 == 0 else m_best))\n",
+    "        color = \"red\" if i % 2 == 0 else \"green\"\n",
+    "        plt.bar(l, value, 0.5, label=get_label(i), color=color)\n",
+    "        plt.text(l, value, \"%s\" % value, ha=\"center\", va=\"bottom\")\n",
+    "    plt.grid(alpha=0.4, linestyle=\":\")\n",
+    "    plt.legend(loc=\"best\")\n",
+    "\n",
+    "    plt.subplot(1, 3, 3)\n",
+    "    plt.title(\"Typical Metrics Epochs\")\n",
+    "    plt.xlabel(\"best mean_dice\")\n",
+    "    plt.ylabel(\"epochs\")\n",
+    "    for i, (l, x) in enumerate(zip(labels, x_values)):\n",
+    "        value = int(get_best_metric_epochs(x, best if i % 2 == 0 else m_best))\n",
+    "        color = \"red\" if i % 2 == 0 else \"green\"\n",
+    "        plt.bar(l, value, 0.5, label=get_label(i), color=color)\n",
+    "        plt.text(l, value, \"%s\" % value, ha=\"center\", va=\"bottom\")\n",
+    "    plt.grid(alpha=0.4, linestyle=\":\")\n",
+    "    plt.legend(loc=\"best\")\n",
+    "    plt.savefig(\"outputs/metric_time_epochs.png\")"
    ]
   },
   {
@@ -783,13 +1020,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "if directory is None:\n",
     "    shutil.rmtree(root_dir)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[<sup id=\"fn1\">1</sup>](#fn1-back) Acknowledgement: This usage is inspired by [Conditional with statement in Python](https://stackoverflow.com/a/68682614) by [Lucas Vasquez](https://stackoverflow.com/users/10712525/lucas-vazquez), used with adaptations under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), accessed June 14, 2022."
+   ]
   }
  ],
  "metadata": {
diff --git a/acceleration/figures/nsys-all-annotated.png b/acceleration/figures/nsys-all-annotated.png
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diff --git a/acceleration/figures/nsys-epoch-long.png b/acceleration/figures/nsys-epoch-long.png
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diff --git a/acceleration/figures/nsys-epoch-short.png b/acceleration/figures/nsys-epoch-short.png
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index 0000000000..ba07e3c760
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diff --git a/acceleration/figures/nsys-fast-transform.png b/acceleration/figures/nsys-fast-transform.png
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diff --git a/acceleration/figures/nsys-transforms-annotated.png b/acceleration/figures/nsys-transforms-annotated.png
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