Repository: tum-pbs/apebench

Issue:
Add Guide how to benchmark foreign models
For example, models trained in PyTorch.

Requires the user to roll out their models given APEBench's initial condition in their environment, cast the trajectories back to JAX and employ the APEBench's metric system.

Discussion:

Solution patch:
diff --git a/README.md b/README.md
index 3c5148e..1541390 100644
--- a/README.md
+++ b/README.md
@@ -126,6 +126,8 @@ by running
 streamlit run explore_sample_data_streamlit.py
 ```
 
+[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://apebench-app-mca2jmqzxmoap6zdm2uvcb.streamlit.app/)
+
 ## Documentation
 
 Documentation is a available at
diff --git a/apebench/_base_scenario.py b/apebench/_base_scenario.py
index 98258ea..502d1e3 100644
--- a/apebench/_base_scenario.py
+++ b/apebench/_base_scenario.py
@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Callable
+from typing import Callable, Optional, Union
 
 import equinox as eqx
 import exponax as ex
@@ -156,6 +156,82 @@ def get_optimizer(self) -> optax.GradientTransformation:
 
         return optimizer
 
+    def _produce_ic_set(
+        self,
+        *,
+        stepper: BaseStepper,
+        num_samples: int,
+        key: PRNGKeyArray,
+    ) -> Float[Array, "num_samples num_channels *num_points"]:
+        """
+        Generate the number of initial conditions as samples requested and
+        discretize them on the grid. Requires the `stepper` to warmup the
+        initial conditions if necessary.
+        """
+        ic_distribution = self.get_ic_generator()
+        ic_set = ex.build_ic_set(
+            ic_distribution,
+            num_points=self.num_points,
+            num_samples=num_samples,
+            key=key,
+        )
+        if self.num_warmup_steps > 0:
+            ic_set = jax.vmap(
+                ex.repeat(
+                    stepper,
+                    self.num_warmup_steps,
+                )
+            )(ic_set)
+        return ic_set
+
+    def get_train_ic_set(
+        self,
+    ) -> Float[Array, "num_train_samples num_channels *num_points"]:
+        """
+        Use the attributes to produce the reference training initial condition set.
+        """
+        return self._produce_ic_set(
+            stepper=self.get_ref_stepper(),
+            num_samples=self.num_train_samples,
+            key=jax.random.PRNGKey(self.train_seed),
+        )
+
+    def get_train_ic_set_coarse(
+        self,
+    ) -> Float[Array, "num_train_samples num_channels *num_points"]:
+        """
+        Use the attributes to produce training initial conditions with the coarse stepper instead.
+        """
+        return self._produce_ic_set(
+            stepper=self.get_coarse_stepper(),
+            num_samples=self.num_train_samples,
+            key=jax.random.PRNGKey(self.train_seed),
+        )
+
+    def get_test_ic_set(
+        self,
+    ) -> Float[Array, "num_test_samples num_channels *num_points"]:
+        """
+        Use the attributes to produce the reference testing initial condition set.
+        """
+        return self._produce_ic_set(
+            stepper=self.get_ref_stepper(),
+            num_samples=self.num_test_samples,
+            key=jax.random.PRNGKey(self.test_seed),
+        )
+
+    def get_test_ic_set_coarse(
+        self,
+    ) -> Float[Array, "num_test_samples num_channels *num_points"]:
+        """
+        Use the attributes to produce testing initial conditions with the coarse stepper instead.
+        """
+        return self._produce_ic_set(
+            stepper=self.get_coarse_stepper(),
+            num_samples=self.num_test_samples,
+            key=jax.random.PRNGKey(self.test_seed),
+        )
+
     def produce_data(
         self,
         *,
@@ -708,6 +784,110 @@ def perform_tests(
 
         return results
 
+    def perform_tests_on_rollout(
+        self,
+        neural_rollout: Union[
+            Float[
+                Array,
+                "num_samples test_temporal_horizon num_channels *num_points",
+            ],
+            Float[
+                Array,
+                "num_seeds num_samples test_temporal_horizon num_channels *num_points",
+            ],
+        ],
+        test_data_no_init: Optional[
+            Float[
+                Array,
+                "num_samples test_temporal_horizon num_channels *num_points",
+            ]
+        ] = None,
+    ) -> Union[
+        dict[str, Float[Array, "test_temporal_horizon"]],
+        dict[str, Float[Array, "num_seeds test_temporal_horizon"]],
+    ]:
+        """
+        Compute all error metrics of the `report_metrics` attribute on an
+        externally produce rollout.
+
+        !!! tip
+
+            Use this function to benchmark external models by producing the
+            initial states with `scenario.get_test_ic_set()`, roll them out in
+            their respective framework for `test_temporal_horizon` steps, and
+            then call this function on the produced rollout. (Some frameworks
+            require different array formats, e.g.,
+            [TensorFlow](https://github.com/tensorflow/tensorflow) and
+            [Flax](https://github.com/google/flax) are typically channels-last.
+            Hence, some reshaping might be necessary.)
+
+        !!! warning
+
+            The `neural_rollout` must **not** contain the initial conditions as
+            the zeroth frame.
+
+        **Arguments:**
+
+        - `neural_rollout`: The neural rollout to be tested.
+        - `test_data_no_init`: The test data without the initial conditions.
+            If not provided, the test data is procedurally generated.
+
+        **Returns:**
+
+        - `results`: A dictionary with the metric names as keys and the error
+            rollouts. The rollout arrays always have a leading `num_seeds` axis,
+            even if the `neural_rollout` did not. This is to ensure
+            compatibility with the `perform_tests` function. Certainly, this
+            will be a singleton axis if `neural_rollout` did not have a leading
+            axis.
+        """
+        if test_data_no_init is None:
+            test_data_no_init = self.get_test_data()[:, 1:]
+
+        if neural_rollout.shape == test_data_no_init.shape:
+            multi_seed = False
+        else:
+            if neural_rollout.shape[1:] == test_data_no_init.shape:
+                multi_seed = True
+            else:
+                raise ValueError(
+                    f"""The shape of the neural rollout is {neural_rollout.shape}
+                    and the shape of the test data is {test_data_no_init.shape}. They should
+                    either be the same or the neural rollout should have an additional
+                    leading axis for the seeds."""
+                )
+
+        metric_function_dict = self.get_metric_fns()
+
+        results = {}
+
+        for metric_config, func in metric_function_dict.items():
+            if multi_seed:
+                func_vectorized = jax.vmap(
+                    jax.vmap(
+                        func,
+                        # Vectorize over time steps
+                        in_axes=(1, 1),
+                    ),
+                    # Vectorize over seeds (but broadcast the reference)
+                    in_axes=(0, None),
+                )
+            else:
+                func_vectorized = jax.vmap(
+                    func,
+                    # Vectorize over time steps
+                    in_axes=(1, 1),
+                )
+
+            metric_rollout = func_vectorized(neural_rollout, test_data_no_init)
+            if not multi_seed:
+                # Add singletone seed axis for compatibility with `perform_tests`
+                metric_rollout = metric_rollout[None]
+
+            results[metric_config] = metric_rollout
+
+        return results
+
     def sample_trjs(
         self, neural_stepper: eqx.Module
     ) -> Float[
diff --git a/docs/examples/benchmark_flax_models_with_linen.ipynb b/docs/examples/benchmark_flax_models_with_linen.ipynb
new file mode 100644
index 0000000..4f3b80f
--- /dev/null
+++ b/docs/examples/benchmark_flax_models_with_linen.ipynb
@@ -0,0 +1,647 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Benchmark `flax.linen` models with APEBench\n",
+    "\n",
+    "This tutorial notebook will be conceptually similar to [the one using the newer\n",
+    "`flax.nnx` API](benchmark_flax_models_with_nnx.ipynb). For more comments, please\n",
+    "refer to that notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/koehler/miniconda3/envs/apebench_dev/lib/python3.12/site-packages/trainax/_general_trainer.py:7: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from tqdm.autonotebook import tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import apebench\n",
+    "\n",
+    "import numpy as np\n",
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "from flax import linen as nn\n",
+    "from tqdm.autonotebook import tqdm\n",
+    "import optax\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy import stats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Create Data with APEBench"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an example, we will use an advection scenario in difficulty mode with mostly\n",
+    "default settings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "advection_scenario = apebench.scenarios.difficulty.Advection(\n",
+    "    # A simple optimization setup that will be mimicked by Flax\n",
+    "    optim_config=\"adam;10_000;constant;1e-4\",\n",
+    "    # The default metric for APEBench scenarios is always `\"mean_nRMSE\"`. Let's\n",
+    "    # add some more metrics to the report. One based on the spectrum up until\n",
+    "    # (including) the fifth mode and a Sobolev-based metric that also considers\n",
+    "    # the first derivative.\n",
+    "    report_metrics=\"mean_nRMSE,mean_fourier_nRMSE;0;5;0,mean_H1_nRMSE\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create train **trajectories** and test **initial conditions** with APEBench."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((50, 51, 1, 160), (30, 1, 160))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data = advection_scenario.get_train_data()\n",
+    "test_ic_set = advection_scenario.get_test_ic_set()\n",
+    "train_data.shape, test_ic_set.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Train `flax.linen` model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rearrange data\n",
+    "We have to rearrange our data to have the channels last convention"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((50, 51, 160, 1), (30, 160, 1))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data = np.moveaxis(train_data, -2, -1)\n",
+    "test_ic_set = np.moveaxis(test_ic_set, -2, -1)\n",
+    "train_data.shape, test_ic_set.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From here on, you are free to do with the `train_data` what you what. The\n",
+    "simplest approach would be **one-step supervised learning**. For this, we slice\n",
+    "windows of length two across both the trajectory and the time axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50, 50, 2, 160, 1)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "substacked_data = jax.vmap(apebench.exponax.stack_sub_trajectories, in_axes=(0, None))(\n",
+    "    train_data, 2\n",
+    ")\n",
+    "substacked_data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we can merge sample and window axis into a joint batch axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2500, 2, 160, 1)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_windows = jnp.concatenate(substacked_data)\n",
+    "train_windows.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Definition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just a simple ReLU feedforward ConvNet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CNN(nn.Module):\n",
+    "    depth: int\n",
+    "    width: int\n",
+    "\n",
+    "    @nn.compact\n",
+    "    def __call__(self, x):\n",
+    "        x = nn.Conv(features=self.width, kernel_size=(3,), padding=\"CIRCULAR\")(x)\n",
+    "        x = nn.relu(x)\n",
+    "        for _ in range(self.depth - 1):\n",
+    "            x = nn.Conv(features=self.width, kernel_size=(3,), padding=\"CIRCULAR\")(x)\n",
+    "            x = nn.relu(x)\n",
+    "        x = nn.Conv(features=1, kernel_size=(3,), padding=\"CIRCULAR\")(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training Loop\n",
+    "\n",
+    "Access the attributes of the APEBench scenario to use the same hyperparameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "10000it [00:13, 750.95it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "cnn = CNN(width=34, depth=10)\n",
+    "params = cnn.init(jax.random.PRNGKey(0), train_windows[:, 0])\n",
+    "optimizer = optax.adam(1e-4)\n",
+    "opt_state = optimizer.init(params)\n",
+    "\n",
+    "\n",
+    "def one_step_supervised_loss(params, batch):\n",
+    "    inputs, targets = batch[:, 0], batch[:, 1]\n",
+    "    predictions = cnn.apply(params, inputs)\n",
+    "    return jnp.mean((predictions - targets) ** 2)\n",
+    "\n",
+    "\n",
+    "@jax.jit\n",
+    "def train_step(params, state, batch):\n",
+    "    loss, grad = jax.value_and_grad(one_step_supervised_loss)(params, batch)\n",
+    "    updates, new_opt_state = optimizer.update(grad, state)\n",
+    "    new_params = optax.apply_updates(params, updates)\n",
+    "    return new_params, new_opt_state, loss\n",
+    "\n",
+    "\n",
+    "loss_history = []\n",
+    "for batch in tqdm(\n",
+    "    apebench.pdequinox.cycling_dataloader(\n",
+    "        train_windows,\n",
+    "        batch_size=20,\n",
+    "        num_steps=10_000,\n",
+    "        key=jax.random.PRNGKey(42),\n",
+    "    )\n",
+    "):\n",
+    "    params, opt_state, loss = train_step(params, opt_state, batch)\n",
+    "    loss_history.append(loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's visualize the loss history. It's a bit noisy towards the end, but let's\n",
+    "stick with a constant learning rate for simplicity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Train Loss')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ISFmNPhAqLCxEQkIC4uPj0aNHD3z22WdqF0mUNyddJSIiUpS32gVwt+DgYGzYsAGBgYEoKytDjx49cPfdd6N58+ZqF81C3RQbbBojIiJSRqOvEfLy8kJgYCAAoLy8HIIgQBC0GWhw0lUiIiJlqR4IbdiwAWPGjEFUVBR0Oh2WL19usUx6ejratWsHf39/JCYmYtu2bbL2UVhYiLi4OLRp0waTJk1CeHi4i0rvWoamMY4sTUREpAjVA6GysjLExcUhPT3d6uuLFy9GWloapk2bhl27diEuLg4pKSkoKCgwLFOX/2P+7+zZswCApk2bYs+ePcjOzsbChQuRn5+vyLHJZWgaY40QERGRImTnCF29ehWCIBiam06ePIlly5YhNjYWt956q+wCjBw5EiNHjhR9ffbs2ZgwYQLGjx8PAJg7dy5WrFiBefPmYfLkyQCAzMxMSfuKiIhAXFwc/vjjD9xzzz1WlykvL0d5ebnhcXFxscQjcZ6Xnt3niYiIlCS7RuiOO+7Al19+CaC2ySkxMRHvvfce7rjjDnzyyScuLVxFRQV27tyJ5ORkw3N6vR7JycnYvHmzpG3k5+ejpKQEAFBUVIQNGzagS5cuosvPnDkToaGhhn/R0dHOHYQM9XONsWmMiIhICbIDoV27dmHw4MEAgO+++w4RERE4efIkvvzyS3zwwQcuLdyFCxdQXV2NiIgIk+cjIiKQl5cnaRsnT57E4MGDERcXh8GDB+Mf//gHevbsKbr8lClTUFRUZPiXm5vr1DHIYZhrjE1jREREipDdNHblyhUEBwcDAFavXo27774ber0eAwYMwMmTJ11eQGf1799fctMZAPj5+cHPzw/p6elIT09HdXW1+wpnxodzjRERESlKdo1Qx44dsXz5cuTm5uLXX3815AUVFBQgJCTEpYULDw+Hl5eXRXJzfn4+IiMjXbovc6mpqcjKysL27dvduh9j9bPPs2mMiIhICbIDoalTp+LFF19Eu3btkJiYiKSkJAC1tUO9e/d2aeF8fX3Rt29fZGRkGJ6rqalBRkaGYb+NiY++LkeINUJERERKkN00ds8992DQoEE4d+4c4uLiDM/ffPPNuOuuu2QXoLS0FMeOHTM8zs7ORmZmJsLCwhATE4O0tDSMGzcOCQkJ6N+/P+bMmYOysjJDLzJ3UaNprK7XWCV7jRERESnCoSk2IiMjDU1TxcXFWLNmDbp06YKuXbvK3taOHTswfPhww+O0tDQAwLhx4zB//nzcf//9OH/+PKZOnYq8vDzEx8dj1apVFgnUrpaamorU1FQUFxcjNDTUrfuq481eY0RERIqSHQjdd999GDJkCCZOnIirV68iISEBOTk5EAQBixYtwl/+8hdZ2xs2bJjdKS8mTpyIiRMnyi1qg1M/sjRrhIiIiJQgO0dow4YNhu7zy5YtgyAIKCwsxAcffIDp06e7vIBqSU9PR2xsLPr166fYPr29ONcYERGRkmQHQkVFRQgLCwMArFq1Cn/5y18QGBiI0aNH4+jRoy4voFrU6DVmqBFi0xgREZEiZAdC0dHR2Lx5M8rKyrBq1SpD9/nLly/D39/f5QX0JIa5xtg0RkREpAjZOULPPfccxo4di6CgILRt2xbDhg0DUNtkZmvEZrKvvkaIgRAREZESZAdCTz/9NPr374/c3Fzccsst0F/v8t2hQ4dGlyOkdPd5bz1zhIiIiJSkE+x12bKhblWdTueyAmlNXff5oqIil4+cbe6HzDN4dlEmBt7QHAsnDHDrvoiIiBozqfdv2TlCAPDll1+iZ8+eCAgIQEBAAHr16oWvvvrK4cJSrboaoctXKlUuCRERkWeQ3TQ2e/ZsvPbaa5g4cSJuvPFGAMDGjRvx5JNP4sKFC3j++eddXkhPUXKtNgA6eK5Y5ZIQERF5BtmB0IcffohPPvkEjzzyiOG522+/Hd27d8e//vUvBkJOOFpQqnYRiIiIPIrsprFz585h4MCBFs8PHDgQ586dc0mhtECNARV9vOrfDidSt4iIiEgi2YFQx44d8e2331o8v3jxYnTq1MklhdICNQdUBAB2HCMiInI/2U1jr7/+Ou6//35s2LDBkCO0adMmZGRkWA2QSDovo0CoqqYGXnovFUtDRETU+MmuEfrLX/6CrVu3Ijw8HMuXL8fy5csRHh6Obdu24a677nJHGT1GhxZNDH8fOMuEaSIiIneTXSMEAH379sXXX39t8lxBQQFmzJiBl19+2SUF80Qp3SMNf58rvAbEqFgYIiIiD+DQOELWnDt3Dq+99pqrNqc6tZOlr1UqN6I1ERGRp3JZINTYqJEsbZQihEBf5gcRERG5GwMhDdHpdIZgKNDPoVZLIiIikoGBkMY0C/QFAFytqFK5JERERI2f5GqHtLQ0m6+fP3/e6cIQcLGsAgDwyfoTGNGjlcqlISIiatwkB0K7d++2u8yQIUOcKgzVaxboo3YRiIiIGj3JgdDatWvdWQ667qEBMfh6yyl0axWidlGIiIgaPeYIiVCj+zwAhDXxAwAUX61UdL9ERESeiIGQCDW6zwNAdU0NAGDB1lOK7peIiMgTMRDSmF/25aldBCIiIo/BQEhjjEeXJiIiIvfiXVdjDueXqF0EIiIij+HQ8MWFhYXYtm0bCgoKUHM9p6XOI4884pKCEVB4pQJNrw+wSERERK4nOxD66aefMHbsWJSWliIkJAQ6Xf0EWTqdjoGQk/41Jhb/+ikLAJB1thgDO4arXCIiIqLGS3bT2AsvvIDHHnsMpaWlKCwsxOXLlw3/Ll265I4yehTj0aRrBBULQkRE5AFkB0JnzpzBM888g8DAQHeUx+MZz0C/+9Rl9QpCRETkAWQHQikpKdixY4c7ykIAjCuB3vvtiGrlICIi8gSyc4RGjx6NSZMmISsrCz179oSPj+mcWLfffrvLCqem9PR0pKeno7q6WtH96uwvQkRERC6iEwRBViaKXi9eiaTT6RQPHNytuLgYoaGhKCoqQkiI++f/Kii+hv4zMgyPc2aNdvs+iYiIGhup92/ZNULm3eXJtfR61gkREREphQMqakx4kJ/J46sVjauGjYiISEsk1Qh98MEHeOKJJ+Dv748PPvjA5rLPPPOMSwpGtUrKKxHg66V2MYiIiBolSTlC7du3x44dO9C8eXO0b99efGM6HU6cOOHSAqpN6RwhAGg3eYXJY+YJERERyePSHKHs7GyrfxMRERE1ZMwR0qBurZSpeSIiUpPMTstEbuHQpKunT5/Gjz/+iFOnTqGiosLktdmzZ7ukYJ5saOcWOHiuWO1iEBG5zfu/H8XCbSfxQ+ogRIb6q10c8mCyA6GMjAzcfvvt6NChAw4dOoQePXogJycHgiCgT58+7iijxzH/lSQIgsnktkREDd3//V47cv77GUcw8+5eKpeGPJnsprEpU6bgxRdfxL59++Dv74/vv/8eubm5GDp0KO699153lNHjNA/yNXm8ZMdplUpCRORebB0jtckOhA4ePIhHHnkEAODt7Y2rV68iKCgIb7zxBv7973+7vICe6JGkdiaP//n9XnUKQkRE1MjJDoSaNGliyAtq1aoVjh8/bnjtwoULriuZi125cgVt27bFiy++qHZR7PL34bhBRERESpCdIzRgwABs3LgR3bp1w6hRo/DCCy9g3759WLp0KQYMGOCOMrrEW2+9penyERERkfJk1wjNnj0biYmJAIDXX38dN998MxYvXox27drhf//7n8sL6ApHjx7FoUOHMHLkSLWLQkQKKCi+hpX7zqGqmnMjEpFtsgKh6upqnD59GjExMQBqm8nmzp2LvXv34vvvv0fbtm1lF2DDhg0YM2YMoqKioNPpsHz5cotl0tPT0a5dO/j7+yMxMRHbtm2TtY8XX3wRM2fOlF02Lcm9dEXtIhA1GLf83wY8vWAX5v+Zo3ZRiEjjZAVCXl5euPXWW3H58mWXFaCsrAxxcXFIT0+3+vrixYuRlpaGadOmYdeuXYiLi0NKSgoKCgoMy8THx6NHjx4W/86ePYsffvgBnTt3RufOnV1WZiUM6hhu8njw22tVKglRw1N0tRIAsO7weZVLQvZwZBBSm+wcoR49euDEiRM25xyTY+TIkTabrGbPno0JEyZg/PjxAIC5c+dixYoVmDdvHiZPngwAyMzMFF1/y5YtWLRoEZYsWYLS0lJUVlYiJCQEU6dOtbp8eXk5ysvLDY+Li9UZ2DB9bB/Evb5alX0TERF5Ctk5QtOnT8eLL76In3/+GefOnUNxcbHJP1eqqKjAzp07kZycXF9gvR7JycnYvHmzpG3MnDkTubm5yMnJwbvvvosJEyaIBkF1y4eGhhr+RUdHO30cjggN8LF47nJZhZUliYiIyFGSA6E33ngDZWVlGDVqFPbs2YPbb78dbdq0QbNmzdCsWTM0bdoUzZo1c2nhLly4gOrqakRERJg8HxERgby8PJfuq86UKVNQVFRk+Jebm+uW/TiihiOPERERuZTkprHXX38dTz75JNaubbi5Ko8++qjdZfz8/ODn54f09HSkp6ejurra/QUT8cnYPnhqwS7D4y0nLmF0r1aqlYeIiMhVpv6wH1U1Ambc1VPVckgOhOrmvxo6dKjbCmMuPDwcXl5eyM/PN3k+Pz8fkZGRbt13amoqUlNTUVxcjNDQULfuS8zInqZBT+rCXRjda7QqZSEiInKVkmuV+HLzSQBA2i2dER7kp1pZZOUIKT3xp6+vL/r27YuMjAzDczU1NcjIyEBSUpKiZSEiIterqGKTvyeqrql/32tq1P0MyOo11rlzZ7vB0KVLl2QVoLS0FMeOHTM8zs7ORmZmJsLCwhATE4O0tDSMGzcOCQkJ6N+/P+bMmYOysjJDLzJ30ULTGBFRY/f9rtN47744tYtBalJ5CAVZgdDrr7/u8maiHTt2YPjw4YbHaWlpAIBx48Zh/vz5uP/++3H+/HlMnToVeXl5iI+Px6pVqywSqF1NC01j1vx6IA8p3d3bLEjkjJoaAXo9B4chInFa6vsjKxD661//ipYtW7q0AMOGDTPkH4mZOHEiJk6c6NL9NhRtmwfi5MX6UaX//tVO5MxinhBp0/OLM7Et+xJ+fX4IgvxkD1PmchuPaXciaCLSBsk5QkrnB6ktPT0dsbGx6Nevn6rlaBlsmUBWrXJ7KpGYZbvP4EzhVazcd07tohARSSI5ELJXa9PYpKamIisrC9u3b1e1HDPvtuxWOPn7vSqUhEi6iipOdkpE0pRXqnu9kBwI1dTUuLxZjOzr2DIYW6bcbPLckp2nVSoNERGR84yrVr5QeXJk2VNskPIiQ/0tnluxl00PRETU8F0oLbe/kBsxEBKhlRwhMakLd9lfiIiISOPUTntlICRCKzlCtnha3hY1HB7Wt4KIZNLS/YuBUAP22HztBmlEREQNAQOhBmzt4fOoqmbvHCIisu5Cabkmh1zRUokYCDVwm45fVLsIRESkQe/8eggJ039Hl1d/UbsomsZASITWkqX7tWtm9fn5m7IVLgmRfTq1Jw8iSUquVeK91YdxOK9E7aKQG6SvPQ4AqNJgjZCxGpXzhRgIidBasvRnjyRYfX7t4fMKl4TIPkFTFd8kZsbKQ/hwzTGkzNmgdlGIVMNAqIFoGuiLqbfFql0MasQqq2tQUHxN7WKQgvaeLlS7CI3ChdJy/O2LHVhzKF/tomjC2sMF2JZ9yeYyxpVAav9sYiDUgDyYGKN2EagRu/vjP9F/RgYOnC1yeltsGpNPjYRWDfVgbtDe/DkLvx/Mx2Pzd6hdFNWdLynH+M+3475PN9tczqTWmOMIkVTeeus3F1fcuIj2nan9HP2QeVblknieHTmX0G3qKnyucM6f2rkZjUU+a1IN1B4l2hEMhERoLVkaALy99JgysqvF86M/2IgFW0+qUCIi7dPSwG1inv82ExVVNXj9pyy1i0LkMg3huwcwEBKltWTpOn8fegN6tg61eP6VZftxrbJahRJRY9NQLl5S1dV0aZlXAxqKe8uJizhTeFXtYpBGGV8+bLb2Gr1WeLXCbeWRgoFQA/TRg72tPj/2v1sVLgmRdVq6rxddrVS7CHbpRZq93U1uzLvr1GX89T9bcOOsNe4pEEn21ZaTmk/OtvWjyviVTcfUHQ/PW9W9k0PaNm9i9fmdJy8rXBIi7dP4ECoAAL1KkaPcHKFdvMZoQtbZYry2fD8AIGfWaJVLY8r4o9wAvnoAWCPU6DS2Zg0iZ/E74Zm+33kaLy/bp8npJZxVUKLd5GyTbvE2Tr2WvpYMhBqZ91YfUbsIpJL84mt446cs5Fwoc2o7WrpAuUJDOBy1grVqmfvVaanN044XluzBwq2n8M/v9qpdFI9lq8ZRS4OuMhBqZD5ae0ztIpBKHvhsC+Ztysawd9epXRRqIE6cdy5obgi+33Xa7fvYcsL24IGu5khAqlSwLTXA0dIPLgZCIrTYfV6qmkZYFUz2ecJNzSEKfh0EQcD6I+dxvkTeWCoN5RurUk43mXHkbVAj8LDZNKZcMexiICRCq93n6+x8NRlPDOlg9bUOL6/EpTJ1uyOSZ9PS/VLJQQN/3HMW4+Ztw7B31iq2T1eRMvyG8ft6taJxDtdxsbS8Uf6YVOp7YDp1RsM4jwyEGqjmQX54eVQ30defW5ypXGGoUVl/pHFN5KvkL+GMgwUAgDK5QYIG7hfZMnPLTl264qaSqGfLiYvoO/13PL1gl9pFscmRXoZqxHbrbUwKrqVODAyEGriEts2sPr/hyHlNfdCo4ThaUKp2EVxKyW/Bj3scm56krKLKxSWRT0qNgXFuSkP5tS/HZxtOAABWHchTuSS2mXRRl3idV6NG6Ke94t8HLd2eGAg1cO8/YH1wRQDYcPSCgiUh0qaGMJ9WfrH68zNJOU38caU9Umt6FAuEjALkhvJxYSDUwLVuGiD62uG8Ypw4X4oj+SUKlojIvSNL51wow8mLTAx3NUmBkMzlG7KSa9odkdz466W1QJ/jCJEqQgN8rD4/Y+Uh3PTeetz6fxtQVq5+1Tt5Dndd5K5VVmPYu+sw9J11qKyukbSOUonbl13UQaG8Sp0kZLk3VC3dyFzF+JA+WqPhoUiMPtSaC4SM/tZa2cQwEGoEerWxnITVXGEDmG+JyJ7CK/Wf4ysSE5KVuhRX1kgLzOz5dnuuS7Yjl5TzZDxKc2PMETJWIHMIBLVoLdaQ2nyqpc8PA6FG4L374uwus3jbKQx/dx1OXWx8PT1Ie9zVNGayXe1cRwGYBmla2I47zF1/wvC31m7ArqbpfCgHiqbUOGPGOUs2J5/X0OllINQItAz2x4kZo2wu88GaY8i+UIbXfzqgUKmIXM80DpJ2JVWqaexDFzWlyJ32wlWknKcLpQ2jlsRRxsGPhu7TFhwp2+8HlZqp3jhZWtrs82pjICSioY0srZc45GuFxLwKIk0y6TYsbRWlLrjmeXiO1ig0xsH8yLWkJiSrQXqytHYKzkBIhNZHlrZmw6Thdpf54+gF/J6l1C8DItfSSai3ME82Vup6az7InYau827RGI/PZJwkDR+flvJrzAkif2sZA6FGJKZ5ILwk1Az97csdCpSGlKSlX1cAUOXiWo2i68n+JgPJiSx7rdK01rMBTZheS6UCy93twXPF7imIilzxPboiYXDMrScuYv+ZIof3obGvuwnjsq05VCC+nAJlkYqBUCPzzj29JC2n5TEySD6tXRg/XnvcZdv6z4bjiHt9Nb7ZdkrS+ClKxBHVNQJyLaaYEGw8km61xkc1rmNr1GBPYh48nb581ebyBSXXcP9/tuC2Dzc6vk+H13Q/yb3GNHQQDIQambv7tMGeabfaXa7nv1Zj3sZs/MZmskZBQ9cUAMCZQts3AzlmrDwEAJiydJ+k5ZVoonpucSYGv73W5pQajtYuHMpz/QCoJ86XYiNHmrfLuDZR6rtnXvlZUWU7DzO/yPmEc9Okbm19+6WXRjvlZiDUCIkNsGjujZ+zMOHLHbhUVuGyweBIHc4OXJbhgh4lcm78FVU1eHnZPqzaf07ePoz+llrx444alp+uB0Afr63vKaalX7jmbnpvPR7631YcOOt4c4wn2Hzioux15Aa8rqix1PBHTdPfAzEMhBqpH1JvlLxsnzd/Q+83f5M8Ui9pjzOBUNGVSjz+hfN5Y3KKsHhHLhZuPYUnv3b9LN/mN6adpy67fB91jHthmh++Fu8Hh86J1zZJSUT3JD9JnEBXlfdZix+u66TWUGkpYGIg1EjFRTfFsbdGwltit3oAKObo0w2WMxeVLzbnuKwcUp0vvua2bZufiqpq911xTcY1cuGV/awLmxaNeXsx2HE1Z952Rz8zxsGG1AE4y+002bmMxoa1kIKBUCPm7aXHsRmj0DRQWlNZ3+m/45ttp/CvHw/g6QU7NdcTicSZ1wjZy1Owta6jZG3FwfYB46LuFel1Y344atV0Xqt0fM6wy1dc11RtPC1GVbWAzNxCjHr/D/x53LmcIV2D647nHs7k6Dj61TNe76H/bZW0zifrXNeBwRW0dHthIOQB/px8k+Rlpyzdh/l/5mDlvjws3HYKAJBXdI1BkcaZJ2zKmbjTVU0icj4jju7R+KazYMspsYUUYzLujNlrS3edUa4gNhh/Fi6WlWPsZ1uQda4YD34m7QYqhteEWnJPg3H86OiPEOPVlJo6w9W0lOTNQMgDBPp6I6yJr+z1Xlm2H//8bg8GzMzApO/2uqFk5CrmF1TznlO2HC1wfS8le+wVb+YvBzHzl4Mmz+l1kBTkmF9glaq4ML+nqTWLvDnjz0JMWBOUiUxWK/c8ya0ROnG+lHmIZhwNBbQ8q7t2SyaOgZCH2P5KMpK7Rche79sdpwEA3+087eoikQsJZvcXOYHQz3vl9dwSLYOcZW0sXHSlEp+uP4FP159AkVH+g9Qbr1r3CK3eAIzPR7C/t+hyn2/KcVsZftl3Dje9tx4TGuFgrrJrhIzqQzUcz7idlo7dIwKhdu3aoVevXoiPj8fw4fanoWiMvPQ6/HdcAgZ3Cnd4G4u3n+JAjBpl/gtRjfQNORc2W4tW1tRHdcYTkOp10oINJa+vdaf5WmU1Nhw5r+CepZPaBPH9Lnk/duQ0jdUFWesOa/McOcOpHCEH19VQDOEwLQVC4j8PGpk///wTQUFBahdDdV+M74/8kmtImrlG9rovfb8P64+cx8dj+7qhZOQM80BIjYuMq9r8jctuHM/poJN0XGrkrvxiZTwkrXRHd9cEnX9wgEYAzvYac3Q9DUURZqRPhqydY/CIGiGqp9fr0Co0wOH1V+7Lw6WyCmRfqE/QO335ilM9ZMh5DW7CcqOr5b7Tpr2/xC6QOp35a9aXM3+25Jr9uZ+ctSfXtQMVuvI+Z3rGGtoHRfvMz2heke2hIezV1tbUCKgx+kLPXX9c8phGDYmWYjnVA6ENGzZgzJgxiIqKgk6nw/Llyy2WSU9PR7t27eDv74/ExERs27ZN1j50Oh2GDh2Kfv36YcGCBS4qecP2y7OD8dKIrtj68s2y1+3z5m8Y/u46vLp8H/afKcKgf69F/7d+d0MpSSrzG5waNzxHm8bGfCRtziW9TmqNkOnjKyLJwa5Qd1Oz9gudvcs9g/l7/6HRaOP217XcVoeXV6LDyyux9lAB9p8pwqxfDuEf3+y2uZ6WNMRgW/WmsbKyMsTFxeGxxx7D3XffbfH64sWLkZaWhrlz5yIxMRFz5sxBSkoKDh8+jJYtWwIA4uPjUVVl+atv9erViIqKwsaNG9G6dWucO3cOycnJ6NmzJ3r1kjY5aWPVrVUIurUKAQCMS2qLLzaflL2Nr7ecMvwSLr5Whd+z8pEcKz8hm5yn5QujNTbLa9w0ZhRMmI8NKrYNVYJAxfconSaaUTQeFP73jxPw8XKsXsD87Mo5VPPPqvGgh+Pnb8dXj/e3up7WaoD3nS7CD5ln8ExypwZ3LQI0EAiNHDkSI0eOFH199uzZmDBhAsaPHw8AmDt3LlasWIF58+Zh8uTJAIDMzEyb+2jdujUAoFWrVhg1ahR27dolGgiVl5ejvLx+Urzi4mI5h9Mg/ev27ogOC0RYE1/8b2M2DpyVfsz7jAa1e/vXQwyEVKLl7rRymc4nVn9b0el00gKOxnMqXIKnw7bLZRWYvuKg/QVFmH/17NUEGr9uWSNk+lhO70811dXqXqmsxojukZLW0dIlS/WmMVsqKiqwc+dOJCcnG57T6/VITk7G5s2bJW2jrKwMJSW146SUlpZizZo16N69u+jyM2fORGhoqOFfdHS0cwfRAOh0OvxtcAfc3acNVjwzGDmzRju0nSP5pfh0/XH8kKmNgeQaih8yz2DAjAzsyS10eBvmvxBVSZaWsM8T50vx8P+2Ymu2xMktje4DOp202g1le43VFtDV59uV4+24K1laLftOFyFxxu9Ytts1Q3pUOHuuZQYvxsG9RScHs42JbUurzU9H8qSPSaalY9B0IHThwgVUV1cjIsK0liEiIgJ5edJmlM7Pz8egQYMQFxeHAQMG4JFHHkG/fv1El58yZQqKiooM/3Jzc506hoZq92u3OLTezF8O4dlFmdhw5Dw+WnPUJOnPlryia9iec8mhfTZEl8oqkLY4E5uPX8SzizKRV3wNT3290+HtST3P7iTlwvbU17vwx9EL2J5zWXw7IpvRmb0mtrfGcLNfuFVk1GxHSDhnDcnEb3Yhv7gczy/e45LtOVvrYjGAp6x1TVVWSXuHlPq6n7xYhoIS980LaEzNa5jqTWPu1qFDB+zZI/0L4+fnBz8/P6SnpyM9PR3V1Z7ZG6pZE1/kzBqN2asP48O1x2TfXB6ZV5vQ/u7qIwhr4ovfnh8CH289Qvytz3s2YGYGAGDp0wPRJ6aZU2VvCN78OQvLdp/B0t31tWeVTlwILKrYHd6S46R8Rs4WOT6ZqF7iBMJa+qXpqNOX3TPpamMgZRJdWdO9ONn65MqmsVUHpA1uqkTe16WyCgx9Zx0AWLQS5BVdw79XHcIjSW3R2+h6rZM41hdgeexHCkrQNTLEiRI7TtM1QuHh4fDy8kJ+fr7J8/n5+YiMlNYO6ajU1FRkZWVh+/btbt2P1qXd2gUnZoxyahuXyirQd/rv6PWv1Xh20W4cKygVXXZ7tmfUCp26dMXiOWeux40rR0ik+7zU9RU8FbZuepfKHJ841ZXBXEMIDF1dG6DkxLsWydJyIiuzlcvKq81erl9A6aT34+frr9OH8kzzRl9csgfLdp/BXR//afK8nLGzzI9GzUuYpgMhX19f9O3bFxkZGYbnampqkJGRgaSkJBVL5ll0Oh2m3hbrkm39kHkWybPXi16YGkhuoNOsHabYsZ+8WIYfMs/YvFlYXlQabs8pk6Ib/W3efV7so6LGfFbWgo2PnZjt25G372JpOR79fBtW7jOtVdBEjGxUhooqy/dnywmJOWPydwcAWLzdfSkOFt81WcNISF9Y6Vyv+z6tz8M1nxrlxHnxH7NSmZ83NT+nqjeNlZaW4tix+nEXsrOzkZmZibCwMMTExCAtLQ3jxo1DQkIC+vfvjzlz5qCsrMzQi8xdPL1pzNxjg9rjoQFtseXERWSdK8asXw45tb1Or/wCfx89XrilC2KaBxqe18povGrILy63+nxd9XR1jYC7+7TBS9cnwP33PfU9H7XQRVpSGZxYxHxARbHA8bnFmfZ34mKuPv2ObO6jtcew7vB5rDt83qQpY9WB+nxKVQJkQcA2o/y/vKJrJt95AKITwTqzT2O2Bjl09pRY/Aix8+4Zf2wdHXtLiVo+47JdLpM4tZLEDg2AtvLVVA+EduzYYTL/V1paGgBg3LhxmD9/Pu6//36cP38eU6dORV5eHuLj47Fq1SqLBGpXS01NRWpqKoqLixEaGurWfTUUvt56DOncAkM6t3A6EAKAa5U1eGulZbfVtYcL0CMqFC2C/Zzeh1Y5UvO1PecyburaEot31P66fWlkV4Q18QWgrYuKK5kGPvYHVFx9IA+7TxW6t1BKcOANFWuKm7J0nzObddr5UusBvjEHh/ARZX6ctpqObd24BUGw29RlkSPkRPNQ54hgm2WBm3op2mNeiyf6Y8XGa/ao2YSreiA0bNgwuxHkxIkTMXHiRIVKRFIse3qgRfuwKyzPPIMDZ4vRNNAHmVNvdfn2tcKRmi+drrZWqI7xxV0TydKu2o7IcZmfsd8PFpg8/uPoeTzxleM975yhdiCaV3QNP2Q6Pw2DlBu/XOWV5jdRy7Pl6hu7eeBjKwXJ2V3LvYGbJkubrhvgaxYRivT4UzwQMmturqox/o46dvHRQCW2gaZzhNSUnp6O2NhYm13tPVnvmGa4q3drl2+3bjDHwiuNe5Z7R3792Lo9yWny+G7nacxefVj2/u1xpmXMuDZDbDt6OwMq7jpZaL8ADYTcz8eUpXulbtj2y264Ob3zq/3Pmpxc6TOF9nvUmR+HrRohpzsaWMQB0rdnftw2AzYNDYNwvqS+ls+4zPJiaO3kCDEQEsFeY/a9dlss7u7TGoueGGB4LrZVCN7/azz+Ny7B6e3PWHkQhVcc73nT2Oh0pj1SbF0YbV1UXlyyBx+sOYZMJwZwtMpsn3LGHxFb1niTecXXbDdjqH57UM/aw+clLWfvHG04Km07cvx53DQR2tpbqGaOm61dOxLc21vH9HsrPRhQsweZVDroJH8PtXQIqjeNUcMV1sQXs++LBwBMSumC4quVmDKqG4DaL2rq8BuQvtbxnjP/2XAC/9lwAvHRTfHV4/0RLDIGkafQ68Qb1MwvKmcuX0VogO3zVXTVvbVuhVcq0TLYX9KyYhdF87wXW9dONS6sdYGpli7qttTY6VD364F8DOvS0q1lUOJUWZvMVIyrh56QVSsio5xq1whdq6yGv4+XxfOO9v7S0leGNULkEqnDOxqCIKD2BjEppStyZo3G34d0cGrbmbmF6PfW7zhwtkizv4SMlVe5p6eh+fXV5Bei2WUl42A+lCbll6CkKTKMFpn1y0HR17TFtQVz13Ha2+w321w4orXkvcoce0fSHmXUtDjbMiZzfUHkb8DKVDlSNqKQ4yJd5o2LUjsNjmPbN563UmkMhEQwR8h1Xkzpgnv7tnFqG9cqazD6g434eN1x7Mi5hLLyKheVzrV2nryELq+uQtLMDJcHbbZuFua7qpawb1cPVLBkh+ncT9aKcKXSfpBofBMTG1LA+nqNh6wx+WR8ztTpPi9lGW0GklJqi+QEXeavW+YZS60RUv599BIZ2d2kQ4MTI0urmRfKQEgEc4Rcx8dLj3fujUPOrNH4PW2oU9t659fDuGfuZjzw2RYXlc613vi5tgbjXNE1ZJj1anIFsYuMnF4y7mJtKARz0m6KNl91buMu5q5Rry6USs+NO5wvZ6JL9VkLelxdLou8HRvL2gp2pORMOfOxW7XfbEoNyTlCju9TCmvvkdicbFcdHAPKfB+uHkJBDgZCpKiOLYMQYKWdWa69p4tQXSPgWEGJtprLjMqy10ZVr1iR31qRhVMXLaffACxnXzfOGJKTE+HIckpyNA9IzSNx9WnMvlAmeVlrc2/97YsduFJhWWuqxtutxvti/rm2Nd2JrXNySepAgsbbs/t6/RL/+inL5DWt9Bqzdk7Epvp74qv6Uad10Dk8oKKXXr1whIEQKW731Fuw+7VbsPBviXg+ubPD23n0821Inr0Bn2444cLSOcekvdyB9T/7Ixv3/2ez1dcycwsljy4rpUr//Yyj6PPmb3KL6Ha2e4bZWs/1ZWkIrP1Q//1gPuZtzLZ4Xq2Rpe35fudpu8vI2qfZ42VGkxubs9m1XkLVqmWvMdvrSO0ZZr6sIPK8O1jbvNg+txrNDymnacycl4qTCrDXGCnO38cL/j5eGNgxHAM7hiPjUD72npafKPfH0QsAgFm/HMKFknKM7BmJvm3DXF1cxZ0TmQ5g96lCi4vRzF8OIiLYH/3ayT/unScvi75WVl6FJn7qXB4crxFSoWlMAzPCiPUlvGwl50KrseLqLGnJ/dJrOqXv2+bnzYEOAM6cY9s1QoLRcu59J50JmCX3GjNbTi9W5aQA1giJYLK0cn6cOAiTUrrgsRvbY++/HBtN+r8bs/GXTzZbndBRCZfLKpBxMN9qM4UrGV+Yt5y4hE/Xn8AbP2e5PAgQq5VyF9MEUqNcCLPjqrLX/1slatZGiQVjB85a/rho6E1jkssvJxCysayUXDvz9a+U286Zsd2DzTyoMv4uKEeJfZl/t9X8TcFASASTpZWVOrwjpo6JRYi/D0Z0j3R4O92mrsIL3+5xYcmkufPjTXj8ix3IOlds8vyViiqkLtyFn/c6P/0BAJMr1DPf7Db8Xe3i7Oj9Z4rtL+QmjnZ1VmHSeQMtDua45cQl+wspQErzklTS4yDp+7RV++HI98pe8rrNnmHmj0V6mKmRIyRlRO/anq1Sq4QsVpa2nhswECLNmftwX+yZeivmj5dfG1ddI+D7Xafx9qpDSF97zOS1mhpB1mjHcpy0kuCs0wH//SMbK/aew8SFu62sJZ/Ydfma2XxODXniUVuXUeOh/c3NXe/44J2O0kLTmBxaDNjkULppzB3vr7waIesP3J3rZe1z8ujn9isFdJDRNCazTO7EQIg0KTTQB8O6tMSxt0bi8UHtZa//8brjeOfXw/hswwlcKC3HobxidHh5Jfq/lYE/j11wQ4ktCYJpb5W9pwud36bI5cN8EEfzaQ0aElsX0peX7RN/UQWuqDm7JmFsJVtk3fQbeNOYVHJGTbc9M7399V15TqVPseG6fcothy16J5Kl2TRGJMLbS4/XbovFHgdnon9r5UEkTP8dI+b8YXjuwf9udVXx7DIee+P2jzah0sn2G7ELlNTkSS12lwfMcyHEyyiWSK42Z06rs82aspqBnNqTg1wZKEhcbrLUSWjhfFDhylo2i5GlBevBj1a/xzqdzuFkaTUxEBLBZGltCQ30Qc6s0TgxY5RLtrfILVMJmDpXdNVi7I26REpHrwFi60lN0nbHII+uZpwPraWLpbvkFTsX3Gn9HFkL0h29kUtdTU5+lM0aIclbkc7RpjHjv909YKqjnyl506yZJUurWCXEQEgEk6W1Sa/XIaV7hNPbmbzU/U0s3+44DS/zwTGc/LKL3UCk1irszhXvMq8VDT2PRa4CGdOIOEuVcYSslsPyOVvDOdRvy/Xlt3lKZM6N53RZbDyhaPd5B89z7ThCjuVxiU8p7X4MhKjB+fThBJyYMQrdo0Kc2s6GI/aHz3eWxbD0ArDlxEWrydVSiF3/iq9Jy4kQGyZfbWKjZGu0uBacuS0F+Do/0rqWWfvMVloZBuGojKlCtETue297PjFp3efNc6C0MlebrKYxx3bhFgyEqEHS63X49u9JTm3jkXnbTB4LgoDnF2di2g/7ba5XVl6F/23MRu4l+8GM+X38UF4x/vqfLbhQ6lgtgNhF5qXvpdVwaTWuaOi1QM7ch8RuYkUSJ6F0dbK0lM+1HNZqL77Zatk0LSVId0dFiLO1K3KDEDnDQ4h1n/9wjWmP2HUu/lHnTMKzw+uyaYxIviZ+3vj1uSH47fkhuC/Bsdnt7537J65WVONaZTWyL5Rh2e4z+GLzSatjnxRdrcSBs0WYsfIg3vw5C7d9uFH2/szHGZLrJyfHI2oI4YZxM5/W819c4YdM6+9pvxm/u3xfUgLO/TbmyDN3KE/+51kAkGOlRnTFvnOWCytAS/PXme/PdOwg8dKcvmx/jB851h5yLJdQTjBjHkCy1xiRg7pEBqNTRDDevicOK58ZLHv97TmX0W3qKvR+4zdcMZpF+btdlnMfJc9ej9EfbMSC679m5XTRrXNVYldpsfGO3vn1sOx9GlMysJBTy2M8g/W7q+uPsSEEQoIgOFWjNf/PHKvPSx0l/c0VWaKvOds1354Ve+0HL7Zu7sbWS6jVcPXnoay8Cl+InH9A2s1ZbpE+35Qt+pp57dR+49HBFfwu/N9vRxxaz5lJV9XEQIgajdioEHz79ySEB/miT0xTWeterazG11tOGh7/8zvL7re2BvMTY/5lf3uVtECm/1sZOFt41WJ8IGdptQnq43X1gyHWzSHXUGw+oe6YTduyxXtImQdZqowj5MKduvrz+68fD2CpjQlZpexN7uEtF6kBtLatM0Y1PUq9dR9kHMWJC2UOrStrAnnzZGk2jWkPu883TP3bh2HHq7dg6dM3yl530fZck8e3ffiH3V/lq/afw+Lt4l3xz0oYll7Mv1cdwv+szCDuDHd3uzUm5wbhzHlSm725pdR02WhAT0DaeyJ2Q1p32LHmEsvPnODW3mtXKqokL7vWwWP689gFh9e1xbxGyPjcCYJ4DZ8rY4jZDtYG1ZZDTrK0efd59hrTHHafb/hOzBiFtS8Oc3j9/WeK8eXmHBRdrRRNIH3y6102E5XF8j+kqKiqwZE81/akkTO6tbNzRP0ucUZxADgkcpzO5lS5g/l5UfOXrL33yKJZSsI2xW5kUxwccsKiJ5RTieX2lym5Jj0QcmR/ldU1ePC/WzH+8+0ovFIBd9bVGAdGf/96J7q+tgrnijT8o0FG93ktYSBEjZZer0P78CaIbeV4N/vpKw4i7vXVGPz2WheWTBpBqD0GV9p0THozzq5Tzo059N5vR5zehhatOpBn+aSbrv32mkar7UQGrqx5sfZJtPfpLCuvshwtGa4fUFRJxsn8RVcrXdbceDivBB+vNZ0vr8poX3tyCwEAS3ZY5i8qdV7sfZ5kDaiohTfzOgZC1OgN6dzC8PcD/aNVLIk8AgRVx/2pcMF07nd//KcLSqIth63UXrnrmm6cRG6N3Ok5pOSciX3kHGm6GGtlOht3DDXgLvb2JwjOv/d172HKnA0WM9dXWxlvSU32Tr/eiSk21LzWMRCiRu+55E6YcVdP/Dn5JvRs3VTt4sjiiolaHfW3L3Y4PTdaYyEIAv7xzW7887s9Fq+58/p9/Lxp0uq27Et44ssdOHM9p8peIHTCbP1XltkeI8vVMq/XYhgTILi1NsCVb8faw+4fdNXWwK7WzpOLK4ldSqeTHuhqqEKIgRA1fv4+XngwMQZRTQMwumcrtYsj2cajF3Akv1S1/V+pqMaPTuQ41Slwci4tNew7XYSU/9tgSIg9W3QNP+05i293nLY6BIK7airMu5Tf9+lmrM7Kx7Pf7AZgv2ksw4HxYMRGPT9jJaH9dwfnrnM0j0TKWvIqyWxHFfa69F+6UoG0bzPl7NCCrZpXa2+vTqeDj/nUPQqxd2rlDKiopYljGQiRRwkN9DFpKtOyMjvNIkq4InEcGltJu7N+OeSq4jhs50npk3ACwGNfbMfh/BKM/7y2s4Tx8Tk7W7wx815dFkRuFjkXyyzK5SozZbxfWeeKZY9ELQiON49JWc/d83AZb//uj//E/jPOJfTb+jyJHUtsVKjJY6XCIrs5QjKqR823pGZNFwMh8jjpD/ZWuwgNUlV1DVIX7MI8K136bY2lk33RsTFJXGnlPisJzjaYD5ZpfH135WSRczcct/m6AGDz8YtWgo3afboyKHNUvswaP6fyaiSsWC5xIEqHi+DiU/70gl2icwWK7UpruUN1dJBe02MxsjQDISLlBPv7YO+/bsUbd3THtpdvxsK/JapdJM16bXl9Tskv+/OwYt85vPGz5UjGxwrEm/AcGYjS1fZYyVVxlHmzzpWKatEb1rEC28Mf2Bunas/pIjzw2RbRXov2msa0yN05QrZGijbnyM3XHUX/RWR6EWs1fjodUFWtzvtut2lMp5PRNOZsaVyHgRB5pBB/HzyS1A4tQ/wxsGM4PnqwN4Y2kCYztZSVi4/PYqs3kqvnQTL3Q+YZjDObQNec3Bue+eLGVf7mN6fUhbtEL+rnS+w0fdlhK5G2tixObd4l5J7b2nPlaI6Q/fWOn5eeV+dIJYSSuS1iexIbd8vd7B26TkaSkAYqMw281S6AVqWnpyM9PR3V1ernaZD73dYrCrf1ikK7ySsAAN1aheCgBgfzc0RZeRU2HbuAjcecm7pC7Ib35/ELmLFSvTygZxdl2l1GbvOV+TXaeG05F3B7TVfONKsBDbNGCHBvjpD4uoJFDosjXbbdccbFPgfWgi5nPzPOsBeI6iQsI3VbSmKNkAiOLO2Zmvh6AQBeG90NL97a2eS1Di2aqFEkp2w8egHdp/2KJ77aiS83n7S/gg1iF+AHP7McK8YR3++0HChOi6wFHz/usd67zt591tm8CHckS8sn/yAcTWh25mh3W2kedahpzD2RkFXW3l41c2nssVY2sbnwNPHRvY41QkRGNr50E05euoL46KYY2DEcfWKaofhaJUb0qO12P/jtNci9pOEh7s28aSWfx2FOXoDtNSm8sGQP7u7TWtY2/zgqcZwXF9485AQf7qqwqbvhVGngbiJ3eATneo3ZX1FsEfMEeMDBj4WCp1zpd9feuGGODKg49Yf9WPXcECvbUv+zW4c1QkRGmjXxRXx0U8PjgR3DDUEQAKx5YZjyhXLQ+ZJyl8xev/Nk7TQZzsYS5pPaWiN31mtrPdjcwfiS7cqeWo6e07r1tNBr7KkFu2QtL6jUKGLtXJ8tkj/GlTu654t9Dqw3jVlZzkXlWL77jFPr63TA6z+Z/vgSy2fSUBzEGiEiOXy89Dg+YxT0utoE2oul5Vieeda1NS8uMuTttVYH/5Nr07EL+OLPHKfmDXv0821Y54ZReqWOWyJlTqzNxy9iUKdw+Pt4mbx2rKAU/j71vxmXyGjCs3fTdLSZo/BKpaTta5EguLdpLKV7hNV8OFfNbq7kGT9qZUBVq4cho8u6rfNw+Ypzyf2ATvI1x2L2eRVzn1gjRCSTl15nuJg0D/LD44PaY8798YbX108ahq8e769S6eq5IggCgO05l/DjnrMmvb/OFF7FrlOXcbFUWtd4qUGQuy6F9u6BqQt34W9f7rA6NMCzi3Y7/Ot18Q7btWCyBqAzKkRFdQ0ulJa7rUaorLwKFyS+t3I50WlM0vvQxM+9v+9fXb7Prds3lmel2bFGAHy9TW/dYqflBrO8xu/sBPH2zq+917/ZdsriObGBEs17PKqZPM0aISIXuCM+CkF+3oiNCkFU0wC0bd4EObNGG3qhNWTWJv68cdYaAICft7q/paSGEbZ+bQqCYAjUFm49hRl39TR5veRaldX8Ein2nymyUy7pury2yuTxmA834rNHEhwolX2JMzJQWl6Fna8mo3mQn8u37+gt7/Rl+6NYu7u1UO7gnFLICYitjdQuNVCf9N1e3Jvg+MTTjgQr3l7WrxFaqstkjRCRC+h0OiTHRiCqaYDJ858+3FelErmOrQuWq0fx3XJC3lQYeifH5a+pEdB+ykqby+QXX8PMXw46tn27A69I35b54Ivniq65rWms9PqYUXX5YY4QHS1ZELDMwVyUuz7+0+4yYudEw52t3FY2LQQbXiJBnvn7xKYxokYqpXskTswYhTfv7GHy/G29Gs7kr87cDOWyNVWHNb9l5Tu1v95v/mb9BaNrdHlVDTYdk1euOu4e8NDdvcacCbTWikz46vabs8gOGlq3c5eQcLL/PH4BX27Owbd2mnEBxxKcRZvotRClXcdAiMjN9HodHh7QFjmzRhue8/XSY+ptsYbHHcIb3hhF7vCTyHg8zhIg4MUlezD6gz9MalasNXl9v/O0zRnB5bpUVmGoYXE1948j5PgdWqy5x9353VpIIJc7pICzxLqi2zsT+04X4cHPtmLqDwfwz+/2YoudHyIHzxXjSoVrPsscUJHIQ/VsXTtr9D0JbfDYoPY49OYIvHtvHBb/PQnv/zUeAJDQtpmKJWycaoTaRNEDZ4uxOst2jscLS/a4bL9nCq+iz5u/oce0X3GuyHL8KadHlnZzIORMTcWq/dbnz3J3VYAGRhTArwdcn0fkKkfyS/Dn8dpedY99YTpgsL3hK+6ZuxljPtzoknJo4X2qw2RpIgV991QS8oquoW3z2hogfx8v3NO3DQDgjvjWuCO+fkBB40TrpA7NMaRzC5wruor7EqLxzDe7ZY+548mMfzFPXLgbt/WKUrwMH605hrRbOmP/2WIM7hgOvV7ndJOI2wOh6/+fumg/SdmcWFKxWjVCSuagyM2dc7bGsO6IV+3Pw3//OIH/uz8ebZoFWK0puvX/NgAA1r44zKEJkY+fd811RwMVdwYeEQhlZ2fjscceQ35+Pry8vLBlyxY0acKmCFKen7eXIQiSY9KILugTU19T9N1TA9FHLL+FLGzPUS7PScyCrafwy/48XCqrwLv3xuHu3q2dvjW7aogEe4a8s1aR/biC2P3VFXk49pqO6shtWp36wwFHimNQF1Q8+fVOAMDLy/ZBp9Mhx0YAm31B+uS07mAesKrZVOYRTWOPPvoo3njjDWRlZWH9+vXw83N9d1AiV5tulGBtHAQBQFgTX6vrTErp4tYyNSa5l+TXcjjrUlntgHUvLtmDm95b53Svu7OF7p3uxdFBCG1N1eDu251ovowLduzMoKLuZH5o54quYcMR+QOYKllLwyk2FHTgwAH4+Phg8ODBAICwsDB4e3tERRg1cGMTYzAppYvksWLSH+yD1OEd3VyqxuGrLScx+G11azlyLl6RPleaiNecrEmQIkdiE+y5oqsY/u46/PePE6iqFr/J/eKGcXiMiSWQT19hOVhma7PhLuyReu/W0D1elBrd1Y2Dn/S1x01e8+ju8xs2bMCYMWMQFRUFnU6H5cuXWyyTnp6Odu3awd/fH4mJidi2bZvk7R89ehRBQUEYM2YM+vTpgxkzZriw9ETuo9PpkDq8I26JjbC53CujuuGTsX0wqmckAGDB3xLRIlh6rec9fdtgwuD2hvU9wWvL96tdBADKNW05Sgdg03HL6SqseffXI8i+UIbpKw7azD2Zt8m988OJpU1Zm/Nq4A3NJW+3ukbA7welDdegdG2H+f6OFdhv9rKWl+TO5qmzhVfRY9qv+CDjKADro2arRfWqkbKyMsTFxeGxxx7D3XffbfH64sWLkZaWhrlz5yIxMRFz5sxBSkoKDh8+jJYtWwIA4uPjUVVl+aauXr0aVVVV+OOPP5CZmYmWLVtixIgR6NevH2655Ra3HxuREgZ0aI6ebUINj2/sGI5tL9+MiuoadHm1fjTi2+OisP9sEU6cL8OonpEI8PHGA/2jkdAuzLBM7qUrOJJfgse/2KHoMXgqNX8FSyW1jMZ5MWrmFLnrVr5w2ynsPlUoaVmxYOyLP3NcVh5n/eOb3RbPuTN+S/s2E2UV1Zj92xE8c3Mni9eNr2FKUz0QGjlyJEaOHCn6+uzZszFhwgSMHz8eADB37lysWLEC8+bNw+TJkwEAmZmZouu3bt0aCQkJiI6uHVZ81KhRyMzMFA2EysvLUV5e/2umuLhY7iERKWLhhEScvnTV6gVEp9PBz9sLsa1CkHWu9jM8/a4eCL4+D5NY7kd0WCCiwwKdKtfjg9rjfwrNCt/QnVIhT0mO8qoayUnGaoR0NTWCxejicmpjpCx5rugqWoUG4HcZg3eKFWHaj+5pyhSb4V1LbI0a//M/BuGGFkEKlsaU6k1jtlRUVGDnzp1ITk42PKfX65GcnIzNmzdL2ka/fv1QUFCAy5cvo6amBhs2bEC3bt1El585cyZCQ0MN/+oCKCKtGXhDOO7rZ/vzuejvA/D144k4PmMUQvx9oNPpXDYLt5gukcE2R86eeXdP0dc8XUp3282gSktduAtz1x+3v6BKducWWjwnZ0BFe5OQAsCv+2tzmsqrpDdjWitDXpH7moKkHIeW9WitXm0QoPFA6MKFC6iurkZEhOnFISIiAnl50hLuvL29MWPGDAwZMgS9evVCp06dcNttt4kuP2XKFBQVFRn+5ebaH3acSKtC/H0wqFM4vGTOybXoiQHoHhWC5G4t8cxNHTEppQs2Tb4Jo3vanxpkTK8ofPDX3qKv358QjfuvT/z4zxHs5WZMi01lJx0YQ8hRckctthZwuLp5p+z6pMNXK6X38DOvlSorr8KAmRkuLZc7uKtp7M9j0vLM1KJ605gS7DW/GfPz84Ofnx/S09ORnp6O6mptJzMSucOADs2x4pnBFs+nj+2DJ08XYcqyvXgkqR3aNA1ATPNACELtKMqJ7cPs1jjp9Tr8+55emHF3TxzKK8bbqw7LKtsd8VH4IVPaVBwfPNAbz1jJhSDX254jb8Jca2Kn/oppY2Ix/sb2kpYvumI5RYqtMSYLSq6hZbC/Q2XzlvFjwrgIK/aeQ+rCXQ7ts7F48L9b1S6CTZquEQoPD4eXlxfy803bZvPz8xEZ6d4eLqmpqcjKysL27dvtL0zkQXq2CcXP/xiM+xKiMbBjONo0q80rGtChuUkQNPu+OIt1l6feaPjbS69D96hQWT13AODGG8IlLffq6G4Y04AmtwW0PTGoLWP/uwXnXNT08/pPlt3cxRgn/JZcq8SJ86U2m8ZeXSa/t2Dd2E9yGJfB04OghkDTgZCvry/69u2LjIz6KsWamhpkZGQgKSlJxZIRkT1392lj+PuTsX1w9K2RiI9uarHcwgkDrK6/6jnLGikA8PaSFi3cHh/l9nwoqrXpmLQRl51RYWXwSePhB4a8vRY3vbce7/wqXsPoSHJ6XeK/nOlMtDSPFtmnetNYaWkpjh07ZnicnZ2NzMxMhIWFISYmBmlpaRg3bhwSEhLQv39/zJkzB2VlZYZeZO7CpjEi571zTy9cLKvASDu5RXMf6oMnv67/5fzV4/3RNTIEAzqEWfQ2iY0KwU1dW2LNoQIAwN5/3Qo/bz2+3Z6L/u2b49cDeejfPszhJhBS34XScoQHmY6Fde+ntjvIXLbSTCbmXNFV/PO7vbLKJKc32ifrjiO2VYjTPTDlqJI5rYeadp50vhnVlVQPhHbs2IHhw4cbHqelpQEAxo0bh/nz5+P+++/H+fPnMXXqVOTl5SE+Ph6rVq2ySKB2tdTUVKSmpqK4uBihoepmtBM1VPcmSOt1OaJHKxyePgJeOh28veorqhf+bQCW7MxFn5hm8PHSI6/4GrpGhuD/7ovHD3vOYHTPVgjx9wEAPJzUDkBtrzVjn4ztg6cWNIzmiSA/b4yJi8JPe6TlQDVWl8oqLAKhPVZ6iNWRGqTULZa+9hj+OCovgbdaZiaxtXF63OlIvvNzhxVekd8M6IjcS+6dGkYu1QOhYcOG2f0QT5w4ERMnTlSoRESkBj9vL4vn9Hod7u8XY3jcLrx2wtrQQB88cj3wsWdkz1bImTW6dv3JKwzP39O3Dc6XlGO9A3MyAUByt5b4/WCB1dfiopvavHFbE+jrhUkjuuCtFQcdKk9jc/x8KW5+bz2aN/GFr7d4FsdPe85KDjqOFpTgnk/+xI6T8ucM03qFS5nMHnfWt6FMC4hxd3+ZHVrdQvVASKvYNEbU+AT5eRumFph+Zw/4+3gh99IVDH57LfQ66bkdnz/aDzd2DMe7qw/jPxtOmLw2ulcrRIX6yw6Est4YAQBoFmh9Ql1PIgjAze+tBwBctJOsLKfmpUaAQ0FQQfE1mxPJaoG1HCqtqhvkFQD0Gsjj0wlamgJWg+qaxoqKihASEqJ2cYjICUfyS/C/P7Ix8aaOJvkb1yqr4eetR/spKw3P1dUirdqfhye/3mmynbrXNhw5j0fm1c99eOytkfD20qPkWiXiXl9tCKwiQ/ztzq1Ut83CKxWIf+M3xw+SPNLXjyfiof9pu5u6Nb5eehx5S9rwNnJJvX9rutcYEZErdY4Ixr/v6WWRxOrv4wWdToe0WzoDAB4zGsdmRI9I5MwajV5WpjIZ1DHcMMHtTxMHGfKbgv19cOytURjWpQUeSWqLe/q2sVjX2AvX9wsATQN9seBviY4dIHksuTlMmqF+hRCbxoiI6vzjpo4Y1bMVOlzPRTJmrSu+Xq/D9leSLZ6ve23++P4AgKKrlfjz+AX0atMUv2Xl40zhVfh46VBZXXvzmnhTR5N1b+xoOlbSe/fG4YUle+yW/407ukMH4LUfTOe0igkLtNl1/N1743CsoFTydBoPD2iLr7aclLQsKSNX4/PWidFCjhBrhESkp6cjNjYW/fr1U7soRKQQnU6Hji2DLCbyBGoHaASAvw/tIHu7oQE+WPr0jfjX7d2xafJNyJk1Gkemj8SnD/fFH/8cbjXIGtAhzPD3X/q2wZ3xUQCARwe2Q9fIYEy9LdZina6RIXg4qR3mPtTH5Hlry/7DKPjq0KIJJo/sKvl4XpKxrD3924fZX4jsenW5/MEitYA5Qg0Ac4SIqM7VimoE+Fr2bnOHvKJr+GDNUYxLamcxJECdLq/+gnKjJNmMF4bihhZBEAQBoz7YiIPnivH+X+Nxe1yUSf7TV4/3x+BOLbBkRy5yLpbhxVu7QKfTmfSqE/PLs4PRrVUI3vgpC/M2ZTt9nNkzR5mUjTxLkJ839r+e4pZtS71/s2mMiEgipYIgAIgM9ceMu3raXCZz6q04fr4UeUXXcLGsHDe0CAJQW7O14h+DUCMIhrylSSld8M6vh/G/cQkY3KkFAMtxnvZMuxWj3v8DZwrFx3np0KK22fDFlM4uCYQ4+rf7tW0e6NDkud1aheCgUQ8vd2DTGBEROSzA1ws9WociOTbCZLwloDZHyXhwytThHXHwjRG4uZv4YLShAT7YNPkmw+Mp15vAukYG4+vHE7HrtVsM4z0F+kr/Hd2tVQiyZ46SvLzS6po9G6sgP8fqPMbf2M61BbHCWjO00hgIERF5CKk1Wpun3IT54/vhiSEdkDNrNFY9NwSDOoUjrInpGEcTh3cU2UK9Ti2D8Muzg6HT6bBn6q0OldvcZ48kuGQ7dR4d2A49W9f3CqzrCah1UgOcu3q3dmj7t8dFObSeHFrIEWIgJILJ0kTkqVqFBmBYl5Z2m61eTOmCnyYOMglwRnSPxCuj6mtYjDcRGuiD9AdNE7kBYMmTSejXrhkW/i0Ro+3MS3d7XBTaNAuQeCS15bHH20uPZU8PxGu3xWLFM4Pw48QbJW9fTaufHyJpOWuTHUuhhSBFCQyERKSmpiIrKwvbt29XuyhERJrVs00oQgN9cGd8FIZ1aYFPHuqDCUPqe9Z1bBlksvzoXq3w/VNJ0OuA9/8aDwDo1y4MS54ciIEdw5E+1jJQMnZX79ZoIqNZ7o07uktazttLj8cHtUf3qFC0Cg2wG5C505DOLSQtF9VUWkDoaB6WBlqtFMFkaSIictqcv/Y2ebzs6YFYvD0Xk1K6WCzbt20YTswcLbqt8Te2w+ebcgyP37ijO4Z0aoFDecUY1qUFdDodnh52A6pqBIspTgDgvoQ2+HZH7XxWLUP80SLYD+dLyuUdkANBwM//GITbPtwof0UzZy67dkwgRyt2lKgR0kLHdQZCRETkcr1jmqF3TDOH1p02pjteGtEVldU1qKwWDLlJ7YwGuvzniNpE7m6tgjF33QksmJCIPbmF6NoqBFcrqvHtjtNIvp4YntwtAt9sO+XkEdnXo7Xl6OOO6N++OY6fL3PJtgDHB2/W6YDvnxqIv3zyp8vKYk79MIiBEBERaZC/jxf8fewnd9/Vuw3u6l07hYlxj7j9r6egyfXk8Ndu64YerUPQt20zjJjzh8n6SR2aW91u3VAE5uKim8qeUFeuCYPbuzRwc7RmR6fToW9bx4JZqTRQIcQcITFMliYiariC/LwNuTGBvt4Ym9gWXSNDsO3lm/HQgNqhBnq2DsW8R61f458aegOeGNIB3z2ZBC+jZBl/b9fcNg++MQJbX77Z4vlbYiPQ3soUL85QcvwruR5Jaqt2ERgIiWGyNBFR49MyxB/T7+yJnFmj8dM/BokGCQG+Xnh5VDcktAvDymcGA6gdmPAJo0TwdS8OQ1SoPwAgtlXtyMVfPtYfjw5sh94xTXFDC/GAJsDXCxEh/jj05gjDcwM6hGH2fXEWyc1v3tlDdDv+PvZv451aBqFHa+VnRnjx1s52l3k+2f4y7samMSIiIhu6RAYjZ1ZtcrcgCPjwgd7oEhmMduFN8MPEQVh7uABjetWOuTOkcwuTXl+Xyiow6v0/kFd8zeq2/X28sPGl4cjMLcSoHq0MAwz2iWmKXacK0atNKB4e0BYPJcZYnYpk40s3IWH67zbLr9PpMGFwBzy7KFPS8T417AbcEV8/hlDdqORy/W1wB7y7+ojNZbQwoCIDISIiIol0Oh3GGA002CLYD/eZTVViLKyJLza+NBz7zhRh6a4z+GrLSXib3fzbNAtEm2aBJs99+nACvt2Ri3sT2hj2a6xuUMnwID9DkAbUzkI/+O21FuW4PS4Ksa1CEBrgg/4zMmwe40sjTCfVTR3e0WogtPPVZPQVCcKGdm4hKcdLC9g0RkRE5EbeXnr0jmmGKaO64uVRXfF72lC767QI9kPq8I5oGexveO6mri0R5OeNPVNvxS2x1qdKiQ4LRM6s0YZ56v4+tLYpT6fToVNEMMKD/BAe5Gt1XVt+TxuCuDamveKaB5mOwL1wQqLh7w8eMB1OQctYI0RERKSAQF9vPDHkBofX/9+4BFRWC/CVkLD9YGIMkru1tJguRK/XYfOUmyEIgI+XDiv35cHXW48rFVV4dlEm+rWz3kusY8tg/DBxEDYdu4Cx/92KAR3CLJZJ6tAcQzu3QNNAH4QG+NgtY6BGkrh1ghZGM9Kw4uJihIaGoqioCCEhyiebERERKeH4+VJENwu0G2jlXrqCyFB/+HjpcfJiGR79fDueGNIBD/SPsVj2nV8PIa+oHONvbIdXlu/H5BFdcbGsHGsOFuCZmzuZjA3lalLv3wyERKSnpyM9PR3V1dU4cuQIAyEiIqIGhIGQi7BGiIiIqOGRev9msjQRERF5LAZCRERE5LEYCBEREZHHYiBEREREHouBEBEREXksBkJERETksRgIERERkcdiIEREREQei4GQiPT0dMTGxqJfv35qF4WIiIjchCNL28GRpYmIiBoejixNREREZAcDISIiIvJYDISIiIjIY3mrXQCtq0uhKi4uVrkkREREJFXdfdteKjQDITtKSkoAANHR0SqXhIiIiOQqKSlBaGio6OvsNWZHTU0Nzp49i+DgYOh0Opdtt7i4GNHR0cjNzWVvNDfjuVYGz7MyeJ6VwfOsDHeeZ0EQUFJSgqioKOj14plArBGyQ6/Xo02bNm7bfkhICL9kCuG5VgbPszJ4npXB86wMd51nWzVBdZgsTURERB6LgRARERF5LAZCKvHz88O0adPg5+endlEaPZ5rZfA8K4PnWRk8z8rQwnlmsjQRERF5LNYIERERkcdiIEREREQei4EQEREReSwGQkREROSxGAipJD09He3atYO/vz8SExOxbds2tYukWTNnzkS/fv0QHByMli1b4s4778Thw4dNlrl27RpSU1PRvHlzBAUF4S9/+Qvy8/NNljl16hRGjx6NwMBAtGzZEpMmTUJVVZXJMuvWrUOfPn3g5+eHjh07Yv78+e4+PM2aNWsWdDodnnvuOcNzPM+ucebMGTz00ENo3rw5AgIC0LNnT+zYscPwuiAImDp1Klq1aoWAgAAkJyfj6NGjJtu4dOkSxo4di5CQEDRt2hSPP/44SktLTZbZu3cvBg8eDH9/f0RHR+Ptt99W5Pi0orq6Gq+99hrat2+PgIAA3HDDDXjzzTdN5p7iuZZvw4YNGDNmDKKioqDT6bB8+XKT15U8p0uWLEHXrl3h7++Pnj17YuXKlfIPSCDFLVq0SPD19RXmzZsnHDhwQJgwYYLQtGlTIT8/X+2iaVJKSorw+eefC/v37xcyMzOFUaNGCTExMUJpaalhmSeffFKIjo4WMjIyhB07dggDBgwQBg4caHi9qqpK6NGjh5CcnCzs3r1bWLlypRAeHi5MmTLFsMyJEyeEwMBAIS0tTcjKyhI+/PBDwcvLS1i1apWix6sF27ZtE9q1ayf06tVLePbZZw3P8zw779KlS0Lbtm2FRx99VNi6datw4sQJ4ddffxWOHTtmWGbWrFlCaGiosHz5cmHPnj3C7bffLrRv3164evWqYZkRI0YIcXFxwpYtW4Q//vhD6Nixo/DAAw8YXi8qKhIiIiKEsWPHCvv37xe++eYbISAgQPj0008VPV41vfXWW0Lz5s2Fn3/+WcjOzhaWLFkiBAUFCe+//75hGZ5r+VauXCm88sorwtKlSwUAwrJly0xeV+qcbtq0SfDy8hLefvttISsrS3j11VcFHx8fYd++fbKOh4GQCvr37y+kpqYaHldXVwtRUVHCzJkzVSxVw1FQUCAAENavXy8IgiAUFhYKPj4+wpIlSwzLHDx4UAAgbN68WRCE2i+uXq8X8vLyDMt88sknQkhIiFBeXi4IgiD885//FLp3726yr/vvv19ISUlx9yFpSklJidCpUyfht99+E4YOHWoIhHieXeOll14SBg0aJPp6TU2NEBkZKbzzzjuG5woLCwU/Pz/hm2++EQRBELKysgQAwvbt2w3L/PLLL4JOpxPOnDkjCIIgfPzxx0KzZs0M571u3126dHH1IWnW6NGjhccee8zkubvvvlsYO3asIAg8165gHggpeU7vu+8+YfTo0SblSUxMFP7+97/LOgY2jSmsoqICO3fuRHJysuE5vV6P5ORkbN68WcWSNRxFRUUAgLCwMADAzp07UVlZaXJOu3btipiYGMM53bx5M3r27ImIiAjDMikpKSguLsaBAwcMyxhvo24ZT3tfUlNTMXr0aItzwfPsGj/++CMSEhJw7733omXLlujduzc+++wzw+vZ2dnIy8szOUehoaFITEw0Oc9NmzZFQkKCYZnk5GTo9Xps3brVsMyQIUPg6+trWCYlJQWHDx/G5cuX3X2YmjBw4EBkZGTgyJEjAIA9e/Zg48aNGDlyJACea3dQ8py66lrCQEhhFy5cQHV1tcmNAgAiIiKQl5enUqkajpqaGjz33HO48cYb0aNHDwBAXl4efH190bRpU5Nljc9pXl6e1XNe95qtZYqLi3H16lV3HI7mLFq0CLt27cLMmTMtXuN5do0TJ07gk08+QadOnfDrr7/iqaeewjPPPIMvvvgCQP15snWNyMvLQ8uWLU1e9/b2RlhYmKz3orGbPHky/vrXv6Jr167w8fFB79698dxzz2Hs2LEAeK7dQclzKraM3HPO2eepQUlNTcX+/fuxceNGtYvS6OTm5uLZZ5/Fb7/9Bn9/f7WL02jV1NQgISEBM2bMAAD07t0b+/fvx9y5czFu3DiVS9e4fPvtt1iwYAEWLlyI7t27IzMzE8899xyioqJ4rsmANUIKCw8Ph5eXl0VPm/z8fERGRqpUqoZh4sSJ+Pnnn7F27Vq0adPG8HxkZCQqKipQWFhosrzxOY2MjLR6zutes7VMSEgIAgICXH04mrNz504UFBSgT58+8Pb2hre3N9avX48PPvgA3t7eiIiI4Hl2gVatWiE2NtbkuW7duuHUqVMA6s+TrWtEZGQkCgoKTF6vqqrCpUuXZL0Xjd2kSZMMtUI9e/bEww8/jOeff95Q48lz7XpKnlOxZeSecwZCCvP19UXfvn2RkZFheK6mpgYZGRlISkpSsWTaJQgCJk6ciGXLlmHNmjVo3769yet9+/aFj4+PyTk9fPgwTp06ZTinSUlJ2Ldvn8mX77fffkNISIjhppSUlGSyjbplPOV9ufnmm7Fv3z5kZmYa/iUkJGDs2LGGv3menXfjjTdaDP9w5MgRtG3bFgDQvn17REZGmpyj4uJibN261eQ8FxYWYufOnYZl1qxZg5qaGiQmJhqW2bBhAyorKw3L/Pbbb+jSpQuaNWvmtuPTkitXrkCvN73NeXl5oaamBgDPtTsoeU5ddi2RlVpNLrFo0SLBz89PmD9/vpCVlSU88cQTQtOmTU162lC9p556SggNDRXWrVsnnDt3zvDvypUrhmWefPJJISYmRlizZo2wY8cOISkpSUhKSjK8Xtet+9ZbbxUyMzOFVatWCS1atLDarXvSpEnCwYMHhfT0dI/q1m2Nca8xQeB5doVt27YJ3t7ewltvvSUcPXpUWLBggRAYGCh8/fXXhmVmzZolNG3aVPjhhx+EvXv3CnfccYfV7se9e/cWtm7dKmzcuFHo1KmTSffjwsJCISIiQnj44YeF/fv3C4sWLRICAwMbbZdua8aNGye0bt3a0H1+6dKlQnh4uPDPf/7TsAzPtXwlJSXC7t27hd27dwsAhNmzZwu7d+8WTp48KQiCcud006ZNgre3t/Duu+8KBw8eFKZNm8bu8w3Jhx9+KMTExAi+vr5C//79hS1btqhdJM0CYPXf559/bljm6tWrwtNPPy00a9ZMCAwMFO666y7h3LlzJtvJyckRRo4cKQQEBAjh4eHCCy+8IFRWVposs3btWiE+Pl7w9fUVOnToYLIPT2QeCPE8u8ZPP/0k9OjRQ/Dz8xO6du0q/Oc//zF5vaamRnjttdeEiIgIwc/PT7j55puFw4cPmyxz8eJF4YEHHhCCgoKEkJAQYfz48UJJSYnJMnv27BEGDRok+Pn5Ca1btxZmzZrl9mPTkuLiYuHZZ58VYmJiBH9/f6FDhw7CK6+8YtIlm+davrVr11q9Jo8bN04QBGXP6bfffit07txZ8PX1Fbp37y6sWLFC9vHoBMFoiE0iIiIiD8IcISIiIvJYDISIiIjIYzEQIiIiIo/FQIiIiIg8FgMhIiIi8lgMhIiIiMhjMRAiIiIij8VAiIiIiDwWAyEiapCGDRuG5557Tu1iEFEDx0CIiNxCLFCZP38+mjZtqnh51q1bB51Oh8LCQqe3lZ2djQcffBBRUVHw9/dHmzZtcMcdd+DQoUMAgJycHOh0OmRmZjq9LyJyL2+1C0BE1JBUVlbilltuQZcuXbB06VK0atUKp0+fxi+//OKSIIuIlMUaISJS1aOPPoo777wTr7/+Olq0aIGQkBA8+eSTqKioMCxTVlaGRx55BEFBQWjVqhXee+89i+189dVXSEhIQHBwMCIjI/Hggw+ioKAAQG0NzfDhwwEAzZo1g06nw6OPPgoAqKmpwcyZM9G+fXsEBAQgLi4O3333nWh5Dxw4gOPHj+Pjjz/GgAED0LZtW9x4442YPn06BgwYAABo3749AKB3797Q6XQYNmyYYf3//ve/6NatG/z9/dG1a1d8/PHHhtfqapIWLVqEgQMHwt/fHz169MD69esdO7lEZBcDISJSXUZGBg4ePIh169bhm2++wdKlS/H6668bXp80aRLWr1+PH374AatXr8a6deuwa9cuk21UVlbizTffxJ49e7B8+XLk5OQYgp3o6Gh8//33AIDDhw/j3LlzeP/99wEAM2fOxJdffom5c+fiwIEDeP755/HQQw+JBh8tWrSAXq/Hd999h+rqaqvLbNu2DQDw+++/49y5c1i6dCkAYMGCBZg6dSreeustHDx4EDNmzMBrr72GL774wmT9SZMm4YUXXsDu3buRlJSEMWPG4OLFizLPKhFJInu+eiIiCYYOHSo8++yzFs9//vnnQmhoqOHxuHHjhLCwMKGsrMzw3CeffCIEBQUJ1dXVQklJieDr6yt8++23htcvXrwoBAQEWN1+ne3btwsAhJKSEkEQBGHt2rUCAOHy5cuGZa5duyYEBgYKf/75p8m6jz/+uPDAAw+Ibvujjz4SAgMDheDgYGH48OHCG2+8IRw/ftzwenZ2tgBA2L17t8l6N9xwg7Bw4UKT5958800hKSnJZL1Zs2YZXq+srBTatGkj/Pvf/xYtDxE5jjVCRKS6uLg4BAYGGh4nJSWhtLQUubm5OH78OCoqKpCYmGh4PSwsDF26dDHZxs6dOzFmzBjExMQgODgYQ4cOBQCcOnVKdL/Hjh3DlStXcMsttyAoKMjw78svv8Tx48dF10tNTUVeXh4WLFiApKQkLFmyBN27d8dvv/0muk5ZWRmOHz+Oxx9/3GRf06dPt9hXUlKS4W9vb28kJCTg4MGDotsmIscxWZqI3CIkJARFRUUWzxcWFiI0NNSl+yorK0NKSgpSUlKwYMECtGjRAqdOnUJKSopJrpG50tJSAMCKFSvQunVrk9f8/Pxs7jM4OBhjxozBmDFjMH36dKSkpGD69Om45ZZbbO7rs88+MwnqAMDLy8vuMRKRe7BGiIjcokuXLhZ5PACwa9cudO7c2eS5PXv24OrVq4bHW7ZsQVBQEKKjo3HDDTfAx8cHW7duNbx++fJlHDlyxPD40KFDuHjxImbNmoXBgweja9euhkTpOr6+vgBgktcTGxsLPz8/nDp1Ch07djT5Fx0dLflYdTodunbtirKyMtF9RUREICoqCidOnLDYV11ytfHx16mqqsLOnTvRrVs3yeUhIulYI0REbvHUU0/ho48+wjPPPIO//e1v8PPzw4oVK/DNN9/gp59+Mlm2oqICjz/+OF599VXk5ORg2rRpmDhxIvR6PYKCgvD4449j0qRJaN68OVq2bIlXXnkFen3977iYmBj4+vriww8/xJNPPon9+/fjzTffNNlH27ZtodPp8PPPP2PUqFEICAhAcHAwXnzxRTz//POoqanBoEGDUFRUhE2bNiEkJATjxo2zOK7MzExMmzYNDz/8MGJjY+Hr64v169dj3rx5eOmllwAALVu2REBAAFatWoU2bdrA398foaGheP311/HMM88gNDQUI0aMQHl5OXbs2IHLly8jLS3NsI/09HR06tQJ3bp1w//93//h8uXLeOyxx1z59hBRHbWTlIio8dq2bZtwyy23CC1atBBCQ0OFxMREYdmyZSbLjBs3TrjjjjuEqVOnCs2bNxeCgoKECRMmCNeuXTMsU1JSIjz00ENCYGCgEBERIbz99tsWydgLFy4U2rVrJ/j5+QlJSUnCjz/+aJGw/MYbbwiRkZGCTqcTxo0bJwiCINTU1Ahz5swRunTpIvj4+AgtWrQQUlJShPXr11s9pvPnzwvPPPOM0KNHDyEoKEgIDg4WevbsKbz77rtCdXW1YbnPPvtMiI6OFvR6vTB06FDD8wsWLBDi4+MFX19foVmzZsKQIUOEpUuXCoJQnyy9cOFCoX///oKvr68QGxsrrFmzxrE3gIjs0gmCIKgdjBGR53r00UdRWFiI5cuXq10U1eXk5KB9+/bYvXs34uPj1S4OkUdgjhARERF5LAZCRERE5LHYNEZEREQeizVCRERE5LEYCBEREZHHYiBEREREHouBEBEREXksBkJERETksRgIERERkcdiIEREREQei4EQEREReaz/B8pXTwSncBJ8AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.semilogy(loss_history)\n",
+    "plt.xlabel(\"Update Step\")\n",
+    "plt.ylabel(\"Train Loss\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Rollout the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rolling out the model requires calling it autoregressively, i.e., feeding it\n",
+    "based on its own output. This can be done by appending to a list and then\n",
+    "calling `jnp.stack`. However, it is more efficient to use `jax.lax.scan` which\n",
+    "is wrapped by `exponax.rollout`.\n",
+    "\n",
+    "⚠️ The neural rollout must be **without** the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(200, 30, 160, 1)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "neural_rollout = apebench.exponax.rollout(\n",
+    "    lambda u: cnn.apply(params, u),\n",
+    "    advection_scenario.test_temporal_horizon,  # 200\n",
+    ")(test_ic_set)\n",
+    "neural_rollout.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Perform tests on the rollout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rearrange data\n",
+    "\n",
+    "Requires to change channel and spatial axes. APEBench also follows the\n",
+    "convention that the zeroth axis is the batch axis, and the next axis is for the\n",
+    "temporal snapshots. This must also be adjusted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(30, 200, 1, 160)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Change to format (batch, time, channels, spatial)\n",
+    "neural_rollout = np.moveaxis(neural_rollout, 0, 1)\n",
+    "neural_rollout = np.moveaxis(neural_rollout, -1, -2)\n",
+    "neural_rollout.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Perform tests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our `neural_rollout` is now of a format supported by `scenario.perform_tests_on_rollout`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_dict = advection_scenario.perform_tests_on_rollout(neural_rollout)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we find the same keys in the `test_dict` as set up for the `report_metrics`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(dict_keys(['mean_nRMSE', 'mean_fourier_nRMSE;0;5;0', 'mean_H1_nRMSE']),\n",
+       " 'mean_nRMSE,mean_fourier_nRMSE;0;5;0,mean_H1_nRMSE')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_dict.keys(), advection_scenario.report_metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For each key, there is an array attached. It is of the shape `(num_seeds,\n",
+    "test_temporal_horizon)`. Since, the `neural_rollout` did not have a leading\n",
+    "`num_seeds` axis, this axis appears as singleton. The axis thereafter is due to\n",
+    "the 200 time steps performed in the test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean_nRMSE: (1, 200)\n",
+      "mean_fourier_nRMSE;0;5;0: (1, 200)\n",
+      "mean_H1_nRMSE: (1, 200)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key, value in test_dict.items():\n",
+    "    print(f\"{key}: {value.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rollout metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's visualize the error over time. Metrics with a full spectrum, even more so\n",
+    "the metric with derivatives (`\"mean_H1_nRMSE\"`) are worse, likely because\n",
+    "**nonlinear** networks applied to linear time stepping problems produce spurious\n",
+    "energy in higher modes. This is problematic since linear PDEs on periodic BCs\n",
+    "remain bandlimited.\n",
+    "\n",
+    "However, this is not a problem of `flax.linen` but a general problem of neural\n",
+    "emulators for linear PDEs. Hence, let's just acknowledge that the model performs\n",
+    "reasonable for at least ~50 time steps which is remarkable since it was only\n",
+    "trained to predict one step into the future and never multiple steps\n",
+    "autoregressively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.1, 1.1)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "time_steps = jnp.arange(1, advection_scenario.test_temporal_horizon + 1)\n",
+    "plt.plot(\n",
+    "    time_steps,\n",
+    "    test_dict[\"mean_nRMSE\"][0],\n",
+    "    label=\"Mean nRMSE\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    time_steps,\n",
+    "    test_dict[\"mean_fourier_nRMSE;0;5;0\"][0],\n",
+    "    label=\"Mean Fourier nRMSE - low freq\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    time_steps,\n",
+    "    test_dict[\"mean_H1_nRMSE\"][0],\n",
+    "    label=\"Mean H1 nRMSE\",\n",
+    ")\n",
+    "plt.xlabel(\"Time Step\")\n",
+    "plt.ylabel(\"error metric\")\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.ylim(-0.1, 1.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregated Metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar to the [APEBench paper](https://arxiv.org/abs/2411.00180), let us\n",
+    "aggregate over the first 100 time steps with a geometric mean."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mean_nRMSE': array([0.06692272], dtype=float32),\n",
+       " 'mean_fourier_nRMSE;0;5;0': array([0.05657782], dtype=float32),\n",
+       " 'mean_H1_nRMSE': array([0.17492542], dtype=float32)}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "UP_TO = 100\n",
+    "test_dict_gmean = {\n",
+    "    key: stats.gmean(value[:, :UP_TO], axis=1) for key, value in test_dict.items()\n",
+    "}\n",
+    "test_dict_gmean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "apebench_dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/examples/benchmark_flax_models_with_nnx.ipynb b/docs/examples/benchmark_flax_models_with_nnx.ipynb
new file mode 100644
index 0000000..51cb16d
--- /dev/null
+++ b/docs/examples/benchmark_flax_models_with_nnx.ipynb
@@ -0,0 +1,1020 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Benchmark Flax Models with APEBench\n",
+    "\n",
+    "While foreign models cannot use advanced features of APEBench like correction\n",
+    "setups or differentiable physics training, it is still possible to benchmark\n",
+    "their performance on purely data-driven supervised learning tasks. This usually\n",
+    "follows a four-step pattern:\n",
+    "\n",
+    "1. Scrape the training data trajectories and the test initial conditions from\n",
+    "   the APEBench scenario.\n",
+    "2. Use the training data trajectories to train the foreign model. (Or use a\n",
+    "   pretrained model.)\n",
+    "3. Rollout the foreign model for `scenario.test_temporal_horizon` steps from the\n",
+    "   test initial conditions.\n",
+    "4. Plug the resulting neural trajectories into\n",
+    "   `scenario.perform_tests_on_rollout` method to obtain a dictionary with the\n",
+    "   error rollouts according to all `scenario.report_metrics`.\n",
+    "\n",
+    "Note that the step from (1.) -> (2.) can require a change in array format or\n",
+    "moving arrays to a different framework. So does step (3.) -> (4.).\n",
+    "\n",
+    "This notebook uses the popular [Flax](https://github.com/google/flax) library in\n",
+    "JAX with its new [nnx](https://flax.readthedocs.io/en/latest/why.html) API.\n",
+    "Benchmarking such models is easy because we can keep JAX arrays. Still, we have\n",
+    "to account for Flax's [channels last convention on Conv\n",
+    "layers](https://flax.readthedocs.io/en/latest/why.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/koehler/miniconda3/envs/apebench_dev/lib/python3.12/site-packages/trainax/_general_trainer.py:7: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from tqdm.autonotebook import tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import apebench\n",
+    "\n",
+    "import numpy as np\n",
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "from flax import nnx\n",
+    "from tqdm.autonotebook import tqdm\n",
+    "import optax\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy import stats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Create Data with APEBench"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an example, we will use an advection scenario in difficulty mode with mostly\n",
+    "default settings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "advection_scenario = apebench.scenarios.difficulty.Advection(\n",
+    "    # A simple optimization setup that will be mimicked by Flax\n",
+    "    optim_config=\"adam;10_000;constant;1e-4\",\n",
+    "    # The default metric for APEBench scenarios is always `\"mean_nRMSE\"`. Let's\n",
+    "    # add some more metrics to the report. One based on the spectrum up until\n",
+    "    # (including) the fifth mode and a Sobolev-based metric that also considers\n",
+    "    # the first derivative.\n",
+    "    report_metrics=\"mean_nRMSE,mean_fourier_nRMSE;0;5;0,mean_H1_nRMSE\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create train **trajectories** and test **initial conditions** with APEBench."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((50, 51, 1, 160), (30, 1, 160))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data = advection_scenario.get_train_data()\n",
+    "test_ic_set = advection_scenario.get_test_ic_set()\n",
+    "train_data.shape, test_ic_set.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Train `flax.nnx` model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rearrange data\n",
+    "We have to rearrange our data to have the channels last convention"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((50, 51, 160, 1), (30, 160, 1))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data = np.moveaxis(train_data, -2, -1)\n",
+    "test_ic_set = np.moveaxis(test_ic_set, -2, -1)\n",
+    "train_data.shape, test_ic_set.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From here on, you are free to do with the `train_data` what you what. The\n",
+    "simplest approach would be **one-step supervised learning**. For this, we slice\n",
+    "windows of length two across both the trajectory and the time axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50, 50, 2, 160, 1)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "substacked_data = jax.vmap(apebench.exponax.stack_sub_trajectories, in_axes=(0, None))(\n",
+    "    train_data, 2\n",
+    ")\n",
+    "substacked_data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we can merge sample and window axis into a joint batch axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2500, 2, 160, 1)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_windows = jnp.concatenate(substacked_data)\n",
+    "train_windows.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Definition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just a simple ReLU feedforward ConvNet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CNN(nnx.Module):\n",
+    "    def __init__(self, width: int, depth: int, *, rngs: nnx.Rngs):\n",
+    "        self.conv_lift = nnx.Conv(\n",
+    "            1, width, kernel_size=(3,), padding=\"CIRCULAR\", rngs=rngs\n",
+    "        )\n",
+    "        self.convs = [\n",
+    "            nnx.Conv(width, width, kernel_size=(3,), padding=\"CIRCULAR\", rngs=rngs)\n",
+    "            for _ in range(depth - 1)\n",
+    "        ]\n",
+    "        self.conv_prj = nnx.Conv(\n",
+    "            width, 1, kernel_size=(3,), padding=\"CIRCULAR\", rngs=rngs\n",
+    "        )\n",
+    "\n",
+    "    def __call__(self, x):\n",
+    "        x = self.conv_lift(x)\n",
+    "        x = jax.nn.relu(x)\n",
+    "        for conv in self.convs:\n",
+    "            x = conv(x)\n",
+    "            x = jax.nn.relu(x)\n",
+    "        x = self.conv_prj(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training Loop\n",
+    "\n",
+    "Inspired by the [nnx MNIST tutorial](https://flax.readthedocs.io/en/latest/mnist_tutorial.html#train-and-evaluate-the-model).\n",
+    "\n",
+    "Access the attributes of the APEBench scenario to use the same hyperparameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "10000it [00:39, 255.96it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "cnn = CNN(width=34, depth=10, rngs=nnx.Rngs(0))\n",
+    "opt = nnx.Optimizer(cnn, optax.adam(1e-4))\n",
+    "\n",
+    "metr = nnx.MultiMetric(\n",
+    "    loss=nnx.metrics.Average(\"loss\"),\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def one_step_supservised_loss(model: CNN, batch):\n",
+    "    inputs, targets = batch[:, 0], batch[:, 1]\n",
+    "    predictions = model(inputs)\n",
+    "    return jnp.mean((predictions - targets) ** 2)\n",
+    "\n",
+    "\n",
+    "@nnx.jit\n",
+    "def train_step(model: CNN, optimizer: nnx.Optimizer, metrics, batch):\n",
+    "    loss, grad = nnx.value_and_grad(one_step_supservised_loss)(model, batch)\n",
+    "    metrics.update(loss=loss)\n",
+    "    optimizer.update(grad)\n",
+    "\n",
+    "\n",
+    "loss_history = []\n",
+    "# Uses a simple infinite dataloader that does as many epochs as necessary\n",
+    "for batch in tqdm(\n",
+    "    apebench.pdequinox.cycling_dataloader(\n",
+    "        train_windows,\n",
+    "        batch_size=advection_scenario.batch_size,  # 20\n",
+    "        num_steps=advection_scenario.num_training_steps,  # 10_000\n",
+    "        key=jax.random.PRNGKey(42),\n",
+    "    )\n",
+    "):\n",
+    "    train_step(cnn, opt, metr, batch)\n",
+    "    for metric, value in metr.compute().items():\n",
+    "        loss_history.append(value)\n",
+    "    metr.reset()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's visualize the loss history. It's a bit noisy towards the end, but let's\n",
+    "stick with a constant learning rate for simplicity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Train Loss')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.semilogy(loss_history)\n",
+    "plt.xlabel(\"Update Step\")\n",
+    "plt.ylabel(\"Train Loss\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Rollout the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rolling out the model requires calling it autoregressively, i.e., feeding it\n",
+    "based on its own output. This can be done by appending to a list and then\n",
+    "calling `jnp.stack`. However, it is more efficient to use `jax.lax.scan` which\n",
+    "is wrapped by `exponax.rollout`.\n",
+    "\n",
+    "⚠️ The neural rollout must be **without** the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(200, 30, 160, 1)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "neural_rollout = apebench.exponax.rollout(\n",
+    "    cnn,\n",
+    "    advection_scenario.test_temporal_horizon,  # 200\n",
+    ")(test_ic_set)\n",
+    "neural_rollout.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Perform tests on the rollout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rearrange data\n",
+    "\n",
+    "Requires to change channel and spatial axes. APEBench also follows the\n",
+    "convention that the zeroth axis is the batch axis, and the next axis is for the\n",
+    "temporal snapshots. This must also be adjusted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(30, 200, 1, 160)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Change to format (batch, time, channels, spatial)\n",
+    "neural_rollout = np.moveaxis(neural_rollout, 0, 1)\n",
+    "neural_rollout = np.moveaxis(neural_rollout, -1, -2)\n",
+    "neural_rollout.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Perform tests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our `neural_rollout` is now of a format supported by `scenario.perform_tests_on_rollout`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_dict = advection_scenario.perform_tests_on_rollout(neural_rollout)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we find the same keys in the `test_dict` as set up for the `report_metrics`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(dict_keys(['mean_nRMSE', 'mean_fourier_nRMSE;0;5;0', 'mean_H1_nRMSE']),\n",
+       " 'mean_nRMSE,mean_fourier_nRMSE;0;5;0,mean_H1_nRMSE')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_dict.keys(), advection_scenario.report_metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For each key, there is an array attached. It is of the shape `(num_seeds,\n",
+    "test_temporal_horizon)`. Since, the `neural_rollout` did not have a leading\n",
+    "`num_seeds` axis, this axis appears as singleton. The axis thereafter is due to\n",
+    "the 200 time steps performed in the test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean_nRMSE: (1, 200)\n",
+      "mean_fourier_nRMSE;0;5;0: (1, 200)\n",
+      "mean_H1_nRMSE: (1, 200)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key, value in test_dict.items():\n",
+    "    print(f\"{key}: {value.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rollout metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's visualize the error over time. Metrics with a full spectrum, even more so\n",
+    "the metric with derivatives (`\"mean_H1_nRMSE\"`) are worse, likely because\n",
+    "**nonlinear** networks applied to linear time stepping problems produce spurious\n",
+    "energy in higher modes. This is problematic since linear PDEs on periodic BCs\n",
+    "remain bandlimited.\n",
+    "\n",
+    "However, this is not a problem of `flax.nnx` but a general problem of neural\n",
+    "emulators for linear PDEs. Hence, let's just acknowledge that the model performs\n",
+    "reasonable for at least ~50 time steps which is remarkable since it was only\n",
+    "trained to predict one step into the future and never multiple steps\n",
+    "autoregressively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.1, 1.1)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "time_steps = jnp.arange(1, advection_scenario.test_temporal_horizon + 1)\n",
+    "plt.plot(\n",
+    "    time_steps,\n",
+    "    test_dict[\"mean_nRMSE\"][0],\n",
+    "    label=\"Mean nRMSE\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    time_steps,\n",
+    "    test_dict[\"mean_fourier_nRMSE;0;5;0\"][0],\n",
+    "    label=\"Mean Fourier nRMSE - low freq\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    time_steps,\n",
+    "    test_dict[\"mean_H1_nRMSE\"][0],\n",
+    "    label=\"Mean H1 nRMSE\",\n",
+    ")\n",
+    "plt.xlabel(\"Time Step\")\n",
+    "plt.ylabel(\"error metric\")\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.ylim(-0.1, 1.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregated Metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar to the [APEBench paper](https://arxiv.org/abs/2411.00180), let us\n",
+    "aggregate over the first 100 time steps with a geometric mean."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mean_nRMSE': array([0.06446283], dtype=float32),\n",
+       " 'mean_fourier_nRMSE;0;5;0': array([0.04139557], dtype=float32),\n",
+       " 'mean_H1_nRMSE': array([0.22861484], dtype=float32)}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "UP_TO = 100\n",
+    "test_dict_gmean = {\n",
+    "    key: stats.gmean(value[:, :UP_TO], axis=1) for key, value in test_dict.items()\n",
+    "}\n",
+    "test_dict_gmean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing against a ConvNet trained within APEBench"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's train a similar ConvNet using the mechanisms of APEBench"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E: 00079, B: 00124: 100%|██████████| 10000/10000 [00:48<00:00, 207.24it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data, trained_net = advection_scenario(\n",
+    "    task_config=\"predict\",  # Means the nextwork fully replaces the simulator\n",
+    "    network_config=\"Conv;34;10;relu\",  # 34 hidden width across 10 hidden depth with relu activation\n",
+    "    train_config=\"one\",  # One-Step supervised learning\n",
+    "    num_seeds=1,  # Only one seed for this example\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Typical APEBench pattern is to melt the wide format `data` into long formats\n",
+    "which are handy to work with when using seaborn. See also\n",
+    "[here](https://pandas.pydata.org/docs/user_guide/reshaping.html#melt-and-wide-to-long)\n",
+    "for a guide in Pandas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss_data = apebench.melt_loss(data)\n",
+    "metric_data = apebench.melt_metrics(\n",
+    "    data, metric_name=advection_scenario.report_metrics.split(\",\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's compare the training loss progress against the `flax.nnx` model. It seems\n",
+    "to converge about an order of magnitude higher."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lineplot(data=loss_data, x=\"update_step\", y=\"train_loss\", hue=\"net\")\n",
+    "plt.plot(loss_history, label=\"Flax\")\n",
+    "plt.xlabel(\"Update Step\")\n",
+    "plt.ylabel(\"Train Loss\")\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.yscale(\"log\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's compare the three metric rollouts. Also, here the `flax.nnx` model seems\n",
+    "to dominate!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IJ598wieffMLt27f5+++/+frrr1m5ciX29vYsXbo0xfp16tRJs0vkizg7O6cahwhInrX6yS+x6aVSqRgxYgRbt25l9+7dqWaCe/r4gNljEEJYXm7Nhc/6XG9nlz1VCnkxFwnbJJVoItd4etDHZy0Dkgd+NhqNBAQEMGvWrDTX8/X1BUwDSDZr1owKFSowa9YsfH19cXBwYNOmTcyePTvFwPzpOW5GZGRfT16gO3fuzMsvv0yfPn24cOECbm5uae6naNGiNGnShJkzZ7J//35+//33dMXl4OBA7dq1qV27Nv7+/gwaNIhVq1Yxfvz4dL4zIURuk1uvw5mV1mQAAAaD4ZnbpKdixGg0olKp2Lx5c5rv8+nrfVYrW9Jblk9+wWrVqhUVKlSgffv2fP31188dK65Pnz7Mnz+fCRMmUK1atRQ3Y56naNGi9OrVi27dulG5cmVWrlzJokWLzPKFrmjRouzatQtFUVL8HR8P/l+sWLFM7ffx+RwZGfnC4z95vCfdvn2b/Pnzv/AmmRDCOiQXWkZezEXCNslfVuRpZcqUISQkhGbNmj3zyw6YZqxMSkpiw4YNKe6CPN1lxpZoNBqmTp1K06ZN+fbbbxkzZswz1+3Tpw9vvPEGXl5etG3bNsPHelxxl9aHfSGEeJ6ceB0uWbIkO3bsICYmJkVrtPPnzye/Dv/dJX+6a0dWWqqBqcwURaFUqVL4+/tnaV+W1K5dOxo3bsyUKVN46623cHV1TXO9l19+mRIlSrB7926+/PLLDB/H3t6eqlWrcvHixeRurVkVGBjITz/9xLlz51J8kTp8+HDy65nxuAvVs1qbPObj44O3tzfHjh1L9dqRI0cyfXwhhG3Kibkwp8jJuUjYJhkTTeRpQUFB3Lx5k//973+pXktISEiekv7xnY8n73RERUWxcOHC7Ak0k5o0aUKdOnWYM2dOcvePtHTv3p3x48fz3XffPbep8+O78k/btGkTAOXLl8960EKIPCUnXofbtm2LwWDg22+/TbF89uzZqFQq2rRpA4CHhwcFCxZMnn3tse+++y5Lx+/atSsajYaJEyemuiYrimJTM3599NFHPHjwIM2/72MqlYq5c+cyfvx4+vfv/8z1Ll68SHh4eKrljx494uDBg+TLl++FlVPp1alTJ+zt7VP8rRRFYf78+fj4+FC/fv3nbh8ZGZmqxaFOp2PatGk4ODika4bUbt26sXHjRq5fv568bOfOnYSGhtKjR48MviMhhC3LibkwJ8mpuUjYJmmJJvK0/v37s3LlSt5++2127dpFgwYNMBgMnD9/npUrV7J161Zq1apFy5YtcXBwoEOHDrz11lvExsbyv//9j0KFCtl866sPP/yQHj16sGjRomcOWO3p6cmECRNeuK/33nuP+Ph4unTpQoUKFdBqtRw4cIAVK1bg5+eXauDqmzdvphoTAExdjTp37pyZtyOEyGVy4nW4Q4cONG3alE8++YSwsDCqVavGtm3bWL9+PcOHD08xGcwbb7zBtGnTeOONN6hVqxZ79uwhNDQ0S8cvU6YMkyZNYuzYsYSFhdG5c2fc3d25evUqa9eu5c0332TUqFFZfZtm0aZNG6pUqcKsWbMYOnRoqvHaHuvUqROdOnV67r5CQkLo06cPbdq0oWHDhuTPn5+bN2+yePFibt26xZw5c1J199m8eXNyC8En1a9fn9KlSwOmiQEWL17M1atXkycXKF68OMOHD2f69OnodDpq167NunXr2Lt3L8uWLUtxnEWLFjFo0CAWLlyYPLnAhg0bmDRpEt27d6dUqVJERkayfPlyTp8+zZQpU1K0UAgLC6NUqVIMGDAgxeQCH3/8MatWraJp06YMGzaM2NhYpk+fTkBAQJYmihBC2J6cmAtf5Ntvv+XRo0fcunULMLWiezypynvvvZdi7DZLywm5SOQcUokm8jS1Ws26deuYPXs2v/zyC2vXrsXFxYXSpUszbNiw5G4y5cuXZ/Xq1Xz66aeMGjWKIkWK8M477+Dt7c1rr71m5XfxfF27dqVMmTLMmDGDwYMHP3M8gfSYMWMGq1atYtOmTfz4449otVpKlCjBkCFD+PTTT/Hy8kqxfnBwcJp3ckqWLCmVaEIIIGdeh9VqNRs2bGDcuHGsWLGChQsX4ufnx/Tp0/nggw9SrDtu3DgiIiJYvXo1K1eupE2bNmzevJlChQplKYYxY8bg7+/P7NmzmThxImAaM6dly5Z07NgxS/s2t1GjRjFw4ECWLVuWagbLjGjUqBFffPEFmzdvZtasWURERODu7k716tX58ssv6datW6ptxo0bl+a+Fi5cmPzFJTY2Fmdn51Q5bNq0aeTLl48ffviBRYsWUa5cOZYuXZpqsOnY2Fjgv3HMAAICAqhUqRJLly4lIiICBwcHAgMDWblyZapWZGltD6a/599//83IkSMZM2YMDg4OtGvXjpkzZ8p4aELkMjkxF77IjBkzUgxfsGbNGtasWQNAv379srUSDWw/F4mcQ6XkxFEFhRBCCCGEMIPChQvz6quvMn369ExtHxQURFhYGEeOHMnU9t999x2jR4/m8uXLFC5cOFP7EEIIIUT2kJZoQgghhBAiTzpz5gwJCQl89NFHmdpeURR2796d5tAF6bVr1y7ef/99qUATQgghcgBpiSaEFdy5c+e5rzs7O2d7E2chhMhL8vJ1ODY2NrkL4bN4e3tnqfu/EEII22fNXCi5SORUUokmhBU8b+pqINXgwkIIIcwrL1+HJ0yYkDyO2rM8Oci+EEKI3MmauVBykcippDunEFawffv2575erFixbIpECCHyprx8HX711Vd5+eWXn7vOk7NHCiGEyJ2smQslF4mcSlqiCSGEEEIIIYQQQgjxAmprByCEEEIIIYQQQgghhK3Lc905jUYjt27dwt3d/YV9wIUQQryYoijExMRQrFgx1Gq5NwOSa4QQwpwkz6QmeUYIIcwrvbkmz1Wi3bp1C19fX2uHIYQQuc7169cpXry4tcOwCZJrhBDC/CTP/EfyjBBCWMaLck2eq0Rzd3cHTAXj4eGR7u10Oh3btm2jZcuW2NvbWyq8XEfKLXOk3DIvr5fdo3gtkzaeZcuZuwAE+HgytVsAfgVcn7tdVsotOjoaX1/f5OurkFyT3aTcMkfKLXOk3Ex369f8c4Mvt5wnXmvE1VHD2DYV6RRY7JmtoiTPmJfkmewl5fYCu7+Eg99AhfbQZX7yYnOWW2Scln4/HSI8MoFKRT1Y9FptXBxyZ3WGnG+Zk9VyS2+uyZ1n3XM8TuweHh4ZTjguLi54eHjIiZwBUm6ZI+WWeXm57PZejGDUqhDuRidh7+zKsGblGNKkDHaaF3d9MUe5SXeS/0iuyV5Sbpkj5ZY5eb3c7scmMXbNKbafvQsqJ14qn5+ZQdXwze/y3O0kz5iX5JnsJeX2Ajd2gaMKqneGJ85Hc5Vbos7AyKWnuBGnwrdwAX55pz6F3J3MELhtkvMtc8xVbi/KNXmuEk0IIXKbBK2BL7ecZ9GBMABKe7syOyiQar5eVo1LCCFE7rLj7F3GrDnJ/Vgt9hoVo1qW542GpdGopXJLiDzr4TW4dwZUGijb3Oy7NxoVRqwI5p/wR3g42bH4tdq5ugJN2D6pRBNCiBzs1I0ohq84weWIOAAG1CvJmDYVcXbQWDkyIYQQuUVckp5Jf57l1yPXAShf2J3ZPQOpVCz9LaCEELlU6BbT7xL1wCW/2Xc/ZdM5Np++g4NGzY+v1qJsIenWLaxLKtGEECIH0huMfL/7Ml/vvIjeqFDI3ZHpParR2N/b2qEJIYTIRY5fi2TEihDCI+NRqWBww9KMbOGPk73crBFCABc2mX6Xb2P2XS/af5Wf9l0FYHqPqrxUuoDZjyFERkklWhoURUGv12MwGJKX6XQ67OzsSExMTLFcPJ+UW/ppNBrs7OxkvA/xQtcexCU3awdoG1CEyZ0DyOfqYN3ARIYYDAZ0Ol2KZXLNzBwpt/Szt7dHo5HKD/FiWr2RuTsv8t3uSxgV8PFyZkaPatQrI19ihRD/SoyCsH2mx2auRNt25g4TN54F4MNW5ekU6GPW/QuRWVKJ9hStVsvt27eJj49PsVxRFIoUKcL169elkiMDpNwyxsXFhaJFi0pZiTQpisJvR6/zxcazxGsNuDva8XnnynQO9JFzJoeJjY3lxo0bKIqSYrlcMzNHyi39VCoVxYsXx83NzdqhCBt26V4Mw1cEc/pmNABdq/swoVNlPJxkgGshxBMu7QCjHgr6Q4EyZttt8PVHvP/bCRQFetfxZUgT8+1biKySSrQnGI1Grl69ikajoVixYjg4OCR/GDcajcTGxuLm5oZa/eKZ7oSJlFv6KIqCVqslIiKCq1ev4ufnZ+2QhI2JiElizO8n2Xn+HgAvlc7PzKBAfLycrRyZyCiDwcCNGzdwcXHB29s7RaWPXDMzR8otfRRFISIighs3blCuXDlrhyNskNGosPhgGNM2nydJb8TLxZ7JnQNoV7WotUMTQtiiC/+Oh2bGVmjhD+J5fdFREnVGGvt780WnKnKDTNgUqUR7glarxWg04uvri4tLymm6jUYjWq0WJycn+YCeAVJu6efs7Iy9vT3Xrl1L1cVL5G3bztxh7JpTPIjT4qBRM7p1eV5rUAq1zIaWI+l0OhRFwdvbG2fnlJWgcs3MHCm39PP29iYsLAydTifdOkUKt6MS+HDVSfZdug9AI39vpnevSmEPmQVPCJEGgw4ubjU99jdPJdrDOC0DFx3hQZyWysU8mNe3BnYayevCtkglWhrkA7iwlsfn3tNdvETeFJuk54s/zrLimGk2tApF3JnTK5AKRWQ2tNxA7qoKa5DzTqTlj5BbfLL2FNGJepzs1XzStiL9Xiop54sQ4tnCD5nGRHPOD751sry7RJ2BN5cc40pEHMU8nVgwsDZujlJdIWyPnJVCCGGDjoVFMmJlMNcjE1Cp4M1GptnQHO2k5YgQQgjziIrXMW7DadYH3wKgWnFPZvUMpIy3jJknhHiB0H+7cvq3BnXWPp8ajQqjVoVwNOwh7o52LBxUR1rBCpsllWhCCGFDtHojc3aEMv/vy8mzoc0KqkZdmdJbCCGEGe2/dJ9Rq0K4HZWIRq3i3aZlefeVsthL1ykhxIsoCpz/0/S4fOss7+7LrefZePI29hoVP/SvSfki7lnepxCWIllSCDOZMGECgYGB1g5D5GAX78bQ5bv9fLfbVIHWrUZxtgxvKBVoQohkkmtEViXqDHz+x1n6/nSY21GJlCroyuq36zGihb9UoAkh0ud+KDy8ChoHKPNKlna15NA1fvj7CgDTulalftmC5ohQCIuRTJmL3Llzh/fee4/SpUvj6OiIr68vHTp0YOfOnVaNa9q0aVSqVAlXV1fy5ctH8+bNOXz4cJrrJiUlERgYiEqlIjg4+Ln73bNnDx06dKBYsWKoVCrWrVuXah1FURg3bhxFixbF2dmZ5s2bc/HiRTO8KyHMx2hUWLDvKu2+2ceZW9Hkc7Hn+741mBlUDXcne8sHEB+Jet9MvKNPW/5YIseTXLMu1TqSa0ROcfpmFB2+2ceC/VcB6Fu3BH++/zLVS+SzcmRCiBzlwibTb7+G4Jj5VmM7z91l/HrT58+RLfzpVrO4OaITwqKsWomWng+mT9u9ezc1atTA0dGRsmXLsmjRIovHmROEhYVRs2ZN/vrrL6ZPn86pU6fYsmULTZs2ZejQoVaNrUyZMsydO5dTp06xb98+/Pz8aNmyJREREanWHT16NMWKFUvXfuPi4qhWrRrz5s175jpfffUVc+fOZf78+Rw+fBhXV1datWpFYmJiuuPXarXpXleIjLodlUD/BYf5fONZtHojTcp7s3V4I9oEFLX8wR9cho0jYVYlNH9PpdzdPyx/zGwmeca8JNekTXKNsHUGo8K8XZfoPG8/F+/FUtDNkYUDazO5SwAuDjK6S1ZJrhF5zoV/x0Mrn/lZOU/eeMS7y09gVCCoVnHee6WsmYITwrKsWomWng+mT7p69Srt2rWjadOmBAcHM3z4cN544w22bt1qsRgVRSFeqydeqydBa0h+nB0/GZmhcciQIahUKo4cOUK3bt3w9/encuXKjBw5kkOHDgEQHh5Op06dcHNzw8PDg6CgIO7evZu8j8ddRJYsWYKfnx+enp706tWLmJgYAH788UeKFSuG0WhMcexOnTrx2muvPTO2Hj160Lx5c0qXLk3lypWZNWsW0dHRnDx5MsV6mzdvZtu2bcyYMSNd77lNmzZMmjSJLl26pPm6oijMmTOHTz/9lE6dOlG1alV++eUXbt269dwPN4/L4aeffqJUqVI4OZkGtXz06BFvvPEG3t7eeHh48MorrxASEvLM/TRp0oThw4enWNa5c2cGDhyYrvcncr/1wTdpNXsP+y89wNlew6TOVVg4sDaFLDmQqqLAtQPwax/4piYc+xn0CSiFAwgv0Nj0ei6S0/JMdueajM4ELLkmNck1wtaFP4gn6IeDTN96Ab1RoVXlwmwb0YimFQpZO7RcIyfkGiHMJu4B3DhieuyfufHQrkfG89qiYyToDDQsV5DJXQJkNmCRY1j11lObNm1o0yb9tdfz58+nVKlSzJw5E4CKFSuyb98+Zs+eTatWrSwSY4LOQKVx1kloZz9vla67g5GRkWzZsoXJkyfj6uqa6nUvLy+MRmPyl5q///4bvV7P0KFD6dmzJ7t3705e9/Lly6xbt46NGzfy8OFDgoKCmDZtGpMnT6ZHjx6899577Nq1i2bNmqU49qZNpia9YWFhlCpVil27dtGkSZNUsWi1Wn788Uc8PT2pVq1a8vK7d+8yePBg1q1bh4uLS5rvU6VSsXDhwnR/Mbh69Sp37tyhefPmycs8PT2pW7cuBw8epFevXoDpC4ifn1+KO4CXLl3i999/Z82aNWg0ptlmevTogbOzM5s3b8bT05MffviBZs2aERoaSv78+dMVkxBgmg3t0/Wn+SPk39nQfL2YHVSN0pacDc2gh3Pr4cC3cOuf/5aXawX130Xv8xI3Nm+mai77ACN55vnSm2dAcs2zSK4RtkpRFFYeu87nf5wlTmvAzdGOCR0r062Gj3xZNbOckGuEMJuL20AxQuEA8PLN8OZR8ToGLTrK/dgkKhRx57u+NWQ8RpGj5Kj22wcPHkzxIRWgVatWqe7APikpKYmkpKTk59HR0QDodDp0Ol2KdXU6HYqiYDQak++AP30nPDs9GcfzhIaGoigK/v7+z1x/+/btnDp1isuXL+Pra7rYLVq0iICAAA4fPkzt2rWT3/uCBQtwdzf1be/Xrx87d+7kiy++wNPTk9atW7Ns2TKaNm0KwMqVKylYsCCNGzfGaDSi0WgoX748Tk5OGI3G5FYOf/zxB3379iU+Pp6iRYuydetW8ufPn7zOgAEDeOutt6hRowZhYWFpvv/y5cvj7u7+zPf49Pq3bpkqKLy9vVMsL1SoELdv305e5uvrS5EiRZKfK4qCVqtl0aJFeHt7A6Zm+keOHOHOnTs4OjoCpu4769atY+XKlbz55pvJ7/XJYz0u0yefP73s6fegKAp6vR4g1TkqXuxxmdlq2e2//ICP1pzmbnQSGrWKoY1L83bjUthr1JaJOSkGdfBS1Ed/RBV1HQBF44gSEISh7jtQ0B/IWrnZallnRmbyDGQt1+SEPAOSa55VZjkt1zwuC51Ol/x6bvofzg62nmcAHsQm8cn6s+w8b+rOXKukF9O7BVA8n3PyZ4zsJnnmP5b+TvM8OeH8tUVSbv/RnN+EGjCUbYnxBeXxdLkl6Y0M/uU4l+7FUtjDkR/7VcdJI+X6NDnfMier5Zbe7XJUJdqdO3coXLhwimWFCxcmOjqahIQEnJ2dU20zdepUJk6cmGr5tm3bUt2FtrOzo0iRIsTGxiaPTaIoCgdHvmTGd5F+uoQ4ohNffKcwNjYWgISEhOSE+rTg4GB8fHzw9PRMXqd48eJ4enpy4sQJypcvT1JSEiVKlEBRlOR18uXLx507d5Kfd+nShWHDhjF16lQcHR1ZsmQJXbp0SY7B3d09uUvPk7HUqlWLPXv28ODBA3755ReCgoLYsWMH3t7e/PDDDzx69IghQ4YQHR2dvK+4uLgU+0hrv096+v3HxcUBEBMTk6LVhF6vR6VSJa/7zTffpNhvUlISvr6+ODo6Ji87fPgwsbGxyV90njzmuXPniI6OJikpCYPBkLyNXq9Hq9WmiEmv16PT6Z75HrRaLQkJCRw4cAAwfSEVmWNrZac1wMZwNX/fMd1p83ZS6F9WT8nEC2zfesHsx3PSPqB0xDb87u9GY0wAIMnOnasFm3G1YHO0Kg84cgm4lGK7zJRbfHy8OUK2CZnJM5C1XJMT8gxIrnksp+eax3lmz549yZUptna9zClstdxORar47bKaWL0KjUqhna+RpkXvc/LgLk6+eHOLy+t5Biz/nSY9bPX8tXV5vdzURh1tQrehBvZFuPHo3xbiL7J9+3YUBZZcUnP8vhpHjcIAvzhO7P+LE5YNOUfL6+dbZmW23NKba3JUJVpmjB07lpEjRyY/j46OxtfXl5YtW+Lh4ZFi3cTERK5fv46bm1vy2CQAnpgq02JiYnB3d7e5JvCPZxgLDw9P9Z4ec3JyQq1Wp3pdpVLh5OSEh4cHjo6OODo6pljncRJ/vCwoKIhhw4axd+9eateuzcGDB/n666+fedzH5VakSBGKFjUNlN6sWTPKly/PqlWrGDNmDAcOHODo0aOpPkw0bdqUPn36pHugVWdn5xRxlClTBjD9Mzy5PDIykmrVqj0zZkdHR9zd3VO8bjAYKFq0KH/99Veq9b28vJLLT6PRJG/n4OCAvb19iv0oipJq2ZMSExNxdnamfv367NmzhxYtWmBvnw2zM+YiOp2O7du321TZnbkVzQerT3E5wvRlu28dX0a3KmeZwZxvh6A5/B2qc+tRGU1fkJUC5TDUfQd1lR6UsXemTBqbZaXcnlXZkJdkNdd4/vua5BrJNZbONY/zTKNGjdBoNDZ3vcwJbDHPAMQm6Zm6+QIrL9wEwL+QGzO6B1CxaOZnzjMnyTNZk5E88zy2ev7aOik3E9WVXdiFJKK4FqJ+96Ggen43zCfL7ZvdYRy/fxU7tYrv+9WgYdmC2RR1ziPnW+ZktdzSm2tyVCVakSJFUgxODKbxTTw8PJ7ZOuDxh/Wn2dvbpypYg8GASqVCrVajVqe8IDzu8vD4dVtSsGBBWrVqxXfffcewYcNSjVXz6NEjKlWqxPXr17l582ZyF5uzZ8/y6NEjqlSpglqtTv7C9uT7e3qZi4sLXbt25ddff+XKlSuUL1+eWrVqPTO2Z5Wb0WhEq9WiVqv55ptvmDx5cvJrt27dolWrVqxYsYK6deumu7yf/ruVKVOGIkWKsGvXLmrUqAGY/jEOHz7MO++888z9plUONWvW5M6dOzg4OODn55eu7by9vblz507yc4PBwJkzZ2jatOkzj/3472BnZ/rXTOs8FeljC2VnMCrM//sys7eHojcqeLs78lX3qjQtb4HBnO+ehZ2fQ+jm/5b5NYT676Eq2wK7dP4fZabcrF3O5pSZPAOSa0ByTU7JNY//Bvb29snjsNnC9TInsqVyO34tkhErQgiPjEelgsENSzOyhT9O9hprh5ZKXs8zYPnvNOlhS+dvTpLny+2yqYWPqnxr7B1Sn4/PsibkLt/vuQrAlK4BvFIxG2ahzwXy/PmWSVm5LqZHjqpEq1evXvKgwo9t376devXqWSki2zFv3jwaNGhAnTp1+Pzzz6latSp6vZ7t27fz/fffc/bsWQICAujbty9z5sxBr9czZMgQGjdu/NwvJmnp27cv7du358yZM/Tr1++568bFxfH555/TvXt3fHx8uH//PvPmzePmzZv06NEDgBIlSqTYxs3NNLh6mTJlKF68+DP3HRsby6VL/3VFu3r1KsHBweTPn58SJUqgUqkYPnw4kyZNoly5cpQqVYrPPvuMYsWK0blz5wy95+bNm1OvXj06d+7MV199hb+/P7du3eLPP/+kS5cuaZbhK6+8wsiRI/nzzz8pU6YMs2bN4tGjRxk6rsi5wh/EM2JlMMevPQSgTZUiTO4SQH5XB/Me6FE47JoKIb8CCqg0UKUb1BsKxQLNe6w8QPLM80mukVwjbItWb+TrnaF8v/syRgV8vJyZ0aMa9coUsHZo4jkk14gcSVHgwhbTY//0T6Rx9qGKnw6fA+D9ZuUIqpXxyQiEsCVWvc0dGxtLcHAwwcHBwH8fTMPDwwFTs+VXX301ef23336bK1euMHr0aM6fP893333HypUrGTFihDXCtymlS5fmn3/+oWnTpnzwwQdUqVKFFi1asHPnTr7//ntUKhXr168nX758NGrUiObNm1O6dGlWrFiR4WO98sor5M+fnwsXLtCnT58Ur4WFhaFSqZJnYdNoNFy8eJEePXrg7+9Phw4dePDgAXv37qVy5coZOq5KpUrR3ebYsWNUr16d6tWrAzBy5EiqV6/OuHHjktcZPXo07733Hm+++Sa1a9cmNjaWLVu2pOiu26RJkxfOwqZSqdi0aRONGjVi0KBB+Pv706tXL65du5aqa9Bjr732GgMGDODVV1+lcePGlC5dOnmQbJF7KYrCiqPhtPl6D8evPcTN0Y6ZParxXd8a5q1Ai3sAWz6Gb2pCyHJAgYodYcgh6PY/qUD7l+QZ85JcI7lG2I6Ld2Po8t1+5u0yVaB1reHD5uENpQLNCiTXiDzh3lmICgc7JyjdJF2bnLkVzaJQNQajQtcaPoxoXs6yMQqRHRQr2rVrlwKk+hkwYICiKIoyYMAApXHjxqm2CQwMVBwcHJTSpUsrCxcuzNAxo6KiFECJiopK9VpCQoJy9uxZJSEhIdVrBoNBefjwoWIwGDJ0vLzmr7/+Ury8vJTIyEhFUcxXbleuXFHs7OyU0NBQc4SZQokSJTJ8HlnK43MwOjpaWbdunaLVaq0dUo6j1WqtVnYRMYnK64uOKiU/2qiU/Gij0mP+ASX8QZx5D5IUqyi7v1KUKcUVZbyH6WdhO0W5fixLu81KuT3vumpt1sgziiK5xtIk12Tek+efNa+XOZm1y81gMCoL9l1R/D/ZpJT8aKNSbeJW5c+Tt6wSS0bk1jyjKLb3neZ5rH3+5lRSboqi/D3d9LlzaY90rX7jYbxSe9J2peRHG5VePxxQknTy2Sa95HzLnKyWW3qvq1btztmkSZPkqdrTktYgv02aNOHECZnDw1Zt2rSJjz/+mHz58pl9v2+++Sblypn37sWZM2fw9PRMcXdQiMzYcfYuH/1+kgdxWhw0aka18uf1l0ujUZtpcHiDDo4vgr+/grh7pmVFAqD5BCjTDGxsEHpbIXkmd5JcI/Kq21EJfLjqJPsu3Qegsb8307tXpZCH0wu2FJYkuUbkCRf+HXe3/Iu7ckYl6Bi08Aj3YpIo4qzwba9qONjZ1livQmRWjhoTTdi+6dOnW2S/Q4cOtch+K1euzMmTtjDhu8ip4pL0TPrzLL8euQ5AhSLuzO4ZSMWi6Z8p64Uu74JNH8KDi6bn+fzglc+gclewscHnhcgOkmtEXrQh5Bafrj1FdKIeJ3s1n7SrRL+6JWxuJl8hRC4Uew9uHjc99m/93FW1eiPvLD1O6N1YCrk78la5ODycZXB8kXtIJZoQQmSSxWdDi7oJ2z6BM2tNz10KQpMxUGMA2Jl5ggIhhBA2KSpex2frT7Mh5BYA1Yp7MqtnIGW83awcmRAizwjdCihQNBA8nj2zpqIojFlzkgOXH+DqoOHHftW5Frwv28IUIjtIJZoQQmSQVm9k7s6LfLf7kmVmQzPo4NB3sPtL0MWBSg113oQmY8HZyzzHEEIIYfP2XbzPqFUh3IlORKNW8W7Tsrz7SlnsNdIKWQiRjUL/nZXzBV05Z++4yJp/bqJRq/i2bw0qF/PgWrDlwxMiO0klmhBCZMClezEMXxHM6ZvRAHSt7sOETpXxcDJTM/U7p2DdO6bfAL51oe0MKFrVPPsXQghh8xJ1Br7ccp6F+8MAKFXQlVlB1ahewrzjAAohxAvpEuHyX6bHz+nKufLYdebuNA09MqlzFZqWL4ROp8uOCIXIVlKJJoQQ6WA0Kiw+GMa0zedJ0hvxcrFncucA2lV9dpP2DDHoYO8s2PMVGPXgnA9aToZqvWXcMyGEyENO34xi+IpgLt2LBaDfSyX4uG1FXBzkY7sQwgrC9oIuHtyLQdFqaa6y92IEH68x3QAe2rQMveuUyM4IhchWko2FEOIF7kQl8uHqEPZeNM2G1ujf2dAKm2s2tKdbn1VoD+1ng1sh8+xfCCGEzdMbjPyw5wqzt4eiNyp4uzvyVfeqNC0vuUAIYUUXNpl+l2+d5mzw525H887Sf9AbFToFFmNUy/LZHKAQ2Usq0YQQ4jn+CLnFp+tOE5WgM82G1rYi/V4qaZ7Z0BQFDn0P28eBUWdqfdZ2BlTpluaHFCGEELnTtQdxjFwZwvFrDwFoXbkIU7oGkN9VJpERQliRovw7qQDgn3o8tNtRCQxaeJTYJD11S+Xnq+5VZcZgketJJZoQQqQhKl7HuA2nWR9sodnQ4h7A+iFPDNTaDjrMkdZnQgiRhyiKwoqj1/l841nitQbcHO2Y2LEyXWv4yBdRIYT13TkJ0TfB3gVKNUrxUkyijkELj3InOpGyhdz4sX8tHO3MNEO9EDZMKtHygCZNmhAYGMicOXOsHYoQOcL+S6bZ0G5HWWg2tLB98PsbEHMbNI7QajLUfkNan4kcS/KMEBkXEZPE2DUn2XHuHgB1SuVnZo9q+OZ3sXJkFvLgEoWiTwJtrR2JECK9Lvx7s7d0U7D/bxgTncHIkGX/cP5ODAXdHFk4sDaeLmaaZEsIGyeVaLnEwIEDWbx4carlFy9etEI0QuRMiToD07de4Od9VwHwK+DC7J6B5psNzWiAPdPh7y9BMUKBctBjIRQJMM/+hbAgyTNCmM/2s3cZ8/tJHsRpcdCoGdXKn9dfLo1GnQtvpjwKh91fYheynOoaN9C+B/Ze1o5KCJEeF/40/S7/X1dORVH4dO1p9l68j7O9hgUDa+Xeyn8h0iCVaLlI69atWbhwYYpl3t7eVopGiJzl9M0oRqwI5uK/s6H1rVuCT9qZcTa06Fvw+2C4ts/0PLAvtJ0ODq7m2b8Q2UDyjBBZE5uk54s/zrLi2HUAKhRxZ3bPQCoW9bByZBYQd9900+jYQjDqUAGPXEpTIDEaXL2sHZ0Q4kUeXYfbIaBSp6hE+273ZVYcu45aBd/0rk7V4l7Wi1EIK5BKtBdRFNOUvkaj6bdWA2ozdel6EXuXDHXvcnR0pEiRIi9cb8mSJXz99ddcuHABV1dXXnnlFebMmUOhQqaxmD7//HPmz5/PqVOnKFCgAADt2rUjPj6enTt3os6u9y9ENjAYFX7Yc5nZ20PRGRQKujnyVfcAXqlQ2HwHCd0Ga9+ChEhwcIN2s6BaT/PtX+Rsj/MMZH+ukTwjRLY5FhbJyJUhhEfGo1LB4IalGdnCHyf7XDaGkD4JDv8Ae2ZAUpRpWanG6BuN4fDJCNp6FLVufEKI9Hk8K6fvS+BaEDBNuDV96wUAxneoTPNKZvy8LEQOIZVoL6KLhynFUANe2X3sj29ZpJWKTqfjiy++oHz58ty7d4+RI0cycOBANm0yXSg/+eQTtmzZwhtvvMHatWuZN28eBw4cICQkRL7YiFzlemQ8I1cGczTMNBtaq8qFmdIlgAJujuY5gNEAu6bA3hmm50WrQfeFUKCMefYvcod/8wyQ/blG8owQFqfVG/l6Zyjf776MUQEfL2dm9KhGvTIFrB2aeSkKnPvDNOP0Q9OwCBSpCi2/gNJNUHQ6OLnJujEKIdLv/EbT7wrtANONgA9WhQDwWoNSDKjvZ6XAhLAuqUTLRTZu3Iib238zB7Zp04ZVq1alWu+1115Lfly6dGnmzp1L7dq1iY2Nxc3NDY1Gw9KlSwkMDGTMmDHMnTuXn376iRIlSmTL+xDC0hRFYdXxG0zccIa4f2dDG9+hEt1rFjffbGhxD+D31+HKLtPzOm9Cy0lgZ6YKOiGsQPKMEBlz8W4Mw1cEc+ZWNABda/gwoWNlPJxy2QDct4Jh6yf/DVngVhiajYNqvUGdy1raCZEXJDyEsP2mxxXaEnY/jsG/HEOrN9KiUmE+aVfRuvEJYUVSifYi9i7w8S2MRiPRMTF4uLtn311y+4wN0Ni0aVO+//775Oeurmm3Ljh+/DgTJkwgJCSEhw8fYjQaAQgPD6dSpUqA6UvPjBkzeOutt+jZsyd9+vTJ5JsQwrY8iE1i7JpTbDt7F4DafvmYFRRo3gFRbx6HlQMg6rrp/7jDXKjaw3z7F7nLv3kGyP5cI3lGCIswGhUWHQhj2pbzaPVGvFzsmdIlgLYBuawrY8wd2PkFBC8DFLBzgvrvQYPh4Oj2oq2FELYqdBsoBihUiYeOxRn0/QEexuuoWtyTr3sF5s5JUIRIJ6lEexGVytTVxWgEe4PpsY12NXF1daVs2bLPXScuLo5WrVrRqlUrli1bhre3N+Hh4bRq1QqtVpti3T179qDRaAgLC0Ov12NnJ6eLyNl2nrvLR7+f5H6sFnuNipEtyvNmIzPOhqYocHwRbB4NBi3kLw09l0LhyubZv8idHucZsPlcI3lGiBe7HZXAqFUh7L/0AIDG/t5M716VQh5OVo7MjHSJcGge7JkJujjTsoAe0Gw8ePlaNzYhRNb925VT79+Gt5Yc5+r9OHy8nPlpQC3zTbolRA5le5/QhUWdP3+eBw8eMG3aNBo2bEiFChW4d+9eqvVWrFjBmjVr2L17N+Hh4XzxxRdWiFYI84hL0jN2zSleX3yM+7Fa/Au7sW5oA95pUsZ8FWi6BFj/LmwcbqpAK98O3twtFWgiz5E8I/Ky9cE3aTV7D/svPcDJXs0XnauwaFDt3FOBpihwbiN8Vxd2fm6qQPOpBa/vgG4/SQWaELmBLgEu7QRg9g1/joRF4u5ox4KBtSnknkuuZUJkgVQj5zElSpTAwcGBb775hrfffpvTp0+n+uJy48YN3nnnHb788ktefvllFi5cSPv27WnTpg0vvfSSlSIXInP+CX/IyBXBhD0wzX74xsulGNWqvHlnQ3sYBiv6w52TpmnAX/nM1JXFBlsSCWFpkmdEXhQVr+PT9af5I8TUNbtacU9m9QykjHcu6tJ47xxsGQNXdpueuxeF5hNNLdAk3wmRe1z5G3RxxDgUYt55N+zUKr7rV4PyRdytHZkQNkEyXh7j7e3NokWLWLVqFZUqVWLatGnMmDEj+XVFURg4cCB16tTh3XffBaBVq1a888479OvXj9jYWGuFLkSG6AxGZm27QPfvDxD2IJ5ink4sf6Mun7avZN4KtCt/w49NTBVoLgWg/1poOFK+UIg8S/KMyGv2Xoyg1Zw9/BFyC41axbBm5Vj9Tv3cU4EWHwmbRsP3DUwVaBoHaPgBvHsMqvWUfCdEbvNvV87f4wMBFZO7VKFhOW+rhiSELZGWaLnEokWLnvna7t27Uzzv3bs3vXv3TrFMUZTkxzt27Ei1j7lz5zJ37twsxShEdrmbAEE/HuH0v7Ohdalumg3N09mMs6EpChz5n+muvGKAYjWg5xLwLG6+YwhhQyTPCJFSgtbApE0XWHzwGgClCroyK6ga1Uvks3JkZmI0wD+LTRMHJESallVob5ppOn8p68YmhLAMowHtuU04ANuMtRjSpAw9a8vM2UI8SSrRhBC5hqIoLDkUzvQQDTolGk9neyZ3qUL7qsXMeyC9FjZ9AP/8YnpetSd0+Brsnc17HCGEEDbpWix0/v4gV+6bhgro91IJPm5bMfcMuH3zH/hzJNw6YXruXRHaTIPSTawalhDCsm6c+pviiQ+IUlwoWLkpo1qWt3ZIQticXJLphRB53d3oREatCmHvxfuAigZlCjAzKJAinmYeADU2Alb2h/CDgApaTIT675tmWBRCCJGr6QxG5v51iXmnNBiJp5C7I9N7VKOxfy7p6pTw0DRhwLGFgAKOHtD0E6j9Bmjka4MQuVlETBJ7/lhEHyDYuS5fBdVEba4JuITIRSQbCiFyvD9P3ubjtaeIStDhaKemfXEdU16tgaOjg3kPdDsEfu0D0TdMXyy6/Qz+Lc17DCGEEDbp0r1YPlgZTMiNKEBFuypFmNw1AC8XM+caazAaIeRX2D4O4u+blgUEmbpuuhe2bmxCCItL1BkYvPgoc7SHQA01WvYz7xjCQuQiUokmhMixohJ0TNhwhrUnbgIQ4OPJ9G5VuHD0b/PfObuwBVYPAl08FCgLvX4Fb3/zHkMIIYTNMRoVfjkYxtTN50nSG/FwsqOzbxKf9ayKvb0Zx9q0ljun4c8P4Poh03PvCtB2BpRqaN24hBDZwmhUGLEimLibZ/BzvItR44h75VbWDksImyWVaGl4cvBjIbLT43NPJV0DX+jA5fuMWhnCrahE1Cp4t2lZ3mtWDowGLpj7YMcWmL5gKEYo3RR6LATnXDJwtLAayTXCGuS8y5jbUQl8uOok+y6ZWmc1LFeQyZ0qcWL/X1aOzAwSo2H3VDj8g2mCHHtXaPIRvDQENLmgclAIkS7Ttpxn8+k7vG9/HAB16Sbg6G7doISwYVKJ9oTHdxPj4+NxdpYBwkX2i483DVBsZyf/ms+SqDMwc9sFftp3FUWBkgVcmBUUSM2SpkotndFgvoMZjfDX57Bvtul5YD/oMEe+XIgs0WhM3SO0Wq3kGpHttFot8N95KJ5tffBNPlt3muhEPU72asa2qUj/l0piMOg5Ye3gsip0K/wxDGJum55X6gStpsgM00LkMUsPXePHPVcAGFTgDDwCKrSzakxC2Dr5pv4EjUaDl5cX9+7dA8DFxSW5RZDRaESr1ZKYmIharbZmmDmKlFv6KIpCfHw89+7dw8vLS77cPMPZW9GMWBHMhbsxAPSuU4JP21XE1dEClzK9FtYPhVMrTc+bjIXGH8kEAiLL7OzscHFxISIiAnt7+xTXRrlmZo6UW/oYjUYiIiJwcXHBzs4OvV5v7ZBs0qN4LZ+uO83Gk6YKpmrFPZnVM5Ay3m4AGMx4rybbJUbB1o/hxFLT8/yloe10KNvcunEJIbLd7gv3GL/hDADjGnmS78hpQAXl21g3MCFsnFSiPaVIkSIAyRVpjymKQkJCAs7OztLVLgOk3DLGy8uLIkWKyBebpxiMCv/be4WZ2y6gMygUdHPgy25VaVbRQoMdJzyCFf0gbC+o7aDDXKje1zLHEnmOSqWiaNGiXL16lWvXrqV4Ta6ZmSPlln5qtZoSJUpIOT3D7gv3GL36JPdiktCoVbz3SlmGNi2LvSYXVM5eOwhrBkPUdUAF9YbCK5+CvbSIFSKvOXsrmqHL/sFgVOhesziDCh4xveBbB9wKWTc4IWyc1SvR5s2bx/Tp07lz5w7VqlXjm2++oU6dOs9cf86cOXz//feEh4dTsGBBunfvztSpU3FycjJLPI+/3BQqVAidTpe8XKfTsWfPHho1apQ7BpHNJlJu6Wdvby8t0NJwPTKeD1aGcCQsEoAWlQozrWsABdwcLXPAqBuwtDtEnAMHdwhaDGWbWeZYItvYWq5xcHCgXLlyyV3rHpNrZuZIuaWfg4ODtNZLQ7xWz5RN51h6KByA0t6uzA4KpJqvl3UDMwejEfbPhr8mm8Y+y+cHnb+HkvWtHVmuYmt5RohnuROVyGuLjhKnNVCvdAGmdAlAtXy86UXpyinEC1m1Em3FihWMHDmS+fPnU7duXebMmUOrVq24cOEChQqlrgFfvnw5Y8aMYcGCBdSvX5/Q0FAGDhyISqVi1qxZZo1No9GkqNDQaDTo9XqcnJzkA3oGSLmJzFIUhdXHbzDxj7PEJulxddAwvkNletQqbrkWFLdPwvIg0xgx7kWhz0ooWtUyxxLZxlZzjVqtTvVlSa6ZmSPlJrLiRPhDRq4M4er9OAAG1vfjo9YVcHbIBTe24u7Dmjfh8k7T84AgaD9LBg03M1vNM0I8LS5Jz+uLj3InOpEy3q7M71cTB120qfcFQIX21g1QiBzAqpVos2bNYvDgwQwaNAiA+fPn8+eff7JgwQLGjBmTav0DBw7QoEED+vTpA4Cfnx+9e/fm8OHDzzxGUlISSUlJyc+jo6MB013rJ1uavcjjdTOyjZByy6y8Xm6RcVo+XX+W7edM3aprlvDiq25VKJHf5YVdXTNbdqoru9H8PhCVNhbFuwL6XivAwwfyyN8gK+ecrZ+nkmtyPym3zMnr5aYzGJm3+wrz91zFYFQo7OHItC5VeLlsAcCITmdMe7scUm6qm8fR/D4IVcwtFDtnDK2moVTrYxrb0wqxS575j+SZnCc3lJveYGTo8mDO3IqmgKsD/+tfHRd70J/ejJ1Rj1KwPHqPEma9PuSGcrMGKbfMyWq5pXc7lWKluc61Wi0uLi6sXr2azp07Jy8fMGAAjx49Yv369am2Wb58OUOGDGHbtm3UqVOHK1eu0K5dO/r378/HH3+c5nEmTJjAxIkT09yXi4uL2d6PEMI8zjxU8etlNTE6FRqVQhtfI82KKagtOHyP74O9BIYvQI2BCLeKHCn1Pno7V8sdMJeJj4+nT58+REVF4eHhYe1wUpBcI4RIy514WHpJw/U4U3KpWdBI91JGXKw+0IkZKAp+9/8i4OZS1IqBWMciHCn1PjHOOXfmTckzkmdE1igK/H5Vzd67auxVCu9WNuD3b4PUWle/xefREUILd+BcsR7WDVQIK0pvrrHaR4X79+9jMBgoXDjlwOCFCxfm/PnzaW7Tp08f7t+/z8svv4yiKOj1et5+++1nJhuAsWPHMnLkyOTn0dHR+Pr60rJlywwlYZ1Ox/bt22nRooV0FckAKbfMyYvlFq/VM21LKL+evwFAWW9XZnQPoHKxjH1YzlDZKQrqvdPRnPgfAMYq3fFq9zUt7Sw03poNy8o59/huuC2SXJM3SLllTl4sN6NR4ZfD4cw6epEkvRFPZzsmdqhEu4Ai6d6HTZebLh7N5lGob5hmljaWb49jh29oaAPdNyXP/EfyTM6T08tt4YFr7D10AZUKZvUMpHXlf89XfRJ2s98BoHTb9yhVrIZZj5vTy81apNwyJ6vllt5ck6Put+3evZspU6bw3XffUbduXS5dusSwYcP44osv+Oyzz9LcxtHREUfH1F+I7e3tM1Wwmd0ur5Nyy5y8Um5Pj0fzWoNSjG5dHif7zI9H88KyM+hg4wg4sdT0/OWRqJuNQ53HZ6zLzDmX285RyTU5l5Rb5uSVcrv5KIEPV4Vw4PIDABr5ezO9e1UKe2RuIHebK7cHl2Hlq3D3NKg00HwC6vrv2VxekzwjeSYny4nltvXMHaZuuQDA2DYV6BD4RKvUsN2gjQP3otj51gYLTTyTE8vNFki5ZU5WrovpYbVKtIIFC6LRaLh7926K5Xfv3qVIkbTvBn722Wf079+fN954A4CAgADi4uJ48803+eSTT2S2KSFyGJ3ByDd/XWLerksYjApFPZ2Y0aMaDcoWtOyBk2Jg5QDTQMsqNbSbCbVes+wxhVVIrhFCKIrCuuCbjFt/hphEPc72Gj5uV5F+dUtYbqKa7Ba6FX4fDElR4OoNPRaB38vWjipPkDwjbFnI9UcM++0EigJ965ZgcMPSKVc4v9H0u3xbi1WgCZHbWO0/xcHBgZo1a7Jz587kZUajkZ07d1KvXr00t4mPj0+VVB7PoGmlod2EEJl0OSKW7t8fYO7OixiMCp0Ci7FlWCPLV6BF34aFbUwVaPYu0OtXqUDLxSTXCJG3PYzTMnT5P4xYEUJMop5AXy82DWtI/5dK5o4KNEWB/V/D8p6mCjTfl+CtvVKBlo0kzwhbdeNhPK8vPkaizkiT8t5M7Fg55XXPaITzm0yPK7SzTpBC5EBW7c45cuRIBgwYQK1atahTpw5z5swhLi4ueWabV199FR8fH6ZOnQpAhw4dmDVrFtWrV09u+vzZZ5/RoUOH5MQjhLBtiqKw9NA1Jm86R6LOiIeTHZO6BNCxWjHLH/zeOVjWA6Kum+7U91kJPuYd+0HYHsk1QuRNuy7cY/Tqk0TEJGGnVjGsWTneaVIGO00uaW2hTzINSxC8zPS85iBoOx000vUnu0meEbYmOlHHa4uOcj82iYpFPfi2T43U176bxyDuHjh6gF9D6wQqRA5k1Uq0nj17EhERwbhx47hz5w6BgYFs2bIleWDO8PDwFHdpPv30U1QqFZ9++ik3b97E29ubDh06MHnyZGu9BSFEBtyLTuTD1Sf5OzQCgJfLFmR6j6oU9XS2/MGv7oXf+pru1BcoC31XQ/5Slj+usDrJNULkLXFJeiZvOsfyw+EAlC3kxuygQAKKe1o5MjOKjYCV/SH8oGlYgtZfQp3BkBta1+VAkmeELdEZjAxZ+g+hd2Mp7OHIgoG1cHNM42v/466c5VqCnUP2BilEDmb1iQXeffdd3n333TRf2717d4rndnZ2jB8/nvHjx2dDZEIIc9p86jZj157iUbwORzs1Y9pUYEA9P9TqbPjAf2o1rHsHDFpTV5fev4JLfssfV9gMyTVC5A3Hrz1k5Mpgrj2IB8wzUY3NuXsGlveCqHBw9IQeC6FsM2tHledJnhG2QFEUPll7in2X7uPioOHnAbXTvlmtKHD+T9Nj6copRIZYvRJNCJG7RSfqmLDhDGv+uQlA5WIezOkZSLnC7pY/uKLA/jmwY4LpeaVO0OUHsM+Glm9CCCGyjVZv5OudoXy/+zJGBYr9O1FNfUuPs5ndLmyG398AbSzkLw29V4C3v7WjEkLYiO92X2blsRuoVTCvTw2q+DyjBe7dM/DgEmgcoGzz7A1SiBxOKtGEEBZz6MoDPlgZws1HCahVMKRJWd5vVg4Hu2wYj8aohz8/gmM/m56/NBRaTpKZh4QQIpcJvRvDiBXBnLkVDUCX6j5M6FgZT+dcNDaYosCBubB9PKCYxi8K+kVaVQshkm0IucX0rRcAmNixMk0rFHr2ymfWmH6XbQFOHtkQnRC5h1SiCSHMLklvYOa2UP639wqKAiXyuzArqBq1/LLnw77GkIRm9QC4uBVQQeup8NI72XJsIYQQ2cNoVFiw/ypfbb2AVm8kn4s9k7sE0DagqLVDMy+DHv4cCf8sNj2XCQSEEE85fi2SUatCAHjj5VL0r+f37JUVBU7/W4lWpavlgxMil5FKNCGEWZ27Hc2IFcGcvxMDQK/avnzavlLaA5paQuw9Glyaijr+Ctg5QdcfTd04hRBC5Bo3HsYzalUIh65EAtC0vDdfdqtKIQ8nK0dmZtp4WP0ahG42TSDQairUfUsmEBBCJAt/EM/gX46j1RtpWakwY9tWfP4Gt4Ph4VWwcwb/1tkSoxC5iVSiCSHMwmBU+HnfFWZsDUVrMFLA1YFp3arSolLh7AviwWXslnQlX3wYinN+VL1/gxJ1s+/4QgghLEpRFH7/5yYTN5whJkmPi4OGT9tVoncdX1S5rWIp7gEsD4Kbx0w3hbr9DBXbWzsqIYQNiUrQMWjRESLjtAT4ePJ1r+poXjRp1+NWaP6twNHN8kEKkctIJZoQIstuPIzng5UhHL5qahHQvGIhpnWrSkE3x+wL4uY/sKwHqvj7xDkUwmHABuyLvOBOnBBCiBzjQWwSH689xdYzdwGoUcKLWUGB+BV0tXJkFvAwDJZ0hcjL4OQFfVZAiZesHZUQwoboDEaGLDvO5Yg4ino68dOAWjg7vGAmYkWBM2tNj6UrpxCZIpVoQohMUxSFNf/cZMITLQLGta9Ez9rZ3CLg8l/wWz/QxaEUqcregoNpVqBs9h1fCCGERe08d5ePfj/F/dgk7NQqRrTw561GpbHT5MLJYm4Fw7IeEHcPPH2h3+/gXd7aUQkhbIiiKIxbf5r9lx7g6qDh5wG1KZye7uw3jkLUdXBwg3ItLR+oELmQVKIJITIlMk7LJ2tPsfn0HcDUImB2z0BKFsjmFgGnf4c1b4FRB6Uao++2iKSde7M3BiGEEBYRm6Rn0saz/Hb0OgD+hd2YFRRIFR9PK0dmIZd3wYp+oI2FwlWg72rwyGUTJQghsuynvVf59ch11CqY27s6lYqlc4bNx105y7cBe2fLBShELiaVaEKIDNt94R4frj5JRIyVWwQcWwgbRwAKVO4KXeaDkgtbJQghRB50NCySD1aGEB4Zj0plmnHug5blcbJ/QXelnOrcRlg9CAxa8GsIvZaBUy6tLBRCZNq2M3eYsvkcAJ+2q0Sziukcf9hohLPrTI8rS1dOITJLKtGEEOkWr9UzddN5lhy6BkDZQm7M6WmlFgH758L2z0yPa70GbWeCWg06XfbHIoQQwmyS9AZmb7/ID3suoyjg4+XMjB7VqFemgLVDs5yTq2DtW6AYoGJH6PYT2GXjuKJCiBzh9M0ohv0WjKJA/5dKMqiBX/o3vrYPYm6DoyeUbWaxGIXI7aQSTQiRLsHXHzFyRTBX7scBMLC+H2PaVMj+FgGKArumwJ6vTM8bDIfmEyC3zcomhBB50Pk70Qz/LZjzd2IA6FajOOM7VsLDyd7KkVnQ8cXwxzBAgWq9oeO3oJGP6EKIlO5EJfL64qMk6Aw08vdmfIdKGRuDOGSF6XflTlJJL0QWZLjfU7du3fjyyy9TLf/qq6/o0aOHWYISQtgOvcHI1zsu0u37A1y5H0dhD0eWvF6HCR0rZ38FmtEIW8b+V4HWbBy0mCgVaLmM5Bkh8h6DUeHHPZfp+M1+zt+JIb+rA/P71WRmULXcXYF28Dv4431AgVqvQ6fvpAItm0iuETlJXJKe1xcf5W50Ev6F3fi2T/WMDaOijf+vK2fVXhaJUYi8IsOVaHv27KFt27aplrdp04Y9e/aYJSghhG24EhFLt/kHmb0jFINRoX3Vomwd3oiG5byzPxhFgU2j4PD3pudtZ0DDD7I/DmFxkmeEyFuuR8bT+3+HmLLpPFqDkeYVC7F1eCNaVyli7dAsR1Fgz3TYOtb0vP770O7fYQlEtpBcI3IKg1Fh2G/BnLkVTUE3B34eUDvjNxcubDJNWOJVAkrUs0ygQuQRGb7VFRsbi4ODQ6rl9vb2REdHmyUoIYR1KYrCssPhTP7zHAk6A+5OdkzqXIVOgT7WCgj+/ACO/QyooPN3ENjHOrEIi5M8I0TeoCgKq4/fYOIfZ4lN0uPqoOGz9pXoWds3Y12UchpFgR0TYP8c0/MmH0Pj0dKqOptJrhE5xdRN59hx7i4Odmp+fLUWvvldMr6TkN9Mv6v2lMp6IbIow/9BAQEBrFixItXy3377jUqVKpklKCGE9dyLTmTQoqN8uu40CToD9csUYOvwRtatQNs0SirQ8hDJM0LkfpFxWt5Z+g8frj5JbJKeWiXzsXlYI3rVKZG7K9CMRtg8+r8KtJaToMlHUoFmBZJrRE6w7PA1ftp3FYCZPapRo0S+jO8k9h5c/sv0WLpyCpFlGW6J9tlnn9G1a1cuX77MK6+8AsDOnTv59ddfWbVqldkDFEJkny2nbzN2zSkexutwsFPzUesKDKrvh1ptpQ/3imL6snH0J0AFneZJBVoeIHlGiNxt1/l7fLj6JPdjk7DXqBjRwp+3GpVBY61ck12MBtjwHgQvA1TQfpZpdmlhFZJrhK3bezGCcevPAPBBC386VCuWuR2dWm2a+denFhQsa8YIhcibMlyJ1qFDB9atW8eUKVNYvXo1zs7OVK1alR07dtC4cWNLxCiEsLCYRB0TNpzl939uAFCpqAdzegXiX9jdekEpCmz+CI78iKkC7Vuo3td68YhsI3lGiNwpXqtnyqZzLD0UDkC5Qm7M7hlIFR9PK0eWDfRaWPsmnFkLKjV0ng/Velo7qjxNco2wZRfvxjBk2T8YjApdq/vw7itZqPw6+W9XzmrSCk0Ic8jU9D/t2rWjXbt25o5FCGEFh688YOTKEG4+SkClgrcbl2FEc38c7Kw4XoKiwJYxcOQHQAUdv4Hq/awXj8h2kmeEyF2Crz9i5IpgrtyPA+C1BqUY3bp89s/ybA26RFg1AEK3gNoeui+ASh2tHZVAco2wTfdjk3ht8VFiEvXU8cvP1G4Bme/mfu8c3A4BtR1U7mreQIXIo2QObSHyqCS9gVnbQ/lxzxUUBYrnc2Z2z0Bq++W3bmCKAlvGwuH5pucd50KN/taNSQghRKboDUbm7brM3L8uYjAqFPFwYkaParxcrqC1Q8seSbHwWx+4+jfYOUHPZVCuubWjEkLYqESdgTd/Ocb1yARKFnBhfv+aONpl4WbDiaWm3+VagWsB8wQpRB6Xrkq0/PnzExoaSsGCBcmXL99za8IjIyPNFpwQwjLO34lm+G/BnL8TA0BQreJ81r4S7hmdLtvcFAW2fQqHvzc97zAXarxq3ZhEtpA8I0Tuc/V+HCNWBBN8/REAHaoVY1KnKni6WDnXZJeER7A8CK4fBgc36LMC/F62dlR5muQaYcsURWH06pP8E/4IDyc7FgysTX7X1DPIppte+9+snHJDWgizSVcl2uzZs3F3N42NNGfOHEvGI4SwIKNR4ed9V5m+9QJag5H8rg5M7RpAq8pFrB2ayd4ZcPBb0+MOX0PNAdaNR2QbyTNC5B6KorD8SDiTNp4jQWfA3cmOSZ2rWG+WZ2uIewBLu5i6UTl5Qr81ULyWtaPK8yTXCFs2Z8dFNoTcwk6tYn7/mpTxdsvaDkO3QPx9cCsCZVuYJ0ghRPoq0QYMMH2R1ev1qFQqWrVqReHChS0amBDCvG4+SuCDlcEcumK6s9qsQiGmdauKt7ujlSP714ml8Nck0+PWX0LNgVYNR2QvyTNC5A73YhIZ8/sp/jp/D4D6ZQowo0c1ink5WzmybBRzB37pBBHnwaUgvLoOigRYOyqB5Bphu9aduMnXOy8CMLlLFeqXMUOX9xNLTL8De4NGRnESwlwy9N9kZ2fH22+/zblz5ywVjxDCzBRFYV3wTcatO0NMkh5new2fta9E7zq+mR+k1NxCt8GG902PXx4BL71t3XiE1UieESLn2nrmDmPXnCIyTouDnZqPWldgUH0/1GobyTXZ4VE4LO4ID6+CezF4dT14+1s7KvEUyTXClhwLi2T06pOAaYKvnrVLZH2n0bfg0g7T40CZnEsIc8pwlXSdOnU4ceIEJUuWtEQ8QggzehSv5ZO1p/nz1G0AqpfwYnZQIH4FXa0c2RNuHDfNWqYYoFpvaDbe2hEJK5M8I0TOEpuk5/M/zrDy2A0AKhb1YE7PQMoXcbdyZNns/iVTC7ToG+BVEgZsgHx+1o5KPIPkGmELwh/E8+aS42gNRlpXLsLoVuXNs+Pg5aAYoUR9KFjWPPsUQgCZqEQbMmQIH3zwATdu3KBmzZq4uqb8Ml61alWzBSeEyLy/QyP4cFUI92KSsFOrGNasHO80KYOdRm3t0P5z/xIs7wG6eCjTDDp+A7bSOk5YjeQZIXKOY2GRjFgZzPXIBFQqeKtRGUa0KJe12eRyortnTRVocfegQDlTCzTPPDQGXA4kuUZYW1SCjkGLjhAZp6VqcU9m9ww0T8tdo/G/WTllQgEhzC7DlWi9evUC4P33309eplKpUBQFlUqFwWAwX3RCiAxL0BqYtvkciw9eA6C0tytzegZStbiXdQN7WsxdWNoV4h9A0UAI+gU0eWTGNvFckmeEsH1avZGvd4by/e7LGBUons+ZWUGB1CmV39qhZb9bJ2BJF0h4CIUDoP9acPO2dlTiBSTXCGvSGYwMWXacyxFxFPV04qdXa+HsYKabD9f2m7qUO7hDpU7m2acQIlmGK9GuXr1qiTiEEGZw8sYjhq8I5kpEHAAD6/vxUesK5kvK5pIUA8u6w6NrkK8U9F0FjlmcgUjkGpJnhLBtF+/GMHxFMGduRQPQvWZxxneohLtTHrwRcv0oLO0GSVHgUwv6rQbnfNaOSqSD5BphLYqiMG79afZfeoCrg4afB9SmkIeT+Q5wbIHpd0A3cLChIVyEyCUy3K/r2rVr+Pj4ULJkyRQ/Pj4+XLt2LcMBzJs3Dz8/P5ycnKhbty5Hjhx57vqPHj1i6NChFC1aFEdHR/z9/dm0aVOGjytEbqI3GJm78yJdvzvAlYg4Cns48strdZjQsbLtVaDptbCiH9w5aZq1rP8acCtk7aiEDTF3ngHJNUKYg9GosHD/Vdp/s48zt6LJ52LP/H41mNGjWp6sQFNdP2RqgZYUBSXqmWbhlAq0HEO+0whr+WnvVX49ch21Cr7pU51KxTzMt/OYu3Bug+lxrdfNt18hRLIMt0Rr2rQpt2/fplChlF96o6KiaNq0aYaaPq9YsYKRI0cyf/586taty5w5c2jVqhUXLlxItX8ArVZLixYtKFSoEKtXr05Ocl5eXhl9G0LkGlfvxzFiRTDB1x8B0C6gKJO7VMHLxcG6gaXFaIT1Q+HKbrB3NbVAy1/a2lEJG2POPAOSa4Qwh9tRCXy46iT7Lt0HoEl5b77qVtW8rSdykAIx59D8Ohd0ceDXEPqskBYfOYx8pxHWsPXMHaZsNs0K+2m7SrxSobB5D3DiFzDqoXgdKCrj+glhCRmuRHs8TsDTHjx4kGpAzheZNWsWgwcPZtCgQQDMnz+fP//8kwULFjBmzJhU6y9YsIDIyEgOHDiAvb3pjqefn19G34IQuYKiKPx65DpfbDxLgs6Au5MdX3SqQqfAYmn+j9qEHePh1EpQ25nGQPOpYe2IhA0yZ54ByTVCZNX64Jt8tu400Yl6nO01fNKuIn3rlrDdXGNhqqt/89LlmagULZR5BXouAwcXa4clMki+04jsdvpmFMN/C0ZRoP9LJRnUwM+8BzAa4Ngi0+Pab5h330KIZOmuROvatStgGnBz4MCBODo6Jr9mMBg4efIk9evXT/eBtVotx48fZ+zYscnL1Go1zZs35+DBg2lus2HDBurVq8fQoUNZv3493t7e9OnTh48++giNJu0ua0lJSSQlJSU/j442jd+h0+nQ6XTpjvfxuhnZRki5ZdaLyi0iJomP151hd6ipRcBLpfLxZdcqFPNyRq/XZ1ucGaE+Mh/NgbkA6Nt9jeLXGCxwXsg5lzlZKTdzlbW58wxIrskrpNwy50XlFpWgY/wf5/jz1B0Aqhb3YEa3AEoVdLXZXGNpqrC9aFb0QaVoMZRujrH7IlDZWySf5Ta2kGdAvtPI9TJzslput6MSeX3RURJ0BhqVK8DHrcuZ/TqqCt2MXfQNFOf86P3b2sR1Sc63zJFyy5ysllt6t0t3JZqnpydgumvj7u6Os7Nz8msODg689NJLDB48ON0B3r9/H4PBQOHCKZuwFi5cmPPnz6e5zZUrV/jrr7/o27cvmzZt4tKlSwwZMgSdTsf48ePT3Gbq1KlMnDgx1fJt27bh4pLxu4bbt2/P8DZCyi2z0iq3k5EqfrusJk6vQqNS6FDCSOPCEQQf2EVw9oeYLsUeHqJ22HcAnC3ag4s33OGGZcf9kHMuczJTbvHx8WY5trnzDEiuyWuk3DInrXK78EjFsstqorQq1Ci0Km6kRfFIzh35m3NWiNEW5Iu7TP1L01AZk7jtWZ2j7n1Qtv1l7bByHGvmGZDvNI/J9TJzMlNuSQb4+rSGu/EqijgrtPW6y7atW8we20uXplMYuORej7M2dm2S8y1zpNwyJ7Pllt5ck+5KtIULFwKmpsajRo3KVJearDIajRQqVIgff/wRjUZDzZo1uXnzJtOnT39mwhk7diwjR45Mfh4dHY2vry8tW7bEwyP9gzjqdDq2b99OixYtkptdixeTcsuctMotJlHP5M3n+f3CLQAqFHFnZvcq+Bd2t2aoL6QK24vmt58AMNR6g3Itp1LOgl2A5JzLnKyU2+O74VllC3kGJNfkRFJumZNWuSXqDEzfdpFfzoUDUKqACzO6B1C1uKc1Q7W+u2ewW/o+KmMShpKNOOY1gOat2sj5lgG2kGfANnKN5JmcJ7PlZjAqDFkezM34CAq4OvDrW3Upns/5xRtm1MOr2J84hYIKv+4T8cvnZ/5jZIKcb5kj5ZY5WS239OaaDI+JNn78ePR6PTt27ODy5cv06dMHd3d3bt26hYeHB25ubunaT8GCBdFoNNy9ezfF8rt371KkSJE0tylatCj29vYpmjlXrFiRO3fuoNVqcXBIPZC6o6Njimbaj9nb22eqYDO7XV4n5ZY5j8vtyNVIRq4M5sbDBFQqeKtRGUa0KIejnY3NvPm0O6dg9QAwaKFSJzRtv0Kjzp6Y5ZzLnMyUm7nL2Vx5BiTX5DVSbpnzuNxO3Yhi+IoTXI6IA+DVeiUZ26ai7c3ynN0eXIZfe0DiIyheB2PQEow7/pbzLZNsIc+AfKeR8zdzMlpuX248y18XInC0U/O/AbUoVciMM3E+6Z9FAKjKNsO+UDnLHCML5HzLHCm3zMnKdTE91Bnd8bVr1wgICKBTp04MHTqUiIgIAL788ktGjRqV7v04ODhQs2ZNdu7cmbzMaDSyc+dO6tWrl+Y2DRo04NKlSxiNxuRloaGhFC1aNM1kI0ROp9Ub+XLLeXr+eJAbDxPw8XJmxZv1GNOmgu1XoD0Kh6XdISkaSjaALj9CNlWgiZzNXHkGJNcIkR56g5Fv/7pIl+/2czkijkLujiwaVJvPO1WRCrSoG/BLZ4i7B4UDTLNKyyycuYJ8pxGWtuzwNX7adxWAmUHVqFEin2UOlBgN//xielz3bcscQwiRLMOVaMOGDaNWrVo8fPgwxRgCXbp0SZE80mPkyJH873//Y/HixZw7d4533nmHuLi45JltXn311RSDdL7zzjtERkYybNgwQkND+fPPP5kyZQpDhw7N6NsQwubdjoduPxzm+92XURToXrM4W4Y3pE6p/NYO7cXiI2FpN4i9A4UqQa/lYO9k7ahEDmHOPAOSa4R4nogE6PPzUWZsC0VvVGgbUIStwxvRpHwha4dmfbERpgq0qHAoUBb6rwFnL2tHJcxEvtMIS9p7MYJx688AMKqlP+2rFrPcwU4sAW0MFCwPZZpZ7jhCCCAT3Tn37t3LgQMHUt0l8fPz4+bNmxnaV8+ePYmIiGDcuHHcuXOHwMBAtmzZkjwwZ3h4OGr1f/V8vr6+bN26lREjRlC1alV8fHwYNmwYH330UUbfhhA2y2hUWHjgGjNOatArMeRzsWdq1wBaVylq7dDSR5cIv/aG+6Hg4QN9V8uXDpEh5swzILlGiLQoisKKYzf46qQGrTEKd0c7Pu9cmc6BPqgsOG5ljpHwCJZ2gQcXwaM49F8HblKxmJvIdxphKRfvxjBk6T8YjApda/gwtGlZyx3MoIdD802P6w0BdYbbyAghMijDlWhGoxGDwZBq+Y0bN3B3z/gA5++++y7vvvtumq/t3r071bJ69epx6NChDB9HiJzg1qMERq0K4cDlB4CKxv4Fmd6jGoXcc0grLqMR1r4F1w+Boyf0+x08fawdlchhzJ1nQHKNEE+KiElizO8n2Xn+HqDipVL5mNmzOj5eFhjsOifSxsHyINO4nq7e8Op68PK1dlTCzOQ7jbCE+7FJDFp0lJgkPXX88jO1a4Blb0yc32hqLetSAKr2tNxxhBDJMlxV3bJlS+bMmZP8XKVSERsby/jx42nbtq05YxMiz1AUhfXBN2k1Zw8HLj/A2V5NUGkD/+tXPedUoAFs/wzOrgO1PfRaCoUqWjsikQNJnhHCcraeuUOrOXvYef4e9hoVnUsaWDywllSgPaZPgt/6wvXD4ORpaoFW0IKtSITVSK4R5paoM/DmL8e48TABvwIu/NC/puXHMD44z/S71utgL9dxIbJDhluizZw5k1atWlGpUiUSExPp06cPFy9epGDBgvz666+WiFGIXO1RvJZP151m48nbAAT6ejG9W2XOHv47Z3WpOTQfDn5retz5eyjVyLrxiBxL8owQ5heTqOPzP86y6vgNACoW9WBGt8pcOr4XtToH5RpLMuhh9WtwZRfYu0Lf36FIFWtHJSxEco0wJ0VR+HD1Sf4Jf4SHkx0/D6xNPlcLTxJx/QjcOAIaB6j9hmWPJYRIluFKtOLFixMSEsJvv/3GyZMniY2N5fXXX6dv374pBuUUQrzY3osRjFoVwt3oJDRqFe+/Uo6hTcugGA2ctXZwGXF2A2wZY3rcbDxU7WHdeESOJnlGCPM6cjWSkSuDufEwAZUK3mpUhhEtyqFWjFyydnC2QlFg43BT1yiNA/ReDr61rR2VsCDJNcKcZu+4yB8ht7BTq5jfvyZlvN0sf9AD35h+B/QA98KWP54QAshEJRqAnZ0d/fr1M3csQuQZiToD0zafZ9GBMABKF3Rlds9Aqvl6AaAzph6jw2aFH4Y1gwHF1JT85RHWjkjkApJnhMi6JL2B2dsv8sMe0yzPxfM5MysoMHmWZ53OaOUIbcjfX5lmuFOpocciKN3E2hGJbCC5RpjDuhM3mbvzIgBTugZQv0xByx/0/iU494fpcb20x+ITQlhGpirRbt26xb59+7h37x5GY8oPYO+//75ZAhMitzp1I4rhK05wOSIOgFfrlWRsm4o4O1h4zARLuH8Jfu0F+kTwbw1tvoKc1AVV2CzJM0JkzYU7MQxfEcy529EABNUqzmftK+HuZG/lyGzQiaWwe4rpcbuZUKGddeMR2UZyjciqo2GRjF59EoC3G5chqFY2TUJy4GtAMX3+Llwpe44phAAyUYm2aNEi3nrrLRwcHChQoECKMZtUKpUkHCGeQW8wMv/vy8zZcRG9UcHb3ZHp3avSpHwha4eWObERsKwbJERCsRrQfQFoMlUvL0QKkmeEyDyjUWHB/qt8teUCWoOR/K4OTOkSQOsqRawdmm26tAP+GGZ63PADqPWadeMR2UZyjciqaw/ieGvJcbQGI60rF2F0q/LZc+Do2xDym+mx9AARIttl+BvvZ599xrhx4xg7dixqdYYn9xQiT7r2II4RK4L5J/wRAG2qFGFKlwDLDzhqKdo4+LUnPAwDr5LQZwU4uFo7KpFLSJ4RInNuPkpg1MoQDl55AECzCoWY2i0gZ83ynJ1uh8DKAWDUQ9We8Mpn1o5IZCPJNSIrouJ1vLboKJFxWqoW92R2z8Dsm6Tl0Hdg0EKJelDipew5phAiWYYr0eLj4+nVq5ckGyHSQVEUfjt6nS82niVea8Dd0Y6JnSrTpbpPzpp580kGPawaBDePg3M+6Pc7uOXQ1nTCJkmeESJjFEVhXfBNxq07Q0ySHhcHDZ+1r0Sv2r45N9dY2qNwWNYDtLFQqjF0/FaGI8hjJNeIzNIZjLyz7DiXI+Io6unET6/Wyr5hWRIewbGFpscNhmfPMYUQKWQ4a7z++uusWrXKErEIkatExCTxxuJjjF1zinitgTql8rN5eEO61iiec7/UKAr8OQIubgU7J+izEgqWs3ZUIpeRPCNE+j2M0/Lu8hOMWBFCTJKe6iW82PR+Q3rXKZFzc42lxUfC0u4QexcKVYaeS8Auh7YMF5kmuUZkhqIofLbuNAcuP8DVQcPPA2pTyCMbW/se+xm0MVCoEpRrmX3HFUIky3BLtKlTp9K+fXu2bNlCQEAA9vYpB6idNWuW2YITIqfafvYuY34/yYM4LQ4aNaNa+fP6y6XRZFczb0v5+0v45xfT7GXdF4BvHWtHJHIhyTNCpM/uC/cYvfok92KSsFOrGN68HG83LoOdRlrWPJMuEX7rC/cvgIcP9F0FTp7WjkpYgeQakRk/77/Gb0evo1bBN32qU6mYR/YdPCkWDs4zPW4wDKQVpRBWkalKtK1bt1K+vGngxKcH4RQiL4tN0vPFH2dZcew6ABWKuDO7ZyAVi2ZjgrWUf36B3VNNj9vOkNnLhMVInhHi+RK0BqZsOseSQ9cAKOPtypye1QkoLpVBz2U0wrq3IfwAOHpC39Xg6WPtqISVSK4RGXUyUsWCQ6EAfNa+Eq9UKJy9ARz9CeIfQP7SUKV79h5bCJEsw5VoM2fOZMGCBQwcONAC4QiRcx0Li2TkyhDCI+NRqeDNhqUZ2dIfR7tsGiPBkkK3wh/DTY8bjoLar1s1HJG7SZ4R4tmCrz9i5IpgrtyPA2BgfT/GtKmAk30uyDWWtv0zOLMW1PbQaykUrmTtiIQVSa4RGXH6ZjRLLqpRFHi1XkkG1vfL3gCSYuHAXNPjRqNBk+Gv8UIIM8nwf5+joyMNGjSwRCxC5EhavZHZO0L54e/LGBXw8XJmZlA1XipdwNqhmceN47BqICgGqNYHXvnU2hGJXE7yjBCp6QxG5u26xDd/XcJgVCjs4ciMHtVoWM7b2qHlDIfmw8FvTY87fw+lGlk3HmF1kmtEet2OSuCtZSfQGlU0KleAce0rZX9rxaP/+68VWkCP7D22ECKFDHekHjZsGN98840lYhEix7lwJ4bO8/bz/W5TBVq3GsXZPLxh7qlAu38JlgeBLh7KNIOOc2X2MmFxkmeESOlKRCzd5x9kzo6LGIwK7asWZevwRlKBll5nN8CWMabHzSdAVfkCKiTXiPSJS9Lz+qJj3ItJoqizwpygatk/7mRSLOyXVmhC2IoM/wceOXKEv/76i40bN1K5cuVUg3CuWbPGbMEJYauMRoUF+6/y1dYLaPVG8rnYM7VrAK2rFLV2aOYTdROWdIb4+1C0GgT9Ahr7F24mRFZJnhHCRFEUlh4OZ/KfZ0nUGfFwsuOLzlXoFCjjeKXb9SOwZjCgQK3XocFwa0ckbITkGvEiBqPCsN9OcPZ2NAVcHXizfDzuTlaowDr6P0iIhPxlpBWaEDYgw1cBLy8vunbtaolYhMgRbjyMZ9SqEA5diQSgaXlvvuxelULu2Ti9taXFPYAlXSDqOhQoC31/B0c3a0cl8gjJM0LAvehERv9+kt0XIgBoULYA07tXo5iXs5Ujy0EeXIblPUGfCP5toM1X0ppaJJNcI15kyqZz7Dh3D0c7NfP7BnLr1IHsDyLhEez/2vS4sbRCE8IWZPi/cOHChZaIQwibpygKa/65yYQNZ4hJ0uNsr+Gz9pXoXcc3d83ilBQDy7rD/Qvg4QP914KbdBkS2UfyjMjrNp+6zdi1p3gUr8PBTs2Y1hUYWN8PtToX5RpLi7tvymUJkVCsOnT/Wb58ihQk14jnWXroGj/vuwrAzKBqBPp6ceuUFQLZNwsSHoJ3BZmRUwgbIZ8mhEiHyDgtn6w9xebTdwCoXsKL2UGB+BV0tXJkZqZPgt/6wq1/wDm/qQLNq4S1oxJCiDwhOlHHhA1nWPPPTQAqF/NgTs9AyhV2t3JkOYw2Hn7tBZFXTDmsz0pwyGX5WghhMXtCIxi/4QwAo1r6075qMXQ6XfYH8ijcNCkKQIvP5UaAEDZC/hOFeIFdF+4xevVJImKSsFOrGN68HG83LpP9g4pamkEPv78OV/8GBzfotxq8y1s7KiGEyBMOXn7AqFUh3HyUgFoF7zQpw7Bm/jjY5bJcY2lGg2kMtBtHwcnLNByBWyFrRyWEyCFC78YwdNk/GIwKXWv4MLRpWesF89dkMCSBX0Mo19J6cQghUpBKNCGeIV6rZ/Kf51h2OByAsoXcmNMzkCo+nlaOzAIUBTYOh3N/gMYBei0Dn5rWjkoIIXK9RJ2Bmdsu8NO+qygKlMjvwqygatTyy2/t0HKmrZ/A+Y2gcYTev4K3v7UjEkLkEPdjk3ht0VFikvTUKZWfqV0DrDdky+0QOLnC9LjF5zKeoxA2RCrRhEjDP+EPGbkimLAH8QC81qAUo1uXx8leY+XILGT7ODixBFRq6L4ASjexdkRCCJHrnbsdzYgVwZy/EwNAr9q+fNq+Em6O8vEsUw5+B4e/Nz3uMh9K1rduPEKIHCNRZ2DwL8e48TABvwIu/NCvJo52Vvrcryimz+YopnHQfGpYJw4hRJoy1EdAp9PRrFkzLl68aKl4hLAqncHIzG0X6P79AcIexFPU04llb9RlXIdKubcCbd9sODDX9LjDXKjYwbrxiDxN8ozICwxGhfl/X6bjt/s4fyeGAq4O/O/VWkzrVlUq0DLr7HrY+rHpcYsvoIrMuiieTXKNeJKiKHy4+iQnwh/h6WzPzwNrk8/VwXoBnf8Truw29Q5p9pn14hBCpClDn9Ts7e05efKkpWIRwqou3Yth+IpgTt+MBqBzYDEmdqqCp7O9lSOzoEPzYccE0+MWn0ON/lYNRwjJMyK3ux4ZzwcrQzgSFglA84qFmdYtgIJujlaOLAcLPwxr3gQUqD0Y6r9n7YiEjZNcI540e8dF/gi5hZ1axfx+NSnj7Wa9YLTxsGWs6XH99yCfn/ViEUKkKcOj1fbr14+ff/7ZErEIYRVGo8LC/VdpN3cfp29G4+lsz7d9qjOnV/XcXYF29CfY8pHpccNR0GCYdeMR4l+SZ0RupCgKq45dp83XezkSFomrg4avulXlf6/WlAq0rLh/yTQTpz4R/NtAmy9l7CCRLpJrBMDaEzeYu9PUInFK1wDqlSlg3YD2zYaocPAoDg0/sG4sQog0ZbjPgF6vZ8GCBezYsYOaNWvi6ppyyvBZs2aZLTghLO12VAKjVoWw/9IDABr5e/NVt6oU8XSycmQWdnwR/PlvYm4wDF751KrhCPEkyTMit3kQm8THa0+x9cxdAGqVzMesoEBKFHCxcmQ5XGwELOsGCZFQrAZ0/xnUuXToBWF2kmvE0bBIPlp9CjDNiBxUy9e6AUVegf1fmx63ngIOrs9fXwhhFRmuRDt9+jQ1apgGNwwNDU3xmtVmLxEiE9YH3+SzdaeJTtTjZK/mk7YV6fdSydx/Hp9YBn8MNz1+aSg0nyh37YVNkTwjcpO/zt9l9OpT3I9Nwk6tYkQLf95uXAaNWs7lLNHGm1qgPQwDr5LQZ4V84RQZIrkmb7v2II43fzmG1mCkTZUifNiyvLVDMnXjNCRB6aZQsaO1oxFCPEOGK9F27dpliTiEyDaP4rV8uu40G0/eBqBacU9m9Qy07vgH2SV4OawfCihQ5y1oNVkq0ITNkTwjcoO4JD2T/jzHr0fCAShXyI3ZPQOp4uNp5chyAaMB1gyGm8fAOR/0+x3cClk7KpHDSK7Ju6LidQxadJSH8TqqFvdkVlAgamvf2Di7HkK3gNoe2nwln8+FsGFZmgLqxo0bABQvXtwswQhhaX+HRjB6dQh3o5PQqFW890pZhjYti70mw8MD5jwH5/03c1mt12XcGJEjSJ4ROdGxsEhGrgwhPDIegNcalGJ06/K5d5bn7KQoptYa5zeCxhF6/QoFy1k7KpHDSa7JO3QGI+8sO86ViDiKeTrx06u1cHaw8rU54SH8Ocr0+OXh4O1v1XCEEM+X4ZoDo9HI559/jqenJyVLlqRkyZJ4eXnxxRdfYDQaMxXEvHnz8PPzw8nJibp163LkyJF0bffbb7+hUqno3Llzpo4r8o4ErYFx608zYMER7kYnUbqgK2veqc/w5v65vwJNUUwzcD6uQHtpKLSdIRVowmZJnhE5lVZv5Mst5wn64SDhkfEU83Ri+Rt1GdehklSgmcvBeXDkB9Pjrj9AyXrWjUfkWObONZJnbJ+iKHy69jQHLj/A1UHDzwNrU8jDBsZB3vopxN2Dgv7Q6ENrRyOEeIEMt0T75JNP+Pnnn5k2bRoNGjQAYN++fUyYMIHExEQmT56cof2tWLGCkSNHMn/+fOrWrcucOXNo1aoVFy5coFChZzfNDwsLY9SoUTRs2DCjb0HkMSHXHzFiRTBX7scBMKBeSca0qWj9u07ZwaCHjcPhxBLT8+YToMFwqUATNk3yjMiJzt+JZsSKEM7djgagaw0fxneonLtnec5uZ9bCtk9Mj1tOgspdrBuPyNHMmWskz+QMP+65wopj11Gr4Js+1alY1MPaIcHlXRC8FFBBx2/ATmZrFsLWZbgSbfHixfz000907PjfYIdVq1bFx8eHIUOGZPjLzaxZsxg8eDCDBg0CYP78+fz5558sWLCAMWPGpLmNwWCgb9++TJw4kb179/Lo0aNn7j8pKYmkpKTk59HRpg+3Op0OnU6X7jgfr5uRbYR1y01nMDL/76vM+/sKBqNCYXdHpnatTMOyBQEjOl3mWrRkB7OUmz4Rzdo3UYduQlGpMbSdhRLYD/R6M0Vpm+R/NXOyUm7mLuuclmdAco21WbPcDEaFBQfCmL3jEjqDQj4Xez7vWInWlQtbLab0yknnm+r6YTRr3kIFGGq9gbHWW2CluHNSudkSW8ozYN5cI3nG9m07e5dpW84D8EnbCjQsk9/65aaNw27D+/9e117HWLSm1a5rlpJXz7esknLLnKyWW3q3y3AlWmRkJBUqVEi1vEKFCkRGRmZoX1qtluPHjzN27NjkZWq1mubNm3Pw4MFnbvf5559TqFAhXn/9dfbu3fvcY0ydOpWJEyemWr5t2zZcXDI+tfz27dszvI3I/nK7lwBLL2m4FmtqcVW9gJEepeKICT3CptAXbGxDMltudoZ46l6ZQ8HY8xhU9hzzG8KdW/nh1iYzR2i75H81czJTbvHx8WaNIaflGZBcYyuyu9weJMKySxoux5hyTeV8RnqVTsB47TibrmVrKFli6+eba+JtGoV+jp0hidue1Tmifxk2b7Z2WDZfbrbKFvIMmC/XSJ6xfddj4eszGhRFRcPCRgpGnmbTptOZ2pc5y63yjWWUjQon3r4Au7S10W/KvZ/T89L5Zk5SbpmT2XJLb67JcCVatWrV+Pbbb5k7d26K5d9++y3VqlXL0L7u37+PwWCgcOHCKZYXLlyY8+fPp7nNvn37+PnnnwkODk7XMcaOHcvIkSOTn0dHR+Pr60vLli3x8Eh/E16dTsf27dtp0aIF9vbSNSO9srvcFEVh+ZHrzNwaSqLOiIeTHRM6VKRD1aIWP7Y5ZancYu9h91tPVLHnURzdUXospUbJBpYJ1AbJ/2rmZKXcHt8NN5eclmdAco21WSPXrP7nFjM3nSdOa8DVQcPHbcrTo6YPqhzUXT5HnG+xd7Fb3A6VIQ5jsRoU7LeOtvYZrzAwpxxRbjbIlvIMmC/XSJ6xbbejEpn8w2F0xiQalSvAD32rY5eJ8ZDNXW6qm8fQnNgGgEO372hZplmW92mL8tr5Zi5SbpmT1XJLb67JcCXaV199Rbt27dixYwf16pkGcz148CDXr19nk4Vrz2NiYujfvz//+9//KFiwYLq2cXR0xNExdd9ye3v7TBVsZrfL67Kj3O5GJ/Lh6pPsCY0AoEHZAszoUY2ins4WPa4lZbjcIq/Cki7w8Cq4eqPq9zt2RTNW6ZBbyP9q5mSm3Mxdzjktz4DkGluRHeUWEZPE2DWn2HHuLgC1SuZjVlAgJQpYt2InK2z2fEuMghW94FEY5PND3WclahdPa0eVzGbLzcbZQp4B6+UayTPZJy5Jz1vLgrkXk0T5wu7M61sTZ6esvWezlJsuATYOAxSo2hO7Cq2ztr8cIC+cb5Yg5ZY5WbkupkeGK9EaN25MaGgo8+bNS7670rVrV4YMGUKxYsUytK+CBQui0Wi4e/duiuV3796lSJEiqda/fPkyYWFhdOjQIXnZ49lz7OzsuHDhAmXKlMnoWxK5wMaTt/hk7WmiEnQ42qkZ06YCA+r5oVbnnBYBWXbnFCztBrF3wask9F8LBeT/QeQ8kmeErdp65g4frznFgzgt9hoVI1uU581GpdHkpVyTXXSJ8GsfU25zLWTKaW7e1o5K5CLmyjWSZ2yTwajw/q8nOHc7moJuDvw8sBbuWaxAM5sdE+D+BXArDK2mWjsaIUQGZagSTafT0bp1a+bPn5/hgZ3T4uDgQM2aNdm5c2fytM5Go5GdO3fy7rvvplq/QoUKnDp1KsWyTz/9lJiYGL7++mt8fX2zHJPIWaISdIxff5p1wbcAqOLjwZyegZQt5G7lyLJZ2H74tRckRUPhKtDvd3BP/cFNCFsneUbYophEHRP/OMvq4zcAqFDEndk9A21jZrfcyGiA31+Ha/vAwR36rYb8pa0dlchFzJlrJM/YpimbzrHz/D0c7dT879VaFM9nI62FL/8Fh+ebHneaB64FrBuPECLDMlSJZm9vz8mTJ80awMiRIxkwYAC1atWiTp06zJkzh7i4uOTZbV599VV8fHyYOnUqTk5OVKlSJcX2Xl5eAKmWi9xv/6X7jFoVwu2oRNQqGNq0LO+9Ug4Hu4yPc5Cjnd8EqweBPhFK1Ifev4Kzl7WjEiJTJM8IW3Pw8gNGrQrh5qMEVCp4q1EZRrQoh6Odxtqh5U6KAhtHwPmNoHEw5bQ8OiyBsBxz5xrJM7ZlyaFr/LzvKgCzggKpXiKflSP6V3wkrBtielz7DSjXwrrxCCEyJcPdOfv168fPP//MtGnTzBJAz549iYiIYNy4cdy5c4fAwEC2bNmSPDhneHg4anUeqxQRz5WoM/DllvMs3B8GgF8BF2b1DKSGrSTI7HRiKWx4HxQD+LeBHgvBPueOAScESJ4RtiFRZ2DG1gv8vP8qigK++Z2ZFRRIbb/81g4td/trEvyzGFRq6PYzlGpo7YhELmXOXCN5xnb8HRrBhA1nAPiwVXna2crkYo9vEMTchgJlocUX1o5ICJFJGa5E0+v1LFiwgB07dlCzZk1cXV1TvD5r1qwMB/Huu++m2dwZYPfu3c/ddtGiRRk+nsi5Tt+MYviKYC7diwWgb90SfNKuIi4OGT6Vc759c2DHeNPjwL7QYS5o8mA5iFxH8oywttM3oxixIpiL/+aaXrV9+bR9Jdwc5RprUYe+h70zTI/bz4ZKHa0bj8jVzJ1rJM9YX+jdGN5d9g8Go0K3GsUZ0sSGxpY7uRLOrgO1HXT9ERxspHupECLDMvxp8PTp09SoUQOA0NDQFK/lpGndRc6iNxiZ//dl5uy4iN6o4O3uyFfdqtK0QiFrh5b9FAW2fwYHvjE9r/8+tPgc5P9P5BKSZ4S1PJ1rCro5MK1rVZpXKmzt0HK/k6tgyxjT41c+hZoDrRqOyP0k1+Qu92OTeG3RUWKS9NQplZ+pXQNs5+/4KBw2jTI9bvwR+NS0bjxCiCzJUCWawWBg4sSJBAQEkC9fHuw6J6wi7H4cI1YGcyL8EQBtqhRhcpcA8rs6WDcwazDoYcN7ELLc9LzF59BgmHVjEsKMJM8Iawm7H8fIlcH882+uaV25CJO7VKGAm6N1A8sLLu6AdW+bHtd9GxqOsm48IteTXJO7JOoMDP7lGDceJuBXwIUf+tW0nTGSDTr4/Q3T5F/Fa8PLI60dkRAiizJUiabRaGjZsiXnzp2ThCMsTlEUlh8JZ9LGcyToDLg72jGxU2W6VPexnTtL2Umvhd9fg3N/gEoDHb+B6n2tHZUQZiV5RmQ3RVFYdjicyX/+l2smdKxM1xp5NNdktxvHYGV/MOqhSndoNVVaVguLk1yTexiNCqNWhXAi/BGezvYsGFibfLZ0o333VLh+GBw9oOv/ZOgVIXKBDP8XV6lShStXrlCqVClLxCMEAPdiEvlo9Ul2XYgA4KXS+ZnRo5rtTE+d3XQJsPJVuLjNNFtZj8VQoa21oxLCIiTPiOxyNzqR0atP8neoKdfUK12AGUHV8PGSCVqyxb3zsKw76OKhTDPo/D3I4Osim0iuyR3m7Ahl48nb2KlVzO9Xk9LebtYO6T+Xd8Hef8fW6/A15JdzTYjcIMOVaJMmTWLUqFF88cUXaQ7C6eHhYbbgRN605fRtxq45xcN4HQ52aka3Ks9rDUqhVufRO9NJsfBbb7i6B+ycofdyKPOKtaMSwmIkz4jssPHkLT5dd5pH/+aaj1pXYFB9v7yba7Jb5BX4pRMkPDSNDxT0C9jZUOsRketJrsn51p64wdy/LgEwpWsA9coUsHJET4i9B2veBBTTGI9Vulo7IiGEmWS4Eq1tW1Prl44dO6bo5qAoCiqVCoPBYL7oRJ4Snahj4oaz/P7PDQAqFvVgTs9Ayhdxt3JkVpQYDSv7wPVD4OAGfVaCXwNrRyWERUmeEZYUFa9j3IbTrA++BUAVHw9mBwVSrnAezjXZLeoGLO4EsXegUCXouxocbaj1iMgTJNfkbEeuRvLR6lMAvNOkDEG1fK0c0ROMRlMFWtw90zWu9TRrRySEMKMMV6Lt2rXLEnGIPO7g5QeMWhXCzUcJqFXwduMyDG/ubzuDglqBvT4WzfKucDsYnDyh3xooXsvaYQlhcZJnhKXsvRjBh6tOcic6EY1axdAmZXj3lXJ5Otdku5i7sLgjRIVD/jLQfx245Ld2VCIPklyTc4Xdj+OtJcfQGoy0qVKED1uWt3ZIKe2fA1d2mXqQdF8I9jJEgBC5SYYr0Ro3bmyJOEQelagzMHPbBX7adxVFgRL5XZgVVI1afnn8A3XsPRpcnIo68Tq4FDB9ySha1dpRCZEtJM8Ic0vQGpi2+RyLD14DoFRBV2YFVaN6CRlQPFvFR8KSzhB5GTxLwIAN4F7Y2lGJPEpyTc4UFa/jtcVHeRivo1pxT2YFBdpWN/zww/DXJNPjtl9BoQrWjUcIYXaZuvW6d+9e+vXrR/369bl58yYAS5YsYd++fWYNTuRuZ25F0enb/fxvr6kCrVdtXzYNaygVaNG3sFvaEc/E6yiuhWDgJqlAE3mO5BlhLsHXH9Fu7t7kCrT+L5Xkz/dflgq07JYYBUu6wL2z4FYEBqwHz+LWjkrkcZJrchat3sjbS49zJSKOYp5O/G9ALZwdNNYO6z8JD+H310ExmGYbrt7//+3dd3hUZdrH8e+kJ4QkQDpFQJqhhI6AYIuUVYoiIKIUXXARXBHb6iqwomJFBBFXV0V8VYoFG6II0jT0KiW0UNMIENJIMpk57x8DwUAgyZDkDOT3ua65yDlzZuaem5ncmXue8zxmRyQi5aDUTbSvvvqK7t274+vry8aNG8nNzQXg1KlTvPzyy2UeoFx9bHaDmcv20XfG78QlZxDs78UHQ9rySr8W+HtX8mWfTx6Ej3tiOb6XbM/q5A/5Xt9gSaWjOiNlwWqzM2XxbvrN/IP9qVmEBXjzyQPtmdS3GX5elbzWVLS8LPhsgGN6Ar8aMORbqF7f7KikklOtubIYhsHzC/4kdv9xqni58+GwdoRW9TE7rHPsdlgwGk4dhmr14I63wOJCI+REpMyUuon24osv8t577/HBBx/g6elZsL9z585s3LixTIOTq8+h49nc834sry7ahdVmcFtUGIvGduW2KJ3OwfF98HFPOHkAI6guqxo955gvRqSSUZ2Ry7U3JZN+M/9g2pI92OwGvaIj+XlsV25sFGJ2aJVPbgZ8PtCxQI5PINz/jb4cEpegWnNleX/FfuauP4ybBd65tzXXRbjY6qmr3oS4H8HdC+7+CHxcLD4RKTOl/io2Li6Orl27XrA/MDCQtLS0sohJrkKGYTB33SFe+H4HWXk2/L09GN8riv5tahVaEanSStkJs/tAZjIENyJ/0FecXrXJ7KhETKE6I86y2w0+WhXPq4t2kZtvJ9DXk0l9m9E7OtLs0Cqn0yfh/+6Go+vBqyoM/goios2OSgRQrbmSLPoziVcW7QJg/B1R3Nwk1OSIzrNnMSx9yfHz7W9CzdbmxiMi5arUTbTw8HD27t1L3bp1C+1ftWoV9etraL5cKMMKoz7fzJJdxwBoX7c6bw6IpnZ1P5MjcxGJW2B2Xzh9AsKaO76l9w4C1ESTykl1RpxxMheGf7KBP/afAKBroxBe69eC8EAXOt2nMsk85pgDLXkb+FaD+76Cmm3MjkqkgGrNlWHbkVOMnbsJw4ChHa9hWOd6ZodU2In9jnnQMKDNcGg9xOyIRKSclbqJNmLECB599FE++ugjLBYLCQkJxMbG8sQTT/D888+XR4xyBft1ZwqvbHYnM/8Ynu4WHu/WmBFd6uPuSqvomOnwOvi/fpB7CiJbOz5k+FUHq9XsyERMozojpWEYBt9uTuDVLe6ctp3Ax9ONf//tOu67/hqNdDbL8X3wWX/HKpxVQh1zoIVFmR2VSCGqNa4vIe00D36yjhyrnZsah/D8HS72eyQvC+bc51g4pVY76Pmq2RGJSAUodRPtX//6F3a7nVtvvZXs7Gy6du2Kt7c3TzzxBI888kh5xChXoMzcfF74fjvz1h8BLDQO8+etga2IitT8AAX2L4cvBoE1C+p0hHvnaf4EEVRnpOROZOXx3IJtLNyWBFiIrhXIWwNbUj/E3+zQKq+DsTDnXsfo6sA6MGQB1ND8nuJ6VGtcW1ZuPg9+sp6UjFwah1Vl+qBWeLiXejrv8mMY8N0/IWW748uCAbPBw9vsqESkApS6iWaxWPj3v//Nk08+yd69e8nMzCQqKgp/f/3BKg7rDpxg3LzNHD5xGosFbo6wM23E9fj7qrAUiFsE84aALRfq3wT3fA5eVcyOSsQlqM5ISSzdlcxTX24jNTMXDzcL3Wrm8+aD7fD1Ua0xzbYvYcEosOU5RlcPmgNVtXCQuCbVGtdlsxv884tN7ExMJ9jfmw+HtaWqj2fxN6xIv78Nf34Jbh4w4BMI0NybIpWF02u8e3l5ERXlYkNqxVS5+TbeWryH/67Yh2FAzSBfXr2rKcd3rsbbw4W+OTLbn1/B1yPBng+Nb3es4OOpOXtEzqc6I0XJys3nxR938MXawwA0DPXn9X7NOLh5lWuNUqhMbPmwZCL8Md2x3eQOuOsD8NLcp+L6VGtcz0s/7mTJrhS8Pdz439C21KrmYr9LdnwHv050/Nx9MlzTydRwRKRiOd1EE/mruKQMxs7dzM7EdADublOLCb2i8HGHhTtNDs6VbJztGPqNAc0HQN93wd3FvlkTEXFR6w6c4PF5Wzh0IhuLBR7sXI8nujfGHTsHN5sdXSWVeQy+HA4HVjq2Oz8Kt04AN3dz4xKRK9Knqw/y0e/xAEwZ0JKWtYPMDeg8loSNji/DMaD9SOgw0uyQRKSCqYkml8VmN/hw1X7e+Hk3eTY71at48fKdzenRLBwAqybIPyf2Xfj5GcfPbYbD7VPATaMmRESKk5tvY8ri3by/Yn/BSOc3+kfT8doaAFitdpMjrKQOrXE00NKPgpc/9JkBTfuaHZWIXKGW7z7GxO+2A/Bk98bc3iLC5IgK881LxX3eE5B/Ghrc5hiFJiKVjppo4rQjJ7N5fN4W1sSfAODWJqFM7tec0Ko6NbEQw4Dlr8Gylx3bnf4Jt70AWjVORKRYOxPTeWzuZnYlZQCOkc7je0UR4Grz41QmhgGxM+DXCY6pCWo0hHs+g5DGZkcmIleo3ckZjPlsIza7Qb/WtXj4JhdbkCQrlY57X8eSmwKhTR3Tsbjro7RIZaR3vpSaYRh8tfEoE7/bTmZuPn5e7jx/RxT3tKuNRY2hwuw2WPgkrP/QsX3zc9D1CTXQRESKYbMbvL9iP1MWx2G1GdSo4sXLdzWne9Nws0Or3E6nwbejYdcPju1m/aDX2+Bd1dSwROTKlZqZywOz1pGRm0/7utV5+a5mrvWZIjcD97n3UDU3ESOgJpbB88AnwOyoRMQkaqJJqZzIyuPZr7exaHsSAK3rBPHWwJZcU0MrS17Aehq++vuZDxoW6Pma5k0QESmBg8ezeHzeFtYfPAlAzHVhvNKvOcH+WnnTVAmbYN5QSDsI7l7QYzK0fVBfDImI03KsNkbOXs+Rk6epW8OP/97fBm8PF5pT0ZoDc+7FLXEzuR5VcRv0JZ6BtcyOSkRMpCaalNjSXck89eU2UjNz8XCz8NhtjXioa32thlaU7BPwxT1weA24e0O/DyCqj9lRiYi4NMMw+GLtYV78cQfZeTb8vT0Y3yuK/m1qudaohMrGMGD9R7DoX2DLg6A60P8TqNna7MhE5ApmGAZPfbmVjYfSCPDx4MNh7ahWxcvssM7Jz4X5wyB+BYZXFVbXe5xOwQ3NjkpETKYmmhQrKzefF3/cyRdrDwHQMNSftwa2pFnNQJMjc1EnD8L/9YPje8AnEAbN0dLXIiLFSMnI4V9fbWPprhQAOtSrzhv9o6ld3c/kyCq53Ez4YSxsm+/Ybvw3x8rSvtVMDUtErnxvL9nDd1sS8HCz8N79bbg2xN/skM7Jz4V5Q2D3IvDwwdb/U9J2ZJodlYi4ADXR5JI2HDzJuHmbOXg8G4AHb6jHk90b4+PpQsOsXUniVvjsbshMhoCacN9XEHqd2VGJiLi0hdsS+fc32ziZbcXLw42nujfmgc71cHPT6DNTHdsNc++D1DiwuEPMROj0iE7fFJHL9u3mo0z9dQ8AL93ZjE7XBpsc0V/k58Lc+2HPz+DhA4PmYNS5AXYsNDsyEXEBaqJJkfLy7by9ZDczl+3DbkBkoA9v9I+mUwMXKnCuZu+vMG8Y5GVAaBQM/hICa5odlYiIyzp12sqEb/9kweYEAJpGBvDWwJY0CtMk9abb8R0seNhR06pGwN0fwzUdzY5KRK4CGw6e4MkvtwLwUNf6DGxXx+SI/iI30zECbd8S8PCFe+dA/ZvAajU7MhFxEWqiyQX2JGcwdu5mtiekA3Bnq5pM7N2UQF9PkyNzYev+BwufAsMGdbvAwP8D3yCzoxIRcVmr9qTy5JdbSDyVg5sFHr6pAf+8tSFeHppn01S2fFg6CX6f6ti+5gbo/zH4h5oalohcHQ6fyGbk7A3k5dvpFhXG0z2amB3SOVmp8Fl/SNgInn6OKVnq32h2VCLiYlziL9UZM2ZQt25dfHx86NChA2vXrr3osR988AFdunShWrVqVKtWjZiYmEseLyVntxt8uCqe26evYntCOkF+nsy4tzVvDWypBtrF2G2w6Fn48XFHAy16kOMUTjXQRFyK6ozrOJ1nY+J327nvwzUknsqhXnAVvhzViSe6N1YDzWxZqfB/d51roHUcA0O+VQNNpARUZ4qXnmPlgVnrOJ6VR7OaAUy9p6XrnLZ/8iB82M3RQPOtDkO/VwNNRIpk+l+rc+fOZdy4cUyYMIGNGzcSHR1N9+7dSUlJKfL4ZcuWMWjQIH777TdiY2OpXbs23bp14+jRoxUc+dUlIe009324hkk/7CAv386NjUL4eWxXbm8RYXZoris30zFXzOoZju1bnoO+M8HD29y4RKQQ1RnXseVwGrdPX8msPw4AcP/11/DjP2+gdR1NUm+6oxvgvzdC/HLwrAJ3fwTdXwJ3nbQgUhzVmeLl2+yM/mwje1IyCQvw5n9D2uHn5SK/Xw6tgf/FwIl9EFgbHvwFarU1OyoRcVGmN9GmTJnCiBEjGD58OFFRUbz33nv4+fnx0UcfFXn8Z599xsMPP0zLli1p0qQJ//vf/7Db7SxZsqSCI786GIbBgk1H6T51BX/sO46vpzsv9m3GrOHtCAvwMTs815WeAB/3hLiF4O7t+LDR9UlNtiziglRnzGe12Xlr8W7umvkH+49lERbgzScPtGdS32au8yGqErNs+hQ+6gHpR6D6tTBiCTTrZ3ZYIlcM1ZlLMwyDid9vZ+WeVHw93flwaDvCA13kc8bGT2HW7ZCVAmHNHQ204IZmRyUiLszUv1zz8vLYsGEDzzzzTME+Nzc3YmJiiI2NLdF9ZGdnY7VaqV69epHX5+bmkpubW7Cdnu6Y58tqtWItxQSRZ48tzW1c3cnsPCZ8t5OfticD0KJWAG/0a0694Crk5+eXyWNcjXkjcTMe8+/HkpGI4ReMrf+nGLXalemEo1dl3iqIcuecy8mbK+e6IuoMqNZcyt6UTJ786k/+PDPP5h3Nw5lwx3UE+XmW2fO8GvNWEaynM4g+9CEem5YDYG/UE1uvGeAToEm0L0GvN+eozlzc1V5nZsUe5P9WH8JigSn9m9M41M/8/1N7Pm6/jsd93fuOzSa9sPV6B7yqFPn7T+975yhvzlHenHO5eSvp7UxtoqWmpmKz2QgLCyu0PywsjF27dpXoPp5++mkiIyOJiYkp8vrJkyfzn//854L9v/zyC35+fqWOefHixaW+jSvaedLC5/vcSLdacMOgR207MTVPsHPtcnaWw+NdFXkzDK45vozmRz7FYuST4RPJ6rqPk731GGwtnyWvr4q8mUS5c44zecvOzi6HSMpGRdQZUK0pit2AFUkWfjjohtWw4Odu0L++ndb+R/hj2ZFyecyrIW8VxS83hXbx06l7+iAGFnZF9GO33x2wdJXZoV0x9HpzjurMha7mOrP9pIUPdrkBFnrXsZEXv56F8RXy0BflmZ9B2wPvEpqxHYCd4Xex26c3/Lq82Nvqfe8c5c05yptznM1bSWvNFX0OxSuvvMKcOXNYtmwZPj5FDwl+5plnGDduXMF2enp6wbwDAQEBJX4sq9XK4sWLue222/D0vHIn2c/Oy+e1n/fw2a7DANQP9uONu5vTvGZguTze1ZI3rNm4//QkbofnAmBv2AOf3jO4yUd5czXKnXMuJ29nvw2/GpWkzoBqzfkS0k7zr2+2E3vgBABdGtRg8p1Ny22agKslbxXFsudn3L+bhCXnFLnu/hj9/keDhjE0MDuwK4Reb85RnSna1VxndiVl8OwHazGwMaBNTV7sE4XF5KlPLEfW4f7NM1gyjmJ4VsHW+10aNLm92N9/et87R3lzjvLmnMvNW0lrjalNtODgYNzd3UlOTi60Pzk5mfDw8Eve9o033uCVV17h119/pUWLFhc9ztvbG2/vCyd69/T0dCqxzt7OFWw6dJJx87YQn5oFwLBOdXm6RxN8vdzL/bGv5LyRsAm+Hgmpu8HiBrdOwK3zo7hVwB8BV3TeTKbcOceZvLlyniuizoBqzVmGYfDNpqNM+HY7Gbn5+Hq68+/br2NwhzoV8sHpSs1bhbHlw7KXYeWbANgj27Cs2n3c0jBGeXOCXm/OUZ0552quMynpOTz0f5vIyrPR6doavHRXCzzdTZyO226H2Omw5AWw50P1+lgGfIpHeLNS3Y3e985R3pyjvDnncn4vloSpCwt4eXnRpk2bQpNonp1Us2PHjhe93WuvvcakSZNYtGgRbdtq5ZTinJ3Q+e73YolPzSI8wIdPH2zPxN5NK6SBdsWy5cPy1x2r9aTuBv9wx3LXN4zVAgIiVwjVmYpzPDOXUf+3kXHztpCRm0+rOkEsfLQL911/jekjDwRIO+SYPPtMA432D2Eb8j05XjXMjUvkCqc6c6HTeTZGzF5Pwqkc6odUYebgNuY20LKOwxcDYfF4RwOt2d3w0AooZQNNRARc4HTOcePGMXToUNq2bUv79u2ZOnUqWVlZDB8+HIAhQ4ZQs2ZNJk+eDMCrr77K+PHj+fzzz6lbty5JSUkA+Pv74+/vb9rzcFX7jmXy2NzNbD1yCoDe0ZFM6tOMQD91tC/pxH74+iE4staxHdUX7ngL/C4+4auIuCbVmfK3ZGcyT3+1jdTMXDzcLDx2WyMe6lofDzM/NMk5f34F3z8GuafAqyr0mgrN79biASJlRHXmHLvd4PH5m9ly5BRBfp58NLSduZ879v0GCx6GjARw94a/vQath+oLcRdgs9k0cf5FWK1WPDw8yMnJwWazmR3OFaO4vHl6euLufvmDiExvog0cOJBjx44xfvx4kpKSaNmyJYsWLSqYnPPQoUO4uZ37I3zmzJnk5eVx9913F7qfCRMmMHHixIoM3aXZ7Qafrj7I5J92kmO1E+DjwYt3Nqd3dKTZobk2Wz6sfR+WvgjWLPAOgL+9AS0GqNiKXKFUZ8pPZm4+L/6wgznrHPNsNgrzZ8qAljQrp3k2pZRyM+Cnp2HzZ47tWu3grg+gej1z4xK5yqjOnDP1190s3JaEp7uF/97XhrrBVcwJJDcTfp0A6/7n2K7RAPp/otFnLsAwDJKSkkhLSzM7FJdlGAbh4eEcPnxYo/lLoSR5CwoKIjw8/LLyanoTDWDMmDGMGTOmyOuWLVtWaPvAgQPlH9AVLulUDk9+uYWVe1IB6NIwmNfubkFEoK/Jkbm4hE3w/aOQuMWxXbcL9J0JQbXNjUtELpvqTNlbG3+Cx+dv5vCJ01gsMKJLfcbd1ggfT00T4BLiV8J3j8DJeMd8nl2egBufAneNRBcpD6oz8P2WBKYt3QvAy3c2p0N9k04XPxgLC0Y5fv8BtBsBt/0HvExq6EkhZxtooaGh+Pn5qUlUBLvdTmZmJv7+/oUa8HJpl8qbYRhkZ2eTkpICQEREhNOP4xJNNCk7329J4LkFf3LqtBVvDzee6dmEIR3r4uamX04XlZsBS1+Ctf8Fww4+gXDbC9BqCOiXlohIIbn5Nqb8spv3V+7HMKBWNV/e7B9t3oclKSznFCyeABs+dmwH1HSMPqvb2dy4ROSqtu3IKZ6Y7/giemTX+vRva8KX0LmZsGwyxM4ADAioBX3egWtvrvhYpEg2m62ggVajhv5uuBi73U5eXh4+Pj5qopVCcXnz9XUMKkpJSSE0NNTpUzvVRLtKnMq28vy3f/LdlgQAmtcM5K2BLWkQemXPq1Cu7DbYMgeWToKMRMe+5v2h+8vgH2pubCIiLmhnYjqPzd3MrqQMAAa2rc1zd1xHVR+NbjKdYcCOBbDoWcfcPwBthjtGX/jo9FoRKT8p6TmMmL2e3Hw7NzcO4ekeTSo+iLifYOGTcMoxvQAt74MeL+v3n4s5Owean5+fyZFIZXX2tWe1WtVEq8xW7UnliflbSErPwd3NwuibG/DILQ3MXQXH1e1fBr88B0nbHNvV6sLtU6DBrWZGJSLikmx2g/dX7GfK4jisNoMaVbx4pV8LbosKMzs0ATi2G3560lHbAKrXh17ToF4XU8MSkatfjtXGiE83kJSeQ4NQf94e1Ar3ijwD5tRRWPQ07PzesR1UB/72JjTqVnExSKnpFE4xS1m89tREu4LlWG288tMuZv1xAIB6wVWYMiCaVnWqmRuYK0veAb9OhD0/O7a9A6HrE9DhIfDwNjU0ERFXdPhENuPmbWbdgZMA3BYVxuS7mhPsr9+ZpsvNhBWvO05dslsdK891GQedHwVPzYMqIuXLMAye+XobWw6nEejryf+GtCWgokYm222w9gPHGSV5meDmAR3HwI1Pg5dGOYlI+VET7Qq19Ugaj83dzL5jWQDcd30dnv3bdfh56b+0SEnbYPlrsPM7x7abB7T7O3R9CqrofHwRkfMZhsG89Yd54fsdZOXZ8Pf2YHyvKPq3qaVvkM1my3esuPnby5CZ5NjXqAf0eEUrb4pIhXlv+X6+2XQUdzcLMwe3rriVOA+tdqw8nLjZsV2rPfSaCmFNK+bxRcQUEydOZMGCBWzevNnUOHS+3xUm32Zn+pI93PXuH+w7lkVIVW8+Ht6OF/s2VwOtKAmbYc5geO+GMw00C0T1hYfXQM9X1UATESnCsYxcRsxez9NfbSMrz0b7etX56dEuDGhbWw00MxkG7FoIMzvB9/90NNCCroFBc+DeuWqgiUiF+XVHMq/9vAuAib2b0qlBcPk/6MmDMH8YfNTd0UDzCYQ73oIHflYDTSpEUlISjzzyCPXr18fb25vatWvTq1cvlixZYmpcEydOpEmTJlSpUoVq1aoRExPDmjVrijw2NzeXli1bYrFYim1GrVixgl69ehEZGYnFYmHBggUXHGMYBuPHjyciIgJfX19iYmLYs2dPGTwr16WuyxXkQGoWj83bzKZDaQD8rXk4L/VtTrUqXuYG5mps+RC3ENa+DwdWntlpgWZ3QdcnIfQ6U8MTEXFlP29P4tmvt3E8Kw8vdzee6N6IB2+oX7Fz3MiFDq+Dxc/DoVjHtm91R01r96CmIxCRChWXlMGjczZhGI6zYe6//pryfcDcDFj1FvzxDthyAQu0HgK3PKfFwKTCHDhwgM6dOxMUFMTrr79O8+bNsVqt/Pzzz4wePZpdu3aZFlujRo145513qF+/PqdPn+att96iW7du7N27l5CQkELHPvXUU0RGRrJly5Zi7zcrK4vo6GgeeOAB7rrrriKPee2115g2bRqffPIJ9erV4/nnn6d79+7s2LEDHx+fEsWfl5eHl9eV09PQSLQrgGEYfLH2EH+btpJNh9Ko6u3BlAHRzLi3tRpof5WVCivfhLejYd79jgaaxR2aD4DRa+Huj9RAExG5iIwcK0/M38JDn27geFYeTcKr8u2Yzozseq0aaGZK+hPm3gcfxjgaaB4+cMM4eHQzdHxYDTQRqVBp2Xn8ffY6svJsdKxfgwm9ynEEmN0GGz+F6W0cf+PbcqFuF/jHSug9TQ20q4RhGGTn5Vf4xTCMUsX58MMPY7FYWLt2Lf369aNRo0Y0bdqUcePGsXr1agAOHTpEnz598Pf3JyAggAEDBpCcnFxwHxMnTqRly5Z8+umn1K1bl8DAQO655x4yMhyrnr///vtERkZit9sLPXafPn144IEHLhrbvffeS0xMDPXr16dp06ZMmTKF9PR0tm7dWui4n376iV9++YU33nijRM+5Z8+evPjii9x5551FXm8YBlOnTuW5556jT58+tGjRgtmzZ5OQkFDkqLXz8/C///2PevXqFTTb0tLS+Pvf/05ISAgBAQHccsstl2z23XTTTYwdO7bQvsGDBzN8+PASPT9naSSaizuWkcszX2/l150pAFxfvzpvDmhJzSBNGAxAfi7s+QW2zIHdPzsmVgbwqwFthkHbByCwlqkhioi4utX7j/P4vC0cTTuNxQIPdb2Wx25riLeHc0t/SxlI3OKYy3PXD45tixu0vBduehYCa5obm4hUSja7wSNfbOLwidPUqe7Hu4Nb4+leTmMy4lfCz89C0pkmQPX60O1FaPw30LQCV5XTVhtR43+u8Mfd8UL3Ek+HdOLECRYtWsRLL71ElSoXzv0XFBSE3W4vaKAtX76c/Px8Ro8ezcCBA1m2bFnBsfv27WPBggX88MMPnDx5kgEDBvDKK6/w0ksv0b9/fx555BF+++032rVrV+ixFy5cCDhGxNWrV4/ffvuNm2666YJY8vLyeP/99wkMDCQ6Orpgf3JyMiNGjGDBggX4+RW9+IbFYuHjjz9m2LBhJcpLfHw8SUlJxMTEFOwLDAykQ4cOxMbGcs899wCOZlfdunWZNWtWwXF79+7lq6++4uuvv8bd3fH3Zv/+/fH19eWnn34iMDCQ//73v9x6663s3r2b6tWrlyimiqAmmgv7dUcyT3+1tdApNX+/oT5ulX1EgGHAkfWwdQ78+RWcPnnuushW0H4kNL0LPEs2fFREpLLKzbfx5i+7+WDlfgwDalf3ZcqAlrSr6zp/qFQqZ+vbqimOaQkAsEDTO+HGpzSaWkRM9cYvcazck4qvpzv/vb9N+ZwRk7gVlvwH9v7q2PYOdPz+az8SPHQGjphj7969GIZBkyZNLnrMkiVL2LZtG/Hx8dSuXRuA2bNn07RpU9atW1fQFLPb7cyaNYuqVasCcP/997NkyRJeeuklqlWrRs+ePfniiy8Kjv/yyy8JDg7m5ptvBsDT05PGjRtf0Aj74YcfuOeee8jOziYiIoLFixcTHOyYq9AwDIYNG8Y//vEP2rZty4EDB4p8Do0bNyYwMLDEeUlKcixuFBYWVmh/WFhYwXUAderUISIiotAxeXl5zJ49u+B001WrVrF27VpSUlLw9naMsn/jjTdYsGABX375JSNHjixxXOVNTTQXlJWbz4s/7uCLtYcBaBJelbcGtuS6iACTIzORYTgmEN35PWxfACf2nbvOPxxaDIDoezSpqIhICe1ISGfcvM3sSnKcQjCwbW2e7xWFv7f+NKhw1tPw59eOuTzPrjZncYNm/aDLExB68T/aRUQqwk/bEpm5zPH396t3tyj7zyUn9sPSl+DPLx3bbh6Os0puegaqVMCiBWIaX093drzQ3ZTHLamSnPq5c+dOateuXdBAA4iKiiIoKIidO3cWNMXq1q1b0EADiIiIICUlpWB78ODBjBgxgsmTJwPw2Wefcc899+Dm5hj1WbNmzSLnX7v55pvZvHkzqampfPDBBwwYMIA1a9YQGhrK9OnTycjI4Jlnnrnkcyived1mz559wb5rrrmm0HxtW7ZsITMzkxo1Ci/8d/r0afbt23f+zU2lv5RdzIaDJxk3bzMHj2djscCILvUZd1sjfErxJr9q2O1wZC3s+M7RPDt16Nx1nn5wXS9oMRDq3wRulTA/IiJOsNkN3l+xnymL47DaDIL9vZh8Vwtuiwor/sZSto7vg42zHZfTJxz73L2h+d1ww2MQ3NDc+EREgN3JGTw+3zEv0ciu9ekdHVl2d56RDCtehw0fgz3fsa/Z3XDzs1Dj2rJ7HHFZFoulxKdVmqVhw4ZYLJYyaTJ5enoW2rZYLIXmQOvVqxeGYfDLL7/QtWtXVq5cyVtvvVXs/VapUoUGDRrQoEEDrr/+eho2bMiHH37IM888w9KlS4mNjS0Y4XVW27ZtGTx4MJ988olTzyU8PBxwnCr615FmycnJtGzZsth4/yozM5OIiIhCp76eFRQUVOR9uLm5XdDgtFqtxQd+mVz71VqJWG12pi3Zw4zf9mI3IDLQhzcHtKTjtTWKv/HVxGaFA6scTbNdP0DmuYkY8fSDBjFwXW9o3AO8q178fkRE5AKHjmfz+PzNrDvgOA3+tqgwJt/VnGB/TU5fYXJOwfZvYPPncPgvy88H1oF2D0CrIVClktV+EXFZp05beejTDWTn2ejcoAZPdW9cNneckQx/TIP1H4E127GvQQzcOh4ioi99W5EKVr16dbp3786MGTP45z//eUEDKC0tjeuuu47Dhw9z+PDhgtFoO3bsIC0tjaioqBI/lo+PD3feeSfz588nISGBxo0b07p161LHbLfbyc3NBWDatGm8+OKLBdclJCTQvXt35s6dS4cOHUp932fVq1eP8PBwlixZUtA0S09PZ82aNYwaNapU99W6dWuSkpLw8PCgbt26JbpNSEgIiYmJBds2m42dO3decOpoWVMTzQXsO5bJY3M3s/XIKQDubFWTib2bEujrWcwtrxI56bBviWNhgN2LCs9x5h0AjXpAVG+49lbwKnoSRBERuTjDMJi77jCTfthBVp4Nf28PxveKon+bWlg0QXP5y8uGvYsd0xHELYT8HMd+i5ujtrV9ABp116hqEXEpdrvBuLmbiU/NomaQL9MHtcbjchcSSE+E3992jDw7+7uwZluImQj1ulx2zCLlZcaMGXTu3Jn27dvzwgsv0KJFC/Lz81m8eDEzZ85kx44dNG/enMGDBzN16lTy8/N5+OGHufHGG2nbtm2pHuvee++ld+/e7N69m/vuu++Sx2ZlZfHSSy/Ru3dvIiIiSE1NZcaMGRw9epT+/fsDjjnJ/srf3x+Aa6+9llq1Lr4IX2ZmJnv37i3Yjo+PZ/PmzVSvXp06depgsVgYO3YsL774Ig0bNqRevXo8//zzREZG0rdv31I955iYGDp27Ejfvn157bXXaNSoEQkJCfz444/ceeedRebwlltuYdy4cfz4449ce+21vPnmm5w6dapUj+sMNdFMZBgG/7f6IC8t3EmO1U6grycv3dmMO1qU4RBpV3Viv6NpFvcTHPzj3Kqa4FhZs8ntcF0fqNdVk4iKiFyG81d5bl+vOm/2j6Z2dX0pUa5y0h2rR+/41jFB9tmRFgAhTaDlYMd8nlXDzYtRROQS3l6yhyW7UvD2cOO/97eh+uUsJHDqCKya6jh93eYYHUOtdnDjv6DBrVpxU1xe/fr12bhxIy+99BKPP/44iYmJhISE0KZNG2bOnInFYuHbb7/lkUceoWvXrri5udGjRw+mT59e6se65ZZbqFatGnFxcdx7772Frjt/dU53d3d27drFJ598QmpqKjVq1KBdu3asXLmSpk1LN1/4+atzrl+/vmBBA4Bx48YBMHTo0IKVNp966imysrIYOXIkaWlp3HDDDSxatAgfn3OL/BW1OmdRj71w4UL+/e9/M3z4cI4dO0Z4eDhdu3a9YOGCsx544AG2bNnCkCFD8PDwYOzYsXTpUv7NeDXRTJKSkcNTX25lWdwxAG5oEMwb/aMJD7xKV5S0noZDq2DfUsdos9Tdha+v0dDxLXzjnlD7enDXS1NE5HIt3ZXMk/MLr/L84A31ca/sqzyXB7sdkrY6Gmb7ljpO1Tw7vw9AUB3HdATN7oLI1vrAKCIu7dcdyby9ZA8AL9/ZnGY1S75iXyHH4hynbW6Ze+5L8zqdHCtu1r9JvwvlihIREcE777zDO++8U+T1derU4dtvv73o7SdOnMjEiRML7Rs7dixjx44ttM/NzY2dO3cSEBBQsKDAWfHx8QQFBREd7Tjt2cfHh6+//rpUz6Nu3boXzCUWHx+Ph4cHnTt3Lth30003FbuogsVi4YUXXuCFF1646DHx8fEFjTkoOg8AVatWZdq0aUybNq3I+zn/dp6enrz77ru8++67gOMU1lGjRhEQUL4LMqpTYYK/fqjx9nDjXz2bMLRjXdyupg81tnxI3Izb3qV02vM1HltHnPvWCRwr7lzTyXGqZqMemjhURKQM5VhtvLxwJ7NjDwJa5blc5GU7VtI8ss5xObQaso4VPqZGA0fjLKo3RLTUh0URuSLsPzPVDMDQjtfQr83FT/cqkmE4fif+/jbs/unc/rpd4ManddqmyGVYuHAhzz77LNWqVSvz+x05ciQNG5btokbbt28nMDCQIUOGlOn9mklNtAqUY7Xxyk+7mPXHAQCuiwhg2j0taRh2FUyQn5cNRzc4vnk/vBYOxUJuOu5AwcK1VSOh/o3QsBtcewv4BpkXr4jIVWpHQjqPztnEnpRMAB7oXI+nejSunKs8l4XTaXBiH6TuheNnLql74NjOwiPNALz8HdMQNLjVMddZ9XqmhCwi4qzM3HxGfrqBjNx82tetznN3lHxCdOw22PWjY+TZkXVndlrgujug06NQu125xCxSmbz++uvlcr+jR48ul/tt2rQpW7duLZf7NouaaBVkT3IGj3yxiV1JGcAV/qHGboeT8ZCwyVEgD6+BpG0XfpjwCcR+zQ1sy6pB1B2j8Ay7Tt/Ci4iUE7vd4KPf43ltURx5NjshVb15o380NzYKKf7GlZ01x1HXjv+lUXZ8n+Pf80eX/ZV/uONDYa0zl5ptNY+niFyxDMPgiXlb2JuSSViAN+8MboVnSRYSsObAli/gj+mOLx0A3L2h5SDo+AgENyjfwEVEKpCaaOXMMAw+W3OIST/sIDffTo0qXrzRP5qbm4SaHVrJGMaZhtlmR9MscTMkbIHcIla9qBoJdTpA7TOXiGhsNjsHFi4kqkZDNdBERMpJcnoOT8zfwso9qQDEXBfGq/2aU8Pf2+TIXIjd5pjYulCj7Mwl7TBwiTk//MMdp2bWuPbcv+EtILCWapuIXDXeXbaPRduT8HJ3Y+Z9bQitWsxczdknYP2HsOa/575w8AmEdiOgw0Pgf4V83hERKQU10crRyaw8nv5qK7/sSAagS8Ng3hwQXXxBMovN6jhFJXk7JG9zNM4St0BO2oXHevhAWDOo2fpc0yyodhH3aS/vqEVEKrWftyfxr6+2cjLbio+nG8/fEcW97R3Ljlc6hgGZx4polO1zrAr917k5z+cdcKZB1uDChpn3VTDtgojIJSyLS+GNX+IA+E+fprSuc4n5lk4dhdgZsGEWWLMc+wJrw/UPQ+v79TtTRK5qaqKVkz/2pTJu7haS0nPwdLfwdI8mPNC5nmssHmAYkJkMyX+eaZhth+QdcGzXuVVz/srdG8KbOSZFjmwFkS0hpAm4e1Z05CIickZ2Xj6TftjJF2sPAdA0MoC372lFg1B/kyMrZ3YbpCc4mmIn4+HEftyP7+emg1vw2D4K8rIuflt3L6he/7wm2ZlLlRCNKhORSunQ8WwenbMZw4BB7WszqH2dog9M3eNYLGDLnHOfGcKaQedHoemd+mwgIpWCmmhlzGqzM/XX3by7bB+GAfVDqjDtnlbOLwt9uXLSIXU3pOw80yw70zg7faLo470DIKwphEZBRLSjaRZ6nYqiiIgL+fPoKf45ZxP7j2VhscDIrvV5/LbGeHmUYO4aV2a3O04JSj/iaJSdOnrez0chI/GCOTjdgHNV1uIYERFcxKiywNrgdgXORSoiUk6y8/IZ+el6Tp220rJ2EBN7N73woIRNsHIK7PyeglPfr+kMN4xzLKSiLyBEpBJRE60MHT6RzSNfbGLz4TQABratzYTeUfh5lXOaDcPxoeNYHKTGOf49FudonmUkFn0bi5vjA0VY0zOXZo5/A2urEIqIuCjDMJj1xwEmL9xFns1OeIAPUwZE06lBsNmhFc8wICvV0RQ7ddTRGDv/5/TEokdEn8/NE6pdA9XqQfX62ALrsG7fcdrc1h/P4Prg6aLTJoiIuBDDMPjXV9vYlZRBsL83793XBm8P97NXQvwKWPUW7P/t3I0a9YQbHnPMgywiUgmpiVZGFv2ZyJNfbiUjJ58AHw8m39WC21tElO2D2O1w6hAc232uWZa62/FvUfOWneUfDiGNzzXKwpo6tj19yzY+EREpNyez8njyyy38ujMFgG5RYbzarwXVqrjAapCG4ZhguqApduZSqEGWALa84u/L4uaoWwGREFgTAmpd+HPV8EIjyuxWK8mpCyG4IXhq5LSISEl8uCqe77Yk4OFm4d3BrQkP9HF83oj70dE8O7rBcaDFHZrfDZ3HQliUqTGLiJhNTbTLlJtvY/LCXcz64wAAreoEMX1QK2pV83P+TvPzHHO9FBpVFgepeyH/9EVuZHF8Kx/cGEIanfm3ieMDhW+Q87GIiIjp1uw/zti5m0k8lYOXuxvP3XEd919/TcUsHlBwiuXZhlgCZCSc+/ns/vycEtyZBfzDim+QaQoBEZFyFbv/OJN/2gXA83dE0b5OVdj8Oaya6vjcAY6FxFrdD50ecXzOEJEi3XTTTbRs2ZKpU6eaHYpUADXRLsOh49mM/nwj246eAuChrvV5ontjPN1LOCeNNcexatixXY5Lyk5Hw+zEfjBsRd/GzdNxGmZIozNNskaOUWU1GmhkmYjIVcZmN3hn6V7eXrIbuwH1g6sw/d5WNI0so3k2bfmQmVS4GXb+JSPhgjnILqpK6JmG2JnL+T/7h4OHC4ycExGpxE7kwsS5W7HZDe5pWY0hbj/B2+84Rg0DeAdC+xHQ4R/gH2JusCIuYtiwYXzyyScX7N+zZ48J0YiZ1ERz0sJtiTz95VYycvMJ8vNkyoBobmkSVvTB1hw4vgdSdp1rmB3bdaZZZi/6Nl7+5xpkBf82hmp1wV3/bSIiV7vk9BwenbOJ1fsdC8H0a12LF/o0pYp3CWvA2VMs0w5C2iHH5explmcbZJnJF69DhVgcI8QCIs9cap77t2qEo0FWNQI8vJ1/wiIiUu4Mw2DWbnf8shN4Lmg5dx1YgmWXY0AA/mHQcTS0GQ4+AeYGKuKCevTowccff1xoX0iIGs2VjUt0Y2bMmMHrr79OUlIS0dHRTJ8+nfbt21/0+Pnz5/P8889z4MABGjZsyKuvvsrf/va3Cok1N9/Oiwv/5JPYgwC0uaYa0we1IjLI19EsOztH2bEzo8pSdsLJ+It/SPEOhNAmjlFlIU0czbKQJo4PJ5rgX0SkTFxJdQZg2e5jPP31dk5k5eHn5c6LfZtxV+tahQ8yDMg+/pcm2eFzzbKzF2tW8Q/m5gFVI//SIPtrk+zMxT9Mp1iKiFzClVJnLBYL030/oGn+Ctxzznw+qX4tdBoD0fdqYRapeIYB1uyKf1xPv1J/3vb29iY8PLzY4z799FPefvtt4uLiqFKlCrfccgtTp04lNDQUgBdeeIH33nuPbdu2UaNGDQBuv/12srOzWbJkSemfi1Qo05toc+fOZdy4cbz33nt06NCBqVOn0r17d+Li4gpeZH/1xx9/MGjQICZPnswdd9zB559/Tt++fdm4cSPNmjUr11hTc2DgB2vYm3CcKEsC/4jK4/bwdNx/esfRNDt54OLNMp9ACLnuvIZZE8c3+2qWiYiUmyupzuTl2/nmgBvLYjcBBteHwZu3+VPTsg5+/6qIJlkJ/uj0D4egOo5LYK3zGmQ1oUoIuJVwGgIREbnAlVRnAPx8fXHPsEO9Gx0jzxrcpjog5rFmw8uRFf+4zyaAV5VyuWur1cqkSZNo3LgxKSkpjBs3jmHDhrFw4UIA/v3vf7No0SL+/ve/88033zBjxgz++OMPtmzZgpubG3Z7Sc4SELOY3kSbMmUKI0aMYPjw4QC89957/Pjjj3z00Uf861//uuD4t99+mx49evDkk08CMGnSJBYvXsw777zDe++9d8Hxubm55ObmFmynp6cDjhe21WotcZxWqxXPk3G8k/o/6vik4I4B+3Bc/sLwCcIIaYIR3BhCGmMEN8EIbuT4Fr+oZll+CeeZuUKdzXFpci3K2+VQ7pxzOXlz9VyXd52Bsqs1KSczGH78DZ71OkZdj+N4ncqBLy99G8M/HCOoDgTWxgisgxFUG87+G1DTMTH0pdhsjssVTO975yhvzlHenKM6c46ZdcZqtbIvtAc1b38Sj8gWjp1XQR0ob3rfO+f8vFmtVgzDwG63n2sU2e2Y0cK12+2OxZNKyDAMfvjhB/z9/Qv29ejRg3nz5hVcf/Y5DRs2rOCYunXrMnXqVDp06EB6ejr+/v5YLBZmz55N69atefrpp5k+fTrvv/8+tWrVwm63YxjGBfcpxStJ3s7m12q14u7uXui6kr6/TW2i5eXlsWHDBp555pmCfW5ubsTExBAbG1vkbWJjYxk3blyhfd27d2fBggVFHj958mT+85//XLD/l19+wc+vdCto1g70pV5SsiN29yqk+9QiwyeSDN+aZPg4LrkegeeaZceAY5nAxlI9ztVo8eLFZodwRVLenKfcOceZvGVnmzAEv4Qqos5A2daaGI/9VLGngx0MLOR4BpHtFUy2VzCnz/yb7RVyZrs6dre/TNR/+swlMRuIO3OpPPS+d47y5hzlzTmqM+bXGTyr8cvmI7D5SOluJ3rfO+ls3jw8PAgPDyczM5O8vDzHlYYBo3dWfFCn8yEnvcSHW61WunTpwptvvlmwz8/Pj/T0dPLz88nLyytobm/evJlXXnmFP//8k1OnThU0dHbs2EGTJk0ACA4O5oUXXuCxxx7jzjvv5I477ii4/VkZGRmX+ywrpUvlLS8vj9OnT7NixQryzxvQVNJaY2oTLTU1FZvNRlhY4Qn5w8LC2LVrV5G3SUpKKvL4pKSkIo9/5plnChWp9PR0ateuTbdu3QgIKPmEmVarlSU/LyRn4Hzcw5tiqRJCoMVCGa2PdtWyWq0sXryY2267DU9PzadTUsqb85Q751xO3s4v+K6kIuoMlG2t2ZY2nOh2nXCvUQ8CauLh4U0AoCmeL07ve+cob85R3pyjOnOO2XVGr9/SU96cc37ecnJyOHz4MP7+/vj4/HWkvOt/qvb09CQgIICWLVtecJ2HhwdeXl4EBASQlZXF3XffTbdu3fjss88ICQnh0KFD9OzZs+CYs9atW4e7uzsJCQn4+fnh4eFozxiGQUZGBlWrVsWiqZ9KrCR5y8nJwdfXl65du573Gix5rTH9dM7y5u3tjbf3hauFeXp6lvoXoN3NE/cGN+sXpxOcybcob5dDuXOOM3lTnsu21iQFtaF1w1uVVyfofe8c5c05yptzVGecU5Z15nJuV9kpb845mzebzYbFYsHNzQ23K2wePovFUhD7xa53c3Nj9+7dHD9+nFdffZXatWsDsHGj48y0vz7vuXPn8s0337Bs2TIGDBjASy+9VDDa9OzItUs9nlyoJHlzc3PDYrEU+V4u6Xvb1P+R4OBg3N3dSU5OLrQ/OTn5oqtehIeHl+p4ERGpvFRnRESkPKnOiMhf1alTBy8vL6ZPn87+/fv57rvvmDRpUqFjjhw5wqhRo3j11Ve54YYb+Pjjj3n55ZdZvXq1SVFLaZjaRPPy8qJNmzaFlnG12+0sWbKEjh07Fnmbjh07XrDs6+LFiy96vIiIVF6qMyIiUp5UZ0Tkr0JCQpg1axbz588nKiqKV155hTfeeKPgesMwGDZsGO3bt2fMmDGAY07EUaNGcd9995GZmWlW6FJCpp/OOW7cOIYOHUrbtm1p3749U6dOJSsrq2B1myFDhlCzZk0mT54MwKOPPsqNN97Im2++ye23386cOXNYv34977//vplPQ0REXJTqjIiIlCfVGZGr36xZsy563bJlywptDxo0iEGDBhXad3blSIBff/31gvuYNm0a06ZNA9CKnC7O9CbawIEDOXbsGOPHjycpKYmWLVuyaNGigsk2Dx06VOh81k6dOvH555/z3HPP8eyzz9KwYUMWLFhAs2bNzHoKIiLiwlRnRESkPKnOiIhUHqY30QDGjBlTMJTxfOd3dQH69+9P//79yzkqERG5WqjOiIhIeVKdERGpHLTUg4iIiIiIiIiISDHURBMRERERERERESmGmmgiIiIiIiIiUiH+Osm+SEUqi9eemmgiIiIiIiIiUq48PT0ByM7ONjkSqazOvvbOvhad4RILC4iIiIiIiIjI1cvd3Z2goCBSUlIA8PPzw2KxmByV67Hb7eTl5ZGTk1NoZV+5tEvlzTAMsrOzSUlJISgoCHd3d6cfR000ERERERERESl34eHhAAWNNLmQYRicPn0aX19fNRlLoSR5CwoKKngNOktNNBEREREREREpdxaLhYiICEJDQ7FarWaH45KsVisrVqyga9eul3XaYWVTXN48PT0vawTaWWqiiYiIiIiIiEiFcXd3L5OGxtXI3d2d/Px8fHx81EQrhYrKm06wFRERERERERERKYaaaCIiIiIiIiIiIsVQE01ERERERERERKQYlW5ONMMwAEhPTy/V7axWK9nZ2aSnp+u85FJQ3pyjvDlPuXPO5eTt7O/Ts79fRbWmoilvzlHenKO8OUd1pmypzlQs5c05yptzlDfnXG7eSlprKl0TLSMjA4DatWubHImIyNUlIyODwMBAs8NwCao1IiJlT3XmHNUZEZHyUVytsRiV7Csdu91OQkICVatWxWKxlPh26enp1K5dm8OHDxMQEFCOEV5dlDfnKG/OU+6cczl5MwyDjIwMIiMjcXPTLAGgWlPRlDfnKG/OUd6cozpTtlRnKpby5hzlzTnKm3MuN28lrTWVbiSam5sbtWrVcvr2AQEBeiE7QXlzjvLmPOXOOc7mTSMDClOtMYfy5hzlzTnKm3NUZ8qG6ow5lDfnKG/OUd6cczl5K0mt0Vc5IiIiIiIiIiIixVATTUREREREREREpBhqopWQt7c3EyZMwNvb2+xQrijKm3OUN+cpd85R3lyD/h+co7w5R3lzjvLmHOXNNej/wTnKm3OUN+cob86pqLxVuoUFRERERERERERESksj0URERERERERERIqhJpqIiIiIiIiIiEgx1EQTEREREREREREphppoIiIiIiIiIiIixVATrYRmzJhB3bp18fHxoUOHDqxdu9bskFzKxIkTsVgshS5NmjQpuD4nJ4fRo0dTo0YN/P396devH8nJySZGbI4VK1bQq1cvIiMjsVgsLFiwoND1hmEwfvx4IiIi8PX1JSYmhj179hQ65sSJEwwePJiAgACCgoJ48MEHyczMrMBnUfGKy9uwYcMueP316NGj0DGVMW+TJ0+mXbt2VK1aldDQUPr27UtcXFyhY0ry3jx06BC33347fn5+hIaG8uSTT5Kfn1+RT6VSUJ25NNWZklGdcY7qjHNUZ64sqjOXpjpTMqozzlOtKT1XrDNqopXA3LlzGTduHBMmTGDjxo1ER0fTvXt3UlJSzA7NpTRt2pTExMSCy6pVqwque+yxx/j++++ZP38+y5cvJyEhgbvuusvEaM2RlZVFdHQ0M2bMKPL61157jWnTpvHee++xZs0aqlSpQvfu3cnJySk4ZvDgwWzfvp3Fixfzww8/sGLFCkaOHFlRT8EUxeUNoEePHoVef1988UWh6ytj3pYvX87o0aNZvXo1ixcvxmq10q1bN7KysgqOKe69abPZuP3228nLy+OPP/7gk08+YdasWYwfP96Mp3TVUp0pGdWZ4qnOOEd1xjmqM1cO1ZmSUZ0pnuqM81RrSs8l64whxWrfvr0xevTogm2bzWZERkYakydPNjEq1zJhwgQjOjq6yOvS0tIMT09PY/78+QX7du7caQBGbGxsBUXoegDjm2++Kdi22+1GeHi48frrrxfsS0tLM7y9vY0vvvjCMAzD2LFjhwEY69atKzjmp59+MiwWi3H06NEKi91M5+fNMAxj6NChRp8+fS56G+XNISUlxQCM5cuXG4ZRsvfmwoULDTc3NyMpKangmJkzZxoBAQFGbm5uxT6Bq5jqTPFUZ0pPdcY5qjPOU51xXaozxVOdKT3VGeep1jjHFeqMRqIVIy8vjw0bNhATE1Owz83NjZiYGGJjY02MzPXs2bOHyMhI6tevz+DBgzl06BAAGzZswGq1FsphkyZNqFOnjnL4F/Hx8SQlJRXKU2BgIB06dCjIU2xsLEFBQbRt27bgmJiYGNzc3FizZk2Fx+xKli1bRmhoKI0bN2bUqFEcP3684DrlzeHUqVMAVK9eHSjZezM2NpbmzZsTFhZWcEz37t1JT09n+/btFRj91Ut1puRUZy6P6szlUZ0pnuqMa1KdKTnVmcujOnP5VGsuzRXqjJpoxUhNTcVmsxVKOEBYWBhJSUkmReV6OnTowKxZs1i0aBEzZ84kPj6eLl26kJGRQVJSEl5eXgQFBRW6jXJY2NlcXOq1lpSURGhoaKHrPTw8qF69eqXOZY8ePZg9ezZLlizh1VdfZfny5fTs2RObzQYobwB2u52xY8fSuXNnmjVrBlCi92ZSUlKRr8mz18nlU50pGdWZy6c64zzVmeKpzrgu1ZmSUZ25fKozl0e15tJcpc54OBG7yAV69uxZ8HOLFi3o0KED11xzDfPmzcPX19fEyKQyuOeeewp+bt68OS1atODaa69l2bJl3HrrrSZG5jpGjx7Nn3/+WWhuD5ErieqMmEl1pniqM3KlU50Rs6nWXJqr1BmNRCtGcHAw7u7uF6zukJycTHh4uElRub6goCAaNWrE3r17CQ8PJy8vj7S0tELHKIeFnc3FpV5r4eHhF0wAm5+fz4kTJ5TLv6hfvz7BwcHs3bsXUN7GjBnDDz/8wG+//UatWrUK9pfkvRkeHl7ka/LsdXL5VGecozpTeqozZUd1pjDVGdemOuMc1ZnSU50pW6o157hSnVETrRheXl60adOGJUuWFOyz2+0sWbKEjh07mhiZa8vMzGTfvn1ERETQpk0bPD09C+UwLi6OQ4cOKYd/Ua9ePcLDwwvlKT09nTVr1hTkqWPHjqSlpbFhw4aCY5YuXYrdbqdDhw4VHrOrOnLkCMePHyciIgKovHkzDIMxY8bwzTffsHTpUurVq1fo+pK8Nzt27Mi2bdsKFezFixcTEBBAVFRUxTyRq5zqjHNUZ0pPdabsqM44qM5cGVRnnKM6U3qqM2VLtcZF68zlrIxQWcyZM8fw9vY2Zs2aZezYscMYOXKkERQUVGh1h8ru8ccfN5YtW2bEx8cbv//+uxETE2MEBwcbKSkphmEYxj/+8Q+jTp06xtKlS43169cbHTt2NDp27Ghy1BUvIyPD2LRpk7Fp0yYDMKZMmWJs2rTJOHjwoGEYhvHKK68YQUFBxrfffmts3brV6NOnj1GvXj3j9OnTBffRo0cPo1WrVsaaNWuMVatWGQ0bNjQGDRpk1lOqEJfKW0ZGhvHEE08YsbGxRnx8vPHrr78arVu3Nho2bGjk5OQU3EdlzNuoUaOMwMBAY9myZUZiYmLBJTs7u+CY4t6b+fn5RrNmzYxu3boZmzdvNhYtWmSEhIQYzzzzjBlP6aqlOlM81ZmSUZ1xjuqMc1RnrhyqM8VTnSkZ1RnnqdaUnivWGTXRSmj69OlGnTp1DC8vL6N9+/bG6tWrzQ7JpQwcONCIiIgwvLy8jJo1axoDBw409u7dW3D96dOnjYcfftioVq2a4efnZ9x5551GYmKiiRGb47fffjOACy5Dhw41DMOxLPTzzz9vhIWFGd7e3satt95qxMXFFbqP48ePG4MGDTL8/f2NgIAAY/jw4UZGRoYJz6biXCpv2dnZRrdu3YyQkBDD09PTuOaaa4wRI0Zc8EdhZcxbUTkDjI8//rjgmJK8Nw8cOGD07NnT8PX1NYKDg43HH3/csFqtFfxsrn6qM5emOlMyqjPOUZ1xjurMlUV15tJUZ0pGdcZ5qjWl54p1xnImMBEREREREREREbkIzYkmIiIiIiIiIiJSDDXRREREREREREREiqEmmoiIiIiIiIiISDHURBMRERERERERESmGmmgiIiIiIiIiIiLFUBNNRERERERERESkGGqiiYiIiIiIiIiIFENNNBERERERERERkWKoiSbihGHDhtG3b1+zwxARkauYao2IiJQn1RmR0lMTTeQ8FovlkpeJEyfy9ttvM2vWLFPi++CDD4iOjsbf35+goCBatWrF5MmTC65XMRQRcX2qNSIiUp5UZ0TKh4fZAYi4msTExIKf586dy/jx44mLiyvY5+/vj7+/vxmh8dFHHzF27FimTZvGjTfeSG5uLlu3buXPP/80JR4REXGOao2IiJQn1RmRcmKIyEV9/PHHRmBg4AX7hw4davTp06dg+8YbbzTGjBljPProo0ZQUJARGhpqvP/++0ZmZqYxbNgww9/f37j22muNhQsXFrqfbdu2GT169DCqVKlihIaGGvfdd59x7Nixi8bTp08fY9iwYRe9fsKECQZQ6PLbb78ZhmEYhw4dMvr3728EBgYa1apVM3r37m3Ex8df8JwmTpxoBAcHG1WrVjUeeughIzc3t0S5EhER56jWqNaIiJQn1RnVGSk7Op1TpIx88sknBAcHs3btWh555BFGjRpF//796dSpExs3bqRbt27cf//9ZGdnA5CWlsYtt9xCq1atWL9+PYsWLSI5OZkBAwZc9DHCw8NZvXo1Bw8eLPL6J554ggEDBtCjRw8SExNJTEykU6dOWK1WunfvTtWqVVm5ciW///47/v7+9OjRg7y8vILbL1myhJ07d7Js2TK++OILvv76a/7zn/+UbaJERMRpqjUiIlKeVGdEimF2F0/ElZXmW5sbbrihYDs/P9+oUqWKcf/99xfsS0xMNAAjNjbWMAzDmDRpktGtW7dC93v48GEDMOLi4oqMJyEhwbj++usNwGjUqJExdOhQY+7cuYbNZrtobIZhGJ9++qnRuHFjw263F+zLzc01fH19jZ9//rngdtWrVzeysrIKjpk5c6bh7+9f6P5FRKRsqdao1oiIlCfVGdUZKTsaiSZSRlq0aFHws7u7OzVq1KB58+YF+8LCwgBISUkBYMuWLfz2228F8xH4+/vTpEkTAPbt21fkY0RERBAbG8u2bdt49NFHyc/PZ+jQofTo0QO73X7R2LZs2cLevXupWrVqwWNVr16dnJycQo8VHR2Nn59fwXbHjh3JzMzk8OHDTmRERETKmmqNiIiUJ9UZkUvTwgIiZcTT07PQtsViKbTPYrEAFBSGzMxMevXqxauvvnrBfUVERFzysZo1a0azZs14+OGH+cc//kGXLl1Yvnw5N998c5HHZ2Zm0qZNGz777LMLrgsJCbn0ExMREZehWiMiIuVJdUbk0tREEzFJ69at+eqrr6hbty4eHs6/FaOiogDIysoCwMvLC5vNdsFjzZ07l9DQUAICAi56X1u2bOH06dP4+voCsHr1avz9/aldu7bT8YmIiHlUa0REpDypzkhlo9M5RUwyevRoTpw4waBBg1i3bh379u3j559/Zvjw4RcUjLNGjRrFpEmT+P333zl48CCrV69myJAhhISE0LFjRwDq1q3L1q1biYuLIzU1FavVyuDBgwkODqZPnz6sXLmS+Ph4li1bxj//+U+OHDlScP95eXk8+OCD7Nixg4ULFzJhwgTGjBmDm5t+VYiIXIlUa0REpDypzkhlo1eRiEkiIyP5/fffsdlsdOvWjebNmzN27FiCgoIu+gs+JiaG1atX079/fxo1akS/fv3w8fFhyZIl1KhRA4ARI0bQuHFj2rZtS0hICL///jt+fn6sWLGCOnXqcNddd3Hdddfx4IMPkpOTU+hbnFtvvZWGDRvStWtXBg4cSO/evZk4cWJFpENERMqBao2IiJQn1RmpbCyGYRhmByEi5hs2bBhpaWksWLDA7FBEROQqpVojIiLlSXVGyptGoomIiIiIiIiIiBRDTTQREREREREREZFi6HROERERERERERGRYmgkmoiIiIiIiIiISDHURBMRERERERERESmGmmgiIiIiIiIiIiLFUBNNRERERERERESkGGqiiYiIiIiIiIiIFENNNBERERERERERkWKoiSYiIiIiIiIiIlIMNdFERERERERERESK8f+cNuxImhBGgAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(15, 3))\n",
+    "\n",
+    "for i, metric in enumerate(advection_scenario.report_metrics.split(\",\")):\n",
+    "    sns.lineplot(data=metric_data, x=\"time_step\", y=metric, hue=\"net\", ax=axs[i])\n",
+    "    axs[i].plot(\n",
+    "        time_steps,\n",
+    "        test_dict[metric][0],\n",
+    "        label=\"Flax\",\n",
+    "    )\n",
+    "    axs[i].legend()\n",
+    "    axs[i].set_title(metric)\n",
+    "    axs[i].set_xlabel(\"Time Step\")\n",
+    "    axs[i].set_ylabel(\"error metric\")\n",
+    "    axs[i].grid()\n",
+    "    axs[i].set_ylim(-0.1, 1.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Naturally, this raises the question: are the two networks actually the same? 🤔\n",
+    "Let's compare their parameter counts.\n",
+    "\n",
+    "Indeed, this is identical giving us reason to believe they are actually the same\n",
+    "ConvNet just at a different point in the parameter space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(31757, 31757)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_params_flax = (\n",
+    "    cnn.conv_lift.kernel.size\n",
+    "    + cnn.conv_lift.bias.size\n",
+    "    + sum(p.kernel.size + p.bias.size for p in cnn.convs)\n",
+    "    + cnn.conv_prj.kernel.size\n",
+    "    + cnn.conv_prj.bias.size\n",
+    ")\n",
+    "num_params_apebench = advection_scenario.get_parameter_count(\"Conv;34;10;relu\")\n",
+    "\n",
+    "num_params_flax, num_params_apebench"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing against multiple seeds with APEBench"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see if this was just an unlucky seed. To check, we will run multiple\n",
+    "seeds. This is one of the big strengths of APEBench. If a network run, does not\n",
+    "fully utilize the GPU (oftentimes the case in 1D), one can get seed statistics\n",
+    "in parallel for a small overhead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E: 00079, B: 00124: 100%|██████████| 10000/10000 [02:03<00:00, 80.78it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_s, trained_net_s = advection_scenario(\n",
+    "    task_config=\"predict\",\n",
+    "    network_config=\"Conv;34;10;relu\",\n",
+    "    train_config=\"one\",\n",
+    "    num_seeds=10,  # Now 10 seeds at the same time\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar melting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss_data_s = apebench.melt_loss(data_s)\n",
+    "metric_data_s = apebench.melt_metrics(\n",
+    "    data_s, metric_name=advection_scenario.report_metrics.split(\",\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Still the `flax.nnx` seems to be better in the one-step error that was used for\n",
+    "training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lineplot(\n",
+    "    data=loss_data_s,\n",
+    "    x=\"update_step\",\n",
+    "    y=\"train_loss\",\n",
+    "    hue=\"net\",\n",
+    "    estimator=\"median\",\n",
+    "    errorbar=(\"pi\", 50),\n",
+    ")\n",
+    "plt.plot(loss_history, label=\"Flax\")\n",
+    "plt.xlabel(\"Update Step\")\n",
+    "plt.ylabel(\"Train Loss\")\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.yscale(\"log\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And this dominance remains for the metric rollout, albeit the APEBench trained\n",
+    "statistics indicate a less severe difference. Yet, it is there.\n",
+    "\n",
+    "This is also good motivation why seed statistics are important. Especially for\n",
+    "autoregressive emulators for which errors compound over time, the effect of an\n",
+    "unlucky seed can be especially pronounced."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(15, 3))\n",
+    "\n",
+    "for i, metric in enumerate(advection_scenario.report_metrics.split(\",\")):\n",
+    "    sns.lineplot(\n",
+    "        data=metric_data_s,\n",
+    "        x=\"time_step\",\n",
+    "        y=metric,\n",
+    "        hue=\"net\",\n",
+    "        estimator=\"median\",\n",
+    "        errorbar=(\"pi\", 50),\n",
+    "        ax=axs[i],\n",
+    "    )\n",
+    "    axs[i].plot(\n",
+    "        time_steps,\n",
+    "        test_dict[metric][0],\n",
+    "        label=\"Flax\",\n",
+    "    )\n",
+    "    axs[i].legend()\n",
+    "    axs[i].set_title(metric)\n",
+    "    axs[i].set_xlabel(\"Time Step\")\n",
+    "    axs[i].set_ylabel(\"error metric\")\n",
+    "    axs[i].grid()\n",
+    "    axs[i].set_ylim(-0.1, 1.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**So what explains the differences?** Likely, this is caused by the different\n",
+    "initialization routines for `flax.nnx` and\n",
+    "[Equinox](https://github.com/patrick-kidger/equinox). The latter follows the\n",
+    "PyTorch conventions, which typically initialize the bias non-zero. For\n",
+    "conservative problems like the advection problem, this can potentially be\n",
+    "disadvantageous."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "apebench_dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/examples/benchmark_pytorch_models.ipynb b/docs/examples/benchmark_pytorch_models.ipynb
new file mode 100644
index 0000000..7a7b8b7
--- /dev/null
+++ b/docs/examples/benchmark_pytorch_models.ipynb
@@ -0,0 +1,1018 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Benchmark PyTorch Models with APEBench\n",
+    "\n",
+    "While foreign models cannot use advanced features of APEBench like correction\n",
+    "setups or differentiable physics training, it is still possible to benchmark\n",
+    "their performance on purely data-driven supervised learning tasks. This usually\n",
+    "follows a four-step pattern:\n",
+    "\n",
+    "1. Scrape the training data trajectories and the test initial conditions from\n",
+    "   the APEBench scenario.\n",
+    "2. Use the training data trajectories to train the foreign model. (Or use a\n",
+    "   pretrained model.)\n",
+    "3. Rollout the foreign model for `scenario.test_temporal_horizon` steps from the\n",
+    "   test initial conditions.\n",
+    "4. Plug the resulting neural trajectories into\n",
+    "   `scenario.perform_tests_on_rollout` method to obtain a dictionary with the\n",
+    "   error rollouts according to all `scenario.report_metrics`.\n",
+    "\n",
+    "Note that the step from (1.) -> (2.) can require a change in array format or\n",
+    "moving arrays to a different framework. So does step (3.) -> (4.).\n",
+    "\n",
+    "This notebook uses Pytorch. Since PyTorch and JAX often conflict when installed\n",
+    "in the same conda environment, please have two separate environments at hand to\n",
+    "run this tutorial. Whenever, a part needs PyTorch, set the `RUN_PYTORCH` flag to\n",
+    "`True`. We will transfer data between PyTorch and JAX by writing the arrays as a\n",
+    "`.npy` file and reading them back in the other framework.\n",
+    "\n",
+    "If you already have a pretrained model in PyTorch, you only have to scrape the\n",
+    "test initial conditions, write them out and then jump directly to step (3.)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_63364/1007276627.py:3: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from tqdm.autonotebook import tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "RUN_PYTORCH = False\n",
+    "\n",
+    "from tqdm.autonotebook import tqdm\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "if RUN_PYTORCH:\n",
+    "    import torch\n",
+    "    import numpy as np\n",
+    "else:\n",
+    "    import apebench\n",
+    "    import numpy as np\n",
+    "    import jax.numpy as jnp\n",
+    "    import seaborn as sns\n",
+    "    from scipy import stats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Create Data with APEBench"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an example, we will use an advection scenario in difficulty mode with mostly\n",
+    "default settings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    advection_scenario = apebench.scenarios.difficulty.Advection(\n",
+    "        # A simple optimization setup that will be mimicked by PyTorch\n",
+    "        optim_config=\"adam;10_000;constant;1e-4\",\n",
+    "        # The default metric for APEBench scenarios is always `\"mean_nRMSE\"`. Let's\n",
+    "        # add some more metrics to the report. One based on the spectrum up until\n",
+    "        # (including) the fifth mode and a Sobolev-based metric that also considers\n",
+    "        # the first derivative.\n",
+    "        report_metrics=\"mean_nRMSE,mean_fourier_nRMSE;0;5;0,mean_H1_nRMSE\",\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create train **trajectories** and test **initial conditions** with APEBench."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(50, 51, 1, 160)\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    train_data = advection_scenario.get_train_data()\n",
+    "    print(train_data.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(30, 1, 160)\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    test_ic_set = advection_scenario.get_test_ic_set()\n",
+    "    print(test_ic_set.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    jnp.save(\"train_data.npy\", train_data)\n",
+    "    jnp.save(\"test_ic_set.npy\", test_ic_set)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Train a Model in PyTorch\n",
+    "\n",
+    "Change the `RUN_PYTORCH` flag to `True` to run the PyTorch code. (Make sure to\n",
+    "change the ipykernel to an environment with PyTorch installed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([50, 51, 1, 160]) torch.Size([30, 1, 160])\n"
+     ]
+    }
+   ],
+   "source": [
+    "if RUN_PYTORCH:\n",
+    "    train_data = torch.from_numpy(np.load(\"train_data.npy\"))\n",
+    "    test_ic_set = torch.from_numpy(np.load(\"test_ic_set.npy\"))\n",
+    "    print(train_data.shape, test_ic_set.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From here on, you are free to do with the `train_data` what you what. The\n",
+    "simplest approach would be **one-step supervised learning**. For this, we slice\n",
+    "windows of length two across both the trajectory and the time axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([50, 50, 2, 1, 160])\n"
+     ]
+    }
+   ],
+   "source": [
+    "if RUN_PYTORCH:\n",
+    "    substacked_data = torch.stack(\n",
+    "        [\n",
+    "            torch.stack([td[i : i + 2] for i in range(td.shape[0] - 1)])\n",
+    "            for td in train_data\n",
+    "        ]\n",
+    "    )\n",
+    "    # substacked_data = torch.tensor(substacked_data)\n",
+    "    print(substacked_data.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we can merge sample and window axis into a joint batch axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2500, 2, 1, 160])\n"
+     ]
+    }
+   ],
+   "source": [
+    "if RUN_PYTORCH:\n",
+    "    train_windows = substacked_data.flatten(0, 1)\n",
+    "    print(train_windows.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up PyTorch Dataloader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if RUN_PYTORCH:\n",
+    "    inputs, targets = train_windows[:, 0], train_windows[:, 1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if RUN_PYTORCH:\n",
+    "    dataloader = torch.utils.data.DataLoader(\n",
+    "        torch.utils.data.TensorDataset(inputs, targets),\n",
+    "        batch_size=20,\n",
+    "        shuffle=True,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Definition\n",
+    "\n",
+    "Just a simple ReLU feedforward ConvNet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if RUN_PYTORCH:\n",
+    "\n",
+    "    class CNN(torch.nn.Module):\n",
+    "        def __init__(self, width: int, depth: int):\n",
+    "            super().__init__()\n",
+    "            module_list = []\n",
+    "\n",
+    "            conv_lift = torch.nn.Conv1d(\n",
+    "                1, width, kernel_size=3, padding=1, padding_mode=\"circular\"\n",
+    "            )\n",
+    "            module_list.append(conv_lift)\n",
+    "            module_list.append(torch.nn.ReLU())\n",
+    "\n",
+    "            for _ in range(depth - 1):\n",
+    "                conv = torch.nn.Conv1d(\n",
+    "                    width, width, kernel_size=3, padding=1, padding_mode=\"circular\"\n",
+    "                )\n",
+    "                module_list.append(conv)\n",
+    "                module_list.append(torch.nn.ReLU())\n",
+    "\n",
+    "            conv_proj = torch.nn.Conv1d(\n",
+    "                width, 1, kernel_size=3, padding=1, padding_mode=\"circular\"\n",
+    "            )\n",
+    "            module_list.append(conv_proj)\n",
+    "\n",
+    "            self.sequential_modules = torch.nn.Sequential(*module_list)\n",
+    "\n",
+    "        def forward(self, x):\n",
+    "            return self.sequential_modules(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training Loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "80.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "if RUN_PYTORCH:\n",
+    "    num_epochs = 10_000 / len(dataloader)\n",
+    "    print(num_epochs)  # 80"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 80/80 [00:38<00:00,  2.10it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "if RUN_PYTORCH:\n",
+    "    DEVICE = \"cuda\"\n",
+    "\n",
+    "    model = CNN(34, 10).to(DEVICE)\n",
+    "    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
+    "\n",
+    "    loss_history = []\n",
+    "    for epoch in tqdm(range(int(num_epochs))):\n",
+    "        for inputs, targets in dataloader:\n",
+    "            inputs, targets = inputs.to(DEVICE), targets.to(DEVICE)\n",
+    "            optimizer.zero_grad()\n",
+    "            outputs = model(inputs)\n",
+    "            loss = torch.nn.functional.mse_loss(outputs, targets)\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "            loss_history.append(loss.item())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's visualize the loss history. It's a bit noisy towards the end, but let's\n",
+    "stick with a constant learning rate for simplicity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if RUN_PYTORCH:\n",
+    "    plt.semilogy(loss_history)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Rollout the Model\n",
+    "\n",
+    "⚠️ The neural rollout must be **without** the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([30, 200, 1, 160])\n"
+     ]
+    }
+   ],
+   "source": [
+    "if RUN_PYTORCH:\n",
+    "    neural_rollout = []\n",
+    "    current_state = test_ic_set.to(DEVICE)\n",
+    "    for i in range(200):\n",
+    "        with torch.no_grad():\n",
+    "            current_state = model(current_state)\n",
+    "        neural_rollout.append(current_state.cpu())\n",
+    "\n",
+    "    neural_rollout = torch.stack(neural_rollout, dim=1)\n",
+    "    print(neural_rollout.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if RUN_PYTORCH:\n",
+    "    # Write to disk\n",
+    "    np.save(\"neural_rollout.npy\", neural_rollout)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Perform tests on the rollout\n",
+    "\n",
+    "Change the `RUN_PYTORCH` flag back to `False` to run the JAX code. (Make sure to\n",
+    "change the ipykernel to an environment with JAX/APEBench installed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(30, 200, 1, 160)\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    neural_rollout = jnp.load(\"neural_rollout.npy\")\n",
+    "    print(neural_rollout.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    test_dict = advection_scenario.perform_tests_on_rollout(neural_rollout)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we find the same keys in the `test_dict` as set up for the `report_metrics`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['mean_nRMSE', 'mean_fourier_nRMSE;0;5;0', 'mean_H1_nRMSE']) mean_nRMSE,mean_fourier_nRMSE;0;5;0,mean_H1_nRMSE\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    print(test_dict.keys(), advection_scenario.report_metrics)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For each key, there is an array attached. It is of the shape `(num_seeds,\n",
+    "test_temporal_horizon)`. Since, the `neural_rollout` did not have a leading\n",
+    "`num_seeds` axis, this axis appears as singleton. The axis thereafter is due to\n",
+    "the 200 time steps performed in the test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean_nRMSE: (1, 200)\n",
+      "mean_fourier_nRMSE;0;5;0: (1, 200)\n",
+      "mean_H1_nRMSE: (1, 200)\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    for key, value in test_dict.items():\n",
+    "        print(f\"{key}: {value.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rollout Metrics\n",
+    "\n",
+    "Let's visualize the error over time. The metric that includes the derivative\n",
+    "(`\"mean_H1_nRMSE\"`) is worse, likely because **nonlinear** networks applied to\n",
+    "linear time stepping problems produce spurious energy in higher modes. This is\n",
+    "problematic since linear PDEs on periodic BCs remain bandlimited.\n",
+    "\n",
+    "However, this is not a problem of PyTorch but a general problem of neural\n",
+    "emulators for linear PDEs. Hence, let's just acknowledge that the model performs\n",
+    "reasonable for at least ~50 time steps which is remarkable since it was only\n",
+    "trained to predict one step into the future and never multiple steps\n",
+    "autoregressively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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gEBLVRCoaKzK8+nAURdE6LCGyjEvhj5i06Tzbz4XRXHeU6Y6rKKbctjbmLma9UlPuDchCf1dS3Aghsqyz987SP6A/8eZ4Xiv4Gs2im6HX6bUOS4gsITwyjunbL7LqSDCVuMhqh+XU0AVZG13yWMfUVOsKBgdN43weUtwIIbKkKxFX6L2tN1GmKKoVqMaU16awY+sOrcMSItOLik9k4e4rfLf7CgUSbzLbsIrW+sPWRoMz1O4HdQeBk7u2gb4AKW6EEFnOrahb9NzakwfxDyiXpxxzGs/BUdFuNlQhsgKT2cKqIzeYsf0ialQ4wwy/09ExAANmUHRQuQM0+hzcC2od6guT4kYIkaXcjb1Lr629CI8Jp1jOYvg39cfVwRVTJpg4TIjMSFVVtp4NY/Lm89y+c58e+k30dfqDHMRaO5RsAU1HQ4FymsZpSzqtA5g7dy4+Pj44OTlRq1YtDh8+/NT+M2bMoHTp0jg7O+Pt7c3HH39MXFzcS4pWCKGliPgIPtr2EcGPginkWoiFzRaSyymX1mEJkWkdu/6Ad/0P0OenI1S7v4HdTp/wqfEXa2HjVRm6/AEdfrGrwgY0vnKzatUqhgwZgr+/P7Vq1WLGjBm0aNGCoKAg8ufPn6L/8uXLGT58OIsWLaJOnTpcuHCBrl27oigK06ZN0yADIcTLEmOKoV9APy48uEBe57wsbLaQAjky5wRiQmjt6t1opmw+z6bTITTUnWSz40pKKTesjR5FrE9AlX8LdJpf48gQmhY306ZNo2fPnnTr1g0Af39/NmzYwKJFixg+fHiK/vv376du3bq0b2+dddTHx4cPPviAQ4cOvdS4hRAvV4I5gUE7B3HqzincHdxZ0GwBRdyLaB2WEJnOvah4ZgVcZNmhYMqol1nusII6ujPWRicPaPAZ1PgQNFyx+2XQrLhJSEjg2LFjjBgxImmbTqejadOmHDhwINV96tSpw88//8zhw4epWbMmV65cYePGjXTq1OmJrxMfH098fHzS95GR1iXZTSaTze7RPz6Ovd7zt/f8wP5zzMr5JVoSGbZ3GAdDDuJscGZ2w9n4uvqmyCUr55hW9p6jvecHGZdjbIKZJQeus2DPVXIlhPKtYRVt9fsBUPWOWGr0xFJnMDh7gEqGLm6ZUTmm53iKqqqqTV89jW7fvk2hQoXYv38/tWv/b8bDzz77jF27dj3xasysWbMYOnQoqqqSmJhI7969mT9//hNfZ/To0YwZMybF9uXLl+Pi4vLiiQghMoxFtfB77O+cSDiBHj2dc3SmuLG41mEJkWlYVDh8R2FjsA7VFE1/wzq6GrbgQCIAN3LV5VzBt4l1yKtxpC8uJiaG9u3bExERgbv70x9Tz1JPSwUGBjJhwgTmzZtHrVq1uHTpEoMGDWLcuHF89dVXqe4zYsQIhgwZkvR9ZGQk3t7eNG/e/Jk/nLQymUxs27aNZs2aYTQabXLMzMTe8wP7zzEr5qeqKt8e+5YTF06gV/RMeW0KjbwbPbF/Vswxvew9R3vPD2yXo6qq7Lp4l2+2XORa+AM66zcz0Gkd7kQBYPGpj7nJaDw9K+Jpq+DTKKPO4+M7L2mhWXGTN29e9Ho9YWFhybaHhYXh6Zn6qfjqq6/o1KkTH35oXRCvQoUKREdH06tXL7744gt0qQyMcnR0xNEx5b1Fo9Fo8z+ejDhmZmLv+YH955iV8pt3ch4rLqwAYFzdcTQv1jxN+2WlHJ+Xvedo7/nBi+X4980IJmw8x8Erd2ijO8Bip18oxB1rY/7y0GwsuhJN0Gm8XIKtz2N6jqVZcePg4EC1atUICAigbdu2AFgsFgICAujfv3+q+8TExKQoYPR661TrGt1dE0JkgJ/O/sT8U9bbzSNqjqBN8TYaRySE9m7cj+HbrUGsO3mb2rozrHdcQQXlirXRrSA0/gIqfQCyBIm2t6WGDBlCly5dqF69OjVr1mTGjBlER0cnPT3VuXNnChUqxMSJEwFo06YN06ZNo0qVKkm3pb766ivatGmTVOQIIbK23y/+zpQjUwDoX7k/7cu21zgiIbT1MCaBOTsu8eOB6/hYrrPIuILG+pPWRgc3eG0wvNoXHGQc6WOaFjft2rXjzp07jBw5ktDQUCpXrszmzZspUMA6d0VwcHCyKzVffvkliqLw5ZdfcuvWLfLly0ebNm34+uuvtUpBCGFD265vY/SB0QB0KdeFXhV7aRuQEBqKM5n58cA15uy4hHNcOGMNv/Ke4250WEBngOrdocEwyJH1BwvbmuYDivv37//E21CBgYHJvjcYDIwaNYpRo0a9hMiEEC/T/lv7+Wz3Z1hUC2+VfItPqn+CovGYASG0YLGorDt1i2+3XCDi4T16Gf6kl9NGnEiwdij3hnUSvjzy5OCTaF7cCCHEyfCTDA4cTKIlkeZFmzPy1ZFS2Ihsae/Fu0zcdI6g2/d5X7+TIU6/kZt/nhLyrgXNx4N3TW2DzAKkuBFCaCrofhB9t/clNjGWuoXqMqneJPQyIFJkM+dCIpm06Ty7LoTTQneEuY6r8FFCrI15SlgXtizzOkjRnyZS3AghNHMt4hq9tvXikekRVfNXZXrD6Rj19v0IsBD/FhIRy9StF/jt+E2qcIHfHJZTTXfB2pgjHzQcDlW7gPxdpIsUN0IITYRGh9JrWy/ux92nTO4yzG4yG2eDs9ZhCfFSPIoz8V3AZRbtvYqX+RbzDCtpqT9ibTS6QO3+UHcgOLppG2gWJcWNEOKlux93n55bexISHYKPuw/+Tf1xd7DNjOFCZGYJiRZ2hSiMnr4XXcxdPjesoYPjDgyYQdFBlY7Q8HNw99I61CxNihshxEsVlRBF7229uRZ5Da8cXnzX/DvyOOfROiwhMpSqqmz4O4TJm85z50EiPfSr6ev0BzmItXYo2cI6rqZAOU3jtBdS3AghXpp4czwDdw7k3P1z5HbKzYJmC/DM8bJXvhHi5Tp05R4TNp3n7xv3eVu/m6GOqymgPLA2elWG5uPAt76mMdobKW6EEC9FoiWRT3d9ypHQI+Qw5mB+0/n45vTVOiwhMsyl8EdM2hTE9nOhNNSdZLPjSkopNwBQcxZBaToKyr8FqayLKF6MFDdCiAxnUS2M3j+anTd24qBzYHbj2ZTLI5ffhX0Kj4xj+vaLrDoSTFmustxhOXV0ZwBQnTw4k6clpTt+i9HZVeNI7ZcUN0KIDKWqKlOPTmXd5XXoFT3fNviWGp41tA5LCJuLjk9k4e4rfLfnCrlNoUw1/MKb+n3WRr0D1PqIxFcHcXnnfkobHLUN1s5JcSOEyFA/nP6BH8/+CMCYOmNoVKSRxhEJYVsms4VVR24wY/tFEqLuMciwju6OWzCSaO1Q4T1o/CXkKgomk7bBZhNS3AghMswvQb8w8/hMAD6t/ilvlHhD44iEsB1VVdl6NozJm89z885DOuu3MtBpHe5EWTv41odmY6FgFW0DzYakuBFCZIgt17Yw/uB4AHpW6Enn8p01jkgI2zke/ICJG89x9No92ugO8JPTagoRbm3MX85a1JRoKsslaESKGyGEze2/tZ/he4ajovJuqXcZUGWA1iEJYRPX7kYzZct5Nv4dSm3dGf5wXMEryhVro5sXNPoCKrcHWR9NU1LcCCFs6tSdU0krfLfwacEXtb6QFb5Flnc/OoFZARf5+eB1fNUbLDKuoLH+hLXRwQ1eGwSv9gMHF20DFYAUN0IIG7r04FLSCt91CtZh4msTZYVvkaXFmcws3X+NOTsv4Rx3h/GG1bxn2I0OC+gMUK0bNBgGrvm0DlX8ixQ3QgibuBV1i4+2fURkQiQV81WUFb5FlmaxqPzx122mbA4i4uE9ehn+pJfTJpyIt3Yo+3/QZBTkLaFtoCJVUtwIIV7Y3di79Nrai/DYcEp4lGBek3m4GOXyvMiaDl25x4SN5zhz8x7v63cyxOk3chNpbSxcE5qPhyK1tA1SPJUUN0KIF/Io4RF9tvch+FEwhVwLsaDZAnI65tQ6LCHS7fKdKCZtOs+2s6E00x1jhuMKfJUQa2Pu4taFLcu2kSegsgApboQQzy0uMY7+Af05f/88uZ1ys7DZQvK75Nc6LCHS5V5UPDMDLrLsUDDl1MuscviZWrrz1kaXPNBwBFTrCnKbNcuQ4kYI8VxMFhNDdw3lePhxXI2uLGi2gCLuRbQOS4g0izOZWbTvKvN2XiZnfAhTjatoq99vbTQ4wat94bXB4CRXIrMaKW6EEOlmUS2M3DeSXTd34ah3ZE6TOZTJXUbrsIRIE4tFZe3JW3y7JYhHEffpb1hHd6fNOPDP0ggV37cul+DhrW2g4rlJcSOESBdVVZlyZAp/XvkTvaJnWsNpVCtQTeuwhEiT/ZfuMmHTOc7fuk8HfQAfO63Bg0fWRp961sHCBStrGqN4cVLcCCHSZcFfC1h2bhkA418bT/3C9TWOSIhnuxj2iImbzrPjfBgtdEeZ47gSn8eDhfOWgmbjoFQLGSxsJ6S4EUKk2crzK5l7ci4Aw2sO5/Vir2sckRBPd+dRPNO3X2Dl4WAqcInVDsuooQuyNubIZx0sXLUL6OXj0J7I2RRCpMnGKxuZcGgCAH0q9aFD2Q4aRyTEk8UmmPl+zxX8d10mlymUGYaV/J/+gLXR4AS1+0PdQeDkrm2gIkNIcSOEeKY9N/fwxd4vUFH5oMwH9KnUR+uQhEiV2aKy5vhNvt0aRGzkPQYa1tHdcQtGEgEFKn1gHSycs5DWoYoMJMWNEOKpToafZEjgEBLVRFr6tmR4zeGyEKbIlPZcvMPXG85xOfQBHfXbGOz0OzmJsjb6NrAOFvaqqG2Q4qWQ4kYI8URB94PoG9CXOHMcrxV6ja9f+xqdotM6LCGSCQp9xISN59h1IRw/3REWOK2kKKHWxnxlrEVNiaYyWDgbkeJGCJGqm49u0md7Hx4lPKJK/ipMazgNo05maBWZR3hkHNO2XeCXozeoyCV+dVhG9aTBwvmh0edQpZMMFs6G5IwLIVK4F3uPj7Z9xJ3YO5TMVZI5TebgbHDWOiwhAIiOT+S7PVdYsOsKeRNvM9Owijb6g9ZGgzPUHQh1BoCjm7aBCs1IcSOESCbaFE3fgL5JC2H6N/XH3UGeKBHaM1tUVh+9wdRtF4h/dI8hhrV0ddz6v8HCVTpAoy/AvaDWoQqNSXEjhEiSYE5g8M7BnL13llyOufBv6i8LYQrNqapK4IU7TNp4nqth9+mk38Ygp7W4Px4sXKwRNB8HnhW0DVRkGlLcCCEA63pRX+z9goMhB3ExuDC/6Xx8cvpoHZbI5s7cjmDixvPsvXSHlrrDfO+0Em/CrI35y1mLmhJNtQ1SZDpS3AghUFWVSYcnsfnaZgw6A9MbTad83vJahyWysZCIWKZuvcBvx29ShQuscVhGVd1Fa6NrAetcNZU7gE6vbaAiU5LiRgjBwr8WsuL8ChQUJrw2gToF62gdksim4s0wI+ASP+y7Rv7EEOYYVtBaf9jaaHSxzipcuz84umobqMjUpLgRIpv79cKvzDk5B4BhNYfR0relxhGJ7MhsUVl97BaTTuhRTH8x1PA7XRy3WQcLKzqo0tE6WNjNU+tQRRYgxY0Q2VjA9QDGHRwHQM8KPWW9KKGJfZfuMn7DOS6H3KOTfiuDndbiRrS1sURTaDYWCshtUpF2UtwIkU0dCT3CZ7s/w6JaeLvk2wyoMkDrkEQ2c/lOFBM3nmP7uTBa6w7xndNKChNubSzwirWoKdFE2yBFliTFjRDZUND9IAbuGEiCJYHG3o358tUvZb0o8dI8iE5gZsBFfj54nYpqULLBwrHGXBhbjMFQtaMMFhbPTYobIbKZm49u0nt7b6JMUVTNX5XJ9Sdj0Mlbgch48Ylmftx/ndk7LuIeH8J0w4r/zSxszIG59gACHhanRaU3pbARL0Te0YTIRu7H3eejbR9xN/YupXKVYnaT2TgZnLQOS9g5VVXZfDqUiZvOc//+XfoZ1tHDcTMOmAAFqnaCRl9gccqDeeNGrcMVdkCKGyGyiXg1ngGBA5KWVZjfdL4sqyAy3KkbDxm/4SzHr92lnT6QoU6ryU2ktdG3PrSY8L+ZhU0mzeIU9kWKGyGygQRzAsuil3El8YosqyBeitsPY/lmSxC/n7hFfd0pNjkuo5Ry09qYpyQ0Hw+lWoCM9RIZQIobIeycRbXw1YGvuJJ4RZZVEBkuOj4R/12XWbj7Ct7mYJYYl9FQf8ra6JwLGo6A6t1Bb9Q2UGHXpLgRwo49XlZhW/A29OiZWn+qLKsgMoTZovLrsRt8u/UClkfhfGn4jfaOO9BjAZ0Ran0E9YdaCxwhMli6i5u3336bmjVrMmzYsGTbp0yZwpEjR1i9erXNghNCvJh/L6vwjss71PKspXVIwg7tvXiX8RvOcjX0Hl31WxjgtA5XYqyNZdtA0zGQp7i2QYpsJd3Fze7duxk9enSK7S1btmTq1Km2iEkIYQOrL6xOWlbh02qf4n5ZBg8L27oUbp2EL+C8dRK+H5xWUIg71kavytbBwj51NY1RZE/pLm6ioqJwcHBIsd1oNBIZGWmToIQQLybgegDjD44HrMsqvF/6fTZelkdshW3cj05g5vYL/HwomArqRX5z+JlqugvWRjcvaDIKKrYDnU7bQEW2le7ipkKFCqxatYqRI0cm275y5UrKlStns8CEEM8ntWUVEhMTtQ5L2IGERAs/HrjGzICLuMWFMtW4krb6/dZGowvUHQx1+oNDDk3jFCLdxc1XX33FW2+9xeXLl2ncuDEAAQEBrFixQsbbCKExWVZBZARVVdlxPpyvN5wj7O5d+hjW09NpE44kAApUbg+NvwJ3L61DFQKAdF8zbNOmDWvXruXSpUv07duXTz75hJs3b7J9+3batm2b7gDmzp2Lj48PTk5O1KpVi8OHDz+1/8OHD+nXrx9eXl44OjpSqlQpNsqMlkJw49ENWVZB2NyFsEd0XnSYnksPU/PBH+xy+oT+hnXWwsanHvQKhLbzpLARmcpzvfO1bt2a1q1bv/CLr1q1iiFDhuDv70+tWrWYMWMGLVq0ICgoiPz5U04wlpCQQLNmzcifPz+//vorhQoV4vr163h4eLxwLEJkZfdi79F7W29ZVkHYzIPoBKZvv8CyQ8G8yl9sdFxGGSXY2pi7mHUSvtKtZBI+kSlp+t+6adOm0bNnT7p16waAv78/GzZsYNGiRQwfPjxF/0WLFnH//n3279+P0WidAMrHx+eprxEfH098fHzS948HPZtMJkw2mur78XFsdbzMxt7zg6ydY7Qpmj4BfQh+FEzBHAWZ3XA2zopzslyycn5pJTna6DXMFpYdvsHsHZfJGx/MAsMymupPAKA6eWCp9ymWat1A7wA2Hssl59A+ZFSO6Tmeoqqq+qxOuXPn5sKFC+TNm5dcuXI99R7+/fv30/TCCQkJuLi48Ouvvya7ndWlSxcePnzIunXrUuzTqlUrcufOjYuLC+vWrSNfvny0b9+eYcOGodenvoLs6NGjGTNmTIrty5cvx8XFJU2xCpFZJaqJ/Bj9I1cSr5BDyUFP157k1efVOiyRRZ19oLD2uo7Y2BgGGtbQ1bAVA2Ys6LmarwlBnm0xGVy1DlNkUzExMbRv356IiAjc3Z8+tUWartxMnz4dNzc3AGbMmPHCAQLcvXsXs9lMgQIFkm0vUKAA58+fT3WfK1eusGPHDjp06MDGjRuTxv2YTCZGjRqV6j4jRoxgyJAhSd9HRkbi7e1N8+bNn/nDSSuTycS2bdto1qxZ0hUle2Lv+UHWzNFsMfP5/s+5EmFdVmFBkwWUy5P6E4tZMb/0khyf36XwKCZtvsDei2G8rw9gqNNqcvEIAEuJZpibjqVInpIUsdkrpk7OoX3IqBzTM91MmoqbLl26AJCYmIiiKLRo0SJFUfIyWCwW8ufPz8KFC9Hr9VSrVo1bt27xzTffPLG4cXR0xNHRMcV2o9Fo81+sjDhmZmLv+UHWyVFVVaYcmsK24G0YdAZmNJpBJc9Kz9wvq+T3IiTHtHsYk8CM7Rf56eB1avE3Gx1/orRyw9qYtzT4TUBXomn6nzx5QXIO7YOtc0zPsdI15sZgMNC7d2/OnTuX7qD+K2/evOj1esLCwpJtDwsLw9PTM9V9vLy8MBqNyW5BlS1bltDQUBISElKdXFAIe7Twr4WsDFqJgsLE1yZSu2BtrUMSWUii2cKyQ8FM336BnLE3mG9YRnP9MWujkwc0+gKqd5PFLUWWle6CvGbNmpw4ceKFX9jBwYFq1aoREBCQtM1isRAQEEDt2qm/UdetW5dLly5hsViStl24cAEvLy8pbES28e9lFYbVHIafr5/GEYmsZPeFO7ScuYdv1x+hd8JStjt+Zi1sFD3U/AgGnoBavaSwEVlaup+W+vfcNtWqVSNHjuQzUVasWDHNxxoyZAhdunShevXq1KxZkxkzZhAdHZ309FTnzp0pVKgQEydOBKBPnz7MmTOHQYMGMWDAAC5evMiECRMYOHBgetMQIkvafn17smUVOpTtoHFEIqu4fCeKCRvOsfN8KO/pA/nUaTV5iLA2Fm9iXQcqfxlNYxTCVtJd3Lz//vsAyQoKRVFQVRVFUTCbzWk+Vrt27bhz5w4jR44kNDSUypUrs3nz5qTxPMHBwej+tTaJt7c3W7Zs4eOPP6ZixYoUKlSIQYMGpVihXAh7dCT0CMN2D0u2rIIQzxIRa2JWwEWW7r9GNfUsfzr8SDnddWtjnpLWoqZkM5mvRtiVdBc3V69etWkA/fv3p3///qm2BQYGpthWu3ZtDh48aNMYhMjsZFkFkV6JZgsrj9xg2rYLuMTcZJZhOa30/8wA75QTGgyHmj3l9pOwS+kubq5fv06dOnUwGJLvmpiYyP79+ylatKjNghNCJF9WoVqBakxpMEWWVRBPte/SXcb+cZabYeH0M6zjQ8eNOJAIig6qdbMOGM6RR+swhcgw6X6HbNSoESEhISmWR4iIiKBRo0bpui0lhHi6/y6rMKvxLBz1Kac2EALg2t1oxm84R8C5EN7R7+Znp1/Ix0NrY7GG0GIiFEh9LiQh7Em6i5vHY2v+6969eykGFwshnl+0KZo+263LKhRyLYR/U3/cHWwz8aSwL5FxJubsuMTifVepZDnHeoefqKD7ZwhB7mLQ/Gso3VLG1YhsI83FzVtvvQVYBw937do12cR4ZrOZv/76izp16tg+QiGyoQRzAoN2DuLc/XPkdsrNgmYLyOeST+uwRCZjtqisOnKDqVuDcIq+xXTjCl43/jMm0dEdGnwGNXuBQa72iewlzcVNzpw5AeuVGzc3N5ydnZPaHBwcePXVV+nZs6ftIxQimzFbzHy+93MOhRzCxeDCvKbzKOouY9lEcgcu32Psn2e5FhJOH8N6PnLaiCMJgALVukCjL8FVCmKRPaW5uFm8eDFgXYV76NChcgtKiAygqiqTDk9iy7UtScsqlM9TXuuwRCZyNw76rTjJtrOhtNXtY4nTSgrwwNroUw/8JoJnBW2DFEJj6R5zM2rUKBITE9m+fTuXL1+mffv2uLm5cfv2bdzd3XF1lRVjhXhesqyCeJKo+ERmbb/ADyf1VGQfvzv8SGXdZWujR1FoPh7KtpFxNULwnI+C+/n5ERwcTHx8PM2aNcPNzY3JkycTHx+Pv79/RsQphN379cKvScsqDK85XJZVEABYLCprTtxi8ubz6B/dZqpxBW31+62NDq5QfyjU6gNGJ20DFSITSXdxM2jQIKpXr86pU6fIk+d/8yS8+eabMuZGiOe0M3gn4w6OA6zLKrQv217jiERmcPLGQ0avP8P5G2F8pP+TPk5/4EQCKgpKlQ7QeCS4FdA6TCEynXQXN3v27GH//v0pFqr08fHh1q1bNgtMiOziZPhJPt39KRbVwpsl3pRlFQThkXFM3hzEb8dv8LruIPMdl+Ol3APgXo5SuLebj7FIdY2jFCLzSndxY7FYUp2o7+bNm7i5udkkKCGyi8sPL9MvoB/x5ngaFG7AyNojZVmFbCw+0czifdeYHXCRIqYrrHJYSi3deWtjziIkNhnF3isGWnlV0jZQITK5dBc3zZs3Z8aMGSxcuBCwznsTFRXFqFGjaNWqlc0DFMJehUaH0nt7byITIqmYryLfNPhGllXIplRVZcf5cMb9eZaH98IYZviVDo4B6LGAwRnqDYE6A1AxwNWNWocrRKaX7nfSqVOn0qJFC8qVK0dcXBzt27fn4sWL5M2blxUrVmREjELYnYj4CPps70NodCg+7j7MbTwXZ4Pzs3cUdudSeBTj/jzLngthtNcHMNRpNR5EWRvLvwnNxoGHt/V7k0m7QIXIQtJd3BQuXJhTp06xcuVK/vrrL6KioujRowcdOnRINrGfECJ1cYlxDNwxkEsPL5HfOT8Lmi3Aw8lD67DESxYZZ2LW9oss2X+NKuo5Njgupaxy3dqYvzy0nAy+9bQNUogs6rmugRsMBjp27GjrWISwe2aLmeF7hnM8/DhuRjfmN5tPQdeCWoclXiKLRWX1sRt8syUIQ1QIU40reOPxo91OHtD4S+vK3Xq5RSnE83quv57bt2+zd+9ewsPDsVgsydoGDhxok8CEsDeqqjLh0AQCggMw6ozMbDyTUrlKaR2WeImOXb/P6PVnuXDrDj30GxngtA5n4gEFqnezLpmQI88zjyOEeLp0FzdLlizho48+wsHBgTx58iR7skNRFCluhHiCBX8t4JcLv6CgMKneJGp41tA6JPGShEbEMWnTOdaevEVT3XHmOf6MtxJmbSxS23oLSp6AEsJm0l3cfPXVV4wcOZIRI0ag0+kyIiYh7M5vF35j7sm5AHxe63Oa+zTXOCLxMsSZzHy/5wpzd17GK/EGSxx+oqHulLXRzcs6WLjCO7JkghA2lu7iJiYmhvfff18KGyHSaGfwTsYeHAtYZx9+v8z7GkckMpqqqmw5E8bXG8/y4P49Bht+p4fjZgyYQe8AtftDvU/AUdbiEyIjpLu46dGjB6tXr2b48OEZEY8QdkVmH85+LoY9YswfZ9l3KZy3dHv53GkleXhobSzVElp8DXmKaxqjEPYu3cXNxIkTef3119m8eTMVKlTAaDQma582bZrNghMiK7vy8IrMPpyNRMaZmPnPo93l1Uv87riUysola2OeEuA3CUo20zZIIbKJ5yputmzZQunSpQFSDCgWQlhnH/5o+0cy+3A2YLGo/H7iFhM3nYeocCYYVtHOEGhtdHCFBp9ZV+02ODz1OEII23muGYoXLVpE165dMyAcIbI+mX04+zh9K4JR689w6vodOuu3McTpN1yJsTZW+gCajgY3T01jFCI7Sndx4+joSN26dTMiFiGyvH/PPpzPOZ/MPmynHsYk8O3WIJYdCqaO8jdbHH+kuHLL2uhVGVp9A941NY1RiOws3cXNoEGDmD17NrNmzcqIeITIslLMPtxUZh+2N2aLysojwXy7JYgcsbeYb1iGn/6ItdElDzQZBVU6gk6vbaBCZHPpLm4OHz7Mjh07+PPPPylfvnyKAcVr1qyxWXBCZBWpzT5cOndprcMSNnTs+gNGrT/NpVt36GNYTx/HP3HABIoeavaChsPB2UPrMIUQPEdx4+HhwVtvvZURsQiRZcnsw/brzqN4Jm8+z6/HbtBKd4jvHJfhpdyzNvrWh5ZTIH9ZbYMUQiST7uJm8eLFGRGHEFnWv2cfHlFrhMw+bCcSzRZ+PHCd6dsuUDDhCiuMP1Jbf9bamLOIdb6asm1kdmEhMiF5NlWIF/Df2Yc/KPOBxhEJWzhw+R6j158hNCyETwy/0slxO3osYHCC1z6GOgPBwUXrMIUQTyDFjRDPSWYftj8hEbFM2HieDadu8r5+J586/UIuHlkby70BzceDRxFtgxRCPJMUN0I8h3/PPly/cH2ZfTiLi080s2jvNWbvuEhZ01nWOyzlFd01a2O+stZVu4s10DRGIUTaSXEjRDqFRYclm3342wbfyuzDWdiuC3cYs/4M0Xdv8LVxBW867rM2OOWERl9A9R6gl/MrRFaSrr9Yk8mEn58f/v7+lCxZMqNiEiLTioiPoPf23kmzD89pPEdmH86ibtyPYdyfZwk8e5Me+k0McFqLC3GoKChVO0OTkZAjr9ZhCiGeQ7qKG6PRyF9//ZVRsQiRqcWb41PMPpzLKZfWYYl0ijOZ8d91mfmBl6lrOco2x58oqoRZGwvXRGk1BQpW0TZIIcQLSfe11o4dO/LDDz8wadKkjIhHiEzJbDEzfLd19mFXo6vMPpwFqarK1rNhjPvzLMaHV/A3/Egjh1PWRldPaDYWKr4nj3YLYQfSXdwkJiayaNEitm/fTrVq1ciRI0ey9mnTptksOCEyg8ezD28P3o5RZ2RW41ky+3AWc+VOFGP+OMvRC8EMMKylh+MmjCSi6owotftB/aHg6KZ1mEIIG0l3cXP69GmqVq0KwIULF5K1ydMiwh4t/GuhzD6cRUXHJzJ7xyUW7b1Ea3UvOx1XkF95aG0s2RylxUTIW0LTGIUQtpfu4mbnzp0ZEYcQmdJvF35jzsk5AAyvOVxmH84iVFXlj79CmLDhHHkfnWWFcSnVdBetjbmLgd8kKNVC2yCFEBnmhZ5vvHnzJgCFCxe2STBCZCb/nX24fdn2Gkck0uJC2CPGbgji0tWrDDX8QjvHQHSoqMYcKA0+hVf7gsFR6zCFEBlIl94dLBYLY8eOJWfOnBQtWpSiRYvi4eHBuHHjsFgsGRGjEC+dzD6c9UTGmlhzVcdb8/ZSNng5Ox0/4QPDTnSoULEdyoBj1qUTpLARwu6l+8rNF198kfS0VN26dQHYu3cvo0ePJi4ujq+//trmQQrxMl15eIX+O/rL7MNZhMWi8tvxm0zadJ5SsedYb1hKaZ31qjKeFaHVN1DkVW2DFEK8VOkubpYuXcr333/P//3f/yVtq1ixIoUKFaJv375S3IgsLTwmnI+2f0REfAQV81bkm/rfyOzDmdjfNyMYuf40YcGXGGNcxusOh6wNzrmtk/BV7Qw6vbZBCiFeunS/a9+/f58yZcqk2F6mTBnu379vk6CE0EKsJZb+O/v/b/bhJnNwMcrKz5nRg+gEvtkaxG+HL9FTt4F+jutwVhJQUbBU646+yZfgklvrMIUQGkl3cVOpUiXmzJnDrFmzkm2fM2cOlSpVsllgQrxM8eZ4lkUv45r5Gvmc8+HfzF9mH86EzBaVFYeD+XbLearHH2Kr8SeK6sIBsBSpzS6X1rzm1xu90ahxpEIILaW7uJkyZQqtW7dm+/bt1K5dG4ADBw5w48YNNm7caPMAhchoZouZL/Z/wTXztaTZhwu5FtI6LPEfx67fZ+S6M8SGnGeG4ScaPp5d2K0gNB+HufT/Eblpk7ZBCiEyhXQXNw0aNODChQvMnTuX8+fPA/DWW2/Rt29fChaU6ehF1qKqKhMPT2THjR3o0TOt/jSZfTiTCX8Ux6RN59ly/BIDDL//M7uwGVXvgFK7P9T7BBxdwWTSOlQhRCbx3KuCy8BhYQ8W/rWQVUGrUFB41+VdqheornVI4h8ms4Wl+68xY/sFmpp2scNxOQWSZhdugeI3EfIU1zRGIUTmJKuCi2zr37MPf1rtU9wvu2sckXhs/+W7jFp3Boc7p1lsXEINh3+Wesnla51duLSftgEKITK1dE/i93hVcCGyssAbgclmH36/9PvaBiQAuP0wln7Lj9P3u+10uT+T9Y5fUkN3AdXoAo2/gr4HpbARQjyTrAousp2T4ScZumsoFtVC2xJtGVBlAImJiVqHla3FJ5r5fs9V5u24wJuWrex0XE0uJcraWP4tlObjIKcs8yKESJt0X7l5vCq4m5sbFy5c4MSJE0lfJ0+efK4g5s6di4+PD05OTtSqVYvDhw+nab+VK1eiKApt27Z9rtcV2c9/Zx8eVXuUzD6ssZ1B4bSYvpudW9fxizKc8cbF1sImf3no8ie8u1gKGyFEuqTryo3ZbGbMmDFUqFCBXLlsMwfIqlWrGDJkCP7+/tSqVYsZM2bQokULgoKCyJ8//xP3u3btGkOHDqVevXo2iUPYv7DoMJl9OBMJvhfD2D/P8te584wwLudNx30AqE45URp9AdV7gF7OjxAi/dJ15Uav19O8eXMePnxoswCmTZtGz5496datG+XKlcPf3x8XFxcWLVr0xH3MZjMdOnRgzJgxFCtWzGaxCPsVmRBJ7+29ZfbhTCA2wcy0bRdoOT2A4he+Z4fjJ7yp34eKAlU7oww4DrU+ksJGCPHc0v3u8corr3DlyhV8fX1f+MUTEhI4duwYI0aMSNqm0+lo2rQpBw4ceOJ+Y8eOJX/+/PTo0YM9e/Y89TXi4+OJj49P+j4yMhKwPtZustG8GI+PY6vjZTZZPb94czwDdg7g0sNL5HXOy5yGc3DVuybLJ6vn+CyZIT9VVdl2LpwJm4IoEXmI9YYfKa4LAcBSsBqWFpNQC1bhn0DTffzMkGNGs/cc7T0/kBxtcdy0UFRVVdNz8M2bNzNixAjGjRuX6oBid/e0P057+/ZtChUqxP79+5NmOwb47LPP2LVrF4cOHUqxz969e3n//fc5efIkefPmpWvXrjx8+JC1a9em+hqjR49mzJgxKbYvX74cFxf5n7u9s6gWVsas5KzpLI448qHbh3jpvbQOK9sJi4XfruqIibzDSMPPNNMfAyDO4M7Zgu24kbsuKOkeAiiEyEZiYmJo3749ERERz6w10n3lplWrVgD83//9X7KBmKqqoigKZrM5vYdMs0ePHtGpUye+++478ubNm6Z9RowYwZAhQ5K+j4yMxNvbm+bNm6erEHsak8nEtm3baNasGUY7XNMmq+anqiqTjk7i7MWzGHVGZjea/cRJ+rJqjmmlVX5R8YnMDbzCyr8u0Eu3ho8cNuComFB1BizVP0Rf7zMqOLlTwQavZe/nEOw/R3vPDyTHF/H4zktapLu42blzZ3p3eaK8efOi1+sJCwtLtj0sLAxPT88U/S9fvsy1a9do06ZN0jaLxQKAwWAgKCiI4sWTz1jq6OiIo6NjimMZjUab/2JlxDEzk6yW38K/FrL64moUFCbWm0jtwrWfuU9WyzG9XlZ+qqqy/tRtvv7zLNVjdrPFuIxCyj1ro28DlJZT0Ocvgz4DXtvezyHYf472nh9Ijs97vLR6rrWlbMXBwYFq1aoREBCQ9Di3xWIhICCA/v37p+hfpkwZ/v7772TbvvzySx49esTMmTPx9va2WWwia1tzcQ2zT8wGYHjN4bTwaaFxRNnHuZBIRq0/w4NrfzHdsJS6DmesDTmLQIuvoWwbkMfvhRAZ6LkeR9izZw8LFizgypUrrF69mkKFCvHTTz/h6+vLa6+9lq5jDRkyhC5dulC9enVq1qzJjBkziI6Oplu3bgB07tyZQoUKMXHiRJycnHjllVeS7e/h4QGQYrvIvgJvBDLmgHWcVc8KPWlftr22AWUTEbEmpm+7wO8HzjBI/xudHbZiUCyoBieUuoOh7iBwkHFuQoiMl+7i5rfffqNTp0506NCB48ePJz2JFBERwYQJE9i4cWO6jteuXTvu3LnDyJEjCQ0NpXLlymzevJkCBQoAEBwcjE4nAw1F2pwMP8mnuz7Folp4o/gbDKgyQOuQ7J7FovLrsZtM2XSWRvHb2e6wknzKP/fGy7yO0mIC5CqqbZBCiGwl3cXN+PHj8ff3p3PnzqxcuTJpe926dRk/fvxzBdG/f/9Ub0MBBAYGPnXfJUuWPNdrCvvzePbhOHMc9QrVY1QdmX04o5268ZCR68/AzaN8b1xKZeNla0PeUtByMhRvrG2AQohsKd3FTVBQEPXr10+xPWfOnDad3E+I9AiLDqP39t5Jsw9/2+BbjDr7HqynpfvRCXyz5TzbjpzmU/0q2jkGAqA6uKE0HAY1PwKDg6YxCiGyr3QXN56enly6dAkfH59k2/fu3SuzBQtNPJ59OCQ6RGYfzmBmi8ryQ9eZvuUcb5g2sMPhN9yVGGtjpQ9Qmo4Gt5RPOgohxMuU7uKmZ8+eDBo0iEWLFqEoCrdv3+bAgQMMHTqUr776KiNiFOKJ4s3xDNwxkEsPL5HPOR/+zfzJ5WSbdc9Eckeu3WfUujPkDDvACsNSShtvWhu8KkHLb6BILW0DFEKIf6S7uBk+fDgWi4UmTZoQExND/fr1cXR0ZOjQoQwYIIM3xctjtpgZsWcEx8KO4Wp0ZX7T+RRyLaR1WHYnPDKOiZvOc+jEKT43LuN1B+vM4apzbpQmI6FqZ9BlxIw1QgjxfNJd3CiKwhdffMGnn37KpUuXiIqKoly5cri6umZEfEKkSlVVJh6eyLbr2zDqjMxsNJPSuUtrHZZdMZktLNl3jfkBZ2ifuI4Ax3U4Kwmoig6leg+URp+DS26twxRCiBSee9ldBwcHypUrZ8tYhEiz7/7+jlVBq5JmH67pVVPrkOzK3ot3Gb3+ND73dvO74SeKGsOtDUXqoLSaAp62WDBBCCEyxnMXN0Jo5feLvyfNPjys5jCZfdiGbj2M5esNZzl3+gSjDD/S0OEUAKqbF0rz8fDK2zK7sBAi05PiRmQpu27sSpp9+MMKH9KhbAeNI7IPcSYz3++5wuKdf9NTXcMMh404KGZUnRGlTn+UekPBUW49CyGyBiluRJZxMvwkQ3cNxayaeaP4GwysMlDrkOzCjvNhjFl/hkoPt7PBuBxP5YG1oWRzFL9JkKf40w8ghBCZjBQ3Iku4GnFVZh+2sev3ohn7x1luBx1hinEptRzOA6Dm8rUWNaX9NI5QCCGejxQ3ItO7E3OHPtv7EBEfQYW8FWT24RcUk5DIvJ2XWbX7LwYoq+jgsB29oqIaXVDqfYJSuz8YnbQOUwghnpsUNyJTi0qIom9AX25F3aKIWxGZffgFqCpsOh3KlE3nqBe9mS2GVeRWoqyN5d+0DhjOWVjbIIUQwgakuBGZlsls4uPAjzl//zy5nXLj38yf3E4yr8rzuBQexbxzOlwP/cp84xIqGK8BoOYvh9JyMvimXC9OCCGyKiluRKZkUS18tf8rDoYcxNngzLym8/B289Y6rCznUZyJWQEX+WPfST7VL+dtxz0AqI7uKI2+QKnxIejlbUAIYV/kXU1kSjOPz2TDlQ0YFAPTGk6jfJ7yWoeUpaiqytqTt5i84TStY9ezzbgGNyXW2lilE0qTUeCaT9sghRAig0hxIzKdZeeWsej0IgBG1xnNa4Ve0ziirOXM7QhGrz+DQ/BufjL8SEnjLQAeuBTD7T1/DD6ywKUQwr5JcSMylW3XtzH58GQABlYZyBsl3tA4oqzjYUwC07ZdYMfBo3xh+JmWDkcAUF3yYm70Fbtv5aRVoaoaRymEEBlPihuRaRwLO8bw3cNRUWlXuh0fVvhQ65CyBItF5ZejN5i5+S/ei1/Ddof1OCkmVEWPUrMnSsMRqIYccHuj1qEKIcRLIcWNyBQuPbjEgB0DSLAk0Ni7MSNqjpBJ+tLg5I2HjFr7NwVCAvjF8DPexjvWBp96KC2nQIF/Frc1mbQLUgghXjIpboTmQqND6b29N48SHlE5X2Um15+MXqfXOqxM7W5UPN9sDuLosYOMMvxIfYe/AVDdC6I0/xrKvykLXAohsi0pboSmIhMi6bO9D2ExYfjm9GV249k4GWR23CdJNFv4+eB15m87RffEXxjvsBmjYkbVO6DUGYhSbwg45NA6TCGE0JQUN0IzCeYEBu8czKWHl8jnnA//pv54OHloHVamdejKPUatO02ZO5tZb1xOAcNDa0MpP5QWE2SBSyGE+IcUN0ITFtXCF3u/4EjoEXIYczCv6TwKuhbUOqxMKSwyjgkbz3Hp1H7GGZdQw+ECAGruYih+k6FUc40jFEKIzEWKG6GJb49+y+ZrmzHoDMxoNIMyuctoHVKmk5BoYfG+qywNOE5fywqmOez43wKX9T9Fqd0PDI5ahymEEJmOFDfipVt6Zik/nf0JgPF1x/Oq16saR5T57Ll4hzHr/qLWgz/ZYPiFXIZ/Frh85W2UZuMgZyFtAxRCiExMihvxUm26uolvj34LwCfVPqF1sdYaR5S53HwQw/g/z3Hn7C5mGJfyyr8XuGz1DfjIbM1CCPEsUtyIl+ZQyCE+3/s5AB3LdqRL+S4aR5R5xJnMLNx9hV92HmGIsoy3HPcCoDrmRGn8JUr17rLApRBCpJG8W4qXIuh+EIN3DibRkkjzos35tManMkkf1gUuA86FM+GPUzSNXMNmw++4KnGoKChV/1ngMkdercMUQogsRYobkeFCokLou70vUaYoqheozoR6E9ApOq3D0tzVu9GM/eMMiRcD+M6wlOLGEADUQtWtt6BkHSghhHguUtyIDBURH0Hv7b0Jjw2nhEcJZjaeiaM+ez/hE5OQyJwdl9i85xDDdD/SwuEoAKpLPpRmY1AqfQA6Kf6EEOJ5SXEjMkxcYhwDdgzgSsQVCrgUYH7T+bg7uGsdlmZUVWXj36F88+cJ3oxZzUbDH/9b4LJWb5SGw8App9ZhCiFElifFjcgQZouZEXtGcCL8BG5GN+Y3nY9nDk+tw9LMhbBHjFp7Gvfrm/nZ+DOFDXcBUH3rWxe4zF9W4wiFEMJ+SHEjbE5VVaYcmcL24O0YdUZmNp5JyVwltQ5LE5FxJmZuv8ie/fv4Sr+Eeg6nAVDdC6G0mIBS7g1Z4FIIIWxMihthcz+e/ZHl55cDMKHeBGp41tA4opfPYlH5/cQtZm08Tsf4FWwwbvlngUtHlLqDUF77GBxctA5TCCHskhQ3wqY2X9ucNEnf0OpD8fPx0ziil+/0rQhGr/ubIjf/4FfjCvIZIqwNpVtZF7jM7attgEIIYeekuBE2cyzsGJ/vsU7S16FsBzqX66xxRC/Xw5gEvt0axMnDuxhjWEI1h4sAqLlLoLScDCWbahyhEEJkD1LcCJu48vAKA3cMxGQx0aRIEz6tnn0m6TNbVH45eoOFmw7T07SMscad6BQVi9EFXYNhKK/2BYOD1mEKIUS2IcWNeGF3Y+/SZ3sfIhMiqZSvEpPqTUKv02sd1ktx8sZDRq89RYXQNfxuWI2HIdraUOFddM3GgntBbQMUQohsSIob8UJiTDH03d6X29G3KepelNmNZ+NkcNI6rAx3Lyqeb7YEcfXYViYallLWGAyAWuAV6+zCRetoHKEQQmRfUtyI55ZoSeSTXZ9w7v45cjvlZn6T+eRyyqV1WBnKbFFZdug6P23ZT3/zT0xy2A+AxckD3eMFLrPJVSshhMispLgRz0VVVcYfHM/eW3tx0jsxp/EcvN29tQ4rQx27fp8xv5/ktTsrWWtYSw59vHWBy2pd0TX+CnLk0TpEIYQQSHEjntPCvxby28Xf0Ck6vmnwDRXyVdA6pAwT/iiOSZvO8+Dkn8w0/IivMQwA1ftVlFZTwKuSxhEKIYT4NyluRLqtu7SOOSfnAPB5zc9p6N1Q24AySKLZwtID1/lt224+sSymicMJACw5CqBrPg6l4nsyu7AQQmRCUtyIdNl/ez+j948GoPsr3WlXpp22AWWQg1fuMXHtUZrf/5nf9Rtx1Cdi0RnRvdoHXYPPwNFN6xCFEEI8gRQ3Is0uPLjAkMAhJKqJtPRtyaCqg7QOyebCIuOYsuVv1NO/4W9cjpfhPgBq8SboWk6GvNlzjSwhhMhKpLgRafLQ8pABgQOINkVTw7MG4+uOR6fotA7LZhISLQTcUlgycznDWUQth/MAmHMWRd9yEkrplnILSgghsggpbsQzPUp4xE9RP3HHcocSHiWY0WgGDnr7mXF378W7fLP2AG9F/Mh0/Xb0iopF74Su/lD0dQaA0f7n7RFCCHsixY14KpPZxCd7PiHMEkY+53zMazIPdwd3rcOyiVsPY/n6j7/JeX4liw2ryG2IAkAt1xZd8/HgYd+PtgshhL2S4kY8kaqqfLX/K46GHcUBB2Y1nIWXq5fWYb2w+EQz3++5yt4dGxmhLKKi8SoADx0L4frObAwlm2gcoRBCiBchxY14olknZrHhygYMioEPXD6gdK7SWof0wnYGhTN77V46RC1ihX4PAGYHd2gwjN13vGjpU1/jCIUQQrwoKW5Eqn4J+oXv//4egC9rfYnhfNb+VblxP4av/zhF4Qs/sdSwBjd9LCoKVOmIvskoTI4eqBs3ah2mEEIIG8gUj7vMnTsXHx8fnJycqFWrFocPH35i3++++4569eqRK1cucuXKRdOmTZ/aX6Tfrhu7+PrQ1wD0rdyX/yv2fxpH9PziTGamb7vAqOmzGHq5O18al+GmxGL2qorSMwDljTngmk/rMIUQQtiQ5sXNqlWrGDJkCKNGjeL48eNUqlSJFi1aEB4enmr/wMBAPvjgA3bu3MmBAwfw9vamefPm3Lp16yVHbp9O3z3Np7s/xaJaeKvkW/Su2FvrkJ6LqqpsOxtG56m/UG53HxbpJ1JCd5tE57zwxlz0PQOgUDWtwxRCCJEBNL/XMG3aNHr27Em3bt0A8Pf3Z8OGDSxatIjhw4en6L9s2bJk33///ff89ttvBAQE0Llz5xT94+PjiY+PT/o+MjISAJPJhMlkskkOj49jq+Np5WbUTfoF9CM2MZbaXrUZVm0YiYmJWS6/a/eimfLHKV65voQf9X/gpDdhUfSo1T9Erf8ZJqecYDZbv/6R1XJML3vPDyRHe2Dv+YHkaIvjpoWiqqpq01dPh4SEBFxcXPj1119p27Zt0vYuXbrw8OFD1q1b98xjPHr0iPz587N69Wpef/31FO2jR49mzJgxKbYvX74cFxeXF4rfnsRYYlgYtZC7lrt46b340PVDHBVHrcNKl3gzbLupYAw7yueGZRRW7gIQ5lqOM4U78si5sMYRCiGEeF4xMTG0b9+eiIgI3N2fPiWJpldu7t69i9lspkCBAsm2FyhQgPPnz6fpGMOGDaNgwYI0bdo01fYRI0YwZMiQpO8jIyOTbmU964eTViaTiW3bttGsWTOMRqNNjvkyxSXG0XdnX+5G3sXTxZMlLZaQz/l/41Aye36qqrLlbDi/b9hOv7iFvGY8A4DJtRBK83HkLtOGes+YXTiz5/ii7D0/kBztgb3nB5Lji3h85yUtNL8t9SImTZrEypUrCQwMxMkp9VlkHR0dcXRMeQXCaDTa/BcrI46Z0SyqhdH7RnPyzkncHNzwb+ZPQfeCqfbNjPldCo9i8trDvBq8kJ/0WzDoLZh1DuheG4zxtY/BIX1X5zJjjrZk7/mB5GgP7D0/kByf93hppWlxkzdvXvR6PWFhYcm2h4WF4enp+dR9v/32WyZNmsT27dupWLFiRoZp16Yfm87W61sx6AzMbDST4h7FtQ4pTaLiE5m9PYiHB5YyQb+CfAZrRW8u1Rp9ywmQy0fbAIUQQmhG06elHBwcqFatGgEBAUnbLBYLAQEB1K5d+4n7TZkyhXHjxrF582aqV6/+MkK1SyvOr2DJmSUAjKs7jhqeNbQNKA1UVWXdyVv0/+Z7/A51ZrJhAfmUSEwexaHjGvTtl0thI4QQ2Zzmt6WGDBlCly5dqF69OjVr1mTGjBlER0cnPT3VuXNnChUqxMSJEwGYPHkyI0eOZPny5fj4+BAaGgqAq6srrq6umuWR1QTeCGTS4UkADKwykNeLpRyMndkEhT7i2zV7aXLbn0X6Xeh0KomGHBgaDcdYqzcY7GcxTyGEEM9P8+KmXbt23Llzh5EjRxIaGkrlypXZvHlz0iDj4OBgdLr/XWCaP38+CQkJvPPOO8mOM2rUKEaPHv0yQ8+yztw9w2e7P8OiWni75Nt8WOFDrUN6qsg4EzO3nkM9/D1T9atxN8QAYK7QDkPzseD29FuYQgghshfNixuA/v37079//1TbAgMDk31/7dq1jA/Ijt18dJO+AX2JTYylbsG6fPHqFyjPeJJIK6qqsub4LbZuWM3HiT9QxnADgIR8r+DQZhr6IrU0jlAIIURmlCmKG/FyRMRH0DegL/fj7lMmdxmmNpyKUZc5R+ufuR3B7DU7eT1sHgv0h0AHJsdcGJuNwqFqZ9DptQ5RCCFEJiXFTTaRYE5g8M7BXI24SgGXAsxtMpccxhxah5VCRIyJmVv+Isex+UzXr8NZn4AFHWr17hgbfwEuubUOUQghRCYnxU02YFEtfLnvS46GHcXV6Mq8pvPI75Jf67CSsVhUVh8N5sDmZXycuJiiBuvaYvGFXsWxzbfgWUHjCIUQQmQVUtxkA7NPzGbT1U0YFAPTGk6jVK5SWoeUzF83HzL/ty20uzuXGfpToIN4F08cW36N4ytvQyYdEySEECJzkuLGzq2+sJrv//4egNF1RlO74JPnD3rZ7kcnMHPjcbxOzWGmfiMOejNmxQh1+uNYfyg4yqP9Qggh0k+KGzu25+Yevj74NQB9KvXhjRJvaByRldmisuLQdc5u/YFBlh8pYHgIQLxvUxxfnwJ5ssYsyUIIITInKW7s1Ll75/hk1yeYVTP/V/z/6FOpj9YhAXDs+gOWrllHpwdz6ai7AArEufng1GYKjqVaaB2eEEIIOyDFjR0KiQqhX0A/YhNjqeVVi9G1R2s+l83dqHhm/3GIkmdmMF2/A71OxaRzQtfgU5zqDgBDysVNhRBCiOchxY2diUyIpG9AX+7E3qGERwmmN5yOUa/dXDbWW1BXubxlPoPV5eQyRAEQX+ZNHFt+DTkLaRabEEII+yTFjR0xmU0M2TmESw8vkd85P/ObzsfNwU2zeE7deMiPv/5G5wdz6Ki7AgrE5CqDyxtTcfR5TbO4hBBC2DcpbuyEqqqMPjCaQ6GHcDG4MLfpXDxzaLPm0oPoBOZuOEiJv6fxjS4QnU4lweCKvsmXuNTsCXr5tRP/Y7FYSEhIeOHjmEwmDAYDcXFxmM1mG0SW+dh7jvaeH0iOz+Lg4JBsPcnnJZ8ydmLeqXmsv7wevaJnasOplMld5qXHYLGorD5yjcub59DfsgIPfTQAceXb4dRyPLhmrokDhfYSEhK4evUqFovlhY+lqiqenp7cuHFD8zFmGcXec7T3/EByfBadToevry8ODg4vFIMUN3bg94u/43/KH4CvXv2K1wq9/Fs+p29F8NPq1XS6P5t2umugQHSucuR4cwZOssClSIWqqoSEhKDX6/H29n7h/61ZLBaioqJwdXW1yf/8MiN7z9He8wPJ8Vn73b59m5CQEIoUKfJCxZ8UN1nc/tv7GXtgLAA9K/Tk7VJvv9TXj4gx4b/xIMVPTWGyfrd1dmGDG4amX5Gj5oeywKV4osTERGJiYihYsCAuLi4vfLzHt7ecnJzs+kPDnnO09/xAcnyWfPnycfv2bRITEzEan/9hGClusrCg+0EMCRxCoppIK99WDKgy4KW9tqqqrDl6ncsbZ9LHshJ3fQwAseU/wLnVeMiR96XFIrKmx/fiX/TysxDCfjx+PzCbzVLcZEeh0aH0DehLtCmaGp41GFd33Eu7f3s+NJJlv6yk/d3ZvK0LBgWicpfH9c2ZOHvXeCkxCPthr+MOhBDpZ6v3AylusqCohCj6BfQjPCacYjmLMb3hdBz0Gf+/30dxiSz54wC+JyYzTr8XdBBncMfQbDSuNbrKLSghhBCZghQ3WYzJYuKTXZ9w4cEF8jjlYV7TeeR0zJmhr6mqKsfDzVyePoye5l9w08diQSH2lY7kaDkGcuTJ0NcXQggh0sM+RzPZKVVVGX9wPPtv78fZ4MzcJnMp5JqxM/xeDHvERP9FfHBjJEMsS3FTYonMUwldzwByvDNHChuR7XTt2hVFUejdu3eKtn79+qEoCl27dn35gT2Hx7koioLRaMTX15fPPvuMuLi4ZP0e9zl48GCy7fHx8eTJkwdFUQgMDEzavmvXLho3bkzu3LlxcXGhZMmSdOnSJWk+o8DAwKRj/vtLr9cTFhaW4XkL+yfFTRay8K+FrLm4Bp2i45v631A+b/kMe63o+ERmr93NuTnvMer+MMrobhBryImp9Qzc+wVCoWoZ9tpCZHbe3t6sXLmS2NjYpG1xcXEsX76cIkWKaBhZ+vn5+RESEsKVK1eYPn06CxYsYNSoUSn6eXt7s3jx4mTbfv/9d1xdXZNtO3v2LH5+flSvXp3du3fz999/M3v2bBwcHFJM6BYUFERISEjS161bt8iXL5/tkxTZjhQ3WcQfl/9gzsk5AIyoOYIG3g0y5HVUVWXTqWAWTfmYbife5f/0+7GgcM6jMYaBRzHW6AZ2+vii0JaqqsQkJL7QV2yC+bn2U1U1XbFWrVoVb29v1qxZk7RtzZo1FClShCpVqiTra7FYmDhxIr6+vjg7O1OpUiV+/fXXpHaz2UyPHj2S2kuXLs3MmTOTHaNr1660bduWqVOnUqZMGfLly0e/fv0wmUxPjHH06NFUrlyZn376CR8fH3LmzMn777/Po0ePkvVzdHTE09MTb29v2rZtS9OmTdm2bVuK43Xp0iVFQbdo0SK6dOmSrN/WrVvx9PRkypQpvPLKKxQvXhw/Pz++++47nJ2dk/XNnz8/np6eyb7s9fFo8XLJmJss4HDIYUbuHwlAt/LdeL/M+xnyOlfuRLHql595J2wWLXW3QIGIPJVxeWMqF07cooRzrgx5XSEAYk1myo3coslrnx3bAheH9L0ddu/encWLF9OhQwfA+kHfrVu3ZLdnACZOnMjPP/+Mv78/JUuWZPfu3XTs2JF8+fLRoEEDLBYLhQsXZvXq1eTJk4f9+/fTq1cvvLy8eO+995KOs3PnTjw9PVm/fj2hoaF88MEHVK5cmZ49ez4xxsuXL7N27Vr+/PNPHjx4wHvvvcekSZP4+uuvU+1/+vRp9u/fT9GiRVO0VatWDR8fH3777Tc6duxIcHAwu3fvZu7cuYwbNy6pn6enJyEhIezevZv69eun50cqhM1IcZPJXXpwicE7B5NoSaSFTwsGVxts89eITTCzdPM+ihz9mhG6g6CDGGMujC3GkrNqR0xmM5y4ZfPXFSIr69ixIyNGjOD69esA7Nu3j5UrVyYrbuLj45kwYQLbt2+ndu3aABQrVoy9e/eyYMECGjRogNFoZMyYMUn7+Pr6cuDAAX755ZdkxU2uXLmYPXs20dHRVK9endatWxMQEPDU4sZisbBkyRLc3KwL6Hbq1ImAgIBkxc2ff/6Jq6sriYmJxMfHo9PpmDNnTqrH6969O4sWLaJjx44sWbKEVq1apbiN9O6777JlyxYaNGiAp6cnr776Kk2aNKFz5864u7sn61u4cOFk3xctWpR9+/Y9MR8h0kqKm0zsTswd+gb05ZHpEVXyV+Hr175Gp9jukq2qqmw/fYNL6ybTxbQaF108FnREVeyKe8tR4Oxh7Wini7uJzMXZqOfs2BbPvb/FYuFR5CPc3N3SfWvD2Zj+aQzy5ctH69atWbJkCaqq0rp1a/LmTT555aVLl4iJiaFZs2bJtickJCS7fTV37lwWLVpEcHAwsbGxJCQkULly5WT7lC9fHr3+f3F6eXnx999/PzVGHx+fpMLm8T7h4eHJ+jRq1Ij58+cTHR3N9OnTMRgMvP126jOdd+zYkeHDh3PlyhWWLFnCrFmzUvTR6/UsXryY8ePHs2PHDg4dOsSECROYPHkyhw8fxsvLK6nvnj17ksX37/yEeBFS3GRSMaYY+gX0IyQ6BB93H2Y1moWj3tFmx79+L5rVq5byZugsmulCQIH7eaqR650ZuHtVtNnrCJFWiqKk+9bQv1ksFhId9Lg4GF7auI3u3bvTv39/wFqg/FdUVBQAGzZsoFCh5E82Ojpa/55XrlzJ0KFDmTp1KrVr18bNzY1vvvmGQ4cOJev/39laFUV55oKjadknR44clChRArDeWqtUqRI//PADPXr0SHG8PHny8Prrr9OjRw/i4uJo2bJlijE8jxUqVIhOnTrRqVMnxo0bR6lSpfD3909xlcrDwyPpe4vFQmRk5FNzEiItpLjJhBItiQzdNZRz98+R2yk385rMw8PJwybHjjOZ+XnLPrwPj2Oo7jDoIMqYG6PfeHJXbQ8yW6wQaebn50dCQgKKotCiRcqrTuXKlcPR0ZHg4GAaNEj9IYB9+/ZRp04d+vbtm7Tt8uXLGRbz0+h0Oj7//HOGDBlC+/btUwwABmtB16pVK4YNG5bmKy25cuXCy8uL6OhoW4csRKqkuMlkVFVl4qGJ7Lm1Bye9E7Mbz8bb3dsmxw48c4OLayfSMWE1zroEzOh4VLE7Hq1GglPGTgQohD3S6/WcO3cu6d//5ebmxtChQ/n444+xWCy89tprREREsG/fPtzd3enSpQslS5bkxx9/ZMuWLfj6+vLTTz9x5MgRfH19X3Y6gHXMzKeffsrcuXMZOnRoinY/Pz/u3LmTYvzMYwsWLODkyZO8+eabFC9enLi4OH788UfOnDnD7Nmzk/UNDw9PNqeOxWJ5ofWEhHhMiptMZtHpRfxy4RcUFCbVn0TFfC9+i+jmgxjWrFpMm9szaagLAwXu5a1O7ndm4uH5ig2iFiL7etKH/GPjxo0jX758TJw4kStXruDh4UHVqlX5/PPPAfjoo484ceIE7dq1Q1EUPvjgA/r27cumTZteRvgpGAwG+vfvz5QpU+jTpw85cuRI1q4oSoqxRf9Ws2ZN9u7dS+/evbl9+zaurq6UL1+etWvXprh6Vbp06RT7b926lSZNmtgmGZFtKWp6J3jI4iIjI8mZMycRERHPfFNKK5PJxMaNG2nVqtUL/a9j09VNfLb7MwCG1RhGx3IdXyiu+EQzq7btp+DB0TRVjgLwyJgXY8uvcarSLs23oGyVX2Zm7zlmxvzi4uK4evUqvr6+ODk5vfDxHo/XcHd3t9u5Uuw9R3vPDyTHZ3na+0J6Pr/lyk0mcSzsGF/s/QKAjmU7vnBhsz/oFmd/m0CH+F9wVhJIRE9kpQ/J3fJLcLJNUSeEEEJkRlLcZAJXIq4wcMdATBYTTYo0YWj1lPe50yr8URyrV/2EX/BU6vzzFNTdPDXI894schcoZ8OohRBCiMxJihuN3Y29S9/tfYlMiKRi3opMrDcRvS79cz2YLSprAg/jvmsk/RTrRHyPDLnRt/yavFU/kKeghBBCZBtS3GgoxhTDgIAB3Iq6hbebN7ObzMbZkPLRy2c5df0Ox1ZNoF30MnIo8ZjR8aB8F/K2GSNPQQkhhMh2pLjRiNliZtieYZy+d5qcjjmZ12QeuZ1yp+sYEbEmVv+2gnoXJtNddxMUCPeoRJ73ZpO3YKUMilwIIYTI3KS40YCqqkw+MpnAG4E46ByY3Xg2Pjl90rX/poMnYetIPlR3Wyfi0+fE0nQs+Wt1llW7hRBCZGtS3Ghg0elFrDi/AoAJ9SZQJX+VZ+zxP5dCH7B3+STeiliKuxKLBYXwUh/g2fZrcEnflR8hhBDCHklx85Ktv7yeGcdnAPBp9U9p4ZO2hQJjE8z8tm4NVU+Po6tyHRQIcytH7ndn41mkegZGLIQQQmQtUty8RHtv7WXUvlEAdC3flc7lO6dpv90nzhH55xd0NAeAAlE6NxIafEmBej3hOZ6sEkIIIeyZDM54Sc7cPcOQwCEkqom0Ltaaj6t9/Mx9bt2P4uc5o6i4tgmvmwMAuOnzNjmGnCB3g95S2AghMiVFUVi7dq3WYdiNl/3zjImJ4e2338bd3R1FUXj48OFLe21bkeLmJQiODKZvQF9iE2N51etVxtUZh0558o/eZLbw2x9/cG9mfTrenYGHEk2YS0liO22icNdFKK75XmL0Qoh/69q1K4qi0Lt37xRt/fr1Q1EUunbt+vID+48lS5agKEqKr++//z7DXzskJISWLVtm+Ouk5vH5URQFo9GIr68vn332WbIFOoGkPgcPHky2PT4+njx58qAoCoGBgUnbd+3aRePGjcmdOzcuLi6ULFmSLl26kJCQAEBgYGCqP29FUQgNDc3wvG1p6dKl7Nmzh/379xMSEkLOnFlvShG5LZXB7sXeo/f23tyPu0/Z3GWZ3nA6Rv2T1/Y5eeEqN34dwZvxm9EpKjGKC4/qDKNA4/6gl9MlRGbg7e3NypUrmT59Os7O1rmp4uLiWL58OUWKFNE4uv9xd3cnKCgo2baM/KBKSEjAwcEBT0/PFz7Oi6w35ufnx+LFizGZTBw7dowuXbqgKAqTJ09O1s/b25vFixfz6quvJm37/fffcXV15f79+0nbzp49i5+fHwMGDGDWrFk4Oztz8eJFfvvtN8xmc7JjBgUFpVj3KH/+/M+dixYuX75M2bJleeWVJy+s/PhcZ1Zy5SYDxZhi6BfQjxuPblDItRDzms7D1cE11b4R0Qms+uFbCi2rT5uETegUlWuFXsdp8HEKNBsshY2wf6oKCdEv9mWKeb790rl+cNWqVfH29mbNmjVJ29asWUORIkWoUiX5048Wi4WJEyfi6+uLs7MzlSpV4tdff01qN5vN9OjRI6m9dOnSzJw5M9kxunbtStu2bZk6dSplypQhX7589OvXD5PJ9NQ4FUXB09Mz2dfjYiw4OJg33ngDV1dX3N3dee+99wgLC0vxmv82ePBgGjZsmPR9w4YN6d+/P4MHDyZv3ry0aNEi6XX/fRvlxo0bvPfee3h4eJA7d27eeOMNrl27luK1JkyYQNmyZSlbtmyq+YwePZrKlSvz008/4ePjQ86cOXn//fd59OhRsn6Ojo54enri7e1N27Ztadq0Kdu2bUtxvC5durBy5UpiY2OTti1atIguXbok67d161Y8PT2ZMmUKr7zyCsWLF8fPz4/vvvsu6ef5WP78+VP8zG29QObff/9N48aNcXZ2Jk+ePPTq1YuoqCgATp8+jU6n486dOwDcv38fnU7H+++/n7T/+PHjee2111I9dsOGDZk6dSq7d+9GUZSk8+3j48O4cePo3Lkz7u7u9OrVC4C9e/dSr149nJ2d8fb2ZuDAgURHRycdLzw8nDZt2uDs7Iyvry/Lli3Dx8eHGTNm2PRn8l/yiZlB4s3xDNo5iDP3zpDLMRf+Tf3J65w3RT9VVdmxdy/uO4bTTj1tfQrKsSgubWfiU7aRBpELoRFTDEwo+Ny76wCP593589vgkCNdu3Tv3p3FixfToUMHwPqh2K1bt2S3MgAmTpzIzz//jL+/PyVLlmT37t107NiRfPny0aBBAywWC4ULF2b16tXkyZOH/fv306tXL7y8vHjvvfeSjrNz5048PT1Zv349oaGhfPDBB1SuXJmePXumO12LxZJU2OzatYvExET69etHu3btUsT/LEuXLqVPnz7s27cv1XaTyUSLFi2oXbs2e/bswWAwMH78ePz8/Pjrr7+S/vcfEBCAm5sba9aswdU19f8EgvWqwtq1a/nzzz958OAB7733HpMmTeLrr79Otf/p06fZv38/RYsWTdFWrVo1fHx8+O233+jYsSPBwcHs3r2buXPnMm7cuKR+np6ehISEsHv3burXr5+eH4/NRUdHJ/08jxw5Qnh4OB9++CH9+/dnyZIllC9fnjx58rBr1y7eeecd9uzZk/T9Y7t27UpWpP7bmjVrGD58OKdPn2bNmjXJrs58++23jBw5klGjrA/GXL58GT8/P8aPH8+iRYu4c+cO/fv3Z8CAAUnFS9euXbl9+zY7d+7EaDQycOBAwsPDM+zn85gUNxkgwZzAxzs/5mDIQZwNzsxpMifVSfqCw+5yYtmXtIz4BQfFTDwOhFUZSJHWw8CQeS/3CSGgY8eOjBgxguvXrwOwb98+Vq5cmaw4iI+PZ8KECWzfvp3atWsDUKxYMfbu3cuCBQto0KABRqORMWPGJO3j6+vLgQMH+OWXX5IVN7ly5WL27NlER0dTvXp1WrduTUBAwFOLm4iIiGSFgqurK6GhoQQEBPD3339z9epVvL29Afjxxx8pX748R44coUaNGmn+OZQsWZIpU6Y8sX3VqlVYLBa+//57lH/WuFu8eDEeHh4EBgbSvHlzAHLkyMF3331HXFxcits6/2axWFiyZAlubm4AdOrUiYCAgGTFzZ9//omrqyuJiYnEx8ej0+mYM2dOqsfr3r07ixYtomPHjixZsoRWrVqRL1/ycY3vvvsuW7ZsoUGDBnh6evLqq6/SpEmTpKsY/1a4cOFk3xctWpQzZ848MZ/0Wr58OXFxcfz444/kyGEtyOfMmUObNm2YPHkyBQoUoH79+gQGBvLOO+8QGBhIt27d+P777zl//jzFixdn//79fPbZZ6ke//GYotRuLzZu3JhPPvkk6fsPP/yQDh06MHjwYMD6uzBr1iwaNGjApEmTCA0NZdOmTRw+fDjpd+qHH3544pU5W5LixsZMFhNDdw1lz609OOmdmNdkHhXzVUzWJyHRwpa1P1Hp7/G8oYSDAtdy1cWr/RyK5CumUeRCaMzoYr2C8pwsFguRjx7h7uaW/tsARpd0v16+fPlo3bo1S5YsQVVVWrduTd68ya/OXrp0iZiYGJo1a5Zse0JCQrLbV3PnzmXRokUEBwcTGxtLQkIClStXTrZP+fLl0ev/94Skl5cXf//991NjdHNz4/jx40nfP/65nDt3Dm9v76TCBqBcuXJ4eHhw7ty5dBU31apVe2r7qVOnuHTpUlIx8lhcXByXL19O+r5ChQo4ODikGPj7Xz4+PsmO5eXlleJKQKNGjZg/fz7R0dFMnz4dg8HA22+/nerxOnbsyPDhw7ly5QpLlixh1qxZKfro9XoWL17M+PHj2bFjB4cOHWLChAlMnjyZw4cP4+XlldR3z549yeIzGp88xrJ8+fJJxXG9evXYtGnTU3MH67mrVKlSUmEDULduXSwWC0FBQRQoUIAGDRqwcOFCwHqVZsKECVy4cIHAwEDu37+PyWSibt26z3yt/6pePfmcaqdOneKvv/5i2bJlSdtUVcVisXD9+nVu376NwWBI9jtSpkwZPDw80v3a6SXFjQ0lWhL5ct+X7LyxEwedA7Maz6K6Z/JfhpNnzhL5+ye0SdwPCtzT5yWx+SR8ar4jK3eL7E1R0n1rKBmLBYxm6zFe0hIk3bt3p3///oC1QPmvx+MgNmzYQKFChZK1OTo6ArBy5UqGDh3K1KlTqV27Nm5ubnzzzTccOnQoWf//fkgqioLFYnlqfDqdjhIlSqQvqX/tq/5nLFJqY3z+/SGbmqioKKpVq5bsA/Cxf18hedZxHkvLzyFHjhxJeS9atIhKlSrxww8/0KNHjxTHy5MnD6+//jo9evQgLi6Oli1bphjD81ihQoXo1KkTnTp1Yty4cZQqVQp/f/8UV97S+uG9cePGpJ/pf8fuvIiGDRsyePBgLl68yNmzZ3nttdc4f/48gYGBPHjwgOrVq+Pikv6C/r/nKCoqio8++oiBAwcm226xWPDw8OD27ef/z8qLkuLGRiyqhdEHR7P1+lYMOgPTG02ndsHaSe0Po2LYs2wCjW5/h6sSRyI6rhbvTIn3xqM4uj3lyEKIzMrPz4+EhAQURUkaTPtv5cqVw9HRkeDgYBo0aJDqMfbt20edOnXo27dv0rZ/X9HICGXLluXGjRvcuHEj6erN2bNnefjwIeXKlQOshcfp06eT7Xfy5MmnXolITdWqVVm1ahX58+d/6u2mjKLT6fj8888ZMmQI7du3T7WI6N69O61atWLYsGHJro49Ta5cufDy8ko2eDa9UhsH9Cxly5ZlyZIlREdHJxUb+/btQ6fTUbp0acB6FSxXrlyMHz+eypUr4+rqSsOGDZk8eTIPHjx44nib9KpatSpnz55NUUBbLBYiIyMpU6YMiYmJHDt2LOlqYFBQ0EuZN0eelrKRowlH2XhtIwbFwNQGU6lf2DroTFVVdgVsJPTb2rQJmY2rEsd1l1eI6bqDkp1mSmEjRBam1+s5d+4cZ8+eTfVD0c3NjaFDh/Lxxx+zdOlSLl++zPHjx5k9ezZLly4FrOMUjh49ypYtW7hw4QJfffUVR44cydC4mzZtSoUKFejQoQPHjx/n8OHDdO7cmQYNGiTdemjcuDFHjx7lxx9/5OLFi4waNSpFsZMWHTp0IG/evLzxxhvs2bOHq1evEhgYyMCBA7l586atU0vVu+++i16vT/XqGliL1Dt37jB27NhU2xcsWECfPn3YunUrly9f5syZMwwbNowzZ87Qpk2bZH3Dw8MJDQ1N9vWsp9rSo0OHDjg5OdGlSxdOnz7Nzp07GTBgAJ06daJAgQKA9WpW/fr1WbZsWVIhU7FiReLj4wkICHhioZ1ew4YNY//+/fTv35+TJ09y8eJF1q1bx4ABAwAoXbo0fn5+fPTRRxw6dIhjx47x4Ycf2vQq1ZNIcWMj1Ryq0dqnNRPrT6RxkcYAXL95ix3fdqDe7vaU4RqRuHK19gSKDt2Du0/aF8sUQmRe7u7uT70iMW7cOL766ismTpxI2bJl8fPzY8OGDfj6+gLw0Ucf8dZbb9GuXTtq1arFvXv3kl3FyQiKorBu3Tpy5cpF/fr1adq0KcWKFWPVqlVJfVq0aMFXX33FZ599Ro0aNXj06BGdO6dtyZh/c3FxYffu3RQpUoS33nqLsmXLJt0CellXcgwGA/3792fKlCmpXmlRFIW8efM+cd6WmjVrEhUVRe/evSlfvjwNGjTg4MGDrF27NkWhULp0aby8vJJ9HTt2zGa5uLi4sGXLFu7fv0+NGjV45513aNKkSYoB0w0aNMBsNicVNzqdjvr166MoynONt0lNxYoV2bVrFxcuXKBevXpUqVKFkSNHUrDg/556XLx4MQULFqRBgwa89dZb9OrV66XM+6Oo/72pauciIyPJmTMnERERNvvDMplMbNy4kVatWmE0Gok3JbJr9RyqBU0ljxIJwLkCr1O8/XQccmatyZwgZX72yN5zzIz5xcXFcfXqVXx9fV9owrbHHl8Kd3d3t/m8IpmFvedo7/mB5AjWQeGDBw9Oesrq3572vpCez+9M8ZOdO3cuPj4+ODk5UatWLQ4fPvzU/qtXr6ZMmTI4OTlRoUIFNm7c+JIifbZTJw5zblJDml8YRR4lkpuGooS8uYayfZZlycJGCCGEyGo0L25WrVrFkCFDGDVqFMePH6dSpUq0aNHiiZP87N+/nw8++IAePXpw4sQJ2rZtS9u2bZ/rXrAtxcXHs2vBYMqu9aOy+W/icOBsuY8pNPwIXpWaaBqbEEIIkZ1oXtxMmzaNnj170q1bN8qVK4e/vz8uLi4sWrQo1f4zZ87Ez8+PTz/9lLJlyzJu3DiqVq36xAmaXhaHkMM0vfszDoqZc251SPjoAOXeG41icNQ0LiGEECIzuXbtWqq3pGxJ00fBExISOHbsGCNGjEjaptPpaNq0KQcOHEh1nwMHDjBkyJBk21q0aPHE5eDj4+OJj49P+j4y0joGxmQy2WwEu8lkQilSl7/NF3Co0o4Sdd8FRbHpCHktPc7DXvJJjb3nmBnzM5lMSRN+PWu+lrR4PHzw8THtkb3naO/5geT4LBaLBVVVMZlMKZ5ATM/7l6bFzd27dzGbzUmPrz1WoEABzp8/n+o+oaGhqfZ/0pLyEydOTDbB0mNbt259rkmMnkTR6bhSvAdEwvk0zDKZFaW28Jy9sfccM1N+BoMBT09PoqKiSEhIsNlxnzQBmz2x9xztPT+QHJ8kISGB2NhYdu/eTWJiYrK2mJiYNB/H7ifxGzFiRLIrPZGRkXh7e9O8eXObPi21bds2mjVrlmmeRLEle88P7D/HzJhfXFwcN27cwNXV1SZPS6mqyqNHj3Bzc0taw8je2HuO9p4fSI7PEhcXh7OzM/Xr10/1aam00rS4yZs3L3q9nrCwsGTbw8LCUizY9Zinp2e6+js6OiZNc/5vRqPR5m/yGXHMzMTe8wP7zzEz5Wc2m1EUBZ1OZ5NHYh9f/n58THtk7znae34gOT6LTqdDUZRU36vS896l6U/WwcGBatWqERAQkLTNYrEQEBCQtILuf9WuXTtZf7Bean9SfyGEEEJkL5rflhoyZAhdunShevXq1KxZkxkzZhAdHU23bt0A6Ny5M4UKFWLixIkADBo0iAYNGjB16lRat27NypUrOXr0aNIKqEIIIYTI3jQvbtq1a8edO3cYOXIkoaGhVK5cmc2bNycNGg4ODk52WatOnTosX76cL7/8ks8//5ySJUuydu1aXnnlFa1SEEIIIUQmkilu+PXv35/r168THx/PoUOHqFWrVlJbYGAgS5YsSdb/3XffJSgoiPj4eE6fPk2rVq1ecsRCiOyqa9euKIpC7969U7T169cPRVHo2rXryw/sP5YsWYKHh0eqbYqiJJs+4+uvv6ZOnTq4uLg8cZ/noShK0pe7uzs1atRg3bp1KeJUFIWyZcum2H/16tUoioKPj0/SNrPZzKRJkyhTpgzOzs7kzp2bWrVq8f333yf1eXyO/vvl5+dns9xE5pYpihshhMhKvL29WblyJbGxsUnb4uLiWL58OUWKFNEwsueTkJDAu+++S58+fWx+7MWLFxMSEsLRo0epW7cu77zzDn///XeyPjly5CA8PDzF/GY//PBDip/nmDFjmD59OuPGjePs2bPs3LmTXr168fDhw2T9/Pz8CAkJSfa1YsUKm+cnMicpboQQmYKqqsSYYl7oKzYx9rn2S+/6wVWrVsXb25s1a9YkbVuzZg1FihShSpUqyfpaLBYmTpyIr68vzs7OVKpUiV9//TWp3Ww206NHj6T20qVLM3PmzGTH6Nq1K23btmXq1KmUKVOGfPny0a9fP5tNyjhmzBg+/vhjKlSokOZ9fHx8mDBhAt27d8fNzY0iRYqkOvbRw8MDT09PSpUqxbhx40hMTGTnzp3J+hgMBtq3b8/ixYuTtt28eZPAwEDat2+frO/69evp27cv7777Lr6+vlSqVIkePXowdOjQZP0cHR3x9PRM9pUrV6405yeyNs3H3AghBEBsYiy1ltd6dscMcKj9IVyM6ZvUs3v37ixevJgOHToAsGjRIrp160ZgYGCyfhMnTuTnn3/G39+fkiVLsnv3bjp27Ei+fPlo0KABFouFwoULs3r1avLkycP+/fvp1asXXl5evPfee0nH2blzJ56enqxfv57Q0FA++OADKleuTM+ePV84/+c1depUxo0bx+eff86vv/5Knz59aNCgAaVLl07RNzExkR9++AGwPin7X927d6dhw4aMHTsWd3d3lixZgp+fX4pJWz09PdmxYwd9+/YlX758GZOYyPLkyo0QQjyHjh07snfvXq5fv87169fZt28fHTt2TNYnPj6eCRMmsGjRIlq0aEGxYsXo2rUrHTt2ZMGCBYB17o4xY8ZQvXp1fH196dChA926deOXX35JdqxcuXIxe/ZsSpUqxeuvv07r1q1TTIvxXxEREbi6uqb4spVWrVrRt29fSpQowbBhw8ibN2+KqzIffPABrq6uODo68vHHH+Pj45OsaHusSpUqFCtWjPXr16OqKkuWLKF79+4p+k2bNo07d+7g6elJxYoV6d27N5tSmRX+zz//TJH3hAkTbJa7yNzkyo0QIlNwNjhzqP2h597fYrEkzYqa3onDnA3O6X69fPny0bp1a5YsWYKqqrRu3Zq8efMm63Pp0iViYmJo1qxZsu0JCQnJbl/NnTuXRYsWERwcTGxsLAkJCVSuXDnZPuXLl0+21o6Xl1eKsSv/5ebmxvHjx1NsL1myZFrTfKqKFSsm/VtRFDw9PQkPD0/WZ/r06TRt2pQrV67w8ccfM2vWLHLnzp3q8bp168ayZcsoVaoU0dHRtGrVKsWiyOXKleP06dMcO3aMffv2sXv3btq0aUPXrl2TDSpu1KgR8+fPT7bvk15X2B8pboQQmYKiKOm+NfRvFouFREMiLkaXlzbza/fu3enfvz9gLVD+KyoqCoANGzZQqFChZG2PZ05fuXIlQ4cOZerUqdSuXRs3Nze++eYbDh1KXuj9d3ZWRVGeuSihTqejRIkS6UsqHdISk6enJyVKlKBEiRIsXryYVq1acfbsWfLnz5/ieO3bt2fYsGGMHTuWTp06YTCk/hGl0+moUaMGNWrUYPDgwfz888906tSJL774Al9fX8A6SDkjcxeZmxQ3QgjxnPz8/EhISEBRFFq0aJGivVy5cjg6OhIcHEyDBg1SPca+ffuoU6cOffv2Tdp2+fLlDItZSzVr1qRatWp8/fXXKQZNg/XKSsuWLfn999/x9/dP83HLlSsHQHR0tM1iFVmbFDdCCPGc9Ho9586dS/r3f7m5uTF06FA+/vhjLBYLr732GhEREezbtw93d3e6dOlCyZIl+fHHH9myZQu+vr789NNPHDlyJOkKxMsQHBzM/fv3CQ4Oxmw2c/LkSQBKlChh0zE6AIMHD+bNN9/ks88+S3E1C6xXwBYsWPDEwcLvvPMOdevWpU6dOnh6enL16lVGjBhBqVKlKFOmTFK/+Ph4QkNDk+1rMBhS3DoU9kmKGyGEeAHu7u5PbR83bhz58uVj4sSJXLlyBQ8PD6pWrcrnn38OwEcffcSJEydo164diqLwwQcf0Ldv31QHyWaUkSNHsnTp0qTvH48H2rlzJw0bNrTpa/n5+eHr68vXX3/NvHnzUrQ7Ozs/9WfaokULVqxYwcSJE4mIiMDT05PGjRszevToZLexNm/ejJeXV7J9S5cuzfnz522XjMi0FDW9EzxkcZGRkeTMmZOIiIhnvimllclkYuPGjbRq1SrTrLhsS/aeH9h/jpkxv7i4OK5evYqvry9OTk4vfDyLxUJkZCTu7u52vdqyPedo7/mB5PgsT3tfSM/nt33+ZIUQQgiRbUlxI4QQQgi7IsWNEEIIIeyKFDdCCCGEsCtS3AghNJXNnmkQQjyFrd4PpLgRQmji8bwwCQkJGkcihMgsHr8fpDZvVHrIPDdCCE0YDAZcXFy4c+cORqPxhR+LtVgsJCQkEBcXZ9eP2NpzjvaeH0iOz9rvzp07uLi4PHHpjbSS4kYIoQlFUfDy8uLq1atcv379hY+nqiqxsbE4OzujKIoNIsx87D1He88PJMdn0el0FClS5IV/NlLcCCE04+DgQMmSJW1ya8pkMrF7927q16+faSYqtDV7z9He8wPJ8VkcHBxsckVLihshhKZ0Op1NZijW6/UkJibi5ORktx8a9p6jvecHkuPLYp83/IQQQgiRbUlxI4QQQgi7IsWNEEIIIexKthtz83iCoMjISJsd02QyERMTQ2RkpF3eQ7X3/MD+c7T3/EBytAf2nh9Iji/i8ed2Wib6y3bFzaNHjwDw9vbWOBIhhBBCpNejR4/ImTPnU/soajab+9xisXD79m3c3NxsNsdAZGQk3t7e3LhxA3d3d5scMzOx9/zA/nO09/xAcrQH9p4fSI4vQlVVHj16RMGCBZ/5uHi2u3Kj0+koXLhwhhzb3d3dbn9Zwf7zA/vP0d7zA8nRHth7fiA5Pq9nXbF5TAYUCyGEEMKuSHEjhBBCCLsixY0NODo6MmrUKBwdHbUOJUPYe35g/znae34gOdoDe88PJMeXJdsNKBZCCCGEfZMrN0IIIYSwK1LcCCGEEMKuSHEjhBBCCLsixY0QQggh7IoUNy9o7ty5+Pj44OTkRK1atTh8+LDWIT2XiRMnUqNGDdzc3MifPz9t27YlKCgoWZ+GDRuiKEqyr969e2sUcfqNHj06RfxlypRJao+Li6Nfv37kyZMHV1dX3n77bcLCwjSMOP18fHxS5KgoCv369QOy5jncvXs3bdq0oWDBgiiKwtq1a5O1q6rKyJEj8fLywtnZmaZNm3Lx4sVkfe7fv0+HDh1wd3fHw8ODHj16EBUV9RKzeLKn5WcymRg2bBgVKlQgR44cFCxYkM6dO3P79u1kx0jtvE+aNOklZ/JkzzqHXbt2TRG/n59fsj5Z9RwCqf5NKorCN998k9Qns5/DtHxGpOU9NDg4mNatW+Pi4kL+/Pn59NNPSUxMtHm8Uty8gFWrVjFkyBBGjRrF8ePHqVSpEi1atCA8PFzr0NJt165d9OvXj4MHD7Jt2zZMJhPNmzcnOjo6Wb+ePXsSEhKS9DVlyhSNIn4+5cuXTxb/3r17k9o+/vhj/vjjD1avXs2uXbu4ffs2b731lobRpt+RI0eS5bdt2zYA3n333aQ+We0cRkdHU6lSJebOnZtq+5QpU5g1axb+/v4cOnSIHDly0KJFC+Li4pL6dOjQgTNnzrBt2zb+/PNPdu/eTa9evV5WCk/1tPxiYmI4fvw4X331FcePH2fNmjUEBQXxf//3fyn6jh07Ntl5HTBgwMsIP02edQ4B/Pz8ksW/YsWKZO1Z9RwCyfIKCQlh0aJFKIrC22+/naxfZj6HafmMeNZ7qNlspnXr1iQkJLB//36WLl3KkiVLGDlypO0DVsVzq1mzptqvX7+k781ms1qwYEF14sSJGkZlG+Hh4Sqg7tq1K2lbgwYN1EGDBmkX1AsaNWqUWqlSpVTbHj58qBqNRnX16tVJ286dO6cC6oEDB15ShLY3aNAgtXjx4qrFYlFVNeufQ0D9/fffk763WCyqp6en+s033yRte/jwoero6KiuWLFCVVVVPXv2rAqoR44cSeqzadMmVVEU9datWy8t9rT4b36pOXz4sAqo169fT9pWtGhRdfr06RkbnI2klmOXLl3UN95444n72Ns5fOONN9TGjRsn25aVzqGqpvyMSMt76MaNG1WdTqeGhoYm9Zk/f77q7u6uxsfH2zQ+uXLznBISEjh27BhNmzZN2qbT6WjatCkHDhzQMDLbiIiIACB37tzJti9btoy8efPyyiuvMGLECGJiYrQI77ldvHiRggULUqxYMTp06EBwcDAAx44dw2QyJTufZcqUoUiRIln2fCYkJPDzzz/TvXv3ZIvEZvVz+G9Xr14lNDQ02XnLmTMntWrVSjpvBw4cwMPDg+rVqyf1adq0KTqdjkOHDr30mF9UREQEiqLg4eGRbPukSZPIkycPVapU4ZtvvsmQS/0ZKTAwkPz581O6dGn69OnDvXv3ktrs6RyGhYWxYcMGevTokaItK53D/35GpOU99MCBA1SoUIECBQok9WnRogWRkZGcOXPGpvFlu4UzbeXu3buYzeZkJwmgQIECnD9/XqOobMNisTB48GDq1q3LK6+8krS9ffv2FC1alIIFC/LXX38xbNgwgoKCWLNmjYbRpl2tWrVYsmQJpUuXJiQkhDFjxlCvXj1Onz5NaGgoDg4OKT4wChQoQGhoqDYBv6C1a9fy8OFDunbtmrQtq5/D/3p8blL7O3zcFhoaSv78+ZO1GwwGcufOneXObVxcHMOGDeODDz5ItiDhwIEDqVq1Krlz52b//v2MGDGCkJAQpk2bpmG0aefn58dbb72Fr68vly9f5vPPP6dly5YcOHAAvV5vV+dw6dKluLm5pbjlnZXOYWqfEWl5Dw0NDU31b/Vxmy1JcSNS6NevH6dPn042HgVIdn+7QoUKeHl50aRJEy5fvkzx4sVfdpjp1rJly6R/V6xYkVq1alG0aFF++eUXnJ2dNYwsY/zwww+0bNmSggULJm3L6ucwOzOZTLz33nuoqsr8+fOTtQ0ZMiTp3xUrVsTBwYGPPvqIiRMnZolp/t9///2kf1eoUIGKFStSvHhxAgMDadKkiYaR2d6iRYvo0KEDTk5OybZnpXP4pM+IzERuSz2nvHnzotfrU4wEDwsLw9PTU6OoXlz//v35888/2blzJ4ULF35q31q1agFw6dKllxGazXl4eFCqVCkuXbqEp6cnCQkJPHz4MFmfrHo+r1+/zvbt2/nwww+f2i+rn8PH5+Zpf4eenp4pBvknJiZy//79LHNuHxc2169fZ9u2bcmu2qSmVq1aJCYmcu3atZcToI0VK1aMvHnzJv1e2sM5BNizZw9BQUHP/LuEzHsOn/QZkZb3UE9Pz1T/Vh+32ZIUN8/JwcGBatWqERAQkLTNYrEQEBBA7dq1NYzs+aiqSv/+/fn999/ZsWMHvr6+z9zn5MmTAHh5eWVwdBkjKiqKy5cv4+XlRbVq1TAajcnOZ1BQEMHBwVnyfC5evJj8+fPTunXrp/bL6ufQ19cXT0/PZOctMjKSQ4cOJZ232rVr8/DhQ44dO5bUZ8eOHVgslqTiLjN7XNhcvHiR7du3kydPnmfuc/LkSXQ6XYpbOVnFzZs3uXfvXtLvZVY/h4/98MMPVKtWjUqVKj2zb2Y7h8/6jEjLe2jt2rX5+++/kxWqj4v1cuXK2Txg8ZxWrlypOjo6qkuWLFHPnj2r9urVS/Xw8Eg2Ejyr6NOnj5ozZ041MDBQDQkJSfqKiYlRVVVVL126pI4dO1Y9evSoevXqVXXdunVqsWLF1Pr/3969hUTV7mEAf+Yrjy3PTmoHM/CQpYkVlVLaQSa7SDugIuEJEdSyBIvoolKk001RXUgImVSEF0V2UQmZlk1OZXlICkFRMxoSC8Xz8b8v9m72Hjz0Ve4G53t+MOB618z7/t+1nDWP78xgWJiJK//7cnJypLKyUlpbW0Wr1UpERIS4urpKZ2eniIikp6eLp6enPHnyRGpqaiQkJERCQkJMXPXPGx8fF09PTzl27JhR+1w9h729vVJbWyu1tbUCQC5cuCC1tbWGbwudO3dOHB0dpbS0VBoaGiQ6OlqWL18ug4ODhj4iIyMlODhYXr58Kc+fPxcfHx+Jj4831ZSMzDS/kZERiYqKkiVLlkhdXZ3Rc/P7t0tevHghFy9elLq6OmlpaZGbN2+KWq2WxMREE8/sv2aaY29vrxw5ckSqq6ultbVVHj9+LGvWrBEfHx8ZGhoy9DFXz+F3PT09YmtrKwUFBZMePxfO4Y9eI0R+fA0dGxuTgIAA0Wg0UldXJ48ePRK1Wi3Hjx+f9XoZbn7TlStXxNPTUywtLWX9+vWi0+lMXdIvATDlraioSEREPn78KGFhYeLs7CxWVlbi7e0tR48elZ6eHtMW/hPi4uLEw8NDLC0tZfHixRIXFyfNzc2G/YODg5KZmSlOTk5ia2sre/bsEb1eb8KKf01ZWZkAkKamJqP2uXoOKyoqpvzdTEpKEpF/fx38xIkT4ubmJlZWVrJ9+/ZJc//69avEx8eLoihib28vKSkp0tvba4LZTDbT/FpbW6d9blZUVIiIyJs3b2TDhg3i4OAg1tbW4u/vL2fOnDEKBqY20xwHBgZEo9GIWq0WCwsLWbZsmaSlpU36I3GunsPvrl69KjY2NtLd3T3p8XPhHP7oNULk711D29raZOfOnWJjYyOurq6Sk5Mjo6Ojs16v6j9FExEREZkFfuaGiIiIzArDDREREZkVhhsiIiIyKww3REREZFYYboiIiMisMNwQERGRWWG4ISIiIrPCcENERERmheGGiP6I5ORk7N6929RlENE/AMMNEf02lUo14y03NxeXLl3C9evXTVJfYWEhgoKCoCgKHB0dERwcjLNnzxr2M3gRmZf5pi6AiOY+vV5v+LmkpAQnT55EU1OToU1RFCiKYorScO3aNWRnZ+Py5csIDw/H8PAwGhoa0NjYaJJ6iOj/jys3RPTb3N3dDTcHBweoVCqjNkVRJq2ObNmyBVlZWcjOzoaTkxPc3NxQWFiI/v5+pKSkwM7ODt7e3nj48KHRWI2Njdi5cycURYGbmxsSEhLQ1dU1bW33799HbGwsUlNT4e3tjVWrViE+Ph6nT58GAOTm5qK4uBilpaWGlabKykoAQEdHB2JjY+Ho6AhnZ2dER0ejra3N0Pf3OeXl5UGtVsPe3h7p6ekYGRmZtWNLRD+P4YaITKa4uBiurq549eoVsrKykJGRgZiYGISGhuLt27fQaDRISEjAwMAAAKC7uxvbtm1DcHAwampq8OjRI3z58gWxsbHTjuHu7g6dTof29vYp9x85cgSxsbGIjIyEXq+HXq9HaGgoRkdHsWPHDtjZ2aGqqgparRaKoiAyMtIovJSXl+PDhw+orKzE7du3cffuXeTl5c3ugSKinzPr/2eciP7RioqKxMHBYVJ7UlKSREdHG7bDw8Nl06ZNhu2xsTFZsGCBJCQkGNr0er0AkOrqahERyc/PF41GY9RvR0eHAJCmpqYp6/n8+bNs3LhRAIivr68kJSVJSUmJjI+PT1ubiMiNGzfEz89PJiYmDG3Dw8NiY2MjZWVlhsc5OztLf3+/4T4FBQWiKIpR/0T0Z3HlhohMZvXq1Yaf582bBxcXFwQGBhra3NzcAACdnZ0AgPr6elRUVBg+w6MoClasWAEAaGlpmXIMDw8PVFdX4927dzh8+DDGxsaQlJSEyMhITExMTFtbfX09mpubYWdnZxjL2dkZQ0NDRmMFBQXB1tbWsB0SEoK+vj50dHT8whEhotnADxQTkclYWFgYbatUKqM2lUoFAIYQ0tfXh127duH8+fOT+vLw8JhxrICAAAQEBCAzMxPp6enYvHkznj59iq1bt055/76+Pqxduxa3bt2atE+tVs88MSIyKYYbIpoz1qxZgzt37sDLywvz5//65WvlypUAgP7+fgCApaUlxsfHJ41VUlKChQsXwt7eftq+6uvrMTg4CBsbGwCATqeDoihYunTpL9dHRL+Hb0sR0Zxx4MABfPv2DfHx8Xj9+jVaWlpQVlaGlJSUSeHku4yMDOTn50Or1aK9vR06nQ6JiYlQq9UICQkBAHh5eaGhoQFNTU3o6urC6Ogo9u/fD1dXV0RHR6Oqqgqtra2orKzEoUOH8OnTJ0P/IyMjSE1Nxfv37/HgwQOcOnUKBw8exF9/8fJKZCp89hHRnLFo0SJotVqMj49Do9EgMDAQ2dnZcHR0nDZMREREQKfTISYmBr6+vti3bx+sra1RXl4OFxcXAEBaWhr8/Pywbt06qNVqaLVa2Nra4tmzZ/D09MTevXvh7++P1NRUDA0NGa3kbN++HT4+PggLC0NcXByioqKQm5v7Jw4HEU1DJSJi6iKIiOai5ORkdHd34969e6YuhYj+B1duiIiIyKww3BAREZFZ4dtSREREZFa4ckNERERmheGGiIiIzArDDREREZkVhhsiIiIyKww3REREZFYYboiIiMisMNwQERGRWWG4ISIiIrPyL0KaFVu4Gd4nAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    time_steps = jnp.arange(1, advection_scenario.test_temporal_horizon + 1)\n",
+    "    plt.plot(\n",
+    "        time_steps,\n",
+    "        test_dict[\"mean_nRMSE\"][0],\n",
+    "        label=\"Mean nRMSE\",\n",
+    "    )\n",
+    "    plt.plot(\n",
+    "        time_steps,\n",
+    "        test_dict[\"mean_fourier_nRMSE;0;5;0\"][0],\n",
+    "        label=\"Mean Fourier nRMSE - low freq\",\n",
+    "    )\n",
+    "    plt.plot(\n",
+    "        time_steps,\n",
+    "        test_dict[\"mean_H1_nRMSE\"][0],\n",
+    "        label=\"Mean H1 nRMSE\",\n",
+    "    )\n",
+    "    plt.xlabel(\"Time Step\")\n",
+    "    plt.ylabel(\"error metric\")\n",
+    "    plt.legend()\n",
+    "    plt.grid()\n",
+    "    plt.ylim(-0.1, 1.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregated Metrics\n",
+    "\n",
+    "Similar to the [APEBench paper](https://arxiv.org/abs/2411.00180), let us\n",
+    "aggregate over the first 100 time steps with a geometric mean."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mean_nRMSE': array([0.17082268], dtype=float32), 'mean_fourier_nRMSE;0;5;0': array([0.16836041], dtype=float32), 'mean_H1_nRMSE': array([0.33509955], dtype=float32)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    UP_TO = 100\n",
+    "    test_dict_gmean = {\n",
+    "        key: stats.gmean(value[:, :UP_TO], axis=1) for key, value in test_dict.items()\n",
+    "    }\n",
+    "    print(test_dict_gmean)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing against a ConvNet trained within APEBench"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's train a similar ConvNet using the mechanisms of APEBench"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E: 00079, B: 00124: 100%|██████████| 10000/10000 [00:47<00:00, 212.56it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    data, trained_net = advection_scenario(\n",
+    "        task_config=\"predict\",  # Means the nextwork fully replaces the simulator\n",
+    "        network_config=\"Conv;34;10;relu\",  # 34 hidden width across 10 hidden depth with relu activation\n",
+    "        train_config=\"one\",  # One-Step supervised learning\n",
+    "        num_seeds=1,  # Only one seed for this example\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Typical APEBench pattern is to melt the wide format `data` into long formats\n",
+    "which are handy to work with when using seaborn. See also\n",
+    "[here](https://pandas.pydata.org/docs/user_guide/reshaping.html#melt-and-wide-to-long)\n",
+    "for a guide in Pandas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    loss_data = apebench.melt_loss(data)\n",
+    "    metric_data = apebench.melt_metrics(\n",
+    "        data,\n",
+    "        metric_name=advection_scenario.report_metrics.split(\",\"),\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's visualize the training loss history (Note that by default, APEBench does\n",
+    "not record the training loss every update step, but only every 100th step.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    sns.lineplot(loss_data, x=\"update_step\", y=\"train_loss\")\n",
+    "    plt.yscale(\"log\")\n",
+    "    plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's compare the three metric rollouts against the results from the PyTorch run. The PyTorch model seems to be particularly bad. What is going on? 🤔"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    fig, axs = plt.subplots(1, 3, figsize=(15, 3))\n",
+    "\n",
+    "    for i, metric in enumerate(advection_scenario.report_metrics.split(\",\")):\n",
+    "        sns.lineplot(data=metric_data, x=\"time_step\", y=metric, hue=\"net\", ax=axs[i])\n",
+    "        axs[i].plot(\n",
+    "            time_steps,\n",
+    "            test_dict[metric][0],\n",
+    "            label=\"PyTorch\",\n",
+    "        )\n",
+    "        axs[i].legend()\n",
+    "        axs[i].set_title(metric)\n",
+    "        axs[i].set_xlabel(\"Time Step\")\n",
+    "        axs[i].set_ylabel(\"error metric\")\n",
+    "        axs[i].grid()\n",
+    "        axs[i].set_ylim(-0.1, 1.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing against multiple seeds with APEBench"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see if this was just an unlucky seed. To check, we will run multiple\n",
+    "seeds. This is one of the big strengths of APEBench. If a network run, does not\n",
+    "fully utilize the GPU (oftentimes the case in 1D), one can get seed statistics\n",
+    "in parallel for a small overhead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E: 00079, B: 00124: 100%|██████████| 10000/10000 [02:03<00:00, 81.18it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    data_s, trained_net_s = advection_scenario(\n",
+    "        task_config=\"predict\",\n",
+    "        network_config=\"Conv;34;10;relu\",\n",
+    "        train_config=\"one\",\n",
+    "        num_seeds=10,  # Now 10 seeds at the same time\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar melting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    loss_data_s = apebench.melt_loss(data_s)\n",
+    "    metric_data_s = apebench.melt_metrics(\n",
+    "        data_s, metric_name=advection_scenario.report_metrics.split(\",\")\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    sns.lineplot(\n",
+    "        data=loss_data_s,\n",
+    "        x=\"update_step\",\n",
+    "        y=\"train_loss\",\n",
+    "        hue=\"net\",\n",
+    "        estimator=\"median\",\n",
+    "        errorbar=(\"pi\", 50),\n",
+    "    )\n",
+    "    plt.xlabel(\"Update Step\")\n",
+    "    plt.ylabel(\"Train Loss\")\n",
+    "    plt.legend()\n",
+    "    plt.grid()\n",
+    "    plt.yscale(\"log\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The seed statistics show that this single seed of before was particularly bad.\n",
+    "In the median performance, the APEBench model is better than the PyTorch model.\n",
+    "Of course, for a conclusive statement, the PyTorch model would have also needed\n",
+    "to be trained using multiple seeds. Ultimately, one would require a hypothesis\n",
+    "test.\n",
+    "\n",
+    "This is also good motivation why seed statistics are important. Especially for\n",
+    "autoregressive emulators for which errors compound over time, the effect of an\n",
+    "unlucky seed can be especially pronounced. This concept of running multiple\n",
+    "seeds is deeply baked into APEBench and can be obtained virtually for free in\n",
+    "case of 1D emulation and low-resolution 2D emulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    fig, axs = plt.subplots(1, 3, figsize=(15, 3))\n",
+    "\n",
+    "    for i, metric in enumerate(advection_scenario.report_metrics.split(\",\")):\n",
+    "        sns.lineplot(\n",
+    "            data=metric_data_s,\n",
+    "            x=\"time_step\",\n",
+    "            y=metric,\n",
+    "            hue=\"net\",\n",
+    "            estimator=\"median\",\n",
+    "            errorbar=(\"pi\", 50),\n",
+    "            ax=axs[i],\n",
+    "        )\n",
+    "        axs[i].plot(\n",
+    "            time_steps,\n",
+    "            test_dict[metric][0],\n",
+    "            label=\"PyTorch\",\n",
+    "        )\n",
+    "        axs[i].legend()\n",
+    "        axs[i].set_title(metric)\n",
+    "        axs[i].set_xlabel(\"Time Step\")\n",
+    "        axs[i].set_ylabel(\"error metric\")\n",
+    "        axs[i].grid()\n",
+    "        axs[i].set_ylim(-0.1, 1.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot the seeds' performances individually."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if not RUN_PYTORCH:\n",
+    "    fig, axs = plt.subplots(1, 3, figsize=(15, 3))\n",
+    "\n",
+    "    for i, metric in enumerate(advection_scenario.report_metrics.split(\",\")):\n",
+    "        sns.lineplot(\n",
+    "            data=metric_data_s,\n",
+    "            x=\"time_step\",\n",
+    "            y=metric,\n",
+    "            hue=\"net\",\n",
+    "            estimator=None,\n",
+    "            errorbar=None,\n",
+    "            units=\"seed\",\n",
+    "            ax=axs[i],\n",
+    "        )\n",
+    "        axs[i].plot(\n",
+    "            time_steps,\n",
+    "            test_dict[metric][0],\n",
+    "            color=\"orange\",\n",
+    "            label=\"PyTorch\",\n",
+    "        )\n",
+    "        axs[i].legend()\n",
+    "        axs[i].set_title(metric)\n",
+    "        axs[i].set_xlabel(\"Time Step\")\n",
+    "        axs[i].set_ylabel(\"error metric\")\n",
+    "        axs[i].grid()\n",
+    "        axs[i].set_ylim(-0.1, 1.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "apebench_dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/use_cases.md b/docs/use_cases.md
index 4ff7710..5f0c3f4 100644
--- a/docs/use_cases.md
+++ b/docs/use_cases.md
@@ -11,9 +11,12 @@ I want to:
        the full pipeline of APEBench (no differentiable physics, for example).
        But you can still [scrape datasets](examples/scrape_datasets.ipynb) for
        purely data-driven training in different ecosystems and you can evaluate
-       your architectures (TODO add link) by rolling them out in your ecosystem
-       of choice and casting the results back to JAX arrays and using APEBench's
-       metric system.
+       your architectures (guides for
+       [PyTorch](examples/benchmark_pytorch_models.ipynb), [Flax with `linen`
+       API](examples/benchmark_flax_models_with_linen.ipynb), and [Flax with
+       `nnx` API](examples/benchmark_flax_models_with_nnx.ipynb)) by rolling
+       them out in your ecosystem of choice and casting the results back to JAX
+       arrays and using APEBench's metric system.
 2. **Create datasets** Consider using the [scraping API](examples/scrape_datasets.ipynb).
 3. **Conduct studies** Learn about the [typical workflow of
    APEBench](typical_workflow.md) and consider [using the command line interface
diff --git a/mkdocs.yml b/mkdocs.yml
index 7a3cc08..286e4da 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -95,6 +95,9 @@ nav:
             - Animate Learning 1D Burgers: 'examples/animate_emulator_learning_burgers.ipynb'
             - Extending Components: 'examples/component_extensions.ipynb'
         - Advanced:
+            - Benchmark Flax nnx models: 'examples/benchmark_flax_models_with_nnx.ipynb'
+            - Benchmark Flax linen models: 'examples/benchmark_flax_models_with_linen.ipynb'
+            - Benchmark PyTorch models: 'examples/benchmark_pytorch_models.ipynb'
             - Randomness & Reproducibility: 'examples/sources_of_randomness_and_reproducibility.ipynb'
             - Difficulty & Receptive Field in 1D Advection: 'examples/difficulty_and_receptive_field_advection_1d.ipynb'
     - Scenarios:


Test patch:
diff --git a/tests/test_data_generation.py b/tests/test_data_generation.py
new file mode 100644
index 0000000..e0b8ed9
--- /dev/null
+++ b/tests/test_data_generation.py
@@ -0,0 +1,32 @@
+import jax.numpy as jnp
+
+import apebench
+
+
+def test_ic_set_is_equal_to_first_snapshot():
+    # Use a scenario with a warmup
+    ks_scenario = apebench.scenarios.difficulty.KuramotoSivashinsky()
+
+    # Train data
+    train_data = ks_scenario.get_train_data()
+    train_ic_set = ks_scenario.get_train_ic_set()
+
+    assert jnp.array_equal(train_data[:, 0], train_ic_set)
+
+    # Test data
+    test_data = ks_scenario.get_test_data()
+    test_ic_set = ks_scenario.get_test_ic_set()
+
+    assert jnp.array_equal(test_data[:, 0], test_ic_set)
+
+    # Coarse train data
+    coarse_train_data = ks_scenario.get_train_data_coarse()
+    coarse_train_ic_set = ks_scenario.get_train_ic_set_coarse()
+
+    assert jnp.array_equal(coarse_train_data[:, 0], coarse_train_ic_set)
+
+    # Coarse test data
+    coarse_test_data = ks_scenario.get_test_data_coarse()
+    coarse_test_ic_set = ks_scenario.get_test_ic_set_coarse()
+
+    assert jnp.array_equal(coarse_test_data[:, 0], coarse_test_ic_set)
diff --git a/tests/test_external_metric_computation.py b/tests/test_external_metric_computation.py
new file mode 100644
index 0000000..61f710d
--- /dev/null
+++ b/tests/test_external_metric_computation.py
@@ -0,0 +1,75 @@
+import equinox as eqx
+import exponax
+import jax
+import jax.numpy as jnp
+
+import apebench
+
+# import pytest
+
+
+def test_external_metric_computation_single_seed():
+    adv_scenario = apebench.scenarios.difficulty.Advection()
+
+    # Just use the randomly initialized model
+    neural_stepper = adv_scenario.get_neural_stepper(
+        task_config="predict",
+        network_config="mlp;16;4;relu",
+        key=jax.random.PRNGKey(0),
+    )
+
+    # Manual path: (1) get test ic, (2) rollout model, (3) compute metrics
+    test_ic = adv_scenario.get_test_ic_set()
+    neural_rollout = jax.vmap(
+        exponax.rollout(neural_stepper, adv_scenario.test_temporal_horizon)
+    )(test_ic)
+    manual_metrics = adv_scenario.perform_tests_on_rollout(neural_rollout)
+
+    # Automated path: Directly use `perform_tests`
+    automated_metrics = adv_scenario.perform_tests(
+        neural_stepper,
+        remove_singleton_axis=True,
+    )
+
+    manual_nRMSE = manual_metrics["mean_nRMSE"]
+    automated_nRMSE = automated_metrics["mean_nRMSE"]
+
+    assert jnp.array_equal(manual_nRMSE, automated_nRMSE)
+    # assert manual_nRMSE == pytest.approx(automated_nRMSE, rel=1e-6, abs=1e-12)
+
+
+def test_external_metric_computation_multiple_seeds():
+    adv_scenario = apebench.scenarios.difficulty.Advection()
+
+    # Just use the randomly initialized model, but multiple seeds
+    neural_stepper_s = eqx.filter_vmap(
+        lambda s: adv_scenario.get_neural_stepper(
+            task_config="predict",
+            network_config="mlp;16;4;relu",
+            key=jax.random.PRNGKey(s),
+        )
+    )(jnp.arange(3))
+
+    # Manual path: (1) get test ic, (2) rollout model, (3) compute metrics
+    test_ic = adv_scenario.get_test_ic_set()
+    # neural_rollout = jax.vmap(
+    #     exponax.rollout(neural_stepper, adv_scenario.test_temporal_horizon)
+    # )(test_ic)
+    neural_rollout_s = eqx.filter_vmap(
+        lambda ns: jax.vmap(exponax.rollout(ns, adv_scenario.test_temporal_horizon))(
+            test_ic
+        )
+    )(neural_stepper_s)
+
+    manual_metrics = adv_scenario.perform_tests_on_rollout(neural_rollout_s)
+
+    # Automated path: Directly use `perform_tests`
+    automated_metrics = adv_scenario.perform_tests(
+        neural_stepper_s,
+        remove_singleton_axis=False,
+    )
+
+    manual_nRMSE = manual_metrics["mean_nRMSE"]
+    automated_nRMSE = automated_metrics["mean_nRMSE"]
+
+    assert jnp.array_equal(manual_nRMSE, automated_nRMSE)