Repository: aeon-toolkit/aeon
Pull request #2920 (2025-06-27 08:34:36)

[ENH] Implement Time-Varying Parameter (TVP) regression forecaster using Kalman filter 
Time-Varying Parameter is a whole sub topic in forecasting, with a strong Bayesian flavour. 

This is a simple implementation using a Kalman filter that also models the parameter variance. It mirrors the RegressionForecaster approach.

Basically you update the parameters of the model each time. My interpretation is that it is a recency weighted regression fitting algorithm, along the lines of discount functions in reinforcement learning or stochastic gradient descent, with a data driven learning rate.



TonyBagnall: ```python
from statsmodels.tsa.statespace.mlemodel import MLEModel

class TVPModel(MLEModel):
    def __init__(self, endog, exog):
        k_states = exog.shape[1]
        super().__init__(endog, k_states=k_states, k_posdef=k_states)
        self.exog = exog
        self['design'] = exog[:, None, :]
        self['transition'] = np.eye(k_states)
        self['selection'] = np.eye(k_states)
        self.k_states = k_states

    def start_params(self):
        return [0.1] + [0.01] * self.k_states

    def update(self, params, **kwargs):
        self['obs_cov'] = np.array([[params[0]]])
        self['state_cov'] = np.diag(np.square(params[1:]))

X = np.column_stack([np.ones(n), x])  # intercept + x
model = TVPModel(y, X)
res = model.fit(disp=False)

filtered_betas = res.filtered_state.T
```
TonyBagnall: > From what I can see it looks good, I have no idea if it matches the model described in the paper, but it looks like it works if the tests pass! It looks like it does what it's supposed to do. I guess the only thing is to check it against any other implementations if they exist

yeah tried that, but the statsmodel one is weird. I can say it forecasts [1,2,3,4,5,6,7,8,9,10] pretty well :)

Patch:
diff --git a/aeon/forecasting/__init__.py b/aeon/forecasting/__init__.py
index 7a331f69..0b134857 100644
--- a/aeon/forecasting/__init__.py
+++ b/aeon/forecasting/__init__.py
@@ -5,9 +5,11 @@ __all__ = [
     "BaseForecaster",
     "RegressionForecaster",
     "ETSForecaster",
+    "TVPForecaster",
 ]
 
 from aeon.forecasting._ets import ETSForecaster
 from aeon.forecasting._naive import NaiveForecaster
 from aeon.forecasting._regression import RegressionForecaster
+from aeon.forecasting._tvp import TVPForecaster
 from aeon.forecasting.base import BaseForecaster
diff --git a/aeon/forecasting/_tvp.py b/aeon/forecasting/_tvp.py
new file mode 100644
index 00000000..83bb9157
--- /dev/null
+++ b/aeon/forecasting/_tvp.py
@@ -0,0 +1,115 @@
+"""Time-Varying Parameter (TVP) Forecaster using Kalman filter."""
+
+import numpy as np
+
+from aeon.forecasting.base import BaseForecaster
+
+
+class TVPForecaster(BaseForecaster):
+    r"""Time-Varying Parameter (TVP) Forecaster using Kalman filter as described in [1].
+
+    This forecaster models the target series using a time-varying linear autoregression:
+
+    .. math::
+
+        \\hat{y}_t = \beta_0,t+\beta_1,t * y_{t-1} + ... + \beta_k,t * y_{t-k}
+
+    where the coefficients $\beta_t$ evolve based on observations $y_t$. At each
+    step, a weight vector is calculated based in the latest residual. This is used to
+    adjust the $\beta$ parameter values and the estimate of parameter variance.
+
+    TVP can be considered as related to stochastic gradient descent (SGD) regression,
+    with the update weight being the dynamically calculated Kalman gain based on the
+    covariance of the parameters rather than a fixed learning rate.
+
+    Parameters
+    ----------
+    window : int
+        Number of autoregressive lags to use, called window to co-ordinate with
+        RegressionForecaster.
+    var : float, default=0.01
+        Observation noise variance. ``var`` controls the influence of recency in the
+        update. A small var (such as the default 0.01) means the parameters will be
+        more
+        affected by
+        recent values. A large var (e.g., 1.0 or more) means the observations are
+        noisy, so the filter will adjust the parameters less to match recent values.
+    beta_var : float, default=0.01
+        State evolution noise variance, applied to all coefficients at each step. Small
+        ``beta_var`` leads to slowly evolving parameters.
+
+    References
+    ----------
+    .. [1] Durbin & Koopman, Time Series Analysis by State Space Methods
+    Oxford University Press, 2nd Edition, 2012
+    """
+
+    def __init__(self, window, horizon=1, var=0.01, beta_var=0.01):
+        self.window = window
+        self.var = var
+        self.beta_var = beta_var
+        super().__init__(axis=1, horizon=horizon)
+
+    def _fit(self, y, exog=None):
+        y = y.squeeze()
+
+        # Create autoregressive design matrix
+        X = np.lib.stride_tricks.sliding_window_view(y, window_shape=self.window)
+        X = X[: -self.horizon]
+        ones = np.ones((X.shape[0], 1))
+        X = np.hstack([ones, X])  # Add intercept column
+
+        y_train = y[self.window + self.horizon - 1 :]
+
+        # Kalman filter initialisation
+        k = X.shape[1]  # number of coefficients (lags + intercept)
+        beta = np.zeros(k)
+        beta_covariance = np.eye(k)
+        beta_var = self.beta_var * np.eye(k)
+
+        for t in range(len(y_train)):
+            x_t = X[t]
+            y_t = y_train[t]
+
+            # Predict covariance
+            beta_covariance = beta_covariance + beta_var
+
+            # Forecast error
+            error_t = y_t - x_t @ beta
+            total_variance = x_t @ beta_covariance @ x_t + self.var
+            kalman_weight = beta_covariance @ x_t / total_variance
+
+            # Update beta parameters with kalman weights times error.
+            beta = beta + kalman_weight * error_t
+            beta_covariance = (
+                beta_covariance - np.outer(kalman_weight, x_t) @ beta_covariance
+            )
+
+        self._beta = beta
+        self._last_window = y[-self.window :]
+        self.forecast_ = (
+            np.insert(self._last_window, 0, 1.0) @ self._beta
+        )  # include intercept
+        return self
+
+    def _predict(self, y, exog=None):
+        y = y.squeeze()
+        x_t = np.insert(y[-self.window :], 0, 1.0)  # include intercept term
+        y_hat = x_t @ self._beta
+        return y_hat
+
+    @classmethod
+    def _get_test_params(cls, parameter_set: str = "default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default='default'
+            Name of the parameter set to return.
+
+        Returns
+        -------
+        dict
+            Dictionary of testing parameter settings.
+        """
+        return {"window": 4}
diff --git a/docs/api_reference/forecasting.md b/docs/api_reference/forecasting.md
index 86a8cc61..cdfae273 100644
--- a/docs/api_reference/forecasting.md
+++ b/docs/api_reference/forecasting.md
@@ -11,4 +11,5 @@
     NaiveForecaster
     RegressionForecaster
     ETSForecaster
+    TVPForecaster
 ```
diff --git a/examples/forecasting/regression.ipynb b/examples/forecasting/regression.ipynb
index e451051b..152cebaa 100644
--- a/examples/forecasting/regression.ipynb
+++ b/examples/forecasting/regression.ipynb
@@ -4,7 +4,7 @@
    "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "# Forecasting using window based regression\n",
+    "# Forecasting using regression\n",
     "\n",
     "All aeon forecasters work by fitting on a series then making a single prediction \n",
     "about a future value. How they make the prediction defines the algorithm.\n",
@@ -33,8 +33,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-07T16:02:32.093458Z",
-     "start_time": "2025-06-07T16:02:32.067219Z"
+     "end_time": "2025-07-10T10:06:22.363194Z",
+     "start_time": "2025-07-10T10:06:21.583141Z"
     }
    },
    "cell_type": "code",
@@ -55,18 +55,18 @@
        "(45, 100)"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 36
+   "execution_count": 1
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-07T16:02:34.104719Z",
-     "start_time": "2025-06-07T16:02:33.936686Z"
+     "end_time": "2025-07-10T10:06:24.483254Z",
+     "start_time": "2025-07-10T10:06:22.771783Z"
     }
    },
    "cell_type": "code",
@@ -123,13 +123,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 37
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-07T16:02:35.782114Z",
-     "start_time": "2025-06-07T16:02:35.777553Z"
+     "end_time": "2025-07-10T10:06:24.514474Z",
+     "start_time": "2025-07-10T10:06:24.509813Z"
     }
    },
    "cell_type": "code",
@@ -150,7 +150,7 @@
      ]
     }
    ],
-   "execution_count": 38
+   "execution_count": 3
   },
   {
    "metadata": {},
@@ -165,8 +165,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-07T16:02:42.494790Z",
-     "start_time": "2025-06-07T16:02:37.913729Z"
+     "end_time": "2025-07-10T10:07:55.008990Z",
+     "start_time": "2025-07-10T10:06:28.466480Z"
     }
    },
    "cell_type": "code",
@@ -189,11 +189,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "LR predicts [463.60934602] DrCIF predicts [425.5]\n"
+      "LR predicts [463.60934602] DrCIF predicts [413.2]\n"
      ]
     }
    ],
-   "execution_count": 39
+   "execution_count": 4
   },
   {
    "metadata": {},
@@ -208,8 +208,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-07T16:02:55.217185Z",
-     "start_time": "2025-06-07T16:02:51.260893Z"
+     "end_time": "2025-07-10T10:07:57.419429Z",
+     "start_time": "2025-07-10T10:07:55.039066Z"
     }
    },
    "cell_type": "code",
@@ -238,7 +238,7 @@
      ]
     }
    ],
-   "execution_count": 40
+   "execution_count": 5
   },
   {
    "metadata": {},
@@ -252,8 +252,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-07T16:03:30.898674Z",
-     "start_time": "2025-06-07T16:03:08.930864Z"
+     "end_time": "2025-07-10T10:08:23.040857Z",
+     "start_time": "2025-07-10T10:07:57.463411Z"
     }
    },
    "cell_type": "code",
@@ -305,6 +305,7 @@
        "  aeon.regression.interval_based._interval_pipelines.RandomIntervalRegressor),\n",
        " ('RandomIntervalSpectralEnsembleRegressor',\n",
        "  aeon.regression.interval_based._rise.RandomIntervalSpectralEnsembleRegressor),\n",
+       " ('RecurrentRegressor', aeon.regression.deep_learning._rnn.RecurrentRegressor),\n",
        " ('RegressorEnsemble', aeon.regression.compose._ensemble.RegressorEnsemble),\n",
        " ('RegressorPipeline', aeon.regression.compose._pipeline.RegressorPipeline),\n",
        " ('ResNetRegressor', aeon.regression.deep_learning._resnet.ResNetRegressor),\n",
@@ -319,30 +320,176 @@
        "  aeon.regression.interval_based._tsf.TimeSeriesForestRegressor)]"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 41
+   "execution_count": 6
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "All aeon forecasters predict a single value. If you want to forecast a range of \n",
-    "values ahead you should use the functions `recursive_foreacast` or `direct_forecast` \n",
-    "(notebooks coming soon). "
+    "All aeon forecasters predict a single value from `fit` (next forecast value from the \n",
+    "train series) and from `predict` (predicted next value from test series). If you want\n",
+    " to forecast a prediction horizon of values ahead you should use the functions \n",
+    " `iterative_foreacast` or `direct_forecast`. "
    ],
    "id": "939c3a82b06e1b95"
   },
   {
    "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "##     Time-Varying Parameter (TVP) Forecaster\n",
+    "\n",
+    "The `aeon` RegressionForecaster defaults to fits a standard linear regression on the windowed series using the sklearn `LinearRegression` estimator. This fits the linear regression parameters with the standard least squares algorithm. An alternative approach in the forecasting literature is to fit the linear regression parameters adaptively, so that more recent observations can have a greater weight in setting the parameters. One way of doing this is to use a Kalman filter. This considers the correlation between parameters in addition to the forecasting error. This is implemented in `aeon` as the `TVPForecaster`.\n"
+   ],
+   "id": "2850a18027127db0"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-10T10:09:38.138944Z",
+     "start_time": "2025-07-10T10:09:38.127369Z"
+    }
+   },
    "cell_type": "code",
+   "source": [
+    "from aeon.forecasting import TVPForecaster\n",
+    "\n",
+    "tvp = TVPForecaster(window=50)\n",
+    "tvp.fit(y_train)\n",
+    "pred = tvp.predict(y_train)"
+   ],
+   "id": "b0787fa71ac848d8",
    "outputs": [],
-   "execution_count": null,
+   "execution_count": 13
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-10T10:14:25.355241Z",
+     "start_time": "2025-07-10T10:14:25.149557Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "print(f\" Forecast for airline with TVP = {pred}\")\n",
+    "direct = tvp.direct_forecast(y_train, prediction_horizon=12)\n",
+    "iterative = tvp.iterative_forecast(y_train, prediction_horizon=12)\n",
+    "plt.plot(np.arange(0, len(y_train)), y_train, label=\"Train\", color=\"blue\")\n",
+    "plt.plot(\n",
+    "    np.arange(len(y_train), len(y_train) + len(direct)),\n",
+    "    direct,\n",
+    "    label=\"Direct\",\n",
+    "    color=\"green\",\n",
+    "    linestyle=\":\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    np.arange(len(y_train), len(y_train) + len(iterative)),\n",
+    "    iterative,\n",
+    "    label=\"Iterative\",\n",
+    "    color=\"red\",\n",
+    "    linestyle=\":\",\n",
+    ")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Value\")\n",
+    "plt.title(\"Iterative and Direct Forecasting with TVP\")\n",
+    "plt.show()"
+   ],
+   "id": "68942e2fa018848a",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Forecast for airline with TVP = 452.44604822530795\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 23
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
    "source": "",
-   "id": "4bf0708606952c16"
+   "id": "495008094eee47b6"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "TVP has two parameters that control the weight given to more recent observations. These are `var` and `coeff_var`. `var` represents the variation in the data. A small value of `var`, such as the default of 0.01 indicates there is less noise in the data and recent values will have a greater effect on parameter updates. It is a bit like the learning rate used in gradient descent or reinforcement learning. A large `var` value (e.g. greater than 1.0) will mean new values will adjust the parameters less at each update.\n",
+    "\n",
+    "The second parameter, `beta_var`, estimates the variation in parameters over time. It has a similar effect on updates to `var` in that it effects how much parameters are changed at each stage, but it is modelling a different type on uncertainty. `TVPRegression` maintains an estimate of the covariance of the parameters in `fit` and this is adjusted after each update and then `beta_var` is added to perturb the matrix. Small `var` means the Kalman weights.\n",
+    "\n",
+    "Add some examples to demonstrate the difference\n"
+   ],
+   "id": "ca7da34828be1c2"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-10T10:16:51.355642Z",
+     "start_time": "2025-07-10T10:16:51.190578Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "tvp2 = TVPForecaster(var=1.0, beta_var=1.0, window=50)\n",
+    "direct2 = tvp.direct_forecast(airline, prediction_horizon=12)\n",
+    "plt.plot(\n",
+    "    np.arange(0, len(direct)),\n",
+    "    direct,\n",
+    "    label=\"Direct 1\",\n",
+    "    color=\"green\",\n",
+    "    linestyle=\":\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    np.arange(0, len(direct2)),\n",
+    "    direct2,\n",
+    "    label=\"Direct 2\",\n",
+    "    color=\"blue\",\n",
+    "    linestyle=\":\",\n",
+    ")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Value\")\n",
+    "plt.title(\"Direct Forecasting with TVP\")\n",
+    "plt.show()"
+   ],
+   "id": "7f1b85ed426c2735",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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bxU716tXZsGEDX331FQEBAQQFBWVaKiAvBAUFUadOHVasWAEgxY7QhnYTwYTI/+5Nqb53s7OzU/z8/JSXXnpJ+fbbb41ThB/0qKnngwYNyvI5YmJilEGDBinFihVTbG1tFT8/P6VJkybKjz/+aHJecnKy8tFHHylBQUHG8zp16mQyLXzHjh1K9erVFTs7uydOdb439fz3339/7Htw4MABJTw8XHFxcVGcnJyURo0aKTt27MjyfXrUFP3Nmzcr4eHhiru7u+Lg4KCUKlVK6dWrl7Jv3z6T844ePaq0b99e8fDwUBwcHJRy5copo0aNMh6/ffu20rt3b8XLy0txcXFRwsPDlZMnTyqBgYEm06w/++wzpUaNGoqHh4fi6OioBAcHK59//rmSlpZmPCcjI0N56623FG9vb0Wn05n8zB5+7+79TK9fv57l6743VV9RFCUpKUkZNGiQ4unpqbi4uCjt2rVTTp06pQDKxIkTH/te3/Piiy8qgLJ7925j2+XLlxVAKVasWKbzs/o7d/LkSaV+/fqKo6OjAhjfn6d5LU/yuKnnD5o2bZoCKDVq1HjkOY/7NyLE85K9sYQQIpdFREQQEhLCr7/+Kj0bQmhAxuwIIUQOunv3bqa2b775BisrK+rXr69BIiGEjNkRQogcNHnyZPbv30+jRo2wsbFh9erVrF69mv79+z9x9poQInfIZSwhhMhB69evZ9y4cRw/fpzExESKFy9O9+7d+eijj3J9Z3IhRNak2BFCCCGERZMxO0IIIYSwaFLsCCGEEMKiyQVk1P17rl69iqura75ZYl0IIYQQj6coCnfu3CEgIOCxG8ZKsQNcvXpVZkkIIYQQZurSpUsULVr0kcel2AFcXV0B9c160l4/QgghhMgfEhISKFasmPFz/FGk2OH+fjVubm5S7AghhBBm5klDUGSAshBCCCEsmhQ7QgghhLBoUuwIIYQQwqLJmB0hhBAFnsFgIC0tTesY4iG2trZYW1s/9+NIsSOEEKJAS0tL4/z58xgMBq2jiCx4eHjg5+f3XOvgSbEjhBCiwFIUhWvXrmFtbU2xYsUeuzCdyFuKopCcnExsbCwA/v7+z/xYUuwIIYQosDIyMkhOTiYgIAAnJyet44iHODo6AhAbG4uPj88zX9KSElYIIUSBpdfrAbCzs9M4iXiUe0Voenr6Mz+GFDtCCCEKPNkXMf/KiZ+NFDtCCCGEsGhS7AghhBAWTKfTsXz5cq1jaEqKHSGEEMLM9OrVC51Oh06nw9bWFl9fX1566SXmzJmTaQr9tWvXaNGiRa7mGTt2LC+88MITzzt27BgdO3akRIkS6HQ6vvnmm1zNdY8UO0IIIYQZat68OdeuXSMqKorVq1fTqFEjhg4dSuvWrcnIyDCe5+fnh729/SMf53kG/j6t5ORkSpYsycSJE/Hz88uz55ViRwghnsGmTfDrr3DnjtZJREFlb2+Pn58fRYoUoVq1anz44YesWLGC1atXM2/ePON5D17GioqKQqfT8dtvv9GgQQMcHBxYsGABALNnz6Z8+fI4ODgQHBzMDz/8YPJ8ly9fpmvXrnh6euLs7ExoaCi7d+9m3rx5jBs3jkOHDhl7mx58/ge9+OKLfPHFF3Tp0uWxBVhOk3V2hBDiGXz5JaxeDWPHwpgxWqcROS0pLQkAJ1sn42ygNH0a6fp0bKxssLexz3Suo60jVjq1DyFdn06aPg1rK2scbByeeK6ttW2O5G7cuDFVq1Zl2bJl9O3b95HnffDBB0yZMoWQkBBjwTN69Gi+//57QkJCOHjwIP369cPZ2ZmePXuSmJhIgwYNKFKkCCtXrsTPz48DBw5gMBh49dVXOXr0KGvWrGHDhg0AuLu758jrySnSsyOEENl05Mj9npy6daFsWeja9f7x/fuhb1/4919t8omc4zLBBZcJLtxIvmFs+2L7F7hMcGHwP4NNzvX50geXCS5cjL9obJu2dxouE1zos7KPybklvi2BywQXTlw/YWybFzEvR7MHBwcTFRX12HOGDRtGhw4dCAoKwt/fnzFjxjBlyhRjW4cOHRg+fDgzZ84EYOHChVy/fp3ly5dTr149SpcuTefOnalduzaOjo64uLhgY2ODn58ffn5+xsUA8wvp2RFCiGxQFOjcGaKi1B6djz6CDz+EB5cA+eUX+OknSE6G+vU1iyoKOEVRnrg2TWhoqPHrpKQkIiMj6dOnD/369TO2Z2RkGHtoIiIiCAkJwdPTM3dC5zIpdoQQIhtu3gS9Xi16qlVT2x7+PHn1Vbh7Fzp1ut92/bpa+Lzyinq5Kwc2cBZ5IHFkIqBexrrn3brvMqzWMGysTD86Y99R925ytL3fmzHoxUH0q9YPayvTH3jU0KhM5/Z6oVdORufEiRMEBQU99hxnZ2fj14mJ6mudNWsWNWvWNDnv3vYM+a2n5mlJsSOEENng5QWnTqk9O25uWZ9Tu7Z6e9Aff8DJk/DPP/DJJ/fbExPBxSXX4orn5GznnKnNztoOO+vM20pkda6ttW2W43AedW5O2bRpE0eOHGH48OHZ/h5fX18CAgI4d+4c3bp1y/KcKlWqMHv2bG7dupVl746dnZ1x6438SIodIYTIJp0OnvAf5kx69AAPD3hwj0m9Xh3vU7o0LFgAxYrlaExRQKSmphIdHY1erycmJoY1a9YwYcIEWrduTY8ePZ7qscaNG8eQIUNwd3enefPmpKamsm/fPm7fvs2IESPo2rUr48ePp127dkyYMAF/f38OHjxIQEAAtWvXpkSJEpw/f56IiAiKFi2Kq6trlrOt0tLSOH78uPHrK1euEBERgYuLC6VLl86R9yVLilDi4+MVQImPj9c6ihAiH0pOVhSDIeceb+9eRdHpFMXTU1FSU++3nz6tKAkJOfc84snu3r2rHD9+XLl7967WUZ5Kz549FUABFBsbG8Xb21tp2rSpMmfOHEWv15ucCyh//vmnoiiKcv78eQVQDh48mOkxFyxYoLzwwguKnZ2dUqhQIaV+/frKsmXLjMejoqKUjh07Km5uboqTk5MSGhqq7N69W1EURUlJSVE6duyoeHh4KIAyd+7cLHPfe/6Hbw0aNHjka33czyi7n9+6/38jCrSEhATc3d2Jj4/H7VH900KIAmv4cFi1CiZMUMfe5ITLl+H4cWjW7H5bWJg6o2vJEmjdOmeeRzxeSkoK58+fJygoCAcHhyd/g8hzj/sZZffzWy5jCSHEYygK/P03REZCTn4WFi2q3u5JTlYHM6ekQEjI/fajRyE2Fho0kMHNQjwrWWdHCCEeQ6eDgwdh8WLTXpic5uQEJ06ovT1Fitxv//praNIE3nsv955bCEsnxY4QQjyBs7M6rTy3V7fX6SA42LStUCF1gHObNvfbLl6E0aPVWV5CiCeTYkcIIfKxL7+EmBh1PM89ixbBp5/CgAHa5RLCnEixI4QQj7BhA7RsCcuWaZvDzg6sHvhtXbUqtGqlTmu/JyUFWrSAGTMgLS3vMwqRn8kAZSGEeIQFC9StIYKCoEMHrdPc17y5envQ6tWwZo06oLl/f21yCZFfSbEjhBCPMHKkWui0bKl1kierVQu++EIdV2QlffZCmJBiRwghHqFsWXUgsDnw94d33jFtS08H25zbiUAIsyX1vxBCWJgLF6BxYyhXTl0nSIiCToodIYR4SFISvP027NplnsWCry/s3g3nz6vr9oiCTafTsXz5cq1jaEqKHSGEeMhff8FXX8EjNoDO9xwc1OnpZ89CxYpapxG5oVevXuh0OnQ6Hba2tvj6+vLSSy8xZ84cDAaDybnXrl2jRYsWuZpn7NixvPDCC088b9asWYSFhVGoUCEKFSpE06ZN2bNnT65mAyl2hBAik6AgeO01eOMNdaE/c9SmDZQqpXUKkZuaN2/OtWvXiIqKYvXq1TRq1IihQ4fSunVrMjIyjOf5+flluQP5Penp6XkRF4AtW7bQtWtXNm/ezM6dOylWrBjNmjXjypUrufq8UuwIIcRDatRQp51/9JHWSYR4NHt7e/z8/ChSpAjVqlXjww8/ZMWKFaxevZp58+YZz3vwMlZUVBQ6nY7ffvuNBg0a4ODgwIIFCwCYPXs25cuXx8HBgeDgYH744QeT57t8+TJdu3bF09MTZ2dnQkND2b17N/PmzWPcuHEcOnTI2Nv04PM/aMGCBQwcOJAXXniB4OBgZs+ejcFgYOPGjbnxFhnJbCwhhLBQO3eqRVuTJtC+vdZpzEtSkvqnk9P93r20NHWGm42N6dYh9851dLw/7T89XT3f2tp0A9lHnZtTs+YaN25M1apVWbZsGX379n3keR988AFTpkwhJCTEWPCMHj2a77//npCQEA4ePEi/fv1wdnamZ8+eJCYm0qBBA4oUKcLKlSvx8/PjwIEDGAwGXn31VY4ePcqaNWvYsGEDAO7u7tnKm5ycTHp6Op6enjny+h9FenaEEOIBq1apu49bgrVrYdo0teART8fFRb3duHG/7Ysv1LbBg03P9fFR2y9evN82bZra1qeP6bklSqjtJ07cb3tEJ8gzCw4OJioq6rHnDBs2jA4dOhAUFIS/vz9jxoxhypQpxrYOHTowfPhwZs6cCcDChQu5fv06y5cvp169epQuXZrOnTtTu3ZtHB0dcXFxwcbGBj8/P/z8/HB0dMxW1vfff5+AgACaNm36vC/7saRnRwgh/t+NG/d7QC5cMN193By1bw/XrkHHjlonEXlJURR0TxhsFhoaavw6KSmJyMhI+vTpQ79+/YztGRkZxh6aiIgIQkJCcrQHZuLEiSxevJgtW7bg8GD3Vy6QYkcIIf7f1asQEgIGg/kXOqDuofX//zEXTykxUf3Tyel+27vvwrBh6mWsB8XGqn8+2JkxaBD066dexnrQvQ6XB8/t1SsHAj/gxIkTBAUFPfYcZ2dn49eJ//9iZ82aRc2aNU3Os/7/F5Ddnprs+vLLL5k4cSIbNmygSpUqOfrYWZFiRwgh/l+VKrB37/0POlFwPVALGNnZqbfsnGtrm/U4nEedm1M2bdrEkSNHGD58eLa/x9fXl4CAAM6dO0e3R6y3UKVKFWbPns2tW7ey7N2xs7NDr9dn6/kmT57M559/ztq1a016mHKTFDtCCPEQFxetE+Ssixdh40a1B8Fcp9KLzFJTU4mOjkav1xMTE8OaNWuYMGECrVu3pkePHk/1WOPGjWPIkCG4u7vTvHlzUlNT2bdvH7dv32bEiBF07dqV8ePH065dOyZMmIC/vz8HDx4kICCA2rVrU6JECc6fP09ERARFixbF1dU1y+nukyZNYvTo0SxcuJASJUoQHR0NgIuLCy65+A9PBigLIQTqJazs/Mf09M3TLDm2JPcD5ZC7d9U9vt54w3RQrDB/a9aswd/fnxIlStC8eXM2b97M1KlTWbFihfHyU3b17duX2bNnM3fuXCpXrkyDBg2YN2+e8XKYnZ0d69atw8fHh5YtW1K5cmUmTpxofJ6OHTvSvHlzGjVqhLe3N4sWLcryeaZPn05aWhqdOnXC39/fePvyyy+f7814Ap2imONi6DkrISEBd3d34uPjcXNz0zqOEEIDderAuXPw22/QoEHW56Tr0wn6NghPR08ODzhsbH9v/XvcSL7BkJpDeMHvhbwJ/BRat4b4eHU2Ua1aWqfJX1JSUjh//jxBQUG5PkhWPJvH/Yyy+/ktPTtCiAIvPh5On1annJcr9+jzztw6Q4YhgyOxR8gw3F+hdtmJZcyNmEtcSpyxbcO5DZSaWoqBfw80eYzbd2+T1//HXLECtm2TQkcUXDJmRwhR4Lm7q5ex9u8HP79Hn1fBuwKXhl/izK0z2Fjd//U5selEjl8/TiWfSsa2E9dPcO72Oa4lXjN5jNo/1eZywmU29NhAraJq9RGdGM2VhCuULVwWV3vXnH1xZJ4RJERBI8WOEEKgzrKpXfvJ59la21LBu4JJW6cKnTKd161KN6r4VsHR9v6UXb1Bz4X4C6RkpFDE9f7c9j9P/MnAfwbSumxrVnVdZWyfHzEfb2dv6gfWx8Xu+QdvpqdDXBx4ez/3QwlhVuQylhCiQMvuFaXbd28/1eN6OnrSoEQDahSpYWyztrIm/oN4Tg46SRG3+8VOuiEdH2cfynqWNbbpDXr+99f/aLWwFbFJscb29ZHreX/9+6yLXPdUeRYtUlf6HTLkqb5NCIsgxY4QokD77DOoVw/++uvR5+gNekJmhlBrdi0ib0U+1/PZWdtRzqscVrr7v36H1BxCzDsxTH5psrEtMS2Rl8u9TDX/agS6Bxrb10auZfKOyfxz5h9jm0Ex0Gh+I3ot72UybuhBgYFqr87+/dkv8AoSmauTf+XEz0YuYwkhCrTFi+H4cbh169HnHIk9wpU7V4hLiSPANSDXslhb3R9c4+7gzu+v/J7pnIYlGpKSkUKToCbGtisJV9gStQUbKxtmvTwry8euWRN27FB3dJe1du67N3U6LS0tx1cJFjkjOTkZANvnWH1Rih0hRIG2bh0sWQJt2z76nBf8XuDy8MscjjlsMgZHC63LtqZ12dYmbR4OHizptITYpFhsrdUPhDR9GlcSrhBUSF0nxdo6e2OSChobGxucnJy4fv06tra2WFnJBY/8QlEUkpOTiY2NxcPD46nXDnqQrLODrLMjhLAsm89vpsOSDpQqVIp9/fdpHSffS0tL4/z58xgMBq2jiCx4eHjg5+eX5eam2f38lp4dIYR4DINiMBlfYw4q+lQkITXBeOnNw8HDeGzSJFi2DBYsgNKltcuYn9jZ2VGmTBnS0tK0jiIeYmtr+1w9OvdIsSOEKJD274dp06BHD2jYMOtzFEXhxVkvUsG7AhObTDSZQZWf+Tj7cKD/ASr5VDIZBwTqZbs9e9QB2cOGaZMvP7KyspIVlC2Yef13RQghcsiCBTB3Lvz446PP2X9tPweuHWDZiWW5sthfbqrqVzVToQNqgTNrFnTpkveZhNCK9OwIIQqkzp3VTTLbt3/0OdX9q7Orzy5O3DiBm735jud78FLcyy9rHEYIDWjes3PlyhVef/11ChcujKOjI5UrV2bfvvsD6nr16oVOpzO5NW/e3OQxbt26Rbdu3XBzc8PDw4M+ffqQmJiY1y9FCGFGatWC6dOhWbNHn6PT6ahZtCa9XuiVZ7ly0pJjS6g2sxqTt09+8slCWDBNe3Zu375N3bp1adSoEatXr8bb25szZ85QqFAhk/OaN2/O3Llzjfft7e1Njnfr1o1r166xfv160tPT6d27N/3792fhwoV58jqEECI/ikuJ42D0QaytrPmg3gfG9sREWLsWbt+Gvn01DChEHtG02Jk0aRLFihUzKWSCgoIynWdvb4/fI3bnO3HiBGvWrGHv3r2EhoYC8N1339GyZUu+/PJLAgJybwEwIYT5yciAb7+Fjh2hRIlHn9fhtw684PcCg2sMxtPRM8/y5aQO5TugQ0f78qbX6vbtg06dwMsLeveWjUKF5dP0MtbKlSsJDQ3llVdewcfHh5CQEGbNyrz655YtW/Dx8aFcuXIMGDCAmzdvGo/t3LkTDw8PY6ED0LRpU6ysrNi9e3eevA4hhPnYtAneeUddSVivz/qcg9cO8ufJP/ns388wKOa79oqXkxf9qvfDy8nLpL1uXQgNhe7d4f8XpxXComnas3Pu3DmmT5/OiBEj+PDDD9m7dy9DhgzBzs6Onj17AuolrA4dOhAUFERkZCQffvghLVq0YOfOnVhbWxMdHY2Pj4/J49rY2ODp6Ul0dHSWz5uamkpqaqrxfkJCQu69SCFEvmJvD40bQ/nyj+7RCPYK5pf2v3D+9vlMhYIlsLWFvXu1TiFE3tG02DEYDISGhjJ+/HgAQkJCOHr0KDNmzDAWO10emB9ZuXJlqlSpQqlSpdiyZQtNmjTJ8nGfZMKECYwbN+75X4AQwuw0aAAbN8LjFst1tHXk9Sqv512oXLbq1CoWHV3EJ40+obSnrCQoCh5NL2P5+/tToUIFk7by5ctz8eLFR35PyZIl8fLy4uzZswD4+fkRGxtrck5GRga3bt165DifkSNHEh8fb7xdunTpOV+JEMLcFKQtkL7b8x2Lji7i92OmG4sqCpw8CQ+MDBDCImn6z71u3bqcOnXKpO306dMEBgY+8nsuX77MzZs38ff3B6B27drExcWxf/9+4zmbNm3CYDBQs2bNLB/D3t4eNzc3k5sQwvLt2wcpKY8/5+21b7P46GJSM1Iff6IZ6VetHyNqjaBlmZYm7V27qpfzlizRKJgQeUTTYmf48OHs2rWL8ePHc/bsWRYuXMiPP/7IoEGDAEhMTOTdd99l165dREVFsXHjRtq2bUvp0qUJDw8H1J6g5s2b069fP/bs2cP27dsZPHgwXbp0kZlYQgijpCT1EpavL5w/n/U5p2+e5qtdX9FtWTeuJ1/P24C56JWKrzAlfApV/aqatIeEgJ0dXLumUTAh8ojmu57/9ddfjBw5kjNnzhAUFMSIESPo168fAHfv3qVdu3YcPHiQuLg4AgICaNasGZ9++im+vr7Gx7h16xaDBw9m1apVWFlZ0bFjR6ZOnYqLi0u2Msiu50JYvkOHoHVrdXBuZCRksYEyMYkx/LD3B6ITo5n58sy8D5nH4uPVQdrZ/FUpRL6T3c9vzYud/ECKHSEKBoMBrlyBYsW0TqKNY7HH2BK1hUE1BmkdRYgckd3Pb9kbSwhRYFhZFdxC5/bd21SdURW9oie8dHimWVkGQ8EatC0KFvmrLYSweElJTz7nu93fsf/q/iefaKYKORaiZZmWtC7bmrvpd43tV69CmzZQtuzjp+MLYc7kMhZyGUsIS9eyJVy+DNOmQVhY5uMX4y9S4psSKCicH3qeEh4l8jxjXlAUBd1Dg5VSU9VtIxITYf9+qFZNo3BCPAO5jCWEEKi9Olu3qtsiPLTYulGaPo1XK71KXEqcxRY6QKZCB9QVpefMUXt2qlTRIJQQeUB6dpCeHSEs3a1b6p5YnTo9/jyDYsBKZ/lX9xPTErkUf4ny3uW1jiLEc8nu57fl/6sWQhR4np5PLnSAAlHobDi3Ae8vvOm6tKvWUYTIM5b/L1sIIR5j5amV3Ei+oXWMPBPiF0K6Pp2k9CTiU+KN7RER8MEH8Oef2mUTIrdIsSOEsFg//givvAJbtmR9PDYplo5LOlLkqyJcii8Ye+QVdirMqcGnOD34NO4O7sb2v/6CSZNg/nwNwwmRS2SAshDCYs2bBzt3qjOwGjbMfPzanWtU9a2KtZU1xdwLzgI8pTxLZWpr2xZOnICOHTUIJEQukwHKyABlISzVwYOweDEMHQqP2yovPiXepJejoFAUhXRDOnbWdlpHEeKZyABlIUSBFxKiXpp50p7ABbHQ+fnQz5T5rgyTt0/WOooQuU6KHSFEgRQRHUGGIUPrGJrJMGQQeTuSVadXmbTfuAG//QbS5y8siYzZEUJYnMhImDsXunSBSpUyH49LiaPOT3Uo5FiIvf32EuD6hK4fC9Q+uD3Ots60KtvK2JaWBkFB6mrKFSpA5coaBhQiB0mxI4SwOAsWwOefq9sfrF6d+fjx68dxsnWikEMh/F388z5gPlDIsRCvVnrVpM3OTh3IffmyuhCjEJZCih0hhMWpUQPatVOnnWelTrE6XBlxhUsJl7LcQqEg++MPdQsJISyJjNkRQlic5s3VxfFee+3R59jb2FPas3TehcqnFhxeQKuFrTh54yQghY6wTFLsCCEKlJvJN7WOkK8sOLKAf878w+/HfjdpNxggLk6bTELkNCl2hBAWQ1HUmUTx8VkfT0pLotTUUtSfW5/YpNi8DZdPvRn6JuMajqNLpS7GthUroGhR6N9fw2BC5CAZsyOEsBh796ozsDw9IToabG1Nj++8vJM7aXe4cucKXk5e2oTMZ9qUa0Obcm1M2vz94do12LFD7eGxkv8WCzMnxY4QwmLExUFwsLqY4MOFDkDTkk25OOwiUXFRBWKH82cVGgrr1kH9+lLoCMsg20Ug20UIYUkUBe7eBScnrZOYD4NiYPfl3WyJ2sLIsJFaxxEi27L7+S09O0IIi6LTZV3o6A16rK2s8z6QGUhITaDBvAakG9JpF9yO8t7ltY4kRI6SDkohhEW4ePHRWxyk6dMo/V1p+qzow+27t/M2mBnwcPCgY4WOvFbZdK7+tGnQpAmcOqVRMCFyiPTsCCHMXkaGOs7E2RnWr4fSDy2fsy5yHVFxUayJXMNM+5nahMznFnVclKltxQrYtAlWrYJy5TQIJUQOkWJHCGH2Tp6E5GS1Z6dEiczHW5Vpxbbe24hJjMHGSn7tZdegQdCypboatRDmTP7VCyHMXqVKEBurFj02WfxW0+l01CteL++DmaHrSde5lHCJav7VaNtW6zRC5AwZsyOEsAhOTlCtmtYpzNuas2vwn+JPjz97aB1FiBwlxY4QwqwZDI8+pjfoaTy/MRP/m0hiWmLehTJTtYrWwtrKGnsbe+JT1GWoU1PVNXfmz9c4nBDPQYodIYRZ690bwsNh9+7Mx9ZFrmNz1Ga+2PGFjNXJBg8HDy4Ou8j+/vtxd3AH1FWpw8Nh+HB1ILgQ5kj+9QshzFZKCixdCklJ8MknmY+HBYYxp80cktKTcLBxyPuAZsjXxdfkfq1aULmy+uedO1CokEbBhHgOUuwIIcyWgwNERKhTo2vUyHzcxc6F3iG98zyXJdAb9KRkpOBs58zhw1qnEeL5yGUsIYRZK11avcSi02mdxHL8uP9HinxVhC92fKF1FCFyhBQ7QgiLoygK/Vb2Y9mJZaTr07WOY3YcbRyJSYphXeQ6k/ZLl+DWLY1CCfEcpNgRQpilP/+Et96C/fszH/v3wr/MPjibnst7kqpPzftwZq5tcFv+ee0ftvTaYmzr1w+KF4cFC7TLJcSzkjE7QgizNHs2/PMPeHlB9eqmx0oWKsnIeiPRocPFzkWbgGbMzd6NFmVamLQFB4OVFZw/r1EoIZ6DTlEetXVewZHdLeKFEPnHunVqL8OHH8q+TXnh9m3Q69XiUoj8Iruf33IZSwhhlpo1Uxe6k0In98zcN5OwuWEciz1GoUJS6AjzJcWOEMJiKIrChG0TOBJzROsoFuHvM3/z38X/+P347ybtcj1AmBspdoQQZiUmBr7/HqKjMx/bf20/H276kBdnvWjc7kA8u8E1BvN1+Nf0rdYXgOvX4bXXoEKFx2/TIUR+IwOUhRBmZckSGDIEFi6EHTtMj9lb29OxfEdc7V2N2x2IZ9esVDOalWpmvO/uDn//DQkJ6jYSNWtqGE6IpyDFjhDCrPj7qx+yr76a+Vhl38r80fkPZN5F7rCzgx9+gBIlIDRU6zRCZJ/MxkJmYwlhjhRFVk3OCxmGDDae28jWC1v5vPHn6ORNF/mIzMYSQli0hz9zfzv6G7fv3tYmjAVLTk+m7eK2TPhvAseuH9M6jhDPRC5jCSHMxvbt6iUsm4d+cx2/fpwuS7vgaufKlRFXcLV31SagBXKzd6N7le7YWNkYd44/eRL++EPdDb1tW40DCpENUuwIIczCmTNQrx4EBEBkpLrj+T03k29S2acypTxLSaGTC2a1mWVy/88/YdQoaNVKih1hHqTYEUKYhTNn1EXtqlQxLXQAwgLDOPTmIZLSk7QJV8C0aQO7d0OHDlonESJ7pNgRQpiFli3h6lW4cSPr4zqd7IOV26LioriccJl6FeuxfLnWaYTIPhmgLIQwG7a26tTzB+29sheDIivc5ba/T/9N0LdB9F3ZV6b2C7MjxY4QIt9LTMy6/fzt89SYXYPSU0uTlCaXsHJTWGAYTrZOBLgGkJCaAMCdO7BypWwfIfI/uYwlhMjXFAVCQtTxOvPnQ9my948dv34cDwcPSnmWwtnOWbuQBYCbvRvX3r6Gm726lklGBgQGqruhHzig/oyEyK+k2BFC5GunTqmzr65ehSJFTI+1KtuKqyOuEpsUq024AuZeoQPq9P/69eHoUYiVt1/kc1LsCCHyteBguHIFIiLAOYvOG0dbRwI9AvM8V0GWkpFCakYqCxa44+QkK1mL/E/zMTtXrlzh9ddfp3Dhwjg6OlK5cmX27dtnPK4oCqNHj8bf3x9HR0eaNm3KmTNnTB7j1q1bdOvWDTc3Nzw8POjTpw+Jj7rIL4QwO/7+0KKFaZv05mhj6u6p+Hzhwxc7vsDZWQodYR40LXZu375N3bp1sbW1ZfXq1Rw/fpwpU6ZQqFAh4zmTJ09m6tSpzJgxg927d+Ps7Ex4eDgpKSnGc7p168axY8dYv349f/31F//++y/9+/fX4iUJIfJAdGI0Rb8qSpOfm5Ccnqx1nALFy8mLO2l32H5pu7FNUSBJxoeL/EzR0Pvvv6/Uq1fvkccNBoPi5+enfPHFF8a2uLg4xd7eXlm0aJGiKIpy/PhxBVD27t1rPGf16tWKTqdTrly5kq0c8fHxCqDEx8c/4ysRQuSGDz5QlB49FOXgQdP2RUcWKbqxOqXOT3U0yVWQ3Um9o2y/uF3RG/SKoijKmjWKUrq0orRvr3EwUSBl9/Nb056dlStXEhoayiuvvIKPjw8hISHMmnV/WfLz588THR1N06ZNjW3u7u7UrFmTnTt3ArBz5048PDwIDQ01ntO0aVOsrKzYvXt3ls+bmppKQkKCyU0Ikb9kZMCcOfDzzxAdbXqsS6UunB96nu9afKdNuALMxc6FOsXqYKVTPz68vODsWdi6Vf2ZCZEfaVrsnDt3junTp1OmTBnWrl3LgAEDGDJkCPPnzwcg+v9/w/n6+pp8n6+vr/FYdHQ0Pj4+JsdtbGzw9PQ0nvOwCRMm4O7ubrwVK1Ysp1+aEOI5WVnBsmUwYgQ0aZL5eKBHINX8q+V9MGGiWjVYvhyiojJv0CpEfqFpsWMwGKhWrRrjx48nJCSE/v37069fP2bMmJGrzzty5Eji4+ONt0uXLuXq8wkhnp6VFdStC1OmqCsn35NhkO6D/GDKjilUnVGVQzERtG0LrrL/qsjHNC12/P39qVChgklb+fLluXjxIgB+fn4AxMTEmJwTExNjPObn50fsQ4s8ZGRkcOvWLeM5D7O3t8fNzc3kJoTI/27dvUWRr4rw5l9vkpKR8uRvELlm+6XtHI45zO/Hftc6ihBPpGmxU7duXU6dOmXSdvr0aQID1TUzgoKC8PPzY+PGjcbjCQkJ7N69m9q1awNQu3Zt4uLi2L9/v/GcTZs2YTAYqFmzZh68CiFETvv3Xxg/Hs6dM21fdmIZsUmx7Lq8C3tre23CCQCG1hzKT21+4u06bwPq2Kq2beH4cY2DCZGVPBownaU9e/YoNjY2yueff66cOXNGWbBggeLk5KT8+uuvxnMmTpyoeHh4KCtWrFAOHz6stG3bVgkKClLu3r1rPKd58+ZKSEiIsnv3buW///5TypQpo3Tt2jXbOWQ2lhD5S7duigKKMnSoabveoFc2ndukrD27VpNc4tFatFB/ZhMnap1EFCTZ/fzWdDjZiy++yJ9//snIkSP55JNPCAoK4ptvvqFbt27Gc9577z2SkpLo378/cXFx1KtXjzVr1uDg4GA8Z8GCBQwePJgmTZpgZWVFx44dmTp1qhYvSQiRA1q2VLcg6NrVtN1KZ0WjoEbahBKP1a8f1KkD7dppnUSIzHSKIvvVJiQk4O7uTnx8vIzfEUKIbErNSGXlqZVsvbCV71p8h06WUxZ5LLuf35pvFyGEEE+SmJZIzdk1+WrnV6Tp07SOI/5fuiGdHst7MG3vNCKiI7SOI8QjyaoIQoh8484dWLdOvYzl6Hi/fcmxJey5sofbd28zvNZw7QIKEy52Lvyv+v+wt7bH09ETvR5274YLFzJfghRCS1LsCCHyjRUroHt3qF4dHtgPmE4VOpGuT8fFzkUuleQz3zT/xvj1zp3q2kgeHtCpk+n6SEJoSYodIUS+oddDYCC0bm3a7mbvxv9C/6dNKJFtNWpAmTIQGgpxceDtrXUiIVRS7Agh8o2ePaFHD0hN1TqJeBqKonA09ijRidGcOvUS0vkm8hsZoCyEyFd0Ori3skRKRgqvLX2NVadWoTfotQ0mHumv039RZUYV3vz7TaDAT/AV+ZAUO0IIzen1sHcvPLwQxvKTy1l0dBED/xmoTTCRLY2DGlPIoRCVfSpzJ+0OADduwO3bGgcT4v9JsSOE0Nzatep4j6ZNTQueFwNe5O3abzOi1gisray1Cygey9nOmWtvX2N5l+W42bsxfDj4+sK8eVonE0IlY3aEEJo7exbs7aFKFUzGe5TyLMWXzb7ULpjINnub+3uVlSgBBoPskyXyD1lBGVlBWYj84NYtyMgAHx+tk4jnkZCaQMz1dByUwhQrpnUaYelkBWUhhFnx9Lxf6GQYMhi9eTQnrp/QNpR4Kl9s/wLvL7yZfWKyFDoiX5FiRwihGb0eYmIyt68+s5pP//2UBvMakGHIyPtg4pmU8ChBmj6Nw7GHtY4ihAkpdoQQmlm7FooVgzffNG33cfahTbk2vBHyBjZWMrTQXLQq24ojA47wz2v/cPs2/O9/ULWqWtQKoSX5LSKE0MyWLZCeDk5Opu01i9ZkRZcVyJBC8+Jk60Qln0oAuLrCkiXqSsq7d0OdOtpmEwWbFDtCCM1MngzdukGhQlkfl32wzJe1tcJXX0FAgI5q1bROIwo6uYwlhNBU1apQvLj6tUEx8POhn0lITdA2lHgu47aMo+TUklQJP0B4+P0VsYXQihQ7Qog8p9erl68etiVqCz2X96T8tPKyPYQZO37jOFFxUfxx/A+towgBSLEjhNDAmjVqb86XD60XmJKRQrBXMG3LtZUVk83Y8FrD+f2V3xnVYBQXLsA338DKlVqnEgWZjNkRQuS5xYshOhquXTNtb1mmJS1Kt+Buxl1tgokcUatoLePXS5bAe+9B8+bQpo2GoUSBJsWOECLP/fQTtG0LISGZj+l0OpxsnTIfEGapdWtYvRpeflnrJKIgk2JHCJHn7OygU6f7909cP8Gpm6doW66tzMCyEHfT77LwyEK2XtjKxo3z5ecqNCVjdoQQeeZRy+Z8tOkj2v/WnpEbR+ZtIJFrFBSGrBnCL4d/Yd/VfVrHEQWcFDtCiDzz99/q4nK//36/TVEUKnpXxM3ejR5Ve2gXTuQoJ1snhtUcxueNP6eoW1FSU2HDBnU3dCHymux6jux6LkReadtWnZXz9tuZZ2IlpSXhbOesTTCRqwwGdVuQq1dh714IDdU6kbAUsuu5ECLfmTkTPv8c+vfPfEwKHctlZQW1a4OfH1y5onUaURBJsSOEyDN+fvDhh1C2rHr5atyWcZy+eVrrWCIXGRQD/138j/bv/c2VK2rvnhB5TYodIYQm1kauZezWsVT/sbpsD2HBVp1aRdjcMD7aMQidrsCPmhAakWJHCJHr1qyBnj1hz577bcXcitG6bGv6V+uPm72MlbNUL5V6iQDXAOoH1icxLRGAtDSNQ4kCR9bZEULkuu+/V2di+fhAjRpqW0Wfiqzqukr2wLJwTrZOXBp+CSudFVu3wrBh6lYhK1ZonUwUJFLsCCFy3ccfq4VOv36Zj8keWJbPSqdeRHB3h4gIOHtW7d2xs9M2lyg45DKWECLX1aoFc+aoA5OXnVjGhG0TSEpL0jqWyGO+paKZ9tNtzp2TQkfkLSl2hBB5Jl2fznvr3+PDTR8yfd90reOIPPTp1k8p8lUA54p+jre31mlEQSPFjhAi12zcqK6rc/Wqet9KZ8WYBmOoVbQWb4a+qW04kacq+VRCQSEqLkrrKKIAkhWUkRWUhcgtrVurA5PfeQe++ELrNEJLKRkpxCTGEOgRyLJlsHQpjBwJlSppnUyYs+x+fssAZSFErunaFeLjs14xWRQsDjYOBHoEAjBvHqxaBRUqSLEj8oZcxhJC5Jpu3WDbNggITKLFghasPrMa6UwWPXrqee89hVattE4iCopnKnYyMjLYsGEDM2fO5M6dOwBcvXqVxMTEHA0nhLAM0/ZOY83ZNQxePZgMQ4bWcYSG3l33LgPP+dP+rd288ILWaURB8dSXsS5cuEDz5s25ePEiqampvPTSS7i6ujJp0iRSU1OZMWNGbuQUQpiR7dvVQclt26pTjN8IeYPrSdcJDQjF1tpW63hCQ1fuXOF68nVWnFxBraK1tI4jCoin7tkZOnQooaGh3L59G0dHR2N7+/bt2bhxY46GE0KYp88/h86d4bPP1PteTl580ewLXq30qrbBhObeqfMOa19fyyeNPiEyEgYMgJs3tU4lLN1T9+xs27aNHTt2YPfQilAlSpTgypUrORZMCGGeFEXdEuLwYejeXQF0WkcS+Ug1/2rGr7t0gX371NW1x43TMJSweE/ds2MwGNDrM+9lc/nyZVxdXXMklBDCfOl0MHYsXLgAs6Lep9fyXrK2isjS++9Dy5bQooXWSYSle+pip1mzZnzzzTfG+zqdjsTERMaMGUPLli1zMpsQwozFp93iuz3fMf/QfE5cP6F1HJGPJKUl8fXOr/k1ox2r/jJQS4buiFz21IsKXr58mfDwcBRF4cyZM4SGhnLmzBm8vLz4999/8fHxya2suUYWFRQiZxw8qPbs3Jtls+fKHpYcW8IXL32BTieXs4QqJSMFny98uJN2h9XdVtO8dHOtIwkzld3P72daQTkjI4PFixdz+PBhEhMTqVatGt26dTMZsGxOpNgRIme0aAFr1sA338DQoVqnEfnZpP8m4WznTJ+QPhjSHPnxRyhUCHr10jqZMCe5WuxYGil2hHh+ej107w7LlsHBQ+mULydTzEX2/PQT9O0LAQFw7hzY22udSJiLXNsu4ueff37s8R49ejztQwohLIC1NSxcCHsiT9F4eUPeu/Uew2oNk8tX4olefx1++UX900rW9Re54KmLnaEP9U2np6eTnJyMnZ0dTk5OUuwIUcD9enoa0YnRbL2wleG1h2sdR+Rz/138j3FbxzF23kc0LNFQ6zjCQj11sXP79u1MbWfOnGHAgAG8++67ORJKCGFezpwBT08oXBimNJtCFd8qsjquyJZFRxax4dwGdOik2BG5Jkc6DMuUKcPEiRMz9foIIQqGIUOgSBFYvBhsrW3pW60vlXxkO2vxZO/Xe583q7/Jjy//iKLA2rXQsycYDFonE5bkqXt2HvlANjZcvXo1px5OCGEmUlPh+nX1z9BQWTFZPJ3i7sWZ3no6AHfuqKsqx8XByy9Dp07aZhOW46mLnZUrV5rcVxSFa9eu8f3331O3bt0cCyaEMA/29rB3L9Se3JPuW0/zo+uPVPatrHUsYYZcXeH99xViY3XUqaN1GmFJnrrYadeuncl9nU6Ht7c3jRs3ZsqUKTmVSwhhRqLiznM443cyrmbgZi/LN4inF5MYw4T/JnC55GX++OAPreMIC/NMe2M9eNPr9URHR7Nw4UL8/f2f6rHGjh2LTqczuQUHBxuPN2zYMNPxN9980+QxLl68SKtWrXBycsLHx4d3332XjIyMp31ZQohnEBOjrq8TVCiIc0PPsbDjQgI9ArWOJczQnbQ7fLfnO5aeWMrR2KNaxxEWJsfG7DyrihUrsmHDBuN9GxvTSP369eOTTz4x3ndycjJ+rdfradWqFX5+fuzYsYNr167Ro0cPbG1tGT9+fO6HF6KA69kTjh+HuXOhSRM/OlWQQRbi2ZT2LM34xuOpHlCdit4VOX8eJk1Sx/A0bKh1OmHuslXsjBgxItsP+NVXXz1dABsb/Pz8HnncycnpkcfXrVvH8ePH2bBhA76+vrzwwgt8+umnvP/++4wdOxY7O7unyiKEyL6EBDhwQOH6dR1BQVqnEZbg/XrvG7/+6iuYOVNd1kCKHfG8slXsHDx4MFsP9iwrpZ45c4aAgAAcHByoXbs2EyZMoHjx4sbjCxYs4Ndff8XPz4+XX36ZUaNGGXt3du7cSeXKlfH19TWeHx4ezoABAzh27BghISFZPmdqaiqpqanG+wkJCU+dW4iCzs0NvlqzlJ5TZzH9bBW+KPmF1pGEBXn7HQPnzlnxwQdaJxGWIFvFzubNm3PlyWvWrMm8efMoV64c165dY9y4cYSFhXH06FFcXV157bXXCAwMJCAggMOHD/P+++9z6tQpli1bBkB0dLRJoQMY70dHRz/yeSdMmMC4ceNy5TUJUZDsit6CIWgdLnYydUbknDkH5zB+23iWz10u6zWJHJGvNgKNi4sjMDCQr776ij59+mQ6vmnTJpo0acLZs2cpVaoU/fv358KFC6xdu9Z4TnJyMs7Ozvzzzz+0aNEiy+fJqmenWLFishGoENmUmnp/s8b/Lv5HFd8qMgtL5JhXfn+FP47/Qd+QvsxqM0vrOCIfy7WNQAH27dvHkiVLuHjxImlpaSbH7vW6PAsPDw/Kli3L2bNnszxes2ZNAGOx4+fnx549e0zOiYmJAXjsOCB7e3vsZVtdIZ5ZmzZqwfPNN1DvhXpaxxEWZnT90dQtVpf+1fuTnAyzZ8PZszB1qtbJhLl66qnnixcvpk6dOpw4cYI///yT9PR0jh07xqZNm3B3d3+uMImJiURGRj5yCntERASA8Xjt2rU5cuQIsbGxxnPWr1+Pm5sbFSpUeK4sQoisxcbCpk0KW7eq43aEyGmVfSszrNYwnGydOHcOhg6F77+HEye0TibM1VMXO+PHj+frr79m1apV2NnZ8e2333Ly5Ek6d+5sMrA4O9555x22bt1KVFQUO3bsoH379lhbW9O1a1ciIyP59NNP2b9/P1FRUaxcuZIePXpQv359qlSpAkCzZs2oUKEC3bt359ChQ6xdu5aPP/6YQYMGSc+NELnEpVAynh+8iPurQ0l2kfVQRO6qVAkGv6Vn+nQoWVLrNMJcPXWxExkZSatWrQCws7MjKSkJnU7H8OHD+fHHH5/qsS5fvkzXrl0pV64cnTt3pnDhwuzatQtvb2/s7OzYsGEDzZo1Izg4mLfffpuOHTuyatUq4/dbW1vz119/YW1tTe3atXn99dfp0aOHybo8Qoicde72ORwL38Cj1grKeJbROo6wYEdijtD81+YkNunL//53f5yYEE/rqcfsFCpUiDt37gBQpEgRjh49SuXKlYmLiyM5OfmpHmvx4sWPPFasWDG2bt36xMcIDAzkn3/+earnFUI8G0WBSj6VODX4FOdun8PeRj59RO65m3GXtZFrcbBx4MuXvqSwU2GtIwkzle2enaNH1e7q+vXrs379egBeeeUVhg4dSr9+/ejatStNmjTJnZRCiHyhQwfo1w+ir9hT3ru81nGEhatRpAZfh3/NsYHHKOxUmA0boFkzuHRJ62TC3GR76rmVlRUvvvgi7dq14/XXX6dYsWIYDAYmT57Mjh07KFOmDB9//DGFChXK7cw5LrtT14QoyA6eiKNaBQ90OoVz53SUKKF1IlHQNGwIW7fCkCHw7bdapxH5QXY/v7Nd7Gzbto25c+fyxx9/YDAY6NixI3379iUsLCzHQmtFih0hnuyD9SOZtHA7NRnGrrkdtI4jCqDV61JZ87c977wDxYppnUbkB9n9/M72ZaywsDDmzJnDtWvX+O6774iKiqJBgwaULVuWSZMmPXbFYiGE+bO2ssKu1G4++sBW6yiigEnXp/P22rfpdtCfdz+9LIWOeGpPPRvL2dmZ3r17s3XrVk6fPs0rr7zCtGnTKF68OG3atMmNjEKIfODzJp8TNTSK1mVbax1FFDC21rbsv7af2ym3WXB4gdZxhBl67u0ikpKSWLBgASNHjiQuLg69Xp9T2fKMXMYS4vH694cyZaBvXzDDYXnCAuy5sodbd28RXiqcS5d0TJoExYvD++8/+XuF5crV7SIA/v33X+bMmcPSpUuxsrKic+fOWe5nJYQwb1+s+pNZs9qj08Err0ixI7RRo0gN49e7dsEPP6h/FwcPBmdnDYMJs/BUxc7Vq1eZN28e8+bN4+zZs9SpU4epU6fSuXNnnOVvmxAW58zNM3ywuyfW7dbQu8gXlCghPZ9Ce23bZ9C9p8IbvWxxctI6jTAH2S52WrRowYYNG/Dy8qJHjx688cYblCtXLjezCSE0lm5Ip0GZUBwrXGbWa1LoCO39ffpvhq4ZSoduHWjYcLLWcYSZyHaxY2tryx9//EHr1q2xtrbOzUxCiHyigncFNvXcRFJaktZRhDCKvB3J78d/Z3yT8dhYPfNoDFGAPPcAZUsgA5SFyNpHH8GLL0Lr1mAjnykiH1AUhbkRc3m14qvYKM7Mng2LFsGmTWBnp3U6kddyfFFBSybFjhCm9l/dz/IdR/nslZ7odHD+PAQGap1KCFMpKVCqFFy9CnPmQO/eWicSeS3XZ2MJISzXh5s+ZF3EYap3KkJFp6ZS6Ih8ycEBPhiTgHWGG127ap1G5GdS7AghTCiKQvvg9py+eZolH5WkpEw1F/nQrbu36PFnD/679R9Rw6JwcPDQOpLIx556BWUhhGXT6XS8GfomZ986S8lCJbWOI0SWPBw8iIqL4k7aHTaf32xsl4EZIitS7AghMvnuOzh1UmZdivzLSmfF7DazOTnoJO3Lt2fbNmjSBP78U+tkIj+Sy1hCCEC9fDXw74HUc+7NkCE1sLKCK1fAz0/rZEJkrVbRWsav161TZ2QlJUGHDhqGEvmSFDtCCACWn1zOjP0z+Pn2Xlq9vANrnZ0UOsJsdO93ixu3XPngPVuto4h8SC5jCSEAde+hN6u/yfvt2/DXSjuWLdM6kRDZ8+2ub6m+IJBS3b6VmYMiS9KzI4QAoIhbEaa3nm68LwulC3PhZu9GYloiG89v5J067wDqQGWdTuNgIt+QYkcIYbR0qTrI08ND6yRCZN/rVV7Hz8WP5qWbExMDn3+uLoS5apXWyUR+IcWOEAXcvIh57L2yl67+Y+jUyQcXF7h8GdzdtU4mRPbYWtvSokwLQF1Vefp0yMiAQ4egalWNw4l8QYodIQqwNH0aozaP4nLCZaz9GlCxYmdKlJBCR5ivosX0vDvmNk1qe1GlitZpRH4hxY4QBZidtR2/tP+F7/d8z+QObbDvDwkJWqcS4tmcvnmalxe9TLJzMmManEWns9c6ksgnZDaWEAVcwxIN+aPzHzjYOKDTSa+OMF+B7oEkpSWRlJbEsevHADAYNA4l8gUpdoQooJQH1tXfvVs+FIT5s7exZ3mX5VwYdoEQv2rMnAllysCJE1onE1qTYkeIAiguJY4XZr7Aj/t/5PjJDGrVgnLl1MGdQpiz0IBQXO1d0elg9Wo4d07d/kQUbDJmR4gCaMa+GRyOOcy3u7/Fy68P7u4QHAwODlonEyLndB10hoaNStK/nywaVdBJsSNEATSs1jCcbJ0o7VmalmWsad4cbt7UOpUQOaffyn7MPjibeW3n4eTUU+s4QmNS7AhRADnYODCk5hDjfScn9SaEpShTuAw6dJy4cX/Ajl4vK4MXVDJmR4gCJF2fbnL/6lWNggiRywa+OJDjg44zselEDhyA8HB45x2tUwmtSLEjRAEydM1Qmv7clEPRhzh5EooWhRYt1NVmhbAkLnYuBHsFA3DjBqxbB3PmQFKSxsGEJuQylhAFRHxKPD8f+pmk9CTiU+M5sVVtt7UFG/lNICxYaL3bvDUyleH9/HB21jqN0IL8ihOigHB3cOfowKMsPb6U+oH1qf8/aNZMppsLy7bp/CbaLW5HYLFAvilxCLmgUTBJsSNEAVLCowRv13nbeD8oSMMwQuSBEL8QdDodVjorohOjCXANID1d7dEUBYeUuEJYML1Bz5t/vcmGcxtM2lNTNQokRB4r5FiI3X13c/B/B3HTBTB8OJQqBYmJWicTeUmKHSEs2Pd7vmfm/pl0+K0Dt+7eAuDkSfD1haFD4YEdI4SwWMFewVjprHB0hL//hkuX4PfftU4l8pJcxhLCgr0Z+iabozbTvUp3PB09AfjtN4iPh6go0Om0zSdEntLp6f7+AWoUDaVZM/nLX5BIsSOEhTEoBqx0aqetvY09f776J7oHqppRo6BuXfDw0CigEBowKAZq/VSLfVf38U/9f9DpWmgdSeQhuYwlhAVJTk+m5YKWfL/ne2Ob7qHuGysraNoUQkPzOp0Q2rHSWdEgsAEeDh7EJMUA6vpSssZUwSDFjhAWZPHRxayNXMvIjSOJTozOdFzG6IiC7KOwj4gaGkWvF3rx229Qrhz8/LPWqURekGJHCAvS+4XejKo/itXdVuPn4mdy7MQJqFQJpk3TKJwQGivkWAh3B3cALl+Gc+dg9myNQ4k8IcWOEGYuITUBg2IA1EtWnzT6hHrF62U676ef4PhxWL8+rxMKkf+EdTjO+59dkX8PBYQMUBbCjN1IvsFLv7xEqH8oM1+eaRyYnJVRo9T1RSpXzsOAQuRDsw/Mpt+qfjQq0Qhn501axxF5QHp2hDBjuy7v4nDMYVaeXsnVO5m3MP/7b4iLU792d4cBA6Be5k4fIQqU8FLh2FvbU9ipMHfT7wKyQailk2JHCDPWumxrFnRYwNZeWynqVtTk2McfQ+vWMGKERuGEyKeKuRfjwrAL/P7K71y75EjLltCwoQzgt2RS7AhhZi7FX+JO6h3j/S6VuhDsFZzpvBYt1N3MvbzAYMjLhELkf74uvgC4usLWrXDgABw5onEokWuk2BHCjETeiqTe3Hq0WtiKpDTTfvfbt2Hv3vv369ZVZ5tMnqyurSOEyMzWNY4+47Zx+jRUqaJ1GpFbZICyEGYkLiWOuJQ47K3tiU+Nx9nOGVBnWb30Euj1cOwYFC6snl+smIZhhcjn7qTeofTU0ty8e5MejnsBWWnTUkmxI4QZqR5QnQ3dN1DUrSj+rv7G9lKl1O0fMjIgOvp+sSOEeDRXe1dalW3F/qv7SU5PBiAhAdzcNA4mcpxOUWRIVkJCAu7u7sTHx+Mmf8tFPhMRHYG7vTtBhYJM2vfvh2rV7m/mee4c+PuDo6MGIYUwUwmpCbjYuaDDig8/hO++g02boEYNrZOJ7Mju57emV/LHjh2LTqczuQUH3x9omZKSwqBBgyhcuDAuLi507NiRmJgYk8e4ePEirVq1wsnJCR8fH959910yZLMTYSEOXDtAo/mNaPxzYy7FXwLUGSMDBqh7W/322/1zS5aUQkeIp+Vm74aVzgqdDq5dU6egL1qkdSqR0zS/jFWxYkU2bNhgvG9jcz/S8OHD+fvvv/n9999xd3dn8ODBdOjQge3btwOg1+tp1aoVfn5+7Nixg2vXrtGjRw9sbW0ZP358nr8WIXKav4s/3k7eeDl54Wav/q9FpwM/P/XPkyc1DiiEhTAoBl7ssp7azcvT/9XiWscROUzTy1hjx45l+fLlREREZDoWHx+Pt7c3CxcupFOnTgCcPHmS8uXLs3PnTmrVqsXq1atp3bo1V69exddXnUY4Y8YM3n//fa5fv46dnV22cshlLJGfXUm4Aqlu2Cqu+Piobenp6lTZmjW1zSaEpXh77dt8tesrXq34Kos7LdY6jsgms7iMBXDmzBkCAgIoWbIk3bp14+LFiwDs37+f9PR0mjZtajw3ODiY4sWLs3PnTgB27txJ5cqVjYUOQHh4OAkJCRw7dixvX4gQOWR95Hq2Rm013r9wtAi1q7vSq9f9Rc9sbaXQESIn9XyhJ+727pQrXA5FUdDrIT5e61Qip2h6GatmzZrMmzePcuXKce3aNcaNG0dYWBhHjx4lOjoaOzs7PDw8TL7H19eX6OhoAKKjo00KnXvH7x17lNTUVFJTU433ExIScugVCfF8dl7aycuLXsbGyoadfXZS2bcyhQpBbCycOgUxMeolLCFEzqriW4Wrb1/FydaJDRtg4EB1raq5c7VOJnKCpsVOixYtjF9XqVKFmjVrEhgYyJIlS3DMxZGWEyZMYNy4cbn2+EI8qxD/EMICw7BNLko5r3IAlC8Pf/0FtWqBi4vGAYWwYE62ToA69fzMGXUaelISODtrHEw8N80vYz3Iw8ODsmXLcvbsWfz8/EhLSyPu3i6G/y8mJga///+vrZ+fX6bZWffu+z3mv78jR44kPj7eeLt06VLOvhAhnlWGAyX++4f/3p3D1Uv3x5w1bSqFjhB5xSXoOH0/38jZs1LoWIp8VewkJiYSGRmJv78/1atXx9bWlo0bNxqPnzp1iosXL1K7dm0AateuzZEjR4iNjTWes379etzc3KhQocIjn8fe3h43NzeTmxBamR8xn1n7ZwFgZwenTthy546Ov//WOJgQBVDkrUgq/VCJuRnhxKaf0zqOyCGaXsZ65513ePnllwkMDOTq1auMGTMGa2trunbtiru7O3369GHEiBF4enri5ubGW2+9Re3atalVqxYAzZo1o0KFCnTv3p3JkycTHR3Nxx9/zKBBg7C3t9fypQmRLbsu76LX74PAJoWKPhWpU6wOc+bAxYvQuLHW6YQoeEp5lqJ56eY42DhgUNQddC9cgMBAjYOJ56JpsXP58mW6du3KzZs38fb2pl69euzatQtvb28Avv76a6ysrOjYsSOpqamEh4fzww8/GL/f2tqav/76iwEDBlC7dm2cnZ3p2bMnn3zyiVYvSYinknK2Jm4/RVG17b/ULqr2WJYurd6EENpY3mU5dtZ2pKZCu3bw999w4oT8uzRnsl0Ess6OyHuKoqDT6fjxR/jf/yA4WOHIER02mi/zKYR4UKtWsGYNzJgB/fppnUY8zGzW2RGioBmz4XPeWv0WiqLQrx98/TXs3i2FjhD5TUJqAsHdZrJ6+0UpdMycFDtC5JGkJHit73U+6dWEabtmsPH8RnQ6GDZMdlkWIj/qs7IPX515kyln+yEXQcybFDtC5JH4eFi91Buu1OINtyU0Ldn0yd8khNDM+MbjKVe4HG/VeAudTsf16+r6O8L8yJgdZMyOyD0GA6AzkKZPw8HGgaVL1fVywsO1TiaEyA6DYsBKZ8XKldCtG1SvDps3qxvxCu3JmB0hNLZ9O1SurNDp289otbAVd9Pv0rGjFDpCmBMrnfoxGRICaWkKd+7AQ2vdCjMgxY4QueTbb+H4cR0rp9dkS9QWtl3cpnUkIcQzOp66Fp9hL/P10v8oVEjrNOJpyfwPIXKQotzv3v7+e/D1hRb9XEm0XkSzUs20DSeEeGZ/HP+Dy05/M/6/DOqXWKN1HPGUpNgRIgfcvQujR6vbPYwel8qtu7fw9/Hnu+8A6mgdTwjxnCa9NAlvZ29G1huJXg9//gnt24O1tdbJRHZIsSNEDli3Dr78EqysFLZ7DeSy9Vb+7f0vAa4BWkcTQuQAT0dPxjcZj6JAgwawbRvMmwc9e2qdTGSHjNkRIge0bQsjR8JPi25x0WozV+9c5cxNmaMqhKXR6aB1a3BzzyA9vcBPZjYb0rMjxDNISoKJE9UCx8lJbRs/HqAwDeM2cSn+EmGBYVpGFELkkpMlB5Hw5gIcanwPvK51HJENUuwI8QzatIFNmyAmBrp/tI3LCZfpWrkrACU8SlDCo4S2AYUQuaaMb1F0jgnSe2tGpNgR4hl8+CGcPg0Vmhyg/rz6uNq50rBEQ/xd/bWOJoTIZW/XeZvw0uFU86/GwYMQGQmdOmmdSjyOFDtCZENiIly6BOXLq/ebNFGXjbezf4Hf5tSiik8V7G3stQ0phMgTdtZ2VPOvxr//qoOVPTzU3wmy/k7+JcWOEE8QGakOSExKghmrdrPk/HR+avMTDg7WgBVbe23FztpO65hCiDxWty5UqJgBvkeIT6xAoULyH578SmZjCfEEPj6g10OG3sCrP73D/EPzmXNwjvG4FDpCFExWVgpK35ocr1+Nn89N0jqOeAzp2REiC+npYGsLiqLg6qpj5UooXNiK386/yqHoYNqXb691RCGExnQ6HaObvsuE/yYQXko2vcvPZNdzZNdzYerwYejcGXq/e4I/DD2Y/fJsqvpV1TqWECIfUhQFvaLnTrwNX34J/fpBiRJapyo4ZNdzIZ7Rr7/CqVMw4RMn9l3ez0ebPtI6khAin9LpdNhY2fDGG+paW2PHFfj+g3xJih0h/p/eoCc1I5XPPoN334Wtm23434v9mdt2rtbRhBD53PvvKxQtfZsNdoO4kXxD6zjiIVLsiALv0CHoMfAaL86qwbit47Czg8mToWrJIsxoPQNvZ2+tIwoh8rnqL2bgMbwBVwKm882ub7SOIx4iA5RFgXbrFtSrB4mJ/tC2Mlfq/cRHYR/hbOesdTQhhBmxtbZlVpsf2Rq1lRG1R2gdRzxEih1RIOkNei4nXCbQM5AxY2DrVoVaA2rRr95kKXSEEM+kVtFa1AioxR9/wJYt8MMPWicS98hsLGQ2VkGzcsslRu3tS5J9JEcHHsXe2gFQdzMWQojnceEClC4NGRkwZ9k5ercvqXUkiyazsYTIwu+/wyvhRTn542iuJ97iSMwRdDopdIQQOSMwEAYPTaNkh/n878CLnLxxUutIAil2RAGgN+jZcG4DAFWqgK2tjhpB5Tn4xkleLPKixumEEJbmqy9sCe64BGuHZA5eO6h1HIGM2REWLk2fRt3pzdl3czObe26mYbmG7N0LwcGe0psjhMgVOp2OGa1mkKZPo5RnKa3jCKRnR1gwRYEfp9tx5MOVOCdU49qda4C6c7kUOkKI3FTMvRip0aXo2BEWL9Y6jXZSM1JZfnK51jGk2BGWxaAY+OnAT1xPuo7BAMuWQeodF3oZttK1clet4wkhCpBly9TbR6PS+HL7V1rH0USaPo3Pt33OzH0zNc0hxY6wKH1X9qXvqr6M3DgSa2tYsECd/vndVy5aRxNCFDDDhsGr3VK41rIu7254m43nNmodKc+52ruyuttqynmV0zSHjNkRFkNRwC3iY+z3eVM5PAAAf38YMEDjYEKIAsnFBRb/6sBb/9TixA13Aj0CtY6UJ6ITozlw7QAty7QEwMvJi4YlGmqaSdbZQdbZMVcGxcDcg3NxtXelc8XOrF4NLVuCjY3CoUM6KlTQOqEQQqjjVuys7TAYdFhba50md8UkxlB3Tl0uxl9kWcd/OLi0Ke+9B/b2ufN82f38lp4dYbZ+PvQzfVf1xdvJm5dKvkTz5oXo0QNCQ3WUL691OiGEUCkZ9nw2AebPh4MHwcVFQWehsyS8nLwIDQjFoBiYOaomfy1TX/OyZdrmkp4dpGfHXKVmpFHhzc95s4cnw+oOxNbaFkWRmVZCiPwlIwMqVoTTp6Ht8PXcrfYlq7utxkpnmcNm0/Rp3L57m+N7fXn1VViyBBo2zJ3nkhWUhUW5d8nqtaWvca8+/19fO879NI6YlUOxtbYFpNARQuQ/Njbw7bcwbc5N1nt2YF3kOlaeWql1rByz6fwmJmybYLxvZ22Hr4svjRrB+fO5V+g8DbmMJczC5YTLDPh7AKn6VLpU6kKbcm1o315dvyIoSOt0QgjxeM2bAxTGdv9X2Frb0rZcW60j5YiouChaLmhJqj6VMoXLcOu/TrRtC76+6nHnfLKvslzGQi5jmYtJ/00mOc6Fj1v0M/bkXLkCRYpoHEwIIZ6CXg9376qztSzBZ/9+xoFrB2hy6zcGD7SldOl7Y5Ny/7llgLIwe5G3IvF09KSQYyFu34btk9/j5El4pwHYuqrnSKEjhDAn27bBwIFQty58Ny2dSwmXKFnI/HZG1xv0WFupU8s+CvsIg2Lg/DlrAgOhR4/8V8hJsSPypUvxl2g0vxGFHAux9vW12Br8OHgQrl+HXbvgpZe0TiiEEM/m6FG4Gq1ne3B9knQxHB14FCdbJ61jZYuiKIzbOo6jsUdZ3GkxNlY26HQ6rHXWlC4NERHg7q51ysyk2BH5UnxqPOmGdFIyUrDSWVG4MPzxB9jZQUiI1umEEOLZhIXBTz9B05bJ1Fl4ibsZdzkWe4wXi7yodbRsOX3zNBP+m0CaPo01Z9fgcrU1Hh7wwgvqcQ8PDcM9hozZQcbs5FenYiOJv+lAjfJyrUoIYXn2XNlDkEcQ3s7eWkd5Kn+e+JOrd65S124QYWFgbQ3//gtVquR9Fpl6LsxOcnoy526fM95fPL0U4XWKsHy5dpmEECK31ChSg6Tr+b/QSclI4UbyDeP99uXbM6jGIIKC1B6d6tWhbFnt8mWHFDsiX0jTp/HK769Qa3YtDl47SEYGrF0LcXGQnKx1OiGEyFmKog7kLVkSdu6EfVf3seyExssMZyEhNYEWC1rw0i8vEZ8Sb3LM3V39Pb18OTg4aJMvu2TMjsgXEtMSiU6MJjEtkaT0JGxsYMsW9R9R585apxNCiJyl06ljEBUF5iw7zxzXmrjauVKnWB38XPy0jmd0Pek6x68f5276XU7fPE0x6xfZvx9atVKPO5nHuGoZswMyZie/iE+J52jsUeoWr6t1FCGEyHWXL0NsLFR9QU+tn2pRrnA5vg7/Ot+N4YmIjkBv0FPauTr166uzyRYuhFdf1TqZrLMjzMTZW2cp7VkaAHcHd06sq8tRPfTvL1s/CCEsW9Gi6g2s2dJzC852+WO54TM3z3A34y5VfNURxy/4vQCoe3yFhqoFWmiohgGfgYzZEZqZvH0yFaZVYOnxpQCcPQtvvQVvvgkrLWfbGCGEeKKMu84cO3b/vlYXXY5fP069ufVo9kszzt46a3LMxgZmz4Z9+6BUKU3iPTMpdoQmFEVh/7X9pBvSjTOwSpaEzz6Dtm3h5Zc1DiiEEHnkv//U33+dO0P83USGrxnOx5s+1iRLgGsA/i7++Lv642bvhl6vrnF2r/bS6cxz5XoZs4OM2dGK3qBn5amVtC/f3qRdUeQSlhCi4IiPVzc09vOD96ZvoveWJthY2RA5JJLi7sXzPE9sUiz21va42bvTv7/am/PeezBpUp5HeSIZs5NP7NoFnp75fw2CvHIx/qLxH6+1lTXty7fn+HEoV05dmAqk0BFCFCzu7uqeWcHBYG3dmEN3hxFeOjzPCp0lx5ZgZ21Hu+B2APg4+xiPVaqk/m6uUSNPouQa6dkh93p2Nm9Wp+f5+qrrKPjln9mEmth2YRvhv4bzQb0PGFV/FDqdjosXoWpV9bZ0KRQurHVKIYQoOLZEbaHx/MbYWtuyt99e46DkB0VG5t8xOrKCcj5QsaJ6bbNCBXB11TqN9nZe3sndjLvsubIHvaIH4PhxSE+Hu3dBriAKIQo6RYE//4SkJPV+YlqiyerFOa1e8Xq0L9+eN154g4reFQFYtw7S0u6fk18LnaeRb4qdiRMnotPpGDZsmLGtYcOG6HQ6k9ubb75p8n0XL16kVatWODk54ePjw7vvvktGRkYep8+ajw/MXnaGnxbexDl/zCjU1Ht13+O3Tr/x+yu/Y2OlXkFt3hwOHoTFi8HWVuOAQgihsVdfhQ4d4Lvv4N8L/1Lxh4oM+HtAjj6HoijG2V42VjYs7riYH1r9gLWVNYsXq7+X27aF1NQcfVpN5YtiZ+/evcycOZMqWewi1q9fP65du2a8TZ482XhMr9fTqlUr0tLS2LFjB/Pnz2fevHmMHj06L+M/UkpGCgP+bcsLsyqy+/JuQL0uazBoHCwPxSbFojfojfc7V+yMo62jyTllyqiD84QQoqBr0wZcXNRp3u727lxJuMLeK3u5mXwzRx5fb9Dzv7/+x4T/JhjbbK1t0f3/YMlChcDRUf2dbGeXI0+ZL2he7CQmJtKtWzdmzZpFoUKFMh13cnLCz8/PeHvwmty6des4fvw4v/76Ky+88AItWrTg008/Zdq0aaQ92AenkWt3rhm/LlO4DF99BfXrw8iRGobKQ7FJsYTNDaPr0q6k6e//PG7ehNat4eRJDcMJIUQ+1LUrnDsH77wDVf2qsqLLCo4NPEZhp5wZ0Lj67GpmHZjFqM2jOHkj8y/h8HB1HZ3vv7esySKaFzuDBg2iVatWNG3aNMvjCxYswMvLi0qVKjFy5EiSH9gVcufOnVSuXBlfX19jW3h4OAkJCRx7cHWmh6SmppKQkGByyw1BhYI48L8DrO++Hk9HT+Pg22vxsRSEYeEHrx3k/O3z7L6y2+Sa87vvwt9/w2uvUSDeByGEyC5ra/B+YLeIVmVb5ejKyq3LtmZsg7H81uk3gr2CAThyBK5fv39O+fJgpXl1kLM0nXq+ePFiDhw4wN69e7M8/tprrxEYGEhAQACHDx/m/fff59SpUyxbpu4MGx0dbVLoAMb70dHRj3zeCRMmMG7cuBx6FY/nYONAZd/KAPTsCSkeBxkQUZ27f3RkUcdFxrErlii8dDj/dPuH4u7FCXANMLZ//jnExKgLCFrS/xyEECInnTqlLl/Ss6d6f/WZ1QS4BlDVr+pTPU58SjyOto7YWavXpcY0HGM8dvo0NGmizoTdsME8FwzMDs0+aS9dusTQoUNZv349Do/YG75///7GrytXroy/vz9NmjQhMjKSUs8xPHzkyJGMGDHCeD8hIYFixYo98+M9jQz/HVgftsbd3h1rnU2+ntL3LNL0aaRkpOBmr15ubFoyc4+dv7/asyOEECJrJ0/eX+OmYUNYGf0dQ9YM4cWAF9nZZyfWVtbZepzoxGjCfw2ngncFfm3/a6bvMxjA3l4dp+PikgsvJJ/QrKNq//79xMbGUq1aNWxsbLCxsWHr1q1MnToVGxsb9Hp9pu+pWbMmAGfPqvt1+Pn5ERMTY3LOvft+j1nUxt7eHjc3N5NbXhlUYxB7+u5hYqMp9OoF1avDzv2JRCc+uifKXOgNerr/2Z1G8xtxPem6ybHkZNi/X6NgQghhZoKDoUEDdWaUXg+dKnTC09GTsOJhpBvSs/04R2OPcvz6cbZEbeHKnStZPs/27bBmjbq4oaXSrNhp0qQJR44cISIiwngLDQ2lW7duREREYG2duWqNiIgAwN/fH4DatWtz5MgRYmNjjeesX78eNzc3KlSokCev41mE+IfgYuvO+fOQmAgfL/qNCtMqsPzkcq2jPZeL8RfZfH4zR2KOcCT2iMmxESOgVi2YPl2jcEIIYWb++gtWrFD3zfJ39efckHNMCZ+Cg03WV0Oy0rRkU35/5Xe29d5mXJE5IQFOnLh/TvHi6lIplkyzy1iurq5UqlTJpM3Z2ZnChQtTqVIlIiMjWbhwIS1btqRw4cIcPnyY4cOHU79+feMU9WbNmlGhQgW6d+/O5MmTiY6O5uOPP2bQoEHY29tr8bKyzcFB/Uu8a28ao6Kmc/vabTwcPLSO9VyCCgWxrfc2Tt44SeOgxsb2jAyIi1P/d1KmjHb5hBDCnDiartKBu0P2ul4OXDtAoHugcQbXvW0gQF3A9eWX1UHJq1fD/18wsXj5dry1nZ0dGzZsoFmzZgQHB/P222/TsWNHVq1aZTzH2tqav/76C2tra2rXrs3rr79Ojx49+OSTTzRMnn2FCkGLZnbs7LOT1d1WU9O3IXfvqscuJ1zGXHbyiE+JN35dzqscbYPbmhy3sYFFi2DPHnjEpDshhBCPkJgI48fDgQPq/ZjEGLou7crKUysznbs1aisN5jWgxYIW3Em9k+l4Soq6OrJeb1nr6DyJ7I1F/tj1/NYtdTEpb2+Y/WscVWZWpKpvVea3m4+3s/eTH0AjM/fNZNzWcax9fa1x1tk9snu5EEI8v4ED1SEA4eHq2JpRm0bx2bbPKOFRgjNvnTGZ1Xvi+gnC5oZR1a8qy19djqt95r2KEhPVWVjVquXlq8gdsuu5mTl1Sl3IydERlm47zM3km0TejsTFLv8Oj0/XpzNj/wyuJV5j5amVmYqdcePgzh31fyT5/KqiEELkW++8A1u2QI8e6n8iR4aN5PiN43xY78NMy5eU9y7Pf2/8RwmPEsaxPYoCR49C5f//Fe3iYhmFztOQnh3yR88OwMqV6jT0ihXh5I2TJKUlUT2guvH49aTr+a6X5/bd28w/NJ+hNYcalxsHdZfcsmXVaY2rVqkrJgshhHg2j+spn7FvBmHFw6joUzHL46NHw4QJMG8edOuWexm1ILuem6E2bdRCByDYK5jKXvcLnaXHl1Jyaklm7Z+lUbr7ktKSjF8XcizEsFrDTAodUIu2ZcvUWVhS6AghxPN5VKETER3B9H3TafZrsyx3RzcYICpKnSiSS5sFmAUpdvKpU6egQoX7i+8tPLqQxLRELsRf0DTXgWsHKDm1JKtOrXriuW3bwpQpeRBKCCEKAEVRPxNef/3+htLHrx/nUvwlBoQOoLBj5v2zrKzUHp21a2FAzm6eblbkMhb55zLWg4YMge++gxdeUBfjU9Az/9B8ulXuhr2NOgDmTuodnO2csdLlXc3aZ0Uf5kTMoUlQE9Z3X5+pR2fDBnUqo2vmMXFCCCGeQ1wcBAaqPTSLFkGXLhAVF0VMYgw1i5rOIT9zpmAs9ZHdz28pdsifxU56Onz4Ibz3nummcPcoikK739oRlxLH3LZzKVmoZJ7kyjBkMGHbBIbWGmrcEuKew4ehRg0oVgy2bYPHLGIthBDiGXz5pbpp53vvYdxc+mEbNkCrVjB8uDpWx5JnxcpsLDNnawtffGHa9uAAtcjbkWw8t5E0fZrJGJrckJqRauxNsrGyYVSDUVmed/euugpnuXLw0P6sQgghcsA77zz5nKNH1bV0zp5VL3dlsSFBgSNjdszE+vXQqJE6lRugtGdpjgw4wrx280ymfN9Nv5ujzxuXEkfN2TWZ+N/EJ55bsyYcOgTz51v2/ySEECI/GzZMnQW7YIEUOvdIsWMGUlKgd2/YuhUmTbrfHlQoiNcqv2a8fyn+EkHfBvHlji/RGzJvpPosfj/2O4diDvHNrm+yHOkP9wfKgboq9KO6VoUQQuSM8+ehZ0/49lv1/tWr6vCHe1q3lvXNHiTFjhlwcIA//4Q33oBRWV9BAmD2gdnEJMWw9MTSHHvuftX78U34N6zrvg4vJ69Mxy9cUKfLr1+fY08phBDiCTZtgp9/hs8/VwufevWgY0f1P8ciMxmgTP4coPwsFEVhbsRc6hSrQ7BXMAAGxYBBMWRaZfNx9AY9Op0uW7O83ngD5s6F2rVh+3a5fCWEEHkhIwPefFO9xcerPTkBAbBjR8EaMymzsZ6CORY706apl4/eeusJ5+2ZxrxD85jbdi6VfCo9/mTUgqn/qv7czbjL3LZzsbW2fez5d++qs8aGDIGgoKd5BUIIIXLKv/+qM2EL2u9hmY1lwbZsgcGD1V6UOnWgevWsz0vTpzFx+0QuJ1xmS9SWbBU7EdERzDs0D4NioH/1/tQPrP/Y8x0d4euvn+FFCCGEyDH1H/+rusCTYscMNWgAgwap69g8bjM3O2s7dvfdzfS90xn44kBju96gx9oq6yH6If4hLH91ObFJsY8sdG7cUAuuTp2e51UIIYQQeUMuY2Gel7Eetync4+gNepr83IRGJRoxMmwkdtZ2xvZHFUAPP2/btuq0xrFjYcyYp88ghBBC5ATZCNTCPVjo6PXw0Udw6dKTv2/V6VVsvbCVKTunEJsUC8DCIwsJmxvGrbu3nvj9BoO6hYWjo1r0CCGEEPmd9Oxgnj07D3r7bfjqK6hUCQ4cUFdffhRFUfjt2G/oDXq6VelGYloipaeWJiYphvGNxzMybGS2njMmpmCN+BdCCJH/yGysp2Duxc7Fi9C4MUyc+GzjaI7FHuOngz/xZbMvHzndPC1NLaJkarkQQoj8Qoqdp2DuxQ6oxYidXe49fr9+am/OnDnglXltQSGEECLPyZidAubBQichQV2HJ6fK2HPn4Jdf4K+/4MiRnHlMIYQQIq/I1HMLk56ubhh64AAkJcF77z3/Y5YsCbt3w7Zt6mMLIYQQ5kSKHQtjawu9esGVK9CkSc49btWq6k0IIYQwN3IZywK99RYcP/7olZWza84cdaNPIYQQwpxJsWOhPD3vf33unHpZ62ls3gx9+6pr6ly9mqPRhBBCiDwll7Es3IkT6jgbg0HdlbxMmex9X2Ag1KgBlSurO+kKIYQQ5kqKHQtXtCgUKaKusuzsnP3vK1lSHZCckZF72YQQQoi8IMWOhXN1hdWrwd4e3N2ffH5CAtxbqsDW9vGrMQshhBDmQMbsFAA+PqaFTkSEOkX9YRERULw4TJ+ec2v0CCGEEFqTYqeA+fNPqFVLHXz8cEHz008QH6/2BAkhhBCWQi5jFTD29uo4nPh4dYsJe/v7x779FsqXh86dZQ8sIYQQlkP2xsIy9sZ6Gtu3q7071tZaJxFCCCGeneyNJR6pbt37hc758zBggDpbSwghhLBEchmrAFMUGDUKFixQL2d9843WiYQQQoicJz07BZjBAC4u6rYSw4ZpnUYIIYTIHdKzU4BZW8OMGVqnEEIIIXKX9OwIIYQQwqJJsSOEEEIIiybFjhBCCCEsmhQ7QgghhLBoUuwIIYQQwqJJsSOEEEIIiybFjhBCCCEsmhQ7QgghhLBoUuwIIYQQwqJJsSOEEEIIiybFjhBCCCEsmhQ7QgghhLBoUuwIIYQQwqJJsSOEEEIIi2ajdYD8QFEUABISEjROIoQQQojsuve5fe9z/FGk2AHu3LkDQLFixTROIoQQQoindefOHdzd3R95XKc8qRwqAAwGA1evXsXV1RWdTpdjj5uQkECxYsW4dOkSbm5uOfa4BY28jzlD3secIe9jzpD3MWcU9PdRURTu3LlDQEAAVlaPHpkjPTuAlZUVRYsWzbXHd3NzK5B/CXOavI85Q97HnCHvY86Q9zFnFOT38XE9OvfIAGUhhBBCWDQpdoQQQghh0aTYyUX29vaMGTMGe3t7raOYNXkfc4a8jzlD3secIe9jzpD3MXtkgLIQQgghLJr07AghhBDCokmxI4QQQgiLJsWOEEIIISyaFDtCCCGEsGhS7OSiadOmUaJECRwcHKhZsyZ79uzROpJZmTBhAi+++CKurq74+PjQrl07Tp06pXUsszZx4kR0Oh3Dhg3TOopZunLlCq+//jqFCxfG0dGRypUrs2/fPq1jmRW9Xs+oUaMICgrC0dGRUqVK8emnnz5xb6OC7t9//+Xll18mICAAnU7H8uXLTY4risLo0aPx9/fH0dGRpk2bcubMGW3C5kNS7OSS3377jREjRjBmzBgOHDhA1apVCQ8PJzY2VutoZmPr1q0MGjSIXbt2sX79etLT02nWrBlJSUlaRzNLe/fuZebMmVSpUkXrKGbp9u3b1K1bF1tbW1avXs3x48eZMmUKhQoV0jqaWZk0aRLTp0/n+++/58SJE0yaNInJkyfz3XffaR0tX0tKSqJq1apMmzYty+OTJ09m6tSpzJgxg927d+Ps7Ex4eDgpKSl5nDSfUkSuqFGjhjJo0CDjfb1erwQEBCgTJkzQMJV5i42NVQBl69atWkcxO3fu3FHKlCmjrF+/XmnQoIEydOhQrSOZnffff1+pV6+e1jHMXqtWrZQ33njDpK1Dhw5Kt27dNEpkfgDlzz//NN43GAyKn5+f8sUXXxjb4uLiFHt7e2XRokUaJMx/pGcnF6SlpbF//36aNm1qbLOysqJp06bs3LlTw2TmLT4+HgBPT0+Nk5ifQYMG0apVK5O/k+LprFy5ktDQUF555RV8fHwICQlh1qxZWscyO3Xq1GHjxo2cPn0agEOHDvHff//RokULjZOZr/PnzxMdHW3y79vd3Z2aNWvKZ87/k41Ac8GNGzfQ6/X4+vqatPv6+nLy5EmNUpk3g8HAsGHDqFu3LpUqVdI6jllZvHgxBw4cYO/evVpHMWvnzp1j+vTpjBgxgg8//JC9e/cyZMgQ7Ozs6Nmzp9bxzMYHH3xAQkICwcHBWFtbo9fr+fzzz+nWrZvW0cxWdHQ0QJafOfeOFXRS7AizMGjQII4ePcp///2ndRSzcunSJYYOHcr69etxcHDQOo5ZMxgMhIaGMn78eABCQkI4evQoM2bMkGLnKSxZsoQFCxawcOFCKlasSEREBMOGDSMgIEDeR5Fr5DJWLvDy8sLa2pqYmBiT9piYGPz8/DRKZb4GDx7MX3/9xebNmylatKjWcczK/v37iY2NpVq1atjY2GBjY8PWrVuZOnUqNjY26PV6rSOaDX9/fypUqGDSVr58eS5evKhRIvP07rvv8sEHH9ClSxcqV65M9+7dGT58OBMmTNA6mtm697kinzmPJsVOLrCzs6N69eps3LjR2GYwGNi4cSO1a9fWMJl5URSFwYMH8+eff7Jp0yaCgoK0jmR2mjRpwpEjR4iIiDDeQkND6datGxEREVhbW2sd0WzUrVs309IHp0+fJjAwUKNE5ik5ORkrK9OPHmtrawwGg0aJzF9QUBB+fn4mnzkJCQns3r1bPnP+n1zGyiUjRoygZ8+ehIaGUqNGDb755huSkpLo3bu31tHMxqBBg1i4cCErVqzA1dXVeO3Z3d0dR0dHjdOZB1dX10xjnJydnSlcuLCMfXpKw4cPp06dOowfP57OnTuzZ88efvzxR3788Ueto5mVl19+mc8//5zixYtTsWJFDh48yFdffcUbb7yhdbR8LTExkbNnzxrvnz9/noiICDw9PSlevDjDhg3js88+o0yZMgQFBTFq1CgCAgJo166ddqHzE62ng1my7777TilevLhiZ2en1KhRQ9m1a5fWkcwKkOVt7ty5WkczazL1/NmtWrVKqVSpkmJvb68EBwcrP/74o9aRzE5CQoIydOhQpXjx4oqDg4NSsmRJ5aOPPlJSU1O1jpavbd68Ocvfhz179lQURZ1+PmrUKMXX11ext7dXmjRpopw6dUrb0PmITlFk2UohhBBCWC4ZsyOEEEIIiybFjhBCCCEsmhQ7QgghhLBoUuwIIYQQwqJJsSOEEEIIiybFjhBCCCEsmhQ7QgghhLBoUuwIIcxer169ZKVYIcQjyXYRQoh8TafTPfb4mDFj+Pbbb5H1UYUQjyLFjhAiX7t27Zrx699++43Ro0ebbMjp4uKCi4uLFtGEEGZCLmMJIfI1Pz8/483d3R2dTmfS5uLikukyVsOGDXnrrbcYNmwYhQoVwtfXl1mzZhk343V1daV06dKsXr3a5LmOHj1KixYtcHFxwdfXl+7du3Pjxo08fsVCiJwmxY4QwiLNnz8fLy8v9uzZw1tvvcWAAQN45ZVXqFOnDgcOHKBZs2Z0796d5ORkAOLi4mjcuDEhISHs27ePNWvWEBMTQ+fOnTV+JUKI5yXFjhDCIlWtWpWPP/6YMmXKMHLkSBwcHPDy8qJfv36UKVOG0aNHc/PmTQ4fPgzA999/T0hICOPHjyc4OJiQkBDmzJnD5s2bOX36tMavRgjxPGTMjhDCIlWpUsX4tbW1NYULF6Zy5crGNl9fXwBiY2MBOHToEJs3b85y/E9kZCRly5bN5cRCiNwixY4QwiLZ2tqa3NfpdCZt92Z5GQwGABITE3n55ZeZNGlSpsfy9/fPxaRCiNwmxY4QQgDVqlVj6dKllChRAhsb+dUohCWRMTtCCAEMGjSIW7du0bVrV/bu3UtkZCRr166ld+/e6PV6reMJIZ6DFDtCCAEEBASwfft29Ho9zZo1o3LlygwbNgwPDw+srORXpRDmTKfIsqNCCCGEsGDy3xUhhBBCWDQpdoQQQghh0aTYEUIIIYRFk2JHCCGEEBZNih0hhBBCWDQpdoQQQghh0aTYEUIIIYRFk2JHCCGEEBZNih0hhBBCWDQpdoQQQghh0aTYEUIIIYRFk2JHCCGEEBbt/wC9hLRCo5U5cgAAAABJRU5ErkJggg=="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 27
   }
  ],
  "metadata": {


Test patch:
diff --git a/aeon/forecasting/tests/test_tvp.py b/aeon/forecasting/tests/test_tvp.py
new file mode 100644
index 00000000..5a31bee2
--- /dev/null
+++ b/aeon/forecasting/tests/test_tvp.py
@@ -0,0 +1,90 @@
+"""Test TVP forecaster.
+
+Tests include convergence properties described in Durbin & Koopman, 2012.
+
+"""
+
+import numpy as np
+
+from aeon.forecasting._tvp import TVPForecaster
+
+
+def test_direct():
+    """Test aeon TVP Forecaster equivalent to statsmodels."""
+    expected = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
+    tvp = TVPForecaster(window=5, horizon=1, var=0.01, beta_var=0.01)
+    p = tvp.forecast(expected)
+    p2 = tvp.direct_forecast(expected, prediction_horizon=5)
+    assert p == p2[0]
+
+
+def test_static_ar1_convergence_to_ols():
+    """Test TVPForecaster converges to the OLS solution for a static AR(1) process."""
+    # Simulate AR(1) data with constant parameters
+    rng = np.random.RandomState(0)
+    true_phi = 0.6
+    true_intercept = 2.0
+    noise_std = 0.5
+    n = 500
+    y = np.zeros(n)
+    # Initialize y[0] near the steady-state mean to avoid startup bias
+    y[0] = true_intercept / (1 - true_phi)
+    for t in range(1, n):
+        y[t] = true_intercept + true_phi * y[t - 1] + rng.normal(0, noise_std)
+    # Fit with beta_var=0 (no parameter drift) and observation variance = noise_var
+    forecaster = TVPForecaster(window=1, horizon=1, var=noise_std**2, beta_var=0.0)
+    forecaster.fit(y)
+    beta_est = forecaster._beta  # [intercept, phi] estimated
+    # Compute static OLS estimates for comparison
+    X = np.vstack(
+        [np.ones(n - 1), y[: n - 1]]
+    ).T  # regress y[t] on [1, y[t-1]] for t=1..n-1
+    y_resp = y[1:]
+    beta_ols, *_ = np.linalg.lstsq(X, y_resp, rcond=None)
+    # The TVP forecaster (with no drift) should converge to OLS estimates
+    assert beta_est.shape == (2,)
+    # Check that estimated parameters are close to OLS solution
+    assert np.allclose(beta_est, beta_ols, atol=0.1)
+    # Also check they are close to true parameters
+    assert abs(beta_est[0] - true_intercept) < 0.2
+    assert abs(beta_est[1] - true_phi) < 0.1
+
+
+def test_tvp_adapts_to_changing_coefficient():
+    """Test TVP adapts its parameters when the true AR(1) coefficient changes."""
+    rng = np.random.RandomState(42)
+    # Piecewise AR(1): phi changes from 0.2 to 0.8 at t=100, intercept remains 1.0
+    n = 200
+    phi1, phi2 = 0.2, 0.8
+    intercept = 1.0
+    noise_std = 0.05
+    y = np.zeros(n)
+    # Start near the mean of first regime
+    y[0] = intercept / (1 - phi1)
+    # First half (t=1 to 99) with phi1
+    for t in range(1, 100):
+        y[t] = intercept + phi1 * y[t - 1] + rng.normal(0, noise_std)
+    # Second half (t=100 to 199) with phi2
+    for t in range(100, n):
+        y[t] = intercept + phi2 * y[t - 1] + rng.normal(0, noise_std)
+    # Fit TVPForecaster with nonzero beta_var to allow parameter drift
+    forecaster = TVPForecaster(window=1, horizon=1, var=noise_std**2, beta_var=0.1)
+    forecaster.fit(y)
+    beta_final = forecaster._beta
+    # Compute OLS on first and second half segments for reference
+    X1 = np.vstack([np.ones(99), y[:99]]).T
+    y1 = y[1:100]
+    beta1_ols, *_ = np.linalg.lstsq(X1, y1, rcond=None)
+    # use points 100..198 to predict 101..199
+    X2 = np.vstack([np.ones(n - 101), y[100 : n - 1]]).T
+    y2 = y[101:n]
+    beta2_ols, *_ = np.linalg.lstsq(X2, y2, rcond=None)
+    # The final estimated phi should be much closer to phi2 than phi1
+    estimated_intercept, estimated_phi = beta_final[0], beta_final[1]
+    # Validate that phi coefficient increased towards phi2
+    assert estimated_phi > 0.5  # moved well above the initial ~0.2
+    assert abs(estimated_phi - phi2) < 0.1  # close to the new true phi
+    # Validate intercept remains reasonable (around true intercept)
+    assert abs(estimated_intercept - intercept) < 0.5
+    # Check that final phi is closer to second-half OLS estimate than first-half
+    assert abs(estimated_phi - beta2_ols[1]) < abs(estimated_phi - beta1_ols[1])