diff --git a/README.md b/README.md
index 2cc1f01d1..19ec5824b 100644
--- a/README.md
+++ b/README.md
@@ -98,6 +98,11 @@ $ conda install -c conda-forge deepxde
 $ git clone https://github.com/lululxvi/deepxde.git
 ```
 
+- If you want to use the new [deepxde.experimental](https://github.com/lululxvi/deepxde/tree/main/deepxde/experimental) module, you can use:
+``` sh
+$ pip install deepxde[experimental]
+```
+
 ## Explore more
 
 - [Install and Setup](https://deepxde.readthedocs.io/en/latest/user/installation.html)
diff --git a/docs/experimental_docs/index.rst b/docs/experimental_docs/index.rst
new file mode 100644
index 000000000..1d4a545ae
--- /dev/null
+++ b/docs/experimental_docs/index.rst
@@ -0,0 +1,38 @@
+Examples of ``deepxde.experimental``
+====================================
+
+
+PINN Forward Examples
+---------------------
+
+.. toctree::
+    :maxdepth: 1
+
+    unit-examples-forward/Beltrami_flow.ipynb
+    unit-examples-forward/diffusion_1d.ipynb
+    unit-examples-forward/Euler_beam.ipynb
+    unit-examples-forward/Helmholtz_Dirichlet_2d.ipynb
+    unit-examples-forward/burgers.ipynb
+    unit-examples-forward/Burgers_RAR.ipynb
+    unit-examples-forward/heat.ipynb
+    unit-examples-forward/heat_resample.ipynb
+    unit-examples-forward/Laplace_disk.ipynb
+
+
+
+PINN Inverse Examples
+---------------------
+
+.. toctree::
+    :maxdepth: 1
+
+    unit-examples-inverse/elliptic_inverse_filed.ipynb
+    unit-examples-inverse/brinkman_forchheimer.ipynb
+    unit-examples-inverse/diffusion_reaction_rate.ipynb
+    unit-examples-inverse/reaction_inverse.ipynb
+    unit-examples-inverse/diffusion_1d_inverse.ipynb
+    unit-examples-inverse/Navier_Stokes_inverse.ipynb
+
+
+
+
diff --git a/docs/experimental_docs/unit-examples-forward/Beltrami_flow.ipynb b/docs/experimental_docs/unit-examples-forward/Beltrami_flow.ipynb
new file mode 100644
index 000000000..263ddeff7
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/Beltrami_flow.ipynb
@@ -0,0 +1,591 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "17296f2332f77ca7",
+   "metadata": {},
+   "source": [
+    "# Three-dimensional unsteady Navier-Stokes Equations\n",
+    "\n",
+    "\n",
+    "\n",
+    "## Problem Statement\n",
+    "\n",
+    "### 1. Momentum Equations\n",
+    "\n",
+    "The Navier-Stokes equations describe the conservation of momentum in fluid dynamics. For an incompressible fluid, the equation is:\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\partial \\mathbf{u}}{\\partial t} + \\mathbf{u} \\cdot \\nabla \\mathbf{u} = - \\nabla p + \\frac{1}{Re} \\nabla^2 \\mathbf{u}\n",
+    "$$\n",
+    "\n",
+    "Where:\n",
+    "- $\\mathbf{u} = (u, v, w)$ is the velocity field,\n",
+    "- $p$ is the pressure field,\n",
+    "- $\\nabla$ is the gradient operator,\n",
+    "- $\\nabla^2$ is the Laplacian operator,\n",
+    "- $\\mu$ is the dynamic viscosity.\n",
+    "\n",
+    "The momentum equations in the code are written for each of the three spatial components (x, y, z). Specifically, the equations correspond to the following:\n",
+    "\n",
+    "\n",
+    "\n",
+    "- **$x$-direction momentum equation** (`momentum_x` in the code):\n",
+    "\n",
+    "$$\n",
+    "\\rho\\left[\\frac{\\partial u}{\\partial t}+\\frac{\\partial u}{\\partial x} u+\\frac{\\partial u}{\\partial y} v+\\frac{\\partial u}{\\partial z} w\\right]=-\\frac{\\partial p}{\\partial x}+\\mu\\left(\\frac{\\partial^2 u}{\\partial x^2}+\\frac{\\partial^2 u}{\\partial y^2}+\\frac{\\partial^2 u}{\\partial z^2}\\right)+\\rho g_x\n",
+    "$$\n",
+    "\n",
+    "- **$y$-direction momentum equation** (`momentum_y` in the code):\n",
+    "\n",
+    "$$\n",
+    "\\rho\\left[\\frac{\\partial v}{\\partial t}+\\frac{\\partial v}{\\partial x} u+\\frac{\\partial v}{\\partial y} v+\\frac{\\partial v}{\\partial z} w\\right]=-\\frac{\\partial p}{\\partial y}+\\mu\\left(\\frac{\\partial^2 v}{\\partial x^2}+\\frac{\\partial^2 v}{\\partial y^2}+\\frac{\\partial^2 v}{\\partial z^2}\\right)+\\rho g_y\n",
+    "$$\n",
+    "\n",
+    "- **$z$-direction momentum equation** (`momentum_z` in the code):\n",
+    "\n",
+    "$$\n",
+    "\\rho\\left[\\frac{\\partial w}{\\partial t}+\\frac{\\partial w}{\\partial x} u+\\frac{\\partial w}{\\partial y} v+\\frac{\\partial w}{\\partial z} w\\right]=-\\frac{\\partial p}{\\partial z}+\\mu\\left(\\frac{\\partial^2 w}{\\partial x^2}+\\frac{\\partial^2 w}{\\partial y^2}+\\frac{\\partial^2 w}{\\partial z^2}\\right)+\\rho g_z\n",
+    "$$\n",
+    "\n",
+    "### 2. Continuity Equation\n",
+    "\n",
+    "The continuity equation represents the conservation of mass, ensuring that the flow is incompressible (i.e., the divergence of the velocity field is zero). The equation is:\n",
+    "\n",
+    "$$\n",
+    "\\nabla \\cdot \\mathbf{u} = 0\n",
+    "$$\n",
+    "\n",
+    "In the code, the continuity equation corresponds to:\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\partial u}{\\partial x} + \\frac{\\partial v}{\\partial y} + \\frac{\\partial w}{\\partial z} = 0\n",
+    "$$\n",
+    "\n",
+    "This guarantees that the volume of the fluid remains constant and the flow is incompressible.\n",
+    "\n",
+    "### 3. Initial and Boundary Conditions (IC and BC)\n",
+    "\n",
+    "The function `icbc_cond_func` defines the initial conditions (IC) and boundary conditions (BC) for the velocity and pressure fields.\n",
+    "\n",
+    "- **Initial velocity fields**:\n",
+    "  The velocity components are given as functions of spatial variables $x$, $y$, and $z$, as well as time $t$. Specifically, the velocity components $u$, $v$, and $w$ are defined as:\n",
+    "\n",
+    "$$\n",
+    "u = -a \\left( e^{a x} \\sin(a y + d z) + e^{a z} \\cos(a x + d y) \\right) e^{-d^2 t}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "v = -a \\left( e^{a y} \\sin(a z + d x) + e^{a x} \\cos(a y + d z) \\right) e^{-d^2 t}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "w = -a \\left( e^{a z} \\sin(a x + d y) + e^{a y} \\cos(a z + d x) \\right) e^{-d^2 t}\n",
+    "$$\n",
+    "\n",
+    "- **Initial pressure field**:\n",
+    "  The pressure field $p$ is given by a more complex expression, involving exponentials and trigonometric functions of the spatial variables $x$, $y$, and $z$:\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "p(x, y, z, t)= & -\\frac{1}{2} a^2\\left[e^{2 a x}+e^{2 a y}+e^{2 a z}\\right. \\\\\n",
+    "& +2 \\sin (a x+d y) \\cos (a z+d x) e^{a(y+z)} \\\\\n",
+    "& +2 \\sin (a y+d z) \\cos (a x+d y) e^{a(z+x)} \\\\\n",
+    "& \\left.+2 \\sin (a z+d x) \\cos (a y+d z) e^{a(x+y)}\\right] e^{-2 d^2 t}\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "### 4. Final Formulation: 3D Navier-Stokes Equations\n",
+    "\n",
+    "The Navier-Stokes equations, based on the code, are as follows:\n",
+    "\n",
+    "#### Momentum Equation (in three dimensions):\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\partial \\mathbf{u}}{\\partial t} + \\mathbf{u} \\cdot \\nabla \\mathbf{u} = - \\nabla p + \\frac{1}{Re} \\nabla^2 \\mathbf{u}\n",
+    "$$\n",
+    "\n",
+    "Where:\n",
+    "- $\\mathbf{u} = (u, v, w)$ is the velocity field,\n",
+    "- $p$ is the pressure field,\n",
+    "- $Re$ is the Reynolds number,\n",
+    "- $\\nabla^2$ is the Laplacian operator.\n",
+    "\n",
+    "#### Continuity Equation (for incompressibility):\n",
+    "\n",
+    "$$\n",
+    "\\nabla \\cdot \\mathbf{u} = 0\n",
+    "$$\n",
+    "\n",
+    "This ensures that the flow remains incompressible.\n",
+    "\n",
+    "### Conclusion\n",
+    "\n",
+    "The Python code essentially implements the 3D Navier-Stokes equations for an incompressible fluid, where the momentum equations are resolved in each spatial direction, and the continuity equation ensures mass conservation. The initial conditions for velocity and pressure are specified, and boundary conditions are likely handled by the methods in `icbc_cond_func`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad99ef67a441b25d",
+   "metadata": {},
+   "source": [
+    "## Dimensional Analysis\n",
+    "\n",
+    "Summary of Physical Units\n",
+    "\n",
+    "- **Velocity ($u, v, w$)**: $[u] = [v] = [w] = \\text{m/s}$\n",
+    "- **Time ($t$)**: $[t] = \\text{s}$\n",
+    "- **Pressure ($p$)**: $[p] = \\text{kg/m} \\cdot \\text{s}^2$\n",
+    "- **Reynolds number ($Re$)**: Dimensionless\n",
+    "- **Laplacian of velocity ($\\nabla^2 \\mathbf{u}$)**: $\\text{s}^{-2}$\n",
+    "- **Density ($\\rho$)**: $\\text{kg/m}^3$\n",
+    "- **Dynamic viscosity ($\\mu$)**: $\\text{kg/m} \\cdot \\text{s}$\n",
+    "\n",
+    "The analysis confirms that the Navier-Stokes equations, including the momentum and continuity equations, are dimensionally consistent and properly describe the physical quantities involved in fluid flow.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2ff0e8c04269c5c",
+   "metadata": {},
+   "source": [
+    "## Code Implementation\n",
+    "\n",
+    "First, we import the necessary libraries for the implementation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9da3a0a3f6e0cfdd",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T13:35:41.570500Z",
+     "start_time": "2024-12-17T13:35:39.215212Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import brainstate as bst\n",
+    "import brainunit as u\n",
+    "import jax.tree\n",
+    "import numpy as np\n",
+    "\n",
+    "import deepxde.experimental as deepxde\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55d79554e448349a",
+   "metadata": {},
+   "source": [
+    "Define the physical units for the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a7d41ee1906c7370",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T13:35:41.578528Z",
+     "start_time": "2024-12-17T13:35:41.574897Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "unit_of_space = u.meter\n",
+    "unit_of_speed = u.meter / u.second\n",
+    "unit_of_t = u.second\n",
+    "unit_of_pressure = u.pascal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7346e281a5e40f06",
+   "metadata": {},
+   "source": [
+    "Define the spatial and temporal domains for the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c1ebb34b6f25d0a8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T13:35:41.632619Z",
+     "start_time": "2024-12-17T13:35:41.629331Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "spatial_domain = deepxde.geometry.Cuboid(xmin=[-1, -1, -1], xmax=[1, 1, 1])\n",
+    "temporal_domain = deepxde.geometry.TimeDomain(0, 1)\n",
+    "spatio_temporal_domain = deepxde.geometry.GeometryXTime(spatial_domain, temporal_domain)\n",
+    "spatio_temporal_domain = spatio_temporal_domain.to_dict_point(\n",
+    "    x=unit_of_space,\n",
+    "    y=unit_of_space,\n",
+    "    z=unit_of_space,\n",
+    "    t=unit_of_t,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a20e646545ec2cfd",
+   "metadata": {},
+   "source": [
+    "Define the neural network model for the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "13fd9b17a2ad3161",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T13:35:41.978316Z",
+     "start_time": "2024-12-17T13:35:41.638695Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "net = deepxde.nn.Model(\n",
+    "    deepxde.nn.DictToArray(x=unit_of_space,\n",
+    "                           y=unit_of_space,\n",
+    "                           z=unit_of_space,\n",
+    "                           t=unit_of_t),\n",
+    "    deepxde.nn.FNN([4] + 4 * [50] + [4], \"tanh\", bst.init.KaimingUniform()),\n",
+    "    deepxde.nn.ArrayToDict(u_vel=unit_of_speed,\n",
+    "                           v_vel=unit_of_speed,\n",
+    "                           w_vel=unit_of_speed,\n",
+    "                           p=unit_of_pressure),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c49c4dd168704ad2",
+   "metadata": {},
+   "source": [
+    "Define the PDE residual function for the Navier-Stokes equations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ba050fc459ae4389",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T13:35:42.026077Z",
+     "start_time": "2024-12-17T13:35:41.988476Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "a = 1\n",
+    "d = 1\n",
+    "Re = 1\n",
+    "rho = 1 * u.kilogram / u.meter ** 3\n",
+    "mu = 1 * u.pascal * u.second\n",
+    "\n",
+    "\n",
+    "@bst.compile.jit\n",
+    "def pde(x, u):\n",
+    "    jacobian = net.jacobian(x)\n",
+    "    x_hessian = net.hessian(x, y=['u_vel', 'v_vel', 'w_vel'], xi=['x'], xj=['x'])\n",
+    "    y_hessian = net.hessian(x, y=['u_vel', 'v_vel', 'w_vel'], xi=['y'], xj=['y'])\n",
+    "    z_hessian = net.hessian(x, y=['u_vel', 'v_vel', 'w_vel'], xi=['z'], xj=['z'])\n",
+    "\n",
+    "    u_vel, v_vel, w_vel, p = u['u_vel'], u['v_vel'], u['w_vel'], u['p']\n",
+    "\n",
+    "    du_vel_dx = jacobian['u_vel']['x']\n",
+    "    du_vel_dy = jacobian['u_vel']['y']\n",
+    "    du_vel_dz = jacobian['u_vel']['z']\n",
+    "    du_vel_dt = jacobian['u_vel']['t']\n",
+    "    du_vel_dx_dx = x_hessian['u_vel']['x']['x']\n",
+    "    du_vel_dy_dy = y_hessian['u_vel']['y']['y']\n",
+    "    du_vel_dz_dz = z_hessian['u_vel']['z']['z']\n",
+    "\n",
+    "    dv_vel_dx = jacobian['v_vel']['x']\n",
+    "    dv_vel_dy = jacobian['v_vel']['y']\n",
+    "    dv_vel_dz = jacobian['v_vel']['z']\n",
+    "    dv_vel_dt = jacobian['v_vel']['t']\n",
+    "    dv_vel_dx_dx = x_hessian['v_vel']['x']['x']\n",
+    "    dv_vel_dy_dy = y_hessian['v_vel']['y']['y']\n",
+    "    dv_vel_dz_dz = z_hessian['v_vel']['z']['z']\n",
+    "\n",
+    "    dw_vel_dx = jacobian['w_vel']['x']\n",
+    "    dw_vel_dy = jacobian['w_vel']['y']\n",
+    "    dw_vel_dz = jacobian['w_vel']['z']\n",
+    "    dw_vel_dt = jacobian['w_vel']['t']\n",
+    "    dw_vel_dx_dx = x_hessian['w_vel']['x']['x']\n",
+    "    dw_vel_dy_dy = y_hessian['w_vel']['y']['y']\n",
+    "    dw_vel_dz_dz = z_hessian['w_vel']['z']['z']\n",
+    "\n",
+    "    dp_dx = jacobian['p']['x']\n",
+    "    dp_dy = jacobian['p']['y']\n",
+    "    dp_dz = jacobian['p']['z']\n",
+    "\n",
+    "    momentum_x = (\n",
+    "        rho * (du_vel_dt + (u_vel * du_vel_dx + v_vel * du_vel_dy + w_vel * du_vel_dz))\n",
+    "        + dp_dx - mu * (du_vel_dx_dx + du_vel_dy_dy + du_vel_dz_dz)\n",
+    "    )\n",
+    "    momentum_y = (\n",
+    "        rho * (dv_vel_dt + (u_vel * dv_vel_dx + v_vel * dv_vel_dy + w_vel * dv_vel_dz))\n",
+    "        + dp_dy - mu * (dv_vel_dx_dx + dv_vel_dy_dy + dv_vel_dz_dz)\n",
+    "    )\n",
+    "    momentum_z = (\n",
+    "        rho * (dw_vel_dt + (u_vel * dw_vel_dx + v_vel * dw_vel_dy + w_vel * dw_vel_dz))\n",
+    "        + dp_dz - mu * (dw_vel_dx_dx + dw_vel_dy_dy + dw_vel_dz_dz)\n",
+    "    )\n",
+    "    continuity = du_vel_dx + dv_vel_dy + dw_vel_dz\n",
+    "\n",
+    "    return [momentum_x, momentum_y, momentum_z, continuity]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7827b37aac032ab8",
+   "metadata": {},
+   "source": [
+    "Define the initial and boundary conditions for the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "30611c1e29ef58b8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T13:35:42.059708Z",
+     "start_time": "2024-12-17T13:35:42.050288Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "@bst.compile.jit(static_argnums=1)\n",
+    "def icbc_cond_func(x, include_p: bool = False):\n",
+    "    x = {k: v.mantissa for k, v in x.items()}\n",
+    "\n",
+    "    u_ = (\n",
+    "        -a\n",
+    "        * (u.math.exp(a * x['x']) * u.math.sin(a * x['y'] + d * x['z'])\n",
+    "           + u.math.exp(a * x['z']) * u.math.cos(a * x['x'] + d * x['y']))\n",
+    "        * u.math.exp(-(d ** 2) * x['t'])\n",
+    "    )\n",
+    "    v = (\n",
+    "        -a\n",
+    "        * (u.math.exp(a * x['y']) * u.math.sin(a * x['z'] + d * x['x'])\n",
+    "           + u.math.exp(a * x['x']) * u.math.cos(a * x['y'] + d * x['z']))\n",
+    "        * u.math.exp(-(d ** 2) * x['t'])\n",
+    "    )\n",
+    "    w = (\n",
+    "        -a\n",
+    "        * (u.math.exp(a * x['z']) * u.math.sin(a * x['x'] + d * x['y'])\n",
+    "           + u.math.exp(a * x['y']) * u.math.cos(a * x['z'] + d * x['x']))\n",
+    "        * u.math.exp(-(d ** 2) * x['t'])\n",
+    "    )\n",
+    "    p = (\n",
+    "        -0.5\n",
+    "        * a ** 2\n",
+    "        * (\n",
+    "            u.math.exp(2 * a * x['x'])\n",
+    "            + u.math.exp(2 * a * x['y'])\n",
+    "            + u.math.exp(2 * a * x['z'])\n",
+    "            + 2\n",
+    "            * u.math.sin(a * x['x'] + d * x['y'])\n",
+    "            * u.math.cos(a * x['z'] + d * x['x'])\n",
+    "            * u.math.exp(a * (x['y'] + x['z']))\n",
+    "            + 2\n",
+    "            * u.math.sin(a * x['y'] + d * x['z'])\n",
+    "            * u.math.cos(a * x['x'] + d * x['y'])\n",
+    "            * u.math.exp(a * (x['z'] + x['x']))\n",
+    "            + 2\n",
+    "            * u.math.sin(a * x['z'] + d * x['x'])\n",
+    "            * u.math.cos(a * x['y'] + d * x['z'])\n",
+    "            * u.math.exp(a * (x['x'] + x['y']))\n",
+    "        )\n",
+    "        * u.math.exp(-2 * d ** 2 * x['t'])\n",
+    "    )\n",
+    "\n",
+    "    r = {'u_vel': u_ * unit_of_speed,\n",
+    "         'v_vel': v * unit_of_speed,\n",
+    "         'w_vel': w * unit_of_speed}\n",
+    "    if include_p:\n",
+    "        r['p'] = p * unit_of_pressure\n",
+    "    return r\n",
+    "\n",
+    "\n",
+    "bc = deepxde.icbc.DirichletBC(icbc_cond_func)\n",
+    "ic = deepxde.icbc.IC(icbc_cond_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5663ac4d9b3ae59",
+   "metadata": {},
+   "source": [
+    "Define the problem as a TimePDE object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3f1612bbaeb56010",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T13:35:43.072978Z",
+     "start_time": "2024-12-17T13:35:42.121586Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: 283 points required, but 343 points sampled.\n",
+      "Warning: 10000 points required, but 12348 points sampled.\n"
+     ]
+    }
+   ],
+   "source": [
+    "problem = deepxde.problem.TimePDE(\n",
+    "    spatio_temporal_domain,\n",
+    "    pde,\n",
+    "    [bc, ic],\n",
+    "    net,\n",
+    "    num_domain=50000,\n",
+    "    num_boundary=5000,\n",
+    "    num_initial=5000,\n",
+    "    num_test=10000,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f472396f52fcb8a5",
+   "metadata": {},
+   "source": [
+    "Train the model using the problem data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71f97c909111b8e1",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2024-12-17T13:35:43.085506Z"
+    },
+    "jupyter": {
+     "is_executing": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.051972 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                                  Test loss                                                                   Test metric \n",
+      "0         [12.974215 * (kilogram / klitre * (meter / second) / second) ** 2,          [14.700201 * (kilogram / klitre * (meter / second) / second) ** 2,          []          \n",
+      "           24.321922 * (kilogram / klitre * (meter / second) / second) ** 2,           29.931065 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           13.350433 * (kilogram / klitre * (meter / second) / second) ** 2,           17.096483 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           1.2527013 * becquerel2,                                                     1.3801537 * becquerel2,                                                                \n",
+      "           {'ibc0': {'u_vel': 2.5884202 * meter / second,                              {'ibc0': {'u_vel': 2.5884202 * meter / second,                                         \n",
+      "                     'v_vel': 1.5904388 * meter / second,                                        'v_vel': 1.5904388 * meter / second,                                         \n",
+      "                     'w_vel': 1.7298671 * meter / second}},                                      'w_vel': 1.7298671 * meter / second}},                                       \n",
+      "           {'ibc1': {'u_vel': 4.1043954 * meter / second,                              {'ibc1': {'u_vel': 4.1043954 * meter / second,                                         \n",
+      "                     'v_vel': 2.08325 * meter / second,                                          'v_vel': 2.08325 * meter / second,                                           \n",
+      "                     'w_vel': 2.6199307 * meter / second}}]                                      'w_vel': 2.6199307 * meter / second}}]                                       \n"
+     ]
+    }
+   ],
+   "source": [
+    "model = deepxde.Trainer(problem)\n",
+    "\n",
+    "model.compile(bst.optim.Adam(1e-3)).train(iterations=30000)\n",
+    "model.compile(bst.optim.LBFGS(1e-3)).train(5000, display_every=200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "becbb0f8333344e9",
+   "metadata": {},
+   "source": [
+    "Verify the results by plotting the loss history and the predicted solution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca417f07847d6081",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y, z = np.meshgrid(np.linspace(-1, 1, 10), np.linspace(-1, 1, 10), np.linspace(-1, 1, 10))\n",
+    "t_0 = np.zeros(1000)\n",
+    "t_1 = np.ones(1000)\n",
+    "X_0 = dict(\n",
+    "    x=np.ravel(x) * unit_of_space,\n",
+    "    y=np.ravel(y) * unit_of_space,\n",
+    "    z=np.ravel(z) * unit_of_space,\n",
+    "    t=t_0 * unit_of_t\n",
+    ")\n",
+    "X_1 = dict(\n",
+    "    x=np.ravel(x) * unit_of_space,\n",
+    "    y=np.ravel(y) * unit_of_space,\n",
+    "    z=np.ravel(z) * unit_of_space,\n",
+    "    t=t_1 * unit_of_t\n",
+    ")\n",
+    "output_0 = model.predict(X_0)\n",
+    "output_1 = model.predict(X_1)\n",
+    "\n",
+    "out_exact_0 = icbc_cond_func(X_0, True)\n",
+    "out_exact_1 = icbc_cond_func(X_1, True)\n",
+    "\n",
+    "f_0 = pde(X_0, output_0)\n",
+    "f_1 = pde(X_1, output_1)\n",
+    "residual_0 = jax.tree.map(lambda x: np.mean(np.absolute(x)), f_0)\n",
+    "residual_1 = jax.tree.map(lambda x: np.mean(np.absolute(x)), f_1)\n",
+    "\n",
+    "print(\"Accuracy at t = 0:\")\n",
+    "print(\"Mean residual:\", residual_0)\n",
+    "print(\"L2 relative error:\", deepxde.metrics.l2_relative_error(output_0, out_exact_0))\n",
+    "print(\"\\n\")\n",
+    "print(\"Accuracy at t = 1:\")\n",
+    "print(\"Mean residual:\", residual_1)\n",
+    "print(\"L2 relative error:\", deepxde.metrics.l2_relative_error(output_1, out_exact_1))\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/experimental_docs/unit-examples-forward/Beltrami_flow.py b/docs/experimental_docs/unit-examples-forward/Beltrami_flow.py
new file mode 100644
index 000000000..a90f90d64
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/Beltrami_flow.py
@@ -0,0 +1,202 @@
+import brainstate as bst
+import brainunit as u
+import jax.tree
+import numpy as np
+
+import deepxde.experimental as deepxde
+
+unit_of_space = u.meter
+unit_of_speed = u.meter / u.second
+unit_of_t = u.second
+unit_of_pressure = u.pascal
+
+spatial_domain = deepxde.geometry.Cuboid(xmin=[-1, -1, -1], xmax=[1, 1, 1])
+temporal_domain = deepxde.geometry.TimeDomain(0, 1)
+spatio_temporal_domain = deepxde.geometry.GeometryXTime(spatial_domain, temporal_domain)
+spatio_temporal_domain = spatio_temporal_domain.to_dict_point(
+    x=unit_of_space,
+    y=unit_of_space,
+    z=unit_of_space,
+    t=unit_of_t,
+)
+
+net = deepxde.nn.Model(
+    deepxde.nn.DictToArray(x=unit_of_space,
+                           y=unit_of_space,
+                           z=unit_of_space,
+                           t=unit_of_t),
+    deepxde.nn.FNN([4] + 4 * [50] + [4], "tanh", bst.init.KaimingUniform()),
+    deepxde.nn.ArrayToDict(u_vel=unit_of_speed,
+                           v_vel=unit_of_speed,
+                           w_vel=unit_of_speed,
+                           p=unit_of_pressure),
+)
+
+a = 1
+d = 1
+Re = 1
+rho = 1 * u.kilogram / u.meter ** 3
+mu = 1 * u.pascal * u.second
+
+
+@bst.compile.jit
+def pde(x, u):
+    jacobian = net.jacobian(x)
+    x_hessian = net.hessian(x, y=['u_vel', 'v_vel', 'w_vel'], xi=['x'], xj=['x'])
+    y_hessian = net.hessian(x, y=['u_vel', 'v_vel', 'w_vel'], xi=['y'], xj=['y'])
+    z_hessian = net.hessian(x, y=['u_vel', 'v_vel', 'w_vel'], xi=['z'], xj=['z'])
+
+    u_vel, v_vel, w_vel, p = u['u_vel'], u['v_vel'], u['w_vel'], u['p']
+
+    du_vel_dx = jacobian['u_vel']['x']
+    du_vel_dy = jacobian['u_vel']['y']
+    du_vel_dz = jacobian['u_vel']['z']
+    du_vel_dt = jacobian['u_vel']['t']
+    du_vel_dx_dx = x_hessian['u_vel']['x']['x']
+    du_vel_dy_dy = y_hessian['u_vel']['y']['y']
+    du_vel_dz_dz = z_hessian['u_vel']['z']['z']
+
+    dv_vel_dx = jacobian['v_vel']['x']
+    dv_vel_dy = jacobian['v_vel']['y']
+    dv_vel_dz = jacobian['v_vel']['z']
+    dv_vel_dt = jacobian['v_vel']['t']
+    dv_vel_dx_dx = x_hessian['v_vel']['x']['x']
+    dv_vel_dy_dy = y_hessian['v_vel']['y']['y']
+    dv_vel_dz_dz = z_hessian['v_vel']['z']['z']
+
+    dw_vel_dx = jacobian['w_vel']['x']
+    dw_vel_dy = jacobian['w_vel']['y']
+    dw_vel_dz = jacobian['w_vel']['z']
+    dw_vel_dt = jacobian['w_vel']['t']
+    dw_vel_dx_dx = x_hessian['w_vel']['x']['x']
+    dw_vel_dy_dy = y_hessian['w_vel']['y']['y']
+    dw_vel_dz_dz = z_hessian['w_vel']['z']['z']
+
+    dp_dx = jacobian['p']['x']
+    dp_dy = jacobian['p']['y']
+    dp_dz = jacobian['p']['z']
+
+    momentum_x = (
+        rho * (du_vel_dt + (u_vel * du_vel_dx + v_vel * du_vel_dy + w_vel * du_vel_dz))
+        + dp_dx - mu * (du_vel_dx_dx + du_vel_dy_dy + du_vel_dz_dz)
+    )
+    momentum_y = (
+        rho * (dv_vel_dt + (u_vel * dv_vel_dx + v_vel * dv_vel_dy + w_vel * dv_vel_dz))
+        + dp_dy - mu * (dv_vel_dx_dx + dv_vel_dy_dy + dv_vel_dz_dz)
+    )
+    momentum_z = (
+        rho * (dw_vel_dt + (u_vel * dw_vel_dx + v_vel * dw_vel_dy + w_vel * dw_vel_dz))
+        + dp_dz - mu * (dw_vel_dx_dx + dw_vel_dy_dy + dw_vel_dz_dz)
+    )
+    continuity = du_vel_dx + dv_vel_dy + dw_vel_dz
+
+    return [momentum_x, momentum_y, momentum_z, continuity]
+
+
+@bst.compile.jit(static_argnums=1)
+def icbc_cond_func(x, include_p: bool = False):
+    x = {k: v.mantissa for k, v in x.items()}
+
+    u_ = (
+        -a
+        * (u.math.exp(a * x['x']) * u.math.sin(a * x['y'] + d * x['z'])
+           + u.math.exp(a * x['z']) * u.math.cos(a * x['x'] + d * x['y']))
+        * u.math.exp(-(d ** 2) * x['t'])
+    )
+    v = (
+        -a
+        * (u.math.exp(a * x['y']) * u.math.sin(a * x['z'] + d * x['x'])
+           + u.math.exp(a * x['x']) * u.math.cos(a * x['y'] + d * x['z']))
+        * u.math.exp(-(d ** 2) * x['t'])
+    )
+    w = (
+        -a
+        * (u.math.exp(a * x['z']) * u.math.sin(a * x['x'] + d * x['y'])
+           + u.math.exp(a * x['y']) * u.math.cos(a * x['z'] + d * x['x']))
+        * u.math.exp(-(d ** 2) * x['t'])
+    )
+    p = (
+        -0.5
+        * a ** 2
+        * (
+            u.math.exp(2 * a * x['x'])
+            + u.math.exp(2 * a * x['y'])
+            + u.math.exp(2 * a * x['z'])
+            + 2
+            * u.math.sin(a * x['x'] + d * x['y'])
+            * u.math.cos(a * x['z'] + d * x['x'])
+            * u.math.exp(a * (x['y'] + x['z']))
+            + 2
+            * u.math.sin(a * x['y'] + d * x['z'])
+            * u.math.cos(a * x['x'] + d * x['y'])
+            * u.math.exp(a * (x['z'] + x['x']))
+            + 2
+            * u.math.sin(a * x['z'] + d * x['x'])
+            * u.math.cos(a * x['y'] + d * x['z'])
+            * u.math.exp(a * (x['x'] + x['y']))
+        )
+        * u.math.exp(-2 * d ** 2 * x['t'])
+    )
+
+    r = {
+        'u_vel': u_ * unit_of_speed,
+        'v_vel': v * unit_of_speed,
+        'w_vel': w * unit_of_speed
+    }
+    if include_p:
+        r['p'] = p * unit_of_pressure
+    return r
+
+
+bc = deepxde.icbc.DirichletBC(icbc_cond_func)
+ic = deepxde.icbc.IC(icbc_cond_func)
+
+problem = deepxde.problem.TimePDE(
+    spatio_temporal_domain,
+    pde,
+    [bc, ic],
+    net,
+    num_domain=50000,
+    num_boundary=5000,
+    num_initial=5000,
+    num_test=10000,
+)
+
+model = deepxde.Trainer(problem)
+
+model.compile(bst.optim.Adam(1e-3)).train(iterations=30000)
+model.compile(bst.optim.LBFGS(1e-3)).train(5000, display_every=200)
+
+x, y, z = np.meshgrid(np.linspace(-1, 1, 10), np.linspace(-1, 1, 10), np.linspace(-1, 1, 10))
+t_0 = np.zeros(1000)
+t_1 = np.ones(1000)
+X_0 = dict(
+    x=np.ravel(x) * unit_of_space,
+    y=np.ravel(y) * unit_of_space,
+    z=np.ravel(z) * unit_of_space,
+    t=t_0 * unit_of_t
+)
+X_1 = dict(
+    x=np.ravel(x) * unit_of_space,
+    y=np.ravel(y) * unit_of_space,
+    z=np.ravel(z) * unit_of_space,
+    t=t_1 * unit_of_t
+)
+output_0 = model.predict(X_0)
+output_1 = model.predict(X_1)
+
+out_exact_0 = icbc_cond_func(X_0, True)
+out_exact_1 = icbc_cond_func(X_1, True)
+
+f_0 = pde(X_0, output_0)
+f_1 = pde(X_1, output_1)
+residual_0 = jax.tree.map(lambda x: np.mean(np.absolute(x)), f_0)
+residual_1 = jax.tree.map(lambda x: np.mean(np.absolute(x)), f_1)
+
+print("Accuracy at t = 0:")
+print("Mean residual:", residual_0)
+print("L2 relative error:", deepxde.metrics.l2_relative_error(output_0, out_exact_0))
+print("\n")
+print("Accuracy at t = 1:")
+print("Mean residual:", residual_1)
+print("L2 relative error:", deepxde.metrics.l2_relative_error(output_1, out_exact_1))
diff --git a/docs/experimental_docs/unit-examples-forward/Burgers.py b/docs/experimental_docs/unit-examples-forward/Burgers.py
new file mode 100644
index 000000000..af7633aaf
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/Burgers.py
@@ -0,0 +1,68 @@
+import brainstate as bst
+import brainunit as u
+import numpy as np
+
+import deepxde.experimental as deepxde
+
+geometry = deepxde.geometry.GeometryXTime(
+    geometry=deepxde.geometry.Interval(-1, 1.),
+    timedomain=deepxde.geometry.TimeDomain(0, 0.99)
+).to_dict_point(x=u.meter, t=u.second)
+
+uy = u.meter / u.second
+bc = deepxde.icbc.DirichletBC(lambda x: {'y': 0. * uy})
+ic = deepxde.icbc.IC(lambda x: {'y': -u.math.sin(u.math.pi * x['x'] / u.meter) * uy})
+
+v = 0.01 / u.math.pi * u.meter ** 2 / u.second
+
+
+def pde(x, y):
+    jacobian = approximator.jacobian(x)
+    hessian = approximator.hessian(x)
+    dy_x = jacobian['y']['x']
+    dy_t = jacobian['y']['t']
+    dy_xx = hessian['y']['x']['x']
+    residual = dy_t + y['y'] * dy_x - v * dy_xx
+    return residual
+
+
+approximator = deepxde.nn.Model(
+    deepxde.nn.DictToArray(x=u.meter, t=u.second),
+    deepxde.nn.FNN(
+        [geometry.dim] + [20] * 3 + [1],
+        "tanh",
+        bst.init.KaimingUniform()
+    ),
+    deepxde.nn.ArrayToDict(y=uy)
+)
+
+problem = deepxde.problem.TimePDE(
+    geometry,
+    pde,
+    [bc, ic],
+    approximator,
+    num_domain=2540,
+    num_boundary=80,
+    num_initial=160,
+)
+
+trainer = deepxde.Trainer(problem)
+trainer.compile(bst.optim.Adam(1e-3)).train(iterations=15000)
+trainer.compile(bst.optim.LBFGS(1e-3)).train(2000, display_every=500)
+trainer.saveplot(issave=True, isplot=True)
+
+
+def gen_testdata():
+    data = np.load("../dataset/Burgers.npz")
+    t, x, exact = data["t"], data["x"], data["usol"].T
+    xx, tt = np.meshgrid(x, t)
+    X = {'x': np.ravel(xx) * u.meter, 't': np.ravel(tt) * u.second}
+    y = exact.flatten()[:, None]
+    return X, y * uy
+
+
+X, y_true = gen_testdata()
+y_pred = trainer.predict(X)
+f = pde(X, y_pred)
+print("Mean residual:", u.math.mean(u.math.absolute(f)))
+print("L2 relative error:", deepxde.metrics.l2_relative_error(y_true, y_pred['y']))
diff --git a/docs/experimental_docs/unit-examples-forward/Burgers_RAR.ipynb b/docs/experimental_docs/unit-examples-forward/Burgers_RAR.ipynb
new file mode 100644
index 000000000..160f8ff73
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/Burgers_RAR.ipynb
@@ -0,0 +1,1424 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Burgers equation with residual-based adaptive refinement\n",
+    "\n",
+    "\n",
+    "## Problem setup\n",
+    "\n",
+    "\n",
+    "We will solve a Burgers equation:\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\partial u}{\\partial t} + u\\frac{\\partial u}{\\partial x} = \\nu\\frac{\\partial^2u}{\\partial x^2}, \\qquad x \\in [-1, 1], \\quad t \\in [0, 1]\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "with the Dirichlet boundary conditions and initial conditions\n",
+    "\n",
+    "$$\n",
+    "u(-1,t)=u(1,t)=0, \\quad u(x,0) = - \\sin(\\pi x).\n",
+    "$$\n",
+    "\n",
+    "## Dimensional Analysis\n",
+    "\n",
+    "### Step 1: Assign Dimensions to Variables\n",
+    "\n",
+    "1. **Spatial Coordinate $x$:**\n",
+    "   - The dimension of $x$ is length:\n",
+    "\n",
+    "     $$\n",
+    "     [x] = L.\n",
+    "     $$\n",
+    "\n",
+    "2. **Time $t$:**\n",
+    "   - The dimension of time is:\n",
+    "\n",
+    "     $$\n",
+    "     [t] = T.\n",
+    "     $$\n",
+    "\n",
+    "3. **Velocity $u$:**\n",
+    "   - Velocity has dimensions of length per unit time:\n",
+    "\n",
+    "     $$\n",
+    "     [u] = L / T.\n",
+    "     $$\n",
+    "\n",
+    "4. **Viscosity $\\nu$:**\n",
+    "   - The term $\\nu \\frac{\\partial^2 u}{\\partial x^2}$ involves the second spatial derivative of velocity, which must have the same dimensions as the time derivative $\\frac{\\partial u}{\\partial t}$.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 2: Analyze the Dimensions of Each Term\n",
+    "\n",
+    "1. **Time Derivative Term:**\n",
+    "   - The time derivative $\\frac{\\partial u}{\\partial t}$ has dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\frac{\\partial u}{\\partial t}\\right] = \\frac{[u]}{[t]} = \\frac{L / T}{T} = \\frac{L}{T^2}.\n",
+    "     $$\n",
+    "\n",
+    "2. **Advection Term:**\n",
+    "   - The advection term $u \\frac{\\partial u}{\\partial x}$ involves the spatial derivative of velocity:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[u \\frac{\\partial u}{\\partial x}\\right] = [u] \\cdot \\frac{[u]}{[x]} = \\frac{L}{T} \\cdot \\frac{L / T}{L} = \\frac{L}{T^2}.\n",
+    "     $$\n",
+    "\n",
+    "3. **Diffusion Term:**\n",
+    "   - The diffusion term $\\nu \\frac{\\partial^2 u}{\\partial x^2}$ involves the second spatial derivative of velocity:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\frac{\\partial^2 u}{\\partial x^2}\\right] = \\frac{[u]}{[x]^2} = \\frac{L / T}{L^2} = \\frac{1}{L T}.\n",
+    "    \n",
+    "     $$\n",
+    "   - Therefore, the diffusion term has dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\nu \\frac{\\partial^2 u}{\\partial x^2}\\right] = [\\nu] \\cdot \\frac{1}{L T} = \\frac{L}{T^2}.\n",
+    "     $$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 3: Determine the Dimensions of $\\nu$\n",
+    "\n",
+    "- The diffusion term $\\nu \\frac{\\partial^2 u}{\\partial x^2}$ must have the same dimensions as the time derivative $\\frac{\\partial u}{\\partial t}$:\n",
+    "\n",
+    "  $$\n",
+    "  [\\nu] \\cdot \\frac{1}{L T} = \\frac{L}{T^2} \\implies [\\nu] = \\frac{L^2}{T}.\n",
+    "  $$\n",
+    "- Therefore, the viscosity $\\nu$ has dimensions of kinematic viscosity:\n",
+    "\n",
+    "  $$\n",
+    "  [\\nu] = \\frac{L^2}{T}.\n",
+    "  $$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 4: Summary of Dimensions\n",
+    "\n",
+    "| Variable/Parameter     | Physical Meaning                   | Dimensions            |\n",
+    "|------------------------|-----------------------------------|-----------------------|\n",
+    "| $x$                   | Spatial coordinate                | $L$                 |\n",
+    "| $t$                   | Time                              | $T$                 |\n",
+    "| $u$                   | Velocity                          | $L / T$             |\n",
+    "| $\\nu$                 | Kinematic viscosity               | $L^2 / T$           |\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 5: Initial and Boundary Conditions\n",
+    "\n",
+    "1. **Boundary Conditions:**\n",
+    "   - The boundary conditions $u(-1,t) = u(1,t) = 0$ are given in meters per second:\n",
+    "\n",
+    "     $$\n",
+    "     [u(-1,t)] = [u(1,t)] = L / T.\n",
+    "     $$\n",
+    "\n",
+    "2. **Initial Condition:**\n",
+    "   - The initial condition $u(x,0) = -\\sin(\\pi x)$ is given in meters per second:\n",
+    "   \n",
+    "     $$\n",
+    "     [u(x,0)] = L / T.\n",
+    "     $$\n",
+    "   - The term $\\sin(\\pi x)$ is dimensionless because $x$ is in meters, and $\\pi$ is a dimensionless constant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implementation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This description goes through the implementation of a solver for the above described Burgers equation step-by-step.\n",
+    "\n",
+    "First, import the libraries we need:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import brainstate as bst\n",
+    "import brainunit as u\n",
+    "import numpy as np\n",
+    "import jax\n",
+    "import deepxde.experimental as deepxde"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We begin by defining a computational geometry and time domain. We can use a built-in class ``Interval`` and ``TimeDomain`` and we combine both the domains using ``GeometryXTime`` as follows:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geomtime = deepxde.geometry.GeometryXTime(\n",
+    "    geometry=deepxde.geometry.Interval(-1., 1.),\n",
+    "    timedomain=deepxde.geometry.TimeDomain(0., 0.99)\n",
+    ").to_dict_point(x=u.meter, t=u.second)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we express the PDE residual of the Burgers equation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = 0.01 / u.math.pi * u.meter ** 2 / u.second\n",
+    "\n",
+    "\n",
+    "def pde(x, y):\n",
+    "    jacobian = approximator.jacobian(x)\n",
+    "    hessian = approximator.hessian(x)\n",
+    "    dy_x = jacobian['y']['x']\n",
+    "    dy_t = jacobian['y']['t']\n",
+    "    dy_xx = hessian['y']['x']['x']\n",
+    "    residual = dy_t + y['y'] * dy_x - v * dy_xx\n",
+    "    return residual"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we consider the boundary/initial condition. ``on_boundary`` is chosen here to use the whole boundary of the computational domain in considered as the boundary condition. We include the ``geomtime`` space, time geometry created above and ``on_boundary`` as the BCs in the ``DirichletBC`` function of DeepXDE. We also define ``IC`` which is the inital condition for the burgers equation and we use the computational domain, initial function, and ``on_initial`` to specify the IC.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uy = u.meter / u.second\n",
+    "\n",
+    "bc = deepxde.icbc.DirichletBC(lambda x: {'y': 0. * uy})\n",
+    "ic = deepxde.icbc.IC(lambda x: {'y': -u.math.sin(u.math.pi * x['x'] / u.meter) * uy})\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we choose the network. Here, we use a fully connected neural network of depth 4 (i.e., 3 hidden layers) and width 20:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "approximator = deepxde.nn.Model(\n",
+    "    deepxde.nn.DictToArray(x=u.meter, t=u.second),\n",
+    "    deepxde.nn.FNN(\n",
+    "        [geometry.dim] + [20] * 3 + [1],\n",
+    "        \"tanh\",\n",
+    "        bst.init.KaimingUniform()\n",
+    "    ),\n",
+    "    deepxde.nn.ArrayToDict(y=uy)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we have specified the geometry, PDE residual, and boundary/initial condition. We then define the ``TimePDE`` problem as\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "problem = deepxde.problem.TimePDE(\n",
+    "    geometry,\n",
+    "    pde,\n",
+    "    [bc, ic],\n",
+    "    approximator,\n",
+    "    num_domain=2540,\n",
+    "    num_boundary=80,\n",
+    "    num_initial=160,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The number 2540 is the number of training residual points sampled inside the domain, and the number 80 is the number of training points sampled on the boundary. We also include 160 initial residual points for the initial conditions.\n",
+    "\n",
+    "Now, we have the PDE problem and the network. We build a ``Trainer`` and choose the optimizer and learning rate:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.003058 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                       Test loss                                                        Test metric \n",
+      "0         [2.1140547 * 10.0^0 * ((meter / second) / second) ** 2,          [2.1140547 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 0.09046214 * meter / second}},                    {'ibc0': {'y': 0.09046214 * meter / second}},                               \n",
+      "           {'ibc1': {'y': 0.23645507 * meter / second}}]                    {'ibc1': {'y': 0.23645507 * meter / second}}]                               \n",
+      "1000      [0.05101593 * 10.0^0 * ((meter / second) / second) ** 2,         [0.05101593 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 0.00244636 * meter / second}},                    {'ibc0': {'y': 0.00244636 * meter / second}},                               \n",
+      "           {'ibc1': {'y': 0.06664469 * meter / second}}]                    {'ibc1': {'y': 0.06664469 * meter / second}}]                               \n",
+      "2000      [0.04578441 * 10.0^0 * ((meter / second) / second) ** 2,         [0.04578441 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 0.00104335 * meter / second}},                    {'ibc0': {'y': 0.00104335 * meter / second}},                               \n",
+      "           {'ibc1': {'y': 0.05536607 * meter / second}}]                    {'ibc1': {'y': 0.05536607 * meter / second}}]                               \n",
+      "3000      [0.04083971 * 10.0^0 * ((meter / second) / second) ** 2,         [0.04083971 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 0.00048144 * meter / second}},                    {'ibc0': {'y': 0.00048144 * meter / second}},                               \n",
+      "           {'ibc1': {'y': 0.05146844 * meter / second}}]                    {'ibc1': {'y': 0.05146844 * meter / second}}]                               \n",
+      "4000      [0.03656165 * 10.0^0 * ((meter / second) / second) ** 2,         [0.03656165 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 0.00020483 * meter / second}},                    {'ibc0': {'y': 0.00020483 * meter / second}},                               \n",
+      "           {'ibc1': {'y': 0.04795899 * meter / second}}]                    {'ibc1': {'y': 0.04795899 * meter / second}}]                               \n",
+      "5000      [0.03201985 * 10.0^0 * ((meter / second) / second) ** 2,         [0.03201985 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 5.5738237e-05 * meter / second}},                 {'ibc0': {'y': 5.5738237e-05 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.04417896 * meter / second}}]                    {'ibc1': {'y': 0.04417896 * meter / second}}]                               \n",
+      "6000      [0.01897248 * 10.0^0 * ((meter / second) / second) ** 2,         [0.01897248 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 3.411638e-05 * meter / second}},                  {'ibc0': {'y': 3.411638e-05 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.02110803 * meter / second}}]                    {'ibc1': {'y': 0.02110803 * meter / second}}]                               \n",
+      "7000      [0.0083445 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0083445 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.9857232e-05 * meter / second}},                 {'ibc0': {'y': 1.9857232e-05 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00840012 * meter / second}}]                    {'ibc1': {'y': 0.00840012 * meter / second}}]                               \n",
+      "8000      [0.00405152 * 10.0^0 * ((meter / second) / second) ** 2,         [0.00405152 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 3.13869e-05 * meter / second}},                   {'ibc0': {'y': 3.13869e-05 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.0031565 * meter / second}}]                     {'ibc1': {'y': 0.0031565 * meter / second}}]                                \n",
+      "9000      [0.00258377 * 10.0^0 * ((meter / second) / second) ** 2,         [0.00258377 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 2.9788125e-05 * meter / second}},                 {'ibc0': {'y': 2.9788125e-05 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00185996 * meter / second}}]                    {'ibc1': {'y': 0.00185996 * meter / second}}]                               \n",
+      "10000     [0.00182265 * 10.0^0 * ((meter / second) / second) ** 2,         [0.00182265 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 1.8763618e-05 * meter / second}},                 {'ibc0': {'y': 1.8763618e-05 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00121202 * meter / second}}]                    {'ibc1': {'y': 0.00121202 * meter / second}}]                               \n",
+      "11000     [0.00131502 * 10.0^0 * ((meter / second) / second) ** 2,         [0.00131502 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 1.1222968e-05 * meter / second}},                 {'ibc0': {'y': 1.1222968e-05 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00087704 * meter / second}}]                    {'ibc1': {'y': 0.00087704 * meter / second}}]                               \n",
+      "12000     [0.00102354 * 10.0^0 * ((meter / second) / second) ** 2,         [0.00102354 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 7.5733656e-06 * meter / second}},                 {'ibc0': {'y': 7.5733656e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00069363 * meter / second}}]                    {'ibc1': {'y': 0.00069363 * meter / second}}]                               \n",
+      "13000     [0.00085795 * 10.0^0 * ((meter / second) / second) ** 2,         [0.00085795 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 6.1193127e-06 * meter / second}},                 {'ibc0': {'y': 6.1193127e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00059203 * meter / second}}]                    {'ibc1': {'y': 0.00059203 * meter / second}}]                               \n",
+      "14000     [0.00075481 * 10.0^0 * ((meter / second) / second) ** 2,         [0.00075481 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 5.336154e-06 * meter / second}},                  {'ibc0': {'y': 5.336154e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00052162 * meter / second}}]                    {'ibc1': {'y': 0.00052162 * meter / second}}]                               \n",
+      "15000     [0.00067973 * 10.0^0 * ((meter / second) / second) ** 2,         [0.00067973 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 4.3045325e-06 * meter / second}},                 {'ibc0': {'y': 4.3045325e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00046943 * meter / second}}]                    {'ibc1': {'y': 0.00046943 * meter / second}}]                               \n",
+      "\n",
+      "Best trainer at step 15000:\n",
+      "  train loss: 1.15e-03\n",
+      "  test loss: 1.15e-03\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 60.923828 s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pinnx.Trainer at 0x308d237d0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer = deepxde.Trainer(problem)\n",
+    "trainer.compile(bst.optim.Adam(1e-3)).train(iterations=15000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After we train the network using Adam, we continue to train the network using L-BFGS to achieve a smaller loss:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.023359 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "15000     [0.00067973 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00067973 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 4.3045325e-06 * meter / second}},                  {'ibc0': {'y': 4.3045325e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00046943 * meter / second}}]                     {'ibc1': {'y': 0.00046943 * meter / second}}]                                \n",
+      "15200     [0.00068126 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00068126 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 4.169301e-06 * meter / second}},                   {'ibc0': {'y': 4.169301e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00046952 * meter / second}}]                     {'ibc1': {'y': 0.00046952 * meter / second}}]                                \n",
+      "15400     [0.00068003 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00068003 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 4.1923004e-06 * meter / second}},                  {'ibc0': {'y': 4.1923004e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00046954 * meter / second}}]                     {'ibc1': {'y': 0.00046954 * meter / second}}]                                \n",
+      "15600     [0.00067914 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00067914 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 4.2128318e-06 * meter / second}},                  {'ibc0': {'y': 4.2128318e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00046957 * meter / second}}]                     {'ibc1': {'y': 0.00046957 * meter / second}}]                                \n",
+      "15800     [0.00067831 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00067831 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 4.2393303e-06 * meter / second}},                  {'ibc0': {'y': 4.2393303e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0004696 * meter / second}}]                      {'ibc1': {'y': 0.0004696 * meter / second}}]                                 \n",
+      "16000     [0.00067745 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00067745 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 4.397528e-06 * meter / second}},                   {'ibc0': {'y': 4.397528e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.0004696 * meter / second}}]                      {'ibc1': {'y': 0.0004696 * meter / second}}]                                 \n",
+      "16200     [0.00067771 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00067771 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 4.1383482e-06 * meter / second}},                  {'ibc0': {'y': 4.1383482e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00046968 * meter / second}}]                     {'ibc1': {'y': 0.00046968 * meter / second}}]                                \n",
+      "16400     [0.00067749 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00067749 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 4.25508e-06 * meter / second}},                    {'ibc0': {'y': 4.25508e-06 * meter / second}},                               \n",
+      "           {'ibc1': {'y': 0.00046969 * meter / second}}]                     {'ibc1': {'y': 0.00046969 * meter / second}}]                                \n",
+      "16600     [0.00067738 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00067738 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 4.3410837e-06 * meter / second}},                  {'ibc0': {'y': 4.3410837e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00046968 * meter / second}}]                     {'ibc1': {'y': 0.00046968 * meter / second}}]                                \n",
+      "16800     [0.0006782 * 10.0^0 * ((meter / second) / second) ** 2,           [0.0006782 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 3.657258e-06 * meter / second}},                   {'ibc0': {'y': 3.657258e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00047006 * meter / second}}]                     {'ibc1': {'y': 0.00047006 * meter / second}}]                                \n",
+      "17000     [0.00067788 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00067788 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 3.8210997e-06 * meter / second}},                  {'ibc0': {'y': 3.8210997e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00046992 * meter / second}}]                     {'ibc1': {'y': 0.00046992 * meter / second}}]                                \n",
+      "\n",
+      "Best trainer at step 16600:\n",
+      "  train loss: 1.15e-03\n",
+      "  test loss: 1.15e-03\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 9.051486 s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pinnx.Trainer at 0x308d237d0>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer.compile(bst.optim.LBFGS(1e-3)).train(2000, display_every=200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because we only use 2500 residual points for training, the accuracy is low. Next, we improve the accuracy by the residual-based adaptive refinement (RAR) method. Because the Burgers equation has a sharp front, intuitively, we should put more points near the sharp front. First, we randomly generate 100000 points from our domain to calculate the PDE residual."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = geomtime.random_points(100000)\n",
+    "err = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will repeatedly add points while the mean residual is greater than 0.005. Each iteration, we use our model to generate predictions for inputs in `X` and compute the absolute values of the errors. We then print the mean residual. Next, we find the points where the residual is greatest and add these new points for training PDE loss. Furthermore, we define a callback function to check whether the network converges. If there is significant improvement in the model’s accuracy, as judged by the callback function, we continue to train the model. As before, after we train the network using Adam, we continue to train the network using L-BFGS to achieve a smaller loss:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean residual: 0.018 * (meter / second) / second\n",
+      "Adding new point: {'t': ArrayImpl([0.31030682], dtype=float32) * second, 'x': ArrayImpl([0.00072277], dtype=float32) * meter} \n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.013387 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "17000     [0.00295319 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00067788 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 3.8210997e-06 * meter / second}},                  {'ibc0': {'y': 3.8210997e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00046992 * meter / second}}]                     {'ibc1': {'y': 0.00046992 * meter / second}}]                                \n",
+      "18000     [0.00108327 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0009432 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 5.890686e-06 * meter / second}},                   {'ibc0': {'y': 5.890686e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00053691 * meter / second}}]                     {'ibc1': {'y': 0.00053691 * meter / second}}]                                \n",
+      "19000     [0.00092558 * 10.0^0 * ((meter / second) / second) ** 2,          [0.000839 * 10.0^0 * ((meter / second) / second) ** 2,            []          \n",
+      "           {'ibc0': {'y': 4.3144605e-06 * meter / second}},                  {'ibc0': {'y': 4.3144605e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00054824 * meter / second}}]                     {'ibc1': {'y': 0.00054824 * meter / second}}]                                \n",
+      "20000     [0.00081564 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00076047 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 3.5706555e-06 * meter / second}},                  {'ibc0': {'y': 3.5706555e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00053703 * meter / second}}]                     {'ibc1': {'y': 0.00053703 * meter / second}}]                                \n",
+      "21000     [0.00096415 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00090121 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 4.095076e-06 * meter / second}},                   {'ibc0': {'y': 4.095076e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00050534 * meter / second}}]                     {'ibc1': {'y': 0.00050534 * meter / second}}]                                \n",
+      "22000     [0.00064556 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00062304 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 3.185172e-06 * meter / second}},                   {'ibc0': {'y': 3.185172e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00046558 * meter / second}}]                     {'ibc1': {'y': 0.00046558 * meter / second}}]                                \n",
+      "23000     [0.00058509 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00056849 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 3.078764e-06 * meter / second}},                   {'ibc0': {'y': 3.078764e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00042695 * meter / second}}]                     {'ibc1': {'y': 0.00042695 * meter / second}}]                                \n",
+      "24000     [0.00053798 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00052545 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.944127e-06 * meter / second}},                   {'ibc0': {'y': 2.944127e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.0003891 * meter / second}}]                      {'ibc1': {'y': 0.0003891 * meter / second}}]                                 \n",
+      "25000     [0.00053603 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00052135 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 3.57443e-06 * meter / second}},                    {'ibc0': {'y': 3.57443e-06 * meter / second}},                               \n",
+      "           {'ibc1': {'y': 0.00035367 * meter / second}}]                     {'ibc1': {'y': 0.00035367 * meter / second}}]                                \n",
+      "26000     [0.00046754 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00046 * 10.0^0 * ((meter / second) / second) ** 2,             []          \n",
+      "           {'ibc0': {'y': 2.5451907e-06 * meter / second}},                  {'ibc0': {'y': 2.5451907e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00032271 * meter / second}}]                     {'ibc1': {'y': 0.00032271 * meter / second}}]                                \n",
+      "27000     [0.00044109 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00043435 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.2023457e-06 * meter / second}},                  {'ibc0': {'y': 2.2023457e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029436 * meter / second}}]                     {'ibc1': {'y': 0.00029436 * meter / second}}]                                \n",
+      "\n",
+      "Best trainer at step 27000:\n",
+      "  train loss: 7.38e-04\n",
+      "  test loss: 7.31e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 38.123230 s\n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.009922 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "27000     [0.00044109 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00043435 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.2023457e-06 * meter / second}},                  {'ibc0': {'y': 2.2023457e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029436 * meter / second}}]                     {'ibc1': {'y': 0.00029436 * meter / second}}]                                \n",
+      "27100     [0.00044241 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00043749 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.3348348e-06 * meter / second}},                  {'ibc0': {'y': 2.3348348e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029449 * meter / second}}]                     {'ibc1': {'y': 0.00029449 * meter / second}}]                                \n",
+      "27200     [0.00044184 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00043681 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.326817e-06 * meter / second}},                   {'ibc0': {'y': 2.326817e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00029447 * meter / second}}]                     {'ibc1': {'y': 0.00029447 * meter / second}}]                                \n",
+      "27300     [0.00044135 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00043622 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.3192913e-06 * meter / second}},                  {'ibc0': {'y': 2.3192913e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029445 * meter / second}}]                     {'ibc1': {'y': 0.00029445 * meter / second}}]                                \n",
+      "27400     [0.00044094 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00043571 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.3149416e-06 * meter / second}},                  {'ibc0': {'y': 2.3149416e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029443 * meter / second}}]                     {'ibc1': {'y': 0.00029443 * meter / second}}]                                \n",
+      "27500     [0.00044026 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00043484 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.307417e-06 * meter / second}},                   {'ibc0': {'y': 2.307417e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00029437 * meter / second}}]                     {'ibc1': {'y': 0.00029437 * meter / second}}]                                \n",
+      "27600     [0.0004401 * 10.0^0 * ((meter / second) / second) ** 2,           [0.00043462 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.3055607e-06 * meter / second}},                  {'ibc0': {'y': 2.3055607e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029435 * meter / second}}]                     {'ibc1': {'y': 0.00029435 * meter / second}}]                                \n",
+      "27700     [0.00044003 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00043452 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.3062646e-06 * meter / second}},                  {'ibc0': {'y': 2.3062646e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029433 * meter / second}}]                     {'ibc1': {'y': 0.00029433 * meter / second}}]                                \n",
+      "27800     [0.00043955 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00043361 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.3343398e-06 * meter / second}},                  {'ibc0': {'y': 2.3343398e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029413 * meter / second}}]                     {'ibc1': {'y': 0.00029413 * meter / second}}]                                \n",
+      "27900     [0.00043953 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00043358 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.3186947e-06 * meter / second}},                  {'ibc0': {'y': 2.3186947e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029415 * meter / second}}]                     {'ibc1': {'y': 0.00029415 * meter / second}}]                                \n",
+      "28000     [0.00070291 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00070311 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.3909661e-05 * meter / second}},                  {'ibc0': {'y': 1.3909661e-05 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00030785 * meter / second}}]                     {'ibc1': {'y': 0.00030785 * meter / second}}]                                \n",
+      "\n",
+      "Best trainer at step 27900:\n",
+      "  train loss: 7.36e-04\n",
+      "  test loss: 7.30e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 4.621998 s\n",
+      "\n",
+      "Mean residual: 0.016 * (meter / second) / second\n",
+      "Adding new point: {'t': ArrayImpl([0.98801452], dtype=float32) * second, 'x': ArrayImpl([-0.00032282], dtype=float32) * meter} \n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.013920 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "28000     [0.00197229 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00070311 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.3909661e-05 * meter / second}},                  {'ibc0': {'y': 1.3909661e-05 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00030785 * meter / second}}]                     {'ibc1': {'y': 0.00030785 * meter / second}}]                                \n",
+      "29000     [0.00051649 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00050734 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.4825522e-06 * meter / second}},                  {'ibc0': {'y': 2.4825522e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0002885 * meter / second}}]                      {'ibc1': {'y': 0.0002885 * meter / second}}]                                 \n",
+      "30000     [0.00048944 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00048262 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.2519919e-06 * meter / second}},                  {'ibc0': {'y': 2.2519919e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00028214 * meter / second}}]                     {'ibc1': {'y': 0.00028214 * meter / second}}]                                \n",
+      "31000     [0.00046857 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00046308 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.1248854e-06 * meter / second}},                  {'ibc0': {'y': 2.1248854e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0002748 * meter / second}}]                      {'ibc1': {'y': 0.0002748 * meter / second}}]                                 \n",
+      "Epoch 31000: early stopping\n",
+      "\n",
+      "Best trainer at step 31000:\n",
+      "  train loss: 7.45e-04\n",
+      "  test loss: 7.40e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 12.569310 s\n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.009631 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "31000     [0.00046857 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00046308 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.1248854e-06 * meter / second}},                  {'ibc0': {'y': 2.1248854e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0002748 * meter / second}}]                      {'ibc1': {'y': 0.0002748 * meter / second}}]                                 \n",
+      "31100     [0.00046857 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00046308 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.1262192e-06 * meter / second}},                  {'ibc0': {'y': 2.1262192e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0002748 * meter / second}}]                      {'ibc1': {'y': 0.0002748 * meter / second}}]                                 \n",
+      "31200     [0.00046849 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00046293 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.1473877e-06 * meter / second}},                  {'ibc0': {'y': 2.1473877e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00027483 * meter / second}}]                     {'ibc1': {'y': 0.00027483 * meter / second}}]                                \n",
+      "31300     [0.00073493 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0007051 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 6.5290965e-06 * meter / second}},                  {'ibc0': {'y': 6.5290965e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029808 * meter / second}}]                     {'ibc1': {'y': 0.00029808 * meter / second}}]                                \n",
+      "31400     [0.00068256 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00065663 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 5.5905107e-06 * meter / second}},                  {'ibc0': {'y': 5.5905107e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029581 * meter / second}}]                     {'ibc1': {'y': 0.00029581 * meter / second}}]                                \n",
+      "31500     [0.00063919 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00061659 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 4.833671e-06 * meter / second}},                   {'ibc0': {'y': 4.833671e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00029365 * meter / second}}]                     {'ibc1': {'y': 0.00029365 * meter / second}}]                                \n",
+      "31600     [0.00060404 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00058418 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 4.2318925e-06 * meter / second}},                  {'ibc0': {'y': 4.2318925e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029175 * meter / second}}]                     {'ibc1': {'y': 0.00029175 * meter / second}}]                                \n",
+      "31700     [0.0005764 * 10.0^0 * ((meter / second) / second) ** 2,           [0.00055869 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 3.7593527e-06 * meter / second}},                  {'ibc0': {'y': 3.7593527e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029007 * meter / second}}]                     {'ibc1': {'y': 0.00029007 * meter / second}}]                                \n",
+      "31800     [0.00055442 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00053848 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 3.3909535e-06 * meter / second}},                  {'ibc0': {'y': 3.3909535e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00028856 * meter / second}}]                     {'ibc1': {'y': 0.00028856 * meter / second}}]                                \n",
+      "31900     [0.00053654 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00052207 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 3.0938609e-06 * meter / second}},                  {'ibc0': {'y': 3.0938609e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00028721 * meter / second}}]                     {'ibc1': {'y': 0.00028721 * meter / second}}]                                \n",
+      "32000     [0.00052238 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00050915 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.8530642e-06 * meter / second}},                  {'ibc0': {'y': 2.8530642e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00028593 * meter / second}}]                     {'ibc1': {'y': 0.00028593 * meter / second}}]                                \n",
+      "\n",
+      "Best trainer at step 31200:\n",
+      "  train loss: 7.45e-04\n",
+      "  test loss: 7.40e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 4.693850 s\n",
+      "\n",
+      "Mean residual: 0.014 * (meter / second) / second\n",
+      "Adding new point: {'t': ArrayImpl([0.40964028], dtype=float32) * second, 'x': ArrayImpl([-0.00796556], dtype=float32) * meter} \n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.013628 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "32000     [0.00079346 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00050915 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.8530642e-06 * meter / second}},                  {'ibc0': {'y': 2.8530642e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00028593 * meter / second}}]                     {'ibc1': {'y': 0.00028593 * meter / second}}]                                \n",
+      "33000     [0.00055445 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0004924 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 2.0238429e-06 * meter / second}},                  {'ibc0': {'y': 2.0238429e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029025 * meter / second}}]                     {'ibc1': {'y': 0.00029025 * meter / second}}]                                \n",
+      "34000     [0.00052658 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00047557 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.0058028e-06 * meter / second}},                  {'ibc0': {'y': 2.0058028e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029186 * meter / second}}]                     {'ibc1': {'y': 0.00029186 * meter / second}}]                                \n",
+      "35000     [0.00050164 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00045865 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.9805689e-06 * meter / second}},                  {'ibc0': {'y': 1.9805689e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0002901 * meter / second}}]                      {'ibc1': {'y': 0.0002901 * meter / second}}]                                 \n",
+      "Epoch 35000: early stopping\n",
+      "\n",
+      "Best trainer at step 35000:\n",
+      "  train loss: 7.94e-04\n",
+      "  test loss: 7.51e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 11.863162 s\n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.009882 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "35000     [0.00050164 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00045865 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.9805689e-06 * meter / second}},                  {'ibc0': {'y': 1.9805689e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0002901 * meter / second}}]                      {'ibc1': {'y': 0.0002901 * meter / second}}]                                 \n",
+      "35100     [0.00050166 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00045857 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.9694924e-06 * meter / second}},                  {'ibc0': {'y': 1.9694924e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029009 * meter / second}}]                     {'ibc1': {'y': 0.00029009 * meter / second}}]                                \n",
+      "35200     [0.00050164 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00045859 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.9775282e-06 * meter / second}},                  {'ibc0': {'y': 1.9775282e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0002901 * meter / second}}]                      {'ibc1': {'y': 0.0002901 * meter / second}}]                                 \n",
+      "35300     [0.00050156 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00045861 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.0046225e-06 * meter / second}},                  {'ibc0': {'y': 2.0046225e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029016 * meter / second}}]                     {'ibc1': {'y': 0.00029016 * meter / second}}]                                \n",
+      "35400     [0.00050155 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00045858 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.0021143e-06 * meter / second}},                  {'ibc0': {'y': 2.0021143e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029016 * meter / second}}]                     {'ibc1': {'y': 0.00029016 * meter / second}}]                                \n",
+      "35500     [0.00050156 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00045858 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.0010755e-06 * meter / second}},                  {'ibc0': {'y': 2.0010755e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029016 * meter / second}}]                     {'ibc1': {'y': 0.00029016 * meter / second}}]                                \n",
+      "35600     [0.00050155 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00045855 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.9991612e-06 * meter / second}},                  {'ibc0': {'y': 1.9991612e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029016 * meter / second}}]                     {'ibc1': {'y': 0.00029016 * meter / second}}]                                \n",
+      "35700     [0.00050155 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00045855 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.9982401e-06 * meter / second}},                  {'ibc0': {'y': 1.9982401e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029016 * meter / second}}]                     {'ibc1': {'y': 0.00029016 * meter / second}}]                                \n",
+      "35800     [0.00050156 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0004586 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 1.9993226e-06 * meter / second}},                  {'ibc0': {'y': 1.9993226e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029015 * meter / second}}]                     {'ibc1': {'y': 0.00029015 * meter / second}}]                                \n",
+      "35900     [0.00050156 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0004586 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 1.9988458e-06 * meter / second}},                  {'ibc0': {'y': 1.9988458e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029015 * meter / second}}]                     {'ibc1': {'y': 0.00029015 * meter / second}}]                                \n",
+      "36000     [0.00050155 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00045858 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.9998658e-06 * meter / second}},                  {'ibc0': {'y': 1.9998658e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029015 * meter / second}}]                     {'ibc1': {'y': 0.00029015 * meter / second}}]                                \n",
+      "\n",
+      "Best trainer at step 36000:\n",
+      "  train loss: 7.94e-04\n",
+      "  test loss: 7.51e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 4.908738 s\n",
+      "\n",
+      "Mean residual: 0.013 * (meter / second) / second\n",
+      "Adding new point: {'t': ArrayImpl([0.2315986], dtype=float32) * second, 'x': ArrayImpl([-0.00680643], dtype=float32) * meter} \n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.015565 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "36000     [0.00074802 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00045858 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.9998658e-06 * meter / second}},                  {'ibc0': {'y': 1.9998658e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029015 * meter / second}}]                     {'ibc1': {'y': 0.00029015 * meter / second}}]                                \n",
+      "37000     [0.00059534 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00048061 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.9475647e-06 * meter / second}},                  {'ibc0': {'y': 1.9475647e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00031009 * meter / second}}]                     {'ibc1': {'y': 0.00031009 * meter / second}}]                                \n",
+      "38000     [0.0005734 * 10.0^0 * ((meter / second) / second) ** 2,           [0.00047273 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.9098984e-06 * meter / second}},                  {'ibc0': {'y': 1.9098984e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00030421 * meter / second}}]                     {'ibc1': {'y': 0.00030421 * meter / second}}]                                \n",
+      "39000     [0.00054813 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00046036 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.8778497e-06 * meter / second}},                  {'ibc0': {'y': 1.8778497e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029654 * meter / second}}]                     {'ibc1': {'y': 0.00029654 * meter / second}}]                                \n",
+      "Epoch 39000: early stopping\n",
+      "\n",
+      "Best trainer at step 39000:\n",
+      "  train loss: 8.47e-04\n",
+      "  test loss: 7.59e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 12.797805 s\n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.010421 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "39000     [0.00054813 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00046036 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.8778497e-06 * meter / second}},                  {'ibc0': {'y': 1.8778497e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029654 * meter / second}}]                     {'ibc1': {'y': 0.00029654 * meter / second}}]                                \n",
+      "39100     [0.00054806 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00046043 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.870236e-06 * meter / second}},                   {'ibc0': {'y': 1.870236e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00029661 * meter / second}}]                     {'ibc1': {'y': 0.00029661 * meter / second}}]                                \n",
+      "39200     [0.00054929 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0004634 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 2.0574782e-06 * meter / second}},                  {'ibc0': {'y': 2.0574782e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029533 * meter / second}}]                     {'ibc1': {'y': 0.00029533 * meter / second}}]                                \n",
+      "39300     [0.00054904 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00046287 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.0242942e-06 * meter / second}},                  {'ibc0': {'y': 2.0242942e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029551 * meter / second}}]                     {'ibc1': {'y': 0.00029551 * meter / second}}]                                \n",
+      "39400     [0.00054891 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00046254 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.004753e-06 * meter / second}},                   {'ibc0': {'y': 2.004753e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00029562 * meter / second}}]                     {'ibc1': {'y': 0.00029562 * meter / second}}]                                \n",
+      "39500     [0.00054882 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0004622 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 1.994075e-06 * meter / second}},                   {'ibc0': {'y': 1.994075e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.0002957 * meter / second}}]                      {'ibc1': {'y': 0.0002957 * meter / second}}]                                 \n",
+      "39600     [0.0005721 * 10.0^0 * ((meter / second) / second) ** 2,           [0.00045579 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.6710437e-06 * meter / second}},                  {'ibc0': {'y': 1.6710437e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00030883 * meter / second}}]                     {'ibc1': {'y': 0.00030883 * meter / second}}]                                \n",
+      "39700     [0.00056654 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00045358 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.5904287e-06 * meter / second}},                  {'ibc0': {'y': 1.5904287e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00030726 * meter / second}}]                     {'ibc1': {'y': 0.00030726 * meter / second}}]                                \n",
+      "39800     [0.00056229 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00045216 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.5325267e-06 * meter / second}},                  {'ibc0': {'y': 1.5325267e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00030581 * meter / second}}]                     {'ibc1': {'y': 0.00030581 * meter / second}}]                                \n",
+      "39900     [0.0005591 * 10.0^0 * ((meter / second) / second) ** 2,           [0.00045143 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.498971e-06 * meter / second}},                   {'ibc0': {'y': 1.498971e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00030449 * meter / second}}]                     {'ibc1': {'y': 0.00030449 * meter / second}}]                                \n",
+      "40000     [0.00055651 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00045115 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.4806845e-06 * meter / second}},                  {'ibc0': {'y': 1.4806845e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00030329 * meter / second}}]                     {'ibc1': {'y': 0.00030329 * meter / second}}]                                \n",
+      "\n",
+      "Best trainer at step 39500:\n",
+      "  train loss: 8.47e-04\n",
+      "  test loss: 7.60e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 4.848265 s\n",
+      "\n",
+      "Mean residual: 0.013 * (meter / second) / second\n",
+      "Adding new point: {'t': ArrayImpl([0.98284292], dtype=float32) * second, 'x': ArrayImpl([0.00254142], dtype=float32) * meter} \n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.014243 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                       Test loss                                                         Test metric \n",
+      "40000     [0.0006681 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00045115 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.4806845e-06 * meter / second}},                 {'ibc0': {'y': 1.4806845e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00030329 * meter / second}}]                    {'ibc1': {'y': 0.00030329 * meter / second}}]                                \n",
+      "41000     [0.00057995 * 10.0^0 * ((meter / second) / second) ** 2,         [0.00048127 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.0367063e-06 * meter / second}},                 {'ibc0': {'y': 2.0367063e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029667 * meter / second}}]                    {'ibc1': {'y': 0.00029667 * meter / second}}]                                \n",
+      "42000     [0.00055732 * 10.0^0 * ((meter / second) / second) ** 2,         [0.000462 * 10.0^0 * ((meter / second) / second) ** 2,            []          \n",
+      "           {'ibc0': {'y': 1.8838784e-06 * meter / second}},                 {'ibc0': {'y': 1.8838784e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0002966 * meter / second}}]                     {'ibc1': {'y': 0.0002966 * meter / second}}]                                 \n",
+      "43000     [0.0005333 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00044418 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.8350134e-06 * meter / second}},                 {'ibc0': {'y': 1.8350134e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00029156 * meter / second}}]                    {'ibc1': {'y': 0.00029156 * meter / second}}]                                \n",
+      "44000     [0.00050091 * 10.0^0 * ((meter / second) / second) ** 2,         [0.0004224 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 1.826639e-06 * meter / second}},                  {'ibc0': {'y': 1.826639e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00027955 * meter / second}}]                    {'ibc1': {'y': 0.00027955 * meter / second}}]                                \n",
+      "Epoch 44000: early stopping\n",
+      "\n",
+      "Best trainer at step 44000:\n",
+      "  train loss: 7.82e-04\n",
+      "  test loss: 7.04e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 14.967950 s\n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.009629 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                        Test metric \n",
+      "44000     [0.00050091 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0004224 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.826639e-06 * meter / second}},                   {'ibc0': {'y': 1.826639e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00027955 * meter / second}}]                     {'ibc1': {'y': 0.00027955 * meter / second}}]                               \n",
+      "44100     [0.00050114 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00042319 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 1.8824794e-06 * meter / second}},                  {'ibc0': {'y': 1.8824794e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00027945 * meter / second}}]                     {'ibc1': {'y': 0.00027945 * meter / second}}]                               \n",
+      "44200     [0.00145139 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00122295 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 8.942031e-06 * meter / second}},                   {'ibc0': {'y': 8.942031e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00027929 * meter / second}}]                     {'ibc1': {'y': 0.00027929 * meter / second}}]                               \n",
+      "44300     [0.00127814 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00108249 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 7.864425e-06 * meter / second}},                   {'ibc0': {'y': 7.864425e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00027888 * meter / second}}]                     {'ibc1': {'y': 0.00027888 * meter / second}}]                               \n",
+      "44400     [0.00113658 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00096735 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 6.9546295e-06 * meter / second}},                  {'ibc0': {'y': 6.9546295e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00027857 * meter / second}}]                     {'ibc1': {'y': 0.00027857 * meter / second}}]                               \n",
+      "44500     [0.00102089 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00087287 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 6.1936867e-06 * meter / second}},                  {'ibc0': {'y': 6.1936867e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00027836 * meter / second}}]                     {'ibc1': {'y': 0.00027836 * meter / second}}]                               \n",
+      "44600     [0.00092632 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00079524 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 5.5573573e-06 * meter / second}},                  {'ibc0': {'y': 5.5573573e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00027821 * meter / second}}]                     {'ibc1': {'y': 0.00027821 * meter / second}}]                               \n",
+      "44700     [0.00084903 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00073138 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 5.0196286e-06 * meter / second}},                  {'ibc0': {'y': 5.0196286e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00027812 * meter / second}}]                     {'ibc1': {'y': 0.00027812 * meter / second}}]                               \n",
+      "44800     [0.0007858 * 10.0^0 * ((meter / second) / second) ** 2,           [0.00067878 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 4.56894e-06 * meter / second}},                    {'ibc0': {'y': 4.56894e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00027808 * meter / second}}]                     {'ibc1': {'y': 0.00027808 * meter / second}}]                               \n",
+      "44900     [0.00073408 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00063543 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 4.191006e-06 * meter / second}},                   {'ibc0': {'y': 4.191006e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00027807 * meter / second}}]                     {'ibc1': {'y': 0.00027807 * meter / second}}]                               \n",
+      "45000     [0.00069176 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00059965 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 3.8737767e-06 * meter / second}},                  {'ibc0': {'y': 3.8737767e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00027808 * meter / second}}]                     {'ibc1': {'y': 0.00027808 * meter / second}}]                               \n",
+      "\n",
+      "Best trainer at step 44000:\n",
+      "  train loss: 7.82e-04\n",
+      "  test loss: 7.04e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 4.975001 s\n",
+      "\n",
+      "Mean residual: 0.014 * (meter / second) / second\n",
+      "Adding new point: {'t': ArrayImpl([0.47824991], dtype=float32) * second, 'x': ArrayImpl([0.00034535], dtype=float32) * meter} \n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.013878 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "45000     [0.00086286 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00059965 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 3.8737767e-06 * meter / second}},                  {'ibc0': {'y': 3.8737767e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00027808 * meter / second}}]                     {'ibc1': {'y': 0.00027808 * meter / second}}]                                \n",
+      "46000     [0.00051184 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00043078 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.951535e-06 * meter / second}},                   {'ibc0': {'y': 1.951535e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00028326 * meter / second}}]                     {'ibc1': {'y': 0.00028326 * meter / second}}]                                \n",
+      "47000     [0.00049468 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00041779 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.8175754e-06 * meter / second}},                  {'ibc0': {'y': 1.8175754e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00027674 * meter / second}}]                     {'ibc1': {'y': 0.00027674 * meter / second}}]                                \n",
+      "48000     [0.00047711 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00040669 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.8042014e-06 * meter / second}},                  {'ibc0': {'y': 1.8042014e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00026785 * meter / second}}]                     {'ibc1': {'y': 0.00026785 * meter / second}}]                                \n",
+      "Epoch 48000: early stopping\n",
+      "\n",
+      "Best trainer at step 48000:\n",
+      "  train loss: 7.47e-04\n",
+      "  test loss: 6.76e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 11.456536 s\n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.009981 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "48000     [0.00047711 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00040669 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.8042014e-06 * meter / second}},                  {'ibc0': {'y': 1.8042014e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00026785 * meter / second}}]                     {'ibc1': {'y': 0.00026785 * meter / second}}]                                \n",
+      "48100     [0.00047705 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00040699 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.8382888e-06 * meter / second}},                  {'ibc0': {'y': 1.8382888e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00026787 * meter / second}}]                     {'ibc1': {'y': 0.00026787 * meter / second}}]                                \n",
+      "48200     [0.00047711 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00040684 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.838244e-06 * meter / second}},                   {'ibc0': {'y': 1.838244e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00026778 * meter / second}}]                     {'ibc1': {'y': 0.00026778 * meter / second}}]                                \n",
+      "48300     [0.00055811 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00051886 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.0354166e-06 * meter / second}},                  {'ibc0': {'y': 2.0354166e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00027301 * meter / second}}]                     {'ibc1': {'y': 0.00027301 * meter / second}}]                                \n",
+      "48400     [0.00054285 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00050191 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.7836052e-06 * meter / second}},                  {'ibc0': {'y': 1.7836052e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00027229 * meter / second}}]                     {'ibc1': {'y': 0.00027229 * meter / second}}]                                \n",
+      "48500     [0.00053009 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00048734 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.5927877e-06 * meter / second}},                  {'ibc0': {'y': 1.5927877e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00027169 * meter / second}}]                     {'ibc1': {'y': 0.00027169 * meter / second}}]                                \n",
+      "48600     [0.00051951 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0004748 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 1.4500241e-06 * meter / second}},                  {'ibc0': {'y': 1.4500241e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00027117 * meter / second}}]                     {'ibc1': {'y': 0.00027117 * meter / second}}]                                \n",
+      "48700     [0.00051096 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00046426 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.344583e-06 * meter / second}},                   {'ibc0': {'y': 1.344583e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00027073 * meter / second}}]                     {'ibc1': {'y': 0.00027073 * meter / second}}]                                \n",
+      "48800     [0.0005042 * 10.0^0 * ((meter / second) / second) ** 2,           [0.00045563 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.2793502e-06 * meter / second}},                  {'ibc0': {'y': 1.2793502e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00027043 * meter / second}}]                     {'ibc1': {'y': 0.00027043 * meter / second}}]                                \n",
+      "48900     [0.00049869 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00044829 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.239052e-06 * meter / second}},                   {'ibc0': {'y': 1.239052e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00027017 * meter / second}}]                     {'ibc1': {'y': 0.00027017 * meter / second}}]                                \n",
+      "49000     [0.00049421 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00044207 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.218723e-06 * meter / second}},                   {'ibc0': {'y': 1.218723e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00026993 * meter / second}}]                     {'ibc1': {'y': 0.00026993 * meter / second}}]                                \n",
+      "\n",
+      "Best trainer at step 48200:\n",
+      "  train loss: 7.47e-04\n",
+      "  test loss: 6.76e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 4.382472 s\n",
+      "\n",
+      "Mean residual: 0.013 * (meter / second) / second\n",
+      "Adding new point: {'t': ArrayImpl([0.32790023], dtype=float32) * second, 'x': ArrayImpl([-0.00789118], dtype=float32) * meter} \n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.013897 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "49000     [0.00057817 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00044207 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.218723e-06 * meter / second}},                   {'ibc0': {'y': 1.218723e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00026993 * meter / second}}]                     {'ibc1': {'y': 0.00026993 * meter / second}}]                                \n",
+      "50000     [0.00050032 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00041502 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.859967e-06 * meter / second}},                   {'ibc0': {'y': 1.859967e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00028114 * meter / second}}]                     {'ibc1': {'y': 0.00028114 * meter / second}}]                                \n",
+      "51000     [0.00047413 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0004074 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 1.8621943e-06 * meter / second}},                  {'ibc0': {'y': 1.8621943e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00027061 * meter / second}}]                     {'ibc1': {'y': 0.00027061 * meter / second}}]                                \n",
+      "52000     [0.00043118 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00039883 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.9477718e-06 * meter / second}},                  {'ibc0': {'y': 1.9477718e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00025804 * meter / second}}]                     {'ibc1': {'y': 0.00025804 * meter / second}}]                                \n",
+      "53000     [0.00039573 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00037706 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.8887133e-06 * meter / second}},                  {'ibc0': {'y': 1.8887133e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00023655 * meter / second}}]                     {'ibc1': {'y': 0.00023655 * meter / second}}]                                \n",
+      "54000     [0.00036355 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00035133 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.6823096e-06 * meter / second}},                  {'ibc0': {'y': 1.6823096e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00021243 * meter / second}}]                     {'ibc1': {'y': 0.00021243 * meter / second}}]                                \n",
+      "55000     [0.00035543 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0003454 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 1.3884645e-06 * meter / second}},                  {'ibc0': {'y': 1.3884645e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00019187 * meter / second}}]                     {'ibc1': {'y': 0.00019187 * meter / second}}]                                \n",
+      "Epoch 55000: early stopping\n",
+      "\n",
+      "Best trainer at step 55000:\n",
+      "  train loss: 5.49e-04\n",
+      "  test loss: 5.39e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 28.337766 s\n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.010395 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                        Test metric \n",
+      "55000     [0.00035543 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0003454 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.3884645e-06 * meter / second}},                  {'ibc0': {'y': 1.3884645e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00019187 * meter / second}}]                     {'ibc1': {'y': 0.00019187 * meter / second}}]                               \n",
+      "55100     [0.00038491 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00036448 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 2.1007363e-06 * meter / second}},                  {'ibc0': {'y': 2.1007363e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00019324 * meter / second}}]                     {'ibc1': {'y': 0.00019324 * meter / second}}]                               \n",
+      "55200     [0.00037617 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00035767 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 2.0267723e-06 * meter / second}},                  {'ibc0': {'y': 2.0267723e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00019314 * meter / second}}]                     {'ibc1': {'y': 0.00019314 * meter / second}}]                               \n",
+      "55300     [0.00036902 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00035212 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 1.9594597e-06 * meter / second}},                  {'ibc0': {'y': 1.9594597e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00019307 * meter / second}}]                     {'ibc1': {'y': 0.00019307 * meter / second}}]                               \n",
+      "55400     [0.00036314 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00034759 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 1.9019135e-06 * meter / second}},                  {'ibc0': {'y': 1.9019135e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00019301 * meter / second}}]                     {'ibc1': {'y': 0.00019301 * meter / second}}]                               \n",
+      "55500     [0.00035832 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00034388 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 1.8527855e-06 * meter / second}},                  {'ibc0': {'y': 1.8527855e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00019295 * meter / second}}]                     {'ibc1': {'y': 0.00019295 * meter / second}}]                               \n",
+      "55600     [0.00035437 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00034088 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 1.8089495e-06 * meter / second}},                  {'ibc0': {'y': 1.8089495e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00019291 * meter / second}}]                     {'ibc1': {'y': 0.00019291 * meter / second}}]                               \n",
+      "55700     [0.00035103 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00033836 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 1.7687397e-06 * meter / second}},                  {'ibc0': {'y': 1.7687397e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00019289 * meter / second}}]                     {'ibc1': {'y': 0.00019289 * meter / second}}]                               \n",
+      "55800     [0.00034844 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00033642 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 1.7341235e-06 * meter / second}},                  {'ibc0': {'y': 1.7341235e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00019286 * meter / second}}]                     {'ibc1': {'y': 0.00019286 * meter / second}}]                               \n",
+      "55900     [0.00034628 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00033482 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 1.7029475e-06 * meter / second}},                  {'ibc0': {'y': 1.7029475e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00019285 * meter / second}}]                     {'ibc1': {'y': 0.00019285 * meter / second}}]                               \n",
+      "56000     [0.00034438 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00033343 * 10.0^0 * ((meter / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 1.6735721e-06 * meter / second}},                  {'ibc0': {'y': 1.6735721e-06 * meter / second}},                            \n",
+      "           {'ibc1': {'y': 0.00019284 * meter / second}}]                     {'ibc1': {'y': 0.00019284 * meter / second}}]                               \n",
+      "\n",
+      "Best trainer at step 56000:\n",
+      "  train loss: 5.39e-04\n",
+      "  test loss: 5.28e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 5.173902 s\n",
+      "\n",
+      "Mean residual: 0.012 * (meter / second) / second\n",
+      "Adding new point: {'t': ArrayImpl([0.5695765], dtype=float32) * second, 'x': ArrayImpl([-0.00540644], dtype=float32) * meter} \n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.015050 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "56000     [0.00045088 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00033343 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.6735721e-06 * meter / second}},                  {'ibc0': {'y': 1.6735721e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00019284 * meter / second}}]                     {'ibc1': {'y': 0.00019284 * meter / second}}]                                \n",
+      "57000     [0.0003596 * 10.0^0 * ((meter / second) / second) ** 2,           [0.00033415 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.4792597e-06 * meter / second}},                  {'ibc0': {'y': 1.4792597e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00019963 * meter / second}}]                     {'ibc1': {'y': 0.00019963 * meter / second}}]                                \n",
+      "58000     [0.00034447 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0003254 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 1.3338099e-06 * meter / second}},                  {'ibc0': {'y': 1.3338099e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0001965 * meter / second}}]                      {'ibc1': {'y': 0.0001965 * meter / second}}]                                 \n",
+      "59000     [0.00033271 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00031697 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.3009029e-06 * meter / second}},                  {'ibc0': {'y': 1.3009029e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00019046 * meter / second}}]                     {'ibc1': {'y': 0.00019046 * meter / second}}]                                \n",
+      "60000     [0.00031924 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030644 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.2355371e-06 * meter / second}},                  {'ibc0': {'y': 1.2355371e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00018091 * meter / second}}]                     {'ibc1': {'y': 0.00018091 * meter / second}}]                                \n",
+      "Epoch 60000: early stopping\n",
+      "\n",
+      "Best trainer at step 60000:\n",
+      "  train loss: 5.01e-04\n",
+      "  test loss: 4.89e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 16.605614 s\n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.009775 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "60000     [0.00031924 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030644 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.2355371e-06 * meter / second}},                  {'ibc0': {'y': 1.2355371e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00018091 * meter / second}}]                     {'ibc1': {'y': 0.00018091 * meter / second}}]                                \n",
+      "60100     [0.00031926 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0003067 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 1.2158225e-06 * meter / second}},                  {'ibc0': {'y': 1.2158225e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00018093 * meter / second}}]                     {'ibc1': {'y': 0.00018093 * meter / second}}]                                \n",
+      "60200     [0.0003193 * 10.0^0 * ((meter / second) / second) ** 2,           [0.00030668 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.2317463e-06 * meter / second}},                  {'ibc0': {'y': 1.2317463e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00018085 * meter / second}}]                     {'ibc1': {'y': 0.00018085 * meter / second}}]                                \n",
+      "60300     [0.00032016 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030574 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 8.9220623e-07 * meter / second}},                  {'ibc0': {'y': 8.9220623e-07 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0001815 * meter / second}}]                      {'ibc1': {'y': 0.0001815 * meter / second}}]                                 \n",
+      "60400     [0.00031992 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030578 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 9.2139237e-07 * meter / second}},                  {'ibc0': {'y': 9.2139237e-07 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00018143 * meter / second}}]                     {'ibc1': {'y': 0.00018143 * meter / second}}]                                \n",
+      "60500     [0.00031974 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030583 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 9.5220133e-07 * meter / second}},                  {'ibc0': {'y': 9.5220133e-07 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00018136 * meter / second}}]                     {'ibc1': {'y': 0.00018136 * meter / second}}]                                \n",
+      "60600     [0.00031961 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030594 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 9.865097e-07 * meter / second}},                   {'ibc0': {'y': 9.865097e-07 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00018126 * meter / second}}]                     {'ibc1': {'y': 0.00018126 * meter / second}}]                                \n",
+      "60700     [0.00031952 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030598 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.0199747e-06 * meter / second}},                  {'ibc0': {'y': 1.0199747e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00018119 * meter / second}}]                     {'ibc1': {'y': 0.00018119 * meter / second}}]                                \n",
+      "60800     [0.00031941 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030598 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.0453716e-06 * meter / second}},                  {'ibc0': {'y': 1.0453716e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00018118 * meter / second}}]                     {'ibc1': {'y': 0.00018118 * meter / second}}]                                \n",
+      "60900     [0.00031924 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030594 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.0795525e-06 * meter / second}},                  {'ibc0': {'y': 1.0795525e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0001812 * meter / second}}]                      {'ibc1': {'y': 0.0001812 * meter / second}}]                                 \n",
+      "61000     [0.00040084 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00037745 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.2552299e-05 * meter / second}},                  {'ibc0': {'y': 1.2552299e-05 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017717 * meter / second}}]                     {'ibc1': {'y': 0.00017717 * meter / second}}]                                \n",
+      "\n",
+      "Best trainer at step 60200:\n",
+      "  train loss: 5.01e-04\n",
+      "  test loss: 4.89e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 4.717136 s\n",
+      "\n",
+      "Mean residual: 0.012 * (meter / second) / second\n",
+      "Adding new point: {'t': ArrayImpl([0.38778442], dtype=float32) * second, 'x': ArrayImpl([-0.0043211], dtype=float32) * meter} \n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.015250 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "61000     [0.00046435 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00037745 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.2552299e-05 * meter / second}},                  {'ibc0': {'y': 1.2552299e-05 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017717 * meter / second}}]                     {'ibc1': {'y': 0.00017717 * meter / second}}]                                \n",
+      "62000     [0.00034305 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00031215 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.2617899e-06 * meter / second}},                  {'ibc0': {'y': 1.2617899e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00018198 * meter / second}}]                     {'ibc1': {'y': 0.00018198 * meter / second}}]                                \n",
+      "63000     [0.0003293 * 10.0^0 * ((meter / second) / second) ** 2,           [0.00030744 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1898037e-06 * meter / second}},                  {'ibc0': {'y': 1.1898037e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017979 * meter / second}}]                     {'ibc1': {'y': 0.00017979 * meter / second}}]                                \n",
+      "64000     [0.00032155 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030236 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1578732e-06 * meter / second}},                  {'ibc0': {'y': 1.1578732e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017604 * meter / second}}]                     {'ibc1': {'y': 0.00017604 * meter / second}}]                                \n",
+      "Epoch 64000: early stopping\n",
+      "\n",
+      "Best trainer at step 64000:\n",
+      "  train loss: 4.99e-04\n",
+      "  test loss: 4.80e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 11.294281 s\n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.010299 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "64000     [0.00032155 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030236 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1578732e-06 * meter / second}},                  {'ibc0': {'y': 1.1578732e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017604 * meter / second}}]                     {'ibc1': {'y': 0.00017604 * meter / second}}]                                \n",
+      "64100     [0.00032129 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030189 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.2672714e-06 * meter / second}},                  {'ibc0': {'y': 1.2672714e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017607 * meter / second}}]                     {'ibc1': {'y': 0.00017607 * meter / second}}]                                \n",
+      "64200     [0.00032125 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030183 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.230208e-06 * meter / second}},                   {'ibc0': {'y': 1.230208e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00017615 * meter / second}}]                     {'ibc1': {'y': 0.00017615 * meter / second}}]                                \n",
+      "64300     [0.00032105 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0003016 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 1.1040507e-06 * meter / second}},                  {'ibc0': {'y': 1.1040507e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0001765 * meter / second}}]                      {'ibc1': {'y': 0.0001765 * meter / second}}]                                 \n",
+      "64400     [0.00032116 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030174 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1353521e-06 * meter / second}},                  {'ibc0': {'y': 1.1353521e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017633 * meter / second}}]                     {'ibc1': {'y': 0.00017633 * meter / second}}]                                \n",
+      "64500     [0.00032118 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030177 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1384761e-06 * meter / second}},                  {'ibc0': {'y': 1.1384761e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0001763 * meter / second}}]                      {'ibc1': {'y': 0.0001763 * meter / second}}]                                 \n",
+      "64600     [0.00032113 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030171 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1360961e-06 * meter / second}},                  {'ibc0': {'y': 1.1360961e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017638 * meter / second}}]                     {'ibc1': {'y': 0.00017638 * meter / second}}]                                \n",
+      "64700     [0.00032106 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030168 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1283665e-06 * meter / second}},                  {'ibc0': {'y': 1.1283665e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017646 * meter / second}}]                     {'ibc1': {'y': 0.00017646 * meter / second}}]                                \n",
+      "64800     [0.00032214 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030261 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1278934e-06 * meter / second}},                  {'ibc0': {'y': 1.1278934e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017566 * meter / second}}]                     {'ibc1': {'y': 0.00017566 * meter / second}}]                                \n",
+      "64900     [0.00032201 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0003025 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 1.1321267e-06 * meter / second}},                  {'ibc0': {'y': 1.1321267e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0001757 * meter / second}}]                      {'ibc1': {'y': 0.0001757 * meter / second}}]                                 \n",
+      "65000     [0.00032184 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030237 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1423148e-06 * meter / second}},                  {'ibc0': {'y': 1.1423148e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017577 * meter / second}}]                     {'ibc1': {'y': 0.00017577 * meter / second}}]                                \n",
+      "\n",
+      "Best trainer at step 64500:\n",
+      "  train loss: 4.99e-04\n",
+      "  test loss: 4.79e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 4.623570 s\n",
+      "\n",
+      "Mean residual: 0.012 * (meter / second) / second\n",
+      "Adding new point: {'t': ArrayImpl([0.40250915], dtype=float32) * second, 'x': ArrayImpl([-0.010427], dtype=float32) * meter} \n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.014968 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "65000     [0.00039208 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00030237 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1423148e-06 * meter / second}},                  {'ibc0': {'y': 1.1423148e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017577 * meter / second}}]                     {'ibc1': {'y': 0.00017577 * meter / second}}]                                \n",
+      "66000     [0.00035875 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00032071 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.144454e-06 * meter / second}},                   {'ibc0': {'y': 1.144454e-06 * meter / second}},                              \n",
+      "           {'ibc1': {'y': 0.00018135 * meter / second}}]                     {'ibc1': {'y': 0.00018135 * meter / second}}]                                \n",
+      "67000     [0.00034588 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00031595 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.0716824e-06 * meter / second}},                  {'ibc0': {'y': 1.0716824e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017891 * meter / second}}]                     {'ibc1': {'y': 0.00017891 * meter / second}}]                                \n",
+      "Epoch 67001: early stopping\n",
+      "\n",
+      "Best trainer at step 67000:\n",
+      "  train loss: 5.26e-04\n",
+      "  test loss: 4.96e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 7.938136 s\n",
+      "\n",
+      "Compiling trainer...\n",
+      "'compile' took 0.014745 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "67001     [0.00034587 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00031594 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.0716063e-06 * meter / second}},                  {'ibc0': {'y': 1.0716063e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017891 * meter / second}}]                     {'ibc1': {'y': 0.00017891 * meter / second}}]                                \n",
+      "67100     [0.00034577 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00031621 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.0872557e-06 * meter / second}},                  {'ibc0': {'y': 1.0872557e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.000179 * meter / second}}]                       {'ibc1': {'y': 0.000179 * meter / second}}]                                  \n",
+      "67200     [0.00034568 * 10.0^0 * ((meter / second) / second) ** 2,          [0.000316 * 10.0^0 * ((meter / second) / second) ** 2,            []          \n",
+      "           {'ibc0': {'y': 1.0810584e-06 * meter / second}},                  {'ibc0': {'y': 1.0810584e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017908 * meter / second}}]                     {'ibc1': {'y': 0.00017908 * meter / second}}]                                \n",
+      "67300     [0.00034568 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00031598 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.0880661e-06 * meter / second}},                  {'ibc0': {'y': 1.0880661e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017907 * meter / second}}]                     {'ibc1': {'y': 0.00017907 * meter / second}}]                                \n",
+      "67400     [0.00034624 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00031566 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1337318e-06 * meter / second}},                  {'ibc0': {'y': 1.1337318e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017865 * meter / second}}]                     {'ibc1': {'y': 0.00017865 * meter / second}}]                                \n",
+      "67500     [0.00034618 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00031566 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1352137e-06 * meter / second}},                  {'ibc0': {'y': 1.1352137e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017867 * meter / second}}]                     {'ibc1': {'y': 0.00017867 * meter / second}}]                                \n",
+      "67600     [0.00034612 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00031567 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1311693e-06 * meter / second}},                  {'ibc0': {'y': 1.1311693e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.0001787 * meter / second}}]                      {'ibc1': {'y': 0.0001787 * meter / second}}]                                 \n",
+      "67700     [0.00034608 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00031568 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1283073e-06 * meter / second}},                  {'ibc0': {'y': 1.1283073e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017872 * meter / second}}]                     {'ibc1': {'y': 0.00017872 * meter / second}}]                                \n",
+      "67800     [0.00034614 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00031567 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1323336e-06 * meter / second}},                  {'ibc0': {'y': 1.1323336e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017869 * meter / second}}]                     {'ibc1': {'y': 0.00017869 * meter / second}}]                                \n",
+      "67900     [0.00034607 * 10.0^0 * ((meter / second) / second) ** 2,          [0.00031569 * 10.0^0 * ((meter / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.1263944e-06 * meter / second}},                  {'ibc0': {'y': 1.1263944e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017873 * meter / second}}]                     {'ibc1': {'y': 0.00017873 * meter / second}}]                                \n",
+      "68000     [0.00034603 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0003157 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 1.1240935e-06 * meter / second}},                  {'ibc0': {'y': 1.1240935e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017874 * meter / second}}]                     {'ibc1': {'y': 0.00017874 * meter / second}}]                                \n",
+      "68001     [0.00034604 * 10.0^0 * ((meter / second) / second) ** 2,          [0.0003157 * 10.0^0 * ((meter / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 1.1240547e-06 * meter / second}},                  {'ibc0': {'y': 1.1240547e-06 * meter / second}},                             \n",
+      "           {'ibc1': {'y': 0.00017874 * meter / second}}]                     {'ibc1': {'y': 0.00017874 * meter / second}}]                                \n",
+      "\n",
+      "Best trainer at step 67300:\n",
+      "  train loss: 5.26e-04\n",
+      "  test loss: 4.96e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 4.620428 s\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "while u.get_magnitude(err) > 0.012:\n",
+    "    f = trainer.predict(X, operator=pde)\n",
+    "    err_eq = u.math.absolute(f)\n",
+    "    err = u.math.mean(err_eq)\n",
+    "    print(f\"Mean residual: {err:.3f}\")\n",
+    "\n",
+    "    x_id = u.math.argmax(err_eq)\n",
+    "    new_xs = jax.tree.map(lambda x: x[[x_id]], X)\n",
+    "    print(\"Adding new point:\", new_xs, \"\\n\")\n",
+    "    problem.add_anchors(new_xs)\n",
+    "    early_stopping = deepxde.callbacks.EarlyStopping(min_delta=1e-4, patience=2000)\n",
+    "    trainer.compile(bst.optim.Adam(1e-3)).train(iterations=10000,\n",
+    "                                                disregard_previous_best=True,\n",
+    "                                                callbacks=[early_stopping])\n",
+    "    trainer.compile(bst.optim.LBFGS(1e-3)).train(1000, display_every=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's visualize and save the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving loss history to /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/loss.dat ...\n",
+      "Saving checkpoint into /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/loss.dat\n",
+      "Saving training data to /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/train.dat ...\n",
+      "Saving checkpoint into /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/train.dat\n",
+      "Saving test data to /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/test.dat ...\n",
+      "Saving checkpoint into /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/test.dat\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trainer.saveplot(issave=True, isplot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also test the model with the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gen_testdata():\n",
+    "    data = np.load(\"../dataset/Burgers.npz\")\n",
+    "    t, x, exact = data[\"t\"], data[\"x\"], data[\"usol\"].T\n",
+    "    xx, tt = np.meshgrid(x, t)\n",
+    "    X = {'x': np.ravel(xx) * u.meter, 't': np.ravel(tt) * u.second}\n",
+    "    y = exact.flatten()[:, None]\n",
+    "    return X, y * uy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean residual: 0.01163746 * (meter / second) / second\n",
+      "L2 relative error: 225.97165\n"
+     ]
+    }
+   ],
+   "source": [
+    "X, y_true = gen_testdata()\n",
+    "y_pred = trainer.predict(X)\n",
+    "f = pde(X, y_pred)\n",
+    "print(\"Mean residual:\", u.math.mean(u.math.absolute(f)))\n",
+    "print(\"L2 relative error:\", deepxde.metrics.l2_relative_error(y_true, y_pred['y']))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pinnx",
+   "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": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/experimental_docs/unit-examples-forward/Burgers_RAR.py b/docs/experimental_docs/unit-examples-forward/Burgers_RAR.py
new file mode 100644
index 000000000..5bab95d61
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/Burgers_RAR.py
@@ -0,0 +1,81 @@
+import brainstate as bst
+import brainunit as u
+import jax.tree
+import numpy as np
+
+import deepxde.experimental as deepxde
+
+geom = deepxde.geometry.Interval(-1, 1)
+timedomain = deepxde.geometry.TimeDomain(0, 0.99)
+geomtime = deepxde.geometry.GeometryXTime(geom, timedomain)
+geomtime = geomtime.to_dict_point(x=u.meter, t=u.second)
+
+net = deepxde.nn.Model(
+    deepxde.nn.DictToArray(x=u.meter, t=u.second),
+    deepxde.nn.FNN([2] + [20] * 3 + [1], "tanh", bst.init.KaimingUniform()),
+    deepxde.nn.ArrayToDict(y=u.meter / u.second),
+)
+v = 0.01 / u.math.pi * u.meter ** 2 / u.second
+
+
+def pde(x, y):
+    jacobian = net.jacobian(x)
+    hessian = net.hessian(x, xi='x', xj='x')
+
+    dy_x = jacobian['y']['x']
+    dy_t = jacobian['y']['t']
+    dy_xx = hessian['y']['x']['x']
+    return dy_t + y['y'] * dy_x - v * dy_xx
+
+
+bc = deepxde.icbc.DirichletBC(lambda x: {'y': 0 * u.meter / u.second})
+ic = deepxde.icbc.IC(lambda x: {'y': -u.math.sin(u.math.pi * x['x'] / u.meter) * u.meter / u.second})
+
+problem = deepxde.problem.TimePDE(
+    geomtime,
+    pde,
+    [bc, ic],
+    net,
+    num_domain=2500,
+    num_boundary=100,
+    num_initial=160
+)
+
+trainer = deepxde.Trainer(problem)
+
+trainer.compile(bst.optim.Adam(1e-3)).train(iterations=10000)
+trainer.compile(bst.optim.LBFGS(1e-3)).train(1000)
+
+X = geomtime.random_points(100000)
+err = 1
+while u.get_magnitude(err) > 0.012:
+    f = trainer.predict(X, operator=pde)
+    err_eq = u.math.absolute(f)
+    err = u.math.mean(err_eq)
+    print(f"Mean residual: {err:.3f}")
+
+    x_id = u.math.argmax(err_eq)
+    new_xs = jax.tree.map(lambda x: x[[x_id]], X)
+    print("Adding new point:", new_xs, "\n")
+    problem.add_anchors(new_xs)
+    early_stopping = deepxde.callbacks.EarlyStopping(min_delta=1e-4, patience=2000)
+    trainer.compile(bst.optim.Adam(1e-3)).train(iterations=10000,
+                                                disregard_previous_best=True,
+                                                callbacks=[early_stopping])
+    trainer.compile(bst.optim.LBFGS(1e-3)).train(1000, display_every=100)
+
+trainer.saveplot(issave=True, isplot=True)
+
+
+def gen_testdata():
+    data = np.load("../dataset/Burgers.npz")
+    t, x, exact = data["t"], data["x"], data["usol"].T
+    xx, tt = np.meshgrid(x, t)
+    X = {'x': np.ravel(xx) * u.meter, 't': np.ravel(tt) * u.second}
+    y = {'y': exact.flatten() * u.meter / u.second}
+    return X, y
+
+
+X, y_true = gen_testdata()
+y_pred = trainer.predict(X)
+print("L2 relative error:", deepxde.metrics.l2_relative_error(y_true, y_pred))
diff --git a/docs/experimental_docs/unit-examples-forward/Euler_beam.ipynb b/docs/experimental_docs/unit-examples-forward/Euler_beam.ipynb
new file mode 100644
index 000000000..1dd7a143e
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/Euler_beam.ipynb
@@ -0,0 +1,469 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Euler-Bernoulli Beam Equation\n",
+    "\n",
+    "## Problem setup\n",
+    "\n",
+    "We will solve a Euler beam problem:\n",
+    "\n",
+    "$$\n",
+    "EI\\frac{d^{4}u}{dx^{4}}=p, \\qquad x \\in [0, 1],\n",
+    "$$\n",
+    "\n",
+    "with two boundary conditions on the right boundary,\n",
+    "\n",
+    "$$\n",
+    "u''(1)=0,   u'''(1)=0\n",
+    "$$\n",
+    "\n",
+    "and one Dirichlet boundary condition on the left boundary,\n",
+    "\n",
+    "$$\n",
+    "u(0)=0\n",
+    "$$\n",
+    "\n",
+    "along with one Neumann boundary condition on the left boundary,\n",
+    "\n",
+    "$$\n",
+    "u'(0)=0\n",
+    "$$\n",
+    "\n",
+    "The exact solution is $u(x) = -\\frac{1}{24}x^4+\\frac{1}{6}x^3-\\frac{1}{4}x^2.$\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dimensional Analysis\n",
+    "\n",
+    "### **Boundary Conditions:**\n",
+    "1. **Right Boundary (at $ x = 1 $):**\n",
+    "\n",
+    "   $$\n",
+    "   u''(1) = 0, \\quad u'''(1) = 0\n",
+    "   $$\n",
+    "\n",
+    "2. **Left Boundary (at $ x = 0 $):**\n",
+    "\n",
+    "   $$\n",
+    "   u(0) = 0, \\quad u'(0) = 0\n",
+    "   $$\n",
+    "\n",
+    "### **Assigning Physical Units:**\n",
+    "\n",
+    "Let's identify and assign physical units to each variable and parameter in the equation.\n",
+    "\n",
+    "| **Variable/Parameter** | **Symbol** | **Physical Quantity**             | **Unit (SI)**       | **Dimension**            |\n",
+    "|------------------------|------------|-----------------------------------|---------------------|--------------------------|\n",
+    "| **Displacement**       | $ u $    | Beam deflection                   | meters (m)          | Length $[L]$           |\n",
+    "| **Position**           | $ x $    | Spatial coordinate along the beam | meters (m)          | Length $[L]$           |\n",
+    "| **Young's Modulus**    | $ E $    | Material property (stiffness)     | pascals (Pa)        | Pressure $[M][L]^{-1}[T]^{-2}$ |\n",
+    "| **Second Moment of Area** | $ I $    | Geometric property of the beam    | meters$^4$ (m$^4$) | Length $^4$ $[L]^4$  |\n",
+    "| **Flexural Rigidity**  | $ EI $   | Product of $ E $ and $ I $    | newton-meter squared (N·m$^2$) | $[EI] = [E][I] = [M][L]^3[T]^{-2}$ |\n",
+    "| **Load per Unit Length** | $ p $    | Distributed load on the beam       | newtons per meter (N/m) | Force per Length $[M][L][T]^{-2}[L]^{-1} = [M][T]^{-2}$ |\n",
+    "\n",
+    "### **Dimensional Consistency Check:**\n",
+    "\n",
+    "To ensure the equation is dimensionally consistent, both sides must have the same dimensions.\n",
+    "\n",
+    "1. **Left Side ($ EI \\frac{d^4 u}{dx^4} $):**\n",
+    "   - $ EI $ has units of N·m$^2$ and dimensions $[M][L]^3[T]^{-2}$.\n",
+    "   - $ \\frac{d^4 u}{dx^4} $ involves four derivatives with respect to $ x $, each introducing a factor of $[L]^{-1}$.\n",
+    "   - Thus, $ \\frac{d^4 u}{dx^4} $ has dimensions $[L]^{-4} \\times [L] = [L]^{-3}$.\n",
+    "   - Multiplying by $ EI $: $[M][L]^3[T]^{-2} \\times [L]^{-3} = [M][T]^{-2}$.\n",
+    "\n",
+    "2. **Right Side ($ p $):**\n",
+    "   - $ p $ has units of N/m and dimensions $[M][T]^{-2}$.\n",
+    "\n",
+    "Both sides have the same dimensions $[M][T]^{-2}$, confirming dimensional consistency.\n",
+    "\n",
+    "### **Summary of Physical Units:**\n",
+    "\n",
+    "- **$ u $** (Displacement): meters (m)\n",
+    "- **$ x $** (Position): meters (m)\n",
+    "- **$ E $** (Young's Modulus): pascals (Pa) = N/m$^2$\n",
+    "- **$ I $** (Second Moment of Area): meters$^4$ (m$^4$)\n",
+    "- **$ EI $** (Flexural Rigidity): newton-meter squared (N·m$^2$)\n",
+    "- **$ p $** (Load per Unit Length): newtons per meter (N/m)\n",
+    "\n",
+    "### **Boundary Conditions Units:**\n",
+    "\n",
+    "1. **$ u''(1) = 0 $:**\n",
+    "   - $ u'' $ involves two derivatives: $[L] \\times [L]^{-2} = [L]^{-1}$\n",
+    "   - Units: 1/m\n",
+    "\n",
+    "2. **$ u'''(1) = 0 $:**\n",
+    "   - $ u''' $ involves three derivatives: $[L] \\times [L]^{-3} = [L]^{-2}$\n",
+    "   - Units: 1/m$^2$\n",
+    "\n",
+    "3. **$ u(0) = 0 $:**\n",
+    "   - Units: meters (m)\n",
+    "\n",
+    "4. **$ u'(0) = 0 $:**\n",
+    "   - $ u' $ involves one derivative: $[L] \\times [L]^{-1} = \\text{dimensionless}$ (often interpreted as radians in small-angle approximations)\n",
+    "\n",
+    "### **Conclusion:**\n",
+    "\n",
+    "All variables and parameters in the Euler-Bernoulli Beam Equation have been assigned consistent physical units, ensuring dimensional integrity of the equation and its boundary conditions.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Code Implementation\n",
+    "\n",
+    "First, we import the necessary libraries and define the physical units for the problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T14:02:06.289401Z",
+     "start_time": "2024-12-17T14:02:02.480109Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import brainstate as bst\n",
+    "import brainunit as u\n",
+    "\n",
+    "import deepxde.experimental as deepxde"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the physical units for the problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T14:02:06.300312Z",
+     "start_time": "2024-12-17T14:02:06.295891Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "unit_of_u = u.meter\n",
+    "unit_of_x = u.meter\n",
+    "unit_of_E = u.pascal\n",
+    "unit_of_I = u.meter ** 4\n",
+    "unit_of_p = u.kilogram / u.second ** 2\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the parameters for the problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T14:02:06.403154Z",
+     "start_time": "2024-12-17T14:02:06.386503Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "E = 1 * unit_of_E\n",
+    "I = 1 * unit_of_I\n",
+    "p = -1. * unit_of_p\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the PDE  for the Euler beam problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T14:02:06.423666Z",
+     "start_time": "2024-12-17T14:02:06.419808Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def pde(x, y):\n",
+    "    dy_xxxx = net.gradient(x, order=4)['y']['x']['x']['x']['x']\n",
+    "    return E * I * dy_xxxx - p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the geometric domain for the problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T14:02:06.431507Z",
+     "start_time": "2024-12-17T14:02:06.428077Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "geom = deepxde.geometry.Interval(0, 1).to_dict_point(x=unit_of_x)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the boundary conditions for the problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T14:02:06.444358Z",
+     "start_time": "2024-12-17T14:02:06.439478Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def boundary_l(x, on_boundary):\n",
+    "    return u.math.logical_and(on_boundary, deepxde.utils.isclose(x['x'] / unit_of_x, 0))\n",
+    "\n",
+    "\n",
+    "def boundary_r(x, on_boundary):\n",
+    "    return u.math.logical_and(on_boundary, deepxde.utils.isclose(x['x'] / unit_of_x, 1))\n",
+    "\n",
+    "\n",
+    "bc1 = deepxde.icbc.DirichletBC(lambda x: {'y': 0 * unit_of_u}, boundary_l)\n",
+    "bc2 = deepxde.icbc.NeumannBC(lambda x: {'y': 0 * unit_of_u}, boundary_l)\n",
+    "bc3 = deepxde.icbc.OperatorBC(lambda x, y: net.hessian(x)['y']['x']['x'] / u.meter, boundary_r)\n",
+    "bc4 = deepxde.icbc.OperatorBC(lambda x, y: net.gradient(x, order=3)['y']['x']['x']['x'] / u.meter ** 2, boundary_r)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the neural network model for the problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T14:02:06.903923Z",
+     "start_time": "2024-12-17T14:02:06.458887Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "net = deepxde.nn.Model(\n",
+    "    deepxde.nn.DictToArray(x=unit_of_x),\n",
+    "    deepxde.nn.FNN([1] + [20] * 3 + [1], \"tanh\"),\n",
+    "    deepxde.nn.ArrayToDict(y=unit_of_u),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the exact solution for the problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T14:02:07.977054Z",
+     "start_time": "2024-12-17T14:02:06.913042Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def func(x):\n",
+    "    x = x['x'] / unit_of_x\n",
+    "    y = -(x ** 4) / 24 + x ** 3 / 6 - x ** 2 / 4\n",
+    "    return {'y': y * unit_of_u}\n",
+    "\n",
+    "\n",
+    "data = deepxde.problem.PDE(\n",
+    "    geom,\n",
+    "    pde,\n",
+    "    [bc1, bc2, bc3, bc4],\n",
+    "    net,\n",
+    "    num_domain=100,\n",
+    "    num_boundary=20,\n",
+    "    solution=func,\n",
+    "    num_test=100,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the model and evaluate the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T14:02:36.927956Z",
+     "start_time": "2024-12-17T14:02:08.206744Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.058168 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                          Test loss                                           Test metric                                        \n",
+      "0         [172697.52 * kilogram ** 2 * second ** -4,          [198196.31 * kilogram ** 2 * second ** -4,          [{'y': Array(0.49183828, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': 0. * meter}},                        {'ibc0': {'y': 0. * meter}},                                                                          \n",
+      "           {'ibc1': {'y': 0.34124637 * meter}},                {'ibc1': {'y': 0.34124637 * meter}},                                                                  \n",
+      "           {'ibc2': 0.02639124 * metre ** -2},                 {'ibc2': 0.02639124 * metre ** -2},                                                                   \n",
+      "           {'ibc3': 1.2099838 * metre ** -4}]                  {'ibc3': 1.2099838 * metre ** -4}]                                                                    \n",
+      "1000      [8.933943 * kilogram ** 2 * second ** -4,           [10.233379 * kilogram ** 2 * second ** -4,          [{'y': Array(1.6421293, dtype=float32)}]           \n",
+      "           {'ibc0': {'y': 9.626521e-11 * meter}},              {'ibc0': {'y': 9.626521e-11 * meter}},                                                                \n",
+      "           {'ibc1': {'y': 0.17711917 * meter}},                {'ibc1': {'y': 0.17711917 * meter}},                                                                  \n",
+      "           {'ibc2': 0.03371554 * metre ** -2},                 {'ibc2': 0.03371554 * metre ** -2},                                                                   \n",
+      "           {'ibc3': 0.3698493 * metre ** -4}]                  {'ibc3': 0.3698493 * metre ** -4}]                                                                    \n",
+      "2000      [5.0301304 * kilogram ** 2 * second ** -4,          [5.7454376 * kilogram ** 2 * second ** -4,          [{'y': Array(1.2512381, dtype=float32)}]           \n",
+      "           {'ibc0': {'y': 5.3002607e-12 * meter}},             {'ibc0': {'y': 5.3002607e-12 * meter}},                                                               \n",
+      "           {'ibc1': {'y': 0.12062849 * meter}},                {'ibc1': {'y': 0.12062849 * meter}},                                                                  \n",
+      "           {'ibc2': 0.02302191 * metre ** -2},                 {'ibc2': 0.02302191 * metre ** -2},                                                                   \n",
+      "           {'ibc3': 0.1910549 * metre ** -4}]                  {'ibc3': 0.1910549 * metre ** -4}]                                                                    \n",
+      "3000      [2.1895514 * kilogram ** 2 * second ** -4,          [2.4801166 * kilogram ** 2 * second ** -4,          [{'y': Array(0.97356707, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': 1.2275463e-11 * meter}},             {'ibc0': {'y': 1.2275463e-11 * meter}},                                                               \n",
+      "           {'ibc1': {'y': 0.08320159 * meter}},                {'ibc1': {'y': 0.08320159 * meter}},                                                                  \n",
+      "           {'ibc2': 0.0165317 * metre ** -2},                  {'ibc2': 0.0165317 * metre ** -2},                                                                    \n",
+      "           {'ibc3': 0.08161929 * metre ** -4}]                 {'ibc3': 0.08161929 * metre ** -4}]                                                                   \n",
+      "4000      [0.72300434 * kilogram ** 2 * second ** -4,         [0.8063759 * kilogram ** 2 * second ** -4,          [{'y': Array(0.8491821, dtype=float32)}]           \n",
+      "           {'ibc0': {'y': 8.0052675e-12 * meter}},             {'ibc0': {'y': 8.0052675e-12 * meter}},                                                               \n",
+      "           {'ibc1': {'y': 0.06257136 * meter}},                {'ibc1': {'y': 0.06257136 * meter}},                                                                  \n",
+      "           {'ibc2': 0.01523586 * metre ** -2},                 {'ibc2': 0.01523586 * metre ** -2},                                                                   \n",
+      "           {'ibc3': 0.03002642 * metre ** -4}]                 {'ibc3': 0.03002642 * metre ** -4}]                                                                   \n",
+      "5000      [0.30583796 * kilogram ** 2 * second ** -4,         [0.3329344 * kilogram ** 2 * second ** -4,          [{'y': Array(0.89385283, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': 4.751356e-12 * meter}},              {'ibc0': {'y': 4.751356e-12 * meter}},                                                                \n",
+      "           {'ibc1': {'y': 0.05717417 * meter}},                {'ibc1': {'y': 0.05717417 * meter}},                                                                  \n",
+      "           {'ibc2': 0.01894331 * metre ** -2},                 {'ibc2': 0.01894331 * metre ** -2},                                                                   \n",
+      "           {'ibc3': 0.01662517 * metre ** -4}]                 {'ibc3': 0.01662517 * metre ** -4}]                                                                   \n",
+      "6000      [0.25211135 * kilogram ** 2 * second ** -4,         [0.24109833 * kilogram ** 2 * second ** -4,         [{'y': Array(0.9684854, dtype=float32)}]           \n",
+      "           {'ibc0': {'y': 6.294266e-10 * meter}},              {'ibc0': {'y': 6.294266e-10 * meter}},                                                                \n",
+      "           {'ibc1': {'y': 0.05709113 * meter}},                {'ibc1': {'y': 0.05709113 * meter}},                                                                  \n",
+      "           {'ibc2': 0.02338268 * metre ** -2},                 {'ibc2': 0.02338268 * metre ** -2},                                                                   \n",
+      "           {'ibc3': 0.01612406 * metre ** -4}]                 {'ibc3': 0.01612406 * metre ** -4}]                                                                   \n",
+      "7000      [0.88259137 * kilogram ** 2 * second ** -4,         [0.624831 * kilogram ** 2 * second ** -4,           [{'y': Array(0.99016815, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': 6.6010295e-09 * meter}},             {'ibc0': {'y': 6.6010295e-09 * meter}},                                                               \n",
+      "           {'ibc1': {'y': 0.05649563 * meter}},                {'ibc1': {'y': 0.05649563 * meter}},                                                                  \n",
+      "           {'ibc2': 0.02561221 * metre ** -2},                 {'ibc2': 0.02561221 * metre ** -2},                                                                   \n",
+      "           {'ibc3': 0.01722029 * metre ** -4}]                 {'ibc3': 0.01722029 * metre ** -4}]                                                                   \n",
+      "8000      [0.21604834 * kilogram ** 2 * second ** -4,         [0.19982578 * kilogram ** 2 * second ** -4,         [{'y': Array(0.98142815, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': 1.7951907e-10 * meter}},             {'ibc0': {'y': 1.7951907e-10 * meter}},                                                               \n",
+      "           {'ibc1': {'y': 0.05653544 * meter}},                {'ibc1': {'y': 0.05653544 * meter}},                                                                  \n",
+      "           {'ibc2': 0.02618827 * metre ** -2},                 {'ibc2': 0.02618827 * metre ** -2},                                                                   \n",
+      "           {'ibc3': 0.01875774 * metre ** -4}]                 {'ibc3': 0.01875774 * metre ** -4}]                                                                   \n",
+      "9000      [0.10451799 * kilogram ** 2 * second ** -4,         [0.10789017 * kilogram ** 2 * second ** -4,         [{'y': Array(0.93547827, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': 4.1144904e-12 * meter}},             {'ibc0': {'y': 4.1144904e-12 * meter}},                                                               \n",
+      "           {'ibc1': {'y': 0.05326409 * meter}},                {'ibc1': {'y': 0.05326409 * meter}},                                                                  \n",
+      "           {'ibc2': 0.02588784 * metre ** -2},                 {'ibc2': 0.02588784 * metre ** -2},                                                                   \n",
+      "           {'ibc3': 0.02023843 * metre ** -4}]                 {'ibc3': 0.02023843 * metre ** -4}]                                                                   \n",
+      "10000     [0.09200532 * kilogram ** 2 * second ** -4,         [0.09282021 * kilogram ** 2 * second ** -4,         [{'y': Array(0.8544506, dtype=float32)}]           \n",
+      "           {'ibc0': {'y': 1.0899624e-12 * meter}},             {'ibc0': {'y': 1.0899624e-12 * meter}},                                                               \n",
+      "           {'ibc1': {'y': 0.04875687 * meter}},                {'ibc1': {'y': 0.04875687 * meter}},                                                                  \n",
+      "           {'ibc2': 0.0242439 * metre ** -2},                  {'ibc2': 0.0242439 * metre ** -2},                                                                    \n",
+      "           {'ibc3': 0.02106431 * metre ** -4}]                 {'ibc3': 0.02106431 * metre ** -4}]                                                                   \n",
+      "\n",
+      "Best trainer at step 10000:\n",
+      "  train loss: 1.86e-01\n",
+      "  test loss: 1.87e-01\n",
+      "  test metric: [{'y': Array(0.85, dtype=float32)}]\n",
+      "\n",
+      "'train' took 27.923877 s\n",
+      "\n",
+      "Saving loss history to D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\loss.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\loss.dat\n",
+      "Saving training data to D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\train.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\train.dat\n",
+      "Saving test data to D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\test.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\test.dat\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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6dCkRERGkpKQcOaQmzjvvPGbMmMG6desICgri3Xffda0/ePBgHnvsMZYtW0b//v154403zuZQeC2vuxz6TLXnqSJwNuiucnRlpHkLRvF6TFk/a/N9iohI25sxYwb3338/UVFRjBkzhoaGBlavXs3hw4eZMmUKs2bNIjExkcGDB2M2m3nrrbdISEggOjoacF5ZlJuby3nnnYfVaiUmJuZH9/mrX/2K2bNnc9999zF58mS2bt3KtGnTmDJlCmazmRUrVpCbm8vll19OXFwcK1as4MCBA/Tr14+CggL+/ve/c/XVV5OUlMTWrVvZvn07t99+exsfKc/wm+DS3nrFh/Mv0gFng67Vs+WIiIib3H333YSGhvLMM8/w8MMPExYWxoABA3jwwQcBiIiI4Omnn2b79u1YLBaGDx/OggULMJudJzGeffZZpkyZwiuvvEJycjK7du360X0mJyezYMECHn74YbKysoiNjeWuu+7i97//PeAc4Vm8eDGzZ8+msrKSrl278uyzzzJ27FhKS0vZsmUL//znPzl06BCJiYlMmjSJX/ziF211iDzKZPxYQ4qPaR5xqaioaPNZdH/1l7m8WP0ATYGRBP6u8KyHKEVEfFF9fT0FBQV069aN4GBdZyknd6qfldb+/VaPy1kIT+1Po2EhsKkSygvbbb8iIiIdld8El/acObdZn+RObDecTVNq0BUREWl7fhNcPCEzKZJ8R/OVRZr6X0REpK0puJyFfomRbDLSAWjae/o3hhQREZHT4zfBxRM9LlEhgRwM7w2AY5+Ci4iISFvzm+DiiR4XgICkLACstcVQW9au+xYREelo/Ca4eEr3lER2OY7cZ0l9LiIiIm1KweUsZSZFHp36v1jBRUREpC0puJylzKQoNjnSAbAruIiIiLQpBZezFB9ppcjaA4CmPXmeLUZEROQ0pKenM3v2bE+XcVr8Jrh44qoicN6t00gYCEBQ+ffQVNeu+xcRkdNnMplO+Zg+ffpZbfu9995zW62nY9euXZhMJvLy8lq1/qpVq7jnnnvatig385ubLLb33aGPlZSczsG9kXQ2VUJpPqQMbdf9i4jI6SkuLnZ9P3/+fKZOncrWrVtdy8LDwz1RVrtpbGwkKCiILl26eLqU0+Y3Iy6elJEcdcwMuprPRUTE2yUkJLgeUVFRmEymFsvmzZtHv379CA4Opm/fvrz44ouu9zY2NjJ58mQSExMJDg6ma9euzJw5E3CeegG49tprMZlMruc/1Dwy8uabb3LBBRcQEhLC8OHD2bZtG6tWrWLYsGGEh4czduxYDhw40OK9//jHP05aW7du3QAYPHgwJpOJiy++GIA77riDa665hj/96U8kJSXRp08fV73HnioqLy/nF7/4BfHx8QQHB9O/f38++uijE36GO++8k6uuuqrFsqamJuLi4vi///u/U/8HOAt+M+LiSZlJUSw00rmQDTiKNygNikiHZxgGdbb2P3UeEhCCyWQ6q228/vrrTJ06lRdeeIHBgwezbt06Jk6cSFhYGOPHj+e5557jgw8+4M033yQtLY2ioiKKiooA56mXuLg45syZw5gxY7BYLKfc17Rp05g9ezZpaWnceeed3HrrrURERPDXv/6V0NBQbrrpJqZOncrf/va3VtW2cuVKRowYwRdffEFmZiZBQUGufeXm5hIZGcnChQtPWIvD4WDs2LFUVVXx73//mx49epCfn3/Sz3D33Xdz4YUXUlxcTGJiIgAfffQRtbW13Hzzzad93FtLwcUNunUOY4fZmXIb96xDN3UXkY6uzlbHyDdGtvt+V9y6gtDA0LPaxrRp03j22We57rrrAOcoRn5+Pi+//DLjx4+nsLCQXr16cf7552Mymejatavrvc2nXqKjo0lISPjRff3mN79h9OjRADzwwAPccsst5Obmct555wFw1113MXfu3FbX1rz/Tp06Hbf/sLAw/vGPf7QIM8f64osvWLlyJZs3b6Z3b+es8N27dz9p7eeeey59+vThX//6F7/97W8BmDNnDjfeeGObnmrT4IAbWMwm6jv3ByDg4GZw2D1ckYiInImamhq+//577rrrLsLDw12PJ598ku+//x5wnnbJy8ujT58+3H///Xz++ednvL+BAwe6vo+Pd05mOmDAgBbL9u/f3+raTmXAgAEnDS0AeXl5pKSkuEJLa9x9993MmTMHgNLSUj755BPuvPPOVr//TGjExU1iUvpQe8hKqL0eDu2ALn08XZKIiMeEBISw4tYVHtnv2aiurgbglVdeYeTIliNGzadMhgwZQkFBAZ988glffPEFN910E9nZ2bz99tunvb/AwEDX982nuH64zOFwtLq2UwkLCzvl6yEhp3/sbr/9dh599FGWL1/OsmXL6NatGxdccMFpb+d0+E1wycnJIScnB7vdM6MdGcmxbM5LY6hpO5RsUHARkQ7NZDKd9SkbT4iPjycpKYmdO3dy2223nXS9yMhIbr75Zm6++WZuuOEGxowZQ1lZGbGxsQQGBrbJ36LW1NY8onIm+x84cCB79uxh27ZtrR516dSpE9dccw1z5sxh+fLlTJgw4bT3e7r8Jrh48nJocE79/52jK0PN2zGK12MacEO71yAiImdvxowZ3H///URFRTFmzBgaGhpYvXo1hw8fZsqUKcyaNYvExEQGDx6M2WzmrbfeIiEhgejoaMB5pU5zn4rVaiUmJqbdaouLiyMkJIRPP/2UlJQUgoODW/038aKLLuLCCy/k+uuvZ9asWfTs2ZMtW7ZgMpkYM2bMSd939913c9VVV2G32xk/fry7PupJqcfFTfokRLCZdAAaNYOuiIjPuvvuu/nHP/7BnDlzGDBgABdddBFz5851XWocERHB008/zbBhwxg+fDi7du1iwYIFmM3OP6nPPvssCxcuJDU1lcGDB7drbQEBATz33HO8/PLLJCUl8dOf/vS0tv/f//6X4cOHc8stt5CRkcFvf/vbHx29yc7OJjExkdGjR5OUlHTGn621TIZhGG2+l3bUPOJSUVFBZGRku+570jP/IKfm1zRaYwh6tADO8pI8ERFfUF9fT0FBAd26dSM4WNdVdjTV1dUkJyczZ84c19VOJ3Oqn5XW/v3WiIsbhaYMwGaYCWo4DJX7PF2OiIhIm3E4HOzfv58nnniC6Ohorr766nbZr9/0uHiD3sld2LE5mb6mImeDblSyp0sSERFpE4WFhXTr1o2UlBTmzp1LQED7RAoFFzfKTIok3+hKX4qg5Dvoc/JmJhEREV+Wnp6OJ7pNdKrIjTKSItl05J5FTXt1zyIRERF3U3Bxo+jQIPaHOq99t+9TcBGRjsXPrvWQNuCOnxG/CS45OTlkZGQwfPhwj9ZhTnJO3xxcXQR15R6tRUSkPTTP9FpbW+vhSsTbNf+MHDs78Onymx4XT09A1yw9JYU9uzqTYjoIpRsh/XyP1SIi0h4sFgvR0dGue+qEhoae9R2axb8YhkFtbS379+8nOjq6VbcoOBm/CS7eIjMpknxHV1IsB6H4OwUXEekQmu9E3BxeRE6ktXfNPhUFFzfLTI7iTUc6l1vWYC/+jjPPlCIivsNkMpGYmEhcXBxNTU2eLke8UGBg4FmNtDRTcHGzpKhgdgX2AJxT/5/dfUpFRHyLxWJxyx8nkZPxm+Zcb2EymXDEDwDAeng72Bo8XJGIiIj/UHBpA/EpPSg3wjAbNti/2dPliIiI+A0FlzaQmRLFJke680nJdx6tRURExJ8ouLSBzKQoNhnpABjFCi4iIiLuouDSBrp3DmO7KR2Ahj15Hq1FRETEnyi4tIEAi5m6TpnO7w9sAofDwxWJiIj4BwWXNhKVmkG9EUiArRYOF3i6HBEREb/gtcGltraWrl278pvf/MbTpZyRvsmxbDFSnU/UoCsiIuIWXhtc/vSnP3HOOed4uowz1jz1P+Cc+l9ERETOmlcGl+3bt7NlyxbGjh3r6VLOWN+ECPKPXFmkBl0RERH3cHtwWbx4MePGjSMpKQmTycR777133Do5OTmkp6cTHBzMyJEjWblyZYvXf/Ob3zBz5kx3l9auQoMCKI/q53xSssGzxYiIiPgJtweXmpoasrKyyMnJOeHr8+fPZ8qUKUybNo21a9eSlZXF6NGjXXcUff/99+nduze9e/du1f4aGhqorKxs8fAWwckDcBgmrPUHoKrU0+WIiIj4PLffZHHs2LGnPMUza9YsJk6cyIQJEwB46aWX+Pjjj3n11Vd59NFH+fbbb5k3bx5vvfUW1dXVNDU1ERkZydSpU0+4vZkzZzJjxgx3fwy36JkSz86tifQ07XOOukTEe7okERERn9auPS6NjY2sWbOG7OzsowWYzWRnZ7N8+XLAGUSKiorYtWsXf/nLX5g4ceJJQwvAY489RkVFhetRVFTU5p+jtTKTIsk3jjTolqz3bDEiIiJ+oF2Dy8GDB7Hb7cTHtxx5iI+Pp6Sk5Iy2abVaiYyMbPHwFhmJka57FjXt05VFIiIiZ8vtp4rc6Y477mj1ujk5OeTk5GC329uuoNPUKdxKcUgvsIF9bx6Bni5IRETEx7XriEvnzp2xWCyUlrZsVC0tLSUhIeGstj1p0iTy8/NZtWrVWW3H3UyJAwEIrtwFDVWeLUZERMTHtWtwCQoKYujQoeTm5rqWORwOcnNzGTVqVHuW0m7SUtMoNmKdT0o3ebYYERERH+f2U0XV1dXs2LHD9bygoIC8vDxiY2NJS0tjypQpjB8/nmHDhjFixAhmz55NTU2N6yqjM+WNp4rg6Ay6iZYy5wy6ab47G7CIiIinmQzDMNy5wUWLFnHJJZcct3z8+PHMnTsXgBdeeIFnnnmGkpISBg0axHPPPcfIkSPdsv/KykqioqKoqKjwikbdorJa3p11L/cHvIdj0M8xX/OCp0sSERHxOq39++32EZeLL76YH8tCkydPZvLkye7etVdKiQmhIKA74Jz6P8TD9YiIiPgyr7xX0ZnIyckhIyOD4cOHe7qUFkwmE7YuAwAIKtsC9iYPVyQiIuK7/Ca4eOtVRQCdU3tTaYRgcTTBga2eLkdERMRn+U1w8WaZydFsds2gq4noREREzpSCSzvITDo6g65RrOAiIiJypvwmuHhrjwtAz7hwtpIOOBt0RURE5Mz4TXDx5h6XQIuZ2k4ZAFj2bwT3XoEuIiLSYfhNcPF2Ecn9aTQsBDZVQfluT5cjIiLikxRc2kmflE5sM1KdT0o2eLYYERERH+U3wcWbe1ygZYMuatAVERE5I34TXLy5xwWgb2Kk65LoRjXoioiInBG/CS7eLtwawKHIvgAYmstFRETkjCi4tKOgJOfU/9baEqg55OFqREREfI+CSzvqnpJIgSPe+USjLiIiIqfNb4KLtzfngrNBN19T/4uIiJwxvwku3t6cC5CZFOW6ssi2T8FFRETkdPlNcPEFXSKs7A3uBUCTriwSERE5bQou7S3hSINuxU5orPVwMSIiIr5FwaWdJad244ARhRkH7M/3dDkiIiI+RcGlnWUmRZHvONKgW7zes8WIiIj4GAWXdpaZFMkmIx0Ah6b+FxEROS1+E1x84XJogLTYUHZaugPQoAZdERGR0+I3wcUXLocGMJtNNHbJBCDo4Gaw2zxckYiIiO/wm+DiS2JT+lJjWLE4GuDQDk+XIyIi4jMUXDwgIznGdadozaArIiLSegouHpCRFMmmI1cWGWrQFRERaTUFFw/oFR/OVtIBNeiKiIicDgUXD7AGWKiKcTbomks3gGF4uCIRERHfoODiIWEpmdgMM0GN5VC519PliIiI+AQFFw/pndyF7Uay80nJBs8WIyIi4iMUXDwkMymS/CMz6KIGXRERkVbxm+DiKzPnNuuXFOm6Z1GjGnRFRERaxW+Ci6/MnNssMjiQA+F9AN2zSEREpLX8Jrj4osCkgQAE1+yBusMerkZERMT7Kbh4UHpKMkWOLs4nJRs9W4yIiIgPUHDxoMzkSPI19b+IiEirKbh4UGZSFJsc6QDY9ym4iIiI/BgFFw+Ki7BSaO0J6MoiERGR1lBw8SCTyYQR3x8Aa/l2aKr3cEUiIiLeTcHFwxJSe1BmhGM27HBgs6fLERER8WoKLh6WmRztmohOM+iKiIicmoKLh2UmRbLpyNT/Dt2zSERE5JQUXDwsvVMYO8zdAGgoWufhakRERLyb1wWX8vJyhg0bxqBBg+jfvz+vvPKKp0tqUxaziYbOzgbdwAObwOHwcEUiIiLeK8DTBfxQREQEixcvJjQ0lJqaGvr37891111Hp06dPF1am4lOzaDuUBAh9joo2wmde3q6JBEREa/kdSMuFouF0NBQABoaGjAMA8MwPFxV2+qXHMNWI9X5pGS9Z4sRERHxYm4PLosXL2bcuHEkJSVhMpl47733jlsnJyeH9PR0goODGTlyJCtXrmzxenl5OVlZWaSkpPDwww/TuXNnd5fpVTKTIl0z6BrFatAVERE5GbcHl5qaGrKyssjJyTnh6/Pnz2fKlClMmzaNtWvXkpWVxejRo9m/f79rnejoaNavX09BQQFvvPEGpaWlJ91fQ0MDlZWVLR6+pnd8BJtxXhLdoBl0RURETsrtwWXs2LE8+eSTXHvttSd8fdasWUycOJEJEyaQkZHBSy+9RGhoKK+++upx68bHx5OVlcU333xz0v3NnDmTqKgo1yM1NdVtn6W9BAdaqIzKAMBUqhEXERGRk2nXHpfGxkbWrFlDdnb20QLMZrKzs1m+fDkApaWlVFVVAVBRUcHixYvp06fPSbf52GOPUVFR4XoUFRW17YdoIyEpA7AbJqz1B6GqxNPliIiIeKV2varo4MGD2O124uPjWyyPj49ny5YtAOzevZt77rnH1ZR73333MWDAgJNu02q1YrVa27Tu9tArJY6dm5PoZdoLJRsgIsHTJYmIiHgdr7scesSIEeTl5Z32+3JycsjJycFut7u/qHaQkRTJJqMrvdgLxeuh12WeLklERMTrtOupos6dO2OxWI5rti0tLSUh4exGGCZNmkR+fj6rVq06q+14SkZipOueRU17dUm0iIjIibRrcAkKCmLo0KHk5ua6ljkcDnJzcxk1atRZbTsnJ4eMjAyGDx9+tmV6RHRoEKWhzl4e2z4FFxERkRNx+6mi6upqduzY4XpeUFBAXl4esbGxpKWlMWXKFMaPH8+wYcMYMWIEs2fPpqamhgkTJpzVfidNmsSkSZOorKwkKirqbD+GR1iSBsJuCKnaDfWVEBzp6ZJERES8ituDy+rVq7nkkktcz6dMmQLA+PHjmTt3LjfffDMHDhxg6tSplJSUMGjQID799NPjGnY7orTUVPbtiiXJVAalm6Dr2Y1CiYiI+BuT4Wfz6TePuFRUVBAZ6VsjFgvzSzH952ayLetg7NMw8heeLklERKRdtPbvt9fdq+hM+XqPCxyZ+t9IB8CuPhcREZHj+E1w8fWrigASo4LZFdgDgMY9Ci4iIiI/5DfBxR+YTCaMeOdke0FlW8DW6OGKREREvIvfBBd/OFUE0CWlF5VGKBbDBge3erocERERr+I3wcUfThUBZCZHk284J6Kj+DvPFiMiIuJl/Ca4+IvMpEg2OdIBMEoUXERERI6l4OJlunUOY5spHYD6ojyP1iIiIuJtFFy8TIDFTF3n/s7v928E/5pmR0RE5Kz4TXDxl+ZcgMjUTBqMAAJt1XB4l6fLERER8Rp+E1z8pTkXoG9SLNuMFOcT9bmIiIi4+E1w8SfHNuhSssGjtYiIiHgTBRcv1Dchks1HLoluKFrn4WpERES8h4KLFwoJslAe1Q8AQyMuIiIiLn4TXPypORfAmjwQh2EiuK4Uag56uhwRERGv4DfBxZ+acwF6pCSwy4h3PlGDroiICOBHwcXfZCZFkW+kO59o6n8RERFAwcVrZSRFku9wNug27c3zbDEiIiJeQsHFS8WGBVEc2gsA2z6NuIiIiICCi1czJQwEILhiJzTWeLgaERERz1Nw8WIpqensN6IxYUBpvqfLERER8Ti/CS7+djk0QEZSlKvPhZL1ni1GRETEC/hNcPG3y6HhyNT/R2bQtavPRURExH+Ciz9KiQlhZ0APABr25Hm2GBERES+g4OLFTCYT9rj+AFgPbQG7zcMViYiIeJaCi5eLTelLtRGMxdEAh7Z7uhwRERGPUnDxcpnJ0Ww20pxPNIOuiIh0cAouXi4zOZJNjnQADAUXERHp4BRcvFyPLuFsM6UDUK8GXRER6eAUXLxcoMVMbWwmAJb9G8AwPFyRiIiI5yi4+ICwlP40GRaCGiugYo+nyxEREfEYvwku/jhzbrM+KZ3ZYSQ7n5Soz0VERDouvwku/jhzbjPnDLrpziclGzxai4iIiCf5TXDxZ30TIsk/MvV/w551Hq5GRETEcxRcfECYNYCyiL4AGMUacRERkY5LwcVHBCYNBCC4Zi/Ulnm4GhEREc9QcPER3VKTKHR0cT4p3ejZYkRERDxEwcVHZCZFHW3Q1Qy6IiLSQSm4+IiMxEjyHc4GXdvePM8WIyIi4iEKLj6iS4SVvcG9AGjaqxEXERHpmBRcfEnCAACsFTugqc7DxYiIiLQ/BRcfkpjanUNGBGbDDvs3e7ocERGRdud1waWoqIiLL76YjIwMBg4cyFtvveXpkrxGZnI0mxzpziea+l9ERDogrwsuAQEBzJ49m/z8fD7//HMefPBBampqPF2WV8hMOjqDrl1XFomISAcU4OkCfigxMZHExEQAEhIS6Ny5M2VlZYSFhXm4Ms9LjQnle0t3ABqK8gj1cD0iIiLtze0jLosXL2bcuHEkJSVhMpl47733jlsnJyeH9PR0goODGTlyJCtXrjzhttasWYPdbic1NdXdZfoks9mErUt/AIIO5oPD7uGKRERE2pfbg0tNTQ1ZWVnk5OSc8PX58+czZcoUpk2bxtq1a8nKymL06NHs37+/xXplZWXcfvvt/P3vf3d3iT4tOrUfdUYQAfY6KNvp6XJERETalduDy9ixY3nyySe59tprT/j6rFmzmDhxIhMmTCAjI4OXXnqJ0NBQXn31Vdc6DQ0NXHPNNTz66KOce+65p9xfQ0MDlZWVLR7+rF9yDFuMNOeT4vWeLUZERKSdtWtzbmNjI2vWrCE7O/toAWYz2dnZLF++HADDMLjjjjv4yU9+ws9//vMf3ebMmTOJiopyPfz9tFJmUiSbjsyga5ToTtEiItKxtGtwOXjwIHa7nfj4+BbL4+PjKSkpAWDp0qXMnz+f9957j0GDBjFo0CA2bDj5H+jHHnuMiooK16OoqKhNP4On9YqLYAvdAKgvWufhakRERNqX111VdP755+NwOFq9vtVqxWq1kpOTQ05ODna7fzesBgWYqY7pB1VgLt0AhgEmk6fLEhERaRftOuLSuXNnLBYLpaWlLZaXlpaSkJBwVtueNGkS+fn5rFq16qy24wtCUgZiN0xYG8qgqsTT5YiIiLSbdg0uQUFBDB06lNzcXNcyh8NBbm4uo0aNas9SfFrvlC58byQ5n2gGXRER6UDcHlyqq6vJy8sjLy8PgIKCAvLy8igsLARgypQpvPLKK/zzn/9k8+bN3HvvvdTU1DBhwoSz2m9OTg4ZGRkMHz78bD+C18tMimKTke58ouAiIiIdiMkwDMOdG1y0aBGXXHLJccvHjx/P3LlzAXjhhRd45plnKCkpYdCgQTz33HOMHDnSLfuvrKwkKiqKiooKIiMj3bJNb1NV38RzT9zP44Fv0Nj7KoJufd3TJYmIiJyV1v79dntz7sUXX8yPZaHJkyczefJkd++6w4gIDuRgRB+oB/s+jbiIiEjH4XU3WTxTHelUEUBA0kAAQqoLob7Cw9WIiIi0D78JLh3pqiKArimp7DU6OZ+UbvJsMSIiIu3Eb4JLR5OZFEW+I935pFini0REpGNQcPFRmUmR5BvOqf9t+/I8W4yIiEg78Zvg0tF6XLpEWCkM6glA4x7dbFFERDoGvwkuHa3HxWQyYSQMAMB6eBvYGj1ckYiISNvzm+DSEcWl9KLcCMNi2ODAFk+XIyIi0uYUXHxYZnIU+Q5nn4tm0BURkY5AwcWHHdug69CVRSIi0gH4TXDpaM25AOmdwthu7g5AQ1GeZ4sRERFpB34TXDpacy6A2WyisXMmAAEHNoLD4eGKRERE2pbfBJeOKiotkwYjkEBbDZTv8nQ5IiIibUrBxcf1TYplq5HifKI+FxER8XMKLj4uMymKTUem/jdKNni2GBERkTbmN8GlIzbnAvSKD2cz6QA0FK3zbDEiIiJtzG+CS0dszgUIDrRQFd3P+aRUIy4iIuLf/Ca4dGTWlAE4DBPBdfuh+oCnyxEREWkzCi5+oGdyAgVGgvNJiW64KCIi/kvBxQ9kJkW5ZtBFDboiIuLHFFz8QEZSJPlHrixq3JPn0VpERETakoKLH4gKCaQ0rDcAtn2ay0VERPyX3wSXjno5dDNz4gAAQioLoKHaw9WIiIi0Db8JLh31cuhmqandKDWiMWHA/nxPlyMiItIm/Ca4dHSZSZGuGXQp1pVFIiLinxRc/ERmcqTryiLbPgUXERHxTwoufiIhMpjdgT0AaNyj4CIiIv5JwcVPmEwmHPHOBl1r2RawN3m4IhEREfdTcPEjnVL7UGWEYHE0wsHtni5HRETE7RRc/EhmcvQxM+hqPhcREfE/Ci5+JDMpknyHM7g4ihVcRETE/yi4+JFuncPZbu4GQEPROg9XIyIi4n5+E1w6+sy5ABazibrYTOf3+zeCYXi4IhEREffym+DS0WfObRaR2p9Gw0JQUyVUFHm6HBEREbfym+AiTn1SOrHDSHE+UZ+LiIj4GQUXP5OZFMWmIw26hq4sEhERP6Pg4mf6xEeQTzoADXvyPFqLiIiIuym4+JmQIAsVkX2dT4o3eLYYERERN1Nw8UNByQMBCK7dB7VlHq5GRETEfRRc/FD31CR2O+KcT9TnIiIifkTBxQ9lJkWxyUh3PinR6SIREfEfCi5+KCPx6NT/jXvzPFuMiIiIGym4+KGYsCBKQnoBYNurU0UiIuI/vDK4XHvttcTExHDDDTd4uhSfZSRmARBc8T001Xm4GhEREffwyuDywAMP8Nprr3m6DJ+WnNqNg0YkZsMOpfmeLkdERMQtvDK4XHzxxURERHi6DJ+WmRzl6nNh55fgcHi2IBERETdwe3BZvHgx48aNIykpCZPJxHvvvXfcOjk5OaSnpxMcHMzIkSNZuXKlu8vo8DKTItlodHM++fJJeLY3vD8Ztn6qU0ciIuKz3B5campqyMrKIicn54Svz58/nylTpjBt2jTWrl1LVlYWo0ePZv/+/We0v4aGBiorK1s8BJKjQ/hvwFW8az8Pe1Ak1ByAdf+C/9wMT3eHebdB3huaoE5ERHxKgLs3OHbsWMaOHXvS12fNmsXEiROZMGECAC+99BIff/wxr776Ko8++uhp72/mzJnMmDHjjOv1VyaTibikrjy0cxK1Y3pzW8Ie2LoAtiyAyj2w5SPnw2SGtFHQ90rocwXEdvN06SIiIifVrj0ujY2NrFmzhuzs7KMFmM1kZ2ezfPnyM9rmY489RkVFhetRVFTkrnJ9Xv/kSAAe/3AbYz608GzA3ay/YQmOiYvgokcgfgAYDti9FD77HTw3CF4cBblPwN416osRERGv4/YRl1M5ePAgdrud+Pj4Fsvj4+PZsmWL63l2djbr16+npqaGlJQU3nrrLUaNGnXCbVqtVqxWa5vW7av+55yubNpXyYqCMraUVLGlpIrnv9xBfKSV7H7juOwndzOqUw3WHZ/B1o9h11LYn+98fPMXiEiEPmOdozHpF0CAjrOIiHhWuwaX1vriiy9O+z05OTnk5ORgt9vboCLf1LVTGG9MPIfy2ka+3LKfLzaX8vXWA5RWNvD6ikJeX1FIWJCFi/oM57KBV/GTqwKJ2rPIGWJ25EJVMax+1fkIioBe2dDnSuh1GYREe/rjiYhIB2QyDMNos42bTLz77rtcc801gPNUUWhoKG+//bZrGcD48eMpLy/n/fffP+t9VlZWEhUVRUVFBZGRkWe9PX9T32Rn+c5DLMwv5Yv8UvZXNbhes5hNjEiPJTsjnst7R5FasQa2fOzsjakuPboRcwB0Pe9oX0x0qgc+iYiI+JPW/v1u1+ACMHLkSEaMGMHzzz8PgMPhIC0tjcmTJ59Rc+4PKbi0nsNhsGFvBQvzS1mYX8rW0qoWr/dNiOCyjHiy+3ZhAN9j3rbAGWIObGm5oYQB0PcqZ4hJGAAmUzt+ChER8QceCy7V1dXs2LEDgMGDBzNr1iwuueQSYmNjSUtLY/78+YwfP56XX36ZESNGMHv2bN588022bNlyXO/L6Tj2VNG2bdsUXM5A4aFaFm4uZWF+Cat2HcbuOPqj4eyLieeyjHhGxVRg3fGp8wqlom+dDb7NotKO9MVc4RyVsQR64JOIiIiv8VhwWbRoEZdccslxy8ePH8/cuXMBeOGFF3jmmWcoKSlh0KBBPPfcc4wcOdIt+9eIi3v8sC+mpvFo75CzL6YLl2XE85NUM1FFXzpDzPdfgu2Yye2Co6DXaGeI6ZkNVs2GLCIiJ+YVp4o8QcHF/Zr7Yr7IL+WLzaWUVp64L2Z0rwhSDq90Nvdu/RRqDx7diCUIul3oPJ3U5wqITPTAJxEREW/V4YKLThW1j2P7Yr7YXMqWkpP0xfTpzABjq7MvZssCKPu+5YaShjibe/teCV36qi9GRKSD63DBpZlGXNpXq/pi+sVxbvQhgrZ/6mzu3bOq5UZiuh29QintHDBb2vlTiIiIpym4KLi0u/LaRr7aup+F+T/SF5PsIKow1xlidn4N9qOnngiJhd5jYPBtzuZejcSIiHQICi4KLh7VYLOz7PtW9MX0DCelbJnzdNK2T6G+/OhGErPgnEmQeS0EBLX/hxARkXbT4YKLely8V+v7YjoxwLYJc/47sH7+0SuUIhJhxEQYOgFCYz3wCUREpK11uODSTCMu3q81fTE3ZYaSVfIOrHzl6Ky9gaGQdQuc8yvo3NND1YuISFtQcFFw8Qmn6osZlBrNxFHJjGYZAStfhJINR9/YewyMmuS8+aP6YEREfJ6Ci4KLz2mw2Vn+/SE+XF/Mh+v30Wh3zsibGBXM7ed05ecJhYSve9nZC9MsYYCzD6b/9eqDERHxYR0uuKjHxb8cqGrg9RW7+fe3uzlY3QhAcKCZ64ekcE+Gna7bX4O8N472wYQnwIi7Ydhd6oMREfFBHS64NNOIi39psNn5IG8fry7dxebiStfyi/t04Z5hMYwq/xDTyr9DVbHzhYAQyPqZsw+mS28PVS0iIqdLwUXBxa8YhsG3O8t4dWkBX2wupfmntldcOHefm8K1QSsJWvU3KF5/9E29RsOoX0G3i9QHIyLi5RRcFFz81u5DNcxZuou3Vhe5mnljQgO5dUQqd6YW0+m7fzgnt+PIj3Z8f+cIzIAbIMDqucJFROSkFFwUXPxeZX0Tb64qYu6yXew57Ox1CTCbuGpgIr8cAH13vwHr/g1Ntc43hMU554MZdieEdfZg5SIi8kMKLgouHYbdYbAwv4RXl+xi5a4y1/JhXWP4xYhYflKzAMuqV6Bqn/OFgGAYeLNzFCaur4eqFhGRY3W44KKrigRgw54K5iwt4MPv9tFkd/5oJ0eHMOGcZG6NWEfompdg37qjb+iZ7ZwPpvsl6oMREfGgDhdcmmnERQD2V9bzr2938/qKQspqnJdThwZZuHFIMr/ofoCk/P+DLR/j6oOJyzjSB3MjBAZ7rnARkQ5KwUXBRYD6Jjvv5+3l1SW72FrqvEeSyQSX9o3j3oFmhhTPx7Tu39BU43xDWBcYfmQ+mPAuHqxcRKRjUXBRcJFjGIbB0h2HeHVpAV9u2e9a3jchgl+M7MRVtoUErnoFKvc4X7BYYeBNztNIcf08VLWISMeh4KLgIiex80A1c5bu4u01e6hrcl5O3SksiJ+PSGZC7HdE5f0d9q45+oYeP3EGmB6Xqg9GRKSNKLgouMiPqKhtYt6qQv65bBf7KuoBCLSYGDcwkUm9yuixfS5s+QgM5z2T6NLX2Qcz8CYIDPFc4SIifkjBRcFFWslmd/DpphJeXVLA2sJy1/KR3WKZNDiA8w/9F/O6f0FjtfOF0E7OPpjhd0N4nGeKFhHxMx0uuOhyaHGHdYWHmbN0Fws2FGNzOP+vkRYbysQRnbjR/BXBa16BiiLnypYgGHCT87YC8ZkerFpExPd1uODSTCMu4g7FFXW8tnw3b6wopKKuCYBwawA3D03k3vjNdN7wCuxZdfQN3S+GC38L6ed5pmARER+n4KLgIm5Q12jnnXV7eHVJAd8fcF4ybTbBZRnx3N/7MBm7/41p8wdH+2D6XQ2XPwEx6Z4rWkTEBym4KLiIGzkcBou3H+DVpbtYvO2Aa3lmUiSTBwdxefk8LGvnOgOMxeq8CumCKWCN8FzRIiI+RMFFwUXayPbSKuYs28U7a/dQ3+QcaekSYeWRwQ6uO/AC5oKvnSuGx8Ol0yDrFjCbPVixiIj3U3BRcJE2drimkTdWFvLa8l2UVjYAkBYTwv8bVMyQrX/BVLbTuWLiIBjzFHQd5bliRUS8nIKLgou0kya7g3fX7uUvn29lf5UzwIzqGsazXVeQtP4FaKh0rph5HVz2R4hO9WC1IiLeScFFwUXaWU2DjZcX7+Tvi793nUL6+YBQHrO+TejG1wEDAoLh3Pvh/AchKMyj9YqIeBMFFwUX8ZDiijqe+XQr76zbC0BwoJnHh9i49fBLWAqXOFeKSILs6c67Uav/RUSk4wUXTUAn3ua7PeU8+dFmVu4qA6BLeBCzBxZx7s7ZmMp3O1dKHgZj/xdShnmwUhERz+twwaWZRlzEmxiGwWebSpj5yRZ2H6oFYGBCMM+nL6dr/t+O3kZg4M3OK5Cikj1YrYiI5yi4KLiIF2mw2fnX8t38NXc7VfU2AK7vFcC0sP8SueVNwIDAUDjvQTj3PggK9Wi9IiLtTcFFwUW8UFlNI8/lbudf3+7G7jAIMJt4eGAdd1W9RMDelc6VIlPgshnQ/3owmTxbsIhIO1Fw6eDBxTAMPtz5IW9ufZPwoHDSI9NJi0hzfo1MIzEsEYvZ4ukyO6wd+6t56pPNfLF5PwCRwRb+X/9d/KTweUyVe5wrpZ4DY2ZC8hAPVioi0j4UXDpwcNlXvY8/Lv8jS/ctPek6geZAUiNSSYtMo2tEV7pGdXV+jexKXGgcJv1Lv10s2X6QJz/OZ0tJFQC9Yy3kdFtGz22vYGpy9sQw6Da4dCpEJHiwUhGRtqXg0gGDi8Nw8J8t/+Gva/9Kna2OIHMQEwdOpHNIZworC9lVuYvCykKKqopodDSedDshASGkRqTSNdIZZNIi0kiPco7YxAbHKtS4md1h8PaaIp75bBsHq50T2I1JczAz8h1idrzjXCkwzHnvo1GTITDYg9WKiLQNBZcOFlx2lu9k2rJp5B3IA2BI3BBmnDuD9Kj049a1O+yU1Jawu3I3uyt3U1hZ6Pp+b/Ve7Ib9pPuJCIwgLTKNtMijp52aR2wigzrO8W4L1Q02Xv76e/6+eCcNNgcmEzzYp5J7G14hqHiNc6XoNLjsCcj4qfpfRMSvKLh0kODS5Gji1Q2v8vJ3L9PkaCIsMIyHhjzEjX1uxGw6/YnNmhxN7Kve5woyx4ab4ppiDE7+4xJjjXGO0ESmuUZrmkdsQgN1lUxr7Suv4+lPt/Be3j4AQgLNzOq7jdElf8NcVexcqet5zv6XxCwPVioi4j4KLh0guGw6uImpy6ay7fA2AC5MuZA/nPMHEsLapheiwd5AUWURu6uOH6k5UHfglO+NC4lrEWiaR2xSIlKwWqxtUq+vyysq58mP8lm9+zAAXSMMXkz/hoyCuZhs9YAJhvwcfvIHCI/zbLEiImdJwcWPg0udrY4X817ktfzXcBgOoq3RPDriUa7odoXH+k9qmmqcQaaqZaDZXbmb8obyk77PhInEsMTjRmpSI1JJDk8myBLUfh/CCxmGwScbS5j5yWaKyuoAuDi+nr/EvEvnXR86VwqKgIsehpG/hACFQBHxTQoufhpcVhavZPry6RRVFQFwRbcreGTEI8QGx3q4spOraKg42hxcVdji9FN1U/VJ32fCRHxYPKkRqaSEpzi/RqS4nkdZozpMo3CDzc4/l+3i+dwdVDU4J7D7ZfeDPGT7P6z71ztXiukGlz8Jfa9U/4uI+ByfDi4fffQRv/71r3E4HDzyyCPcfffdrX6vvwaXqsYqZq2Zxdvb3gYgLjSOqedM5aLUizxc2ZkzDIOy+rKWvTRHgk1RVRF1trpTvj88MNwVZlIiUlqEm8SwRALMAe30SdrPoeoGZn+xnTdWFmJ3GASaDZ7pvZmrD7yCuabUuVK3C2HMUxCf6dliRUROg88GF5vNRkZGBl999RVRUVEMHTqUZcuW0alTp1a93x+Dy1eFX/Hkt0+yv845WdnNfW7mwSEPEh4U7uHK2k5zqCmqKmJP9R7n16o9rkfzsTgZi8lCYlhiy1GaY0ZrfP3YbS+t4s8LNvPVVmdvUUKwjZfTv2Zg0b8x2RvAZIahd8Alj0NYZ88WKyLSCj4bXJYtW8YzzzzDu+++C8CDDz7IyJEjueWWW1r1fn8KLofqDvHUyqf4dNenAHSN7Mr0UdMZlqA7CdfZ6thXvY89VXtOGG5ONU8NOK+Aah6l+WGwiQuNO6Mrsjxh8bYD/OnjzWwtdU5gNyq2mv8X+y4Jez5xrmCNgosfgeETIaBj9wuJiHdr7d9vt4+lL168mGeeeYY1a9ZQXFzMu+++yzXXXNNinZycHJ555hlKSkrIysri+eefZ8SIEQDs27eP5OSjd8hNTk5m79697i7TqxmGwUc7P+J/V/0vFQ0VWEwWxmeO596sewkO0ORj4Jwkr0d0D3pE9zjuNYfh4EDtgROP1lTvoay+jMMNhznccJgNBzcc9/5AcyDJ4cnHjdKkRqSSHJFMSEBIe3zEVrmwdxfO7dGJt9bs4dnPt7K8LJxzyn7OhORL+S1zCTm0ET77Hax+FS7/E/Qerf4XEfFpbg8uNTU1ZGVlceedd3Ldddcd9/r8+fOZMmUKL730EiNHjmT27NmMHj2arVu3Ehd3+pd0NjQ00NDQ4HpeWVl5VvV72r7qffzx2z+ydK9zuv6+sX2Zce4MMjpleLgy32E2mYkPiyc+LJ5hHD86Vd1Yzd7qva5Ac2zAKa4upsnRxK7KXeyq3HXC7XcJ6XJcT01qRCqdQjoRZY0iIjCiXZuGAyxmbhmRxlUDE3np6+955ZsC5uxN4jXTo8zs9h3Xl7+K5dAO+M/N0OMnMHomxPVtt/o6MsMwqLfX02hvxGE4sBt2HIbj6PeOo8tO+tVhx27YW77X8SPvOcG2j93ej+3vYHU9jXY78ZHBrtFHEybMJjMmkwkTzp9vs8mMCdPRZSYwc4J1jjw/0TrHfT3RspOt84PnZpOZIHMQgZZA19dAcyBBliDnV3OQ6/tjXwsyB+nebT6kTU8VmUym40ZcRo4cyfDhw3nhhRcAcDgcpKamct999/Hoo4+e8FTRiBEjuPXWW0+4j+nTpzNjxozjlvvaqSKH4WD+1vnMXjObWlstQeYg7h10L+MzxxNoDvR0eR2GzWGjpKbkuJGa5u+rmqp+dBsWk4UoaxRR1iiirdFEBR39Pjo42vla0JHXjlnurvls9hyu5ZnPtvL+kQnsOgc28FLaVwwtmYfJ3ggmCwy/Cy5+DEK992q09uIwHNTb6qm11VJnq2vxqG06flmLR9MP1v/BNupt9aectFG8x7Ghp7Vh54TLThaajtnesa9ZLVZCA0IJDTzyCAj1ywsLWsMrelx+GFwaGxsJDQ3l7bffbhFmxo8fT3l5Oe+//z42m41+/fqxaNGiVjXnnmjEJTU11aeCy86KnUxfNp11+9cBzun6p587nW5R3TxcmfxQRUPFSftqyurLqLfXn/G2gy3BR4PMkVDjCj8nWR4ZFHnSfymuLTzMkx/ls7awHIChEeU81+kdkku+OLLDaDjvARhyu9c28BqGgc1ho9HRSKPd+ai31x8XHE4UOloTNOpsdWf13+xMWEwWzCZzy6/mo8+Pe+0E65z0q9nS6vce+73NbiKvqIKNe6qwG0dHMRyGgcVsYljXaIZ0jcFiBgMDwzBw/c84wfNTfT2ddU7zPXaHnSZHE02OJhrtjS2+NtmbaHS0XOatfhhmwgLCnF8DwwgJCCE0wPl98zLXuke+/nBZSECIT0wd4bEel1M5ePAgdrud+Pj4Fsvj4+PZsmWLs6CAAJ599lkuueQSHA4Hv/3tb095RZHVasVq9c1Jt5ocTczdOJe/rf8bTY4mQgNCeWjoQ9zU5yafaQ7taJpDQ2bnE19qXG+rp6KhgvKG8qNfGyuc39eXt3x+ZJ2Khgrshp16ez31tfWU1pa2uh4TJiKCIk4YcCKtkdx4STRD9xt8sLacdRUBnL/3Gm6KOZ8/WP5D+OGtkDsDFs103vto2F04UkfQ6GhyBYXmX/aN9kbnL317k+v7ky1rcjTRYG9wBY3mPxSt3aZr/SPL2lNIQEiLR/MvfdcjMOS4dU72CA0Ida3ffCrC2/5/3Whz8K9vd/Nc7nYq6px/yC/o1ZnfXdEPa4CZaR9s4pvtB/lmP+z+PpQZV2dySV//mKX52FD8w1DTaG887rVjfzZtDtvRn98fhKNjf95PGJyO2VeTvYl6ez21TbXUNtViM5xzNDXYG2iwN3C44bBbPqsJU4sAFBIQcjT4HFnWHHJahJ4fLGt+T2hAKIEWz50J8MrxqKuvvpqrr776tN6Tk5NDTk4OdvvJbxDoTTYd2sS0pdPYengrAOcnn8/Uc6aSGJ7o4crkbAQHBBMcEEx8WPyPr3yEYRhUN1W3CDvHhpoTPa9oqKC6qRoDg8rGSiobK12TEp5QLIQdOSu0APjYsBAe2ZsQGmgybDRWLqfpq2+xefm/ygJMAVgDrK0PD0d+SR8XQE4QRIItwT7xr1J3MAyDTzeW8NSnW9h9qBaA3vHh/O6Kflzc52gwee3OESzYUMITH+VTWFbLhLmrGJ0Zz9RxmSRHe0+T+pkwmUzOUzaWQPCSs/FN9iZqmmqotdW2+FrXVEeNrcYZcJpfO/J9bdPRdU+0DJyjZDVNNdQ01cCpp8dqtcdGPMat/U7cwtHW2jW4dO7cGYvFQmlpy39RlpaWkpBwdvfXmTRpEpMmTXINNXmrels9L65/kX9u+qdruv5HRjzCld2u7DC/NKUlk8k5ahIRFEFqRGqr39fkaKKioYLKhsoW4eaEXxsrqKiv4HBDOU2ORkwmOzUmOzUAnLwpMdAUQFCAtcW5+yBLUIvz+VbLMa8fWX7sef1TLTtum8es+8NtBpoD1UDpBusKD/Onjze77oHVJcLKry/rzQ1DUwiwtBwRMplMXDkwkYv6dOG53O3835ICPttUyuJtB7nv0p7cfX53ggK8axTJlwVaAom2RBNNtFu2d2z/VnPYcYWc5qDT9IOQZKtrEYx+uKx5FNSTV1e2a3AJCgpi6NCh5ObmunpcHA4Hubm5TJ48uT1L8YhVJauYvmw6hVWFAIxNH8sjIx6hU0jrJtcTOVagOZDOIZ3pHNL6/hTDMKiz1bFu717+tngD3xYcwmG3YBgBxAfZeShhK9k1nxNR/j2B4LwuJHUkDLvLeTopUJfj+6qislr+99MtfPSd8w7jwYFm7rmwB7+4sDth1lP/KQi3BvC7K/px/ZAU/vD+RlYWlPH0p1v575o9PPHT/pzb0zt7pDo6s8nsOuVzOr8nTqXJ0URtU61H7yPn9ubc6upqduzYAcDgwYOZNWsWl1xyCbGxsaSlpTF//nzGjx/Pyy+/zIgRI5g9ezZvvvkmW7ZsOa735XQce6po27ZtXtWcW9VYxf9b8/94a9tbgHO6/j+c8wcuTr3Ys4VJh1daWc9bq4uYv7rIdRNHMLgtrpB7whaRVpqLyeE8705ILAz+Hxg2AWK7e6xmOT0VtU3kLNrB3KW7aLQ7MJngxqEpTLmsDwlRpx9EDcPg3XV7+fOCzRysdv7r++qsJH5/ZT/iIhVs5cx57KqiRYsWcckllxy3fPz48cydOxeAF154wTUB3aBBg3juuecYOXKkW/bvbTPnLipaxBPfPsH+WucU9Tf2vpGHhj5ERFCEZwsTOYbDYbD0+4PMW1nE5/klNNmdvxbSgqp4PHE1l1R/TFDNvqNv6HGp85LqXqPB4pWtch1eo83Bv7/dzXNfbqe81tl4e35PZ+NtRtJJfjfWHIK8f8Paf4G9AYbd6RxtCz5+/Yq6JmZ9vpV/fbsbh+EclXnost6MH9X1uFNOIq3hFZdDe4K3BJdDdYf435X/yye7nFOvp0WkMf3c6QxPGO6xmkRa41B1A/9du4d5q4rYecDZBWPGwe2dtnB38FckH1qGqXlukshk5z2RhtwOEWfXpybucarG24t6dzm+l84woGgFrPo/yH8P7D+4kssa5Qyp5/wKwrsct7+Neyv4/XsbySsqB6BvQgRPXtOfYemaI0hOT4cLLt5yqqh5uv6nVz1NeUM5ZpOZ8Znj+VXWrzRdv/gUwzBYWVDGvFVFLNhQTIPNAUCPgAM8Hv8tF1R/RmBDmXNlcwD0vdL5r/NuF+q2Ah7yw8bbzuFWfn15b248QeMtDVXw3XxY9Srs33R0eeIgZ1AxB8CS2XDQeeUjAcEw+Odw7n0Q07XFphwOg/mri/jfT7e4RnduGJrCY2P70incN6erkPbX4YJLM0+OuBRXF/PHb//Ikr1LAOgT04cZ580gs9OJ5/wQ8RUVtU28l7eX/6wsZEuJc/bgIJq4IzqPO61fkVCRd3TlTr2cpxgG3QIhMZ4puIM5rcbbko2w+v/guzehsdq5LCAY+t8Aw++E5KFH13U4YNsn8M0s2Lvaucxkgf7Xw/kPQXzLW5GU1TTy9KdbmLfKeWl+VEggD4/uwy0j0rCYFWbl1BRc2jG4/HC6/kBzIPdm3csd/e/QdP3iVwzDYP2eCuatLOSD9fuobXTOm9TfUshvOy/j3JpcAmzO00sEhDj/wP3wj6G4zYkab28YksKvL/9B421TPeS/7wwsRSuOLm9tyDQM2LUElsyC7788urz3GDh/CqS17FFcs/swf3hvI/nFznvHDUyJ4slr+jMwJdoNn1r8lYJLOwWXgooCpi+bztr9awEY1GUQM86bQfcoXXUh/q26wcaH6/cxb2Uh6/dUABBGHRMiVnJH0Jd0rtl+dOWkwc7TSP2vh6BQD1XsPxptDl5fsZu/5v5I423ZTuedwde9DnXHnta7ynk6KP2C0z+tty8Plvw/ZxBq7nVKOxcumAI9s13bs9mdzcHPfr6NqgYbJhPcOiKN347uS1So/kEnx+twwaW9e1yap+t/af1LNDoaCQ0I5YEhD/Czvj/zumm9Rdpa/r5K5q0q5N11e6mqtwEGIyzbmRL9DSPqFmNuvi9McBRk3er8V36X3h6t2RcZhsFnm0p46pMt7DrSeNsrLpzfXdmPi5sbb+022Papc3Tl2NGRyJQjjdQ/d08j9cEdsOyvkPcfaP7vGz8Azn8QMq5xXW22v6qemQu28O66vQDEhgXx6Ni+3DAkBbNOH8kxOlxwadYeIy75h/KZunSqa7r+85LPY+o5U0kKT2qT/Yn4irpGOws2FDNvVSGrdjkbRGOp5M6wpfxPwJdEN+w9unL6Bc5/9fe9Cjx43xNfkVdUzp8+zncd187hVqZc1pubhh1pvK0shrWvwdp/QmXzcTZBz0udo129Lm+bS9cr98HyHFg9B5qOnCaM6Qbn3e8MqUcmLVz+/SGmvr+R7fudfTXDusbwxDX96Zfo+WkrxDsouLRBcGmerv+1Ta9hN+xEWaN4ZPgjXNX9Kk3XL/IDO/ZXMW9lEf9du4fDtU2YcHCheQMPRC1mcN0KTDivUiI83nk59dA7ICrFozV7o6KyWp7+bCsfrnfOoxMcaOaeC7pzz0U9CA80Q8HXztGVLQvAOHKvttBOziuAht4Bse10l/naMlj1D/j2b0dPS4XHOy+jHnYnBEfSZHfw6pIC/pq7ndpGOxazifGj0nnosl5EBCu8dnQKLm4OLqtKVjFj+Qx2V+4GYEz6GB4d8aim6xf5EQ02O59vKmXeqkKW7jgEQBIHuTPka34WsIjwJucyTGZns+ewu6DHT8DcsU+5VtQ18eJXO5hzosbbwFrIe8PZv1L2/dE3pY06cnuGqyHAQ5chN9Y4J7Bb9jxU7nEus0bBiLth5L0Q3oXiijqe+CifBRtKAIiLsPL4lf24OitJ/wjswDpccGnrHpd7v7iXJXuXEBcSx+PnPM5P0n7itm2LdBS7D9Uwf1URb63Zw4GqBgKwcbl5Nb8K/5r+jeuPrhiTDkMnOEcNwjrWPw4abQ7eONJ4e/hI4+15PTvxu7F9yTR2OCeK2/QO2OqdbwiKgKybnaMa8V409YKtETa+fcq5YL7edoBp72909euc26MTf/xpJj3jNLN4R9Thgkuzthpx2Ve9j1c3vsr9Q+4nMkjnZEXORpPdwZdb9jNvZSFfbzuAw4Aepr3caf2K6y2LCbYfmV/EYoXMa5yjCKkj/HpiO2fjbSn/++kWCg46e0V6xYXzh8vTuKB+EabVr0LJd0ffkDDAeVwG3AjWcA9V3QoOB2xd4LyUeu8a5zKTBQbcAOc9SH1sH/6+eCc5X+2gweYg0GLi7gu6c99PehIapNtJdCQKLl5yryIRObV95XW8ubqIN1cVsa+inmAaGGdZzi9CvqKn7ZhLquP7O0cV+l4F4XF+FWLyisr588ebWbnL2RvSOTyIGedYGFv/MeYNb0KDcz4ULFbof50zsKQM861jYBiw6xvnZHY7vzq6vPdYuGAKhaH9mfHhJnK3OO/rlhwdwh+uymB0ZrxOH3UQCi4KLiI+xe4wWLz9APNWFpK7eT82h8FA0/dMCMrlKvMyAo1j7qETEAzRaRCV6vzqenR1fvWRYFNUVsszn23lgyONtxGBdp7qu4sx9QuwFC0/umJs9yMTxd0GoX5wD6C9a2HpbMj/ANdcMF3Ph/MfYmFjf6Z/mM/ecufdyi/u04UZV2fStVOYx8qV9qHgouAi4rP2V9Xz9po9zF9VxO5DtURSzQ2Wbxgf/DVp9qKjN3k8GYsVon8QaqKO+T483qPNvz9svE017WdG8kouqvkMS11zs7IF+l5x5P5PF/lns/LB7bD0r7B+3tG5YBIG0HjOAzxXksHL3+ymyW4QFGDmVxf34JcX9SA40OLZmqXNdLjg4i03WRQR93E4DL7deYj/rCris40lNNodBGIj0XSIZNNBUkwHjnkcJNl0kEQOYTGd+teawxxIY3gyjsg0TDFpBHXqiiU2/Zhgk9AmQaHJ7uD1b52NtxW1DVxiXsd9EYvJalh9NIxFJMHQ8c5LxCM7yNxQFXvh2xePmwtmf9Yv+e32TBZ97zxV1rVTKNOvzuSSPnEeLFbaSocLLs004iLin8pqGvlw/T4KDtZQUdfE4dpGymubXN9X1DVhGBCAjQRTGak/CDTNAac1wcZGAIcD46i0JlIbmkxDeAqOqDQsMakEdkontFMqMeHBRIUEHn/X5RMwDIPP80t56pMtVB/cy02WRdwe9BXxxoGjK/X4ifN0UO+xbTNRnC+oLYOVr8CKl1xzwRjhCWxO/x8mbR1EQZXzWI/OjGfquEySo0M8Wa24mYKLgotIh+JwGFTWN1Fe20R5c5ipbaK8tpHDRwJOeW0jlTV1WGpKCKndS2R9MZ1sJaTgDDgppgMkmg4RYHKccl9NhoV9Rif2GF0otcRxODCeCmsStSFJNEakYopIICoshKjQICKsAby9pghL4RJus3zBGMtqAjgyUVxIDAz+H+el3516tMNR8hGNNc5ZgJc975oF2AiOYmnMtTy0exQHHBGEBFq479Ke3H1+d4IC/PA0Wgek4KLgIiKtYHcYVNY5w055bSMV1XXUH96D43AhpooirFVFhNbuI6KhmNimEro4DhwNHifRZFgoMWLZY3RhH53IMn1PT/O+oyukjHDe7iDjGteU+HICtkbY8JazkffgNgAcAcF8FnQZT5Zls5cu9OgSxhPX9OfcHp09W6ucNQUXBRcRaQsOO1QVYy/bTd2BAhoP7cJxuBBzRSFBVXsIqSvGYtiOf1tgGObmieISBnigcB/mcMCWj5xzwexb51xksvAJ5zG7/iq2GylcnZXE76/sR1ykgqCvUnBRcBERT3DYoboUyguPPHZDWBxkXgvB+p10VgzDeW+mJf8Pdi5yLV5oH8qLtqvZaO5N/+QoRqTHMjw9lmHpMUSHBnmuXjktCi4KLiIi/mvvGuftBDZ/SPNcMOscPVnl6MMGRzc2GN3YbcTTOz6K4d1iGH4kzCSpoddrdbjgosuhRUQ6oAPbYNlfMdbPx9Q8F8wRlUYIG4+EmA2O7mwwumGLTGdE904MT49lRLcYenQJ18y8XqLDBZdmGnEREemAKvfB91/CvjxnH0zpxqM3ojxGhRHaIswUBfcmoWtfV5jJSIoksBWXuIv7KbgouIiIdFz2Jjiw5WiQKc7DKNmIyd5w3KrlRhgbHelsNLqz1dwDI2kQ6T0yGNGtE4PTYggJ0my97UHBRcFFRESOZW+C/ZuhOA/2rcOxLw9KNmJ2NB63arkRxgZHN/LpTnlMf8K7DaN370yGpccSE6aG37ag4KLgIiIiP8bWCAc2w748jH3rqC9cS9DBfCxG03GrHjbC2eDoxt6Q3jgSB9Ol90gyM/qTHBPqgcL9j4KLgouIiJwJWyPsz8fYt46aXatpKlpLROU2Ak4wP0+ZEc52S08qYvoTnDaE1MxzSe/eB5M/3hSzjSm4KLiIiIi72Bpgfz7VBas4vGMVAaV5dKn9/oSzKB8mgr0hfWmMG0h0j+Gk9j+XwJg00NVLp6TgouAiIiJtydZA3Z7v2LtpOXWFq4ko20hy4y4CTceHmQpzFGWRGQSkDCGuz0isaUMhMllh5hgKLgouIiLSzpoaatm5cQX7t66AfeuIq95MD6PohDfurA6IoS6mDwRHQ3Ak5uBIAkKiCAiNJCg0ioDQaEzBEWCNBGvEkUckBIWDH56KUnBRcBEREQ9zOAy+Lz7Ajg0rqC5YTcjB7+jRtINepj0/ehfyU6k3h9JgCaMxIBxbQDj2wHAcQeEY1khM1gjMwZFYQqIICI0iKCyKoLBorKFRmEOijoYgLwtAHS64aOZcERHxBXvL61izYx97t6zEdmAHlsZqAmzVBNpqCLZXE2zUEk4dEaY6InB+H37k+6ATnIY6Uw5M1JtCqDeH0WAJpdESTlNgcwiKwAiKcI0EWYIjnSNBYdEEhkUTkdCLkJh4t9UCHTC4NNOIi4iI+DK7w6C20UZNg52aRhs1DUe+b7BRV1dDY00FttoKbPWVGHUVGA1V0FCFubEKS2OVKwRZ7c4gFHIkCDnDUC0R1J2wD+d0fNv3Uc752WNu+sROrf37HeDWvYqIiMhZsZhNRAQHEhEc6JbtGYZBfZODmkYbVQ02iuubqKuro76mnKaaCmy15TTVVmLUV+Cor8LUWImpoQpLUzUBTdUE2qqx2p1BKMSoJcyoxQjt7JbazoSCi4iIiB8zmUyEBFkICbLQOdx6ZGk0kHjG20zx4Mka7+nKEREREZ/gyTtqK7iIiIiIz1BwEREREZ+h4CIiIiI+Q8FFREREfIaCi4iIiPgMBRcRERHxGQouIiIi4jO8Mrhce+21xMTEcMMNN3i6FBEREfEiXhlcHnjgAV577TVPlyEiIiJexiuDy8UXX0xERISnyxAREREvc9rBZfHixYwbN46kpCRMJhPvvffecevk5OSQnp5OcHAwI0eOZOXKle6oVURERDq4077JYk1NDVlZWdx5551cd911x70+f/58pkyZwksvvcTIkSOZPXs2o0ePZuvWrcTFxQEwaNAgbDbbce/9/PPPSUpKOq16GhoaaGhocD2vrKw8zU8kIiIivuK0g8vYsWMZO3bsSV+fNWsWEydOZMKECQC89NJLfPzxx7z66qs8+uijAOTl5Z1ZtScwc+ZMZsyY4bbtiYiIiPc67eByKo2NjaxZs4bHHnvMtcxsNpOdnc3y5cvduSuXxx57jClTprieV1RUkJaWppEXERERH9L8d9swjFOu59bgcvDgQex2O/Hx8S2Wx8fHs2XLllZvJzs7m/Xr11NTU0NKSgpvvfUWo0aNOuG6VqsVq9Xqet78wVNTU8/gE4iIiIgnVVVVERUVddLX3Rpc3OWLL7444/cmJSVRVFREREQEJpPJbTVVVlaSmppKUVERkZGRbtuuHE/Hun3oOLcPHef2oePcPtryOBuGQVVV1Y/2uro1uHTu3BmLxUJpaWmL5aWlpSQkJLhzVydlNptJSUlps+1HRkbq/xTtRMe6feg4tw8d5/ah49w+2uo4n2qkpZlb53EJCgpi6NCh5ObmupY5HA5yc3NPeqpHREREpLVOe8SlurqaHTt2uJ4XFBSQl5dHbGwsaWlpTJkyhfHjxzNs2DBGjBjB7NmzqampcV1lJCIiInKmTju4rF69mksuucT1vPmKnvHjxzN37lxuvvlmDhw4wNSpUykpKWHQoEF8+umnxzXs+hqr1cq0adNaNAJL29Cxbh86zu1Dx7l96Di3D284zibjx647EhEREfESXnmvIhEREZETUXARERERn6HgIiIiIj5DwUVERER8hoJLK+Xk5JCenk5wcDAjR45k5cqVni7Ja82cOZPhw4cTERFBXFwc11xzDVu3bm2xTn19PZMmTaJTp06Eh4dz/fXXHzdxYWFhIVdeeSWhoaHExcXx8MMPH3dX8UWLFjFkyBCsVis9e/Zk7ty5bf3xvNZTTz2FyWTiwQcfdC3TcXaPvXv38j//8z906tSJkJAQBgwYwOrVq12vG4bB1KlTSUxMJCQkhOzsbLZv395iG2VlZdx2221ERkYSHR3NXXfdRXV1dYt1vvvuOy644AKCg4NJTU3l6aefbpfP5y3sdjt/+MMf6NatGyEhIfTo0YMnnniixb1rdKxP3+LFixk3bhxJSUmYTCbee++9Fq+35zF966236Nu3L8HBwQwYMIAFCxac/gcy5EfNmzfPCAoKMl599VVj06ZNxsSJE43o6GijtLTU06V5pdGjRxtz5swxNm7caOTl5RlXXHGFkZaWZlRXV7vW+eUvf2mkpqYaubm5xurVq41zzjnHOPfcc12v22w2o3///kZ2draxbt06Y8GCBUbnzp2Nxx57zLXOzp07jdDQUGPKlClGfn6+8fzzzxsWi8X49NNP2/XzeoOVK1ca6enpxsCBA40HHnjAtVzH+eyVlZUZXbt2Ne644w5jxYoVxs6dO43PPvvM2LFjh2udp556yoiKijLee+89Y/369cbVV19tdOvWzairq3OtM2bMGCMrK8v49ttvjW+++cbo2bOnccstt7her6ioMOLj443bbrvN2Lhxo/Gf//zHCAkJMV5++eV2/bye9Kc//cno1KmT8dFHHxkFBQXGW2+9ZYSHhxt//etfXevoWJ++BQsWGI8//rjxzjvvGIDx7rvvtni9vY7p0qVLDYvFYjz99NNGfn6+8fvf/94IDAw0NmzYcFqfR8GlFUaMGGFMmjTJ9dxutxtJSUnGzJkzPViV79i/f78BGF9//bVhGIZRXl5uBAYGGm+99ZZrnc2bNxuAsXz5csMwnP9HM5vNRklJiWudv/3tb0ZkZKTR0NBgGIZh/Pa3vzUyMzNb7Ovmm282Ro8e3dYfyatUVVUZvXr1MhYuXGhcdNFFruCi4+wejzzyiHH++eef9HWHw2EkJCQYzzzzjGtZeXm5YbVajf/85z+GYRhGfn6+ARirVq1yrfPJJ58YJpPJ2Lt3r2EYhvHiiy8aMTExruPevO8+ffq4+yN5rSuvvNK48847Wyy77rrrjNtuu80wDB1rd/hhcGnPY3rTTTcZV155ZYt6Ro4cafziF784rc+gU0U/orGxkTVr1pCdne1aZjabyc7OZvny5R6szHdUVFQAEBsbC8CaNWtoampqcUz79u1LWlqa65guX76cAQMGtJi4cPTo0VRWVrJp0ybXOsduo3mdjvbfZdKkSVx55ZXHHQsdZ/f44IMPGDZsGDfeeCNxcXEMHjyYV155xfV6QUEBJSUlLY5RVFQUI0eObHGco6OjGTZsmGud7OxszGYzK1ascK1z4YUXEhQU5Fpn9OjRbN26lcOHD7f1x/QK5557Lrm5uWzbtg2A9evXs2TJEsaOHQvoWLeF9jym7vpdouDyIw4ePIjdbj9u5t/4+HhKSko8VJXvcDgcPPjgg5x33nn0798fgJKSEoKCgoiOjm6x7rHHtKSk5ITHvPm1U61TWVlJXV1dW3wcrzNv3jzWrl3LzJkzj3tNx9k9du7cyd/+9jd69erFZ599xr333sv999/PP//5T+DocTrV74iSkhLi4uJavB4QEEBsbOxp/bfwd48++ig/+9nP6Nu3L4GBgQwePJgHH3yQ2267DdCxbgvteUxPts7pHnO33h1a5IcmTZrExo0bWbJkiadL8TtFRUU88MADLFy4kODgYE+X47ccDgfDhg3jz3/+MwCDBw9m48aNvPTSS4wfP97D1fmXN998k9dff5033niDzMxM8vLyePDBB0lKStKxFheNuPyIzp07Y7FYjrsSo7S0lISEBA9V5RsmT57MRx99xFdffUVKSopreUJCAo2NjZSXl7dY/9hjmpCQcMJj3vzaqdaJjIwkJCTE3R/H66xZs4b9+/czZMgQAgICCAgI4Ouvv+a5554jICCA+Ph4HWc3SExMJCMjo8Wyfv36UVhYCBw9Tqf6HZGQkMD+/ftbvG6z2SgrKzut/xb+7uGHH3aNugwYMICf//znPPTQQ64RRR1r92vPY3qydU73mCu4/IigoCCGDh1Kbm6ua5nD4SA3N5dRo0Z5sDLvZRgGkydP5t133+XLL7+kW7duLV4fOnQogYGBLY7p1q1bKSwsdB3TUaNGsWHDhhb/Z1m4cCGRkZGuPyKjRo1qsY3mdTrKf5dLL72UDRs2kJeX53oMGzaM2267zfW9jvPZO++88467nH/btm107doVgG7dupGQkNDiGFVWVrJixYoWx7m8vJw1a9a41vnyyy9xOByMHDnStc7ixYtpampyrbNw4UL69OlDTExMm30+b1JbW4vZ3PLPksViweFwADrWbaE9j6nbfpecVitvBzVv3jzDarUac+fONfLz84177rnHiI6ObnElhhx17733GlFRUcaiRYuM4uJi16O2tta1zi9/+UsjLS3N+PLLL43Vq1cbo0aNMkaNGuV6vfky3csvv9zIy8szPv30U6NLly4nvEz34YcfNjZv3mzk5OR0qMt0T+TYq4oMQ8fZHVauXGkEBAQYf/rTn4zt27cbr7/+uhEaGmr8+9//dq3z1FNPGdHR0cb7779vfPfdd8ZPf/rTE15OOnjwYGPFihXGkiVLjF69erW4nLS8vNyIj483fv7znxsbN2405s2bZ4SGhvrtJbonMn78eCM5Odl1OfQ777xjdO7c2fjtb3/rWkfH+vRVVVUZ69atM9atW2cAxqxZs4x169YZu3fvNgyj/Y7p0qVLjYCAAOMvf/mLsXnzZmPatGm6HLotPf/880ZaWpoRFBRkjBgxwvj22289XZLXAk74mDNnjmuduro641e/+pURExNjhIaGGtdee61RXFzcYju7du0yxo4da4SEhBidO3c2fv3rXxtNTU0t1vnqq6+MQYMGGUFBQUb37t1b7KMj+mFw0XF2jw8//NDo37+/YbVajb59+xp///vfW7zucDiMP/zhD0Z8fLxhtVqNSy+91Ni6dWuLdQ4dOmTccsstRnh4uBEZGWlMmDDBqKqqarHO+vXrjfPPP9+wWq1GcnKy8dRTT7X5Z/MmlZWVxgMPPGCkpaUZwcHBRvfu3Y3HH3+8xSW2Otan76uvvjrh7+Tx48cbhtG+x/TNN980evfubQQFBRmZmZnGxx9/fNqfx2QYx0xJKCIiIuLF1OMiIiIiPkPBRURERHyGgouIiIj4DAUXERER8RkKLiIiIuIzFFxERETEZyi4iIiIiM9QcBERERGfoeAiIiIiPkPBRUTc6sCBAwQFBVFTU0NTUxNhYWGuOymfTG1tLY899hg9evQgODiYLl26cNFFF/H++++71klPT2f27NltXL2IeLsATxcgIv5l+fLlZGVlERYWxooVK4iNjSUtLe2U7/nlL3/JihUreP7558nIyODQoUMsW7aMQ4cOtVPVIuIrNOIiIm61bNkyzjvvPACWLFni+v5UPvjgA373u99xxRVXkJ6eztChQ7nvvvu48847Abj44ovZvXs3Dz30ECaTCZPJ5HrvkiVLuOCCCwgJCSE1NZX777+fmpoa1+vp6ek88cQT3HLLLYSFhZGcnExOTo7rdcMwmD59OmlpaVitVpKSkrj//vvddThExM10k0UROWuFhYUMHDgQcJ72sVgsWK1W6urqMJlMBAcHc+utt/Liiy+e8P19+/YlKyuLf/zjH0RERBz3ellZGVlZWdxzzz1MnDgRgISEBL7//nuysrJ48sknufLKKzlw4ACTJ08mKyuLOXPmAM7gUlZWxu9+9zuuu+46PvvsMx566CE++eQTLrvsMt5++23uuusu5s2bR2ZmJiUlJaxfv961HxHxLgouInLWbDYbe/bsobKykmHDhrF69WrCwsIYNGgQH3/8MWlpaYSHh9O5c+cTvn/x4sXcdtttlJaWkpWVxfnnn88NN9zQYrQmPT2dBx98kAcffNC17O6778ZisfDyyy+7li1ZsoSLLrqImpoagoODSU9Pp1+/fnzyySeudX72s59RWVnJggULmDVrFi+//DIbN24kMDDQ/QdHRNxKp4pE5KwFBASQnp7Oli1bGD58OAMHDqSkpIT4+HguvPBC0tPTTxpaAC688EJ27txJbm4uN9xwA5s2beKCCy7giSeeOOV+169fz9y5cwkPD3c9Ro8ejcPhoKCgwLXeqFGjWrxv1KhRbN68GYAbb7yRuro6unfvzsSJE3n33Xex2WxncTREpC2pOVdEzlpmZia7d++mqakJh8NBeHg4NpsNm81GeHg4Xbt2ZdOmTafcRmBgIBdccAEXXHABjzzyCE8++SR//OMfeeSRRwgKCjrhe6qrq/nFL35xwp6UH2sIbpaamsrWrVv54osvWLhwIb/61a945pln+PrrrzUCI+KFFFxE5KwtWLCApqYmLr30Up5++mmGDh3Kz372M+644w7GjBlzRgEgIyMDm81GfX09QUFBBAUFYbfbW6wzZMgQ8vPz6dmz5ym39e233x73vF+/fq7nISEhjBs3jnHjxjFp0iT69u3Lhg0bGDJkyGnXLSJtSz0uIuIWJSUlpKenU15ejslkIjo6mp07d5KYmPij77344ou55ZZbGDZsGJ06dSI/P58pU6aQnJxMbm4uAJdffjkhISG8+OKLWK1WOnfuzHfffcc555zDnXfeyd13301YWBj5+fksXLiQF154AXD2xhw+fJjHH3+ca665hoULF/LAAw/w8ccfM3r0aObOnYvdbmfkyJGEhoYyZ84cnn32WYqKiujUqVObHjMROX3qcRERt1i0aBHDhw8nODiYlStXkpKS0qrQAjB69Gj++c9/cvnll9OvXz/uu+8+Ro8ezZtvvula549//CO7du2iR48edOnSBYCBAwfy9ddfs23bNi644AIGDx7M1KlTSUpKarH9X//616xevZrBgwfz5JNPMmvWLEaPHg1AdHQ0r7zyCueddx4DBw7kiy++4MMPP1RoEfFSGnEREb92oquRRMR3acRFREREfIaCi4iIiPgMnSoSERERn6ERFxEREfEZCi4iIiLiMxRcRERExGcouIiIiIjPUHARERERn6HgIiIiIj5DwUVERER8hoKLiIiI+Iz/D8FGshXCp+pWAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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QxiG/dq9ZQ6OOHQEI37CBlq1a4ezsbGnZannmjCWReht4V7dIyVf3YHbS0tI4cOAAGzZsYOPGjYSHh3PlypUMx5QrV46OHTvSsWNH2rRpk2FZCyGMThIqoSdJqAzElgnVFWdniqWl8ddPP9GwZ0+rXrsg7FyxgiaPP04a4HTXj2JqaipbNm2i1LRpBP32GwBzgZfRp+aWeSyYeRkLW43PSElJYffu3ZYEa+vWrRnWhixUqBAtW7akU6dOdOzYkaCgIGm9EoYmCZXQk7USKqdsnxV2z1yJJCXdF66RmOPOrEKWs7MzIY8+StCKFTBjBsrZmX7A/rJlqaRD1XLz3x7Tp0+ndevWVKhQgaVLl1r9Pi4uLjRr1ow333yT9evXExcXx8qVKxk6dCiVKlUiOTmZjRs3Mnr0aGrVqkWFChV45ZVXWL16dYbESwghRMGRhMrgUm+3TBg1oUo1J1T3a2EZNAjTihXg4UG106c56u9PxIIFzJ8/n4kTJxIYGFgA0WZkXsl+6dKlpKamEh4ezoIFCwgPD89TEdWsuLu706lTJz777DOOHj3K4cOHmT59Ou3bt8fNzY1Tp04xc+ZMOnXqRIkSJejRowfz588nPj7eajEIIYTInnT5FQBbdvldLFSIEikpbPnsM1pmsxaUvQqfM4eQ/v257uSEe06SkL17oXNnuHAB1q+H29WP0w98z03h0vzKakB7iRIlePbZZ23aNQhw/fp1wsLC+O2331i+fDlnz561POfi4kJISAhPPPEEjz/+uBQXFXZLuvyEnmQMlYHYMqE67+ZGyaQkwqdOJWTkSKteuyCsnzGDtoMHa2PBUlLufwLAmTMQGQlduuTocHOy9euvv/LDDz9w8eJFy3OBgYHcuHGDuLg4my2xExgYyIABA6w+W/BuaWlp7Nq1i19//ZVffvmFgwcPZni+UaNGPP300zz99NNUqlTJ6vcXIq8koRJ6koTKQGyZUMUULkzArVusf+892o4da9VrF4Tl33zDgZdewr9sWfrdZ4HQLO3bB2FhMGwY3Kfr8O4SDukLkIJtlta5m61mC97tyJEjluTqjz/+yPDaGjdubEmuKlasaNM4hLgfSaiEnmRQugBA3V67MO0+i2vaq2vu7rwJ/FC1at4uEB+vdQEOHw5Dh8J9WrnMVeF79+5NSEgIzs7OdOvWjcWLF1OmTJm8xZBL6cde2VLVqlUZPXo0W7duJSYmhpkzZ1rWo9y1axdvvPEGlSpVomnTpkyZMoWTJ0/aNB4hhHBkklAZ3G4/PxYB1wy6bElyslYA4e7V1HPMy0tLpkwm+PJLeOIJuJr7RWu6devGiRMnCAsLs/lAd3NLUWhoKElJSTYbzJ5eqVKlePnll1m/fj0xMTHMmDGD1q1b4+TkxM6dO3nttdeoUKECDz30EJ9++innz5+3SRxCOAqTyZTtNmHCBL1DFAUtn2sKihyw2eLISqnHHntMAWr27NlWv3ZBmPPll6oyqL5t2uTvQosXK1W4sLZYdIMGSkVH5zs286LRoaGhNlmkGVAlSpS457E1Fm7OqdjYWPXll1+qkJCQDAtZOzs7q44dO6offvhBXb161eZxiAebNRZHLmgxMTGWbfr06crT0zPDvitXrliOTUtLU8nJyTpGK7JjrcWRpYXK4MwL6iYZtMvP8/hxjgIfRETk70Ldu2vjqPz8YM8eaNYM/v47X5c0dw9OmzaNJUuW3NNiFRgYiK+vb76KaqYfIG9+bOs6V+mVKlWKV155hbCwMM6ePcsnn3xC06ZNSU1NZfXq1Tz77LOUKlWK5557jjVr1pCS04kDQjg4f39/y+bl5YXJZLI8PnToEMWKFWP16tU0atQINzc3tm7dSt++fXniiScyXCc0NDTD4vRpaWlMnjyZihUrUqRIEerVq8fixYsL9sWJPHHROwCRP+ausmSDJlSpt7v80pyskNs/9BD8+ac2purQIRg/HpYty/91yXol+/QD2q0tOjqa7t2727wyu5m/vz+vvvoqr776Kv/88w8//vgjP/zwA//++y8//PADP/zwAyVLlqR379707dvXbteOFManlOL69eu63Nvd3d1qKw+MGTOGKVOmUKlSJYoXL56jcyZPnswPP/zAzJkzqVq1Kps3b+bZZ5/Fz8+PVq1aWSUuYSM2aD0Td7Fll99RX1+lQC158UWrX7sgLHjlFaVAnS1WzHoXjYtTasAApS5ftt41s7FkyRIVGBhos25BdOoOVErrqoiIiFBDhgxRvr6+GeJp0KCB+vTTT9XFixcLJBbhuO7ucrl69arNf5+y2vLSxT137lzl5eVleRwWFqYA9csvv2Q47oUXXlBdu3bNsG/48OGqVatWSimlbt68qdzd3dUff/yR4Zj+/fur3r175zoukTPS5ScALH9JmVt6jCb1dstamjVbXooXh1mzwNv7zr5Vq8BGJRHSD2gPDQ3Fz8/PJve5uztw0qRJNh/MbjKZeOihh/j888+JiYlhxYoVPPXUUxQqVIg9e/bw6quvUrp0aZ5++mlWr15tsziEMKLGjRvn6vijR49y/fp1/vOf/+Dh4WHZvvvuO44dO2ajKIW1SJefwanbiYhRyyak3U4ElTW6/LIyfTqMGAHPPQdffw02mBFpHm8VEhLClClTLF2DR44cYdasWRmqqPv5+XHhwoV83e/MmTOMHz/e8rggalsVKlSILl260KVLFy5dusT8+fOZM2cOkZGRLF68mMWLF1O6dGmef/55+vXrR7Vq1WwWi3Bs7u7uXM3DbF1r3dtaihYtmuGxk5PTPbXuktP9MWx+zStXrrynjIubm5vV4hK2IQmVwVkSKoO2UFkSKhuODcLTE5yd4fvvIToali7Vyi3YiDm5MnvrrbcyjL1q3rw5lStXJjo62mqFRM+cOUP37t2ZOHEib731lk3HWgH4+voybNgwhg0bRmRkJHPnzuXHH3/k7NmzvP/++7z//vuEhIQwcOBAunXrJl8GIldMJtM9yYgj8PPzY//+/Rn2RUZGWsbC1qxZ07I+p4yXMh7p8jO62y07yqgJ1e2WNZu2UL34IqxcCR4esHEjtGgBp0/b7n53ubuYqKurK5988olN7jV+/HjKly9fIN2BZvXr1+eTTz4hOjqaxYsX06lTJ5ycnAgPD+eZZ56hTJkyjB49msOHD9s0DiHs3aOPPsquXbv47rvvOHLkCOPHj8+QYBUrVozRo0czYsQI5s2bx7Fjx/jrr7/47LPPmDdvno6Ri5yQhMrgjN5CdcHdnc+AfTVq2PZG7dvD5s0QEAD792szAvfute09s2Guzm6L4qHR0dGMHz+eZ555psDKL4DWJdG9e3dWrlzJiRMnmDBhAoGBgVy6dImpU6dSo0YNWrduzYIFC7h165bN4xHC3rRv357//ve/vP766zRp0oQrV67w/PPPZzjmnXfe4b///S+TJ08mKCiIDh06sHLlSlkiygisP15e3M2Ws/z+qVxZKVDf5bcwpk6GDh2qAPX2228XzA1PnlSqVi2tAKiXl1I6z1BLXzzUz8/PJrOWTCaTMplMasmSJQX++pKTk9WKFStUly5dlJOTkyUmX19fNXr0aHX06NECj0nYHyMW9hSOQ2b5CQBiAwNZBVwy6BiVfC89k1vlysHWrRASAhMngq9vwdw3C+mLh8bExFhmClqT0mGpGzMXFxe6dOnCihUr7mm1mjJlClWrVqVz586sWrWKtLQ0m8YihBC2JAmVwW3r0IHOwN+lSukdSt5cv44/4FGQFbi9vWHdOm0NQLPLl21WViGn7leZPT+UUpw+fZoyZcrQunXrAu8OBChbtizjx4/nxIkTrFixgo4dO6KUYtWqVXTu3Jlq1aoxdepU4uLiCiQeIYSwJkmoDM5SKd2gY6jqHztGDNDtp58K9sYu6Sa4xsdDy5bQrx/YSfkJW9W2unupG/PswEmTJhVYDSlnZ2e6dOnCqlWrOHLkCCNHjsTb25tjx44xevRoAgMDeemll9izZ0+BxCOEENYgCZXBGX0tP2VumXLRsYLHli3aUjXz5mnL1iQm6hdLOpl1B86fP5+JEydSunRpq95Lj9mBAFWqVGHq1KlER0cze/Zs6tWrx40bN/jmm29o2LAhLVq0YPHixbKGoBDC/ll9dJe4hy0HpR9s3lxdAzWrbl2rX7sgfNGggVKgjteurW8gK1cqVbSoNli9bl2lTp/WN577SElJURMnTrTpEhyBgYEFPpA9LS1Nbd26VfXu3VsVKlTIEkuFChXUxx9/bJPfIaE/GZQu9CSD0gUAhdLScAecDNrlZxctVACdOsGmTVCqFPz9t1ZW4e+/9Y0pG87OzowbN87qY63Si46O5qmnniqwMVagFXR8+OGHmT9/PidPnuS///0vvr6+nDhxgpEjRxIYGMiIESM4fvx4gcUkhBA5IQmV0d1ORJRB11Czm4QKoFEj+PNPqFFDq6jesqU2I9COpR9rZe4OvHvJiryOv1LpZgfqsUZfQEAAkyZN4vTp08yaNYugoCCuXLnC9OnTqVKlCk899RTbtm2zWrV5IYTID0moDM5kLjdg0BYqbidUJntIqAAqVIA//oBHHtFmA1aqpHdE95W+Evu4ceM4efKkJcEKCwvjzJkzBAYGWhbSzg11e3ZgeHh4gZZbSK9IkSIMGDCAAwcOsHr1atq1a0daWhpLliyhRYsWNGvWjAULFhh2YoYQwjFIQmVwlkTEqC1Ut+O2m4QKoHhx+P13CA8HKw/+Lgi2WOqmR48eGcot+Pv7M2LEiAJNrkwmEx06dGDt2rXs27eP/v374+bmxs6dO3nmmWeoVKkSH3zwAZcvXy6QeIQQIj1JqAzO6AnVkUKF+AaIq1tX71AycnOD9Es9zJ8PAwYYtiXQvNTN3d2BOXV3baiLFy8yffr0Aq9lZVa7dm2+/vprTp06xcSJEylZsiRnzpxhzJgxBAYGEhoayqlTpwo0JiHsjclk4pdffgHgxIkTmEwmIiMj83w9a1wjJ/r27YvJZMoQf15NmDDBcq3p06dbJb6sSEJlcJYuP4NOK99WtCgvAWc7d9Y7lKydOwcvvQRffw2PPQZXrugdUZ5069aNkydPMnHiRKteNzo6mu7duxd4ixVAyZIlGTduHKdOnWLu3LnUqVOH69ev88knn1C5cmX69u3LwYMHCyweIe7H/OVuMpnw8vLi4YcfZuPGjTa/b9myZYmJiaF27do5Or5v37488cQT+bpGfnTo0IGYmBg6duyYr+uMHj2amJgYm03eSc8wCVVcXBx9+vTB09MTb29v+vfvz9WrV7M95+bNmwwZMgRfX188PDzo3r07586dszy/d+9eevfuTdmyZSlSpAhBQUH3dI2Eh4dn+AUwb7GxsTZ5nbl1q2xZNgGnby+SbDTmcS/melp2qVQp+PlncHeHtWu18VVnz+odVZ7YYnageVC4ni1Wbm5u9O3bl71797J27Vpat25NSkoK8+bNo1atWnTt2pWIiIgCjUmIrMydO5eYmBi2bdtGiRIl6NKlC//++2+mx1prbKCzszP+/v645GN4hTWukVNubm74+/vjls9l1Tw8PPD398e5AL4jDZNQ9enThwMHDrBu3Tp+++03Nm/ezMCBA7M9Z8SIEaxYsYJFixaxadMmzp49S7du3SzP7969m5IlS/LDDz9w4MAB3nrrLcaOHcvnn39+z7UOHz5MTEyMZStZsqTVX2NenOvRgxBgYbFieoeSJ063blEMcLX3mVpdumhjqkqWhMhIrazCgQN6R5VnOZkd6JvHdQ71KLdgZjKZaNeuHRs3bmT79u1069YNk8nE8uXLad68Oa1atWLVqlUyM1Dk23fffYevry+3bt3KsP+JJ57gueeey/Zcb29v/P39qV27NjNmzODGjRusW7cO0H6GZ8yYweOPP07RokV59913Afj1119p2LAhhQsXplKlSkycODFDwdsjR47wyCOPULhwYWrWrGm5nllm3XUHDhygS5cueHp6UqxYMVq2bMmxY8eYMGEC8+bN49dff7U0IoSHh2d6jU2bNtG0aVPc3NwICAhgzJgxGeIKCQnh1Vdf5fXXX8fHxwd/f38mTJiQm7c6Q/w///wzLVu2pEiRIjRp0oR//vmHnTt30rhxYzw8POjYsSMXLlzI9fWtwsr1sWzi4MGDClA7d+607Fu9erUymUwqOjo603Pi4+NVoUKF1KJFiyz7oqKiFKAiIiKyvNfgwYNV69atLY/DwsIUoC5fvpzjeG/evKkSEhIs2+nTp21W2POPP/5QgKpUqZLVr10QPvX2VgrU2W7d9A4lZ/79V6nq1bUCoF5eSm3cqHdEVpOSkqLCwsLU/PnzVVhYmFq/fn2ei4KaTCZVtmxZdevWrQzXTElJKfDXFRUVpV588cUMhULr1q2rfvzxR5WcnFzg8Yh7ZVlY8erVrLfcHHv9es6OzYXr168rLy8v9fPPP1v2nTt3Trm4uKiN2XwuAGrZsmWWx3FxcQpQn376qeX5kiVLqjlz5qhjx46pkydPqs2bNytPT0/17bffqmPHjqnff/9dVahQQU2YMEEppVRqaqqqXbu2atOmjYqMjFSbNm1SDRo0yHCv48ePK0Dt2bNHKaXUmTNnlI+Pj+rWrZvauXOnOnz4sJozZ446dOiQunLliurRo4fq0KGDiomJUTExMerWrVuZXsPd3V0NHjxYRUVFqWXLlqkSJUqo8ePHW15fq1atlKenp5owYYL6559/1Lx585TJZFK///57lu/RCy+8oLp27Zphn/neNWrUUGvWrFEHDx5UDz30kGrUqJEKCQlRW7duVX/99ZeqUqWKGjRo0D3XLF++vJo2bVqm97NWYU9DJFTffPON8vb2zrAvOTlZOTs7q6VLl2Z6zoYNGzJNhMqVK6c+/vjjLO/Vp08f1b17d8tjc0JVvnx55e/vr9q2bau2bt2abbzjx4/P9EvGFgnVzp07LVWtjejTYsWUAhXTq5feoeTcxYtKtWihJVVvvaV3NDaTkpKiAgMDlclkynNiVaJECd2rr5udOXNGjRo1Snl4eGSowP7555+ra9eu6RKT0GT5haYtWZ751qlTxmPd3bM+tlWrjMeWKJH5cbn0yiuvqI4dO1oeT506VVWqVEmlpaVleU76JOfatWtq8ODBytnZWe3du9fyfGhoaIZz2rRpo957770M+77//nsVEBCglFJq7dq1ysXFJUMDw+rVq7NNqMaOHasqVqyokpKSMo0zu6TGfI0333xTVa9ePcPr/eKLL5SHh4dKTU1VSmkJVYsWLTJcp0mTJuqNN97I8j3K7t5ff/21Zd+CBQsUoDZs2GDZN3nyZFW9evV7rlkQCZUhuvxiY2Pv6WJzcXHBx8cny7FMsbGxuLq64u3tnWF/qVKlsjznjz/+YOHChRm6EgMCApg5cyZLlixhyZIllC1blpCQEP76668s4x07diwJCQmW7fTp0zl8pblXesECzgFj75qFZRSmtDQAnMyD643A1xfWrYPPP4dJk/SOxmacnZ3zXW4hq8WY9RjAXqZMGaZMmcKpU6f43//+h5+fHydOnGDo0KGUL1+e9957j0Q7WcdRGMOAAQP4/fffiY6OBuDbb7+1zFDLTu/evfHw8KBYsWIsWbKEb775hrrpZjo3btw4w/F79+5l0qRJeHh4WLYBAwYQExPD9evXiYqKomzZshnW+AwODs42hsjISFq2bEmhfHz2RkVFERwcnOH1Pvzww1y9epUzZ85Y9tW9axZ3QEAA58+fz9M901+rVKlSANSpUyfDvrxeO790TajGjBmT6YDv9NuhQ4cKJJb9+/fTtWtXxo8fT7t27Sz7q1evzssvv0yjRo1o3rw5c+bMoXnz5kybNi3La7m5ueHp6Zlhs5VCSUmUBNwNWjbBXO7BUAkVQOHCMGQION3+Fbp5E6ZMMexsy6yYyy1Ye4aMngPYixcvzltvvcXJkyf54osvqFChAhcvXuStt96ifPnyTJw4UWpZ2YurV7PelizJeOz581kfu3p1xmNPnMj8uFxq0KAB9erV47vvvmP37t0cOHCAvn373ve8adOmERkZSWxsLLGxsbzwwgsZni9atOhdb8NVJk6cSGRkpGXbt28fR44coXDhwrmOG7SCuQXl7qTNZDKRdvuP6fxcy5zI3b0vr9fOL10TqlGjRhEVFZXtVqlSJfz9/e/JOFNSUoiLi8Pf3z/Ta/v7+5OUlER8fHyG/efOnbvnnIMHD9KmTRsGDhzI22+/fd+4mzZtytGjR3P3Ym3E6fbsOJNOP0D55WTUhOpu/frBa69B1655+mC2Z+kHsIeGhuZ5KZvMmFusJk2aVODL2xQpUoTBgwdz5MgRfvjhB4KCgoiPj2fChAlUqFCBt99+m0uXLhVoTOIuRYtmvd2dSGR37N3JQ1bH5cFLL73Et99+y9y5c2nbti1ly5a97zn+/v5UqVIlx79LDRs25PDhw1SpUuWezcnJiaCgIE6fPk1MTIzlnD///DPba9atW5ctW7ZkOYvQ1dX1vr+TQUFBREREZJjksW3bNooVK1YgZQrsja4JlZ+fHzVq1Mh2c3V1JTg4mPj4eHbv3m05d+PGjaSlpdGsWbNMr92oUSMKFSrEhg0bLPsOHz7MqVOnMjSFHjhwgNatW/PCCy9YZlPcT2RkJAEBAXl81dZlSagM2kJl6fLL59RY3fXqpX1or1oFISFgJ2U1rMVcfX3atGnExMRYkitrGT9+POXLl9dlZqCLiwt9+vRh3759LFy4kNq1a5OYmMi7775LhQoVeOONN3TrQhD275lnnuHMmTPMnj2bF1980Sb3GDduHN999x0TJ07kwIEDREVF8dNPP1kaANq2bUu1atV44YUX2Lt3L1u2bOGtt97K9ppDhw4lMTGRXr16sWvXLo4cOcL333/P4cOHAahQoQJ///03hw8f5uLFi5kmXoMHD+b06dMMGzaMQ4cO8euvvzJ+/HhGjhyJk5MhRhRZlSFecVBQEB06dGDAgAHs2LGDbdu2MXToUHr16mXpM46OjqZGjRrs2LEDAC8vL/r378/IkSMJCwtj9+7d9OvXj+DgYB566CFA6+Zr3bo17dq1Y+TIkZbm1/RTLqdPn86vv/7K0aNH2b9/P6GhoWzcuJEhQ4YU/BuRCefbCZWTQVuoDDmGKjNdu0JYGJQoAbt3Q3AwFFB3dUFLn1xlVs8qry1YepZbAO119ejRg71797J06VLq16/P1atX+fDDD6lQoQKjRo3K0AIgBGjfNd27d8fDw+OeQpjW0r59e3777Td+//13mjRpwkMPPcS0adMoX748AE5OTixbtowbN27QtGlTXnrppfs2EPj6+rJx40auXr1Kq1ataNSoEbNnz7Z0nw0YMIDq1avTuHFj/Pz82LZt2z3XKFOmDKtWrWLHjh3Uq1ePQYMG0b9//xz19Dik+w5btxOXLl1SvXv3Vh4eHsrT01P169dPXblyxfK8eQZAWFiYZd+NGzfU4MGDVfHixZW7u7t68sknVUxMjOX5rGbjlS9f3nLMBx98oCpXrqwKFy6sfHx8VEhISLZTYjOTm1kCuXXlnXeUAvUjWGZVGEVqaqp6EdQCUPHffKN3ONZx5IhSlStrM4aKF1dqyxa9I7K5u8st3Lp1K8+zA+2p3EJaWppasWKFatKkiSU+Nzc3NWzYMHX69OkCj8eRZTfLyggeffRRNWzYML3DcBiZzfLLLymb4CBsmVBd//BDpUD9BOrmzZtWv74t3bx50/JFlZs6X3bv/HmlmjXTkqrSpe+tl/MAWLJkiTKZTHkuuWBP5RbS0tLUmjVrVPPmzS3xuLq6qkGDBqkTJ07oEpOjMWpCFRcXp5YuXaqcnJzUoUOH9A7HYbzwwgvK2dlZFS1aVK1YsSJf13r33XdV0aJFlclkkoTKEdgyobr5ww9qJ6jJoBITE61+fVu6cuWK5QvK4eoAXbumVM+eSt2nZpkjW7JkiQoMDMxzDavMttDQUF1brDZs2KBatWplicfFxUW9/PLL6tSpUwUejyMxakJVvnx55enpqT766CO9Q3Eo586dU0eOHFFHjhxRV3NZcPVuly5dslwrPj4+02OslVCZlJI1GGwtMTERLy8vEhISrF5CISUlxdLnfenSJXx8fKx6fVu6fPkyfj4+KODmrVsUsuf1/KwhKgqqVQODrruYF6mpqWzZsoVff/3Vqiu9BwYG8sknn2RYSqogbd68mUmTJlkmvbi6uvLyyy8zduxYu5mwYiQ3b97k+PHjVKxYMc9lAITIq+x+/nLz/W2IQekia+kXfLTWIpoFJSkpiR+AVMBlxgy9w7GtvXuhWTPo0QNu3NA7mgJzvwHseaVnuQWARx55hPXr17Np0yYeeeQRkpKS+Oyzz6hUqRKjR4/Wby0xIYRuJKEyOJPJZGmhMlpClZycjDkdNBXA6uW6+vdfuHULli6Ftm3hrgriDwJzPauJEyda7Zp6llsALbEKDw9n/fr1BAcHc/PmTaZOnUrFihV58803iTPoCgZ6kQ4ToQdr/dxJQmV0v/7KkZQUfkBr8TGS5ORkLGmUoydUTz6pLVfj7Q1//AHNm2tJ1gPG2dmZcePGOVS5BZPJRJs2bdi2bRurVq2icePGXLt2jcmTJ1OhQgXGjx9/T4FhkZH5j8Lr16/rHIl4EJm/O53zORxDxlAVAFuOoeKnn6B3bzYAgYcOUb16dete34b++ecfDlWvzuMAs2fDSy/pHZLtHTwIHTvCqVNQsiT89hs0aaJ3VLowj6+KiYkhICCA5s2bU7lyZaKjo3P9F6PJZCIwMJDjx4/n+0Mxv5RSLF++nHHjxvH3338D4O3tzejRo3n11VcpVqyYrvHZq5iYGOLj4ylZsiTu7u73XQ9PCGtIS0vj7NmzFCpUiHLlyt3zc5eb729JqAqATROqRYugRw82Ab779lG7dm3rXt+G9u/fz6k6degEMHcu5GANLIdw9ix07gyRkeDuDps3Q6NGekdlF5YuXcpTTz0F5K0Zfv369Tg7O1uStJYtW+qWYKWlpbF06VLGjRtHVFQUoBVTfOONNxgyZAju7u66xGWvlFLExsZKa54ocE5OTlSsWBHXTCZGSUJlZ2yaUC1bBt26sRUo+tdfNGjQwLrXt6E9e/ZwvmFD2gN8/z08+6zeIRWcK1fg6achLQ1WrgSjV4q3oqVLlzJ8+PAMq9XnlI+PT4ZxS2XKlGHgwIFUrVpVtwQrNTWVhQsXMmHCBI4cOQJAQEAA48aNo3///vcsHPugS01NNdx4UGFsrq6uWS6VIwmVnbFpQrV8OXTtyp+A6c8/s1zb0B7t2LGDK82a0QZg/nzo3VvvkApWcrI2UN3DQ3ucmgpOTiBdHQ5ZbiElJYUffviBiRMncuLECQAqV67MO++8Q8+ePR/Itc+EsHdSNuFBcvuvbWeMOcsvAtjo7g4P4MrkFCp0J5lSCoYPh+eeA4NNLrAFRyy34OLiQt++fTl06BCfffYZJUuW5NixYzzzzDM0bNiQ1atXyyw3IQxMEiqjuz07zogJVVJSEv8FhpYvDy1b6h2Ovg4cgK++gh9/hA4dQMaRWDhauQU3NzeGDh3KsWPHeOedd/D09GTv3r106tSJkJAQ/vjjjwKPSQiRf5JQGV2xYhxzc+M4xkuozPHKGBKgdm1tLJWHB4SFQYsWcPq03lHZjezKLfj6+ubpmnqXW/Dw8ODtt9/m33//ZfTo0bi5ubF582YefvhhHn/8cfbt26dLXEKIvJGEyuiaN6d33bo8hTHrUAGZzqx4ILVrB1u2QECA1mL10ENahXVhYW6tCgsLY/78+YSFhbFw4cJ8XTM0NFSXautmvr6+fPTRRxw9epQBAwbg7OzMihUrqFevHs8//zzHjx/XLTYhRM5JQuUAjFwpfTuwdfdu+P13vcOxD/Xrw59/Qs2aWnmFli21gqDCwjy+qnfv3oSEhBASEkJgYGCe6hYppTh9+jTh4eGEh4ezYMECwsPDdUmwAgMDmTVrFgcOHODpp59GKcX3339P9erVGTZsGOfOnSvwmIQQOScJlQMwakKVlJSEG+CmlMxsS69cOdi6FVq1gmvXHqi1//LC2dmZTz75BCDPxSB79OhB69ateeaZZ2jdujUVKlTQrSuwevXq/Pzzz+zatYt27dqRnJzM559/TuXKlZk4cSJXr17VJS4hRPYkoTK6ffuYu2sX6zFeQpV+LT+HX3omt4oXh7VrYfVqePxxvaOxe926dWPx4sWUKVMmT+ffveaeeTbgiBEjdGuxatSoEWvXrmXjxo00bdqUa9euMWHCBKpWrcqsWbNISUkp8JiEEFmThMrokpKoeO0a1TDmGCpLGqXzciF2yc1NG1dldvw4vPYayBdppu4eXzVx4sQ8J1hm06dP173FqnXr1vz555/8/PPPVK5cmdjYWF5++WXq1KnD8uXLpdSCEHZCEiqju52IuCAtVA4tJQUeewymTNFarKTbJ1Ppx1eNGzeOkydPWqXcgp71q0Drynz66ac5ePAgn376Kb6+vhw6dIiuXbvSqlUrtm/fXuAxCSEykoTK6Axeh0paqHLIxQXeew+KFNG6AVu1gthYvaOye9Yut6Bn/SrQZsQOGzaMY8eOMXbsWAoXLsyWLVt46KGH6NGjB0ePHtUlLiGEJFTGZ/BK6dJClQuPP67VqPLzg7/+guBgOHRI76gMwZrlFvSuXwXg5eXFe++9x5EjR+jXrx8mk4lFixZRs2ZNhg8fzoULF3SLTYgHlSRURnc7EXHBmGOotgEH/f3By0vvcIyhWTOIiIAqVeDECWjeXKtdJe7LmuUWQP/6VaCVWpgzZw579+6lY8eOJCcn8+mnn1KlShUmT57M9evXdY1PiAeJJFRGZ+AWqqSkJJ4BPu7cWUsQRM5Urgx//KEV/rx8GcaO1dYCFLmSn3IL9lS/CqBOnTqsWrWK9evX07BhQxITE3nzzTepVq0a8+bNIy0tTZe4hHiQSEJldG5uxHl4cAbjJVSy9Ew++PnBhg0wZAgsXix1vPIov+UW7Kl+FUCbNm3YuXMnP/zwA+XLlyc6Opq+ffvStGlTNm/erFtcQjwIJKEyujJlmPjiiwRh3IRKlp7JI3d3+Pxz8Pe/s+/330HnbiijST++KjQ0NFfn2mP9KicnJ/r06cOhQ4f44IMPKFasGLt376ZVq1Z0796dY8eOFXhMQjwIJKFyAOaExIhjqM4A782apY0HEvnz3XfQvj08/bRUV88l8/iqadOmZTojMLfsoX5V4cKFef311zl69CiDBg3CycmJpUuXEhQUxOjRo4mPj9clLiEclSRUDsDIS8/4AEVv3pQuK2soXBhcXWHZMmjTBi5e1DsiQzK3WDlC/SqAkiVLMmPGDPbu3WtZymbq1KlUqVKFL774QiquC2ElklAZ3ZUrDJ43j11A6s2bekeTK1I2wcp69NAWUvb21mYCNm8O0r2TJ45Wvwqgdu3arF27llWrVhEUFMSlS5cYOnQodevWZfXq1VJxXYh8koTK6JQi8OxZGgEpt27pHU2uyNIzNvDII9oMwPLl4cgRrVbVjh16R2VY1q5fpff4KoCOHTvy999/88UXX1CiRAmioqLo1KkTHTp0YP/+/brEJIQjkITK6NK17KQZbAxVSlLSnR9AaaGynqAgrYWqYUO4cAFat4bTp/WOyrCsXb/KHsZXubi4MHjwYI4cOcLo0aMpVKgQv//+O/Xq1WPQoEGcP39el7iEMDJJqIwuXctOqtESqvQtatJCZV0BAbBpE3ToACNHQtmyekfkMPJTvyo9exhf5e3tzUcffURUVBTdu3cnLS2Nr776iipVqvDBBx9w02DDCITQkyRURpeuZSfVYIPSMySA0kJlfR4esGIFTJp0Z19iIkiRx3zLb/2q9OxhfFXlypVZvHgxmzZtolGjRly5coUxY8YQFBTEzz//LOOrhMgBSaiMzunO/8I0g42hSklO5g/gXIUKIMU9bcPF5c4MyuvXtbIKzz4LBvtZsUf5qV91N3sZX/XII4+wY8cO5s2bR+nSpTlx4gQ9e/akRYsW7Nq1S5eYhDAKwyRUcXFx9OnTB09PT7y9venfvz9Xr17N9pybN28yZMgQfH198fDwoHv37pw7dy7DMSaT6Z7tp59+ynBMeHg4DRs2xM3NjSpVqvDtt99a++XlnclE2u0vTKN1+V1LS+NhYOV//6tN+Re2FREBu3bBggVaV6DUIco3R6xf5eTkxPPPP88///zDhAkTcHd3548//qBp06a89NJLMr5KiCwYJqHq06cPBw4cYN26dfz2229s3ryZgQMHZnvOiBEjWLFiBYsWLWLTpk2cPXuWbt263XPc3LlziYmJsWxPPPGE5bnjx4/TuXNnWrduTWRkJKGhobz00kusXbvW2i8xz256eXEOrcXHSMyFSGXpmQLSpg2sWgXFikF4ODz8MJw6pXdUDsOa9avsocWqaNGijB8/nn/++Ydnn30WpRTffPMNVatW5eOPPzZcIWEhbE4ZwMGDBxWgdu7cadm3evVqZTKZVHR0dKbnxMfHq0KFCqlFixZZ9kVFRSlARUREWPYBatmyZVne+/XXX1e1atXKsK9nz56qffv2OY4/ISFBASohISHH5+TGTz/9pAAVEhJik+vbSkhIiALUggUL9A7lwRIZqVTp0kqBUgEBSu3Zo3dEDmfJkiUqMDBQAVbZAgMD1ZIlS3R9Tdu2bVONGjWyxFS9enW1evVqXWMSwtZy8/1tiBaqiIgIvL29ady4sWVf27ZtcXJyYvv27Zmes3v3bpKTk2nbtq1lX40aNShXrhwREREZjh0yZAglSpSgadOmzJkzJ8MAzIiIiAzXAGjfvv0910jv1q1bJCYmZthsydzCY7S/GD2uXSMaeGz4cL1DebDUqwd//gm1akFMjFa7KixM76gcijXHV4HWYvXUU0/pOnC9efPm7Nixg6+//pqSJUty+PBhOnbsyGOPPcaRI0d0i0sIe2GIhCo2NpaSJUtm2Ofi4oKPjw+xsbFZnuPq6oq3t3eG/aVKlcpwzqRJk/j5559Zt24d3bt3Z/DgwXz22WcZrlOqVKl7rpGYmMiNLNZLmzx5Ml5eXpatrI2nrJvX8jPa0jMkJVEaKHzXArOiAJQtC1u3ajWq3NykrIINWHN8lfmPvNDQUN0GrIM2vqp///78888/jBw5EhcXF3777Tdq1arFG2+8wZUrV3SLTQi96ZpQjRkzJtNB4em3Q4cO2TSG//73vzz88MM0aNCAN954g9dff52PPvooX9ccO3YsCQkJlu20jYsqNpk8mc2A330G6dsb8yB6JTWo9OHtDatXw5YtUKWK3tE4NGuMr1JKcfr0acLDwwkPD2fBggW6ja/y8vJi6tSp7Nu3jw4dOpCcnMyHH35ItWrVmDdvHmlSmkM8gHRNqEaNGkVUVFS2W6VKlfD3979nZklKSgpxcXH4+/tnem1/f3+SkpLuWVH93LlzWZ4D0KxZM86cOcOt29PK/f3975kZeO7cOTw9PSlSpEim13Bzc8PT0zPDZkveUVG0BNwM1uWnzC1qklDpx80NatS483j1anj5ZTBaa6cBZLc+YG706NGD1q1b88wzz+g+I7BGjRqsWrWKFStWUKVKFWJjY+nbt6+le1CIB4muCZWfnx81atTIdnN1dSU4OJj4+Hh2795tOXfjxo2kpaXRrFmzTK/dqFEjChUqxIYNGyz7Dh8+zKlTpwgODs4ypsjISIoXL46bmxsAwcHBGa4BsG7dumyvUeBuF8U0WtkEdXuVe2mhshOXL0Pv3jBrFjz+OEj3jU1kNr4qNxXX4+7qIte74rrJZKJLly7s37+fDz74AA8PD7Zv306zZs3o27cvMTExBR6TELqw9Qh5a+nQoYNq0KCB2r59u9q6dauqWrWq6t27t+X5M2fOqOrVq6vt27db9g0aNEiVK1dObdy4Ue3atUsFBwer4OBgy/PLly9Xs2fPVvv27VNHjhxRX375pXJ3d1fjxo2zHPPvv/8qd3d39dprr6moqCj1xRdfKGdnZ7VmzZocx27rWX43/f2VAvV4QIBNrm8r7cqUUQpUkre33qEIs+XLlXJ312YANmig1Nmzekfk8Kw5I9AeZgPGxMSovn37WmLy8PBQH3zwgbp586aucQmRF7n5/jZMQnXp0iXVu3dv5eHhoTw9PVW/fv3UlStXLM8fP35cASosLMyy78aNG2rw4MGqePHiyt3dXT355JMqJibG8vzq1atV/fr1lYeHhypatKiqV6+emjlzpkpNTc1w77CwMFW/fn3l6uqqKlWqpObOnZur2G2eUN1OTB7z87PJ9W2lbcmSWkLl66t3KCK9HTuU8vPTkqry5ZU6eFDviBxeSkqKCgsLU6GhoVZJrEJDQ1VYWJhKSUnR7TVt375dNWvWzBJTlSpV1G+//aZbPELkhUMmVEZm84SqfHmlQHUxWEtPcx8ftQfUlaZN9Q5F3O3oUaWqVtWSKm9vpTZt0juiB0ZmLVa+vr6GbLFKTU1V8+bNU/7+/paYunTpoo4ePapbTELkhsPVoRL3cXsMUtrtMUlGEaUUDYAz8+bpHYq4W+XK8McfEBysLVFz13JMwnbSj7GaP38+YWFhLFy4ME/X0rt+VfplbF5//XUKFSpkKbMwbtw4rl+/rktcQtiCSSlZRtzWEhMT8fLyIiEhwSYz/m7Vrk3ygQM8Vbgwa7KojWWPPDw8uHbtGkePHqVy5cp6hyMyc+MGfPIJjBolC1jrKDU1lQoVKhAdHU1uP7JNJhOBgYEcP34cZ50ngBw+fJhhw4axbt06AMqXL8/06dPp2rVrrgbmC1FQcvP9LS1UDuD86tUUA8IMVvvFXIhU1vKzY0WKwJgxd5KplBT46ivQsbjkg8jZ2ZlPPvkkT+eq2/WrtmzZYuWocq969eqsXbuWJUuWUK5cOU6ePMmTTz5Jp06d+Oeff/QOT4h8kYTKAZgTkuTk5Fz/9aqnBklJ/AOUfPFFvUMROTVsGAwaBE89BdJdU6C6devG4sWLKVOmTJ7Oj46O1r0gKGgtZt26dSMqKoq33noLV1dX1qxZQ+3atRk7dizXrl3TJS4h8ksSKgdgXnpGKaXrshS5kZqaSlGgKuBy9qze4YicevRRrRjoL79AmzZw4YLeET1QunXrxsmTJ/NUcT00NNRuCoICuLu787///Y/9+/fTsWNHkpOTef/996lRowaLFi0y1B+HQoAkVA7BY+JEVgHBGGc9v6SkJMyjOUy3C5MKA3j6aVi/HooX1xZYbt4cjh7VO6oHSl4rrl+8eDHDY70LgppVrVqVlStX8uuvv1KhQgXOnDlDjx49+M9//kNUVJRucQmRW5JQOQCXXbvoCARgnIQqOTkZcxolCZXBtGihzQCsUEFLpoKDYft2vaN64GRWcf1uORnoPX78eN1bq0wmE48//jgHDx5k/PjxuLm5sWHDBurWrctrr70miy4LQ5CEygGYbo+hcsFYCZW0UBlYjRoQEQGNGsHFi/DYYyBjXwqcs7MzISEhTJs2LdMWqxIlSuToOubWqhEjRug6vqpIkSJMmDCBgwcP8vjjj5OSksKUKVOoXr068+fPl25AYdckoXIA5oTEGa0rzQgytFDJLD9j8veH8HBt3b9vv4WiRfWO6IGWWf2qadOm5eoa06dPt4vxVZUqVeLXX39l5cqVVK5cmZiYGPr06UPr1q3Zv3+/bnEJkR1JqBzB7doyRmqhSj+GClkc2bg8PLQB6p063dl38iQYrISHozC3WPXu3ZuQkJB8zQjUsyCoWadOndi/fz//+9//KFKkCJs2baJ+/fqMGDGCxMREXWMT4m6SUDmCdC1URkmokpOTuQ4cNZkgFwNrhR1KP07n2DFo0gT69IFbt/SLSQDQsmVLAgMDc10009y1FhoaqvvM4cKFC/PWW28RFRVFt27dSE1NZfr06dSoUYOFCxdKN6CwG5JQOQIDtlAlJyezGmjq7Q3z5+sdjrCWyEi4fFlbqqZ9e+3fQjfWKAgaHh5uF/Wrypcvz5IlS1i7di1VqlQhJiaGXr160a5dOykKKuyCJFSO4HZC5YSxxlCBVEl3ON27w+rVUKwYbNqkzQg8eVLvqB5o+S0I2qNHD7uqX9WuXTv27dvHxIkTcXNzY/369dSpU4dx48Zxw0BLbwnHIwmVI1iyhLKBgczCOC1U5sRPEioH1LYtbN0KZcrAwYNaWYXISL2jeqDlpyBoXFxchsf2UL+qcOHCjBs3jgMHDtChQweSkpJ45513qFWrFqtWrdIlJiEkoXIETk4Zlp8xguTkZLoBv58/DyNH6h2OsLa6dbXCn7VrQ0wMtGwJO3fqHdUDLa8FQbNiD/WrKleuzKpVqyyv6fjx43Tu3Jlu3bpx6tQp3eISDyZJqByEERMqP6BmcrJ0CTmqwECtperRR6FmTahVS++IBDkrCJpT9lC/Kv3agK+99houLi4sW7aMoKAgPvzwQ8N8Jgrjk4TKEXz5JZ/HxvIExhpDZSnnKWUTHJeXlzamatUqcHfX9iklZRV0dr+CoL6+vrm6nj3Ur/Lw8ODDDz9kz549tGjRguvXr/PGG29Qv359Nm/erEtM4sEiCZUj2L2b/yQmUgPjtFBlqEMlldIdm6srpP+CnjgRevUCGUBsFzIrCLpw4cI8Xcse6lfVrl2bzZs38+2331KiRAkOHjxIq1ateOGFFzh37pxucQnHJwmVI7jdwmO0OlTSQvUAOn4c3nsPFi2CNm3gwgW9IxLcWxA0JCTE0PWrTCYTL7zwAocPH2bQoEGYTCa+++47atSowYwZM3SvrSUckyRUjuB2C48LxurykxaqB1DFivD77+Dtra0F+NBDcPiw3lGJu1ijftWWLVusHFXu+fj4MGPGDP78808aNmxIfHw8gwcP5qGHHmLXrl16hyccjCRUjkBaqISRhITAH39oydW//2plFWSMi93Jb/2q6OhouygICtC0aVN27NjBZ599hqenJ7t27aJp06YMGTKE+Ph43eISjkUSKkdgwKVnkpKSuAZccnWF4sX1DkcUtKAgraxCs2ZaNfX//Ecq5tuh/NSvCg0NtauCoM7OzgwdOpTDhw/Tp08flFJ8+eWXVK9enR9++EGWsBH5JgmVIzDo0jOfAi/85z/w0Ud6hyP0ULIkbNwI3bpBUhIY5Gf3QZPX+lUXL17M8NgeCoIC+Pv788MPP7Bx40Zq1KjB+fPnee6552jdujUHDx7ULS5hfJJQOYJ0XX5GGkMFUin9gefurg1QX7sWXnhB72hENnJSvyong9jtoSAoQOvWrdm7dy/vvfceRYoUYdOmTdSrV48333yT69ev6xqbMCZJqBzBpEk8260bb2CsFiqQhEoATk7Qrt2dx+fOQb9+IGNb7M796leVKFEiR9exh4KgAK6urowdO5aDBw/y2GOPkZKSwuTJk6lduzZr1qzRJSZhXJJQOQI3NyhShFSMk1AlJSUxBPhfWBjMmKF3OMKePPMMfPutLKxs5zKrXzVt2rRcXcMeCoICVKhQgeXLl7Ns2TLLEjYdO3akZ8+exMTE6BaXMBZJqByEEZeeqQRUu3gRZM0tkd6UKVC6NBw4oJVVkOntduvu+lX5mRGod0FQgCeeeIKDBw8yYsQInJyc+Pnnn6lRowZffPGF1K4S9yUJlSP47TcGbNvGixhrDJWUTRCZatAAtm/XFliOjYVWreDXX/WOSuRAy5YtDV0QFKBYsWJ8/PHH7Nq1iyZNmpCYmMjQoUMJDg5mz549usYm7JskVI5g/36aHznCwxinhUqWnhHZCgyELVugQwe4fh2efBLyWGhSFBxrFAQNDw+3i/pVDRo0ICIigs8//xxPT0927txJ48aNGTlyJFevXtUlJmHfJKFyBFLYUzgiT09YsQJefllbUHnGDC25EnYtvwVBe/ToYTf1q5ydnRkyZAhRUVH06NGDtLQ0pk2bRlBQEL/88osuMQn7JQmVI0i39IyREippoRL35eKiJVKffAKrVmllFoTdy09B0Li4uAyP7WF8VenSpVm4cCGrV6+mYsWKnDlzhieffJKuXbtySsaAitskoXIEBq1DJS1UIkdMJnj1VahU6c6+RYtAZl/ZtbwWBL2bUgqlFIMGDdL9861Dhw7s37+fsWPH4uLiwvLly6lZsyZTp04lJSVF19iE/iShcgQGrJSelJTETeDm7ZIPQuTYqlXQq5c2A3D/fr2jEfeRk4KgOXHhwgUCAwN1nwno7u7Oe++9R2RkJC1atODatWuMHj2axo0bs337dl1jE/oyTEIVFxdHnz598PT0xNvbm/79+993YODNmzcZMmQIvr6+eHh40L17d86dO2d5/ttvv8VkMmW6nT9/HoDw8PBMn4+NjbXp680VA67ll5yczCvA1P/+F4YN0zscYSTVq0OVKlq5jYcfhnXr9I5I3Mf9CoL6+vrm6DoXLlywi4KgALVq1WLTpk18/fXX+Pj4sHfvXoKDgxkyZAgJCQm6xSX0Y5iEqk+fPhw4cIB169bx22+/sXnzZgYOHJjtOSNGjGDFihUsWrSITZs2cfbsWbp162Z53ly0Lf3Wvn17WrVqRcmSJTNc6/DhwxmOu/t5XRmwhcocp6urq86RCMOpXBkiIqBlS0hMhI4dYfZsvaMSOZRZQdCFCxfm6hr2UhDUycmJ/v37c+jQIZ5//nnLgss1atRg4cKFsuDyg0YZwMGDBxWgdu7cadm3evVqZTKZVHR0dKbnxMfHq0KFCqlFixZZ9kVFRSlARUREZHrO+fPnVaFChdR3331n2RcWFqYAdfny5RzHe/PmTZWQkGDZTp8+rQCVkJCQ42vkyvXrauY77ygPUL169bLNPaysd+/eClDTpk3TOxRhVDdvKtWnj1LaHEClRo1SKiVF76hEHqSkpKjAwEBlMpkUkOPNZDIpk8mklixZovdLUEoptXHjRlWtWjVLfO3bt1fHjh3TOyyRDwkJCTn+/jZEC1VERATe3t40btzYsq9t27Y4OTll2We9e/dukpOTadu2rWVfjRo1KFeuHBEREZme89133+Hu7s5TTz11z3P169cnICCA//znP2zbti3beCdPnoyXl5dlK1u2bE5eZt4VKUKqjw9XMU4LVVJSEpOAbl99BbJmlsgLNzf4/nswzySbOhWWL9c3JpEnea1fpeyoIChoCy7//fffTJw4ETc3N9auXUutWrV47733dB9QL2zPEAlVbGzsPV1sLi4u+Pj4ZDmWKTY2FldXV7y9vTPsL1WqVJbnfPPNNzzzzDMUSTdIOiAggJkzZ7JkyRKWLFlC2bJlCQkJ4a+//soy3rFjx5KQkGDZTp8+ncNXmndGXHqmAVDu0CE4e1bvcIRRmUwwbhwsWADDh8MTT+gdkcgjc/2qnC6wbKbsrCCom5sb48aNY9++fbRp04abN2/y1ltv0aBBg/v+MS6MTdeEasyYMVkOCjdvhw4dKpBYIiIiiIqKon///hn2V69enZdffplGjRrRvHlz5syZQ/PmzbNdBNTNzQ1PT88Mm03t2UPLBQsYhbESKkvZBKlDJfKrVy+YPl1LsAASEuDvv3UNSeRet27diI6Oxs/PL9fn2lNBUICqVauybt06fvjhB/z8/Dh48CAtWrTglVdeIT4+Xre4hO3omlCNGjWKqKiobLdKlSrh7+9vmXVnlpKSQlxcHP7+/ple29/fn6SkpHt+cM+dO5fpOV9//TX169enUaNG9427adOmHD16NOcv1NaOH6dGWBhPYKw6VFLYU9hESgr07AnBwdIFaECurq7MnDnT8kd1Tt1dEPTMmTO6zwg0mUz06dOHQ4cO8eKLLwIwc+ZMgoKCWLRokQxadzC6JlR+fn7UqFEj283V1ZXg4GDi4+PZvXu35dyNGzeSlpZGs2bNMr12o0aNKFSoEBs2bLDsO3z4MKdOnSI4ODjDsVevXuXnn3++p3UqK5GRkQQEBOThFduIAZeeSUpKksKewjZu3tSGqV+/rnUBTp2qPRaGkd/la9KzhxmBPj4+fPPNN4SFhVGtWjViY2Pp0aMHjz/+uFRadyS2HiFvLR06dFANGjRQ27dvV1u3blVVq1ZVvXv3tjx/5swZVb16dbV9+3bLvkGDBqly5cqpjRs3ql27dqng4GAVHBx8z7W//vprVbhw4Uxn8k2bNk398ssv6siRI2rfvn1q+PDhysnJSa1fvz7HsedmlkCe/PabUqC2Q6avzx4FBwerTebZWYsX6x2OcDTJyUoNGnRnBuDAgUolJekdlcillJQUFRYWpkJDQ3M1+y+zzV5mBN64cUONGzdOFSpUSAGqaNGiatq0aSpFZqjapdx8fxsmobp06ZLq3bu38vDwUJ6enqpfv37qypUrluePHz+uABUWFmbZd+PGDTV48GBVvHhx5e7urp588kkVExNzz7WDg4PVM888k+l9P/jgA1W5cmVVuHBh5ePjo0JCQtTGjRtzFbvNE6rVq5UCtRtU48aNbXMPK2vcuLHaZv6yW7ZM73CEI0pLU2raNKVMJu3nrE0bpXJR/kTYlyVLlqjAwMAMSZKvr2+uEys/Pz9169YtvV+OOnDggGrRooUlrkaNGqm//vpL77DEXXLz/W1SStrCbS0xMREvLy8SEhJsM0B93Tpo1469wPN167J3717r38PK6tevz+d799LcyQmnX36Bxx7TOyThqFasgN694do1+M9/4Pff9Y5I5FFqaipbtmwhJiaGgIAAUlNTM5TGyakSJUrw1VdfZSj0rIe0tDS++eYbXnvtNRISEnByciI0NJSJEyfi4eGha2xCk5vvb0OUTRD3YcClZ5KSkmgJbFq/XpIpYVuPPQZbt0KNGvDhh3pHI/LBvIRN7969CQkJISQkhMDAwFwNXge4ePEiTz31lO7rAjo5OTFgwAAOHTpEz549SUtL4+OPP6Z27dqsWrVK19hE7klC5Qhk6Rkhsle/vraQcv36d/YVQH04YVvpC4LmNqkC+ykI6u/vz08//cTKlSspX748J0+epHPnzvTs2dO+1o0V2ZKEyhE0bUrkL78QgvESKnNBUiFsLv1s0j/+gGrVYMIEmQFocHmdEajsrCAoQKdOnThw4ACjRo3CycmJn3/+maCgIGbNmkVaWppucYmckYTKERQujCpXjnMYqw7VF0C1MWPgwAG9wxEPmvBwrbzCxInQp4/2b2FY6RdcDg0NzdW59lYQtGjRokyZMoWdO3fSqFEj4uPjefnll2nVqhVRUVG6xSXuTxIqB2HuOjNKC1VSUhJtAe+wMLh8We9wxIPmzTfh66+18YcLFsCjj8JdxYOFsZjHV02bNo0lS5bkuNq6PRYEBWjYsCF//vknH3/8MUWLFmXr1q3Uq1eP8ePHc1P+ALBLklA5grNnCfj4YyZgnIRKKqUL3fXvr8348/aGiAho1gwOHtQ7KmEF3bp148yZM7leFzA9eygI6uLiwogRIzhw4ACdO3cmOTmZSZMmUa9ePcLDw3WJSWRNEipHcOkSPnPmMAhjJVRSKV3ornVr+PNPqFwZTpzQlqs5fFjvqIQVuLq68tVXX+V6CZu7RUdH6z4jsHz58qxYsYKff/4Zf39//vnnH1q3bk3//v3vaWET+pGEyhGkm+VnlDFUSUlJ0kIl7EP16lpS1bKlVqeqalW9IxJWktWAdV9f3xxfQ2kFsBk0aJCun68mk4mnn36aqKgoXn75ZQDmzJlDjRo1mD9/vqwLaAeksGcBsHlhz3/+gerViQeKoxWLy89fZLamlMLJyYkYwB9g716oW1fnqMQD79YtSE0Fd/c7j11cpAXVAThaQVCArVu38vLLL3Pwdjd1u3btmDFjBpUqVdI5MscihT0fNOlaqABSUlL0iyUHzPFJC5WwK25ud5IppaBfP3j8cUhM1DcukW/WLAhqDwPWAVq0aMGePXt45513cHNz4/fff6d27dp88MEHhhn64WgkoXIE6Sqlg/2PozLHJ2OohN06eBCWLYNVq7RxVf/+q3dEworyWxDUHgasgzZO7O233+bvv/8mJCSEGzduMGbMGJo0acKuXbt0i+tBJQmVI7idkJjTEnsfR2WOzw+4lZAgY1aE/alVCzZvhoAALblq2lR7LBxGXguCpmcusTBp0iRdW6uqVavGxo0bmTt3LsWLF2fv3r00a9aMUaNGce3aNd3ietBIQuUIbrdQmVt8jNJClQoU8vAAJ/kxFHaoSRPYuRMaNYJLl6BtW/jmG72jElaUn4Kg6Y0fP57y5cvr2lplMpno27cvhw4donfv3hnWBVy7dq1ucT1I5JvMEZQoAfv3U+d2YmKUhMrZ2RknSaaEPStTRmuZevppSE6Gl17SqqsLh5HXgqB3i46OtovxVSVLlmT+/PmsXLmScuXKceLECTp06MBzzz3HxYsXdYnpQSHfZo7AxQVq1eLf29XS7b3Lz5xQ/aiUtuxHfLy+AQmRHXd3WLhQW/fP1VWrXSUckqMUBIU76wIOHz4ck8nEDz/8QI0aNfj++++lxIKNSELlQIyy/Iw54euZlgbz54OdJ4BCYDLB+PFaiZJHHrmz385/10TuOVJBUA8PD6ZPn05ERAR16tTh0qVLPP/883To0IHjx4/rFpejkoTKESQnw/jxjEtOphD2n1AlJydn/MGTsgnCKMqXv/PvAwe0oqAyWN3hWGPAur0UBAVo1qwZu3fv5t13381QYuHjjz+2+zI7RiIJlSNIS4NJkxh14waFMUZClSGFkrIJwogmTIDjx2WwuoOy1oD1CxcuUKZMGV1bqgAKFSrEm2++yd9//02rVq24fv06o0aN4qGHHiIyMlLX2ByFJFSOIF1CYoTlZzIsjAzSQiWMad486NHjzmD1UaO0SuvCYdw9YD0wMDBP17l48aLu3X9m5hILs2fPxsvLi927d9O4cWPeeOMNrl+/rnd4hiYJlSNIl1A5Y/8tVElJSdJCJYzP3R1++klrqQL4+GOprO7AzC1WE/MxyzM0NFTXelVmTk5OvPTSS0RFRfHUU0+RmprKhx9+SN26ddmwYYPe4RmWJFSOwGSy1HIyQkIlLVTCYZgHq//8MxQpolVWb94cYmP1jkzYgLOzM+PGjctTa5VSitOnTxMeHk54eDgLFizQffmagIAAFi1axC+//EKZMmU4duwYbdu25cUXXyQuLk63uIxKEipHkW49PyMkVNJCJRzK009rg9NLl4ZSpcDXV++IhA3lZ3xVjx49aN26Nc8884xdlFcA6Nq1KwcPHmTw4MEAzJ07l6CgIH766ScpsZALklA5inTLzxhhDNUl4JF69bQK1PmYmiyE3WjcGHbsgEWLoFAhbZ98GTmsvBYEvbvlx7x8jd4FQT09Pfniiy/YunUrQUFBnD9/nt69e9OlSxdOnTqlS0xGIwmVo0i3QLK9t1AlJSWhgKTChcHHR+9whLCeMmUy/kwPH64NVpep6Q7NkQqCPvzww+zZs4cJEyZQqFAhVq1aRc2aNfn000/tYvyXPZOEylFs2sRLDRoQjf0nVOb4zIVIhXBIu3fDZ59pg9W7dIHLl/WOSNiQIxUEdXNzY/z48URGRtK8eXOuXbvG8OHDefjhh9m3b59ucdk7SagcRcOGHC9enCSMkVD5AWOPHtX+ghfCETVqdGew+tq10KwZHDqkd1TChrIqCOqbizF19lQQtGbNmmzZsoUvv/ySYsWKsX37dho2bMjbb7/NzZs3dY3NHklC5UAK3R63ofcv4f0kJyfjDXSMidFq+QjhqJ5+GrZtg7Jl4cgRLalauVLvqIQNpR+wPn/+fMLCwli4cGGur2MvBUGdnJx45ZVXOHjwIF27diUlJYV3332XevXqsWnTJl1jszeSUDmKL7+kz8mTlML+W6iSkpLulE2QkgnC0TVoALt2QYsWWo2qxx6D6dP1jkrYkHnAeu/evQkJCSEkJITAwMBcdwXaU0HQwMBAli1bxuLFi/H39+eff/4hJCSEgQMHEi8L3AN5SKheeOEFNsvaVfbn/fd57tAhArH/hCpD2QQpmSAeBCVLwoYNMHCg9rhSJX3jEQXK2dmZTz75BCBP46vspSCoyWSie/fuREVFMWDAAABmz55NUFAQS5YseeBLLOQ6oUpISKBt27ZUrVqV9957j+joaFvEJXIrXdkEIyRU0kIlHjiurjBzptZa9fjjd/anpekXkygweV1w2R4Lgnp7ezNr1izCw8OpVq0asbGxPPXUUzz55JMPdE6Q64Tql19+ITo6mldeeYWFCxdSoUIFOnbsyOLFi+3+i9yh3U5MjLKWn7RQiQeSyQQNG955fPIk1KsHf/yhX0yiwDhaQdBWrVqxd+9e3nrrLVxcXPj1118JCgpixowZpD2AfyjkaQyVn58fI0eOZO/evWzfvp0qVarw3HPPUbp0aUaMGMGRI0esHae4HwO1UMkYKiFue+st2L8fQkJgzhy9oxEFwNEKghYuXJj//e9//PXXXzRr1owrV64wePBgHnnkEQ4ePKhLTHrJ16D0mJgY1q1bx7p163B2dqZTp07s27ePmjVrMm3aNGvFCGg/TH369MHT0xNvb2/69+/P1atXsz1n1qxZhISE4OnpiclkynTgXE6u+/fff9OyZUsKFy5M2bJl+fDDD6350qwjXQuVvSdU0kIlxG0zZ0L37pCcDP37a2VEpAjoA8ORCoLWqVOHbdu28cknn1C0aFG2bdtG/fr1mThxIrdu3dItroKU64QqOTmZJUuW0KVLF8qXL8+iRYsIDQ3l7NmzzJs3j/Xr1/Pzzz8zadIkqwbap08fDhw4wLp16/jtt9/YvHkzA80DPLNw/fp1OnTowJtvvpnn6yYmJtKuXTvKly/P7t27+eijj5gwYQKzZs2y2muzCgO1UCUnJ7MTeKtfP239MyEeVB4eWq2qiRO1x59+Ch06aEsyiQeCIxUEdXZ25tVXX+XgwYN06tSJ5ORkJkyYQIMGDdi2bZtucRUYlUu+vr6qePHiavDgwWrPnj2ZHnP58mVVoUKF3F46SwcPHlSA2rlzp2Xf6tWrlclkUtHR0fc9PywsTAHq8uXLub7ul19+qYoXL65u3bplOeaNN95Q1atXz3H8CQkJClAJCQk5PifXGjZUClR7UK+//rrt7mMFr7/+ugLUiBEj9A5FCPuxdKlSRYsqBUpVqqTUsWN6RyQK0JIlS1RgYKACLJuvr2+GxznZ/Pz8Mnxf6SUtLU0tWLBAlSxZ0hLbK6+8YtvvQRvIzfd3rluopk2bxtmzZ/niiy+oX79+psd4e3tz/Pjx3F46SxEREXh7e9O4cWPLvrZt2+Lk5MT27dttet2IiAgeeeSRDMuktG/fnsOHD3M5i6Ukbt26RWJiYobN5mbNYuYzz7ADY7RQwZ1CpEII4MknISICKlYEb2/w99c7IlGAHK0gqMlkolevXkRFRdGvXz8AZsyYQc2aNVm+fLmusdlKrhOq5557jsKFC9silizFxsZSsmTJDPtcXFzw8fEhNjbWpteNjY2lVKlSGY4xP87q3pMnT8bLy8uylS1bNs8x5lijRkRXqsRljJFQBQFPhIXB1Kl6hyOE/ahTB3bsgOXLwd1d25eWBg94fZ8HhSMWBPXx8WHOnDmsX7+eypUrEx0dTdeuXenRo0e+vr/tka6V0seMGWPpN85qO2TAta/Gjh1LQkKCZTt9+nSB3Nfc4mOEhKoSELxzJ+ThLzAhHFqJEpC+VtE770DPnnDtmn4xCV04SkFQgDZt2vD333/zxhtv4OzszKJFiwgKCmLOnDkOUxBU1znro0aNom/fvtkeU6lSJfz9/Tl//nyG/SkpKcTFxeGfj2bxnFzX39+fc+fOZTjG/Dire7u5ueHm5pbnuPLkl18I3rmTqth/HaoMZRNklp8QWTt9Gt57D5KS4PBh+OUXrUtQPDDMBUGHDx/OmTNncnyeSlcQ1NnZmZiYGAICAmjZsiXOOn3uuru78/7779OjRw9eeukl9uzZQ//+/fnxxx+ZNWsWlStX1iUuq7H5iC4rMA8e37Vrl2Xf2rVrrTYoPbvrmgelJyUlWY4ZO3as/Q1Kf/RRpUD1AvXss8/a7j5W8Oyzz6puWieGUi1a6B2OEPZtyxalSpbUfl98fJT6/Xe9IxI6SElJUWFhYSo0NDRXg9R9fHwyPA4MDFRLlizR++Wo5ORk9eGHH6rChQsrQBUpUkR9+OGHKjk5We/QMsjN97chEiqllOrQoYNq0KCB2r59u9q6dauqWrWq6t27t+X5M2fOqOrVq6vt27db9sXExKg9e/ao2bNnK0Bt3rxZ7dmzR126dCnH142Pj1elSpVSzz33nNq/f7/66aeflLu7u/rqq69yHHuBJFTt2ikFqg+onj172u4+VtCzZ0/1tDmhatVK73CEsH+nTinVpIn2O+PkpNQHHyiVlqZ3VEInS5YsUX5+frmeAZh+Cw0NVWFhYSolJUXX13LkyBH16KOPWuJq2LBhlhUE9OCQCdWlS5dU7969lYeHh/L09FT9+vVTV65csTx//PhxBaiwsDDLvvHjx2f6gzR37twcX1cppfbu3atatGih3NzcVJkyZdT777+fq9gLJKHq2FEpUC+A6tatm+3uYwXdunVTvc0JVZs2eocjhDHcuKFUv37a7w0o9cILekckdHTr1i1VokSJfCVV9tJilZaWpr755hvl7e2tAOXs7KzGjBmjrl+/rmtcSjloQmVkBZJQdemiFKgXQXXp0sV297GCLl26qGfNXwrt2ukdjhDGkZam1BdfKOXiolS6PwzFg2nJkiXKZDIpk8mU54TKfL7eSZVSWq/S008/bYmtSpUqGRpJ9GDTOlTCTt1eesYoldItsyFkLT8hcs5kgsGDtQHq6Sf0XL+uW0hCP+YB62XSzwoFfH19c3wNpTWsMHDgQDZs2KDrrEB/f39+/vlnfvnlF0qXLs3Ro0dp3bo1AwYMyHTpOHsjCZWjuD1rwyhr+S0GVnz8MdjbEj5CGEGlSnf+feEC1KoF778v9aoeQNYqCHrp0iXatm2r+5qAAF27duXgwYMMGjQIgK+//pqgoCDd47ofSagchcHW8rsK3CpbNmO9HSFE7s2fDydOwNix0KMH3GfReOF4rFUQFOxjTUAALy8vZsyYwebNm6lWrRqxsbF0796dbt26cfbsWV1jy4okVI5izBgixo1jOcaoQwWy9IwQVjF8OHz1FRQqBIsXw0MPwdGjekcldJSfgqDqdiunvRQFbdmyJXv37uWtt97CxcWFZcuWUbNmTWbPnk1aWpre4WUgCZWjaNCA+Ice4hTGaKFqDtT+8Uf4+We9wxHC+AYOhPBwbf2/AwegSRNYvVrvqISOshpflRMqXVHQ8PBwFixYQHh4uG4JVuHChfnf//7H7t27adKkCQkJCQwcOJBHH32Uf/75R5eYMiMJlQMx0tIzTYHKixZpa5YJIfKveXPYvRuCgyE+Hjp3hkWL9I5K6Cj9+KrQ0FAgdy1WPXr0oHXr1jzzzDO0bt1a9/FVdevWJSIigmnTpuHu7s6mTZuoW7cukydPtovvPUmoHMUff1B27VoaYP9dfhlm+cnSM0JYT+nSWkvVoEFQpQr85z96RyR0Zh5fNW3aNJYsWZKrFqu4uLgMj8+cOUP37t2ZNGmSbq1Vzs7OhIaGcuDAAdq1a8etW7d48803adKkCXv37tUlJjNJqBzF999TfcoUHsP+W6gyrOUnZROEsC5XV5gxA3bsAG9vbZ9SEBOja1hCf+YWq/Xr1+Pj45Pn64wfP1731qoKFSqwZs0avvvuO3x8fNi7dy+9evXSLR6QhMpxGGyWn7RQCWFj5mQK4IsvICgIVq7ULRxhH5ydnWnTpg2zZ8/GZDLlaSYgaK1Ves8GNJlMPPfcc6y8/XOdkJCgWywgCZXjuN3SY5Q6VNJCJUQBSUuDpUshIQEeewwmTdL2iQeatYqC2kNB0CJFiuh27/QkoXIU6VqoZAyVEMLCyQnWrNEqrCsF48dridVd42PEg8caRUHtqSCo3qR5wFEYaOkZGUMlRAFzddW6/Zo0gVdegVWroFEjWLIEGjbUOzqhI/OgdbPU1FQCAwOJjo621KTKCXNB0MWLF9OtWzcbRGr/pIXKURhs6ZlPgbOrVsGoUXqHI8SDo29fiIiAihW16uqPPKItXSPEbemLguaGvRUE1YM0DzgKgw1KPwdQr542zVsIUXDq19fqVT33HLRqBX5+ekck7Ix5fNWrr75KdHR0js9LXxDU2dmZmJgYAgICaNmyJc4PwPAOaaFyFL16kTBnDl+jNdnaW0l+s7S0NMtfL7L0jBA6KV5cK6o7evSdfUeOaK1WQqAlVSdPnmTixIm5PtfeCoIWFEmoHEWtWtCtG3/ffmivrVTmuDoCHp98Alu26BuQEA8qJycwT5m/ehW6dtXGVa1Zo29cwm44Ozszbtw4lixZQmBgYI7Ps8eCoAVBEioHkr7Fx94TqseAIu++C2Fh+gYkhIArV8DDQ5v516mTlFYQGThSQVBbkoTKURw+jOvSpTx0+6G9J1RSNkEIOxIQoLUWv/yylFYQmXKkgqC2IgmVo1i5EpfnnmPI7Yf2WovKHJeUTRDCzri5wcyZ8O23ULjwndIKe/boHZmwI45UENTaJKFyFLdbegrd/qvB3luoXM1/3UgLlRD25YUXtNIKlSppg9TfeEPviISdkYKgmZPmAUdxu6WnkJMTpKbafUJljlNaqISwQ/Xrw65dMHIkvPee3tEIOyQFQe8lLVSOwmAtVIWcbv/oSQuVEPapeHGYO1cbX2X26adSWkFkSgqCSkLlOMwJ1e1Exd7HUJkTP2mhEsIgFi+G4cOltILIUlbjq+4nfUHQ8PBwFixYQHh4uOESLEmoHIW5y88gLVQfeHnBtm3wxBP6BiSEyJmmTbW1AM2lFcaP17rthUjnQS4IKgmVozBYl98Zd3do3jxjd4IQwn6VK6eVVhg0SCutMGkSdOgA58/rHZmwM9YqCGoeX2WUpEoSKkfx8MMwbx5f3y66Zu8JlSw7I4QBubnBjBnw/ffg7g7r10ODBrB1q96RCTuU34KgSimUUgwaNMhuh7GkJwmVo6hcGZ5/nl1eXoD9j6HqcuMGTJsGhw7pHJEQIteefRZ27oSgIDh7VlqpRJasURD0woULBAYG2n1LlSRUDsbV1RWw/xaqXgkJ2pRsKRoohDHVrKklVfPnQ/rp7rmYMi8eHPktCHrhwgW6d+/OiBEj7HbAuiRUjuLcOVi+nGY3bwL2n1BZOvxklp8QxlW0KPTufedxdLQ2eH3HDv1iEnbLGgVBp0+fbrcD1uXbzFHs3AlduzK8aFFmYf8JlbOUTRDC8YwdqxUEbdECpkyBYcMgj2u+Ccdki4KglStXtkGkuSctVI7i9iw/c6Ji72OoZHFkIRzQZ59B9+6QnKzVrOrRAxIS9I5K2DFHKggqCZWjuN3SY05U7L2FykVaqIRwPF5esGgRfPIJFCqkFQNt3BgiI/WOTNgx8/iqEiVK5Oo8c0HQFStWABATE4OTkxP79u2zRZj3JQmVozC3UN1+aPcJlblpV1qohHAsJhO8+qpWs6pcOTh6FB56CMLD9Y5M2LFu3boRHR2Nn59frs+dMGGC5d9KKerWrZun2YT5ZZiEKi4ujj59+uDp6Ym3tzf9+/fn6tWr2Z4za9YsQkJC8PT0xGQyER8fn+H5EydO0L9/fypWrEiRIkWoXLky48ePz9BdduLECctUz/Tbn3/+aYuXmXcGS6gsaZS0UAnhmJo102bxdu4MVapoVdaFyIarqyszZ87Mc3mFuxV0UmWYhKpPnz4cOHCAdevW8dtvv7F582YGDhyY7TnXr1+nQ4cOvPnmm5k+f+jQIdLS0vjqq684cOAA06ZNY+bMmZkev379emJiYixbo0aNrPK6rOZ2YmJOVOx9DNVXjRvD779rq9oLIRyTjw8sXw5hYdqMQIC0NDhyRN+4hN3K63qAWSnI7j+Tys2wep1ERUVRs2ZNdu7cSePGjQFYs2YNnTp14syZM5QuXTrb88PDw2ndujWXL1/G29s722M/+ugjZsyYwb///gtoLVQVK1Zkz5491M/hl/+tW7e4deuW5XFiYiJly5YlISEBT0/PHF0j1/78E4KDOV+0KKWuXSMoKIiKFSva5l75cPz4caKioujduzfz58/XOxwhREH74ANtHcDp0+Hll2UWoMhUamoqW7Zs4ddff2X69Ol5vo7JZCItLS3P5ycmJuLl5ZWj729D9LdERETg7e1tSaYA2rZti5OTE9u3b+fJJ5+02r0SEhIyLZH/+OOPc/PmTapVq8brr7/O448/nuU1Jk+enKeFIfOlYkWYMYN1v/0GK1cSFRVFVFRUwcaQC9b660MIYSBKQUQE3LoFr7wCGzfC7NnaYHYh0jGXVwgJCaFly5YMHz6cM2fO5Po6BdlmZIiEKjY2lpIlS2bY5+Ligo+PD7GxsVa7z9GjR/nss8+YMmWKZZ+HhwdTp07l4YcfxsnJiSVLlvDEE0/wyy+/ZJlUjR07lpEjR1oem1uobKpUKRg0iK7PPstPK1dy48YN294vHwoXLkzXK1dg1ix48knIwyBEIYQBmUywdKm27NSYMdqMwN274aefZIyVyFK3bt3o2rUrW7ZsISYmhoCAAB599NEcJUsFOY5K14RqzJgxfPDBB9keU1CtLNHR0XTo0IGnn36aAQMGWPaXKFEiQ3LUpEkTzp49y0cffZRlQuXm5oabm5vNY86Mh4cHPXv21OXeuVK2LJw5A40aSUIlxIPEyQlGjdKKf/bsCf/+qy3u/uGHWu0q6QIUmbi7IOjevXupW7fufc/bu3evDaPKSNeEatSoUfTt2zfbYypVqoS/vz/n71p8MyUlhbi4OPz9/fMdx9mzZ2ndujXNmzdn1qxZ9z2+WbNmrFu3Lt/3tapr17RxVE5O0Lq13tHcn7kQm8zyE+LBZJ4F2L8/LFsGb7yhzQisWlXvyIQB1KlTx6rHWYOu32Z+fn45qjkRHBxMfHw8u3fvtsyu27hxI2lpaTRr1ixfMURHR9O6dWsaNWrE3LlzcXK6/8THyMhIAgIC8nVfqztzBtq2heLFIS5O72juLyVF+6/UoRLiwVW8OCxZAl98of1xJcmUyAWlVLZdegU9584QzQNBQUF06NCBAQMGMHPmTJKTkxk6dCi9evWyzPCLjo6mTZs2fPfddzRt2hTQxl7FxsZy9OhRQJs+WaxYMcqVK4ePjw/R0dGEhIRQvnx5pkyZwoULFyz3NLd8zZs3D1dXVxo0aADA0qVLmTNnDl9//XVBvgX3Z05MzImKvTO3UElCJcSDzWSCoUMz7tu9G9avh9de01rdhciCUop9+/ZRr149S4K1d+/eAm2ZMjNEQgXw448/MnToUNq0aYOTkxPdu3fn008/tTyfnJzM4cOHuX79umXfzJkzM8y2e+SRRwCYO3cuffv2Zd26dRw9epSjR48SGBiY4X7pM9t33nmHkydP4uLiQo0aNVi4cCFPPfWUrV5q3pgTEztZ0+i+zImfdPkJIdK7fl0bW3XsmFa/6rvv4K5JSUKkV6dOnXyVRrAWQ9ShMrrc1LHIs9OntWUeXF21Kcn2zsNDG/d17BhUqqR3NEIIe6EUzJ2rtVrduAEBAfDDD/Doo3pHJh5Aufn+lrZUR2HUFirp8hNCpGcywYsvws6dULMmxMRo40PffBPsdEktIUASKsdh7jpLTdX+wrN3S5ZoM3ukZIIQIjO1asGOHTBggPaZNnkytGxpjEk34oEkA1gcRfqWnrQ0+2/56dxZ7wiEEPauaFGtAHC7dlpiVbw43Gf5MCH0IgmVoyhaFD76yP4TKSGEyK2nnoKmTcHN7c6sv2vXtD8eixXTNzYhbpOEylEULgyjR+sdRc6kpmqDTJ2doUcPbSC9EEJkp1y5jI+HDYMtW2DBAki3zqsQepExVKLgJSVB377w3HPGmJEohLAvly5pdaqOHoXmzWHKFK21SggdSULlKJTSlp7Zts3+Z8Kkn4koXZRCiNzy9YW9e6F7d+3z7rXXoGNHiI3VOzLxAJOEylEoBcHB2oKjCQl6R5O99NXcpbCnECIviheHRYvgq6+gSBH4/XeoVw9Wr9Y7MvGAkoTKUTg53Vml3d6Xn5EWKiGENZhMMHAg7NoFderA+fPQr582YF2IAibNA47E2VlLpuy9uGf6hE/W6RJC5FfNmlrNqtdeg06dtFnPQhQwSagciYuLMRKq9AsjZ7NSuBBC5FjhwvDZZxn3LV6stVq98op81gibk+YBR2LuPrP3Lj9ZdkYIYWuxsVox0CFD4LHH4Nw5vSMSDk4SKkdilPX8fH3hp5+0VeSFEMIWSpaEiRO1YqArV2pjrH77Te+ohAOThMqRmGfM2XsLVdGi0LOntgkhhC04OcGrr2oD1uvWhQsXtJaqwYPh+nW9oxMOSBIqRzJ2LPzvf1CihN6RCCGEfahdWxuwPnKk9njGDK2yuswEFFYmg9IdiVGWnklIgHXrtJaqjh31jkYI4ejc3GDqVO3z5oUXoG1bmQkorE4SKlHwTpyAp58Gf3+IidE7GiHEg6JtW/j7b3B3v7Pv9Glt2Zry5fWLSzgE6fJzJIcPQ2Sk/Tdlpy+bIIQQBcnXV6usDtpnUZ8+WoX1BQv0jUsYniRUjqRzZ2jQQFvjyp6ZB83LsjNCCD1dvqytBZiQAM88oyVX8fF6RyUMShIqR2KUOlTSQiWEsAclSsCWLTBhgvZ5NH++Vl5h/Xq9IxMGJAmVIzG3+Nh7HSop7CmEsBcuLjB+PGzdClWqwJkz8J//wLBhcOOG3tEJA5GEypEYpbCnOT7p8hNC2IuHHtLGoL7yivZ40yZZa1TkinyjORKjFPaUFiohhD0qWhS+/BK6doWAAK3cAmifWUpBoUL6xifsmiRUjsQoLVRBQfDNN+DlpXckQghxr/btMz6ePBl++QW+/x5q1tQlJGH/pD3TkRhlDFWZMvDii9C9u96RCCFE9q5ehS++gL/+goYNYdo0rW6VEHeRhMqRPP+8tvxMlSp6RyKEEI7Bw0NLpjp2hFu3tCVs2rSBkyf1jkzYGZNSSukdhKNLTEzEy8uLhIQEPD099Q5Hf7GxsGePVmCvaVO9oxFCiPtTCmbP1hKqa9egWDH49FNtKRuTSe/ohI3k5vtbWqhEwduyBTp1gtde0zsSIYTIGZMJBg7UCic//DBcuaLNCDx9Wu/IhJ2QQemOJCZG6+8vVQrsuSVMCnsKIYyqcmWtpMLUqVp3YLlyekck7IS0UDmSF1+EatVg2TK9I8meLD0jhDAyZ2d4/XUYPPjOvq1boWdPuHBBv7iEriShciRGmeUnLVRCCEeSlgYvvww//wy1asHixXpHJHQgCZUjMcpaflLYUwjhSJyctBpVdepoLVRPP621Vl28qHdkogAZJqGKi4ujT58+eHp64u3tTf/+/bl69Wq258yaNYuQkBA8PT0xmUzEZ7KKeIUKFTCZTBm2999/P8Mxf//9Ny1btqRw4cKULVuWDz/80JovzXqMUthTlp4RQjiahg1h1y54+23ts9jcWrV0qd6RiQJimISqT58+HDhwgHXr1vHbb7+xefNmBg4cmO05169fp0OHDrz55pvZHjdp0iRiYmIs27BhwyzPJSYm0q5dO8qXL8/u3bv56KOPmDBhArNmzbLK67IqWXpGCCH04+oK77wDf/6pJVPnz2sFjMPC9I5MFABDNBFERUWxZs0adu7cSePGjQH47LPP6NSpE1OmTKF06dKZnhcaGgpAeHh4ttcvVqwY/v7+mT73448/kpSUxJw5c3B1daVWrVpERkby8ccf3zehK3BGaaFq2VKr31Kpkt6RCCGE9TVuDLt3w6RJsG8fhIToHZEoAIZooYqIiMDb29uSTAG0bdsWJycntm/fnu/rv//++/j6+tKgQQM++ugjUtK18ERERPDII4/g6upq2de+fXsOHz7M5cuXM73erVu3SExMzLAVCKMMSq9TB4YNg86d9Y5ECCFsw80N3n1XWwPQXPgzPh5efRXi4vSMTNiIIVqoYmNjKVmyZIZ9Li4u+Pj4EBsbm69rv/rqqzRs2BAfHx/++OMPxo4dS0xMDB9//LHl3hUrVsxwTqlSpSzPFS9e/J5rTp48mYkTJ+Yrrjxp1w6KF4cGDQr+3kIIIe7llK7dYtQomDMHFi2Cr76Cxx/XLy5hdbq2UI0ZM+aeAeF3b4cOHbJpDCNHjiQkJIS6desyaNAgpk6dymeffcatW7fyfM2xY8eSkJBg2U4XVCXdZ5+FTz6BRx8tmPvl1cmTsHkzHD2qdyRCCFFwXn4ZatTQlt/q2hWeeUbqVjkQXROqUaNGERUVle1WqVIl/P39OX/+fIZzU1JSiIuLy3LsU141a9aMlJQUTpw4AYC/vz/nzp3LcIz5cVb3dnNzw9PTM8Mm0vnxR2jVCj74QO9IhBCi4DRtqq1j+vrrWsvVggVQs6b2X1lW1/B07fLz8/PDz8/vvscFBwcTHx/P7t27adSoEQAbN24kLS2NZs2aWTWmyMhInJycLF2MwcHBvPXWWyQnJ1OoUCEA1q1bR/Xq1TPt7tNVYqK29EzRouDlpXc0WZNZfkKIB1Xhwtofk08/Df37w99/ay1V587B7YlUwpgMMSg9KCiIDh06MGDAAHbs2MG2bdsYOnQovXr1sszwi46OpkaNGuzYscNyXmxsLJGRkRy93bW0b98+IiMjibs9IDAiIoLp06ezd+9e/v33X3788UdGjBjBs88+a0mWnnnmGVxdXenfvz8HDhxg4cKFfPLJJ4wcObKA34UcePttKFMG7LVOlplUShdCPOgaN4adO7UyC2XLwnPP6R2RyCdDJFSglS+oUaMGbdq0oVOnTrRo0SJDLajk5GQOHz7M9evXLftmzpxJgwYNGDBgAACPPPIIDRo0YPny5YDWNffTTz/RqlUratWqxbvvvsuIESMyXNfLy4vff/+d48eP06hRI0aNGsW4cePsr2QCGGeWn6zlJ4QQWt2qt9+Gf/4BX19tn1IwYYKMMTUgw3yj+fj4MH/+/Cyfr1ChAuquPugJEyYwYcKELM9p2LAhf/75533vXbduXbZs2ZLjWHVjlKVnpIVKCCHuKFz4zr8XLoSJE7Wehnfe0boB5bPSEAzTQiVywGgtVPIhIYQQGTVtCm3awI0bMHo0BAfD/v16RyVyQBIqR2KUSumylp8QQmSuUiVYtw6+/lqbXLRzp7ZO4MSJkJSkd3QiG5JQORKjrOXXpQtMngzt2+sdiRBC2B+TSZsBePCgVvwzOVkbV/X003pHJrIhTQSOxCgtVG3aaJsQQoislS6tLV3z88/akjVSVsGuSQuVI2nYUPurpkULvSMRQghhDSYT9OwJx49D69Z39n/7LSxbpltY4l4mdffUOGF1iYmJeHl5kZCQIFXTQftgiIuDwEC4vS6iEEKIHDp1Squwfu2atoTN559rn6fC6nLz/S0tVKLgTZyoFbWbN0/vSIQQwnj8/LTuPxcX+PVXLbn67DP7H+7h4CShciRJSZCQoC0/Y8+ksKcQQuRdkSLwv/9p6wIGB8OVK9oYq+bNtaVshC4koXIks2aBtze8+KLekWRPCnsKIUT+1a4NW7fCl1+Cpyfs2AEPPQQXLugd2QNJmggciVFm+UkLlRBCWIeTE7zyijaW6tVXoVo1rUtQFDhpoXIkRqmULi1UQghhXaVLw+LF2nI1ZpGR8PzzcP68bmE9SCShciRGWctPlp4RQgjbMH+uKqW1XH3/PdSooQ0JSUvTNzYHJwmVIzFaC5V0+QkhhG2YTPDpp1C/Ply+DC+/rA1a37NH78gcliRUjsQoLVS9e8Pbb0O9enpHIoQQjqtJE20twOnToVgx2L5dK1kzfDgkJuodncORhMqRGGVQ+rPPav38DRvqHYkQQjg2FxctgTp0SKu4npamtVz99JPekTkcSagcSYUK0KsXPPqo3pEIIYSwJ6VLa0nU779riVX//neeS07WLy4HIkvPFABZeuYux4/DzZvaUgnFiukdjRBCPLiuX4dGjeDpp2HsWK1oqLCQpWeEfevTR1sqYcMGvSMRQogH288/a92B77yjFQpdvVrviAxLEipHopQ2ID0pSe9IsieFPYUQwj688IJWv6pMGfj3X+jUCbp3h9On9Y7McCShciTr1kGhQtC0qd6RZE8KewohhH0wmbQEKioKRo3SPpeXLoWgIPjgA+0PdZEjklA5EqPM8pPCnkIIYV+KFYMpU7Q6VQ8/DNeuwZ9/agmXyBHpc3EkUthTCCFEftSpA1u2wA8/QIsWd/afOwdXr0LlyvrFZuekhcqRGKWwp7RQCSGE/TKZ4LnnoGLFO/tefx1q1YJx47SZgeIeklA5EqN0+UkLlRBCGEdSEsTEwK1b2mzAoCBtILuMr8pAEipHYpQuv/79YcQIbVaJEEII++bqCmvXaklUuXJw6pRWt+o//4GDB/WOzm5IQuVIjNLlN2YMfPyxVtldCCGE/Us/G3DcOHBz02oJ1qsHy5frHZ1dkITKkRQvDo89Bu3a6R2JEEIIR+TuDhMnai1TXbuCry+0aqV3VHZBlp4pALL0zF3MBeP8/bW6WUIIIYwpNlb7LAdtTNXQodqA9oce0jcuK5GlZ4R9a9BA64c/ckTvSIQQQuSHOZkCbRmbL7+E4GB49tkHrtq6JFSi4MksPyGEcDyPPAL9+mnjrX78EapX17oHH5AyC5JQOZITJ7TZGPberSh1qIQQwvEEBMCcObBzp1YU9MYNmDBBS6zmz3f4MguSUDkSJydITrb/xZFlLT8hhHBcjRrB5s1aF2D58nDmjLYuYFqa3pHZlPS5OBKjFPY0t1BJl58QQjgmk0mrVdWlC0ybBs2b3/mOunEDLl2CwEB9Y7Qyw7RQxcXF0adPHzw9PfH29qZ///5cvXo123NmzZpFSEgInp6emEwm4uPjMzwfHh6OyWTKdNu5cycAJ06cyPT5P//801YvNe/MCYq916GSFiohhHgwFCkCb74JISF39n38MVSr5nDjqwyTUPXp04cDBw6wbt06fvvtNzZv3szAgQOzPef69et06NCBN998M9PnmzdvTkxMTIbtpZdeomLFijRu3DjDsevXr89wXKNGjaz22qwmfYJir02rSt2JTRIqIYR4sCgFW7dmHF/144/2+52VC4boc4mKimLNmjXs3LnTkuh89tlndOrUiSlTplC6dOlMzwsNDQW0lqjMuLq64p9uymdycjK//vorw4YNw2QyZTjW19c3w7HZuXXrFrdu3bI8TkxMzNF5+Za+Cy0lRRugbm+Uglde0Vqp3N31jkYIIURBMplg1SpYtEhbcPnkSa3EwvTpMGWKoYuEGqKFKiIiAm9v7wytRm3btsXJyYnt27db7T7Lly/n0qVL9OvX757nHn/8cUqWLEmLFi1Yfp8y+5MnT8bLy8uylS1b1moxZit9i4+9jqNyctLqlHz1FXh46B2NEEKIgmYyQY8e2jI2776rfRfs2qV1C370kd7R5ZkhEqrY2FhKliyZYZ+Liws+Pj7ExsZa7T7ffPMN7du3JzDdQDkPDw+mTp3KokWLWLlyJS1atOCJJ57INqkaO3YsCQkJlu10QRU3K1QIHn1Ulp4RQghh/8zjq44e1XouihSBJ5/UO6o807XLb8yYMXzwwQfZHhMVFVUgsZw5c4a1a9fy888/Z9hfokQJRo4caXncpEkTzp49y0cffcTjjz+e6bXc3Nxwc3OzabyZKlxYW6zSnqWlwcWLWmuaj4/2l4oQQogHV6lSWs/FO+9oawOaDRumVWIfMcIQQ0R0TahGjRpF3759sz2mUqVK+Pv7c/78+Qz7U1JSiIuLy/G4pvuZO3cuvr6+WSZJ6TVr1ox169ZZ5b4PnPh47ZcHtJpZUjpBCCEEZEymDhyAL77Qxt3OmAH/+5+2RqAdT2bS9dvMz88PPz+/+x4XHBxMfHw8u3fvtsyu27hxI2lpaTRr1izfcSilmDt3Ls8//zyFcrBYb2RkJAEBAfm+7wMp/dguO/7FEEIIoaOgIG3239ix2sD1fv20elYffWS3w1oMMYYqKCiIDh06MGDAAHbs2MG2bdsYOnQovXr1sszwi46OpkaNGuzYscNyXmxsLJGRkRw9ehSAffv2ERkZSVxcXIbrb9y4kePHj/PSSy/dc+958+axYMECDh06xKFDh3jvvfeYM2cOw4YNs+ErzodSpbSlZ86e1TuSzJlrZDk5SXefEEKIzDk5Qe/ecOiQlkR5ecHff0P79tpmhwsvGyKhAvjxxx+pUaMGbdq0oVOnTrRo0YJZs2ZZnk9OTubw4cNcT1ckbObMmTRo0IABAwYA8Mgjj9CgQYN7BpR/8803NG/enBo1amR673feeYdGjRrRrFkzfv31VxYuXJjpTEC7kJAAV67Yb3FPKeophBAipwoXhtGj4dgxCA3VJl/t2aMlWHbGpJSDr1ZoBxITE/Hy8iIhIQFPWy9cXLSoVnn233+hYkXb3isvTpzQ4ipSxKEq5AohhCgAx45pswLbt9ceKwWffALPP69NdLKy3Hx/G6aFSuSQeZC3vdahkhYqIYQQeVW58p1kCmDpUm0WYKVKMG+efnEhCZXjMScq9trlZ45LEiohhBD55esLdepow13GjdM1FJmz7mjMiYq9tlAVKwYvvKB1+QkhhBD5ERIC338P9etrpXh0JAmVozF3+dlrC1Xp0vDtt3pHIYQQQliVJFSOpkkTiIuTFiAhhBCiAElC5Wjus3Cz7lJT4eZNrSVNj+V5hBBCCBuQQemiYO3cqa0sHhSkdyRCCCEcQdGi2liq5s11DUNaqETBkrIJQgghrKlKFQgL0zsKaaFyOK1bQ0AA/PGH3pFkzjxYXhZFFkII4UAkoXI0Fy9CbCzcuKF3JJmTFiohhBAOSBIqR2PvdaiksKcQQghr2r8f/Pygdm1dw5B+F0dj73WozImedPkJIYSwhtRUrXemUCFdw5AWKkcjLVRCCCFEgZNmAkdj72v5+ftD9+7arAwhhBDCQUhC5WjMXWn22kLVpAksXqx3FEIIIYRVSULlaKpVgytXwNNT70iEEEKIB4YkVI7m66/1jiB7Smn/NZn0jUMIIYSwIhmULgrW99+DkxN06qR3JEIIIRxBkSLacJL69XUNQ1qoRMEyj+2SFiohhBDWUK0a7NihdxTSQuVwhgzRZtD99JPekWRO6lAJIYRwQJJQOZrYWDh2DC5f1juSzEkdKiGEEA5IEipHY++FPaWFSgghhDWdOAFPPw0DB+oahnyrORpzQnXhApw8CSVKQNGi2r5r17Ty/Fnx9QUPD+3fN27A+fNZH1u8+J3SDDdvwrlzWR/r7Q1eXtq/r1/PGKcQQgiRHxUqwKJFekchLVQOx9zyM2mS9kO2YsWd59as0fZltS1ceOfYTZuyP3bevDvH7tyZ/bEzZtw59upV7b+SUAkhhHAgklA5mq5dtdajwoW1LX3i4uR0Z39mm62OTd+917Wr1mLVtavN3wohhBCioJiUMldaFLaSmJiIl5cXCQkJeEoFcyGEEMIQcvP9LS1UQgghhBD5JAmVEEIIIUQ+SUIlhBBCCJFPklAJIYQQQuSTJFRCCCGEEPkkCZUQQgghRD4ZJqGKi4ujT58+eHp64u3tTf/+/blqLhKZxfHDhg2jevXqFClShHLlyvHqq6+SkJCQ4bhTp07RuXNn3N3dKVmyJK+99hop5vXmbgsPD6dhw4a4ublRpUoVvv32W1u8RCGEEEIYlGESqj59+nDgwAHWrVvHb7/9xubNmxmYzbo9Z8+e5ezZs0yZMoX9+/fz7bffsmbNGvr37285JjU1lc6dO5OUlMQff/zBvHnz+Pbbbxk3bpzlmOPHj9O5c2dat25NZGQkoaGhvPTSS6xdu9amr1cIIYQQxmGIwp5RUVHUrFmTnTt30rhxYwDWrFlDp06dOHPmDKVLl87RdRYtWsSzzz7LtWvXcHFxYfXq1XTp0oWzZ89SqlQpAGbOnMkbb7zBhQsXcHV15Y033mDlypXs37/fcp1evXoRHx/PmjVrcnRfKewphBBCGI/DFfaMiIjA29vbkkwBtG3bFicnJ7Zv357j65jfEJfbS6FERERQp04dSzIF0L59exITEzlw4IDlmLZt22a4Tvv27YmIiMjyPrdu3SIxMTHDJoQQQgjHZYiEKjY2lpIlS2bY5+Ligo+PD7GxsTm6xsWLF3nnnXcydBPGxsZmSKYAy2PzdbM6JjExkRs3bmR6r8mTJ+Pl5WXZypYtm6MYhRBCCGFMuiZUY8aMwWQyZbsdOnQo3/dJTEykc+fO1KxZkwkTJuQ/8PsYO3YsCQkJlu306dM2v6cQQggh9OOi581HjRpF3759sz2mUqVK+Pv7c/78+Qz7U1JSiIuLw9/fP9vzr1y5QocOHShWrBjLli2jUKFCluf8/f3ZsWNHhuPPnTtnec78X/O+9Md4enpSpEiRTO/p5uaGm5tbtnEJIYQQwnHomlD5+fnh5+d33+OCg4OJj49n9+7dNGrUCICNGzeSlpZGs2bNsjwvMTGR9u3b4+bmxvLlyylcuPA913333Xc5f/68pUtx3bp1eHp6UrNmTcsxq1atynDeunXrCA4OztVrFUIIIYTjMsQsP4COHTty7tw5Zs6cSXJyMv369aNx48bMnz8fgOjoaNq0acN3331H06ZNSUxMpF27dly/fp1ly5ZRtGhRy7X8/PxwdnYmNTWV+vXrU7p0aT788ENiY2N57rnneOmll3jvvfcArWxC7dq1GTJkCC+++CIbN27k1VdfZeXKlbRv3z5HsSckJODt7c3p06dllp8QQghhEImJiZQtW5b4+Hi8vLyyP1gZxKVLl1Tv3r2Vh4eH8vT0VP369VNXrlyxPH/8+HEFqLCwMKWUUmFhYQrIdDt+/LjlvBMnTqiOHTuqIkWKqBIlSqhRo0ap5OTkDPcOCwtT9evXV66urqpSpUpq7ty5uYr99OnTWcYim2yyySabbLLZ93b69On7ftcbpoXKyNLS0jh79izFihXDZDJZ9drm7Flav2xL3ueCIe9zwZD3ueDIe10wbPU+K6W4cuUKpUuXxskp+3l8uo6helA4OTkRGBho03t4enrKL2sBkPe5YMj7XDDkfS448l4XDFu8z/ft6rvNEHWohBBCCCHsmSRUQgghhBD5JAmVwbm5uTF+/Hipe2Vj8j4XDHmfC4a8zwVH3uuCYQ/vswxKF0IIIYTIJ2mhEkIIIYTIJ0mohBBCCCHySRIqIYQQQoh8koRKCCGEECKfJKEygC+++IIKFSpQuHBhmjVrxo4dO7I9ftGiRdSoUYPChQtTp06dexZ3FpnLzfs8e/ZsWrZsSfHixSlevDht27a97/8Xocntz7PZTz/9hMlk4oknnrBtgA4it+9zfHw8Q4YMISAgADc3N6pVqyafHTmU2/d6+vTpVK9enSJFilC2bFlGjBjBzZs3Cyha49m8eTOPPfYYpUuXxmQy8csvv9z3nPDwcBo2bIibmxtVqlTh22+/tXmchlnL70H1008/KVdXVzVnzhx14MABNWDAAOXt7a3OnTuX6fHbtm1Tzs7O6sMPP1QHDx5Ub7/9tipUqJDat29fAUduLLl9n5955hn1xRdfqD179qioqCjVt29f5eXlpc6cOVPAkRtLbt9ns+PHj6syZcqoli1bqq5duxZMsAaW2/f51q1bqnHjxqpTp05q69at6vjx4yo8PFxFRkYWcOTGk9v3+scff1Rubm7qxx9/VMePH1dr165VAQEBasSIEQUcuXGsWrVKvfXWW2rp0qUKUMuWLcv2+H///Ve5u7urkSNHqoMHD6rPPvtMOTs7qzVr1tg0Tkmo7FzTpk3VkCFDLI9TU1NV6dKl1eTJkzM9vkePHqpz584Z9jVr1ky9/PLLNo3T6HL7Pt8tJSVFFStWTM2bN89WITqEvLzPKSkpqnnz5urrr79WL7zwgiRUOZDb93nGjBmqUqVKKikpqaBCdBi5fa+HDBmiHn300Qz7Ro4cqR5++GGbxukocpJQvf7666pWrVoZ9vXs2VO1b9/ehpEpJV1+diwpKYndu3fTtm1byz4nJyfatm1LREREpudERERkOB6gffv2WR4v8vY+3+369eskJyfj4+NjqzANL6/v86RJkyhZsiT9+/cviDANLy/v8/LlywkODmbIkCGUKlWK2rVr895775GamlpQYRtSXt7r5s2bs3v3bku34L///suqVavo1KlTgcT8INDre1AWR7ZjFy9eJDU1lVKlSmXYX6pUKQ4dOpTpObGxsZkeHxsba7M4jS4v7/Pd3njjDUqXLn3PL7G4Iy/v89atW/nmm2+IjIwsgAgdQ17e53///ZeNGzfSp08fVq1axdGjRxk8eDDJycmMHz++IMI2pLy818888wwXL16kRYsWKKVISUlh0KBBvPnmmwUR8gMhq+/BxMREbty4QZEiRWxyX2mhEiKf3n//fX766SeWLVtG4cKF9Q7HYVy5coXnnnuO2bNnU6JECb3DcWhpaWmULFmSWbNm0ahRI3r27Mlbb73FzJkz9Q7N4YSHh/Pee+/x5Zdf8tdff7F06VJWrlzJO++8o3doIp+khcqOlShRAmdnZ86dO5dh/7lz5/D398/0HH9//1wdL/L2PptNmTKF999/n/Xr11O3bl1bhml4uX2fjx07xokTJ3jssccs+9LS0gBwcXHh8OHDVK5c2bZBG1Befp4DAgIoVKgQzs7Oln1BQUHExsaSlJSEq6urTWM2qry81//973957rnneOmllwCoU6cO165dY+DAgbz11ls4OUk7R35l9T3o6elps9YpkBYqu+bq6kqjRo3YsGGDZV9aWhobNmwgODg403OCg4MzHA+wbt26LI8XeXuf/9/e/bukt8dxHH/fiGNDvyapwQKFDCpwiBoapH+gmtrEraFaBTeDfhARIkQ0FjgU4VgRURREEkRoCElR0Q+ooKFBKKjofYdLcu/tckk/9+u5wvMBZ9EjvM4b0Rcfj+eIiMzMzMj4+Lhsbm5KZ2dnKaKWtULn3NraKplMRtLpdH7r6+uT3t5eSafT4nK5Shm/bBTzfu7p6ZGLi4t8YRUROT8/l8bGRsrUvyhm1i8vL99K01eRVW6t+5+w7Xvwl57yDmMrKyvqcDh0aWlJT09PdWhoSOvr6/Xx8VFVVQOBgIbD4fz+BwcHWllZqbOzs5rNZjUSiXDZhB8odM7T09NqWZYmEgl9eHjIb7lczq5DKAuFzvnv+JffzxQ659vbW62pqdHR0VE9OzvTtbU1dTqdOjExYdchlI1CZx2JRLSmpkaXl5f16upKt7a21OPx6ODgoF2H8L+Xy+U0lUppKpVSEdFoNKqpVEpvbm5UVTUcDmsgEMjv/3XZhFAopNlsVufn57lsAv4wNzenTU1NalmWdnV16eHhYf45v9+vwWDwL/uvrq5qS0uLWpalbW1tur6+XuLE5amQOTc3N6uIfNsikUjpg5eZQt/Pf0ah+rlC55xMJrW7u1sdDoe63W6dnJzUj4+PEqcuT4XM+v39XcfGxtTj8WhVVZW6XC4dHh7W5+fn0gcvE7u7u//4efs112AwqH6//9trfD6fWpalbrdbFxcXf3nO31RZYwQAADDBOVQAAACGKFQAAACGKFQAAACGKFQAAACGKFQAAACGKFQAAACGKFQAAACGKFQAAACGKFQAAACGKFQAAACGKFQAAACGKFQAUISnpydpaGiQqamp/GPJZFIsy5KdnR0bkwGwAzdHBoAibWxsyMDAgCSTSfF6veLz+aS/v1+i0ajd0QCUGIUKAAyMjIzI9va2dHZ2SiaTkaOjI3E4HHbHAlBiFCoAMPD6+irt7e1yd3cnx8fH0tHRYXckADbgHCoAMHB5eSn39/fy+fkp19fXdscBYBNWqACgSG9vb9LV1SU+n0+8Xq/EYjHJZDLidDrtjgagxChUAFCkUCgkiURCTk5OpLq6Wvx+v9TV1cna2prd0QCUGD/5AUAR9vb2JBaLSTwel9raWqmoqJB4PC77+/uysLBgdzwAJcYKFQAAgCFWqAAAAAxRqAAAAAxRqAAAAAxRqAAAAAxRqAAAAAxRqAAAAAxRqAAAAAxRqAAAAAxRqAAAAAxRqAAAAAxRqAAAAAz9Dp0uYHn/Q5u1AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trainer = deepxde.Trainer(data)\n",
+    "trainer.compile(bst.optim.Adam(0.001), metrics=[\"l2 relative error\"]).train(iterations=10000)\n",
+    "trainer.saveplot(issave=True, isplot=True)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/experimental_docs/unit-examples-forward/Euler_beam.py b/docs/experimental_docs/unit-examples-forward/Euler_beam.py
new file mode 100644
index 000000000..111ddce50
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/Euler_beam.py
@@ -0,0 +1,63 @@
+import brainstate as bst
+import brainunit as u
+
+import deepxde.experimental as deepxde
+
+unit_of_u = u.meter
+unit_of_x = u.meter
+unit_of_E = u.pascal
+unit_of_I = u.meter ** 4
+unit_of_p = u.kilogram / u.second ** 2
+
+geom = deepxde.geometry.Interval(0, 1).to_dict_point(x=unit_of_x)
+
+E = 1 * unit_of_E
+I = 1 * unit_of_I
+p = -1. * unit_of_p
+
+
+def pde(x, y):
+    dy_xxxx = net.gradient(x, order=4)['y']['x']['x']['x']['x']
+    return E * I * dy_xxxx - p
+
+
+def boundary_l(x, on_boundary):
+    return u.math.logical_and(on_boundary, deepxde.utils.isclose(x['x'] / unit_of_x, 0))
+
+
+def boundary_r(x, on_boundary):
+    return u.math.logical_and(on_boundary, deepxde.utils.isclose(x['x'] / unit_of_x, 1))
+
+
+bc1 = deepxde.icbc.DirichletBC(lambda x: {'y': 0 * unit_of_u}, boundary_l)
+bc2 = deepxde.icbc.NeumannBC(lambda x: {'y': 0 * unit_of_u}, boundary_l)
+bc3 = deepxde.icbc.OperatorBC(lambda x, y: net.hessian(x)['y']['x']['x'] / u.meter, boundary_r)
+bc4 = deepxde.icbc.OperatorBC(lambda x, y: net.gradient(x, order=3)['y']['x']['x']['x'] / u.meter ** 2, boundary_r)
+
+net = deepxde.nn.Model(
+    deepxde.nn.DictToArray(x=unit_of_x),
+    deepxde.nn.FNN([1] + [20] * 3 + [1], "tanh"),
+    deepxde.nn.ArrayToDict(y=unit_of_u),
+)
+
+
+def func(x):
+    x = x['x'] / unit_of_x
+    y = -(x ** 4) / 24 + x ** 3 / 6 - x ** 2 / 4
+    return {'y': y * unit_of_u}
+
+
+data = deepxde.problem.PDE(
+    geom,
+    pde,
+    [bc1, bc2, bc3, bc4],
+    net,
+    num_domain=100,
+    num_boundary=20,
+    solution=func,
+    num_test=100,
+)
+
+trainer = deepxde.Trainer(data)
+trainer.compile(bst.optim.Adam(0.001), metrics=["l2 relative error"]).train(iterations=10000)
+trainer.saveplot(issave=True, isplot=True)
diff --git a/docs/experimental_docs/unit-examples-forward/Helmholtz_Dirichlet_2d.ipynb b/docs/experimental_docs/unit-examples-forward/Helmholtz_Dirichlet_2d.ipynb
new file mode 100644
index 000000000..ac1491782
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/Helmholtz_Dirichlet_2d.ipynb
@@ -0,0 +1,356 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Helmholtz equation over a 2D square domain\n",
+    "\n",
+    "## Problem setup\n",
+    "For a wave number $k_0 = 2\\pi n$ with $n = 2$, we will solve a Helmholtz equation:\n",
+    "\n",
+    "$$\n",
+    "- u_{xx}-u_{yy} - k_0^2 u = f, \\qquad  \\Omega = [0,1]^2\n",
+    "$$\n",
+    "\n",
+    "with the Dirichlet boundary conditions\n",
+    "\n",
+    "$$\n",
+    "u(x,y)=0, \\qquad (x,y)\\in \\partial \\Omega\n",
+    "$$\n",
+    "\n",
+    "and a source term $f(x,y) = k_0^2 \\sin(k_0 x)\\sin(k_0 y)$.\n",
+    "\n",
+    "Remark that the exact solution reads:\n",
+    "$$\n",
+    "u(x,y)= \\sin(k_0 x)\\sin(k_0 y)\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "## Dimensional Analysis\n",
+    "\n",
+    "### **Assigning Physical Units:**\n",
+    "\n",
+    "To perform dimensional analysis, we will assign physical units to each variable and parameter in the equation. We'll ensure that both sides of the Helmholtz equation have consistent dimensions.\n",
+    "\n",
+    "#### **Variables and Parameters:**\n",
+    "\n",
+    "| **Variable/Parameter** | **Symbol** | **Physical Quantity**             | **Unit (SI)**                                                                   | **Dimension**            |\n",
+    "|------------------------|------------|-----------------------------------|---------------------------------------------------------------------------------|--------------------------|\n",
+    "| **Field Variable**     | $ u $    | Scalar field (e.g., displacement, pressure) | **Dimensionless** or [U] [Depends on Physical Context]                          | $[U]$                 |\n",
+    "| **Spatial Coordinate** | $ x, y $  | Position in space                  | meters (m)                                                                      | Length $[L]$           |\n",
+    "| **Wave Number**        | $ k_0 $  | Spatial frequency                  | inverse meters (1/m)                                                            | $[L]^{-1}$             |\n",
+    "| **Source Term**        | $ f $    | External forcing or source        | Depends on $ u $'s units (e.g., if $ u $ is dimensionless, f has units of 1/m²) | $[U][L]^{-2}$          |\n",
+    "\n",
+    "> **Note:** The units of $ u $ can vary based on the physical context of the problem. However, based on the exact solution provided, $ u(x,y) = \\sin(k_0 x) \\sin(k_0 y) $, it suggests that $ u $ is **dimensionless**. Therefore, for this analysis, we'll assume $ u $ is dimensionless.\n",
+    "\n",
+    "#### **Detailed Assignments:**\n",
+    "\n",
+    "1. **Field Variable ($ u $):**\n",
+    "   - **Physical Quantity:** Scalar field (e.g., displacement, pressure)\n",
+    "   - **Unit:** **Dimensionless**\n",
+    "   - **Dimension:** $[1]$\n",
+    "   \n",
+    "2. **Spatial Coordinates ($ x, y $):**\n",
+    "   - **Physical Quantity:** Position in space\n",
+    "   - **Unit:** meters (m)\n",
+    "   - **Dimension:** Length $[L]$\n",
+    "   \n",
+    "3. **Wave Number ($ k_0 $):**\n",
+    "   - **Physical Quantity:** Spatial frequency\n",
+    "   - **Unit:** inverse meters (1/m)\n",
+    "   - **Dimension:** $[L]^{-1}$\n",
+    "   \n",
+    "4. **Source Term ($ f $):**\n",
+    "   - **Physical Quantity:** External forcing or source\n",
+    "   - **Unit:** inverse meters squared (1/m²)\n",
+    "   - **Dimension:** $[L]^{-2}$\n",
+    "   \n",
+    "#### **Dimensional Consistency Check:**\n",
+    "\n",
+    "To ensure the Helmholtz equation is dimensionally consistent, both sides of the equation must have the same dimensions.\n",
+    "\n",
+    "1. **Left Side ($ -u_{xx} - u_{yy} - k_0^2 u $):**\n",
+    "   - $ u_{xx} = \\frac{\\partial^2 u}{\\partial x^2} $: \n",
+    "     - Dimension: $\\frac{[U]}{[L]^2}$ \n",
+    "     - Since $ u $ is dimensionless: $[U] = 1$, so $ u_{xx} $ has dimension $[L]^{-2}$.\n",
+    "     \n",
+    "   - $ u_{yy} = \\frac{\\partial^2 u}{\\partial y^2} $:\n",
+    "     - Dimension: Same as $ u_{xx} $, i.e., $[L]^{-2}$.\n",
+    "     \n",
+    "   - $ k_0^2 u $:\n",
+    "     - Dimension: $[k_0]^2 [U] = [L]^{-2} \\times 1 = [L]^{-2}$.\n",
+    "     \n",
+    "   - **Combined Left Side:** Each term has dimension $[L]^{-2}$, ensuring consistency.\n",
+    "   \n",
+    "2. **Right Side ($ f $):**\n",
+    "   - Dimension: $[L]^{-2}$.\n",
+    "   \n",
+    "   - **Conclusion:** Both sides of the equation have the same dimension $[L]^{-2}$, confirming dimensional consistency.\n",
+    "\n",
+    "### **Summary of Physical Units:**\n",
+    "\n",
+    "| **Symbol** | **Physical Quantity**                     | **Unit (SI)**       | **Dimension**            |\n",
+    "|------------|-------------------------------------------|---------------------|--------------------------|\n",
+    "| $ u $    | Scalar field (dimensionless)              | Dimensionless       | $[1]$                  |\n",
+    "| $ x, y $  | Spatial coordinates                       | meters (m)          | Length $[L]$           |\n",
+    "| $ k_0 $  | Wave number                               | inverse meters (1/m)| $[L]^{-1}$             |\n",
+    "| $ f $    | Source term                               | inverse meters squared (1/m²)| $[L]^{-2}$          |\n",
+    "\n",
+    "### **Boundary Conditions Units:**\n",
+    "\n",
+    "1. **Dirichlet Boundary Conditions ($ u(x,y) = 0 $):**\n",
+    "   - **Units:** Same as $ u $, which is **dimensionless**.\n",
+    "   \n",
+    "2. **Exact Solution ($ u(x,y) = \\sin(k_0 x) \\sin(k_0 y) $):**\n",
+    "   - **Units:** Dimensionless, consistent with $ u $'s units.\n",
+    "\n",
+    "### **Conclusion:**\n",
+    "\n",
+    "All variables and parameters in the Helmholtz equation have been assigned consistent physical units, ensuring the dimensional integrity of the equation and its boundary conditions. Specifically:\n",
+    "\n",
+    "- **$ u $** is dimensionless.\n",
+    "- **$ x $** and **$ y $** are measured in meters (m).\n",
+    "- **$ k_0 $** has units of inverse meters (1/m).\n",
+    "- **$ f $** has units of inverse meters squared (1/m²).\n",
+    "\n",
+    "This dimensional assignment ensures that the Helmholtz equation is dimensionally consistent and the boundary conditions are appropriately defined.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Code Implementation\n",
+    "\n",
+    "First, import the necessary libraries and modules for the problem setup and solution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T14:18:10.846957Z",
+     "start_time": "2024-12-17T14:18:07.057723Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import brainstate as bst\n",
+    "import brainunit as u\n",
+    "import numpy as np\n",
+    "\n",
+    "import deepxde.experimental as deepxde"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the physical units and parameters for the Helmholtz equation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T14:18:10.855991Z",
+     "start_time": "2024-12-17T14:18:10.850982Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "unit_of_u = u.UNITLESS\n",
+    "unit_of_x = u.meter\n",
+    "unit_of_y = u.meter\n",
+    "unit_of_k0 = 1 / unit_of_x\n",
+    "unit_of_f = 1 / u.meter ** 2\n",
+    "\n",
+    "# General parameters\n",
+    "n = 2\n",
+    "precision_train = 10\n",
+    "precision_test = 30\n",
+    "hard_constraint = True  # True or False\n",
+    "weights = 100  # if hard_constraint == False\n",
+    "iterations = 5000\n",
+    "parameters = [1e-3, 3, 150]\n",
+    "\n",
+    "learning_rate, num_dense_layers, num_dense_nodes = parameters\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the PDE function for the Helmholtz equation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T14:18:10.873024Z",
+     "start_time": "2024-12-17T14:18:10.867395Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "geom = deepxde.geometry.Rectangle([0, 0], [1, 1]).to_dict_point(x=unit_of_x, y=unit_of_y)\n",
+    "k0 = 2 * np.pi * n\n",
+    "wave_len = 1 / n\n",
+    "\n",
+    "hx_train = wave_len / precision_train\n",
+    "nx_train = int(1 / hx_train)\n",
+    "\n",
+    "hx_test = wave_len / precision_test\n",
+    "nx_test = int(1 / hx_test)\n",
+    "\n",
+    "\n",
+    "def pde(x, y):\n",
+    "    hessian = net.hessian(x)\n",
+    "\n",
+    "    dy_xx = hessian[\"y\"][\"x\"][\"x\"]\n",
+    "    dy_yy = hessian[\"y\"][\"y\"][\"y\"]\n",
+    "\n",
+    "    f = k0 ** 2 * u.math.sin(k0 * x['x'] / unit_of_x) * u.math.sin(k0 * x['y'] / unit_of_y)\n",
+    "    return -dy_xx - dy_yy - (k0 * unit_of_k0) ** 2 * y['y'] - f * unit_of_f\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the boundary conditions for the Helmholtz equation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T14:18:11.385027Z",
+     "start_time": "2024-12-17T14:18:10.883839Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "if hard_constraint:\n",
+    "    bc = []\n",
+    "else:\n",
+    "    bc = deepxde.icbc.DirichletBC(lambda x: {'y': 0 * unit_of_u})\n",
+    "\n",
+    "net = deepxde.nn.Model(\n",
+    "    deepxde.nn.DictToArray(x=unit_of_x, y=unit_of_y),\n",
+    "    deepxde.nn.FNN([2] + [num_dense_nodes] * num_dense_layers + [1],\n",
+    "                   u.math.sin,\n",
+    "                   bst.init.KaimingUniform()),\n",
+    "    deepxde.nn.ArrayToDict(y=unit_of_u),\n",
+    ")\n",
+    "\n",
+    "if hard_constraint:\n",
+    "    def transform(x, y):\n",
+    "        x = deepxde.utils.array_to_dict(x, [\"x\", \"y\"], keep_dim=True)\n",
+    "        res = x['x'] * (1 - x['x']) * x['y'] * (1 - x['y'])\n",
+    "        return res * y\n",
+    "\n",
+    "\n",
+    "    net.approx.apply_output_transform(transform)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the problem and train the model to solve the Helmholtz equation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T14:18:11.811162Z",
+     "start_time": "2024-12-17T14:18:11.398719Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "problem = deepxde.problem.PDE(\n",
+    "    geom,\n",
+    "    pde,\n",
+    "    bc,\n",
+    "    net,\n",
+    "    num_domain=nx_train ** 2,\n",
+    "    num_boundary=4 * nx_train,\n",
+    "    solution=lambda x: {'y': u.math.sin(k0 * x['x'] / unit_of_x) * u.math.sin(k0 * x['y'] / unit_of_y) * unit_of_u},\n",
+    "    num_test=nx_test ** 2,\n",
+    "    loss_weights=None if hard_constraint else [1, weights],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the model using the Adam optimizer and the specified learning rate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T14:19:14.913372200Z",
+     "start_time": "2024-12-17T14:18:11.822636Z"
+    },
+    "jupyter": {
+     "is_executing": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.059387 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                         Test loss                        Test metric                                       \n",
+      "0         [5213.1675 * metre ** -4]          [6450.17 * metre ** -4]          [{'y': Array(1.0007389, dtype=float32)}]          \n",
+      "1000      [115.11537 * metre ** -4]          [164.17776 * metre ** -4]        [{'y': Array(0.50004345, dtype=float32)}]         \n"
+     ]
+    }
+   ],
+   "source": [
+    "trainer = deepxde.Trainer(problem)\n",
+    "trainer.compile(bst.optim.Adam(learning_rate), metrics=[\"l2 relative error\"]).train(iterations=iterations)\n",
+    "trainer.saveplot(issave=True, isplot=True)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pinnx",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/experimental_docs/unit-examples-forward/Helmholtz_Dirichlet_2d.py b/docs/experimental_docs/unit-examples-forward/Helmholtz_Dirichlet_2d.py
new file mode 100644
index 000000000..ca7ab60a2
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/Helmholtz_Dirichlet_2d.py
@@ -0,0 +1,81 @@
+import brainstate as bst
+import brainunit as u
+import numpy as np
+
+import deepxde.experimental as deepxde
+
+unit_of_u = u.UNITLESS
+unit_of_x = u.meter
+unit_of_y = u.meter
+unit_of_k0 = 1 / unit_of_x
+unit_of_f = 1 / u.meter ** 2
+
+# General parameters
+n = 2
+precision_train = 10
+precision_test = 30
+hard_constraint = True  # True or False
+weights = 100  # if hard_constraint == False
+iterations = 5000
+parameters = [1e-3, 3, 150]
+
+learning_rate, num_dense_layers, num_dense_nodes = parameters
+
+geom = deepxde.geometry.Rectangle([0, 0], [1, 1]).to_dict_point(x=unit_of_x, y=unit_of_y)
+k0 = 2 * np.pi * n
+wave_len = 1 / n
+
+hx_train = wave_len / precision_train
+nx_train = int(1 / hx_train)
+
+hx_test = wave_len / precision_test
+nx_test = int(1 / hx_test)
+
+def pde(x, y):
+    hessian = net.hessian(x)
+
+    dy_xx = hessian["y"]["x"]["x"]
+    dy_yy = hessian["y"]["y"]["y"]
+
+    f = k0 ** 2 * u.math.sin(k0 * x['x'] / unit_of_x) * u.math.sin(k0 * x['y'] / unit_of_y)
+    return -dy_xx - dy_yy - (k0 * unit_of_k0) ** 2 * y['y'] - f * unit_of_f
+
+
+
+if hard_constraint:
+    bc = []
+else:
+    bc = deepxde.icbc.DirichletBC(lambda x: {'y': 0 * unit_of_u})
+
+net = deepxde.nn.Model(
+    deepxde.nn.DictToArray(x=unit_of_x, y=unit_of_y),
+    deepxde.nn.FNN([2] + [num_dense_nodes] * num_dense_layers + [1],
+                   u.math.sin,
+                   bst.init.KaimingUniform()),
+    deepxde.nn.ArrayToDict(y=unit_of_u),
+)
+
+if hard_constraint:
+    def transform(x, y):
+        x = deepxde.utils.array_to_dict(x, ["x", "y"], keep_dim=True)
+        res = x['x'] * (1 - x['x']) * x['y'] * (1 - x['y'])
+        return res * y
+
+
+    net.approx.apply_output_transform(transform)
+
+problem = deepxde.problem.PDE(
+    geom,
+    pde,
+    bc,
+    net,
+    num_domain=nx_train ** 2,
+    num_boundary=4 * nx_train,
+    solution=lambda x: {'y': u.math.sin(k0 * x['x'] / unit_of_x) * u.math.sin(k0 * x['y'] / unit_of_y) * unit_of_u},
+    num_test=nx_test ** 2,
+    loss_weights=None if hard_constraint else [1, weights],
+)
+
+trainer = deepxde.Trainer(problem)
+trainer.compile(bst.optim.Adam(learning_rate), metrics=["l2 relative error"]).train(iterations=iterations)
+trainer.saveplot(issave=True, isplot=True)
diff --git a/docs/experimental_docs/unit-examples-forward/Laplace_disk.ipynb b/docs/experimental_docs/unit-examples-forward/Laplace_disk.ipynb
new file mode 100644
index 000000000..ca2547cf5
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/Laplace_disk.ipynb
@@ -0,0 +1,535 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Laplace equation on a disk\n",
+    "## Problem setup\n",
+    "We will solve a Laplace equation in a polar coordinate system:\n",
+    "\n",
+    "$$\n",
+    "r\\frac{dy}{dr} + r^2\\frac{dy^2}{dr^2} + \\frac{dy^2}{d\\theta^2} = 0,  \\qquad r \\in [0, 1], \\quad \\theta \\in [0, 2\\pi]\n",
+    "$$\n",
+    "\n",
+    "with the Dirichlet boundary condition\n",
+    "\n",
+    "$$\n",
+    "y(1,\\theta) = \\cos(\\theta)\n",
+    "$$\n",
+    "\n",
+    "and the periodic boundary condition\n",
+    "\n",
+    "$$\n",
+    "y(r, \\theta +2\\pi) = y(r, \\theta).\n",
+    "$$\n",
+    "\n",
+    "The reference solution is $y=r\\cos(\\theta)$.\n",
+    "\n",
+    "# Dimensional Analysis for the Laplace Equation on a Disk\n",
+    "\n",
+    "## Problem Setup\n",
+    "\n",
+    "We will solve the Laplace equation in a polar coordinate system:\n",
+    "\n",
+    "$$\n",
+    "r\\frac{dy}{dr} + r^2\\frac{d^2y}{dr^2} + \\frac{d^2y}{d\\theta^2} = 0, \\qquad r \\in [0, 1], \\quad \\theta \\in [0, 2\\pi]\n",
+    "$$\n",
+    "\n",
+    "with the Dirichlet boundary condition:\n",
+    "\n",
+    "$$\n",
+    "y(1,\\theta) = \\cos(\\theta)\n",
+    "$$\n",
+    "\n",
+    "and the periodic boundary condition:\n",
+    "\n",
+    "$$\n",
+    "y(r, \\theta + 2\\pi) = y(r, \\theta).\n",
+    "$$\n",
+    "\n",
+    "The reference solution is:\n",
+    "\n",
+    "$$\n",
+    "y = r\\cos(\\theta).\n",
+    "$$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Dimensional Analysis\n",
+    "\n",
+    "### Step 1: Assign Dimensions to Variables\n",
+    "\n",
+    "1. **Radial Coordinate $r$:**\n",
+    "   - The dimension of $r$ is length:\n",
+    "\n",
+    "     $$\n",
+    "     [r] = L.\n",
+    "     $$\n",
+    "\n",
+    "2. **Angular Coordinate $\\theta$:**\n",
+    "   - The dimension of $\\theta$ is dimensionless:\n",
+    "\n",
+    "     $$\n",
+    "     [\\theta] = 1.\n",
+    "     $$\n",
+    "\n",
+    "3. **Solution $y$:**\n",
+    "   - The solution $y$ represents a physical quantity, which we assume to be in volts (V):\n",
+    "\n",
+    "     $$\n",
+    "     [y] = V.\n",
+    "     $$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 2: Analyze the Dimensions of Each Term\n",
+    "\n",
+    "1. **First Derivative Term $r\\frac{dy}{dr}$:**\n",
+    "   - The first derivative $\\frac{dy}{dr}$ has dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\frac{dy}{dr}\\right] = \\frac{[y]}{[r]} = \\frac{V}{L}.\n",
+    "     $$\n",
+    "   - Therefore, the term $r\\frac{dy}{dr}$ has dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[r\\frac{dy}{dr}\\right] = [r] \\cdot \\frac{V}{L} = L \\cdot \\frac{V}{L} = V.\n",
+    "     $$\n",
+    "\n",
+    "2. **Second Derivative Term $r^2\\frac{d^2y}{dr^2}$:**\n",
+    "   - The second derivative $\\frac{d^2y}{dr^2}$ has dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\frac{d^2y}{dr^2}\\right] = \\frac{[y]}{[r]^2} = \\frac{V}{L^2}.\n",
+    "     $$\n",
+    "   - Therefore, the term $r^2\\frac{d^2y}{dr^2}$ has dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[r^2\\frac{d^2y}{dr^2}\\right] = [r]^2 \\cdot \\frac{V}{L^2} = L^2 \\cdot \\frac{V}{L^2} = V.\n",
+    "     $$\n",
+    "\n",
+    "3. **Second Derivative Term $\\frac{d^2y}{d\\theta^2}$:**\n",
+    "   - The second derivative $\\frac{d^2y}{d\\theta^2}$ has dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\frac{d^2y}{d\\theta^2}\\right] = \\frac{[y]}{[\\theta]^2} = \\frac{V}{1^2} = V.\n",
+    "     $$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 3: Verify Dimensional Consistency\n",
+    "\n",
+    "The Laplace equation in polar coordinates is:\n",
+    "\n",
+    "$$\n",
+    "r\\frac{dy}{dr} + r^2\\frac{d^2y}{dr^2} + \\frac{d^2y}{d\\theta^2} = 0.\n",
+    "$$\n",
+    "\n",
+    "Each term in the equation has dimensions of $V$:\n",
+    "\n",
+    "- $r\\frac{dy}{dr}$: $V$\n",
+    "- $r^2\\frac{d^2y}{dr^2}$: $V$\n",
+    "- $\\frac{d^2y}{d\\theta^2}$: $V$\n",
+    "\n",
+    "Since all terms have the same dimensions, the equation is dimensionally consistent.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 4: Summary of Dimensions\n",
+    "\n",
+    "| Variable/Parameter     | Physical Meaning                   | Dimensions            |\n",
+    "|------------------------|-----------------------------------|-----------------------|\n",
+    "| $r$                   | Radial coordinate                 | $L$                 |\n",
+    "| $\\theta$              | Angular coordinate                | $1$ (dimensionless) |\n",
+    "| $y$                   | Solution (e.g., voltage)          | $V$                 |\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 5: Initial and Boundary Conditions\n",
+    "\n",
+    "1. **Boundary Condition $y(1,\\theta) = \\cos(\\theta)$:**\n",
+    "   - The boundary condition $y(1,\\theta) = \\cos(\\theta)$ is given in volts:\n",
+    "   \n",
+    "     $$\n",
+    "     [y(1,\\theta)] = V.\n",
+    "     $$\n",
+    "   - The term $\\cos(\\theta)$ is dimensionless because $\\theta$ is dimensionless.\n",
+    "\n",
+    "2. **Periodic Boundary Condition $y(r, \\theta + 2\\pi) = y(r, \\theta)$:**\n",
+    "   - The periodic boundary condition ensures that the solution is periodic in $\\theta$ with period $2\\pi$.\n",
+    "   - Since $\\theta$ is dimensionless, the condition is dimensionally consistent.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implementation\n",
+    "This description goes through the implementation of a solver for the above described Heat equation step-by-step.\n",
+    "\n",
+    "First, import the libraries we need:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import brainstate as bst\n",
+    "import brainunit as u\n",
+    "import numpy as np\n",
+    "\n",
+    "import deepxde.experimental as deepxde"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We begin by defining a computational geometry. We can use a built-in class `Rectangle` as follows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geom = deepxde.geometry.Rectangle(\n",
+    "    xmin=[0, 0],\n",
+    "    xmax=[1, 2 * np.pi],\n",
+    ").to_dict_point(r=u.meter, theta=u.radian)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we express the PDE residual of the Laplace equation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pde(x, y):\n",
+    "    jacobian = net.jacobian(x)\n",
+    "    hessian = net.hessian(x)\n",
+    "\n",
+    "    dy_r = jacobian[\"y\"][\"r\"]\n",
+    "    dy_rr = hessian[\"y\"][\"r\"][\"r\"]\n",
+    "    dy_thetatheta = hessian[\"y\"][\"theta\"][\"theta\"]\n",
+    "    return x['r'] * dy_r + x['r'] ** 2 * dy_rr + dy_thetatheta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first argument to `pde` is 2-dimensional vector where the first component(`x[:,0:1]`) is $r$-coordinate and the second componenet (`x[:,1:]`) is the $\\theta$-coordinate. The second argument is the network output, i.e., the solution $y(r, \\theta)$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we consider the Dirichlet boundary condition. We need to implement a function, which should return `True` for points inside the subdomain and `False` for the points outside. In our case, if the points satisfy $r=1$ and are on the whole boundary of the rectangle domain, then function `boundary` returns `True`. Otherwise, it returns `False`. (Note that because of rounding-off errors, it is often wise to use u.math.allclose to test whether two floating point values are equivalent.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def boundary(x, on_boundary):\n",
+    "    return on_boundary and u.math.allclose(x['r'], 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The argument `x` to `boundary` is the network input and is a $d$-dim vector, where $d$ is the dimension and $d=2$ in this case. To facilitate the implementation of `boundary`, a boolean `on_boundary` is used as the second argument. If the point $r,\\theta$ (the first argument) is on the entire boundary of the rectangle geometry that created above, then `on_boundary` is `True`, otherwise, `on_boundary` is False."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using a lambda funtion, the `boundary` we defined above can be passed to `DirichletBC` as the second argument. Thus, the Dirichlet boundary condition is"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uy = u.volt / u.meter\n",
+    "bc = deepxde.icbc.DirichletBC(\n",
+    "    lambda x: {'y': u.math.cos(x['theta']) * uy},\n",
+    "    lambda x, on_boundary: u.math.logical_and(on_boundary, u.math.allclose(x['r'], 1 * u.meter)),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we rewrite this problem in cartesian coordinates, the variables are in the form of $[r\\sin(\\theta), r\\cos(\\theta)]$. We use them as features to satisfy the certain underlying physical constraints, so that the network is automatically periodic along the $\\theta$ coordinate and the period is $2\\pi$.\n",
+    "\n",
+    "Next, we choose the network. Here, we use a fully connected neural network of depth 4 (i.e., 3 hidden layers) and width 20:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use [r*sin(theta), r*cos(theta)] as features,\n",
+    "# so that the network is automatically periodic along the theta coordinate.\n",
+    "def feature_transform(x):\n",
+    "    x = deepxde.utils.array_to_dict(x, [\"r\", \"theta\"], keep_dim=True)\n",
+    "    return u.math.concatenate([x['r'] * u.math.sin(x['theta']),\n",
+    "                               x['r'] * u.math.cos(x['theta'])], axis=-1)\n",
+    "\n",
+    "net = deepxde.nn.Model(\n",
+    "    deepxde.nn.DictToArray(r=u.meter, theta=u.radian),\n",
+    "    deepxde.nn.FNN([2] + [20] * 3 + [1], \"tanh\", input_transform=feature_transform),\n",
+    "    deepxde.nn.ArrayToDict(y=uy),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we have specified the geometry, PDE residual, and boundary condition. We then define the `PDE` problem as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The argument `solution` is the reference solution to compute the error of our solution, and we define it as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def solution(x):\n",
+    "    r, theta = x['r'], x['theta']\n",
+    "    return {'y': r * u.math.cos(theta) * uy / u.meter}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "problem = deepxde.problem.PDE(\n",
+    "    geom,\n",
+    "    pde,\n",
+    "    bc,\n",
+    "    net,\n",
+    "    num_domain=2540,\n",
+    "    num_boundary=80,\n",
+    "    solution=solution\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we have the PDE problem and the network. We bulid a `trainer` and choose the optimizer and learning rate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.093740 s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pinnx.Trainer at 0x141369fd0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer = deepxde.Trainer(problem)\n",
+    "trainer.compile(bst.optim.Adam(1e-3), metrics=[\"l2 relative error\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then train the model for 15000 iterations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                            Test loss                                                             Test metric                                       \n",
+      "0         [3.4136772 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,          [3.4136772 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,          [{'y': Array(1.9016247, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': 1.2442183 * volt / meter}}]                            {'ibc0': {'y': 1.2442183 * volt / meter}}]                                                                             \n",
+      "1000      [0.00209501 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,         [0.00209501 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,         [{'y': Array(0.03482116, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 9.6204014e-05 * volt / meter}}]                        {'ibc0': {'y': 9.6204014e-05 * volt / meter}}]                                                                         \n",
+      "2000      [0.00059394 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,         [0.00059394 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,         [{'y': Array(0.02848322, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 1.8821578e-05 * volt / meter}}]                        {'ibc0': {'y': 1.8821578e-05 * volt / meter}}]                                                                         \n",
+      "3000      [0.0003004 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,          [0.0003004 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,          [{'y': Array(0.01964555, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 1.1315266e-05 * volt / meter}}]                        {'ibc0': {'y': 1.1315266e-05 * volt / meter}}]                                                                         \n",
+      "4000      [0.0001739 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,          [0.0001739 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,          [{'y': Array(0.0129396, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': 8.526638e-06 * volt / meter}}]                         {'ibc0': {'y': 8.526638e-06 * volt / meter}}]                                                                          \n",
+      "5000      [0.00010057 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,         [0.00010057 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,         [{'y': Array(0.00673189, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 6.3102784e-06 * volt / meter}}]                        {'ibc0': {'y': 6.3102784e-06 * volt / meter}}]                                                                         \n",
+      "6000      [5.6971734e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,      [5.6971734e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,      [{'y': Array(0.00247048, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 4.33217e-06 * volt / meter}}]                          {'ibc0': {'y': 4.33217e-06 * volt / meter}}]                                                                           \n",
+      "7000      [3.255158e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,       [3.255158e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,       [{'y': Array(0.0019735, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': 2.83005e-06 * volt / meter}}]                          {'ibc0': {'y': 2.83005e-06 * volt / meter}}]                                                                           \n",
+      "8000      [2.4938374e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,      [2.4938374e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,      [{'y': Array(0.00377944, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 4.333744e-06 * volt / meter}}]                         {'ibc0': {'y': 4.333744e-06 * volt / meter}}]                                                                          \n",
+      "9000      [1.1950426e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,      [1.1950426e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,      [{'y': Array(0.00174527, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 1.2896384e-06 * volt / meter}}]                        {'ibc0': {'y': 1.2896384e-06 * volt / meter}}]                                                                         \n",
+      "10000     [8.125793e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,       [8.125793e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,       [{'y': Array(0.00141424, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 9.607884e-07 * volt / meter}}]                         {'ibc0': {'y': 9.607884e-07 * volt / meter}}]                                                                          \n",
+      "11000     [7.288996e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,       [7.288996e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,       [{'y': Array(0.00191965, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 1.3772864e-06 * volt / meter}}]                        {'ibc0': {'y': 1.3772864e-06 * volt / meter}}]                                                                         \n",
+      "12000     [5.566375e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,       [5.566375e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,       [{'y': Array(0.00146894, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 9.980397e-07 * volt / meter}}]                         {'ibc0': {'y': 9.980397e-07 * volt / meter}}]                                                                          \n",
+      "13000     [4.0166346e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,      [4.0166346e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,      [{'y': Array(0.00078307, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 4.9924233e-07 * volt / meter}}]                        {'ibc0': {'y': 4.9924233e-07 * volt / meter}}]                                                                         \n",
+      "14000     [3.4733355e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,      [3.4733355e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,      [{'y': Array(0.00065782, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 4.2479667e-07 * volt / meter}}]                        {'ibc0': {'y': 4.2479667e-07 * volt / meter}}]                                                                         \n",
+      "15000     [4.31375e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,        [4.31375e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2,        [{'y': Array(0.0016097, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': 1.0574405e-06 * volt / meter}}]                        {'ibc0': {'y': 1.0574405e-06 * volt / meter}}]                                                                         \n",
+      "\n",
+      "Best trainer at step 14000:\n",
+      "  train loss: 3.90e-06\n",
+      "  test loss: 3.90e-06\n",
+      "  test metric: [{'y': Array(0., dtype=float32)}]\n",
+      "\n",
+      "'train' took 56.701270 s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pinnx.Trainer at 0x141369fd0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer.train(iterations=15000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We also save and plot the best trained result and loss history."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving loss history to /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/loss.dat ...\n",
+      "Saving checkpoint into /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/loss.dat\n",
+      "Saving training data to /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/train.dat ...\n",
+      "Saving checkpoint into /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/train.dat\n",
+      "Saving test data to /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/test.dat ...\n",
+      "Saving checkpoint into /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/test.dat\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trainer.saveplot(issave=True, isplot=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pinnx",
+   "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": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/experimental_docs/unit-examples-forward/Laplace_disk.py b/docs/experimental_docs/unit-examples-forward/Laplace_disk.py
new file mode 100644
index 000000000..73acafb9e
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/Laplace_disk.py
@@ -0,0 +1,64 @@
+import brainstate as bst
+import brainunit as u
+import numpy as np
+
+import deepxde.experimental as deepxde
+
+# geom = experimental.geometry.Rectangle(xmin=[0, 0], xmax=[1, 2 * np.pi])
+# geom = geom.to_dict_point("r", "theta")
+
+geom = deepxde.geometry.Rectangle(
+    xmin=[0, 0],
+    xmax=[1, 2 * np.pi],
+).to_dict_point(r=u.meter, theta=u.radian)
+
+uy = u.volt / u.meter
+bc = deepxde.icbc.DirichletBC(
+    lambda x: {'y': u.math.cos(x['theta']) * uy},
+    lambda x, on_boundary: u.math.logical_and(on_boundary, u.math.allclose(x['r'], 1 * u.meter)),
+)
+
+
+def solution(x):
+    r, theta = x['r'], x['theta']
+    # TODO: Why add more divide u.meter?
+    return {'y': r * u.math.cos(theta) * uy / u.meter}
+
+
+def pde(x, y):
+    jacobian = net.jacobian(x)
+    hessian = net.hessian(x)
+
+    dy_r = jacobian["y"]["r"]
+    dy_rr = hessian["y"]["r"]["r"]
+    dy_thetatheta = hessian["y"]["theta"]["theta"]
+    return x['r'] * dy_r + x['r'] ** 2 * dy_rr + dy_thetatheta
+
+
+# Use [r*sin(theta), r*cos(theta)] as features,
+# so that the network is automatically periodic along the theta coordinate.
+def feature_transform(x):
+    x = deepxde.utils.array_to_dict(x, ["r", "theta"], keep_dim=True)
+    return u.math.concatenate([x['r'] * u.math.sin(x['theta']),
+                               x['r'] * u.math.cos(x['theta'])], axis=-1)
+
+
+net = deepxde.nn.Model(
+    deepxde.nn.DictToArray(r=u.meter, theta=u.radian),
+    deepxde.nn.FNN([2] + [20] * 3 + [1], "tanh", input_transform=feature_transform),
+    deepxde.nn.ArrayToDict(y=uy),
+)
+
+problem = deepxde.problem.PDE(
+    geom,
+    pde,
+    bc,
+    net,
+    num_domain=2540,
+    num_boundary=80,
+    solution=solution
+)
+
+trainer = deepxde.Trainer(problem)
+trainer.compile(bst.optim.Adam(1e-3), metrics=["l2 relative error"]).train(iterations=15000)
+trainer.saveplot(issave=True, isplot=True)
diff --git a/docs/experimental_docs/unit-examples-forward/burgers.ipynb b/docs/experimental_docs/unit-examples-forward/burgers.ipynb
new file mode 100644
index 000000000..d50c1c462
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/burgers.ipynb
@@ -0,0 +1,637 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e0159dcbb63a3365",
+   "metadata": {},
+   "source": [
+    "# Burgers equation\n",
+    "\n",
+    "\n",
+    "## Problem setup\n",
+    "\n",
+    "\n",
+    "We will solve a Burgers equation:\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\partial u}{\\partial t} + u\\frac{\\partial u}{\\partial x} = \\nu\\frac{\\partial^2u}{\\partial x^2}, \\qquad x \\in [-1, 1], \\quad t \\in [0, 1]\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "with the Dirichlet boundary conditions and initial conditions\n",
+    "\n",
+    "$$\n",
+    "u(-1,t)=u(1,t)=0, \\quad u(x,0) = - \\sin(\\pi x).\n",
+    "$$\n",
+    "\n",
+    "## Dimensional Analysis\n",
+    "\n",
+    "### Step 1: Assign Dimensions to Variables\n",
+    "\n",
+    "1. **Spatial Coordinate $x$:**\n",
+    "   - The dimension of $x$ is length:\n",
+    "\n",
+    "     $$\n",
+    "     [x] = L.\n",
+    "     $$\n",
+    "\n",
+    "2. **Time $t$:**\n",
+    "   - The dimension of time is:\n",
+    "\n",
+    "     $$\n",
+    "     [t] = T.\n",
+    "     $$\n",
+    "\n",
+    "3. **Velocity $u$:**\n",
+    "   - Velocity has dimensions of length per unit time:\n",
+    "\n",
+    "     $$\n",
+    "     [u] = L / T.\n",
+    "     $$\n",
+    "\n",
+    "4. **Viscosity $\\nu$:**\n",
+    "   - The term $\\nu \\frac{\\partial^2 u}{\\partial x^2}$ involves the second spatial derivative of velocity, which must have the same dimensions as the time derivative $\\frac{\\partial u}{\\partial t}$.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 2: Analyze the Dimensions of Each Term\n",
+    "\n",
+    "1. **Time Derivative Term:**\n",
+    "   - The time derivative $\\frac{\\partial u}{\\partial t}$ has dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\frac{\\partial u}{\\partial t}\\right] = \\frac{[u]}{[t]} = \\frac{L / T}{T} = \\frac{L}{T^2}.\n",
+    "     $$\n",
+    "\n",
+    "2. **Advection Term:**\n",
+    "   - The advection term $u \\frac{\\partial u}{\\partial x}$ involves the spatial derivative of velocity:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[u \\frac{\\partial u}{\\partial x}\\right] = [u] \\cdot \\frac{[u]}{[x]} = \\frac{L}{T} \\cdot \\frac{L / T}{L} = \\frac{L}{T^2}.\n",
+    "     $$\n",
+    "\n",
+    "3. **Diffusion Term:**\n",
+    "   - The diffusion term $\\nu \\frac{\\partial^2 u}{\\partial x^2}$ involves the second spatial derivative of velocity:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\frac{\\partial^2 u}{\\partial x^2}\\right] = \\frac{[u]}{[x]^2} = \\frac{L / T}{L^2} = \\frac{1}{L T}.\n",
+    "    \n",
+    "     $$\n",
+    "   - Therefore, the diffusion term has dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\nu \\frac{\\partial^2 u}{\\partial x^2}\\right] = [\\nu] \\cdot \\frac{1}{L T} = \\frac{L}{T^2}.\n",
+    "     $$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 3: Determine the Dimensions of $\\nu$\n",
+    "\n",
+    "- The diffusion term $\\nu \\frac{\\partial^2 u}{\\partial x^2}$ must have the same dimensions as the time derivative $\\frac{\\partial u}{\\partial t}$:\n",
+    "\n",
+    "  $$\n",
+    "  [\\nu] \\cdot \\frac{1}{L T} = \\frac{L}{T^2} \\implies [\\nu] = \\frac{L^2}{T}.\n",
+    "  $$\n",
+    "- Therefore, the viscosity $\\nu$ has dimensions of kinematic viscosity:\n",
+    "\n",
+    "  $$\n",
+    "  [\\nu] = \\frac{L^2}{T}.\n",
+    "  $$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 4: Summary of Dimensions\n",
+    "\n",
+    "| Variable/Parameter     | Physical Meaning                   | Dimensions            |\n",
+    "|------------------------|-----------------------------------|-----------------------|\n",
+    "| $x$                   | Spatial coordinate                | $L$                 |\n",
+    "| $t$                   | Time                              | $T$                 |\n",
+    "| $u$                   | Velocity                          | $L / T$             |\n",
+    "| $\\nu$                 | Kinematic viscosity               | $L^2 / T$           |\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 5: Initial and Boundary Conditions\n",
+    "\n",
+    "1. **Boundary Conditions:**\n",
+    "   - The boundary conditions $u(-1,t) = u(1,t) = 0$ are given in meters per second:\n",
+    "\n",
+    "     $$\n",
+    "     [u(-1,t)] = [u(1,t)] = L / T.\n",
+    "     $$\n",
+    "\n",
+    "2. **Initial Condition:**\n",
+    "   - The initial condition $u(x,0) = -\\sin(\\pi x)$ is given in meters per second:\n",
+    "   \n",
+    "     $$\n",
+    "     [u(x,0)] = L / T.\n",
+    "     $$\n",
+    "   - The term $\\sin(\\pi x)$ is dimensionless because $x$ is in meters, and $\\pi$ is a dimensionless constant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f173a598aa4fb4",
+   "metadata": {},
+   "source": [
+    "## Implementation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a491f73861dcaa4",
+   "metadata": {},
+   "source": [
+    "This description goes through the implementation of a solver for the above described Burgers equation step-by-step.\n",
+    "\n",
+    "First, import the libraries we need:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a6e9a11ec74e35dd",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-26T08:31:14.883767Z",
+     "start_time": "2024-11-26T08:31:12.197302Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import brainstate as bst\n",
+    "import brainunit as u\n",
+    "import numpy as np\n",
+    "import deepxde.experimental as deepxde"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95245422ff39b28d",
+   "metadata": {},
+   "source": [
+    "We begin by defining a computational geometry and time domain. We can use a built-in class ``Interval`` and ``TimeDomain`` and we combine both the domains using ``GeometryXTime`` as follows:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2b87ed2d174e56cf",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-26T08:31:14.937721Z",
+     "start_time": "2024-11-26T08:31:14.888260Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "geometry = deepxde.geometry.GeometryXTime(\n",
+    "    geometry=deepxde.geometry.Interval(-1., 1.),\n",
+    "    timedomain=deepxde.geometry.TimeDomain(0., 0.99)\n",
+    ").to_dict_point(x=u.meter, t=u.second)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "271c9ad81e74bf98",
+   "metadata": {},
+   "source": [
+    "Next, we express the PDE residual of the Burgers equation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "89d86ee9fcaa2e22",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-26T08:31:15.009040Z",
+     "start_time": "2024-11-26T08:31:14.993264Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = 0.01 / u.math.pi * u.meter ** 2 / u.second\n",
+    "\n",
+    "\n",
+    "def pde(x, y):\n",
+    "    jacobian = approximator.jacobian(x)\n",
+    "    hessian = approximator.hessian(x)\n",
+    "    dy_x = jacobian['y']['x']\n",
+    "    dy_t = jacobian['y']['t']\n",
+    "    dy_xx = hessian['y']['x']['x']\n",
+    "    residual = dy_t + y['y'] * dy_x - v * dy_xx\n",
+    "    return residual"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d8df2efba443bb4",
+   "metadata": {},
+   "source": [
+    "Next, we consider the boundary/initial condition. ``on_boundary`` is chosen here to use the whole boundary of the computational domain in considered as the boundary condition. We include the ``geomtime`` space, time geometry created above and ``on_boundary`` as the BCs in the ``DirichletBC`` function of DeepXDE. We also define ``IC`` which is the inital condition for the burgers equation and we use the computational domain, initial function, and ``on_initial`` to specify the IC.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ce3ebafdc08158a0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-26T08:31:15.018480Z",
+     "start_time": "2024-11-26T08:31:15.015094Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "uy = u.meter / u.second\n",
+    "\n",
+    "bc = deepxde.icbc.DirichletBC(lambda x: {'y': 0. * uy})\n",
+    "ic = deepxde.icbc.IC(lambda x: {'y': -u.math.sin(u.math.pi * x['x'] / u.meter) * uy})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0d5bb9643b9573b",
+   "metadata": {},
+   "source": [
+    "Next, we choose the network. Here, we use a fully connected neural network of depth 4 (i.e., 3 hidden layers) and width 20:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6c6eefc678fcc466",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-26T08:31:15.418417Z",
+     "start_time": "2024-11-26T08:31:15.025837Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "approximator = deepxde.nn.Model(\n",
+    "    deepxde.nn.DictToArray(x=u.meter, t=u.second),\n",
+    "    deepxde.nn.FNN(\n",
+    "        [geometry.dim] + [20] * 3 + [1],\n",
+    "        \"tanh\",\n",
+    "        bst.init.KaimingUniform()\n",
+    "    ),\n",
+    "    deepxde.nn.ArrayToDict(y=uy)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48a114491365b25a",
+   "metadata": {},
+   "source": [
+    "Now, we have specified the geometry, PDE residual, and boundary/initial condition. We then define the ``TimePDE`` problem as\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "aa60a6f8cad0dace",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-26T08:31:16.586859Z",
+     "start_time": "2024-11-26T08:31:15.430286Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "problem = deepxde.problem.TimePDE(\n",
+    "    geometry,\n",
+    "    pde,\n",
+    "    [bc, ic],\n",
+    "    approximator,\n",
+    "    num_domain=2540,\n",
+    "    num_boundary=80,\n",
+    "    num_initial=160,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de04e3c7d5dce9cb",
+   "metadata": {},
+   "source": [
+    "The number 2540 is the number of training residual points sampled inside the domain, and the number 80 is the number of training points sampled on the boundary. We also include 160 initial residual points for the initial conditions.\n",
+    "\n",
+    "Now, we have the PDE problem and the network. We build a ``Trainer`` and choose the optimizer and learning rate:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "29fa25c853bbc6f6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-26T08:33:20.343840Z",
+     "start_time": "2024-11-26T08:31:16.598749Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.047883 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                  Test loss                                                   Test metric\n",
+      "0         [0.15803409 * 10.0^0 * ((meter / second) / second) ** 2,    [0.15803409 * 10.0^0 * ((meter / second) / second) ** 2,    []    \n",
+      "           {'ibc0': {'y': 0.25960198 * meter / second}},               {'ibc0': {'y': 0.25960198 * meter / second}},                    \n",
+      "           {'ibc1': {'y': 1.1659584 * meter / second}}]                {'ibc1': {'y': 1.1659584 * meter / second}}]                     \n",
+      "1000      [0.04754296 * 10.0^0 * ((meter / second) / second) ** 2,    [0.04754296 * 10.0^0 * ((meter / second) / second) ** 2,    []    \n",
+      "           {'ibc0': {'y': 0.00308682 * meter / second}},               {'ibc0': {'y': 0.00308682 * meter / second}},                    \n",
+      "           {'ibc1': {'y': 0.06809452 * meter / second}}]               {'ibc1': {'y': 0.06809452 * meter / second}}]                    \n",
+      "2000      [0.04182805 * 10.0^0 * ((meter / second) / second) ** 2,    [0.04182805 * 10.0^0 * ((meter / second) / second) ** 2,    []    \n",
+      "           {'ibc0': {'y': 0.00125541 * meter / second}},               {'ibc0': {'y': 0.00125541 * meter / second}},                    \n",
+      "           {'ibc1': {'y': 0.05376936 * meter / second}}]               {'ibc1': {'y': 0.05376936 * meter / second}}]                    \n",
+      "3000      [0.03440975 * 10.0^0 * ((meter / second) / second) ** 2,    [0.03440975 * 10.0^0 * ((meter / second) / second) ** 2,    []    \n",
+      "           {'ibc0': {'y': 0.00054205 * meter / second}},               {'ibc0': {'y': 0.00054205 * meter / second}},                    \n",
+      "           {'ibc1': {'y': 0.04500021 * meter / second}}]               {'ibc1': {'y': 0.04500021 * meter / second}}]                    \n",
+      "4000      [0.0215442 * 10.0^0 * ((meter / second) / second) ** 2,     [0.0215442 * 10.0^0 * ((meter / second) / second) ** 2,     []    \n",
+      "           {'ibc0': {'y': 0.00029352 * meter / second}},               {'ibc0': {'y': 0.00029352 * meter / second}},                    \n",
+      "           {'ibc1': {'y': 0.03042006 * meter / second}}]               {'ibc1': {'y': 0.03042006 * meter / second}}]                    \n",
+      "5000      [0.01140877 * 10.0^0 * ((meter / second) / second) ** 2,    [0.01140877 * 10.0^0 * ((meter / second) / second) ** 2,    []    \n",
+      "           {'ibc0': {'y': 0.00016095 * meter / second}},               {'ibc0': {'y': 0.00016095 * meter / second}},                    \n",
+      "           {'ibc1': {'y': 0.02001206 * meter / second}}]               {'ibc1': {'y': 0.02001206 * meter / second}}]                    \n",
+      "6000      [0.00863622 * 10.0^0 * ((meter / second) / second) ** 2,    [0.00863622 * 10.0^0 * ((meter / second) / second) ** 2,    []    \n",
+      "           {'ibc0': {'y': 9.4245006e-05 * meter / second}},            {'ibc0': {'y': 9.4245006e-05 * meter / second}},                 \n",
+      "           {'ibc1': {'y': 0.01318286 * meter / second}}]               {'ibc1': {'y': 0.01318286 * meter / second}}]                    \n",
+      "7000      [0.00690631 * 10.0^0 * ((meter / second) / second) ** 2,    [0.00690631 * 10.0^0 * ((meter / second) / second) ** 2,    []    \n",
+      "           {'ibc0': {'y': 5.483637e-05 * meter / second}},             {'ibc0': {'y': 5.483637e-05 * meter / second}},                  \n",
+      "           {'ibc1': {'y': 0.00822749 * meter / second}}]               {'ibc1': {'y': 0.00822749 * meter / second}}]                    \n",
+      "8000      [0.00483667 * 10.0^0 * ((meter / second) / second) ** 2,    [0.00483667 * 10.0^0 * ((meter / second) / second) ** 2,    []    \n",
+      "           {'ibc0': {'y': 2.3079416e-05 * meter / second}},            {'ibc0': {'y': 2.3079416e-05 * meter / second}},                 \n",
+      "           {'ibc1': {'y': 0.00571595 * meter / second}}]               {'ibc1': {'y': 0.00571595 * meter / second}}]                    \n",
+      "9000      [0.00386771 * 10.0^0 * ((meter / second) / second) ** 2,    [0.00386771 * 10.0^0 * ((meter / second) / second) ** 2,    []    \n",
+      "           {'ibc0': {'y': 1.4185833e-05 * meter / second}},            {'ibc0': {'y': 1.4185833e-05 * meter / second}},                 \n",
+      "           {'ibc1': {'y': 0.00445467 * meter / second}}]               {'ibc1': {'y': 0.00445467 * meter / second}}]                    \n",
+      "10000     [0.00332004 * 10.0^0 * ((meter / second) / second) ** 2,    [0.00332004 * 10.0^0 * ((meter / second) / second) ** 2,    []    \n",
+      "           {'ibc0': {'y': 1.3227182e-05 * meter / second}},            {'ibc0': {'y': 1.3227182e-05 * meter / second}},                 \n",
+      "           {'ibc1': {'y': 0.00389429 * meter / second}}]               {'ibc1': {'y': 0.00389429 * meter / second}}]                    \n",
+      "11000     [0.00295054 * 10.0^0 * ((meter / second) / second) ** 2,    [0.00295054 * 10.0^0 * ((meter / second) / second) ** 2,    []    \n",
+      "           {'ibc0': {'y': 1.3391712e-05 * meter / second}},            {'ibc0': {'y': 1.3391712e-05 * meter / second}},                 \n",
+      "           {'ibc1': {'y': 0.00342724 * meter / second}}]               {'ibc1': {'y': 0.00342724 * meter / second}}]                    \n",
+      "12000     [0.00252938 * 10.0^0 * ((meter / second) / second) ** 2,    [0.00252938 * 10.0^0 * ((meter / second) / second) ** 2,    []    \n",
+      "           {'ibc0': {'y': 1.0651251e-05 * meter / second}},            {'ibc0': {'y': 1.0651251e-05 * meter / second}},                 \n",
+      "           {'ibc1': {'y': 0.00329811 * meter / second}}]               {'ibc1': {'y': 0.00329811 * meter / second}}]                    \n",
+      "13000     [0.00229796 * 10.0^0 * ((meter / second) / second) ** 2,    [0.00229796 * 10.0^0 * ((meter / second) / second) ** 2,    []    \n",
+      "           {'ibc0': {'y': 8.255725e-06 * meter / second}},             {'ibc0': {'y': 8.255725e-06 * meter / second}},                  \n",
+      "           {'ibc1': {'y': 0.00314907 * meter / second}}]               {'ibc1': {'y': 0.00314907 * meter / second}}]                    \n",
+      "14000     [0.00211558 * 10.0^0 * ((meter / second) / second) ** 2,    [0.00211558 * 10.0^0 * ((meter / second) / second) ** 2,    []    \n",
+      "           {'ibc0': {'y': 6.807138e-06 * meter / second}},             {'ibc0': {'y': 6.807138e-06 * meter / second}},                  \n",
+      "           {'ibc1': {'y': 0.002992 * meter / second}}]                 {'ibc1': {'y': 0.002992 * meter / second}}]                      \n",
+      "15000     [0.002326 * 10.0^0 * ((meter / second) / second) ** 2,      [0.002326 * 10.0^0 * ((meter / second) / second) ** 2,      []    \n",
+      "           {'ibc0': {'y': 7.791486e-06 * meter / second}},             {'ibc0': {'y': 7.791486e-06 * meter / second}},                  \n",
+      "           {'ibc1': {'y': 0.00277965 * meter / second}}]               {'ibc1': {'y': 0.00277965 * meter / second}}]                    \n",
+      "\n",
+      "Best trainer at step 15000:\n",
+      "  train loss: 5.11e-03\n",
+      "  test loss: 5.11e-03\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 123.689102 s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pinnx.Trainer at 0x24d2849f7d0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer = deepxde.Trainer(problem)\n",
+    "trainer.compile(bst.optim.Adam(1e-3)).train(iterations=15000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cff205141601ec3",
+   "metadata": {},
+   "source": [
+    "After we train the network using Adam, we continue to train the network using L-BFGS to achieve a smaller loss:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5013a7d8bcac6ee9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-26T08:33:36.821381Z",
+     "start_time": "2024-11-26T08:33:20.374591Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.105205 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                Test loss                                                 Test metric\n",
+      "15000     [0.002326 * 10.0^0 * ((meter / second) / second) ** 2,    [0.002326 * 10.0^0 * ((meter / second) / second) ** 2,    []    \n",
+      "           {'ibc0': {'y': 7.791486e-06 * meter / second}},           {'ibc0': {'y': 7.791486e-06 * meter / second}},                \n",
+      "           {'ibc1': {'y': 0.00277965 * meter / second}}]             {'ibc1': {'y': 0.00277965 * meter / second}}]                  \n",
+      "15200     [0.00468681 * 10.0^0 * ((meter / second) / second) ** 2,  [0.00468681 * 10.0^0 * ((meter / second) / second) ** 2,  []    \n",
+      "           {'ibc0': {'y': 6.556747e-06 * meter / second}},           {'ibc0': {'y': 6.556747e-06 * meter / second}},                \n",
+      "           {'ibc1': {'y': 0.00299743 * meter / second}}]             {'ibc1': {'y': 0.00299743 * meter / second}}]                  \n",
+      "15400     [0.00374917 * 10.0^0 * ((meter / second) / second) ** 2,  [0.00374917 * 10.0^0 * ((meter / second) / second) ** 2,  []    \n",
+      "           {'ibc0': {'y': 5.420788e-06 * meter / second}},           {'ibc0': {'y': 5.420788e-06 * meter / second}},                \n",
+      "           {'ibc1': {'y': 0.00296684 * meter / second}}]             {'ibc1': {'y': 0.00296684 * meter / second}}]                  \n",
+      "15600     [0.00311677 * 10.0^0 * ((meter / second) / second) ** 2,  [0.00311677 * 10.0^0 * ((meter / second) / second) ** 2,  []    \n",
+      "           {'ibc0': {'y': 4.825496e-06 * meter / second}},           {'ibc0': {'y': 4.825496e-06 * meter / second}},                \n",
+      "           {'ibc1': {'y': 0.00294279 * meter / second}}]             {'ibc1': {'y': 0.00294279 * meter / second}}]                  \n",
+      "15800     [0.00269283 * 10.0^0 * ((meter / second) / second) ** 2,  [0.00269283 * 10.0^0 * ((meter / second) / second) ** 2,  []    \n",
+      "           {'ibc0': {'y': 4.55498e-06 * meter / second}},            {'ibc0': {'y': 4.55498e-06 * meter / second}},                 \n",
+      "           {'ibc1': {'y': 0.00292296 * meter / second}}]             {'ibc1': {'y': 0.00292296 * meter / second}}]                  \n",
+      "16000     [0.00241696 * 10.0^0 * ((meter / second) / second) ** 2,  [0.00241696 * 10.0^0 * ((meter / second) / second) ** 2,  []    \n",
+      "           {'ibc0': {'y': 4.4667136e-06 * meter / second}},          {'ibc0': {'y': 4.4667136e-06 * meter / second}},               \n",
+      "           {'ibc1': {'y': 0.00290674 * meter / second}}]             {'ibc1': {'y': 0.00290674 * meter / second}}]                  \n",
+      "16200     [0.00223442 * 10.0^0 * ((meter / second) / second) ** 2,  [0.00223442 * 10.0^0 * ((meter / second) / second) ** 2,  []    \n",
+      "           {'ibc0': {'y': 4.4755107e-06 * meter / second}},          {'ibc0': {'y': 4.4755107e-06 * meter / second}},               \n",
+      "           {'ibc1': {'y': 0.00289299 * meter / second}}]             {'ibc1': {'y': 0.00289299 * meter / second}}]                  \n",
+      "16400     [0.00212654 * 10.0^0 * ((meter / second) / second) ** 2,  [0.00212654 * 10.0^0 * ((meter / second) / second) ** 2,  []    \n",
+      "           {'ibc0': {'y': 4.4758385e-06 * meter / second}},          {'ibc0': {'y': 4.4758385e-06 * meter / second}},               \n",
+      "           {'ibc1': {'y': 0.00288151 * meter / second}}]             {'ibc1': {'y': 0.00288151 * meter / second}}]                  \n",
+      "16600     [0.00205081 * 10.0^0 * ((meter / second) / second) ** 2,  [0.00205081 * 10.0^0 * ((meter / second) / second) ** 2,  []    \n",
+      "           {'ibc0': {'y': 4.5549314e-06 * meter / second}},          {'ibc0': {'y': 4.5549314e-06 * meter / second}},               \n",
+      "           {'ibc1': {'y': 0.00287196 * meter / second}}]             {'ibc1': {'y': 0.00287196 * meter / second}}]                  \n",
+      "16800     [0.00199925 * 10.0^0 * ((meter / second) / second) ** 2,  [0.00199925 * 10.0^0 * ((meter / second) / second) ** 2,  []    \n",
+      "           {'ibc0': {'y': 4.6707073e-06 * meter / second}},          {'ibc0': {'y': 4.6707073e-06 * meter / second}},               \n",
+      "           {'ibc1': {'y': 0.00286368 * meter / second}}]             {'ibc1': {'y': 0.00286368 * meter / second}}]                  \n",
+      "17000     [0.00197535 * 10.0^0 * ((meter / second) / second) ** 2,  [0.00197535 * 10.0^0 * ((meter / second) / second) ** 2,  []    \n",
+      "           {'ibc0': {'y': 4.7530243e-06 * meter / second}},          {'ibc0': {'y': 4.7530243e-06 * meter / second}},               \n",
+      "           {'ibc1': {'y': 0.00285702 * meter / second}}]             {'ibc1': {'y': 0.00285702 * meter / second}}]                  \n",
+      "\n",
+      "Best trainer at step 17000:\n",
+      "  train loss: 4.84e-03\n",
+      "  test loss: 4.84e-03\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 16.232710 s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pinnx.Trainer at 0x24d2849f7d0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer.compile(bst.optim.LBFGS(1e-3)).train(2000, display_every=200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dc20d3bc5b2e106",
+   "metadata": {},
+   "source": [
+    "Let's visualize and save the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5c9f7a8ec63d3638",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-26T08:33:37.376974Z",
+     "start_time": "2024-11-26T08:33:36.834728Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving loss history to D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\loss.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\loss.dat\n",
+      "Saving training data to D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\train.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\train.dat\n",
+      "Saving test data to D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\test.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\test.dat\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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7kR0/Pz+ef/55RowYQXJyMvv27WPx4sVYrcc+tl988UWWLFlCdHQ0w4YNO6Nt9ujRg8WLF7NmzRoSExO54447uPXWW3n88ceBY0eSVq5cyYQJE+jfvz+PP/44L774IuPHj8fb25vt27dz9dVX079/f26//XamT5/Or3/96/bZQW3MYvz0eFZbP7nF0mLMzvHxOD/88AOpqan29X7zm9+wYsUKMjMzAfj000956KGHsNls/OY3v+H222//2W2c7MhOdHQ0FRUV+Pv7t88L+4/VGbcx6uCHrA67kVF3vt6u2xIR6arq6urIzc2lV69eeHp6mh1HzsGpfpeVlZUEBAS0y+e3w516DnD55Zdz+eWXn9G6Hh4eeHh4tHOik7OGD4KDH+JTvt2U7YuIiMjpdejXWCEhIbi4uFBSUtJieUlJSauuFeAoAnomAhBRn2tyEhEREfk5HVp23N3dSUpKYtmyZfZlNpuNZcuWtfhaq7OIGjAcm2EhhHKOlDre6fEiIiLSDl9jVVdXs3v3j9eeyc3NJTs7m6CgIGJiYpgxYwZTpkxhxIgRjBw5kpdffpmamhr72VmdiY9fIPutYUQZxRTuzCKoe4/TP0hEREQ6VJuXnXXr1nHhhRfaf54xYwYAU6ZMYf78+Vx//fUcPHiQJ554guLiYoYOHcoXX3xBWFjYOW03IyODjIyMVl934Fwd9OpDVG0x1fmbgDMbZyQiIq3XjufTSAcx63fYrmdjmaE9R3OfzOq3ZjBq/19YEziBkfe/1+7bExHpapqbm9m5cyfdu3d3uHkSpXUOHz5MaWkp/fv3P2EC0y53NlZn4haZAPshsFrTRoiItAcXFxcCAwPtV9r39vbuNLNtyzGGYVBbW0tpaSmBgYFnPFN7W1HZOUehfYfBGohqzMPW3Iy1g3+BIiJdwfEzdjvb1ELSUmtnam8rKjvnqEfvwdQZbnhb6tmfu42ovoPNjiQi4nQsFgsRERF0796dxsZGs+PIWXBzc+vwIzrHqeycIxdXV3JdY+jbvIfSPRtUdkRE2pGLi4tpH5jSeXXodXacVZlvPwDqD2w2OYmIiIj8N6cpOxkZGcTHx5OcnNzh224OjQPA4/C2Dt+2iIiInJrTlJ3p06eTk5PD2rVrO3zbPtFDAAit1RlZIiIijsZpyo6ZIgaMACDSVkRdbbXJaUREROSnVHbaQHD3KMrwx8ViULBzg9lxRERE5CdUdtqAxWql0L0XAGW52eaGERERkRZUdtpIVUB/AGzFW01OIiIiIj+lstNGrOGDAPAp325yEhEREfkppyk7Zp56DhDQMxGAiPpcU7YvIiIiJ6dZz9tITVU5Xi/EYrUYHLkrh6DuPTps2yIiIp1de35+O82RHbP5+AVSaA0DoHBnlslpRERE5DiVnTZ00KsPANX5m0xOIiIiIsep7LSh+qCBAFhLdUaWiIiIo1DZaUNukQkABFZr2ggRERFHobLThkL6DAMgqjEPW3OzyWlEREQEVHbaVI/e8dQZbnhb6inM1QzoIiIijkBlpw25urmz3zUGgNI9miNLRETEEThN2TH7ooLHlfn2A6D+wGZTc4iIiMgxTlN2pk+fTk5ODmvXrjU1R3NoHAAeh/U1loiIiCNwmrLjKHyihwAQWqszskRERByByk4bi+iXBECkrYi62mqT04iIiIjKThsLDo+mDH9cLAYFOzVIWURExGwqO23MYrVS6N4LgLLcbHPDiIiIiMpOe6gK6A+ArVjTRoiIiJhNZacdWMMHAeBTvt3kJCIiIqKy0w4CeiYCEFGfa3ISERERUdlpB1EDhmMzLIRQzpHSA2bHERER6dJUdtqBj18ghdYwAAp3ZpmcRkREpGtzmrLjKNNFHHfQqw8A1fmbTE4iIiLStTlN2XGU6SKOqw8aCIC1VGdkiYiImMlpyo6jcYtMACCwWtNGiIiImEllp52E9BkGQFRjHrbmZpPTiIiIdF0qO+2kR+946gw3vC31FOZqBnQRERGzqOy0E1c3d/a7xgBQukdzZImIiJhFZacdlfn2A6D+wGaTk4iIiHRdKjvtqDk0DgCPw/oaS0RExCwqO+3IJ3oIAKG1OiNLRETELCo77SiiXxIAkbYi6mqrTU4jIiLSNanstKPg8GjK8MfFYlCwU4OURUREzKCy044sViuF7r0AKMvNNjeMiIhIF6Wy086qAvoDYCvWtBEiIiJmcJqy42gTgR5nDR8EgE/5dpOTiIiIdE1OU3YcbSLQ4wJ6JgIQUZ9rchIREZGuyWnKjqOKGjAcgBDKOVJ6wOQ0IiIiXY/KTjvz8QtkvyUcgMKdWSanERER6XpUdjrAQa8+AFTnbzI5iYiISNejstMB6oMGAmA9mGNyEhERka5HZacDuEUmABBYtcvkJCIiIl2Pyk4HCOkzDICoxjxszc0mpxEREelaVHY6QI/e8dQbbnhb6inM1QzoIiIiHUllpwO4urlT4BoDQOkezZElIiLSkVR2OkiZbz8A6g9sNjmJiIhI16Ky00GaQ+MA8Disr7FEREQ6kspOB/GJHgJAaO1uk5OIiIh0LSo7HSSiXxIAkbYi6mqrTU4jIiLSdajsdJDg8GjK8MfFYlCwU4OURUREOorKTgexWK0UuvcCoCw329wwIiIiXYjTlJ2MjAzi4+NJTk42O8rPqgroD4CteKvJSURERLoOpyk706dPJycnh7Vr15od5WdZwwcB4FO+3eQkIiIiXYfTlJ3OIKBnIgAR9bkmJxEREek6VHY6UNSA4QCEUM6R0gMmpxEREekaVHY6kI9fIPst4QAU7swyOY2IiEjXoLLTwQ569QGgOn+TyUlERES6BpWdDlYfNBAA68Eck5OIiIh0DSo7HcwtMgGAwKpdJicRERHpGlR2OlhIn2EARDXmYWtuNjmNiIiI81PZ6WA9esdTb7jhbamnMFczoIuIiLQ3lZ0O5urmToFrDAClezRHloiISHtT2TFBmW8/AOoPbDY5iYiIiPNT2TFBc2gcAB6H9TWWiIhIe1PZMYFP9BAAQmt3m5xERETE+ansmCCiXxIAkbYi6mqrTU4jIiLi3FR2TBAcHk0ZfrhYDAp2apCyiIhIe1LZMYHFaqXQvTcAZbnZ5oYRERFxcio7JqkK6A+ArXiryUlEREScm8qOSazhgwDwKd9uchIRERHnprJjkoCeiQBE1OeanERERMS5qeyYJGrAcABCKOdI6QGT04iIiDgvlR2T+PgFcsASBkDhziyT04iIiDgvlR0TlXr1BaA6f5PJSURERJyXyo6J6oMGAmA9mGNyEhEREeflNGUnIyOD+Ph4kpOTzY5yxtwiEwAIrNplchIRERHn5TRlZ/r06eTk5LB27Vqzo5yxkD7DAIhqzMPW3GxyGhEREefkNGWnM+rRO556ww1vSz2FuZoBXUREpD2o7JjI1c2dAtcYAEr3aI4sERGR9qCyY7Iy334A1B/YbHISERER56SyY7Lm0DgAPA7raywREZH2oLJjMp/oIQCE1u42OYmIiIhzUtkxWUS/JAAibUXU1VabnEZERMT5qOyYLDg8mjL8cLEYFOzUIGUREZG2prJjMovVSqF7bwDKcrPNDSMiIuKEVHYcQFVAfwBsxVtNTiIiIuJ8VHYcgDV8EAA+5dtNTiIiIuJ8VHYcQEDPRAAi6nNNTiIiIuJ8VHYcQNSA4QCEUM6R0gMmpxEREXEuKjsOwMcvkAOWMAAKd2aZnEZERMS5qOw4iFKvvgBU528yOYmIiIhzUdlxEPVBAwGwHswxOYmIiIhzUdlxEG6RCQAEVu0yOYmIiIhzUdlxECF9hgEQ1ZiHrbnZ5DQiIiLOQ2XHQfToHU+94Ya3pZ7CXM2ALiIi0lZUdhyEq5s7Ba4xAJTu0RxZIiIibUVlx4GU+fYDoP7AZpOTiIiIOA+VHQfSHBoHgMdhfY0lIiLSVlR2HIhP9BAAQmt3m5xERETEeajsOJCIfkkARNqKqKutNjmNiIiIc1DZcSDB4dGU4YeLxaBgpwYpi4iItAWVHQdisVopdO8FQFlutrlhREREnITKjoOpChgAgK14q8lJREREnIPKjoOxhg8CwKd8u8lJREREnIPKjoMJ6JkIQEz9TrZlfolhs5mcSEREpHNT2XEwMXHJVBteBFBD3OfXsesPKaz77E2aGhvMjiYiItIpqew4GC8fP45M/oI13f6HesON/k07GbH2IQ79Pp7V7z5JZflhsyOKiIh0KhbDMAyzQ7SlyspKAgICqKiowN/f3+w45+RwyX52fvYnBuS/TxCVANQYnmwOu4KY8TOI7DXQ5IQiIiJtoz0/v1V2OoG6ozVsWvwm3be+RaytAIBmw8JGvzF4j72PgclpJicUERE5Nyo7reCMZec4w2Zj88qFsDqDIXVZ9uU7XAdSPfwOEi+ZjKubu4kJRUREzk57fn475JidSZMm0a1bN6655hqzozgUi9XKkAuuZsgjX5N77VesCZxAg+HKgKbtJK25n4O/j2f135+mquKI2VFFREQchkMe2Vm+fDlVVVW88847fPTRR616rDMf2TmZQ8UF7PrsZQYWfEC3/4zrqTa82BJ+JT0nPEBEzwEmJxQRETm9Lndk54ILLsDPz8/sGJ1CSHg0qbe+iNdvtrFm8CzyrFH4Wo4yquQ9Qt8eRdYLV7Bj3ddmxxQRETFNq8vOypUrmThxIpGRkVgsFhYtWnTCOhkZGcTGxuLp6UlKSgpr1qxpi6xyCp7evoy8ZgbRv93ExrFvsdljGK4WG0nVyxnw70ls/30q67+YT3NTk9lRRUREOlSry05NTQ2JiYlkZGSc9P4FCxYwY8YMZs2axfr160lMTCQ9PZ3S0lL7OkOHDmXw4MEn3AoLC1v9Aurr66msrGxx68qsLi4kXngtCY8uZ+81X7E2cDwNhgsDG3MYvvo+Sn4fz+p/PEN1ZZnZUUVERDrEOY3ZsVgsLFy4kCuvvNK+LCUlheTkZObOnQuAzWYjOjqae+65h0ceeeSMn3v58uXMnTv3tGN2nnzySZ566qkTlneVMTtn4lBhHrsWv8zA/R/SjSoAKvEmJ3wSsRMeIDymn8kJRUSkq+s0Y3YaGhrIysoiLe3H675YrVbS0tJYtWpVW27K7tFHH6WiosJ+KygoaJftdGYhkT1Jve2PeD68jcxB/0e+tQf+1DKq+O+E/GUk61+YyNYfFmseLhERcUqubflkhw4dorm5mbCwsBbLw8LC2L79zGfxTktLY+PGjdTU1BAVFcWHH35IamrqSdf18PDAw8PjnHJ3FV4+fqRc+xC25gfYuOJDXDJfY3B9NsOrV8JXK9m3NIaSgTczePw0fPwCzY4rIiLSJtq07LSVpUuXmh3BqVldXEi86Aa46AZyt2ZS+nUGCYe+INaWT2zOM1RtfZHM7pcRnnYPPQcMNTuuiIjIOWnTshMSEoKLiwslJSUtlpeUlBAeHt6Wm5I20mtQCr0GpVBRdojVn79Oj11/J5pCUg5+BO99xGaPYTQl3UbChdfp6swiItIptemYHXd3d5KSkli2bJl9mc1mY9myZT/7NZQ4hoBuIYy66XF6PL6FzRfNZ4P3L2g2LCTUb2DYD9M59Pt4Vr3zGEdKD5gdVUREpFVafWSnurqa3bt323/Ozc0lOzuboKAgYmJimDFjBlOmTGHEiBGMHDmSl19+mZqaGm655ZY2Df7fMjIyyMjIoLm5uV234+ysLi4knD8Jzp9E4b4d5H05l4FFCwnnIOG5GTRkvMHawIsIGDud/sMvMDuuiIjIabX61PPly5dz4YUXnrB8ypQpzJ8/H4C5c+cye/ZsiouLGTp0KK+88gopKSltEvh0utp0ER2h7mgNm7+cT8CW+fRv2mlfvsu1H2WDpjAk/RY8vX1NTCgiIp2dZj1vBZWd9rVz/XIqVrzKkPKv8bA0AlCOL9sirqRn+r1ExmouLhERaT2VnVZQ2ekYZQeL2P75q8TufY8IDgJgMyxs8hmFdeQ0Bo+5EquLi8kpRUSks1DZaQWVnY7V3NTE5m8+wJr1FkPqsuzLCyyRHOh3E3Hj7ySgW4iJCUVEpDNQ2WkFlR3zFOzayIElcxlU8i/8LEcBqDU82BycTuhFd9N7cMeM2xIRkc5HZacVVHbMV1NVzpYv3qL7tr/Ry7bPvnyTZzI9b3+PgKBQ88KJiIhD6jRzY5kpIyOD+Ph4kpOTzY7S5fn4BZJy7UPEPr6BnPT3yfK9gEbDhSF1azkyN41DxflmRxQRkS5ER3akQ+zdkon/R9cRQjn7LeFYb/6EyF4DzY4lIiIOQkd2pNPrPTiF+psXU2gJI8ooxvWd8ezbts7sWCIi0gWo7EiH6dF7EG7TvmKfNYbuHCFwwRXsXL/c7FgiIuLkVHakQ4VGxhJ41xJ2uvYnkGqiPrmOLd9+YnYsERFxYio70uECQ8Lpcd8StngMxdtST/+lv2L9l38zO5aIiDgplR0xhY9fIP0e+Jz1PmNwtzSR+MM9rFk4x+xYIiLihFR2xDQent4Muf9j1gROwMViMHLj46z+x+/MjiUiIk7GacqOrrPTObm6uZN8799ZHXYjAKN2vsCqtx7AsNlMTiYiIs5C19kRh2DYbKz+62Ok7nsNgMyQq0i+8y1NJioi0kXoOjvi9CxWK6lTnyMz7jFshoWUQx+z/k/X0dhQb3Y0ERHp5FR2xKGkXD+T9cnP02i4MKJyKTl/nEhdbbXZsUREpBNT2RGHM+J/bidn7KvUGW4kHs1k78vjqCw/bHYsERHppFR2xCElXnQDe8e/S5XhRXzDZkrnXMLhkv1mxxIRkU5IZUccVvyocZRc9U+O4E/f5j3Uvn4Jxfm7zI4lIiKdjMqOOLS+iaOpmfxvigkl2iiEt8eRvzPb7FgiItKJqOyIw4vulwi3fkGeNYpwDuH3j4ns3vid2bFERKSTcJqyo4sKOrfw6L743fEVu1z60o1Kwj++hpxVn5sdS0REOgFdVFA6laqKI+RnXM6ghs3UGW7sGDuXxItuMDuWiIicI11UUOQ//AKC6HP/F2R7p+JpaSR+xV2s+/R1s2OJiIgDU9mRTsfT25dB93/COv9LcLM0M2L9TDIXPGd2LBERcVAqO9Ipubl7MPy+BWSGXgNAyrZnWTVvpiYQFRGRE6jsSKdldXFh5J1vsip6GgCpea+T+cad2JqbTU4mIiKORGVHOjWL1UrqrS+wuv/DAIwqeZ+sOZNpamwwOZmIiDgKlR1xCqNuepy1Q39Pk2ElufxzsufcpCM8IiICqOyIE0m+8m42j57znxnTl7Dmz9M1hkdERFR2xLkMu/R/yR7+DACjSt4j8+9PmhtIRERM5zRlR1dQluOSr7iL1X0fAGDUnj+xdtFckxOJiIiZdAVlcVqrX7+LUcV/p8mwsnXsa7rSsoiIA9MVlEXOwshpc1gbkI6rxUb/Ffewfe1SsyOJiIgJVHbEaVldXBg6/W9s9EzGy9JAxGc3k7cty+xYIiLSwVR2xKm5uXvQ7+5/ssN1IAHU4LngWooLdpsdS0REOpDKjjg9b98Awu74hDxrFGEcpn7elVQcLjE7loiIdBCVHekSAkPC8Zi6iBKC6WkroOi1KzhaU2V2LBER6QAqO9JlhMf04+j1H1CBDwObtrFz7tU0NtSbHUtERNqZyo50KbFxIyia8A5HDXcSj2aSnXGzrrIsIuLkVHakyxk48hJ2nD/n2DxaFV+w+s17zI4kIiLtSGVHuqShF9/A+qFPA5Ba9C6r//6UyYlERKS9qOxIlzVy0j2s6n0vAKN2vcS6T18zOZGIiLQHlR3p0kb971OsDjs2jURi1m/Z9M1HJicSEZG25jRlRxOBytmwWK2MvP1V1vmn4WZppu/yu9ix7muzY4mISBvSRKAiQEN9Hdv/eBlD6tZRhh+VN/6bngOGmh1LRKTL0ESgIu3M3cOTPtP/yU7X/nSjCo/3rqH0QK7ZsUREpA2o7Ij8h49fICG3L6LAEkk4B6n5yxVUHDlodiwRETlHKjsiPxHUvQeuUxdRShC9bHkUvnYFdbXVZscSEZFzoLIj8l8ieg6g5tr3qcSbuMatbJt7LU2NDWbHEhGRs6SyI3ISvQalsH/cPOoMN4bV/sD6V6dqWgkRkU5KZUfkZ8SPGse20S/TbFgYWfYZq//ygNmRRETkLKjsiJzCsEv/l6whswBIPTCf1e/93uREIiLSWio7Iqcx8uoHWB07/di/t89m3WdvmpxIRERaQ2VH5Ayk3PwMmaHXYLUYDFkzk80rF5odSUREzpDKjsgZsFitJN/xZ7L8LsTd0kzvZXewa8NKs2OJiMgZUNkROUNWFxcGT/8Hmz2G4WOpI/iTyRTs3mx2LBEROQ2VHZFW8PD0ptf0hexy6UsQlbi9O4ncrZlmxxIRkVNQ2RFpJV//bgTd/ol9WomwDyaStXie2bFERORnqOyInIXgsCj8pn/DZo/heFvqSVpzP6veuIfmpiazo4mIyH9R2RE5S4Eh4cQ99CWrwycDkFr0V7a+kE7F4RKTk4mIyE85TdnJyMggPj6e5ORks6NIF+Lq5s6oO14lK/lFag0PhtSto3ruGI3jERFxIBbDMAyzQ7SlyspKAgICqKiowN/f3+w40oXs2bwar49vJtIoodbwYFvKsyRNuNXsWCIinUJ7fn47zZEdEbP1SRiF9/SVPxnHM4NVb0zXOB4REZOp7Ii0oePjeFZF/C8AqUXvsvWFSzWOR0TERCo7Im3M1c2d1F9n/GQcTxbVc8ewd4vG8YiImEFlR6SdJF12G8XX/otCSxg9jBLCP5xI1mdvmR1LRKTLUdkRaUe9B6fgc/e3bPJMOjaOZ+2DrH79Lo3jERHpQCo7Iu0sIDiMQQ99ZR/HM6r47+TMvkTjeEREOojKjkgHcHF1bTGOJ6F+PTUaxyMi0iFUdkQ60PFxPAcsYUT+ZxzPus/eNDuWiIhTU9kR6WC9B6fg+5NxPCPWPsTq1++iqbHB7GgiIk5JZUfEBD+O47kZODaOZ9sLl1J+qNjkZCIizkdlR8Qkx8bxzCFr5Ev/GcezgdqM89mzebXZ0UREnIrKjojJkibc2mIcT+RHGscjItKWVHZEHMCP43hG4GVp0DgeEZE2pLIj4iCOjeP5klWRGscjItKWVHZEHIiLqyupt88ha+TLPxnHM0bjeEREzoHKjogDSppwCyXXHR/HU3psHM+//2x2LBGRTkllR8RB9Rr0X+N41j2scTwiImdBZUfEgWkcj4jIuVPZEXFwJx/Ho+vxiIicKZUdkU4iacItFF/7Lwr/cz2eiI8uJ2vxX8yOJSLi8FR2RDqR3oNT8Ln7WzZ7DMfbUk/SmhmsemM6zU1NZkcTEXFYKjsinUxAcBhxD33J6vDJAKQWvcvWFy6l4nCJyclERByT05SdjIwM4uPjSU5ONjuKSLtzdXNn1B2vsi75BY4a7gypy6J67hhyt2aaHU1ExOFYDMMwzA7RliorKwkICKCiogJ/f3+z44i0uz2bV+P18S+JNEqpNTzYPur/MXz8LWbHEhFplfb8/HaaIzsiXVWfhFF4T/+WLR5D8bbUMzzzflb9+V6N4xER+Q+VHREnEBgSzsCHlvw4jqfwHba+MI6KIwdNTiYiYj6VHREnYR/Hk/T8f8bxrKVqznns27bO7GgiIqZS2RFxMiMm/poDVy2iiFCijGK6vz+B9V/MNzuWiIhpVHZEnFDfxNF4/nQcz+r7WPXm/RrHIyJdksqOiJPqFhpxbBxP2A0ApB6Yx5YXx1NRdsjkZCIiHUtlR8SJubq5M+rON1g3/DnqDDcSj66h8pUx5G3LMjuaiEiHUdkR6QJGXH4n+69aRDGhRBuFhLw/gfVf/s3sWCIiHUJlR6SL6Jt4Hu53rWCr+xB8LHUMX3U3q9+aga252exoIiLtSmVHpAsJ6t6D/g8tZXX36wAYtf8vbHphApXlh01OJiLSflR2RLoYN3cPRt31JmuH/oF6w42hR1dT/soY8nZkmx1NRKRdqOyIdFHJV04n/8qPKSGYGNsBgv8xjuwl/zA7lohIm1PZEenC+g07H9c7V5LjnoCv5ShDv7+TVX95SON4RMSpqOyIdHHBYVH0e2gZmSFXA5Ba8CabXpig6/GIiNNQ2RER3Nw9SLn7bdYkPmMfx1P5yhjNqyUiTkFlR0TsRk66h/xJC+3X4+n+/gSyFs8zO5aIyDlR2RGRFvoNHYP7XSvs82olrbmfVW9Mp6mxwexoIiJnRWVHRE4Q1L3HsXm1wicDkFr0LtteuJSyg0UmJxMRaT2VHRE5KVc3d0bd8SpZyS9Sa3iQUL+Buowx7N74ndnRRERaRWVHRE4p6bLbKLn+M/ZbIojgIFEfX8naRRlmxxIROWMqOyJyWr3ik/G79zs2eqXgaWkkOfsxMufeQkN9ndnRREROS2VHRM5IQLcQEh76nFXR0wBIOfQxe164iEOFeSYnExE5NZUdETljVhcXUm99gezzXqfK8CKucSvGn8eyfc0Ss6OJiPwslR0RabWhaTdS/r9fss8aTShl9P7sejI/eB7DZjM7mojICVR2ROSsRPdLJPSB71jvez7ulmZScn7P2lcmU3e0xuxoIiItqOyIyFnz8Qtk2IxPWN37XpoNCyPLF1Pw4liK83eZHU1ExE5lR0TOicVqZdTNvyPn4ncow49+TbvwePsitnz/L7OjiYgAKjsi0kYSzr+Co1OXsdulD92oJO6rX7L63Sc1jkdETKeyIyJtJjJ2AFEPrmRtQDouFoNRu//I+j9eTW11hdnRRKQLU9kRkTbl6e3LiPveJ3PgIzQaLiRVfU3JS+dzYO9Ws6OJSBelsiMibc5itZJyw6PsnvAehwikl20ffn+9hI1ff2B2NBHpglR2RKTdxKWkY9y+gu2ucfhTQ8KK21k1bya25mazo4lIF+JwZaegoIALLriA+Ph4hgwZwocffmh2JBE5B6GRsfR+eDmZwVditRik5r3Oxhf/h8ryw2ZHE5EuwmIYhmF2iJ8qKiqipKSEoUOHUlxcTFJSEjt37sTHx+eMHl9ZWUlAQAAVFRX4+/u3c1oRaY21H/+JIRt/h4elkQJLJHWXv0G/YeebHUtEHEB7fn473JGdiIgIhg4dCkB4eDghISEcOXLE3FAi0iaSr7qPvCv+STEhRBuF9PtkIutnTyRvW5bZ0UTEibW67KxcuZKJEycSGRmJxWJh0aJFJ6yTkZFBbGwsnp6epKSksGbNmrMKl5WVRXNzM9HR0Wf1eBFxPP2Hj8X9rpWs878Em2FheM1Kot6/mLV/vJYDe7eZHU9EnFCry05NTQ2JiYlkZGSc9P4FCxYwY8YMZs2axfr160lMTCQ9PZ3S0lL7OkOHDmXw4MEn3AoLC+3rHDlyhJtvvpk///nPZ/GyRMSRBXXvwYgZH5F/w1LW+4zBxWKQXPEV3d8ZTeacKZQeyDU7oog4kXMas2OxWFi4cCFXXnmlfVlKSgrJycnMnTsXAJvNRnR0NPfccw+PPPLIGT1vfX09l1xyCdOmTeOXv/zladetr6+3/1xZWUl0dLTG7Ih0Irs2rOTol08xpG4dAHWGG9nh1zDgmll0C40wOZ2IdIROM2anoaGBrKws0tLSftyA1UpaWhqrVq06o+cwDIOpU6dy0UUXnbboADz77LMEBATYb/rKS6Tz6TfsfIY8soyc9PfZ5jYIT0sjo0rew33uUFa/NUNnbonIOWnTsnPo0CGam5sJCwtrsTwsLIzi4uIzeo7vv/+eBQsWsGjRIoYOHcrQoUPZvHnzz67/6KOPUlFRYb8VFBSc02sQEfPEp45n4KPfsWnsX9jl0hcfSx2j9v8F4+UEVr3zW007ISJnxdXsAP/tvPPOw9aKiQM9PDzw8PBox0Qi0pEsVitDLrwGY+xVbFjyN7plzibWVkBq7lwOvfAumwb8mmGT7sfD09vsqCLSSbTpkZ2QkBBcXFwoKSlpsbykpITw8PC23JSIODmL1cqw9ClEP5bN2mHPcsASRgjljNrx/yh7bghr/vkyTY0NZscUkU6gTcuOu7s7SUlJLFu2zL7MZrOxbNkyUlNT23JTItJFuLi6knzFXXR/dDOZg/6PUoII5yAjN8+i6A+JrPvsTU0/ISKn1OqyU11dTXZ2NtnZ2QDk5uaSnZ1Nfn4+ADNmzODNN9/knXfeYdu2bdx5553U1NRwyy23tGlwEela3Nw9SLn2Ifx/s5nV/R6kDH+ijUJGrH2Ifb8fTvaSf2C04itwEek6Wn3q+fLly7nwwgtPWD5lyhTmz58PwNy5c5k9ezbFxcUMHTqUV155hZSUlDYJ/HMyMjLIyMigubmZnTt36tRzESdXXVnG5n8+x6C8v+JPLQA7XAfQOPa3DB5zhcnpRKS12vPUc4ebG+tcaW4ska6l4nAJOf98hsQDC/C2HLvm1lb3RFwueYKByWmnebSIOAqVnVZQ2RHpmg4V57P7n08zvHQh7pYmADZ6peA7/kn6DPmFyelE5HRUdlpBZUekayvO30X+wicZfmQxrpZjY3g2eI/GNvhq+o+ehF9AkMkJReRkVHZaQWVHRAAKdm+m5JNZDK/8Gqvl2J+5BsOVbV7DqOsznj5jriUkPMbklCJynMpOK6jsiMhP5easpfjb+UQVf0208eNkwzbDwk63gZTHXEKP1GuI7pdoYkoRUdlpBZUdETkZw2Yjf2c2hZkfEVywhP5NO1vcn2eNpjD8IoKGX0m/YWOxuriYlFSka1LZOQM69VxEWqP0QC6533+I194viDuajZvlxwsTHqQbe4PH4pVwOQNTL8Pdw9PEpOJsDJsNi7VNr+nrFFR2WkFHdkSktSrKDrHr+4+x7FjMgMrV+FqO2u+rMrzY6Z8KAy+j/3lXaYCznJOd61cQ9umNbOt9K6Nu/p3ZcRyKyk4rqOyIyLmor6tlx6rFHN3yKX0OryCEcvt9DYYL272Gc7TPOPqMvpaQyJ7mBZVOafWr0xhV+gHNhoVdE//JwBEXmx3JYajstILKjoi0FVtzMzs3LKcsaxFRxUtbDHAG2OE6kCMxlxKZcjU9Bww1J6R0KjufSbaPFyuwRBLy0Bq8fPxMTuUYVHZaQWVHRNpL3o5sCld/SFDBEgY07WhxX761BwfCLiZgyAT6Dr9Q43zkBHW11Vj/XwzulmbK8SWQalZ3v55Rd/3Z7GgOQWWnFVR2RKQjHCzcx97vjg1wHnh0A+4/GeBca3iwy3soR6PPJ3zYOHoOGK4BqcK2zC+J+/w6DhHIgbEvkLjiNgC2XvIPBo2+zOR05mvPz2/XNn02EZEuIjQyltDrHgYeprL8MJu+/xi2f06fqrV0s1SSeDQTdmbCztmUEkReQDKWPhcSO/IyXcywi6rY+T0ABT6DGXbhtazZ/Akjj/yLbkvvpzrhF/j6dzM5ofNymiM7OvVcRByBrbmZvVtWc2jTl/js/5Z+dZvxtDS2WCfXGktJaCreA9Pom3wJ3r4BJqWVjrRh9mUMq/mO1X3vZ9T/PkV1ZRlVLyUTwUEyg68g5Z6/mh3RVPoaqxX0NZaIOJK62mp2rVtG9bavCC1dRd/mPS3ubzBc2eURT1XkGIKGpNNnyGhcXHXQ3dkYNhuHn+5FCOVsH/8hA1MuBWDL9/9i8JL/BWDTBW8z5IKrzYxpKpWdVlDZERFHdqT0ALlrP6d599fElK0hnIMt7q/Ahz2+STT2vICopAn06B1nUlJpS4W524l8J4UGwwXbIwV4evnY71udcRujDn7IIQJpvOUrInoOMDGpeVR2WkFlR0Q6C8NmY/+ezRSu/xz3vBX0rdmA308uaAhwwBLG/qBRuPW7iD4jLyMgKNSktHIu1n36OiPWz2SH6wAGPL6mxX1Ha6oofvE8etn2kWeNInD61wQEh5mU1DwqO62gsiMinVVTYwO7s1dStvkrAou+o2/D9hbTWDQbFva49eNw2Gj84i4iZvBo/AODTUwsZypz7i2kHPr4Z081L9m/B966hDAOs80tnl4PLMHT29eEpOZR2WkFlR0RcRbVlWXsXvMF9TuWEn54NT1t+09YZ78lghLfgTSGJuDbawTRg36hoz8OaPfvhtO3eQ9ZI18iacKtJ11n37Z1BC2YiD+1rPcZQ+IDi7rU+C2VnVZQ2RERZ1Wyfw95axdjzV1Oj8qNRPzXeJ/jCi1hFPsMoD50CL6xSUQP+gWBIeEdnFaOq62uwH12LK4WGyW3rScsqs/Prrv1h8X0+/KXuFuayAy5mpF3vdVlrtGkstMKKjsi0lWUHSxif84qqvdl4XFwE+E1O4g0Sk66bhGhFPsMoC40AZ+eI+gRP4rgsKgOTtw1bf3+MwYtuYkSggl7cu9p189a/BeS1swAYHXve7vMhKG6qKCIiJygW2gE3cZeBWOvsi+rOHKQgq0/UJ27DreDmwmr3k6UUUQEB4moOQg138E+YAWUEEyh9wDqQhLwjh1Oj/hf6IKH7aBy97GLCR7wHcyZDDtOmnArq8sLGbXzBUbtfYVVb9cxaur/6zJHeNqD0xzZ0UUFRUROrqLsEPtzMqnKXYtrySa612wnqrkQq+XEP/8H6cYBrwEcDUnAK2Y4kfGphEb01AftOcj+f+kMPbqa1f0fYtRN/3fGj1v15n2kHpgPQJbvBcTf+a5TTxqqr7FaQV9jiYicXlXFEQpyMqnMXYdr8UZCq7cT3bz/pAWoEh+KXXtQ6d2Txm59cO/ej4CoOCJ6D8LHL7Djw3cihs1G+dMxdKOKHf+zkAEjLmrV49f8848M2/Q73CzN7HLtR8AtH9K9R692SmsulZ1WUNkRETk7NVXlFOSsoXzvWlyKNxFStY2Y5nxcTlKAjisliIPuUVT7xmIE9cEzfABBMXGE9xyomd+Bgl0bif77+dQbblge239W+2TrD4uJ/Op2ulHFQbpRdvk79B8+th3SmktlpxVUdkRE2k5dbTVF+7ZRXpBDXfFOXMr24lezj7DG/QRR+bOPazKsFFvDOOwZzVG/XlhC+uITMYCQ2EF0j+yF1cWlA1+FedYumkty9m/Z5hZP3G9XnfXzHNi7jcZ3ryXWVkCd4Ub2wAcYcc3DuLq5t2Fac6nstILKjohIx6g4cpCSfVup3L+NptJduFXsJaA2n8im/Xhb6n/2cUcNd4pcIin3jqE+oDeuof3w6zGQsNhBBAaHOdX4oMw5N5Ny+BNWh09m1B2vntNzVVUcYe/r15N49NgVmPdZY6i+8BkGj7miLaKaTmWnFVR2RETMZdhsHCrOpzR3K9WF2zEO7cKzah9BdflENBe3uCr0f6vEhxLXSCq9omkIiMU1pA9+kQMI7RlHUGhkpytCuU8n0su2jw2przAsfco5P19zUxPrFr5M/60v040qANb7jCH82heJjO3cc2qp7LSCyo6IiONqamygOG8nh/NzOFq8A8vh3fhU7yOkfj/hHDrlY6sNL4pdI6n0iqLe/1gR8onoT/ee8QSHRztcEaqqOILPS72xWgwO3b6JkMiebfbcFUcOsv29R0gq/RhXi416w40N4dcQdcndRPUd3Gbb6UgqO62gsiMi0jkdramiOG875ft3UF+6C0tZLj7VeQTXHyDMOHTSM8WOqzU8KHaJoNwrmnr/nrgE98E7oh+hPeMJjYg1ZYzQ5pWfkPD1zRRauhM5a1e7bCM3Zy01nzzE4Pps+7JNnkk0D/8VCRde16nG9KjstILKjoiI86mvq6U4bydlBduoKzlWhLyq8wiu30+4rfSUZ4zVGW4Uu0RQ5hlFvV9PLMF9cPE6i88Hi6V16+/8khGVS1nndzEjHvy49ds7Q4bNxqblH8GaP5NwdJ29FJYQzN6oKwgYnE7fYRc4/NlxKjutoLIjItK1NNTXUVKwiyMF2zlavBPLkb14VuURVH+AcFvJKccIdYTVA2Yy6sbHOmRbB/ZuI3/JqwwsWkS3n5wtV2t4sNtrMDWRowkefDE940fi4endIZnOlMpOK6jsiIjIcU2NDZQU7OFIwTZqi3dh/KcIuTTXnfJxFtrmo7HeLZC+v3qrw2eir6+rZfOSv2HZ+Tm9qrJOuExAo+HCfpdoDvn1p7n7YHxjhtK99xBCwmNMuyyAys4Z0HQRIiIiJzJsNvZtz6Jk41d4FHxPbO0m+5lc/63OcKPEJZxyj0jqfKMx/CKx+gTh6tMNd79gvPxD8QkIJjA0Ek8vnzbNqbLTCjqyIyIi8vMMm43SwlyKdqzlaP4GPA7l0L12J+G2UlwttjN6jtX9H2bUTY+3aS7Nei4iIiJtwmK1EhbVh7CoPsAN9uWNDfUc2L+XI/t3UluyG9uRvbjWHsStoQLPpkq8mqvwsVXhb1Th4hNk3gs4Cyo7IiIigpu7Bz16x9Gjd9wp1zNsNpI62ZdCKjsiIiJyxixWK608Cd90jnW5SREREZE2prIjIiIiTk1lR0RERJyayo6IiIg4NZUdERERcWoqOyIiIuLUVHZERETEqansiIiIiFNzmrKTkZFBfHw8ycnJZkcRERERB6KJQEVERMR07fn57TRHdkRERERORmVHREREnJrKjoiIiDg1p5v1/PgQpMrKSpOTiIiIyJk6/rndHkOJna7sVFVVARAdHW1yEhEREWmtqqoqAgIC2vQ5ne5sLJvNRmFhIX5+flgsljZ73srKSqKjoykoKOjyZ3lpXxyj/XCM9sOPtC+O0X44RvvhR2eyLwzDoKqqisjISKzWth1l43RHdqxWK1FRUe32/P7+/l3+TXuc9sUx2g/HaD/8SPviGO2HY7QffnS6fdHWR3SO0wBlERERcWoqOyIiIuLUVHbOkIeHB7NmzcLDw8PsKKbTvjhG++EY7YcfaV8co/1wjPbDj8zeF043QFlERETkp3RkR0RERJyayo6IiIg4NZUdERERcWoqOyIiIuLUVHbOUEZGBrGxsXh6epKSksKaNWvMjnTWnn32WZKTk/Hz86N79+5ceeWV7Nixo8U6F1xwARaLpcXtjjvuaLFOfn4+l112Gd7e3nTv3p2HH36YpqamFussX76c4cOH4+HhQd++fZk/f357v7xWefLJJ094nQMHDrTfX1dXx/Tp0wkODsbX15err76akpKSFs/hDPshNjb2hP1gsViYPn064Lzvh5UrVzJx4kQiIyOxWCwsWrSoxf2GYfDEE08QERGBl5cXaWlp7Nq1q8U6R44cYfLkyfj7+xMYGMitt95KdXV1i3U2bdrEmDFj8PT0JDo6mueff/6ELB9++CEDBw7E09OThIQEFi9e3Oav91ROtS8aGxuZOXMmCQkJ+Pj4EBkZyc0330xhYWGL5zjZ++i5555rsY6j74vTvSemTp16wmscN25ci3Wc4T1xuv1wsr8XFouF2bNn29dxqPeDIaf1/vvvG+7u7sbbb79tbN261Zg2bZoRGBholJSUmB3trKSnpxvz5s0ztmzZYmRnZxsTJkwwYmJijOrqavs6Y8eONaZNm2YUFRXZbxUVFfb7m5qajMGDBxtpaWnGhg0bjMWLFxshISHGo48+al9n7969hre3tzFjxgwjJyfHmDNnjuHi4mJ88cUXHfp6T2XWrFnGoEGDWrzOgwcP2u+/4447jOjoaGPZsmXGunXrjFGjRhm/+MUv7Pc7y34oLS1tsQ+WLFliAMY333xjGIbzvh8WL15s/Pa3vzU+/vhjAzAWLlzY4v7nnnvOCAgIMBYtWmRs3LjRuPzyy41evXoZR48eta8zbtw4IzEx0Vi9erXx7bffGn379jVuvPFG+/0VFRVGWFiYMXnyZGPLli3Ge++9Z3h5eRlvvPGGfZ3vv//ecHFxMZ5//nkjJyfHePzxxw03Nzdj8+bN7b4PjjvVvigvLzfS0tKMBQsWGNu3bzdWrVpljBw50khKSmrxHD179jSefvrpFu+Tn/5d6Qz74nTviSlTphjjxo1r8RqPHDnSYh1neE+cbj/89PUXFRUZb7/9tmGxWIw9e/bY13Gk94PKzhkYOXKkMX36dPvPzc3NRmRkpPHss8+amKrtlJaWGoCxYsUK+7KxY8ca9913388+ZvHixYbVajWKi4vty1577TXD39/fqK+vNwzDMH7zm98YgwYNavG466+/3khPT2/bF3AOZs2aZSQmJp70vvLycsPNzc348MMP7cu2bdtmAMaqVasMw3Ce/fDf7rvvPqNPnz6GzWYzDKNrvB/++w+6zWYzwsPDjdmzZ9uXlZeXGx4eHsZ7771nGIZh5OTkGICxdu1a+zqff/65YbFYjAMHDhiGYRivvvqq0a1bN/t+MAzDmDlzpjFgwAD7z9ddd51x2WWXtciTkpJi/PrXv27T13imTvbh9t/WrFljAEZeXp59Wc+ePY0//vGPP/uYzrYvfq7sXHHFFT/7GGd8T5zJ++GKK64wLrroohbLHOn9oK+xTqOhoYGsrCzS0tLsy6xWK2lpaaxatcrEZG2noqICgKCgoBbL//73vxMSEsLgwYN59NFHqa2ttd+3atUqEhISCAsLsy9LT0+nsrKSrVu32tf56X47vo6j7bddu3YRGRlJ7969mTx5Mvn5+QBkZWXR2NjY4jUMHDiQmJgY+2twpv1wXENDA++++y6/+tWvWkym21XeD8fl5uZSXFzcInNAQAApKSktfv+BgYGMGDHCvk5aWhpWq5XMzEz7Oueffz7u7u72ddLT09mxYwdlZWX2dTrTvoFjfzcsFguBgYEtlj/33HMEBwczbNgwZs+e3eKrTGfZF8uXL6d79+4MGDCAO++8k8OHD9vv64rviZKSEj777DNuvfXWE+5zlPeD000E2tYOHTpEc3Nziz/iAGFhYWzfvt2kVG3HZrNx//33M3r0aAYPHmxfftNNN9GzZ08iIyPZtGkTM2fOZMeOHXz88ccAFBcXn3SfHL/vVOtUVlZy9OhRvLy82vOlnZGUlBTmz5/PgAEDKCoq4qmnnmLMmDFs2bKF4uJi3N3dT/hjHhYWdtrXePy+U63jSPvhpxYtWkR5eTlTp061L+sq74efOp77ZJl/+pq6d+/e4n5XV1eCgoJarNOrV68TnuP4fd26dfvZfXP8ORxNXV0dM2fO5MYbb2wxqeO9997L8OHDCQoK4ocffuDRRx+lqKiIl156CXCOfTFu3DiuuuoqevXqxZ49e3jssccYP348q1atwsXFpUu+J9555x38/Py46qqrWix3pPeDyk4XN336dLZs2cJ3333XYvntt99u/3dCQgIRERFcfPHF7Nmzhz59+nR0zHYzfvx4+7+HDBlCSkoKPXv25IMPPnC4D9+O8pe//IXx48cTGRlpX9ZV3g9yeo2NjVx33XUYhsFrr73W4r4ZM2bY/z1kyBDc3d359a9/zbPPPus0UybccMMN9n8nJCQwZMgQ+vTpw/Lly7n44otNTGaet99+m8mTJ+Pp6dliuSO9H/Q11mmEhITg4uJywhk4JSUlhIeHm5Sqbdx99938+9//5ptvviEqKuqU66akpACwe/duAMLDw0+6T47fd6p1/P39HbZIBAYG0r9/f3bv3k14eDgNDQ2Ul5e3WOenv3tn2w95eXksXbqU22677ZTrdYX3w/Hcp/pvPzw8nNLS0hb3NzU1ceTIkTZ5jzja35jjRScvL48lS5a0OKpzMikpKTQ1NbFv3z7AufbFcb179yYkJKTFfwtd6T3x7bffsmPHjtP+zQBz3w8qO6fh7u5OUlISy5Ytsy+z2WwsW7aM1NRUE5OdPcMwuPvuu1m4cCFff/31CYcRTyY7OxuAiIgIAFJTU9m8eXOL/6iP//GLj4+3r/PT/XZ8HUfeb9XV1ezZs4eIiAiSkpJwc3Nr8Rp27NhBfn6+/TU4236YN28e3bt357LLLjvlel3h/dCrVy/Cw8NbZK6srCQzM7PF77+8vJysrCz7Ol9//TU2m81eCFNTU1m5ciWNjY32dZYsWcKAAQPo1q2bfR1H3zfHi86uXbtYunQpwcHBp31MdnY2VqvV/rWOs+yLn9q/fz+HDx9u8d9CV3lPwLEjwUlJSSQmJp52XVPfD60aztxFvf/++4aHh4cxf/58Iycnx7j99tuNwMDAFmeedCZ33nmnERAQYCxfvrzFKYG1tbWGYRjG7t27jaefftpYt26dkZuba3zyySdG7969jfPPP9/+HMdPNb700kuN7Oxs44svvjBCQ0NPeqrxww8/bGzbts3IyMgw/VTj//bggw8ay5cvN3Jzc43vv//eSEtLM0JCQozS0lLDMI6deh4TE2N8/fXXxrp164zU1FQjNTXV/nhn2Q+Gcewsw5iYGGPmzJktljvz+6GqqsrYsGGDsWHDBgMwXnrpJWPDhg32M4yee+45IzAw0Pjkk0+MTZs2GVdcccVJTz0fNmyYkZmZaXz33XdGv379WpxmXF5eboSFhRm//OUvjS1bthjvv/++4e3tfcLpta6ursYLL7xgbNu2zZg1a1aHn3p+qn3R0NBgXH755UZUVJSRnZ3d4u/G8TNpfvjhB+OPf/yjkZ2dbezZs8d49913jdDQUOPmm2/uVPviVPuhqqrKeOihh4xVq1YZubm5xtKlS43hw4cb/fr1M+rq6uzP4QzvidP9t2EYx04d9/b2Nl577bUTHu9o7weVnTM0Z84cIyYmxnB3dzdGjhxprF692uxIZw046W3evHmGYRhGfn6+cf755xtBQUGGh4eH0bdvX+Phhx9ucV0VwzCMffv2GePHjze8vLyMkJAQ48EHHzQaGxtbrPPNN98YQ4cONdzd3Y3evXvbt+Eorr/+eiMiIsJwd3c3evToYVx//fXG7t277fcfPXrUuOuuu4xu3boZ3t7exqRJk4yioqIWz+EM+8EwDOPLL780AGPHjh0tljvz++Gbb7456X8LU6ZMMQzj2Onn//d//2eEhYUZHh4exsUXX3zC/jl8+LBx4403Gr6+voa/v79xyy23GFVVVS3W2bhxo3HeeecZHh4eRo8ePYznnnvuhCwffPCB0b9/f8Pd3d0YNGiQ8dlnn7Xb6z6ZU+2L3Nzcn/27cfxaTFlZWUZKSooREBBgeHp6GnFxccYf/vCHFiXAMBx/X5xqP9TW1hqXXnqpERoaari5uRk9e/Y0pk2bdsL/+DrDe+J0/20YhmG88cYbhpeXl1FeXn7C4x3t/WAxDMNo3bEgERERkc5DY3ZERETEqansiIiIiFNT2RERERGnprIjIiIiTk1lR0RERJyayo6IiIg4NZUdERERcWoqOyIiIuLUVHZERETEqansiEibOnjwIO7u7tTU1NDY2IiPjw/5+fmnfExtbS2PPvooffr0wdPTk9DQUMaOHcsnn3xiXyc2NpaXX365ndOLiDNyNTuAiDiXVatWkZiYiI+PD5mZmQQFBRETE3PKx9xxxx1kZmYyZ84c4uPjOXz4MD/88AOHDx/uoNQi4sx0ZEdE2tQPP/zA6NGjAfjuu+/s/z6VTz/9lMcee4wJEyYQGxtLUlIS99xzD7/61a8AuOCCC8jLy+OBBx7AYrFgsVjsj/3uu+8YM2YMXl5eREdHc++991JTU2O/PzY2lt/97nfceOON+Pj40KNHDzIyMuz3G4bBk08+SUxMDB4eHkRGRnLvvfe21e4QEQegiUBF5Jzl5+czZMgQ4NhXUi4uLnh4eHD06FEsFguenp7cdNNNvPrqqyd9/MCBA0lMTOStt97Cz8/vhPuPHDlCYmIit99+O9OmTQMgPDycPXv2kJiYyDPPPMNll13GwYMHufvuu0lMTGTevHnAsbJz5MgRHnvsMa666iq+/PJLHnjgAT7//HMuueQSPvroI2699Vbef/99Bg0aRHFxMRs3brRvR0Q6P5UdETlnTU1N7N+/n8rKSkaMGMG6devw8fFh6NChfPbZZ8TExODr60tISMhJH79y5UomT55MSUkJiYmJnHfeeVxzzTUtjgrFxsZy//33c//999uX3Xbbbbi4uPDGG2/Yl3333XeMHTuWmpoaPD09iY2NJS4ujs8//9y+zg033EBlZSWLFy/mpZde4o033mDLli24ubm1/c4REdPpaywROWeurq7Exsayfft2kpOTGTJkCMXFxYSFhXH++ecTGxv7s0UH4Pzzz2fv3r0sW7aMa665hq1btzJmzBh+97vfnXK7GzduZP78+fj6+tpv6enp2Gw2cnNz7eulpqa2eFxqairbtm0D4Nprr+Xo0aP07t2badOmsXDhQpqams5hb4iIo9EAZRE5Z4MGDSIvL4/GxkZsNhu+vr40NTXR1NSEr68vPXv2ZOvWrad8Djc3N8aMGcOYMWOYOXMmzzzzDE8//TQzZ87E3d39pI+prq7m17/+9UnH2JxuUPRx0dHR7Nixg6VLl7JkyRLuuusuZs+ezYoVK3SkR8RJqOyIyDlbvHgxjY2NXHzxxTz//PMkJSVxww03MHXqVMaNG3dWpSE+Pp6mpibq6upwd3fH3d2d5ubmFusMHz6cnJwc+vbte8rnWr169Qk/x8XF2X/28vJi4sSJTJw4kenTpzNw4EA2b97M8OHDW51bRByPxuyISJsoLi4mNjaW8vJyLBYLgYGB7N27l4iIiNM+9oILLuDGG29kxIgRBAcHk5OTw4wZM+jRowfLli0D4NJLL8XLy4tXX30VDw8PQkJC2LRpE6NGjeJXv/oVt912Gz4+PuTk5LBkyRLmzp0LHBvrU1ZWxm9/+1uuvPJKlixZwn333cdnn31Geno68+fPp7m5mZSUFLy9vZk3bx4vvvgiBQUFBAcHt+s+E5GOoTE7ItImli9fTnJyMp6enqxZs4aoqKgzKjoA6enpvPPOO1x66aXExcVxzz33kJ6ezgcffGBf5+mnn2bfvn306dOH0NBQAIYMGcKKFSvYuXMnY8aMYdiwYTzxxBNERka2eP4HH3yQdevWMWzYMJ555hleeukl0tPTAQgMDOTNN99k9OjRDBkyhKVLl/Kvf/1LRUfEiejIjog4tZOdxSUiXYuO7IiIiIhTU9kRERERp6avsURERMSp6ciOiIiIODWVHREREXFqKjsiIiLi1FR2RERExKmp7IiIiIhTU9kRERERp6ayIyIiIk5NZUdERESc2v8H3SSqCyN17S0AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trainer.saveplot(issave=True, isplot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "295c375c641f4943",
+   "metadata": {},
+   "source": [
+    "We can also test the model with the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40109d87837d8e20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gen_testdata():\n",
+    "    data = np.load(\"../dataset/Burgers.npz\")\n",
+    "    t, x, exact = data[\"t\"], data[\"x\"], data[\"usol\"].T\n",
+    "    xx, tt = np.meshgrid(x, t)\n",
+    "    X = {'x': np.ravel(xx) * u.meter, 't': np.ravel(tt) * u.second}\n",
+    "    y = exact.flatten()[:, None]\n",
+    "    return X, y * uy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "600622e0fc0ccf2e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-26T08:33:39.149077Z",
+     "start_time": "2024-11-26T08:33:37.402855Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean residual: 0.02894243 * (meter / second) / second\n",
+      "L2 relative error: 224.70277\n"
+     ]
+    }
+   ],
+   "source": [
+    "X, y_true = gen_testdata()\n",
+    "y_pred = trainer.predict(X)\n",
+    "f = pde(X, y_pred)\n",
+    "print(\"Mean residual:\", u.math.mean(u.math.absolute(f)))\n",
+    "print(\"L2 relative error:\", deepxde.metrics.l2_relative_error(y_true, y_pred['y']))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/experimental_docs/unit-examples-forward/diffusion_1d.ipynb b/docs/experimental_docs/unit-examples-forward/diffusion_1d.ipynb
new file mode 100644
index 000000000..45f0d5ef2
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/diffusion_1d.ipynb
@@ -0,0 +1,433 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# One-dimensional Diffusion Equation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Problem setup\n",
+    "We will solve a diffusion equation:\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\partial y}{\\partial t} = C \\frac{\\partial^2y}{\\partial x^2} - e^{-t}(\\sin(\\pi x) - \\pi^2\\sin(\\pi x)),   \\qquad x \\in [-1, 1], \\quad t \\in [0, 1]\n",
+    "$$\n",
+    "\n",
+    "with the initial condition\n",
+    "\n",
+    "$$\n",
+    "y(x, 0) = \\sin(\\pi x)\n",
+    "$$\n",
+    "\n",
+    "and the Dirichlet boundary condition\n",
+    "\n",
+    "$$\n",
+    "y(-1, t) = y(1, t) = 0.\n",
+    "$$\n",
+    "\n",
+    "The reference solution is $y = e^{-t} \\sin(\\pi x)$.\n",
+    "\n",
+    "\n",
+    "\n",
+    "## Dimensional Analysis\n",
+    "\n",
+    "\n",
+    "Below is the dimensional analysis of the given **diffusion equation** with an unknown parameter $C$:\n",
+    "\n",
+    "\n",
+    "### Step 1: Assign Dimensions to Variables\n",
+    "\n",
+    "1. **Spatial Coordinate $x$:**\n",
+    "   - Spatial coordinate has the dimension of length:\n",
+    "\n",
+    "     $$\n",
+    "     [x] = L.\n",
+    "     $$\n",
+    "\n",
+    "2. **Time $t$:**\n",
+    "   - Time has the dimension:\n",
+    "\n",
+    "     $$\n",
+    "     [t] = T.\n",
+    "     $$\n",
+    "\n",
+    "3. **Function $y(x, t)$:**\n",
+    "   - $y$ is the solution of the diffusion equation and depends on the context. For this case, $y$ has no explicit physical quantity associated with it, but we assume it to be **dimensionless** since the reference solution is given as $ y = e^{-t} \\sin(\\pi x) $, where both $e^{-t}$ and $\\sin(\\pi x)$ are dimensionless.\n",
+    "\n",
+    "     $$\n",
+    "     [y] = 1 \\quad \\text{(dimensionless)}.\n",
+    "     $$\n",
+    "\n",
+    "4. **Parameter $C$:**\n",
+    "   - The term $C \\frac{\\partial^2 y}{\\partial x^2}$ must have the same dimension as $\\frac{\\partial y}{\\partial t}$ for consistency.\n",
+    "\n",
+    "     - First, consider the time derivative:\n",
+    "\n",
+    "       $$\n",
+    "       \\left[\\frac{\\partial y}{\\partial t}\\right] = \\frac{[y]}{[t]} = \\frac{1}{T}.\n",
+    "       $$\n",
+    "\n",
+    "     - Next, consider the second spatial derivative:\n",
+    "\n",
+    "       $$\n",
+    "       \\left[\\frac{\\partial^2 y}{\\partial x^2}\\right] = \\frac{[y]}{[x]^2} = \\frac{1}{L^2}.\n",
+    "       $$\n",
+    "     - Multiplying by $C$, the dimensions of $C$ must satisfy:\n",
+    "\n",
+    "       $$\n",
+    "       [C] \\cdot \\frac{1}{L^2} = \\frac{1}{T} \\implies [C] = \\frac{L^2}{T}.\n",
+    "       $$\n",
+    "\n",
+    "5. **Source Term:** $e^{-t} \\left(\\sin(\\pi x) - \\pi^2 \\sin(\\pi x)\\right)$\n",
+    "   - The exponential term $e^{-t}$ and the sine functions are dimensionless. Therefore, the source term is dimensionally consistent with:\n",
+    "   \n",
+    "     $$\n",
+    "     \\text{Source Term} = \\frac{1}{T}.\n",
+    "     $$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 2: Initial and Boundary Conditions\n",
+    "\n",
+    "- **Initial Condition:** $y(x, 0) = \\sin(\\pi x)$.\n",
+    "  - $\\sin(\\pi x)$ is dimensionless, consistent with $ [y] = 1 $.\n",
+    "\n",
+    "- **Boundary Condition:** $y(-1, t) = y(1, t) = 0$.\n",
+    "  - The boundary values are dimensionless.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 3: Summary of Dimensions\n",
+    "\n",
+    "| Variable/Parameter     | Physical Meaning                   | Dimensions            |\n",
+    "|------------------------|-----------------------------------|-----------------------|\n",
+    "| $x$                  | Spatial coordinate                | $L$                 |\n",
+    "| $t$                  | Time                              | $T$                 |\n",
+    "| $y$                  | Solution (dimensionless)          | $1$                 |\n",
+    "| $C$                  | Diffusion coefficient             | $L^2 / T$           |\n",
+    "| Source term            | Forcing function                  | $1 / T$             |\n",
+    "\n",
+    "---\n",
+    "\n",
+    "In conclusion,\n",
+    "\n",
+    "- The unknown parameter $C$ has dimensions of $L^2 / T$, which is consistent with the physical meaning of a diffusion coefficient.\n",
+    "- The function $y$ and the boundary/initial conditions are dimensionless, ensuring the consistency of the problem setup.\n",
+    "\n",
+    "\n",
+    "## Implementation\n",
+    "\n",
+    "Import the required libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T13:42:57.489721Z",
+     "start_time": "2024-12-17T13:42:53.900651Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import brainstate as bst\n",
+    "import brainunit as u\n",
+    "\n",
+    "import deepxde.experimental as deepxde"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the physical units for the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T13:42:57.520303Z",
+     "start_time": "2024-12-17T13:42:57.494778Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "unit_of_x = u.meter\n",
+    "unit_of_t = u.second\n",
+    "unit_of_f = 1 / u.second\n",
+    "\n",
+    "c = 1. * u.meter ** 2 / u.second"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the geometry and time domain:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T13:42:57.577592Z",
+     "start_time": "2024-12-17T13:42:57.573968Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "geom = deepxde.geometry.Interval(-1, 1)\n",
+    "timedomain = deepxde.geometry.TimeDomain(0, 1)\n",
+    "geomtime = deepxde.geometry.GeometryXTime(geom, timedomain)\n",
+    "geomtime = geomtime.to_dict_point(x=unit_of_x, t=unit_of_t)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the initial condition and boundary condition functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T13:42:57.589705Z",
+     "start_time": "2024-12-17T13:42:57.586185Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def func(x):\n",
+    "    y = u.math.sin(u.math.pi * x['x'] / unit_of_x) * u.math.exp(-x['t'] / unit_of_t)\n",
+    "    return {'y': y}\n",
+    "\n",
+    "\n",
+    "bc = deepxde.icbc.DirichletBC(func)\n",
+    "ic = deepxde.icbc.IC(func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the neural network model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T13:42:58.048271Z",
+     "start_time": "2024-12-17T13:42:57.611872Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "net = deepxde.nn.Model(\n",
+    "    deepxde.nn.DictToArray(x=unit_of_x, t=unit_of_t),\n",
+    "    deepxde.nn.FNN([2] + [32] * 3 + [1], \"tanh\"),\n",
+    "    deepxde.nn.ArrayToDict(y=None),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the PDE function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T13:42:58.061945Z",
+     "start_time": "2024-12-17T13:42:58.057100Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def pde(x, y):\n",
+    "    jacobian = net.jacobian(x, x='t')\n",
+    "    hessian = net.hessian(x, xi='x', xj='x')\n",
+    "    dy_t = jacobian[\"y\"][\"t\"]\n",
+    "    dy_xx = hessian[\"y\"][\"x\"][\"x\"]\n",
+    "    source = (\n",
+    "        u.math.exp(-x['t'] / unit_of_t) * (\n",
+    "        u.math.sin(u.math.pi * x['x'] / unit_of_x) -\n",
+    "        u.math.pi ** 2 * u.math.sin(u.math.pi * x['x'] / unit_of_x)\n",
+    "    )\n",
+    "    )\n",
+    "    return dy_t - c * dy_xx + source * unit_of_f\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the problem and train the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T13:42:59.334380Z",
+     "start_time": "2024-12-17T13:42:58.070890Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: 10000 points required, but 10082 points sampled.\n"
+     ]
+    }
+   ],
+   "source": [
+    "problem = deepxde.problem.TimePDE(\n",
+    "    geomtime,\n",
+    "    pde,\n",
+    "    [bc, ic],\n",
+    "    net,\n",
+    "    num_domain=40,\n",
+    "    num_boundary=20,\n",
+    "    num_initial=10,\n",
+    "    solution=func,\n",
+    "    num_test=10000,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T13:43:08.334394Z",
+     "start_time": "2024-12-17T13:42:59.359833Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.066982 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                   Test loss                                                    Test metric                                       \n",
+      "0         [39.28094 * becquerel2,                                      [42.6258 * becquerel2,                                       [{'y': Array(2.4083016, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': Array(0.82120794, dtype=float32)}},           {'ibc0': {'y': Array(0.82120794, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(1.7334808, dtype=float32)}}]            {'ibc1': {'y': Array(1.7334808, dtype=float32)}}]                                                             \n",
+      "1000      [0.00716378 * becquerel2,                                    [0.03221128 * becquerel2,                                    [{'y': Array(0.08175115, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(0.00580588, dtype=float32)}},           {'ibc0': {'y': Array(0.00580588, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(0.00591157, dtype=float32)}}]           {'ibc1': {'y': Array(0.00591157, dtype=float32)}}]                                                            \n",
+      "2000      [0.00374766 * becquerel2,                                    [0.01406009 * becquerel2,                                    [{'y': Array(0.0635821, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': Array(0.0034853, dtype=float32)}},            {'ibc0': {'y': Array(0.0034853, dtype=float32)}},                                                             \n",
+      "           {'ibc1': {'y': Array(0.00233263, dtype=float32)}}]           {'ibc1': {'y': Array(0.00233263, dtype=float32)}}]                                                            \n",
+      "3000      [0.00207005 * becquerel2,                                    [0.00866511 * becquerel2,                                    [{'y': Array(0.04210193, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(0.00153627, dtype=float32)}},           {'ibc0': {'y': Array(0.00153627, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(0.00113975, dtype=float32)}}]           {'ibc1': {'y': Array(0.00113975, dtype=float32)}}]                                                            \n",
+      "4000      [0.00109155 * becquerel2,                                    [0.00996974 * becquerel2,                                    [{'y': Array(0.02316353, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(0.00045823, dtype=float32)}},           {'ibc0': {'y': Array(0.00045823, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(0.00050199, dtype=float32)}}]           {'ibc1': {'y': Array(0.00050199, dtype=float32)}}]                                                            \n",
+      "5000      [0.00051956 * becquerel2,                                    [0.01253793 * becquerel2,                                    [{'y': Array(0.01541764, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(0.00017889, dtype=float32)}},           {'ibc0': {'y': Array(0.00017889, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(0.00024536, dtype=float32)}}]           {'ibc1': {'y': Array(0.00024536, dtype=float32)}}]                                                            \n",
+      "6000      [0.00026679 * becquerel2,                                    [0.01434105 * becquerel2,                                    [{'y': Array(0.01198213, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(0.00010253, dtype=float32)}},           {'ibc0': {'y': Array(0.00010253, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(0.00014029, dtype=float32)}}]           {'ibc1': {'y': Array(0.00014029, dtype=float32)}}]                                                            \n",
+      "7000      [0.00015957 * becquerel2,                                    [0.01430503 * becquerel2,                                    [{'y': Array(0.00955142, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(6.4639426e-05, dtype=float32)}},        {'ibc0': {'y': Array(6.4639426e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(8.0856196e-05, dtype=float32)}}]        {'ibc1': {'y': Array(8.0856196e-05, dtype=float32)}}]                                                         \n",
+      "8000      [0.00011952 * becquerel2,                                    [0.01355371 * becquerel2,                                    [{'y': Array(0.00870061, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(5.1637177e-05, dtype=float32)}},        {'ibc0': {'y': Array(5.1637177e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(4.7870093e-05, dtype=float32)}}]        {'ibc1': {'y': Array(4.7870093e-05, dtype=float32)}}]                                                         \n",
+      "9000      [2.9594898e-05 * becquerel2,                                 [0.01290757 * becquerel2,                                    [{'y': Array(0.00810704, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(3.8509617e-05, dtype=float32)}},        {'ibc0': {'y': Array(3.8509617e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(2.8898923e-05, dtype=float32)}}]        {'ibc1': {'y': Array(2.8898923e-05, dtype=float32)}}]                                                         \n",
+      "10000     [1.6880738e-05 * becquerel2,                                 [0.01241341 * becquerel2,                                    [{'y': Array(0.00777998, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(3.39993e-05, dtype=float32)}},          {'ibc0': {'y': Array(3.39993e-05, dtype=float32)}},                                                           \n",
+      "           {'ibc1': {'y': Array(2.0438354e-05, dtype=float32)}}]        {'ibc1': {'y': Array(2.0438354e-05, dtype=float32)}}]                                                         \n",
+      "\n",
+      "Best trainer at step 10000:\n",
+      "  train loss: 7.13e-05\n",
+      "  test loss: 1.25e-02\n",
+      "  test metric: [{'y': Array(0.01, dtype=float32)}]\n",
+      "\n",
+      "'train' took 8.116077 s\n",
+      "\n",
+      "Saving loss history to D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\loss.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\loss.dat\n",
+      "Saving training data to D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\train.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\train.dat\n",
+      "Saving test data to D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\test.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\examples-pinn-forward\\test.dat\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trainer = deepxde.Trainer(problem)\n",
+    "trainer.compile(bst.optim.Adam(0.001), metrics=[\"l2 relative error\"]).train(iterations=10000)\n",
+    "trainer.saveplot(issave=True, isplot=True)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/experimental_docs/unit-examples-forward/diffusion_1d.py b/docs/experimental_docs/unit-examples-forward/diffusion_1d.py
new file mode 100644
index 000000000..d41538b6e
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/diffusion_1d.py
@@ -0,0 +1,61 @@
+import brainstate as bst
+import brainunit as u
+
+import deepxde.experimental as deepxde
+
+unit_of_x = u.meter
+unit_of_t = u.second
+unit_of_f = 1 / u.second
+
+c = 1. * u.meter ** 2 / u.second
+
+geom = deepxde.geometry.Interval(-1, 1)
+timedomain = deepxde.geometry.TimeDomain(0, 1)
+geomtime = deepxde.geometry.GeometryXTime(geom, timedomain)
+geomtime = geomtime.to_dict_point(x=unit_of_x, t=unit_of_t)
+
+
+def func(x):
+    y = u.math.sin(u.math.pi * x['x'] / unit_of_x) * u.math.exp(-x['t'] / unit_of_t)
+    return {'y': y}
+
+
+bc = deepxde.icbc.DirichletBC(func)
+ic = deepxde.icbc.IC(func)
+
+net = deepxde.nn.Model(
+    deepxde.nn.DictToArray(x=unit_of_x, t=unit_of_t),
+    deepxde.nn.FNN([2] + [32] * 3 + [1], "tanh"),
+    deepxde.nn.ArrayToDict(y=None),
+)
+
+
+def pde(x, y):
+    jacobian = net.jacobian(x, x='t')
+    hessian = net.hessian(x, xi='x', xj='x')
+    dy_t = jacobian["y"]["t"]
+    dy_xx = hessian["y"]["x"]["x"]
+    source = (
+        u.math.exp(-x['t'] / unit_of_t) * (
+        u.math.sin(u.math.pi * x['x'] / unit_of_x) -
+        u.math.pi ** 2 * u.math.sin(u.math.pi * x['x'] / unit_of_x)
+    )
+    )
+    return dy_t - c * dy_xx + source * unit_of_f
+
+
+problem = deepxde.problem.TimePDE(
+    geomtime,
+    pde,
+    [bc, ic],
+    net,
+    num_domain=40,
+    num_boundary=20,
+    num_initial=10,
+    solution=func,
+    num_test=10000,
+)
+
+trainer = deepxde.Trainer(problem)
+trainer.compile(bst.optim.Adam(0.001), metrics=["l2 relative error"]).train(iterations=10000)
+trainer.saveplot(issave=True, isplot=True)
diff --git a/docs/experimental_docs/unit-examples-forward/heat.ipynb b/docs/experimental_docs/unit-examples-forward/heat.ipynb
new file mode 100644
index 000000000..3728f756a
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/heat.ipynb
@@ -0,0 +1,550 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Heat equation\n",
+    "\n",
+    "## Problem setup\n",
+    "We will solve a heat equation:\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\partial u}{\\partial t}=\\alpha \\frac{\\partial^2u}{\\partial x^2}, \\qquad x \\in [0, 1], \\quad t \\in [0, 1]\n",
+    "$$\n",
+    "\n",
+    "where $alpha=0.4$ is the thermal diffusivity constant.\n",
+    "\n",
+    "With Dirichlet boundary conditions:\n",
+    "\n",
+    "$$\n",
+    "u(0,t) = u(1,t)=0,\n",
+    "$$\n",
+    "\n",
+    "and periodic(sinusoidal) inital condition:\n",
+    "\n",
+    "$$\n",
+    "u(x,0) = \\sin (\\frac{n\\pi x}{L}),\\qquad 0<x<L, \\quad n = 1,2,.....\n",
+    "$$\n",
+    "\n",
+    "where $L=1$ is the length of the bar, $n=1$ is the frequency of the sinusoidal initial conditions.\n",
+    "\n",
+    "The exact solution is $u(x,t) = e^{\\frac{-n ^2\\pi ^2 \\alpha t}{L^2}}\\sin (\\frac{n\\pi x}{L})$.\n",
+    "\n",
+    "\n",
+    "## Dimensional Analysis\n",
+    "\n",
+    "### Step 1: Assign Dimensions to Variables\n",
+    "\n",
+    "1. **Spatial Coordinate $x$:**\n",
+    "   - The dimension of $x$ is length:\n",
+    "\n",
+    "     $$\n",
+    "     [x] = L.\n",
+    "     $$\n",
+    "\n",
+    "2. **Time $t$:**\n",
+    "   - The dimension of time is:\n",
+    "\n",
+    "     $$\n",
+    "     [t] = T.\n",
+    "     $$\n",
+    "\n",
+    "3. **Temperature $u$:**\n",
+    "   - Temperature has dimensions of temperature:\n",
+    "\n",
+    "     $$\n",
+    "     [u] = \\Theta.\n",
+    "     $$\n",
+    "\n",
+    "4. **Thermal Diffusivity $\\alpha$:**\n",
+    "   - The term $\\alpha \\frac{\\partial^2 u}{\\partial x^2}$ involves the second spatial derivative of temperature, which must have the same dimensions as the time derivative $\\frac{\\partial u}{\\partial t}$.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 2: Analyze the Dimensions of Each Term\n",
+    "\n",
+    "1. **Time Derivative Term:**\n",
+    "   - The time derivative $\\frac{\\partial u}{\\partial t}$ has dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\frac{\\partial u}{\\partial t}\\right] = \\frac{[u]}{[t]} = \\frac{\\Theta}{T}.\n",
+    "     $$\n",
+    "\n",
+    "2. **Diffusion Term:**\n",
+    "   - The diffusion term $\\alpha \\frac{\\partial^2 u}{\\partial x^2}$ involves the second spatial derivative of temperature:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\frac{\\partial^2 u}{\\partial x^2}\\right] = \\frac{[u]}{[x]^2} = \\frac{\\Theta}{L^2}.\n",
+    "     $$\n",
+    "   - Therefore, the diffusion term has dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\alpha \\frac{\\partial^2 u}{\\partial x^2}\\right] = [\\alpha] \\cdot \\frac{\\Theta}{L^2} = \\frac{\\Theta}{T}.\n",
+    "     $$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 3: Determine the Dimensions of $\\alpha$\n",
+    "\n",
+    "- The diffusion term $\\alpha \\frac{\\partial^2 u}{\\partial x^2}$ must have the same dimensions as the time derivative $\\frac{\\partial u}{\\partial t}$:\n",
+    "\n",
+    "  $$\n",
+    "  [\\alpha] \\cdot \\frac{\\Theta}{L^2} = \\frac{\\Theta}{T} \\implies [\\alpha] = \\frac{L^2}{T}.\n",
+    "  $$\n",
+    "- Therefore, the thermal diffusivity $\\alpha$ has dimensions of thermal diffusivity:\n",
+    "\n",
+    "  $$\n",
+    "  [\\alpha] = \\frac{L^2}{T}.\n",
+    "  $$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 4: Summary of Dimensions\n",
+    "\n",
+    "| Variable/Parameter     | Physical Meaning                   | Dimensions            |\n",
+    "|------------------------|-----------------------------------|-----------------------|\n",
+    "| $x$                   | Spatial coordinate                | $L$                 |\n",
+    "| $t$                   | Time                              | $T$                 |\n",
+    "| $u$                   | Temperature                       | $\\Theta$            |\n",
+    "| $\\alpha$              | Thermal diffusivity               | $L^2 / T$           |\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 5: Initial and Boundary Conditions\n",
+    "\n",
+    "1. **Boundary Conditions:**\n",
+    "   - The boundary conditions $u(0,t) = u(1,t) = 0$ are given in temperature units:\n",
+    "\n",
+    "     $$\n",
+    "     [u(0,t)] = [u(1,t)] = \\Theta.\n",
+    "     $$\n",
+    "\n",
+    "2. **Initial Condition:**\n",
+    "   - The initial condition $u(x,0) = \\sin \\left(\\frac{n\\pi x}{L}\\right)$ is given in temperature units:\n",
+    "   \n",
+    "     $$\n",
+    "     [u(x,0)] = \\Theta.\n",
+    "     $$\n",
+    "   - The term $\\sin \\left(\\frac{n\\pi x}{L}\\right)$ is dimensionless because $\\frac{n\\pi x}{L}$ is dimensionless ($n$ and $\\pi$ are dimensionless constants, and $x$ and $L$ have the same dimensions).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implementation\n",
+    "This description goes through the implementation of a solver for the above described Heat equation step-by-step.\n",
+    "\n",
+    "First, import the libraries we need:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import brainstate as bst\n",
+    "import optax\n",
+    "import brainunit as u\n",
+    "\n",
+    "import deepxde.experimental as deepxde"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We begin by defining the parameters of the equation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 0.4 * u.meter2 / u.second # Thermal diffusivity\n",
+    "L = 1 * u.meter  # Length of the bar\n",
+    "n = 1 * u.Hz # Frequency of the sinusoidal initial conditions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we define a computational geometry and time domain. We can use a built-in class `Interval` and `TimeDomain` and we combine both the domains using `GeometryXTime` as follows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geomtime = deepxde.geometry.GeometryXTime(\n",
+    "    geometry=deepxde.geometry.Interval(0., 1.),\n",
+    "    timedomain=deepxde.geometry.TimeDomain(0., 1.)\n",
+    ").to_dict_point(x=u.meter, t=u.second)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we express the PDE residual of the Heat equation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@bst.compile.jit\n",
+    "def pde(x, y):\n",
+    "    jacobian = approximator.jacobian(x)\n",
+    "    hessian = approximator.hessian(x)\n",
+    "    dy_t = jacobian['y']['t']\n",
+    "    dy_xx = hessian['y']['x']['x']\n",
+    "    return dy_t - a * dy_xx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first argument to pde is 2-dimensional vector where the first component(`x[:,0]`) is x-coordinate and the second componenet (`x[:,1]`) is the t-coordinate. The second argument is the network output, i.e., the solution `u(x,t)`, but here we use `y` as the name of the variable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we consider the boundary/initial condition. We include the `geomtime` space, time geometry created above. We also define `IC` which is the inital condition for the burgers equation and we use the computational domain and initial function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uy = u.kelvin / u.second\n",
+    "# Initial and boundary conditions:\n",
+    "bc = deepxde.icbc.DirichletBC(\n",
+    "    lambda x : {'y': 0. * uy}\n",
+    ")\n",
+    "ic = deepxde.icbc.IC(\n",
+    "    lambda x: {'y': u.math.sin(n * u.math.pi * x['x'] / L, unit_to_scale=u.becquerel) * uy},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we choose the network. Here, we use a fully connected neural network of depth 4 (i.e., 3 hidden layers) and width 20:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "approximator = deepxde.nn.Model(\n",
+    "    deepxde.nn.DictToArray(x=u.meter, t=u.second),\n",
+    "    deepxde.nn.FNN(\n",
+    "        [2] + [20] * 3 + [1],\n",
+    "        \"tanh\",\n",
+    "        bst.init.KaimingUniform()\n",
+    "    ),\n",
+    "    deepxde.nn.ArrayToDict(y=uy)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we have specified the geometry, PDE residual, boundary/initial condition and approximator. We then define the TimePDE problem as"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: 2540 points required, but 2550 points sampled.\n"
+     ]
+    }
+   ],
+   "source": [
+    "problem = deepxde.problem.TimePDE(\n",
+    "    geomtime,\n",
+    "    pde,\n",
+    "    [bc, ic],\n",
+    "    approximator,\n",
+    "    num_domain=2540,\n",
+    "    num_boundary=80,\n",
+    "    num_initial=160,\n",
+    "    num_test=2540,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The number 2540 is the number of training residual points sampled inside the domain, and the number 80 is the number of training points sampled on the boundary. We also include 160 initial residual points for the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we have the PDE problem and the network. We build a Model and choose the optimizer and learning rate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.057718 s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pinnx.Trainer at 0x140369fd0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer = deepxde.Trainer(problem)\n",
+    "trainer.compile(bst.optim.Adam(1e-3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then train the model for 10000 iterations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                        Test loss                                                         Test metric \n",
+      "0         [1.7518853 * 10.0^0 * ((kelvin / second) / second) ** 2,          [1.5275408 * 10.0^0 * ((kelvin / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 0.32626745 * kelvin / second}},                    {'ibc0': {'y': 0.32626745 * kelvin / second}},                               \n",
+      "           {'ibc1': {'y': 1.0854373 * kelvin / second}}]                     {'ibc1': {'y': 1.0854373 * kelvin / second}}]                                \n",
+      "1000      [0.00440458 * 10.0^0 * ((kelvin / second) / second) ** 2,         [0.00259006 * 10.0^0 * ((kelvin / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 0.00315431 * kelvin / second}},                    {'ibc0': {'y': 0.00315431 * kelvin / second}},                               \n",
+      "           {'ibc1': {'y': 0.0065606 * kelvin / second}}]                     {'ibc1': {'y': 0.0065606 * kelvin / second}}]                                \n",
+      "2000      [0.00167008 * 10.0^0 * ((kelvin / second) / second) ** 2,         [0.00116817 * 10.0^0 * ((kelvin / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 0.00121897 * kelvin / second}},                    {'ibc0': {'y': 0.00121897 * kelvin / second}},                               \n",
+      "           {'ibc1': {'y': 0.00086062 * kelvin / second}}]                    {'ibc1': {'y': 0.00086062 * kelvin / second}}]                               \n",
+      "3000      [0.00081106 * 10.0^0 * ((kelvin / second) / second) ** 2,         [0.00056661 * 10.0^0 * ((kelvin / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 0.00062208 * kelvin / second}},                    {'ibc0': {'y': 0.00062208 * kelvin / second}},                               \n",
+      "           {'ibc1': {'y': 0.00032557 * kelvin / second}}]                    {'ibc1': {'y': 0.00032557 * kelvin / second}}]                               \n",
+      "4000      [0.00046928 * 10.0^0 * ((kelvin / second) / second) ** 2,         [0.000322 * 10.0^0 * ((kelvin / second) / second) ** 2,           []          \n",
+      "           {'ibc0': {'y': 0.00029971 * kelvin / second}},                    {'ibc0': {'y': 0.00029971 * kelvin / second}},                               \n",
+      "           {'ibc1': {'y': 0.00014895 * kelvin / second}}]                    {'ibc1': {'y': 0.00014895 * kelvin / second}}]                               \n",
+      "5000      [0.00028848 * 10.0^0 * ((kelvin / second) / second) ** 2,         [0.00019277 * 10.0^0 * ((kelvin / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 0.00011767 * kelvin / second}},                    {'ibc0': {'y': 0.00011767 * kelvin / second}},                               \n",
+      "           {'ibc1': {'y': 6.189949e-05 * kelvin / second}}]                  {'ibc1': {'y': 6.189949e-05 * kelvin / second}}]                             \n",
+      "6000      [0.00018016 * 10.0^0 * ((kelvin / second) / second) ** 2,         [0.00011896 * 10.0^0 * ((kelvin / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 3.331493e-05 * kelvin / second}},                  {'ibc0': {'y': 3.331493e-05 * kelvin / second}},                             \n",
+      "           {'ibc1': {'y': 1.836832e-05 * kelvin / second}}]                  {'ibc1': {'y': 1.836832e-05 * kelvin / second}}]                             \n",
+      "7000      [0.00010137 * 10.0^0 * ((kelvin / second) / second) ** 2,         [6.64615e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,        []          \n",
+      "           {'ibc0': {'y': 1.1578003e-05 * kelvin / second}},                 {'ibc0': {'y': 1.1578003e-05 * kelvin / second}},                            \n",
+      "           {'ibc1': {'y': 6.0143293e-06 * kelvin / second}}]                 {'ibc1': {'y': 6.0143293e-06 * kelvin / second}}]                            \n",
+      "8000      [5.2904743e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,      [3.3663386e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,      []          \n",
+      "           {'ibc0': {'y': 5.493999e-06 * kelvin / second}},                  {'ibc0': {'y': 5.493999e-06 * kelvin / second}},                             \n",
+      "           {'ibc1': {'y': 2.8610739e-06 * kelvin / second}}]                 {'ibc1': {'y': 2.8610739e-06 * kelvin / second}}]                            \n",
+      "9000      [2.8543282e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,      [1.8309089e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,      []          \n",
+      "           {'ibc0': {'y': 3.213987e-06 * kelvin / second}},                  {'ibc0': {'y': 3.213987e-06 * kelvin / second}},                             \n",
+      "           {'ibc1': {'y': 1.6611314e-06 * kelvin / second}}]                 {'ibc1': {'y': 1.6611314e-06 * kelvin / second}}]                            \n",
+      "10000     [1.7808488e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,      [1.16582605e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,     []          \n",
+      "           {'ibc0': {'y': 2.0270465e-06 * kelvin / second}},                 {'ibc0': {'y': 2.0270465e-06 * kelvin / second}},                            \n",
+      "           {'ibc1': {'y': 1.0902334e-06 * kelvin / second}}]                 {'ibc1': {'y': 1.0902334e-06 * kelvin / second}}]                            \n",
+      "\n",
+      "Best trainer at step 10000:\n",
+      "  train loss: 2.09e-05\n",
+      "  test loss: 1.48e-05\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 47.118451 s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pinnx.Trainer at 0x140369fd0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer.train(iterations=10000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After we train the network using Adam, we continue to train the network using L-BFGS to achieve a smaller loss:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.128015 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                            Test loss                                                              Test metric \n",
+      "10000     [1.7808488e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,          [1.16582605e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 2.0270465e-06 * kelvin / second}},                     {'ibc0': {'y': 2.0270465e-06 * kelvin / second}},                                 \n",
+      "           {'ibc1': {'y': 1.0902334e-06 * kelvin / second}}]                     {'ibc1': {'y': 1.0902334e-06 * kelvin / second}}]                                 \n",
+      "11000     [0.00920797 * 10.0^0 * ((kelvin / second) / second) ** 2,             [0.00422679 * 10.0^0 * ((kelvin / second) / second) ** 2,              []          \n",
+      "           {'ibc0': {'y': 0.00172434 * kelvin / second}},                        {'ibc0': {'y': 0.00172434 * kelvin / second}},                                    \n",
+      "           {'ibc1': {'y': 6.910529e-05 * kelvin / second}}]                      {'ibc1': {'y': 6.910529e-05 * kelvin / second}}]                                  \n",
+      "12000     [0.0007039 * 10.0^0 * ((kelvin / second) / second) ** 2,              [0.00046745 * 10.0^0 * ((kelvin / second) / second) ** 2,              []          \n",
+      "           {'ibc0': {'y': 0.00013835 * kelvin / second}},                        {'ibc0': {'y': 0.00013835 * kelvin / second}},                                    \n",
+      "           {'ibc1': {'y': 3.9120117e-05 * kelvin / second}}]                     {'ibc1': {'y': 3.9120117e-05 * kelvin / second}}]                                 \n",
+      "\n",
+      "Best trainer at step 10000:\n",
+      "  train loss: 2.09e-05\n",
+      "  test loss: 1.48e-05\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 10.107104 s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pinnx.Trainer at 0x140369fd0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer.compile(bst.optim.OptaxOptimizer(optax.lbfgs(1e-3, linesearch=None)))\n",
+    "trainer.train(iterations=2000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's visualize and save the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving loss history to /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/loss.dat ...\n",
+      "Saving checkpoint into /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/loss.dat\n",
+      "Saving training data to /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/train.dat ...\n",
+      "Saving checkpoint into /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/train.dat\n",
+      "Saving test data to /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/test.dat ...\n",
+      "Saving checkpoint into /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/test.dat\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trainer.saveplot(issave=True, isplot=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pinnx",
+   "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": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/experimental_docs/unit-examples-forward/heat.py b/docs/experimental_docs/unit-examples-forward/heat.py
new file mode 100644
index 000000000..a4ade0305
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/heat.py
@@ -0,0 +1,133 @@
+import os
+
+import brainstate as bst
+import numpy as np
+import optax
+import brainunit as u
+
+import deepxde.experimental as deepxde
+
+
+def heat_eq_exact_solution(x, t):
+    """Returns the exact solution for a given x and t (for sinusoidal initial conditions).
+
+    Parameters
+    ----------
+    x : np.ndarray
+    t : np.ndarray
+    """
+    a_value = a.to_decimal(u.meter2 / u.second)
+    n_value = n.to_decimal(u.Hz)
+    L_value = L.to_decimal(u.meter)
+    return np.exp(-(n_value ** 2 * np.pi ** 2 * a_value * t) / (L_value ** 2)) * np.sin(n_value * np.pi * x / L_value)
+
+
+def gen_exact_solution():
+    """Generates exact solution for the heat equation for the given values of x and t."""
+    # Number of points in each dimension:
+    x_dim, t_dim = (256, 201)
+
+    # Bounds of 'x' and 't':
+    x_min, t_min = (0, 0.0)
+    x_max, t_max = (L.to_decimal(u.meter), 1.0)
+
+    # Create tensors:
+    t = np.linspace(t_min, t_max, num=t_dim).reshape(t_dim, 1)
+    x = np.linspace(x_min, x_max, num=x_dim).reshape(x_dim, 1)
+    usol = np.zeros((x_dim, t_dim)).reshape(x_dim, t_dim)
+
+    # Obtain the value of the exact solution for each generated point:
+    for i in range(x_dim):
+        for j in range(t_dim):
+            usol[i, j] = heat_eq_exact_solution(x[i], t[j])
+
+    # Save solution:
+    np.savez("heat_eq_data", x=x, t=t, usol=usol)
+
+def gen_testdata():
+    if os.path.exists("heat_eq_data.npz"):
+        return
+    """Import and preprocess the dataset with the exact solution."""
+    # Load the data:
+    data = np.load("heat_eq_data.npz")
+    # Obtain the values for t, x, and the excat solution:
+    t, x, exact = data["t"], data["x"], data["usol"].T
+    # Process the data and flatten it out (like labels and features):
+    xx, tt = np.meshgrid(x, t)
+    X = {'x': np.ravel(xx) * u.meter, 't': np.ravel(tt) * u.second}
+    y = exact.flatten()[:, None]
+    return X, y * uy
+
+
+# Problem parameters:
+a = 0.4 * u.meter2 / u.second # Thermal diffusivity
+L = 1 * u.meter  # Length of the bar
+n = 1 * u.Hz # Frequency of the sinusoidal initial conditions
+
+gen_exact_solution()
+
+# Computational geometry:
+geomtime = deepxde.geometry.GeometryXTime(
+    geometry=deepxde.geometry.Interval(0., 1.),
+    timedomain=deepxde.geometry.TimeDomain(0., 1.)
+).to_dict_point(x=u.meter, t=u.second)
+
+uy = u.kelvin / u.second
+# Initial and boundary conditions:
+bc = deepxde.icbc.DirichletBC(
+    lambda x : {'y': 0. * uy}
+)
+ic = deepxde.icbc.IC(
+    lambda x: {'y': u.math.sin(n * u.math.pi * x['x'][:] / L, unit_to_scale=u.becquerel) * uy},
+)
+
+
+@bst.compile.jit
+def pde(x, y):
+    """
+    Expresses the PDE residual of the heat equation.
+    """
+    jacobian = approximator.jacobian(x)
+    hessian = approximator.hessian(x)
+    dy_t = jacobian['y']['t']
+    dy_xx = hessian['y']['x']['x']
+    return dy_t - a * dy_xx
+
+approximator = deepxde.nn.Model(
+    deepxde.nn.DictToArray(x=u.meter, t=u.second),
+    deepxde.nn.FNN(
+        [2] + [20] * 3 + [1],
+        "tanh",
+        bst.init.KaimingUniform()
+    ),
+    deepxde.nn.ArrayToDict(y=uy)
+)
+
+# Define the PDE problem and configurations of the network:
+problem = deepxde.problem.TimePDE(
+    geomtime,
+    pde,
+    [bc, ic],
+    approximator,
+    num_domain=2540,
+    num_boundary=80,
+    num_initial=160,
+    num_test=2540,
+)
+
+trainer = deepxde.Trainer(problem)
+
+# Build and train the trainer:
+trainer.compile(bst.optim.Adam(1e-3))
+trainer.train(iterations=10000)
+trainer.compile(bst.optim.OptaxOptimizer(optax.lbfgs(1e-3, linesearch=None)))
+
+# Plot/print the results
+trainer.saveplot(issave=True, isplot=True)
+
+X, y_true = gen_testdata()
+y_pred = trainer.predict(X)
+f = pde(X, y_pred)
+print("Mean residual:", u.math.mean(u.math.absolute(f)))
+print("L2 relative error:", deepxde.metrics.l2_relative_error(y_true, y_pred['y']))
+# np.savetxt("test.dat", u.math.hstack((X, y_true, y_pred)))
diff --git a/docs/experimental_docs/unit-examples-forward/heat_resample.ipynb b/docs/experimental_docs/unit-examples-forward/heat_resample.ipynb
new file mode 100644
index 000000000..008d4a156
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/heat_resample.ipynb
@@ -0,0 +1,565 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Heat equation with training points resampling\n",
+    "\n",
+    "## Problem setup\n",
+    "We will solve a heat equation:\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\partial u}{\\partial t}=\\alpha \\frac{\\partial^2u}{\\partial x^2}, \\qquad x \\in [0, 1], \\quad t \\in [0, 1]\n",
+    "$$\n",
+    "\n",
+    "where $alpha=0.4$ is the thermal diffusivity constant.\n",
+    "\n",
+    "With Dirichlet boundary conditions:\n",
+    "\n",
+    "$$\n",
+    "u(0,t) = u(1,t)=0,\n",
+    "$$\n",
+    "\n",
+    "and periodic(sinusoidal) inital condition:\n",
+    "\n",
+    "$$\n",
+    "u(x,0) = \\sin (\\frac{n\\pi x}{L}),\\qquad 0<x<L, \\quad n = 1,2,.....\n",
+    "$$\n",
+    "\n",
+    "where $L=1$ is the length of the bar, $n=1$ is the frequency of the sinusoidal initial conditions.\n",
+    "\n",
+    "The exact solution is $u(x,t) = e^{\\frac{-n ^2\\pi ^2 \\alpha t}{L^2}}\\sin (\\frac{n\\pi x}{L})$.\n",
+    "\n",
+    "\n",
+    "## Dimensional Analysis\n",
+    "\n",
+    "### Step 1: Assign Dimensions to Variables\n",
+    "\n",
+    "1. **Spatial Coordinate $x$:**\n",
+    "   - The dimension of $x$ is length:\n",
+    "\n",
+    "     $$\n",
+    "     [x] = L.\n",
+    "     $$\n",
+    "\n",
+    "2. **Time $t$:**\n",
+    "   - The dimension of time is:\n",
+    "\n",
+    "     $$\n",
+    "     [t] = T.\n",
+    "     $$\n",
+    "\n",
+    "3. **Temperature $u$:**\n",
+    "   - Temperature has dimensions of temperature:\n",
+    "\n",
+    "     $$\n",
+    "     [u] = \\Theta.\n",
+    "     $$\n",
+    "\n",
+    "4. **Thermal Diffusivity $\\alpha$:**\n",
+    "   - The term $\\alpha \\frac{\\partial^2 u}{\\partial x^2}$ involves the second spatial derivative of temperature, which must have the same dimensions as the time derivative $\\frac{\\partial u}{\\partial t}$.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 2: Analyze the Dimensions of Each Term\n",
+    "\n",
+    "1. **Time Derivative Term:**\n",
+    "   - The time derivative $\\frac{\\partial u}{\\partial t}$ has dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\frac{\\partial u}{\\partial t}\\right] = \\frac{[u]}{[t]} = \\frac{\\Theta}{T}.\n",
+    "     $$\n",
+    "\n",
+    "2. **Diffusion Term:**\n",
+    "   - The diffusion term $\\alpha \\frac{\\partial^2 u}{\\partial x^2}$ involves the second spatial derivative of temperature:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\frac{\\partial^2 u}{\\partial x^2}\\right] = \\frac{[u]}{[x]^2} = \\frac{\\Theta}{L^2}.\n",
+    "     $$\n",
+    "   - Therefore, the diffusion term has dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\alpha \\frac{\\partial^2 u}{\\partial x^2}\\right] = [\\alpha] \\cdot \\frac{\\Theta}{L^2} = \\frac{\\Theta}{T}.\n",
+    "     $$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 3: Determine the Dimensions of $\\alpha$\n",
+    "\n",
+    "- The diffusion term $\\alpha \\frac{\\partial^2 u}{\\partial x^2}$ must have the same dimensions as the time derivative $\\frac{\\partial u}{\\partial t}$:\n",
+    "\n",
+    "  $$\n",
+    "  [\\alpha] \\cdot \\frac{\\Theta}{L^2} = \\frac{\\Theta}{T} \\implies [\\alpha] = \\frac{L^2}{T}.\n",
+    "  $$\n",
+    "- Therefore, the thermal diffusivity $\\alpha$ has dimensions of thermal diffusivity:\n",
+    "\n",
+    "  $$\n",
+    "  [\\alpha] = \\frac{L^2}{T}.\n",
+    "  $$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 4: Summary of Dimensions\n",
+    "\n",
+    "| Variable/Parameter     | Physical Meaning                   | Dimensions            |\n",
+    "|------------------------|-----------------------------------|-----------------------|\n",
+    "| $x$                   | Spatial coordinate                | $L$                 |\n",
+    "| $t$                   | Time                              | $T$                 |\n",
+    "| $u$                   | Temperature                       | $\\Theta$            |\n",
+    "| $\\alpha$              | Thermal diffusivity               | $L^2 / T$           |\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 5: Initial and Boundary Conditions\n",
+    "\n",
+    "1. **Boundary Conditions:**\n",
+    "   - The boundary conditions $u(0,t) = u(1,t) = 0$ are given in temperature units:\n",
+    "\n",
+    "     $$\n",
+    "     [u(0,t)] = [u(1,t)] = \\Theta.\n",
+    "     $$\n",
+    "\n",
+    "2. **Initial Condition:**\n",
+    "   - The initial condition $u(x,0) = \\sin \\left(\\frac{n\\pi x}{L}\\right)$ is given in temperature units:\n",
+    "   \n",
+    "     $$\n",
+    "     [u(x,0)] = \\Theta.\n",
+    "     $$\n",
+    "   - The term $\\sin \\left(\\frac{n\\pi x}{L}\\right)$ is dimensionless because $\\frac{n\\pi x}{L}$ is dimensionless ($n$ and $\\pi$ are dimensionless constants, and $x$ and $L$ have the same dimensions).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implementation\n",
+    "This description goes through the implementation of a solver for the above described Heat equation step-by-step.\n",
+    "\n",
+    "First, import the libraries we need:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import brainstate as bst\n",
+    "import numpy as np\n",
+    "import optax\n",
+    "import brainunit as u\n",
+    "\n",
+    "import deepxde.experimental as deepxde"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We begin by defining the parameters of the equation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 0.4 * u.meter2 / u.second # Thermal diffusivity\n",
+    "L = 1 * u.meter  # Length of the bar\n",
+    "n = 1 * u.Hz # Frequency of the sinusoidal initial conditions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we define a computational geometry and time domain. We can use a built-in class `Interval` and `TimeDomain` and we combine both the domains using `GeometryXTime` as follows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geomtime = deepxde.geometry.GeometryXTime(\n",
+    "    geometry=deepxde.geometry.Interval(0., 1.),\n",
+    "    timedomain=deepxde.geometry.TimeDomain(0., 1.)\n",
+    ").to_dict_point(x=u.meter, t=u.second)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we express the PDE residual of the Heat equation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@bst.compile.jit\n",
+    "def pde(x, y):\n",
+    "    jacobian = approximator.jacobian(x)\n",
+    "    hessian = approximator.hessian(x)\n",
+    "    dy_t = jacobian['y']['t']\n",
+    "    dy_xx = hessian['y']['x']['x']\n",
+    "    return dy_t - a * dy_xx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first argument to pde is 2-dimensional vector where the first component(`x[:,0]`) is x-coordinate and the second componenet (`x[:,1]`) is the t-coordinate. The second argument is the network output, i.e., the solution `u(x,t)`, but here we use `y` as the name of the variable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we consider the boundary/initial condition. We include the `geomtime` space, time geometry created above. We also define `IC` which is the inital condition for the burgers equation and we use the computational domain and initial function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uy = u.kelvin / u.second\n",
+    "# Initial and boundary conditions:\n",
+    "bc = deepxde.icbc.DirichletBC(\n",
+    "    lambda x : {'y': 0. * uy}\n",
+    ")\n",
+    "ic = deepxde.icbc.IC(\n",
+    "    lambda x: {'y': u.math.sin(n * u.math.pi * x['x'][:] / L, unit_to_scale=u.becquerel) * uy},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we choose the network. Here, we use a fully connected neural network of depth 4 (i.e., 3 hidden layers) and width 20:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "approximator = deepxde.nn.Model(\n",
+    "    deepxde.nn.DictToArray(x=u.meter, t=u.second),\n",
+    "    deepxde.nn.FNN(\n",
+    "        [2] + [20] * 3 + [1],\n",
+    "        \"tanh\",\n",
+    "        bst.init.KaimingUniform()\n",
+    "    ),\n",
+    "    deepxde.nn.ArrayToDict(y=uy)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we have specified the geometry, PDE residual, boundary/initial condition and approximator. We then define the TimePDE problem as"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: 2540 points required, but 2550 points sampled.\n"
+     ]
+    }
+   ],
+   "source": [
+    "problem = deepxde.problem.TimePDE(\n",
+    "    geomtime,\n",
+    "    pde,\n",
+    "    [bc, ic],\n",
+    "    approximator,\n",
+    "    num_domain=2540,\n",
+    "    num_boundary=80,\n",
+    "    num_initial=160,\n",
+    "    num_test=2540,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The number 2540 is the number of training residual points sampled inside the domain, and the number 80 is the number of training points sampled on the boundary. We also include 160 initial residual points for the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we have the PDE problem and the network. We build a Model and choose the optimizer and learning rate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.055764 s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pinnx.Trainer at 0x148ceacd0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer = deepxde.Trainer(problem)\n",
+    "trainer.compile(bst.optim.Adam(1e-3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following code is to apply mini-batch gradient descent sampling method. The period is the period of resamping. Here, the training points in the domain will be resampled every 10 iterations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": "pde_resampler = deepxde.callbacks.PDEPointResampler(period=10)"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then train the model for 20000 iterations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                            Test loss                                                           Test metric \n",
+      "10000     [2.6207555e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,          [2.11663e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 6.1262945e-06 * kelvin / second}},                     {'ibc0': {'y': 6.1262945e-06 * kelvin / second}},                              \n",
+      "           {'ibc1': {'y': 4.302944e-06 * kelvin / second}}]                      {'ibc1': {'y': 4.302944e-06 * kelvin / second}}]                               \n",
+      "11000     [1.9524387e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,          [1.6359398e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,        []          \n",
+      "           {'ibc0': {'y': 4.0165432e-06 * kelvin / second}},                     {'ibc0': {'y': 4.0165432e-06 * kelvin / second}},                              \n",
+      "           {'ibc1': {'y': 3.168525e-06 * kelvin / second}}]                      {'ibc1': {'y': 3.168525e-06 * kelvin / second}}]                               \n",
+      "12000     [1.5614694e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,          [1.34647535e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,       []          \n",
+      "           {'ibc0': {'y': 2.8513743e-06 * kelvin / second}},                     {'ibc0': {'y': 2.8513743e-06 * kelvin / second}},                              \n",
+      "           {'ibc1': {'y': 2.5143677e-06 * kelvin / second}}]                     {'ibc1': {'y': 2.5143677e-06 * kelvin / second}}]                              \n",
+      "13000     [1.5810221e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,          [1.08873655e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,       []          \n",
+      "           {'ibc0': {'y': 2.6305897e-06 * kelvin / second}},                     {'ibc0': {'y': 2.6305897e-06 * kelvin / second}},                              \n",
+      "           {'ibc1': {'y': 2.057085e-06 * kelvin / second}}]                      {'ibc1': {'y': 2.057085e-06 * kelvin / second}}]                               \n",
+      "14000     [1.0092881e-05 * 10.0^0 * ((kelvin / second) / second) ** 2,          [8.765763e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 1.7242603e-06 * kelvin / second}},                     {'ibc0': {'y': 1.7242603e-06 * kelvin / second}},                              \n",
+      "           {'ibc1': {'y': 1.4643564e-06 * kelvin / second}}]                     {'ibc1': {'y': 1.4643564e-06 * kelvin / second}}]                              \n",
+      "15000     [9.48981e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,            [8.021057e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,         []          \n",
+      "           {'ibc0': {'y': 1.8745583e-06 * kelvin / second}},                     {'ibc0': {'y': 1.8745583e-06 * kelvin / second}},                              \n",
+      "           {'ibc1': {'y': 1.6735984e-06 * kelvin / second}}]                     {'ibc1': {'y': 1.6735984e-06 * kelvin / second}}]                              \n",
+      "16000     [8.69476e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,            [6.61613e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 1.2975498e-06 * kelvin / second}},                     {'ibc0': {'y': 1.2975498e-06 * kelvin / second}},                              \n",
+      "           {'ibc1': {'y': 8.5685264e-07 * kelvin / second}}]                     {'ibc1': {'y': 8.5685264e-07 * kelvin / second}}]                              \n",
+      "17000     [6.9987154e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,          [5.5678443e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,        []          \n",
+      "           {'ibc0': {'y': 1.1903414e-06 * kelvin / second}},                     {'ibc0': {'y': 1.1903414e-06 * kelvin / second}},                              \n",
+      "           {'ibc1': {'y': 1.0031221e-06 * kelvin / second}}]                     {'ibc1': {'y': 1.0031221e-06 * kelvin / second}}]                              \n",
+      "18000     [5.6217564e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,          [4.7939957e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,        []          \n",
+      "           {'ibc0': {'y': 7.706846e-07 * kelvin / second}},                      {'ibc0': {'y': 7.706846e-07 * kelvin / second}},                               \n",
+      "           {'ibc1': {'y': 7.064452e-07 * kelvin / second}}]                      {'ibc1': {'y': 7.064452e-07 * kelvin / second}}]                               \n",
+      "19000     [5.385209e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,           [4.3364116e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,        []          \n",
+      "           {'ibc0': {'y': 6.759552e-07 * kelvin / second}},                      {'ibc0': {'y': 6.759552e-07 * kelvin / second}},                               \n",
+      "           {'ibc1': {'y': 5.627439e-07 * kelvin / second}}]                      {'ibc1': {'y': 5.627439e-07 * kelvin / second}}]                               \n",
+      "20000     [5.322141e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,           [4.2963425e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,        []          \n",
+      "           {'ibc0': {'y': 7.620727e-07 * kelvin / second}},                      {'ibc0': {'y': 7.620727e-07 * kelvin / second}},                               \n",
+      "           {'ibc1': {'y': 5.87999e-07 * kelvin / second}}]                       {'ibc1': {'y': 5.87999e-07 * kelvin / second}}]                                \n",
+      "\n",
+      "Best trainer at step 19000:\n",
+      "  train loss: 6.62e-06\n",
+      "  test loss: 5.58e-06\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 47.065386 s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pinnx.Trainer at 0x148ceacd0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer.train(iterations=20000, callbacks=[pde_resampler])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After we train the network using Adam, we continue to train the network using L-BFGS to achieve a smaller loss:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.140131 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                            Test loss                                                             Test metric \n",
+      "20000     [5.1988213e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,          [4.2963425e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 7.620727e-07 * kelvin / second}},                      {'ibc0': {'y': 7.620727e-07 * kelvin / second}},                                 \n",
+      "           {'ibc1': {'y': 5.87999e-07 * kelvin / second}}]                       {'ibc1': {'y': 5.87999e-07 * kelvin / second}}]                                  \n",
+      "21000     [4.5359056e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,          [3.7426814e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 6.414695e-07 * kelvin / second}},                      {'ibc0': {'y': 6.414695e-07 * kelvin / second}},                                 \n",
+      "           {'ibc1': {'y': 4.3834766e-07 * kelvin / second}}]                     {'ibc1': {'y': 4.3834766e-07 * kelvin / second}}]                                \n",
+      "22000     [4.472149e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,           [3.7037253e-06 * 10.0^0 * ((kelvin / second) / second) ** 2,          []          \n",
+      "           {'ibc0': {'y': 6.1141054e-07 * kelvin / second}},                     {'ibc0': {'y': 6.1141054e-07 * kelvin / second}},                                \n",
+      "           {'ibc1': {'y': 4.3972565e-07 * kelvin / second}}]                     {'ibc1': {'y': 4.3972565e-07 * kelvin / second}}]                                \n",
+      "\n",
+      "Best trainer at step 22000:\n",
+      "  train loss: 5.52e-06\n",
+      "  test loss: 4.75e-06\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 10.094850 s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pinnx.Trainer at 0x148ceacd0>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer.compile(bst.optim.OptaxOptimizer(optax.lbfgs(1e-3, linesearch=None)))\n",
+    "trainer.train(iterations=2000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's visualize and save the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving loss history to /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/loss.dat ...\n",
+      "Saving checkpoint into /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/loss.dat\n",
+      "Saving training data to /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/train.dat ...\n",
+      "Saving checkpoint into /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/train.dat\n",
+      "Saving test data to /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/test.dat ...\n",
+      "Saving checkpoint into /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/test.dat\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trainer.saveplot(issave=True, isplot=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pinnx",
+   "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": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/experimental_docs/unit-examples-forward/heat_resample.py b/docs/experimental_docs/unit-examples-forward/heat_resample.py
new file mode 100644
index 000000000..96829c889
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-forward/heat_resample.py
@@ -0,0 +1,147 @@
+import brainstate as bst
+import numpy as np
+import optax
+import brainunit as u
+import os
+
+import deepxde.experimental as deepxde
+
+
+def heat_eq_exact_solution(x, t):
+    """Returns the exact solution for a given x and t (for sinusoidal initial conditions).
+
+    Parameters
+    ----------
+    x : np.ndarray
+    t : np.ndarray
+    """
+    a_value = a.to_decimal(u.meter2 / u.second).item()
+    n_value = n.to_decimal(u.Hz).item()
+    L_value = L.to_decimal(u.meter).item()
+    return np.exp(-(n_value ** 2 * np.pi ** 2 * a_value * t) / (L_value ** 2)) * np.sin(n_value * np.pi * x / L_value)
+
+
+def gen_exact_solution():
+    """Generates exact solution for the heat equation for the given values of x and t."""
+    if os.path.exists("heat_eq_data.npz"):
+        return
+    # Number of points in each dimension:
+    x_dim, t_dim = (256, 201)
+
+    # Bounds of 'x' and 't':
+    x_min, t_min = (0, 0.0)
+    x_max, t_max = (L.to_decimal(u.meter), 1.0)
+
+    # Create tensors:
+    t = np.linspace(t_min, t_max, num=t_dim).reshape(t_dim, 1)
+    x = np.linspace(x_min, x_max, num=x_dim).reshape(x_dim, 1)
+    usol = np.zeros((x_dim, t_dim)).reshape(x_dim, t_dim)
+
+    # Obtain the value of the exact solution for each generated point:
+    for i in range(x_dim):
+        for j in range(t_dim):
+            usol[i, j] = heat_eq_exact_solution(x[i], t[j])
+
+    # Save solution:
+    np.savez("heat_eq_data", x=x, t=t, usol=usol)
+
+    # Save solution:
+    np.savez("heat_eq_data", x=x, t=t, usol=usol)
+    # Load solution:
+    # data = np.load("heat_eq_data.npz")
+
+
+def gen_testdata():
+    """Import and preprocess the dataset with the exact solution."""
+    # Load the data:
+    data = np.load("heat_eq_data.npz")
+    # Obtain the values for t, x, and the excat solution:
+    t, x, exact = data["t"], data["x"], data["usol"].T
+    # Process the data and flatten it out (like labels and features):
+    xx, tt = np.meshgrid(x, t)
+    X = {'x': np.ravel(xx) * u.meter, 't': np.ravel(tt) * u.second}
+    y = exact.flatten()[:, None]
+    return X, y * uy
+
+
+# Problem parameters:
+a = 0.4 * u.meter2 / u.second  # Thermal diffusivity
+L = 1 * u.meter # Length of the bar
+n = 1 * u.Hz # Frequency of the sinusoidal initial conditions
+
+# Generate a dataset with the exact solution (if you dont have one):
+gen_exact_solution()
+
+uy = u.kelvin / u.second
+
+# Computational geometry:
+geomtime = deepxde.geometry.GeometryXTime(
+    geometry=deepxde.geometry.Interval(0., 1.),
+    timedomain=deepxde.geometry.TimeDomain(0., 1.)
+).to_dict_point(x=u.meter, t=u.second)
+
+# Initial and boundary conditions:
+bc = deepxde.icbc.DirichletBC(lambda x: {'y': 0. * uy})
+ic = deepxde.icbc.IC(
+    lambda x: {'y': u.math.sin(n * u.math.pi * x['x'][:] / L, unit_to_scale=u.becquerel) * uy},
+)
+
+
+@bst.compile.jit
+def pde(x, y):
+    """
+    Expresses the PDE residual of the heat equation.
+    """
+    jacobian = approximator.jacobian(x)
+    hessian = approximator.hessian(x)
+    dy_t = jacobian['y']['t']
+    dy_xx = hessian['y']['x']['x']
+    return dy_t - a * dy_xx
+
+
+# Define the PDE problem and configurations of the network:
+
+approximator = deepxde.nn.Model(
+    deepxde.nn.DictToArray(x=u.meter, t=u.second),
+    deepxde.nn.FNN(
+        [2] + [20] * 3 + [1],
+        "tanh",
+        bst.init.KaimingUniform()
+    ),
+    deepxde.nn.ArrayToDict(y=uy)
+)
+
+problem = deepxde.problem.TimePDE(
+    geomtime,
+    pde,
+    [bc, ic],
+    approximator,
+    num_domain=2540,
+    num_boundary=80,
+    num_initial=160,
+    num_test=2540,
+)
+
+trainer = deepxde.Trainer(problem)
+
+# Build and train the trainer:
+trainer.compile(bst.optim.Adam(1e-3))
+
+pde_resampler = deepxde.callbacks.PDEPointResampler(period=10)
+trainer.train(iterations=10000, callbacks=[pde_resampler])
+trainer.compile(bst.optim.OptaxOptimizer(optax.lbfgs(1e-3, linesearch=None)))
+# TODO: train method must has iteration param
+# losshistory, train_state = trainer.train(callbacks=[pde_resampler])
+trainer.train(iterations=1000, callbacks=[pde_resampler])
+
+# Plot/print the results
+# TODO: losshistory, train_state params for saveplot?
+# trainer.saveplot(losshistory, train_state, issave=True, isplot=True)
+trainer.saveplot(issave=True, isplot=True)
+
+X, y_true = gen_testdata()
+y_pred = trainer.predict(X)
+f = trainer.predict(X, operator=pde)
+print("Mean residual:", u.math.mean(u.math.absolute(f)))
+print("L2 relative error:", deepxde.metrics.l2_relative_error(y_true, y_pred['y']))
+# np.savetxt("test.dat", np.hstack((X, y_true, y_pred)))
diff --git a/docs/experimental_docs/unit-examples-inverse/Navier_Stokes_inverse.ipynb b/docs/experimental_docs/unit-examples-inverse/Navier_Stokes_inverse.ipynb
new file mode 100644
index 000000000..96c9c5a3f
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-inverse/Navier_Stokes_inverse.ipynb
@@ -0,0 +1,894 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Inverse problem for the Navier-Stokes equation of incompressible flow around cylinder\n",
+    "\n",
+    "## The problem we are solving\n",
+    "\n",
+    "We will solve an inverse problem for the Navier-Stokes equation of incompressible flow around a cylinder. Let us consider the Navier–Stokes equations in two dimensions2 (2D) given explicitly by\n",
+    "\n",
+    "$$\n",
+    "\\rho u_t + \\rho λ_1(uu_x + vu_y) = −p_x + \\rho λ_2(u_{xx} + u_{yy}),\n",
+    "$$\n",
+    "$$\n",
+    "\\rho v_t + \\rho λ_1(uv_x + vv_y) = −p_y + \\rho λ_2(v_{xx} + v_{yy}),\n",
+    "$$\n",
+    "\n",
+    "for $x \\in [1, 8]$, $y \\in [-2, 2]$, and $t \\in [0, 7]$ Spatio-temporal domain\n",
+    "\n",
+    "\n",
+    "where $u(t, x, y)$ denotes the $x$-component of the velocity field, $v(t, x, y)$ the y-component, $\\rho$ is the fluid density, and $p(t, x, y)$ the pressure. Here, $λ = (λ_1, λ_2)$ are the unknown parameters. Solutions to the Navier–Stokes equations are searched in the set of divergence-free functions; i.e.,\n",
+    "\n",
+    "This extra equation is the continuity equation for incompressible fluids that describes the conservation of mass of the fluid. We make the assumption that\n",
+    "\n",
+    "$$\n",
+    "u = ψ_y, v = −ψ_x,\n",
+    "$$\n",
+    "\n",
+    "for some latent function $ψ(t, x, y)$ Under this assumption, the continuity equation will be automatically satisfied. Given noisy measurements\n",
+    "\n",
+    "$$\n",
+    "\\{t^i, x^i , y^i, u^i , v^i \\}^N_{i=1}\n",
+    "$$\n",
+    "\n",
+    "of the velocity field, we are interested in learning the parameters $λ$ as well as the pressure $p(t, x, y)$. We define $f (t, x, y)$ and $g(t, x, y)$ to be given by\n",
+    "\n",
+    "$$\n",
+    "f := u_t + λ_1(uu_x + vu_y) + p_x − λ_2(u_{xx} + u_{yy}),\n",
+    "$$\n",
+    "$$\n",
+    "g := v_t + λ_1(uv_x + vv_y) + p_y − λ_2(v_{xx} + v_{yy}),\n",
+    "$$\n",
+    "\n",
+    "## Dimensional Analysis\n",
+    "\n",
+    "### Step 1: Assign Dimensions to Variables\n",
+    "\n",
+    "1. **Spatial Coordinates $x$ and $y$:**\n",
+    "   - The dimensions of $x$ and $y$ are length:\n",
+    "\n",
+    "     $$\n",
+    "     [x] = [y] = L.\n",
+    "     $$\n",
+    "\n",
+    "2. **Time $t$:**\n",
+    "   - The dimension of time is:\n",
+    "\n",
+    "     $$\n",
+    "     [t] = T.\n",
+    "     $$\n",
+    "\n",
+    "3. **Velocity Components $u$ and $v$:**\n",
+    "   - Velocity components have dimensions of length per unit time:\n",
+    "\n",
+    "     $$\n",
+    "     [u] = [v] = L / T.\n",
+    "     $$\n",
+    "\n",
+    "4. **Pressure $p$:**\n",
+    "   - Pressure has dimensions of force per unit area, which can be expressed as mass per unit length per unit time squared:\n",
+    "\n",
+    "     $$\n",
+    "     [p] = M / (L T^2),\n",
+    "     $$\n",
+    "     where $M$ represents mass.\n",
+    "\n",
+    "5. **Density $\\rho$:**\n",
+    "   - Density has dimensions of mass per unit volume:\n",
+    "\n",
+    "     $$\n",
+    "     [\\rho] = M / L^3.\n",
+    "     $$\n",
+    "\n",
+    "6. **Parameters $\\lambda_1$ and $\\lambda_2$:**\n",
+    "   - The term $\\rho \\lambda_1(uu_x + vu_y)$ involves the advection term, which must have the same dimensions as the time derivative $\\rho u_t$.\n",
+    "   - The term $\\rho \\lambda_2(u_{xx} + u_{yy})$ involves the diffusion term, which must have the same dimensions as the time derivative $\\rho u_t$.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 2: Analyze the Dimensions of Each Term\n",
+    "\n",
+    "1. **Time Derivative Terms:**\n",
+    "   - The time derivatives $\\rho u_t$ and $\\rho v_t$ have dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     [\\rho u_t] = [\\rho v_t] = [\\rho] \\cdot \\frac{[u]}{[t]} = \\frac{M}{L^3} \\cdot \\frac{L}{T^2} = \\frac{M}{L^2 T^2}.\n",
+    "     $$\n",
+    "\n",
+    "2. **Advection Terms:**\n",
+    "   - The advection terms $\\rho \\lambda_1(uu_x + vu_y)$ and $\\rho \\lambda_1(uv_x + vv_y)$ involve spatial derivatives of velocity:\n",
+    "\n",
+    "     $$\n",
+    "     [uu_x] = [vu_y] = [uv_x] = [vv_y] = \\frac{[u]^2}{[x]} = \\frac{(L / T)^2}{L} = \\frac{L}{T^2}.\n",
+    "     $$\n",
+    "   - Therefore, the advection terms have dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     [\\rho \\lambda_1(uu_x + vu_y)] = [\\rho \\lambda_1(uv_x + vv_y)] = [\\rho] \\cdot \\frac{L}{T^2} = \\frac{M}{L^3} \\cdot \\frac{L}{T^2} = \\frac{M}{L^2 T^2}.\n",
+    "     $$\n",
+    "\n",
+    "3. **Pressure Gradient Terms:**\n",
+    "   - The pressure gradient terms $-p_x$ and $-p_y$ have dimensions:\n",
+    "\n",
+    "     $$\n",
+    "     [p_x] = [p_y] = \\frac{[p]}{[x]} = \\frac{M / (L T^2)}{L} = \\frac{M}{L^2 T^2}.\n",
+    "     $$\n",
+    "\n",
+    "4. **Diffusion Terms:**\n",
+    "   - The diffusion terms $\\rho \\lambda_2(u_{xx} + u_{yy})$ and $\\rho \\lambda_2(v_{xx} + v_{yy})$ involve second spatial derivatives of velocity:\n",
+    "\n",
+    "     $$\n",
+    "     [u_{xx}] = [u_{yy}] = [v_{xx}] = [v_{yy}] = \\frac{[u]}{[x]^2} = \\frac{L / T}{L^2} = \\frac{1}{L T}.\n",
+    "     $$\n",
+    "   - Therefore, the diffusion terms have dimensions:\n",
+    "   \n",
+    "     $$\n",
+    "     [\\rho \\lambda_2(u_{xx} + u_{yy})] = [\\rho \\lambda_2(v_{xx} + v_{yy})] = [\\rho] \\cdot [\\lambda_2] \\cdot \\frac{1}{L T} = \\frac{M}{L^3} \\cdot \\frac{L^2}{T} \\cdot \\frac{1}{L T} = \\frac{M}{L^2 T^2}.\n",
+    "     $$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 3: Determine the Dimensions of $\\lambda_1$ and $\\lambda_2$\n",
+    "\n",
+    "1. **Advection Parameter $\\lambda_1$:**\n",
+    "   - The advection term $\\rho \\lambda_1(uu_x + vu_y)$ must have the same dimensions as $\\rho u_t$:\n",
+    "\n",
+    "     $$\n",
+    "     [\\rho \\lambda_1] \\cdot \\frac{L}{T^2} = \\frac{M}{L^2 T^2} \\implies [\\lambda_1] = \\frac{M}{L^2 T^2} \\cdot \\frac{L^2 T^2}{M} = 1.\n",
+    "     $$\n",
+    "   - Therefore, $\\lambda_1$ has no dimensions.\n",
+    "\n",
+    "2. **Diffusion Parameter $\\lambda_2$:**\n",
+    "   - The diffusion term $\\rho \\lambda_2(u_{xx} + u_{yy})$ must have the same dimensions as $\\rho u_t$:\n",
+    "   \n",
+    "     $$\n",
+    "     [\\rho \\lambda_2] \\cdot \\frac{1}{L T} = \\frac{M}{L^2 T^2} \\implies [\\lambda_2] = \\frac{M}{L^2 T^2} \\cdot \\frac{L^4 T}{M} = \\frac{L^2}{T}.\n",
+    "     $$\n",
+    "   - Therefore, $\\lambda_2$ has dimensions of mass per unit length ($L^2 / T$).\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 4: Summary of Dimensions\n",
+    "\n",
+    "| Variable/Parameter     | Physical Meaning                   | Dimensions            |\n",
+    "|------------------------|-----------------------------------|-----------------------|\n",
+    "| $x, y$                | Spatial coordinates               | $L$                 |\n",
+    "| $t$                  | Time                              | $T$                 |\n",
+    "| $u, v$               | Velocity components               | $L / T$             |\n",
+    "| $p$                  | Pressure                          | $M / (L T^2)$       |\n",
+    "| $\\rho$               | Density                           | $M / L^3$           |\n",
+    "| $\\lambda_1$          | Advection parameter               | $1$           |\n",
+    "| $\\lambda_2$          | Diffusion parameter               | $L^2 / T$             |\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 5: Given Values\n",
+    "\n",
+    "- $\\lambda_1$ has no dimensions ($M / L^3$).\n",
+    "- $\\lambda_2$ has dimensions of length $^2$ per unit time ($L^2 / T$).\n",
+    "\n",
+    "These dimensions are consistent with the Navier-Stokes equations for incompressible flow, incorporating the density term $\\rho$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implementation\n",
+    "\n",
+    "Below is the implementation of the inverse problem for the Navier-Stokes equation of incompressible flow around a cylinder using PINNx.\n",
+    "\n",
+    "We first import the required libraries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "\n",
+    "import brainstate as bst\n",
+    "import brainunit as u\n",
+    "import numpy as np\n",
+    "from scipy.io import loadmat\n",
+    "import jax\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import deepxde.experimental as deepxde\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the physical units for the problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unit_of_x = u.meter\n",
+    "unit_of_y = u.meter\n",
+    "unit_of_t = u.second\n",
+    "unit_of_rho = u.kilogram / u.meter**3\n",
+    "unit_of_c1 = u.UNITLESS\n",
+    "unit_of_c2 = u.meter2 / u.second\n",
+    "unit_of_u = u.meter / u.second\n",
+    "unit_of_v = u.meter / u.second\n",
+    "unit_of_p = u.pascal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define Navier Stokes Equations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Navier_Stokes_Equation(x, y):\n",
+    "    jacobian = net.jacobian(x)\n",
+    "    x_hessian = net.hessian(x, y=['u', 'v'], xi=['x'], xj=['x'])\n",
+    "    y_hessian = net.hessian(x, y=['u', 'v'], xi=['y'], xj=['y'])\n",
+    "\n",
+    "    u = y['u']\n",
+    "    v = y['v']\n",
+    "    p = y['p']\n",
+    "    du_x = jacobian['u']['x']\n",
+    "    du_y = jacobian['u']['y']\n",
+    "    du_t = jacobian['u']['t']\n",
+    "    dv_x = jacobian['v']['x']\n",
+    "    dv_y = jacobian['v']['y']\n",
+    "    dv_t = jacobian['v']['t']\n",
+    "    dp_x = jacobian['p']['x']\n",
+    "    dp_y = jacobian['p']['y']\n",
+    "    du_xx = x_hessian['u']['x']['x']\n",
+    "    du_yy = y_hessian['u']['y']['y']\n",
+    "    dv_xx = x_hessian['v']['x']['x']\n",
+    "    dv_yy = y_hessian['v']['y']['y']\n",
+    "    continuity = du_x + dv_y\n",
+    "    x_momentum = unit_of_rho * du_t + unit_of_rho * C1.value * (u * du_x + v * du_y) + dp_x - unit_of_rho * C2.value * (du_xx + du_yy)\n",
+    "    y_momentum = unit_of_rho * dv_t + unit_of_rho * C1.value * (u * dv_x + v * dv_y) + dp_y - unit_of_rho * C2.value * (dv_xx + dv_yy)\n",
+    "    return [continuity, x_momentum, y_momentum]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the neural network model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = deepxde.nn.Model(\n",
+    "    deepxde.nn.DictToArray(x=unit_of_x, y=unit_of_y, t=unit_of_t),\n",
+    "    deepxde.nn.FNN([3] + [50] * 6 + [3], \"tanh\"),\n",
+    "    deepxde.nn.ArrayToDict(u=unit_of_u, v=unit_of_v, p=unit_of_p),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define Spatio-temporal domain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Rectangular\n",
+    "Lx_min, Lx_max = 1.0, 8.0\n",
+    "Ly_min, Ly_max = -2.0, 2.0\n",
+    "# Spatial domain: X × Y = [1, 8] × [−2, 2]\n",
+    "space_domain = deepxde.geometry.Rectangle([Lx_min, Ly_min], [Lx_max, Ly_max])\n",
+    "# Time domain: T = [0, 7]\n",
+    "time_domain = deepxde.geometry.TimeDomain(0, 7)\n",
+    "# Spatio-temporal domain\n",
+    "geomtime = deepxde.geometry.GeometryXTime(space_domain, time_domain)\n",
+    "geomtime = geomtime.to_dict_point(x=unit_of_x, y=unit_of_y, t=unit_of_t)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the true values of the $\\lambda_1, \\lambda_2$ and the function to load training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# true values\n",
+    "C1true = 1.0\n",
+    "C2true = 0.01 * unit_of_c2\n",
+    "\n",
+    "\n",
+    "# Load training data\n",
+    "def load_training_data(num):\n",
+    "    data = loadmat(\"../dataset/cylinder_nektar_wake.mat\")\n",
+    "    U_star = data[\"U_star\"]  # N x 2 x T\n",
+    "    P_star = data[\"p_star\"]  # N x T\n",
+    "    t_star = data[\"t\"]  # T x 1\n",
+    "    X_star = data[\"X_star\"]  # N x 2\n",
+    "    N = X_star.shape[0]\n",
+    "    T = t_star.shape[0]\n",
+    "    # Rearrange Problem\n",
+    "    XX = np.tile(X_star[:, 0:1], (1, T))  # N x T\n",
+    "    YY = np.tile(X_star[:, 1:2], (1, T))  # N x T\n",
+    "    TT = np.tile(t_star, (1, N)).T  # N x T\n",
+    "    UU = U_star[:, 0, :]  # N x T\n",
+    "    VV = U_star[:, 1, :]  # N x T\n",
+    "    PP = P_star  # N x T\n",
+    "    x = XX.flatten()[:, None]  # NT x 1\n",
+    "    y = YY.flatten()[:, None]  # NT x 1\n",
+    "    t = TT.flatten()[:, None]  # NT x 1\n",
+    "    u = UU.flatten()[:, None]  # NT x 1\n",
+    "    v = VV.flatten()[:, None]  # NT x 1\n",
+    "    p = PP.flatten()[:, None]  # NT x 1\n",
+    "    # training domain: X × Y = [1, 8] × [−2, 2] and T = [0, 7]\n",
+    "    data1 = np.concatenate([x, y, t, u, v, p], 1)\n",
+    "    data2 = data1[:, :][data1[:, 2] <= 7]\n",
+    "    data3 = data2[:, :][data2[:, 0] >= 1]\n",
+    "    data4 = data3[:, :][data3[:, 0] <= 8]\n",
+    "    data5 = data4[:, :][data4[:, 1] >= -2]\n",
+    "    data_domain = data5[:, :][data5[:, 1] <= 2]\n",
+    "    # choose number of training points: num =7000\n",
+    "    idx = np.random.choice(data_domain.shape[0], num, replace=False)\n",
+    "    x_train = data_domain[idx, 0]\n",
+    "    y_train = data_domain[idx, 1]\n",
+    "    t_train = data_domain[idx, 2]\n",
+    "    u_train = data_domain[idx, 3]\n",
+    "    v_train = data_domain[idx, 4]\n",
+    "    p_train = data_domain[idx, 5]\n",
+    "    # return [x_train, y_train, t_train, u_train, v_train, p_train]\n",
+    "    return [x_train * unit_of_x, y_train * unit_of_y, t_train * unit_of_t,\n",
+    "            u_train * unit_of_u, v_train * unit_of_v, p_train * unit_of_p]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the Parameters to be identified:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C1 = bst.ParamState(0.0)\n",
+    "C2 = bst.ParamState(0.0 * unit_of_c2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the training data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "[ob_x, ob_y, ob_t, ob_u, ob_v, ob_p] = load_training_data(num=7000)\n",
+    "ob_xyt = {\"x\": ob_x, \"y\": ob_y, \"t\": ob_t}\n",
+    "ob_yv = {\"u\": ob_u, \"v\": ob_v, }\n",
+    "observe_bc = deepxde.icbc.PointSetBC(ob_xyt, ob_yv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the problem and solve the inverse problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "problem = deepxde.problem.TimePDE(\n",
+    "    geomtime,\n",
+    "    Navier_Stokes_Equation,\n",
+    "    [observe_bc],\n",
+    "    net,\n",
+    "    num_domain=700,\n",
+    "    num_boundary=200,\n",
+    "    num_initial=100,\n",
+    "    anchors=ob_xyt,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Setup the model trainer:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": "model = deepxde.Trainer(problem, external_trainable_variables=[C1, C2])"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the callbacks for storing results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fnamevar = \"variables.dat\"\n",
+    "variable = deepxde.callbacks.VariableValue([C1, C2], period=100, filename=fnamevar)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compile, train and save trainer:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.096312 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                                   Test loss                                                                    Test metric \n",
+      "0         [0.28003943 * becquerel2,                                                    [0.28003943 * becquerel2,                                                    []          \n",
+      "           0.08384456 * (kilogram / klitre * (meter / second) / second) ** 2,           0.08384456 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           0.14824082 * (kilogram / klitre * (meter / second) / second) ** 2,           0.14824082 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 0.7059334 * meter / second,                                   {'ibc0': {'u': 0.7059334 * meter / second,                                              \n",
+      "                     'v': 1.947519 * meter / second}}]                                            'v': 1.947519 * meter / second}}]                                             \n",
+      "1000      [0.00180862 * becquerel2,                                                    [0.00180862 * becquerel2,                                                    []          \n",
+      "           0.00279982 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00279982 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           0.00574982 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00574982 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 0.01001318 * meter / second,                                  {'ibc0': {'u': 0.01001318 * meter / second,                                             \n",
+      "                     'v': 0.02458837 * meter / second}}]                                          'v': 0.02458837 * meter / second}}]                                           \n",
+      "2000      [0.00110196 * becquerel2,                                                    [0.00110196 * becquerel2,                                                    []          \n",
+      "           0.001189 * (kilogram / klitre * (meter / second) / second) ** 2,             0.001189 * (kilogram / klitre * (meter / second) / second) ** 2,                        \n",
+      "           0.00160737 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00160737 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 0.00394361 * meter / second,                                  {'ibc0': {'u': 0.00394361 * meter / second,                                             \n",
+      "                     'v': 0.00928425 * meter / second}}]                                          'v': 0.00928425 * meter / second}}]                                           \n",
+      "3000      [0.00075236 * becquerel2,                                                    [0.00075236 * becquerel2,                                                    []          \n",
+      "           0.00077494 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00077494 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           0.00097048 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00097048 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 0.00139976 * meter / second,                                  {'ibc0': {'u': 0.00139976 * meter / second,                                             \n",
+      "                     'v': 0.00430239 * meter / second}}]                                          'v': 0.00430239 * meter / second}}]                                           \n",
+      "4000      [0.00042566 * becquerel2,                                                    [0.00042566 * becquerel2,                                                    []          \n",
+      "           0.0004626 * (kilogram / klitre * (meter / second) / second) ** 2,            0.0004626 * (kilogram / klitre * (meter / second) / second) ** 2,                       \n",
+      "           0.00049891 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00049891 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 0.00073693 * meter / second,                                  {'ibc0': {'u': 0.00073693 * meter / second,                                             \n",
+      "                     'v': 0.00118131 * meter / second}}]                                          'v': 0.00118131 * meter / second}}]                                           \n",
+      "5000      [0.00027099 * becquerel2,                                                    [0.00027099 * becquerel2,                                                    []          \n",
+      "           0.00032169 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00032169 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           0.00030988 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00030988 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 0.00051542 * meter / second,                                  {'ibc0': {'u': 0.00051542 * meter / second,                                             \n",
+      "                     'v': 0.0007421 * meter / second}}]                                           'v': 0.0007421 * meter / second}}]                                            \n",
+      "6000      [0.00019304 * becquerel2,                                                    [0.00019304 * becquerel2,                                                    []          \n",
+      "           0.00024095 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00024095 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           0.00022211 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00022211 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 0.00037054 * meter / second,                                  {'ibc0': {'u': 0.00037054 * meter / second,                                             \n",
+      "                     'v': 0.00047546 * meter / second}}]                                          'v': 0.00047546 * meter / second}}]                                           \n",
+      "7000      [0.00014662 * becquerel2,                                                    [0.00014662 * becquerel2,                                                    []          \n",
+      "           0.00019332 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00019332 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           0.00016124 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00016124 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 0.00027189 * meter / second,                                  {'ibc0': {'u': 0.00027189 * meter / second,                                             \n",
+      "                     'v': 0.00031798 * meter / second}}]                                          'v': 0.00031798 * meter / second}}]                                           \n",
+      "8000      [0.00011527 * becquerel2,                                                    [0.00011527 * becquerel2,                                                    []          \n",
+      "           0.00016216 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00016216 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           0.00014464 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00014464 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 0.00026401 * meter / second,                                  {'ibc0': {'u': 0.00026401 * meter / second,                                             \n",
+      "                     'v': 0.0002146 * meter / second}}]                                           'v': 0.0002146 * meter / second}}]                                            \n",
+      "9000      [9.65281e-05 * becquerel2,                                                   [9.65281e-05 * becquerel2,                                                   []          \n",
+      "           0.00012925 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00012925 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           0.00011425 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00011425 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 0.0002264 * meter / second,                                   {'ibc0': {'u': 0.0002264 * meter / second,                                              \n",
+      "                     'v': 0.00015709 * meter / second}}]                                          'v': 0.00015709 * meter / second}}]                                           \n",
+      "10000     [8.203531e-05 * becquerel2,                                                  [8.203531e-05 * becquerel2,                                                  []          \n",
+      "           0.00010743 * (kilogram / klitre * (meter / second) / second) ** 2,           0.00010743 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           8.810719e-05 * (kilogram / klitre * (meter / second) / second) ** 2,         8.810719e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                    \n",
+      "           {'ibc0': {'u': 0.00013619 * meter / second,                                  {'ibc0': {'u': 0.00013619 * meter / second,                                             \n",
+      "                     'v': 0.00012556 * meter / second}}]                                          'v': 0.00012556 * meter / second}}]                                           \n",
+      "\n",
+      "Best trainer at step 10000:\n",
+      "  train loss: 5.39e-04\n",
+      "  test loss: 5.39e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 91.177866 s\n",
+      "\n",
+      "Saving loss history to /home/brainpy/codes/SichaoHe/pinnx/docs/unit-examples-inverse/loss.dat ...\n",
+      "Saving checkpoint into /home/brainpy/codes/SichaoHe/pinnx/docs/unit-examples-inverse/loss.dat\n",
+      "Saving training data to /home/brainpy/codes/SichaoHe/pinnx/docs/unit-examples-inverse/train.dat ...\n",
+      "Saving checkpoint into /home/brainpy/codes/SichaoHe/pinnx/docs/unit-examples-inverse/train.dat\n",
+      "Saving test data to /home/brainpy/codes/SichaoHe/pinnx/docs/unit-examples-inverse/test.dat ...\n",
+      "Saving checkpoint into /home/brainpy/codes/SichaoHe/pinnx/docs/unit-examples-inverse/test.dat\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.068254 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                                     Test loss                                                                      Test metric \n",
+      "10000     [8.203531e-05 * becquerel2,                                                    [8.203531e-05 * becquerel2,                                                    []          \n",
+      "           0.00010743 * (kilogram / klitre * (meter / second) / second) ** 2,             0.00010743 * (kilogram / klitre * (meter / second) / second) ** 2,                        \n",
+      "           8.810719e-05 * (kilogram / klitre * (meter / second) / second) ** 2,           8.810719e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 0.00013619 * meter / second,                                    {'ibc0': {'u': 0.00013619 * meter / second,                                               \n",
+      "                     'v': 0.00012556 * meter / second}}]                                            'v': 0.00012556 * meter / second}}]                                             \n",
+      "11000     [7.96016e-05 * becquerel2,                                                     [7.96016e-05 * becquerel2,                                                     []          \n",
+      "           0.00010362 * (kilogram / klitre * (meter / second) / second) ** 2,             0.00010362 * (kilogram / klitre * (meter / second) / second) ** 2,                        \n",
+      "           8.5908185e-05 * (kilogram / klitre * (meter / second) / second) ** 2,          8.5908185e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                     \n",
+      "           {'ibc0': {'u': 0.00012997 * meter / second,                                    {'ibc0': {'u': 0.00012997 * meter / second,                                               \n",
+      "                     'v': 0.00011991 * meter / second}}]                                            'v': 0.00011991 * meter / second}}]                                             \n",
+      "12000     [7.62613e-05 * becquerel2,                                                     [7.62613e-05 * becquerel2,                                                     []          \n",
+      "           9.886667e-05 * (kilogram / klitre * (meter / second) / second) ** 2,           9.886667e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           8.261804e-05 * (kilogram / klitre * (meter / second) / second) ** 2,           8.261804e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 0.00012325 * meter / second,                                    {'ibc0': {'u': 0.00012325 * meter / second,                                               \n",
+      "                     'v': 0.00011304 * meter / second}}]                                            'v': 0.00011304 * meter / second}}]                                             \n",
+      "13000     [7.160572e-05 * becquerel2,                                                    [7.160572e-05 * becquerel2,                                                    []          \n",
+      "           9.2203576e-05 * (kilogram / klitre * (meter / second) / second) ** 2,          9.2203576e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                     \n",
+      "           7.7911514e-05 * (kilogram / klitre * (meter / second) / second) ** 2,          7.7911514e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                     \n",
+      "           {'ibc0': {'u': 0.00011437 * meter / second,                                    {'ibc0': {'u': 0.00011437 * meter / second,                                               \n",
+      "                     'v': 0.00010453 * meter / second}}]                                            'v': 0.00010453 * meter / second}}]                                             \n",
+      "14000     [6.619561e-05 * becquerel2,                                                    [6.619561e-05 * becquerel2,                                                    []          \n",
+      "           8.488368e-05 * (kilogram / klitre * (meter / second) / second) ** 2,           8.488368e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           7.294095e-05 * (kilogram / klitre * (meter / second) / second) ** 2,           7.294095e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 0.00010421 * meter / second,                                    {'ibc0': {'u': 0.00010421 * meter / second,                                               \n",
+      "                     'v': 9.4588875e-05 * meter / second}}]                                         'v': 9.4588875e-05 * meter / second}}]                                          \n",
+      "15000     [6.082786e-05 * becquerel2,                                                    [6.082786e-05 * becquerel2,                                                    []          \n",
+      "           7.7803525e-05 * (kilogram / klitre * (meter / second) / second) ** 2,          7.7803525e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                     \n",
+      "           6.7992296e-05 * (kilogram / klitre * (meter / second) / second) ** 2,          6.7992296e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                     \n",
+      "           {'ibc0': {'u': 9.468454e-05 * meter / second,                                  {'ibc0': {'u': 9.468454e-05 * meter / second,                                             \n",
+      "                     'v': 8.5897096e-05 * meter / second}}]                                         'v': 8.5897096e-05 * meter / second}}]                                          \n",
+      "16000     [5.6113255e-05 * becquerel2,                                                   [5.6113255e-05 * becquerel2,                                                   []          \n",
+      "           7.168196e-05 * (kilogram / klitre * (meter / second) / second) ** 2,           7.168196e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           6.354173e-05 * (kilogram / klitre * (meter / second) / second) ** 2,           6.354173e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 8.729596e-05 * meter / second,                                  {'ibc0': {'u': 8.729596e-05 * meter / second,                                             \n",
+      "                     'v': 7.8841156e-05 * meter / second}}]                                         'v': 7.8841156e-05 * meter / second}}]                                          \n",
+      "17000     [5.2216485e-05 * becquerel2,                                                   [5.2216485e-05 * becquerel2,                                                   []          \n",
+      "           6.676306e-05 * (kilogram / klitre * (meter / second) / second) ** 2,           6.676306e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           5.993753e-05 * (kilogram / klitre * (meter / second) / second) ** 2,           5.993753e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 7.945047e-05 * meter / second,                                  {'ibc0': {'u': 7.945047e-05 * meter / second,                                             \n",
+      "                     'v': 7.2727926e-05 * meter / second}}]                                         'v': 7.2727926e-05 * meter / second}}]                                          \n",
+      "18000     [4.8839163e-05 * becquerel2,                                                   [4.8839163e-05 * becquerel2,                                                   []          \n",
+      "           6.259088e-05 * (kilogram / klitre * (meter / second) / second) ** 2,           6.259088e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           5.6764642e-05 * (kilogram / klitre * (meter / second) / second) ** 2,          5.6764642e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                     \n",
+      "           {'ibc0': {'u': 7.3541385e-05 * meter / second,                                 {'ibc0': {'u': 7.3541385e-05 * meter / second,                                            \n",
+      "                     'v': 6.803875e-05 * meter / second}}]                                          'v': 6.803875e-05 * meter / second}}]                                           \n",
+      "19000     [4.608007e-05 * becquerel2,                                                    [4.608007e-05 * becquerel2,                                                    []          \n",
+      "           5.8930185e-05 * (kilogram / klitre * (meter / second) / second) ** 2,          5.8930185e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                     \n",
+      "           5.403013e-05 * (kilogram / klitre * (meter / second) / second) ** 2,           5.403013e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 6.8380396e-05 * meter / second,                                 {'ibc0': {'u': 6.8380396e-05 * meter / second,                                            \n",
+      "                     'v': 6.40664e-05 * meter / second}}]                                           'v': 6.40664e-05 * meter / second}}]                                            \n",
+      "20000     [4.3594566e-05 * becquerel2,                                                   [4.3594566e-05 * becquerel2,                                                   []          \n",
+      "           5.553491e-05 * (kilogram / klitre * (meter / second) / second) ** 2,           5.553491e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           5.129298e-05 * (kilogram / klitre * (meter / second) / second) ** 2,           5.129298e-05 * (kilogram / klitre * (meter / second) / second) ** 2,                      \n",
+      "           {'ibc0': {'u': 6.40729e-05 * meter / second,                                   {'ibc0': {'u': 6.40729e-05 * meter / second,                                              \n",
+      "                     'v': 6.0597904e-05 * meter / second}}]                                         'v': 6.0597904e-05 * meter / second}}]                                          \n",
+      "\n",
+      "Best trainer at step 20000:\n",
+      "  train loss: 2.75e-04\n",
+      "  test loss: 2.75e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 76.299114 s\n",
+      "\n",
+      "Saving loss history to /home/brainpy/codes/SichaoHe/pinnx/docs/unit-examples-inverse/loss.dat ...\n",
+      "Saving checkpoint into /home/brainpy/codes/SichaoHe/pinnx/docs/unit-examples-inverse/loss.dat\n",
+      "Saving training data to /home/brainpy/codes/SichaoHe/pinnx/docs/unit-examples-inverse/train.dat ...\n",
+      "Saving checkpoint into /home/brainpy/codes/SichaoHe/pinnx/docs/unit-examples-inverse/train.dat\n",
+      "Saving test data to /home/brainpy/codes/SichaoHe/pinnx/docs/unit-examples-inverse/test.dat ...\n",
+      "Saving checkpoint into /home/brainpy/codes/SichaoHe/pinnx/docs/unit-examples-inverse/test.dat\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.compile(bst.optim.Adam(1e-3)).train(iterations=10000, callbacks=[variable],\n",
+    "                                          display_every=1000, disregard_previous_best=True)\n",
+    "model.saveplot(issave=True, isplot=True)\n",
+    "model.compile(bst.optim.Adam(1e-4)).train(iterations=10000, callbacks=[variable],\n",
+    "                                          display_every=1000, disregard_previous_best=True)\n",
+    "model.saveplot(issave=True, isplot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the mean residual error:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean residual: [0.00478853 * becquerel, 0.00537193 * kilogram / klitre * (meter / second) / second, 0.0051398 * kilogram / klitre * (meter / second) / second]\n"
+     ]
+    }
+   ],
+   "source": [
+    "f = model.predict(ob_xyt, operator=Navier_Stokes_Equation)\n",
+    "print(\"Mean residual:\", jax.tree.map(lambda x: u.math.mean(u.math.abs(x)), f))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot Variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2729976/4284654551.py:6: DeprecationWarning: string or file could not be read to its end due to unmatched data; this will raise a ValueError in the future.\n",
+      "  np.fromstring(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# reopen saved data using callbacks in fnamevar\n",
+    "lines = open(fnamevar, \"r\").readlines()\n",
+    "# read output data in fnamevar\n",
+    "Chat = np.array(\n",
+    "    [\n",
+    "        np.fromstring(\n",
+    "            min(re.findall(re.escape(\"[\") + \"(.*?)\" + re.escape(\"]\"), line), key=len),\n",
+    "            sep=\",\",\n",
+    "        )\n",
+    "        for line in lines\n",
+    "    ]\n",
+    ")\n",
+    "l, c = Chat.shape\n",
+    "plt.semilogy(range(0, l * 100, 100), Chat[:, 0], \"r-\")\n",
+    "plt.semilogy(range(0, l * 100, 100), Chat[:, 1], \"k-\")\n",
+    "plt.semilogy(range(0, l * 100, 100), np.ones(Chat[:, 0].shape) * C1true, \"r--\")\n",
+    "plt.semilogy(range(0, l * 100, 100), np.ones(Chat[:, 1].shape) * C2true, \"k--\")\n",
+    "plt.legend([\"C1hat\", \"C2hat\", \"True C1\", \"True C2\"], loc=\"right\")\n",
+    "plt.xlabel(\"Epochs\")\n",
+    "plt.title(\"Variables\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the velocity distribution of the flow field:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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YuXMnVq9ejUWLFmH16tWOHwNJk/wFKQ1dGoS8GFq9EYi4HQ3tdNjFyYuzUX8WqA35EWIldYpg5Hiox067HbRrEfDq9zdzOUJevsjM8fNFpNF+PMxu4iJCwOjc5bkdIk6HU+c8q5/GCIFJsSFxmIMHDyI/vz6t1Gl3Y+3atfjpT3+KP//5zxgxYoRu2/33349p06bhhhtuAAD06dMHX331FRYsWIBx48Y5ehwk8ldEY0d8iIZYjN7TqSdemkwvzll+EqwVGPFi1sZUkNCiQbeNE2Ix2t9KaIVuSxLyZc+hEM01UtuZCQ8R0WEGvU91lJ8YbRUrjp1dAS2RWCHkBdyMpNG6ZPP8/Hyd4HCSP/7xj5gwYQLWrl2rJYaS1NTUpNWO8Xg8SCaTaW2dRAoOI4zi2TRmoqMpuRysizNLJGRRbIgIDVHIvrjiw8ztiFACArDvdNBuBdnWaZeDhZ3EZjPhISI6suVyNDbS3ZA0IlVVVdi7d6/2ev/+/di1axfatWuHbt26Yfr06Th06BCee+45AKkwyrhx4/DEE0+gtLRUy4EMhUIoKCgAAIwePRrz5s1Dt27d0Lt3b3z88cdYvHgxJkyYkNXP0iRzOJocorkeZhd6o5sJr/+mls2fgdioDfm1JVuY9s/L72B9LrOcDlq4ke2N8jnIp3ojMWvmmmXDFTPKTWqIUVU8N8gOTggFgz5qg81kdJOkcQh5UzVj6MWiCN++fTv69++vDWktKytD//79MXv2bADAN998gwMHDmjtV6xYgXg8jsmTJ+tyIKdOnaq1efLJJ3HttdfizjvvxNlnn42f//zn+NnPfoZHHnnEgQ/Op1nMpWKHiooKFBQUoPyi7s4U/iIRudAb3Sx4JxyvX5btzHIpWP3SN81suRtGF+YsCgwRuK4HL8ySVuDLpDBYWoEvoj0tMsm2pJgkb+bkPvTNX9eOc3N2aug27xymz1P6Nb0ffV6GOOJLhT5HRXI4RM9r3v4Wz+vakB8V1VEUj34G5eXlWbPGJc0P7d5zdgfke9LvPRWJJAr+c6xVnjct3yvkFUrK5OlQJNRiFGLhWc3ZzOVwgiyLjZjf+unoi5qHINRjSBMe5HGTAoJOKDULsag3uzCjvfp3riXassIrZD4HGVrJ9fMFRDZzQIDMc5NEcDKPQwQrOU0GYkMiMSXHz7730GUaWhGtN6RCD421M0TWiVEC2UTk4irqbth4AhS5MMf8Xm2xA7m/WR+Gx8QKtdBDftv49d8NvU/Qp3/S5oVYRMIrZPtcThu71MTSy6zzYJ3D9HlPv27s894qMllUImkQWq/g4GFVfJi15cbDOU/mjV2V0SGxYUSmIkO0b17/hrkkTgsPsm3IWy8knBIdVgUIKTRU4WEmPuyIDt02BxJhjcg0TMhCuhuSTAn5+EsrRQoOI6yIDydFR7ZwsoqjjQtytkSGEWYChCs+WCXTMxUeKqToULeLiI5sYiY8mmNJfxEsuBtSbEgkmdHyczicQiRvw6gNLx7OyucQyeVwogy03flHGPuZCQ1RohkKEr9JTgd5LHT+B3d4LZ3rQX4nkZheTNRE04fgqnkbvNwOdbsqOqqj7JwOMp9Dt53K5TDK+zCjJmY8UzGJ0XnaEPkf2UIOg5U4QdDLnoCRNXliK6H1/LJYF2A7F0RR4ZGp6KBpiOQ6mzkfmYqNTEWGSF8sIUIfGylA6M+kCRBR8UELDzKxlBYetdR2NZk0E9GRCTzRYVprxkCANHZNDiuhQgbS3ZBIMqflh1SqDeoKqDUH6EUEu7NrWum/saEvyBbEhln4JOr3aktDQL4f732Nwi/M0AsddiFDLmS4hWzDCrOwcjtUcUnGfNU2IvkcmboLvPBKNhJCnc5RysShsHiOSyRccv38xQJbtmzB6NGj0aVLF7hcLrz88suG7b/55hvcdNNNOPPMM+F2u3HPPfektVm5ciUuuugitG3bFm3btsWIESOyPjU90BoEhx2sCBAj4cHbxkzCa+DEOvrC6uD8FGYXYisiIxzwZ7SYHYeREKEFiPq5SPGh3YxYwiPgqxcebfxs4UEnlarbgJToIIUH2cau6BANl4hiJ3epqWJBqEixIWkoqqur0bdvXyxdulSofSQSQceOHTFz5kz07duX2WbTpk248cYb8e6772Lr1q0oKSnB5ZdfjkOHDjl56GnIX40I5IXTqPQz4Oy03tmce8IMm+6GmathhJlAsINon8FI/d+YdZxqSIaVA6Kr88ELuajhFlaoJUxsEwmxmIVXSFj5HG184sNirdIUa8lkEE6xeo5LJBpBL+Bn5HB4GPOrGDBq1CiMGjVKuH2PHj3wxBNPAACeeeYZZpsXXnhB9/p3v/sdXnzxRWzcuBG33nqrpeOzgvzlWMVMfGQyw6ZVnEgcZeGw2GgIoRH16d/DH7PmGPGOQRUi9GfwR+OGNx6d+KBzPQC98OBBJ5SaiQ7tzR3K57CSQNpQiFTOlUiaARUVFbrXgUDA8RljRampqUEsFkO7du2y+j4tX3DUxtkz9olgdrE1mvCKN9kVYG3Cq2w8MWYYThEVG04JDVpMZGsfElWwsI4xGIkaChBfNM52PVThwUouNXM7REQHL4nUaZcj7Zx1YOZYp8jyyCvpbkiECfk4Dkcqk6GkpES3es6cOZg7d24DHFg6DzzwALp06ZI2jb3TyF+PEeQF2Uh88ISHqNthZZZN0bCK3ZwME3fDCbFhmlthUyxEMhAZAcoR4R2DPxZPO35SgPCcj7RwC5AeZsm26AD0wkM9p7MVXqFpjJEqDo68kmJD4iQHDx7UzaXSWO7GwoULsXbtWmzatAnBYDCr7yV/QaLQF2XesEGALTysig6z/Z0iw2RRK2LDSGiIioxMREUm/aqChBW64X0ulvhgCg8RnBAdPLKZ05EpTgsUWcZc0lC08bF/697UtSQ/P7/RJ29btGgRFi5ciL/97W8477zzsv5+rUtwiIwEEb3AGbkfLOHBcjsaqm6BaJzborshKjYyERpWBUbE72ziaSAa5R5HIBbXHT8pPsxcD0140GEWnttB4nR4BWC7HU0tf8MMO0IuA3cjxirqJJE0E375y19i3rx5eOuttzBw4MAGec+WLzis5nDwRInRzV+9SLOEh4jbYdTeaWzWKHBabBgJDTORYVVUWBUtZHiF9V4sEUKKDzPhoeZ4WBYdZIXSbIgOoHmJDDMhTQs1AXfDUsgw2norRkoECHIcDpe1nMKqqirs3btXe71//37s2rUL7dq1Q7du3TB9+nQcOnQIzz33nNZm165d2r5Hjx7Frl274Pf7cc455wAAHnvsMcyePRtr1qxBjx49cPjwYQBAbm4ucnNzLX5QcVq+4HAKWoiwBAhLePDcDlV0GIVWSJeDbEfmcWQyUsUonGJRmIiKDZ7QMBIFIgLDyVCLUV+BWDzteALRqLaPmfCgRQcAhAwPxkB0qDgtOsxoriXLaWQJc0kzYPv27bjkkku012VlZQCAcePGYdWqVfjmm29w4MAB3T79+/fX/r9jxw6sWbMG3bt3x5dffgkAWLZsGaLRKK699lrdftlOXJW/OJWqCJBrIWmHFCD0DYAnPERFh10yCcMYPP2ZuRuZiA27QsNMYMS8zp3avjjheFCuBlB/nGbCgxYdAMzdDsA4mRTIjtORDchz0875bkUg28hNcmr0lUQCIFWHI8hyOKx1M3z4cCiKwt2+atWqtHVG7QFowqOhaR2/nKqIM+14gkS98LOEhx3RIeJyZBODJz8zsZGp0OCJDCOBYSYuwl5731kwHmP2rwoQUlwAqWMnwy1koiktOgALIRYgu+EVIDPhkek5SY66ylZdDYHaMjRSbEgkztLyS5tXC4oNEaoi9QuL2nh66IWe9pu+sJMjCGoN2jmNblp1MXcjm2Ij4veniY2Iz6stJDGvV7fojsHrS1tYRLzetIWG1w/9vuQxkp+DXK9+D+R3pH5/ZMl0APoS6UB9WXSAPQ8La/4VdRtQLyp4JdDVNmahEhvzQGSEkVvnsDCRk7NJJNlHynW7kKKDdj5YjgfpdlhNDmWFSuyUORcqilTfxugibCY2rAgNkXY8F8NIUFiFt0+gztEg3ysYj2nHRDoeRm4H6XQA/LyORnE6VMjzUqSkf1PELFlUuhuShqCNjy2M7RaibAG03l9QjcXJ0toYfFWq+GAJDxHRIRJa0fpsnLkqjIoemYkNJ4WGHYERc1sfvuhL1o9AUPsOELkcYa8vLeTii8d1YRYj0UEiLDoA/ugVwFqdDiD9vKMRERn0udgY9WIyTP4UcTesDveWSCTptC7BYVVkGO3LEiAs4UG7HVZFR7Yhn/447oZRKMWq2BARGiw3gyU0WCLDTFzUcgRLKK6/4ZL9qOKDFh7qMZHCg0wwBcydDnLSOAB80QGY1+mwOvcK7XYA1uZgMTtHRQSLnckIjcIpWXI3aMIBPxBzMFwraXkEOA6HcT5ni6bl53CoZCI2eP3x+mTleJC5HbycDqOLvVHRsrDBfqwTPoPqoka2slWxQednGOVkaPtQORcxt0e3qNR6fcyFh1Fbum8654PO76A/O0tk0TkdpnY9mc8BsHM6WNPbA/ycDnIb+VpE7LLaGO0nMkKFJyQa0N2QoRRJU2Xp0qXo0aMHgsEgSktLsW3bNm7bWCyGhx9+GKeddhqCwSD69u2LDRs2pLU7dOgQbr75ZrRv3x6hUAh9+vTB9u3bs/YZWo/gyBY84cFKLhURHVrbGH+bSrWDiaUC7oZR3oaR2OAlhGrvQQkNOlHTSGRox2wgLGJur9BCk96P3j3hiQ7ed0Cv0/alRIdQEim5nrxRWxEdZDIpT3jwFhpWEmomOFFl18a8QDQylCKxTcDLXyyybt06lJWVYc6cOdi5cyf69u2LkSNH4ttvv2W2nzlzJp5++mk8+eST+Oyzz3D77bfjRz/6ET7++GOtzYkTJzB06FD4fD68+eab+Oyzz/CrX/0Kbdu2tf2RzXApZgN2mykVFRUoKChAea4P+S6X8w4HD1aohc7tIC+maniF9dQZYm3z6repN5QgdUNpQ60nT3LthmUcThENpZiJDd42AGlCQ9eWcjvoGz7LsWAJBzv4kvrzhQy5kPkdZF5HkGxDrFcTSdXQCrlOzecgQyv+aGqdr+7fUG3dNjW0ouZz1BDrw9Q6Vdyq61VxqgpZUvyKumw8WAKEdc6y2vOGxIYY5yvdBmCf19o2a4JDxN0gz/vKqgjOGfoEysvLG31ODEnTQbv3PPUD5DN+GxW1MRRMedXSeVNaWopBgwbhqaeeAgAkk0mUlJTgrrvuwrRp09Lad+nSBQ8++CAmT56srbvmmmsQCoXw/PPPAwCmTZuGv//973jvvffsfExbNFmHY8uWLRg9ejS6dOkCl8uFl19+ObMOjZI+nYTndphh5GRke4iswMgUEbFBh0isuhpaOypkQboZtIvBcymiLq/lhYQWLnSIhTxW1mdgIRJa0R2DqNMBsIfLkut5TgeQLnZFwypkfyRG7kYmYoPG6EkxC+6GdDYklgj5U+cvvVgcgh2NRrFjxw7d1PFutxsjRozA1q1bmftEIpG0mV9DoRDef/997fWrr76KgQMH4rrrrkNRURH69++PlStXWjo2qzRZwVFdXY2+ffti6dKlznXamKKDhBda0bbH9P86iWD+Bi+UYiQ2SMibKytXQ+uPETrR2jGERv02Y4FBEnZ7mQsNva+o6GBB1+mgEQmtCGNUo4NczxIdrBCLillYRURsZFpdlMRKsqhFRKrn0ojOcCyRsKioqNAtkQj7wfTYsWNIJBLo1KmTbn2nTp20OVBoRo4cicWLF+OLL75AMpnE22+/jZdeegnffPON1mbfvn1YtmwZzjjjDLz11lu44447cPfdd2P16tXOfUiKJis4Ro0ahUcffRQ/+tGPnO24jbd+aUgydTmchlHsi/XU54TYUDFzNbR2HKFBOxksgSEiLFhtdZ/ZQHSwsFLzw8jl0K3LNJ9DVHSQbYDMinsZiQ2aJuRuCI9KkUisEPCmzsW0JXW+lZSUoKCgQFsWLFjg2Fs/8cQTOOOMM9CrVy/4/X5MmTIF48ePh9tdf8tPJpM4//zzMX/+fPTv3x+TJk3CxIkTsXz5cseOg6blS/ScQKrQCuuGr4qObOR31MSNRQ1Zo4Mugd6QMC7crAuwiNgwC6FofRkIDRXazSChBQYL2uWg8Sv6v3nY7UWQyNuIurxpbUQg63OYQRYK0/a3MlTWygyzvCGzQP2wWaDefeMVAKPhiROjvA07ZNHdoBF2NyJytliJfQ4ePKjL4QgE2FNndOjQAR6PB0eOHNGtP3LkCIqLi5n7dOzYES+//DLC4TC+++47dOnSBdOmTUPPnj21Np07d9Zmj1U5++yz8eKLL9r9SKY0WYfDKpFIJM2i0pEbqF9oGsv1aCgEsqJZiaKsC6+I2DALoWjtGGKD5WgA6W4G7UwYhVRY8EIvLEjBYzS0Nm0/C84H+b0ahVYcdTp4bgctFlTXg7WwMBMbIu6GEVay/LPgbshQikQIk1Eq+fn5uoUnOPx+PwYMGICNGzdq65LJJDZu3IgLLrjA8BCCwSC6du2KeDyOF198ET/84Q+1bUOHDsXu3bt17ffs2YPu3bvb/cSmtJhfzoIFC/DQQw+lbwh5xUrJqg4IT3RYcUEaW7jQI1To9epNyuDCbVTcCzAWGypOuBpGooAlLCIu49yKgKJ/KrXrZFjFrOw5D7oKqY5MnQ6A7XYA6Y6HCCzBICo2aIxGpvDaAYbzAokgE0UlTZGysjKMGzcOAwcOxODBg7FkyRJUV1dj/PjxAIBbb70VXbt21cIyH374IQ4dOoR+/frh0KFDmDt3LpLJJH7xi19ofd57770YMmQI5s+fjx//+MfYtm0bVqxYgRUrVmTtc7QYwTF9+nSUlZVprysqKlBSUpK6CHoII6cmln5hrI2znQ8yDJOpiDAaGtvI8IbBAsbDXwFnxAYvfMILndBCw0xksNqSwoMUHXRopSEhy54LhVZYWBEdgD7EAvCFhx2MxEZa28bP3ZCJohInqQ364WPkxdUmrfd1/fXX4+jRo5g9ezYOHz6Mfv36YcOGDVoi6YEDB3T5GeFwGDNnzsS+ffuQm5uL73//+/jDH/6AwsJCrc2gQYOwfv16TJ8+HQ8//DBOPfVULFmyBGPHjrV+gIK0mF9PIBBgW1I5fsBrEDniCRBALxJEp7hvTBwSMeqF1yxvw67YsOJq8IQGS2TETE5nH+qFRMTlSXM7aLLpfpAuhxmqywEI5HMA4qID0Od1AHzhYQVWvgYtNkRDKQ2Yu0EjQymSpsSUKVMwZcoU5rZNmzbpXg8bNgyfffaZaZ9XXXUVrrrqKicOT4gm+wuqqqrC3r17tdf79+/Hrl270K5dO3Tr1k28o7YhIE5ISvpCqsahycS4Nr704arkRTpT6AtspgmjIlN1c8IpRu6GSqZiw46rISI0zAQGTQxeneggaYjQCi98Qq7nuRyk6FAxFB0qRqIDYLsdQLrwoKHnYOHBcjWMxIaou+FwkS87oZSojRmJJa2HmN/LvK7GYjYsjhZCk/3FbN++HZdccon2Wg2XjBs3DqtWrRLvKNcP+Iin4SqedcwQInR2PkuIAOJChPUkR4oNq8MRRSa+slhGl3Y37IgNOyGUTIVGmHMqBymBQYoOEZejoRARHSpkPofpdPZGk73x3A5Af26xSuhnKjQAc7FBts9ioqgMpUgkDUOT/RUNHz4cjlRdbxsC/HU3K97wP1JY0DNomlnKrJCMCEauhtnF3Gg7L2EUMHU3jC68ZsWrjMQGK4Ri5GqICg2eyGC1oYWHCE44HqwZZFWshFUAdmhFh5XwCrmN5XYAbPGhQosQM/GbidigcThRlEbE3Yj4vEC4aYhVSdMk5vMwr6mxaOs9b5qs4HCMdm3qn3jIWVXJCyZ58VOn8AZSQoTlhJAihOWEWIU3eyc9l4qRsDHaZjHWTbsbZpOQ2RUbrFwNdR1PaPBERkSpbx9wpf+gw/BqosMotGIGPY29k1gNrTCTSEVFB8B2OwC946G2oxGdVp4lHKyKjQYu8kVjNguyRCIRo+X/ctqF9BcwdXKrtqH0Ca4AthBRRYhoOMYOrNk6gXSxwZu0jYQ1YZuKibvR0GKDFUJRxYaR0CAFBo26jSU8GhuzYbAkVkUHgPTwCpA+ZBbgux0AX3iQsESIWS4RM6RoUWxkuciXHAYrcYpwwA8f43wKx1rkfKlCtHzB0akAAPEHZlnGNdH07ayKjGbhGCNYYsRsmm/RnA56hlgS1sywFKxRKTR2xYZIvoaRq0EKDSORwTxmxdMoosNStVEqrEILEiuiA2DkdADmeR1AutsBpDt/JCKJyioiQoPVp5nYaIRhsNLdkEjs02IqjXLJDwLFBamlbU7dv21SS+cCoCCU+rddTmpp3ya1tA2llvY5qSXHD3TISS3tQqkl5AU6tkktuX6gY05qEZn0ipyCnl5HVnEkKz9myd2god0NEbFBTrxGiw26YijAFxsxeIXERkzxMhcaUZFilK9BT1XvNEYl4Wl0Q5WJvx/5t9RVI6UrkgL6qqRkZVKyOqm6qKjnIWsxa0PTCGJDBDkMVuIkUZ+Xu9hh6dKl6NGjB4LBIEpLS7Ft2zbD9kuWLMFZZ52FUCiEkpIS3HvvvQiHwxn1mSkt/hf1Xfs8RHMD2tMgmWgXqo0C+aH6J8EI4XgUEOtpJ4SObasuiFGVRtqtqIqaTONNXsiJiystNljuRoBaR1ycrYZSRMWGto0hNgCxfA0zocESFDQxxQufy1ggGOVvNETRL5GwCi+fA7DodAD8EAvADrMA+lALSQ3D0RNJmuaFQpjJzc6HUey4GzTS3ZA0FuvWrUNZWRmWL1+O0tJSLFmyBCNHjsTu3btRVFSU1n7NmjWYNm0annnmGQwZMgR79uzBbbfdBpfLhcWLF9vq0wla/C/oWGE+lGD9TUu9cAcjUVTnBnVCpP4CTVyYAb7QMBIggPEoF5GLNEtokO9Hi40g6ymW73iIig2STMSG0SgUWmxYFRq6YxQQHWawHA8yYdSXFA/VGI1UUTEbsWJFdABg53UA7IRSgC88AH2+hlM5FCJCg/d+TWQ22Ijf2dExEgmPxYsXY+LEiVop8+XLl+P111/HM888g2nTpqW1/+CDDzB06FDcdNNNAIAePXrgxhtvxIcffmi7Tydo8YKjMieEZF4QALQnxkA0iso2Qe01KUJoJyR1sTYQICJlowF2LQMRzCa6osVGBqEUHqoAcVJsGCWG8sRGbZJ/3CG3dYGh1uBoiLlUaFguh0g+BwBuTgcAe24HoE8aJYUHkC4OWAmjIhjlfTgkNkSQuRuShiDq9TLPm6jXWl5ZNBrFjh07MH36dG2d2+3GiBEjsHXrVuY+Q4YMwfPPP49t27Zh8ODB2LdvH9544w3ccssttvt0ghb/K/ouJxe1uSEE4nFUBoFgPIaqUFB74mSJEFKAkE+MAEOAGLkfZvUNRDESGuR2VijF5qgUOpTCExtWRqJYCaGICg2yjR3RQcIKp4jkbwRM3AsrsEQHAMNEUgCZuR2APswC6B0P7WAMRq3YhScYbIqNhnM3/EAkbN5QIuFAz2jOm57j2LFjSCQS2rwpKp06dcLnn3/O7Pumm27CsWPHcOGFF0JRFMTjcdx+++2YMWOG7T6doMULjuPBXNQGQ9qNQ7XFVUs8EI/rRIgVAaLLATEKv/Cy/0UqlBrVLGC5Gs1UbNhxNUSwM0olU8dDeIQKr9Q5I7xiNHoFsOZ2ACbCA0gXHwBfgFjBzJHghWxsiA0WsqqopKGI+H3wMcLSEX+qtHlJSYlu/Zw5czB37lxH3nvTpk2YP38+fvvb36K0tBR79+7F1KlT8cgjj2DWrFmOvIcdWvwv6ztvDvI9fsCTeoKt9gS1G4ovGQf8KREiIkDIizxTfAD88ItRQh4rEU/FLKOflyCaZbFB4pTYMBIaMc5oE5+goFCLfqkJo1ZLmtvN37CLqOgAYOh2ANCmtwfAD7MAeuEBGIsPcj8nEBUanPdliQ2n3A0ZTpFkg4MHDyI/P197zZx8FECHDh3g8Xhw5MgR3fojR46guLiYuc+sWbNwyy234Kc//SkAoE+fPqiursakSZPw4IMP2urTCVr8L+k7VxvE3ak/pM9df7PxK3FNhKg3Sl8yjpA7hip/AL5kQheGIS/2qvgAUhd7MgFVF3oB2ALELEZuhFEGfwOJjWw4GzyxwRMaTqGKT7vhFFF4iaNGI1Z4ogNID7EA1oUHeZNOczwAc/GhYiSYeZgln/LqxmQgNpxyN1jJ1BIJTdTHyeGoW5efn68THDz8fj8GDBiAjRs3YsyYMQCAZDKJjRs3cmePramp0U1XDwAeT+paqiiKrT6doMULjupkALFkTt2ohQACrgQqXUDQFYcPcVS6A3oB4onDr8R17od6o1XdD/KizxIfANLdD6OhiYD4RduoHgElNMjjaA5ioyGFBg+r4RQn8jesig51H8AZ4QFwQi2AsfhQcbL6p9G8KDbDKDxkVVFJc6GsrAzjxo3DwIEDMXjwYCxZsgTV1dXaCJNbb70VXbt2xYIFCwAAo0ePxuLFi9G/f38tpDJr1iyMHj1aEx5mfWaDFi84vouFkJMIAAho9nvIHa+7CaYESMwVB1wB+BDXbpJ+Jc50P0jxEfMaiw/AKPGU8zRJX8xJBJ76WCNRnBYbdqqHNqTYUIfEqvkbvHCKaLGvhginmIkOAA0qPABB8aE7GBujV0QmXuOEbnhiw667wUKGUyR2iXm92rWUXm+V66+/HkePHsXs2bNx+PBh9OvXDxs2bNCSPg8cOKBzNGbOnAmXy4WZM2fi0KFD6NixI0aPHo158+YJ95kNXIojU7I2PSoqKlBQUIDL/vcefPm5CLjrbj7eBPx1/ycFiM8V192gyJuTKj7I3A8170N9wg3GY7qRL+rNgU46BcAvQmYDo6x8K1PNWxUbIrO9kmJDJITCEhrRpH6d+rdTIXM41FEqVgWHGk4hBYio4GA5HLykUbNaHACE5lkxqtfB2t9PraOnu/dH9dvpWWi556adpFErGOSIZCo2WO4GK5zCnCW57jdTVRnGRec+ivLyciFrXNI6UO89G/77KHLqSjKQVFeGccVpM1vledPi5XtVxA9v2K+NfY6oyaHuBIJ162KKB766myTL/fC5PHrnw5VyPuiwi3rzYTkfQPqTJ3nhJy+UadOOG2BUQdGK0EgdtzWx4WS+Bik2aJEhAi02VJx0N2isiA1RWI5FWhsih8DpHA8g/Vykb+5pyaa6A8hQhAgkojaEs8FD5m9IRAl7fPB40x28sKfpTSrZULR4wfFdeRA5Xh+q4IPflxqO5PcmEPUmUBlN/Z90P1I3vlT4RRUfAZc3lfeBevFBJ52Kig8ATAFC3gToiyT9BKpi9uTGEhoA39UAGk5s8EIodsQGC9HhsFZKmTfE6BQVEeEB8MUH+fcmS6SrkOcckH7e8cQHwAi7kDg5coXCKF/DSjE70dwNGU6RSJylxf+iqmu9iFfXxcDrBEfQH9cEiN+bQCWAgDeR5n6wnI+Ay5uW86G6HgA08RFyp550afHhi8e1eL2aEEjeXOinUcBacptRhUSn50VxUmzYERpkSIyFFXdDN1S6DquhFDNESpzT0Dc9O86Hkfgwcz0AcfGhYjc8yMIsKdRIaFhxN2TtDYnTRL0eeBn5GlFv4yTENwVa/K+sssqHiC91wwwF6sIqsVRyTcCXRNCvXoA98NeFWKJ14oN2PnyuVDXLKgTqcj68WsiFHO2ihlwAGIoPgH0zIEUIuV4ElsBgbcvW1PINJTZY0LkbZrByNzIh03CKCKICxEx8iLoegLn4APRhQCsjR1RxYme0iR2xIUemSCSNR4sXHBUVXgTV0EE0JTR8dZXeQoGEofgg8z5I1wOAJj5U1wOAYb4HkC4+AOiSTlURQIsNXtxYdyPhtKFvUEZCA8g8ORQwFxtGIZRwvP510GDOAV7BL6uJoro+s5Aomm1Ytj9rjhZtW905Y8X1AIzFB2AsQIxwWmgA1vM2pLshyQYxt0dXRoBc31pp8b+0nAoPYp7UxywP1IVUQql/Y1G3ofiI+lInhio+eCEX1dLXhVwAfb4HkCY+ADAFiCoGSBHCKxplBj0Ey0xoAOYhFHJdtsQGC3qECsBPFhXFiVBKY4kNHkYuiIj4oF0PwFx8AMYCRMVKQjSJaI6Gkdiw4m7I/A2JxHnc5k2aN4FaN/JPeJF/wovcitSSPOlFuNaN8nIvqqpSS0WlDxVVqaWqxovyaj8q6paqWh8qw/76JepHedSPiqgf1Qk/jsXaoCIRQEUiiKpkAOVKaqlEEFWuACrdqaXCE9SW6rqlwh9CrdeHCn8QMbcHVf4AqvwBRLxelAdDKA+GEPb6UBkMaYs6vttsqazbl1wiXq+2AHoVXuv1odbrQ8ztNQyhRF1eRFyerIoNEXfDyWGwKtkclWI1f8MpInUVD+mbaMTv1xa6rUrU59UWlXDAry0kUb9XW3jE/F5biwhOiQ0ecoSKxArq9ZS12GHp0qXo0aMHgsEgSktLsW3bNsP2J0+exOTJk9G5c2cEAgGceeaZeOONN5htFy5cCJfLhXvuucfWsYnS4mV8boUH7qgHkaACX8SNWCCJYI0b4TYpZyMaSf1bHkimOR9kzgcv5CKaaArUh1wApIVdAOjcj1rU5Z3EY4j568I4de9Fz2ei3ghZ85zQ0HYeK0+DPD4rrgZgXWyYuRoqdO0UGqdqbpCQ7kamFUXtig07RYKM3pOXG0QXFxMZ5QKwnQ/A2P1wEpHwiZHYkOEUSXNg3bp1KCsrw/Lly1FaWoolS5Zg5MiR2L17N4qKitLaR6NRXHbZZSgqKsJf/vIXdO3aFV999RUKCwvT2n700Ud4+umncd5552X9c7T4X1vecQ8ShW4Ea1Kvw21cAABfJGXuxAJJhNsk4Y+4EY0kEQ0kEa51IxhK6nI+eCEXVqIpXd+DDrkAdeIDKUFCig8AmgDxK0QlSEqEkKiCRBRaYbOEBpCeq6F+BsBejQ2rIRQVq6EUpwt8NbSzkYnIMOuHPBaWqLCb7wHwxQfAFwZ2hIiVHA2ZJCppLOKEU0yvt8rixYsxceJErez48uXL8frrr+OZZ57BtGnT0to/88wzOH78OD744AP46gZN9OjRI61dVVUVxo4di5UrV+LRRx+1fFxWafGCwx92wXPCjWgwVVDVX+tBNKQgWAOE2yQRCHu4zofqekSibma+hxXXA4Ch+EhtT3c/gNSNMuqpX6cbnmhjgjH6R0AKCkDM1QAaRmyQiIZSAOeSRO2KDTuOhlNCw8p7qMdpJD6sjHJRoW/0tABRcbowF4mZ2JDuhqQxqaio0L0OBALMGWOj0Sh27NiB6dOna+vcbjdGjBiBrVu3Mvt+9dVXccEFF2Dy5Ml45ZVX0LFjR9x000144IEHtLlUAGDy5Mm48sorMWLECCk4nCDvOzeSBS4Eq10I59SJjrAL0aBCiY9654MUH3TIJeBP6kIuoq4HABPxkUAYXgQRR8zl1dyPgJLQiQ31Jq/eQNVt6nYRaIEBsEWGemwqVkIoqXX2xAaZv8EqQ596bZy3AWReb8OO2GiM0AmgTwbmwTp28n1p8WHX9QDSy6kbuR/ZIBvORiAalXkcEmGiLg88jGtttO6aWlJSols/Z84czJ07N639sWPHkEgk0uY46dSpEz7//HPme+/btw/vvPMOxo4dizfeeAN79+7FnXfeiVgshjlz5gAA1q5di507d+Kjjz6y8/Fs0eIFhz/sgjfhRjSkwF+bEhXRkKIJEJ74IHM+yJALOdLFiusB6IfXRuHhig8A6e4H4vXbKBGiQgoBXgXNMMfOo0UIS2iQ7xEhcjKczNdgiQ0a0SRRrU8qlMISG2ajUbIlNAB7YkNEYIjsQ34uekSU1ZALq5w6YCw+tONwQIRkUiCPRI5QkTQEBw8e1M2lwnI37JJMJlFUVIQVK1bA4/FgwIABOHToEB5//HHMmTMHBw8exNSpU/H2228jGEyf7yVbtPhfVqDKBZ/PhWCVC+FcNazi0gSIkfggQy5Afb5HNJBEMuK25HoA/OG1LPERU7zwueKIoN79AKBzQADoRAgJ6Xyo+JU4091QIUUGwBYagL05UayKDRJ6kj1ALEmUFF202LAy9NVIbDgx8sQXjwuLDjtCQ7Q/9XOKuB6AWKIpYCw+tOPgiAUjIWLXxZChFElDEHZ74WI84KkPffn5+UKTt3Xo0AEejwdHjhzRrT9y5AiKi4uZ+3Tu3Bk+n08XPjn77LNx+PBhLUTz7bff4vzzz9e2JxIJbNmyBU899RQikYhuX6do8b88f9gFXzzlbKgCA4AmQIzEB+mI0PkeZq5HKJBALObWuR7RmJsZcgH44gPQhxFYAgSAToTwoAUFixh1SogKjdR65+ZE4YVSgOyJDVGh0VjDW0XEhshIJYAdLiLrv6hYcT2Mcj1UWMmmRsikT0lrx+/3Y8CAAdi4cSPGjBkDIOVgbNy4EVOmTGHuM3ToUKxZswbJZFKbtn7Pnj3o3Lkz/H4/Lr30Uvz73//W7TN+/Hj06tUrLc/DSVq84PDVAvq8NFdaG1HxQYZczFwPsxEufl8S0XhKfATqhAZLfPjdCcQSHvhcCdRC734A0AQIgDQRYocwdUqQoRPAXGzYTQ4VzduwIjZYo1EyERuNJTQAc7EhKjR47cnPb+R6sEa5iAgPsh2Q7jKICpBMke6GpDlSVlaGcePGYeDAgRg8eDCWLFmC6upqbdTKrbfeiq5du2LBggUAgDvuuANPPfUUpk6dirvuugtffPEF5s+fj7vvvhsAkJeXh3PPPVf3Hjk5OWjfvn3aeidp0r++pUuX4vHHH8fhw4fRt29fPPnkkxg8eLClPoLVLvg99SIjWAUqtALi/4qp+OCFXFiuBz3ChXY9AKSFXFjiA4BuqC2gdz9UEUCKEEB8ThEaI5EBiLkaVkahOCE2eENfzfI1mrrQMMOq0BDphyU+MhUerHYkImGXTHBKaMjEUYkoMZeXmTQaMwhr87j++utx9OhRzJ49G4cPH0a/fv2wYcMGLZH0wIEDmpMBpBJS33rrLdx7770477zz0LVrV0ydOhUPPPCA/Q/kAC5FUZRGPQIO69atw6233qordPLnP/+ZW+iEpqKiAgUFBfhpj5Pwu/Vxsmib+v+r4kPbFlL/VXTb1dfRkKKNdokGlfp2bZKIBBXE6sIrqusBAFEi1wOodz18daKDTDRVZ7AFUjPYAqh/TYkPVjEseuZUuyW/jURGarv9EuUkmYgNszobVlwNu0KDdfO0k3RolMPBczfMxIaVORvoPBYVM0HG+o7o74QWHrx2LDIVH3aEhtnfTxUcVZVhXHTuoygvLxeKxUtaB+q9Z97JPyCY3yZte7iiBg8W3tIqzxuhX+PTTz+Nn/3sZ9k+Fh1WC53w8NW64HfXOxzRkAJ/Tf12f41LJ0AAVYCkh160fQxdD+MRLmauRziKtFEukbhHcz783gQiUQ8C7gTC8OhCLwC08IuKGoaxg5HIULHragANKzasuBpGQkPkJqlLrGxgC9/uxFDqfrTwoCcZFHU8jJJLyXaA8XfKEwxmQiSboRPpckhEiLg8cDHy5kRy6VoqQr/KV155BRs3bsTvfve7BlFkdgqd8AhWA4E67RDOhZYIqqfe5VAFiBp6UcMuZiEXOteDV1TMrJopgDTxQeZ7mIkPADoBAqSLECuYiQwVltiIEO0C1BBXM7FhNBqFJTaM8jVEXQ2e0BARGTy0MIPNG6AVd0NUbKiVZlnzxpB9kN+bVeEhMqpFWy8gPGgaOxcjEI2iqlGPQCJpfgj9at944w0sW7YMF154IX7/+99j0KBBWT0oO4VOIpEIIpGI9pqu4gakRAQNLUL0DohL289IfAD6ETCsJNNIXaVTTXhQSaZA/RwutRGPFnKJxPy6US4i4gOAToAA6SLEKrxRJyKuhhNiQyQ51I7QEAkJOEEgFjcUHVaGxlrN2+BNFkWvTyuZz3A9Il5vWo4HParFyO0AnBUe2cDsb6W1izatWYIlTYsYvPAwbrH0SMDWhPAnv+OOO3DJJZfgoosuQklJCRRFgcvlws6dO7N5fMIsWLAADz30kOX90kUI6YAojPXpIRcj4ZEqqe4WKiimhlvUXA9VeADQhAcZcmGhig8ASBus4tB5biQyIibDYTMVG1ZDKI0tNOj+sxVi4bkbVmamJNuS4oMWHk64HUBKeLDyO5qK8JBIJM4ifPV7//33MXHiREyePFnLq8gWdgqdTJ8+HWVlZdrriooKlJSUwF8D+PnpGBpqHodegLDEh971YAkPf60L/lpFeHQLAObkcXRBsVjMXZdo6tWEB+166D4TqHUm1++gN2E5D8NIYNDOBomZ2DAbiWLF1TASGkY3NV6yoxFmsf1siA4nxAZvX1p4NJTbobYHGkd4ZFMcSloHqdGC6eeQOoqwNSL0i7r//vvx6quvYtWqVbjggguyfUy2Cp1wJ75pAwRqBd6TSCSlxYc+7KJobYJVrrRwi65PstAYOZdLnfAAOMIjkEQY0IQHAF0ZdVV40K6HkfgAGAKkDi0MYyI2zNwLM3ija4zEhp0Qil2hYUdg8PowEh6Z3MxEwyk8scGavRLgTwJICw8Rt8NMdADibofRPtlGig6JxFnc5k2Ao0ePYseOHQ0iNlTKysqwcuVKrF69Gv/5z39wxx136AqdWCGaPjLJEH+NXoAEq+rFR8q9cOnapISHC/5aIPd4anv9Ohdyj7tT66pdyP/OreV45H/nQbDGjUDYhdxyD/JOeBGsccMfcSO3wgt/xI1wrRvl5V5tZEtFpQ+1EQ9iMbc2uiUScyMc9SIc9aaqmcbciMY9iMY9iFBLlFqAlJAQWaxAuhtBb4IpNkLuuKnY8CvxNLHhS6aWUDyGUDwGXzIBXzKBQDyOQDyOYDym3fR88bh2wwvE4toCpMQBuTiJWZ+sm6dT9T6sig11G7mY9Um7KqQQCnt9umRXVm4K60Zu5g5FfN4GFwDk+SKRWCGieLiLHZYuXYoePXogGAyitLQU27ZtE9pv7dq1cLlc2sO7SlVVFaZMmYJTTjkFoVAI55xzDpYvX27r2EQR+vWuWrUqqwfBwqzQiVVU0UEKCTPUtiKOR4r6dXYdD8BtyfEg8zxSqH/SuqG1da4Hi4A3oYkO5ufnzG1iF1pspP4fF8rXIF0Nq46Gk3UhWBjdBLM9hFJ0ZIqR2OC1p12PWq/PdoiFdjoA6yEWo/2yTaajjSSSTFi3bh3Kysp0dalGjhxpWpfqyy+/xM9//nNcdNFFadvKysrwzjvv4Pnnn0ePHj3wf//3f7jzzjvRpUsX/OAHP8jK52iyhb8yRS2+ckencgTc+cwRKoA1AUI7JeFcYltISWsTzlWYhcREi4gB9aXTgVQBsSgxbwtZSAxAWjExFbKomO6zU4IiYFFg8AQJ7W4AKbFhVmPDKIQiGj4h3QzdMTFuYE7ftIxuRjzRQe9DuwH0sFg6pEILDpa7YVVs0LDCLXYnv7OSrCviOjWm+/BdOIHB5z/WKgs4Sfio956fnXgN/vyctO3Rimo83fYqS+dNaWkpBg0ahKeeegpAKsWgpKQEd911F7cuVSKRwMUXX4wJEybgvffew8mTJ/Hyyy9r288991xcf/31mDVrlrZuwIABGDVqFB599FELn1icFi/XYyEFgYheHAD1bgUpEKyID7UPtV+tLgfhigSrXBApJJaOGym3Q/1/OmHqtep20KRyPPSuBwAt1wOoFw4RhtthVYRYFRusEApg3dVgCQ2rIkOkqqVR/Qejp+DGKhZlJDZYMweT9UzIPozcDjOnQ9eXoNMBGOd1mO3bEPibcMl7SdOHLt3AzUO0WZfq4YcfRlFREX7yk5/gvffeS9s+ZMgQvPrqq5gwYQK6dOmCTZs2Yc+ePfj1r3+dwacypsULDqDeXQDqQyGqUCCdD16uh5EQYYkOdR9VdKSSSqHV8BBDFRpJiIoOI0jhoSaYAnrhoUI6F6oIYQkPOjmVJzZUrIoN0tWwKzRYN6NMymWT+/LEBy/ZUER00PU4gvGY41PSA2yxQa6nhYcqXEjhISo6zBJJgeYrOiQSHrVJLxLJ9N9ZtG5dSUmJbv2cOXMwd+7ctPZ26lK9//77+P3vf49du3Zxj+/JJ5/EpEmTcMopp8Dr9cLtdmPlypW4+OKLTT6ZfVq84KjooCCfuDNH2+hLmxuJAJYLYgY5pT1dQKyh3A4jeMIDqA+50M4HYCw8ADFng04OpfM16BAK6WqYCQ0jkWFFYAQj9f2YTY2u9ssSHsLFo7Jam8NYVBhhJDx4I1po0UGSieiQSFoiBw8e1IVUWO6GHSorK3HLLbdg5cqV6NChA7fdk08+iX/84x949dVX0b17d2zZsgWTJ09Gly5dMGLECEeOhabFC45YCKjISzkMKuFcNdyhwktjsSIM7EHW7gCg1e4AUFe7wwXAXVe7I/V/tVQ6kMrr8EfcOtFB1u9Qk0pjMb1YoYUHkJ5gyhteq8KbVA4QExt0vgYdQiFdDatCgyUySDEhAqs9S4TwhAdLTGQztCJSd0NEbNDtjUSHUSKpWZ0OUaTLIWmOxBQPXIwRKep0Efn5+UI5HFbrUv33v//Fl19+idGjR2vrksnUdd3r9WL37t3o0qULZsyYgfXr1+PKK68EAJx33nnYtWsXFi1aJAWHXaJBBYpPYTgZ9SIjXYDUhURCeiEiHg5hwwqv0POzAPZFh1o0jAdZPEydrwUAwlEvV3TYgSU2WMmhrBAKz9XgCQ0jkWEmMPzR9BtU1G/8k1D75AkPO6LDissRiMcdm5JeFJboIKFFB4lT+RwSSWvFal2qXr164d///rdu3cyZM1FZWYknnngCJSUlCIfDiMViuintAcDj8WjiJBu0eMFR2SEJrz8Jf219+fFglX7Iqi73os4JYYkQOhyTCTzRQR6PFdGhHSNRqVQtk85yO0RFB+lykOEUnruhig2RfA0rroao0GCJDJaw4CEqQnjCQ1R0GGFlXhUrWHU36H1J0UGHVozyOUhEQissRFyOTLCT15NJLpCk5RNLugFG/aJYUqj8lY6ysjKMGzcOAwcOxODBg7FkyRJdXapbb70VXbt2xYIFCxAMBnHuuefq9i8sLAQAbb3f78ewYcNw//33IxQKoXv37ti8eTOee+45LF682PLxidLyBUf7JDyBpHbzBoBoqP7/PCHCEiEAmWxq3e1Q38cIehZagJyTpX7yN1p0qENnSacjE9GhHY+J2OAV9Er9P24aQqETQ0WFBk9k8MSFT1B0xBjiguyTFh/BSFRIdNBYCa2IJo6G4rGMypmbYSY6SIxCK2Y0lMshBYOkuWBWl+rAgQNpboUZa9euxfTp0zF27FgcP34c3bt3x7x583D77bdn4yMAaAV1OM6YeRghpUBb7w8Tc59U64UH6/+ksKBDKn6DkunqfjyRQTosZI0O8rW2vm4uFnWdKi54tTrUf9UiYax6HQC0mh1Afd0OumYHLThYzgYtNnj5GrwQCu1q6CqDcoSGkcgQFRdmsMQHwHY8WCEWWnTQLgctOMjtRvU4yJCKUR0OOmnUyOEIU22DHCFBh1ZowcGrz2FUmwNwpvS8FZHihNiorIrgnKFPyDocEh3qveeqrzfBl5+btj1WUYXXugxvledNi3c4KtomUBWK1zkCKdQwRFXbehHBEyKqG0I7Ialt0LaloxcadNXR+vVieo/nctDT3qsOBwBhl0P73Aaz0ALiYoOXr8ELobBcDTOhQYoMlsAI1Vq332tDegFA9kuKD/W9SeEh4nRYCa2IhlV8yYRwtVEetNhQ17FEhxWXg8Sqy5FNpLMhkTQOLV5wVBXG4WoTRyUIoVGQgC9Sbz+pYkRUiBiFZFR4AoMF6YLQ7oa2PuicEUU7HAG6AinD3eCJDVZyqFG+hpGroZvjxERo0CJDWGBEiP0C+tOf7IMlPmjHwx+Nm4oOI+wmjzZU4qio6CCxm8tBwyt7nklZ+kyEBis3KGZx1JOkdRFJepBk5nDI2WJbLPGiGCJ1OQVV+akcBxX1/6oYIYUIyxGJBsXdEKvwnA619LkTqCEVI8zEhkhyqEgIhRc+ERUaTIERsXhDodsTAkTtnxQe6nvTboeR6MjE5SCxUwDMl4zrwip+JW45cZQnOkhEXQ7DPgSTR+1iR2xYHUYtkUiMafGCIycnjrzc1IUjFq0XG5GoG3FAG0ZKihF/xK1zRNSwBe2E+Gvrwxz+cGoytmC1C6zKpqKQ+5LzrJDb6BwO3f4Ba0Oa6NwNwDmxYTTclXQ1yPAJKTRYboYmNFjiImKxxkOAuoGrfQoIj0xEh+4QDJJH7YxWyXbiqIrZUFkWjRVWsVv4TSKROEuLFxwdCiPw5PkRIQpfkUWw1PlHVDFCCpGqunweUogAKcHBc0LIcAzAd0NY0G4GGUbhiQ11Vll1PQDdBG9AKmmUlTDKShQ1Ehu8fA1e1VAjV4MOnxgJDabIMBIXNYybRhvGTZ3sgxQfHOFhRXQYYeRy2AmrOJHHYYRVl0M0rOIUToxokUJD4jSxuAcKY36quMEM3S0d6wOCmxntC8LoUFCLjoVhbelALB3bRtCxbQTt26WWju1TS4eOUXToGEVBQRyhoijchXHEO0VRlR9HRds4viuOorJtHMc6x1BVkEBVQQLl7VJLRfv6paptElVtk4gGFVS0Txou0aCiW4CU0FBHppiJDdLdoEeoAJmLDZ8rwRUbASWBvGQE+YmwkNgIRKPIr67RxEYwEkV+ZQ380Th8dUuoNpoSG5F43RKrX4CUsGAtLMzakf1q64zzROg8EjKRlb6BGT1lG42+IMMMdqp0tnZE3A2rYsMfjafO01h2hZREQrJ06VL06NEDwWAQpaWl2LZtG7ftSy+9hIEDB6KwsBA5OTno168f/vCHP2jbY7EYHnjgAfTp0wc5OTno0qULbr31Vnz99ddZ/Qwt3uFoH6qFr40HESJRR50rJBL3IDcU061T5xYJR72aK6I6IrURD3JzU24I7YSQLkiMKMZFh2MApBXrMoJ0LsgQCk9s8IbDsobCGokNVnKo1RCKiKtB5mjoHA31Zk+KgDSRYOPJlkwUVfsj3Q/1/VTHg3I7zJwOEqMkUidcDhFE8jiCyThzpEq2sVvqXBQzsWFFaFgpHieRAEAk4UGC5XAkrDsc69atQ1lZGZYvX47S0lIsWbIEI0eOxO7du1FUVJTWvl27dnjwwQfRq1cv+P1+vPbaaxg/fjyKioowcuRI1NTUYOfOnZg1axb69u2LEydOYOrUqfjBD36A7du32/q8IrT4Ohy3frcB/vwcrX49AETrxEeYOBlUQRKNe7SJykRFCBmOAVIihA7DAPVCgxwhQ6KKElZuRozKzWAJDcBaGIVMECXrbGQrhMILnxgKDVJksARG2OSGFTTIZ6BGqaSFXcgwC9WWFB204CBDK7TgIHM5jOpyZFqTw2o9DjPBIVKXg0wcpUud82pykILDbi0OXkjFSHCIiA0zkVFRHUXJyBWtsp6ChI9677ngvx/Cm5dehyNeWYWtp5VaOm9KS0sxaNAgPPXUUwBSpc1LSkpw1113Ydq0aUJ9nH/++bjyyivxyCOPMLd/9NFHGDx4ML766it069ZNqE+rtHiHo4O3GgEvECEER0yp+9i+1BTCqXV14iLp0YQILUJyQzGdCAlHvchtE0ck5jZ1QQAgGkgXIUC9EIlxJgskXY5UP8b5GoC+yBc5/LUxXA169IkloUGKDDNxwfzyqH2CBrkaNdF0t4N0Ojg5HUb5HHZdDpJMS503hsthNL+KkzSW2JBIzIjGPUg6kMMRjUaxY8cOTJ8+XVvndrsxYsQIbN261XR/RVHwzjvvYPfu3Xjssce47crLy+FyubQy6NmgxQuO9koNVMMg4qpLECUutupFVhUkMcWLWo+xCInGPUAwFZKxIkCA9NExQL0QEYHO0wjW/d+omqgVVwPQj0Jx0tWgE0K5QoMnMnj5GWaQIkLtjxYepOgg9zEQHSSioRXRESt2wipk8qid0SqNFVoRxYm5VOxM6Efjo5KaJRI7VFRU6F4HAgHmFPXHjh1DIpHQypirdOrUCZ9//jm3//LycnTt2hWRSAQejwe//e1vcdlllzHbhsNhPPDAA7jxxhuz6tY13auLQ7SPVyMUrws71AkN8qKqriPFCClCYooX8EAnQsiQjKgAAVJhGHVUDC1CaFRBojoXKrTAAPgly0USQ7PtarDCJ2lCg3YzjERGrYWLfIgSEUC9kKCFh4GY4IkOOp/DDnZcDrImh2gRMNGaHCzRYTZCpalht8CXmdiQAkNihVjMjWQs/dqeqFtXUlKiWz9nzhzMnTvXsffPy8vDrl27UFVVhY0bN6KsrAw9e/bE8OHDqeOM4cc//jEURcGyZcsce38WLV5wtAtXoY0/dVNVn/7UJz/1AqwXIhGdCIkR23QuSNKLHE+9AAnHPcjzizkgAJi5ICQBf12+R11JcvX/NGbzoogU8jLL1eBNumbF1dCEBlA/4gTQOxphRt4GLTCs5GyQ+9LigxQeLNFBh1cEIF0Oo2GyIpO7Adl3OYxEByA2HNZJsl38y8jdMBIbUmhIssHBgwd1bgLL3QCADh06wOPx4MiRI7r1R44cQXFxMbd/t9uN008/HQDQr18//Oc//8GCBQt0gkMVG1999RXeeeedrOcitXjB0b66Cjl1T+zqE6L6dKg+GbKECClCoi4vchFJCRB4AVckXYB4vGkhGJYAAVKxPTIRFYDOCVFRxYgqKlR8VClyZvEuTugEMBcaAH8elIzDJyJCgxQKVvM2yPa0+AgRpzspKEREhw2Xw04uh2ghMCdcDjPMxAZd+MuJiqNWsFJ/gyc2pKshyRbhqAeeaPrvLRFN3Qfy8/OFbvB+vx8DBgzAxo0bMWbMGACppNGNGzdiypQpwseTTCYRiUS016rY+OKLL/Duu++iffv2wn3ZpeULjvJK5CWiuidF9YKuriOFCClCar0+5CT0ToiZACFDMCIChExEBepHxKiEGScsa5I1PyFCWDO82p3d1WzCNSFXw0homImMahtx+xxO2ER9jxBDUFh1OjiiwyiXg6SpuxzNFSvhlExcjVBtFLGwLBYmaRjKysowbtw4DBw4EIMHD8aSJUtQXV2N8ePHAwBuvfVWdO3aFQsWLAAALFiwAAMHDsRpp52GSCSCN954A3/4wx+0kEksFsO1116LnTt34rXXXkMikcDhw4cBpIbU+jkPPZnScq40HPKrapCH1A1WfcJUn4zUC7kqQHy+OPKQEiBhrw+50QhXgES1XI9UCMbniiPPVZcDAi9y3RFNgNS66sMvQLoAAeqH4JJOCACtTojaRhUTNAFiPWuiNQDMydYAvtAA4Fz4xExoGImMWgGXI0TcVNX9jYQHS3SYQbocNnDC5dD155DL4ZToaEh3wwpWC3sZiQ07sxBLWifRuBtuRg5HMm693ub111+Po0ePYvbs2Th8+DD69euHDRs2aImkBw4cgNtd3291dTXuvPNO/O9//0MoFEKvXr3w/PPP4/rrrwcAHDp0CK+++iqAVLiF5N13303L83CKFl+Ho/zPN8PXvn4stPr0qVrd6g1AfdqM+LxMB4QMw5AhGJb7AaDe/QAQhj78AqSG45ICRIVVG0QEVVwAeoEBQCcyUq/5oRPAfLI1ltAAwHY1yIRQI6FBigwRgcEjRAmCHOrGTYZZVNFBCg5yO5lEqraxUZvDTl0OWnDw6nLwanIA/LocqW3pAsOq6KBDKrTgIIfFitThAPS1OOiQCT1Khd7OczdYgoPnblgRGxU1URRc97yswyHRod57emz7N9y5eWnbk1WV+HJwn1Z53rR4hwMVYYTidRe7gA8hQLtR1Ib8yKkK60RIOOBHXk04TYDkIoyIz4sgEX5RL6Is9wMwyP+gHBDU3RfIRFSVqKDo8BOCQxUYgF5kAOBOHw9Yn9VVOHxC5mjwhAYpMqyMRNE+qFffjyo8aLeDBS+0wkJwmCwP0SnsnXY56NBKpk6H1YnbGgsnxIZ0NSR2iMU4DgdjXWuh5QuOkzVAxJe6Mag3lrqbRqjuhqHG4H11cyQA9eLDH4vrQjBk+MVIfACAX/EyR8CoN/+gq16AAPWiACCKkxFihIcqKlTIfmgnAwBz1AnAztEAYD98YkVosERGlcmFPpe4IdP5GbWxdLdDfX9WaIVFBqNW7IxYES13blQIzMqkbnZFR3MRG1awJTbslNaXSFoxLV9wnKgBXG3qbzR0dcm6m4gV8RHxeXVPoLT4UHM/yNCLX+EPwVUdkLy66VZUJwSAJkZy3fXZxUao7gUApsAAwMzPAGBZaABID5/QeRosocFzM8wEBo3anhYeLNFRHdXndNAuht1cDht1ObI5YiWtnUkCKU90qNDigyc2mmL+hhV3gwVXbEihIRGgNuKB25cu+JMR8VB5S6PlC45wHPiuJvV/MmZPChDS/UCd+AAQCni54gMA1/kAgCCR90G6HwCYo18AsiBZfS0QVYyIoAoLFZbASB1P3eerczMACIdOAMYwV6M8DZbQ4IkMq7kbqpighYeI6NDek+Fy8EasZEBD1uWwEloBjIfL2nUzsl3WXDR/QwSWuyHFhkTiPC1fcByvAdqG2NtUkUGLDwLa+QDqbx7qkyrL+QjE4jrxAcBQgADQiRAg/enSqOw0XTOBNakW6WQA4LoZAMQSQtXvMBOhkUmCKB02qYqyRQcLOy5HBmEVmmy7HEahFauioyngRFlzlrthqc6GFBsSC8RjbrgYVaQVmcPRtJg3bx5ef/117Nq1C36/HydPnrTfWW0M8Fu0sBiuBwCEAF3IJeb3pomPqM/LFB8RX30VRZ4AAZAWcydvDDnsEbFp8GbqVEcH0CIDAHeCNcCkcBedp2FHaNh1NtR9WbkaJCJtaGwmj4oWAkvb5sAcK6KhFcBZ0dFcwimiMN0NKTYkkoxpklIrGo3iuuuuwx133OFMh7Wx1FIdTS3hWP1SG08tNdHUoq6PxFNLTRQ4UZ26wVTUApE4QidrEKqNIr+8Br5oHDlVYfijceRX1iC/KrXk1YSRVxNGfnWNbgnE4sitDSO3Noy8cC3ywrUoIJYONVXIjUa0pWNNFfKjYeGlI7V/h5oqrW/1/dT3J48rryasHXt+ZQ1yqsLwRePIL69B6GRN3WevczFO1ADltfWuxnc1wIm61+p3XEt8t1XR+kX9W5B/FxKyrdHfk/daF6Jh3CRYhcTMRsVkcLMxeoI2uikaVdEkt9GlwMlhpgFqGylsAXbYw5eMWxIQToqNbJY1Z76faCiF9/eXIkRiQKRuxnDWYoelS5eiR48eCAaDKC0txbZt27htP/30U1xzzTXo0aMHXC4XlixZwmx36NAh3HzzzWjfvj1CoRD69OmD7du32zo+EZqkw/HQQw8BAFatWpV5Z+E4kOCUGsnx25vynIATrNEgwy6q+wFAc0CA9JoLaihG60NwanLWBZu8OZHhEgBpE6sBMJ/zhDfyhBx1oq4XcTOMhAW9TZccGrPmdLC2Z5o8ysHupG6iLgeN6KgVQMzpSLUzdzt4YoPnsDUmjk83H8nsuiGRWGHdunUoKyvD8uXLUVpaiiVLlmDkyJHYvXs3ioqK0trX1NSgZ8+euO6663Dvvfcy+zxx4gSGDh2KSy65BG+++SY6duyIL774Am3bts3a52iSgsNRqqL1ORxqXF+9+ZA1GtSbj9qGld9hgCo86JALCW+aclIUkEJEWydYZpYV56bFhXocAPgiA2AngwLGQgOodzRUSDdDxepoFHI/I9HBa+ckAvOrGGEUVqERHSZLY5TPAVgTHS2VjNwNKTYkAkTCbsDNcDPC1h2OxYsXY+LEiVop8+XLl+P111/HM888g2nTpqW1HzRoEAYNGgQAzO0A8Nhjj6GkpATPPvustu7UU0+1fGxWaDGCIxKJ6CamqaioqN/ISiZkjV5giQ4SelQLB9r1oGsyANDyPgB99UlSGLAEiRF0pj5p2ZNPeLohrYBeZAD8idXMhAZQHzZRMRIaNQIX7jbUjTCbYoLGLI9DECvJo1ZcDqPaHHZEB5BekdQq2R6dIkIm+RumSLEhaWCi0Sh27NiB6dOna+vcbjdGjBiBrVu32u731VdfxciRI3Hddddh8+bN6Nq1K+68805MnDjRicNm0mCCY9q0aXjssccM2/znP/9Br169bPW/YMECLRTDhCc6gHq3gxViod0OEjPhYZBMyBIhKuQMo1ag+6Gf4rgCAzCfvdWoYJeRo2FHZNDtadFBQgpHluPBGq0iOjw2A6wkj4qOWAGshVasig7AOeFBvke2yWRIrIpMFJU0NLqHYqSmp2dNUX/s2DEkEglt3hSVTp064fPPP7f9/vv27cOyZctQVlaGGTNm4KOPPsLdd98Nv9+PcePG2e7XiAYTHPfddx9uu+02wzY9e/a03f/06dNRVlamva6oqEBJSYm+kdmwSVZxKNrtYIVZAtR29UYe0IdizPI9VFShYDTrqKUJpsgLZ4QSE+Q2O5VB6TwNnqNhVWiQ0KKjIV0OFYvT1tOIziILpLscVkIrTogOwJ7wEHE36ETWhsCx/A3pbkgs4Iu44PIwhsVGXIgBafenOXPmYO7cuQ1zcEhNVz9w4EDMnz8fANC/f3988sknWL58efMXHB07dkTHjh2z1j9PHSIcBxjV3nSYVaRkzS5KzkBqJjw4hEzi/pbmcKCfxuj3Jp0Y2skgt7PCJkB2hAZrdIiDLkNj45TLQWMUWgHsiQ4gfUg2ICY8eEKjKSSLshCqvSHibsg5ViQZcPDgQd3kbcz7F4AOHTrA4/HgyJEjuvVHjhxBcXGx7ffv3LkzzjnnHN26s88+Gy+++KLtPs1oklf3AwcO4Pjx4zhw4AASiQR27doFADj99NORm5vr3BvZrUhJux0AX3ioCAgQYYz6od+X5WCQ7WoZ283cDN02QaEhOiEbPSdKC8JJl8Np0QEYz73SFHIzsoWQsJfuhsQi/oib43C4EQOQn58vNFus3+/HgAEDsHHjRowZMwZAyp3YuHEjpkyZYvv4hg4dit27d+vW7dmzB927d7fdpxlN8qo+e/ZsrF69Wnvdv39/AMC7776L4cOHZ9a5VTveaLIv3iyj5E0+QIkUFdKStwovb4R+KmMJDMBYZABiBbusOBp2Zn9tTDIZGmuhFLqZy9FYogNgux1WYLkbdDiFnpq+ySDibhjkbkkkTlNWVoZx48Zh4MCBGDx4MJYsWYLq6mpt1Mqtt96Krl27YsGCBQBSiaafffaZ9v9Dhw5h165dyM3Nxemnnw4AuPfeezFkyBDMnz8fP/7xj7Ft2zasWLECK1asyNrnaJKCY9WqVc7U4GAhKjZ4k32ZiQ5AP7KBJT7U/ezCuiDSCa10/yyRARhPD5+Jo0G/Z0NgtZqoU5B5HBRGycKAtWGyLMymsbcjOoDMhIcToRTRkVkNglF4UiLhEKh1w82orZmsdaPaYl/XX389jh49itmzZ+Pw4cPo168fNmzYoCWSHjhwAG5iCO7XX3+tPagDwKJFi7Bo0SIMGzYMmzZtApAaOrt+/XpMnz4dDz/8ME499VQsWbIEY8eOtfxZRXEpisKpitW8qaioQEFBAcovOw35ZA4HLTho6568adFhFVJIsCx/1lOxA8Mq02AVK7MqMACxWVsbS2jQ3y+ZNEr+Dcm/V4jRhuyH9bfl/U3JvyXZJsBpQwsOoh2dx0GHVWjBQedy0MNk6QRSWnCwEkzpwmCsEui8Cd8AMeFhJDRYyaK0w0EWrqMFB11jhtxuNBwcsDB6S0UwH6qiNoaCKa+ivLxcyBqXtA7Ue0+7pw7CHUo/L5K1FTg+paRVnjdN0uFoFrBGubCsePJmb1V8GFVBZT1l8UQGYE1o2K2j4ZSj0cLyN6y6HE6EVgBYCq8A9aLAyPFo7lgWGxKJTXwRt851UElGmuSMIg1Cy7qyNzQ80QGw3Y5MyqibCQzWe9DzhlhxNOj/N2b4xKgOR1PBIKxih0xFB6sNS3QA6W6HkfCwg1V3w0kyHhIrwykSiWO0LsFhJZwCpI9WYVWe5BWMMhIeZhhd1MxEBuCs0KDbZVtsmLkbIjk4Zm3oUJldjJJLqeRRJ3I5siE6AP4ss04Ij8aouyGRNAUCERfcLlfa+mQkfV1rofUIDqcKRfFEB2AsPDKBd0N3QmiktbNYijzbiaFG7gYvf6OZYqc2h13RAUDI7QD0okFUfBgJjUxHpzSZhFIZfpFILNE6BAdLbDSHPAEnhAbdj12x0RA44W5w+3ZIkBgNe6XDKhZdDhYic63YER0A3+0A2MIDyNyxYImNhp6WXhgZTpFkQKDGDU8yPV8jYWPytpZCy//k2SiBzcvFqI1n/sSv9sELnTSE2GCRSWlyM0JezqgfG+6GbgRL0xeVdBIjK+cgbdQF4wmfNarDaGSH9v7xOPOGH4zHHK+TYbc/1izI3PfIZOI26VhIJFml5QsOFtm+EVkRHqTAMHI0WCInW2Ij0wnXrMD7W9BigzcUtiGwMlqIfiqmbmIiVS2dEh0AezgpT3iwcEp48PpgvW9DhUwsTR1AI8WJxAR/2AV/LWMJ28vhWLp0KXr06IFgMIjS0lJs27bNsP2f//xn9OrVC8FgEH369MEbb7yh264oCmbPno3OnTsjFAphxIgR+OKLL2wdmyitT3DwbnBWb2IiI05oMcFa7L6PFbGRdlwWbyDZGCXCczVY72fkUvHcjWYEa24PkdEVdkUHrx3P7QDqhYdVAWLUvsmGUljIcIqkEVm3bh3KysowZ84c7Ny5E3379sXIkSPx7bffMtt/8MEHuPHGG/GTn/wEH3/8McaMGYMxY8bgk08+0dr88pe/xG9+8xssX74cH374IXJycjBy5EiEw+GsfY7WJziaE9kQG05jJBystm/jMxcbosKQV/CrMbDhcrBghQsyFR1WhYd2LJQA4S08eP03mYRQiSRD/GEXd7HK4sWLMXHiRIwfPx7nnHMOli9fjjZt2uCZZ55htn/iiSdwxRVX4P7778fZZ5+NRx55BOeffz6eeuopACl3Y8mSJZg5cyZ++MMf4rzzzsNzzz2Hr7/+Gi+//HImH9uQ1iM4jG50vBuS2fDJTOpqmPUrKjZEsJoAynIKzFwO9fs1W3iw+jcTG3bcDSeGxNJ2ullYhQEtOkRdDiuiQySvQ23LQhUeTjsRVvozy99gfXbu+1qpyWH0N5ThFIkDVFRU6JZIJMJsF41GsWPHDowYMUJb53a7MWLECGzdupW5z9atW3XtAWDkyJFa+/379+Pw4cO6NgUFBSgtLeX26QStQ3A0g+RBDSMRwxIbVsMjTQ2WqwFYExs0on/vbJSd5yFwk3JadACZux3asTkgPsz2F3E3suaASBEhcZhAjQvB6vQlUJNyOEpKSlBQUKAt6sRrNMeOHUMikdDmTVHp1KkTDh8+zNzn8OHDhu3Vf6306QTN6E5sk6DJR8zUbmfV5cikLx52nA1RQj4x4aIKAyeSSHmOCXMIs4nYEM3xaGLQw2QB9lBZVlEwVo0OVXSYDZsF6kUHa/gswJ6PRTtGSjTQQ2uN2vIQFUaNgszfkGSBgwcP6uZSCQQCjXg0DUPLFxwNQaaiw25ohiUSnMrfyPXzQzGkWBAVHyKJp3bERlr7Rjql6aqjrFLnjBoeTosOgF+rA0gXErzZZkkBYCQ+gMySP604FlbdjYzLmkskGeCvdcGbSM/XcEdT6/Lz84Umb+vQoQM8Hg+OHDmiW3/kyBEUFxcz9ykuLjZsr/575MgRdO7cWdemX79+psdkl9YRUuFhdvOy4irYEQ1GuRp2j0ME1udmrcv1m+dGqCERs8UI3vuIuBNWRqY4VdIcaBQLXjS8AlgPsfDyO9R9shHKMOpTxN0wmyXWCNtDYmXoRdLA+P1+DBgwABs3btTWJZNJbNy4ERdccAFznwsuuEDXHgDefvttrf2pp56K4uJiXZuKigp8+OGH3D6doPU6HNmw2lXxYOZ2ZCvZNFsYuR2Z9stDRBRZnRvHiGw4Iw67HADf6QDSp7U3CrEAbOeC53iQ++k+jon7IdIH6xjs7OcYAkm/ltpJWiX+MMfhiFkfpVJWVoZx48Zh4MCBGDx4MJYsWYLq6mqMHz8eAHDrrbeia9euWh7I1KlTMWzYMPzqV7/ClVdeibVr12L79u1YsWIFAMDlcuGee+7Bo48+ijPOOAOnnnoqZs2ahS5dumDMmDH2P7QJrVdwZBMnBYUT7gZLMLDyNoxyOdSbe6bCw8yR4IkEK3kb2UAkbGY0mZsJmYoOwFqIBTAOs6jwxAfdhxNYydmwMjoFsDhChUTmb0iaANdffz2OHj2K2bNn4/Dhw+jXrx82bNigJX0eOHAAbnd9wGLIkCFYs2YNZs6ciRkzZuCMM87Ayy+/jHPPPVdr84tf/ALV1dWYNGkSTp48iQsvvBAbNmxAMBjM2udwKYqiZK33RqSiogIFBQUov7EP8v0e/UYrT75O2vBWMRMbPHHAyuNgCQWjRFEro1+MRIioMBAVGrw+zdwN+u9Iiwd6f5ZwoPdhzanC2o81bT1jX1pwqPDmW+HNLsub8I0lOlREnAoz8ZEJRmKDJWrMwil0+IkWHLqQCh0mIZ2LtCHP9W0rTtagYMbbKC8vF4rFS1oH6r1nxI++g9eXfl7EYxX42/r2rfK8aX0ORxMetZBVRF0OkW2svu1iOLzVRpEvp8jArbALy+UA7DkdgHiIBRAbnWLF+RBBxNEQERtWMczfkPU3JA4RqHLB50sPn8RshFRaCq1LcDQnsZHNYbAkZqIDyE6tD9PRJhaGzVrtO5uwhIpgLgfgnOgAjEMsgLHwAMTFB4mRELESMuGFalhiw6q74QjNLQ9LImkCtB7B0ZzERrbgJX+aCQvyu8tEfIj8DYzaiIRSWNgJi4m4G0ZT1YvgoOgA2CEWntsBGAsPwNrQWG0fB2pnZCI2LGPkWsj8DUkG+MMu+OLpboaLsa610HoER3MiU3cj5LVXj0MkjMISBKzkUztYdTVYYkPkvRuywqgKy+UAHBMdgD23AzAXHoA98WEFs+RT0TCKpdwNK8hwikSSMa1DcGTiblRHGzdx1GnMhrhayd0g98kEq64GIC42GuNvl+X8j0xEB8BPKhURHkDmw2OtjGwxEhoZuxs0osNcZThFIoCvFmD+FFuxdm35hb+aWyilIXI3RIamNsT3ZvY+TW26edaNxsqTL++GxunD6GncKC/BH40bVtkMRqKGN2t/LK4toqjFwUQWEczenzmfjFV3Q4ZTJC2M48ePY+zYscjPz0dhYSF+8pOfoKqqynCfFStWYPjw4cjPz4fL5cLJkyeZ7V5//XWUlpYiFAqhbdu2tup1tHzB4QQNlcDpJGa5DaLJl9kQHiL9GhYFawB3o4nccOyKDsC8tLeZ8ADsiY9MEBEaImLDMnJ0isRhgtUuBKsYS3X2cjjGjh2LTz/9FG+//TZee+01bNmyBZMmTTLcp6amBldccQVmzJjBbfPiiy/illtuwfjx4/HPf/4Tf//733HTTTdZPr7WEVJxgmyHVhpD1IhWEHUiadSKcLEqNqzAyt9oyGG1FnM5AH4+B1AvOoxCLAC/ZgdgHmrR+mIIAbPwixmiQsbQkWGIjay5G6TL5cQkhhKJQ/znP//Bhg0b8NFHH2HgwIEAgCeffBLf//73sWjRInTp0oW53z333AMA2LRpE3N7PB7H1KlT8fjjj+MnP/mJtv6cc86xfIzS4bBCtkRBtvoVuZGKzJei69NnbxHB7Fh4nycbuRsNXIMDgOFN0CzZMVO3AxBzPNL6JRwQO0smx8QLHVkWG9LdkGQBfy3gr2Estdl5v61bt6KwsFATGwAwYsQIuN1ufPjhh7b73blzJw4dOgS3243+/fujc+fOGDVqFD755BPLfUnBYZXqqHMCwcm+MqWx8yVM80osiI2GxupNyeYNzgnRYUV4OJ6UKYjI+/M+R8ajUoTdjSbyu5U0WyoqKnRLJBLJqL/Dhw+jqKhIt87r9aJdu3Y4fPiw7X737dsHAJg7dy5mzpyJ1157DW3btsXw4cNx/PhxS31JwWGXTIRCQwoNK+ECq26HE4i8p1Mhj4YeDpuFm5KI6HBKeAD6m382RYho/0auhpDYkO6GpIHw1brgZyy+2lQOR0lJCQoKCrRFnXiNZtq0aXC5XIbL559/nrXPkUwmAQAPPvggrrnmGgwYMADPPvssXC4X/vznP1vqS+ZwZAItGkRs/MZwNKzW5cjW7LCs9zHDSGw0BXfDLrxcDsC0oJhRToeK0dBZFZH8DhZGosAoDyQTsWIkkFgCy5bYEHU3JBIHOHjwoG4ulUAgwGx333334bbbbjPsq2fPniguLsa3336rWx+Px3H8+HEUFxfbPs7OnTsD0OdsBAIB9OzZEwcOHLDUV5MTHF9++SUeeeQRvPPOOzh8+DC6dOmCm2++GQ8++CD8WZw8yhGaSniEhR3RATT8tPQkdp2NllA3xSHRAfATSlXIm7lV8UHjpANi5sLwnBwhsUFjMEmbaVuJhEGwGggwBqS46qZLzc/PF5q8rWPHjujYsaNpuwsuuAAnT57Ejh07MGDAAADAO++8g2QyidLSUkvHTjJgwAAEAgHs3r0bF154IQAgFovhyy+/RPfu3S311eQEx+eff45kMomnn34ap59+Oj755BNMnDgR1dXVWLRoUWMfXvPGTgVSUhxke2p6EjOx4ZS7kY0RKiRGRcCMXA5ASHQA/FlmVUTcDhX6Jp+pALGKSKjHKGQkLDashFKkuyFpBpx99tm44oorMHHiRCxfvhyxWAxTpkzBDTfcoI1QOXToEC699FI899xzGDx4MIBU7sfhw4exd+9eAMC///1v5OXloVu3bmjXrh3y8/Nx++23Y86cOSgpKUH37t3x+OOPAwCuu+46S8fY5ATHFVdcgSuuuEJ73bNnT+zevRvLli2TgoPGVlVQm2XPgXTB4MS09CwyEQHZdjfCMXYuiN15VTIUHYCzbgcNSwA4JUKs1s6wLDQAsZwNK6EUsm1Y5nhI+PhrAD/D4VCU7L3nCy+8gClTpuDSSy+F2+3GNddcg9/85jfa9lgsht27d6OmpkZbt3z5cjz00EPa64svvhgA8Oyzz2qhnMcffxxerxe33HILamtrUVpainfeeQdt27a1dHwuRcnmx3eGmTNnYsOGDdi+fTu3TSQS0WX5lpeXo1u3bjh4c1/k+z0NcZiNg926GE31YhkUuJkZuRttONt4CaO89+P1A/AFgpEwMBIEIkJFoE1t0JrYsio+GgOzBNhQWFBo8NZZGTJL1d2oKA+j5Ml/4OTJkygoKDA8TknroaKiAgUFBbjHfRABpIdMIqjAkmQJysvLhUIqLYkmf8XZu3cvnnzySVN3Y8GCBTqVplLy/D+zdWgSiUSC7777TgoOiYbf70dxcTGWHC7htikuLm76OYlZoMEcjmnTpuGxxx4zbPOf//wHvXr10l4fOnQIw4YNw/Dhw/G73/3OcF/a4Ugmkzh+/Djat28Pl6vpTQdcUVGBkpKStExliRjy+8sc+R1mhuqinjhxAoWFhY19OJImRDgcRjRqUEfG70cwGGzAI2oaNJjgOHr0KL777jvDNj179tRU39dff43hw4fje9/7HlatWgW3u2WVDFFtt9ZoqzmB/P4yR36HmSG/P4nEGg0WUhEd2gOknI1LLrlEKzDS0sSGRCKRSCStjSaXw3Ho0CEMHz4c3bt3x6JFi3D06FFtWybFSyQSiUQikTQeTU5wvP3229i7dy/27t2LU045RbetGQyoESYQCGDOnDnc6nISY+T3lznyO8wM+f1JJNZoFsNiJRKJRCKRNG9kcoREIpFIJJKsIwWHRCKRSCSSrCMFh0QikUgkkqwjBYdEIpFIJJKsIwVHA7NgwQIMGjQIeXl5KCoqwpgxY7B79+7GPqxmy8KFC+FyuXDPPfc09qE0Gw4dOoSbb74Z7du3RygUQp8+fQznKZLoSSQSmDVrFk499VSEQiGcdtppeOSRR1rUKDqJJBs0uWGxLZ3Nmzdj8uTJGDRoEOLxOGbMmIHLL78cn332GXJychr78JoVH330EZ5++mmcd955jX0ozYYTJ05g6NChuOSSS/Dmm2+iY8eO+OKLLyzP+tiaeeyxx7Bs2TKsXr0avXv3xvbt2zF+/HgUFBTg7rvvbuzDk0iaLHJYbCNz9OhRFBUVYfPmzdq0wBJzqqqqcP755+O3v/0tHn30UfTr1w9Llixp7MNq8kybNg1///vf8d577zX2oTRbrrrqKnTq1Am///3vtXXXXHMNQqEQnn/++UY8MomkaSNDKo1MeXk5AKBdu3aNfCTNi8mTJ+PKK6/EiBEjGvtQmhWvvvoqBg4ciOuuuw5FRUXo378/Vq5c2diH1awYMmQINm7ciD179gAA/vnPf+L999/HqFGjGvnIJJKmjQypNCLJZBL33HMPhg4dinPPPbexD6fZsHbtWuzcuRMfffRRYx9Ks2Pfvn1YtmwZysrKMGPGDHz00Ue4++674ff7MW7cuMY+vGbBtGnTUFFRgV69esHj8SCRSGDevHkYO3ZsYx+aRNKkkYKjEZk8eTI++eQTvP/++419KM2GgwcPYurUqXj77bdb5fTOmZJMJjFw4EDMnz8fANC/f3988sknWL58uRQcgvzpT3/CCy+8gDVr1qB3797YtWsX7rnnHnTp0kV+hxKJAVJwNBJTpkzBa6+9hi1btqTNGSPhs2PHDnz77bc4//zztXWJRAJbtmzBU089hUgkAo/H04hH2LTp3LkzzjnnHN26s88+Gy+++GIjHVHz4/7778e0adNwww03AAD69OmDr776CgsWLJCCQyIxQAqOBkZRFNx1111Yv349Nm3ahFNPPbWxD6lZcemll+Lf//63bt348ePRq1cvPPDAA1JsmDB06NC0Ydh79uxB9+7dG+mImh81NTVwu/Xpbx6PB8lkspGOSCJpHkjB0cBMnjwZa9aswSuvvIK8vDwcPnwYAFBQUIBQKNTIR9f0ycvLS8t3ycnJQfv27WUejAD33nsvhgwZgvnz5+PHP/4xtm3bhhUrVmDFihWNfWjNhtGjR2PevHno1q0bevfujY8//hiLFy/GhAkTGvvQJJImjRwW28C4XC7m+meffRa33XZbwx5MC2H48OFyWKwFXnvtNUyfPh1ffPEFTj31VJSVlWHixImNfVjNhsrKSsyaNQvr16/Ht99+iy5duuDGG2/E7Nmz4ff7G/vwJJImixQcEolEIpFIso6swyGRSCQSiSTrSMEhkUgkEokk60jBIZFIJBKJJOtIwSGRSCQSiSTrSMEhkUgkEokk60jBIZFIJBKJJOtIwSGRSCQSiSTrSMEhkUgkEokk60jBIWnVbN68GaeffjpOnjwJAAiHw7jgggvw9ttvN+6BSSQSSQtDCg5Jq2bYsGEYP368Vlb+Zz/7GcaMGYPLLruscQ9MIpFIWhiytLmk1aMoCkaNGgWv14tgMIi//OUvjX1IEolE0uKQs8VKWj0ulwt33303rrzyShlKkUgkkiwhQyqSVs+3336Le++9FytWrMCkSZPw3XffNfYhSSQSSYtDOhySVk08HsePf/xjzJgxA+PGjcORI0dwyy234PXXX4fL5Wrsw5NIJJIWg8zhkLRq7r33XlRUVOD3v/89ACCZTOKKK67A8OHDMWPGjEY+OolEImk5SMEhkUgkEokk68gcDolEIpFIJFlHCg6JRCKRSCRZRwoOiUQikUgkWUcKDolEIpFIJFlHCg6JRCKRSCRZRwoOiUQikUgkWUcKDolEIpFIJFlHCg6JRCKRSCRZRwoOiUQikUgkWUcKDolEIpFIJFlHCg6JRCKRSCRZRwoOiUQikUgkWUcKDolEIpFIJFlHCg6JRCKRSCRZRwoOiUQikUgkWUcKjmZENBrFDTfcgHbt2iE3NxeRSKSxD6lJ0KNHD6xatSqjPl544QX07t3bmQMy4N1338X555+PZDKZ9fdqjQwfPhxz584FANTW1qJnz57Ys2dP4x6URCIBIAVHgzF37lx4PB7k5uYiLy8P3bt3x3333YdoNCrcx1/+8hf8/e9/x1dffYWqqioEAoEsHnHL5bbbbsNtt92mWzd27Fh8+umnWX/vqVOn4uGHH4bb7eYeixW+/PJLuFwu5OTkIDc3V1vKy8tN9x0+fLjt920OhEIh3Hfffbj//vsb+1AkEgmk4GhQLrroIlRVVaGyshJ//etf8fzzz+Oxxx4T3n/fvn047bTTkJeXZ/sYYrGY7X0lmfH222/jxIkT+P73v+94359++imqqqq0paCggNnu448/xv3334/KykoAQGVlJe6//37s3LnT8WNqCtx8883429/+hv/+97+NfSgSSatHCg4bsCx8l8uFTZs2Cfdx3nnn4aKLLsKHH36orTt06BBuuukmdO3aFUVFRbjxxhtx9OhRAMBPf/pTPPzww3jvvfeQm5uLH/3oR6b7AKmn2LvvvhvXXnstCgsLMX36dADAc889h759+6KgoAC9e/fG2rVrtX02bdoEl8uF9evX48wzz0R+fj5GjhyJb775RmtTU1OD6dOn4/TTT0deXh7OOOMMvPjii9r2N954A6WlpWjbti3OOOMM/OY3v+F+F4qiYPbs2TjllFOQl5eHU045BTNmzNC2f/rpp7j88svRvn17dO/eHT//+c8RDoeZfanHTrJq1Sr06NEDADB//ny88MILeOGFFzQ34LvvvtO1AVJ2/H333Yfu3bujffv2uPzyy/HZZ59p2+fOnYvhw4fjoYceQufOndGuXTvccccdSCQS3M/50ksvYcSIEZq7wTuWbNK/f39cddVVuO2227B7927ccsstGDVqFM4//3xm+z/96U/o3bs38vPz0aFDB4wYMULbFg6HMWPGDJx22mlo27YtLr74Ynz88ce6/Z999lntPOvcuTNmzpypbXv//fcxZMgQFBYW4vTTT8fChQt135/L5cJvf/tbDBkyBLm5uTjvvPPwwQcfaNvj8Th+8YtfoLi4GB07dsS0adOgKIru/QsKCjBo0CC8/PLLmXxtEonECRSJZbp37648++yzunUAlHfffZe7z5w5c5Rhw4YpiqIoyWRS2bFjh9KhQwelrKxMURRFCYfDyllnnaX84he/UKqqqpTKykrl5ptvVkaMGMHsQ3SfYcOGKTk5OcqGDRuURCKhVFdXK88++6zSvXt3Zfv27UoikVDee+89JT8/X3nvvfcURVGUd999VwGg3HLLLcrJkyeVEydOKN/73veUCRMmaP3ecMMNSmlpqbJ7925FURTlwIEDyj//+U9FURTlnXfeUQoLC5WNGzcqiURC+fe//62ccsopyvPPP8/8bv7v//5P6dq1q/LVV18piqIo3333nfLBBx8oiqIo5eXlSnFxsTJjxgyltrZW2b9/v9K3b1/lrrvuYv491GMnUT+vyrhx45Rx48YZtrnjjjuU/v37K19++aVSW1urPPDAA0qXLl2UiooK7W/h9XqVX//610o0GlU+//xzpbCwUHnuueeYn1FRFGXw4MHKokWLdOtYx/Lee+8pBQUFhovK/v37FQBKly5dlPbt2ysXXHCB8tJLL3GPQVEUZfPmzcrVV1+tFBcXK2PGjFE2bdrEbFddXa34fD5l48aNiqIoSm1trfZ/9dgvv/xy5X//+58Si8WUp556SunYsaNy4sQJRVEUZfny5UpRUZGyceNGJR6PKydPntTOsS+//FIJhULKsmXLlGg0qvzzn/9USkpKlF/96lda/wCU/v37K3v37lVisZgyZcoUpWfPntr2efPmKaeeeqry2WefKeFwWPubzJkzR/c5pkyZolx//fWG34lEIsk+UnDYwK7g8Hg8SkFBgRIIBBQAynXXXadUVlYqiqIoL774olJSUqIkk0ltn8OHDysAlIMHD2p9kIJDZJ9hw4YpN998s+5Y+vTpo/zud7/Trbv99tuVn/zkJ4qi1N+0Dx06pG1/8sknlXPPPVdRFEX59ttvFQDKxx9/zPyso0ePVmbOnKlbt3DhQuXSSy9ltt+0aZPSvn175Y033lBqamp029asWaMUFxcr8XhcW/fXv/5VCYVC2ud2WnAkEgklGAwqr7/+urY9Ho8rnTp1Uv74xz8qipL6W5xxxhm6Pq655hplypQpzM+oKIpyxhlnKCtXrtStYx2LFSorK5UPPvhAiUQiSk1NjfLCCy8ogUBAd+wkO3fuVO677z6loqJCGTZsmFJRUaHcd999ys6dO9PaVldXK23atFGeeuop5ejRo7ptx44dUwAoe/fu1a3v1auX8oc//EFRFEU555xzlF//+tfM45g/f74yaNAg3bonn3xSOeuss7TXAJQXXnhBe/3vf/9bAaAcO3ZMURRFOf3005Xf/va32vZEIqEUFxenCY4ZM2Yol112GfM4JBJJwyFDKlmgd+/emkV+++23a+svvPBCnDx5ElVVVXj66afxwQcf4OTJkwCAL774Al9//TXatm2LwsJCFBYW4qyzzkIgEMCBAweY7yO6z6mnnpq239SpU7V9CgsL8Yc//AFff/21rl2XLl20/+fm5mpx/y+//BIAcNZZZ3GP61e/+pWu/0cffVQXkiEZNmwYfvnLX2LhwoUoLi7G8OHDsXHjRgDAwYMH0b17d3g8Hq396aefjtraWl3oyEmOHTuGcDiMnj17aus8Hg969Oih+17J7wfQf0cs2rVrJ5TMaYXc3FxccMEF8Pv9CIVCuOmmm3DzzTfj+eefZ7bv378/Fi1apOUB5eXlYdGiRejfv39a2zZt2mDDhg3429/+hrPOOgvnnXcennrqKQDA3r17AQADBgzQ/Z2/+uor/O9//wOQOk9458jBgwd13y+Q+rvS5zp9DgLQvuP//e9/unPb7Xaje/fuae9VUVGBdu3aMY9DIpE0HN7GPoDmSF5eHqqrq7XX9I3abLSD1+vFpEmTsHnzZtx1111Yv349iouLLQ/hE91HzRkg95s3bx5uuukm4fciUXMd9uzZg759+zKP65ZbbtHlYZgxYcIETJgwAZFIBEuXLsXo0aNx7NgxlJSU4KuvvkIymdQ+x3//+1+EQiF07NgxrR/1RlpdXY2cnBwA6X8ft9ttOCy1Q4cOCAaD2L9/P3r16gUASCQS+Oqrr9CtWzfhz0QzYMCAtHODdSzvvfceRo0aZdhXVVUVd5vb7U7LZWAhknN00UUX4aKLLoKiKNi8eTOuuOIK9O7dWxMLn332WZrwUunRowf27NnD/CwlJSXYsWOHbt1///tfS9/vKaecoolfAEgmk0xx/sknn+DKK68U7lcikWQH6XDYYODAgVizZg3Ky8tRXl6OadOm2ernoYcewmuvvYYPPvgAV199NcLhMObOnas9BX/77bdYt24dd387+wDAPffcg4ceegjbt29HMplEJBLBRx99lHYD4NGxY0fceOONuPPOO/HFF18ASD1t/utf/wKQGvr5xBNP4J133kE8Hkc8Hscnn3yCLVu2MPvbtm0btmzZgtraWvj9fk00uN1uXHnllVAUBXPmzEEkEsFXX32FWbNmYcKECWnJoQBw5plnIi8vD08//TSSySR27dqFFStW6NoUFxdj79693ARPt9uN2267DbNmzcKBAwcQDocxe/ZsuFyujG5cV199Nf72t7/pBAbrWNTRTEaLynvvvYfPPvsM8Xgc0WgU69atwx/+8AfceOONto9T5fDhw/jzn/+MkydPwuVyobCwEC6XCx6PB927d8eYMWNw55134quvvgKQch7efPNNzcmaOnUqFixYgM2bNyORSKC8vBzvv/8+AODGG2/Ev//9b6xYsQKxWAyffPIJfvnLX+KnP/2p8PGNGzcOjz/+OHbv3o1oNIpHH300zfWqqKjAtm3b8MMf/jDj70MikWSGFBw2ePTRR5GTk4NTTjkFAwcO1EaMWOX000/HrbfeigceeAB5eXnYunUr9u/fjz59+iA/Px9Dhgzh3qQB2NoHSN0IZs+ejZ/97Gdo164dunbtivvvv1/n2pixcuVKDB06FCNHjkRubi4uueQSzWYfM2YMVq9ejVmzZqGoqAhFRUX46U9/imPHjjH7qqqqQllZGYqKilBYWIgVK1Zg/fr1CAaDyM/Px9tvv42tW7eic+fOuOiiizB8+HA8/vjj3O9k9erVWLp0KfLz8zF9+nRMmjRJ12bSpElIJBLo0KEDCgsLcfz48bR+fvWrX+Giiy7ChRdeiC5duuDDDz/E//3f/2U0JPnSSy9F27Zt8cYbb1g6FiN2796N0aNHo6CgAMXFxfj1r3+N5557Dj/4wQ9sH6eKoihYvnw5evbsidzcXFx77bWYP38+Lr74YgDAmjVr0K9fP1x22WXIy8vDWWedhZUrV2ruyqRJk/Doo49iypQpKCwsRK9evfDWW28BSLkfGzZswLPPPosOHTrghz/8ISZNmoR7771X+PgeeOABjBkzBhdffDG6du2K6upqDBkyRNfm+eefx//7f/8PZ5xxRsbfh0QiyQyXIuK9SiQSR3j33Xdx3333Yfv27WmhLomz1NbWonfv3njzzTe5uSQSiaThkIJDIpFIJBJJ1pGPWBKJRCKRSLKOFBwSiUQikUiyTpMUHAsWLMCgQYOQl5eHoqIijBkzBrt3727sw5JIJBKJRGKTJik4Nm/ejMmTJ+Mf//gH3n77bcRiMVx++eWWRlFIJBKJRCJpOjSLpNGjR4+iqKgImzdv1obkSSQSiUQiaT40SYeDRi1qJcsTSyQSiUTSPGnyDkcymcQPfvADnDx5UqtSyCISiSASiej2O378ONq3b8+sSCmRSCSZoCgKKisr0aVLF1lTRaIjHA4jGo1yt/v9fgSDQaG+tmzZgscffxw7duzAN998g/Xr12PMmDHc9i+99BKWLVuGXbt2IRKJoHfv3pg7dy5GjhyptUkkEpg7dy6ef/55HD58GF26dMFtt92GmTNnZvV+2eTnUpk8eTI++eQTQ7EBpBJNH3rooQY6KolEIklx8OBBnHLKKY19GJImQjgcxqmhEA4btCkuLsb+/fuFREd1dTX69u2LCRMm4OqrrzZtv2XLFlx22WWYP38+CgsL8eyzz2L06NH48MMPtUkaH3vsMSxbtgyrV69G7969sX37dowfPx4FBQW4++67RT+qZZq0wzFlyhS88sor2LJlS9qMpzS0w1FeXo5u3brh4MGDyJ8wPLMDCdnQZTl+gX59/G0Bznvy+g1y2vPWA4Cfs62NwbEHDI5Za+MxbyNCiHEctfynBlMi7LlTUtti6etqGO8Vjetfh01eVzP6iBBtaqn3ZbWvjaevo98nGxidOwD7d8E6P1nnuZXzm3UcvGNjndN2zmejc/iHKwGk5mkpKSnByZMnUVBQwG8vaVVUVFSgoKAAB90u5LO2AyhJKigvL0d+PqsFH5fLZepwsOjduzeuv/56zJ49GwBw1VVXoVOnTvj973+vtbnmmmsQCoW4M007QZN0OBRF0WZR3bRpk6nYAIBAIIBAIJC2Pn/CcOT7MrwBxglNJio+ogkg10R0+A2Oi3dB5q3nCg7eBdXgc/CEEK8v0X6Z7yUgzEhYNw9REdIG+ps9CcsRD/nSRYebshuDPiBMiIagVy8QAt50EUFq/AQ1a22UOidq4wB9/tbGAK+JhU8LGR5GojeeNG4TV9J/D6zzPpFM70NR2OcT6/xhCg4L57Wd85l3Hof8wPv3At+vv1DLkK2ERX4bL/JZ54aiAFWCv08HSCaTqKys1OVADhkyBCtWrMCePXtw5pln4p///Cfef/99LF68OKvH0iQFx+TJk7FmzRq88soryMvLw+HDKXOqoKAAoVCocQ9OvZmICI+qqLHoqI3xL4bhGPuCWB0Vc0/swnsaNBMbVoSGVZFhpT8z8RHwskUHLRys7t9QmAkJUaHBas87F9U2rO218fTfgtl5ny2sit1M+nnjJ8CFv3bm/SStkoqKCt1r3kNzpixatAhVVVX48Y9/rK2bNm0aKioq0KtXL3g8HiQSCcybNw9jx451/P1JmmSm07Jly1BeXo7hw4ejc+fO2mI27XqDUhtnW900VRmEALKFUxdmq/2F/M6LDd57GL1PwMt5kmbcUI3seKehzxWR80trG7MuNnh98Prhrhc4Tta+IgIvW4i4dTT0OfV/k505FknLJCcA5DKWnJSoKCkpQUFBgbYsWLDA8UNYs2YNHnroIfzpT39CUVGRtv5Pf/oTXnjhBaxZswY7d+7E6tWrsWjRIqxevdrxYyBpkg6Ho2kl4Xi9NaxiZCNbRcTxMHris+NyZAs77oaI2Mi2yDB6XyPHg+VWsJyONn59aIXej94n5LUmFkRh3bQzFRlm70Wfm0bnK0ljuRw0VgWjUShFInGQgwcP6nI4nHY31q5di5/+9Kf485//jBEjRui23X///Zg2bRpuuOEGAECfPn3w1VdfYcGCBRg3bpyjx0HSJAVH1jG7SNsRJCxrmcSu6GgosiE2MrhIRxnJf346YVME9Rh4wsMp0dEYmJ3Hou6a1bAfc53J+c/bTyJpqYS86TlfAJBMPVDn5+dbThoV5Y9//CMmTJiAtWvX4sorr0zbXlNTkzaU2+PxIJlMprV1ktYpOMwQiWsz9zNxO7IpOkQu+E7ioNhgiQur7UzFiJHwEBUdRhi1z/GzR5/Q5PqdC8FZ6Ydsyzo/7YoOOy5HtnOUAOuJ1NLdkDQiVVVV2Lt3r/Z6//792LVrF9q1a4du3bph+vTpOHToEJ577jkAqTDKuHHj8MQTT6C0tFTLgQyFQtpoqtGjR2PevHno1q0bevfujY8//hiLFy/GhAkTsvpZmmQOR5PCLK7N3Mfg5mf1hsK6iYncvHhYyd+wNcJFLE8j6vdqixOQ/Rn2yTs+kZwO2gWi9yHb0+KPvInq2jn8xF8VzUy08PZvyHBOMyHqk89rEgNCXqCNL32x+GC4fft29O/fX6uhUVZWhv79+2tDXL/55hscOHBAa79ixQrE43FMnjxZlwM5depUrc2TTz6Ja6+9FnfeeSfOPvts/PznP8fPfvYzPPLIIw58cD5Nug5HJqhjocsv6o58syGEdhC5URidWLwnP1a/rBs/s96BwQ1QhXVjtRJOMRMbJjglMEQxdT5YjgftdtCij1Wfg9yHbE+KT1Io6tpQ/ZM3fHJ/up3Rfk7AOkdZ5ye9jj4P6X7o9vR5Rp/brN8RvU+2zmvOOR31e1FRHUXHMats1VOQtFy0e8/ZHZDvSb/3VCSSKPjPsVZ53kiHwy4iT3dOOh0NjdVkVROx4aSbYQUhx4OG5VyQ30cbv7HbwXM6GsrlcApRp6M5YeW8lqEUSSbk+FNim16yHTJswrR8T5AukuToCBWD+gRaG4u5FazYOGu0ilOxbitZ/DZi3FZERlSkiikHP6tSKOM4mI4HazQL+VlV90L9G6juhPrdqY4HmQtC5nSof//aeP3frDqq70/9m9fG9Lkc5IiXkC97N/yaWMrupWHlYdDnaNrrBs4nyhSLw8QbQzhLJC2B1vfLsXLBFhUnZsKDdwFuKkMHaRyynEXIRGSY9cMSIeRx6cQH/TlqqREpgLHw4IkOsh0pHshEUp048aWLDh60ADHap0bgvFfb0MLDjugw2z8TGmKoeIbntUSCkI9dEZgu09CKkCEVI6wmjBq144VXRG1rJ4okOV3wK4OLcjTgc0xs2H0PwyRTVgExumAYL8xi1M5KiEW9SYe89fvZcehExIZZe6shQKNwYkOEZVjOndWcJAopNiSSzJC/IFFEh8paLQPdmGRyUbYhNuwIDF5/VmpykO/LC70Y1v2gh9SSjgf5fYVj+jALq104Jh5i4YVXSGcjW2EWVoiFdiqcqqvR0AXuzJC5GxInCHrT50ECgJjBJJItHOlw2EHE9bBSBropJ+c5IDZE3Qx6aKuheGG0FUlMVY+FXozeQ0PE8VBvnDzHg2xj5naQTgfpdpDb6P87Gbqw6nQYnbPZTJK269xJd0MiaVCk4MgEM+GRqeigyfbcEyJPmTbEhhFO1+Og+7SSS8ITIVzhEaIEBUtUmAkPMlxiJDoAcdHhJGbhGDthxMZA1EGxUrAuyyFBSTNH/W2zFgts2bIFo0ePRpcuXeByufDyyy8btv/mm29w00034cwzz4Tb7cY999yT1mblypW46KKL0LZtW7Rt2xYjRozAtm3bLB2XHVq+4KiO1hczsrOIYDbhFbNoks0Jr0isFACz8xRI72NBbIg4Bw311GjkhpiFgFjCQ1h8qKIi6DMWHkD9hSiHGDZHig46r8MIug1r9IkVaNFh1+VoKByecI93fkskDUF1dTX69u2LpUuXCrWPRCLo2LEjZs6cib59+zLbbNq0CTfeeCPeffddbN26FSUlJbj88stx6NAhJw89DekTmkFfXM3mnQA4+RtZKgVt1J8R9EVZpJASA6sXYxGRUevgxTxkMlwWMM8ToT+PPxJL28cfjesFWS0nh0OFzPFQUfM71NwONa+DHsECmOdzOFkm3Qw7uRwNOa9KBnlJMpQisU2OH/Azcjii1nI4Ro0ahVGjRgm379GjB5544gkAwDPPPMNs88ILL+he/+53v8OLL76IjRs34tZbb7V0fFaQvyarqBdxqxNeGa1vDghejI1cDSOcFBmi/ZqJEaaoADsJNa3OB5loKiI81G080QHUCw9VTFgRHW181kerSJhId0PiBBUVFbrXgUDA8RljRampqUEsFkO7du2y+j5ScNjFTHiITu9t5nLYFSlWsv7N3I0MxIaR0MiWyBBF9P1DlKhQ8UfjaeIjrY32ZgzhQY5qMYMUHiq06ADqzxenRQc9akV0IkLy/HaiHofZuSryfWbgbkixIREm5GM7HHXlzktKSnSr58yZg7lz5zbAgaXzwAMPoEuXLmnT2DtNyxcctXH2FMEiiMS+RYRHpqKDpKGHEGZBbIje6GP+xr24+6KpmzPreEMMcUGjChBduKWWE0pRi4eJuB1h1jlFVSOVTkeKpjTcViIhOHjwoG4ulcZyNxYuXIi1a9di06ZNCAaDWX2vli84MoF1YeaJECPhkc1QClni3G7+BolJ7oaI2LArNBpbYNDwjscXjaV9FlqAqO4H6XowhYeo28ETHWqtDrIdKTqA9Hoe2RAd2TrHna5bI90NSRMhPz+/0SdvW7RoERYuXIi//e1vOO+887L+flJwWIVX/lmF505YnX+C7CebgsXoCZC6EGdDbFgRGU1hOnB/LJ52zLQAYbkfTOFhxe0AzEWHWV5Ha3Y6LCDu4DHscolEha7To+Ky6bg7zC9/+UvMmzcPb731FgYOHNgg79n4V/DmipHw4LkdVuafaAysTmIlIDbsCo1MxEXU6/xp7Y/XJYxSx0ULEFJ8hIiQivpd6YSHuhPP7WCJDpWmIDoaey4gs/yNDOrKSCRNhaqqKuzdu1d7vX//fuzatQvt2rVDt27dMH36dBw6dAjPPfec1mbXrl3avkePHsWuXbvg9/txzjnnAAAee+wxzJ49G2vWrEGPHj1w+PBhAEBubi5yc3Oz9llal+CwUohI1Mo1Ex5WRIfosFYyj0NkxljywiyapGjibtgVG0ZCQ1Rk2BEU4QzCNcFoLO09WQKEFB+q8AhRzgYtPNKG02oHbJLXATgnOkiasuuRaT6GgKC2lJ/UiktUSwTI8bPPOdaEbgZs374dl1xyifa6rKwMADBu3DisWrUK33zzDQ4cOKDbp3///tr/d+zYgTVr1qB79+748ssvAQDLli1DNBrFtddeq9sv24mrLV9w2E0a5YkTniAwmmkTEB95wsvqz4YbIngBtyo2rAgNEZEhKjDsioqIQf+BeFzXb7AukdTomFThwXI7AJP8DjLMYiQ6AOfCKyTk+UuKj0yLhxnRWImdNt0NWZtD0pAMHz4ciqJwt69atSptnVF7AJrwaGjkL8cqpBBhiQ/RKb7ppL7GDq2QStzgQuyU2DASGmYCQ0RYGIkIK6j9BOocDfq9VQECpI7bH48j6vNy3Q4A3DCLJdEBOJvTwcJMZGQSTuGd7yKOHWA9nOKguyGRCBH0phaappHC0ShIwZEJRuKDJTxERUcTmlWWvAhbFRtWhEYmIkNEXMRsChBfndAg30MVH+Rxkc6HKjoA625Ho4gOwHpVUlpsZGNel2z+BgTcDat1ZSQSiTGt89dTFbG3X67BOGmtDgJDeIgWTGJhFlYhh8VagXwC5LgbZrO1ktgVG0ZCgyUyzMSFkbCo9RnfCEOx9PwFsj8j8RH2+9JEBwBLbkejiQ4g/Zx0qiw6+XvIxBHJpJCdAFJISBwn6JV1YChaz6/Mrsgw6oMlQFjCw0h0NAOXA0DaJGYkZmLDitDgORm00DBzLMzEhZV9VCHCEx880QHA0O0QFh0Af/QKYFwczIroILEiDho7HGiWCC04CSGJdDckEudp+b+g6kj2xj2rAoQnPOyIDub7MFwROxVHWRdmAXfDSbFhRWiw3Aye0GCJhZjb/untS9aHTci+SfFhJjp4sESHIUZDZgHziqR2RYcdGkJ82Jn52AARIcFqk8nIJ0krwO9ln6tJ44TOlkzLn57eCjXx9EWEqgjbQaFHuogMOXR6em/WCe+QzZep2Aj7fdpCEvF6dWIj5vVqC5ASAPQCpAQGuWjv6/EyFyN4fZHigyd+yM9DfmaW06N+h+oNLUq91k17D9T/7UjxSE5xD7CntwfqRavajs67UBcrGLUXcejIc5EMDZL7Gp2vVpNFbbobNI09D5Ck9bF06VL06NEDwWAQpaWl2LZtG7dtLBbDww8/jNNOOw3BYBB9+/bFhg0b0todOnQIN998M9q3b49QKIQ+ffpg+/btWfsMrVdwiIoLK+KDJTxq43rhQYoOMk7OEhqsoblOCxIVi+6GFbER9XqZYoNEFRksoaG9JyEutDaUKBARFhGDtrz9zEQH7caYiQ5mjotV0cFaxxMdKizRQYuGbIgP0qHLxAVpIu6GRGJKGz9/sci6detQVlaGOXPmYOfOnejbty9GjhyJb7/9ltl+5syZePrpp/Hkk0/is88+w+23344f/ehH+Pjjj7U2J06cwNChQ+Hz+fDmm2/is88+w69+9Su0bdvW9kc2w6WYDdhtplRUVKCgoADluT7kqyEVUcfCjDYCFyA6zEI/7ZHhFe0mwMj0V/djXbDp4l+sp0L1As16IjYJp9gRGyKuBolI2IQlMnTvYSAqRAgkjc8Lf6J+OxlqUcMrPmLUCjmCBdAPm1UTSf0xor+67WpoRZ3e3k+EWvzReH0uh5q3odbiqOGsV8VqdZRYxxC7pKjNVMzqzl/q++cJDqsOh9FwWAdmPRbJ3VDP/8qqCM6+6EmUl5c3+pwYkqaDdu956gfIZ4jritoYCqa8aum8KS0txaBBg/DUU08BAJLJJEpKSnDXXXdh2rRpae27dOmCBx98EJMnT9bWXXPNNQiFQnj++ecBANOmTcPf//53vPfee3Y+pi1avsNhNTziVN+02yFS5dTI5XBq1IAJZk9zdsUGL3Si68vE0eA5GUBKYJALAEQ8PtOFtS/Zh/pe5DGQx6ceN+8zsbASWtEwC63wnA7dOsK1IJ0O2u2w40DYcTecDqdYxI67IUMpEmECvtT5Sy8Wz6FoNIodO3bopo53u90YMWIEtm7dytwnEomkzfwaCoXw/vvva69fffVVDBw4ENdddx2KiorQv39/rFy50tKxWaXJCo4tW7Zg9OjR6NKlC1wuF15++eXGPiRjjISHikhoRWubpdAJ6wLOevIzSBRVsSI2SKyGTlhCQ+vLQGCIILqPac4HR3TwQivafk6EVniiA9CHVng5HUC6I2FFeKSFZLIUgrASTrERehGdiFAicYKKigrdEomwR1IeO3YMiUQCnTp10q3v1KmTNgcKzciRI7F48WJ88cUXSCaTePvtt/HSSy/hm2++0drs27cPy5YtwxlnnIG33noLd9xxB+6++26sXr3auQ9J0WQFR3V1Nfr27YulS5c29qGwMcr5oBERHdp2B4QGK2RCw9jGrLTICaXYERtOCQ2ekwEAEZfH0kJDig5eWIaXz2EFI5eD2d5KEqkKnc9BtidFB8vt0PrwmS+s91ThDa/NdrIojcDMx2ZId0NiiYCnfsZY3ZK67pSUlKCgoEBbFixY4NhbP/HEEzjjjDPQq1cv+P1+TJkyBePHj4fbXX/LTyaTOP/88zF//nz0798fkyZNwsSJE7F8+XLHjoOmyUr4UaNGYdSoUZl31MYL1Do8yZJZeKYmbpznwaqx0Vgzb5q4GyoiYsMoX8OojoZRjgbtZmj/p9wIWjzE3F5dvgUPdb+Akvk5whsqa7pf3TBZALoJ38jaHDrI+hw0rIne1OGyAHvILKAvc06em6ITHrJcDaNqpFbJxN2wMTJFRJCYzXgskRhx8OBBXQ5HIMAuLNmhQwd4PB4cOXJEt/7IkSMoLi5m7tOxY0e8/PLLCIfD+O6779ClSxdMmzYNPXv21Np07txZmz1W5eyzz8aLL75o9yOZ0mQdDqtEIpE0i0qINl79ki2cKDxmlzaErc7BzN1QEREbdL4Gz9Ww6mgAfDeDNYyVHtJqBClYRFwOK1gZJkvCDa2o2MnnYA2ZZbkdKqrrQS/0NhojsdHQ7gaFSKIojXQ3JJYJ+fkLgPz8fN3CExx+vx8DBgzAxo0btXXJZBIbN27EBRdcYHgIwWAQXbt2RTwex4svvogf/vCH2rahQ4di9+7duvZ79uxB9+7d7X5iU1qM4FiwYIHOniopKanfSIsKI4GRTdFhlVpqxIFTiaMGT4usm5t6sRUVGyqsEIrWJ0doABAWGuT+Wr8ur7bQ/bOWpgL53bJubhnnc/BEBx1iAeqFh5HjxhMZTNGS4c06y+4GjXQ3JE2NsrIyrFy5EqtXr8Z//vMf3HHHHaiursb48eMBALfeeiumT5+utf/www/x0ksvYd++fXjvvfdwxRVXIJlM4he/+IXW5t5778U//vEPzJ8/H3v37sWaNWuwYsUK3cgWp2k6V9wMmT59OsrKyrTXFRUVKdGRE6ifnl7UZWjjzc6oFlEaavbYuosxd2QEzJ/sjMSGitXwCS90whMZWt8uL/d1SGH/PcnQS8TlMQytRD1e3RBZJ1DnWuFuV8ucsyDnW+FVIqXDK4C+IimgD7EA+jwikTlWDIUJdf60AHdDig2JCFGfl+0cx5KW+7r++utx9OhRzJ49G4cPH0a/fv2wYcMGLZH0wIEDuvyMcDiMmTNnYt++fcjNzcX3v/99/OEPf0BhYaHWZtCgQVi/fj2mT5+Ohx9+GKeeeiqWLFmCsWPHWv+wgrQYwREIBLiWlAZv8jWWECGdDqviw8wlaUJzpLBQfyRmeRuZiA2zPI1MhEaMOK19iKe14YkPkojHh0CCn8ArmiOiwpvcTdcnI5cDMJhrBbAmOgDjvA5tO0N4qIjkGfHEsojYoDFyNxyoKkojh8FKmipTpkzBlClTmNs2bdqkez1s2DB89tlnpn1eddVVuOqqq5w4PCGa9p3PCdoFAY+bX1a8Nm4uRJwIsxjNNEujFf2i7HArSaV0wS965ArH3bCSt6HCEhtOuBpGYRMWMcbprK5ThYe6v4joyBTRxFHS5eCJDq0tS3SomIkOQNztANKFg9kIKhGhARjPbtyAdTdE3A0a6W5IRIkGfOxCcvEWWWtTiCYrOKqqqrB3717t9f79+7Fr1y60a9cO3bp1E++oQw7gJVJVamN6W7gNdUKQwoSVra8+HYrMHNuQqMfq8DwpZnkbVsSGHVdD3ceKyIgita8f/PAIKTqsuhVGkCNVjOC5HIDxqBWAITrIUStmE72R2wBjtwOobwfYC/OZiQ2jUArpWGQ4Z4os8iWRND5NVnBs374dl1xyifZazc8YN24cVq1aJd5R2yDgI4ZM1lK1A+inNnJ4IAktTNQLNU2m4RIn3A0ai+4GM2kxC2LDyNVgORoscaE7RnjSXquiIwavzuVwklqfTytzboTRTLJmuRwAMhcd9DaA7XYAesdD9yFMPqeR4LUrNszeI0sTtNHQ7kbMZJSRpHUT9Xs4ORwOl2loRjTZX8zw4cPhyDQvXQsAsh9VYFRF66fpprexYtjkOp4oITFyTuhtgLig4M2jQsKaP0XbXy82jGBWwbQoNpxwNUihQYsKMxpKdNjBqssBZCg6AH6IBWALD6BefKjtrcI6R63kbcgS5hJJi6DJCg7H6FpXWIV0I6qjxvFpTUyE0l0MtVgSWTRJZLgqS2QAbKHBczfMLG2jmwHniZHnbhgliTotNli5GrTYEBUaZDtVaDRl0UFCuxy2RQeQnkgKGIdY1O2AXngA6YKh2uB8N8rPIPtWMXMrGmn6eRrmbMiR1vukKjEn7PfBxzjXwjZGqbQUWr7g6FSQqjZSE61/qqMveuTMmgDQto3xDJvaBZkKf9gtSy4y8RWrcBJrllgVVrEvC4miKqwRKSpOig0RV8Oqu9EUoBNH6bAK7XI4IjqA9NErAN/tAIyFB6AX3maiggXrHHVabDDIZDZYiUTiLC2m8BePaMc8oLgA6Fy3tG1T//+CUGopzkstpxQAHdqkllMK6pbC1D4hH9Axh1rapJZ2obqlTWoRnXcC0OdskG14lrOZpc26CAsWTqLdDaPhr6xZUY3EBl3ECzAXG1F4TMVGWPHpFhJyn+YkVuiRQLyiYOSNkhSPzOJgZCEwskhYG4M2OrHqNa4symvHa5+p2GC+d+aJoiyY7oZEYkLM7+Mudli6dCl69OiBYDCI0tJSbNu2zbD9kiVLcNZZZyEUCqGkpAT33nsvwuFwRn1mSov/5VTlBeHOCcAfjcMfidU/AdZGU2IDqH8CBOqf7tQkuyAx7wkdXlEdEdrhUPNDWNBtQ9RF3WgfEtrdIC/IBqXMzRJFabFBwhIbZJlywDg51ExoAOkhFPVfWkxYxWjESmNjlMuhYsXp0K1n5XUA+twNnuOhtlPRjVixeelgCQc7YiNLoRTpbkiaIuvWrUNZWRmWL1+O0tJSLFmyBCNHjsTu3btRVFSU1n7NmjWYNm0annnmGQwZMgR79uzBbbfdBpfLhcWLF9vq0wlavOA43jYf0dwA/LE4fNEYQqroyEuJDXJirDQxol50w7HUxThI53NQQoQUIGb5HeS+VmfY5IkN8iLMGZkCiIVStLZU3ka2xAZPaADWxEZY8SHoshbaYg2JNSr6lSms0SpmoRUgQ9EB1IdYAHYIhSU81LaA2IgVK0mlLOfNIbGRLXdDIhEl5vMyH9xiPusPP4sXL8bEiRO1UubLly/H66+/jmeeeQbTpk1La//BBx9g6NChuOmmmwAAPXr0wI033ogPP/zQdp9O0OIFR0UoiNr8XATicQSjMVTH49qF3BeNacIjRAkP9aLtj8b1AoS8yJIuCKCvaUC7HwC/ciNrplheSWgzsWFjZIpZKKWxxAZLaEST7NCI3238IyYTR5sLVkUHAK0iKQAtr0Ndb0t4AGzxAdiv+SIiNHj928jbAJxzN2Q4ReIU9ASjvGrZ0WgUO3bs0M2V4na7MWLECGzdupXZ95AhQ/D8889j27ZtGDx4MPbt24c33ngDt9xyi+0+naDF/3qqg0G4Q3WiIhaDLx7XkvjUp0x/PI7qnDrHgxAjIUJ0pIVjgPQLNemCsNwPqwWVeEWTzMQGw/GwGkppDLHBczV4IqOhsTuPCqviKM/lAGDZ6QAg7nYA6WEWQC88AH1yKcAWH9oHJI7PykRrKpmIDYG8DZFEURbS3ZBkQtTjYSbbRz2p34tuglEAc+bMwdy5c9PaHzt2DIlEQps3RaVTp074/PPPme9900034dixY7jwwguhKAri8Thuv/12zJgxw3afTtDiBcd3oVx4ArkAgIAvDn8iDl8yjlAshqpgEAC0ypA6IRIKam6I6oSo4Rg/+dQI6AUIKT7oibNYmf5WhhiyCiUJlDBvCLFhdySKkathV2jYCavwCHAqkJJhGJGiX1awGl4BMnA7gHTHA+C7Hio1xHnrlMgg35eE17+NuVIAttiQ7oakoTl48CDy8/O116ZzgVlg06ZNmD9/Pn7729+itLQUe/fuxdSpU/HII49g1qxZjr2PVVr8L6jSF4TbG9ReB5QEAokYAr7UhdWfiAN1f2f1JsJyQvzxuHZj1uWCAPCzEvRo8QHUux908h0vwVTdTkJekAXnSwHsiw0Sp8SGSAiFJTYiin5dwOVsiMRopthsYFh5VFB0ABB2OwCG8ADYoRYg3fUA+AIEqD/P7RTq4oVnLIgNu6EUFtLdkGSb/Px8neDg0aFDB3g8Hhw5ckS3/siRIyguLmbuM2vWLNxyyy346U9/CgDo06cPqqurMWnSJDz44IO2+nSCFi84jnlzEPQEdQWfQp64Ji7Um4yaJBhIxlM3xwA0J4S0xIPRGPw+r158EBd2rvgA0gUILxnPCNb8EjZLlzvpbIiUKbcrNmihwSKa9BjmcVjJ38hmwqgVWKIDgJDwYLkdgAXhAaS7HoBx4mgjCQ3A2VCKKFFP0wj1SZomYb8PXoZwDfutPdj4/X4MGDAAGzduxJgxYwAAyWQSGzdu5M4eW1NTo5uuHgA8deeroii2+nSCFi84ahBALdrUvfLDjwSqXQH4PQldxUlVhASUBOBL3XRU8eHz1TsfVsUHwAm9AOwEVCN4GfwmQoNc1xhiQzQ5VMTVoLdZdTk00Wlxtlgyf8Opid4AY5cDYA+ZJa19oyJhACwJD207L9yi4kTiKA+j8EyWxIadcErq99I0K9ZKWh5lZWUYN24cBg4ciMGDB2PJkiWorq7WRpjceuut6Nq1KxYsWAAAGD16NBYvXoz+/ftrIZVZs2Zh9OjRmvAw6zMbtHjB8V2iDfyJoHbjUm9QfndCi/P7kUDM5YXPU38zirg8qfCLW+98+HxxLWavig8t+bROfKgX/RBxkfdHYgDtfhgl7NGYxbYFqohaFRtGFUR5yaFASmxYHYlCig2eyAgnvAh6nL3IG4kHXv6GHYymqle/d6PwCgBmrQ5WZVLAOMwCsIUHa3ua+ACMBYgdzHJADHI1nHQsVGQ4ReIEUa+XWSCRlUhqxvXXX4+jR49i9uzZOHz4MPr164cNGzZoSZ8HDhzQORozZ86Ey+XCzJkzcejQIXTs2BGjR4/GvHnzhPvMBi7FkRnSmh4VFRUoKCjAdd9uhJKrj5Op1nvQE0fAlUgTH36k3I+Qog+9qK6H+rSrhlzIpFNy5IuWcAroRrwAnPofQP1FXQQyR0Ng5leR+VFoscGa8dXpYa+q2DASGiSk6CAdDu3vSiSMqqEU9V/a4eCF1gC94OA5HKyEUd709DzBQWLkdpDwCoXR4RYf1R85/Bugzj11nUAbS+epXWwIDV7OhhV3Q6SyaNTrRVVlGBf0XYDy8nKhWLykdaDee9764mHk5AXTtldXhjHyjNmt8rxp8Q7HiUgAvoAP4VjqowZ9ccTcbvg8SS3uH/TEEXWl/h9GaoSDHx5LrgdQH3IB6sWHv+5iRT95ku6H7imTfsJkYGQhiwoNQFxsZLPGBk9s0CLDDmZiwwgRscGCJzZEMXM7VHiuB53nIep4AOauB9kmTQw4KUAayNEQERvMY7DxhCppfcS8Xp1LTK5vrbT4T14R9iPpqqux4U0gEvcj4E0i6IvD704glnDrhEfAlUgViapzPfx1N0hVfKjCA4AmPtSbExlyidXZaWk1P+oscPpGALBFCKB/4jR7gmPN9gqICQ2gYWts0GIjE5FhVviLBe1uZBujsAqNVeEBpNfvANKFB5A658ibLU98sM5BpvgA7A1RVUWKSFlyE6FhNBolE5Eih8JKJM7R4n9NxysD8NTd4PxeDwL+BKJxDyJxtyY8okkPYm63bqQD6XoEXbG6SpUenfCIwVvveiRiiLi99aNcAGa+B5kk6I/HdTcGNf+DHGUAiF0wzeZCYU3Aph4XIJavAWQmNlgjUZwQGyRkaIz8V2RKenKkkoqRu+F0/Q0W9PBkswRTwFh4AHzXAzDP9QDY4oNsK4ygSMmG2JBzpkiyTdjrhduXfp6Fva032bjFC46qah/cLh8C/iSiMQ+icTf83iQiUQ+i/oQmPMLwWg63AOC6HgAMxQegLzSm3SyoEAwJHY/nteNNKS8qNAD7w14BY7HBCqGQYkPdLuJY2KnDwcvdsNyPhdwNFVF3wwgRAcJyPVgjW1guGy0+jEIuAOW+MW7ulkWIQV9pbUxEg1WxIZNFJZLs0vIFR5UPfp8H4YgHwUACkagbAX8Sfl+iTnykhIdIuAWAzvUAwM31AIzFBwCmAFEdEKOnVTPM3AxAXGgAfFeDbG9HbNCuhkhlUdYoFTpZVDRRlMSqu2EHKyEVUcwEiJH44IVbVKzke2jrqYTTbIwiESnmlY33BWT+hkScmNurC0mT61srbvMmzZuqKg+qK72IRd2orPQhHPGgvNKHymofojEPqmp9qKzxobLGj2jcg8qwD+W1flREAogmPaiO+VAd86EiFkA44UV5PIDKhB8ViSDCig8VShBV8KMaAVQjgApXEBWeIGJuL6o8AVR4g6nFF0KlL4hKXxDVvtT2ikAotYRSS1UwiKpgEOVtQihvE0LY70NFm5BuiXq9hktF3X7qEqnLJSEdDTJPgwyfsFwNoxCKWmMjU7ERTXoslzGn3Q07oRTWyBQVq6GUTJNFnYL829Oo54j2um42S1LIxvw+bVGpDfi0RdvX79UW3XsEfLrFKaz0aSQ2rLgbMn9D0pRYunQpevTogWAwiNLSUmzbts2w/cmTJzF58mR07twZgUAAZ555Jt544w1m24ULF8LlcuGee+7JwpHX0+J/UYFaNyJtXIhEPQj4FUQjbvgDScSiboT9SZ3rEY2n9JfqekTqXquuR6xunDPP9QDADbmo+R5AuvMR9XjhT8QRC3jhS8Y1EaDe2MibGWtctxl0VjQrIRQwDp8Azo1EsZuvYeRu6NYxqoqKhFJE6m5kIjacdjfM4LkfVkMugFi+h7Y/EUbhCQTaCaGxI1bMXA2ZtyFpSKIeL7ye9HMyylhnxrp161BWVobly5ejtLQUS5YswciRI7F7924UFRWlv0c0issuuwxFRUX4y1/+gq5du+Krr75CYWFhWtuPPvoITz/9NM477zzLx2WVFi84/BEXAie9iAYUoBYIhxI68RGLuuHzJ+tCLm4q1yM9yRSoH1pL5huwRrgAEBIfAHQCBIAmQID6EAyJlYTFWipxiTXyRIVXyIve1ymxYWeCNrJ4G5Dubqg0RCilIZ0NEbFpJGpYI1/MQndWxQdgMJKFwEn3g35PFkZiQ+ZuSJo6ixcvxsSJE7UqoMuXL8frr7+OZ555BtOmTUtr/8wzz+D48eP44IMP4Ku7/vfo0SOtXVVVFcaOHYuVK1fi0UcfzepnAFqB4Mg74UXU7YY/DESDSfgjLPGRci5EXA861wMAd4QLQFc01YsPAFpxsYg3NdpFvdHTDoiKejPUxAjjhmgWI+SJDO21Qa4GkHmNDRGRQc+NorobZmJDJG+DNX+OSmOPSrHjYInsTwoR0vkwcj0AsVEuQGbiww7ZytGQSJwi4vbCw7gW09dbM6LRKHbs2IHp06dr69xuN0aMGIGtW7cy93n11VdxwQUXYPLkyXjllVfQsWNH3HTTTXjggQe00uYAMHnyZFx55ZUYMWKEFBxO4Iu6EDjhQSygIFjjRrhN0lR8xKIpkZESH3VhlTrx4ffW3Vw5I1wACIsPVkl1AGwBUndzJE9WWoyIwjrhzRwNIF1oANkJo9BhEqvlzBtCbFhxNkRCKZkKDav9q8ckKj5EXQ9ATHxofTFECDnLLb3eKmZhFOluSBqLiooK3etAIMCcov7YsWNIJBJpJcc7deqEzz//nNn3vn378M4772Ds2LF44403sHfvXtx5552IxWKYM2cOAGDt2rXYuXMnPvroI4c+kTktXnD4w2544EYgDESCSfgi9eIDAMJtUmESf8QFAIgGFFSGEoYhFwC6ES4AmCEXcngtYCw+ADDdD6BegJCoYsTOzKakuNCtNxEaAN/VAMRKlVsNobDKmJu5GzzsiA27ORsNla9B5udYEUH00GzAOOQCWB9iC/DFh9Y/r0S5Aw5GJjkbMmFUkilRjxcexrU26kn9DkpKSnTr58yZg7lz5zry3slkEkVFRVixYgU8Hg8GDBiAQ4cO4fHHH8ecOXNw8OBBTJ06FW+//TaCwfTy69mixf+q8r9zI9bOVfeqzq2oEx8AdAJEdT/8EVfK+QDqxIc+5IL/397ZR0lRnfn/22813cwb8iYvIoLkHPAVYYAgvuARF103Wfa4aFxWkHAwUVBgXJeXKGiMzrqyhgQNiGs0iXF1sy6Gg8rG3/iCGiMIIUcSRYxRCJ4BFJhmZvq9+/dHz62+deveqlvV1S8zcz/n1GGm6lb17Z6m7re+z3OfCxTyPZiQCwBDUTEAUuIDAN/9QBoxxsUgeSA8IUIjqjFBCwsaNhTDExqAvasB8OtsuMnXIBQbSimX2HAjNLxIBBbt4yFKQrZyPQC5wmIi1wOwFx9eohJEFdXOoUOHDGup8NwNABg0aBACgQCOHDli2H/kyBEMHTqUe86wYcMQCoUM4ZPx48ejra1ND9EcPXoUEydO1I9nMhns2LEDjz76KBKJhOFcr+j1giOU8CHylR/JcA7hTh/itXkhocW6B81IDjXxQlsASNX4Dc6HlfhgQy6AeJYLAGHYhU44JQXGgIL7kf85354VIqK1QYgo4R4TxBFpkUGuQaDdjFKJjVLkbXghNspR1EsGL9ZhYK9B3pus+JCp7QHIiw/AOwHiRGiocIqilCR8Afg5D3fkga+hoUFq8TZN0zBp0iS0trZi9uzZAPIORmtrK5YsWcI9Z/r06Xj22WeRzWb1VWQ//vhjDBs2DJqm4corr8QHH3xgOGfBggUYN26cKc/DS3q94NBiPgQzvsLv8e7QSTgvIsKd+d/jtTlosYAuQEIJn8H5AAriw22+BwBTOXXALD4AmKqbAoVBXkPG8DMRIDxigSBXkLDCgibFfC14QgOQrx4qSznFhlf5GuWc6upWbMRCIcuEV3JdN84H63rwhAcgDrnofeQIBVkR4sbNUGJD0ZNobm7G/Pnz0dTUhClTpmD9+vXo7OzUZ63MmzcPI0aMQEtLCwDg1ltvxaOPPoqlS5fi9ttvx4EDB/Dggw/ijjvuAADU19fjvPPOM7xGbW0tBg4caNrvJb1ecABAuMOHcIcP8bqcvk+LdQuPSLfjEfcZXBAiPgCz+HCb7wHIi484ggbxofebI0KIABHlMOi5IRxRwooLAi0s2N+drIlCOxupTKHOXCiQ5b4u4E5sWNXZEIkNt0Kj3PU0AHuxwU59ljnOvl9RPohMvkcyGLSd3QJYux6mPquwiKIHkxZUGk27qDR6ww034NixY1izZg3a2towYcIEbN++XU8kPXjwoO5kAPn8kP/7v//D8uXLccEFF2DEiBFYunQpVqxY4f4NeYAvl8vl7Jv1PKLRKBobG3HVN79CbbrRdJyICQItRsixZDinh2DIvkQ4i1RNTnc9kuGsHnKJd4sPANBqsgjp+R4ZXXxooQy07uTSmu5ptoAx7EIGWDIos+6HaCAmWCVP8o6x4kK030poAPw1UQhEbIiEhux7dCI26IRaK7FRaaFhl8NhJTbshIYTRC4I+3mwnwNbTl0TfE6s+DC8ho348BKpJegtkkaJq9NxKo5pF7agvb1dyhpX9A3I2PPQyZ8h3NDPdDwe7cKK/vP75Pemqh2Oxx57DA8//DDa2tpw4YUXYsOGDZgyZYqraxFHg/6dFh3078QN0WI+aPEc1/lg8z3oabZ0yEVU1ZRey4UuLgYYnQ+ypguddErcj0QmYMj9INAOCIvICeGJDlpg6O1c5GqQ/otwIzaKydcgbSstNOjru0kclREbvCcsUV4PfT36s2FDLqzjwXM7CDKuB2AfclEoehoxXxA5Tug6bhHO7u1U7Tt3WsrVCi3mgxbjHSmIkGQkZxIl9PnJSI4Ju/iYfI/CNFs90TQG25ALqWoKgFvZlNT4kBUf+vthRIjgbZtgRQZPQMi6GoAxjMLiNGej2HwNkavBExoyIoO3UitvDZNSYCU27Aq/yYgQtrw+ICc8RIvHVYvwkM3d0FJpocuhpdNqETeFwgVS/2sef/xxfOc73yl1Xww4LeUqgoiIcAdvlKXDKnwHhPxLux60+ACgi49CnQ951wMAp6R6FomkubKprPjQEZkKNkv22YkMgp2rQeC5G16KDSchFCuhYScyeAJD1MYr4eEkUbSYVSjpc2nxIRIebI6HzJRaNtTCq+ehv4aDXA9ZVKKoopykEESAM8SK8ub6AlLv/Ne//jVaW1vxn//5n2WJObkp5ZpIJJBIJPTf6SpuWgzQunhn0SKD5GoU9hCxYic+ABhcD8AoPAy1PRjXw0nIhSc+CLT4AMAXIMgP6rJTVHkiA3DmahQjNtzka7hxNayEhozIkDnPS+eD527IiA1eiXwe5FpWwsPO7QDMjgfP7dCPhYLCHA9WKLgRIF6LDS2dhpaxLjSnUCiMSAmOl19+GRs3bsQll1yCJ598EpMnTy5pp9yUcm1pacF9991n2q/FfQJ3oyBCkv1oB4SfQ5sXF+bcj8KxguthDrcUZrjwXY/89UjIBYAuPAAYXI8ajb7JBQ3Cg11/hIvD6dWi6a2ipFAaGbHhZXKoU1dDJDTcigwrwsmUpehwm8chA6/8PbuPJ0C8EB6yC8YB1qLD0C+H7odbsWEVVlEo7EghAD/nhptyehPuRUj/b7r11ltxxRVX4NJLL8XIkSORy+Xg8/mwZ8+eUvZPmlWrVqG5uVn/PRqN6qVjtS5+kmgBcWjFSE7YhpdoSieZsuEWK9dDlOuhhbormSb5rgcAgLm3sgmbUqLEBhmxwcON2JAJoXghNNyIDENegoRYsBMdMsi6G07X2LESICLhIRtmAcT5HaIwi4zwAEofJlGiQ6HwDrmRAsDbb7+Nf/iHf8DixYuxZcsWvPjii9iyZUtJOuWmlGtNTY1euY2u4BbiJosaySeVdm9dEG6knocWK5xTd9yv/1w47kPdCT8avvIj3OlDw5d+1J8IoCbuR117APUnAgh3+dFwPIi69gC0hA8NJ4PwtQeRSPqQTPjReSqYD7UkAmg/FUIi6UcyFUBHLIRk2o9TXaF8nkc6oCeZJtJ+xFNBxFNBJLMBfUtl/IaNPkY2Hrx2pC19PRmciA2y0WIjlE2bxEZNNo2abBpaJr+RNpFUSh8MQ+k0Quk0aqgNyA/+9Gbqbzptu/Ha2+G1e+KF2OCRDARN10kxdQVioZBBAKWCQUPOCevY8MSWSKipQV7R00nCjyQCnE162DXw2GOP4ayzzkI4HMbUqVOxc+dOqfOee+45+Hw+vUopoaOjA0uWLMEZZ5yBSCSCc845B5s2bXLVN1mk/lffdddd2Lp1K55++mlMmzatpB0C3JVytbyeYPYJIdyR/zdeZ91Wi5F6HD6EO+jaHdZTbs34UcjopL98WQABffVawK/neQDodjxk3QnqT0vd50myqX6o2/2wy+uQFRbsdclrEuicElG+hheuBnnatqsZAYjrRrhBr7hp4Xh44XQUC7vqsIhkIGgKt6T8QWm3w63TAciHWEqNcjkUlcbtrM3PPvsM//Iv/4JLL73UdKy5uRmvvfYannnmGZx11ln4zW9+g9tuuw3Dhw/HN7/5zZK8D6n/RceOHcPu3btRV1dXkk7wsCvlKgspXQ7YCwEiPHjE6wrXMIdWcvoxOunUGjKAF1awBQAtni8mJis86FALj5pgFvGUnPhwAi1Q2BCNqOYGnSBqJTascjXcCA0nIkNmgJMZfOymTvJEhxd5HFbuRkKQWGonPsg12TBLMaID8Davo9Qo0aFwSjwXQo5TyyjB2WeHm1mbmUwGc+fOxX333Ye33noLJ0+eNBz/7W9/i/nz52PGjBkAgFtuuQWPP/44du7cWVnB8fTTT5fkxa2wK+UqC6/+hp3joXXlE0lp+C5It0jo8lHtxXke/P4FmJktZuER1wt1icWBnfAowBcfpUCmoJeV2OC5GlazT1ixQQ9oPIHhdiCjz7MahGTcjnIhEhuidiLhYZXb4UR0AHy3g4fTvI5SoUSHwkvomZRAPi2At2Ksm1mbAPD9738fQ4YMwcKFC/HWW2+Zjl988cXYunUrvv3tb2P48OF444038PHHH+OHP/xhEe/Kmqr+37NkyRJXIRQn8FwNVmwQd8N8XmE9FiJSjLNdLMIzVB2PPAXHgxtmAbqFhztHAkB3gmmhmintfNBTbItBphy7qL4GydUA8q6GVfiElxDKCg0vRYYIcj074cETHTKhlVA6LV2Lg+duyAoN0Xms8LBzO6wWipOdOityn6rB7ZD5eysUAJDKBuDjlQ3o3kcmNRDWrl2Le++919TezazNt99+G08++ST27t0r7N+GDRtwyy234IwzzkAwGITf78cTTzyByy67zOaduadP/K+xCqPwxIQM8Tp053FQ02JN02ytRQcAvXIp6WdNnJffQX6Xczt4kFktgFl4ADCGXQTYiRKe2JAt5kXEhoyrYedomBI6OYOU22JSolkRdgORbHXKUk6PdYOV8JARHezsFYDvdhiuXeWiA8j/vUNV0A9Fz+XQoUOGulY8d8MNp06dwk033YQnnngCgwYNErbbsGEDfve732Hr1q0YNWoUduzYgcWLF2P48OGYOXOmJ31hqZ47W4lIRmCoPaF1WQsQwBxyEU2npcUKvfqs0e0ohFhEOSS06Mjj755G64Os20Gm0QIwTKMlZdMJIuGhHw+KypPmRQkrOqwWmmNzNQDxlFfeDBQA+uwTwJnQYAclr6pV0tfhiY9qst2FORsBvmiiK7XyrmUnOmjsRAeNk0RSoHpEh0JRDPRsSiucztr885//jM8++wzf+MY39H3ZbP7+HAwGsX//fgwfPhyrV6/Gli1bcO211wIALrjgAuzduxfr1q1TgsMtpwblEArm9FwOIhKsioHZCRIZaNGRr88BvXCYFfnaHXmhIRIdyZocwrG820Fqd1j2xUZ4ANBXrWWdD8vr+jPc2Sg8sWFXyIsNofBcDTuhQQ9CPIERSbgXHbyl0slrsMJDJDp4Lkc1zFgh0EKEJz7sRIdVIqlXdTr0Y0p0KKqcRC6AHKdac1JQwVmE01mb48aNwwcffGDYd/fdd+PUqVP40Y9+hJEjRyIejyOVShmWtAeAQCCgi5NS0OsFRzKcQy6U02ePGJyIbhFCiw86f4NXDl1cPEwsVHiig10sjlQqNVIIqRSSSfMhF57oYGexkGqlyVS3o6GXS89XLQWgVy6lXQ8A+uJxhvchCKmI8jUAa7HB5mvYuRp2QoMVGSKBoSW7wx+a/NefXEskPHiiAzCHWGRCK1ZhlUgqVdSS9CJ3g9dOVnTQsKLDcEyJDoXCFXazNufNm4cRI0agpaUF4XAY5513nuH8/v37A4C+X9M0XH755bjrrrsQiUQwatQovPnmm/j5z3+ORx55pGTvo9cLjlODsghqWV1UEFHAK1VuntFidiNoEeLECRGJDnKMRpzX4d7p4IVZRMKDiA5D/23yN0TJoaKF13ghFF5SKM/VsBIarMgg4oKH1TECK0pEwsOp20FTTS4HjazokM3nqDachtm8WNNF0XdIZILIcpaGSAmWi7DCbtbmwYMHTW6FHc899xxWrVqFuXPn4vjx4xg1ahQeeOABfPe733XcP1l8uVyu+PhBFRKNRtHY2IhJtx5FJNeo72ddCV5ohW4jXtpeHJbhUSgSlhcctFghP8frcvrPpDQ6fTwRzjsd8X7dxbrCedEB5Ot1kDVZiNMR0lej7R7Yu3+np89qlItBRActOIjLwcvdoPM22ARRGbHBczXcCA1aZPBEhGYRSklyHAtuO4EbwnM8eLkdrOhgXQ5WcLAOBz1TxVDdk8rTYGepsDkcsu4GC090sC4Hm8vBuhxOVul1WpzNicvhpUg41ZHA+Es3oL29vSyLWip6BmTsmXO0FaGGWtPxVLQTvxpyZZ/83vR6hyM6MItULltwDKipqFpcXAiMXtaeFSAEOiwjC1sYjBYb/PYFsQFAz+nIr8dSCK8AQCLpM4VXaOgQCxEdJMSSP25co0UEXejLSmwQRPkaPFfDC6FhJTBM70VSjIjCMJFEiut22K3xwYZWWJejmmar8JwOJy5HtaAcCUU5SWb9yHGnxbovbdDTqY47WglJhrNIhzNIRgp/ZCIaWAeBPgbkq5QmI4Ul6sMd5twLc1lzi75IViE11ufgQ1afBQAt4QMQ6F591mdafZauTGonOlhILgeZoULPSmErjNI1NgBjMS9RCIV1NZwIDSuRIRMuoeE5GOSarPBwIzqqafaKG9yIDppSJpDKoMSGQlF5eu4dUJJT/TPwRzJIdVGDeKO4PRqJiwAkI/6C00GJD4ARJh38/XSCajEQdwOAnjyqv0bc373kfUF0ANBdDlnRIYIOqbBigy1VLqoc6sTVoBNCeUJDJDKEAiOW5O+PaMbPkTqfFRRaImXrdrgRHXYuh+FaVAEwOnE0lE3rYRUtk3a1cFvCZ3wKI8JQ6lyLJNJqdDkUinKRyvgBztIRbpeT6A30esHR2ZCBr18GHY3dA2HCnHdBQhL0MS3uz4uUbgFCiw/AuEaLjBBxSrw251qo0C6H4Zo2i7/xcjiAgthwmrMhEhs8V0NWaFiKDJG44MG2pQQIT1DIuB080VEMlQqrJHwBrugQJZHSWLkcNHa1OZxQidkqkUQKKeWaKBSO6PWCI9eQAWop+9aiLTmWSPoQjgW61zIxiw8AOHUaumeP5KGdkPw5vPCLNTyBIXI3SDiFJRwTuxxOqQlmDWLDrs4G62oAEIoNq/CJldCwFBkJh4NODfX1J9eREB5ORIdTl6PcsO4GvV9WdNhNlSXYlT0vdVjFDcXUblH0bZLZgCCHw1kdjt5Er/d2BgxMYOAg8VZbnzZt9fUZ5BrTiPbPbx2NGXw5LIXogDROnZbRt47Gwkb2RQdl89vA/NZxWn5LRnK2G2CcnSKCFhvJsCD3ImkvdEg4hc7fIO4GT2yEA2mD2Aj7UkKxUZPLoCEdR0MqhvpUHLWpuElshJMpXWxoqTRqO2MIJVOIJFK62NASqfyWTBfERixZ2BLpwqZ/QCnxZviQOOeS69KfEyNy7PJF2EGKzR+wehq3WsyMdgQMsz6qOGyRcrmWi5e4yd8g30GFolp47LHHcNZZZyEcDmPq1KnYuXOnsO3//u//oqmpCf3790dtbS0mTJiAX/ziF/rxVCqFFStW4Pzzz0dtbS2GDx+OefPm4Ysvvijpe6j83aDEDGhIIFBvjNfTVTfra5mnqFQAiaQfcS2AWuQTL1GfQTLhRwJAtNv9AIyDvdbtdpBckVC4kAcCmB0QEbxEVsCcu8G+Pj09FoAhpMKbHkuLDTaUIhIbgLt8DStXgw6fWDoaVk4GKySsoNuGqfBHIm12PBi3w4nT4QQrl8NpWIXO46jJpm0Xb6vJZTxxOWgqEVbxEiU0FF6QSAeR4axRlU47v088//zzaG5uxqZNmzB16lSsX78es2bNwv79+zFkyBBT+wEDBuB73/sexo0bB03TsG3bNixYsABDhgzBrFmz0NXVhT179uCee+7BhRdeiBMnTmDp0qX45je/iffff9/V+5Wh19fhuPQvv0Owvs5wjF0/hC7xTQphEVFCqnQmkn59rZJU0q8X2SJOAhEhJA9Eo8It4a78z6GEjxuG0fvBiAxeOIXnbiTpUIugHkexYkOUr2EnNACYXA02fELPOhEKDdbBoOlykLvRTzPvCzN5FzXMDYESHqyooEUHe4zN52CnytKhFau6HLTgcFqPQ6YWh0hwAOIEUlZwWNXlkK3JUUw9DivXSNbhcCI0tGQa0c4kRlzzRJ+sp6AQYzX2AED6VAfeGv11R9+bqVOnYvLkyXj00UcB5Eubjxw5ErfffjtWrlwpdY2JEyfi2muvxf333889vmvXLkyZMgWff/45zjzzTKlrOqX3OxyRGEL9jDdUNks42R1Ti6eCqA+nDIKEiJFEMmAQIUSAEBck2T3IR6lQBuuEaHE/TiFTECBh+7AHL2+D52wA7sQGW+RLJDbs8jWcuhqiPA0tmXYuMuxyN2gBQZ9HxAe5NhEerNtBYeVkFJNEKutyOJ2tIuNyWCHrcngxW0Umj8MpXosNp9OtFX2XRNqPTNqctZDm7LMimUxi9+7dWLVqlb7P7/dj5syZePfdd23Pz+VyeO2117B//3489NBDwnbt7e3w+Xx6GfRS0OsFx7BwB3ya+Q+coBbQiXeXmk2Guh2MbkHCEyJ5AZKyFSAADKGYBCcUQ7sggLG2BotV+ATIh1DI4sZaTVYYRiH5GrTYYKe+8sQGu6x8Qy7u2NWwCp8YHA0iIIoRGTR0W1Z80I5HPMUXHRbhFSehFSe1OcpZ7twqrNITELkbMmJDRmgokaEoBdFo1PB7TU0Nd4n6L7/8EplMRi9jTjj99NPx0UcfCa/f3t6OESNGIJFIIBAI4Cc/+Qmuuuoqbtt4PI4VK1bgxhtvLKlb1+sFx0BfF8IBIAnjDZX+Pd5tMxOBQcQIT4gkswFLAUKHYAAgrnWHWqhckCgThgHyoRiRc0FD52jQAoNAz0YJ12QM5cxFYkNUY4PN13DqarBTXR0LDTuB4SR3gxYS+TdeeA0XooOGFR00drNWDNcpg8tRk0k5KnHupCYHjWweRyVRYkNRSlLpALJps5DPdO8bOXKkYf/atWtx7733evb69fX12Lt3Lzo6OtDa2orm5maMGTMGM2bMMPYzlcL111+PXC6HjRs3evb6PHq94BiQ6UAkkx9o6Th3zEfFxbt/Tga6HYhuMUILkUQuAITyIsROgCSSAdRF8nkgZMCnBYjW/XOSSuyMCmaVsPU0amAtMArnGddNYcUGz9UA4KnYEIVPpIWGVUgFAGI2g0GE+npbhU3I69EhFhvRYZVE6iS00tddjkoljtqJDTuhQWZPKRRuOXTokMFN4LkbADBo0CAEAgEcOXLEsP/IkSMYOnSo8Pp+vx9jx44FAEyYMAEffvghWlpaDIKDiI3PP/8cr732WslzkXq94BiU7ES/ZGFQJk965CmPvskSQULESMoXzIuPbocknguhPpDkCpBUxg/UQBcgJA+EFSD1tSk9BIN66ImohlCMDWy4BCgIDEAsMgB++ATwXmgAMLkahoRQGaEhIzI6mYTRWo3flogPkZiwQlJ00DgJrRjOk6zLUQqXgxUdbt2NascLsaFQ2JFI+hFIchyO7vt8Q0OD1ACvaRomTZqE1tZWzJ49G0A+abS1tRVLliyR7k82m0UiUahERcTGgQMH8Prrr2PgwIHS13JLrxccA2MdqA0WbiCxUAgpfxD1qbgwm58WIyl/EDFfEBqC0LpnaogESDwTRC1SugChc0CMAiRrEiD6a0uKDp7AAIwiI/97xrTiq9tCXk5dDduEUFpo8ESGyMFgRYboGCs+7EQHHV6h21hglc9B44XL4WXlUSvR0RsQ5W8UIzaU0FBUiubmZsyfPx9NTU2YMmUK1q9fj87OTixYsAAAMG/ePIwYMQItLS0AgJaWFjQ1NeHss89GIpHAyy+/jF/84hd6yCSVSuEf//EfsWfPHmzbtg2ZTAZtbW0A8lNqNY0fPi6WXi84hp04CS2XXyI4roVQF4/rN23yhBgLhUAWEabFSMIfRCIQQsiXNrkfGoKo8yWNDkggZMgDiWeCSGYDqA2lLAUIyQEBoAsRJ7AiI78vo+dnAOC6GQCESaFuhQYAk6thCp9YCQ07B4Nglb9BhAI5lwiPYkSHZD4HTaVcDlmc5nP0dpTYUHhJMuNHgHMvz7hYS+WGG27AsWPHsGbNGrS1tWHChAnYvn27nkh68OBB+P2F63Z2duK2227DX//6V0QiEYwbNw7PPPMMbrjhBgDA4cOHsXXrVgD5cAvN66+/bsrz8IpeX4fjw7duR31djeHmngwF9Rs6eXpkax0QJ4QID8AchqFFSKpbuyURKOSA5EKF8AugCxAAJgECGOuDJDnJRiLYqa0Af7E1AI6EBmBfmlzkaljmaXRxXA4iNHgCw0pcxFJARDBo0g4F7XbQuR10Gzq8QieS8towgoMWFrK1Oaq1LocMvMJf5ajFwavDwZulwnM4rNwNN2IjfrILjXOeUXU4FAbI2DN2zx8QqKs3Hc90nMInEy/sk9+bXu9wDPrqFMK5LJJawdIOJVMFR6P7Rk5u9slgEHEtZHh65IVhaBFSh4Qh/FILIEVCMHb5H5QDQqadJKla+2RZeFlokUF+p0WGvs+iUijgXmgATPjEqdAQiYuYjegg0OKDdig6k/ZOhxMkZ62UwuXwsi5HJVwO0Xoqbih20TY3QoOcEy/qlRW9nVQqgEzK/OCY5ezrK/R6waFFu1AX8iOpBbk3l6RWuKnEakLQtJD+FMUTH0AhBMMLv9DiA4Ce/0HCL2QmDE+AANDzQADoYsTR++U4GWQ/62YA5lVdARSVFGqbp0ELDdrNMORuWLznDkGIpY6Iie5zifCQER2EIvI5ZEubV+OMld4WWnHqbvCwExsKhcI5vV5wIBoHAn5oNUFoEc00KLDLnRMBQosPNvxCW8Ak/EIECBuCCfnybdnwC5v/ARhFCIEuUGYHERcADAmgAExuBgBTjgYAR5VCHQkNIL+PdjR08cG5uYuEBcAXCx3Jgugg17QTHQQnM1dc1OaodpcDcCY67Jao7ymIxIMSGwovSCT98HMmAWQlJwb0Rnq/4DjZBeRy+afVRBqaxWBCCxIiPmI13WJCC6EuFjeFXoCCACHiQ/MXbuwAdPcDAFeAkBAMLUIAc7EymnjOODgQYQEUxAX9MysyAGPYBIDj4l22CaGA2NWIp4xCgycwrOps8Ka8yooO+hqscKGRcTlcTJO1KgZmtXy9rMtRjOgA3Od19AYciY2YhShWKBQmer/giKcBxApPpv00sT1OCRIiPsiNhnY+9MGBcT9q0mmD+ACgCxC7/A/AXAeECBEedT7xzY4WFwAMAgMA180A5BZZcyQ0gIKrwRMatMgQiQtReEWUKCqDncthh2T9jnJOk2XDfsI+SayzYuV2iNwNq4RRr2ATRotJFuUJCCU2FF6SSAbgS5gfGnOc2hx9hb4hOMLB/IAST1kPFv20wsDTLT607qfXpBZEsiZkcD5SWkgfHOi8D8AcfiHuBwCuA0IgIgTgFyWzgp0JwBMYALgiA4C7wl0yU1zZ8IlIaFjlbrDQ7gXtVHjhcsgWBONQ7cXAWKxEB2B0O3p7KEW6rRIbCoUrer/g6EwaBw8ywDh8mtViSW7IBShMdWQFSDIYNDyR8twPAHr+B2B8UuQNBNxiTRZTFMmTJi1GeCIDgLNS5FbJoIB51gntaohEhozgoJ0NkejwkiLrclSyGJhMaAXgC1/9mITIEK0SWyxOV4otJlmU524osaEohlTSD1/InK+Rc5nD8dhjj+Hhhx9GW1sbLrzwQmzYsAFTpkzhtv3jH/+INWvWYPfu3fj888/xwx/+EMuWLTO1O3z4MFasWIFXXnkFXV1dGDt2LJ566ik0NTW56qMdVSk4HnjgAbz00kvYu3cvNE3DyZMni7sgW9uBtdQdDFS08CCDCXlyJQMIeVplxQc9SADgFiED+EKEUJ+yn4zHigsABoEBwCQygELBLgClFRpORQYNOwuFV4fDyuUgFBtWsaDcLodsaMVKdADFL2Uvwmpp+nKvoyIbSlFiQ1FNPP/882hubsamTZswdepUrF+/HrNmzcL+/fsxZMgQU/uuri6MGTMGc+bMwfLly7nXPHHiBKZPn44rrrgCr7zyCgYPHowDBw7gtNNOK9n7qErBkUwmMWfOHEybNg1PPvlkcRdLpPNJox6jdT/VkgHETnwA/KJFbAgGMAoRgkx8nr1500WUaIEBwFJkkJ9Ns05kEkEBc44GKzSczkoBjAKCXIMVEVahFad4WH201C4Hi1UFUq9FR6ncDS9wOhWWICU2Emkg0TvKwCtKQzLhhy/IcTgSzh2ORx55BIsWLdJLmW/atAkvvfQSfvrTn2LlypWm9pMnT8bkyZMBgHscAB566CGMHDkSTz31lL5v9OjRjvvmhKoUHPfddx8A4Omnny7+YrEUkMmKj4dDhaddMmBReRwykGFGJD4Ao91LP8WyOSAA9FAMjczsBJ4FzRMYAMQiA7Av2CXjZgD2QsNOZPDa8kSEVbVRFjezVVxQDpfDKrRSDtEhEhvlWJaeTRgVrZ1iOMdBoqgB5WwoSkA0GjX8XlNTw10xNplMYvfu3Vi1apW+z+/3Y+bMmXj33Xddv/7WrVsxa9YszJkzB2+++SZGjBiB2267DYsWLXJ9TTuqUnB4SmcSoLOC6QJRkVBhAOKJDqAgPGqChSddi1LbPPFRmOlSmBYJwFD5lEBCMabr2ljPooqLPIEBwF5kAHI1NABnQkMkMro4n2k/GxFRCpejmLCK5BorLE5cDtnQiuk1bEQHAG5eByDI7XDpbFhVGWXLmheDp+4Gi+SDiKJvk0z5gKTPfCCV3zdy5EjD7rVr1+Lee+81Nf/yyy+RyWT0dVMIp59+Oj766CPX/fv000+xceNGNDc3Y/Xq1di1axfuuOMOaJqG+fPnu76uFb1GcCQSCcPSu7p6jKWBdI4/CPFEBw0tPFhshAdQEB/679RNkDgg+u+MGKEhoRkRMmtEcAUGILdEvF2xLjuh4URk8I7TwkMkIpy4HAReHgeN07CKBWxYxYnL4SS0YpXPYSU6rGDFh5XYKJW7YSe4XV3TjbuhxIbCIw4dOmRYS4XnbpSSbDaLpqYmPPjggwCAiy66CPv27cOmTZt6vuBYuXIlHnroIcs2H374IcaNG+fq+i0tLXoohovoyZcVHYDZ7QCMYRbidgBG4SEIw2gST77k5seL9ctO47NMfmMFBmAtMgC5/AzSjj5mJTTsREapcCNIikS23DlgXQzMdF2L0ArgXnTwnA4Wp86GVcJoOZH5P2RqYyc21AqyiiJoaGiQWrxt0KBBCAQCOHLkiGH/kSNHMHToUNevP2zYMJxzzjmGfePHj8cLL7zg+pp2lE1w3Hnnnbj55pst24wZM8b19VetWoXm5mb992g0arKsbHEiOgCx8ADM7eibV3eyKQ+e6yGLSVwQWCEBWIsMQFwRFHDmaNgJDLbgFy+XoitlH16pFhwkjzoRJHahFdkkUsDe6aBdCivxYeqjh+6G0ymxTmHFuSpbrvCampgfPh+n8FfMjwSnvQhN0zBp0iS0trZi9uzZAPLuRGtrK5YsWeK6f9OnT8f+/fsN+z7++GOMGjXK9TXtKJvgGDx4MAYPHlyy64sSborCSnQAYuFB2nE7GjQLAgGaqJ3ILeHlYQCFvtF9ptvYJYECZrcilhYLDZHIsCpVTrcpRT2NUr6uRVjFS5ejmHwOt+EVGdeDbucEr6bEsuFGt/kbBuzcDZtwqkLhJc3NzZg/fz6ampowZcoUrF+/Hp2dnfqslXnz5mHEiBFoaWkBkE80/dOf/qT/fPjwYezduxd1dXUYO3YsAGD58uW4+OKL8eCDD+L666/Hzp07sXnzZmzevLlk76MqczgOHjyI48eP4+DBg8hkMti7dy8AYOzYsairq/P+Be2qUQJm0QHw4/j04M6KDzbkUhN0FxOmhQ67TxQmoY+R9wPwl4WXKT3OExs8oSEjMsqBF+EU2RVki3A5nIRWWKxCK0BxOR1WwkMkNthwilfL0jtdkp51L6RyN6xQYkMhgZbwwRcwJ43mEj5HDgcA3HDDDTh27BjWrFmDtrY2TJgwAdu3b9cTSQ8ePAi/vzDd9osvvsBFF12k/75u3TqsW7cOl19+Od544w0A+amzW7ZswapVq/D9738fo0ePxvr16zF37lzH71UWXy5XgiIVRXLzzTfjZz/7mWn/66+/jhkzZkhdIxqNorGxEe1XnY2GULetRT/BsomH9IBEDyRsUiH7FMwOOuyTbj+BG8Ge5/YmFqaEEmAfKgH4y8JbJYGyyIiNYoQGz2mgQyr034783ei/H+/vLPP3pc+j29B/U/rvKWoDmAQHLSrYPB3WAWFnrLCCg00gZV0ONrRiV9OFFh16myILgPFyN1jBQTsc7AwVNqRCJ43aTYllHQ47weE4d6P7/0w0lkLjkq1ob2+XisUr+gZk7Kl/4nP4+pm/F7muKE4tGtUnvzdV6XA8/fTT3tTg8AJ2JgNrvbNPuqy93sUJi0jMcDE5Jbzr8K7Pm11C3gfdZ4JMEigPkdgo1tGwExuGtlWU1+Fg/ZViXQ6n+RwyTgcAk9sBuBMeXieKlmKGimuUu6GQJJTwG1wHQtZF4a/eQlUKjpLADmRWNRrsajHIiA5APADJigm2vUz4hVeYi+wn2LkZsqXHvRYbMhRTPdRrHIRVnORy8Ci16ADMIRZAfuos3Z6HlbvhJSV3N2js/t8qFAoDfUdwsMiUyraCJzoAvvAgEAHCSzZ1ksshespyIzTo8+g2MlVBidgopdAoZnZKNYkTBqcuB/caHogOAJZ5HYBRRFiJD6+cDSczVGQqjCoUlSDc5Yc/x3E4Ysrh6N0UO+tBVCCKN7OBJzwIpnn8NjdoK/uWN8h7ITTYn3mUytVgP0tWbMgIiFLPcKETR1nYsIrLyqMENwmkTkUHIBdi0du6EBVeJYtWHBVOUSiKom8IDjewdrkT0UHOJ8iUypa5mbGDO1sZlb2OjNBg29k5G+USG7btQ/yfS4GD/AwrWBfDTV0OmSqkMqIDgJTbAfCFhyxeiw2nM1Qsr6XCKYoSEkr64PebZ6lkeeXO+wi939vx8omXN8AD3TUpLG5O8ZT9JoJc205ssNeRWb+EbVfM4mp9lSKfeu1yCng1JdgwgszaO7wwBW/tEl5uRSSV0jcZ7NpbrWrsJZ5Ph1UoKshjjz2Gs846C+FwGFOnTsXOnTst2//qV7/CuHHjEA6Hcf755+Pll182HM/lclizZg2GDRuGSCSCmTNn4sCBA6V8C31AcHiNSHQA3j3xi0QGeX2e2DCcb1Owi9fOLowCmKe+eiXmZGam8KbCsj9XA+xTMfPU7KaipUwhK1nRwQqPmnTaNOCH0mlhUqdITDgVJbI4maHiScEvESqconCIFvejhrNpcefD7vPPP4/m5masXbsWe/bswYUXXohZs2bh6NGj3Pa//e1vceONN2LhwoX4/e9/j9mzZ2P27NnYt2+f3ubf//3f8eMf/xibNm3Ce++9h9raWsyaNQvxeNz1e7ajbwoOXi4Ab+AS3WTsRIeVYJA5T4SdqwHIOxtOKcUaKJGgc7Fhd71y4LGdLlNmmx1MecmSsqsMO3E7rGaTuBEZpZqd4jkqnKKoIh555BEsWrQICxYswDnnnINNmzahX79++OlPf8pt/6Mf/QhXX3017rrrLowfPx73338/Jk6ciEcffRRA3t1Yv3497r77bvz93/89LrjgAvz85z/HF198gRdffLFk76PvCQ6vZi5YiQ4aVoAYHAZJYcJzNbivZXPjr4aqn0RkiMSBndiQdTdEjkgpYEWfBy6HTJtSiA43wsMOcj7vGnYFvyyvW8QMFdv8DYWiSLS4D1qMs8Wd5XAkk0ns3r0bM2fO1Pf5/X7MnDkT7777Lvecd99919AeAGbNmqW3/8tf/oK2tjZDm8bGRkydOlV4TS9QSaOVwMnALxIadhavVSgFcB5O6Rfiuxy0cBC9LxnngTf91UpsuHkNJ9jVYvEYNoGUB2+qLG/miiiRFIApmRQwVyblJZUCZneCreNh1bZHo8IpihIQjUYNv4vWA/vyyy+RyWT0MuaE008/HR999BH32m1tbdz2bW1t+nGyT9SmFPQth8PrugyyLofbazsRGzKiQYRXDgDtXtg5GTRuxIasu8FSRhFhhVuXQyaJFMiLDiduhxPHQ39dyrlgNxmcJosWM0NFJYwqyk240yfcAGDkyJFobGzUN7LwWm9GORxAcQMuEQW8KbPFXrPaELkcxVyPh1NhaCVqKpVU6qImB8/l4E2VlXU6ALlpswTREve0MGBdD7fwxIbV+ilOKWq5eZW/oSgDhw4dMqylIlrtfNCgQQgEAjhy5Ihh/5EjRzB06FDuOUOHDrVsT/49cuQIhg0bZmgzYcIEx+9Flr7jcLh1N2TtVC9Egkyuhoy74SZZVHZg7hcqrvonOd+J2PDK3WBxIhKLKdImgDco8p7Ei3E6AHFehxO3g0BcD7dTWYs5V6HoSYQS+XwNdgsl8g5HQ0ODYRMJDk3TMGnSJLS2tur7stksWltbMW3aNO4506ZNM7QHgFdffVVvP3r0aAwdOtTQJhqN4r333hNe0wv6hsNRrhLXxbgdMoKl3LHkOk0sXljBQJwPt2JE9DeyExvlmpnCYlVxVATH5ShmjZVinQ6An9sBGN0GnusBmF0KnvshKy6cJIsC9ivEOsJtwmgxLopC4YDm5mbMnz8fTU1NmDJlCtavX4/Ozk4sWLAAADBv3jyMGDFCD8ssXboUl19+Of7jP/4D1157LZ577jm8//772Lx5MwDA5/Nh2bJl+MEPfoCvfe1rGD16NO655x4MHz4cs2fPLtn76BuCo9zYCQ83bohw/RSXN9pISO5cK9FBU4o1T5w4GzLnlzt/w2WFUtnQCuBcdADm5e0BsfAA5MQH4K6Al0hosO6LlxVGHaESRhUu0WI+BDPmGSl+F5VGb7jhBhw7dgxr1qxBW1sbJkyYgO3bt+tJnwcPHjSsTHvxxRfj2Wefxd13343Vq1fja1/7Gl588UWcd955ept//dd/RWdnJ2655RacPHkSl1xyCbZv345wOOzi3crhy+VyuZJdvYJEo1E0Njai/cbz0aAFrBvLDGSVTDZ0KjZ4AoE3g4R3vt3qsF4iKzSE+5jB0S7RlP0bsoKQvR7bnhUPPIfD7hyAm8vBignRjBWRG8Jb6M1q3RWe6DAcl8jVsBIfMli5GnaCw87hsKoy6qikOft/j8rhiLbH0Hj3/0N7e7shFq/o25Cx57Ibv0RQM38v0skodvzXoD75vVEOR7VVqvQCnisRCcpNx7VyPugB3a34sAtvyf49KhVKKROiabJOnQ6ALzys3A7A2vEgsIJBVoA4ERoKRU+lptOHEMfNSKX67loqvfuu3RtwY+nKigGRuJAJt3idF2NZY0Mib8NJzY5ywgurSOZyeCE6AOtVZmWFB2DvejjNw7B6LcP+SoVTFAqFpyjBUc2UI35sJTqA4up7OOmDk/0sMuLHaThFBjeJow7xUnQA4jCLnfAAnIkPpzgRG3YVRotatE1NiVV4hBbzIZQ2uxm+Puxw9J1psTyq5SmYRzFiQ5gbIRgk7NyFUnxO5LpOxYaMMKjk31X278aZGSGqHSEaMEXtrWZshJIpywGbFAyzcxXItFp2c4LdebLORkkXbVMoFJ6hHA5ZylzquuzYhVG8cjxkxIATsVGuKc/F4GC2itNpslZOB8BPJgXsHQ/AOODbJZnq53iUgyESG8Wsn6JQlBMtLnA4OPv6Cn1XcPRWd8MOq+RRmdwNu8+NnO/m83W6Vorsqr+lFIrFhFUkqo8SrNZasRIpohALQUZ4AO7EhxucCo2i3Q21aJtCUTb6dkjFKb1lTn4pS4G7DcGUc2E2L8vQ8+B9T+wqlVI4Da1YnQPkB2W7gdku1GJ4LSrsIhuCkb2WqG88eO/JLn/DUclzq//vDv6eir5JKAZonC0UK91rHj9+HHPnzkVDQwP69++PhQsXoqOjw/KczZs3Y8aMGWhoaIDP58PJkye57V566SVMnToVkUgEp512mqsCYX1TcPQFd6OYUEOp8jZEr+NGbFTC3SjFICN4wvZadABybgARHm5CFyIhYrXJ9If3PmTEhkLR15g7dy7++Mc/4tVXX8W2bduwY8cO3HLLLZbndHV14eqrr8bq1auFbV544QXcdNNNWLBgAf7whz/gnXfewT/90z857l/fC6n0BbEhg0xdjlLNVJHK47D4avaEvA0eLiuPsrgNrwD2uR007GBvF3bxCiuxIxJNsuvSWKJcC4WHhDt90AKcSqOc6qNe8OGHH2L79u3YtWsXmpqaAAAbNmzA3/7t32LdunUYPnw497xly5YBAN544w3u8XQ6jaVLl+Lhhx/GwoUL9f3nnHOO4z72LYejmsVGKbAtsiU5+NFOhNvP0Mn5bsRGT/7bOnQ5AHunQ8btcJr/QLsfxTghdte06jMPWWfDtsKoQlFGotGoYUskEkVd791330X//v11sQEAM2fOhN/vx3vvvef6unv27MHhw4fh9/tx0UUXYdiwYbjmmmuwb98+x9fqO4LDqwGpVC5EpfJD3ORHsAJEZitlf0Twwim8/A3eaxYbihH9PUVP0R6LDrtzCUR4FJN8KRIiMpsMViEU2dCT43CLRUlzhUIGLQZoXZytO4dj5MiRaGxs1Dey8Jpb2traMGTIEMO+YDCIAQMGoK2tzfV1P/30UwDAvffei7vvvhvbtm3DaaedhhkzZuD48eOOrtU3BEdPfvotFpnwQyRY+VLhMn2oVnejxINRsaJDdrD1Qnx4hVVf7ISGVCjFav0UO1ToReEBhw4dQnt7u76tWrWK227lypXw+XyW20cffVSyfmazWQDA9773PVx33XWYNGkSnnrqKfh8PvzqV79ydK3en8NRisHI65oc1TL7RXa9lVK8rh1OxUY11Exx+j2xmCZrlZthldNBnw+IF39j4Q30MnkfbnAicNyIr6JDKcrdUJSAhoYGqcXb7rzzTtx8882WbcaMGYOhQ4fi6NGjhv3pdBrHjx/H0KFDXfdz2LBhAIw5GzU1NRgzZgwOHjzo6Fq9X3BUO8WIDa+XmAcKg385hIesq+KV2Cj1dFhZrJJHixAdgHiVWfoagLzwoJEVBrQw8cotsXNpihIbrGOhpsMqPCAU80HzmxNEc1lnSaODBw/G4MGDbdtNmzYNJ0+exO7duzFp0iQAwGuvvYZsNoupU6c6ek2aSZMmoaamBvv378cll1wCAEilUvjss88watQoR9equpDKZ599hoULF2L06NGIRCI4++yzsXbtWiSTvfApo5zOhtOZHaUMszi5dk+dkWKHy4HL7cDLu06pppF6FZohfbRzNZyWfjdgJzZE7sadLwO3v2B/fYWiTIwfPx5XX301Fi1ahJ07d+Kdd97BkiVL8K1vfUufoXL48GGMGzcOO3fu1M9ra2vD3r178cknnwAAPvjgA+zdu1fPz2hoaMB3v/tdrF27Fr/5zW+wf/9+3HrrrQCAOXPmOOpj1TkcH330EbLZLB5//HGMHTsW+/btw6JFi9DZ2Yl169ZVunveUYkwihOng0ALA6euRzGCxe0y9tUQSqFxE36zqUBqN/VV1u0g1yK4cT28wqn4cVWPpJi8Dbr9nS87O0/RJwl3AjUcM8OXK91r/vKXv8SSJUtw5ZVXwu/347rrrsOPf/xj/XgqlcL+/fvR1dWl79u0aRPuu+8+/ffLLrsMAPDUU0/poZyHH34YwWAQN910E2KxGKZOnYrXXnsNp512mqP++XK5XAnfvjc8/PDD2Lhxo54tK0M0GkVjYyPavz0RDVqgNB0rZnDzUnA4rZPhVHSUE6kkV4vP3U04xcksFas6GqIS51bfE6vr2ZQ9lxEIMqKjmOu7wa2z4npWjkwYBZBzN5Zs1X/U7zHt7VKxeEXfgHwvlte1o8Zn/l4kclH8sKNvfm+qzuHg0d7ejgEDBlS6G95QDQmibpyOclCs2HCDUxfGo+JdUtcr0ukAnLkdvOtXGpkQkWU/vRIblNBQKGTQugCN43BU/yN+6ah6wfHJJ59gw4YNtuGURCJhKJzS3t4OAIgmM6XrXDIjP/iUMtnMzXskrk9nFQgP4j7YvY9IyLpNTVD8OQcF6Upuiv5lBXcMn+BisRRgNeBnsuJjXTYLvHUl5RZT60wW5XaUC1ZgxK3adpdG57YRzURJcL4/rKhhxcZ3ngeiUdNp0e59PcAkVlSABKIA56uRgPm71GfIlYkVK1bkkP/4hduHH35oOOevf/1r7uyzz84tXLjQ9vpr1661vb7a1KY2tXm9/fnPfy7VbVPRA4nFYrmhQ4dafmeGDh2ai8Vile5q2SlbDsexY8fw1VdfWbYZM2YMNC3/NPfFF19gxowZ+PrXv46nn34afr/1hBrW4chmszh+/DgGDhwIn+jJs4JEo1GMHDkShw4d6nNxPC9Qn1/xqM+wONrb23HmmWfixIkT6N+/f6W7o6gi4vG45cxKTdMQDofL2KPqoGwhFdm5xEB+6s4VV1yhVzSzExtAvhBJTU2NYV9PuAnIFn9R8FGfX/Goz7A4ZO5Pir5FOBzuk4LCjqrL4Th8+DBmzJiBUaNGYd26dTh27Jh+rJhqaQqFQqFQKCpH1QmOV199FZ988gk++eQTnHHGGYZjZYr+KBQKhUKh8Jiq8wJvvvlm5HI57tabqKmpwdq1a01hIIUc6vMrHvUZFof6/BQKZ/SIwl8KhUKhUCh6NlXncCgUCoVCoeh9KMGhUCgUCoWi5CjBoVAoFAqFouQowVFmWlpaMHnyZNTX12PIkCGYPXs29u/fX+lu9Vj+7d/+DT6fD8uWLat0V3oMhw8fxj//8z9j4MCBiEQiOP/88/H+++9Xuls9hkwmg3vuuQejR49GJBLB2Wefjfvvv7/XJbYrFF5TddNieztvvvkmFi9ejMmTJyOdTmP16tX4m7/5G/zpT39CbW1tpbvXo9i1axcef/xxXHDBBZXuSo/hxIkTmD59Oq644gq88sorGDx4MA4cOOB4mem+zEMPPYSNGzfiZz/7Gc4991y8//77WLBgARobG3HHHXdUunsKRdWiZqlUmGPHjmHIkCF48803cdlll1W6Oz2Gjo4OTJw4ET/5yU/wgx/8ABMmTMD69esr3a2qZ+XKlXjnnXfw1ltvVborPZa/+7u/w+mnn44nn3xS33fdddchEongmWeeqWDPFIrqRoVUKgxZ1XbAgAEV7knPYvHixbj22msxc+bMSnelR7F161Y0NTVhzpw5GDJkCC666CI88cQTle5Wj+Liiy9Ga2srPv74YwDAH/7wB7z99tu45pprKtwzhaK6USGVCpLNZrFs2TJMnz4d5513XqW702N47rnnsGfPHuzatavSXelxfPrpp9i4cSOam5uxevVq7Nq1C3fccQc0TcP8+fMr3b0ewcqVKxGNRjFu3DgEAgFkMhk88MADmDt3bqW7plBUNUpwVJDFixdj3759ePvttyvdlR7DoUOHsHTpUrz66qtqcSQXZLNZNDU14cEHHwQAXHTRRdi3bx82bdqkBIck//3f/41f/vKXePbZZ3Huuedi7969WLZsGYYPH64+Q4XCAiU4KsSSJUuwbds27Nixw7RmjELM7t27cfToUUycOFHfl8lksGPHDjz66KNIJBIIBAIV7GF1M2zYMJxzzjmGfePHj8cLL7xQoR71PO666y6sXLkS3/rWtwAA559/Pj7//HO0tLQowaFQWKAER5nJ5XK4/fbbsWXLFrzxxhsYPXp0pbvUo7jyyivxwQcfGPYtWLAA48aNw4oVK5TYsGH69Ommadgff/wxRo0aVaEe9Ty6urpMS9IHAgFks9kK9Uih6BkowVFmFi9ejGeffRa//vWvUV9fj7a2NgBAY2MjIpFIhXtX/dTX15vyXWprazFw4ECVByPB8uXLcfHFF+PBBx/E9ddfj507d2Lz5s3YvHlzpbvWY/jGN76BBx54AGeeeSbOPfdc/P73v8cjjzyCb3/725XumkJR1ahpsWXG5/Nx9z/11FO4+eaby9uZXsKMGTPUtFgHbNu2DatWrcKBAwcwevRoNDc3Y9GiRZXuVo/h1KlTuOeee7BlyxYcPXoUw4cPx4033og1a9ZA07RKd0+hqFqU4FAoFAqFQlFyVB0OhUKhUCgUJUcJDoVCoVAoFCVHCQ6FQqFQKBQlRwkOhUKhUCgUJUcJDoVCoVAoFCVHCQ6FQqFQKBQlRwkOhUKhUCgUJUcJDoVCoVAoFCVHCQ5Fn+bNN9/E2LFjcfLkSQBAPB7HtGnT8Oqrr1a2YwqFQtHLUIJD0ae5/PLLsWDBAr2s/He+8x3Mnj0bV111VWU7plAoFL0MVdpc0efJ5XK45pprEAwGEQ6H8T//8z+V7pJCoVD0OtRqsYo+j8/nwx133IFrr71WhVIUCoWiRKiQiqLPc/ToUSxfvhybN2/GLbfcgq+++qrSXVIoFIpeh3I4FH2adDqN66+/HqtXr8b8+fNx5MgR3HTTTXjppZfg8/kq3T2FQqHoNagcDkWfZvny5YhGo3jyyScBANlsFldffTVmzJiB1atXV7h3CoVC0XtQgkOhUCgUCkXJUTkcCoVCoVAoSo4SHAqFQqFQKEqOEhwKhUKhUChKjhIcCoVCoVAoSo4SHAqFQqFQKEqOEhwKhUKhUChKjhIcCoVCoVAoSo4SHAqFQqFQKEqOEhwKhUKhUChKjhIcCoVCoVAoSo4SHAqFQqFQKErO/wdpvRsWuJcFQgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for t in range(0, 8):\n",
+    "    t = t * unit_of_t\n",
+    "    [ob_x, ob_y, ob_t, ob_u, ob_v, ob_p] = load_training_data(num=140000)\n",
+    "\n",
+    "    xyt_pred = {\"x\": ob_x, \"y\": ob_y, \"t\": t * np.ones((len(ob_x),))}\n",
+    "    uvp_pred = model.predict(xyt_pred)\n",
+    "\n",
+    "    x_pred, y_pred, t_pred = xyt_pred['x'], xyt_pred['y'], xyt_pred['t']\n",
+    "    u_pred, v_pred, p_pred = uvp_pred['u'], uvp_pred['v'], uvp_pred['p']\n",
+    "    x_true = ob_x[ob_t == t]\n",
+    "    y_true = ob_y[ob_t == t]\n",
+    "    u_true = ob_u[ob_t == t]\n",
+    "    fig, ax = plt.subplots(2, 1)\n",
+    "    cntr0 = ax[0].tricontourf(x_pred.mantissa, y_pred.mantissa, u_pred.mantissa, levels=80, cmap=\"rainbow\")\n",
+    "    cb0 = plt.colorbar(cntr0, ax=ax[0])\n",
+    "    cntr1 = ax[1].tricontourf(x_true.mantissa, y_true.mantissa, u_true.mantissa, levels=80, cmap=\"rainbow\")\n",
+    "    cb1 = plt.colorbar(cntr1, ax=ax[1])\n",
+    "    ax[0].set_title(\"u-PINN \" + \"(t=\" + str(t) + \")\", fontsize=9.5)\n",
+    "    ax[0].axis(\"scaled\")\n",
+    "    ax[0].set_xlabel(\"X\", fontsize=7.5, family=\"Arial\")\n",
+    "    ax[0].set_ylabel(\"Y\", fontsize=7.5, family=\"Arial\")\n",
+    "    ax[1].set_title(\"u-Reference solution \" + \"(t=\" + str(t) + \")\", fontsize=9.5)\n",
+    "    ax[1].axis(\"scaled\")\n",
+    "    ax[1].set_xlabel(\"X\", fontsize=7.5, family=\"Arial\")\n",
+    "    ax[1].set_ylabel(\"Y\", fontsize=7.5, family=\"Arial\")\n",
+    "    fig.tight_layout()\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pinnx",
+   "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": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/experimental_docs/unit-examples-inverse/Navier_Stokes_inverse.py b/docs/experimental_docs/unit-examples-inverse/Navier_Stokes_inverse.py
new file mode 100644
index 000000000..16dd31860
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-inverse/Navier_Stokes_inverse.py
@@ -0,0 +1,211 @@
+"""
+An inverse problem of the Navier-Stokes equation of incompressible flow around cylinder with Re=100
+
+References: https://doi.org/10.1016/j.jcp.2018.10.045 Section 4.1.1
+"""
+
+import re
+
+import brainstate as bst
+import jax.tree
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.io import loadmat
+import brainunit as u
+import deepxde.experimental as deepxde
+
+unit_of_x = u.meter
+unit_of_y = u.meter
+unit_of_t = u.second
+unit_of_rho = u.kilogram / u.meter**3
+unit_of_c1 = u.UNITLESS
+unit_of_c2 = u.meter2 / u.second
+unit_of_u = u.meter / u.second
+unit_of_v = u.meter / u.second
+unit_of_p = u.pascal
+
+# true values
+C1true = 1.0
+C2true = 0.01 * unit_of_c2
+
+
+# Load training data
+def load_training_data(num):
+    data = loadmat("../dataset/cylinder_nektar_wake.mat")
+    U_star = data["U_star"]  # N x 2 x T
+    P_star = data["p_star"]  # N x T
+    t_star = data["t"]  # T x 1
+    X_star = data["X_star"]  # N x 2
+    N = X_star.shape[0]
+    T = t_star.shape[0]
+    # Rearrange Problem
+    XX = np.tile(X_star[:, 0:1], (1, T))  # N x T
+    YY = np.tile(X_star[:, 1:2], (1, T))  # N x T
+    TT = np.tile(t_star, (1, N)).T  # N x T
+    UU = U_star[:, 0, :]  # N x T
+    VV = U_star[:, 1, :]  # N x T
+    PP = P_star  # N x T
+    x = XX.flatten()[:, None]  # NT x 1
+    y = YY.flatten()[:, None]  # NT x 1
+    t = TT.flatten()[:, None]  # NT x 1
+    u = UU.flatten()[:, None]  # NT x 1
+    v = VV.flatten()[:, None]  # NT x 1
+    p = PP.flatten()[:, None]  # NT x 1
+    # training domain: X × Y = [1, 8] × [−2, 2] and T = [0, 7]
+    data1 = np.concatenate([x, y, t, u, v, p], 1)
+    data2 = data1[:, :][data1[:, 2] <= 7]
+    data3 = data2[:, :][data2[:, 0] >= 1]
+    data4 = data3[:, :][data3[:, 0] <= 8]
+    data5 = data4[:, :][data4[:, 1] >= -2]
+    data_domain = data5[:, :][data5[:, 1] <= 2]
+    # choose number of training points: num =7000
+    idx = np.random.choice(data_domain.shape[0], num, replace=False)
+    x_train = data_domain[idx, 0]
+    y_train = data_domain[idx, 1]
+    t_train = data_domain[idx, 2]
+    u_train = data_domain[idx, 3]
+    v_train = data_domain[idx, 4]
+    p_train = data_domain[idx, 5]
+    # return [x_train, y_train, t_train, u_train, v_train, p_train]
+    return [x_train * unit_of_x, y_train * unit_of_y, t_train * unit_of_t,
+            u_train * unit_of_u, v_train * unit_of_v, p_train * unit_of_p]
+
+
+# Parameters to be identified
+C1 = bst.ParamState(0.0)
+C2 = bst.ParamState(0.0 * unit_of_c2)
+
+
+# Define Navier Stokes Equations (Time-dependent PDEs)
+def Navier_Stokes_Equation(x, y):
+    jacobian = net.jacobian(x)
+    x_hessian = net.hessian(x, y=['u', 'v'], xi=['x'], xj=['x'])
+    y_hessian = net.hessian(x, y=['u', 'v'], xi=['y'], xj=['y'])
+
+    u = y['u']
+    v = y['v']
+    p = y['p']
+    du_x = jacobian['u']['x']
+    du_y = jacobian['u']['y']
+    du_t = jacobian['u']['t']
+    dv_x = jacobian['v']['x']
+    dv_y = jacobian['v']['y']
+    dv_t = jacobian['v']['t']
+    dp_x = jacobian['p']['x']
+    dp_y = jacobian['p']['y']
+    du_xx = x_hessian['u']['x']['x']
+    du_yy = y_hessian['u']['y']['y']
+    dv_xx = x_hessian['v']['x']['x']
+    dv_yy = y_hessian['v']['y']['y']
+    continuity = du_x + dv_y
+    x_momentum = unit_of_rho * du_t + unit_of_rho * C1.value * (u * du_x + v * du_y) + dp_x - unit_of_rho * C2.value * (du_xx + du_yy)
+    y_momentum = unit_of_rho * dv_t + unit_of_rho * C1.value * (u * dv_x + v * dv_y) + dp_y - unit_of_rho * C2.value * (dv_xx + dv_yy)
+    return [continuity, x_momentum, y_momentum]
+
+
+net = deepxde.nn.Model(
+    deepxde.nn.DictToArray(x=unit_of_x, y=unit_of_y, t=unit_of_t),
+    deepxde.nn.FNN([3] + [50] * 6 + [3], "tanh"),
+    deepxde.nn.ArrayToDict(u=unit_of_u, v=unit_of_v, p=unit_of_p),
+)
+
+# Define Spatio-temporal domain
+# Rectangular
+Lx_min, Lx_max = 1.0, 8.0
+Ly_min, Ly_max = -2.0, 2.0
+# Spatial domain: X × Y = [1, 8] × [−2, 2]
+space_domain = deepxde.geometry.Rectangle([Lx_min, Ly_min], [Lx_max, Ly_max])
+# Time domain: T = [0, 7]
+time_domain = deepxde.geometry.TimeDomain(0, 7)
+# Spatio-temporal domain
+geomtime = deepxde.geometry.GeometryXTime(space_domain, time_domain)
+geomtime = geomtime.to_dict_point(x=unit_of_x, y=unit_of_y, t=unit_of_t)
+
+# Get the training data: num = 7000
+[ob_x, ob_y, ob_t, ob_u, ob_v, ob_p] = load_training_data(num=7000)
+ob_xyt = {"x": ob_x, "y": ob_y, "t": ob_t}
+ob_yv = {"u": ob_u, "v": ob_v, }
+observe_bc = deepxde.icbc.PointSetBC(ob_xyt, ob_yv)
+
+# Training datasets and Loss
+problem = deepxde.problem.TimePDE(
+    geomtime,
+    Navier_Stokes_Equation,
+    [observe_bc],
+    net,
+    num_domain=700,
+    num_boundary=200,
+    num_initial=100,
+    anchors=ob_xyt,
+)
+
+# Neural Network setup
+model = deepxde.Trainer(problem, external_trainable_variables=[C1, C2])
+
+# callbacks for storing results
+fnamevar = "variables.dat"
+variable = deepxde.callbacks.VariableValue([C1, C2], period=100, filename=fnamevar)
+
+# Compile, train and save trainer
+model.compile(bst.optim.Adam(1e-3)).train(iterations=10000, callbacks=[variable],
+                                          display_every=1000, disregard_previous_best=True)
+model.saveplot(issave=True, isplot=True)
+model.compile(bst.optim.Adam(1e-4)).train(iterations=10000, callbacks=[variable],
+                                          display_every=1000, disregard_previous_best=True)
+model.saveplot(issave=True, isplot=True)
+
+# trainer.save(save_path = "./NS_inverse_model/trainer")
+f = model.predict(ob_xyt, operator=Navier_Stokes_Equation)
+print("Mean residual:", jax.tree.map(lambda x: u.math.mean(u.math.abs(x)), f))
+
+# Plot Variables:
+# reopen saved data using callbacks in fnamevar
+lines = open(fnamevar, "r").readlines()
+# read output data in fnamevar
+Chat = np.array(
+    [
+        np.fromstring(
+            min(re.findall(re.escape("[") + "(.*?)" + re.escape("]"), line), key=len),
+            sep=",",
+        )
+        for line in lines
+    ]
+)
+l, c = Chat.shape
+plt.semilogy(range(0, l * 100, 100), Chat[:, 0], "r-")
+plt.semilogy(range(0, l * 100, 100), Chat[:, 1], "k-")
+plt.semilogy(range(0, l * 100, 100), np.ones(Chat[:, 0].shape) * C1true, "r--")
+plt.semilogy(range(0, l * 100, 100), np.ones(Chat[:, 1].shape) * C2true, "k--")
+plt.legend(["C1hat", "C2hat", "True C1", "True C2"], loc="right")
+plt.xlabel("Epochs")
+plt.title("Variables")
+plt.show()
+
+# Plot the velocity distribution of the flow field:
+for t in range(0, 8):
+    t = t * unit_of_t
+    [ob_x, ob_y, ob_t, ob_u, ob_v, ob_p] = load_training_data(num=140000)
+
+    xyt_pred = {"x": ob_x, "y": ob_y, "t": t * np.ones((len(ob_x),))}
+    uvp_pred = model.predict(xyt_pred)
+
+    x_pred, y_pred, t_pred = xyt_pred['x'], xyt_pred['y'], xyt_pred['t']
+    u_pred, v_pred, p_pred = uvp_pred['u'], uvp_pred['v'], uvp_pred['p']
+    x_true = ob_x[ob_t == t]
+    y_true = ob_y[ob_t == t]
+    u_true = ob_u[ob_t == t]
+    fig, ax = plt.subplots(2, 1)
+    cntr0 = ax[0].tricontourf(x_pred.mantissa, y_pred.mantissa, u_pred.mantissa, levels=80, cmap="rainbow")
+    cb0 = plt.colorbar(cntr0, ax=ax[0])
+    cntr1 = ax[1].tricontourf(x_true.mantissa, y_true.mantissa, u_true.mantissa, levels=80, cmap="rainbow")
+    cb1 = plt.colorbar(cntr1, ax=ax[1])
+    ax[0].set_title("u-PINN " + "(t=" + str(t) + ")", fontsize=9.5)
+    ax[0].axis("scaled")
+    ax[0].set_xlabel("X", fontsize=7.5, family="Arial")
+    ax[0].set_ylabel("Y", fontsize=7.5, family="Arial")
+    ax[1].set_title("u-Reference solution " + "(t=" + str(t) + ")", fontsize=9.5)
+    ax[1].axis("scaled")
+    ax[1].set_xlabel("X", fontsize=7.5, family="Arial")
+    ax[1].set_ylabel("Y", fontsize=7.5, family="Arial")
+    fig.tight_layout()
+    plt.show()
diff --git a/docs/experimental_docs/unit-examples-inverse/brinkman_forchheimer.ipynb b/docs/experimental_docs/unit-examples-inverse/brinkman_forchheimer.ipynb
new file mode 100644
index 000000000..029c1e39b
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-inverse/brinkman_forchheimer.ipynb
@@ -0,0 +1,452 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c43b16089a8ea201",
+   "metadata": {},
+   "source": [
+    "# Inferring the effective viscosity and permeability for the Brinkman-Forchheimer model\n",
+    "\n",
+    "\n",
+    "## The problem we need to solve\n",
+    "\n",
+    "The Brinkman-Forchheimer model can be viewed as an extended Darcy’s law and is used to describe\n",
+    "wall-bounded porous media flows:\n",
+    "\n",
+    "$$\n",
+    "-\\frac{\\nu_e}{\\epsilon}\\nabla^2u+\\frac{\\nu u}{K}=g,\\quad x\\in[0,H],\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "where the solution $u$ is the fluid velocity, $g$ denotes the external force, $\\nu$ is the kinetic viscosity of fluid, $\\epsilon$ is the porosity of the porous medium, and $K$ is the permeability. The effective viscosity, $\\nu_e$, is related to the pore structure and hardly to be determined. A no-slip boundary condition is imposed, i.e., $u(0)=u(1)=0.$ The analytic solution for this problem is\n",
+    "\n",
+    "$$u(x)=\\frac{gK}\\nu\\left[1-\\frac{\\cosh\\left(r\\left(x-\\frac H2\\right)\\right)}{\\cosh\\left(\\frac{rH}2\\right)}\\right]$$\n",
+    "\n",
+    "with $r=\\sqrt{\\nu\\epsilon/\\nu_eK}.$ We choose $H=1,\\nu_e=\\nu=10^{-3},\\epsilon=0.4$, and $K=10^{-3}$, and $g=1.$ To infer $\\nu_e$,we\n",
+    "collect the data measurements of the velocity $u$ in only 5 sensor locations.\n",
+    "\n",
+    "\n",
+    "## Dimensional analysis\n",
+    "\n",
+    "**Variables and Parameters:**\n",
+    "\n",
+    "1. **Spatial Coordinate, $x$**\n",
+    "   - Typical dimension: length $[x] = L$.\n",
+    "\n",
+    "2. **Fluid Velocity, $u(x)$**\n",
+    "   - Velocity dimension: $[u] = L/T$.\n",
+    "\n",
+    "3. **Kinematic Viscosity of the Fluid, $\\nu$**\n",
+    "   - Kinematic viscosity has dimensions: $[\\nu] = L^2/T$.\n",
+    "\n",
+    "4. **Effective Viscosity, $\\nu_e$**\n",
+    "   - This is also a (generalized) viscosity term, so it has the same dimension as kinematic viscosity: $[\\nu_e] = L^2/T$.\n",
+    "\n",
+    "5. **Porosity, $\\epsilon$**\n",
+    "   - Porosity is a dimensionless measure of void fraction: $[\\epsilon] = 1$ (no units).\n",
+    "\n",
+    "6. **Permeability, $K$**\n",
+    "   - Permeability has dimensions of area: $[K] = L^2$.\n",
+    "\n",
+    "7. **External Force, $g$**\n",
+    "   - The right-hand side $g$ must match the dimensions of the left-hand side terms.\n",
+    "   - Consider the term $-\\frac{\\nu_e}{\\epsilon}\\nabla^2 u$:\n",
+    "     $\\nabla^2 u \\sim u/L^2 = (L/T)/L^2 = 1/(T L)$.\n",
+    "     Multiplying by $\\nu_e = L^2/T$ yields $(L^2/T)*(1/(T L)) = L/T^2$.\n",
+    "   - Similarly, $\\frac{\\nu u}{K} = (L^2/T)*(L/T)/(L^2) = L/T^2$.\n",
+    "   - Thus both terms on the left-hand side have dimension $L/T^2$.\n",
+    "   - Hence, $[g] = L/T^2$.\n",
+    "\n",
+    "**Summarized Dimensions:**\n",
+    "\n",
+    "- $[x] = L$\n",
+    "- $[u] = L/T$\n",
+    "- $[\\nu] = L^2/T$\n",
+    "- $[\\nu_e] = L^2/T$\n",
+    "- $[\\epsilon] = 1$ (dimensionless)\n",
+    "- $[K] = L^2$\n",
+    "- $[g] = L/T^2$\n",
+    "- $[H] = L$\n",
+    "\n",
+    "**Given values (with dimensions):**\n",
+    "- $H=1\\,L$\n",
+    "- $\\nu_e=10^{-3}\\,L^2/T$\n",
+    "- $\\nu=10^{-3}\\,L^2/T$\n",
+    "- $\\epsilon=0.4\\,(\\text{dimensionless})$\n",
+    "- $K=10^{-3}\\,L^2$\n",
+    "- $g=1\\,L/T^2$\n",
+    "\n",
+    "All chosen parameters are dimensionally consistent with the governing equation.\n",
+    "\n",
+    "\n",
+    "## Implementation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9de2453b30e99ba",
+   "metadata": {},
+   "source": [
+    "Import the package we need:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a91eb38957b21a99",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T09:29:04.620209Z",
+     "start_time": "2024-12-17T09:29:01.698006Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import brainstate as bst\n",
+    "import brainunit as u\n",
+    "import numpy as np\n",
+    "\n",
+    "import deepxde.experimental as deepxde\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43f988a9be346d26",
+   "metadata": {},
+   "source": [
+    "Define the parameters with physical units and the analytical solution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d1f0b74f440942cc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T09:29:04.682837Z",
+     "start_time": "2024-12-17T09:29:04.627220Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "g = 1 * u.meter / u.second ** 2\n",
+    "v = 1e-3 * u.meter ** 2 / u.second\n",
+    "e = 0.4\n",
+    "H = 1 * u.meter\n",
+    "\n",
+    "v_e = bst.ParamState(0.1 * u.meter ** 2 / u.second)\n",
+    "K = bst.ParamState(0.001 * u.meter ** 2)\n",
+    "\n",
+    "\n",
+    "def sol(x):\n",
+    "    x = x['x'].mantissa\n",
+    "    r = (v.mantissa * e / (1e-3 * 1e-3)) ** 0.5\n",
+    "    y = (\n",
+    "        g.mantissa * 1e-3 /\n",
+    "        v.mantissa *\n",
+    "        (1 - u.math.cosh(r * (x - H.mantissa / 2)) /\n",
+    "         u.math.cosh(r * H.mantissa / 2))\n",
+    "    )\n",
+    "    return {'y': y * u.meter / u.second}\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7aa2f75d15d059c",
+   "metadata": {},
+   "source": [
+    "Define the geometry of the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b1be0e60a93b48fc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T09:29:04.943829Z",
+     "start_time": "2024-12-17T09:29:04.940952Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "geom = deepxde.geometry.Interval(0, 1).to_dict_point(x=u.meter)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5934eeb99a17bb8d",
+   "metadata": {},
+   "source": [
+    "Define the PDE and generate training data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "10584bcbcabe6fc5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T09:29:04.953902Z",
+     "start_time": "2024-12-17T09:29:04.950832Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def pde(x, y):\n",
+    "    du_xx = net.hessian(x)[\"y\"][\"x\"][\"x\"]\n",
+    "    return -v_e.value / e * du_xx + v * y['y'] / K.value - g\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "74d955175466677f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T09:29:05.126179Z",
+     "start_time": "2024-12-17T09:29:04.963231Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def gen_traindata(num):\n",
+    "    xvals = np.linspace(1 / (num + 1), 1, num, endpoint=False)\n",
+    "    x = {'x': xvals * u.meter}\n",
+    "    return x, sol(x)\n",
+    "\n",
+    "ob_x, ob_u = gen_traindata(5)\n",
+    "observe_u = deepxde.icbc.PointSetBC(ob_x, ob_u)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d611b1435ebcc2e",
+   "metadata": {},
+   "source": [
+    "Define the neural network model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b859b04b8c319673",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T09:29:05.538907Z",
+     "start_time": "2024-12-17T09:29:05.164654Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "net = deepxde.nn.Model(\n",
+    "    deepxde.nn.DictToArray(x=u.meter),\n",
+    "    deepxde.nn.FNN(\n",
+    "        [1] + [20] * 3 + [1], \"tanh\",\n",
+    "        output_transform=lambda x, y: x * (1 - x) * y\n",
+    "    ),\n",
+    "    deepxde.nn.ArrayToDict(y=u.meter / u.second),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "109687da566db88f",
+   "metadata": {},
+   "source": [
+    "Define the problem and solve it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f5ecb219b9c78d4d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T09:29:05.783667Z",
+     "start_time": "2024-12-17T09:29:05.551786Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "problem = deepxde.problem.PDE(\n",
+    "    geom,\n",
+    "    pde,\n",
+    "    approximator=net,\n",
+    "    solution=sol,\n",
+    "    constraints=[observe_u],\n",
+    "    num_domain=100,\n",
+    "    num_boundary=0,\n",
+    "    train_distribution=\"uniform\",\n",
+    "    num_test=500,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "730abad10e358c29",
+   "metadata": {},
+   "source": [
+    "Train the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "387fb624a8715cc5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T09:29:21.188062Z",
+     "start_time": "2024-12-17T09:29:05.797627Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.066302 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                Test loss                                                 Test metric                                       \n",
+      "0         [1.2322745 * 10.0^0 * metre ** 2 * second ** -4,          [1.2312626 * 10.0^0 * metre ** 2 * second ** -4,          [{'y': Array(1.0370464, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': 1.0472959 * meter / second}}]              {'ibc0': {'y': 1.0472959 * meter / second}}]                                                               \n",
+      "1000      [0.00070408 * 10.0^0 * metre ** 2 * second ** -4,         [0.00077674 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.26238587, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 0.0429582 * meter / second}}]              {'ibc0': {'y': 0.0429582 * meter / second}}]                                                               \n",
+      "2000      [0.00269285 * 10.0^0 * metre ** 2 * second ** -4,         [0.00285477 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.24537273, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 0.03586509 * meter / second}}]             {'ibc0': {'y': 0.03586509 * meter / second}}]                                                              \n",
+      "3000      [0.00576497 * 10.0^0 * metre ** 2 * second ** -4,         [0.00623961 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.20437899, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 0.02151751 * meter / second}}]             {'ibc0': {'y': 0.02151751 * meter / second}}]                                                              \n",
+      "4000      [0.00459755 * 10.0^0 * metre ** 2 * second ** -4,         [0.00526482 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.15171465, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 0.0091022 * meter / second}}]              {'ibc0': {'y': 0.0091022 * meter / second}}]                                                               \n",
+      "5000      [0.00277259 * 10.0^0 * metre ** 2 * second ** -4,         [0.0033845 * 10.0^0 * metre ** 2 * second ** -4,          [{'y': Array(0.11733841, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 0.00427992 * meter / second}}]             {'ibc0': {'y': 0.00427992 * meter / second}}]                                                              \n",
+      "6000      [0.00165531 * 10.0^0 * metre ** 2 * second ** -4,         [0.00217546 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.09476856, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 0.00225442 * meter / second}}]             {'ibc0': {'y': 0.00225442 * meter / second}}]                                                              \n",
+      "7000      [0.00104619 * 10.0^0 * metre ** 2 * second ** -4,         [0.00148209 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.07885406, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 0.00126963 * meter / second}}]             {'ibc0': {'y': 0.00126963 * meter / second}}]                                                              \n",
+      "8000      [0.00072733 * 10.0^0 * metre ** 2 * second ** -4,         [0.00110027 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.06720789, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 0.00075283 * meter / second}}]             {'ibc0': {'y': 0.00075283 * meter / second}}]                                                              \n",
+      "9000      [0.00063628 * 10.0^0 * metre ** 2 * second ** -4,         [0.00099117 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.05862389, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 0.00049126 * meter / second}}]             {'ibc0': {'y': 0.00049126 * meter / second}}]                                                              \n",
+      "10000     [0.00038361 * 10.0^0 * metre ** 2 * second ** -4,         [0.00070372 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.05175733, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 0.00034356 * meter / second}}]             {'ibc0': {'y': 0.00034356 * meter / second}}]                                                              \n",
+      "11000     [0.00030018 * 10.0^0 * metre ** 2 * second ** -4,         [0.00058416 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.04615347, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 0.00025155 * meter / second}}]             {'ibc0': {'y': 0.00025155 * meter / second}}]                                                              \n",
+      "12000     [0.00021172 * 10.0^0 * metre ** 2 * second ** -4,         [0.00048861 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.04148087, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 0.00018796 * meter / second}}]             {'ibc0': {'y': 0.00018796 * meter / second}}]                                                              \n",
+      "13000     [0.00019228 * 10.0^0 * metre ** 2 * second ** -4,         [0.00044553 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.03766144, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 0.00014382 * meter / second}}]             {'ibc0': {'y': 0.00014382 * meter / second}}]                                                              \n",
+      "14000     [0.00017108 * 10.0^0 * metre ** 2 * second ** -4,         [0.00044543 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.0341393, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': 0.00010903 * meter / second}}]             {'ibc0': {'y': 0.00010903 * meter / second}}]                                                              \n",
+      "15000     [0.00013393 * 10.0^0 * metre ** 2 * second ** -4,         [0.00039608 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.03076173, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 8.177792e-05 * meter / second}}]           {'ibc0': {'y': 8.177792e-05 * meter / second}}]                                                            \n",
+      "16000     [8.0808204e-05 * 10.0^0 * metre ** 2 * second ** -4,      [0.0003569 * 10.0^0 * metre ** 2 * second ** -4,          [{'y': Array(0.02793985, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 6.29709e-05 * meter / second}}]            {'ibc0': {'y': 6.29709e-05 * meter / second}}]                                                             \n",
+      "17000     [0.00011505 * 10.0^0 * metre ** 2 * second ** -4,         [0.00042251 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.0253933, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': 4.9956318e-05 * meter / second}}]          {'ibc0': {'y': 4.9956318e-05 * meter / second}}]                                                           \n",
+      "18000     [9.197639e-05 * 10.0^0 * metre ** 2 * second ** -4,       [0.00039912 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.02277111, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 3.684857e-05 * meter / second}}]           {'ibc0': {'y': 3.684857e-05 * meter / second}}]                                                            \n",
+      "19000     [0.0010154 * 10.0^0 * metre ** 2 * second ** -4,          [0.00129051 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.02021311, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 3.3709657e-05 * meter / second}}]          {'ibc0': {'y': 3.3709657e-05 * meter / second}}]                                                           \n",
+      "20000     [5.1668612e-05 * 10.0^0 * metre ** 2 * second ** -4,      [0.00041443 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.01882938, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 2.288534e-05 * meter / second}}]           {'ibc0': {'y': 2.288534e-05 * meter / second}}]                                                            \n",
+      "21000     [3.3456847e-05 * 10.0^0 * metre ** 2 * second ** -4,      [0.00041545 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.0172698, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': 1.875132e-05 * meter / second}}]           {'ibc0': {'y': 1.875132e-05 * meter / second}}]                                                            \n",
+      "22000     [5.7533292e-05 * 10.0^0 * metre ** 2 * second ** -4,      [0.00046078 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.01577936, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 1.50083115e-05 * meter / second}}]         {'ibc0': {'y': 1.50083115e-05 * meter / second}}]                                                          \n",
+      "23000     [2.343095e-05 * 10.0^0 * metre ** 2 * second ** -4,       [0.00048284 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.01447075, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 1.2138024e-05 * meter / second}}]          {'ibc0': {'y': 1.2138024e-05 * meter / second}}]                                                           \n",
+      "24000     [1.470709e-05 * 10.0^0 * metre ** 2 * second ** -4,       [0.00047367 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.01338609, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 1.0363932e-05 * meter / second}}]          {'ibc0': {'y': 1.0363932e-05 * meter / second}}]                                                           \n",
+      "25000     [0.00023094 * 10.0^0 * metre ** 2 * second ** -4,         [0.00074617 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.01255435, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 9.4853285e-06 * meter / second}}]          {'ibc0': {'y': 9.4853285e-06 * meter / second}}]                                                           \n",
+      "26000     [1.3408353e-05 * 10.0^0 * metre ** 2 * second ** -4,      [0.00052996 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.01138956, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 7.0691417e-06 * meter / second}}]          {'ibc0': {'y': 7.0691417e-06 * meter / second}}]                                                           \n",
+      "27000     [2.6520533e-05 * 10.0^0 * metre ** 2 * second ** -4,      [0.00054236 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.01050647, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 5.9204394e-06 * meter / second}}]          {'ibc0': {'y': 5.9204394e-06 * meter / second}}]                                                           \n",
+      "28000     [1.0871769e-05 * 10.0^0 * metre ** 2 * second ** -4,      [0.00052523 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.00997761, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 5.3106005e-06 * meter / second}}]          {'ibc0': {'y': 5.3106005e-06 * meter / second}}]                                                           \n",
+      "29000     [5.7753732e-05 * 10.0^0 * metre ** 2 * second ** -4,      [0.00058264 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.00915718, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 4.4464914e-06 * meter / second}}]          {'ibc0': {'y': 4.4464914e-06 * meter / second}}]                                                           \n",
+      "30000     [1.193763e-05 * 10.0^0 * metre ** 2 * second ** -4,       [0.00057735 * 10.0^0 * metre ** 2 * second ** -4,         [{'y': Array(0.00868338, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': 3.8816847e-06 * meter / second}}]          {'ibc0': {'y': 3.8816847e-06 * meter / second}}]                                                           \n",
+      "\n",
+      "Best trainer at step 30000:\n",
+      "  train loss: 1.58e-05\n",
+      "  test loss: 5.81e-04\n",
+      "  test metric: [{'y': Array(0.01, dtype=float32)}]\n",
+      "\n",
+      "'train' took 14.610238 s\n",
+      "\n",
+      "Saving loss history to D:\\codes\\projects\\pinnx\\docs\\examples-pinn-inverse\\loss.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\examples-pinn-inverse\\loss.dat\n",
+      "Saving training data to D:\\codes\\projects\\pinnx\\docs\\examples-pinn-inverse\\train.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\examples-pinn-inverse\\train.dat\n",
+      "Saving test data to D:\\codes\\projects\\pinnx\\docs\\examples-pinn-inverse\\test.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\examples-pinn-inverse\\test.dat\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "variable = deepxde.callbacks.VariableValue([v_e, K], period=200, filename=\"../examples-pinn-inverse/variables1.dat\")\n",
+    "trainer = deepxde.Trainer(problem, external_trainable_variables=[v_e, K])\n",
+    "trainer.compile(bst.optim.Adam(0.001), metrics=[\"l2 relative error\"]).train(iterations=30000, callbacks=[variable])\n",
+    "trainer.saveplot(issave=True, isplot=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55206d2d7c0aac3",
+   "metadata": {},
+   "source": [
+    "The complete code is available in the [Brinkman-Forchheimer model example](https://github.com/lululxvi/deepxde/blob/master/docs/unit-examples-forward/brinkman_forchheimer.py)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/experimental_docs/unit-examples-inverse/brinkman_forchheimer.py b/docs/experimental_docs/unit-examples-inverse/brinkman_forchheimer.py
new file mode 100644
index 000000000..bec4aa0c6
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-inverse/brinkman_forchheimer.py
@@ -0,0 +1,72 @@
+"""
+Implementation of Brinkman-Forchheimer equation example in paper https://arxiv.org/pdf/2111.02801.pdf.
+"""
+
+import brainstate as bst
+import brainunit as u
+import numpy as np
+
+import deepxde.experimental as deepxde
+
+g = 1 * u.meter / u.second ** 2
+v = 1e-3 * u.meter ** 2 / u.second
+e = 0.4
+H = 1 * u.meter
+
+
+v_e = bst.ParamState(0.1 * u.meter ** 2 / u.second)
+K = bst.ParamState(0.001 * u.meter ** 2)
+
+
+def sol(x):
+    x = x['x'].mantissa
+    r = (v.mantissa * e / (1e-3 * 1e-3)) ** 0.5
+    y = (
+        g.mantissa * 1e-3 /
+        v.mantissa *
+        (1 - u.math.cosh(r * (x - H.mantissa / 2)) /
+         u.math.cosh(r * H.mantissa / 2))
+    )
+    return {'y': y * u.meter / u.second}
+
+geom = deepxde.geometry.Interval(0, 1).to_dict_point(x=u.meter)
+
+
+def pde(x, y):
+    du_xx = net.hessian(x)["y"]["x"]["x"]
+    return -v_e.value / e * du_xx + v * y['y'] / K.value - g
+
+
+def gen_traindata(num):
+    xvals = np.linspace(1 / (num + 1), 1, num, endpoint=False)
+    x = {'x': xvals * u.meter}
+    return x, sol(x)
+
+ob_x, ob_u = gen_traindata(5)
+observe_u = deepxde.icbc.PointSetBC(ob_x, ob_u)
+
+net = deepxde.nn.Model(
+    deepxde.nn.DictToArray(x=u.meter),
+    deepxde.nn.FNN(
+        [1] + [20] * 3 + [1], "tanh",
+        output_transform=lambda x, y: x * (1 - x) * y
+    ),
+    deepxde.nn.ArrayToDict(y=u.meter / u.second),
+)
+
+problem = deepxde.problem.PDE(
+    geom,
+    pde,
+    approximator=net,
+    solution=sol,
+    constraints=[observe_u],
+    num_domain=100,
+    num_boundary=0,
+    train_distribution="uniform",
+    num_test=500,
+)
+
+variable = deepxde.callbacks.VariableValue([v_e, K], period=200, filename="./variables1.dat")
+trainer = deepxde.Trainer(problem, external_trainable_variables=[v_e, K])
+trainer.compile(bst.optim.Adam(0.001), metrics=["l2 relative error"]).train(iterations=30000, callbacks=[variable])
+trainer.saveplot(issave=True, isplot=True)
diff --git a/docs/experimental_docs/unit-examples-inverse/diffusion_1d_inverse.ipynb b/docs/experimental_docs/unit-examples-inverse/diffusion_1d_inverse.ipynb
new file mode 100644
index 000000000..3f1161182
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-inverse/diffusion_1d_inverse.ipynb
@@ -0,0 +1,713 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2302dbabe1f9d091",
+   "metadata": {},
+   "source": [
+    "# Inverse problem for the diffusion equation\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ade6fe17e09dcdf4",
+   "metadata": {},
+   "source": [
+    "## Problem setup\n",
+    "\n",
+    "We will solve an inverse problem for the diffusion equation with an unknown parameter $C$ :\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\partial y}{\\partial t}=C \\frac{\\partial^2 y}{\\partial x^2}-e^{-t}\\left(\\sin (\\pi x)-\\pi^2 \\sin (\\pi x)\\right), \\quad x \\in[-1,1], \\quad t \\in[0,1]\n",
+    "$$\n",
+    "\n",
+    "with the initial condition\n",
+    "\n",
+    "$$\n",
+    "y(x, 0)=\\sin (\\pi x)\n",
+    "$$\n",
+    "\n",
+    "and the Dirichlet boundary condition\n",
+    "\n",
+    "$$\n",
+    "y(-1, t)=y(1, t)=0\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "The reference solution is $y=e^{-t} \\sin (\\pi x)$.\n",
+    "\n",
+    "\n",
+    "## Dimensional Analysis\n",
+    "\n",
+    "\n",
+    "Below is the dimensional analysis of the given **diffusion equation** with an unknown parameter $C$:\n",
+    "\n",
+    "\n",
+    "### Step 1: Assign Dimensions to Variables\n",
+    "\n",
+    "1. **Spatial Coordinate $x$:**  \n",
+    "   - Spatial coordinate has the dimension of length:  \n",
+    "\n",
+    "     $$\n",
+    "     [x] = L.\n",
+    "     $$\n",
+    "\n",
+    "2. **Time $t$:**  \n",
+    "   - Time has the dimension:  \n",
+    "\n",
+    "     $$\n",
+    "     [t] = T.\n",
+    "     $$\n",
+    "\n",
+    "3. **Function $y(x, t)$:**  \n",
+    "   - $y$ is the solution of the diffusion equation and depends on the context. For this case, $y$ has no explicit physical quantity associated with it, but we assume it to be **dimensionless** since the reference solution is given as $ y = e^{-t} \\sin(\\pi x) $, where both $e^{-t}$ and $\\sin(\\pi x)$ are dimensionless.  \n",
+    "\n",
+    "     $$\n",
+    "     [y] = 1 \\quad \\text{(dimensionless)}.\n",
+    "     $$\n",
+    "\n",
+    "4. **Parameter $C$:**  \n",
+    "   - The term $C \\frac{\\partial^2 y}{\\partial x^2}$ must have the same dimension as $\\frac{\\partial y}{\\partial t}$ for consistency.  \n",
+    "\n",
+    "     - First, consider the time derivative:\n",
+    "\n",
+    "       $$\n",
+    "       \\left[\\frac{\\partial y}{\\partial t}\\right] = \\frac{[y]}{[t]} = \\frac{1}{T}.\n",
+    "       $$\n",
+    "\n",
+    "     - Next, consider the second spatial derivative:  \n",
+    "\n",
+    "       $$\n",
+    "       \\left[\\frac{\\partial^2 y}{\\partial x^2}\\right] = \\frac{[y]}{[x]^2} = \\frac{1}{L^2}.\n",
+    "       $$\n",
+    "     - Multiplying by $C$, the dimensions of $C$ must satisfy:  \n",
+    "\n",
+    "       $$\n",
+    "       [C] \\cdot \\frac{1}{L^2} = \\frac{1}{T} \\implies [C] = \\frac{L^2}{T}.\n",
+    "       $$\n",
+    "\n",
+    "5. **Source Term:** $e^{-t} \\left(\\sin(\\pi x) - \\pi^2 \\sin(\\pi x)\\right)$\n",
+    "   - The exponential term $e^{-t}$ and the sine functions are dimensionless. Therefore, the source term is dimensionally consistent with:  \n",
+    "   \n",
+    "     $$\n",
+    "     \\text{Source Term} = \\frac{1}{T}.\n",
+    "     $$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 2: Initial and Boundary Conditions\n",
+    "\n",
+    "- **Initial Condition:** $y(x, 0) = \\sin(\\pi x)$.  \n",
+    "  - $\\sin(\\pi x)$ is dimensionless, consistent with $ [y] = 1 $.\n",
+    "\n",
+    "- **Boundary Condition:** $y(-1, t) = y(1, t) = 0$.  \n",
+    "  - The boundary values are dimensionless.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 3: Summary of Dimensions\n",
+    "\n",
+    "| Variable/Parameter     | Physical Meaning                   | Dimensions            |\n",
+    "|------------------------|-----------------------------------|-----------------------|\n",
+    "| $x$                  | Spatial coordinate                | $L$                 |\n",
+    "| $t$                  | Time                              | $T$                 |\n",
+    "| $y$                  | Solution (dimensionless)          | $1$                 |\n",
+    "| $C$                  | Diffusion coefficient             | $L^2 / T$           |\n",
+    "| Source term            | Forcing function                  | $1 / T$             |\n",
+    "\n",
+    "---\n",
+    "\n",
+    "In conclusion,\n",
+    "\n",
+    "- The unknown parameter $C$ has dimensions of $L^2 / T$, which is consistent with the physical meaning of a diffusion coefficient.  \n",
+    "- The function $y$ and the boundary/initial conditions are dimensionless, ensuring the consistency of the problem setup.\n",
+    "\n",
+    "\n",
+    "## Implementation\n",
+    "\n",
+    "First, we import the necessary libraries and define the diffusion coefficient $C$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b14466f59910c8ca",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:55:52.923053Z",
+     "start_time": "2024-12-17T11:55:45.342106Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import brainstate as bst\n",
+    "import brainunit as u\n",
+    "\n",
+    "import deepxde.experimental as deepxde"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57750359145b44ed",
+   "metadata": {},
+   "source": [
+    "Define the physical units for the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5cdd092e713648c6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:55:52.961722Z",
+     "start_time": "2024-12-17T11:55:52.951823Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "unit_of_x = u.meter\n",
+    "unit_of_t = u.second\n",
+    "unit_of_f = 1 / u.second"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77d9da26ed289f2a",
+   "metadata": {},
+   "source": [
+    "Define the diffusion coefficient $C$ and the PDE function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5f2555c7ead2367f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:55:53.147610Z",
+     "start_time": "2024-12-17T11:55:53.094464Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "C = bst.ParamState(2.0 * u.meter ** 2 / u.second)\n",
+    "\n",
+    "\n",
+    "def pde(x, y):\n",
+    "    jacobian = net.jacobian(x, x='t')\n",
+    "    hessian = net.hessian(x, xi='x', xj='x')\n",
+    "\n",
+    "    dy_t = jacobian[\"y\"][\"t\"]\n",
+    "    dy_xx = hessian[\"y\"][\"x\"][\"x\"]\n",
+    "    source = (\n",
+    "        u.math.exp(-x['t'] / unit_of_t) *\n",
+    "        (u.math.sin(u.math.pi * x['x'] / unit_of_x) -\n",
+    "         u.math.pi ** 2 * u.math.sin(u.math.pi * x['x'] / unit_of_x))\n",
+    "    )\n",
+    "\n",
+    "    return dy_t - C.value * dy_xx + source * unit_of_f\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a928939bb63c740c",
+   "metadata": {},
+   "source": [
+    "Define the geometry:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e908c2d526aa2ca8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:55:53.195884Z",
+     "start_time": "2024-12-17T11:55:53.187882Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "geom = deepxde.geometry.Interval(-1, 1)\n",
+    "timedomain = deepxde.geometry.TimeDomain(0, 1)\n",
+    "geomtime = deepxde.geometry.GeometryXTime(geom, timedomain).to_dict_point(x=unit_of_x, t=unit_of_t)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c719b37b9d0b7c3c",
+   "metadata": {},
+   "source": [
+    "Define the initial and boundary conditions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9ca35cce65b759c5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:55:53.227949Z",
+     "start_time": "2024-12-17T11:55:53.219564Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def func(x):\n",
+    "    y = u.math.sin(u.math.pi * x['x'] / unit_of_x) * u.math.exp(-x['t'] / unit_of_t)\n",
+    "    return {'y': y}\n",
+    "\n",
+    "\n",
+    "bc = deepxde.icbc.DirichletBC(func)\n",
+    "ic = deepxde.icbc.IC(func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c46ecd2824e7271",
+   "metadata": {},
+   "source": [
+    "Define the observation points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8f0fa4abcf0de76e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:55:53.697829Z",
+     "start_time": "2024-12-17T11:55:53.276581Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x = {\n",
+    "    'x': u.math.linspace(-1, 1, num=10) * unit_of_x,\n",
+    "    't': u.math.full((10,), 1) * unit_of_t,\n",
+    "}\n",
+    "observe_y = deepxde.icbc.PointSetBC(x, func(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "905e64508fde15d2",
+   "metadata": {},
+   "source": [
+    "Define the neural network model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6d210547e792f151",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:55:54.575794Z",
+     "start_time": "2024-12-17T11:55:53.813933Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "net = deepxde.nn.Model(\n",
+    "    deepxde.nn.DictToArray(x=unit_of_x, t=unit_of_t),\n",
+    "    deepxde.nn.FNN([2] + [32] * 3 + [1], \"tanh\"),\n",
+    "    deepxde.nn.ArrayToDict(y=None),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f56e4b92ccfbd623",
+   "metadata": {},
+   "source": [
+    "Define the inverse problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1a80b4edda3cd3db",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:55:56.553747Z",
+     "start_time": "2024-12-17T11:55:54.680989Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: 10000 points required, but 10082 points sampled.\n"
+     ]
+    }
+   ],
+   "source": [
+    "problem = deepxde.problem.TimePDE(\n",
+    "    geomtime,\n",
+    "    pde,\n",
+    "    [bc, ic, observe_y],\n",
+    "    net,\n",
+    "    num_domain=40,\n",
+    "    num_boundary=20,\n",
+    "    num_initial=10,\n",
+    "    anchors=x,\n",
+    "    solution=func,\n",
+    "    num_test=10000,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c55cae7178a45e1",
+   "metadata": {},
+   "source": [
+    "Train the neural network model and solve the inverse problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "94928f8ed0e68c95",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:56:47.459476Z",
+     "start_time": "2024-12-17T11:55:56.672102Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.106258 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                   Test loss                                                    Test metric                                       \n",
+      "0         [35.679718 * becquerel2,                                     [45.503433 * becquerel2,                                     [{'y': Array(0.8310661, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': Array(0.7570488, dtype=float32)}},            {'ibc0': {'y': Array(0.7570488, dtype=float32)}},                                                             \n",
+      "           {'ibc1': {'y': Array(0.02982451, dtype=float32)}},           {'ibc1': {'y': Array(0.02982451, dtype=float32)}},                                                            \n",
+      "           {'ibc2': {'y': Array(0.25618497, dtype=float32)}}]           {'ibc2': {'y': Array(0.25618497, dtype=float32)}}]                                                            \n",
+      "0 [2.00e+00 * meter2 / second]\n",
+      "1000      [0.03602879 * becquerel2,                                    [0.04448503 * becquerel2,                                    [{'y': Array(0.38794544, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(0.02735445, dtype=float32)}},           {'ibc0': {'y': Array(0.02735445, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(0.06501726, dtype=float32)}},           {'ibc1': {'y': Array(0.06501726, dtype=float32)}},                                                            \n",
+      "           {'ibc2': {'y': Array(0.01995897, dtype=float32)}}]           {'ibc2': {'y': Array(0.01995897, dtype=float32)}}]                                                            \n",
+      "1000 [1.97e+00 * meter2 / second]\n",
+      "2000      [0.01038404 * becquerel2,                                    [0.01745677 * becquerel2,                                    [{'y': Array(0.37819982, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(0.02291086, dtype=float32)}},           {'ibc0': {'y': Array(0.02291086, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(0.05443568, dtype=float32)}},           {'ibc1': {'y': Array(0.05443568, dtype=float32)}},                                                            \n",
+      "           {'ibc2': {'y': Array(0.01564121, dtype=float32)}}]           {'ibc2': {'y': Array(0.01564121, dtype=float32)}}]                                                            \n",
+      "2000 [1.93e+00 * meter2 / second]\n",
+      "3000      [0.00719014 * becquerel2,                                    [0.01219053 * becquerel2,                                    [{'y': Array(0.37957639, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(0.01583208, dtype=float32)}},           {'ibc0': {'y': Array(0.01583208, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(0.03816846, dtype=float32)}},           {'ibc1': {'y': Array(0.03816846, dtype=float32)}},                                                            \n",
+      "           {'ibc2': {'y': Array(0.01334066, dtype=float32)}}]           {'ibc2': {'y': Array(0.01334066, dtype=float32)}}]                                                            \n",
+      "3000 [1.87e+00 * meter2 / second]\n",
+      "4000      [0.00574172 * becquerel2,                                    [0.01916098 * becquerel2,                                    [{'y': Array(0.39443684, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(0.00819715, dtype=float32)}},           {'ibc0': {'y': Array(0.00819715, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(0.01961347, dtype=float32)}},           {'ibc1': {'y': Array(0.01961347, dtype=float32)}},                                                            \n",
+      "           {'ibc2': {'y': Array(0.01037939, dtype=float32)}}]           {'ibc2': {'y': Array(0.01037939, dtype=float32)}}]                                                            \n",
+      "4000 [1.79e+00 * meter2 / second]\n",
+      "5000      [0.00397846 * becquerel2,                                    [0.03868386 * becquerel2,                                    [{'y': Array(0.403426, dtype=float32)}]           \n",
+      "           {'ibc0': {'y': Array(0.00524556, dtype=float32)}},           {'ibc0': {'y': Array(0.00524556, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(0.00908885, dtype=float32)}},           {'ibc1': {'y': Array(0.00908885, dtype=float32)}},                                                            \n",
+      "           {'ibc2': {'y': Array(0.00787205, dtype=float32)}}]           {'ibc2': {'y': Array(0.00787205, dtype=float32)}}]                                                            \n",
+      "5000 [1.70e+00 * meter2 / second]\n",
+      "6000      [0.00152285 * becquerel2,                                    [0.07867768 * becquerel2,                                    [{'y': Array(0.3750372, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': Array(0.00280839, dtype=float32)}},           {'ibc0': {'y': Array(0.00280839, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(0.00285654, dtype=float32)}},           {'ibc1': {'y': Array(0.00285654, dtype=float32)}},                                                            \n",
+      "           {'ibc2': {'y': Array(0.00617229, dtype=float32)}}]           {'ibc2': {'y': Array(0.00617229, dtype=float32)}}]                                                            \n",
+      "6000 [1.59e+00 * meter2 / second]\n",
+      "7000      [0.00042557 * becquerel2,                                    [0.16960475 * becquerel2,                                    [{'y': Array(0.32183775, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(0.00137896, dtype=float32)}},           {'ibc0': {'y': Array(0.00137896, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(0.00036345, dtype=float32)}},           {'ibc1': {'y': Array(0.00036345, dtype=float32)}},                                                            \n",
+      "           {'ibc2': {'y': Array(0.00481733, dtype=float32)}}]           {'ibc2': {'y': Array(0.00481733, dtype=float32)}}]                                                            \n",
+      "7000 [1.49e+00 * meter2 / second]\n",
+      "8000      [0.00017074 * becquerel2,                                    [0.19776653 * becquerel2,                                    [{'y': Array(0.26631635, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(0.00086597, dtype=float32)}},           {'ibc0': {'y': Array(0.00086597, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(7.828907e-05, dtype=float32)}},         {'ibc1': {'y': Array(7.828907e-05, dtype=float32)}},                                                          \n",
+      "           {'ibc2': {'y': Array(0.00348265, dtype=float32)}}]           {'ibc2': {'y': Array(0.00348265, dtype=float32)}}]                                                            \n",
+      "8000 [1.38e+00 * meter2 / second]\n",
+      "9000      [0.00010915 * becquerel2,                                    [0.14258294 * becquerel2,                                    [{'y': Array(0.19723569, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(0.00049594, dtype=float32)}},           {'ibc0': {'y': Array(0.00049594, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(7.553954e-05, dtype=float32)}},         {'ibc1': {'y': Array(7.553954e-05, dtype=float32)}},                                                          \n",
+      "           {'ibc2': {'y': Array(0.00193216, dtype=float32)}}]           {'ibc2': {'y': Array(0.00193216, dtype=float32)}}]                                                            \n",
+      "9000 [1.26e+00 * meter2 / second]\n",
+      "10000     [0.00038679 * becquerel2,                                    [0.07152588 * becquerel2,                                    [{'y': Array(0.12039716, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(0.00016165, dtype=float32)}},           {'ibc0': {'y': Array(0.00016165, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(7.3326264e-05, dtype=float32)}},        {'ibc1': {'y': Array(7.3326264e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(0.0007023, dtype=float32)}}]            {'ibc2': {'y': Array(0.0007023, dtype=float32)}}]                                                             \n",
+      "10000 [1.13e+00 * meter2 / second]\n",
+      "11000     [6.400991e-05 * becquerel2,                                  [0.02994728 * becquerel2,                                    [{'y': Array(0.05713889, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(8.80569e-05, dtype=float32)}},          {'ibc0': {'y': Array(8.80569e-05, dtype=float32)}},                                                           \n",
+      "           {'ibc1': {'y': Array(4.1765466e-05, dtype=float32)}},        {'ibc1': {'y': Array(4.1765466e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(0.00013529, dtype=float32)}}]           {'ibc2': {'y': Array(0.00013529, dtype=float32)}}]                                                            \n",
+      "11000 [1.06e+00 * meter2 / second]\n",
+      "12000     [4.424834e-05 * becquerel2,                                  [0.01685221 * becquerel2,                                    [{'y': Array(0.03156196, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(4.9093396e-05, dtype=float32)}},        {'ibc0': {'y': Array(4.9093396e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(1.958303e-05, dtype=float32)}},         {'ibc1': {'y': Array(1.958303e-05, dtype=float32)}},                                                          \n",
+      "           {'ibc2': {'y': Array(2.9790845e-05, dtype=float32)}}]        {'ibc2': {'y': Array(2.9790845e-05, dtype=float32)}}]                                                         \n",
+      "12000 [1.03e+00 * meter2 / second]\n",
+      "13000     [4.0583745e-05 * becquerel2,                                 [0.01293554 * becquerel2,                                    [{'y': Array(0.02287465, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(3.074142e-05, dtype=float32)}},         {'ibc0': {'y': Array(3.074142e-05, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(1.0277039e-05, dtype=float32)}},        {'ibc1': {'y': Array(1.0277039e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(1.312225e-05, dtype=float32)}}]         {'ibc2': {'y': Array(1.312225e-05, dtype=float32)}}]                                                          \n",
+      "13000 [1.01e+00 * meter2 / second]\n",
+      "14000     [2.6840251e-05 * becquerel2,                                 [0.01152143 * becquerel2,                                    [{'y': Array(0.01827432, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(2.6315587e-05, dtype=float32)}},        {'ibc0': {'y': Array(2.6315587e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(6.6273933e-06, dtype=float32)}},        {'ibc1': {'y': Array(6.6273933e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(5.9816884e-06, dtype=float32)}}]        {'ibc2': {'y': Array(5.9816884e-06, dtype=float32)}}]                                                         \n",
+      "14000 [1.01e+00 * meter2 / second]\n",
+      "15000     [0.00012371 * becquerel2,                                    [0.01070947 * becquerel2,                                    [{'y': Array(0.01927234, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(2.5944038e-05, dtype=float32)}},        {'ibc0': {'y': Array(2.5944038e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(7.3463248e-06, dtype=float32)}},        {'ibc1': {'y': Array(7.3463248e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(1.579595e-05, dtype=float32)}}]         {'ibc2': {'y': Array(1.579595e-05, dtype=float32)}}]                                                          \n",
+      "15000 [1.00e+00 * meter2 / second]\n",
+      "16000     [1.8782257e-05 * becquerel2,                                 [0.010138 * becquerel2,                                      [{'y': Array(0.01481783, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(2.1143982e-05, dtype=float32)}},        {'ibc0': {'y': Array(2.1143982e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(4.4593826e-06, dtype=float32)}},        {'ibc1': {'y': Array(4.4593826e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(3.7475588e-06, dtype=float32)}}]        {'ibc2': {'y': Array(3.7475588e-06, dtype=float32)}}]                                                         \n",
+      "16000 [1.00e+00 * meter2 / second]\n",
+      "17000     [1.5538082e-05 * becquerel2,                                 [0.00959209 * becquerel2,                                    [{'y': Array(0.01383823, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(1.9341458e-05, dtype=float32)}},        {'ibc0': {'y': Array(1.9341458e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(4.0253426e-06, dtype=float32)}},        {'ibc1': {'y': Array(4.0253426e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(3.624856e-06, dtype=float32)}}]         {'ibc2': {'y': Array(3.624856e-06, dtype=float32)}}]                                                          \n",
+      "17000 [1.00e+00 * meter2 / second]\n",
+      "18000     [1.4605197e-05 * becquerel2,                                 [0.00903223 * becquerel2,                                    [{'y': Array(0.01343373, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(1.7818755e-05, dtype=float32)}},        {'ibc0': {'y': Array(1.7818755e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(3.7411664e-06, dtype=float32)}},        {'ibc1': {'y': Array(3.7411664e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(3.8074975e-06, dtype=float32)}}]        {'ibc2': {'y': Array(3.8074975e-06, dtype=float32)}}]                                                         \n",
+      "18000 [1.00e+00 * meter2 / second]\n",
+      "19000     [0.00020008 * becquerel2,                                    [0.00830668 * becquerel2,                                    [{'y': Array(0.01487183, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(3.245175e-05, dtype=float32)}},         {'ibc0': {'y': Array(3.245175e-05, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(1.4154613e-05, dtype=float32)}},        {'ibc1': {'y': Array(1.4154613e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(1.382081e-05, dtype=float32)}}]         {'ibc2': {'y': Array(1.382081e-05, dtype=float32)}}]                                                          \n",
+      "19000 [1.00e+00 * meter2 / second]\n",
+      "20000     [1.2453206e-05 * becquerel2,                                 [0.00797975 * becquerel2,                                    [{'y': Array(0.01158835, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(1.5657448e-05, dtype=float32)}},        {'ibc0': {'y': Array(1.5657448e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(3.3342942e-06, dtype=float32)}},        {'ibc1': {'y': Array(3.3342942e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(2.7141502e-06, dtype=float32)}}]        {'ibc2': {'y': Array(2.7141502e-06, dtype=float32)}}]                                                         \n",
+      "20000 [1.00e+00 * meter2 / second]\n",
+      "21000     [1.0425839e-05 * becquerel2,                                 [0.00742463 * becquerel2,                                    [{'y': Array(0.01137655, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(1.4240931e-05, dtype=float32)}},        {'ibc0': {'y': Array(1.4240931e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(3.172249e-06, dtype=float32)}},         {'ibc1': {'y': Array(3.172249e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc2': {'y': Array(2.8847342e-06, dtype=float32)}}]        {'ibc2': {'y': Array(2.8847342e-06, dtype=float32)}}]                                                         \n",
+      "21000 [1.00e+00 * meter2 / second]\n",
+      "22000     [1.3698635e-05 * becquerel2,                                 [0.00690996 * becquerel2,                                    [{'y': Array(0.01074506, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(1.3334024e-05, dtype=float32)}},        {'ibc0': {'y': Array(1.3334024e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(3.3594083e-06, dtype=float32)}},        {'ibc1': {'y': Array(3.3594083e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(2.9274486e-06, dtype=float32)}}]        {'ibc2': {'y': Array(2.9274486e-06, dtype=float32)}}]                                                         \n",
+      "22000 [1.00e+00 * meter2 / second]\n",
+      "23000     [1.260023e-05 * becquerel2,                                  [0.00642458 * becquerel2,                                    [{'y': Array(0.01045308, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(1.2438575e-05, dtype=float32)}},        {'ibc0': {'y': Array(1.2438575e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(2.9666814e-06, dtype=float32)}},        {'ibc1': {'y': Array(2.9666814e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(2.9300436e-06, dtype=float32)}}]        {'ibc2': {'y': Array(2.9300436e-06, dtype=float32)}}]                                                         \n",
+      "23000 [1.00e+00 * meter2 / second]\n",
+      "24000     [8.246606e-06 * becquerel2,                                  [0.00602244 * becquerel2,                                    [{'y': Array(0.00997281, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(1.1696405e-05, dtype=float32)}},        {'ibc0': {'y': Array(1.1696405e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(2.636282e-06, dtype=float32)}},         {'ibc1': {'y': Array(2.636282e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc2': {'y': Array(2.374021e-06, dtype=float32)}}]         {'ibc2': {'y': Array(2.374021e-06, dtype=float32)}}]                                                          \n",
+      "24000 [1.00e+00 * meter2 / second]\n",
+      "25000     [1.1195777e-05 * becquerel2,                                 [0.00564192 * becquerel2,                                    [{'y': Array(0.01005104, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(1.1166664e-05, dtype=float32)}},        {'ibc0': {'y': Array(1.1166664e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(2.4298395e-06, dtype=float32)}},        {'ibc1': {'y': Array(2.4298395e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(2.711555e-06, dtype=float32)}}]         {'ibc2': {'y': Array(2.711555e-06, dtype=float32)}}]                                                          \n",
+      "25000 [1.00e+00 * meter2 / second]\n",
+      "26000     [9.503363e-06 * becquerel2,                                  [0.0052563 * becquerel2,                                     [{'y': Array(0.00906924, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(1.0266521e-05, dtype=float32)}},        {'ibc0': {'y': Array(1.0266521e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(2.6634286e-06, dtype=float32)}},        {'ibc1': {'y': Array(2.6634286e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(2.141071e-06, dtype=float32)}}]         {'ibc2': {'y': Array(2.141071e-06, dtype=float32)}}]                                                          \n",
+      "26000 [1.00e+00 * meter2 / second]\n",
+      "27000     [6.5200206e-06 * becquerel2,                                 [0.00498187 * becquerel2,                                    [{'y': Array(0.00870219, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(1.0096963e-05, dtype=float32)}},        {'ibc0': {'y': Array(1.0096963e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(2.105438e-06, dtype=float32)}},         {'ibc1': {'y': Array(2.105438e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc2': {'y': Array(1.7404262e-06, dtype=float32)}}]        {'ibc2': {'y': Array(1.7404262e-06, dtype=float32)}}]                                                         \n",
+      "27000 [1.00e+00 * meter2 / second]\n",
+      "28000     [6.0612156e-06 * becquerel2,                                 [0.00469941 * becquerel2,                                    [{'y': Array(0.00836593, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(9.4506e-06, dtype=float32)}},           {'ibc0': {'y': Array(9.4506e-06, dtype=float32)}},                                                            \n",
+      "           {'ibc1': {'y': Array(1.9978488e-06, dtype=float32)}},        {'ibc1': {'y': Array(1.9978488e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(1.6832313e-06, dtype=float32)}}]        {'ibc2': {'y': Array(1.6832313e-06, dtype=float32)}}]                                                         \n",
+      "28000 [1.00e+00 * meter2 / second]\n",
+      "29000     [0.00027426 * becquerel2,                                    [0.00477805 * becquerel2,                                    [{'y': Array(0.01418097, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(1.648501e-05, dtype=float32)}},         {'ibc0': {'y': Array(1.648501e-05, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(6.1632722e-06, dtype=float32)}},        {'ibc1': {'y': Array(6.1632722e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(2.5690164e-05, dtype=float32)}}]        {'ibc2': {'y': Array(2.5690164e-05, dtype=float32)}}]                                                         \n",
+      "29000 [1.00e+00 * meter2 / second]\n",
+      "30000     [5.3968092e-06 * becquerel2,                                 [0.00423714 * becquerel2,                                    [{'y': Array(0.00768072, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(8.529474e-06, dtype=float32)}},         {'ibc0': {'y': Array(8.529474e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(1.7662005e-06, dtype=float32)}},        {'ibc1': {'y': Array(1.7662005e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(1.4625474e-06, dtype=float32)}}]        {'ibc2': {'y': Array(1.4625474e-06, dtype=float32)}}]                                                         \n",
+      "30000 [1.00e+00 * meter2 / second]\n",
+      "31000     [5.086522e-06 * becquerel2,                                  [0.00405679 * becquerel2,                                    [{'y': Array(0.00739116, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(8.098868e-06, dtype=float32)}},         {'ibc0': {'y': Array(8.098868e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(1.6670391e-06, dtype=float32)}},        {'ibc1': {'y': Array(1.6670391e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(1.3792062e-06, dtype=float32)}}]        {'ibc2': {'y': Array(1.3792062e-06, dtype=float32)}}]                                                         \n",
+      "31000 [1.00e+00 * meter2 / second]\n",
+      "32000     [9.037222e-06 * becquerel2,                                  [0.00388475 * becquerel2,                                    [{'y': Array(0.006628, dtype=float32)}]           \n",
+      "           {'ibc0': {'y': Array(8.564745e-06, dtype=float32)}},         {'ibc0': {'y': Array(8.564745e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(1.6539137e-06, dtype=float32)}},        {'ibc1': {'y': Array(1.6539137e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(1.3912552e-06, dtype=float32)}}]        {'ibc2': {'y': Array(1.3912552e-06, dtype=float32)}}]                                                         \n",
+      "32000 [1.00e+00 * meter2 / second]\n",
+      "33000     [4.528527e-06 * becquerel2,                                  [0.00375882 * becquerel2,                                    [{'y': Array(0.0068511, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': Array(7.359239e-06, dtype=float32)}},         {'ibc0': {'y': Array(7.359239e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(1.4748082e-06, dtype=float32)}},        {'ibc1': {'y': Array(1.4748082e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(1.2449651e-06, dtype=float32)}}]        {'ibc2': {'y': Array(1.2449651e-06, dtype=float32)}}]                                                         \n",
+      "33000 [1.00e+00 * meter2 / second]\n",
+      "34000     [4.695375e-06 * becquerel2,                                  [0.00363632 * becquerel2,                                    [{'y': Array(0.00661771, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(6.8333384e-06, dtype=float32)}},        {'ibc0': {'y': Array(6.8333384e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(1.4989963e-06, dtype=float32)}},        {'ibc1': {'y': Array(1.4989963e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(1.2258452e-06, dtype=float32)}}]        {'ibc2': {'y': Array(1.2258452e-06, dtype=float32)}}]                                                         \n",
+      "34000 [1.00e+00 * meter2 / second]\n",
+      "35000     [0.00031871 * becquerel2,                                    [0.00399784 * becquerel2,                                    [{'y': Array(0.01087699, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(5.1616953e-05, dtype=float32)}},        {'ibc0': {'y': Array(5.1616953e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(1.8963277e-05, dtype=float32)}},        {'ibc1': {'y': Array(1.8963277e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(1.1703542e-05, dtype=float32)}}]        {'ibc2': {'y': Array(1.1703542e-05, dtype=float32)}}]                                                         \n",
+      "35000 [1.00e+00 * meter2 / second]\n",
+      "36000     [3.8406342e-06 * becquerel2,                                 [0.00347829 * becquerel2,                                    [{'y': Array(0.0061718, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': Array(6.3839334e-06, dtype=float32)}},        {'ibc0': {'y': Array(6.3839334e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(1.249506e-06, dtype=float32)}},         {'ibc1': {'y': Array(1.249506e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc2': {'y': Array(1.0979719e-06, dtype=float32)}}]        {'ibc2': {'y': Array(1.0979719e-06, dtype=float32)}}]                                                         \n",
+      "36000 [1.00e+00 * meter2 / second]\n",
+      "37000     [7.074466e-05 * becquerel2,                                  [0.00337066 * becquerel2,                                    [{'y': Array(0.00761305, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(1.2363613e-05, dtype=float32)}},        {'ibc0': {'y': Array(1.2363613e-05, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(6.2355684e-06, dtype=float32)}},        {'ibc1': {'y': Array(6.2355684e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(4.710876e-06, dtype=float32)}}]         {'ibc2': {'y': Array(4.710876e-06, dtype=float32)}}]                                                          \n",
+      "37000 [1.00e+00 * meter2 / second]\n",
+      "38000     [3.465391e-06 * becquerel2,                                  [0.00336986 * becquerel2,                                    [{'y': Array(0.00574788, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(5.8357455e-06, dtype=float32)}},        {'ibc0': {'y': Array(5.8357455e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(1.135399e-06, dtype=float32)}},         {'ibc1': {'y': Array(1.135399e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc2': {'y': Array(1.0042679e-06, dtype=float32)}}]        {'ibc2': {'y': Array(1.0042679e-06, dtype=float32)}}]                                                         \n",
+      "38000 [1.00e+00 * meter2 / second]\n",
+      "39000     [1.0268762e-05 * becquerel2,                                 [0.0033686 * becquerel2,                                     [{'y': Array(0.00545561, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(8.097706e-06, dtype=float32)}},         {'ibc0': {'y': Array(8.097706e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(1.4723436e-06, dtype=float32)}},        {'ibc1': {'y': Array(1.4723436e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(9.431725e-07, dtype=float32)}}]         {'ibc2': {'y': Array(9.431725e-07, dtype=float32)}}]                                                          \n",
+      "39000 [1.00e+00 * meter2 / second]\n",
+      "40000     [4.2823112e-06 * becquerel2,                                 [0.00333381 * becquerel2,                                    [{'y': Array(0.0054828, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': Array(5.738444e-06, dtype=float32)}},         {'ibc0': {'y': Array(5.738444e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(9.648383e-07, dtype=float32)}},         {'ibc1': {'y': Array(9.648383e-07, dtype=float32)}},                                                          \n",
+      "           {'ibc2': {'y': Array(9.519137e-07, dtype=float32)}}]         {'ibc2': {'y': Array(9.519137e-07, dtype=float32)}}]                                                          \n",
+      "40000 [1.00e+00 * meter2 / second]\n",
+      "41000     [3.582141e-06 * becquerel2,                                  [0.00330048 * becquerel2,                                    [{'y': Array(0.00508944, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(5.472003e-06, dtype=float32)}},         {'ibc0': {'y': Array(5.472003e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(9.464065e-07, dtype=float32)}},         {'ibc1': {'y': Array(9.464065e-07, dtype=float32)}},                                                          \n",
+      "           {'ibc2': {'y': Array(7.4483506e-07, dtype=float32)}}]        {'ibc2': {'y': Array(7.4483506e-07, dtype=float32)}}]                                                         \n",
+      "41000 [1.00e+00 * meter2 / second]\n",
+      "42000     [7.568629e-06 * becquerel2,                                  [0.00326802 * becquerel2,                                    [{'y': Array(0.0046458, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': Array(5.431604e-06, dtype=float32)}},         {'ibc0': {'y': Array(5.431604e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(1.0235344e-06, dtype=float32)}},        {'ibc1': {'y': Array(1.0235344e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(7.4398366e-07, dtype=float32)}}]        {'ibc2': {'y': Array(7.4398366e-07, dtype=float32)}}]                                                         \n",
+      "42000 [1.00e+00 * meter2 / second]\n",
+      "43000     [4.5585504e-05 * becquerel2,                                 [0.00319802 * becquerel2,                                    [{'y': Array(0.00467657, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(5.951851e-06, dtype=float32)}},         {'ibc0': {'y': Array(5.951851e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(2.2854892e-06, dtype=float32)}},        {'ibc1': {'y': Array(2.2854892e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(3.2231546e-06, dtype=float32)}}]        {'ibc2': {'y': Array(3.2231546e-06, dtype=float32)}}]                                                         \n",
+      "43000 [1.00e+00 * meter2 / second]\n",
+      "44000     [1.7233344e-05 * becquerel2,                                 [0.00329324 * becquerel2,                                    [{'y': Array(0.00528376, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(5.196705e-06, dtype=float32)}},         {'ibc0': {'y': Array(5.196705e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(1.7772775e-06, dtype=float32)}},        {'ibc1': {'y': Array(1.7772775e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(1.7591086e-06, dtype=float32)}}]        {'ibc2': {'y': Array(1.7591086e-06, dtype=float32)}}]                                                         \n",
+      "44000 [1.00e+00 * meter2 / second]\n",
+      "45000     [2.5381134e-06 * becquerel2,                                 [0.00331884 * becquerel2,                                    [{'y': Array(0.00453062, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(4.551063e-06, dtype=float32)}},         {'ibc0': {'y': Array(4.551063e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(8.1471774e-07, dtype=float32)}},        {'ibc1': {'y': Array(8.1471774e-07, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(6.6565326e-07, dtype=float32)}}]        {'ibc2': {'y': Array(6.6565326e-07, dtype=float32)}}]                                                         \n",
+      "45000 [1.00e+00 * meter2 / second]\n",
+      "46000     [9.146097e-05 * becquerel2,                                  [0.00332611 * becquerel2,                                    [{'y': Array(0.00681314, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(1.28242e-05, dtype=float32)}},          {'ibc0': {'y': Array(1.28242e-05, dtype=float32)}},                                                           \n",
+      "           {'ibc1': {'y': Array(6.6814805e-06, dtype=float32)}},        {'ibc1': {'y': Array(6.6814805e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(5.115698e-06, dtype=float32)}}]         {'ibc2': {'y': Array(5.115698e-06, dtype=float32)}}]                                                          \n",
+      "46000 [1.00e+00 * meter2 / second]\n",
+      "47000     [2.7379099e-06 * becquerel2,                                 [0.00336373 * becquerel2,                                    [{'y': Array(0.00429155, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(4.344931e-06, dtype=float32)}},         {'ibc0': {'y': Array(4.344931e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(7.400954e-07, dtype=float32)}},         {'ibc1': {'y': Array(7.400954e-07, dtype=float32)}},                                                          \n",
+      "           {'ibc2': {'y': Array(5.937529e-07, dtype=float32)}}]         {'ibc2': {'y': Array(5.937529e-07, dtype=float32)}}]                                                          \n",
+      "47000 [1.00e+00 * meter2 / second]\n",
+      "48000     [0.00013315 * becquerel2,                                    [0.00377896 * becquerel2,                                    [{'y': Array(0.00817893, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(7.9199735e-06, dtype=float32)}},        {'ibc0': {'y': Array(7.9199735e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(2.2944569e-06, dtype=float32)}},        {'ibc1': {'y': Array(2.2944569e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc2': {'y': Array(1.2221748e-05, dtype=float32)}}]        {'ibc2': {'y': Array(1.2221748e-05, dtype=float32)}}]                                                         \n",
+      "48000 [1.00e+00 * meter2 / second]\n",
+      "49000     [2.3180658e-06 * becquerel2,                                 [0.00342008 * becquerel2,                                    [{'y': Array(0.00412427, dtype=float32)}]         \n",
+      "           {'ibc0': {'y': Array(3.955222e-06, dtype=float32)}},         {'ibc0': {'y': Array(3.955222e-06, dtype=float32)}},                                                          \n",
+      "           {'ibc1': {'y': Array(6.883399e-07, dtype=float32)}},         {'ibc1': {'y': Array(6.883399e-07, dtype=float32)}},                                                          \n",
+      "           {'ibc2': {'y': Array(6.2362477e-07, dtype=float32)}}]        {'ibc2': {'y': Array(6.2362477e-07, dtype=float32)}}]                                                         \n",
+      "49000 [1.00e+00 * meter2 / second]\n",
+      "50000     [3.457769e-06 * becquerel2,                                  [0.00347083 * becquerel2,                                    [{'y': Array(0.0041982, dtype=float32)}]          \n",
+      "           {'ibc0': {'y': Array(3.7689515e-06, dtype=float32)}},        {'ibc0': {'y': Array(3.7689515e-06, dtype=float32)}},                                                         \n",
+      "           {'ibc1': {'y': Array(6.474415e-07, dtype=float32)}},         {'ibc1': {'y': Array(6.474415e-07, dtype=float32)}},                                                          \n",
+      "           {'ibc2': {'y': Array(8.447834e-07, dtype=float32)}}]         {'ibc2': {'y': Array(8.447834e-07, dtype=float32)}}]                                                          \n",
+      "50000 [1.00e+00 * meter2 / second]\n",
+      "\n",
+      "Best trainer at step 49000:\n",
+      "  train loss: 7.59e-06\n",
+      "  test loss: 3.43e-03\n",
+      "  test metric: [{'y': Array(0., dtype=float32)}]\n",
+      "\n",
+      "'train' took 49.581476 s\n",
+      "\n",
+      "Saving loss history to D:\\codes\\projects\\pinnx\\docs\\unit-examples-inverse\\loss.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\unit-examples-inverse\\loss.dat\n",
+      "Saving training data to D:\\codes\\projects\\pinnx\\docs\\unit-examples-inverse\\train.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\unit-examples-inverse\\train.dat\n",
+      "Saving test data to D:\\codes\\projects\\pinnx\\docs\\unit-examples-inverse\\test.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\unit-examples-inverse\\test.dat\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "variable = deepxde.callbacks.VariableValue(C, period=1000)\n",
+    "trainer = deepxde.Trainer(problem, external_trainable_variables=C)\n",
+    "trainer.compile(bst.optim.Adam(0.001), metrics=[\"l2 relative error\"]).train(iterations=50000, callbacks=[variable])\n",
+    "trainer.saveplot(issave=True, isplot=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/experimental_docs/unit-examples-inverse/diffusion_1d_inverse.py b/docs/experimental_docs/unit-examples-inverse/diffusion_1d_inverse.py
new file mode 100644
index 000000000..3b49cda9e
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-inverse/diffusion_1d_inverse.py
@@ -0,0 +1,69 @@
+import brainstate as bst
+import brainunit as u
+
+import deepxde.experimental as deepxde
+
+unit_of_x = u.meter
+unit_of_t = u.second
+unit_of_f = 1 / u.second
+
+C = bst.ParamState(2.0 * u.meter ** 2 / u.second)
+
+
+def pde(x, y):
+    jacobian = net.jacobian(x, x='t')
+    hessian = net.hessian(x, xi='x', xj='x')
+
+    dy_t = jacobian["y"]["t"]
+    dy_xx = hessian["y"]["x"]["x"]
+    source = (
+        u.math.exp(-x['t'] / unit_of_t) *
+        (u.math.sin(u.math.pi * x['x'] / unit_of_x) -
+         u.math.pi ** 2 * u.math.sin(u.math.pi * x['x'] / unit_of_x))
+    )
+
+    return dy_t - C.value * dy_xx + source * unit_of_f
+
+
+geom = deepxde.geometry.Interval(-1, 1)
+timedomain = deepxde.geometry.TimeDomain(0, 1)
+geomtime = deepxde.geometry.GeometryXTime(geom, timedomain).to_dict_point(x=unit_of_x, t=unit_of_t)
+
+
+def func(x):
+    y = u.math.sin(u.math.pi * x['x'] / unit_of_x) * u.math.exp(-x['t'] / unit_of_t)
+    return {'y': y}
+
+
+bc = deepxde.icbc.DirichletBC(func)
+ic = deepxde.icbc.IC(func)
+
+x = {
+    'x': u.math.linspace(-1, 1, num=10) * unit_of_x,
+    't': u.math.full((10,), 1) * unit_of_t,
+}
+observe_y = deepxde.icbc.PointSetBC(x, func(x))
+
+net = deepxde.nn.Model(
+    deepxde.nn.DictToArray(x=unit_of_x, t=unit_of_t),
+    deepxde.nn.FNN([2] + [32] * 3 + [1], "tanh"),
+    deepxde.nn.ArrayToDict(y=None),
+)
+
+problem = deepxde.problem.TimePDE(
+    geomtime,
+    pde,
+    [bc, ic, observe_y],
+    net,
+    num_domain=40,
+    num_boundary=20,
+    num_initial=10,
+    anchors=x,
+    solution=func,
+    num_test=10000,
+)
+
+variable = deepxde.callbacks.VariableValue(C, period=1000)
+trainer = deepxde.Trainer(problem, external_trainable_variables=C)
+trainer.compile(bst.optim.Adam(0.001), metrics=["l2 relative error"]).train(iterations=50000, callbacks=[variable])
+trainer.saveplot(issave=True, isplot=True)
diff --git a/docs/experimental_docs/unit-examples-inverse/diffusion_reaction_rate.ipynb b/docs/experimental_docs/unit-examples-inverse/diffusion_reaction_rate.ipynb
new file mode 100644
index 000000000..42921f4c1
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-inverse/diffusion_reaction_rate.ipynb
@@ -0,0 +1,544 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2fe892610f90c7e5",
+   "metadata": {},
+   "source": [
+    "# Inferring the space-dependent reaction rate in a diffusion-reaction system\n",
+    "\n",
+    "\n",
+    "## The problem we need to solve\n",
+    "\n",
+    "\n",
+    "We consider a one-dimensional diffusion-reaction system in which the reaction rate $k(x)$ is a space-dependent function:\n",
+    "\n",
+    "$$\\lambda\\frac{\\partial^2u}{\\partial x^2}-k(x)u=f,\\quad x\\in[0,1],$$\n",
+    "\n",
+    "where $\\lambda=0.01$ is the diffusion coefficient, $u$ is the solute concentration, and $f=\\sin(2\\pi x)$ is the source term.\n",
+    "The objective is to infer $k(x)$ given measurements on $u.$ The exact unknown reaction rate is\n",
+    "\n",
+    "$$k(x)=0.1+\\exp\\left[-0.5\\frac{(x-0.5)^2}{0.15^2}\\right].$$\n",
+    "\n",
+    "In addition, the condition $u(x)=0$ is imposed at $x=0$ and 1.\n",
+    "\n",
+    "\n",
+    "## Dimensional analysis\n",
+    "\n",
+    "Below is a dimensional analysis for each variable and parameter in the given one-dimensional diffusion-reaction equation:\n",
+    "\n",
+    "**Governing equation:**\n",
+    "$$\n",
+    "\\lambda \\frac{\\partial^2 u}{\\partial x^2} - k(x)u = f, \\quad x \\in [0,1].\n",
+    "$$\n",
+    "\n",
+    "**Physical meanings and typical dimensions:**\n",
+    "\n",
+    "1. **Spatial coordinate, $ x $**  \n",
+    "   - Typically, a spatial coordinate has the dimension of length:\n",
+    "\n",
+    "     $$\n",
+    "     [x] = L.\n",
+    "     $$\n",
+    "\n",
+    "2. **Solute concentration, $ u(x) $**  \n",
+    "   - Concentration is commonly expressed as mass per unit volume (assuming a mass-based concentration):  \n",
+    "\n",
+    "     $$\n",
+    "     [u] = M / L^3.\n",
+    "     $$\n",
+    "\n",
+    "3. **Diffusion coefficient, $ \\lambda $**  \n",
+    "   - A diffusion coefficient typically has dimensions of length squared per time:  \n",
+    "\n",
+    "     $$\n",
+    "     [\\lambda] = L^2 / T.\n",
+    "     $$\n",
+    "\n",
+    "4. **Reaction rate, $ k(x) $**  \n",
+    "   - Consider the term $ k(x)u $. This must have the same dimension as $\\lambda \\frac{\\partial^2 u}{\\partial x^2}$.  \n",
+    "   - The Laplacian term $\\frac{\\partial^2 u}{\\partial x^2}$ has dimension $(M/L^3) / L^2 = M / (L^5)$.  \n",
+    "   - Multiplying by $\\lambda = L^2/T$ gives $(L^2/T)*(M/L^5) = M/(T L^3)$.  \n",
+    "   - Therefore, $k(x)u$ must also have dimension $M/(T L^3)$. Given $ [u] = M/L^3 $, it follows:  \n",
+    "\n",
+    "     $$\n",
+    "     [k(x)] [u] = M/(T L^3) \\implies [k(x)] (M/L^3) = M/(T L^3) \\implies [k(x)] = 1/T.\n",
+    "     $$\n",
+    "\n",
+    "   Thus, the reaction rate $k(x)$ has units of inverse time, which makes sense physically.\n",
+    "\n",
+    "5. **Source term, $ f $**  \n",
+    "   - The source term $ f $ appears on the right-hand side of the equation and must have the same dimension as the left-hand side terms. We have already found that $\\lambda \\frac{\\partial^2 u}{\\partial x^2}$ and $k(x)u$ both have dimensions $ M/(T L^3) $. Hence:\n",
+    "   \n",
+    "     $$\n",
+    "     [f] = M/(T L^3).\n",
+    "     $$\n",
+    "\n",
+    "**Summary of Dimensions:**\n",
+    "- $[x] = L$\n",
+    "- $[u] = M/L^3$\n",
+    "- $[\\lambda] = L^2/T$\n",
+    "- $[k(x)] = 1/T$\n",
+    "- $[f] = M/(T L^3)$\n",
+    "\n",
+    "These dimensions are consistent with a diffusion-reaction system where:\n",
+    "- $u$ is a concentration (mass per volume),\n",
+    "- $\\lambda$ is a diffusion coefficient (length²/time),\n",
+    "- $k(x)$ is a reaction rate (1/time),\n",
+    "- $f$ is a source term with the dimension of a generation/consumption rate of mass per volume per time.\n",
+    "\n",
+    "## Implementation\n",
+    "\n",
+    "\n",
+    "First, import the necessary libraries:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "initial_id",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T10:11:19.069774Z",
+     "start_time": "2024-12-17T10:11:16.062094Z"
+    },
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import brainstate as bst\n",
+    "import brainunit as u\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from scipy.integrate import solve_bvp\n",
+    "\n",
+    "import deepxde.experimental as deepxde\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33d07fdf14aee443",
+   "metadata": {},
+   "source": [
+    "Define the units for the variables and parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b8b8a351a1ba24c6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T10:11:19.106898Z",
+     "start_time": "2024-12-17T10:11:19.079977Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "unit_of_x = u.meter\n",
+    "unit_of_u = u.mole / u.meter ** 3\n",
+    "unit_of_f = u.mole / (u.second * u.meter ** 3)\n",
+    "\n",
+    "l = 0.01 * unit_of_x ** 2 / u.second\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98c91402924a57ba",
+   "metadata": {},
+   "source": [
+    "Define the solution for the solute concentration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "30f6bd1a201542af",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T10:11:19.241025Z",
+     "start_time": "2024-12-17T10:11:19.160967Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def k(x):\n",
+    "    return 0.1 + np.exp(-0.5 * (x - 0.5) ** 2 / 0.15 ** 2)\n",
+    "\n",
+    "\n",
+    "def fun(x, y):\n",
+    "    return np.vstack((y[1], (k(x) * y[0] + np.sin(2 * np.pi * x)) / l.mantissa))\n",
+    "\n",
+    "\n",
+    "def bc(ya, yb):\n",
+    "    return np.array([ya[0], yb[0]])\n",
+    "\n",
+    "\n",
+    "num = 100\n",
+    "xvals = np.linspace(0, 1, num)\n",
+    "y = np.zeros((2, xvals.size))\n",
+    "res = solve_bvp(fun, bc, xvals, y)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d1b117970b778b7",
+   "metadata": {},
+   "source": [
+    "Define the PDE for the neural network model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "833da26094bea1de",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T10:11:19.304593Z",
+     "start_time": "2024-12-17T10:11:19.298448Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "geom = deepxde.geometry.Interval(0, 1).to_dict_point(x=unit_of_x)\n",
+    "\n",
+    "\n",
+    "def pde(x, y):\n",
+    "    hessian = net.hessian(x, y='u')\n",
+    "    du_xx = hessian[\"u\"][\"x\"][\"x\"]\n",
+    "    f = u.math.sin(2 * np.pi * x['x'].mantissa) * unit_of_f\n",
+    "    return l * du_xx - y['u'] * y['k'] - f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad654d3c6c4a245e",
+   "metadata": {},
+   "source": [
+    "Define the neural network model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "339f530f4a5cb4f9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T10:11:19.703511Z",
+     "start_time": "2024-12-17T10:11:19.324536Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "net = deepxde.nn.Model(\n",
+    "    deepxde.nn.DictToArray(x=unit_of_x),\n",
+    "    deepxde.nn.PFNN([1, [20, 20], [20, 20], 2], \"tanh\", bst.init.KaimingUniform()),\n",
+    "    deepxde.nn.ArrayToDict(u=unit_of_u, k=1 / u.second),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8df5ae51e9be3f9a",
+   "metadata": {},
+   "source": [
+    "Define the training data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e967dfbb9d3ed8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T10:11:19.720423Z",
+     "start_time": "2024-12-17T10:11:19.716718Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def gen_traindata():\n",
+    "    x = {'x': xvals * unit_of_x}\n",
+    "    y = {'u': res.sol(xvals)[0] * unit_of_u}\n",
+    "    return x, y\n",
+    "\n",
+    "\n",
+    "ob_x, ob_u = gen_traindata()\n",
+    "observe_u = deepxde.icbc.PointSetBC(ob_x, ob_u)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f9dee3f6a33fa88",
+   "metadata": {},
+   "source": [
+    "Define the boundary conditions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b90cf6294b4d76",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T10:11:19.786147Z",
+     "start_time": "2024-12-17T10:11:19.783554Z"
+    }
+   },
+   "outputs": [],
+   "source": "bc = deepxde.icbc.DirichletBC(lambda x: {'u': 0 * unit_of_u})\n"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90a1b4482cb27968",
+   "metadata": {},
+   "source": [
+    "Define the PDE problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "70aab77e50447359",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T10:11:20.046406Z",
+     "start_time": "2024-12-17T10:11:19.816754Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "problem = deepxde.problem.PDE(\n",
+    "    geom,\n",
+    "    pde,\n",
+    "    constraints=[bc, observe_u],\n",
+    "    approximator=net,\n",
+    "    num_domain=50,\n",
+    "    num_boundary=8,\n",
+    "    train_distribution=\"uniform\",\n",
+    "    num_test=1000,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a3c42e5568fcc06",
+   "metadata": {},
+   "source": [
+    "Train the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "1918ddc952ba62c2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T10:11:28.606169Z",
+     "start_time": "2024-12-17T10:11:20.073429Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.046593 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                                                         Test loss                                                                          Test metric \n",
+      "0         [0.42841953 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,          [0.48738545 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,          []          \n",
+      "           {'ibc0': {'u': 0.04377487 * mmolar}},                                              {'ibc0': {'u': 0.04377487 * mmolar}},                                                         \n",
+      "           {'ibc1': {'u': 0.76479465 * mmolar}}]                                              {'ibc1': {'u': 0.76479465 * mmolar}}]                                                         \n",
+      "1000      [0.00161212 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,          [0.00137106 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,          []          \n",
+      "           {'ibc0': {'u': 0.00100709 * mmolar}},                                              {'ibc0': {'u': 0.00100709 * mmolar}},                                                         \n",
+      "           {'ibc1': {'u': 0.0077876 * mmolar}}]                                               {'ibc1': {'u': 0.0077876 * mmolar}}]                                                          \n",
+      "2000      [0.00086259 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,          [0.00051533 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,          []          \n",
+      "           {'ibc0': {'u': 4.4753404e-05 * mmolar}},                                           {'ibc0': {'u': 4.4753404e-05 * mmolar}},                                                      \n",
+      "           {'ibc1': {'u': 0.00127289 * mmolar}}]                                              {'ibc1': {'u': 0.00127289 * mmolar}}]                                                         \n",
+      "3000      [0.00062482 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,          [0.00038163 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,          []          \n",
+      "           {'ibc0': {'u': 1.1394843e-05 * mmolar}},                                           {'ibc0': {'u': 1.1394843e-05 * mmolar}},                                                      \n",
+      "           {'ibc1': {'u': 0.00050347 * mmolar}}]                                              {'ibc1': {'u': 0.00050347 * mmolar}}]                                                         \n",
+      "4000      [0.00029539 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,          [0.00020541 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,          []          \n",
+      "           {'ibc0': {'u': 1.0836469e-06 * mmolar}},                                           {'ibc0': {'u': 1.0836469e-06 * mmolar}},                                                      \n",
+      "           {'ibc1': {'u': 0.0001564 * mmolar}}]                                               {'ibc1': {'u': 0.0001564 * mmolar}}]                                                          \n",
+      "5000      [0.00010554 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,          [8.63514e-05 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,         []          \n",
+      "           {'ibc0': {'u': 5.5338234e-08 * mmolar}},                                           {'ibc0': {'u': 5.5338234e-08 * mmolar}},                                                      \n",
+      "           {'ibc1': {'u': 4.1387873e-05 * mmolar}}]                                           {'ibc1': {'u': 4.1387873e-05 * mmolar}}]                                                      \n",
+      "6000      [3.261345e-05 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,        [3.0046363e-05 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       []          \n",
+      "           {'ibc0': {'u': 4.189261e-08 * mmolar}},                                            {'ibc0': {'u': 4.189261e-08 * mmolar}},                                                       \n",
+      "           {'ibc1': {'u': 1.1832751e-05 * mmolar}}]                                           {'ibc1': {'u': 1.1832751e-05 * mmolar}}]                                                      \n",
+      "7000      [1.0917592e-05 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       [1.06402085e-05 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,      []          \n",
+      "           {'ibc0': {'u': 6.6463612e-09 * mmolar}},                                           {'ibc0': {'u': 6.6463612e-09 * mmolar}},                                                      \n",
+      "           {'ibc1': {'u': 3.1595891e-06 * mmolar}}]                                           {'ibc1': {'u': 3.1595891e-06 * mmolar}}]                                                      \n",
+      "8000      [5.7016923e-06 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       [5.549621e-06 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,        []          \n",
+      "           {'ibc0': {'u': 1.4118614e-06 * mmolar}},                                           {'ibc0': {'u': 1.4118614e-06 * mmolar}},                                                      \n",
+      "           {'ibc1': {'u': 2.000358e-06 * mmolar}}]                                            {'ibc1': {'u': 2.000358e-06 * mmolar}}]                                                       \n",
+      "9000      [3.0069625e-06 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       [2.992862e-06 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,        []          \n",
+      "           {'ibc0': {'u': 6.584769e-09 * mmolar}},                                            {'ibc0': {'u': 6.584769e-09 * mmolar}},                                                       \n",
+      "           {'ibc1': {'u': 5.5307254e-07 * mmolar}}]                                           {'ibc1': {'u': 5.5307254e-07 * mmolar}}]                                                      \n",
+      "10000     [2.0530574e-06 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       [2.0282348e-06 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       []          \n",
+      "           {'ibc0': {'u': 1.3601655e-08 * mmolar}},                                           {'ibc0': {'u': 1.3601655e-08 * mmolar}},                                                      \n",
+      "           {'ibc1': {'u': 5.196718e-07 * mmolar}}]                                            {'ibc1': {'u': 5.196718e-07 * mmolar}}]                                                       \n",
+      "11000     [1.5368364e-06 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       [1.4977886e-06 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       []          \n",
+      "           {'ibc0': {'u': 8.276815e-08 * mmolar}},                                            {'ibc0': {'u': 8.276815e-08 * mmolar}},                                                       \n",
+      "           {'ibc1': {'u': 5.301843e-07 * mmolar}}]                                            {'ibc1': {'u': 5.301843e-07 * mmolar}}]                                                       \n",
+      "12000     [1.1885431e-06 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       [1.1730367e-06 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       []          \n",
+      "           {'ibc0': {'u': 1.5201218e-08 * mmolar}},                                           {'ibc0': {'u': 1.5201218e-08 * mmolar}},                                                      \n",
+      "           {'ibc1': {'u': 5.8795325e-07 * mmolar}}]                                           {'ibc1': {'u': 5.8795325e-07 * mmolar}}]                                                      \n",
+      "13000     [9.736607e-07 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,        [9.640654e-07 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,        []          \n",
+      "           {'ibc0': {'u': 1.3303917e-08 * mmolar}},                                           {'ibc0': {'u': 1.3303917e-08 * mmolar}},                                                      \n",
+      "           {'ibc1': {'u': 6.254742e-07 * mmolar}}]                                            {'ibc1': {'u': 6.254742e-07 * mmolar}}]                                                       \n",
+      "14000     [8.2432285e-07 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       [8.188007e-07 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,        []          \n",
+      "           {'ibc0': {'u': 1.3247805e-08 * mmolar}},                                           {'ibc0': {'u': 1.3247805e-08 * mmolar}},                                                      \n",
+      "           {'ibc1': {'u': 6.5696383e-07 * mmolar}}]                                           {'ibc1': {'u': 6.5696383e-07 * mmolar}}]                                                      \n",
+      "15000     [6.123539e-06 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,        [6.9687558e-06 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       []          \n",
+      "           {'ibc0': {'u': 8.313561e-06 * mmolar}},                                            {'ibc0': {'u': 8.313561e-06 * mmolar}},                                                       \n",
+      "           {'ibc1': {'u': 8.073111e-06 * mmolar}}]                                            {'ibc1': {'u': 8.073111e-06 * mmolar}}]                                                       \n",
+      "16000     [6.50423e-07 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,         [6.7078685e-07 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       []          \n",
+      "           {'ibc0': {'u': 8.236504e-08 * mmolar}},                                            {'ibc0': {'u': 8.236504e-08 * mmolar}},                                                       \n",
+      "           {'ibc1': {'u': 8.032191e-07 * mmolar}}]                                            {'ibc1': {'u': 8.032191e-07 * mmolar}}]                                                       \n",
+      "17000     [5.692829e-07 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,        [5.758455e-07 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,        []          \n",
+      "           {'ibc0': {'u': 1.7372976e-09 * mmolar}},                                           {'ibc0': {'u': 1.7372976e-09 * mmolar}},                                                      \n",
+      "           {'ibc1': {'u': 7.4665815e-07 * mmolar}}]                                           {'ibc1': {'u': 7.4665815e-07 * mmolar}}]                                                      \n",
+      "18000     [5.349737e-07 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,        [5.5260807e-07 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       []          \n",
+      "           {'ibc0': {'u': 4.742464e-09 * mmolar}},                                            {'ibc0': {'u': 4.742464e-09 * mmolar}},                                                       \n",
+      "           {'ibc1': {'u': 8.103133e-07 * mmolar}}]                                            {'ibc1': {'u': 8.103133e-07 * mmolar}}]                                                       \n",
+      "19000     [4.710544e-07 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,        [4.7181476e-07 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       []          \n",
+      "           {'ibc0': {'u': 1.8345938e-08 * mmolar}},                                           {'ibc0': {'u': 1.8345938e-08 * mmolar}},                                                      \n",
+      "           {'ibc1': {'u': 7.362613e-07 * mmolar}}]                                            {'ibc1': {'u': 7.362613e-07 * mmolar}}]                                                       \n",
+      "20000     [4.3603515e-07 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       [4.3779076e-07 * 10.0^0 * (meter2 / second * (mmolar / meter) / meter) ** 2,       []          \n",
+      "           {'ibc0': {'u': 1.614938e-08 * mmolar}},                                            {'ibc0': {'u': 1.614938e-08 * mmolar}},                                                       \n",
+      "           {'ibc1': {'u': 7.4421376e-07 * mmolar}}]                                           {'ibc1': {'u': 7.4421376e-07 * mmolar}}]                                                      \n",
+      "\n",
+      "Best trainer at step 20000:\n",
+      "  train loss: 1.20e-06\n",
+      "  test loss: 1.20e-06\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 8.478413 s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pinnx.Trainer at 0x1b2eee86c10>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = deepxde.Trainer(problem)\n",
+    "model.compile(bst.optim.Adam(1e-3)).train(iterations=20000)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b26a4e8563104c1",
+   "metadata": {},
+   "source": [
+    "Verify the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "362b2bf854d85584",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T10:11:28.904291Z",
+     "start_time": "2024-12-17T10:11:28.670596Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "l2 relative error for k: 0.030291751\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "l2 relative error for u: 0.00095011527\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = geom.uniform_points(500)\n",
+    "yhat = model.predict(x)\n",
+    "uhat, khat = yhat['u'].mantissa, yhat['k'].mantissa\n",
+    "x = x['x'].mantissa\n",
+    "\n",
+    "ktrue = k(x)\n",
+    "print(\"l2 relative error for k: \" + str(deepxde.metrics.l2_relative_error(khat, ktrue)))\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(x, ktrue, \"-\", label=\"k_true\")\n",
+    "plt.plot(x, khat, \"--\", label=\"k_NN\")\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "utrue = res.sol(x)[0]\n",
+    "print(\"l2 relative error for u: \" + str(deepxde.metrics.l2_relative_error(uhat, utrue)))\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(x, utrue, \"-\", label=\"u_true\")\n",
+    "plt.plot(x, uhat, \"--\", label=\"u_NN\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/experimental_docs/unit-examples-inverse/diffusion_reaction_rate.py b/docs/experimental_docs/unit-examples-inverse/diffusion_reaction_rate.py
new file mode 100644
index 000000000..892619582
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-inverse/diffusion_reaction_rate.py
@@ -0,0 +1,100 @@
+"""
+Implementation for the diffusion-reaction system with a space-dependent reaction rate in paper https://arxiv.org/abs/2111.02801.
+"""
+
+import brainstate as bst
+import brainunit as u
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.integrate import solve_bvp
+
+import deepxde.experimental as deepxde
+
+unit_of_x = u.meter
+unit_of_u = u.mole / u.meter ** 3
+unit_of_f = u.mole / (u.second * u.meter ** 3)
+
+l = 0.01 * unit_of_x ** 2 / u.second
+
+
+def k(x):
+    return 0.1 + np.exp(-0.5 * (x - 0.5) ** 2 / 0.15 ** 2)
+
+
+def fun(x, y):
+    return np.vstack((y[1], (k(x) * y[0] + np.sin(2 * np.pi * x)) / l.mantissa))
+
+
+def bc(ya, yb):
+    return np.array([ya[0], yb[0]])
+
+
+num = 100
+xvals = np.linspace(0, 1, num)
+y = np.zeros((2, xvals.size))
+res = solve_bvp(fun, bc, xvals, y)
+
+geom = deepxde.geometry.Interval(0, 1).to_dict_point(x=unit_of_x)
+
+
+def pde(x, y):
+    hessian = net.hessian(x, y='u')
+    du_xx = hessian["u"]["x"]["x"]
+    f = u.math.sin(2 * np.pi * x['x'].mantissa) * unit_of_f
+    return l * du_xx - y['u'] * y['k'] - f
+
+
+net = deepxde.nn.Model(
+    deepxde.nn.DictToArray(x=unit_of_x),
+    deepxde.nn.PFNN([1, [20, 20], [20, 20], 2], "tanh", bst.init.KaimingUniform()),
+    deepxde.nn.ArrayToDict(u=unit_of_u, k=1 / u.second),
+)
+
+
+def gen_traindata():
+    x = {'x': xvals * unit_of_x}
+    y = {'u': res.sol(xvals)[0] * unit_of_u}
+    return x, y
+
+
+ob_x, ob_u = gen_traindata()
+observe_u = deepxde.icbc.PointSetBC(ob_x, ob_u)
+
+bc = deepxde.icbc.DirichletBC(lambda x: {'u': 0 * unit_of_u})
+
+problem = deepxde.problem.PDE(
+    geom,
+    pde,
+    constraints=[bc, observe_u],
+    approximator=net,
+    num_domain=50,
+    num_boundary=8,
+    train_distribution="uniform",
+    num_test=1000,
+)
+
+model = deepxde.Trainer(problem)
+model.compile(bst.optim.Adam(1e-3)).train(iterations=20000)
+
+x = geom.uniform_points(500)
+yhat = model.predict(x)
+uhat, khat = yhat['u'].mantissa, yhat['k'].mantissa
+x = x['x'].mantissa
+
+ktrue = k(x)
+print("l2 relative error for k: " + str(deepxde.metrics.l2_relative_error(khat, ktrue)))
+
+plt.figure()
+plt.plot(x, ktrue, "-", label="k_true")
+plt.plot(x, khat, "--", label="k_NN")
+plt.legend()
+plt.show()
+
+utrue = res.sol(x)[0]
+print("l2 relative error for u: " + str(deepxde.metrics.l2_relative_error(uhat, utrue)))
+
+plt.figure()
+plt.plot(x, utrue, "-", label="u_true")
+plt.plot(x, uhat, "--", label="u_NN")
+plt.legend()
+plt.show()
diff --git a/docs/experimental_docs/unit-examples-inverse/elliptic_inverse_field.py b/docs/experimental_docs/unit-examples-inverse/elliptic_inverse_field.py
new file mode 100644
index 000000000..c563de087
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-inverse/elliptic_inverse_field.py
@@ -0,0 +1,85 @@
+import brainstate as bst
+import brainunit as u
+import matplotlib.pyplot as plt
+import numpy as np
+
+import deepxde.experimental as deepxde
+
+unit_of_x = u.meter
+unit_of_u = u.newton
+unit_of_q = u.newton / unit_of_x ** 2
+
+
+def pde(x, y):
+    du_xx = net.hessian(x, y='u')['u']['x']['x']
+    return -du_xx + y['q']
+
+
+geom = deepxde.geometry.Interval(-1, 1).to_dict_point(x=unit_of_x)
+
+
+def sol(x):
+    # solution is u(x) = sin(pi*x), q(x) = -pi^2 * sin(pi*x)
+    # return {'u': u.math.sin(u.math.pi * x['x']), }
+    return {
+        'u': u.math.sin(u.math.pi * x['x'] / unit_of_x) * unit_of_u,
+        'q': -u.math.pi ** 2 * u.math.sin(u.math.pi * x['x'] / unit_of_x) * unit_of_q
+    }
+
+
+bc = deepxde.icbc.DirichletBC(sol)
+
+
+def gen_traindata(num):
+    # generate num equally-spaced points from -1 to 1
+    xvals = np.linspace(-1, 1, num)
+    uvals = np.sin(np.pi * xvals)
+    return {'x': xvals * unit_of_x}, {'u': uvals * unit_of_u}
+
+
+ob_x, ob_u = gen_traindata(100)
+observe_u = deepxde.icbc.PointSetBC(ob_x, ob_u)
+
+net = deepxde.nn.Model(
+    deepxde.nn.DictToArray(x=unit_of_x),
+    deepxde.nn.PFNN([1, [20, 20], [20, 20], [20, 20], 2], "tanh", bst.init.KaimingUniform()),
+    deepxde.nn.ArrayToDict(u=unit_of_u, q=unit_of_q),
+)
+problem = deepxde.problem.PDE(
+    geom,
+    pde,
+    [bc, observe_u],
+    net,
+    num_domain=200,
+    num_boundary=2,
+    anchors=ob_x,
+    num_test=1000,
+    loss_weights=[1, 100, 1000],
+)
+
+model = deepxde.Trainer(problem)
+model.compile(bst.optim.Adam(0.0001)).train(iterations=20000)
+model.saveplot(issave=True, isplot=True)
+
+# view results
+x = geom.uniform_points(500)
+yhat = model.predict(x)
+uhat = yhat['u'] / unit_of_u
+qhat = yhat['q'] / unit_of_q
+x = x['x'] / unit_of_x
+
+utrue = np.sin(np.pi * x)
+print("l2 relative error for u: " + str(deepxde.metrics.l2_relative_error(utrue, uhat)))
+plt.figure()
+plt.plot(x, utrue, "-", label="u_true")
+plt.plot(x, uhat, "--", label="u_NN")
+plt.legend()
+
+qtrue = -np.pi ** 2 * np.sin(np.pi * x)
+print("l2 relative error for q: " + str(deepxde.metrics.l2_relative_error(qtrue, qhat)))
+plt.figure()
+plt.plot(x, qtrue, "-", label="q_true")
+plt.plot(x, qhat, "--", label="q_NN")
+plt.legend()
+
+plt.show()
diff --git a/docs/experimental_docs/unit-examples-inverse/elliptic_inverse_filed.ipynb b/docs/experimental_docs/unit-examples-inverse/elliptic_inverse_filed.ipynb
new file mode 100644
index 000000000..a44abc0b6
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-inverse/elliptic_inverse_filed.ipynb
@@ -0,0 +1,482 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Inverse problem for the Poisson equation with unknown forcing field\n",
+    "\n",
+    "## Problem setup\n",
+    "\n",
+    "We will solve\n",
+    "\n",
+    "$$\n",
+    "\\frac{d^2u}{dx^2} = q(x), \\quad x \\in [-1, 1]\n",
+    "$$\n",
+    "\n",
+    "with the Dirichlet boundary conditions\n",
+    "\n",
+    "$$\n",
+    "u(-1) = 0, \\quad u(1) = 0\n",
+    "$$\n",
+    "\n",
+    "Here, both $u(x)$ and $q(x)$ are unknown. Furthermore, we have the measurement of $u(x)$ at 100 points.\n",
+    "\n",
+    "The reference solution is $u(x) = \\sin(\\pi x), \\quad q(x) = -\\pi^2 \\sin(\\pi x)$."
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Dimensional analysis\n",
+    "\n",
+    "\n",
+    "**Assumptions on dimensions:**\n",
+    "- Let $[x] = L$, where $L$ represents length.\n",
+    "- Let $[u] = U$, where $U$ represents the dimension of the unknown field $u$. For instance, $u$ could represent displacement, temperature, potential, etc. The exact choice of $U$ depends on the physical context.\n",
+    "- The dimension of $q(x)$ is to be determined.\n",
+    "\n",
+    "**Dimensional Analysis:**\n",
+    "\n",
+    "1. Consider the left-hand side (LHS): $\\frac{d^2 u}{dx^2}$\n",
+    "   Its dimension is:\n",
+    "   \n",
+    "   \n",
+    "   $$\n",
+    "   \\left[\\frac{d^2 u}{dx^2}\\right] = \\frac{[u]}{[x]^2} = \\frac{U}{L^2}.\n",
+    "   $$\n",
+    "\n",
+    "2. For the equation to be dimensionally consistent, the right-hand side (RHS) $q(x)$ must have the same dimension as the LHS:\n",
+    "   $$\n",
+    "   [q(x)] = \\frac{U}{L^2}.\n",
+    "   $$\n",
+    "\n",
+    "**Summary:**\n",
+    "- If the dimension of $u$ is $U$ and the dimension of $x$ is $L$, then the source term $q(x)$ must have the dimension $U/L^2$.\n",
+    "\n",
+    "In other words, the source term $q(x)$ is the field quantity $u$ per unit length squared. The physical meaning depends on the specific problem. For example, if $u$ represents temperature, then $q(x)$ might represent a thermal energy source density. If $u$ represents an electric potential, then $q(x)$ may relate to charge distribution (scaled appropriately by physical constants).\n",
+    "\n"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Implementation\n",
+    "\n",
+    "Import the necessary package:\n"
+   ]
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T08:43:36.188442Z",
+     "start_time": "2024-12-17T08:43:36.184546Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import brainstate as bst\n",
+    "import brainunit as u\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "import deepxde.experimental as deepxde\n"
+   ],
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Define the units for the problem:"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T08:43:36.206960Z",
+     "start_time": "2024-12-17T08:43:36.202803Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "unit_of_x = u.meter\n",
+    "unit_of_u = u.newton\n",
+    "unit_of_q = u.newton / unit_of_x ** 2\n"
+   ],
+   "outputs": [],
+   "execution_count": 7
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Define the problem."
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T08:43:36.216484Z",
+     "start_time": "2024-12-17T08:43:36.213121Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "def pde(x, y):\n",
+    "    du_xx = net.hessian(x, y='u')['u']['x']['x']\n",
+    "    return -du_xx + y['q']\n",
+    "\n",
+    "\n",
+    "geom = deepxde.geometry.Interval(-1, 1).to_dict_point(x=unit_of_x)\n"
+   ],
+   "outputs": [],
+   "execution_count": 8
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Define the Dirichlet boundary conditions."
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T08:43:36.231014Z",
+     "start_time": "2024-12-17T08:43:36.226846Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "def sol(x):\n",
+    "    # solution is u(x) = sin(pi*x), q(x) = -pi^2 * sin(pi*x)\n",
+    "    # return {'u': u.math.sin(u.math.pi * x['x']), }\n",
+    "    return {\n",
+    "        'u': u.math.sin(u.math.pi * x['x'] / unit_of_x) * unit_of_u,\n",
+    "        'q': -u.math.pi ** 2 * u.math.sin(u.math.pi * x['x'] / unit_of_x) * unit_of_q\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "bc = deepxde.icbc.DirichletBC(sol)\n"
+   ],
+   "outputs": [],
+   "execution_count": 9
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Define the observation points and the training data generator."
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T08:43:36.239114Z",
+     "start_time": "2024-12-17T08:43:36.235392Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "def gen_traindata(num):\n",
+    "    # generate num equally-spaced points from -1 to 1\n",
+    "    xvals = np.linspace(-1, 1, num)\n",
+    "    uvals = np.sin(np.pi * xvals)\n",
+    "    return {'x': xvals * unit_of_x}, {'u': uvals * unit_of_u}\n",
+    "\n",
+    "\n",
+    "ob_x, ob_u = gen_traindata(100)\n",
+    "observe_u = deepxde.icbc.PointSetBC(ob_x, ob_u)"
+   ],
+   "outputs": [],
+   "execution_count": 10
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Define the neural network model, adding the unit information to the input and output:"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T08:43:36.630314Z",
+     "start_time": "2024-12-17T08:43:36.297275Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "net = deepxde.nn.Model(\n",
+    "    deepxde.nn.DictToArray(x=unit_of_x),\n",
+    "    deepxde.nn.PFNN([1, [20, 20], [20, 20], [20, 20], 2], \"tanh\", bst.init.KaimingUniform()),\n",
+    "    deepxde.nn.ArrayToDict(u=unit_of_u, q=unit_of_q),\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": 11
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "The final problem:"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T08:43:37.054584Z",
+     "start_time": "2024-12-17T08:43:36.656295Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "problem = deepxde.problem.PDE(\n",
+    "    geom,\n",
+    "    pde,\n",
+    "    [bc, observe_u],\n",
+    "    net,\n",
+    "    num_domain=200,\n",
+    "    num_boundary=2,\n",
+    "    anchors=ob_x,\n",
+    "    num_test=1000,\n",
+    "    loss_weights=[1, 100, 1000],\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": 12
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Train the model:"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T08:44:20.614511Z",
+     "start_time": "2024-12-17T08:43:37.087754Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "model = deepxde.Trainer(problem)\n",
+    "model.compile(bst.optim.Adam(0.0001)).train(iterations=20000)\n",
+    "model.saveplot(issave=True, isplot=True)\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.049581 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                     Test loss                                      Test metric \n",
+      "0         [57.104668 * pascal2,                          [58.55549 * pascal2,                           []          \n",
+      "           {'ibc0': {'q': 71.43462 * pascal,              {'ibc0': {'q': 71.43462 * pascal,                         \n",
+      "                     'u': 21.93853 * newton}},                      'u': 21.93853 * newton}},                       \n",
+      "           {'ibc1': {'u': 1383.2239 * newton}}]           {'ibc1': {'u': 1383.2239 * newton}}]                      \n",
+      "1000      [9.099975 * pascal2,                           [9.277513 * pascal2,                           []          \n",
+      "           {'ibc0': {'q': 0.02584232 * pascal,            {'ibc0': {'q': 0.02584232 * pascal,                       \n",
+      "                     'u': 39.094223 * newton}},                     'u': 39.094223 * newton}},                      \n",
+      "           {'ibc1': {'u': 89.13055 * newton}}]            {'ibc1': {'u': 89.13055 * newton}}]                       \n",
+      "2000      [17.813923 * pascal2,                          [17.973166 * pascal2,                          []          \n",
+      "           {'ibc0': {'q': 0.03595128 * pascal,            {'ibc0': {'q': 0.03595128 * pascal,                       \n",
+      "                     'u': 22.458023 * newton}},                     'u': 22.458023 * newton}},                      \n",
+      "           {'ibc1': {'u': 49.399963 * newton}}]           {'ibc1': {'u': 49.399963 * newton}}]                      \n",
+      "3000      [20.421053 * pascal2,                          [20.553577 * pascal2,                          []          \n",
+      "           {'ibc0': {'q': 0.03406093 * pascal,            {'ibc0': {'q': 0.03406093 * pascal,                       \n",
+      "                     'u': 12.548685 * newton}},                     'u': 12.548685 * newton}},                      \n",
+      "           {'ibc1': {'u': 26.532593 * newton}}]           {'ibc1': {'u': 26.532593 * newton}}]                      \n",
+      "4000      [18.949293 * pascal2,                          [19.105434 * pascal2,                          []          \n",
+      "           {'ibc0': {'q': 0.03712923 * pascal,            {'ibc0': {'q': 0.03712923 * pascal,                       \n",
+      "                     'u': 6.2132597 * newton}},                     'u': 6.2132597 * newton}},                      \n",
+      "           {'ibc1': {'u': 12.868485 * newton}}]           {'ibc1': {'u': 12.868485 * newton}}]                      \n",
+      "5000      [15.683843 * pascal2,                          [15.849516 * pascal2,                          []          \n",
+      "           {'ibc0': {'q': 0.03507624 * pascal,            {'ibc0': {'q': 0.03507624 * pascal,                       \n",
+      "                     'u': 2.9483395 * newton}},                     'u': 2.9483395 * newton}},                      \n",
+      "           {'ibc1': {'u': 6.0103345 * newton}}]           {'ibc1': {'u': 6.0103345 * newton}}]                      \n",
+      "6000      [11.3106 * pascal2,                            [11.44179 * pascal2,                           []          \n",
+      "           {'ibc0': {'q': 0.02415182 * pascal,            {'ibc0': {'q': 0.02415182 * pascal,                       \n",
+      "                     'u': 1.4232403 * newton}},                     'u': 1.4232403 * newton}},                      \n",
+      "           {'ibc1': {'u': 2.9394844 * newton}}]           {'ibc1': {'u': 2.9394844 * newton}}]                      \n",
+      "7000      [6.889275 * pascal2,                           [6.9675927 * pascal2,                          []          \n",
+      "           {'ibc0': {'q': 0.01458644 * pascal,            {'ibc0': {'q': 0.01458644 * pascal,                       \n",
+      "                     'u': 0.7137065 * newton}},                     'u': 0.7137065 * newton}},                      \n",
+      "           {'ibc1': {'u': 1.5752933 * newton}}]           {'ibc1': {'u': 1.5752933 * newton}}]                      \n",
+      "8000      [3.0519495 * pascal2,                          [3.085175 * pascal2,                           []          \n",
+      "           {'ibc0': {'q': 0.00667563 * pascal,            {'ibc0': {'q': 0.00667563 * pascal,                       \n",
+      "                     'u': 0.33465862 * newton}},                    'u': 0.33465862 * newton}},                     \n",
+      "           {'ibc1': {'u': 0.84809256 * newton}}]          {'ibc1': {'u': 0.84809256 * newton}}]                     \n",
+      "9000      [1.0699911 * pascal2,                          [1.0805568 * pascal2,                          []          \n",
+      "           {'ibc0': {'q': 0.0019603 * pascal,             {'ibc0': {'q': 0.0019603 * pascal,                        \n",
+      "                     'u': 0.14555798 * newton}},                    'u': 0.14555798 * newton}},                     \n",
+      "           {'ibc1': {'u': 0.529897 * newton}}]            {'ibc1': {'u': 0.529897 * newton}}]                       \n",
+      "10000     [0.36260056 * pascal2,                         [0.36515456 * pascal2,                         []          \n",
+      "           {'ibc0': {'q': 0.00053385 * pascal,            {'ibc0': {'q': 0.00053385 * pascal,                       \n",
+      "                     'u': 0.06022839 * newton}},                    'u': 0.06022839 * newton}},                     \n",
+      "           {'ibc1': {'u': 0.4085685 * newton}}]           {'ibc1': {'u': 0.4085685 * newton}}]                      \n",
+      "11000     [0.18483813 * pascal2,                         [0.1858395 * pascal2,                          []          \n",
+      "           {'ibc0': {'q': 0.00012002 * pascal,            {'ibc0': {'q': 0.00012002 * pascal,                       \n",
+      "                     'u': 0.02717585 * newton}},                    'u': 0.02717585 * newton}},                     \n",
+      "           {'ibc1': {'u': 0.3386068 * newton}}]           {'ibc1': {'u': 0.3386068 * newton}}]                      \n",
+      "12000     [0.12419797 * pascal2,                         [0.12520623 * pascal2,                         []          \n",
+      "           {'ibc0': {'q': 6.49303e-05 * pascal,           {'ibc0': {'q': 6.49303e-05 * pascal,                      \n",
+      "                     'u': 0.01508655 * newton}},                    'u': 0.01508655 * newton}},                     \n",
+      "           {'ibc1': {'u': 0.252522 * newton}}]            {'ibc1': {'u': 0.252522 * newton}}]                       \n",
+      "13000     [0.07476345 * pascal2,                         [0.07559747 * pascal2,                         []          \n",
+      "           {'ibc0': {'q': 0.00022872 * pascal,            {'ibc0': {'q': 0.00022872 * pascal,                       \n",
+      "                     'u': 0.00766858 * newton}},                    'u': 0.00766858 * newton}},                     \n",
+      "           {'ibc1': {'u': 0.15152633 * newton}}]          {'ibc1': {'u': 0.15152633 * newton}}]                     \n",
+      "14000     [0.0346879 * pascal2,                          [0.0351154 * pascal2,                          []          \n",
+      "           {'ibc0': {'q': 1.1295616e-05 * pascal,         {'ibc0': {'q': 1.1295616e-05 * pascal,                    \n",
+      "                     'u': 0.00236588 * newton}},                    'u': 0.00236588 * newton}},                     \n",
+      "           {'ibc1': {'u': 0.07225788 * newton}}]          {'ibc1': {'u': 0.07225788 * newton}}]                     \n",
+      "15000     [0.01340395 * pascal2,                         [0.01357405 * pascal2,                         []          \n",
+      "           {'ibc0': {'q': 5.2584824e-06 * pascal,         {'ibc0': {'q': 5.2584824e-06 * pascal,                    \n",
+      "                     'u': 0.00018877 * newton}},                    'u': 0.00018877 * newton}},                     \n",
+      "           {'ibc1': {'u': 0.03148581 * newton}}]          {'ibc1': {'u': 0.03148581 * newton}}]                     \n",
+      "16000     [0.00504076 * pascal2,                         [0.00508512 * pascal2,                         []          \n",
+      "           {'ibc0': {'q': 2.1729034e-05 * pascal,         {'ibc0': {'q': 2.1729034e-05 * pascal,                    \n",
+      "                     'u': 3.5908473e-05 * newton}},                 'u': 3.5908473e-05 * newton}},                  \n",
+      "           {'ibc1': {'u': 0.01453501 * newton}}]          {'ibc1': {'u': 0.01453501 * newton}}]                     \n",
+      "17000     [0.00211953 * pascal2,                         [0.00213015 * pascal2,                         []          \n",
+      "           {'ibc0': {'q': 3.1532704e-06 * pascal,         {'ibc0': {'q': 3.1532704e-06 * pascal,                    \n",
+      "                     'u': 0.00025964 * newton}},                    'u': 0.00025964 * newton}},                     \n",
+      "           {'ibc1': {'u': 0.00791687 * newton}}]          {'ibc1': {'u': 0.00791687 * newton}}]                     \n",
+      "18000     [0.0009769 * pascal2,                          [0.00098521 * pascal2,                         []          \n",
+      "           {'ibc0': {'q': 3.8374005e-06 * pascal,         {'ibc0': {'q': 3.8374005e-06 * pascal,                    \n",
+      "                     'u': 0.00030924 * newton}},                    'u': 0.00030924 * newton}},                     \n",
+      "           {'ibc1': {'u': 0.00499536 * newton}}]          {'ibc1': {'u': 0.00499536 * newton}}]                     \n",
+      "19000     [0.00052247 * pascal2,                         [0.00052762 * pascal2,                         []          \n",
+      "           {'ibc0': {'q': 3.1709257e-07 * pascal,         {'ibc0': {'q': 3.1709257e-07 * pascal,                    \n",
+      "                     'u': 0.00027461 * newton}},                    'u': 0.00027461 * newton}},                     \n",
+      "           {'ibc1': {'u': 0.00339381 * newton}}]          {'ibc1': {'u': 0.00339381 * newton}}]                     \n",
+      "20000     [0.00034573 * pascal2,                         [0.00034509 * pascal2,                         []          \n",
+      "           {'ibc0': {'q': 8.5156114e-07 * pascal,         {'ibc0': {'q': 8.5156114e-07 * pascal,                    \n",
+      "                     'u': 0.00021139 * newton}},                    'u': 0.00021139 * newton}},                     \n",
+      "           {'ibc1': {'u': 0.00248489 * newton}}]          {'ibc1': {'u': 0.00248489 * newton}}]                     \n",
+      "\n",
+      "Best trainer at step 20000:\n",
+      "  train loss: 3.04e-03\n",
+      "  test loss: 3.04e-03\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 42.773905 s\n",
+      "\n",
+      "Saving loss history to D:\\codes\\projects\\pinnx\\docs\\examples-pinn-inverse\\loss.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\examples-pinn-inverse\\loss.dat\n",
+      "Saving training data to D:\\codes\\projects\\pinnx\\docs\\examples-pinn-inverse\\train.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\examples-pinn-inverse\\train.dat\n",
+      "Saving test data to D:\\codes\\projects\\pinnx\\docs\\examples-pinn-inverse\\test.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\examples-pinn-inverse\\test.dat\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Visualize the results:"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T08:44:20.926785Z",
+     "start_time": "2024-12-17T08:44:20.648711Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "# view results\n",
+    "x = geom.uniform_points(500)\n",
+    "yhat = model.predict(x)\n",
+    "uhat = yhat['u'] / unit_of_u\n",
+    "qhat = yhat['q'] / unit_of_q\n",
+    "x = x['x'] / unit_of_x\n",
+    "\n",
+    "utrue = np.sin(np.pi * x)\n",
+    "print(\"l2 relative error for u: \" + str(deepxde.metrics.l2_relative_error(utrue, uhat)))\n",
+    "plt.figure()\n",
+    "plt.plot(x, utrue, \"-\", label=\"u_true\")\n",
+    "plt.plot(x, uhat, \"--\", label=\"u_NN\")\n",
+    "plt.legend()\n",
+    "\n",
+    "qtrue = -np.pi ** 2 * np.sin(np.pi * x)\n",
+    "print(\"l2 relative error for q: \" + str(deepxde.metrics.l2_relative_error(qtrue, qhat)))\n",
+    "plt.figure()\n",
+    "plt.plot(x, qtrue, \"-\", label=\"q_true\")\n",
+    "plt.plot(x, qhat, \"--\", label=\"q_NN\")\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "l2 relative error for u: 0.0022317795\n",
+      "l2 relative error for q: 0.02959022\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Complete code please see the code in [elliptic_inverse_field.py](https://github.com/chaobrain/pinnx/blob/main/docs/unit-examples-forward/elliptic_inverse_field.py)."
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "language": "python",
+   "display_name": "Python 3 (ipykernel)"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/experimental_docs/unit-examples-inverse/reaction_inverse.ipynb b/docs/experimental_docs/unit-examples-inverse/reaction_inverse.ipynb
new file mode 100644
index 000000000..65d0560bc
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-inverse/reaction_inverse.ipynb
@@ -0,0 +1,653 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "279084cb29c58e51",
+   "metadata": {},
+   "source": [
+    "# Inverse problem for the diffusion-reaction system\n",
+    "\n",
+    "## The problem we are solving\n",
+    "\n",
+    "We will solve an inverse problem for diffusion-reaction systems for unknowns $D$ and $k_f$:\n",
+    "\n",
+    "$$\\frac{\\partial C_A}{\\partial t}=D\\frac{\\partial^2C_A}{\\partial x^2}-k_fC_AC_B^2,$$\n",
+    "$$\\frac{\\partial C_B}{\\partial t}=D\\frac{\\partial^2C_B}{\\partial x^2}-2k_fC_AC_B^2$$\n",
+    "\n",
+    "for $x\\in[0,1]$ and $t\\in[0,10]$ initial conditions\n",
+    "\n",
+    "$$C_A(x,0)=C_B(x,0)=e^{-20x}$$\n",
+    "\n",
+    "and boundary conditions\n",
+    "\n",
+    "$$C_A(0,t)=C_B(0,t)=1\\\\C_A(1,t)=C_B(1,t)=0.$$\n",
+    "\n",
+    "The expected values of $D$ and $k_f$ are $2\\cdot10^{-3}$ and 0.1, respectively.\n",
+    "\n",
+    "\n",
+    "\n",
+    "Below is the dimensional analysis for the given **diffusion-reaction system** with unknowns $ D $ and $ k_f $:\n",
+    "\n",
+    "The equations are:  \n",
+    "$$\n",
+    "\\frac{\\partial C_A}{\\partial t} = D \\frac{\\partial^2 C_A}{\\partial x^2} - k_f C_A C_B^2, \\quad \\frac{\\partial C_B}{\\partial t} = D \\frac{\\partial^2 C_B}{\\partial x^2} - 2k_f C_A C_B^2.\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "\n",
+    "## Dimensional Analysis\n",
+    "\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 1: Assign Dimensions to Variables\n",
+    "\n",
+    "1. **Spatial Coordinate $x$:**  \n",
+    "   - The dimension of $x$ is length:  \n",
+    "\n",
+    "     $$\n",
+    "     [x] = L.\n",
+    "     $$\n",
+    "\n",
+    "2. **Time $t$:**  \n",
+    "   - The dimension of time is:  \n",
+    "\n",
+    "     $$\n",
+    "     [t] = T.\n",
+    "     $$\n",
+    "\n",
+    "3. **Concentrations $C_A$ and $C_B$:**  \n",
+    "   - Concentrations typically have dimensions of **moles per unit volume**:  \n",
+    "\n",
+    "     $$\n",
+    "     [C_A] = [C_B] = N / L^3,\n",
+    "     $$\n",
+    "     where $N$ represents the amount of substance (moles).\n",
+    "\n",
+    "4. **Diffusion Coefficient $D$:**  \n",
+    "   - The diffusion term involves $\\partial^2 C_A / \\partial x^2$, which has dimensions: \n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\frac{\\partial^2 C_A}{\\partial x^2}\\right] = \\frac{[C_A]}{[x]^2} = \\frac{N}{L^5}.\n",
+    "     $$\n",
+    "   - The time derivative $\\partial C_A / \\partial t$ has dimensions:  \n",
+    "\n",
+    "     $$\n",
+    "     \\left[\\frac{\\partial C_A}{\\partial t}\\right] = \\frac{[C_A]}{[t]} = \\frac{N}{L^3 T}.\n",
+    "     $$\n",
+    "   - For dimensional consistency in the diffusion term $D \\frac{\\partial^2 C_A}{\\partial x^2}$, $D$ must have dimensions: \n",
+    "\n",
+    "     $$\n",
+    "     [D] \\cdot \\frac{N}{L^5} = \\frac{N}{L^3 T} \\implies [D] = \\frac{L^2}{T}.\n",
+    "     $$\n",
+    "\n",
+    "5. **Reaction Rate Constant $k_f$:**  \n",
+    "   - The reaction term $k_f C_A C_B^2$ must have the same dimensions as $\\partial C_A / \\partial t$, which is $N / (L^3 T)$.  \n",
+    "   - The product $C_A C_B^2$ has dimensions:  \n",
+    "\n",
+    "     $$\n",
+    "     [C_A C_B^2] = \\left(\\frac{N}{L^3}\\right)\\left(\\frac{N}{L^3}\\right)^2 = \\frac{N^3}{L^9}.\n",
+    "     $$\n",
+    "   - To balance dimensions, $k_f$ must satisfy:  \n",
+    "   \n",
+    "     $$\n",
+    "     [k_f] \\cdot \\frac{N^3}{L^9} = \\frac{N}{L^3 T} \\implies [k_f] = \\frac{L^6}{N^2 T}.\n",
+    "     $$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 2: Initial and Boundary Conditions\n",
+    "\n",
+    "- **Initial Conditions:** $C_A(x,0) = C_B(x,0) = e^{-20x}$  \n",
+    "  - The exponential function is dimensionless, so the initial condition is consistent with $C_A$ and $C_B$ having dimensions $N / L^3$.\n",
+    "\n",
+    "- **Boundary Conditions:** $C_A(0,t) = C_B(0,t) = 1$ and $C_A(1,t) = C_B(1,t) = 0$  \n",
+    "  - These conditions specify dimensionless values for $C_A$ and $C_B$ scaled appropriately.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 3: Summary of Dimensions\n",
+    "\n",
+    "| Variable/Parameter     | Physical Meaning                   | Dimensions            |\n",
+    "|------------------------|-----------------------------------|-----------------------|\n",
+    "| $x$                  | Spatial coordinate                | $L$                 |\n",
+    "| $t$                  | Time                              | $T$                 |\n",
+    "| $C_A, C_B$           | Concentrations (solute)           | $N / L^3$           |\n",
+    "| $D$                  | Diffusion coefficient             | $L^2 / T$           |\n",
+    "| $k_f$                | Reaction rate constant            | $L^6 / (N^2 T)$     |\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step 4: Given Values\n",
+    "\n",
+    "- $D = 2 \\cdot 10^{-3}$: The value is consistent with $[D] = L^2 / T$.  \n",
+    "- $k_f = 0.1$: The value is consistent with $[k_f] = L^6 / (N^2 T)$.  \n",
+    "\n",
+    "These values and dimensions are appropriate for the given system.\n",
+    "\n",
+    "\n",
+    "## Implementation\n",
+    "\n",
+    "Below is the implementation of the inverse problem for the diffusion-reaction system using PINNx.\n",
+    "\n",
+    "We first load the dataset and define the PDE and neural network model. We then define the boundary and initial conditions, create the geometry, and set up the problem. Finally, we generate training data and solve the inverse problem to estimate the diffusion coefficient $D$ and reaction rate constant $k_f$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "205911c6d4d69526",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:57:30.259168Z",
+     "start_time": "2024-12-17T11:57:27.822591Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import brainstate as bst\n",
+    "import brainunit as u\n",
+    "import numpy as np\n",
+    "\n",
+    "import deepxde.experimental as deepxde\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d742a533bc1b9f40",
+   "metadata": {},
+   "source": [
+    "Define the physical units for the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "99a72a216707e048",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:57:30.268480Z",
+     "start_time": "2024-12-17T11:57:30.264509Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "unit_of_x = u.meter\n",
+    "unit_of_t = u.second\n",
+    "unit_of_c = u.mole / u.meter ** 3\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15e1eaa719f3e5fb",
+   "metadata": {},
+   "source": [
+    "Define the parameters and the PDE:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7f12f0c9983f84a4",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:57:30.349029Z",
+     "start_time": "2024-12-17T11:57:30.326235Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "kf = bst.ParamState(0.05 * u.meter ** 6 / u.mole ** 2 / u.second)\n",
+    "D = bst.ParamState(1.0 * u.meter ** 2 / u.second)\n",
+    "\n",
+    "\n",
+    "def pde(x, y):\n",
+    "    jacobian = net.jacobian(x, x='t')\n",
+    "    hessian = net.hessian(x)\n",
+    "    ca, cb = y['ca'], y['cb']\n",
+    "    dca_t = jacobian['ca']['t']\n",
+    "    dcb_t = jacobian['cb']['t']\n",
+    "    dca_xx = hessian['ca']['x']['x']\n",
+    "    dcb_xx = hessian['cb']['x']['x']\n",
+    "    eq_a = dca_t - 1e-3 * D.value * dca_xx + kf.value * ca * cb ** 2\n",
+    "    eq_b = dcb_t - 1e-3 * D.value * dcb_xx + 2 * kf.value * ca * cb ** 2\n",
+    "    return [eq_a, eq_b]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11b3d9b47bc88f60",
+   "metadata": {},
+   "source": [
+    "Define the neural network model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b775fc99b5ee8c55",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:57:30.701936Z",
+     "start_time": "2024-12-17T11:57:30.355769Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "net = deepxde.nn.Model(\n",
+    "    deepxde.nn.DictToArray(x=unit_of_x, t=unit_of_t),\n",
+    "    deepxde.nn.FNN([2] + [20] * 3 + [2], \"tanh\"),\n",
+    "    deepxde.nn.ArrayToDict(ca=unit_of_c, cb=unit_of_c),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef5efb7f3bd9906b",
+   "metadata": {},
+   "source": [
+    "Define the geometry:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d498ccf2539f5328",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:57:30.716369Z",
+     "start_time": "2024-12-17T11:57:30.713200Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "geom = deepxde.geometry.Interval(0, 1)\n",
+    "timedomain = deepxde.geometry.TimeDomain(0, 10)\n",
+    "geomtime = deepxde.geometry.GeometryXTime(geom, timedomain)\n",
+    "geomtime = geomtime.to_dict_point(x=unit_of_x, t=unit_of_t)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2f8765abd40ccb8",
+   "metadata": {},
+   "source": [
+    "Define the boundary condition function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4a0af6e60e78144b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:57:30.730191Z",
+     "start_time": "2024-12-17T11:57:30.726746Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def fun_bc(x):\n",
+    "    c = (1 - x['x'] / unit_of_x) * unit_of_c\n",
+    "    return {'ca': c, 'cb': c}\n",
+    "\n",
+    "\n",
+    "bc = deepxde.icbc.DirichletBC(fun_bc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d88ddda62f92154",
+   "metadata": {},
+   "source": [
+    "Define the initial condition function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bf5b45de593f59ee",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:57:30.743564Z",
+     "start_time": "2024-12-17T11:57:30.740694Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def fun_init(x):\n",
+    "    return {\n",
+    "        'ca': u.math.exp(-20 * x['x'] / unit_of_x) * unit_of_c,\n",
+    "        'cb': u.math.exp(-20 * x['x'] / unit_of_x) * unit_of_c,\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "ic = deepxde.icbc.IC(fun_init)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e20907e3949fec2",
+   "metadata": {},
+   "source": [
+    "Define a function to generate training data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e04b9f425939fbd9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:57:30.761991Z",
+     "start_time": "2024-12-17T11:57:30.754627Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def gen_traindata():\n",
+    "    data = np.load(\"../dataset/reaction.npz\")\n",
+    "    t, x, ca, cb = data[\"t\"], data[\"x\"], data[\"Ca\"], data[\"Cb\"]\n",
+    "    X, T = np.meshgrid(x, t)\n",
+    "    x = {'x': X.flatten() * unit_of_x, 't': T.flatten() * unit_of_t}\n",
+    "    y = {'ca': ca.flatten() * unit_of_c, 'cb': cb.flatten() * unit_of_c}\n",
+    "    return x, y\n",
+    "\n",
+    "\n",
+    "observe_x, observe_y = gen_traindata()\n",
+    "observe_bc = deepxde.icbc.PointSetBC(observe_x, observe_y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "980a88d6abc9efb3",
+   "metadata": {},
+   "source": [
+    "Define the problem and solve the inverse problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ca9735601273941",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T11:57:31.371099Z",
+     "start_time": "2024-12-17T11:57:30.820203Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: 50000 points required, but 50055 points sampled.\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = deepxde.problem.TimePDE(\n",
+    "    geomtime,\n",
+    "    pde,\n",
+    "    [bc, ic, observe_bc],\n",
+    "    net,\n",
+    "    num_domain=2000,\n",
+    "    num_boundary=100,\n",
+    "    num_initial=100,\n",
+    "    anchors=observe_x,\n",
+    "    num_test=50000,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6077c109557ca484",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T12:22:33.088002Z",
+     "start_time": "2024-12-17T11:57:31.527399Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compiling trainer...\n",
+      "'compile' took 0.055027 s\n",
+      "\n",
+      "Training trainer...\n",
+      "\n",
+      "Step      Train loss                                              Test loss                                               Test metric \n",
+      "0         [0.09352963 * 10.0^0 * (mmolar / second) ** 2,          [0.08070567 * 10.0^0 * (mmolar / second) ** 2,          []          \n",
+      "           0.30233935 * 10.0^0 * (mmolar / second) ** 2,           0.28232208 * 10.0^0 * (mmolar / second) ** 2,                      \n",
+      "           {'ibc0': {'ca': 2.5078168 * mmolar,                     {'ibc0': {'ca': 2.5078168 * mmolar,                                \n",
+      "                     'cb': 2.447632 * mmolar}},                              'cb': 2.447632 * mmolar}},                               \n",
+      "           {'ibc1': {'ca': 1.8000234 * mmolar,                     {'ibc1': {'ca': 1.8000234 * mmolar,                                \n",
+      "                     'cb': 0.0453864 * mmolar}},                             'cb': 0.0453864 * mmolar}},                              \n",
+      "           {'ibc2': {'ca': 3.1355276 * mmolar,                     {'ibc2': {'ca': 3.1355276 * mmolar,                                \n",
+      "                     'cb': 1.4068981 * mmolar}}]                             'cb': 1.4068981 * mmolar}}]                              \n",
+      "1000      [0.00314551 * 10.0^0 * (mmolar / second) ** 2,          [0.0021539 * 10.0^0 * (mmolar / second) ** 2,           []          \n",
+      "           0.00141168 * 10.0^0 * (mmolar / second) ** 2,           0.00109254 * 10.0^0 * (mmolar / second) ** 2,                      \n",
+      "           {'ibc0': {'ca': 0.0053007 * mmolar,                     {'ibc0': {'ca': 0.0053007 * mmolar,                                \n",
+      "                     'cb': 0.0027947 * mmolar}},                             'cb': 0.0027947 * mmolar}},                              \n",
+      "           {'ibc1': {'ca': 0.00446298 * mmolar,                    {'ibc1': {'ca': 0.00446298 * mmolar,                               \n",
+      "                     'cb': 0.00301004 * mmolar}},                            'cb': 0.00301004 * mmolar}},                             \n",
+      "           {'ibc2': {'ca': 0.00534556 * mmolar,                    {'ibc2': {'ca': 0.00534556 * mmolar,                               \n",
+      "                     'cb': 0.00337772 * mmolar}}]                            'cb': 0.00337772 * mmolar}}]                             \n",
+      "2000      [0.00084838 * 10.0^0 * (mmolar / second) ** 2,          [0.00054396 * 10.0^0 * (mmolar / second) ** 2,          []          \n",
+      "           0.00066913 * 10.0^0 * (mmolar / second) ** 2,           0.0005198 * 10.0^0 * (mmolar / second) ** 2,                       \n",
+      "           {'ibc0': {'ca': 0.00092106 * mmolar,                    {'ibc0': {'ca': 0.00092106 * mmolar,                               \n",
+      "                     'cb': 0.00056548 * mmolar}},                            'cb': 0.00056548 * mmolar}},                             \n",
+      "           {'ibc1': {'ca': 0.00122206 * mmolar,                    {'ibc1': {'ca': 0.00122206 * mmolar,                               \n",
+      "                     'cb': 0.00080589 * mmolar}},                            'cb': 0.00080589 * mmolar}},                             \n",
+      "           {'ibc2': {'ca': 0.00065078 * mmolar,                    {'ibc2': {'ca': 0.00065078 * mmolar,                               \n",
+      "                     'cb': 0.00079521 * mmolar}}]                            'cb': 0.00079521 * mmolar}}]                             \n",
+      "3000      [0.00039193 * 10.0^0 * (mmolar / second) ** 2,          [0.00028126 * 10.0^0 * (mmolar / second) ** 2,          []          \n",
+      "           0.00028135 * 10.0^0 * (mmolar / second) ** 2,           0.00023285 * 10.0^0 * (mmolar / second) ** 2,                      \n",
+      "           {'ibc0': {'ca': 0.00026876 * mmolar,                    {'ibc0': {'ca': 0.00026876 * mmolar,                               \n",
+      "                     'cb': 0.00018014 * mmolar}},                            'cb': 0.00018014 * mmolar}},                             \n",
+      "           {'ibc1': {'ca': 0.00043916 * mmolar,                    {'ibc1': {'ca': 0.00043916 * mmolar,                               \n",
+      "                     'cb': 0.0003322 * mmolar}},                             'cb': 0.0003322 * mmolar}},                              \n",
+      "           {'ibc2': {'ca': 0.00018346 * mmolar,                    {'ibc2': {'ca': 0.00018346 * mmolar,                               \n",
+      "                     'cb': 0.00025419 * mmolar}}]                            'cb': 0.00025419 * mmolar}}]                             \n",
+      "4000      [0.00021093 * 10.0^0 * (mmolar / second) ** 2,          [0.00015969 * 10.0^0 * (mmolar / second) ** 2,          []          \n",
+      "           0.00016543 * 10.0^0 * (mmolar / second) ** 2,           0.00013996 * 10.0^0 * (mmolar / second) ** 2,                      \n",
+      "           {'ibc0': {'ca': 0.00010608 * mmolar,                    {'ibc0': {'ca': 0.00010608 * mmolar,                               \n",
+      "                     'cb': 9.404449e-05 * mmolar}},                          'cb': 9.404449e-05 * mmolar}},                           \n",
+      "           {'ibc1': {'ca': 0.00023372 * mmolar,                    {'ibc1': {'ca': 0.00023372 * mmolar,                               \n",
+      "                     'cb': 0.0001386 * mmolar}},                             'cb': 0.0001386 * mmolar}},                              \n",
+      "           {'ibc2': {'ca': 9.680791e-05 * mmolar,                  {'ibc2': {'ca': 9.680791e-05 * mmolar,                             \n",
+      "                     'cb': 0.00014055 * mmolar}}]                            'cb': 0.00014055 * mmolar}}]                             \n",
+      "5000      [0.00014335 * 10.0^0 * (mmolar / second) ** 2,          [0.00011242 * 10.0^0 * (mmolar / second) ** 2,          []          \n",
+      "           0.00011522 * 10.0^0 * (mmolar / second) ** 2,           0.00010192 * 10.0^0 * (mmolar / second) ** 2,                      \n",
+      "           {'ibc0': {'ca': 5.779891e-05 * mmolar,                  {'ibc0': {'ca': 5.779891e-05 * mmolar,                             \n",
+      "                     'cb': 5.5820285e-05 * mmolar}},                         'cb': 5.5820285e-05 * mmolar}},                          \n",
+      "           {'ibc1': {'ca': 0.00013723 * mmolar,                    {'ibc1': {'ca': 0.00013723 * mmolar,                               \n",
+      "                     'cb': 6.1033348e-05 * mmolar}},                         'cb': 6.1033348e-05 * mmolar}},                          \n",
+      "           {'ibc2': {'ca': 7.234841e-05 * mmolar,                  {'ibc2': {'ca': 7.234841e-05 * mmolar,                             \n",
+      "                     'cb': 0.00010172 * mmolar}}]                            'cb': 0.00010172 * mmolar}}]                             \n",
+      "6000      [0.00011602 * 10.0^0 * (mmolar / second) ** 2,          [9.5097355e-05 * 10.0^0 * (mmolar / second) ** 2,       []          \n",
+      "           7.968601e-05 * 10.0^0 * (mmolar / second) ** 2,         7.246479e-05 * 10.0^0 * (mmolar / second) ** 2,                    \n",
+      "           {'ibc0': {'ca': 3.963989e-05 * mmolar,                  {'ibc0': {'ca': 3.963989e-05 * mmolar,                             \n",
+      "                     'cb': 3.690526e-05 * mmolar}},                          'cb': 3.690526e-05 * mmolar}},                           \n",
+      "           {'ibc1': {'ca': 8.915077e-05 * mmolar,                  {'ibc1': {'ca': 8.915077e-05 * mmolar,                             \n",
+      "                     'cb': 3.357489e-05 * mmolar}},                          'cb': 3.357489e-05 * mmolar}},                           \n",
+      "           {'ibc2': {'ca': 5.7701498e-05 * mmolar,                 {'ibc2': {'ca': 5.7701498e-05 * mmolar,                            \n",
+      "                     'cb': 7.855295e-05 * mmolar}}]                          'cb': 7.855295e-05 * mmolar}}]                           \n",
+      "7000      [9.821482e-05 * 10.0^0 * (mmolar / second) ** 2,        [8.3726925e-05 * 10.0^0 * (mmolar / second) ** 2,       []          \n",
+      "           5.896915e-05 * 10.0^0 * (mmolar / second) ** 2,         5.3390148e-05 * 10.0^0 * (mmolar / second) ** 2,                   \n",
+      "           {'ibc0': {'ca': 3.0593033e-05 * mmolar,                 {'ibc0': {'ca': 3.0593033e-05 * mmolar,                            \n",
+      "                     'cb': 2.6512069e-05 * mmolar}},                         'cb': 2.6512069e-05 * mmolar}},                          \n",
+      "           {'ibc1': {'ca': 6.14094e-05 * mmolar,                   {'ibc1': {'ca': 6.14094e-05 * mmolar,                              \n",
+      "                     'cb': 2.1582948e-05 * mmolar}},                         'cb': 2.1582948e-05 * mmolar}},                          \n",
+      "           {'ibc2': {'ca': 4.6279834e-05 * mmolar,                 {'ibc2': {'ca': 4.6279834e-05 * mmolar,                            \n",
+      "                     'cb': 6.889654e-05 * mmolar}}]                          'cb': 6.889654e-05 * mmolar}}]                           \n",
+      "8000      [8.164347e-05 * 10.0^0 * (mmolar / second) ** 2,        [7.186554e-05 * 10.0^0 * (mmolar / second) ** 2,        []          \n",
+      "           4.8710255e-05 * 10.0^0 * (mmolar / second) ** 2,        4.3789907e-05 * 10.0^0 * (mmolar / second) ** 2,                   \n",
+      "           {'ibc0': {'ca': 2.6550331e-05 * mmolar,                 {'ibc0': {'ca': 2.6550331e-05 * mmolar,                            \n",
+      "                     'cb': 3.1876243e-05 * mmolar}},                         'cb': 3.1876243e-05 * mmolar}},                          \n",
+      "           {'ibc1': {'ca': 4.271252e-05 * mmolar,                  {'ibc1': {'ca': 4.271252e-05 * mmolar,                             \n",
+      "                     'cb': 1.5517275e-05 * mmolar}},                         'cb': 1.5517275e-05 * mmolar}},                          \n",
+      "           {'ibc2': {'ca': 4.1019546e-05 * mmolar,                 {'ibc2': {'ca': 4.1019546e-05 * mmolar,                            \n",
+      "                     'cb': 6.213671e-05 * mmolar}}]                          'cb': 6.213671e-05 * mmolar}}]                           \n",
+      "9000      [6.772583e-05 * 10.0^0 * (mmolar / second) ** 2,        [6.0895793e-05 * 10.0^0 * (mmolar / second) ** 2,       []          \n",
+      "           4.3536915e-05 * 10.0^0 * (mmolar / second) ** 2,        3.854337e-05 * 10.0^0 * (mmolar / second) ** 2,                    \n",
+      "           {'ibc0': {'ca': 2.3455248e-05 * mmolar,                 {'ibc0': {'ca': 2.3455248e-05 * mmolar,                            \n",
+      "                     'cb': 1.5344798e-05 * mmolar}},                         'cb': 1.5344798e-05 * mmolar}},                          \n",
+      "           {'ibc1': {'ca': 3.100229e-05 * mmolar,                  {'ibc1': {'ca': 3.100229e-05 * mmolar,                             \n",
+      "                     'cb': 1.36119e-05 * mmolar}},                           'cb': 1.36119e-05 * mmolar}},                            \n",
+      "           {'ibc2': {'ca': 2.8181494e-05 * mmolar,                 {'ibc2': {'ca': 2.8181494e-05 * mmolar,                            \n",
+      "                     'cb': 6.931232e-05 * mmolar}}]                          'cb': 6.931232e-05 * mmolar}}]                           \n",
+      "10000     [5.5303026e-05 * 10.0^0 * (mmolar / second) ** 2,       [5.092607e-05 * 10.0^0 * (mmolar / second) ** 2,        []          \n",
+      "           3.807514e-05 * 10.0^0 * (mmolar / second) ** 2,         3.359254e-05 * 10.0^0 * (mmolar / second) ** 2,                    \n",
+      "           {'ibc0': {'ca': 1.35118025e-05 * mmolar,                {'ibc0': {'ca': 1.35118025e-05 * mmolar,                           \n",
+      "                     'cb': 1.4254373e-05 * mmolar}},                         'cb': 1.4254373e-05 * mmolar}},                          \n",
+      "           {'ibc1': {'ca': 2.082289e-05 * mmolar,                  {'ibc1': {'ca': 2.082289e-05 * mmolar,                             \n",
+      "                     'cb': 1.0991285e-05 * mmolar}},                         'cb': 1.0991285e-05 * mmolar}},                          \n",
+      "           {'ibc2': {'ca': 2.1368152e-05 * mmolar,                 {'ibc2': {'ca': 2.1368152e-05 * mmolar,                            \n",
+      "                     'cb': 5.3593893e-05 * mmolar}}]                         'cb': 5.3593893e-05 * mmolar}}]                          \n",
+      "\n",
+      "Best trainer at step 10000:\n",
+      "  train loss: 2.28e-04\n",
+      "  test loss: 2.19e-04\n",
+      "  test metric: []\n",
+      "\n",
+      "'train' took 1500.885740 s\n",
+      "\n",
+      "Saving loss history to D:\\codes\\projects\\pinnx\\docs\\unit-examples-inverse\\loss.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\unit-examples-inverse\\loss.dat\n",
+      "Saving training data to D:\\codes\\projects\\pinnx\\docs\\unit-examples-inverse\\train.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\unit-examples-inverse\\train.dat\n",
+      "Saving test data to D:\\codes\\projects\\pinnx\\docs\\unit-examples-inverse\\test.dat ...\n",
+      "Saving checkpoint into D:\\codes\\projects\\pinnx\\docs\\unit-examples-inverse\\test.dat\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+bqwREREOjsYGgwFKpRKTk5MONiu08MTHx7N6o2HbGY2vqTNv8dSZmq6As1gsMyJXe+EhQ+CCDxGaEOOuN4aiKPT09KCnpweLFy9GTk4OtFptUKz3fU2djY6OoqmpCVlZWViyZEnQHaEDTVRUFNLT05Gens7YrNDC09/fDwAOB9IxMTEhXrEjbBOaUJ0ZuXOm7u/vh8FgwBdffDGnMzVApo8GEyI0IYT+IjtHMQaDAY2NjTNsZIJhFUM/rzeiYLVa0dbWhtHRUaxYscJl1dtcQuOtCAWjqdT5+WmblaysLNhsNqbpUC6Xo6urCwKBAAKBAFwuFwaDAVFRUUFbjzfrZgN0uTEb1kOXxKvValAUhaysLK+dqQEyfTSQEKEJAfa9MfZzY4BvbWSSkpJm2MjQghDoH7Q3m7hOp0NdXR04HA6qqqrcXuUHI6KZzwjJvukwNzeXKcHt6+uDRqPBV1995dYqZ75gU8RIr4VNZ1w2mw08Hi+gztSupo/aCw+ZPuoaIjTzzGw2Mh0dHRgcHERJSQkyMjJmPJb+8tM/oEDhaUQzNjaGpqYmZGRkoLCwcNZNJdBCE+ofLF2COz09jcjISCxZsoS5SqYPpO3TMyKRKOhWOWyJIIBvhYYt6wHO/k5cVaP54kw91yweMn10dojQzCPuRix7aiND338+Lf2Bsz/Y9vZ2DA8PY9myZcywLH+e0xfYdAXvzipncnISbW1tTHommFY5bBIaOiJmy3oAOFSducNTZ2r7Hh53ztQAER53EKGZB5x7Y+y/YCMjI2hpafHIRsY+ogkks6XOaBGkKGrWVJmr51xIEc1c2E+stE/P2F8lB8Mqhy3vC1tTZ96ux50ztX05tafO1MBM4RkYGIBarUZBQcE5NX2UCE2QcR6xTH8JLRYLWltbMT4+7rGNTLCExl3qTCaTobGx0ScvtYUc0cy1GbhKzzhvVlwu12EGjy8OD2x5PwD2ps78TV+6cp9Qq9WYnJz02pkaANPGQBf2nCvTR4nQBAn7hjDnVJm9o/G6des8rl6iv7DBjmjo86KhoSEsXboUaWlpPj1noPtowhV3VjmTk5MYGxtDR0eHg7eXWCz2yCqHbakzXzzsgokvEc1ccLlcCIVCphLUW2dqesyF/bpmmz66UISHCE0QmK03ZnBwEO3t7cjNzfXJRiYYJc72EY1er0ddXR1zXhQbG+vz8y7EiCYQa3DlZkwXFgwMDKClpYWxWBGLxbNa5bBl42GT6NEEQ2ic8daZ2mg0zoiy3DlT2wvPddddh5tuugnXXXddUP+eYEGEJsDYbDaMjY0BAMRiMfPjM5lMaG5uhkqlQkVFBWMg6C3BHLs8Pj6OxsZGSKVSFBcX+5V2CPToAbZtYoGEx+PN8Pai02z2Vjm08NBuxGza3OfLFcAb5kNonJnLmXp6ehp8Ph9Wq9UrZ+qxsTGvffnYRPiunGXY28iMjIwgMjKS2TjsbWTWrVvnl4NwMISGFoT6+nosXboU6enpAX1eV/iyKbEhopkPnC1WDAYDMwqhubmZ6fswGAzQ6/WsEBy2OUkDoREaZ5ydqZuamsDhcCAQCLxyptbpdKxzpvAGIjQBwDlVxuPxYLVaXdrI+LshBPqQ3WAwoL6+HgCwdu1aZiqiv9iXYi+k6qpQEBUVhbS0NKbhkO776O3txeDgIIaHh5lmQ1eH0fMBG8TOGavVGnKhcQV9VgfM7UydkJCAyMhIaLVaxMfHh3jlvsO+TyHMoHOoFovFwXrcbDbj9OnTGB4expo1a5CbmxuQH2IgIxq5XI4vv/ySuVIK5JCwQPf8hIN32nxA931kZmYiJiYGhYWFKC0tRUJCAuRyOU6fPo0vv/wSLS0tGB0dZfo4gg1JnXmGs/jR/ViLFy/GmjVrsH79euTl5YGiKPT29uKqq65CeXk5FAoFqqurMTU15dfrf/7559iyZQvS09PB4XDwzjvvzPmY48ePo7y8HJGRkSgoKMDevXu9fl12fQphhP0YWeeqMr1ej7GxMURFRaGqqoqpUAkEgRAauqqsrq4ORUVFWLZsGYDApqY8ERoiHP5DW+Xk5uairKwM69evR0lJCSIjIzE8PIyTJ0/i66+/Rnt7O8bHx5npk4GGpM48g646cwedNi0sLMTatWvxyiuvYMeOHbBYLHj22WchFouxZs0a/OMf//Dp9bVaLVauXIkXXnjBo/v39vZi8+bN2LBhA+rq6nDvvffixz/+MT766COvXpekznzAecSys42MTCZDQkICVqxYEfDX9ldo6FSZyWTCeeedh/j4eOb5Ann2M5fQ+GKqSYTJEVfvh6tplc6zW4JhlUMiGs+YS2icyczMxI9+9CPcd999+PLLL0FRFI4dO8ac/3rLZZddhssuu8zj++/Zswd5eXn4y1/+AgAoLi7GiRMn8Ne//hWbNm3y+HmI0HiBuxHLwNkrBfqsIy8vD1qtNihr8EdoFAoFGhoakJSUhIqKCqaKJRjWNsF4TiI0jnhyLsLn891a5bS3t8NoNDr4eiUkJPi0ObMtoqF/q2xaE+BbE6lGowEAxMXFISEhAT/60Y+CsTSXnDx5Ehs3bnS4bdOmTbj33nu9eh4iNB7ifOBvLzL0COPMzEwUFhZiaGgI09PTQVmHLw2bFEWhq6sLfX19KC4uRkZGhsvOZTYLDZuulsN5Ld5Y5bjy9XIH2yIaNlriAL4VKGg0GsZtYr4ZGxub4VoilUoxPT0NvV7v8bkuERoPsB+xbH8WY28jQ48wBs6mL4I1O8XbiMZoNKK+vh4Gg4FJlbki0I4DcwkNbTDqzdUdiWgc8bfSy1urnMTERIcu90CuJdDQ32U2Co23EQ1d2sy2v8UbiNDMwmwjlqenp1FfX+/SRobL5TKRT6DxphFyYmICDQ0NEIvFKC8vn7XhK1gNlq6eU6VSoba2FkajEfHx8cyZgvMAKlfPR/iWYLh4u7PKkclkM6xy7JsN2ZY6W0hCo9FoAmbC6i2pqamQyWQOt9Fn0N5UqRKhccNsNjIDAwPo6OhAXl4e8vPzZ3wBQh3R2PfvFBYWIisryyMjyGBHNPYWPIsWLYJEImG6pulGRLofxJXDMRsiGjasgSbYUcRsVjl0syHd88Gm9wVgp9DQ50beCo1Wq/XLCsofKisrcejQIYfbPvnkE1RWVnr1PERoXOBuxLLJZEJTUxOmp6dntZEJ1shlT57bZDKhvr4eer3eqwbMYEc0FosFLS0tmJiYQEVFBRISEmA2mx0aEek5IEqlEr29vYydh1gsZkYsEByZz6vc2axy6NLpM2fOMNGOUCgM+vA3dzj/dtkA/bsNZUSj0WjQ1dXF/H9vby/q6uogFouRnZ2Nhx56CMPDw3jttdcAAHfeeSeef/55PPDAA7j11ltx9OhR/Pvf/8YHH3zg1esSobFjthHLtI1MQkICqqqqZrWRCZXQKJVK1NfXIzExccYY6LkIRvkw/ZxarRa1tbUQCASoqqpCZGTkjNSi8xwQm82GqakpKJVKxiOKy+Wio6NjTqPJc4VQn4vYW+XExMRAoVAgNTUVk5OTaGlpYaxy7Ie/zdd62VhxZp8d8QadThewiObMmTPYsGED8/87d+4EANx0003Yu3cvRkdHMTAwwPx7Xl4ePvjgA9x333145plnkJmZif/5n//xqrQZIELD4K43hqIodHd3o7e3F0uWLEF2dvacP5ZgntG4SnHRXcTd3d0er9GT5/UXDocDuVyOrq4uZGZmYsmSJR7/yOwPoQFgaGgIw8PDoCiKMZpMSEhg0mzBmGDJdtiUrqIoCnw+36VVjlKpZDav+bLKWUhCQ0c0geCiiy6a9Xvjquv/oosuQm1trV+ve84LzWy9MQaDAQ0NDTAYDF6loebzjMZkMqGxsREajQZr1qzx2YUg0Kkzm83GiMLy5cs9Gv88GzweDwKBAIWFhQDOui8olUoolUoMDg4CwIxBYmxKmwSDUEc09jhv7LRVjv2IZLVaDaVSyVx80HNbaOHxdC6TJ7DR54wuBPD2M9NqtW7Hu4cL57TQOI9YthcZ2jI/OTl5zootZ4KdOqMFgU7nCYVCVFVVQSAQ+Py8gUydGY1G1NXVgaIoh7Jv59fzdn32REdHIyMjgynLpTex8fFxdHZ2MtVRdMTjz3vDZtgiNHOJHofDQUJCAmOXQ89toVOjbW1tiIqKYkQnMTHRr8+MjRGNrxM/dTodEZpwxb43xn72g81mQ3t7OzNd0hfLfFpognHFyeVyYTKZ0Nvbi66uroC5QgdKHCcnJ5nDRT6fP+tVqrdrdieErjYxlUoFpVKJvr4+NDc3M2XUoT6kDiRsimi8LW92nttisViYz4y2yomLi2M+M2+tctgoNL5GWaGsOgsU55zQ0Af+fX194HK5jIsp4GgjU1VV5fOHS/8gAjGz3BmKoqBQKKBQKLB69WqIRKKAPK+/EQ1FUejv70dnZydzTnTs2LGQOAM4V0fRtitKpZI5pLY/K/C0+51tsOmMxl9nAD6fj6SkJCQlJQE4mxJWKpUOVjn0mZwnVjlsFRpf9gONRhPWIwKAc0xo7Htj1Gq1Q1WZs42MP19S++gokEKjUqkwODgIHo/n9wA1Z/wpBrBYLGhqaoJKpXIQv0Cm4/x5LmfbFZ1Ox5zv0N3vdIrNk7MCtogS2yKaQK4lIiKC+cyAb8/kPLXKWUhCo9VqkZaWFoQVzR/njNA498bQw8no/g65XO72PMFb6C+41WoNyNmAfbRAj4cOpMgAvhcDaDQa1NbWMiMR7NfFRsdl+0Nquoza+awgOjqaER62l1GzSWiCubE7n8lptVpGeFxZ5bCxGMCfM5pwnq4JnANC4643hsfjQaPR4KuvvkJUVNQMGxl/oH/8gTjzMJvNaGpqwtTUFFatWoXp6WkoFAq/n9cZX0RhdHQUTU1NyMnJweLFi2dsemyJaGbDvvt90aJFzMRDpVLpsow6WEUevsCmiCYYaWJ32Pdc0VY5dDEIbZXD4/HA4/EwNjbmYJUTSvyJaEgxAIuZzUaG3rALCgqwaNGigP5gaSHzd1OamppCXV0d4uLimGhBo9EEZbPzphiALpgYHh6eNQoMtDjMR3RETzykbfVpd2OlUomhoSFYLBZERkZiaGgo5GXUbIoWQ+l1xuVyIRQKIRQKGaucjo4OTE1NzbDKof8LRZTqTzEAERqWQkcx7mxklEolEhISkJ+fH5TX96eKy95PLT8/H3l5ecz6A93vQuOpKBgMBtTV1cFqtaKqqmrWkD7QEU0oiI6ORnR0NNLT00FRFFpbW6HVaiGXy9HZ2YmIiAiH851ApzRng20RDVvWwuPxEBUVBYqiUFJS4mCV093dDb1e7zD8bb6qEH2NaEh5Mwtx7o2xFxnaokUoFKKgoADj4+NBW4ev7gBmsxnNzc2YnJx06acWrB4dT4oBJiYmUF9fj+TkZJSUlMz5ownHiGY2OBwOIiMjweVyUVRU5GAy2d/fj+bmZqYkl3ajDvYGxpbNnY3uzfR7b2+VA5y9WKKFZz6tcnxJL9LnUaS8mUXQvTH0hmk/0MvZRmZsbCxoNjGAb+4A09PTqKurQ3R0tNuqsmAJzWyRkr3FTVFRETIzMz36IS6EiGY2nMuo6ZJcpVKJ1tZWmM1mCIVCRngCXUYdauG1h03RFTB71VlUVJRLq5zJyUkHqxxaeAJllWO1Wn1K2ZHyZpZgbyPjnCozGAyor6+HyWRysJEJpk0M4J0gONvnz3ZmFMyIxtXGZTab0djYCLVa7bXFzUKLaGjcfTb2Jbn2ZdSTk5NM35a9W4E/xSf0e8GWzZ1NqTPg7Ho82dTdWeVMTk5CoVCgu7ubscqhPztfPzer1epTapWc0bAAdwf+wLc2MikpKaioqHD44tHlzcHC09SZxWJBc3MzlEolysvLmavj2Z53vooB1Go1amtrERMTg8rKSq9/JAs9opmN2cqoR0ZG0N7ezpRR+3JAzTahYWPqzJf12LtM5OTkMC7ik5OTzOcWFRXFiI5IJPL4d+HLGQ19wUJSZyHE3YhlT2xkgi00nkRM9EYeHR3N2OfPRTBclunntReF4eFhtLS0uB3u5stz+gtbIhpfcC6jtlgsTDUbfUCdkJDAbGBzdb7TsEVo2BjRBEL47Ptz6M+NPpfr7e1log36cxMKhW4vGHw5ozEYDLBarSR1FgpmG7HsqY1MKFNnFEVheHgYra2tXm/kwaw6s9lssNlsaG1txdjYGEpLS5kyX1+f81yNaOaCz+c7lFEbDAbmfGd4eBg2m83B2dj5nICNEQ1b1gIEzxnAlVUOfcFgb5Xj6oLBl4hGq9UCAEmdzTezpcroq/CsrKw5Z58Ec2bMbM9POxEoFAqUlZUxX1hvnjdYqTOz2YxvvvkGFEWhqqrKq5ngriARjedERUUhPT2dKaPWaDQOlvoCgYARHbFYzKo0FbBwUmfeEhERAalUCqlUCsCx74q+YKALQkwmk08jArhcrt+/xVBhs9nQ3NwcXkLjrjfG3kbG06twOqIJ1pWYuzOPuro6REREoKqqyqdDxWAJjV6vh1wuR0ZGBoqLiwPyIyURjW9wOBzEx8cjPj4eOTk5sFqtzLTRgYEBpgEROOuWLZFIQu5GzbbUWagsaJz7ruzHk+t0OrS1tWF8fJy5aJir4ZcubWbTe+sNCoUCN9xwQ3gIjX1vjPOI5ampKdTX13ttI0P/MH0tOfTk+e0FgY62cnJyUFBQ4POPINBCQ5d+j4+PQyQSYenSpQF77nCwoPGWUKyBx+MxlWrA2XSNXC5He3s7Ojs70dLSEtQyak84VyOa2XAeT37ixAnk5ubCbDYzVjl0wy99DuR8TktP1wxXoRkeHkZDQwP7hWa2Ecu00eRcJcGuCLbQ0IJgtVrR0tKC8fFxv8886OelKCogkZjJZEJDQwN0Oh0yMzODMsqZDeKw0IiIiEBycjLa29uxdu1ahzEIzmXU9FVzsGFbRMMGoXGGoigIhULEx8czVjl0pOrKKkcgEAS0WfOFF17AU089hbGxMaxcuRLPPfcc1qxZ4/b+u3fvxksvvYSBgQEkJSXh+9//Pnbt2uVVJobD4eDKK69kr9DMNmKZHl+sVquxatUqZniSN9DPFaxzGi6XC71ej5MnT0IgEATMtDNQIwhoH7X4+HhUVlZiYGAAGo3G7/XZsxAjGrZAvxdcLtehD8TeYHJ0dJQpx7W3yQnGhdW5UgzgD87pPOdI1d7QtampCd///vchlUphMBhw9OhRv/aQffv2YefOndizZw/Wrl2L3bt3Y9OmTWhvb3fpVfjmm2/iwQcfxKuvvoqqqip0dHTg5ptvBofDwdNPP+3x6xYUFOCJJ54Auz6J/4M2vRwdHZ0hMkqlEl9++SW4XC6qqqp8EhkgcMaX7tDr9RgdHUVycjJWr14dcGdofzbdoaEhnDp1CllZWSgrK4NAIAhKNRsRh+DjvLnTBpN5eXmoqKjA+vXrUVBQAA6Hg+7ubnzxxRc4c+YMenp6oFKpAvb9Z9vGzrb10BfOs10c0oauhYWF2LBhA6qrq7Fu3ToAwE033QSRSITvfOc76Ojo8Pr1n376adx+++245ZZbUFJSgj179iAmJgavvvqqy/t/9dVXWLduHa6//nrk5ubiu9/9Lq677jqcOnXKq9eNi4vD0qVL2RfR0FHM5OQkenp6mDfaZrOhu7sbfX19KCwsRFZWlt9XUMHopbFarWhtbcXExATEYjEKCwsD+vz2EY0va6OLJpybQ4MhCiSiCR6elje7K6OenJxEY2MjM0CMvrL21W6FjRFNqAsk7KH3GW/WlJubi7Vr12JiYgIff/wxOjo6cOTIEa9nZplMJlRXV+Ohhx5ibuNyudi4cSNOnjzp8jFVVVV4/fXXcerUKaxZswY9PT04dOgQbrjhBq9eGzgbqbFGaJx7YwQCAfPh6PV6NDQ0wGQy4bzzzgtY81KghUar1aKurg48Hg/Z2dkwGo0Be24aX4VGp9Ohrq6OiQSdI6xgVLPNJg4URcFgMHhls88WoWHDhurrxu6ujHpiYgLd3d0QCAQONjmedr2TYoDZoX9bvvTR0MUdhYWFPl24KhQKWK1WpgSbRiqVoq2tzeVjrr/+eigUCpx//vlMMdadd96Jhx9+2OvX5/P57BAaV70xfD4fVquVsZGRSqUzbGT8JZBCQw8Bo3t4BgYGoNfrA/Lc9tBpRG9EYXx8HA0NDcjIyHA7pno+Ixqz2Yz6+nooFApERkYym5pYLHY7kZQNmzubCMRnNVsZNX04bd/1LhKJ3G6UpBhgdqxWq8MRgKeEyn7m+PHj+MMf/oAXX3wRa9euRVdXF+655x787ne/w69//WuvnouiqNALjfOIZfqD4HA4TFXU0qVLgzIzOxBX8VarFW1tbRgdHcWKFSuYq4Zg9bt489wURaGzsxP9/f1YtmzZrO9hMKxtXAkNbbsTGxuLdevWMVfUfX19aG5udphm6WzDwpaIhg0EI1XlqoyarmZra2uDyWRycDW2t9NnU+qMPg9hm9DY72+eotFo/HYFSEpKAo/Hg0wmc7hdJpMhNTXV5WN+/etf44YbbsCPf/xjAMDy5cuh1Wrxk5/8BI888ojX723IhMbdiGXg7Jvb0NAAiqJQWVkZNEX3N6Kh01EcDmfGELBQC43JZEJ9fT0MBoNH6cb5KAago768vDzk5eXBbDZDLBYzZ0VGo5FJ4wwPD4OiKGZTC9fO6GAS7I3dvuudoijo9XrGJqe/vx8cDoepZGNT6sy+Io8t+DPG2deCJ5qIiAhUVFTgyJEj2LZtG4CzF/hHjhzBjh07XD5Gp9PNeP/o9Xu7T3C53NAIzWwjlkdGRtDS0oKMjAxotdqAVWu5wh+hGRsbQ1NTk9t0VDAtbuYSBZVKhbq6OgiFQlRWVnpslx6siMZms6GzsxODg4PM6GdXrxUZGekwJ8R+DvzU1BQoikJHRweTxgnFOF62MN/RHYfDQUxMDGJiYmaUUY+NjQEAampqIJFIHPpAQgH93VoIQqPX65GVleX36+/cuRM33XQTVq1ahTVr1mD37t3QarW45ZZbAAA33ngjMjIysGvXLgDAli1b8PTTT6OsrIxJnf3617/Gli1bfPo7QvJLtU+POdvIKBQKlJaWQiwWY2BgwOcPyBN8ERraGXp4eBjLli1zG3oGs3TanSjYz7VZvHgxcnJyQmrWyeFwYLFYUF1dDYPB4FV0am/Xnpubi+npaVRXVzPpQIPBwHTDSySSeeuGZ0v6LtSpKrqMWigUIjs7G5999hkWLVqE6elp9PT0MOOS6YhHKBTO28bPRqHxtQpOo9HMOi7dU7Zv3w65XI7HHnuMMcw9fPgwk+ofGBhweL8effRRcDgcPProoxgeHkZycjK2bNmCJ554wqfXD9klof3GRtvI0Hb59Lxv4KwA+TqHvWFoCv9tHIVi2gQA0JutkKmNMFqskMZHgjJqIRDokJtqxJYVqViROftQL51Oh/r6esZ0crYvwHynzmihnpiYcDkCei6CUQxgNpshl8shkUg8jqzcQT+Wrrqhh4rRaRwul+tQVODJyIVwhy1nIvR3MSkpibnwoscl0/N3rFYrU0admJgYVFsV+uKRLe8P4Lv3WiCHnu3YscNtquz48eMO/8/n8/H444/j8ccfD8hrhzT3QNvIdHR0ID8/38FGhsPhMJVn3nKsTY7H3m/FuNrk9j7dCroizIzP+wfx2jeDSBdGojRDiKT4yBnCI5PJ0NjYiPT0dBQWFs55dRLs1Jm90Gi1WtTW1kIgEHg818aZQKfORkZGMDY2BqFQiNLSUr9/9M6Pd07jTE9PY2JiAkNDQ2htbUVcXBwjOiKRiFVXt4Eg1BGNPa56epzHJWu1Wocyaj6f7+BWEMgLA+fCIjbgzxlNuI8IsNlsoRMao9GI+vp6qNVqrF692uWBly+pre2vnEbd0JRPaxqZMmJkahwA8No3g4iL4uFnF+SiSmJkhqh5Wv0WzNSZvdDQApiZmTnnaIS5njMQEQ2dWhwZGUFKSgoiIyNd/uB9bQp0hf1Qsfz8fKZaamJiAi0tLbBYLA69IYGaAR9K2JLCA+Y+fLc3l8zOzmbKqCcnJx08vuwvDPxJl7Ot4gzwXWgWwnTNkJY3Dw4OgsvlYt26dW4PDb0VmmNtcp9FxhUagxV//LgbPADbK6RIscbA0yLrYKbOOBwOrFYr2tvbMTg4OOtZkTfP6e96jUYj6urqYDabUVlZicHBQVgslllf05v1eYpztZTz1TQ924U+uA7VobU/sCmiob83nq7Hvow6Pz+fcQKxHx5m70ZtX0bt6XrYJjS+nNHQ391wn67J4XBCJzQFBQWwWCyzfoH4fP6sG5UzDxxsCsTSZmAF8Ga1DG9Wy5AujMTGopQ5z3SCPVitu7sbHA4nYOXf/kY0U1NTqK2thUgkYhprgzGPxtsN1tXVNG1c2Nvbi+bmZubQWiKRID4+fs5Nig0bPJuExt/SZoFAgJSUFKSkpDiUUU9OTmJgYAAAHCLSuUrd2Sg0bDijCRUhFRpPumS9iWiOtckxbQjexk4zMmXEa9+cPdORxkegIkvk8kwnWIPVJicnMT09jYSEBKxevTpgJb7+iAJ9LlJQUIDc3FyHczY2pXiAs5+LRCJhenfsRygPDQ0B8G5TIwTWFWC2Mmp6hou9m4SriJStQnMuntHQv/+QCs1ceCM0n3Uq3P5bXAQXFyxOOlt1Nm2EyWJFSnwkIigzmmRaKHx0ipGpTTjU8u2ZTn5SNJ68ahlWZH5byhkoobGfvxMbG4v09PSA9pH4kuqz2WyMK4KrsdRsiGjmwtn7S61WY2JiAmNjY+jo6EB0dLTDpsYWFlJEMxv2ZdR5eXmwWCwOEWlTUxMTkYrFYgiFQtYZagJnhcbbFC2dOgvnMxr6O8rqjjcej+dx6uzCxUn415nhGbcXpsTgvZ9VuXzM8PAwhoeHEZVeiH+f6sWpbgX6/RjJ0q3Q45pXTiNdGImLl0iQog/M1ZXFYkFTUxNUKhVWr16N3t7ekDstGwwG1NXVwWazobKy0mWpdzAimmBGSPa9O/SmRp8ddHR0wGg0IiIiAlFRUVCr1SGZZEnDNqGZr7Xw+XwkJSUxFzW0m4RSqURzczMsFgtiYmJgsVhYNZ3SF/HT6XSgKCqsz2gaGxsRERHBbqHxprx5Q1EySjOFDsUAS1Ji8d7PKt0+hhayKJ0MF8bLcdf1JeDFSXCsXY4XPuuGXOP5+ZA9I1NGvH56BAAP/xr4CpcUSz3q03GFRqNBbW0toqKiUFVVhYiIiKA5LXv6nJOTk6irq4NEIsHSpUvd/oCCEdHMJ84W+/TMd4PBgJqaGvB4PCQmJkIikXjldBwo2LCBAqE11HR2k9Bqtejr64NSqcSZM2fA5/MdUqGh6q/yJXWm1WoBIGxTZwaDAddeey2EQuHCSZ0BwL7bV+NYmxyfdylwQUESNhS5HpvcMDSFYx1yGDQaqJUaCPqMyM7Kg3zIBlGMChsKk3Hdmiw0DE3h/cZRfNoqx/CUL5b/HIxOm5gzHU8LCWhob7CcnBwsXryYec+CITSeFgMMDg6ira3NI+eBcIto5iImJoZxO87Pz3fpdEwXFQS7E55tEQ0bzkTowo/ExERYrVYsX76c+Yzoc0S6jDoxMXFebYx8KQbQarXg8Xhh23zM5XJx2223wWKxsDui8aWPZkNR8gyBGZsyoGZQBZXWjHfqR1E/PG33r1wAVqC9z+ExG4uSkC6MgiQ2AvdfshjRAh5O9k7gnfoxTOl9j3TsCwlWZiTg6rKMGeu1t7mhvcHsCZYvGeB+A7PZbGhpacH4+LjHzgPhHtG4gn5/uFwu4+lF9+7Yp3DsO+GD0bvDpiILto4IcP6M7Muo6VSoUChkIp6EhISguhX4EtHExsayQsR9ISIiAjt37gTA8jMaPp/v9/Cwv5/ow58+6fL6cZ+2zSwu2LJcins35GNYZUDn+DTax7UYm3bvPjAbMrUJH7cp8HGbAgmRPBSlxiM7MRpXrUyBZbwbVqvVrc1NsCIawHUu2WAwoLa2FgBQWVnpcSXWQotoZiMiIgKpqalITU116N1RKBTo7u5GRETErJVS3kIiGve4ix7sy6gBOLhRDw4OAvi24jAxMdGroXxz4csZTSBGBIQaq9Ua2obNYKTO7BmbMuDPn3bivw2yue/sIf9tlOG/jd8+38aiJJRlcqHUmdCv1PksOtNGK071q3CqX4W360YhjAC+UyxFvNKMFS7s1IJlgAnM3MiVSiXq6uqQnJyMkpISr34sgdwI2bKpeoInvTvOc3d8+fvY8p6wSfQAzwtwoqOjkZGRgYyMjBlu4Z6UUXuDPxFNOEP/zayOaHwVmv9UD+PR91qDsCJHnKOe9fmJMFsptMs0mPQxvQYAUybgQL0MB+pliOIDRakJuHN9HpNi43K5MJvNfq3dGXqjoCMliqIwMDCAjo4OFBYWIisry+vNJBgpPrZGNLMxW++O/ZU0XVTgyWgMNm3ubE2deYOzW7jzxYGrMmpvXsOXMxqdThf2dkn09zSkQjNXasWb8maasSnDvIiMK77onnT4f2kUoLNxoDb5vjkaLEDd0DTu/Fc9YiK4WJuTiASeEZVpAiz1d8F22Pf9WK1WNDc3Y2JiAqtWrfK5f2SuH4g3qbVw/rE549y7Mz09DaVSidHRUbS3tzO9OxKJxK3vF5sEl22ps0C0FDhfHBiNRuZ8hy6jtj+Dm6uM2peIZiGkzlghNHPhi3vzL9723Ibm4iViSExyFBUXgsfloWZAhfcaA5dqkxkAIHAbgs5kw7HOCQDAu23AX898jhxxDLITo7F9daZP5dM09I9Er9ejpaUFXC4XlZWVfg2eW6gRTaBTgvYNiWazGSqVChMTE4zvl/2GRvfukIjGPTabLeD+dZGRkTPO4Gjh6e3tZUrd3ZVR+3JGsxBSZzSsFhpvU2cNQ1M4M6By+++XFCVhXb4EohgByrJESImPwMcff4wNZWmIjIzEdWuy8ItLFqN2UIX+CR2UOhNGVAZ84qIwwF8ieIDJSgHw/QcqU5sgU5uYs53ZxhzMBb1RVFdXIzU1FcXFxX5fFZ6rZzT+IBAImN4de9+viYkJ9PX1MYaUwTin85WFGNHMhv0ZXFZWFmw2G+NGPTw8jNbWVsTExPjtVhDu9jMAS5wBPEmdeSM01bOIzF3rc3HvxgKH2+irQvvXSBVG4TKhoxPy2JQBtYMqqHTmgEU9JitgLzJnhce/53Qec+Bp3w5FUejr6wMA5ObmIj8/37+F/B8LNaKZL1z5ftF9IWNjYzAajTh9+rTP5waBgo0RzXy+D/Zl1IsWLWLKqCcnJ5lpsMBZT8CkpCSPjFuBhRHRsEJo5sLbM5oV6a4/lB+tyZwhMgAgmzaidoKL4TPDiIp0TBFN682Y0JkgiY1AQtS3YXhZlghlWSJwOMCU3oxv+ibxVc+k81N7jb8i4wrnvp1NJTMdCqxWK5qamqBUKsHlcmf07PgDiWgCi/2GFhUVBZlMhvT0dIfeHWdD0Pkab82mzyfUpprOZdRTU1Oorq5mJvQCcEiHuvucFkJEQ8NqoaHPaDz5Ik9OTkLd14QLsiLw+eC3ZcabilPw681FDvcdmzLgz590/l+pMgdo7/dpfRuLklCQHIuS1HiodCaAw8GU3hyUVJu/yNSODgWFKXHgcmxYGqPFqvRIVFVV4cSJEwGNGALdR8NGN+hQQVEUeDyew7mBRqOBUqmEXC5HZ2cnU55Lz90JVhd8qDd2Z9i2HoFAAA6Hg+XLlzuUUY+PjzOfk33/Dm1lpNFoZhjVhishT53NBp3TnC2/Sad9urq6sHjxYrx8YQ6OtyvwWZcCFzrZ0IxNGfDi5z3Yd2YkIOt31dQJnBUgURQfw3IlpoxAi8K3/ppgcTbFdrYR9giAmGYzMk9XY1kchVWriDCEC/a/Hw6Hw9jj5OTkMOW59LA3vV7P9O7Qc3cCFYWQiGZ27Pev2cqo+/v70dzcjOHhYZw+fRqdnZ3IyMgIyBpeeOEFPPXUUxgbG8PKlSvx3HPPYc2aNW7vr1Kp8Mgjj+DAgQNQKpXIycnB7t27cfnll/v0+qyOaOgPx11poMlkQmNjIzMOWiQS4T/Vw3jsv62wUcC+M8P47ZZirC+QBFRg5sKVAGWLIkFxOBicNMzLGrxBZ6bQIdehQw6899fTWF8ggYDHcWmP4w0LMaIJ9evTzLW5u+vdmZiYYHp36NSNp707vq5lvmHbmIDZemicPyeTyYQTJ07g0KFDOH36NE6dOoXTp0/jkksuwSWXXIKVK1d6/fr79u3Dzp07sWfPHqxduxa7d+/Gpk2b0N7e7jJVbjKZcMkllyAlJQVvv/02MjIy0N/fD5FI5PVrh0V5M5fLBYfDgcVimeGMq1KpUFdXh/j4eMbVeGzKwIgMANgohKynxpkB1UwrnXRhJBNZsAULBaaE+uM2BWIiuLi8ROpT+fRswmA2m9HV1YXIyEhIJBKXVjuuYMtGH2q8fR/m6t2xr5Jy17vjDjZGEGxajzc9NBEREbj44otx8cUXY8uWLbjwwgshkUjw6aef4sMPP8SRI0e8fv2nn34at99+O2655RYAwJ49e/DBBx/g1VdfxYMPPjjj/q+++iqUSiW++uorpkw8NzfX69cFvhV9VgsNh8OZUXlmPwDMeaJjn1LHiEw44EpkYiO40JoCW6nlDzqTDW/XjeLtulFIYvhYkSFEljjGo/Jpd0Kj0WhQU1PDXDx0dXUhKiqKubJzt9Gx6ao51PgTRbjq3aF7Qtrb22EymSAUChmngrmaEdkY0YSr0Nij1+uxePFi/OhHP8LPf/5zny6yTCYTqqur8dBDDzG3cblcbNy4ESdPnnT5mPfeew+VlZX42c9+hnfffRfJycm4/vrr8atf/crnSJHVZzSAY4mz2WxGU1MTpqamXHas54pjwOXAa7EpSorEtWtzUTugwte9SuhMFtgA2GwUuFwO7L+yJisFYxAnRrNJZJyZ0Fn+L9qZcCgqcJdmcyU0crkc9fX1yMrKQm5uLiiKgs1mm7HRiUQiZqOzt+EgEc1ZArm521dJURQFnU7HWOT09PSAz+c7pNmcswvnWh+Nt/gzxtm+vNmXz1uhUMBqtUIqlTrcLpVK0dbW5vIxPT09OHr0KH74wx/i0KFD6Orqwk9/+lOYzWY8/vjjXr1+WHidAd+WOE9NTaGurg5xcXFMqsyZVGEUfrul2CF9NhuVeSLwzFoMqK34zQftbu5FNjZ32BcV0C7U932nABcXJSNVGOUgNPZFG0uXLkVaWhrMZvNZZ1e7AWP2Gx19kB0REQGJRMLY44QaNl29BxoOh4PY2FjExsY6NCNOTExgYGAALS0trB+dzDah8WU9tPtAKKZr2mw2pKSk4OWXXwaPx0NFRQWGh4fx1FNPeS00NGEhNGNjYxgdHUV+fj7y8vJm/aFfU5GBT1rH8dn/nTO4YstyKcamDTjZqwrCis9dpo1W/L9D7fh/h9qxLC0OS6UxyONYsd6uV2fNmjUQCoVuIxPnjc6+espms6GmpobpQZBIJKwZ1TvfzFe6yr53BwAzd2diYgJNTU2w2Wzg8/mIjY1lTCBDDduExp+Ixt8+mqSkJPB4PMhkjk3mMpkMqampLh+TlpYGgUDgsObi4mKMjY3BZDL5NEmW1akzs9kMg8EAvV7v8bCthqGpWUXm0pJkB6t/QnBoGtWgaVQDAHix8RgWiTgoyU2DRA2s8KKmwL4qRyaToaioCEajERMTE+jt7YVAIGBERywWz9vExFATqnMR57k7Go0Gra2t0Ol0+Oabb5jiDronJBSfx0IRGp1O57czQEREBCoqKnDkyBFs27YNwNn358iRI9ixY4fLx6xbtw5vvvmmw/vY0dGBtLQ0n8eVs/ZXOT09jbq6OgDAokWLPBIZYHYbmvhILg63yAOwOoI3TFuAOgWFOsUI3jwzwkwX3bIiBRfke/a50kRGRiI5ORmZmZmwWq1MWsd+zgstTLQB5UIl1H8b3btDj7jOzMxkztqC3bvjDvrML9yFxmazBcwZYOfOnbjpppuwatUqrFmzBrt374ZWq2Wq0G688UZkZGRg165dAIC77roLzz//PO655x7cfffd6OzsxB/+8Af8/Oc/93kNrBMaiqIwODiI9vZ2LFq0CFNTU159OSuyRS5v5wFQG9l70H4uYT9dND6Si3WLxJDERWDzshQsT09w+zjn7wFtMElfhBgMBkxMTDDNb/S/01fYgXb0DSVsqvSiiwF4PB6SkpKYbnbnCZYcDofpgJdIJDMcjgMB7a3HJqHx1VATQEDOaLZv3w65XI7HHnsMY2NjKC0txeHDh5kCgYGBAYf3KysrCx999BHuu+8+rFixAhkZGbjnnnvwq1/9yuc1cKgQlvFQFAWT6duueYvFgubmZiiVSqxYsQISiQSNjY2Ijo5GQcFMrzJ3/OpAM96pHw3GkglBJi6CizvWZ+Pm87Jn/NuJEyewfPlyCIVz597sD7GVSiVzsEpHO75eXbe3t4PP5wfMeNRXurq6YLVaUVhYGNJ1AEB9fT0kEgkyMzPd3sdms0GtVjOfh1qt9qt3xx1msxlffPEFLrjgAtakUdva2iAQCLz6zoyOjqKwsBA6nc7j0elsxWg0sieiUavVqK2tRXR0NKqqqpirHV+mbP7x6qX44ZpM1Ayo8PSRThh9H3ZJmGc0Jhv+cqQPzxztw8aiJIdIxxthcD7Eps917K+u6U1OIpF4nHtmS3k12yKaudbC5XKZ3h17h+OJiQm0tbXBbDZ7NUjMHWyMaKxWq9fOCzqdDgKBIChR33xjtVpDLzQURTEzHPLy8pCfn+/wBfNlyiYArMgUYsdbDT6JzJblUhQkx0KpM0EcE4Eong2Dg0PgcXnIzMqEgM/HlN6MwUkddG76XmRqI6oHprx/cQKAsw4Fh1vPWvm8cXoEKXF8pEdacV2MCpeXeT/gLTIykumMt7+6HhoaQmtrK1OyK5FIkJCQwKqNyh1sERpfzkTm6t2hizzo/zxNe9IjC9j0+fk6XXOhVFTGxMSEVmisVisaGxuhUChQVlbm0qmUz+fDaPTepuWmvdWQqT1/3JblUnynKBllWSKkCs9efYxNGXC0sQ9tPUNITBQhJSUFHHBAUUDT8DQ+aZ/bpTk7DliWM9NPaHBSj8YRted/0DnOuMaCcQ1Qd6gfvz8yiKLUeGSKonBNedqs5zqucL66ti/ZbWxsBEVRDtEOG68qwy2imQ1XvTvORpOeXgiwrRAA8O2MhhaahULIy5t5PB6qqqrchpa+pM4ahqbwda9nM2I2FiTg8kwzNm9YDuCsuBxqGsMHDWP41EFIVP/3n3cMaICB5nGP789G/zO2oTbacLp/Cqf7p3CwXgZxNB8XLpH4JDrAzJJdOtoZGRlBe3s7YmNjmU2OTakztmyogV4Ll8t1KPIwGo1MtNPY2AibzeZQVGB/hsFGoZnNVNMdOp1uwVRNms3m0AoNj8fDsmXL5pyy6W3q7FiHZyXMv7ykAFsLY9He3h7wEQK+QkTGe5R6Cw7Wy3CwXoY0YQRWpid4VMXmCnsbd9oHjI52mpubYTabERUVxXiz+eN67C9s2YSCPWEzMjISaWlpSEtLY3p3JiYmmHkuUVFRjDDxeDxWCo0vVWdsaH71BzrSra+vD/0ZzVz4EtEkx82e6qhalIhd25YiVRiF1r5R7G024otPTvizTAJLGJ0yYXTK8WynPEvks/AIBAJIpVJIpVJQFIWmpiaYzWbIZDJ0dHQgOjqaqWQTCoXzZsXCttTZfG3u9nN3cnNzYbFYmDRbd3c3dDoduFwu+vr6IBaL56V3Zy58PaMJ9+ma9He0tbWV/UJDT9n0hrSE2a8yaZH566dd2PNFnx+rI7CdcY3FoaggPUGAP39vqU8pNg6HA4FAgNjYWCxatAgWi4WJdlpbW2E2m5GYmOhgBhos2JLCA4If0cwGn8936N0ZGRlBT08P1Go1BgYGHKoLxWJxSM7bfO2jCfczGvo7kZGREXqhmWuYlU9nNCPuq70euKQAqcIo3PF6LY7PYlXjDSvTE3BVWRrz/wfrRlE/PB2Q5yYElpFpM67/R53fkQ5wdpOzr5zSarUOo5Tp9BptxxLIaIdtEQ1b1kKXBC9fvhw2m42Zu0NXttqft81XBOpr6myhCM3FF18ceqGZC59SZ7Gur1ouKUrC5uWp+Nm/6rwSGWchmdKb0S5TQ2u0YnFKPDJEUbDXyouXJCFdGIkJnQlalRJZadIZqQW92QqdyQouhwPZtAE9E3qv/kaCfzhHOilxfCzPSMC2lam4aLH7Oe2zmYHGxcUhLi4O2dnZTEpnYmICHR0ds44+8AU2be5sKkywP3jncrkQiUQQiUQzenfoCNTeoNXfz8STNXlKoOxn2IBSqWS/0PD5fK+LAS4uSsb/OzTT9j8/OQ4XPu3ZWczGoiQUSuNw0ZJkpMRHomZQBZXWjJpBlYMp52ddyjmeiYdm1dxl0ITQMq6x4Ei7EkfalYiP5PpVPg04pnRmG30QSvPJQBHK1Jkzs1WduevdmZiY8Kt3Z671UBR1TkY0dCS3Z8+e0AvNXF9QOqLx5gouVRiF319ZjF+/1woKAAfABYslHp3HbK9Ix08vXAQAqBlU4bWvB4jb8znGbOXTvvxgZht90NXVBYPBwEy09HT0AYloXONpebOrz2Rqasqhd4c2BBWLxT438dJOBd4KjU6nQ0rKzP67cIKO/sOm6gzw/kDtmooMrC+QoF+pw8luJV6aVWQoLE+Px/PXliJVGIW/n+jDnz7p8m/hhAWDffl0UjSQJ4pEQo1uzjSbO+xHHwBwuLJ2Hn2QmJjo8sqaTUITLhHNbDgbtLrq3bGPdjz1H6PT/t6uSaPRYNGiRd79ESyD3q+vvfZa9gsNnVKwWCxeXxXQHf437q2Z9X7rUyk8e8MKTJuB+/c34r8NJIIhuEahBxR6IzBqZNJsV65I9bmgADhr0RETE4PMzEymK96T0Qds2dzZJnqBiK6ce3fUajWUSiVT1m5f6CESidymPq1Wq0+WOIGYRRNq6O+FwWAIvdDM9QXlcDjgcDg+j/B98bOeWf/9l5cUQDjVhd992IkDDWRWDcE71EYb3jg9gjdOj0AczceKjHhkJEb7LDzOXfHuRh8YDAYmLRNKKIoKy9SZN9g38dr37kxMTKCzs5NJfdJRqP3FgK9DzxZCHw3N559/HnqhmQvapsYXoRmbMmBftetOf7pp81+nh/DUKQAgIkPwD6XeguNdkwAmmUq2S4pT/Ip2oqKikJGRgYyMDGb0AV1C3d3dDblczlxZJyR453AdCOg8PJsimmCXLDv37tBzdyYmJtDf3+9wseA8EtlTaAuacMX+4uOxxx5jv9AAvjs4P3Cgwe2/7dq2FI//tzVgvTQEgjPjGkvAox169IFarWbOb/wdfeAPtNAs5IhmLqKjox0uBux7d6anp8HhcNDV1QWxWOxx7064V53RFx42mw1pab4V0QQUT66EfIlojtR24Zu+KZytOXPkgUsK8D8n+ojIEOYN52hHGifAj8/PxkWLk5Ca4H23OkVREAgEIR99QKfv2BTRhLJU3Ll3Z3R0FD09PTCbzQ7uEfQFgaveHbr5NxDTNUPFHXfcgccffxzp6ekAWDjK2RXe2NDYbDa0trbi04ZRuBKZDUskUBss+OepoQCvkkDwHJnGjCcOd+OJw91YkhKN1TmJfqXYPBl9QNvjBHL0ARtTZ2yJroCz70tUVBSKi4sd3CPofip3FYbhnjp75ZVXcNFFF2HNmjUAwkRoPI1o9Ho96urqAABXn78CB3pmps4SYwRzlDoTCPNLx7geHeN65lwnRxI7Z7PoXJVe3ow+EAqFPm/OJHU2O/bFAM7uEXTvjn2F4eHDh2G1WjE9PR2Qi4EXXngBTz31FMbGxrBy5Uo899xzzOY/G2+99Rauu+46bN26Fe+8847XrxsdHY3f/va3EIlEiIiICB+hmeuMRqFQoL6+HqmpqSguLgaXy8W2lWl4p36Uuc+SlFgcqBsL9nJnkB0HpMZyEBMngt5KEdsZglvODnj7tlk0TRiBi5ckzYh2vCkpnmv0gdVqdTAD9WZGPRtTZ2wSmtmKE1z17gwNDeHAgQMwmUxYv349LrnkEmzatAmbN29GRkaGV6+9b98+7Ny5E3v27MHatWuxe/dubNq0Ce3t7bM2g/b19eH+++/H+vXrvXo9e1577TXIZDKo1WpoNBpwqBDbwFIUBZPJNOt96urqIBQKkZeX5/Lx3d3d6O3tRUlJyYwPo2FoCsc75GiXafBpW/CsYDYWJUEUxcfwlAG9EzqMTc/+NxEI3pImjEBhcix4PA6Wx2hxRUUupFKpX89pP99FqVRiamrKq9EHOp0Op06dwkUXXeTXOgJFbW0tpFIpczYQavr6+qDVarF06VKPH6NSqZCdnY1PP/0UX3/9NT766CNcd911uOuuu7x67bVr12L16tV4/vnnAZwVvaysLNx999148MEHXT7GarXiggsuwK233oovvvgCKpXKp4jGmbCJaFylzkwmExoaGqDT6XDeeee5PDxrl2nw4md9CIaaLkuLR1FqHKb0ZnwSRBEjEAB61s7ZC5gjAF5t7kBV/oRfDtSu5rvQHfGejD5gkysAwL6Ixpc+Gp1OBwBYv349vvOd7+CRRx7x+nVNJhOqq6vx0EMPMbdxuVxs3LgRJ0+edPu43/72t0hJScFtt92GL774wuvXpXHer8NWaKamplBXV4f4+HhUVla6tOkYmzLgsf+2BlRkzs9PhFJrRsuYBk2jajSNqgP47ASC50ybKAcH6iuXp+CJK4v8ek5vRx+wyRUAWBhCo9VqERkZ6Vf1nEKhgNVqnRHxSqVStLW1uXzMiRMn8Pe//5055/YH57855ELjaXmz/RkNXb6Zn5+PvLw8t8/Rp9TBFgCVKU6Nw/aKDBysG8WJ7kn/n5BACALvNY7j2lXpPleuOePJ6IO4uDhGkIJls+8NbBQabw/1NRqNR8aqgUStVuOGG27AK6+8wjSiBpKQC40n8Pl8GI1GWK1WtLS0QC6Xo7y8nDEldIfe6JttjT0/WpMJs82G33wwc+xAoBBG8zGl974hlUBwpm5wOmBC44zz6AO9Xo+hoSGo1WqcPn0aERERDqW6oehnYZvQ+LIeWmj8ISkpCTweDzKZo2+jTCZDamrqjPt3d3ejr68PW7ZsYW6jCz34fD7a29uRn5/v83pYITSeTNk0Go345ptvwOVyUVVVhaio2cc1A2cjGl+5pCgJUQIeXvez36YgUYApgwVyvfu/j4gMIVCUZgVHZJzhcDiIiYlBcnIyFAoF1q5dy0Q73d3d0Ov1Xo8+CARsExpfz2j8fb8iIiJQUVGBI0eOYNu2bQDOvjdHjhzBjh07Zty/qKgIjY2NDrc9+uijUKvVeOaZZ5CVleXzWgCWCM1c0Dbq2dnZKCws9PiLVJEt8un1Llws8flwPzGaj0k74eiaNPv0PASCL3SOa4MW0biC3tidRx/o9Xqmkq23txd8Pp/5d3ejDwK5HrYQyjHOO3fuxE033YRVq1ZhzZo12L17N7RaLW655RYAwI033oiMjAzs2rULUVFRWLZsmcPjRSIRAMy43Vva2trYLTQURaGzsxNDQ0OIjo5GcXGxV49fkSmc0UszF1eXpnrVa7NYxEFkdCyaRjUA4CAyBMJ88/8OdaJqkdgnWxtfcFcMEB0djczMTK9HH/jLfJhqeoMvQhMo5+bt27dDLpfjsccew9jYGEpLS3H48GGmQGBgYCBookx/L7q7u3HvvfeyQ2hcpc5MJhPq6+thMBhQWFiIoSHfUliXlqR4LDTn5Yo8FpmkSEBhBDpVFKDS+LQ2AiHQ2ChgcFI/r0Iz12bl6egD+j9/zEDZFtH4InyBnEWzY8cOl6kyADh+/Pisj927d6/Pr2u1WsHn8/HJJ5+gq6uLHULjjEqlQl1dHUQiEcrKyjA1NeXzPJoPmz0bYpYeC3zdp5rzfmlxPIxqLFAY2VPSSSDQcDlAVqLnnf3+4ksfjbvRBwMDA2hpaWFGKNNmoJ4+P9tm4wBnN1xv1xPuzs3At5ZEU1NTKCoqYpfQUBSFwcFBtLe3Y/HixcjJyQGHwwGfz/dpTAAALEry7AMb0c7+78XSWLTKtBjVWOHKrJNAYANXLEuZt2gG8H+6pv3og/z8fGaEMu1C7c3oA7pKim1CE6rUWaigBR8ALrnkEnR2drJDaDgcDiwWC1paWjAxMYFVq1YhMTGR+XdfB58BwLbSNPz1aLdf6ytMjkarbA4lIhBYwPtN47j7orx5E5tAp6rsRyh7O/pgoQhNuEc0zzzzDP72t78hLy8PYrEYR48eZYfQaDQaVFdXQyAQoKqqakaDEy00vlw9pQqjkCOORr/SewPLnMRo9E/q0S4n5peE8CAUZzTBKlv2dvSB/ePYgq9nNGlpaUFaUfApKCjA+eefDx6PB4VCgfXr17NDaIaGhpCcnIzFixe7/JLQHxR9wOQNDUNTPokMAPRPEoEhhB97vujH6hzRvLzWfB6+zzX6gHadVqlUfo0+CBQ2mw0URfkU0YRz6uyKK67AFVdc4XAbK4SmqKho1tQYLS6+CE31gMqfpREIYcep/ik0jgTPIcCeUHmduRp9MDo6iu7ubr9HHwQKek/zpRggnIUG+PZ70dvbC4VCwQ6hmeuLyuFwwOFwfDqnyRXHzH0nO4qlMWiV+e4oQCCwgWBa0djDliovgUDA2N6sW7eOGX0gk8nQ0dHBjD4Qi8UQiUTz0mtDnxmdaxEN8G3K8LnnnoNAIGCH0MwFh8PxuSAgOtLTD5kCwCEiQ1gQzJcVDZvGBNBpvNlGH7S1tTGjD+iiAufRB4GCLm329v1ZCEJDc/jwYTzwwAPsEBpfHJw9JVccAy4HAXFxJhDCgZUZ8fNmQ8OmMQHuelbcjT5QKBTo6uqaMfogUNGOLz00QPhXnQHf7ukmkwlRUVHsEBpP4PP5PkU0qcIo/HZLMX793lxzadjxYyEQ/KVxRI2xaeO8VJ6xqRPfk7V4MvrA3gzUn9EHvpQ200LoaohjOEG/ZxdddBH27t0bPkLjTy/NNRUZ+Hf1MBqGpwO8KgKBfcxniTObIhpfRM/V6IOJiQlMTEygp6fHr9EHvggNcLa8OVjpvPmC/k7cf//9+MEPfsAOofE0dear0DQMTRGRIZxTNI2o56XEmS3FAID/0RU9+iAmJgZZWVmwWq1+jT7w1eBzIUQ0NEVFRdizZw87hMYT/LGhISXOhHON3cd6cdnS4NvRsMktOdBpPH9HH/hyRmM2m2E0GhdMMQBFUaiqqgofofEnovF1Lg2BEK7MV/qMbamzYIqeq9EHtOjYjz4Qi8WIj4/32ecMwIKJaOjvxjkhNCsyhdhcIsEHLRMBXhWBwF4mpjSgKGFQhSDcigEChf3og4KCghmjD7hcLjMF2GQyeTz6QKc7214R7lVnzrBCaIJZ3gwAw8PDuEyswJLKZPz1pG+TMwmEcONkYxfi1f1ISkpi0juBvuJnW0QTKtFzNfqgp6cHWq0WJ06c8Hj0gVarRXR0NGvSkYGCFUIDuB5+Zo8vQmOz2dDW1obR0VEMRWTjr0cH/V0mgRA2XFhRgqJkHiYmJtDZ2Qmj0QiRSORQuusvC6kYIFDQow9EIhHi4uKQm5vrMPoAACM6zqMPNBrNnEUG4QhrhGYu+Hw+jEajx/c3Go2oq6uDxWLBoqXl+NlL1UFcHYHAPmIj+ZBIzm5o9qW7dKMibcsikUggEol82qTZ6AzAFuhigLlGH8TFxUEikWB8fBwGgyFgabMXXngBTz31FMbGxrBy5Uo899xzWLNmjcv7vvLKK3jttdfQ1NQEAKioqMAf/vAHt/f3lrARGm/OaFQqFWprayEWi7Fs2TKcHpiao1mTQFh4NA5PoywjDlwuF1wu16F012KxYHJyEhMTE2htbYXFYnGw3KfPF+aCpM7cY7VaZ4w8cTf6QCaT4frrr4dGo4FAIMCrr76KSy+9FBkZGT699r59+7Bz507s2bMHa9euxe7du7Fp0ya0t7cjJSVlxv2PHz+O6667DlVVVYiKisIf//hHfPe730Vzc7PPa7CHQ82Wr5pHzGYzY0LnisHBQYyNjWH16tWzPs/g4CDa2tocJnSOTRlw4dMnAr1kAoHVcDnAoTsrkBz3bdkt7b3F4XCYTZnuRqejnenpacTExDCiM5vlfk1NDXO1HmpaW1sRGRmJRYsWhXopAICmpibEx8cjJyfHo/tbrVbs2rULr7/+OnJzc/HNN9+gpKQE77//PrKzs7167bVr12L16tV4/vnnAZwV4aysLNx999148MEHPVpLYmIinn/+edx4441evbYrwiaimcuCxmazoaWlBTKZDOXl5Q6DkFKFUbikKAmftJFCAMK5g40C/nKsH7dWZqEkNZaZj0Jf0NkLDm3LkpOTA7PZzJwpNDU1gaIot2cKJKJxj7fl1jweD5mZmSgsLMTRo0cxOTmJTz/9FOnp6V69rslkQnV1NR566CHmNi6Xi40bN+LkyZMePYdOp4PZbIZYLPbqtd0RNkIzW+rMYDCgtraWaQ5yNXvi8mWpRGgI5xyftCnwSZsCUXzgvNxE3LE+F8vSz/Z42Gw25j/g22iHx+NBKpVCKpU6DBgbHh5mxilLJBIkJSWxanNn01oA3xo27e1nEhMTcc0113j9ugqFAlarFVKp1OF2qVSKtrY2j57jV7/6FdLT07Fx40avX98VYSU0rqrOJicnUVtbi+TkZJSUlLi9gijPFoEDkLMawjmJwQIc75rE8a5JxAg4yJHEoFgah2vK091GO7Tw2A8YM5lMjBdYXV0drFYrhoaGmKjHuTt+PmGj0ITjLJonn3wSb731Fo4fP+7xWd1csEZo5gq/nVNnFEVhcHAQ7e3tKCwsRFZW1qzPkSqMQklaPJpH1QFbM4EQjujMFFrHtGgd0+JAvQyJ0XxcsVyKy5emOEQ79r83uqAgIiLCoYLq66+/hkAgQH9/P1paWpjueIlEgri4uHlNqxGhOUtSUhJ4PB5kMpnD7TKZDKmpqbM+9s9//jOefPJJfPrpp1ixYoVf67CHNUIzF/apM6vVipaWFsjlclRUVHiUR2wYmiIiQyC4YFJvwT9PDeOfp4YhjOShLCsBmYnR2LxM6hDt0BkF+2iHy+UiIyMDYrGY6Y6fmJhAf38/+Hw+xGIxkpKSvHY+9gUiNGeJiIhARUUFjhw5gm3btgE4+94cOXIEO3bscPu4P/3pT3jiiSfw0UcfYdWqVX6twZmwExqdToe6ujpwOBymFM8TiLEmgTA3U0YrjndNApjE66dHkBQrwF0X5OLCxRKkxAmYMx2r1QqKohzOepy7452dj52bRQMd7bDJ4BPwbT1arTYgB/A7d+7ETTfdhFWrVmHNmjXYvXs3tFotbrnlFgDAjTfeiIyMDOzatQsA8Mc//hGPPfYY3nzzTeTm5mJsbAwAmCIRf2GN0Mz1paM/sJMnT0IqlaKkpMSrqxdirEkgeI9Ca8bvPuzE7z7sRJ4kCgXJcdi6QooLCsTo6OgARVGIjo52Ge3QXmCLFy+GTqdjKtnoOS+0NY5IJAqIQLAxovF2PYE6o9m+fTvkcjkee+wxjI2NobS0FIcPH2YKBAYGBhzW9tJLL8FkMuH73/++w/M8/vjj+M1vfuP3elgjNLNBURSGh4cBAIsWLUJeXp7Xz7EiU4jz88U40a0M9PIIhHOC3gkDeicM+KRNAT4HWCsFfvKdEsTFxTERjruzHbpZNDMzE1arlWkWbW9vh8lkcmgWdVU16glsEho62vMlogmUc/OOHTvcpsqOHz/u8P99fX0BeU13sF5orFYrmpqaoFSeFQjnkj1veGJrCWncJBACgIUCvhwDvnyjhSkmuGK5FEvT4mek1JzLpzkcjsNUS51Oh4mJCYyPj6Ozs9Nnaxw2CQ39N3srNAthuqYrWCM0rlJnOp0OtbW14PP5qKqqwhdffOHzqADgbOVZflIsuhVaf5ZKIBDssC8miBFwsDQ9AbdUZuGiJclMhDNb+XRsbCxiY2ORnZ0Ni8XCpNhaWlpgtVodmkWdLV3sWQhCo9FoFswsGntYIzTOKBQK1NfXIy0tDUVFReByuX5N2QTOVp4RkSEQgofOTOF0/xRO908hms/BZUtTsH1VBpZnCBmxma18msvlIiUlBSkpKaAoChqNBhMTExgdHUV7eztiY2OZZlFnu302CQ39t/lyRrPQZtEALBQaiqLQ29uL7u5uFBcXIzMzk/k3f4afAaTyjECYT/QWCgfqZThQL0MUn4PlGd9GOgBmRDuuCgri4+MRHx+P3NxcmM1mpny6oaEBFEUxkY5YLGad0NB/g6fQnnMkogkiHA4HFosFTU1NUKlUWLNmDYRCocN9/BUaUnlGIIQGg8V9pAPA4TzHXbQjEAiQmpqK1NRUUBSF6elpTExMYHBwEC0tLQDODjlMSUlBfHx8SD3YfC21Jmc0QUan0+Hrr79GREQEKisrXeZi/ZmyCZytPNu2Mg3v1I/6s1QCgeAH9pFOfCQXuZIYXLsqA1eVnjWPtD/PmS3asbfb12q1+Oabb5hzXR6P5xDtBLtZ1BlfKs6AwFadsQnWCE1ERATS09ORm5vrNvydy8HZE364JpMIDYHAEtRGGxpHNGh8rx2P/7cdS9PjkZ8U41O0AwBLly4Fh8PB1NQUJiYm0Nvbi+bmZgiFQkZ45mOCpS89NCaTCWazmQhNMBEIBHPOkfA3dQYAxzrkfj2eQCAEBwsF1A+rUT+sxoF6GYRRPEjjI7GtNBU3V56d6WJfUEBRFBPt0NN36bEFiYmJSExMREFBAfR6PVPJ1tvbC4FAwDSLJiYmBsVNwNceGgAhN9UMBqwRGk/wN3UGAMlx7ssjCQQCe5gyWDFl0OFPn/TgqU96sCLDdbRjNpvR19eHuLi4GX07XC4X0dHRjDWO1WplrHE6OzthNBpnWOMEAl+ERqPRAAA5owk2HA4Hsw38DEREc3FhMv7fB+1+PQeBQJhfKDhGO0kxfCxKjkWGKAplCTpkRJlRVlYGgUAwZ7MoLSoAmGZRhUKBrq4uREVFMf+emJjocxWbrz5nsbGxrKmcCySsEpq54PP5MJvNfj1HqjAKv7+yGI++1xqgVREIhPlGobNA0T8F9E/hIABpHB/fnRrAFculDtHOXM2itDVOVlYWLBYLY43T1tbGTJikhceb2Sy+nNFoNJp5OT8KBWElNDweDwaDwe/nKZQuvBwogXAuI9PYjzrgIjcp1iHN5kmzKJ/PR3JyMpKTk5melomJCchkMnR0dCAmJoYRHaFQOKuQ+JI6W6ilzQDLhMaT1Jm/ZzQAadwkEBYyU0abY1GBG+GZq1mUtsjPycmB2WxmCgqampqYiaK08ERERDiswdczGhLRsIBAlDcDQI44MONJCQQC+3EWHlEUH6WZ8cgSxzBGoJ6UT0ulUkilUlAUBbVajYmJCQwPD6O1tRXx8fGM6CQkJPh8RrMQK86AMBOaQBQDmEwm9Pd0BWhFBAIh3FAZLMxwt3+eGoY0ToDvlqQ4uE/P1SyakJCAhIQE5OXlwWQyMdY4Q0ND4HA44PP5iI2NhdlsZnp85mKh+pwBLBMaT4af+ZM6m56eRk1NDbIT4wHofX4eAoGwcJBpzMz5TlIMH6tyREiOj2SEZ65ZOxEREUhLS0NaWhpsNhump6fR1taG6elpnDhxAgkJCUy0ExcX53afI0LDEvyJaMbGxtDY2IhFixZh0aJFuKinDsc7JgK8QgKBEM4odBYcblUAAP55ahgJUTw8ua2YGXlg3yxqXz5tH+2IRCJmpk5ycjIT7fT39zPWOElJSUhMTHSwxlnIqbOwKtj25YyGoih0dnaisbERK1asQH5+PjgcDn524ewuBAQCgTBtsOKnbzWh7IljuHLPKTxwoAXtcj0iIyMRGRkJPp/PnMVYrVZYLBaYzWZYLBZwOBxERkYiPT0dy5cvx/r161FSUgI+n4/u7m588cUXqK2tRUdHB+rq6qBWqwMiNC+88AJyc3MRFRWFtWvX4tSpU7Pe/z//+Q+KiooQFRWF5cuX49ChQ36vwZkFHdFYLBY0NDRAo9HgvPPOc/AQmtCYgrFEAoGwADFagR6FHj0KPT5sVbgsKKCjHa1WC61WCz6fD5PJ5NAsKhaLIRaLsXjxYuj1ekxMTODzzz/Hz3/+c/D5fBQUFODQoUPYsGGDTyOt9+3bh507d2LPnj1Yu3Ytdu/ejU2bNqG9vR0pKSkz7v/VV1/huuuuw65du3DFFVfgzTffxLZt21BTU4Nly5YF4q0DAHCo2eqJ5xn6isAder0en332GTZt2jTneY5Op0NNTQ0iIyOxcuXKGeWHD+xvxLsNsoCsm0AgnNtsWynFH7aWQKfT4cyZM5BKpcjLy2P82Oht1j7FZt+Ho1arsX37dhgMBigUCoyNjeGaa67Ba6+95tU61q5di9WrV+P5558HcLZpNSsrC3fffTcefPDBGfffvn07tFot3n//fea28847D6WlpdizZ48vb4VLwi51BmDOqGZiYgInT56ERCJBRUXFDJEBgEWShXnoRiAQ5p936mU43S1DdXU1pFIplixZgoiICERGRiIiIgIREREzUmwmkwkWiwU2m40pj/7e976Hnp4e1NTUYPv27V6twWQyobq6Ghs3bmRu43K52LhxI06ePOnyMSdPnnS4PwBs2rTJ7f19JayExv6DcgVFUejr60NNTQ0KCwtRXFzstnt3W1l60NZJIBDOPf52pAUpKSlYsmSJQ8aFy+WCx+MxgkOf7XC5XKZ82mQyYWRkBDweDxwOB8XFxdi8ebNXr69QKGC1WiGVSh1ul0qlGBsbc/mYsbExr+7vK6wSmrnSYXTI6UpobDYbmpub0dPTg1WrVjmMgHZFqjAKly2dmbMkEAgEXzBQghki44x9Aygd7QgEAnz99deora3FxMTCrIRlldB4Ap/Pn3GOYzQacerUKUxPT6OqqgqJiYkePdfWFWnBWCKBQDgHWV+c4bV9DJfLxenTp3Hdddfh2Wefxa5du3x+/aSkJPB4PMhkjmfPMpkMqampLh+Tmprq1f19JeyExrnybGpqCidPnkR0dDTWrl3rlcNqdGTgBx4RCIRzk62l3l+4nj59Gt/73vfw+9//HnfeeadfPmcRERGoqKjAkSNHmNtsNhuOHDmCyspKl4+prKx0uD8AfPLJJ27v7yusKm/25E22F5qRkRE0NzejoKAAubm5Xn9IueKF6ZRKIBDmEwrfW8TF5HAveMYkiMVij3zOamtrsW3bNjz66KO4++67A2KmuXPnTtx0001YtWoV1qxZg927d0Or1eKWW24BANx4443IyMhgIqd77rkHF154If7yl79g8+bNeOutt3DmzBm8/PLLfq/FHlYJjSfweDyYzWa0t7djcHAQpaWlSE5O9um5UoVRuLQkGYdbyHhnAoHgGzsvzsO2ongoFAq0tbXBZDJBLBYjKSkJSUlJLvthGhsbceWVV+L+++/HL37xi4A5Nm/fvh1yuRyPPfYYxsbGUFpaisOHDzMH/gMDAw4FUlVVVXjzzTfx6KOP4uGHH8bixYvxzjvvBLSHBmBZHw1FUTCZZm+k/Prrr2G1WmG1WlFeXu53J+1v/tuKf50Z9us5CATCucm+28qZQWsAmDk2CoUCCoUCKpUKMTExSE5OxvT0NJYtW4bu7m5cfvnluOuuu/Cb3/xmQY4FcIZVEc1cb7hWq4VarUZ0dDQqKys9dkWdjQsXJxGhIRAIXvO7LYUOIgPAYY5Nbm4uM8dGLpfjlltuwcjICGw2GzZu3Iif/exn54TIAGFUDCCXy3Hy5ElERUUhPT09ICIDABuKkrEkhTRvEggEz9l3Wzm+50EvHj3HZtmyZXj77bcRExODFStWYGxsDGlpaTjvvPPQ3Nw8DysOLawTGmeFpygKvb29qKurQ0lJCUQiUUCGn9lzNWneJBAIHnL/xkUzIpm56Ovrw9atW3Httdfi66+/xpkzZzA8PIw77rgD6ekLf/9hVerMGavViubmZkxMTGDNmjUQCoWYmpoKuNBUZIsC+nwEAmFhcsf52bi1KserxwwPD2Pz5s249NJL8eyzzzKH8ampqUw12EKHtUJjMBhQW1sLDoeDyspKpj+GrjoLJCsyhZDGR0CmJo7OBALBNXecn417Ls736jGjo6O4/PLLcdFFF+HFF190a4m10GHdX83hcKBSqXDy5EnExcVhzZo1Dk2YgRjn7IpNxI6GQCC44f6Ni7wWGZlMhiuuuAJr1qzBK6+84lFvzUKFdUIzNDSE06dPIy8vD8uWLZtxBeDvOGd3bFlO7GgIBMJM9t1W7nW6TKFQYMuWLVi2bBn27t3rMEnzXIR1QqNWq1FeXu6209+XKZuesCJTiAsXSwL+vAQCIXzx5eB/cnISW7duRUFBAd54442AVciGM6wTmpKSEkgk7jf8YKXOAODlH5UhknXvCIFACAVXrZR6HclMTU1h69atSE9Px759+1zOwjoXCbttNVipM5pV5KiGQDjnyZNE44mtJV49Rq1W46qrroJYLMb+/fsRGRkZpNWFH2EpNMGIaOh+nbVJwRMxAoHAfsoyE/DBz87z6jFarRbf//73ER0djYMHD3rlIn8uwDqhmcuSIRhnNDabDU1NTejr68OKTCFK02aa4BEIhIXPD1en441bK7x6jF6vxw9+8AMAwHvvvYfYWOI04gzrhGYuAh3RmEwmnD59Gmq1GpWVlYiIiMCTm9KRJiRhL4FwLvHD1el45LJCrx5jMBhw3XXXQa/X4/3330d8fHyQVhfehK3QBMJ0Wq1W4+TJk4iMjMTatWsRGRkJgUCA4eFh/OOqTGQSsSEQzgnW5gq9Fhmj0YgbbrgBSqUSH374IYRC76rTziVYJzSepM4A+B3VyOVyfPPNN0hPT8fKlSvB5XJhtVqRn5+P9PR0jI+P44GlOnwni3VvEYFACCDn5YrwjxvLvXqM2WzGzTffjKGhIXz00Ucej48/Vwm7LiK6u9ZqtfrUBEVRFPr7+9HZ2Ylly5YhLS0NNpuN+S8yMhI5OTnIycmByWRCsVyOrf1j2NeoQo2cC70t0H8RgUAIFVuLhfjNld5Vl1ksFtx2223o7u7G0aNHZ23HIJyFVYPPgLMH83N5mX300Uc4//zzvT50s9lsaGlpgVwuR1lZGYRCISiKYqIjDofjNqKyWCyYmJjA+Pg45HI5BAIBkpOTkZKSAovFgqamJuTl5TGNpntP9uNvn/dhykiUiUBgIz9YmoDLs6yYmppCQkICkpOTkZycjLi4OLf7gNVqxR133IHa2locO3YMqamp87zq8CQshebIkSNYvXo1EhISPH5ek8mEuro6WCwWlJeXIzIy0uGsxxuzO5vNBqVSifHxcYyNjcFqtSIxMRE5OTkezwt3x96T/Xjps16oTaz6WAiEBcVVK6VMn4zRaGQmYk5MTIDP5yM5ORlJSUkOv2er1Yq7774bX375JY4fP46MjIxQ/glhBeuExpNxzsePH8fKlSs9zotqNBrU1NQgPj4ey5cvB4/Hg81mg9VqBZfL9WnKnc1mQ1tbG8bHx1FQUACtVovx8XGYzWYkJSUxV0fz4XE0Nm3Ag/ubcWpwOuivRSCEO3mSaLd9MjabDZOTk5DL5ZDL5TCZTNi7dy9WrlyJlpYWnDp1CseOHUNOjneOAec6YSk0X3zxBYqKipCcnDzn8ykUCtTV1SE7OxuLFy8GcPbKxGaz+SwyZrMZDQ0NMJlMKC0tRXR0NLN2jUaD8fFxjI+PQ6vVQiwWIyUlBcnJySHrFB6bNmDnvkbUjWpC8voEAlsoy0zwuE+Goiio1Wo8+eSTeP311zExMYFly5bhe9/7Hq644gqUl5efs7b/3hKW75InvTT0oX9tbS1KSkqwZMkS5jzGH5HR6XQ4ffo0uFwuVq9ezYgMcPaMJz4+Hvn5+aisrERVVRXEYjFGRkbwxRdf4PTp0+jv74der/f6df0hNSEKb96+GjUPVOLV70bjn5sT0PDwerQ8tgEtj23AA5csQuS562BOOEfwthmTw+Ew5zWRkZE4efIkHnjgAbS2tuI73/kO6uvrg7LOzz//HFu2bEF6ejo4HA7eeeedOR9z/Phx5kigoKAAe/fuDcrafIV1EQ1wNmc6G6dOnUJGRobbHKnNZkNraytkMhnKysogEok8PvSfDZVKhbq6OqSlpWHJkiVePYfBYIBcLsf4+DgmJycRFxfHFBPMdvgYKDQaDWprayEWi1FcXOzxldjYtAG37q1Bn2r2z4RAYDO+DC2jKAq/+93vsHfvXhw7dgzFxcXMv5lMJggEgqD8bj/88EN8+eWXqKiowNVXX42DBw9i27Ztbu/f29uLZcuW4c4778SPf/xjHDlyBPfeey8++OADbNq0KeDr84WwFJrq6mokJycjOzt7xr+ZTCbU19fDZDKhvLwcUVFRDof+vorM6OgoWlpasGTJEmRlZXn9eHvMZjMUCgXGx8ehUCgQGRmJlJQUpKSkQCgUBvzLq1QqUV9fj+zsbCxatChgz984PIUH9jejn4gQgcX40vFPURT++Mc/4qWXXsLRo0exfPnyIK1udjgczpxC86tf/QoffPABmpqamNuuvfZaqFQqHD58eB5WOTes7KPhcDizdv67S51ptVpUV1cjLi4Oa9euZe7nT6qMoij09PRgYGAAK1euRFJSktfP4YxAIEBaWhrS0tJgtVqZsuna2lpwuVxGdBITE/3OAdMCWVxcjPT0dL/Xbs/yDCE+/HmVy387WDeC33/YDn1gp24TCF6xqTjJJ5H561//iueffx5HjhwJmch4ysmTJ7Fx40aH2zZt2oR77703NAtyASuFZi74fP6MUQETExOoq6tDZmYmlixZAsD/Q3+r1Yrm5mZMTU1h9erViIuLC8j67eHxeIyw0BUv4+PjaG5uhtVqZarXkpKSvCqbpt2o+/v7UVpaOu9NZVeVpuOq0pnCdrxDjgcPtmCa9BcRgsy+28q9HlpGURSef/55/OUvf8FHH32EsrKyIK0ucIyNjUEqlTrcJpVKMT09Db1e73COHCrCUmicI5qBgQG0t7ejpKQEGRkZDp3+voqM0WhkDvvWrl07LwOMuFwuJBIJJBIJioqKMD09jfHxcXR1daGpqQkSiYSpYJttah9deq1QKLBq1SpWGf1dtCQZX//qwhm37z3Zj7980oNvP1UbwrRWhcACjt5bidQE76z6KYrCyy+/jD/84Q/48MMPsWbNmiCt7tyDlULjSerMbDYzG+rY2BhWrVoFkUjE9McA8Flk6INzkUiEkpISvxowfYXD4UAoFEIoFDr06QwMDKClpQWJiYmM6NjPvrBYLGhoaIDRaMSaNWvCZi7GzZU5uOm8bPT19aGvrw8rV5ZBLBbjp/+qw/HOyVAvjxBG3L9xkU8is3fvXjz++ON4//33UVXlOiXMRlJTUyGTyRxuk8lkSEhIYEU0A7BUaOaCx+Mx5zFGoxHnnXceoqOjmSgG8P3QX6FQoLGxMeAH5/5Al1nGxcVh0aJF0Ov1jCtBe3s7Y58hEonQ3t4OgUCAVatWhdWscoqi0N7eDplM5hCFvXhd6Yz7PnesG698OQALyb4RnLh/4yKvxy9TFIU33ngDDz74IN59911ccMEFQVpdcKisrMShQ4ccbvvkk09QWVkZohXNhJVVZ3S04o6Ojg709/dDLBZj5cqVDof+HA7H5wP0wcFBdHR0oKSkBGlpab4uf14xmUwYHx/H6OgoVCoV+Hw+MjMzIZVKER8fzwqhnAt68Nz09DQqKip8ugo73iHHAweaoSHWPeckCZFcvHPXWp8imf/85z/YsWMH3n77bVx66aVBWqHnaDQadHV1AQDKysrw9NNPY8OGDRCLxcjOzsZDDz2E4eFhvPbaawC+LW/+2c9+hltvvRVHjx7Fz3/+c1LePBcWi8VtQ+bExARqamogEAhw4YVnc/2BqCxrb2/H2NgYSktLIRKJ/Fn+vEOXL2dmZiIuLg5yuRwKhQICgYApNBCJRKwUHYvFgvr6elgsFpSVlQX8LOzW12rwdd9UQJ+TwC4ieRzUPnKRT489ePAgfvKTn2Dfvn244oorArswHzl+/Dg2bNgw4/abbroJe/fuxc0334y+vj4cP37c4TH33XcfWlpakJmZiV//+te4+eab52/RcxBWQjM4OIi2tjakpaVBrVbjvPPO81tkLBYLGhsbodfrUVZWxpqcpqfQ5ctFRUUODaxWq5Ux/pTL5QDANIhKJBJWWGeYTCbmomHlypXz4gsHAI++14IDdbK570hgPcmxAnz2i/N9euz777+PW265Ba+//jquuuqqAK+MYE9YCA1FUWhra8PIyAjKyspgsVjQ0dGB8847DxRF+Xweo9frUVdXh4iICKxYsSLszjT6+vrQ29uLFStWzNrfQ1EUVCoV48FGG3+mpKQgKSlp3jZ4e3Q6HWpqaiAUCrF06dKQC9/BuhH85r/tMLPu10Bwx5alSfjj93zrcTl8+DBuuOEGvPrqq9i+fXuAV0ZwhpVCY7VamT4Zs9mM+vp6GAwGlJeXIzo6GiqVCqdPn0ZqaqrPV+hTU1Ooq6tDSkoKCgsLQ77ReYPNZkN7ezvGx8dRVlbm1bgE2iiQFh2dTudQNj0fZdxqtRo1NTWQSqUoLCxkZUoPOGu/c+M/ajA0RZwP2MbPL8zCnRcW+PTYo0eP4tprr8WePXvwwx/+kLXfv4UEq4WGvuqNiopiUiu0nYxKpWK8wywWi0NaaK5yZJlMhubmZuTn5yM7OzusvmhWqxUNDQ0BS/XRZdNyuRzT09MQCoXMuU4w0oj0eVJubi4zJC5coAfn3f+xHAPECDtk/O8PS7A6Xzr3HV3w+eef45prrsEzzzyDW265Jay+f+EMa4WGtmRJT09nrnppkbFPlVEUxTQ2ymQymEwmt2khulu+r68Py5cv92jMAJswGo2oq6sDj8fDypUrA57qc2X8SYtObGys3z9KmUyGpqamGedJ4YDNZkNjYyO0Wi0qKipmjHzYdbgd/zw1EqLVnStQuGVpJH6wKhPJycmIiYnx6tFfffUVrr76avzpT3/CHXfcQURmHmGl0IyMjKCmpgZFRUXIyspinJfnOo+xnwcjk8mg1+shFoshlUohkUjQ2dkJpVKJsrIyVnXLe4JWq0VNTQ1EItG8nGmYzWZGdCYmJhAVFcWITkJCgtc/0sHBQXR2doalwFutVmY6qzeVcY3DU7j1tRpoHfzeiOOBL6RFAy9sL0Ys5+z3UqlUIiYmhrFomsuM9vTp09i6dSt+97vfYceOHURk5hlWCo1er4dKpUJiYiIoivLZToZOC42NjUGj0YDH42HRokVIT0+fl7OIQDE5OYm6ujpkZWUhPz9/3n8kVqsVCoWCmTrI4/GYVOVcxp/2pqT0yIZwwmw2M2anpaWlfhVOUBQFrVaLH75ai06VBYAVABkENBeXZgG/vcbRa9BisWBiYoIp5QfAiI5z+ry2thZXXHEFHn30UezcuZOITAhgpdDYbDaYTKaAdPprtVrU1tYiJiYGiYmJzFmESCRirtDZbNMyNjaG5uZmFBYWIjMzM9TLcTD+HB8fh81mc3s+RlEUWltboVAoUF5eHhRT0mBiNBqZM8IVK1YE3IqITlXK5XK8fEaJz8eI6NgTKwAeLgcuWTe7oa3NZsPU1BTzXhoMBjQ3N8NsNmPZsmX48Y9/jPvvvx8PPvggEZkQwUqhsVgsMBgMzP/7miayb2QsKChgvmQGg4HZKFUqFRISEpCSkgKpVMqaPhp6QmhPTw9r000URWFqaop5L41GI3M+lpiYiPb2dmi12rDsT9Lr9aipqUFCQsK8pSrpcREKhQJ8Ph9TPBEePz4Bw+zDZBckxUkRuLvEjFWrVnl9gaLVavHPf/4Te/bsQWdnJ9LT03HnnXdi69atWL58ORGbEMBKobnpppvQ3d2Nbdu24corr0RGRobXX46hoSG0t7fPefBMW7iMj49DqVQyB+BSqRSxsbH+/ik+Ye/75W35cqiwPx8bHx+HRqMBn89HXl4e0tLSZhyesxn6PCwpKQlFRUXzvjHZbDYolUrmCt1qtSIpKYkZF/H9V86gQz6/48Dnkx8sTcBFEjUqKip8Pkttb2/HZZddhh/84AcoLy/Hf//7X3z00Ue4+eab8fzzzwd4xd/ywgsv4KmnnsLY2BhWrlyJ5557blYX6N27d+Oll17CwMDA2c/2+9/Hrl27WJ1l8QVWCs3Q0BDefvttHDhwAF999RVWrVqFrVu3YuvWrcjJyZn1h09RFDo7OzEyMoIVK1ZALBZ7/LrOB+DR0dGM6MzHuGXg7HlIY2MjdDpdWEYCdLpJIBBAIpFAoVBgamqKiRpTUlK8rhaaT+gen/T0dIcoOFTQVZX091Kn00EsFjN9T7SAO7pch2fBQUFSNB67MAka+ZBfItPd3Y1LL70U1157LZ566ikmGjUYDFCpVEhNTQ3kshn27duHG2+8EXv27MHatWuxe/du/Oc//0F7eztSUlJm3P/NN9/ErbfeildffRVVVVXo6OjAzTffjGuvvRZPP/10UNYYKlgpNDQURWF0dBQHDx7EgQMH8Pnnn2PFihWM6DhvBPQmrdVqUVpa6ldEYrFYHMYtR0REQCqV+lx15Qkmkwm1tbVBK18ONnQkkJiYiJKSEuYHbjQamY1SqVQiNjaWEZ35EnBPUKlUqK2tRW5uLvLy8kK9HJdotVom0qEFnD4E12g0aG5uxrJlyyCVSrH3ZD+e/rQHFtb+wr9l323lSLCo0NPTg4qKCp+j+L6+Plx22WXYsmULnn322XltxF67di1Wr17NREw2mw1ZWVm4++678eCDD864/44dO9Da2oojR44wt/3iF7/AN998gxMnTszbuucDVguNPRRFQaFQMKJz9OhRFBUVMaITFRWFO++8E7/85S9x0UUXBXSTth+3TFdd0ZFOoMwq6aKFhIQELFu2LKycCoCzTgt039PixYvdvidms5mpYKMFnC4mCKXx58TEBOrr67F48WJkZWWFZA3eYjKZHCJwiqKQkpKCnJwct+W+e0/248+f9IAtExZS4gU4ft/5GBgYQHd3t18iMzQ0hE2bNuG73/0uXnrppXn9DZlMJsTExODtt9/Gtm3bmNtvuukmqFQqvPvuuzMe8+abb+KnP/0pPv74Y6xZswY9PT3YvHkzbrjhBjz88MPztvb5IGyExh6KojA5OYn33nsP+/fvx0cffQSbzYb8/Hy8/PLLqKioCNqXjM6f02cRHA4HycnJkEqlc5b6ukOlUqGurg4ZGRmsSNd4C71J5+fnIyfH81kgzsaf9HuZkpICsVg8bxvF+Pg4Ghsbw2o8hD0jIyNobW1FTk4ODAYDFAoF814mJydDLBbPWjHXODyFH/+zFup5HLGQK47CA98twEVLkhmRKS8vh1Do3ehlmtHRUVx66aVYv349XnnllXkfVjgyMoKMjAx89dVXDnNgHnjgAXz22Wf45ptvXD7u2Wefxf333w+KomCxWHDnnXfipZdemq9lzxthOfiMw+FALBbj5ptvhkgkwvHjx7F582YYjUZcfvnlSEtLw9atW7Ft2zaUlZUFdMPicrlISkpiDopps8qmpiZQFOW1QzJthxNOV9L20OXXvmzSdD9OcnIybDYb8162trbCYrEwFWwSiSRoxp8jIyNoa2vD8uXLXebR2c7w8DDa29tRWloKiUQCAMx7KZfL0dbWBrPZDIlEwrzXztH+8gwhvnnwIofbxqYN2PbS15g2Bk58OAC2l6fiJxfkMXNjBgcH/RYZmUyGzZs3Y+3atSERGV85fvw4/vCHP+DFF1/E2rVr0dXVhXvuuQe/+93v8Otf/zrUywsoYRnR0NTX1+P888/Ha6+9xth8azQaHDp0CPv378eHH34IsViMK6+8Etu2bcPq1auD9iWkS31lMhnjkEyLTlJSksvX7e/vR3d3N2vLl+diYGAAXV1dc7pHe4vzATjt8BBo4096/StXrmQ26XCCdlsoKytDYmKiy/vYVwPK5XJoNBqmhyw5OdmrYpPjHXLsfLsJBot36yzLjMeDmxZjeYajkAwNDaGjowPl5eU+N/IqFApcfvnlKCkpwZtvvhkSJ3LAt9TZ+vXrcd555+Gpp55ibnv99dfxk5/8BBqNJuzS57MR1kIDnP2xuYsEdDodPvroI+zfvx8ffPABYmNjsWXLFmzbtg2VlZVB+1LSDsm06BgMBubqPDk5GTweDx0dHcygNV+v5EIFRVHo6urC8PAwysrKgr5+2uFhfHwcarXa72Zb2vOuv78/LN0KADDpJm/Xr9frmWIC2s+OviDypTCjcXgKez7rRd2wGjqTBQIuFzmSGFTkCLF5mXSGuNDQIjObSM6FUqnE5s2bsWjRIuzbty/kbh9r167FmjVr8NxzzwE4G1lmZ2djx44dLosBKioqsHHjRvzxj39kbvvXv/6F2267DWq1OmwiM08Ie6HxFIPBgE8//RQHDhzAu+++Cz6fjy1btuCqq67C+eefH7QKL9p2hBYdrVbLvFa49MjYY7PZ0NraCqVSifLy8nnvNXJuto2Pj2cE3JPGPrr8fXR0FOXl5WHneQeAmUPkT7oJ+LYwgy4mEAgEDoUZwbqiptN9/oiMSqXCli1bkJqaigMHDrCiT2vfvn246aab8Le//Q1r1qzB7t278e9//xttbW2QSqW48cYbkZGRgV27dgEAfvOb3+Dpp5/Gyy+/zKTO7rrrLlRUVGDfvn0h/msCyzkjNPaYzWYcO3YM+/fvxzvvvAOr1YrNmzdj27ZtuOiii4L2pTWZTKiurobFYoFAIIBGo0FiYiJzdc6GH8ts0CMKDAYDysrKQt5UZjKZHDbK6OhoZqN0VYJOW+JMTEyERCQDAe0bV15eHtCLFLowg452aGsh2jssUNE/fSZWWlrqVY+bPdPT09i2bRsSEhLw3nvvhfx7aM/zzz/PNGyWlpbi2Wefxdq1awEAF110EXJzc7F3714AZ1sonnjiCfzzn//E8PAwkpOTsWXLFjzxxBNhGWXPxjkpNPZYLBacOHEC//nPf/DOO+9Aq9Vi8+bN2Lp1K77zne8ErGGSnq1DW5rweDzo9Xrm6nxqagpCoRBSqdTr3Pl8YDabUVdXBwAoLS1lXY8PbbJI9z3RJej01TkANDU1Qa0+23HOps3JEyiKQnd3N4aHh4MeidHnjbTo0GdktPD4ekE0OjqK1tZWv0RGo9Hg6quvRkREBN5//31WN/8SvuWcFxp7rFYrTp48ibfffhsHDx7E5OQkLr30UmzduhXf/e53fb4CpsuXZ+sxMRqNjOhMTk4yKSGpVBryH5PBYEBNTQ1iYmKwfPly1ueO7UvQ5XI5KIoCl8sFl8tFRUUF60R8LugzsZGREVRUVMy7OSndJDo+Po7p6WnG5SE5Odnj3wQtMv4UXuh0Onz/+9+HzWbDoUOHws6k9VyGCI0bbDYbTp8+zYjO6Ogovvvd72Lr1q247LLLPL6ipEufvSlfdm7Eozvpaf+1+eyz0Wg0qK2thUQiQVFRUdhVwpjNZlRXV8NkMoHD4TClvnQ1INsiM2coikJHRwdkMhkqKipCnu6jXR7omTB0unK2mTBjY2NoaWnxS2QMBgO2b98OjUaDjz76KOzONs91iNB4gM1mQ11dHSM6fX19+M53voOtW7di8+bNbn9gdPnssmXLfO7RsD+wVSgUiIqKYqxw4uPjgyo6dCSWmZkZkjk4/kJb+ggEAqxcuRJcLtfB+FOr1br0DWMLFEWhra0NCoUCFRUVIY9snbGfCSOXy8Hlcmc03NJTVVeuXOlzCbzRaMQPf/hDyOVyfPzxxz4XEBBCBxEaL6EoCs3NzYzpZ1tbGzZs2IBt27Zh8+bNkEgksNls+Mc//oGCgoKAlv/SA8hkMhlj30KfQ8w1YdBb5HI5GhsbUVBQgOzs7IA973xBp/tiY2OxfPlyl5GYTqdjRGd6ehpCoZDZKEO9qVMUhZaWFkxOToZFuo+eU0SLjtlsRlxcHKanp7F06VKfHRfMZjNuvPFG9Pf348iRI2HZ70QgQuMXdFpj//792L9/PxoaGlBVVYWxsTGo1Wp8+eWXkEqlQXltukpIJpM5+K/Rs2D8ER3a0mTp0qVBc7oNJnThRWJiIoqLiz1K97HJ+JO+mJmamgrbwoX+/n50dnYiKioKRqMRiYmJjIh7+vdYLBbceuutaGtrw7Fjx8KyqZlwFiI0AYKiKJw5cwbXXHMNFAoF9Ho91q1bhyuvvBJbt25Fenp60DYr+mqSFh3aXNEXz7C+vj709PSEbbe8RqNBdXU1UlNTsWTJEp/ec+d0ZWRkZNAiR2dsNhuampqg0WhQUVHBunSeJ8jlcjQ0NDC2PnSTKN37RM98onufXL2fVqsVd9xxB2pra3Hs2LGwvOAhfAsRmgDR29uLTZs2YcWKFXjttdegUCiwf/9+HDhwACdPnsTq1asZK5zs7OygbVa04SidErJarW5HLTs/jm5kDMdGUuBbB+msrCwsWrQoIO+xvXO3vVllMIw/bTYbM4uooqIi5J3uvkCLDD2qwBm694l2746MjGSKCegmUavVirvvvhtffvkljh8/PuvgQkJ4QIQmQIyMjODvf/87HnnkEYfNh6IojIyMMOMNvvjiC6xYsQLbtm3D1q1bg3rITnuG0a4EJpOJscJJSkpimvBsNhtaWlqgUqlQXl4e8vMJX6DHdi9atMgrB2lvsDf+pEXc/v30p+zbZrOhvr4eRqMR5eXlYSkyCoUC9fX1bkXGGWf37t/97ncQi8XQarXo6+vDZ599FrTPkjC/EKGZRyiKwvj4ON555x0cOHAAx44dQ1FRESM6wRwbTJsr0qKj1+shkUiQlJSEsbExWCwWlJWVhW2qprGxEYWFhfN29UuLOC06BoOBKZt25ZA8G1arFfX19TCbzSgvL2d9ybUrFAoFGhoaUFJS4lOai6IofPLJJ3jooYfQ2dkJPp+PTZs2YevWrbjyyisDatrqCm9HMKtUKjzyyCM4cOAAlEolcnJysHv3blx++eVBXWe4QoQmRNAprnfffRf79+/Hp59+ikWLFjHjDZYuXRrUnhWNRoPR0VEMDAzAZrNBLBYzZdPhdDVNjynw9Co6WNiXTdtbCyUnJ896+G21WlFXVwebzYaysrKQuQ/7Az2PyFeRAc5GdI8++ij+85//4NixY7BYLHj33Xfxzjvv4Cc/+Qluu+22AK/6W7wdwWwymbBu3TqkpKTg4YcfRkZGBvr7+yESibBy5cqgrTOcIULDEqampvDf//6XGeSWkZHBiE5paWnARUev16OmpgZxcXHIz89nDr+np6chEokYKxw2VzzRDsCBHlPgL86H37TLQ0pKikPDpcViQW1tLTgcDkpLS8NSZJRKJerq6lBcXOxzCTNFUfjtb3+L//3f/8Xx48dRVFQ049+DWYDh7QjmPXv24KmnnkJbW1tYRp+hgAgNC1Gr1Q4zdZKSkhin6dWrV/stOmq1GrW1tUhOTp6RrqPdkWUyGTOTno502NTLQTsYl5aWsrqBz9nlISYmhikkoFNEpaWlrLf1cQUtMkVFRUhPT/fpOSiKwpNPPok9e/bg2LFjWLZsWYBXOTu+zJG5/PLLIRaLERMTg3fffRfJycm4/vrr8atf/SosP8f5IPwuoc4B4uPjsX37dmzfvh06nQ6HDx/G/v37cdVVVyEuLo6pXqusrPT6iz05OYm6ujrk5OQgLy9vxpViVFQUsrOzkZ2dzfSWyGQydHZ2Ii4ujhGdUFmh0OaSQ0NDfs2Xny8iIiKQkZGBjIwMWCwWpuG2t7cXXC4X6enpmJqa8rv3ab6hv0f+isxf//pXvPjiizhy5Mi8iwxw9mzJarXOSLtKpVK0tbW5fExPTw+OHj2KH/7whzh06BC6urrw05/+FGazGY8//vh8LDvsIELDcmJiYnD11Vfj6quvhsFgwCeffIIDBw7g2muvRUREBBPprFu3bs4wnvZdW7JkCTIzM+d87cjISGRmZiIzMxNms5kRne7u7pA0NFIUhfb2doyPj2PVqlVhZ6rI5/MhFovR19eHpKQkZGRkMIfoABzKptl8ZTw5OYna2loUFhb6JTLPP/88nn76aXz00UcoLS0N7CKDiM1mQ0pKCl5++WXweDxUVFRgeHgYTz31FBEaNxChCSOioqKwZcsWbNmyBSaTiZmpc/PNN8Nms+GKK65gZuo4H+jT5xm++q4JBAKkp6cjPT3d4cq8r68PUVFRjOi4mgMTCOxLsFevXs2qNJ6n0POIaBdsLpeLlJQUFBcXM2XTbW1tMJvNLsvQ2YBKpUJtbS2WLFnic4UfRVF4+eWXsWvXLnz44YdYvXp1gFfpOXRZukwmc7hdJpO5LWxIS0uDQCBwuBgoLi7G2NgYTCZTWBXTzBfkjGYBYLFY8MUXXzAzdfR6PTNTZ8OGDfjtb38Li8WCRx55JODnGXRDI+2/xufzHebABEJ0bDYbGhoaoNfrUV5eHpYl2EajEdXV1YiPj5+1opAeA073ltgbf4a6IpAWmYKCAo+dyJ2hKAp79+7FQw89hPfffx8XXHBBgFfpPd6OYH744Yfx5ptvoqenh/kcn3nmGfzxj3/EyMjIvK49XCBCs8CwWq346quvGKfpsbExcDgcPPzww/jZz34W1GZMm83GdNHL5XJwOBwH/zVfihjo8l+LxRK2PSYGgwHV1dUQCoVYunSpV+LrPAtGKBQy7+l8RnVTU1OoqanxW2Ref/113H///XjvvfewYcOGAK/SN7wdwTw4OIilS5fipptuwt13343Ozk7ceuut+PnPf45HHnkkxH8NO2G10HjbREX4FqPRiBtuuAHffPMNLrnkEhw7dgwymQyXXHIJtm3bhksvvTSoUxpp/zW6t4SiKAcrHE9Ex2w2o7a2FlwuN2zLf/V6PaqrqyEWi1FcXOxXhGcwGBjRmZycZDzD6OKMYJ2T0SKTn5/vs5M3RVH497//jbvvvhv79+/Hpk2bArxK//BmBDMAnDx5Evfddx/q6uqQkZGB2267jVSdzQJrhcbbJiqCI7fccgtaWlrw/vvvIzk5GTabDbW1tcx4g4GBAWzcuBFbt27F5ZdfHlSzSIqiHKxbLBYLkpKSIJVK3fqvGY1G1NTUICoqCitWrAjLH7BOp0N1dTWSk5NRWFgY0PeXLs6w9wyje58C+VlOT0+jurrab2ufAwcO4I477sC+fftwxRVXBGRthPCBtULjbRMVwZHe3l7GHdcZiqLQ1NTEiE5HR4fDTB2xWBx0/zW6V8doNDKiQx98082kCQkJQXdICBZarRbV1dWQSqU+u0h7ir3xJz0ygo4efU1ZAt+KTF5eHnJzc31e3/vvv49bbrkFr7/+Oq666iqfn4cQvrBSaHxpoiL4Bl0yTM/UaWxsxAUXXICtW7diy5YtSElJCbr/Gi06er0eQqEQGo0GycnJKCkpCaveEhp6VEF6ejoKCgrm9W9wTlnabDaP3LudUavVqK6uRm5url8ic/jwYdxwww34xz/+gR/84Ac+Pw8hvGGl0IyMjCAjIwNfffUVKisrmdsfeOABfPbZZ/jmm29CuLqFC90MSY83qK6uRlVVFWNsGMyZOsDZPp/Gxkbw+XyYzWbWVFt5A71BB3JUga9QFIWpqSnmXMdgMCApKYmx5XdXWEH/DXRTr68cOXIE1113Hf72t7/h+uuvD8uLhkChVCoxOjqKpUuXhnopIYEIDcElFEVhYGCAEZ2vv/4aq1evxtatW7F169aAz9ShS2dzc3ORl5cHvV7POE3T/mu06LDVf216eho1NTV+b9DBgKIoaLXaOY0/NRoNzpw5g+zsbCxatMjn1/v8889xzTXX4Nlnn8XNN998TouMXC5HeXk5hoeHcezYMVx44YWhXtK8w0qhIakzdkHP1Dlw4AAOHDiAEydOYOXKlcx4A3+v3Gn338WLF7ssnaX912iTyoSEBEZ02DI7h67M8vc8Y77Q6/XMe0p72gmFQoyOjiIrKwv5+fk+P/dXX32Fq6++Gn/+859x++23n9MiY7PZUFJSAqlUisWLF+PgwYN46623cMkll4R6afMKK4UG8L6JijA/UBQFmUzGzNQ5fvw4iouLGdHxtrqKTpeVlJR45P5rMpmYDVKpVDqU+IbKkoaOxvwp/w0lJpMJQ0ND6OnpAUVRDvZC8fHxXn2ep06dwrZt2/D73/8eP/vZz85pkaEZGRlBeno6uru78cQTT+DgwYN44403zqnZNawVmrmaqAihh6IoKJVKh5k6BQUFzHiDkpKSWSueRkZG0NbW5rMtDl3iSzsjR0dHIyUlBVKpdN7812gHY0/949iIVqvFmTNnkJGRgZycHIfR1QKBwGOnh5qaGmzZsgW//vWvcd999xGRwdkLZA6Hw7wXXV1deOqpp7Bv3z787//+L7Zu3RriFc4PrBUaYPYmKgK7oA+e6Zk6H3/8MTIzMxnRWblypYPodHd3o7+/H6WlpRCLxX6/Pu2/Rm+QERERjOgEy3+NTvnN52TPQEOXYaelpc2okHMetQyAOdNxbrptaGjA5s2b8ctf/hK/+tWviMg4YT9Tp6enB3/5y1/w+uuv43/+539wzTXXhHh1wYfVQkMIX9RqNT744APs378fhw8fRlJSEq688kps3boVBw4cwJkzZ3Dw4EEIhcKAv7arvhJ7K5xAbIK067I/A79CjU6nw5kzZ5CamorFixfP+r7YbDaoVComgjSbzWhsbERcXBxKSkpw7bXXYseOHXjssceIyODsd9C5lNxmszHi3NfXh6effhp79+7Fnj17cP3114dimfNG+HXCEcKC+Ph4XHvttfjPf/6DsbEx/PnPf4ZcLsell16Kl156CRkZGWhqaoLVag34a9PCsmzZMlx44YUoKSlhjDk///xztLS0YGJiAjabzafnHx8fR0NDg8fnSmyEdi2gD6nnEgculwuxWIzCwkKcf/75WLVqFbRaLZ588kls3LgRIpGIGXswX7zwwgvIzc1FVFQU1q5di1OnTnn0uLfeegscDseh0CiQ2Gw2RmQOHTqEN954g4kIaXJzc3H//ffj9ttvx5133on//d//Dcpa2AIRmjnYtWsXVq9ezYzj3bZtG9rb20O9rLAiNjYW27ZtQ1xcHNLS0vDiiy8yw92WLFmCe++9F8ePH4fZbA74a3O5XCQlJaGkpAQXXHABY8/f1NSEzz77DE1NTZDL5R4LnkwmQ2NjI5YtW+bWRp7t0P5rKSkpPrkWcDgcJCQkMA2Yt9xyC2677Ta88sorSEtLwy9/+ctgLNuBffv2YefOnXj88cdRU1ODlStXYtOmTRgfH5/1cX19fbj//vuxfv36oK2Njlpuu+023Hzzzbj33ntRWlqKvXv3YnJykrlfdnY27rvvPtxxxx2477778PLLLwdtTaGGpM7m4NJLL8W1116L1atXw2Kx4OGHH0ZTUxNaWlpCNmUyHPmf//kf/OUvf8Enn3zCHJqbTCYcPXoU+/fvxzvvvAMAzEydCy+8MKhNmvSZEt2rQ8+Aoa1wXHXQj46OorW1FcuXL0dycnLQ1hZM9Ho9zpw547f/Wl9fHy699FJs27YNu3fvZjbX4eFhTE5OBn1api8WVVarFRdccAFuvfVWfPHFF1CpVMz3LhDQ5zAURaG2thZ333039uzZg7y8PPzyl7/ERx99hHvuuQc/+tGPIJFImMfZbDbs2LEDe/bsQU1NTVgNgfMUIjReIpfLkZKSgs8++4wVszTCBavViunpabfzcCwWCz7//HNmpo7BYMAVV1yBrVu34uKLLw5qk6b9DBiZTMZ00NODxwQCAVMht3LlSodNIpwwGAw4c+YMJBIJioqKfBaZwcFBbNq0CZdeeilefPHFefei87XP7vHHH0dDQwMOHjyIm2++OaBCY3/+otfrMTg4iFdeeQVPPfUUc5/77rsPBw8exD333IMbbrgBSUlJAM42ySYlJeG2227DM888E5Yu5XOx8P6iIDM1NQUAAamUOpfg8XizDl3j8/m4+OKLcfHFF+P555/Hl19+ibfffhs7d+7E1NQUc/V8ySWXBLxJk04FJSQkID8/H1qtlpke2tzcjJiYGOh0OqxYsSLsRUYsFvslMqOjo9i8eTMuvvhivPDCCyExPFUoFLBarTPaHKRSKdra2lw+5sSJE/j73/+Ourq6oKyJfh8ef/xxfPLJJ2hra8PSpUsxOTnJfO//+te/IjIyEs8++yxUKhV+8YtfICEhAQKBAA899BAeffTRsHQp9wRyRuMFNpsN9957L9atWxf01MC5DI/HwwUXXIBnn30WfX19OHz4MDIzM/HII48gNzcXP/rRj/D2229DrVYH/LU5HA7i4uKQn5+PyspKxg4nOjoaDQ0NqK6uxuDgIIxGY8BfO1jQg9f8nYkjk8mwefNmVFZW4pVXXgmbTVGtVuOGG27AK6+8wkQRgcI+IfTGG2/g2WefxbXXXosNGzagp6cHTz75JEZHR5n7PPnkk7jiiiswPj6OhIQE2Gw2REZG4vHHHw+b99MXSOrMC+666y58+OGHOHHiRNg254UzNpsNNTU1zHiDwcFBbNy4Edu2bcPll18e8H6Z/v5+9PT0oKysDCKRaIZtS6imXXqD0WjEmTNnIBKJ/HLDlsvl2Lx5M5YuXYo33ngjpOkdb1NndXV1KCsrc9jI6YpDLpeL9vZ2vyx3AODYsWM4dOgQysrKmFLlxx9/HO+//z42btyIe+65B+np6TMeZ99fs5AhQuMhO3bswLvvvovPP/+cdYaJ5yI2m81hpk5nZycuvvhibN26NSAzdXp7e9HX14fy8nKXvT5Go5ERncnJSaYqkZ52yQaMRiOqq6uZuT6+vh9KpRKXX3458vPz8e9//5sV47S9sagyGAzo6upyuO3RRx+FWq3GM888gyVLlnhVeHLPPffg+uuvZ5rHq6urcfvtt2NgYADPPfccrrvuOua+v/3tb/Huu+9i48aNuOuuuxx88M4VkQGI0MwJRVG4++67cfDgQRw/fhyLFy8O9ZIITlAUhba2NkZ0mpqacOGFFzIzdZKTk736QXd3d2NwcBAVFRUejbs2mUwOVji0V5hUKg3qiOW51nTmzBm/RUalUmHLli1IS0vDgQMHWDOuYS6LqhtvvBEZGRnYtWuXy8f7Wgzwox/9CF988QWqq6uZNBxFUfjrX/+KF198ETk5OXj99dcd+quefPJJPP/883j00Udx5513+vw3hzNEaObgpz/9Kd588028++67KCwsZG4XCoWsTZecy9AzdWjRqa2tdZipk5aW5nbTpR87PDyMiooKn0w6zWazgxVOVFQUIzreGlT6Ci0y8fHxWLZsmc+vOT09jW3btkEoFOLdd99l3XiG2SyqLrroIuTm5mLv3r0uH+uL0AwPD2Px4sW44IIL8P7774PP5ztUm73wwgt4/fXXUVRUhN///vcOtkRvvfUWrr32Wp//1nCHCM0cuPuR/uMf/8DNN988v4sheAVFUejv78f+/ftx8OBBfP3111izZg0zUycrK4v5fG02G9rb2zE+Po5Vq1YFJP1ltVqhUCggk8kcDCqlUimEQmFQRMdkMqG6uhqxsbFYtmyZz1VhGo0GV199NSIiIvDBBx+Qi6r/4/Tp09iyZQvWrl2Lf/zjHxCLxQ4psBdeeAH/+te/kJeXhyeeeGKGm7cra5pzASI0hHMCiqIwPDzMzNT58ssvUVpaim3btmHLli3YtWsXjEYjXn755aDMuLE3qBwfH3fwXxOJRAEpE6ZFJiYmhnFA8AWdTofvf//7oCgKH3zwQcjGL7CV2tpaXHbZZaioqMBrr70GiUTiIDZ79uzBvn37EBUVhb///e8uiwDONYjQEM456Jk6Bw8exP79+3H06FHweDzcfvvtuP32232yZfEGm82GyclJyGQyyOVyUBTFiI5YLPZJIMxmM6qrqxEdHe2XyBgMBmzfvh1arRaHDx9GQkKCT8+z0GlsbMSll16KpUuX4vXXX0dKSoqD2DzzzDNoaGjAnj17WFE8EWqI0IQxTz75JB566CHcc8892L17d6iXE3ZYrVb8+Mc/xvHjx3HXXXfh+PHjOHLkCBYvXowrr7wSV111FYqLi4PalEhRFFQqFWOFY7VakZycjJSUFEgkEo/SLLTIREVFYcWKFT6v12g04oc//CEUCgU+/vhjiEQin57nXKGtrQ2bNm3CokWL8K9//QupqakOYmOxWGac45yrEKEJU06fPo0f/OAHSEhIwIYNG4jQ+MCf/vQnvPrqqzhy5AgyMjIY/7P33nuPmamTnZ3NiI4/m7gnUBSF6elpRnRMJpODFY6r3hWz2YyamhpERETMmPnjDSaTCTfeeCMGBwdx5MgR4nzhIV1dXdi0aRPS09Px1ltvzZhLdK6eyThDhCYM0Wg0KC8vx4svvojf//73KC0tJULjAxqNBlqt1u3E1unpaYeZOikpKYzoVFRUBF10NBoNIzp6vR4SiYQZPCYQCGCxWFBTUwOBQOCXyJjNZtx2221ob2/H0aNHw9YwNNDQkYjZbJ41/dXb24tLL70UIpEIH3zwAZKSknDs2DFs2LBhHlfLbv5/e/ceFXP+/wH8Wakp2m9iVymVsEqxhTSljl0rcu2y61itrU5rZddqya0cJ+1ZItYlO1nCrthFaCctFYd0kcRmktIyOHZN1ihdaFym+sz794dv8zPKft2mmanX45z+6PN5T/P6lOM5n/fnfaGg0UGhoaHo0aMHNm7ciA8++ICCph08ePAAWVlZEAqFyMjIgJmZGfz8/BAQEAA+n6/2T60ymUw5kEAmk8Hc3ByPHj2CsbFxq1nvL6O5uRmzZ89GaWkpcnJyaJv0Z+Tm5uLkyZP47rvv/rWdRCJRhk2PHj1QX1+Po0ePas3kXU3r3B2HOiglJQUikei5E9GIenTr1g1Tp07F3r17IZVKIRAI0NDQgGnTpsHBwQGRkZHIy8tDc3OzWt7f1NQU/fr1g4eHB/h8Ph4+fIimpibU19ejpKQEN2/exOPHj1/qZ3Ich4iICJw/fx4nTpygkGlDUVERjh49CgD/umeRjY0NTpw4gbq6OpSVlWH37t0UMk+h1Zt1iEQiwbx583D8+HGtmzzXmZiYmCjn4jQ2NuLEiRMQCoUICQmBnp4eJk2ahMDAQIwaNeqNz6Rvbm7G5cuX0bVrV7i6uqKpqUm5vYFYLMZ//vMf5Vydf5v7olAoEBkZiYKCAuTk5NAQ3P96dlkYKysr/PPPPyq7Zj5P7969cerUKcjlcvp9PoO6znTIoUOHEBgYqPIPnuM46OnpQV9fH3K5nB48alBTU5NyT5309HTI5XJMmjQJAQEBGD169Gt/OOA4DiUlJdDT04Orq2urv7VcLkd1dTXu3LmDuro6mJqawsLCotX6awqFAkuWLEFGRgZyc3Np7b5nSKVS1NTUwNnZGWVlZZg5cyb27dunXHjz6VFknWm9stdBQaNDGhoa8Pfff6scCwsLg6OjI6KiomjrAi3CcRwKCgqQmpqKQ4cO4f79+5gwYQICAgLg4+Pz0pNCW0IGwAs9k2lqalKGTk1NDfT09JCVlYWPP/4Y6enpEAqFyMnJobX7nlFTU4OhQ4fi8ePHsLS0hFwux/Xr17FkyRJ4eXlh+PDhMDY2homJCXg8nqbL1RkUNDqOBgNoP4VCgaKiImXoVFdXY9y4cQgICICvr+//nHnPcRwuXLgAhUKBYcOGvfRda3NzM8rKyhATE4P8/HwATz6ghIeHw83Nrd0+kW/evFm5NpmLiwsEAgHc3d3bbLt9+3bs3r0b5eXlAIDhw4dj1apVz23/Jl24cAGmpqbIz8+HTCbD/PnzwePx4OrqCrFYDD09PXh7e+OXX355oUVXCQWNzqOg0S0KhQLnz5/Hb7/9BqFQiMrKSvj4+MDf37/NPXU4jkNpaSk4jsPQoUNfeR8YxhhWr16NpKQkLFu2DMXFxTh8+DC6d++OzMxMODs7v6lLbNP+/fsREhKCrVu3gs/nIyEhAQcPHsSVK1fQq1evVu1nzJgBLy8vjBw5EsbGxlizZg3S0tJw6dKlVnNV1C0yMhJVVVXYtm0bLl68CJlMhl69esHFxaVd69BlFDSEaIhCocDFixeVoXPt2jWMGTMGfn5+mDx5MoyMjBAeHo4ZM2bA19f3tUJmw4YNSEhIwMmTJ5X/QcrlcmRnZ2P06NFqXzSTz+djxIgRSExMBPDk2m1sbBAREdFq/5i2cBwHc3NzJCYmIiQkRK21Pv2eBgYGiIqKQn5+Ps6cOaNynmb8vzj6LRGiIfr6+nB1dcWKFStQXl6OkpISeHh4YOvWrbC3t4eDgwPOnTuHgQMHvvIgD8YYBAIBNm7ciGPHjql8CufxeJg4caLaQ6ZlsU8fHx/lMX19ffj4+LT6z/t5WoZzt+eKBS2/84kTJ6Kurq7V1uEUMi+OflPktdy6dQufffYZevbsqVzQsbi4WNNl6Rw9PT04OTlh+fLlOHv2LEaNGoVu3brBwsICbm5umDhxIpKSknD79m28aCcEYwxJSUmIj49HZmYm3Nzc1HwVbbt79y44jms1T8fCwgJSqfSFfkZUVBSsrKxUwqq9GBoa4vr166ivr2/39+4oKGjIK6urq4OXlxcMDQ2RlZWFiooKrF+/Hubm5pouTWdxHIdPPvkE9+7dQ3l5OUQiEcRiMaZMmYLU1FQ4ODhg3LhxSExMhEQieW7oMMawc+dOfPvttzh8+DA8PDza+UrenPj4eKSkpCAtLU0j88fc3NxQXl4OGxubdn/vDoMR8oqioqKYt7e3psvocHbs2MFqampaHVcoFOzmzZssISGBjRo1ihkYGLARI0awuLg4Vl5ezmQyGXvw4AGTyWRsy5YtzNTUlOXk5LT/BTxDLpczAwMDlpaWpnI8JCSE+fn5/etrv//+e2ZmZsb++OMPNVZI1I0GA5BX5uTkBF9fX1RWViIvLw/W1taYM2cOZs2apenSOjzGGKRSKdLS0iAUCpGXl4fBgwfD398fPB4PcXFxEAqFGDdunKZLBfBkMIC7uzsEAgGAJw/SbW1tMXfu3OcOBli7di3i4uJw7Ngxnb4jI6A7GvLqeDwe4/F4bOnSpUwkErGkpCRmbGzMkpOTNV1ap6JQKFh1dTXbsWMH+/DDDxkA9uuvv2q6LBUpKSmMx+Ox5ORkVlFRwcLDw1n37t2ZVCpljDEWHBzMoqOjle3j4+OZkZERS01NZbdv31Z+NTQ0aOoSyGugoCGvzNDQkHl6eqoci4iIYB4eHhqqiCgUCiaRSDRdRpsEAgGztbVlRkZGzN3dnRUVFSnPvf/++yw0NFT5vZ2dHQPQ6is2Nrb9CyevjbrOyCuzs7PD2LFjsWPHDuWxLVu2YOXKlbh165YGKyOEaBMadUZemZeXF65cuaJyTCwWw87OTkMVEUK0EQWNDlEoFJouQUVkZCSKioqwatUqXLt2DXv37sW2bdvw9ddfa7o0QogWoa4z8lqOHDmCpUuX4urVq7C3t8eCBQto1BkhRAUFjQ6QSCSYPXs2IiIiMGHCBE2XQwghL4W6zrRcbW0tDhw4gOrqauXmVf+2pSwhhGgbChotd+TIEcTExKCiogJCoRASiaTVAostwZOTk4OPPvoIO3fuRGNjoybK1TiO4xATEwN7e3uYmJigf//+WLFixQuvD0YIefNebd1x0m4mTJgAR0dH2NjYoKSkBPb29rh8+TIGDBjQqu3u3buRl5eHrKwsiMVirF69WgMVa9aaNWuwZcsW7Nq1C87OziguLkZYWBjMzMzwzTffaLo8QjolChotd/jwYXTp0gVz587F2LFj0dDQ0GpXv5Y7nNOnTyM6OhqLFy/utN1rhYWF8Pf3x6RJkwAAffv2xb59+3Du3DkNV0ZI50VdZ1ouJycH1tbWGDhwIAC02va3Zcjz8ePH0djYCAcHBwBos3tN24ZHq8PIkSORnZ0NsVgMACgtLUVBQQENoiBEgyhotNjt27dx9epVODs7KydBPru/e8uzh4MHD2LAgAFwcnIC8P8B1PKsxsDAoNVGTRKJBC4uLli7di1qamrUei3tJTo6GtOnT4ejoyMMDQ0xdOhQzJ8/HzNmzNB0aYR0WhQ0Wuz06dPgOE65YVVbdyQtdy75+fnw9PREnz59ADzZ/a+goAALFy6Ei4sLwsLCVHYzZIyhT58+8Pf3x/LlyxEUFNQOV6R+Bw4cwJ49e7B3716IRCLs2rUL69atw65duzRdms7ZvHkz+vbtC2NjY/D5/P/Z/Xjw4EE4OjrC2NgYQ4YMQWZmZjtVSrQdBY0WMzIygkwmg7W1NYDWW8e2BE9ubi4ePnwINzc35cZQcrkcs2bNwo0bNzBv3jw0NjYiKCgIP/30E5qbmwE8uTuaOnUq3N3dNbJzoTosXrxYeVczZMgQBAcHIzIyslMOjHgd+/fvx4IFCxAbGwuRSAQXFxf4+vqiqqqqzfaFhYUICgrCzJkzUVJSgoCAAAQEBKC8vLydKyfaiIJGi3l6esLc3BxBQUFYtGgR5HK5yvmnu8369++PQYMGKc/dvHkTpqammDZtGj7//HPs2bMH+/btQ79+/dClSxdlF9ypU6dQVVWlfHiu6x4+fNgqkA0MDDrF86k3acOGDZg1axbCwsLg5OSErVu3omvXrvj555/bbL9p0yaMHz8eixcvxqBBg7BixQoMGzYMiYmJ7Vw50UYUNFrsnXfeQWFhIWJjY/HgwYNWD/hbvs/JyYGnp6fKVrN9+vTBsGHDsHDhQqxfvx61tbXw9PTEyJEjlW1qa2tRUlICa2trODs7t89FqdmUKVMQFxeHjIwM/PXXX0hLS8OGDRsQGBio6dJ0RmNjI86fP69yl6uvrw8fHx+V7tennTlzptVdsa+v73Pbk86FhjdrMYVCAX19fQQHByM4OLjNc4WFhWhoaICbmxtMTEyU501MTJCUlAQPDw+kpqaisrIScXFx6Nq1q/K1YrEYFRUVHeZuBgAEAgFiYmIwZ84cVFVVwcrKCrNnz8by5cs1XZrOuHv3LjiOg4WFhcpxCwsLXL58uc3XSKXSNttLpVK11Ul0BwWNFmvpAuI4rtXdzLOjzVqGPwNAeXk5KisrMX78eISFhaFnz5744osvMHjwYMycOVP5c0tLS1FbW4spU6a00xWp31tvvYWEhAQkJCRouhRCyH9R15kOeDZknj72+++/w8XFBYMHD1aeO3XqFNatW4fU1FQAQK9evdCvXz9UVFQo29TX10MkEsHCwgLvvfeemq+A6JK3334bBgYGuHPnjsrxO3fuwNLSss3XWFpavlR70rlQ0Oiw6upq2NnZQSAQwN/fX/nA28/PD3w+H3PnzkXv3r3x1VdfwdLSEp9++qnytVevXsWlS5cwevRoTZVPtJSRkRGGDx+O7Oxs5TGFQoHs7Gx4enq2+RpPT0+V9sCTScTPa086GQ1tIU3ekObmZiYSiVh6ejpjjDGO41TOi0QilpmZyZqbm1WOb9++nTk4OLDi4uJ2q7UzycvLY5MnT2a9e/dmAFhaWprKeYVCwWJiYpilpSUzNjZmY8aMYWKxWDPFtiElJYXxeDyWnJzMKioqWHh4OOvevTuTSqWMMcaCg4NZdHS0sv3p06dZly5d2Lp169iff/7JYmNjmaGhISsrK9PUJRAtQkHTAXEcx5qamto8ztiTcPryyy+Zt7d3e5fWaWRmZrJly5YxoVDYZtDEx8czMzMzdujQIVZaWsr8/PyYvb09e/TokWYKboNAIGC2trbMyMiIubu7s6KiIuW5999/n4WGhqq0P3DgABs4cCAzMjJizs7OLCMjo50rJtqKNj7r4BhjKsvWZGdn49GjR9i0aRP4fD5Wrlypweo6Bz09PaSlpSEgIADAk7+JlZUVFi5ciEWLFgEA7t27BwsLCyQnJ2P69OkarJaQN4+e0XRwT4dMY2MjfvjhBwQGBqKgoKBDDWvWJTdu3IBUKlWZd2JmZgY+n0/zTkiHRMObOxEjIyOkp6dDIpGgoqKCHtRqSMvcEpp3QjoLCppOyMbGRmUVAUIIUSfqOiOknbXMLaF5J6SzoKAhpJ3Z29vD0tJSZd7J/fv3cfbsWerOJB0SdZ0RogYymQzXrl1Tfn/jxg1cuHABPXr0gK2tLebPn4+VK1fi3Xffhb29PWJiYmBlZaUcmUZIR0LDmwlRg9zc3DZXXQgNDUVycjIYY4iNjcW2bdtQX18Pb29v/Pjjjypr1hHSUVDQEEIIUSt6RkMIIUStKGgIIYSoFQUNIYQQtaKgIYQQolYUNIQQQtSKgoYQQohaUdAQQghRKwoaQgghakVBQwghRK0oaAghhKgVBQ0hhBC1+j+XCXpcZ3POKwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "variable = deepxde.callbacks.VariableValue([kf, D], period=1000, filename=\"./variables.dat\")\n",
+    "model = deepxde.Trainer(data, external_trainable_variables=[kf, D])\n",
+    "model.compile(bst.optim.Adam(0.001)).train(iterations=80000, callbacks=[variable])\n",
+    "model.saveplot(issave=True, isplot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ea32d35cae6152a",
+   "metadata": {},
+   "source": [
+    "The inverse problem is solved, and the estimated values of $D$ and $k_f$ are obtained. The results are as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9b2219b56e66045f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T12:22:33.123566Z",
+     "start_time": "2024-12-17T12:22:33.118684Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ParamState(\n",
+       "  value=0.01828403 * 10.0^0 * metre ** 6 * second ** -1 * mole ** -2\n",
+       ")"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "38de8e25966dffa6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-17T12:22:33.201927Z",
+     "start_time": "2024-12-17T12:22:33.195370Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ParamState(\n",
+       "  value=1.4558828 * meter2 / second\n",
+       ")"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "D"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/experimental_docs/unit-examples-inverse/reaction_inverse.py b/docs/experimental_docs/unit-examples-inverse/reaction_inverse.py
new file mode 100644
index 000000000..a87aa3399
--- /dev/null
+++ b/docs/experimental_docs/unit-examples-inverse/reaction_inverse.py
@@ -0,0 +1,85 @@
+import brainstate as bst
+import brainunit as u
+import numpy as np
+
+import deepxde.experimental as deepxde
+
+unit_of_x = u.meter
+unit_of_t = u.second
+unit_of_c = u.mole / u.meter ** 3
+
+kf = bst.ParamState(0.05 * u.meter ** 6 / u.mole ** 2 / u.second)
+D = bst.ParamState(1.0 * u.meter ** 2 / u.second)
+
+
+def pde(x, y):
+    jacobian = net.jacobian(x, x='t')
+    hessian = net.hessian(x)
+    ca, cb = y['ca'], y['cb']
+    dca_t = jacobian['ca']['t']
+    dcb_t = jacobian['cb']['t']
+    dca_xx = hessian['ca']['x']['x']
+    dcb_xx = hessian['cb']['x']['x']
+    eq_a = dca_t - 1e-3 * D.value * dca_xx + kf.value * ca * cb ** 2
+    eq_b = dcb_t - 1e-3 * D.value * dcb_xx + 2 * kf.value * ca * cb ** 2
+    return [eq_a, eq_b]
+
+
+net = deepxde.nn.Model(
+    deepxde.nn.DictToArray(x=unit_of_x, t=unit_of_t),
+    deepxde.nn.FNN([2] + [20] * 3 + [2], "tanh"),
+    deepxde.nn.ArrayToDict(ca=unit_of_c, cb=unit_of_c),
+)
+
+geom = deepxde.geometry.Interval(0, 1)
+timedomain = deepxde.geometry.TimeDomain(0, 10)
+geomtime = deepxde.geometry.GeometryXTime(geom, timedomain)
+geomtime = geomtime.to_dict_point(x=unit_of_x, t=unit_of_t)
+
+
+def fun_bc(x):
+    c = (1 - x['x'] / unit_of_x) * unit_of_c
+    return {'ca': c, 'cb': c}
+
+
+bc = deepxde.icbc.DirichletBC(fun_bc)
+
+
+def fun_init(x):
+    return {
+        'ca': u.math.exp(-20 * x['x'] / unit_of_x) * unit_of_c,
+        'cb': u.math.exp(-20 * x['x'] / unit_of_x) * unit_of_c,
+    }
+
+
+ic = deepxde.icbc.IC(fun_init)
+
+
+def gen_traindata():
+    data = np.load("../dataset/reaction.npz")
+    t, x, ca, cb = data["t"], data["x"], data["Ca"], data["Cb"]
+    X, T = np.meshgrid(x, t)
+    x = {'x': X.flatten() * unit_of_x, 't': T.flatten() * unit_of_t}
+    y = {'ca': ca.flatten() * unit_of_c, 'cb': cb.flatten() * unit_of_c}
+    return x, y
+
+
+observe_x, observe_y = gen_traindata()
+observe_bc = deepxde.icbc.PointSetBC(observe_x, observe_y)
+
+data = deepxde.problem.TimePDE(
+    geomtime,
+    pde,
+    [bc, ic, observe_bc],
+    net,
+    num_domain=2000,
+    num_boundary=100,
+    num_initial=100,
+    anchors=observe_x,
+    num_test=50000,
+)
+
+variable = deepxde.callbacks.VariableValue([kf, D], period=1000, filename="./variables.dat")
+model = deepxde.Trainer(data, external_trainable_variables=[kf, D])
+model.compile(bst.optim.Adam(0.001)).train(iterations=80000, callbacks=[variable])
+model.saveplot(issave=True, isplot=True)
diff --git a/pyproject.toml b/pyproject.toml
index 96ae3e45b..150ad2f54 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -54,3 +54,14 @@ exclude = ["docker", "docs*", "examples*"]
 
 [tool.setuptools_scm]
 write_to = "deepxde/_version.py"
+
+[project.optional-dependencies]
+experimental = [
+    "brainstate",
+    "brainunit",
+    "braintools",
+    "jax",
+    "optax",
+    "flax",  # for compatibility
+]
+