diff --git a/README.md b/README.md
index 49fac5fe..9189e63b 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,7 @@
   <img src="figs/Neuromancer.png" width="250">  
 </p>
 
-# NeuroMANCER v1.4.2
+# NeuroMANCER v1.5.0
 
 **Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations (NeuroMANCER)**
 is an open-source differentiable programming (DP) library for solving parametric constrained optimization problems, 
@@ -16,13 +16,21 @@ differentiable models and algorithms embedded with prior knowledge and physics.
 ![Static Badge](https://img.shields.io/badge/pip_install-neuromancer-blue)
  ![PyPI - Version](https://img.shields.io/pypi/v/neuromancer)
 
-### New in v1.4.2
-New feature: `GradientProjection` now supports updating violation energy at each gradient step, as in the original [DC3 paper](https://arxiv.org/pdf/2104.12225.pdf). See the full [release notes](RELEASE_NOTES.md) for more information.
 
-**New Colab Examples:**  
-> ⭐ [Physics-Informed Neural Networks (PINNs) for solving partial differential equations (PDEs) in NeuroMANCER](#physics-informed-neural-networks-pinns-for-partial-differential-equations-pdes)
+### New in v1.5.0
+![Lightning](https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white)
+Now supports integration with PyTorch Lightning (https://lightning.ai/docs/pytorch/stable/), bringing:
+* User workflow simplifications: zero boilerplate code and increased modularity 
+* Ability for user to define custom training logic easily 
+* Easy support for distributed GPU training
+* Weights and Biases hyperparameter tuning 
 
-> ⭐ [System identification for ordinary differential equations (ODEs)](#ordinary-differential-equations-odes)
+Please refer to the Lightning folder and its [README](examples/lightning_integration_examples/README.md).
+
+**New Colab Examples:**
+> ⭐ [Various domain examples, such as system identification of building thermal dynamics, in NeuroMANCER](#domain-examples)
+
+> ⭐ [PyTorch lightning integration Examples ](#lightning-integration-examples)
 
 
 ## Features and Examples
@@ -111,8 +119,40 @@ Part 5: Using Cvxpylayers for differentiable projection onto the polytopic feasi
 + <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/control/Part_5_neural_Lyapunov.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 5: Learning neural Lyapunov function for a nonlinear dynamical system.
 
+### Domain Examples 
+
++ <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/DPC_building_control.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 1: Learning to Control Indoor Air Temperature in Buildings.
+
++ <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/DPC_PSH.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 2: Learning to Control an Pumped-Hydroelectricity Energy Storage System.
+
++ <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/NODE_building_dynamics.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 3: Learning Building Thermal Dynamics using Neural ODEs.
+
++ <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/NODE_rc_networks.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 4: Data-driven modeling of a Resistance-Capacitance network with Neural ODEs.
+
++ <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/NODE_swing_equation.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 5: Learning Swing Equation Dynamics using Neural ODEs.
+
++ <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/NSSM_building_dynamics.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 6: Learning Building Thermal Dynamics using Neural State Space Models.
+
+### Lightning Integration Examples 
+
++ <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/lightning_integration_examples/Part_1_lightning_basics_tutorial.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 1: Lightning Integration Basics.
+
++ <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/lightning_integration_examples/Part_2_lightning_advanced_and_gpu_tutorial.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 2: Lightning Advanced Features and Automatic GPU Support.
 
++ <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/lightning_integration_examples/Part_4_lightning_wanb_hyperparameter_tuning.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 3: Hyperparameter Tuning With Lightning & WandB
 
++ <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/lightning_integration_examples/other_examples/lightning_custom_training_example.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 4: Defining Custom Training Logic via Lightning Modularized Code.
+ 
 ## Documentation
 The documentation for the library can be found [online](https://pnnl.github.io/neuromancer/). 
 There is also an [introduction video](https://www.youtube.com/watch?v=YkFKz-DgC98) covering 
diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md
index d5f31e89..4f58c45f 100644
--- a/RELEASE_NOTES.md
+++ b/RELEASE_NOTES.md
@@ -1,6 +1,14 @@
 
 ##  Release notes
 
+### Version 1.5.0 Release Notes 
++ New Feature: PyTorch Lightning Integration with NeuroMANCER core library. All these features are opt-in. 
+  + Code simplifications: zero boilerplate code, increased modularity 
+  + Added ability for user to define custom training logic 
+  + Easy support for GPU and multi-GPU training
+  + Easy Weights and Biases (https://wandb.ai/site) hyperparameter tuning and Tensorboard Logging
+
+
 ### Version 1.4.2 Release Notes 
 + New feature: Update violation energy for projected gradient #110 (based on idea #86).
 + Reverted `psl.nonautonomous.TwoTank` `(umin, umax)` bounds to `(0.5, 0.5)` for numerical stability #105
diff --git a/examples/ODEs/Part_1_NODE.ipynb b/examples/ODEs/Part_1_NODE.ipynb
index 5c283ab9..2df8081d 100644
--- a/examples/ODEs/Part_1_NODE.ipynb
+++ b/examples/ODEs/Part_1_NODE.ipynb
@@ -55,7 +55,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2dca093f",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/ODEs/Part_1_NODE_lightning.ipynb b/examples/ODEs/Part_1_NODE_lightning.ipynb
new file mode 100644
index 00000000..2cd0ed7f
--- /dev/null
+++ b/examples/ODEs/Part_1_NODE_lightning.ipynb
@@ -0,0 +1,980 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a2d86e51",
+   "metadata": {},
+   "source": [
+    "# Neural ODEs in NeuroMANCER  \n",
+    "\n",
+    "\n",
+    "This tutorial demonstrates the use of [Neural Ordinary Differential Equations](https://arxiv.org/abs/1806.07366) (NODE) for system identificaiton of dynamical systems. \n",
+    "\n",
+    "\n",
+    "## System Identification Problem Setup\n",
+    "\n",
+    "<img src=\"../figs/NODE.PNG\" width=\"600\">  \n",
+    "\n",
+    "\n",
+    "Starting from a given initial condition  $x_{0}$, the next state of the system  $x_{k+1}$\n",
+    "is obtained by feeding the current state $x_{k}$ into the neural network $N$ that generates a derivative to be\n",
+    "integrated using an integration scheme $\\int$. In system identification, the loss $\\mathcal{L}$ is evaluated by comparing the  trajectory generated by the model with the training trajectory. The process can be repeated for multiple trajectories to improve the generalization of the model.\n",
+    "\n",
+    "\n",
+    "### References\n",
+    "\n",
+    "[1] [Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud, Neural Ordinary Differential Equations, NeurIPS 2018](https://arxiv.org/abs/1806.07366)  \n",
+    "[2] http://implicit-layers-tutorial.org/neural_odes/  \n",
+    "[3] https://github.com/Zymrael/awesome-neural-ode  \n",
+    "[4] https://rkevingibson.github.io/blog/neural-networks-as-ordinary-differential-equations/  \n",
+    "[5] [Christian Legaard, Thomas Schranz, Gerald Schweiger, Ján Drgoňa, Basak Falay, Cláudio Gomes, Alexandros Iosifidis, Mahdi Abkar, and Peter Larsen. 2023. Constructing Neural Network Based Models for Simulating Dynamical Systems. ACM Comput. Surv. 55, 11, Article 236 (November 2023), 34 pages.](https://dl.acm.org/doi/10.1145/3567591)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2e3300d",
+   "metadata": {},
+   "source": [
+    "## NeuroMANCER and Dependencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39dc8323",
+   "metadata": {},
+   "source": [
+    "### Install (Colab only)\n",
+    "Skip this step when running locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb524e98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b2b86eb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<torch._C.Generator at 0x7f9dd8206f10>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from neuromancer.psl import plot\n",
+    "from neuromancer import psl\n",
+    "import matplotlib.pyplot as plt\n",
+    "from torch.utils.data import DataLoader\n",
+    "\n",
+    "from neuromancer.system import Node, System\n",
+    "from neuromancer.dynamics import integrators, ode\n",
+    "from neuromancer.trainer import Trainer\n",
+    "from neuromancer.problem import Problem\n",
+    "from neuromancer.loggers import BasicLogger\n",
+    "from neuromancer.dataset import DictDataset\n",
+    "from neuromancer.constraint import variable\n",
+    "from neuromancer.loss import PenaltyLoss\n",
+    "from neuromancer.modules import blocks\n",
+    "\n",
+    "torch.manual_seed(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56b94c2f",
+   "metadata": {},
+   "source": [
+    "## Generate trajectories from ODE system \n",
+    "\n",
+    "Consider the [VanDerPol system](https://en.wikipedia.org/wiki/Van_der_Pol_oscillator) defined by the [ordinary differential equations](https://en.wikipedia.org/wiki/Ordinary_differential_equation): \n",
+    "\n",
+    "$$\n",
+    " \\frac{dx_1}{dt} = \\mu (x_1 - \\frac{1}{3}x_1^3 - x_2)   \\\\  \n",
+    " \\frac{dx_2}{dt} = \\frac{x_1}{\\mu} \n",
+    "$$  \n",
+    "\n",
+    "In this example we don't assume any prior knowledge on the system dynamics. We will only have access to limited measurements of the system states $x$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "34498f23",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1600 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": [
+    "# %%  ground truth system\n",
+    "system = psl.systems['VanDerPol']\n",
+    "modelSystem = system()\n",
+    "ts = modelSystem.ts\n",
+    "nx = modelSystem.nx\n",
+    "raw = modelSystem.simulate(nsim=1000, ts=ts)\n",
+    "plot.pltOL(Y=raw['Y'])\n",
+    "plot.pltPhase(X=raw['Y'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6be88219",
+   "metadata": {},
+   "source": [
+    "## Create training data of sampled trajectories\n",
+    "\n",
+    "We will obtain a dataset of sampled trajectories of the system dynamics to model: \n",
+    "$$\\hat{X} = [\\hat{x}^i_0, ..., \\hat{x}^i_{N}], \\, \\, i \\in [1, ..., m]$$\n",
+    "where $N$ represents the prediction horizon, $m$ represents number of measured trajectories, and $i$ represents an index of the sampled trajectory.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "168db8f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(sys, nsim, nsteps, ts, bs):\n",
+    "    \"\"\"\n",
+    "    :param nsteps: (int) Number of timesteps for each batch of training data\n",
+    "    :param sys: (psl.system)\n",
+    "    :param ts: (float) step size\n",
+    "    :param bs: (int) batch size\n",
+    "\n",
+    "    \"\"\"\n",
+    "    train_sim, dev_sim, test_sim = [sys.simulate(nsim=nsim, ts=ts) for i in range(3)]\n",
+    "    nx = sys.nx\n",
+    "    nbatch = nsim//nsteps\n",
+    "    length = (nsim//nsteps) * nsteps\n",
+    "\n",
+    "    trainX = train_sim['X'][:length].reshape(nbatch, nsteps, nx)\n",
+    "    trainX = torch.tensor(trainX, dtype=torch.float32)\n",
+    "    train_data = DictDataset({'X': trainX, 'xn': trainX[:, 0:1, :]}, name='train')\n",
+    "    train_loader = DataLoader(train_data, batch_size=bs,\n",
+    "                              collate_fn=train_data.collate_fn, shuffle=True)\n",
+    "\n",
+    "    devX = dev_sim['X'][:length].reshape(nbatch, nsteps, nx)\n",
+    "    devX = torch.tensor(devX, dtype=torch.float32)\n",
+    "    dev_data = DictDataset({'X': devX, 'xn': devX[:, 0:1, :]}, name='dev')\n",
+    "    dev_loader = DataLoader(dev_data, batch_size=bs,\n",
+    "                            collate_fn=dev_data.collate_fn, shuffle=True)\n",
+    "\n",
+    "    testX = test_sim['X'][:length].reshape(1, nsim, nx)\n",
+    "    testX = torch.tensor(testX, dtype=torch.float32)\n",
+    "    test_data = {'X': testX, 'xn': testX[:, 0:1, :]}\n",
+    "\n",
+    "    return train_loader, dev_loader, test_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4fb7c36e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nsim = 600   # number of simulation steps in the dataset\n",
+    "nsteps = 2   # number of prediction horizon steps in the loss function\n",
+    "bs = 100     # minibatching batch size\n",
+    "train_loader, dev_loader, test_data = get_data(modelSystem, nsim, nsteps, ts, bs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66e81939",
+   "metadata": {},
+   "source": [
+    "## NODE system model in Neuromancer\n",
+    "\n",
+    "Here we construct a continuous-time NODE model  $\\dot{x} = f_{\\theta}(x)$ with trainable parameters $\\theta$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c41df1bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define neural network of the NODE\n",
+    "fx = blocks.MLP(nx, nx, bias=True,\n",
+    "                 linear_map=torch.nn.Linear,\n",
+    "                 nonlin=torch.nn.ReLU,\n",
+    "                 hsizes=[60, 60, 60])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01e9b524",
+   "metadata": {},
+   "source": [
+    "Next we need to solve the continuous-time NODE model with suitable ODE solver, e.g., [Runge–Kutta integrator](https://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods).  \n",
+    "$x_{k+1} = \\text{ODESolve}(f_{\\theta}(x_k))$ \n",
+    "\n",
+    "For training we need to obtain accurate reverse-mode gradients of the integrated NODE system. This can be done in two ways, either by unrolling the operations of the ODE solver and using the [backpropagation through time](https://en.wikipedia.org/wiki/Backpropagation_through_time) (BPTT) algorithm, or via [Adjoint state method](https://en.wikipedia.org/wiki/Adjoint_state_method).\n",
+    "\n",
+    "Schematics illustrating the adjoing method used in the [Neural Ordinary Differential Equations](https://arxiv.org/abs/1806.07366) paper:\n",
+    "<img src=\"../figs/NODE_backprop.png\" width=\"500\">  \n",
+    "\n",
+    "Neuromancer provides a set of ODE solvers implemented in [integrators.py](https://github.com/pnnl/neuromancer/blob/master/src/neuromancer/dynamics/integrators.py).\n",
+    "For adjoint method we provide the interface to the [open-source implementation](https://github.com/rtqichen/torchdiffeq) via DiffEqIntegrator class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "23e4e146",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# integrate NODE with adjoint-based solver\n",
+    "fxRK4 = integrators.DiffEqIntegrator(fx, h=ts, method='rk4')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f630931d",
+   "metadata": {},
+   "source": [
+    "Next we construct an open-loop system composed of a smbolic NODE model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f62e2ab0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create symbolic system model in Neuromancer\n",
+    "model = Node(fxRK4, ['xn'], ['xn'], name='NODE')\n",
+    "dynamics_model = System([model], name='system', nsteps=nsteps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "a3bcd5eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# visualize the system\n",
+    "# dynamics_model.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd4f33d0",
+   "metadata": {},
+   "source": [
+    "## Define system identification loss function terms\n",
+    "\n",
+    "Here we define loss function terms to fit the ODE parameters from given time-series data.\n",
+    "\n",
+    "**Tracking loss:**  \n",
+    "$$\\ell_x = Q_x||x^i_k - \\hat{x}^i_k||_2^2$$ \n",
+    "**Finite difference loss:**  \n",
+    "$$\\ell_{dx} =  Q_{dx}||\\Delta x^i_k - \\Delta \\hat{x}^i_k||_2^2$$\n",
+    "where $\\Delta x^i_k = x^i_{k+1} - x^i_k$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c28dac38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %% Constraints + losses:\n",
+    "x = variable(\"X\")\n",
+    "xhat = variable('xn')[:, :-1, :]\n",
+    "# finite difference variables\n",
+    "xFD = (x[:, 1:, :] - x[:, :-1, :])\n",
+    "xhatFD = (xhat[:, 1:, :] - xhat[:, :-1, :])\n",
+    "\n",
+    "# trajectory tracking loss\n",
+    "reference_loss = (xhat == x)^2\n",
+    "reference_loss.name = \"ref_loss\"\n",
+    "\n",
+    "# finite difference loss\n",
+    "fd_loss = 2.*(xFD == xhatFD)^2\n",
+    "fd_loss.name = 'FD_loss'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd0f573a",
+   "metadata": {},
+   "source": [
+    "## Construct System ID learning problem\n",
+    "\n",
+    "Given the training dataset $\\hat{X} = [\\hat{x}^i_0, ..., \\hat{x}^i_{N}]$ we want to solve the following problem:\n",
+    " \n",
+    "$$\n",
+    "\\begin{align}\n",
+    "&\\underset{\\theta}{\\text{minimize}}     && \\sum_{i=1}^m \\Big(\\sum_{k=1}^{N}  Q_x||x^i_k - \\hat{x}^i_k||_2^2  +  \\sum_{k=1}^{N-1}  Q_{dx}||\\Delta x^i_k - \\Delta \\hat{x}^i_k||_2^2  \\Big) \\\\\n",
+    "&\\text{subject to}    && x^i_{k+1} =  \\text{ODESolve}(f_{\\theta}(x^i_k)) \\\\\n",
+    "\\end{align}\n",
+    "$$  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a36553e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# aggregate list of objective terms and constraints\n",
+    "objectives = [reference_loss, fd_loss]\n",
+    "constraints = []\n",
+    "# create constrained optimization loss\n",
+    "loss = PenaltyLoss(objectives, constraints)\n",
+    "# construct constrained optimization problem\n",
+    "problem = Problem([dynamics_model], loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "70f8a337",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot computational graph\n",
+    "# problem.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d39e880",
+   "metadata": {},
+   "source": [
+    "## Solve the problem\n",
+    "\n",
+    "We fit the unknown NODE parameters $\\theta$ using stochastic gradient descent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a629deae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n",
+      "Number of parameters: 7622\n",
+      "epoch: 0\ttrain_loss: 0.04616\tdev_loss: 0.04439\teltime:  0.16038\n",
+      "epoch: 1\ttrain_loss: 0.04229\tdev_loss: 0.04218\teltime:  0.21043\n",
+      "epoch: 2\ttrain_loss: 0.04167\tdev_loss: 0.04180\teltime:  0.29876\n",
+      "epoch: 3\ttrain_loss: 0.03413\tdev_loss: 0.03351\teltime:  0.35262\n",
+      "epoch: 4\ttrain_loss: 0.03059\tdev_loss: 0.02892\teltime:  0.40529\n",
+      "epoch: 5\ttrain_loss: 0.02564\tdev_loss: 0.02503\teltime:  0.46167\n",
+      "epoch: 6\ttrain_loss: 0.02355\tdev_loss: 0.02029\teltime:  0.50992\n",
+      "epoch: 7\ttrain_loss: 0.01770\tdev_loss: 0.01689\teltime:  0.55860\n",
+      "epoch: 8\ttrain_loss: 0.01419\tdev_loss: 0.01275\teltime:  0.61264\n",
+      "epoch: 9\ttrain_loss: 0.00927\tdev_loss: 0.00872\teltime:  0.66223\n",
+      "epoch: 10\ttrain_loss: 0.00631\tdev_loss: 0.00595\teltime:  0.71307\n",
+      "epoch: 11\ttrain_loss: 0.00430\tdev_loss: 0.00368\teltime:  0.76417\n",
+      "epoch: 12\ttrain_loss: 0.00309\tdev_loss: 0.00285\teltime:  0.80996\n",
+      "epoch: 13\ttrain_loss: 0.00317\tdev_loss: 0.00305\teltime:  0.85653\n",
+      "epoch: 14\ttrain_loss: 0.00260\tdev_loss: 0.00232\teltime:  0.90060\n",
+      "epoch: 15\ttrain_loss: 0.00224\tdev_loss: 0.00163\teltime:  0.94695\n",
+      "epoch: 16\ttrain_loss: 0.00149\tdev_loss: 0.00127\teltime:  0.99168\n",
+      "epoch: 17\ttrain_loss: 0.00103\tdev_loss: 0.00086\teltime:  1.03394\n",
+      "epoch: 18\ttrain_loss: 0.00104\tdev_loss: 0.00094\teltime:  1.07595\n",
+      "epoch: 19\ttrain_loss: 0.00108\tdev_loss: 0.00077\teltime:  1.11803\n",
+      "epoch: 20\ttrain_loss: 0.00068\tdev_loss: 0.00074\teltime:  1.17413\n",
+      "epoch: 21\ttrain_loss: 0.00057\tdev_loss: 0.00059\teltime:  1.22149\n",
+      "epoch: 22\ttrain_loss: 0.00046\tdev_loss: 0.00044\teltime:  1.26699\n",
+      "epoch: 23\ttrain_loss: 0.00043\tdev_loss: 0.00042\teltime:  1.31041\n",
+      "epoch: 24\ttrain_loss: 0.00038\tdev_loss: 0.00030\teltime:  1.35351\n",
+      "epoch: 25\ttrain_loss: 0.00038\tdev_loss: 0.00032\teltime:  1.40338\n",
+      "epoch: 26\ttrain_loss: 0.00027\tdev_loss: 0.00022\teltime:  1.44938\n",
+      "epoch: 27\ttrain_loss: 0.00021\tdev_loss: 0.00022\teltime:  1.49686\n",
+      "epoch: 28\ttrain_loss: 0.00021\tdev_loss: 0.00020\teltime:  1.54071\n",
+      "epoch: 29\ttrain_loss: 0.00021\tdev_loss: 0.00017\teltime:  1.58779\n",
+      "epoch: 30\ttrain_loss: 0.00016\tdev_loss: 0.00016\teltime:  1.63995\n",
+      "epoch: 31\ttrain_loss: 0.00015\tdev_loss: 0.00015\teltime:  1.69397\n",
+      "epoch: 32\ttrain_loss: 0.00013\tdev_loss: 0.00012\teltime:  1.75597\n",
+      "epoch: 33\ttrain_loss: 0.00011\tdev_loss: 0.00011\teltime:  1.82028\n",
+      "epoch: 34\ttrain_loss: 0.00010\tdev_loss: 0.00010\teltime:  1.87982\n",
+      "epoch: 35\ttrain_loss: 0.00010\tdev_loss: 0.00010\teltime:  1.92920\n",
+      "epoch: 36\ttrain_loss: 0.00010\tdev_loss: 0.00009\teltime:  1.97770\n",
+      "epoch: 37\ttrain_loss: 0.00009\tdev_loss: 0.00008\teltime:  2.02799\n",
+      "epoch: 38\ttrain_loss: 0.00008\tdev_loss: 0.00008\teltime:  2.07789\n",
+      "epoch: 39\ttrain_loss: 0.00007\tdev_loss: 0.00006\teltime:  2.13011\n",
+      "epoch: 40\ttrain_loss: 0.00006\tdev_loss: 0.00007\teltime:  2.17664\n",
+      "epoch: 41\ttrain_loss: 0.00006\tdev_loss: 0.00005\teltime:  2.22458\n",
+      "epoch: 42\ttrain_loss: 0.00006\tdev_loss: 0.00005\teltime:  2.26983\n",
+      "epoch: 43\ttrain_loss: 0.00005\tdev_loss: 0.00005\teltime:  2.31570\n",
+      "epoch: 44\ttrain_loss: 0.00004\tdev_loss: 0.00004\teltime:  2.36237\n",
+      "epoch: 45\ttrain_loss: 0.00004\tdev_loss: 0.00005\teltime:  2.40865\n",
+      "epoch: 46\ttrain_loss: 0.00005\tdev_loss: 0.00004\teltime:  2.46579\n",
+      "epoch: 47\ttrain_loss: 0.00003\tdev_loss: 0.00004\teltime:  2.51981\n",
+      "epoch: 48\ttrain_loss: 0.00004\tdev_loss: 0.00003\teltime:  2.57553\n",
+      "epoch: 49\ttrain_loss: 0.00003\tdev_loss: 0.00003\teltime:  2.63117\n",
+      "epoch: 50\ttrain_loss: 0.00004\tdev_loss: 0.00003\teltime:  2.69091\n",
+      "epoch: 51\ttrain_loss: 0.00003\tdev_loss: 0.00003\teltime:  2.74260\n",
+      "epoch: 52\ttrain_loss: 0.00003\tdev_loss: 0.00003\teltime:  2.78911\n",
+      "epoch: 53\ttrain_loss: 0.00003\tdev_loss: 0.00002\teltime:  2.83822\n",
+      "epoch: 54\ttrain_loss: 0.00003\tdev_loss: 0.00003\teltime:  2.88335\n",
+      "epoch: 55\ttrain_loss: 0.00002\tdev_loss: 0.00003\teltime:  2.93104\n",
+      "epoch: 56\ttrain_loss: 0.00003\tdev_loss: 0.00002\teltime:  2.97834\n",
+      "epoch: 57\ttrain_loss: 0.00002\tdev_loss: 0.00002\teltime:  3.03161\n",
+      "epoch: 58\ttrain_loss: 0.00003\tdev_loss: 0.00002\teltime:  3.08001\n",
+      "epoch: 59\ttrain_loss: 0.00002\tdev_loss: 0.00002\teltime:  3.12587\n",
+      "epoch: 60\ttrain_loss: 0.00002\tdev_loss: 0.00002\teltime:  3.17198\n",
+      "epoch: 61\ttrain_loss: 0.00002\tdev_loss: 0.00002\teltime:  3.21516\n",
+      "epoch: 62\ttrain_loss: 0.00002\tdev_loss: 0.00002\teltime:  3.26029\n",
+      "epoch: 63\ttrain_loss: 0.00002\tdev_loss: 0.00001\teltime:  3.30706\n",
+      "epoch: 64\ttrain_loss: 0.00002\tdev_loss: 0.00002\teltime:  3.35702\n",
+      "epoch: 65\ttrain_loss: 0.00001\tdev_loss: 0.00002\teltime:  3.40993\n",
+      "epoch: 66\ttrain_loss: 0.00002\tdev_loss: 0.00002\teltime:  3.46307\n",
+      "epoch: 67\ttrain_loss: 0.00002\tdev_loss: 0.00002\teltime:  3.52965\n",
+      "epoch: 68\ttrain_loss: 0.00002\tdev_loss: 0.00001\teltime:  3.58147\n",
+      "epoch: 69\ttrain_loss: 0.00002\tdev_loss: 0.00001\teltime:  3.63851\n",
+      "epoch: 70\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  3.69374\n",
+      "epoch: 71\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  3.74680\n",
+      "epoch: 72\ttrain_loss: 0.00001\tdev_loss: 0.00002\teltime:  3.79985\n",
+      "epoch: 73\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  3.84956\n",
+      "epoch: 74\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  3.90265\n",
+      "epoch: 75\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  3.95661\n",
+      "epoch: 76\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  4.00938\n",
+      "epoch: 77\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  4.06648\n",
+      "epoch: 78\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  4.12690\n",
+      "epoch: 79\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  4.18396\n",
+      "epoch: 80\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  4.24829\n",
+      "epoch: 81\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  4.31332\n",
+      "epoch: 82\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  4.37796\n",
+      "epoch: 83\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  4.43459\n",
+      "epoch: 84\ttrain_loss: 0.00002\tdev_loss: 0.00001\teltime:  4.49275\n",
+      "epoch: 85\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  4.55135\n",
+      "epoch: 86\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  4.60696\n",
+      "epoch: 87\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  4.66731\n",
+      "epoch: 88\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  4.73613\n",
+      "epoch: 89\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  4.80034\n",
+      "epoch: 90\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  4.87084\n",
+      "epoch: 91\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  4.93945\n",
+      "epoch: 92\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  5.00196\n",
+      "epoch: 93\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  5.06704\n",
+      "epoch: 94\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  5.13279\n",
+      "epoch: 95\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  5.19972\n",
+      "epoch: 96\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  5.26567\n",
+      "epoch: 97\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  5.32878\n",
+      "epoch: 98\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  5.39835\n",
+      "epoch: 99\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  5.45780\n",
+      "epoch: 100\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  5.52953\n",
+      "epoch: 101\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  5.59927\n",
+      "epoch: 102\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  5.66790\n",
+      "epoch: 103\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  5.73531\n",
+      "epoch: 104\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  5.80419\n",
+      "epoch: 105\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  5.87252\n",
+      "epoch: 106\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  5.94288\n",
+      "epoch: 107\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  6.01258\n",
+      "epoch: 108\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  6.08189\n",
+      "epoch: 109\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  6.15377\n",
+      "epoch: 110\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  6.21849\n",
+      "epoch: 111\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  6.28719\n",
+      "epoch: 112\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  6.35769\n",
+      "epoch: 113\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  6.42865\n",
+      "epoch: 114\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  6.49967\n",
+      "epoch: 115\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  6.57254\n",
+      "epoch: 116\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  6.64414\n",
+      "epoch: 117\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  6.71589\n",
+      "epoch: 118\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  6.78852\n",
+      "epoch: 119\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  6.85679\n",
+      "epoch: 120\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  6.96864\n",
+      "epoch: 121\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  7.03789\n",
+      "epoch: 122\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  7.10152\n",
+      "epoch: 123\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  7.16305\n",
+      "epoch: 124\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  7.23209\n",
+      "epoch: 125\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  7.29530\n",
+      "epoch: 126\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  7.35682\n",
+      "epoch: 127\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  7.41950\n",
+      "epoch: 128\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  7.48362\n",
+      "epoch: 129\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  7.54421\n",
+      "epoch: 130\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  7.60300\n",
+      "epoch: 131\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  7.66515\n",
+      "epoch: 132\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  7.72719\n",
+      "epoch: 133\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  7.78659\n",
+      "epoch: 134\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  7.84881\n",
+      "epoch: 135\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  7.90191\n",
+      "epoch: 136\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  7.97050\n",
+      "epoch: 137\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  8.03053\n",
+      "epoch: 138\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  8.08793\n",
+      "epoch: 139\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  8.14178\n",
+      "epoch: 140\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  8.20170\n",
+      "epoch: 141\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  8.26024\n",
+      "epoch: 142\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  8.31864\n",
+      "epoch: 143\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  8.38089\n",
+      "epoch: 144\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  8.44255\n",
+      "epoch: 145\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  8.50269\n",
+      "epoch: 146\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  8.57896\n",
+      "epoch: 147\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  8.64763\n",
+      "epoch: 148\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  8.70655\n",
+      "epoch: 149\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  8.76210\n",
+      "epoch: 150\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  8.81191\n",
+      "epoch: 151\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  8.86962\n",
+      "epoch: 152\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  8.91922\n",
+      "epoch: 153\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  8.97028\n",
+      "epoch: 154\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  9.02913\n",
+      "epoch: 155\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  9.08353\n",
+      "epoch: 156\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  9.14146\n",
+      "epoch: 157\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  9.19106\n",
+      "epoch: 158\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  9.24578\n",
+      "epoch: 159\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  9.29943\n",
+      "epoch: 160\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  9.35683\n",
+      "epoch: 161\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  9.40362\n",
+      "epoch: 162\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  9.45162\n",
+      "epoch: 163\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  9.49777\n",
+      "epoch: 164\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  9.54631\n",
+      "epoch: 165\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  9.59413\n",
+      "epoch: 166\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  9.64375\n",
+      "epoch: 167\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  9.69809\n",
+      "epoch: 168\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  9.75023\n",
+      "epoch: 169\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  9.80049\n",
+      "epoch: 170\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  9.85900\n",
+      "epoch: 171\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  9.91969\n",
+      "epoch: 172\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  9.97619\n",
+      "epoch: 173\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  10.02524\n",
+      "epoch: 174\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  10.07156\n",
+      "epoch: 175\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  10.11613\n",
+      "epoch: 176\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  10.16775\n",
+      "epoch: 177\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  10.21629\n",
+      "epoch: 178\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  10.26330\n",
+      "epoch: 179\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  10.30787\n",
+      "epoch: 180\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  10.35293\n",
+      "epoch: 181\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  10.39793\n",
+      "epoch: 182\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  10.44606\n",
+      "epoch: 183\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  10.49746\n",
+      "epoch: 184\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  10.54719\n",
+      "epoch: 185\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  10.59285\n",
+      "epoch: 186\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  10.63997\n",
+      "epoch: 187\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  10.68542\n",
+      "epoch: 188\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  10.72965\n",
+      "epoch: 189\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  10.77951\n",
+      "epoch: 190\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  10.82258\n",
+      "epoch: 191\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  10.86793\n",
+      "epoch: 192\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  10.91599\n",
+      "epoch: 193\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  10.96088\n",
+      "epoch: 194\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.00804\n",
+      "epoch: 195\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.05360\n",
+      "epoch: 196\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.10130\n",
+      "epoch: 197\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.15230\n",
+      "epoch: 198\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.20806\n",
+      "epoch: 199\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.25747\n",
+      "epoch: 200\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  11.31264\n",
+      "epoch: 201\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.37374\n",
+      "epoch: 202\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  11.43194\n",
+      "epoch: 203\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  11.48854\n",
+      "epoch: 204\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.53751\n",
+      "epoch: 205\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.58567\n",
+      "epoch: 206\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.63547\n",
+      "epoch: 207\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.68058\n",
+      "epoch: 208\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.72346\n",
+      "epoch: 209\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.77305\n",
+      "epoch: 210\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.82031\n",
+      "epoch: 211\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.86632\n",
+      "epoch: 212\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  11.90878\n",
+      "epoch: 213\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  11.95508\n",
+      "epoch: 214\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  12.00535\n",
+      "epoch: 215\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  12.05152\n",
+      "epoch: 216\ttrain_loss: 0.00001\tdev_loss: 0.00001\teltime:  12.09826\n",
+      "epoch: 217\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  12.14500\n",
+      "epoch: 218\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  12.19114\n",
+      "epoch: 219\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  12.23603\n",
+      "epoch: 220\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  12.28645\n",
+      "epoch: 221\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  12.33571\n",
+      "epoch: 222\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  12.38290\n",
+      "epoch: 223\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  12.42762\n",
+      "epoch: 224\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  12.47080\n",
+      "epoch: 225\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  12.51964\n",
+      "epoch: 226\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  12.56681\n",
+      "epoch: 227\ttrain_loss: 0.00001\tdev_loss: 0.00000\teltime:  12.61113\n",
+      "epoch: 228\ttrain_loss: 0.00000\tdev_loss: 0.00001\teltime:  12.65651\n",
+      "epoch: 229\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  12.70190\n",
+      "epoch: 230\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  12.75103\n",
+      "epoch: 231\ttrain_loss: 0.00000\tdev_loss: 0.00000\teltime:  12.80334\n",
+      "Early stopping!!!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n",
+    "logger = BasicLogger(args=None, savedir='test', verbosity=1,\n",
+    "                     stdout=['dev_loss', 'train_loss'])\n",
+    "# define neuromancer trainer\n",
+    "trainer = Trainer(\n",
+    "    problem,\n",
+    "    train_loader,\n",
+    "    dev_loader,\n",
+    "    test_data,\n",
+    "    optimizer,\n",
+    "    patience=50,\n",
+    "    warmup=100,\n",
+    "    epochs=500,\n",
+    "    eval_metric=\"dev_loss\",\n",
+    "    train_metric=\"train_loss\",\n",
+    "    dev_metric=\"dev_loss\",\n",
+    "    test_metric=\"dev_loss\",\n",
+    "    logger=logger,\n",
+    ")\n",
+    "\n",
+    "# %% train\n",
+    "best_model = trainer.train()\n",
+    "problem.load_state_dict(best_model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "import lightning.pytorch as pl \n",
+    "from lightning.pytorch.callbacks import ModelCheckpoint\n",
+    "from lightning.pytorch.callbacks.early_stopping import EarlyStopping\n",
+    "\n",
+    "\n",
+    "checkpoint_callback = ModelCheckpoint(\n",
+    "    monitor='train_loss',\n",
+    "    mode='min',\n",
+    "    save_top_k=1,\n",
+    "    dirpath='./foo',\n",
+    "    save_weights_only=True,\n",
+    "    filename='best_model-{epoch:02d}-{val_loss:.2f}'\n",
+    ")\n",
+    "\n",
+    "early_stopping_callback = EarlyStopping(monitor='train_loss', patience=50)\n",
+    "\n",
+    "class LitProblem(pl.LightningModule): \n",
+    "    def __init__(self, problem, train_metric, dev_metric): \n",
+    "        super().__init__()\n",
+    "        self.problem = problem \n",
+    "        self.train_metric = train_metric\n",
+    "        self.dev_metric = dev_metric\n",
+    "        self.training_step_outputs = []\n",
+    "    \n",
+    "    def training_step(self, batch): \n",
+    "        output = self.problem(batch)\n",
+    "        loss = output[self.train_metric]\n",
+    "        self.training_step_outputs.append(loss)\n",
+    "        self.log('train_loss', loss, on_epoch=True, enable_graph=True, prog_bar=False)\n",
+    "        return loss \n",
+    "    \n",
+    "    def on_train_epoch_end(self):\n",
+    "        epoch_average = torch.stack(self.training_step_outputs).mean()\n",
+    "        print(\"EPOCH AVERAGE \", epoch_average)\n",
+    "        self.log(\"training_epoch_average\", epoch_average)\n",
+    "        self.training_step_outputs.clear()  # free memory\n",
+    "\n",
+    "    \n",
+    "    def validation_step(self, batch): \n",
+    "        output = self.problem(batch)\n",
+    "        loss = output[self.dev_metric]\n",
+    "        self.log('val_loss', loss)\n",
+    "    \n",
+    "    def configure_optimizers(self): \n",
+    "        optimizer = torch.optim.Adam(problem.parameters(), 0.001, betas=(0.0, 0.9))\n",
+    "        return optimizer \n",
+    "    \n",
+    "\n",
+    "class LightningDataModule(pl.LightningDataModule): \n",
+    "    def __init__(self,data_setup_function, batch_size, *args, **kwargs): \n",
+    "        super().__init__()\n",
+    "        self.data_setup_function = data_setup_function\n",
+    "        self.batch_size = batch_size \n",
+    "        self.data_setup_args = args\n",
+    "        self.data_setup_kwargs = kwargs\n",
+    "\n",
+    "    def setup(self, stage=None): \n",
+    "        train_data, dev_data,test_data = self.data_setup_function(*self.data_setup_args, **self.data_setup_kwargs)\n",
+    "\n",
+    "        self.train_data = train_data\n",
+    "        self.test_data = test_data\n",
+    "        self.dev_data = dev_data\n",
+    " \n",
+    "    def train_dataloader(self): \n",
+    "        return DataLoader(self.train_data, batch_size=self.batch_size, collate_fn=self.train_data.collate_fn)\n",
+    "    \n",
+    "    def dev_dataloader(self): \n",
+    "        return DataLoader(self.dev_data, batch_size=self.batch_size, collate_fn=self.dev_data.collate_fn)\n",
+    "\n",
+    "    def test_dataloader(self): \n",
+    "        return DataLoader(self.test_data, batch_size=self.batch_size, collate_fn=self.test_data.collate_fn)\n",
+    "\n",
+    "def data_setup_function(sys, nsim, nsteps, ts, bs):\n",
+    "    \"\"\"\n",
+    "    :param nsteps: (int) Number of timesteps for each batch of training data\n",
+    "    :param sys: (psl.system)\n",
+    "    :param ts: (float) step size\n",
+    "    :param bs: (int) batch size\n",
+    "\n",
+    "    \"\"\"\n",
+    "    train_sim, dev_sim, test_sim = [sys.simulate(nsim=nsim, ts=ts) for i in range(3)]\n",
+    "    nx = sys.nx\n",
+    "    nbatch = nsim//nsteps\n",
+    "    length = (nsim//nsteps) * nsteps\n",
+    "\n",
+    "    trainX = train_sim['X'][:length].reshape(nbatch, nsteps, nx)\n",
+    "    trainX = torch.tensor(trainX, dtype=torch.float32)\n",
+    "    train_data = DictDataset({'X': trainX, 'xn': trainX[:, 0:1, :]}, name='train')\n",
+    "\n",
+    "\n",
+    "    devX = dev_sim['X'][:length].reshape(nbatch, nsteps, nx)\n",
+    "    devX = torch.tensor(devX, dtype=torch.float32)\n",
+    "    dev_data = DictDataset({'X': devX, 'xn': devX[:, 0:1, :]}, name='dev')\n",
+    "    \n",
+    "\n",
+    "    testX = test_sim['X'][:length].reshape(1, nsim, nx)\n",
+    "    testX = torch.tensor(testX, dtype=torch.float32)\n",
+    "    test_data = {'X': testX, 'xn': testX[:, 0:1, :]}\n",
+    "\n",
+    "    return train_data, dev_data, test_data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: False, used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "/Users/birm560/opt/anaconda3/envs/neuromancer/lib/python3.10/site-packages/lightning/pytorch/trainer/configuration_validator.py:74: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\n",
+      "/Users/birm560/opt/anaconda3/envs/neuromancer/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:630: Checkpoint directory /Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/examples/ODEs/foo exists and is not empty.\n",
+      "\n",
+      "  | Name    | Type    | Params\n",
+      "------------------------------------\n",
+      "0 | problem | Problem | 7.6 K \n",
+      "------------------------------------\n",
+      "7.6 K     Trainable params\n",
+      "0         Non-trainable params\n",
+      "7.6 K     Total params\n",
+      "0.030     Total estimated model params size (MB)\n",
+      "/Users/birm560/opt/anaconda3/envs/neuromancer/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n",
+      "/Users/birm560/opt/anaconda3/envs/neuromancer/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:293: The number of training batches (5) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|██████████| 5/5 [00:00<00:00, 56.52it/s, v_num=13]EPOCH AVERAGE  tensor(6.4759e-05, grad_fn=<MeanBackward0>)\n",
+      "Epoch 1: 100%|██████████| 5/5 [00:00<00:00, 60.71it/s, v_num=13]EPOCH AVERAGE  tensor(6.8348e-05, grad_fn=<MeanBackward0>)\n",
+      "Epoch 2: 100%|██████████| 5/5 [00:00<00:00, 62.22it/s, v_num=13]EPOCH AVERAGE  tensor(4.8949e-05, grad_fn=<MeanBackward0>)\n",
+      "Epoch 3: 100%|██████████| 5/5 [00:00<00:00, 58.35it/s, v_num=13]EPOCH AVERAGE  tensor(7.6849e-05, grad_fn=<MeanBackward0>)\n",
+      "Epoch 4: 100%|██████████| 5/5 [00:00<00:00, 41.43it/s, v_num=13]EPOCH AVERAGE  tensor(7.4435e-05, grad_fn=<MeanBackward0>)\n",
+      "Epoch 5: 100%|██████████| 5/5 [00:00<00:00, 51.07it/s, v_num=13]EPOCH AVERAGE  tensor(7.4478e-05, grad_fn=<MeanBackward0>)\n",
+      "Epoch 6: 100%|██████████| 5/5 [00:00<00:00, 59.22it/s, v_num=13]EPOCH AVERAGE  tensor(3.6759e-05, grad_fn=<MeanBackward0>)\n",
+      "Epoch 7: 100%|██████████| 5/5 [00:00<00:00, 58.63it/s, v_num=13]EPOCH AVERAGE  tensor(8.1271e-05, grad_fn=<MeanBackward0>)\n",
+      "Epoch 8: 100%|██████████| 5/5 [00:00<00:00, 60.34it/s, v_num=13]EPOCH AVERAGE  tensor(4.5861e-05, grad_fn=<MeanBackward0>)\n",
+      "Epoch 9: 100%|██████████| 5/5 [00:00<00:00, 56.67it/s, v_num=13]EPOCH AVERAGE  tensor(8.3935e-05, grad_fn=<MeanBackward0>)\n",
+      "Epoch 9: 100%|██████████| 5/5 [00:00<00:00, 46.24it/s, v_num=13]\n"
+     ]
+    }
+   ],
+   "source": [
+    "mymodel = LitProblem(problem, train_metric='train_loss', dev_metric='train_loss')\n",
+    "data_module = LightningDataModule(data_setup_function, 64, modelSystem, nsim, nsteps, ts, bs)\n",
+    "trainer = pl.Trainer(max_epochs=50, num_sanity_val_steps=0, callbacks=[early_stopping_callback, checkpoint_callback])\n",
+    "trainer.fit(mymodel, data_module)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/examples/ODEs/foo/best_model-epoch=09-val_loss=0.00.ckpt\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "The classmethod `LitProblem.load_from_checkpoint` cannot be called on an instance. Please call it on the class type and make sure the return value is used.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/examples/ODEs/Part_1_NODE lightning.ipynb Cell 27\u001b[0m line \u001b[0;36m4\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/examples/ODEs/Part_1_NODE%20lightning.ipynb#X44sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mprint\u001b[39m(best_weights_path)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/examples/ODEs/Part_1_NODE%20lightning.ipynb#X44sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m# Load the best weights using the model class\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/examples/ODEs/Part_1_NODE%20lightning.ipynb#X44sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m best_model \u001b[39m=\u001b[39m mymodel\u001b[39m.\u001b[39;49mload_from_checkpoint(best_weights_path)\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer/lib/python3.10/site-packages/lightning/pytorch/utilities/model_helpers.py:93\u001b[0m, in \u001b[0;36m_restricted_classmethod_impl.__get__\u001b[0;34m(self, instance, cls)\u001b[0m\n\u001b[1;32m     91\u001b[0m is_scripting \u001b[39m=\u001b[39m \u001b[39many\u001b[39m(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(\u001b[39m\"\u001b[39m\u001b[39mtorch\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mjit\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39min\u001b[39;00m frameinfo\u001b[39m.\u001b[39mfilename \u001b[39mfor\u001b[39;00m frameinfo \u001b[39min\u001b[39;00m inspect\u001b[39m.\u001b[39mstack())\n\u001b[1;32m     92\u001b[0m \u001b[39mif\u001b[39;00m instance \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m is_scripting:\n\u001b[0;32m---> 93\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\n\u001b[1;32m     94\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mThe classmethod `\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mcls\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmethod\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m` cannot be called on an instance.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     95\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m Please call it on the class type and make sure the return value is used.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     96\u001b[0m     )\n\u001b[1;32m     97\u001b[0m \u001b[39mreturn\u001b[39;00m MethodType(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmethod, \u001b[39mcls\u001b[39m)\n",
+      "\u001b[0;31mTypeError\u001b[0m: The classmethod `LitProblem.load_from_checkpoint` cannot be called on an instance. Please call it on the class type and make sure the return value is used."
+     ]
+    }
+   ],
+   "source": [
+    "best_weights_path = checkpoint_callback.best_model_path\n",
+    "print(best_weights_path)\n",
+    "# Load the best weights using the model class\n",
+    "best_model = mymodel.load_from_checkpoint(best_weights_path)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f17ad938",
+   "metadata": {},
+   "source": [
+    "## Parameter estimation results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c76e9b09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# update the rollout length based on the test data\n",
+    "dynamics_model.nsteps = test_data['X'].shape[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "0b3d2839",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Test set results\n",
+    "test_outputs = dynamics_model(test_data)\n",
+    "\n",
+    "pred_traj = test_outputs['xn'][:, :-1, :]\n",
+    "true_traj = test_data['X']\n",
+    "pred_traj = pred_traj.detach().numpy().reshape(-1, nx)\n",
+    "true_traj = true_traj.detach().numpy().reshape(-1, nx)\n",
+    "pred_traj, true_traj = pred_traj.transpose(1, 0), true_traj.transpose(1, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "25e6f8aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 2500x2500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot rollout\n",
+    "figsize = 25\n",
+    "fig, ax = plt.subplots(nx, figsize=(figsize, figsize))\n",
+    "labels = [f'$y_{k}$' for k in range(len(true_traj))]\n",
+    "for row, (t1, t2, label) in enumerate(zip(true_traj, pred_traj, labels)):\n",
+    "    if nx > 1:\n",
+    "        axe = ax[row]\n",
+    "    else:\n",
+    "        axe = ax\n",
+    "    axe.set_ylabel(label, rotation=0, labelpad=20, fontsize=figsize)\n",
+    "    axe.plot(t1, 'c', linewidth=4.0, label='True')\n",
+    "    axe.plot(t2, 'm--', linewidth=4.0, label='Pred')\n",
+    "    axe.tick_params(labelbottom=False, labelsize=figsize)\n",
+    "axe.tick_params(labelbottom=True, labelsize=figsize)\n",
+    "axe.legend(fontsize=figsize)\n",
+    "axe.set_xlabel('$time$', fontsize=figsize)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de31932a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neuromancer",
+   "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/ODEs/Part_2_param_estim_ODE.ipynb b/examples/ODEs/Part_2_param_estim_ODE.ipynb
index a2dfdd40..b6a2908d 100644
--- a/examples/ODEs/Part_2_param_estim_ODE.ipynb
+++ b/examples/ODEs/Part_2_param_estim_ODE.ipynb
@@ -48,7 +48,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "901105f0",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/ODEs/Part_3_UDE.ipynb b/examples/ODEs/Part_3_UDE.ipynb
index 987646d8..fc7acf97 100644
--- a/examples/ODEs/Part_3_UDE.ipynb
+++ b/examples/ODEs/Part_3_UDE.ipynb
@@ -54,7 +54,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a386539",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/ODEs/Part_4_nonauto_NODE.ipynb b/examples/ODEs/Part_4_nonauto_NODE.ipynb
index 7a2ef3ec..39fa9509 100644
--- a/examples/ODEs/Part_4_nonauto_NODE.ipynb
+++ b/examples/ODEs/Part_4_nonauto_NODE.ipynb
@@ -54,7 +54,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7d0fa0d",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/ODEs/Part_5_nonauto_NSSM.ipynb b/examples/ODEs/Part_5_nonauto_NSSM.ipynb
index 785e9fdd..893938d3 100644
--- a/examples/ODEs/Part_5_nonauto_NSSM.ipynb
+++ b/examples/ODEs/Part_5_nonauto_NSSM.ipynb
@@ -60,7 +60,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99c494f8",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/ODEs/Part_6_NetworkODE.ipynb b/examples/ODEs/Part_6_NetworkODE.ipynb
index 873ad5b7..03fb1c3f 100644
--- a/examples/ODEs/Part_6_NetworkODE.ipynb
+++ b/examples/ODEs/Part_6_NetworkODE.ipynb
@@ -47,6 +47,30 @@
     "Our task is to leverage the known structure of RC networks to estimate capacitances and coupling resistivities from data. We will use the NeruoMANCER package for this task; let's do our imports:"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Install (Colab only)\n",
+    "Skip this step when running locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/examples/ODEs/Part_7_DeepKoopman.ipynb b/examples/ODEs/Part_7_DeepKoopman.ipynb
index f49c2363..0f4fc6f2 100644
--- a/examples/ODEs/Part_7_DeepKoopman.ipynb
+++ b/examples/ODEs/Part_7_DeepKoopman.ipynb
@@ -63,7 +63,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fd58f0c",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/ODEs/Part_8_nonauto_DeepKoopman.ipynb b/examples/ODEs/Part_8_nonauto_DeepKoopman.ipynb
index 48ba8d7a..ca095d8c 100644
--- a/examples/ODEs/Part_8_nonauto_DeepKoopman.ipynb
+++ b/examples/ODEs/Part_8_nonauto_DeepKoopman.ipynb
@@ -62,7 +62,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89ee6302",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
@@ -1796,9 +1804,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "neuromancer",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "neuromancer"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1810,7 +1818,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,
diff --git a/examples/PDEs/Part_1_PINN_DiffusionEquation.ipynb b/examples/PDEs/Part_1_PINN_DiffusionEquation.ipynb
index 12321176..95443f8b 100644
--- a/examples/PDEs/Part_1_PINN_DiffusionEquation.ipynb
+++ b/examples/PDEs/Part_1_PINN_DiffusionEquation.ipynb
@@ -43,10 +43,17 @@
    },
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\"\n",
+    "!pip install neuromancer\n",
     "!pip install pyDOE"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/examples/PDEs/Part_2_PINN_BurgersEquation.ipynb b/examples/PDEs/Part_2_PINN_BurgersEquation.ipynb
index 3f39d255..9215211c 100644
--- a/examples/PDEs/Part_2_PINN_BurgersEquation.ipynb
+++ b/examples/PDEs/Part_2_PINN_BurgersEquation.ipynb
@@ -41,10 +41,17 @@
    },
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\"\n",
+    "!pip install neuromancer\n",
     "!pip install pyDOE"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/examples/PDEs/Part_3_PINN_BurgersEquation_inverse.ipynb b/examples/PDEs/Part_3_PINN_BurgersEquation_inverse.ipynb
index 809b0efb..2cb0cd1b 100644
--- a/examples/PDEs/Part_3_PINN_BurgersEquation_inverse.ipynb
+++ b/examples/PDEs/Part_3_PINN_BurgersEquation_inverse.ipynb
@@ -41,10 +41,17 @@
    },
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\"\n",
+    "!pip install neuromancer\n",
     "!pip install pyDOE"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/examples/control/Part_1_stabilize_linear_system.ipynb b/examples/control/Part_1_stabilize_linear_system.ipynb
index d2908fab..86a13823 100644
--- a/examples/control/Part_1_stabilize_linear_system.ipynb
+++ b/examples/control/Part_1_stabilize_linear_system.ipynb
@@ -39,7 +39,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54e902d0",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/control/Part_2_stabilize_ODE.ipynb b/examples/control/Part_2_stabilize_ODE.ipynb
index a409b8bb..f0fe2b79 100644
--- a/examples/control/Part_2_stabilize_ODE.ipynb
+++ b/examples/control/Part_2_stabilize_ODE.ipynb
@@ -175,7 +175,15 @@
     }
    ],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62795a33",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/control/Part_3_ref_tracking_ODE.ipynb b/examples/control/Part_3_ref_tracking_ODE.ipynb
index f9e5f62d..0719ae19 100644
--- a/examples/control/Part_3_ref_tracking_ODE.ipynb
+++ b/examples/control/Part_3_ref_tracking_ODE.ipynb
@@ -36,7 +36,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3995d5f",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/control/Part_4_NODE_control.ipynb b/examples/control/Part_4_NODE_control.ipynb
index 2206d062..9676ed6d 100644
--- a/examples/control/Part_4_NODE_control.ipynb
+++ b/examples/control/Part_4_NODE_control.ipynb
@@ -44,7 +44,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "652c96fb",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/control/Part_5_neural_Lyapunov.ipynb b/examples/control/Part_5_neural_Lyapunov.ipynb
index 467fb6ac..5eb91cbe 100644
--- a/examples/control/Part_5_neural_Lyapunov.ipynb
+++ b/examples/control/Part_5_neural_Lyapunov.ipynb
@@ -54,7 +54,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff70f066",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/domain_examples/DPC_building_control.ipynb b/examples/domain_examples/DPC_building_control.ipynb
index cb91797f..b8053d17 100644
--- a/examples/domain_examples/DPC_building_control.ipynb
+++ b/examples/domain_examples/DPC_building_control.ipynb
@@ -115,7 +115,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/domain_examples/NODE_building_dynamics.ipynb b/examples/domain_examples/NODE_building_dynamics.ipynb
index e65d73b9..5dba2ae4 100644
--- a/examples/domain_examples/NODE_building_dynamics.ipynb
+++ b/examples/domain_examples/NODE_building_dynamics.ipynb
@@ -98,7 +98,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/domain_examples/NODE_swing_equation.ipynb b/examples/domain_examples/NODE_swing_equation.ipynb
index 3ea073e3..6ce23c1d 100644
--- a/examples/domain_examples/NODE_swing_equation.ipynb
+++ b/examples/domain_examples/NODE_swing_equation.ipynb
@@ -95,7 +95,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf689b12",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/domain_examples/NSSM_building_dynamics.ipynb b/examples/domain_examples/NSSM_building_dynamics.ipynb
index 3d4423c1..f5ba1cb5 100644
--- a/examples/domain_examples/NSSM_building_dynamics.ipynb
+++ b/examples/domain_examples/NSSM_building_dynamics.ipynb
@@ -100,7 +100,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/lightning_integration_examples/Part_1_lightning_basics_tutorial.ipynb b/examples/lightning_integration_examples/Part_1_lightning_basics_tutorial.ipynb
new file mode 100755
index 00000000..c4a4f8d1
--- /dev/null
+++ b/examples/lightning_integration_examples/Part_1_lightning_basics_tutorial.ipynb
@@ -0,0 +1,905 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "34qVD_ntSKLF"
+   },
+   "source": [
+    "# Lightning Integration Basics\n",
+    "\n",
+    "The following notebook is equivalent to /parametric_programming/Part_1_basics.ipynb, but now showcasing the use of **PyTorch-Lightning** to simplify the user workflow. \n",
+    "\n",
+    "We will demonstrate the capability of learning to optimize (L2O)\n",
+    "for solving [parametric nonlinear programming problem (pNLP)](https://en.wikipedia.org/wiki/Parametric_programming) defined as:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "&\\text{minimize } &&  f(x, \\theta) \\\\\n",
+    "&\\text{subject to} && g(x, \\theta) \\le 0\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "with  parameters $\\theta$ and decision variables $x$.\n",
+    "\n",
+    "In L2O train a neural network mapping problem parameters onto the primal decision variables $x = \\pi(\\theta)$ by using gradients of the optimizaiton problem to minimize the objective function and satisfy the constraints.\n",
+    "\n",
+    "### References\n",
+    "[1] [F. Fioretto, et al., Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods, 2019](https://arxiv.org/abs/1909.10461)  \n",
+    "[2] [S. Gould, et al., Deep Declarative Networks: A New Hope, 2020](https://arxiv.org/abs/1909.04866)  \n",
+    "[3] [P. Donti, et al., DC3: A learning method for optimization with hard constraints, 2021](https://arxiv.org/abs/2104.12225)  \n",
+    "[4] [J. Kotary, et al., End-to-End Constrained Optimization Learning: A Survey, 2021](https://arxiv.org/abs/2103.16378)  \n",
+    "[5] [M. Li, et al., Learning to Solve Optimization Problems with Hard Linear Constraints, 2022](https://arxiv.org/abs/2208.10611)  \n",
+    "[6] [R. Sambharya, et al., End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization, 2022](https://arxiv.org/abs/2212.08260)  \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OCn3zpaIqgMc"
+   },
+   "source": [
+    "## NeuroMANCER and Dependencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Qzy5Wot5k2Gf"
+   },
+   "source": [
+    "### Install (Colab only)\n",
+    "Skip this step when running locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "X_3EvkSz0Fnz",
+    "outputId": "23c06f6b-ab48-4763-c43c-40a325cacf87"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\"\n",
+    "!pip install lightning \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LWyvndXlz0Fv"
+   },
+   "source": [
+    "### Import"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(The user might need to install PyTorch Lightning). If so, please run \n",
+    "\n",
+    "```\n",
+    "pip install lightning\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "KbP0n-4evRqt"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import numpy as np\n",
+    "import neuromancer.slim as slim\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patheffects as patheffects\n",
+    "import casadi\n",
+    "import time\n",
+    "import lightning.pytorch as pl \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "POL27EJZxJmI"
+   },
+   "outputs": [],
+   "source": [
+    "from neuromancer.trainer import Trainer, LitTrainer\n",
+    "from neuromancer.problem import Problem\n",
+    "from neuromancer.constraint import variable\n",
+    "from neuromancer.dataset import DictDataset\n",
+    "from neuromancer.loss import PenaltyLoss\n",
+    "from neuromancer.modules import blocks\n",
+    "from neuromancer.system import Node\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Problem formulation\n",
+    "\n",
+    "In this example we will solve parametric constrained [Rosenbrock problem](https://en.wikipedia.org/wiki/Rosenbrock_function):\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "&\\text{minimize } &&  (1-x)^2 + a(y-x^2)^2\\\\\n",
+    "&\\text{subject to} && \\left(\\frac{p}{2}\\right)^2 \\le x^2 + y^2 \\le p^2\\\\\n",
+    "& && x \\ge y\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "with parameters $p, a$ and decision variables $x, y$.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lightning Workflow\n",
+    "\n",
+    "The workflow when using Lightning consists of three parts: \n",
+    "\n",
+    "1. Defining a \"data_setup_function() -- this function should return 4 values (train, dev, test datasets, and batch size). The datasets should be named Neuromancer DictDatasets. \n",
+    "2. Defining the Problem -- consisting of Nodes, System, Loss. \n",
+    "3. Instantiating the PyTorch-Lightning -based Trainer (LitTrainer class)\n",
+    "\n",
+    "For this notebook, we assume all operations are done on the CPU. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_WH7o7Wu1epw"
+   },
+   "source": [
+    "### Lightning Dataset\n",
+    "\n",
+    "We constructy the dataset by sampling the parametric space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "_r6p2p6myHAh"
+   },
+   "outputs": [],
+   "source": [
+    "data_seed = 408  # random seed used for simulated data\n",
+    "np.random.seed(data_seed)\n",
+    "torch.manual_seed(data_seed)\n",
+    "nsim = 5000  # number of datapoints: increase sample density for more robust results\n",
+    "\n",
+    "# create dictionaries with sampled datapoints with uniform distribution\n",
+    "a_low, a_high, p_low, p_high = 0.2, 1.2, 0.5, 2.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JZ9qrw0tlJhs"
+   },
+   "source": [
+    "We define the **data_setup_function()** below. It randomly sample parameters from a uniform distribution: $0.5\\le p\\le2.0$;  $0.2\\le a\\le1.2$. It takes these parameters as inputs and outputs Neuromancer DictDatasets() for train, dev, and test data (or None type otherwise), as well as batch size (such as if we want to minibatch w.r.t time on the DictDatasets) We have hardcoded batch size to be 64 in this case. \n",
+    "\n",
+    "It is important to define both training and dev/validation datasets. Training datasets will be used for the training step; dev datasets will be used for model checkpointing (if desired)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "Nu58M-8JyHy6"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def data_setup_function(nsim, a_low, a_high, p_low, p_high): \n",
+    "\n",
+    "    \n",
+    "    samples_train = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
+    "                    \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
+    "    samples_dev = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
+    "                \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
+    "    samples_test = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
+    "                \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
+    "    # create named dictionary datasets\n",
+    "    train_data = DictDataset(samples_train, name='train')\n",
+    "    dev_data = DictDataset(samples_dev, name='dev')\n",
+    "    test_data = DictDataset(samples_test, name='test')\n",
+    "\n",
+    "    batch_size = 64\n",
+    "\n",
+    "    # Return the dict datasets in train, dev, test order, followed by batch_size \n",
+    "    return train_data, dev_data, test_data, batch_size \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now define the **Problem()**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Y2htUaWMDjsk"
+   },
+   "source": [
+    "## Primal Solution Map Architecture\n",
+    "\n",
+    "A neural network mapping problem parameters onto primal decision variables:  \n",
+    "$$x = \\pi(\\theta)$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "Ta_I_pjyyLzf"
+   },
+   "outputs": [],
+   "source": [
+    "# define neural architecture for the trainable solution map\n",
+    "func = blocks.MLP(insize=2, outsize=2,\n",
+    "                bias=True,\n",
+    "                linear_map=slim.maps['linear'],\n",
+    "                nonlin=nn.ReLU,\n",
+    "                hsizes=[80] * 4)\n",
+    "# wrap neural net into symbolic representation of the solution map via the Node class: sol_map(xi) -> x\n",
+    "sol_map = Node(func, ['a', 'p'], ['x'], name='map')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Lxj77EFj7EO-"
+   },
+   "source": [
+    "## Objective and Constraints in NeuroMANCER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "bcoVjphjyPp9"
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "variable is a basic symbolic abstraction in Neuromancer\n",
+    "   x = variable(\"variable_name\")                      (instantiates new variable)  \n",
+    "variable construction supports:\n",
+    "   algebraic expressions:     x**2 + x**3 + 5     (instantiates new variable)  \n",
+    "   slicing:                   x[:, i]             (instantiates new variable)  \n",
+    "   pytorch callables:         torch.sin(x)        (instantiates new variable)  \n",
+    "   constraints definition:    x <= 1.0            (instantiates Constraint object) \n",
+    "   objective definition:      x.minimize()        (instantiates Objective object) \n",
+    "to visualize computational graph of the variable use x.show() method          \n",
+    "\"\"\"\n",
+    "\n",
+    "# define decision variables\n",
+    "x1 = variable(\"x\")[:, [0]]\n",
+    "x2 = variable(\"x\")[:, [1]]\n",
+    "# problem parameters sampled in the dataset\n",
+    "p = variable('p')\n",
+    "a = variable('a')\n",
+    "\n",
+    "# objective function\n",
+    "f = (1-x1)**2 + a*(x2-x1**2)**2\n",
+    "obj = f.minimize(weight=1.0, name='obj')\n",
+    "\n",
+    "# constraints\n",
+    "Q_con = 100.  # constraint penalty weights\n",
+    "con_1 = Q_con*(x1 >= x2)\n",
+    "con_2 = Q_con*((p/2)**2 <= x1**2+x2**2)\n",
+    "con_3 = Q_con*(x1**2+x2**2 <= p**2)\n",
+    "con_1.name = 'c1'\n",
+    "con_2.name = 'c2'\n",
+    "con_3.name = 'c3'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 496
+    },
+    "id": "n7VPa9Wc8JRB",
+    "outputId": "0da17c45-6370-4f46-f626-bd5686b94bfc"
+   },
+   "outputs": [],
+   "source": [
+    "# constrained optimization problem construction\n",
+    "objectives = [obj]\n",
+    "constraints = [con_1, con_2, con_3]\n",
+    "components = [sol_map]\n",
+    "\n",
+    "# create penalty method loss function\n",
+    "loss = PenaltyLoss(objectives, constraints)\n",
+    "# construct constrained optimization problem\n",
+    "problem = Problem(components, loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Define Lightning Modules and Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now instantiate the trainer and data modules as shown below. We will then train our Neuromancer **problem** on this data setup function using the **fit** function. The original problem will already have its weights updated. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (cuda), used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/trainer/setup.py:187: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n",
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:639: Checkpoint directory ./ exists and is not empty.\n",
+      "\n",
+      "  | Name    | Type    | Params\n",
+      "------------------------------------\n",
+      "0 | problem | Problem | 19.8 K\n",
+      "------------------------------------\n",
+      "19.8 K    Trainable params\n",
+      "0         Non-trainable params\n",
+      "19.8 K    Total params\n",
+      "0.079     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                                           "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=159` in the `DataLoader` to improve performance.\n",
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/utilities/data.py:77: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=159` in the `DataLoader` to improve performance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|██████████| 79/79 [00:00<00:00, 126.64it/s, v_num=15, train_loss_step=0.903]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/utilities/data.py:77: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 8. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|██████████| 79/79 [00:01<00:00, 70.31it/s, v_num=15, train_loss_step=0.903, dev_loss=0.815, train_loss_epoch=5.690]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, global step 79: 'dev_loss' reached 0.81470 (best 0.81470), saving model to './epoch=0-step=79.ckpt' as top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: 100%|██████████| 79/79 [00:00<00:00, 81.24it/s, v_num=15, train_loss_step=0.528, dev_loss=0.421, train_loss_epoch=0.881] "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1, global step 158: 'dev_loss' reached 0.42123 (best 0.42123), saving model to './epoch=1-step=158.ckpt' as top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: 100%|██████████| 79/79 [00:00<00:00, 81.72it/s, v_num=15, train_loss_step=0.144, dev_loss=0.111, train_loss_epoch=0.286]  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2, global step 237: 'dev_loss' reached 0.11081 (best 0.11081), saving model to './epoch=2-step=237.ckpt' as top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3: 100%|██████████| 79/79 [00:00<00:00, 87.08it/s, v_num=15, train_loss_step=0.228, dev_loss=0.187, train_loss_epoch=0.171]  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3, global step 316: 'dev_loss' was not in top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: 100%|██████████| 79/79 [00:00<00:00, 86.42it/s, v_num=15, train_loss_step=0.159, dev_loss=0.105, train_loss_epoch=0.147]  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4, global step 395: 'dev_loss' reached 0.10479 (best 0.10479), saving model to './epoch=4-step=395.ckpt' as top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5: 100%|██████████| 79/79 [00:00<00:00, 88.72it/s, v_num=15, train_loss_step=0.103, dev_loss=0.140, train_loss_epoch=0.128]  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5, global step 474: 'dev_loss' was not in top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6: 100%|██████████| 79/79 [00:00<00:00, 80.26it/s, v_num=15, train_loss_step=0.155, dev_loss=0.104, train_loss_epoch=0.138]  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6, global step 553: 'dev_loss' reached 0.10385 (best 0.10385), saving model to './epoch=6-step=553.ckpt' as top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7: 100%|██████████| 79/79 [00:00<00:00, 83.75it/s, v_num=15, train_loss_step=0.110, dev_loss=0.0644, train_loss_epoch=0.120] "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7, global step 632: 'dev_loss' reached 0.06439 (best 0.06439), saving model to './epoch=7-step=632-v1.ckpt' as top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8: 100%|██████████| 79/79 [00:00<00:00, 79.68it/s, v_num=15, train_loss_step=0.140, dev_loss=0.0949, train_loss_epoch=0.124]  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8, global step 711: 'dev_loss' was not in top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9: 100%|██████████| 79/79 [00:00<00:00, 86.22it/s, v_num=15, train_loss_step=0.142, dev_loss=0.0917, train_loss_epoch=0.146]  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9, global step 790: 'dev_loss' was not in top 1\n",
+      "`Trainer.fit` stopped: `max_epochs=10` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9: 100%|██████████| 79/79 [00:00<00:00, 84.98it/s, v_num=15, train_loss_step=0.142, dev_loss=0.0917, train_loss_epoch=0.146]\n"
+     ]
+    }
+   ],
+   "source": [
+    "lit_trainer = LitTrainer(epochs=10, accelerator='cpu', patience=3)\n",
+    "lit_trainer.fit(problem, data_setup_function, nsim=nsim,a_low=0.2, a_high=1.2, p_low=0.5, p_high=2.0)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Commentary\n",
+    "\n",
+    "The idea for PyTorch Lightning integration is to streamline the user experience such that the user can focus solely on the Problem and Data. Lightning emphasizes modularity; the aim here is to easily be able to swap out different problems and/or datasets, akin to running different experimental configurations. For example, we could easily procure different datasets by varying one of the data parameters (such as a_low) and easily fit our Problem to each of these datasets.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# After Training \n",
+    "\n",
+    "We can use the newly-trained Problem just as in the original version of the notebook: "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0hhUw4PVBWmb"
+   },
+   "source": [
+    "## Get pNLP solution from trained neural network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# selected problem parameters\n",
+    "p = 1.0\n",
+    "a = 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "os3I3I8L3HbE",
+    "outputId": "50c13f99-7693-4102-b65c-4f3707f90c29"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7884852\n",
+      "0.56312114\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution to mpNLP via Neuromancer\n",
+    "datapoint = {'a': torch.tensor([[a]]), 'p': torch.tensor([[p]]),\n",
+    "             'name': 'test'}\n",
+    "model_out = problem(datapoint)\n",
+    "x_nm = model_out['test_' + \"x\"][0, 0].detach().numpy()\n",
+    "y_nm = model_out['test_' + \"x\"][0, 1].detach().numpy()\n",
+    "print(x_nm)\n",
+    "print(y_nm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "g-KmqqUj-Q3E"
+   },
+   "source": [
+    "## Get pNLP solution from CasADi for comparison\n",
+    "\n",
+    "[CasADi](https://web.casadi.org/) is an open-source tool for constrained optimization and optimal control that has influenced the development of NeuroMANCER."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "dmJERFP2yYuC"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "******************************************************************************\n",
+      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
+      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
+      "         For more information visit https://github.com/coin-or/Ipopt\n",
+      "******************************************************************************\n",
+      "\n",
+      "This is Ipopt version 3.14.11, running with linear solver MUMPS 5.4.1.\n",
+      "\n",
+      "Number of nonzeros in equality constraint Jacobian...:        0\n",
+      "Number of nonzeros in inequality constraint Jacobian.:        6\n",
+      "Number of nonzeros in Lagrangian Hessian.............:        3\n",
+      "\n",
+      "Total number of variables............................:        2\n",
+      "                     variables with only lower bounds:        0\n",
+      "                variables with lower and upper bounds:        0\n",
+      "                     variables with only upper bounds:        0\n",
+      "Total number of equality constraints.................:        0\n",
+      "Total number of inequality constraints...............:        3\n",
+      "        inequality constraints with only lower bounds:        1\n",
+      "   inequality constraints with lower and upper bounds:        0\n",
+      "        inequality constraints with only upper bounds:        2\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   0  1.0000000e+00 2.50e-01 1.67e+00  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
+      "   1  9.5811923e-01 2.49e-01 1.71e+01  -1.0 5.59e-01    -  5.26e-01 3.81e-02h  1\n",
+      "   2  5.1823940e-01 1.40e-02 1.95e+01  -1.0 2.12e+01    -  1.25e-03 3.12e-02f  6\n",
+      "   3  3.2701557e-01 0.00e+00 5.64e-01  -1.0 2.47e-01    -  1.00e+00 1.00e+00f  1\n",
+      "   4  1.5594126e-01 0.00e+00 9.02e-01  -1.7 4.58e-01    -  3.11e-01 1.00e+00f  1\n",
+      "   5  5.3473898e-02 0.00e+00 2.99e-01  -1.7 5.51e-01    -  1.00e+00 1.00e+00h  1\n",
+      "   6  5.7915701e-02 0.00e+00 6.95e-03  -1.7 2.57e-02    -  1.00e+00 1.00e+00h  1\n",
+      "   7  4.4884709e-02 0.00e+00 7.47e-03  -3.8 9.13e-02    -  8.77e-01 1.00e+00h  1\n",
+      "   8  4.1207939e-02 0.00e+00 2.60e-04  -3.8 3.58e-02    -  1.00e+00 1.00e+00h  1\n",
+      "   9  4.0921509e-02 0.00e+00 8.22e-07  -5.7 2.98e-03    -  1.00e+00 1.00e+00h  1\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  10  4.0919039e-02 0.00e+00 7.11e-11  -8.6 2.53e-05    -  1.00e+00 1.00e+00h  1\n",
+      "\n",
+      "Number of Iterations....: 10\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   4.0919038633377619e-02    4.0919038633377619e-02\n",
+      "Dual infeasibility......:   7.1118860800467587e-11    7.1118860800467587e-11\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   2.6584596762648413e-09    2.6584596762648413e-09\n",
+      "Overall NLP error.......:   2.6584596762648413e-09    2.6584596762648413e-09\n",
+      "\n",
+      "\n",
+      "Number of objective function evaluations             = 18\n",
+      "Number of objective gradient evaluations             = 11\n",
+      "Number of equality constraint evaluations            = 0\n",
+      "Number of inequality constraint evaluations          = 18\n",
+      "Number of equality constraint Jacobian evaluations   = 0\n",
+      "Number of inequality constraint Jacobian evaluations = 11\n",
+      "Number of Lagrangian Hessian evaluations             = 10\n",
+      "Total seconds in IPOPT                               = 0.043\n",
+      "\n",
+      "EXIT: Optimal Solution Found.\n",
+      "      solver  :   t_proc      (avg)   t_wall      (avg)    n_eval\n",
+      "       nlp_f  |   1.74ms ( 96.72us) 202.69us ( 11.26us)        18\n",
+      "       nlp_g  |   1.97ms (109.56us) 210.76us ( 11.71us)        18\n",
+      "  nlp_grad_f  |   1.55ms (129.42us) 150.41us ( 12.53us)        12\n",
+      "  nlp_hess_l  |   1.25ms (125.10us) 147.25us ( 14.73us)        10\n",
+      "   nlp_jac_g  |   1.58ms (132.08us) 159.06us ( 13.25us)        12\n",
+      "       total  | 709.78ms (709.78ms)  56.32ms ( 56.32ms)         1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# instantiate casadi optimizaiton problem class\n",
+    "def NLP_param(a, p, opti_silent=False):\n",
+    "    opti = casadi.Opti()\n",
+    "    # define variables\n",
+    "    x = opti.variable()\n",
+    "    y = opti.variable()\n",
+    "    p_opti = opti.parameter()\n",
+    "    a_opti = opti.parameter()\n",
+    "    # define objective and constraints\n",
+    "    opti.minimize((1 - x) ** 2 + a_opti * (y - x ** 2) ** 2)\n",
+    "    opti.subject_to(x >= y)\n",
+    "    opti.subject_to((p_opti / 2) ** 2 <= x ** 2 + y ** 2)\n",
+    "    opti.subject_to(x ** 2 + y ** 2 <= p_opti ** 2)\n",
+    "    # select IPOPT solver and solve the NLP\n",
+    "    if opti_silent:\n",
+    "        opts = {'ipopt.print_level': 0, 'print_time': 0, 'ipopt.sb': 'yes'}\n",
+    "    else:\n",
+    "        opts = {}\n",
+    "    opti.solver('ipopt', opts)\n",
+    "    # set parametric values\n",
+    "    opti.set_value(p_opti, p)\n",
+    "    opti.set_value(a_opti, a)\n",
+    "    return opti, x, y\n",
+    "\n",
+    "# construct casadi problem\n",
+    "opti, x, y = NLP_param(a, p)\n",
+    "# solve NLP via casadi\n",
+    "sol = opti.solve()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "MvXjYHNjISrC",
+    "outputId": "69a77f30-0ba5-411e-9d15-3cb7f15d9154"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x = 0.8081695826847699\n",
+      "y = 0.588949838491767\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"x = {sol.value(x)}\")\n",
+    "print(f\"y = {sol.value(y)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pcNq5fOVE4lR"
+   },
+   "source": [
+    "## Compare: NeuroMANCER vs. CasADi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 281
+    },
+    "id": "kvYCfjq6zxxC",
+    "outputId": "322e5b72-b93d-4d9e-a913-8703aad67d1a"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_373406/1714511162.py:21: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed two minor releases later.\n",
+      "  plt.setp(cg1.collections,\n",
+      "/tmp/ipykernel_373406/1714511162.py:24: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed two minor releases later.\n",
+      "  plt.setp(cg2.collections,\n",
+      "/tmp/ipykernel_373406/1714511162.py:27: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed two minor releases later.\n",
+      "  plt.setp(cg3.collections,\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7884852\n",
+      "0.56312114\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Plots\n",
+    "\"\"\"\n",
+    "x1 = np.arange(-0.5, 1.5, 0.02)\n",
+    "y1 = np.arange(-0.5, 1.5, 0.02)\n",
+    "xx, yy = np.meshgrid(x1, y1)\n",
+    "\n",
+    "# eval objective and constraints\n",
+    "J = (1 - xx) ** 2 + a * (yy - xx ** 2) ** 2\n",
+    "c1 = xx - yy\n",
+    "c2 = xx ** 2 + yy ** 2 - (p / 2) ** 2\n",
+    "c3 = -(xx ** 2 + yy ** 2) + p ** 2\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1)\n",
+    "cp = ax.contourf(xx, yy, J,\n",
+    "                 levels=[0, 0.05, 0.2, 0.5, 1.0, 2.0, 4.0, 6.0, 8.0],\n",
+    "                 alpha=0.6)\n",
+    "fig.colorbar(cp)\n",
+    "ax.set_title('Rosenbrock problem')\n",
+    "cg1 = ax.contour(xx, yy, c1, [0], colors='mediumblue', alpha=0.7)\n",
+    "plt.setp(cg1.collections,\n",
+    "         path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
+    "cg2 = ax.contour(xx, yy, c2, [0], colors='mediumblue', alpha=0.7)\n",
+    "plt.setp(cg2.collections,\n",
+    "         path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
+    "cg3 = ax.contour(xx, yy, c3, [0], colors='mediumblue', alpha=0.7)\n",
+    "plt.setp(cg3.collections,\n",
+    "         path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
+    "\n",
+    "# Solution to mpNLP via Neuromancer\n",
+    "datapoint = {'a': torch.tensor([[a]]), 'p': torch.tensor([[p]]),\n",
+    "             'name': 'test'}\n",
+    "model_out = problem(datapoint)\n",
+    "x_nm = model_out['test_' + \"x\"][0, 0].detach().numpy()\n",
+    "y_nm = model_out['test_' + \"x\"][0, 1].detach().numpy()\n",
+    "print(x_nm)\n",
+    "print(y_nm)\n",
+    "\n",
+    "# plot optimal solutions CasADi vs Neuromancer\n",
+    "ax.plot(sol.value(x), sol.value(y), 'g*', markersize=10, label='CasADi')\n",
+    "ax.plot(x_nm, y_nm, 'r*', fillstyle='none', markersize=10, label='NeuroMANCER')\n",
+    "plt.legend(bbox_to_anchor=(1.0, 0.15))\n",
+    "plt.show(block=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "neuromancer",
+   "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/examples/lightning_integration_examples/Part_2_lightning_advanced_and_gpu_tutorial.ipynb b/examples/lightning_integration_examples/Part_2_lightning_advanced_and_gpu_tutorial.ipynb
new file mode 100644
index 00000000..6755183e
--- /dev/null
+++ b/examples/lightning_integration_examples/Part_2_lightning_advanced_and_gpu_tutorial.ipynb
@@ -0,0 +1,497 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e017c9a3",
+   "metadata": {},
+   "source": [
+    "# Lightning Integration: Advanced Tutorial and Multi-GPU Automatic Support\n",
+    "\n",
+    "The following notebook is equivalent to /control/Part_1_stabilize_linear_system.ipynb, but now showcasing the use of **PyTorch-Lightning** to simplify the user workflow. \n",
+    "\n",
+    "This notebook also describes how to use **multi-GPU** distributed training.\n",
+    "\n",
+    "*For performance reasons, we only demonstrate single-GPU training. For a multi-GPU training example please refer to Part 6_lightning_multi_gpu.py*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff3ffdcd",
+   "metadata": {},
+   "source": [
+    "## NeuroMANCER and Dependencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9bac7825",
+   "metadata": {},
+   "source": [
+    "### Install (Colab only)\n",
+    "Skip this step when running locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89a187ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\"\n",
+    "!pip install lightning \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cabfefdc",
+   "metadata": {},
+   "source": [
+    "## Import"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46d1282a",
+   "metadata": {},
+   "source": [
+    "(The user might need to install PyTorch Lightning). If so, please run \n",
+    "\n",
+    "```\n",
+    "pip install lightning\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1a9b6ec8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import lightning.pytorch as pl\n",
+    "\n",
+    "from neuromancer.system import Node, System\n",
+    "from neuromancer.modules import blocks\n",
+    "from neuromancer.dataset import DictDataset\n",
+    "from neuromancer.constraint import variable\n",
+    "from neuromancer.loss import PenaltyLoss\n",
+    "from neuromancer.problem import Problem\n",
+    "from neuromancer.trainer import Trainer, LitTrainer\n",
+    "from neuromancer.plot import pltCL, pltPhase\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77e52bff",
+   "metadata": {},
+   "source": [
+    "# Problem formulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfe1b7ed",
+   "metadata": {},
+   "source": [
+    "## Node and System classes\n",
+    "\n",
+    "The Node class is a simple wrapper for any callable pytorch function or nn.Module which provides names for the inputs and outputs to be used in composition of a potentially cyclic computational graph. \n",
+    "\n",
+    "The Node, System and Problem formulation is exactly the same as in the original notebook *except* in how hardcoded tensors are handled in Lightning when wanting to use the GPU. The back-end will automatically handle all device management, so the user should *never* have to specify cuda() or .to(device) commands as in original PyTorch. However, hardcoded tensors need to be handled differently: \n",
+    "* In the original code, tensors A and B are specified without a device. In traditional PyTorch, we would provide .to(device) in the definitions of A and B to run on desired GPU device\n",
+    "* In lightning, we cannot use .to(device) as this interrupts the automated GPU support. Instead, we need to use the **type_as()** functionality\n",
+    "* Furthermore, we need to wrap the original callable *xnext()* within a PyTorch-Lightning *LightningModule()* class\n",
+    "\n",
+    "This is understandably a lot of extra overhead when using hardcoded tensors on the front end. \n",
+    "\n",
+    "For more information please see https://pytorch-lightning.readthedocs.io/en/1.4.9/advanced/multi_gpu.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cd824de1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Double integrator parameters\n",
+    "nx = 2\n",
+    "nu = 1\n",
+    "import numpy as np \n",
+    "\n",
+    "# neural control policy\n",
+    "mlp = blocks.MLP(nx, nu, bias=True,\n",
+    "                 linear_map=torch.nn.Linear,\n",
+    "                 nonlin=torch.nn.ReLU,\n",
+    "                 hsizes=[20, 20, 20, 20])\n",
+    "policy = Node(mlp, ['X'], ['U'], name='policy')\n",
+    "\n",
+    "A = torch.tensor([[1.2, 1.0],\n",
+    "                  [0.0, 1.0]])\n",
+    "B = torch.tensor([[1.0],\n",
+    "                  [0.5]])\n",
+    "\n",
+    "# linear state space model when using CPU \n",
+    "xnext = lambda x, u: x @ A.T + u @ B.T\n",
+    "\n",
+    "\n",
+    "# linear state space model when using Lightning for automated (multi) GPU support \n",
+    "class XNextLightning(pl.LightningModule):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "    def forward(self, x,u): \n",
+    "        A = torch.tensor([[1.2, 1.0],\n",
+    "                  [0.0, 1.0]]).type_as(x)\n",
+    "\n",
+    "        B = torch.tensor([[1.0],\n",
+    "                  [0.5]]).type_as(x)\n",
+    "        \n",
+    "        return x @ A.T + u @ B.T\n",
+    "        \n",
+    "# If using GPU: \n",
+    "double_integrator = Node(XNextLightning(), ['X', 'U'], ['X'], name='integrator')\n",
+    "\n",
+    "# If using CPU, use this line below and uncomment it\n",
+    "#double_integrator = Node(xnext, ['X', 'U'], ['X'], name='integrator').to\n",
+    "\n",
+    "# closed loop system definition\n",
+    "cl_system = System([policy, double_integrator])\n",
+    "\n",
+    "# Rollout of two steps\n",
+    "cl_system.nsteps = 2\n",
+    "# cl_system.show()\n",
+    "\n",
+    "# Define optimization problem\n",
+    "u = variable('U')\n",
+    "x = variable('X')\n",
+    "action_loss = 0.0001 * (u == 0.)^2  # control penalty\n",
+    "\n",
+    "regulation_loss = 10. * (x == 0.)^2  # target position\n",
+    "loss = PenaltyLoss([action_loss, regulation_loss], [])\n",
+    "problem = Problem([cl_system], loss)\n",
+    "\n",
+    "# we define optimizer specific to the policy instead of the overall problem\n",
+    "optimizer = torch.optim.AdamW(policy.parameters(), lr=0.001)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa938158",
+   "metadata": {},
+   "source": [
+    "# Data Setup Function\n",
+    "\n",
+    "We define the data_setup_function: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b74ee8c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Training dataset generation\n",
+    "def data_setup_function():\n",
+    "    train_data = DictDataset({'X': 3.*torch.randn(3333, 1, nx)}, name='train')  # Split conditions into train and dev\n",
+    "    dev_data = DictDataset({'X': 3.*torch.randn(3333, 1, nx)}, name='dev')\n",
+    "    test_data = None\n",
+    "    return train_data, dev_data, test_data, 3333\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d394587",
+   "metadata": {},
+   "source": [
+    "# GPU Options and Set-Up\n",
+    "\n",
+    "Assuming the appropriate callable (XNext_lightning) is used as the callable for the double integrator as discussed above, we can now pass in apropriate keyword arguments into our *LitTrainer* to automatically handle running on the GPU and/or multiple GPUs and distribute the training workload. \n",
+    "\n",
+    "We go over the keyword arguments below: \n",
+    "\n",
+    "#### To Run on CPU: \n",
+    "* accelerator = \"cpu\" is all that is necessary\n",
+    "\n",
+    "#### To Run on GPU: \n",
+    "* accelerator = \"gpu\" is required \n",
+    "* devices can be a list, integer, or \"auto\"\n",
+    "    * ex) [1,2,3] will distribute training over cuda:1, cuda:2, and cuda:3\n",
+    "    * ex) 7 will distribute training over the 7 GPUs automatically selected \n",
+    "    * ex) \"Auto\" for automatic selection based on the chosen accelerator. We do not recommend this. \n",
+    "* strategy is either \"auto\", \"ddp\" or \"ddp_notebook\"\n",
+    "    * \"auto\" will utilize a single GPU \n",
+    "    * \"ddp\" will run distributed training across devices desginated under \"devices\" assuming len(devices) > 1. This keyword should *NOT* be used in notebooks, only scripts \n",
+    "    * \"ddp_notebook\" is akin to \"ddp\" and should only be used in notebook environments\n",
+    "    \n",
+    "    \n",
+    "\n",
+    "For more information please see: https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78378ad6",
+   "metadata": {},
+   "source": [
+    "# Lightning Training with GPU\n",
+    "\n",
+    "We define the LitTrainer and fit our problem to the data_setup_function. Note that below, we specify accelerator and strategy to run on single GPUs. We also specify the optimizer by using the custom_optimizer keyword argument. \n",
+    "\n",
+    "**Note: Running distributed training within a notebook setting (ddp_notebook strategy) may take significant setup time. It is recommended to run distributed training within a .py script and setting strategy=\"ddp\". Nonetheless, running single and multi-GPU training is shown below**\n",
+    "\n",
+    "Please run either of the training blocks, not both"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "017d2163",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (cuda), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/2\n",
+      "Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/2\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "distributed_backend=nccl\n",
+      "All distributed processes registered. Starting with 2 processes\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "\n",
+      "You are using a CUDA device ('NVIDIA RTX A6000') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py:54: Detected KeyboardInterrupt, attempting graceful shutdown...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# train with single GPU \n",
+    "lit_trainer = LitTrainer(epochs=200, accelerator='gpu', strategy='auto', devices=[1], custom_optimizer=optimizer)\n",
+    "lit_trainer.fit(problem, data_setup_function)\n",
+    "\n",
+    "# train with two GPU. \n",
+    "lit_trainer = LitTrainer(epochs=200, accelerator='gpu', strategy='ddp_notebook', devices=[1,2], custom_optimizer=optimizer)\n",
+    "lit_trainer.fit(problem, data_setup_function)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "082a2e70",
+   "metadata": {},
+   "source": [
+    "# Saving and Loading Problem Weights\n",
+    "\n",
+    "By defauly, Problems() passed into LitTrainer (as well as base Neuromancer trainer) will automatically have the best weights at end of training, so there should be no need to manually load best_weights at the end of training.\n",
+    "\n",
+    "That said, we can save and load weights as follows: \n",
+    "* Set save_weights argument to True (this is default)\n",
+    "* Specify directory where to save weights (optional, by default is the current working directory)\n",
+    "* Use *load_state_dict_lightning()* function to properly ingest weights into Problem\n",
+    "\n",
+    "By default, weights will be saved with the following convention: '{epoch}-{step}.ckpt', where “epoch” and “step” match the number of finished epoch and optimizer steps respectively. The weights file can be given a custom name by changing the \"weight_name\" argument to LiTrainer. E.g. \"test_weights\" will save to \"test_weights.ckpt\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff131c5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lit_trainer = LitTrainer(epochs=200, accelerator='cpu', custom_optimizer=optimizer, weight_name='test_weights')\n",
+    "lit_trainer.fit(problem, data_setup_function)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3aa0543d",
+   "metadata": {},
+   "source": [
+    "#### Recreate a fresh problem and load weights: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c108787d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Double integrator parameters\n",
+    "nx = 2\n",
+    "nu = 1\n",
+    "import numpy as np \n",
+    "\n",
+    "# neural control policy\n",
+    "mlp = blocks.MLP(nx, nu, bias=True,\n",
+    "                 linear_map=torch.nn.Linear,\n",
+    "                 nonlin=torch.nn.ReLU,\n",
+    "                 hsizes=[20, 20, 20, 20])\n",
+    "policy = Node(mlp, ['X'], ['U'], name='policy')\n",
+    "\n",
+    "A = torch.tensor([[1.2, 1.0],\n",
+    "                  [0.0, 1.0]])\n",
+    "B = torch.tensor([[1.0],\n",
+    "                  [0.5]])\n",
+    "\n",
+    "# linear state space model when using CPU \n",
+    "xnext = lambda x, u: x @ A.T + u @ B.T\n",
+    "\n",
+    "\n",
+    "# linear state space model when using Lightning for automated (multi) GPU support \n",
+    "class XNextLightning(pl.LightningModule):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "    def forward(self, x,u): \n",
+    "        A = torch.tensor([[1.2, 1.0],\n",
+    "                  [0.0, 1.0]]).type_as(x)\n",
+    "\n",
+    "        B = torch.tensor([[1.0],\n",
+    "                  [0.5]]).type_as(x)\n",
+    "        \n",
+    "        return x @ A.T + u @ B.T\n",
+    "        \n",
+    "# If using GPU: \n",
+    "double_integrator = Node(XNextLightning(), ['X', 'U'], ['X'], name='integrator')\n",
+    "\n",
+    "# If using CPU, use this line below and uncomment it\n",
+    "#double_integrator = Node(xnext, ['X', 'U'], ['X'], name='integrator').to\n",
+    "\n",
+    "# closed loop system definition\n",
+    "cl_system = System([policy, double_integrator])\n",
+    "\n",
+    "# Rollout of two steps\n",
+    "cl_system.nsteps = 2\n",
+    "# cl_system.show()\n",
+    "\n",
+    "# Define optimization problem\n",
+    "u = variable('U')\n",
+    "x = variable('X')\n",
+    "action_loss = 0.0001 * (u == 0.)^2  # control penalty\n",
+    "\n",
+    "regulation_loss = 10. * (x == 0.)^2  # target position\n",
+    "loss = PenaltyLoss([action_loss, regulation_loss], [])\n",
+    "problem = Problem([cl_system], loss)\n",
+    "\n",
+    "# we define optimizer specific to the policy instead of the overall problem\n",
+    "optimizer = torch.optim.AdamW(policy.parameters(), lr=0.001)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cee97195",
+   "metadata": {},
+   "source": [
+    "#### Load weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "deb9276a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neuromancer.utils import load_state_dict_lightning\n",
+    "load_state_dict_lightning(problem, 'test_weights.ckpt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb5ef578",
+   "metadata": {},
+   "source": [
+    "# After Training\n",
+    "\n",
+    "Like before, we can now use our trained problem/system\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c1360ed",
+   "metadata": {},
+   "source": [
+    "# Evaluate best model on a system rollout \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "937b9143",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1600 with 2 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": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Test best model with prediction horizon of 50\n",
+    "\n",
+    "data = {'X': torch.ones(1, 1, nx, dtype=torch.float32)}\n",
+    "nsteps = 30\n",
+    "cl_system.nsteps = nsteps\n",
+    "trajectories = cl_system(data)\n",
+    "pltCL(Y=trajectories['X'].detach().reshape(nsteps+1, 2), U=trajectories['U'].detach().reshape(nsteps, 1), figname='cl.png')\n",
+    "pltPhase(X=trajectories['X'].detach().reshape(nsteps+1, 2), figname='phase.png')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neuromancer3",
+   "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/lightning_integration_examples/Part_3_multi_GPU_example.py b/examples/lightning_integration_examples/Part_3_multi_GPU_example.py
new file mode 100644
index 00000000..fea3adc5
--- /dev/null
+++ b/examples/lightning_integration_examples/Part_3_multi_GPU_example.py
@@ -0,0 +1,104 @@
+"""
+The following is a Neuromancer example that demonstrates the need for distributed training across GPUs.
+The objective function here is complex and this example cannot be solved on the CPU without error. 
+Note that this script still takes about ~90 minutes to run on 2 GPUs. Most of that time is "setup time"/preprocessing.
+The training loop should execute in a manner of seconds once the preprocessing is complete
+"""
+
+
+
+import numpy as np
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+
+import neuromancer as nm
+from neuromancer.dataset import DictDataset
+
+import lightning.pytorch as pl 
+from lightning.pytorch.callbacks import ModelCheckpoint
+from lightning.pytorch.callbacks.early_stopping import EarlyStopping
+from torch.utils.data import Dataset, DataLoader
+
+from neuromancer.trainer import Trainer, LitTrainer
+from neuromancer.problem import Problem
+from neuromancer.constraint import variable
+from neuromancer.dataset import DictDataset
+from neuromancer.loss import PenaltyLoss
+from neuromancer.modules import blocks
+from neuromancer.system import Node
+
+
+# Define the data setup function
+def data_setup_function(exp_returns): 
+        p_low, p_high = max(min(exp_returns),0), max(exp_returns)
+        data_train = DictDataset({"p": torch.FloatTensor(1000, 1).uniform_(p_low, p_high)})
+        data_test = DictDataset({"p": torch.FloatTensor(100, 1).uniform_(p_low, p_high)})
+        data_dev = DictDataset({"p": torch.FloatTensor(100, 1).uniform_(p_low, p_high)})
+        return data_train, data_dev, data_test, 32
+
+def main(): 
+        num_vars = 5
+
+        # expected returns
+        exp_returns = np.random.uniform(0.002, 0.01, num_vars)
+        print("Expected Returns:")
+        print(exp_returns)
+
+        # covariance matrix
+        A = np.random.rand(num_vars, num_vars)
+        # positive semi-definite matrix
+        cov_matrix = A @ A.T / 1000
+        print("Covariance Matrix:")
+        print(cov_matrix)
+
+
+        # parameters
+        p = nm.constraint.variable("p")
+        # variables
+        x = nm.constraint.variable("x")
+
+        # objective function
+        f = sum(cov_matrix[i, j] * x[:, i] * x[:, j] for i in range(num_vars) for j in range(num_vars))
+        obj = [f.minimize(weight=1.0, name="obj")]
+
+        # constraints
+        constrs = []
+        # constr: 100 units
+        con = 100 * (sum(x[:, i] for i in range(num_vars)) == 1)
+        con.name = "c_units"
+        constrs.append(con)
+        # constr: expected return
+        con = 100 * (sum(exp_returns[i] * x[:, i] for i in range(num_vars)) >= p[:, 0])
+        con.name = "c_return"
+        constrs.append(con)
+
+        # define neural architecture for the solution map
+        func = nm.modules.blocks.MLP(insize=1, outsize=num_vars, bias=True,
+                                linear_map=nm.slim.maps["linear"], nonlin=nn.ReLU, hsizes=[5]*4)
+        # solution map from model parameters: sol_map(p) -> x
+        sol_map = nm.system.Node(func, ["p"], ["x"], name="smap")
+        # trainable components
+        components = [sol_map]
+
+        # merit loss function
+        loss = nm.loss.PenaltyLoss(obj, constrs)
+        # problem
+        problem = nm.problem.Problem(components, loss)
+
+        # training
+        lr = 0.001  # step size for gradient descent
+
+        # set adamW as optimizer
+        optimizer = torch.optim.AdamW(problem.parameters(), lr=lr)
+
+        # Define lightning trainer. We use GPU acceleration utilizing 2 GPUS. We tell Lightning to 
+        # distribute training parallely (strategy=ddp)
+        lit_trainer = LitTrainer(epochs=10, accelerator="gpu", devices=[1,2], strategy="ddp", dev_metric='train_loss')
+
+        # Train problem to the data_setup_function
+        lit_trainer.fit(problem, data_setup_function, exp_returns=exp_returns)
+
+if __name__ == "__main__": 
+        main()
+        
\ No newline at end of file
diff --git a/examples/lightning_integration_examples/Part_4_lightning_wanb_hyperparameter_tuning.ipynb b/examples/lightning_integration_examples/Part_4_lightning_wanb_hyperparameter_tuning.ipynb
new file mode 100644
index 00000000..8f17e1f5
--- /dev/null
+++ b/examples/lightning_integration_examples/Part_4_lightning_wanb_hyperparameter_tuning.ipynb
@@ -0,0 +1,482 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lightning Integration: Hyperparameter Tuning Examples with WandB\n",
+    "\n",
+    "We demonstrate using wandb library for hyperparameter tuning. In this case we showcase how to easily tune learning rate and batch size, as well as certain layer sizes of Neuromancer MLP blocks. \n",
+    "\n",
+    "This notebook is equivalent to NSSM_building_dynamics.ipynb\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NeuroMANCER and Dependencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Install (Colab only)\n",
+    "Skip this step when running locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\"\n",
+    "!pip install lightning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Imports\n",
+    "(The user might need to install PyTorch Lightning). If so, please run \n",
+    "\n",
+    "```\n",
+    "pip install lightning\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "from neuromancer.psl import plot\n",
+    "from neuromancer import psl\n",
+    "import matplotlib.pyplot as plt\n",
+    "from torch.utils.data import DataLoader\n",
+    "\n",
+    "from neuromancer.system import Node, System\n",
+    "from neuromancer.trainer import Trainer, LitTrainer\n",
+    "from neuromancer.problem import Problem\n",
+    "from neuromancer.dataset import DictDataset\n",
+    "from neuromancer.constraint import variable\n",
+    "from neuromancer.loss import PenaltyLoss\n",
+    "from neuromancer.modules import blocks\n",
+    "\n",
+    "import lightning.pytorch as pl \n",
+    "\n",
+    "torch.manual_seed(0);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate trajectories\n",
+    "\n",
+    "In this example we don't assume any prior knowledge on the system dynamics of the swing equation. We will only have access to limited measurements of the system states $x$ of an unknown [ordinary differential equations](https://en.wikipedia.org/wiki/Ordinary_differential_equation) (ODE)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ground truth system\n",
+    "system_name = \"SimpleSingleZone\"\n",
+    "system = psl.systems[system_name]\n",
+    "modelSystem = system()\n",
+    "ts = modelSystem.ts\n",
+    "nx = modelSystem.nx\n",
+    "ny = modelSystem.ny\n",
+    "nu = modelSystem.nu\n",
+    "nd = modelSystem.nd\n",
+    "raw = modelSystem.simulate(nsim=1000)\n",
+    "plot.pltOL(Y=raw['Y'], U=raw['U'], D=raw['D'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Setup Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalize(x, mean, std):\n",
+    "    return (x - mean) / std\n",
+    "\n",
+    "def data_setup_function(sys, nsim, nsteps, ts, bs):\n",
+    "    \"\"\"\n",
+    "    :param nsteps: (int) Number of timesteps for each batch of training data\n",
+    "    :param sys: (psl.system)\n",
+    "    :param ts: (float) step size\n",
+    "    :param bs: (int) batch size\n",
+    "\n",
+    "    \"\"\"\n",
+    "    train_sim, dev_sim, test_sim = [sys.simulate(nsim=nsim, ts=ts) for i in range(3)]\n",
+    "    nx = sys.nx\n",
+    "    nu = sys.nu\n",
+    "    nd = sys.nd\n",
+    "    ny = sys.ny\n",
+    "    nbatch = nsim//nsteps\n",
+    "    length = (nsim//nsteps) * nsteps\n",
+    "\n",
+    "    mean_x = modelSystem.stats['X']['mean']\n",
+    "    std_x = modelSystem.stats['X']['std']\n",
+    "    mean_y = modelSystem.stats['Y']['mean']\n",
+    "    std_y = modelSystem.stats['Y']['std']\n",
+    "    mean_u = modelSystem.stats['U']['mean']\n",
+    "    std_u = modelSystem.stats['U']['std']\n",
+    "    mean_d = modelSystem.stats['D']['mean']\n",
+    "    std_d = modelSystem.stats['D']['std']\n",
+    "\n",
+    "    trainX = normalize(train_sim['X'][:length], mean_x, std_x)\n",
+    "    trainX = trainX.reshape(nbatch, nsteps, nx)\n",
+    "    trainX = torch.tensor(trainX, dtype=torch.float32)\n",
+    "    trainY = normalize(train_sim['Y'][:length], mean_y, std_y)\n",
+    "    trainY = trainY.reshape(nbatch, nsteps, ny)\n",
+    "    trainY = torch.tensor(trainY, dtype=torch.float32)\n",
+    "    trainU = normalize(train_sim['U'][:length], mean_u, std_u)\n",
+    "    trainU = trainU.reshape(nbatch, nsteps, nu)\n",
+    "    trainU = torch.tensor(trainU, dtype=torch.float32)\n",
+    "    trainD = normalize(train_sim['D'][:length], mean_d, std_d)\n",
+    "    trainD = trainD.reshape(nbatch, nsteps, nd)\n",
+    "    trainD = torch.tensor(trainD, dtype=torch.float32)\n",
+    "    train_data = DictDataset({'X': trainX, 'yn': trainY[:, 0:1, :],\n",
+    "                              'Y': trainY,\n",
+    "                              'U': trainU,\n",
+    "                              'D': trainD}, name='train')\n",
+    "\n",
+    "\n",
+    "    devX = normalize(dev_sim['X'][:length], mean_x, std_x)\n",
+    "    devX = devX.reshape(nbatch, nsteps, nx)\n",
+    "    devX = torch.tensor(devX, dtype=torch.float32)\n",
+    "    devY = normalize(dev_sim['Y'][:length], mean_y, std_y)\n",
+    "    devY = devY.reshape(nbatch, nsteps, ny)\n",
+    "    devY = torch.tensor(devY, dtype=torch.float32)\n",
+    "    devU = normalize(dev_sim['U'][:length], mean_u, std_u)\n",
+    "    devU = devU[:length].reshape(nbatch, nsteps, nu)\n",
+    "    devU = torch.tensor(devU, dtype=torch.float32)\n",
+    "    devD = normalize(dev_sim['D'][:length], mean_d, std_d)\n",
+    "    devD = devD[:length].reshape(nbatch, nsteps, nd)\n",
+    "    devD = torch.tensor(devD, dtype=torch.float32)\n",
+    "    dev_data = DictDataset({'X': devX, 'yn': devY[:, 0:1, :],\n",
+    "                            'Y': devY,\n",
+    "                            'U': devU,\n",
+    "                            'D': devD}, name='dev')\n",
+    "\n",
+    "\n",
+    "    testX = normalize(test_sim['X'][:length], mean_x, std_x)\n",
+    "    testX = testX.reshape(1, nbatch*nsteps, nx)\n",
+    "    testX = torch.tensor(testX, dtype=torch.float32)\n",
+    "    testY = normalize(test_sim['Y'][:length], mean_y, std_y)\n",
+    "    testY = testY.reshape(1, nbatch*nsteps, ny)\n",
+    "    testY = torch.tensor(testY, dtype=torch.float32)\n",
+    "    testU = normalize(test_sim['U'][:length], mean_u, std_u)\n",
+    "    testU = testU.reshape(1, nbatch * nsteps, nu)\n",
+    "    testU = torch.tensor(testU, dtype=torch.float32)\n",
+    "    testD = normalize(test_sim['D'][:length], mean_d, std_d)\n",
+    "    testD = testD.reshape(1, nbatch*nsteps, nd)\n",
+    "    testD = torch.tensor(testD, dtype=torch.float32)\n",
+    "    test_data = {'X': testX, 'yn': testY[:, 0:1, :],\n",
+    "                 'Y': testY, 'U': testU, 'D': testD,\n",
+    "                 'name': 'test'}\n",
+    "\n",
+    "    return train_data, dev_data, test_data, bs\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nsim = 2000   # number of simulation steps in the dataset\n",
+    "nsteps = 2   # number of prediction horizon steps in the loss function\n",
+    "bs = 100       # minibatching batch size\n",
+    "train_data, dev_data, test_data, bs = data_setup_function(modelSystem, nsim, nsteps, ts, bs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NSSM system model in Neuromancer\n",
+    "\n",
+    "Here we construct a state space model  $x_{k+1} = A_{\\theta}(x_k) + B_{\\theta}(u_k) +D_{\\theta}(d_k)$ with $A$, $B$, $D$ parametrized by neural networks with trainable parameters $\\theta$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SSM(nn.Module):\n",
+    "    \"\"\"\n",
+    "    Baseline class for (neural) state space model (SSM)\n",
+    "    Implements discrete-time dynamical system:\n",
+    "        x_k+1 = fx(x_k) + fu(u_k) + fd(d_k)\n",
+    "    with variables:\n",
+    "        x_k - states\n",
+    "        u_k - control inputs\n",
+    "    \"\"\"\n",
+    "    def __init__(self, fx, fu, fd, nx, nu, nd):\n",
+    "        super().__init__()\n",
+    "        self.fx, self.fu, self.fd = fx, fu, fd\n",
+    "        self.nx, self.nu, self.nd = nx, nu, nd\n",
+    "        self.in_features, self.out_features = nx+nu+nd, nx\n",
+    "\n",
+    "    def forward(self, x, u, d):\n",
+    "        \"\"\"\n",
+    "        :param x: (torch.Tensor, shape=[batchsize, nx])\n",
+    "        :param u: (torch.Tensor, shape=[batchsize, nu])\n",
+    "        :return: (torch.Tensor, shape=[batchsize, outsize])\n",
+    "        \"\"\"\n",
+    "        # state space model\n",
+    "        x = self.fx(x) + self.fu(u) + self.fd(d)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_hidden = 80\n",
+    "n_layers = 2\n",
+    "# instantiate neural nets\n",
+    "fx = blocks.MLP(ny, ny, bias=True,\n",
+    "                 linear_map=torch.nn.Linear,\n",
+    "                 nonlin=torch.nn.ReLU,\n",
+    "                 hsizes=n_layers*[n_hidden])\n",
+    "fu = blocks.MLP(nu, ny, bias=True,\n",
+    "                linear_map=torch.nn.Linear,\n",
+    "                nonlin=torch.nn.ReLU,\n",
+    "                hsizes=n_layers*[n_hidden])\n",
+    "fd = blocks.MLP(nd, ny, bias=True,\n",
+    "                linear_map=torch.nn.Linear,\n",
+    "                nonlin=torch.nn.ReLU,\n",
+    "                hsizes=n_layers*[n_hidden])\n",
+    "# construct NSSM model in Neuromancer\n",
+    "ssm = SSM(fx, fu, fd, ny, nu, nd)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For training we need to obtain accurate reverse-mode gradients by unrolling the operations of the NSSM and using the [backpropagation through time](https://en.wikipedia.org/wiki/Backpropagation_through_time) (BPTT) algorithm. Numner of steps (nsteps) to rollout the NSSM model can be specified in the symbolic System model in Neuromancer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# construct symbolic model\n",
+    "model = Node(ssm, ['yn', 'U', 'D'], ['yn'], name='NSSM')\n",
+    "dynamics_model = System([model], name='system')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define system identification loss function terms\n",
+    "\n",
+    "Here we define loss function terms to fit the ODE parameters from given time-series data.\n",
+    "\n",
+    "**Tracking loss:**  \n",
+    "$$\\ell_x = Q_N||x^i_k - \\hat{x}^i_k||_2^2$$ \n",
+    "**One-step loss:**  \n",
+    "$$\\ell_x = Q_1||x^i_1 - \\hat{x}^i_1||_2^2$$ "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %% Constraints + losses:\n",
+    "y = variable(\"Y\")\n",
+    "yhat = variable('yn')[:, :-1, :]\n",
+    "\n",
+    "# trajectory tracking loss\n",
+    "reference_loss = 10.*(yhat == y)^2\n",
+    "reference_loss.name = \"ref_loss\"\n",
+    "\n",
+    "# one-step tracking loss\n",
+    "onestep_loss = 1.*(yhat[:, 1, :] == y[:, 1, :])^2\n",
+    "onestep_loss.name = \"onestep_loss\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Construct System ID learning problem\n",
+    "\n",
+    "Given the training dataset $D$ we want to solve the following problem:\n",
+    " \n",
+    "$$\n",
+    "\\begin{align}\n",
+    "&\\underset{\\theta}{\\text{minimize}}     && \\sum_{i=1}^m \\Big(Q_1||x^i_1 - \\hat{x}^i_1||_2^2 + \\sum_{k=1}^{N}  Q_N||x^i_k - \\hat{x}^i_k||_2^2   \\Big) \\\\\n",
+    "&\\text{subject to}    &&  x^i_{k+1} = A_{\\theta}(x^i_k) + B_{\\theta}(u^i_k) +D_{\\theta}(d^i_k)\n",
+    "\\end{align}\n",
+    "$$  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "objectives = [reference_loss, onestep_loss]\n",
+    "constraints = []\n",
+    "\n",
+    "# create constrained optimization loss\n",
+    "loss = PenaltyLoss(objectives, constraints)\n",
+    "\n",
+    "# construct constrained optimization problem\n",
+    "problem = Problem([dynamics_model], loss)\n",
+    "\n",
+    "# plot computational graph\n",
+    "# problem.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hyperparameter Tuning \n",
+    "\n",
+    "We now will tune learning rate and batch size hyperparameters. Recall in previous tutorials we use the `LitTrainer.fit()` function to fit a problem to data_setup_function. To tune hyperparameters, we use `LitTrainer.hyperparameter_sweep()` instead. The syntax is similar: \n",
+    "\n",
+    "#### Fit\n",
+    "\n",
+    "```bash\n",
+    "lit_trainer = LitTrainer()\n",
+    "lit_trainer.fit(problem, data_setup_function, *kwargs)\n",
+    "```\n",
+    "\n",
+    "#### Tuning \n",
+    "\n",
+    "```bash\n",
+    "lit_trainer = LitTrainer()\n",
+    "lit_trainer.hyperparameter_sweep(problem, data_setup_function, sweep_config, *kwargs)\n",
+    "```\n",
+    "\n",
+    "The only difference is the addition of a sweep configuration file in the format required by wandb. An example of such a config is shown below: \n",
+    "```bash\n",
+    "sweep_config = {\n",
+    "    'method': 'random',\n",
+    "    'parameters': {\n",
+    "        'learning_rate': {\n",
+    "            'min': 0.001,\n",
+    "            'max': .007\n",
+    "        },\n",
+    "        'batch_size': {\n",
+    "            'values': [16, 64, 128]\n",
+    "        }\n",
+    "    }\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "We go over the parameters for sweep below. Again the function signature is: \n",
+    "\n",
+    "```bash\n",
+    "def hyperparameter_sweep(self, problem, data_setup_function, sweep_config, count=10, project_name='run_sweep', **kwargs):\n",
+    "```\n",
+    "* problem: Neuromancer problem \n",
+    "* data_setup_function: A data setup function \n",
+    "* sweep_config: Dictionary of sweep parameters. \n",
+    "* Count: Number of iterations to sample from the param distributions. E.g. 10 will execute 10 runs \n",
+    "* project_name: Name of the project. Generally unnecesssary\n",
+    "* **kwargs: Any keyword arguments needed for data setup function \n",
+    "\n",
+    "Using the above config, we now tune hyperparameters with count=20\n",
+    "\n",
+    "#### WandB Visualization\n",
+    "\n",
+    "Upon launching the sweep, we will see a supplied link(s) to view the sweeps and runs in stdout\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "sweep_config = {\n",
+    "    'method': 'random',\n",
+    "    'parameters': {\n",
+    "        'learning_rate': {\n",
+    "            'min': 0.001,\n",
+    "            'max': .1\n",
+    "        },\n",
+    "        'batch_size': {\n",
+    "            'values': [16, 32, 64, 128]\n",
+    "        }\n",
+    "    }\n",
+    "}\n",
+    "lit_trainer = LitTrainer(epochs=100, accelerator='cpu')\n",
+    "lit_trainer.hyperparameter_sweep(problem, data_setup_function, sweep_config, count=20, sys=modelSystem, nsim=nsim, nsteps=nsteps, ts=ts, bs=bs) \n",
+    "# note that even though we supply batch_size argument for data_setup_function, it will be overriden by wandb hyperparameter sweep values\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neuromancer3",
+   "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/lightning_integration_examples/README.md b/examples/lightning_integration_examples/README.md
new file mode 100644
index 00000000..929b7d56
--- /dev/null
+++ b/examples/lightning_integration_examples/README.md
@@ -0,0 +1,250 @@
+# Additional Instructions for Lightning Integration Features
+
+## Motivation for PyTorch Lightning 
+
+PyTorch Lightning is a framework built on top of PyTorch designed to simplify the implementation of complex models in PyTorch. It promotes cleaner, more organized code by providing a high-level interface for PyTorch that handles many training intricacies automatically. 
+
+Why might I want to use Lightning in the context of NeuroMANCER? For the user, Lightning simplifies the workflow, allowing one to focus on solving NeuroMANCER problems with their associated datasets. How does it do this? Let's take a look at the added functionality when using Lightning 
+
+## New Features
+
+* PyTorch boilerplate code is removed, thus increasing the accessibility of the NeuroMANCER library. For example, the user doesn't need to deal with details instantiating a PyTorch Dataloader() class. 
+* Increased modularity on the data portion: Lightning caters towards modularity often found in "traditional" scientific experimental design. In the Lightning workflow, the user is expected to define a "data_setup_function" that is fed into the LightningTrainer(). The user can easily swap out, or modify generation of, the datasets from run-to-run. 
+* Automatic GPU support: Lightning allows for easy migration to running training on the GPU. It even allows for multi-GPU training with a simple keyword argument change. Most of all, .to(device) calls are no longer required. 
+* Other features include hyperparameter tuning with wandb, automatic logging, easy profiling integration, and ability for the user to define own training logic without boilerplate
+
+
+
+
+## Data Setup Function
+
+The user is expected to define a *data_setup_function(kwargs)*. This function takes in arbitrary number of keyword arguments (such as number of simualations, batch_size, or other parameters defining the parametric space to sample from) and return for entities: 
+1. Train DictDataset -- used for model training. 
+2. Dev DictDataset -- used for validation and model checkpointing 
+3. Test DictDataset -- currently unsupported
+4. Batch Size 
+
+The signature of this function will look like: 
+
+```
+def data_setup_function(*kwargs): 
+    # insert data generation code here
+```
+
+## Problem Formulation
+
+For almost all use cases, there is no change in how NeuroMANCER problems are defined when using Lightning. There may be a minor change in how hardcoded tensors need to be handled in the event the user wants to utilize GPU training. For more information on this please refer to the example found in the notebook "Part_2_lightning_advanced_and_gpu_tutorial.ipynb". 
+
+## Training 
+
+The main change when using Lightning is how Problem training gets done. Currently there is a Neuromancer Trainer() classes that handles training a Problem on various dataloaders. As an alternative training mechanism, we introduce a Lightning Trainer (LitTrainer) class which is designed to simplify the training process for the user as well as include a couple more key features. 
+
+The user will invoke simply invoke:
+
+```
+lit_trainer = LitTrainer(*kwargs)
+lit_trainer.fit(problem, data_setup_function, *kwargs)
+```
+
+Which will then train that particular Problem to data governed by the data_setup_function. 
+
+
+## Tutorials 
+
+The user will find several Lightning-Neuromancer tutorials in this folder. There are two main tutorials
+* Part 1: Goes over how basics on how a Neuromancer set-up can be converted into the Lightning version
+* Part 2: Goes over more advanced / nuanced cases when migrating towards Lightning. Also showcases automated GPU support, loading/saving weights
+* Part 3: Is a Python script that demonstrates solving a computationally expensive problem with automated multi-GPU distributed training
+* Part 4: Demonstrates how to do hyperparameter tuning with wandb library
+
+Other domain-specific examples can be found in the "other_examples" folder: 
+
+* Part 3: Goes over solving a PINN with the Lightning workflow 
+* lightning_nonauto_DeepKoopman: Goes over using Koopman Operators with the Lightning workflow. Also showcases how to easily visualize training progress with Tensorboard
+* lightning_cvxpy_layers: Goes over using cvxpy with Lightning workflow. 
+* lighting_custom_training_example: Demonstrates how the user can define their own training logic to replace default training logic, if desired
+
+
+
+
+## LitTrainer Parameters
+* epochs: Number of training epochs 
+* eval_metric: This metric will be monitored every epoch during training and will save the best weights accordingly. Default 'dev_loss'
+* train_metric: Metric for training. Defaults to 'train_loss'.
+* dev_metric: Metric for development/validation. Defaults to 'dev_loss'.
+* test_metric: Metric for testing. Defaults to 'test_loss'. Currently unused
+* patience: Number of epochs to wait for improvement before early stopping. Defaults to None (no patience)
+* warmup: Number of warmup epochs. Defaults to 0.
+* clip: Gradient clipping value, by norm. Defaults to 100.0
+* custom_optimizer: 
+    * If the user wants to pass in their own optimizer. For example: 
+    ```
+    optimizer = torch.optim.AdamW(policy.parameters(), lr=0.001)
+    lit_trainer = LitTrainer(custom_optimizer=optimizer)
+    ```
+    By default the optimizer used is: `torch.optim.Adam(self.problem.parameters(), 0.001, betas=(0.0, 0.9))`
+
+* save_weights: Set to True if best Problem weights should be saved to disk. Default true 
+* weight_path: Folder to save weights, defaults to ./
+* weight_name: Name of the weight file. By default, filename is None and will be set to '{epoch}-{step}', where “epoch” and “step” match the number of finished epoch and optimizer steps respectively.
+* devices: Please refer to "Device Management" below
+* strategy: Please refer to "Device Management" below 
+* accelerator: Please refer to "Device Management" below. 
+* profiler: Lightning integrates easily with PyTorch profilers such as "simple" or "pytorch"
+* custom_training_step: Custom training step function, if desired. Defaults to None, in which case the standard training step procedure is executed. See Custom Training Logic section below
+
+## Example
+The following is basic pseudocode that outlines the steps required to utilize this Lightning integration. In this example, we define a data_setup_function (DSF) that takes in "nsim" as an argument and returns the Neuromancer DictDatasets (which take in nsim as a parameter) and a batch_size. We then instantiate a LitTrainer to run for 10 epochs on the GPU with device=1 (akin to cuda:1) and train it on a problem and DSF
+```
+def data_setup_function(nsim=5000): 
+    train_data = DictDataset(nsim, ... , name='train')
+    dev_data = DictDataset(nsim, ... , name='dev)
+    batch_size = 32
+    return train_data, dev_data, None, batch_size 
+
+problem = Problem(...)
+lit_trainer = LitTrainer(epochs=10, accelerator='gpu', devices=[1])
+lit_trainer.fit(problem, data_setup_function, nsim=100)
+```
+
+## Device Management
+#### To Run on CPU: 
+* accelerator = "cpu" is all that is necessary
+
+#### To Run on GPU: 
+* accelerator = "gpu" is required 
+* devices can be a list, integer, or "auto"
+    * ex) [1,2,3] will distribute training over cuda:1, cuda:2, and cuda:3
+    * ex) 7 will distribute training over the 7 GPUs automatically selected 
+    * ex) "Auto" for automatic selection based on the chosen accelerator. We do not recommend this. 
+* strategy is either "auto", "ddp" or "ddp_notebook"
+    * "auto" will utilize whatever hardward is "best" available  
+    * "ddp" will run distributed training across devices desginated under "devices" assuming len(devices) > 1. This keyword should *NOT* be used in notebooks, only scripts 
+    * "ddp_notebook" is akin to "ddp" and should only be used in notebook environments
+    
+    
+For more information please see: https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api
+
+
+## Saving and Loading Problem Weights
+By default, Problems() passed into LitTrainer (as well as base Neuromancer trainer) will automatically have the best weights at end of training, so there should be no need to manually load best_weights at the end of training.
+
+That said, we can save and load weights as follows: 
+* Set save_weights argument to True (this is default)
+* Specify directory where to save weights (optional, by default is the current working directory)
+* Use *load_state_dict_lightning()* function to properly ingest weights into Problem
+
+By default, weights will be saved with the following convention: '{epoch}-{step}.ckpt', where “epoch” and “step” match the number of finished epoch and optimizer steps respectively. The weights file can be given a custom name by changing the "weight_name" argument to LiTrainer. E.g. "test_weights" will save to "test_weights.ckpt"
+
+For example, the following code would save the weights to ./test_weights.ckpt. It then loads the weights into a desired Problem using load_state_dict_lightning()
+```
+lit_trainer = LitTrainer(epochs=200, accelerator='cpu', custom_optimizer=optimizer, monitor_metric='dev_loss', weight_name='test_weights')
+lit_trainer.fit(problem, data_setup_function)
+```
+
+Load weights. Note that unless the problem specified here is untrained, this step is redundant
+```
+load_state_dict_lightning(problem, 'test_weights.ckpt')
+```
+
+# Other Features 
+
+## Wandb Hyperparameter Tuning 
+
+
+We now will tune learning rate and batch size hyperparameters. Recall in previous tutorials we use the `LitTrainer.fit()` function to fit a problem to data_setup_function. To tune hyperparameters, we use `LitTrainer.hyperparameter_sweep()` instead. The syntax is similar: 
+
+### Fit
+
+```bash
+lit_trainer = LitTrainer()
+lit_trainer.fit(problem, data_setup_function, *kwargs)
+```
+
+### Tuning 
+
+```bash
+lit_trainer = LitTrainer()
+lit_trainer.hyperparameter_sweep(problem, data_setup_function, sweep_config, *kwargs)
+```
+
+The only difference is the addition of a sweep configuration file in the format required by wandb. An example of such a config is shown below: 
+```bash
+sweep_config = {
+    'method': 'random',
+    'parameters': {
+        'learning_rate': {
+            'min': 0.001,
+            'max': .007
+        },
+        'batch_size': {
+            'values': [16, 64, 128]
+        }
+    }
+}
+```
+
+We go over the parameters for sweep below. Again the function signature is: 
+
+```bash
+def hyperparameter_sweep(self, problem, data_setup_function, sweep_config, count=10, project_name='run_sweep', **kwargs):
+```
+* problem: Neuromancer problem 
+* data_setup_function: A data setup function 
+* sweep_config: Dictionary of sweep parameters. 
+* Count: Number of iterations to sample from the param distributions. E.g. 10 will execute 10 runs 
+* project_name: Name of the project. Generally unnecesssary
+* **kwargs: Any keyword arguments needed for data setup function 
+
+### WandB Set-Up
+
+Please ensure you have a wandb account setup and provided API key. When running on VS Code, one will need to provide said API key upon launching hyperparameter_sweep()
+
+### WandB Visualization
+
+Upon launching the sweep, we will see a supplied link(s) to view the sweeps and runs in stdout
+
+For more information please refer to: https://docs.wandb.ai/guides/sweeps/define-sweep-configuration
+
+
+
+## Tensorboard
+Lightning automatically will log training history to a *lightning_logs* found in the current working direcotyr. The latest "version" should correspond to the most current training run. As a result it is easy to view training progress, etc. with Tensorboard. For example, assuming one is in VS Code environment with Tensorboard plug-in installed, one can launch a Tensorboard session to view latest training progress with: 
+```
+%reload_ext tensorboard
+%tensorboard --logdir=lightning_logs/
+``````
+## Profiling 
+One can profile training run easily by passing in the "profiler" keyword argument to LitTrainer, for example: 
+
+```
+lit_trainer = LitTrainer(profiler='simple)
+```
+
+Will output profiling report at end of training. 
+Profiling options include "simple", "pytorch" and "advanced". For more information please see https://pytorch-lightning.readthedocs.io/en/1.5.10/advanced/profiler.html#pytorch-profiling
+
+## Custom Training Logic
+Training within PyTorch Lightning framework is defined by a `training_step` function, which defines the logic going from a data batch to loss. For example, the default training_step used is shown below (other extraneous details removed for simplicity). Here, we get the problem output for the given batch and return the loss associated with that output.
+
+```
+def training_step(self, batch):
+    output = self.problem(batch)
+    loss = output[self.train_metric]
+    return loss
+```
+While rare, there may be instances where the user might want to define their own training logic. Potential cases include test-time data augmentation (e.g. operations on/w.r.t the data rollout), other domain augmentations, or modifications to how the output and/or loss is handled. 
+
+The user can pass in their own "training_step" by supplying an equivalent function handler to the "custom_training_step" keyword of LitTrainer, for example: 
+
+```
+def custom_training_step(model, batch): 
+    output = model.problem(batch)
+    Q_con = 1
+    if model.current_epoch > 1: 
+        Q_con = 1 
+    loss = Q_con*(output[model.train_metric])
+    return loss
+```
+
+The signature of this function should be `custom_training_step(model, batch)` where model is a Neuromancer Problem
\ No newline at end of file
diff --git a/examples/lightning_integration_examples/other_examples/lightning_custom_training_example.ipynb b/examples/lightning_integration_examples/other_examples/lightning_custom_training_example.ipynb
new file mode 100755
index 00000000..818702b1
--- /dev/null
+++ b/examples/lightning_integration_examples/other_examples/lightning_custom_training_example.ipynb
@@ -0,0 +1,607 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "34qVD_ntSKLF"
+   },
+   "source": [
+    "# Custom Training Logic with Lightning Integration\n",
+    "\n",
+    "In this example, we showcase the ability for the user to define own training logic and easily integrate into Lightning workflow\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OCn3zpaIqgMc"
+   },
+   "source": [
+    "## NeuroMANCER and Dependencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Qzy5Wot5k2Gf"
+   },
+   "source": [
+    "### Install (Colab only)\n",
+    "Skip this step when running locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "X_3EvkSz0Fnz",
+    "outputId": "23c06f6b-ab48-4763-c43c-40a325cacf87"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\"\n",
+    "!pip install lightning \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LWyvndXlz0Fv"
+   },
+   "source": [
+    "### Import"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(The user might need to install PyTorch Lightning). If so, please run \n",
+    "\n",
+    "```\n",
+    "pip install lightning\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "KbP0n-4evRqt"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import numpy as np\n",
+    "import neuromancer.slim as slim\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patheffects as patheffects\n",
+    "import casadi\n",
+    "import time\n",
+    "import lightning.pytorch as pl \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "POL27EJZxJmI"
+   },
+   "outputs": [],
+   "source": [
+    "from neuromancer.trainer import Trainer, LitTrainer\n",
+    "from neuromancer.problem import Problem\n",
+    "from neuromancer.constraint import variable\n",
+    "from neuromancer.dataset import DictDataset\n",
+    "from neuromancer.loss import PenaltyLoss\n",
+    "from neuromancer.modules import blocks\n",
+    "from neuromancer.system import Node\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Problem formulation\n",
+    "\n",
+    "In this example we will solve parametric constrained [Rosenbrock problem](https://en.wikipedia.org/wiki/Rosenbrock_function):\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "&\\text{minimize } &&  (1-x)^2 + a(y-x^2)^2\\\\\n",
+    "&\\text{subject to} && \\left(\\frac{p}{2}\\right)^2 \\le x^2 + y^2 \\le p^2\\\\\n",
+    "& && x \\ge y\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "with parameters $p, a$ and decision variables $x, y$.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lightning Workflow\n",
+    "\n",
+    "The workflow when using Lightning consists of three parts: \n",
+    "\n",
+    "1. Defining a \"data_setup_function() -- this function should return 4 values (train, dev, test datasets, and batch size). The datasets should be named Neuromancer DictDatasets. \n",
+    "2. Defining the Problem -- consisting of Nodes, System, Loss. \n",
+    "3. Instantiating the PyTorch-Lightning -based Trainer (LitTrainer class)\n",
+    "\n",
+    "For this notebook, we assume all operations are done on the CPU. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_WH7o7Wu1epw"
+   },
+   "source": [
+    "### Lightning Dataset\n",
+    "\n",
+    "We constructy the dataset by sampling the parametric space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "_r6p2p6myHAh"
+   },
+   "outputs": [],
+   "source": [
+    "data_seed = 408  # random seed used for simulated data\n",
+    "np.random.seed(data_seed)\n",
+    "torch.manual_seed(data_seed)\n",
+    "nsim = 5000  # number of datapoints: increase sample density for more robust results\n",
+    "\n",
+    "# create dictionaries with sampled datapoints with uniform distribution\n",
+    "a_low, a_high, p_low, p_high = 0.2, 1.2, 0.5, 2.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JZ9qrw0tlJhs"
+   },
+   "source": [
+    "We define the **data_setup_function()** below. It randomly sample parameters from a uniform distribution: $0.5\\le p\\le2.0$;  $0.2\\le a\\le1.2$. It takes these parameters as inputs and outputs Neuromancer DictDatasets() for train, dev, and test data (or None type otherwise), as well as batch size. We have hardcoded batch size to be 64 in this case. \n",
+    "\n",
+    "It is important to define both training and dev/validation datasets. Training datasets will be used for the training step; dev datasets will be used for model checkpointing (if desired)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "Nu58M-8JyHy6"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def data_setup_function(nsim, a_low, a_high, p_low, p_high): \n",
+    "\n",
+    "    \n",
+    "    samples_train = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
+    "                    \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
+    "    samples_dev = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
+    "                \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
+    "    samples_test = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
+    "                \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
+    "    # create named dictionary datasets\n",
+    "    train_data = DictDataset(samples_train, name='train')\n",
+    "    dev_data = DictDataset(samples_dev, name='dev')\n",
+    "    test_data = DictDataset(samples_test, name='test')\n",
+    "\n",
+    "    batch_size = 64\n",
+    "\n",
+    "    # Return the dict datasets in train, dev, test order, followed by batch_size \n",
+    "    return train_data, dev_data, test_data, batch_size \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now define the **Problem()**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Y2htUaWMDjsk"
+   },
+   "source": [
+    "## Primal Solution Map Architecture\n",
+    "\n",
+    "A neural network mapping problem parameters onto primal decision variables:  \n",
+    "$$x = \\pi(\\theta)$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "Ta_I_pjyyLzf"
+   },
+   "outputs": [],
+   "source": [
+    "# define neural architecture for the trainable solution map\n",
+    "func = blocks.MLP(insize=2, outsize=2,\n",
+    "                bias=True,\n",
+    "                linear_map=slim.maps['linear'],\n",
+    "                nonlin=nn.ReLU,\n",
+    "                hsizes=[80] * 4)\n",
+    "# wrap neural net into symbolic representation of the solution map via the Node class: sol_map(xi) -> x\n",
+    "sol_map = Node(func, ['a', 'p'], ['x'], name='map')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Lxj77EFj7EO-"
+   },
+   "source": [
+    "## Objective and Constraints in NeuroMANCER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "bcoVjphjyPp9"
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "variable is a basic symbolic abstraction in Neuromancer\n",
+    "   x = variable(\"variable_name\")                      (instantiates new variable)  \n",
+    "variable construction supports:\n",
+    "   algebraic expressions:     x**2 + x**3 + 5     (instantiates new variable)  \n",
+    "   slicing:                   x[:, i]             (instantiates new variable)  \n",
+    "   pytorch callables:         torch.sin(x)        (instantiates new variable)  \n",
+    "   constraints definition:    x <= 1.0            (instantiates Constraint object) \n",
+    "   objective definition:      x.minimize()        (instantiates Objective object) \n",
+    "to visualize computational graph of the variable use x.show() method          \n",
+    "\"\"\"\n",
+    "\n",
+    "# define decision variables\n",
+    "x1 = variable(\"x\")[:, [0]]\n",
+    "x2 = variable(\"x\")[:, [1]]\n",
+    "# problem parameters sampled in the dataset\n",
+    "p = variable('p')\n",
+    "a = variable('a')\n",
+    "\n",
+    "# objective function\n",
+    "f = (1-x1)**2 + a*(x2-x1**2)**2\n",
+    "obj = f.minimize(weight=1.0, name='obj')\n",
+    "\n",
+    "# constraints\n",
+    "Q_con = 100.  # constraint penalty weights\n",
+    "con_1 = Q_con*(x1 >= x2)\n",
+    "con_2 = Q_con*((p/2)**2 <= x1**2+x2**2)\n",
+    "con_3 = Q_con*(x1**2+x2**2 <= p**2)\n",
+    "con_1.name = 'c1'\n",
+    "con_2.name = 'c2'\n",
+    "con_3.name = 'c3'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 496
+    },
+    "id": "n7VPa9Wc8JRB",
+    "outputId": "0da17c45-6370-4f46-f626-bd5686b94bfc"
+   },
+   "outputs": [],
+   "source": [
+    "# constrained optimization problem construction\n",
+    "objectives = [obj]\n",
+    "constraints = [con_1, con_2, con_3]\n",
+    "components = [sol_map]\n",
+    "\n",
+    "# create penalty method loss function\n",
+    "loss = PenaltyLoss(objectives, constraints)\n",
+    "# construct constrained optimization problem\n",
+    "problem = Problem(components, loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Custom Training Logic\n",
+    "Training within PyTorch Lightning framework is defined by a `training_step` function, which defines the logic going from a data batch to loss. For example, the default training_step used is shown below (other extraneous details removed for simplicity). Here, we get the problem output for the given batch and return the loss associated with that output.\n",
+    "\n",
+    "```\n",
+    "def training_step(self, batch):\n",
+    "    output = self.problem(batch)\n",
+    "    loss = output[self.train_metric]\n",
+    "    return loss\n",
+    "```\n",
+    "While rare, there may be instances where the user might want to define their own training logic. Potential cases include test-time data augmentation (e.g. operations on/w.r.t the data rollout), other domain augmentations, or modifications to how the output and/or loss is handled. \n",
+    "\n",
+    "The user can pass in their own \"training_step\" by supplying an equivalent function handler to the \"custom_training_step\" keyword of LitTrainer, for example: \n",
+    "\n",
+    "```\n",
+    "def custom_training_step(model, batch): \n",
+    "    output = model.problem(batch)\n",
+    "    Q_con = 1\n",
+    "    if model.current_epoch > 1: \n",
+    "        Q_con = 1/10000\n",
+    "    loss = Q_con*(output[model.train_metric])\n",
+    "    return loss\n",
+    "```\n",
+    "\n",
+    "The signature of this function should be `custom_training_step(model, batch)` where model is a Neuromancer Problem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (cuda), used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Missing logger folder: /home/birm560/neuromancer/examples/lightning_integration_examples/other_examples/lightning_logs\n",
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:639: Checkpoint directory ./ exists and is not empty.\n",
+      "\n",
+      "  | Name    | Type    | Params\n",
+      "------------------------------------\n",
+      "0 | problem | Problem | 19.8 K\n",
+      "------------------------------------\n",
+      "19.8 K    Trainable params\n",
+      "0         Non-trainable params\n",
+      "19.8 K    Total params\n",
+      "0.079     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                                           "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=159` in the `DataLoader` to improve performance.\n",
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/utilities/data.py:77: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=159` in the `DataLoader` to improve performance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|██████████| 79/79 [00:00<00:00, 121.77it/s, v_num=0, train_loss_step=0.903]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/utilities/data.py:77: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 8. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|██████████| 79/79 [00:01<00:00, 73.36it/s, v_num=0, train_loss_step=0.903, dev_loss=0.815, train_loss_epoch=5.690]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, global step 79: 'dev_loss' reached 0.81470 (best 0.81470), saving model to './epoch=0-step=79.ckpt' as top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1:  51%|█████     | 40/79 [00:00<00:00, 121.57it/s, v_num=0, train_loss_step=0.714, dev_loss=0.815, train_loss_epoch=5.690]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py:54: Detected KeyboardInterrupt, attempting graceful shutdown...\n"
+     ]
+    }
+   ],
+   "source": [
+    "def custom_training_step(model, batch): \n",
+    "    output = model.problem(batch)\n",
+    "    Q_con = 1\n",
+    "    if model.current_epoch > 1: \n",
+    "        Q_con = 1/10000    \n",
+    "    loss = Q_con*(output[model.train_metric])\n",
+    "    return loss\n",
+    "\n",
+    "lit_trainer = LitTrainer(epochs=100, accelerator='cpu', patience=3, custom_training_step=custom_training_step)\n",
+    "lit_trainer.fit(problem=problem, data_setup_function=data_setup_function, nsim=nsim,a_low=0.2, a_high=1.2, p_low=0.5, p_high=2.0)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below is another example of a dummy custom_training_step. Here we want to add the loss of the previous batch and accumulate into the \"current\" loss. (Again this is a dummy example and not necessarily propel ML techniques). Any sort of variables, such as \"past_loss\" can be defined by setting them as attributes of \"model\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (cuda), used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/trainer/setup.py:187: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n",
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:639: Checkpoint directory ./ exists and is not empty.\n",
+      "\n",
+      "  | Name    | Type    | Params\n",
+      "------------------------------------\n",
+      "0 | problem | Problem | 19.8 K\n",
+      "------------------------------------\n",
+      "19.8 K    Trainable params\n",
+      "0         Non-trainable params\n",
+      "19.8 K    Total params\n",
+      "0.079     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                                            "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=159` in the `DataLoader` to improve performance.\n",
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=159` in the `DataLoader` to improve performance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|██████████| 79/79 [00:01<00:00, 75.73it/s, v_num=3, train_loss_step=0.0857, dev_loss=0.0834, train_loss_epoch=0.137]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, global step 79: 'dev_loss' reached 0.08340 (best 0.08340), saving model to './epoch=0-step=79-v3.ckpt' as top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: 100%|██████████| 79/79 [00:01<00:00, 74.35it/s, v_num=3, train_loss_step=0.213, dev_loss=0.0887, train_loss_epoch=0.282] "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1, global step 158: 'dev_loss' was not in top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: 100%|██████████| 79/79 [00:01<00:00, 75.86it/s, v_num=3, train_loss_step=0.309, dev_loss=0.138, train_loss_epoch=0.284]  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2, global step 237: 'dev_loss' was not in top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3: 100%|██████████| 79/79 [00:01<00:00, 75.04it/s, v_num=3, train_loss_step=0.206, dev_loss=0.0908, train_loss_epoch=0.243]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3, global step 316: 'dev_loss' was not in top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3: 100%|██████████| 79/79 [00:01<00:00, 73.78it/s, v_num=3, train_loss_step=0.206, dev_loss=0.0908, train_loss_epoch=0.243]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def custom_training_step(model, batch): \n",
+    "    with torch.no_grad(): \n",
+    "        if model.current_epoch == 0: \n",
+    "            model.past_loss = 0\n",
+    "    \n",
+    "    output = model.problem(batch)\n",
+    "    loss = (output[model.train_metric]) + 0.5*model.past_loss\n",
+    "    model.past_loss = loss.item()\n",
+    "    return loss\n",
+    "\n",
+    "lit_trainer = LitTrainer(epochs=100, accelerator='cpu', patience=3, custom_training_step=custom_training_step)\n",
+    "lit_trainer.fit(problem=problem, data_setup_function=data_setup_function, nsim=nsim,a_low=0.2, a_high=1.2, p_low=0.5, p_high=2.0)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "neuromancer",
+   "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/examples/lightning_integration_examples/other_examples/lightning_cvxpy_layers.ipynb b/examples/lightning_integration_examples/other_examples/lightning_cvxpy_layers.ipynb
new file mode 100644
index 00000000..27ca801b
--- /dev/null
+++ b/examples/lightning_integration_examples/other_examples/lightning_cvxpy_layers.ipynb
@@ -0,0 +1,945 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "34qVD_ntSKLF"
+   },
+   "source": [
+    "# Cvxpylayers + Neuromancer + Lightning\n",
+    "\n",
+    "This notebook is equivalent to parametric_programing/Part_5_cvxpy_layers.ipynb. In this version we showcase the use of **PyTorch-Lightning** to simplify the user workflow. \n",
+    "\n",
+    "This example demonstrates integration of Cvxpylayers into Neuromancer.\n",
+    "We will demonstrate this capability on learning to optimize\n",
+    "for [parametric nonlinear programming problem (pNLP)](https://en.wikipedia.org/wiki/Parametric_programming) defined as:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "&\\text{minimize } &&  f(x, \\theta) \\\\\n",
+    "&\\text{subject to} && g(x, \\theta) \\le 0\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "with  parameters $\\theta$ and decision variables $x$.\n",
+    "\n",
+    "\n",
+    "### Cvxpy Layes References\n",
+    "[1] [Agrawal, A. and Amos, B. and Barratt, S. and Boyd, S. and Diamond, S. and Kolter, Z., Differentiable Convex Optimization Layers, NeurIPS 2019](https://arxiv.org/abs/1910.12430)  \n",
+    "[2] https://github.com/cvxgrp/cvxpylayers/tree/master  \n",
+    "[3] https://locuslab.github.io/2019-10-28-cvxpylayers/  \n",
+    "\n",
+    "### Learning to Optimize References\n",
+    "[1] [F. Fioretto, et al., Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods, 2019](https://arxiv.org/abs/1909.10461)  \n",
+    "[2] [S. Gould, et al., Deep Declarative Networks: A New Hope, 2020](https://arxiv.org/abs/1909.04866)  \n",
+    "[3] [P. Donti, et al., DC3: A learning method for optimization with hard constraints, 2021](https://arxiv.org/abs/2104.12225)  \n",
+    "[4] [J. Kotary, et al., End-to-End Constrained Optimization Learning: A Survey, 2021](https://arxiv.org/abs/2103.16378)  \n",
+    "[5] [M. Li, et al., Learning to Solve Optimization Problems with Hard Linear Constraints, 2022](https://arxiv.org/abs/2208.10611)  \n",
+    "[6] [R. Sambharya, et al., End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization, 2022](https://arxiv.org/abs/2212.08260)  \n",
+    "[7] [Parametric programming in Neuromancer](https://github.com/pnnl/neuromancer/tree/master/examples/parametric_programming)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OCn3zpaIqgMc"
+   },
+   "source": [
+    "## NeuroMANCER and Dependencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Qzy5Wot5k2Gf"
+   },
+   "source": [
+    "### Install (Colab only)\n",
+    "Skip this step when running locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "X_3EvkSz0Fnz",
+    "outputId": "23c06f6b-ab48-4763-c43c-40a325cacf87"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master\n",
+      "  Cloning https://github.com/pnnl/neuromancer.git (to revision master) to /tmp/pip-install-53vktbvg/neuromancer_65ef9f1060f0401cb2ec971b8342f147\n",
+      "  Running command git clone --filter=blob:none --quiet https://github.com/pnnl/neuromancer.git /tmp/pip-install-53vktbvg/neuromancer_65ef9f1060f0401cb2ec971b8342f147\n",
+      "  Resolved https://github.com/pnnl/neuromancer.git to commit e0f02e592ce32584a03fba52d422aad4bbb4276d\n",
+      "  Installing build dependencies ... \u001b[?25ldone\n",
+      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
+      "\u001b[?25h  Installing backend dependencies ... \u001b[?25ldone\n",
+      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
+      "\u001b[?25hRequirement already satisfied: dill in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.3.7)\n",
+      "Requirement already satisfied: graphviz in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.20.1)\n",
+      "Requirement already satisfied: matplotlib in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.8.2)\n",
+      "Requirement already satisfied: mlflow==2.5.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.5.0)\n",
+      "Requirement already satisfied: networkx==3.0.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.0)\n",
+      "Requirement already satisfied: numpy<1.24.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.23.5)\n",
+      "Requirement already satisfied: pandas in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.1.3)\n",
+      "Requirement already satisfied: plum-dispatch==1.7.3 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.7.3)\n",
+      "Requirement already satisfied: scikit-learn in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.3.2)\n",
+      "Requirement already satisfied: scipy in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.11.4)\n",
+      "Requirement already satisfied: six in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.16.0)\n",
+      "Requirement already satisfied: pydot==1.4.2 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.4.2)\n",
+      "Requirement already satisfied: pyts in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.13.0)\n",
+      "Requirement already satisfied: torch in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.0.0)\n",
+      "Requirement already satisfied: torchdiffeq in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.2.3)\n",
+      "Requirement already satisfied: toml in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.10.2)\n",
+      "Requirement already satisfied: casadi in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.6.4)\n",
+      "Requirement already satisfied: cvxpy in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.4.1)\n",
+      "Requirement already satisfied: imageio in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.33.0)\n",
+      "Requirement already satisfied: cvxpylayers in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.1.6)\n",
+      "Requirement already satisfied: click<9,>=7.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (8.1.7)\n",
+      "Requirement already satisfied: cloudpickle<3 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.2.1)\n",
+      "Requirement already satisfied: databricks-cli<1,>=0.8.7 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.18.0)\n",
+      "Requirement already satisfied: entrypoints<1 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.4)\n",
+      "Requirement already satisfied: gitpython<4,>=2.1.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.1.40)\n",
+      "Requirement already satisfied: pyyaml<7,>=5.1 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (6.0.1)\n",
+      "Requirement already satisfied: protobuf<5,>=3.12.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (4.23.4)\n",
+      "Requirement already satisfied: pytz<2024 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2023.3.post1)\n",
+      "Requirement already satisfied: requests<3,>=2.17.3 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.31.0)\n",
+      "Requirement already satisfied: packaging<24 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (23.2)\n",
+      "Requirement already satisfied: importlib-metadata!=4.7.0,<7,>=3.7.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (6.11.0)\n",
+      "Requirement already satisfied: sqlparse<1,>=0.4.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.4.4)\n",
+      "Requirement already satisfied: alembic!=1.10.0,<2 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.13.0)\n",
+      "Requirement already satisfied: docker<7,>=4.0.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (6.1.3)\n",
+      "Requirement already satisfied: Flask<3 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.3.3)\n",
+      "Requirement already satisfied: querystring-parser<2 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.2.4)\n",
+      "Requirement already satisfied: sqlalchemy<3,>=1.4.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.0.23)\n",
+      "Requirement already satisfied: pyarrow<13,>=4.0.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (12.0.1)\n",
+      "Requirement already satisfied: markdown<4,>=3.3 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.5.1)\n",
+      "Requirement already satisfied: gunicorn<21 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (20.1.0)\n",
+      "Requirement already satisfied: Jinja2<4,>=2.11 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.1.2)\n",
+      "Requirement already satisfied: pyparsing>=2.1.4 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from pydot==1.4.2->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.1.1)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from matplotlib->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.2.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from matplotlib->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from matplotlib->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (4.46.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from matplotlib->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.4.5)\n",
+      "Requirement already satisfied: pillow>=8 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from matplotlib->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (10.1.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from matplotlib->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.8.2)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from pandas->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2023.3)\n",
+      "Requirement already satisfied: joblib>=1.1.1 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from scikit-learn->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.3.2)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from scikit-learn->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.2.0)\n",
+      "Requirement already satisfied: osqp>=0.6.2 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from cvxpy->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.6.3)\n",
+      "Requirement already satisfied: ecos>=2 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from cvxpy->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.0.12)\n",
+      "Requirement already satisfied: clarabel>=0.5.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from cvxpy->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.6.0)\n",
+      "Requirement already satisfied: scs>=3.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from cvxpy->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.2.4.post1)\n",
+      "Requirement already satisfied: pybind11 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from cvxpy->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.11.1)\n",
+      "Requirement already satisfied: diffcp>=1.0.13 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from cvxpylayers->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.0.23)\n",
+      "Requirement already satisfied: numba>=0.55.2 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from pyts->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.58.1)\n",
+      "Requirement already satisfied: filelock in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.13.1)\n",
+      "Requirement already satisfied: typing-extensions in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (4.8.0)\n",
+      "Requirement already satisfied: sympy in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.12)\n",
+      "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (11.7.99)\n",
+      "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (11.7.99)\n",
+      "Requirement already satisfied: nvidia-cuda-cupti-cu11==11.7.101 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (11.7.101)\n",
+      "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (8.5.0.96)\n",
+      "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (11.10.3.66)\n",
+      "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (10.9.0.58)\n",
+      "Requirement already satisfied: nvidia-curand-cu11==10.2.10.91 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (10.2.10.91)\n",
+      "Requirement already satisfied: nvidia-cusolver-cu11==11.4.0.1 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (11.4.0.1)\n",
+      "Requirement already satisfied: nvidia-cusparse-cu11==11.7.4.91 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (11.7.4.91)\n",
+      "Requirement already satisfied: nvidia-nccl-cu11==2.14.3 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.14.3)\n",
+      "Requirement already satisfied: nvidia-nvtx-cu11==11.7.91 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (11.7.91)\n",
+      "Requirement already satisfied: triton==2.0.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.0.0)\n",
+      "Requirement already satisfied: setuptools in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (68.2.2)\n",
+      "Requirement already satisfied: wheel in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.42.0)\n",
+      "Requirement already satisfied: cmake in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from triton==2.0.0->torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.27.9)\n",
+      "Requirement already satisfied: lit in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from triton==2.0.0->torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (17.0.6)\n",
+      "Requirement already satisfied: Mako in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from alembic!=1.10.0,<2->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.3.0)\n",
+      "Requirement already satisfied: pyjwt>=1.7.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from databricks-cli<1,>=0.8.7->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.8.0)\n",
+      "Requirement already satisfied: oauthlib>=3.1.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from databricks-cli<1,>=0.8.7->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.2.2)\n",
+      "Requirement already satisfied: tabulate>=0.7.7 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from databricks-cli<1,>=0.8.7->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.9.0)\n",
+      "Requirement already satisfied: urllib3<3,>=1.26.7 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from databricks-cli<1,>=0.8.7->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.1.0)\n",
+      "Requirement already satisfied: websocket-client>=0.32.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from docker<7,>=4.0.0->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.7.0)\n",
+      "Requirement already satisfied: Werkzeug>=2.3.7 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from Flask<3->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.0.1)\n",
+      "Requirement already satisfied: itsdangerous>=2.1.2 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from Flask<3->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.1.2)\n",
+      "Requirement already satisfied: blinker>=1.6.2 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from Flask<3->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.7.0)\n",
+      "Requirement already satisfied: gitdb<5,>=4.0.1 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from gitpython<4,>=2.1.0->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (4.0.11)\n",
+      "Requirement already satisfied: zipp>=0.5 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from importlib-metadata!=4.7.0,<7,>=3.7.0->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.17.0)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from Jinja2<4,>=2.11->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2.1.3)\n",
+      "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from numba>=0.55.2->pyts->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.41.1)\n",
+      "Requirement already satisfied: qdldl in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from osqp>=0.6.2->cvxpy->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (0.1.7.post0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from requests<3,>=2.17.3->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from requests<3,>=2.17.3->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.6)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from requests<3,>=2.17.3->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (2023.11.17)\n",
+      "Requirement already satisfied: greenlet!=0.4.17 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from sqlalchemy<3,>=1.4.0->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (3.0.1)\n",
+      "Requirement already satisfied: mpmath>=0.19 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from sympy->torch->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (1.3.0)\n",
+      "Requirement already satisfied: smmap<6,>=3.0.1 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from gitdb<5,>=4.0.1->gitpython<4,>=2.1.0->mlflow==2.5.0->neuromancer[examples]@ git+https://github.com/pnnl/neuromancer.git@master) (5.0.1)\n",
+      "Building wheels for collected packages: neuromancer\n",
+      "  Building wheel for neuromancer (pyproject.toml) ... \u001b[?25ldone\n",
+      "\u001b[?25h  Created wheel for neuromancer: filename=neuromancer-1.4.2-py3-none-any.whl size=153920 sha256=6b6ea703a02095068498e2871644edbf895e203fa32d9e06f74c131b00c2f9f2\n",
+      "  Stored in directory: /tmp/pip-ephem-wheel-cache-nqxsquma/wheels/88/db/69/58e642f880e17cca6125cfc7a8bcc17b296381c19d2b6598d1\n",
+      "Successfully built neuromancer\n",
+      "Installing collected packages: neuromancer\n",
+      "  Attempting uninstall: neuromancer\n",
+      "    Found existing installation: neuromancer 1.4.2\n",
+      "    Uninstalling neuromancer-1.4.2:\n",
+      "^C\n",
+      "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n",
+      "\u001b[0mRequirement already satisfied: lightning in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (2.1.2)\n",
+      "Requirement already satisfied: PyYAML<8.0,>=5.4 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from lightning) (6.0.1)\n",
+      "Requirement already satisfied: fsspec<2025.0,>2021.06.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from fsspec[http]<2025.0,>2021.06.0->lightning) (2023.12.1)\n",
+      "Requirement already satisfied: lightning-utilities<2.0,>=0.8.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from lightning) (0.10.0)\n",
+      "Requirement already satisfied: numpy<3.0,>=1.17.2 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from lightning) (1.23.5)\n",
+      "Requirement already satisfied: packaging<25.0,>=20.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from lightning) (23.2)\n",
+      "Requirement already satisfied: torch<4.0,>=1.12.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from lightning) (2.0.0)\n",
+      "Requirement already satisfied: torchmetrics<3.0,>=0.7.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from lightning) (1.2.1)\n",
+      "Requirement already satisfied: tqdm<6.0,>=4.57.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from lightning) (4.66.1)\n",
+      "Requirement already satisfied: typing-extensions<6.0,>=4.0.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from lightning) (4.8.0)\n",
+      "Requirement already satisfied: pytorch-lightning in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from lightning) (2.1.2)\n",
+      "Requirement already satisfied: requests in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from fsspec[http]<2025.0,>2021.06.0->lightning) (2.31.0)\n",
+      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from fsspec[http]<2025.0,>2021.06.0->lightning) (3.9.1)\n",
+      "Requirement already satisfied: setuptools in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from lightning-utilities<2.0,>=0.8.0->lightning) (68.2.2)\n",
+      "Requirement already satisfied: filelock in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (3.13.1)\n",
+      "Requirement already satisfied: sympy in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (1.12)\n",
+      "Requirement already satisfied: networkx in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (3.0)\n",
+      "Requirement already satisfied: jinja2 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (3.1.2)\n",
+      "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (11.7.99)\n",
+      "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (11.7.99)\n",
+      "Requirement already satisfied: nvidia-cuda-cupti-cu11==11.7.101 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (11.7.101)\n",
+      "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (8.5.0.96)\n",
+      "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (11.10.3.66)\n",
+      "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (10.9.0.58)\n",
+      "Requirement already satisfied: nvidia-curand-cu11==10.2.10.91 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (10.2.10.91)\n",
+      "Requirement already satisfied: nvidia-cusolver-cu11==11.4.0.1 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (11.4.0.1)\n",
+      "Requirement already satisfied: nvidia-cusparse-cu11==11.7.4.91 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (11.7.4.91)\n",
+      "Requirement already satisfied: nvidia-nccl-cu11==2.14.3 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (2.14.3)\n",
+      "Requirement already satisfied: nvidia-nvtx-cu11==11.7.91 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (11.7.91)\n",
+      "Requirement already satisfied: triton==2.0.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from torch<4.0,>=1.12.0->lightning) (2.0.0)\n",
+      "Requirement already satisfied: wheel in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch<4.0,>=1.12.0->lightning) (0.42.0)\n",
+      "Requirement already satisfied: cmake in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from triton==2.0.0->torch<4.0,>=1.12.0->lightning) (3.27.9)\n",
+      "Requirement already satisfied: lit in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from triton==2.0.0->torch<4.0,>=1.12.0->lightning) (17.0.6)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>2021.06.0->lightning) (23.1.0)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>2021.06.0->lightning) (6.0.4)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>2021.06.0->lightning) (1.9.4)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>2021.06.0->lightning) (1.4.0)\n",
+      "Requirement already satisfied: aiosignal>=1.1.2 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>2021.06.0->lightning) (1.3.1)\n",
+      "Requirement already satisfied: async-timeout<5.0,>=4.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>2021.06.0->lightning) (4.0.3)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from jinja2->torch<4.0,>=1.12.0->lightning) (2.1.3)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from requests->fsspec[http]<2025.0,>2021.06.0->lightning) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from requests->fsspec[http]<2025.0,>2021.06.0->lightning) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from requests->fsspec[http]<2025.0,>2021.06.0->lightning) (2.1.0)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from requests->fsspec[http]<2025.0,>2021.06.0->lightning) (2023.11.17)\n",
+      "Requirement already satisfied: mpmath>=0.19 in /home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages (from sympy->torch<4.0,>=1.12.0->lightning) (1.3.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\"\n",
+    "!pip install lightning "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports\n",
+    "\n",
+    "(The user might need to install PyTorch Lightning). If so, please run \n",
+    "\n",
+    "```\n",
+    "pip install lightning\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LWyvndXlz0Fv"
+   },
+   "source": [
+    "### Import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "KbP0n-4evRqt"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import numpy as np\n",
+    "import cvxpy\n",
+    "import lightning.pytorch as pl\n",
+    "from cvxpylayers.torch import CvxpyLayer\n",
+    "\n",
+    "# plotting\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patheffects as patheffects\n",
+    "\n",
+    "# neuromancer\n",
+    "from neuromancer.trainer import Trainer, LitTrainer\n",
+    "from neuromancer.problem import Problem\n",
+    "from neuromancer.constraint import variable\n",
+    "from neuromancer.dataset import DictDataset\n",
+    "from neuromancer.loss import PenaltyLoss\n",
+    "from neuromancer.modules import blocks\n",
+    "from neuromancer.system import Node\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Problem Formulation\n",
+    "\n",
+    "Specifically we will focus on solving the [Rosenbrock problem](https://en.wikipedia.org/wiki/Rosenbrock_function):\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "&\\text{minimize } &&  (1-x_1)^2 + p(x_2-x_1^2)^2\\\\\n",
+    "&\\text{subject to} && Ax \\le b\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "with parameters $p, b$ and decision variables $x= [x_1, x_2]$.\n",
+    "\n",
+    "In this tutorial, we will use neuromancer to train the neural network to minimize the nonlinear objective function,\n",
+    "and cvxpy layers to project the solution onto feasible region."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nx = 2              #  number of decision variables\n",
+    "n_con = 4           #  number of constraints\n",
+    "n_p = 1             #  number of objective parameters\n",
+    "\n",
+    "# generate fixed parameters of the inequality constraints: Ax <= b\n",
+    "torch.manual_seed(7)\n",
+    "A = torch.FloatTensor(n_con, nx).uniform_(-4, 4)\n",
+    "x0 = torch.full([nx], 0.5)       # controls center of the polytope\n",
+    "s0 = torch.full([n_con], 0.2)    # controls offset from the center of the polytope\n",
+    "b0 = A.mv(x0) + s0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_WH7o7Wu1epw"
+   },
+   "source": [
+    "## Dataset\n",
+    "\n",
+    "We constructy the dataset by sampling the parametric space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "_r6p2p6myHAh"
+   },
+   "outputs": [],
+   "source": [
+    "data_seed = 408  # random seed used for simulated data\n",
+    "np.random.seed(data_seed)\n",
+    "torch.manual_seed(data_seed);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JZ9qrw0tlJhs"
+   },
+   "source": [
+    "Randomly sample parameters from a uniform distribution: $0.5\\le p\\le2.0$;  $0.2\\le a\\le1.2$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "Nu58M-8JyHy6"
+   },
+   "outputs": [],
+   "source": [
+    "nsim = 1000  # number of datapoints: increase sample density for more robust results\n",
+    "# create dictionaries with sampled datapoints with uniform distribution\n",
+    "p_low, p_high = 0.2, 5.0,\n",
+    "b_low, b_high = 0.0, 1.0,\n",
+    "\n",
+    "def data_setup_function(nsim, p_low, p_high, b_low, b_high): \n",
+    "    # we sample objective and constraints parameters\n",
+    "    samples_train = {\"p\": torch.FloatTensor(nsim, n_p).uniform_(p_low, p_high),\n",
+    "                    \"b_param\": torch.FloatTensor(nsim, n_con).uniform_(b_low, b_high)}\n",
+    "    samples_dev = {\"p\": torch.FloatTensor(nsim, n_p).uniform_(p_low, p_high),\n",
+    "                \"b_param\": torch.FloatTensor(nsim, n_con).uniform_(b_low, b_high)}\n",
+    "    samples_test = {\"p\": torch.FloatTensor(nsim, n_p).uniform_(p_low, p_high),\n",
+    "                    \"b_param\": torch.FloatTensor(nsim, n_con).uniform_(b_low, b_high)}\n",
+    "    # create named dictionary datasets\n",
+    "    train_data = DictDataset(samples_train, name='train')\n",
+    "    dev_data = DictDataset(samples_dev, name='dev')\n",
+    "    test_data = DictDataset(samples_test, name='test')\n",
+    "    batch_size = 32\n",
+    "\n",
+    "    return train_data, dev_data, test_data, batch_size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Y2htUaWMDjsk"
+   },
+   "source": [
+    "## Primal Solution Map Architecture\n",
+    "\n",
+    "A neural network mapping problem parameters onto primal decision variables:  \n",
+    "$$x = \\pi(\\theta)$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "Ta_I_pjyyLzf"
+   },
+   "outputs": [],
+   "source": [
+    "# define neural architecture for the trainable solution map\n",
+    "# mapping problem parameters to decitionv ariables\n",
+    "func = blocks.MLP(insize=n_con+n_p, outsize=nx,\n",
+    "                bias=True,\n",
+    "                linear_map=torch.nn.Linear,\n",
+    "                nonlin=nn.ReLU,\n",
+    "                hsizes=[80] * 4)\n",
+    "# wrap neural net into symbolic representation of the solution map via the Node class:\n",
+    "# sol_map(p, bparam) -> xy\n",
+    "sol_map = Node(func, ['p', 'b_param'], ['xy'], name='map')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Lxj77EFj7EO-"
+   },
+   "source": [
+    "## Objective in NeuroMANCER\n",
+    "\n",
+    "\n",
+    "We want to minimize the objective:\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "&\\text{minimize } &&  (1-x_1)^2 + p(x_2-x_1^2)^2\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "with $x = [x_1, x_2]$ being decision variables and $p$ being problem parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "bcoVjphjyPp9"
+   },
+   "outputs": [],
+   "source": [
+    "# define decision variables\n",
+    "xy = variable(\"xy\")\n",
+    "x = variable(\"xy\")[:, [0]]\n",
+    "y = variable(\"xy\")[:, [1]]\n",
+    "# problem parameters sampled in the dataset\n",
+    "p = variable('p')\n",
+    "b_param = variable('b_param')\n",
+    "\n",
+    "# objective function\n",
+    "f = (1-x)**2 + p*(y-x**2)**2\n",
+    "nm_obj = f.minimize(weight=10.0, name='obj')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cvxpy projection layer\n",
+    "\n",
+    "We will use cvxpy layer to implement the projection onto the polytopic constraints:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "& \\hat{x}  = \\text{argmin}_x && ||x -  \\hat{x}  ||_2^2 \\\\\n",
+    "&\\text{subject to} && A \\hat{x} \\le b\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "with $x$ being solution obtained from the neural network $x = \\pi(\\theta)$, and $\\hat{x}$ being projected solution onto the feasible set satisfying $A \\hat{x} \\le b$. Here the problem constraints are parametrized by $b$ and solution from neural network $x$. Hence we can compactly represent this projection operator as:  \n",
+    "\n",
+    "$$\\hat{x} = \\text{proj}_{A x \\le b}(x, b) $$\n",
+    "\n",
+    "Cvxpy layers allow us to use this projection operator as a differentiable layer in our end-to-end solver pipeline.\n",
+    "\n",
+    "\n",
+    "<img src=\"./figs/cvxpylayers_overview.png\" width=\"600\">  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# constants, parameters, and variables\n",
+    "A_cvxpy = A.detach().numpy()\n",
+    "b0_cvxpy = b0.detach().numpy()\n",
+    "b_cvxpy = cvxpy.Parameter(n_con)\n",
+    "xy_net = cvxpy.Parameter(nx)      # primal decision from neural net\n",
+    "xy_cvxpy = cvxpy.Variable(nx)     # cvxpy decision variable\n",
+    "\n",
+    "# projection problem formulation\n",
+    "cvxpy_obj = cvxpy.Minimize(1.0 * cvxpy.sum_squares(xy_net - xy_cvxpy))\n",
+    "cvxpy_cons = [xy_cvxpy@A_cvxpy.T <= b0_cvxpy + b_cvxpy]\n",
+    "cvxpy_prob = cvxpy.Problem(cvxpy_obj, cvxpy_cons)\n",
+    "\n",
+    "# cvxpy layer\n",
+    "cvxpy_layer = CvxpyLayer(cvxpy_prob,\n",
+    "                   parameters=[b_cvxpy, xy_net],\n",
+    "                   variables=[xy_cvxpy])\n",
+    "\n",
+    "# symbolic wrapper: sol_map(bparam, xy) -> xy\n",
+    "project = Node(cvxpy_layer, ['b_param', 'xy'], ['xy_cvx'], name='proj')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that in the above code, we did not need to wrap A_cvxpy, b0_cvxpy etc within a LightningModule class"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Self-supervised loss\n",
+    "\n",
+    "We can use the correction form the cvxpylayer as a supervisory signal for the neural network alongside with the objective minimization loss function term. This extra term can be used as a guide for the neural network to satisfy constraints of the problem by following the projected solution. The larger the weigthing factor of this loss function term the closer the neural network will be to feasible region on its own without the subsequent projection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# corrected variable by the cvxpy layer\n",
+    "xy_cvx = variable(\"xy_cvx\")\n",
+    "# cvxpy-supervised loss for the neural net\n",
+    "residual = torch.abs(xy - xy_cvx)\n",
+    "cvxp_loss = 1.*(residual == 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Construct differentiable parametric optimization problem \n",
+    "\n",
+    "Here we put all the pieces together and construct differentiable parametric constrained optimization problem with neural network and cvxpy projection layer as solvers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 496
+    },
+    "id": "n7VPa9Wc8JRB",
+    "outputId": "0da17c45-6370-4f46-f626-bd5686b94bfc"
+   },
+   "outputs": [],
+   "source": [
+    "# constrained optimization problem construction\n",
+    "objectives = [nm_obj, cvxp_loss]\n",
+    "constraints = []\n",
+    "nodes = [sol_map, project]\n",
+    "\n",
+    "# create penalty method loss function\n",
+    "loss = PenaltyLoss(objectives, constraints)\n",
+    "# construct constrained optimization problem\n",
+    "problem = Problem(nodes, loss)\n",
+    "# plot computational graph\n",
+    "# problem.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "icWSMeG28SKc"
+   },
+   "source": [
+    "## Parametric Problem Solution in NeuroMANCER\n",
+    "\n",
+    "Here we will use stochastic gradient descent to optimize the neural network $x = \\pi({\\theta})$ to solve a distribution of sampled problem instances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "rk1bRczByUvl"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (cuda), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "You are using a CUDA device ('NVIDIA RTX A6000') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:639: Checkpoint directory ./ exists and is not empty.\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n",
+      "\n",
+      "  | Name    | Type    | Params\n",
+      "------------------------------------\n",
+      "0 | problem | Problem | 20.1 K\n",
+      "------------------------------------\n",
+      "20.1 K    Trainable params\n",
+      "0         Non-trainable params\n",
+      "20.1 K    Total params\n",
+      "0.080     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sanity Checking DataLoader 0:   0%|          | 0/2 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=159` in the `DataLoader` to improve performance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sanity Checking DataLoader 0:  50%|█████     | 1/2 [00:05<00:05,  0.18it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/birm560/neuromancer/src/neuromancer/constraint.py:169: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([32, 2])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
+      "  loss = F.l1_loss(left, right)\n",
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/utilities/data.py:77: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 32. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                                           "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=159` in the `DataLoader` to improve performance.\n",
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:293: The number of training batches (32) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|██████████| 32/32 [00:12<00:00,  2.53it/s, v_num=24, train_loss_step=0.539]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/birm560/neuromancer/src/neuromancer/constraint.py:169: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([8, 2])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
+      "  loss = F.l1_loss(left, right)\n",
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/lightning/pytorch/utilities/data.py:77: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 8. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|██████████| 32/32 [00:21<00:00,  1.48it/s, v_num=24, train_loss_step=0.539, dev_loss=0.614, train_loss_epoch=2.680]epoch: 0  : 2.6311771869659424\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, global step 32: 'dev_loss' reached 0.61403 (best 0.61403), saving model to './epoch=0-step=32.ckpt' as top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: 100%|██████████| 32/32 [00:21<00:00,  1.46it/s, v_num=24, train_loss_step=0.423, dev_loss=0.590, train_loss_epoch=0.789]epoch: 1  : 0.7808655500411987\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1, global step 64: 'dev_loss' reached 0.59040 (best 0.59040), saving model to './epoch=1-step=64.ckpt' as top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: 100%|██████████| 32/32 [00:21<00:00,  1.47it/s, v_num=24, train_loss_step=0.555, dev_loss=0.646, train_loss_epoch=0.618]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2, global step 96: 'dev_loss' was not in top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch: 2  : 0.6163569688796997\n",
+      "Epoch 3: 100%|██████████| 32/32 [00:22<00:00,  1.40it/s, v_num=24, train_loss_step=0.220, dev_loss=0.256, train_loss_epoch=0.552]epoch: 3  : 0.5445245504379272\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3, global step 128: 'dev_loss' reached 0.25570 (best 0.25570), saving model to './epoch=3-step=128-v1.ckpt' as top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: 100%|██████████| 32/32 [00:23<00:00,  1.39it/s, v_num=24, train_loss_step=0.221, dev_loss=0.303, train_loss_epoch=0.483]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4, global step 160: 'dev_loss' was not in top 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch: 4  : 0.47689807415008545\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: 100%|██████████| 32/32 [00:23<00:00,  1.36it/s, v_num=24, train_loss_step=0.221, dev_loss=0.303, train_loss_epoch=0.483]\n"
+     ]
+    }
+   ],
+   "source": [
+    "lit_trainer = LitTrainer(epochs=5, patience=10, accelerator='gpu', devices=[1])\n",
+    "lit_trainer.fit(problem, data_setup_function, nsim=nsim, p_low=p_low, p_high=p_high, b_low=b_low, b_high=b_high)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0hhUw4PVBWmb"
+   },
+   "source": [
+    "## Plot the solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 281
+    },
+    "id": "kvYCfjq6zxxC",
+    "outputId": "322e5b72-b93d-4d9e-a913-8703aad67d1a"
+   },
+   "outputs": [],
+   "source": [
+    "def plot_pNLP(p, b_param, savedir=None):\n",
+    "    # sample primal solution domain\n",
+    "    x1 = np.arange(-0.5, 1.5, 0.02)\n",
+    "    y1 = np.arange(-0.5, 1.5, 0.02)\n",
+    "    xx, yy = np.meshgrid(x1, y1)\n",
+    "    xy_samples = np.stack([xx.flatten(), yy.flatten()])\n",
+    "\n",
+    "    # eval constraints Ax - b <= 0\n",
+    "    A_np = A.detach().numpy()\n",
+    "    b = b0 + b_param\n",
+    "    b_np = b.detach().numpy()\n",
+    "    C_samples = np.subtract(np.matmul(A_np, xy_samples).T, b_np).T\n",
+    "    C_samples = C_samples.reshape(n_con, x1.shape[0], y1.shape[0])\n",
+    "\n",
+    "    # eval objective\n",
+    "    p_np = p.detach().numpy()\n",
+    "    J = (1 - xx) ** 2 + p_np * (yy - xx ** 2) ** 2\n",
+    "\n",
+    "    # plot objective and constraints\n",
+    "    fig, ax = plt.subplots(1, 1)\n",
+    "    cp = ax.contourf(xx, yy, J,\n",
+    "                     levels=[0, 0.05, 0.2, 0.5, 1.0, 2.0, 4.0, 6.0, 8.0, 16],\n",
+    "                     alpha=0.6)\n",
+    "    fig.colorbar(cp)\n",
+    "    ax.set_title('Rosenbrock problem')\n",
+    "    for k in range(n_con):\n",
+    "        cg1 = ax.contour(xx, yy, -C_samples[k], [0],\n",
+    "                         colors='mediumblue', alpha=0.7)\n",
+    "        plt.setp(cg1.collections,\n",
+    "                 path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
+    "\n",
+    "    # Solution to pNLP via Neuromancer\n",
+    "    datapoint = {'p': p, 'b_param': b_param,\n",
+    "                 'name': 'test'}\n",
+    "    # evaluate neuromancer model\n",
+    "    model_out = problem.step(datapoint)\n",
+    "    # neural net solution\n",
+    "    x_nm_net = model_out[\"xy\"][0].detach().numpy()\n",
+    "    y_nm_net = model_out[\"xy\"][1].detach().numpy()\n",
+    "    # cvxpy projected solution\n",
+    "    x_nm_cvx = model_out[\"xy_cvx\"][0].detach().numpy()\n",
+    "    y_nm_cvx = model_out[\"xy_cvx\"][1].detach().numpy()\n",
+    "\n",
+    "    # plot optimal solutions CasADi vs Neuromancer\n",
+    "    ax.plot(x_nm_net, y_nm_net, 'r*', fillstyle='none',\n",
+    "            markersize=16, label='net')\n",
+    "    ax.plot(x_nm_cvx, y_nm_cvx, 'g*', fillstyle='none',\n",
+    "            markersize=14, label='cvxpy')\n",
+    "    # plot projection step\n",
+    "    ax.plot([x_nm_net, x_nm_cvx],\n",
+    "            [y_nm_net, y_nm_cvx], '--', c='orange')\n",
+    "    plt.legend(bbox_to_anchor=(1.0, 0.15))\n",
+    "\n",
+    "    if savedir is not None:\n",
+    "        if not os.path.exists(savedir):\n",
+    "            os.mkdir(savedir)\n",
+    "        figure_path = os.path.join(savedir, f'pNLP_p={p}_bsum={b_np}.png')\n",
+    "        plt.savefig(figure_path)\n",
+    "        return figure_path\n",
+    "    else:\n",
+    "        plt.show(block=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "id": "yD5kAnjy4CUL"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_229275/2558518230.py:29: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed two minor releases later.\n",
+      "  plt.setp(cg1.collections,\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# sample random scenario\n",
+    "p = torch.FloatTensor(n_p).uniform_(p_low, p_high)\n",
+    "b_param = torch.rand(n_con)\n",
+    "# plot single random scenario\n",
+    "plot_pNLP(p, b_param)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "neuromancer3",
+   "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/examples/lightning_integration_examples/other_examples/lightning_nonauto_DeepKoopman_logging_tensorboard.ipynb b/examples/lightning_integration_examples/other_examples/lightning_nonauto_DeepKoopman_logging_tensorboard.ipynb
new file mode 100644
index 00000000..373b78dd
--- /dev/null
+++ b/examples/lightning_integration_examples/other_examples/lightning_nonauto_DeepKoopman_logging_tensorboard.ipynb
@@ -0,0 +1,885 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a2d86e51",
+   "metadata": {},
+   "source": [
+    "# Lightning Integration: Logging/Tensorboard + Deep Koopman Operators Example \n",
+    "\n",
+    "The following notebook is equivalent to /ODEs/Part_8_nonauto_DeepKoopman.ipynb, but now showcasing the use of **PyTorch-Lightning** to simplify the user workflow. \n",
+    "\n",
+    "We also show how to easily use **Tensorboard**\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2e3300d",
+   "metadata": {},
+   "source": [
+    "## NeuroMANCER and Dependencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39dc8323",
+   "metadata": {},
+   "source": [
+    "### Install (Colab only)\n",
+    "Skip this step when running locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb524e98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\"\n",
+    "!pip install lightning "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a0df979",
+   "metadata": {},
+   "source": [
+    "## Imports\n",
+    "\n",
+    "(The user might need to install PyTorch Lightning). If so, please run \n",
+    "\n",
+    "```\n",
+    "pip install lightning\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b2b86eb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<torch._C.Generator at 0x7f870826d4d0>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import numpy as np\n",
+    "import lightning.pytorch as pl \n",
+    "\n",
+    "from neuromancer.psl import plot\n",
+    "from neuromancer import psl\n",
+    "import matplotlib.pyplot as plt\n",
+    "from torch.utils.data import DataLoader\n",
+    "\n",
+    "from neuromancer.system import Node, System\n",
+    "from neuromancer.slim import slim\n",
+    "from neuromancer.trainer import Trainer, LitTrainer\n",
+    "from neuromancer.problem import Problem\n",
+    "from neuromancer.dataset import DictDataset\n",
+    "from neuromancer.constraint import variable\n",
+    "from neuromancer.loss import PenaltyLoss\n",
+    "from neuromancer. modules import blocks\n",
+    "\n",
+    "torch.manual_seed(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56b94c2f",
+   "metadata": {},
+   "source": [
+    "## Generate trajectories from ODE system \n",
+    "\n",
+    "In this example we don't assume any prior knowledge on the system dynamics. We will only have access to limited measurements of the system states $x$ of an unknown [ordinary differential equations](https://en.wikipedia.org/wiki/Ordinary_differential_equation) (ODE).\n",
+    "\n",
+    "Select the system_name from the available list of [nonautonomous ODE systems](https://github.com/pnnl/neuromancer/blob/master/src/neuromancer/psl/nonautonomous.py):\n",
+    "- TwoTank\n",
+    "- CSTR\n",
+    "- SwingEquation\n",
+    "- IverSimple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "34498f23",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1600 with 2 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": [
+    "# %%  ground truth system\n",
+    "system = psl.systems['CSTR']\n",
+    "modelSystem = system()\n",
+    "ts = modelSystem.ts\n",
+    "nx = modelSystem.nx\n",
+    "ny = modelSystem.ny\n",
+    "nu = modelSystem.nu\n",
+    "raw = modelSystem.simulate(nsim=1000, ts=ts)\n",
+    "plot.pltOL(Y=raw['Y'], U=raw['U'])\n",
+    "plot.pltPhase(X=raw['Y'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6be88219",
+   "metadata": {},
+   "source": [
+    "## Create training data of sampled trajectories\n",
+    "\n",
+    "We will obtain a dataset of sampled trajectories of the system dynamics in the form of input-state tuples: \n",
+    "$$D = \\big[(u^i_0, \\hat{x}^i_0), ..., (u^i_N, \\hat{x}^i_{N}) \\big], \\, \\, i \\in [1, ..., m]$$\n",
+    "where $N$ represents the prediction horizon, $m$ represents number of measured trajectories, and $i$ represents an index of the sampled trajectory.\n",
+    "Variables $x_k$ represent system states, and $u_k$ are exogenous inputs at time $k$.\n",
+    "\n",
+    "The **data_setup_function()** is defined here based off the base version of this notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "168db8f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalize(x, mean, std):\n",
+    "    return (x - mean) / std\n",
+    "\n",
+    "def data_setup_function(sys, nsim, nsteps, ts, bs):\n",
+    "    \"\"\"\n",
+    "    :param nsteps: (int) Number of timesteps for each batch of training data\n",
+    "    :param sys: (psl.system)\n",
+    "    :param ts: (float) step size\n",
+    "    :param bs: (int) batch size\n",
+    "\n",
+    "    \"\"\"\n",
+    "    train_sim, dev_sim, test_sim = [sys.simulate(nsim=nsim, ts=ts) for i in range(3)]\n",
+    "    nx = sys.nx\n",
+    "    nu = sys.nu\n",
+    "    nbatch = nsim//nsteps\n",
+    "    length = (nsim//nsteps) * nsteps\n",
+    "\n",
+    "    mean_x = modelSystem.stats['Y']['mean']\n",
+    "    std_x = modelSystem.stats['Y']['std']\n",
+    "    mean_u = modelSystem.stats['U']['mean']\n",
+    "    std_u = modelSystem.stats['U']['std']\n",
+    "    \n",
+    "\n",
+    "    trainX = normalize(train_sim['Y'][:length], mean_x, std_x)\n",
+    "    trainX = trainX.reshape(nbatch, nsteps, nx)\n",
+    "    trainX = torch.tensor(trainX, dtype=torch.float32)\n",
+    "    trainU = normalize(train_sim['U'][:length], mean_u, std_u)\n",
+    "    trainU = trainU.reshape(nbatch, nsteps, nu)\n",
+    "    trainU = torch.tensor(trainU, dtype=torch.float32)\n",
+    "    train_data = DictDataset({'Y': trainX, 'Y0': trainX[:, 0:1, :],\n",
+    "                              'U': trainU}, name='train')\n",
+    "\n",
+    "\n",
+    "    devX = normalize(dev_sim['Y'][:length], mean_x, std_x)\n",
+    "    devX = devX.reshape(nbatch, nsteps, nx)\n",
+    "    devX = torch.tensor(devX, dtype=torch.float32)\n",
+    "    devU = normalize(dev_sim['U'][:length], mean_u, std_u)\n",
+    "    devU = devU[:length].reshape(nbatch, nsteps, nu)\n",
+    "    devU = torch.tensor(devU, dtype=torch.float32)\n",
+    "    dev_data = DictDataset({'Y': devX, 'Y0': devX[:, 0:1, :],\n",
+    "                            'U': devU}, name='dev')\n",
+    "\n",
+    "\n",
+    "    testX = normalize(test_sim['Y'][:length], mean_x, std_x)\n",
+    "    testX = testX.reshape(1, nbatch*nsteps, nx)\n",
+    "    testX = torch.tensor(testX, dtype=torch.float32)\n",
+    "    testU = normalize(test_sim['U'][:length], mean_u, std_u)\n",
+    "    testU = testU.reshape(1, nbatch*nsteps, nu)\n",
+    "    testU = torch.tensor(testU, dtype=torch.float32)\n",
+    "    test_data = {'Y': testX, 'Y0': testX[:, 0:1, :],\n",
+    "                 'U': testU}\n",
+    "    \n",
+    "\n",
+    "    return train_data, dev_data, test_data, bs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4fb7c36e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nsim = 2000   # number of simulation steps in the dataset\n",
+    "nsteps = 20   # number of prediction horizon steps in the loss function\n",
+    "bs = 100      # minibatching batch size\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66e81939",
+   "metadata": {},
+   "source": [
+    "## Deep Koopman model in Neuromancer\n",
+    "\n",
+    "Here we construct a discrete-time encoder-decoder Koopman model with control:   \n",
+    "\n",
+    "<img src=\"../figs/Koopman_model.png\" width=\"500\">  \n",
+    "\n",
+    "\n",
+    "Encoder: $${x}_{k} = f_y(y_k) +f_u(u_k)$$ \n",
+    "Koopman: $${x}_{k+1} = K(x_k)$$ \n",
+    "Decoder: $$\\hat{y}_{k+1} = f_y^{-1}(x_{k+1})$$  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c41df1bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model parameters\n",
+    "nx_koopman = 50\n",
+    "n_hidden = 60\n",
+    "n_layers = 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad920aa1",
+   "metadata": {},
+   "source": [
+    "**Encoder and Decoder networks** "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "23e4e146",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# instantiate output encoder neural net f_y\n",
+    "f_y = blocks.MLP(ny, nx_koopman, bias=True,\n",
+    "                 linear_map=torch.nn.Linear,\n",
+    "                 nonlin=torch.nn.ELU,\n",
+    "                 hsizes=n_layers*[n_hidden])\n",
+    "# initial condition encoder\n",
+    "encode_Y0 = Node(f_y, ['Y0'], ['x'], name='encoder_Y0')\n",
+    "# observed trajectory encoder\n",
+    "encode_Y = Node(f_y, ['Y'], ['x_latent'], name='encoder_Y')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6b36f15f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# instantiate input encoder net f_u\n",
+    "f_u = blocks.MLP(nu, nx_koopman, bias=True,\n",
+    "                 linear_map=torch.nn.Linear,\n",
+    "                 nonlin=torch.nn.ELU,\n",
+    "                 hsizes=n_layers*[n_hidden])\n",
+    "# initial condition encoder\n",
+    "encode_U = Node(f_u, ['U'], ['u_latent'], name='encoder_U')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f62e2ab0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# instantiate state decoder neural net f_y_inv\n",
+    "f_y_inv = blocks.MLP(nx_koopman, ny, bias=True,\n",
+    "                linear_map=torch.nn.Linear,\n",
+    "                nonlin=torch.nn.ELU,\n",
+    "                hsizes=n_layers*[n_hidden])\n",
+    "# predicted trajectory decoder\n",
+    "decode_y = Node(f_y_inv, ['x'], ['yhat'], name='decoder_y')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "417990ee",
+   "metadata": {},
+   "source": [
+    "**Standard Koopman Operator** without stability guarantees is parametrized by linear layer of the latent size. \n",
+    "\n",
+    "**Stable Koopman Operator:**  For provably stable Koopman operator we can choose a range of linear algebra factorization that allow to constrain the eigenvalues of the resulting linear operator. \n",
+    "In this example we use the following [Singular Value Decomposition (SVD)](https://en.wikipedia.org/wiki/Singular_value_decomposition) factorization of the operator given as:\n",
+    "$$K = U \\Sigma V$$ \n",
+    "$$\\Sigma =  \\text{diag}(\\lambda_{\\text{max}} - (\\lambda_{\\text{max}} - \\lambda_{\\text{min}}) \\cdot \\sigma(\\Lambda)) $$\n",
+    "where $\\sigma$ is logistic sigmoid activation function, $\\cdot$ is a dot product, $\\Lambda$ is a vector of eigenvalues of the linear operator, while $\\lambda_{\\text{max}}$ and $\\lambda_{\\text{min}}$ are constraints on maximum and minimum value of SVD factorized linear operator.\n",
+    "\n",
+    "<img src=\"../figs/SVD_layer.png\" width=\"300\">  \n",
+    "\n",
+    "In order for the SVD factorization to hold the left and right matrices $U$ and $V$, respectively, needs to be [orthogonal](https://en.wikipedia.org/wiki/Orthogonal_matrix).\n",
+    "This can be achieved either via [Householder reflectors](https://arxiv.org/abs/1803.09327), or via penalties in the loss function given as:  \n",
+    "$$\\ell_{U} = || I - UU^T||_2 + || I - U^TU||_2  $$\n",
+    "$$\\ell_{V} = || I - VV^T||_2 + || I - V^TV||_2  $$\n",
+    "$$\\ell_{\\text{stable}} = \\ell_{U} + \\ell_{V} $$\n",
+    "\n",
+    "For more details on the SVD and other linear algebra factorizations of trainable linear layers see the references [[7]](https://ieeexplore.ieee.org/document/9482930) and [[8]](https://ieeexplore.ieee.org/abstract/document/9809789), with Pytorch implementations in the [slim submodule](https://github.com/pnnl/neuromancer/tree/master/src/neuromancer/slim) of the Neuromancer library. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a3bcd5eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# instantiate Koopman operator matrix\n",
+    "stable = True     # if True then provably stable Koopman operator\n",
+    "if stable:\n",
+    "    # SVD factorized Koopman operator with bounded eigenvalues: sigma_min <= \\lambda_i <= sigma_max\n",
+    "    K = slim.linear.SVDLinear(nx_koopman, nx_koopman,\n",
+    "                          sigma_min=0.01, sigma_max=1.0, bias=False)\n",
+    "    # SVD penalty variable\n",
+    "    K_reg_error = variable(K.reg_error())\n",
+    "    # SVD penalty loss term\n",
+    "    K_reg_loss = 1.*(K_reg_error == 0.0)\n",
+    "    K_reg_loss.name = 'SVD_loss'\n",
+    "else:\n",
+    "    # linear Koopman operator without guaranteed stability\n",
+    "    K = torch.nn.Linear(nx_koopman, nx_koopman, bias=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44579415",
+   "metadata": {},
+   "source": [
+    "Below is the base class for Koopman control. Note that if we want to use any hardcoded tensors, one would need to have this class inherit from **pl.LightningModule** and use **type_as()**. Please refer to Part_1_stabilize_linear_system_lightning.ipynb notebook for more information "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "64f9e6a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Koopman_control(nn.Module):\n",
+    "    \"\"\"\n",
+    "    Baseline class for Koopman control model\n",
+    "    Implements discrete-time dynamical system:\n",
+    "        x_k+1 = K x_k + u_k\n",
+    "    with variables:\n",
+    "        x_k - latent states\n",
+    "        u_k - latent control inputs\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, K):\n",
+    "        super().__init__()\n",
+    "        self.K = K\n",
+    "\n",
+    "    def forward(self, x, u):\n",
+    "        \"\"\"\n",
+    "        :param x: (torch.Tensor, shape=[batchsize, nx])\n",
+    "        :param u: (torch.Tensor, shape=[batchsize, nx])\n",
+    "        :return: (torch.Tensor, shape=[batchsize, nx])\n",
+    "        \"\"\"\n",
+    "        x = self.K(x) + u\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a155d21d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# symbolic Koopman model with control inputs\n",
+    "Koopman = Node(Koopman_control(K), ['x', 'u_latent'], ['x'], name='K')\n",
+    "\n",
+    "# latent Koopmann rollout\n",
+    "dynamics_model = System([Koopman], name='Koopman', nsteps=nsteps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "235d9f50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dynamics_model.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9bbe7f89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# put all nodes of the Koopman model together in a list of nodes\n",
+    "nodes = [encode_Y0, encode_Y, encode_U, dynamics_model, decode_y]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd4f33d0",
+   "metadata": {},
+   "source": [
+    "## Define Koopman system identification loss function terms\n",
+    "\n",
+    "Here we define loss function terms to fit the encoded-decoder Koopman parameters $\\theta$ from given time-series data.\n",
+    "The loss function terms follow the implementation as given in the reference [[2]](https://www.nature.com/articles/s41467-018-07210-0). \n",
+    "\n",
+    "**Output trajectory prediction loss:**  \n",
+    "$$\\ell_y = \\sum_{k=1}^{N} Q_y||y^i_{k+1} - \\hat{y}^i_{k+1}||_2^2$$ \n",
+    "**One step output prediction loss:**  \n",
+    "$$\\ell_{y_1} = Q_{y_1}||y^i_2 - \\hat{y}^i_2||_2^2$$ \n",
+    "\n",
+    "where $\\hat{y}^i_{k+1} = \\phi^{-1}_{\\theta_3}(K^k_{\\theta_2}(\\phi_{\\theta_1}(y^i_1))) $, \n",
+    "and $N$ is defining length of the rollout (prediction) horizon.  \n",
+    "\n",
+    "**Latent trajectory prediction loss:**  \n",
+    "$$\\ell_{\\text{lin}} = \\sum_{k=1}^{N}  Q_x||\\phi_{\\theta_1}(y_{k+1}^i) - K^k\\phi_{\\theta_1}(y_1^i)||_2^2$$ \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c28dac38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# variables\n",
+    "Y = variable(\"Y\")  # observed\n",
+    "yhat = variable('yhat')  # predicted output\n",
+    "x_latent = variable('x_latent')  # encoded output trajectory in the latent space\n",
+    "u_latent = variable('u_latent')  # encoded input trajectory in the latent space\n",
+    "x = variable('x')  # Koopman latent space trajectory\n",
+    "\n",
+    "xu_latent = x_latent + u_latent  # latent state trajectory\n",
+    "\n",
+    "# output trajectory tracking loss\n",
+    "y_loss = 10. * (yhat[:, 1:-1, :] == Y[:, 1:, :]) ^ 2\n",
+    "y_loss.name = \"y_loss\"\n",
+    "\n",
+    "# one-step tracking loss\n",
+    "onestep_loss = 1.*(yhat[:, 1, :] == Y[:, 1, :])^2\n",
+    "onestep_loss.name = \"onestep_loss\"\n",
+    "\n",
+    "# latent trajectory tracking loss\n",
+    "x_loss = 1. * (x[:, 1:-1, :] == xu_latent[:, 1:, :]) ^ 2\n",
+    "x_loss.name = \"x_loss\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd0f573a",
+   "metadata": {},
+   "source": [
+    "## Construct System ID learning problem\n",
+    "\n",
+    "Given the training dataset $\\hat{X} = [\\hat{x}^i_0, ..., \\hat{x}^i_{N}]$ we want to solve the following problem:\n",
+    " \n",
+    "$$\n",
+    "\\begin{align}\n",
+    "&\\underset{\\theta}{\\text{minimize}}     && \\sum_{i=1}^m \\Big( \\ell_{y} + \\ell_{y_1}  +\\ell_{\\text{lin}} + \\ell_{\\text{recon}} + \\ell_{\\text{stable}} \\Big) \\\\\n",
+    "&\\text{subject to}    && \\hat{y}^i_{k+1} = \\phi^{-1}_{\\theta_3}(K^k_{\\theta_2}(\\phi_{\\theta_1}(y_1^i))) \\\\\n",
+    "\\end{align}\n",
+    "$$  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a36553e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# aggregate list of objective terms and constraints\n",
+    "objectives = [y_loss, x_loss, onestep_loss]\n",
+    "\n",
+    "if stable:\n",
+    "    objectives.append(K_reg_loss)\n",
+    "\n",
+    "# create constrained optimization loss\n",
+    "loss = PenaltyLoss(objectives, constraints=[])\n",
+    "\n",
+    "# construct constrained optimization problem\n",
+    "problem = Problem(nodes, loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "70f8a337",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot computational graph\n",
+    "# problem.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52654dc6",
+   "metadata": {},
+   "source": [
+    "# Tensorboard Visualization\n",
+    "\n",
+    "We can invoke tensorboard directly using the following commands, as Lightning automatically will log training history to *lightning_logs*. The latest \"version\" should correspond to the most current training run. Please launch training in the 2nd cell below and then launch Tensorboard. This notebook was developed in VS Code and assumes the user "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "97061f27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Launching TensorBoard..."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%reload_ext tensorboard\n",
+    "%tensorboard --logdir=lightning_logs/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be0ee43b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lit_trainer = LitTrainer(epochs=300,accelerator='cpu', devices='auto')\n",
+    "lit_trainer.fit(problem, data_setup_function , sys=modelSystem, nsim=nsim, nsteps=nsteps,ts=ts, bs=bs)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae692736",
+   "metadata": {},
+   "source": [
+    "# Wandb Logging\n",
+    "\n",
+    "We can also use a wandb logger to visualize using wandb library. To do that we run: \n",
+    "\n",
+    "For more information please see: https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.loggers.wandb.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80b64c92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lightning.pytorch.loggers import WandbLogger\n",
+    "\n",
+    "wandb_logger = WandbLogger()\n",
+    "lit_trainer = LitTrainer(epochs=300,accelerator='cpu', logger=wandb_logger)\n",
+    "lit_trainer.fit(problem, data_setup_function , sys=modelSystem, nsim=nsim, nsteps=nsteps,ts=ts, bs=bs)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ca14454",
+   "metadata": {},
+   "source": [
+    "## After Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e58d5511",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "problem = problem.to('cpu')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f17ad938",
+   "metadata": {},
+   "source": [
+    "## System Identification results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "8504c4d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, _, test_data, _ = data_setup_function( sys=modelSystem, nsim=nsim, nsteps=nsteps,ts=ts, bs=bs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c76e9b09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# update the rollout length based on the test data\n",
+    "problem.nodes[3].nsteps = test_data['Y'].shape[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0b3d2839",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Test set results\n",
+    "test_outputs = problem.step(test_data)\n",
+    "\n",
+    "pred_traj = test_outputs['yhat'][:, 1:-1, :].detach().numpy().reshape(-1, nx).T\n",
+    "true_traj = test_data['Y'][:, 1:, :].detach().numpy().reshape(-1, nx).T\n",
+    "input_traj = test_data['U'].detach().numpy().reshape(-1, nu).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "25e6f8aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2500x2500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot trajectories\n",
+    "figsize = 25\n",
+    "fig, ax = plt.subplots(nx + nu, figsize=(figsize, figsize))\n",
+    "\n",
+    "x_labels = [f'$y_{k}$' for k in range(len(true_traj))]\n",
+    "for row, (t1, t2, label) in enumerate(zip(true_traj, pred_traj, x_labels)):\n",
+    "    axe = ax[row]\n",
+    "    axe.set_ylabel(label, rotation=0, labelpad=20, fontsize=figsize)\n",
+    "    axe.plot(t1, 'c', linewidth=4.0, label='True')\n",
+    "    axe.plot(t2, 'm--', linewidth=4.0, label='Pred')\n",
+    "    axe.tick_params(labelbottom=False, labelsize=figsize)\n",
+    "axe.tick_params(labelbottom=True, labelsize=figsize)\n",
+    "axe.legend(fontsize=figsize)\n",
+    "\n",
+    "u_labels = [f'$u_{k}$' for k in range(len(input_traj))]\n",
+    "for row, (u, label) in enumerate(zip(input_traj, u_labels)):\n",
+    "    axe = ax[row+nx]\n",
+    "    axe.plot(u, linewidth=4.0, label='inputs')\n",
+    "    axe.legend(fontsize=figsize)\n",
+    "    axe.set_ylabel(label, rotation=0, labelpad=20, fontsize=figsize)\n",
+    "    axe.tick_params(labelbottom=True, labelsize=figsize)\n",
+    "\n",
+    "ax[-1].set_xlabel('$time$', fontsize=figsize)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "de31932a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute Koopman eigenvalues and eigenvectors\n",
+    "if stable:\n",
+    "    eig, eig_vec = torch.linalg.eig(K.effective_W())\n",
+    "else:\n",
+    "    eig, eig_vec = torch.linalg.eig(K.weight)\n",
+    "# Koopman eigenvalues real and imaginary parts\n",
+    "eReal = eig.real.detach().numpy()\n",
+    "eImag = eig.imag.detach().numpy()\n",
+    "# unit circle\n",
+    "t = np.linspace(0.0, 2 * np.pi, 1000)\n",
+    "x_circ = np.cos(t)\n",
+    "y_circ = np.sin(t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "195e3042",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0.98, 'Koopman operator eigenvalues')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot Koopman eigenvalues\n",
+    "fig1, ax1 = plt.subplots()\n",
+    "ax1.plot(x_circ, y_circ, 'c', linewidth=4)\n",
+    "ax1.plot(eReal, eImag, 'wo')\n",
+    "ax1.set_aspect('equal', 'box')\n",
+    "ax1.set_facecolor(\"navy\")\n",
+    "ax1.set_xlabel(\"$Re(\\lambda)$\", fontsize=figsize)\n",
+    "ax1.set_ylabel(\"$Im(\\lambda)$\", fontsize=figsize)\n",
+    "fig1.suptitle('Koopman operator eigenvalues')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "dbc080a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/birm560/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\n",
+      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# compute Koopman state eigenvectors\n",
+    "y_min = 1.1*test_data['Y'].min()\n",
+    "y_max = 1.1*test_data['Y'].max()\n",
+    "y1 = torch.linspace(y_min, y_max, 1000)\n",
+    "y2 = torch.linspace(y_min, y_max, 1000)\n",
+    "yy1, yy2 = torch.meshgrid(y1, y1)\n",
+    "plot_yy1 = yy1.detach().numpy()\n",
+    "plot_yy2 = yy2.detach().numpy()\n",
+    "# eigenvectors\n",
+    "features = torch.stack([yy1, yy2]).transpose(0, 2)\n",
+    "latent = f_y(features)\n",
+    "phi = torch.matmul(latent, abs(eig_vec))\n",
+    "# select first 6 eigenvectors\n",
+    "phi_1 = phi.detach().numpy()[:,:,0]\n",
+    "phi_2 = phi.detach().numpy()[:,:,1]\n",
+    "phi_3 = phi.detach().numpy()[:,:,2]\n",
+    "phi_4 = phi.detach().numpy()[:,:,3]\n",
+    "phi_5 = phi.detach().numpy()[:,:,4]\n",
+    "phi_6 = phi.detach().numpy()[:,:,6]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "a247b97a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0.98, 'first six eigenfunctions')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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RI0fyc889l//2t7/lAPhDDz2klH3sscf4cccdx3fddVfe3NzMJ0yYwD/96U/z5cuXyzLi+m/ZskWpq08fFedjmu65ZcsWfvzxx/PBgwfzXXbZhZ955pn8X//6l3Hq65AhQ2L1TTbQ09PDL7/8cr7PPvvwpqYmPnLkSD579my+cuVKWWb16tX8sMMO44MGDeIA5DRY07lzHkx13WeffXhjYyMfPXo0P+uss/jrr7+ulLF9x7lz5/IJEybI/R//+Mf8sMMOk9d28uTJ/Otf/zrv7OyM1XVwKBLu3SgODg51wZVXXolzzjkHL774InbffffePh0HB4dehCMbDg4OVePtt99WAku3b9+Ogw46COVyGf/+97978cwcHBz6AlzMhoODQ9U47rjjsMcee2DatGno7OzEDTfcgNWrV1un0jo4OLyz4MiGg4ND1Zg1axZ+9rOf4cYbb0S5XMaUKVNw880344QTTujtU3NwcOgDcG4UBwcHBwcHh7rCrbPh4ODg4ODgUFc4suHg4ODg4OBQVziy4eDg4ODg4FBXOLLh4ODg4ODgUFc4suHg4ODg4OBQVziy4eDg4ODg4FBXOLLh4ODg4ODgUFc4suHg4ODg4OBQVziy4eDg4ODg4FBXOLLh4ODg4ODgUFc4suHg4ODg4OBQVziy4eDg4ODg4FBXOLLh4ODg4ODgUFc4suHg4ODg4OBQVziy4eDg4ODg4FBXOLLh4ODg4ODgUFc4suHg4ODg4OBQVziy4eDg4ODg4FBXOLLh4ODg4ODgUFc4suHg4ODg4OBQVziy4eDg4ODg4FBXOLLh4ODg4ODgUFc4suHg4ODg4OBQVziy4eDg4ODg4FBXOLLh4ODg4ODgUFc4suHg4ODg4OBQVziy4eDg4ODg4FBXNPT2CTg4ODg4OAwEbN++HTt27Ki6naamJrS0tNTgjPoOnLLh4ODg4OBQJbZv345JE4aira2t6s+kSZOwffv2TMf961//imOOOQZjx44FYwy///3vE8v/5S9/AWMs9uno6KjBVbDDKRsODg4ODg5VYseOHejYXMYLKyeidVjl4/iuN31MmP48duzYkUnd2LZtGw488EB8/vOfx3HHHZf5OGvWrEFra6vcHzVqVEXnmxWObDg4ODg4ONQIQ4cxDB3GKq7vI1/d2bNnY/bs2bmPM2rUKAwfPjx3vUrh3CgODg4ODg41Qpn7VX8AoKurS/l0d3fX9DynTZuGMWPG4CMf+QgeeOCBmrZtgiMbDg4ODg4OfQzjx49X4jgWL15ck3bHjBmD6667Dr/97W/x29/+FuPHj8cRRxyBf/7znzVp3wbnRnFwcHBwcKgRfHD44FXVB4ANGzYoMRXNzc1VnxsA7L333th7773l/vve9z6sXbsWV1xxBX7961/X5BgmOLLh4ODg4OBQI/jw4VdZHwBaW1sVslFPHHroofj73/9e12M4N4qDg4ODg8M7GKtWrcKYMWPqegynbDg4ODg4ONQIZc5R5pW7UfLW3bp1K5599lm5v27dOqxatQojRozAHnvsgYULF+Kll17Cr371KwDAlVdeiUmTJmG//fbD9u3b8bOf/Qz33nsv7r777orPOQsc2XBwcHBwcKgRahWzkRX/+Mc/8KEPfUjuL1iwAAAwd+5cLFmyBBs3bsT69etl/o4dO3DuuefipZdewuDBg3HAAQdg2bJlShv1AOO8Cgrm4ODg4ODggK6uLrS1teGF1WOrX9Rrn5fR2dlZWMxGEXDKhoODg4ODQ43gg6NcoLLRX+DIhoODg4ODQ41QtBulv8CRDQcHBwcHhxqh6ADR/gI39dXBwcHBwcGhrnDKhoODg4ODQ43gh59q6g9EOLLh4ODg4OBQI5SrDBCtpm5fhnOjODg4ODg4ONQVTtlwcHBwcHCoEco8+FRTfyDCkQ0HBwcHB4cawcVsmOHcKA4ODg4ODg51hVM2HBwcHBwcagQfDGWwquoPRPSasnH11Vdj4sSJaGlpwYwZM/DII4/01qk49AM4e3HIA2cvDr0Fn1f/GYjoFbJxyy23YMGCBVi0aBH++c9/4sADD8SsWbOwefPm3jgdhz4OZy8OeeDsxcGh76FX3vo6Y8YMHHLIIbjqqqsAAL7vY/z48fjyl7+Mb3zjG0WfjkMfh7MXhzxw9uLQGxBvfX34yXYMreKtr1vf9DFjvw731tdqsWPHDqxcuRILFy6UaZ7n4aijjsKKFSuMdbq7u9Hd3S33fd/Ha6+9hl133RWMDUz/1jsNnHO8+eabGDt2LDwv+qE6e3EwwdmLQx7Y7KUeKFcZs1FN3b6MwsnGK6+8gnK5jNGjRyvpo0ePxurVq411Fi9ejIsuuqiI03PoZWzYsAHjxo2T+85eHJLg7MUhD3R7qQd8zuDzKgJEq6jbl9EvZqMsXLgQCxYskPudnZ3YY489sNfPvwpvUAs4Z+Ac4SfYhvKXBQvAcgbuAxwM0MoE+2TbD294mM5oGZ8F3FPmhWlcSyN/4QdtK+l+vBzzo7+0TJTP4/mijE/yZR4n+RysHJbxAcY5WJnLesF2EKHEyj4YD9sqc8D35V+UfYBzMN8P9jkP0sJ0XvYRXGgO+GXA5+Bc1C0H19znQRkA3Ofo8bvxd9yJYcOG9S17Ccsk2kt4b2P24kdlhL0wmibuUSxP7DPVhkQZGOzDV9ug9iLzFTtR80FtxM9gKzxYuSgoT+0mvM8+AvsoRzbCqK34fmAX4V9wUc6P7KVcBnxfsRceRs8VYS9HjDoVDV4T4GkPf13t0Pf18rZyWfPqgSze87QyeiSjXt5Uv9Lox6R7wFiUz8hfxsDDvz18B+5/7tqa2ItDZSicbOy2224olUrYtGmTkr5p0ya0t7cb6zQ3N6O5uTmWXhrcDG9QM+k8mLYNcD+QzFi4z9I6Dz/KA2DvPPQOQCcSRlLB1M4gD9kgnYPM18rKjoCmy84j3Ga0DAdjpANhpNNgfrBd5mCloDEmTzDsPBBtg4cnUS6Dh3/DiwvwMrioWwo6I8541MmUwhvqIyZb91l7EQSDkg6I607TDfddTxPf3WRPsXTzvnrPDbYi2jIRU2ofsgwH8wjZ8DVbKfPQHjiYF5IMYiNMnmjYoLAX3we8MB0+uPhR8ZBohF+YgwNeeLLUXlhIOJgfnHsd7aXBawrIBgDo0nsa4QDMpCONVBRBOrgwuirK+Dw+vSAL2fAQ2EBWJF13se2pBEOkc4/si991AdfXuVHMKHw2SlNTE6ZPn47ly5fLNN/3sXz5csycOTNXW/HOQu04ZAeQqbGU8hZCzvT0hH2lrKE9kR9rU6tjzNfSrG1oZYJPSuFKYogZMS2PBT9yzwtHIV64z2Q5ZhkN9iV7UYipsQAS72tUjtltIcu26biWMsyUp9mk1Z4IEbaWA0LSaMjMYDfKw9+mCCgVvMBWmPnRVUt7Uc5f7yBNHaueZhrFm8qZ8usRt5+13XrPGUiLmfC86CNASITc9sQzRP1wj4GXSBtU+SgAZXhVfwYiesWNsmDBAsydOxcHH3wwDj30UFx55ZXYtm0bTjvttELPI7XzACJVw5CeRCyAbB1+pZ2I8RiVHF+WrdEDjrGoHcbiJyXgeWC+D+4hHHXYf2C9Zi9cVTVsZbLmMYPNML08LaunG/JjZCJLWXluaplEQiHLcbutJAxYWVb5nHmA5wvhIiKn0lZ4WIxZj1dTe+E86uQE4RCdoGLnhvKIzjfW2Znqmo4tUM2IPM/vuqjJiVmDNE1KBhBXM8JtrpMPse8PzA68P6FXyMYJJ5yALVu24IILLkBHRwemTZuGpUuXxoK66oasqkdWNcOUZhjNJtXPQixofpr6QY+VSb2Q9XM+bAS5ICSDMRbI4EDwY/dJB+J5UZwH7UQS7ket7CVN1dBjNewNwUpSldgevU6sDVqP5uWzlVTioZVNV9i4dKUkk2Eet6us9qN3HGVDGWErsgqLyGnCYWr+fIkRCF/tLPV8E5GohnTQchSmOkWQhczEkVV+PjY3lS02w+RKIcSDFxgTw6sMEOUuQLS2mD9/PubPn19VG3luigwfyEQykkYbph94cpu2h3vSKNLagaS1n3QeWTqQTA1ZHiJ6B+J7APzQT2sgHIAkHGl3pRb2Ug2qcqGkKU5pigYS7MFKPHisDbU9rqanSfu+3W5i34cqDpnJhwdwlXEE5MKLOnffjwgHTx6p1ub5Qq+RRgpMhIPmizS9k/O5WdLPSjpMdWqF4pdcUmH67kkkI/xrVTMEyfAsbdcJLmbDjH6vLWUdqWZrDNncGpWkJSCPu0NpP6mTSlVI7AWyqiBcfwAAGfyxJH6DxHBkllULgEJMk1SwvHlaGhNpND9tOweyxGvEzy/leBxm90VFioYh+E/EY5hmH3gkyK8gH3xszUM9jiNLLIeOpDWp6xWvkYZ6HZO6OZLyTUpGipqRi2i49VJ6Hf1i6mvdkdRpyPx4stGdYqqbtq2npY1Wbe3H8lIeILVchN9jQDkanTGAxGMQdQOIFI7QlRI8sOv/MKgJMa1E4eBZ3GzRZiHxGiQ/KUDUlp+bINtgVckMSpggHL5faN/BOVcDWWvhVgHsKkdSnVojL8mo9JmR9XvY4jK0v5JkiHQb0fA88ILdKGXuoZyivCXXr+HJ9CG8s8lGWnAoYA4OBbIpGvU0GmMHYCEjedWaJIigT0OsRjQSCTsEmAgHjzoSsGBNBc9LlcV7E1ldKLqtWF0oWsefFq+RRD7yxmvE2iHHSnPrxeMz9DL2BrjHgumxgEpMIdwliNtJWFYSDiDq2IuwFzK9UygcknRkIRxBBTXNRDiAdNKht1UL9LbbhMI2K0knFEAuNUPu55htWy18MPhVOA38unYcvYd+TTboSDV7JSA1AFCWS8hLSdMf3EVNe80aOJo5D7CPPml+UkN6/AYQUzlYocqGXdXI7EJBQp6tnsmtQpDVRtIJMk9vQ55nvK6M7UlqWyBtpJsU32OzKdlBeAD3I8JRsBIGIKY+KCqHKY4DqFzlAJLdQ70xO6We0L+DjWSEfzMFgepEoxRuFxh06WI2zOjXZIMicweS2lBymYok5TzqiJ6XMtrMk14zwixGpkLloOliBEHUDUBTOACVdJQAuWJrX4dttglgTU+d8gotP4V0xPKyEFGFkOoKheUg3JJXzQwDvcM0qWDcVwkHIEkpgOKVsCTCESTkc6vY0sSxgPSYFP0eZCEf9SYZaYMSUUaHiWSIbZOaIfJ0olFiFtKB9OvpUHcMGLJRE6Q92LPORMlLEHI+A6pa1KuK5w1nLNZR8XDUKUElSzLNVZXKgRjpKKDzoKpG9jpIJhiA0YUi0o2uNV2JsilTRqKglbP8ZaY2kxSwpO+Xpl7k6cQSOiR1aislHFy63gAUpoSJ5fQBxEhARYQDyKZyiOPl6SD7qlqRBNsS5NWqGXS/FKTxAtWC6mM2+si9rDH6NdnI23HYG2KVuRnydvpJD3uSbw0mrAUpSUg3woO6BgKN1QCSpXBAWSuBAUCJBXK4TjoKjhavmQvFeB9ZNgUsth8drypSmkXpCPMzk45YuSoIhu5HTyOlBtdbrtWBqwH3oaxWSkiAMY4jSAjLZnCr2NLEsYC+NSr3WPWB5RlIBoDsQaCEWOhqRvBuFBT6fAliNio/XjV1+zL6NdkQ0F0o2SqljFZlOXNy7pkoFSDrkuNJx6tk1gBnDCzGmojLRNvmHuLqBqB2JoCZdKAUPsBLqDeqJqcZFA67e0XvYNTdzOtrkPxMa7Ek1Dcem5RJJUdpsNmMqZyRcISKhsn1VuSs/QTCARSkcgB9i3RUgrSXqYV/Y2qG2M7hNuElT5IMzmo0KHWoCgOCbOhIG61mGpTZpPEwL/9JmbcrnkJYhNIWjkil+4SqHNpy0THCAUSjVzGyNZEOCA9KcQ+DrKpGJhcKEK0aGjuQucNm2n6ueA2dWMSIB7e2kz4bJX7Amk1xBewuN9E5ayoY1xUNSjqKiPFRFvUSCpyw4SoJh6mMLU220UdIRx51I+1ldCY1Q+zncZt4nlHN4OJvqUhlo7r3m7jZKH0QPCtxyN1wBXnVjgKTRqcp7WZZfjoRdNBp86lT4mE6mEnhAFSVQ7RdKkXHCFeFtBK7WqLWx7CQEFNAqCyvl7Pt10KlSrMFU37aFMEsUwhNpNQW0GgjHMTOjKSjKFk8NpuEqByVEA4gm1sF6NukQxzbRDps52UiGWS7Zm4TLyIZ8AKl1rgcfp3gYjbM6NdkQ6Cit7waG0quX2nQZUWjQ9uItSiYZpzoUjhVN8KHppFwAHHXikwXD+5iHpy5F/FSKotPguJlCSKuOF4jiXSkqBy246ntm9QMru0b2lMKaAoYEA8mlh1KaB+0Q9XXQUhUwBCQDstbX+sCvfPXCQeQPXAUyOZWsdVV2ulDpCMJ+nfIomaItrO4TUoJagYLbNG5UXofA4JsUFTViSSWMbeXeypsLTlDheTHCpuqoYxUAe4TdcOwYI7oFBhVM2j5IDf0yQu1o4jZKGZnTSYXSgUrzMq8lLRMQcRZlS+tbq62E86xIlD1QhJXqIRUKhrhfmbSUZAbhaoXSmB0FYGjQD63il5XR18gHSbYSAbZzq1mJLhNZFtCzWDkb8FuFLeoVxz9mmykBfxV3YnIhmzpplFshjIaauEXz6N8cEaOqYkVNI0zBFMMdfeJx1MJB2AhHZR8UOJR2OwCompU3RYS43pS4zXCNOM2QRKhTVw5NKF+DLV6vskOxeA2IR1nogJmVb9C0lGU202gAsIRFK1RHEdSun6eQO+TjqwkQ+xTspHXbeKRfUE0KAER5Qp1ozCUq7DPaur2ZfRrsiFQExeKsWFUNWLVUTOffB2Ir5iBIoiIMiOFMcDjqt/TpHAAamyGaJuSDpB8Wq4AZSOGrAHEJnKaRAyyqhk2pcHWfl77SGmj6tkmFDEXW4ICltflZor1KWI2CvcjdthfCIfhHAqB6bwsMRpGNQMwE43w3SapbpMSM6oZ0qXSXxYNHMAYEGRDoCIXigVJI6fMr3Sv9OFtGa3WcmYAgCguQ4d4uHsM3OfyuIq6Ec5OkR2KrBPmG9QMXorIhZF41Bt5YjWyTo2W5S1t5ozXMOabjkX+ZonrybXeSxKES43YiLQhYQ+287DE+AAa6ZDkgpDR3rAXANaAUJ1wAOZyyEg4gOxxHKZ0HUWoHEnnkKZmiL81dJsE+0TNEESj4JnS5Spno5SdG6UPImE2ijJizdUmkh/2thFtvqOktldLKMF62gBUyaexGbScHHlo7hOSJqoYSQdNNxCPQt0oBDFVIydYGiHJqnDY9g2kILf6lfd7VXAdFLcckGIvSHe5maATj6LIBpCNcCSVQwbCAfQPlSPtmFnVDPE367tNBIHQ3CY0CFQ8RjitE36KfOurzz34VcxG8d1slD4MnhJtTOVxXRLX5fHE4+RITxjN1lyhqASi0xcdhSAXxLXBGQPzEKgbosuwEA4AZtKRZXTKefRWzzpC2og1ziKjjWQgpBUt+mazkST7yqF+2aa9VmyPFkIaEVam2gsNBrWQCqvLjaLEUMhsFJ8H7+0B3tmEI8sxkkgGTaNqBtmnRIN7XrLbxIvUDKFwGNUMSkLqv2aghFM2zBgYZCNEJnm8QvQJglAtDKoGhRK3ERbmsBAOsGC5A4AQCwTEIcyDUExMxEPMRpH1i43ZqFj5SkBivEaqKyXDASq1wSptN5WQageQ7jZJLgwEVdiCjXTYXG603SKgEIt+TDgqRR4lg+xzSm5oXEZWt4kkFZa1M7QgUKOa4RHSMUCDLvsTcj3hFy9ejEMOOQTDhg3DqFGjcOyxx2LNmjVKmSOOOAKMMeXzpS99SSmzfv16zJkzB4MHD8aoUaPw9a9/HT09PblP3hYYWvOOJE01SUurZ0eS1qzp1PUBPpUZxQ9dr0d/7HJ0EtTjNI3kPbflAaxY+wsse+py3Lv6Sjy2/jfYtvP14EHKGFDy8Mj6m3DX6v/G8meuAAC0tbXVz15sF8SkfOVpNEEZyzI1OimmI9NMlBz5tSLNytfVbUOOOmkHY7GVkhd0PiUPz732EFas/xWWPXMl7lt7FR7b+Hts2/kaeMkLPh7Dwy/fgqXPfQ/Lnr8KQH3tRYIuXGV6ORsQV19s5QA5NdZaF4jiONLKJaVXA3r/bHm6mqETDeNzIYVolFgQnyGIhiQcCIJAS0wSDe4FJEMSjTA2g4f1uLZdFHxEM1Iq+WRZN68/Ipeycf/992PevHk45JBD0NPTg29+85s4+uij8dRTT2HIkCGy3BlnnIGLL75Y7g8ePFhul8tlzJkzB+3t7XjwwQexceNGnHLKKWhsbMSll15a0ZdInMpIO5LcDSOx4+mzaodJwZAEI1Ab5GiVBvgxBEoGnYUSqhsA4goHkKhmvL5tPcbvdgjaBo8B5z6e2Xgf/vH8TXjfu7+EBq9JNjFul2mYuNtM/P2Za/Hvf/8bo0ePlnn1sRekk9EUF4p1iXK9vild2WfJ+WnpNK8Ae4zNUhLql5KG7O42gtfeWo89RkxHW8sY+H4Zz2z5K/7x4m14/6TTA3sJ2x7XdgAmDj8Uf3/hZ3W1F04XZunrCkca8rhSktq3KBlABjWDpCe6TRjUd5sQt0ksCFQSXcTVDOpOKZRsVLvORi/MzCsAub7V0qVLceqpp2K//fbDgQceiCVLlmD9+vVYuXKlUm7w4MFob2+Xn9bWVpl3991346mnnsINN9yAadOmYfbs2bjkkktw9dVXY8eOHfnOPvagTiEVGWM0UjuSBKQSkFr4yjMiVdgRIxHxYxWg6ob0lSKSM8MpZZyMUqQvXow2GMN79vocdt9tGoYOHo1hQ8Zg/wkfx/adXejq7pBlwQCv1ISmxqEAgNGjR9fVXuLTWkl6LZQwnnBfa3G/a+lKMf1Y4j+peLroGCjEQ1+2k6BmCDvR8g6e9FnsvsuBGDpoFFqHjMHU3f8Xtvd0oWvHZqXz8rwmNDcNA1Bne4GmQvSGwpEVtVA3sqgZln3rKqBUzQgVTVXB8EDdJoFS4UVEQqR5hGhQhUNXM0qamsGifYfeRVUUqrOzEwAwYsQIJf3GG2/Ebrvthv333x8LFy7EW2+9JfNWrFiBqVOnKqORWbNmoaurC08++aTxON3d3ejq6lI+AsbRaVYXimXUmgkZO6ai1I/EaGvx9Tx1P6gHOepQ5qkTwhGRCkS+UiJvcu1hAvkwiYgHZww7/Z0AgMaGwUHZsPzG1/+Fv60JZPELL7ywrvYSfNEE+6A2kdUexPTWBJJhOlremSix86wnTD8rzcYoGY351U3EQtiHdKPFbYQTG9rJA3LQ2DAocLeUQnvpehJ/fe5aAMXYS68Sjlq5U9I626wkQyurdOSUYFjcJrzE4suOp7hNqGJBiYdCJkoIPsK9Ij+9MPU1fDdKNZ+BiIoDRH3fx9lnn433v//92H///WX6Zz/7WUyYMAFjx47F448/jvPOOw9r1qzB7373OwBAR0eH8iAAIPc7OjqMx1q8eDEuuugi67mk+eJrjnqOXKsFQ9CZMihBfXJ6a7iPsC+N1tAQWWGQKBiYx+U7S4RYDrHHwgZKoVsGkMuPR6uTMojAUc59rHlpKYYPHY+hQ0eH5YH2EVPR0tSGBq8Zj/77F7jlllvw/PPP18VeqCoefo10NUxcHFmeKF9ZyEhWW6mTkpaYL+6PBYmuttA2IrtQg4mDouRq21woxEZk0CkYeJljdcc9GD54HIYObg9tlWPM8P0wqDGwl4fX/bKu9gLfl7NRFLdH0S6VWrpTTEgiGpb9mMtE/5vgNoEgHTndJkoQKJ2NUqIDpXBQJZXZkHQUKGz4YPArXwyhqrp9GRVTqHnz5uFf//oXbr75ZiX9i1/8ImbNmoWpU6fipJNOwq9+9SvcfvvtWLt2bcUnuXDhQnR2dsrPhg0bAMRHp9zy8K8o8M+C3ozTMEraeSCeg8I1ApAfL/lxktEqL0UPCR5TMsiogxH5U8icckTCsHr9n7D17S2Y+q5PKSPbcaMPxm677IWhQ0cBAK677rq62Uvw5WFeVyPvNOg0cJbdnVKNwiHz9BFyyvnRoppdmVSymKstdgKqwmFVvQxqRjQKjtSupzcuxdbtW3DAxOMV99y4kdOx6/B3YciQguyFKAtVKxwUaQpHrHydwgarIRq6u8SgZpjiMxSiIdwm5FmhqxdWt0mKmqGnOfQuKlI25s+fjzvuuAN//etfMW7cuMSyM2bMAAA8++yzmDx5Mtrb2/HII48oZTZt2gQAaG9vN7bR3NyM5uZmY57ZFZ1ALColHnlcLLWGUCMs6XEVQ+wHo0a66BL3AEYW7gqqhSNUj4UBoJHCgXAbpbBDI0oHQEavDOGxyLUNVY/Vz92BLW/8Gwfvdzqam9qCbP1hFrZ78MEHA6iTvRgIhvUZXwnx4Eh2p6AAslqr9oXNMaFehM2zuPKl2oIaUAxYlA5ADS4G5DTY1S/+GVu6nsEhe81FS3Ob+fyKsBd5rEhZqErhSFnaXEEW5aJadcNUtxo1A6huWitdclyQ1QrUjChAFIoLpihU/4r5gelGyfWtOOeYP38+br/9dtx7772YNGlSap1Vq1YBAMaMGQMAmDlzJp544gls3rxZlrnnnnvQ2tqKKVOm5DkdcmKWTiOto6Cdic0vnjRC1cvWGtWQcTlKjfbliJWoF9GP0qBwkLnsMaVDTjezKB5e0AmtXncntrz+NN6z/+fRMmhENI2RfKSvFsATTzwBoH72kqpqWCsCRhdKWryGVl9HRe8nqSNhUZ5z1P4sypc+iyBV8aJ2QhWPcHS6+sU/YfMbqzF971MwaPCIaFQr1TIS24E624uiZNRI4eB2haKi+I0sMBGaWhMNj6UTDdu0Vo9JoqGoG6YprTnVDJpeFMSiXtV8BiJyKRvz5s3DTTfdhP/5n//BsGHDpA+0ra0NgwYNwtq1a3HTTTfhYx/7GHbddVc8/vjjOOecc3DYYYfhgAMOAAAcffTRmDJlCk4++WRcdtll6OjowPnnn4958+Yljy4MEIN5JUEvk2d0qncm1aKejsI0VQNqXuDzhvRhMl9XLzSFAwCEyiHUCoZw2my4L9QOcUI++cqcY826O7Bpy+OYOuUklBqa0d2zFQDQUGpBqdSIt95+FZu2/D/susu74bHAOX7mmWfWzV4UJBHUGqtYVitIsY/UVUTTDpqoepn3aV1xiknKl1Q4AIPKEZ24VC9ojA8g43wAYPW6O9Hx6hM4cK8T0dDQgu6ebQCAhlIzSl4j3tr+GjpeewK7te0FFioEdbcXqh7UQ+FIi98oClndJqa/gmSEaVnjM+TS42TJ8YBYRAOYqFxEINLUDGV6rKxXl6vmkAO5yMa11wYR4EcccYSSfv311+PUU09FU1MTli1bhiuvvBLbtm3D+PHjcfzxx+P888+XZUulEu644w6cddZZmDlzJoYMGYK5c+cq63LkQixuwzByBWregVBU+migxMD2sI/KagGexvYMZUJXimhXdhJJhIMB4CrpQHiuRuIhKofBdAEBYXhpYyBnP/bEz5Xz3Pfdx2NM+3vASiW89sZzWP/yCpTLwcyDj3/847jkkktk2VraC4fppWgWexF54rvltB2pdljq1W2GSYJ9pNWh79BR1tMwtUdtSbpNQsMQpDPsYCMyE7VNXW1AYDMvbn4UALBy9fXKoabseSzGjjwIrNSAV7uew/pND6GnAHuRyEI4crdpJxzWY9cKCcSi6iBQIJphUo3bRJKHQM1QyAgprwSA6mVIfpFigc8Z/CoGmtXU7ctgPDUyqe+hq6sLbW1tmPDz8+ENGqQEhhpfFa4H/8l0xDqURJmc004kSjfnA8w31PVJGQQdvmzHN5eT6eINrEpaUC6qy5V2gm2unCvzedQGonq0c5SdoVgyWuz72j6iOkG6zpJMdzBerqdnO/7690vQ2dmprJtQCwh7Gf+TC+C1tBRqGwi5n14muC9aOV8rY7Gb+P0l91SzDdVGuLJvsilhB6ys2kRQJixLbUDaC1e3AXUpcnK/VduxGIglWaCnZzvuf+TbdbWXD7d8Gg2lliiDdsoh4VDIhtI5e5b0DGX0dnVyQGemJBEHQ7tVEw2qZpC0TG9rFUoGIRrK2hmKipGiZlCCQWM5FOKBMM4DKO/cjid+8b/rYi8Cwm7++9HD0TK08jeBbN/ag28ccn/mc/3rX/+Kyy+/HCtXrsTGjRtx++2349hjj02s85e//AULFizAk08+ifHjx+P888/HqaeeWvE5Z0H/FpdMqkZKmSg9IS/XOVTfhGyKno7p1BLSaF06c0CJx5Bp4gcOZQSB8MdJf7TyAUFHHrEZJ1BHHFIGZep+yqfu0O9VogpWp/MhHTRFtYGjmdZZITE7UR6LlRPbSXE9NIYjGlHSUSqJ7yExPqY4H8UGZCxHhk8RoHESBmJUs/gNv4YPEhvyEg3G4h8gcbZJLD6DQS41LxUO02wTGcMBNTaDxmKQGSnp8RoI2yL7BUG89bWaTx5s27YNBx54IK6++upM5detW4c5c+bgQx/6EFatWoWzzz4bX/jCF3DXXXdV8nUzo9+/iM24ImSskCU9a35SvSQEfgctTaun7xvbicqYfOxqbAaVwqNisfU0QilbcaloLhLq1w/SmfqVOCcyuPY1fDGkt10WjSgW1XmYYjWSyAVVNapE0dOmFTddQp6wCelKKcTNBsQMxLffhtj5FzmV0bTORd74jWpQqSsl6RplIRr63yS3iSk+Q3OTKPEZNreJJA4sk5qhxmpAIcfqsuUF2kvBmD17NmbPnp25/HXXXYdJkybhe9/7HgBg3333xd///ndcccUVmDVrVr1Os/+TDQDGEWsls1PyLNZUt47DRCK09IhBqGmSVDA1W0yDFd51QR6CP2HnUoraSiUeEA0TokEvLSEhRhiuXeFvZTTOShGfZOKRm5hWSmYrhIlkWIOFgTgRTYnrAeKkQ+bpxENkiH6KEhB5cuHfHK8BL8JeODlvJbGS+I2cwaI1DxS1uWXC7aoX6coYn5HqNiEzTWyxGXEiAsVlortWZH5BKIOhXHEkH2RdfSXb1CnaGbFixQocddRRStqsWbNw9tlnV912Evq1G0VRJG1TGmOVkP3Bn+CTrwZ69cR9RtNZrAx1j4jydP55lAb5IKA+TSlDKg+CMF0EZmkuEeluoS4X45Qz88cvGdJr+WC1oRJVI6GtzPEaqeeVpRCBYhPmdLCEtNi2ahOKDdncbAabirnZFBsDGblW7mor1O1GYXOnmKajZnGn1APW+JGMRMPkNhFEI81tohMN+rdStwnZ9kvxNPmc0reFMuKp+UWhVm6U8ePHo62tTX4WL15ck/OzrbLb1dWFt99+uybHMKH/KxtpBMNGLgqiuklStgIG9TzJfqLCgWBfcZlALSNVjrCAmEMgR6BhOaMMThQPgKgeoAcj+bE1wc3g2kahU9OSVI1YWdTEhVI1dPvIAdVlkuxmU+tpCgdxvZlcbABRuTR1K+ZmE8jbAUsVpaDfr+IiIe4Ug2vD6k4xN5yqbtQcaUTD9JcSDcAen8GEW8QyrTWr20RXLyz7KhHWSLEpSFQsZNiPsGHDBiVAtBaqRm+i/5MNwLxseayQ5QdcoLytwNR5JBAOE7FQyAhUohGt9Eja4aRJEX9BSQdpRCceACEfAqZr51f2oOQV1st1DJ0H5bWT3rIVE3TbsBBTo8uEqbYSlCVutrBuYB+qmy3JxSaOT8kHQGxLR5q7Tfm+2m+8CCVMHMtGOATS3CkVkomKXSk2VUNLKyI+I8ltoiqngjAwQjDUfIVsJMxGURQ6or4WhTJQpRslQGtra11mzrS3t8tVdQU2bdqE1tZWDBo0qObHExgQZAOAYaRquNm2zqLenYiJWFigKyFxYhH6zynpgE40xCFZpFDQ05AdCSLSwSB7YzUINPwKgnxAP6D+XSu7mAkLK9YWuqqRNCPFWD+oU7OYnRztGG3DongpakUsL25Dwe2MCAcQ3WIlmDghtgdAXNmyEQ3xBWxIeVYX0nn43PwiNnkSRJ0wkpAc6ka1SCAVynalRMPiNlGIhiE+I7ZIl+IKiQhCFjVDCQDVyugKh66CFLvORv4ZJXr9emLmzJn405/+pKTdc889mDlzZl2P27/JBoeqaphcKIY6JjBbp1MrWAiHTiZEWV3azqJm6Omx0WmYQVUMVdEIr584Lu04YmTO8j0rdDkU7oM3fR+D/SS6UOqsdhhdcLodkX1KRGVeeJ4ml4luQ5JwhMSTgdgJVGKR6mIDabgO4FkVkWphIgx53CkyMWVmSiUzV0wEw/Q7ykI0dLdJmJ45PkPug8TiQHGbJAaBWtQMUwCo1WWSNjNlgGLr1q149tln5f66deuwatUqjBgxAnvssQcWLlyIl156Cb/61a8AAF/60pdw1VVX4b/+67/w+c9/Hvfeey9uvfVW3HnnnXU9z/5NNgRop5ylU0zrQOoEI7FIyiN9vyqNRy4SWSfqB1QiIdNYNDrVexhyXBP5MMrglsWYdJKUFYU8DEyRuFnONYVU1CpwOCsSFQ7EbSlGNAw2FLMdC+lQXGzkGJlcbNWC8ph6q5EG5HGnRGkmspKieORFkqpRCdFIis/woKyfobtNIhdJitskp5pB/xpdJhbS0Rtko+gXsf3jH//Ahz70Ibm/YMECAMDcuXOxZMkSbNy4EevXr5f5kyZNwp133olzzjkHP/jBDzBu3Dj87Gc/q+u0V2CgkA1AfdCbOpVcbVV/OokgxCB2LFseTUfYWWiBfmCQq6maOxDD6DTWicgDRMhJ2Cq5fH4BMRsAzMTBqHJY6qYg00vZlAop5cXtyqJwQE2jbraYiwUGwqHYidiOSIfRRhg5GEWtfkMWsyh2nY2IHKS6U2RSRnUjrytFJzNKnplg5CUaqfEZFqKhxGdY3CYm5cKqZigEA+R4ehrMpCP8GvIYBYGDwa9C0ssb/HzEEUfEX+JHsGTJEmOdxx57LO+pVYV+TTaChyXZT+og83YeBc5AsCkeppiMVDWDlJcVDCqGKQBUXkBd/q6w482FomXODITU6ELJGq/Bkf2RkUY4RBlo5QzElAYGq+kaAZV/NRcbzKQjycVmVr4yfO+072oBZ5W/ADUXuB+dS5o7JSmtHtAJRUo5K9GoRXxGFreJQi5UwqDsawGgJpeJLS5DIR2Il3PoXfRrsgEghTBkKJsmjxeIRHIhEgWYpaMQ5cMdQTr02SZm94nWocjjWi5EDa9PIdwuiz2k1clTN0e5JBdblnKq7RhUL5iIhqp2VeJiS/yOWX0dKR2mkfMVpWwY1Iis6oZEPaezCthUDdSGaCTGZ5BprYluE4NykaZmUCKiEwoj6UC8nChTFIp2o/QX9H+yAahPo7QRa9YRbCVgKjkItrmiwMTzg40snU0sKJRBLmZmmuYqSAaA6GV1lHjQ4yWqQinXJ2vHm9BMYQF/HJq9mPINaba2ag2m2oUNJhJhUz1MNqfbCiUdgY1kdLElnmfl9zTR5BjUl7wVhazqRojEqasZCEjmaa8J7pOqiYbpRWpeQnyGwW2iTGnNo2boLpOkYFBE+7ScLFMqbuTo3vpqxsAgG0B+F0qsgZzpWhEmn+DVIU3dsLtNWNRJAEY1QyEeMDQMw7YoVuffauFkXvm+GQio5fvX/broBBP2+xFTNxAPABX1pX2AFFCIhc6cyQEr+X2lIJVc5Clfa2RVNwSSXCnVzDoRbSa5UEx5JqKRNuMkZaGuWHwGjbswuE3S1IxYAKgtQFQnHcycHhGR8CFZZIAoPJSrOGA1dfsy+jfZqMaFUqtOohqSQZ/nRN2wjURlGiEatOMA7SSAoINIGJkqMxiSyJo4fo2umamj8ItYTrhaG+hLIw6dC8BkO3HCodgK2efUjo3uE/LdjWQ038XM8pZac73gr1+AEsbJOhsSSYpELdfNyApduSDbXCMVCtEwzDjhnmeOzxBEQ1s/Q4/PECRDIRWENKSpGVlcJqaZJjQ9RjJofYdeRf8mG4DaAfTVWSjM4kpJGJ0mSd9SoSD7Sl5YOEY8YGgYhm1YRs6pX7QKFPUwSLKRDGTESLh6S93IlGZYYwXQCGpUKRbbY/p+RtuoopPNQC5i6QW+6yI4YIIikSUotFZxG1mUC6QTjYoDQfX4DIvbJE4saB5ipEKqGUkuExKXEQv+lMSDG+M1ihwnODeKGf2fbCB9VB7l6R1NlQemo0FTHlIUAVpGPx9meN4TYsHI8dNGpQCixc+Ua2WXxuvpOuk1ZUOeQI68OhMJAHE7gMUmLEh1vdEA0PB4+sJu8hx0O7KdQzXXpQJyodcp6l0X3I/eaisREocsy4lX/fZW3YVCQc9LDwitkGikBoKaprXSF6ixNNIBhVTo+TaXiVAmKiIZrFhy6sODX8XoqZq6fRkDgmzEkJV8VHMIMTLUIUhCgmohTiv2DoswQzxwlU5DEAuaQUajMeJBOxRxQO28Yq/pNhCO2NezLeZVpXzc6yuIEqTGa+SxqURCGipeOQiFjZAYCQclEyCkgyfMOhG2p/NQ2zmmnXvabbXkp5GOwpUNAJniLYQrpdIpsOHvwEhQklSNcJvrBCQr0dACQdPiMxLdJgY1w0QojAGgliBQGwkB49Z4DUpE4BUxanBIQv8mG0kulN6AqWORRIKr56gRjNg+1A5GNK+TjiRCYVR8KnUVyOKVX+ekW1SITzXxBAo4Pj2cjawCuRUv47YgGnRb2JmuclEXGxDfNu1XgqTLn0IudBQRsyFgVDd0VLu+RhKJoe1KpYPF1Atl5klWomELBM0Yn0EVjFgQaJKaYQsA1V0mMXIhymQkGYwcpyCUOUO5iv6omrp9GbnIxoUXXoiLLrpISdt7772xevVqAMD27dtx7rnn4uabb0Z3dzdmzZqFa665BqNHj5bl169fj7POOgv33Xcfhg4dirlz52Lx4sVoaKgx70nyxROkTns1EgiNOEB90IvDxzsKErthIhhaRyL5CSUVVM0AkmeWkGMrcR4pHYlZ0YinpV26TSuWYsvDdytpTbuMwrvnfgMA4PfsRMff/oDONY/B7+kBAGzevFl502HN7UXpUOtLPnSbABC/R1q6ropRO5IExUA4RBOUhIr6ioKhq2EwkA95HpaLkOXapNiGSQnreGQpNj+q2kvz8FHY+3PfAFhgLxv//ge88cxj4EXYC13US0cOV0oiEgNlmXnbpF7o+5UQjYSFuhR3ikI01JVA1emuNF3sMzuxoOQEcQISbHNzUCigqB1xN0xxowkXs2FG7l/gfvvth2XLlkUNkB/xOeecgzvvvBO33XYb2traMH/+fBx33HF44IEHAADlchlz5sxBe3s7HnzwQWzcuBGnnHIKGhsbcemll1b0BTLHa9QKRvKhpRn2aSeiEBGdYJCqMdIRJpqC+IwzS3KOVJMVjQrgAc27tmPCp74UHYN5UgLfeN//YOtzT2HcMXPBmIfnb70an/vc5/DQQw8BqI+9JCLpS9bgAWBUMwyuFOoS0ZmEkXCIvHDbHjBM2rPYhuo+YRXeeP072rOUy8qA5hHtmPQfX4rs2ovs5eW//w/efP4pjJ8zF2Aenv9NL9hLJVNXc0IhLyZVQ2yb4jSyEA3Tq+FNgaBp8Rm620SbfZKoZhiUDWW9jBghqYxkiLQBGgbRr5CbbDQ0NKC9vT2W3tnZiZ///Oe46aab8OEPfxgAcP3112PffffFQw89hPe+9724++678dRTT2HZsmUYPXo0pk2bhksuuQTnnXceLrzwQjQ1NRmP2d3dje7ubrnf1dWV97RV1JKUkAc4B5T1NnR1I9gPnvpKh0JOSek8CKFgpJOgxMP4nWqoaFSD4EfvoWFYNPIUD4hy99t4/YmHMe6Yz2HIpL1Q7t4OAHj44Yfrby+9QUoNJDVGPsT91NimJKrULrQmraTDQkqtJD2RcCXkZeBiqXEYHsBKHhqEUkG+f7n7bbz+5MMY97HPYcjEAu0lJBeZXCl5YGrLEvAZUzVMcRrVEg3TQl2G+AyTghELAk1RMxJdJsbgUE6UDeQjGbJeccoG59W9Yp4P0Hm6ub/VM888g7Fjx2LPPffESSedJN8mt3LlSuzcuRNHHXWULLvPPvtgjz32wIoVKwAAK1aswNSpUxW3yqxZs9DV1YUnn3zSeszFixejra1NfsaPH68WsLlMskrmGRGLG9RGZQr0PPHglD8ErpIG8aNg0Q9P/bFEIwH6g9Xr6WWUT8mwH37gVfehbck2GbDjjVfw72svxDM/+T948c4bsOPN18E94O1NLwJ+GYP3fHdUHsD48ePrby8JYDZbyoMkWxD7in3wmC3ptqHYhTg97eEs8+moMsPHeB9t9mP7ZChntRViz92vv4LVP7kQa37xf7Dhzzege+vr8EvAW1teBPfLGDzp3fIYQO/bS26YVBGbUmJTNcQ+JRqxNu1Eg5IJaCqGX2LwG/IRDe4Fs8n82D0N2y1pdkGeRX5JsxulHA9W/qRpwtbDfHg8VgceJ7YWtVEUymBVfwYict2CGTNmYMmSJVi6dCmuvfZarFu3Dh/84Afx5ptvoqOjA01NTRg+fLhSZ/To0ejo6AAAdHR0KA8CkS/ybFi4cCE6OzvlZ8OGDXlOu2IkkwluzON039BpqB0DVwiC7CgM5IJ2IkoZy2giRlJSiIh4WFT6MbXZMm4Cxvyvz2DcZ76I0bM/iZ2dr+H5G69Cz87t2Pl2F1ipBG/QoOh6ABg5cmT97MVGPqsd9CQ8G2Jiko1UiLwMtqOTFZMcrdgOJRMG4mm8h0nEIu2TYlO29lt2n4Dd53wGe3z6ixhz9Cexo/M1PH/zVShTexkySB4DqLO99AJi8R8mVUOP0xDliKqRRDRMS4/TQFBJMEoMvAERYSgR8lCCtg3NvphiC1y/9yYiK//ykGiQNEoySskkQxAMXhLqBnezUfoAcrlRZs+eLbcPOOAAzJgxAxMmTMCtt96KQYMG1fzkBJqbm9Hc3BxLJy+erL7DyAMWHU+Rwkm63GZqEg3WE4QjcnUwWZAG8CmuE0RpsdPK4TJJgh5XUKkoNGSvfeV2MxuLlvET8NwPL0HXmlXwGhqDtsMRai2nMtrspQhY7UHf121Id6+ZbIccI9G9Fqtg2Qa0GA1zmVSk2Eea+0RgaGgvnAFNGItx4ydg7dWXoPPfq8AaQ3sJSUYtleZC7MXkNkkK+tRVDYOCYXOfmFYC1YmGPrWVEg0an+GXTPEZ9iDQqD7UoE9tEKW7UYSrIzbzRBk4JcdkcOo6AcljKJRs+Ly6IE9/gPKiqn6yw4cPx7vf/W48++yzaG9vx44dO/DGG28oZTZt2iRjPNrb27Fp06ZYvsjrdSgPa4tykVaGdBA0TdY1fCLVgUO6WAS7t6kRBtdHVpdJmtukmrJJo11v8CA07joSO15/BaXWVvByGT073lZGqlu2bOk/9pIHJhsiD0b54JTbMNpImnst0b4MtmRUpeqhZJg+FtsW38sbPAhNI0ZixxuvoDQ0tJedb0d2iWLthYtegFf5FjiFWESPYKOqkeY+McRpZCIaJQYu3CUNDLzBk8qF3xB+UohG3FYS1Axha4zUI3/9ElfThS1QpcKkZIRKBxeuE/E7Khk+BcHnXtWfgYiqvtXWrVuxdu1ajBkzBtOnT0djYyOWL18u89esWYP169dj5syZAICZM2fiiSeewObNm2WZe+65B62trZgyZUo1p6IiB6vkKSsqWUeLhD3HAjZDwiFdKkZiQdLJtvIgFmzf8vFLWpr4QRoeBBWThJRPnnbLPd3Y+foraGhrRdO4cUCphG0v/DuSPAFs2LChvvZScRxGjcgnJRemNBLPk5lc2AinjXhqxMRIAkwfW/saSbB+bETFYlflcrckps3jxgFeCdue/7dCTutqL7x6cpFpWmySqiHyCbFIDAgtxbeNRMMjRMMUn5HBbSLIhC/KWZ478n4xw33X4jKEDUtbps+0NJLhEWIh4zY4WPgpCj5Y1Z+BiFxulK997Ws45phjMGHCBLz88stYtGgRSqUSTjzxRLS1teH000/HggULMGLECLS2tuLLX/4yZs6cife+970AgKOPPhpTpkzBySefjMsuuwwdHR04//zzMW/evGJlb4bkTseUT9KkVJ6UFv6V3hCD/SiHCPMVCT4D4i4TrvwxHqsgvLL0Dxiyz35oGL4Lero68dryuwDmYei0g+ANaUHr9EPxytI/wBsyWD6UDz300L5nL1mQZh8gt1TYBi2HqFxQJFQ5dNea4VhK5QS7ibnHYhs5kHCcRK5vy2PAlrv+gCF7T0Fj2wj0vNmJV++7C8zzMPTA98AbMght02dgy91/gDd0sBzx9yt7oYGgGgmxqhoJAaBVEw3dhSKIhqZg+JRI6KQ2VFJixJOQYsX1IUmp5jLxoM4wiU11TXCXyNkqJJ3x4LKx4smGgxm5yMaLL76IE088Ea+++ipGjhyJD3zgA3jooYcwcuRIAMAVV1wBz/Nw/PHHK4t6CZRKJdxxxx0466yzMHPmTAwZMgRz587FxRdfXPk3qMEsEwAWgkH86NqDXu0o1I5BPyM6zVB5boht6jev9Osk/Jbq+bbWJPR0daLjlhtQfmsbSkOGYtDESRg37yvwWoeCA9j1mE8AdzJ03LRELtJ0ww03yPp1sZc8MNhEoi0AyTYD7f7rdkTIQqSGkJgehugmmPqflPtcFbkwoWLCET8BzoCdb76Bjb8h9jJhEsZ96SsotQ4BB8eucz4OeMDGm/uovVBQdYIqFxYXSrAfqhoJ5EKZfaJPcTXOOvHCDttCNKiS4bHQ7WF3m6jEghnTdQVOEgaZxgmJQIxUqKobN24H3z9OMph0RQNM/kWhZMOtIGoG4zznu6H7ALq6utDW1oZx1y6C1xIGppJAOfmko2kg6Voes5aP6jBTnjFdK0/zbPs1RJFmWtHXsJygv307nr/gf6Ozs1NZEbIWkPbyw4vgtbQEiRZbAEJ7yGoLCXZgtRnrcclJJ9lPRmQmmUkPtwyNZA0ATUvP046/fTvWXVhfezkCn0CDF67NERIDudYG8yRxkKoEJQoh2WBCfaB1mLov61OyocdhmNwnJS8iGoJgZCQafkkoEpZA0BjpUEmGUc1g8W01jUMnI7pyQZWPzCTDi1QMnWSABUNA/vbbWPf5b9fFXgSE3Xxm+efQNNS8pksW7Ni6AzcfeUNdz7U30L/fjWKDYTnxJHDGgw4mi7pBR6DGkakmf8PwrKwjI0jsGmhmjc8hNxnXOjFeYABXxTDZlc0OYFEteLSp3wPlCjByifRrm/E+Gq9o9kQVKfe3ZoQj4/F7NYYu4wqiimskpmIkqBp1JBpiDQ140NbTgBp3QdbPSFUzmLodUzNMLhONSKjuFq7kVUoyvDD+iTHAL1UZ1OtQNQYm2cgCE7HQ8yiBSCEcolqEqHFF/k48pyRfSEK9evTTFZKRihRABjm7oFdgJJlQbYAgE/GsxLWmHUteS9O5mbaLQF5yY6tjIxtWctLHyantJWw2F0q4z0wzTjRYp7jmIBpREKeBaBCSkRifYXGbJKkZZjIRkQp9P6ZmANlJhhf82hghGUzEbhQdIFrN1NfCf9TFoP+TjSQVI6HD0MvE1I0shINCO43YGdVDji4SFZxDnt9bn3BTZiKgFShdSXYDlXiAmWzHfEqZHp+0btbnbdq9qJIoVN4OKdOb5DQNOvHQA0NNykiCqhHVC/M8D3TRLhGvYSIafskjhIKpgaDEVRKL1VBmMDF7EGgWNYMQDZM7JVh0S5Th0RxJmW4nGSzMt5GM4C9pswBwVDejpJo3a/dl9H+yIZDUUcgyBkagdBQZCAfIaNXSeSDDqcjjJ+2jjp1wpVGjlZxPljpFjVQz2ElFxLNa1xo9fp7vEjv3rJWztJ9faat//EZ4LXthdoHp3Siqq4SZ04FkVSOr+0RfHVSQjlIGoqGvDBqSiGgdDG2NDEPchlXNYHoeUTM8RLNMdJVDKBZe7UmG5/kK2WCec6P0NgYO2RDIQjoSkNjRQGxHnQ09bOw8RHljAXE8Wkc78aSOo9YkpIL20tYoyXOcwmXxnHE9NmR2rRHSEeyqxpM6kyRGTHPYiq2NrKgFUcipbqTafm8rG6YVQQH7LBQgUjVoUKiONKIhVgethGg0UCXDEquhKRp6emxKq0JEVDVDCQC1uEyUuAxCLkSaPrvERjI8j0fEgpIMAF7BZMO9Yt6MgUc2BERnYhmVRuW0fFgIB7RtjdFYpa+0B69CNpLrVdS5J51HLcvnhf5densefIwsZFO5rAoHRDtR8yo0+0m73iQ/TjySy2dB+vFzkIXc5CQnEUHB5DQtIFRzm8QCQ5PWytBVjTBdIRolWgYxohG0H5KDBk+NxdCIhuIuUaa7QpKEaKaKpmLobhNF4eBqOUk0IjUj5jIxrY9B0piXTDI8TyMWJpIhlY0CYzZ4dauADtQVRAcu2TAhC+EA4p0N4vnxB2GKMds6CKUTsZAQ0341yEta8hy7kvNkvBg3Cr3mGYhnJSqX7evHvh1Lvg2JhCKReOTvuDOXy0OAcxIO+zFt7RdhLyaC4JFsZi/rsThJoapGkvtEtKd9ZBCoRjSi1UAzEg25RLhJ0QiIRnwWikXNEKQhTc2wuUwo4RCEgpAMoW5USjK8MI8z50bpbfRrshFF8aeoGGJfqWzoaKDWFw9ShXRYT4YcI8ODVj6kDaQjXs/wYK1F52Fru5r2Ki3b18h8EuEA4nYGqKTDSEhVVKRmZFHFTPvKcTPcc1P9mhGFHMe0tVOw280UrwFAc5t45lgN4j6JN6wRDSZcIuG+mOIqZ5t4cgZKTYiG8i4TQ3yGyGdQCIZ0m9DyUNMp6TC6TExxGaE7pBKSUfJ8hWB4knBwlJ0bpdfRr8lGImyEQyEFBr+93qkA4BpTYTlcJrEHu41cZO5Eqhi51pB4pMzWy90uK2oevI2Q6vlkO1HlUmCJyQjbqjguw2QPJuKql01Ky4I8JDcPUbCVz0O4e3PqqykWQ8+3uF5iqoaonxYQqhGNaKlxC9EoaYt1lVgCyYDmWkl3m1A1A4yWsagZhumrgniIuIxI0YhIRFpMRsmgYlCSwdALMRuobjaKm/raR8EYgqmDWVQM4+jUQjhAy6jZOvkwn5hlm54TzUsbuRr37eeRSgbSer4c9s7yqCM2ntYbMRtJhANAqsqV6FqLf59MA5YEYhFTw/RtpZ2calgt2snRhtU+k2yJ3qLCZi95xu24gpGuaqS5T2JEQ4nN8KKproRoRAt2VUA0UmeiADG3CVUzGGLEIiprUDMk+YiIhyQYZMYJXYyLSUWjMpIhXSpO2eh19HuyoSCLimEYuSoPuCTikXr8lHOjZZghTcnX1BSlTL6OJJEMZPh+mVWMnC4ZpdkiyAa93mluNVqOEIrMrjXTMWzlDPsxtSILYTW12RfIgo1gVqDSyeMWSE4VF4qy7cUvhFA1KiUanmcnGp4XIxqxWSdJRCNGMlTXientwIlTWsU24woZSVQzRFxGuB8L/iRTVmtBMsS272I2eh39m2yIBz8DYm94SSMcAGKj0TTiQdvOcF5KWUoiTGlIIBR0RJcmmevtJJ0bTU6oYjx2auF8nYFXmBulQpXLQjqiYoYrmJGkGd0gMQKRUwmz1Re7eciCqT1Y7CGvTSapc7Z0xoFeXDfBqmrY4jrUynaiobtOZGwGKiYaeqwGjdmIxWWIbUooDG6T2CqgUungGslATM2IuUxIXAYj5CK2TgaSYzIEySh5viQYHqL8Hqds9Dr6N9kAZAcRc6cA9g4FUEkHtHJ62UznYdmnnYSNXGgdiY1QJKkbiQ9mY0YVLpikditsr1ykGyWLypVT4crkWrO1Z2o7wW6MhCSNsFqOk0ddMN5Hk+phbrGmtliI2013oaSpGrScTdUAYgGh8iOCQbXYjFoSDZFGZ5woM08YcaGYgkBDe5RBoIR0xNQMT/2ru0xoXAYN/lTiMxAqFZ5vJRkNkoTESYZUORzZ6HX0f7IBmAkHYFcxTApGpfc3xwjURC6YXh5xEqL0H7EOJLvEbXrYJxGBrIQie7mEzCKnvgIq4QCSSWlSOzQt6SskfXebyyQPUdWOkYmwmo6fcspmNaM6G6yoTkFKmD4LhVHSAKiqRlb3iWnmCSUaJDYjN9GQbpOIaFBSYVI5JOFgCfEZSqAonW2SUc1IcJnowZ+e50uSwRhHySPkIifJ8MDhMR/MK9fbVBxS0L/JhqHzUFwqusoB5FAwknrhBPk4qVOgyoXc1sqDlNGORx/G+gPYOMiydSLWdGNyYp1KywHaFS5q5GEjFDb70BlE0qgjC2FNcpmkkYsMCphN2UgkrPp5KWVNadnIRTKxyGeDsfJFL+plmoFiCgqN1dWIhs19QolGSDBMRMM4vdVENMh2EtFQAkGVmA2D28Q0pdVDtG6GeK7R2AyLmmFzmShBnSHRyOMu0UmGUzb6Dvo32QDUESUhHICmcgDprhOl3QwPM9uDXesYmLKtlmeg+XZSYSMaWUiHqZwtTZyTDV4dSAcAsFIBIw/RiSepGGn2wXiygmE6pm3fpIKlkQsLSVWaMRBVpY6pnKWMvVz1akY19ldE58G0KayMEgf1pGBVNXSi4QkFg+SVvMxEw6dqRriORhLRiEiFbSaKIT6jpLpIjEGgVNkQtioIiFAzBKkI69NZJkLNoKqGHvxZIsqFIBkK0chAMqJyxZENjuqmrxZIowtFvyYbDNrDWus46DMhlXjkOrClg08iGBnIhU4sov0syobh4W84dRNZyNsZRG0lZudqq9iYDaixGkA+1xqtb8ozpicrGpkUMJv6lZGkKnUs+6Y69nLZbA6wk9RK1A8Axa3LAkTEQUnL6D6xKRqEaEALAu0VolFKd5vEprQKHha6SYSqkaZmeLqqgXjwpyAUlGQwxtHg+blIRrDtgzHnRult9GuyAQSGyY2uErXj0J8VuQP6YHgIm0ae4X4SwaBkIo1Y6OnkcADiD/GsnYJ5MUP7NUlTNPIoGUbCU4RPNRxZZVO4Ur5PFqLFbNtm9SLJfoLyPDNBNeXp6bZ9/XQFshJVmy0kkVN7HXN6uYiRqr4wlyAIeeM0yLRW0ztPKMGoC9EwuU5YtK3EZ5QMbpOMQaDWmSZabIb8S0iEHpdR0vI9xtHAtBiNDCSj1AvKhnOjmJGLbEycOBEvvPBCLP0///M/cfXVV+OII47A/fffr+SdeeaZuO666+T++vXrcdZZZ+G+++7D0KFDMXfuXCxevBgNDRXwHu0BGyMdgDkmw0A+ko+j1TeQi2A/KisJBnngM+N2Qlp4OPrANZGPoIx2ikaCkadjyC9x63X/cfKP0b2pK5bffsw0TP7yR/DE125G1+MblLyzzz4bv/jFL+R+Le3FGFRZjcIVO4ApTevwTQRVsx8buciqfBkJh3Za1RCHrEQ1r5qxau612GGwl9HHHIQ9538ET379/xZqL+GXgNV9YitrIho0RsOLCIpCNBgLSUBlRMOnZCIpRoORWA0P8EuCRFiIRloQqLbUOPPEQCquZkhVA6qaQV0mgmhQYlEyEop0kiGVjQIDRB3ZMCPXL/DRRx9FuRzdtH/961/4yEc+gk996lMy7YwzzsDFF18s9wcPHiy3y+Uy5syZg/b2djz44IPYuHEjTjnlFDQ2NuLSSy+t7BsQNUN/iHFrR5JhFJ72QE0iGAjSIgkxqp+HXOj5HmkHWlnjOdrKxL+atWNIUjTS1IyDrj4J8KPvvG3dK3j8v36L0UfshcZSGYxxjJkzFRNPfR/Kb3XjkblLFNuph71kV7gy/OCtnamhjE39CsvYbIe2p9sPbS/NhqJzo7ZjOvfKSaqxXPwQ1rLTfnQyuO/L89r2/Cv413m3YdThewUzD8DR/rGpmHjq+9HzVjf+cer19bWXnO6TGNEg7ziRs07ool2CaBAywktebYmGxXUSpfHIjcIyuE0EOdaCQPOqGXQqq+4yMQV/NpA4jTwkozeUDQczcpGNkSNHKvv//d//jcmTJ+Pwww+XaYMHD0Z7e7ux/t13342nnnoKy5Ytw+jRozFt2jRccsklOO+883DhhReiqakp18mL3zY3yeAG8hFlxR+BiR0no+XERjLBiM4v8k+K45g6hjRiYeo8FMVDO2XTw7yaTiPNjeIZOu2mEc3K/ov/dy0GjW3DyIPGgLEyPHA0tpQwdLcW9GwLvkFra6ssX2t7kW4UgSSFK+X7ppavQv3KQ05txBQwk1Mgfi9rSVDzqhm0fOOuzUr6S7c8i0Fj27DbQWPBWBmMAQ0tDcXZizyZbO4ThWiQTyrRKBGiUSL7XoVEIyVGI9rnkRvFRjRMbpOEKa0iCNTzkmMz6CwT6jIxBX+KOI1KSIaogwJjNpyyYUbFMRs7duzADTfcgAULFijs/8Ybb8QNN9yA9vZ2HHPMMfjWt74l1Y0VK1Zg6tSpGD16tCw/a9YsnHXWWXjyySdx0EEHGY/V3d2N7u5uud/VFUqt2ihPwEg+CLLGF5hGqJJraARDpCURjDRyYSMWnnZsmqZvZ+k4jJ2EgSzkdbEk5fs7y+hYvhqTPjUNjSUOILg+HctWY+Oy1WgaPggA8NZbb8kOpPb2gugicuQnFDo0G7MqXwCyEAxqE7H9sBlPKZNMTPVz6m1yarIxW5v+zjI2LXsaEz99EJrCYFAGjk3Ln8amZU+jaZf620vgGsgRp0GJRkkjHTrRYCxGNKBMb2XwGwjpqIBoKIt2MY106DNOShncJrYg0ApiM6iaQdfLMMVlNLByxSSjhJBsuKmvvY6Kycbvf/97vPHGGzj11FNl2mc/+1lMmDABY8eOxeOPP47zzjsPa9aswe9+9zsAQEdHh/IgACD3Ozo6rMdavHgxLrroInOm/qDKG4+R0p7CNzIQjCDNTjDSyIVOLDxDGWh51n39bbVZSEeO0aqt81DrBj/ylx54Fj1buzFxzrvR5PUAAMZ/5F0YPHoaWnYbgtee2oT/d9n9OOOMM/DHP/4RQO3thSG6/iZ1KwuMBCxF+ZL1ctiNqFtPYmr6PpUShzwxG1nSX77/GfRs68YeH9tbjmzHf2QvDGp/D1p2HYLXn9qExy+/r672YozTyDvzpOSZiUa4SiglGrzBQDRKKURDIRsZiEaJKypHYnwGCf40BYGa1AypYlSoZuguFI/5mgslH8kQ20UqG5yzKH6wwvoDERWTjZ///OeYPXs2xo4dK9O++MUvyu2pU6dizJgxOPLII7F27VpMnjy54pNcuHAhFixYIPe7urowfvx4ZaAKhJEYFY5W9durjlJVBcWoWuQgGGnkwtaBKJ0GedindRiVKBuexcdZSWwHAKy/82mMfu94tI1uARD88N/9H3vL/LbdB+P/XXY/7rjjjrrZCzUYloEoSRh++8a4DFK0UmJqUy6oLdiIRVZimkZK9bqmOrnSLNc6ycZe+tOTGD1jD7SOagEQkNPJx+4j22/bfRAevxz1tRd5Qtq7T0xEo1SKiIZYS0O6T4ohGvry41aioc84kcGiuqLBI7cJDQI1TGmtVM3Qp7KaXCYNcjs/yfBYEFxaJNlwMKMisvHCCy9g2bJlUrGwYcaMGQCAZ599FpMnT0Z7ezseeeQRpcymTZsAwBrnAQDNzc1obm6OpdMHMufGV2GlIjYiox2HJk/bCIZMQ3aCYeogTOTCRCpsHUdaJ6I/3OvRgZjqbNv4Jrb84yV8YPFRaIidQyiRh2oHUE97ocoD4i/vQ3wgq2ZmU72iY1kUCwPBYIp92MlFErEw2UK9SWlQLk4acilkWvq2jW9i8z9ewgcuPQpNllkEdHZBvewFVvdJDqIRKhtFEg3qLomtoUGJRikhPkO4TVKCQCtRM6h6oc8yMblMGrxyxSTDQ/CXFxmzAVbVol7V1O3LqIhsXH/99Rg1ahTmzJmTWG7VqlUAgDFjxgAAZs6ciW9/+9vYvHkzRo0aBQC455570NraiilTpuQ/EcZjcnWuunRXV0stBEOkZeksTB2FjVzonYNN1bCpG0lEojJlI1unAgAlSyci8MKfVqNllxZM+sAYeIRUUHBEbRdhL0AKsaDV6Db9rhZiWq3NRGmRbee1G8BiMymENChTG1JqVzSSlY7Vf3oaLbu0YPwHdocXdjQ6dpDOo272YovTsE1x7ctEQ6SHSoceCMrD6a/B946Ihh4EStUMRcnwIkKRR83QZ5mYXCaNRmUjG8mQZMNNfe115CYbvu/j+uuvx9y5c5W562vXrsVNN92Ej33sY9h1113x+OOP45xzzsFhhx2GAw44AABw9NFHY8qUKTj55JNx2WWXoaOjA+effz7mzZtnHlmkQJ2Nki6LmzoXfbQXVzMso1OoKkaSgqF3FCblwtiBaB0J7QQqVzeS1Q7Tg93cIWWTxrnPsfaOZ7HXnD3R1MQBBJ1H54tv4tml67DH+3dHS1szNj2xBQDw/ve/v372guykNIviRcv1BimthpDWTtVItifATkZN7XGf47k7n8FecyZjUGgvAND1Yheeu2sd9nj/WDS3NWNzEfZiCQhViAaJx+hLRENZrCu2YiiXJMQYn2FymyQEgZpWAZXKBovHZpRiLpJkl4mqbOQjGR7zUQIvVNlwMCM32Vi2bBnWr1+Pz3/+80p6U1MTli1bhiuvvBLbtm3D+PHjcfzxx+P888+XZUqlEu644w6cddZZmDlzJoYMGYK5c+cqc+VzQXb29qAaU+BakG4vp3cWIs1EMEQeJRJZCEYauRAP7aTRaRKRSCMRlSgZWYkIrb/+4Y3Y2rENU4+diGaiajQ1crz8yEY88X9Xo+ftHgwZGcwuuPnmm6Pj1dheKBEwF4jnJRELNc0QxxOWsZFSk3qhk1UbuUgjpPUio0A2O6hUGdvw6MvY1rENUz6xp2Ivg5o4Nj76Mp68+anQXoIZbvW0FyPRUKax9gLRICuDxsiHRjTkYl3KiqEqwbASDc1t4rFAFaRTWmuhZjR4xG1icZk0EkKSl2SIv74LEO11MM5Nnuu+ja6uLrS1tWHyrxaiNLglV924kmGWwsXfrB0GLUfT9c5CIR0ZyEW8M4mrHHmIRCUEIqtPvgRzZ5JUR6B7605c/cHfo7OzU1k7oRYQ9jLp+m/CI/Zic6OkqV10O2mKqs1eZD5UewHZp4pXXkJqIqN5Fa1KyUNaO7Z6tmMAZrvq3roT1x52e13t5chd5qKh1FxToqGvo1EY0dCntgplQ4vP0GebULdJ2gJdtVAzbC6TRhLLkYdkBH99dG/biQUHP1gXe9Ht5uDfnY2GIfmVNIGebd34x3FX1vVcewP9+t0oqSNVray6H8+jLhJA7TCCfbuKkaRg6ASDjkJt5EOkiXJA9CDP2omkdyDJvnn9IW/ucLJ3EIl1LbEctYR4YFrzDTYR347SkuzFFORZqb0AcXJRKRlNsh29nl5Xr2+rY2rbZg95bEpppwB7gZzOWkeikWXBrloSDapsmIiGwW2SNQi0FKab1s3Io2Y0emWjy0QQjjwkQ9TnrAB7cUhEPycbdjcJLaPuWzqR8C8dZSapGPRhr7D8jARD72QAtaPQiYVJnbB1IrRMtB/lp5GILKqFsUzCksBps1eKkDmV2SgyLbvSRbepDWR1q6URDD3dRi6yklGbolUJgchCHrISh6x1g3M125RfyIv79MDQjEQjZWXQXEuQ24iGRjLo8uNRvIY6C8U446TEpauEEg2PcaPbxKZmBCSgNmqGmJFicpk0snIukhGULXY2Sm+5Ua6++mpcfvnl6OjowIEHHogf/ehHOPTQQ41llyxZgtNOO01Ja25uxvbt2ys6dhYMALJhy7N3IoBKLmh+3k6DPvyVNAvBUEeq6eTC3pHYg/3owzyNSKSRCP1hb+qYTB1CErkwdiDeTmv5WkFKwoaOLUnpApLJaLRtthWan4Vg2JWN/PZCy9B8WkcgCwmoJRHNakum8oWMVMUUV0kyKiAaDR4qftdJ+PFLDEoQaDVEo8St8Rl0kS7dbVJPNcMUANqoEQ5KMIK8bCSjFO735FlXp0rwKmejVEI2brnlFixYsADXXXcdZsyYgSuvvBKzZs3CmjVr5MwsHa2trVizZo3cj70HqMbo52RD7TiMnYi2byIXQXrUBu1Q0khGJQSDdgi0s0gjFiZ1IqkzSOoE0khExSTDFgSYEkjKC5DFg/7ComwZiEVwbqp6EaRFdSq1FVGePmDj6WZykZWIZrWFSghEpeQhD8FItKUi3ChpRMP0rhPyt6q3t9aCaJDFuuTU1gSiEcRmRLNNPOlCsQeBxtbOQLRdrZoRUzE0NSMiHXaSIeyoEQN7Nsr3v/99nHHGGVKtuO6663DnnXfiF7/4Bb7xjW8Y6zDGEtefqTUGBNkw8TH94WjqWPROg26ndRog+Vk6DZ1gJI9So9GtiVTYOpGsZCI30dD9+Bn99jaCYSrbU4gbJa5smIiFKEv/pi201Zu2Qs89j73oecEx89lG5jIZ7SMrWS0XYS/9hWgoi3NpRKPEjTNOWCkkG0wlGlndJjrJoO80oauA6uqGTc2wBYA2hi9szOMyKck2gnIl5sMvMGaDA8YFA/PUB8g7nULYFp/bsWMHVq5ciYULF8o0z/Nw1FFHYcWKFdbjbN26FRMmTIDv+3jPe96DSy+9FPvtt1/lJ56Cfk02SqHRUyS5T2wvrFLIBvKpGCbZO2unkYVcJHUi9KGelUwkEQn9gV45ycjukxfli5DF9c5dIA8RFXXzuEmyEgxdvTArYNltRUmrUslKI6DBcdPtIx/BsNuRX5Cy0aeIhgz4pGRDIxp0nxKNhEBQj3Flka48bhNBLOiqoHQV0Dxqhi0AtNHrqZhkeGF+kVNffTBUtp51VB+AumQ+gEWLFuHCCy+MlX/llVdQLpeN7wVavXq18Rh77703fvGLX+CAAw5AZ2cnvvvd7+J973sfnnzySYwbN67ic09CvyYbyowApneUarko3U4wRH7ejoN2GkEb8TX8AShMXhzL1GHoaSZSkZloZCQT6UQjg1/e0DmYR8ImZaMYslEib3/UiYWaVls7EXVMBEN3pamkQyUXaSRUXG+bTVSrYmUhGdUQ0Kw2VC5ipNpfiYa2lkYWoiEJRjjbhLpNbGqGJAasdmqGLQA0i8vERDJK4PDgF2MvIWoVILphwwZl6mslC9PZMHPmTMycOVPuv+9978O+++6LH//4x7jkkktqdhyKfk82SoapjDZyoeyT/LSOQ5ZLIBk6wQjq+KkEw9SR6B2GbV8cQ6BSMpEqrWv58VGwqYPI1pGIcyli5OGRFQ6B7HZiIxgAcpNRm4JhUi+qtROaJtqV6TUgEbUioCb7CcrZ2ytkpPoOIxo0PoO6TWxqBg0CraWakRQAGpGRbCRDbJct6llfRmtra6Z1NnbbbTeUSiX5HiCBTZs2ZY7JaGxsxEEHHYRnn322onPNgn5NNkSwkoBNGrcRDFGuUhXD5CZJIhg29YJ2EtYOxNJRZEtPCBTNQSSyEIis8rp+nCJiNoJRWly5UPbDsiYbkekF24mJXNTKTvS8ID8f+aTHl3UyEFCz2pHNfgoJ+PO8aMaJ5wElQTySiYZf8uRU1boRDZIu33Mi19fg6hoaJXMgqCk+g7pNbEGgpimttVYzGlk51WXSyHpSSYb87WVY96dW8DkDK/DdKE1NTZg+fTqWL1+OY489NmjD97F8+XLMnz8/UxvlchlPPPEEPvaxj+U93czo12RDPLithENLq2XnYeo4onNSF6MRx9E7DtpR2DoN8aDV92lakG4OFNXzVEldJxr2emnyelZ3iW3mQUsRbhREbhQTuVDSLTYi96uwExvB0NWLNHJhsxF7Wv0VrCwEIgv5tB8vVDYKWGeDezUmGqESUXOiQd9zQomGCAJNIBp6fAadbWILAjVNaa1GzZBkw6BmBKQjn8ukxFQ1JFA2ilxno8oA0QrqLliwAHPnzsXBBx+MQw89VL42RMxOOeWUU7D77rtj8eLFAICLL74Y733ve/Gud70Lb7zxBi6//HK88MIL+MIXvlD5iaegf5ONUOITMM0sSOo8aHotOg+T9G0iGMZOhHQcyZ2I2lnSNHEM5RpZlIk0RYJ2CGmdjp5vDAJMIR5FPAyELxow24eeXiuCEXzXuJ1QGxHHSCKhJrswkc80GxHtKdcmB/Gkx4ry0wlEVvKZxX4ailA2ak00JMGoA9Ggy4+XhOskG9HI6jYxTWmNYjXoyp/qKqC6miHy8qgZgnTkJRmifE+BykZv4IQTTsCWLVtwwQUXoKOjA9OmTcPSpUtl0Oj69evheZ4s//rrr+OMM85AR0cHdtllF0yfPh0PPvhgZW9Hzoh+TTYYVJ86kNx5AEiMx9AXnqmm80gjGEkdh63TyKJQ2AhFpWQijUhURDwMHUyjRfGoJUrafdcJKAArwRB51RLRJIKRZCN2pctsI1nsg5aXZWtAIrIQiKqIB/NR9upvL32GaJSixblyE41SeiCoFyoaQsWwuU0EsaDPSUE8SrHtytSMRtajkAxdzWhi5VwkoxT+HnsKeL4I9NYKovPnz7e6Tf7yl78o+1dccQWuuOKKio5TKfo12RA/AMAco5HWgdSy8wBU+TtP52HqOEzEwpMdSTqhSHJzZCUTeYlEmu/fVEfUKyKAS4zabAqXTNPy66F06dP7ACgPX3Fd1O1kYpHHPoK8ZFdYLRSsSslnmu3sLGKkWvICV4qHuhANEdxZE6KhBYQyEqehEw19/Qw9PsPmNhEEIouaoRAMi5oRDwQN6jWxnswukyYyS8VEMuTvx2B39YJ766sZ/Zps0HX5AU3hMBAMuV3jEapN/jYRDFvnkdZxmDqNSghF0ggzkXikEIlK1uigaTsK6DwaPF8+KAGLskHutdyvsY2YSGgSAaXkolL7SI7Vya9E5CWe+jma6tjrxcs1F+B2qzfRiKVlIBpKEKgXJxpMi9OwEQ1TfEaa28QUBErVDJmeQ82I3COVB4AmEY3gN8bRVKCy4WBGvyYbovMAoHQggNZ5EIWjXiQjD8EwjUxtnYfeceQlFEmdRFYykZdIxMsn5wNFuVF8KQmL89Jtg6ZT5YPahP6AzeNKSyMYOgE1kQubbYhj0fI0TS+rl9PLGstXQCKyEAgz8bAT1EJk8b5CNOSqobUlGnp8RprbxBYE2siiGA79nSb6TJNK1YyY+yQjyQi2gZ0FigVFz0bpL+jXZMMLjSoLwaDpNpJheiFQXhUjC8GgHYKJXCQRC1PHUQmhSOogEuM7chIJXb40T58txqda8iJJWJybbhtyu04k1EYwdPvIQi7iRCROPnLF31SpXpnKVEI8TZK3bjdFkNNKiEZQxkI0vBoSDfpCtdBlIoiG59kDQem0Vj0+I81tYpvSWis1o4kQkSxqhggoTSIZYUgMGutvLRK9MRulP6Bfkw2TDx6IHlZUxRD5tNPQp2hV04GI41XTgeTpPGpJKJI6iKTOIY1IxImHihIh8A0FkHkqCYt9k20E+9lIRoPnG+0DMLtJdPvQ1S1KPvPYRh67EO2r+/VXr7IST7VOHCUG7DTUrTUqIRoqwdCIhknlqCHR8Eo8dcaJHghKXSU2t4nYr6WakbY4l3ChVKNmCKJRAlCq8xtNHdLRr8kGY4i5UcQPINiur6vE1IGI46YRDDo6zdqBZCEVWQhFUkeRpEwkkYkkImHM1/aLGHk0sDIaPC9GMOR2AfZhIqA6wbDZRh67yOoGS4ulEdcmMT8n6QzqaMc09AVJNlPISNVj1RMNr05EI9zPSzRiagaJz7C5TbJMaU0jGNWoGXKtjQpJRu8oG9UEiNbwZPoQ+jXZaAiNu7ekcNMoVXeR6ARDzY+TiywdSC0JRVJHkZVMpBEJfVRhGq22MFNqbaEHiFJbCM6rGPvIaxtWMmqxi1qoVrJMDdUrU37Qhl4n/qDW6zUWMFIVJCMgFJ7iIkkjGirJsMRt1IBosJKfaQ0NSjRoICiNz7C5TWxTWqmqV62a0ch6El0mOtFolMdKJhklMHiMgWlKXV3txs1GMaJ/kw2vLFk4UJuRqm3BmawqRqWdSFoHouZZCEFBhCKpc4gTD41oaJ2EyN9RwMOggfmKGyXL+im6RFwtwQi+czwALji2ql4otpJCLEw2UY0LTC8f1NHyc6pXWUhnmr2IMjuKJBsVEA1VwTAQDa/2RKNU4plmnNBAUBqfYXObFKlmSPeJhWTkUTME0SiBoTFmjXW0m/BTTf2BiH5NNgQLB6JRaZCenWTYXmtci04kOC+/Jp2IiVRUSyiSOouSUs7eSSR1DrE8rXuh7RYxUm3wymj0vARbqa+KkUQwYvsGu6jGJmz2EJRTkdelEdRJJhJZSafaRpyOiOMUpmzkIRpeCtHwakQ0Sr6yKqhX8nPNOLHFZ+huE1MQqB6j1MjKyu9BXwXUpmZkXZwraq8ykuHBQ4kxNA5MsaBfoV+SDR46tfhb21EOfyCc+eDhQ5SDw2ccYIFcyAH4zA9+sAj+llgZYDxIR1CWi78s4KY8zPOZDx7+wHj4o+AIynng8MMfho+gs/Dhh+0GD/ayRjDKhJyIB79Y1ErOctFGp6ZOJKkDob+tahUKvRxTCAVt204mgnz7/ttbg21eB4eltJdt3cHbQkO7AGC0DbrNiH14rGy0DQYfPuNg8EM7DGzDD1oHM9hGCUF5MB9lQD5Qg23VLjxw9MBuE9QexBtmqD0wADtFOou2g7pxZCEStI0SGHbS46UQiSQSkVSmaHvp4Tvg+8GV5IyBlxk4WLBfZvDDdCCYrshLQGgt4YfBD03J90NCQf+WAYglyMuEaJShvetEJRoyOJQF6gb3ePRhHPB8wAufZV74nAr/yjSPg4ODe4FNc+YH26FNg/koC/tnPPzdhPbslUGfmY3hvvhN8HAVUB7+HjwEKknwj8vfgSAXDaGaURYkHZzYvZloMJiIBkcJDIwFaR5YSJYYtm3ldbOXuP04N4oJ/ZJsvPrqqwCAPx77f3v5TBxqjTfffBNtbW01bVPYy20fv62m7Tr0PuppLw8+dFlN23XofdTDXmJwfhQj+iXZGDFiBIDg5TJ1N5xeRFdXF8aPH48NGzagtbW1t0+nruCc480338TYsWNr3razl4EHZy/Vw9mLQ5Hol2RDvL2ura1twP9IAKC1tfUd8T3r9WB39jIw4eylNnD2UmNU6UaBc6M4ODg4ODg4JMGtIGpG/Rc3cHBwcHBwcHhHo18qG83NzVi0aBGam5t7+1TqinfK96w33inX8Z3yPeuNd8p1fKd8z6LhZqOYwXgRc4EcHBwcHBwGMLq6utDW1oaJP/8WvMEtFbfjv7Udz59+CTo7OwdULI1zozg4ODg4ODjUFf3SjeLg4ODg4NAX4QJEzXBkw8HBwcHBoVZwi3oZ4ciGg4ODg4NDjeACRM3olzEbV199NSZOnIiWlhbMmDEDjzzySG+fUmZceOGFYIwpn3322Ufmb9++HfPmzcOuu+6KoUOH4vjjj8emTZuUNtavX485c+Zg8ODBGDVqFL7+9a+jp6dHP5RDCGcvzl7ywNmLsxeH2qPfkY1bbrkFCxYswKJFi/DPf/4TBx54IGbNmoXNmzf39qllxn777YeNGzfKz9///neZd8455+CPf/wjbrvtNtx///14+eWXcdxxx8n8crmMOXPmYMeOHXjwwQfxy1/+EkuWLMEFF1zQG1+lz8PZi7OXPHD24uylJuBVfAYqeD/DoYceyufNmyf3y+UyHzt2LF+8eHEvnlV2LFq0iB944IHGvDfeeIM3Njby2267TaY9/fTTHABfsWIF55zzP/3pT9zzPN7R0SHLXHvttby1tZV3d3fX9dz7I5y9OHvJA2cvzl4qRWdnJwfAx/94EZ/wq8UVf8b/eBEHwDs7O3v7K9UU/UrZ2LFjB1auXImjjjpKpnmeh6OOOgorVqzoxTPLh2eeeQZjx47FnnvuiZNOOgnr168HAKxcuRI7d+5Uvt8+++yDPfbYQ36/FStWYOrUqRg9erQsM2vWLHR1deHJJ58s9ov0cTh7cfaSB85enL041A/9imy88sorKJfLyg8BAEaPHo2Ojo5eOqt8mDFjBpYsWYKlS5fi2muvxbp16/DBD34Qb775Jjo6OtDU1IThw4crdej36+joMH5/kecQwdmLs5c8cPbi7KUmqMaFMoBdKW42SsGYPXu23D7ggAMwY8YMTJgwAbfeeisGDRrUi2fm0Bfh7MUhD5y99AWw8FNN/YGHfqVs7LbbbiiVSrHo6U2bNqG9vb2Xzqo6DB8+HO9+97vx7LPPor29HTt27MAbb7yhlKHfr7293fj9RZ5DBGcvzl7ywNmLsxeH+qFfkY2mpiZMnz4dy5cvl2m+72P58uWYOXNmL55Z5di6dSvWrl2LMWPGYPr06WhsbFS+35o1a7B+/Xr5/WbOnIknnnhCiY6/55570NraiilTphR+/n0Zzl6cveSBsxdnLzWBc6MY0e/cKAsWLMDcuXNx8MEH49BDD8WVV16Jbdu24bTTTuvtU8uEr33tazjmmGMwYcIEvPzyy1i0aBFKpRJOPPFEtLW14fTTT8eCBQswYsQItLa24stf/jJmzpyJ9773vQCAo48+GlOmTMHJJ5+Myy67DB0dHTj//PMxb9489/ZGA5y9OHvJA2cvzl6qhltB1Ih+RzZOOOEEbNmyBRdccAE6Ojowbdo0LF26NBbU1Ffx4osv4sQTT8Srr76KkSNH4gMf+AAeeughjBw5EgBwxRVXwPM8HH/88eju7sasWbNwzTXXyPqlUgl33HEHzjrrLMycORNDhgzB3LlzcfHFF/fWV+rTcPbi7CUPnL04e3GoD9wr5h0cHBwcHKqEeMX8+KsvgjeoilfMv70dG+YtGnCvmO93yoaDg4ODg0NfhXvrqxmObDg4ODg4ONQKLmbDiH41G8XBwcHBwcGh/8EpGw4ODg4ODrUCZ8GnmvoDEI5sODg4ODg41AiMB59q6g9EODeKg4ODg4ODQ13hlA0HBwcHB4dawQWIGuHIhoODg4ODQ63gYjaMcG4UBwcHBwcHh7rCKRsODg4ODg61gnOjGOHIhoODg4ODQ63gyIYRzo3i4ODg4ODgUFc4ZcPBwcHBwaFWcMqGEY5sODg4ODg41ApuNooRjmw4ODg4ODjUCG4FUTNczIaDg4ODg4NDXeGUDQcHBwcHh1rBxWwY4ZQNBwcHBwcHh7rCkQ0HBwcHB4d+jquvvhoTJ05ES0sLZsyYgUceeSSx/G233YZ99tkHLS0tmDp1Kv70pz/V9fwc2XBwcHBwcKgRGKIg0Yo+FRzzlltuwYIFC7Bo0SL885//xIEHHohZs2Zh8+bNxvIPPvggTjzxRJx++ul47LHHcOyxx+LYY4/Fv/71r6q+exIY53yAeogcHBwcHByKQVdXF9ra2jDhv78Nr6Wl4nb87dvxwjf+Nzo7O9Ha2pqpzowZM3DIIYfgqquuCtrwfYwfPx5f/vKX8Y1vfCNW/oQTTsC2bdtwxx13yLT3vve9mDZtGq677rqKzz0JTtlwcHBwcHDoY+jq6lI+3d3dxnI7duzAypUrcdRRR8k0z/Nw1FFHYcWKFcY6K1asUMoDwKxZs6zlawFHNhwcHBwcHGoFXoMPgPHjx6OtrU1+Fi9ebDzcK6+8gnK5jNGjRyvpo0ePRkdHh7FOR0dHrvK1gJv66uDg4ODgUCvUaOrrhg0bFDdKc3NzVafV23Bkw8HBwcHBoY+htbU1U8zGbrvthlKphE2bNinpmzZtQnt7u7FOe3t7rvK1gHOjODg4ODg41AhVzUSpYKnzpqYmTJ8+HcuXL5dpvu9j+fLlmDlzprHOzJkzlfIAcM8991jL1wJO2XBwcHBwcKgVemEF0QULFmDu3Lk4+OCDceihh+LKK6/Etm3bcNpppwEATjnlFOy+++4y7uOrX/0qDj/8cHzve9/DnDlzcPPNN+Mf//gHfvKTn1Rx4slwZMPBwcHBwaFW6AWyccIJJ2DLli244IIL0NHRgWnTpmHp0qUyCHT9+vXwvMiR8b73vQ833XQTzj//fHzzm9/EXnvthd///vfYf//9qzjxZLh1NhwcHBwcHKqEWGdj4iXVr7Px/LfyrbPRH+CUDQcHBwcHhxrBvWLeDEc2HBwcHBwcagXOgk819Qcg3GwUBwcHBwcHh7rCKRsODg4ODg61Qi8EiPYHOLLh4ODg4OBQI7iYDTOcG8XBwcHBwcGhrnDKhoODg4ODQ63g3ChGOLLh4ODg4OBQK1TpRhmoZMO5URwcHBwcHBzqCqdsODg4ODg41ArOjWKEIxsODg4ODg61giMbRjiy4eDg4ODgUCO4qa9muJgNBwcHBwcHh7rCkQ0HBwcHBweHusK5URwcHBwcHGoFF7NhhFM2HBwcHBwcHOoKp2w4ODg4ODjUCC5A1AxHNhwcHBwcHGqJAUoYqoFzozg4ODg4ODjUFU7ZcHBwcHBwqBVcgKgRjmw4ODg4ODjUCC5mwwznRnFwcHBwcHCoK5yy4eDg4ODgUCs4N4oRjmw4ODg4ODjUCM6NYoYjGw4ODg4ODrWCUzaMcDEbDg4ODg4ODnWFUzYcHBwcHBxqBadsGOHIhoODg4ODQ43gYjbMcG4UBwcHBwcHh7rCKRsODg4ODg61gnOjGNFrysbVV1+NiRMnoqWlBTNmzMAjjzzSW6fi0A/g7MUhD5y9OPQaeA0+AxC9QjZuueUWLFiwAIsWLcI///lPHHjggZg1axY2b97cG6fj0Mfh7MUhD5y9ODj0PTDOeeE8asaMGTjkkENw1VVXAQB838f48ePx5S9/Gd/4xjeKPh2HPg5nLw554OzFoTfQ1dWFtrY27POVS1Fqbqm4nXL3dqz+4TfR2dmJ1tbWGp5h76LwmI0dO3Zg5cqVWLhwoUzzPA9HHXUUVqxYYazT3d2N7u5uue/7Pl577TXsuuuuYIzV/Zwd6g/OOd58802MHTsWnhcJbs5eHExw9uKQBzZ7qc/B4GI2DCicbLzyyisol8sYPXq0kj569GisXr3aWGfx4sW46KKLijg9h17Ghg0bMG7cOLnv7MUhCc5eHPJAtxeH4tAvZqMsXLgQCxYskPudnZ3YY489cMSoU9HgNQWJnjYC0UckphGKXsdWLk9+LZDFs5VWxjfk63VsbZjq2pB03cW2KMNY9AHAyX6PvwP3P3cNhg0blv3YFtjs5T/+8GmwQYPgg8HnwafHL8l9zoGy78n9su/JchyAH+ZxzlD2g78yP6zv+8FfcAbui3SRBnDOZJ4cAcnt4C8T+36QxsJyzI/yaFpQjuT5pDwpI/bpNvN52G6Q7pXFcXhUthyVYT4HK4dpnJM8sc0Bn4OVfTDOgTLAfB8o8/CvD/h+sC32gwsP7vuB7fnlMK0MHv6F7wfX0Ofg5TJ6/G78HXfW1V7G/eC/wAaFcri4p+I+ybTovtFyNJ/Fyof3VSlD0kk5cxlDWZD7rqUp7fma7Yhj+AjuF9fsJGZPkV1As59om8v2GIe0C+bzwA7C+8jKfnjswHbg+6FtErvwQzvSbAWcg5d9gIty5cA2DPYC7mNneQf+zv9YE3tJg1tnw4zCycZuu+2GUqmETZs2KembNm1Ce3u7sU5zczOam5tj6Q2sMSIbAKDLYyZioKeZCIetbiVl8kB0/mnNcp5exqTFZSUbHoIftw0mGTKNZIi/jIF7hHSIPO6FRdQvVkt7aRzSBDaoSSEbjJCNYD8iG4yQjTJnYOGHcwaERIKmcQ4w35OkI0onZMOvgGzQhz0lGwp5YCqRIB2PQjZoHu0gBMkoGdI9tUPxGAe8sAwjZIOFnUrZBwPpKOCH9zY6EQY/+gJlH+BlgHFw+ACCjgMlP+hQvKAcLwdlUCoFN9Svr714g5rhDW4OCSIkieDkXkUkg+xTcgFxH2g+4qRCIysmwiHuW0QoDG3QclDJgmIPRvKRkWwoNkRsg4dEVJCNkLBKu/Aju4HPwZgfHIeF9uKFZIT54Xbwl5U1W2EBoeCMR+Q0PEFqL/D9oIwoy+P2UhdQu6i0/gBE4bNRmpqaMH36dCxfvlym+b6P5cuXY+bMmfkbpB2mGC3RvLQONjR8Y7tp6oEoU22MbZ42iorn9Tz7h4ISBrptUjNMRMNjQMnQboia20tfA8/28Kv1aCc2SjbBkM4SOGhNfguAQlyZvm0bHISoqb3ohzLdhKTTSbm1nIVlWDYzyFpez0vcN7XD1L+cxcvGjm9sR03kCQM9bhqsmNrS/2rbgY14Wr4HlmI3NYX+26rkMwDRK26UBQsWYO7cuTj44INx6KGH4sorr8S2bdtw2mmnVdYg56px+b7aeen5UkGgdcI03ShNZW3noMNUpwiykNUNwljl52NzUyWpGWRfEg2Rn3B9a24vdQJP6TES8y15uUhGLU3LZM4JtpKUZ69E7M/zAvnbY0AZ0V+RRwcRovOwEJ+62AuDek2CIXn8OgkZwnavZXJCmcznxMFB3DGJ55uyj6DDr+w+ph8/1jbNZ2THQ3TfQ/vgHgvcLKJc+KxgjAWCqLAX3wtUjXJkU8z3gzLcLZbd2+gVsnHCCSdgy5YtuOCCC9DR0YFp06Zh6dKlsaCuNCizdnVSYCIcNF+k6Z2cz82jp6ykw1SnVih+lrKKpBGHiWSEf41qBiUZnqXtELWyF597KOWqYWsn2q75HeHa36QyWU6gkhM0Eg1z0cA/XSe7ZJV1HrWyF3kajCtE0cjRbeQDIjmkBfX6CYdN6/eJM5JmIgV6GS0tKc8GnVjo5a316YWlxIKTvx4L7EGQUyCwE0S+o4CEeLIPYL6fqojVEkKAqqb+QESvBYjOnz8f8+fPr7odzrnqh6MEQoyG0lQOE+EAakc6aoG6PdCFVmrrTWwjNJJuUTMAZCcaKdezVvYy0GF8iPOMColFwk0iGpVCjlaBeIdi6zxE55IBNbMXk6KhKxKWTjw1j5YJi4W/mkixgPqIMhGK1OORNCv5ENu28w3T4wqFyry4B7Ck20SVC/18ZFp4DEEs9GORv8Fpked1qG4wH7ltpmao1hUyQN0oA0Jbiq1LFovL8JPzbb7mJHdErfzTach7nDwzSSi02AorAdDjMnQyQdwmvOSlEw3PA/c88NJA5fM1RJWye2InlZNQ5CIaSURSU8KUgYPHQuJBynpeptiNWiB22rF9bh6GMiRfIBaVqeiWhvWtdQ15tjiNxOMz7a8tX7YVL5gUH6KUF7c5ST2l2yI2A4jsRD5jTDZj+Q4OhaF/kw3SsXLOzW4VWdZAOLLMzrAFkOrt1IN49LbbhMJEMkQ6zdfdJp4XBICGQaCSWJSCvEjd6JVv1SuoyW2lAz69vTRSEX7k1MS8hKLa80978usdhsfUwL9e6DyYdjGMx7eRDyAkAAn5FZ8YjwJHDce07RtJBkvI09OTyEyG8iYCYQwS1RVSIBq0hPnMRDIMNlMUxG+qms9ARP8mG0CMCMQIR9JsFVFG36+EdNC6Rc5OqSd0hUMnGSY1I4fbhDONdPRheOQJUGRge8Wo0nxqRTQS76se7xObRWDoRHoDJkVDz8+iEGQ4TjTjhJAIlvGyZzlOFlJC0zKUTyIuAJLX4tEHLUBc5TCRELGdZDM64SgKvAafAYj+TzaAZMIRJGjlUwiHLU0cK4urghKPPNNa60kysvzgTC4UE8nQykqS4RHXiU40BLGg6gZjwcOqX/Tg+VGTu5mhkVqOhqwBn7U4RkIgsTx+HxqpZnKlxCqJTw5XSlaioBCSeBFTW0nuFr2MjRxmUjvIOUb7agFVvTC0oascxF6UGW1U3YBmMzbC4dCr6BcriFrBCWnQgjoTA0eB7LNV9DR6vDwdZF9QKoB8DN82ImGWB0BWNYPul4I03k/9KAx9dyBSCQGpqYTLtKvDmPl3IMqJTsH31d+eh3jgX7XTRjOdv+46SZmVwgCkTHut9awUzhAsiGUIWuUgX4HcitQZKmGacUaKrR1TW9o+DS6N2mbB+QNR8Ci1ByU6VnuGazbDgGCSkh8GGFO7KcJeKPrqQ6EX0f/pHtdUirQ4jrxuFVuaOFalAZn1Qi0UAqpkACqRsMVmZCEaNFbDi4hGMBoqnmz4BTyA+hOFqiSA1Fpc/+L0SUP98DK/j49UmbZtcqUY6/HkfMNxbK6UJGSJ20gNEk07R1tbhrq6QqKUNwWDxhQNkq6rG1T1kM+dwE6S7KYouJgNM/o/2QASCQdQoVslayyHOF5fIx2VwEYyxHb41xibkcVtIvMAXgoIBzzSXh+DHhRYOFLIUJbTq8lXqDZOw+Rz111xNJ92HH0A6unpagfMFzmNIFASkdGVwkm9PDNRYscV7ZnIB9PyDEQk8f4CyhIonGltMUMdi2uN688f3U60Z04q4XDoVfTvO6CoFr7ZrSKL5iQcpjK2NHrMvkA68nTcgmAkkQxdzdDTbbNNmD7FNVQzSp5UM7j4OwCmvhZCTipRH4qM+UggCJn98HpHZFM3igC9MLHRvI1kWC6mJAo1uthJxEMjL9aZITkvYy5Fg+ZZlybX2lUUDbqt2Yw+yLFB2E6RxDUt+DPLZwCif5MNwKBAVEk4srpV+jrp0AmEKc8Uk2EaSVDZu1K3CQuOy8M6Us0oheX6P9fov6gn0bDNLDCNUhWS0fuyuNInp3SyFa+5QRB790lYN1MAqWXNjkzra2gdviQDzJwv07T27ITGTDTix2PRfpK6oRMOmqfbDbWdguDcKGb0f7IBxDt/nXBocRyxujqyuFVsdZV2+hDpsBEMIP6g137AMbeJRx/8hGjY3CahmiHdJgxReh92owwoVKF6pDYt7r8O2mEAZuLBmDl2ow/L4jVdc4OQivQDZ3ClmIhJCslQ0pMIhqGesUyWY+i2oChcsGwT29CfVxa7Mc5sqjf6sLLx2muv4aSTTkJrayuGDx+O008/HVu3bk2sc8QRR8hrKT5f+tKXch+7n89G4eqsECVy2VcNjJQThENKscYXs2mzVfT2aZpeV0fS8ue9Cf2cDYRDCcjKomZQtwlDpGYw0paHKE38HQBulKxgLJ2n1gX6Mas8BzHiNvaTHguWmmZMOZCyFLXg9PrvSqxWDthnGhQpi9MlytWvo+aJfCB+beW0DkNeVecWHS72/hHTeYg6nJSLnWuQT/Pktnb+sZkqStvBvZZ2oh1b/hDo8RCkBbNUoG4DkW2IF7TRcaFuN4C0nT737O0lnHTSSdi4cSPuuece7Ny5E6eddhq++MUv4qabbkqsd8YZZ+Diiy+W+4MHD8597P5NNgQqIBxB0ZzTY01l0tL18wR63/CzkgyxT8lGmttE5kG6S+S+IBqUgIhyvfAKg7xgpGPRp0HGy9aAUJjew1GDJlne0ROLf2wv0+LCXLjeEZFemG6HedxDcsehE46CyIapbzRlMobgHR2m96YAyddbVtHeiSKz1HRrWywsZzlHhYAoX8yQpp+flh/8frl6f+W0VgZ4PHhHCm0zJKDit888HnKN4PnAPEQE1ePRNFiPg/vk+xMbstqNOB9hO73gRqmmfj3w9NNPY+nSpXj00Udx8MEHAwB+9KMf4WMf+xi++93vYuzYsda6gwcPRnt7e1XH799uFFt8hs2lopdDBrdK1sDRpHQdveFascmPumoBmGMzbEQjzW0iiEaJuE2ImqGk9VNkDQzt9dkt9YYyC4Ep6dSVogSKEvtKdKWZXCp1hnK/tEBRax4pkylQtAKzz+tK0WM+olgJrY6Sx5T0RFdMUp7cZmo+U9OV49NnjHz2pNiN/pwyuX2LQo3cKF1dXcqnu7u7qtNasWIFhg8fLokGABx11FHwPA8PP/xwYt0bb7wRu+22G/bff38sXLgQb731Vu7j939lg6oXSQoHYC6HjAoHgNRFwJLSdRShciSdQ5qaIf7W0G0S7BM1I3ywcA+F094i1tioC2yjz94GPS8qAdgUDUI+hCyulid5ct/rFVk88ZLrmcLXYHJNJC34VQ3EY04cPkQmVwo5/5irhEWnHHOnIF4Xeh6C332gCJLzke0GC3oJ94lQN7gfKiYeVEXDI9dPsw1F4ZCqBinT22pyBRg/fryyv2jRIlx44YUVt9fR0YFRo0YpaQ0NDRgxYgQ6Ojqs9T772c9iwoQJGDt2LB5//HGcd955WLNmDX73u9/lOn7/JxtANsKRVA4ZCAdQe7eK4TyqRtoxDSQDQDw2Q/xNIxrakuO620TMNKFuE07rhJ/+rGxQGN0rdXCH9Bl44YPd5If3WLzjCDuB3LK43C9eFqdQeVTGFUVN5AMiWXGQQE+xuVLEJY63mcGVopEFI4HISTRMcRvydfOMlCGuFCYJQWQzwXekZJS4UGzxG+KalIJ2GFWORZkiF4Ej6kTF9QFs2LABra2tMrm5udlY/Bvf+Aa+853vJDb59NNPV3w6X/ziF+X21KlTMWbMGBx55JFYu3YtJk+enLmd/k02fA6Uwu13MuHIcowkkkHTqJpB9inR4J4Xi9WgK4EGs04IwfAIGZFloJKQEhxsIA92ClvcRBLkCLTavjq8j7R3VUaqZORqVjQ4IR/MPkoFItVLieEohmwohMIWKCpIhR4omqRuWO5pvpML/khCEiM6UZotXsMW+Gk8ligniUhQWY/bEL9xRoI/5bMBQRoL2xTkQgaHgkckVXwJYSuMxeM39BgNGOynJE66GNQqZqO1tVUhGzace+65OPXUUxPL7Lnnnmhvb8fmzZuV9J6eHrz22mu54jFmzJgBAHj22WffQWQD0IhFPyYclSKPkkH2Ff+4IBhiO4vbRJIKsnaG6IAMQaBGNcMjpKOPjvw9xq0uF5amWGRRNOjLJ6q9Blk7DLqf85DcA8Cjd1wAUDsXD1FgHycdh0kWN7lTyHESZfFSASNV0mMo3IJwLKO6ESMeCeoGAM4qUDesSona0dlcKUbyIUkEIYs6YUhTM/R0RO0Y1Q1E6gYHj44r3CmUcMAD832VcDBxstxsP1Jp65vPl1pg5MiRGDlyZGq5mTNn4o033sDKlSsxffp0AMC9994L3/clgciCVatWAQDGjBmT6zz7d4CogBIcmiFoNKkcUNlaHLZySenVgBIEW56uZphiM/RPFreJpy41LomGFvDJPcggUBqbEa2toW73B3iWIUvm4E9xS5KCB+uBpHZZMs8RCzaZXF3mJa+Z3FcWbBJElBAMbrE9SWZN9lkqeJGmhEBRtaB2T21kTpSrgOzRtrmtDdMCX2Q/OTBUK2/IV8qkpXskDYieQUpd9Tkj7YYOYOQgxgsGeuJZY7Mfz1OC1AslG7wGnzpg3333xUc/+lGcccYZeOSRR/DAAw9g/vz5+MxnPiNnorz00kvYZ5998MgjjwAA1q5di0suuQQrV67E888/jz/84Q845ZRTcNhhh+GAAw7Idfxcj/jFixfjkEMOwbBhwzBq1Cgce+yxWLNmjVLmiCOOQNoCIOvXr8ecOXMwePBgjBo1Cl//+tfR09OT68QBjRT0dcKRhjwzVJJ+OCYlg5AMK9HQIrh1t4n+SnhltolYCZQxhTysf/4vWPnQVXjgnguwYvnFeOofv8S2t7aAlxB8PIZ//fVaPPjbr+PR//kWAKCtra1u9lIUTLcn97MuqZMyIFEUyXhsSiqibXubMi8M/A3aoJ0Jk/lKpwGoHUi4/dwrK7Di+SVY9swVuG/tVXjs5duxtfy60mE83HEblj5/BZa9eC2AAuxFUzfkdkJnrteT+1nIhShDVw4V1zqFEMba1/aVNkzkI4VoxNrW6wMKmYhcq9G2tCmPKWVidpNEOJTnloGwCjIqnmWlYMZcUWCcV/2pF2688Ubss88+OPLII/Gxj30MH/jAB/CTn/xE5u/cuRNr1qyRs02ampqwbNkyHH300dhnn31w7rnn4vjjj8cf//jH3MfO5Ua5//77MW/ePBxyyCHo6enBN7/5TRx99NF46qmnMGTIEFkuaQGQcrmMOXPmoL29HQ8++CA2btyIU045BY2Njbj00ktzfwHF7dEbLpXsJ1o9u85LMujhTbEZ4q/G/tXAT0o6xI9ffQjEgkBLQd4br6/D2AkzMXSXcfA5xwurl+LJFT/DtI98HaXGJvnwG7XnDLTvewQev/M7+Pe//43Ro0fL8661vRSNtPU40mCNy2BIHQHFZgjUAqLDsvnpdVmcIZD+U2YZcJ/htbfWY48RB6OtpR2+X8YzW/6Kf7x4G94/8fNo8BqlW2XcsKmY1DYdf3txSWH2Yo3dMOQxMOt6HMbrCUQOkrR7xdIDRQMXSXSONXelkDKg+VpduU8CRYM2QtsIv78MCqV2ExZWbIe6VAz2I8+FutxC+xzIbpQ8GDFiROICXhMnTlQG2uPHj8f9999fk2PnontLly7Fqaeeiv322w8HHngglixZgvXr12PlypVKObEAiPjQIJe7774bTz31FG644QZMmzYNs2fPxiWXXIKrr74aO3bsqOhL9KrCUSt3SlqAqO3HYpIIyX5MzaB1NKLBBXGwvETN5jah8Rd0CfL9Z5yO0XscjMFtYzBkxFjsdfCn0f32G9ja+WLoQgmePV5DExoHDwMAjB49uu72UktQt0rFz7Mqn4PWtRZqCdGZyGMyua+PfKXdALFRatIIdfqkz2L3EQdi6KBRaB0yBlN3/1/Y3tOFru7NcoQKxlDyGtHcWH97MSpUSQl6pmkdDXEd87wvJQ1hm8b1NLTzU9wlsfumHTMhP9GVoqgYzKhuiGcPJ3nKwEZphyFV4SiZ0rzI5npjNkofc6P0Nqq6A52dnQACtkSRtADIihUrMHXqVGU0MmvWLHR1deHJJ580Hqe7uzu2wAkApaPvN4SjEiQRDX0/i8skxW0i1QryErVUtwkjRINBjc0IXSY7y8GiNKWWweR9K8ArL/wTq/4nGHVeeOGF9bOXTJc6ft/0OI0s8RmJZajP3gDTW0GNsRHW9lPys0J2jIS8MjW+RnGlQCUheqchyydJ4qVwthNj2OkH9tLYMAh0YaaXtz6N+1/4KYCC7MUap0O31bgOo5sl6yJfOiHRyYTmYjG1p9uLzX7SCEYWV4pwuyWVF4Hg9LuoM9QQBZXnIRzEXqxpgnQUBNvL1fJ8BiIqJhu+7+Pss8/G+9//fuy///4y/bOf/SxuuOEG3HfffVi4cCF+/etf43Of+5zM7+joUB4EAOS+bWGRxYsXo62tTX6UxU5qSTiUL5hCOGLlDYSjFshDNEJYlxo3qBlxV4l5NdBIvWBQX6AGdSVQGgQqYjMYg898rHv8Dxi220QM2mWMVDZ2nfge7PmBz2KvD58OALjlllvqay+9ABbbyFsxe5k8HhsamwHTR2s31mERMkLbsY1SU0eo8gV/gQK+ZtMyDB88DsMGj5Y2O6ZtPxww5n/hPbt/EkD97SWJUCjXwrQtyhmJh9ZGFeCkTfUecfM9E/USCIZ+P2PlTKRCqcfIPlOIiW43lHBExILk64SjJBTWyF54+OxS0nTSURScsmFExWRj3rx5+Ne//oWbb75ZSf/iF7+IWbNmYerUqTjppJPwq1/9CrfffjvWrl1b8UkuXLgQnZ2d8rNhwwa1QK0IR8rS5gqyKBfVqhtGHVf74SSpGTRf7FM1gwZ+hsuO0/gMGe0t2k1xm5jUDFHuuVW3463ODkx+3+ciF4oHjNz7vWgdtw9aRgTzvK+77rr620uNoTzP83YgOQlFosKR0JaRVGSAlWQg6oSoupHYaZimSTNmnFnw9MY/483tW3DAHscpI9TxIw7Cbq3vwtCWYKpfXe3FQi7U+02366NuGANFabqlLdu+MVBUIxh6npIPgz2B2IFhXypihHxEJBRRwKjHQAc1UJ4vQjnVbCicnWIjrpJ0OPQqKroD8+fPxx133IH77rsP48aNSyxLFwABgPb2dmzatEkpI/ZtC4s0NzfLBU6UhU4UYlEnwgFarAJ3ShaYJD4b0bDsx9bN0P+muU0Ys09rNblNbFNaiZohCMVzq27HGy8/jX2P/BKahg4nZIOoI6ElinX762IvNUQWVdZ8C7k5z/SAz4OUTiwP6IyUqB2mkAxdFqedhihjJRxeoJbJEaqmcjz94lJs6XoWB7/rZLQ0t6ojVPk3OE4R9tLn1Q2mqhsCnJISuR+vbiUYIg+aumEoo8ZmxNUNmZeBcKj5mv2QdGlDMp3FiSshHUXBuVHMyHULOOeYP38+br/9dtx7772YNGlSah19AZCZM2fiiSeeUFYyu+eee9Da2oopU6bkOR1xUtF2PQhHXndKvZDHbaJ/EtwmSnyGkMPD+IxEt4lCFMzbPuN47rHb8dpL/8I+R34Jza27GkkGJRtPPPEEgDraSwZ4Bh0zb98fb6BCu7F0aEprtg4k8zEQG7nqeaJNYzCo1mnk6TCUWU0AVr/4J2zuXI3pe52CwS0j4iNUKpej/vZiIxc6D7OV7w11wxjPoZGEqgNFFVIRz4u7Yog9CMLhVUY4xCDI6FoxkA5qL4XAuVGMyDX1dd68ebjpppvwP//zPxg2bJj0gba1tWHQoEFYu3YtbrrpJnzsYx/DrrvuiscffxznnHOOsgDI0UcfjSlTpuDkk0/GZZddho6ODpx//vmYN2+ede33VNCpTWR1z9jU1Vxt2qfEWo9dKyQQi9xqhtgWJAOIZpjQH7FpWitZhpwGgUo1g4kRBtkmD5J1K3+HV194DHsdfhq85mbs6O4CZwxe8yB4jY3Y/uYreO25x9C6xz5gpcAUzzzzzPrbC5C4MmgloNMfk6ZJBmmAXOqRM8NfxB84NI1si2rWsrETjToAxixlaQfCSXkepXMwwONgfrDNGA9nRobEwOdh9XA5ajAwj9unNgJY/fyf0PHqEzjwXZ9BQ1MztvvbAB9oKDWjwWvAW9tfxcbX/4WRw94FxoL17YuyF8ai8UjmJczpdTWtIirNwmAjuU9QexRR+7BNgzXYkcynxATh9w+NQOaTNgRhCI5F2xH3G9JuGDjECp/cY2B+ZDscHMxjcfsJn0OMA/AR2ZC4LxzSjuCLyy2+FA+eWw69ilxk49prg4V0jjjiCCX9+uuvx6mnnioXALnyyiuxbds2jB8/HscffzzOP/98WbZUKuGOO+7AWWedhZkzZ2LIkCGYO3eusi5HZvg+eTeKmXBEZQlhyLoGhwWJJMZ07CToJKZaoiEJhYFo6G4TSToQuUuYSjTk6KNERhXKX3WEwiV5ATY/uwIAsHrZtcpXmvDBE7Druw8FGkp4c+O/sfnJv8LvCaYlfvzjH8cll1wiy9bUXqqExzjKpFPIu4YG7bCyVYCBCNg7Ji5uv+7tSyIWCWWUd1+EnUns3RZhpyFHtgDghVQiC+EgncWLmx8FAKxcs0Q5t/0mfgJjR0wDKzXita3rsH7LIyj79beX6GdlJhcKt0ggI8Z1N2wEQ17H6pYwl509ORTdlyTCRDAMHNhELpjgTtQeCCGJ8kK7EQTDRDg4D+2AKceBz2WaYkNggQDNk0hH8DxnnKkv86szqnWFDFQ3CuO95heoHF1dXWhra8OHWz6NBtakdu6ys/VIEvnRKp20Z0lPL8MsJEA/diJ5SDpu7DwMRMOkZog2SZo1PoO4TWRnQgM9hVJBprfKyG+LmiElUA9k25xO98s7t+NfP/vf6OzsrHmMhbCXTy47BaXBzfDB4HOGHt+Dzz25Lz6cM/gI8jlNB1D2vagMB3yxDwRpfpAXbQMI64MHD0jOWdDhioerr/3lAPPDh26YxvywUxd5fviA19JlOTG645Bpep7c9rmaT+twrqVztd3wja+sHG2Di/Y5QMuLsuKRIzqL4OIFaSI/dF/KB69YrCms11Puxn3/77/rai+Tf7UQpcEt4aGZPA2pEoi/CNLkk5SUB9fzmLxGUMrE95koq5URo3ump4l6YXlxz8T9EHYk96mNcLUO46otqH+5NV/ed72N8B6raVw7JtfON7QNYUPQ6pnsSNykcFuU6dnxNv6ycnFd7EVA2M30T38bpaaWitsp79iOlbfW51nYm+j/L2IDLEqGxZ1iUziqQaWulKQow0qIRpLbhMZnkG3qJonyYXebkNgMk5qhkgrqbgFoUCHdlwpKwQhcKfnq6C9fS30ZW6wBQBlSWsAZDzobOUSE6jIh6bpiYVQ4WHRvTOoHvS/UXRJJ5EiUxZVt0bxHzp+MUGWHIlSOsEzw3YRkHo52AXWECpCLUBxM6kaSgpGobsjbrtmAIjMg0ixMXzdMZmpJ2SygNKXaitgXbdDDkvw014qwCZov7rtwjURqBhSFI7AJTeHQ7YeRLylIBxCqJ1DtiCgdga0E2zxsgxfx4j6HRPRrssGjX5uaWEn8hs2dYnG5VBUPYoJNKQm3K4rPMLlNUuIzUt0mZKaJTbWIExEQVYSWZUon19egu02ywCanG/t3RmzY9FcpHKXZSQePHtCWugKmuA3dlaL44ZUORnWnxOI3SIeRzQ9vIB3i2nAApTjxKOIV83TmUHRPxT2LiIJyC8R1MtyfIM9AMCJ2oZ1AjEZATxGnAqUEeXyRU5VuCEogNHuT/DaRfERLlItjKeQj/LKhlRgJB0P4bJBuEy4JCFUvFJJSYhHpCM+DkleUEMV00Otu6ifqjIHqCqkG/ZpsKKDqRi3iN+oBqzsnI9Goxm2iEw2hZOR1m8j58KI9nVRQIqKSCzq6ViPRAxLTG/CYDz/nwXUSkkRKTHEamWM9JPkgHRQhJMp7MXRCwTRSIsowrSwTpC+KzVCVDDGSpGpG0AFABoWK+8nByimEA4j74SnpMI1QQa6h+E0UQDaCw8UDf5PiM+h9YkgIFKZsQUkjTCBrhxXWs74zRSMeKlOJ81yablIvRJuyvCGNe4iUC4CoGRrhkMeOqxzCJoTrKuAjGulARDoYj9uSIB6FKqfU5Vdp/QGIfk82VBeJhXAYy6YQiwzqRs1RDdEQ5TPGZ1TsNtHVC8u+TjCU+fW0DEOwX67PJc0CDxy+ZeiT1U2i9N82MmEkDcqQEcqwU1c5LOSDkoqYC4V2GnoZvW16TK2ziXU+gCKXiw4DpfgIVQn845HKIXsx+j2NI1RCPML9ot1ugmBUFCwqLx7i6kasd6cHDf7kDhblen16GDIXiCFqldiGvOeUjOj3n7pKZBqIghEnHDJNs4dInWDyHMGhKmXimvJImZEX3WZL4tx4SFAKggsQNaPfkw0ggXAIpLlTKiQTFbtSkoJCSVoR8RlJbpPMaoYpANTiMokrHORvLyJpKqwpL42EWDsl0hFFhRF/SAJR3AbifESpF26nkQ5TGTDRkYQZouOIkYxo5CmCU22EI6sfXijnuixOR6ji8gGIlr/hKIRsRD83k6KBTOqGcv0VkmEgGLTnt9kWKWOahULLyPGSRjwZUU/kfUsgGIy0qdZRXWxWwsF1G6F/zSpHYK8acVVIh36CiIgHECMevRET5qCif5MNn8upr2YSIX5tSHenmFDLNTQSSIWyXSnRsLhNFKJhiM8wuk0qVDOUAFCtjOjoTGUQHre3kUY4dFeJx4AyedDrioa1Q5IFkglLVA4xMiLJB/QOhSsP4VRXimHbNJIFI8SBaafjhV9UkhCNcBASAUEwpL+dK7K47PkAlXhwyN+6mFlRBCKikbyOiiCfPEyTAaEs6CR7NVhU65OhNq2WoXnRbZB2oJCJ0CaieB0L4QBCGwFUJUN84nmBbZtJh1QrxCXT1A5KYoMvUHAAuvhe1dQfgOjfZAMwE4Y87hSZmDIzpZKZKyaCYTL6LESjTvEZ1G2SGARqUTNMAaBWl0nazJQ+gMT4C1NajGBkiMcg5EASEp1QxP6SXsBGPsL9NFeKqmjQbQYam6HUpb50vXMBgg7Uox0J7b0spCOto9CIBxCRjyKVDT1NuFMgCUVEFJTLLu4tkO5OQdgO1O8rG0WMRsRSKOEQ7UoyCnI7BAGCwZ1CbAhaurzfMeJhJhySOCi2orlVlL8iSDiZdMRsSVxnhSVH1w0cudbDqRZyqngV9Qci+j/ZIMjjTonSTGTFkFYNklSNSohGUnyGB7lQV2J8RprbJKeaoQaFMjWNEJFYeh8jGwASXSQmMhKfDgujoqF2RAbpPSVuQ3WhaMNTMToVZcS2RjooibBt20ayOuGIdyYGSVyqLJV3FABUeRwohGwI6OpGXdwp0Mto5WInFZVJW9RLGW9R4kEkDp1IgEN1mVG1wkMw9lLSiU342u0jxMOmZNCPlXRQWxKN+9EXEmtqKGuapFxGh+IwMMhG2pTUatSNvK6UpNVDU4I9sxKN1PgMC9FQ4jMsbhOTcmFVM4yzTpghDWbSIZ4F4hh9CHmDRpNWF1XiNGydkKyopsXW2yCEwuhKYeQY5DrLZkldSSiUbUNsBq2vxWLEO5N4R2IiHQAydxTyupD9okaqVMmwuVOoStFX3Smcq/cyPJK872KfEglpb4jbgY1wSNuhJIHE+Kh/xRToqHwa6ZAnQm1dnCuxp+DScXk5eZ5neLUQ51pN/QGI/k02OKHQae6UpLR6QCcUKeWsRKMW8RlZ3CYKuVAJg7o0uUoaTC4TW1yGQjoQL1dveDn1ybwxHDp3yBQkqqsYtKHYX5VE6GUoCaGkIkndkIKCIBcewH0tNoN2LAAhF5bAP9oh2DoK+d3DbVNHgTj5iC5D/TsPfZ0NE4Gs6+wUkEZAkypcypzp+cE5x+pSgkH/anYQIxwcAfnSbcJDpF7FPiwkFhbSgaiMvIimPJ14AFGMB1BoTJibjWJG/yYbAExqRFZ1Q6Le62sAdlUDtSEaifEZZFprotvEoFykqRmUiOiEwkg6EC+ndIB1hhd2cDbVwlTeB8jJRzApHJmDRElvFIvbAOIkJPyrqBqmMpSYsOhYMXUDUZqRfICpfviww8ga+GciHfJy6R2F+L4geRbyAXGogjqPpLU1jO6UMOaFcxEPEXxp6SGipA80TzBGkU8pgWZnLB/hkK4SYhLyHnoIyQGpawjw1IlDol3YbIKnk47w4ql5jJQRJ28jHso15nLbzUbpffR/skGRVd0IkfxCtXQCknnaa4L7pGqiYXqRmpcQn2FwmyhTWvOoGbrLJCkYFNE+LSfLlIql8x4ZPmRZstymcugzUgAT4cgZt0E7HcDoSlHUDECVyUV1hZhEHESqGLQcSCeT4IenErmxs0jqKGSnYCEeoBdHJR8cLAqc48V0HpGyYXKdRIQDqHCxL0I4ZDld0ZBfUyOW0PPD8zAQDsV1YiIc4pyyEg4QO7DYReA+MQeDAuTec0M9YZjClkDKyG0L8VD+RmX8hgLJhlvUy4iBQTayqhsCSa6UamadiDaTXCimPBPRoITERDRSFuqKxWfQuAuD2yRNzYgFgNoCRHXSwczpEREJe8lejNnwwh42Te2wEQ4mO0ZKMNRnhu46ob58Y0difMIDccWD7IPUDY9D1Q29XUouYoGAQhb3DX74pE5I6ygUUkE7AUI8AJpHykBLIwSE5/OIVYwk10mh8Ru6nSCkFhY3n/olDPEblHBIdcJAOMJrLU0z3BDELzE+w0PwHQ0kVPnJ6KRDnJhuJzbiIch5gk0VGRPm3Chm9Guywck6GxJJikQt183ICl25INtcIxUK0RBlFFeJZ47PEERDWz9Dj88QJEMhFYQ0pKkZWVwmppkmND1GMmj9XgZVO5LeiWKbHmsKEgWgdSp0H/KBqLhSZKcSdS4cEXmWXEP/G5o3TQ9cISxeVhw+iXAg6jAUP7z0zyPeUdDhsj7qpA9/QF0vQ+soZL6SF11bXq7/71jhb5rrJGv8hpVwiDZFRynyNEIhLyglXDrhUM40Q/wGDIQDgjjwcNVPkkdcJZL/hCTDTDAQIxaJpIPF60SKmDiRBOIBQ1klj/eJ58s7Hf2abChIUiSyBIXWKm4ji3KBdKJRcSCoHp9hcZvEiQXNQ4xUSDUjyWVC4jJiwZ+SeHBjvEaWAVqRYIzD4/bYDsY4PMRJCQOC75ikcoD0HVqHo4xkuVZB/qUkJGoj6vREOuTATwbzib+0SQvhAKf79s4i1lGA7pMHv1XFoOSCGfLjBKTId+nYpr5SMqmmZVh/I/xCFQWM1pFwAEC0Xgozrp0hQiHSCYYpzWBH0OpArafsA1abkvYRsx+3qFdfQL8nG9wP3rWgICQOWZYTr/rtrboLhYKelx4QWiHRSA0ENU1rFW4TEo9hJx1QSIWeHyMP1J2CCkkGK7bzyAOhdiQFiXpgxrgN2ekY9uPSudqBRE9/Sizi6oZSTrdjsUvJCDIQDg67TJ4wQuX6g17bl+/t0EiEcZQqzs1CQIrqPOQ1shAOKHlmwsGAxIBRSThA83j0vU2Eg54gshEOMASLovkZCEfwDcA9ptxbm1slmWCY0ogdQc0HLMTDuK1dExv5cG6UXke/JxsKssRbiF9XpVNgw4eckaAkqRrhNtcJSFaioQWCpsVnJLpNDGqGiVAYA0BNioZpX5bj1ngNSkTg9f1fmCAeJheKKW5DpCtrQhhcKcJ/r4xq9UBRygzCdhQCgYiAxNwpHgI3iMfMCzOBlAs7E0UmFw9sZbEmcjCtIwGiOsoDH+pfnXwEZS0ERNZBFORaZ4j7nUQ4gCQSgvoRDmEniJJshAMsugfBvefh/WFKpy7PV7nvQSUWzg6StkPL0YuUl3SI70eOJQ+UgXjE7ctMPniP6Q7XCS5A1IgBQTaM6oaOatfXSCIxtF2pdDCVfFCiIUhGFqJhCwTNGJ9BFYxYEGiSmkEVC8tfhTDESEhGksHIceoMDzxca8NTn9sAgOyvmg84n3lKrAgipY8LMdIVHZNoQ3Q6MkESjbAij6fLoECRLpQORB2WHGFTQkEJR0goYutnMERrbdBOwaJyAEiVxQF7R2H+q3YWJgLCAaCgtwSL+5mXcERpyEw4ACj3OxPhABSbiRGOkA1U5VYBwllJBpWjWtIBmk4Obon1kcex2ZLFntzU195HLrJx4YUX4qKLLlLS9t57b6xevRoAsH37dpx77rm4+eab0d3djVmzZuGaa67B6NGjZfn169fjrLPOwn333YehQ4di7ty5WLx4MRoaKuA9dFEvHTlcKYlIqmtTMEzqhb5fCdFIWKhLcacoRIPOOlFJBpR0sc/sxIKSE8QJSLDNJaF49d678Pryu5VL1jhyJMZ94zyAAeWenXj9D3/Etn+uAu8Jhh6bN29Ga2urLF9Te0mBB67Yk811otQJSYfuSpFKB3WVADGfvN7ZGNUNkc9kkxJKLKHo5ISCYSMcYGoMhyAUXCMUZJnyTB2FHGlysk2+G1e/q1IGwMsr70LHKtVemltHYv/jvgHGAb+8Exse/QNeW7cK3K+/vYh7WBThEBQ1t0vFFsMhWaSSCqUFll/lqBnpgJomTiwr8QCgnLNuT7S8m43S+8j9C9xvv/2wbNmyqAHyIz7nnHNw55134rbbbkNbWxvmz5+P4447Dg888AAAoFwuY86cOWhvb8eDDz6IjRs34pRTTkFjYyMuvfTSGnwdoKKpqzmhkBeTqiG2TXEaWYiG6dXwpkDQtPgM3W2izT5JVDMMyob8wWrpOsmg5KNxdDvGfPFMqWSwUkm2/9pv/4C3n3oau51+MliJYdOV1+Fzn/scHnroIQAF2UsKKAER3gXbqqKycwKSZ6WINKKA6DJ6xCAI8RDqBiPdBos6ERF3YSQcgjx4gJxxEEuPOgHZXtaOQnn4M0SdQnJHQYuAAS27tGOvj55JrpMnz239Q/+Dzhefxp4fOgWMefj30mvqbi+CPBSpcOR3qUCWlYQjvElJbhVJEEgeC+9DvUmHJAU0HVFZsa27WoJ6XClDywJm8lFoTBj9DpXWH4DI3Ss3NDSgvb1dfnbbbTcAQGdnJ37+85/j+9//Pj784Q9j+vTpuP766/Hggw/Kh8Hdd9+Np556CjfccAOmTZuG2bNn45JLLsHVV1+NHTt2VPYNwgn3PMvKTHlgkt0sAZ8xVcMUp1Et0fAYeIMXJxqhsuE3hJ8Sg18C+RtsK8SjFHz8EmR9XlLzxDa8aB8sns89wC/xiKCIOix89pU8lIYPQ8PwYSgNb4XXNgS8BJS738LWhx7BLscdg0H7vguNe4wFADz88MP1tZcawAs7DI9xomCoYAg6DZof3FYemIdIl/sRKZH5VMlQ6oSdSUgypctKIXrkniDKU8ihx1VlixJTUl7Pk3aj16EfaTsROQ7skNqNakfcA5jnoWFYKxqGBp/SkKHgHrCz/DZefeYRjJvxcQwbtxcG7bY7gPrai7h/VOEQt0Pkq3/T0kTFqE2hLkT3nbQt7YCrNkHtQd9X7IXYibAVjyv2AuUec2XQIPv4WDmxHdmQOV+cg8U2tDT6DDHZWlRPe/7pZUlbyrk49Cpy34JnnnkGY8eOxZ577omTTjoJ69evBwCsXLkSO3fuxFFHHSXL7rPPPthjjz2wYsUKAMCKFSswdepUxa0ya9YsdHV14cknn7Qes7u7G11dXcqnbjCpIjalxKZqiH1KNGJt2okG/TFBIxd+SCxMgaAmV4nYFsRDIQuiXQvRMP3Y1XI8WPmTpomHSwkA49j5yhasv+hibPj2pdh8w43Y+cZrgMfR/eJLQLmMlv3eJdsAgPHjx9fNXjxwlFgQt+ExHsZw0I+vpGWFIB0e6VjiphAnJoxxpbMJEhF1LmRfdhgyLZlwmOJmFEJCy5g6iDwdhZ5eSsgvxe1Z5jOgu/MVPHHDRXjy5m/j+ftuQPe218FLwFuvvgjulzF03Luj8qizvZCOn8l7nI1w0PutpomKOQiHLC9sgZIMyz61k5AUKMTERCx00iE67FqRDovd2MiFThbMxMQw+LKRj4Igb0kVn4GIXLdgxowZWLJkCZYuXYprr70W69atwwc/+EG8+eab6OjoQFNTE4YPH67UGT16NDo6OgAAHR0dyoNA5Is8GxYvXoy2tjb5GT9+fJ7Trili8R8mVUOP0xDliKqRRDRMS49TFUMSjBIDb0BEGEqEPJSgbUP94VKSEf4gTSNNI9EQDxeaRkhGQB44mibugd0++xmMOusLGHHCcSi/9ho6rrwG5Z1vo7y1C2gowRsyKPh1hbNRRo4c2WfsRZIIQkBEh5NERkQZoW6oeYg6HU31kOqGtRNBNGsnhXBEpELtaGiHEXUA8c4iS0ehkExjR5BgT0pnEtj0oDETMO7Iz2DSMV/E7ocfj+43X8Mzf7gaPT3bsWP7m2BeCaXBg5TOo972QgkHvbfi/up58l5CJZhqmqgYkRJJOIjSpdoDojxZFojZR4rKwT0uCYRCOkSacn800kFJaw7SoduIkcQaiKxZ1bDk6+TDi5OPwuDz6j8DELliNmbPni23DzjgAMyYMQMTJkzArbfeikGDBtX85AQWLlyIBQsWyP2urq7aEw6T2yQp6FNXNQwKhs19YloJVCca+tRWSjRofIYcGVqUjXhHwaIfrf7QYCSdqWlyhERGyrLzIiMo2qEN2n9f0kmOQdPk8Xjpfy/GW/98HKypMbyuWidaAxRiL4B0pchYDsQDRQHRCTEgitBQ19/gTMZhBCW1qbCyWwu3PR68qIyJ1NDb7gHgWsCfDA7lURoXaUGTIk364kUAgeHDwiJitMv09HBf5sGQD3OZYZP2JfxrLAaPmYinf3kJ3nju/4E1BPYiOo1a+uBt9hK5T4Lnvx6noSxfT4ijaeGvsJCyDkfwtcP7IuwgbIvJfEAPHGW0rrSNsDQDQM9JSBLid8rDegxgIraGQbGbIA8k/oKTOIgcgaR6eVoGZB/kawhbQ7xsWnyGKSZIxHvwkuH57lAoqgrpHz58ON797nfj2WefxUc+8hHs2LEDb7zxhqJubNq0Ce3t7QCA9vZ2PPLII0obmzZtknk2NDc3o7m5OfV85BTYaoNEFWIRtWNUNdLcJ4Y4jUxEg844qcBtYpIy49Nf44QEIPUIsZBEgoxMbCSDTmsN9oNtb+ggNI7eDT1bXkHLfnsBPWX43W8F6kb4IrYtW7YUZi+AGgAaQLwYREkJuBCzB4dS0EBRX84qQTxANKAVwaEo4ZB9CFnC2kQ4xFOahbavdTxy1oneaXAWTrVJ6SwSAgABO/FQ8izkI1YGUTmR5w0ehObhI9Hd9QqGjd8b3C+jp2c7Sk2DIKYy1tteImKRTDgElbSRC1GH3keVUELaQfBHkJDoGsnA0fDCsXCbEzuICkdtWUmHaIeTumJdCsP9D5pKn70Sfr2oPVqe5GUiHnqexZ5MtqTYVZFcgx670voDEFWJS1u3bsXatWsxZswYTJ8+HY2NjVi+fLnMX7NmDdavX4+ZM2cCAGbOnIknnngCmzdvlmXuuecetLa2YsqUKflPQEa6V/5WpkzTYpNUDZFPiEViQGgpvm0lGl4C0cjgNhGuEV+U02XNTAGgalxGTGYvcbVdjwcdYUnkcSnb+ju2o2fLqyjtMgxNk3YHSiVsf/pZMI+DhcrGhg0b6mYvIk6Dxm1U0oYupdO0rLEbujtFKS/qyzxE5M20DYT3g1xvgwwekUuSRkliFllcd8lR20mTwG3xQFpAsijb09ONHZ2voGFoK5rHjAPzSnjzxX8rbpS62osgAUThoPtZAkejx0D2OI40t4o9eJRLgi/zaayGx9Vy8r5zg+2o9qO7MWy2FbM18pzwS1od8qyJ7Qt7Ygn2ZLM32h610YKg3I5KPsWdaqHIpWx87WtfwzHHHIMJEybg5ZdfxqJFi1AqlXDiiSeira0Np59+OhYsWIARI0agtbUVX/7ylzFz5ky8973vBQAcffTRmDJlCk4++WRcdtll6OjowPnnn4958+ZlHonWFVQN0UiIVdVICACtmmjoLhRF0WDkh5agZoRKiu4fjU1tJR2O0WXihYRb7uszIKIHnVAyXv/tHRh0wL5o2G04yp1d6PzDPYDnYfB7D4Q3tBlDDzsYr998J0ptLXJRtkMPPbT/2EsKgngPVd0gw0+yzSLXhlA3glRFOjcqHIA6JGQAF6s+CplcH40i2wiVKh1yJKmVN4069VOSZ0tHndDqAei49w8Y9q4paBo2Aj1bO7H5wbsAz0PrlPegNGgQdtl/Bjb+7Q/wBg+GGCfV015oQKgYznjhF/K14ad4ctTErSL2iMrBQqUishypiQGg+cS2pHmFF5ob0qKjRbYTNBnZTyglmOwlUi7ieVSVUZWMwPYUe9LLkX1xOak9GV0mJC1mbwWSDQczcpGNF198ESeeeCJeffVVjBw5Eh/4wAfw0EMPYeTIkQCAK664Ap7n4fjjj1cW9RIolUq44447cNZZZ2HmzJkYMmQI5s6di4svvri23yoLqDpBlQuLCyXYD1WNBHKhzD7Rp7gaZ514YYdtIRpUyfBYyODzuU3iI1t11CAJg0zjhEQgRipEOnWh6NsAUH7jDbz6s5tQ3vYWSsOGoHmviWi/4Cw07DIYYBwjTp6N128CtvzgJvCdwSJNN9xwg7yk9bSXEuOQK2fQPhxAmisFUc1gW8jpUGM3RIdkIxymZcyN8RuhXM7Ch690hRCJOgCPOpQE0iFdNTyFdIRNphIPUi78Kua0iFuJs1U6hJ5tb+ClO25A+e1tKA0aisHjJmHS576K0tCh8AGMOvIT4PcxbLhjCfyeYuwl5i6R+5W5VaJvbkuLSIe0Dko6ZH7YNoeRdEj3itxHdHO52q4SY6ETD3n/NOIa1o/sgtiSaJve/4zEA6SMjUiYbCmNfPg1jPFJBacHr7D+AATjvP99s66uLrS1teEIfAINXlOQGBIDuWw58yRxkKoEJQoh2WBCfaB1mLov61OyocdhmNwnJS8iGoJgZCQafoklB4LGSIdKMoxqBotvq2kcOhnRlQuqfNhIRiTdhn891RXA5F/ITpa//TbWff7b6OzsVFaErAWEvZxy3wloGNICnzP4YChzBp97ct/n9OPF0nhYDoCaJrbDdK7kAb7vyXwepgfPI30bAdkQnQvEPkKSIR7gUVlZUBIQsi0gH/Bhvg/ZUdCHP+2ElDzQPL0sJQxMHU2SBz+0Nozp0NJNZcO0cvd2rPnBN+tqLwf/7qvwBrcEhyTXU6gXIk1MHuCxdHIfEd3vYNtch6YrHTcto6SJMmp7OqHQ29LvhTHdZkNknyoaajm1jarsyZgXpZvsSE/zt2/HmivrYy8Cwm4+8OEL0dDQUnE7PT3b8fd7L6zrufYGBsS7UWLIGByquEZiKkaCqlFHoiHW0IAHbT0NqHEXZP2MVDWDqdsxNcPkMtGIhOpu4agFyfCIz9kvVR53Uy086VugqXZ1w/baeRuyulMiF0Y4dDOoHAjLKCoHA2JKB90W91IoHeGBopVBKUkhI1STxM20fSWfqB4w1Cd/E9MZSdP6QNF2EStCMiDmRqmvyqGnC3LBlRypXABEyUDkkgmrcmFvPBo/RaSEtEUTaLqSFh4Dwq4o8VBtSXezROpHfnvSCTAtC+jlSZq86GH1It0olCBVWn8AYmCSDRNsL2GzuVDCfWaacaLBOsU1B9GIgjgNRIOQjMT4DIvbJEnNMJOJiFTo+zE1A8hOMjyy9gTTgiQLeOurCAxVXSc+/AxPItrp1NqdAoQPXfKEjM1SCTuVwA2ik46w8zERDVOHkRbTIR+WUcdiJA56f5mBfAApRANqmj56FZeIDrrrCXFHKMEAyD0P/1YSyxEdATCRDhbeU2kbcj+4AAwkHRbiQZrmxPb0kX8i+chKPMg9jxGPDPakEw8o28TdAkP9NPLhYjZ6He8cskGhEw89MNSkjCSoGlG9MM/zQBftEvEaJqLhlzxCKJgaCEpcJbFYDSX6mtmDQLOoGYRomNwpYFxGuHOxDZpuJxkszLeRDBld38sPgzR1wzbllZILoDLCIToVsa8QDha85ZWbSIfw2YsHvuiIddIBfT+FdITlkzoKAFE+i46vkwi1Q+GymVQCQrZNJKQIZUPMOBL3XhJOTb0A6ks61HQW7nEl10g8AEk+gm3StkYyEslHAcQjaj/6Qkw5jwQia7IhzXYKnY3CORi91hXUH4gYUGTD9Jp51VXCzOlAsqqR1X1CFQ0PEekoZSAa+sqgIYmIpnQxjWAgFrdhVTOYnsdV4gFIAmKMy/BqTzI8z1fIBvOKcaOYA0PN6oYnyiYEi+okRHROWQmH6LQikgFQAsJ5nHTI6y46EkIqYi4WOlKNkRBCOoBsxCNsS32wa50FI39FOvmbSkAQtWlTO4roPASRoO4Qmm5zrQC1Jx1C3TCpHcE5CPJJXxnP1PapqeoKhjxk6AoykY+sxKNSezLlm4gstSOTDRnIR6HKho/IOCqtPwAxoMiGAgPxCNI9exmhatCgUB1pREOsDloJ0WiIyIPiLtHXyDC4TZT56gphoEREVTOiuAxYXSbqOxUiciHSmJy7n0wyPI+sIUBJBoKHdxFkw/a8sc1MMREJAZs7xScdTxLhKIUkw6xyRASEkg76RGY8mgYpiIeudjDyYLa6WHhUn6od4hjWEarcDzb0ziKor3YYcrQq6pK/cVIRdSAin7ZdROdBp74KJJEOSiTykg4mFQmy2BfXVS9xXvE8K/kQIOcNkOvK6HXXCAi5p1ZbEo2Z9pPsyUo8AOOKpcpJRxybfhmdfIC04dwovY+BQTbSAkI1t0ksMDRprQxd1QjTFaJRomUQIxpB+yE5kG9uNRMNxV2iTHeFJAnRTBX1E3ObKAoHV8tJoiFIRVROukz0WAyFaIQLcSWQDM/TiIWJZEhlQ3ui1gnRiqERudADPm2riopyafEbaYQDjMsZDKI8Yxyc6SSDdjxB+RjxEL57ROehqhdkpCoexraOghIPwDxCBQkGDI9lJR+0fWiKhomAgLYbXf2YEgJx4esPPUZDIAvpUNPMpKMk73lc7UgiHjQPiPIFdIISU+ZJggj0lCnEjphyv6nqEdhepJ6RDt5kX0ggHqScbktMyxeXx0hiFcKi2UxB9hKcn3OjmNC/yYaRIHgkm9nLeixOUqiqkeQ+Ee1pHxkEqhGNaDXQjERDrrBoUjQCohGfhSLIAmKkQqwMmKhm2FwmlHAIQkFIhlA3KiUZYrVNzgpQNgzHUGM1Ajqg++gDxOM3JJEwEA5ZSyMcECTEonJQ0iFIhhilmokH5I7SaRDSIIhEFGCndRS22A7RBgPMsxCgdhZh/dhIVOl4yHEEKMHQOhCZTW8FR+ExGzbSASAe00HbEGXkfkQo9Pdt6XYjygHJ5EJXMWi5KM2+LwkMxDosCOxDFNBJLJBsTybiQe0J2YgH5HZ0olkUNFPsUKExYfScK60/ANG/yQaBKV4DgOY28cyxGsR9Em9YIxpMuESYompEs008OQOlJkRDWabXEJ8h8hkUgiHdJrQ81HRKOowuE6F0MERxGV60XHJeklHyfIVg0GW+y0W4UVg4G0XGYESxGkmKR0Q61PgNhUhohCNoUy0XEQ2zyhEjHYg6GYCSDNHJMCQRD6WzsHUUpH7uYMAwPY18APpIM/oy8XStvMhjpAxDIZ2H6e2+1nuLOOkwlRc2EKSpxIPea5PiIWxCbAPZiEWQxrV9RrbjdQPSEScfUUeekXhUoaBJJS48nlVBs5Wh5KPIRb0cjBgwZEOBKRZDz7e4XmKqhqifFhCqEQ3lVccmohHOOolIBUsgGdBcKxrhYNq+UCqo2pGmZhimr+pxGZGiEZGItJiMkkHFUN4lguJiNih0cuGTh7wev6F2IvGAUdkKIRxRy2SfkBNKLErhM1h0OFKhEJ1NWJ+TDiaS1e3EQ3YWto4ifCpTN4uoHxuhAtE27SzSyIfoNEDbsRGQ4L8Y0SB1ZL0iyAa44lJTiIUsY0aMeCgkA4Y0lXgAEfkokfuuH0NduwOSoJqg1qcr2HJiO/+/vfMP0qMq9/z3dL/vOzGEmQgkmWRvCD/Em8tvF6+5syCiBFKY5SpG1l+lca0CjWxtQShFtoBgFKPc3YKyFrHKiwQvWihVFveCmCuJyC4SRGFzQWBzBSMDayYRvJNJomQy3Wf/OH1OP+f0Of12v2+/PfMO55uaeuc9/fOd90mfT3+f55w2wZa8GvVCNJ5ouqUUeND3NJbMOMpLp0Cu73bZjD9db6WRf4fbz0L1P2xow1YdKRSgkKvRLn2SAQ2tNiOAGupKQCOdsKsD0Gg7EgXKndCKQA3IMEeaON0M+rj3BDwUYJARJ3QyLqYcjc4gQ6VUaikQjRUwSEfDhIvUzXBNZQ6YKRWzhkMARQoK5l1qmNyhasuYPtqE3uma28tUC6B3DrmOh9FR2K1xR04+sx6yMJIDH/K46fsCAKItS9+oziPs/QVZxa88lKXDsqVMtOWkl7OBB/3+0zYCBso10SGCQgiVCSSucwWgYkjfTsaaDiB5IAv1GVAcPGjstHPQaAzR+LFAhQ69KXzUES9STH3mzrefjep/2AD0FIr2O3En6HJaFFoWNILADRpBkAGNzKiTPNDIQAbgHolCAUP+pEChj0rhGozkuhmyLiN5nyn+JENWq4AMlRevoWYDAELECZSaRZ86XEScZdooWMjtKVgkrWJ/BqRIl0PWAFDokB0NhYrU3Ui2J7Ag2lOYAUCKS9uDR9oZFLhDBbQLPr2T1B0P0oHQNsUYBQDE3D/kfuX6pK+pMY2ifafqZNJ1YuO7AEjJE1kOQIcMta/0+weg4ENfH+myZBtTJpBkPg/dHtn9qenVLSAr4SMFEUbaBSxUBh42iEUa07muB2CFDz8aZfo1K2DDlNPVcNV16Bu3dzRIESjoCJQOQMOs1aA1G5m6DPk7BQpL2kS5GQkwmCNN8tyMTMqE1GUwAheZeTKQX5MhISMMYnURD5Aun6qpZkP/PXUobHBha9OVnDNJqyiYQNbloJ1KkAAHACd4AOkFvwh82NMtgKujyL1DBQybOnE9xO7aw4etTXYcNgAh29kgxHRCeA2jC8R/9/Q4Gky0AQ+re2GuAzd8yGXUvcjMm2EohO6k2EXTJ+J9bP7dybHk0GwOM370GOsIPMQOUvAQuzDihqVBRgC2dN1QnbDh0yhW9TdsmCmUdq4GXc/lagAaaGg/shjUqM2oEjRkGx1xoo08YSl8WItAE4hQRaAEOjJuRqC/mikTWpdBiz+1+gwkd4BB7ISMhoKQLGQol6O2AlEO8BgRgkLAIUTaQNvJcovLocABPBl9kg6JlWkTAAgIdJi7l+BgOho2+JB3rtT1yIKHuEPMvUMVB3a7HsmB8+GD3KGqdYw2AFqHUBRCgFo6D1uBqFQ78MioIHwAOoBISRBtp2LAkcaR2K38nabrLHGXnKsZU3YXzRJPbcEjfY9km9KpOxuwcl4LnEqxWPx0s32vdNNNN+FHP/oRduzYgVarhfHx8bbbcM6xYcMGfOtb38L4+DjOPvts3H777TjppJNKHbu/YQPIjEJhFBoA3dUomj6xjTyhoEFqM0qDhkqbpKBBocLmcijgYHDXZ9C0iTbapKCbkZMyMYs/gyBOCzsZRxjwjiEjgBghwoKo16GCUNVswAkcQja4IOWeyYVO7yh0l0ObZyNxHcLkYi6hhIIHvYsNSEdgAkgx+EhqPkzXo8AdqtyHdTQCZ8XhI7HK03NC+vfMvFrAhJFlFELkrmvIwZsFoubU5VYXAwZIEPrIcz4AaNtJFQEHU2GB7Wm9hgkonDPSJguZs3Fnh9nURbPVDWXAA8RBo3FjAqwJGsl+NBiBBVjlut7ZAABMTk7i0ksvxcjICO64445C29x88834+te/jrvuugvHH388rr/+eqxatQrPPfcc5swp/nTbvocNAFlXA1AA4ZwJ1FivbfqEgkYCGDbQsA5vtYEG+T0PNLRCUK1mQ0IEMkNd07qNZB3qYrC0Pc/NcKVMtKLOBDTKpEtMyKjT2QBozQYAnhyT1HCI94ANLkIWIVJ/bJB1aUcSJxN16U5HBiwIeABATOKUXvhd7kdo3GXa4MPpejjuUGVHAbouCsIHSKch/2YoAiAW0IBjGfkunA5ChRLxSeIC7uPmwQegg0R2SDWMmWTTNlsRaBnJEZ+2wlEaZyE5Z1tMSfigMZUHs+3AI69uiK7cUcGybKPwUWOB6EzWF7/4RQDA5s2bC63POcett96K6667Du973/sAAN/5znewaNEi3Hffffjwhz9c+Nh9DRvMGMLKKDhQ5bkaJmgE0sEgy8KgMGjE1M1I5tHIA40UKlwjUSQ4kPehniKxFoFSZ4OJ5ZAAksCFhA3pZtBRJtLNoK6GWfwZEudCQoYGGgUgI12vvpoNChyi4ydFowBSl4MMcDRcjoAJoADPdiDC0YjInBrJsZgODFrNBulRA9KjmdvQu1AKIHnwoXcU+a6HbHPVeojldviQOXr1STLuB9R+cgFE7i8v5VKHs6HSKGlsSpCUy13ORSY9YgEQQIcQKRuMVCUNMIz3pqOmACSJDRozzHA9rDALPXaK1A2Z4EHTLSpWTNcjWZSbsuvB39Ipei6dbg9gYmJCax4YGMDAwEAXOy6vXbt2YWxsDCtXrlRtQ0NDWLFiBbZv3/7GgQ0l27NMiqZPXI4GAQ2zCHRaQCNE27RJZkir5LAkTSJdjXZuRmC6GsgWf0qgoJDBGEcjiEtBhvg9BmM1pFGYnkbRan609460CmDAhR06zBSKBh7E8VCuhnHXaXU2cgDE5n7kwYero5DL7fAhtzHb5AVfQkR6xyrhg7ofbQGEtNGRKBknpEbYyNZV2OFDbpOZ1t4GFMQFMdvCrnqqfGnDagGAQQOmojGVAqwbZk0XzZ2+0+uGXAXLVtdDbKbDh83lqDGNUtV05UuXLtXaN2zYgBtvvLGbUyutsbExAMCiRYu09kWLFqllRdXfsGFOzCUBoWydBhnWanvmCQWMnoCGLXXC0t+1+oxkmZY2UakUbtRnSIdDvDpHmhi1GeqVQIRZlxEaywPG0WBxacgI63Q2wBGyGFHS4VuBQ8VVmg7JpFAAJ3QEoM9EsYMHoANFbFwJXSmVTEeQXIWLwEd6vbZ3FOXhAzDhQ3c/uLau6BQcAIK0z7VBCADdCQFqeZYOLRDNcy0kTKr3ySt1QKScKRSjrddyORzp5GLpucYKUC0x5XA9nC4aAeDSBabIdz3MlIvYj1i3DjitWi+//DIGBwfVe5er8YUvfAFf+9rXcvf1/PPPY/ny5ZWeX1mVgo3jjjsOL730Uqb9s5/9LG677Tacd955eOSRR7Rln/70p/HNb35TvR8dHcW6devw8MMPY968eVi7di02bdqERqND7jHTIUXWtYGGbS4NEzQYSyCgM9CIKUzk1Wiw9L2o55AQ4QCNdkWgxlTjLHC7GcrVgO5m0JSJBA0KFqEFKLb+p+/gz2P7M1/DSWuWY+TzI/jndQ9iz1N7tGVXXnklvv3tb/ckXgIWJwV/ND1CASOR4XLYoMPmaJjLKHgAOhTonY1+IXSBiCsNIzsLddeadBQUPkKkAGF2FGXhg9rkqg30ztXWrncq4lXuQOzzpf/yPzD16jhMHXnBChz9yb/F7i//PQ49v0tb1st4ach4SVTEtTAhJNNG9m+DkU5ke+YPFXVelAhMpOs5oBX5MWW6HnkuWpk6D+p0FHE98uCjrgc9Jh+OBHeH2wMYHBzUYMOlq6++Gp/85Cdz1znhhBM6OpXh4WEAwJ49e7B48WLVvmfPHpx55pml9lXqf+Avf/lLRFFqd//617/GBRdcgEsvvVS1XXbZZdi4caN6P3fuXPV7FEVYvXo1hoeH8dhjj2H37t34xCc+gWazia985SulThxA6fRJBjTIM07UqBM6aZcEDQIjPAyqBQ1H6iRt42kahUkAyUmbMAoZUE5GWTeDDmU1Uya24s8GqdOQTsb5f38JGI/BEidj4rev4af/9Z9xwvnLhAsCYPklb8GKT5+OqT9N4rsfeECLnarjRTobaSEoTY/oLkeA5G7VAh1FHI0w6WgidQEPVMeVucgbMWxa9mYnAAk6xt0nfWUF4UMb7VIAPmx3q+Z8DKq9EIBAvV/61U8jjmTdDMPky3ux+6bNmPc3p4j4BMeR73k75l96PvifJvHK1bf0Nl5YjEYQ6WkSmK6G/p0WcSzS9s5Th+UAJdaOazsXsZb8f6HHlS2mKPhS1yMv3dJZnYfb9bCl7OwpF9T7OAQOmmnrbPsSWrBgARYsWNDFAd06/vjjMTw8jG3btim4mJiYwC9+8QusW7eu1L5KwYb5gb761a/ixBNPxLve9S7VNnfuXEVDpn7yk5/gueeew9atW7Fo0SKceeaZ+NKXvoRrrrkGN954I1qtVqmTVyqYPsmMPCkKGiEBjZC8DzoEjTY1Gul7nqZRXKBhS5vkDGmVRaBBkF+bQUeZ0JSJrfhT1mmY6ZLWUQNaumTnP4ziyL84En/x9gVoBBECcLTmBBhc2MLUgZTmexUvoels8EBY4AKjLICRQkfIgCgp90y3dTsagO5gxKRziKwXfXL3yYxOwHA0xO/6nXS7IlQXfChXBO3hA8jerUr4cLkfZQAkmJ/emHAO7Pun/4vmoqMw99Rl4u/OgGCggdab5yEeeB1Ab+NFwCknKbSkncnvQIcQ+vcW6xmQUUHhZ1C2J6KS5+1wNGxgGyMLtTRuTNdDtZP9MKBwnQeQxmHRyelUTLlSLkAm4/5G1ejoKP74xz9idHQUURRhx44dAIC3vOUtmDdvHgBg+fLl2LRpEy655BIwxnDllVfiy1/+Mk466SQ19HXJkiV4//vfX+rYHddsTE5O4u6778b69es1d+G73/0u7r77bgwPD+Piiy/G9ddfr9yN7du347TTTtOKTVatWoV169bh2Wefxdve9jbrsQ4dOoRDhw6p97JKV6QGStRpUNCgT261gQZjGdCANryVIW4Q6OgANLRJu5gBHeaIE/relTZxFYF2UJtB3Qw6X4atLqPBorY1GXxqCr/d8luc/rG/QiuQRaEcv/nxS/jXH/8Oc98s8pF/+tOfVAdSdbzIdE8IIOKBBh00tZIFjEQ8Vm22ok8ghQoJH5DLqR1vGT8ZGx0Q7dxUp0YgJFvXYXQUzO5+lIUPuZ3tbhXJOi73oxMAEW0APxxh///+F7z54hGEIQCIWDzw6NPY/+jTCAfFNaWn8QKuvjfTwQCQca/Eevq+bUDSqdqlS8rIBbdZWLVALYkXuSzP9Sha5+FyPcrOEUNBRMFHjfUwVRWI9kI33HAD7rrrLvVe/p94+OGHcd555wEAdu7ciX379ql1Pv/5z+PgwYO4/PLLMT4+jnPOOQdbtmwpNccG0AVs3HfffRgfH9dyRR/96EexbNkyLFmyBE8//TSuueYa7Ny5Ez/84Q8BiMpWW1WrXObSpk2b1PhgTbY6jbIjT8LADhrJLKEUNHjDAhphG9DQYKMAaISpc0HhwgoaMm1CXvOGtMpZQAMFHeXdDDOFIqzm2AkZ8v3v/tdLmDwwiVP+dhkaQYQQMf7qomMxtORNGFowgN3PvIYfb9yByy67DPfff39P4iVMzleJpFMkgFCnQ6xDAUM+Ij6FEQCISL7ehA8gHySoaAfQIB0AWKw6ORuEKJgo0VHI9jz4kOvkjUxwuR+dAAiQ3omOP/Y8ooOvY/75ZyIMxXc2dO6paCwYQuPNg/jzv76CPd+8v6fx0ggiaxol/fsnfxdLRyb/fmEFo6zyIKWoS5JJoZCYAixxJeMvEy9pTNncNNP1MMHDVefRzvXQCpZJ3GVdjix8cA4EYc1plK5qNio7k4w2b97cdo4Nbpw7YwwbN27UUpadqGPYuOOOO3DRRRdhyZIlqu3yyy9Xv5922mlYvHgxzj//fLz44os48cQTOz7Ja6+9FuvXr1fvJyYm9GFB5rNPbKARhiloyLk0VPqkHtAwpx93goY54kQVi+akTWgRqGVIa6duhjmU1ZYyaajfs5Ah3+/8x99g2X9YjPkLBxAkTshZH1ym3IYF/66FH2/cgQceeKBn8RKwOB1WmEzYFPPA6nRI6AhZAiGG20ELAl3wkS7X1ZCug6Ei7oY+BJbb1yvQUQB2+JCvtuJAuayd+1EGQMS2UOvI133b/g+OPOstmHPMEQBicABHX/Q2tc7A8CD2fPP+nsZLyNI0ivl3BnrrYqTHsHeSnUz2lZe+o3BrxpUGtZqrEWvAGiPIBY8i6ZaO5/RwpFxUW50Fol5WdQQbL730ErZu3aocC5dWrFgBAHjhhRdw4oknYnh4GE888YS2zp49YjSCq84DyJnMxJk+KQEaibNRJ2jQdIkq/rQNbSUuRwY0ZNokcS9cRaCduBnUvTBHmdhSJo0gckJGwGIc2H0ArzyxBxf/9xE0glhNGy5rKEIWg5M7wF7FSwiOJosQiYraJIZislx3NzLvCXgAsoO1w4eUmR9PYzfSUzRyfWrJayNRsjUfnXYU6XGynYXYRxY+XCNeMstRHEAAO4Qc2juOA/+yC8f/tzVohFHGCQGg2eK9ihfbaBQq87utwsWgsgFLN6mURnK6egrF7ZrJuLLGlFqW43poYNI+3dKp61FkgrqwzqGvM3i68ulUR7Bx5513YuHChVi9enXuerL4RA6ZGRkZwU033YS9e/di4cKFAICHHnoIg4ODOPnkk8ufiKtOwzXEdSaDhmxPnA6zEJQnw1/F505BwywCbTekVc4CWtTNMEeZ2FImTauzkQAFYuy8/0XMPWoAf3nuQjRYpEGGgg3ybJRexQsj00+HCXTItId6b7gdNvAAoOBDfB3EyTA6oCinMq1B6iys2xtAomx7pKBkdhQZAEnWscIHTLAoVvchX7sBkPQYWQgZ27oDzaG5OPpvTkjuSLNOSCPsfbwIJ08HCB0GeyNbaiRsM7zBmsqxOBkAnHFFHTcbYNjgo5Drkey3166HNeUit6tzNEoMuO4xCm8/C1UaNuI4xp133om1a9dqY9dffPFFfO9738N73/teHH300Xj66adx1VVX4dxzz8Xpp58OALjwwgtx8skn4+Mf/zhuvvlmjI2N4brrrsMVV1zR0TSshYa4knqMmQQa2mRdxmgUqDZHfYYtbZJTBGqbBVQ5G5bajDCTIslPmejORgoZAeNgPMZz/7QLp128FAMtjgAxQhZj/OUDeObB/4fl5x6NI+c38fLT/wYAOPvss3sWL00WIUQgJvaC/GNDuRtFwAOAgg/Y2o2LTNDmLiXjfDC9KNUKMqSzyHQUDgAx4cNcXqruw7qeeC0KIHJdE0KiCHj1oWew4IJT0WgCSFJUr//+3/Daw89j6K9PRGNwDg48vxtAb+OlwSKE6psWMr9fm2yOlfi87XsRGzS46jLa7o+5z0cDXZZ+J7T+CEjB1gYfAhIsMFuT6yHhg6bqnLPj1ggbM7lAdDpVGja2bt2K0dFRfOpTn9LaW60Wtm7diltvvRUHDx7E0qVLsWbNGlx33XVqnTAM8cADD2DdunUYGRnBEUccgbVr13ZeeGIDDW0Y6zSAhoINC3wYoKEm60pSJ7JuI7cQVIKGkTaxDWmtws1oBCRt4kiZNAmQSMiQv//u8TFM7P4zzrpkKZosUk7GQCvGbx9/FY/9w+8w+ecIQ4vEsMR77rmnZ/FiPvVVQoeEDAC5jgdkW7I/ChqAPWXSVPl+x30wy27nckqoS6LW6bCjEMuZe7mCE3vqRb23uB/0HFwOCP1sdJ19T/0Ok3snsPiiU9AMYtW5TDUDTOzYhT33/RLR64fROkYM0+tlvMgaJZdczgFNo5n7y5MJD1aHo4M0ShP2GLTFloIQ9f0LsLXFVBnXIxc8gNKuR5mUC5A4wV7TKsbN0tM+0MTEBIaGhnD+m9eiEQ5UChrmPBq1gYY5tFU6G4l7oaVNVI1GsSJQOUFXFW6GK2XSJLUctpoM9ZoUaeqvMQ4dPIz1b38M+/btKzRrXifx8j+ffAfeNK+hLrqmO6Bby3Idlmkz281lLjmBA1ngoOvqoway9rf5OfKWmWmUTHpFORjFlpvOhn4OQe669HdO23LWl+tNHpjELy75ek/jZf3P/yMG5jUz59Cp8mDDhAhbvUYnoGEqFzi4/f8D/R5s8WR1ykicuNrpfm3xaVvOHdtz27ryM3KGqQOTeOqDt/QkXqRUv3TK50S/1KGmokPY9uzf9fRcp0N9/myUah0NK2gUmbCrStCgzoYNNCxpk6JFoGHSbps3o4yb0Uwm5DLdDAkcZSBDbs/ZVM/DJRD18iI9ggAhxHNSVJGnVviZuh6iLdAKAM25MuICow9UcapFJqxoQyzJNlp7cjdodzkCbZnqELp0PzLrOB0QgE7Dbrog9JyKpGLM9ZthPQ/ukx18kfRJnvJGp7QDjTzIcBWMuuC3ySInyKqJyYy4SmMnUM6HLZ5Kux6q3e56uGo9ABRKuag2oN6aDV8gatUsgA2SRikKGm1mBi01BbkLNAzISF/JrKAJVKQgYgGNME2VUNBwpU1cbkZAHI1u3Qw5IsVMmUjgKAMZYl19NEqv1EzOHwBCyFk/U/AAYIUPAIiJxU0hJF1e7ALRRNYVAQAwYz4HoxORy2hnIS/4Wpujo5C1IC6bXByDta39ADoDELl/GEWxRSBE/k4hhAW9j5cGi9CsYOpJF2jYIML83m1PgC00IoWm9wzwMKE3/V71mMmFD/l/hLRR8JBxZEuriPk3snCbLTIFXLUeqs2RcpHLJXzUWbPhZVefw4YAC6YgowPQaHTxrJPkJw6ZXgTaDWiE3FmfQSfpMtMmvXQzbAWgTQM4KGCIZcUgI0zeT/VyJptEAWI02ZS66EmoCJHOo2DCh2gP0jk0DAiRojDSTk3Yi/YosJhQQpfRzsbVUSgAIRdf252qvV13P8oASN46HUOI3JZsU8e8Cc2qYMMCByZEVAYZNhkgC+jOnIwtCiExDzJga4OPUq6HpZjUVjfkcj1kjGjFyhk4ccBHDU6Yknc2rOpv2GgHGrZnnZDXrp7eWgVohHT0SX4hqKrPIKNNApVCcReBZubOILOAdutmZFwMw81IocMNGeJrjFURWy/VUqNRErgAQwBS+KmAQr9jpxf50AIJYptynVIT2Xk2TGDR5tlwTHcu24sASDub3Ol+oByAyHOwjYCh69kghJ6P7GDocVU+Pqgh7cZ4IQjI30cnoGFzPLrsgJLp9aUo8Npiy4wr24MExXOGEhCp0PUoOsJFgw/ijtngg1dQ71JYfuirVX0NG30DGtrkXAZohNw64oSFCWwwHTSKpk1MyKDPNKGzgJruhsvNcBWA0hEmZd0MuV7IYsQ11Ww0k+MI9yItigvJXZ0NQADdBbHtu6wy4ML0Aj4NSEhnYesoqBPiAhDzTrWI+1EEQMT+mXWeBiB/DhB6LOdMqMl56M5G76/Iwtmw9xpFoMMOGvkjTszlVRSFSplwq9I7JLYCI64ofEjnwwaznbge2jEtgJE3qZhtqLat3kPGTeRHo0y7+ho2FGDMFNBQBZ8UNgzQoO8paOQUgsq0SZoyKZ42kWBBZwWls4CWcTNcBaDNYKpjyJDzbsQ11Gy0WIQwuTjJ4lB154Z0SCsgUyfpOVEXxKYm6ZOKjEyRe812AOZdbkDWJjUdBTqKPADpJv2SARBtWbE5QIAcJwRo64bw2mo23LeoefNcFJmYK1sYai7PKSptA7c2p61pPL3WFltpPMi5Z7LwYYslGTftXA8tdZd83+1cD3kMZ8oFqaOi13Ok+63DCZPy82zY1eew0aegYcylUQQ0FGAwIAhiLW3icjNoEWhVboarALRIysQGGSHERF9RDc4GQ5quiY10ilmjkbW2S6RJKKS0AQ+zg2kyHVZoSoeum9dRFL1TFcfT8/R0P0UAhKZA5HnYXJBcCAFy0zHpuqkbUk9BceewYS3+pN9fG0ejSPolT8IVy567HjfZ2JLLc+EjqQGxpVzauR5N6FBSZoQL4E65mMtM+PCjUaZffQ4bbyzQoPUZNG3icjNoEWiVbkZeAWgKI8UgQ/4e1ZBTbbEILabDhXjWSSoKIVImjJRRk+UDRzM5pn48Wiiadho6iESkQ4gz+3J1FioFQ4oGywCIWI/BNgJGdiRyHQClIEQcj+nP8HA4HK0a7lRbbAotN2s4nS6bI0Hjp10dSDtQKSpbbIF8bzS2UsiQUNoePqwgK0GkjetRdISLdVIxB2DkwUcd1xevfPU5bATpiJMgEKNOGGsLGnEYqKGqPQMN0q6ec6Lm1+Dp0NZkeKutENRWn0HTJq4iUNuQ1qrdDDH1d37KpMmm2kKGvCh12pmXkfhs6VNYzbkzbFBhwkinx81c9OkxzLtaY92id6N0eQZACPS4rPKyAJI7DJfcVVIIyXVC6PLkfFwgwi0detXqxNko4khQmMimTrorSM0q63DQ+DIhQwKIBNs8+JA1RbaUSyeuB2CmV/JTLuqYBeCjzjQKxNPhutt+FqqvYYOrh6xVBBqJE1E5aMjZQE3QkEWgOaBh1mfQ0SauIlDbkNZu3AwFGxY3Q0BHuZRJyHQ3RDgbNUzSBI4WSOqEXNitc2fwrMvRudwTegWG82HCCYURW0dB71YVZBAXxNVhSAChhahFAERryxkFI/ZnQEhy3BhMn0Zbg4z2IBIHh9FrNRBptThULghoX4dBU2G88LrdyIwv6rxE5vcNHUAkfOQ5H1W7HkD7lEtZ+IjrdDZ8GsWqvoaNykFDAUYPQEO+MijIKAoaRdMmtiGtaa0GnflTnwXUdDPksjJuhoSOspAh15+qwdkQBX9cPftESjzvxJI6qfgC5fJImpZ5EDTAIMtsHQXdd57LkS7LpmCKAEjRUTCuSaNcs1cWdkPk+SFAVEMOXqbdbLKlNdq5EnrNRpt128RexoHLcc7M+LLFVh6ACKBwORy8EtcDkEWger2Qc3gtysFHHQXFqbqEja7drJmp/oaNmQIaYTo5V2nQCNsXggaJoxE4HA2zCJRO0CXBI8z83pmb0WRTuQWgLRaVgowwgZ+pGu485PHohV1ARRY0uk2d2NREFirUMTN3xOl6emdBhuhaIYQO4TUAxOKAlAEQscxdhEodD21SMtpeAkS0h9gx0o4IrAZnQwyVdjgYlg6hqIvRbl6NvNSJC0JU4XOB+NJdjKTwMgdAIuJU2ByOSlyP5H3eaCnlghSYTj1N14m4bviajWlXf8NGGIhUSoCegIYs7qwENIyCUBbmF4LS+TPM+gxX2kQCRBE3w5wF1OZmZAtBxXYtNlU4ZdIio1RskBGqC04dOfhYs8UjkjOWEpMA9fBcWGxNp5jPWgmNOyNbZ0EhJP9utToAsRWhFpkPpGoQqSPt1mIRmrYRHQXqMvSJ4NwwUXTSsMIxWSC+aGzJuMoDkIAHGnjQ9at2PWRsFBmqXQQ+ZHtcZ82GT6NY1dew0WvQyLQVAA2tCDTIggYz6jRcoGGrz2iXNrEVgVI3Q7WXcDPS9EjnBaB5oCHAhqNVw52HhI1I3ZEboAGWO7dBVTLBAkg6cTLk0+w02nUWtGMvcrdaFkBsNSB03gZt5IPNCUGajjHPtxMQqQM2GmwKLct05e0m68oHD56zXpegIfdjAyRO/3Y8s24egITgGngAvXM91HFzhtcC+fBhS7vUMY+PUszRVSrEF4jOQM0U0AjSmo0qQcOsz2iXNnEVgTZJDYf5TBNzpEmnbkYmfVIQMsTvwGF30X9lasg0iuWiLorg6vlPHnD7h42NkQ86YOR3FmKdpAOoEEB0J0PWaWSHTNLlYvusE0IhRKxD/t4lHZGBGu5Um8n/HVNlnmuiuxg5oGGJvU7Btwl9rhJAjy1bXOUBSMQoUFTnekhYzRteaw7V7hg+aq3Z8LKpz2GjPGiIdRygEVQIGvSBaknKRIJGELgLQemwVrM+o13axDWktSo3o0VApIibIQtK8yAjKYlBs4ZwCZk+06dUxOtJ40g1GbeWw2aGKNLJrdp0FkDaYVQLIOlyOmeIzQUpMieI0w0BNBhpl5qZqiONYhmNYgPSIkBhxleYAx5i/e5kgormklniygUgsn7JjKMqXA/ZXmZuj07h43CdzgaPxU83289C9TVsdAIaOmAYoGFzOSoEjSDkbUecmIWgNFXiSpvI91W6Ge0m55IplG7cDAkaIYAwZz6DqtSEDjXy8hPU4KqYsl1OAu7ukNp1FkDaYdCUTKcA0kkhqtje7nQUARGxrH1qpokIvIYZZwfYFFpGt599lonboXABhQkSoRF/3YIGkI0vGlu2uJIxJVOJsp7JdD6qdD1Ee7HhtaK9PXwo98sYZtusFTZ8zYZNfQ0bCFj3oBH0CDSS92VBI+NmkPoMV9qkyJDWdoDRjZuh5troEDLqcjaajKlJmuLk+NOlyHJBMaGHdhjtOguxjvuOtSyAiOWpG5FXiKrPC6JDBU2baFO+OxwRuV07V2SqBtgwC4qB4g5FoK0Da7vYJqsqwNuMLxpbtrhKocCADyOObPDRqeuhr5ufcrG359R8QC84rSPt5pWvvoYNCRkCKAItRdIONHTIcNRtVAAaLIwLzaFBQYMWgtL6DFfaxDWktRGkNRfduhlNNlWqALSpjpUPGSEYAsbAaigQbbAAoao1mF7Z3BTznGiH4QKRonesQDEAoXUR7QpRXRAiagbyQUQso26FmVLJT8/UMVTaBhtFHQq5zPxOQ82JyipE96ABZOOFnoctrsx4KgIfcuRWp66HfpxiKZf8djd81JF2U/IFolbNDtjoADR0B8MCGkH1oBGGxUac0EJQWp/hSpvU6Wao9EmXKRMKGiEYmjV0/yEYmpbRBZ3KNklXjGKdoOi09YtKnHM36gKRJkvTQXl3rEAxAAFIHQgZEVNmNIw432zNh1rPMUmZCSPaXCOgKZWoltEFDXA0tbSIvlxzL+jvDqAwQSIwHIyqQEMc1x1btriS8VQGPrp1PcT+UviQYJqXchHt5eGjVtjwaRSr+h82yoBG0AY0gopAI4y1WUGDMC414sRVn2GmTWxFoNTNUIAhoYGlRZvt3Iyik3Ol++sMMgIECBlzTgtdpZosrAw23E9MCQsDh1mjYU6p3GmHYS43Ow5aoBoRlyMy7l7FObhdELoc0CEEZKiqCSKuScrS87C5Ivq6deTgm0wvKC7iUlBgoDCRAQ0jfoqkTsxt8uIsL7bUnT/n6jPJeCkCHzGg6jpEW2euB/1MFD6KpFxEux0yZOpNpt2a5Hhe06e+hA2eBOwUn0QcizDljIFHDBxMvI8Y4qQdEBdKHor/NFz9MOFYxUAcJ0BBXyMAcgryiIBGBONZJzpoqOJQJtwNHvD0h3EgiIGAJ9PoxuBJKoUz0hZwcHDwIAJnHJyJKXc5uLhwsBgR4yL9wLi402Piw7AgEp+QxeAQcAKIfYhXMQsoRwyegEaTxRD/BJjEBC4aiZsRSQcEPLFIOaYcoMFgAw2OEAyMibYgKUYLwHDwANe+217Ey58PMDQqunvM6xzMYYc2iep5ZrTp7yNOl+kXS9nV0r8XdUoUTGjLYVmetnG1Xnoe2uPeabs2+Zaj3TJBl2098d7oSB37FOcUYPKA+DS9jJfJgxyHNLBKFYLhcPIXYxpUgKxP2jUwyYKqbW4MqtCA5IjHMOOHKtZ+T9cV8SDrlmRnnn5G+dllG40j00GTsUPjJiKFprINIKAKfXmahmMqBsx1JXxyBNZ2sb9Aa0uPI95PHYy1z9dTcXTpbFR2JjNKfQkbr732GgDgscdvnuYz8apa+/fvx9DQUKX7lPFy/Fmjle7Xa/rVy3j593/9h0r36zX96kW8ZOTTKFb1JWwcddRRAIDR0dHeB840amJiAkuXLsXLL7+MwcHB6T6dnopzjv3792PJkiWV79vHy+yTj5fu5eOlR4pj2Ae3l9l+9qkvYSMIhDU2NDQ06/+TAMDg4OAb4nP26sLu42V2ysdLNfLx4lWH+hI2vLy8vLy8ZqR8GsUqDxteXl5eXl5VycOGVdVNOlCjBgYGsGHDBgwMDEz3qfRUb5TP2Wu9Uf6Ob5TP2Wu9Uf6Ob5TP6TUzxHgtY4G8vLy8vLxmryYmJjA0NISVR/1nNIJWx/uZiiex9Y93Yt++fbOqlsanUby8vLy8vCoS5zF4F09u7Wbbmay+TKN4eXl5eXl59Y+8s+Hl5eXl5VWVOO/uYWqztLLBw4aXl5eXl1dV4l0+9XWWwoZPo3h5eXl5eXn1VH0JG7fddhuOO+44zJkzBytWrMATTzwx3adUWDfeeCMYY9rP8uXL1fLXX38dV1xxBY4++mjMmzcPa9aswZ49e7R9jI6OYvXq1Zg7dy4WLlyIz33uc5iamqr7o/SNfLz4eCkjHy8+XrpSHHf/MwvVd7Dx/e9/H+vXr8eGDRvw1FNP4YwzzsCqVauwd+/e6T61wjrllFOwe/du9fPoo4+qZVdddRXuv/9+3HvvvXjkkUfw+9//Hh/4wAfU8iiKsHr1akxOTuKxxx7DXXfdhc2bN+OGG26Yjo8y4+XjxcdLGfl48fHSteSkXt38zEbxPtM73vEOfsUVV6j3URTxJUuW8E2bNk3jWRXXhg0b+BlnnGFdNj4+zpvNJr/33ntV2/PPP88B8O3bt3POOX/wwQd5EAR8bGxMrXP77bfzwcFBfujQoZ6eez/Kx4uPlzLy8eLjpVPt27ePA+DvmfthfuERn+j45z1zP8wB8H379k33R6pUfeVsTE5O4sknn8TKlStVWxAEWLlyJbZv3z6NZ1ZOv/nNb7BkyRKccMIJ+NjHPobRUfHo8yeffBKHDx/WPt/y5ctx7LHHqs+3fft2nHbaaVi0aJFaZ9WqVZiYmMCzzz5b7weZ4fLx4uOljHy8+Hjx6p36CjZeffVVRFGk/UcAgEWLFmFsbGyazqqcVqxYgc2bN2PLli24/fbbsWvXLrzzne/E/v37MTY2hlarhfnz52vb0M83NjZm/fxymVcqHy8+XsrIx4uPl0rk0yhW+aGvNeuiiy5Sv59++ulYsWIFli1bhh/84Ad405veNI1n5jUT5ePFq4x8vMwAxRxgfuirqb5yNo455hiEYZipnt6zZw+Gh4en6ay60/z58/HWt74VL7zwAoaHhzE5OYnx8XFtHfr5hoeHrZ9fLvNK5ePFx0sZ+Xjx8eLVO/UVbLRaLZx11lnYtm2baovjGNu2bcPIyMg0nlnnOnDgAF588UUsXrwYZ511FprNpvb5du7cidHRUfX5RkZG8Mwzz2jV8Q899BAGBwdx8skn137+M1k+Xny8lJGPFx8vlYhzgMdd/MxOZ6PvRqPcc889fGBggG/evJk/99xz/PLLL+fz58/Xqqdnsq6++mr+s5/9jO/atYv//Oc/5ytXruTHHHMM37t3L+ec88985jP82GOP5T/96U/5r371Kz4yMsJHRkbU9lNTU/zUU0/lF154Id+xYwffsmULX7BgAb/22mun6yPNaPl48fFSRj5efLx0Kjka5d2ND/ILmh/p+OfdjQ/OytEofVez8aEPfQh/+MMfcMMNN2BsbAxnnnkmtmzZkilqmql65ZVX8JGPfASvvfYaFixYgHPOOQePP/44FixYAAC45ZZbEAQB1qxZg0OHDmHVqlX4xje+obYPwxAPPPAA1q1bh5GRERxxxBFYu3YtNm7cOF0faUbLx4uPlzLy8eLjxas3YpzPVs/Gy8vLy8urHk1MTGBoaAjvDj+ABmt2vJ8pfhgPRz/Evn37MDg4WOEZTq/6ztnw8vLy8vKaqeIxB+9iNMpsvf/vqwJRLy8vLy8vr/6Tdza8vLy8vLwq0hQ/JEaVdLo9Dld4NjNHHja8vLy8vLy6VKvVwvDwMB4de7DrfQ0PD6PValVwVjNHvkDUy8vLy8urAr3++uuYnJzsej+tVgtz5syp4IxmjjxseHl5eXl5efVUvkDUy8vLy8vLq6fysOHl5eXl5eXVU3nY8PLy8vLy8uqpPGx4eXl5eXl59VQeNry8vLy8vLx6Kg8bXl5eXl5eXj2Vhw0vLy8vLy+vnur/A2wg8Ez1AQhxAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot eigenvectors\n",
+    "fig2, axs = plt.subplots(2, 3)\n",
+    "im1 = axs[0,0].imshow(phi_1)\n",
+    "im2 = axs[0,1].imshow(phi_2)\n",
+    "im3 = axs[0,2].imshow(phi_3)\n",
+    "im4 = axs[1,0].imshow(phi_4)\n",
+    "im5 = axs[1,1].imshow(phi_5)\n",
+    "im6 = axs[1,2].imshow(phi_6)\n",
+    "fig2.colorbar(im1, ax=axs)\n",
+    "fig2.suptitle('first six eigenfunctions')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neuromancer3",
+   "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/parametric_programming/Part_1_basics copy.ipynb b/examples/parametric_programming/Part_1_basics copy.ipynb
new file mode 100644
index 00000000..d0903339
--- /dev/null
+++ b/examples/parametric_programming/Part_1_basics copy.ipynb	
@@ -0,0 +1,1109 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "34qVD_ntSKLF"
+   },
+   "source": [
+    "# Learning to optimize parametric nonlinear programming problem (pNLP) using Neuromancer\n",
+    "\n",
+    "\n",
+    "This is an interactive notebook based on the python script [Part_1_LearnToOptimize_tutorial.py](./Part_1_LearnToOptimize_tutorial.py).  \n",
+    "\n",
+    "We will demonstrate the capability of learning to optimize (L2O)\n",
+    "for solving [parametric nonlinear programming problem (pNLP)](https://en.wikipedia.org/wiki/Parametric_programming) defined as:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "&\\text{minimize } &&  f(x, \\theta) \\\\\n",
+    "&\\text{subject to} && g(x, \\theta) \\le 0\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "with  parameters $\\theta$ and decision variables $x$.\n",
+    "\n",
+    "In L2O train a neural network mapping problem parameters onto the primal decision variables $x = \\pi(\\theta)$ by using gradients of the optimizaiton problem to minimize the objective function and satisfy the constraints.\n",
+    "\n",
+    "### References\n",
+    "[1] [F. Fioretto, et al., Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods, 2019](https://arxiv.org/abs/1909.10461)  \n",
+    "[2] [S. Gould, et al., Deep Declarative Networks: A New Hope, 2020](https://arxiv.org/abs/1909.04866)  \n",
+    "[3] [P. Donti, et al., DC3: A learning method for optimization with hard constraints, 2021](https://arxiv.org/abs/2104.12225)  \n",
+    "[4] [J. Kotary, et al., End-to-End Constrained Optimization Learning: A Survey, 2021](https://arxiv.org/abs/2103.16378)  \n",
+    "[5] [M. Li, et al., Learning to Solve Optimization Problems with Hard Linear Constraints, 2022](https://arxiv.org/abs/2208.10611)  \n",
+    "[6] [R. Sambharya, et al., End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization, 2022](https://arxiv.org/abs/2212.08260)  \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OCn3zpaIqgMc"
+   },
+   "source": [
+    "## NeuroMANCER and Dependencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Qzy5Wot5k2Gf"
+   },
+   "source": [
+    "### Install (Colab only)\n",
+    "Skip this step when running locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "X_3EvkSz0Fnz",
+    "outputId": "23c06f6b-ab48-4763-c43c-40a325cacf87"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LWyvndXlz0Fv"
+   },
+   "source": [
+    "### Import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "KbP0n-4evRqt"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import numpy as np\n",
+    "import neuromancer.slim as slim\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patheffects as patheffects\n",
+    "import casadi\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "POL27EJZxJmI"
+   },
+   "outputs": [],
+   "source": [
+    "from neuromancer.trainer import Trainer\n",
+    "from neuromancer.problem import Problem\n",
+    "from neuromancer.constraint import variable\n",
+    "from neuromancer.dataset import DictDataset\n",
+    "from neuromancer.loss import PenaltyLoss\n",
+    "from neuromancer.modules import blocks\n",
+    "from neuromancer.system import Node"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Problem formulation\n",
+    "\n",
+    "In this example we will solve parametric constrained [Rosenbrock problem](https://en.wikipedia.org/wiki/Rosenbrock_function):\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "&\\text{minimize } &&  (1-x)^2 + a(y-x^2)^2\\\\\n",
+    "&\\text{subject to} && \\left(\\frac{p}{2}\\right)^2 \\le x^2 + y^2 \\le p^2\\\\\n",
+    "& && x \\ge y\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "with parameters $p, a$ and decision variables $x, y$.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_WH7o7Wu1epw"
+   },
+   "source": [
+    "## Dataset\n",
+    "\n",
+    "We constructy the dataset by sampling the parametric space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "_r6p2p6myHAh"
+   },
+   "outputs": [],
+   "source": [
+    "data_seed = 408  # random seed used for simulated data\n",
+    "np.random.seed(data_seed)\n",
+    "torch.manual_seed(data_seed);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JZ9qrw0tlJhs"
+   },
+   "source": [
+    "Randomly sample parameters from a uniform distribution: $0.5\\le p\\le2.0$;  $0.2\\le a\\le1.2$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "Nu58M-8JyHy6"
+   },
+   "outputs": [],
+   "source": [
+    "nsim = 5000  # number of datapoints: increase sample density for more robust results\n",
+    "# create dictionaries with sampled datapoints with uniform distribution\n",
+    "a_low, a_high, p_low, p_high = 0.2, 1.2, 0.5, 2.0\n",
+    "samples_train = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
+    "                 \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
+    "samples_dev = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
+    "               \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
+    "samples_test = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
+    "               \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
+    "# create named dictionary datasets\n",
+    "train_data = DictDataset(samples_train, name='train')\n",
+    "dev_data = DictDataset(samples_dev, name='dev')\n",
+    "test_data = DictDataset(samples_test, name='test')\n",
+    "# create torch dataloaders for the Trainer\n",
+    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, num_workers=0,\n",
+    "                                           collate_fn=train_data.collate_fn, shuffle=True)\n",
+    "dev_loader = torch.utils.data.DataLoader(dev_data, batch_size=32, num_workers=0,\n",
+    "                                         collate_fn=dev_data.collate_fn, shuffle=True)\n",
+    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=32, num_workers=0,\n",
+    "                                         collate_fn=test_data.collate_fn, shuffle=True)\n",
+    "# note: training quality will depend on the DataLoader parameters such as batch size and shuffle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize taining and test samples for 2D parametric space\n",
+    "a_train = samples_train['a'].numpy()\n",
+    "p_train = samples_train['p'].numpy()\n",
+    "a_dev = samples_dev['a'].numpy()\n",
+    "p_dev = samples_dev['p'].numpy()\n",
+    "plt.figure()\n",
+    "plt.scatter(a_train, p_train, s=2., c='blue', marker='o')\n",
+    "plt.scatter(a_dev, p_dev, s=2., c='red', marker='o')\n",
+    "plt.title('Sampled parametric space for training')\n",
+    "plt.xlim(a_low, a_high)\n",
+    "plt.ylim(p_low, p_high)\n",
+    "plt.grid(True)\n",
+    "plt.xlabel('a')\n",
+    "plt.ylabel('p')\n",
+    "plt.legend(['train', 'test'], loc='upper right')\n",
+    "plt.show()\n",
+    "plt.show(block=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Y2htUaWMDjsk"
+   },
+   "source": [
+    "## Primal Solution Map Architecture\n",
+    "\n",
+    "A neural network mapping problem parameters onto primal decision variables:  \n",
+    "$$x = \\pi(\\theta)$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "Ta_I_pjyyLzf"
+   },
+   "outputs": [],
+   "source": [
+    "# define neural architecture for the trainable solution map\n",
+    "func = blocks.MLP(insize=2, outsize=2,\n",
+    "                bias=True,\n",
+    "                linear_map=slim.maps['linear'],\n",
+    "                nonlin=nn.ReLU,\n",
+    "                hsizes=[80] * 4)\n",
+    "# wrap neural net into symbolic representation of the solution map via the Node class: sol_map(xi) -> x\n",
+    "sol_map = Node(func, ['a', 'p'], ['x'], name='map')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Lxj77EFj7EO-"
+   },
+   "source": [
+    "## Objective and Constraints in NeuroMANCER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "bcoVjphjyPp9"
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "variable is a basic symbolic abstraction in Neuromancer\n",
+    "   x = variable(\"variable_name\")                      (instantiates new variable)  \n",
+    "variable construction supports:\n",
+    "   algebraic expressions:     x**2 + x**3 + 5     (instantiates new variable)  \n",
+    "   slicing:                   x[:, i]             (instantiates new variable)  \n",
+    "   pytorch callables:         torch.sin(x)        (instantiates new variable)  \n",
+    "   constraints definition:    x <= 1.0            (instantiates Constraint object) \n",
+    "   objective definition:      x.minimize()        (instantiates Objective object) \n",
+    "to visualize computational graph of the variable use x.show() method          \n",
+    "\"\"\"\n",
+    "\n",
+    "# define decision variables\n",
+    "x1 = variable(\"x\")[:, [0]]\n",
+    "x2 = variable(\"x\")[:, [1]]\n",
+    "# problem parameters sampled in the dataset\n",
+    "p = variable('p')\n",
+    "a = variable('a')\n",
+    "\n",
+    "# objective function\n",
+    "f = (1-x1)**2 + a*(x2-x1**2)**2\n",
+    "obj = f.minimize(weight=1.0, name='obj')\n",
+    "\n",
+    "# constraints\n",
+    "Q_con = 100.  # constraint penalty weights\n",
+    "con_1 = Q_con*(x1 >= x2)\n",
+    "con_2 = Q_con*((p/2)**2 <= x1**2+x2**2)\n",
+    "con_3 = Q_con*(x1**2+x2**2 <= p**2)\n",
+    "con_1.name = 'c1'\n",
+    "con_2.name = 'c2'\n",
+    "con_3.name = 'c3'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 496
+    },
+    "id": "n7VPa9Wc8JRB",
+    "outputId": "0da17c45-6370-4f46-f626-bd5686b94bfc"
+   },
+   "outputs": [],
+   "source": [
+    "# constrained optimization problem construction\n",
+    "objectives = [obj]\n",
+    "constraints = [con_1, con_2, con_3]\n",
+    "components = [sol_map]\n",
+    "\n",
+    "# create penalty method loss function\n",
+    "loss = PenaltyLoss(objectives, constraints)\n",
+    "# construct constrained optimization problem\n",
+    "problem = Problem(components, loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "icWSMeG28SKc"
+   },
+   "source": [
+    "## Parametric Problem Solution in NeuroMANCER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "rk1bRczByUvl"
+   },
+   "outputs": [],
+   "source": [
+    "lr = 0.001      # step size for gradient descent\n",
+    "epochs = 400    # number of training epochs\n",
+    "warmup = 100    # number of epochs to wait before enacting early stopping policy\n",
+    "patience = 100  # number of epochs with no improvement in eval metric to allow before early stopping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "x4oS2N2ZyWtD"
+   },
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.AdamW(problem.parameters(), lr=lr)\n",
+    "\n",
+    "# define trainer\n",
+    "trainer = Trainer(\n",
+    "    problem,\n",
+    "    train_loader,\n",
+    "    dev_loader,\n",
+    "    test_loader,\n",
+    "    optimizer,\n",
+    "    epochs=epochs,\n",
+    "    patience=patience,\n",
+    "    warmup=warmup)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "27ASQD3q-M0A",
+    "outputId": "04eec20c-51c3-4a71-e570-9636af9421c0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch: 0  train_loss: 3.3431482315063477\n",
+      "epoch: 1  train_loss: 0.29509055614471436\n",
+      "epoch: 2  train_loss: 0.12004771828651428\n",
+      "epoch: 3  train_loss: 0.09695008397102356\n",
+      "epoch: 4  train_loss: 0.10390646010637283\n",
+      "epoch: 5  train_loss: 0.08398374170064926\n",
+      "epoch: 6  train_loss: 0.10284885764122009\n",
+      "epoch: 7  train_loss: 0.09823866188526154\n",
+      "epoch: 8  train_loss: 0.07910480350255966\n",
+      "epoch: 9  train_loss: 0.07298458367586136\n",
+      "epoch: 10  train_loss: 0.11471019685268402\n",
+      "epoch: 11  train_loss: 0.08374786376953125\n",
+      "epoch: 12  train_loss: 0.08673930913209915\n",
+      "epoch: 13  train_loss: 0.08220476657152176\n",
+      "epoch: 14  train_loss: 0.1439075917005539\n",
+      "epoch: 15  train_loss: 0.07737980037927628\n",
+      "epoch: 16  train_loss: 0.0750625804066658\n",
+      "epoch: 17  train_loss: 0.12319241464138031\n",
+      "epoch: 18  train_loss: 0.07722419500350952\n",
+      "epoch: 19  train_loss: 0.07891330122947693\n",
+      "epoch: 20  train_loss: 0.08825673907995224\n",
+      "epoch: 21  train_loss: 0.1074901893734932\n",
+      "epoch: 22  train_loss: 0.0737956091761589\n",
+      "epoch: 23  train_loss: 0.06982501596212387\n",
+      "epoch: 24  train_loss: 0.07347986847162247\n",
+      "epoch: 25  train_loss: 0.07330764085054398\n",
+      "epoch: 26  train_loss: 0.07121726870536804\n",
+      "epoch: 27  train_loss: 0.06778355687856674\n",
+      "epoch: 28  train_loss: 0.0902303159236908\n",
+      "epoch: 29  train_loss: 0.0762813463807106\n",
+      "epoch: 30  train_loss: 0.06578237563371658\n",
+      "epoch: 31  train_loss: 0.07065192610025406\n",
+      "epoch: 32  train_loss: 0.07241765409708023\n",
+      "epoch: 33  train_loss: 0.06881008297204971\n",
+      "epoch: 34  train_loss: 0.07727275788784027\n",
+      "epoch: 35  train_loss: 0.10817047953605652\n",
+      "epoch: 36  train_loss: 0.0718890130519867\n",
+      "epoch: 37  train_loss: 0.06794296950101852\n",
+      "epoch: 38  train_loss: 0.09400344640016556\n",
+      "epoch: 39  train_loss: 0.07327811419963837\n",
+      "epoch: 40  train_loss: 0.06426447629928589\n",
+      "epoch: 41  train_loss: 0.07742034643888474\n",
+      "epoch: 42  train_loss: 0.08132495731115341\n",
+      "epoch: 43  train_loss: 0.06514927744865417\n",
+      "epoch: 44  train_loss: 0.06611117720603943\n",
+      "epoch: 45  train_loss: 0.0720672756433487\n",
+      "epoch: 46  train_loss: 0.0670868381857872\n",
+      "epoch: 47  train_loss: 0.06496549397706985\n",
+      "epoch: 48  train_loss: 0.10737393796443939\n",
+      "epoch: 49  train_loss: 0.09823333472013474\n",
+      "epoch: 50  train_loss: 0.07376237213611603\n",
+      "epoch: 51  train_loss: 0.0744401142001152\n",
+      "epoch: 52  train_loss: 0.06866953521966934\n",
+      "epoch: 53  train_loss: 0.06074123457074165\n",
+      "epoch: 54  train_loss: 0.10845162719488144\n",
+      "epoch: 55  train_loss: 0.06688561290502548\n",
+      "epoch: 56  train_loss: 0.07066939026117325\n",
+      "epoch: 57  train_loss: 0.06284123659133911\n",
+      "epoch: 58  train_loss: 0.07488392293453217\n",
+      "epoch: 59  train_loss: 0.05865217372775078\n",
+      "epoch: 60  train_loss: 0.056915514171123505\n",
+      "epoch: 61  train_loss: 0.06498084217309952\n",
+      "epoch: 62  train_loss: 0.07205688208341599\n",
+      "epoch: 63  train_loss: 0.07458624243736267\n",
+      "epoch: 64  train_loss: 0.06615233421325684\n",
+      "epoch: 65  train_loss: 0.0639859139919281\n",
+      "epoch: 66  train_loss: 0.06653369218111038\n",
+      "epoch: 67  train_loss: 0.0768393948674202\n",
+      "epoch: 68  train_loss: 0.0896148830652237\n",
+      "epoch: 69  train_loss: 0.06078161299228668\n",
+      "epoch: 70  train_loss: 0.06107955798506737\n",
+      "epoch: 71  train_loss: 0.0626201257109642\n",
+      "epoch: 72  train_loss: 0.06813453137874603\n",
+      "epoch: 73  train_loss: 0.07850225269794464\n",
+      "epoch: 74  train_loss: 0.07709406316280365\n",
+      "epoch: 75  train_loss: 0.06747493147850037\n",
+      "epoch: 76  train_loss: 0.07129170745611191\n",
+      "epoch: 77  train_loss: 0.06171644479036331\n",
+      "epoch: 78  train_loss: 0.0636206716299057\n",
+      "epoch: 79  train_loss: 0.06365420669317245\n",
+      "epoch: 80  train_loss: 0.061640478670597076\n",
+      "epoch: 81  train_loss: 0.08616021275520325\n",
+      "epoch: 82  train_loss: 0.06648426502943039\n",
+      "epoch: 83  train_loss: 0.11166501045227051\n",
+      "epoch: 84  train_loss: 0.0678570494055748\n",
+      "epoch: 85  train_loss: 0.0645548552274704\n",
+      "epoch: 86  train_loss: 0.06504324823617935\n",
+      "epoch: 87  train_loss: 0.05727844312787056\n",
+      "epoch: 88  train_loss: 0.06676892936229706\n",
+      "epoch: 89  train_loss: 0.05544864013791084\n",
+      "epoch: 90  train_loss: 0.05851911008358002\n",
+      "epoch: 91  train_loss: 0.08444546163082123\n",
+      "epoch: 92  train_loss: 0.07609823346138\n",
+      "epoch: 93  train_loss: 0.0778665691614151\n",
+      "epoch: 94  train_loss: 0.07745882868766785\n",
+      "epoch: 95  train_loss: 0.05701448395848274\n",
+      "epoch: 96  train_loss: 0.05955454334616661\n",
+      "epoch: 97  train_loss: 0.07137059420347214\n",
+      "epoch: 98  train_loss: 0.06247563660144806\n",
+      "epoch: 99  train_loss: 0.05710960179567337\n",
+      "epoch: 100  train_loss: 0.06280960142612457\n",
+      "epoch: 101  train_loss: 0.07082230597734451\n",
+      "epoch: 102  train_loss: 0.06900833547115326\n",
+      "epoch: 103  train_loss: 0.07604509592056274\n",
+      "epoch: 104  train_loss: 0.07385031878948212\n",
+      "epoch: 105  train_loss: 0.08383945375680923\n",
+      "epoch: 106  train_loss: 0.07010190188884735\n",
+      "epoch: 107  train_loss: 0.06499896943569183\n",
+      "epoch: 108  train_loss: 0.057489365339279175\n",
+      "epoch: 109  train_loss: 0.06130903214216232\n",
+      "epoch: 110  train_loss: 0.05912163481116295\n",
+      "epoch: 111  train_loss: 0.06301431357860565\n",
+      "epoch: 112  train_loss: 0.07039793580770493\n",
+      "epoch: 113  train_loss: 0.0647478699684143\n",
+      "epoch: 114  train_loss: 0.08488962799310684\n",
+      "epoch: 115  train_loss: 0.09969013184309006\n",
+      "epoch: 116  train_loss: 0.06109851226210594\n",
+      "epoch: 117  train_loss: 0.06529794633388519\n",
+      "epoch: 118  train_loss: 0.06595301628112793\n",
+      "epoch: 119  train_loss: 0.07320734113454819\n",
+      "epoch: 120  train_loss: 0.055339764803647995\n",
+      "epoch: 121  train_loss: 0.06297348439693451\n",
+      "epoch: 122  train_loss: 0.056549739092588425\n",
+      "epoch: 123  train_loss: 0.06267240643501282\n",
+      "epoch: 124  train_loss: 0.07376105338335037\n",
+      "epoch: 125  train_loss: 0.05744027718901634\n",
+      "epoch: 126  train_loss: 0.05655454471707344\n",
+      "epoch: 127  train_loss: 0.06369715183973312\n",
+      "epoch: 128  train_loss: 0.05851554870605469\n",
+      "epoch: 129  train_loss: 0.060357026755809784\n",
+      "epoch: 130  train_loss: 0.06795436888933182\n",
+      "epoch: 131  train_loss: 0.05705079063773155\n",
+      "epoch: 132  train_loss: 0.06090143695473671\n",
+      "epoch: 133  train_loss: 0.07615236192941666\n",
+      "epoch: 134  train_loss: 0.054762762039899826\n",
+      "epoch: 135  train_loss: 0.05837693065404892\n",
+      "epoch: 136  train_loss: 0.060538388788700104\n",
+      "epoch: 137  train_loss: 0.058495841920375824\n",
+      "epoch: 138  train_loss: 0.05660303682088852\n",
+      "epoch: 139  train_loss: 0.05731586739420891\n",
+      "epoch: 140  train_loss: 0.07366020232439041\n",
+      "epoch: 141  train_loss: 0.06555984914302826\n",
+      "epoch: 142  train_loss: 0.057349637150764465\n",
+      "epoch: 143  train_loss: 0.08015884459018707\n",
+      "epoch: 144  train_loss: 0.0677114725112915\n",
+      "epoch: 145  train_loss: 0.07407013326883316\n",
+      "epoch: 146  train_loss: 0.06757846474647522\n",
+      "epoch: 147  train_loss: 0.06481954455375671\n",
+      "epoch: 148  train_loss: 0.06345803290605545\n",
+      "epoch: 149  train_loss: 0.060742754489183426\n",
+      "epoch: 150  train_loss: 0.06050338223576546\n",
+      "epoch: 151  train_loss: 0.0577315054833889\n",
+      "epoch: 152  train_loss: 0.06013864278793335\n",
+      "epoch: 153  train_loss: 0.059723228216171265\n",
+      "epoch: 154  train_loss: 0.05805044248700142\n",
+      "epoch: 155  train_loss: 0.06691598892211914\n",
+      "epoch: 156  train_loss: 0.07555340975522995\n",
+      "epoch: 157  train_loss: 0.06746435165405273\n",
+      "epoch: 158  train_loss: 0.057914577424526215\n",
+      "epoch: 159  train_loss: 0.05615909770131111\n",
+      "epoch: 160  train_loss: 0.055125679820775986\n",
+      "epoch: 161  train_loss: 0.06525089591741562\n",
+      "epoch: 162  train_loss: 0.05403207615017891\n",
+      "epoch: 163  train_loss: 0.05500206723809242\n",
+      "epoch: 164  train_loss: 0.06962452083826065\n",
+      "epoch: 165  train_loss: 0.05859214812517166\n",
+      "epoch: 166  train_loss: 0.07691822201013565\n",
+      "epoch: 167  train_loss: 0.06408967822790146\n",
+      "epoch: 168  train_loss: 0.059578247368335724\n",
+      "epoch: 169  train_loss: 0.061754580587148666\n",
+      "epoch: 170  train_loss: 0.06840617209672928\n",
+      "epoch: 171  train_loss: 0.05595561861991882\n",
+      "epoch: 172  train_loss: 0.06401976943016052\n",
+      "epoch: 173  train_loss: 0.054142531007528305\n",
+      "epoch: 174  train_loss: 0.058156631886959076\n",
+      "epoch: 175  train_loss: 0.0910397469997406\n",
+      "epoch: 176  train_loss: 0.08194470405578613\n",
+      "epoch: 177  train_loss: 0.06770230829715729\n",
+      "epoch: 178  train_loss: 0.05700008571147919\n",
+      "epoch: 179  train_loss: 0.060024309903383255\n",
+      "epoch: 180  train_loss: 0.06890615075826645\n",
+      "epoch: 181  train_loss: 0.05353209748864174\n",
+      "epoch: 182  train_loss: 0.0628865510225296\n",
+      "epoch: 183  train_loss: 0.0552230030298233\n",
+      "epoch: 184  train_loss: 0.059697456657886505\n",
+      "epoch: 185  train_loss: 0.08943110704421997\n",
+      "epoch: 186  train_loss: 0.062282465398311615\n",
+      "epoch: 187  train_loss: 0.05895530804991722\n",
+      "epoch: 188  train_loss: 0.05498126149177551\n",
+      "epoch: 189  train_loss: 0.061980750411748886\n",
+      "epoch: 190  train_loss: 0.059062786400318146\n",
+      "epoch: 191  train_loss: 0.055834975093603134\n",
+      "epoch: 192  train_loss: 0.0781770572066307\n",
+      "epoch: 193  train_loss: 0.05961065739393234\n",
+      "epoch: 194  train_loss: 0.06025943532586098\n",
+      "epoch: 195  train_loss: 0.06063732132315636\n",
+      "epoch: 196  train_loss: 0.05604882538318634\n",
+      "epoch: 197  train_loss: 0.06419660896062851\n",
+      "epoch: 198  train_loss: 0.055826686322689056\n",
+      "epoch: 199  train_loss: 0.07758674025535583\n",
+      "epoch: 200  train_loss: 0.059960201382637024\n",
+      "epoch: 201  train_loss: 0.07817187905311584\n",
+      "epoch: 202  train_loss: 0.05648574233055115\n",
+      "epoch: 203  train_loss: 0.06254447251558304\n",
+      "epoch: 204  train_loss: 0.07737401127815247\n",
+      "epoch: 205  train_loss: 0.054320164024829865\n",
+      "epoch: 206  train_loss: 0.06252673268318176\n",
+      "epoch: 207  train_loss: 0.061384834349155426\n",
+      "epoch: 208  train_loss: 0.062424782663583755\n",
+      "epoch: 209  train_loss: 0.060497745871543884\n",
+      "epoch: 210  train_loss: 0.07003450393676758\n",
+      "epoch: 211  train_loss: 0.058020226657390594\n",
+      "epoch: 212  train_loss: 0.0571184940636158\n",
+      "epoch: 213  train_loss: 0.058880534023046494\n",
+      "epoch: 214  train_loss: 0.07074400782585144\n",
+      "epoch: 215  train_loss: 0.06494082510471344\n",
+      "epoch: 216  train_loss: 0.06047496199607849\n",
+      "epoch: 217  train_loss: 0.0779847726225853\n",
+      "epoch: 218  train_loss: 0.0685817301273346\n",
+      "epoch: 219  train_loss: 0.08113349974155426\n",
+      "epoch: 220  train_loss: 0.05932389944791794\n",
+      "epoch: 221  train_loss: 0.061256274580955505\n",
+      "epoch: 222  train_loss: 0.06109828129410744\n",
+      "epoch: 223  train_loss: 0.0698949545621872\n",
+      "epoch: 224  train_loss: 0.0666641891002655\n",
+      "epoch: 225  train_loss: 0.07610601931810379\n",
+      "epoch: 226  train_loss: 0.055644333362579346\n",
+      "Early stopping!!!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train NLP solution map\n",
+    "best_model = trainer.train()\n",
+    "best_outputs = trainer.test(best_model)\n",
+    "# load best model dict\n",
+    "problem.load_state_dict(best_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0hhUw4PVBWmb"
+   },
+   "source": [
+    "## Get pNLP solution from trained neural network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# selected problem parameters\n",
+    "p = 1.0\n",
+    "a = 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "os3I3I8L3HbE",
+    "outputId": "50c13f99-7693-4102-b65c-4f3707f90c29"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7896563\n",
+      "0.5546384\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution to mpNLP via Neuromancer\n",
+    "datapoint = {'a': torch.tensor([[a]]), 'p': torch.tensor([[p]]),\n",
+    "             'name': 'test'}\n",
+    "model_out = problem(datapoint)\n",
+    "x_nm = model_out['test_' + \"x\"][0, 0].detach().numpy()\n",
+    "y_nm = model_out['test_' + \"x\"][0, 1].detach().numpy()\n",
+    "print(x_nm)\n",
+    "print(y_nm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "g-KmqqUj-Q3E"
+   },
+   "source": [
+    "## Get pNLP solution from CasADi for comparison\n",
+    "\n",
+    "[CasADi](https://web.casadi.org/) is an open-source tool for constrained optimization and optimal control that has influenced the development of NeuroMANCER."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "dmJERFP2yYuC"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "******************************************************************************\n",
+      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
+      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
+      "         For more information visit https://github.com/coin-or/Ipopt\n",
+      "******************************************************************************\n",
+      "\n",
+      "This is Ipopt version 3.14.11, running with linear solver MUMPS 5.4.1.\n",
+      "\n",
+      "Number of nonzeros in equality constraint Jacobian...:        0\n",
+      "Number of nonzeros in inequality constraint Jacobian.:        6\n",
+      "Number of nonzeros in Lagrangian Hessian.............:        3\n",
+      "\n",
+      "Total number of variables............................:        2\n",
+      "                     variables with only lower bounds:        0\n",
+      "                variables with lower and upper bounds:        0\n",
+      "                     variables with only upper bounds:        0\n",
+      "Total number of equality constraints.................:        0\n",
+      "Total number of inequality constraints...............:        3\n",
+      "        inequality constraints with only lower bounds:        1\n",
+      "   inequality constraints with lower and upper bounds:        0\n",
+      "        inequality constraints with only upper bounds:        2\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   0  1.0000000e+00 2.50e-01 1.67e+00  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
+      "   1  9.5811923e-01 2.49e-01 1.71e+01  -1.0 5.59e-01    -  5.26e-01 3.81e-02h  1\n",
+      "   2  5.1823940e-01 1.40e-02 1.95e+01  -1.0 2.12e+01    -  1.25e-03 3.12e-02f  6\n",
+      "   3  3.2701557e-01 0.00e+00 5.64e-01  -1.0 2.47e-01    -  1.00e+00 1.00e+00f  1\n",
+      "   4  1.5594126e-01 0.00e+00 9.02e-01  -1.7 4.58e-01    -  3.11e-01 1.00e+00f  1\n",
+      "   5  5.3473898e-02 0.00e+00 2.99e-01  -1.7 5.51e-01    -  1.00e+00 1.00e+00h  1\n",
+      "   6  5.7915701e-02 0.00e+00 6.95e-03  -1.7 2.57e-02    -  1.00e+00 1.00e+00h  1\n",
+      "   7  4.4884709e-02 0.00e+00 7.47e-03  -3.8 9.13e-02    -  8.77e-01 1.00e+00h  1\n",
+      "   8  4.1207939e-02 0.00e+00 2.60e-04  -3.8 3.58e-02    -  1.00e+00 1.00e+00h  1\n",
+      "   9  4.0921509e-02 0.00e+00 8.22e-07  -5.7 2.98e-03    -  1.00e+00 1.00e+00h  1\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  10  4.0919039e-02 0.00e+00 7.11e-11  -8.6 2.53e-05    -  1.00e+00 1.00e+00h  1\n",
+      "\n",
+      "Number of Iterations....: 10\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   4.0919038633377619e-02    4.0919038633377619e-02\n",
+      "Dual infeasibility......:   7.1118860800467587e-11    7.1118860800467587e-11\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   2.6584596762655841e-09    2.6584596762655841e-09\n",
+      "Overall NLP error.......:   2.6584596762655841e-09    2.6584596762655841e-09\n",
+      "\n",
+      "\n",
+      "Number of objective function evaluations             = 18\n",
+      "Number of objective gradient evaluations             = 11\n",
+      "Number of equality constraint evaluations            = 0\n",
+      "Number of inequality constraint evaluations          = 18\n",
+      "Number of equality constraint Jacobian evaluations   = 0\n",
+      "Number of inequality constraint Jacobian evaluations = 11\n",
+      "Number of Lagrangian Hessian evaluations             = 10\n",
+      "Total seconds in IPOPT                               = 0.008\n",
+      "\n",
+      "EXIT: Optimal Solution Found.\n",
+      "      solver  :   t_proc      (avg)   t_wall      (avg)    n_eval\n",
+      "       nlp_f  |        0 (       0)  12.00us (666.67ns)        18\n",
+      "       nlp_g  |        0 (       0)  15.00us (833.33ns)        18\n",
+      "  nlp_grad_f  |        0 (       0)  24.00us (  2.00us)        12\n",
+      "  nlp_hess_l  |        0 (       0)  22.00us (  2.20us)        10\n",
+      "   nlp_jac_g  |        0 (       0)  36.00us (  3.00us)        12\n",
+      "       total  |  11.00ms ( 11.00ms)   8.18ms (  8.18ms)         1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# instantiate casadi optimizaiton problem class\n",
+    "def NLP_param(a, p, opti_silent=False):\n",
+    "    opti = casadi.Opti()\n",
+    "    # define variables\n",
+    "    x = opti.variable()\n",
+    "    y = opti.variable()\n",
+    "    p_opti = opti.parameter()\n",
+    "    a_opti = opti.parameter()\n",
+    "    # define objective and constraints\n",
+    "    opti.minimize((1 - x) ** 2 + a_opti * (y - x ** 2) ** 2)\n",
+    "    opti.subject_to(x >= y)\n",
+    "    opti.subject_to((p_opti / 2) ** 2 <= x ** 2 + y ** 2)\n",
+    "    opti.subject_to(x ** 2 + y ** 2 <= p_opti ** 2)\n",
+    "    # select IPOPT solver and solve the NLP\n",
+    "    if opti_silent:\n",
+    "        opts = {'ipopt.print_level': 0, 'print_time': 0, 'ipopt.sb': 'yes'}\n",
+    "    else:\n",
+    "        opts = {}\n",
+    "    opti.solver('ipopt', opts)\n",
+    "    # set parametric values\n",
+    "    opti.set_value(p_opti, p)\n",
+    "    opti.set_value(a_opti, a)\n",
+    "    return opti, x, y\n",
+    "\n",
+    "# construct casadi problem\n",
+    "opti, x, y = NLP_param(a, p)\n",
+    "# solve NLP via casadi\n",
+    "sol = opti.solve()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "MvXjYHNjISrC",
+    "outputId": "69a77f30-0ba5-411e-9d15-3cb7f15d9154"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x = 0.8081695826847699\n",
+      "y = 0.588949838491767\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"x = {sol.value(x)}\")\n",
+    "print(f\"y = {sol.value(y)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pcNq5fOVE4lR"
+   },
+   "source": [
+    "## Compare: NeuroMANCER vs. CasADi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 281
+    },
+    "id": "kvYCfjq6zxxC",
+    "outputId": "322e5b72-b93d-4d9e-a913-8703aad67d1a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7767074\n",
+      "0.5304637\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Plots\n",
+    "\"\"\"\n",
+    "x1 = np.arange(-0.5, 1.5, 0.02)\n",
+    "y1 = np.arange(-0.5, 1.5, 0.02)\n",
+    "xx, yy = np.meshgrid(x1, y1)\n",
+    "\n",
+    "# eval objective and constraints\n",
+    "J = (1 - xx) ** 2 + a * (yy - xx ** 2) ** 2\n",
+    "c1 = xx - yy\n",
+    "c2 = xx ** 2 + yy ** 2 - (p / 2) ** 2\n",
+    "c3 = -(xx ** 2 + yy ** 2) + p ** 2\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1)\n",
+    "cp = ax.contourf(xx, yy, J,\n",
+    "                 levels=[0, 0.05, 0.2, 0.5, 1.0, 2.0, 4.0, 6.0, 8.0],\n",
+    "                 alpha=0.6)\n",
+    "fig.colorbar(cp)\n",
+    "ax.set_title('Rosenbrock problem')\n",
+    "cg1 = ax.contour(xx, yy, c1, [0], colors='mediumblue', alpha=0.7)\n",
+    "plt.setp(cg1.collections,\n",
+    "         path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
+    "cg2 = ax.contour(xx, yy, c2, [0], colors='mediumblue', alpha=0.7)\n",
+    "plt.setp(cg2.collections,\n",
+    "         path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
+    "cg3 = ax.contour(xx, yy, c3, [0], colors='mediumblue', alpha=0.7)\n",
+    "plt.setp(cg3.collections,\n",
+    "         path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
+    "\n",
+    "# Solution to mpNLP via Neuromancer\n",
+    "datapoint = {'a': torch.tensor([[a]]), 'p': torch.tensor([[p]]),\n",
+    "             'name': 'test'}\n",
+    "model_out = problem(datapoint)\n",
+    "x_nm = model_out['test_' + \"x\"][0, 0].detach().numpy()\n",
+    "y_nm = model_out['test_' + \"x\"][0, 1].detach().numpy()\n",
+    "print(x_nm)\n",
+    "print(y_nm)\n",
+    "\n",
+    "# plot optimal solutions CasADi vs Neuromancer\n",
+    "ax.plot(sol.value(x), sol.value(y), 'g*', markersize=10, label='CasADi')\n",
+    "ax.plot(x_nm, y_nm, 'r*', fillstyle='none', markersize=10, label='NeuroMANCER')\n",
+    "plt.legend(bbox_to_anchor=(1.0, 0.15))\n",
+    "plt.show(block=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "id": "yD5kAnjy4CUL"
+   },
+   "outputs": [],
+   "source": [
+    "def eval_constraints(x, y, p):\n",
+    "    \"\"\"\n",
+    "    evaluate mean constraints violations\n",
+    "    \"\"\"\n",
+    "    con_1_viol = np.maximum(0, y - x)\n",
+    "    con_2_viol = np.maximum(0, (p/2)**2 - (x**2+y**2))\n",
+    "    con_3_viol = np.maximum(0, x**2+y**2 - p**2)\n",
+    "    con_viol = con_1_viol + con_2_viol + con_3_viol\n",
+    "    con_viol_mean = np.mean(con_viol)\n",
+    "    return con_viol_mean\n",
+    "\n",
+    "def eval_objective(x, y):\n",
+    "    obj_value_mean = np.mean((1 - x) ** 2 + a * (y - x ** 2) ** 2)   \n",
+    "    return obj_value_mean\n",
+    "\n",
+    "# select n number of random samples to evaluate\n",
+    "n_samples = 1000\n",
+    "idx = np.random.randint(0, nsim, n_samples)\n",
+    "p_samples = samples_test['p'][idx]\n",
+    "a_samples = samples_test['a'][idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "id": "r85kJ1Dr_nQ2"
+   },
+   "outputs": [],
+   "source": [
+    "# create named dictionary for neuromancer\n",
+    "datapoint = {'a': a_samples, 'p': p_samples, 'name': 'test'}\n",
+    "\n",
+    "# Solve via neuromancer\n",
+    "t = time.time()\n",
+    "model_out = problem(datapoint)\n",
+    "nm_time = time.time() - t\n",
+    "x_nm = model_out['test_' + \"x\"][:, [0]].detach().numpy()\n",
+    "y_nm = model_out['test_' + \"x\"][:, [1]].detach().numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "gemVhNU2AALr"
+   },
+   "outputs": [],
+   "source": [
+    "# Solve via solver\n",
+    "t = time.time()\n",
+    "x_solver, y_solver = [], []\n",
+    "for i in range(0, n_samples):\n",
+    "    prob, x, y = NLP_param(p_samples[i].numpy(), a_samples[i].numpy(), opti_silent=True)\n",
+    "    sol = prob.solve()\n",
+    "    x_solver.append(sol.value(x))\n",
+    "    y_solver.append(sol.value(y))\n",
+    "solver_time = time.time() - t\n",
+    "x_solver = np.asarray(x_solver)\n",
+    "y_solver = np.asarray(y_solver)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5n-SG-TuADib",
+    "outputId": "6a251657-0b4d-41f9-80c0-537966f1f6f0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Solution for 1000 problems via Neuromancer obtained in 0.0038 seconds\n",
+      "Neuromancer mean constraints violation 0.3537\n",
+      "Neuromancer mean objective value 0.0610\n",
+      "\n",
+      "Solution for 1000 problems via solver obtained in 9.5317 seconds\n",
+      "Solver mean constraints violation 0.0746\n",
+      "Solver mean objective value 0.2036\n",
+      "\n",
+      "Solution speedup factor 2540.9132\n",
+      "MSE primal optimizers: 0.11051816632586187\n",
+      "mean objective value discrepancy: 70.02 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate neuromancer solution\n",
+    "print(f'Solution for {n_samples} problems via Neuromancer obtained in {nm_time:.4f} seconds')\n",
+    "nm_con_viol_mean = eval_constraints(x_nm, y_nm, p)\n",
+    "print(f'Neuromancer mean constraints violation {nm_con_viol_mean:.4f}')\n",
+    "nm_obj_mean = eval_objective(x_nm, y_nm)\n",
+    "print(f'Neuromancer mean objective value {nm_obj_mean:.4f}\\n')\n",
+    "\n",
+    "# Evaluate solver solution\n",
+    "print(f'Solution for {n_samples} problems via solver obtained in {solver_time:.4f} seconds')\n",
+    "solver_con_viol_mean = eval_constraints(x_solver, y_solver, p)\n",
+    "print(f'Solver mean constraints violation {solver_con_viol_mean:.4f}')\n",
+    "solver_obj_mean = eval_objective(x_solver, y_solver)\n",
+    "print(f'Solver mean objective value {solver_obj_mean:.4f}\\n')\n",
+    "\n",
+    "# neuromancer solver comparison\n",
+    "speedup_factor = solver_time/nm_time\n",
+    "print(f'Solution speedup factor {speedup_factor:.4f}')\n",
+    "\n",
+    "# Difference in primal optimizers\n",
+    "dx = (x_solver - x_nm)[:,0]\n",
+    "dy = (y_solver - y_nm)[:,0]\n",
+    "err_x = np.mean(dx**2)\n",
+    "err_y = np.mean(dy**2)\n",
+    "err_primal = err_x + err_y\n",
+    "print('MSE primal optimizers:', err_primal)\n",
+    "\n",
+    "# Difference in objective\n",
+    "err_obj = np.abs(solver_obj_mean - nm_obj_mean) / solver_obj_mean * 100\n",
+    "print(f'mean objective value discrepancy: {err_obj:.2f} %')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "neuromancer",
+   "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/examples/parametric_programming/Part_1_basics.ipynb b/examples/parametric_programming/Part_1_basics.ipynb
index cef97bdf..511eea88 100644
--- a/examples/parametric_programming/Part_1_basics.ipynb
+++ b/examples/parametric_programming/Part_1_basics.ipynb
@@ -67,7 +67,14 @@
    },
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/parametric_programming/Part_1_basics_lightning.ipynb b/examples/parametric_programming/Part_1_basics_lightning.ipynb
new file mode 100755
index 00000000..133f1208
--- /dev/null
+++ b/examples/parametric_programming/Part_1_basics_lightning.ipynb
@@ -0,0 +1,1079 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "34qVD_ntSKLF"
+   },
+   "source": [
+    "# Learning to optimize parametric nonlinear programming problem (pNLP) using Neuromancer\n",
+    "\n",
+    "\n",
+    "This is an interactive notebook based on the python script [Part_1_LearnToOptimize_tutorial.py](./Part_1_LearnToOptimize_tutorial.py).  \n",
+    "\n",
+    "We will demonstrate the capability of learning to optimize (L2O)\n",
+    "for solving [parametric nonlinear programming problem (pNLP)](https://en.wikipedia.org/wiki/Parametric_programming) defined as:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "&\\text{minimize } &&  f(x, \\theta) \\\\\n",
+    "&\\text{subject to} && g(x, \\theta) \\le 0\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "with  parameters $\\theta$ and decision variables $x$.\n",
+    "\n",
+    "In L2O train a neural network mapping problem parameters onto the primal decision variables $x = \\pi(\\theta)$ by using gradients of the optimizaiton problem to minimize the objective function and satisfy the constraints.\n",
+    "\n",
+    "### References\n",
+    "[1] [F. Fioretto, et al., Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods, 2019](https://arxiv.org/abs/1909.10461)  \n",
+    "[2] [S. Gould, et al., Deep Declarative Networks: A New Hope, 2020](https://arxiv.org/abs/1909.04866)  \n",
+    "[3] [P. Donti, et al., DC3: A learning method for optimization with hard constraints, 2021](https://arxiv.org/abs/2104.12225)  \n",
+    "[4] [J. Kotary, et al., End-to-End Constrained Optimization Learning: A Survey, 2021](https://arxiv.org/abs/2103.16378)  \n",
+    "[5] [M. Li, et al., Learning to Solve Optimization Problems with Hard Linear Constraints, 2022](https://arxiv.org/abs/2208.10611)  \n",
+    "[6] [R. Sambharya, et al., End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization, 2022](https://arxiv.org/abs/2212.08260)  \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OCn3zpaIqgMc"
+   },
+   "source": [
+    "## NeuroMANCER and Dependencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Qzy5Wot5k2Gf"
+   },
+   "source": [
+    "### Install (Colab only)\n",
+    "Skip this step when running locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "X_3EvkSz0Fnz",
+    "outputId": "23c06f6b-ab48-4763-c43c-40a325cacf87"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LWyvndXlz0Fv"
+   },
+   "source": [
+    "### Import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "KbP0n-4evRqt"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import numpy as np\n",
+    "import neuromancer.slim as slim\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patheffects as patheffects\n",
+    "import casadi\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "POL27EJZxJmI"
+   },
+   "outputs": [],
+   "source": [
+    "from neuromancer.trainer import Trainer\n",
+    "from neuromancer.problem import Problem\n",
+    "from neuromancer.constraint import variable\n",
+    "from neuromancer.dataset import DictDataset\n",
+    "from neuromancer.loss import PenaltyLoss\n",
+    "from neuromancer.modules import blocks\n",
+    "from neuromancer.system import Node"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Problem formulation\n",
+    "\n",
+    "In this example we will solve parametric constrained [Rosenbrock problem](https://en.wikipedia.org/wiki/Rosenbrock_function):\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "&\\text{minimize } &&  (1-x)^2 + a(y-x^2)^2\\\\\n",
+    "&\\text{subject to} && \\left(\\frac{p}{2}\\right)^2 \\le x^2 + y^2 \\le p^2\\\\\n",
+    "& && x \\ge y\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "with parameters $p, a$ and decision variables $x, y$.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_WH7o7Wu1epw"
+   },
+   "source": [
+    "## Dataset\n",
+    "\n",
+    "We constructy the dataset by sampling the parametric space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "_r6p2p6myHAh"
+   },
+   "outputs": [],
+   "source": [
+    "data_seed = 408  # random seed used for simulated data\n",
+    "np.random.seed(data_seed)\n",
+    "torch.manual_seed(data_seed);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JZ9qrw0tlJhs"
+   },
+   "source": [
+    "Randomly sample parameters from a uniform distribution: $0.5\\le p\\le2.0$;  $0.2\\le a\\le1.2$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "Nu58M-8JyHy6"
+   },
+   "outputs": [],
+   "source": [
+    "nsim = 5000  # number of datapoints: increase sample density for more robust results\n",
+    "# create dictionaries with sampled datapoints with uniform distribution\n",
+    "a_low, a_high, p_low, p_high = 0.2, 1.2, 0.5, 2.0\n",
+    "samples_train = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
+    "                 \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
+    "samples_dev = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
+    "               \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
+    "samples_test = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
+    "               \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
+    "# create named dictionary datasets\n",
+    "train_data = DictDataset(samples_train, name='train')\n",
+    "dev_data = DictDataset(samples_dev, name='dev')\n",
+    "test_data = DictDataset(samples_test, name='test')\n",
+    "# create torch dataloaders for the Trainer\n",
+    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, num_workers=0,\n",
+    "                                           collate_fn=train_data.collate_fn, shuffle=True)\n",
+    "dev_loader = torch.utils.data.DataLoader(dev_data, batch_size=32, num_workers=0,\n",
+    "                                         collate_fn=dev_data.collate_fn, shuffle=True)\n",
+    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=32, num_workers=0,\n",
+    "                                         collate_fn=test_data.collate_fn, shuffle=True)\n",
+    "# note: training quality will depend on the DataLoader parameters such as batch size and shuffle\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lightning.pytorch as pl \n",
+    "\n",
+    "def data_setup_function(): \n",
+    "    data_seed = 408  # random seed used for simulated data\n",
+    "    np.random.seed(data_seed)\n",
+    "    torch.manual_seed(data_seed)\n",
+    "\n",
+    "    nsim = 5000  # number of datapoints: increase sample density for more robust results\n",
+    "    # create dictionaries with sampled datapoints with uniform distribution\n",
+    "    a_low, a_high, p_low, p_high = 0.2, 1.2, 0.5, 2.0\n",
+    "    samples_train = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
+    "                    \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
+    "    samples_dev = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
+    "                \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
+    "    samples_test = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
+    "                \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
+    "    # create named dictionary datasets\n",
+    "    train_data = DictDataset(samples_train, name='train')\n",
+    "    dev_data = DictDataset(samples_dev, name='dev')\n",
+    "    test_data = DictDataset(samples_test, name='test')\n",
+    "\n",
+    "    return train_data, test_data\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize taining and test samples for 2D parametric space\n",
+    "a_train = samples_train['a'].numpy()\n",
+    "p_train = samples_train['p'].numpy()\n",
+    "a_dev = samples_dev['a'].numpy()\n",
+    "p_dev = samples_dev['p'].numpy()\n",
+    "plt.figure()\n",
+    "plt.scatter(a_train, p_train, s=2., c='blue', marker='o')\n",
+    "plt.scatter(a_dev, p_dev, s=2., c='red', marker='o')\n",
+    "plt.title('Sampled parametric space for training')\n",
+    "plt.xlim(a_low, a_high)\n",
+    "plt.ylim(p_low, p_high)\n",
+    "plt.grid(True)\n",
+    "plt.xlabel('a')\n",
+    "plt.ylabel('p')\n",
+    "plt.legend(['train', 'test'], loc='upper right')\n",
+    "plt.show()\n",
+    "plt.show(block=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Y2htUaWMDjsk"
+   },
+   "source": [
+    "## Primal Solution Map Architecture\n",
+    "\n",
+    "A neural network mapping problem parameters onto primal decision variables:  \n",
+    "$$x = \\pi(\\theta)$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "Ta_I_pjyyLzf"
+   },
+   "outputs": [],
+   "source": [
+    "# define neural architecture for the trainable solution map\n",
+    "func = blocks.MLP(insize=2, outsize=2,\n",
+    "                bias=True,\n",
+    "                linear_map=slim.maps['linear'],\n",
+    "                nonlin=nn.ReLU,\n",
+    "                hsizes=[80] * 4)\n",
+    "# wrap neural net into symbolic representation of the solution map via the Node class: sol_map(xi) -> x\n",
+    "sol_map = Node(func, ['a', 'p'], ['x'], name='map')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Lxj77EFj7EO-"
+   },
+   "source": [
+    "## Objective and Constraints in NeuroMANCER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "bcoVjphjyPp9"
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "variable is a basic symbolic abstraction in Neuromancer\n",
+    "   x = variable(\"variable_name\")                      (instantiates new variable)  \n",
+    "variable construction supports:\n",
+    "   algebraic expressions:     x**2 + x**3 + 5     (instantiates new variable)  \n",
+    "   slicing:                   x[:, i]             (instantiates new variable)  \n",
+    "   pytorch callables:         torch.sin(x)        (instantiates new variable)  \n",
+    "   constraints definition:    x <= 1.0            (instantiates Constraint object) \n",
+    "   objective definition:      x.minimize()        (instantiates Objective object) \n",
+    "to visualize computational graph of the variable use x.show() method          \n",
+    "\"\"\"\n",
+    "\n",
+    "# define decision variables\n",
+    "x1 = variable(\"x\")[:, [0]]\n",
+    "x2 = variable(\"x\")[:, [1]]\n",
+    "# problem parameters sampled in the dataset\n",
+    "p = variable('p')\n",
+    "a = variable('a')\n",
+    "\n",
+    "# objective function\n",
+    "f = (1-x1)**2 + a*(x2-x1**2)**2\n",
+    "obj = f.minimize(weight=1.0, name='obj')\n",
+    "\n",
+    "# constraints\n",
+    "Q_con = 100.  # constraint penalty weights\n",
+    "con_1 = Q_con*(x1 >= x2)\n",
+    "con_2 = Q_con*((p/2)**2 <= x1**2+x2**2)\n",
+    "con_3 = Q_con*(x1**2+x2**2 <= p**2)\n",
+    "con_1.name = 'c1'\n",
+    "con_2.name = 'c2'\n",
+    "con_3.name = 'c3'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 496
+    },
+    "id": "n7VPa9Wc8JRB",
+    "outputId": "0da17c45-6370-4f46-f626-bd5686b94bfc"
+   },
+   "outputs": [],
+   "source": [
+    "# constrained optimization problem construction\n",
+    "objectives = [obj]\n",
+    "constraints = [con_1, con_2, con_3]\n",
+    "components = [sol_map]\n",
+    "\n",
+    "# create penalty method loss function\n",
+    "loss = PenaltyLoss(objectives, constraints)\n",
+    "# construct constrained optimization problem\n",
+    "problem = Problem(components, loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Define Lightning Modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "\n",
+    "class LitProblem(pl.LightningModule): \n",
+    "    def __init__(self, problem, train_metric, dev_metric): \n",
+    "        super().__init__()\n",
+    "        self.problem = problem \n",
+    "        self.train_metric = train_metric\n",
+    "        self.dev_metric = dev_metric\n",
+    "        self.training_step_outputs = []\n",
+    "    \n",
+    "    def training_step(self, batch): \n",
+    "        output = self.problem(batch)\n",
+    "        loss = output[self.train_metric]\n",
+    "        self.training_step_outputs.append(loss)\n",
+    "        self.log('train_loss', loss, on_epoch=True, enable_graph=True, prog_bar=False)\n",
+    "        return loss \n",
+    "    \n",
+    "    def on_train_epoch_end(self):\n",
+    "        epoch_average = torch.stack(self.training_step_outputs).mean()\n",
+    "        print(\"EPOCH AVERAGE \", epoch_average)\n",
+    "        self.log(\"training_epoch_average\", epoch_average)\n",
+    "        self.training_step_outputs.clear()  # free memory\n",
+    "\n",
+    "    \"\"\"\n",
+    "    def validation_step(self, batch): \n",
+    "        output = self.problem(batch)\n",
+    "        loss = output[self.dev_metric]\n",
+    "        self.log('val_loss', loss)\n",
+    "    \"\"\"\n",
+    "    def configure_optimizers(self): \n",
+    "        optimizer = torch.optim.Adam(problem.parameters(), 0.001, betas=(0.0, 0.9))\n",
+    "        return optimizer \n",
+    "    \n",
+    "\n",
+    "class LightningDataModule(pl.LightningDataModule): \n",
+    "    def __init__(self,data_setup_function, batch_size ): \n",
+    "        super().__init__()\n",
+    "        self.data_setup_function = data_setup_function\n",
+    "        self.batch_size = batch_size \n",
+    "\n",
+    "    def setup(self, stage=None): \n",
+    "        train_data, test_data = self.data_setup_function()\n",
+    "\n",
+    "        self.train_data = train_data\n",
+    "        self.test_data = test_data\n",
+    " \n",
+    "    def train_dataloader(self): \n",
+    "        return DataLoader(self.train_data, batch_size=self.batch_size, collate_fn=self.train_data.collate_fn)\n",
+    "\n",
+    "    def val_dataloader(self): \n",
+    "        return DataLoader(self.test_data, batch_size=self.batch_size, collate_fn=self.test_data.collate_fn)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train Using PyTorch Lightning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: False, used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "/Users/birm560/opt/anaconda3/envs/neuromancer/lib/python3.10/site-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.\n",
+      "\n",
+      "  | Name    | Type    | Params\n",
+      "------------------------------------\n",
+      "0 | problem | Problem | 19.8 K\n",
+      "------------------------------------\n",
+      "19.8 K    Trainable params\n",
+      "0         Non-trainable params\n",
+      "19.8 K    Total params\n",
+      "0.079     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/birm560/opt/anaconda3/envs/neuromancer/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|██████████| 79/79 [00:01<00:00, 63.25it/s, v_num=20]EPOCH AVERAGE  tensor(0.0596, grad_fn=<MeanBackward0>)\n",
+      "Epoch 1: 100%|██████████| 79/79 [00:00<00:00, 85.24it/s, v_num=20]EPOCH AVERAGE  tensor(0.0588, grad_fn=<MeanBackward0>)\n",
+      "Epoch 2: 100%|██████████| 79/79 [00:00<00:00, 94.03it/s, v_num=20]EPOCH AVERAGE  tensor(0.0612, grad_fn=<MeanBackward0>)\n",
+      "Epoch 3: 100%|██████████| 79/79 [00:00<00:00, 102.20it/s, v_num=20]EPOCH AVERAGE  tensor(0.0605, grad_fn=<MeanBackward0>)\n",
+      "Epoch 4: 100%|██████████| 79/79 [00:00<00:00, 105.07it/s, v_num=20]EPOCH AVERAGE  tensor(0.0595, grad_fn=<MeanBackward0>)\n",
+      "Epoch 5: 100%|██████████| 79/79 [00:00<00:00, 101.22it/s, v_num=20]EPOCH AVERAGE  tensor(0.0591, grad_fn=<MeanBackward0>)\n",
+      "Epoch 6: 100%|██████████| 79/79 [00:00<00:00, 105.14it/s, v_num=20]EPOCH AVERAGE  tensor(0.0616, grad_fn=<MeanBackward0>)\n",
+      "Epoch 7: 100%|██████████| 79/79 [00:00<00:00, 107.24it/s, v_num=20]EPOCH AVERAGE  tensor(0.0606, grad_fn=<MeanBackward0>)\n",
+      "Epoch 8: 100%|██████████| 79/79 [00:00<00:00, 105.59it/s, v_num=20]EPOCH AVERAGE  tensor(0.0612, grad_fn=<MeanBackward0>)\n",
+      "Epoch 9: 100%|██████████| 79/79 [00:00<00:00, 96.76it/s, v_num=20] EPOCH AVERAGE  tensor(0.0613, grad_fn=<MeanBackward0>)\n",
+      "Epoch 10: 100%|██████████| 79/79 [00:00<00:00, 96.55it/s, v_num=20] EPOCH AVERAGE  tensor(0.0615, grad_fn=<MeanBackward0>)\n",
+      "Epoch 11: 100%|██████████| 79/79 [00:01<00:00, 70.57it/s, v_num=20]EPOCH AVERAGE  tensor(0.0600, grad_fn=<MeanBackward0>)\n",
+      "Epoch 12: 100%|██████████| 79/79 [00:01<00:00, 72.41it/s, v_num=20]EPOCH AVERAGE  tensor(0.0609, grad_fn=<MeanBackward0>)\n",
+      "Epoch 13: 100%|██████████| 79/79 [00:01<00:00, 67.49it/s, v_num=20]EPOCH AVERAGE  tensor(0.0606, grad_fn=<MeanBackward0>)\n",
+      "Epoch 14: 100%|██████████| 79/79 [00:01<00:00, 67.66it/s, v_num=20]EPOCH AVERAGE  tensor(0.0612, grad_fn=<MeanBackward0>)\n",
+      "Epoch 15: 100%|██████████| 79/79 [00:01<00:00, 61.54it/s, v_num=20]EPOCH AVERAGE  tensor(0.0604, grad_fn=<MeanBackward0>)\n",
+      "Epoch 16: 100%|██████████| 79/79 [00:01<00:00, 77.00it/s, v_num=20]EPOCH AVERAGE  tensor(0.0623, grad_fn=<MeanBackward0>)\n",
+      "Epoch 17: 100%|██████████| 79/79 [00:00<00:00, 80.32it/s, v_num=20]EPOCH AVERAGE  tensor(0.0616, grad_fn=<MeanBackward0>)\n",
+      "Epoch 18: 100%|██████████| 79/79 [00:00<00:00, 87.52it/s, v_num=20]EPOCH AVERAGE  tensor(0.0596, grad_fn=<MeanBackward0>)\n",
+      "Epoch 19: 100%|██████████| 79/79 [00:00<00:00, 99.25it/s, v_num=20]EPOCH AVERAGE  tensor(0.0607, grad_fn=<MeanBackward0>)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=20` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19: 100%|██████████| 79/79 [00:00<00:00, 96.78it/s, v_num=20]\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = LitProblem(problem, train_metric='train_loss', dev_metric='train_loss')\n",
+    "data_module = LightningDataModule(data_setup_function=data_setup_function, batch_size=64)\n",
+    "trainer = pl.Trainer(max_epochs=20, num_sanity_val_steps=0)\n",
+    "trainer.fit(model, data_module)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "icWSMeG28SKc"
+   },
+   "source": [
+    "## Parametric Problem Solution in NeuroMANCER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "id": "rk1bRczByUvl"
+   },
+   "outputs": [],
+   "source": [
+    "lr = 0.001      # step size for gradient descent\n",
+    "epochs = 400    # number of training epochs\n",
+    "warmup = 100    # number of epochs to wait before enacting early stopping policy\n",
+    "patience = 100  # number of epochs with no improvement in eval metric to allow before early stopping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "id": "x4oS2N2ZyWtD"
+   },
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.AdamW(problem.parameters(), lr=lr)\n",
+    "\n",
+    "# define trainer\n",
+    "trainer = Trainer(\n",
+    "    problem,\n",
+    "    train_loader,\n",
+    "    dev_loader,\n",
+    "    test_loader,\n",
+    "    optimizer,\n",
+    "    epochs=epochs,\n",
+    "    patience=patience,\n",
+    "    warmup=warmup)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "27ASQD3q-M0A",
+    "outputId": "04eec20c-51c3-4a71-e570-9636af9421c0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch: 0  train_loss: 0.06656379252672195\n",
+      "epoch: 1  train_loss: 0.06053765490651131\n",
+      "epoch: 2  train_loss: 0.06149037927389145\n",
+      "epoch: 3  train_loss: 0.06822418421506882\n",
+      "epoch: 4  train_loss: 0.07178667932748795\n",
+      "epoch: 5  train_loss: 0.05971056967973709\n",
+      "epoch: 6  train_loss: 0.06326249986886978\n",
+      "epoch: 7  train_loss: 0.05923514813184738\n",
+      "epoch: 8  train_loss: 0.06209183484315872\n",
+      "epoch: 9  train_loss: 0.0609293170273304\n",
+      "epoch: 10  train_loss: 0.058502864092588425\n",
+      "epoch: 11  train_loss: 0.06029532477259636\n",
+      "epoch: 12  train_loss: 0.0610412135720253\n",
+      "epoch: 13  train_loss: 0.0586276613175869\n",
+      "epoch: 14  train_loss: 0.07020330429077148\n",
+      "epoch: 15  train_loss: 0.08000552654266357\n",
+      "epoch: 16  train_loss: 0.0636485144495964\n",
+      "Interrupted training loop.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train NLP solution map\n",
+    "best_model = trainer.train()\n",
+    "best_outputs = trainer.test(best_model)\n",
+    "# load best model dict\n",
+    "problem.load_state_dict(best_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0hhUw4PVBWmb"
+   },
+   "source": [
+    "## Get pNLP solution from trained neural network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# selected problem parameters\n",
+    "p = 1.0\n",
+    "a = 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "os3I3I8L3HbE",
+    "outputId": "50c13f99-7693-4102-b65c-4f3707f90c29"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7767074\n",
+      "0.5304637\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution to mpNLP via Neuromancer\n",
+    "datapoint = {'a': torch.tensor([[a]]), 'p': torch.tensor([[p]]),\n",
+    "             'name': 'test'}\n",
+    "model_out = problem(datapoint)\n",
+    "x_nm = model_out['test_' + \"x\"][0, 0].detach().numpy()\n",
+    "y_nm = model_out['test_' + \"x\"][0, 1].detach().numpy()\n",
+    "print(x_nm)\n",
+    "print(y_nm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "g-KmqqUj-Q3E"
+   },
+   "source": [
+    "## Get pNLP solution from CasADi for comparison\n",
+    "\n",
+    "[CasADi](https://web.casadi.org/) is an open-source tool for constrained optimization and optimal control that has influenced the development of NeuroMANCER."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "dmJERFP2yYuC"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "******************************************************************************\n",
+      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
+      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
+      "         For more information visit https://github.com/coin-or/Ipopt\n",
+      "******************************************************************************\n",
+      "\n",
+      "This is Ipopt version 3.14.11, running with linear solver MUMPS 5.4.1.\n",
+      "\n",
+      "Number of nonzeros in equality constraint Jacobian...:        0\n",
+      "Number of nonzeros in inequality constraint Jacobian.:        6\n",
+      "Number of nonzeros in Lagrangian Hessian.............:        3\n",
+      "\n",
+      "Total number of variables............................:        2\n",
+      "                     variables with only lower bounds:        0\n",
+      "                variables with lower and upper bounds:        0\n",
+      "                     variables with only upper bounds:        0\n",
+      "Total number of equality constraints.................:        0\n",
+      "Total number of inequality constraints...............:        3\n",
+      "        inequality constraints with only lower bounds:        1\n",
+      "   inequality constraints with lower and upper bounds:        0\n",
+      "        inequality constraints with only upper bounds:        2\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   0  1.0000000e+00 2.50e-01 1.67e+00  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
+      "   1  9.5811923e-01 2.49e-01 1.71e+01  -1.0 5.59e-01    -  5.26e-01 3.81e-02h  1\n",
+      "   2  5.1823940e-01 1.40e-02 1.95e+01  -1.0 2.12e+01    -  1.25e-03 3.12e-02f  6\n",
+      "   3  3.2701557e-01 0.00e+00 5.64e-01  -1.0 2.47e-01    -  1.00e+00 1.00e+00f  1\n",
+      "   4  1.5594126e-01 0.00e+00 9.02e-01  -1.7 4.58e-01    -  3.11e-01 1.00e+00f  1\n",
+      "   5  5.3473898e-02 0.00e+00 2.99e-01  -1.7 5.51e-01    -  1.00e+00 1.00e+00h  1\n",
+      "   6  5.7915701e-02 0.00e+00 6.95e-03  -1.7 2.57e-02    -  1.00e+00 1.00e+00h  1\n",
+      "   7  4.4884709e-02 0.00e+00 7.47e-03  -3.8 9.13e-02    -  8.77e-01 1.00e+00h  1\n",
+      "   8  4.1207939e-02 0.00e+00 2.60e-04  -3.8 3.58e-02    -  1.00e+00 1.00e+00h  1\n",
+      "   9  4.0921509e-02 0.00e+00 8.22e-07  -5.7 2.98e-03    -  1.00e+00 1.00e+00h  1\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  10  4.0919039e-02 0.00e+00 7.11e-11  -8.6 2.53e-05    -  1.00e+00 1.00e+00h  1\n",
+      "\n",
+      "Number of Iterations....: 10\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   4.0919038633377619e-02    4.0919038633377619e-02\n",
+      "Dual infeasibility......:   7.1118860800467587e-11    7.1118860800467587e-11\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   2.6584596762655841e-09    2.6584596762655841e-09\n",
+      "Overall NLP error.......:   2.6584596762655841e-09    2.6584596762655841e-09\n",
+      "\n",
+      "\n",
+      "Number of objective function evaluations             = 18\n",
+      "Number of objective gradient evaluations             = 11\n",
+      "Number of equality constraint evaluations            = 0\n",
+      "Number of inequality constraint evaluations          = 18\n",
+      "Number of equality constraint Jacobian evaluations   = 0\n",
+      "Number of inequality constraint Jacobian evaluations = 11\n",
+      "Number of Lagrangian Hessian evaluations             = 10\n",
+      "Total seconds in IPOPT                               = 0.008\n",
+      "\n",
+      "EXIT: Optimal Solution Found.\n",
+      "      solver  :   t_proc      (avg)   t_wall      (avg)    n_eval\n",
+      "       nlp_f  |        0 (       0)  12.00us (666.67ns)        18\n",
+      "       nlp_g  |        0 (       0)  15.00us (833.33ns)        18\n",
+      "  nlp_grad_f  |        0 (       0)  24.00us (  2.00us)        12\n",
+      "  nlp_hess_l  |        0 (       0)  22.00us (  2.20us)        10\n",
+      "   nlp_jac_g  |        0 (       0)  36.00us (  3.00us)        12\n",
+      "       total  |  11.00ms ( 11.00ms)   8.18ms (  8.18ms)         1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# instantiate casadi optimizaiton problem class\n",
+    "def NLP_param(a, p, opti_silent=False):\n",
+    "    opti = casadi.Opti()\n",
+    "    # define variables\n",
+    "    x = opti.variable()\n",
+    "    y = opti.variable()\n",
+    "    p_opti = opti.parameter()\n",
+    "    a_opti = opti.parameter()\n",
+    "    # define objective and constraints\n",
+    "    opti.minimize((1 - x) ** 2 + a_opti * (y - x ** 2) ** 2)\n",
+    "    opti.subject_to(x >= y)\n",
+    "    opti.subject_to((p_opti / 2) ** 2 <= x ** 2 + y ** 2)\n",
+    "    opti.subject_to(x ** 2 + y ** 2 <= p_opti ** 2)\n",
+    "    # select IPOPT solver and solve the NLP\n",
+    "    if opti_silent:\n",
+    "        opts = {'ipopt.print_level': 0, 'print_time': 0, 'ipopt.sb': 'yes'}\n",
+    "    else:\n",
+    "        opts = {}\n",
+    "    opti.solver('ipopt', opts)\n",
+    "    # set parametric values\n",
+    "    opti.set_value(p_opti, p)\n",
+    "    opti.set_value(a_opti, a)\n",
+    "    return opti, x, y\n",
+    "\n",
+    "# construct casadi problem\n",
+    "opti, x, y = NLP_param(a, p)\n",
+    "# solve NLP via casadi\n",
+    "sol = opti.solve()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "MvXjYHNjISrC",
+    "outputId": "69a77f30-0ba5-411e-9d15-3cb7f15d9154"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x = 0.8081695826847699\n",
+      "y = 0.588949838491767\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"x = {sol.value(x)}\")\n",
+    "print(f\"y = {sol.value(y)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pcNq5fOVE4lR"
+   },
+   "source": [
+    "## Compare: NeuroMANCER vs. CasADi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 281
+    },
+    "id": "kvYCfjq6zxxC",
+    "outputId": "322e5b72-b93d-4d9e-a913-8703aad67d1a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7767074\n",
+      "0.5304637\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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EAoFAIBAkHEKgCAQCgUAgSDiEQBEIBAKBQJBwCIEiEAhizo4dO0T+iUAwCPD5fHz729+msLAQk8nEuHHj+O53vxuxyeCOHTuYM2cOBoOB8ePH8+qrr3Zb8+KLLzJmzBiMRiMLFy5k//79vTo3IVAEAoFAIBimPPfcc/ziF7/ghRde4PTp0zz33HP84Ac/4Oc//3nIfUpLS7nrrrtYuXIlR48e5cknn+TRRx/lvffeC6558803Wbt2LevXr+fw4cPMnDmT1atXdxsfEQ5RxSMQCAQCwTDl7rvvJicnh9/85jfBbR//+McxmUz87//+b4/7/Md//AcbN27s4pJ+6lOform5mS1btgD+gZnz588PNlBUFIWRI0fyL//yLzz11FNRnduwbNSmKAqVlZUkJyfHdG6JQCAQCIYeqqrS1tZGfn5+1NO8+4LT6cTtdt/wcVRV7XZtMxgMXQaoBliyZAkvv/wy586dY8KECRw7dozdu3cHO1D3RHFxcbfxEKtXr+bJJ58E/F2jDx06xLp164KPazQaVq1aRXFxcdSvY1gKlMrKym7TRwUCgUAgCEdZWRkjRoyIy7GdTicjRmfRUNt9fEZvSUpK6jaGY/369cGO2Z156qmnaG1tZdKkSciyjM/n4z//8z/5zGc+E/L41dXV5OTkdNmWk5NDa2srDoeDpqYmfD5fj2vOnDkT9esYlgIlOTkZgK/94nUMJvMAn41AEHtOnqwAYFFKToSVg59Th0uZMTZ7QM/h5L6LTJkWvvV/LDi11z8AccrcMXF7jpN7/BeQKQvCD5KMxKn9F5i6ZFIsTqnjmIcuM2XRhMgLozlWif//yNSF47o95vXBjv0plFUZkICFM6t59S8PB68d8cDtdtNQ286mo/+OJbm70xEttjYXd876EWVlZVit1uD2ntwTgLfeeov/+7//4/XXX2fq1KnBnJL8/PyQU777i2EpUALWl8Fkxmi2RFgtEAwujh8vQ2cwcVNq7kCfStwpOXAJvc6E0TBwXzRO7DmPXmvEaDDF/bn0soFpC8bG/zkW35gIKCk+x9wVs2N0Rh3oZUPM3me91sj0JUXdtrs9Ejv2pFJZq0evg1sXN5Ob6Q/r9EdKgCXZQFKy8YaPY7VauwiUUHz961/nqaee4lOf+hQA06dP58qVK2zYsCGkQMnNzaWmpqbLtpqaGqxWKyaTCVmWkWW5xzW5udF/LokqHoFgCHH8eBnAsBAnAWaNH3iXaNrM+Nj+nSnZHb013ldO7DoV9+dIZJwuiY070qis1aPTqtyxrIkxBa7IOw5i7HZ7t7waWZZRFCXkPosXL2bbtm1dtm3dupXFixcDoNfrmTt3bpc1iqKwbdu24JpoGJYOikAwlBku4qTkwKUBFycn9pzvF3ESIJ7uSUCc3Kh7Ei9K9l+K6/Ftdg1/eC+T6jo9ZpOP+26rIzvdG9fnTATuuece/vM//5NRo0YxdepUjhw5wk9+8hMeeeSR4Jp169ZRUVHB7373OwAee+wxXnjhBb7xjW/wyCOPsH37dt566y02btwY3Gft2rU89NBDzJs3jwULFvD8889js9l4+OGHoz43IVAEgiHC8eNlw0qcDDQn9pzvt+cq2X0m7qEdSFxxEmDa0tjktJQcK+8S3mlpk9n4YRqNzVocTolJY13DQpwA/PznP+fb3/42X/nKV6itrSU/P58vfelLPP3008E1VVVVXL16NXi/sLCQjRs38rWvfY2f/exnjBgxgl//+tesXr06uOaTn/wkdXV1PP3001RXVzNr1iy2bNnSLXE2HEKgCARDgEBoZzgx0O4J9E9opz84setUwouTeNHQrGXTh2k4nBpMBgWj3keyxTfQp9VvJCcn8/zzz/P888+HXNNTl9gVK1Zw5MiRsMd+4okneOKJJ/p8biIHRSAY5Ay3vBPhnsSW4Zx3Ul2v428fpONwashI9TJnajtaLciy6F+aCAiBIhAMYoarOBku7kl/JMZC4od2SvZfinl4p6xKz6YP03C7JXIyPdy9sjEoTLRCoCQEIsQjEAxShps4CTDQ4qQ/3RPon8TYWFJSfC7mx4w1F8sMfLA3FUWBkXluVi1pRqdV8fn8ZcSyPMAnKACEQBEIBjXDSZwkQmgnwFBwT+JZtTN92ZSYHzNWlDdmUF6eiqrC2JFOVi5qQb4WS/BeEyjCQUkMRIhHIBiEDKeKnc4I9yTGx0/w0A7Etrz4b++7OVk+ClWFyeMc3LK4Q5yAECiJhnBQBIJBxnCs2EmEnicBhpJ7Mli40fwTVYUDp1M4V6XHmmZh1mQb86e3c31j2I4QjxAoiYAQKALBIGI45p0kSmhnqDRlS/SGbLFGUeHDIxmcuZyEqjpYMKOdWZNtPa71CoGSUIgQj0AwSBiO4iTAQLsn/V1WHG/iJU5inSB7o+EdnwI7DmVw4FQKV6p0pJpDixPocFBEiCcxEA6KQDAIGK7iZLiFdoLPFWf3JJ7EOkG2r+Edr1di64FMymv9wwWtJjuz51qA9tD7XOvPphVVPAmBcFAEggRnOIuTREC4J4MPl0diU3E25bUmZFllwsh2DDpPRGdEhHgSCyFQBIIEZriKkwDCPYkdw6Wdvd2pYeNHOdQ0GtDrFO5cUhNsXR9JoIgQT2IhBIpAkOAMR3Ei3JPYMtiqdqBv3WPb7TJ/251DQ4seo8HH3TfVkJvuprKiDWuaJaIz4vUKgZJIiBwUgSBBGa69ThKpnT0MDfcE4h/aGegOss1tWjYVZ2NzaEkye7lzcS0pSf6JxD5FA3IUDopyTaBohUBJBIRAEQgSkOHY66QziSBOhspAwP50Twaqg2xds54txVk43TIpSR7uXFJLkqljIrFP8QcLIgkPkYOSWIgQj0CQYAznvJNECe0EGApN2WDwJcb2pry4qt7Axo+ycbplMlPdfGxpTRdxUnKsHEX1X+pkjQjxDCaEQBEIEojhLE4CJIp7MhRCO4Mx9yRANPknV6uNbN6bjcerITfDyV1LajAalG7rUrNTgfBDAFUVfNd0jRgWmBiIEI9AkCAMd3GSKD1Phlpi7GBzT6LlfJmZd3bl4vZKTBhlY83iupDOR3DGTpgQj6+TrhEOSmIgBIpAkAAIcTL8QjvB5xrEibEBSorPxSz/JJrwzsnSJPYcT8fh0iDLKjPGtvYoKkqOlQPRlQ/LGvjC/TV4vRI6kSSbEIgQj0AwwAx3cRJAuCexYzCHdiB0eEdV4fBZK3uOpwOQkeImw+pBpwstKKYvKYpKoEiSX6QY9Gq3IYKCgUEIFIFgABHiJHFCOwGEe5KYqCrsO5nKoTOpAMye2EJWqhtJihySEdU5gxMhUASCAUKIk8QK7Qwl92SwipNQ4R1FgZ1H0zlx0QrAomlNzJvUEuxb0pPwCIR3FMV/A5FbMtgQOSgCwQAynMVJAOGeDG5imX8C3cM7Ph9sP5TJ5SozSCrLZzUyYZR/InGwsVqI8uHpS4pwezriNUKgDC6EQBEIBoDh2iW2M4kU2unPsmLhnkSPxyvx9/1ZVNYZ0WhUbp1Xz5g8R/BxbxS5Jb6OliiifHiQIQSKQNDPCHEyfEM7AeLhngz2xNjrcbo1vLc3i9omA1qtwu0L6ijIcnVZ44sit6Qj/4Q+J78ev1Tbtx0FN4QQKAJBPzLcW9h3JlHcE+i/0E68u8b2t3sSy/k7nYcD2pwym/dk09Smw6D3cceiOrLT3F3Wqyp4Q+SglBwrZ/qSIiA6ESNITIRAEQj6CZEU60e4J0PLPYn1/J1Wm5a/fJiDzaHFavFy15Ja0qyebusUBVADIZ7u3WMDRBMGEiQmQqAIBP2AECd+Em1SMQj3JJFobNWxuTibS5UWJFTuuqmmR3ECHe4JhBcfJoPCnKm2PguUoxdq+rSf4MYRAkUgiDNCnHQlUcTJUHJPBrs4Kdl/iawpM/jb7mzcHg06rUKG1U1KkjfkPl7ftS4ZkoqmU25J5/AOgMWsMG9a+w2d35Q5hfDnGzqEoA8IgSIQxBEhTjpIpNBOgP50T4ZKWXGAWOafNNhTOFCcjderITvNhYqKhBShOqcjdCM6vw5NRKM2gSBOCHHSQaKFdvp7WnG8GGj3JBb5J6VVFg5XTsbr1VCQ5eSOxbVIhO9vAp0ESpg1N4oI7wwswkERCOKAECfdSSRx0p/Eyz0ZCmXFZ8uS2bjTjIpEYb6dlXPqUdQOOyS68uGONdeHd2LBtPljcTrtMT2mIDqEQBEIYowQJ10Z7qGdeDKYc09OlKaw91QGKnbmzYJls+rRSOB2RZf8Kqpzhj5xDfHs3LmTe+65h/z8fCRJ4u233w67/s9//jO33XYbWVlZWK1WFi9ezHvvvddlzTPPPIMkSV1ukyb1PPlSIOhvhDjpSiKGdvqboeie3Ej+iarCwbNp7D2VAcCYtApuntUYTHQNhG40mvC5JaF6oMQKEd4ZeOIqUGw2GzNnzuTFF1+Mav3OnTu57bbb2LRpE4cOHWLlypXcc889HDlypMu6qVOnUlVVFbzt3r07HqcvEPQKIU56JlHESQDhnsSGvuSfqCrsOZnBkQtpAOTIp5mQcaWLEAkIj0jOyPU5KIHhgLFk2vyhldg82IhriGfNmjWsWbMm6vXPP/98l/vf//73+etf/8rf/vY3Zs+eHdyu1WrJzRUXAUHiIMRJdxIttCPck4HFp8CHx7K5WJkEEtw0tR6lpqLbcMCeckt6PF4PIZ5Y558IBpaEruJRFIW2tjbS09O7bD9//jz5+fmMHTuWz3zmM1y9ejXscVwuF62trV1uAkGsEOKkO4kW2gkg3JMbpy/hHa9P4r0DuRSfyqC+Vc+KGTVMGd3z53C01Tk+VQJJFS3sY0BFRQWf/exnycjIwGQyMX36dA4ePBh2nx07djBnzhwMBgPjx4/n1Vdf7bbmxRdfZMyYMRiNRhYuXMj+/ft7dV4JLVB+9KMf0d7ezgMPPBDctnDhQl599VW2bNnCL37xC0pLS1m2bBltbW0hj7NhwwZSUlKCt5EjR/bH6QuGAUKchCaRxMlAlBUPZfekN+Edt0di8/5crtRacHpkLEYf4wtslOzv2WGLdnbOhJE2Hr2njNsX1MU8vHP0Qs2wCe80NTVx0003odPp2Lx5M6dOneLHP/4xaWlpIfcpLS3lrrvuYuXKlRw9epQnn3ySRx99tEvO6JtvvsnatWtZv349hw8fZubMmaxevZra2ugHLyZsFc/rr7/Od77zHf7617+SnZ0d3N45ZDRjxgwWLlzI6NGjeeutt/jCF77Q47HWrVvH2rVrg/dbW1uFSBHcMEKc9MxwD+0MZfektzhcGjYfyKOhxYBOVshOcZJk8gZzTq4P70DvqnMkqWNCsQjv9I3nnnuOkSNH8tvf/ja4rbCwMOw+L730EoWFhfz4xz8GYPLkyezevZuf/vSnrF69GoCf/OQnfPGLX+Thhx8O7rNx40ZeeeUVnnrqqajOLSEdlDfeeINHH32Ut956i1WrVoVdm5qayoQJE7hw4ULINQaDAavV2uUmEMQCIU66MtxDO8HnEy3taXfI/K24gIYWA0a9j5WzajHqlYjCI1idE8cGbMOB69MaXC5Xj+veeecd5s2bx/333092djazZ8/mV7/6VdhjFxcXd7s2r169muLiYgDcbjeHDh3qskaj0bBq1argmmhIOAfl97//PY888ghvvPEGd911V8T17e3tXLx4kX/6p3/qh7MTCPwcP14mxEkIEkmcDBX3JFFCOyXF56IK7zS369i0Pw+bQ4vF5OXOBVV4vP7vw7KshgzvQKccFG3oCcVdzikO4Z2BZFPFdAxJ5j7v72r3N5W7Pkqwfv16nnnmmW7rL126xC9+8QvWrl3LN7/5TQ4cOMC//uu/otfreeihh3p8jurqanJyuv4/z8nJobW1FYfDQVNTEz6fr8c1Z85E/38krgKlvb29i7NRWlrK0aNHSU9PZ9SoUaxbt46Kigp+97vfAf6wzkMPPcTPfvYzFi5cSHV1NQAmk4mUlBQA/v3f/5177rmH0aNHU1lZyfr165FlmQcffDCeL0UgCCLESc8kWmgnwFBwT2DwhHbqW/Rs3p+H0y2TYvFw58IqkkxeqhqNQEfya0/hHegYAtibFvbx6B472CkrK+sSLTAYDD2uUxSFefPm8f3vfx+A2bNnU1JSwksvvRRSoPQXcQ3xHDx4kNmzZwdLhNeuXcvs2bN5+umnAaiqqupSgfPyyy/j9Xp5/PHHycvLC96++tWvBteUl5fz4IMPMnHiRB544AEyMjLYu3cvWVlZ8XwpAgEgxEkoEjG0I9yT/qeq0ci7e/NxumUyrC7uWVxBksk/kbgj+TW8M9JT+bCg91yf1hBKoOTl5TFlSldXbPLkyWGrY3Nzc6mp6eo01dTUYLVaMZlMZGZmIstyj2t60yIkrg7KihUrUNXQf2TXlyXt2LEj4jHfeOONGzwrgaBvCHESnkQSJwGEexI7IpUXX6018f7hXHw+idx0B7fPq8Gg6xAjgdyS5tpmCFOj0JsOsUMtvDMQ3HTTTZw9e7bLtnPnzjF69OiQ+yxevJhNmzZ12bZ161YWL14MgF6vZ+7cuWzbto377rsP8Ds127Zt44knnoj63BIySVYgSDSEOAlNIoZ2+ruseLgMBAyVf3Kx0sLfD/nFychsO2sWVHcRJ9Cphb2khAzvQKdGbVGGeER458b42te+xt69e/n+97/PhQsXeP3113n55Zd5/PHHg2vWrVvH5z73ueD9xx57jEuXLvGNb3yDM2fO8D//8z+89dZbfO1rXwuuWbt2Lb/61a947bXXOH36NF/+8pex2WzBqp5oSLgkWYEg0RDiJDQitBN/EsE9CcfxS1Z2nshGg8qkUW0sn1mL3MNX30BuiayJMsSjFSGe/mD+/Pn85S9/Yd26dTz77LMUFhby/PPP85nPfCa45vp0jMLCQjZu3MjXvvY1fvaznzFixAh+/etfB0uMAT75yU9SV1fH008/TXV1NbNmzWLLli3dEmfDIQSKQBAGIU4ik0jiJIBwT2JLT+EdVYVjF1PZdSKTmmYj2alOVs6qDTngz+uTaG22k5OkAKGnAI7MdmDQKWSl9VwWGzwnEd6JGXfffTd33313yMd76hK7YsWKbnPyrueJJ57oVUjneoRAEQhCIMRJeBI1tDOUSCT3pHN4R1Vh/5l0jl9KRUUixeyhINMRdvqw71puScG4TKAh5Lr8LBf5WeHFSfCcRHhnSCNyUASCHhDiJDyJGNoJINyT+KKosPNEFscvpQIweVQrqUkedJEasEU5Y0cgCCAEikBwHUKcREeiiZOhUlYcIFHck87hHZ8Pth/J4VxZMkhw84w6xuTagMhlwVfO1wPRVedEPCcR3hkWCIEiEHRCiJPIJGJoJ8BQKCtORPdk+rIpeLwS7x3KpbTKgkajsmp2DRNHtnWanRMh+VXVYE1Pirgu6nMS4Z0hj8hBEQiuIcRJZBI1tDMQZcXxJFHckwAuj4YtB3KpbTKi1SrcNreGEZkOoCO3JJIzoqiBKh4R4hFEh3BQBAKEOOkNiShOBoLh4J6UFJ/D5dPz7t58apuM6HUKdy6oCooT6Fw+HFp4lOy/hE+51sL+BkM8IrwzfBACRTDsEeIkOkRox89wck8cHiMXWUljqx6TwcfdiyrJua781xtla/qs0blRrYsGEd4ZHogQj2BYI8RJdCRyaGcgGA7uSVO7nv3VszGk6Eg2e1izoIoUi7fbumBjtQgN2KIVMgJBACFQBMMWIU56R6KJkwDCPYk9tc0GfrcpkzZ3EvnJcM/iSixGX49rI83OKdnvF7eyRkUrKzckUGId3hEkNkKgCIYlQpxET6KGdoaae5Io4qSywcTfD+fj8qroDAbG5jeEFCfQkYMSTnhMWzqJadTH5PxiGd45eqFGhHcSGCFQBMOK48fLAIQ4iZJEDe0EGEruSSJwpdbCtqN5+BQJi74Vc6oWoy60OIGOEI+ozhHEGiFQBMMGIU76RiKKk/4uKw4wlN2T8xXJ7CzJQVElTK7LpCc7aJJSIoZkAmXGPa0r2X8p7OTi3iDCO8MPIVAEwwIhTnqPCO10MNTdk5LLqRSfyQJgQkEraW2nsGcupak0cn+TaBu1xQIR3hleiDJjwZBHiJPeI0I7PTznEHRPVBUOXUgPipPpY5q4eVoNGkntcEYihG5EdY4gXggHRTCkEeKk7ySiOBHuSexQVSg+k8XJK6kAzCtqYNbYRk7u9c/eCQiPSA5KoAHb9TkoIrwjuFGEgyIYsghx0jcSNbQTQLgnN46iwIcncoLiZMnkWmaPa0TyaxKmL5sSdegmWiFzo4jwzvBDOCiCIYkQJ30jkUM7wj2JDV6fxPZjuVypTUIjqSyfXsP4/LZu64IzdiKEeDpCQR1CJtD7RCC4EYRAEQw5hDi5MRJRnAQQ7smN4fZK/G3fSKoaTRh0CqtmVzE62xZ8vKT4XPDfvij6m0DoHBQR3hHcKEKgCIYUQpz0nZIDlxJWnAxEWfFQc0+cbg1bDhVwoTIZu0vL8uk1XcRJgOnLpgCdhEcEB2X+xEZcHpkkU/c2+LEi1uEdweBACBTBkEGIk76TyHknA9UxFoaOe9Lu1LLlYAFN7XpkjUpWipOsFGfYfTpa2IfPQZkwor3L/cEQ3hH5J4MDIVAEQwIhTm6cRHVPoP9DOyW7z8RFnAwELTYdmw8W0ObQkWT0kJvqoLrZ1L3qpvhc0D2BTkMA+5D8KsI7glggqngEgx4hTm6MRA/tDCX62z1paNXzt30jaHPoSDG7uXthOTqt3xGJXJ3Tc/lwfyPCO8MX4aAIBjVCnNwYiRzaCSDck75R02TkvUP5uLwyGVYXa+ZWYDL4el+d0wsHRYR3BLFECBTBoEWIk9gg3JP+oT/dk7J6M+8fzsOraMhNc3D7nEoMOr9j4lW6V+dcH96BvrewF+EdQawQAkUwKBHi5MZJ5NBOAOGe9J5L1UnsOJ6LT5EYmWlj1eyqLmKkozonvPCI1mmJJyK8M7wRAkUw6BDi5MZJ9NDOQE0rjhf95Z6cKbOy+2Q2KhJjc9tYMaMa+bpMw+s7v3bufRJAUUDpZYgnlq3t44UI7wwuhEARDCqEOLlxErlbLAxcaGewuyfHLqWx/1wmAJNHtrBkSi0aqfu6nmbndAvvKB07DsQQwJJj5TF1TwSDEyFQBIMGIU5iR6KKkwBDqSnbiV2n4nZs8A/9O3A+gw+O5eLxalg4sZ6bptQG5+pcTzTlw4oiYTF58fqkqEI8iZ4cK8I7gxMhUASDguPHy4QwiQGDIbQzUMTTPYlXeEdRYc+pbE6XpeD2ajAbfEwc0RpSnEDXHJSekmMBjHqFT99ytVfnIsI7glgjBIogoRGuSexI9NBOAOGeRIdPgR3Hc7lUnYyESm6avzNsuKobVe2o4on39OG+IsI7ggCiUZsgYRHiJPYksjgR7kn0eH0SWw/nc6k6GVmjcsusapJNHiB86MbXKbfk7MHuybF9QYR3BPFCCBRBQiLESWxJ9NBOAOGeRMbl0bDpQAFl9Ra0GoXbZlcyNre9U1lwaAels0CRJaXH8E5fSPTeJyK8MziJq0DZuXMn99xzD/n5+UiSxNtvvx1xnx07djBnzhwMBgPjx4/n1Vdf7bbmxRdfZMyYMRiNRhYuXMj+/ftjf/KCAUOIk9gyGEI7A1lWPJjcE4dLZuP+EdQ0mzBofdw5v4KRWXagI/k1XOgmkH8ioSKRmCEeEd4RBIirQLHZbMycOZMXX3wxqvWlpaXcddddrFy5kqNHj/Lkk0/y6KOP8t577wXXvPnmm6xdu5b169dz+PBhZs6cyerVq6mtrY3XyxD0I0KcxIdEFycDwWBzT9ocWt7ZN4KGNgMmvZe7FpSTk9YxkbinDrHXE1hjaw6fSBstid77RIR3BjdxTZJds2YNa9asiXr9Sy+9RGFhIT/+8Y8BmDx5Mrt37+anP/0pq1evBuAnP/kJX/ziF3n44YeD+2zcuJFXXnmFp556KvYvQtBvCHESe0RoJ8LzDhL3pKldz+aDBdicWpJNHtbMqyDF4gk+rqidZueEC/H4OsJAsQrvxAoR3hFcT0JV8RQXF7Nq1aou21avXs2TTz4JgNvt5tChQ6xbty74uEajYdWqVRQXF4c8rsvlwuVyBe+3trbG9sSHMYoCbqeEywFuh4Tr2s3jkvB6JHxegj99HvB6/f9WvF2/vpVfbUdnyGWkycJmQJJAq1XRyiBrVbRa/zdDbeDfWhWDUcVoUDEaFQwGFYPB/5jAz2AJ7QwEg8k9qWsxsOVQAU63TFqSmzXzyrEYfV3WBIQHhG9N71Mk2praMWp7N1+nJ+KRHCvCOwPLf/3Xf7Fu3Tq++tWv8vzzz4dc94c//IFvf/vbXL58maKiIp577jnuvPPO4OOqqrJ+/Xp+9atf0dzczE033cQvfvELiop69/tNqI/z6upqcnK6fpjm5OTQ2tqKw+GgqakJn8/X45ozZ0J/4GzYsIHvfOc7cTnnoYqqgsshYW+VsLdJ2Fo12Nsk7Nd+Otol3A4Jt0siEMpWFGiokknJUtDro49vN9S5aW9MJd0KFakK0TrPigqNjTJWqw+9zr9Nq/ULFZNJwWxRSbIoWK67mc0qsty792OwksjiJIBwT0JT2Wji7T0j8SkaCjJs3Dm/AqO+u7gIhG4gQojH51+XlZcC3PgXNRHeGTocOHCAX/7yl8yYMSPsuj179vDggw+yYcMG7r77bl5//XXuu+8+Dh8+zLRp0wD4wQ9+wH//93/z2muvUVhYyLe//W1Wr17NqVOnMBqNUZ9TQgmUeLFu3TrWrl0bvN/a2srIkSMH8IwSg4AIaW2UaGvU0NqoobVBQ1ujhporMkaLGnWcWqtXaaiSQQW3A8bP9KLTq8g6v2iQddeckGs/NRpAgqP7WlFrU8nNUMnL9zJrphNJAkW95rr4JLxef3Kf1+v/t88nYWuXOHvOgMct0dwsk53tBTWwRsJm00B9iJOV/EmC2Tle0lIVUlIUUlJ8pKQoJCUp/nMb5AyGQYBD0T2JJVdqLWw7mkd1kwmDTuHTK6p7FCfQOXQT/v9sisXNtMwzFE0cFY9T7jMivDOwtLe385nPfIZf/epXfO973wu79mc/+xl33HEHX//61wH47ne/y9atW3nhhRd46aWXUFWV559/nm9961vce++9APzud78jJyeHt99+m0996lNRn1dCCZTc3Fxqarqq3pqaGqxWKyaTCVmWkWW5xzW5uaHzFgwGAwaDIS7nPFhwu6C5VqapRkNTrYaWer8g8Ti7f5q1NWtordegN6qMmODFkqJiTlYwW1UsVgVzsoopScVgUjGYVXQGlX2bjHjdEjqjym2fdZCaGdlC3rG5gdpL2eQm6xg71sXy5faoxEFDg4a/b03CmqyQne3ltlU2srJ8uN3gcmlwuSQcDr9IaW/XYLNpsNkD/5ZobJRpa9PQ0CiTntb1PDWyijVZISXVR0a6j4wMH+kZPizm6MXaQDMY8k4C4mSouSexGgp4vjKZnSdyUFQJk95HhtUVUpxAx+ycSBOKLx0+TX4SjM3LuKHzE+GdxOf6VIZw18HHH3+cu+66i1WrVkUUKMXFxV2+8IM/FSNQpVtaWkp1dXWXdI2UlBQWLlxIcXHx4BUoixcvZtOmTV22bd26lcWLFwOg1+uZO3cu27Zt47777gNAURS2bdvGE0880d+nm7A47dBY7Rcj1ZdlbK0a2ptCXPklsFhVktMVrOkKXg+c2qcnZ7SXBXe4mDTf0/N+nTi+W8/lk1okDSy9zxm1ODn7UTZpspGxY91Ri5Oyci3bt1vweiRSUn2svt1GcrL/+QwGMBhCP3drq4bt2814PRJmk0rBCA8F+V5aWjS0tMq0tmrwef2OTHOzzJXLHfsajAoZGT5SU31kZ/nIzPRhtSoJK1oS3T2BgREng8E9OXklhT2nswEYl9eGql7LyYoidBNNd9hE630SDwZzeEe6ZERjNvV9f7v/M/D6KMH69et55plnuq1/4403OHz4MAcOHIjq+KFSMaqrq4OPB7aFWhMtcRUo7e3tXLhwIXi/tLSUo0ePkp6ezqhRo1i3bh0VFRX87ne/A+Cxxx7jhRde4Bvf+AaPPPII27dv56233mLjxo3BY6xdu5aHHnqIefPmsWDBAp5//nlsNluwqme4oarQ2ihRXyFTV+6/tTX6P6y8Xqi9KmMwQ1q2j6RUldRsH+k5CqlZCsnp/lsgsbS9WWLLa2aMJpWxMzxMnBdZnFwq0VKyWw/Agjuc5I3xRdij7+Lk9Gk9e4rNoEJevodbb7FjMESX63Lxoo6PPjLj8UiYLQo3L7MzapS3yxpVhfZ2iZZWmeYmmYYG/625RYPTqeH8eZmmJpmsLB8GvYrBqJCT7SM720tOjpfMTN+AJ+mK0E5kEtU9UVU4cjGdQxf87sbU0c3MGdfAxapkILoOseHWlBTHpnNsrBHhnfhQVlaG1WoN3u/JPSkrK+OrX/0qW7du7VVuSH8R14/TgwcPsnLlyuD9gC300EMP8eqrr1JVVcXVqx0DqQoLC9m4cSNf+9rX+NnPfsaIESP49a9/HSwxBvjkJz9JXV0dTz/9NNXV1cyaNYstW7Z0U2tDFX8iqobaMpn6cpm6Chm3o/vX+OR0hcYqmeQ0hexRPlY/5MCcFPrDy+OGnX824XZIpOcpzLvdFdEdqLkqs3+z/496ymI342Z4w+9A38SJosCBA0ZKSvzPVVTk5qab7FElunq9ULzXxLmz/v+cOTleVqy0kWTp/l5IEiQnqyQnexlR0PFa2tokPthhpvSSHrNZQVX9eTQup4arVzVcverP0NXIKhkZPnJyvOTmeMnN9dKfkcXBENoJINyTrqgq7DubyYnLaQDMHd/A7HGN2F3+P3IJFY0URqB0Kh8ORyzck3j0PhHhndhjtVq7CJSeOHToELW1tcyZMye4zefzsXPnTl544QVcLhfydR+0oVIxAmkWgZ81NTXk5eV1WTNr1qxevYa4CpQVK1agqqH/U/XUJXbFihUcOXIk7HGfeOKJYRXSaWuSqLiopfaqTM1VuVveiKyFjHwfmQU+sgr8Py+f1HHofQ3GJFj16fDiRFVh/xYjzbUaDGaVZf/giOgEtDRI7PyzEcUHoyZ5mXmzO+Lr2LG5gbr9I0mTpajFiccDH35o4coVvwiYO9fBzJmRxRNAU5OG7R9YaG6SQYJZM53Mnu3sVRLslStadn9kxunQkGxVmD3LyYwZ/pL1hgaZmlottTX+nw67hrpaLXW1WkpOgKRRycr0UVDgpaDAQ2amL27VQ4OhpBiGtnvSVxQFdp3M4VyF/2KyeFId08Y0Ax3OiCyHz4EKhHjCOSjDhcEc3ulvbr31Vk6cONFl28MPP8ykSZP4j//4j27iBPypGNu2bQu2/4CuqRiFhYXk5uaybdu2oCBpbW1l3759fPnLX+7V+SVUDorAj9sJ1Ve0VJfKVF+WaW/2J7b6vBIpWT4sySrZo3xkjfDf0nKULhe+1kaJozv8X91nr3SRnBb+Q+vMAR1XTnXkkFis4dc77RIf/sGExymRWeBj0d3OsB+ex4+X0Vhhpm7/SFQlenFit0tsfd9CfZ0Wjaxy8zI748ZFDjupKpw7p6d4rwmfV8JkVlix3E5+fmSHJ4DLBXv3mblw3h++Sk3zsfxmO5mZHSGs7Gwf2dk+mOZ/zrY2DbW1MtU1WqqrtLS0+IVL6WUdTqeZ3FwfeXleRhR4KCjwYrXeeC+KziS6OAkwVN2TvoR3vD6JD47ncrkmCY2kcvO0GooK2oKP+wLdYcP0NoGOJNlQPVBiFd6JdXJsvCYXD/fwTrQkJycHS4MDWCwWMjIygts/97nPUVBQwIYNGwD46le/yvLly/nxj3/MXXfdxRtvvMHBgwd5+eWXAZAkiSeffJLvfe97FBUVBcuM8/Pzg7mj0SIESoJgb5coP6el/LyWmisyaqdrl8ftb3pmNKvc9DEnY6d7Q17cFQWK3zXi80LuGB9Fs8Nf0KuvyBz5wC9m5tziImdU+BwSrxd2/slIe7OGpFSFmz/uDOu29FWcNDb6K3Vs7RoMRoVVt9rIzY2c3+J2w0cfmbl0yS8sCgo8LF9ux2SK/ptlRYWWXbvM/lJlCaZPdzJ3jjOs+yFJYLUqWK0K48f73/PLl7Xs+NCMw6HD6ZRobVXxuHVcveYGpab6GD3Gw+hRfnelrwm3gyW0I9yTrri9Eu8fyaeiweyfSDyzijE5ti5rArNztHJ4MeuLYt1wSI4VxJ6rV6+i6fSBvWTJEl5//XW+9a1v8c1vfpOioiLefvvtLkLnG9/4BjabjX/+53+mubmZpUuXsmXLll7nuQiBMoC0NkqUndNSfk5LQ2XXq19yukLeWB+5o72c+EiPTi9TOM3D+JnhXYDT+3U0VMroDCoL7wzvbLS3SOx+2wgqFE7zMGFueDGjqrD3XSP1FTI6o8ry+x0YzaEv/H0VJxUVWrZtt+BxS1hT/JU60bgN9fUyH3xgprVVRtKozJ3jD8dEe+H3eODAAROnT/sFm9Xq4+ab7eTkRBZGnbHZJA4eNHHhgl8k5eZ6mTHdSV6el5oaLRUVOqpr/JVCzUdljh01YrYojB7lYfRoD7m53qhDQYMltBNAuCd+nG4N7x0qoLbFiE5WuH1OJfkZjm7rvL7wzkhwXRgHZTglxx69UCPckxtkx44dYe8D3H///dx///0hjyFJEs8++yzPPvvsDZ2LECj9TEu9hkslWiouaGmt73qlzsj3MXKClxETvFjT/R80l09paaqWkXUwc3n4PI+mWg0ndvkvrnNXucKGarxe2HUtKTYtV2H+HZEv5Md26rl6RotGhpv/0UlKRuzFydmzej7aY0JVJHJyvay61YbRGP7DWVXh5EkDBw4aUXwSliSFlStt5GRHLyyqq2V27fKLG4ApU1zMm+dAp4v6EHi9cOKEgePHjXivtfIvKnIzb54D8zUhl5vrY+ZMFy4XlJfruHJFR1m5DrtNw+nTBk6fNqA3qIwa6WHsODcF+aHdsgCDQZwM5LRiSCz3xOaU+du+kZyrsKLTKnzh9vNkp7p6XOuLYgBg4HGr2Y3F2PMXmFglx8YakRwrCIcQKP2Ao13iymktl0/qqCvXUFcuk5Tq7z2SM9ovSgqKvN0SWb0egrkkUxe7MSeHy+KHvRv9SasFRV4Kp4V2WgJJsU010SfFXjyu5VRxRzlxuFBQX8SJqsLBg0aOH/dbgOPGuVm2LHKljtMpsWu3ORg2GT3GzbKljqjLj71eOHT4WoWQChaLws039y5fRVXh0iUdBw6Y/GEhIDvHy6KFDrKyen6fDAYYN87DuHEevF6orNRy5YqOq2U6nA4NZ8/qOXjQSEaGj0mT3Iwf7yYjo2sYSIR2IpNo7kmLXcfmAwU0tetRVIksqzOkOIGuSbLhKMpvoyi/rdv2WLsnIrwj6E+EQIkTHjeUn/eLkqpSOTivpq1Jg86gklHg454v2tCHCcmd3q/H3iphtqpMWhDePTm5R09TjQa9SWXB6vBuyLlDOi6X+JNib7rXSVJK+A+/qssy+7f4T3TaTW7GTg998e6LOPH54MMPzZSW+gXQ7Nn+aptIjk51tcyOHRZsNg0aWWXhAgeTJ7ujDunU1cns3GmmudmvgoomuFi4wNGr0uC6Opm9+0zU1vj/K1mSFBbMd1BY6Il+TIAWRo3yMmqUl5YWFx/t8YeZ2m0aFAV8PgMnTxpISfUxfpybcePcXDlzERgc7gkMXMdYiO/Mnd7Q2OafSGx3abEYvOSkOjAZIuR8BXJLIpQPhyMR3RMR3hFEgxAoMURVoa5c5uIxLWXndHg7aYqMfB85o72U7DGgkWD5xx1hxYmtVeLU3msX7JWusA5HQ5WGk9fcjfmrXZjClBTXXJU5vL2jwid3dPgPyOZ6Dbv/YkRVYPQUL9OXhhZKx4+X4XVraDrcu7CORgN6vYpGVlm21B5MMo3EiRIjNpsGa4qPW1bayciILqTj88HRY0aOHTOgKhImk8LSpd0bt4XDbpc4cNAUrPLRalVmzHQyfVr431UoGhpkjh0zUHpZDyqkpiiMH+8mP99DW5vM1as6WpplDh0yceiQCVkdxfK5El6fE20CDz4cyu5Jbxqz1TYb2XIoH5dHJj3Zxbyiev5+uCBybkkvOsTGG9H7RNDfCIESA5x2KC3RcfGYjtaGjqtxUppC4VQvY6Z6SE5T+egdIxoJCsZ7ycwP/43o2E4DPg9kjvAxalLoC6fXC8UbOwTE6DBrba3+pFhVgTFTvRE7xQbLiV0SWSN8LAqTdHv8eBkAy7OzqbzVxsWLOm66yRFVzxFJgsWLHUya5O5SxhuJZUvtHEkyMm9u9LkiLS0aPvjAQkOD/6peONbNksWOiHkuAbxeOFFi4PixjjyT8UVu5s11YOmh+Vs4VNXvAh07bqSivOMFjBzpYcYMZ5eqJbcbLl/Rc+GCjnOn3VjNqXywD/YctTJxjIPJ4+ykJPcumbe/GO7uSUW9ib8fycfr05CT6mD13EoaWv1fEiLllgQ7xEYQMj1RUnwuZpU7AsFAIATKDdBYreHsIR1XTutQrukCWQejJ3sYN8NDZkHHrJamWg1XTvnf7hkRmprVV2q4XOJfO/fW8OGa4zv1tNZrMCapzLvNGXKd1wu7/mLCZZdIy1FYcEfkEIreqJI/zkv1ZZmbP+5ADvHXEhAnN6X6Owjm53t7lcMBIMv0SpwAGI0qixd1r3wIh0YDLa3+0uUlix2MHRudW6OqUFqqY/8BE7b2a3km2V4WLQqdZxLuWFevajl23Ehd7bU3VYKxY93MnOEkPb27eNXrYUKRG3fzGbIXaNFLozh90YzNruH4WTPHz5opyHEzZbydUfku5ASYyDzU3ZNoKK1O4oPjufgUiRGZNlbNqkKnVTuckQihm2jLjOPJYOh9IsI7QxMhUHqJovhzS84d0lF+TktjjYakFJWRk7yMn+Vh9GQP+h5yGI7v9IcCRk/xkpYd+sNGVeHQ+/4DFE73kJEXem3NVZkzB/zHXXiHE0OY+VIlu/U0VvlzVJb9owNtFI6DRgPzbnPhdoEhRDjqenGS6CQnK9x6i430dF+wsiYSjY0a9uwxUxPIM7EozJ/vFze96V3i8/mTaY8fNwbzXmRZZcIEN9OmuSKWUgeSYhdNzQBszJpso6zKwOmLJsqqDJRVGzh9yURKko9Zk21MGW/HYhq4CxsMbfckUnjnbLmVXSXZqEiMzW1jxYyaYEjHG8XsHOgU4umlgyKSYwVDASFQosTlgIvHdJw77E9cBbC3SeiNKoUz3Kx6MLQjUV+poeKCPyl1+tLQGfvgLytuqJTR6sOXFXvcsHeTAVQYO8NDwfjw3+InL3LTXK9h4jxPxKTYzkjS0BEnAUaM6J27oygSNbVaf57JDCfTp/cuz8Tr9ZdPnygxBt0XnV5lymQXU6e6etVErnNSrEaC0fkuksw+ZI3KsbMWWtu1uFwajpyycOyMhaIxDmZMtJFm7d/wz0CWFSeCe3K8NJV9Z7MAmDiihaVTa9F0+nzwRej82m1dH3JQhktyrGDoIgRKBNqaJU7v1VNaosN37bpmMKuMmerhzH49kgSzloevHDn24TVHZJon2N+kJzzujrLiKYvcYefnHPnAgK1Zg9mqMufW8KIH/CJj+ccjh3WiZbCKk76Qmelj6VI7BQWeHocMhsLlkjh1Ws/JkwZcTr8wMZkUpk51MWmSq1fVQteXFPsUuFJh4OQFM1W1fhfNavExOs9FRpqHNpuWmnodZy+ZOHvJxKh8FzMm2sjL6p3r0xcGumMsDJx7oqpw8HwGRy+lAzCjsIkFE+q7vedRd4hVeh/iSXT3RIR3BNEiBEoIWuo1nNyr58opbbDtfFqOwsR5bkZN9nLpuA5JgpRMJWzCa/VlmZorMhrZX6IbjtP79TjaJCwp4cuKqy/LXDjij9EsusvZY0ipJ4Q46TsTJ0QehtiZU6f0HDhowuvxv+nJVh/Tp7koKnL3usqnc7dYu1PDmUumYP4J+H+vY0Y4mTre3kWAVNfrOH7GwpVKA1ev3bLSPcyYZKNwhKvLN/pYM5ChnYFCVeGD4zmcKE1H1igsnVbLrLFNPa71RRm6CQ4B7GWIRyTHCoYCQqBcR2ONv2S37Kw22Lskb6yPKYvcZI/0N8pSVbhw1C8Qxs8K/Y1UVf3dVwHGzw4fWrG1+p0agFkRyooz8n2Mn+1BIxOxTDjWDEdx0hcMBhWvRyIt3cfMGU4KCz29mqLcGVWF/LQRbN9r5lKZEeWaHjYaFSaP9VfwJJm7i+TcTA+5S5tpbpU5cc7Cucsm6hp1bNuTivVansqEMdFVWkXLQLsnJbvPxNU9CVVa7FNg54lcSi6nUt9moDCnLaQ4gehzUCaNbCE/w06qJTqBHEv3ZDCEd8Tk4qGNECjXqKvQcHyXnprLHW/JiAlepi52d0tUbajS0FyrQdbCmKmhq0AqLso0VPrb1E9dFP4D5ugOAz4vZI30MWpi+BwJnR4WrHah9nNrBCFOoqew0IPB2E5BvrfPzpXXC9s3N1BWOQZJtQa352R6mDreTuGI8AMMA6RafSyb18q8aW2cvGDh5AUzre0yOw9YOXrawtyp7Ywb7YyZozLc3BOvT2Lb0Tyu1lkAyEh2kpMauqIOohvuB5BpdZFpjRzC7Uws3ZNED++AmFw8lBn2AqW+UsPxnQYqLspUX9ZiMKnMXuli2k3ukNU2Afdk5CRvyMoZVYXjO/2xl4lz3WGbp9VVXCtBliKXFXcm3rkEnRHipHdoNDCioHfJuAHa2iROnzFwoNiDx5uL1WxAlmHcKAdTx9vJSu/bcU1GlXnT2pk5ycapCyaOnrZQ06Djj+9lMjrfycKZ7Ywd2fc8peHgnlyPy6Ph74fzqW4yodUozB7XzJny1CiG+/WtOqe/iMfcHYGgtwxrgbLnb0aqS82AvymZOVlh5EQPy/4h9LcftwuungmEd0K7IldOa2mu1aAzqkxeGHqdqsLhbX4hM3a6h/TcgS0L7QkhTvqHykotJ08ZuHpVByp4vD7ys7RMGd/OpEI7xijnC4XD64OKGj2NLTp8ioTbI2F3aqiq17OtOIUjpyzMndbOmILohXJnhrp70jm843DLbD5YQEOrAb1W4fY5FTS1R9mALcopxb0h1o3ZYumeiPCOoC8Ma4FScUGLrPVX1zRUa2itk5kwJ/y30yun/S3srZkKWQU9iwlFgRO7/fkkkxd4wvYnkSSYf7uLYzsNzIzQwG0gEOKk/zh9Wh8ceqiT6pg5pYlbF1huOPTi8UqUVekpLTdytdKAx9txwNxMD/OmtQNwpdJIY4uWrR+lkpHmZeGMNkbkRvc3OdDTiuPtnlxPu0PLpoMFtNj0mPQ+7phXQabVRV2LvyY/UujG24fqnMGOCO8IesuwFigjJniZs9KOrFN55xcWkGDU5PACJZgcOzN0cmxpiZa2Rv+k4InzIn/Ap+cqrHygdx1R+wMhTvqXqVNdmC0qGtd5LGb3DQ0CdLklrlYZKC03UlZlwNcpl9piVigc4aRwhJOcTE9QALncbZw4Z+HEOTMNTVo2fZjGqHwXi2a2kRqmj8pAh3b6g87Jsc02/0TidqeOJKOHNfMrSLX4c9F8UYZuAlU8kZyWaIl1cmyiuyeC4cGwFiiL73JiNMuc3OsXHTmjfGF7jzRUa2iq1qCR/a5LT/i8cGK33+adutiNTh/78+4PhDjpf3JzfeTmOig50Ddx4nRJXK4wUlpuoKLGEKz2AbAm+RgzwknhCBfZ6T2La4Pen6MyrcjG4ZNJnLpo5mqlgfJqA1PG2ZkztT1kmGm4uCf1rQa2HCzA4ZZJsbi5c34FScaOLzWB/iaRGqt5b2DGTigSubQ4Hr1P+pO9LSKcNBAMa4ES4Mopv0AZPSX8XJaLgeTYiaGTYy8c1WFvlTBbVcbPjm7OS6IhxMnAUXLgUq/Eid2h4XKF3ymprNV3qexKtfqCTklGavTVREaDypI5bUwZb2fvsWSuVhooOW/m/BUTc6a2M2W8PTjrZ7i4JwBVjSb+fjgft1dDptXJHfMqMem7OkvRNlYL5qDEIMSTyKXF8USEd4Y+w16gNNf7S4Y1sl94hMLjhsunOnqfhEQCnVFl6pLeN+RKBIQ4GTiu7xYbiSOnLRw8kdRFlGSkev2iZKTzhtvbp1p93LGsmfJqPXuPJdPYrKX4SDKnLphZOLON0fn+8teBdk/6A+u42Ww5mItX0ZCX7uD2OZXotd3FRbBDbJRVPLEK8Qyn5Nj+5qPm6oE+hWHLILyExpbAhOG8sd6QM2fAX5XjdUNyukL2yNAf/BPnehgzxYN2EIZ2hDgZeHrjnqSneFFVyEr3UDjCReEIJynJsW/cNyLXzZqURvYfT+bIqSSq6/VcqTSQYypjUsHA/6HHO7xTZcvm/T2jaLHpGJll4465FSGFRbTD/ZJNHlzJGoy6G/t9DQb3ZLCHdwCmTi3gb/3+rILhLVDUDoEyOmJy7LWOsGE6xwYIV7WTqAhxMrD0NrQDMCLHxYN315FsiW0liKpCu11DdZ2eqjo91fV6mlv9HeHMRh9er4TLLVHnteKtmYv+XAvTx7cGwz79RX+4J5s2NnOmcTI+vYRBpzA+vzWs69Ex3C/872T59NhdZBPVPYknIrwzPBjWAqWxRkN7swZZBwXjQwuUxhoNjVWB5Ni+NclKZIQ4GVh6G9oJIMvERJyoKjS2aKmu11F9TZAE5vx0Ji3F62+fn+Wm4kwpl+py8fpMHDidyvlyC8tmNZCb3r+l8vFyT1QVjl5M5XRjFslpSZgNdmxOHXptlLNzYhS6CUeshwLGmpJj5XEpLe5PRHhnYBnWAiXQcG1EkTdstc3FY9fWTfBiNCdm58e+IsTJwNJ5EGB/4VOgvlFHVb2e6jodNfV6XO6utqBGA5lpnqAgyc10d6ngcVa0c/NNTi6U17P3ZBrNbTr+tjuHaWPbmD+pBW2EC/mNEk/3RFVh35l0Pjrod41mj2vE5dFw6mrkDrG+OFTnhCNW7slgSY4diMnFM2aMxGm39etzCvwMa4FSds7/8seEqd7xuOHySf+68TMHZ1VOKIQ4SQz6S5xcuGrk9EUTtQ36Ln1RALRalZyMgBjxkJ3hQRdCZHRuylY00s6oHCd7T6Zy7moSJRetXK02cfOsRvIyezdDprfEwz1RVNh1IotzZclAG7ctdjB9TDM7S7KByM6INw4dYnsiHu6JSI4VJBrDWqA4bRImi0puYehENVWFyQs91FyVyennycHxRIiTgaevoZ2+0m6Xqar1W4VGg9LhjmS5yUj1RpVD0lNZsUGvsHx2I2Pz7ew6lk6rTce7H+UwdWwb8yc3hxQ6fSVe7onPB9uP5nC52t+0cWrGWaaP8Q9pjLZ8uKM6J/4dYhPdPRHhHcGNMqwFCvgH/oWbCKs3wLQlbqYt6b9zijdCnAw8AxHaGZPvxKBTyM1yk5rs6/NQwFBlxSNznHx8ZRX7TqZx9koSJy8lc7XGxM2zGsiPsZsSa/fE45XYeiiXinoTGo3KaGkfOZZ64JpAibI6xxdlo7YbIdHdk3gxUOEdwcDRz3n3iceYKUMv6TUcQpwkDv0pTsDf12TyOAdp1r6Jk2iashl0KjfPamTN4losJi/NbVr+9EEe2w5mdJkB1Ffi4Z64PBo27cujot6EVquwen41OZb6LoMBg/1NIjVg66cclETuGivCO4JYMawdFJNFJWvE0AnbREKIk8Sgv0M7saQn90RVwe6SaWnT0tSmC948XomWdh1tdi1uj4aGFj0r5zaQlXpjlT6xdE9sTpktB/JobNWj1yncMb+KnDQXTdet80Y5Y6ejiic+IZ5YuyexnrsTYCj0PhEMPMNaoIyY6EWjGR5vgRAnicFAhHZiwYk956/1R5FpatfRfE2EBH66PT2bselWDxmpblwuDS3tOt7ZlcP8yc1MH9fW5xBTrGiza/nDzpG02HSkWtzcs7iC9GRPsLV9Z3xBByW6GTvxDPEMV/ekP8M7HzVXi/BOAjA8rs4hGD3Jw3B4C4Q4SSwGgzhRVCirMtDUqqW5Veb8+YnIpjT2XgkRFZZUrGYvackeUpM91356SU3yVwM53Rp2HU3ncpWZfSfTKK81sWJOPWZj9E5DLIcCNrXp2LQ/j5omAy6PzE3T6khP7qjS6xzegc7D/cKf722zK/H6NFgMsQ8dx8M9iQeDPTlWkDgM/atzGNKy459pnygIcTLwDKbQjgRsL07B45VobbIBZqw6DZKkkpLUSYgk+X+mJHnDugtGvcKq+fWcuWKhuCSdijojf96Rxy1z68nPim858vXUNBl472AuLreMUaeQavGQbAovKKKdUlyQ4YjZefZErN2TwZIcKxieDGuBwgBbzP3B8eNlQpwkAIMttCNJMLrAhapCm76KGdOSSU1uwmqJrhw51DEnj7GRm+Fi28FMmlr1bCzOZu7EFmZPaA0b8omVe1JRb+Lvh3LwejVkpzrJSYOaJmNQXPUU3gHwRZmDEi8Gg3siwjuCWDPsq3iGMoHQjiAxGCziJMAti1rIVg9y7+1aCvMdpCX3XZx0Ji3Zy33Lapgwqh1UiUNnUtm8NwunK74fR6XVZrYcyMXr1VCQ6eDOhVXBxzpX3Vwf3oHoc1DiyWBwT0R4RxBL+kWgvPjii4wZMwaj0cjChQvZv39/yLUrVqxAkqRut7vuuiu45vOf/3y3x++4447+eCmDBpF3kjgMptBOf6HVqiyf3cjy2Q3IskpFrYm/7MyloUXXbW0sSovPlSfx/uFcFEViTK6N1fOq0GnVTkmtERqwRVlmHA9Kis/FVJwMJvdEhHeGN3EXKG+++SZr165l/fr1HD58mJkzZ7J69Wpqa2t7XP/nP/+Zqqqq4K2kpARZlrn//vu7rLvjjju6rPv9738f75cyaBDiJHEYbKGdznRuaR8vJoyycd/N1VgtHtrtWt7ZlcOlCnO3dTcS3ikptfLhsWxQYcKINm6dXRNszhh0RjRqyPCOqnbqENvPIZ54DQQcTO6JCO8MX+IuUH7yk5/wxS9+kYcffpgpU6bw0ksvYTabeeWVV3pcn56eTm5ubvC2detWzGZzN4FiMBi6rEtLS4v3SxkUCHGSeAxWcRIvfD5os8tUN+q5VGGmos5IfpYTj1eios7IHz/I5cDpFBT1xtwTVYWD59IoPpUJwPTCZm6eUYem06deoG9JIPm1x/CO0pEcE8/y4VAkclmxYPCzYcMG5s+fT3JyMtnZ2dx3332cPXs24n5/+MMfmDRpEkajkenTp7Np06Yuj6uqytNPP01eXh4mk4lVq1Zx/nzvPlfimiTrdrs5dOgQ69atC27TaDSsWrWK4uLiqI7xm9/8hk996lNYLJYu23fs2EF2djZpaWnccsstfO973yMjI6PHY7hcLlyujkqB1tbWPryawYMQJ4nBYA/t9NY9UVVwuTXYnLL/5pCxO7XYA/edMnaHjNPd82wJrayi1ym4vRqOnkuhoUVPju88sxeP7vW5qyoUn8rg5OUUAOZNbGTWuOZuibgdM3ZCC49AeAdAjlBmHEvi4Z7EozFbPMM7/d3afjjy4Ycf8vjjjzN//ny8Xi/f/OY3uf322zl16lS3626APXv28OCDD7JhwwbuvvtuXn/9de677z4OHz7MtGnTAPjBD37Af//3f/Paa69RWFjIt7/9bVavXs2pU6cwGo1RnVtcBUp9fT0+n4+cnK7fIHNycjhzJvI3o/3791NSUsJvfvObLtvvuOMO/vEf/5HCwkIuXrzIN7/5TdasWUNxcTFyD4N1NmzYwHe+850bezGDAFGxkzgM9tBOb/nLhzk0tupRlOhK42SNitnow2z0YTF5/T+v3RrbdJy4YKWsxsTl9hkUtttJTYp+krhPgZ3Hs7hQkQzAkqn1TB3T85eSgPg4f/Aslu7pL9eO51+jkdSYJAn3hsHingyF5NjhGt7ZsmVLl/uvvvoq2dnZHDp0iJtvvrnHfX72s59xxx138PWvfx2A7373u2zdupUXXniBl156CVVVef755/nWt77FvffeC8Dvfvc7cnJyePvtt/nUpz4V1bkldJnxb37zG6ZPn86CBQu6bO/84qZPn86MGTMYN24cO3bs4NZbb+12nHXr1rF27drg/dbWVkaOHFp/iKJiJ/EYjOIkQG/dE0WVguLEaPALjYDoMBu9WExdtxn0Ssiy4nHAmFwHb72rQ5+UwV/3pHHbnGryM50Rz8Prk9h2JJv9ZzLw+STWLKgKKU6gQ3zIktJjeCdAXnp8+5tcT7zck5gfUyTHJizXRwoMBgMGgyHifi0tLYA/3SIUxcXFXa6pAKtXr+btt98GoLS0lOrqalatWhV8PCUlhYULF1JcXJwYAiUzMxNZlqmp6frHVlNTQ25u+G/6NpuNN954g2effTbi84wdO5bMzEwuXLjQo0CJ9hczWBF5J4nFYA7t9DX35Ja59WhlFbPBF3Y6eLRkpblZNPI05SyktsnI5gN53DyjjqKC9pD7uD0Sfz+US1WDCVWF9GQ3o3LsIdcrKkFRpZFCz+SyGH3cvaD/B+DFwz0RybGJT3K5F6MxesfwenROf9PB67+Er1+/nmeeeSbsvoqi8OSTT3LTTTcFQzU9UV1d3WNkpLq6Ovh4YFuoNdEQV4Gi1+uZO3cu27Zt47777gP8b8C2bdt44oknwu77hz/8AZfLxWc/+9mIz1NeXk5DQwN5eXmxOO1BhRAnicVgDu0E6EvlTlpybFu7l+w+g0ELd82tYsexbEqrLOw4mk2bXcvs8d1zSZxuDZsP5FHfbECnVcjPdKAqUtiy4EAFT2tjG5qkxOkqPVjck6HEUAzvlJWVYbVag/ej+ZL++OOPU1JSwu7du+N5alET94jq2rVr+dWvfsVrr73G6dOn+fKXv4zNZuPhhx8G4HOf+1yXJNoAv/nNb7jvvvu6Jb62t7fz9a9/nb1793L58mW2bdvGvffey/jx41m9enW8X05CIsRJYjFYxUk8K3f6wrQFY9HKKrfOrmH62GYADp1LZ+eJLJROeqLdIfNOcT71zQaMeh93LapEr/UvCFcW3Dn5deaS8XF5Db0lIE4Gg3siwjuJjdVq7XKLJFCeeOIJ3n33XT744ANGjAj/JSU3NzdsZCTwsy/Rk87EXaB88pOf5Ec/+hFPP/00s2bN4ujRo2zZsiVo/Vy9epWqqqou+5w9e5bdu3fzhS98odvxZFnm+PHjfOxjH2PChAl84QtfYO7cuezatWtIh3F6QiTFJhaDObQTIN59T6Lh+tJiSYJFkxu5aVo9SHCuLJn3D+fg9Um02LS8U1xAS7sei8nLPYsryUpxR9X5NdC+XiOFzocZCGItTuLpnojwzuBHVVWeeOIJ/vKXv7B9+3YKCwsj7rN48WK2bdvWZdvWrVtZvHgxAIWFheTm5nZZ09rayr59+4JroqFfkmSfeOKJkCGdHTt2dNs2ceJEVLXnDxaTycR7770Xy9MblIik2MRisId2EtE9uZ4po1sxG71sP5LDlRoLf95VgN0l4/HKWC0e7lxQRbLZ26WxWrjZOQEHRSMlRngnXk3ZYPC4JwPBUAzv9IbHH3+c119/nb/+9a8kJycHc0RSUlIwmUyAP9JRUFDAhg0bAPjqV7/K8uXL+fGPf8xdd93FG2+8wcGDB3n55ZcBkCSJJ598ku9973sUFRUFy4zz8/OD6R7RkNBVPIKeEXkniclgFScBBsI98Sl+oeD1afD6JE7tvYRPTaKy3ujfpkhdHvf6JEZk2jl8Po2LlUkY9V6mjG7lnsWVmAx+oaEowDVdEq6xmtcn0drYhmEA2teHYri7J6L3Sf/zi1/8AvCPmenMb3/7Wz7/+c8D/kiHplOHwyVLlvD666/zrW99i29+85sUFRXx9ttvd0ms/cY3voHNZuOf//mfaW5uZunSpWzZsiXqHiggBMqgRYiTxGGwh3b60z0pqzGy82hGUGxc3zeltdGKNdXM6X2hj+Fwy7Q5tHgVCQUJr6K5Fq7xC42AewLhZ+cESowzc5OBhj6/plgwmNwTwdAiVLSiMz1FOu6///5uHd47I0kSzz77bFSVuKEQAmWQIfJOEovBHtoJ0F/uiQrYnT3UIUuqv5Os7CHJ5EUrK2i1KlqNglZWr90Umtt1XKhMJtXiIcViQ6dTsTm0/K04n7sXVZJs9nYkv0qgCZNbEshT6c/usD0Rr8TYeLknQyk5driHdxIdIVAGESLvJDEZzOKkv3NPctJd/MOKqqDo0F0THhoNnPzoDKSEHgx4+moyh8+nIWtUphc2c8vsWhwumY378mm16fjb3nzuWlgZXK+Vwye/BubwhEuk7S/i1TE2Xu6JSI4V9Af93LhZ0FdE3kniMdhDOwH6M/fEoFPJTPGQmuQlyeTvKCvLBIVEKHFy9GIqu05k0dSux+PVsGRqPbIGkkw+7l5USUqSG5tDy7t782lq9/esjzR5uP70UebnHuGmKT1PVu8P4hnaiQdDKTlWkPgIgTKIEOIk8Rjs7kkilBVD6KnFqgr7zqRz4Ew6qgopZg9pSW702g7xYTH6uHtRFWnJbuxOLe8fysXjlSJOHtbJXpatzCUrxRV2XbyIZ8+TeAwFDBCv5Nj+RoR3Eh8hUAYBIrSTeJQcuDSoxUkicr17oqiwuyST4xdTAZhb1ERqkgdJ6l6dYzb4uGthJelWFw63TG2zsUszt0RlsAwD7A9EeEdwPUKgJDgitJN4DIXQTiK5Jz3hU2D7kWzOXLWCBMum1zFplH/4mUaj9pj8ajIo3LmgiiSTF68iUV5nxtZTQm4CEM/QTjyTY4fC1GLB4EEIlEGAECeJh3BPYkfJ7jNd3BOvT+LvB3MprUpCo/G3up80qi2qpFaTQWHRJP/gQo9Pw8Z9eThcifkxF0/3ZDCVFg9E7xMR3hkcJOb/XAEgQjuJyFAI7SSye+LyaNi0L4+rtWYkSeGW2TWMzrahqh19S8J1hwXQ61RyUp0YtD5a2vVs2p+Hy9P1o+7ErlNxew2RKCk+FzdxMthKiwWCcIgy4wRFhHYSj6EQ2kk0OifH2l0ym/fn0thqwO3VABreP9Tx9+/2aqhuMqLXKvx/W0cHQz2yRr32bxVZo9Js09PQ6p/N09Suo67FgMen4R+XlndJrp22eEJ/vlSgf6p2RHJseD5qru735xT0DeGgJDBCnCQewj2JPdMWjKXN7m+21thqwGTwsWBiI0Zd1yxXRfFX9SgKON0ydqeWdoeWFpuOpjY9Da0GapuNNLTqcXpkHC4tFqMXl1tDXbOB9w/l4hvAxNl4Vu3A4HVPBiI5VoR3BgfCQUlARGgn8RDuSfxoatOxaX8edqeWJJOXOxdWkmLxMn9SI4oioSigqBJXasxsP5JDWrKb1fOq8SkSPkVCVf1rfIqET5U4X57Eycsp5Gc4mD62heZ2PScvW6moN7HzeBYrZtYN2GuNd9XOYHJPBIJICIGSoAj3JHEYKu3sE809Kdl9hpwJk/nTrnyu1FgwG7x8+tYrWIw+wN+mXiOrIAOo6LT+7rNmo4+0ZE/I4yqKf7hgQaaDyaPaABiRaee9g7lcqEjGYvRh6ofX15l4h3biORQwXojwjiASQqAkGGLWTmIy2MVJItJot3JgXx5Oj4xeqzAiyx4UJz0RSJKN1CF2bJ6NsXm2LttGZju4eUYdHx7L5tjFVPIpYFqI/WNNvEM7AeLhnsS7tFiEdwThEDkoCYQI7SQeQyW0k2juyfYtNRyqnILXqyErxUV2qhO9LrzwCAwBDDehOBwTRrQzb2IjAGcbx3O5xtKn4/SG/hAng9E9EQiiQQiUBEO4J4mHcE9iy/kyM0erJ5GUksToHBuLJtej0UQe2ucLCpS+D/ebNa4Zq+ccKvDB8VzqWw3Bx0p1F/lp5nOU6i72+fg90R/dYuPlnsSLgep9IhhcCIGSIAj3JPEQ7knsKbmUxI7DmaiqRNGINlbNqUElELoJ74x4Ff/HVaQ+KOGQJJiUfp7JRf48la2H87C7/N1md1k+4Kq+lF2WHX0+fmfi2e8k+Bxxdk+GWnKsCO8MLoRASSCEe5I4DJXE2ERBVeHwWSvFJ9JpbWxndGoVy2fUodF0OCORhvt5r1unqlBeb6K5XRd8LBo0ksotM6tJsbhpd+rYeiSfFtXGMdNhAI6ZDtGuae/LywzSn1OKB6N7IhBEg0iSTQCEe5KYDAVxkgjuiarC3pI0Si4lAzA+4yofW61HuqYpAi3sIzkj1+egON0aNu/LCz5uMvhIMnlJMnlINnlJNntJMnpJMntIMnm7NGkz6BRWz63kr8UjqW028lbrCdT8a8IHlYOmvaywrerT6+2vpNjB7J6I8I4gGoRASRCEe5I4DJXQTiKgKLDzaDrny5IAWDy9ESrLkaSOC1SwOifaHJRrQsbt1ZCa5KbdqcXr1eBwyThcMnXNhh73N+h9JBm9NNmauVreisngI2dyHaeuplJV8B4qHQJlt2UH49xdO80m+5JJVdLCnmN/iZMAg2nmzkAjwjuDDyFQBhhRVpyYCPfkxvH6JLYfyuBKlRlJUlk+uwHXlcNdBgMG1kFkgRLMQbm2LsXi5f7l5aiqf4ZPu0NLm11H+7UOs232az8dOtweDS63jMstc/62VzgxotMsnnmACgSiRBI0yPX8JOv7XZ5/rKuIf2n4t5Dn15/iJJ7uyVBLjhUMXoRAEQg6IdyT2OD2SryzM4fLVWb0OoWPLathTJ6Dkivd1wZzSyIkyYaq4pEkMOoVjHo3mSnuns/HI9Hm0GFzaOHSDE7nnUfReLqIkq4H7fRvFbRoWWS/Kez5Qf85JxBf92QoJceK8M7gRQiUAUS4J4nFUEqMPbHn/IA9t9OtYXNxFuW1JlptWqaObWNMniPk+mhDPAsmNTK9sAWz0dvrc9LrVDJ0bjKsbr6Ys4qd2wzsWrmZem0dqhT6eSVVIsubzcNNXyLXmx9yXX9U7ASfaxC7JwOFCO8MTkQVj6DfaGnRcO68HrXvVaJ4Qnc4jwlDQZwEGIjwjs0h87fdOdQ3G9BqFbLTXcG29CW7z3QL70BHkmwkgZJk8pKV6grbbTZa0toy+Lf6/8dMx9yw62Y55vJv9f8vYcRJgMHqnojwjqA3CIEyQAw398Tngx0fmtm108zx4z0nMUaiqUnDH/5g5cIFXYzPbmiFdgbKPWmxaXlndw7NbTrMRi/zJjdj0KkRy4cDDkqkdbHGoBoY7y6CUE+rwjj3BPSqPuQx+luclOy/FDdxEu+pxQOBCO8MboRAEUSNwyFx/IQBb+8ddo4cNVJfp0VvUBk/vuc8gXC0tGjYvDkJh0PDqVMGlL51Ow/r3gj3pO80tOj4264c2u1arBYPH1tWg1Hvf7NljUrJ7jMh9w0myUbIQYkHZbqraAIfg4G/jeBPmVJN6BYAA+GcxJt4uScivCPoC0KgDACD1T05dszIgf0mtm/v3QyTqiqZY8eMACxdasdi6d035ba2DnGSlu7j9tttaPrwl1teoeXdd5NwubpuF+7JjVHTqOfdj3JwuGTSrW7uWVpDstnXLam1p/AOdG/AFm/yqi7wjYtvkld9kSv6SyiSgkbVoEXL8vZb0aIFRQMaH2e4gnLdaZUUnxsQcRJP96Q/EOEdQW8RAkUQFa2tGk6f8VvdU6e6IqzuwOWCD3daQIWiCS4Kx/QuiaTdJrFpcxI2m4aUVB9r7mjHaOzdhUxVoeSkgffeS6K2Vsvx48Zua4R70jfKa41s2pON26MhJ93F3UtrMBv9Tki05cPRTinuC212LQ2tenydzJmZJduZYK9g1M53qNH6v9lnerP4t7pvcl/r/fxb3TfJ8GYBYEsq59ClpOC+/d3npL+I59TigXJPPmquFu7JIEdU8fQzg7Vr7KFDRhSfREGBh4KC6GM8e4rN2No1WK0+Fi8KXcnRE3a7xJbNSbS3+fe/c007JlPvLmI+H+wpNnHurD/vpWiCizlznMHHSw5cGjLipL/dk0uVJj44lImiSIzIdrBqfj26Tt1aAwKl6mIlRRmhj+Pzde1vEktOX7Vy7GIqskYlzeqm0FTHVy4eps6ax7yW84yrTiPdOoGPtz4YzDXJ9ebz9YZv8qruT5RKFRy+ksSINC8NZ48DAyNOhHsiGI4IB2UAGMjwTlWVTF2d3KtKmvp6mUuX/B/e8+c7I6zu4OJFHZcu6pE0KsuX29H1IrfV6ZTYsiWJlhYZS5LCHXe0Yzb37gLmcEhs3pLkFycSLFjoYNlSB7J/NtyQCu0E6C/35OwVC9sO+sVJYb6d2xfWdREn0Cl0I/lChnc6r4uHg6KooNMq+BSJ+mYDU87uQVUkHknagKrKPPr/rUbzv5/n/JUsapqNwXMxqAa+5P40qw/8AFuNxF+2mSlaOG3AxElcjz8Ek2MFQwPhoPQjieCe7D9gor5Oy9JldiZOiC5Z9cBBf0hk3Dg3GRnRlXi2tUl8tMcMwOxZTrKzoy8Ndblgy5YkmppkzGaFO9e0k5zcu4tXY6OGre/73RedXmXlShsjR3R3fnrjnqiqv+GXQd+/1SbR0J/uydHzyew/lYYETBrdzk0zG9H0MKsvELrRhOkzAt1n7MQKS3sT92Re4e4MsDllWmx6PrPvHT5KW0KlpYD3zUt5wL2TD1vmUbHTRpPVxAXAk2yBjGRsVaWYtafIK1iAzZHMRyeN3Dq7NqbnGC3xdk9EeEeQiAiB0s8MpHtSXy9TX6dFI6uMGhldLkh5hZbKCh0aWWXu3OhCNIrizzvxuCWyc7zMnBl9zorHA+/9PYmGBhmjSeGONe1Yrb27cF25omXHhxa8Hgmr1ceq22ykpXY9Rm/dk/omLbsPWdFpVe5c3hQcdJdIxNs9UVU4cDqFD49k0NKuY0phG0tnNoZ8L4JTiqXwv7/8TAdJJi/mGPQ36czt219hVPnpLtsUScPu1f/EP+Veoe7KYu7YtINN3rX+B+3+H7vqZvKZ6mexps8CL7gcGhpa9TS26SnMtTE2zxbT8wzHUHBPRHhH0FeEQBlGnDnrD9OMGe2JKpdDVeHgARMAkye7o3Yxjh83UFOtRadTWX6zPeqKG68X/r41ibpafznyHavbuwmLSOd7/LiBg4dMoEJ+voeVK+0hk2qjcU9cbomDJUmcumBGVUGnVWltl0lJju3FNNFRVfjoeBqnLyejqpCa7GFcgT2sUPP6JFob2xm3PBtoD7lu0eTG2J8wcHrCYvKqL6JoZD5Y9mka0/NxGcy0WrOQgOYxY3nts9/H4LKT3ljJyl2vIyk+ymYtZmWah/oWG/WtBkCL2eDD7dXwUUkmeekOTIb+K4kW7knvEL1Phg5CoPQTA11a7PHAxYt+gTJpUnShnYsXdTQ0yOj0KrNmRpd7Ulcnc/iIPyS0aLEjavfD64Wt71uortKi0/vFSUZG9BcBrxd27TZz6dprnDzZxcKFHfkmnYnGPVFVOH/FyN5jyTidfoU1bpSTRbPasJj6v19HOOI9FNCnwIeHM7hYYQFJZWy+naY2feThfsEZOwMTEjszaQm12WO4872XuPXD/4/tN3+WM5OWdFnTas1i8pmPWLnz/2ixZrL59sdoTM9nDs3BNQ6XhrpmAzuOZ+N0y+wuyWLVnJq4u2jCPek7IrwzNBBJssOEixf1eD0SKSk+cnMjV+H4fHDosN89mTHdGVVpr8cDH35oRlUkCgvdFEXZkM3ngw8+sFBZoUOrU1l9eztZWdE7FHa7xMZNScGE3CVL7CxZ0rM4CRDOPWlo1vK3D9LZsS8Fp1NDqtXHXSuauHVxS8KJk3jj9Ups3Z/FxQoLkqRyy9wGMlP9v9dE7RDbmcb0fN74xP/j0phZrN7+CmZbc5fHzbZmbt/+Wy4VzuLNj/8/GtO7t7Q3GRRG5Ti4c0EVkkblcrWF0ure9QLqK4PVPREIYkG/CJQXX3yRMWPGYDQaWbhwIfv37w+59tVXX0WSpC43o7Fr3wpVVXn66afJy8vDZDKxatUqzp8fuOFokYhlcqzDIXHqlJ6GhjBX3x44ey28M3GiO6pvfqdPG2hv02A2K0ybFl0Oyb79Jn/VjUVhyRJHVM+jKH5Rc/WqDlmrctsqGzk50YuTujqZv/41mfo6LQajwh2rbUyeHFoYhXNP3B6J4iPJ/PnvGVTX6dBqVRbMaOfjq+spyOl999v+IJ7JsS6PxOa9WZTVmJBlldUL6xhXYO803C+8WKstb8aaao7otMQbr85ARX4RPo2My+AXFjq33xF0GSz4NDIVeRPw6sKPYMhMcTNrXDMAe05m4vLE7+Mz3u5JvBHhHUEsiLtAefPNN1m7di3r16/n8OHDzJw5k9WrV1NbGzob3mq1UlVVFbxdudJ1RvsPfvAD/vu//5uXXnqJffv2YbFYWL16NU5n9CWw/U2swjvV1VqKi83s2mWOep/6epn6en9ybFFR5AutywVHj/k/rOfMcaKNIhB45YqOs2f85bw33xw676Mzqgq7dpkpLdWjkVVuvdVGfn70PVYuXtSxcVMSdruG1FQfH7unPar9r3dPVBUuXDHy1uZMTpzz55oUjnTywJp6Zk22ISe4zxiP8I7DpWHjRzlUNxjR6xTuXFzLyBz//6/rO8SGwqf4RXQ8yod7S3bdFRrS85FUhdu2v8JXfv0Eq7b/FklVaEzLJ7vuSuSDALPGNWO1eHC4ZA6eS4vrOcfTPYlnY7YAIrwjuFHi/tH7k5/8hC9+8Ys8/PDDTJkyhZdeegmz2cwrr7wSch9JksjNzQ3ecnI6LiiqqvL888/zrW99i3vvvZcZM2bwu9/9jsrKSt5+++14v5wBp7bO/6GflRX9hbxzcmw0wuHECSMup79zazSCxm6X2LXbHw6aPs3ZRSSoqr+nSUuLhro6mYoKLaWXdZw7r+Mvbydx9KiBdpvEmDEe2ts0nD2r59x5PZcu6Sgr11JTI9PUpMFul4Lzd1QVDh4ysmOHBZ9XYuRID/fc0xYx36Un96SpVWbjjjS2703B7tCQkuxjzc1N3LakhSRzbMI5za0yuw5aqWmI7ZDDeLkn7Xb/ROKGFj1Gg4+7bqohN6PDRQtMH46UW6Ko8Skf7gvZdVfw6Ix86o//SdGFgxyeeRsTLhzgk3/6T9x6I9l1l6M6jlZWWTatDoBTV1Koa+7b4MtwDPambAM5d0fQe3bu3Mk999xDfn4+kiRFdR3dsWMHc+bMwWAwMH78eF599dVua3oTOQlFXJNk3W43hw4dYt26dcFtGo2GVatWUVxcHHK/9vZ2Ro8ejaIozJkzh+9///tMnToVgNLSUqqrq1m1alVwfUpKCgsXLqS4uJhPfepT3Y7ncrlwdRrA0traGouXFxWxTo6tq/P/yrKi7CvSNTk2cqjGZpMoOen/0J0/zxGxAkdR4P1tFlqaZUxmBVWFbdv93WPtDg0Oh4Ti6znW09auoalJQ2aGj0sX9cEE15BIYDQq2O0a6utltFqVcePcjC9y096uISVFCZt3Ah3uiccrceikhZJzFhQFZBnmTGlnxkRbxGNEg6pCZa2eE+fMXK30v59Ot8RtS1pu/OCdiLV70tyuZdOebGwOLRaTlzuX1JKa1FUMR9PCvmT3GRR1PjBwSbIBZK+HjMZKZMVHfXo+b3ziWzSm53Ny8jJWvPMrRjaexyfJ2FsVzNbI39nyM52ML2jjQkUyu05kct9NFX2aDdUT/RHaGaruiQjv9A2bzcbMmTN55JFH+Md//MeI60tLS7nrrrt47LHH+L//+z+2bdvGo48+Sl5eHqtXrwY6IicvvfQSCxcu5Pnnn2f16tWcPXuW7OzsqM8trgKlvr4en8/XxQEByMnJ4cyZnqebTpw4kVdeeYUZM2bQ0tLCj370I5YsWcLJkycZMWIE1dXVwWNcf8zAY9ezYcMGvvOd78TgFQ0siuIP10D0DkrX5NjIoubwESM+r79/yahRXZ/D54PGRn8n2oZGmaYmmfp6mYoKHT4f5OaqlJR0n3MDoNOr6PUqep2KTq+i06poNOB2S5ivORWqCorid0q8PgmPW8Lj8Zf6ut0SqiLhdPivBKoCBoNKU6OWD7b7/4wljUqKVSEtzUdamo/MLB9ZmT6MRjXY0l5V4VK5gb1Hrdjs/mONLnCxZHYryZYb/6bvU+BSmZETZy3UN107LwlG5buYXmS/4ePHk/pmHZuL/ZUqKUke7lxSS5Kp+99MtDN2LKlWfL7I6+KN1uumLnMUDekF7Fj2YDDXpCEtn88UvMDXKn/BWM8V3vkwF9mqZ2SWnRHZDvLSQidaL5rcwNVaCw2tBk5eSWF6YeyE52B2TwYaEd7p4Pov4gaDAYOhu+O3Zs0a1qxZE/VxX3rpJQoLC/nxj38MwOTJk9m9ezc//elPgwKlc+QksM/GjRt55ZVXeOqpp6J+roQrM168eDGLFy8O3l+yZAmTJ0/ml7/8Jd/97nf7dMx169axdu3a4P3W1lZGjhx8f8hNTTI+r4TeoJKaEt3FNBDemTgpcnJsU7OGc+f8f8AL5jtwuSSqqrVUV2uprZVpbJR7dEPy8z3odJCb4yXZqpCcrJCcpGAyKZjNCiaTesOuRCBU5HBI2B0a2ts0tNs0tLVqaGvT0NIq43ZJNDfLNDfLlJZ27Jts9eFz5uNxGLlaaaC+SYckQbLFx5I5bYzOj76RXChcbokzl0yUnLcEhY8sw8RCO9Mn2GPeNyXWpcVV9Qb++EEeTreGgiwnH1tagzFEr49oqnNUtVOjtgEWKC6jhTc/vg6k7jbH8gUt/LXuUcprjbQ3m1DbJZrb9ZwoTUUrK+RlOBmVbWdklp1kc4dgNxkUFkxqYPeJLA6eS6Mwt71HMdcb+ss9iScivHPjaJttaA19/z+jdfm/CF1/jVu/fj3PPPPMjZwaAMXFxV0iGACrV6/mySefBPoeOemJuAqUzMxMZFmmpqbrH21NTQ25udGFPXQ6HbNnz+bChQsAwf1qamrIy8vrcsxZs2b1eIxQyjHexDq8U1vrv8pnZnqjqpCpq5NpCCTHRlHyu3+/CafD72h89JGZpqbuqsJgVMjK9JGZ6SMt3e9UWJMjh1ZuFEkCk0nFZFJJp/uFU1X9uTBNTX5np6HB7+60tMhUljmxmjPZd0xDdb0OWYaxIx3MmtxOdvqNVee02TSUnLNw5pIJj9f/SzEZFaYW2Zkyzo7xBj5o+our1UbeP5hFq02LqsKM8a0hxQlE39/kgRVX8SkSRl0CNLXrQZxIkr8yJzPFzezx4PJoqKg3UVZrprzehN2ppazWTFmtPyE9xeJh5DWxkpvuZNLINs6XJ1PTZKT4VCa3zb3xi3N/uCcivDM8KCsrw2q1Bu/H6hpYXV3dYwSjtbUVh8NBU1NTryMnoYirQNHr9cydO5dt27Zx3333AaAoCtu2beOJJ56I6hg+n48TJ05w5513AlBYWEhubi7btm0LCpLW1lb27dvHl7/85Xi8jIQhkH+SHWWPkGBy7JjQybFuN1y9quPCRT0HDpjweiEvTw2Kk9Q0H3m5XnJyvGRn+0hKUhKyzbskgcWiYrF4GdFp5o7LJbH3wypSzblU1erx+vzdYF0uDbsOprD7EGRneBiV72LsCGfUTkdtg47jZ82UlhuDgxfTUrxMn2Bn/GgH2jgKtlgmx14oN7PjcAaqKpFs8WLQKZj04d25SFU8JbvPMH3hWCD6RO5EwKBTGJvnb2WvqtDQqqe8zkxZnZmaJgMtNh0tpSmUlKYE3ZXsVCeVDUYuV1uorDeSn9m3SkLhntw4IrzTFavV2kWgDEbiHuJZu3YtDz30EPPmzWPBggU8//zz2Gy2YGzqc5/7HAUFBWzYsAGAZ599lkWLFjF+/Hiam5v54Q9/yJUrV3j00UcBf4XPk08+yfe+9z2KioooLCzk29/+Nvn5+UERNFioqpLRaiEtzRdVKW9dLyp43G6CE4gnTewawvB4/KLkUqmeinJt8IKTk+NFVWHKZBcjRnjJzfVG1RI/kTl//CK3zs8BbDDVhs8HtY06yqsNXK0y0NCkpaZeR029jgPHk8hM8zJ2pJOxI51Yk7qKFUWFq5UGjp+1UF3XUZFTkONmxkQbI3Kj6zETC2IR3jlVmsRHJ9JAlRg/0kZzm5b6ZkPEkEy0OSiDmc7uyqzxzbg8GirrTVytM1Ne18ldwUyLTY/TLbP9aDafufVqn/8GhHsiGAzk5ub2GBWxWq2YTCZkWb7hyEmAuAuUT37yk9TV1fH0009TXV3NrFmz2LJlS9D+uXr1KppOKfBNTU188YtfpLq6mrS0NObOncuePXuYMqVjzPk3vvENbDYb//zP/0xzczNLly5ly5Yt3Rq6DSTRhHd27LBgt2u49942MjPDf3N3uaC5OSBQIn/LDybHpvqTY1XV30Pl/Hk9pZd1eD0dn6IpqT4Kx3gYNcpDZqYvIR2SWCHLkJflIS/Lw/zp7bTbNZRVGSgtN1JRo6e+SUt9UxL7jyeRl+1mwhgHo/JcXCr3J762tvt/BxqNv/X9jIk2MlL7zymIhXuiqnD0vJWDp1MBmFLYxpLpTfx5h//vNXJ/k8TILelPDDqFwjwbhdfclcY2PWV1ZspqTSioVNSZaXPoOF+RxIQRoecO9UR/lBX3R1v7gUJMLu5fFi9ezKZNm7ps27p1azB3NBaRkwD9kiT7xBNPhDyxHTt2dLn/05/+lJ/+9KdhjydJEs8++yzPPvtsrE6x31EUsF+rSDFH0W+jvt7/q0q2+iK6GqoKZ8/6443jxropOWng9Gk9ba0dcQer1cfYsR4KC92kpSVm2OZGCVTuhCPJrDB5nIPJ4xw4XRKl5UYulRmprNVTde3W2OLPzUi2+EiyKEwZZ2fqeDuWGPVJCYeq0u13cyPuiarCvlOpnLjgt35nT2xh7sQWJCk6Z0RR/JVWkdYNZSQJMqxuMqz+zrIuj4bdJZlcqkziwNl0xubZon5v+rNjbDzdk4EO7wj6Tnt7ezDHE/xlxEePHiU9PZ1Ro0axbt06Kioq+N3vfgfAY489xgsvvMA3vvENHnnkEbZv385bb73Fxo0bg8eIFDmJloSr4hkuOJ0SqFzr7RH5wyzYoC2C0wL+UuTqahmbTcORo0bUaxcUnV5l7Fg3RUVusrOGtlPSF4wGNShW2u0azl82cbbUhNOloaFFi16nMjLPTVa6B1McZ/J4vBKVNXrKqvVcrTJw1/KmmFQBKQrsOpbOuatJACya1sT0cW3Bx6NxRrxKxx9NTx1iS3afYdqC4WXzG3QKy2fUUdtkpN2h5URpCrPHN0e9/1BxT0Ry7ODk4MGDrFy5Mng/UPH60EMP8eqrr1JVVcXVq1eDjxcWFrJx40a+9rWv8bOf/YwRI0bw61//OlhiDJEjJ9EiBEociGb2juOae2IyKVE1eaqrjdygLRDG+ftWC1XVWixmFVWRSEv3MWWKi/Hj3FHlugwFonFPwpFkVpg9xcasyTaq6nScumjmcrmRmnodW+tTSbL4mDnJxsTCG0+IVVVobpMpqzJQVmWgqk4f7JoLUFZlICXZfkPhHZ8Pth/K5HKVGSSV5bMamTDK1mVNh4MSWnz5OpWZD6cQTyS0ssqCSQ1sP5LD0YupTBzRhtkYXlQK9yQ2iPDOjbFixQpUNfT/5Z66xK5YsYIjR46EPW64yEm0DJPLVf8TKf/EZvN/0JujSEJV1Q4HJbuHBFlVhbIyLceOGam9JmTy87wUjPAyd46T3NzoypIF3ZEkyM/2kJ/dgs3exqmLZk5dNNNuk/nokJXDJ5OYMdHG5HEO9LroL9huj0Rlrd4vSqr1tNu6qpwki49ReS5G5rrJ7zSosC/hHc+1icQVdUY0GpVb5tZTmO/oti5YnROmfLhzibH4m+rK2Dwbx0td1DcbOHQ+jWXT6yPuI9wTgSA0QqAMEEEHJYo8hrY2DS6nBo2skpHR9VtZVZWWAweNQYdFllUmTHAzfbqL5OSBn4EyEISbWHwjWMwK86e3M3tKO2cvmTh2Jol2u4Z9x5I5eiaJOZPbmVJk73G4oKpCU6uWsiq/KKmu7+qS+JN33YzMdTEyz0VKctcQXF/dE6dbw8Y92dQ369FrFW5fWEdBVvfGdKraacZOGGckUZqv9QcNrXpSLJ6o80kkyd9h9t3ifM6UWZle2EJqkqfHtf05byfelTsDhQjvDH2EQBkg7PZrDkoUAiVQXpyR7gs2RGto0HDwoInycn+5q1arMnmyi2nTXJjNQ//iEYkbCe9EQivD1CIHk8Y5uHDFyNHTSbS0yRQfTebkBTMLZ7YxpsCFxytRURNwSQzBDrMBrEk+RuS6GJnnJj/bjU4b/vfWW/fE5pTZXJxFycVkFFXiU6sqehQn0JF/AuHFh1arMmFUe49N2kp2964JUyKjKLBpXx4+RWJ0jp3CvHZGZtojNiTMS3cyKsfG1RoLRy6ksnJWXbc1/RXa6Q/35OiFmgF1T0R4Z2gjBEqMibZ7rCNYwRNZTHQeEGi3Sxw4YOLCRT2o/vkzkya6mTXLKYQJ8XNPekLWwMRCJ0VjnJwrNXGwJInWdpmtH6Xi9khIEl1EhyxDfnZXlyRetNq0bCrOps2mRZYhK9lFTpiuuZ1zS8I5BkkmH8tnN4Z8fKgkyLY5tMiyitMtc6EiiQsVSei0CmNybYzNtVGQ1bNTBjCnqImrNRYuVCYze3xzjy6KcE8EgsgIgTJABL5Nm6OoBqmtk1FVaGuV+MMfrcEeJoVj3cyb68RqHZ6hnFDE0z3pCY0Ek8Y6GDfKybEzFo6dsWBrkWm1yWSkepk1qZ2iMU7yst19SqjtbXinsdU/9M/ulEm2eJEkFVWVoqvOkVQ0IreEFIuXB1depbbZwKWqJC5VWbA7tZwvT+Z8eTJ6ncLoHBvj8trJz3R0EStZKW5GZtspqzV3c1H6MzE23gxkcqwI7wwPhECJAV4vtDVqUHrxhdgRDPFEaIrlg8pKLfX1Wv9kWC1kZXtZvMgRVcO24UR/uic9odOqzJvWzuSxdnYfsnKxzIhWVrlcaSQjzUtBTt/n/kQb3qlp1LNlbzZuj4Y0q5s1i+t4Y2s+qhreGencA0Ukv/qRJMhJc5GT5mLR5AZqmoxcqrJwqSoJh0vuIlbG5NoYm9dOfoZfrMwtaqKs1syFymTmFDWRYvEGxUl/uCclx8r7xT0R4R1BPBECJQa01Gl47zUzZqvK2KXR7WO3R27S5vXCR3tM1NfLKD6wWBQWLHRSNL7/WqoPNvrbPekJi1lh9bJm6pu07DlipbpOx75jyVy8amL5gpa4dZ4trzWy9UAmXq+G7DQXqxfVodcpHY3VwlTnRJqvM9yRJMhNd5Kb7mTRlAZqGv1ipbTaL1bOlSVzriwZg97HmBwbY/NtjMiyU15n5siFNFbM9Lso/RXaiTcDXVosGB4IgRID9NcardVWO/inKPJPVBUczmuTb0MIlMpKLR99ZKK1VSYv18eYQjdLFjsG/WyceDHQ7klPZKZ5uWdlI+cumyg+mkx9k5a/bM1g9pR2Zk22hcxh6Ey04Z3SShPbD2WiKBIFWU5uW1CHTqsGJyxDlNU5ESYUh2IoJchGQiNBXoaTvAwni6c2UN1o5FJVEqVVFpxumbNlVs6WWQGVhjY9rtIUzG3HMOv75/yGunsiWtsPH4RAiQGGa6JB8Ul4vURshuZySSjXLgim67rIejywb7+Js2f8reotFoUlS+yMGjW4JsMOBIngnlyPJMHEQgcjcl18dMjK5QoDh0qSKC03cuviZtKskcN0kcI7Z69Y2H4oA49XQ0aKm0mj24LJud4ok19jMQBwqCTI9gaNBPkZTvIznCyZUk9Vo+mas2LB5Zbx+SRq6iUuySP41MfFx61A0BvE/5gYoDOAdO3bsNstoY1QLhooMTYYlS5li42NGj74wBIcCjh5sot58xzo++mblyB+WEwKt93UzMUyIx8dttLY7HdTbprTysRCZ8T9FRVsdpkWm45Wm5YWm5aWdh2Xq0xU1BlRVYkkkxejXsu5siTGFvgbsQWcEU2ExmrDYUJxvNFooCDTQUGmg5um1lPZaOLYhRSOnjXQriuk3VFJkim+eWP94Z6I5FhBfyEESgyQpGthnhb/jJ1Iia8d+Sf+daoKZ8/p2bvXhM8rYTYrLF9uJz9fuCbRcKNt7fsLSYLxo5zkZ7v5YG8KFTV6PtyfQlWdnpvmtCHLKja7hpY2LS3tMqeONpCUNptT27S02bXBXBLwj3FqafeLFICUJA9jch2kJHnISe/odeKNcrCfdxg1YOsPNBoYkemg+dJJapOn4lULOHkpmYVTmwf61GKCSI4V9AdCoMSIdnsboMPl0gDhy347TzH2emHXbjOXLvptkoIRHpbfbBe5JkMYs1HhzuVNHD1t4WBJUrCHitmodBESrU0yVrcpeF+jUbFavFjNXmqb9FQ5DZgMXpbPbmTx9KYec1qiFR6+KBJpBb0jULVz6wod7+2D05eTmD2hpVcjEXr1fEPcPREMP4RAiRFag0K6w4jTZYu4NlBiLGvg3XeTaWiQkTQq8+Y6mT7dJSp0ekEiJsdGgyTB7Ck2cjLdbN+bSm2jjuZWLVnpHvKy3KQk+WiTa5k8KRmrxUtKkgeLyYeqws4jGdhdMl6fhmSzl8XTehYnEN18HYAxeQ4+vboi1i9zSNLu0CJJKpYIwwDBX7Wjqk5Skjy0tOs4cyWJGePbIu6XyAxkcqxgeCEESozQ6v0fVi5XZHVht2twuSTOnNVjNqkYjAq33mIjL0/0NekLgyG8E4r8bA8fv72eTTvTqKnXI8sqRWOcyI1HuX3NCKA9uNbrk3j/QCZlNX5XJcPqxmL2hp2GHe3sHK2sopX79vdXsvvMsEqQ3X8mndJqC0UFbcwY2/O8nc4N2SQJpo9rZfexDE5dTmb6uLaYfwkZLu6JCO8ML6IodBREg07vD+u4nJE/ea5e1VJbK+PzQVq6j3s/1ibEyTDGZFT5h1WNzJpkQwIOHE/iZPlofJ3+JNweic3FWZTVmJBllZtnNWIx+SI2VhPJr7FFUfwzjhRF4myZlT/sHMn7h3OoazYE1/TUkK1ohB29TqHNpqW81tjv5x0rxNRiQX8iBEqM0AYEShgHRVXh6FEDjU1aMjJ8FI13c8/dbSQni4tHXxgsybHRoNHAkjlt3DS3FUmCiqZ0NhVn43RpcLg0bNyTQ3WDEZ1WYc2iWnKvJcJGCt10JMmKcQixQKOBexZXcc/iCkbl2ECF0ioLb39UwKZ9eZTXm1DV7g3ZAkMWAU6WJsf0nPqr78lAInqfDE9EiCdGaA3hQzyqCvv2mTh50oBepzJ/npO5c50i30TQhanjHdReLOUyU6lu0PDnD3NRVbA7tRgNPtYsqiUz1UNdkz+pOmLyq6jOiQu56S5y02tobNNx7GIqFyuTqKg3cfqCSophBpZKmTF5ji5zjSaPaafkopWyWiNtdplk8+BxTRMhvCMYfggHJQROO1w5o+XKmcga7vjxMmakpfj3c3V/S30+2PGhmZMn/TbwwkUO5s0T4uRGGKzJsdGQmdzGvctq0GkVzl21cL7Mgl7n456lNWSm+vMdoi8f9v89iuqc+JCe7GHlrDo+uaKMNC4iSwqqJZdtB7L4w7Y8Tl+2BEN1qUleCrKcoEqcvpwUk+fvT/dEJMcK+hshUEJQV67lo7eNnNwTXZc0w7WOsNc7KF4vvL/NwqWLeiSNyvLlNqZNdfV0CEEvGSrhnZ7wqf7mbJLENSEr+ZufBB6PtnxY5KD0C8lmL5OySvnSg63MntiCXqfQatOx+1gGb7xfwLELybg9ElMK/RU8Z68kdeny2xdKjpXH4tQjkgjuiQjvDE+EQAlBRr7/a09znQZPFENojYZrAqVTkqzPB9u3Wygv0yFrVW67zcb48d0z/gWCACf2nKfJZuHd3Tn4fBomjW5n4qh23B4Nf/soh4YWf2M2b5TlwwGnRYR44ksgMdZkUJg3qYVP317BomlNWExe7E6Z/SfT+P3WAqob9Rj0PpxumdJKU4SjRmaouyeC4Y3IQQmBOUnFbFWxt0o0VMnkjg4fLzYYujooPh988IGFsmviZPXtNvLyRGfYWDCUkmOvp7bFyoWmaagK5Gc5uX1hHaoisXlvNvXNet79KIdFU5tos2txeyS8XokWmxZZo6LVqGhkFVmjornmvHhvcAigIDI9Ve3otCrTx7UxpbCNi+UWjp630tKu48SFFNrsMh6vhhMXkykaae/bcw4T90SEd4Y3QqCEITPfx9VWLQ2VmsgCxeivknC6JHw++PBDM1eu6JBlldtWCXEiiMzFq0YOlhbR6jSRkuThC4vqrs11UrlzSQ2b9mRTVW/k91sLSDJ5aG7X09yuo7Gt5zCkrFFxeSQcLpmTpclU1BmRNf6OtNprQmbCqPY+XyQFPYuTzsgamDDKRtFIG1eqTRw7b8XrM9LYqqes1kS7XSapj8myw8U9EeGd4YsI8YQhs8D/wVFfKUdY2RHiUXwSH35oprRUj0ZWufVWGwUFQpwIwnP6oom3N2lRFA0Wo5fRuY4uQycNOpU1i+tISfYgSSptdi2SRkWnVdBqFSSpu0PiUyS0MiSbfWgkaLXpaGrT0dCip6bRQGW9kXaH+I5yo4QSJ52RJH+33o8tq+G+m6spyHJiMihcKLf0+vn6yz0RCAYa8ekUhoxrzdMaKmVUlR6rbo4fLwNAlv1x/oYGmfMX9Oh0KrestDFypBAnsWQohneOnraw/3gSKjamFvmoqvf0mNRq1Cvcd3MN7+7OprzWRLtDZtaEVm5fUA/4S9l9ioRP8SfH+v8t4fNJKIrkT7z1SXg7bUu3ipyovtK5W2y0SBLkZbq4ZW49O49mcL7cwsyi1l5X9PWHe3L0Qs2Auiei94lAOChhSMtV0MjgtEnYWkN/gtyUmosk+ScZt7ZpUHwSS5c6GD1aiBNBaFQV9h1LYv9xf8np2Oxqphb626CHqrqxGH3cuaQWvU7B49VwsdyC71oPtsB+Bp2K2aiQbPaRmuQlI8VDVpqb3HQ3+VkuRuU4Kcx3MG6EnTQhUPpEpNBOJMbk25FlleY2XTDxOarnFe6JYBghBEoYtFpIzfZ/+tdXhA/zlFdo8Xgk0tN9zJrtYEJRFKU/gl4xlHqfKCrsOmjl2Bm/xb9wZhtFuVUdU4XDVN0km31MHduKRqPSatey+1g6qsiB7Td6EienSpP8PU+ibNhr0KmMynEAcL6XYZ7+ck8GEpEcKwAhUCISyENpCJOH8v+3d97xbdT3/3/eacu2vHecxM7eCRnGISEhMYlD2GWWlvGlUErp+NIfLVBm0pZRSumX0kIpq2UEaNkJISELkjiD7D2d2Bm24ylL1tbn98fFihXLtpx4yPY9Hw89bJ0+d/qcTrp73XtWVcksXxaF2SwYO8bFhbnOzpper6MnuHd8PlheGMvewyYkCS6eaEWu2sbIMX0a9c5p+UpnMvhJinUjS4L9xdFsO2DpjKmrnKaxOHG5ZTbuiWP1tkT+szydQ8fMYQnGwVlK6ftDx6LwhyFsOtt6ogbHqnQ1qkBphaSMhkDZ0B+VxyWzZGk0Ho9EWrqXKVPq1QqxKs3i8Up8tTqewyVGZBlm5tUwNMcReN0XZt0Sr0/GZPAz5HR/l4174jhy8vzraqi0zM4Nh5v22dH4GT+0BqPBh9WuY/mmJD5amcbRUlOLQiUzxYnR4MPh0nD8VHgNBHuD9URFpQFVoLRCQ6BsdZkG31khJULAgXXJ2G0yllgfM2fY0bSe8KNyDvQE947LLbFoVTzHSvVotYKCqdXkZAVXFW4oTd9a3ZKGCrH90hyMyFGqk67akkhdvfoF7CiaC4rVaGBkjo2b8k8wYVgNep2fKqueJeuT+ezbVE6cMoReT4bsdCXF+8hJc8vv3YusJ6p7R6UBVaC0QnScwGAW+H1QXR78ce0q1FNTakKjFcycYcdoVAMBOpLu7N6pd8h8viKBsgoder1g7rRq+qQ1jVPyhlmavvG43BHVJMW5cHtklm9KCjsOQiV8wgmK1WkF4wZbuSn/BGMG1aLV+CmvNrBwbSoL16ZQVtW0Xk3/dMV6dqTUhL+V00dvsp6o7h0V6O0CJQw9IUlnyt43rodSelTD9m+VE87kPAcJCepVQSU0dXaZBYuSOHDEhNsjccUlVaQmncme2bH2ACPH9AHC753TWKBoZMifUIFe56e8ysCmvbEdtCe9k7Zm7Bj0fiYNr+XG/BOMyKlDlgUnThn57Ns0lqxPCsraSU9yotf5cbo0lFeHLrinWk9Ueiu9WqBUlYW3+0kZivhoCJR11kPh50YQkNLfxuDBasaOSmiqrRo+XZZItVWL0y3RL8NFYlzz6efeMJsAXjrxFLfOOcaAPoqLICbKx8VjKwHYdiCWHYeicXnUYKjz5XzSic1GP5NHVXPDzBMM7msDSXC01MxHq9JYvinxdIsCAtk8Lbl5OqtqbCSgWk9UGujVhdoO79SREcbNQuNAWSFgw2IjDpuER7IybHwl0H1dD92B7lqcrbxKy5ffJOBySZiNfkwGH1Hmli1t4bp4lMKAwdvKznAwPLuOnYdj+M/yDNISncSYfViivMRGe4iN9hIbpfy1RHnVDsdhcq61ThqIMfuYNq6KMQOtfLc3jqITZg4di+LwcTND+tlJSXBx8FgUR0+ayB1eExRkv3PbsU4TJ11dmE1F5Ww6xYLy0ksv0b9/f4xGI7m5uWzYsKHZsa+++ipTp04lPj6e+Ph48vPzm4y//fbbkSQp6FFQUNDmeZXs1YXVqTgh3QcS2GtkDmzVcWy/FlkDgy88xcVJ3e/CqdLxHC/Ts3CFIk6SEz1cMNyGVtO68AinDkpL5I6owRLlBQmqrHrqnRrKqgzsL45m4+44vt6YzH9XpPPGwj68tzSDRWtTWL0tnp2HYiguM1Jr14aV8tobCJWxcz7ExXjJn1jBNdNPkpXqQAiJvUeiKdweT61NS3Wd0oqgt6K6d1TOpsMtKO+//z73338/L7/8Mrm5ubzwwgvMnj2bffv2kZKS0mT8ypUrufnmm5k8eTJGo5FnnnmGWbNmsWvXLjIzMwPjCgoKeOONNwLPDYbQkfIt4fVA8V4tA0a3XPFVb4DYRD/VZTLrFxnQaGDEZDciRnXtqDTlyHEDywrj8PkgI8XNrCk1gYJsZwuPxvEncCYG5Vy7D2s1goILy3E4NXh8EmMGW0myuKm1a6m16aixabHadbg9MrZ6LbZ6bZMUV0kSWKIUK0tctIeczHpS4nvXd/1cytiHS1Ksh4ILT1FapWfj7jhKK424PDK2SgNFJ02B9gOdbT2JBFT3jkpjOtyC8vzzz3PXXXdxxx13MHz4cF5++WXMZjOvv/56yPHvvPMO9957L2PHjmXo0KH885//xO/3s2zZsqBxBoOBtLS0wCM+Pv6c5ndoW3h3LIkZPmorZWy1MrFJfoZf2LtO2F1Fd0sv3n/EyNI1ijjpn+mi4OJq9DqB1xtufZPzs6AAxMd4GT+sBlmCvUeiSU9ycsEQK5eMr+SaaWXcOucYPyg4xhVTS5k2rpIxg2rpn15PgsWNRiMQQqLWpqOkzMSOQxZqetld/fmWsQ+XtAQ3l19Uzpy8ctISXcSYfRwrU2rZdEVJe9W907tpi6fjzTffbOLFMBqDb3SEEDz22GOkp6djMpnIz8/nwIEDbZpTh1pQ3G43mzZt4qGHHgosk2WZ/Px8CgsLw9pGfX09Ho+HhISEoOUrV64kJSWF+Ph4ZsyYwe9+9zsSExNDbsPlcuFynak3YbVaAZBkpYR9TYVMXFLLdm1ZI3DWS0TFCibNcar1TjqR7hJ/snO/mbVbYgAYnO3g4glW5NO3AOEGv7aHQAEYNaCOQ8eiqKzVs35XPJeMrwy8JklKJVqTQenP0xghwO7UUGtTLC61di3J8a6zN99j6Sxx0oAkQZ8UJzfMOMF7SzMpr9EHgpt7k/VEbQzYtbTV0wFgsVjYt29f4Ll0VoXSZ599lv/7v//jrbfeIjs7m0cffZTZs2eze/fuJmKmOTrUglJRUYHP5yM1NfgCk5qaSmlpeP7G3/zmN2RkZJCfnx9YVlBQwL/+9S+WLVvGM888w6pVq5gzZw4+ny/kNp566iliY2MDj6ws5YeQkaO4dlqzonjccPKwlvT+PiZc6iI5U3XSq5xBCNi0KyogTkYNrmfaxDPiBAjUJtG2ImzDFTKtIUswdUwVSIKDx6I43kyxsLORJIg2+chMdjE820beyBriY3pH08v2FCe2eg3HysM7CYOShRUX4wEhsabQet7v31ZU60nLbJa7lyW3rbTV0wGKIGnsxWh8nRdC8MILL/DII49w1VVXMXr0aP71r39x4sQJPvnkk7DnFdFpxk8//TQLFizg448/DlJcN910E1deeSWjRo3i6quv5osvvmDjxo2sXLky5HYeeughamtrA4+SkhIAskcovt6indomVWIbs2O1HnutjNkiGHNx77mbVGkdIaBwSwybdiodiSeMtHHh2Lom7Q4aXDyNLSM71jY1dwZK3Z9jDEpjkuPdDO+vlMJfvS0hIH46go6M2egM2lOcCAGrtyfwZWEKKzcn4nSHd5rNSFJ6eFXaYnqd9USl/bFarUGPxl6ExjR4OhobAcLxdNhsNvr160dWVhZXXXUVu3btCrxWVFREaWlp0DZjY2PJzc0N23sCHSxQkpKS0Gg0lJUF/wjKyspIS0trcd3nnnuOp59+miVLljB69OgWx+bk5JCUlMTBgwdDvm4wGLBYLEEPgLT+PkwxArdD4tjB0N6uumqJ/ZuUAkoTZznRha6lpNIBRHp6sd8PS9fGsWVPFH4hMXlcHReMsIfsxeRrxjLSOEC28Tittn2sdBOG1WA2Kj1idh6KaZdtnk1nuUM6ivZ26/gFxEZ5QRIcKIniwzAbCGYmO7FaHVTaOuY4NUckWE8i2b2zUQ59XekohN2JsDvO46EI3aysrCDPwVNPPRXy/c7F0zFkyBBef/11Pv30U95++238fj+TJ0/m2DEldqphvfPxnkAHCxS9Xs/48eODAlwbAl7z8vKaXe/ZZ59l/vz5LF68mAkTJrT6PseOHaOyspL09PQ2zU+SIWeUYkU5tDW0m2fbKgN+H6Tn+MgcGNqFpNL78PpOi5NdUZw8pScz1cXIwfUtjA8vtmRofxujBliJNrbPd82gE0waXg3A1gMWnK6INpp2Oh0Rc6KRIW9UNVdOKSPe4sbp0rB8UxJfrU/G1kKvpIwkFxKgMSdiq+/44xQJ1pPuwrj+GV09hTZTUlIS5DloHAt6vuTl5XHrrbcyduxYpk2bxkcffURycjKvvPJKu70HdIKL5/777+fVV1/lrbfeYs+ePfzkJz/Bbrdzxx13AHDrrbcGfXDPPPMMjz76KK+//jr9+/entLSU0tJSbDbFVG2z2XjggQdYt24dR44cYdmyZVx11VUMHDiQ2bNnt3l+A0YrAqX0iAZbTfCt76njMsV7tSDB2Omqa0dFwe2RWPxNPEePG0CCpHgPfVJbzuoKCBRtywJlzMA6LhxZQ7S5/cTwwD71JMa68XhlNu9Xy+A30NEBsakJbq6ZVsr4oTXIsqCkzMSHK9LZeTg6ZN+dA3uKyRmomGiPl3WOqbarrSdqcGzHcbbXoLlSHOfj6WhAp9Mxbty4gBejYb3z2SZ0gkC58cYbee6553jssccYO3YsW7duZfHixQHTT3FxMSdPngyM//vf/47b7ea6664jPT098HjuuecA0Gg0bN++nSuvvJLBgwdz5513Mn78eL799ttzqoUSHSdI669cDA7vOGNFEQK2rlC2lzPKQ3yKGhirAk6XxMKV8Zwo16PTCYZkOzAb/a0GtYbbY6cjkCTIHaFYUXYXRVNn75gUtO4Uh9JZ2ToaGS4YYuV700+SmuDC65Up3JHA59+mUmVtarVtELonytt+LlNpfzrbvdMVnKunozE+n48dO3YEvBjZ2dmkpaUFbdNqtbJ+/fqwtwmdVOr+vvvu47777gv52tmBrUeOHGlxWyaTia+++qqdZqYwYIyH0iMaDm3XMfIiN7KsNAM8dUyDrIXRU9WaJ51NJMaf2OtlFq6Kp8aqxWAQzLm4im17lODY1tOHCWtcR5GZ7CIz2cnxU0a2HoxVMnzakZFThrJz9d523WZH0dmpxKBUkb1iShl7jkSzYXcc5dUGPl6VxphBVsYNqmXPTqUo27FSN1uIoqyiY2vPREJZ++4SHDs+JxOXrXn3bU/g/vvv57bbbmPChAlMmjSJF154oYmnIzMzMxDHMm/ePC688EIGDhxITU0Nf/zjHzl69Cg/+tGPACXD55e//CW/+93vGDRoUCDNOCMjg6uvvjrsefXqXjwN9BnkRW8SOOokThZpyMjxsWO1YmIdONaDOUbtWdLbqa3TsHBVPDa7hiizn8umVRFv8YUdW3ImfVh5fnYF2c7ggqE1HD+VRuGOOOwOmeH9bWSmONH0orCUrhAnDUgSDM+20S/NwZrt8RwtNbNlXyzfbfczoo9SaTg5wYMkgdWmweGUMBl79rlHde9EBjfeeCOnTp3iscceo7S0lLFjxzbxdMiN6iZUV1dz1113UVpaSnx8POPHj2ft2rUMHz48MObXv/41drudu+++m5qaGqZMmcLixYvDroECqkABQKOF7JFe9m3UcWibDlkDFaetJ2rFWJXKGi2LVsXjcMpYon3MnV5FTJTi8muwjLRW36QrXTwNpCW4SUt0UlIex7YDsZSUmdHr/PRPrycns56MpPMTKw1WlJGTuj4rJBRdKU4aE2XycemkCo6cNLFmRwJlViN7q8Yhb6pn4igbsTFeaqxayiv19Mts/9i3SLCedAd6g3unMW3xdPz5z3/mz3/+c4vbkySJefPmMW/evHOekypQTjNgtId9G3UcP6ilvk65mAwc68Ec3bPvYFRaprRCx+Jv43G7JRLjvMyZVo3ZeCYeqbn04bOJBIECMH5ILUdLTThcGnRaP26PzP7iaPYXR2PQ+8jOcDAgw05akgu548qmdDqRIk4akCSl+3TNye/QejOpE1HsPmjm6Akj5tMZXOVVug4RKJFAd3LvqHQdqkA5TVyyn8QMHyeLNBzdo8OS4GfYJNV60hVESv+dkpN6lq6Nw+uVSE3yUDC1GoM+WGC03cXTtQIlI9lFvzQHpZVGhvZT3A2HjpspOmnG6dKw90g0e49EYzL4yM5QLCtpCa6QtV2aY+eGwxFjRYk0YXI2Oq2Pm28wcLysmm+/s2C1aSg7pcPhljlWqmfiqPZ9v0hKLVbdOyqt0Yu8z60zcKwHe61MvVUia4iXKItqPekqujpA9lCJga9Wx+P1SmSlu5k7raqJOIHQFWLPxu9XHq2N6yxGD6wDYF9xNElxbqaMqeaWWceZk1fOkH42DHofDpeG3UUxfLE6lXeXZFK4I56yKn2rxcYiSQhEujhp3K04M9XNdQUVjB1mx2Dw43DKlFboQ6Yjny9d7d7pDtaT3ubeiVRUC0ojUrJ8IEFskp8hE1TrSW9l72ETn69IwOORGD6wnllTqpuNzQiUpm9BeDSMgcgQKH1THViiPFjtOg6URDE824YsK03r+qQ4uWg0nDhl5NBxM0dLzdQ7New8HMPOwzFEmbzkZNYzIKOepDh3SMtKJMSiRJo4EYKgzypUt2KtBiaNtpHdx8k7nyejkQXVtVoS49qnF5JqPWkbqnun61EFSiOK92qJP+3qURsC9k627TWzflsMTpeMVuNn1GB7i4Gj4RRg02kFd91Qhs+v1MUI1YOnM5EkGJFto3BnPHuORDOsvy3o4qmRISvVSVaqE5+vimOnTKfFigm7Q8uOgxZ2HLQQG+3he9NPNtvZuytcPZEmTABq6rSs2JzI9HFVxFs8geXN9dtJTvAysK+TE+V6TlXp2k2gQNdbT1RU2oLq4jmNEHBou1J7YNA4TyujVXoaQsCG7dGs36b0QUmM85AQ60XbioRvcPG0lv0iScodcoMQ6OwU47MZlGVHIwuqrHoqapqvWqrRQL80BzPGV/LDguPkTzxFdkY9Go0g2uRrVpw0CITOKt62c8PhiBQnAGt3xlNRY+CTb1I5eMwc0npyNg2ipLq2fe4hI8V6orp3VNqCKlBOU16iwVYto9ULsob0jvbykUhXBMj6BazeZGHrHqUWxaTRNpLiPadFRfOWESHAF0gz7nrXTVsw6P30z1CKT+0tjg5rHa1GkJ3hIH9iBT8sOMaUVoq9dZZIaSxMIk2cAFxyQSUZyU68PpkVm5LYfTyL4bktdyuOj1XOQVXtJFAgcqwnqntHJVxUgXKaButJv+FetWNxF9OZAbI+P6xYF8ueQyYkCaZOsDJ2mL1RV+GWYkvO/N/V2TnnwtC+Sn+rQ8fMAUtQuOi0AktU60K+I0VKg9UkUoVJAyaDnzl55YwbXIvV6qDW34/PViRQZ2/+9NsgUKqt5y9QVOuJSndFFSiA1wPH9isngobuxio9H68XlqyO51CxEVmGGXk1DBvgAM4EtrZkGWkQMdB6E8BIJD3JRbTZi8crU1xm6rD3aSxSzleoNGwjUt05zSFLYHTv4oL+hzDoBacqdXy0NImSk6HvhhJOC5R6h4zTdf4FaVTrSXio7p3IQhUowPFDWrxuiIoVJGWowbG9AZdbYtE3CZSc1KPRwOwp1QzIOlMUK1C3RG5eeDSMkSS6ZVEzSYIBmXYADh03d+h7NbZynItIOVuURLrVpDlmFKRy7awKkhK8uFwSi7+N57udTbsb67SCmCjFf3g+bp5IsZ50J1T3TuSgZvEAxXuUj6HfME+bClKpdE/qnTJffhNPZbUWvV5QMLWatKRgy1mgvkkLlpHGNVC66/dmQGb96bL3JtweCb2uYy1BAZHSxsaC3VGMNKZxYGxMlJ8rZ1RSuMXCnkMmNu+KorxSx4wLazAaznz+CbFe6uwaqmp1ZKScu2U3EqwnqntH5Vzo9QLF41YsKAB9h6nBsT2dOrvMolUJ1NZpMBr9zJ1W3SSN0y/CK6wWqIHSDd07DSRYPMRGe6is1fPttgQuGV/ZKdag7i44zoXGacVajRLvlJbk5pvvYjlWque/S5LIn1xDaqIiRhLivBw9YTjnTJ5Is56o7h2VttKrXTx2q0TZUQ1+L0TF+YlPUd07PZkaq4YPFydRUa0jyuzjqhlVIWtMNI4taS6NFho1CuzGvyKlJ0w9NXVaNuyK46OVaZSUh99tVKV1GleMPZtB/Z1cc2klsTE+7PUyny9PYOd+M0KAJVr5blptrXSibAHVetI2VPdOZNGNT63nz9aVBk4WKXcnGTm+bmum7yns3Hi4wzJ4TlVp+Wx5AkeOGTlVpWXaxFpiY3whx3obaZaWLChmo58Jo2yMGmxv7+l2Kn2Snej1ArdXpqpWz+LCFL4sTKayVtfVU+v2hFPzJCHWyzWXVpKd5cTvh7VbYli+LhbT6aaUdfa2CxTVeqLSE+jVLp7jB7WU6ZSTcEaO6t7pqZwo1/HV6ng8Hgmdzk9SvAdLdPPWsgYLiizTomiNNvu5YHj3FicAqQkukmLdRBll+qXXc6zcpDxOGRnS1874obVEGUOLOZXWac560hi9TpCfV8vORA/rt8dwqNhIaYUOj1eizq7B71e+j20hEqwn3QXVvROZ9GoLit8DpUc0SDKk9FVPwD2RoycMfPmN0lcnPcVNSoIHjdxy3ZJwytf3JGQZMpOdaGRIjnNz3YyTZGfUg5DYdzSaD5als3mfBU8ba6X0dlpy7YRCkmDUkHouv6QKs8mPza6hrEKPrV6D3RH+qTqSrCeqe0flfOjVAkWSQfghKtavFmfrgRw4amTJ6jh8PuiX6WLWRdUBi0hLrpuAQOmGxdfOlYwkJwCllUZio7zkT6zgiqmlpMS78HplNu2N44NlGewrjuqQDrs9jeZcOy536yIvLcnD92ZVkJnqRpYFPh9YbW0zdkeS9UR176icK71aoKRm+0jt56Ofmr3T49h1wMSKdbEIoQQi5k+uAcLrKuzrQIGyY+2BLu/DE4q0RKUGTFmVPlAhNy3BzZVTy5g5oYIYs5d6p4ZvtiTy8ao0jqmBtK1ytvWkxqrh/S+T2LG/9ZozJqPgsunVDOznxBLtCzsORbWetB3VvRO59GqBUm+VkGVIyVLdOz0FIWDz7ijWbLYAMHJQPdMn1aKRzyqsFkaH4u5Yvv5ciY/xYND78PpkKmvPmBMlCXIy67l+xglyR1Sj1/mpqtXz5elA2mqrGkh7Ns1ZTw4fM+J0yhRuiWHjjmhEK18vWYJ+GYpwbEugrGo9aTuqeycy6dUCxV4rgwRJmapA6QkIAeu2xfDdDqX53QUjbOSNqwu4dRpbRloKfu2NLh5JgtQENwBlVYYmr2s0MHpgHTfmn2BkTh2SJDhWbuI/K9P4dlsC9c5efSoJ0CBOQsWejBtmZ+Iopf/Rlt1RrN5kadVd1lBN1lbfukCJJOuJikp70OvPKpZENf6kJ+D3w6qNFnbsU8zneePqmDDSHiREGuqWtGYZOTOuI2YauSTHKXfrFbXN/yCMej95o6q5fsZJ+qcrgbR7j0Tz/rIMNu+zBDVQ7K00FxgrSTBuuJ2pE6xIEuw5ZGLZ2rhAR+xQmE5nTzld4Z2qI8V6sqamtFtYTzbKB1XrSQTT6wVKQqp6Ro0EzqcGis8Hywrj2F+kdCSeNqmWUYPrm4w747ppbXu9z4ICkBSrWFAqa1pX7LHRXi6dVMHlU8pIilMCaY+cNPfqWkLh1DwBGDbAwcy8GmQZio4Z+PKbeNye0B+cyaCcn1qzUKnWE5WeSK+ugwIQn6q6d7ozHq/EolXxbN9nRiPDzZefIruPK+TYxr1zWkIIRcT0NoGSGKeUWK+2afF4JXRhpFmnJ7q4+uIyDh83Yzb5umXTxPagJddOKHKyXBj01SxZHceJcj1frExgztQqTMbgz7yhWFs4FpRIsp6oqLQHvd6Copa37744XRILV8ZzvEyP2y0TE+1tVpxA+JaRIdlO7ryujEsvqmnP6UY8UUYfRoMPhERtG9JaJQkG9KknPbH5z7430JaaJwCZqW7mXlKF0eCnokrLZ8sTqbMHn5IbBIrDKTcbVBuJ1hPVvaPSHvR6gWJJVAVKd8TukPl8RQLllTr0OkFKopuYqJaPZaC5X5iWkd7oroiNUlLua21qdk64hOvaCUVKgpcrZ1QRbfZTW6fh02WJVDVqDmg87eLx+2nWDQSq9USlZ9KrBYpWB6bo3mXG7wlYbRo+W5ZAda0Ws8nPjAtrMOhFq5aR3pid01Zioz34/LCrqPU0WJW2u3ZCEWfxcdXMSuIsXuodSsPA0gpFIGo1Shl8UKwoZ6NaT84NtfZJ96BXC5ToeP953yWrdwydS1Wt0vSvzq7BEu3jyhlVRJmVu0yN3IpA8fa++iZtJS7aQ2Wtnl2HY/hqfXLY2SO9mfMRJw1Emf1cOaOKlEQPLrfEwpUJlJxUgpUbrCiOZo5FpFhPuhuqeyfy6dVnn+jY83PvdIc7hZ5EWaWOz5cnUO+QSYjzcsWMKizRvrCFR0M6Z1f22Bk1edB5uQQ6mmizF5PBh19IlJSZ+O/KdE5WNK2LotL2XjutYTQI5k6vJivdjc8HX62O58BRI3q98n31nOXiiTTriXqzptLe9GqBorp3ug/HSvUsWhmPyy2RmuThikuqiDIpAvNMc7+Wt3HGxdOhU+3WRJt8xJh9ZCQ7iY32UO/U8MXaFDbtjcWvhmsF6CiRqdMKZk1RStz7/bBiXSw1VuUL6/E1NfdGmvWkO9y0qe6d7kOvFijmGPWM2x0oOmZg8bfxeLwSfdLcXDatGoP+jLgMWEZataCoMSitEWVSPkyfT+Kqi0sZ3NcGQmLzvlgWrk3B5lDVXQPnaj1xuqQWC9ppZLgkt5aRg5RaPifK9NTUafE2sqCo1pPzQ3XvdA96tUAxRqkXqkhn72ETX6+Nw++H7Cwns6dUN6nP0SA8Wo1B6YU9dtqK2eADSSCEhM8nMW1cFdMvqECr9VNaaeSjlWkcLTV19TS7lPNx7VhtGj75OpFvNsa2GIQsSaerIY+yIclK/NTZFhTVeqLS0+nVAsVgUi9UkcymXVEsWROHyyMzJMfBzLzakFVgz7h41Cye80WWQX/6c3R5lNPDoKx6rp1eSlKcG5dbw5L1yRTuiG+xRHtP5XxdOzVWDXV2DQeOGFm3LaZVkXLBcDvDcupJjPPg9SnHI9KsJ90J1b3TvejVAkVvVC9UkYgQsHFHNGs2WSir0AGCiydYm61SGq7w6J/pJHdMHVnpvbugWGsY9Ir/oUGggFIf5cqppYwaYAVg5+EYPv02jZo2FHTr7rRHSnHfDDcXT6wFYMc+M9v2RrW6TkqiB0k6k4UGkWU96S59dxpQ3Tvdh14tULR6VaBEGkLAms0xbNkdhV9AnMVLZoq7xXRwX5ium4wUD2OG1pOe7GnPKfc49FpFoLjdwacHjQwXjqxhdm45Br2Pylo9H69K40CJuSum2SW0R9bOkGxFKANs2B7N3sMtu8waLINer6RaT1R6FZ0iUF566SX69++P0WgkNzeXDRs2tDj+ww8/ZOjQoRiNRkaNGsWiRYuCXhdC8Nhjj5Geno7JZCI/P58DBw60eV46NXsyovD5YcX6WHYfVJrOjRxcT2y0T3XddDJ6nSJQPN7Qp4e+aU6+N72U9CQnXq/Mys1JrNyciMfbc0vvtnfWzpih9YwdZgfg2+8sFB1r/mTUkHXW0GU70qwn3QXVvdP96HCB8v7773P//ffz+OOPs3nzZsaMGcPs2bMpLy8POX7t2rXcfPPN3HnnnWzZsoWrr76aq6++mp07dwbGPPvss/zf//0fL7/8MuvXrycqKorZs2fjdDrbNDdZvaBFDD6fxNI1cRw8akSW4ZILa8lKU7rrtl4hVvnbWpdilfCQTwcb+1v42KNMPi6bXM74oTUgCQ6URPHxqjQqanteifz2cO2EYuIoG0NyHAgBy9fFcaK85c/u2Km6dn3/9kJ17/QM2mJIePXVV5k6dSrx8fHEx8eTn5/fZHx7GBI6XKA8//zz3HXXXdxxxx0MHz6cl19+GbPZzOuvvx5y/F/+8hcKCgp44IEHGDZsGPPnz+eCCy7gr3/9K6Ds9AsvvMAjjzzCVVddxejRo/nXv/7FiRMn+OSTT9o0t97aeTXScLlgy64sik8Y0Gjg0otqGNjXGRAerdUtUdOH25eG34Xf3/IPRJbggiFWLr+onCiTl1qbjk+/SWPn4ehOmGXn0FHiBJQg2KnjrfTPdAUKs1VUtxzTo1pPVDqCthoSVq5cyc0338yKFSsoLCwkKyuLWbNmcfz48cCY9jAkdKhAcbvdbNq0ifz8/DNvKMvk5+dTWFgYcp3CwsKg8QCzZ88OjC8qKqK0tDRoTGxsLLm5uc1u0+VyYbVagx7KXM5r9wDl7kH9oZ47DofEl4ujqak1odcJLptWRb8MJYi1IShQdfF0Lg3p2r5WBEoD6Ykurp1eSr+0evx+iZq6nmVF6Qhx0oAsw4y8GtJT3Hg8Eou+iae2rqkit9ZHZmB3d7GeqJ2LW6athoR33nmHe++9l7FjxzJ06FD++c9/4vf7WbZsGdB+hoQOFSgVFRX4fD5SU1ODlqemplJaGvqiXlpa2uL4hr9t2eZTTz1FbGxs4JGVdfpHpVpQuhSbXWLhwmgqK7TodD4uv6QqKIA10H24lfom3c2CEunl7s8Fo97PpZMqmHZBJReOrOnq6bQL7V3Kvjm0Gph1UQ2J8V6cTpmFq+KxO5qemjP6JXX4XFR6DmfflLtcoUXuuRgSzqa+vh6Px0NCQgJwboaEUPSKLJ6HHnqI2trawKOkpAQAfy+s4xAp1NbKfPF5DLW1GqKi/EwYXUxSvDdoTLjCQy3A1r74/BJ+cebzDxdJgsFZ9m4jFFuiswWkQS+Yc3E1lmgfNruGL1cpbR0AjpbWEhPbejpyZ9LdUou7FR43uM/j4VFi97KysoJuzJ966qmQb3cuhoSz+c1vfkNGRkZAkJyLISEUHVrEICkpCY1GQ1lZcGpcWVkZaWlpIddJS0trcXzD37KyMtLT04PGjB07NuQ2DQYDBkPTKPmWggBVOo7KSg2Lv4rC6ZCJjfVRMMfGkd3uJuPCdfGEm2asEh4en8Spaj0b9sQyuK89UBelt9CRcSctYTb6uWxaNZ8tT6CqVsvib+Pp02cvkNip8+hp9Fb3TklJCRaLJfA81DWwPXj66adZsGABK1euxGg0tuu2O9SCotfrGT9+fMAvBQT8VHl5eSHXycvLCxoPsHTp0sD47Oxs0tLSgsZYrVbWr1/f7Dabw9+D0yIjldJSDR98EMPRozr8AubOtRHdTMuBcC0jWo3AoBfodapAaQ9cbhmPV6auXsfidcm4e+HvpLPFSQOWaB9zLq5GrxeUVejYsTeTjOyULplLc6gxd90Di8US9GhOoJyLIaGB5557jqeffpolS5YwevTowPLGhoS2brMxHe7iuf/++3n11Vd566232LNnDz/5yU+w2+3ccccdANx666089NBDgfG/+MUvWLx4MX/605/Yu3cvTzzxBN999x333XcfAJIk8ctf/pLf/e53fPbZZ+zYsYNbb72VjIwMrr766jbNzdP0pl2lAykp0bL4q2icTuVrNyDHjamFdgNnBErL2730ohpuu6ac7D6RGUjY3ZAlSElwYdT7KK828NW65KAqpj2Zzog78foIuG9CkRjnpWBKNTank4qqKPbu0SMATSuWxM6ku7h31NonrXMuhgRQsnTmz5/P4sWLmTBhQtBr7WVI6PA61TfeeCOnTp3iscceo7S0lLFjx7J48eKAb6q4uBi5UTrN5MmTeffdd3nkkUd4+OGHGTRoEJ988gkjR44MjPn1r3+N3W7n7rvvpqamhilTprB48eI2m5c8rt5x0o0EDh3SseobM8IvkZDgw+ORMBhac90of7WtBMl2V3ZuO8bIMX26ehpNcHlk9FrB1LGVrNsZT2mlkSUbk5g96VSPrjXTGXEnVpuGrwvjMBn8FEytbrZCclqyh1FDj1N0cjClZRokCbQR0FWgO1pPeqN7p63cf//93HbbbUyYMIFJkybxwgsvNDEkZGZmBuJYnnnmGR577DHeffdd+vfvH4griY6OJjo6OsiQMGjQILKzs3n00UfbbEjolK/8fffdF7CAnM3KlSubLLv++uu5/vrrm92eJEnMmzePefPmnde83E5VoHQGe/boWVtoBgE5A9yYTH527TSiaeXbF24TwO7IqMmD2LG27dWPOwP36R48aYkuCvLK+XJtCsfLTSz7LomZEyvQ9MDQ+s6KO/F4JaprtVT4YMvuKC4YYQ85buvBMpIToc+Aej7+2ILH13o2W2fRXawnKuHTVkPC3//+d9xuN9ddd13Qdh5//HGeeOIJoH0MCRGgybsOl6P9BMqamlIuigvft9YbEAK2bzfw3XdKr5Fhw1zk5TlYt155rga/Rh4+/xmBYtD5iYv2Miv3FF+tS+FoqZlVmxOZPr6yRxU57Myg2MQ4L1PG17JqQyybdkWTkuihT1poX7NSlM1DzgAXNdXaVi2OKsGo7p220RZDwpEjR1rdXnsYEnrgvVD4OGztc5ZV7yiaIgRs3GgMiJOxY53k5TlOd2VVxoSbPtwT0la7C/VOxYcjywLj6eydzGQX+RNPIcuCQ8ej+HZrAqKHHJKuyNgZku1k6OkS98vWxWGrDz4Nn90QMCZaIEm0anHsaLpjarHq3une9HKB0qt3v8Pw+2H1ahM7diimvEm5DsaPdwb87b7TAZdhu3h6sECJtIJt9tMCJcroC4qP6Jvm5JLxFSAJ9hdHs3ZHfI8RKV2RsTP5AitJ8V5cLomv18bhOyuTu3FJe49HORC6HujqVFFpiV59hW4vC4rKGXw+WLHSzP79BpBgytR6Ro10NRkDrQuPnu7i6apU1pawOxTVGGXyNnktJ8PB9HGVIAl2F8WwYXdctxYpnVUpNhRaDeRPrkGvF5RX6li/NQZQrCdn99sJ1APqwjT67hYcq7p3ega9WqDYa3v17rc7Hg8sXRrFkSI9skYwY4adIYObL8DWugVF+duTM0cijTq7clCizaHLLA/KqmfK6GoAth+0sGW/JeS4SCcSLFeWaB+X5NYCsPOAmcVr60OOc5/u/tDVdX5U945KZ9Orr9B11XK3vgOMJFwuiU8+ieHgIT1IglmX2snu7wk5NlzXTW9w8UQaNTZFoMRGhT52AMP627hwpCJSNu2NY/vBmE6ZW3vRVZViQ9Evw8XYYUomz54D6WQOGthkjON0X56WagZ1JN3NeqLSc+jVAsXnBbtVdfOcL/X1EgsXRVN0RE9lpYZBA91kZjZ1ETTgO/1Sa66bvLF1XDTe2uzdfE8g0hoH1tqUTsRx0c0fP4BRA+oYP7QGgPW74tldFN3RU2sXIkmcNDBhpA2NrhqfX2L5sig8jbSh1wve0zEoRmPXtRzoTtaT3lravifSqwUKgLWyfT6C0aOzeuWdRl2dzMKF0VRXadBqBSnJXhITWxYUZ+qbtLztQf2cjBjowKBXLSidgRBQc1qgxEY3b0FpYNxgK2MGKS6KNdsT2F8cWQ3tziYSxQmALMPIoSdITTdRU6Nh9WpzwLLrcDS4QwU6XefPrTee01Qih14vUKrLe/1HcM5UV8t88UU0VquG6Bg/gwe70etbjy0J14Ki0rnYHRrcHhlJEsS2YkEBpXvxxGG1jMipA2DV1gSOnDR19DTPiUgVJ6AExhr0Pi65xI4kCw4f1rNnjx4g0BbCaBTNVp3taLqb9USl59Drr87VpWoE5rlw6pSGhYuiqa+XiYv3ccXldQHBEXZsSa8uE3iGSHHznKrRU++SqbLqAseoNSQJ8kZWM6SfDYvZS2Js5DW4imRx0sDIiTmkpfmYONEJwPoNJsrLNQELisnUuzpKnw+qe6fn0OsFSlVZr/8I2syJE1oWfRmNyymTnOxl7mU2zGYRdnZOQ5px4+ZnIyfmNClQpdK5lFfrqanT4fdLFO6MD3s9SYIpY6q4amoZMREWLxRp4sQvoN5x5pxzdlrxyBEu+vd34/dJLF8ehdV6xoLS2XTHwmwqPYtef3W2Vcu4HF09i+7D0aM6vloShdcjkZHhYc4cW+DkGW59k0BdB9XFE0RXW1GqrHoSY93o9X4OlkS1yV0jS2A0RNZdfqSJk9o6DZ8tS+DLb+Px+ZtWjAVF7E2dWo8l1ofdLvPdJhMCMHWBQOluqO6dnkevFiimGOVHf+q46uYJhwMHdCxbbsbvk+jX382sWfagwL1AhdgWPk4hzoxTXTxn6OqLqN8PZVUGDDrB2IFK4OvqbQk43d3zFBFp4gRApxPU1mmprNayY78SUHx2UTYAvR5mzrCj0QpOlWuw1spER3eu+OuuwbGqe6dn0T3PPu1EebGMrVam7Gj7CJSenMmzc5eBb76JQvglBg1yM+OS+iZCJJzuw75GHgA1SDZyqLTq8Xhl9Do/U8ZWExfjweHSsHZ7+K6eSCESxQmA2ejnwrFWAJav11LvaD4tJyHBz5SL6vF6JVxuiaiozrdOqe4dla6mVwsUrRZqK2ROHFJv5ZtDCNi82cj6dYq5f8RIF1On1iOH+OaEk53T4N5RxrXrVLs9XRksW1ppACA1wYVeK5g+rhJJUpoDFp2IzMycUESqOGlgcH8nmalu/H6JCvvQFgtFDhzoITXNS3Kyj9jYzovt6Y43Wap7p2fSqwXKhFkuLIl+bDUybmdXzybyEALWrTex9OsoSss0DBjgIneSo9l0x3Cyc4SA6Bg/UVF+VaBEEMdPKY0d0xOVH0JyvJsxg5S7/dXbE3C4Iv9UEeniBJQYk/jEQ8iy4OQJHQcO6Jsd6z9tNJFQfjOdSXe0nqjunZ5H5J91OpDhF7rJHOBF+OHkEdWK0hifD775xszuXQY8Hokos2DgIE+LtRhCZeecjckkuPEGKzfdZG3nGfccOtuK4vVJnKxQLCh9Us4o9QsG1xJvceN0aVizPaFT59RWuoM4acBs8nDJTEWdr99gor4+9I/KbpcQfglZI4gyd447VLWeqEQSvVqgAKTnKFfVk4fV2/kGvF5YviKKgwf1SLIgLc1DTIy/xawbvx/8gd45nTXTnkdXXGBLKw14fTJmo48Ey5kKshoNTBtXhSQJik6YOXzc3OlzC4fuJE4a0opHjHCRmOTF7ZJYty60C62uTvkhRUf7O7VIm2o9UYkUer1AychRAidOHta2W+PA7ngX0oDHA0uWRlF8VIdGK8ifaSc66nQBthaMTI2DX1sKkm0JtRaKQmfHopSUK+6dPilN3XfJcW7GDlasXWu2x0ecq6e7iZMGZBmmTnEgyYKiIj1Hjzb9cdXZlM86ppPcO935vKXSM4mss00XkJzlQ6sHh02i4vj5fxzd8e6jAadTYtGX0Zw8oUOnE8yeZaNvXy/eMFw3avBr+9MZIkUIKDqhWEb6poYuCDRucC0JFjdOt4bV2xIipgN4dxInDTROK05M9DFypAuAtYVm3GcV4a2tUc5Hlk6MP+lu5y/VvdOz6fUCRauFPoMVK8rRvV3QjStCsNklFi6MpuKUFoPRz2WX2UhPV5RJoG5JC8IjEH+i6bqeIT2JzrrollfrsTu0aLV+slJDR4prZJh2gZLVc+SkmcMnut7V093ESXOWwQvGObFYfNTbZb77LtjVU12t/OASEiKrOm+kobp3ei69XqAA9Buq+N2L92oDkfO9idpamYULY6ip0RAV5efyuTaSkpSTohBnsnNaSh/2+cIrc6/SNjrainLouFIwrH+6o8UYo6RYD+MauXrqnV136uiu4iRUUTatFi66SLFc7dljoKzszF1Ag0CJj+94gaKWtVeJRFSBAqRl+9AbBU6bRHlx7/JPVFbKLFwYja1OxhLrY+7cOuLizqg0vx84fd0Kp76JWr6+/ejoC7DfD4dPmPELGJBhb3X82MG1JMa6cbk1rN7eNa6e7iZOGgglThrIyPAyeLDi6lm92ozPBy4X2O3K6TkuTrWghEJ17/R8VIGCEjPRd6ji5jm0vX3cPN0h4KysTMOiL2NwOGQSE31cPtdGTEzwVadxbElLQbLhxKmotJ2ODJgtLjNx4pSB0koDUSZvq+M1MkwbV4ksC46eNHOoE7N6dm47xs5txxg1eVC3EicN1hOfjxYF3cSJTkwmPzU1GrZtMwasJ1FRfgyGjp1jdzhXNYfq3unZqALlNAPGKG6ekv1aXOdZtK07mEqPHdPyn/9aqKlROhLPmVOHydT0DBrIzpEIWT02MC6MPjzhoGbyhKYjRMr+4ii8PhmTwcfuI5aw1kmM9TBusNKrZ832BOzOjrc4dlerSQNRyUP4z38tlJQ0r/CNRsGFeYqrZ9t2AyUlyo1SZ7h3oHucs1R6H71aoFSckAN3NQlpfuJS/Pi9cHR3zw6WPVykY+nXUZw6pcFmk7n4Ynuzd2kB14225eDXuDgf06fbmTBebQ3d3jRcmNtTpNQ7ZYrLTMSYvUSbfBwoiQq7MeCYQVYSY91oNAJbfccKlO4sThpqnpw6pcFWJ7NhgykoHf9ssvt76NvPg98nsWmzEQHEd3CAbHe1nmyUD6rWk15ArxYoK943s/A1M3s26HA5YMBoxYpyYIsuYlIp25u9+/SsWBGFzydhMgmSk3wtmpC9YfTXATCbBQMGeOjXr3VXgUrbaW+Rsq84GiEk+qY6SEt04fNJ7DkSHda6GhnyJ1Zw3SUnSU1wt77COdLdxUkDY0Y7MZr81NZq2Lev+dL2kgR5efXodIKqKg02m0RCJ1hQVOuJSqTSqwWKRgvWCpktyw188lI0pUc1eD0StadkynpgsOz27QbWrDaDgMGDXCQm+JCk1roPq9VhI4X2ulD7/LC7KAaA4dl1jBqgZOfsLorBF2YWmyXKi1HfMSlv3TXe5GwaAmMNBiWdGGDzFiMuV/OmyOgowfjxDjweCSEkkpM7TqB0Z+uJSu+gVwuUK35sY2KBi4Q0P34fHD+gxVYjUXpUw/Zvmr/TCZdIOQEIARs3Gtm4UamzMGaMk4kTnQGXTUtxIw0WlHOtDqvSvrRH0Ozh42bqnRrMRh85mfXkZNZjNvqod2q6vJx9d7aaNBAqhmrIEDdx8T5cTpmtW1uOek1J8ZGa6iUpyYfF0rF1D7qr9UR17/QOerVA0elh0FgPBbfXU3BHPYMu8BCX7EP44dRxDdaqc684Fik/fL8f1qw1sX27Us584kQHEyY4A3fKska0HPzaBfVN1EDZljkfkSIE7DysBMQOz65DIysum+HZdQDsOGTpsvThnmA1aa7miSxD7iQlPmv3HgNWa/M/ulOnNOi0kJLs7bCih5Fy86Si0hK9WqA0JiHVz8RZLq6/387wC91IwK7C87eidCU+H6xcZWbfXgNIcNGUekaPVuotBKrDtiI8fGEUaVPpfM5VpBw7ZaSiRo9GIxjWzxZYPqyfDY1GUFmrp7Sqg/Naz6InWE0a01zNkz59vPTpowTBbthobHb9U6eUH2VySsfGn0TKTVRbUN07vQtVoJyFTg8TZysX8SO7dNRVd8+67V4vfP11FEWH9cgawSWX2Bk6xB30OrQuPFQXT+TSIFLCFSpCwOa9sQAM71+H0XDGfWA0+BmUpRRr23kopv0nG4KeYjVpIByr36RcpUHg0SN6Tp4M7VstL1eWpyR3TMB5d7eeqO6d3oMqUEKQlOEnPUdx9exce35WlK44Gbhc8OXiaI4d06HVCi7Nt5OT7Qkac6Z8fcvb8qpBshFNW7J7jp0yUl5tQKMRjB5obfL6yBxl2ZFSE1Z7x/n0GouqniBMGtNSxViA+Dh/4EZh/QZTE3ea0ylhtSo/tuQOEiigWk/aSoZxUZe9d29GFSjNMGrKGSvKucaidMVJwOGQWPRlDOVlWvQGQUGBjT59mp7ofGGWpvc1WFq6wIKixqGERzgi5WzridnYNPgyPsZLnxQHCIldh9vfinK2MOlJ4qSh5kk4jBvnRKcXVFZoOXAw+AaowXoSF9dy+v+5olpPVLoTqkBphqQMPxkDvQg/bFvVuT75c6WuTuLjj2PYudNARaWGuZfVkZoa2o8drutGrxfExfmIju7cLorhnuxVFBou+M25fIpOmiivNqDV+ENaTxoYOUAJlt1bHIXL0z7uzZ4sTKDtQtpkEowdq6Qdb/rOiKeRcfNkqWK5SklVrSeRQh/jZ109hV6LKlBaYOw0N0hQsk/LqeOR/VFV18h8sTBGyQ4QkJLiISGheVHhC9PFM3Cgh+99r47cSedZ/1+lU2hsTWkQBT4/bNwdhxAwemBo60kDfZKdxMV48Hpl9heHV7itOXq6MGlMWwX18GEuomP81NfL7NhxJmD2+HGlinVmRvsLlO5sPenq4Nixiaqo6wo67KpbVVXFLbfcgsViIS4ujjvvvBObzdbi+J/97GcMGTIEk8lE3759+fnPf05tbW3QOEmSmjwWLFjQIfsQl+wnZ5Rye7NlhSFiq8ueOqVh4cJo6u0y0TF+UlK8mJpPEgDO9NhRm/v1PBqLgZ3bjvHlcheHjkdxstLAgMyWuxZL0plYlJ2HY/C38evRIEp6izBpzrUjhPK7bA6tVkn5B9i+w4DdLuFwSFRXKeukp3eMBaU7W09U907H8tJLL9G/f3+MRiO5ubls2LChxfEffvghQ4cOxWg0MmrUKBYtCo7Tuf3225tcqwsKCto0pw6LhLvllls4efIkS5cuxePxcMcdd3D33Xfz7rvvhhx/4sQJTpw4wXPPPcfw4cM5evQo99xzDydOnOA///lP0Ng33ngjaEfj4uI6ajcYfbGbo3t0VBzTcHSPlv7D23biGD06izXbS7goLq1D5nfypJalS6PweCSSkr2MHePk66+jWxUe3jDTjLuarQfLGDswtaun0S0ZNXkQ9U6Z5f824HD4MOjcrPuujoJLTC2uNyirno174rDVazl60kR2Rsv9lc52KXWGINFoJXRGDRJdl2W3p7iCuAQjJvNZQkTAqm/MVJzSMG2anaTk0BarEcP9lJf7qKrUUHTETFqql/R0iIvzk5DY/veOiV4DUbrItgQ3R5JsJFp0zMlKIHDixyc1PWf2FvfO+++/z/3338/LL79Mbm4uL7zwArNnz2bfvn2kpKQ0Gb927VpuvvlmnnrqKS6//HLeffddrr76ajZv3szIkSMD4woKCnjjjTcCzw1tDKzqkCO+Z88eFi9ezMaNG5kwYQIAL774IpdddhnPPfccGRkZTdYZOXIk//3vfwPPBwwYwO9//3t+8IMf4PV60Ta6ksbFxZGWdv4XfH8YZQbM0YIReW62f6Nn09cG0nO8GFqxTnQWxcVali9X+uqkZ3i4NN9OWbnyOYWfnRO5FpSRE3PYufFwV0+jW1O4JQaTxUifaJAkHRV1FnZuKwoaM3JMn6DnWo1gWH8bW/fHsuOQJUigNBeI25lWkj5DYknvH4uskemwSmZhMMyTjs4Q+hQ6YqSExyOh0RhDdglvYMxYcDhkwIRGI5iZL6HTCQyGlkVkW3H5fYzVJbfrNjsLt+ThIu2oDn0Pn/Bz1G9jPzbO1rzjk/phq+vZLu7nn3+eu+66izvuuAOAl19+mYULF/L666/z4IMPNhn/l7/8hYKCAh544AEA5s+fz9KlS/nrX//Kyy+/HBhnMBjO61rdIQKlsLCQuLi4gDgByM/PR5Zl1q9fzzXXXBPWdmpra7FYLEHiBOCnP/0pP/rRj8jJyeGee+7hjjvuQGrhROVyuXC5XIHnVqtiwv74pWgS0kwkpPqIT/WTkOYjLtmP9qxmxsMmuTmyW4u1QmbbSgOTClx0NQcP6vjmWzPCL9G3n4dLptvRas9k3bQW/NqV2TkqncPREwYOFRuRJJh+YQ3fbIjFq89g+IVaNKdvpHesPRBSdEieMmx1I7BaJdZoq4g11wde60qXTZ8hsWQNTiQxMRG91tDkYtJZuFw+9KbmSxAIP9TUKh9ybKy/xWrN9XYZl1up+izLEBMj2r3uUL3fi8nQPbu010suTPqOm7sQAq/Tja6iArwoIqUH0HCda8BgMIS0YLjdbjZt2sRDDz0UWCbLMvn5+RQWFobcdmFhIffff3/QstmzZ/PJJ58ELVu5ciUpKSnEx8czY8YMfve735GYmBj2PnSIQCktLW1iFtJqtSQkJFBaGl6gVkVFBfPnz+fuu+8OWj5v3jxmzJiB2WxmyZIl3HvvvdhsNn7+8583u62nnnqKJ598sslyvw+qS2WqSxudPSSITfQTn+YPCJf4VB8TZ7lY9q6Jg1t19B/pIaVP52a1NGb3bj2FhUrPlIGD3EydUh84AZ5x3bTi4lHrm/Ro3B6J1ZuUkvajhtgZ0t/Jhu0xOJ0ypaf0ZKYqtThaEhs2o5YDR4z4Y4cxamLzmT+dhUYrkd4/lsTERKJMnVNMLhQOpxeNRkana7lGkskk43JJuN2CmJjmzxcxFvBUafB6JTQagdHoa1fDkM3nISYqqv022InYJScadGgNHVvVW2c0kAh4yr0c8tvxSaLL3DvC60XI5x6DJE6naGZlBccbPf744zzxxBNNxldUVODz+UhNDXalp6amsnfv3pDvUVpaGnJ84+t7QUEB1157LdnZ2Rw6dIiHH36YOXPmUFhYiKY1E/9p2iRQHnzwQZ555pkWx+zZs6ctmwyJ1Wpl7ty5DB8+vMkH+uijjwb+HzduHHa7nT/+8Y8tCpSHHnooSO1ZrVaysrK47E479loN1WUyVaXKX6ddorZCprZC5sjOMx9PdLwfJKirlvnmPyauvs8edvxGe8WhCAFbtxrYvFkx/w4f4eLCXEfQySzc7BxvN7GgjJyYw9aNh9U4lDayYXs09noZS7SPCSNsSBL0TXexv8hEyUlDQKC0xOghdtKT3Qzs13IMSmehM2qQNbJiOekiHE7lh2OMan0OJpMfl0uD2y0FrCOhkGXQ6QQ+n/K3C71WEYm5g8VJA1qjHo0kY0TGjuL/H5/Ur1PeuyMoKSnBYrEEnrc1/uN8uemmmwL/jxo1itGjRzNgwABWrlzJzJkzw9pGmwTKr371K26//fYWx+Tk5JCWlkZ5eXnQcq/XS1VVVav+qLq6OgoKCoiJieHjjz9Gp2vZtJebm8v8+fNxuVzNHoDmTFtRFkFimpe+Q84sq7dJVJfKVJWdES71VglbtYzfB/YaCb9Pw7ZVBsbP7DxXjxBK5cldO5X9GHeBk3FjnU1OZg3ZOa0XYFMtKD2V0godew4pFrapE6wBIZ11WqAUn9Rz4djWt5MY5yUxruPqcbQVCUmJOWmnC/jGsg08vu4Rnrzwd0xMnRT2euGIE1AC0PV6gdst4aiXiWqllpBGAwZD+94w2HwezC24olTO0BAm0JWB1+2JxWIJEijNkZSUhEajoawsuJ5PWVlZs9frtLS0No0HRRskJSVx8ODBjhEoycnJJCe3HmiVl5dHTU0NmzZtYvz48QAsX74cv99Pbm5us+tZrVZmz56NwWDgs88+w2hsPRp169atxMfHt5s6NEcLzAN9ZA48E0HrcqAIllKZI7t1lB7RsO87HZkDvaT169iGXqD4pr9eZmbPHgMaDUyfXs/IEaHFUbjZOYE04wgOklVpOx6vxMr1sQgBQ3IcQZaSPqluJAlqrFrq7DIxUV3npowEXtv1KptPbeL1Xa+GJVAarCdtwWRSBIrTJWE2gxTCiiLEmd+tQa/+HhuwS10XmNpbsncA9Ho948ePZ9myZVx99dUA+P1+li1bxn333Rdynby8PJYtW8Yvf/nLwLKlS5eSl5fX7PscO3aMyspK0tPTw55bh+ScDRs2jIKCAu666y42bNjAmjVruO+++7jpppsCGTzHjx9n6NChgVxrq9XKrFmzsNvtvPbaa1itVkpLSyktLcV3+mr6+eef889//pOdO3dy8OBB/v73v/OHP/yBn/3sZx2xGwEMJkjv72P4hR4u+596Rkx2g4DCL4y4Otj67fXC8hVm9u0zUFWlIdbia1acNIyH1oXHiBEu8vPt9O/vaXFcpKCWvQ+PtVtisNo0RJn9XDimLug1g16QmqQc7+KT3aM6ckdR6azkiyLlIvR50WdUOitbHN8W105jdDqBTisQAhzO0HfmbreEEErcmNyOFs3ztZ5kJsayeOEX7Tehc6Cz3Duh6M7unbZy//338+qrr/LWW2+xZ88efvKTn2C32wNZPbfeemtQEO0vfvELFi9ezJ/+9Cf27t3LE088wXfffRcQNDabjQceeIB169Zx5MgRli1bxlVXXcXAgQOZPXt22PPqsKT4d955h6FDhzJz5kwuu+wypkyZwj/+8Y/A6x6Ph3379lFfr2QHbN68mfXr17Njxw4GDhxIenp64FFSUgKATqfjpZdeIi8vj7Fjx/LKK6/w/PPP8/jjj3fUboTkghkuYhL8OOokChcawyrgNnp0VpsrOXo8sHRpFEeP6JEkSErykZTcssUmEIPSigUlIcFPv34e4uIi/y5aLXsfHgeOGNl32KRk7UyqDXk33jddEbclvVygfLB/AT6h/JZ8wseHB95vdZ22ipMGTGblOJSUnOLhhx8gN3cM/fqlMH78CG699UZWrlwFKO6g9uCmm64lMzOB7Vu3NHntlz/9CZmJsWQmxtIvNZExQwdy07VXseCdf+P3B58LtuzezyX5l7bLnNpKV1pPeiM33ngjzz33HI899hhjx45l69atLF68OBAIW1xczMmTJwPjJ0+ezLvvvss//vEPxowZw3/+8x8++eSTQA0UjUbD9u3bufLKKxk8eDB33nkn48eP59tvv22Tt6PDynQlJCQ0W5QNoH///ohGV/bp06cHPQ9FQUFBmyvRdQQ6PVx0lZMl/zZz4qCW3et1jLiwfS0RTqfEkiVRnDqlRasTjBvmYu8eQ6uum+5Q30Sl/am2avj2dNbOuOG2ZoNgs9JdbNgezYlyPV5f74hBOmk/wSnHqaBlb+55Lej5G7v/yeT0i4KWJZuSSY/KOCfXTmP0ekFp6RFuuWUWsbGxPProPIYNG4HX62HFimU8+eSv+Oyzze0iUI4dK+G77zbwg9vv5D/vv8uFeU1d6pfMzOf5F/+Gz+ej4lQ5K5Z9zWMPPcjCzz7ljXcWBMo6pKR2bWB6V1lPUg3L6fpCEp3Pfffd16xLZ+XKlU2WXX/99Vx//fUhx5tMJr766qvznlOE1xGNXBJS/UzId7FhsYHt3xhIyvCT2rd94lHq6yW+XBxNTbUGg9HP7Fn2QJfT1lw3Z7Jz2mUqEYVaVTY0Hq/E0jVxeDwSfdLcXDCi+XL2CbFeosx+7PVKunGftNazebo79yy/i/Vl64KWSUgIlN+SQHC07iizPpkRNObCtDzey/8EOHfrSQO///39gMS7766kT58zsXXZ2cOZNes2NBr45z//yvvvv8vRo0eIj4/n0ksLePTRJ4mKUnoilZQU89vfPsCGDetwuz1kZfXlscfmMXPmrMD23n//HS69dDbfv/UObrhyDvOfehqTKbjom15vCIiP9IwMRo0ZywUTJnLjNVfywXvv8P0f3gYoLp7X/vUOBXMvP6997470JvdOJNM96x5HCAPGeOg/Uul4vPYzI3Zr69Hfrbl5rFaZz7+IoaZag9nsZ+5cG8nJvjPBr63c8Ta4eNq70FNXo7p5QiMErNxgYfdBM8fL9UwcVYfcwtdQkiArTbk/7C1xKLcM/SEGjSEoO6NBnIRCQsKgMfD9IT8Azl+cVFdXs2rV13z/+3dhNEbhahSL4nZLWCxx6PUCWZb53e+eZtWqQv7yl7+xevU3zJ9/xn398MMP4HK5+fjjRaxYsZZHHnkCs/lMfRMhBAsWvMNl11zLgIGD6J+dzcLPPg1rjlMunsbwkaP48ovPz2tf2wPVvaPSgCpQzgNJgomznMQm+3HYJFb9x4SnhRvS1hp1VVXJfLEwGludjMXi4/LL64g/HSMSrvAIVJJVXTy9go07oikqUarFJsV5cHta/0ln9bI4lBsG3cSSq5eTE5uDHCqNphGyJDMgdgBLrl7OFVnXhS1OHA6J6mo5YMFszJEjhxFCMHSIUhSv3nFaoAhwuZT/9Xo/d999LxdddDFZWf2YMmUaDz74CJ999nFgO8ePlzBpUi7Dho2gX7/+XHppAXl5Z9xS33yzEofDwdRpMzCb9Fx7w40seOffYc0fYOCgQZQUF4c9viPpKveORoqMmj8qCqpAOU90eph2nQODWVBTLrPmMyP+c4g7LS/XsHBRDI56mfgEH3Pn2oiJOSMyws3OabC09EQXD6jZPI3Ze9jE1j3KHfSAvg6MBoHV1npQSWaqG1mG2joNtXW9IAgFGBI/lCVXr+CK7KtaHHdl9tUsuWYFfU0D27R9r1fC55NwuZqeUhti63R6gSwrNxtut4Tbo2TvaDSg0ykC4/rrr2TcuGEMHNiHn/3sx1RXVwUSCe688x5eeOE5rrxyNn/84x/YvXtn0PssWPA2l11xFZYYpQbO1ddex8b16zhSFF4/KyFEiy1Degsj4pv2ilPpGlSB0g5ExwqmXedAo4UTB7VsXt62O9Pjx7V8+WU0bpdESqqXuZfZMJuDhUi42TnhFmrrjqhunjMcK9Xz7XdKUOwFI+zkZClWEautdWWq1wnSkhRTX2+xogBE6aLIS5vcbCEuCYm89MlIXuXuvS2unYYCaw0WkcZkZw9AkiQOHjyA0ajcvdTXSzhPu3oMBj8lJUe59dYbGTZsBP/857/46quV/OEPfwTAc9ose8stt7Ju3Va+970b2bNnNwUFl/Daa68Aihvpyy+/4J1/vUHflAT6piQwfuRQvF4vC955O6x9OLh/P1n9ujb2wi45uzS1WCWyUAVKO5GU4efCyxXf6f7vdOzZELoC7tnpxkVHdCxZGoXXK5HZx0PBbFvIapLeMF03AQtK77gx7pVU1mhZujYOIWBQfyfjR9iwRCnK1GoP78A3uHl6SxxKA9srtqGRQn9GGknD9optQNvjTvSnrSN+P3g8wSIlPj6e6dNn8uab/0QIpe2A1ysFxIzTWc22bVvx+/088cTvGT9+IgMGDAzZtywzsw+33fY/vP762/z4x/fxzjtvAfDRRx+QmpbO0m/WsGTV6sDjsfm/58P33g3UkmqO1d+sYs/uXcy9/Mo27XdPQifVYZB7qOm5m6IejUYc2q7l6B4dskY52cgyyFqQZdH0f43ykGSBJvA/pOd4ObRdx9rPjMgawZDxzaco7tuv58svo7FaZfr3d3Npvr1ZYeEN04ISF+fD7ydwp9bT6O29eWqsGhatisfjkUhPcXPxhFokCSzRyvcsHBcPQN8MF+u3xXDylB6vt/XKwz2FTeXf4RVeNJIGrazl9mH/w5t7Xsfr9+IVXjaWbjznoFiDQeBwKJYRnS74RuKppxTXzGWXzeTnP/8tffuOxOPxsWnTMj744DVefvk1PB4Pr732CrNmzWHDhnX8+99vBG3j0UcfZMaMSxkwYAA1NTWsXfstgwYpfTree+/fFMy9gqHDhgetk5GZydPzn2TFsq/Jn6UUyHK7XZSXlQWlGf/1hT+TP7uA6266+Zz2vT1Qg2NVzqaXnJbCo65KprTo/EwPQoDHKWGrkdmw2IjB5KT/8KYiZccOAxs2mPD5wGT0M3Cgp0WrR7ium6lT1SCvnkptnYYvVibgcMokxHm5dHJN4DtjiVa+IHU2DULQasO5uBgf0VE+6h0aKmt0gQqzPRmn18nB2gMAZFuyeS3/LYbED+X7Q37A/3x9K4dqD3HIegCn14lR23qbjbMxGPw4HJpAZdjGx6Bfv/4sWbKKv/zlTzz99MOUlZURH5/E6NFjeeaZPzFixCieeOL3vPTSX/jDH+Zx4YWTeeihx/j5z+8JbMPv9/Hww/+PkydPEB0dwyWXzOTJJ59i27at7Nq1k2df+L8mc7JYYply8TQWvP2vgEBZsexrxg0fjFarJTYujuEjRjL/qWe4/ubvIzfX0bCTUN07Ko1RBUoj+g7zEpvkx+8Hv0/C71NMtk2eB/2vPBd+RUQIP6T1lzhxWLlyFH5hRNY46TtEESlCQPH2eI7tkUjQQkaGF7tNDiM7p2cHv7aV3lYTpc4us3BlAvUOmTiLl7nTqjA2cgXGRPmQJKUmisMlY27FgiZJMHtKDTFRPvS6nhevFAqnz8nQ+GGMShrNHyY/g1mrBJMOiR/KZ3OW8viGh9lTuwuX79wEilarBLE3BMGe7apNTU3jD3/4I/Pn/5Hqag2SBAnxvkB/nh//+Kf8+Mc/DVrn+uvPdIT9/e//GPJ9ExISOHDsVLNl7f/9/n8C/7/w0t954aW/h7U/xytrwxrXE9BLNWg0LTemVel81MtdIxJS/SSkto9rRAhYt8hA0Q4daz41wpVOsoZ4+W6pAeuxaMDBhAkObDaZvXsNrdY3USvEnmHkxBx2bgwvM6EnYKuX+WJFArZ6mdgYH5dPr8ZkDP4eaDQQZfJjq5ex2jStChQgojoVdwZxhjiWXrMiZKqxWRvFi5e+jF/4W01FbgmDQVBfr8SXNNeZuKEOik4nQjYPbCs9oWOxGhyrEgo1SLaDkCTIneOi/wilkNu3nxj5/B9mDmzWgQQ5EyoZM8Z1JjunFeHhCzPNuDfRG1KOa6waPlueQJ1dgyXax+XTqzCbQouPhjiUujDjUHojocSHw+kNxJ2cjzgBAlYtt1tqttyA83QqstGo/pYjAb1U09VTUGkGVaB0ILIMF8510n+Eh6qTGvZt1GO3Slx0pZO0AUqn2UB2TisunoAFRbV5Ab0j5bi8Ssu7XyRzokyPQS+4fHoVUebmLSMNcSi1qkAJm/Pts3M2soZAgGyolGOXSxEuGrl9mgP2FOtJV2PWdu/PsKeiCpQOxusGW62M9rQ5V6OFmlMyo0Yp6ca+MIWHL0who9IzOF6mZ+GKBKpqtdgdGsYOsxEd1bLbJhAoa1dVbDg0iJPzLWV/Ni3VRGmofdJTs+zOFdW9oxIKVaB0IM56iWXvmak4piExw0feFQ6MZsGutXrWLTTg94H3dHZOqxVifWp9k1D0RDfPwWIjX34Tj8crEWXykZrobtFy0kBMG1ONezMdJU5AESgNtU4alx/xeZUaKZIEhnZw7/QE60lXo7p3Ihv1VquDsFslli8wUVclYzALLrnRQUKqn9QsPxuXGCjaqYMD6aRo2lYhVqNaUAL0tGBZIeC7ndFs2a2Ur8/OcmKtU9KAdWEc91jVxdMmzlWcuFxKrRODQYSMI5EkxX3TMC4qShnjcCr3gw1F3c4Hm69npIVHQnCs6t6JXFQLSgdgrZL49O9RnCzSojUILv1BfSA7aOBYD9Ouc6AzCKwVBrbtlXG7w4hB8apZPM3RE6wobo/EkjVxAXEyakg9M/Nq8fnDP+4xp6vJOp0ybk8rhVB6MY2DYs+Fhmqxbnfzn/EZN49yihWNGgOa2sm9o1pPVHo6qkBpZ6pKZZa+babqpAZ7rcTQiR4sCcEXl4wcH7N+6CCjrwGvR0NZuZaTJ1s2oRTMtjF7tg2TSRUojekJwbJWm4bPliVw9LgBWYZpk2rJG1uHLIHXq/xEdbrWL2oGvQhcGFU3T2gaB8XqNq4n4eo56Daub9M2GoJgG24aQnF26XuXUyneptUKtOdZbqMnWU+6EtW9E/moAqUdKSvWsOw9E656CYNZkJTpI8py5sLi9UJ1uUzxPi0l+7XEp/rweSQcDplVq8wUFppCtmsHSEvz0aePV83iaYbuakUpOmbgo6WJVNVqMZv8XDGjiiHZZ07cntOxR+G4eKBRqnGYPXl6E2fHnZjfeBX9+kLMb/yzTdvRagmIj+Z+r3AmS8fpVH7j0H6pxT3FeqK6d1RaQr3ctRPHD2pY/YkJrwfiU30YzH4qT2g4sEXH0d06rFUy9damd1ySLED24fNJ7N5toKxMyyWX2ImNVaP8w6U7xqJ4fbBuawy7DyrVTFMSPeRPriH6rGDYhrt0XZiuPUuUj1OVOmptWsDVrnPuzpwtTqTKSowLP8M7YBDGhZ9irXwakZgY9vZ0WoHLLeHxSM26Z40GP06nBqdTCYyV5fMXKD3FeqKiEg6qBeUc8fuhvETDoe1alr5j4uO/RnHisIbqcpnqMg3H9uuordBw/KCW0iOagDjRGQWJGT76j/Qy+mI36X0hLqOey+fWYTD6qazU8MmnMRw8qEOo3pw20V2sKDVWDZ9+nRgQJ2OH2bliRlUTceL3N+rBFLYF5UxPHhWFUBk7pg/fBaD6jXdOP3+vTdvUnbaOnN25uDFanZJ15/VK/Pa3P2bEiBhefPHPQWO+/PIL0tPj2vTeHWU9yUyMJTMxlk0bNwYtd7lcjBjYn8zEWNau/rbJer++/xdkJcfz+acfN3ntT888RWZiLL/51S+Dlm/c+R2DE1IpOVoctHzhJ59xXcHlDEvvy+CUPuRPuog/P/Us1VXVAHzw73fpExXf5DEgIS2wjf+9+97A8v6xyeQNH8PvfvsYTucZy6Tq3ukeqBaU82D5AhPWKpnaChkEmGIE8al+NFpFiMhawYAxHrIGe7Ek+IlJEBhMIqiJ2I7VepweQf9sDzk5HlasjKL0pJZVq6I4csTD5Mn1mM2qUmmN7mBF8flh+94oVm2MxeeHeIvS8C8r3R1yvKdRjEO4Lp7MVBdCQHpy6G32RjSlJzHZa4KWmd9+C+fcK/ENGozzsiswv/0m7slTgsb4k1Pwp2eE3GZDHEpLAgUaygc01D4x8tJLL/DDH95BXFxcm/fjXK0nHo8HnS68wJeMzD68/97bjJ84MbBs8cLPiYqKpqa6usl4R309n330Eff+7Be8/87bXHHVNU3GGI1GFrz9b35878/IGTCg2fd+5on5/O35v/Cj+37Cg088Smp6OkUHD/Hv197gv++9z49+qjROjLHEsGpLsIiSzuqOOf3SmTz/8kt4vR62b9nG/979EyRJ4re/ezIwRnXvRD6qBeUckSSQZHDaJaIsfoblurn2PhtX/cTODffbyBrsJTHNz/AL3QwY7SW5jx+jOVic+P1Kc0GA72xlmM2COQU2LrjAgSQLjh7V8d+PLBw4oFpTwiVSrSinqrR8sjSRjTuiqbFqcDhkpk6obVacwJnaNw3ugXDISPEwabStxe32JhxOLykP/YKk2dODHpojRdTf/iMA6m//EZqiw03GxP7y3ma3q9Eox0SIlkWK36+IGa0Wpk6dRnJyKi+++Hyz49evL+Sqq+aQnZ3G+PEjeOSRX1Nfbw+8PqhPMosXfhG0zrDsvrz/rmIJKik+SmZiLJ9+/F++d8Vl5GSk8NGHH+D3+/nzH59h/MhhZKcnc+m0KaxY9nWT97/+ppv57KOPcDjOdEVf8M7bXH/TzSHn+/mnnzBoyBB++sv/ZV3hWo4fP9ZkTM7AQUyeMpVnfj8PCB0cu+W7Tbz4x+d59Knf8egf5jPhwlyy+vXl4pmX8Oq7/+L6W868vyRJpKSlBj2SU1OCtmcwGEhJSyWjTx8KrpjLlEum8+3ylSH3QSVyUQXKOSAEbFmhx+eBpAwfU652MvdH9WQM8BEdq0Tvh9N9uKE6rCXGhHw6xkCWYdw4F1dfVUdSkhe3S+Kbb6JYsiSKujr1cLVEJGb0eL2wbls0n3ydSGWNFoNeEGfxkpzgIS2p5TvihgufThcsbFXCo8G147juJvwmM36LhZoXX6Hiq5VUrN2EZ9KFAHhy86go3EzFVyupefEV/BYLfpMZx/dubHH7Z6wooV/3eCS8XiX+RKsRyLKGhx56lNdf/wcnThxvMv7IkSK+//3rmDv3CpYtW8PLL7/O+vXrePjhB9psPXlq3hPcefc9rCzcwPQZM/nnK3/nlZf+ymPz5rP0m7VMv2QGd9xyE4cPHQpab/SYsfTp25dFn38GwPFjJawvXMv3brgpxLvAgnf+zfeuvxGLJZZLZubzwXvvhhz38GNPsOjzz9i2ZTMAprMsOh8v+JCo6Ghuu/vOkOvHxsW2ZfeD2LtrN5vWbUCnVywmeqlGtZ50E9QrXhvx+2H9lwb2blC+4BfMdDF6qrvJBcR7+nzSUtaNr5EJXz4rCDIhwc8VV9iYMMGBrBEcO6bjvx/FsGWrocXMAZXIsaKUnNTzn6+S2L43CiFgQF8ncy6uwmz0o9XSJObkbNQO1udO47gT5w03U/nlcnxpGVh+/b9o9+7B17d/0Hhf3/5o9+7G8uv/xZeeSeWXy3HeENpq0IC+lTgUh+O0a8cgGrw8XHbZFYwYMYrnnnuqyfgXX3yea6+9nrvvvpecnAFMnJjL7373DB9+uACX09mm2JMf3XMvl11xJX379Sc1LY1X/voi9/78F1x17XUMHDSI3z4xjxEjR/HPl//WZN2bbvkBC975NwAfvPcuM/IvJTGpaQDx4UOH2PzdRq685loAvnf9jXzw7tuIEObeUWPGcsXV1zBv3qMh51t06BB9+/cLyxVlrbUyOKVP0OMHV18XNObrL79icEofBiSkkT/pIipOneKeX/6s1W2rRBZqDEob8Hlh7edGSvZpQYLcOU4GjA6tFnyeBgtK8xeXBhEjaxUz/pqaUi6KOxPsJcswZoyLfv08rC00cfKEjs2bTOzfbyB3koN+/TzqnfVZREIsSm2dhnXbYjh6XAnKjDL7mTLeSr8MFyUnlYtMbIy31WPXEIMSbvyJikKooFjfkKFUfbkMy69+TtwvfkL5tEvwpzb6rZWVEveLe3Fccx21z/0fmM2tvk/jeihCEHQ8vV6lo7EkgeksIfrb3z7B9ddfyT33BF8wd+3ayZ49u/joow8Dy4QQ+P1+SkqKiY8fGfZnMGbsuMD/dVYrpaUnmZh7YdCYCbkXsnvnjibrXnv9jTw17wmOHinig/feYd5Tz4Z8j/ff+TfTZswk4XT204xLZ/GrX9zH6m9WMXXa9Cbjf/3wI0zPm8TGb1eTlJwc9FooUdMc0TExfLlmZdAyo8kY9HzyxVP5w1/+hMNu59W//h2NVsvcq69Ug2O7GapACROPG779yMT21Xp8Xpj1g/pmxYkQZ9w3Lbl4ZBnS+vuQZBg9Oovt20tCjouL8zOnwM7hwzo2bDBRVaXho49j6N/fzUWTHaSm+kKu11sZOTGHrRsPM3Zgaqe+b71DZvPuKPYeNuP3K8d3xKB6xo+woT99MaupU74QDWXpWyJQPVgVKGHTUo8dYY7CfeFkjF98ij82DgDJVoeIjsEfG4fQanHnTg5LnIByfDUaJdPK65ECmT0AdrtinDYYmpa1z8u7iOnTZ/KHPzzJjTd+v9E6dn74w9u58857gsbX+zwMGKi4LyVJanIx93ibun9MYe5DKBISEpg5aza/+sXPcDldzMi/FJutLmiMz+fjwwXvUV5eRt+UhKDl77/7dkiB0j87h+tv/QFPPfYkz/3txaDXcgYOZGPh+rACemVZIntAy+5cc5Q5MOZPL/+VS3On8N5b/+a2269Q3TvdCNXFEwYup5KxU3pEAwLiU/yk5zR/gWncIKwlC4o5RjDjJgeX3OBodgwod2MlJVpOlmrxC6ivl7BaZUqKdXzxRQxffx1FTY16KM+ms1w9bo/Exh3RLFiUxO6DijjJSnfzvdkV5I2tC4gTUKwroFhQWkO1oLSNcBoA6rZvxTt0GJLfh+WX95I6KAvL//4U/H68Q4ai2761Te/ZYEVxN9IIHo8UaApobsaN99vfPs7SpYvZtOlMNsqoUWPYv38f2dk5gUdy3yz6ZeegPx0/kZiURFlZaWCdw4cO4aivb3GOMRYLaWnpbFy/Lmj5d+vXMXjI0JDr3HTLDylc/S3X3XgTmhAdSpctXYLNZuOrld+yZNXqwONvr77Gl198Tm1tTZN17JKT+x64n8MHD/Hpf/4b9NrVN16H3WbjrX+8FnI+tTW1Le5jS8iyzM8euJ8/Pvl7HI6urV6r0jZUC0or1NskVn5goqZcRm8UpPTzgT+84FcATRvLWje4eWx2iZJiHSUlOk6c1AbFqyQl+hg21IXPL1FeruXoUR1Hi3XkZLsZM8ZJQoJa5K0zXD0ut8TOA2Y2bI9RMm0kQUqih0mj68hICR3UWNtgQVEFSrsSbndi3fatiKhoEubMRFNSjP3uezH9+010WzYjYuPQbd/SpvfV6QROp3Q6DkU5TnZ7Q1qxv9nsq2HDRnDttdfz2muvBJbdd98vmDv3Uh5++AG+//0fYjZHsX3vLtav/ZbfP/scABdNvZg3//kqEyZOwufz8fsnHw8rbuOen/2cPz39FP2ysxkxcjQfvPs2u3bu4MVXQlfRvWRmPjv2HyY6Jibk6wve/jczZ81ixMhRQcsHDxnKE488zMcffsjtP7qryXp9s/pw18/u5eUXgi0oF0ycwE/+9+fMf+gRSk+cYM6Vl5OansaRQ0X8+7U3mJh3YSDNWAhBeWnTm4+klGTkZj7wy6+9mt//9lFef+VfPPD/7gs5RiXyUAVKC9hqJJa/b8JWLWOKVjoSL33HhMcptWgZCYiJNqSHCgEZSf1Yt9xKeVUMVZXBdy3RMX6y+njI6ushPe1MyfvqGpnvvjNRfFTH4cN6Dh/W07evh9GjnarrB8WK0t6unnqHzM4DZnYdNFPvkDlRrsdgENxyeTk5Wa4WY0saBEpcTFtcPKrgbIlwxQlOJ9q9e5C8XjxDhimBs0OGUv/9W4m7+3b0GwoRWi04nWA0tryt0+jPikNpnLnTWv2iX//6YT777Exxs+HDR/Lxxwt56qn5XH31ZQjhp192Dldefaa2yGPzfs/9P7uXay6fQ2paGvP+8Aw7tm1rdZ533n0PdVYr8x59hMqKUwwaMpQ33lnQbF0SSZICsSVnc6q8nGVLv+Kv/2gqbmRZpuCyy3nvnX8FCZTGqcX3/OI+/v3P13E5g60Zv/3dk4weN5Y3//FP3n7tTfx+P/2y+zP3mquC0ozrrHVcMKCp5Wfzob2kpIX+rWu1Wu6851Ze+vM/uPcn/0NU1Lm7wFQ6D0m0JTqph2C1WomNjeXBNz/BaI4KOaamQmbFAhMOm0R0nJ9LbnIQEydY8Mdo/D646l47UZbQH11dtcTnr0Sh1cMN99uanYfbBaVHtBw/qOHEIS2ueglrnYMErREkSEnx0jfLQ1aWh/h4f4sXvspKDdu2GSg6om+4kSM52cuIES769/cQwkrbK9jZTrEoQkB5lY5dB8wcLjHiP60Z/H5wuGQG93cyd3rTQlaN8Xrh9f8qc7n16nIlu6MFtu6NYsO2aAZnO5g+yXre+9DdMUXrGD2tD5kZmehOxxGELU4AqaaG+O9/D+/QYVh/92xQrIlUbyfmt79Gu38f1e/+B3E6RiUcampkvF6JmBg/9fUSPp+E2ew/rwKLDWnFPaXnjl1ydnnfndbSi11OLyVHT+BNKETS2YNes9U5mTbg99TW1mKxWDpkfg3Xpadu/zNGvemct+N0O3jozf/t0Ll2FqoFJQQVJ2RWfmjC7ZCITfZzyY0OzNECvx/8p298w7GghBpTVyNx/KCWEwe1lJdoAtsDpfrsyGE6XFIJBUMtberbkZjoY8aMemprnWzfbuDQIT2nTmlZuVKL2exn6FAXgwe7iYrqXXr0fANmvT4oKjGy84CZU1VnTOmpSR7GDrWzdW8UZRU6+me27ttW+uMogZOtiRNoZEFR04xbJBxxAiDi4qj6YmlIs6YwR2H980sEopvbgE6niE+HQxEnsky7dB3vKeIkElCzd7onqkA5i9IjGr75yITXDYkZPqZf78BwWswGxZaEEYMSaszudXoObT1zoYtJ8JM50EvGAB/JfXxoNLB9ux2jMbTvtzViY/1MnepgwgQne/fq2bPXQH29zObNJjZvMZGa6mX4MBd9+3p6VWfktrh6hIBTVTr2HzFysNiE231acGpgQF8HIwfVkxTvxemSKK+MA6BvRuuN+QIBstHhFbIZOcjOgL4ONQalGRxOb9jiJEBr4qON4gSUOBSHQ8LplNFqBSZTy9bO1uhpDQFDVY7tCtTsne5HL7pEtU7Jfi1rPjXi9ynpv1OvdaBr9J1uHKiqbSEuzdtCDZQ+A73YquXTosSLJaFjLj4mk2DcOBejR7soKtKxdZuRw4f1lJRoKS7WER3tp18/D9n93WRmenu0CyicgFkhoKpWy+ESI4dLjAExAUodk+ED6hmaU4+pkVWr5KQBISAhzktMVOtxImcCZMOLDTIaBEaDGkcUinMSJx2ETifw+c4YX0zn2bEYep71pKvdOyrdE1WgnObwDi3rFhlBQNYQL5OvcDaxgDRYRmQNLd4hNYwLJWIyB/rIHNhyWvHo0Vms2V4SVLTtXBACSku1HDykp7ZGgyXGh0YjK8F8bomDB/QcPKBHbxD066sE4PbJ9BBmX7FuRShXj/+0paT4hIGiYwbKKvVU1WiJjVEER3YfJ4P7O8hMbVopGODoCeUC2S8M6wlA7ekOw3FhZPCoNI/L5SPKcu4++vZG+JXzgU4niI7yB6rGnguq9aT9Ud073RdVoAB7N+pY9R8TkgwjJ7u4cK4rpKU34LrRtXyH1FIMSmfg80FRkY4dO41nsoEkGDzYzciRLpKTfZSVaSgq0lN0RIejXubAAT0HDuiRNYK0NC9Zfbykp3uJj/edi9U7Ylm3q5Kk6EyOlekpPmnA6Tyzc1abBq1G0CfVxZUzq4Pql5yNzw8lpW0TKDXW8FOMVZoiELg9XvSmyFLQdruMLCul7/VhxBa1hmo96YA5hOHeUfJFGh4qkUCHXXqqqqq45ZZbsFgsxMXFceedd2KzNZ/RAjB9+nQkSQp63HNPcFXF4uJi5s6di9lsJiUlhQceeADvuTanEbD9Wz2bvjZgrZTxeWB8fmhxAo1dNy1vNqWvl1m31jNxdngXruZYU1Pa+qBGuFywY4eBDz+0sGpVFFWVGrRawfARLq6/zsqMGfWkpPiQJEhL85GX5+CmG61cdpmNESNdWCw+/D6JE8d1rF9v4pNPYnjn3ViWLo1i+w4DJ09qmm2MFon4/VBVJbNvv55vvzWx58gYVm8YwCfLjewvMuF0yuh1gpwsJ2OH20iM9ZCS6OHSi2pbFCcAJ8v1eDwSZpOf5ITwPpSGINlwXTwqwWzaXYpGq8HjOb/fVXvi8Ui4Tpe0jwrDzdcSPc160t1wOT348YMmcr5fnclLL71E//79MRqN5ObmsmHDhhbHf/jhhwwdOhSj0cioUaNYtGhR0OtCCB577DHS09MxmUzk5+dz4MCBNs2pwywot9xyCydPnmTp0qV4PB7uuOMO7r77bt59N3S3ywbuuusu5s2bF3hubpQK6PP5mDt3Lmlpaaxdu5aTJ09y6623otPp+MMf/tDmOW5ZZaBoux4hwJLoJzrO32JsyZng15YvXgYjGDLO72TVUun7s7HZJXbtMrBvryHQuMxk8jN8uIuhQ90tZgPJMqSnK9aSC3OhtlamuETHieNaysq0uF0SxcU69u3TY6+XSUryERfnIynJR0K8j7h4H/HxPqLMXdtx1+2G6hoN1dXK4/hxLXa7HBCVDcTERYG3ktGDfGSlu0hLdqORYfG3ccgyDOznDEtAFJ9272Slt1z3pAGnS8LlUgaGGySrotBQEXjkxBxOHLej01UAoNMZOC9/Sjtgtcr4/UrZFL/fH0g/Pxd8fi8mgw6v291+E+xC6iUXJr0Or6vr9kcn1aHR6HC1cBMrhMDl9FBxqgq/sRhJ7n03EO+//z73338/L7/8Mrm5ubzwwgvMnj2bffv2kZKS0mT82rVrufnmm3nqqae4/PLLeffdd7n66qvZvHkzI0cqPaOeffZZ/u///o+33nqL7OxsHn30UWbPns3u3bsxhllnqEMEyp49e1i8eDEbN25kwoQJALz44otcdtllPPfcc2RkZDS7rtlsJi0tdOzFkiVL2L17N19//TWpqamMHTuW+fPn85vf/IYnnngiUA46XA5u0aHRwpjpLnZ8Y1AKq7UQLHrGddOmt+kwKis17Nhh4HCRDuFX5hYX52PkKBcDB7jPKfA1NtbPqFgXo0a68PvhxAkt33xrpqJCh8stYbNJIDTUVAdvXKsTWCx+YmJ8xMT4iY5S6kCYTMpfo9GPTtdy7E4ohFBcVi6XhMMhU1+v/LXbZerqZKx1yl9H/Rmzl8slUVauwWxS3FVJyT6Sk72kpXpJTfVyYPtRADJTlXiUU1Vaik8YkCS4YHjLVr6GOR1pa/zJ6QDZKLO/V2VPnS+NxQlAcbFS1t3j8SHLcheLYinQENBsPr/MHZffh07Xs74YbsmDXtu10fcayYFBbu1zFfjx4zcWg+VQp8wr0nj++ee56667uOOOOwB4+eWXWbhwIa+//joPPvhgk/F/+ctfKCgo4IEHHgBg/vz5LF26lL/+9a+8/PLLCCF44YUXeOSRR7jqqqsA+Ne//kVqaiqffPIJN910U1jz6pBfRGFhIXFxcQFxApCfn48sy6xfv55rrrmm2XXfeecd3n77bdLS0rjiiit49NFHA1aUwsJCRo0aRWrqmUDH2bNn85Of/IRdu3Yxbty4kNt0uVy4XGcuJLW1Sl8HIexMuNRJQrqPrSui0GjB5bCH3AaAo06Dz+cH4cfZSv+L9sDjcrCyrIgLY8/srxBw/ISGPbsNnDwpAx7AQ1q6h+HDXGRmKi4cj4fzcscIAUeLtWzYoMHpcBCf4GDECCeDB7kCloqaag01NRqsdTIup8QpJ5wqP3tLSlBunU1HTIxf8dPrBBqt0kRNlpW/Oq3A7ZHw+yX8PiXGw+2SqKnVYDKJs+6TBeA7/TiD2ezHYvFxtFhHXJzE4EEuLrnEEeSyEwIGjkpj9+YiNuw5wuicFNZvj8XntzOgrxODvg5nK5qj2qqlts6JRobEuBqcrtZ91uVVRnx+O9EmN05Xx393egLbDytfpuEXZON0nvnM9u+v59ChSgwGDVIXKRSvF5YsNeN2y4wd66R/v3O3im2tUyxCgzq5uWVHsltWrL/DMpvefXcWKYZvABgaRrKBkJ0geaGZ+xN7nXJS6Iy6pk73+QUWN6xvtQYXdzQYDBgMTTPf3G43mzZt4qGHHgosk2WZ/Px8CgsLQ75HYWEh999/f9Cy2bNn88knnwBQVFREaWkp+fn5gddjY2PJzc2lsLAwbIGC6AB+//vfi8GDBzdZnpycLP72t781u94rr7wiFi9eLLZv3y7efvttkZmZKa655prA63fddZeYNWtW0Dp2u10AYtGiRc1u9/HHH28c/aQ+1If6UB/qQ320+VFSUnIOV8TwcDgcIi0trV3mGR0d3WTZ448/HvJ9jx8/LgCxdu3aoOUPPPCAmDRpUsh1dDqdePfdd4OWvfTSSyIlJUUIIcSaNWsEIE6cOBE05vrrrxc33HBD2J9JmywoDz74IM8880yLY/bs2dOWTQZx9913B/4fNWoU6enpzJw5k0OHDjGgmZ4R4fDQQw8Fqb2amhr69etHcXExsbGx57zdrsZqtZKVlUVJSUm3Lmms7kdkoe5H5NFT9qW77ocQgrq6uhbDE84Xo9FIUVER7naIQRJCNLEuhrKeRDptEii/+tWvuP3221sck5OTQ1paGuXlwbZ+r9dLVVVVs/ElocjNzQXg4MGDDBgwgLS0tCaRxWVlio+6pe02Z9qKjY3tVj+S5rBYLOp+RBDqfkQWPWU/oOfsS3fcj864mTUajWEHkLYXSUlJaDSawLW0gbKysmavq2lpaS2Ob/hbVlZGenp60JixY8eGPbc2pRknJyczdOjQFh96vZ68vDxqamrYtGlTYN3ly5fj9/sDoiMctm7dChDYwby8PHbs2BEkLfHTlQAADsxJREFUfpYuXYrFYmH48OFt2RUVFRUVFZVej16vZ/z48SxbtiywzO/3s2zZMvLy8kKuk5eXFzQelGtxw/js7GzS0tKCxlitVtavX9/sNkMStjOojRQUFIhx48aJ9evXi9WrV4tBgwaJm2++OfD6sWPHxJAhQ8T69euFEEIcPHhQzJs3T3z33XeiqKhIfPrppyInJ0dcfPHFgXW8Xq8YOXKkmDVrlti6datYvHixSE5OFg899FCb5lZbWysAUVtb2z4720Wo+xFZqPsRWfSU/RCi5+xLT9mPnsaCBQuEwWAQb775pti9e7e4++67RVxcnCgtLRVCCPHDH/5QPPjgg4Hxa9asEVqtVjz33HNiz5494vHHHxc6nU7s2LEjMObpp58WcXFx4tNPPxXbt28XV111lcjOzhYOhyPseXWYQKmsrBQ333yziI6OFhaLRdxxxx2irq4u8HpRUZEAxIoVK4QQQhQXF4uLL75YJCQkCIPBIAYOHCgeeOCBJl/kI0eOiDlz5giTySSSkpLEr371K+HxeNo0N6fTKR5//HHhdDrPez+7EnU/Igt1PyKLnrIfQvScfekp+9ETefHFF0Xfvn2FXq8XkyZNEuvWrQu8Nm3aNHHbbbcFjf/ggw/E4MGDhV6vFyNGjBALFy4Met3v94tHH31UpKamCoPBIGbOnCn27dvXpjlJQnRC3pSKioqKioqKShvoQV1WVFRUVFRUVHoKqkBRUVFRUVFRiThUgaKioqKioqIScagCRUVFRUVFRSXiUAWKioqKioqKSsTRIwVKVVUVt9xyCxaLhbi4OO68805stpa71E6fPh1JkoIe99xzT9CY4uJi5s6di9lsJiUlhQceeABvC228O3s/qqqq+NnPfsaQIUMwmUz07duXn//854HmiA2cvZ+SJLFgwYJ2nftLL71E//79MRqN5ObmNqkAfDYffvghQ4cOxWg0MmrUKBYtWhT0uhCCxx57jPT0dEwmE/n5+Rw4cKBd5xyKtuzHq6++ytSpU4mPjyc+Pp78/Pwm42+//fYmn31BQUFH70ab9uPNN99sMsezq1t2h+MR6jctSRJz584NjOmK4/HNN99wxRVXkJGRgSRJgQZrLbFy5UouuOACDAYDAwcO5M0332wypq2/ufOlrfvx0Ucfcemll5KcnIzFYiEvL4+vvvoqaMwTTzzR5HgMHTq0A/dCJaI5p4TpCKegoECMGTNGrFu3Tnz77bdi4MCBQUXiQjFt2jRx1113iZMnTwYejWuwNBSJy8/PF1u2bBGLFi0SSUlJbS4S15H7sWPHDnHttdeKzz77TBw8eFAsW7ZMDBo0SHzve98LGgeIN954I2hf21I8pzUWLFgg9Hq9eP3118WuXbvEXXfdJeLi4kRZWVnI8WvWrBEajUY8++yzYvfu3eKRRx4JWfQnNjZWfPLJJ2Lbtm3iyiuvbHPRn47ej+9///vipZdeElu2bBF79uwRt99+u4iNjRXHjh0LjLnttttEQUFB0GdfVVXVYftwLvvxxhtvCIvFEjTHhoJNDXSH41FZWRm0Dzt37hQajUa88cYbgTFdcTwWLVokfvvb34qPPvpIAOLjjz9ucfzhw4eF2WwW999/v9i9e7d48cUXhUajEYsXLw6Maetn0xX78Ytf/EI888wzYsOGDWL//v3ioYceEjqdTmzevDkw5vHHHxcjRowIOh6nTp3qsH1QiWx6nEDZvXu3AMTGjRsDy7788kshSZI4fvx4s+tNmzZN/OIXv2j29UWLFglZloNO1H//+9+FxWIRLperXebemHPdj7P54IMPhF6vDypmF87J5HyYNGmS+OlPfxp47vP5REZGhnjqqadCjr/hhhvE3Llzg5bl5uaKH//4x0IIpeBPWlqa+OMf/xh4vaamRhgMBvHee+91wB4otHU/zsbr9YqYmBjx1ltvBZbddttt4qqrrmrvqbZIW/fjjTfeELGxsc1ur7sejz//+c8iJiZG2Gy2wLKuOB6NCee3+Otf/1qMGDEiaNmNN94oZs+eHXh+vp/N+XKu55Thw4eLJ598MvD88ccfF2PGjGm/ial0a3qci6ewsJC4uDgmTJgQWJafn48sy6xfv77Fdd955x2SkpIYOXIkDz30EPX19UHbHTVqFKmpqYFls2fPxmq1smvXrojaj8bU1tZisVjQaoP7Qv70pz8lKSmJSZMm8frrryPaqV6f2+1m06ZN5OfnB5bJskx+fj6FhYUh1yksLAwaD8pn2zC+qKiI0tLSoDGxsbHk5uY2u83z5Vz242zq6+vxeDwkJCQELV+5ciUpKSkMGTKEn/zkJ1RWVrbr3Btzrvths9no168fWVlZXHXVVUHf8e56PF577TVuuukmoqKigpZ35vE4F1r7fbTHZ9MV+P1+6urqmvw+Dhw4QEZGBjk5Odxyyy0UFxd30QxVupo2dTPuDpSWlpKSkhK0TKvVkpCQQGlpabPrff/736dfv35kZGSwfft2fvOb37Bv3z4++uijwHYbixMg8Lyl7Z4r57ofjamoqGD+/PncfffdQcvnzZvHjBkzMJvNLFmyhHvvvRebzcbPf/7z8553RUUFPp8v5Ge1d+/ekOs099k27GfD35bGtDfnsh9n85vf/IaMjIygC0dBQQHXXnst2dnZHDp0iIcffpg5c+ZQWFiIRqNp132Ac9uPIUOG8PrrrzN69Ghqa2t57rnnmDx5Mrt27aJPnz7d8nhs2LCBnTt38tprrwUt7+zjcS409/uwWq04HA6qq6vP+7vaFTz33HPYbDZuuOGGwLLc3FzefPNNhgwZwsmTJ3nyySeZOnUqO3fuJCYmpgtnq9IVdBuB8uCDD/LMM8+0OGbPnj3nvP3GF/FRo0aRnp7OzJkzOXToEAMGDDjn7Z5NR+9HA1arlblz5zJ8+HCeeOKJoNceffTRwP/jxo3Dbrfzxz/+sV0EiorC008/zYIFC1i5cmVQgOlNN90U+H/UqFGMHj2aAQMGsHLlSmbOnNkVU21CXl5eUMfRyZMnM2zYMF555RXmz5/fhTM7d1577TVGjRrFpEmTgpZ3h+PRE3n33Xd58skn+fTTT4NuxObMmRP4f/To0eTm5tKvXz8++OAD7rzzzq6YqkoX0m0Eyq9+9Stuv/32Fsfk5OSQlpZGeXl50HKv10tVVRVpaWlhv19ubi4ABw8eZMCAAaSlpTWJii8rKwNo03Y7Yz/q6uooKCggJiaGjz/+GJ1O1+L43Nxc5s+fj8vlwmAwhLUfzZGUlIRGowl8Ng2UlZU1O++0tLQWxzf8LSsrIz09PWjM2LFjz2u+zXEu+9HAc889x9NPP83XX3/N6NGjWxybk5NDUlISBw8e7JAL4vnsRwM6nY5x48Zx8OBBoPsdD7vdzoIFC5g3b16r79PRx+NcaO73YbFYMJlMaDSa8z7GncmCBQv40Y9+xIcfftjEdXU2cXFxDB48OPDdU+lddJsYlOTkZIYOHdriQ6/Xk5eXR01NDZs2bQqsu3z5cvx+f0B0hMPWrVsBAifgvLw8duzYESQali5disViYfjw4RGzH1arlVmzZqHX6/nss8+apIc2t6/x8fHnLU4A9Ho948ePZ9myZYFlfr+fZcuWBd2VNyYvLy9oPCifbcP47Oxs0tLSgsZYrVbWr1/f7DbPl3PZD4Bnn32W+fPns3jx4qD4oeY4duwYlZWVQRf69uRc96MxPp+PHTt2BObYnY4HKCnsLpeLH/zgB62+T0cfj3Ohtd9HexzjzuK9997jjjvu4L333gtK924Om83GoUOHIup4qHQiXR2l2xEUFBSIcePGifXr14vVq1eLQYMGBaXnHjt2TAwZMkSsX79eCCHEwYMHxbx588R3330nioqKxKeffipycnLExRdfHFinIc141qxZYuvWrWLx4sUiOTm5w9OM27IftbW1Ijc3V4waNUocPHgwKFXP6/UKIYT47LPPxKuvvip27NghDhw4IP72t78Js9ksHnvssXab94IFC4TBYBBvvvmm2L17t7j77rtFXFxcIAPqhz/8oXjwwQcD49esWSO0Wq147rnnxJ49e8Tjjz8eMs04Li5OfPrpp2L79u3iqquu6pS01rbsx9NPPy30er34z3/+E/TZ19XVCSGEqKurE//v//0/UVhYKIqKisTXX38tLrjgAjFo0KAObT/f1v148sknxVdffSUOHTokNm3aJG666SZhNBrFrl27gvY10o9HA1OmTBE33nhjk+VddTzq6urEli1bxJYtWwQgnn/+ebFlyxZx9OhRIYQQDz74oPjhD38YGN+QZvzAAw+IPXv2iJdeeilkmnFLn00k7Mc777wjtFqteOmll4J+HzU1NYExv/rVr8TKlStFUVGRWLNmjcjPzxdJSUmivLy8w/ZDJXLpkQKlsrJS3HzzzSI6OlpYLBZxxx13BC4SQghRVFQkALFixQohhBDFxcXi4osvFgkJCcJgMIiBAweKBx54IKgOihBCHDlyRMyZM0eYTCaRlJQkfvWrXwWl73b1fqxYsUIAIR9FRUVCCCVVeezYsSI6OlpERUWJMWPGiJdffln4fL52nfuLL74o+vbtK/R6vZg0aZJYt25d4LVp06aJ2267LWj8Bx98IAYPHiz0er0YMWKEWLhwYdDrfr9fPProoyI1NVUYDAYxc+ZMsW/fvnad8/nuR79+/UJ+9o8//rgQQoj6+noxa9YskZycLHQ6nejXr5+46667OvQici778ctf/jIwNjU1VVx22WVBtSqE6B7HQwgh9u7dKwCxZMmSJtvqquPR3O+0Ye633XabmDZtWpN1xo4dK/R6vcjJyQmq5dJAS59NJOzHtGnTWhwvhJI+nZ6eLvR6vcjMzBQ33nijOHjwYIfuh0rkIgnRTvmlKioqKioqKirtRLeJQVFRUVFRUVHpPagCRUVFRUVFRSXiUAWKioqKioqKSsShChQVFRUVFRWViEMVKCoqKioqKioRhypQVFRUVFRUVCIOVaCoqKioqKioRByqQFFRUVFRUVGJOFSBoqKioqKiohJxqAJFRUVFRUVFJeJQBYqKioqKiopKxPH/AbUzEji69dUiAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Plots\n",
+    "\"\"\"\n",
+    "x1 = np.arange(-0.5, 1.5, 0.02)\n",
+    "y1 = np.arange(-0.5, 1.5, 0.02)\n",
+    "xx, yy = np.meshgrid(x1, y1)\n",
+    "\n",
+    "# eval objective and constraints\n",
+    "J = (1 - xx) ** 2 + a * (yy - xx ** 2) ** 2\n",
+    "c1 = xx - yy\n",
+    "c2 = xx ** 2 + yy ** 2 - (p / 2) ** 2\n",
+    "c3 = -(xx ** 2 + yy ** 2) + p ** 2\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1)\n",
+    "cp = ax.contourf(xx, yy, J,\n",
+    "                 levels=[0, 0.05, 0.2, 0.5, 1.0, 2.0, 4.0, 6.0, 8.0],\n",
+    "                 alpha=0.6)\n",
+    "fig.colorbar(cp)\n",
+    "ax.set_title('Rosenbrock problem')\n",
+    "cg1 = ax.contour(xx, yy, c1, [0], colors='mediumblue', alpha=0.7)\n",
+    "plt.setp(cg1.collections,\n",
+    "         path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
+    "cg2 = ax.contour(xx, yy, c2, [0], colors='mediumblue', alpha=0.7)\n",
+    "plt.setp(cg2.collections,\n",
+    "         path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
+    "cg3 = ax.contour(xx, yy, c3, [0], colors='mediumblue', alpha=0.7)\n",
+    "plt.setp(cg3.collections,\n",
+    "         path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
+    "\n",
+    "# Solution to mpNLP via Neuromancer\n",
+    "datapoint = {'a': torch.tensor([[a]]), 'p': torch.tensor([[p]]),\n",
+    "             'name': 'test'}\n",
+    "model_out = problem(datapoint)\n",
+    "x_nm = model_out['test_' + \"x\"][0, 0].detach().numpy()\n",
+    "y_nm = model_out['test_' + \"x\"][0, 1].detach().numpy()\n",
+    "print(x_nm)\n",
+    "print(y_nm)\n",
+    "\n",
+    "# plot optimal solutions CasADi vs Neuromancer\n",
+    "ax.plot(sol.value(x), sol.value(y), 'g*', markersize=10, label='CasADi')\n",
+    "ax.plot(x_nm, y_nm, 'r*', fillstyle='none', markersize=10, label='NeuroMANCER')\n",
+    "plt.legend(bbox_to_anchor=(1.0, 0.15))\n",
+    "plt.show(block=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "id": "yD5kAnjy4CUL"
+   },
+   "outputs": [],
+   "source": [
+    "def eval_constraints(x, y, p):\n",
+    "    \"\"\"\n",
+    "    evaluate mean constraints violations\n",
+    "    \"\"\"\n",
+    "    con_1_viol = np.maximum(0, y - x)\n",
+    "    con_2_viol = np.maximum(0, (p/2)**2 - (x**2+y**2))\n",
+    "    con_3_viol = np.maximum(0, x**2+y**2 - p**2)\n",
+    "    con_viol = con_1_viol + con_2_viol + con_3_viol\n",
+    "    con_viol_mean = np.mean(con_viol)\n",
+    "    return con_viol_mean\n",
+    "\n",
+    "def eval_objective(x, y):\n",
+    "    obj_value_mean = np.mean((1 - x) ** 2 + a * (y - x ** 2) ** 2)   \n",
+    "    return obj_value_mean\n",
+    "\n",
+    "# select n number of random samples to evaluate\n",
+    "n_samples = 1000\n",
+    "idx = np.random.randint(0, nsim, n_samples)\n",
+    "p_samples = samples_test['p'][idx]\n",
+    "a_samples = samples_test['a'][idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "id": "r85kJ1Dr_nQ2"
+   },
+   "outputs": [],
+   "source": [
+    "# create named dictionary for neuromancer\n",
+    "datapoint = {'a': a_samples, 'p': p_samples, 'name': 'test'}\n",
+    "\n",
+    "# Solve via neuromancer\n",
+    "t = time.time()\n",
+    "model_out = problem(datapoint)\n",
+    "nm_time = time.time() - t\n",
+    "x_nm = model_out['test_' + \"x\"][:, [0]].detach().numpy()\n",
+    "y_nm = model_out['test_' + \"x\"][:, [1]].detach().numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "gemVhNU2AALr"
+   },
+   "outputs": [],
+   "source": [
+    "# Solve via solver\n",
+    "t = time.time()\n",
+    "x_solver, y_solver = [], []\n",
+    "for i in range(0, n_samples):\n",
+    "    prob, x, y = NLP_param(p_samples[i].numpy(), a_samples[i].numpy(), opti_silent=True)\n",
+    "    sol = prob.solve()\n",
+    "    x_solver.append(sol.value(x))\n",
+    "    y_solver.append(sol.value(y))\n",
+    "solver_time = time.time() - t\n",
+    "x_solver = np.asarray(x_solver)\n",
+    "y_solver = np.asarray(y_solver)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5n-SG-TuADib",
+    "outputId": "6a251657-0b4d-41f9-80c0-537966f1f6f0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Solution for 1000 problems via Neuromancer obtained in 0.0038 seconds\n",
+      "Neuromancer mean constraints violation 0.3537\n",
+      "Neuromancer mean objective value 0.0610\n",
+      "\n",
+      "Solution for 1000 problems via solver obtained in 9.5317 seconds\n",
+      "Solver mean constraints violation 0.0746\n",
+      "Solver mean objective value 0.2036\n",
+      "\n",
+      "Solution speedup factor 2540.9132\n",
+      "MSE primal optimizers: 0.11051816632586187\n",
+      "mean objective value discrepancy: 70.02 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate neuromancer solution\n",
+    "print(f'Solution for {n_samples} problems via Neuromancer obtained in {nm_time:.4f} seconds')\n",
+    "nm_con_viol_mean = eval_constraints(x_nm, y_nm, p)\n",
+    "print(f'Neuromancer mean constraints violation {nm_con_viol_mean:.4f}')\n",
+    "nm_obj_mean = eval_objective(x_nm, y_nm)\n",
+    "print(f'Neuromancer mean objective value {nm_obj_mean:.4f}\\n')\n",
+    "\n",
+    "# Evaluate solver solution\n",
+    "print(f'Solution for {n_samples} problems via solver obtained in {solver_time:.4f} seconds')\n",
+    "solver_con_viol_mean = eval_constraints(x_solver, y_solver, p)\n",
+    "print(f'Solver mean constraints violation {solver_con_viol_mean:.4f}')\n",
+    "solver_obj_mean = eval_objective(x_solver, y_solver)\n",
+    "print(f'Solver mean objective value {solver_obj_mean:.4f}\\n')\n",
+    "\n",
+    "# neuromancer solver comparison\n",
+    "speedup_factor = solver_time/nm_time\n",
+    "print(f'Solution speedup factor {speedup_factor:.4f}')\n",
+    "\n",
+    "# Difference in primal optimizers\n",
+    "dx = (x_solver - x_nm)[:,0]\n",
+    "dy = (y_solver - y_nm)[:,0]\n",
+    "err_x = np.mean(dx**2)\n",
+    "err_y = np.mean(dy**2)\n",
+    "err_primal = err_x + err_y\n",
+    "print('MSE primal optimizers:', err_primal)\n",
+    "\n",
+    "# Difference in objective\n",
+    "err_obj = np.abs(solver_obj_mean - nm_obj_mean) / solver_obj_mean * 100\n",
+    "print(f'mean objective value discrepancy: {err_obj:.2f} %')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "neuromancer",
+   "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/examples/parametric_programming/Part_2_pQP.ipynb b/examples/parametric_programming/Part_2_pQP.ipynb
index c92f354f..3aeaec8a 100644
--- a/examples/parametric_programming/Part_2_pQP.ipynb
+++ b/examples/parametric_programming/Part_2_pQP.ipynb
@@ -90,7 +90,14 @@
     }
    ],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/parametric_programming/Part_3_pNLP.ipynb b/examples/parametric_programming/Part_3_pNLP.ipynb
index 2ad87043..3c0c041e 100644
--- a/examples/parametric_programming/Part_3_pNLP.ipynb
+++ b/examples/parametric_programming/Part_3_pNLP.ipynb
@@ -97,7 +97,14 @@
     }
    ],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
@@ -111,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 1,
    "metadata": {
     "id": "KbP0n-4evRqt"
    },
@@ -119,6 +126,7 @@
    "source": [
     "import torch\n",
     "import torch.nn as nn\n",
+    "import numpy as np \n",
     "import neuromancer.slim as slim\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.patheffects as patheffects\n",
@@ -1066,7 +1074,7 @@
   "kernelspec": {
    "display_name": "neuromancer",
    "language": "python",
-   "name": "neuromancer"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1078,7 +1086,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/examples/parametric_programming/Part_4_projectedGradient.ipynb b/examples/parametric_programming/Part_4_projectedGradient.ipynb
index 56572fd3..e133bbb3 100644
--- a/examples/parametric_programming/Part_4_projectedGradient.ipynb
+++ b/examples/parametric_programming/Part_4_projectedGradient.ipynb
@@ -60,7 +60,14 @@
    },
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/parametric_programming/Part_5_cvxpy_layers.ipynb b/examples/parametric_programming/Part_5_cvxpy_layers.ipynb
index 06f1c739..8f6eeb71 100644
--- a/examples/parametric_programming/Part_5_cvxpy_layers.ipynb
+++ b/examples/parametric_programming/Part_5_cvxpy_layers.ipynb
@@ -70,7 +70,14 @@
    },
    "outputs": [],
    "source": [
-    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/tutorials/part_1_linear_regression.ipynb b/examples/tutorials/part_1_linear_regression.ipynb
index ae9be8bb..759b5a8e 100644
--- a/examples/tutorials/part_1_linear_regression.ipynb
+++ b/examples/tutorials/part_1_linear_regression.ipynb
@@ -26,7 +26,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install git+https://github.com/pnnl/neuromancer.git@master"
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a53dec3e",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/tutorials/part_2_variable.ipynb b/examples/tutorials/part_2_variable.ipynb
index 51d63cac..17e7459f 100644
--- a/examples/tutorials/part_2_variable.ipynb
+++ b/examples/tutorials/part_2_variable.ipynb
@@ -31,7 +31,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install git+https://github.com/pnnl/neuromancer.git@master"
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06def2bb",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/examples/tutorials/part_3_node.ipynb b/examples/tutorials/part_3_node.ipynb
index 2d9cbc2b..8b1fb47d 100644
--- a/examples/tutorials/part_3_node.ipynb
+++ b/examples/tutorials/part_3_node.ipynb
@@ -27,7 +27,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install git+https://github.com/pnnl/neuromancer.git@master"
+    "!pip install neuromancer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22e70f37",
+   "metadata": {},
+   "source": [
+    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
    ]
   },
   {
diff --git a/pyproject.toml b/pyproject.toml
index 5633d357..1154aa20 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,6 +9,7 @@ authors = [
     { name = "Jan Drgona", email = "jan.drgona@pnnl.gov" },
     { name = "James Koch", email = "james.koch@pnnl.gov" },
     { name = "Madelyn Shapiro", email = "madelyn.shapiro@pnnl.gov"},
+    { name = "Rahul Birmiwal", email = "rahul.birmiwal@pnnl.gov"},
     { name = "Draguna Vrabie", email = "draguna.vrabie@pnnl.gov" }
 ]
 description = "Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularization"
@@ -48,10 +49,14 @@ dependencies = [
     "pyts",
     "torch",
     "torchdiffeq",
-    "toml"
+    "toml", 
+    "lightning", 
+    "cvxpy", 
+    "cvxpylayers", 
+    "casadi"
 ]
 
-version = "1.4.2"
+version = "1.5.0"
 
 [project.urls]
 homepage = "https://github.com/pnnl/neuromancer/"
diff --git a/src/neuromancer/__init__.py b/src/neuromancer/__init__.py
index 97d0c362..c826cb26 100644
--- a/src/neuromancer/__init__.py
+++ b/src/neuromancer/__init__.py
@@ -13,6 +13,7 @@
 from neuromancer import problem
 from neuromancer import trainer
 from neuromancer import plot
+from neuromancer import utils 
 
 from neuromancer.dynamics import *
 from neuromancer.modules import *
diff --git a/src/neuromancer/constraint.py b/src/neuromancer/constraint.py
index 53deb6b9..a39f8bd9 100644
--- a/src/neuromancer/constraint.py
+++ b/src/neuromancer/constraint.py
@@ -15,6 +15,10 @@
 import torch.nn as nn
 import torch.nn.functional as F
 from neuromancer.gradients import gradient
+from neuromancer.utils import handle_device_placement
+import lightning.pytorch as pl
+
+
 
 
 class Loss(nn.Module):
@@ -77,6 +81,7 @@ def __init__(self, norm=1):
     def __str__(self):
         return 'lt'
 
+    @handle_device_placement
     def forward(self, left, right):
         """
 
@@ -113,6 +118,7 @@ def __init__(self, norm=1):
     def __str__(self):
         return 'gt'
 
+    @handle_device_placement
     def forward(self, left, right):
         """
 
@@ -147,6 +153,7 @@ def __init__(self, norm=1):
     def __str__(self):
         return 'eq'
 
+    @handle_device_placement
     def forward(self, left, right):
         """
 
@@ -154,6 +161,8 @@ def forward(self, left, right):
         :param right: torch.Tensor
         :return: zero dimensional torch.Tensor, torch.Tensor, torch.Tensor
         """
+        #right = right.type_as(left)
+        
         value = left - right
         if self.norm == 1:
             penalty = torch.abs(value)
@@ -347,7 +356,7 @@ def __init__(self, input_variables=[], func=None, key=None, display_name=None, v
         self._is_input = key is not None
         self.key = key
         self._display_name = display_name
-
+    
     def make_graph(self, input_variables):
         """
         This is the function that composes the graph of the Variable from constituent input variables which
@@ -390,7 +399,10 @@ def make_graph(self, input_variables):
         # self Can't be part of ordered nodes since this will make a loop when retrieving parameters
         ordered_nodes = nn.ModuleList(nx.topological_sort(g))[:-1]
         return g, ordered_nodes
+   
 
+    
+            
     @property
     def display_name(self):
         name = self._display_name
diff --git a/src/neuromancer/dataset.py b/src/neuromancer/dataset.py
index bb168e37..56edfe1d 100644
--- a/src/neuromancer/dataset.py
+++ b/src/neuromancer/dataset.py
@@ -9,8 +9,81 @@
 import torch
 from torch.utils.data import Dataset, DataLoader
 from torch.utils.data.dataloader import default_collate
+import sys 
 
+import lightning.pytorch as pl 
+from lightning.pytorch.callbacks import ModelCheckpoint
+from lightning.pytorch.callbacks.early_stopping import EarlyStopping
 
+class LitDataModule(pl.LightningDataModule):
+    """
+    A Neuromancer-specific class inheriting from PyTorch Lightning LightningDataModule
+    This class converts a data_setup_function (which yields Neuromancer DictDatasets associated with a Neuromancer Problem)
+    to a LightningDataModule such that it integrates with LitProblem and LitTrainer
+    """
+    def __init__(self,data_setup_function, hparam_config=None, **kwargs):
+        """
+        Minimial required input is the data_setup_function (callable) (see README.md as well as examples)
+        If the data_setup_function requires any arguments, they should also be passed in here as keyword arguments
+
+        :param data_setup_function: Function that generates Neuromancer DictDatasets
+        """
+        super().__init__()
+        self.data_setup_function = data_setup_function
+        self.data_setup_kwargs = kwargs
+        self.train_data = None
+        self.dev_data = None
+        self.test_data = None
+        self.hparam_config = hparam_config
+        self.param_sweep_batch_size = None #to be used for wandb param sweep
+        self._load_from_config()
+
+    def _load_from_config(self): 
+        if self.hparam_config: 
+            if "batch_size" in self.hparam_config: 
+                self.param_sweep_batch_size = self.hparam_config.batch_size
+        
+
+    def setup(self, stage=None):
+        """
+        Setup is a preprecessing stage required by LightningDataModules. Here we create the data splits from the data setup function, 
+        and we do data splitting and check that the user has properly named the DictDatasets
+        """
+        train_data, dev_data, test_data, batch_size = self.data_setup_function(**self.data_setup_kwargs)
+
+        try:
+            assert dev_data is None or dev_data.name == 'dev', f"Invalid name '{dev_data.name}' for dev_data DictDataset. Expected 'train'."
+        except AssertionError as e:
+            print("AssertionError:", e)
+            sys.exit(1) 
+
+        try:
+            assert train_data is None or train_data.name == 'train', f"Invalid name '{train_data.name}' for train_data DictDataset. Expected 'dev'."
+        except AssertionError as e:
+            print("AssertionError:", e)
+            sys.exit(1) 
+
+        self.train_data = train_data
+        self.dev_data = dev_data
+        self.test_data = test_data
+        self.batch_size = batch_size if not self.param_sweep_batch_size else self.param_sweep_batch_size
+        print("USING BATCH SIZE ", self.batch_size)
+
+    def train_dataloader(self):
+        return DataLoader(self.train_data, batch_size=self.batch_size, collate_fn=self.train_data.collate_fn)
+
+    def val_dataloader(self):
+        if self.dev_data is not None:
+            return DataLoader(self.dev_data, batch_size=self.batch_size, collate_fn=self.dev_data.collate_fn)
+        else:
+            # Return an empty DataLoader if dev_data is None
+            return DataLoader(dataset=[], batch_size=self.batch_size)
+
+    # currently unused
+    def test_dataloader(self):
+        return DataLoader(self.test_data, batch_size=self.batch_size, collate_fn=self.dev_data.collate_fn)
+    
+    
 class DictDataset(Dataset):
     """
     Basic dataset compatible with neuromancer Trainer
diff --git a/src/neuromancer/problem.py b/src/neuromancer/problem.py
index 731c9146..febdf51f 100644
--- a/src/neuromancer/problem.py
+++ b/src/neuromancer/problem.py
@@ -1,6 +1,3 @@
-"""
-
-"""
 # python base imports
 import os
 import pydot
@@ -8,6 +5,7 @@
 import matplotlib.image as mpimg
 import matplotlib.pyplot as plt
 import warnings
+import lightning.pytorch as pl 
 from typing import Dict, List, Callable
 
 # machine learning/data science imports
@@ -15,6 +13,92 @@
 import torch.nn as nn
 
 
+
+class LitProblem(pl.LightningModule):
+    """
+    A PyTorch-Lightning Module wrapper for the Neuromancer Problem class. 
+    As is customary with LightningModules, steps for training and validation are outlined here, as well as the optimizer
+    Logging metrics are also defined here, such as 'train_loss'. 
+    """
+    def __init__(self, problem, train_metric='train_loss', dev_metric='train_loss', test_metric='train_loss', custom_optimizer=None, \
+                custom_training_step=None, hparam_config=None):
+        """
+        :param problem: A Neuromancer Problem()
+        :param train_metric: metric to be used during training step. Default to train_loss
+        :param dev_metric: metric to be used during validation step. Default to train_loss
+        :param test_metric: metric to be used during testing step (currently not supported yet)
+        :param custom_optimizer: Optimizer to be used during training. Default is None, in which an 
+                                 Adam optimizer is used with learning rate = 0.001
+        :param custom_training_step: Custom training step function, if desired. Defaults to None, in which case the standard training step procedure is executed
+        """
+        super().__init__()
+        self.problem = problem
+        self.train_metric = train_metric
+        self.dev_metric = dev_metric
+        self.test_metric = test_metric
+        self.custom_optimizer=custom_optimizer
+        self.custom_training_step = custom_training_step
+        self.hparam_config = hparam_config
+        self.lr = .001
+
+        self.training_step_outputs = []
+        self.validation_step_outputs = []
+
+        self._load_from_config()
+
+    
+    def _load_from_config(self): 
+        if self.hparam_config: 
+            if "learning_rate" in self.hparam_config: 
+                self.lr = self.hparam_config.learning_rate
+    
+
+
+    # Defines training step logic for a Neuromancer problem. Registers train_loss
+    def training_step(self, batch):
+        if self.custom_training_step is not None: 
+            loss = self.custom_training_step(self, batch)
+        else: 
+            output = self.problem(batch)
+            loss = output[self.train_metric]
+        self.training_step_outputs.append(loss)
+        self.log('train_loss', loss, on_epoch=True, enable_graph=True, prog_bar=True)
+        return loss
+
+    # Defines what to do after each training epoch
+    def on_train_epoch_end(self):
+        epoch_average = torch.stack(self.training_step_outputs).mean()
+        #print(f'epoch: {self.current_epoch}  : {epoch_average}')
+        self.log("training_epoch_average", epoch_average) #log to lightning_logs
+        self.training_step_outputs.clear() 
+
+    # Defines validation step logic for a Neuromancer problem. Registers dev_loss
+    def validation_step(self, batch):
+        output = self.problem(batch)
+        loss = output[self.dev_metric]
+        self.validation_step_outputs.append(loss)
+        self.log('dev_loss', loss, prog_bar=True)
+
+    # Defines what to do after each validation epoch
+    def on_validation_epoch_end(self):
+        epoch_average = torch.stack(self.validation_step_outputs).mean()
+        self.log("validation_epoch_average", epoch_average)
+        self.validation_step_outputs.clear()  # free memory
+
+    # Defines the optimizers
+    def configure_optimizers(self):
+        if self.custom_optimizer is None: 
+            print("USING LEARNING RATE ", self.lr)
+            optimizer = torch.optim.Adam(self.problem.parameters(), self.lr, betas=(0.0, 0.9))
+        else: 
+            optimizer = self.custom_optimizer
+        return optimizer
+    
+    # Returns the original Neuromancer problem
+    def get_problem(self):
+        return self.problem
+    
+
 class Problem(nn.Module):
     """
     This class is similar in spirit to a nn.Sequential module. However,
@@ -197,6 +281,20 @@ def show(self, figname=None):
             fig.axes.get_yaxis().set_visible(False)
             plt.show()
 
+    def freeze(self):
+        """
+        Freezes the parameters of all nodes in the system
+        """
+        for node in self.nodes:
+            node.freeze()
+
+    def unfreeze(self):
+        """
+        Unfreezes the parameters of all nodes in the system
+        """
+        for node in self.nodes:
+            node.unfreeze()
+
     def __repr__(self):
         s = "### MODEL SUMMARY ###\n\nnodeS:"
         if len(self.nodes) > 0:
diff --git a/src/neuromancer/slim/linear.py b/src/neuromancer/slim/linear.py
index 5ec2bc86..34f9ba66 100644
--- a/src/neuromancer/slim/linear.py
+++ b/src/neuromancer/slim/linear.py
@@ -48,10 +48,14 @@ def __init__(self, insize, outsize, bias=False, provide_weights=True):
         """
         super().__init__()
         self.in_features, self.out_features = insize, outsize
-        self.bias = nn.Parameter(torch.zeros(1, outsize), requires_grad=not bias)
-        if bias:
+        self.use_bias = bias 
+        if bias: 
             bound = 1 / math.sqrt(insize)
+            self.bias = nn.Parameter(torch.zeros(1, outsize), requires_grad=bias)
             torch.nn.init.uniform_(self.bias, -bound, bound)
+        else: 
+            self.register_parameter('bias', None)
+
         if provide_weights:
             self.weight = nn.Parameter(torch.Tensor(insize, outsize))
             torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
@@ -89,12 +93,13 @@ def effective_W(self):
 
     def forward(self, x):
         """
-0-
         :param x: (torch.Tensor, shape=[batchsize, in_features])
         :return: (torch.Tensor, shape=[batchsize, out_features])
         """
-        return torch.matmul(x, self.effective_W()) + self.bias
-
+        if self.use_bias: 
+            return torch.matmul(x, self.effective_W()) + self.bias
+        else: 
+            return torch.matmul(x, self.effective_W())
 
 class Linear(LinearBase):
     """
diff --git a/src/neuromancer/system.py b/src/neuromancer/system.py
index 26d46375..43f31730 100644
--- a/src/neuromancer/system.py
+++ b/src/neuromancer/system.py
@@ -52,6 +52,20 @@ def forward(self, data):
             output = [output]
         return {k: v for k, v in zip(self.output_keys, output)}
 
+    def freeze(self):
+        """
+        Freezes the parameters of the callable in this node
+        """
+        for param in self.callable.parameters():
+            param.requires_grad = False
+
+    def unfreeze(self):
+        """
+        Unfreezes the parameters of the callable in this node
+        """
+        for param in self.callable.parameters():
+            param.requires_grad = True
+
     def __repr__(self):
         return f"{self.name}({', '.join(self.input_keys)}) -> {', '.join(self.output_keys)}"
 
@@ -254,4 +268,18 @@ def forward(self, input_dict):
                 indata = {k: data[k][:, i] for k in node.input_keys}  # collect what the compute node needs from data nodes
                 outdata = node(indata)  # compute
                 data = self.cat(data, outdata)  # feed the data nodes
-        return data  # return recorded system measurements
\ No newline at end of file
+        return data  # return recorded system measurements
+
+    def freeze(self):
+        """
+        Freezes the parameters of all nodes in the system
+        """
+        for node in self.nodes:
+            node.freeze()
+
+    def unfreeze(self):
+        """
+        Unfreezes the parameters of all nodes in the system
+        """
+        for node in self.nodes:
+            node.unfreeze()
\ No newline at end of file
diff --git a/src/neuromancer/trainer.py b/src/neuromancer/trainer.py
index 337e923c..1afda9c0 100644
--- a/src/neuromancer/trainer.py
+++ b/src/neuromancer/trainer.py
@@ -6,17 +6,183 @@
 
 import torch
 from torch.optim.lr_scheduler import ReduceLROnPlateau
+
 import numpy as np
+import wandb
+import lightning.pytorch as pl 
 
 from neuromancer.loggers import BasicLogger
 from neuromancer.problem import Problem
 from neuromancer.callbacks import Callback
+from neuromancer.problem import LitProblem
+from neuromancer.dataset import LitDataModule
+
+from lightning.pytorch.callbacks import ModelCheckpoint
+from lightning.pytorch.callbacks.early_stopping import EarlyStopping
+from lightning.pytorch.loggers import WandbLogger
+
 
 
 def move_batch_to_device(batch, device="cpu"):
     return {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}
 
 
+class CustomEarlyStopping(EarlyStopping):
+    """
+    Custom early stopping callback inherited from PyTorch Lightning Early Stopping. 
+    Needed to support proper warmup functionality (early stopping cannot occur within warmup grace period)
+    """
+    def __init__(self, monitor, patience, warmup=0):
+        self.warmup = warmup
+        self.monitor = monitor 
+        self.patience = patience 
+        super().__init__(monitor=monitor, patience=patience)
+    
+    def _run_early_stopping_check(self, trainer) -> None:
+        if trainer.current_epoch < self.warmup: 
+            trainer.should_stop = False
+            return None 
+        else: 
+            # If not in the warm-up period, perform early stopping as usual
+            super()._run_early_stopping_check(trainer)
+
+
+class LitTrainer(pl.Trainer):
+    def __init__(self, epochs=1000, train_metric='train_loss', dev_metric='dev_loss', test_metric='test_loss', eval_metric='dev_loss',
+                 patience=None, warmup=0, clip=100.0, custom_optimizer=None, save_weights=True, weight_path='./', weight_name=None, devices='auto', strategy='auto', \
+                    accelerator='auto', profiler=None, custom_training_step=None, logger=None, hparam_config=None):
+        
+        """
+        A Neuromancer-specific custom trainer class inheriting from PyTorch Lightning's Trainer. 
+        This class is mainly a wrapper to interface with the user through fit()
+
+        For more information please see: https://lightning.ai/docs/pytorch/stable/common/trainer.html
+
+        :param epochs: Number of epochs for training. Defaults to 1000.
+        :param train_metric: Metric for training. Defaults to 'train_loss'.
+        :param dev_metric: Metric for development/validation. Defaults to 'dev_loss'.
+        :param test_metric: Metric for testing. Defaults to 'test_loss'. Currently unused
+        :param eval_metric: Metric for model checkpointing. Defaults to 'dev_loss'.
+        :param patience: Number of epochs to wait for improvement before early stopping. Defaults to None (no patience)
+        :param warmup: Number of warmup epochs. Defaults to 0.
+        :param clip: Gradient clipping value, by norm. Defaults to 100.0.
+        :param custom_optimizer: Optimizer to be used during training. If None (default), an Adam optimizer with learning rate of 0.001 will be used. 
+        :param save_weights: Whether to save weights. Defaults to True.
+        :param weight_path: Path to save weights. Defaults to './'.
+        :param weight_name: Name of the weight file. By default, filename is None and will be set to '{epoch}-{step}', where “epoch” and “step” match the number of finished epoch and optimizer steps respectively.
+        :param devices: Device assignment strategy. Defaults to 'auto'.
+        :param strategy: Strategy for distributed training. Defaults to 'auto'.
+        :param accelerator: Accelerator type. Defaults to 'auto'.
+        :param profiler: Profiler to use. Defaults to None (no profiling)
+        :param custom_training_step: Custom training step function, if desired. Defaults to None, in which case the standard training step procedure is executed
+
+        """
+        self.epochs = epochs
+        self.train_metric = train_metric
+        self.dev_metric = dev_metric
+        self.test_metric = test_metric
+        self.eval_metric = eval_metric
+        self.patience = patience
+        self.warmup = warmup
+        self.clip = clip
+        self.save_weights = save_weights
+        self.weight_path = weight_path
+        self.weight_name = weight_name
+        self.devices = devices
+        self.custom_optimizer = custom_optimizer
+        self.profiler = profiler 
+        self.custom_training_step = custom_training_step
+        self.logger = logger
+        self.hparam_config = hparam_config 
+
+        self.problem_copy = None #store copy of base Neuromancer problem
+        self.lit_problem = None
+        self.lit_data_module = None 
+
+
+        callbacks = []
+        if self.save_weights:
+            callbacks.append(ModelCheckpoint(save_weights_only=True, monitor=self.eval_metric, dirpath=self.weight_path, filename=self.weight_name, \
+                                             mode='min', every_n_epochs=1, verbose=True))
+        if self.patience:
+            callbacks.append(CustomEarlyStopping(monitor=self.eval_metric, patience=self.patience, warmup=self.warmup))
+
+        super().__init__(max_epochs=self.epochs, callbacks=callbacks, devices=self.devices, strategy=strategy, accelerator=accelerator, \
+                         gradient_clip_val=clip, profiler=self.profiler, logger=self.logger)
+
+
+    def get_weights(self):
+        # Get state dict of best model
+        best_model = self.lit_problem.problem.state_dict()
+        return best_model
+
+
+    def hyperparameter_sweep(self, problem, data_setup_function, sweep_config, count=10, project_name='run_sweep', **kwargs):
+        """
+        Performs hyperparameter tuning sweep using wandb
+        """
+        self.problem_copy = deepcopy(problem) # store the original problem so that the original is used to train each sweep
+        self.data_setup_function = data_setup_function
+
+        # A nester LiTrainer class is required to circumvent a PyTorch lightning constraint that "current_spoch" cannot be set. So this 
+        # allows epoch counter to be reset after each sweep
+        class TempTrainer:
+            def __init__(self, parent, epochs=1000, train_metric='train_loss', dev_metric='dev_loss', test_metric='test_loss', eval_metric='dev_loss',
+                 patience=None, warmup=0, clip=100.0, custom_optimizer=None, save_weights=True, weight_path='./', weight_name=None, devices='auto', strategy='auto', \
+                    accelerator='auto', profiler=None, custom_training_step=None, logger=None):
+                self.parent = parent
+                self.problem_copy = deepcopy(parent.problem_copy)
+                self.data_setup_function = parent.data_setup_function
+                self.epochs = epochs
+                self.train_metric = train_metric
+                self.dev_metric = dev_metric
+                self.test_metric = test_metric
+                self.eval_metric = eval_metric
+                self.patience = patience
+                self.warmup = warmup
+                self.clip = clip
+                self.save_weights = save_weights
+                self.weight_path = weight_path
+                self.weight_name = weight_name
+                self.devices = devices
+                self.custom_optimizer = custom_optimizer
+                self.profiler = profiler 
+                self.custom_training_step = custom_training_step
+                self.accelerator = accelerator
+
+            def train_model(self):
+                wandb.init()
+                self.parent.hparam_config = wandb.config
+                trainer = LitTrainer(epochs=self.epochs, train_metric=self.train_metric, dev_metric=self.dev_metric, test_metric=self.test_metric, eval_metric=self.eval_metric, \
+                                                  patience=self.patience, warmup=self.warmup, clip=self.clip, save_weights=False, devices=self.devices, \
+                                                  custom_optimizer=self.custom_optimizer, profiler=None, custom_training_step=self.custom_optimizer, accelerator=self.accelerator, \
+                                                  hparam_config=wandb.config)
+                trainer.fit(self.problem_copy, self.data_setup_function, **kwargs)
+
+        sweep_id = wandb.sweep(sweep_config, project=project_name)
+        trainer_within_itself = TempTrainer(self, epochs=self.epochs, train_metric=self.train_metric, dev_metric=self.dev_metric, test_metric=self.test_metric, eval_metric=self.eval_metric, \
+                                                  patience=self.patience, warmup=self.warmup, clip=self.clip, save_weights=False, devices=self.devices, \
+                                                  custom_optimizer=self.custom_optimizer, profiler=None, custom_training_step=self.custom_optimizer, accelerator=self.accelerator)
+        wandb.agent(sweep_id=sweep_id, function=trainer_within_itself.train_model, count=count)
+
+
+    def fit(self, problem, data_setup_function, **kwargs):
+        """
+        Fits (trains) a base neuromancer Problem to a data defined by a data setup function). 
+        This function will also instantiate a Lightning version of the provided Problem 
+        and LightningDataModule associated with the data setup function
+
+        :param problem: A Neuromancer Problem() we want to train/fit
+        :param data_setup_function: A function that returns train/dev/test Neuromancer DictDatasets as well as batch_size to use
+        """
+        self.problem_copy = deepcopy(problem)
+        self.data_setup_function = data_setup_function
+        self.lit_problem = LitProblem(problem,self.train_metric, self.dev_metric, self.test_metric, custom_training_step=self.custom_training_step, hparam_config=self.hparam_config )
+        self.lit_data_module = LitDataModule(data_setup_function,self.hparam_config ,**kwargs)
+        super().fit(self.lit_problem, self.lit_data_module)
+
+
+
 class Trainer:
     """
     Class encapsulating boilerplate PyTorch training code. Training procedure is somewhat
@@ -157,6 +323,9 @@ def train(self):
 
         self.callback.end_train(self, output)  # write training visualizations
 
+        # Assign best weights to the model
+        self.model.load_state_dict(self.best_model)
+
         if self.logger is not None:
             self.logger.log_artifacts({
                 "best_model_state_dict.pth": self.best_model,
diff --git a/src/neuromancer/utils.py b/src/neuromancer/utils.py
new file mode 100644
index 00000000..288fbc72
--- /dev/null
+++ b/src/neuromancer/utils.py
@@ -0,0 +1,36 @@
+
+import torch
+import functools
+import lightning.pytorch as pl
+from collections import OrderedDict
+
+def handle_device_placement(func):
+    """
+    This is a decorator to handle automated GPU support for Neuromancer constraints. 
+    It decorates a forward method that takes in two tensors (left and right) and ensures 
+    both tensors reside on the same non-cpu device (if a GPU is available)
+    """
+    @functools.wraps(func)
+    def wrapper(self, left, right):
+        # Check if either tensor is on the CPU and the other on a non-CPU device
+        if left.device.type == 'cpu' != right.device.type:
+            left = left.type_as(right)
+        elif right.device.type == 'cpu' != left.device.type:
+            right = right.type_as(left)
+        
+        return func(self, left, right)
+    
+    return wrapper
+
+def load_state_dict_lightning(problem, weight_path): 
+    """
+    This function handles loading problem weights when said problem was trained using 
+    LitTrainer method
+
+    :param problem: A Neuromancer Problem
+    :param weight_path: (str) Path to weights saved by LitTrainer method
+    """
+    weights = torch.load(weight_path)['state_dict']
+    weights = OrderedDict({key.replace('problem.', '', 1): value for key, value in weights.items()})
+    problem.load_state_dict(weights)
+    return problem
\ No newline at end of file
diff --git a/tests/test_constraints.py b/tests/test_constraints.py
index e3690c0d..a7f2ac1d 100644
--- a/tests/test_constraints.py
+++ b/tests/test_constraints.py
@@ -4,6 +4,30 @@
 import torch
 
 
+
+@given(constraint_class = st.sampled_from[cn.LT, cn.EQ, cn.GT], device=st.sampled_from(['cpu', 'cuda:1']))
+def test_constraint_device_placement(constraint_class, device):
+    left_tensor = torch.tensor(10.)
+    right_tensor = torch.tensor(7.)
+    left_tensor = left_tensor.to(device=device)
+    right_tensor = right_tensor.to('cpu')
+
+    test_constraint = constraint_class()
+    loss, value, penalty = test_constraint(left_tensor, right_tensor)
+    if device == 'cuda:1':
+        assert value.device.type == 'cuda'
+        assert value.get_device() == 1
+    else: 
+        assert value.device.type == 'cpu'
+
+    loss, value, penalty = test_constraint(right_tensor, left_tensor)
+    if device == 'cuda:1':
+        assert value.device.type == 'cuda'
+        assert value.get_device() == 1
+    else: 
+        assert value.device.type == 'cpu'
+
+
 @given(st.lists(st.integers(1, 100), min_size=1, max_size=4))
 @settings(max_examples=10, deadline=None)
 def test_add_two_variables(shape):
@@ -660,4 +684,6 @@ def test_variable_expression_slicing2(shape):
 def test_variable_expression_slicing_shape(shape):
     x = cn.variable('x')
     data = {'x': torch.rand(shape)}
-    assert (x+x)[1:](data).shape[0] == (x + x)(data).shape[0] - 1
\ No newline at end of file
+    assert (x+x)[1:](data).shape[0] == (x + x)(data).shape[0] - 1
+
+
diff --git a/tests/test_trainer.py b/tests/test_trainer.py
new file mode 100644
index 00000000..f051de56
--- /dev/null
+++ b/tests/test_trainer.py
@@ -0,0 +1,299 @@
+import torch
+import torch.nn as nn
+import pytest
+import neuromancer.slim as slim
+from unittest import TestCase
+from neuromancer.trainer import Trainer, LitTrainer
+from neuromancer.problem import Problem, LitProblem
+from neuromancer.constraint import variable
+from neuromancer.dataset import DictDataset, LitDataModule
+from neuromancer.loss import PenaltyLoss
+from neuromancer.modules import blocks
+from neuromancer.system import Node
+
+
+
+
+data_seed = 408  # random seed used for simulated data
+torch.manual_seed(data_seed)
+nsim = 5000  # number of datapoints: increase sample density for more robust results
+
+# create dictionaries with sampled datapoints with uniform distribution
+a_low, a_high, p_low, p_high = 0.2, 1.2, 0.5, 2.0
+
+def data_setup_function(nsim=nsim, a_low=a_low, a_high=a_high, p_low=p_low, p_high=p_high): 
+
+    
+    samples_train = {"a": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),
+                    "p": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}
+    samples_dev = {"a": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),
+                "p": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}
+    samples_test = {"a": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),
+                "p": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}
+    # create named dictionary datasets
+    train_data = DictDataset(samples_train, name='train')
+    dev_data = DictDataset(samples_dev, name='dev')
+    test_data = DictDataset(samples_test, name='test')
+
+    batch_size = 64
+
+    # Return the dict datasets in train, dev, test order, followed by batch_size 
+    # If dev dataset is not wanted to be used, return None 
+    return train_data, dev_data, test_data, batch_size 
+
+
+def sample_problem(): 
+    # define neural architecture for the trainable solution map
+    func = blocks.MLP(insize=2, outsize=2,
+                    bias=True,
+                    linear_map=slim.maps['linear'],
+                    nonlin=nn.ReLU,
+                    hsizes=[80] * 4)
+    # wrap neural net into symbolic representation of the solution map via the Node class: sol_map(xi) -> x
+    sol_map = Node(func, ['a', 'p'], ['x'], name='map')
+    # define decision variables
+    x1 = variable("x")[:, [0]]
+    x2 = variable("x")[:, [1]]
+    # problem parameters sampled in the dataset
+    p = variable('p')
+    a = variable('a')
+
+    # objective function
+    f = (1-x1)**2 + a*(x2-x1**2)**2
+    obj = f.minimize(weight=1.0, name='obj')
+
+    # constraints
+    Q_con = 100.  # constraint penalty weights
+    con_1 = Q_con*(x1 >= x2)
+    con_2 = Q_con*((p/2)**2 <= x1**2+x2**2)
+    con_3 = Q_con*(x1**2+x2**2 <= p**2)
+    con_1.name = 'c1'
+    con_2.name = 'c2'
+    con_3.name = 'c3'
+
+    # constrained optimization problem construction
+    objectives = [obj]
+    constraints = [con_1, con_2, con_3]
+    components = [sol_map]
+
+    # create penalty method loss function
+    loss = PenaltyLoss(objectives, constraints)
+    # construct constrained optimization problem
+    problem = Problem(components, loss)
+
+    return problem
+
+
+
+@pytest.fixture(params=[sample_problem()])
+def get_problem(request): 
+    return request.param
+
+
+@pytest.fixture(params=[data_setup_function])
+def get_data(request): 
+    return request.param
+
+@pytest.fixture(params=[10])
+def get_num_epochs(request): 
+    return request.param
+
+@pytest.fixture(params=[10])
+def get_num_epochs(request): 
+    return request.param
+
+
+
+
+
+
+
+
+def compare_state_dicts(dict1, dict2):
+    # Check if keys are the same
+    if dict1.keys() != dict2.keys():
+        return False
+
+    # Check if values (tensors) are equal for each key
+    for key in dict1.keys():
+        if torch.equal(dict1[key], dict2[key]):
+            return False
+    return True
+
+
+
+def test_trainer_initialization(get_problem, get_data): 
+
+    epochs = 400
+    patience = 11
+    warmup = 100
+    clip = 1.0 
+    train_metric = 'dev_loss'
+    dev_metric = 'train_loss'
+
+
+
+
+    lit_data_module = LitDataModule(data_setup_function=get_data, nsim=nsim,a_low=0.2, a_high=1.2, p_low=0.5, p_high=2.0)
+    
+
+    train_data, dev_data, test_data, batch_size = get_data(nsim=nsim,a_low=0.2, a_high=1.2, p_low=0.5, p_high=2.0)
+    train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, num_workers=0,
+                                        collate_fn=train_data.collate_fn, shuffle=True)
+    dev_loader = torch.utils.data.DataLoader(dev_data, batch_size=batch_size, num_workers=0,
+                                            collate_fn=dev_data.collate_fn, shuffle=True)
+    test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, num_workers=0,
+                                            collate_fn=test_data.collate_fn, shuffle=True)
+
+
+
+
+    lit_trainer = LitTrainer(epochs=epochs, accelerator='cpu', train_metric=train_metric, dev_metric=dev_metric,clip=clip, \
+                             warmup=warmup, patience=patience )
+    base_trainer = Trainer(
+        get_problem,
+        train_loader,
+        dev_loader,
+        test_loader,
+        patience=patience,
+        epochs=epochs, 
+        clip = clip, 
+        train_metric=train_metric, 
+        dev_metric=dev_metric
+
+    )
+
+    assert base_trainer.epochs == epochs 
+    assert base_trainer.patience == patience 
+    assert base_trainer.clip == clip 
+    assert base_trainer.train_metric == train_metric 
+    assert base_trainer.dev_metric == dev_metric 
+
+    assert lit_trainer.epochs == epochs 
+    assert lit_trainer.patience == patience 
+    assert lit_trainer.clip == clip 
+    assert lit_trainer.train_metric == train_metric 
+    assert lit_trainer.dev_metric == dev_metric 
+
+def test_train_runs_for_epochs(get_problem, get_data, get_num_epochs):
+    
+    lit_data_module = LitDataModule(data_setup_function=get_data, nsim=nsim,a_low=0.2, a_high=1.2, p_low=0.5, p_high=2.0)
+    
+
+    train_data, dev_data, test_data, batch_size = get_data(nsim=nsim,a_low=0.2, a_high=1.2, p_low=0.5, p_high=2.0)
+    train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, num_workers=0,
+                                        collate_fn=train_data.collate_fn, shuffle=True)
+    dev_loader = torch.utils.data.DataLoader(dev_data, batch_size=batch_size, num_workers=0,
+                                            collate_fn=dev_data.collate_fn, shuffle=True)
+    test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, num_workers=0,
+                                            collate_fn=test_data.collate_fn, shuffle=True)
+
+
+    num_epochs = get_num_epochs
+
+    lit_trainer = LitTrainer(epochs=num_epochs, accelerator='cpu', save_weights=False)
+    base_trainer = Trainer(
+        get_problem,
+        train_loader,
+        dev_loader,
+        test_loader,
+        patience=99999,
+        epochs=num_epochs
+    )
+
+    # Test for Standard PyTorch Trainer
+    _ = base_trainer.train()
+    assert base_trainer.current_epoch == num_epochs
+    print("HELLO")
+    # Test for PyTorch Lightning Trainer
+    lit_trainer.fit(problem=get_problem, datamodule=lit_data_module)
+    assert lit_trainer.current_epoch == num_epochs
+
+
+def test_weight_updates(get_problem, get_data):
+    problem = get_problem
+    lit_data_module = LitDataModule(data_setup_function=get_data, nsim=nsim,a_low=0.2, a_high=1.2, p_low=0.5, p_high=2.0)
+    
+
+    train_data, dev_data, test_data, batch_size = get_data(nsim=nsim,a_low=0.2, a_high=1.2, p_low=0.5, p_high=2.0)
+    train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, num_workers=0,
+                                        collate_fn=train_data.collate_fn, shuffle=True)
+    dev_loader = torch.utils.data.DataLoader(dev_data, batch_size=batch_size, num_workers=0,
+                                            collate_fn=dev_data.collate_fn, shuffle=True)
+    test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, num_workers=0,
+                                            collate_fn=test_data.collate_fn, shuffle=True)
+    num_epochs = 10
+
+    
+
+    
+
+    # Train for  1 epochs
+    lit_trainer = LitTrainer(epochs=1, accelerator='cpu', save_weights=False)
+    base_trainer = Trainer(
+        problem,
+        train_loader,
+        dev_loader,
+        test_loader,
+        patience=99999,
+        epochs=1
+    )
+    _ = base_trainer.train()
+    lit_trainer.fit(problem=problem, datamodule=lit_data_module)
+
+    lit_trainer_initial_weights = lit_trainer.get_weights().copy()
+    print("lit trainer initial weights ", lit_trainer_initial_weights)
+    base_trainer_initial_weights = base_trainer.best_model.copy()
+
+    # Train for 9 more epochs
+    lit_trainer = LitTrainer(epochs=9, accelerator='cpu', save_weights=False)
+    base_trainer = Trainer(
+        problem,
+        train_loader,
+        dev_loader,
+        test_loader,
+        patience=99999,
+        epochs=9
+    )
+    _ = base_trainer.train()
+    lit_trainer.fit(problem=problem, datamodule=lit_data_module)
+    lit_trainer_final_weights = lit_trainer.get_weights().copy()
+    base_trainer_final_weights = base_trainer.best_model.copy()
+
+    compare_state_dicts(base_trainer_initial_weights, base_trainer_final_weights)
+    compare_state_dicts(lit_trainer_initial_weights, lit_trainer_final_weights)
+
+"""
+def test_early_stopping(get_problem, get_data): 
+    problem = get_problem
+    lit_data_module = LitDataModule(data_setup_function=get_data, nsim=nsim,a_low=0.2, a_high=1.2, p_low=0.5, p_high=2.0)
+    
+
+    train_data, dev_data, test_data, batch_size = get_data(nsim=nsim,a_low=0.2, a_high=1.2, p_low=0.5, p_high=2.0)
+    train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, num_workers=0,
+                                        collate_fn=train_data.collate_fn, shuffle=True)
+    dev_loader = torch.utils.data.DataLoader(dev_data, batch_size=batch_size, num_workers=0,
+                                            collate_fn=dev_data.collate_fn, shuffle=True)
+    test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, num_workers=0,
+                                            collate_fn=test_data.collate_fn, shuffle=True)
+
+
+    num_epochs = 400
+
+    lit_trainer = LitTrainer(epochs=num_epochs, accelerator='cpu', patience=3, save_weights=False)
+    base_trainer = Trainer(
+        get_problem,
+        train_loader,
+        dev_loader,
+        test_loader,
+        patience=3,
+        warmup=0,
+        epochs=num_epochs
+    )
+
+    _ = base_trainer.train()
+    lit_trainer.fit(problem=problem, datamodule=lit_data_module)
+
+    assert base_trainer.current_epoch == 5 
+    assert lit_trainer.current_epoch == 5
+"""
\ No newline at end of file
diff --git a/tests/test_utils.py b/tests/test_utils.py
new file mode 100644
index 00000000..0d2be8cf
--- /dev/null
+++ b/tests/test_utils.py
@@ -0,0 +1,50 @@
+import pytest 
+import unittest 
+from neuromancer.utils import handle_device_placement
+import torch 
+import lightning.pytorch as pl
+
+torch.manual_seed(0)
+
+class DummyConstraint(pl.LightningModule):
+    def __init__(self, callable):
+        super().__init__()
+        self.callable = callable
+
+    @handle_device_placement
+    def forward(self, left, right):
+        return self.callable(left, right)
+
+def callable_1(left, right):
+    return left - right
+
+def callable_2(left, right):
+    return left + right
+
+def callable_3(left, right):
+    return left * right
+
+@pytest.mark.parametrize("callable", [
+    callable_1,
+    callable_2,
+    callable_3,
+])
+def test_handle_device_placement(callable):
+    
+    cons = DummyConstraint(callable)
+
+    left_tensor = torch.randn(3, 3)  
+    right_tensor = torch.randn(3, 3).to(device='cuda:1')  
+
+    result = cons(left_tensor, right_tensor)
+
+    if left_tensor.device.type == 'cpu' != right_tensor.device.type:
+        expected_result = callable(left_tensor.type_as(right_tensor), right_tensor)
+    elif right_tensor.device.type == 'cpu' != left_tensor.device.type:
+        expected_result = callable(left_tensor, right_tensor.type_as(left_tensor))
+    else:
+        expected_result = callable(left_tensor, right_tensor)
+        
+    assert (torch.allclose(result, expected_result))
+    assert result.device.type == 'cuda'
+    assert result.get_device() == 1
\ No newline at end of file