Repository: pnnl/neuromancer (language: python)

Issue:
added random walk signals to signals dictionary, modified signals tes…
…t to test for any numpy data type


Discussion / hints:

Fix patch:
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 4e688fc4..63956c7f 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,5 +1,5 @@
 
-## Community Development
+## Community Development Guidelines
 
 We welcome contributions and feedback from the open-source community!
 
@@ -34,4 +34,22 @@ Before creating a PR, we encourage developers to discuss and document the intend
 in [Ideas](https://github.com/pnnl/neuromancer/discussions/categories/ideas) discussion category.
 
 ### Reporting issues or bugs
-If you find a bug in the code or want to request a new well-developed feature, please open an issue.
+If you find a bug in the code or want to request a new well-developed feature, please open an [issue](https://github.com/pnnl/neuromancer/issues).
+
+
+## NeuroMANCER development plan
+Here are some upcoming features we plan to develop. Please let us know if you would like to get involved and 
+contribute so we may be able to coordinate on development. If there is a feature that you think would
+be highly valuable but not included below, please open an issue and let us know your thoughts. 
+
++ Control and modelling for networked systems
++ Support for stochastic differential equations (SDEs)
++ Easy to implement modeling and control with uncertainty quantification
++ Proximal operators for dealing with equality and inequality constraints
++ Interface with CVXPYlayers
++ Online learning examples
++ Benchmark examples of DPC compared to deep RL
++ Conda and pip package distribution
++ More versatile and simplified time series dataloading
++ Discovery of governing equations from learned RHS via NODEs and SINDy
++ More domain science examples
diff --git a/README.md b/README.md
index e3f562e6..06f23b0d 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,8 @@
+
+<p align="center">
+  <img src="figs/Neuromancer.png" width="250">  
+</p>
+
 # NeuroMANCER v1.4.1
 
 **Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations (NeuroMANCER)**
@@ -71,6 +76,12 @@ Part 4: Learning to solve a constrained optimization problem with projected grad
 + <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_6_NetworkODE.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 6: Data-driven modeling of resistance-capacitance (RC) network ODEs
 
++ <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_7_DeepKoopman.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 7: Deep Koopman operator
+
++ <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_8_nonauto_DeepKoopman.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 8: control-oriented Deep Koopman operator
+
 
 ### Physics-Informed Neural Networks (PINNs) for Partial Differential Equations (PDEs)
 + <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/PDEs/Part_1_PINN_DiffusionEquation.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 1: Diffusion Equation
@@ -253,154 +264,22 @@ It should take about 2 minutes to run the tests on CPU.
 There will be a lot of warnings that you can safely ignore. These warnings will be cleaned 
 up in a future release.
 
+## Community Information
+We welcome contributions and feedback from the open-source community!  
 
-## Community Development
-
-We welcome contributions and feedback from the open-source community!
-
-### Discussions
-
-[Discussions](https://github.com/pnnl/neuromancer/discussions) should be the first line of contact for new users to provide direct feedback on the library.
-Post your [Ideas](https://github.com/pnnl/neuromancer/discussions/categories/ideas) for new features or examples, 
-showcase your work using neuromancer in [Show and tell](https://github.com/pnnl/neuromancer/discussions/categories/show-and-tell),
-or get support for usage in [Q&As](https://github.com/pnnl/neuromancer/discussions/categories/q-a),
-please post them in one of our  categories.
-
-
-### Contributing examples
-If you have an example of using NeuroMANCER to solve an interesting problem, or of using 
-NeuroMANCER in a unique way please share them in [Show and tell](https://github.com/pnnl/neuromancer/discussions/categories/show-and-tell)
-discussions.
-The best examples might be incorporated into our current library of examples. 
-To submit an example, create a folder for your example/s in the example folder if there isn't 
-currently an applicable folder and place either your executable python file or notebook file there. 
-Push your code back to Github and then submit a pull request. Please make sure to note in a comment at 
-the top of your code if there are additional dependencies to run your example and how to install 
-those dependencies. 
-
-### Contributing code
-
-We welcome contributions to NeuroMANCER. Please accompany contributions with some lightweight unit tests
-via pytest (see test/ folder for some examples of easy to compose unit tests using pytest). 
-In addition to unit tests
-a script utilizing introduced new classes or modules should be placed in the examples folder. 
-To contribute a new well-developed feature please submit a pull request (PR). 
-Before creating a PR, we encourage developers to discuss and document the intended feature
-in [Ideas](https://github.com/pnnl/neuromancer/discussions/categories/ideas) discussion category.
-
-### Reporting issues or bugs
-If you find a bug in the code or want to request a new well-developed feature, please open an issue.
-
-## NeuroMANCER development plan
-Here are some upcoming features we plan to develop. Please let us know if you would like to get involved and 
-contribute so we may be able to coordinate on development. If there is a feature that you think would
-be highly valuable but not included below, please open an issue and let us know your thoughts. 
-
-+ Control and modelling for networked systems
-+ Support for stochastic differential equations (SDEs)
-+ Easy to implement modeling and control with uncertainty quantification
-+ Proximal operators for dealing with equality and inequality constraints
-+ Interface with CVXPYlayers
-+ Online learning examples
-+ Benchmark examples of DPC compared to deep RL
-+ Conda and pip package distribution
-+ More versatile and simplified time series dataloading
-+ Discovery of governing equations from learned RHS via NODEs and SINDy
-+ More domain science examples
-
-
-##  Release notes
-
-### Version 1.4.1 Release Notes
-+ To simplify integration, interpolation of control input is no longer supported in `integrators.py`
-  + The `interp_u` parameter of `Integrator` and subclasses has been deprecated
-+ Additional inputs (e.g., `u`, `t`) can now be passed as `*args` (instead of as a single tensor input stacked with `x`) in:
-  + `Integrator` and subclasses in `integrators.py`
-  + `Block` - new base class for all other classes in `blocks.py`
-  + `ODESystem` in `ode.py`
-+ New Physics-Informed Neural Network (PINN) examples for solving PDEs in `/examples/PDEs/`
-+ New system identification examples for ODEs in `/examples/ODEs/`
-+ Fixed a bug in the `show(...)` method of the `Problem` class
-+ Hotfix: `*args` for `GeneralNetworkedODE`
-
-###  Version 1.4 Release Notes
-+ Refactored PSL
-  + Better PSL unit testing coverage
-  + Consistent interfaces across system types
-  + Consistent perturbation signal interface in signals.py
-+ Refactored Control and System ID learning using Node and System class (system.py)
-  + Classes used for system ID can now be easily interchanged to accommodate downstream control policy learning
-
-###  Version 1.3.2 Release Notes
-+ Merged Structured Linear Maps and Pyton Systems Library into Neuromancer
-  + The code in neuromancer was closely tied to psl and slim.
-  A decision was made to integrate the packages as submodules of neuromancer.
-  This also solves the issue of the package names "psl" and "slim" already being taken on PyPI.
-
-*Import changes for psl and slim*
-
-```python
-# before
-import psl
-import slim
-
-# now
-from neuromancer import psl
-from neuromancer import slim
-```
-
-### Version 1.3.1 release notes
-+ New example scripts and notebooks
-  + Interactive Colab notebooks for testing Neuromancer functionality without setting up an environment 
-    + See [Examples](#examples) for links to Colab
-  + RC-Network modeling using Graph Neural Time-steppers example:
-    + See neuromancer/examples/graph_timesteppers/
-  + Baseline NODE dynamics modeling results for all nonautonomous systems in Python Systems Library
-    + See neuromancer/examples/benchmarks/node/
-  + Updated install instructions for Linux, Windows, and MAC operating systems
-    + New linux_env.yml, windows_env.yml, osxarm64_env.yml files for installation of dependencies across OS
-+ Corresponding releases of SLiM and PSL packages
-  + Make sure to update these packages if updating Neuromancer
-  + Release 1.4 will roll SLiM and PSL into core Neuromancer for ease of installation and development
-
-###  Version 1.3 release notes
-+ Tutorial [YouTube videos](https://www.youtube.com/channel/UC5oWRFxzUwWrDNzkdWLIb7A) to accompany tutorial scripts in examples folder:
-  + [examples/system_identification/duffing_parameter.py](https://www.youtube.com/watch?v=HLuqneSnoC8)
-+ Closed loop control policy learning examples with Neural Ordinary Differential Equations
-  + examples/control/
-      + vdpo_DPC_cl_fixed_ref.py
-      + two_tank_sysID_DPC_cl_var_ref.py
-      + two_tank_DPC_cl_var_ref.py
-      + two_tank_DPC_cl_fixed_ref.py
-+ Closed loop control policy learning example with Linear State Space Models. 
-  + examples/control/
-      + double_integrator_dpc_ol_fixed_ref.py
-      + vtol_dpc_ol_fixed_ref.py
-+ New class for Linear State Space Models (LSSM)
-    + LinearSSM in dynamics.py
-+ Refactored closed-loop control policy simulations
-  + simulator.py
-+ Interfaces for open and closed loop simulation (evaluation after training) for several classes 
-    + Dynamics
-    + Estimator
-    + Policy
-    + Constraint
-    + PSL Emulator classes
-+ New class for closed-loop policy learning of non-autonomous ODE systems
-  + ControlODE class in ode.py
-+ Added support for NODE systems
-  + Torchdiffeq integration with fast adjoint method for NODE optimization
+### Contributions, Discussions, and Issues
+Please read the [Community Development Guidelines](https://github.com/pnnl/neuromancer/blob/master/CONTRIBUTING.md) 
+for further information on contributions, [discussions](https://github.com/pnnl/neuromancer/discussions), and [Issues](https://github.com/pnnl/neuromancer/issues).
 
+###  Release notes
+See the [Release notes](https://github.com/pnnl/neuromancer/blob/master/RELEASE_NOTES.md) documenting new features.
 
-## Development team
+###  License
+NeuroMANCER comes with [BSD license](https://en.wikipedia.org/wiki/BSD_licenses).
+See the [license](https://github.com/pnnl/neuromancer/blob/master/LICENSE.md) for further details. 
 
-**Lead developers**: Jan Drgona, Aaron Tuor   
-**Active core developers**: James Koch, Madelyn Shapiro, Draguna Vrabie  
-**Notable contributors**: Seth Briney, Bo Tang, Ethan King, Shrirang Abhyankar, 
-Mia Skomski, Stefan Dernbach, Zhao Chen, Christian Møldrup Legaard
 
-
-## Publications
+## Publications 
 + [James Koch, Zhao Chen, Aaron Tuor, Jan Drgona, Draguna Vrabie, Structural Inference of Networked Dynamical Systems with Universal Differential Equations, arXiv:2207.04962, (2022)](https://aps.arxiv.org/abs/2207.04962)
 + [Ján Drgoňa, Sayak Mukherjee, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie, Learning Stochastic Parametric Differentiable Predictive Control Policies, IFAC ROCOND conference (2022)](https://www.sciencedirect.com/science/article/pii/S2405896322015877)
 + [Sayak Mukherjee, Ján Drgoňa, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie, Neural Lyapunov Differentiable Predictive Control, IEEE Conference on Decision and Control Conference 2022](https://arxiv.org/abs/2205.10728)
@@ -426,6 +305,21 @@ Mia Skomski, Stefan Dernbach, Zhao Chen, Christian Møldrup Legaard
 }
 ```
 
+## Development team
+
+**Lead developers**: Jan Drgona, Aaron Tuor   
+**Active core developers**: James Koch, Madelyn Shapiro  
+**Scientific advisors**: Draguna Vrabie  
+**Notable contributors**: Seth Briney, Bo Tang, Ethan King, Shrirang Abhyankar, 
+Mia Skomski, Stefan Dernbach, Zhao Chen, Christian Møldrup Legaard
+
+Open-source contributions made by:  
+<a href="https://github.com/pnnl/neuromancer/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=pnnl/neuromancer" />
+</a>
+
+Made with [contrib.rocks](https://contrib.rocks).
+
 ## Acknowledgments
 This research was partially supported by the Mathematics for Artificial Reasoning in Science (MARS) and Data Model Convergence (DMC) initiatives via the Laboratory Directed Research and Development (LDRD) investments at Pacific Northwest National Laboratory (PNNL), by the U.S. Department of Energy, through the Office of Advanced Scientific Computing Research's “Data-Driven Decision Control for Complex Systems (DnC2S)” project, and through the Energy Efficiency and Renewable Energy, Building Technologies Office under the “Dynamic decarbonization through autonomous physics-centric deep learning and optimization of building operations” and the “Advancing Market-Ready Building Energy Management by Cost-Effective Differentiable Predictive Control” projects. 
 PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL0-1830.
diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md
new file mode 100644
index 00000000..b3d8dcbe
--- /dev/null
+++ b/RELEASE_NOTES.md
@@ -0,0 +1,82 @@
+
+##  Release notes
+
+### Version 1.4.1 Release Notes
++ To simplify integration, interpolation of control input is no longer supported in `integrators.py`
+  + The `interp_u` parameter of `Integrator` and subclasses has been deprecated
++ Additional inputs (e.g., `u`, `t`) can now be passed as `*args` (instead of as a single tensor input stacked with `x`) in:
+  + `Integrator` and subclasses in `integrators.py`
+  + `Block` - new base class for all other classes in `blocks.py`
+  + `ODESystem` in `ode.py`
++ New Physics-Informed Neural Network (PINN) examples for solving PDEs in `/examples/PDEs/`
++ New system identification examples for ODEs in `/examples/ODEs/`
++ Fixed a bug in the `show(...)` method of the `Problem` class
++ Hotfix: `*args` for `GeneralNetworkedODE`
+
+###  Version 1.4 Release Notes
++ Refactored PSL
+  + Better PSL unit testing coverage
+  + Consistent interfaces across system types
+  + Consistent perturbation signal interface in signals.py
++ Refactored Control and System ID learning using Node and System class (system.py)
+  + Classes used for system ID can now be easily interchanged to accommodate downstream control policy learning
+
+###  Version 1.3.2 Release Notes
++ Merged Structured Linear Maps and Pyton Systems Library into Neuromancer
+  + The code in neuromancer was closely tied to psl and slim.
+  A decision was made to integrate the packages as submodules of neuromancer.
+  This also solves the issue of the package names "psl" and "slim" already being taken on PyPI.
+
+*Import changes for psl and slim*
+
+```python
+# before
+import psl
+import slim
+
+# now
+from neuromancer import psl
+from neuromancer import slim
+```
+
+### Version 1.3.1 release notes
++ New example scripts and notebooks
+  + Interactive Colab notebooks for testing Neuromancer functionality without setting up an environment 
+    + See [Examples](#examples) for links to Colab
+  + RC-Network modeling using Graph Neural Time-steppers example:
+    + See neuromancer/examples/graph_timesteppers/
+  + Baseline NODE dynamics modeling results for all nonautonomous systems in Python Systems Library
+    + See neuromancer/examples/benchmarks/node/
+  + Updated install instructions for Linux, Windows, and MAC operating systems
+    + New linux_env.yml, windows_env.yml, osxarm64_env.yml files for installation of dependencies across OS
++ Corresponding releases of SLiM and PSL packages
+  + Make sure to update these packages if updating Neuromancer
+  + Release 1.4 will roll SLiM and PSL into core Neuromancer for ease of installation and development
+
+###  Version 1.3 release notes
++ Tutorial [YouTube videos](https://www.youtube.com/channel/UC5oWRFxzUwWrDNzkdWLIb7A) to accompany tutorial scripts in examples folder:
+  + [examples/system_identification/duffing_parameter.py](https://www.youtube.com/watch?v=HLuqneSnoC8)
++ Closed loop control policy learning examples with Neural Ordinary Differential Equations
+  + examples/control/
+      + vdpo_DPC_cl_fixed_ref.py
+      + two_tank_sysID_DPC_cl_var_ref.py
+      + two_tank_DPC_cl_var_ref.py
+      + two_tank_DPC_cl_fixed_ref.py
++ Closed loop control policy learning example with Linear State Space Models. 
+  + examples/control/
+      + double_integrator_dpc_ol_fixed_ref.py
+      + vtol_dpc_ol_fixed_ref.py
++ New class for Linear State Space Models (LSSM)
+    + LinearSSM in dynamics.py
++ Refactored closed-loop control policy simulations
+  + simulator.py
++ Interfaces for open and closed loop simulation (evaluation after training) for several classes 
+    + Dynamics
+    + Estimator
+    + Policy
+    + Constraint
+    + PSL Emulator classes
++ New class for closed-loop policy learning of non-autonomous ODE systems
+  + ControlODE class in ode.py
++ Added support for NODE systems
+  + Torchdiffeq integration with fast adjoint method for NODE optimization
diff --git a/examples/ODEs/README.md b/examples/ODEs/README.md
index 776645e0..d68bf682 100644
--- a/examples/ODEs/README.md
+++ b/examples/ODEs/README.md
@@ -21,6 +21,12 @@ showcasing system identification capabilities for ordinary differential equation
 + <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_6_NetworkODE.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 6: Data-driven modeling of resistance-capacitance (RC) network ODEs
 
++ <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_7_DeepKoopman.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 7: Deep Koopman operator
+
++ <a target="_blank" href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_8_nonauto_DeepKoopman.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Part 8: control-oriented Deep Koopman operator
+
 
 ## System Identification
 
diff --git a/examples/Pytorch_ecosystem/Part_1_cvxpy_layers.ipynb b/examples/Pytorch_ecosystem/Part_1_cvxpy_layers.ipynb
new file mode 100644
index 00000000..1e83e173
--- /dev/null
+++ b/examples/Pytorch_ecosystem/Part_1_cvxpy_layers.ipynb
@@ -0,0 +1,665 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "34qVD_ntSKLF"
+   },
+   "source": [
+    "# Cvxpylayers + Neuromancer\n",
+    "\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": 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": 1,
+   "metadata": {
+    "id": "KbP0n-4evRqt"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import numpy as np\n",
+    "import cvxpy\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\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",
+    "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": 2,
+   "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": 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 = 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",
+    "# 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",
+    "# 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": "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",
+    "# 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'], ['x'], 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": 6,
+   "metadata": {
+    "id": "bcoVjphjyPp9"
+   },
+   "outputs": [],
+   "source": [
+    "# 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",
+    "b_param = variable('b_param')\n",
+    "\n",
+    "# objective function\n",
+    "f = (1-x1)**2 + p*(x2-x1**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": 7,
+   "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": [
+    "## 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": 8,
+   "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": 15,
+   "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": 10,
+   "metadata": {
+    "id": "rk1bRczByUvl"
+   },
+   "outputs": [],
+   "source": [
+    "lr = 0.001      # step size for gradient descent\n",
+    "epochs = 10    # number of training epochs\n",
+    "warmup = 10    # number of epochs to wait before enacting early stopping policy\n",
+    "patience = 10  # number of epochs with no improvement in eval metric to allow before early stopping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "x4oS2N2ZyWtD"
+   },
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.AdamW(problem.parameters(), lr=lr)\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,\n",
+    "    train_metric=\"train_loss\",\n",
+    "    dev_metric=\"dev_loss\",\n",
+    "    test_metric=\"test_loss\",\n",
+    "    eval_metric=\"dev_loss\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "27ASQD3q-M0A",
+    "outputId": "04eec20c-51c3-4a71-e570-9636af9421c0"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\drgo694\\OneDrive - PNNL\\Documents\\anaconda3\\envs\\neuromancer\\lib\\site-packages\\threadpoolctl.py:1010: RuntimeWarning: \n",
+      "Found Intel OpenMP ('libiomp') and LLVM OpenMP ('libomp') loaded at\n",
+      "the same time. Both libraries are known to be incompatible and this\n",
+      "can cause random crashes or deadlocks on Linux when loaded in the\n",
+      "same Python program.\n",
+      "Using threadpoolctl may cause crashes or deadlocks. For more\n",
+      "information and possible workarounds, please see\n",
+      "    https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md\n",
+      "\n",
+      "  warnings.warn(msg, RuntimeWarning)\n",
+      "C:\\Users\\drgo694\\OneDrive - PNNL\\Documents\\Codes_dev\\neuromancer\\neuromancer\\src\\neuromancer\\constraint.py:160: 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",
+      "C:\\Users\\drgo694\\OneDrive - PNNL\\Documents\\Codes_dev\\neuromancer\\neuromancer\\src\\neuromancer\\constraint.py:160: 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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch: 0  train_loss: 3.2589502334594727\n",
+      "epoch: 1  train_loss: 0.3854717016220093\n",
+      "epoch: 2  train_loss: 0.2311873733997345\n",
+      "epoch: 3  train_loss: 0.19490069150924683\n",
+      "epoch: 4  train_loss: 0.18166537582874298\n",
+      "epoch: 5  train_loss: 0.17738522589206696\n",
+      "epoch: 6  train_loss: 0.17531147599220276\n",
+      "epoch: 7  train_loss: 0.1747892051935196\n",
+      "epoch: 8  train_loss: 0.17444591224193573\n",
+      "epoch: 9  train_loss: 0.17344164848327637\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 12,
+     "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": [
+    "## 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": 18,
+   "metadata": {
+    "id": "yD5kAnjy4CUL"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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gEAgEgrBEiBiBQCAQCARhiRAxAoEg4OzevVvkwwgEYcCnn37Kpk2bSE1NRZIk3nrrrT5rioqK+MIXvkB0dDQmk4lly5ZRVVU16HVfe+01Zs2ahV6vZ/78+X6jXcDbBuGRRx4hJSUFg8HA2rVr++2TNRRCxAgEAoFAcIliNptZuHAhzz77bL/Pl5WVsWrVKmbNmsXu3bs5ceIEP/3pT/vMXevNvn37uOOOO/jGN77BsWPH2Lx5M5s3b/b7w+aXv/wlv/3tb9m6dSsHDhzAZDKxbt26Pr2rhkJUJwkEAoFAIECSJN588002b97sO3b77bej0Wj4v//7v2Ff57bbbsNsNvPuu+/6jl122WXk5eWxdetWFEUhNTWVf/u3f+OHP/whAB0dHSQlJfHCCy9w++23D/u1Lslmdx6Ph7q6OiIjIwM6Z0YgEAgEkw9FUejq6iI1NXXYU9xHg81mw+FwjPk6iqL0ubfpdDq/objDwePxsG3bNh566CHWrVvHsWPHyMnJ4eGHH/YTOhezf/9+HnzwQb9j69at84WqKioqaGho8Bv3ER0dzYoVK9i/f78QMUNRV1fXZ+qsQCAQCASDUV1dTXp6elCubbPZSM5MpqN57JPPIyIi+oxEefTRR32dyYdLU1MT3d3d/Od//ic///nPefLJJ9mxYwdf+tKX+Pjjj1m9enW/5zU0NJCUlOR3LCkpiYaGBt/zPccGWjNcLkkRExkZCcD3X3gJndE4wbsJHzxuePd3Jhw2iTVftRCf4ZnoLQUVRYFjH+goP+Ztkb/wWjvTlzlHfb380joAlsemBGR/ocSpwlqWJCdP9DaGxO2GN9+MxGJRsfJyC9Nzvd/PU0crWDA1cVz2cPKgd/bUnHmDj20YilMHSpizOHt0e9jnHYQ6Z8XgA0CHOn/uZdP7f/7zEuZePnPE1z11oNR73RGce+roWeas6H8fA55TWMvc5dOGvd5mt/Lo777ju3cEA4fDQUdzB/+999cYIkY/1d3abeWHV/yA6upqoqKifMdH6sLAhUnvN910k2/UR15eHvv27WPr1q0Dipjx5JIUMT02m85oRGc0DbFa0Jv0WXoqC9S0VGtImzl22zPUuWwzGCM1FO3VUrjbBDiYe5WDkUQhj56pAeCqtNHdMEKdghPVaHQG9PrQ/4Pg9GktdruRqCgPc2Y7kWUNhYfK0WoM6HXjs3+tWs+8hWP7a75wbzFaWYdeO7qbnVbWMe/yGUMv7IeCz4rQyjrmXzGw0Bjt3rSyjvlXzh564XkKD5V7X0s3/NcqPF7Dkivnj3hvwLikHxgiDBgiRy9ieoiKivITMaMhPj4etVrNnDlz/I7Pnj2bPXv2DHhecnIyjY2NfscaGxtJPv+HTs+/jY2NpKSk+K3Jy8sb0R5FdZJgRKTOcAFQeyZ89K/bCXYLWLskutskOppVtDWoaKlR0XRWpr5MprZYpvqUmrMn1JQfU1NySEPx5xqK9mpQqSEqwUNHs4o9r+o59r6W4abD9wiYlXFj+6s7VCk4UQ3A8pTQd5c8HjhR4P1rdN58O72HguflJg1wVmAp2D/yEtKBmLds6uj28FnRmF97MAEz3swLob1MNrRaLcuWLaO4uNjv+JkzZ8jKyhrwvJUrV7Jr1y6/Yzt37mTlypUA5OTkkJyc7Lems7OTAwcO+NYMl/C5EwlCgpRcF5IMnc0qus5JRMaGfnHbkfcuhITGgqxR6GySKf5ci8spsWyjHWmAPwMmu3iB8BIwABUVGro6ZXR6DzNn2AHvX/LjJWB6CIQLM+Y9jMGFGXLNKPdXuO/MqM4b0Wscrwn6a4Qb3d3dlJaW+h5XVFSQn59PbGwsmZmZ/OhHP+K2227jqquu4uqrr2bHjh2888477N6923fOnXfeSVpaGlu2bAHg+9//PqtXr+app55i48aNvPzyyxw+fJjnnnsO8DpaDzzwAD//+c+ZPn06OTk5/PSnPyU1NXXQhOH+ECJGMCK0ekjIdNNQJpP/gY5Vt9oGvJGHCqrzP+WSClSyV4zIsvdzlawgq0FWK6jU559XK+f/7f08qNQKLdUyzZUy5Uc1uBwSl222oZL9X08ImNBDUSD/uB6HU2LBQgcajVfAjCeh4sKMVcAMx4UZSUhotOeN9vs3f+XI8mcmO4cPH+bqq6/2Pe6pKrrrrrt44YUX+OIXv8jWrVvZsmUL//qv/8rMmTN54403WLVqle+cqqoqv6qtyy+/nJdeeomf/OQn/Pu//zvTp0/nrbfeYt68eb41Dz30EGazmW9961u0t7ezatUqduzYMWj/mf4QIkYwYtJmuCg7ouH4Lh3dbSoWXmsnZbp7RHki48nidXaWrB/YNRkpVSfVfP6mnqpCNS6HnitutiFrLg3xAuEnYACqq9WUlmqxWSW0mgsJ6ZeiCzMWQimMBCKUFAjWrFnDUO3i7rnnHu65554Bn+/tyvRwyy23cMsttwx4jiRJPPHEEzzxxBPD3mt/CBEjGDGpM1yoVOByQFu9ik//biA+083CtXYSQrBi6WKnZKxkznWh0VnZ86qBujNqXnnWyfTrmpA1QsCEIj0ujE6roJYVqqq0yI7T47qHgv0lYxYwPUykCxMsRChJMFpCPBAgCEUiYxVScl0kZrtJnuZC1kBLlcyu54189rKejubJ/2OVkutm9VesdDssdNUZ6Po4l8WmyS1geggnAQPQ0KCmuUlNZJSbyCgPZSV2LFZN2CXzTpQLM5Iw0mjzYUCEkgSjY/LfbQRBoceNMUTCxvvMTF3sBAlqi9W893sjB9/WYekI0fhSADh6poYaRyUzb2ggKVJHS4PMzteMWM2T9z0XnKgOOwEDkH/cW5E0e5aDjAxvXxi9PL7NLsPZhYGRhZFGmw8zUkQoSQBCxAhGSeoMNwD1pTL6CIXlm+xs+K6F9NkuUKD8mIZtz5o4/qEWh3WCNxtAjp6p8ct9WTsrgetvsaA3KrS1yHzwmhFz5+QTMj1hpHCjuVmmrlaDpFKYP9+G7KoEoLTSMOwy+bEQCi7MWEJBwQ4jgQglCcaGEDGCURGf7kZrVHBYJVqqvUkn0QkeVt1qY+09FhKy3LidULRXyzu/NVG0V4N79M1uQ4Le4qV37suUBA/rbjVjivTQ2abi/VeNdLVPHiETjnkwPRw/4XVhpk1zUnm6jMS4bqZE6mjvlGltH5+UwIl2YWD0JdUwPsm8IpQkGC1CxAhGhUqG1FyvG1N3xj9zNj7DwzV3WbnqK1Zikjw4bRLHP9Tx7jMmyo+p8bgnYsej52L3pT+ipiisu81C1BQP5i4V779ioq0l/P97hbOAaWtXUXlWCxIsWGADYOmsBLJSvZ+XVI69K+pgXGouzFjyYUaKCCUJehDVSYJRkzrDxdkTamrPqMm73n8EgSRB6nQ3KdMsnC1QU/CxN0fm4Nt6Tu/3sOAaO2kzQ6ss22GFc/Uybid43BKnzzbjcUso7khmmOJwuyWOn/HOkHK7Je+/LgmPxzuTx+2S0Bk8dFXINNXK/PPPJm78f2bik0OvYms4hLOAAThxwttvIivLQW3JhWZeuVk2yqv1lFXpWbGwC1UQfwbD1YUZSTJvb0aaD1O470zQc2hEKGlyI0SMYNQkT3OhkqGrVUVni0RUfN8kA0kFOQtdZM5xUXpEw8nPtHQ2q9jzioH4DDcrNttCputve6PM7v8z0GG2nT+SwBSt90Z4eATXMUUqWM1gtUjsfN3INZutJKWHl/0U7gKmq0uirMzbpdkgeQVMTzVSRoodnVbBYlVR36QlLSnwM8ACVVJduLd4wsYLhFpPGPCGkkbjwow1lHSivGlM5wuCxyUtYvJL61ixQMRJR4tWDwlZbhrLZerOqImKHzjpRdbAzMuc5OQ5Ob1fS/F+LV2tKvTG0BAwAGfq63DoEjAZFdJNEec797qQ5fMdfHt38+3p9tvzuVrxO6YocOqIlnNNMrveNLD6RitpOeEhZMJdwAAUFOhRPBKpaU6iI21+5dSyCqZm2CgqM1BSqQ+4iAlkZ96xMhYXRiAIBy5pEQMXkjUXzwiM7XupkTbDRWO5TO0ZNbMuHzpzV6uHBVc7mL7USWeLCs3IOkwHhZ6fAWMcfPObEiABljFfNyPXxafvGqitULP7bSNXbLCSfX6AZqgyGQSMxSJx5owWAJNc1u+aaZlWisoMnK3R41rSiTrADREvNRdmNPkwIw0ljSahNxChpPzSxqEXCSaM8M88HAPLY1N8iZq9kzcFw6dnqnVLtTyiUmpDpELSBDsTFyfsBrrbrloNqzdZyZ7pxOOBPdsMlJ4c+yDKYDEZBAzAyZM63G4JydPKlGhLv03tUhKcmIweHE6JqjpdwF47FJJ5exhvF2Y8+sNMRCgJYM7inDFfQxAcLmkR00PvG5gQMyMjYopCdKIHxQP1peFh7AVbvPRGluGK9TZy5zlRgP0f6DkdgInagWayCBi7HQoKdXR2qUhLbmfR9P678koSTM/yqu7SAFcpTXQybzjkwoxHbxjBpUF43HXGid43s/29hIwINQ1O6gwXbQ1a9r6mJyLWQlxaaFbj9Ban4znjSKWCy9ba0GgVio5qObRbj9MhMW+5IySqsyaLgAEoKtLR0KDGbnPhdA3+N9q0TBv5RSaq6nXYHRI67djysyaDCzOeybzjEUoaqwsjQkmhj3BiBkC4M8MndYaL7nMqGs/KfPBHI3tf19PVGgJ35/OMp/MyEJIES66ys3ClHYD8fTqOfqYbl66xgzGZBIzTCYUndUiKFYPegcc5+HuKi3ERG+PC44Hy6rElZ/UImHB1YcbUT2ZvcciGkgLyumMobxcEH+HEDEHPDW9/a62fkBHuzAXi0zxMSXHjdqlx2CSqT6qpOa1m2iInc69yYIgc/zv1RLkugyFJsOAyBxqtwuFP9Jw6osXlhOXX2CfEkZlMAgag+IwOu01FdKQVvcZEyzlo75SJiRo49yo308bB9ghKq/TMnja2+RiBSuYdK6PtzjteLowIJQkCiRAxw8Qv1NRL0Agx4+0FkznXhdspkTHHhcsJ9SVqSg9rqDiuYeZKB7NXOsalEikUxcvFzF7sRK2BAx/qOXNCi9MusXKdDTnAFTKDMdkEjNsNhQU6ujrMzMptRVbUVNfrKK0ysHRe94Dn5WZaOXgigvomLd0WFRHGkYdCA11SHW4uzGgQoSRBoBAiZhQId6YvqTPcVORraGtQsfF+C82VMsd3aWmtkTn1qZbSwxrmXukgd6kTOcA/dRMhXIrzNdis0qDde3uOud0Xdfk9/7ndBs31aprrZJxOias2WgP+temPySZgAErLtDTUWdFqXWy4XEdFjc0rYir1LJnbPaDTFWHykJLooL5JS2mVgbxZ5lG9/qXswohQkmAiESJmDAzkzsClJ2iSp3q793afU9HVKpGY7WbtPVZqT6s5vsvb2O7Y+zrOHNAy/2o7WfNcSGPIyLo4R2m8XZeTR7SYO8eWUqbVQ2ySm+4OFTXlaj7+p4HVm6xotAHaZD9MRgHj8cCJEzrAxpqlHmQZstLsaNQKnd0yTec0JMUN3MNoTq6FpDgn2am2AdcMRCi5MOGQzCvGDAgCjRAxAeJSFzQaHSRmu2ko8za+i4p3IkmQPttF6gwXFcfVFO7WYW6X+PxNPaf3eVhwrZ2U3OHPT5po4dKb7JkuHLaezr3eTr5yr06+/R3zdvbF1wFYfb77b2uDir3vG6ivUrPrTSPXbLagDVzrEh+TUcAAnK3UUFtlQ6P2+PJaNGqFrDQ7pZV6Siv1g4qYaRl2pmXYR/36E93YbjIzUWMGRCgpfBAiJghcqoImdYaLhjLvCILZvbr3qmSYtthF1nwXZw5oKNqrpb1RxacvGUjIcrNwrZ349P5zEUJJuPRm8arR3/QuxhTpxhBh4aM3jTTXyex8zci1X7IGdCTDZBUwigIffWAHdKxa7EajvvA1m55lpbRST1m1nsvyupADXIsZqPlIY97HBLkw41WVNFEIURkeCBETZAYTNDC5RE3aDBdH39PRXCVjt4DO6P+8WgNzVjmZtthJ0V4tZw5qaa6U+fB/jaTPdrHgGjtR8UrICpdgkpDi4fpbLHz4hpFzzTIfvGrk2i9bMAWgsmuyChiAmlo13WaJuCgtc3M7/J5LS3Kg13uw2VTUNWrJSAncjKRAhpEmwoWZiPlI4TJmQBBeCBEzjlx8M55sosYUoxCT5KG9UUV9qZrsBf3PCdIZIe86BzOWOynYrSV/n5uOw3DqiIrc65vYkDf5RUt/TEnwsO5WCzvfMNDRpuL9V41c92ULkTGjFzKTWcAAfLjDRpQxhtnTLOh1/l8nlQqmZdg4WWKkpNIQUBEDoZHMO1oXBkJzSvXFTFQoSbgw4YMQMRPIZBQ1qTNctDdqqT3Tv4i5+P1pZsOdszM4tkdHa6OK6+bGjtdWQ5KoWA/rbrPw4etGujq8Qmbtl63ExI2i9HeSC5jPPqynozOTmAiYP7P/qqLcLK+IqazV4XRJfuGm0RJKybzjeZ7fNUYovkRvGEGwECImhOgvbLJ/gE7BoSpuUme4OPWZlvpSmcNFNaj66X3S9316uPomK3arN+R0qRMRpXiFzBtG2ltV3tDSlyzEJQ1fyEx2AQNwtjqOKKOOmTlWTIb+vzaJsU6iItx0dstU1urIzRp5BVJ/hIILAxPb2G6k+TAilCQIBkLEhDj9Cpt+HJuLGQ+R098eFAUsngyc52SusWaSkjX8SdW6wM7hC2sMJoXrbzGz600jrY0yO183cvVNVpLSh/56XgoCZv/HtbS3z8FsUbHp6nMDrpMkrxtz9KSJkkr9mEVMoJN5J8KFCYcwEohQkmB4CBEThgyV6DockRPMfUiztJSe1FBZ4hiRiBF4RWBPQzxFkbh8nZU92w001cm893dvsm/G1IG/ppeCgCk8VE5FdSrdFhWySqGxVUtK4sAl1LmZVo6eNFHToMNqkzDoRxdSCnQy71gZrQszVkQoSRBKCBEzCZnoap70qS7OnNCwd4cBlQwLVjgCWi4cTlScVnO2WOMTJh635N/R9+Jj/URFFA90tMrYrBIfv2lkzResZE7vm290qQgYs0WL3RqPyeBBq/FQWqUnb/bAnXZjotzEx7poOaemvFrP3Omjn5EkXBgvwQ4ljdSFEWMGLl3EFGtBwEnOdGGzSjjsEoUHtbz1vIkTn2txBrY4JCzoOOftxltXqaaxRk1zvcy5JpmOcyq6OlRYulXYrBJOR/8CRqUCrU4hNcdFdKwbtwc+fddA2Sn/vz8uBQHTg8uehaLArKlW9DqFc+1qWtsH/3tseqZXuJRWjS5mGWol1SN1YSaipBrCz4URoaTwQzgxgoCj0ULuPCcVp7xdaZ1OieP7dRQf17LgMju585zjOuxwIkmf6sIYoSDLCrJ64O69Fz+vOt/pt3c3Y48HPv9QT9lJDfveN+By2pi50HnJCJjCQ+XYbGpKznqFyJJ53ahPmzhbo6OsSk9czMCDHqdm2vj8eCSNLRo6u2WiIkYe5gyVxnajJRAuzGga3ImEXkEwESJGEBTSp7qoO6smIcXDrEUO8vfq6OpQcfAjPUVHtORdYSdrhmvYIwfClfhkD/HJIy+P7g+VClZeZ0OjUTidr+XgR3rKSlrJmHFpCBgAjyMbjwdSEx0kxTmZnmXlbI2OkkoDy+YPPOjRZPCQmuigtlFLRY2OhbMsw37tQCbzXkouzGgRYwYEI0GEkwRBIX2qN2ejpV4mOcPNprvMLL/aht6g0NWh4rPtBra/ZKS+8hKxZAKEJMHSNXbmr7DT1Wnl7MlYVLU5KJdAytHM9GSKyr1toHtyYDJS7Gi1CmaLivrmwevzl8zrZuOaNubPHJmACRSBaGw3WgLlwowEEUoKD9xuNz/96U/JycnBYDAwbdo0fvazn6EM8Utl9+7dLF68GJ1OR25uLi+88EKfNc8++yzZ2dno9XpWrFjBwYMHA75/IWIEQcEUqRCb4EYBaitkZBlm5jnZfE83C1faUWsUzjXJfPgPIx++YaC1UfwoDhdJAjmijJx554jR6Tl+XM/+/YZJK2QKD5WTl5tEYYkJtxsSYp2kJXkTrNQy5KR7y6bLhsh3SY73nqcaofsXCsm8vvMn2IURoaTJx5NPPsnvf/97nnnmGYqKinjyySf55S9/ye9+97sBz6moqGDjxo1cffXV5Ofn88ADD3Dvvffy/vvv+9a88sorPPjggzz66KMcPXqUhQsXsm7dOpqamgK6/6DeOT799FM2bdpEamoqkiTx1ltvDbr+H//4B9dddx0JCQlERUWxcuVKvy8KwGOPPYYkSX4fs2bNCuK7EIyWtPNuTE35hailRgsLLnPwxXvMzMpzoFJBfZWa7S+Z+Gy7nq72SR5fGiMFJ6opOFHN8pQUvnSVkSuusIAERUU6Pv3M2G9ycDjTc2OzOyROll5wYXqHjXIzvSKmvFqPO4DvP9RcmMk8XqAHEUoaf/bt28dNN93Exo0byc7O5uabb+b6668f1DXZunUrOTk5PPXUU8yePZv777+fm2++mV//+te+Nb/61a/45je/yd13382cOXPYunUrRqOR559/PqD7D6qIMZvNLFy4kGeffXZY6z/99FOuu+46tm/fzpEjR7j66qvZtGkTx44d81s3d+5c6uvrfR979uwJxvYFY6QnpFRXqcZ9UUWw3qiw7Go7N329m5xZTiTgbLGGt1+M4OBHOqxmIWYupr8E3lmzHKy+yoykUigt0fLxx0bck6Q1T4+AyctNoqjMiMMhERPlIjvNf4J4SqIDo8GD3SFRXa8LyGv3CJhQcmFGykQ2thOhpImns7PT78Nut/e77vLLL2fXrl2cOeP9nh0/fpw9e/awYcOGAa+9f/9+1q5d63ds3bp17N+/HwCHw8GRI0f81qhUKtauXetbEyiCmti7YcOGQb8QF/P000/7Pf7FL37BP//5T9555x0WLVrkO65Wq0lOTg7UNgVBIi7Jg8GoYLVINNbKpPbT+C4iWmHVBhtzljg4tldH3Vk1xce1lJ3SMHuxgzlLHGgDc18KawarQMrNdaLRmPnoYxNnz2rZ+aHE2mvNqCdB2n5ebhIuN5w443VhFl3kwgCoJK8bc6LYSGmlvo/IGS2BTOYdC6NxYQIdRhqPqqSJ6A0TilQWJ6MzmkZ9vt1yPl8sI8Pv+KOPPspjjz3WZ/2Pf/xjOjs7mTVrFrIs43a7+Y//+A+++tWvDvgaDQ0NJCUl+R1LSkqis7MTq9VKW1sbbre73zWnT58e5Tvrn5D+NefxeOjq6iI21n8oYElJCampqej1elauXMmWLVvIzMwc8Dp2u91PhXZ2dgZtz4ILSJLXjSkp1FBTru5XxPQQm+jh2i9aaaiSObpHR2ujTMEBHXqjwqy8gbuxTmZ6uveeOF6Dx6MiLyGZzs7zjfHcEm7PhUZ5AHPm2Dl82EBxsZbuLhWbNnWhC1MB2JMHA1BcbsBmUxFhcjM1s/+xAblZVk4UG6ms0+NwdqLVjGHyd4AHPML4N7aDiQsjhZMLM5lDSdXV1URFRfke6wb4ZfDqq6/yt7/9jZdeeom5c+f6clxSU1O56667xmu7oyakRcx///d/093dza233uo7tmLFCl544QVmzpxJfX09jz/+OFdeeSWFhYVERkb2e50tW7bw+OOPj9e2Bb3wiZgyNcvW2IcsqU7OdLPhDgtVJWpKCjRMn3dpCJjONomdrxt9osRzXqh0dVqBbGJ0eobz97wEtLfLuN0SO3ZEsG6dGf0o2+xPFL0TPN0eOH46AkWBhbPMyAMEwONiXMREuWjvVFNRo2NmzthmJImS6l7XG4WTFOyE3kAyGUNJAFFRUX4iZiB+9KMf8eMf/5jbb78dgPnz51NZWcmWLVsGFDHJyck0NvoLwMbGRqKiojAYDMiyjCzL/a4JdBQlZEtCXnrpJR5//HFeffVVEhMTfcc3bNjALbfcwoIFC1i3bh3bt2+nvb2dV199dcBrPfzww3R0dPg+qqurx+MtCPB275XVCuYuFe0tw/txkyTImuFi7ZetyCEtswOHJOHXvfeCgIEYnR7wNsFTaxR0eg9Gk4fIKDcxMW7i4twkJLpITnExdaqDvIU2jEYPLS1qtm2LwGIJn/yi3nkwAKVn9Zyt01HfrCU5fuCWzz2DHgFKK0c/STTUknlHS6BdmJGGkkaKCCVNHBaLBZXK/3ezLMt4BqkSWLlyJbt27fI7tnPnTlauXAmAVqtlyZIlfms8Hg+7du3yrQkUIXmLePnll7n33nt57bXX+iQPXUxMTAwzZsygtLR0wDU6nW5AK00QXNQaSM5wU1uhpqZczZSES3D2wDAwRijc8BUzKhlKztQiqRSWpSahUinIsq1P996haG9X8d6OCNrbZd7dFsGG9WYiI8OjdKlHwCgKHC+OwOOGCKObylo9cTEDz0jKzbRxuCCCuiYtZqsKk2Fk7zcUk3kn2oUZKSKUFH5s2rSJ//iP/yAzM5O5c+dy7NgxfvWrX3HPPff41jz88MPU1tbyl7/8BYDvfOc7PPPMMzz00EPcc889fPTRR7z66qts27bNd86DDz7IXXfdxdKlS1m+fDlPP/00ZrOZu+++O6D7Dzkn5u9//zt33303f//739m4ceOQ67u7uykrKyNlkncsDWd6qpTOnNDg6ju3UADIam8idE1dJYYIF1dNS8RgUNDpQK0emYABiInxcOPGbiKj3HR1eoVMe3vI/Xf34+KwwtlaHe2dMtFRbiKMbkoqB++FExXhJineiaJAWZV+VHsIpWTe0RJIF2ayjxmYrKGkkfC73/2Om2++me9973vMnj2bH/7wh3z729/mZz/7mW9NfX09VVVVvsc5OTls27aNnTt3snDhQp566in+9Kc/sW7dOt+a2267jf/+7//mkUceIS8vj/z8fHbs2NEn2XesBNWJ6e7u9nNIKioqyM/PJzY2lszMzD7q7qWXXuKuu+7iN7/5DStWrKChoQEAg8FAdHQ0AD/84Q/ZtGkTWVlZ1NXV8eijjyLLMnfccUcw34pgDKRPdbHvfSgp0PDGH00sXuVg2lwnqtC+p447gZ6BFBnpFTLvvefvyMTFhV4N9sVhJEWBY0URACyd18WpUhMdXTLNbWoSYwdWwrmZVhpbNJRWGlgwQZ15exAuzPCYiN4wggtERkby9NNP96kO7k1/3XjXrFnTp/3Jxdx///3cf//9Y9zh4AT1NnL48GEWLVrkK49+8MEHWbRoEY888gjQV90999xzuFwu7rvvPlJSUnwf3//+931rampquOOOO5g5cya33norcXFxfP755yQkJATzrQjGgDFCISLag0qGtmaZzz/U8+5fTFSVqidtl9mREqwhjkajwsaN3cTHu7DbVGx/L4LGxtAa9XCxgAGobdTSck6NWq2waLaZ7LTzXXmHyHeZmmFDkqClTU1758jep3BhLtrHJE7ozS9tFC7MJCGoTsyaNWsGnb9wsbrbvXv3kNd8+eWXx7grwUQwfb4TS7cKg9GDokh0tKn45B0D8cluFl9pJyk99NyB8aBHvEDwhjjq9Qrr13ezc2cEjY1qduyIYO1aM2lpoRPb6y1gAI4VeftkzJ5qRa9TyM20UValp6xaz4q8rgFHBxj0CunJdhqatbR1qomJGvrnKpADHnuYLC5MKCb0CgS9EYa+YFxIn+qdWO1ySmy6s5v5y+2o1QotDTIfvGbko7cMtDVfWj+Ovd2XYE+h1ulg/fpu0tKduFwSH+w0UVk58Xn9/f013tCiob5Ji0oF82d6E3nTU+zodAoWq4q6Ru2g17xySSf/76YmctKHbnoX6DDSWEuqQ8WFGSnjmdArQkmC3lxadw3BhBGb6MEY4cHlkjjXJJN3hYOb7jYzY4EDSYLaCjXb/mpi7w493Z3hUxI8WoIVPhoMtRquW2smO9uBxy2x6yMTpaWDT34OJv2FkQDyz7sw07OtRBi9FUayCqaeH/RYOkTSboTJM6JuxaESRuphNC5MoAWMCCUJwoWJ/1NMcEkgSZCW46KkQEtNuZq0HDfGCIUV19qZvchJ/n4tlWc0lBdpOFusYeZCB/NWONAbJl/SzFgFjM0m4XJf6NZ7cfdetxvcHsnXMM/d+1+PRESEB41WobFR5u13IrlhQzezZo1v6ftAAqa1XU1VnQ5JgrxZ/uXU07OsFJUZqKjRs2pJJ+oxpvaEWjLvRCfmXkwohpKECyO4GCFiBONG+tQLImb5NRe690bFerhqo42WJQ6O7dHRUK2m6JiW0pMa5i51MGuRA83gEYSwIFDuy473TbS2jO2/bk+qmtmsYu9eIw6nxIL5gZk5NFwuFjBwwYWZmmEjOtI/nyUp3kmEyU23WaayTse0jNHvN9A9YSaTCzNSCvedCbrgCRSiN8zkQ4gYwbiRnOlGVitYulW0NauITfRvRhaf7GHtl63UV8kc+0zHuWaZ/H06ivO1LLjMzrR5TuTQKqwZNoEMH8kqkFQKsgyyrCCrQHX+c7Xs7ewry6BS9XqsOr9WPr9WpaBSQW2tmuZmNYcOGnA6JRYvso24J81IGSiU0NElU17tDRUtnNW3qZ10ftBjfpGJ0krDmEQMBLapHYzdhQmVZN7R9IYZCaMd9hgoRChpciFEjGDcUKshJdNNTbm3e29sYt8QhiRBapablEwLZ4vV5O/V0d2p4sBHek4d1bJ0tY30qeFVyRTo/Jcbb+wOmNBYsgSOH9dx+LCB/GN6nA6JFSusQRMyA4WRAI6fNqEokJFiJ35K/5VTuVlW8otMVNfrsNkl9LqRhxuDkcw7FsI1mRdEQq9g4hGJvYJxJWOa9+ZUUz64fpYkyJnl4gtfN7NsjQ29QaGrXYWlO3x+ZAtOVFNwojrg1UeBFhgLF9q5/HJvY7iTJ3Xs2WNgkLEpo2YwAWO2qDhz1oDdoWLRnIFHC8RGu4mLceHxQEXNyLvyBmO0AEyekurJntArmHyEzx1BMClIy/GKmNZGGUv30HdjWYZZi5xsvqebJVfZmDY3PKZaT0T10ViYPdvBVVeZQYIzZ3Ts3m3EHQTDqz8BA5Bf7HVX2jtldNrBFVRulncw5lBVSgMRSAEzGUuqhytKxqtDrwglCQZDiBjBuGIwKcQne++OtRXDj2ZqtDBnSejnxPS4LxA+AqaH6dOdXHONGZWsUFGh5cMPTQGbdVV4qHxAAWOzSxSXG9BoPERFuIecQj0t05u3U9+kpds8/F9hoRZG6iFUXJjRMF4JvSKUJBgIIWIE407PQMihQkrhxng2rwsWOdlOrltrRlYr1NRoeP+DCJxjNL+GCiEUlphwuSRSEx3odR5KK/WDjqOIMHpIPj8NvbRqcMFzMaEURgo1F2YyJ/SKUNLkRYgYwbiT1jPV+riGqhI57OcnhbP70h/p6S7Wr+tGo1VoqFfz3nsR2GyjS8QZLA8GwOGUOFliBODKJR1oNQpdZpnG1sGb8E3PGl7jux4CPVrgUndhwjGhV4SSJidCxAjGnSnxHrQ6hbZmmR2vmtj5mpHm+vD8UZwM7kt/JCe7uWFDNzq9h+ZmNdu3R2CxjEzIDCVgAIrKDNgdEtGRbqZl2ck+PyqgtHJwcZKTbkOlgnPtalrbB3f0gtHUDiafCzPifYRJQq9gcjO5/HxBWCBJkDHNSXOdjMMKjbUyO142kZHrYtEVdqJjg1AaE2DGY3DjWOnqUnHunOpC916PhNvVTzdf32NwuXs95wGDQaGuVkV9gxqLVeKmL3QRGTl862wwAeNyQ8EZb3O7vNlmVBLkZlopOaunvFrPykVdyANoW51WITvNhtOlGpaTN1lcmGCWVE/WhF4xZmByI0SMYELImuGi7JS3/X1mrovyUxqqS9XUlKrJnedkwUo7xojQjDOFS+ioskrDgc9HljPSH9HRHpqaVHR2ymzbFsmGDd1ERw8uNAdL5O3hTIUBi1WFyejxVRylJTnQ6z3YbCpqGnRkpQ7c0O7alR1DlpsHY0I1jH9ju1BDJPQKQgUhYgQTQlKGG7VGwemQmJnnYM5S78iBmnI1JYUayk+rmbXIydyldnSjq6QNOOHgvvTGZPSQkOjyde+VVSCr++/e29P9VyWD+vy/surCY7tD4uhRPd1dMu9ui2D9um7i4voXMsMJHXg83uZ2AAtnmn2Oi0oFuRk2CkuMlFbqBxUxwxEwgWasJdWjJZguTCgm9AYKkdA7+REiRjAhqNWQkuX2ui/lahaudHD1TVaa6rwjB5rqZE4e0lJyQsO85Q5m5jlGNJk40ISL+9KbnBwnOTmB66uTnubi/fcjaG2V2f5eJOuu7yYx0b+ZzHDyYADKqvV0mWX0eg+zplr8nsvN8oqYyjodTpeERj16Ry7UwkihNF5gNIzXnKRADnsUoaTJTXhmUwomBf2VWiemurn+VgtXf8FKTJwHh13i6Gc6/vlnE6UnNUHpJDsYweq6G44YDAobNnSRmOTCYZd4b0cEdXUXvnfDFTCKcmHQ4/zplj7iNCHWSVSEG5dL4myNblR7Fcm8w2M0Cb0jQST0CoKNEDGCCSM9x4UEnGvy794rSZA+zcXGr5m5/HorxggPlm4V+z/Q8+7/maguUwe9LHuylU0HCp0O1q/rJjXNicsp8cEHJqqqLqiQoQQMQGWdjrYONVqNwpxcS5/nJal3V96R5/QEY7TAZC6pnswJvYLJjxAxgglDb1SIO9+9t7/GdyoVTJvr4qa7zSy50o5Or9BxTsXutw28/6qRptrAt++9WLwIAdMXjQauW2smM8uJ2y3x4S4TH25vHpaAURQ4dt6FmZNrQaftX4329IGpadBiHUWPmlBM5h0toeTChFtCrwglTX6EiBFMKMPp3qtWw5ylDjbf0828ZXZktUJzncz7rxo5sGt04Yb+EOJl+KjVcM3VZqZNc9DZZqHwdCqny4d2TeqatNQ26Oi2yMybPvCgx+hINwmxThQFykbgxoRyMm84uzCjYSITegWXDkLECCaU9PNTrRuqZVxD5KBqdbBolYPNd5uZPt+BJEF8ytinFIq8l9EhyxBnLCQtuZ0oo45PD0VRUGwc9JwjJyNoaNHgcEpY7YM7abmj6MoLoRdGCkUXZqSMZ0JvIBChpEsHIWIEE0pMnAdTlAe3S6K+anjlR8YIhcvW2vnCXd3kzBr9hEKR9zI2Cg+VI0lw+3pYMMvrquzPj+RwganfnKWmVg0NzRqMeg9RJhclQ3TlnZbhHfTY1Kqho2t4ocNQCyP5rhFiLsx4lFWPFhFKEowEUWItmFAkCTKmujidr6WmTE3GtOGLkqgpo8vuDbd+L8HG6YSCAn2f7r09XXs9bgmXGzweb4dfz/lOv80NU4mJMHK80NsJuNMs09ahpq1TjcutYsXCLr9eLj0VSTOnWmht01BWZWDFgu4B+70YDR7SkhzUNGgpq9KzeO7A4adghZHGymRxYUbDRCX0Ci4thIgRTDjpPSKm3Ft1NFQTs9EixEv/eDwSx46NrKNgV4eZKGMEFuuFY1EmNxLgdEmcKDbicEqsWtqJSoJzHTJna3VIEqxe1sG7u+MwW1TUN2tITRw4jpibZaWmQUtJpYFFc8z9/mwEI4zUw2R1YUZKOCX0ijEDlxZCxAgmnMR0NxqNQkO1zPuvGrhinY3ImMDVUAvxMjhqtcLs2fY+XXr76+YrqxQqTteiylSYmx17vvOvgko63wVYpVBRq2fvkShOlxtwuiTWrOjg+OkIALLT7MRPcZOTbqO43EBppWFQEZOdZkeWoaNLprlNTWJs/05doAVMIJJ5xzJeINguTDDLqkVCr2A8ESJGMOHIMiSkuTlbrKG0UEtrg5rp8x3MX+HAYBq9mBHiZXjIMlx+uXXohXhvUPGxPf1g+hcfc6ZZ0Wk9fPx5DGVVeswWFQ0tWgDyZncDkJvpFTHlNXquWNyJPEDKi1ajsHCWGZ3GQ6SxbxL3ZAsjXaoujEjoFYwWIWIEIUH2TCeVxWrsdgmPB4qPayk7pWH2YgdzljjQjqCSWoiX4DDcjrwA0zLsaOQ2du6bwqkyI06XRN5sMwnnnZTURAcmowezRUVVvY6c9IFnJC2d193v8ckWRuohVFyY0SASegXjjahOEoQEaTlutDqIiFJYtd5KfLIbl1Oi4ICOt/4cweljGtxD5PyKRnXBYyQCpofMVAdXr2jDYpWx2VV0dMnYHd6kFknyVh8BlFaOftJ2MMJIY2UsLkwwBcxIXZjRllWLUJJgPBEiRhAS6A0K8anecIHDIbH+dgurb7QSNcWD3SpxaLeef75oorzIf+RAj3Dp3edFiJfAMhoB00PzOS0JcU6MBg9mi8y7H8f6OvD2jBaoqtf5xM1wCdZsJJhYFybYhKILE6hhjyKUNHpqa2v52te+RlxcHAaDgfnz53P48OFBz9m9ezeLFy9Gp9ORm5vLCy+80GfNs88+S3Z2Nnq9nhUrVnDw4MGA712IGEHI0Lt7ryRB5nQXN/4/MyuutWEweTB3qti7w8C2vxn5ZGcLJ44L12W8GI2AsTskTpUa0Wk8bLr6HEaDh9Z2NW9/FEe3RUVcjIuYKBduN1TUDL86KlhhpEAl8472vFApqYbxK6sONCKUNHLa2tq44oor0Gg0vPfee5w6dYqnnnqKKVOmDHhORUUFGzdu5OqrryY/P58HHniAe++9l/fff9+35pVXXuHBBx/k0Ucf5ejRoyxcuJB169bR1NQU0P0LESMIGXpETEOVGqfDe0yWYcYCJ5vvNrPoCjsWq5mqMgcn9yXTmj+dHHXg8yEEFyg8VD4qAQNwssSbCxMb42L+DAtfuOYcEUYPHV0yb++Ko7Nb9s1IKh2i8d3FhGIYqYdQK6mGyZ/QKxg9Tz75JBkZGfz5z39m+fLl5OTkcP311zNt2rQBz9m6dSs5OTk89dRTzJ49m/vvv5+bb76ZX//61741v/rVr/jmN7/J3XffzZw5c9i6dStGo5Hnn38+oPsXIkYQMkTHeoiI9uDx4Ne9t+BENUVF1Sj6Mr7z/5ysWgqxRh319WrefjuSXbuMdHSIH+VAM5YkTadLouCMt3Pv3FwLVrsKWaWwZkU7ep2H1g41r+2IJ36Kt8KpvlmL2TL09zCUw0ih3tgu2GXVoyUcesMcaWgI2rWDRWdnp9+H3d5/8vzbb7/N0qVLueWWW0hMTGTRokX88Y9/HPTa+/fvZ+3atX7H1q1bx/79+wFwOBwcOXLEb41KpWLt2rW+NYFCVCcJQgZJOt/47piWQ3u66LC0+J67EC5SWLHcxtw5do4eNVBSquXsWS2VVRpmzHCweJENozFwPWYuVQbKgzlcaKKpVYvb09PR93wXX8+Fz91uidZ2Nc1tGsA7uVrVS5+43XCuXY3LLfHRgRgiI9x0dcuUVulZOMsy4J6CGUYKFJeyCzPRoaTJgvacgs46+t9hyvlzMzIy/I4/+uijPPbYY33Wl5eX8/vf/54HH3yQf//3f+fQoUP867/+K1qtlrvuuqvf12hoaCApyf93Q1JSEp2dnVitVtra2nC73f2uOX369KjfW39c0iLmVGEtGp23MmL+gowhVguCTcGJaqwuPV2dKdjsRpYlpwzYvTciQuGqqyzMm2fjyBEDVVUaik/raGpS88XNXUHr+nspMFgib2ubhpoG7aDnKwp0dsu4XBJqtYLTJaHTnm+ap1JQ6RVyjDYaWrTY7RLtHRokFZRWGQYVMRCccmoIjAsTqiXVIBJ6L0Wqq6uJioryPdbp+u9T4fF4WLp0Kb/4xS8AWLRoEYWFhWzdunVAERNKXNIiZklyMnq9kYP19X69RYSgGT96f90B1s6bQuMJHU6HRHNzF4mJg0+pjo31cN11ZhoaZA4dNjB3jl0ImDEwVCXS3OkWpmbYUMkKsuq8KFF5O/zKsoJKpVBRrcftiaTb7CEyws38GWauWNxXWDqcEh/siaG6QUtdkw5FgbZOmSlR/Te1C4aAmehk3mBzqZRVBzOUdLC+njnz0vhH0F4hOERFRfmJmIFISUlhzpw5fsdmz57NG2+8MeA5ycnJNDb6i8fGxkaioqIwGAzIsowsy/2uSU5OHsG7GJqgJhJ8+umnbNq0idTUVCRJ4q233hrynIko2+pdmrs8JcWvbPfim6xg7Fz8te39tZdlSE/35klUVWuGfc3kZDc3buwmJ2fgFvaCwRlOKXV6soPp2TamZdjJTrOTkeIgLclBUryT+CkuYqLclFQaUMsKS+Z1e8cQ1Oj7nWqt1Sisv6qNnHQ7Oq2H5nMa9h6N7LMuWHkwExlG6iHcXZjRIhJ6Q4crrriC4mL//wtnzpwhKytrwHNWrlzJrl27/I7t3LmTlStXAqDValmyZInfGo/Hw65du3xrAkVQRYzZbGbhwoU8++yzw1ofKmVbF/cbEaJmbFz89btYNF5MZsZ5EVM5fBED3pwa4cKMjrH0gulNRbWezm4ZnU7hiiWd6HUerDYVdU39h6DUMlx3RTszsqwoChwqiOR0ed/md6EcRhrP80b0GqNwYUbKWOYkhUMo6WB9fVCvHwr84Ac/4PPPP+cXv/gFpaWlvPTSSzz33HPcd999vjUPP/wwd955p+/xd77zHcrLy3nooYc4ffo0//M//8Orr77KD37wA9+aBx98kD/+8Y+8+OKLFBUV8d3vfhez2czdd98d0P0HNZy0YcMGNmzYMOz1vcu2wGtp7dmzh1//+tesW7cO8C/b6jln27ZtPP/88/z4xz8O+Hu4+CZ7ceipBxGCukB/X5+R9HFJT3chqRRaWmVaWlTEx3sCuT3BAIxVwCgKHCsyATB/uhm9VmFqho1TpUZKKg2kJzv6PU9WwebrWjlbp6ejS2bn3mhcLol5MyxBDSMFisnkwoSiazMUwe4NM39BBjarOaivMZEsW7aMN998k4cffpgnnniCnJwcnn76ab761a/61tTX11NVVeV7nJOTw7Zt2/jBD37Ab37zG9LT0/nTn/7ku08D3HbbbTQ3N/PII4/Q0NBAXl4eO3bs6JPsO1ZCKidmoLKtBx54ALhQtvXwww/7nh9O2ZbdbvcrL+vs7Bz1Hvu7GV/KwmasgqU/9HqFpCQ3J0/q+PvL0Vy5ysLcuXY0IzNmBMNkLL1gelNVr+NcuxqNWmFOrjdBNzfLK2LO1uhwLQH1AL9xtBr48vUtVNToKa/Ws+9YJGWnGshJHPO2BmQyuzAjZTzLqgOV0CsIHDfeeCM33njjgM/3l9axZs0ajh07Nuh177//fu6///6xbm9QQkrEBKtsa8uWLTz++ONB2TOMTNhAeIqbwcJoweiWm57mJD9fh9ul4sgRA6eKdCzKszFjhmPAiceCkRMoAaMokH/ehZmda0Gv8ybBJMU5iTS56TLLVNbpmZZpG/Aa0zLtTM2wMyXaxcefSXSSSlJaAorSHtAwYSCSeXsIVRemYG/xuLgwE5nQG+zeMJdCKGkyEFIiJlg8/PDDPPjgg77HnZ2dfWroA81AN/bBxE0PEyFyhpPrM56t/bOznSQlubHZJAxGD1aLin37jBSe1LFkiY2cbKfIfxkjY2lQdjH1zRoaWzTIMsyfcaFMWpK8bsyxUyZKKwcXMT3rl8w1U1fWQX13LidKDDhdElcsaAvI9ztQYSThwozehQknwvEPzkuNkBIxwSrb0ul0A9bIjzfDEQIHJyh5OJTmD0VHe4iOdiMhs2K5FZtNIv+4ns4OmY8/MlGQ4GLZUhupqUOMthb0S6ASeXvIL4oAYEa2FZPBP4cpN8vKsVMmKut1dHSr0KjB48bbHK+nQZ5Hwu2W8HjgzPEadGoVOalmCsqiOHAyhm6rzPXLW/ya5o2WS8GFGSnj6cKEQ0KvIHwIKRGzcuVKtm/f7ndsoLKtzZs3AxfKtoIddxtPQklMTCSZGU4KO2RqajWsvsrCjBkOCgr0FBTqaGlW8957EaSlO1m21EZc3OD9ZAQXCLSAaT6npqZBiyTBwlneBMiKGh2fHY7yiZP6Zi0Op8SfXk0m0jTw96qzzQxMJSrKAO0gAe1dGs5URqCS4NqlLaMOJwbShRmNgBlPFyaYIwZCBRFKEkCQRUx3dzelpaW+xxUVFeTn5xMbG0tmZiYPP/wwtbW1/OUvfwG8ZVvPPPMMDz30EPfccw8fffQRr776Ktu2bfNd48EHH+Suu+5i6dKlLF++nKeffjooZVuCiScz00VhIVRXq/F4QKOBxYttzJ5tJz9fz+liLbU1GmprNEyd5mDJYhtRUaKSaTACLWAA8k97c2Fys2xERXgFikcBm/2CbWIyuHE41ZitKiJNblQqkM83yLvQNE8Bm5XEJBOyyoas8j7faVbT3Kajst7I+wcSuW55Mxr1yNqy9wiYiZyPBJMjF2a0ZdXhltArQknhQVBFzOHDh7n66qt9j3vyUu666y5eeOGFkC7bEkw8iYkutDoFu01Fc7NMUpL3BmkwKKxcaWXuXDtHjugpL9dSXqbl7FkNs2Y5yFtow2AQ85MuJhgCpr1T5uz5ZnYLZ3X7jqcnObh5fYtXoMgKNrvEG+/HA3D7xmaiIvqKzYL9JRDTf0+YumYd7x9IoLZJz3v7Elm3sgmdZmTf44kMIxV8VjQuJdWXAiKUJOhNUEXMmjVrUPpr1XmeUC7bEkw8suytUiov11JVpfGJmB6iojxcfbWF+fPtHD6sp7ZWw6mTOs6c0TJ/vp3582yiLPs8wRAwAAeOR9LQosGg8/iNC9BpFXTaC48jjJCW5KCuSUtZtYFFs/37bgw13DE1wc4NlzexY38ijed0bNuTxIbLmzDohnbeJjqZd7wYjxEDoZLQK0JJgh6C2rFXIBgrmZnnu/dWDaxG4uPdrF9vZsOGbuLjXbicEseO6nn1tShOndLiucQjTMESMN1mFZX1OhwOFTqth8aWwRVjbpa3Mqm0sv8xBEM1tUuKdXDjqkb0OjetHVre2ZOE2Tq8BJlLxYUZj2Z1E5nQO16IUFL4IESMIKTp6d7b3i7T2Tn4j2tqqosvfKGbq68xExXlxmZVUVQUGlVpE0WwBAzAiWITKJCc4ECnVSip7DsuoDc56TZkGdo61JzruGACj6Qrb1y0k01XNmIyuOjo0vDOniQ6zQMbyoHqCTMZXZiREgouTLB7wwjCDyFiBCGNTqeQfD6MNJyBkJIEU3OcfOlLXVx+uYUVK6wBKcsNR4IpYKw2iaJyIwArF3UBUF6jxz2I66XTKmSmeDtnl1TqgdENd4yJcLHpykaiTE66zGre+SyJtq6+QiaQowVg8rkw4VZWPR6IUFL4cYn+eheEEz0hpeqq4adwyTLMnu0gPf3S7CMTTAEDUHDGhNsNCXFOFs40Y9B7sNslauoHd75ys6wAlFUZOLFv8DyYwYg0utl0ZSNTohxYbDLvfJZEc3vfIZOBcmFG2xNmPBgPFyYUGK+EXhFKCi+EiBGEPD0ipr5BTa8RWIIBCLaAsTskTpV6XZhFs83IMr5OvKVV+kHPzUyxo9UqKApYndoxDXc06j3cuKqJ+Cl27A6ZbXsTaWj1iqhQSOYdyoWJO1fLDTufI+5c7ahfo4dQLqsOJCKUJLiYkGp2JxD0R1SUh+gYN60tMvs/N3LVlZZLNkQ0FMEWMACnyow4nBJTol1kpXpVZW6WlcIzRs7W6nE4O9EOUP4sy7D52laqCk8jSd6ZS2arfKF77/kOvh6/xz2f4+3qq/Q+JhEf7aChRU9Lh46Xd6aQF/s58aaJT+YdipyqQmZUHKUxIYvW2LTRbC0sXJhwCiUJFyb8ECJGEBZkZjgpK9Ny8ICB1haZpUutZGa6xPykXoyHgHG5oOCM14XJm2X2ff0TpriIjnTT0SVztlbHjOyBZyRVn/QKmHkL01EU+PvOVFDG9o1UqRQ8HgmLTc2xutlsvqYbsAx53mAEu7Fdep1XgGTUn+FI3rpB1w76OuPgwoyGQCf0CgT9IUSMICzIyHCi1Sp0d0u0tcl8+GEEiUkuli21kpwsRg6Mh4ABOF1uxGZTEWFy+w1z9A56tHKkMILSSsOAIubifjCSBFq1gqL0dO9Vznfvxdext+eYLPc67nfM+7kEHDrioNthZNeRJFYvbGZ6ene/+xguwXJhVB43qQ1lmA1RpDaUovK48ahGNk9hPF2YUEjoFaEkQX8IESMIC5KS3CQkuIgwqcjOdlBTq6GpUc22bZFkZjpZssRKbOyl2RBmvASM2wPHi70jBvJmmfuE9HIzbRwpjKC2UYvFqsJo6P/7cXEezF0bA/cXu6almHO65Zypgd3HE3C6JOZkd434OsF2YRKbK9G67Oxb9gXW7H+NxJYqGhJzRv46IerCBJLxcGFEKCl8EZkFgrBApYKMdBcqFURGebj1lk5mzrIjqRSqqjS8+VYUn35qpLv70oovjZeAAW+TOrPFK05m5Fj7PB8d6SYhzomiQFl13wTf0ZRTj4TCvcXMXz6Vqxa0MDe7E4C9J+PJL40e1fWCOeQxvf4MDo2OgtlX4lDrSK8bmUsymknVoyUU5iQJF0YwEELECMKGDF+ptQajUWHVFVa+/KUusrMdoEBJiZbXX4/iwEE9NtvkFzPjKWA8CuQXRQAwf4YZ9QCRj+mZF7ry9maosQJjpXc1kiTByjmtLMptB+BQcSwHT0/pt0twf4y1pHo4fWHS685Ql5yLW62lLnka6fUjD/WMxFUZzxED4YboDRPeiHCSIGxIS3UiqRQ6OmQ6OlRER3uIjvZw7bUWmprsHDpsoKFeTWGBnjNndCyYb2PuXDvqSfhTPp4CBuBsjY62TjWSpDB7Wl8XpoepmTb250fSfE5DR5dMdOSFfKVgCRjf9Xv9tS5JsHRmGxq1h4OnYzleFoPTpeLyua1BSwbvcWFM5jaM1oFDWJKikNpQxoHFNwBQkzqT5Ue3k9hciTLI5iyGSMymKWHhwgQK0RtGMBST8Ne7YLKi00FKsou6Og1V1RrmR19oGpOY6OaGDd3U1Ko5dMhA2zmZw4cNnDqlY9EiGzNmOCZNWfZ4CxhFgf3HIqlt0BIf60StHtjSMOo9pCU5qGnQUlKpZ+k884jGCoyGwXrCLJzWgUbtYW9hPKcqo3C6VVw1v3nAn4VAuDDXvP97plUeH3SdWyVTmTEXgLMZc1l5+G2+8uaWQc8pzV7Iu9d/1/s6QXZhxooIJQnGCyFiBGFFRqZXxFRXaZg/z7/znSR582bS07ooK9Nw5KiB7i4Ve/caKSzUsWSpjewsZ1iXZY+3gAGoadDSbZFRqUCr8VDfpCUtyTHg+ulZVmoatJRWGtB25gf1690jYAa70c3J6kKjVvgkP4GSmgicLolr8pqQLwqJjbWxne/z2auIba9nSkcT9Yk5fL7kRiyGSL/1dp2Rzsh4AFri0nnxtsfR2f1Lwo3WLi478i4pTRWci06icNaqcXNhQqW5XbARoaTwR4gYQViRmeHkwOcGGhpl7HYJna6vKyBJkJvrJCfHSdFpHfn5Ojo6ZD7aZSIh0cV1a80YDMNMkAghJkLAAOSfNnlnUmVYsTtUlFbpBxUxWWl2ZBlqqm2k641csTI2qPsbzl/q09O60cgedh1N5GyDiQ8OJ3Pd0kbUsv/PQSByYc5mzqcqbTYLT+7msqPbuGHXn/h88UaOz12DR/b+yo3SN6N1WnG4vUMzOyPj4bzOUbldvnMVJD5ZeQvH56z2nltdfMm4MOM17FGEksIbIWIEYUVUlIeYGDft7TI1NWqmTXMOuFaWYd5cOzOm2yko1FNYqMPjltDrhYAZLg0tGuqbtKhUsGpJJ7v2x1BRo+eKxZ0DJvdqNQrZaTbaWkAyTQPaArKX5nYtXWY1bg+43BIVhXV4lFScJTG+7r0eD/6dfpWe4xIut4RW4+Fsg4m6FgNOt8S6ZY3oNJ6AuTA9eGQ1xxaspWjGZaw8/DZXHniDBUWf8snKWzDPjmHzgt/RZknizRP/ilu5MNg0u6qA1ftfI7qzmYLZV7F/6SZsem9CdcHekQmY0XKpJPQKJgdCxAjCjsxMJ+3tMlVVmkFFTA9aLSxZbGP2LDs2mxR24aSJEjAA+UXevjAzsq1MzbDz+XEPZouK6nodOekDD7KSOs8QFTWPslo3l81rC0g+0snyCEqqvDf0zrZuIIeoGCMjLeyZEuGgqV1H/Tk92w8ks2F5AxAYF+ZibPoIPl71FU7MWc21n/2NTZX/g+t2HTqNDYsjCq3ahtXpFTFah5UvvP97GhKzefe6b496FEEP493cLtwSekUoaXIgRIwg7MjMdHLihJ6aWg1uN31yGwbCaFQwGsPLhZlIAdPSpqaqTockwcLzIwZyM60cP22ipNIwoIgp2F9CXIREg9VFSrwdh0uFXjv2RoRTIp0kx9uQVQp6RxspmTHIqm5Ukrdzr1p1oXtv366+3k6/apUHlQq6LGoOFU+hpUPHi//QsySp7wTs4TBcB6c1Ng3Xcg2qLyvoNDZq2qfzduH3fOEkAIdGR2dELG3RyX0EzGhdmPEuqw63hF4RSgp/hIgRhB0JCW50eg92m4qmJjUpKa6J3lJQmEgBA3D8tNeFmZph85VK52bZOH7aRFWdDrtDQqf1F4U9/WAWLEpjnlKHKoCu18LpXSyc3kXh3mIy02De4rHd5JJjbWw/mEJDq4lS5SpmWOuINIz8Z2k4fWEWJOwm48piJBWUtSxge9G9uD1adHYLdp13FhWSiqIZK1ly/AN2X3ErTs3gE8EHI5xdGIFgJEySolPBpYRKBZkZLpxO2LbNRHPzyGbOhAMTLWA6umTKz3fdzZtt9h2Pi3ERG+3C44GKmv5vsj3l1IEUMH1eIwB/pU+JdDJd+oS0NC2dFg3v7E+jvVsz9InnGa4LsyDlE66Z8zKSCs5ULebdk99GtrlZ/9H/8t0XH2TdR8+jdXh77xTNWIHWZSe3Iv/C61xiLsx4JPSKMQOTByFiBGFJRqaTjg6Z+gYN//xnJLt2GenomBw/zhMtYMDrwigKZKbaiYvxdydys7xdeUv66cob7IZ2hXuLA3aDK/isCIPGxo2X1TLF5MBsV/PugTRaOocfWhqOC1PbkYvboqJz9xS2V3yT5IYKvvrGz8mpLODQwnVMrTzBV9/4OSkNZXRGxlOdOpPZZ/Z79ziKkuqJGPQoEEwUk+O3vuCSIz3NSWycG51OweWGs2e1vPGPSPbsNWCxhFnmbi9CQcB0W1ScOevN1ejtwvQwLdPrGjQ0a+m2eH+FBHsuEgze1G60zLt8Bia9mxsvqyU+yo7VIbP9QBqNbYOHcnaf+IQPL/8nterqIV/D2ahD/pGHz89s4rLD73LLO0/RbYzhbzf/hL0rvsjfvvwTzMZobnnnv7ns8DsU5S4ns66YyK5WYOSOymjPGS2BnJMkEnoFI0WIGEFYotFARrqTuFg3C+bbyMx0ongkik/rePW1KA4f0WMfuHgmJAkFAQNQUGzC44GURAfJ8X2rvyJNHpITzg96rNIHfS5SbwLpwvRGr/Vww/I6kqfYsLtUbD+USm2LYYCz4Wx6KVVp5ZwwHOvznKxycMPsP5Ie7RVds88cgHZYVLCL5ce2c2DxRl7f9KCv2V1nVDyvbfo3DizeyPJj21lU+BEAsR++O+L3NVoXZrTN7YKBSOgVjAQhYgRhS+b5gZDn2tRcd52ZjRu7SExy4XZJHM/X89rrURQU6HCFQd5vqAgYm12iqNx7817UjwvTw/QsrxtTWuldG05hpB4uLqnWaTxsWFZHerwFl1vi/cMpVDYa+5xX8FkRNckVABTpTvo9p5WtfHH+75iReIQNc/6EWrIx53xoSOu08dqmH3JgyY0oKv88LkUlc2DJjby26Ydond5w3RXtp5i/ataI39d4uzCCycN//ud/IkkSDzzwwKDrXnvtNWbNmoVer2f+/Pls377d73lFUXjkkUdISUnBYDCwdu1aSkqC49YKESMIWzIyvCKmsVGNzSaRnOzmxo3drF3bTUyMG7tNxcGDBl5/PYozJVo8Y6/yDTiFh8opPFROXm7ShAsY8Low3WYZtVoZtCtvTroNlQoqKuyk5eQEdU+BDiMNlpCrlhWuX1xPTpIZt0fig6MpnKmJ8Ftj1ZlpjW1ihn0W1dpKulSdABg1ndy88Fekx5Rgd+nZfupbKE4VnRGxFOWu4G9f/gn1ydMG3Vt98jT+9uWfsD96Fuc0kcjuofsg9TAWF2YshFtCr6B/Dh06xB/+8AcWLFgw6Lp9+/Zxxx138I1vfINjx46xefNmNm/eTGFhoW/NL3/5S37729+ydetWDhw4gMlkYt26ddhstoDvW5RYC8KWyEiFKbFu2s55u/fm5nrnImVlucjI6KKkVMuxo3rMZhWffWqksEDH0qVWMjJcIdHwLlTclx4cTolDhRE0tmpQazwoCgN+nfQ6Ba2zDoimpMbI8jkdQd3bSG5uHWY1p6ui/Lv39vpoaFATmxpP2T7vY9f5jr7uXv+63BKtXTrMNjXbD6Xh9tQzO9MrVmqSKwHY3HELv0z8Gad1p7hWmcaXFvyWGEMzZkcUb534F5rNGaCGN258AKTh/73o0Br4U/oG5q+aOaLzYPQuzKVUVi1CSX3p7u7mq1/9Kn/84x/5+c9/Puja3/zmN6xfv54f/ehHAPzsZz9j586dPPPMM2zduhVFUXj66af5yU9+wk033QTAX/7yF5KSknjrrbe4/fbbA7p34cQIwpqekFJVlX9prEoFM2c4uPnmTpYtt6LVKbS1yezcGcG2bRE0Nk5sWXaoCRiAU2VGZFlBr/MgS1DbOHCVTsH+ElKmnCMqykBZjbeSKRiMxoUxW9WcKI+m8Gw0RVVRnKmJpKwugrMNJk4WeXAY0mlo09PUoaO1S0uHWUOXVY3FLmNzqnC6JRQgNtJOhMGb+7PnZAInKmIo+KyI7vmtZDiySHGlke7IxBV9kNvy/osYQzMd1nhePfYjr4DpYYRCxFeRNILzJoMLIwgsnZ2dfh/2QZIE77vvPjZu3MjatWuHvO7+/fv7rFu3bh3793vDphUVFTQ0NPitiY6OZsWKFb41gUQ4MYKwJjPDyfH8gbv3qtWwYL6dmTMcnDih4+QpHY2Nat59N5KsLCdLllqZEjO+caZQFDAuNxQUG5GAObkWWts0lFYZyEjpG1LqSeRdsyqSmvc8dFvUNJ7TkRwX2Ezq4Uyo7o8Ig4t5OR19OvdWF1WREa8wbV7qhc6+8oXne3f7NWvOYVF3oJIUimujOFMbye5mmKKWOa07xZXmNQDMts9jXdoHmHQu6syJPHfqdrrcVtBUARDpjiTGM2XYe+8RMONZkXSpJPSGSyjJ0OJGrx99Ip9k8zamzMjwd5weffRRHnvssT7rX375ZY4ePcqhQ4eGdf2GhgaSkvx/dyUlJdHQ0OB7vufYQGsCiRAxgrAmIcGN3uDBZlXR2KgmNbX///w6ncKyZTZmz7Fz7JieM2d0VFZqqKzSsPmmLuLi3OOy31AUMADFFQasNhURRg+X53XxzsexnK3R4VwioVH3tVm8ibwK2akWSqoiKK0xBlzEwOhubFEmFyvnnOv7RG39+WTe7iGv8X9TnqNUd97dSAaWeD+tACSPzDxrHgDzbQv5XtN7NHtgS1sTHTG/9btOrn0m97X+YET7H3GTuglwYQJZVj2eXEqhpOrqaqKionyPdTpdv2u+//3vs3PnTvT60XeInkiEiBGENZLkTfAtOaOjqkozoIjpIcKkcOUqK/Pm2TlyRI/VqiI29tIWMG7PhREDC2aZSU5wEhXhprNbprJOR27mhWS8i/vB5KZ7RUx5rZGV89uQAxSgHs9k3v5YYltBhbacCE8Et3R+jSh3NHv3mmliJrI9CsPscjyxHjKcWTzU9DOaWix84/y5naoOXov5K90qM0usy4e/xzG853B2YcYjofdSJCoqyk/E9MeRI0doampi8eLFvmNut5tPP/2UZ555BrvdjnyRvZ2cnExjo3/4r7GxkeTkZN/zPcdSUlL81uTl5Y3lLfWLyIkRhD2ZGV7hUlWtHnZuxpQYD2uvtbBhffe4JPmGqoABKK/S022W0es9zJpqQZJg2nnhUtqrK29//WBSE2xMSzezcn5bwPYz2jDSUIxkSvVl1iu5/9wPUVB4LeqvnDlexu3LZrE+NQpddyLXz32H3OjtuD0Kse54MpyZZDgzcePi1Zi/AnB/y4NcZrliRHsMFxcm3BBjBvrn2muvpaCggPz8fN/H0qVL+epXv0p+fn4fAQOwcuVKdu3a5Xds586drFy5EoCcnBySk5P91nR2dnLgwAHfmkAinBhB2JOW5kSWFbo6Zdo7VCPKcVEH+X9AKIsXAEWBY0XnXZgZFtTnf2dNz7Jy7JSJmgYdNrtEyVHvzfLifjAqCa5Z2hrwfQVSwIzUhekh2zmVB1v/f7wQ8we2r3kNU5eay9JWoZEVfvT3n3LTkh18WpLK2rwG1LLC58Y9vB79dzId2Xy97VtEeaKHv8cwc2FEQu/kIDIyknnz5vkdM5lMxMXF+Y7feeedpKWlsWXLFgC+//3vs3r1ap566ik2btzIyy+/zOHDh3nuuecAfH1mfv7znzN9+nRycnL46U9/SmpqKps3bw74exBOjCDs0WjwTbIuKx3+3JtgE+oCBqCyTkd7pxqtRmH2NIvveEyUm/gp3kGPZecHQY5HR95gjBaAkbkwvYn2xLD67Y3k1EznlZi/0q3qIie5m1Xz23jl881UN0ew40ga5zxdvBLzV/KsS/le6w9GJGB6uFRdGJHQG9pUVVVR3+trePnll/PSSy/x3HPPsXDhQl5//XXeeustPzH00EMP8S//8i9861vfYtmyZXR3d7Njx46g5N0IJ0YwKcjMdFJeoWXnhyacLom8hTYMhiDV/Q6DcBAwigLHTnldmLnTLei0/l+v3CwrLW2R7N9v4xtfHj8BE2gXZrQCpgdZkZkaO5V6TzVGj/frlRFvYf3SWj44mkb9OQOfHpiJIcVInDsO9Qh/rY52SjWEtwsznohQ0vDZvXv3oI8BbrnlFm655ZYBryFJEk888QRPPPFEgHfXF+HECCYFGZlOLBYJm02i4IR3ftLRY3qcw294GjDCQcAA1DVpaT6nQZZh3vT+Bj3a6Go302420WUen746oRBG6u8aRbpCZljn4HR6+8l0WmUwdHDFnEZAoaYlEn31Mk5qTw5+wYuvP0rnaTK4MCKhVxAIhBMjmBREmBSmTXVSW6dGq/Pgcqo4dlRPUZGWRXk2Zs509OkhEwzCRcDABRdm9lQLBn1f16o8v5hYUy5OVSKlNSYWzewM2l4mKoy061gSFrv6Qqfei7r3nmuKR5/ipkb7azyf38FfSqfh0rdRv/I3dOV8RmTFVcSrv8+51lTcp1dRm/0JDQ4Lydq+M5cGQrgwwUOEkiY/4+LEPPvss2RnZ6PX61mxYgUHDx4ccO2aNWuQJKnPx8aNG31rvv71r/d5fv369ePxVgQhTEamE71OIT3dxdXXmImKdmOzqti/38gb/4ikrEwTvM6yITYDaSgaWzXUNWlRqWDBzL4uTE8l0solXuVXOg5deSfChWlq1w/Yvbe5wYLbI9OZdhiAiJpldOR8TNmX7sGScoLk41/DmnKc6lvvIXbFO0TXLQXgtbo62s2awV7Wu8dxdmFCifFM6A1EKOngOSGGQpWgOzGvvPIKDz74IFu3bmXFihU8/fTTrFu3juLiYhITE/us/8c//oHDcaFLaGtrKwsXLuwTf1u/fj1//vOffY/7a+QjuLTIzHCSf0xPba2Gq9dYyMp0cuaMlmP5ero6ZXbvNlFQ4GbpMitpqYGbnxRO7ksPB45H0tapJjXRQYSp/2queQvTsTst7D0RS3uXhtYODfExwYnPBSOsMJxcmJVzWvB48HXsVcsXuveWHC5h7vIcXk/+CKszHu3mX3DGeJQF1kXc3HEHkQlRdLUv4PXov3Piql8wd/ESWrqTaEk6yrv7r+eGZTXERg48RBPG14UpPFQ+ahcmGM3tRChJEAiCLmJ+9atf8c1vfpO7774bgK1bt7Jt2zaef/55fvzjH/dZHxsb6/f45Zdfxmg09hExOp3O11RHIACIj3djMHqwWlTUN6hJT3Mxe7aD3FwHJ0/qOHFCT2urzPs7IkhJdbJsqY2EhLE1ugtHAXOuQ83ZGh1d3TK2GBVuD35N6gr2l/gqkXQahcwkKxV1RkprTMTHtAd0L8EII40kFyY7qa8L1XONCA2YjHbKjCexqCw4VDbuPHcvi2xLfesiPVHc3fZtjlkP80b0y9jlbtwaCxYnvHsgnfVL60iM6Tu5d7TJvIX7zoxa+FxqBKo3zP7W2gDsRhAsghpOcjgcHDlyxG8QlEqlYu3atcMeBPW///u/3H777ZhMJr/ju3fvJjExkZkzZ/Ld736X1taBe1XY7fY+w7AEk4+e7r0A1b0GQmo0kJdn59ZbO5k7z45KVqiv0/D225F89JGRjo6R/zfoCR9BeAkYgPwiEzqdh+goN4oHqusvuJgXd+QFyM3w3ujLakx4ghBSmigXZijmXzETp+QkwZXEAusi/r/mR/wETG8W2Zby/zU/wgLrIlJJID6uHbtLZvuhNOpaDX5rx9ITZrSM1YUJJOGY0JuXmzrRWxAMQFBFTEtLC263e9SDoA4ePEhhYSH33nuv3/H169fzl7/8hV27dvHkk0/yySefsGHDBtzu/v+q3rJlC9HR0b6PiwdjCSYPmedFTFV13/wXvV7hshVWbv5yJ7nTHSBBRYWWN/4Ryd69BiyW4cWXeouXcBMwnd0yZVV6JCBvlneGUE9X3v468gJkJFrRajxYbDINLYEL2xbuLQ74zSwQFUm90St6/rXlR9zd9m0iPYO3cO9xZb7f+kM25bWRFmfB6Vax40gaVU3+f4SFmwsjEnoFoUpIl1j/7//+L/Pnz2f5cv/5I7fffjtf+MIXmD9/Pps3b+bdd9/l0KFD/dazAzz88MN0dHT4Pqqrq8dh94KJIC3NhaxW6O5S0dbW/493ZKTC6qssfHFzJxkZThSPxOnTOl57LYrDR/QMMrE+bN2XHo6f9iboZqQ4yJvtdVgq6/Qc21MG9N/QTpZhapq3EV5JjanP86MhWNVIMHYXpuCzIub3ci1UI/w1qUKFRq1w/eI6shK7cXskdh5Loaw+YkxhpNESai7MeCFCSZcGQRUx8fHxyLI86LCogTCbzbz88st84xvfGHQdwNSpU4mPj6e0tLTf53U6nW8Y1nCGYgnCF7UaUs93762qGrxCJDbWw/XXm7nhhi4SEl24XBLH8/U0N/efKhbuAsZsVVFc4Q1t5M3uJn6Ki+hIN243NHbEDNqRNzfdK3jO1htxD3+qw6AEw4UJRBgpUKhlhWvz6slN6cKjSPxzdyS13aPP45ssLky4hZIWzwh+o0fB6AmqiNFqtSxZssRvEJTH42HXrl1DDoJ67bXXsNvtfO1rXxvydWpqamhtbfWbmCm4dMnMvBBSGg4pKW423djNtdeamTnLTlqa/yTscCufHoiCYhMeDyTFO0lJcCJJ3hlJnW1m3LrsQc9NjrNz+YJz3HxN/ZgnVYdyGCnQ4ShZBWsWNDA7owMUqGYpBRUjG0kgXJiRIUJJlxZBr0568MEHueuuu1i6dCnLly/n6aefxmw2+6qVLh4u1cP//u//snnzZuLi4vyOd3d38/jjj/PlL3+Z5ORkysrKeOihh8jNzWXdunXBfjuCMKAnube5WY3VKg1r/IAkQXa2k+xs/xLicHdferDZJYrKvC7MotndvuPO5lPAXGqbdVhsKoz6/m0WSYK5U7v7fW4khHIYqYf5Q9z0FQXc5xvi9f7w9HPM7VbhUaC7uoQobSqdlil8cCQFm0Nm6Yxzwy7zFy7MyBChpEuHoIuY2267jebmZh555BEaGhrIy8tjx44dvmTfqqoqVCr/P+2Ki4vZs2cPH3zwQZ/rybLMiRMnePHFF2lvbyc1NZXrr7+en/3sZ6JXjAAAk0khPt5FS4ua6moNM2YM3qtjICaLgAE4VWrE6ZKIi3GRkeL9ehTsL8Gog9xsFU3nJMpqTcyf1hX0vYSqCzMYR0piKTg7xStWlJE1GOo61w3MIiI2Ao8FOsxajpTE4lEkls9sHVTIjNWFEYwNEUoKfcZl7MD999/P/fff3+9z/SXjzpw5E2WA9qAGg4H3338/kNsTTEIyMr0i5vhxHVOnOlCP4Cd9MokXAKdLoqDEm5CbN6cbSfKvRJLKzTSd01FaYwyqiAlGGKmHQLgwFyf09sajSDjd/cfR1CrP+QZ5IJ//vKdhnqxSUFvaSMmKQ1aZUakUmjt0dJi1nKiIweFSccXcZlSDCJmxuDCh0txuPENJYtjjpYWYnSSYlGRmOPn8cwMnT+lwOCSWLLExY4YD1RD5HJNNwAAUlRmw2yWiI93kpNv7lFJPTbOwv2AKLW062rvVxES4BrvcqAhWGGk8XBiAedntzEjv7CVYzgsViUGdlIK9xeQkwfxl/kKkuDqSzwoTOV0dhdOlYvWCxj65RpPNhQmnhF4RSgofQrrEWiAYLXFxbnQ6D7IMbW0ye/ca+cc/Iqk42//8pHBuXjcYbjecKPa6MAtnmX1/8feuRDLoPKQlervKllYHpoS6P0LdhRkMg9ZNtNGJSe9Gr/WgVSvIqqEFDPTvpMzM6OKavAZUEpTVR7DrWDIud9+LTZQLE0jCNaFXhJLCAyFiBJMSSYKZMx2kpLhITnGh03vo6JD5aJeJd96NoL7+ggkZzs3rhuLMWQMWqwqjwcP0bKvfSIHe9JRQl9UYAz7oMVhhpECXVA+V0Duqaw4iQqammLlucT1qlUJlk4n3D6fgdHmFzES7MJdyQq8gvBAiRjBpycxwIgEup8QtN3eSt8iGWqPQ3KRm+/YI3n/fxP6PvbbxZBMvAB4P7DsWSX2zFlmlcOpA35ECPWSnWFHLHjrNGpratAHbQ7iHkUbLcJvaZSZaWL+sDo2sUHfOwPZDqdid3l/Lk8GFCUdEKCm8ECJGMGlJTfV27zWbVXR3yyxZbOOWmzuZPdtOd6eZopMOTp+ZSVvrDDq75YnebsApr9Fjtsq43RL1dRbcHmnAhnYatUJWihXwzkgKJKEcRoLBE3pHdb0RCreUWBs3LK9Fp/HQ1K7nL28ZyV0+d1SvHWouzHjNSRKhpEsXIWIEkxa1Gl/juqoqb/jIaFSI0pxk5ZJyFs1SUBQoOavn1ffi2XcsEqttZOWzoYqieAc96rUejOoOPB4VkYmD35x8IaVaI54AdOUNZhgpVBksD2YwEmPs3Li8FntHG12OCN7Zn0q3dXTC+lJ1YUQo6dJEVCcJJjWZGU6qKjVUVWvQuLw3vwuhow4WzDRz8EQktY1aCs8YKa4wsHCmmfkzLWjUQRjZPE5U1es4167G0tnF1KRW2h2ZlNYovhlI/ZGWaEOvc2Ozy9Q268lIso369YPZ1A4C58IEg9GGgWKjHCxLOU65ciUdZjXv7E/lhhX1RJuGVy0Wii5MuLG/tVa4MGGGEDGCSU1P996KUjvpsWpWzPXvAJ0Q62LjmjZqGrQcPBFJS5uaw4URnCw1snhON7OmWcfcZn+8URQ4dsobEsqIa2bN5Tre+AiqGw3YHCr02v5tFlkFU1MtnKqIpLTGNGoR0yNgwsGFCWQoqWBvMXMun017t8bXvdejSLjcA3f07X2s6kwjHiWVxAw7hWf1tHRoefmjTG5ZXU1slHPoDRB6Lsx4hZKEC3PpIkSMYFJTfrIMyZ1FlDGaaEM6YO13XXqyg7SkVsqr9RwqiKCzW2bv0SgKzphYtaST9OTRdf2dCOqbNTS1auju6OarN6sx6p3ERjs416Glos7I7OyBxwfkZpg5VRFJl1mNogxeQjwYwbx5haIL0xNG6jBreWPv6G6oXR1aouIiaG8AvcZFp1lPl1XNu5+nsmF5PQkxA/8MChdm7IiE3vBEiBjBpKXnF/vSOTKHC6GyTsesqf2LGPDesKdl2shOt3G6zMCRU14xo1KFV1gpvyiCzjYzGXGtGPXeveemmznYoaW0ZnARkzjFwc3X1DNlmH/5X0www0ihmgvTOw+mw6ygU3suNMM7/+/FH37HZYXGigbiohUyc5N9xz0eOF0dRbdVzbbPU1i3rJGUuIHdsUvVhQkkIpQUfggRI5h0XNy0rqXNzuHCCGoadLjcoB4iX1JWwdzpVmbk2DhbqyM1cXQ39Img6ZyaU0XeCdUbrlYBbgCmpVs4eCqGhhY93RaZCKO73/MliTELmHBxYQIZSurJg4k2ObnzuooRn1/YWXb+Gm1+x+fmdLLzcBJ1rQbeO5jMdUsayUj0F+Kh5sKMNyKUdGkTZtF+gWBgBuq6GxfjwmT04HZDbePwh4Rq1ArTs0af3DoRvP++d7+L5niI7CVUIgxukuPsAJQGuIS6N8ESMKHswox1wnThvjMDXkOrVli3rJGMRG+J/AeHkyiv7/v9G60LEwzGq6w6kIhQUvgiRIxgUjBY111JgqxU7829qm7yTjrf/3E1jR0xREUZyJvR0ef53iXUgSbY1UgQerkwI+0H0x/D6cyrlhWuW9LItJRuPIrErqOJnKmO8J4/Rhcm0IMexxMRShKAEDGCMGe4M4+yUr0uxOHCCM7W6gLeWn+iKdhfwtnmJKKiDGSnWpgS2bcsNyfVikqlcK5DS2uHJmCvHewwUqDHC/Rcc0znj7IfTH8M5xqyCtYsamZmhnfK+CcnEiisiAJCz4UZT0QoSSBEjCBsGcnMo5REBy63REubmnc/nsI7H8fS0BK4G/lEY3Vo6XanApA3vbPfNXqth4yknq68gXVjwjGMNNp8mEAJmJHOR1JJcOX8FubleF229/cZcMYvG7UgD5YLI0JJgvFEiBhB2NHjvoxkYKNahpx0G9GRbmwOFQ3NGt7eFcsHe2Jo6wzvkQMF+0twaGfickvEx9hJmDJwKW5uurfZXWmNKSBu1KUWRgqkAzOa60gSXDb7HIunexOADxfFcPBUTEg4i+PpwgS6N4wIJYUvQsQIwobhho4GYmqGnZhIFxnJDmZNtSJJcLZWx+s74vnkUBTdlvD771CwvwS7U82homhqmvUwRF+XzGQrGrUHs1VNQ+vY8oPGI4wUSgRSwIxlSrUkga7jKNdd6a0iO1ESxd4TU0YkZMK5rFog6E34/dYWXJKMJHQ0EJkpNiQJOrpklszt5ub1LWSn21EUKC438Mr2BA4cj8DuCI/5SQX7vVOp3YaZqGUFnVqhrUOD0zXw/tWyQnbqBTdmrAT7phUqLkwwBEzMzMWcqY6gqDKSwooojpdFc7QkhsPFU/j8VCz7TsbxWUE8u48n8NHRBHYeTuS9g8ls+zyZA1XzKKs1IkkKdS06Pjkax+6jcSOaeSUSesWYAYAtW7awbNkyIiMjSUxMZPPmzRQXD+2wvvbaa8yaNQu9Xs/8+fPZvn273/OKovDII4+QkpKCwWBg7dq1lJSUBHz/QsQIQprRhI4GwqBXSIj1/vVaVa9jSpSb669o56Zrz5GS6MDthuOnTfx9WwL5p024+m+lEhL0CJjpczI4VRGJVqOQEm/F7VFxtt4w6Lk9IaXyOiPuUQ56DHYYKZRcmECHkHqudfB0LJ+cSGBPYTz7T8Vx8HQsR85M4VhpDAUV0Zw8G8XpqkhKaiIoq4/gbKOJmmYDp8sUPIYUWtp0KIpEtMmF2yNRWm1i1+F43EP83AbDhRnvsmqR0Bs4PvnkE+677z4+//xzdu7cidPp5Prrr8dsNg94zr59+7jjjjv4xje+wbFjx9i8eTObN2+msLDQt+aXv/wlv/3tb9m6dSsHDhzAZDKxbt06bLbAtq0Qze4EIUnv0tGxipfeZKXaaWrVUFmnY/Y0b5JrUryTG9e0UV2v5WBBJOfa1Rw8HsHJEiNL5nYzI9uKKoTkfo+AmbcwnWPFkThdKmKjHGQlW8k/E01pjYnpGQMPekxNsGHUu7HYZKobDWSnDNzFuD/Go6kdhIYLE2gB0zuMlBRrw6hzI8sDdPaVz3f3lRTU59dUl9ShSlbIXZh+vvMvyCqF5nYth4tiOFtn5H1XItctbx50gGm4ujCCwLNjxw6/xy+88AKJiYkcOXKEq666qt9zfvOb37B+/Xp+9KMfAfCzn/2MnTt38swzz7B161YUReHpp5/mJz/5CTfddBMAf/nLX0hKSuKtt97i9ttvD9j+hYgRhBTBEi89ZKbaOVQQQW2jDpcL1Of/B0gSZKY6SE9ppaxSz6GCSLotKj49FMWJYiPLF3STlWof9SyhQNFbwLhcEoXlkQDkzegkPsZB/ploapv0WO0qDLr+bRaVBFPTzBSWRVFaYxqxiIHgCphguzDD7dQbDAem9/VWzWsd8bmepubzJdX+37OUeDvx0Q7eP5BAbZOe9/Ylsm5lEzqNv5AJlgszXgQyoXeyh5I6O/2rFHU6HTrd0HlwHR3e6rfY2NgB1+zfv58HH3zQ79i6det46623AKioqKChoYG1a9f6no+OjmbFihXs378/oCImhP6+FFzKXJy0GwwBAxAb7SLC5Mbthpp+uveqJJiebePWG5pZmdeFTqfQ3qnmgz0xvP1RLPXNE1eW3VvAAJyuNGGzy0SaXExNtRAT4SJ+ih1FkSgfoqFdboYFo95NtGlkIwbGoxoJguvCzL9y9pCN6oIhYAbrzDus84dobJeaYOeGy5vQajw0ntOxbU8SVnvfX/GirDq0UbebUbeN4aPdGwbKyMggOjra97Fly5YhX9vj8fDAAw9wxRVXMG/evAHXNTQ0kJTk/zs6KSmJhoYG3/M9xwZaEyiEEyOYcMZScTRSvN177ZwsMVJVpyM7zd7vOrUM82damJFj5USxiYIzRhpbNLzzUSyL5phZNn/gIYrBpEfAuD1wotTb7Gzh9E5fuCs33UJLm47SGhNzpw68x/hoB3esq0U1AmdpPMJIoZALEywBEwiGamyXFOvgxlWNbN+XSGuHlnf2JLHx8iZMBvekcGECxaXQG6a6upqoqCjf4+G4MPfddx+FhYXs2bMnmFsLKELECCaM8RQvvfGJmHpv597BQkQ6rcKy+d3MybVw7JSJ0+VGMlP7Fz7BpGB/iU/AAJRWmzBb1Rj1bqZnXEjAm5Zm5vPCGJrO6ejoVhMd0bdzL3jf82giY+PxF/d45cL4xMp5YRCs8FEPwXRhehMX7WTTlY1s35tIR5eGd/YkccPlTd49hLkLI3rDDJ+oqCg/ETMU999/P++++y6ffvop6emDf22Sk5NpbPQXsI2NjSQnJ/ue7zmWkpLityYvL2/YexoOIpwkGHfG2u9lrKQkONCoFSxWFc1tw9PxJoOHVUu6uOPGZpLixneqdU8YqQePAsdLvL+c5ud2opYv5D0Y9R7SErwiK5AzksYjjDSeLsz8K2f7REXB3uKgCpjxcmF6ExPhYtOVjUSZnHSZ1fzf2xF02/QB2YdgcqEoCvfffz9vvvkmH330ETk5OUOes3LlSnbt2uV3bOfOnaxcuRKAnJwckpOT/dZ0dnZy4MAB35pAIZwYwbgR7KTd4SLLkJ7soKJGR1WdnsTY4YeGTIZR1iSPkovzYADO1hnp6Nag1XqYnd1377npZmqb9JRWm1g0o3PMycjjVY0E41+RFCzXJZCvM9ohj5FGNzde2cR7+xKo7NRQ0rGY6W1txE/p350bKeNZVi1CScHjvvvu46WXXuKf//wnkZGRvpyV6OhoDAZvu4Y777yTtLQ0X17N97//fVavXs1TTz3Fxo0befnllzl8+DDPPfccAJIk8cADD/Dzn/+c6dOnk5OTw09/+lNSU1PZvHlzQPcvnBhB0BmvpN2RkHl+qvXZmtCdat2fgFEUyD9zfvDf1K5+y2izUyzIskJHtyZggx6DfbMKhVyYYDDWZN4eRjvk0aR3kxN1jCiDBYtN5p+7YqlrCs+ZYSKUFBx+//vf09HRwZo1a0hJSfF9vPLKK741VVVV1PcSkpdffjkvvfQSzz33HAsXLuT111/nrbfe8ksGfuihh/iXf/kXvvWtb7Fs2TK6u7vZsWMHen1gHUHhxAiCRqg4L/2RmWIHCYrKDWzbHcOqJV1ER4ZOd7v+BAxATZOe1g4tarWHuVO7+j1Xq1HITLZSUWuktMZEfEz7qPcxXtVIEBp9YQJJIMJIhYfKabVEse/EFNweCbcHPB7J+7m717/K+ePui57zSLS3J2CKMtHcqsbmUPHPXXFsuvoc6ckDz9gainBN6BX0RRnGvIrdu3f3OXbLLbdwyy23DHiOJEk88cQTPPHEE2PZ3pAIESMIOKEsXnow6BWiItxU1ek4XW6kvlnHrKkWFs8xYxznkNHFDCRgAI6ejsZsk5mdbUGvHXifuelmKmqNlNWYWD63fURVSD2MVxip4LOiSSdgegiECzMlZwYHCiNHdW5np7efjEoFCXFOWs5pcLkldnw2hWtXtpOTPvok9XBM6J3svWEuRYSIEQSMcBAvvZmVY6WlTYPiAY8HTpUaOXPWwPwZFhbOMqPVjP9o4MEETH2LjuIqE10WNW734KokI8mKTuvtylvfovMl+44U0f9jdATKhZl3xUwaWu3kzejwdvaV8HXy7d3Vt3f3Xt8xWaGkqI65y3OQVWbUsoIiwScHoimv1vPhvhjWLO9gevbI2sCPpwsjEAyFEDGCMRNu4qWHrDQbB09EoFLB9Vec4+ipSJpaNRw7ZeJUmZHFs72l1bI8PvsZTMAA5JdEYTK48SgSDef02J1Sn46sPcgqyEm1cvpsBKXVphGLmMK9xeMiYCajC9MjYAKVNJwc5yA5buShn8LjNRi0fZPRr1nZgVqtcKbCwMcHonG4JObmjqxrs0joFYQKIrFXMGpCMWF3JMREuomKcOPxgNuj4qZrz3HdFe1ER7qx2yX250fyynvxlJzV4xknU2YgAdPSrqGm0YBW4yEj0YrbLXG2boiuvOne/jEV9UZcQzg3vRnPPJjJylgFTI8LM+Z99NMXRiXB6mWdzJvuna+190gU+UXDm2g+ES6MSOgVDIYQMYIRE+7ipQfvvCSvQ1FZp0OSICfdzi3rW7hqWSdGg4dus8zHB6L5xwdxVNVpGUYO3Ki4uJndxeSXRAMwLc3CnBxvWXVpzeA3nuQ4OyajC6dTRVXDyCoChAszOgIVRhrzNYbozitJsHJRF4vmeIXuwRMRHCqIGNbPdzi6MGPlnOnIRG9BMABCxAiGzWQRL73JOi9ierr3gjcJctZUK7ff0MzyBd1otQrn2tXs+GwK7+6eQmNrYEtUL25mdzHt3Woq6rz9GhZO7/Q5LHUtOsy2gWNdkgS5ad61QwmeHsYrjDQZCWQYKVguTG8kCZbN72b5Qq8oPnbKxL5jkUET6qMhkAm9gsmJyIkRDEm45rwMh+QEB1qNgs2moumcxq8br1oNebPNzJpqIf+0iZMlJuqbtPzzw1hy0u0sm99FTNTYyrKHyoOB8915FYnMZCtx0d79JcXaaTyno6zGyILc/kutAaZnWDDb1H6jCQZiPMNIk9GFgcCEkcbKSGck5c0yo1V72Hs0ipMlRpwuiauWXpjH1cN4NrcLBqMNJbVGHISh//sIJgghYgQDMpnFSw+yCtKT7ZRX66mq0/U7UkCvU7hsYTfzpls4cjKCMxUGKmp0nK3VMTPHypK53ZiMIy/LHo6A6bbKlFZ7XZS8GR2+47kZZhrPeQc9DiZipkQ5uXpJ67D3FM43qYkkUKMFYHxcmIuZk2tFo1HYfSCaMxUGnC6Jay7rQJ4grz6UQkkAi9LTJnoLggEYlx/RZ599luzsbPR6PStWrODgwYMDrn3hhReQJMnv4+IOf4qi8Mgjj5CSkoLBYGDt2rWUlAxuyQuGz2QMGw1GVq+8mMGIMHpYvayTL69rISvNjqLA6XIDe44Of8haD8MRMAAFpVF4PBKpCTaSYi9UqExNtSCpFFrbtbR3jf1vkfEMI002FyZQYaTxcmE8HnC6JGx2CbNVRWe3THunTGy0i6XzunG4JIpKjby9KxbX+QkF4ZzQK0JJk5ugOzGvvPIKDz74IFu3bmXFihU8/fTTrFu3juLiYhITE/s9JyoqiuLiC9a2dNHwl1/+8pf89re/5cUXX/TNZFi3bh2nTp0KeEvjS4lLwXnpj4wUO5IE59rVdJlVRJoGd1Vio92sW9VOQ4uGgyciWTJ3+LOXYPgCxmpXUXQ2AvDmwvRGr/OQnmijusFAaY2JpbM7+rvEsBDVSGMnUOXUY3FhbA4VB0tnEJs8heL3e7r2Xujk63JLeDwMmfPidku0nNNgtavY/mks669s8+4tjBN6xxRKEoQ0QXdifvWrX/HNb36Tu+++mzlz5rB161aMRiPPP//8gOdIkkRycrLvIynpwg1VURSefvppfvKTn3DTTTexYMEC/vKXv1BXV8dbb70V7LczKbnUnJeL0esUkuK9YaTKuuGL4OR4J1+45tyIBuoNV8AAFJZF4nZLxE+xk5bQtyHZ9PQLSbtjTcYcTxdmMhGoMFIgXJhTBbW0mU00n9Nwrl1NR5dMt1nGYlVhd0i43X0FjCR5B6LqtApGg4cIk5vkeCfTs63otB4amjW88JYOp3N840qBLKseK0szRVl2KBNUJ8bhcHDkyBEefvhh3zGVSsXatWvZv3//gOd1d3eTlZWFx+Nh8eLF/OIXv2Du3LkAVFRU0NDQwNq1a33ro6OjWbFiBfv37+f222/vcz273Y7dfqHZV2dnZ581lyKXqvPSH1mpNhqaNVTV6Xz9MwLNSASMwylxqsLbaj5vev+TqDOTrajVHrrMapratH7hpuEyEdVIkyWUFOimdmPNhVGrPHz5RicqVXuvzr0XOvyqen0uy/iODURLm5rtn0yhqU1PecN8Zlq6MRpDqHRpGIgxA5OfoMrrlpYW3G63n5MCkJSU5Bv3fTEzZ87k+eef55///Cd//etf8Xg8XH755dTUeGO9PeeN5JpbtmwhOjra95GREToqfyK41J2X/uidF9PZFfj/FiMRMACnKiJxOFXERDrJTum/m6pGrfie60n+HQnjHUaabC4MBEbABKKxXeHxGlQqhZx0O1mpdtKTHaQkOEmMcxIX4yImytvY0WTwoNcpaNSDCxiA+Ckupk49g07ror1NZtu2CLq6RjGEawSEUkJva8RB4cKEASHXJ2blypXceeed5OXlsXr1av7xj3+QkJDAH/7wh1Ff8+GHH6ajo8P3UV1dHcAdhw9CvAxM9Pnuvc3nNPzln4kcPWnC6QrsL+zhChiXW+LQqWhaOjQkx9n6dWF66OkZU15nxD2KuZXChRkdhfvOBEzABIqRViQNhwijg6/dJREZ5aazU2bbtkg6OoJ72xAJvYKRENSfxvj4eGRZprHRP7O9sbGR5OTkYV1Do9GwaNEiSktLAXznjeSaOp2OqKgov49LCSFehkaSIDXRW3HUZZY5XBjBy9viOVliGJU46M1Q3XgvprjSRFu3BrtDRbd18IhvWoINvc6NzS5T2zT8fJ7xDiNNJhcmkOXUMPYw0kj7woyUyEgPN27sJibGjdms4t1tEbS2Bn6gWCgl9ArCh6CKGK1Wy5IlS9i1a5fvmMfjYdeuXaxcuXJY13C73RQUFJCSkgJATk4OycnJftfs7OzkwIEDw77mpYIQLyMjN8tGSoKD6Ag3kSY3VpuKvUejeO29eMqq9KNKnh2pgHF74ERpFBF6N1EmF7XNemyOgf+bqlTeUQQwsq68E8FkcGECmQcT6i5M7+Z2RqPCxo3dxMW7sFlVbH8vgsamwAuZUEnoFaGk8CHo4aQHH3yQP/7xj7z44osUFRXx3e9+F7PZzN133w3AnXfe6Zf4+8QTT/DBBx9QXl7O0aNH+drXvkZlZSX33nsv4K1ceuCBB/j5z3/O22+/TUFBAXfeeSepqals3rw52G8nLBDiZXQkxTvRahXUaoXVyzu4Ykkner2Hzm6ZXfujeXNnHDUN2mFfb6QCBqCsxkS3RU2kyUVGkhXFI1FeO/igx2nnQ0qV9YZhh8CECzN6ApXIC6HvwvRGr1fYsL6bpCQXDrvEjh0R1NWFZr9UEUq6dAj6T+Btt91Gc3MzjzzyCA0NDeTl5bFjxw5fYm5VVRWqXhlmbW1tfPOb36ShoYEpU6awZMkS9u3bx5w5c3xrHnroIcxmM9/61rdob29n1apV7NixQ/SIAT/xIhgZsgoyku2UVempadCxfEE3M7JtFBQbOX7a5KvWSEtysGJh16Cl1UPNQ+oPRYH8Em+oc/60LiRJ4eBJLaU1Jt/Qx/5InOIgyuSk06zhbL2B6RkDV1dN1GykyeTCBORaAZpSDcF3YXqj08H69d18+KGJ2loN739g4pqrzWRlDb/NQH+EUihJ9IYJL8ZFRt9///3cf//9/T63e/duv8e//vWv+fWvfz3o9SRJ4oknnuCJJ54I1BbDHiFeAkNWmlfEnK31ihiNWmHxXDOzp1k4VhTBqVIjtY1a/vFBHNMybSyd1010pP/8pJFWIvVwtt5AR5cGrcbDnJwuHC4VB0/F0Niqo8siE2nsf06TJMG0dAvHiqOpqDMOKGJEU7vRE4phpEC4MB4FPG5vMrnb422G53ZLdHXraG6Wcbu9j91uvM+ff5yR4eTcOZnGRjVvvhXFphu7mDat78iOkRAqoSQQvWHCidD0AgXDRoiXwJKR7O3e296pprNbJirCKxwMeoXLF3X55ieVVuopq9JTUaNn1lQLi+eaMeo9oxYwvV2YuVO70GoUtBo3KXF26lv0lNWYyJsxcH+jmZndREc4yRqgHLuH8XZhJtOIgVAKI/UwWhdmx/+fvT+Pj7Mu9//x533PvmSyJ5OkWZom3fctFAoUGmhZFBAQOCJSFY540OOpBxC/CgoqoqjokZ8c8SD4UaSu7BbaQKGlofu+p82+75PZt/v3x51JkyaZSdpMMpPez8djHmlm7vuee9JZXnNdr/fr2ppEbb/J7f2xOT1AAkfPhPdYSRL4fAIut8CWD034fE5mzhx9VtFYV2GUbJiLC0XExClKUF100GklrOleGlu0VDfomDd9YFXDYg5wVUk386Y72HUogdpGLUcrjJyqMpAknqEgXWTh4uxR3299q562Th0qlcScwrMDHYtyHTS26amoM7FgmNA7gARTgART7LWRJgNjtZwaYqcKI0lDp/eKIqhVQVLSTajUQTkQT8WA4Lz+vxcXQ129GrdL5OOPjfh8AvPmeYa+0zDEShVGaSXFH4qIiUOU6kt0yc/20NiipWYIERMiLdnPdVd0Ut+sZechM6crvLRjpSeYh/p0N3MK7WHzXc5l/0m5CjOrwI5Bd3ZN99RsJx8fSKHTpqHDpiE1cfQl+4lqI00GQ+9YL6eGia/CAFy53IYk0ZfeK4oSogAHTsvRFaMRvJIEu3frOXhQz86dBrw+gcWLwucbRYuxMPQqraT4IubC7hSGJ7TqSFlxFF1C6b2NrVo83vDvxDmZXqYZd7CwoJLcbDVuj4rGdv2o3sCbO7Q0tukRRYl5RQNbRjqNRJ61N5V3hEuoh2KiqjDx3Eoa67ECsVKFATDqgwPSe1Uifc/Z0T5XBAGWLXOzdKn8PN2/T8+OnYYRRRLEkqFXIT5RKjFxgNI6Gl8SEwIkJgTo7lFR16xlWu7w5fFD5acQBFh9hZlAsJGTNWayUgcPawzH/pOJgNw6MhsGm3eLpjioajByus7I8tldoxJIShXmwhhrARMLVZjh2F/RHHmjMCxY4EGjkSgvN3LksA6fFy67zBVxvIHSSlK4EJRKTIyj5L1MDKFqTE2YqdbnmnhVotwOSkoY+XLT9m4NNU0GECQWFA9t3M3NdKPVBHG41DS260Z87JCAUaowo2csfTAhxkLARDsX5kKfK7Nne7n8CicIcPKkji1bjASGXlQXFUPvhaK0kuIPRcTEMIr3ZeLIy5arKTWNOoJhxg6MdhXSuRw4ZUECCrJcJJmHFj9qlURBdm8q7ygHPSpm3tEz1j6YsUzmhdiswvRnerGXq692IKokKiu1lJWZ8A+j68e6CqO0ki4+FBETgyjel4nHmuZDp5Xo7lHxyYGEQULmfNJ4z6Xbrmb/SQsNrTqSE8IvTS3uTeWtbBj+m21/JjITJp5bSWPtgwkxVlWYaAiYEGMpeKcW+Lim1IFKLVFbq+G998z4LixGJiwXWoVRxgzEL4qIiTGU6ktsIIqQm+Whs1vN1t0W/vZuGlX1cq7GWAgYkGckBYICGrVEa1f4NpE1zYNRH8DrE6lpNoTddqLbSBCfraRoCJixTOaNN6ZM8bN2jR2NRqKxUc2//mXG7ZYNXTsbG5UqjMKYoBh7YwhFwMQWuVluTEYTLreKLpuK97YlIXpbKLaaL/jYDpeKkzUmTIYAem2Q+hY9Lo84YHl1f0RBnpF0qMJCRZ2JqdmxFWo3WRhrATNmx4piFWa4EQMAHg84HOLZ1N4h0ntDvwf8yIm/wd7r/AKBIKSm+jl+XEdrmw67Q+Tmm3qGvK+JQjH0xjeKiIkBFPESm+RleUlKCGA2BplR6GLvPj+BoIkTrfNxfuJi2ayu88ptATh0OoFgUCA3w0UgKNDaqeN0nZG504afkVTUK2Jqmw14fAI6zeA1rBM9WiBeW0nRMPJCfJh5w1Fdo2XrR+EHkI4EkylIS6uajg4Vv/+byLzLxm4CtmLovbhR2kkTjCJgYhedViIr3YsoSNgaa1k58yglC/wIgkRtk4F/bLGyZW8qPc7RvSG7vSLHquRqzsLpNop6/S6n68ObdlMTfSQl+AgEBKoahv9gmegqTLy1kqIRaBcPZl4IX4UB0GokDIYgJnMQS2KA5JQAael+MjL9ZGX7mDLFR36+j8JCL8XFXmbM9DB7joe589wsXOhm8RIXy5a7uPxyJzfe0ENSUgC3XUPNoWJsnWOXhqe0ks6fjz76iE996lNkZ2cjCAKvvfZaxH22bNnC4sWL0el0FBUV8dJLLw3a5rnnnqOgoAC9Xk9JSQk7d0an4qVUYiYQRcDEPnnZHo4d96H2JXJjqRboZO60HnYfS6Ky3sipGhOn64zMntrDouk29MO0g/pz5EwCfr9IaqKXKRluUhNFPjmcTEuHjm67msRhVikJglyN2X08kS67ZtDtymiB0RMtIy/EfxUGoKDAR0HB2DlyHYm12LZn4bQbePcvJko/4yQ5PfJrJloorSRwOBwsWLCAL37xi3zmM5+JuH1lZSU33HADX/nKV/jTn/5EWVkZX/7yl8nKymLNmjUAbNiwgfXr1/P8889TUlLCs88+y5o1azhx4gQZGRljev5KJWaCUARMfOBskNsjAVUaHq/8ckky+yld1sbNVzaRne4mGBQ4fNrCq5uz2XfCgs8//DdMr1/g8JkEQK7CCIKcnpqTLi/pPl0fvnQ/q8DOv13bQMmcrgHXT3QbCeKvlRQtATPWZt6JqsJEA50xwF1fEUlJD+B2Crz3VyOtjef/MTQWwx4v9lbSddddxw9+8ANuueWWEW3//PPPM3XqVH72s58xa9YsHnzwQW677TZ+8Ytf9G3z85//nPvuu49169Yxe/Zsnn/+eYxGIy+++OKYn78iYiYARcDEB4fKT2HUecnLUSNJAnUtA4Pv0pO9XH9pC2tXtJCa6MXnE9l9LIkNm7M5WmkmMMQXzONVZrxekUSzry/7BeS0XpBzYMLFtet1QUxDpPrCxLeRIH5aSdEUMGN2rAmuwkQLvVHimtudpGcF8HoENv/dSFPt2HlkFGRsNtuAi8cz+sGcQ1FeXk5paemA69asWUN5eTkAXq+XPXv2DNhGFEVKS0v7thlLFBEzzigCJj7on8abb5XFRnXT4KXNgiAn6t6yqomrl7aRYPLjcqv4+EAKf3s/izP1xj5R4g8IHKqQBz0uKLYh9ivY5Ge5UKkkuu0a2rq0ozrXWKjCxCPRaCHB2I0WgOhWYcab/gm9Wh2s/owTa64fv0/g/dcM1J0ZnZAZi2yYWERyupGcrgu4yFXd3NxcEhMT+y5PPfXUmJxfU1MTmZkDP78yMzOx2Wy4XC7a2toIBAJDbtPU1DQm59AfRcSMI4qAiS9CWTChAYy1zYYhqysgi5lpU5zcvrqBFfM60OsC2Owaynal8cbWTPwBgVO1JpxuFSaDv6/yEkKrlijI6k3lrRv9apCJrsLEUyspWiuRxrKNNB5VmIl4zvTPhtFo4aqbXUwp9BPwC3z4ppGqE6OzaSqtpOGpra2lu7u77/Loo49O9ClFBUXEjBOKgIkfzg2zy0jxotfJQXPNHeFD6VQizJ1m545rGlg8sxu1OkiC0Y8oSBw4JVdh5hfZUA3xyuu/Sik4ggnAEFtm3nhoJUVTwIw1k7UK0x+1Gq640cXUGT6CQdj2joGKw4NN6wqjx2KxDLjodCOfuxYOq9VKc/PA51BzczMWiwWDwUBaWhoqlWrIbaxW65icQ38UETOOKAIm9hkqjVfsbRkB8rDGEaBVSyyZ2c2d1zRQMreLMw1Gehxq9LoAM/MdQ+6Tk+FGpw3gcqtoaB1+8GQIpY00OqKxlLo/ShUmPMMl9KpUcNl1borneZGA8k16ju0NL2SUMQMTx4oVKygrKxtw3aZNm1ixYgUAWq2WJUuWDNgmGAxSVlbWt81YoiyxHgdCc5AUYpuQD2Yo8jKdnKoxUd1k4JK5XSM+pkEXRJJg/0l51dLC6XbU6qHLLCoRCnOcHKtMoKLOyJQMd8Tjx0oVJtaJ5lJqpQoDkgSnTmnPSfM9m957uiMVR7OeQACCAfD7BYJB+lJ/g375Oo9LoKNFZPM/jPi8LuaVeBGGWeynZMOMDXa7nYqKir7fKysr2b9/PykpKeTl5fHoo49SX1/PH/7wBwC+8pWv8Otf/5qHH36YL37xi7z//vv85S9/4e233+47xvr16/nCF77A0qVLWb58Oc8++ywOh4N169aN+fkrIibKROMNTmHs6W/kHYopGW5EUTbedvaoSU4YZizvEFQ3GThVY8LlVWHQhZ/eWDTFwbHKBKoajPgXdKJWDS14YqkKE+t+mPEQMGNZhYnmkEeInvDdunVoL1eXx02CJZNTbZGPodVLmCwSfh8cKNfh8wosvtwzQMhMVkPvRLF7926uuuqqvt/Xr18PwBe+8AVeeuklGhsbqamp6bt96tSpvP322/zXf/0Xv/zlL5kyZQq/+93v+jJiAO644w5aW1t57LHHaGpqYuHChWzcuHGQ2XcsUERMFFF8MPFBJAEDcnJpVpqHU7VG/rHFyo2XtZCZEn7yNMjfUA+csqDRSKhVfqqbwo8WyEzxYjb6sTvV1DQZKMxxDrttLFVhYtUPE00BEyJe2kjR9MIIAuTl+RBECZUot4hElYRKBdU93eTni6jUIKpApZIG/lQP/LcoStSeVnN4p46je7T4vLD8ag9iP/ODYugdO1atWoUUJtdhqDTeVatWsW/fvrDHffDBB3nwwQcv9PQiooiYKKMImNhmJAImRF6mk93HEulxqHnjIysF2U6WzeoiKUxVprFNR0uHDovJTzAADW06HC7VsFkvoVTe/ScTqagzDiliYsnMG8tEW8DEUxspRDSfN9dcM9jrtbOxkUJg3vzwIzXOJc3qJSFR4pNNek4d0uLzCly6xo1KiZNROAfF2BsllDZS7DMaAQNylkt6she1WiIgSVQ1GPnr+1l8tC8Fh2vod9f9pxIBmDO1h+x0D0hCxFTe0PLr2mYDbu/Al2gstZFimfESMEoVJjLDGXojUTTXx8obXIgiVJ3Q8NFbBj5ubrigc1FaSZMPRcREEaUKE/uMVMAAJBgDZCR7SLX4KJndTX6WEySBE9VmNmzOZueRxL7RBACtnVrqW/QIosT8YtuAVN5wJCf4SU30EgwKVA4x6FGpwoRnPFpIMLahdhDfVZihGG5Z9WgomO5n1aedqNQSdWfUnHovg/kFSitJ4SyKiIkCymqk2GeopdQjIS9TDr5rt2m5tqSNT13eRGaKh0BA4MCpRF7dnM2BUwn4AwL7e3NhiqY4SDAGKMxxIogS7d1aOnvCd3KLch2kJHrRqs+m6ylVmMiMh4AZ6yqrUoUJT87UAKtvcaHWSNgaDGz5owFv5IV7ChcJiogZY5Q2UuwTbil1JELpvXUtegJBsKZ6+dTlzVx7SSvJFh9er8jOI8n8v39lc/h0AiCxoNgGgF4bJLd32XSkaszcaT3celUT06YM9MQoVZjhGU8Bo1RhxpfMKQHSVleSnqajrVbF+y8bcTuGH7Q6FEoraXKiiJgooFRhYpfR+mDOJSNZTu/1+USa2uUETEGAfKuLz1zVyJWL2zEZ/DS162nv1mJ3qem2a/rmJ4VSeSvqwg96FM95f1aqMOEZrxYSjK2AifaS6omqwuxsbByTKkx/zBlerv6CC71ZoqtJpOz3BpzdoxMySitp8qGIGIWLhgsVMNC7lLS3pVTTNNCvIgowPc/BdZe2oNMEEUUJjTrIph3pvLk1k6Z2HflWF2p1ELtTTXPH6AY9xvu36WgRGiUQbQETb22kEJPheRPKhknMCHLV553oEyS6W0TefcFIT8fohIzC5EJZYj2GKF6Y2GUsBEyIPKuLkzXm3vTezkGJokfOJJBgDFCc5yAzxcPh0xaaO3S8uTWTPKuLzBQP9S0GKupMWFMjZ80oVZihGc/qSzy2kSayChOO00fUtDaqCPiFvsTeUJJvKL03dF3AD8GAQIsrj0SjkZN+QAK/D9rrVQR8AmW/N7Lq8y6SMoaZzooyZmAyo4gYhYuGsRAwcDa9t8ehpss+ML3X6RY5WWMGYOnMbrLSPMwptLP3eCInqk3UNBnw+ARUKjhTb2TFvM4hh0EOOvdJ8G16LBlPARNirNtI48FEPW/CtZKaatWcOTa6IY8Br0ig3y5qDaRNCdDRoMJlF3j/ZQNX/puL1JzhhYzC5EQRMWOEYuiNXc53JdJwaNQS2elu6poNVDcaSU6wnb2v0xYCAYGMFA/WVA8AJn2Ayxd2MK/Ixq6jSWjUQeqaDbg8Kupb9ORZh19qoQTbDWa8BczhXWfGvAIDF2cVBiCvyE9CUhCVClTqc9J7B1wnp/4e6GlibZEVldqBqJZvV6lAVIPPDR++YqC9TsUH/8/AFXe6ySgYGCSpGHonN4qIGUOUVlLscSErkcKRb3VR12ygpsnAwumyiPF4RQ5VJGBzqlm1uG1QmynJ7Oea5W0EJdhxOJnDpxOoqDOFFTEKA5kIATPmx7yIqzAAuUV+cotGfjyjxoclbWgXvNYAq+52sXWDgZZKFR++YmDlZ11kFQ0UMkorafKiGHsVJi1j6YM5l5C5t7lTi9sjv4yOVJppaNPjcKnw+od/aYnC2VVKVY1GvP6hjYlKFeYsh7efHDcDb999RskHA5OzChMNRjLsUaODK+9ykT3dT8AHW181UHtU+X5+saCImDFAMfTGHtEUMABmY4CURC+SJHCixoTPL3D4dAImQwCL0c/puvA5MGlJXixmH4GAQHWjISrnOFmYCP9LiLEWMOMxpRompgoTjWXVMLJhjyoNrPysm7w58oyyj/+mp3K/WmklXQSMi4h57rnnKCgoQK/XU1JSws6dwz+xXnjhBS6//HKSk5NJTk6mtLR00Pb33nsvgiAMuKxduzbaD0MhzoiWgAmRb3XhdKt4Z3sG/9xixelWkZniwWjw09yho8c5/LQ6oV81Zqjgu3iqwsy7fFaf0BhLQtWX0H2MJ/HaRppMVZjRIqpgxWfcFC72gQQ7XtdTtT1daSVNcqIuYjZs2MD69et5/PHH2bt3LwsWLGDNmjW0tLQMuf2WLVu46667+OCDDygvLyc3N5drr72W+vqBZcW1a9fS2NjYd/nzn/8c7YeiECdEywdzLnlWFx6viNOt4siZBBra9FiMPqwpsqE3UipvUW8ab12rHqdbKYr2p794mSgBE29tpBATVYUZa8rb60dUhemPIMKyGz3MuMQHwJE3c9n1jmXMz00hdoj6O+fPf/5z7rvvPtatW8fs2bN5/vnnMRqNvPjii0Nu/6c//YmvfvWrLFy4kJkzZ/K73/2OYDBIWVnZgO10Oh1Wq7XvkpycHO2HMiTKqqTYItptpP6kJ3nJyXBjNsjhFYIATe16alsM2JwqTtWGT+VNNPtJT5YnW5/pN9k6nqowIcaqGjMR3pehiEYbKdpMdBUmGq2k80EQYOG1HgrWHiPJoGf7P5L4+O9JYV+LCvFLVN1PXq+XPXv28Oijj/ZdJ4oipaWllJeXj+gYTqcTn89HSkrKgOu3bNlCRkYGycnJXH311fzgBz8gNTV1yGN4PB48Hk/f7zabbcjtzhfFDxNbjIeAAfnNMjfDxek6I0ZDkCUzuuiwaem0aejq0dDjVLPnuIUlM22DViqFmD3VTkeqh5x0z9AbxBHzLp/Foa3HAJh76fQR79df/EykcIHoLacGpQozGkZi6JUkkIKhQDzOhuf1/kwrtpGGnT0bE/lwQzK2dhVr72sf9rWoEJ9EVcS0tbURCATIzBz4IZ+Zmcnx48dHdIxHHnmE7OxsSktL+65bu3Ytn/nMZ5g6dSqnT5/m29/+Ntdddx3l5eWoVIN9CE899RTf//73L+zBKMQ8Y50HMxLUKgmfX0QU4YpFHahUEidrzLy1LYNuu4b3d6dR3WRk+ewupmS4B72BTs9zDPg93tN5Q0Lm8PaTEYVMLIkXiF5VdTzMvJOxCpNryOO9F/QDREro38GA/G+Gqa541V3ADJIMenxeaK/XsvfdREQRrlnXjji8XU0hzojpdWg//vGPefXVV9myZQt6vb7v+jvvvLPv3/PmzWP+/PlMmzaNLVu2sHr16kHHefTRR1m/fn3f7zabjdzc2Ch9KowN4+WD6Y8kQWObDkGQMGgD9DjVpCb6mFVgR6MK8tpHVhxuFe1dWjaWZ5Cd7mbZ7C4yksOPGoi3VtK59BcykbaLFaLlgxmvTBiI/+fNuQR8Ah0NI3c8CCKo1CCqJUS9j3SLEbXaT9oUH6k5PmxtGo5/YsLnEVl7fyvq0YUGK8QoURUxaWlpqFQqmpsHfktobm7GarWG3feZZ57hxz/+MZs3b2b+/Plhty0sLCQtLY2KioohRYxOp0On043+ASjEBePpg+lPbbOezh4tRn0As9FPTZOB1ETZUDg120l6khezVyTP6qS+1UBDq57XP7QyNdvJ0tldJJn9A44X71WY/sSSQBkp8dpGmsgqTDSWVYcMvR5ngCvucvWl9IqqXpGiknrFSm/Sr1pO9xX66Z1286FBq5LOHDDwzm/SOL3PwBv/k8Gn/qMVjU4xysQ7UTX2arValixZMsCUGzLprlixYtj9fvKTn/Dkk0+yceNGli5dGvF+6urqaG9vJysra0zOWyH+GG8BI0mw/2QiADPz7ahEqGk+m/eiUslCRiWAQR/kjtIGivPsIEhUNhj5W1kWW/en4HAPrGtPtm/T8UC0fDBKFebC0Bkhe3oAa2GA9LwgqTlBkjKDWNIkTEkSBrOE1iDPURooYIaO8Chc4OKm/2xBo5OoParnn7/IwONUDDLxTtRXJ61fv54XXniBl19+mWPHjvHAAw/gcDhYt24dAPfcc88A4+/TTz/Nd7/7XV588UUKCgpoamqiqakJu90OgN1u56GHHuKTTz6hqqqKsrIybrrpJoqKilizZk20H84AlJC7iWci2kgATe06mjt0iKLEygUdALR0anF5zr6kinJ7U3kbjOh1QVYt7uDWq5rIs7qQJIHjVWY2bMpm19FE9n1UMSGP42In2qsLJ3sVZqwZiaF3JAyXDZM7y8Mt32xGZwzSWKHj789k4rQp8QbxTNT/9+644w6eeeYZHnvsMRYuXMj+/fvZuHFjn9m3pqaGxn4vht/85jd4vV5uu+02srKy+i7PPPMMACqVioMHD/LpT3+a6dOn86UvfYklS5awdetWpWV0kTFRbSSA/afk7IkZ+XbSk32kJnlBEqhpOluNyUr1YDL48fpEaptlT1eKxceaS1r51OXNZKZ4CAQEDlRY8AU0k/LbdCwTzTyY8UrmhYmtwkxUQu+FkFXo5daHmjEkBGmt0fL3n2Zi71ScvvHKuBh7H3zwQR588MEhb9uyZcuA36uqqsIey2Aw8O67747RmSnEOxMhYFo7NZyoNuNwq/h0rlwhzLe6aO/SUtNsYEa+XIERBJg2xcnBUxYq6kxMzXb1HcOa6uFTlzdT3WSg265G1aIMgZwI4rmNNNmqMGNBu3nniBJ603N93P5IE//4WSYdjRr++nQmn/lmC4np/oj7KsQWSh1NIS6ZqDYSwIFTifQ41YiCRGunXGHJs8oCpa5Fjz9wts8eGi1Q02TA4xvYfxcEKMhyoWrZPU5nrhAimnkwMD6ZMDC5qjDnk9B7ISRb/dz+SBOJGX5sbWr++nQmHQ3KkqV4QxExCnHLRFRhunrUVDYaMOr9WIx+TtXJSbtpiV6M+gB+v0hj29m2ZorFR7LFRzAoUNVgHO6wSitpHImmD0apwkwc5zPs0ZIW4LaHm0nN8eHoUvG3n2TSUq2NwtkpRIuYzolRUBiKiQi1C3HglAUkgZn5dhrb9LR16ui2q0k0+8mzujhWZeaTw0lYU5vRqKW+QY+7jiZRUWfqazUpTAzR9sGAUoU5Hy7U0CtJEPQLLMqZgqtH6A3GEwj4BPx++v7d/3r5J/h9AsVLHbTXJdFwWsfvv5XN559sJLso/lO0LwYUEaMQV0xkG8nuVHGqTh7quHRWN3tPQF2zgYo6E0tmdpNvdbL7WCINrRbcXhVLZnYzI9/OtF4R09Cmw+FSYTIE+o4Zj3OS4pVoCpgQ4yFgJjqdN1pcSCvptWdM9HgXUW7QR964H64eFV0tajLyvQiChNsu4nGI/PPnGXzqwVbyZitetVhHaScpxB0TVYU5WGFBCgpkp7vJTPH2+V0qao1IEmSne9CoJRAkbHY1Hx9I4W/vZ9HaqSMzpXfQY5iWkkL0iZaAGc9MGJi4Kky0wu0uFLfYgVE4+9oSVaDRSehNQUxJARJSAyRn+knL9ZE51Ut2sYeMfC8Om4iokrebe4WD1fe0kz/Phd8r8PqvMqjYawhzrwqxgFKJUYgbJrIK4/KIHK82A7Bwejcgm3LVqiA2h4bWLi0ZyV6m59nRNhrJTHFjc2iw2TWU7UpDr/OzZGYXc6b2TNhjuJiJtpEXxq8KMxkrdxdShQn4QZ/oQeM0csMDrRTMcyNG+Hru8wj85SkraTk+Mgu83PZwM2qtnN57tb+DjS+kUbHHyDvPp3PNunYK5rrCH1BhwlAqMQpxxURVYQ6fTiAQEEhP9pCdJvfKNWqJ/Cz5za2iVm4z5VldCIAkCdxxTQOLZ3ajVgdxe9TsOZ7Eu5+k09Ylr4BQWknjQ7QD7cYzE2YiiUYV5lz2v6fl5A4N0ginAUgSfFTWTE+zEY1WIjXbH1HASBJs+n0qbXUaDAlBbvhqa5+AAXm0wXX3tzHrUgdSEN77v1QOfWS6gEcVHzz33HMUFBSg1+spKSlh587hjdIvvfQSgiAMuPSfbwggSRKPPfYYWVlZGAwGSktLOXVq7L+IKiJGIS6YyCqMxydwpDIBgIXTbQMmUYdaSqfrjQSDkJsp99BbO3X4/QJLZnZzR2kDswt7EEWJuhYD/9ySxfu7U3F6lXDGaBNtH8x4tpHiqQrT0SISDEbern8rqXK/muPlWvZu1NHZOLKPpordGmp3p5Js1HP9V9pGlPOy518WTu02Iqrghq+2kpASGLSNqJKnXS9YLVdO3/x1xojOJ17ZsGED69ev5/HHH2fv3r0sWLCANWvW0NLSMuw+FouFxsbGvkt1dfWA23/yk5/wq1/9iueff54dO3ZgMplYs2YNbvfY+owUEaMQN0xUFeZYZQI+n0hSgo9868Cyck6GG70ugNujor5Vj0kfIC1ZrtSEZikZ9UEum9/JbVc3Mi0keupMfFy9iO1HUgeMKlAYO8bDyAsXh5l3NMuqO1tF3v2LkbJ/GPCOYIHP4ulTaK8X2fW2/E1+zhVeUrIjK6CWKhV735W/CFx2W+eITLhVh/Rs/2cSAFfe1UFO8fAnKAhw5Z3ycaXBOmdS8fOf/5z77ruPdevWMXv2bJ5//nmMRiMvvvjisPsIgoDVau27hFL4Qa7CPPvss3znO9/hpptuYv78+fzhD3+goaGB1157bUzPXXn3VIh5JnJJtd8vcOh0qArTPaAKA6AS5UGPABW9K5dCQqf/CAKARLOfq5e2c8uqRrSeOoKSwJEqC69+kMeek0l4/cowurFiPATMeLeRxrsK4/PB++8bsfXOFhpJK8njgi1vGPD3Bjuqw2THhaowbrvAtr8YCPohZ4afuau8Ee/H0S3w8d/0eIQushd0sPjayF6zzmY1G19IQ5Jg7hV25l1pj7hPe72GxtM6krN8EbeNNWw224CLxzO0YPN6vezZs4fS0tK+60RRpLS0lPLy8mGPb7fbyc/PJzc3l5tuuokjR4703VZZWUlTU9OAYyYmJlJSUhL2mOeDImIUFMJwosaE26PCbPQzLcc55DahllJVowG/XyAvM5TeaxiQ3hsiLcnHkinHuHNNN2mJHvwBgb2nktnwQS5HqiwERlCGV4hMtAXMeHGhVRhJgu7u0b3VSxJ8+KGJykotf3qDEXlUgkHY+o4Bu03EZAlyxQ2uiP6UhdOmsO0velw2AUtakEtudg/6onAuAR9s22DA4xCwZDu59z8DEffxugXe+nU6HqdI1jQPV97VEXEfl13krefS8XkEpswcx6XWPh94L+DikwVXbm4uiYmJfZennnpqyLtra2sjEAgMqKQAZGZm0tTUNOQ+M2bM4MUXX+T111/nj3/8I8FgkEsvvZS6Ovl1EdpvNMc8X5TVSQoxzUR6YQJBOdxOAhYU24Z9Q85M8ZJg8tPjUFPdZKAwx4nJ4MfhUtPQqiPPOvQbYE6am5sva6Cy0cSuE8nYnBq2H0nlUGUiVy1sITNZCds6H8ZjJRKMX6gdXFgV5sgRHbt267nsUhfTp0eucgDs36+julqDqJIoXtTG/AWRqzD7tulorFGjVkus+rQL3QhWJ+/dqKOtVoVGL7HyDheaCDEvkgS73tLT2SiiM0osufsMGl14v4okwbv/l0ZHowZTUoDrH2gLWyECCAbgX79No7tVTUJqgGvWdfDuC5EfTyxRW1uLxWLp+30sBySvWLGCFStW9P1+6aWXMmvWLP73f/+XJ598cszuZyQolRiFmGeiWkmnak3UNBlobNORnT78EktBgGk5cjXmVJ0JQTg7S6m6KXwujCBAYbaD266s47I5bRi0AZxuFSa9MojufBgPATNRVZjmZhV79+pHvHIHoL5ezY6dBoIBIfQFPSLV1Wr29uajpM6sw5ISWUxXHldzdI8c179ijZuU9PDlxPL2eiw9U6nYrQEBVtzixpIW+YGd3KGh6qAaQYQ5X9jFFQsiG253vpXImX0GRLVs5DUnRTa4fPz3JGqP6lFrJT71YAsGU/yVRy0Wy4DLcCImLS0NlUpFc/PAil9zczNWq3VE96XRaFi0aBEVFRUAfftdyDFHiiJiFGKWiazCBCU4eMqCPyBgNgSobgwvRopyZRFT16LH7RHP+mKaB3/oHP74xKD9VSLMLujhjqtquXZpM2bDJHcSRoFoL6WGsRkt4PYI+Efx3zt3WSEOh0DZ+yb27dNz+PDIvlH39Ih8sMUIEhQXe5k9O3IVprNL5MMPZW/X7NkerPn2iF6Y9maR8k29ptxlXgqmRxbgPc069v5LfhzzrvKSPT3yH6TpjIp978n7LLrWQ2phZE/L6f0GPnk9EYCr724nqzDy3+DEDiN735MrGNesayc9N/78MKNBq9WyZMkSysrK+q4LBoOUlZUNqLaEIxAIcOjQIbKysgCYOnUqVqt1wDFtNhs7duwY8TFHiiJiFGKaiarCVDca6LZrSDT7MRv8fabd4UhO8JOa6EUKyqm8WWke1OogTpea9u7Btevh2gMatcSUMFUfhaEZr5VIcGECJhCETduTeOP9VOzO8G+/oSqM3w9lZSZcTpHklAAzZ0aujPh8sGmzCY9bJC3dz6WXOiN6QDwe2LzZhM8nYM3ys3x55Oeh2ynw4ZsGAn6B7AI/Cy+NfG4f1jRxuiyDYAByZ/uZvTKysLB3Cmz/ux4kmLrQR8rVH0fcp6NBw7u/SwNgwdU9zFkZeW5ZS7WWzS+lArD0OhvTlw3tg5tsrF+/nhdeeIGXX36ZY8eO8cADD+BwOFi3bh0A99xzD48++mjf9k888QTvvfceZ86cYe/evdx9991UV1fz5S9/GZBXLn3jG9/gBz/4AW+88QaHDh3innvuITs7m5tvvnlMz13xxCjEJBNZhZEk2HdS/va2dFYXRysT6OjW0mnTkGwZ/ltZUa6D9m4tFbUmZk+1k5PuprrRSHWTgbSkyf1tbiIZLwFzbhvp0AkjWRle0pJH3vrbsT+BxhYtGrWEbwSr0eYsLWTrViOtrWq0OonS1Q40EfwckgRbtxrp7FBhMARZvdqBOsI7fTAIWz40YetWYTIFufoqB3tawofbBQLw0dt6HD0iCYlBVl4X2cjr90PF5gyMgonEjCAlN0U28vq9spHX6xRIyQ6y9HoPXQIszRv+C47HKfDmc+n43AI50z1c/tnO8HcCOG2ykdfvE8ib42bxmm46GjT0dKrweSZ3ZfSOO+6gtbWVxx57jKamJhYuXMjGjRv7jLk1NTWI/f5zOzs7ue+++2hqaiI5OZklS5awfft2Zs+e3bfNww8/jMPh4P7776erq4uVK1eycePGQaF4F4oiYhRilomqwtS16Gnv0qJWBVk03UaPU011o5FTdUaWz+4edr9pOU52HEmiuUNHj1NFvtVFVaORrftTSU2UM2YivWErjI6xEDAer4hOG97zcG4b6UytjvL9CajVEp+9rg2zMbJn4kSlnsOn5LbkVZd0k2wZ/oMxVIU5dkzLqVNaEODqqxxYLJHv5+BBHZWVWkSVxOrVDsymyF6Tvfv01NVqUKklSksdGAwSdEXY5yMdzXVq1BqJVTe50I3AlPuXN904WhNIT5ONvGpt5H12vKGnq1lEb5ZY+VkXqkim3CBsfCGNrmY15uQA13+lFVW/TzpJAlePiL1Tjb1Lhb1Dhb1LzY43E2mu1KJSS4gq+O03zgq4jIKO8Hc6CXjwwQd58MEHh7xty5YtA37/xS9+wS9+8YuwxxMEgSeeeIInnnhirE5xSBQRo6BwDvtPyf3wmQV29LogRVOcVDcaOV1nYtmswVkxIUyGANlpHpo7dLR1acnNdGF3qejo1vKv7enkpHtYPqdr/B7IJGcsBEyXXc0bH2Uyv6iHBcW2sCIzJGDau9Rs2SlX6mZPc41IwLS0a9i2R95n8Rw7BTmR2y6pU6bzr42ywXb5Mhc5OZErPrV1anbvkfdZcYmLzMzIFYTKSg0H9ssKZOVlTtLSAhFHDJw+oub4flmBXLbWTVJq5L/ByQMa2k5qSDTrufQ2FwkpkcXVsY811B5RI6rgss+6MCZKtJt3DluFCQZgyyspHP/EBBLMv6qHPRst2LvU2DtU9HSqcXSrCA7xpwz4BIJBgdRMH8HeP5vOGMScHBhRErDCxKCIGIWYYyJbSU3tWpra9IiixLwiOUArz+pCow5id6ppateRlTb8B9DKBR0Y9AG0avkNOt/qwh8Q8PpEmjt0vLk1E713JtkzJFLCtKYUwnOugPH7BQ6fSWBekQ3VCJ1+Xp/Aph3peLwqapoM8r5DiJj+bSS3R+C9bUn4/QI5mV6Wz48csuZ0iWzankQgAPk5HpbMCe/N2F/RTMGsabz2uhEpKFA4zcvcuZFFT3e3yJYt8of3jJkeZs6M7DXp6BD5aKtcHZo7101RUeTnZFuTyI4yWfTMK/GQVxT5A765TsXuD/WAm4WlHqyFkcVVY4WKg+/LRt7F13lIsQbp6RBobzFzrN6Eo1OFvatXmPT+29GlovG0DrddJD3Px8EPEoY8tiCAwRIgITmAKSmAOSmAOcUv/0z2Y04OYE4OoNHJr2NXj4u/PR3xlBUmAEXEKMQkE9VK2n9K/rZcnOvoWyGkVkkUZDs5VWOmos4UVsQkmge+oRdmO+no1mJNc5Nk9nOi2kSLI5m/bzVSPMXO0ukdykqkc/AHBNSq4b+lnytgJAm2HkihotZEa5eWa5a3RbwPSYIte1Pp6tFg1AdYvbxtSPHTv40UDEJZeRI9DhUJpgCrV3RF9ICEjLwOp0iSxc9VJcNX8kAWMIGAwObNZjxukdTUAJevjGzK9flkU67XI5CR6WfFJSMw5boFNpeZ8PsEsnN8LFsm5xmFGzHgcvQaeQMCUwr9LFgxgnRdm8CHbxlod7tJmWZnxopzBwWCzwMum4jTJuDqEehuEdn9jg6vS8ScEuTg+1p2v6XDq+4CpnPcMHzvKrvIgyRBVpGnV5QMFCbmZD+mxMCAFpNC/KL8N14g+yuaWViUGXlDhZinvVtDbZMBBIkFxbYBtxVNkUVMZYOBS+cz4m/7eVYXu48l0dqp47pLWplXZOOtf/lwYeRUnZkzDSZm59tYWNSFPoIv42LgWHUChyoTuWZJM8kJg6sCQ7WQDp1OoKLWhCBIzCmMXBkB2HvCQnWjEVGUuKakFZN+eCEZaiPtPJhAfbMWtVri2pVd6HWR2yHb91pobtOg1Upce1kXWk34fSQJOt1zaG9XodMHWb3aHtGUG0rX7epSYTQGWX21A5Uq/D7BIHywxUiPTYU5IchVq5wDBNlQraRAAD58y4DTLpKYHOSytZE9Xj4vbP67gZ4uAZU2yLwFCRz6QOgVKyLObvmnfwgtJAgCkgRag4TXKd+RShMkL0eLKdndWz0JYE4aKFCMlqDiPbuIUETMBTB3WeG4ZFNcTExkK+lArxdmWo5zUEUlO92NUS8H0dU2GyjIGtky6BSLD7PRj92ppr5NT8/p/dx9UyHNnQ3sPJ5CU4eeQ5WJnKhNYMG0LuZOtYWtQsQLbd1afH6RrNSRx7U3dejYfiSVoCRQ02IkOWGgiXooAVPXomfHkSQALpnbSXaYKlmIqkYDe4/L+6xc0EFG8tDVhP6zkSqq9Rw8IbddrlzeTWpS5BbKsdMGjp02IAhwdUkXSWGMvCB/IaqpT6apQ4sgSqy+2klCQuTnQv903dJSB0Zj5H127dbTUK9BrZa4ptSOXi/vE64Ks3uLjtYGFRqtnMirUstVFoddxGkXcNkFHD2i/LP3uu42keZ6FT4pQIJVxf53h8+40RoljAkShgQJgyWI0SJhMAcxJkoYLBJGSxBb6n6W5U9MlVYhNlFEjELMMRGtpG67mtP18ofUuVUYAFGAwhwHh09bqKgzjVjEhNJ7j55JoLrRQErv9ZnJHm68pJG6VgM7j6fQ0aNl14kUjlQlsri4kxm5PRFbFeNBMMioz8PlEXlvdyZOt5prljaTnxk5a8PhVrF5byZBSWBqloP5hZEFjM2h5v3dqSAJFOfZmTOC8LPOHjUf7JFzQOYU9jAjf2h/Sn8fTFunmg93yW3GRbMdTMuNLJSa2jR8vFcWxUvn2cnLjtx2ae800tQ5FYCS5S6ysiILpf7pupdd6iI9PXJr8vRpDYcPye2Yy69wkpIysAI4b34ufh84e2Qh4nQInDmm4dgeLQE/ZBf4ee+vRtxOgUhySa2FjJwAdsHF1CIdBotfFiohkZIgixODRYo4CqDdvJNlYZZVK1ycKCJGQYHeKowkkGt1kZo4tLmxKNfJ4dMWapoMeH1CxNZAiPxeEVPTbCDZfPZ6QYDcDBdT0uupqDez+2QydpeabYfTOFSZyLIZHRRYI/shokVQgvd2Z5Jk9rF8ZseIxEwgCJv3ZuJwq7EYfVhTIldi/AGBTbszcXlUpCR4uXJ+64DHPJSA8fkFNu1Mw+NVkZ7sYeWCzshhbj6B93ak4/eLWNPclMwdOjukvw/G5RZ4b1sygQDkZnlYMjeyUHI4RTZ9nEQwCFNz3SycGTlkbfuhDg4fL0BvOs903TmeEc1Fam1VseVDI14vTJvmxecV2LtXj8MpcKrZiceVw9FtZryegX/MgB+8HgGDUcLjPvtEEEXkaolZwpQg/zSa+/1MkNAbJXZ21bN4uiJAFMYeRcQoXPQ4XCpO1Zrw+AQWFg+fA5OW6CXR7KPbrqGq0cj0vMgfTgBZqW7U6iAutwqbZnDyryBA8RQ7hVl2jtVY2HcqiW6Hhs17M8lI8rBsZgfZo2jLDEVVk5GcNBca9chbVbtPJFPbaqShXWJGbs+QHpVz2XEslaYOPRpVkGuXNqPThPf5SBJsO5xGa7cOnSbINUuaB5zjUAJGkuDDfal0dGsx6AJcs7wtYgsuKMH7u9Ow2TWYjH5Klw1t5A0xb0WxLMjKk7A7RRITAlx9STdipGC2ALy3PQmXWyQlyc+q5eGXbYMsyA4ezUFvSjj/dN1lLjn/xCXgcIg4naGfIg6HiMMp4HSI1NRo6LaJ6PUSNbVaamvPBrV0eSQSLAa8vYUmtUYaIErml3gwWSRMCVKfcNEbpYjnWt5+4QKm3bzzgvZXmLwoIkYhZjhUfmpCWkn7T1qobdajVkkY9cN/6AqCnMq751gSFXWmEYsYlQqmZLipajDSak8Ju93cqTamT+nh4JkkDlUm0tKl4+1Psrh0TjtzCmxIEqOuzNS0GNi0J5OUBC+furShb/l3OM40mDhwOgmAy+e3jUjAnKg1c6RKbqGsWtg6on2OVls4VSeXp65e1ILFdLaFMlwOzIFTFirrZVNu6fJWTCNY3bX7WCJ1zQZUKolrl7di0A39/9y/jdSXrquRuHZlJzptZFPutj0WWts16HSykTeSaJQk+PO/ROxOHRnW4dN1AwFwOgWcThG7XWR7uYHmJjUqNaSkwt/+bsHhEJCC4Z8clsQgogqSk/0kJEiYTEFMxiCN3g7SDX7mzE/DmCALF4129M+1aBIuoVfh4kURMQoXNW6PyMkaE2q1hMXop6LOyOIZgz0xIablONlzLIn6Vh1OtxhW9PQn3+qiqsFI0FLI4V0fDjs7CUCrkVg6o5PZ+Tb2ViRxut5MgdVBj1PNe7szWTmvjczkyL4MgC67hg/2yZN+M5I9IxIw7TYtHx5MB2De1G6KcyK3UJo7dWw7JM+pWVzcSYE1sg+moV1P+RHZn1Iyq2PAzKjhBExNk55dx2R/yqXzO7GmRm6hnKk3cqB3jMTlC9uHHQHRv400IF23JHy6bogjFUZOVspG3tWXdGExR97nwHETza0aTBYj8+a5aGxQn62gOEWcDrmi4nINLBvZHQIOp0hGeoCO9n5v4wIYDUGMpmCvQJEwGnv/bQpiMsm/nyuUdjZ29a5IGtvl/uXt9WN6PAWFc1FEzAUyd1kh+3edUZZZxymHzyTgD4hkp3nw+wUq6kwsmj58CyDR7CcjxUNLh47T9SbmTRvZkt7cTBcIEu3dWpyJOhra9GSnhW8RGfUBVs5tZ/kM2Y/yxvZsOnq0fHI0lU9f2hDxW7LXJ7BpTyZev0hmsptL50TOT3F7RTbtzsQfEMhJc7F8ZuS4dadbxeY9sik3P9PJ4uKuiPvYXSrK9mQgAUXZduZNPdvGG07AdNnVfLAnDSSBmQV2ZhVEFlft3Ro+3CtXv+YV2SjODS+u5q0oHpCuu2TuyNJ1G1o0lO+Tg9VKFvQwxepFksDjFXC4VDhdInanCodL7P1dhc2u4sAJLYKoQVQH2Lkz/KR0USVhNEqYjEGmmoLodUEsFqlPoBhNQQx6KeLy6nMJtyJpLFBaSQrRRBExChctXr/AkTPyB8/KBe1sO5BKd4+G9m5N2IGNRVMctHToqKgzjljEGHRBMpO9NHfo2NcwkwNdaSyb0cHCouE9OCE0aokP9qXTbtOi1wZYvbg5ooCRJNhyIIMuuxzmVrq4JWK2TTAI7+/LoMelJsHo5+pFLSMKc9u8NwOnR0WS2ceqBS2RB/oFBN7bbcXtU5Fq8XL5/La+fYYTMKF0Xa9PJDPFw6XzI4srt1dk0850/AGRnAw3y2d3DbttaDn1uem6i2cP3TIMSuByizhcIm3tGjZ/kojbrSIp0U91g45jp43YnSoCYQobNqeHxIQgolpFUnKwr7VjMklyJcUYxGgMYjbL1RO9PrL/5HwJN2LgfBnLKozSSlIYDkXEKFy0HKs04/WJJJp9FOc6qWk2UllvpKLORFpS17D7FeY4KT+UTHWjkX+Vp4/IWAryUuuKeiOd3ZnkGQOkWiK3QgAOVSZyutGMAJQubhlRwu/eU0lUNxtRiRLXLmnCGCbMLcTO4ynUtxlQqySuWdI8ovC97UfSaO7Uo1UHuXZp04jC3LYeTOsTZNcuber72w0nYM5N1y1d3joyQbY7jR6HmgSTn6uXtg0ryEJtpFC6rt0hYjIGmD3NyelavdzWcZ2totidcmVFkkL3JWDrURMICiQGobFl4FRDvS6IyRDEZAzIPw0BjIYg9e0tzF+ejcnYgTbCIMR4RanCKEQbRcQoxATjber1BwQOnZZNqMW5DjxekaIpDirr5UGPy+d0DbsSxaALkpLopaVTx5k6IycyTSPKKDHoAnTYNLi8AhqVjQRj5ByQulYDO47J7ZAVc9pHFB5X1WRk76lkAFbObSM9KbJYOlVv5lCl3EK5ckHriATWseoEjtfIlayrF7WQaIr8eA5VJlLRMFiQhRvmGErXVankdN2R+JB2Hk2ivkWPWhXkmuWtfYLM55fbOw63CodLzfHjXbh9uSRZrez8h5bqej0SYE3z8q+PksPehyCAQS+Lk8I8NzpNkERLALMhgNEQwGwMYjQEUA/R3tlf0UxqMiQnTWxKc6RBj7GAUoVRCIciYhQuSk7WmHC5VRj08jyj49Vmri1pQasN4nSraGzTkZM+tBfC5RFxuVWkJ3nJtbqYNTWygLG7VOw8koQACIJEl0PL3z6cwoy8HhYXdw4Ze9/tUFPWa8qdPqWH2fnDG45DdPZo+GC/vM+cAhvTcyOfW1u3lq0HZVPuwqIuCrMir7oKpesCLJvRQW5G5PC/4QRZOAEz0nRdSQKPT66UHK8ysfNIMv6gwLQcJzuOJOF0q3G4VHh9A8sxNpsRS7KJ7gb590SLH7VKQqeVMBvlionJEMBkDGDuraaErjPqgxcUSBjO3B3vjMWyagWFkaCImDFAMfdOPJIEW/enMDXbSW5m+GpFMCgv05WAQFDA5VZjNvox6oMUZjs5XmWmotY0pIgJBGHzrjScbjWJZh9XLWkbQXaIHMzm9qrITPHg6HZi0OqQgOM1CZyqMzOvsJv5hd19uSo+f68p1yeSnujhsrntIwhzk5Ny/QGBrBQ3JbPaw+/A2XTdQFAgN93JkulDB8D1x+46a+SdmuVgwbTIvh6bQ8375wiycOIFzqbrShIU59lJSvBR2WDoraSocbpV2HtNsg63mkBA6HtMnXYNZqOf9u7BfRqNJohJH8Bh6yQnxUfRLHqFSq9gMQTQ66LnP9lf0RydA4+SWK/CKK0khZGgiBiFmKOzR01yQuTWRH/2n7RwolqeMn3nNfVhWw6n643YnWqcbtnXoFFLXNObHVI0xcHxKjOVjUYuC3QO8rrsOJxMU5sejTrINSWt6EbgAdl2IJm2Th06bYArF3fx4d5UPLZObr3Gwa4TKTR36tlfkcSxagsLi7qYlWdjy4F0Onu0GLQBrlnaPLIwt30Z2JwaTHo/qxc3R/SN9E/XTTT5uGpR68gE2Z5MXN6h03WHIiTIPP0E2ZHdsoCZdckMehwq7C5ZlITaPA6XikOnE+i2a1CrgyCYqag1h78jQK8LkJropSDbSVKCH7PBL7d39AFMBj8mQwCtWpJ9MGmh4Y6Rq1VjRUjATPYqzFihtJIUIqGIGIWYoqFVxzvbM5hZYOfSeZ0jKtfXNOnZfVz2c6yY1xFWwEgS7D+ZiMOtwh8QEIUgVy46mx1iTfVgMvhxuNTUNOkpzDnbJjlRbepbzbRqSfuIhNaRM2ZO1ZhBkLh6aTtZaW62H0rGFtAgivCpFY1UNxvZdSKFLruGHcdS+OhAGr6AQILBT+mS5rATlkPsPpFMXatBNvIubR42zK0//dN1r1ky8nTdtmHSdc/F6xewO9V8cCCdmhYDKhESjD7+/KYGt38++uQ0yt8cfj2wQRfA7RVJTvAhClKvEOkVJL2iJCRQQreNxGDdPw9mIogFARPtKoxi6FUYLxQRoxAVHC6RXYfMrFjYEzHpNESPU8XmXWlIkoDPL46onN9lV/N+b3bIrKk9zCoI7+eobjLQ0qmls0dNdqqH+cU2pk05mx0iCDBtipODp+RBjyER09yhZdsB2c+xZFbXiAZANrTp+OSwbA4tmdPFlAy5zTUlw017C9S0GMlM9lBgdZKX4eRUvZmPDqTT1CkP5zMbAnh9YsSU3v7pulcuaCUtMbIp93zSdY9Uyem6kgSXzW3DFxCpaTbgcKt7L6oB//b55XNvt2lxutVkJLvZf0wEkrEkm3H3dutUKgmT3o/REBInAUx6f19rx2QIYNAFIlaJRsNECJhYaSNFE6UKozDejIuIee655/jpT39KU1MTCxYs4H/+539Yvnz5sNv/9a9/5bvf/S5VVVUUFxfz9NNPc/311/fdLkkSjz/+OC+88AJdXV1cdtll/OY3v6G4eGK+WYXYX9E86XwxwaDsMTAZRr6KIhCAzduTaG7T4PWKXLuyK/I+QTkHxONVkZrk5fKFHSMKc3tvRzq+3uyQFfPC+zkkSY6fb+3SYtYHyLW6WDZEdkjRFAcHT1mobTbg8Ypy22VnOsGgQEG2k0XTIxtse5wqynoFWVGuY0CeTL7VyYHDevYd9LOs1w4iivJka5U6SLLZi4T84N/dbSUz2c3ymR1YUwZ7dPqn684v7GZadmRTbv903SXTB6brBoPg8vZv78iipMOmYc+pZAIBEYvJ1+dviYROE6Qo20FnaxcWtYOiS1J7RUpLn0DRaYLjFm/ff6zARBArVZhoohh6FcaTqIuYDRs2sH79ep5//nlKSkp49tlnWbNmDSdOnCAjY/Ab4fbt27nrrrt46qmnuPHGG3nllVe4+eab2bt3L3PnzgXgJz/5Cb/61a94+eWXmTp1Kt/97ndZs2YNR48eRa/XR/shDcncZYV9RsXJxI6DCZyq1lO6oovsjMjf1iUJPt5roblNg1YrUbIgchicJMGR2nwcQS16XYBrl7dGbAuEskO6ezQYDf4RZYfUteg4WpnQt5z66qXtQ367T030kWzx0mnTUlFn4HSdGadbRVKCjysXRzbY+v2yIHN7hhZkuZluLCmp2Dqgx+kiweiXBdnuTAIBkeIpdkqXNHO4MpHDlYk0d+p5szyb/Ewny2Z09FVNzk3XXRYhXdcfEGjv1vJGeTYOt4qUBB8uj4pNezJwutXYe82yw2E2+HF7VZj0chtNr+2tnOj9vZdQBeXsvzVqSX5dmEIG3sgm4GgxkW2kWKvCKIZehclC1EXMz3/+c+677z7WrVsHwPPPP8/bb7/Niy++yLe+9a1B2//yl79k7dq1PPTQQwA8+eSTbNq0iV//+tc8//zzSJLEs88+y3e+8x1uuukmAP7whz+QmZnJa6+9xp133hnthxSXVNXr8PsFivJHPg35VJWeQyfkKHSPd2RrSY+dNnD8zNn5MYkJkf0cB08YaewSSUySKF3Whtk4gjC3E4l92SHXLm8bUXbIWx9n0m1Xo1EHuWx+Z9gwt6IpTnYd1fLRvjTUKgmtJsi1Ja0RZw9JEny0P4X27uEFmV4bxJriwdYBW7fZuO4aI1sOZNDtOJuua9QFWT6zd37SqWRO1CZQ3WykutnI9Ck9LCrqZNvh9L503cvntdJt15xdtdOvrRNaXuz2qfD4RFq7dIgiGLQBjlZbBj0GATAOI0pCYsWo80eMt+8v6odbgTReTLQPBiZ/FWYsl1UrrSSFkRJVEeP1etmzZw+PPvpo33WiKFJaWkp5efmQ+5SXl7N+/foB161Zs4bXXnsNgMrKSpqamigtLe27PTExkZKSEsrLy4cUMR6PB4/nbCneZovcDohV3B4BnXZ0yz87ulV88EkiPr+AWi2NaBZMa4eaj3bLZtlFsx1MnRJ5n8ZWDdv3yR+Ky+bZyc2K7M2oa9Ky82AC4GDF3E6y0iLfT2WDgb29Rt6VCzpIHyY7pD87jyZS3yIbXzOSPbR3a5maPbyvZdoUB+/vSaWjW0tOhou1S9tINI8gzO10AqfrTAhieEGWZ3XR1J5MqyOZPSeNw6brmvQBls3oICfNyd6TydS2Gtl5PIX9FUn0uNRIkkBQcvPqB3kRzw3AqAtQnGOXV/EkeDEZ/Bh1skAxG/wY9WPjP4m0fHoimCgBs7+iOSYETAilCqMwmYiqiGlrayMQCJCZOdAnkpmZyfHjx4fcp6mpacjtm5qa+m4PXTfcNufy1FNP8f3vf/+8HsNoiaYvxucXeOuDFBItflYtt4VdGRLC4xV4b1syPr9AdoaXvKzIIsHlFnjv42QCAcjL9rB07gjC3JzyzJlgEApz3SyYGdmb0d2joqw8CUmCnJR2Zk+NXE3ptGnYslcOWZs7rYfpeZHvp7VLS9ku2QNSkOVEo4aKOhNLZnYPKwYdLjWu3tZKVqonYvYMQF2Lnh1HkgAiCrJ8q4sdR5Kp786g4ZMARrOKmXk2TjeYOXRGhcOjlpcau1UEpbMnqVUH6XZqMOl9iIIak8HXJzi06uDZSkmokmLwY9b7e1f0+MfFfxJrAiY0F2kiiKU2UrSrMGOFUoVRGA0XxeqkRx99dEB1x2azkZs79t9GRuqLkSQ4cNxEcYFrxIZZSYItOxLp6Fbj9op4fUJEEROU4P1PkrDZVZhNAUov7RrZQL/yJBxOkcSEAFeVDP9BH8IfgE0fJ+F2i6Qk+bly+fBToEP4/AKbPk7C4xXISPVRYKhFEHLC7uPxiry3Iw2/XyQ73c3yOZGD2Vwekbe2ZuBwqTHoA9y6upF3Ps6kx6GmpVNLZsrgKo7dpWLzzjQMugCCIHtJItFtV1O2W14lNT3fzvQ8B932s4FsjgE5KPJ1jW06nD4jUsCPN+CmpsU07PEN2kBfW8eo92PSDe0/mUhiTbzAxBt5ITbaSCFieVm1gsL5EFURk5aWhkqlorl54LeR5uZmrFbrkPtYrdaw24d+Njc3k5WVNWCbhQsXDnlMnU6HTqc734cRFp8PNJrR7XP4lJGdB80crTDy2etaUY/gf2H/MROVdbKX4ZpLu0YkfnYfMlPbqEWlgmsv60Kvi/whV74vgcYWLRqNxJqVnRGXR0sSbNtjobVDg04nce1lXRE/TPsLMqMhyDWXdXFm/wgE2Z5UbA45ifXqpW0jC3PblUZThw6NOsiSmV1kJvsoyHJRUWuios40SMSE0nVdHhU5GS5sDg2dNi0dNg0pFh+SJOefOPuFstldKsoPJtPt0KDVBKlqMHKyOnIwm0EXxB8QMJkl0rQtFGSZBhpk+1VVIj3WiSSWvC/9mWgfjFKFGR3t5p1KFUZh1ERVxGi1WpYsWUJZWRk333wzAMFgkLKyMh588MEh91mxYgVlZWV84xvf6Ltu06ZNrFixAoCpU6ditVopKyvrEy02m40dO3bwwAMPRPPhDKKlRcV7m0xcvtJFfv7ZlTvhWkr1zVo+2S8Hps2b7hiRgKlu0LH7sPyhuHKJjcy0yKuETtfq2H9M/mZ/5fJu0pIj+zmOnzFwtEI28l5d0k2SJbLB9sgpIycrZSNv6YouLObI+5yXIDuaSF2zQR4C2JuuG4lPDidT22TA5VFhTfGwdKa8MqZoioOKWhNn6o1cMrezTyAEg/DBnhQaWvSIokRRjpOTtSaCksBH+1Lw+uXZPH7/YEURRMAfFEgxBvrm86hV8gDAvsTY/tkn+gA2p4qyXfLy6LvW+qnc8wlzZ8fOt/aREIvVF5h4ARNCqcIoKESXqLeT1q9fzxe+8AWWLl3K8uXLefbZZ3E4HH2rle655x5ycnJ46qmnAPjP//xPrrzySn72s59xww038Oqrr7J7925++9vfAiAIAt/4xjf4wQ9+QHFxcd8S6+zs7D6hNB44nQJlZSY8bpGKCg15eT4EIXxLqcchsrnXA1Jc4GbudOeQ2/WnyyabciUJZhc5mVkYOWStvUvNhztl4+uCmQ6K8iL7OZrbNWzbI5tyl861kz8C8299s5byXkF2yYIecjIjG2zPR5CdqTdy4FTvhOV+6brhOFFt4uiZBHqcajmGPsuJXhekuUOLxyvi9op09qh57cNMNGpwuFT0ONU0tOnwBwQyk73sPZHUdzyna+BLRasJnm3lGEJJsnKCbEikaCP4T1ISQaMOUtVo5A/vTCFb3YN/RxULSwoiPr6JJlbFS38mUsDEkpk3XqowCgrnQ9RFzB133EFrayuPPfYYTU1NLFy4kI0bN/YZc2tqahD7GTUuvfRSXnnlFb7zne/w7W9/m+LiYl577bW+jBiAhx9+GIfDwf33309XVxcrV65k48aN45YREwhAWZkJp1MkKTnAFVc4R5AdAu9tS8bjEUhL8XP50sheE49X4N1tyXh9AtZ0HysWRc5ccXsE3t2WhN8vMMXqZdn8yKZch0tk08eyKbdgiodFsyObZcdNkHVr+HCvnJR7brpuCH9A6B0KKPtNmtp1lB9KxusTcftEnB4Vpxsk6lsNffs43bJoOVljJi1RFkUCsolXkiAzxTtApBj1AXn1Tq9AGQv/iUqEjGQPtc0G7A41LQmzqKm00tBdx5pSY0y2kOJBvEykkRdiq40UIh6qMEorSeF8GBdj74MPPjhs+2jLli2Drrv99tu5/fbbhz2eIAg88cQTPPHEE2N1iiNGkmB7uYGWFjVancQ1pY6InhhJgg93JdLepUavD3LtZZ2oI2RshHwj3T0qTMYgpZd2RfxQCwahrDwJu0OFxRzg6ku6Ii6VDaXrOl0iSRY/q5aPwMjbX5Alj0yQeX3yiqfRCDKXR+Rf5ek43XJonMXkY89xixzM5lL1+lJUeLwD/5iSBEFJwOMTQRJQq4Lo1BKiKPXN3UlP9nCyxoRWI3Hl4nYsJl+fWBlP8TAj30F978wjs8EPJFPvT+a3f2ujKLWG0qsTxy3NNhzxIF4gNoy8EDttpGin8yooTDQXxeqkseT4cS0nT+hAgKtWObBYBnsz5i4rZP+uM32+mIMnjJyu0SOKsm/EbIzs59hzxEx1gw6VCq65rHNEYW47DiZQ36xFo5a4dmVkI++56bprVnahHcFU5vMRZB98kkiX7awgEwVwukWcLhG7U0VtWxquo4lyQJtLFifNnTqaO7RoVBIqtcS2/anD3odaFezX1gmgUQfYcywJBFi1uJ3iXAcG3dn2jiTBXzZnYXNoUIkS1tTIrbBokJvhRhQkgkGBNZe00NAmZ+C4SONgk56qvzu47soAOWmucRczsWrYHY5Y8MFcTFWYsQq3Uwy9scFoxgO98MIL/OEPf+Dw4cMALFmyhB/96EcDth+v8UCKiBkFTU0qyj+RWxLLlrqYMiWyWba2MRTmBisW2UYU3V9Zp2PvEdmUe/nSbjJSIt9P/3TdVSXdpCRG3ud803UHCDLTYHEVCILLJfYuLRY5eNzEySoDwaBAgtnNa5tTcLpUBPvtauvUYOkxDDiOXhMkI8mLUR8g2eLrben0LjHuPyzQ4EerHhgAuPeEBaM+SEqil/lFPYMEQGjQ474TiZyqMw3ZphoPdNog1lQPjW166loMzJ1mpzjXwaHTCRw8ZaG9FTa8B6lGiRuuCpKeFH2xFW/iBWJLwChVGIV4Y7TjgbZs2cJdd93FpZdeil6v5+mnn+baa6/lyJEj5OTIURnjNR5IETEjxO4QKHvfhBQUmFroZd68yMbX7h4VZZ/IvpEZhS5mT4vsAenoVrFlh2xinTfdyfSCyKbcC07XnT+ydN3aRnlllT8gMG+6A4dTxb5jWhxOsa964nSJuNzy9OIQHp+I06UiyeKnx362bCMIYNDLFRR9sBu9ppXiIstAw6w+gHqU/hOfX+DwaVk4LiwePrOmKNfBvhOJ1LXocXtE9CNY8RQN8qwuGtv01DQbmTvNjkYtsXiGjdkFdvadtHC0MoH2dvjD22A1t3NjKSSaIovU0RCPwiVELAiYELEiYEJEswozFiiG3thgtOOB/vSnPw34/Xe/+x1///vfKSsr45577hnX8UCKiBkBfj+UbTbhdomkpAa4fGVkI++MhYW89H8SKuQwt5WLIwfAnZuuO5LhiRearjstz82CGQ45/8QnYHfKQsThUmF3yuLD4VLhcIqcrDbgcIqYjUGOnzFyPEyunygiLy82yiJFNytIojmA0RjAbJCvMxiC/fwnKg6V1zF3DMrTh0+b5flAgsTUnOErLElmP2lJXtq6tJxpMDJ7auS/XTTIt7rYcTiZhjYdHp+Arrelp9cFWTGvi7nTeth9LImKOiNOycyLr/WQk9hCYWoterXvvD44z11BF2/C5VwmWsDEWhtpPKowiqE3tjl3vM5weWnnMx7oXJxOJz6fj5QUeRHG+YwHOl8UERMBSYKPtxtpa1Oj0wcpXW0fkZH3o4+MOJw+rKlymFukYXlBSTblRiNdV5LA5Raxu0R6HPIKoY4uDTptEIdLxV/+lYbdqSIQoZuUmuhDrVKTluTDZAxiNgYwGkI/e8WJUa6g6HWjm+80VgQCcOi0ha4eNSkJPjptGlITh2/hFeU6aOvSUlFrmjARk2j2k2j20W3XUN9ioPAc4ZVgDHDVknbmF9nYdTSJWhLoJoFDtqkkBU/i/6QatSpyK7A/8S5aQkz0SqT+KFWY0aFUYYZGCgSQIr0ZR9gfGJRK//jjj/O9731v0PbnMx7oXB555BGys7P7RMv5jAc6XxQRE4EjR3VUnNIiiBJXX+UkISFya+PAAR1VVVoEwcc1l408XbeuaXTpup/sT6ChWYsowuLZPdQ1a3E4VfKlt5ricKpwusU+/4kEdPeosTtlsdTUOlCR6XRSr89EFicmgyxUTIZAr0AJotVMjEAZCSdrTXi8Kiwm2Tdzus5IamL3sNsX5jj55HASzR06ehwqEkzn/+ZxIeRZXRyq0FDdNFjEhEhN9LF2RSuNbTp2Hk2ipUNHGzPpsRezdFb3hImwiSJWViLFUiYMKFUYBZna2loslrNT6qOVWv/jH/+YV199lS1btoxbzEl/FBEThoYGNTt3yv8py5e5yc6O7EOoqVGzZ69sUF17vUBjZx2ZqeEHQp6u0Q+ZruvzC/38JuIAcdLRpeb4GSOBIKSn+PhgR1LY++jznxgDFE7xoNUESUwI9FVOTL1VlEgrjWKZYBAOnpJftAund1PVII8WWDZ7+CXgJn2AnHQP9S16KupMLJoxMRPO860uDlVYqG3WEwwStgqXlebh05c3U9VoYNfRJLrtGhyuOP6POw9ixQcTa22kELFehVGIPhaLZYCIGY7zGQ8U4plnnuHHP/4xmzdvZv78+X3Xn894oPNFETHD0NMj8P4HRqSgQFGRlzlzIptlu7pEtnxoAglmzfIwY4aXw7sGbydJsv/F4VLR0KKhrDwZrw+saT5OVhrYf9SE3aXC6w1f7khL9uHxCZgMwQH+E1P/yknvdUZ9MGJ7KhY4fKCOuQvO79tZZYMRm0ODThtg5YJO6lsNOFxqmtp1YSdKF01x9ImYhdMje5eiQWaKB602iMerorkj/PmCLEqnZrvIt7o4VWuiIHtiVldNBLEiYEIoVZjRo7SSYofzGQ8E8uqjH/7wh7z77rssXbp0wG3jOR5IETFD4PfD5jIzHrdIWpqfyy6LbOT1eGDzZhNej0Bqmp/CaV6qqjTUNiRxqjJIakJyv2rKWf+Jzy/QZVOhVkm4PCJ1TdoBx9Wopb42Tt9PQ//fJ85/MtbMW1HMofJT57WvJMH+3irM3Gk9GHRBpmY5OVljpqLOFFYUFGQ52aZKoatHQ0cED020EEXIy5QHU9Y0GyKKmP77zciPnLA8WYglAXOxVWHGGqWVFDuMdjzQ008/zWOPPcYrr7xCQUFBn8/FbDZjNpvHdTyQImLOQZJg61YjHe0q9IYgq0vPDmkMBOSZSQ6HiNMp4nCIcrvHIVBZqaWhQY0gyCbdt99K6D1iIT3dDjqNg/uRel2Q1OQgBTkejPoAiZZzqii9/hOFyNQ26+no1qJWB5nT6w0pynVwssbMmQYjl87vGDaJV6uRWLmgg2SLjxTL+AuYECERU91koGRO14SdR6wSiwLmYqrCjGW4nUJsMdrxQL/5zW/wer3cdtttA47T3zw8XuOBFBHTi88HDofI/gM6Dh7SEQxCdk6A8u1GHL3Cxe0K348xGoOYjBIqFbIZ1iRfulo7mZFvloPaJon/JJaQJNh3Us7JmT3Vjk4ru5iz0mRx6HSrqG02UJA1fE7P9LyJr2ZMyXQhiBL1LXp2H09k8fTuuGgBjgexJGBCxJKACaFUYRTOl9GMB6qqqop4vPEaD3RRi5jNm414PAk4nCK+Xv9JT49IZ5dIcnKAhvrBa6lVKgmjKYjRKMkixSgLFaNJwmQMyrcZpAEfPod3tQAtFOWHN/gqyIzWF9PUrqOlQ4dKJTFv2lljrijAtCkODlVYqKgzhRUxsYBOI5Ge5KGmycCHe1I5U2dk2ewuCrLGf+RALBFrAiYW20jjUYVRUIhFLmoR09CgQdUvwEWjkcjN9ZGfL5GSGsTcW0kxGoOYTLJo0Z2H/2TussJB4WIKQ3M+vph9J2UvzPQ8+6AZU0W9IqamyYDXJ8R8e67A6qKizoTPL9Jt17B5ZzoZKR6Wze4ie4Q+mclErAqYi7EKo8xJUohFLmoRc+llTpKT5MqKyRhEq428j0Js0dqlpb7FgCBILCgavDw6NdFHYoKP7h4NVY3GmGgbhaMg24XFGAD8zJvWw7FqMy0dOt7elsmUTBfLZ3dNiPF4IokVARMi1gSMUoVRuJi5qDvuRdN85OT4SU6KvoCZu6wwJsvQscpIg8z291Zhpk1xDhlUJwhyNQagos40dicYJRLNfpISfIBAerKXO0obmDW1B0GUqGs28I8tVj7Yk0qPc/IbqmIpjRdis40UIl6qMAoKY81FLWIUYpORfnB19qipajQgIQ96HI6i3gnV9a06HO7Y//DPt8rnW91kwKgPsnJBJ7df3UhhjgMkgYpaE3/ZnE35oWRcnsn5Eo5VAaNUYS4MpZWkMNZMznfAGCaWv83FGpGqMTuPJNHSqcPhUvVWL4bGYvKTkeIBSeBMvXGsT3PMybPKBuTaFkPfuIhEs5/Vy9q5eVUTORlugkF5UveGTdnsPWHB5588zt9YEzAhYk3AhFCqMAoXM4qIGUdi9U0wFon0IdbjVFHTZMDtEdGog7R1he8H9rWUamO/pZSR4kWvC+D1ijR3DMwXSk/ycv2lLVx3aQupSV58fpE9x5LYsCmbo5VmApHHdMU0sShgYvWLx87GxqgKGKUKoxAPKCJGIS45WGEBBKypHnQaiYq68BWWwhwngiDR1qWlyx7bfnZRgNxMNyC3lIZiSoabW65s4uqlbVhMPlweFR8fSOFvZVmcrjMixfYirCGJZQFzsX4BGatBjwoK0UIRMeOMYvAdHUO1lJxukRPVZgCW9ybbnq43EQzzwW3QBZmSIQuDeKjG5GWe9cUMhyDIhubbVjdy2YIO9LoANoeG93en8c72jLgSMrEoYELEooCJdhVmLFGWVStEE0XEKMQsw32oHT6dQCAgkJHiYdH0bnTaAC63iobW8HHWRblnVynF+gf8lAw3oihhs2siVo5UopxUfOc1DSyZ1YVaHSQrzR03AXmxKmAu5i8bYzViQEEh2igiZoK4mN8gR0v/aozHJ3C0Sp5LtaDYhlolT3MGIraU8q0u1OogPr88QTyW0WqkviGQNWGqMf3RqCUWz7Bx5zUNzJvWE83TGzNiXcAoVZgLQzH0KkQbRcRMALH4xhirnPsBd/RMAj6fSLLFS37vKp6QabeqwYg/MHz5QaOW+PTlzfzbmnrMxsGZMrHGSFpKQ2HQBdGoY7vUdPhAXcwKmBCx+DqN9pJqGPsqjNJKUogmioiZQJRqzMg5fKAOv1/g8JmzVZhQu8Sa6sFk9OPzixGrFqmJvmGnWcca+b2znpradZMqDybWRgmcS6y/LpUqjILCWSbPO2OcEYvf8mKV0Ifd8WoTbo+KBJOfaTnOvtsFAYpyQn6X2M+BGSkJxgDJFi/ddg1/2ZxNU3v8z8WIFwETi6/P8arCjCVKFUYh2igiZoKJ9W99scLskmI+3CkbXBcU2wZMCQcoypVFTW2zAbd38jytczNd9DjVNLTqeXOrlfd2pNHZE9tLxIcj1gVMiFgUMCHGowqjGHoV4onJ824fh8Tym2WsUVFtwO3T4HHZKM4dPMQxxeIjJdFLMChQ2TB5qjEFWS6sqR406iASEtWNRv72fhYf7kvBHuPm5P7Eg4DZX9Ecs6/JeKvCTLZl1XcX3DXRp6AwDIqIiQGUakx4ghLsP2bCkmyiIL0FtWpo0+rZVN7JI2LSk72YDX4SzX6uWNhBQbYTJIGT1Wb+sjmbHUeSYr7yFC8CJtZRqjAKCoOJ7Xe/i4BY/eYXS1TW6eiyqRAEmJLSNuxMpWlTnCBINLXrJ82UZzm9Vzb4dvZouGZ5G5++oglrqptAQODgKQsbNmVz4FRC2JVZE0U8CJgQsfpajMcqzGRi3dR7JvoUFMKgiJgYIR6+CU4EkgTley3UNetwewQWXjYNGDrJ12wIYE2Vs1VO18V+Ku9ICS0lr2kyIEmQmeLlxpUtrLmkhWSLF69PZOeRZDZszuJ4talvaOREEy8CJpbbSCHirQozmVpJCrGNImJigFh/A51I6pq0OFwiggBqtURzuybsh2JfS2kSrVLqS+91nE3vFQTIs7r5zFVNXLm4HZPRj9OlZuu+VP72QRZVjYYJSyXunwETDwImllGqMAoK4VFETAwR62+oE8G+Y2YEAaZOcaMSoaJaHi0wb0XxkNWYwmwXoijRadPS3q0Z79ONChq1RHa6PPfp3BwcUYDpeQ4+u7qRS+Z1otMG6O7RsGlHOm9szaSxTTfUIaNGvFRfILaXU/dHqcJMHEorKfZRREyMEOtvpBNBU6uGplYNogiXLbIBcKZWT6Bfu+RcIaPTBvs8JBWTsKVU3Th0hUmtkpg3rYc7rmlg4fRuVCqJlg4db23LZOMn6eMi6OJJwISI5dddvFVhFBQmAkXExBhKNeYs+47JImTGVBdF+W70+iBuj0hdk1xdCH1YnitkQi2l03XGmB/0OFLyeoVZc6cWd5j0Xp1GYtnsbu64poFZU3sQBInaJgNvf5wRVeNvvAmYeHmdxVMVZrItq1aIDxQRE0PE8rfC8aatU01tow5BgPkzHIgiFOXKLZVQSwmGFjJ5VjcGfYDMFA9e3+R4ipuNAVISvSAJ1DRHnqVk0gdYuaCT21Y3MjXbycLptmGXpl8I8eR/CREPbaTxGPKoVGHCo7SS4oPJ8Q4/yYiXb4nRZH9vFaYw101igjyssShfFjHVDTp8/rNVhXM/PNUqibuurWf1snZ02hhZqjMG9F+lNFKSzH5Kl7dFZap1vFVfIH4EzHgxllUYBYWJIGoipqOjg8997nNYLBaSkpL40pe+hN1uD7v91772NWbMmIHBYCAvL4+vf/3rdHd3D9hOEIRBl1dffTVaD2PcieU31/Giy6aisk6utiycdTadNz3Fh8UcwO8XqKobaFg91+gbL0MeR0Ner4ipaxnoCxoJwhh3kuJRwISIh9dYvAx57I/SSlKYCKI2hOVzn/scjY2NbNq0CZ/Px7p167j//vt55ZVXhty+oaGBhoYGnnnmGWbPnk11dTVf+cpXaGho4G9/+9uAbX//+9+zdu3avt+TkpKi9TAmjP0VzSwsypzo05gQDpwwIUmQl+0hNcnfd70gQFG+i71HzFTUGCgucA/Yb96KYg6Vn2Lugsn5Zpqe5MWgD+BwqThVa2Jm/uDxC9EmnsVLPFQ4h6vCCAIYNCJjpUX3d7Vwxay8MTlWl/EQiZjQBeJzQKkE+EU/QeHsNwOllRQ/REXEHDt2jI0bN7Jr1y6WLl0KwP/8z/9w/fXX88wzz5CdnT1on7lz5/L3v/+97/dp06bxwx/+kLvvvhu/349affZUk5KSsFqt0Tj1mGDuskIO7zoz0acxIdidIqeq5HbJolmDP6SL893sPWKmrkmLyy1g0A/0eUxmISP0pvfuPJLEG1szaevqZNEMGyZ9YFzufzIImHiswujUAkvyLei16gsSMYbTFUx54bfU3Xc/ixYVodWMzdt/UChBq1aBa0wONyEEpCDt6nZa9G2MmVJUGBeiImLKy8tJSkrqEzAApaWliKLIjh07uOWWW0Z0nO7ubiwWywABA/Af//EffPnLX6awsJCvfOUrrFu3DiFMvdzj8eDxePp+t9lso3xEE8PFWI05eEJOnM3K8JKZ5ht0e2JCgPQUH60dGk7XGJg73Tlom8ksZPIzXWw/kIzHJ3L0TAKnakzMK+phfpENrSY6S7HiWbxA/AiY4aowxZlGki1mUlLTL6gvmPDaayS/X0bgkktIuvrq8z5Of/wq+fVn1MRvJpMkSXhdXjRtanBDi6Ftok9JYRRERcQ0NTWRkZEx8I7UalJSUmhqahrRMdra2njyySe5//77B1z/xBNPcPXVV2M0Gnnvvff46le/it1u5+tf//qwx3rqqaf4/ve/P/oHMoFcjNUYl1vg+JnhqzAhivLdtHZoqKjRDyli4KyQASaVmMnJcJOZ6sXhUpFo9mFzaNh3IpGjlWYWzbAxu6AH1RiOjYp3ARMi1gVMiHOrMFqVQHqCjsSkZDTaCwsuNJRvB8BUXo7voYcv6Fh9qHyYtPErYEJo9XIrzNfiZ23emgk+G4XRMCr747e+9a0hjbX9L8ePH7/gk7LZbNxwww3Mnj2b733vewNu++53v8tll13GokWLeOSRR3j44Yf56U9/GvZ4jz76KN3d3X2X2traCz7H8SIe+vhjxeFTJuxOFVqNRE6md9jtpuW6EQRoadfQ3TP8J/ZwOTLxjEYtkZ3mRq8NMj3PQenyVhLNPjxeFZ8cSuYvZdmcqjUSvMCiTDwunR6KeHn9DFeFUasEREFApbrA75s+H9odn+DPyMCwayf4Blc5L3a0Bi0qQcR3kf5tnnvuOQoKCtDr9ZSUlLBzZ/gVZ3/961+ZOXMmer2eefPm8c477wy4/d577x2kD/p7WceKUYmYb37zmxw7dizspbCwEKvVSktLy4B9/X4/HR0dEb0sPT09rF27loSEBP75z3+iiVCmLCkpoa6ubkC76Fx0Oh0Wi2XAJR6Il2+PY4HXJ7DroJnmNg1ub/iSudEQ7BM5p2v0YbedjEImtEqpptnA1GwXt13dyOWL2jHqA9idarbsSeOfW6zUNOlHHfYXEi8Q/9WXeGkjhRhqRVLfK+ECl5dpDuxHdDro/NrXEe12NAcOXNDxAPwqx6SowoQQBAGz2jzRpzEhbNiwgfXr1/P444+zd+9eFixYwJo1awZ9jofYvn07d911F1/60pfYt28fN998MzfffDOHDx8esN3atWtpbGzsu/z5z38e83MflYhJT09n5syZYS9arZYVK1bQ1dXFnj17+vZ9//33CQaDlJSUDHt8m83Gtddei1ar5Y033kCvD/8BBbB//36Sk5PR6cZ3Rsx4MXdZYdx8m7wQjlYYUakk9DoJAWhsDf/mWJQvf5Cfqo486HCyCZmQiGnp1OLyiIgizMx3cEdpA8tmd6HVBOno1vLuJxm89XEGzR0jWzXSX7woAmb8GI9gO+3HWwmaTHi/9GWCJhO6bVujen8K8cXPf/5z7rvvPtatW8fs2bN5/vnnMRqNvPjii0Nu/8tf/pK1a9fy0EMPMWvWLJ588kkWL17Mr3/96wHb6XQ6rFZr3yU5OXnMzz0qaRqzZs1i7dq13HfffezcuZOPP/6YBx98kDvvvLNvZVJ9fT0zZ87sK1mFBIzD4eD//u//sNlsNDU10dTURCAgr7548803+d3vfsfhw4epqKjgN7/5DT/60Y/42te+Fo2HEVNMZiHjD8Chk0YEAWYVOhEEqKgOH+hWkONBpYLuHhWtnZFL7aEP5v6VhnjFbAiQmiSn99b2S+9VqyUWTrdxxzUNzC+2oVJJNLXpeeMjK5t2ptHVM/TfabK0js4lXgTMeKD9eBuu5SVgMOC95BJ027Zd0PH8qvFf3q8wOmw224DLcN0Kr9fLnj17KC0t7btOFEVKS0spLy8fcp/y8vIB2wOsWbNm0PZbtmwhIyODGTNm8MADD9De3n6Bj2owUcuJ+dOf/sSDDz7I6tWrEUWRW2+9lV/96ld9t/t8Pk6cOIHTKRsz9+7dy44dOwAoKioacKzKykoKCgrQaDQ899xz/Nd//ReSJFFUVNSnICczk93ke6LSgMstYjYFuGShjXc+TOFMnZ7LFtuGNalqNRIFOW5O1+g5XW0gI2VkibQhw+/hA3VxbfjNt7po79JS3WRget7ADxS9NkjJnC7mFPaw51giJ2tNVDUYqWo0MDPfweIZ3ZgMgQFibjKJl/0VzXEhYEJcaBVGbGxAHKbsLyOh3VFOz8OPAOBZeTkJz/wUzf59YdtUgfQMgkPEYYSIZivpJ0/+hHfefIctO7dE7T7OJVmbTBdd43Z/wxIMypcL2R/IzR34vHr88ccHeUxBXkQTCATIzBy4EjYzM3NYj2tTU9OQ2/dfuLN27Vo+85nPMHXqVE6fPs23v/1trrvuOsrLy1GN4eqDqImYlJSUYYPtAAoKCpD69QFWrVo14PehWLt2bVSMQfHCZFxyHQjCgePyiIEFMxzkZPowGYM4nCI1jTqmThne61SU1ytiavWULOxBHKFtoL+QgfhcvZRndbH3eCJ1LXr8AWHIuUhmQ4ArF3cwv6iHXccSqW40crzKzJ7DInlpLRRmiCxaOW0Czj56xFPFcqyqMImP/Df6f70ddhtJo8F99WoA3KtLsfzoh2RctSrsPq4bbqTjj38adL1ShYkPamtrB/g/x9tyceedd/b9e968ecyfP59p06axZcsWVq9ePWb3EzURozC2TNZqzJkaPXaHCr0+yIxCF4IA0/JcHDxuoqLaEFbETMnykJ/jIT/bI/tiRuF9DFUe4rUqk5boxagP4HSraGzTkZvpHnbbZIuPa0va2Lq9k5ONOQSCJhyqAuaUmJHzSicH8eSDCTEWXhjn5+9FfeI46jOn8S5dRs9D3yKYmtZ3uyvoQ5uZQSAvHwD/vHk0796D2Nk54DhiWxuWp3+MdtcufEVFOO75wrD3GakKc9M1NzF73mz0ej1//P0f0Wg13Pvle3n4u/LS7u6ubh7/1uNsfGsjHo+HhYsX8uRPn2Tu/Ln8+Q9/5qc/lFecpuvTAfjVb3/FXffcNfo/zkXMSBexpKWloVKpaG4e+AWgubl52IU4Vqt1VNsDFBYWkpaWRkVFxZiKmEk4YWbyMtlMvpIE+3oHPc6f7kTdW2Es7h30WNOowxNmpZJKhDUru5hZ6DrvWUnx6pURhLMG3+oIAyFDjy3Z5OBLd0vc/ikfly2OXjjeRBBvAmYsvTCea66ldesn2J78EeqTJ0m+74voyj/GP2s2/gUL8cxf0CdgQgTy8vEtWChfZs1Gt20bKV/8IuqTJ+n60VO0fFyO59prB93XaKowG/64AaPRyMatG3n8h4/zzI+eYcvmLQB86d++RFtrG6++/iqbyzczf9F8br3uVjo7Orn59pv56je+yszZMzlcdZjDVYe5+fabL+RPFJE0XVrkjSYpWq2WJUuWUFZW1nddMBikrKyMFStWDLnPihUrBmwPsGnTpmG3B6irq6O9vZ2srKyxOfFeFBETh0wWIVNVr6PLpkarlZhddDa0LiXRT3Kin0CAvkGQ0ab/CqZ4ETP5VvlvVtM0eIVW6HH0N+zOW1GMIEB+jodpucNXuOKVeBEwIcZ0RZJWi+Mr/0HLzn24br2NhMe/Q/rllxDc+BZG3fCr03TvvkvmpZdgeey7OG+/neY9+3A88FXQDr/PSL0ws+fO5qHvPMS0omnccfcdLFyykI+2fMQnH3/C3t17+b9X/o+FSxYyrWga3//x97EkWnjzH29iMBgwmUyo1CoyrZlkWjMxGEY+uV1h9Kxfv54XXniBl19+mWPHjvHAAw/gcDhYt24dAPfccw+PPvpo3/b/+Z//ycaNG/nZz37G8ePH+d73vsfu3bt58MEHAbDb7Tz00EN88sknVFVVUVZWxk033URRURFr1oxtmKDSToozJktbSZJgf28VZk6Rc0BVQB706GbXQTMV1XpmFo7PUJb+5tZQ2i/ErmcmO92DSiXhcKnpsGlorKoccPtkMuuGI96MvNFcUi2lpmL7yc9xrvsyid/8BtnrvkBjxRmkxMRB2wrd3aR+7i68S5bS/oc/4p89e0zPZfa8gcfLtGbS1tLGkUNHcNgdTM+ePuB2t8tNVWXVmJ6Dwsi44447aG1t5bHHHqOpqYmFCxeycePGPvNuTU0Noni25nHppZfyyiuv8J3vfIdvf/vbFBcX89prrzF37lwAVCoVBw8e5OWXX6arq4vs7GyuvfZannzyyTH35igiJk6Jd5NvfbOW1g4NKhXMLR5coi7Kc7HroJnGVi0Op4jJeAFu/fPgXM8MxJ6YUaskNIFmOrqT+McmLcsKJ59RNxLxVpUcryXV/lmzcRdOhZZmpGF8EVJCAoHcXPzFxSMSMKMNtzs3qFQQBILBIA67g8ysTF5777VB+yQOIbaizcXcSurPgw8+2FdJOZctW7YMuu7222/n9ttvH3J7g8HAu+++O5anNyyKiIlDQtWYeBYyoSrMrELnoEnUAAmmINZ0H02t8oykBTOHnpEUbWKxOtO/3ZVuSaXRkUdTTxKH21PRVtgvyCMUT8SbDyZEtIPtAAS7HfObb2D/xn8NWEYtdHchJSbJv4gizrv+DfOvfkn3j59GMo9PWu38hfNpaWpBrVKTV5A35DYarYZgYHy/uCjEJxfBW93kJN7euPvT3KahoUWLKML8mcMbBaflyW2kiprY6If395b095xE00Nz7v2cm6p7xeo0dNogwSDYHSIf77Hw13+lcbpWN+qRA/FEPAqY8arCAPDGPxCdTpx3yMtche5uku/7MtkF+STffx9CdzcAzjvvRHQ40L/1ZtjDjeWy6itXX8nSkqXc89l7+GDTB9RU1bCzfCc/fOyH7N+zH4C8/Dyqq6o5dOAQ7W3tYcfKXAhKFSb+USoxcU48VmN2HDTT1aNmSqYHc5g20bRcN+X7LLR3qum0qUi2BMbxLMNzrt+kf5VmKIar3IxEAIXztpgMQfKzPRj0QQpy3DS3a7HZVZRtT+JAip+S+T1hh2nGI/EoYEKMRxUGwPKXDbivuIJAXh7aTz4h+d/vQ+zsouc/v4HpxRfJ2PEJnb/9Hd6SEjyXX47xz6/gujP8EuaxCrcTBIFXX3+VHz7+Q77+71+nvbWdjMwMVqxcQXqGvKT6xltu5K3X3+KWNbfQ3dWtLLFWGBZFxMQx8dhWau9SU1mnx9ajwpkkEgyCOEw9UK+TmGL1UNOgo6LawLJ59vE92VEQyUQ7nMgZC/NtfraH1g4NIHDXDW0cOmHkwHETbR1q3t6STE6ml5IFPaQl+y/4vmKFeBMw4zEfKYS7soKsj7fR+evnSPjxUyT89Cd4ly2n7c23COTl41i3juT77yft+rX0PPQwzs/eQfLXHkRVU0Mgb3B753yqMK9ven3QdX/46x/6/m1OMPPUz5/iqZ8/NeT+Op2O3//596O+X4WLD0XExDnxtlrpwHETel2QBHOAYECgvllLbtbwlYKifHeviNGzdK79Qof5ThjRXCmUl+1h92Ez9c1aBEFi8RwHs6Y52XfMzNEKI/XNWv7xXirT8twsnWsnMSF2KlqjJd5WIsE4t5EAy1//AoD5N79BfewoPQ8/Qs83/xvU8tt9IL+AtrffIeFnz5Dwk6fxz5JNvcYNr9Lz0MNDHnMyTasOobSSJgeKJ2aSEA+rNLp7VJyu0SMAC2bKVZVIfpeCbDcatUSPQ0Vz++R7Ix0LUpP8mIxB/H6BhmY538Ogl7h0UQ+fva6N4gI3ggCna/T8dWMa2/Yk4HTH30s/Hp7jwzFeVRi7z03iX14FQOix0fbORnoe+VafgOlDrabnkW/R9s5GhB4bAMZX/sS5RiplxIBCrBN/72QKgwh9M431N/mDJ0xIEuRmeVk4S15tVFWnwx+my6FWQ0Hv6IGK6vEJvos3BAHys+WU4+qGgX8jiznAVSXd3HJNO7lZXoJBOFphZMPbaew+bMbri4/SVrz6YMa7CiN4PARy83B+9g5aPtqGt6Qk7PbekhJaPtqG8/bPEpiSC+7B4ysmYxVGYfKgiJhJQqy/uTtcIicq5arLoll2MlN9mE0BfH6Bqobw4qSod5XSmTo9wUm84uZCyM+WhV5N49CrktKS/Vx3RSc3rOokPdWHzy+w94iJV99O4/BJI4EY7jDFq4AJMW5VmIAXQ1Iiba+/Qef//nbIgLuhkBIT6fztC7S9/gb0S8adzFUYpZU0eVBEzCQilmcrHTphIhgEa7oPa7oPQTg7IylShSVkTL2ltH3Ek6ovNrIyvGjUEg6nSHvX8Fa3nEwvN6/u4JrLukhMCOD2iGzfl8CGf6XR0a0axzMeGfEsYMa7CtPHME75I60H+eKbd3Gk9eCI91OqMAqxjiJiJiGxJmTcHoFjp89WYUIU5csVlromHW7P8OpEFGHBTCcJJiX8ajjUKvqWUlc3hI/1FgSYOsXDbWvbuHypDaMhiCQJWMyxVY6ZDAJmPKsw4WYkAbxT8QbvnXmHdyrCCA5NNAAAIhZJREFUZ8JMdpQqzORCETGTjFj0xxypMOLzC6Qm+5liPbsSKdkSIDXZTzAIp2sVv8uFktfbUjrXFzMcKhFmTXNx5/WtrFnZ2TdFPBaIZwETYrwEzEj5oGoTAFt6f4ZjtCMGFBQmCkXETEJi6Y3f6xM4fFIeMbBw1uAl0iG/S0V1bKTyxjN52R4EAdo61DhcI39pq9XEVIZMvAuY8W4jjaQK0+po4WDLfi7PXcWBln20OVvH6ewUFKKLImImMbFQjTl22oDHK5CYEGDqlMHR4UV58vLf5jYNPQ7l6XghGPVB0lN8ANREaCnFOvEqYELEWhXmw5oyAB6/Qg6X21JdNuy2k7kKo7SSJh/Kp8YkJRbaSoEAHOqtwiyY6RjSlGsyBslKl1tMSjXmwsnP9hAICmzcmkRVffzNT4rHMLv+jGcyL4ysCgPwftV7LMhYxMy02czPWMgHVe+Nw9kpKEQfJbF3EjPRYwlOVhlwukRMhiDFBa5htyvKd9PQouVUtZ6Fsxxxm8obC+Rle3jv4yR6HCo2bk0iK93Hsnk9ZGf4JvrUIjIZBMx4Yg8MTLputDfQ5mwZtJ0kSXxY/T73LrgfgKsKruHlg7/jYPM+hHNebAHRRZoxnWmpQ0+XVlCINRQRM8mZqLEEwSBs35tAU5uWmYVOVGFqflOnuNm2x0KXTU1Ht5rUpNjxZ8QbKYl+sjO8NLZq8flEmts0vPVBCnnZHpbNs8fs3zbeBUyI8W4j9a/CfP3d+ymv2zrkdmpRzdppNwCwpvAGntv9C65/ddWQ267MW8k//+2fY36uE43SSpqcKO2ki4CJyI85U6vH4VLh9ws4XCoCYVZH67RSX1jbKSWV94KQl0+7SUrwM2uak1nTXAiC7JH5x3upfLAjMea8R7Hg3bpQJroKA3DLjNvRqrRkmrJ4+dN/4V93fdh32fqFvczLWAjA/MxFbP3C3gG3v/zpv5BpsqJVabl1zq1jco7BYJD/+dn/sGz2MnIsOSwsWsjPf/xzrl91PU/8f08M2LattY0scxbbt27n1IlT5CXn8fdX/953+2t/e43cpFxOHDsBwINffpB7br+Hn/7gp8ycMpOp6VP57wf/G69X/rts+OMGpmdPx+MZ6MO7+eab+fznPz8mj08hNoitdzOFqDJeHxaSBPuPmdDpgqQl+/D5BOqawhtNQ5kxp2sMcefjiDVCgrCpTcvKJTZuX9tGYa4bSYJTVXo2vJNO+b6EsNk840W8r0Tqz0RWYQD+be4X+Out7wASD5f9J96Aj3kZC5mXsZBcS/6AbXMt+X23eQNeHi77OgCv/9vr3L3g7jE5vx989wf86plf8c1Hv8m2fdt4/uXnSc9I59Y7b+Wff/knUr8X+mt/ew1rlpUVK1dQPKOY7/34ezz8nw9TV1NHQ10DD33tIb77w+8yY9aMvn0++uAjTp44yWvvvcb//uF/efv1t/npD34KwKdv/TSBQICNb20E5CpMS0sLb7/9Nl/84hfH5PEpxAaKiLlIGE+jb02Djo5uNVqN1BduFymVNzfLg1YrJ842tk7OlRHjRXZveq/TJdLaqSbJEqD00m5uvqad7Ax5ftKhk0ZefTudfUdN+PwTI2Ymi4CZCDPvcCzJWsY7d31IriWP2/9+Pa8cfjnssV45/DK3/e16chNzefPujSzNWTo259hj57e//i2P/fAx7vz8nUydNpVLLruEz3/x89x06000NTbxycef9G3/jw3/4JY7bunz6Hzx379IyaUlPLDuAb5239dYuGQh9331vgH3odVq+eX//pKZs2dy7XXX8shjj/DC/+8FgsEgBoOBW++4lT//4c992//xj38kLy+PVatWjcljVIgNFBFzETEeQkaSYN8xeUXSnCInM6bJFZaqen3YYYNqldwGAWWV0oWiUtEXKljTL/guI8XPDas6ue6KTlKT/Xh9ArsOmdnwThpHKwxhW35jzWQSMBNBuBVJmSYrf7n1LW4svoWHy75Ou7NtyO3anK08XPZ1PjX9M7xy+1+ZmpwzZud38vhJPB4PV1x1xaDb0tLTWFW6qq9dVF1Zza5PdnHbnbcN2O6X//tLjh4+ysH9B/mfF/5nkAl5zrw5GI3Gvt+XlizFYXdQX1sPwN1fvJstm7fQWC//H7300kvce++9g46jEN8oIuYiI9ofGg0tWlraNahUMHe6k/RkP4kJAQIBqK6P0FLKk0VMR7daaSldIHl9U60H/s0FQZ4i/plr2rl6RTcJpgBOl8i2PRb+tjGN07XRX5Y9WQRMiFipwvRHq9KSa8kjSZ9Mkj55yG2S9Skk6pKYkpiFVhV5mfZo0BvCV15vu/M23vznm/h8Pv6+4e/Mnjub2XNnD9jmyMEjOB1OnA4nzU2j/+I1f+F85syfw1sb3mLPnj0cOXKEe++9d9THUYhtFBFzERJNo+/+3irMjKlOjPpg76BHuRpzKkKFJTvDy2eubeem1R3KMusLJC9LTu9t71Rjdw5+mQuCLBo/e30bly7qQa8P0t2jomx7Eq9tTqG+eWw/1EJMJgEz3m2kECPJhQF5zMCVeVejEuV5EpIk0enu6LtdJaq4Mv9qtlR9MObhdoVFhRgMBj764KMhb1/7qbW43W7ef+99/rHhH9x650AzcWdHJ1+7/2v81yP/xZ2fv5MH7n0Al2tgTMORQ0cGXLdnxx5MZhM5uWcrSnevu5uXXnqJ3//+95SWlpKbG1shhAoXjiJiLlKiIWRaOtTUN2sRBDncLsS03gpLfbMWp3v4p5wgyPH3ioC5cAx6iYzUyOm9KlGumN15fRuL59jRqCVaOzS8vSWZyrqxTf2dbAJmvBlpFQbOjhm4quAaQG4d/fs79zDvf6fy72/f0zd24IqpV3Cw+QCtjrEdQ6DX6/naN7/GE//fE2z44wYqT1eye8du/vj7PwJgMpm4/lPX89T3n+Lk8ZN85rOfGbD/f3/tv8mZksP6R9fzxE+eIBAI8Pi3Hh+wjdfr5Rv//g1OHDvBpo2bePoHT/Olr3wJsd807lvvuJW6ujpeeOEFxdA7SVFEzEXOWAqZ/cfMgBxe13/idGJCgIxUH5IEZ2qUJdTjRf4oBkJqNRJL5zq484ZW5hY7SUwIkJc1eEzE+TKZBEyIWK7ChMYMrMov5Y2Tf+eq/7ecT+o/5hvLH6G8fhtX/7GEN0/+gysLrgLgg8oPxvxcv/ntb/LAfz7A0088zWULL+O+u++jrfWsP+fWu27lyMEjXHLZJUzJm9J3/YY/bmDzxs089+JzqNVqTCYTv/n9b/jji39k87ub+7a74qorKCwq5NOln+a+u+9j7Q1refi7Dw84h8KMQm699VbMZjM333zzmD9GhYlHCbu7iBnLRN9Om4qqOh2CIA96PJeiPDct7RpO1eiZO915QfelMDLyst3sPGimoUWLzy+gUUc2uxj0Epcu7iEQ7AkbUDgaJpuAmYgqjDPgQyWOvOXzQdUm8iz5fPuD9bxT8QbXF32aH131c9KM6dy74D6+/cF6HvjXOq4vvpG8xDzKzpTx2bmfHdNzFkWR9d9az/pvrR/y9tI1pbS6B1eA7rj7Du64+44B1y1etpiGnoZB2z7y2CM88tgjYc+jvr6ez33uc+h08T1PTGFolErMRc5YrVgKVWEKcjwkWwKDbp+WJ4eutbZr6O5RXdB9KYyMZEuABJNsqh6tx2UsBczcZYWTTsDEchXGH/SzpbqMGls1n9R/zG+u+z2/veH/kWZMByDNmM5vb/h//PqG59lRV05Ndw0fnPmAQHDw6zaeUTlV/POf/2TLli38x3/8x0SfjkKUUESMwgULmR6H2JcDs2CIKgzI3/BDy36VVN7xQRDOtpROVY3v33x/RfOkGSVwLuMtYDyjFBduv4vC5CKuL/o079+9g09N/8ygbfwqBzfO+DRbv7yVG2fcyLSUabh8w883i0cWLVrEvffey9NPP82MGTMi76AQlyjtJAXgwlpLB46bCAQEMtO9ZKQMP5unKM9FbaOWimoDS+Yogx7Hg7xsDweOm9i2N5GgJFAyv4ekISplY8lkax+FmKhMGADDKFYPmbUJvHb7e32rkobDpNVg0qbz+1t+TyAYiLh9LPHr3/064jZVVVXRPxGFCUepxCj0cT4VGadLZNehBOqatajE8J6Lgike1GoJm11Fa4eSyjseZGV48QUEAgE4WannrxvT+GiXBccQy67HgskqYEKMdxVmf1cLWs3ov2uGEyR+lWPQdfEkYEaCMuzx4kERMQoDGK2QOXzKiEolodUEae/U4A/zJV+jPjvoMdIYAoWxQSXC7GlOstK9mIxBJAmOnzHw6jvp7DxoxuMdu3LYZBYwE5EJU95ej4QESL0/x46xzoWZDEhKwmZcEjUR09HRwec+9zksFgtJSUl86Utfwm4f2i8RYtWqVQiCMODyla98ZcA2NTU13HDDDRiNRjIyMnjooYfw+4dvYSiMnpEKGY9X4EiFEb02SGaaD59foLYx0qDH3tECtXqC4xhzfzGTn+1Bo5awmAN8enUHmWk+AgE5mPDVt9M5cNwYVnyOhMkuYCaK4vxMgkGJoPIeF3WcTnnVpEZzcQq85557joKCAvR6PSUlJezcuTPs9n/961+ZOXMmer2eefPm8c477wy4XZIkHnvsMbKysjAYDJSWlnLq1KkxP++oeWI+97nP0djYyKZNm/D5fKxbt47777+fV155Jex+9913H088cXZMe//ZGIFAgBtuuAGr1cr27dtpbGzknnvuQaPR8KMf/ShaD+WiZCQemSMVRnw+gZQkP1MyPRw8YeJUtYGpU4bPF5li9aDXBXG7ReqbteRmjTzAS+H8yO1N7+3oUmMyBPj01R3UNOjYcdBMl03NjgMJHD5lYulcO8UFLsRRFmcms4AJMRFVGABvUKLF7cXU2UmSSn3Bc3/8KidGUYPP4xuL04xJkrXJuN3uEW8vSRJOp5OWlhaSkpJQqSZXa20kbNiwgfXr1/P8889TUlLCs88+y5o1azhx4gQZGRmDtt++fTt33XUXTz31FDfeeCOvvPIKN998M3v37mXu3LkA/OQnP+FXv/oVL7/8MlOnTuW73/0ua9as4ejRo+j1Y1eJF6Qo1NCOHTvG7Nmz2bVrF0uXylNRN27cyPXXX09dXR3Z2dlD7rdq1SoWLlzIs88+O+Tt//rXv7jxxhtpaGggM1P+YH3++ed55JFHaG1tRasd2RJEm81GYmIiP/zB39DrjZF3uIg5vOsMwCAh4/ML/PmtNNwekatXdJNs8fP3d1MRRfj8TS3otMM/rbbtSeBohZHiAjdXlXRH9fwVZN54P4WmVg2XLbYxp1hehRKU5FVLuw8n9Hlkkix+ls+3k5/tGZHxerILmIkaLVDeXs/i6XIAnF4UuCTFgl6tumARExQ8aNWT+0ParDaf135JSUlYrdYh/8ahz4zu7m4sFsuFnuKQhO7jqXt/gV57/kNw3V4Xj770X6M615KSEpYtW8avfy0bpoPBILm5uXzta1/jW9/61qDt77jjDhwOB2+99VbfdZdccgkLFy7k+eefR5IksrOz+eY3v8l///d/A9Dd3U1mZiYvvfQSd95553k/vnOJSiWmvLycpKSkPgEDUFpaiiiK7Nixg1tuuWXYff/0pz/xxz/+EavVyqc+9Sm++93v9lVjysvLmTdvXp+AAVizZg0PPPAAR44cYdGiRUMe0+Px4PGcrQ50d8sfnG63EroWiaJ5Vo7urWTnsSrmF55V5CcqDThcLhJMAbLTOxBFsJjVdNrUnKgMMr1g+OWauVk+TlUH0KpduD3K/8F4kJUO9c1mTtcEmJZ39m+en+0kJ7ODY6eNHDphor1L4F8fachIgaVz7WSmDf2N/eCZFgBmL54KTM7X0p6mJgDcrsFG2Giys0NuX3mc8v16gE12O8YLFDHdhiPMsmbCJG7jfjrnhvPaT61WI4oiPT09Q95us9mA8fHNuL0jryKF2z90ziF0Ot2QgX9er5c9e/bw6KOP9l0niiKlpaWUl5cPeR/l5eWsXz8wxHDNmjW89tprAFRWVtLU1ERpaWnf7YmJiZSUlFBeXh77IqapqWlQCUqtVpOSkkJT7xvDUPzbv/0b+fn5ZGdnc/DgQR555BFOnDjBP/7xj77j9hcwQN/v4Y771FNP8f3vf3/Q9U/+4J4RPyYF+NMw1+86NPD342dGdrz9x+AvGy/olBRGyfEzUPbJyLb7aPcIDviPCz6lmGeiHuJ7E3S/CsPT09NDYmJiVI6t1WqxWq18/5VHI28cAbPZPGjY5eOPP873vve9Qdu2tbURCASG/Gw9fvz4kMcf7rM49Dkc+hlum7FiVCLmW9/6Fk8//XTYbY4dO3beJ3P//ff3/XvevHlkZWWxevVqTp8+zbRp0877uI8++ugA1djV1UV+fj41NTVRe0KOBzabjdzcXGpra6NW4hwPlMcRWyiPI/aYLI8lXh+HJEn09PQMa4UYC/R6PZWVlXi9F+4TlCRpUNVuso5dGJWI+eY3v8m9994bdpvCwkKsVistLS0Drvf7/XR0dGC1Wkd8fyUlJQBUVFQwbdo0rFbrIMd0c7Pckw933OHKaImJiXH1QhoOi8WiPI4YQnkcscVkeRwweR5LPD6O8fjCq9frx9T0OhLS0tJQqVR9n6Uhmpubh/1ctVqtYbcP/WxubiYrK2vANgsXLhzDsx/lEuv09HRmzpwZ9qLValmxYgVdXV3s2bOnb9/333+fYDDYJ0xGwv79+wH6/ggrVqzg0KFDAwTSpk2bsFgszJ49ezQPRUFBQUFB4aJHq9WyZMkSysrK+q4LBoOUlZWxYsWKIfdZsWLFgO1B/iwObT916lSsVuuAbWw2Gzt27Bj2mOeNFCXWrl0rLVq0SNqxY4e0bds2qbi4WLrrrrv6bq+rq5NmzJgh7dixQ5IkSaqoqJCeeOIJaffu3VJlZaX0+uuvS4WFhdIVV1zRt4/f75fmzp0rXXvttdL+/fuljRs3Sunp6dKjjz46qnPr7u6WAKm7u3tsHuwEoTyO2EJ5HLHFZHkckjR5HstkeRyTjVdffVXS6XTSSy+9JB09elS6//77paSkJKmpqUmSJEn6/Oc/L33rW9/q2/7jjz+W1Gq19Mwzz0jHjh2THn/8cUmj0UiHDh3q2+bHP/6xlJSUJL3++uvSwYMHpZtuukmaOnWq5HK5xvTcoyZi2tvbpbvuuksym82SxWKR1q1bJ/X09PTdXllZKQHSBx98IEnS/7+9O42Jq2rjAP6wzQAtw5IBBlSw7IJs1TChsWBkpINESWpSsLWBplKXxtaIWKpSCnyQtkQ/EFzSsPihQqihQlKktViiEqCK1LIVGZxKMIVakK0IFvi/Hxruy2WzDDOFIc8vmTBz7nMv5zln7swh3HMu0NPTg4iICDg4OEAqlcLLywupqakL3uw3btxATEwMrKysIJfLkZKSgrt3766obhMTE8jIyMDExMSq81xLnMf6wnmsLxslD2Dj5LJR8tiI8vLy4ObmBolEgrCwMDQ0NAjbIiMjkZiYKIovKyuDj48PJBIJAgICcP78edH2mZkZpKenw9nZGVKpFFFRUejs7NR7vQ2yTgxjjDHGmKHxvZMYY4wxZpR4EMMYY4wxo8SDGMYYY4wZJR7EMMYYY8wo8SCGMcYYY0ZpQw5iBgcHac+ePSSTycjOzo72799PY2Njy+7z9NNPk4mJiejx2muviWJ6enooNjaWrK2tycnJiVJTU2lqamrd5DE4OEhvvvkm+fr6kpWVFbm5udGhQ4eEG17Omp+niYkJlZaW6rXu+fn59Oijj5KlpSUplcoFKy3Pd/bsWfLz8yNLS0sKDAykqqoq0XYAdOzYMXJxcSErKytSqVTU1dWl1zovZiV5nD59mrZv30729vZkb29PKpVqQXxSUtKCtler1YZOY0V5FBcXL6jj/FVEjaE/FjunTUxMKDb2/zcJXIv++P777+n5558nV1dXMjExEW6at5za2lraunUrSaVS8vLyouLi4gUxKz3nVmuleZSXl9Ozzz5Ljo6OJJPJKDw8nC5cuCCKOX78+IL+8PPzM2AWzOjpfdL2OqBWqxEcHIyGhgb88MMP8PLyEi20t5jIyEgkJyfj5s2bwmPuGjWzC+2pVCo0NzejqqoKcrl8xQvtGTKPlpYW7Ny5E5WVldBoNKipqYG3tzdefPFFURwRoaioSJSrPhcgKi0thUQiQWFhIdra2pCcnAw7Ozv09/cvGl9XVwczMzOcPHkS7e3t+OCDDxZdOMnW1hZff/01fv31V7zwwgsGWThpNXns3r0b+fn5aG5uRkdHB5KSkmBra4ve3l4hJjExEWq1WtT2g4ODBstBlzyKioogk8lEdZxd9GqWMfTHwMCAKIfW1laYmZmhqKhIiFmL/qiqqsL777+P8vJyEBHOnTu3bPzvv/8Oa2trvP3222hvb0deXh7MzMxQXV0txKy0bdYij8OHD+PEiRO4cuUKfvvtNxw9ehQWFhb45ZdfhJiMjAwEBASI+uOvv/4yWA7M+G24QUx7ezuICD/99JNQ9s0338DExAR//vnnkvtFRkbi8OHDS26vqqqCqamp6MP8008/hUwmw+TkpF7qPpeuecxXVlYGiUQiWhDwfj5wViMsLAwHDx4UXk9PT8PV1RUffvjhovG7du1CbGysqEypVOLVV18FcG/RJIVCgVOnTgnbh4aGIJVKUVJSYoAM7llpHvNNTU3BxsYGX3zxhVCWmJiIuLg4fVd1WSvNo6ioCLa2tksez1j74+OPP4aNjQ3GxsaEsrXoj7nu51x89913ERAQICqLj4/Hjh07hNerbZvV0vUzxd/fH5mZmcLrjIwMBAcH669ibMPbcP9Oqq+vJzs7O3ryySeFMpVKRaamptTY2LjsvmfOnCG5XE6PP/44HT16lMbHx0XHDQwMFN1afMeOHTQyMkJtbW3rKo+5hoeHSSaTkbm5+F6fBw8eJLlcTmFhYVRYWEjQ05qH//77LzU1NZFKpRLKTE1NSaVSUX19/aL71NfXi+KJ7rXtbLxWq6W+vj5RjK2tLSmVyiWPuVq65DHf+Pg43b17lxwcHETltbW15OTkRL6+vvT666/TwMCAXus+l655jI2Nkbu7Oz3yyCMUFxcneo8ba38UFBRQQkICbdq0SVT+IPtDF/91fuijbdbCzMwMjY6OLjg/urq6yNXVlTw8PGjPnj3U09OzRjVkxmBFd7E2Bn19feTk5CQqMzc3JwcHB+rr61tyv927d5O7uzu5urrStWvX6MiRI9TZ2Unl5eXCcecOYIhIeL3ccXWlax5z3b59m7Kzs+nAgQOi8qysLHrmmWfI2tqaLl68SG+88QaNjY3RoUOHVl3v27dv0/T09KJtdf369UX3WaptZ/Oc/blcjL7pksd8R44cIVdXV9GXi1qtpp07d9KWLVuou7ub3nvvPYqJiaH6+noyMzPTaw5EuuXh6+tLhYWFFBQURMPDw5Sbm0vbtm2jtrY2evjhh42yP65cuUKtra1UUFAgKn/Q/aGLpc6PkZER+ueff+jvv/9e9Xt1LeTm5tLY2Bjt2rVLKFMqlVRcXEy+vr508+ZNyszMpO3bt1NrayvZ2NisYW3ZemU0g5i0tDQ6ceLEsjEdHR06H3/uF31gYCC5uLhQVFQUdXd3k6enp87Hnc/QecwaGRmh2NhY8vf3p+PHj4u2paenC89DQ0Ppzp07dOrUKb0MYtg9OTk5VFpaSrW1taKLYhMSEoTngYGBFBQURJ6enlRbW0tRUVFrUdUFwsPDRXea3bZtGz322GP0+eefU3Z29hrWTHcFBQUUGBhIYWFhonJj6I+N6Msvv6TMzEyqqKgQ/bEWExMjPA8KCiKlUknu7u5UVlZG+/fvX4uqsnXOaAYxKSkplJSUtGyMh4cHKRQKunXrlqh8amqKBgcHSaFQ3PfvUyqVRESk0WjI09OTFArFgqv9+/v7iYhWdNwHkcfo6Cip1WqysbGhc+fOkYWFxbLxSqWSsrOzaXJykqRS6X3lsRS5XE5mZmZC28zq7+9fst4KhWLZ+Nmf/f395OLiIooJCQlZVX2Xokses3JzcyknJ4cuXbpEQUFBy8Z6eHiQXC4njUZjkC/N1eQxy8LCgkJDQ0mj0RCR8fXHnTt3qLS0lLKysv7z9xi6P3Sx1Pkhk8nIysqKzMzMVt3HD1JpaSm98sordPbs2QX/JpvPzs6OfHx8hPceY/MZzTUxjo6O5Ofnt+xDIpFQeHg4DQ0NUVNTk7Dvd999RzMzM8LA5H5cvXqViEj4kA4PD6eWlhbRwOLbb78lmUxG/v7+6yaPkZERio6OJolEQpWVlQumxi6Vq729/aoHMEREEomEnnjiCaqpqRHKZmZmqKamRvTX/Vzh4eGieKJ7bTsbv2XLFlIoFKKYkZERamxsXPKYq6VLHkREJ0+epOzsbKqurhZdz7SU3t5eGhgYEA0G9EnXPOaanp6mlpYWoY7G1B9E96bvT05O0ssvv/yfv8fQ/aGL/zo/9NHHD0pJSQnt27ePSkpKRFPdlzI2Nkbd3d3rqj/YOrPWVxYbglqtRmhoKBobG/Hjjz/C29tbNDW5t7cXvr6+aGxsBABoNBpkZWXh559/hlarRUVFBTw8PBARESHsMzvFOjo6GlevXkV1dTUcHR0NPsV6JXkMDw9DqVQiMDAQGo1GNE1xamoKAFBZWYnTp0+jpaUFXV1d+OSTT2BtbY1jx47prd6lpaWQSqUoLi5Ge3s7Dhw4ADs7O2Fm1969e5GWlibE19XVwdzcHLm5uejo6EBGRsaiU6zt7OxQUVGBa9euIS4u7oFM6V1JHjk5OZBIJPjqq69EbT86OgoAGB0dxTvvvIP6+npotVpcunQJW7duhbe3NyYmJtZNHpmZmbhw4QK6u7vR1NSEhIQEWFpaoq2tTZTreu+PWU899RTi4+MXlK9Vf4yOjqK5uRnNzc0gInz00Udobm7GH3/8AQBIS0vD3r17hfjZKdapqano6OhAfn7+olOsl2ub9ZDHmTNnYG5ujvz8fNH5MTQ0JMSkpKSgtrYWWq0WdXV1UKlUkMvluHXrlsHyYMZtQw5iBgYG8NJLL2Hz5s2QyWTYt2+f8EUCAFqtFkSEy5cvAwB6enoQEREBBwcHSKVSeHl5ITU1VbRODADcuHEDMTExsLKyglwuR0pKimjq8lrncfnyZRDRog+tVgvg3jTtkJAQbN68GZs2bUJwcDA+++wzTE9P67XueXl5cHNzg0QiQVhYGBoaGoRtkZGRSExMFMWXlZXBx8cHEokEAQEBOH/+vGj7zMwM0tPT4ezsDKlUiqioKHR2duq1zqvNw93dfdG2z8jIAACMj48jOjoajo6OsLCwgLu7O5KTkw36RaNLHm+99ZYQ6+zsjOeee060lgdgHP0BANevXwcR4eLFiwuOtVb9sdR5Olv3xMREREZGLtgnJCQEEokEHh4eorVuZi3XNushj8jIyGXjgXtTx11cXCCRSPDQQw8hPj4eGo3GoHkw42YC6GluLWOMMcbYA2Q018QwxhhjjM3FgxjGGGOMGSUexDDGGGPMKPEghjHGGGNGiQcxjDHGGDNKPIhhjDHGmFHiQQxjjDHGjBIPYhhjjDFmlHgQwxhjjDGjxIMYxhhjjBklHsQwxhhjzCj9DyJTu3Kf65nHAAAAAElFTkSuQmCC",
+      "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": "neuromancer",
+   "language": "python",
+   "name": "neuromancer"
+  },
+  "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/Pytorch_ecosystem/Part_1_cvxpylayers.py b/examples/Pytorch_ecosystem/Part_1_cvxpylayers.py
new file mode 100644
index 00000000..af7b4fcc
--- /dev/null
+++ b/examples/Pytorch_ecosystem/Part_1_cvxpylayers.py
@@ -0,0 +1,278 @@
+"""
+This example demonstrates integration of Cvxpylayers into Neuromancer
+
+We will demonstrate this capability on learning to optimize
+for parametric nonlinear programming problem (pNLP).
+
+Formulation of the parametric Rosenbrock problem:
+
+    minimize     (1-x)^2 + p*(y-x^2)^2
+    subject to   Ax <= b
+
+    varying problem parameters:     p, b
+    fixed problem parameters:       A
+    problem decition variables:     x, y
+
+https://en.wikipedia.org/wiki/Rosenbrock_function
+
+
+We will use neuromancer to minimize the objective function,
+and cvxpy layers to project the solution onto feasible region.
+
+"""
+
+import torch
+import torch.nn as nn
+import numpy as np
+import cvxpy
+from cvxpylayers.torch import CvxpyLayer
+
+# plotting
+import matplotlib.pyplot as plt
+import matplotlib.patheffects as patheffects
+import os
+import imageio.v2 as imageio
+
+# neuromancer
+from neuromancer.trainer import Trainer
+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
+
+
+if __name__ == "__main__":
+    """
+    # # #  Problem dimensions
+    """
+    nx = 2              #  number of decision variables
+    n_con = 4           #  number of constraints
+    n_p = 1             #  number of objective parameters
+
+    # generate fixed parameters of the inequality constraints: Ax <= b
+    torch.manual_seed(7)
+    A = torch.FloatTensor(n_con, nx).uniform_(-4, 4)
+    x0 = torch.full([nx], 0.5)       # controls center of the polytope
+    s0 = torch.full([n_con], 0.2)    # controls offset from the center of the polytope
+    b0 = A.mv(x0) + s0
+
+    """
+    # # #  Dataset
+    """
+    # We synthetically sample a distribution of the optimization problem parameters
+    # Each element in the set of sampled parameters represents one instance of the problem
+    data_seed = 408
+    np.random.seed(data_seed)
+    nsim = 1000  # number of datapoints: increase sample density for more robust results
+    # create dictionaries with sampled datapoints with uniform distribution
+    p_low, p_high = 0.2, 5.0,
+    b_low, b_high = 0.0, 1.0,
+    # we sample objective and constraints parameters
+    samples_train = {"p": torch.FloatTensor(nsim, n_p).uniform_(p_low, p_high),
+                     "b_param": torch.FloatTensor(nsim, n_con).uniform_(b_low, b_high)}
+    samples_dev = {"p": torch.FloatTensor(nsim, n_p).uniform_(p_low, p_high),
+                   "b_param": torch.FloatTensor(nsim, n_con).uniform_(b_low, b_high)}
+    samples_test = {"p": torch.FloatTensor(nsim, n_p).uniform_(p_low, p_high),
+                    "b_param": torch.FloatTensor(nsim, n_con).uniform_(b_low, b_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')
+    # create torch dataloaders for the Trainer
+    train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, num_workers=0,
+                                               collate_fn=train_data.collate_fn, shuffle=True)
+    dev_loader = torch.utils.data.DataLoader(dev_data, batch_size=32, num_workers=0,
+                                             collate_fn=dev_data.collate_fn, shuffle=True)
+    test_loader = torch.utils.data.DataLoader(test_data, batch_size=32, num_workers=0,
+                                             collate_fn=test_data.collate_fn, shuffle=True)
+    # note: training quality will depend on the DataLoader parameters such as batch size and shuffle
+
+    """
+    # # #  pNLP primal solution map architecture
+    """
+    # define neural architecture for the trainable solution map
+    # mapping problem parameters to decitionv ariables
+    func = blocks.MLP(insize=n_con+n_p, outsize=nx,
+                    bias=True,
+                    linear_map=torch.nn.Linear,
+                    nonlin=nn.ReLU,
+                    hsizes=[80] * 4)
+    # wrap neural net into symbolic representation of the solution map via the Node class:
+    # sol_map(p, bparam) -> xy
+    sol_map = Node(func, ['p', 'b_param'], ['xy'], name='map')
+
+    """
+    # # #  Neuromancer variables and objective function 
+    """
+    # define decision variables
+    xy = variable("xy")
+    x = variable("xy")[:, [0]]
+    y = variable("xy")[:, [1]]
+    # problem parameters sampled in the dataset
+    p = variable('p')
+    b_param = variable('b_param')
+
+    # objective function
+    f = (1-x)**2 + p*(y-x**2)**2
+    nm_obj = f.minimize(weight=10.0, name='obj')
+
+    """
+    # # #  cvxpy layer
+    """
+    # cvxpy projection problem
+    A_cvxpy = A.detach().numpy()
+    b0_cvxpy = b0.detach().numpy()
+    b_cvxpy = cvxpy.Parameter(n_con)
+    xy_net = cvxpy.Parameter(nx)      # primal decision from neural net
+    xy_cvxpy = cvxpy.Variable(nx)     # cvxpy decision variable
+    cvxpy_obj = cvxpy.Minimize(1.0 * cvxpy.sum_squares(xy_net - xy_cvxpy))
+    cvxpy_cons = [xy_cvxpy@A_cvxpy.T <= b0_cvxpy + b_cvxpy]
+    cvxpy_prob = cvxpy.Problem(cvxpy_obj, cvxpy_cons)
+
+    # cvxpy layer
+    cvxpy_layer = CvxpyLayer(cvxpy_prob,
+                       parameters=[b_cvxpy, xy_net],
+                       variables=[xy_cvxpy])
+    # symbolic wrapper: sol_map(bparam, xy) -> xy
+    project = Node(cvxpy_layer, ['b_param', 'xy'], ['xy_cvx'], name='proj')
+
+    # corrected variable by the cvxpy layer
+    xy_cvx = variable("xy_cvx")
+    # cvxpy-supervised loss for the neural net
+    residual = torch.abs(xy - xy_cvx)
+    cvxp_loss = 1.*(residual == 0)
+
+    """
+    # # #  Differentiable Parametric optimization problem
+    """
+    # constrained optimization problem construction
+    objectives = [nm_obj, cvxp_loss]
+    constraints = []
+    components = [sol_map, project]
+
+    # create penalty method loss function
+    loss = PenaltyLoss(objectives, constraints)
+    # construct constrained optimization problem
+    problem = Problem(components, loss)
+    # plot computational graph
+    problem.show()
+
+    """
+    # # #  pNLP problem solution in Neuromancer
+    """
+    optimizer = torch.optim.AdamW(problem.parameters(), lr=0.001)
+    # define trainer
+    trainer = Trainer(
+        problem,
+        train_loader,
+        dev_loader,
+        test_loader,
+        optimizer,
+        epochs=10,
+        patience=10,
+        warmup=10,
+        train_metric="train_loss",
+        dev_metric="dev_loss",
+        test_metric="test_loss",
+        eval_metric="dev_loss",
+    )
+
+    # Train problem solution map
+    best_model = trainer.train()
+    best_outputs = trainer.test(best_model)
+    # load best model dict
+    problem.load_state_dict(best_model)
+
+    """
+    Plots
+    """
+    def plot_pNLP(p, b_param, savedir=None):
+        # sample primal solution domain
+        x1 = np.arange(-0.5, 1.5, 0.02)
+        y1 = np.arange(-0.5, 1.5, 0.02)
+        xx, yy = np.meshgrid(x1, y1)
+        xy_samples = np.stack([xx.flatten(), yy.flatten()])
+
+        # eval constraints Ax - b <= 0
+        A_np = A.detach().numpy()
+        b = b0 + b_param
+        b_np = b.detach().numpy()
+        C_samples = np.subtract(np.matmul(A_np, xy_samples).T, b_np).T
+        C_samples = C_samples.reshape(n_con, x1.shape[0], y1.shape[0])
+
+        # eval objective
+        p_np = p.detach().numpy()
+        J = (1 - xx) ** 2 + p_np * (yy - xx ** 2) ** 2
+
+        # plot objective and constraints
+        fig, ax = plt.subplots(1, 1)
+        cp = ax.contourf(xx, yy, J,
+                         levels=[0, 0.05, 0.2, 0.5, 1.0, 2.0, 4.0, 6.0, 8.0, 16],
+                         alpha=0.6)
+        fig.colorbar(cp)
+        ax.set_title('Rosenbrock problem')
+        for k in range(n_con):
+            cg1 = ax.contour(xx, yy, -C_samples[k], [0],
+                             colors='mediumblue', alpha=0.7)
+            plt.setp(cg1.collections,
+                     path_effects=[patheffects.withTickedStroke()], alpha=0.7)
+
+        # Solution to pNLP via Neuromancer
+        datapoint = {'p': p, 'b_param': b_param,
+                     'name': 'test'}
+        # evaluate neuromancer model
+        model_out = problem.step(datapoint)
+        # neural net solution
+        x_nm_net = model_out["xy"][0].detach().numpy()
+        y_nm_net = model_out["xy"][1].detach().numpy()
+        # cvxpy projected solution
+        x_nm_cvx = model_out["xy_cvx"][0].detach().numpy()
+        y_nm_cvx = model_out["xy_cvx"][1].detach().numpy()
+
+        # plot optimal solutions CasADi vs Neuromancer
+        ax.plot(x_nm_net, y_nm_net, 'r*', fillstyle='none',
+                markersize=16, label='net')
+        ax.plot(x_nm_cvx, y_nm_cvx, 'g*', fillstyle='none',
+                markersize=14, label='cvxpy')
+        # plot projection step
+        ax.plot([x_nm_net, x_nm_cvx],
+                [y_nm_net, y_nm_cvx], '--', c='orange')
+        plt.legend(bbox_to_anchor=(1.0, 0.15))
+
+        if savedir is not None:
+            if not os.path.exists(savedir):
+                os.mkdir(savedir)
+            figure_path = os.path.join(savedir, f'pNLP_p={p}_bsum={b_np}.png')
+            plt.savefig(figure_path)
+            return figure_path
+        else:
+            plt.show(block=True)
+
+    generate_gif = False  # if True generate pNLP sensitivity gif
+
+    if not generate_gif:  # plot single pNLP scenario
+        # sample random scenario
+        p = torch.FloatTensor(n_p).uniform_(p_low, p_high)
+        b_param = torch.rand(n_con)
+        # plot single random scenario
+        plot_pNLP(p, b_param)
+
+    else:  # plot pNLP sensitivity gif
+        p_set = torch.arange(p_low, p_high, 0.05)
+        n_samples = p_set.shape[0]
+        savedir = './figs/'
+        images = []
+        b_values, _ = torch.FloatTensor(n_samples, n_con)\
+                      .uniform_(0.0, 1.0).sort(0)
+        for i, p in enumerate(p_set):
+            # plot snapshot
+            figure_path = plot_pNLP(p.unsqueeze(0),
+                                    b_values[-(i+1)], savedir)
+            # get image
+            images.append(imageio.imread(figure_path))
+        # get gif
+        gif_path = os.path.join(savedir, 'cvxpy_layer.gif')
+        imageio.mimsave(gif_path, images)
+
diff --git a/examples/Pytorch_ecosystem/figs/cvxpy_layer.gif b/examples/Pytorch_ecosystem/figs/cvxpy_layer.gif
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diff --git a/examples/Pytorch_ecosystem/figs/cvxpylayers_overview.png b/examples/Pytorch_ecosystem/figs/cvxpylayers_overview.png
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diff --git a/examples/parametric_programming/Part_1_basics.ipynb b/examples/parametric_programming/Part_1_basics.ipynb
index a54ce00b..cef97bdf 100644
--- a/examples/parametric_programming/Part_1_basics.ipynb
+++ b/examples/parametric_programming/Part_1_basics.ipynb
@@ -10,17 +10,29 @@
     "\n",
     "\n",
     "This is an interactive notebook based on the python script [Part_1_LearnToOptimize_tutorial.py](./Part_1_LearnToOptimize_tutorial.py).  \n",
-    "We demonstrate how the NeuroMANCER toolbox can be used to solve parametric constrained [Rosenbrock problem](https://en.wikipedia.org/wiki/Rosenbrock_function):\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",
     "$$\n",
     "\\begin{align}\n",
-    "&\\text{minimize } &&  (1-x)^2 + a(y-x^2)^2\\\\\n",
-    "&\\text{subject to} && \\left(\\frac{p}{2}\\right)^2 \\le x^2 + y^2 \\le p^2\\\\\n",
-    "& && x \\ge y\n",
+    "&\\text{minimize } &&  f(x, \\theta) \\\\\n",
+    "&\\text{subject to} && g(x, \\theta) \\le 0\n",
     "\\end{align}\n",
     "$$\n",
     "\n",
-    "with parameters $p, a$ and decision variables $x, y$."
+    "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"
    ]
   },
   {
@@ -102,13 +114,34 @@
     "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"
+    "## Dataset\n",
+    "\n",
+    "We constructy the dataset by sampling the parametric space."
    ]
   },
   {
@@ -171,7 +204,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -200,22 +233,16 @@
     "plt.show(block=True)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Q6DqbTdL6S4o"
-   },
-   "source": [
-    "# pNLP Formulation in NeuroMANCER"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "Y2htUaWMDjsk"
    },
    "source": [
-    "## Primal Solution Map Architecture"
+    "## Primal Solution Map Architecture\n",
+    "\n",
+    "A neural network mapping problem parameters onto primal decision variables:  \n",
+    "$$x = \\pi(\\theta)$$"
    ]
   },
   {
@@ -266,21 +293,21 @@
     "\"\"\"\n",
     "\n",
     "# define decision variables\n",
-    "x = variable(\"x\")[:, [0]]\n",
-    "y = variable(\"x\")[:, [1]]\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-x)**2 + a*(y-x**2)**2\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*(x >= y)\n",
-    "con_2 = Q_con*((p/2)**2 <= x**2+y**2)\n",
-    "con_3 = Q_con*(x**2+y**2 <= p**2)\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'"
@@ -577,7 +604,8 @@
       "epoch: 198  train_loss: 0.0564209446310997\n",
       "epoch: 199  train_loss: 0.05408472940325737\n",
       "epoch: 200  train_loss: 0.06316656619310379\n",
-      "epoch: 201  train_loss: 0.06245981156826019\n"
+      "epoch: 201  train_loss: 0.06245981156826019\n",
+      "Early stopping!!!\n"
      ]
     },
     {
@@ -727,16 +755,16 @@
       "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                               = 0.007\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  |   1.00ms ( 55.56us)  16.00us (888.89ns)        18\n",
-      "       nlp_g  |        0 (       0)  19.00us (  1.06us)        18\n",
-      "  nlp_grad_f  |        0 (       0)  26.00us (  2.17us)        12\n",
-      "  nlp_hess_l  |        0 (       0)  26.00us (  2.60us)        10\n",
-      "   nlp_jac_g  |        0 (       0)  33.00us (  2.75us)        12\n",
-      "       total  |   8.00ms (  8.00ms)   7.64ms (  7.64ms)         1\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"
      ]
     }
    ],
@@ -827,7 +855,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -914,7 +942,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 25,
    "metadata": {
     "id": "r85kJ1Dr_nQ2"
    },
@@ -933,7 +961,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 26,
    "metadata": {
     "id": "gemVhNU2AALr"
    },
@@ -954,7 +982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 27,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -967,15 +995,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Solution for 1000 problems via Neuromancer obtained in 0.0035 seconds\n",
+      "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 10.0548 seconds\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 2872.2249\n",
+      "Solution speedup factor 2540.9132\n",
       "MSE primal optimizers: 0.11051816632586187\n",
       "mean objective value discrepancy: 70.02 %\n"
      ]
@@ -1013,6 +1041,13 @@
     "print(f'mean objective value discrepancy: {err_obj:.2f} %')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1026,9 +1061,9 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "neuromancer",
    "language": "python",
-   "name": "python3"
+   "name": "neuromancer"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1040,7 +1075,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,
diff --git a/figs/Neuromancer.png b/figs/Neuromancer.png
new file mode 100644
index 00000000..c782dc51
Binary files /dev/null and b/figs/Neuromancer.png differ
diff --git a/src/neuromancer/problem.py b/src/neuromancer/problem.py
index dc0525a7..731c9146 100644
--- a/src/neuromancer/problem.py
+++ b/src/neuromancer/problem.py
@@ -180,7 +180,7 @@ def graph(self, include_objectives=True):
         return graph
 
     def show(self, figname=None):
-        graph = self.problem_graph
+        graph = self.graph()
         if figname is not None:
             plot_func = {'svg': graph.write_svg,
                          'png': graph.write_png,
diff --git a/src/neuromancer/psl/signals.py b/src/neuromancer/psl/signals.py
index e1c47490..4371c9c3 100644
--- a/src/neuromancer/psl/signals.py
+++ b/src/neuromancer/psl/signals.py
@@ -434,7 +434,7 @@ def nd_walk(nsim, d, min:_float_or_npd=0., max:_float_or_npd=1., x0:_float_or_np
 
 periodic_signals = {f'{stype}': functools.partial(periodic, form=stype) for stype in _periodic_functions}
 signals = {**periodic_signals, 'walk': walk, 'noise': noise, 'step': step, 'spline': spline,
-           'sines': sines,  'arma': arma, 'prbs': prbs}
+           'sines': sines,  'arma': arma, 'prbs': prbs, 'beta':beta, 'beta_walk_mean':beta_walk_mean, 'beta_walk_max_step':beta_walk_max_step}
 
 
 if __name__ == '__main__':


Test patch:
diff --git a/tests/psl/test_signals.py b/tests/psl/test_signals.py
index 1288903d..4bc12210 100644
--- a/tests/psl/test_signals.py
+++ b/tests/psl/test_signals.py
@@ -17,7 +17,7 @@ def test_signal_shape(signal, nsim, d):
     """
     out = signal(nsim, d)
     assert isinstance(out, np.ndarray)
-    assert np.issubdtype(out.dtype, np.float64)
+    assert np.issubdtype(out.dtype, np.floating)
     assert out.shape == (nsim, d)