tutorials/actor_critic_and_rc_safety_module/actor_critic_and_safe_actor_critic.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Actor-critic algorithms with safety constraints\n",
        "\n",
        "Here is a [better rendering](https://nbviewer.org/github/facebookresearch/Pearl/blob/main/tutorials/actor_critic_and_rc_safety_module/actor_critic_and_safe_actor_critic.ipynb) of this notebook on [nbviewer](https://nbviewer.org/).\n",
        "\n",
        "The class of actor-critic algorithms is known for its practical success. Pearl includes several popular algorithms in this class, such as PPO, DDPG, SAC, and TD3.\n",
        "\n",
        "In this tutorial, we will demonstrate how to use the TD3 algorithm with Pearl. Implementing other algorithms is just as simple; you only need to modify the Policy Learner to the appropriate algorithm.\n",
        "\n",
        "Pearl also supports safe training, which allows an algorithm designer to optimize a reward function while adhering to additional constraints. We will show you how to use safe training by instantiating the Safety Module with the Reward Constraint Safety Module.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8NNfwWXGvn_o",
        "outputId": "051fdcce-06d3-403b-8f16-48fa4fc7efd3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The autoreload extension is already loaded. To reload it, use:\n",
            "  %reload_ext autoreload\n"
          ]
        }
      ],
      "source": [
        "%load_ext autoreload\n",
        "%autoreload 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YY0f_c_WLNGP"
      },
      "source": [
        "## Installation\n",
        "If you haven't installed Pearl, please make sure you install Pearl with the following cell. Otherwise, you can skip the cell below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1uLHbYlegKX-",
        "outputId": "f287ac37-b45b-481c-dadb-6fbee3a957a2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "/content\n"
          ]
        }
      ],
      "source": [
        "%pip uninstall Pearl -y\n",
        "%rm -rf Pearl\n",
        "!git clone https://github.com/facebookresearch/Pearl.git\n",
        "%cd Pearl\n",
        "%pip install .\n",
        "%cd .."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nCSJf8nALNGQ"
      },
      "source": [
        "## Import Modules"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vcb70ZC_h3OA"
      },
      "outputs": [],
      "source": [
        "from pearl.utils.functional_utils.experimentation.set_seed import set_seed\n",
        "from pearl.replay_buffers.sequential_decision_making.fifo_off_policy_replay_buffer import FIFOOffPolicyReplayBuffer\n",
        "from pearl.utils.functional_utils.train_and_eval.online_learning import online_learning\n",
        "from pearl.pearl_agent import PearlAgent\n",
        "\n",
        "from pearl.user_envs.wrappers.gym_avg_torque_cost import GymAvgTorqueWrapper\n",
        "from pearl.utils.instantiations.environments.gym_environment import GymEnvironment\n",
        "import gymnasium as gym\n",
        "from pearl.policy_learners.sequential_decision_making.td3 import TD3\n",
        "from pearl.neural_networks.sequential_decision_making.actor_networks import VanillaContinuousActorNetwork\n",
        "from pearl.neural_networks.sequential_decision_making.q_value_networks import VanillaQValueNetwork\n",
        "from pearl.policy_learners.exploration_modules.common.normal_distribution_exploration import (\n",
        "    NormalDistributionExploration,\n",
        ")\n",
        "from pearl.safety_modules.reward_constrained_safety_module import (\n",
        "    RCSafetyModuleCostCriticContinuousAction,\n",
        ")\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "\n",
        "import torch\n",
        "import numpy as np\n",
        "\n",
        "set_seed(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UYIoDAGSLNGR"
      },
      "source": [
        "Let's dive into the code. First, we will create a MuJoCo environment with an additional torque cost function. This environment is designed as a wrapper on top of Gym, so at each step, the environment returns the usual reward and an additional cost function. The cost function we use is $c(s,a)= ||a ||^2$, which represents the squared norm of the action. This cost function represents the average power of the action, and often, algorithm designers wish to restrict this quantity. In later sections, we will use the additional cost function to place additional constraints on the agent.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "td6gL3ZXisy6",
        "outputId": "539f4877-db9d-4cdf-974b-c88835167347"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# create a HalfCheetah with an additional torque cost by using the GymAvgTorqueWrapper wrapper\n",
        "env = GymEnvironment(GymAvgTorqueWrapper(gym.make(\"HalfCheetah-v4\")))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FIXrgNvCh-1T"
      },
      "source": [
        "## TD3 algorithms in Pearl\n",
        "\n",
        "With the environment ready, we now test the performance of the TD3 algorithm. We set the Policy Learner to be TD3 and configure the replay buffer, but do not add any special safety module at this point.\n",
        "\n",
        "The following code demonstrates the performance of TD3 in the environment with the additional torque cost. As the learning progresses, we track and print both the cumulative reward and the cumulative cost."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vfCofCSiNxQ",
        "outputId": "d1289958-4702-4901-8b3c-b9c5a4bbd983"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "episode 20, step 20000, agent=PearlAgent with TD3, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 49.970514269312844\n",
            "return_cost: 806.2538956999779\n",
            "episode 40, step 40000, agent=PearlAgent with TD3, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: -14.736769429975539\n",
            "return_cost: 787.3026733063161\n",
            "episode 60, step 60000, agent=PearlAgent with TD3, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 1430.6730238944292\n",
            "return_cost: 813.093893378973\n",
            "episode 80, step 80000, agent=PearlAgent with TD3, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 2032.4104736470617\n",
            "return_cost: 794.4528419971466\n",
            "episode 100, step 100000, agent=PearlAgent with TD3, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 2657.43493815884\n",
            "return_cost: 793.5689518451691\n",
            "episode 120, step 120000, agent=PearlAgent with TD3, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 2887.5689703784883\n",
            "return_cost: 798.2803127765656\n",
            "episode 140, step 140000, agent=PearlAgent with TD3, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 3716.0947114322335\n",
            "return_cost: 803.4706705212593\n",
            "episode 160, step 160000, agent=PearlAgent with TD3, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 3800.822272643447\n",
            "return_cost: 800.8424595594406\n",
            "episode 180, step 180000, agent=PearlAgent with TD3, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 3853.1925881505013\n",
            "return_cost: 805.224086523056\n",
            "episode 200, step 200000, agent=PearlAgent with TD3, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 4108.776982480951\n",
            "return_cost: 812.8720943331718\n"
          ]
        }
      ],
      "source": [
        "# setup TD3 algorithm\n",
        "td3_agent = PearlAgent(\n",
        "                    policy_learner=TD3(\n",
        "                        state_dim=env.observation_space.shape[0],\n",
        "                        action_space=env.action_space,\n",
        "                        actor_hidden_dims= [256, 256],\n",
        "                        critic_hidden_dims= [256, 256],\n",
        "                        training_rounds= 1,\n",
        "                        batch_size= 256,\n",
        "                        actor_network_type= VanillaContinuousActorNetwork,\n",
        "                        critic_network_type= VanillaQValueNetwork,\n",
        "                        actor_soft_update_tau= 0.005,\n",
        "                        critic_soft_update_tau= 0.005,\n",
        "                        actor_learning_rate= 1e-3,\n",
        "                        critic_learning_rate= 3e-4,\n",
        "                        discount_factor= 0.99,\n",
        "                        actor_update_freq= 2,\n",
        "                        actor_update_noise= 0.2,\n",
        "                        actor_update_noise_clip= 0.5,\n",
        "                        exploration_module=NormalDistributionExploration(\n",
        "                            mean=0.0,\n",
        "                            std_dev=0.1,\n",
        "                            ),\n",
        "                    ),\n",
        "                    replay_buffer=FIFOOffPolicyReplayBuffer(\n",
        "                        capacity=100000,\n",
        "                        has_cost_available=True\n",
        "                    ),\n",
        "                    safety_module=None,\n",
        "                )\n",
        "\n",
        "# Run TD3 on the environment\n",
        "number_of_steps = 200000\n",
        "print_every_x_steps = 20000\n",
        "record_period = 1000\n",
        "\n",
        "td3_info = online_learning(\n",
        "    td3_agent,\n",
        "    env,\n",
        "    number_of_steps=number_of_steps,\n",
        "    print_every_x_steps=print_every_x_steps,\n",
        "    record_period=record_period,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WDxQIjW1tZFn"
      },
      "source": [
        "## TD3 with Reward Constrained safety module in Pearl\n",
        "\n",
        "The modular design of Pearl enables us to easily combine the TD3 algorithm with a safety module. One such generic safety module we developed is based on the Reward Constrained (RC) Policy Optimization (PO) framework [1].\n",
        "\n",
        "This approach is versatile and can be applied to any actor-critic algorithm. To try its Pearl implementation yourself, simply instantiate the Pearl agent safety module with the RC safety module.\n",
        "\n",
        "To integrate TD3 with the RC safety module, simply instantiate `PearlAgent` with TD3 as before, and set the `safety_module` parameter to `RCSafetyModuleCostCriticContinuousAction`. The following code demonstrates this.\n",
        "\n",
        "[1] Reward Constrained Policy Optimization, C. Tessler, D. Mankowitz, S. Mannor, 2019, https://arxiv.org/abs/1805.11074.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WSyhE54tpTL",
        "outputId": "0741734a-aeaf-4d65-fe9c-dbd718d5fea7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "episode 20, step 20000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 946.3039617876057\n",
            "return_cost: 612.9130087792873\n",
            "episode 40, step 40000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 1735.8694469803013\n",
            "return_cost: 557.5508826673031\n",
            "episode 60, step 60000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 2104.1888732789084\n",
            "return_cost: 462.54283828660846\n",
            "episode 80, step 80000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 2863.3212767671794\n",
            "return_cost: 478.60841024667025\n",
            "episode 100, step 100000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 3986.5186753571033\n",
            "return_cost: 469.34288113564253\n",
            "episode 120, step 120000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 4531.529906626791\n",
            "return_cost: 458.11196183785796\n",
            "episode 140, step 140000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 4801.1537653543055\n",
            "return_cost: 442.866158567369\n",
            "episode 160, step 160000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 5312.129231594212\n",
            "return_cost: 431.6710966601968\n",
            "episode 180, step 180000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 5500.471403837204\n",
            "return_cost: 425.44032253697515\n",
            "episode 200, step 200000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, FIFOOffPolicyReplayBuffer, env=HalfCheetah-v4\n",
            "return: 5173.863486928865\n",
            "return_cost: 419.25983215868473\n"
          ]
        }
      ],
      "source": [
        "# setup RCTD3 algorithm, TD3 with reward constraint safety module\n",
        "rctd3_agent = PearlAgent(\n",
        "                    policy_learner=TD3(\n",
        "                        state_dim=env.observation_space.shape[0],\n",
        "                        action_space=env.action_space,\n",
        "                        actor_hidden_dims= [256, 256],\n",
        "                        critic_hidden_dims= [256, 256],\n",
        "                        training_rounds= 1,\n",
        "                        batch_size= 256,\n",
        "                        actor_network_type= VanillaContinuousActorNetwork,\n",
        "                        critic_network_type= VanillaQValueNetwork,\n",
        "                        actor_soft_update_tau= 0.005,\n",
        "                        critic_soft_update_tau= 0.005,\n",
        "                        actor_learning_rate= 1e-3,\n",
        "                        critic_learning_rate= 3e-4,\n",
        "                        discount_factor= 0.99,\n",
        "                        actor_update_freq= 2,\n",
        "                        actor_update_noise= 0.2,\n",
        "                        actor_update_noise_clip= 0.5,\n",
        "                        exploration_module=NormalDistributionExploration(\n",
        "                            mean=0.0,\n",
        "                            std_dev=0.1,\n",
        "                            ),\n",
        "                    ),\n",
        "                    replay_buffer=FIFOOffPolicyReplayBuffer(\n",
        "                        capacity=100000,\n",
        "                        has_cost_available=True\n",
        "                    ),\n",
        "                    safety_module=RCSafetyModuleCostCriticContinuousAction(\n",
        "                        state_dim=env.observation_space.shape[0],\n",
        "                        action_space=env.action_space,\n",
        "                        critic_hidden_dims= [256, 256],\n",
        "                        constraint_value=0.4,\n",
        "                        lambda_constraint_ub_value=200.0,\n",
        "                        lr_lambda=1e-3\n",
        "                    ),\n",
        "                )\n",
        "\n",
        "# Run RCTD3 on the environment\n",
        "number_of_steps = 200000\n",
        "print_every_x_steps = 20000\n",
        "record_period = 1000\n",
        "\n",
        "rctd3_info = online_learning(\n",
        "    rctd3_agent,\n",
        "    env,\n",
        "    number_of_steps=number_of_steps,\n",
        "    print_every_x_steps=print_every_x_steps,\n",
        "    record_period=record_period,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RR5SQJlvcTrL"
      },
      "source": [
        "## More on RC-PO and Its Pearl Implementation\n",
        "\n",
        "Next we elaborate on the RC-PO framework and its Pearl implementation. The RC-PO framework is based on the Lagrangian formulation of Constraint MDPs (CMDP). Effectively, it translates the solution of a CMDP to a min-max optimization problem. The min-player solves an MDP with a modifed reward of\n",
        "$$\n",
        "r_\\lambda(s,a) = r(s,a) + \\lambda (c(s,a)-\\alpha),\n",
        "$$\n",
        "where $r(s,a)$ and $c(s,a)$ are the immediate reward and cost, $\\alpha$ is the constraint, and $\\lambda$ is the Lagrange multiplier. On the other hand, the max-player maximizes $\\lambda$, and increases it as long the agent does not satisfy the constraint.\n",
        "\n",
        "In Pearl, when the safety module is instantiated with RC safety, the baseline actor optimizes the effective reward $r_\\lambda(s,a)$ while updating the Lagrange multiplier."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VA1xdInBvQE2"
      },
      "source": [
        "## Comparing the results\n",
        "\n",
        "Let us now compare the results. As we can see, the cumulative cost of the RCTD3 agent is much better controlled than that of the TD3 agent. Additionally, the cumulative reward of the RCTD3 agent is higher in this particular problem, which may be due to the extra regularization provided by the RC module. This is not always the case, but it is possible in certain situations.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "slEdjytvvU8k",
        "outputId": "664e0200-a7f9-4152-9d1b-280f4698e44b"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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JgpIzJBPkTqWDIEdsP6GyQA0jA+nVNAqA2eaSmN7kftsl8QCsOXiGszmFDPvwX4Z++C8nMypXHjOZNGasTQJgUNsYGkYGkl9k4rs16lh2QTG/bzpOfpE1i1VYbKKw2FTmuUpMGlPm7mLBzhSH3qeouuyCYp75eStL96R6eihCCAdIEORKAXoQdBZN00jNtM0ESRDkTnZBUE7ROc4sS+8H6lA/jKs7xgHw55YTHE3L5eDpHIwGGHtFc3y8DJzMyOexmZs5nV1IfpGJv7clV+o1/t1/mqS0XEL9vfng1i5MGNwKgG9XHya3sJgH/28Dj8/czPO/bbe8n8HvL2fAu0s5m2Mf1CUeOcvnyw/y8l+yFIO7zFx/lJkbjvLsL9soMUlzvPCs7cczKv0HWG0nQZAr6Zmg3DSyC4rJs/krXjJB7nVBmSBzP1CHBmFc1T4WXy8ju05mcsOnq8zHw4kO9adLvCp/Lt97yvLYv7efLPN8R87kkJFrH4h9t+YIACO6NSDA14sh7WOpHx7AmZxCbvtijaXs9svGYyQeSWPK3F0cOp3D0bQ8JpUKdvTes+PpeXaZI+E6i3errFtyZr7dv784vxKTxrgfNjHmu0SKS8pmNoVjth3L4NqPV3DbF2swuSggT8nM5+qP/uXtfyq3LMjh0znsS8lyyVgulARBLnS8wB+Akpwzdv1AACczJQhyp0ybICjdwSBo6/F0ADrWDyMyyJcPbu1MsJ+35d+0b3NVIuvVLMrymEtb1AFgw5GzpGbmk1dYwrvz99D/v0vp9/ZS+v93KasPqMDmZEYei3apD9E7ejYEwNvLyL19GgOwxZyJam3uIRs3YxM/rj8KqB6lPzaf4J8d1oxTmjkzpGmQlJbr0HsV6hf8rpOZlT4/K7+ItQfTLLdnmv9taqK9KVnc/816NhxOO//JTjJrw1H+2nKCv7cn83PiMbe97sXqqxUHMWlw+Ewuaw9V/t8xI7eIn9YfZcPhtPP+8fTx4v1sP57J1CUHLH8kViQzv4hR36zn+k9WsfbgmUqPx10kCHKhd1eofpH8rDN2/UAAJ9MlVelOtkFQmgPlsLM5hZZp7+3qhwEwpEM95j56KV0ahuPjZWBoh3oA9G2uAp8QP2/+e3MnOseHo2nwz45knvllKx8u3s/BUzkAnMkp5M6v1jJ+5mZu/3ItJg16NY2kebS1Wf6WHvGE+Km5Czd1a8D39/ck1N+bE+Ys4l29GjH6smYAPP/bdku260y2Ncg7fDrHge9SzZRTUOy0ElR+UQnXTV3JkA/+5f5v1nOo1PevoLikzGv9u+80xSaN8EAfABbuSqkxq4efSM8jydz0n1dYwkPfJbJwVyrjf9pCQbHrs4hZ+UW8M3+P5fZ7C/eed/bm4t0pPPrDpjJ/WJZH0zTmbjvJpqSztWINr9TMfObYzF79fdPxSj0up6CYO75aw9O/bOXGz1bTYdI/fGgzScPWyYw8u0D/1Tk7K/zelpg0xs3YxIFTOQT7edOkTpAD78Y9JAhyodDIaABMOdYgqE6wLyDlMHerajlMb4puHBVIWICP5XjDqEB+HdObLS8Nol2cCo56NI7gnZs68X/39yQ6xJ+hHWIBeG/hPv7ccgIvo4G3buzIuucHcF3nOEpMGr9uOs6h0zn4+xh5fGBLu9cO8ffhvVs6M/qypkwe3p6oYD+eMvcKxYX588yQ1jw+sAUNIwM5nV3Av+ZFG9NseoQOn6meQZCmaaw7lHbBH7Q7TmTQZfICJv66tVLnHzmTw95zpOVnbThq6ddbuCuVwe8tZ3eyygrlFhZz2VtLGPz+crt+i0W7VDP0jV0b0KlBGMUmjd82Vu7DJ7+ohBPpeR75gF6yO5X+/1VZya9WHGLK37ssQXpSWi7/t1qVaLPyi/hxXRK3fL6a7q8udOqK9x8v2c/p7EKa1gmifngAKZkFTFt1yHL/mewCxs7YyFvzdpNbWMyS3amM/jaRP7ec4N0Fe87xzMoP647y8Pcbuf6TVfR7eynvLdhbqeCppvpuzRGKSjTL58zc7SfPm9UpLjExdsZGth/PJMTfmzrBfhSVaLy7YC/zzOX8JbtTefuf3ZzMyOPzZQcpLDHRtl4ovt5G1hxMY/Fu64SAOVtP0unl+Vw3dSX3f7OeZXtP4e9j5Mu7uxMd6u+6N19FHg2CSkpK+M9//kOTJk0ICAigWbNmvPLKK3a/EDRN48UXX6RevXoEBAQwcOBA9u2zj1DT0tK44447CA0NJTw8nFGjRpGdne3ut1NGVB31IehdkG75j9exQTgAWQXFZOU71qDrSt+vPcKo6evL9KpcLDKqWA5bf1it1dPB/O9my2AwEOjrbXf7xm4N6Byvzh3SXmWI9KDkiYEtuLl7PNEh/rx3S2dev74Dd/ZqyNTbu7Jm4gDLzDNbA9vG8NzQNgT4egEq+zP19q78ODqBYD9v/H286NBABWEp5sZ7+yDI9eWw/anZ7E917P/bz4nHuPnz1Uz8ddsFvfasDccoLDExK/EYR84T8CVn5HP1hysY9uG/5Za7iktMfL78IACjL2tK23qhFJaYWLBDlSr3pWSTklnA/tRsbv9yLSmZ+ZSYNMuMsAFtYri5h5olOH3VYZbvPUVqZj5T/t7FoPeWWUqeoDJK36w6zKVvLaH3G4u5+qMV/LTh6Dl7YjLzi5gydxfP/rKVZ37eytv/7Gb21hMcr0JW+Y/Nx3ng2w3kF5koNmm8Mnsn35qDnhu61gfgo8X7mbE2iX5vL+XZX7ex9lAap81BSVI5P1d5hSW8Mnsnl7+9pFJlj9UHzjBtxWEAnh/WhicHqT8CPl16gP2p2eQUFHPf9PXM2XqST5YeYNB7y3nou0SKzZm4nxOPnfO9a5rGdHNAZTSowO6DRfvo88ZinvttW7XrlysxaczZevK82du9KVnl/g7LLyrhe/MM00nXtqNemD9Z+cUs3ZNKYbGJ+TuSeWrWFga+u4yX/thOZn4Rp7MLePTHTSzdowKV/xvVk/XPD2D0ZU0BmPDzVp79ZSv3Tl/P1CUHuOKdpZZZrC8Ma8Oovk0AmDx7J/tTs1hz8AxPzNxMRl4Rm4+ms2SP+sPsnZs6WX5PVTceXSfozTff5NNPP+Wbb76hXbt2bNiwgXvvvZewsDAeffRRAN566y0+/PBDvvnmG5o0acJ//vMfBg8ezM6dO/H3V1HlHXfcwcmTJ1mwYAFFRUXce++9jB49mhkzZnjy7RFbrx7sgoCSLE5lqh/sxlFBhPh7k5VfTEpmPiH+Pud5FtfbeSKTF//YQYlJ44f1STzUr5mnh+R0tkFQWiUDvVNZBUxboX6JXt6yrsOvGR8ZSPv6oWw/nsklTSIZc3lzy30Gg4Hbzf0/jjAYDAzrWM/uWHSIWkdIb4g+k2P9S7eiX6iFxSbu/notLWNCmDy8vcPj0B07m8vVH/2Ll8HAimf6ExHkW6nH6X85/r7pOI8NaEGjKGuafMnuVD5ffoCYUH8aRwVxU/cGNIgILPMcmqYx39wLpWkq8HjpmnYVvuYrs3daVmp/7rdt/PJQbxbsSuGNv3fTv3U0TeoEcexsHlFBvjwxsCWBvl7sPJnJCXPWx3a7lEOncxjx6SoGtI7mTE4hIf7edG8cQbv6oby/cB/H0/O4++t1GAxYFtN87MfN/DWuL37eRu7+ep1d4LjjRCZP/7yVdYfSeOemTuWOf8rc3fywLqnMcaMB7u3ThMcHtmBPchZL95wiwNeLxlFB9GkeRXig9d/kVFYBb87bbem9Gd45jo4NwpkydxfFJo17ejfmP1e3ZeeJTHYnZ/HcbypIbVIniJu7xzNv+0m2HMtg9P9t4NeHe1v+CEg8ksZTs7ZayocTft7K/Ccuw9/Hi0OnVfbN22jA38eL8EAf1h1K49U5uygxaVzRqi79W0dj0uDLfw+x62QmQz5YTuOoIPalZhMR6EOAjxfHzqrv/8A20WTlF7P2UBqfLT3AK9e1R9M0NA2MRoPlva49lMbelGwCfb1Y+tTlrD54hmkrD7P5aDoz1iaRmlnAZ3d2xdvL9bkATdNYfeAMTesGExtWNhuiaRrP/7aNH9cfJcDHi7dv6sjQ9vVYfziN1KwChnaoh5fRwN/bTjLm+41EBvny2Z3duKSJmnyz5Wg6b/+zhzM5hdQPD+CqdrFsO57B58sO8uGi/bw6Z5fl+wfqD5d5O5LJKywhM78YowE+uq2r5Q+4CYNbseFwGhuT0i39hy2ig9ln/pnt3iiChGZRtG8Qxi+JxzhyJpchH/yLn7cXhSUmBreLYUCbGNYcPEOPxpGWWbXVkUeDoFWrVjF8+HCGDRsGQOPGjfnhhx9Yt24doH4w3n//fV544QWGDx8OwLfffktMTAy///47t956K7t27WLevHmsX7+e7t27A/DRRx8xdOhQ3nnnHeLiPPfNb9SggeV6zlmVQo4O9SMuLIA9+VmczMi36wHxhBKTxnO/Waf1/pJ4jAcva4rBYDjPI2uWqmSCpvy9i6yCYjo2COO6LvWr9LovXdOOnzccY/yglngZXfM9rWsOgvRso20m6EgFmaBdJzNZczCNNQfTuKtXI1rEVO3n8L/z95JfpLIXf209wd0JjcnKL2LWhmP0a1WXZuUsFqmXwgBMGny+/CCvX98BUP0Gj/64iax867Yyy/ed4tcxvcv8TG47nsGJjHxLoPHT+qM8cWVLQsv5w2LZ3lPM2XYSowH8fbzYlJTOYzM3M3fbSUpMGl+tsJZg7u3TmABfL+LC1YKax9Ots+1AlT1PpOdz7Gwe35izJ/1a1sXHy4iPl5E/H+nDl8sP8cO6JPKKSujSMByTSWPLsQwe+r9EsguKOZ6eR90QPx4b0IJB7WL4OfEY7/yzh58Tj3FVu1gGto2xG/+uk5nMXK8CoAf7NSXEz5vj6XlsP57JtuMZfLXiEN+sOmzJkuhiQv2Y99hlRAT5snL/aR76v0RLIDiqbxOeH9oGo9HAJY0j2Xz0LDf3iMfLaOD5YW2466t1+HoZeXRAc0Zf1gxfbyPXdYnjmo9Wsjs5i5s+W81r13dg5f7TvLtgLyUmjdhQf0yaRlJaLp8sPUCjyECe/mVrhT1b13WOY8oNHTEYDHgZ4KuR3Xnh9+0s3p3KvlQVwEy79xKaRwfz0eJ95BaU8PywNmxKSue2L9dYelP+3HKC/KIS6kcE0LlBOBOHtrGU867rUp/oUH+Gd67P8M71WbInlQf/L5GFu1KY+Os2hnSIJSWzgF5No8r0rJzKKuCL5Qc4lVVAdkExwzrW4/ouDcq8j/KUmDTL//n/W3OEF//YgY+XgRFdG/Dw5c1pGGUN7N+Zv8cSbOQVlfDIjE3UCd5p6S1bdeAMjw9swURzUJqWU8jtX67h6o712J2cxe5kVeL1Nhp4+qpWeHsZua5zfT5fdpCd5qxnnWA/rulUj3ZxYXy8eJ8lS9y+fiivDG9Pl4YRlvH4eBn5+PauXP/JSopKNP57Uycub1WXf3akMH9HMg9f0RyDwUCovw+/PtybF//YweLdqRSVFNO9UQQf3NoFfx8vbu4eX6nvlSd5NAjq3bs3X3zxBXv37qVly5Zs2bKFFStW8O677wJw6NAhkpOTGThwoOUxYWFh9OzZk9WrV3PrrbeyevVqwsPDLQEQwMCBAzEajaxdu5brr7++zOsWFBRQUGD9azkzs/IzQRzRIjacTC2QUEMuZ06nAMHUDfYjNsyfPSlZ1WKtoO/XHmHz0XRC/LwpLDGxLzWb7cczq23qsqoq0xOkaZrlr/jYUH9+3XgcgwFeGd6+ygFMj8aR9GgcWaXHVlZ0iPrLsrwg6ESGmibv7+Nl9xjbc2asS6owg3IyIw8/by8izRmezPwiFu9K5bKWdTmRnsdvNo2XvyQe4+6Exjz/23b+3HIC77kG7undmEcHtrALTA6cyuZMTiFGgwqCft5wjMcHtKBuiB/P/LKNrPxi2tcPZViHON5fuJdNSemsOnCGPs3roGkahSUm/Ly9mLddZYGuahfL/tRs9qVm89P6o9x/aVO795BfVMJLf6j1le7p3YSGkQFM+msnf205AcDANjHsTckiKS2XYD9v7kpoDEB9PQg6qz4sTpiDoa4NI/jf3c2Zve0E87Ynszcli3t6N7a8Xr2wAF68pi2PDWjB2dxCGkUFqr/mP/iXPeZ+pKZ1gvju/p6WQOvhy5uTkVvE58sPMvG3bXRvHGHJ4GiaxqtzdmLSYFiHekwc0sbu/S3dk8pLf+7gyBk1/gFtolVmbv9pUjILeG/hXp4a3IrxP20mq0B9bycPb09Xmw+9Dg3C7P7PX9qiLn8+0ofIIF+7LFy9sAA+v6sr905bz44TmVw3daXlvus6x/Hy8Pas2n+aMd9v5JMl+y1BWauYEPx8jOQVlpCeV4TJpDHm8maM6tvELriNCw/gq5HdWbAzhZ8Tj3Ff3yaW7ITt++7VNJJLGkey7nAa/2deXgLg4KkcDp7KYeWB05YJAnf1amT3/bqiVTQf3daFMd8lMivxGLPMWTE/byMvXtOW2y9paBmTbdYMYPne03SOjzhng+++lCwen7mZM9mFzHigJ3VD/Hh/oWrhKCrR+HH9UWZvPcnnd3WjZ5NI/rtgL58uVXsavnpde46ezeXzZQc5nV1AiJ83OYXF/LAuiQU7k0nPLaJdXChN6gQxe+tJft+sfoaNBri+SwMeG9DCEly1qRfKgNbRbEw6y/2XNuXePo0tmburO9bj29WHCQ/wZUS3BuX+fosLD2DJU5fjbTTi662yZVe1j+Wq9rF25zWICOSrkd35Z0cyG5PSefjyZmV+31RnHg2Cnn32WTIzM2ndujVeXl6UlJTw2muvcccddwCQnKx+ycXE2P9VFBMTY7kvOTmZ6Ohou/u9vb2JjIy0nFPalClTePnll539dsoID/TluCGYUHI5lXoSaEF0qB/1zOlQT+8flldYwtvzVHPh01e1Yt3hs/y15QS/bDxG4zqBfL7sIAnNouhjnvVUU+UXlVBgs7JyRYslzt2WbLdtBcCtPRrSyfxLuLqKtskEmUwaZ83lPj1DkpSWS8tSmZ4zNkHQL4nHeOaq1mV+cc1cn8Tzv20n0NeLGQ/0olFUILd/uYbtxzMJ9vO2ZKAub1WXFftOs+VYBr9vOs6f5uCi2KTxvxWHmLcjmS/u6k7buFAA1pink/dsEkVhiYnEI2eZ8PNWYkL9WL73FH7eRt6/pQvNo4NJzlDZlo8X76djgzBGfbOBXScz+eDWzpZlAa5qH0tuYQkTf93G9FWHubdPE7tf6lPm7uLwmVyiQ/x44soWBPp68/vmE2w+ms4t3eN5/YYOFJWY+GXjMVrHhloa4PUg6ER6PpqmWcphceEBhAX6cEfPRtzR0/4D1lZYoA9h5hljMaH+vH9rZ+7/ZgPNo4OZfu8llu+f7okrW7JwVwoHTuUw5IN/qRfmT4i/D15GAyv3n8HXy8izQ1qXeZ3LW0Xzz+NRHDiVTbO6wZZ/x1UHTnP7l2v5bs0RDp/JJSWzgMZRgfz8UO9KfUh1LKcPDqBbo0gWPXk5r83Zye+bTxDg48Xk4e24sVsDDAYDV7WP5bKWdS3rJT1waRMmDmljV6o6F4PBwKB2sQxqF3vOc54f1oZHfthI69hQ7uzViCZRQRw4nc1rc3ZZSo09GkfQpl5omccPbhfLGyM68ta8PdQJ9sXby8D245k8/9t21h1K472bO3Mmp5A/zUHGuP7NWXPwDOsPn2XSnzuYfm8PDAYDxeafm18SjxMV7Ev98AC+X5tkWRPuoe8SuaxFXdJyVPO3es3dbDhylnumraNVbAjbj6s/wicMbsWd5oCtX8u65BSUcGmLOvy55QRP/7yV09mF5v8bnWkeHczlraLZl5pFl/hwujWKLPPzBPDVPT3K/f75+3hZZpaei23P47mof/d6XNW+3vlPrmY8GgT99NNPfP/998yYMYN27dqxefNmHn/8ceLi4hg5cqTLXnfixImMHz/ecjszM5P4eNek7fJ9wqEolRBN/QUYHeJvqQknZ3p2mvyag2fIKiimfngAt/dsRIPIQP7acoI/Nh9nzcEz7E7OYt6OZBaO7+fRcV6ozFIN6NkFxRQWmyx/3YAKlN6YtwuA/q2jLR+iz1zVyn0DraK6lp6gAjLyiiylh1YxIexOzuLw6ZwyQVCaTd9QZn4xc7ed5IauKs2vaRpv/L3b0iScmV/MHf9bS/PoYLYfz8RoUN/D7IJifL2MvDK8PZNn72TBzhSemrUFgGs7xXFD1/r854/tHE3LY8Snq3jnpk4M61jPUgrr2TSS9nFh3P/tBpbZLDD49FWtaR6tymij+zVjxrokVh88w/WfrLJ8uN3/zQZMGvh4GbiidTS+XkbemrebY2fzWLAz2fLLeMHOFEvJ6q0bO1p68GY80JOdJzLp2jACo9GAl9GrTECj/z/NKyohPbfI0hukZ28cdWmLuqx7biAh/t7lBgT+Pl68c1Mnbv9yLScz8stkiu/r24T4yLK9Ufpj9VmKut7N6jCkfSx/b0+2BCRvjOjolL/S64b48f6tXXjgsqZEBPrafU8MBgOvX9+eF//YweWt6nK3ObPmbJ3iw/n36f52xxpGBXJJ40ie/20bC3am8NiAlhU8Gm7uHm8p15hMGl+vPMSb83bzx+YTdG0YQVpOIYUlJro0DOfJQa04eCqbwe8vZ9neU/y04SiFJRrfrDpc7qSA3s2i2Jeazd4U9QXqD81LmkTy/QM9efKnLczeepLtxzMJ8vXijREduaZTnM3jrX943tw9nsJiEx8s2sfTg1tZStc3dqtcWU6cm0eDoAkTJvDss89y6623AtChQweOHDnClClTGDlyJLGx6i+BlJQU6tWzRpgpKSl07twZgNjYWFJT7ffrKS4uJi0tzfL40vz8/PDzc8+mlFpABBRBBOo/QnSITSbIw+Uw/YOnX6u6eBkNXNq8DnVD/DiVVWDJJhw+nUNxicktzYOuoq8RFOLvTU5BMSYN0vMKLWUkgG9WHeZoWh7RIX58fHuXSv8FVB3omaC0nEJSzM3RIX7etNCDoDM5/Jx4jG9XH+azO7sRZ16JGiDAx4u8ohJmrE2yBEGLd6daAqBHrmjOygOn2ZSUTuKRswT5evHD6F4cOZPL/605wrWd4oiPDGRE1wYs2JlCsUnD22jgyUEtaRQVxF+P9GXcD5v4d99pHvlhIzGhCZYg6JImkSQ0jeKV69pzIDWbgmITDSICuNemtFQ/PIAbujRg5oaj7E/NJtTfm55Noyz7ovVuVsdSarujZyM+XrKfr1Yc4qr29TiZkceEn1VQ9sClTbi8lTVjHOjrTffzlCn9fbyoE+zH6ewCjqfn2WSCqj7NV88MVaRLwwiWPX05+1OyycxXgWZWfhHeRoNl5pkjnhvahsW7UykoNnF7z4blzkC8EKUDL12DiEC+riAL4WpBft68f2sXTCat0tkno9HA/Zc2xdfbyIt/7GDK37sIMAeL9/VRM6Ca1g3m/kub8unSAzzzi3VWY0SgD6Mva4a30cDu5Cw6Ngjjzl6NSDxyltu+XEOJSaNzfDiDzZktP28vPry1Cy2iQ9h2PJ3nhrY570bLd/ZqxB09G150vZrVgUd/0+fm5mI02n+4enl5YTKp0kWTJk2IjY1l0aJFlqAnMzOTtWvXMmbMGAASEhJIT08nMTGRbt26AbB48WJMJhM9e/Z035upgHdQJGRCuCELHy8D4YE+xIapv5o8vVaQJQgyz3zy9jIyomsDPlt2gBbRwRxJy6Ww2MTx9Dy72Ts1jd4PFBHoi7fRwNncIs7mFFmCoLScQj5evB9QKemaFACB9X0VmzT2mBskI4N9aWzuDVh3KI2V+8+QV1TC/B3J3NOnCWnmfombuzfgu7VJbDhylqNpucRHBlqmjw/vHMdTg1sxOr8p901bz57kLD6/qxsdG4TTsUG43V+u/VtHExHow9ncIm7v2dDy8xIe6Mv0ey/h8Zmb+WvLCR7+fiOpWQX4eBno2jACg8FQpmejtDGXN+O3zcfx9TLyzX2X0KlBOG/O2830VYftHntXQiM+X36A9YfPmmcf7SQ9t4gO9cOYMLhsGaky6kcEcDq7gAOnsjlt/p41CC8/G+Ms0SH+dgH6hYiPDOTtmzqxav9pJpZTSruYVTYAsnVnz0bM257MqgNnyC8yERfmzxCbHphx/Zvz97aTHD6TS9eG4VzVPpZbL2lYbjP+JU0ief369nz57yFeGd7eLoAxGg08NrCFQ2OTAMg1PPrb/pprruG1116jYcOGtGvXjk2bNvHuu+9y3333Aeof/fHHH+fVV1+lRYsWlinycXFxXHfddQC0adOGq666igceeIDPPvuMoqIiHnnkEW699VaPzgzTBYTVhZMQbsihbpAfBoOBuGqQCTpyJodDp3PwNhrobbPdw2MDWtCxQRiXtqjDiE9XsTclm0Oncy6KICgswMcaBNk0Ry/dk0pWQTEtooMZ0bXmpZiNRgN1gv1Izsy3BEFRQb40Nv+bLdxlzZSmlmqebl0vlBbRwexOzmL/qWziIwMts6D0x4f6+zDroQQKik0VllJ8vY28PLw983ck80SpRR+9jAZeva49Gw6nWX7mOzUIr3RZpnGdIOY+eikBvl6WPp2JQ9vw9FWt7Xp/YkL9ubpjHL9tOs7dX68lv8hERKAPH9/exa706Yj64f5sOao2pQUI8vUiNKBmBcnXdorj2k6e/11YExiNBt4c0ZGr3l9OTmEJI3s3tsuCB/p6M/exSykoMlVqOYhbejTklh6OL4Uh3MejNY6PPvqIG2+8kYcffpg2bdrw1FNP8eCDD/LKK69Yznn66acZN24co0ePpkePHmRnZzNv3jzLGkEA33//Pa1bt2bAgAEMHTqUvn378sUXX3jiLZURFqmauiPIoq55tcx64QEYDerD2XYBNXfSewS6NYqwW6sowNeLoR3qEeLvY/kQrK5bLxQUq8xG6Z6f0myDIP0Xl+00+WTzPm4dGoRV6a/H6iA6VJXE9KmykUF+NK5TNmOhL6iol8NsZ/8cM+8zpq8nUj/Cvs/jfEHLtZ3i+Pj2ruV+OIQF+PDmiI6W2z2bOjZjrnl0sCUA0pU3o0UvXeQXmfDxMvD5Xd0vKICPM2dt9RJeXHiA/EV+kYuPDOSLu7vz4GVNGWlTmtUF+npXej0sUf15NAgKCQnh/fff58iRI+Tl5XHgwAFeffVVfH2tP2AGg4HJkyeTnJxMfn4+CxcupGVL+780IyMjmTFjBllZWWRkZPD1118THHzuGqu7+IepUlO4IdvSuxHs521pFnxy1pYqrfh6oWz7gSqiTwN1x6rDVfFz4jFG/18iU5fsP+d5+irYYQE+RJh7Mmz3D0s1BwbOKkF4Qt1g9bNVXiYIVD8UWBdU1DNBUUG+xEeqD3o9+NF/HksHHRfqspZ1GXtFM0L9vbm2U9XWXTqfDuYsJsDr13ewLCZXVXrDrz61vapN0aJm6dO8DhOHtqlRU71F1dTcbteaIkCtxRGONQgCmDi0NZ0ahJGeW8QjMzZSdI7l8p2toLiEVeYdzPudYyXkxuYgqPQmktXFcfOH9rG0cweRGXlqcbjQAG/L2iu25TA9MIguZ4ppTaFngvQAJjLYl8ggX5rWCSLI14tnrlL9IKmlttaIDPIl3pwJOno2124quLODIIAJg1uz5aVBtIp13SKhn9/VjWUTLucmJyzUpmfD9FWfJQgS4uIiQZCrBai/RCMM2Xar5/p5e/Hx7V0J9fdmU1I6v9gsyOVK+UUlTJm7m9zCEuqG+NG2nDU0dNZMUPUMgnLMK9/aLoRYHr1cFhrgY1n0z7YcpgcGMdVwc7/K0jNBuqggXwwGA78+3JtFT15uWbAxJSufguISss3fu6ggPxqYP+iPpuWRllNoWQG63gXMgjoXV5eTAn29ndbDVjoQrO+i74kQwjMkCHK1QJUJahZcyB297Bvk4iMDGddfzRD4euUhl+8kfexsLsM/Xsn0VYcBNW34XB9IehB01DxLrLrJqmQQZNsTFF5eOczcLKxnU2qiuqUCOD3YCw/0JTbMnxjze0vPLbLMSvQ2GggN8LasPXPsbK4lkxQd4oeft5QCSmd+JBMkxMVFgiBXM2eC/Isyyv1QublHPIG+XuxNyWbF/tMuHcq7C/ayJyWLqCBfvry7+3lXDI0O8SPQ1wuTpkolusz8IlbsO01eoWd3Ya5sJsiuMTrQPhOkadrFUQ4rNfbIUo2bYQE+lhlSu06q/pYIc7ZIzwSdzS2y9BTZNkXXZvrmnToJgoS4uEgQ5GrmniCKcqGo7JT4sAAfy6qlX9ts4uhsmqax0hxkvX9rZ64stUFjeQwGg6WscPh0DsfT83jypy1c8tpC7vxqLW/9s9tl462MnAIVhDk0O6xUT1BmfrGl/FOjG6NDSpfD7G8bDAZLoLQ7OdN8jvpehPhbM2RrzbOgXNEPVBMZDAa7xRHl+yLExUWCIFfzDwOD+S/JvLPlnnJP78YYDLBkz6lyl2B3hkOnc0jJLMDXy+jQhp5NzNOsD53OYdT09fyy8ZglaJi/I8XlJbxz0ftaMs0bMlYkM6/s7LB084yxU/oKy/7eBPjW3PJPmUxQcNkpvHrPk74Yom22SG+OXntINczLh72Vnv0xGKxbaQghLg4SBLmawWDNBuWllXtK4zpBDGitMjPvLdzrkmGsPqg+3Do3rPwidWBdMG/6qsPsTs4ixM+bGff3xNfbyPH0PA6c8lzTtB4EmTTILiyu8Dw9ExTqb10nKM2cCbJOj6+5pTCAOuU0RpdmzQTpawlZz7FtjgYph9nSA8KYEH98avD2MUKIsuR/tDtYgqDyM0EAjw9sgdEAc7aeZOFO5y+guNo8Jd52dejK0KfJ62vI3Nu3Cb2b1+ESczbp332nKnysq+k9QWDN9pSnvHKYvtGopSm6BpfCQO1zpe9+HujrVW6gq2eCjpjXfbINlEpvzCmZICv9e3Ehe4YJIaonCYLcIdBcfsotPxME0L5+GA9c2hSAF37fft4+F0domsaag+q1ExzcQFGfIQaqZDSqr1qRV1+QbvlezwVB2TZBUEXN0UUlJnLNDdy2s8M0TQVOlqboGjwzTKdneko3RVvuDy3dPG29HV8q8yOZIKtO8eF2l0KIi4cEQe5gniFWUTlM9/jAljSOCiQ5M5+RX6/j/9YcITXzwvcX25+azensAvy8jXRuGO7QY21XHR7Vt4kl23BpC7XI4pqDaRQUu3+WmKZpdpmgioIg2wxRaIAPPl5Gy+rJabmFlm0kavIaQTq9Obq8UhiUzXbZ9g3pW2foJBNkdVnLuiyfcAXPD23j6aEIIZxMgiB3qEQmCNS+XW+M6IiPl4FNSen85/ftDHh32XmngJ+Pvjp098YRDq/9UifYly4Nw2lSJ4j7zFkggDb1QqgT7EdeUQmJhysu87lKXlEJtr3QFZXD9O9diJ+3Za8pa1kox6YcdvFngmJCK+4b0rfOAAj197bbT05Aw6hAu400hRAXB/lf7Q5+5lWZC7LOe2qvplHMe/wynr6qFXWCfcnKL7bs82UrNSvfLhNSEU3TWLJH7SLuaCkM1BThX8f0ZsETlxFq88FoMBi4TC+J7XPt+kblyS713jPzyv9eWJqiA6xj79QgHICNR9ItmbbSU8xromhzcBcZVP57KZMJCio/EyRr4QghagsJgtzBx/zhU1y50lazusE8fHlzRnRrAFBmp/mV+0/T940l9H5jMT+sS6pweviZ7ALum76epXvMm6W2jK7S8A0GQ7l/BV9m3nds/s5kt+59BtY1gnQVZcvKC4K6NgoHYGPSWU5dJI3RAIPaxtAoKpChHWLLvf9cmSB/Hy/LDLMG0g8khKglvD09gFrBx/xXdpFju7EPbBPD58sOsnTPKYpLTHh7GdmfmsVD3yVSWGKiMM/ExF+38UviMT69sxt1Q/xIzcrnw0X72HI0gz0pWRQWm/D1NvLi1W3p0CDMqW/r8lZ1CQvw4eCpHKYu2c/jA1s69fnPJTvfPvNzviAoLMD6o96tkZqtt+VoOnr4WDpAqIm6N45k2YQrKrxfXzVa3wKldNksPjKA09kF0g8khKg1JBPkDt7mLEM5K0afS9eGEYQH+pCRV0TikbOcyS7g3unrycovpnujCF4Y1oYgXy82HDnLTZ+tYt72ZK79aCXfrUli2/EMCotNtIoJ4Y+xfbizVyOnv63wQF8mD28HwEeL97P1WLrTX6MiZcphFcym06f2237gt4gOIdjPm5zCEsvMseiLoDH6fGxXjTYY1L+fLX2D38Y2MwKFEOJiJpkgd/Ax/2XtYCbIy2jgilbR/LbpOPN3pvDh4n0cTcujYWQgn9/VjahgPwa0ieGur9Zy+EwuD32XCECzukE8NagVbeNCiY8IxGh03a7d13aKY/7OFOZsPcn4n7Yw77FL3dJAWrofqqJM0JytJwHo27yu5ZiX0UDn+HDLXm2Bvl4E+9WO/woxof4cO5tHRKCvpVFc9/jAFrSMCeYm8zYuQghxsZNMkDvoQVAle4JsDWij+nimrTzEyv1nCPDx4n8juxNl7t9oUieIX8b0plVMCABXtKrLb2P7MKRDPRpFBbk0AAKVXXh1eHtC/LzZn5pt2ZzT1XIKzx8E7UvJYufJTHy8DAxpb98n09VcEoOLY2ZYZellv/JmkDWICGT0Zc1qTUAohBDy284dLJmgPIcfelnLungbDRSbm5/fvLEjLc0Bjy4m1J/fx/Zhx4kMujSMKPMXvqtFBPnSMjaExCNnOZKW4/Teo/JkmXuCvIwGSkxauVPk/9xyAoB+LetatsvQdbVZL6k2lMJ0egN4RdPohRCiNpFMkDt4Vz0ICvX3oU9zNRX9nt6NubZTXLnnBfh60b1xpNsDIF1D87YLSWmOlfyqSi+HxZoDmNKZIE3T+GOzCoKu7Vy/zOO7xNfWTJD6ftUpZ4NVIYSobSQT5A4XkAkCeOvGjiQeOcugtjFOHJRzWYKgM+4NguLC/TmenkdGqXWCNh1NJyktl0BfL65sU/b7FhboQ4voYPalZl8U0+Mr6+qO9Vh76Ax39nR+o7wQQtQ0EgS5g6UnqGpBUEyoP0M71HPigJyvUZQKgo64KQjKsgRBAcBZMvOK0DQNg0Flwv40Z4EGt4slwLf8VbL7NK/DvtRsmkXXntlQ8ZGBTL/3Ek8PQwghqgUJgtzhAjNBNYEeBLm7HKavblxYYqKg2IS/jxfFJSZmb9VLYeWXDwGeHNSSy1rWsZs5JoQQovaQniB3uICeoJoi3lwOO5GRZ1mMD1RvzlcrDvH3tpNOfT19xejoED9LH5TeF7TqwBlOZxcSGeRLX3M/VXlC/H3o3zoGX2/5byCEELWR/PZ3h1qQCaob7EegrxeaBsfOWrNBK/ef4ZXZO3n2121OfT19scRgP29CzbvC60GQ3hA9rEM9fGTTSyGEEBWQTwh30IOgkgIwuXePLXcxGAyW5ugjNiWxGeuOACpAKalgj7Oq0IOgEH9vwsz7gmXmFZFfVMI/O5IBGH6OUpgQQgghQZA7+NjsxVTF5uiaIL7UDLHUrHzm77Bu/ppbeP5d7ytL7wkK8vO2bI6akVfEol2pZBcUUz88gK4NI871FEIIIWo5CYLcwdsmCHJw/7CapFGptYJmbThmWeQRsOzT5QzZNkFQmE0Q9Mfm44BqiHb1atlCCCFqNgmC3MFoBC/zgnwO7h9Wk9hOkzeZNH5Yl2R3f+lNTy+EpRxmkwk6fjaPpXtOAVIKE0IIcX4SBLmLj3lBvirsH1ZTNIxS6+0kpeWwZE8qx87mEervbdmiIbfAeZkgu3KYvwqCftt8nMISE83qBln2UhNCCCEqIkGQu/ioLMnFnAmy3Trjjb93A3DbJQ2JCFRBirMyQQXFJRSVqDKbbTns4KkcAIZ2qGdZNFEIIYSoiARB7uJtzgRdxD1B9cMDMBogv8jEvtRsIoN8efiK5gSZdyV3VmN0jk1GKcjXyxIE6a4qtWO8EEIIUR4JgtylJmWCqjiN39fbaFnBGeCpQa0IC/AhyFcFQTlOaozONu8gH+DjhbeX0S4IahQVSNt6oU55HSGEEBc3CYLcpab0BO2ZB2/Ew84/qvRwvTm6Tb1QbukRD0CQn9q7K8dJ5TDbmWEAoQHW3V+GtJdSmBBCiMqRIMhdakom6PC/UJgNh1dU6eE3dmtA07pBTLmhg2U7Cz1YcVYQlFOorxatgivbTNDQDlIKE0IIUTmygaq71JSeoELVXFzVYO36Lg24vksDu2OBejnMSbPDLFtmmLfLaBwVhNEATeoE0aF+mFNeQwghxMVPgiB3sewfVs0zQfr4nLjPWZCvytg4qzFa7wnSe43iIwP57eE+RIf6SSlMCCFEpUk5zF30IKi69wRZMkFODILM5TA9g3PgVDaT/txBalbVvhc5Npun6jrFh1MvLKCihwghhBBlSBDkLjVlJ3lLJsh5GSu9MVrfNuOrFYeYvuowX604VKXnK90YLYQQQlSFBEHu4l1TgqA8+0snKJ0JOp1VAMC6Q2lVer7SPUFCCCFEVXg0CGrcuDEGg6HM19ixYwHIz89n7NixREVFERwczIgRI0hJSbF7jqSkJIYNG0ZgYCDR0dFMmDCB4mLn7VHlNDUlE3SBjdHl0Xt39J6g9LwiALYdyyCvCmsHlVcOE0IIIRzl0SBo/fr1nDx50vK1YMECAG666SYAnnjiCf766y9mzZrFsmXLOHHiBDfccIPl8SUlJQwbNozCwkJWrVrFN998w/Tp03nxxRc98n7OydITVM2DIFc0RlsyQSrgyTQHQcUmjU1Hzzr8fPrz6MGVEEIIURUeDYLq1q1LbGys5Wv27Nk0a9aMfv36kZGRwVdffcW7775L//796datG9OmTWPVqlWsWbMGgPnz57Nz506+++47OnfuzJAhQ3jllVeYOnUqhYWFnnxrZdWYTFCu/aUTWGaHmTM46blFlvs2HHY8CMqRcpgQQggnqDY9QYWFhXz33Xfcd999GAwGEhMTKSoqYuDAgZZzWrduTcOGDVm9ejUAq1evpkOHDsTExFjOGTx4MJmZmezYsaPC1yooKCAzM9Puy+Us6wRV8yCoyAXlMMveYSqDk55nDVDXH3a8LygrXwVR+mKJQgghRFVUmyDo999/Jz09nXvuuQeA5ORkfH19CQ8PtzsvJiaG5ORkyzm2AZB+v35fRaZMmUJYWJjlKz4+3nlvpCKWFaOreRBU6IpymApWsguKyS8qIb/IujfZxiNnKS6p/F5lP65LYtneUwDUDw902hiFEELUPtUmCPrqq68YMmQIcXFxLn+tiRMnkpGRYfk6evSoy1+zRuwdVlIEJnOpqqQATM5Z4TnQpjFa7wcyGiDU35ucwhJ2nqxcJu7rFYd49tdtmDS47ZJ4ejeLcsr4hBBC1E7VIgg6cuQICxcu5P7777cci42NpbCwkPT0dLtzU1JSiI2NtZxTeraYfls/pzx+fn6EhobafblcTdg7TJ8ZpnNSNkgvhxWVaKSap8eHBfjQo3EkULmp8ln5Rbw+dxcAD/VrxuvXd8BolNWhhRBCVF21CIKmTZtGdHQ0w4YNsxzr1q0bPj4+LFq0yHJsz549JCUlkZCQAEBCQgLbtm0jNTXVcs6CBQsIDQ2lbdu27nsDlVET9g4rHaA5KwjytfbuHE9Xzxke6Et3cxBUmeboLUczKDZpNIgI4NkhrWV7DCGEEBfM49NrTCYT06ZNY+TIkXh7W4cTFhbGqFGjGD9+PJGRkYSGhjJu3DgSEhLo1asXAIMGDaJt27bcddddvPXWWyQnJ/PCCy8wduxY/Pz8PPWWylcTMkGlgx4njdXby4ift5GCYhPHz6rXCAvwoXl0MADJmecPDBOPqECpW6MIp4xJCCGE8HgQtHDhQpKSkrjvvvvK3Pfee+9hNBoZMWIEBQUFDB48mE8++cRyv5eXF7Nnz2bMmDEkJCQQFBTEyJEjmTx5sjvfQuX41IDZYS4qh4EqiRUUF3LCkgnysTRM61Pez2VjkgqCujaUIEgIIYRzeDwIGjRoEJqmlXufv78/U6dOZerUqRU+vlGjRsydO9dVw3MePRNUnRdLLFMOc+7+YWk5cCLDHAQF+BDi5wNYt8GoiMmkWYIgyQQJIYRwlmrRE1Qr1IR1glyZCTLPEDuerkpfYQE+lsUOs/PPHQQdOJVNVn4xAT5etI4NcdqYhBBC1G4SBLmLngkqKbSfel6QBSk7PTOm0lzUGA3WGWJ6OSws0Ney91d2YTEmU/nZQLD2A3WKD8PbS35khRBCOId8oriL3hME1uAieTtM7QWfJsCJzR4Zlp3SW2U4sRwWaJ4hdso8RT48wIcQcyZI0yC3qOI1ifQgSPqBhBBCOJMEQe7iHWC9XpwPe+fD14Mh85g6ln7EM+OyVeT6cpguPNAHP28j3ua1fs5VEpN+ICGEEK4gQZC7GI3gZZ62n3cWfhkFhdnW+4urwYavLswE6eUwXXigDwaDwWaH+fKDoPTcQg6cUsFZF8kECSGEcCIJgtxJ30k+dRcUZIJfKDS9XB0rKfDYsCxc2hNkv9lpWICaGRZ8niDo50SVKWtaN4jIIF+njUcIIYSQIMid9CDo1G51GdEIfNWCgRRXgyCozOww12WCwgJUQBNyjhliR87k8M78PQDc37ep08YihBBCQDVYJ6hWsc0EAUQ0BqPKiFBSDcphZVaMdmZPkH0mKDywdCaoyO5+TdN49pdt5BeZSGgaxW2XxDttLEIIIQRIJsi9vEtlgsIbgbe5T6g6ZIIsjdHmfblcMEVeZymHmTNBWTaZoILiEibP3snqg2fw9zHyxogOsleYEEIIp5NMkDvpmaDT+9RlRGO1ThBUj0yQ3hgdGAm5Z8rOFrsAtrPDgny98DGv91O6MfrgqWwe/n4ju5PV92XikDY0igpy2jiEEEIInWSC3EkPgkzm0k9E42qWCdKDoDrm287LBAXaNEaHB1obnEPMQZC+f9iLf+xgd3IWUUG+fHFXN0b2buy0MQghhBC2JAhyJ58A+9vhjcDLHBBUh9lhemN0kB4EuaYxWi+FgbUnKMsmEwTw6Z3dGNQu1mmvL4QQQpQmQZA7efvb3w5vaA2CqsM6QZZMUJT5tmsWS9SbooEy+4el5arvQ2xoqe+VEEII4WQSBLmTvn8YQEg9tZWGXg6rFpkgFwZBduWwspmg7IJicguLyS8yARAZLGsCCSGEcC0JgtzJdv+w8EbqslplglxYDvMtvxxmu05QWo76Hvh6GctMqRdCCCGcTYIgd7LNBEU0VpfVMhPk/MZo+54g3zLHswqsQVBkkK9MiRdCCOFyEgS5k21PUESpTFB1mCKvBz0uaYw+dzksxyYIipDtMYQQQriBBEHuZJsJ0sthlinyHg6CNM2ljdEBPl7oyZ3w8sphNkFQlARBQggh3ECCIHey7QnSy2Fe1aQcVpQHaOp6kPPLYQaDwdIXZJ8JUtdte4IkEySEEMIdJAhyJ9t1gvRymHc1aYy2LX1ZMkG5KkPkJIHmZufQgLJT5LMkEySEEMLNJAhyJ33vMKOPmiIP1ScTpC+U6B0AvuZtKjSTU3uVBrSJIS7Mn3b1wizHgs3ZocJiEymZ6nsQEShBkBBCCNeTvcPcSc8EhTcEo7lRuLplgnwC7HuXinKtfUsXaMoNHdA0zW7ml23D9NE0NQZZI0gIIYQ7SCbInWI7qgCjxZXWY9UmE2QOgnyDwMtHZavAqX1BQJmp795eRgJ8VCCUpAdBkgkSQgjhBpIJcqc6zeGZw/aZleqygaq+UKKeBfIJhIIMpwdB5Qn29yavqITkzHxArRMkhBBCuJpkgtytdGmpuqwTZMkE6UGQuXTnxLWCKhLiZx+LSxAkhBDCHSQI8rRqlwkyN0VbgiD3ZIJsSRAkhBDCHSQI8rTqkgnSgx1fm3IYWGeNuZDtvmJgv46QEEII4SoSBHmaZQPVatIY7VO6HObeTFBYgA8+XvJjKYQQwvXk08bT9HKYqQhMJs+NQy+H+ZYuh7m3J0hKYUIIIdxFgiBP87L50PdkSazQZp0gsGaE3JwJkiBICCGEu0gQ5Gm2s8U8GQQVebAcZpMJktWihRBCuIsEQZ7mVU2CoMLS5TA9E+T6cphtJkj2DRNCCOEuEgR5mtEIRnMQ4Mnm6OqSCZIgSAghhJtIEFQdVIetMyyZIPcvlmgbBEkmSAghhLs4HATdd999ZGVllTmek5PDfffd55RB1TrVYRNVSyaodDlMMkFCCCEuTg4HQd988w15eWU/GPPy8vj222+dMqhapzpkgsosluiZdYIkEySEEMJdKr2BamZmJpqmoWkaWVlZ+Pv7W+4rKSlh7ty5REdHu2SQF73qkAnKOqku/cPVpd4gXZQLR1apy+YDXfLSIX7WFaJlirwQQgh3qXQQFB4ejsFgwGAw0LJlyzL3GwwGXn75ZacOrtbwdCYo5wycPayu1+uoLvVM0PFEmP4HGIwwYT8EhDv95YP8vCzXJQgSQgjhLpUuhy1ZsoRFixahaRo///wzixcvtnytWLGCpKQknn/+eYcHcPz4ce68806ioqIICAigQ4cObNiwwXK/pmm8+OKL1KtXj4CAAAYOHMi+ffvsniMtLY077riD0NBQwsPDGTVqFNnZ2Q6PxWM8vYnq8UR1GdUcAiLUdb0nKP0IaCVqReuc0y55edtymPQECSGEcJdKZ4L69esHwKFDh2jYsCEGg+GCX/zs2bP06dOHK664gr///pu6deuyb98+IiIiLOe89dZbfPjhh3zzzTc0adKE//znPwwePJidO3daSnJ33HEHJ0+eZMGCBRQVFXHvvfcyevRoZsyYccFjdAtPb6KqB0H1u1uP6ZkgWwUZLnn5usF+XNk2hmA/b7smaSGEEMKVHP7E2bVrF0ePHqVv374ATJ06lS+//JK2bdsydepUuwDmfN58803i4+OZNm2a5ViTJk0s1zVN4/333+eFF15g+PDhAHz77bfExMTw+++/c+utt7Jr1y7mzZvH+vXr6d5dfYh/9NFHDB06lHfeeYe4uDhH36L7eTwTZM68NbAJgnyD1aXBCH4hkJ+hvlzAYDDw5d3dz3+iEEII4UQOzw6bMGECmZmZAGzbto3x48czdOhQDh06xPjx4x16rj///JPu3btz0003ER0dTZcuXfjyyy8t9x86dIjk5GQGDrQ25IaFhdGzZ09Wr14NwOrVqwkPD7cEQAADBw7EaDSydu1aR9+eZ3gyE6RpNpmgrtbjDROgy51w3WcQ3U4dc1EQJIQQQniCw5mgQ4cO0bZtWwB++eUXrrnmGl5//XU2btzI0KFDHXqugwcP8umnnzJ+/Hiee+451q9fz6OPPoqvry8jR44kOTkZgJiYGLvHxcTEWO5LTk4uMyvN29ubyMhIyzmlFRQUUFBgzbroQZ3H6EGQJzJBaQch76xqzo7pYD3u7QvDp6rrO35Tl/ke/j4JIYQQTuRwJsjX15fcXLWw3sKFCxk0aBAAkZGRDgcTJpOJrl278vrrr9OlSxdGjx7NAw88wGeffebosBwyZcoUwsLCLF/x8fEufb3z8vbg7DA9C1Svo3Wqfmn+YepSMkFCCCEuIg4HQX379mX8+PG88sorrFu3jmHDhgGwd+9eGjRo4NBz1atXz5JV0rVp04akpCQAYmNjAUhJSbE7JyUlxXJfbGwsqampdvcXFxeTlpZmOae0iRMnkpGRYfk6evSoQ+N2Oks5rMj9r33M3A9U/xw9Of6h6tI2CMrPhJJi141LCCGEcDGHg6CPP/4Yb29vfv75Zz799FPq168PwN9//81VV13l0HP16dOHPXv22B3bu3cvjRo1AlSTdGxsLIsWLbLcn5mZydq1a0lISAAgISGB9PR0EhMTLecsXrwYk8lEz549y31dPz8/QkND7b48ypON0eU1RZemZ4IKzJm+/QvhjXiY0gC+vgp2/O7SIQohhBCu4HBPUMOGDZk9e3aZ4++9957DL/7EE0/Qu3dvXn/9dW6++WbWrVvHF198wRdffAGoWUOPP/44r776Ki1atLBMkY+Li+O6664DVOboqquuspTRioqKeOSRR7j11ltrxsww8FxjdHEBJG9T1+t3q/i80uWwI6vNj8+DpNVwZj+0u85lwxRCCCFcoUqLspSUlPD777+za9cuANq1a8e1116Ll5fXeR5pr0ePHvz2229MnDiRyZMn06RJE95//33uuOMOyzlPP/00OTk5jB49mvT0dPr27cu8efPstu34/vvveeSRRxgwYABGo5ERI0bw4YcfVuWteYanMkEpO1TgFRAJEY0rPs+vVDks17xoYpc7YdP3kHNKLaQYVMelwxVCCCGcyeEgaP/+/QwdOpTjx4/TqlUrQDUax8fHM2fOHJo1a+bQ81199dVcffXVFd5vMBiYPHkykydPrvCcyMjImrMwYnksmSA3B0EnN6vLuM5wrsUvLZkgczks94y6rNcZDq9QW26c2iNBkBBCiBrF4Z6gRx99lGbNmnH06FE2btzIxo0bSUpKokmTJjz66KOuGOPFz5IJcnM57MQmdRnX5dznlS6H5aapy8AoqNtaXT+12/njE0IIIVzI4UzQsmXLWLNmDZGRkZZjUVFRvPHGG/Tp08epg6s1PLWBaqWDoNLlMHMmKDAK6raCvfNUJkgIIYSoQRzOBPn5+ZGVlVXmeHZ2Nr6+svlllejr87gzE1SUB6mqp4t6nc99rn+4uiwoVQ6TTJAQQogazOEg6Oqrr2b06NGsXbsWTdPQNI01a9bw0EMPce2117pijBc/T2SCUnaAqRgC60DYedZ3sp0iX1Jcqhym+sIkEySEEKKmcTgI+vDDD2nWrBkJCQn4+/vj7+9Pnz59aN68OR988IErxnjx8/bAthm2pbBzNUWDdXYYQMZR0ErU9cBIqNNSXc9OVttvCCGEEDWEwz1B4eHh/PHHH+zfv98yRb5NmzY0b97c6YOrNSyZIDeWw05sVpdxnc9/rrcveAeodYHSDqpjfqGqodvbD8LiVXB0ai80LH+BSiGEEKK6qdI6QQDNmzeXwMdZPLFOUGWbonX+oZBtEwQFWhvjqdvKHATtliBICCFEjeFwOWzEiBG8+eabZY6/9dZb3HTTTU4ZVK3j7hWjC3PhlLkputJBkLkvKO2QugyMst5naY6WviAhhBA1h8NB0PLlyxk6dGiZ40OGDGH58uVOGVSt4+XmnqCU7aCZICgaQupV7jF6EHS2vCBIb4724AyxnDOe2XtNCCFEjeVwEFTRVHgfHx8yMzOdMqhax9vNu8if2a8uY9qevylapzdHnzmgLqtTJujUHvigI7zfEQ4u88wYhBBC1DgOB0EdOnRg5syZZY7/+OOPtG3b1imDqnXcPUU+84S6DK1f+cdYMkGH1aVtEKTPEMs8Zt1a40KYTCqrY7tuUkkx/Doafri97Gus/BAKs9UMtW+Hw5LXL3wMQgghLnoON0b/5z//4YYbbuDAgQP0798fgEWLFvHDDz8wa9Yspw+wVnB3Y3RWsrqsbCkMrEGQHqjZBkEB4eq5sk6qklj8JVUf21+PQ+I0dd3bHwZOgl5jYOX7sNUcfP/6ANw6A4xe6r3ox1tepVavXvYmNOsPDXtVfRxCCCEueg5ngq655hp+//139u/fz8MPP8yTTz7JsWPHWLhwIdddd50LhlgLuLsxOuukugyJrfxj/EPtb9sGQQAx7dRlyo6qj2vbz9YACKA4H+Y9C4tegaVvqGMGowp0Fk5St9d9AaYiiO8Jt8+ETrep4xu/rfo4hBBC1AoOB0EAw4YNY+XKleTk5HD69GkWL15Mv379nD222sPdmSBLOSyu8o/RM0G60kFQtLkUWtUgKPMEzBmvrl82AZ5Ngt7j1O1/31GBTquhcMOX6tiqD+GrQbD+f+q2fm63e9Xljt+se50JIYQQ5ahSECSczGOZoCqUw3RlMkHt1WVVgiBTCfz+sApa4rpAv2fU6w2cbM3sBETA1e9Dhxvhyslg9IGja9VjIpuqAAlUKa5uayjKVZklIYQQogISBFUH7swEmUogO0VddyQI8isVBAXVsb+tl8NSd4CmOTamhS/BwSWqB+j6L8DLRx03GuHaj+Daj+HeeRASo473eQye2AGXPa02fx3ytuoPAjXbrevd6rqUxIQQQpyDBEHVgT47zFSkZka5UnaqWiPI4AXB0ZV/3PkyQXVagtFbZWYyj1f+eRO/gVUfqevDp0Ldlvb3e/lA17sgurX98ZAY6P88PLgMWgy0v6/jrSq7dnIznNxS+bEIIYSoVSQIqg68bdZdcnVJLMvcDxQcY82eVIZtEGQwlg2KvH2tU+UrUxI7exjmPGXtA7p8oip1OUNQFDS9Ql1PWms9Xlzg+iBTCCFEjVHlIKiwsJA9e/ZQXFzszPHUTnomCFy/VpA+PT7UgVIY2M8OC4goP4CyzBDbrtbyWfgyJG8re962n+HDrrD+SzAVQ+c7VB+QM+mlM705ujBHLab4f9c593WEEELUWA4HQbm5uYwaNYrAwEDatWtHUlISAOPGjeONN95w+gBrBS+bTFCxizNB+swwR/qBwD7zE1in/HMsM8R2qgULV7wL342A7FP25+2eDVoJNOgBI2erMlhlV66uLH2F6wJzEHTmgFpMMWm1c19HCCFEjeVwEDRx4kS2bNnC0qVL8ff3txwfOHBguStJi0owGtVsJ3BDJqgKM8OgVBAUVf45+gyxI6tgw9fqenYK/D7GvgyVcUxd9n4Umlzq/ADIdrx6Jki/LCmUPcaEEEIAVQiCfv/9dz7++GP69u2LwebDq127dhw4cMCpg6tV3DVDLNMcBDlaDvMJVM3UAIGR5Z+jl8OyTqhgLrqtmvG1fwGs+cR6nh4EhTVwbAyOsARB5i02bNcMKshy3esKIYSoMRwOgk6dOkV0dNlZRTk5OXZBkXCQu9YKqmomyGCwBhYVZYJC48A/3Hp76Nsw+DV1fdlbaup8caG1Lyks3rExOMJSDisvCJKNfoUQQlQhCOrevTtz5syx3NYDn//9738kJCQ4b2S1TXUPguD8QZDBYM0GNbkMGveFLner2WQFGao0lnUC0FQzeOm1hpxJb+QuXQ4DyQQJIYQAqrCB6uuvv86QIUPYuXMnxcXFfPDBB+zcuZNVq1axbNkyV4yxdtCnybu8MVovhzmwZYZODyzOFbz0GKUyLYOnqNvevirjk35ENSdjXkgxrIFreoEsY5VymBBCiHNzOBPUt29fNm/eTHFxMR06dGD+/PlER0ezevVqunXr5oox1g76NHlXNkYX5lhnSzmyeaouqK66DI6p+Jz2I+ChFRDb3nosqpm6TDvonn4gKFsOsy2B5Us5TAghRBUyQQDNmjXjyy+/dPZYajd3NEbrvTg+QdYgwRFXPKdmgOn7dFVWZFM4sBjSDoBvkDrmyn4gsCmHSSZICCFE+RzOBA0cOJDp06eTmSl/TTuVO3qCLLvH16taKap+N7jyZfANdOxxkR7MBBXnqRKjNEYLIYQoxeEgqF27dkycOJHY2Fhuuukm/vjjD4qKilwxttrFLZmgC2iKvhCRTdXlGQ8EQaCCHskECSGEKMXhIOiDDz7g+PHj/P777wQFBXH33XcTExPD6NGjpTH6QrgjE+SpIMi2Jyj9qLru6iDIyxt8g9X1/AzIT7feJ0GQEEIIqrh3mNFoZNCgQUyfPp2UlBQ+//xz1q1bR//+/Z09vtrDnT1Bji6UeKHCG6lp8kU5cGafOubqniCwZoPyM6QcJoQQoowL2kU+OTmZzz77jDfffJOtW7fSo0cPZ42r9rFkglwYBOWlq8uAClZ8dhV9mjyAZt4+I6y+619XnyZfUTlsx+/wXnv7neaFEELUGg4HQZmZmUybNo0rr7yS+Ph4Pv30U6699lr27dvHmjVrXDHG2sGSCXJhOawwW13qM7TcSS+JgdqA1SfA9a+pzxDLS7efFq8HQTv/gIyjsGWG68cihBCi2nF4inxMTAwRERHccsstTJkyhe7du7tiXLWPW9YJ0oOgYNe9RkX0afLg+n4gnV4OyzyOZZFGsAZBuWfUpWSChBCiVnI4CPrzzz8ZMGAARuMFVdJEae5YMbowR136eSIIsskEuSsI0jNBejO2Tu8J0oOgU7sg7ywERLhnXEIIIaoFhyOZK6+8UgIgV3BHJqjAg+UwfZo8uDEIMvcEZZQKgvTSWM5p67FjG9wzJiGEENVGpTJBXbt2ZdGiRURERNClS5dz7ha/ceNGpw2uVrFkgtxRDgtx3WtUJMoDmSC9HJaeZD5gADRVDtM0ayYIIGkNtLjSPeMSQghRLVQqCBo+fDh+fn6W6+cKgkQVWTJBLlx4Ui+HeSITpE+T10weKIeZg6DgGMhOVkFQfgaYbL7XR6UvSAghaptKBUEvvfSS5fqkSZNcNZbazduNjdGe6Any9oU6LeHUbnXpDpad5NPVZVgDFQSVFFgXjtQd26ACUC8f94xNCCGExznc3NO0aVPOnDlT5nh6ejpNmzYt5xEVmzRpEgaDwe6rdevWlvvz8/MZO3YsUVFRBAcHM2LECFJSUuyeIykpiWHDhhEYGEh0dDQTJkyguLjY0bflefqHr6sao0uKoThfXffE7DCAm6bDLd9BTDv3vJ5fmP1t27WJ0g6py/CG4B+u9hhL3uqecQkhhKgWHA6CDh8+TElJSZnjBQUFHDt2zOEBtGvXjpMnT1q+VqxYYbnviSee4K+//mLWrFksW7aMEydOcMMNN1juLykpYdiwYRQWFrJq1Sq++eYbpk+fzosvvujwODzO1Y3RehYIPFMOA4huA22ucd/r+Yfa3w6MAh/z5q9nD6vLoLoQ31Ndl6ny51eQrRaZdGXZVggh3KTSU+T//PNPy/V//vmHsDDrX9klJSUsWrSIJk2aOD4Ab29iY2PLHM/IyOCrr75ixowZlu04pk2bRps2bVizZg29evVi/vz57Ny5k4ULFxITE0Pnzp155ZVXeOaZZ5g0aRK+vr4Oj8djXN0YrfcDGX2spbeLnV+pIMg/DPxCoCgXzpozQYFR0LAn7PsHNnwNjftCvY7uH2tN8e87sOI9uPRJGFAD/9gQQggblQ6CrrvuOgAMBgMjR460u8/Hx4fGjRvz3//+1+EB7Nu3j7i4OPz9/UlISGDKlCk0bNiQxMREioqKGDhwoOXc1q1b07BhQ1avXk2vXr1YvXo1HTp0ICYmxnLO4MGDGTNmDDt27KBLly7lvmZBQQEFBdZgIzOzGuwlZckEuagc5snVoj3FP6zsbb9QyE5Rm7mCWr26/Y2w8kO1r9kXl0PvR6Dfs+Ab6PYhV3t6tmzTd3D5c2qjWiGEqKEqXQ4zmUyYTCYaNmxIamqq5bbJZKKgoIA9e/Zw9dVXO/TiPXv2ZPr06cybN49PP/2UQ4cOcemll5KVlUVycjK+vr6Eh4fbPSYmJobkZLURaHJysl0ApN+v31eRKVOmEBYWZvmKj3fDZp7n4+oNVC1N0R6YHu8ppctheiYIrD1BQVEQ0QjGroO214FWAis/gE8T4OBSd462+tM0SNmhrmenwP6Fnh2PEEJcIId7gg4dOkSdOnWc8uJDhgzhpptuomPHjgwePJi5c+eSnp7OTz/95JTnr8jEiRPJyMiwfB09evT8D3I1ywaqrsoEeXB6vKdUVA4D67T5QPPPckgM3PwN3PYjhNZXPUPfXgcpO9012uov4ygU2GxEu/k7z41FCCGcoEq57JycHJYtW0ZSUhKFhfYf2o8++miVBxMeHk7Lli3Zv38/V155JYWFhaSnp9tlg1JSUiw9RLGxsaxbt87uOfTZY+X1Gen8/Pws6x5VG67OBBV4cN8wT/ENtq5NBPZBkL5GUFCpgL7VEGjUB768As7sV8FQTFvVCPz3M2pBxVZD3PYWqhU9C+QfrpYd2DMPcs6obJqt5G0w/wW1DUlkM1VWNJWoQD80Duq0gLjyS9VCCOFODgdBmzZtYujQoeTm5pKTk0NkZCSnT5+2TFG/kCAoOzubAwcOcNddd9GtWzd8fHxYtGgRI0aMAGDPnj0kJSWRkJAAQEJCAq+99hqpqalER0cDsGDBAkJDQ2nbtm2Vx+ERkglyPqNRBT355uyFf3jZ7FBgOVlN/1AIi1dBkL7Z6sGlsOEr2D0bWgxWz13bJG9Xly0Hq/WeTm6BbT9BrzHWczQN/nocjp9nG5Lbf1LPo8tNU0FmdjLcPgt8/J0+fCGEKM3h3+RPPPEE11xzDWfPniUgIIA1a9Zw5MgRunXrxjvvvOPQcz311FMsW7aMw4cPs2rVKq6//nq8vLy47bbbCAsLY9SoUYwfP54lS5aQmJjIvffeS0JCAr169QJg0KBBtG3blrvuuostW7bwzz//8MILLzB27Njql+k5H5f3BJk/zGtTJgjs1wryDyvbJ1Q6E2R5nPn7pH/f8s6qy+wUSN7i3DFWR6f2WPdY06WYg6CY9tDlLnV94cuw8f9U8AOwZ64KgLwDYOAk6H4fdL4Tut0DHW+xLpS5dab1eY9tgM/7qYDq0HJIWuXKdyaEEBYOZ4I2b97M559/jtFoxMvLi4KCApo2bcpbb73FyJEj7dbxOZ9jx45x2223cebMGerWrUvfvn1Zs2YNdevWBeC9997DaDQyYsQICgoKGDx4MJ988onl8V5eXsyePZsxY8aQkJBAUFAQI0eOZPLkyY6+Lc9z+TpBHtxB3pP8Q0FvY7Eth+kCo8o8BLBmjPRMUIFNQLD3n4u7nLN5Bvw+BnyCoONN0PtRtfebJQhqp0qGe/6GA4vgz0fgwGK4cjIsekWd02sM9H2i7HMf2wD/G6C+h0X5kHUCpg+zLuQJcDwRmvV37Xs8sQl2zYbLngKfANe+lhCi2nI4CPLx8bHsIh8dHU1SUhJt2rQhLCzM4QbjH3/88Zz3+/v7M3XqVKZOnVrhOY0aNWLu3LkOvW61ZFknyEXlME/uIO9JttPk/ULLBkEVZoLM51mCoCzrfXv/gcufdd4Yq5OcM/DPc+p6UQ4kToedf8KYlXDmgDoe20GVq+74GVa+D4tfhR2/ws4/1Ow6/zDoU0FZPK4rhMSp4OfQMtg3XwVA8T2h2QBY+jocSzz/OIsL1Ka3jS+tWmnynxfgyIpzj1UIcdFz+LdHly5dWL9+PQD9+vXjxRdf5Pvvv+fxxx+nffv2Th9greGuFaNrXTnMnNHx9lcf3LZBkJdfxd8P/Xh5QdCJjZCVUvYxF4OFL6rSX3Q7uPtP1diclwZ/jAU0tcJ2sOq/w2iES8fD/QuhYW8VAAH0eVw1RZfHaIQ25qU0EqfDpu/V9f4vQLMr1PXjG1R5LXUXvNtOlcoWTrKfqbdoMnx7rVq80VGaZs1q7fjN8ccLIS4aDgdBr7/+OvXq1QPgtddeIyIigjFjxnDq1Cm++OILpw+w1tAzQa7ajsDSGF3LgiC9B0jPCNk2RgfVAYOh/MdZMkHm4NE2CALYv8B5Y3SnojxY8ylkHC9735HVahFEgKvfhab9VIkLVLkLVD9QafW7wr1z1fICg16FhEfOPQZ965Q9c9WebfU6q4xObAcwekPOKTUdf+3nkHkMTm5Wq1RPGwKFuWAywbZZ6jnWfKrekyOyU6yb6p7YaF0zSghR6zgcBHXv3p0rrlB/sUVHRzNv3jwyMzNJTEykU6dOTh9greHlrsUSa1sQFGZ/aZsJqqgfyPY8vRdIbxLWn2fvPOeN0Z2WvQnznoU/x9kfT90NM+9U17vcBQ3V5ANaD7Pvf6po81uDQS0d0HucNaCvSMPeEBBpvd17nHq8T4A1yEpaC7vMW/X0HQ8h9VTgsns2HF2rAhlQWaot5y6rl5G6y/72zt8de7wQ4qJRC+f5VlP67DBTkfpL19lq4xR5sGZ+KsoEne9xpcthbYerywNLrFmimqIwFzZMU9cPLIazR9T1MwdUaSn3tMrKDH7N+hiDQZWqdLEdLnwcXt4quAK1FEHb66z3NeiuLld+ALlnVKB6xfNqdhmoTJUeHOkz/9Z84tj/mVO7zeMwB2tSEhOi1qpUY3SXLl0wVFQ2KGXjxo0XNKBay8vHer2kEIxOXieloJZOkS9TDqtsJqh0T5A5E9T4Mji8Qu09tvrjmtUgvfVHaxkIDTb9n5rB9d0IlVmJaQ93/VZ2z7VmA6D11Wr6epPLnDOW3uMgdSdcNsF+/7H63WD9/yBlm7rd5lp1f6fbYOkU1UydbL5v6Nsw9yk4vVdt4dFyUOVeW88EdbkTEr9R6x2t+xKOrVdlua53Oec9CiGqvUoFQfrmqcKFvGzWNSopcP5icbW1JyjWvCO8nsGwC4LOlQkyn1dYqifIPwz6/wd+vldtutr9PmujcHVmMqn+GYBGfdXMqE3fqSbos4dURuau3yEwsuxjDQa4+Vu18rZtsH4h6raCBxaXPV6/u/3t9uYlNyIaqQDs0HJVAvMNhrbXQvJWFYyu/rjyQZCeCWrUR60IfmCxCqZAZYVaDyv/+yCEuOhUKgh66aWXXD0O4WXTR+GKafK1cRd5UDOOxu+GYPNGu7ZBUOntHmxVNEXeL0RtnbHqI9VUu+xNGPZf54/b2Q4uVhkT3xAV0EztAVknVdYF4JoPILhuxY83egFerh9nVHNV5irIgKBoFajoOt+pgiBQq037BEDPB1Vwp2eIzleu0zTV/wQQ3QYuGa2CoLB4MBWr78m2Wep5hRAXPekJqi6MRjCa/8p2xTT52rpYIkBoPetaMpXNBJWZIp9pfbzBYJ01lThdlcaqu8Tp6rLrXSr463Sb9b4ud0HzAR4ZVhlGo5ptBqr/ymgTeLW5xtqr1eZadRneUGWEAFZbF1IF1M/8voVwaq91ReuskyrAMnipgKvVEHj2KDy2VTVggyoTnk9xQdkZg0KIGsfhIEhfKbqiL3EBXLl1Rm1dJ6g0Lx/wCVTXK9MYXZitSkmWcpj5eJNLoUk/lT2o7o21phI4aM6gdLhRXXa/T5Vgw+LtG6Grg37PqICn9IrTvoEw4n/Q71nrNHuwTsnfNguyklXAs+1n+Kg7fD9CZb3ebq76fvR+oKhm1v9v/qEq+Opwo/qeJG9TfULn8sOt8N82kHHMOe9ZCOERDq8Y/dtv9r/wi4qK2LRpE9988w0vv/yy0wZWK7lqE1VNq527yFfELwSKcivXEwSqb0bf1sH2eJtrVBlm/2K49EnXjNUZTm5R2Q+/MDX7C1QQMHaNOla6EdrTGiWor/K0HGy/8SqoGWXxPdXU+d8fVoFQqnnH+6C6KoDNPQ1/Pw0dblLH67Yu+9yBkWohx+2/wIav1V5nGceh3XX2vVDJ26zrJu1faJ25JoSocRwOgoYPH17m2I033ki7du2YOXMmo0aNcsrAaiVXZYKKC6yr+da2nqDyXPIAHF5pLbuUx9tPlSdNRWqLB52vTRDUfKC6PLpGrSNUenPW6kLvo2ncx768FNnUM+NxhYSxKgg6sEjd9glU5a3ej4DBCL89pLb20DdujW5T/vN0uVMFQYnTrSXE4xtgyJvWc/QFJUFt3SFBkBA1ltN6gnr16sWiRYuc9XS1k6syQXopDCQIAjUt++7frUFneQwGa/9UpjkI8gm0n84d2UQFEqZia6BxPlkp8Pll8Md5VlV2Jn1szpreXh21vlpl5hr0gKHvwPid0G+Cap729oOr3rCuKwTlZ4IAmlxuvU9fQmHtZ3BwmbpeXGANpEAFQUKIGsspQVBeXh4ffvgh9evXd8bT1V6uygTpQZBPoH0mQJybXvrKPG5/25aeDdq/UJVdfh+rSinlMZngt9GqPLXpO/cstlhcCEmr1fWLOQgyesEt36l9zC55oOzeZSExMNBmlmtFmSCjEe77Bx5JhAkHoNu96vgfYyE/A3bPUeXRoGiVYTp7SJXfhBA1ksPlsIiICLuFEzVNIysri8DAQL777rtzPFKcl6s2Ua2tq0VfKL05Ws8E+ZVT7mo+ENZ9AfsXqYUId/wGW2aojIQ+XTvvLPgEwaoP4eBS8wM1tcZNo97OG+/pfbDgRdWfpK+8fDzR2v9Ut4IP/tqi271wdJ3ar6xOy4rPCwhXX6D2Qju4RK0n9Flf6wzOrnfDvn9Uf1DSGtU35AwlRWpbk7iu0OUO5zynEKJCDgdB77//vt1to9FI3bp16dmzJxERFewcLSpH33PJ2esESVN01fiWKoeVlwlq3FeVMTOS1BeoRQXnPKVKbr89VHZvqsA6qlH3xCbnBkGLX1WbkiZvV03PvkE2pbBLrcsE1FZGI9zwuWOP8QuGG/4H398I6UnW451vV5khZwdB+xeptZt8g6HTrZK5FcLFHA6CRo4c6YpxCHBhJkiCoCqxlMPOEQT5BqlARs/w9H0C1n6hmqU/6aUyCLY63a56iZa8Bic2299nMsHev9WqySExjo0157Qq1YAKxpa9BQMnWWcxNb7UsecTVvE9YPwu9f3d8RvEdVaz6xr2gvVfWsuNznD4X3VZmK22FXHGXm1CiAo5HAQB5Ofns3XrVlJTUzGV2rjw2muvdcrAaiU9E1RS5Nznra07yF+oygRBAO1uUEFQh5tgwEvgHw4LX1IBkG8w3DpDfZjlp0N4Y+sMphOb7J9n2Zuw7A21/tDIPx0b69aZaiZbYJTaeHT1x+rD+ehadX/Tyx17PmHPNxA63qS+dA3N0/iTt6lsq6P/vzRNPTb3jPr3MRjUvnS6o2slCBLCxRwOgubNm8ddd93FmTNnytxnMBgoKSlxysBqJS9XNUZLT1CVlG6Mrmg9na53Q/wlUKeV+iDr9TDsnad2ab/lO2jQTZ2n70elr9VzZp91av2JTbD8bXX80DIVQEU0rtw4NQ02fquu939BlVR2z1Yfoj6BMOgVlbkQzhVWXy02mXEUlrwOUU0hN00FzXGdK546r2nw739V2SvrpDp249eqvyx5q/W8pLXQ435XvwshajWHg6Bx48Zx88038+KLLxIT42DKXpybJRPk5CDI0hMkQZBDSm+iWlEmyGCwn23k7Qv3zAEM5ffhBNe1fnie3KKaqH97yLyWkwHQYMtMuPyZyo3z2Aa1Kah3ALQfAS0Gq/6VsAZw1ZTKB1PCcY16qyzcmqn2xxOBuC5Qr1PZx2yeAYtfMd8w/3uv+1JlDTWTmnWmmaxZPHfLTVOz6/QJMEvfUKXbjjerpQiykyH9qHp/voGeGaMQTuJwEJSSksL48eMlAHIFLxc1Rlt6gir4EBflKx30VBQEled8Da31OpmDoM2wb74KYoKiVU/RPxPVDLN+T1s/iM5lkzkL1O46la3yD4OH/q38WEXVXf6sCl7yM9Sq4gERcGoPHFsHS9+E22bYn3/mAMydoK5f+qTat+2jbqp0ud7889X2OtV7lH5ETb8PiVXPv2+Byhg2H6g2BnaFjf8Hfz6iyrqXjlczDpdOUfft/VvtuaYvvNrlLhj+sWvGIYSbOBwE3XjjjSxdupRmzSS97nQyRb56uZAg6HziuqiS1abvVQAEaif3pv1U0/TZw+qD8Xyzx0wm2D1XXbfdFFW4R2RTuPpd+2On9sLUS2DPHJXpyzyhdrr39ocz+6EoRzWqX/G8CpZbXqXO3TdfPb7VUDi9F1K2q2xQVgr885zq+QLV79XlLjV9X5/Kn5eu1iyq17lygXN5ctNg/gvq+or31XpL+qrZUc1VRjk7GYzeaoHQ7b+YF6EMhsJcKMpTm/N6wuGVEBwNdVp45vVFjeVwEPTxxx9z00038e+//9KhQwd8fHzs7n/00UedNrhax1VT5KUxumpKz6ZzdhAEcMq8oWfnO6D1UHW97XWw+TtVNjlfEHRys5pu7xvi3On2ourqtlSbsW6bBT/cZu0p0wVEwPWfW7OF3e5RQZCucR9IWqWCoLVfqGBYK1FrG9VtDbv+Ujvdb/lR9aL5BKgVrU1FcNWb0Ouhyo81N01dBkaqJRby09XtggxVotv8vbo96DVoPkBtGBtaX818TDugxtLhJpg2RGWNRi9V79+ddvwOs0ZCSBw8sd01ywqYSmDNJ+r/beO+zn9+4TEOB0E//PAD8+fPx9/fn6VLl9otnGgwGCQIuhAunyIvmSCHlMkEOXFvMD0IAgg19+7oOt+mgqBtP6vG2LjOFT/PfvNMs6b97Df5FJ512QT176cHQJc8CDHtVMDabIBqqtY1H2DtEYtsBqFxakPYDV/DEfNssQ43w4gv1fUjq+Cvx+H0Hjiy0v51V74P3e9VpfXlb6veot7j7P/vm0wquNk6U81GMxihxSC1+CNAx1th648qKNJK1M9niytVcBHZRJ3T6VaVsdz6o9rm5+RmdXz5WzDif078RqIaySvKbqUdgj/HqetZJ1R/XMOezn19UBMP5r+g1vh6crf8X7uIOBwEPf/887z88ss8++yzGGv74mvOZskEuaoxWnqCHFI66HFmEBQYqaY/p+xQfRW2M88a9oZm/dUaPzNuVltBhDcs/3n2L1CX+vYdonqo20r1/Oz4Tc3Oaz2s4nONXtBjFCycBC0Hq2PxNh/kEY1h2H+ttxv1hrFrIe2gWleoIFv1CH03Qs022zZL9ScteU2dv/l7GPaeCrZMJarnZ8sP1ufTSlS/D0Db4aose2Ax5KSqY91Gls2udLxZPf/BZWpxTt22n+GypyufDSrMUX1PeekqMAyJtb//xGaVTYtoDMPeUYFk9ikVdBXmwIr3oCDTev7evysXBJUUVT6QKS6Ef80lz9zTajmMFldC4jfq9f1C1NIUYfXVEhjxl6hyp3w+1ggOB0GFhYXccsstEgC5giUT5OxymPQEVUnp8qEzy2EAd/4GeWnqA9OW0Qg3TYevh0DqDvj+Jhi1oOwu9Xln4dh6dV2CoOpnwH/UV2X0fgxiO6oFGEF96NdpqXrDRnxd9t/eYFDLHtgufdBrjNo2ZdlbavFMUJvGpifB9yNUv1BQHbXPncFLNXV3vEX9flj/P9WIfdUb4OMPPUerTJDBS/UflRbRWK2TlLRaBQbhjdTP8b75lcsGFeWpAGLlBypgA7W+1qObrEtJnN6nArvc0yrL8/llENNe9VmhWZ8rIAL6PKaCyD3z1CKh5/LraLWExa0/qNLj+Wz5wboaPKggs0EPlRmyDcBshTVUGd3Ot6vvlcmkgsqgus4r1xXlqX8f/Y9nUSUORzIjR45k5syZ5z9ROM7VG6hKEOQYVzZGg5oqXzoA0vmHwR2zIKSeapz+c5wqC9g6uFSVO+q2hvB4545NuJfRqDI1+v9Rg0Ft5Dpuo3WdqfPpdo/KVqYfUc3XjfqqHpleY9UfWCc3qwDIyxdu/lbNPoxoBDFtVXP3nb+oUhzAJaNV2e7yZyG0Xvmv1/Fm6/UBL6o1qkBlg1a8r/ZpKyku+7iTW1Tj+LI3VQDkG6yWd8hPV/vwgeo9+vY6FQDV66Sm5puKzWU3Tf3MN+ytmshvn6Xeu8FL9djZrtKenwkLX4bjG9Xt3DQVxORnwA+3wsmtnFNJEfz7jrre7gZ1uWu2Ct4KMtU47vgZrvsULn9OlS39wsyrtr8JH3SCT3rDm43gv63gTwfbRTRNBTulFeXDl/3hnebWVeFFlTicCSopKeGtt97in3/+oWPHjmUao999990KHinOS58i7/RMkDRGV4mrg6DzCauvFlv8+iq1/9j6/6kZO7p9C9WlZIEuToGR1qxIZfiHqX6glR+oDXuHf6wySFe9rkpzG6fDgSVq6nuz/ud/rrt+Pfc57UfAms9UINXuBhXItb5azXpc+JI6J6iuOq/bPWotrbx0+PFOFSSENlBja3OtKhv+fC+s/UwtNvrzKMg8prJhd/6qMlgHl6ngqOnl9j1VuoYJqodqzzxrc/i8Z1U5cM9ceHiNygBp5l0OCjLhuxvUHxu2PXqggo8jq1S2Kj1JLV8x/GM4sVEFWSvMn3OXTVClMVtFeSpQ2vydGnPqDut9m7+DDiPO//0H9Tq/PKCCxgH/UcGsXoHZ/J3aVgVUpvjq96FrORm7qkhaC/OeURM0+j7unOesxhwOgrZt20aXLuoHZvv27Xb3Gao6NVMoLssE6eUwCYIcUjroKV2ScIcG3eHKyWrtoH+eg/rdoH5X9TOyXw+CBrh/XKJ66vsE5JxRvT16EzOoqeuXPqm+nMU/DB5ZZ3/shi/UrLKja1WpLOeUCmzWf6Wa/4+sUgFQRGMYvcw6xb/tcIhooqb5Tx+qthPxDYHbf1IBEKjm/3NpdZUKgvb+rYKgA0uss9tO7ValY31/vZ5j1LnJ21RG5ZIHof/z1v/z8ybC2k+tzz1wksrSdbjJurJ7ZDNod33ZcfgEWLdYST+qAqeo5rBhmtprbs6TMGa1Kjva0jQ4uEQ1e+enw4oP1Cw9UKW3g0vVrEK/UPj3PXW8TivVIP/nIypr3OIC/yDa+Sf8+oDK0J3YpGYC2m4VcxFyOAhasmSJK8YhwHWZINlFvmpKN5J7qrG81xg1C2j3bJh1Dzy4XG27kJ2sZqs0lKnxwiwgAq6bev7zXMU3yJo9KClSgci6L1QD/9yn1HGjN4z4yhoAgeqT6fMYzH5cBSagGqFtA7nzaXmVChYOr1QZHH2NI58gVR5c+5l1NmXn21UW5+8Jar2jtZ+qLUtGzlbb2az9TJ3X7R6VgdEbvTvcbA2CLh1//v6e8HhrqXrAi+r/cNpB9f+3//P25y59Q+0daKvBJdDmGtWEvn+hCtjaXaeyZMGx8OAy9X3d9B2s+rBsELTrL5VlyzypGuATxqqAszxbZsJvDwKamoiRnqSCqzotzj1DtYaT7ubqxOWZIOkJcoiXt+pVALUHl1eV9hu+cAYDDJ+qmk/Tj8D/XQ+rPlL3Xfth2b8ohagOvHyg5SBVbhr0murZAbjiOZXhLK3TbRBs3omg/Y2qadsRdVqo5nJTkWqSPntYldxu/kbdv/0XKM5TTcuxHVR27MavVbnNJ0j9obH5e9XLg6ZKe9d8YD/TrW5LSHikauPzD1WN56DKaUdWW+/b8Zs1AGp+pcowXfkK3DsX+jwKDyxRmbL0I6rcCeq4TwD0e0Ytc3BoGaTutj5nxjH4+T71vpNWqezcT3fDj3dARqm1q1J2wF+Pqvfd7R54JFGNozgfZt5pbbSvDJMJDi1XC2jWAA4HQfn5+bz99tsMHTqU7t2707VrV7svcQFcMTvMZFJ/BYH7e1ouBvr3zNPfu4BwNWPMy1el1/VfVueaei1EdWAwQO9H4IHFcMP/oM8T5Z/n4w83fKkyL1e/W7WVr++ZrQKXJpepfqTrpqqeuTo2gUybq+2fu/kAuGKiuv7P87Dd3At1+bPlv8bg1+DGr6q2VlDb4SqAMhWrrG5WisrW/DZG3d9rLNz5s/q/3udR62vEtIX7F1mzvoF1oNu96np4Q9UgDtbGclDT+ksKVb/TjV+rUqjRW2WjPuwCfz+rtnjJTYOfRqqAp9kAtZyCt6+a4RfZTK1fNeseldk7tReWv6PKfBX5ZyJ8c4119fFqzqBppaecnNsdd9zB/PnzufHGG4mJiSnTB/TSSy85dYDukJmZSVhYGBkZGYSGeqDvQ7fzDxWpN0yA++Y55zkLsmBKA3X9+WT1l4OovA+7qPR1VAsYt8HTo1H9FnOfUj0GDy6X7J4QlbHqI+uH8j1zyq76XFIEn/ezNjG3uUZNSnCFgmxV1jq9R2WYi8wZk2YDVA/UuTLOxQVqyn79biqbpTu0XAUePkEwfqf6vf9hF5UVGzkbmlyqzkveDn8/XXaRTVArbj/0r7UHC1Rm6X8D1OSauK6qSVsrURm7238qWybbOx9mmHuI/ELhqb0u/8y50M9vh/P7s2fPZu7cufTpU4n1FYRjvFxQDtP7gQxGtXeRcEx1yQTpLnlALRgX1UICICEqq9NtKjMSEAHxvcre7+Wjsk9fmxer7FdBFsgZ/IJVgPXlFSq48A2BHvepRSbPV3L39lMZ4NIaXwrRbdWMsV8fUKVHU5E6rgdAALHtVRB4cKnqSzqeqIIwL1+V3bINgACiW6tm7Jl3mDPQqIUhs1Ng2lBVatRnx2Wnwh8PWx9bkAl7/ob2N6imcFMxdB9V7RaRdDgIql+/PiEh1eQD4WLj7YLGaEs/UEjVN1aszfRVoqtLEASyR5gQjgqqA49sUI3MFQUaDXvBLd+r35Ox7V07nrotYeSfagZW+xEqOLsQBoMqd/0yyroRL5Rf0jMY1Arjza5QM9KyU9Wx4Ojyn7vN1TDsXfW8CWPVops/3aUCqRm3qOAxvpd67ZxTKhhr1l9t9Lt1plrnad5E1Y8VHANtr72w9+pkDgdB//3vf3nmmWf47LPPaNSokSvGVHu5IhMkCyVemOqWCRJCVE1ldrhvc7Xrx6Gr3019OUuHG9XSA1tnwu65KgN0vs1eDQYIiTn/c/cYpb50t89SjdRbfoC/HgOjj8o8BZqbzQ1eKgjav1BljYrzVJ9Wazd+fyvJ4SCoe/fu5Ofn07RpUwIDA8sslpiWlua0wdU6linyLgiCZKHEqtGXFXDmvmFCCOEKDbqrr6Fvu/Z1vH3VKtlh8WqbFFORakAf/ok1qIrrojJdJzap36PXflztSmFQhSDotttu4/jx47z++uvlNkaLC2DZQNUV5TDJBFWJngHyxEKJQghRXRkMaq2jBj2gMEutGm4bD3S8RQVAAINeVSuLV0MOB0GrVq1i9erVdOrUyRXjqd0sU+Sd2RidpS5locSq0ZfTr9fZo8MQQohqqeWg8o93vEUtWBnbofxm7mrC4SCodevW5OWVs6GbuHAuzQRJEFQlXe9Sa/E4soeTEELUdoGRMHatp0dxXg4X6N544w2efPJJli5dypkzZ8jMzLT7qqo33ngDg8HA448/bjmWn5/P2LFjiYqKIjg4mBEjRpCSkmL3uKSkJIYNG0ZgYCDR0dFMmDCB4uJydi6uCVyRCZLG6AsnAZAQQlyUHM4EXXXVVQAMGGC/aaOmaRgMBkpKShwexPr16/n888/p2LGj3fEnnniCOXPmMGvWLMLCwnjkkUe44YYbWLlSLfRUUlLCsGHDiI2NZdWqVZw8eZK7774bHx8fXn/9dYfH4XH6thmmYrXSszOayPRMkDRGCyGEEHY8voFqdnY2d9xxB19++SWvvvqq5XhGRgZfffUVM2bMoH///gBMmzaNNm3asGbNGnr16sX8+fPZuXMnCxcuJCYmhs6dO/PKK6/wzDPPMGnSJHx9fZ06VpfzshlvSQEYnbDSpiUTJEGQEEIIYcvhIKhfv35OHcDYsWMZNmwYAwcOtAuCEhMTKSoqYuBA6664rVu3pmHDhqxevZpevXqxevVqOnToQEyMdZ2DwYMHM2bMGHbs2EGXLl3Kfc2CggIKCqwlpwsp4zmVngkCtVaQM5Yblx3khRBCiHI5HAQtX778nPdfdtlllX6uH3/8kY0bN7J+/foy9yUnJ+Pr60t4eLjd8ZiYGJKTky3n2AZA+v36fRWZMmUKL7/8cqXH6TZ2mSAnNUfLFHkhhBCiXA4HQZdffnmZY7ZrBVW2J+jo0aM89thjLFiwAH9/9+5pNXHiRMaPH2+5nZmZSXx8vFvHUC6DQQVCJYVODIKkMVoIIYQoj8Odt2fPnrX7Sk1NZd68efTo0YP58+ef/wnMEhMTSU1NpWvXrnh7e+Pt7c2yZcv48MMP8fb2JiYmhsLCQtLT0+0el5KSQmxsLACxsbFlZovpt/VzyuPn50doaKjdV7Xh7K0zLCtGy7YPQgghhC2HM0FhYWFljl155ZX4+voyfvx4EhMTK/U8AwYMYNu2bXbH7r33Xlq3bs0zzzxDfHw8Pj4+LFq0iBEjRgCwZ88ekpKSSEhIACAhIYHXXnuN1NRUoqPV5m8LFiwgNDSUtm3bOvrWqgdvXyhEymFCCCGEizkcBFUkJiaGPXv2VPr8kJAQ2re336k3KCiIqKgoy/FRo0Yxfvx4IiMjCQ0NZdy4cSQkJNCrVy8ABg0aRNu2bbnrrrt46623SE5O5oUXXmDs2LH4+fmVec0a4UIzQSYTHFmhdu71D5PGaCGEEKICDgdBW7dutbutaRonT57kjTfeoHPnzs4aFwDvvfceRqORESNGUFBQwODBg/nkk08s93t5eTF79mzGjBlDQkICQUFBjBw5ksmTJzt1HG6lrxpd1UzQnrkw8w7ocicMnyorRgshhBAVcDgI6ty5MwaDAU3T7I736tWLr7/++oIGs3TpUrvb/v7+TJ06lalTp1b4mEaNGjF37twLet1q5UIzQSe3qMvT+9RloXnvMFksUQghhLDjcBB06NAhu9tGo5G6deu6fYbXRUufJl/VrTPOHlaX2anqUnqChBBCiHI5HAQ1atTIFeMQugvdRFUPgnJOqefQy2oSBAkhhBB2Kj1FfvHixbRt27bc1ZUzMjJo164d//77r1MHVytd6CaqehBUmK0CIZ30BAkhhBB2Kh0Evf/++zzwwAPlrqkTFhbGgw8+yLvvvuvUwdVKF5IJKsyBnFTr7bPm0qWXH3j5XPjYhBBCiItIpYOgLVu2WHaQL8+gQYMqvUaQOIcLyQSdPWJ/O80cBElTtBBCCFFGpYOglJQUfHwqziZ4e3tz6tSpCu8XlWTJBFUlCDpc6rY5CJJ+ICGEEKKMSgdB9evXZ/v27RXev3XrVurVq+eUQdVqlkxQFcphpYMgPRPkK1tmCCGEEKVVOggaOnQo//nPf8jPzy9zX15eHi+99BJXX321UwdXK3lfwDpBZYKgg+pSMkFCCCFEGZWeIv/CCy/w66+/0rJlSx555BFatWoFwO7du5k6dSolJSU8//zzLhtorWFZJ6jI8cfqQVBAJOSlSTlMCCGEOIdKB0ExMTGsWrWKMWPGMHHiRMuK0QaDgcGDBzN16lRiYmJcNtBaw/tCGqMPq8v4S2DvPMjPULelMVoIIYQow6HFEvUtKs6ePcv+/fvRNI0WLVoQERHhqvHVPl5VbIw2mSDdPDusQQ8VBOlkjSAhhBCijCrtIh8REUGPHj2cPRYBNpkgBxujs1OgOB8MXhDXxf4+CYKEEEKIMirdGC3cpKobqOqlsLAGEBpnf5/0BAkhhBBlSBBU3ejrBDmaCdKDoIjGEBRtf5/0BAkhhBBlSBBU3VxoJiiiMQREqLKYTsphQgghRBkSBFU3+h5fjmaC9KboiEZgNEJQHet9Ug4TQgghypAgqLqp6mKJ+ZnqMiBSXdqWxCQTJIQQQpQhQVB1U9UNVIty1aVPoLoMrmu9T4IgIYQQogwJgqobywaqDpbDivLUpU+AugyyCYKkMVoIIYQoQ4Kg6sZZmSDbIEh6goQQQogyJAiqbpyVCQqWniAhhBDiXCQIqm68/dVlcb5jjytTDpMgSAghhDgXCYKqG710pZe3KkvKYUIIIYRDJAiqbnzMAUuho0FQ6XKYHgQZrIGREEIIISyqtIGqcCFfc8BSmA2aBgbD+R9jMkGxHgSZHx/RBHxD1D5iRol1hRBCiNIkCKpu9CBGK1GrRuuLJ56Lbf+QngnyD4VxieDj7/wxCiGEEBcBCYKqG9v+ncKcygVBeikMrEEQQEiM88YlhBBCXGSkTlLdePmAl3mafGWbo/XzvPzA6HXuc4UQQggBSBBUPeklsco2R5duihZCCCHEeUkQVB3pJbHC7MqdX3p6vBBCCCHOS4Kg6kgPZipdDpNMkBBCCOEoCYKqI19Hy2GSCRJCCCEcJUFQdaQvmFiUU7nzJRMkhBBCOEyCoOrI18FVo/UgyFcyQUIIIURlSRBUHVnKYZXNBEk5TAghhHCUBEHVkZTDhBBCCJeTIKg6qnJjtARBQgghRGVJEFQdVXmKvJTDhBBCiMqSIKg68g1Wlw73BEkmSAghhKgsjwZBn376KR07diQ0NJTQ0FASEhL4+++/Lffn5+czduxYoqKiCA4OZsSIEaSkpNg9R1JSEsOGDSMwMJDo6GgmTJhAcXGxu9+KczncGC2ZICGEEMJRHg2CGjRowBtvvEFiYiIbNmygf//+DB8+nB07dgDwxBNP8NdffzFr1iyWLVvGiRMnuOGGGyyPLykpYdiwYRQWFrJq1Sq++eYbpk+fzosvvuipt+QcsmK0EEII4XLennzxa665xu72a6+9xqeffsqaNWto0KABX331FTNmzKB///4ATJs2jTZt2rBmzRp69erF/Pnz2blzJwsXLiQmJobOnTvzyiuv8MwzzzBp0iR8fX098bYunGWdIJkiL4QQQrhKtekJKikp4ccffyQnJ4eEhAQSExMpKipi4MCBlnNat25Nw4YNWb16NQCrV6+mQ4cOxMTEWM4ZPHgwmZmZlmxSeQoKCsjMzLT7qlYkEySEEEK4nMeDoG3bthEcHIyfnx8PPfQQv/32G23btiU5ORlfX1/Cw8Ptzo+JiSE5ORmA5ORkuwBIv1+/ryJTpkwhLCzM8hUfH+/cN3WhHF4xWhqjhRBCCEd5PAhq1aoVmzdvZu3atYwZM4aRI0eyc+dOl77mxIkTycjIsHwdPXrUpa/nMN+qLpYo5TAhhBCisjzaEwTg6+tL8+bNAejWrRvr16/ngw8+4JZbbqGwsJD09HS7bFBKSgqxsbEAxMbGsm7dOrvn02eP6eeUx8/PDz8/Pye/EyfyqersMMkECSGEEJXl8UxQaSaTiYKCArp164aPjw+LFi2y3Ldnzx6SkpJISEgAICEhgW3btpGammo5Z8GCBYSGhtK2bVu3j91pqlwOk0yQEEIIUVkezQRNnDiRIUOG0LBhQ7KyspgxYwZLly7ln3/+ISwsjFGjRjF+/HgiIyMJDQ1l3LhxJCQk0KtXLwAGDRpE27Ztueuuu3jrrbdITk7mhRdeYOzYsdU703M+lsboHNA0MBjOfb5kgoQQQgiHeTQISk1N5e677+bkyZOEhYXRsWNH/vnnH6688koA3nvvPYxGIyNGjKCgoIDBgwfzySefWB7v5eXF7NmzGTNmDAkJCQQFBTFy5EgmT57sqbfkHPpiiZoJigvAx//c50tPkBBCCOEwg6ZpmqcH4WmZmZmEhYWRkZFBaGiop4cDJcXwSpS6/vQhCIw89/mT64CpCMbvgtA4149PCCGEqAYu9PO72vUECcDLG7zM5bzC7HOfW1KkAiCQcpgQQgjhAAmCqivL/mHnaY7WS2Eg5TAhhBDCARIEVVc+lVwrSJ8ZZjCCVw3dJkQIIYTwAAmCqqtKZ4JspsefbxaZEEIIISwkCKquLKtGV7IcJv1AQgghhEMkCKqu9HLY+RqjJQgSQgghqkSCoOqqKuUwIYQQQlSaBEHVlWXVaCmHCSGEEK4gQVB1Zdk/rJKzwyQTJIQQQjhEgqDqShqjhRBCCJeSIKi60jM7lc4ESRAkhBBCOEKCoOqq0uUw2TxVCCGEqAoJgqoraYwWQgghXEqCoOpKpsgLIYQQLiVBUHVV6b3DJBMkhBBCVIUEQdWVw1PkJQgSQgghHCFBUHVV6XKYNEYLIYQQVSFBUHUl5TAhhBDCpSQIqq6kMVoIIYRwKQmCqiuZIi+EEEK4lARB1ZVvsLoszAFNq/g8yQQJIYQQVSJBUHWll8PQrNme8kgmSAghhKgSCYKqK9vMzrlKYhIECSGEEFUiQVB1ZfSy2UQ1u/xzTCbIS1fX9fKZEEIIISpFgqDqTA9sCrLKv//ULijMUtPpI5u6b1xCCCHERUCCoOrML0RdVhQEHV6pLuMvAS8f94xJCCGEuEhIEFSdnS8IOrJCXTbu457xCCGEEBcRCYKqs3MFQZoGR1ap640kCBJCCCEc5e3pAYhz8AtVl+UFQaf3Qc4p8PaH+t3cOy4hhKiFSkpKKCoq8vQwahUfHx+8vLxc9vwSBFVn58oE6aWwBj3A2899YxJCiFpG0zSSk5NJT0/39FBqpfDwcGJjYzEYDE5/bgmCqjO/c8wO05uiG/V233iEEKIW0gOg6OhoAgMDXfJhLMrSNI3c3FxSU1MBqFevntNfQ4Kg6qyiTJD0AwkhhFuUlJRYAqCoqChPD6fWCQhQCwGnpqYSHR3t9NKYNEZXZxUFQelJkHUCjD6qHCaEEMIl9B6gwEDZn9FT9O+9K/qxJAiqziyN0Zn2xzOPq8vweJs9xoQQQriKlMA8x5XfewmCqjM9E1R624yc0+oysI57xyOEEEJcRCQIqs4qKoflnlGXQRIECSGEKMtgMJzza9KkSRw+fNjuWEhICO3atWPs2LHs27fP7vlWrFhBnz59iIqKIiAggNatW/Pee+956N05jzRGV2cV7R2mB0GBke4djxBCiBrh5MmTluszZ87kxRdfZM+ePZZjwcHBnD6tqgoLFy6kXbt25Obmsm3bNj744AM6derEX3/9xYABAwAICgrikUceoWPHjgQFBbFixQoefPBBgoKCGD16tHvfnBNJEFSdVZgJSlOXgTJTQQghRFmxsbGW62FhYRgMBrtjgCUIioqKstzXtGlTrrnmGgYMGMCoUaM4cOAAXl5edOnShS5dulge27hxY3799Vf+/fffGh0EebQcNmXKFHr06EFISAjR0dFcd911dpEqQH5+PmPHjiUqKorg4GBGjBhBSkqK3TlJSUkMGzaMwMBAoqOjmTBhAsXFxe58K65R0YrRlkyQBEFCCOFumqaRW1js9i9N09zy/oxGI4899hhHjhwhMTGx3HM2bdrEqlWr6Nevn1vG5CoezQQtW7aMsWPH0qNHD4qLi3nuuecYNGgQO3fuJCgoCIAnnniCOXPmMGvWLMLCwnjkkUe44YYbWLlSLRZYUlLCsGHDiI2NZdWqVZw8eZK7774bHx8fXn/9dU++vQtnmwnSNNA75CUIEkIIj8krKqHti/+4/XV3Th5MoK97PrZbt24NwOHDh7nkkkssxxs0aMCpU6coLi5m0qRJ3H///W4Zj6t4NAiaN2+e3e3p06cTHR1NYmIil112GRkZGXz11VfMmDGD/v37AzBt2jTatGnDmjVr6NWrF/Pnz2fnzp0sXLiQmJgYOnfuzCuvvMIzzzzDpEmT8PX19cRbcw49CEKDwhzrCtISBAkhhHAhPetUenr6v//+S3Z2NmvWrOHZZ5+lefPm3HbbbZ4YolNUq56gjIwMACIjVcNvYmIiRUVFDBw40HJO69atadiwIatXr6ZXr16sXr2aDh06EBMTYzln8ODBjBkzhh07dtjVMGscnwAweIFWorJBEgQJIYTHBfh4sXPyYI+8rrvs2rULgCZNmtgd12936NCBlJQUJk2aJEGQM5hMJh5//HH69OlD+/btAbVfi6+vL+Hh4XbnxsTEkJycbDnHNgDS79fvK09BQQEFBQWW25mZmeWe53EGgwp88jPMfUHmfVOkMVoIITzGYDC4rSzlCSaTiQ8//JAmTZqcM5FgMpnsPktromrzrzh27Fi2b9/OihUrXP5aU6ZM4eWXX3b56ziFX6hNEAQUF0Ch+bpMkRdCCHGBzpw5Q3JyMrm5uWzfvp3333+fdevWMWfOHMteXVOnTqVhw4aWXqHly5fzzjvv8Oijj3py6BesWgRBjzzyCLNnz2b58uU0aNDAcjw2NpbCwkLS09PtskEpKSmW6XyxsbGsW7fO7vn02WOlpwPqJk6cyPjx4y23MzMziY+Pd9bbcS5Lc7Q5W6VngQxe4BfmmTEJIYS4aOgtJ4GBgTRq1IgrrriCL774gubNm1vOMZlMTJw4kUOHDuHt7U2zZs148803efDBBz01bKfwaBCkaRrjxo3jt99+Y+nSpWVqj926dcPHx4dFixYxYsQIAPbs2UNSUhIJCQkAJCQk8Nprr1l2mAVYsGABoaGhtG3bttzX9fPzw8/Pz4XvzIlKrxVku1CiURb8FkIIcW733HMP99xzT5njjRs3rvS0+3HjxjFu3Dgnj8zzPBoEjR07lhkzZvDHH38QEhJi6eEJCwsjICCAsLAwRo0axfjx44mMjCQ0NJRx48aRkJBAr169ABg0aBBt27blrrvu4q233iI5OZkXXniBsWPH1pxA51xK7x8mTdFCCCGEU3g0CPr0008BuPzyy+2OT5s2zRK1vvfeexiNRkaMGEFBQQGDBw/mk08+sZzr5eXF7Nmz+f/27jwqqvP8A/h32IZNGBTZFBFFRRFRaURs1BgJQj3GNB41xBoxxkSKRqMxFtsEY5qCWjU5xlib49aTNpo0ak6FuEPccEFFxQUFUdII4sYWRECe3x/+uOUKCEbGmWG+n3M4zrz3nXuf575w7+Nd5sbExCA0NBQODg6YNGkSFi5c+LTS0K96R4JqH57KIoiIiOhJGPx0WFNsbW2xcuVKrFy5stE+Pj4+SE5ObsnQjIfy/LCHrgniRdFERERPhBeVGLuHH52hnA7jE+SJiIieBIsgY9fohdE8HUZERPQkWAQZOxZBREREesEiyNgpRRDvDiMiImpJLIKMHY8EERER6QWLIGPX2DdG8+4wIiKiJ8IiyNjVPRIkwiNBRERELYRFkLGrWwRVlQPVFQ/eswgiIqJHiI6OhkajgUajgbW1NXx9ffHee++hoqJC6ZOdnY3JkyejY8eO0Gq18PX1RVRUFNLT07F+/Xrl8439XLlyBQsWLFDeW1lZwdXVFUOGDMEnn3xS7ynzCxYsgL+/PxwcHODi4oKwsDAcOXLkaa8aBYsgY1e3CKo9CmSpBWwcDBcTERGZhIiICOTn5+Py5ctYvnw5Vq9ejfj4eABAeno6goODcfHiRaxevRrnzp3Dli1b4O/vjzlz5mD8+PHIz89XfkJDQzF16lRVW+3DxwMCApCfn4+8vDykpKRg7NixSEhIwKBBg1BaWqrE0717d3z22Wc4c+YMDhw4gM6dOyM8PBw3btwwyPoxiqfI0yPUFkH37wGlD56tBvt2gEZjuJiIiMgkaLVaeHh4AAC8vb0RFhaGXbt2ITExEdHR0ejWrRv2798PizoP5O7bty9mzpwJOzs72NnZKe02Njawt7dX5leXlZWV0u7l5YXAwEC88MILCAoKwqJFi/DnP/8ZAPDqq6+qPrds2TKsWbMGp0+fxvDhw1s8/6awCDJ2Nm3+9/rO1Qf/8lQYEZHhiDy4POFps7Z/ov8AZ2Zm4tChQ/Dx8UFGRgbOnj2Lf/3rX6oCqJZOp3uCQB/w9/dHZGQkNm/erBRBdVVWVuLvf/87nJ2dERQU9MTL+yVYBBk7SyvAyg6ovgvcyX3QxjvDiIgMp6oc+IvX01/u/GuPfSnEtm3b4OjoiOrqaty7dw8WFhb47LPPcOnSJQAPChV98vf3x86dO+vF9Morr6C8vByenp7YtWsXXF0N8ygoXhNkCmpPiV058OBfHgkiIqJmGDZsGDIyMnDkyBFMmjQJkydPxpgxY5r1APOWICLQPHT0qjamQ4cOISIiAuPGjUNhYeFTiedhPBJkCrRtgJ8LgdwfHrwPeMmg4RARmTVr+wdHZQyx3Mfk4OAAPz8/AMDatWsRFBSENWvWoH///gCACxcuoF+/fi0aZl3nz5+Hr69vgzH5+flh4MCB6NatG9asWYO4uDi9xdEYFkGmQFvnuqCgKKDXaMPFQkRk7jQak7xD18LCAvPnz8fs2bNx+fJl9OrVC0uXLsX48ePrXRdUVFT0xNcFXbhwAdu3b2+yuKmpqal3K/3TwtNhpqC2CHLpDPxmiUFDISIi0zV27FhYWlpi5cqVWLduHS5evIjBgwcjOTkZly9fxunTp/Hxxx9j9OjH+892dXU1CgoKcO3aNZw5cwYrVqzA0KFD0bdvX8ydOxcA8PPPP2P+/Pk4fPgwrl69iuPHj+P111/HTz/9hLFjx+oj3SbxSJAp6D0GKLsO/Ha1+qgQERHRY7CyssL06dOxePFixMTEID09HR9//DGmTp2KmzdvwtPTE4MGDcInn3zyWPM9e/YsPD09YWlpCWdnZ/Tq1QtxcXGIiYmBVqsFAFhaWuLChQvYsGEDbt68iXbt2uGZZ57B/v37ERAQoIdsm6aRp3V1lBErKSmBs7MziouL4eTkZOhwiIjISFRUVCA3Nxe+vr6wtbU1dDhm6VFj8KT7b54OIyIiIrPEIoiIiIjMEosgIiIiMkssgoiIiMgssQgiIiIis8QiiIiIqAm8kdpw9LnuWQQRERE1wtraGgBQXm6Ap8YTgP+t+9qxaEn8skQiIqJGWFpaQqfTKQ/4tLe3r/dAUNIPEUF5eTkKCwuh0+lgaWnZ4stgEURERPQIHh4eAGCwJ52bO51Op4xBS2MRRERE9AgajQaenp5wc3NDVVWVocMxK9bW1no5AlSLRRAREVEzWFpa6nWHTE8fL4wmIiIis8QiiIiIiMwSiyAiIiIyS7wmCP/7IqaSkhIDR0JERETNVbvf/qVfqMgiCEBpaSkAwNvb28CREBER0eMqLS2Fs7PzY39OI/wucNTU1ODatWto06ZNi34JVklJCby9vfHjjz/CycmpxeZrTFp7jq09P4A5tgatPT+AObYG+shPRFBaWgovLy9YWDz+FT48EgTAwsICHTt21Nv8nZycWuUvdF2tPcfWnh/AHFuD1p4fwBxbg5bO75ccAarFC6OJiIjILLEIIiIiIrPEIkiPtFot4uPjodVqDR2K3rT2HFt7fgBzbA1ae34Ac2wNjDE/XhhNREREZolHgoiIiMgssQgiIiIis8QiiIiIiMwSiyAiIiIySyyC9GjlypXo3LkzbG1tERISgqNHjxo6JCQkJOCZZ55BmzZt4ObmhpdeeglZWVmqPs899xw0Go3qZ9q0aao+eXl5GDlyJOzt7eHm5oa5c+eiurpa1Sc1NRX9+/eHVquFn58f1q9fXy8efayjBQsW1Ivf399fmV5RUYHY2Fi0a9cOjo6OGDNmDK5fv24y+XXu3LlefhqNBrGxsQBMc/z27duHUaNGwcvLCxqNBlu3blVNFxF88MEH8PT0hJ2dHcLCwnDp0iVVn9u3b2PChAlwcnKCTqfDlClTUFZWpupz+vRpDB48GLa2tvD29sbixYvrxfLNN9/A398ftra2CAwMRHJy8mPH8rg5VlVVYd68eQgMDISDgwO8vLzw2muv4dq1a6p5NDT2iYmJRpFjU2MYHR1dL/aIiAhVH1MeQwAN/l1qNBosWbJE6WPMY9ic/YMxbT+bE0uThPRi48aNYmNjI2vXrpWzZ8/K1KlTRafTyfXr1w0a14gRI2TdunWSmZkpGRkZ8pvf/EY6deokZWVlSp+hQ4fK1KlTJT8/X/kpLi5WpldXV0vv3r0lLCxMTp48KcnJyeLq6ipxcXFKn8uXL4u9vb3Mnj1bzp07JytWrBBLS0vZvn270kdf6yg+Pl4CAgJU8d+4cUOZPm3aNPH29pY9e/ZIenq6DBw4UAYNGmQy+RUWFqpy27VrlwCQlJQUETHN8UtOTpY//vGPsnnzZgEgW7ZsUU1PTEwUZ2dn2bp1q5w6dUpefPFF8fX1lbt37yp9IiIiJCgoSA4fPiz79+8XPz8/iYqKUqYXFxeLu7u7TJgwQTIzM+Wrr74SOzs7Wb16tdLn4MGDYmlpKYsXL5Zz587Jn/70J7G2tpYzZ848ViyPm2NRUZGEhYXJpk2b5MKFC5KWliYDBgyQ4OBg1Tx8fHxk4cKFqrGt+7dryBybGsNJkyZJRESEKvbbt2+r+pjyGIqIKrf8/HxZu3ataDQaycnJUfoY8xg2Z/9gTNvPpmJpDhZBejJgwACJjY1V3t+/f1+8vLwkISHBgFHVV1hYKADkhx9+UNqGDh0qM2fObPQzycnJYmFhIQUFBUrbqlWrxMnJSe7duyciIu+9954EBASoPjd+/HgZMWKE8l5f6yg+Pl6CgoIanFZUVCTW1tbyzTffKG3nz58XAJKWlmYS+T1s5syZ0rVrV6mpqRER0x+/h3cuNTU14uHhIUuWLFHaioqKRKvVyldffSUiIufOnRMAcuzYMaXP999/LxqNRn766ScREfn888/FxcVFyVFEZN68edKjRw/l/bhx42TkyJGqeEJCQuStt95qdiy/JMeGHD16VADI1atXlTYfHx9Zvnx5o58xlhwbK4JGjx7d6Gda4xiOHj1ann/+eVWbqYyhSP39gzFtP5sTS3PwdJgeVFZW4vjx4wgLC1PaLCwsEBYWhrS0NANGVl9xcTEAoG3btqr2f/7zn3B1dUXv3r0RFxeH8vJyZVpaWhoCAwPh7u6utI0YMQIlJSU4e/as0qdu/rV9avPX9zq6dOkSvLy80KVLF0yYMAF5eXkAgOPHj6Oqqkq1XH9/f3Tq1ElZrinkV6uyshJffvklXn/9ddXDf019/OrKzc1FQUGBalnOzs4ICQlRjZlOp8OvfvUrpU9YWBgsLCxw5MgRpc+QIUNgY2OjyikrKwt37txpVt7NiaWlFBcXQ6PRQKfTqdoTExPRrl079OvXD0uWLFGdZjD2HFNTU+Hm5oYePXogJiYGt27dUsXemsbw+vXrSEpKwpQpU+pNM5UxfHj/YEzbz+bE0hx8gKoe3Lx5E/fv31f9EgCAu7s7Lly4YKCo6qupqcGsWbPw61//Gr1791baX331Vfj4+MDLywunT5/GvHnzkJWVhc2bNwMACgoKGsytdtqj+pSUlODu3bu4c+eO3tZRSEgI1q9fjx49eiA/Px8ffvghBg8ejMzMTBQUFMDGxqbejsXd3b3J2I0lv7q2bt2KoqIiREdHK22mPn4Pq42poWXVjdfNzU013crKCm3btlX18fX1rTeP2mkuLi6N5l13Hk3F0hIqKiowb948REVFqR40+fbbb6N///5o27YtDh06hLi4OOTn52PZsmVGn2NERARefvll+Pr6IicnB/Pnz0dkZCTS0tJgaWnZ6sZww4YNaNOmDV5++WVVu6mMYUP7B2PafjYnluZgEWTGYmNjkZmZiQMHDqja33zzTeV1YGAgPD09MXz4cOTk5KBr165PO8zHFhkZqbzu06cPQkJC4OPjg6+//hp2dnYGjKzlrVmzBpGRkfDy8lLaTH38zF1VVRXGjRsHEcGqVatU02bPnq287tOnD2xsbPDWW28hISHBqB5F0JBXXnlFeR0YGIg+ffqga9euSE1NxfDhww0YmX6sXbsWEyZMgK2trardVMawsf1Da8PTYXrg6uoKS0vLelepX79+HR4eHgaKSm369OnYtm0bUlJS0LFjx0f2DQkJAQBkZ2cDADw8PBrMrXbao/o4OTnBzs7uqa4jnU6H7t27Izs7Gx4eHqisrERRUVGjyzWV/K5evYrdu3fjjTfeeGQ/Ux+/2vk9alkeHh4oLCxUTa+ursbt27dbZFzrTm8qlidRWwBdvXoVu3btUh0FakhISAiqq6tx5cqVR8ZfN3ZD51irS5cucHV1Vf1etoYxBID9+/cjKyuryb9NwDjHsLH9gzFtP5sTS3OwCNIDGxsbBAcHY8+ePUpbTU0N9uzZg9DQUANG9uC2yenTp2PLli3Yu3dvvcOuDcnIyAAAeHp6AgBCQ0Nx5swZ1QardoPdq1cvpU/d/Gv71Ob/NNdRWVkZcnJy4OnpieDgYFhbW6uWm5WVhby8PGW5ppLfunXr4ObmhpEjRz6yn6mPn6+vLzw8PFTLKikpwZEjR1RjVlRUhOPHjyt99u7di5qaGqUIDA0Nxb59+1BVVaXKqUePHnBxcWlW3s2J5ZeqLYAuXbqE3bt3o127dk1+JiMjAxYWFsppJGPPsa7//ve/uHXrlur30tTHsNaaNWsQHByMoKCgJvsa0xg2tX8wpu1nc2JplmZfQk2PZePGjaLVamX9+vVy7tw5efPNN0Wn06mumDeEmJgYcXZ2ltTUVNUtmuXl5SIikp2dLQsXLpT09HTJzc2V7777Trp06SJDhgxR5lF7C2R4eLhkZGTI9u3bpX379g3eAjl37lw5f/68rFy5ssFbIPWxjubMmSOpqamSm5srBw8elLCwMHF1dZXCwkIReXBbZadOnWTv3r2Snp4uoaGhEhoaajL5iTy4U6JTp04yb948Vbupjl9paamcPHlSTp48KQBk2bJlcvLkSeXOqMTERNHpdPLdd9/J6dOnZfTo0Q3eIt+vXz85cuSIHDhwQLp166a6vbqoqEjc3d1l4sSJkpmZKRs3bhR7e/t6tx5bWVnJX//6Vzl//rzEx8c3eOtxU7E8bo6VlZXy4osvSseOHSUjI0P1t1l7R82hQ4dk+fLlkpGRITk5OfLll19K+/bt5bXXXjOKHB+VX2lpqbz77ruSlpYmubm5snv3bunfv79069ZNKioqWsUY1iouLhZ7e3tZtWpVvc8b+xg2tX8QMa7tZ1OxNAeLID1asWKFdOrUSWxsbGTAgAFy+PBhQ4ckABr8WbdunYiI5OXlyZAhQ6Rt27ai1WrFz89P5s6dq/qeGRGRK1euSGRkpNjZ2Ymrq6vMmTNHqqqqVH1SUlKkb9++YmNjI126dFGWUZc+1tH48ePF09NTbGxspEOHDjJ+/HjJzs5Wpt+9e1d+//vfi4uLi9jb28tvf/tbyc/PN5n8RER27NghACQrK0vVbqrjl5KS0uDv5aRJk0TkwS2/77//vri7u4tWq5Xhw4fXy/3WrVsSFRUljo6O4uTkJJMnT5bS0lJVn1OnTsmzzz4rWq1WOnToIImJifVi+frrr6V79+5iY2MjAQEBkpSUpJrenFgeN8fc3NxG/zZrv//p+PHjEhISIs7OzmJrays9e/aUv/zlL6oiwpA5Piq/8vJyCQ8Pl/bt24u1tbX4+PjI1KlT6xXMpjyGtVavXi12dnZSVFRU7/PGPoZN7R9EjGv72ZxYmqL5/8SJiIiIzAqvCSIiIiKzxCKIiIiIzBKLICIiIjJLLIKIiIjILLEIIiIiIrPEIoiIiIjMEosgIiIiMkssgojIYKKjo/HSSy8ZOgwiMlN8ijwR6YVGo3nk9Pj4eHz66acwtu9rTU1NxbBhw3Dnzh3odDpDh0NEesQiiIj0Ij8/X3m9adMmfPDBB8jKylLaHB0d4ejoaIjQiIgA8HQYEemJh4eH8uPs7AyNRqNqc3R0rHc67LnnnsOMGTMwa9YsuLi4wN3dHV988QV+/vlnTJ48GW3atIGfnx++//571bIyMzMRGRkJR0dHuLu7Y+LEibh582ajsV29ehWjRo2Ci4sLHBwcEBAQgOTkZFy5cgXDhg0DALi4uECj0SA6OhrAg6dYJyQkwNfXF3Z2dggKCsK///1vZZ6pqanQaDRISkpCnz59YGtri4EDByIzM7PlVioRtSgWQURkVDZs2ABXV1ccPXoUM2bMQExMDMaOHYtBgwbhxIkTCA8Px8SJE1FeXg4AKCoqwvPPP49+/fohPT0d27dvx/Xr1zFu3LhGlxEbG4t79+5h3759OHPmDBYtWgRHR0d4e3vj22+/BQBkZWUhPz8fn376KQAgISEB//jHP/C3v/0NZ8+exTvvvIPf/e53+OGHH1Tznjt3LpYuXYpjx46hffv2GDVqFKqqqvS0tojoiTzW41aJiH6BdevWibOzc732SZMmyejRo5X3Q4cOlWeffVZ5X11dLQ4ODjJx4kSlLT8/XwBIWlqaiIh89NFHEh4erprvjz/+KAAafWp2YGCgLFiwoMFptU8Kv3PnjtJWUVEh9vb2cujQIVXfKVOmSFRUlOpzGzduVKbfunVL7OzsZNOmTQ0ui4gMi9cEEZFR6dOnj/La0tIS7dq1Q2BgoNLm7u4OACgsLAQAnDp1CikpKQ1eX5STk4Pu3bvXa3/77bcRExODnTt3IiwsDGPGjFEt92HZ2dkoLy/HCy+8oGqvrKxEv379VG2hoaHK67Zt26JHjx44f/78o1ImIgNhEURERsXa2lr1XqPRqNpq7zqrqakBAJSVlWHUqFFYtGhRvXl5eno2uIw33ngDI0aMQFJSEnbu3ImEhAQsXboUM2bMaLB/WVkZACApKQkdOnRQTdNqtc3MjIiMDYsgIjJp/fv3x7fffovOnTvDyqr5mzRvb29MmzYN06ZNQ1xcHL744gvMmDEDNjY2AID79+8rfXv16gWtVou8vDwMHTr0kfM9fPgwOnXqBAC4c+cOLl68iJ49e/6CzIhI33hhNBGZtNjYWNy+fRtRUVE4duwYcnJysGPHDkyePFlVyNQ1a9Ys7NixA7m5uThx4gRSUlKUQsXHxwcajQbbtm3DjRs3UFZWhjZt2uDdd9/FO++8gw0bNiAnJwcnTpzAihUrsGHDBtW8Fy5ciD179iAzMxPR0dFwdXXlF0ISGSkWQURk0ry8vHDw4EHcv38f4eHhCAwMxKxZs6DT6WBh0fAm7v79+4iNjUXPnj0RERGB7t274/PPPwcAdOjQAR9++CH+8Ic/wN3dHdOnTwcAfPTRR3j//feRkJCgfC4pKQm+vr6qeScmJmLmzJkIDg5GQUEB/vOf/yhHl4jIuGhEjOzrWomITBC/aZrI9PBIEBEREZklFkFERERklng6jIiIiMwSjwQRERGRWWIRRERERGaJRRARERGZJRZBREREZJZYBBEREZFZYhFEREREZolFEBEREZklFkFERERkllgEERERkVn6Pw4SMeZTzr/1AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Plot the cummulative return and cummulative cost\n",
        "steps = np.arange(200) * record_period\n",
        "plt.plot(steps, td3_info[\"return\"], label='TD3')\n",
        "plt.plot(steps, rctd3_info[\"return\"], label='RCTD3')\n",
        "plt.xlabel('Time step')\n",
        "plt.ylabel('Cummulative return')\n",
        "plt.title(\"Cummulative return\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "# Create the second plot\n",
        "plt.plot(steps, td3_info[\"return_cost\"], label='TD3')\n",
        "plt.plot(steps, rctd3_info[\"return_cost\"], label='RCTD3')\n",
        "plt.xlabel('Time step')\n",
        "plt.ylabel('Cummulative cost')\n",
        "plt.title(\"Cummulative cost\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pm18iQ_2LNGS"
      },
      "source": [
        "## Summary\n",
        "In this tutorial, we showed how to use the TD3 algorithm and its safe variant, we refer as RCTD3, in Pearl. Further, we elaborated on the RC framework, and empirically tested the performance of TD3 and RCTD3.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LaOXrDCtzPKD"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "V100",
      "provenance": []
    },
    "custom": {
      "cells": [],
      "metadata": {
        "custom": {
          "cells": [],
          "metadata": {
            "accelerator": "GPU",
            "colab": {
              "gpuType": "T4",
              "include_colab_link": true,
              "provenance": []
            },
            "fileHeader": "",
            "fileUid": "4316417e-7688-45f2-a94f-24148bfc425e",
            "isAdHoc": false,
            "kernelspec": {
              "display_name": "pearl (local)",
              "language": "python",
              "name": "pearl_local"
            },
            "language_info": {
              "name": "python"
            }
          },
          "nbformat": 4,
          "nbformat_minor": 2
        },
        "fileHeader": "",
        "fileUid": "1158a851-91bb-437e-a391-aba92448f600",
        "indentAmount": 2,
        "isAdHoc": false,
        "language_info": {
          "name": "plaintext"
        }
      },
      "nbformat": 4,
      "nbformat_minor": 2
    },
    "fileHeader": "",
    "fileUid": "5d9dd93d-37cb-4030-9b38-47b755e7db0c",
    "indentAmount": 2,
    "isAdHoc": false,
    "kernelspec": {
      "display_name": "pearl",
      "language": "python",
      "name": "bento_kernel_pearl"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}