diff --git a/.gitignore b/.gitignore
index da2e426..72fc0ce 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,12 @@
-# Taken from https://github.com/github/gitignore
+### Manual additions
+# Generated Data
+*.npz
+*.json
+
+# Model weights
+weights/
+
+### Taken from https://github.com/github/gitignore
 
 ### Python
 # Byte-compiled / optimized / DLL files
diff --git a/README.md b/README.md
index e55026f..a79628d 100644
--- a/README.md
+++ b/README.md
@@ -90,15 +90,12 @@ and **Bonferroni** correction for planned comparisons.
 NeuroAI-Project13/
 ├── README.md
 ├── environment.yml          # pinned conda environment
-├── configs/                 # experiment configs (task × condition × seed)
 ├── src/
 │   ├── models/              # CTRNN definition + Euler integration
-│   ├── tasks/               # NeuroGym task wrappers (dt=20 ms)
 │   ├── training/            # training loop, loss functions, BPTT
-│   ├── analysis/            # PID / ΦID / MI / Fisher pipelines
-│   └── stats/               # permutation tests, bootstrap, effect sizes
-├── notebooks/               # exploratory analysis + figure generation
-├── results/                 # checkpoints, saved activations, metric outputs
+│   └── analysis/            # PID / ΦID / MI / Fisher pipelines
+├── notebooks/               # exploratory analysis + throwaway/example files
+├── results/                 # saved model weights/activations, metric outputs
 ├── figures/                 # final figures
 └── docs/                    # technical note, references
 ```
@@ -148,14 +145,6 @@ python -m src.stats.run_tests --metrics results/metrics/
 Planning and progress are tracked on the repo's **Projects** tab (Roadmap / Board / Table views).
 Key dates: final roadmap **8 June**, midway presentation **17 June**, final presentation **15 July 2026**.
 
-## Team
-
-Group project (3 members):
-
-- Jan Casas Gendra
-- Jean-Pasqual Sindermann
-- Wang Chak Ip
-
 ## References
 
 - Williams & Beer (2010), *Nonnegative Decomposition of Multivariate Information* — [arXiv:1004.2515](https://doi.org/10.48550/arXiv.1004.2515)
diff --git a/environment.yml b/environment.yml
new file mode 100644
index 0000000..5abccbd
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,17 @@
+name: neuroai-project13
+channels:
+  - nvidia
+  - pytorch
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.11
+  - pytorch=2.4.1
+  - pytorch-cuda=12.1
+  - pip:
+    - numpy
+    - scipy
+    - matplotlib
+    - seaborn
+    - neurogym==2.3.1
+    - jupyter
\ No newline at end of file
diff --git a/figures/.gitkeep b/figures/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/figures/learning_curves/context/CTRNN_01.png b/figures/learning_curves/context/CTRNN_01.png
new file mode 100644
index 0000000..d20c2a3
Binary files /dev/null and b/figures/learning_curves/context/CTRNN_01.png differ
diff --git a/figures/learning_curves/perceptual/CTRNN_01.png b/figures/learning_curves/perceptual/CTRNN_01.png
new file mode 100644
index 0000000..6e844fe
Binary files /dev/null and b/figures/learning_curves/perceptual/CTRNN_01.png differ
diff --git a/notebooks/01_poc_training.ipynb b/notebooks/01_poc_training.ipynb
new file mode 100644
index 0000000..50eeb18
--- /dev/null
+++ b/notebooks/01_poc_training.ipynb
@@ -0,0 +1,317 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "83552712",
+   "metadata": {},
+   "source": [
+    "# Training Notebook\n",
+    "\n",
+    "This Notebook is a proof of concept for the training approach. It uses random generated data and **not** NeuroGym. The sole purpose is to see a functional training loop using the custom code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3b48e7de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import torch\n",
+    "import torch.optim as optim\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "from src.models.ctrnn import CTRNN\n",
+    "from src.training.loss import compute_loss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6c900af",
+   "metadata": {},
+   "source": [
+    "### 1. Task Parameters & Dimensionality\n",
+    "\n",
+    "**What:** Setting up the tensor shapes for our Continuous-Time RNN (CT-RNN).\n",
+    "\n",
+    "**Why:** To ensure our architecture maps directly onto the *ContextDecisionMaking-v0* task. We use 7 input channels (e.g., 2 for context cues, 4 for sensory stimuli, 1 for fixation), 3 output classes, and the 80 hidden units specified in our proposal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b5e226a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1. Setup Dummy Task Parameters (approximating Mante task dimensions)\n",
+    "batch_size = 64\n",
+    "seq_len = 50       # 50 timesteps * 20ms = 1000ms trial\n",
+    "input_size = 7     # e.g., 2 context, 4 stimuli (2 color, 2 motion), 1 fixation\n",
+    "output_size = 3    # e.g., Fixate, Choice 1, Choice 2\n",
+    "hidden_size = 80"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9eb37e9a",
+   "metadata": {},
+   "source": [
+    "### 2. Mocking the Environment Data\n",
+    "\n",
+    "**What:** Generating dummy inputs and targets (`torch.randn`).\n",
+    "\n",
+    "**Why:** Before we fight with the `NeuroGym` installation and dataset wrappers, we need to prove that our custom loss functions and network gradients actually compile and flow. This isolates our ML engineering from environment bugs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0b7a9989",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 2. Generate dummy data\n",
+    "inputs = torch.randn(batch_size, seq_len, input_size)\n",
+    "# Random targets (0, 1, or 2) for the 16 trials\n",
+    "targets = torch.randint(0, output_size, (batch_size,))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "333b408a",
+   "metadata": {},
+   "source": [
+    "### 3. Model Initialization\n",
+    "\n",
+    "**What:** Instantiating the CT-RNN and the Adam optimizer.\n",
+    "\n",
+    "**Why:** Our CT-RNN uses explicit Euler integration to simulate biological time ($\\tau=100$ ms, $\\Delta t=20$ ms). We also initialize the recurrent weights orthogonally and the input/output weights with Xavier uniform, matching the standards from Mante (2013) and Yang (2019)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8d70cca3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3. Initialize Model and Optimizer\n",
+    "model = CTRNN(input_size=input_size, hidden_size=hidden_size, output_size=output_size)\n",
+    "optimizer = optim.Adam(model.parameters(), lr=1e-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "023a9088",
+   "metadata": {},
+   "source": [
+    "### 4. Training Loop & Custom Losses\n",
+    "\n",
+    "**What:** The core training loop applying our three experimental conditions.\n",
+    "\n",
+    "**Why:** This proves we can successfully apply normative training pressures. \n",
+    "\n",
+    "* **Vanilla:** Standard cross-entropy.\n",
+    "* **Efficient:** Adds an $L_2$ penalty to the hidden states to simulate metabolic cost.\n",
+    "* **Predictive:** Uses a secondary linear head to force the hidden state to predict the $t+1$ input.\n",
+    "We extract and save the continuous hidden states ($h_t$) here because they are the raw material for our Information Theory analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4a4aee85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--- Testing VANILLA Condition ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1000: 100%|██████████| 1000/1000 [00:23<00:00, 42.07it/s, loss=4.16e-5]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[VANILLA] Final Loss: 0.0000\n",
+      "\n",
+      "--- Testing EFFICIENT Condition ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1000: 100%|██████████| 1000/1000 [00:24<00:00, 40.37it/s, loss=9.72e-5]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[EFFICIENT] Final Loss: 0.0001\n",
+      "\n",
+      "--- Testing PREDICTIVE Condition ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1000: 100%|██████████| 1000/1000 [00:26<00:00, 37.95it/s, loss=0.097]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[PREDICTIVE] Final Loss: 0.0970\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 4. Proof of Concept Training Loop (10 steps per condition)\n",
+    "conditions = [\"vanilla\", \"efficient\", \"predictive\"]\n",
+    "epochs = 1000\n",
+    "\n",
+    "# Dictionaries to store results for plotting\n",
+    "loss_history = {cond: [] for cond in conditions}\n",
+    "final_hidden_states = {cond: None for cond in conditions}\n",
+    "\n",
+    "for condition in conditions:\n",
+    "    print(f\"\\n--- Testing {condition.upper()} Condition ---\")\n",
+    "    \n",
+    "    # Reset model to ensure clean test\n",
+    "    model.init_weights()\n",
+    "    \n",
+    "    pbar = tqdm(range(epochs))\n",
+    "    for epoch in pbar:\n",
+    "        optimizer.zero_grad()\n",
+    "        \n",
+    "        # Forward pass (need dynamics for custom losses)\n",
+    "        outputs, predictions, hidden_states = model(inputs, return_dynamics=True)\n",
+    "        \n",
+    "        # Compute loss\n",
+    "        loss = compute_loss(\n",
+    "            outputs=outputs, \n",
+    "            targets=targets, \n",
+    "            hidden_states=hidden_states, \n",
+    "            predictions=predictions, \n",
+    "            inputs=inputs, \n",
+    "            condition=condition\n",
+    "        )\n",
+    "        \n",
+    "        # Backward pass\n",
+    "        loss.backward()\n",
+    "        \n",
+    "        # Gradient clipping (as requested in README)\n",
+    "        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+    "        \n",
+    "        optimizer.step()\n",
+    "\n",
+    "        loss_history[condition].append(loss.item())\n",
+    "\n",
+    "        pbar.set_description(f\"Epoch {epoch + 1}\")\n",
+    "        pbar.set_postfix(loss=loss.item())\n",
+    "    pbar.close()\n",
+    "    # Save the final hidden states for analysis (saving trial 0)\n",
+    "    final_hidden_states[condition] = hidden_states[0].detach().numpy()\n",
+    "    print(f\"[{condition.upper()}] Final Loss: {loss_history[condition][-1]:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b089343a",
+   "metadata": {},
+   "source": [
+    "### Plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "282ea9aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
+    "\n",
+    "# Plot 1: Training Losses\n",
+    "for cond in conditions:\n",
+    "    axes[0].plot(loss_history[cond], label=cond)\n",
+    "axes[0].set_title(\"Training Loss by Condition\")\n",
+    "axes[0].set_xlabel(\"Epoch\")\n",
+    "axes[0].set_ylabel(\"Loss\")\n",
+    "axes[0].legend()\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 2: Hidden State Dynamics (Vanilla Condition)\n",
+    "vanilla_states = final_hidden_states[\"vanilla\"] # Shape: (50 timesteps, 80 neurons)\n",
+    "# Plotting just the first 5 neurons for clarity\n",
+    "for neuron_idx in range(5):\n",
+    "    axes[1].plot(vanilla_states[:, neuron_idx], label=f\"Neuron {neuron_idx}\")\n",
+    "axes[1].set_title(\"Neural Dynamics (Vanilla) over 1 Trial\")\n",
+    "axes[1].set_xlabel(\"Timestep (dt=20ms)\")\n",
+    "axes[1].set_ylabel(\"Activation (tanh)\")\n",
+    "axes[1].legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Save states for Notebook 2\n",
+    "np.save(\"poc_hidden_states.npy\", final_hidden_states)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neuroai-project13",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/02_poc_analysis.ipynb b/notebooks/02_poc_analysis.ipynb
new file mode 100644
index 0000000..8471fbb
--- /dev/null
+++ b/notebooks/02_poc_analysis.ipynb
@@ -0,0 +1,252 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ae5b91d7",
+   "metadata": {},
+   "source": [
+    "# Analysis Notebook\n",
+    "\n",
+    "This Notebook is a proof of concept to show a simple analysis of the Network states learned in Notebook `01_poc_training.ipynb`\n",
+    "\n",
+    "## Phase 2: From Neural Dynamics to Information Theory\n",
+    "\n",
+    "Classical Shannon entropy (and by extension, PID and $\\Phi$ ID) was designed for discrete symbols, not the continuous floating-point matrices generated by neural networks. This notebook builds the data pipeline to bridge that gap."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1885aa0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.decomposition import PCA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55de1b71",
+   "metadata": {},
+   "source": [
+    "### 1. Loading the Dynamics\n",
+    "\n",
+    "**What:** Importing the frozen $h_t$ states from our trained Vanilla model.\n",
+    "\n",
+    "**Why:** Information decomposition is performed *after* training. We analyze the network's settled strategy, treating the weights as static and looking only at the state activations over time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fff675c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully loaded network dynamics.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1. Load the data generated by the training notebook\n",
+    "try:\n",
+    "    hidden_states = np.load(\"poc_hidden_states.npy\", allow_pickle=True).item()\n",
+    "    print(\"Successfully loaded network dynamics.\")\n",
+    "except FileNotFoundError:\n",
+    "    print(\"Run notebook 01 first to generate data!\")\n",
+    "\n",
+    "vanilla_data = hidden_states[\"vanilla\"] # Shape: (50 timesteps, 80 neurons)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db0b3d1e",
+   "metadata": {},
+   "source": [
+    "### 2. Dimensionality Reduction (TDR Proxy)\n",
+    "\n",
+    "**What:** Using PCA to crush the 80-neuron space down to 2 axes. \n",
+    "\n",
+    "**Why:** This is the most crucial step. If we discretize 80 neurons into 4 bins each, the network has $4^{80}$ possible states. Our probability tables would be entirely zeros (the \"curse of dimensionality\"). By projecting the data into a low-dimensional space (mimicking Mante's Targeted Dimensionality Reduction), we isolate the functional components of the network and make the PID math tractable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9174451f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variance explained by top 2 components: 97.2%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 2. Dimensionality Reduction (Proxy for Mante's TDR)\n",
+    "# We want to reduce 80 neurons down to 2 functional macro-components\n",
+    "pca = PCA(n_components=2)\n",
+    "reduced_states = pca.fit_transform(vanilla_data) # Shape: (50, 2)\n",
+    "\n",
+    "print(f\"Variance explained by top 2 components: {pca.explained_variance_ratio_.sum()*100:.1f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ac61bfc",
+   "metadata": {},
+   "source": [
+    "### 3. Discretization Pipeline\n",
+    "\n",
+    "**What:** Converting continuous coordinate values into discrete bins (e.g., States 0, 1, 2, 3).\n",
+    "\n",
+    "**Why:** Standard PID libraries (`phyid`, `JIDT`) require discrete input variables to calculate mutual information and synergy. We digitize the principal components to map the continuous trajectory into a finite alphabet of states."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1dad30b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3. Discretization Pipeline (Continuous to Discrete)\n",
+    "# We will chop each continuous component into 4 distinct bins/states\n",
+    "num_bins = 4\n",
+    "discrete_states = np.zeros_like(reduced_states, dtype=int)\n",
+    "\n",
+    "for i in range(2): # For PC1 and PC2\n",
+    "    component_data = reduced_states[:, i]\n",
+    "    # Define bin edges evenly between the min and max values\n",
+    "    bins = np.linspace(component_data.min(), component_data.max(), num_bins + 1)\n",
+    "    # Digitize converts continuous values to bin indices (1 to 4)\n",
+    "    # We subtract 1 so indices are 0 to 3\n",
+    "    discrete_states[:, i] = np.digitize(component_data, bins) - 1\n",
+    "\n",
+    "# Ensure bounds just in case\n",
+    "discrete_states = np.clip(discrete_states, 0, num_bins - 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3408fc3f",
+   "metadata": {},
+   "source": [
+    "### 4. Building the Probability Landscape\n",
+    "\n",
+    "**What:** Counting the frequency of joint states to build a Probability Mass Function (PMF).\n",
+    "\n",
+    "**Why:** Synergy is calculated based on how often different variables overlap. This matrix tells us exactly how the two principal components co-occur across the trial. This 2D array is the final artifact we will hand off to the heavy mathematical libraries to compute the actual Redundancy/Synergy split."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d95fd665",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 4. Building the Probability Landscape (The basis for PID)\n",
+    "# Count how often the network visits each joint state (PC1_state, PC2_state)\n",
+    "joint_probabilities = np.zeros((num_bins, num_bins))\n",
+    "\n",
+    "for t in range(len(discrete_states)):\n",
+    "    state_c1 = discrete_states[t, 0]\n",
+    "    state_c2 = discrete_states[t, 1]\n",
+    "    joint_probabilities[state_c1, state_c2] += 1\n",
+    "\n",
+    "# Normalize to sum to 1.0\n",
+    "joint_probabilities /= joint_probabilities.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62a623ba",
+   "metadata": {},
+   "source": [
+    "### Plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c2d9be0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABVoAAAHqCAYAAAD1QvWBAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3Xd8U9X7B/DPzW6aNJ1pgZa0Ze9RECgyFBARxIXiYopfNyrqTxEXKOIXB6J+AQdTRXEgDgQEZMqUIciebaF7N02TZpzfH7WR0BY6UtLSz9tXXpKbk3ufnN42J0/OfY4khBAgIiIiIiIiIiIiomqT+ToAIiIiIiIiIiIiovqOiVYiIiIiIiIiIiKiGmKilYiIiIiIiIiIiKiGmGglIiIiIiIiIiIiqiEmWomIiIiIiIiIiIhqiIlWIiIiIiIiIiIiohpiopWIiIiIiIiIiIiohphoJSIiIiIiIiIiIqohJlqJiIiIiIiIiIiIaoiJVqJasmjRIkiSBI1Gg4SEhDKP9+/fH+3bt6/WvpcuXYr333+/hhF612uvvQZJkpCZmVmt5+/cuRO33XYbmjZtCrVajfDwcPTq1QvPPPOMR7s5c+Zg0aJFNYr1zTffxIoVK2q0jwuV/qwvd4uOjq7xsaKjozF27Nga76ci3ujf2iJJEl577TWv7a/0nC29qVQqxMTE4Mknn0Rubm6Z9gcOHMC4ceMQExMDjUYDnU6Hrl27YubMmcjOzi73GF27doUkSXjnnXeqFFtSUhIeffRRtGzZEn5+fggODkaHDh3w4IMPIikpqTovl4iIfKx0vPDnn39W63lnz56t8jGTk5Px2muvYf/+/ZV+zpEjRzBq1CjExsZCo9EgNDQUXbt2xeOPP478/Hx3O2+MR2tr3DF27FiP93i1Wo1WrVrh1VdfhdVq9dpxNm7cCEmS8N1333ltn1U5T8aOHVtmfHnxWPHs2bOQJMmjn7dt24bXXnut3PFOTZT2u16vh9lsLvN4QkICZDJZtcd0FosFr732GjZu3Fil55WO+bxtwIABePjhh933S8+HC8eWYWFh6N27N6ZMmVLuZ8Ka/H7Xtkv1t6/ivvicz8nJQWBgoFc/2xHVFBOtRLXMZrPhpZde8uo+62KitSZWrlyJ+Ph45OfnY+bMmfjtt98we/Zs9O7dG8uWLfNoWxcTrUOHDsX27ds9bgAwYsQIj20//PBDjY/1ww8/4OWXX67xfipSlxOt27dvx4QJE7y+39WrV2P79u1YuXIlbr31Vnz44YcYMmQIhBDuNp9++ini4uKwe/duPPfcc1i9ejV++OEH3HnnnZg3bx4eeOCBMvvdv38/9u3bBwCYP39+peM5d+4cunbtirVr12LSpEn49ddfsWDBAtxzzz3YvXs3Tp8+XfMXTURE9UbpOKNRo0ZVfm5ycjKmTp1a6UTrvn37EBcXh8OHD+OVV17B6tWrMW/ePAwdOhRr1qzx+GKxLidaAcDPz889BluxYgV69OiBadOmYcyYMbVyPF94+eWXLzu+bNSoEbZv346hQ4e6t23btg1Tp071eqIVAJRKJRwOR5kxPAAsXLgQer2+2vu2WCyYOnVqlROtEyZMcI/PveXHH3/EH3/8Ue64/M0338T27duxYcMGzJ8/H/3798eCBQvQpk0bfPnllx5ta/L7Xdsu1d91Je6goCA8/fTTeO6551BcXOzTWIhKKXwdANHV7sYbb8TSpUvx7LPPolOnTr4Ox+uKioqg0WhqtI+ZM2ciJiYGa9asgULx75+lu+++GzNnzqxpiLUuLCwMYWFhZbaHh4ejZ8+eFT7P6XTC4XBArVZX+lhdunSpVoy+VJ3XWZ5L9WVNxMXFITQ0FAAwaNAgZGVl4fPPP8e2bdvQu3dvbN++HY888ggGDRqEFStWeLyOQYMG4ZlnnsHq1avL7Pezzz4DUDIQXblyJbZt24b4+PjLxvPpp58iMzMTu3btQkxMjHv7rbfeihdffBEul6umL5mIiOqRisYZteH999+HTCbDxo0bPRJiI0aMwOuvv+7xJWRdJ5PJPMYOQ4YMwdmzZ/HNN9/gvffeQ5MmTcp9XlFREfz8/K5UmDXSrFmzy7ZRq9W1NoYqj0qlws0334wFCxZ4fBEthMCiRYswcuRIfPrpp1ckFovFAq1Wi8jISERGRnp132+++SZuu+22cs+jFi1aePT58OHD8cwzz2DgwIEYO3YsOnbsiA4dOgC4sr/fgPfO7ysd96U8/PDDeOONN/Ddd9/h3nvv9XU4RJzRSlTb/u///g8hISF4/vnnL9tWCIE5c+agc+fO8PPzQ1BQEEaMGOExg61///5YuXIlEhISPC5NAYDu3bt7fFsNAB06dIAkSdi9e7d72/LlyyFJEg4ePOjetnXrVgwYMAB6vR5arRbx8fFYuXKlx75KLxH57bffMH78eISFhUGr1cJms5X7eo4ePYrY2Fj06NED6enpFb7urKwshIaGeiRZS8lk//6Zio6OxqFDh7Bp06Yyl+NbrVY888wz6Ny5MwwGA4KDg9GrVy/8+OOPHvuTJAmFhYVYvHixex/9+/d3P56amoqHHnoIkZGR7kvJp06dCofDUWH8lVF62dbMmTPxxhtvICYmBmq1Ghs2bKh07KV9cHHpgPz8fDz77LOIiYmBSqVCkyZN8NRTT6GwsNCjncvlwocffug+vwIDA9GzZ0/89NNPl+1fAEhMTMT9998Po9EItVqNNm3a4N133/VI/FX0OteuXYvAwEA89NBD5faNXC7H22+/fck+vPgys9LzccOGDXjkkUcQGhqKkJAQ3H777UhOTr7kvi6ldGBcennXm2++CUmS8Mknn5SbLFapVBg+fLjHNqvViqVLlyIuLg6zZs0CACxYsKBSx8/KyoJMJoPRaCz38Qt/J8aOHQudTodDhw5hwIAB8Pf3R1hYGB5//HFYLBaP5/3vf/9D3759YTQa4e/vjw4dOmDmzJmw2+1ljrF69WoMGDAABoMBWq0Wbdq0wYwZMzza/Pnnnxg+fDiCg4Oh0WjQpUsXfPPNN5V6jUREVOKnn35Cr169oNVqodfrMWjQoDIz78q7RLe0BNXu3bvRp08faLVaxMbG4q233nK/L2/cuBHdu3cHAIwbN8793n6pS7azsrIQEBAAnU5X7uOlY85LjUcBYOrUqejRoweCg4MREBCArl27Yv78+R6J2suNOyo7vqmKi9/jo6OjMWzYMCxfvhxdunSBRqPB1KlTAQB///03brnlFgQFBUGj0aBz585YvHhxufu1Wq2YNGkSIiIi4Ofnh379+rmvain1559/4u6770Z0dDT8/PwQHR2Ne+65p9zLyYGSS6LHjRuH4OBg+Pv74+abby5zVUt5pQMudnHpgNdeew3PPfccACAmJsbd9xs3bsQDDzyA4ODgMmMIALj++uvRrl27Sx6r1Pjx47Ft2zYcO3bMvW3dunVISEjAuHHjyrTPyMjAo48+irZt20Kn08FoNOL666/Hli1bPF5HaWJv6tSp7rhLx8Wl5QH27t2LESNGICgoyJ2Ivrh0wNatW6FUKvHss896xFH6u3a5K5H27duHXbt2YdSoUZXqDwAIDg7Gxx9/DIfD4R4bXnjMC3+/9+3bh2HDhrnH3I0bN8bQoUNx7tw5d5vLjeuBS5/fl/vMc7n+vjjui8smXKp82rJly9CrVy/4+/tDp9Nh8ODBZX5fSo/RqlUr9+eOJUuWlNu34eHhGDRoEObNm1e5HwZRLWOilaiW6fV6vPTSS1izZg1+//33S7Z96KGH8NRTT2HgwIFYsWIF5syZg0OHDiE+Ph5paWkASi6x6t27NyIiIspcqj5w4EBs3rzZnThJS0vD33//DT8/P6xdu9Z9nHXr1iE8PNz9TeqmTZtw/fXXIy8vD/Pnz8dXX30FvV6Pm2++udzLfsaPHw+lUonPP/8c3333HZRKZZk2mzZtQnx8PDp27IgNGzZUmDQCgF69emHnzp2YOHEidu7cWW7iByi5bD42NhZdunQpczm+zWZDdnY2nn32WaxYsQJfffUVrr32Wtx+++0eb8rbt2+Hn58fbrrpJvc+5syZA6BkwHHNNddgzZo1eOWVV7Bq1So88MADmDFjBh588MGKf3BV8MEHH+D333/HO++8g1WrVqF169aVjr08FosF/fr1w+LFizFx4kSsWrUKzz//PBYtWoThw4d7fKAZO3YsnnzySXTv3h3Lli3D119/jeHDh7sHSJfq34yMDMTHx+O3337D66+/jp9++gkDBw7Es88+i8cff/yyr7N9+/YYP348vvzyS+Tl5Xm0nTNnDlQqFcaPH1+tPp0wYQKUSiWWLl2KmTNnYuPGjbj//vurtS8AOHnyJICSb+qdTid+//13xMXFISoqqtL7WL58OXJycjB+/Hi0aNEC1157LZYtW1ZuvbKL9erVCy6XC7fffjvWrFnjUQ+vPHa7HTfddBMGDBiAFStW4PHHH8fHH3+MkSNHerQ7deoU7r33Xnz++ef45Zdf8MADD+Dtt98uk/yeP38+brrpJrhcLsybNw8///wzJk6c6DG437BhA3r37o3c3FzMmzcPP/74Izp37oyRI0fW2dITRER1zdKlS3HLLbcgICAAX331FebPn4+cnBz0798fW7duvezzU1NTcd999+H+++/HTz/9hCFDhmDy5Mn44osvAJTUCV+4cCEA4KWXXnK/t1+qDE+vXr2QkpKC++67D5s2bUJRUVG57S41HgVKkjQPPfQQvvnmGyxfvhy33347nnjiCbz++uvuNpcad1RlfFMVF77Hl9q7dy+ee+45TJw4EatXr8Ydd9yBY8eOIT4+HocOHcIHH3yA5cuXo23bthg7dmy5V1u9+OKLOH36ND777DN89tlnSE5ORv/+/T0So2fPnkWrVq3w/vvvY82aNfjvf/+LlJQUdO/evdw1Dh544AHIZDJ3iYZdu3ahf//+Nb7cf8KECXjiiScAlIxXSvu+a9euePLJJ5GTk4OlS5d6POfw4cPYsGEDHnvssUodY+DAgTCZTB5fMs+fPx99+/ZFixYtyrQvLUnx6quvYuXKlVi4cCFiY2PRv39/92XrjRo1cl9B9MADD7jjvvjS/dtvvx3NmzfHt99+W2Hi7dprr8Ubb7yBd999152YPHToEB577DHcf//95ZaEutAvv/wCuVyOvn37Vqo/SnXv3h2NGjXC5s2bK2xTWFiIQYMGIS0tDf/73/+wdu1avP/++2jatCkKCgrc7S43ri9V3vldmc88le3vUl27di1TSm3JkiVQKpUeCfo333wT99xzD9q2bYtvvvkGn3/+OQoKCtCnTx8cPnzY3W7RokUYN24c2rRpg++//x4vvfQSXn/99Qo/T/fv3x9//PFHrZTDIKoyQUS1YuHChQKA2L17t7DZbCI2NlZ069ZNuFwuIYQQ/fr1E+3atXO33759uwAg3n33XY/9JCUlCT8/P/F///d/7m1Dhw4VJpOpzDHXrVsnAIjNmzcLIYT44osvhF6vF48++qi47rrr3O1atGgh7r33Xvf9nj17CqPRKAoKCtzbHA6HaN++vYiMjHTHXPqaRo8eXebYr776qgAgMjIyxOeffy5UKpWYOHGicDqdl+2rzMxMce211woAAoBQKpUiPj5ezJgxwyMmIYRo166d6Nev32X36XA4hN1uFw888IDo0qWLx2P+/v5izJgxZZ7z0EMPCZ1OJxISEjy2v/POOwKAOHTo0GWPWwqAeOyxx9z3z5w5IwCIZs2aieLi4mrHbjKZPGKfMWOGkMlkYvfu3R7tvvvuOwFA/Prrr0IIITZv3iwAiClTplzy2BX17wsvvCAAiJ07d3psf+SRR4QkSeLYsWOXfZ2nTp0SMplMzJo1y72tqKhIhISEiHHjxl0yLiFK+vTVV1913y89Hx999FGPdjNnzhQAREpKyiX3V3rOpqamCrvdLnJycsQXX3wh/Pz8RFRUlCgqKhKpqakCgLj77rsvG9+Frr/+eqHRaEROTo5HrPPnz7/sc10ul3jooYeETCYTAIQkSaJNmzbi6aefFmfOnPFoO2bMGAFAzJ4922P79OnTBQCxdevWco/hdDqF3W4XS5YsEXK5XGRnZwshhCgoKBABAQHi2muvdf/el6d169aiS5cuwm63e2wfNmyYaNSoUaV+74mIGpoLx4ZOp1M0btxYdOjQweNvZkFBgTAajSI+Pr7M8y58D+jXr1+578tt27YVgwcPdt/fvXu3ACAWLlxYqRitVqu49dZb3WMyuVwuunTpIqZMmSLS09M92lY0Hr1Y6XvOtGnTREhIiMf7S0XjjsqObyoyZswY4e/vL+x2u7Db7SIjI0PMnj1bSJIkunfv7m5nMpmEXC53j2NK3X333UKtVovExESP7UOGDBFarVbk5uYKIYTYsGGDACC6du3q8brOnj0rlEqlmDBhQoUxOhwOYTabhb+/v8f7eOnP+7bbbvNo/8cffwgA4o033vB4nRf/DC4eK5aOzS48B95+++0y51Spfv36ic6dO3tse+SRR0RAQECZcfnFSvtdiJJxVkREhLDb7SIrK0uo1WqxaNEikZGRUWZMd7HSsfCAAQM8+uFSzy0d173yyisVPnYhl8slbrrpJhEYGCj+/vtv0bZtW9G6dWthNpsv+RqFKDkPWrduXWZ76fnw7bffVvjcHj16CD8/P/f9i3+///zzTwFArFixosJ9VHZcX9H5XdnPPJfq7/L+Ll0oLS1NxMbGinbt2rnHw4mJiUKhUIgnnnjCo21BQYGIiIgQd911lxBCuP8+VvR7Vd7fnbVr1woAYtWqVZfqEqIrgjNaia4AlUqFN954A3/++WeFl9b+8ssvkCQJ999/PxwOh/sWERGBTp06Varoe+/evaHRaLBu3ToAwNq1a9G/f3/ceOON2LZtGywWC5KSknDixAkMHDgQQMm3pjt37sSIESM8LhOTy+UYNWoUzp0753HZDwDccccdFcYwffp0jB07Fm+99RZmz57tcZlzRUJCQrBlyxbs3r0bb731Fm655RYcP34ckydPRocOHcr9lr883377LXr37g2dTgeFQgGlUon58+fjyJEjlXr+L7/8guuuuw6NGzf2+BkMGTIEQMks3ZoaPnx4uTOAqxv7L7/8gvbt26Nz584eMQ8ePNh9GRgArFq1CgAqPRPhYr///jvatm2La665xmP72LFjIYQo8+1yea8zNjYWw4YNw5w5c9wzUZYuXYqsrKxyZ8VW1sWX7Xfs2BEAKrwU72IRERFQKpUICgrC/fffj65du2L16tXVrj185swZbNiwAbfffjsCAwMBAHfeeSf0en2lygdIkoR58+bh9OnTmDNnDsaNGwe73Y5Zs2ahXbt25Z6H9913n8f90vpUGzZscG/bt28fhg8fjpCQEMjlciiVSowePRpOpxPHjx8HULI4Rn5+Ph599NEKV+c9efIkjh496j7mhefdTTfdhJSUlDJ/M4iIyNOxY8eQnJyMUaNGeYyVdDod7rjjDuzYsaPcy7cvFBERUeZ9uWPHjpV+/yuPWq3GDz/8gMOHD2PWrFm4++67kZGRgenTp6NNmzaV/vv++++/Y+DAgTAYDO73nFdeeQVZWVmXLCdVqrLjm0spLCyEUqmEUqlEWFgYnnrqKQwZMqTM4lEdO3ZEy5Yty8Q/YMCAMlezjB07FhaLpUx5h3vvvdfjfdNkMiE+Pt7jfdhsNuP5559H8+bNoVAooFAooNPpUFhYWO547+L39vj4eJhMJo991oYnn3wS+/fvxx9//AGgpITD559/jjFjxlRYUqI848aNQ1paGlatWoUvv/wSKpUKd955Z4Xt582bh65du0Kj0bjHwuvXr6/0OL7UpT6nXEiSJCxZsgR6vR7dunXDmTNn8M0338Df3/+yz01OTr7k1XqXIi4zG7t58+YICgrC888/j3nz5nnM8ixVlXF9eed3bX/mKSwsxNChQ2G1WrFq1Sr3eHjNmjVwOBwYPXq0x3E1Gg369evn/r0u/ftY0e9VeUp/HufPn69R7ETewEQr0RVy9913o2vXrpgyZUq5l8anpaVBCIHw8HD3oLD0tmPHjkolGzUaDXr37u1OtK5fvx6DBg1C//794XQ6sWXLFncJgdJEa05ODoQQ5a4Y2bhxYwAl9boudKnVJb/44gs0adIEd99992XjvVi3bt3w/PPP49tvv0VycjKefvppnD17tlILYi1fvhx33XUXmjRpgi+++ALbt2/H7t27MX78eFit1kodPy0tDT///HOZ/i+93KWyCd9LKa/vahJ7WloaDhw4UCZmvV4PIYQ75oyMDMjlckRERFQr7qysLK+cI08++SROnDjhPg//97//oVevXujatWu14gJKEvUXKq2jWtHljhdbt24ddu/ejf379yMzMxNbt25F27ZtAQChoaHQarU4c+ZMpeNZsGABhBAYMWIEcnNzkZubC7vdjuHDh+OPP/7A0aNHK7Ufk8mERx55BPPnz8eJEyewbNkyWK1Wd121UgqFokwflP6cS38uiYmJ6NOnD86fP4/Zs2e7v9j43//+B+DfvsrIyACASy4YUVrG5Nlnny1z3j366KMAvPO7QkR0NSv9+1zRe6vL5UJOTs4l93Hx336g5D2wsu9/l9KmTRs89dRT+OKLL5CYmIj33nsPWVlZFV42fKFdu3bhhhtuAFCywOMff/yB3bt3Y8qUKQAq9/5c2fHNpfj5+WH37t3YvXs3Dhw4gNzcXKxcubLM4kXl/QyqOu4pb3wVERHh0e7ee+/FRx99hAkTJmDNmjXYtWsXdu/ejbCwsHL7pDL7rA233HILoqOj3WOERYsWobCwsMpf1ptMJgwYMAALFizAggULcPfdd0Or1Zbb9r333sMjjzyCHj164Pvvv8eOHTuwe/du3HjjjVU+ny/1OeViISEhGD58OKxWK2688UZ3WbXLqcliwImJie7zqDwGgwGbNm1C586d8eKLL6Jdu3Zo3LgxXn31VfdnyKqM68vrj9r8zONwODBixAgcP34cv/76q8eXFaVjyO7du5c59rJly9zHLT3HK/odKE/pz8Mbf/+IaqrsyjNEVCskScJ///tfDBo0CJ988kmZx0NDQyFJErZs2VLugjuVXbF9wIABeOWVV7Br1y6cO3cOgwYNgl6vR/fu3bF27VokJyejZcuW7je9oKAgyGQypKSklNlX6YJCpSuyX/haKrJ69WqMHDkSffr0wfr162EymSoV98WUSiVeffVVzJo1C3///fdl23/xxReIiYnBsmXLPOKraKGu8oSGhqJjx46YPn16uY9falBUWeX1XU1iDw0NhZ+fX4UzJUt/dqX1RlNTU6s0AC0VEhLilXPk+uuvR/v27fHRRx9Bp9Nh79697lpyvtKpU6cy8ZeSy+UYMGAAVq1ahXPnzl12xVqXy+WuUXr77beX22bBggWV+vLgYnfddRdmzJhR5vfB4XAgKyvL4wN3amoqgH8/hK9YsQKFhYVYvny5x+/k/v37PfZVWrPuwnqsFyvtq8mTJ1f4Glu1alXJV0VE1DCV/n2u6L1VJpMhKCjoSodVLkmS8PTTT2PatGmVGpN9/fXXUCqV+OWXXzySUStWrKj0MSs7vrkUmUyGbt26XbZdeWOWqo57St93L95W+nPOy8vDL7/8gldffRUvvPCCu01pnf7yVLTP5s2bX+LV1JxMJsNjjz2GF198Ee+++y7mzJmDAQMGVOu9ffz48bj//vvhcrkwd+7cCtt98cUX6N+/f5k2F9YkraxLfU652Nq1azF37lxcc801+OGHH/D9999XakZsaGhohT+3S9m1axdSU1MvWwO2Q4cO+PrrryGEwIEDB7Bo0SJMmzYNfn5+eOGFF6o0ri+vP2rzM89//vMfrF+/Hr/++is6depU5rgA8N13313yM2Lp701FvwPlKf15VOZvA1Ft44xWoito4MCBGDRoEKZNm1ZmUZxhw4ZBCIHz58+jW7duZW4XfsN6qdkKAwcOhMPhwMsvv4zIyEi0bt3avX3dunXuS7lK+fv7o0ePHli+fLnHPl0uF7744gtERkaWudzkUkwmkztZ3KdPH5w4ceKyzylvIAvAfanQhW/2Fb12SZKgUqk8BhOpqan48ccfy7StaB/Dhg3D33//jWbNmpX7M/BGorU8VYm9vJhPnTqFkJCQcmMuXeWz9FKgSw1ygYr7ZsCAATh8+DD27t3rsX3JkiWQJAnXXXfdZWMtNXHiRKxcuRKTJ09GeHj4JS8jqwsmT54MIQQefPBBFBcXl3ncbrfj559/BlBySdS5c+fw2GOPYcOGDWVu7dq1w5IlS9wrupanot8Hs9mMpKSkcs/DL7/80uN+6SIW/fv3B/DvIPvCL2yEEPj00089nhcfHw+DwYB58+ZVeGlbq1at0KJFC/z111/lnnPdunWDXq+v8PUREVHJ39ImTZpg6dKlHn9vCwsL8f3336NXr14Vzv6riqpe5VHRe1BycjLy8/MrPSZTKBSQy+XubUVFRfj888/Lja+iMVllxje1ZcCAAfj999/didVSS5YsgVarRc+ePT22f/XVVx4/x4SEBGzbts3jfVgIUWbixGeffQan01luDBe/t2/btg0JCQnufdbE5c6LCRMmQKVS4b777sOxY8eqXeLptttuw2233Ybx48eX6bMLSZJUpm8OHDhQpkRDVc/nS0lJScH999+Pfv36Ydu2bRg+fDgeeOCBSl3F1Lp1a4+FziojOzsbDz/8MJRKJZ5++ulKPUeSJHTq1AmzZs1CYGCgexxe2XF9RSr7maeq/f3SSy9h4cKF+Oyzzzw+b5YaPHgwFAoFTp06VeEYEij5+9ioUaMKf6/KU/rzKL0qjciXOKOV6Ar773//i7i4OKSnp3uswNi7d2/85z//wbhx4/Dnn3+ib9++8Pf3R0pKCrZu3YoOHTrgkUceAVDyLefy5csxd+5cxMXFeXxjHxcXh6CgIPz2228YN26ce/8DBw50r/R68RvfjBkzMGjQIFx33XV49tlnoVKpMGfOHPz999/46quvqvTNMFByicqmTZswePBg9O3bF2vXrkX79u0rbD948GBERkbi5ptvRuvWreFyubB//368++670Ol0ePLJJ91tS7/hXbZsGWJjY6HRaNChQwcMGzYMy5cvx6OPPooRI0YgKSkJr7/+Oho1alQm2duhQwds3LgRP//8Mxo1agS9Xo9WrVph2rRpWLt2LeLj4zFx4kS0atUKVqsVZ8+exa+//op58+ZddkZjdVQl9os99dRT+P7779G3b188/fTT6NixI1wuFxITE/Hbb7/hmWeeQY8ePdCnTx+MGjUKb7zxBtLS0jBs2DCo1Wrs27cPWq3WvfpsRf379NNPY8mSJRg6dCimTZsGk8mElStXYs6cOXjkkUeqlIy///77MXnyZGzevBkvvfQSVCpVjfqvtvXq1Qtz587Fo48+iri4ODzyyCNo164d7HY79u3bh08++QTt27fHzTffjPnz50OhUODFF18sNyH60EMPuRPNt9xyS7nHmz59Ov744w+MHDkSnTt3hp+fH86cOYOPPvoIWVlZePvttz3aq1QqvPvuuzCbzejevTu2bduGN954A0OGDMG1114LABg0aBBUKhXuuece/N///R+sVivmzp1b5rJUnU6Hd999FxMmTMDAgQPx4IMPIjw8HCdPnsRff/2Fjz76CADw8ccfY8iQIRg8eDDGjh2LJk2aIDs7G0eOHMHevXvx7bffeqPriYiuSpIkQSaTYebMmbjvvvswbNgwPPTQQ7DZbHj77beRm5uLt956yyvHatasGfz8/PDll1+iTZs20Ol0aNy4cYVfHv/nP/9Bbm4u7rjjDrRv3x5yuRxHjx7FrFmzIJPJ8Pzzz7vbVjQeHTp0KN577z3ce++9+M9//oOsrCy888475V6dVdG4o7Ljm9ry6quvuutYvvLKKwgODsaXX36JlStXYubMmTAYDB7t09PTcdttt+HBBx9EXl4eXn31VWg0GkyePBkAEBAQgL59++Ltt99GaGgooqOjsWnTJsyfP99dv/Jif/75JyZMmIA777wTSUlJmDJlCpo0aeIu01MTpRM4Zs+ejTFjxkCpVKJVq1buL0oDAwMxevRozJ07FyaTCTfffHO1jqPRaPDdd99dtt2wYcPw+uuv49VXX0W/fv1w7NgxTJs2DTExMR5fTuv1ephMJvz4448YMGAAgoOD3f1ZFU6nE/fccw8kScLSpUshl8uxaNEidO7cGSNHjsTWrVsvOT7t378/FixYgOPHj5c7Bj5x4gR27NgBl8uFrKws7Ny5E/Pnz0d+fj6WLFni8RnwYr/88gvmzJmDW2+9FbGxsRBCYPny5cjNzcWgQYMAoNLj+opU9jNPVfr722+/xfTp0zFixAi0bNkSO3bscD+mVqvRpUsXREdHY9q0aZgyZQpOnz6NG2+8EUFBQUhLS8OuXbvg7++PqVOnQiaT4fXXX8eECRPcv1e5ubl47bXXKiwdsGPHDoSEhFS6/ANRrbrSq28RNRQXrix7sXvvvVcAEO3atSvz2IIFC0SPHj2Ev7+/8PPzE82aNROjR48Wf/75p7tNdna2GDFihAgMDBSSJJVZRfO2224TAMSXX37p3lZcXCz8/f2FTCZzr/x4oS1btojrr7/efdyePXuKn3/+udKvqXQ1z4yMDPe23Nxc0bt3bxEcHFzuc0otW7ZM3HvvvaJFixZCp9MJpVIpmjZtKkaNGiUOHz7s0fbs2bPihhtuEHq9XgDwWHXyrbfeEtHR0UKtVos2bdqITz/9tNxVRvfv3y969+4ttFqtAOCx2m1GRoaYOHGiiImJEUqlUgQHB4u4uDgxZcqUSq1CWgqAeOyxx9z3S1d8ffvtt8ttX9nYTSaTGDt2rMc2s9ksXnrpJdGqVSuhUqmEwWAQHTp0EE8//bRITU11t3M6nWLWrFmiffv27na9evXy+Dlfqn8TEhLEvffeK0JCQoRSqRStWrUSb7/9tsdqyZd7naXGjh0rFAqFOHfu3CXbXQgXrXpa0flYuuLrhg0bLrm/8s7ZS9m/f78YM2aMaNq0qVCpVMLf31906dJFvPLKKyI9PV1kZGQIlUolbr311gr3kZOTI/z8/MTNN99cYZsdO3aIxx57THTq1EkEBwcLuVwuwsLCxI033lhmleXS1X0PHDgg+vfvL/z8/ERwcLB45JFHypyvP//8s+jUqZPQaDSiSZMm4rnnnhOrVq0qt69+/fVX0a9fP+Hv7y+0Wq1o27at+O9//+vR5q+//hJ33XWXMBqNQqlUioiICHH99deLefPmVao/iYgamv/9738CgDh48KB724oVK0SPHj2ERqMR/v7+YsCAAeKPP/7weF55q3v369ev3HFkeavQf/XVV6J169ZCqVRedrX3NWvWiPHjx4u2bdsKg8EgFAqFaNSokbj99tvF9u3bPdpeajy6YMEC0apVK6FWq0VsbKyYMWOGmD9/fpnXcalxR2XHN+UpfX+8HJPJJIYOHVruYwcPHhQ333yzMBgMQqVSiU6dOomFCxd6tCkdc3z++edi4sSJIiwsTKjVatGnTx+PsbsQQpw7d07ccccdIigoSOj1enHjjTeKv//+W5hMJjFmzBh3u9Kf92+//SZGjRolAgMDhZ+fn7jpppvEiRMnyrzOi3/eF++vdGx2ceyTJ08WjRs3FjKZrNyxwMaNGwUA8dZbb1XYfxerTL+Xt5K9zWYTzz77rGjSpInQaDSia9euYsWKFeW+vnXr1okuXboItVotALhf66XGdRePqadMmSJkMplYv369R7tt27YJhUIhnnzyyUu+hry8PKHT6cTMmTM9tpeeD6U3hUIhQkJCRK9evcSLL74ozp49W2ZfF/9+Hz16VNxzzz2iWbNmws/PTxgMBnHNNdeIRYsWeTyvMuP6S53flf3MU1F/Xxx3aR+Xd7v4Z7hixQpx3XXXiYCAAKFWq4XJZBIjRowQ69at82j32WefiRYtWgiVSiVatmwpFixYUO454XK5hMlkEk888US5r5XoSpOEuMyyd0REVGcEBwdj/PjxeOedd3wdSrUVFxcjOjoa1157Lb755htfh1OvjR07Ft99912ZUiRERFT3PPnkk/joo4+Qm5vLEitU5z3zzDOYO3cukpKSyl14raF74oknsH79ehw6dKjKV/+Rd61fvx433HADDh065C6bR+RLLB1ARFQPHDhwAL/++itycnLQq1cvX4dTLRkZGTh27BgWLlyItLQ0j8UgiIiIrlZ79uzB7t27sWDBAgwfPpxJVqrTduzYgePHj2POnDl46KGHmGStwEsvvYQlS5bg+++/x4gRI3wdToP2xhtvYPz48UyyUp3BRCsRUT3w5JNP4ujRo3j22WcrXOm9rlu5ciXGjRuHRo0aYc6cOejatauvQyIiIqp1I0aMQF5eHoYPH44PPvjA1+EQXVLpQmzDhg3DG2+84etw6qzw8HB8+eWXZerd05WVk5ODfv36eaV2MZG3sHQAERERERERERERUQ3JfB0AERERERERERERUX3HRCsRERERERERERFRDTHRSkRERERERERERFRDDWoxLJfLheTkZOj1ekiS5OtwiIiIiKgGhBAoKChA48aNIZNx/gBVDj8TEBGRN9WF8YjVakVxcbHX9qdSqaDRaLy2v4akQSVak5OTERUV5eswiIiIiMiLkpKSEBkZ6eswqJ7gZwIiIqoNvhqPWK1W+OlDAIfFa/uMiIjAmTNnmGythgaVaNXr9QBKTv6AgAAfR3NpLpcLGRkZCAsL4wyNi7BvKsa+qRj7pmLsm4qxbyrGvqkY+6Zi3u6b/Px8REVFucd4RJVRer6o2o6BJFf5OBq60OwPnvJ1CET1QqRe6+sQ6AIWcwHu6t/RZ+OR4uJiwGGBuu0YwBvva85ipB5ejOLiYiZaq6FBJVpLLw0KCAioF4lWq9WKgIAAfki7CPumYuybirFvKsa+qRj7pmLsm4qxbypWW33Dy7+pKkrPF0muYqK1jvHT8UsTosrw1zHRWhf5fDyi0HjlfU1IHL/WBHuPiIiIiIiIiIiIqIYa1IxWIiIiIiIiIiKiq44EwBuzanmhUI0w0UpERERERERERFSfSbKSmzf2Q9XGRCsRERFRLXE6nbDb7b4Oo85wuVyw2+2wWq2VqtGqVCohl8uvQGRERERERDXHRCsRERGRlwkhkJqairy8PF+HUqcIIeByuVBQUFDpBSMCAwMRERHh+wUmiIiIiOoySfJS6QCOuWqCiVYiIiIiLzObzXA4HDAajdBqtUwS/kMIAYfDAYVCcdk+EULAYrEgPT0dANCoUaMrESIRERFR/cTSAXUCE61EREREXuR0OmG1WtGoUSOEhIT4Opw6pSqJVgDw8/MDAKSnp8NoNLKMABERERHVaUy0EhEREXmR3W6HJEnQarW+DuWqUNqPdrudiVYiIiKiirB0QJ1Qb+cDz5gxA5Ik4amnnvJ1KERERERlsFyAd7AfiYiIiCpD9m/5gJrc6m+qsE6olzNad+/ejU8++QQdO3b0dShEREREtUIIgayiLJiLzdCpdAjxC2HSkYiIiIioDqt3aWqz2Yz77rsPn376KYKCgnwdDhEREZFX5VpzMXvHbLT4sAXC3g5DzOwYhL0dhhYftsDsHbORa831dYhEREREVNeUlg7wxo2qrd4lWh977DEMHToUAwcO9HUoRERXLZdL4GhqPnaezsLR1Hy4XMLXIRE1CGtOrkHke5F4es3TOJ1z2uOx0zmn8fSapxH5XiTWnFxTK8dPT0/HQw89hKZNm0KtViMiIgKDBw/G9u3bAZRcxr9ixQqvHOvs2bOQJAn79+/3yv6IiIiIiHytXpUO+Prrr7F3717s3r27Uu1tNhtsNpv7fn5+PgDA5XLB5XLVSoze4nK5IISo83H6AvumYuybirFvKnZx3+xJyMGS7Qk4lWGGzeGCWiFDszAdRvcyIc7UsK4k4HlTMfZNxS7sEyEq/yXFmpNrMOyrYRBCQKDs80q3FdmLMHTpUPxyzy8Y3HxwzQO+wB133AG73Y5FixYhNjYWaWlpWL9+PbKystyvRQhRpdd1MSEEiouLK72/0sfLG7/x/CMiIiL6h7vGqhf2Q9VWbxKtSUlJePLJJ/Hbb79Bo9FU6jkzZszA1KlTy2zPyMiA1Wr1dohe5XK5kJeXByEEZDKe5Bdi31SMfVMx9k3FLuybv1Mt+GDzORTYHAjWKmHQymF1uHAgKRtT0/MxsW8kOjbW+TrkK4bnTcXYNxUrLi6Gy+WC3W6HQlG5oVauNRcjvh1RklDEpZOHLrggEzKM+HYEzjxxBoGaQC9EDeTm5mLr1q1Yt24d+vTpAwBo0qQJunbtCgCIiYkBANx+++0AAJPJhBMnTuDUqVN47rnnsGvXLhQWFqJ169Z44403MGDAAPe+W7RogXHjxuHUqVP48ccfMXz4cHzxxRcA4N5/3759sW7dujJxORwOuFwuZGVlQalUejxWUFDglddOREREVO9567J/lg6okXqTaN2zZw/S09MRFxfn3uZ0OrF582Z89NFHsNlskMvlHs+ZPHkyJk2a5L6fn5+PqKgohIWFISAg4IrFXh0ulwuSJCEsLIwfYC/CvqkY+6Zi7JuKlfZNSEgoft54EBYHEBOmR7FTQAIQqJEhUKdBYlYRfjmWj+s7xkAmaxhvvjxvKsa+qZjFYkF+fj6USmWlE61fHvoSFrul3Jms5XHBBYvdgqWHlmJij4k1CdctMDAQOp0OP//8M3r37g21Wu3x+K5duxAeHo4FCxbgxhtvhFwuh0KhgNVqxdChQzF9+nRoNBosXrwYt912G44ePYqmTZu6n//ee+/hpZdewgsvvAClUoknnngCPXr0wNq1a9GuXTuoVKpy+0uhUEAmkyEkJKTMl+2V/fKdiIiIiOhKqDeJ1gEDBuDgwYMe28aNG4fWrVvj+eefL5NkBQC1Wl3mQwIAyGSyevGhUJKkehPrlca+qRj7pmLsm/IJIZBlcWDLuRTsScgBABxOKXDXZZXJJGiUcsgkYH9SHjadyESfFmFQKRpGP/K8qRj7pnwX9odUiRkBQgh8tOujah3rw10fYmKPiZU6zuUolUosWrQIDz74ID7++GN07doV/fr1w913342OHTvCaDQCAIKCgtCoUSP38zp37ozOnTu770+fPh0rVqzAzz//jMcff9y9/frrr8ezzz4Lh8MBhUKBhIQEAEBoaKjH/i4mSVKF5xrPPSIiIqJ/sHRAnVBvEq16vR7t27f32Obv74+QkJAy24mIqGI5hcU4k1WIs5kltzOZhcgusMDiBHKL7PBTyP5JakgQomRhLIvNASEEihwu/G/DSXy1KwmNAzVoGqxFVLAWTf+5+avrzdsKUZ2RVZSFUzmnqvw8AYFTOaeQXZSNEG2IV2K54447MHToUGzZsgXbt2/H6tWrMXPmTHz22WcYO3Zsuc8pLCzE1KlT8csvvyA5ORkOhwNFRUVITEz0aNetWzevxEhERERE5WDpgDqBn4iJiK5iZpvDnUw9m1mIM1mFyLPYPdoICChkEqIDtUjJtSFIq0SwvwpqZcmVAja7E0V2J3KLiiEVORCgUUIIgfM5RTifU4Ttp7Lc+wrRqTySr6YQfwRplV6ZbUd0tTIXm2v0/ILiAq8lWoGSy/EHDRqEQYMG4ZVXXsGECRPw6quvVphofe6557BmzRq88847aN68Ofz8/DBixAgUFxd7tPP39/dajEREREREdVG9TrRu3LjR1yEQEdUZVrsTCVkWnLkgsZpptpVpJ0lAk0A/RIf6IzrUH6ZgP6jsZkQYjZj07QEcSs6DWiFDaWpUo5RDrZAhr8iO3s1C8d5dnZBndSAx24LEbAuSsi1IyCpElrnYfduXmOs+nr9a4Z7xGhWshSlEi/AADeQNpM4r0eXoVDVbYE6v0nspkvK1bdsWK1asAFBSXsDpdHo8vmXLFowdOxa33XYbAMBsNuPs2bOX3a9KpQKAMvsjIiIiompg6YA6oV4nWomIGqpihwvnciw4m1WIM5kWnMk0IzXPClHOOjrGAA1iQrWIDvFHbJg/IoO00Cj/rWvtcrmQnl4ImUzCmHgTpq88goRsC8J0amiUcljtTmSYbTD4KTE63gS5XIZgfxWC/VXoHBXo3o+l+J/ka9a/CdjzuVYU2hw4kpKPIyn57rZKuQyRQX5oGvLv7NfIID+oFWXrbVeWyyVwPL0AeRY7DFolWhr1DWbRLqrfQvxC0CyoGU7nnK70YlgAIEFCbFAsgv2CvRJHVlYW7rzzTowfPx4dO3aEXq/Hn3/+iZkzZ+KWW24BAERHR2P9+vXuxbKCgoLQvHlzLF++HDfffDMkScLLL78Ml8t12eMZjUb4+flh9erViIyMhEajgcFg8MprISIiIiLyBSZaiYjqOKdLIDm36J+kasntfE4RnK6yCZkgfxViQv0RHeKP6H+Sq1WpmxpnCsaUoW2weFsCTqabkWm2QaWQo31jA0bHmxBnqjiho1Up0DoiAK0jAtzbih0uJOcWISnHgoSskuRrUo4FNrvL/VpKSRIQYdBcNPvVH7pKxL8nIdsdc7HDCZVCjuZGHcZcJmaiukCSJDxxzRN4es3TVX6utxbCAgCdTocePXpg1qxZOHXqFOx2O6KiovDggw/ixRdfBAC8++67mDRpEj799FM0adIEZ8+exaxZszB+/HjEx8cjNDQUzz//PPLz8y9zNEChUOCDDz7AtGnT8Morr6BPnz68WomIiIiouiTJSzNaOVmlJiQhypv/dHXKz8+HwWBAXl4eAgICLv8EHyqZYZYOo9HIFXUvwr6pGPumYle6b6o7u1IIgbR8G85kFiLhn8RqYrYFxY6ys8N0GgWiQ/wRE+rvTq4atMpqxFq2b2pzdmjpayxNvpbMgi1EgdVRbvsgf1WZ0gMh/ip3cmlPQjamrzyCXIsdRn3ZWbhThrapdrKVv1MVY99UzGKx4PTp02jWrBn8/Pwq9Zxcay4i34tEkb0ILlx+NqhMksFP4Ydzk84hUBNYw4ivHCEEHA4HFApFpRPEVqsVZ86cQUxMDDQajcdj9WlsR3VH6Xmj7vAgJLnK1+HQBT7+9Hlfh0BULzTVa30dAl2g0FyAYd1ifDYecb+vXfsiJIXm8k+4DOGwwrb1TY6vqokzWomIvKyysyuFEMix2HEm04wzmRaczSzE2axCFBWXrVeoUcrdM1Sj/0msXphs9DaZTPKYmepNkiQhwqBBhEGD7tH/9keupRhJ2UVIyC50lx5Iz7chp7AYOYXF+Csp193WTyV3lxv45a8UZJqLERuqhfyfhJ+/WgGtSo6EbAuWbEtAl6gglhGgOi1QE4jv7/oeQ5cOhUzILplslUEGCRKWj1xer5KsRERERERXOyZaiYi8qOzsSjWsdicOJedh6s+HcV+PptAo5TibWVJfNb/IXmYfSrkMUcF+iAnVITpUi5hQf0QEaGotqVpXBGpVCNSq0CHy3xqNRcVOJOX8W/c1MduC5NwiFBU7cSy1AHvO5uBQSh4UMhkOJedDo5RDr1HCqFdDLpMQplPjRLoZx9MLai1xTOQtg5sPxsp7V+KOb+6AxW4BAI+ardI/S9T5Kf2wfORy3NDsBp/ESURERER1EBfDqhOYaCUi8hKXS2DxtgTkWuwwhWhhtbuQVWiDpdiJQpsDKXlWvLf2ONo1CnAnTSVJQmSQX8ml/6H+iAnxR+NADRRyvrkBJTNXW4br0TL831XVHU4XknOtSMqxYOOxDBxLL4BSJkGIksRsUbET2YXFaGTQIMBPiWKzDXmWsgltorpocPPBODfpHJb8tQQf7PwAp3JOuR+LDYrFxB4TMabTGBg0XDSKiIiIiC4gSd6pr3qVT/CpbUy0EhF5ydHUfPydnAcB4GhqAewX1VVVyWUodrjQ3KjDNTEhiA71R9NgLVQKJlWrQiGXoWmIFk1DtAjRqfDHyUzoNQoo5TJYih1Izbei2O5CUrYFSoUM/mpFtWrXEvlKoCYQE3tMxBPXPIHsomwUFBdAr9Ij2C/4qp/ZTkRERERUnzHRSkRUA3anC4eT87E3MQfrjqQhNc8KP4UMkiRBJpOg15TUCvVTKaCWy5CSV4QBbcLRIzbE16FfFVoa9Whu1OFQch5MwVqotSoY/JTIMNuQmmdFXpEdLiGw+2w2mgZroVXxbY/qD0mSEKINQYiWfy+IiIiI6DJYOqBO4CdOIqIqstqdOHg+D3sScnDwXB6s9pLFqxwOAYVMgk6jRFiAGnq1ArILZp8V2hxQKeScXelFMpmEMfEmTF95BAnZFoTp1NAo5dCpFNCpFVApZGgU4IeNRzOw52wORsRFoXfzEM4KJCIiIiKiqwtLB9QJTLQSEVWC2ebA/sRc7E3MwaHkPDic/y5QE6hVoaspEJ2jAjFv4ykcTslHgFrhkcwTQiDDbEP7xga0NOrLOwRVU5wpGFOGtsHibQk4mW5GptkGlUKOTpGBGB1vgp9SgaW7EpCSa8XCP85g84kM3NejKUwh/r4OnYiIiIiIiK4iTLQSEVUgu7AY+xJzsDcxB8dSzRDi3+SqMUCNrk2D0NUUhNhQf3dSdWzv6DKzK612JzLMNhj8lBgdb4JMxm8IvS3OFIwuUUE4nl6APIsdBq0SLY16d1+/dnM7rDuShp/+SsapdDNe/+Uw+rUy4rYuTaBT862QiIiIiIjqOZYOqBPYe0REF0jLt2LVwRRMX3kYz337F5buTMTRlAIIIRAVrMUtXZpg6i3t8OZtHXBntyg0C9N5zFwtnV3ZrrEB+VYHzuVYkG91oH1jA6YMbYM4U7APX93VTSaT0DoiAD1iQ9A6IsAjoa2Qy3Bj+0aYfmsHXBMTDCGAjUfTMeWHg9h8PMMjiU5UZwgBZGYCZ8+W/J/nKRERERHVUXPmzEFMTAw0Gg3i4uKwZcuWCtsuX74cgwYNQlhYGAICAtCrVy+sWbOmTLvvv/8ebdu2hVqtRtu2bfHDDz/U6LhXAqfxEFGDJoRAUnYR9v4zc/V8TpH7MUkCmoXp0KVpELqaAmHUayq1z8vNriTfCfJX4aF+zdC3ZRi+3FlSTmDxtrPYfDwD9/U0ISaU5QSoDsjNBRYvBj78EDh16t/tzZoBTzwBjBkDBAb6Kroq27hxI6677jrk5OTAYDD4OhwiIiKiq5MPa7QuW7YMTz31FObMmYPevXvj448/xpAhQ3D48GE0bdq0TPvNmzdj0KBBePPNNxEYGIiFCxfi5ptvxs6dO9GlSxcAwPbt2zFy5Ei8/vrruO222/DDDz/grrvuwtatW9GjR49qHfdKkEQDmsaTn58Pg8GAvLw8BAQE+DqcS3K5XEhPT4fRaIRMxonHF2LfVIx9U7EL+0aSJJzKMGNvQi72JOQg02xztyuZFalHV1MQukQFIlCr8mHUV0ZDPW8cThfWH03HT/uTYbU7IUlA35ZhuL1rpLucQEPtm8pg31TMYrHg9OnTaNasGfz8/Kr25DVrgDvuACyWkvsXDtNKB71aLfD998Dgwd4J+AJjx47F4sWLy2wfPHgwVq9eXa19XpxodTgcUCgUlV6Uzmq14syZM+6ZCheqT2M7qjtKzxt1hwchya/+9/n65ONPn/d1CET1QlO91tch0AUKzQUY1i3GZ+MR9/vagOmQFJWbHHQpwmGFbf2UKr2eHj16oGvXrpg7d657W5s2bXDrrbdixowZldpHu3btMHLkSLzyyisAgJEjRyI/Px+rVq1yt7nxxhsRFBSEr776ymvH9TbOaCWiBsHhdOFYugVrTifgr6Q85BXZ3Y8p5TK0bxKArqYgdIoMhD9rdjYICrkMg9tFoEdMML798xx2nM7CpmMZ+PNsDm7v2gR9W4T5OkRqaNasAYYOLUmulvc9eOm2oqKSditX1kqy9cYbb8TChQs9tqnVaq8fh4iIiIjqrvz8fI/7arW63DFhcXEx9uzZgxdeeMFj+w033IBt27ZV6lgulwsFBQUIDv631N727dvx9NNPe7QbPHgw3n//fa8dtzYwm0BE9Y7LJSp1Wb7N4cSh5HzsTcjB/sRc5JgtUKtVkCBBo5Kjc2QgupoC0a6xARql3AevhOqCQK0KD/aNRd+WYVi6MwHncorw+fYEbDmRiXuuiYLO1wFSw5CbWzKTVQjA5bp0W5cLkMlK2p875/UyAmq1GhEREeU+JkkSPv30U6xcuRJr1qxBkyZN8O6772L48OHuNr/++iueeuopJCUloWfPnhgzZoxX4yMiIiKicni5dEBUVJTH5ldffRWvvfZameaZmZlwOp0IDw/32B4eHo7U1NRKHfLdd99FYWEh7rrrLve21NTUS+7TG8etDUy0ElG9sichG4u3JeBkuhnFDidUCjmaG3UYE29CnCkYlmIH/krKw97EHBw8lwe7syRhISCgU8vRq0UYukUHo3WEHgo5L3emf7WK0OOVm9vh96PpWLH/PM5mFmLGr0fRKUKNMX2DYNByRh/VosWLS8oFVLaik8tV0n7JEmDixNqN7SJTp07FzJkz8fbbb+PDDz/Efffdh4SEBAQHByMpKQm33347Hn74YTzyyCP4888/8cwzz1zR+IiIiIgaJhkgeeMzbsk+kpKSPEoHXO4Kp4vLQgkhKlUq6quvvsJrr72GH3/8EUajscr7rO5xawsTrURUb+xJyMb0lUeQa7HDqFdDo1TDanfi4Llc/N93eejaNAh5RXY4Xf8mKkJ0KnRtGoTOUQYEwIKI8HDWk6QKyWUSBrUNxzXRwfh2TxK2ncrEzoR8HF9xCHd0jUS/lmFc1Iy8T4iSha+q44MPShbI8uJg8pdffoFO5zmX+/nnn8fLL78MoKSO6z333AMAePPNN/Hhhx9i165duPHGGzF37lzExsZi1qxZkCQJrVq1wsGDB/Hf//7Xa/ERERERUe0LCAioVI3W0NBQyOXyMrNI09PTy8w2vdiyZcvwwAMP4Ntvv8XAgQM9HouIiLjkPmty3NrERCsR1Qsul8DibQnItdgRHaKFwyWQXViM3CI7Cm12FNld2HwiA+0aBaBxkB/iTEHo2jQITYO1kCTpn4V7inz9MqieMGiVmNAnFn1bhOLTjUeRZXXgix0J2HwiA/f1MKG5kQUFyIuysoBTp6r+PCFKnpedDYSEeC2c6667zmNBAQAe9bI6duzo/re/vz/0ej3S09MBAEeOHEHPnj09ZhH06tXLa7ERERERUQW8XDqgslQqFeLi4rB27Vrcdttt7u1r167FLbfcUuHzvvrqK4wfPx5fffUVhg4dWubxXr16Ye3atR51Wn/77TfEx8fX6Li1jYlWIqoXjqcX4GS6GQY/JZJyipBjKQb+mbgqQUKARgGFXIZx18agDxcxIi9pbtThqb5ROJwr4cf9yUjMsmDGr0fQu3koRnSLRIBG6esQ6WpgNtfs+QUFXk20+vv7o3nz5hU+rlR6nvelX2YBJZdqEREREVHDMmnSJIwaNQrdunVDr1698MknnyAxMREPP/wwAGDy5Mk4f/48lixZAqAkyTp69GjMnj0bPXv2dM9K9fPzg8FgAAA8+eST6Nu3L/773//illtuwY8//oh169Zh69atlT6uLzDRSkR1nhACexNykJJXBBn+rcGiVSsQqFXC4KeEXJJwLscCFeuukpfJZRIGtDaiR2wIvvvzHP44mYk/TmZib2IObuvSBP1bGSFnOQGqCV0NZ0jr9d6Jwwvatm2LFStWeGzbsWOHb4IhIiIiakgkyTs1WqsxK3bkyJHIysrCtGnTkJKSgvbt2+PXX3+FyWQCAKSkpCAxMdHd/uOPP4bD4cBjjz2Gxx57zL19zJgxWLRoEQAgPj4eX3/9NV566SW8/PLLaNasGZYtW4YePXpU+ri+wEQrEdVZxQ4Xtp/OwtrDqTiRaobd6YJCLkOIVokwvRr+qn//hBXaHFAp5DBoOcOQakeARonx18agb8swfLEjAUnZFizdmYgtJzJxf8+maG6sO8kuqmdCQoBmzYDTpyu/GBZQMgiOjQUuuKzfG2w2W5laVwqFAqGhoZd97sMPP4x3330XkyZNwkMPPYQ9e/a4B8tEREREVIskLy2GVc19PProo3j00UfLfezi8eDGjRsrtc8RI0ZgxIgR1T6uL3DqFxHVOXlFdqzYdx7PffcXlmw7i5RcK0L0KjQP0yHQTwlTsNYjySqEQIbZhhZGHVoy2UW1rLlRh1eGtcX9PU3QqhVIyrZgxq9H8dmW08grsvs6PKqPJKlkQavqmDjRqwthAcDq1avRqFEjj9u1115bqec2bdoU33//PX7++Wd06tQJ8+bNw5tvvunV+IiIiIiI6irOaCWiOuN8bhF+O5SKHaez4HCWzOoK0akwsE04+rQIw+GUPExfeQQJ2RaE6dTQKOWw2p3IMNtg8FNidLyJK8LTFSGTSbiutRFx0UFYvucctpzIxPZTWdiXlItbOzfB9a1ZToCqaMwYYMoUoKgI+Kfe6SXJZICfHzB6tFfDWLRo0SVnoJZXgzU3N9fj/rBhwzBs2DCPbePGjavw+URERETkBT5aDIs8MdFKRD4lhMDhlHysOZSGQ+fz3Ntjw/xxQ7sIdG0a5E5YxZmCMWVoGyzeloCT6WZkmm1QKeRo39iA0fEmxJm8e/ks0eUEaJQY2zsGfVqG4csdiUjIKsTXuxKx9UQG7u1hQqsIzrCmSgoMBL7/Hhg6tCSJeqlkq0xWMgBevrzkeUREREREPi4dQCWYaCUin7A7Xdh5Ohu/HU7F+ZwiACV5gy5NgzC4XXiF9S7jTMHoEhWE4+kFyLPYYdAq0dKo50xW8qlmYTq8NLQNNp/IwPK953EupwgzVx9Fz9gQ3NktEoFalbutyyV4/lL5Bg8GVq4E7rgDsFhKtl04A7R0doGfX0mS9YYbrnyMRD42d+5czJ07F2fPngUAtGvXDq+88gqGDBni28CIiIiIwEQrEV1hBVY7NhzLwIaj6cj/p56lWilDnxZhGNDGCKNec9l9yGQSWkcE1HaoRFUik0no38qIbtHBWL73HDYfz8CO01nYn5SL4Z0bY0BrI/46l+uekV3scEKlkKO5UYcxnJFNpQYPBs6dA5YsAT74ADh16t/HYmNLarKOGQMYDL6LkciHIiMj8dZbb6F58+YAgMWLF+OWW27Bvn370K5dOx9HR0RE5EMsHVAnMNFKRFdESl4R1h5Ow7aTWbA7Sy6JDfJXYWAbI/q2DINWxT9HdHXQqRUY3SsafVqE4csdCTiTWYhvdidh+d5zSMyywOZwwahXQ6NUw2p34lBySe3hKUPbMNlKJQIDSxKqTzwBZGcDBQWAXg8EB3PgSw3ezTff7HF/+vTpmDt3Lnbs2MFEKxEREfkcMxtEVGuEEDiaWoDfDqXhwLlc93ZTiD8GtwtHnCkICjnrv9DVKSbUH1OGtsGWE5n49s8k7DqTjQKbA40C1FApZJDLJPirFdCq5EjItmDJtgR0iQpiGQH6lyQBISElNyIqw+l04ttvv0VhYSF69epVYTubzQabzea+n5+ffyXCIyIiurJYo7VOYKKViKqkMvUlHU4Xdp3Nxm+H0pCUXVJnUJKATpGBuKFdBFqG6yBxVhY1AJIkoW/LMOg0Cuw+mwOVQoa8IgcKbAUI0qrgr5bDX61AqE6NE+lmHE8vYFmMq4jrUgtaUaWxH+liBw8eRK9evWC1WqHT6fDDDz+gbdu2FbafMWMGpk6degUjJCIi8gGWDqgTmGglokrbk5B9yfqSZpsDm45lYP3RNORZSuqvqhQy9G4eikFtwxEecPn6q0RXI7vDBZ1aDlOIFil5RbDYnMgy25BlLnlcIZdgdwr8fiQdWqUCTYL8IOfM1npLpSpZ/CwlJQVhYWFQqVT8cukfQgg4HA4oFIrL9okQAsXFxcjIyIBMJnP3K1GrVq2wf/9+5Obm4vvvv8eYMWOwadOmCpOtkydPxqRJk9z38/PzERUVdaXCJSIiogaEiVYiqpQ9CdmYvvIIci32MvUlX/3pEOJjQ5GUY0Gxo2TmkUGrxIDW4ejXKgw6Nf/UUMNm0CqhUsghAWhu1CPfaofZ6kChzYEiuxNWuwsOlwubjmVgT0IO1EoZmoXp0NxYcmsWpoNGKff1y6BKkslkCAoKgt1uR3Jysq/DqVOEEHC5XJDJZJVOPmu1WjRt2hQyGS9joxIqlcq9GFa3bt2we/duzJ49Gx9//HG57dVqNdRq9ZUMkYiI6IqTJMk7X+5zgkCNMPtBRJflcgks3paAXIsd0SFaSJIEAUD889jpjEJkFNjQrlEAmob444Z24bgmOpj1V4n+0dKoR3OjDoeS82BSyWHQKGHQKAEADpcLpzMKYQzQ4JrYYJzOLIS12InDyfk4nFxSR1CSgMggLVqG693J12B/zu6ry+RyOSIiIuByueB0On0dTp3hcrmQlZWFkJCQSiVO5XJ5pWa/UsMmhPCowUpERNQQMdFaNzDRSkSXdTy9ACfTzTDq1RAAcgqLkWm2oai4JHmgksvgcgEjukVhSPsIfiAmuohMJmFMvAnTVx5BQrYFYTo1NEo5rHYnMsw2hOnVePGm1ogzBcPlEjifW4ST6Wac+Od3L8tcjKRsC5KyLVh/JA0AEKJToblRhxb/JHGbBPpxIa06RpIkKJVKKJVKX4dSZ7hcLiiVSmg0Gs5QpWp58cUXMWTIEERFRaGgoABff/01Nm7ciNWrV/s6NCIiIiImWono8vIsdhQWO+BwuZCbaYfLJQCUJBGC/JUI9lchs8CGEH/WISSqSJwpGFOGtnHXOc4026BSyNG+sQGj/6lzDJQkZaOCtYgK1uK61kYAQHZhMU6mm93J16RsC7LMxcgyZ2Pn6WwAgEYld5cbaGHUISbU/7LlBiqzuB0RUV2SlpaGUaNGISUlBQaDAR07dsTq1asxaNAgX4dGRETkW9I/N2/sh6qNiVYiqlCxw4U/z2Zj+b7zyDLboJDJoJBJUCpkCPFXIVinglImQ6HNAZVCDoOWs7aILiXOFIwuUUFVTm4G+6twTUwwrokpScZa7U6cyjC7k6+nMsywFjtx6HweDp3PA1DyRYgpRIsWxn9rvQZq/y03cLnF7YiI6qL58+f7OgQiIqI6iaUD6gYmWomojPO5Rdh0LAPbTmWiqNgJIQT81QrYnQLRoVoE+KncX3IJIZBhtqF9YwNaGvU+jZuoPpDJJLSOCKjRPjRKOdo1NqBdYwMAwOkSOJ9ThBPpBTjxT/I1p7AYZzMLcTazEGsPl5QbCNOr0dyogxACP+5PhqXYWWZxu+krj2DK0DZMthIREREREVURE61EBOCf2asJ2dh0LAMn083u7SE6Ffq2DMNotQIfrD+BHIsdCpnMo76kwU+J0fEmXnJM5CNymYSmIVo0DdFiQJtwAECW2YaT6WYcTzfjVLoZ53IsyCiwIT3fikPJ+SiwOeCvkkMA0GsUCPFXwRSsRUK2BUu2JaBLVBB/p4mIiIiI6gnOaK0bmGglauCSc4uw8YLZq0DJH+guTQPRr2UY2jUOcP+x1msUl60vSUR1Q4hOjRCdGj1iQwAAlmIHTmcUYvPxDBw4nwe1UgYhgIIiOwqK7MgpLEZUsBZhOjVOpJtxPL2gxjNviYiIiIiIGhImWokaIPfs1eMZOJlWdvbqtc1DPWo5lqpufUki8j2tSoH2TQwotDmwfO85NA70g90pYLY5kF5gRVGxE8fTChAeoIbN4UKexe7rkImIiIiIqJI4o7VuYKKVqAFJzi3CpuMZ2HYqCxabA8C/s1f7tiiZvXq5pKk36ksSke8YtEqoFHIUO1zwVyugVckRpFUiKacIBUV2nM+xQi6TYHcKX4dKRERERESVxERr3VBvEq1z587F3LlzcfbsWQBAu3bt8Morr2DIkCG+DYyojqto9mqwf8ns1T4typ+9SkRXp5ZGPZobdTiUnAetSg5JkqCUyxAT6o9ssw0nMsxQKxX4fPtZWO1ODGhj9M6AjYiIiIiI6CpXbxKtkZGReOutt9C8eXMAwOLFi3HLLbdg3759aNeunY+jI6p7Kpq92jnKgH4tjZWavUpEVx+ZTMKYeBOmrzyChGwLwnRq9+J2BTYHYkN1aBmuQ67Fjq92JWJfUg7G9Y5BqE7t69CJiIiIiKgi0j83b+yHqq3eJFpvvvlmj/vTp0/H3LlzsWPHDiZaif5R7HBhT1IB/tqbjVPphe7tnL1KRBeKMwVjytA2FS5u17VpEDYey8A3fybhaEoBXvnxb9zdvSn6tAjl7FYiIiIiojqIpQPqhnqTaL2Q0+nEt99+i8LCQvTq1cvX4RD5XOns1T9OZiKnwAK1WgWZJOPsVSKq0OUWt7uudcnfjvlbz+BkuhmLt53FnoQcjOsdzS9siIiIiIiIylGvEq0HDx5Er169YLVaodPp8MMPP6Bt27YVtrfZbLDZbO77+fn5AACXywWXy1Xr8daEy+WCEKLOx+kLDaFvXC6B4+lm5BfZEeCnREujrkyitNjhwp6EHGw6kYGT6SW1V4UAAv0UuKFDI1zbIgxB7mSIgMvVsBe2aQjnTXWxbyrWEPqmpVF3wT3PvxWhOhWeu6El1h5Jww/7knHwfC5eWvE37rsmCt1MgVd931RXQzhvqsvbfcM+JiIiIiohSfDSjNaa76Ihq1eJ1latWmH//v3Izc3F999/jzFjxmDTpk0VJltnzJiBqVOnltmekZEBq9Va2+HWiMvlQl5eHoQQkMlkvg6nTrna++ZAshnf7k9HQo4VxQ4BlUKCKUiDOzsb0bGxDmkFxdiRkI8/E/NhsZd8wJRJQJtwf/Q06RGuKkZQoBx2cy7SzZc5WANytZ83NcG+qRj7pkSXMBka9zLiq71pSMq14KP1x9AhQouB0eoG3zfl4XlTMW/3TUFBgReiIiIiIiLyjnqVaFWpVO7FsLp164bdu3dj9uzZ+Pjjj8ttP3nyZEyaNMl9Pz8/H1FRUQgLC0NAQMAVibm6XC4XJElCWFgYP6Rd5Grumz0JOZi7PQ15RXaE6TXuBWpOZRdj5sZzaNfIgAKbvaSxTIFGwSr0aR6Ka1uEIkirgsvlQkZGxlXZNzV1NZ83NcW+qRj75l9GI9AmujFW/Z2KXw6k4Fi2A2eyrXignxHdokN8HV6dwvOmYt7uG41G44WoiIiIiOo/CV6q0coprTVSrxKtFxNCeJQGuJharYZaXXaVZJlMVi8++EiSVG9ivdKuxr5xuQQ+35GIvCI7okO0kCQJVrsTeUUOmG12mG1OFFgdaN/YgM5RgejXKgztGxvKlBS4GvvGW9g3FWPfVIx98y+VTIZbukSiS9NgfLrlFE6n5WHe5jPoda4A9/ZoCn91vR5WeBXPm4p5s2/Yv0REREQluBhW3VBvPhG9+OKLGDJkCKKiolBQUICvv/4aGzduxOrVq30dGpFXHE8vwMl0M8L0KuQW2ZFlLkahzeF+3F8lh0Imw0N9Y3FNLGePEZHvNA3R4qWhbfDFlmPYlmjBjtNZOJKaj3HxMegQafB1eERERERERD5RbxKtaWlpGDVqFFJSUmAwGNCxY0esXr0agwYN8nVoRF5xPqcIWYU2ZJkBZ+liNBIQoFEiRKeCVqXA+RwLGvaSVkRUVyjlMgxtG4J+7aKwYFsC0vKseH/dcfRtGYaR3aOgUcp9HSIRERERUcMhwTtX/XNCa43Um0Tr/PnzfR0CkdcJIXAy3Yz1R9Ox8Vg6Cm0OKGQyaJRyhOhUCPFXQSkvuSyy0OaASiGHQav0cdRERP+KDdPh1ZvbYvne81h3OA2bj2fgUHIexl8bg9YRdbseOhERERHRVcNLpQMESwfUSL1JtBJdTWwOJ3aezsbvR9ORlG0BAGiVcoQHaFBU7ETLcB3kF9SdE0Igw2xD+8YGtDTqfRU2EVG51Ao57rmmKbo0DcSCrWeQZS7G26uPYUCbcNwR1wRqBWe3EhERERHR1Y+JVqIrKD3fig3H0rHlRCaKip0ASi6/7RkbjOtbhyPDbMX0lUeQlFOEMJ0aGqUcVrsTGWYbDH5KjI43lVn8ioiormgdEYBpt7THN38mYdOxDKw/koaD5/PwwLUxaG7U+To8IiIiIqKrlrcWw/LKgloNGBOtRLVMCIG/z+dj/dE0/H0+D+KfIqthejX6tzLi2hah0P2zUnfTEC2mDG2DxdsScDLdjEyzDSqFHO0bGzA63oQ4U7APXwkR0eVplHKM7hWNLlFBWLjtDNLzrXhr1RHc2L4Rbunc2F0OxeUSOJ5egDyLHQatEi2Nen6RRERERERE9RoTrUS1pNDmwB8nM7HhWDrS823u7e2bGHB9ayM6NDGUm1SIMwWjS1QQExBEVK91iDTg9Vva46tdidh+KgurDqbgwLlcPHBtDDLNNvcXSsUOJ1QKOZobdRjDL5SIiIiIiKqFM1rrBiZaiaqgMjOwkrIt+P1oOrafyoLd6QIA+KnkuLZ5KK5rbUR4gOayx5HJJC4iQ0T1nr9agQl9YtGlaRA+334W53OK8Oy3fyGjwAaZJMGoV0OjVMNqd+JQch6mrzyCKUPbMNlKRERERFRV0j83b+yHqk12+SZEBAB7ErLx1LL9mLTsL0z54SAmLfsLTy3bjz0J2XA4Xdh9NhtvrTqK1346hM3HM2B3uhAZ5IdRvUx4585OuPuappVKshIRXW3iTEGYdmt7dGkaiMQsC9ILbCh2OCGXSZDLJPirFTAFa5FXZMeSbQlwuYSvQyYiIiIioiqYM2cOYmJioNFoEBcXhy1btlTYNiUlBffeey9atWoFmUyGp556qkyb/v37u2fpXngbOnSou81rr71W5vGIiIjaeHmVxhmtRJWwJyEb01ceQa7F7jED6+C5XDzzTS5MIVooZCXfW0iShDhTEK5vbUTLcB2n3RMRAQjQKDGgjRHf7jkHh8sFq92FY2kFiAjQIEyvhkySEKZT40S6GcfTCzirn4iIiIioCnxZOmDZsmV46qmnMGfOHPTu3Rsff/wxhgwZgsOHD6Np06Zl2ttsNoSFhWHKlCmYNWtWuftcvnw5iouL3fezsrLQqVMn3HnnnR7t2rVrh3Xr1rnvy+XyKsfvTUy0El2GyyWweFsCci12RIdoAUmCpdiBTLMN+VY7iuwuFNmd6BUbgv6tjOjXMgxB/ipfh01EVOfkFzmglEloExGA83lWFBTZkZpnRY6lGJFBWvgp5cg025Bnsfs6VCIiIiKiesWXidb33nsPDzzwACZMmAAAeP/997FmzRrMnTsXM2bMKNM+Ojoas2fPBgAsWLCg3H0GB3uWE/v666+h1WrLJFoVCoXPZ7FeiKUDiC7jeHoBTqabYdSrYXW4cCKtACfTzMi12CFBgsFPCT+lHA/2icWtXZowyUpEVAGDVgmVQg6HSyAm1B9RIVrI5RJsdhdOpZtxJrMQCrkMBq3S16ESERERETVo+fn5HjebzVZuu+LiYuzZswc33HCDx/YbbrgB27Zt81o88+fPx9133w1/f3+P7SdOnEDjxo0RExODu+++G6dPn/baMauDiVaiy8iz2GFzOFFod+BEmhlFxU5IkoRgnQotI/RoFa6HQibBbHP4OlQiojqtpVGP5kYdMsw2QAgEa1VoHRGAYJ0KQghkmG0osNqRlm+FEKzTSkRERERUWeXVM63uDQCioqJgMBjct/JmpgJAZmYmnE4nwsPDPbaHh4cjNTXVK69t165d+Pvvv90zZkv16NEDS5YswZo1a/Dpp58iNTUV8fHxyMrK8spxq4OlA4guQ62UocDqQJa5GAqZBL2fElHBflD+U5O10OaASiHnDCwiosuQySSMiTdh+sojSMi2IEynhkYpR7BWhaJiJ2QyJyIC/LBkWwK2n8rGqF4mNAn083XYRERERER1nrdLByQlJSEg4N91E9RqdaWeV0oI4bU1a+bPn4/27dvjmmuu8dg+ZMgQ9787dOiAXr16oVmzZli8eDEmTZrklWNXFWe0El3CmcxCfLUzERKAYqcLEQYNYkL93UnW0hlYLYw6tDTqfRssEVE9EGcKxpShbdCusQH5VgfO5ViQb3Wga9Mg/O/erniwbyxUChlOpBXgtZ8O4bs952BzOH0dNhERERFRgxIQEOBxqyjRGhoaCrlcXmb2anp6eplZrtVhsVjw9ddfl5nNWh5/f3906NABJ06cqPFxq4szWonKIYTA2sNp+G7POThdAu2aBCA1z4oiuxMWmwMapRxWuxMZZhsMfkqMjjdBJvPONzVERFe7OFMwukQF4Xh6AfIsdhi0SrQ06t1/R7tHB2HpzkTsT8rFqoMp2HUmC/f3NKFjZKBvAyciIiIiqqukf27e2E8VqFQqxMXFYe3atbjtttvc29euXYtbbrmlxuF88803sNlsuP/++y/b1maz4ciRI+jTp0+Nj1tdTLQSXaTAaseCrWdx4FwuACAuOghj46NxJCUfi7cl4GS6GZlmG1QKOdo3NmB0vAlxpuBL75SIiDzIZBJaRwSU+1iITo0nBrTAvsQcfLkzEVnmYsxedwJdTUG455qmCOaig0REREREdcakSZMwatQodOvWDb169cInn3yCxMREPPzwwwCAyZMn4/z581iyZIn7Ofv37wcAmM1mZGRkYP/+/VCpVGjbtq3HvufPn49bb70VISEhZY777LPP4uabb0bTpk2Rnp6ON954A/n5+RgzZkztvdjLYKKV6ALH0wrw8abTyLUUQyGXcPc1TdG/ZRgkSbrsDCwiIvKuLk2D0KZRAH7an4zfDqdhb0IODiXn4bYukbi+tRHyf/7+ulyCf5uJiIiIqEHzdo3Wqhg5ciSysrIwbdo0pKSkoH379vj1119hMpkAACkpKUhMTPR4TpcuXdz/3rNnD5YuXQqTyYSzZ8+6tx8/fhxbt27Fb7/9Vu5xz507h3vuuQeZmZkICwtDz549sWPHDvdxfYGJViKUfEj/5WAKftp/HkIA4QYNHunXDFHBWo92l5qBRURE3qdRynFX9yj0ahaCJdvP4nRGIb7elYg/TmZidC8TcizF7qsNih1OqBRyNDfqMIZXGxARERFRA+LLRCsAPProo3j00UfLfWzRokVltgkhLrvPli1bXrLd119/Xen4rhQmWqnBy7UU49Mtp3E0pQAAEN88FPf1aAqNUu7jyIiIqFRUsBYv3tQGm45n4Ls955CUbcH/fXcAaflWKOQSwvUaaJRqWO1OHErOw/SVRzBlaBsmW4mIiIiI6IphopUatL/P5+GzLadRYHVArZTh/p4mxDcL9XVYRERUDkmS0L+VEV1NQVi2KxGfbTmDApsDerUCxVoXtGoF/NUKaFVyJGRbsGRbArpEBbGMABERERFd9Xw9o5VKMNFKDZLD6cKK/clYdTAFQMlMqYf7NUOEQePjyIiI6HICNEr0aRmGr3YlQUDA6RJIzLIgu7AYkUFaqBUyhOnUOJFuxvH0ApZ8ISIiIqKrn/TPzRv7oWpjopUanEyzDZ9sPo1T6WYAQP/WRozsFgWVQubjyIiIqLLyLHbIJKB1RACyCouRnm+F2erAsdQCRBjUCPZXo9hsQ57F7utQiYiIiIiogWCilRqUPQk5WPjHGRQVO+GnkmNc72jW7yMiqocMWiVUCjmKHS5EBGgQpFXhXI4FZqsDKblWpBfYoFcrYNAqfR0qEREREVGtY+mAuoGJVmoQih0ufLsnCb8fSQcAxIb546F+zRCqU/s4MiIiqo6WRj2aG3U4lJwHrUoOtUKG2DAdcgqLcT7XggKrAwCwPzEX0SH+XOCQiIiIiIhqHROtdNVLzbNi3qZTSMq2AABubB+B27o0gULOUgFERPWVTCZhTLwJ01ceQUK2BWE6NTTKkoSrv0oBpVyGiAA/rD2chn2JuRjVy4T2TQy+DpuIiIiIqFZwRmvdwEQrXdW2ncrEFzsSYLO7oNMoMOHaWHSI5AdtIqKrQZwpGFOGtsHibQk4mW5GptkGlUKOjpGBGB1vglIuw+fbE5BptmHW2uPo1SwEI7tHQa9hOQEiIiIiurpI8FKilath1QgTrVTvuVwCx9MLkGexw6BVoqVRj2KnC1/uTMS2k5kAgFYRevynbywCtSofR0tERN4UZwpGl6igMu8DMlnJAPH1W/X4Yd95rD+Shu2nsnDwfB5Gdo9Cr9gQfltPRERERERexUQr1Wt7ErLdM5mKHU6oFHI0DtRAJklwugQkCRjeuQmGdWjk/tBNRERXF5lMQuuIgHIf0yjluOeaprgmJhhLtp3FuZwizN9yBjtOZ2NUTxPC9KzVTURERET1H0sH1A0sUkn11p6EHExfeQR/n89DgEaBJkFaOIUL209lYcfpLDhcLjw3uDWGd2rMJCsRUQPXLEyHl4e1xW1dm0Ahl3DofB5e+fFvrDmUCqdL+Do8IiIiIqKakbx4o2pjopXqJZcQWLI9AbkWO6JDtNAo5TiXY0FWQTE0ChnkMgn+agVaGHW+DpWIiOoIhVyGYR0bY+rw9mgVoUexw4Vvdidh+sojSMyy+Do8IiIiIiKq51g6gOql01lWnMoww6hXw+4SOJVuRrHDBUhA40AttCo5zmZacDy9oMLLSYmIqGGKMGjw3OBW2HoyE8t2JyEhqxDTfjmMwe3CMbxzY6gVcl+HSERXwMZlr0Gn5zixLmkewUkSRJXxzf4kX4dAFygy140v7Fk6oG7gjFaqlwqsDtgcLijlMpzOKEmyKhUyNDfqYNSr4aeUo9jhRJ7F7utQiYioDpIkCX1ahGH6rR3QLToYQgis/jsVr/10CIeT830dHhERERER1UNMtFK9pNcooJLLcCrDDJv93ySrv6pkkrbVXrIwlkGr9HGkRERUlxm0SjzSvxmeGNACQf4qpOfb8O5vxzB/6xmYbQ4AgMslcDQ1HztPZ+Foaj5crOlKRERERHVM6YxWb9yo+lg6gOqlmOCSVaJzi+zwV8kRE+oPlbzkewMhBDLMNrRvbEBLo96XYRIRUT3ROSoQrcL1WL7vHDYcTce2k5k4eC4XXZsGYXdCNk6lF6LYUfIlXnOjDmPiTYgzBfs6bCIiIiIiAIAkldy8sR+qPs5opXrpl8PZ0KrkUMolaJRyuFwCTpdAoc2BhGwLDH5KjI43QSbjXwgiIqocP5Uc9/Uw4YUhbdA40A9J2UV457dj2HoiE34qGSKDtAjQKHAoOQ/TVx7BnoRsX4dMRERERER1CBOtVO+sPZyGzadyYdAqMWlQK3RpGoR8qwPncizItzrQvrEBU4a24UwjIiKqluZGHV4e2gYCAg6XgAxAUnYRsi3F0KoVMAVrkVdkx5JtCSwjQERERER1QsmMVm+UDvD1K6nfWDqA6pXdZ7PxzZ/nAAB3dI3E0I6NMaqnCcfTC5BnscOgVaKlUc+ZrEREVCOnswphtbvQKkKPbHMxCm0OJOcUoajYiahgLcJ0apxIN+N4egFaR3DVciIiIiLyMS+VDgDTKTXCRCvVG8fTCvDZltMQEOgdY8CN7cIBADKZxA+5RETkVXkWO4odThj1agRpVcgy23A+twg5hcVwugQig/xQ7HAiz2L3dahERERERFRHMNFK9UJybhE+/P0kHE6BLlGBuLWtnivhERFRrTFolVAp5LDanfBXKxCqU0MplyEhqxD5RXacdDhh0Chh0Cp9HSoRERERkfvSf2/sh6qPNVqpzsu1FGPW2uOw2BxoZtThwT6xkPEXn4iIalFLox7NjTpkmG0QoqQOq8FPiZhQHSQJyLXYUex0oYnBz8eREhERERGV1mj1zo2qj4lWqtOsdifeX3cC2YXFMAZo8MT1zaFS8LQlIqLaJZNJGBNvgsFPiYRsCwptDjhdAjIJ0CjlUCtkCPRT4Z3fjiGviOUDiIiIiIiIiVaqwxxOF+ZsOImkbAv0GgWeHtQCeg0v0SQioisjzhSMKUPboF1jA/KtDpzLsSDf6kDXpkGYflsHmEK0OJdThLdWHUWm2ebrcImIiIioAZPJJK/dqPpYo5XqJCEEFm9PwKHkfKgUMjw5sCWMeo2vwyIiogYmzhSMLlFBOJ5egDyLHQatEi2NeshkErqZgvDOb8eQnm/FW6uO4tkbWiHCwPcqIiIiIqKGqt7MaJ0xYwa6d+8OvV4Po9GIW2+9FceOHfN1WFRLftyfjG0nMyFJEh7p3wwxof6+DomIiBoomUxC64gA9IgNQeuIAPe3/MYADV4Y0gYRBg1yCovx1qojSMyy+DhaIiIiImqIWKO1bqg3idZNmzbhsccew44dO7B27Vo4HA7ccMMNKCws9HVo5GWbjmfg57+SAQCjepnQMTLQtwERERFVINhfheeHtEbTEC0KrA7MXHMUJ9PNvg6LiIiIiBoYSZK8dqPqqzelA1avXu1xf+HChTAajdizZw/69u3ro6jI2/5KysXn2xMAADd3aox+LcN8HBEREdGlBWiUeG5wK3yw/iROpBXg3bXHcU+nYBiNvo6MiIiIiIiupHqTaL1YXl4eACA4OLjCNjabDTbbv4tT5OfnAwBcLhdcLlftBlhDLpcLQog6H6c3ncksxNyNJ+ESLsQ3C8HNHSPKff0NsW8qi31TMfZNxdg3FWPfVIx940mjkOHJAc0wd+NpHDyfhwU7k6HV6dEtOsTXodUp3j5veP4RERERlfDWZf+c0Foz9TLRKoTApEmTcO2116J9+/YVtpsxYwamTp1aZntGRgasVmtthlhjLpcLeXl5EEJAJqs3FR6qLavQjg+2nIPZ5kQroxY3NtMiIyOj3LYNrW+qgn1TMfZNxdg3FWPfVIx9U7672gfAbrVgT2IhPlh7FHd1Ccc1TQN8HVad4e3zpqCgwAtREREREdV/3rrsn6UDaqZeJloff/xxHDhwAFu3br1ku8mTJ2PSpEnu+/n5+YiKikJYWBgCAur2hx6XywVJkhAWFnbVf4AtsNrxxR/HYIccLSL0ePbGVtAo5RW2b0h9U1Xsm4qxbyrGvqkY+6Zi7JuKPX1jKOauP4z9qTasOJwLjb8eA9uE+zqsOsHb541Go/FCVERERERE3lHvEq1PPPEEfvrpJ2zevBmRkZGXbKtWq6FWq8tsl8lk9eJDoSRJ9SbW6rI5nPhowymkF9gQqlPjqUEtoVUrL/u8htA31cW+qRj7pmLsm4qxbyrGvimfQgHc1dkIY1Ix1h1Ox7Ld51Bkd2F4p8acIQDvnjc894iIiIhKcEZr3VBvRqdCCDz++ONYvnw5fv/9d8TExPg6JKohl0vg082ncTqjEFq1Ak8NbIlArcrXYREREdWYJEm4Ky4St3RpAgD4aX8ylu1OghDCx5EREREREVFtqTczWh977DEsXboUP/74I/R6PVJTUwEABoMBfn5+Po6OqkoIgaW7ErEvMRcKuYSJ1zdH40D+HImI6OohSRKGd2oMrVKOr3YlYu3hNFiKnRgTHw0JwPH0AuRZ7DBolWhp1EMm4+wBIiIiIqoeLoZVN9SbROvcuXMBAP379/fYvnDhQowdO/bKB0RV4nIJjw+Up9LN2HA0HZIEPNgnFi3C9b4OkYiIqFYMbBsOP5UcC/84gz9OZuJkuhn5VjtOZxSi2OGESiFHc6MOY+JNiDMF+zpcIiIiIqqHJHipdACYaa2JelU6oLwbk6x1356EbDy1bD8mLfsLU344iIc+34NpPx9GrsWOu7pFoVs0P1QSEdHVrXfzUDzSvxkKrHb8ciAZW09mQqeRIzJIiwCNAoeS8zB95RHsScj2dahERERERFU2Z84cxMTEQKPRIC4uDlu2bKmwbUpKCu699160atUKMpkMTz31VJk2ixYtctedvfBmtVqrfdwrocqJ1nPnzsFsNpfZbrfbsXnzZq8ERVePPQnZmL7yCP4+n4cAjQKBWhXyiuwosDmQYbYhRMearERE1DB0iQqCn0oOp0tADiA1zwYA8FcrYArWIq/IjiXbEuBysY4rEREREVVNaekAb9yqatmyZXjqqacwZcoU7Nu3D3369MGQIUOQmJhYbnubzYawsDBMmTIFnTp1qnC/AQEBSElJ8bhpNJpqH/dKqHSiNSUlBddccw1MJhMCAwMxZswYj4RrdnY2rrvuuloJkuonl0tg8bYE5FrsiA7RQiaTkJhtgUKSEBGghlIu8QMlERE1GMfTC5BRUIzmRh3kchksNgdOZxZCoKSea5hOjRPpZhxPL/B1qERERERUz5Q3+7O6t6p677338MADD2DChAlo06YN3n//fURFRbnLgF4sOjoas2fPxujRo2EwGC75miIiIjxuNTnulVDpROsLL7wAuVyOnTt3YvXq1Th8+DD69++PnJwcdxuupEsXOp5egJPpZhj1athdAqczCuFyiZKZOyH+/EBJREQNSp7FjmKHE0FaFZobdZDJJFhsDmQVlsxs1SjlKHY4kWex+zhSIiIiIqLKKS4uxp49e3DDDTd4bL/hhhuwbdu2Gu3bbDbDZDIhMjISw4YNw759+67IcWui0onWdevWYfbs2ejWrRsGDhyIrVu3IjIyEtdffz2ys0vqiXmj6C5dPUo/UCrlEk5lmOFwuqBWyhAd6g+ZJPEDJRERNSgGrRIqhRxWuxN+SjkiDCWXPaXmWWF3uWC1lyyMZdAqfRwpEREREdU33i4dkJ+f73Gz2WzlHjczMxNOpxPh4eEe28PDw5Gamlrt19O6dWssWrQIP/30E7766itoNBr07t0bJ06cqNXj1lSlE615eXkICgpy31er1fjuu+8QHR2N6667Dunp6bUSINVfBq0SSrkMJ9LNKLa7oFTIEBumg0JW8lvLD5RERNSQtDTq0dyoQ4bZBiEEQnVqaFRyOJ0CyTlFyDDb0MKoQ0uj3tehEhEREVEDFxUVBYPB4L7NmDHjku0vnnwphKjRhMyePXvi/vvvR6dOndCnTx988803aNmyJT788MNaPW5NVTrRGhsbiwMHDnhsUygU+PbbbxEbG4thw4Z5PTiq32JD/OEUAvlWB2QyIDbUHyp5ySknhOAHSiIialBkMglj4k0w+CmRkG2BxeZAY4MGDiGQnGeFSiHD6HgTZDJeIUREREREVePtGq1JSUnIy8tz3yZPnlzucUNDQyGXy8vMIk1PTy8z27QmZDIZunfv7p7ReqWOW1WVTrQOGTIEn3zySZntpcnWzp07ezMuqueEEFi47Sz0aiVUchnUipJVlp0ugUKbAwnZFhj8lPxASUREDUqcKRhThrZBu8YG5FsdyC4shkYhh16jQNNgLTpFBvo6RCIiIiKqh7xdOiAgIMDjplaryz2uSqVCXFwc1q5d67F97dq1iI+P99rrE0Jg//79aNSo0RU9blUpKttw+vTpsFgs5e9EocDy5ctx7tw5rwVG9ZcQAl/vTsKuM9kI1qkwsnsbbD2ZiZPpZmSabVAp5Gjf2IDR8SbEmYJ9HS4REdEVFWcKRpeoIBxPL0CexQ6lQoZFf5xBoc2JdUfScGP7Rr4OkYiIiIio0iZNmoRRo0ahW7du6NWrFz755BMkJibi4YcfBgBMnjwZ58+fx5IlS9zP2b9/P4CSBa8yMjKwf/9+qFQqtG3bFgAwdepU9OzZEy1atEB+fj4++OAD7N+/H//73/8qfVxfqHSiVaFQICAgoMLH5XI5TCaTV4Ki+u3Xg6lYdzgNADC+dwx6NQvBiLhI9wdKg1aJlkY9Z7ISEVGDJZNJaB3x77iqqNiJBVvP4Mf9yegeHYwQXfkzBoiIiIiIynPhZf813U9VjRw5EllZWZg2bRpSUlLQvn17/Prrr+48YUpKChITEz2e06VLF/e/9+zZg6VLl8JkMuHs2bMAgNzcXPznP/9BamoqDAYDunTpgs2bN+Oaa66p9HF9odKJVqLK2Hw8A8v3lsxsHtk9Cr2ahQAo+4GSiIiI/hXfLASbT2TgZJoZX+9OwmPXNfd1SERERERUn1xw2X9N91Mdjz76KB599NFyH1u0aFGZbUKIS+5v1qxZmDVrVo2O6wuVrtFKdDl7E3OwZPtZAMBNHRrhhnYRvg2IiIionpAkCaN6miBJEvYm5OCvpFxfh0RERERERFXERCt5xbHUAny86RSEAK5tEYrbuzbxdUhERET1SmSQFje0LVkh9cudCbA5nD6OiIiIiIjqi9LSAd64UfVVOdGamJhY7vReIUSZegvUMCRlW/Dh7yfgcAp0jgrE6F7R/MUkIiKqhuGdGyPIX4UsczF+PZji63CIiIiIqJ6QJO/dqPqqnGiNiYlBRkZGme3Z2dmIiYnxSlBUf6QXWDFr7XEUFTvRIlyPh/o1g5yLXBEREVWLRinHPddEAQBWHUxFap7VxxEREREREVFlVTnRKoQod7ai2WyGRqPxSlBUN7lcAkdT87HzdBaOpuYj11KMWWuPI6/IjsggP0wc0BwqBatREBER1UTXpkFo38QAp0vgix0Jl10ogIiIiIiIpQPqBkVlG06aNAlAyQ/u5ZdfhlardT/mdDqxc+dOdO7c2esBUt2wJyEbi7cl4GS6GcUOJxRyGWwOF4K1KjQz+uPpQS2hVVX6dCIiIqIKSJKE+3o2xSsrDuFISj52nclGj9gQX4dFRERERESXUenM2L59+wCUzGg9ePAgVCqV+zGVSoVOnTrh2Wef9X6E5HN7ErIxfeUR5FrsMOrVUClUOJVhRo7FjlxLMR7uH4tAreryOyIiIqJKMeo1uKljI/y47zyW7U5Ch0gDv9AkIiIiogp5q74qJ7TWTKVH7Bs2bAAAjBs3DrNnz0ZAQECtBUV1h8slsHhbAnItdkSHaAFJQkJWIWx2F/xVcvip5PjlrxQMaB0OGWuzEhERec2Q9hHYfioL6flWrNiXjHt7NPV1SERERERUR3nrsn+WDqiZKhfUXLhwIZOsDcjx9AKcTDfDqFdDkiSczylCnsUOSEBMqA6NDX44kW7G8fQCX4dKRER0VVHKZbi/Z0ly9fejaUjIKvRxREREREREdClVvgatsLAQb731FtavX4/09HS4XC6Px0+fPu214Mj38ix2FDuc0CjVyC2yI8tsAyTAFOIPvUYBp0sg02wrSb4SERGRV7VrbMA1McHYdSYbS7adxR1xkSiwOmDQKtHSqOfVJEREREQEgDNa64oqJ1onTJiATZs2YdSoUWjUqBF/AFc5g1YJlUIOc7Ed53KKAJTUjQv0UwIArHYnVAo5DFqlL8MkIiK6ao3sHoWNxzLwy4EUrD2SBpVcBpVCjuZGHcbEmxBnCvZ1iERERETkY6zRWjdUOdG6atUqrFy5Er17966NeKiOaWnUo5nRH3+czIIkBPzUCoQHqAGULIyWYbahfWMDWhr1Po6UiIjo6nQqw4y0fCsKbA7YXTK0bRQAh1PgUHIepq88gilD2zDZSkRERERUB1S5RmtQUBCCgzmYbyhkMgndTMFwOF0ocrgQqlNDCKDQ5kBCtgUGPyVGx5t46SIREVEtKF2UEgCCtErIICEt3wZ/tQKmYC3yiuxYsi0BLpfwcaREV8aMGTPQvXt36PV6GI1G3HrrrTh27JivwyIiIvK50tIB3rhR9VU50fr666/jlVdegcViqY14qI7JtRTjz4QctDDq0TpCj2KHC+dyLMi3OtC+sYGzaIiIiGrRhYtSRgVrAQnIKSxGYbEDkiQhTKfmopTUoGzatAmPPfYYduzYgbVr18LhcOCGG25AYSEXiyMiIiLfq3LpgHfffRenTp1CeHg4oqOjoVR61ubcu3ev14Ij3xKiZBaNxeZAp6hAvHBjK5zOKkSexc5FOIiIiK6ACxellMskBPurkG0uRnKuFc2NOmiUci5KSQ3K6tWrPe4vXLgQRqMRe/bsQd++fX0UFRERke+xRmvdUOVE66233loLYVBd9MfJLBw4lwu5TMIDfWKgUsrROiLA12ERERE1GKWLUlrtTvirFYgI0CCn0A6LzYF8qx0KSeKilNSg5eXlAQBLmxERUYPnrcv+WTqgZqqcaH311VdrIw6qY7LMNny1OxEAcGuXJmgS6OfjiIiIiBqelkY9mht1OJScB61KDqVchlC9Ghn5ViTnFMFPKUf7JlyUkuo2IQQ2bdqELVu24OzZs7BYLAgLC0OXLl0wcOBAREVFVXu/kyZNwrXXXov27dtX2M5ms8Fms7nv5+fnV+t4RERERJdT5RqtAJCbm4vPPvsMkydPRnZ2NoCSkgHnz5/3anDkG0IILNp2FtZiJ2LD/HFjuwhfh0RERNQgyWQSxsSbYPBTIiHbgkKbA6H+KggI5BbZIZNJXJSS6qyioiK8+eabiIqKwpAhQ7By5Urk5uZCLpfj5MmTePXVVxETE4ObbroJO3bsqPL+H3/8cRw4cABfffXVJdvNmDEDBoPBfatuYpeIiKguk/Bv+YAa3Xz9Quq5Ks9oPXDgAAYOHAiDwYCzZ8/iwQcfRHBwMH744QckJCRgyZIltREnXUEbj2XgcHI+lHIZHrg2lh/eiIiIfCjOFIwpQ9tg8bYEnEw3o9jhhJ9KAYXchSZBfujQJNDXIRKVq2XLlujRowfmzZuHwYMHl1nbAQASEhKwdOlSjBw5Ei+99BIefPDBSu37iSeewE8//YTNmzcjMjLykm0nT56MSZMmue/n5+cz2UpERFcdmSRB5oXL/r2xj4asyonWSZMmYezYsZg5cyb0+n8vUxsyZAjuvfderwZHV156gRXf7kkCANwRF4kIg8bHEREREVGcKRhdooJwPL0AeRY7tCoFFm07g1yLHb8fTceN7Xn1CdU9q1atuuQl/QBgMpkwefJkPPPMM0hISLjsPoUQeOKJJ/DDDz9g48aNiImJuexz1Go11Gp1peMmIiIiqq4qlw7YvXs3HnrooTLbmzRpgtTUVK8ERb4hhMCCrWdhs7vQMkKPgW2Mvg6JiIiI/iGTSWgdEYAesSHoEGnAbV1KZvGtPJgCS7HDx9ERlXW5JOuFVCoVWrRocdl2jz32GL744gssXboUer0eqampSE1NRVFRUU1CJSIiqve8UjbgnxtVX5VntGo0mnILyB87dgxhYWFeCYp8Y+3hNJxIK4BaKcO43tFcaY6IiKgOi28WgjWHUpGcW4RVB1NxR9ylL58m8rXc3Fzs2rUL6enpcLlcHo+NHj26UvuYO3cuAKB///4e2xcuXIixY8d6I0wiIqJ6SZIkr+RxmAuqmSonWm+55RZMmzYN33zzDYCSH0BiYiJeeOEF3HHHHV4PkK6M1Dwrlu8tWczsrm5RMOpZMoCIiKguk8kk3N61CT76/STWHk7DgDZGBGpVvg6LqFw///wz7rvvPhQWFkKv13t8iJMkqdKJViFEbYVIREREVGNVLh3wzjvvICMjA0ajEUVFRejXrx+aN28OvV6P6dOn10aMVMucLoH5W0/D7nShXeMA9GvJmclERET1QeeoQDQ36mB3uvDTX8m+DoeoQs888wzGjx+PgoIC5ObmIicnx33Lzs72dXhERET1nkzy3o2qr8ozWgMCArB161b8/vvv2Lt3L1wuF7p27YqBAwfWRnx0Baw5lIrTGYXQqOQY2zuG08SJiIjqCUmScGe3SMz49Sg2H8/EDW0juJAl1Unnz5/HxIkTodVqfR0KERERUa2pcqK11PXXX4/rr7/em7GQDyRlW7BiX0nJgHuvaYpgf15ySEREVJ80N+rROSoQ+5Ny8f3ec3jsuua+DomojMGDB+PPP/9EbGysr0MhIiK6Okleqq/KuXc1Uq1E6/r167F+/fpyC9kvWLDAK4FR7XG5BI6nFyDbXIxlu5PgcLrQKSoI8c1CfB0aERERVcPtcZH461wu9ibk4FSGGc3CdL4OicjD0KFD8dxzz+Hw4cPo0KEDlEqlx+PDhw/3UWRERERXB0kquXljP1R9VU60Tp06FdOmTUO3bt3QqFEjXmZez+xJyMbibQk4mW5GVqENhTYHDH5KjI6P5s+SiIionmoS6If4ZqH442QmvttzDv83uBXf16lOefDBBwEA06ZNK/OYJElwOp1XOiQiIiIir6tyonXevHlYtGgRRo0aVRvxUC3ak5CN6SuPINdiR4CfEg6nCwqZDEIAH6w/Ab1GgThTsK/DJCIiomq4tUsT7DyTheOpBfj7fD46RBp8HRKR28VXwREREZF3Sf/85439UPXJqvqE4uJixMfH10YsVItcLoHF2xKQa7GjabAfMgpskCAhRKdCqwg98orsWLItAS6X8HWoREREVA3B/ioMaB0OAPhuTxKE4Hs6ERERUUMhk7x3o+qr8ozWCRMmYOnSpXj55ZdrIx6qJcfTC3Ay3QyjXo20fBtsdifkcglNgvwgkySE6dQ4kW7G8fQCtI4I8HW4REREVA03dWyEzScycC6nCNtPZyG+WaivQ6IG7IMPPsB//vMfaDQafPDBB5dsO3HixCsUFREREVHtqXKi1Wq14pNPPsG6devQsWPHMoXs33vvPa8Fd7HNmzfj7bffxp49e5CSkoIffvgBt956a60d72qSZ7Gj2OGE3SlDRoENABAVpIVSVjKpWaOUI9NsQ57F7sswiYiIqAZ0agVu6tAI3+85hxX7zqN7dDCU8ipfwETkFbNmzcJ9990HjUaDWbNmVdhOkiQmWomIiGpIkiSv1Ohnnf+aqXKi9cCBA+jcuTMA4O+///Z4rLZ/GIWFhejUqRPGjRuHO+64o1aPdbUxaJWQy2VIyLIAAEJ0ahj8/k2SW+1OqBRyGLTKinZBRERE9cCANkasO5KGLHMxNh7LwKC24b4OiRqoM2fOlPtvIiIioqtVlROtGzZsqI04KmXIkCEYMmSIz45fn7UI0wECsNidCPRToHGgxv2YEAIZZhvaNzagpVHvwyiJiIioptQKOf6fvXuPi6pO/wD+OWeuDDDDdUAUB7xfS8TNwKzcyjK3rNSsNrXrb83KkrYts1Jbzd2yspuWraXultp1L+lu2c0uaBfULoriFVTA4aIMwzDXc35/IJMIowwMnAE+b17n9WLOnPmeh0eKM898z/OdMKw71uQewoc/FeOCPgmI0KqUDouIiIiI2pAg1G2hGIdaLuhC66mOHDkCQRDQvXv3UMUTUi6XCy6Xy//YZrMBqFv1NNxXPpUkCbIshyzOLwqsiNKpoFEJUKtEONxe6DUqOD0+lFe7YTRocPP5PQHIYb8gVqhz05kwN4ExN4ExN4ExN4ExN4GFQ26ye8Xhf7+U4JjNif/+UoxrhoXHtVqoc8Pfv45h7969+OmnnzB8+HCkp6djw4YN+Otf/4ra2lpcc801eOSRR3ibIhERUSuJggAxBH9PQzFGR7Jq1Spcf/31MBgMIRkv6EKrJElYuHAhnnnmGdjtdgBAdHQ0HnjgAcydOxeiGD59wBYvXowFCxY02l9WVgan06lARM0nSRKqqqogy3Krc1pqc+Pv3xyGXiXj98OTsLfMgcLjTri9MrRqAb3j9Zh0rhmpER5YrdYQ/QRtJ5S56WyYm8CYm8CYm8CYm8CYm8DCJTdj0gxY/b0NH24vwpA4AUZ9qz5fD4lQ56a6ujoEUVFb+uCDD3D99ddDFEUIgoAVK1bg//7v/zBmzBgYjUbMnz8farUaDz30kNKhEhERUSssW7YMTz/9NEpKSjB48GAsXboUo0ePbvLYkpISPPDAA8jLy8PevXsxa9YsLF26tMExr732GtasWeNvW5qZmYknn3wS5513nv+Y+fPnN6r7JSUlobS0tNlxz5kzB7NmzcLkyZNx++23Izs7u9mvbUrQV9xz587FypUr8Ze//AWjRo2CLMv45ptvMH/+fDidTixatKhVAYXSnDlzkJOT439ss9mQmpqKxMREGI1GBSM7O0mSIAgCEhMTW/VGxO2VsGzrbohqDTJ7mnDfJX0gy0CB1Q5brQfGCA36maMgih3nE4tQ5aYzYm4CY24CY24CY24CY24CC5fcXJKYiO+K3ThQbkfuUTfOS49W/G9/qHOj1+vPfhApatGiRfjTn/6EhQsXYtWqVZgxYwb+8pe/4P777wcArFixAs899xwLrURERK2kZOuA9evX4/7778eyZcswatQovPrqqxg3bhx27dqFnj17Njre5XIhMTERc+fODbhg5hdffIEbb7wR2dnZ0Ov1eOqppzB27Fjs3LmzwZ31gwcPxieffOJ/rFIF1zLryJEj2LBhA1atWoUxY8YgPT0dt956K6ZPn47k5OSgxgJaUGhdvXo1/va3v+Hqq6/27zv33HPRvXt3zJw5M6wKrTqdDjqdrtF+URQ7xJtCQRBaHev724/g6IlaGPUa3D66l/8XblCKKVRhKiIUuemsmJvAmJvAmJvAmJvAmJvAwiU3k0ek4pH3f8byzQfw1neHIUkytGoV+pijMD3bgkxLXLvHFMrcKJ1fOrs9e/Zg/fr1EAQB06dPx5133olLL73U//zYsWP9RVciIiJqOUEQQtKKpyVjPPvss7j99ttxxx13AACWLl2Kjz76CMuXL8fixYsbHZ+Wlobnn38eAPD66683Oeabb77Z4PFrr72Gd999F59++immTZvm369Wq1tUEK2nUqlw9dVX4+qrr4bVasU//vEPrFq1Co899hiuuOIK3H777bjqqquafd0Z9NVpZWUlBgwY0Gj/gAEDUFlZGexw1IZ+OnICn+YfAwDcdkE6TBEahSMiIiKi9mR3eXD0RC2qnV7Uun3oEWuAUa/GzuIqLNqQj7xCXrtR26qpqUF0dN1iq6IoIiIiokEPtIiIiAZrKhAREVHH4na7kZeXh7FjxzbYP3bsWOTm5obsPA6HAx6PB3FxDScK7N27FykpKUhPT8cNN9yAAwcOtPgcZrMZo0aNQlZWFkRRxM8//4xbbrkFvXv3xhdffNGsMYIutJ577rl46aWXGu1/6aWXcO655wY7XFDsdjt27NiBHTt2AAAOHjyIHTt2oKioqE3P2xFV1Xrw+tcHAQCXDEzCOT1ilA2IiIiI2pUkyVidWwitWkSERkSt2weXV0KkTg1LnAFVtR6syS0M+0UwqWM7fXZNqGbbEBERUUP1rQNCsQF17TdP3QJ9MFpeXg6fz4ekpKQG+4PtlXo2Dz/8MLp3797gzpiRI0dizZo1+Oijj/Daa6+htLQU2dnZqKioCGrsY8eOYcmSJRg8eDAuvvhi2Gw2fPjhhzh48CCKi4tx3XXXYfr06c0aK+jWAU899RTGjx+PTz75BFlZWRAEAbm5uTh8+DA2btwY7HBB+eGHHzBmzBj/4/r+q9OnT8eqVava9NwdiSzLeP3rg6h2etEjNgKTMnsoHRIRERG1swJrNfZZ7Ugx6VFRI+J4jRv7yuww6tUwRWgQF6nFXqsdBdZqDEgO79711HHJsox+/fr5i6t2ux0ZGRn+2+9kmYV+IiKicJSamtrg8bx58zB//vyAx5/+QaosyyH7cPWpp57C2rVr8cUXXzTo0T9u3Dj/90OHDkVWVhZ69+6N1atXN1iz6UyuuuoqfPTRR+jXrx/uvPNOTJs2rcGs2YiICDzwwAMBe8meLuhC60UXXYSCggK8/PLL2L17N2RZxnXXXYeZM2ciJSUl2OGCcvHFF/NirBk+ybfil6NV0KhE3HlhL2jV7F9GRETU1VQ5PHB7fdBrdEg26eFwe+HySKhyeFDl8ECGDFkGvi4oR49YA6J0Z74slCQZBdZqVDk8MBk06GeO7lCLaZIy3njjDaVDICIi6hJEQYAYgsJm/RiHDx9usJB8U2sgAUBCQgJUKlWj2atWq7XRLNeWWLJkCZ588kl88sknOOecc854bGRkJIYOHYq9e/c2e3yz2YzNmzcjKysr4DHdunXDwYMHmzVe0IVWAEhJSQmrRa/oV4crHXjnh8MAgCm/SUWPWMNZXkFERESdkcmggVatgtPjQ6ROjf7JRtS6faiq9eBErRsOlw9eScKmXcew9WAlBiRHI9MSi+E9Y2EyNOzrnldYidW5hdhntcPt9Sm+oBZ1HM29zY6IiIhaRzi5hWIcADAajQ0KrYFotVpkZmZi06ZNuPbaa/37N23ahAkTJrQqlqeffhoLFy7ERx99hBEjRpz1eJfLhfz8fIwePbrZ57joooswfPjwRvvdbjfWrVuHadOmQRAEWCyWZo3XokLr8ePHsXLlSuTn50MQBAwcOBC33npro4a01L7cXgmvfrkfPknGuakxuLh/otIhERERkUL6maPRxxyFncVVMGhVEAQBBq0KBq0KSUYdDpTVICFah/7JUTh6won8EhvyS2x489tC9DZHIbNnLIZbYlFYUYNFG/JxwuGBOVoHvUYHp8fnX1Br7viBLLZSSITyFkMiIiJqPzk5OZg6dSpGjBiBrKwsrFixAkVFRZgxYwYAYM6cOTh69CjWrFnjf039+kt2ux1lZWXYsWMHtFotBg0aBKCuXcBjjz2Gt956C2lpaf4Zs1FRUYiKigIA/PGPf8RVV12Fnj17wmq1YuHChbDZbEF90HvrrbfiiiuugNlsbrC/uroat956K6ZNmxZULoIutG7evBkTJkyA0Wj0V5NfeOEFPPHEE/j3v/+Niy66KNghqYVOv4Xv24OVKDnhhClCg1tGpfFClYiIqAsTRQHTsy1YtCEfhZUOJEbpoNfUzXAts7sQH6XFI1cOQKYlDlabE3mFx5FXeBwHy2uw75gd+47Zse67IhyqcMDu8qKPOQoRGhUAIFKnhkGrQmGlA2tyC5GRGss2AtTIwIED8dhjj2HSpEnQarUBj9u7dy+effZZWCwWPPzww+0YIRERUecRqgUnWzLGlClTUFFRgSeeeAIlJSUYMmQINm7c6J8FWlJS0mgh+4yMDP/3eXl5eOutt2CxWHDo0CEAwLJly+B2uzFp0qQGrzu1V+yRI0dw4403ory8HImJiTj//POxdevWZs8+BQJ/0HvkyBGYTKZmj1Mv6ELr3Xffjeuvvx7Lly+HSlV3se3z+TBz5kzcfffd+OWXX4IOgoJ3+i18PlmGw+VDj1gDcsb2g1GvOfsgRERE1KllWuIwd/xA/zVDud0FrVqFISkmTDvltn+zUY9xQ7th3NBuqKxxY1vhceQVHcf2wuOwVjuhFkUUlFZDr1UhPkqLhEgdBEFAYpSOC2pRQC+//DIeeugh3H333Rg7dixGjBiBlJQU6PV6HD9+HLt27cLXX3+NXbt24Z577sHMmTOVDpmIiKjDEoW6LRTjtMTMmTMD/i1vagH7s63BVF9wPZN169Y1J7QmZWRk+IvTl1xyCdTqX0ukPp8PBw8exBVXXBH0uEEXWvfv34/33nvPX2QFAJVKhZycnAZTgKnt5BVWNriFTy1qkF9qQ61HQpndBafHp3SIREREFCYyLXHISI1t9kJWcZFaXDooCZcOSsJnu49h7ge/QKMS4HD74HT7cPR4LWIjtFCJAvQaFcrtLlQ5PO38U1FH8Nvf/hbff/89cnNzsX79erz11ls4dOgQamtrkZCQgIyMDEybNg0333wzYmJilA6XiIiIupBrrrkGQF0Lg8svv9zfjgCo6zublpaGiRMnBj1u0IXW4cOHIz8/H/3792+wPz8/H8OGDQs6AAqOJMlYnVuIEw4P0uINgCDgQJkdAgTEGjRQiwJv4SMiIqIGRFFo0YzTlJgIxBq0MOrV0GlUyC+xQZJkeCQZKlGA01O3MNbpi2cRnSo7OxvZ2dlKh0FERNSpKdk6oCOaN28eACAtLQ1TpkyBXq8PybhBF1pnzZqF++67D/v27cP5558PANi6dStefvll/OUvf8FPP/3kP/acc84JSZD0qwJrNfZZ7TBH192yV2Z3we70QhAEpCVEwueTeQsfERERhcSpC2pZ4gxQiwLckgyfJEGW665DhqSY0M8crXSoRERERF1eF6mRhlQwC2c1R9CF1htvvBEA8Kc//anJ5wRB8DeS9fl4C3uoVTk8cHt90Gt08EkyjtmcAICUGD30ahV8osxb+IiIiCgkTl9QS5JlyLKMaqcXZdUumCI0mJZt4V00RERERNRhxMXFoaCgAAkJCYiNjT3jLN7Kysqgxg660Hrw4MFgX0IhZDJooFXXrRhc7fLC55Oh04iIj9IBAG/hIyIiopA6dUGt3P3lqPVKsDu9GJYa02BBLSIiIiJSDlsHNN9zzz2H6Oho//eh/JmDLrRaLJaQnZyCV38L389Hq2B31s1aTTJGQEDdim28hY+IiIhCrX5Brb/8Nx8/FB7H+KEpuHVUGmeyEhEREVGHc2q7gFtuuSWkYwddaAWAo0eP4ptvvoHVaoUkSQ2emzVrVkgCo6bV38L3wNs/osbtQ7RejWi9GjUuL8rsvIWPiIiI2oYoCkhPjML+shqYDBpeaxARERGFEVGo20IxTmdns9mafazRGNz6R0EXWt944w3MmDEDWq0W8fHxDabXCoLAQms76J9sRDdTBGrdPkRoVDh63AGtWoUhKSbewkdERERtJkpXd+lYf1cNUTD279+PN954A/v378fzzz8Ps9mM//3vf0hNTcXgwYOVDo+IiKhDY+uA5ouJiTnrz9nS9aeCLrQ+/vjjePzxxzFnzhyIohjsyykE/vdLKQxaFa4YkowbzkuFrdYLk0GDfuZozi4hIiKiNuMvtLq8CkdCHc3mzZsxbtw4jBo1Cl9++SUWLVoEs9mMn376CX/729/w7rvvKh0iERERdRGff/55m40ddKHV4XDghhtuYJFVITanB5/mHwMAXDu8BwZ2MykcEREREXUVUfr6Qmtwn+wTPfzww1i4cCFycnL8i08AwJgxY/D8888rGBkREVHnIJzcQjFOZ3fRRRe12dhBF1pvv/12vPPOO3j44YfbIh46i//9XAq3V0JaQiTO7cEiKxEREbWfX2e0snUABefnn3/GW2+91Wh/YmIiKioqFIiIiIiocxEFAWIIbvsPxRjh7qeffsKQIUMgiiJ++umnMx57zjnnBDV20IXWxYsX43e/+x3+97//YejQodBoNA2ef/bZZ4MdkprphMONz3ZbAQDXDOveJfpmEBERUfj4tUcrWwdQcGJiYlBSUoL09PQG+7dv347u3bsrFBURERF1RcOGDUNpaSnMZjOGDRsGQRAgy3Kj49qlR+uTTz6Jjz76CP379/ef9NQAqO1s/LkUHp+E3uYoDOke3KpnRERERK0VfUrrgPoFAoia46abbsJDDz2Ed955B4IgQJIkfPPNN/jjH/+IadOmKR0eERFRhycIdVsoxunsDh48iMTERP/3oRR0ofXZZ5/F66+/jltuuSWkgdCZVda48cUezmYlIiIi5USenNEqyzIcbp//MdHZLFq0CLfccgu6d+8OWZYxaNAg+Hw+3HTTTXj00UeVDo+IiIi6EIvF0uT3oRD01bFOp8OoUaNCGgSd3YafS+CTZPRLjsbAbtFnfwERERFRiGlUInQaES6PhBqXl4VWajaNRoM333wTf/7zn7Ft2zZIkoSMjAz07dtX6dCIiIg6BUEQQjIprytO7NuzZw9efPFF5OfnQxAEDBgwAPfee6//bv5giMG+4L777sOLL74Y9Imo5crtLnxVUAaAs1mJiIhIWfV9Wqtd7NNKzffEE0/A4XCgV69emDRpEq6//nr07dsXtbW1eOKJJ5QOj4iIqMOrbx0Qiq0reffddzFkyBDk5eXh3HPPxTnnnINt27ZhyJAheOedd4IeL+hpCN999x0+++wzfPjhhxg8eHCjxbDef//9oIOgM/vwx2L4JBkDuxnRP5mzWYmIiEg5UToNKuxuLohFQVmwYAFmzJgBg8HQYL/D4cCCBQvw+OOPKxQZERERdWV/+tOfMGfOnEYf/M6bNw8PPfQQJk+eHNR4QRdaY2JicN111wX7Mmohq82Jr/dVAACuyUhROBoiIiLq6qJ0KgCAnTNaKQiBFk/78ccfERcXp0BEREREnYsoCBBDMB01FGN0JKWlpU0uzHnzzTfj6aefDnq8oAutb7zxRtAnoZb78OcSyLKMId1N6GPmbFYiIiJSVpT+ZOsAzmilZoiNjfX3jOvXr1+DYqvP54PdbseMGTMUjJCIiKhzCNVt/12szoqLL74YX331Ffr06dNg/9dff43Ro0cHPV6LVzAoKyvDnj17/BdNiYmJLR2KArDa3diyvxIAcE1Gd4WjISIiIqprHQAANZzRSs2wdOlSyLKM2267DQsWLIDJZPI/p9VqkZaWhqysLAUjJCIioq7m3//+t//7q6++Gg899BDy8vJw/vnnAwC2bt2Kd955BwsWLAh67KALrTU1Nbj33nuxZs0aSJIEAFCpVJg2bRpefPHFRn2XKDiSJKPAWo0TNW78a9sxSLKEjJ5xSE+IVDo0IiIiIv+MVrYOoOaYPn06ACA9PR3Z2dmN1ncgIiKi0Ki/gyQU43R211xzTaN9y5Ytw7Jlyxrsu/vuu4O+8yboQmtOTg42b96M//znPxg1ahSAuum0s2bNwgMPPIDly5cHOySdlFdYidW5hdhntaPG5UW53YkonQaTMnsqHRoRERERAMCgFWF3epFfYsPuUhv6maMhip3/gpxa56KLLvJ/X1tbC4/H0+B5o9HY3iERERFRF1U/cbQtBF1ofe+99/Duu+/i4osv9u+78sorERERgeuvv56F1hbKK6zEog35OOHwwBytQ63HC5UgwOOTsPLrA0g26ZBp4UIBREREpJy8wkq88fUh7Cypwh5rNX46UoU+5ihMz7bwOoXOyOFw4E9/+hPefvttVFRUNHre5/O1e0y9kqJgNEa1+3mJiFqrZzTvJA4nNUL7/w1rinhyC8U41HJBF1odDgeSkpIa7TebzXA4HCEJqquRJBmrcwtxwuFBWrwBTq8EW60XKpWA/snRsFa7sCa3EBmpsZwxQkRERIqo/1DYanNBLYowaFQw6tXYWVyFRRvyMXf8QBZbKaAHH3wQn3/+OZYtW4Zp06bh5ZdfxtGjR/Hqq6/iL3/5i9LhERERdXhsHdByNTU12Lx5M4qKiuB2uxs8N2vWrKDGCrrQmpWVhXnz5mHNmjXQ6/UA6m7/WbBgARvZt1CBtRr7rHaYo3UQBAHHbE4AgEmnRoRWhcQoHfZa7SiwVmNAMm+rIiIiovZ16ofCqXER2HvMDkkGInVqGLQqFFY6+KEwndF//vMfrFmzBhdffDFuu+02jB49Gn369IHFYsGbb76J3//+90qHSERERF3Q9u3bceWVV8LhcKCmpgZxcXEoLy+HwWCA2WwOutAa9Izg559/Hrm5uejRowcuueQSXHrppUhNTUVubi6ef/75YIcjAFUOD9xeH/QaFSRZRlVtXc+qhKi6xQL0GhXcXh+qHJ4zDUNERETUJk79UFitqrt89EoSJFmGIAgNPhQmakplZSXS09MB1PVjraysBABccMEF+PLLL5UMjYiIqFMQBEAMwdbVJrTOnj0bV111FSorKxEREYGtW7eisLAQmZmZWLJkSdDjBV1oHTJkCPbu3YvFixdj2LBhOOecc/CXv/wFe/fuxeDBg4MOgACTQQOtWgWnx4catw+QAY1KhE5d99vt9PigVatgMnCVViIiImp/p34orFGJdcVWGbC7vAD4oTCdXa9evXDo0CEAwKBBg/D2228DqJvpGhMTo1xgREREnUQoiqz1W1eyY8cOPPDAA1CpVFCpVHC5XEhNTcVTTz2FRx55JOjxgm4dAAARERG48847W/JSakI/czT6mKOws7gKOnVd7TtSpwIgQJZllNldGJJiQj9ztLKBEhERUZd06ofCkTo1TAYNKqpdOOHwwKjX8ENhOqtbb70VP/74Iy666CLMmTMH48ePx4svvgiv14tnn31W6fCIiIioi9JoNP6+tElJSSgqKsLAgQNhMplQVFQU9HjNntGal5eHMWPGwGazNXquqqoKY8aMwY8//hh0AASIooDp2RaYIjQoPuGEV5Jh0KrgcPtQVFELU4QG07It7HlGREREiqj/ULjM7oIsy4iJqCuo2pwe+CQJZXYX+pqj+KEwBTR79mx/j7MxY8Zg9+7dWLt2LbZt24b77rtP4eiIiIg6vvrFsEKxdSUZGRn44YcfANRdozz++ON48803cf/992Po0KFBj9fsQuszzzyD3/72tzAaGy/GZDKZcNlll+Hpp58OOgCqk2mJw5+uGAC9VoRXkmBzelHj9mFwdyNX8SUiIiJFnfqhcGGl4+Q+wOWRsK+shh8K01mtWbMGLpfL/7hnz5647rrrMHDgQKxZs0bByIiIiDoHtg5omSeffBLdunUDAPz5z39GfHw87rrrLlitVqxYsSLo8ZrdOuDbb7/Fww8/HPD5q666Cn/729+CDoB+FWvQYlCyERAE3HlBGnxOO84f0BNqtUrp0IiIiKiLy7TEYe74gVidW4h9VjskuW5BrFiDlh8K01ndeuutuOKKK2A2mxvsr66uxq233opp06YpFBkRERF1ZSNGjPB/n5iYiI0bN7ZqvGYXWo8ePYro6MC3g0VFRaGkpKRVwXR1e45VQxAEjEiLw8he8bBafZwZQkRERGEj0xKHjNRYFFirsaPoBD7YdhTJJh0yUmOVDo3CnCzLTd6KeOTIEZhMJgUiIiIi6lwEoW4LxThdkdVqxZ49eyAIAvr374/ExMQWjdPsQmtiYiL27NmD9PT0Jp/fvXs3EhISWhQE1SkorQYA9E+OUjgSIiIioqaJooAByUb0SYzCN/srYHd5sddqR/9k9melxjIyMvz93i655BKo1b++/fD5fDh48CCuuOIKBSMkIiKirsxms+Huu+/GunXr4PP5AAAqlQpTpkzByy+/HPQHws0utF566aVYtGhRkxdCsizjySefxKWXXhrUyelXXp+EfVY7AKBfEt+oEBERUXhTq0QMS41B7r5y5BUeZ6GVmnTNNdcAAHbs2IHLL78cUVG/TijQarVIS0vDxIkTFYqOiIio8xAFAWIIpqOGYoyO5I477sCOHTvw4YcfIisrC4IgIDc3F/fddx/uvPNOvP3220GN1+xC66OPPorMzEyMHDkSDzzwAPr37w9BEJCfn49nnnkGBQUFeOONN4L+gajOoYoaeHwSInVqdI+JgCzLSodEREREdEaZllh/ofXG81K73Cq1dHbz5s0DAKSlpWHKlCnQ6/UKR0RERNQ5iQhixfuzjNOVbNiwAR999BEuuOAC/77LL78cr732Wovuuml2obV379745JNPcMstt+CGG27wX0jLsoxBgwZh06ZN6NOnT9ABUJ09pXWzWfsnR0MQBBZaiYiIKOwN6maEXqPCCYcbB8pr0DuR7Y+oadOnT/d/73Q6sX79etTU1OCyyy5D3759FYyMiIiIurL4+Pgm2wOYTCbExga/DkFQheoRI0bgl19+wbZt27Bu3TqsXbsW27Ztwy+//ILf/OY3QZ+cfrXnWF1/VrYNICIioo5CqxZxTo+6C9O8Q8cVjobC0YMPPoj77rvP/9jtduP888/HnXfeiUceeQQZGRnYsmWLghESERF1DvWLYYVia4lly5YhPT0der0emZmZ+OqrrwIeW1JSgptuugn9+/eHKIq4//77mzzuvffew6BBg6DT6TBo0CB88MEHrTpvUx599FHk5OSgpKTEv6+0tBQPPvggHnvssaDGAlo4I3jYsGGYPHkyrr/+egwbNqwlQ7RYaxMYjnySjH3WkwthsdBKREREHciItFjIsoxP8o9h64EK7C61QZJ4Zw7V+e9//4tLLrnE//jNN99EUVER9u7di+PHj2Py5MlYuHChghESERF1DiIEf5/WVm0IvtK6fv163H///Zg7dy62b9+O0aNHY9y4cSgqKmryeJfLhcTERMydOxfnnntuk8ds2bIFU6ZMwdSpU/Hjjz9i6tSpuP766/Htt9+2+Lz1MjIyMHz4cAwfPhyvvPIKtm7dCovFgj59+qBPnz7o2bMncnNz8eqrrwadi2a3DggH9QlctmwZRo0ahVdffRXjxo3Drl270LNnT6XDa7HCihq4PBIMOjVS4yKUDoeIiIio2VxeCfml1ah2erCntBqROjX6mKMwPduCTEuc0uGRwoqKijBo0CD/448//hiTJk2CxWIBANx333248sorlQqPiIiIQuDZZ5/F7bffjjvuuAMAsHTpUnz00UdYvnw5Fi9e3Oj4tLQ0PP/88wCA119/vckxly5dissuuwxz5swBAMyZMwebN2/G0qVLsXbt2hadt179Yp1toUMVWluawHBXUN82wBzFRSSIiIiow8grrMSSj/bA5ZGgFkVEaFUw6tXYWVyFRRvyMXf8QBZbuzhRFBusPbB169YGt+HFxMTg+HG2nSAiImqt1tz2f/o4AGCz2Rrs1+l00Ol0jY53u93Iy8vDww8/3GD/2LFjkZub2+I4tmzZgtmzZzfYd/nll2Pp0qWtPm/9Yp1tocMUWluSQJfLBZfL5X9c/0siSRIkSWq7YIO0u8QGGTL6JkX545IkCbIsh1Wc4YK5CYy5CYy5CYy5CYy5CYy5Cayr5EaSZKz65hBOODywxEegqLIWNqcHKSY9esZFoKiiFqtzD+Hc7iaIonDyNaHNTWfPcWcwYMAA/Oc//0FOTg527tyJoqIijBkzxv98YWEhkpKSFIyQiIiImpKamtrg8bx58zB//vxGx5WXl8Pn8zX6e56UlITS0tIWn7+0tPSMY4b6vHl5ecjPz4cgCBg0aBAyMjJaFHeHKbS2JIGLFy/GggULGu0vKyuD0+lskziDJckyfi6qgMsrIV7tgtVqrdsvSaiqqoIsyxDFFrXS7bSYm8CYm8CYm8CYm8CYm8CYm8C6Sm72lddiT8kJmHQq6FUyZFlCrUtCtcMJrVqEUSdgd/EJbN1dhD4Jda2RQp2b6urqVo9BbevBBx/EjTfeiA0bNmDnzp248sorkZ6e7n9+48aNOO+88xSMkIiIqHMQhbotFOMAwOHDh2E0Gv37m5rNeqrT79CWZbnVd203Z8zWntdqteKGG27AF198gZiYGMiyjKqqKowZMwbr1q1DYmJiUDE3q9D6008/NXvAc845J6gAghVMAufMmYOcnBz/Y5vNhtTUVCQmJjb4ZVFSYUUNZJUaMXoVMvqkNpjxIQgCEhMTO/WbtJZgbgJjbgJjbgJjbgJjbgJjbgLrKrk5VFMJH0QYI/VQCQJUohOSDGi1Wug0ItQaGTZ3LdQR0TCb69oHhDo3er2+1WNQ25o4cSI2btyIDRs2YOzYsbj33nsbPG8wGDBz5kyFoiMiIuo8BAEQQ9A7oH4Io9HYrNpZQkICVCpVo0mQVqu1VXetJCcnn3HMUJ333nvvhc1mw86dOzFw4EAAwK5duzB9+nTMmjXL3w+2uZpVaB02bBgEQWjQX+lU9c8JggCfzxdUAM3VkgQG6h8himLYvPEpsNZAgIC+SdFQq1UNnhMEIaxiDSfMTWDMTWDMTWDMTWDMTWDMTWBdITcxkVro1Cq4PBK0ahGyXHdhrlWLECDA5fFBp1YhJlLbIA+hzE1nzm9ncumll+LSSy9t8rm27JFGREREbU+r1SIzMxObNm3Ctdde69+/adMmTJgwocXjZmVlYdOmTQ36tH788cfIzs4O6Xn/97//4ZNPPvEXWQFg0KBBePnllzF27Nig425WofXgwYNBDxxqbfUPp7SC0rpb3vonRSscCREREVHz9TNHo485CjuLq5AQpQUAaFQixJMfwJfZXRiSYkI/M69xiIiIiNpaqBfDCkZOTg6mTp2KESNGICsrCytWrEBRURFmzJgBoO6O86NHj2LNmjX+1+zYsQMAYLfbUVZWhh07dkCr1WLQoEEAgPvuuw8XXngh/vrXv2LChAn417/+hU8++QRff/11s8/bHJIkQaPRNNqv0WhatB5AswqtFosl6IHbQigSGE5kWUaB1Q4A6JfMNyFERETUcYiigOnZFizakI/DlbXwSjIMKgE1Li/K7C6YIjSYlm3xt0UiIiIiorYT6h6twZgyZQoqKirwxBNPoKSkBEOGDMHGjRv99cSSkhIUFRU1eM2pi03l5eXhrbfegsViwaFDhwAA2dnZWLduHR599FE89thj6N27N9avX4+RI0c2+7zN8dvf/hb33Xcf1q5di5SUFADA0aNHMXv2bFxyySVB56LFi2Ht2rULRUVFcLvdDfZfffXVLR3yrEKRwHBy5HgtHC4vdBoRljiD0uEQERERBSXTEoe54wdi0YZ8FByrhsPtg83pxZAUE6ZlW5BpiVM6RCIiIiJqBzNnzgzYd33VqlWN9gVqT3qqSZMmYdKkSS0+b3O89NJLmDBhAtLS0pCamgpBEFBUVIShQ4fiH//4R9DjBV1oPXDgAK699lr8/PPPDfq21i9I1VY9Wuu1NoHhZM/JtgF9EqOgVrHHGBEREXU8mZY4jBuSDI0oIrtPPC4fkox+5mjOZCUiIiJqR8LJr1CM05WkpqZi27Zt2LRpE3bv3g1ZljFo0KCA/eXPJuhC63333Yf09HR88skn6NWrF7777jtUVFTggQcewJIlS1oURFe151hdoZVtA4iIiKgjK7O7EaVXI7tPAgYkn311WiIiIiIipXm9Xuj1euzYsQOXXXYZLrvsslaPGfQ0yi1btuCJJ55AYmKif8XYCy64AIsXL8asWbNaHVBXIEky8kuqkLu/HHanF/2SopQOiYiIiKjFyqpdAIDEKJ3CkVA4+vHHH7Fw4UIsW7YM5eXlDZ6z2Wy47bbbFIqMiIio86jv0RqKratQq9WwWCwhvTs/6EKrz+dDVFRdYTAhIQHFxcUA6hbM2rNnT8gC66zyCitx//odmLV2B7YXncCuUhte/HQf8gorlQ6NiIiIKGhOjw+2Wg8AwGxkoZUa+vjjj3Heeedh3bp1+Otf/4qBAwfi888/9z9fW1uL1atXKxghERFR58BCa8s8+uijmDNnDiorQ1OXC7p1wJAhQ/DTTz+hV69eGDlyJJ566ilotVqsWLECvXr1CklQnVVeYSUWbcjHCYcHGpWACLUIvVaFXSU2LNqQj7njB3LRCCIiIupQ6mezGnRqGLQtXmeVOqn58+fjj3/8IxYtWgRZlrFkyRJcffXVeOedd3DFFVcoHR4RERF1cS+88AL27duHlJQUWCwWREZGNnh+27ZtQY0X9NXwo48+ipqaGgDAwoUL8bvf/Q6jR49GfHw81q9fH+xwXYYkyVidW4gTDg/S4g0orHRAEATEGLRIitahsNKBNbmFyEiN5eIRRERE1GGU2esKreZozmalxnbu3Im///3vAOoWz33wwQfRo0cPTJo0CWvXrsV5552ncIRERESdgyAI/oXqWztOV3LNNddAEATIshyS8YIutF5++eX+73v16oVdu3ahsrISsbGxXe4fIxgF1mrss9phjtZBEATUuOr6P0Tp1BAEAYlROuy12lFgreYiEkRERNRhWG0n+7Oy0EpN0Ol0OHHiRIN9N954I0RRxA033IBnnnlGmcCIiIg6mVDd9t9V5v45HA48+OCD+Oc//wmPx4NLLrkEL774IhISElo1bqvu7zp8+DAEQUCPHj1aFURXUOXwwO31Qa+pexPikyQAgFZd1yZXr1Gh3O5ClcOjWIxEREREwaqf0cqFsKgpw4YNw+eff47MzMwG+6dMmQJJkjB9+nSFIiMiIqKubN68eVi1ahV+//vfIyIiAm+99RbuuusuvPPOO60aN+jFsLxeLx577DGYTCakpaXBYrHAZDLh0UcfhcfDImEgJoMGWrUKTk/dTFaVWJd6n1Q3Ndnp8UGrVsFk0CgWIxEREVGw6nu0ciEsaspdd92Fo0ePNvncjTfeiNWrV+PCCy9s56iIiIg6H0EI3dYVvP/++1i5ciVWrFiB559/Hhs2bMA///lP+Hy+Vo0b9IzWe+65Bx988AGeeuopZGVlAQC2bNmC+fPno7y8HK+88kqrAuqs+pmj0ccchZ3FVTBoVRBFAL66QqssyyizuzAkxYR+5milQyUiIiJqtrJqJwC2DqCmXXvttbj22msDPn/jjTfixhtvbMeIiIiIOidRECCGoEoaijE6gsOHD2P06NH+x+eddx7UajWKi4uRmpra4nGDntG6du1arFq1Cn/4wx9wzjnn4JxzzsEf/vAHvP7661i7dm2LA+nsRFHA9GwLTBEaFFY6IMt1BVa7y4vCSgdMERpMy7ZwISwiIiLqMHySjHK7GwBbB1DTjh8/jhdffBE2m63Rc1VVVQGfIyIiImpLPp8PWq22wT61Wg2v19uqcYOe0arX65GWltZof1paWqMAqaFMSxzmjh+I1bmFyN1fjlqvBLvTi2GpMZiWbUGmJU7pEImIiIia7bjDDUmSoRIFxBp4HUiNvfTSS/jpp59w7733NnrOZDLhq6++gs1mw9y5cxWIjoiIqPPgYljBkWUZt9xyC3S6XycLOJ1OzJgxA5GRkf5977//flDjBj2j9e6778af//xnuFwu/z6Xy4VFixbhnnvuCXa4LifTEoelU4bh6mEpGJAcjVtHpeG5KcNYZCUiIqIOx2qrux5MiNbxrhxq0nvvvYcZM2YEfP4Pf/gD3n333XaMiIiIiAiYPn06zGYzTCaTf7v55puRkpLSYF+wgp7Run37dnz66afo0aMHzj33XADAjz/+CLfbjUsuuQTXXXed/9hgq75dhSgKSIuPRGG5A3FRfGNCREREHZO1vj8r2wZQAPv370ffvn0DPt+3b1/s37+/HSMiIiLqpEK1kFUXKVG98cYbbTJu0IXWmJgYTJw4scG+1jSJ7aoM2rrUO1yt6/1AREREpJT6/qxmIwut1DSVSoXi4mL07NmzyeeLi4shikHfZEdERESnESFADEGVNBRjdGVBF1rbquLb1Ri0KgCAw+1TOBIiIiKiluGMVjqbjIwM/POf/8T555/f5PMffPABMjIy2jkqIiIiorYRdKGVQiPiZKG11sNCKxEREXVM9T1aE6NZaKWm3XPPPbjhhhvQo0cP3HXXXVCp6q6BfT4fli1bhueeew5vvfWWwlESERF1fEKIWgeEpP1AF9asQuvw4cPx6aefIjY2FhkZGRDOkPVt27aFLLjOzKCpn9HK1gFERETU8ciyjDJ7XaHVbNQrHA2Fq4kTJ+JPf/oTZs2ahblz56JXr14QBAH79++H3W7Hgw8+iEmTJikdJhERUYcnCnVbKMahlmtWoXXChAnQ6epmKlxzzTVtGU+XUd+jtcbFGa1ERETU8dS4fXCebIGUEKVVOBoKZ4sWLcKECRPw5ptvYt++fZBlGRdeeCFuuukmnHfeeUqHR0RERBQyzSq0zps3r8nvqeUMupOtA9ijlYiIiDogq62uP6vJoIFOrVI4Ggp35513HouqREREbUgUBIghuO8/FGN0ZUEv8fn999/j22+/bbT/22+/xQ8//BCSoLoC/2JY7NFKREREHVBZNfuz0tk5HA7cfffd6N69O8xmM2666SaUl5crHRYRERFRmwi60Hr33Xfj8OHDjfYfPXoUd999d0iC6gp0ahF2pxfFJxzIL6mCJMlKh0RERETULJIkY8eREzjucEOWwesYCmjevHlYtWoVxo8fjxtuuAGbNm3CXXfd1aoxv/zyS1x11VVISUmBIAj45z//GZpgiYiIOrD6xbBCsVHLNat1wKl27dqF4cOHN9qfkZGBXbt2hSSozi6vsBJvfHMIO0uqIMnA7PU/ol9SNKZnW5BpiVM6PCIiIqKA8gorsTq3EN8erEC104tjNhdKq5y8jqEmvf/++1i5ciVuuOEGAMDNN9+MUaNGwefzQaVqWcuJmpoanHvuubj11lsxceLEUIZLRETUYYkIUesAsNLaGkHPaNXpdDh27Fij/SUlJVCrg67bdjl5hZVYtCEfu4ptUKtERKhFROpU2FlchUUb8pFXWKl0iERERERNqr+O+eVoFVSigAi1CFOEmtcxFNDhw4cxevRo/+PzzjsParUaxcXFLR5z3LhxWLhwIa677rpQhEhEREQUMkEXWi+77DLMmTMHVVVV/n0nTpzAI488gssuuyykwXU2kiRjdW4hTjg8SIs3QKcWIQgCdGoVLHEGVNV6sCa3kLffERERUdg5/TpGlgFBEGCM0PA6hgLy+XzQarUN9qnVani93naLweVywWazNdiIiIg6G7YOCA9BT0F95plncOGFF8JisSAjIwMAsGPHDiQlJeHvf/97yAPsTAqs1dhntcMcrYMgCFCJAnw+GT5JhiAISIzSYa/VjgJrNfqZo5QOl4iIiMjv1OsYGYDHJwEAtCc/OD71OmZAslHZYClsyLKMW265BTrdr4umOZ1OzJgxA5GRkf5977//fpvFsHjxYixYsKDNxiciIgoHIlowmzLAONRyQRdau3fvjp9++glvvvkmfvzxR0RERODWW2/FjTfeCI1G0xYxdhpVDg/cXh/0mroLTdXJjwkkuW7mh16jQrndhSqHR7EYiYiIiJpy6nWM2ysBMiCKAtRi3eU4r2OoKdOnT2+07+abb27XGObMmYOcnBz/Y5vNhtTU1HaNgYiIiLqGFjVVjYyMxP/93/+FOpZOz2TQQKtWwenxIVKnhkqsK7T6Tt5i5/T4oFWrYDKwYE1ERETh5dTrGO/Jaxet+tflEngdQ0154403lA4BOp2uwYxaIiKizkgQBAghuO8/FGN0ZS0qtBYUFOCLL76A1WqFJEkNnnv88cdDElhn1M8cjT7mKOwsroJBq/KvBueTZciyjDK7C0NSTOhnjgbA/mZEREQUPk69jonQ1K0Wr1PXzWZtfB1DRERERNT1BF1ofe2113DXXXchISEBycnJDSrdgiCw0HoGoihgerYFizbko7DSAZ9UV2B1uLywO70wRWgwLdsCURS4kAQRERGFlVOvY4oqHfBKMjQqETUuL8rsrgbXMURtyW63Y9++ff7HBw8exI4dOxAXF4eePXsqGBkREZFyhJNbKMahlgu60Lpw4UIsWrQIDz30UFvE0+llWuIwd/xArM4txPeHKlHrlVDj9uE3ljhMy7Yg0xKndIhERERETaq/jpnz/s8oPlELm9MDURAwJMXE6xhqNz/88APGjBnjf1zff3X69OlYtWqVQlEREREpSxQE/53TrR2HWi7oQuvx48cxefLktoily8i0xCEjNRbLvtiHz3ZbcUGfRNx/aV/OACEiIqKwl2mJw7k9TIiN0OL63/TA4O517QJ4HUPt5eKLL4Ys8+4vIiIiCj9isC+YPHkyPv7447aIpUsRRQG9EqMQa9DCZNDwzQkRERF1CG6vhOMOD6L0alw6KBkDko28jiEiIiIKA0IINmqdoGe09unTB4899hi2bt2KoUOHQqNpuLLsrFmzQhZcZ1e/kITT41M4EiIiIqLmKbe7IMuATiPCqG/RuqpEREREFGKCULeFYhxquaCvjlesWIGoqChs3rwZmzdvbvCcIAgstAZBz0IrERERdTDHbE4AgDla32BRVCIiIiKiri7o1gEHDx4MuB04cKAtYuy0IrR1hdZaNwutRERE1DFYq10AgCSjXuFIiIiIiKieIAgh21pi2bJlSE9Ph16vR2ZmJr766qszHr9582ZkZmZCr9ejV69eeOWVVxo8f/HFFzcZ2/jx4/3HzJ8/v9HzycnJLYo/VIIutFLo1LcOqOWMViIiIuogrCdntCYZdQpHQkRERET1xBBuwVq/fj3uv/9+zJ07F9u3b8fo0aMxbtw4FBUVNXn8wYMHceWVV2L06NHYvn07HnnkEcyaNQvvvfee/5j3338fJSUl/u2XX36BSqXC5MmTG4w1ePDgBsf9/PPPLfgJQqdZrQNycnLw5z//GZGRkcjJyTnjsc8++2xIAusKWGglIiKijuaYjTNaiYiIiOhXzz77LG6//XbccccdAIClS5fio48+wvLly7F48eJGx7/yyivo2bMnli5dCgAYOHAgfvjhByxZsgQTJ04EAMTFxTV4zbp162AwGBoVWtVqteKzWE/VrELr9u3b4fF4AADbtm0LOI2YfbqCo9fUfU7AHq1ERETUUVir63u0ckYrERERUbhozW3/p48DADabrcF+nU4Hna7x9Z/b7UZeXh4efvjhBvvHjh2L3NzcJs+xZcsWjB07tsG+yy+/HCtXroTH44FGo2n0mpUrV+KGG25AZGRkg/179+5FSkoKdDodRo4ciSeffBK9evU6+w/aRppVaP3888/933/xxRdtFUuXo1WLsDu9OO6QsKu4CgOSjRBFFquJiIgoPLm9Eipr3AAAM2e0EhEREXVaqampDR7PmzcP8+fPb3RceXk5fD4fkpKSGuxPSkpCaWlpk2OXlpY2ebzX60V5eTm6devW4LnvvvsOv/zyC1auXNlg/8iRI7FmzRr069cPx44dw8KFC5GdnY2dO3ciPj6+uT9qSDWr0FrP6/VCr9djx44dGDJkSFvF1CXkFVbijW8OYWdJFSQZyHn7R/RLisb0bAsyLXFnH4CIiIionZXbXZBlQK9RwagP6jKSiIiIiNqQcHILxTgAcPjwYRiNRv/+pmazNnjdabNpZVk+4wzbpo5vaj9QN5t1yJAhOO+88xrsHzdunP/7oUOHIisrC71798bq1avP2vq0rQTV41atVsNiscDn463urZFXWIlFG/Kxq9gGtUpEhFpEpE6FncVVWLQhH3mFlUqHSERERNTIsZMLYSVG69gyioiIiCiM1LcOCMUGAEajscEWqNCakJAAlUrVaPaq1WptNGu1XnJycpPHq9XqRjNRHQ4H1q1b5+//eiaRkZEYOnQo9u7de9Zj20rQi4k9+uijmDNnDior27cYuGjRImRnZ8NgMCAmJqZdzx1KkiRjdW4hTjg8SIs3QKcWIQgC9GoVLHEGVNV6sCa3EJIkKx0qERERUQPWai6ERURERES/0mq1yMzMxKZNmxrs37RpE7Kzs5t8TVZWVqPjP/74Y4wYMaJRf9a3334bLpcLN99881ljcblcyM/Pb9R6oD0Ffc/XCy+8gH379iElJQUWi6VRE9pt27aFLLhTud1uTJ48GVlZWY16MnQkBdZq7LPaYT45E0SAAECG7+SU6sQoHfZa7Siw2hEXdBmciIiIqO1YT85oTTJyISwiIiKicCKiBbMpA4wTrJycHEydOhUjRoxAVlYWVqxYgaKiIsyYMQMAMGfOHBw9ehRr1qwBAMyYMQMvvfQScnJycOedd2LLli1YuXIl1q5d22jslStX4pprrmmy5+of//hHXHXVVejZsyesVisWLlwIm82G6dOnt+CnCI2gC60TJkxQ5FaxBQsWAABWrVrV7ucOpSqHB26vD3qNDnaXF16fBAh1C2MBdT3Pyu0u2Go9iIs8y2BERERE7eiYjTNaiYiIiMLRqbf9t3acYE2ZMgUVFRV44oknUFJSgiFDhmDjxo2wWCwAgJKSEhQVFfmPT09Px8aNGzF79my8/PLLSElJwQsvvICJEyc2GLegoABff/01Pv744ybPe+TIEdx4440oLy9HYmIizj//fGzdutV/XiUEXWhtaoWxcOVyueByufyPbTYbAECSJEiSpEhM0Xo1tGoRtR4vSqtckAEkRGqhFgXIkFHr8UKrFhGtU0GWPYrFGc4kSYIsy8xNE5ibwJibwJibwJibwJibwDprbiRJRsGxalQ6XLA7PfB6fRDF4C7EQ52bzpZjIiIioo5q5syZmDlzZpPPNTVp8qKLLjrrXfH9+vXzL5LVlHXr1gUVY3todqHV4XDgwQcfxD//+U94PB5ceumleOGFF5CQkNCW8bXK4sWL/TNhT1VWVgan06lARECMIKN7tBo7S2vgcNe9QYnRiXC53JBlGaVVLvQ3G2ASHDhxwgZZliGK7CFwKkmSUFVVxdw0gbkJjLkJjLkJjLkJjLkJrDPm5qdiO97ecQxbDtkgycBT/92Fd787iMnDzDgnJarZ44Q6N9XV1a0eg4iIiKgzEE5uoRiHWq7ZhdZ58+Zh1apV+P3vfw+9Xo+1a9firrvuwjvvvNPik8+fP7/JQuipvv/+e4wYMaJF48+ZMwc5OTn+xzabDampqUhMTITRaGzRmKHwf2O0uOet7XBLgDlSC71eC6fHh3K7G3HREbjz4v5ITjJBJYpITEzsNG/SQkWSpLp+tsxNI8xNYMxNYMxNYMxNYMxNYJ0tN3mFx7F8yzFU1LigUamgUQlIMEbgQKUby7ccwyNXxiLTEtussUKdG72eLQyIiIiIKHw0u9D6/vvvY+XKlbjhhhsAADfffDNGjRoFn88HlUrVopPfc889/vECSUtLa9HYAKDT6aDTNV6sQRRFRd/4REdo0CPWgKMnHNCqRRw9XgutWoUh3U2Ylm1BpiXO/0ZE6VjDFXMTGHMTGHMTGHMTGHMTGHMTWGfJjSTJ+PvWIlTVepAYpYPD5YNeo0KUToNIrRqFlQ78Y2sRMi1xzW4jEMrcdPT8EhEREYWKINRtoRiHWq7ZhdbDhw9j9OjR/sfnnXce1Go1iouLkZqa2qKTJyQkhHXrgbbyrx3FiDFoMCGjD85Lj0OVwwOTQYN+5uige50RERERtZUCazX2We0wR+tQ4/YC+HUBT0EQkBilw16rHQXWagxIVu5uISIiIqKuToQAMQQ3/odijK6s2YVWn88HrVbb8MVqNbxeb8iDakpRUREqKytRVFQEn8+HHTt2AAD69OmDqKjm9wZT2u5SGwpKq6ESBfzunBTERWrP/iIiIiIiBVQ5PHB7fdBpdDh83AEAMGh/vXzUa1Qot7tQ5fAoFSIRERERUdhodqFVlmXccsstDW7FdzqdmDFjBiIjI/373n///dBGeNLjjz+O1atX+x9nZGQAAD7//HNcfPHFbXLOtvCvHcUAgAv7JbLISkRERGHNZNBAq64rpro8EkRRQPwp1y9Ojw9atQomg0bBKImIiIiIrQPCQ7MLrdOnT2+07+abbw5pMGeyatUqrFq1qt3O1xZOnc165dBuSodDREREdEb9zNHobY7E5oIyaAQBidF6qE62OZJlGWV2F4akmNDPHK1wpERERERdm3DyKxTjUMs1u9D6xhtvtGUcXcK/OZuViIiIOhBRFHBBnwRs3lMGpyTBoFXBJ8lwenwos7tgitBgWraFPeaJiIiIiBBEoZVaZ3epDXs4m5WIiIg6EFmWsb+sBn3N0fBJEmpcPhyvcUOrVmFIignTsi3ItMQpHSYRERFRl8fWAeGBhdY2JEkyCqzVqHJ48P62I5BlGaP7mTmblYiIiDqE3aXV2G+1IyFai8XXDYW1um7hK5NBg37maM5kJSIiIgoTAgSIbB2gOBZa20heYSVW5xZin9WOaqcHxx1uROnUmJqVpnRoRERERM2y4acSAMAFfRMRF6lDXKTuLK8gIiIiIuq6WGhtA3mFlVi0IR8nHB6Yo3WwuzxQiyK8kowXP9sLY4Sat9kRERFRWNtfZkd+iQ2iKGDckGSlwyEiIiKiM2DrgPAgKh1AZyNJMlbnFuKEw4O0eANkAA6XD2qVgAHJ0aiq9WBNbiEkSVY6VCIiIqKAPvyxbjZrVq94JERxJisRERER0dmw0BpiBdZq7LPaYY7WQRAElFW7AADxkTro1CokRumw12pHgbVa4UiJiIiImna40oGfjpyAIICLeBIRERF1APUzWkOxUcuxdUCIVTk8cHt90GvqZn64fRIAwBRRl2q9RoVye91CEkRERETh6MOTvVl/kxaHZJNe4WiIiIiI6GyEk1+hGIdajjNaQ8xk0ECrVsHp8QEAJLmuRUD9qrxOjw9atQomg0axGImIiIgCKamqRV5hJQDOZiUiIiIiCgYLrSHWzxyNPuYolNldkGUZJ+usECBAlmWU2V3oa45CP3O0soESERERNWHjz6WQZWBYagxS4wxKh0NEREREzSAKoduo5VhoDTFRFDA92wJThAaFlQ64vT7Isgynx4fCSgdMERpMy7b4Z7gSERERhYtyuwtb9lcAAMafw9msRERERB2FEMIvajkWWttApiUOc8cPxOAUEzw+GbVeCTVuL4akmDB3/EBkWuKUDpGIiIiokf/+XAJZljEoxYheiVFKh0NERERE1KFwMaw2kmmJw7AeMfj9376F2ydhzrgByLTEcSYrERERhaUTDje+2lsOAPjdOSkKR0NEREREwRCEui0U41DLsdDahmQAkTo1IgEMSjGxyEpERERh66OdpfBJMvqYo9AvibNZiYiIiIiCxUJrG/L4ZP/3GhW7NBAREVF4qnZ68MWeMgB1s1kFTmUgIiIi6lAEICT9VXkV2DostLYhjyT5v9eo+KtKRERE4WnTrmNweyVY4iMxpLtR6XCIiIiIKEiiULeFYhxqORZa25DHW1doVasEzgwhIiKisCJJMgqs1bDanPhg+1FoRAHjz0nmNQsRERERUQux0NqG6lsHsG0AERERhZO8wkqszi3EPqsdFTUu1Li8SIjSQZbP/loiIiIiCj/Cya9QjEMtxwpgG/L46ma0alloJSIiojCRV1iJRRvy8cvRKkTpVZBlGWpRhNsr4cmN+cgrrFQ6RCIiIiIKkiCEbqOWYwWwDblPFlo5o5WIiIjCgSTJWJ1biBMOD9LiDXC6JUgSYNCq0D85GlW1HqzJLYQkcWorEREREVGwWAFsQ/UzWjVqfhxAREREyiuwVmOf1Q5ztA5un4xj1U4AgNmohygISIzSYa/VjgJrtcKREhEREVEwhBBu1HIstLYhj5c9WomIiCh8VDk8cHt9EADss9rh88nQaUTEGjQAAL1GBbfXhyqHR9lAiYiIiIg6IC6G1Ybc7NFKREREYcRk0ACCgAJrNQQI0GtV6JUYCfFkMy6nxwetWlV3HBERERF1GCIE/zVda8ehlmOhtQ3Vtw5Qq/hLSkRERMrTqETUur2o9UiINWjQOzESarHuA2FZllFmd2FIign9zNEKR0rU9q5bsRVqfaTSYdApLhyYqHQIdJo+8RFKh0BN6BltUDoECkOhuu2fFazWYaG1DXn8M1pVCkdCREREXd0+azWe+2Qvko0R8PhkaNUquDwSBI0Ap8eHMrsLpggNpmVbIIq8xCYiIiIiChYLrW3I66vr0coZrURERKSk3aU2vPDpXrg8Es7rFYcH+vbDuu8OY5/VjnK7C1q1CkNSTJiWbUGmJU7pcImIiIgoWJzSGhZYaG0jkiTjQFkNjjvcqHK4IUkyZ4cQERFRu/vlaBVe+mwfPD4Jg1KMuOe3faBTq3B+ejwKrNWocnhgMmjQzxzNaxUiIiKiDko4+RWKcajlWGhtA3mFlVidW4htRcdRWeNG8YlalNpcmM5ZIkRERNSOthcdx/Iv9sMnyTinRwzuurg3tOq6nqyiKGBAslHhCImIiIiIOg9R6QA6m7zCSizakI9fjlZBpxYRoRYRoVVjZ3EVFm3IR15hpdIhEhERURfw/aFKLDtZZB1uicXdY34tshIRERFRJyMAQgi2lk5oXbZsGdLT06HX65GZmYmvvvrqjMdv3rwZmZmZ0Ov16NWrF1555ZUGz69atQqCIDTanE5nq87b1ni1HUKSJGN1biFOODxIizdAqxIhCAL0GhGWOAOqaj1Yk1sISZKVDpWIiIg6sdz95Xh1835Ikozze8VjxkW9oVbxso+IiIiosxJCuAVr/fr1uP/++zF37lxs374do0ePxrhx41BUVNTk8QcPHsSVV16J0aNHY/v27XjkkUcwa9YsvPfeew2OMxqNKCkpabDp9foWn7c98Io7hAqs1dhntcMcrYMgCKgvp0oSIAgCEqN02Gu1o8BarWicRERE1HlIkozdpTZ8e6ACu0tt+HyPFa9/fRCyDFzQNwG3X5AOFXuvEhEREVEbefbZZ3H77bfjjjvuwMCBA7F06VKkpqZi+fLlTR7/yiuvoGfPnli6dCkGDhyIO+64A7fddhuWLFnS4DhBEJCcnNxga8152wN7tIZQlcMDt9cHvUYHAIjSq1Fe7UJVrQfdYyOg16hQbnehyuFROFIiIiLqDOr7wu+z2uH2+uDySnB7JfSINeDa4d3x+5E9IQgsshIRERF1eq247b/ROABsNluD3TqdDjqdrtHhbrcbeXl5ePjhhxvsHzt2LHJzc5s8xZYtWzB27NgG+y6//HKsXLkSHo8HGo0GAGC322GxWODz+TBs2DD8+c9/RkZGRovP2x44ozWETAYNtGoVnB4fACBap4ZKJcDrk1Dt9MDp8UGrVsFk0CgcKRERdUWyLKPcUY5DJw6h3FEOWWYrm47s1L7wRr0aOo2IaqcX1S4vyuwuDEiOZpGViIiIiFokNTUVJpPJvy1evLjJ48rLy+Hz+ZCUlNRgf1JSEkpLS5t8TWlpaZPHe71elJeXAwAGDBiAVatW4d///jfWrl0LvV6PUaNGYe/evS0+b3vgjNYQ6meORh9zFHYWV8GgVUEUBMREaFFhd6Gyxg0AGJJiQj9ztMKREhFRV3LCeQKrd6zGi9+9iP3H9/v3947tjXvPuxfTh01HjD5GuQApaKf2hbfEG2CtduFYlQtqUUBKjAEur4S/bynE8J6xENk2gIiIiKjTE05+hWIcADh8+DCMRqN/f1OzWRu87rQP+GVZPuOH/k0df+r+888/H+eff77/+VGjRmH48OF48cUX8cILL7T4vG2NM1pDSBQFTM+2wBShQWGlAzUuL0wRanglGaVVTkTrNZiWbeEbHiIiajcf7fsIPZ7tgdkfzcaB4wcaPHfg+AHM/mg2ejzbAx/t+0ihCKklTu0LX1btwrGqutVXk016dDNFsC88ERERURcjCKHbgLqFqE7dAhVaExISoFKpGs0itVqtjWab1ktOTm7yeLVajfj4+CZfI4oifvOb3/hntLbkvO2BhdYQy7TEYe74gRicYoLN6UVFjRsQ6vq1XpORgkxLnNIhEhFRF/HRvo8w/q3xqPXUQj75dar6fbWeWox/azyLrR1IfV94p1dC6ckia7eYCCQZ61Zh1WtUcHt97AtPRERERG1Kq9UiMzMTmzZtarB/06ZNyM7ObvI1WVlZjY7/+OOPMWLECH9/1tPJsowdO3agW7duLT5ve2DrgDaQaYlDRmosCqzVqHJ4sP3wCXx3oAKVdrfSoRERURdxwnkCE9+eCFmWIUE647ESJIiyiIlvT8SRnCNsI9ABmAwayAAKK2qgEgQkROtgjv51lgH7whMRERF1LSFeCysoOTk5mDp1KkaMGIGsrCysWLECRUVFmDFjBgBgzpw5OHr0KNasWQMAmDFjBl566SXk5OTgzjvvxJYtW7By5UqsXbvWP+aCBQtw/vnno2/fvrDZbHjhhRewY8cOvPzyy80+rxJYaG0joihgQHJdL4v0hEh8f7ASu0psOOFwI8agVTg6IiLq7FbvWA2Hx9FoFmsgEiQ4PA6s+XENZo2c1cbRUWvFGrRwenxweSUkReuQEhPhf06WZZTZXewLT0RERNSVKFhpnTJlCioqKvDEE0+gpKQEQ4YMwcaNG2GxWAAAJSUlKCoq8h+fnp6OjRs3Yvbs2Xj55ZeRkpKCF154ARMnTvQfc+LECfzf//0fSktLYTKZkJGRgS+//BLnnXdes8+rBBZa24HZqEdvcxT2W+349mAlLh+crHRIRETUicmyjBe/e7FFr33h2xdw73n3crX6MOZwe/HiZ3uRbIyA2ytBEAQ4XF7oNSo4PT6U2V0wRbAvPBERERG1n5kzZ2LmzJlNPrdq1apG+y666CJs27Yt4HjPPfccnnvuuVadVwns0dpOsnrVNfPdsr9C4UiIiKizq6itwP7j+5s9m7WeDBn7j+9HZW1lG0VGreX1SVj+xX6UnHAiLSEST006F0O61/WFP3LcAZvTiyEpJswdP5B94YmIiIi6ECGEX9RynNHaTn6THoe13xXhcKUDR4470CPWoHRIRETUSdnd9la9vtpdjXhD06t9knJkWcab3xZhV7ENOo2I+y7pi57xBlzUL9HfF95k0KCfOZozWYmIiIiIFNAhZrQeOnQIt99+O9LT0xEREYHevXtj3rx5cLs7zuJSUTo1hnY3QZZlfLD9KL49UIHdpTZIUnCzjYiIiM4mShvVqtdHa9nXMxx9tPMYviwogyAAf7iwN3rG131oW98XfmSveAxINrLISkRERNQFCULoNmq5DjGjdffu3ZAkCa+++ir69OmDX375BXfeeSdqamqwZMkSpcNrtthILXYW25BXdAL/3H4UOrUKfcxRmJ5t4e19REQUMvER8egd2xsHjh8Iqn2AAAG9YnshLoJ/k8JNXuFxvJt3GABww2964tzUGGUDIiIiIqKwouBaWHSKDjGj9YorrsAbb7yBsWPHolevXrj66qvxxz/+Ee+//77SoTVbXmEl3s07ArvbCwF1qwUb9WrsLK7Cog35yCtkPzwiIgoNQRBw73n3tui1s0bO4kJYYeZgeQ1e+/IAZBkYM8CMSwaalQ6JiIiIiIia0CFmtDalqqoKcXFnnnHjcrngcrn8j202GwBAkiRIktSm8Z1KkmSs+uYQbLUepBj1qHR4UFXrQWpcBHpqI1BUUYvVuYdwbneT/3Y/SZIgy3K7xtlRMDeBMTeBMTeBMTeBdeTcTD1nKuZ+Nhe1nlpIOHv8oiAiQh2Bm4fe3KyftyPnpq2FMjcVdhde+LQAbp8PQ1JMmDKiB2RZhix3zNZDof694e8fERER0Umc0hoWOmShdf/+/XjxxRfxzDPPnPG4xYsXY8GCBY32l5WVwel0tlV4jewrr8WekhMw6VQAgHK7hOM1LiQYRIiCAKNOwO7iE9i6uwh9EiIA1L1xqKqqgizLEMUOMfG43TA3gTE3gTE3gTE3gXX03Lx22WuY+t+pEGXxjMVWESIECPjbZX+D2+aG1WY969gdPTdtKVS5cXokvPT1EVhtbnQzanHdwGhUlJeFMNL2F+rfm+rq6hBERURERNTxCSe/QjEOtZyihdb58+c3WQg91ffff48RI0b4HxcXF+OKK67A5MmTcccdd5zxtXPmzEFOTo7/sc1mQ2pqKhITE2E0GlsXfBAO1VTCBxHGSD1UgoBjdi9cXgkVtRJ6xEZArZFhc9dCHRENs7lulq4kSRAEAYmJiXwDexrmJjDmJjDmJjDmJrCOnpsp5imIiYnB5Hcmw+FxAECDnq31F1ERmgi8O/ldjO09ttljd/TctKVQ5MYnyXjxs32odAHmmEg8NG4A4qN0IY60/YX690av14cgKiIiIiKi0FC00HrPPffghhtuOOMxaWlp/u+Li4sxZswYZGVlYcWKFWcdX6fTQadr/KZEFMV2fVMYE6mFTq2CyyMhUqdGjzgDDpTZUWl3I1qvgUYUoFOrEBOpbRCXIAjtHmtHwdwExtwExtwExtwE1tFzM67vOBzJOYI1P67BC9++gP3H9/uf62FMwwNZ9+GWYbfApDcFPXZHz01bam1u1n5fiJ3FNmhVKsy6pB8SjREhjlA5ofy94e8eERERUR1BqNtCMQ61nKKF1oSEBCQkJDTr2KNHj2LMmDHIzMzEG2+80aEurPuZo9HHHIWdxVUwaFWI1qmRGK1Dmc2Fw5U1MGjVOLdHDPqZo5UOlYiIOqEYfQxmjZyF7KTf4+lNedh+pARqwQCLkIT9B6Oxr5sPmRalo+y6JElGgbUaVQ4PTAYNiioc+CzfCkEA7rywF9ITIpUOkYiIiIiImqFD9GgtLi7GxRdfjJ49e2LJkiUoK/u1P1lycrKCkTWPKAqYnm3Bog35KKx0IDFKh8QoHY7XuGFzeiEKAqZmWfwLYREREYVaXmElnty4G+V2DQxiN2hUAkwRGuwsrsKiDfmYO34gMi2BF5k8vRjYh8W/kMgrrMTq3ELss9rh9vrgkwGH24seMQbceWEvZFpilQ6RiIiIiDoAroUVHjpEofXjjz/Gvn37sG/fPvTo0aPBcx1l1d1MSxzmjh/Y4M1UtF4DQRCQYopAud2F3aU2voElIqKQkyQZq3MLccLhQXqCATuP2iDLgF6rgiXOgMJKB9bkFiIjNbbJD/1OLwZq1Sr0TozEVQOMuNRsVuAn6hzyCiuxaEM+Tjg8MEfrAKhRYK2G0yPhWLUTCVFapUMkIiIioo6Cldaw0CEKrbfccgtuueUWpcNotUxLHDJSYxvMCDp6ohbLPtuP+f/eiSi9BpBlvoElIqKQKrBWY5/VDnO0DmpRhCgKkCQZR4/XIkKjQoRGhfxSG3aX2jAopWGv1tOLgXqNDk6PD7uKbThotSE2Nha/SY9X6CfruE4tfqfFG+CRZOw9ZocIAYlRWqhEEX/fUojhPZsufhMRERERUfjpEIXWzkQUBQxINvof251eFFfVwub0QpKBAcnRcHslvoElIqKQqXJ44Pb6oNfULRAZoVWhxunF8Ro3jqPu7pBar4TH/7UT/ZKjkWzUI9moR0K0FmtyC1FZ40Z6QiTEk53xI3VqRGhFHLBW4+9bC5FpiWMxMEj1xe/EaB0qatwoqXJCkmToNCLSE6Lg9Piw12pHgbW6wXUDEREREVFThJNfoRiHWo6FVgVJkow1WwqhU4uI1qnhk2RYq13oHhPBN7BERBQyJoMGWrUKTo8PkTo10uIjYav1wOWV4PL6YHd5oZJkqAQBx6qcOFblxI+o+zBwZ0kV1KKIncU26NQikox6mCLqWt/EGTTY14mKgaf3oe1njm6zv79VDg8cbi+qnR7Uun0AAINOBUt8JFSiAL1GhXK7C1UOT5ucn4iIiIg6F0Go20IxDrUcC60Kqp/NkmzUwyfLOFhWg/JqF0wRGkTqVJ3uDSwRESmjnzkafcxR2FlcBYNWBbUoIC6yrv+nLMsorHRgZHo8Hr9qEKw2F47ZnDhmc+KHwuPIL7VBJdYVIWvdPpRU1cIUoQEA6NQibA5fpygGNtWHto85CtOzLWdcJKwlPD4JeYXHUWF3QyUK0KpFdDPpER+l888fcHrqYjAZNCE9NxERERERtR0WWhV06q2cKlGAyaBBlcODaqcXkTpVp3oDS0REyhFFAdOzLVi0IR+FlQ4kRumg19TNcC2z133ANz3bgoQoHRKidBiUUvfh3rCeMdhVbEO0XgWVKKLgWDVcHgkenwS1SoDLK0GnFjt8MTBQH9qdxVVYtCEfc8cPDFmxteBYNVbnHkLJiVoYtCq4fRL6JUVBp1b5j5FlGWV2F4akmNDPHB2S8xIRERFR58a1sMKDqHQAXdmpt3ICgPrk7Yn107Q7yxtYIiJSXqYlDnPHD8TgFBNsTi+OHHfA5vRiSIopYCGxfiZsud0NvVqEXlNXDLS7vJBlGZUOD/qYozp0MfD0RakidWqoRAGROjUscQZU1XqwJrcQkiS36jwOtxdrthzCX/+7G6VVTpgMWtx7SV+kJUSipMqJGpcXPklGjcuLwkoHTBEaTMu2sHUQERERETWPEMKNWowzWhV0+q2c9e/hBMD/BvbcnnEd+g0sERGFj0xLHDJSY5vdh/T0mbBalYBaWUZljRtVDg+i9WpMPb9jFwPr2/iYo3Wwu304ZnPCoFUjIUoLrUpEYpSuVYtSybKMbUXH8ea3Rf47VEb3TcDkEamI1KkxsFu0v2VBud0FrVqFISkmTGuDlgVERERERNS2WGhV0OlvYD0+CbIsw+WVUFRR2ynewBIRUXgRRSGogmH9TNjVuYX4+WgVar0SfLUeXNg3Ab/rb0SmJbYNo217VQ7PyQXBgFKbE5CBGqcXZdVOGPUaxBo0cHlb1sbnuMONt749jB2HTwAAkkx6TM9KQ//kXz9ADbb4TURERETUFOHkVyjGoZZjoVVhp76B3XqgArVeCU6vDxmpMZ3iDSwREXV89cXAn49W4YkPd0EtCHjkygFArU3p0FrNoFXD7vKiwu6G+mS/dJ8kw+70wlbrQWWNGxCAncU2DOluQqSu4aWTJMmNiqSyLCP3YBU+2X8ULq8EURRw5dBkjB+aAq26cdemYIvfREREREQUnlhoDQP1b2Dn/XsndhZXYeLwHpgyogfKy8uUDo2IiAhAXTHw3NQYDO8ZgwNlNdh7zI6+AWqDTRUfw3GGZoXdhfe2HQZkwO2TkBoXicRoPQQATq+E8monjpyoRZROja/3luG7g5UY2SsOvx1ghiU+EnmFlf7b/t1eH7RqFbrHRiBCI6LC5oBOp0XvxChMz05Dj1iD0j8uEREREXVigvDrmj+tHYdajoXWMCGKAmINGsQatEhPjAzLN6REREQDuxmx32rHlwVlsCdrYZEiMCDZ6P+71VTxsY85CtPDrOfo7lIbln+xH3anF/2So1Fe7YLD7YPD5YVeo4LPJ8Eryehrjsb4ock4crwWR47X4uu95fh6bzkidSrkl1TDJ8kwR+ugVWtRXFWLb/aVQy0K6Jugxy2je+KSAUn8m05EREREbS5U61jxyrV1WGgNI56Tq2FpVI1vKyQiIgoHklx3G31e0Qls0qtg0Jegjzka07MtAIBFG/JxwuGBOVoHvUYHp8eHncVVWLQhH3PHD1S82CrLMj7Nt2Ld94chyzJS4wy457d9UFhRc8ZFqWRZxv4yOz7Nt+KHQ5X49kAlql1eROlU0KpF2JweuD0SItQiJABGvRpj+iWyyEpERERE1IWw0BpGvD4JAKDmmzIiIgpDeYWV+MfWQlS7vNCqRMRHqqHWqLGzuAoLP9yFSJ0aJxwepMUbAEGAJMmI1Klh0KpQWOnAmtxCZKTGKlZ8dHsl/H1rIXL3lQMAzu8Vj2nZFujUKiRE6c64KJUgCOhjjkYfczQyLbG4f/0ORMoqSBJQXu0CAKhVIrrHRkAtAEernCiw2jEoxaTIz0pEREREXQyntIYFFlrDiMdXN6NVLXJGKxERhRdJkrE6txC2Wg8SorSocflQ65FgNqhgjtZhn9UOu8uL+Egd9hyrhssrATLQM96AWIMWiVE67LXaUWCtbpeFn07vE5sQpcPyL/bjUHkNBAGYPCIVYwclQTilCVVzF6XySTL0ahG9E6NQ4/KissYNjVpEslEPtSjAK0lwV8mw1Xra8kckIiIiIvITTn6FYhxqORZaw4hXOjmjVcVfaiIiCi8F1mrss9phjtah2uVFjcuHY9VuWO1eAIDT44PTI6Ha6WnQAue4w4NYgxZ6jQrldheqHG1ffDy9T6x8Mr5kYwS6x0bgDxf1wuBWzDQ1GTTQqlVweXwwRWhgitA0eN7p8UGrFmA8bT8REREREXVunDoZRnz+Hq0stBIRUXipcnjg9vqg16hg1NcVECUZkAGoVAIi9WqIIhCt1yA9MRJpiZEAgBqX11/o1KpVMBnatviYV1iJRRvy8cvRKkTr1YjQqlBR48ZxhwdFlTW4elhKq4qsANDPHI0+5iiU2V2QZbnBc7Iso7zajbQ4PfqZo1p1HiIiIiKiZhMAIQQbJ7S2DgutYaS+dYCKrQOIiCjM1M/idHp8MGhV6JsUhbQ4PQanGDEkxYS0OAMitWoIAhCtU8Oo10AU6/q0OlxelNld6GuOQj9zdJvFWN/e4ITDg2SjDpU1bpSccEItCEg26hCt1+A/O4ohSfLZBzsDURQwPdsCU4QGhZUO1Li88EkyalxeFFY6YDRoMOlcMxfCIiIiIiLqYljRCxOSJKPC7sJxhxuFFTWtfhNIREQUSqfP4jRoVTBoVVCrhLpZnDVunNsjBglROhRWOuBweWHQquCVZByqqIEpQoNp2Zagi4+SJGN3qQ3fHqjA7lJbwL+PPknGx7uO4YdDlbC7PCg4ZsfxGjcgAN1i9EhLiII5+tc+sa2VaYnD3PEDMTjFBJvTiyPHHbA5vRiSYsIj4wbgnBTOZiUiIiKi9iOEcKOWY4/WMFDfSy6v8Di8kownN+Sjf3I0rhpgxKVms9LhERER+WdxLtqQj8JKBxKitBBOzuIst7thitDg/sv6AoC/P6rTI8ErSTBG6DB3/EBkWuKCOufpvVa1ahX6mKMwPduCTEscnB4fdhZXYXvRCfx0pApHjjtwotaDCLUIQRQQrdcgMVqHaF3d5U6o+8RmWuKQkRrbYNGtuhm7MqxWa0jOQURNW7ZsGZ5++mmUlJRg8ODBWLp0KUaPHq10WERERMoJVZWUldZWYaFVYfW95E44PFCJAjSigOgINXYV23DQakNsbCx+kx6vdJhERET+WZx1xc9qOJweGPQaDEkxYdrJ4icAf/Fx7zE7/rGlEHFRGpzbIyaoc53699EcrYNeo4PT48NPR07gT+9WIdMSi6paD7y+X2e4GvUaROvVSIjSITFaB5XQ8CqxLfrEiqKAAcnGBvt4VwpR21q/fj3uv/9+LFu2DKNGjcKrr76KcePGYdeuXejZs6fS4REREVEXxkKrgk7tJWeJN8BWWzfDJlKrhlGvxgFrNf6+tRCZljj2eSMiorBQP4tzd6kNhcVWWFLMGJBsbPB3qr742D8pGpt2HfP3Lu2d2Lzb6U/9+5gWb4DLK9XNRK31wOHyotYrYXNBGQZ3MyLJpMew1BgMS41Fr4RIPPDOj9hZXNWoN5IsyyizuzAkxdSmfWKJqO09++yzuP3223HHHXcAAJYuXYqPPvoIy5cvx+LFixWOjoiISBnCya9QjEMtx0Krggqs1dhntcMcrYNwyqybwkoHEqN0iDOose9kL7nTZ8sQEREppa6QGo04sRZmc3TADwMFQUD/5GhsKzyO3SXVzS60Flirsae0GmpRQIHVDqfb12BMU4QaKlHEraPScUHfhAZ/Q09tb5AYpYNeU7eAV5nd1eI+sUQUPtxuN/Ly8vDwww832D927Fjk5uYqFBUREZHyBKFuC8U41HIstCqoyuGB2+uDXqODACAxWocyuws1Ti/sTi/UggSVSo2KajeQrHS0REREwRuQHI28Q5X4el8ZEqK0/j6mTRU73V4JPx45gbe/P4zDxx11vVYFARCAaL0GpggNjHo1REHAkeMOaOufP0XD9gZ2lNtd0KpVjdobEFHHVF5eDp/Ph6SkpAb7k5KSUFpa2uRrXC4XXC6X/7HNZmvTGImIiKjrOv3OOmpHJoMGWnXdTBsASImJwMBuxrq+cqKAWo8Mm9ODv319AP/+sRjVztAs3kFERNRePD4ZO4tt+PCnEjzywc/IWf8j7l+/A3mFlQDqbunfX2bH37cW4oF3fsQrX+xHUYUDogBoNSK6x0VgcIoJvRIiER+phUYlnrXXaqYlDkunDMOzU87FomuH4tkp5+K5KcNYZCXqRE7/kEWW5Ub76i1evBgmk8m/paamtkeIRERE7UoI4dYSy5YtQ3p6OvR6PTIzM/HVV1+d8fjNmzcjMzMTer0evXr1wiuvvNLg+ddeew2jR49GbGwsYmNjcemll+K7775rcMz8+fMhCEKDLTlZ2ZmKnNGqoH7maPQxR2FncRUMWhUEQYBWJSIlJgKJ0VrkF9ug16ohAPjX9qPY+FMJRvVNwNhBSUgy6pUOn4iI6IzyCivx6pf7UeP2QS0KiDdoIYoCdhZXYf6/d2HsoCSU2Jw4VuX0vyY2UosrhiTDuEuDA2V2xBu0DYonze212tQiVUTU8SUkJEClUjWavWq1WhvNcq03Z84c5OTk+B/bbDYWW4mIiEIo2IUqDx48iCuvvBJ33nkn/vGPf+Cbb77BzJkzkZiYiIkTJwIAvvjiC9x4443Izs6GXq/HU089hbFjx2Lnzp3o3r27f6zBgwfjk08+8T9WqVRt/wOfAQutChJFIXAvuWoXkoxaPHbVEAACPtp5DIUVNfhitxWb91gxLDUGlw9ORh9zVIM3oJIko8BajSqH54y3ZxIREbWl+gWtqhwemKO1sNV6Ue32Qa8R4fVJ2F9mxz++dWJwNyO0ahUyLbHI7hOPgScX1uqVGMleq0TUiFarRWZmJjZt2oRrr73Wv3/Tpk2YMGFCk6/R6XTQ6XTtFSIREZEyWjMd9fRxghTsQpWvvPIKevbsiaVLlwIABg4ciB9++AFLlizxF1rffPPNBq957bXX8O677+LTTz/FtGnT/PvVarXis1hPxUKrwgL1khvc3Yjf9TfiN2lxEEUR56XHoeCYHf/7pRQ/HTmB7UV1W6/ESIwdnIzhPWOx4/Bx/zhub91tlX3MUZjOnnRERNTOTl3wsdbrg63WizLbrzNXtSoRXp+MSwcl4dqMHojQNvzkmb1WiSiQnJwcTJ06FSNGjEBWVhZWrFiBoqIizJgxQ+nQiIiIFCOc/ArFOMFoyUKVW7ZswdixYxvsu/zyy7Fy5Up4PB5oNI1bhDkcDng8HsTFNXwfsHfvXqSkpECn02HkyJF48skn0atXr6B+hlBioTUMZFrikJEa22Amap+ESJSXl/mPqV+5uX9yNEqqavHxzmPI3V+OA2U1eOWL/ZAh41C5AzKApGgd9BodnB4fdhZXYdGGfMwdP5BvSomIqN2cuuCjWiUCQi0gA1q1iNhILYx6DcqqneidGNWoyFqvqb+PvFODiKZMmYKKigo88cQTKCkpwZAhQ7Bx40ZYLBalQyMiIuo0Tl88MtAdIi1ZqLK0tLTJ471eL8rLy9GtW7dGr3n44YfRvXt3XHrppf59I0eOxJo1a9CvXz8cO3YMCxcuRHZ2Nnbu3In4+Phm/6yhxEJrmDi9l5wkSQGP7WaKwPTsNFw7vDs+y7fis93H8O2BSlS7vIjUqqDXqKBVi4jUqWHQqlBY6cCa3EJkpMbyzSkREbWLUxd8jNSp0cccBQAwnOw9XuPynnFBq3rstUpETZk5cyZmzpypdBhERERhQwAQYF3IoMcB0Kif+bx58zB//vzArwtiocpAxze1HwCeeuoprF27Fl988QX0+l/XLBo3bpz/+6FDhyIrKwu9e/fG6tWrG/Rnb08stHZgRr0G12R0R+/EKOw4fAIyVJAkoMzmRGWNC6mxBpgiNEiM0mGv1Y4CazXfrBIRUbs4fcHHSO2vlxzNXdCKiIiIiIiaJ9QtWg8fPgyj8dcaUqB+5y1ZqDI5ObnJ49VqdaOZqEuWLMGTTz6JTz75BOecc84ZY4+MjMTQoUOxd+/eMx7XlkTFzkwh43B7oVWJGNjNhLSESOi1Kvh8Mg6V1+DI8Vpo1SLcXh+qHB6lQyUioi6ifsFHU4QGhZUO1Li88EkyalxeFFY6uKAVEREREVEYMxqNDbZAhdZTF6o81aZNm5Cdnd3ka7Kyshod//HHH2PEiBEN+rM+/fTT+POf/4z//e9/GDFixFljdrlcyM/Pb7L1QHthobUTqL890+XxwRShQV9zFBKNdf8BVNhd2HOsGoIgnPX2TCIiolCqX9BqcIoJNqcXR447YHN6MSTFxN7hREREREQhJAih24KVk5ODv/3tb3j99deRn5+P2bNnN1iocs6cOZg2bZr/+BkzZqCwsBA5OTnIz8/H66+/jpUrV+KPf/yj/5innnoKjz76KF5//XWkpaWhtLQUpaWlsNvt/mP++Mc/YvPmzTh48CC+/fZbTJo0CTabDdOnT295IluJrQM6gdNvzxQFASmmCETp1CisqEG10wtREHDM5kT/pOgz9sggIiIKJS5oRURERETUHkLdPKD5zrZQZUlJCYqKivzHp6enY+PGjZg9ezZefvllpKSk4IUXXsDEiRP9xyxbtgxutxuTJk1qcK5Te8UeOXIEN954I8rLy5GYmIjzzz8fW7duVXSBTBZaO4H62zMXbchHYaUDiVE66DUqqAQBBq3aX3hdk1uI3SXVmJaVFnCFZyIiolDjglZERERERJ3bmRaqXLVqVaN9F110EbZt2xZwvEOHDp31nOvWrWtueO2GrQM6iUC3Z57bIwYv3TQct49OhyAI+O5gJRb8ZycOltcoHTIREREREREREYWAkq0D6Fec0dqJnO32zH5J0Vjx5X6UVbvw5MZ8TBzeA5cPToIsg7d0EhERERERERERtQILrZ3MmW7P7GOOwvyrB2N1biF+OFSJd344jE/zj8Hu8qKwwgG31wetWoU+5ihMz7ZwkRIiIiIiIiIiog5AuQ6tdCq2DuhiDFo1ZlzUC1OzLLC7vPhoZym+2lsOlQj0iDXAqFdjZ3EVFm3IR15hpdLhEhERERERERHRWbB1QHhgobULEgQBF/ZNRLS+bkKzTiWgpMqJY9UuRGhVsMQZUFXrwZrcQkiSrHC0RERERERERERE4Y+F1i6qwFqN4hNODOwWjfhoHSADZTYniiodgCAgMUqHvVY7CqzVSodKRERERERERERnIITwi1qOhdYuqsrhgdvrQ4RGjdRYA3rGRwJC3f6jxx3QaVRwe32ocniUDpWIiIiIiIiIiM5ECOFGLcbFsLook0EDrVoFp8eHSJ0asQYNgEgUVdagwu6GT5KhVatgMmiUDpWIiIiIiIiIiCjsdZgZrVdffTV69uwJvV6Pbt26YerUqSguLlY6rA6rnzkafcxRKLO7IMt1fVhjDRp0j42ALMsornIiSq9GP3O0wpESEREREREREdGZcEJreOgwhdYxY8bg7bffxp49e/Dee+9h//79mDRpktJhdViiKGB6tgWmCA0KKx2ocXnhk2REqFVQq0WoRQE+n4ytByuUDpWIiIiIiIiIiCjsdZjWAbNnz/Z/b7FY8PDDD+Oaa66Bx+OBRsPb21si0xKHueMHYnVuIfZZ7Si3u6BVqzAyPR7dY/QoOGbH618fgkGrxrDUGKXDJSIiIiIiIiKiJghC3RaKcajlOkyh9VSVlZV48803kZ2dfcYiq8vlgsvl8j+22WwAAEmSIElSm8fZGpIkQZblNo8zIzUG5042ocBqh63WA2OEBv3MURAEYOU3h7D1QAWWf7EPsy/ti35J4dFGoL1y0xExN4ExN4ExN4ExN4ExN4ExN4GFOjfMMREREVEd4eRXKMahlutQhdaHHnoIL730EhwOB84//3x8+OGHZzx+8eLFWLBgQaP9ZWVlcDqdbRVmSEiShKqqKsiyDFFs+w4PcSIQFwkAXpSX1wIAruxjQPlxG3aW1uDpjTsx84Lu6G7StXksZ9PeuelImJvAmJvAmJvAmJvAmJvAmJvAQp2b6urqEERFRERERBQaihZa58+f32Qh9FTff/89RowYAQB48MEHcfvtt6OwsBALFizAtGnT8OGHH0IIMK95zpw5yMnJ8T+22WxITU1FYmIijEZj6H6QNiBJEgRBQGJioqJv0nLGJeK5T/Zir7Uaa7ZX4uEr+iPJqP/1AFkGKioAux2IigLi49t8nnm45CYcMTeBMTeBMTeBMTeBMTeBMTeBhTo3er3+7AcRERERdQWhWsmKE1pbRdFC6z333IMbbrjhjMekpaX5v09ISEBCQgL69euHgQMHIjU1FVu3bkVWVlaTr9XpdNDpGs/AFEWxQ7zxEQRB8Vj1WhH3XdoXT/1vDw5XOvDcJ3vxyJUDEeN2AKtXAy++COzf/+sLevcG7r0XmD4diIlps7jCITfhirkJjLkJjLkJjLkJjLkJjLkJLJS5YX6JiIiI6rDOGh4ULbTWF05bQpZlAGjQg5XahkGrxuzL+uEv/82H1ebCv596A1OXPADB4Wh88IEDwOzZwNy5wHvvAZdf3v4BExERERERERERtbMOMQ3gu+++w0svvYQdO3agsLAQn3/+OW666Sb07t074GxWCi1ThAY5l/XHeQXf4/d/ngnZ4ahrG3Cy4O1Xv6+2Fhg/HvjoI2UCJiIiIiIiIiLqIgQhdBu1XIcotEZEROD999/HJZdcgv79++O2227DkCFDsHnz5iZbA1DbSPTV4s4X/gRBliGeXmA9nSTVFVwnTgROnGiX+IiIiIiIiIiIuiYhJF9sHtA6irYOaK6hQ4fis88+UzoMWr0aYv1M1uaQJMDhANasAWbNatvYiIiIiIiIiIiIFNQhZrRSGJDluoWvWuKFF5pfnCUiIiIiIiIioqCwdUB4YKGVmqeiAti/P/iCqSzXva6ysm3iIiIiIiIiIiIiCgMstFLz2O2te311dWjiICIiIiIiIiIiCkMdokcrhYGoqNa9Pjo6NHEQEREREREREVEDobrtn60DWoczWql54uOB3r2D/y9OEOpeFxfXNnERERERERERERGFARZaqXkEAbj33pa9dtYsfiRCRERERERERNRGhBB+Ucux0ErNN306YDAAYjN/bUSx7vhp09o2LiIiIiIiIiKiLqy+dUAoNmo5Flqp+WJigPfeq/uv7mzFVlGsO+799+teR0RERERERERE1Imx0ErBufxyYMMGICKi6Y866vdFRAAbNwJjxyoTJxERERERERFRFyGEcKOWY6GVgnf55cCRI8DSpUCvXg2f69Wrbv/RoyyyEhERERERERFRl6FWOgDqoGJi6ha5uvdeoLISqK4GoqOBuDg29CAiIiIiIiIiak+hmo7Kkk6rsNBKrSMIQHx83UZERERERERERO1OOPkVinGo5dg6gIiIiIiIiIiIiKiVOKOViIiIiIiIiIioA2tqvfKWjkMtx0IrERERERERERFRB8YWreGBrQOIiIiIiIiIiIiIWomFViIiIiIiIiIioo5MCOHWAsuWLUN6ejr0ej0yMzPx1VdfnfH4zZs3IzMzE3q9Hr169cIrr7zS6Jj33nsPgwYNgk6nw6BBg/DBBx+0+rxtjYVWIiIiIiIiIiIiapH169fj/vvvx9y5c7F9+3aMHj0a48aNQ1FRUZPHHzx4EFdeeSVGjx6N7du345FHHsGsWbPw3nvv+Y/ZsmULpkyZgqlTp+LHH3/E1KlTcf311+Pbb79t8XnbAwutREREREREREREHZgQwq9gPfvss7j99ttxxx13YODAgVi6dClSU1OxfPnyJo9/5ZVX0LNnTyxduhQDBw7EHXfcgdtuuw1LlizxH7N06VJcdtllmDNnDgYMGIA5c+bgkksuwdKlS1t83vbAQisREREREREREVEHJgih24LhdruRl5eHsWPHNtg/duxY5ObmNvmaLVu2NDr+8ssvxw8//ACPx3PGY+rHbMl524NasTMrQJZlAIDNZlM4krOTJAnV1dXQ6/UQRdbDT8XcBMbcBMbcBMbcBMbcBMbcBMbcBBbq3NRf09Vf4xE1R/3vi9dZo3AkdDpXTYTSIdBpanVepUOgJtQIPqVDoFM47NUAlL8eCVWtq36c08fT6XTQ6XSNji8vL4fP50NSUlKD/UlJSSgtLW3yHKWlpU0e7/V6UV5ejm7dugU8pn7Mlpy3PXSpQmt1dd0vf2pqqsKREBEREVGoVFdXw2QyKR0GdRD17wm+XXCdwpHQ6b5ROgAiolZQ6npEq9UiOTkZfdNDV+uKiopqVDubN28e5s+fH/A1wmlTYWVZbrTvbMefvr85YwZ73rbWpQqtKSkpOHz4MKKjoxVNenPYbDakpqbi8OHDMBqNSocTVpibwJibwJibwJibwJibwJibwJibwEKdG1mWUV1djZSUlBBER11FR3pPcDb8/0344b9JeOK/S3jqLP8uSl+P6PV6HDx4EG63O2RjNlWsbGo2KwAkJCRApVI1mkVqtVobzTatl5yc3OTxarUa8fHxZzymfsyWnLc9dKlCqyiK6NGjh9JhBMVoNHbo/+G0JeYmMOYmMOYmMOYmMOYmMOYmMOYmsFDmhjNZKVgd8T3B2fD/N+GH/ybhif8u4akz/LsofT2i1+uh1+sVObdWq0VmZiY2bdqEa6+91r9/06ZNmDBhQpOvycrKwn/+858G+z7++GOMGDECGo3Gf8ymTZswe/bsBsdkZ2e3+LztoUsVWomIiIiIiIiIiCh0cnJyMHXqVIwYMQJZWVlYsWIFioqKMGPGDADAnDlzcPToUaxZswYAMGPGDLz00kvIycnBnXfeiS1btmDlypVYu3atf8z77rsPF154If76179iwoQJ+Ne//oVPPvkEX3/9dbPPqwQWWomIiIiIiIiIiKhFpkyZgoqKCjzxxBMoKSnBkCFDsHHjRlgsFgBASUkJioqK/Menp6dj48aNmD17Nl5++WWkpKTghRdewMSJE/3HZGdnY926dXj00Ufx2GOPoXfv3li/fj1GjhzZ7PMqgYXWMKXT6TBv3ryAPTC6MuYmMOYmMOYmMOYmMOYmMOYmMOYmMOaGKLT431T44b9JeOK/S3jiv0vnMnPmTMycObPJ51atWtVo30UXXYRt27adccxJkyZh0qRJLT6vEgS5flkvIiIiIiIiIiIiImoRUekAiIiIiIiIiIiIiDo6FlqJiIiIiIiIiIiIWomFViIiIiIiIiIiIqJWYqG1AygoKMCECROQkJAAo9GIUaNG4fPPP1c6rLCxYcMGjBw5EhEREUhISMB1112ndEhhxeVyYdiwYRAEATt27FA6HMUdOnQIt99+O9LT0xEREYHevXtj3rx5cLvdSoemmGXLliE9PR16vR6ZmZn46quvlA5JcYsXL8ZvfvMbREdHw2w245prrsGePXuUDissLV68GIIg4P7771c6lLBw9OhR3HzzzYiPj4fBYMCwYcOQl5endFiK83q9ePTRR/3/7+3VqxeeeOIJSJKkdGhEHRb/foeXL7/8EldddRVSUlIgCAL++c9/Kh0Sgdd04Wj58uU455xzYDQaYTQakZWVhf/+979Kh0UUMiy0dgDjx4+H1+vFZ599hry8PAwbNgy/+93vUFpaqnRoinvvvfcwdepU3Hrrrfjxxx/xzTff4KabblI6rLDypz/9CSkpKUqHETZ2794NSZLw6quvYufOnXjuuefwyiuv4JFHHlE6NEWsX78e999/P+bOnYvt27dj9OjRGDduHIqKipQOTVGbN2/G3Xffja1bt2LTpk3wer0YO3YsampqlA4trHz//fdYsWIFzjnnHKVDCQvHjx/HqFGjoNFo8N///he7du3CM888g5iYGKVDU9xf//pXvPLKK3jppZeQn5+Pp556Ck8//TRefPFFpUMj6pD49zv81NTU4Nxzz8VLL72kdCh0Cl7ThZ8ePXrgL3/5C3744Qf88MMP+O1vf4sJEyZg586dSodGFBKCLMuy0kFQYOXl5UhMTMSXX36J0aNHAwCqq6thNBrxySef4JJLLlE4QuV4vV6kpaVhwYIFuP3225UOJyz997//RU5ODt577z0MHjwY27dvx7Bhw5QOK+w8/fTTWL58OQ4cOKB0KO1u5MiRGD58OJYvX+7fN3DgQFxzzTVYvHixgpGFl7KyMpjNZmzevBkXXnih0uGEBbvdjuHDh2PZsmVYuHAhhg0bhqVLlyodlqIefvhhfPPNN5xV1oTf/e53SEpKwsqVK/37Jk6cCIPBgL///e8KRkbUMfHvd3gTBAEffPABrrnmGqVDodPwmi48xcXF4emnn+b7euoUOKM1zMXHx2PgwIFYs2YNampq4PV68eqrryIpKQmZmZlKh6eobdu24ejRoxBFERkZGejWrRvGjRvHT8JOOnbsGO688078/e9/h8FgUDqcsFZVVYW4uDilw2h3brcbeXl5GDt2bIP9Y8eORW5urkJRhaeqqioA6JK/J4HcfffdGD9+PC699FKlQwkb//73vzFixAhMnjwZZrMZGRkZeO2115QOKyxccMEF+PTTT1FQUAAA+PHHH/H111/jyiuvVDgyoo6Hf7+JWo7XdOHF5/Nh3bp1qKmpQVZWltLhEIWEWukA6MwEQcCmTZswYcIEREdHQxRFJCUl4X//+1+XvxWxfvbh/Pnz8eyzzyItLQ3PPPMMLrroIhQUFHTpP56yLOOWW27BjBkzMGLECBw6dEjpkMLW/v378eKLL+KZZ55ROpR2V15eDp/Ph6SkpAb7k5KS2JrkFLIsIycnBxdccAGGDBmidDhhYd26ddi2bRu+//57pUMJKwcOHMDy5cuRk5ODRx55BN999x1mzZoFnU6HadOmKR2eoh566CFUVVVhwIABUKlU8Pl8WLRoEW688UalQyPqcPj3m6hleE0XPn7++WdkZWXB6XQiKioKH3zwAQYNGqR0WEQhwRmtCpk/fz4EQTjj9sMPP0CWZcycORNmsxlfffUVvvvuO0yYMAG/+93vUFJSovSP0Saam5v6BTTmzp2LiRMnIjMzE2+88QYEQcA777yj8E/RNpqbmxdffBE2m63VjgsAABcLSURBVA1z5sxROuR209zcnKq4uBhXXHEFJk+ejDvuuEOhyJUnCEKDx7IsN9rXld1zzz346aefsHbtWqVDCQuHDx/Gfffdh3/84x/Q6/VKhxNWJEnC8OHD8eSTTyIjIwN/+MMfcOeddza4tberWr9+Pf7xj3/grbfewrZt27B69WosWbIEq1evVjo0og6Lf7+JgsNruvDRv39/7NixA1u3bsVdd92F6dOnY9euXUqHRRQSnNGqkHvuuQc33HDDGY9JS0vDZ599hg8//BDHjx+H0WgEULfC6KZNm7B69Wo8/PDD7RFuu2pubqqrqwGgwSdfOp0OvXr16rQLATQ3NwsXLsTWrVuh0+kaPDdixAj8/ve/75RvbJubm3rFxcUYM2YMsrKysGLFijaOLjwlJCRApVI1mv1itVobzZLpqu699178+9//xpdffokePXooHU5YyMvLg9VqbdC+xufz4csvv8RLL70El8sFlUqlYITK6datW6PZGAMHDsR7772nUETh48EHH8TDDz/s///00KFDUVhYiMWLF2P69OkKR0fUsfDvN1HweE0XXrRaLfr06QOg7j3q999/j+effx6vvvqqwpERtR4LrQpJSEhAQkLCWY9zOBwAAFFsOPlYFEX/jM7Oprm5yczMhE6nw549e3DBBRcAADweDw4dOgSLxdLWYSqiubl54YUXsHDhQv/j4uJiXH755Vi/fj1GjhzZliEqprm5AYCjR49izJgx/lnQp//31VVotVpkZmZi06ZNuPbaa/3769uVdGWyLOPee+/FBx98gC+++ALp6elKhxQ2LrnkEvz8888N9t16660YMGAAHnrooS5bZAWAUaNGYc+ePQ32FRQUdNq/ScFwOByN/l+rUqk67bUMUVvi32+i5uM1XccgyzJcLpfSYRCFBAutYS4rKwuxsbGYPn06Hn/8cUREROC1117DwYMHMX78eKXDU5TRaMSMGTMwb948pKamwmKx4OmnnwYATJ48WeHolNWzZ88Gj6OiogAAvXv37vKf4BYXF+Piiy9Gz549sWTJEpSVlfmfS05OVjAyZeTk5GDq1KkYMWKEf3ZvUVERZsyYoXRoirr77rvx1ltv4V//+heio6P9s4ZMJhMiIiIUjk5Z0dHRjfqaRUZGIj4+vsv3O5s9ezays7Px5JNP4vrrr8d3332HFStWdNlZ86e66qqrsGjRIvTs2RODBw/G9u3b8eyzz+K2225TOjSiDol/v8OP3W7Hvn37/I8PHjyIHTt2IC4urtG1ObUfXtOFn0f+v717j4qqevsA/p1Buc2MIIjihRiBQZSbGGqokYqGYaZmKupSEdOMtIvg1C9cIaYsrTAxL0UpGivFDLxklCUgmTcuMiCCZgh2USrFLFBBZL9/sDivEzCNgkH1/aw1a8mZffZ+ztGl22c/Z59XX8Vjjz0GBwcH/PHHH0hKSsKhQ4fwxRdftHVoRK1DULuXnZ0tHn30UWFjYyNUKpV46KGHRGpqaluH1S7U1NSI8PBw0bVrV6FSqcSoUaNEYWFhW4fV7pSWlgoAIi8vr61DaXMJCQkCQJOf/6oNGzYIR0dHYWpqKgYMGCAyMzPbOqQ219yfkYSEhLYOrV165JFHxAsvvNDWYbQLn376qfDw8BBmZmbCzc1NxMfHt3VI7cLvv/8uXnjhBfHAAw8Ic3Nz4eTkJCIjI0V1dXVbh0b0j8V/v9uXjIyMJucOs2fPbuvQ/tM4p2t/QkNDpb+77OzsREBAgPjyyy/bOiyiViMTQoi/L61LRERERERERERE9O/z39yYkIiIiIiIiIiIiKgVMdFKRERERERERERE1EJMtBIRERERERERERG1EBOtRERERERERERERC3ERCsRERERERERERFRCzHRSkRERERERERERNRCTLQSERERERERERERtRATrUREREREREREREQtxEQrEf0rDR8+HC+++GKr9bds2TL079+/1foDgLKyMshkMuh0ulbtl4iIiIiIiIj+fky0ElG7FhISAplMBplMho4dO8LJyQkRERGoqqoyeF5KSgpef/31VosjIiICaWlprdbf3fjuu+8wZ84c9OrVC2ZmZujduzemTZuGnJycNomnvTI2uZ6SkoLAwEB06dKFiW4iIiKiNpCeng43NzfU1dXd1XlqtRpr1641uv2pU6fQq1evv/y/AxFRa2GilYjavTFjxuDSpUs4f/48VqxYgY0bNyIiIqLJtrdu3QIA2NjYQKVStVoMSqUStra2rdafsXJycvDggw/i22+/xXvvvYeioiLs3r0bbm5uCA8P/9vj+TeoqqrC0KFDsWrVqrYOhYiIiP5ljC0SSE5OxvDhw2FlZQWlUgkvLy8sX74cFRUVAIBLly5h+vTp6NOnD+RyudFPaiUnJ2Pw4MGwsrKCSqWCu7u73pzxXp/S2rp1K6ytre/6vOZotVpERkZCLpdL/TfcN5lMBqVSiQcffBApKSl652VnZ2P+/PlGj+Pp6YlBgwbh7bffbrXYiYgMYaKViNo9MzMz2Nvbw8HBAdOnT8eMGTOwZ88eAP8/WdyyZQucnJxgZmYGIUSj6ka1Wo2YmBiEhoZCpVLhgQceQHx8vN44P/74I4KDg2FjYwOFQgFfX1+cOHFCb5wGISEhmDBhAqKjo9G1a1d06tQJzzzzDGpqaqQ2X3zxBYYNGwZra2vY2tri8ccfR0lJidHXLYRASEgINBoNDh8+jLFjx8LZ2Rn9+/dHVFQU9u7dK7U9deoURo4cCQsLC9ja2mL+/PmorKxsFG9MTAy6desGa2trREdHo7a2FkuWLIGNjQ169eqFLVu2SOc0bG2QlJSEIUOGwNzcHO7u7jh06JBenJmZmRg0aBDMzMzQvXt3vPLKK6itrZW+Hz58OJ5//nlotVrY2NjA3t4ey5Yt0+vj2rVrmD9/vnQvR44cifz8fOn7hvufmJgItVoNKysrBAcH448//pCuLzMzE3FxcdIEvaysrMn7OnPmTLz22msYNWqU0b8XRERERMb6qyKByMhITJ06FQMHDsTnn3+OwsJCxMbGIj8/H4mJiQCA6upq2NnZITIyEt7e3kaNe/DgQQQHB+Opp55CVlYWcnNzsXLlSr35aXtw9OhRnDt3DpMnT9Y73qlTJ1y6dAmXLl1CXl4eAgMDMWXKFJw9e1ZqY2dnB0tLy7sab86cOdi0aRNu377dKvETERnCRCsR/eNYWFhIlatA/aP1H3/8MZKTkw0+Bh4bGwtfX1/k5eUhLCwMzz77LM6cOQMAqKysxCOPPIKLFy9i3759yM/Ph1arNfg4U1paGoqLi5GRkYEdO3Zg9+7diI6Olr6vqqrC4sWLkZ2djbS0NMjlckycONHoR6R0Oh1Onz6N8PBwabX/Tg1VBdevX8eYMWPQuXNnZGdnY9euXTh48CAWLlyo1z49PR0XL17E119/jTVr1mDZsmV4/PHH0blzZ5w4cQILFizAggUL8MMPP+idt2TJEoSHhyMvLw9DhgzBE088gStXrgAAfvrpJwQFBWHgwIHIz8/Hpk2bsHnzZqxYsUKvj23btkGhUODEiRN44403sHz5cnz11VcA6hPKY8eORXl5OVJTU5Gbm4sBAwYgICBAquoAgJKSEuzZswf79+/H/v37kZmZKVWlxsXFwc/PD/PmzZMm6A4ODkbdZyIiIqLWZKhIICsrCzExMYiNjcWbb76JIUOGQK1WY/To0UhOTsbs2bMB1BcJxMXFYdasWbCysjJq3P3792PYsGFYsmQJ+vTpA1dXV0yYMAHvvPMOgPqq0ejoaOTn50sL01u3bgUArFmzBp6enlAoFHBwcEBYWJi0aH/o0CHMmTMH165dk85rWDSvqamBVqtFz549oVAoMHjw4EaL8n+WlJSERx99FObm5nrHZTIZ7O3tYW9vD41GgxUrVkAul6OgoEBq8+etA2QyGT744ANMnDgRlpaW0Gg02Ldvn16/gYGBuHLlCjIzM426j0RELcFEKxH9o2RlZWH79u0ICAiQjtXU1CAxMRE+Pj7w8vKCTCZr8tygoCCEhYXBxcUFL7/8Mrp06SJNBLdv345ff/0Ve/bswbBhw+Di4oIpU6bAz8+v2VhMTU2xZcsWuLu7Y+zYsVi+fDnWrVsnJVInTZqEJ598EhqNBv3798fmzZtx6tQpFBUVGXWt586dAwC4ubkZbPfRRx/hxo0b+PDDD+Hh4YGRI0di/fr1SExMxM8//yy1s7Gxwbp169CnTx+EhoaiT58+uH79Ol599VVoNBr873//g6mpKY4cOaLX/8KFCzFp0iT07dsXmzZtgpWVFTZv3gwA2LhxIxwcHLB+/Xq4ublJVb6xsbF6CWUvLy9ERUVBo9Fg1qxZ8PX1lfa8zcjIwKlTp7Br1y74+vpCo9HgrbfegrW1NT755BOpj7q6OmzduhUeHh54+OGHMXPmTKkPKysrmJqawtLSUpqgm5iYGHWfiYiIiO6nO4sEPvroIyiVSoSFhTXZtiWP59vb2+P06dMoLCxs8vupU6ciPDwc7u7u0sL01KlTAQByuRzr1q1DYWEhtm3bhvT0dGi1WgDAkCFDsHbtWr2K04YK3Tlz5uDIkSNISkpCQUEBJk+ejDFjxkjz2KZ8/fXX8PX1NXgtt2/fxrZt2wAAAwYMMNg2OjoaU6ZMQUFBAYKCgjBjxgy9xXpTU1N4e3vj8OHDBvshImoNTLQSUbu3f/9+KJVKmJubw8/PD/7+/tLKPAA4OjrCzs7uL/vx8vKSft2wYv7LL78AqK8e9fHxgY2NjdFxeXt76z265Ofnh8rKSqkitKSkBNOnT4eTkxM6deqE3r17AwC+//57o/oXQkixGlJcXAxvb28oFArp2NChQ1FXV6f3qJW7u7teZWy3bt3g6ekp/WxiYgJbW1vpntx5XQ06dOgAX19fFBcXS2P7+fnpxTh06FBUVlbixx9/lI7dee8BoHv37tI4ubm5qKyshK2tLZRKpfQpLS3V22pBrVbr7bt7Zx9ERERE7dGfiwTOnTsHJycndOzYsdXHWrRoEQYOHAhPT0+o1WoEBwdjy5YtqK6uBlCf8FUqlejQoYO0MG1hYQEAePHFFzFixAj07t0bI0eOxOuvv46PP/4YQH2i0srKSq/iVKlUoqSkBDt27MCuXbvw8MMPw9nZGRERERg2bBgSEhKajbOsrAw9evRodPzatWvSPNDU1BTPPvss4uPj4ezsbPC6Q0JCMG3aNLi4uCAmJgZVVVXIysrSa9OzZ89mt5UiImpNHdo6ACKivzJixAhs2rQJHTt2RI8ePRpNTO9MMBry5/NkMplUddkwyWwNDUnHcePGwcHBAe+//z569OiBuro6eHh4GL1PlqurK4D6ZKahlxYIIZpNxt55vKnrN3RPDGnot6mxm0oQGxqnrq4O3bt3b/IxszurOu41ViIiIqK/U0ORQG1tLW7duoXx48dLRQKG5m0tpVAo8Nlnn6GkpAQZGRk4fvw4wsPDERcXh2PHjhnc2zQjIwMxMTEoKirC77//jtraWty8eRNVVVXNzrVPnjwJIYQ0Z21QXV1t8CWyN27caLRtAACoVCqcPHkSQP3WWAcPHsQzzzwDW1tbjBs3rtn+7lzQVygUUKlUjRbjLSwscP369Wb7ICJqLaxoJaJ2T6FQwMXFBY6Ojvdl9R+on6DpdDq9x4z+Sn5+Pm7cuCH9fPz4cSiVSvTq1QtXrlxBcXExli5dioCAAPTt2xdXr169q5j69++Pfv36NXoMv8Fvv/0GAOjXrx90Op3e22yPHDkCuVzeaOJ7L44fPy79ura2Frm5udJ2Bv369cPRo0el5CpQ/4IDlUqFnj17GtX/gAEDUF5ejg4dOsDFxUXv06VLF6PjNDU15UsOiIiIqM2NGDECOp0OZ8+exc2bN5GSkoKuXbsCqF9ILykp0XvfQGtzdnbG008/jQ8++AAnT55EUVERdu7c2Wz7CxcuICgoCB4eHkhOTkZubi42bNgAAAbjrKurg4mJCXJzc6HT6aRPcXEx4uLimj2vS5cuTc6L5XK5NAf08vLC4sWLMWLECKxevdrg9RqzGF9RUWHUE3BERC3FRCsREYBp06bB3t4eEyZMwJEjR3D+/HkkJyfj2LFjzZ5TU1ODuXPnoqioCJ9//jmioqKwcOFCyOVydO7cGba2toiPj8d3332H9PR0LF68+K5ikslkSEhIwLfffgt/f3+kpqbi/PnzKCgowMqVKzF+/HgAwIwZM2Bubo7Zs2ejsLAQGRkZWLRoEWbOnIlu3bq16L4AwIYNG7B7926cOXMGzz33HK5evYrQ0FAAQFhYGH744QcsWrQIZ86cwd69exEVFYXFixc3+QKvpowaNQp+fn6YMGECDhw4gLKyMhw9ehRLly5FTk6O0XGq1WqcOHECZWVluHz5crPVrhUVFdDpdNJeuWfPnoVOp0N5ebnRYxERERE1x1CRwPTp01FZWYmNGzc2eW7DQnprUavVsLS0lBbkm1qYzsnJQW1tLWJjY/HQQw/B1dUVFy9e1GvT1Hk+Pj64ffs2fvnll0aL5fb29s3G5OPjY/Q7C0xMTPQKG+5VYWEhfHx8WtwPEdFfYaKViAj1k8cvv/wSXbt2RVBQEDw9PbFq1SqDL1QKCAiARqOBv78/pkyZgnHjxklvYJXL5UhKSkJubi48PDzw0ksv4c0337zruAYNGoScnBw4Oztj3rx56Nu3L5544gmcPn1aeuOqpaUlDhw4gIqKCgwcOBBPPfUUAgICsH79+nu5FY2sWrUKq1evll4isHfvXqnStGfPnkhNTUVWVha8vb2xYMECzJ07F0uXLjW6f5lMhtTUVPj7+yM0NBSurq4IDg5GWVnZXSWKIyIiYGJign79+sHOzq7ZvXD37dsHHx8fjB07FgAQHBwMHx8fvPvuu0aPRURERHQvBg8eDK1Wi/DwcGi1Whw7dgwXLlxAWloaJk+eLL0ACoBUIVpZWYlff/1Vb6G4KcuWLYNWq8WhQ4dQWlqKvLw8hIaG4tatWxg9ejSA+sRraWkpdDodLl++jOrqajg7O6O2thbvvPMOzp8/j8TExEbzIrVajcrKSqSlpeHy5cu4fv06XF1dMWPGDMyaNQspKSkoLS1FdnY2Vq9ejdTU1GbjDAwMxDfffNPouBAC5eXlKC8vR2lpKeLj43HgwAGpuOBelZWV4aeffsKoUaNa1A8RkVEEERHdtdmzZ4vx48e3dRj3VWlpqQAg8vLy2joUIiIion8EY+eIO3fuFP7+/kKlUgmFQiG8vLzE8uXLxdWrV6U2ABp9HB0dm+0zPT1dTJo0STg4OAhTU1PRrVs3MWbMGHH48GGpzc2bN8WkSZOEtbW1ACASEhKEEEKsWbNGdO/eXVhYWIjAwEDx4YcfCgB68SxYsEDY2toKACIqKkoIIURNTY147bXXhFqtFh07dhT29vZi4sSJoqCgoNk4KyoqhIWFhThz5ox0LCEhQe86zczMhKurq1i5cqWora2V2jk6Ooq3335b7x7t3r1br38rKyvpuoQQIiYmRgQGBjYbDxFRa5IJccfGekREZJSQkBD89ttv2LNnT1uHct+UlZWhd+/eyMvLM/gyLiIiIiKiu6HVanHt2jW8995793Wc6upqaDQa7NixA0OHDr2vYxERAdw6gIiIiIiIiIj+RpGRkXB0dLzvLzK9cOECIiMjmWQlor8NK1qJiIiIiIiIiIiIWogVrUREREREREREREQtxEQrERERERERERERUQsx0UpERERERERERETUQky0EhEREREREREREbUQE61ERERERERERERELcREKxEREREREREREVELMdFKRERERERERERE1EJMtBIRERERERERERG1EBOtRERERERERERERC30fzmRFowYzZuwAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1400x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This joint probability matrix is exactly what will be fed into the JIDT or phyid libraries to compute PID.\n"
+     ]
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "# Plot 1: 2D Trajectory of the Network\n",
+    "axes[0].plot(reduced_states[:, 0], reduced_states[:, 1], '-o', alpha=0.7)\n",
+    "axes[0].set_title(\"Network State Trajectory in PCA Space\")\n",
+    "axes[0].set_xlabel(\"Principal Component 1\")\n",
+    "axes[0].set_ylabel(\"Principal Component 2\")\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "# Mark start and end points\n",
+    "axes[0].scatter(reduced_states[0, 0], reduced_states[0, 1], c='green', s=100, label='Start', zorder=5)\n",
+    "axes[0].scatter(reduced_states[-1, 0], reduced_states[-1, 1], c='red', s=100, label='End', zorder=5)\n",
+    "axes[0].legend()\n",
+    "\n",
+    "# Plot 2: The Joint Probability Matrix (Discrete State Space)\n",
+    "im = axes[1].imshow(joint_probabilities, cmap='Blues', origin='lower')\n",
+    "axes[1].set_title(\"Joint State Probability Matrix (Discretized)\")\n",
+    "axes[1].set_xlabel(\"PC1 State (Bin)\")\n",
+    "axes[1].set_ylabel(\"PC2 State (Bin)\")\n",
+    "axes[1].set_xticks(range(num_bins))\n",
+    "axes[1].set_yticks(range(num_bins))\n",
+    "plt.colorbar(im, ax=axes[1], label='Probability')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"This joint probability matrix is exactly what will be fed into the JIDT or phyid libraries to compute PID.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neuroai-project13",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/03_neurogym_tour.ipynb b/notebooks/03_neurogym_tour.ipynb
new file mode 100644
index 0000000..0ffcee2
--- /dev/null
+++ b/notebooks/03_neurogym_tour.ipynb
@@ -0,0 +1,189 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "71919846",
+   "metadata": {},
+   "source": [
+    "# Phase 3: The NeuroGym Data Tour\n",
+    "Before we train the RNN, we need to understand what the data actually looks like. NeuroGym generates cognitive tasks with specific temporal phases:\n",
+    "1. **Fixation:** The network must output a 'fixation' action (usually class 0) and wait.\n",
+    "2. **Stimulus:** The noisy evidence is presented. The network must continue to fixate while internally accumulating evidence.\n",
+    "3. **Decision:** The stimulus turns off, and the network must output its choice based on the accumulated evidence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9b11e2b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading datasets...\n",
+      "Data loaded successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import torch\n",
+    "from src.tasks.data_loader import load_mante_data\n",
+    "\n",
+    "BASE_PATH = '../src/tasks/data/mante_style'\n",
+    "\n",
+    "print(\"Loading datasets...\")\n",
+    "# We use subsample_step=20 just for visual plotting clarity (fewer data points on the X axis)\n",
+    "context_loader = load_mante_data(f'{BASE_PATH}/context/train.npz', batch_size=1, shuffle=True, subsample_step=20)\n",
+    "perceptual_loader = load_mante_data(f'{BASE_PATH}/perceptual/train.npz', batch_size=1, shuffle=True, subsample_step=20)\n",
+    "\n",
+    "# Pull one single trial from each\n",
+    "ctx_obs, ctx_labels, ctx_periods, ctx_coh, ctx_ctx = next(iter(context_loader))\n",
+    "per_obs, per_labels, per_periods, per_coh, per_ctx = next(iter(perceptual_loader))\n",
+    "\n",
+    "# Remove batch dimension for plotting: shape becomes (Timesteps, Features)\n",
+    "ctx_obs = ctx_obs.squeeze(0).numpy()\n",
+    "ctx_labels = ctx_labels.squeeze(0).numpy()\n",
+    "\n",
+    "per_obs = per_obs.squeeze(0).numpy()\n",
+    "per_labels = per_labels.squeeze(0).numpy()\n",
+    "\n",
+    "print(\"Data loaded successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59b3f248",
+   "metadata": {},
+   "source": [
+    "### Visualizing Perceptual Decision Making (Low Integration)\n",
+    "In this task, the network receives a single stream of noisy evidence (e.g., dots moving left or right). Notice how the target changes from `0` (Fixate) to the correct choice (e.g., `1` or `2`) at the very end of the trial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "eb6e9d25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(3, 1, figsize=(12, 10), sharex=True)\n",
+    "\n",
+    "# 1. Plot the Rules (Context Cues) - Notice how it is Clamped!\n",
+    "axes[0].plot(per_obs[:, 5], label=\"Context 1 (Attend Modality 1)\", color=\"#1f77b4\", linewidth=2)\n",
+    "axes[0].plot(per_obs[:, 6], label=\"Context 2 (Attend Modality 2)\", color=\"#ff7f0e\", linewidth=2)\n",
+    "axes[0].set_title(\"Perceptual Task: The Rules (Clamped to Context 1)\", fontsize=14)\n",
+    "axes[0].legend(loc=\"upper right\")\n",
+    "\n",
+    "# 2. Plot the Noisy Sensory Streams - Notice how Distractor is Dead!\n",
+    "axes[1].plot(per_obs[:, 1], label=\"Modality 1 - A\", color=\"#1f77b4\", alpha=0.8)\n",
+    "axes[1].plot(per_obs[:, 2], label=\"Modality 1 - B\", color=\"#ff7f0e\", alpha=0.8)\n",
+    "axes[1].plot(per_obs[:, 3], label=\"Modality 2 - A (Dead Wire)\", color=\"#2ca02c\", linestyle='--', alpha=0.8)\n",
+    "axes[1].plot(per_obs[:, 4], label=\"Modality 2 - B (Dead Wire)\", color=\"#d62728\", linestyle='--', alpha=0.8)\n",
+    "axes[1].set_title(\"Noisy Sensory Streams (Distractor Masked out)\", fontsize=14)\n",
+    "axes[1].set_ylabel(\"Signal Strength\")\n",
+    "axes[1].legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\")\n",
+    "\n",
+    "# 3. Plot the Target Output\n",
+    "axes[2].plot(per_labels, color=\"red\", linewidth=2.5, label=\"Target Output\")\n",
+    "axes[2].set_title(\"Target Output\", fontsize=14)\n",
+    "axes[2].set_yticks([0, 1, 2])\n",
+    "axes[2].set_yticklabels([\"Fixate (0)\", \"Choice 1\", \"Choice 2\"])\n",
+    "axes[2].set_xlabel(\"Timesteps (Subsampled)\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17fb7136",
+   "metadata": {},
+   "source": [
+    "### Visualizing Context Decision Making (High Integration)\n",
+    "This is where synergy is expected to emerge! The network now receives **four** noisy stimulus channels (e.g., Motion Left, Motion Right, Color Red, Color Blue) AND **two** context channels. The network must use the context channel to figure out which pair of stimulus channels to pay attention to, and actively ignore the other pair."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d7ac620b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(3, 1, figsize=(12, 10), sharex=True)\n",
+    "\n",
+    "# 1. Plot the Rules (Context Cues)\n",
+    "axes[0].plot(ctx_obs[:, 5], label=\"Context 1 (Attend Modality 1)\", color=\"#1f77b4\", linewidth=2)\n",
+    "axes[0].plot(ctx_obs[:, 6], label=\"Context 2 (Attend Modality 2)\", color=\"#ff7f0e\", linewidth=2)\n",
+    "axes[0].set_title(\"Context Task: The Rules (High Integration)\", fontsize=14)\n",
+    "axes[0].legend(loc=\"upper right\")\n",
+    "\n",
+    "# 2. Plot the Noisy Sensory Streams (Both are active!)\n",
+    "axes[1].plot(ctx_obs[:, 1], label=\"Modality 1 - A\", color=\"#1f77b4\", alpha=0.7)\n",
+    "axes[1].plot(ctx_obs[:, 2], label=\"Modality 1 - B\", color=\"#ff7f0e\", alpha=0.7)\n",
+    "axes[1].plot(ctx_obs[:, 3], label=\"Modality 2 - A\", color=\"#2ca02c\", linestyle='--', alpha=0.7)\n",
+    "axes[1].plot(ctx_obs[:, 4], label=\"Modality 2 - B\", color=\"#d62728\", linestyle='--', alpha=0.7)\n",
+    "axes[1].set_title(\"Noisy Sensory Streams (Conflict Present)\", fontsize=14)\n",
+    "axes[1].set_ylabel(\"Signal Strength\")\n",
+    "axes[1].legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\")\n",
+    "\n",
+    "# 3. Plot the Target Output\n",
+    "axes[2].plot(ctx_labels, color=\"red\", linewidth=2.5, label=\"Target Output\")\n",
+    "axes[2].set_title(\"Target Output (Withhold decision until the end)\", fontsize=14)\n",
+    "axes[2].set_yticks([0, 1, 2])\n",
+    "axes[2].set_yticklabels([\"Fixate (0)\", \"Choice 1\", \"Choice 2\"])\n",
+    "axes[2].set_xlabel(\"Timesteps (Subsampled)\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neuroai-project13",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/04_minimal_product.ipynb b/notebooks/04_minimal_product.ipynb
new file mode 100644
index 0000000..cf7c37f
--- /dev/null
+++ b/notebooks/04_minimal_product.ipynb
@@ -0,0 +1,666 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a22658ed",
+   "metadata": {},
+   "source": [
+    "# Minimal Product: Task Integration Demand & Information Geometry\n",
+    "**Hypothesis:** Tasks requiring the integration of multiple streams (Context) will force the network to encode information synergistically. Simple accumulation tasks (Perceptual) will be solved redundantly. This effect should peak exactly at the end of the stimulus period.\n",
+    "\n",
+    "We test this by training both an Elman RNN and a CTRNN to compare how temporal architectures affect the `gaussian_pid` structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7205bf02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import copy\n",
+    "import os\n",
+    "\n",
+    "#import sys\n",
+    "#sys.path.append('../src/tasks') # Ensure Python can find data_loader.py\n",
+    "from src.tasks.data_loader import load_mante_data\n",
+    "from src.models.ctrnn import CTRNN\n",
+    "from src.analysis.RNN import ElmanRNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "275176ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading Perceptual Datasets...\n",
+      "Loading Context Datasets...\n",
+      "All datasets loaded successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Configuration\n",
+    "BASE_PATH = \"../src/tasks/data/mante_medium\"\n",
+    "BATCH_SIZE = 1024\n",
+    "SUBSAMPLE_STEP = 10 # Subsample for Elman RNN (simulate tau=10ms)\n",
+    "\n",
+    "print(\"Loading Perceptual Datasets...\")\n",
+    "per_train_loader = load_mante_data(f'{BASE_PATH}/perceptual/train.npz', batch_size=BATCH_SIZE, shuffle=True, subsample_step=SUBSAMPLE_STEP)\n",
+    "per_val_loader = load_mante_data(f'{BASE_PATH}/perceptual/val.npz', batch_size=BATCH_SIZE, shuffle=False, subsample_step=SUBSAMPLE_STEP)\n",
+    "\n",
+    "print(\"Loading Context Datasets...\")\n",
+    "ctx_train_loader = load_mante_data(f'{BASE_PATH}/context/train.npz', batch_size=BATCH_SIZE, shuffle=True, subsample_step=SUBSAMPLE_STEP)\n",
+    "ctx_val_loader = load_mante_data(f'{BASE_PATH}/context/val.npz', batch_size=BATCH_SIZE, shuffle=False, subsample_step=SUBSAMPLE_STEP)\n",
+    "\n",
+    "print(\"All datasets loaded successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70862864",
+   "metadata": {},
+   "source": [
+    "### 1. The Architectures\n",
+    "We define both the Elman RNN and the CTRNN. We configure both to use `batch_first=True` for easier tensor slicing, and output the full sequence of hidden states for the PID analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b489acba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class WrappedCTRNN(nn.Module):\n",
+    "    def __init__(self, input_dim, hidden_size, output_size=3):\n",
+    "        super().__init__()\n",
+    "        # Use the existing CTRNN\n",
+    "        self.model = CTRNN(input_size=input_dim, hidden_size=hidden_size, output_size=output_size)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        # CTRNN expects (Batch, Seq, Dim). \n",
+    "        # We grab outputs and hidden_states, ignoring predictions.\n",
+    "        outputs, _, hidden_states = self.model(x, return_dynamics=True)\n",
+    "        return outputs, hidden_states\n",
+    "\n",
+    "class WrappedElman(nn.Module):\n",
+    "    def __init__(self, input_dim, hidden_size, output_size=3):\n",
+    "        super().__init__()\n",
+    "        # Use teammate's ElmanRNN, override to 80 units\n",
+    "        self.model = ElmanRNN(dim=input_dim, hidden_dim=hidden_size)\n",
+    "        # Teammate's code hardcoded output=input_dim. We overwrite it to 3 for our task classes.\n",
+    "        self.model.readout = nn.Linear(hidden_size, output_size) \n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        # x is (Batch, Seq, Dim). \n",
+    "        # Teammate's ElmanRNN forward() assumes batch=1 and seq_first.\n",
+    "        # We bypass their forward() and just call their internal layers directly to support batching.\n",
+    "        x_seq_first = x.transpose(0, 1) # Convert to (Seq, Batch, Dim)\n",
+    "        \n",
+    "        h_seq, _ = self.model.rnn(x_seq_first) \n",
+    "        outputs = self.model.readout(h_seq)\n",
+    "        \n",
+    "        # Transpose back to (Batch, Seq, Features) for the training loop\n",
+    "        return outputs.transpose(0, 1), h_seq.transpose(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9572bdc5",
+   "metadata": {},
+   "source": [
+    "### 2. The Data Wrapper & Masked Loss\n",
+    "We implement the explicit context fix for NeuroGym and write a custom loss function that only penalizes the network during the decision period (ignoring the fixation targets of `0`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b3a2a686",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def masked_cross_entropy(outputs, targets):\n",
+    "    \"\"\"Computes loss only on the decision timesteps (where target != 0)\"\"\"\n",
+    "    mask = targets != 0\n",
+    "    if mask.sum() == 0:\n",
+    "        return torch.tensor(0.0, requires_grad=True).to(outputs.device)\n",
+    "    \n",
+    "    # Flatten everything and apply the mask\n",
+    "    masked_outputs = outputs[mask]\n",
+    "    masked_targets = targets[mask]\n",
+    "    \n",
+    "    return torch.nn.functional.cross_entropy(masked_outputs, masked_targets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a395f752",
+   "metadata": {},
+   "source": [
+    "### 3. The Training Loop\n",
+    "We will train 4 total models: Elman vs. CTRNN on Perceptual vs. Context. We train for 500 batches to get a functional Proof of Concept."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a11be7d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_rnn(model, train_loader, val_loader, device, num_epochs=50, lr=1e-3, patience=5, use_early_stopping=True):\n",
+    "    \"\"\"\n",
+    "    Trains an RNN with validation and early stopping.\n",
+    "    Only computes loss during the decision period (period == 2).\n",
+    "    \"\"\"\n",
+    "    criterion = nn.CrossEntropyLoss()\n",
+    "    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
+    "    \n",
+    "    best_val_loss = float('inf')\n",
+    "    patience_counter = 0\n",
+    "\n",
+    "    # Store the initial weights just in case it completely fails to learn\n",
+    "    best_model_weights = copy.deepcopy(model.state_dict())\n",
+    "    \n",
+    "    history = {'train_loss': [], 'val_loss': []}\n",
+    "    \n",
+    "    for epoch in range(num_epochs):\n",
+    "        # --- TRAINING PHASE ---\n",
+    "        model.train()\n",
+    "        running_train_loss = 0.0\n",
+    "        train_batches = 0\n",
+    "        \n",
+    "        for obs, labels, periods, cohs, ctxs in train_loader:\n",
+    "            obs = obs.to(device)\n",
+    "            labels = labels.to(device)\n",
+    "            periods = periods.to(device)\n",
+    "            \n",
+    "            optimizer.zero_grad()\n",
+    "            \n",
+    "            # Forward pass\n",
+    "            outputs, _ = model(obs) # outputs shape: (Batch, Time, Classes)\n",
+    "            \n",
+    "            # Mask for decision period\n",
+    "            mask = (periods == 2)\n",
+    "            if mask.sum() > 0:\n",
+    "                masked_outputs = outputs[mask]\n",
+    "                masked_labels = labels[mask]\n",
+    "                \n",
+    "                loss = criterion(masked_outputs, masked_labels)\n",
+    "                loss.backward()\n",
+    "                optimizer.step()\n",
+    "                \n",
+    "                running_train_loss += loss.item()\n",
+    "                train_batches += 1\n",
+    "                \n",
+    "        avg_train_loss = running_train_loss / max(1, train_batches)\n",
+    "        history['train_loss'].append(avg_train_loss)\n",
+    "        \n",
+    "        # --- VALIDATION PHASE ---\n",
+    "        model.eval()\n",
+    "        running_val_loss = 0.0\n",
+    "        val_batches = 0\n",
+    "        \n",
+    "        with torch.no_grad():\n",
+    "            for obs, labels, periods, cohs, ctxs in val_loader:\n",
+    "                obs = obs.to(device)\n",
+    "                labels = labels.to(device)\n",
+    "                periods = periods.to(device)\n",
+    "\n",
+    "                outputs, _ = model(obs)\n",
+    "                \n",
+    "                mask = (periods == 2)\n",
+    "                if mask.sum() > 0:\n",
+    "                    masked_outputs = outputs[mask]\n",
+    "                    masked_labels = labels[mask]\n",
+    "                    \n",
+    "                    loss = criterion(masked_outputs, masked_labels)\n",
+    "                    running_val_loss += loss.item()\n",
+    "                    val_batches += 1\n",
+    "                    \n",
+    "        avg_val_loss = running_val_loss / max(1, val_batches)\n",
+    "        history['val_loss'].append(avg_val_loss)\n",
+    "        \n",
+    "        # --- EARLY STOPPING CHECK ---\n",
+    "        if avg_val_loss < best_val_loss:\n",
+    "            best_val_loss = avg_val_loss\n",
+    "            patience_counter = 0\n",
+    "            # Save model weights to keep the best version\n",
+    "            best_model_weights = copy.deepcopy(model.state_dict())\n",
+    "        else:\n",
+    "            patience_counter += 1\n",
+    "            \n",
+    "        # Print progress every 5 epochs\n",
+    "        if (epoch+1) % 5 == 0 or epoch == 0:\n",
+    "            print(f\"Epoch [{epoch+1:03d}/{num_epochs}] | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f}\")\n",
+    "            \n",
+    "        if use_early_stopping and patience_counter >= patience:\n",
+    "            print(f\"Early stopping triggered at epoch {epoch+1}! Best Val Loss: {best_val_loss:.4f}\")\n",
+    "            break\n",
+    "\n",
+    "    print(\"Training complete. Restoring best model weights...\")\n",
+    "    # Load the best weights back into the model before returning\n",
+    "    model.load_state_dict(best_model_weights)\n",
+    "            \n",
+    "    return history, model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "03e2fd19",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using compute device: CUDA\n",
+      "   GPU Name: NVIDIA GeForce GTX 1080\n",
+      "--- Training Elman RNN on Perceptual Task ---\n",
+      "Epoch [001/50] | Train Loss: 0.7098 | Val Loss: 0.6835\n",
+      "Epoch [005/50] | Train Loss: 0.3116 | Val Loss: 0.3443\n",
+      "Epoch [010/50] | Train Loss: 0.3052 | Val Loss: 0.3211\n",
+      "Epoch [015/50] | Train Loss: 0.3054 | Val Loss: 0.3172\n",
+      "Epoch [020/50] | Train Loss: 0.3041 | Val Loss: 0.3145\n",
+      "Early stopping triggered at epoch 22! Best Val Loss: 0.3018\n",
+      "Training complete. Restoring best model weights...\n",
+      "Saved Perceptual Model to ../src/models/weights/mante_medium\\elman_perceptual_best_h100.pth\n",
+      "\n",
+      "\n",
+      "--- Training Elman RNN on Context Task ---\n",
+      "Epoch [001/50] | Train Loss: 0.7135 | Val Loss: 0.6937\n",
+      "Epoch [005/50] | Train Loss: 0.6942 | Val Loss: 0.6945\n",
+      "Epoch [010/50] | Train Loss: 0.6935 | Val Loss: 0.6930\n",
+      "Epoch [015/50] | Train Loss: 0.6934 | Val Loss: 0.6920\n",
+      "Early stopping triggered at epoch 18! Best Val Loss: 0.6268\n",
+      "Training complete. Restoring best model weights...\n",
+      "Saved Context Model to ../src/models/weights/mante_medium\\elman_context_best_h100.pth\n",
+      "\n",
+      "\n",
+      "--- Training CTRNN on Perceptual Task ---\n",
+      "Epoch [001/50] | Train Loss: 0.3412 | Val Loss: 0.3320\n",
+      "Epoch [005/50] | Train Loss: 0.2991 | Val Loss: 0.3045\n",
+      "Epoch [010/50] | Train Loss: 0.2980 | Val Loss: 0.2998\n",
+      "Epoch [015/50] | Train Loss: 0.2982 | Val Loss: 0.3106\n",
+      "Epoch [020/50] | Train Loss: 0.2970 | Val Loss: 0.3003\n",
+      "Epoch [025/50] | Train Loss: 0.2977 | Val Loss: 0.2997\n",
+      "Epoch [030/50] | Train Loss: 0.2969 | Val Loss: 0.3016\n",
+      "Epoch [035/50] | Train Loss: 0.2965 | Val Loss: 0.2998\n",
+      "Early stopping triggered at epoch 36! Best Val Loss: 0.2983\n",
+      "Training complete. Restoring best model weights...\n",
+      "Saved Perceptual Model to ../src/models/weights/mante_medium\\ctrnn_perceptual_best_h100.pth\n",
+      "\n",
+      "\n",
+      "--- Training CTRNN on Context Task ---\n",
+      "Epoch [001/50] | Train Loss: 0.3975 | Val Loss: 0.3047\n",
+      "Epoch [005/50] | Train Loss: 0.3076 | Val Loss: 0.2863\n",
+      "Epoch [010/50] | Train Loss: 0.3027 | Val Loss: 0.2885\n",
+      "Epoch [015/50] | Train Loss: 0.3035 | Val Loss: 0.2830\n",
+      "Epoch [020/50] | Train Loss: 0.3015 | Val Loss: 0.2862\n",
+      "Epoch [025/50] | Train Loss: 0.3010 | Val Loss: 0.3076\n",
+      "Epoch [030/50] | Train Loss: 0.2999 | Val Loss: 0.2852\n",
+      "Epoch [035/50] | Train Loss: 0.2991 | Val Loss: 0.2834\n",
+      "Early stopping triggered at epoch 37! Best Val Loss: 0.2819\n",
+      "Training complete. Restoring best model weights...\n",
+      "Saved Context Model to ../src/models/weights/mante_medium\\ctrnn_context_best_h100.pth\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAAGJCAYAAAC90mOkAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAAyz5JREFUeJzsnQd4FNXbxU96J/Tee29S7IhKUwEVARUVe8EGov71E7FjQcXexYINEQEVRVGKCkqv0nvvEAjpyXzPuZNJNsumkbKb5Px4LlN35s6d2c0985brZ1mWBSGEEEIIIYQQ2eKf/SYhhBBCCCGEEBJOQgghhBBCCJEHZHESQgghhBBCiFyQcBJCCCGEEEKIXJBwEkIIIYQQQohckHASQgghhBBCiFyQcBJCCCGEEEKIXJBwEkIIIYQQQohckHASQgghhBBCiFyQcBJCiCLmggsuMOV0uPHGG1G/fv1Cr5PInbS0NLRu3RrPPfdckTbXk08+CT8/Pxw6dKhU3pY5c+aY6+O0sNustPLII4+ga9eu3q6GEMINCSchRJHw6aefmo6NUwIDA1GrVi0jBHbv3n3K/hQW3K9v376nbNu2bZvZ9vLLL5/SGWNZsmTJKZ/heSIjI7Otn3PMvBTuW5aZMmUK+vTpg8qVKyM4OBg1a9bEoEGDMGvWLJRmvv76a+zcuRP33HNPlvWrVq3CVVddhXr16iE0NNQ81z169MCbb77ptboKz6xfvx4jRozA2Wefbe5Vbt/nH374AR07djT71q1bF0888QRSUlKKvXmHDx+OFStWmPoIIXyHQG9XQAhRunn66afRoEEDJCQk4N9//zWC6u+//8bq1atN58Sdn376yQihM844I19vn3/88cd81atKlSqYMGFClnWvvPIKdu3ahXHjxp2yb0H47bffTvuzH374obF8eAPLsnDzzTebe9ahQwc88MADqF69Ovbu3WvE1EUXXYR58+aZTmlpZOzYsbj66qsRHR2dsW7+/Pno3r276VTfdtttpj0orvhsv/7667j33nu9WmeRlX/++QdvvPEGWrZsiRYtWmD58uXZNtEvv/yCyy+/3LzEoQimQH722Wdx4MABvPvuu8XatHyu+vfvb14W9evXr1jPLYTIHgknIUSRQktFp06dzPytt95qrBYvvviieZNKq4Ur7IyeOHECTz31VJ7ftLZv396IraVLl5o3xXklIiIC1113XZZ133zzDY4ePXrKencxQREYFhaW53PRSnO6BAUFwVtQSFI08e33q6++msU16rHHHjPCk5bEgnI6bVrULFu2zLzxZxu4Qrc9CqlFixahfPnyWbaxgy18C4qOY8eOISoqyoiQnITTgw8+iLZt25oXHc5zXa5cOYwZMwb3338/mjdvXow1h/l9HDhwILZs2YKGDRsW67mFEJ6Rq54Qolg577zzzHTz5s2nbGPnhm41tB5RCOUFvuGvUKGCsToVBYwvuuyyy/Drr78aAcjO/fvvv2+2ffLJJ7jwwgtRtWpVhISEmLfant5Mu8c4OW6G3377remI165d21jfaMHZtGlTjjFOrm6LH3zwARo1amTO3blzZ9OZd2fSpEmmXjw+43VoKcpL3FR8fDyef/5501nkuTzFk1x//fXo0qVLjjEnjsumq3tUdm3K+tGa4w4tbnSHo3uc67rXXnsNrVq1MtdWrVo13HHHHUb4urJ48WL06tXLCHaeh9ZPWtFyY+rUqUbwnn/++VnW87nlOd1FE+Fz4H6feP3ucL2n55UxTuwss7NeqVIl01mnoHRl5syZOPfcc8356YrarFkz/N///V/G9qSkJIwePdpYbCnw+IKA37nZs2dnOY7rc/T222+bjnl4eDh69uxpLGgUs88884x5NtlutH4cOXIkyzGc+0ihwRcYvA981r7//nvkhQULFqB3796mnjx3t27djAXTHVqo+Xzz+Hzene9fXqhYsaL5XcmNNWvWmHL77bdneRkwbNgw0xbffffdKW7AO3bsMNfPeT6fbEdCSxV/F9j2dOf86quvspwrOTnZvBxq0qSJuSbea95T3ltXLr74YjOdNm1anq9XCFG0SDgJIYoVpwNNseMJdhbzI4TYycyv2DqdOIlrrrnGxLHQHYudREKRxI4RO660TNSpU8d0tJwOVG688MILRsjwTfejjz5q3L2GDBmSp8+yM0ZXMooFuhOxXa+88krTKXOYPn06Bg8ebKxWFEHcfsstt3iMCfPUWWVH+dprr0VAQACKo01Z1z///BP79u07pS579uwxbnMOvO6HHnoI55xzjvn8TTfdhC+//NKIJKcNaAGiEGDbMNie7ldsX7ZzbtAlj0LO3eLH+832o6tpYUPRRKHEe3XJJZcYFzN25B3+++8/01FPTEw0LrB85mhRcRUbx48fx0cffWSEOi27/B4dPHjQtIsnawvb7J133jEvIEaOHIm5c+eaeowaNQozZszA//73P1MHfr/4nLqzceNGc99oWWa9KTpoJXEXAe4wPo6ilPVlHBGtOrQMUXAsXLgwYz+KEN5D3kteC+8z9+f3prAtjMSxjjswno/i0dnukJqaaq6Z3/mXXnrJiEjGwlEoUwzyOGx/irYbbrgBW7duzfgsr4PCiS8J3nrrLWO9pbXd/feLgpJC0ZOYFEJ4CUsIIYqATz75xOJPzO+//24dPHjQ2rlzp/Xdd99ZVapUsUJCQsyyK926dbNatWpl5p966inz2SVLlpjlrVu3muWxY8dm7D979myzbtKkSdaxY8esChUqWP369cvYPnToUCsiIiJfdb700kutevXqZVnHZZ5nxowZp+wfFxd3yrpevXpZDRs2POXaWNzr3qJFCysxMTFj/euvv27Wr1q1Kst1uNbJaYtKlSpZR44cyVg/bdo0s/7HH3/MWNemTRurdu3a1okTJzLWzZkzx+znfp3uOHWZMmWKlReeeOIJs392zwHrnVubrl+/3qx/8803s6wfNmyYFRkZmdHef/31l9nvyy+/zLIfj+e6nnXn8qJFi6z8wnYbMGDAKet/++03KyAgwJSzzjrLevjhh61ff/3VSkpKyrKfc594/e5wPdvLve1cn1/nurl+xYoVZnncuHFmmd+n7EhJScnyTJGjR49a1apVs26++eZT6sfvI78/Do8++qhZ365dOys5OTlj/TXXXGMFBwdbCQkJp9zHyZMnZ6yLiYmxatSoYXXo0OGU551TkpaWZjVp0sR8VzjvwPvboEEDq0ePHhnrLr/8cis0NNTavn17xro1a9aY9s9vF4a/H+7Povu2HTt2nLKtc+fO1plnnpnlO8l9x4wZk6WNw8LCLD8/P+ubb77JWL9u3bpT7jfblr81eaFnz57md0II4RvI4iSEKFLobsLkCnwzS1cruq8wfolvcbPDsTrxrWxe4JtZxuHwuO5vhgsDunfxjb07rjE5MTExxtWK7kaMSeBybvDtuWv8k+PGyM/nBt/yu1rt3D9LCw3f1vNtt2t2QdavTZs2uR6flgCSFzenwmrTpk2bGsvTxIkTs7zZp5sUsy067U33Q95zWqvY5k6hexqv1XFLc9zpGAPnaonLC4cPH/ZoFeU5mXCAlh7GQNHawOugq1ZBM6DdfffdWZadRBM///xzluuh61Z2CUNoHXSeKe5DqyGzwtEC4skiS+uQa/ILJwU24/xcXda4nm6A7hkxaZG54oorsliA+czxe+huOXSg5YuWKloz2c7O/Tt58qRxV6XVkXXnvac7JxM20CLjwCQPnr6PBYGuqYRur+7Qnc7Z7gpjNh14b+g2yd8319hNruM21+80l2k9ZBvkBp/B0pqmXoiSiISTEKJIodsa3XbY+aX7ETsBnjonBRVCFFvskBRFrBM7+Z6gCw2FITtLPDcFohNvkhfh5NoZJE5H3T1O53Q+u337djNt3LjxKZ/1tM4ddoAJk3UUBdm1KQUh29XpoDMejG5aXO/ADifblzFFbHPXEhsbm5GkgSJxwIABRoAzxolxOoxLo6tbXrCNQ6fCeBvG8bCt6VZGN0u2E18MME7mdGHMiyt00/L3989wb2Ub0DWRHXbGdNF1kXFy7iLqs88+M0kOnPgZtgvdNj09k+7PkSOi+KLD03r3Z5PPkntsGwUwyS7ttyMYhg4desr9o5sh7w/rShdDChb3dnEESWHiiHJPz4anxCVsW/dsm2wjvhBybw+ud203ulnSLZHtxJcYdDlduXJlts9gaR6vSoiShoSTEKJIYfIAigt2YCmEGDfCN83s4OZFCPmC1clTtjcmCeDbcQpBZpxjx5QCkfFWJC8pxLOLHcquw15Yn80LTgYxWq3yQnadO1oNPJFdBj2KA14DrUqEwoD3lnEjDmxbiia2t6fCjqlTJwp2WogYf0IxxsQQtEzl9vxRcOQmYGnZoYhifA7j3WjVcuqd3/bwhPsx2Ga0xvz+++8mMQc722wvWsGc437xxRcmeQFF18cff2zilNgmjB3y9Exm9xwV5fPl1IMxetndw5zGYCsKatSoYaZMte8O19GyVljtxtgu/n6MHz/e/B5SLDIjKKfu8Bmk6BdC+AYSTkKIYoOdCgaQ042MQdF5EUJ0S8qrEOL++RFbBYHB8nw7TaHGRAW0plEg+kpKbSYxIO5Z+rJb5w6zfNGKxUFg89LZdyxefJPuimP5yo8limKb7np0MaNlh65arlZKigK6eNH6wjZ3L+3atctyzDPPPNNkL2SGPSZDoJsUU8/nJhxdA/pzw0kq4HS8T6c93F23eJ8oMlwzINICRcFOsU7rFq+LiRYc90QKRWbIY7tRXNGljW3inp2vsGAd3cXUhg0bzDS7zI28f45V09P9Y2FSDlp0+H3y5NLG5CKFiZPwhc+IK/yt4thuzvbCgtn+6KrrDLJMC6EnazmfQbomCiF8AwknIUSxwmxf7BgzlXRunTlHCDkWhNxwFVs5jddSGDhvll07jXQvoiuYL8A35Hyb/fnnn2exrjBrWl6sSEwPzYxqa9euNVNPlgZaN5wMaE5nmBYRB8as0G0sv9CKwsx3fCNPi56rmx5hDAnFHNNlu0Ox5YgVvq13r7fTAc7NXe+ss84ymfPc96NA8dQWThyS40JGUUBLgWt7EGawyw73bIzMAkiYvY24pwP3dD2enkum/abVrSigsHDNcMfYOD5zrBcHcfUELX58XpgK3ZPljy56zrVQ+DE1PFN/O/CZZOxTYcIU8xTLTPHv+qKAlkRa/lxT4RcUin5XaF2jy6P7s8bfE1qmSusA00KURDQArhCi2KFPP4PSmbr3zjvvzFEI0WUvPxYk7j9u3DgTuM/Yo6KCKZLpqsWkBbQ4sQP44YcfGhcyT+4+3oAuZIzroWWGb7cpJGjpo6DKzVXNuU+0zjDtNQUDO4/sDDPon51Ziiam7Xbag/EyTHfOz7HTS+FDq4FrpzcvUBgx9TUL38w749k4MHaJbU7rJQUyz00LBS0TdJVjenLWlaKNQoXJC9hRZxwS7xFFDS2EOcF2ozCj0OTxXRM2xMXFmWOyo82ECWwDWshoYWE7OzAWiSnnOaVFiiLKscZ4gtYFJp2gWyKFDoUp3VodCxpfIPAYl156qbEoMpaL18e4GloICdOV09rE+nE/HvO9994z4yvl5Z7nF8bp8J5zDDHGXfGe79+/P8cXCLSa0S2NgpCChW3G5Bp0peRzxvtDiy7hd5/uhkx+wlT/FMYUlPxcdnFB7uLDEaBOWm9+B/hChoUunA50HWT7834zfozCmfvy/hWm1Yf3gi+QKCD5fNPKRUuha10IXTIpgPksCiF8BG+n9RNClE6cNNSeUkGnpqZajRo1MoXpk93TkbvCNL/R0dE5piN3x0nvXFjpyLNLHfzDDz9Ybdu2NemS69evb7344ovW+PHjT0l5nF06cve6e0phnV06cte2cHBPe0yYGrl58+YmBXzr1q1NnZlmm+vyCtPIMy1yxYoVrcDAQJNuevDgwSa1uStMH9+1a1eTtrpu3brWq6++mm068tzSMZ9zzjnmc7feemu2+3zwwQfWGWecYdJAR0VFmfTrTA++Z88es33p0qUmjTbrwuuvWrWqddlll1mLFy/O03Xz3t5yyy1Z1v3yyy8mrTfbjynSea2NGze27r33Xmv//v1Z9mV6bX6ezy/rN2jQIOvAgQPZpiNnmu2rrrrK7Mv0+vfcc48VHx+fsd8ff/xh9e/f36pZs6Y5L6e8vg0bNmTsw/TeTJPNNuY1My34Tz/9lOfnKLtn09P32bmPTMfOtuL52C7un3VPR+6wbNky68orrzSp9flZHo9txOt0Ze7cueY+85qZ6v+9997LNv29O851eiqeUvIzhX379u1NfZiSftSoUaekms9uqIPsfsPcn/dnn33W6tKli1W+fHnz7LLNnnvuuVPOw+/Yueeem+s1CiGKDz/+523xJoQQovigGxUtQbkNUlrWmTBhgkkRTouZkwpcZEILG62XTPcuChdadRnvx1g8WZyE8B0U4ySEEKUUZnmja5MrTO9NN0a6ComcGTJkiHE/dI89EqKoYQwoU5VLNAnhW8jiJIQQpRSOo8P4IA5mymQR69atM/EujB1j/AZTbgtxusjiJIQoayg5hBBClFKYEpsB6AzEZ6YyJstgwgAmLJBoEkIIIfKHLE5CCCGEEEIIkQuKcRJCCCGEEEKIXJBwEkIIIYQQQohcKHMxTmlpaWak86ioKDMauBBCCCGEEKJsYlmWGSCdSZQ4QHdOlDnhRNFUp04db1dDCCGEEEII4SPs3LkTtWvXznGfMiecaGlyGqdcuXLero4QQgghhBDCSxw/ftwYVRyNkBNlTjg57nkUTRJOQgghhBBCCL88hPAoOYQQQgghhBBC5IKEkxBCCCGEEELkgoSTEEIIIYQQQuRCmYtxEkIIIYTwNVJTU5GcnOztaghRKgkKCkJAQECBjyPhJIQQQgjhRWJjY7Fr1y4znowQomgSPzDVeGRkZIGOI+EkhBBCCOFFSxNFU3h4OKpUqZKnzF5CiLzDFxIHDx4037MmTZoUyPIk4SSEEEII4SXonseOHUVTWFiY7oMQRQC/X9u2bTPft4IIJyWHEEIIIYTwMrI0CeH73y8JJyGEEEIIIYTIBQknb5IUA6x+DkhN8Go1hBBCCCGEEDkj4eRNlv8PWDkK+LkdcOBvr1ZFCCGEEEIU3CVs6tSpxdqM9evXx2uvvVas5yyrSDh5kxq9gdDqwIkNwO/nAYvuAZJPeLVKQgghhBC5ceONNxqRwBIcHIzGjRvj6aefRkpKis83nreEhtNe2ZUnn3zytI67aNEi3H777QWq2wUXXIDhw4cX6BhlAQknL/LU+MvRZMQarIi9xV6x8W1geitgzy/erJYQQgghRK707t0be/fuxcaNGzFy5EjT8R87duxpp2VPS0sr1a3OtnIKhVu5cuWyrHvwwQcz9mWmxbyKUGaMYzp7UfRIOHkR/j5s2lEBby/+CLjwdyCyIRC3E5hzCTD/eiDhkDerJ4QQQohihmPgnjzpnZLf8XdDQkJQvXp11KtXD3fddRcuvvhi/PDDD2ZbYmKiEQK1atVCREQEunbtijlz5mR89tNPP0X58uXN/i1btjTH2rFjh/nc//73P9SpU8esoyXr448/zvjc6tWr0adPHzOQabVq1XD99dfj0KFDWSwn99xzjynR0dGoXLkyHn/88YzBhbl9+/btGDFiRIalh1D0tW/fPsv1UdzQOuVq2enRo4c5Jo/drVs3LF26NM/txbZyCj/PczvL69atQ1RUFH755RecccYZ5tr//vtvbN68Gf379zfXymvu3Lkzfv/99xwtaDzuRx99hCuuuMIIKo5d5NyX02Xy5Mlo1aqVqRfP98orr2TZ/s4775jzhIaGmrpeddVVGdu+++47tGnTxqTbr1SpknlOTvKBK4FIOHmRBg3s6bZt/DZdBFyyEmj+AODnD2z7ApjeEtg+Mf+/ZEIIIYQokcTFAZGR3ik8d0FgxzgpKcnMU7j8888/+Oabb7By5UoMHDjQWKhoncq81ji8+OKLppP/33//oWrVqrjhhhvw9ddf44033sDatWvx/vvvG8FAjh07hgsvvBAdOnTA4sWLMWPGDOzfvx+DBg3KUo/PPvsMgYGBWLhwIV5//XW8+uqr5hzk+++/R+3atY1boWPpySsnTpzA0KFDjaD5999/jVC45JJLzPrC4pFHHsELL7xgrr1t27aIjY015/jjjz+wbNky04Z9+/Y1IjMnnnrqKdMubHt+fsiQIThy5Mhp1WnJkiXmWFdffTVWrVplRCbFKMUv4b247777TJuuX7/e3Jfzzz/fbGP7XnPNNbj55pvNNVE8X3nllRlCtsRhlTFiYmJ4p8zU28yZw6fGsho3dttwcIFl/dTasr6EXeb0s6yTu7xUSyGEEEIUFfHx8daaNWvMlMTG2n0DbxSeO68MHTrU6t+/v5lPS0uzZs6caYWEhFgPPvigtX37disgIMDavXt3ls9cdNFF1qOPPmrmP/nkE9MfW758ecb29evXm3U8lieeeeYZq2fPnlnW7dy503yGnyXdunWzWrRoYerk8L///c+sc6hXr541bty4LMd54oknrHbt2mVZx324b3akpqZaUVFR1o8//pixjnWZMmWKlRu8/ujo6Izl2bNnm89OnTo118+2atXKevPNN7O9Hh5n1KhRGcuxsbFm3S+//JLtMdlu999/v8dt1157rdWjR48s6x566CGrZcuWZn7y5MlWuXLlrOPHj5/y2SVLlphzb9u2zfKl79npagOfsDi9/fbbxuxH8x5NuXxDkB00sXoKqLv00ktR0nCsv9u307fXZUPlLkDvJUCbpwD/IGD3D7b1adMHgFW6/X+FEEKIsgxDVWJjvVPyGybz008/GWsQ+290nxs8eLCxRtAqwZilpk2bmu1OmTt3rnE9c2BSCVpVHJYvX46AgADjAueJFStWYPbs2VmO2bx5c7PN9bhnnnlmlgFPzzrrLGPpYp0KAq1bt912m7E00dWOMUq0COVm/ckPnTp1yrLM49PlsUWLFsa1kddMy01u53RtV7pKsq4HDhw4rTrxfOecc06WdVx22pTui3TXbNiwoXGd/PLLL401kbRr1w4XXXSRcdWj1fHDDz/E0aNHUVIJ9HYFJk6ciAceeADvvfeeEU300ezVq5cx9dFk6w5NrI4ZmBw+fNjcFN6Mkkbt2kBgIJCcDOzZA9Sp47IxIBhoMxqoMwBYcAtweAGw8A5g29dA1w+BqMZerLkQQgghigL29yMiSkbbdu/eHe+++64RQDVr1jTucU5nnwKILl6cuuK43Tmufa4Ch8s5wePSTY3ufe7UqFGjQNfi7+9/ivtYMjtoLtBNj/1Ouv9RKDDeh6LMtV9aUChyXKFomjlzJl5++WUT78U2YvxQbucMCgrKssx2LqrkG1FRUSbWi254v/32G0aPHm0ENGPCKPZY//nz55ttb775Jh577DEsWLAADZyYlRKE1y1O9Duler/ppptMcCAFFAPZxo8f73H/ihUrZgmu483g/tkJJwYZHj9+PEvxFfhbUreuS5yTJ8q3AnrMAzqOAwLCgQNzgJ/bAGvGAmm+n/JTCCGEEKUTdvLZma9bt26GaCKMQaIlghYObnct7LtlB60S7NzTMuWJjh07mlgoeim5H9dVcLBT7ooTj+SIOAo9d+sTM9Pt27cvi3iiBcyVefPmmVgexgw5iRJcE1MUBTwnU78z0QPbh+23LdtOY9FAaxfr4QqXaVF02pT3n0kfXnrpJRNXxTrOmjUrQ7TRQsW4K8Zpsf2nTJmCkohXhRPVMt9GsKEzKuTvb5YZUJgXmGmFwWruCt3h+eefN+ZUpzBLiy/hiO2tW3PYyT8AaD4cuHQ1UP1iIDUBWP4w8NuZwNEVxVVVIYQQQohcYYeayQiY6IGeQlu3bjVhGOyTTZ8+PdvPURDRqsNEAhxElp+jFePbb7812++++26T4IDJBmjNoHver7/+al6+uwohurHRm4neS0w0QSvH/fffn+U8f/75J3bv3p0hfBgKcvDgQdPx53EZRsIMd65QfE2YMMG4rlGc8Rpzs5IVFJ6TbUgRR1fFa6+9tsgsR7x+nse10D2RqeaZnOKZZ57Bhg0bTPKNt956KyN9Ol02mcyD+zNj4eeff27q2KxZM9NOY8aMMQkkeF94LTwPxVhJxKvCiQ8rH3SmLXSFy1T9ucEvIdNS3nrrrdnu8+ijjyImJiaj7Ny5E74Y55SjcHKIbAB0/w3oOh4IKg8cWQLM6ASseBxITSzqqgohRP5JPKxWE6IM8sknnxjhxE43O9CXX365ETu0TuUEXf/oijZs2DATv0SvJCd1Nd0Baelg37Fnz57GAsNBW+kOxhfvDjxvfHw8unTpYsQWRZPrALHM/kaLSKNGjYylibAjz5TaFEwMAWEf03VcJedlPeNzaPliLA+tT57CSgrbM6tChQo4++yzjZsiw1l4/qLgq6++MtZC18KYJJ6P4vWbb75B69atjSse25CWMML2pyBixkO2I73HKFhplWNsFUUqrXQU1KNGjTKpzBkTVxLxY4YIb518z549Jr8//R7pI+rw8MMPGzOtu6nVnTvuuMNYpmgSzCt01aPliSKKN9PbPPccMGoUR+Dmj0w+Phi/F1h8D7Dze3u5XHOg68dAlbNRJuBj6+IXLYRPwSQuSUdt0ZB4CIioD4TXtLcdWwWsfx3wDwUi6gDh6SWiLhBW004IUxqunwN5r38NOLoMGODiyrL8ESBuDxBey75eZxrGafXScf1C5IOEhARjWWG8B5MsiIJByxHHY3Id10iIhBy+Z/nRBl5NDsEBxOgbSTOgK1zOyQeW8O0DlS8Vb0mma4O5GH3lHDSulAwsSwbSkgErfcrS+nEgMt0stWMSsHl81n0iGwEntwPH1wEzzwFq9QUCI4C43cCJDYBfIOAXcGphZyWQAZoWkBIPpMQw6soeQ4rbkT7lGxz/EMA//TgZOttlO+fpTmimwXbHxxyDUOCk78vOFM+Vml4Ys8UkGFYKkHgUOLkVSE0C0hKztgEL6xtc3o7rSjoCnOA4EH6ZxzaF84FASBUgpLItrHjshIP2enMNgen1S58PCM2srzP1C8q8XtdjB1cEQqsCASFAWqrdvhmYTJbp7WMBtfsBNS+Bz5F8AjixKb2tXNouooF9L0hSDJASm/VZcO4vlwPD7W0JB4CD/wJp8fa9MAWZxyZ8Hpz2Y5tZqZmC10xdCp9bp9PM+8x7Z+4xP899/NOneXzX4/GdkOVhH9YrLb1u6c8J16clAcnH7fUsfA5TTthtyFKhvZ2khfU7thJY+zKQfAxISi9wcaXo8ArQ+FZ7X7702Jw5mGNW/OzvfKNb7OuP2wfs+dF+7kJYKgFB0faxTZ3YTun1DyoHBEfb1mhO+f0q7pcLKSeBLZ/ZwtD5fvCljuuLjt0/ATH/ef48v7sDXLI+rX/DvgeOsKL4jGwMBBata4wQQgjhc8KJwWEcHZl+kzThEvpEcpkDp+XEpEmTTOKH6667DiWZZtGzcfGAp+yFtR52aHx7pnCK3QbsnZHzAXf/mLcTH1+DEkUMO6LuWJmdWleSY4DYTfAqmz4Euv0A1PKhNPl05/ylPRC75dRtl20AyjWx59e8CKx5Pvvj1B0IHFsNHPf0wAqPLBtpl1yxgNVP26WgUAQ6Qopiiy8egtPnHXGV3ZQihS8I8srJncCGt+whEygeCYVco9uApvdkFXBtnrCfQb7cid9tW584paAMc8uKtel9IMbtt+qiWUC17gVpGSGEEKJkpiNn8B4DAZm3nr6oNK3SmsRAP8dPle58DCh09zOl2KpUqRJKMhF1O+PdcXciJS0Iw+4JQkBgULrFI72E187cuWYf+82zs811PxZ2jGl54lvrxAN2Z4ZvzU1JzpxaSUC1i9I7KX62K82en132TbItP7T6kMZ3AhXa2m+1mRZ964TsL6h2fyC6tS1mjq8HdrlnTXGsUsF256diR/sNP12a9s8FAkPTLVFh9pTWDVoiaFmjW5Nj9UqJs+uTehJIjQOS06dcTyuAaTcLiNsJ7Pgu08rFkmH1SgDqXAHU6G3Xl525/55Nr6dj6XCxwvHNeVRT2yJGi8KRpbaxxLEymWlaZhvO7WtbD1qPTrfYeJntX9vXyLan9cyxWrAdeQ8c2Mnl80QLkavVxIGWTwdaQfisZUdUM/sesn3i9wMJOYzQHlrdtgCyUWllSMohNia0mv1cEFp/Eg9mvy/rGBRlz9OSlrA/530pHMy+cXaH3rGkZVg4062stHqwLR1rFO+5eQ7Yrumi3lP75UoAEJBu/TSPVnL6MdNOPR4tMbSukoRDQMK+zH2MJffw6cUYUVy1eBBodn9m2+XEvpnA2pfseVqE+LmGQz1/lsLbE7w+3ktXGtxgW5eNyEoXWBR1QgiRDUwmIUSpjHFyYGaOsWPHmoQQ9EtlZg6O6eT4qjL7yaeffpqxP7OkMGCQ+eA56FZ+8LUYJ7Y+B5xLSODgbUDDhvAdnA4gO9KO65Xj0mTmnUfH5REyrk7pIoFuRKYz6WxLd/vz1dgkCgWKIiPsCvBOgcJs6XD77TuhSD37SyAsaxKUYufXrsDhhUD7F4GWD5+6PW6XLV6Z8v7A3HR3SDfKtwWqdgeqXQBUPc8WuBShGW56Lq53nKf4ddrScbvMbl9HpDr7moQnLuLOdUrh57hr0bWQ1opT9qXrnb/thhhSMdOVjC6G5lwu7piOa6bjulmYuNbHtZjrTj9vhmtoLvl62C4UEHwpwpcClc/KtEivfBJYnW69dsRP5bOBSp2Bci1tl0QKflpks5saV8Oj9ksFQlHW8hGgybDM9mYddky2XTvrXGmv4/7zhwANbrStrLldhxAiA8U4CVFyYpx8QjgVJ74mnAgHvV6/Hvj9d+Cii7xdG1FobJkALLwdSEuwrSnnfANU8zwaerFAywNdn9gJpsvWyR22QNqfLpRiM0ddN7DzW759ukjqZgul4Areqr3IDVrp9v5mxxDtmW4npXCgKOu1CKjYIW8vEHZ8C6x6IlM80zrdbIT9ImTju7blh5alvuslkoQoIBJOQhQ9pSI5hMgcy4nCKU8pyUXJgHEZa1+wrUy0ujAeaNaFQNtngZb/805nk1YaulEuHWFblpiMwxXWqUJHWyRRLFU51xZYomTAmKK6V9mF4odutRRRjHuk0CnfJnPf/8bYljcmk6lyXmZiEEKLcf1rbJe6rZ8DK0bZFj2OHefqKtngeltIGfdKIYQQovQj4eRDg+AW80DQoigJr2sHvdP1iZam3dOBbROAFf8HHPwbOOtzO0NacUDXQbrfrXzcHvvL1QpR8Yx0axKF0jmZ8T2iZEPxw6EJWNqPARKPZLpM0smAbqTMxsnsdxRcjPOrdZmdCdJ5Lrk/3QI9xaUFRtnxfnRrFUIIIcoIckQvaYPgipJBUCTQ5E57ntnGzvoM6PqR/XaeiTh+6QAc+rfo60E3vGl1gTmX2KKJCRWaDQcu+AW46ijQawHQ4SWg1iUSTaUZJ8bLYAEdxwENb7KTzdDFj655/9wAfF8VmHdt5q60RjEWq/blwAUzgPYv2cKKWSvnX2Nnadw1LZvU70IIIUTpQsLJi0yYADRubMc2EQmnUkbTe+1EA7QwMSkDx+bp+S8Q1cQO7J95HrBuXNF0OinKZvUA/uieGetS/zqg31bgjHFAzd55y5YmSh90yWQ2yTPHA1fstZ/JVo/ZiT+YyMLVElr1fKD/VuD8KUDNXkDLh+xnqM3TtqWKg/n+ebmdeITxVRJQQgghSjESTl6EicSYSe9werZgueqVMjhYZ730t/frXrGnFdoBvRcDdQfZ6aKXPgD8NSB9wNRC4OhyYE5f4LezgH2/Z37FK58DnD0BCK1SOOcRpUdEVe4KtHsWuGQF0H+7nYY8Y7sfEFEv62couNs8bguolo/aMXxHFgGzewG/dwMO/FV49aOQY7bHA38CWz4FVjwOzBtirxNCCB/Ez88PU6dOLdJzMOP08OHDi/QcwjMSTl6kSfp4o3v3Zk7jmdlZlB6aP2BPd04GYtN9MfmmnnFPnd62Y0Q41tUvHbPGH+WXmHXA34NtF8A9P9kdYoo2pgsnrR8rhIsRpZ6IuqcKpZzc/xg/RYsUM+5xqIGDfwG/nw/M6gUcXpS343C8rGP/Abt+BNa9Biy+D5hzGfBTC2BiODC1ji3I/r3JHmdt+1fp49UJIbzJjTfeaEQCS3BwMBo3boynn34aKSkpPn9jOMwNxw0tbvr27YvevXt73PbXX3+Ztly5cmWBz8MhfMqXV3KnokDJIXxEOEVEACdPAtu32+nJRSmBAwdX7wns+w3Y9lWmgOGb/KbDgEpdgL8H2hnufjsbOON1oPEdeR/rigParnoK2PZF+gCpAOpdDbR50o49YSeTmfQY/C9EUcA4qTNeBVo8AKx+Dtj8kf28s3BAbLr1cR8+q0x5b6Yu82YMrhxgEhOKuciG9kDYZupLA94JUXahCPjkk0+QmJiIn3/+GXfffTeCgoLw6KOP5vtYqampRjj4+5fed/q33HILBgwYgF27dqF27dpZtrEdO3XqhLZt23qtfiJ3Su/TWQKoWBGokD4sTs2a9lTueqUQukF1/w1o9X+nbqvUCeiz1O5gMrXzorvsgUSTT+R8TLoqLbwT+LGZnTKaoonH6LMCOOdre9BXZkwjLUb67qDDovQQXhvo8q49tlODobbVk+L9l3bAlBrAzHPsBBSrnrSf2YPzMkUTB+tlKvw6VwEtHga6vA9cOBPotxkYnGBPudzlPXvwZgknUQbgy9TsSkJC3vd192TJbr/TISQkBNWrV0e9evVw11134eKLL8YPP/xgtlFMPfjgg6hVqxYiIiLQtWtXzJkz5xSrCPdv2bKlOdaOHTvM5/73v/+hTp06Zh0tWR9//HHG51avXo0+ffogMjIS1apVw/XXX49Dhw5lcWO75557TOHYPJUrV8bjjz8OZ9hSbt++fTtGjBiRYTEjTz75JNq3b5/l+miVonXKYdGiRejRo4c5Jo/drVs3LF26NM/tddlll6FKlSrm2l2JjY3FpEmTjLA6fPgwrrnmGtNu4eHhaNOmDb7++msUJmzn/v37mzbkuEWDBg3C/v37M7avWLEC3bt3R1RUlNl+xhlnYPHixWYb246WswoVKpj72qpVKyOaywoSTj5idYpOzwKtBBGlkEqdgRo9shcvHFT2vClAh1cAv0Bg+9fAjE524L078fuBJSOAHxrbg9kyTooWrZ4LgPOn2hYuxzWQqaQ5cGm9a4r2+oRwhaLmrE+BS/6zY/kIRRStRtW620lS2o2x3VU5KO+Aw3aGxz5LgPMmAR1eBBrfDlS/2D6Wk0ZdiDJGZGT2ZcCArPtWrZr9vn36ZN2XOsDTfoVBWFgYkpKSzDyFyz///INvvvnGuJ8NHDjQWKg2btyY+Q4wLg4vvvgiPvroI/z333+oWrUqbrjhBiMU3njjDaxduxbvv/++6eCTY8eO4cILL0SHDh1MR37GjBmmw8+OvyufffYZAgMDsXDhQrz++ut49dVXzTnI999/b6w9dCvcu3evKXnlxIkTGDp0KP7++2/8+++/aNKkCS655BKzPi+wTrw+CidHyBGKJlrcKJg4UCuFyvTp041IvP3224045LUUBmlpaUY0HTlyBHPnzsXMmTOxZcsWDB48OGOfIUOGmDaiUFyyZAkeeeQRY0kktCpS3P75559YtWqVuX/O/SkL6C+Sl2FWPX4XgtOHQ5FwKuUkx9pxTa4DjhKKKro6VT4TmDcYOLHBzlTGOKhGNwFJR4E1Y20rUmpcZqpoWrOY+cwdDl7KzmpaChAQUjzXJoQr0c2BcycCSR/YsXbuz7wQotRAEfDHH3/g119/xb333mssGnQ947RmuksNrU8UOlw/ZswYsy45ORnvvPMO2rVrZ5Y3bNiAb7/91nTmab0iDRtmuua+9dZbRjQ5nyfjx4831il+tmnTpmYdl8eNG2esSc2aNTMdfC7fdtttqFixIgICAow1hday/EDR5soHH3xgrGYUILQm5YWbb74ZY8eONZ+h9YuwTejCRysWC9vKge3JdmW7dOnSBQWF94ntsXXrVtNO5PPPPzeWIwqlzp07m/v20EMPoXl67AgFosOOHTtMXWkJc78/ZQEJJy/ToQPAly+Oq6tc9Uoxa18BVj9rxzE1vMHzPhywtPcy4J/rgb0zgAU3A9u/AQ4vAJJj7H0qdrYFU/UcrFh8S18v8+2REF5DgyoLcVrExma/LSAg6/KBA9nv6x4yVJj9jJ9++slYGyiAaMm49tprjcsbXfJoQXGEjAMtFZUqZQ55wKQSrjE9y5cvN6KGLnCeoAvZ7NmzPVo4Nm/enHG+M888M8MFj5x11ll45ZVXTJ14/NOF1q1Ro0aZ6ztw4IA5Hq1mFBN5hWLk7LPPNoKPwmnTpk0mMQQtYITHpDCkUNq9e7ex4LHd6LZXGNCKR8HkiCZCV0kKQG6jcHrggQdw6623YsKECUbA0lrYqFEjs+99991n3DJ/++03s40iqizFZclVz8vwpQItTtemZ62WxakUk5YMJB+zU5PnNN5NaGXggulAu+dsqxGD7Cmayrex3fE4aG2NntmLprTUIrsEIYQQxQOTRmVXQkPzvm9YWN72PR0YB0OxQ/e7+Ph44yLHuBfG7FCg0M2L253Cjjld51xd+1wFDpdzgsdlfI3rMZ3zn3++B++LfMCkFK7uc4SC0BW66fF8vIb58+ebeQpBxz0xrzCWafLkycbFj9YmihJHLNIaxeMzzosikefo1atXvs9RECh+6Tp56aWXYtasWUZYTZkyxWyjoKJrH90HabliQos333wTZQUJJx+hQQN7KuFUimlyBxAYARxbmT7GUg5QMDGZxIWz7DiRs78G+iy3E0DklOjhyDLgh/r2wLpCCCFEEUKRxOQNdevWNfE7DnSno+WEVhludy05ucfR/YuWK7qxeaJjx46mQ8+EDe7HZV0cFixYkOVzTjySY22ipYv1c4VJG/bt25dFPFG0uDJv3jxjcWFcE13bmLzCNTFFXmFMFoXaV199Zdzk6L7nCEiegzFI1113nXFhpCsc3RALixYtWmDnzp2mOKxZs8bEj1EgOdB6xwQatCxdeeWVRuA50Fp15513mnixkSNH4sMPP0RZQcLJR3Bc9TgYbh5jDEVJg0kgGt6SdUDc3KjWzY4TqX+1LaZyg8dlxr28jqEjhBBCFDLsdDPBABMhsHPNeBomN3j++edN0oPsoCCiVYdCgoPI8nN0i6PbmpOYgEkNmESB8Th0z2P8z0033ZRFCNF1ju5m69evN4kmaBG5//77s5yHyQ3oCucIH7rNHTx4EC+99JI57ttvv41ffvklS/0ovui+RssZxRmvMTcrmSfoashkDEzbzuQUHBPL9RyM8aJFi+e54447smS8yytsD3fLHI9H9zoKVNadGQF5X3ifaPGi9YiWQyb2YLszgx6FHNuagotw4F22Oe8NP0+rmLOtLCDh5AOceSbAFzDlytnLinMqxTQfbgugvb8CRws+yF0WTu6046GcFORCCCGEl6CFgh1yWiSYoOHyyy83HXBap3Li3XffxVVXXYVhw4aZeCAmdDiZni+diSbYkaco6NmzpxEA7MgzPsd1/CeelwKAyRQotiiamJ3OgfFE27ZtMy5ytDQRdv6ZqIKCiZYeCgrXJA2EadGPHj1qLF90VaP1iZkATwe66/FYdMNzEmgQxlDx+FxPMUcLHdsuv9CtkZY/10I3R1q2pk2bZtKJ072RQopWrYkTJ5rP0SrHlOhsQwpgWseY/v2pp54y29n2bFO2F7Mkch+2W1nBz3J36CzlHD9+3GQsiYmJMbnpfQEmSVm0iJlJgC1bgGnTgH79vF0rUWT8PQjYMcke64ZpmwuLpSOBda/aKZ8vmlV4xxVCCFFkMP003943aNAAoe7BSyLfUGxwPCaOwSREXr5n+dEGsjj5SEpy4iRMUZxTKad5ujVoB1M1HyucY/I4mz5IP37WN2RCCCGEEKLgKB25D+Ckx3eszHLVK+VU7gp0eNlO9BBcvnCOuelDICUWiG4J1HQb7VAIIYQQQhQYCScfsjglJNhTWZzKAIUZg8RBbjkwrmNtyinrnhBCCFGKYVIDIYoKuer5kMXpyBF7KuFUxkiJL9jnOdhtt2lA49uB+ukDggkhhBBCiEJFwsmHLE7OUAB01StbKTvKKPF7gb8GAj+3sa1GBaHiGUCX94GAkMKqnRBCCCGEcEHCyQeoVAnggNEDB9rLx48DR496u1aiyAmKBg7MBmI3A7vsEbnzjRS2EEIIIUSxIOHkAzAkhS65HN/NGVBb7nplgMBwoMkwe37ty6cngv7sDyy8wx70VgghhBBCFBkSTj5G/fr2VJn1yghN7gb8Q4DDC4GD8/L32aPLgd0/Aps/LrirnxBCCCGEyBEJJx8iNRWoVcuel8WpjBBWDWhwvT2/7pX8fXZt+v51BwKR6YpbCCGEEEIUCRJOPsLUqfYAuIsX28sSTmWI5g/Y013TgOMb8/aZkzuB7d+kf74QU5sLIYQQosjx8/PDVHb+RIlCwslHqFYNSEqyE0MQCacyRHQLoOalzPQAbHgrb5/huE1WClD1AqBSp6KuoRBCCHEK+/btw7333ouGDRsiJCQEderUQd++ffHHH38UamtdcMEFGD58eKHfgdyOu23bNiNwciqffvrpaZ1779696NOnYAPW169fH6+99lqBjiHyhwbA9bGxnJxseopxKmO0egyofjHQ6Jbc902KATZ9YM+3eKjIqyaEEEJ4EhXnnHMOypcvj7Fjx6JNmzZITk7Gr7/+irvvvhvr1q0r8Y1GIUiB4/Dyyy9jxowZ+P333zPWRUdHZ8ynpqYaMeXvn7tdorqTDUyUKGRx8qGU5C7fPY3lVNaochbQfDgQFJX7vps/BFJOANEtgZq9i6N2QgghigtmWE056Z2Sj+yuw4YNMyJh4cKFGDBgAJo2bYpWrVrhgQcewL///pux344dO9C/f39ERkaiXLlyGDRoEPbv35+x/cknn0T79u0xYcIEY0GhELn66qtx4sQJs/3GG2/E3Llz8frrr2dYeSjayOrVq43VhseuVq0arr/+ehxKHxRzzpw5CA4Oxl9//ZVxrpdeeglVq1Y158/puA4BAQFG4DiF5wkMDMxYpoiqUaMGfvjhB7Rs2dJY3Xi9ixYtQo8ePVC5cmVzPd26dcPSpUuzddVzLFvff/89unfvjvDwcLRr1w7//PMPCsK7776LRo0amXZo1qyZaWMHy7JM29etW9fUu2bNmrjvvvsytr/zzjto0qQJQkNDTdteddVVBapLaUEWJx9KSU6rkxPjFB8P8HdFLyTKIPzDZaUB/gGetze4AUhOF05+evchhBClitQ44NtI75x7UCwQGJHrbkeOHDGi4bnnnkNExKn70wpF0tLSMkQTRUpKSoqxRg0ePNgIG4fNmzcbEfHTTz/h6NGjRly98MIL5vgUNhs2bEDr1q3x9NNPm/2rVKmCY8eO4cILL8Stt96KcePGIT4+Hv/73//MZ2fNmpXhhkcxtWLFCmzZsgWPP/44Jk2aZIRAdsfNL3FxcXjxxRfx0UcfoVKlSkaY8VxDhw7Fm2++aQTKK6+8gksuuQQbN25EVFT2L0gfe+wxY9WiYOH8Nddcg02bNhmxll+mTJmC+++/37jyXXzxxaZtb7rpJtSuXduIs8mTJ5t2++abb4zgpdsl24ksXrzYiCgKrbPPPtvcb1cBWpaRcPIhGje2hRMtTzExttVJwqmMwQQRK58Amt4DNL7V8z6hVYG2TxV3zYQQQggDO/MUBM2bN8+xRRjrtGrVKmzdutW4vZHPP//cdNRplencuXOGwGKskCMqKHb4WQonWmxoMaEVxtW97a233kKHDh0wZsyYjHXjx48356EgogXs2WefxcyZM3H77bcb6xTFTL9+/cy+2R03v9A9kdYZWogcKOhc+eCDD4yYpHi87LLLsj3Wgw8+iEsvZcwz8NRTT5l2Ylvn1s6eoACjVY2WQeJYArmewomWMV43RVVQUJCxPHXp0sXsy20UxKwr70m9evVMWwsJJ58TTiQszBZOTBBx5pnerpUoVk5sAo6tANa9CjS6WRYlIYQoawSE25Yfb507D1A05YW1a9caIeOIJkKXNooIbnOEE130XC0xdH87cOBAjsemdWT27NnGmuUOLVgUThRGX375Jdq2bWs6/7SwFDY8B4/vCl0BR40aZaxqvA7GPtEyRUGSE67HYRsQfv50hBPbl4LRFcak0dJGBg4caKxRTOzRu3dvYxFjYg9at+hmyPZytrFcccUVRmSWdeTn40NQJNGFtEEDe1mZ9cogjW8DgsoBx9cCe37Jum3vTOCPC4G9v3mrdkIIIYrDd5/uct4oPHceoCsZY3IKKwEELR5Zm8DPWKFyIjY21nT0ly9fnqXQHe7888/P2G/+/PlmSnczlsImLCzM1NcVWrZYF4oUnp/zdONLYvrkPLaDc8zc2uF0oZhdv369sZbxGmiZYrvRgkYRy5isr7/+2gi40aNHG4vasWPHUNaRcPIhaJ2dNAno2dNeVma9MghFU6PbPA+Iu3YssH82sHu6V6omhBBCkIoVK6JXr154++23cfLkyVMaxelgt2jRAjt37jTFYc2aNWY7LU/5serQauNKx44d8d9//xlrVePGjbMUJ+6KlqcRI0bgww8/RNeuXY2gcRUino5bGMybN8/ECNGKQ3c7Jl9wklYUF2x71sO9Xq7tTsFE8fnGG28Y6xiTUdC1ktDyRDc+JtRYuXKlSWAxa9YslHUknHwQWZzKOM3uB/wCbZF0JD0Lz9EVwL6Ztute8xHerqEQQogyDkUTRQfjYphogJYeuoexE37WWWeZfdjxZpryIUOGGAsGM/DdcMMNJstcp055H4OQ4mjBggWm804BQvHDJBO0IDGBAuOlKJKYCp0JEFgvluuuu84IPK775JNPjABgooacjlsY0CLHxApsDx6f10+RUhTs3r37FKsbE2w89NBDJm6MmfV4b1599VWTtY9xVITbPv74YxP7xWQWX3zxhakjXfSYSIL3kcfavn27iUtj2zRr1gxlHQknH4PfWSdBjVz1yigRdYC6g+z5ta9kndYZCETW917dhBBCCMDEv1AMMdHAyJEjTXY6xsYwqQM764672bRp01ChQgXjBkYhxc9NnDgxX23Izj5Tg9Nawsx3jBVi+mxaUCiQevbsaQQas+gxforjKDGxBDv977//vjkGXc6YpIGxR072OE/HLQwoSCheaBVjogtan5htryhgsgcmbnAt06dPx+WXX25cBbmdVi+2A8Ujsw0SthMtcYx7YmwVx6b68ccfjUsht1FkMckFLVfvvfeecdtr1aoVyjp+Vl4j/EoJx48fN5lUYmJizHgCvsbZZwNO2n66ujIteUA2WalFKYaWphlnAH4BwMV/Ab+fD1gpQK9FQKW8v6UTQgjh2yQkJJiscw0aNDBj5gghivd7lh9tIIuTj1G3rj3loNPJycCePd6ukfAKFTsCLR4Ezp8G7PzOFk1Vu0k0CSGEEEJ4CQknH01J7mTXlLteGabDWKDKucCmD+3lFg95u0ZCCCGEEGUWCScfo0kTe+q450k4lXECw4HO7wB1rgJq9vF2bYQQQgghyiyB3q6A8GxxopseUUryMo5/ENDgOrsIIYQQQgivIYuTj1qcnGERZHESQgghhBDC+0g4+RhVqgBRUYCT61DCSQghhBBCCO8j4eRj+PkBt98ODBliL8tVTwghhBBCCO8j4eSDvPyyXciuXZnxTkIIIYQQQgjvIOHko1SrBnB8rrQ0oJAGshZCCCGEEEKcJhJOPgjjm/buBapXt5flrieEEEIIUXKpX78+XnvtNW9XQxQQCScfZP58oFYtYP9+e1kJIoQQQgjha+zbtw/33nsvGjZsiJCQENSpUwd9+/bFH3/8UajnueCCCzB8+PBCPWZej9umTRvceeedHrdNmDDBXPehQ4cKXJcnn3wS7du3L/BxRNEi4eTDYznFx9tTCSchhBBC+BLbtm3DGWecgVmzZmHs2LFYtWoVZsyYge7du+Puu+9GaeGWW27BN998g3inU+bCJ598gn79+qFy5cpeqZsofiScfJCqVe2U5A4STkIIIUQZI+Vk9iU1Ie/7prh1+LPbL58MGzYMfn5+WLhwIQYMGICmTZuiVatWeOCBB/Dvv/9m7Ldjxw70798fkZGRKFeuHAYNGoT9jkuNi6WF1hu6s0VHR+Pqq6/GiRMnzPYbb7wRc+fOxeuvv27Ox0LRRlavXo0+ffqYY1erVg3XX399hvVnzpw5CA4Oxl9//ZVxrpdeeglVq1Y158/puK5cd911RjRNnjw5y/qtW7eac1BYbd682Vwj68C6dO7cGb///jsKEwrTCy+8EGFhYahUqRJuv/12xMbGZmxnXbp06YKIiAiUL18e55xzDrZv3262rVixwgjaqKgocw8oeBcvXlyo9SsrSDj5aEpyx+pEFOMkhBBClDG+jcy+/DUg676Tq2a/75w+WfedVt/zfvngyJEjxrpEyxI76u6w407S0tKMoOD+FCkzZ87Eli1bMHjw4Cz7U3hMnToVP/30kync94UXXjDbKGzOOuss3Hbbbdi7d68pdAk8duyYERIdOnQwIoD1oSCiMHN1w6OYiomJwbJly/D444/jo48+MgInu+O6Q2sSr2H8+PFZ1n/66aeoXbs2evbsaQTMJZdcYlwUeZ7evXsbl0WKxsLg5MmT6NWrFypUqIBFixZh0qRJRpjdc889ZntKSgouv/xydOvWDStXrsQ///xjhBXFIBkyZIipKz+7ZMkSPPLIIwgKCiqUupU1Ar1dAeGZJk2AZcvseVmchBBCCOErbNq0CZZloXnz5jnuRyFBSwmtM44o+fzzz41lip14WmYcgUUhQosIodjhZ5977jljgaLlKDw8HNWdrFkA3nrrLSOaxowZk7GO4obn2bBhg7GAPfvss0asUUTQOjV06FDjWkeyO64naFWiZYvX0aBBA3Ptn332mTmev78/2rVrZ4rDM888gylTpuCHH37IEDcF4auvvkJCQoJpO0eo8vopzl588UUjgigOL7vsMjRq1Mhsb9GiRcbnKeAeeuihjPvVhJ1McVpIOPkorhanffvseKewMG/WSAghhBDFxqBMN6xT8AvIujzgQN6di/qf6o6WXygc8sLatWuNkHG15LRs2dJYpLjNEU500XNEE6lRowYOHMjpmmz3s9mzZxvXOHdowaJwojD68ssv0bZtW9SrVw/jxo3D6dCjRw9jsWFM09NPP21EHcXITTfdZLbT4kSXw+nTpxvLFS1AdO8rLIsT24rCzNW6R1c8Cs7169fj/PPPN66HtEqxrhdffLGxvLEdCd0nb731VuMOyW0DBw7MEFiihLnqvf322+YLExoaiq5duxpf2ZygaZamYT4MzGTCL8bPP/+M0iqcAtJ/G9PdVIUQQghRFgiMyL4EhOZ930C3t67Z7ZcPaLGgG9i6desK4UJxitsYj01RkBMUK7S4LF++PEvZuHGjERIO85mqON29kOV0oFWJwoRWJtaLAooxQ8wmSB588EFjYaL1izFVrAez8SUlJaG4YJ3oonf22Wdj4sSJpn/sxJpR1P3333+49NJLTTIPilfWV5Qw4cQbSxX8xBNPYOnSpUZNUy1n95aBDyCVNIP3vvvuO6OyP/zwQ9Ri7u5SRteuwMiRdlpyInc9IYQQQvgCFStWNP01vvxm/I2nl9yOu9jOnTtNcVizZo3Zzs57XqHlKDU1Ncu6jh07GjHAl++NGzfOUhzLDC1PI0aMMH1Fvpyna52rIPN03OygdYnX8f333xvRQfc9h3nz5hlhdcUVVxjBRNc/T4kmThe2Iy1srm3Nc1LQNWvWLGMdXRcfffRRIxZbt25tXPwcKKTYFr/99huuvPJKI7RECRNOr776qgnK48PIL9B7771nfE3dA/AcuJ5vCxhASBMlvywMhHP1Ky0t8Pfk5Zf5JbCXJZyEEEII4StQNFF0MJMbM87R0kOXsjfeeMMkXSB0C6OQYHICviCnV9ENN9xg+m6dOnXK87nY31uwYIERI8yaR/FD7yP2Ca+55hoTL0WR9Ouvv5o+JevFwox4FHhcR6HAxAmvvPJKjsfNDsY2MRkF46Xo8UTx4WqBo6CipYkC59prr83VYuYJuve5W9B4XWw/emZR+DFWiy6KHD+LsWBMdMHYKwomWpyYSY/iiPeDgovHZJwVs+5xGwUX28s1BkqUAOFE6xEze/BLlVEZf3+zzBvvCQbZ8cvILwsfFKppmkVzeluQmJiI48ePZyklifr17aky6wkhhBDCV6CbGsUQXdZGjhxp+mT0CmL8z7vvvpvhcjdt2jSTDY7uc+zj8XP0OMoPdIULCAgwL9mrVKliYodq1qxpRAD7gMxsR4HGLHqMn2J/koklKBTef/99cwyGeHzwwQcYNWqUETfZHTcnaGU6evSoEUYUMq6GAF4j3eToPkixRotYfmFSC1qNXMsdd9xhjAoUhRSKjAu76qqrcNFFF5kEEYTb6TbppIWnuGNfmZ/l9R0+fNgIVm5j7BMTXTz11FP5rp8A/Ky8RvgVMnv27DEudjQnOm8myMMPP2zSUPINgDvMBsK3AlTeHD+AWV04ve+++4y7nyfo1+np4WD2Eeay92UOHgSefRZ44w3gqquASZO8XSMhhBBCFCbMluZka3PtjAshiud7RqMKsyzmRRt4PTlEfqDZkwOX8Y0BB+/iOACPPfaYcfHLDpou2RBOcfWz9XWGDbNFE5GrnhBCCCGEEGUwHTkHFKP50HX0aMLl7PLp08zKzCv8nAN9NPft22dc/xjk5w79UFlKIq5p9uWqJ4QQQgghhPfIt8WJQWZxcXEZy/Qffe2110wgWn6gyKHViL6wrhYlLru67rnChBB0z3MNuKM/KAWVJ9FU0nEVTocPAydOeLM2QgghhBBClF3yLZz69+9vRi4mTCfJ9I7MUML1TjBgXmEqcqaIZF58ZmK56667TKpFZ0AxBrLR1c6B2xkYd//99xvBxIHGmByCAXClEWcsJ//0uyR3PSGEEEIIIUqIcGIGlfPOO8/McywlZrej1Yliiiko8wNjlF5++WWMHj0a7du3N2kXZ8yYYY5JmN2EIzA7cORpZhVhGkWOAs2kEBRRjzzyCEojjsXJMbBJOAkhhBBCCFFCYpzophcVFWXmnUG0mPbxzDPPNAIqvzC3PIsnmHPeHbrxOSMhl3aoHyMjOTq2vaw4JyGEEEIIIUqIxYkjMnMAWmano/WHufPJgQMHfD69d0nDzy/TXY/I4iSEEEIIIUQJEU50q+OAYRxtmfFNTiIHWp84UJcoXO6/ny6N9ryEkxBCCCGEECXEVY+jFZ977rkm9qhdu3YZ6zmC8RVXXFHY9Svz3Hij7bLHQbblqieEEEIIIYR3OK0BcDnOEq1LjG3iaLt03WPcU/PmzQu/hgINGmRanCxLDSKEEEII4Yv4+fmZfnFxQi8wDg0kfFA4DRo0CG+99VbGmE6dOnUy65jlbvLkyUVRxzJNYiKwb589z3Gcjhzxdo2EEEIIUda58cYbjUhg4ViajIF/+umnkZKSAl/HW0LDaa/sypNPPnlax2W26dtvv71AdbvgggswfPhw+AqTJ082dYqOjkZkZKTRGXy+OCwR1+fUjtzuM8Lpzz//zEhHPmXKFFiWZcZzYiryZ599tijqWKahUOrePXNZ7npCCCGE8AV69+5tQjc2btyIkSNHmo7/2LFjT+tYqampSHPGXymlsK2cQuHGpGqu65hDwIH967yK0CpVqiA8PBylhccee8wMWdS5c2f88ssvWL16tRkzdsWKFZgwYQK+//77jDZbuHCh+czvv/+esY7bfUY4xcTEoGLFimaeYy4NGDDA3KxLL73UfHFE4VK9OhARkbmsBBFCCCFEKYY++SdPeqfkMx4gJCTEhG/Uq1cPd911Fy6++GL88MMPZltiYqIRArVq1UJERIRJKOY6zMynn36K8uXLm/1btmxpjsXxO/m5//3vf2bsTq6jJevjjz/O+Bw70X369DFWCI77ef311+PQoUMZ22ltcIa6obWicuXKePzxx40QcbZz+JwRI0ZkWCgIRR/HFHWF4obWKVfLTo8ePcwxeexu3bqZ8U3zCtvKKfw8z+0sr1u3zoS9UCicccYZ5tr//vtvbN68Gf379zfXymummKBIyMmCxuN+9NFHJvcA++hNmjTJuC8FsQC1atXK1Ivno5Bx5Z133jHnCQ0NNXVlTgQHjvvapk0bhIWFoVKlSuY5OcnnzQMUQmPGjDHHpwg/++yzzfnY7qzD0KFDjQ5x2o2ikfC4zjpHp/iEcOKD/M8//5gLpnBy0pEfPXrUNJYoXJSSXAghhChDxMXZgzh6o/DcBYAd46SkJDNP4cL+4jfffIOVK1di4MCBxkLl+pKdY4O++OKLppP/33//oWrVqrjhhhvw9ddfG0+mtWvX4v333zeCgdDD6cILLzRx9osXLzb90P3795uQEVc+++wzBAYGmk7466+/jldffdWcg9AaUbt2beP25Vgo8sqJEydMx52ChmOKUihccsklZn1h8cgjj+CFF14w1073tNjYWHOOP/74A8uWLTNt2LdvXyMyc+Kpp54y7cK25+eHDBli3NxOhyVLlphjXX311Vi1apURmRSjFL+E9+K+++4zbbp+/XpzX84//3yzje17zTXX4OabbzbXRPHMMWAdIevOl19+ae73sGHDPG6n2C5RWfXo/8jG50XxDYPjR0gXPqpJUfhwLKcVK+x5WZyEEEII4UuwE8yOPcf3vPfee02n/pNPPjHTmjVrmn1ofWKHmutpUSDJycnGUuFkad6wYQO+/fZbzJw501glSMOGDTPOwxh7iibn82T8+PHmpT4/27RpU7OOy+PGjTOWl2bNmpnOPpdvu+02Y40ICAgw1h1aJ/IDRZsrH3zwgenIz507F5dddhkKA4oPWlccWF/XLNbPPPOMCZWhBYniNKcYNAoWwvaiEKWQpPDKLxSezJ79+OOPm2W285o1a4xFiOfhfaZVkW3AdqU+cIYoonCiyyHFEteTnPQChTXveVBQEHyRfAsnKsAuXbqYAXB5Y5lZj/AiFeNUNDRpkjmvGCchhBCiFMNYldhY7507H/z000/mRToFEOOTrr32WmONoFWBMUuOkHGgGx5dqhyYVIJWFYfly5cbUUMXOE8wxmX27NkZFihX6NLmnO/MM8/McMEjHHOUrl+sE49/utC6NWrUKHN9Bw4cMMej1Sw3609+YNI1V2hxYptOnz49Q4QwOVtu53RtV4oaxlOxzqcDLUV0F3TlnHPOMe6BbAPqAYoiagEKMxbHTZCij6KLYqlXr17GU41ufBUqVIAnsrNElVjh5NxUFl4cCx9OxjiJorM4OcjiJIQQQpRi2OF3DW72Ybp37453333XCCBaluge53T2KVDo4uUuVFxFD137XAUOl3OCx6WbGt373KlRo0aBroWGAPdOOwWhK3TTO3z4sHH/o1BgvA9FmeOeWBhQ5LhCSx0tcC+//LKJ92IbUXjkdk53iw3buaiSb0RFRZlYLwrK3377DaNHjzZijzFhtMix/vPnzzfb3nzzTZP8YcGCBWjgjLfjAsUvXSHZ9r5odTqtcZw+//zzjCAvFqpaZrkQxWNx8nExLoQQQogyADv57MzXrVs3QzQRumnREkELB7e7lpzc49i3ZOeerm+e6Nixo4mFYrIA9+O6Cg52yl1x4pEcEUehx/q5wiQD+/btyyKeaAFzZd68eSaWhzFDTqIE18QURQHPSXc4WnDYPmy/bcXsftSiRQtTD1e4TJHjtCnvP90rX3rpJRNXxTrOmjUrQ7TRQsW4K8Zpsf3pbugJWi0pkOnC6QnGuZUo4UQ/R2ZO4UNDP1QWmuTuvPNO4z8qCp/WrZnZxX4JlZBAU7FaWQghhBC+CTvUjIdnogcmY9i6dauJr3n++eeNy1l2UBDRqsNEAhxElp+jFYN9TXL33XebBAeM3aE1g+55jKu66aabsgghurE98MADJlEBE03QynH//fdnOQ9j83fv3p0hfBizf/DgQdPx53Hffvttk+HOFYovGgroukZxxmvMzUpWUHhOtiFFHF0VKSyKynLE6+d5XAvdE5lqnjFszzzzjIklY/INxps56dPpsskYKu7PjIU0sLCOjC9jOzHGigkkeF94LTwPxZgnmH3x4YcfNufklAlGeEyenwlGeG6vYuWT+vXrW5999tkp6z/99FOzzdeJiYnhqwQzLWnUrcvXIJY1f763ayKEEEKIwiA+Pt5as2aNmZYkhg4davXv3z/b7UlJSdbo0aNN3zAoKMiqUaOGdcUVV1grV6402z/55BMrOjr6lM+xHUaMGGH2Dw4Otho3bmyNHz8+Y/uGDRvMccqXL2+FhYVZzZs3t4YPH26lpaWZ7d26dbOGDRtm3XnnnVa5cuWsChUqWP/3f/+XsZ38888/Vtu2ba2QkBDTJ3R49913rTp16lgRERHWDTfcYD333HNWvXr1MrYvXbrU6tSpkxUaGmo1adLEmjRpktk+bty4jH14vClTpuTafu7XP3v2bPPZo0ePZtlv69atVvfu3c21sm5vvfWWucb7778/Y5+81IHn4jmzg8fk59zLM888Y7Z/9913VsuWLc29rFu3rjV27NiMz/7111/m82xr1pNtO3HiRLONz3avXr2sKlWqmPZu2rSp9eabb+baPvz8+eefb0VFRZn7wWM+/fTTHtuH9Vy2bNlpf8/yow38+F9+hBZTjjOHPs2i7lkwaEJMoEnEhzl+/LjJnc/xqBgoV5JgAkNar7/8kqZMb9dGCCGEEAWF/SZaVhjvoWFdCg4tRxyPyXVcIyEScvie5Ucb5NtVj4LJMZm6MnHiRGNOFEXDhg2AY5lVZj0hhBBCCCF8PKseA7sGDx5sfEMZ6OUEiNH30JOgEoXD+PHAX3/Z88qsJ4QQQgghhI8LpwEDBphALyaCYOAeYYAXg/6cwa5E4eNqzJNwEkIIIYQ4FSaTEMKnxnE644wz8MUXX2RZx5STzJrxf//3f4VVN+GCa0iZXPWEEEIIIYQoAeM4eYKjGT/++OOFdTiRg8Vp+3bAbfgBIYQQQgghREkQTqJo4YDY4eH2fEoKsHu3WlwIIYQQQojiQsKphMDBb13d9RTnJIQQQgghRPEh4VSCUJyTEEIIIYQQPp4c4oEHHshx+8GDBwujPiIHRowATp4Efv1VFichhBBCCCF8UjgtW7Ys133OP//8gtZH5MC55wLdukk4CSGEEEL4In5+fpgyZQouv/zyIjvHBRdcgPbt2+O1114rsnOIArrqzZ49O09FFC3169tTpSQXQgghhLe48cYbjUhgCQ4ORuPGjfH0008jhRmsfJz69et7RXT07dsXvXv39rjtr7/+Mm25cuXKAp/n008/Rfny5eErbNq0CTfddBNq166NkJAQNGjQANdccw0WL15s6uo8R9mVbT7U6VWMUwmCv0WbNtnzW7Z4uzZCCCGEKMtQBHA4mo0bN2LkyJF48sknMXbs2NM6VmpqKtLS0lCaueWWWzBz5kzs2rXrlG2ffPIJOnXqhLZt26I0sXjxYjP+64YNG/D+++9jzZo1xiLXvHlz88wMHjzYPENOOeuss3DbbbdlWVenTh34ChJOJQh/f+D55+15fueSkrxdIyGEEEIUCQxqzq4kJOR93/j4vO17GtB6UL16ddSrVw933XUXLr74Yvzwww9mW2JiIh588EHUqlULERER6Nq1K+bMmXOKVYT7t2zZ0hxrx44d5nP/+9//TGeZ62jJ+vjjjzM+t3r1avTp0weRkZGoVq0arr/+ehw6dCiLG9s999xjSnR0NCpXrmzGGbUsK2P79u3bMWLEiAyLBqHoo/ubK7RK0TrlsGjRIvTo0cMck8fu1q0bli5dmuf2uuyyy1ClShVz7a7ExsZi0qRJRlgdPnzYWGPYbuHh4WjTpg2+/vprFCZs5/79+5s2LFeuHAYNGoT9+/dnbF+xYgW6d++OqKgos53ChwKIsO1oOatQoYK5r61atcLPP/8MT7DNaZls0qSJsahdeumlaNSokWnnJ554AtOmTUNYWJh5hpxC6yWv23VdQEAAfAUJpxImnFwz6+3c6c3aCCGEEKLIiIzMvgwYkHXfqlWz37dPn6z7Ugh42q8QYCc4Kf2tLoXLP//8g2+++ca4nw0cONBYqGidcoiLi8OLL76Ijz76CP/99x+qVq2KG264wQiFN954A2vXrjVWCnbwybFjx3DhhReiQ4cOpiM/Y8YM0+Fnx9+Vzz77DIGBgVi4cCFef/11vPrqq+Yc5PvvvzcuY3QrdCwaeeXEiRMYOnQo/v77b/z7779GEFxyySVmfV5gnXh9FE6OkCMUTbS4UTAlJCQYoTJ9+nQjEm+//XYjDnkthQGtehRNR44cwdy5c40FbMuWLcby4zBkyBDTRhSKS5YswSOPPIKgoCCz7e677zbi9s8//8SqVavM/XPujzvLly8395WWJX92Yt3wJXfCQk8OIXwDCqdVqzLHcmrUyNs1EkIIIURZhiLgjz/+wK+//op7773XWDToesZpzZo1zT60PlHocP2YMWPMuuTkZLzzzjto166dWaY717fffms687RekYYNG2ac56233jKiyfk8GT9+vLFO8bNNmzY167g8btw4Y01q1qyZ6eBzmS5gFStWNBYMWlNozcgPFG2ufPDBB6bzTwFCa1JeuPnmm407Iz9D6xdhmwwYMMBYsVjYVg5sT7Yr26VLly4oKLxPbI+tW7dmuMB9/vnnxnJEodS5c2dz3x566CHjTkcoEB127Nhh6kpLmPv9cccRyc5xSgMSTiUMl2dXg+AKIYQQpZXY2Oy3ubsuHTiQ/b7ub/oLMdD+p59+MtYGCiBaMq699lrj8kaXPFpQHCHjQEtFpUqVMpbpluUa00MLBUUNXeA8QRcyJiLzZOHYvHlzxvnOPPPMDBc8wriZV155xdSpIG5ftG6NGjXKXN+BAwfM8Wg1o5jIKxQRZ599thF8FE5MnEA3NlrACI9JYUihtHv3bmPBY7vRfa0woBWPgsk1boiukhSA3EbhxCGIbr31VkyYMMEIWFoL6WJH7rvvPuOW+dtvv5ltFFHZxWW5WtXKrHCiryfVMn0W69atWzS1EtmiQXCFEEKIMkBEhPf3zQXGwbz77rtGANGyRFc0J2aHAoVuXu5CxVX00LXPVeBwOSd4XMbX0D3MnRo1ahToWuhK5t7RpyB0hW56jEGi+x/juhiDRVHmuCfmFcYy0ZL09ttvG2sTRYkjFmmN4vEZX0WrDuOIhg8fnu9zFASKX4pgugv+8ssvJh6JLpdXXHGFEVS9evUy2yienn/+eSNKeT3uOEJ23bp1xlJYGsh3jBNvHv1DaZpjgBwbkkpYFA+yOAkhhBDCF2Cnnskb+CLdEU2EnWRaTmiV4XbXkpN7HIUCLVd0Y/NEx44dTcwMX+K7H5d1cViwYEGWzznxSI6Io9Bj/Vxh0oZ9+/ZlEU+0gLkyb948Y3FhXBNd2yicXBNT5BXGZFGoffXVV8ZNjgYJR0DyHIxBuu6664wLI/vbdEMsLFq0aIGdO3ea4sBMd4wfo+XJVfQwgQbF0ZVXXmkEngOtVXfeeafRA4xf+vDDD+EJJoHgMSmsPGVM5DnLhHDig8QgNTY+FSZVPoMA85NZRBTc4rR5s1pRCCGEEL4FO91MMMBECOxcM56G/UZaJ2ipyA4KIlp1KCSmTp1qPke3OLqtOYkJmNSASRQYj0P3PMb/cIwgVyFE1zm6m61fv94kmnjzzTdx//33ZzkPkxvQFc4RPnSbO3jwIF566SVzXFqDaG1xheKL7mt0aaM44zXmZiXzBK1uTMbw6KOPmuQU9OJyPQdjvObPn2/Oc8cdd2TJeJdX2B7sr7sWHo/udRSorDv77bwvvE+0eDEdenx8vOnTs92ZQY9Cjm3NPr+jA9jmvDf8PF0nnW3uUAxScFH4nXfeeSb7HhNRMFnIc889ZwRiSeO0s+pR9TPjyZ49e4wJj9lK6BdJdUm/zdLo1+gLMMby1VfteR8aD0wIIYQQIgN2mNkhp0WCCRouv/xy0wHPLcyDrn9XXXUVhg0bZuKBmNDhZHq6dLoDsiNPUdCzZ08jANiRZ3yOa9Y2npcCgMkUKLYompidzoHxRBxUlS5ytDQRdv6ZqIKCiZYeCgrXJA2EadGPHj1q+sDMdEfrEzMBng501+Ox6PbmJNAgjKHi8bmeYo4WOrZdfqFbIy1/roVujhQzTAPOdOLnn3++EVK0ak2cONF8jlY5uiOyDSmAaR1j+vennnrKbGfbs03ZXsySyH3YbtnBe8AMiLQK8l7yc/369TOWQ28MQlxQ/KzTVDj0++QAVvxiUBkzEI8PAQf14kPHzCM0Qfoax48fNxlLYmJiTG76ksiRI4ATWxkXR59gb9dICCGEEKcD00/z7X2DBg0QGhqqRiwgFBt8iV8SO+XCO9+z/GiDfCeHoFmOYommT6p7KlKmeHRNNcjgMVqfRNFQoQLA+3r8uG11ysZCKoQQQgghhCgk8i2cKIiYFIKmVJoOnQGxXKGau/rqqwurjsKNlSsBZqWkcOJYThJOQgghhBBC+JhwYlAXUzDmBDObuGbfEIULk8Xs22fPK85JCCGEEMKGSQ2E8Bnh5IgmBnoxOwdhoBczcYjiQSnJhRBCCCGE8HHhxOQPTAPJrCbMYuLkYecoyBzTqXbt2kVRT+GCUpILIYQQQgjh4+nIOWIwM+rR2sRc+iyc58BW3CaKnlq1OHibPb9xo1pcCCGEEEIIn7M4cTRnDsrFnPwOnOfgYhzcShQ9HKqgTh17AFzFOAkhhBBCCOGDFqc6deoYi5M7HBDLdQAvUbQ4mfRiY+3sekIIIYQQQggfEk5jx47Fvffea5JDOHCeozK//PLLhV0/kQ0uw2aZlORCCCGEEKJk4Ofnh6lTp3q7GqKohdONN96I5cuXo2vXrggJCTGF8xwY9+abb0bFihUziig67r4baNXKnpe7nhBCCCGKm3379pmX6Q0bNjT9QXol9e3bF3/88UehnueCCy7A8OHDC/WYeTnutm3bjMDJqXz66aende69e/eiT58+Bag9UL9+fbz22mvwBSzLwgcffGA0QWRkpEkgx4zbrF9cXJypa07tSH1RKmOcfOUGlXXq1wdatgT++08WJyGEEEIULxQV55xzjukg0xupTZs2JpTj119/xd13341169aV+FtCIUiB40DPqhkzZuD333/PWBcdHZ0lbIUiwJ/B6LlQvXp1lCauv/56fP/99xg1ahTeeustVKlSBStWrDC6gaJp0aJFpn0IcyUMGDAA69evR7ly5cy6sLAwlAisMkZMTIzFy+a0pPPww5bFO3jffd6uiRBCCCFOh/j4eGvNmjVmWpLo06ePVatWLSs2NvaUbUePHs2Y3759u9WvXz8rIiLCioqKsgYOHGjt27cvY/sTTzxhtWvXzvr888+tevXqWeXKlbMGDx5sHT9+3GwfOnSo6be5lq1bt5ptq1atsnr37m2OXbVqVeu6666zDh48aLbNnj3bCgoKsv7888+Mc7344otWlSpVzPlzOm52OHV1+OSTT6zo6Ghr2rRpVosWLayAgABzjIULF1oXX3yxValSJXM9559/vrVkyZIsx+L5pkyZYub5GS5PnjzZuuCCC6ywsDCrbdu21vz583OsD9tr3Lhx2W5/5513rIYNG5p2aNq0qWljh7S0NHM9derUsYKDg60aNWpY9957b8b2t99+22rcuLEVEhJi2nbAgAHZnmfixImm/lOnTj1lG89z7NixLOt4b7i/63Pize9ZfrRBvl31CBXj5MmT8eyzz5oyZcqUDBUpio8tW+xp+jjEQgghhCjhsE99MumkV4rdn88dDkVDywstSxEREadsd8b55FA1/fv3N/szK/PMmTOxZcsWDB48OMv+mzdvNvE+P/30kync94UXXjDbXn/9dZx11lm47bbbjPWHhZYgjiF64YUXokOHDibWnvXZv38/Bg0alMUNj5aQmJgYLFu2DI8//jg++ugjVKtWLdvj5he6ob344ovmuP/99x+qVq2KEydOYOjQofj777/x77//okmTJrjkkkvM+px47LHH8OCDD5qQmKZNm5pxU1NSUnA6sG/O/AMjR47E6tWrcccdd+Cmm27C7NmzzXb248eNG4f3338fGzduNO1PqyFhe9533314+umnjVWIbXv++edne64vv/zSZNjmvXaHFjhXq1xJJ9+ueps2bTI3f/fu3RkpyZ9//nnzsE2fPh2NGjUqinoKDyxaZE81lpMQQghROohLjkPk85FeOXfso7GICD5VCHnqC1JkNXfNVOUBxjqtWrUKW7duzRAln3/+OVq1amVctzp37pwhsBgrFBUVZZYpdvjZ5557znS6g4ODER4ensW9je5gFE1jxozJWDd+/Hhzng0bNhjhwZf7FGu33367EQ8UM/369TP7Znfc/EL3xHfeeQft2rXLWEdB5wpjfygmKQgvu+yybI9F0XTppZea+aeeesq0E9s6t3b2BN0KGTc0bNgws/zAAw8YEcf13bt3x44dO8x1X3zxxQgKCkLdunXRpUsXsy+3URCzrrwn9erVM22dHRRersMUlWbybXGiAqU42rlzp0kIwcIGbtCggdkmio/Gje3pnj18Q6WWF0IIIUTRk1fL1Nq1a42QcbXktGzZ0ogIbnNgDIwjmkiNGjVw4MCBHI/N+BlaT5iIwCmOwKAFi1AY0RpC60pCQoKxsBQ2PEfbtm2zrKPli5YsWpoo0BjHExsba/rLOeF6HLYBya0dsoPtyxg0V7jstPvAgQMRHx9vEnuwrrRQOdatHj16GLHEbRSxbENa1gr6PJTZAXCpWF2z5lWqVMmYVN1vkChaaFFl4pqkJJrNeR/U4kIIIURJJjwo3Fh+vHXuvEBBQBeswkoAQYuHKzw2rVA5QSHCDH50k3PHER1OIgJCd0EWT66FBYFJDVhfV2jZOnz4sHEHpABhxkG6BSaxw5bHdnCOmVs7nC4Us3TDY6ILWuVomWKSD/bzKWJpGJkzZw5+++03jB49Gk8++aSxEjpumK7QulcakoEUicWJN9+TjyYfYKpuUXxoLCchhBCidMEOM93lvFHcBUB28OV5r1698Pbbb+PkyZOnbGf8EWnRooXxUGJxWLNmjdlOy1NeYf/SPZa+Y8eOJqaI1qrGjRtnKY44ouVpxIgR+PDDD02abAoaVyHi6biFwbx584wXFkNb6G7HvvOhQ4dQnLDtWQ/3erm2O0Ufxecbb7xhRNI///xjXCtJYGCgceN76aWXsHLlSpNFcdasWR7Pde211xr3yGnTpnm0RjHGrMwKJ/o70ld0wYIFpjFYaIG68847M/xGRfHQpEnmvAbBFUIIIURxQdFE0cG4GLrCMc6FbmDshNO6QtjxZsKBIUOGGAvGwoULccMNN6Bbt25mjJ+8QnHEfic77xQgFD9MTEELEhMo0BJCkcRU6EyAwHqxXHfddUbgcd0nn3xiBMArr7yS43ELA1rkJkyYYNqDx+f1F1W6beYcYDIJ13L06FE89NBDJm7s3XffNffm1VdfNenCGUdFuO3jjz82sV9M2PHFF1+YOtJCxgQdvI881vbt201cGtumWTZxTEzIwYQfvBeMOWNyCX6Ox+Ez4CSkKJPCiQ3JGCd+KUJDQ02hix4VPk2Sp/vl48PLY/GNAL9Y2cEb7T5oFj9XlmOcSLo7rxBCCCFEkcP4F4ohJhpg5rbWrVub2BgmdWBnnbCPRitEhQoVTFY2dqL5uYkTJ+brXOzsBwQEGGsJxwdirFDNmjWNBYUCqWfPnkagMYseXck4jhITS7DzzqxxjvsekzRwnCHGR2V33MKAgoTihVYxxgjR+sRse0UBkz0wcYNrYbK2yy+/3PTLuZ1WL7YDxSOzDRK2Ey1x7MMztoouez/++KMJv+E2iiwmuaDl6r333sPXX39tjuMJ3uevvvrKiDNm56Mw5jHp3sdMexSvpQU/5iTP687cleZWPlxUuE6AGRuVwul04JeHbx94UyiaOFDWpEmTjN+lp4eMwonpFbk94yL8/Exqybxw/PhxE6hHs6Ez6FZJhS9G6B1JK/OQIcAXX3i7RkIIIYTID0xawKxzTLJVVl8EC+HN71l+tEG+LE4UThRIu3btMlP6RbKcrmgiVKfM5kEzKhU/BRRTQzKlZHZQKDGFolPyKppKGxyY+skn7fnDh71dGyGEEEIIIUov+RJONH3Sb5OZQgoDZhdZsmSJMd26noPLDFDLDiaioA8mM4LQBMjgwOxITEw0StK1lCbOPtueKsZJCCGEEEIIH4pxYtpxBpwxmKygMBCPvqnuFiMu79u3z+NnGJhGaxR9ZhnIxmC1s88+21jBPMHBeWl+c8rpjArty9Svb0+3b9dYTkIIIYQQQvjMOE6MR+IgWBwhmWkc3bOEMMNJUcKkFE62FkLRxBgrBr0988wzp+z/6KOPmtGSHWhxKk3iyWnuhASAWtNl6AIhhBBCCCGEt4QTR13Oa57/3KhcubLJZsIRll3hMmOX8gIHC2MGkU2bNnncztz5LKWV9KESMtz1JJyEEEIIIYTwAeF04403FtrJabE644wzTOpKpk0kdL3j8j333JOnY9DVj4N1cZCxsj6W05YtmTFPQgghhBBCCC/GONFCdODAgVPWM2EEt+UXutExj/xnn31m0pvfddddZhRqZtlzXAPpbufw9NNP47fffjODdXH8AA5uxjz9t956K8oi9Dpkdj2ybJm3ayOEEEIIIUTpJN8Wp+yGfWL2OlqQ8gtHGj548CBGjx5tEkK0b98eM2bMyEgYwcHImGnPgQOKMX059+WAarRYzZ8/36QyL4uwaSpVAg4eBHJILiiEEEIIIYQoDuH0xhtvmCnjmz766CNERkZmcZf7888/0bx589OqBN3ysnPNmzNnzikxViwik9q1beG0bZtaRQghhBBCCK8KJ0es0OLEQWpd3fJoaapfv75ZL7wT50Q3vb171fpCCCGEEL4G+8nDhw83RZSBGKetW7ea0q1bN6xYsSJjmWX9+vX49ddf0bVr16KtrfBI+/b29MQJWv/USEIIIYQoehg2ce+996Jhw4YmgzGHe+nbt69J8lWYXHDBBUUiOPJy3DZt2uDOO+/0uG3ChAnmujkuaUF58sknTbiKr7Bs2TIMHDjQhM6EhoaiSZMmJlRmw4YNpq70QMuplFbynRxi9uzZJrZI+A7DhgGBgfYAuNmMAyyEEEIIUWhs27bNxJnPmjULY8eONRmOGaPevXt33H333aWmpW+55RZ88803iI+PP2XbJ598gn79+pnhdUoTP/30E84880yTv+DLL780ydu++OILREdH4/HHH8eDDz6IvXv3ZpTatWub5G2u60or+RZOjGf6+OOPce211+Liiy/GhRdemKWI4ic6miZge15xTkIIIYQoaoYNG2YsCwsXLsSAAQPQtGlTtGrVymRL/vfffzP2Y5Kv/v37m9j4cuXKYdCgQVnG73QsLbTe0J2NnfOrr74aJ+hGkz4Mzty5c/H6669nWDMo2sjq1avRp08fc2xaRq6//voM6w9j5BlK8tdff2Wc66WXXkLVqlXN+XM6rivM3kzRNHny5Czr6XHFc1BYbd682Vwj68C6dO7cGb///nuhtjeFKfvZYWFhqFSpEm6//XbExsZmbGddunTpgoiICJQvXx7nnHOOyTpN6ClGQRsVFWXuAQXv4sWLPZ4nLi7OZLbmMD8//PCD6es3aNDAeJW9/PLLeP/99801crxVpzB8h8d2XVdaybdwuv/++02hgGrdujXatWuXpQjv0KBB5iC4QgghhCjZnEw6mW1JSEnI877xyVktJdntlx+OHDlirEu0LLGj7g477s7YnBQU3J8iZebMmWY4GWZUdoXCY+rUqcbSwcJ9X3jhBbONwuass84ybmKONYMugceOHTNCokOHDkYEsD4URBRmrm54FFMxMTHG9YzWEiY4o8DJ7rju0JrEaxg/fnyW9Z9++qmxtPTs2dMIGAoNuijyPL179zYuixSNhQGH6enVq5fx+Fq0aBEmTZpkhJmTWC0lJcWMh8pwmpUrV+Kff/4xwspxmRsyZIipKz+7ZMkSPPLIIwgKCvJ4LobeUHw+/PDDHreXT7+3ZZV8pyOnufLbb78tswPO+ipHj9rTuXP5dsbbtRFCCCFEQYh8PjN7sTuXNLkE06+dnrFc9eWqiEuO87hvt3rdMOfGzAzF9V+vj0Nxp8bkWE94Hm7GE5s2bTLJwnLLpkwhQUsJrTOOKPn888+NZYqdeFpmHIFFIUKrBaHY4Wefe+45Y4Gi5Sg8PDyLJeOtt94yomnMmDEZ6yhueB7G4dAC9uyzzxqxRhFB69TQoUONax3J7rieoFWJli1eB60vvHaOP8rjccgcd+PBM888gylTphiLTXZZo/PDV199hYSEBNN2jlDl9VOcvfjii0YEURxedtllaNSokdneokWLjM9TwD300EMZ94vxStmxceNGMz3dTNmlnXxbnPiQNW7cuGhqI04bx1q7dq0aUQghhBBFR3ZjerrD2BgKGVdLDsfdpNWC2xzooueIJlKjRg0cOHAgx2PT/Yxx93Qbc4rT2acFy+mzMkaHbnYUHqc7nE2PHj2MxYYxTYSijmKELm2EFifG/VCs8NpYF15fYVmceCwKM1frHl3xKDiZoK1ixYrG9ZBWKYopWtNc44zoPnnrrbcatzta8pz2Kci9Lavk2+I0cuRIc0OodEtz1oySRsOGwLp1Sg4hhBBClAZiH82MX3EnwD9zSBhy4MHsRYa/X9Z35NvuL/igj7RYsA+4jh2PQsDdbYzHpijICYoVx+LiDoWXw/z5882U7oIsnlwLc4NWJQoTWpkYk0UBxZghZhMkFE20bDEGiMYFxiFdddVVSEpKQnHBOt13333GZXHixIkYNWqUqROTPLDOzE0wffp0/PLLL3jiiSeMB9kVV1xxynFoqSO8t3RlFAW0OP39999GvdMUyAf2yiuvzFKEd2jVyp4ePqw7IIQQQpR0IoIjsi2hgaF53jcsKCxP++YHWjho3Xj77bdN/I07jD8itMDs3LnTFIc1a9aY7bQ85RVajhhb70rHjh3x33//GWsVxYprccQRLSsjRozAhx9+aJIb0LXOVZB5Om520LrE6/j++++NGx7d9xzmzZtnhBWFCNOX0/XPU6KJ04XtSAuba1vznBR0zZo1y1hH18VHH33UiEXmIaCLn6sgYlv89ttvpr/uWM/cYcwW47qYSMMTx9LvbVkl38KJJkg+GAxAY8PSR9S1CO/QqZM9TUgAivEFhxBCCCHKIBRNFB3M5EZXOMbG0KXsjTfeyLBU0DWMQoLJCZYuXWoy8N1www2mD9nJ6bjkAYqjBQsWGDHCxAUUP0xMQQvSNddcY+KlKJKY2IACh/ViYUY8Cjyuo1Bg4oRXXnklx+NmB2ObmIyC8VIcu8nVWEALHAXV8uXLjcChdSc3i5knmL2Px3AtvC62H8dSovBjrBZdFDl+FmPBmOiCsVcUTEwKwUx6FEe8HxRcPCbjrJh1j9souNherjFQrlB0MoEGrVOMB2MSCrYPE3A8/PDD2Y5pVWawyhgxMTF03jTT0sSWLXRKtcuyZd6ujRBCCCHyQnx8vLVmzRozLWns2bPHuvvuu6169epZwcHBVq1atax+/fpZs2fPzthn+/btZl1ERIQVFRVlDRw40Nq3b1/G9ieeeMJq165dluOOGzfOHNNh/fr11plnnmmFhYWZPtzWrVvN+g0bNlhXXHGFVb58ebOtefPm1vDhw620tDTrqaeesmrUqGEdOnQo4ziTJ0829Vy+fHmOx82Or776yuw3bNiwLOv5ue7du5vj1KlTx3rrrbesbt26Wffff3/GPrweXld2sB14bPdy0UUXme0rV6405wgNDbUqVqxo3XbbbdaJEyfMNrbn5Zdfbq6X18dzjR492kpNTbUSExOtq6++2tSL22rWrGndc889uT5vixYtsq688kqrSpUqVkhIiNW4cWPr9ttvtzZu3HjKvrldm69/z/KjDfz4X14EFoP0mPs+O5gKkW8T+ObBlzl+/LixjDH7CHPZlxZoaQ4OZmYa4KmngNGjvV0jIYQQQuQGkxY42dpoVRBCFO/3LD/aIM+ueu4ZTmh6dfVZPXz4sILIvEhAAFC3rj0/b543ayKEEEIIIUTpI8/Cyd0wRX/H5OTkHPcRxUv6WHEoxHhEIYQQQgghxOkkh8gJpSf3Lj162NMNG4BDp45tJ4QQQgghhPAF4SS8S8WKHOnZnp89W3dDCCGEEEKIYhdOtCadOHHCBFAxeIrLHHyMy04R3qdSJXv68sverokQQgghhBClh8C87sj4JWc0YWeZA225LstVzzcGwmVyiNWrvV0TIYQQQuQVxYkL4fvfrzwLJw62JXyfoUOBDz4A4uKALVuAhg29XSMhhBBCZEdQUJB58Xzw4EFUqVJFL6GFKALRxO8Xv2f8vhWEPI/jVFooreM4OXAcJ47nxHGdRo0CnnnG2zUSQgghRE4w9GHXrl2yOglRRFA01a5dG5GRkQXSBnm2OImSgb8/0KABsGkT8NNPEk5CCCGEr8POXJMmTU4Z5kUIUTjQ0hTAQU8LiIRTKaR7d1s4/fcfkJICBOouCyGEED4NO3WF0bETQhQdSkdeCrnqKnvKF1cLFni7NkIIIYQQQpR8JJxKIWedlTnv5+fNmgghhBBCCFE6KLBwYkDV1KlTsXbt2sKpkSgwUVFAu3b2/N69alAhhBBCCCGKXTgNGjQIb731lpmPj49Hp06dzLq2bdti8uTJBa6QKBzOPtuezp+vFhVCCCGEEKLYhdOff/6J8847z8xPmTLFpM48duwY3njjDTz77LMFrpAoXHe9n38GFi1SqwohhBBCCFGswok5zitWrGjmZ8yYgQEDBiA8PByXXnopNm7cWKDKiMK3OK1bB7z2mlpWCCGEEEKIYhVOderUwT///IOTJ08a4dSzZ0+z/ujRowgNDS1QZUTh0bAhUL68Pf/LL/bAuEIIIYQQQohiEk7Dhw/HkCFDzOi7NWvWxAUXXJDhwtemTZvTrIYobJhNL92jEkePAsuXq42FEEIIIYQoNuE0bNgwY3EaP348/v77b/j724do2LChYpx8jHPPzZz/9Vdv1kQIIYQQQoiSjZ/F7A4FIDU1FatWrUK9evVQoUIF+DpMnx4dHW1itcqVK4fSzN9/Z1qdzj8fmDvX2zUSQgghhBCiZGqD03LV+/jjjzNEU7du3dCxY0cT+zRnzpzTr7UodM44AwgIyExLfvy4GlkIIYQQQojTId/C6bvvvkO79NFVf/zxR2zduhXr1q3DiBEj8Nhjj51WJUTREBZmiyeSkgL89ZdaWgghhBBCiGIRTocOHUL16tXN/M8//4yBAweiadOmuPnmm43LnvDNtORDhgCXXOLt2gghhBBCCFFGhFO1atWwZs0a46bHdOQ9evQw6+Pi4hDg+IUJnxsId+1aO9OeEEIIIYQQIv8E5vcDN910EwYNGoQaNWrAz88PF198sVm/YMECNG/e/DSqIIrD4rRiBXDyJBARofYWQgghhBCiyIXTk08+idatW2Pnzp3GTS8kJMSsp7XpkUceyXcFRNFSu7Zddu0CLr3Udtm77Ta1uhBCCCGEEMWajrykUZbSkTsMHgx8+60936sXMGOGt2skhBBCCCFEKU9HTubOnYu+ffuicePGpvTr1w9/KWWbz7vrEY7lFB/vzdoIIYQQQghR8si3cPriiy9MXFN4eDjuu+8+U8LCwnDRRRfhq6++KppaikJJEOHvDyQkAH/+qQYVQgghhBCiSF31WrRogdtvv92M2+TKq6++ig8//BBrmb7NhymLrnpJSUB0tC2ayPDhwLhx3q6VEEIIIYQQpdhVb8uWLcZNzx2663EwXOF7BAcDnTtnLv/6qzdrI4QQQgghRMkj38KpTp06+OOPP05Z//vvv5ttwrfd9TiWE42C27d7u0ZCCCGEEEKU4nTkI0eONHFNy5cvx9npWQfmzZuHTz/9FK+//npR1FEUYoKIsDCgTRvg0CGgXj01rRBCCCGEEEUinO666y5Ur14dr7zyCr5Nz3HNuKeJEyeif//++T2cKGaLU1ycnY68fHk1vRBCCCGEEEUinFJSUjBmzBjcfPPN+Pvvv/PzUeFlqlYFGjUCNm8GFiywx3MSQgghhBBCFEGMU2BgIF566SUjoETJddebPx+IiQEOHvR2jYQQQgghhCilySE4XhMHwBUlVzh98QVQqZJSkgshhBBCCFFkMU59+vTBI488glWrVuGMM85ARETEKWnJhW/HOe3ZA6Sm2mnJx4zxdq2EEEIIIYQohQPg+vtnb6Ty8/NDKnvkPkxZHADXgbeGSSFiYzPX7dsHVKvmzVoJIYQQQghRCgfATUtLy7b4umgq6wQEAGeeac87Q2799ptXqySEEEIIIUSJIN/CSZQOd73oaHtKdz0hhBBCCCFEIQmnWbNmoWXLlsac5Q5NW61atcKff/6Z18MJLyeIOHo0Uzilpel2CCGEEEIIUSjC6bXXXsNtt93m0fePfoF33HEHxo0bh9Ph7bffRv369REaGoquXbti4cKFefrcN998Y+KqLr/88tM6b1nEcdXbvRuIjAQOHQKWLvV2rYQQQgghhCglwmnFihXo3bt3ttt79uyJJUuW5LsCEydOxAMPPIAnnngCS5cuRbt27dCrVy8cOHAgx89t27YNDz74IM4777x8n7Msw+QQLVva81deCbz3HlC/vrdrJYQQQgghRCkRTvv370dQUFCOg+MePI0RVV999VVjybrpppuMK+B7772H8PBwjB8/PtvPMAnFkCFD8NRTT6Fhw4b5PmdZx3HXq1EDuOMOoHJlb9dICCGEEEKIUiKcatWqhdWrV2e7feXKlajBnng+SEpKMlaqiy++OLNC/v5m+Z9//sn2c08//TSqVq2KW265JddzJCYmmrgs11LWcYTT/PnerokQQgghhBClTDhdcsklePzxx5GQkHDKtvj4eONqd9lll+Xr5IcOHTLWo2puAwlxeR8HGPLA33//jY8//hgffvhhns7x/PPPmxgsp9Rx8nD7ACcST+DT5Z9i05FNXsmst2gRsHEj8M47wLx5xVoFIYQQQgghSqdwGjVqFI4cOYKmTZvipZdewrRp00x58cUX0axZM7PtscceK9LKnjhxAtdff70RTZXz6F/26KOPmqx/Ttm5cyd8hZum3WTKx0s/LtbzNm0KVKwIUAOPHg3cfTfw6afFWgUhhBBCCCFKFIF53ZFWoPnz5+Ouu+4yYsSyLLOeWe2YzIGZ8dwtR7lB8RMQEGDip1zhcvXq1U/Zf/PmzSYpRN++fTPWceBdcyGBgVi/fj0aNWqU5TMhISGm+CJXt74ak9dOxpervsRzFz0Hf7/iGVbL39+2Ok2fnjme04wZAG+pn1+xVEEIIYQQQogSRb566vXq1cPPP/9sXOwWLFiAf//918xzXYMGDfJ98uDgYJxxxhn4448/sgghLp/l+JO50Lx5c6xatQrLly/PKP369UP37t3NvC+54eWFy5pehuiQaOw8vhNzt80t1nM7zct05KGhwK5dwNq1xVoFIYQQQgghSp/FyZUKFSqgc+fOhVIBpiIfOnQoOnXqhC5dupjxok6ePGmy7JEbbrjBJKZgrBLHeWrdunWWz5dnfm3glPUlgdDAUAxqNQgfLv0QE1ZOQPcG3Ys9QQSHzOrWzR4Il1YnJ1W5EEIIIYQQIpPi8Q3LgcGDB+Pll1/G6NGj0b59e2M5mjFjRobb344dO7B3716UVq5ve72ZfrfmO8QlxxXbeal7AwIAhnw5g+JSOAkhhBBCCCFOxc9ygpXKCExHzux6TBRRrlw5b1cHaVYaGr3RCNuObcPXA742cU/FRceOwLJlHEuLlj/GgwFHjgDh4cVWBSGEEEIIIUqENvC6xamsw4QQ17W5zszTXa84cdz1tm8H6tblwMLAihXFWgUhhBBCCCFKBBJOPsB1bW3h9OumX7E/NmuGweIQThxreNo04PDhzKQRQgghhBBCiEwknHyAZpWboXPNzki1UjHxv4nFdl5HJNFdr1kzwAc8F4UQQgghhPBJJJx8LElEcbrr1a8PcLis5GRgyZJiO60QQgghhBAlDgknH4FJIQL9A7F4z2KsO7SuWM7JwW4dd73584FPPwW6dgU++KBYTi+EEEIIIUSJQcLJR6gSUQW9G/c28xNWTCh2dz3GOe3ZY4/r9PPPxXZ6IYQQQgghSgQSTj7orvfFqi9MmvLiwNXi1KuXPf/HH0BSUrGcXgghhBBCiBKBhJMP0bdpX5QLKYcdMTvw1/a/im0sp+Bg4MABOzlElSpAbKwtpIQQQgghhBA2Ek4+RFhQGAa2HFisSSJCQ23xRBYsyLQ6/fprsZxeCCGEEEKIEoGEk4+6601aMwnxyfHF7q7X2w6zwowZxXJqIYQQQgghSgQSTj7GefXOQ93oujieeBw/bvix2IVTz552tr3ly4F9+4rl9EIIIYQQQvg8Ek4+hr+fP65rc12xuus5mfVWrbJd93r0AAYPtmOdhBBCCCGEEBJOPsn17Wx3vRmbZuDgyYNFfr6aNYF69YC0NDsdOeObvvkGaNy4yE8thBBCCCFEiUAWJx+keeXm6FSzE1LSUvDN6m+KfTwnIYQQQgghRFYknHw8SURxueu5xjkRywLWrAF27y6W0wshhBBCCOHTSDj5KFe3vhoBfgFYtGcR1h9aX2zCiRYnuuzdcQfQqhXw0UdFfmohhBBCCCF8HgknH6VqRFX0bty72KxObdsCYWHAsWPA+vWZrnvvvQccP17kpxdCCCGEEMKnkXAqAe56X676EmlWWpGeKygI6NIl013v2mvt5BBMSf7000V6aiGEEEIIIXweCScfpl+zfogKjsK2Y9swb8e8Yo1zCgkB3njDXn79dTveSQghhBBCiLKKhJMPExYUhqtaXlVs7nrumfX69AH69QNSUoD77rMTRgghhBBCCFEWkXAqIe563/73LRJSEopFOK1dCxw5Ys+PG2dbn/74A5g8uUhPL4QQQgghhM8i4eTjdKvfDXXK1UFMYgx+2vBTkZ6rcmWgaVN7/t9/7WnDhsAjjwCNGgHlyxfp6YUQQgghhPBZJJx8HH8/fwxpM8Rr7nqEwmn1auDii4v89EIIIYQQQvgkEk4lgOvb2e56P2/8GYfiDhXrQLgkNNQuQgghhBBClFUknEoALau0RMcaHZGSloKJqycWi3BasMBOCuEKl5lp77rrirQKQgghhBBC+BwSTiUsSURRu+u1bAmUKwecPGm757mybRvw4IPAl18CPxVtuJUQQgghhBA+hYRTCeGa1tcgwC8AC3YvwIbDG4rsPP7+wJlnnuquRzgg7gMP2PP33w8kFG2SPyGEEEIIIXwGCacSQrXIaujZqKeZ/2LlF8Ue5+QwahRQsyawZQvw8stFWg0hhBBCCCF8BgmnEuiuR+FkFeFotJ4y6zlERgKvvGLPjxkDbN+OYmf3buCKK4BHH7VdCoUQQgghhChqJJy8TXx8nnft37w/ooKjsPXYVszbOa/IqtS1K+DnZ1uV9u07dfvgwUC3bnbVR45EsUK92L8/MHUq8MILQJs29uC8QgghhBBCFCUSTt5kzhx7hNm//87T7uFB4RjQcoCZn7Ci6JJEREcDrVtnb3WiqHrzTSAgwBYwG4ou5MrjuV97DWjVCqhTB9i61R5f6tZbgWPHiq8eQgghhBCibCHh5E3ee8826Vx+ObB5c77c9b5d8y0SUhKK3F2P2fM8eQXS0kPxtHQp0LQpipVzzwVWrAD++w+4+2573ccfAy1aAIsWFW9dhBBCCCFE2UDCyZuMHw906gQcPgxceilw9GiuH7mg/gWoXa42jiUcMwPiFhV9+mRWkfFEhzyMu3vXXUDbtihyaEnq3RtYvjxzHa1dUVHAW28Bf/6ZKd6Y+U8IIYQQQojCRsLJm4SHAz/8YPucrV8PXHklkJSU40f8/fwxpM2QIh/TiXFE48YBwcHAtGlAu3bArFnZ70/rz969hV+P48eBXr2AX38Frr4aSE09dZ/zzrMtUL/9BlSoYK+jlYyfKcIcGkIIIYQQogwh4eRtatSw/eFoPmHM0x135Nrbv67tdWY6fcN0HI47XGSxRMOHA//+CzRrBuzZY8cSMZNdcnLWfd95xxZWHBy3MImNtS1fCxcCFSsC335rW5o8ERpquw86cJBeWqlYOHCvEEIIIYQQBUHCyRegv9vEifbos59+Cjz/fI67t67aGu2rt0dyWjK+/e/bIq1ahw7AkiXAbbfZeo6Z7M45J2tIFrPwpaUBX31lu80VBnFxwGWX2WNJlS8PzJyZP7fAmBggJMS2QjHRBeOxPFmrhBBCCCGEyAsSTt6GJp1HHgGqVgXeeMNe99hjtnklD0kiitJdzyEiAvjgA2DSJFvEMAFD+/bAhPRTn3EGcPvt9vw99wApKQU7H9Oc01Vw7lygXDlb/HTsmL9jMGnEypXA+efbYz3dd5/t0rdmTcHqJoQQQgghyiYSTt7m88+BF1+0k0RwZFmqEHL99baoyoZrWl9j4p3+2fUPNh3ZVCxVveqqTDFCN7obbgCuu8627jz3nO1Ot2oV8O67BTvPM88Av/9uD7b7yy9A586ndxwmjJg9264PPSGZWp0WNIpAIYQQQggh8oOEk7dhNr2BA+1EERyUiH5xhEkiLrwQWLvW48dqRNVAj4Y9zPwXK78otuoyjwWTRFDcMN6IsUQUIxs32uKJPP44cODA6Z/j//7PdtObPh04++yC1Zfej3feaSevYFOzWRmzJYQQQgghRH6QcPI27M3TLY9Kg75wgwbZIsrxWWOmPWdkV2Y5cEkc4bjrUThZxZg+joJp1Cjgr7+A+vVtvcexlQ4etEUULVBMIpEfGCPlQEvTjz/alq3CFHw8JhNNdOuWuZ4xWYynEkIIIYQQIicknHwFBhLRF45JIqhA3n/fzq29bp1tkWLvnoE+VCojRxo3vsub9UdEUAQ2H91sXPaKGw6Sy7GVrrnGTrwwerSt6+gW55rhLjcYEzVkiG3FKkqYKdDV7W/LFjvrHpNO0KVPCCGEEEKI7JBw8kVocWK2hT/+sAUVA34Y80Q/sx07gFdfNaoloklLDDhZ13xkworPvVLV6GjbXe+zz2xLEYUULVJ17WrlCgXXzTcD33xjCycOZ1Vc7NsHVKpkZwikVyRFFN0QNfaTcIXxfHkYm1oIIYQQpRwJJ1+Gfm9ff20H6nz/vR38w+m119oqZedOXP+NHQM1ccnnSExJ9Eo1aclhoohly+wcF/QsHDDAHpIqJzc4uudRHzI7H8UWjW3FGX/E+CnGPt11l93EHDD3ootsqxS9JwuaHVCUfPh88HlgEhTHnXTXLtvDltkeJbKFEEKIsoOEk6/Tt69tYSIMLGJPjSYeuvNNnYruZ12Dmif8cBTx+Hnjz/Z++/cDR44Ue1UbNwbmzQP+9z9bTDF7XeXKnsd24mUwZfj48bZo4RhQV1xR7FU26c45gO+mTXYq9bAwOz/HLbfYlgaRN2gMdY1TKw3QikrRRG9ZWlJ37rTXv/UW8PPPQK9etkvqxx8DCQnerq0QQgghihoJp5IAByGiyqDa4KtvDqQUGmoGOwr44isMuWh41jGdmEaueXNbYBXzK/HgYHuQ3BkzgMBAO79F9+7A669nVoXT4cOB996zBRYzsjMnhjdp0MAeJJeekE8+aYs/jlnl1JchZ4cPe7eOvgIti3362JkUHZjyvV49+1GdM6dkW+toJaX76I032s9vz572NfP6CK2k999vG31pkbr1Vts1lc8N31kIIYQQonTiZxVnOjYf4Pjx44iOjkZMTAzK0dxQUmBPtF8/e2CjatXs9HDpgUSr9q9C2/faIsg/CPtuXYeKF/fNHOm1Rw+7V9uoUbFXedo04PLLM5cpoLhMcUX3OEKL0003waehxYyZ+Bh6RkvUAw/YOTo8cuIE8OGHtimL/ou82FLC9u220fOL9Oz3gwfbsWmE1he6rjnQ0shBjJkUku6PISEoETi5WFavti2hTz1le8hy3h1mj/zoI3vcagpuwpi53buL93qPH7fdB+nmSpdXIYQQQhSRNrDKGDExMRSKZlriOH7cstq0odK1rNateTEZm9q9287Ck7DeXfSuZSUmWtazz1pWSIi9b2ioZT33nL2+mLn6arsKfn721Cn+/pZVp45l3XGHZX30kWWtXGlZycmWTzJ3rmV16JBZ94AAy7rmGstautRlp4QEy3r9dcuqUiVzx4YNLevLLy0rNdUqyRw5YlkPPmhZwcGZl3bttZa1ZUvmPvHxlvXjj5Z1002WVbFi1nvNJvHCo5dveJucr1f16pY1a1bePsfnduJEyzrzTMu6997M9WlplvXnn4V7+0+etKw//rCssWPt71aTJpnt3KKFfQ+EEEIIUTTaQMKppLF9u92rY0+pd+8MtfHyvJeNcOr0QSdr69GtVhp7bRs3WtZFF2X2rFq1stcVIzt3WlZEhH36vn0t67LLLKtq1awda6eEh1vWueda1gMPWNbXX1vW5s1259MXYD1+/92yevbMWucePSxr/37Lsv75J3Nl48aZ94ilbVvL2rrVKom8845lVaiQeSndu1vW4sU5f4aPJDv3d99tWTVr2vfclREjLOvTTy3r8GHL51i0yP5a7d17ep93FYjz5tlt1ry5Zb3/vmXFxeXvWGyfmTMta8mSzHXLlnn+7jii9oMPTq/eouC/D3yRUsLfkQghRJkkJh/CSa56JZHFi+3RYRmAQZ+3t9/G3th9qD2uNtIsO0K/SngVdK7VGV1qdkbn9bHo/OwnqBJYzvZBYorzYuTFF4FHHrHnGQNSpYodaM9QLXoccspLopebO3R9YoC+U7p0sT0VvQkTBYx9ycLyieuR1LC5ce8yLlKMLeNYW/Q9ZLYE+nDx4um3tnYtEBSEksaYMcBjjwGtWwMvvWSnbGdcWl5hwgim8uZ9dMZwZjwZYZvRfZPufHThrFGjkCvPh4yBahxYLBuYFIT3k0OoFTbMFsnQROe5Zhvw6zps2KnXyvi5pUvtxCRO4cDShI8TXVpJcrKdkIL344wz7MeNha6BdBtkghPHO3TqVODkSeDqq+XCV1TQLZOhpIzTZLwbx4O74AJ724YNdmwcXVX5EyCEEKLku+pJOJVU2Ctij5Mvmpl1b8QIvLPoHYxfNh4r9q9AStqp0fkNwmuhc4Nz0KVmF3SucQY6/ncEkZdekb+e8GmQmGh37hh2xc4d44Q8dbA5hpOrmGKHlvrDnTp1bBHFYH0+3yzsG+c0X6iahUFPjzyCtKXLsPjrTehyRa2M6+R4UBwQmIkFmDzAZDdkcBBTyxNeEHu3LBx518fgOFbshJ9zTmaihO++swcoLoz4GY6dxUQbzKq/cmXmej6CHFCZ8URM9X3aMBUi89ozpSMfJAamjRgBPPvsKbtOmmQ/i7wl//yTeYsKO/6IoofJUSgaCZ9FPiNcR11HYcXx0DxFm1JkMuaK+js/UGAxPwwHeW7a1I5N4zmZsEUUDIpR/vwy6yKH2HPuG783r71mvz8hTBbCGDk+2xS5TDLCwue8FIU+CiFEiUfCqZAax+d55RXgwQftv8z8S87kEQASUhKwYt8KLNy9EIv2LDLT9YdPHVnWPw1oFR+Jzu36oEuzi9ClVhe0rtoaQQFBRdKBpH7g2/K8QiGyalWmkOKUhpvTSWfCJIQ5iSv2rynemIODg/Jy6hRnufbhFbh29aPodOAXc8wE/zA82+JLzCl/hdlOUcBrJLwltDAwRTtF43nn2fNNf38H5R69296BaoQ9q4YN4W3YzswkyNwj7drZFo+iTjRAa8+UKbaI+vdfex2HLaOFhOzZY+vOVq3yoO35ap9iia//3U2X7ME+8YQ9n5yMxMOxePC5CiatOOG9YZKLmjVRZPD5YLIUvuOYP98WRMxK6LQxEzvwOWMH2yl8bipUyMPBmcqQg5BR+TEjR58+SAgtbzrxL7+cmQ2ySRNbQHEYOAmo04ODZbdvn3WognPPtfPAUOA6mTgJc/Iwc6jrCwJCgz8trUxjX7XqaVZECCFEoSHhVEiN4/NQQfD1JjuM7Pn/9Zfd2/LAsYRjWLJnSaaYWj8Lu62YU/YLDQxFh+od0LlmZ3Sq2QlVI6oiLCjMrGcJC8ycN8tBYQj0L77X2OwTL1liYdHiNBw65I8Tx/2MKON6Tp3iLNObsaA0xGY8jdEYgq/MchIC8Fr49Xgp8nocjkoGovYCkXuBqD2Z86khwNGGwNEG6VO7NIo7ijH+ozEobaI5Vop/EBZ3uB0bBo1ClTbVjTWNhRaIvFoWmN3NKRx82HXqaR0760zIyELRyGx47HfzcWKHmu5kTCnPR6qw4XhHtLzQDY3WEKfQrYmdUopXioYzz7TdoGgVYoefxlWO80VL4ykZ7vghqlLHt43zzBnO3izVCTNKpvvG7X9vCqKHXYtvrYH4ELfhvEfOxdPP+BWrkFiwwBaETOnu+pIgz5n46O/q6q/KIQooGB14MUwD2b8/Yi/sh7d/qoexYzMFFJvn7bdt64fIGT6XdMVNfydlviN8HvnI8fG6/vrcE5bu3Wtbpvg9YzlwwBZYHIrPee7o1smXO3Trq1hRd6WgMH7br4g9KYQQpQcJp0JqnBIBe870bZo5035lzl5Z7dp5+uie5X9i0bN3Y+HR1VhUC1hUJwDHglPzXYUAv4AsQio7kUVLVnJqsnEjTE5Ln+awnNM2wvTr5ULKITo0GtEh0adOQ6IRFRyNYCsaQanRCEiJhl9SNPwSo5EWF43Uk+WRGBuGEyf8jPtNQGAaEgMPIi5gL+L99+Kk/x7Ep21HnUUvYH94MvZGATsrheNAWDJSYNch3yRFGAEVfbQiLjy6Dd2PbkfDo0D1o6H44dh9eDZlDNIQYAQNhQ1FVPXqtgD0JIjoSleYMP6MHUGem66QjsBy5tnhy6k/wo4lLW+uoshVJFEMFQTGiow4fwmux+eoOeElBISHZFpfGShHwUTR4CF/OI2yhwYPw61J72aupD+bI7KcQCxfhIqTJqtPPrG/6/Rjdcy3jFukQKRJlvvQLOvA34IdOxB70s8M9EwBdegQMHeuHSYpToWilt6ejFuiJZTPPJ9pR9jyGeZP7en0yym4aN3ld8EZ8JvfGVogaa3mMflywHHr4zwFlcgbaw+uxT2/3IP1h9bj8ys+x4UNLlTTCSFyRcKpkBqnxLwZYy+aQSmMTmZPm4E2TgQ5p3zbnt05+FebzvojRyLt6BFsqggsengIFraphOX7l+N44nHj+hefHG+mZj4lHkmpHoKPSiC0llF8hQSE4GDcQY+xYdlRObwyakTWQI2oGqgZVdOeT19mO205uiWjbDy0FfvjdsNCLn6GJ2qcaqmKqWcLLlqxUlhCM+fTpxHhAaaDR0tVdlNnnjE9HKDYGYMpLMx2E/MUT+aOI+gcMcUOJN+cO8KI1qTcrHyM/aI4o4ciO4ycOoV6h/1/dlhZ6IHHukXhOK7B17gdH+AMLDXH+eDCbxByw2BjnaIVJTfXQoY5Pf64hVtaL8RrrT5A5I/fZCpPBp3QrEVh4tJTpYsdO9IUGxSq1C+eCq85u23ZFX71WGdaHVhc582yv4XmJ5eg5+5PcO6urxGVfDSjXl91fR1z292XZX+6gLFta8dvRNP1P6D+ih8Q16Qtdv/vTbM+KjwVNfp1wpZqZ6H5w/3h1/0CowbGjbOfDVpPCjt/Ca+R948/P7y3hW0EoBDhSw93S7PrfE7rOKUg4nNNqx+tQRRGPC5hnfkTygGP+cyynfhnw5kWhqWS52XyHFqjnOH3XKEL4LffZi7zHQENjtTELLVq2d/hskxiSiKe//t5U5y/Tfxtf+/S93BLRw9BtaLkwB8R9nH49oJ/cHyg3yZKHyVOOL399tsYO3Ys9u3bh3bt2uHNN99EF6ZP88D333+PMWPGYNOmTUhOTkaTJk0wcuRIXM+/+iVQOP24/ke8/M/LGHHmCPRt2hcB/qcZWMIeK6OO+ePiDn0/HBHlOnX1B2Pvd+RI4Kuv7J6r4/LH3jR7B25v8Jm9j3+sKKIyBJWLuHIElus2WopoJaLliX/UgvwDEZiUiqD4BASeTEBQbDyCTsYjMDAYQeddYO/DfV94CUFbtyPw2AkEHY1BYHqJLxeGmKb1EPPZB4hJjEFMQgxi5v2OmPgYxIT7IyYEiAlMQYxfImKSTmTukxhjBKGTgdAVP/ihShxQs1pj1GCJrIGakdVRo1wte54CKaoGqkdWR3BA/iK82Q7bj203QmrRpq2Yt3YL1u7dgn2JW2BFbkBKyOn7FdLqFxIYgtTEEFgugorFSg5BWnIIQgNDcP45IcYqGBEUgQV/R6BFowi0axGJiOAIWEkRSDgeidgjETh+KAJH90fg8N5IHNgdgT3bI3BkbySQHAGk5dxb5KNCYeUqiFwFEg07eepAWxYS/l6MY2M/QMVfv0Zw0kmzOhHBmIwBeBkPYhns55Sig9YyxpswhoeZzRgfxF8351yMS2FcE92h2Gk+tuM4Kv/2FZrO/RA19i7F5kpdcE/nBcaljSX+0EnsPV68GSgdGmALpqE/2mB1xrqdqI3PMBSf4kZsRmPALxWI3AdE7wTCDwKJ0cDJKkBcZSC+ImDxt4Q/73YDnIu/8BcyzUzHEYU5ob0xKaEfpuMSxIdWNIkkGG9F7UhjNgt/Apx515Lbek4pPF1xBBTvl1O4jlPX9c68+75cphBzRI9rrJE3oFh1hBQLBRjFp1P405ndsqdtFIGM/WOiHBoOeX0cw5xJUyh+2QaevLH5neI7M7oUMmzSYc4cu69JgVUUrreFkvnyjz9si6kTFOq8VcmHGvx317846+OzzPwlTS5BVHAUJv5nu0P/37n/h+cueq7ILkEUAH6ZHR9xwuBaZiPiiN58g8HCeecFF9/2ceR18uOPdopS97eDTuF+TsYfvvnil8p1P36h5M4pSqJwmjhxIm644Qa899576Nq1K1577TVMmjQJ69evR1UPkbNz5szB0aNH0bx5cwQHB+Onn34ywmn69Ono1atXiRNOPSf0xMwtM818g/INcF/X+3Bzh5uNFSTfMO8z8+HSdYf+IJzSSd95feoO/9K6iym+eW/ZMnMf/tWlkz5/YNz/4jNKmm5DDnQZ5A+dp54Be9KMxXJgIAujpt17VoS9N/7IOfA8K1Z4vgb28hiE4MAfSroxefLx4r5//20W+djHTvoSMXFHEFMxHIk7tqLa6x+j6pb9CEpzywFdxPAbuHWrhemzjmDGwi04am1Fv4qvY8va+dhSMwzLwsuD0WhpASmwApOAgEQgMJHfXniDIL9gBCECgWmR8EuJQGhAOKJCwhEdEY4KkWGoVC4ckSHhxlUzPCjcFIq1jPkc1lOMUtCypG7ZhLSLLkSqH5DmB6Q2aoDUgQOwr+ulWL69HFauSsOq1an4b20aUoxZI80WE5z6p6JcNMV9EsKjExAbl4hki+2WkNl+GfMJqBS4GwEBcTgQGG22BQbE4uzAWdgfWB57AyriZEgI/AMsBCVXRkhKVYSmVENYWlWEoyoi/aqhnH9VRAdWRYXgaogKiURYqJ8RH9kVWjkcEWASkCQkI3TPFhyr1swsJ8cn4Oz/q4V9wSewrG0XLG/eGlsrhuNw2m4cS9uJY2m7cAJ7kIZsXGstPwQmV0RAYmUgrgqsk5WB4xVR+0QsOp7cgfPi1qJJXIx5SVA5Dqhw0h8jk9/Fx7i9cB4S5174p9glc0NG/bLOw/NyrtuyKnD2iygS+NPOnz0n6YgnmJvltttsgUeRMnq0/fNB703eI4oz1/hBU45biDmehoQEXh9VeVpmoUg182Y4kMztzrzHqdv+7tPUYCAp0i6czwX+yWRyF/YJ+Yw5VmVCRwSKKr5cKFb4QDOAkX+TWJjAxFF/NKU5HWF3aEqjbynTQTp/3+j3m276o3eAa3ztqFmj0LZaWwxsOdAsPz33aTw590l81Pcjj1Yn/sbQOsUXWnwJmJiamDFvpqmJGet5Hv4+Ob9X7vMeEyrxgeGDxb9l/BvFKQuFAP+eMkuM87eWZl+aEvmj4PrWwJln1hzeWPLFF3ZKTmc7BaaT6YiFYyC0aGHvy/ZiP8B1u6P0Czu1o2Ne5lsT5+8/2bHD/rvrKoScKfsWP/1kj3PhBPvRbdoTrPfkycDFF9vL/PvsKUWvg2umIWYgGjAg63bWj2Kdb9yefjrTd5YvoSnKuJ6FfQdnqnSYpZYSJZwoljp37oy30lNcpaWloU6dOrj33nvxiDP4Ty507NgRl156KZ555pkSJ5x2Hd9l0oi/v+R9HIk/YtbxbdktHW7BvV3vRcMKBcy4Rp8g/ni7iilO+aPlCf5IM/qZQorpzJiWK7vXup062enuHOrXz0wr5w57I66xF65iiD+yNA84heYJWr4cmCGAb5y4jdYzTtlD4utZ+kg5f1AIswzyPLS8sTCQnj/mudXBgQKPP6IMuC/qtHI5wYAUZoLjmzI39kQ0xkOXb0B4RAqq107E8JOPIXHfLiRGR2Ld4XDEBIUjOToUKeVCkVI+HAktm8MvKBH+wYkIj4xHfEoiTiafRGxSLE4mncycTz6ZddltPtXKf/xbWYTxfEyqUi2impmeMh9pz7NztmvVPOycMw271i3Ezmg/7OrWATuP78SeE3vy5DZKSyOtoDzeiaQTOBR3KON3JL8EpoUgNbaqEVnVk46jqv8eWCEBSA32R0qAHxIsf5xM9kOKP3DCPxRp/pYRRX7+yYB/MgKD02D5pxgx58maW2RQQFl+8IdlLMb+/vyff9z8kJpiC6s0BCIN/madv+WHAKQiKMgPwUF+xiaXhjTT7+M0zZ/rrAwBb6WZrbm72RYxfqmB8KfLbhKtw5GwEsvBSiqXKaxyK8kRGHptGB558NQ4VL6wKLRkCuwUs9PqCCX6H9If0YEDwj30kD3PF3tMcMSXWvSJpcCiyY2ig9bl7ybiRJ8LjYfAiZ+n4sT/jcTxYODX1qH4smUK7jjRDCEVq+BE+TAcr14RJ4LSzPeAv1f8fh1NOGq+I44QoucDxRLnC9PdPAD+CEMQwsPKISw4/YXQ/iMI230AYclAWAoQzmn6PKehN9+GsBp17fvw2yyE/vCL2RbqbOfUWf7yW4R27GLHEL/1HsL+7wkEUnN7qgzNjIzzdP6OUEh5gsqaaU3Ts9RYfAn62rhM8eNe3ngzMzCSQYD33etmdnb5vaJiHzTIfH9SvvgMKbfeYn43Uv2ZEClrSR3zLFIGXGl+71I2rEXq998hpWoVpFSuiJQqlZBSqTxSK1ZAanCQ8ZRw4prLxachetchBJ046Tkb0s03Zw758cMP9rAUzj7uL2xdLVmeRJYD+4zsqzoeThTCTI9JYZU+ZqAVFYWUqAgkR4YhuVZ1JHM+NTkjbjsvU3bLKcrpCcJrdqaRwbZ3SHEm5PL0siHeLXTD3esoT+tSM7dNuGICokKyH2+xOCgxwikpKQnh4eH47rvvcDlHwExn6NChOHbsGKYx0DkHWPVZs2ahX79+mDp1KnrQp8GNxMREU1wbh8LMV4STQ1xyHL5Y+QVe+/c1rD1kd+6Z1W7RbS7CpDDhjwtFlLug4lu90/VV4R9evqrlGzBnyh9nvqWh2GEclumdpNmihm/LuB+3O+uzK562E+ePPaeeCvejuKLI4o8lhZGzjT+QFFb0+eHxmSOYVjNeC6+Lvi2epqcrqHgO4yN2zG5n1+JpHf3FGDjEbU4bEL5Ovuwy2y+IhW8gGZyR3StoikMn5zr/8DGBiGu7OVP++PPtoAPfAjoWOj8gKQCIDQJOBgMny4Xh5B+/GFHFH7+4Jx5F3H8rEB8ExKWX+MD0+WA/xN9yg3nG+eMZt+RfxB075HFfniMgMAj+fv6msNNjpv4Bnpc9rE9O9EfMsQDExPgjIiQY9WqFIjI0BJFhIYgMCUGIfzBCA4IR4hdkz/sHI8Q/CCEIRCinfkEI5bY9BxDyz0KE/rsYISdsN8pD4cCBCGD/LYNxoGoE9scfwoHNK3Dg4Hbsj7TbprDg9VAU1SlXB3Wi66B2VG17Wq62WccpXUbd3XvZATkSdxgHT+zDoeP77enJAzgYe8AIq4Nxh3Ao4QgOJhzGoYSjOJh0FIlppSNm0dehpKNAoTHJPzXNNkqZP8Ru0+Bg+AWHZHTyi6NeoXz++ez7ByOM3wtaTqMq2CIrIARhsYn2fBCTAIUjMDEZaYcPmdjYtJo1kFavri04Dx1E2vTpxkrM3w1O0wIDkBZdDmnRUbBq1UZa1coZwtQp/C05kXjCCJ/j8TFmmmzlPea0oNi/A8HmGun2HBLEaw03opLfKf52xZ+MQVz8ccSnJSHePzXTAOoF/OGHML9gBMDP9IXMP06DAo3EN8vpJu2MZec9Q4bR135JUJLh/WIiqAxBxamz7Gl9cBSi/UIRcjIB8Yf3I+7IfsTVroa4yBDzNypu3SrE/TULcQnHEZcYizj+fePfLefvVftWiK8YZe979ADiDu0z6xMDgWSKwWJ43xrsF4SIEFtERaQFImLvYUQm+yEiGYhIYrEQkZiGiIQ0RJzdDX5nnmVbUg/tQ+KMH5EYHGBKQpAfElkC/ZAQaCGxXCQSI0Nt62tSPBLjjiPRSkGilYyEtKQi+z7uHbnX/C3zJiVGOO3Zswe1atXC/PnzcRbjc9J5+OGHMXfuXCxgB88DvDB+joIoICAA77zzDm7m2wUPPPnkk3jK1enb5Ri+JJwc+Adk5uaZeG3BaxjSZgiua3udWc83btPWTcPg1oPzHVuTZ/gocPCc1ath0Y1u3Vr4HT6SGVnNNzXOPC1ZZRUKPQqonMQV38S5CyJHAHkLJ04tuzpQEDpZFrgv3TwoOLM7lpO9joKUz4sn0e0IM4oyZ2AsWg89uWieLq6ZBxz3FWee1+r4wxVF2/McdPlIz6xxMsgWVk6hmDrQvgn2Vw7FgaBkHIg7gP2JR7A/AghJBeocB+pY5VA7ogbqRNZE7ZQI1EkMQe2EYFSPDzCdU/O2nsfn1Cmuy868+xviPMI/ABR8B8NtYXgwwu4gBKXSCpVZAhiK4Bdg3nYGVq6KwKhoBEZEIXD/QQRu2YbApBQEuOyf8bm334Ff02awaIWaOBEWR8F28bozf4BCQ2Dxu/Pww7BoGWYHcMli8/bc4suXsFB7GhoKKzAAFjuDXbogrWIFWGmpsDZsgLV0CazUFHsbp6mp6SUFVvcLYNWoYaxH1vp1SPtzrr0tLRX8VhhLlbFGAX59+8K/dRv4BwbBb/16+E+cZK/n7abgMXtz3g/+t90G/4t6wC8gAP7LV8Dv6Wds4ZNmwZ+Jf9Is/pG1ve/4IoKWfL7IoWWFLwadF0Kcur4c4veFvyNpaUhOTsTJfTsQG2QhNjDNngYjs9SsjNjqFe31qfGIPbAr63aXkhAWhPgQfyT4pSIeKV7t+OeHMCvQWHVOIsnUme3ZJDEC7Y+FoXxcGsrFJiOqdkNEhZVHlBWEiG27EbZyLUJS7O8ZrTYvngP80Ny25ryZ0gP92gxESGgEQv/+F0GvvenZeuMMyMUXVfyeMZESPRLSn1t2luMjghFfqxria1RB3AVnI75KBcQnxSEu6aQtttjp5pQWL86nJSKBHVOKL86nJSMeyUiwUuwp741fKhL8UxHvn4YE/zTEB1hIYCn8IRa9QoDzO8LiF+CybE/Nsss+9osxP5xMjsNxxisnHTdtWlLg8xpk+SMoJc38rjIcgNNgl/mgqGgERZZDkF8g/BISEHdwr/2SMv1lJb+/tNj5Cn4U7kFhGVZrTxmVs8uy7L792jbXGmuaNyn1wonufFu2bEFsbCz++OMP46JHi9MFjAgvoRan3Hj939cx/NfhRpUP6zQMd3a6E1UiqhTKsWkeppWLg+au2J9e9q0w1q565euZfdYcXGNSvHKQ3FrlatkdNSc1laugymmeHQb3jq2nkt02944xi9PhcC0kL+tc17Nj7Vim2LF3nTrzhfVVYQfQ1TXRcT3Mbh2tRawb29u9OFHyuRWRO65ZCJw0d66F8D7wWchLCkJfxDUbgafixCXy++Cajq4wha7wCfhrlhwAJLDzH2hPzXxQ9utc1/OzFMpGTLoKS2fZZT67fVz349vyqESgXCIQlWTPc7q+EjDsUmBp+gDV3bYB7/8INEsflyyvMFnQoIHAb8yrYgEvzQRGzs/G3c1HccSacx94X2jRI9laL9P/bOV1W6ELBr48cXmR4jwHBYVufsdD7BLjTEPzvsx2pPukp0Jxnd02j/tToKe4iCC3KV86FRQegh4ZrmLKVVSZ+bAAu4Q6U3+cDA00L5tC0/ztFwkJyRkvE0IyimUvh0UiJCIaIZY/QuOTEbJ5O0KSLYQkpSI0Kc1MQ+o0QNhDjyLwxltK1Vhp+RFO3nGUTKdy5crGYrSf7lIucLk6B6/JBn9/fzTmm3ETptIea9euxfPPP+9ROIXQNSfPI0v6LlTjtaJqYfeJ3Rg9ZzSe++s5Y426v+v9aFMtfTyXfKY///a/bzHmrzFGFDljI7lCAeUIp69WfWXOSVgPCiindGrZ6fSSWZQkKJpoZXMXU54EFqe0SmUnhoo7d7CTs9kRUbwOV5dHWmQKskxR4XS6nY53TtOctlEUu759d30Ln991LI5FKDdRlN9c2Y7lzP0ZcJ3mtM2x5PG3yXFn9TSf12WnZCeEWJwUdqf77HsaYTqvxUlvmJtrbW7bnW2u96ygL2CcFydOcSyUOa3Lbh8+c/x+Z1doVcvPNlerqaeS0zb37SbrSGbqQ7+kJAQnJyM4KQnl+CLANR2i+7yzHO8yz2O6fqcLUlzvp9v9nhG+EEvDF6JCWgheTjgfN1VoBb+h/jk/H7ynrnVPSkJ0UhKmxyfg/gPL8E7VbXioJ7C+ZVW8s6I2ghJTsu7P5911mXV0/Z4530H3dXndx/X7m25JPWWdh338QkIQml7Kcx3rxWv19LfodJfdnxv3Zyyv24rqnXz6b3hgQAAqBgaiIuN90gKA5PRpUrqq9DTWg/s6T3+HIrL5G5XTOufFWl5/M3L7PeH3iy+nXcavoBUqhCUxERW5LjZ9WxYYh1yQWGR6jezMeZe1W4BNW8t0RkKvCidmxTvjjDOM1ciJcaI1icv33HNPno/Dz7halUojzAp0Q7sb8N2a7zDu33FYtGcRPl72sSk9G/XE9GunZwkYTE1LxcYjGzOsSMv3LTfTzy//HBc1vMjsQ79triMUPu2qtbNLdXvqKsiqhFcxGYtWH1htxNuUdVNMcUy2m+7blJHIgnEUPF6RuRR6A/5IOJ0ZXx4o1RPskDjxUKJw4B9MJ0NVacf12ecAQkIUMYx1coLFH0lJROysUXjw7AdNYpWCwL+Qb1kWmi18EyN+HYGPah/AlvNa47uB36FCWAWU2O8n415ZfAlXr47COl5RDAZXknFeEOQ2aCDFWG7eN3ksVloa/JzB18soXhVO5IEHHjDJIDp16mTGbmI68pMnT+ImpoMGM1PeYNz5aFEinHLfRo0aGbH0888/Y8KECXiXfsilHKY8vabNNbi69dX4Z9c/RkB9v/Z7I1Ic0fTX9r8w8reRRuDQn9odCiVHOHFU9amDpxqhVC+6Xo5m1/vPvN8UZitauncpFuxegIW7F5pyLOEY6pevn7Hvvb/ciylrp6BDjQ7oUrMLutbuaixTjSo0KlWmXVEwmFSCQdhMgkBmbZ1lBD6fk6aVmhohzu1CiLLB3hN7cf+M+81LP7qK8+8afwPG9hxbaOfg3yAO+8HfmasnX42dMTuLNwtkWcHVCljK2R+73wh9ZsIrVti+jkXSdVzOQoReSvy7PH3jdFMW71mMR2o/gmdgj5HFpBP8vvJvdql6We7Lwmnw4ME4ePAgRo8ebQbApevdjBkzUC39zeaOHTuMa54DRdWwYcOwa9cuhIWFmfGcvvjiC3OcsgJ/+M+uc7YpHFDV1c2Of2RojSL8EtNK5GpJ4rID46X6N++fr3Mzi8t59c4zxYGDyjqdX7Lh8AaTlYWDErJgob2eAq9D9Q6Yc+OcLPtWCK2AyuGVS6yo4h9d3gf+aJj4r/R17y9+32RGZLuXlR8UJ2TSuZcLdi3A3zv+xt7YvXY5kTnlQMQb7tmAJpWamH1nb52NZ/96NuNYfKYoyPmD3LRiUzxw1gMZrqNCiNIDfy8/WvoRHp75sPldYDIA/m5cUP9U9/vC4tKml2LezfNMcHql8BLmRSB8gnWH1qH/N/1NP4Yiv3319jinzjmmb8ap0x8oaTBF+K+bfsVPG37Cz5t+NsNjuFLL5br4Mr7rR13N9Ter1Aytq7bOKG2qtkGDCg2y9A9LA14fx6m48bVxnIriLT4fdnbW+UbNPVVxccBHatORTRkWqYV7FmLZ3mVGTFHALb8zc4Datu+2xaoDq0ya0MYVG5tOdOMK9rR55ebGUuVLMLvhqv2rsHL/SrscWGmWmTr30XMfxZiLxpj91h5ci5bv2IMbhgSEGOtb11pd7VK7qxnsuCQIRd5LdmQYY+dYNfnGaf7O+TgcdxiH4w9jX+y+LKJoxZ0rjNgho2ePxjN/Zj++2twb5+L8evbYIPyhHr98vHl2+IeI6YldcRVZL817CZ+t+Mycp0nFJra4Si8cM6kktK0QZRm+qeb4hbT4/LXjL+PFQM6ocQY+7Puh+c0sbj5c8qF5+Ui3eCFc/w4yFnzGphnGpfPmDnYWZ6Ykr/hixWyHDBhx5gi82utVM++k3ffWGEy5wWtxLGa0oFV/JTPPAMeQurjhxbi0yaU4p+45ZpiM8qHlzbYf1/+I66ZcZ/pGnhjXaxyGnzk847i0XlFU8Ri+9He6xCSHEIUP0zsObGWPnO4t+GVgB5dlSNshZh3HI2FnmF9O1x8jZxBCds6X7F1iikOrKq2wetjqjOVHfn8EQf5BRmCxcNBPmsc5YDC/8IX5JWSM2Oajm80PHQUc2RGzA/Ve82zxoEWJfvkOvK7ejXsb4cgBSTOsb+k8fcHTeLzb4xlvd8wPcFhFFCWsE+PPWCh6zqx9pnleCFPdT1s/zQghRxBxyrpz4Ns1w9agRRV7NHoK86fmnpri34FCyhFOnWt2NqlGa0TWMIVWzhpR9jynFMwOvRr3MsV5NngcugDwuWFxdQelKyr/kLG4w+eB4o1vusjUdVONuHUdUNB1nuLccQnkiwc+RxS7hf080TLMqRnoMS3FiFFvuyKy87rt2DYz6C7/MDo8MfsJ/L719yzjY7nOT7xqYkb6WFpWZ2+b7XlcLb8AvNjjxYw/stM3TDffA7od87vMKTsSzvygVoMy9uW9pYh2tjlT3l8+C94eMNFTW9JtmdlOfeEN69H4oyZbKl/isC35vD/d3U6lTd5d9K753eIYN2xz18LvJds6v/D5pis3B3bnM+U6ZeFLks8u/8zsy/v+wK8P/H979wJtY53/cfx7RJJQyC1JLhHFf3WRk0o5xm2SWxdlGpcZjdC/y1JWSjQ0TM00Q9fpglVxEgvJn8TJMInoIhJncpmhJNS4RNTK/q/Pj98zzz6dY6fOOc8+e79faz1r7332Pvs85/k9v9/z+/5uT3BjbeXFh9o8ZINbDI6kwU+Ne7f+361uf1Te6FglQzqmMpWDyjNanlrnp2j0xrwN89z5qxsY63096vqqyrauJ+Gyqqjo7y/ctNAFS/M3zndzu0WjdnzgpOvIgpsXuJ+pDrN0y1LXqLh061LXE6M6jKeAofWk1q7x1I8Y0jXYl3fFTaspaz9VJs/5ZI47tjm/znHvaS7hdU2uc9drBUut67Z2PbP56dyos+0eutvlb12X3bbzyKPKHQVJXs7mHOs140idUP+33pvSfYq7P2FJQuCEYqELdDgDiSqm6wevd5XVTf/Z5CpJqij7R/UkeKpIP77icdv/Xf73FVK3+Fv9jtysVTpO7ugCElXuVNFyW9kK7rXmzoRbFBXcqHdj7Y61rhdJBZ6fI6bC45XrXnGf0w1H9T2qaKigbFat2ZGhkDWau30NVzT0s3m95sX1vqlFVZsKUHXpezmbcuzq7Kvdd6g3yvdMJRrip+O288BO27l/Z/DY/dzuwQXomfeesUmrJgXvqWAPWztwrTU5/UivmPZp4qqJBf4tBVCe9l3HRcMrq5Sr4gpZHwipoNVxCheq2o6Xzg0XYFWoGfRIhalnT/c584GVf1QQoHNEFwFP8wBfXP1igX/riyFfWLXS1dzzuxfcbU+sfMJVmPwd212QVaa8u3DM7TXX/d8yavEom/ThpCAQCgdFbuGVAR9a/cr13Wfve/M+++PSP/7gb+v4adiD7pzuh9GqoqugXatX6j39vcKowGn+o85DnY8b/rPBPaoxQI0DWuDlm/uOzDmT9V+udxWAgoTnhOg7p66dWuBnR145MqgcqBLy+MrHC/ys5l36z77w4Qv5HjNPw6xU+ZCVn610PdfK29p07Aq78q30VeVg8+7Ntvk/m13jghYs8Nq+2NYNL1OeVY+y3xdten31OVf/pGDkeCpCWvBAlRUFTGp4CNPfD9Nc2PzmwcpldS6zf/T9R/Baw5F0TrvAquyR4EoNAQqKGldpbCOuHBF8NvP5zCAYyitcNih9VAFV3qpTqY5d2+TaSCtQKm+Hthpqf3jrD24FWZUnCvJ841IyUeOXbg+iIVRKh7z3xVFjn89HCuj1Gf385/Z2hFflVSCj8iO4sbkedbNY3Rj2uwOusu0b0NRYolECCoB8QKRHjdSQSV0mWe//6e2er9251gWwBdHwdx84Kd//etav3f+r89A9Vm1sjao2+lkBSbep3Wx27uy4ck7HT0NHO9TvEHcc/LQF1QvqnF/HzUOXvCMmlm1d5n6m4EGbqNxtWq2pXVr7UhvUYlDcVIqioHqAglLNVdIIj3CdQAGr0s+f777e82NkZGS4vKutY8OOcWVm3hssn1v1XJe3FBAr0CzqBuOiQOCEyCmjqvDQVhBdtFUBc5W+o5sq8yqIlDHz3jxNGdIXynll1s6MC5y6T+0etCbF7Vfp+AumKq+f3fXZcbV059f7pgtZuDcj98tc96jKv7aXVr8UN8RvXIdxwZDFP739JxdA6sKZXxB5Ya0Lg2BIFSctIpL3f1BBpcq4KlqeLkSq8Gmsvyrz4Ud9Ptza1LVxV7dFSRUwbe3tSA+Vp2Oryly4J0eVcaWljpc2XdS1yIl/roqb54+pLpg6f/KeQwrGPZ1/CviPdc56BVVYXM/eN1/GHV8FIeEePfW0KID0gdSYrDGux1U0PFL7qDRSpT6cPxR8zf/V/CD4fu6D51wwkpfyjr5P++EDziGZQ6xn057uOKgSrAugf67HcN5QC7Aqne790Of883B+UWVKVJHT+adHHSf/OnxbA6Wvznv9PPwZXXC1r1rQxtNqow+//XDwWv+zeqVc4HJqPRt2+bC4+Yf5BaKqDClfhe+Pp8qeWpsVKOm8CqepvkO3g/DBkP6eAif17CpP+3zt01+BabhH759f/dPtWzjA0v+cX8CnfdaQNgVELjBSL9Kuj90xmNJjypHz5IQy7hYTaigJH0OVB6qstK/fPu771MiiY6nKkx79pjI1b6VTN2U/VpDlAyftu8ogVQhViapdofaRx6P5NdxrLM90fsaShdLzoayHXGW//2v9bdrH0+zfe/5tr/Z81TUIFTWdfzqvP9v7mbse6VGBkXu+7zP7c7s/B6MfJn4w0e5ZeE+B3zXnxjluDpe/lUi/2f2C8zDvDUjHdxgf9PSrMj3mrTFB8BMOirRl98h2Aa7M/WSuW1yjIBOumRAETior/Sq8+QmXszqndX3R3Gedh3rU8DiVUwrUwg1pyguaZ6QtL6XZYx0fC/ZXc7EVsClI9/lfx1f/sxqJ/tb5b8HPVQ4pjyjvKFDS8bm8zuXHFUTnrZPo3pvad/2ttz9929VRVEb7XpobzvvvPP3sNdk2Y/0MV/arLNPmnyufq9zx5ZmCR9+LH/6c/6wal3xw0ufVPi7dPNUDOjXs5HqVtDpzYTcSnJCnLNO1Qpuu0yofN361MWjoLUmY44QSTYWbCnTfGuqpcNCYW1UC1DLmKsGH9rnXungPvWxo8FlNbFTrlwpJ15N0dCvOOWIaFud7pfyj7+UJ9wypl0P38fJUSKqip+Xi9aiLoB9Spx40XVDC76sAZfhJwXQeKahywZUCrdBzBU1ZZ2cFFxdVphWc+rvbuzvcH73zvTZVFH3Qou9QpT/8OZ2fvpJ0Vd2rgmBPgbF6//Tejv07ftBilzs4N6iQqAL++yX/HX6VV/izEz6YYAs2LXBzCP1wVz/kNZnGmv8YqgSpcuP3W3NTpq+b7gJZ9TqGAxzZeufWoLfj3oX3uiDS9wSpbFBa6vcU9By8/2AQ6P5qxq9s8prJwfcoPVWxUxmi39Vqbz7Y0/mh31MAq/3Qd+px0+5NrqIw44YZwfe0fK5lMKcnTL+vcufjQR8H+bTtC21dA0h4mLOnffnXHf8KXj/97tOuUqwyQJXsn3J/PR077a+v0KhCr0qvjrkPrlQB1TBMBUX6O6p8pZIl/17ieh1UBqvBYlHvRcH8Sg37VXoofRQg6hwMP/9FvV+4hg5RA4bmhIbfVzngAyINS/S38VCjmHq8C/Laja8FvYYKkDV0XZVnNbDp3FOAox4DPX+h2wsuoPVDMgfOHVjg9yowvKbRNUGF/aYZNxX4Wd3O5ObmN7vn8z6Z53p7dL6pV16bykb3WLqc+99UGRflCc2FUQDkAyEfFOnxp/bE6rqpoaE+eFJPuR79Ygav93o9CAoVQGqYmAJGlYmqOyhg8T743QfBSBBV6JXXi7oHVHN+/NA+NQz7YEtl1NilYwv8vVW/W+Uaq2T0ktE2fNGRYf+JeuafXPmkPfv+s3Z1w6tdYK2h9FEMi01GxzPHicAJSEKqrKg1SkGUlp/3lShV7tTL4IOhcOURqUeVLAVn4Rbo317w2yB4uy/nPhv3zjgX2Kn1MAiIjgZHqtCW2PvT/MwhdS5oObqNajMqyEPXT7ve9SbkR5VbBSJqlfYrPaqnSUGS5sypR64wGh40ZFGt5do35XMf8ClwU0V64/9uDD576fOXuoq6GklU4fM9SAqOfGMPCp+Cnl9O+aUbVvR2v7ct88xM9/NHlz3qhjkWRHNe/FAyBbLHGnY2u+fsYCizr9irXFdApHPN9TIf7WlWEOLPy+OhAEHBlAuujgZW4SBL55BfVVA9Ouq98IGQD4L8c32uoLkuyUSNUhrKqCF7vvFg/DvjXWDq51X7/K6hf5qPrHL1pxzfoqA0eG/be66XXfur64B7PPpaQ4R9L6iGoiuQDr8f/p2JXSYGo3nCQwwRj8ApjVfVA5B+1EMQ9UITJYUqVT6gUrDi5z0qOFILc1S3DlDAp8BYvRy+NdkvWqBKq+bLJeuKXKlKIxG0KqiGWfmeWy2kox5h9QQrKFFlNPx8bNuxQc+Fell0v0X33tHPKA01J1QBkXpvfOCrPCzk46Kj3lTleQVVWglPQ+f8nFWkt730OBXOwQEAAACQuo4nNmCtTQAAAABIgMAJAAAAABIgcAIAAACABAicAAAAACABAicAAAAASIDACQAAAAASIHACAAAAgAQInAAAAAAgAQInAAAAAEiAwAkAAAAAEiBwAgAAAIAESluaicVi7nHv3r1R7woAAACACPmYwMcIx5J2gdO+ffvc45lnnhn1rgAAAABIkhihUqVKx/xMRuzHhFcp5PDhw7Zt2zarUKGCZWRkJEWUqyBu69atVrFixah3Jy2RBsmBdEgOpENyIB2SA+mQHEiH6KVyGsRiMRc01apVy0qVOvYsprTrcdIBqV27tiUbnYSpdiKWNKRBciAdkgPpkBxIh+RAOiQH0iF6FVO0vpqop8ljcQgAAAAASIDACQAAAAASIHCKWNmyZW3EiBHuEaRBOiMvJAfSITmQDsmBdEgOpEP0SIM0XRwCAAAAAI4XPU4AAAAAkACBEwAAAAAkQOAEAAAAAAkQOAEAAABAAgROEXriiSesbt26dtJJJ9kll1xiK1asiHJ30s7IkSMtIyMjbmvcuHHUu5XylixZYp07d3Z36NYxnzVrVtz7Wq/mgQcesJo1a1q5cuWsbdu29sknn0S2v+maDn369PlB/ujQoUNk+5uKxowZYxdffLFVqFDBqlWrZl27drXc3Ny4zxw8eNAGDRpkVapUsVNOOcV69OhhX3zxRWT7nK7pcOWVV/4gPwwYMCCyfU5FTz31lDVr1iy4wWpmZqbNmzcveJ+8kBzpcGWa5wUCp4hMnTrV7rrrLrcU+fvvv2/Nmze39u3b244dO6LapbTUtGlT+/zzz4PtrbfeinqXUt7+/fvd+a6Gg/w8/PDDNn78eHv66aftnXfesfLly7u8oYsmii8dRIFSOH9kZ2eTBIVo8eLFLihavny5LViwwL777jtr166dSxvvzjvvtNdee82mTZvmPr9t2zbr3r076VDM6SD9+/ePyw8qq1B4ateubWPHjrX33nvP3n33XWvTpo116dLF1q5d694nLyRHOli65wUtR47i16JFi9igQYOC199//32sVq1asTFjxpAcxWTEiBGx5s2bc7wjpCJo5syZwevDhw/HatSoEXvkkUeCn+3evTtWtmzZWHZ2dkR7mX7pIL1794516dIlsn1KRzt27HBpsXjx4uDcL1OmTGzatGnBZ9atW+c+s2zZsgj3NL3SQVq3bh27/fbbI92vdHTaaafFnnvuOfJCkqSDpHteoMcpAt9++62L5DUEyStVqpR7vWzZsih2KW1pCJiGKtWrV8969eplW7ZsiXqX0trmzZtt+/btcXmjUqVKbigreaP4/f3vf3dDlxo1amS33nqrffnllxHsRfrYs2ePe6xcubJ71HVCvR/h/KDhxHXq1CE/FGM6eJMnT7aqVavaeeedZ/fee68dOHCgKHcjrX3//ff28ssvu14/DRUjLyRHOnjpnBdKR70D6WjXrl3uZKxevXrcz/V6/fr1ke1XulFlfNKkSa5SqK7mBx980C6//HL76KOP3Fh3FD8FTZJf3vDvoXhomJ6GhJ199tm2ceNGGzZsmHXs2NFV2E844QSSoZAdPnzY7rjjDmvVqpWrjIjO+RNPPNFOPfXUuM+SH4o3HeSmm26ys846yzW0rV692oYOHermQc2YMaMI9yb9rFmzxlXQNTRbc/pmzpxpTZo0sVWrVpEXkiAdJN3zAoET0pYqgZ4mQiqQUmHwyiuv2G9+85tI9w2IWs+ePYPn559/vssj9evXd71QWVlZke5bKtIcGzXaMM8yOdPhlltuicsPWrxG+UCNCsoXKBxqyFSQpF6/6dOnW+/evd0cNCRHOjRp0iTt8wJD9SKg7k212OZdGUmva9SoEcUuwcy16p5zzjm2YcMGjkdE/PlP3kg+Gs6qsov8UfgGDx5sc+bMsUWLFrmJ2eH8oKHdu3fvjvs814riTYf8qKFNyA+FSz2sDRo0sAsvvNCtdqgFbMaNG0deSJJ0yE+65QUCpwjohNTJmJOTEzc8QK/DY0hRvL7++mvXeqiWRERDw8JUWQznjb1797rV9cgb0fr000/dHCfyR+HRuhyqrGsYzJtvvunO/zBdJ8qUKROXHzQkRnMxyQ/Flw75UWu8kB+KlupGhw4dIi8kSTrkJ93yAkP1IqKlyNX1edFFF1mLFi3sr3/9q5t817dv36h2Ke0MGTLE3cdGw/O0xK+WhldP4I033hj1rqV8gBpumdKCECp4NRFbk941v2D06NHWsGFDV4EZPny4G0ute6ugeNJBm+b86Z5BCmTVoHDPPfe4FkgtDY/CGxY2ZcoUe/XVV928Sj+PTwui6B5metSwYV0vlCa6p8ptt93mgqaWLVuSDMWUDjr/9X6nTp3c/bQ0r0NLY19xxRVuCCsKhxYZ0BB6XQf27dvnjrmGBs+fP5+8kCTpsJG8wHLkUXrsscdiderUiZ144oluefLly5dHuj/p5oYbbojVrFnTHf8zzjjDvd6wYUPUu5XyFi1a5Jb6zbtp+Wu/JPnw4cNj1atXd8uQZ2VlxXJzc6Pe7bRKhwMHDsTatWsXO/30091y2GeddVasf//+se3bt0e92yklv+OvbeLEicFnvvnmm9jAgQPdcsAnn3xyrFu3brHPP/880v1Ot3TYsmVL7IorrohVrlzZlUkNGjSI3X333bE9e/ZEvesppV+/fq6s0TVZZY/K/jfeeCN4n7wQfTpsIS/EMnQgijOSBQAAAICShjlOAAAAAJAAgRMAAAAAJEDgBAAAAAAJEDgBAAAAQAIETgAAAACQAIETAAAAACRA4AQAAAAACRA4AQAAAEACBE4AAByHjIwMmzVrFscMANIMgRMAoMTo06ePC1zybh06dIh61wAAKa501DsAAMDxUJA0ceLEuJ+VLVuWgwgAKFL0OAEAShQFSTVq1IjbTjvtNPeeep+eeuop69ixo5UrV87q1atn06dPj/v9NWvWWJs2bdz7VapUsVtuucW+/vrruM9MmDDBmjZt6v5WzZo1bfDgwXHv79q1y7p162Ynn3yyNWzY0GbPnl0M/zkAIEoETgCAlDJ8+HDr0aOHffjhh9arVy/r2bOnrVu3zr23f/9+a9++vQu0Vq5cadOmTbOFCxfGBUYKvAYNGuQCKgVZCooaNGgQ9zcefPBBu/7662316tXWqVMn93e++uqrYv9fAQDFJyMWi8WK8e8BAPCz5ji99NJLdtJJJ8X9fNiwYW5Tj9OAAQNc8OO1bNnSLrjgAnvyySft2WeftaFDh9rWrVutfPny7v25c+da586dbdu2bVa9enU744wzrG/fvjZ69Oh890F/4/7777dRo0YFwdgpp5xi8+bNY64VAKQw5jgBAEqUq666Ki4wksqVKwfPMzMz497T61WrVrnn6nlq3rx5EDRJq1at7PDhw5abm+uCIgVQWVlZx9yHZs2aBc/1XRUrVrQdO3b87P8NAJC8CJwAACWKApW8Q+cKi+Y9/RhlypSJe62AS8EXACB1MccJAJBSli9f/oPX5557rnuuR8190vA6b+nSpVaqVClr1KiRVahQwerWrWs5OTnFvt8AgORGjxMAoEQ5dOiQbd++Pe5npUuXtqpVq7rnWvDhoosusssuu8wmT55sK1assOeff969p0UcRowYYb1797aRI0fazp077bbbbrObb77ZzW8S/VzzpKpVq+ZW59u3b58LrvQ5AED6InACAJQor7/+ulsiPEy9RevXrw9WvHv55Zdt4MCB7nPZ2dnWpEkT956WD58/f77dfvvtdvHFF7vXWoHv0UcfDb5LQdXBgwftL3/5iw0ZMsQFZNdee20x/5cAgGTDqnoAgJShuUYzZ860rl27Rr0rAIAUwxwnAAAAAEiAwAkAAAAAEmCOEwAgZXBPdwBAUaHHCQAAAAASIHACAAAAgAQInAAAAAAgAQInAAAAAEiAwAkAAAAAEiBwAgAAAIAECJwAAAAAIAECJwAAAACwY/t/F6dBuk51aOQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 7 input channels, 100 hidden units, 3 output choices (Fixate, Choice 1, Choice 2)\n",
+    "input_size = 7 \n",
+    "hidden_size = 100\n",
+    "output_size = 3\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(f\"Using compute device: {device.type.upper()}\")\n",
+    "if device.type == \"cuda\":\n",
+    "    print(f\"   GPU Name: {torch.cuda.get_device_name(0)}\")\n",
+    "\n",
+    "WEIGHTS_DIR = \"../src/models/weights/mante_medium\"\n",
+    "os.makedirs(WEIGHTS_DIR, exist_ok=True)\n",
+    "\n",
+    "print(\"--- Training Elman RNN on Perceptual Task ---\")\n",
+    "perceptual_model = WrappedElman(input_size, hidden_size, output_size).to(device)\n",
+    "per_history, perceptual_model = train_rnn(perceptual_model, per_train_loader, per_val_loader, device=device, num_epochs=50, patience=10)\n",
+    "perceptual_model.to('cpu')\n",
+    "\n",
+    "# Save the best perceptual weights to disk\n",
+    "perceptual_path = os.path.join(WEIGHTS_DIR, f\"elman_perceptual_best_h{hidden_size}.pth\")\n",
+    "torch.save(perceptual_model.state_dict(), perceptual_path)\n",
+    "print(f\"Saved Perceptual Model to {perceptual_path}\\n\")\n",
+    "\n",
+    "print(\"\\n--- Training Elman RNN on Context Task ---\")\n",
+    "context_model = WrappedElman(input_size, hidden_size, output_size).to(device)\n",
+    "ctx_history, context_model = train_rnn(context_model, ctx_train_loader, ctx_val_loader, device=device, num_epochs=50, patience=10)\n",
+    "context_model.to('cpu')\n",
+    "\n",
+    "# Save the best context weights to disk\n",
+    "context_path = os.path.join(WEIGHTS_DIR, f\"elman_context_best_h{hidden_size}.pth\")\n",
+    "torch.save(context_model.state_dict(), context_path)\n",
+    "print(f\"Saved Context Model to {context_path}\\n\")\n",
+    "\n",
+    "print(\"\\n--- Training CTRNN on Perceptual Task ---\")\n",
+    "perceptual_model_2 = WrappedCTRNN(input_size, hidden_size, output_size).to(device)\n",
+    "per_history_2, perceptual_model_2 = train_rnn(perceptual_model_2, per_train_loader, per_val_loader, device=device, num_epochs=50, patience=10)\n",
+    "perceptual_model_2.to('cpu')\n",
+    "\n",
+    "# Save the best perceptual weights to disk\n",
+    "perceptual_path = os.path.join(WEIGHTS_DIR, f\"ctrnn_perceptual_best_h{hidden_size}.pth\")\n",
+    "torch.save(perceptual_model_2.state_dict(), perceptual_path)\n",
+    "print(f\"Saved Perceptual Model to {perceptual_path}\\n\")\n",
+    "\n",
+    "print(\"\\n--- Training CTRNN on Context Task ---\")\n",
+    "context_model_2 = WrappedCTRNN(input_size, hidden_size, output_size).to(device)\n",
+    "ctx_history_2, context_model_2 = train_rnn(context_model_2, ctx_train_loader, ctx_val_loader, device=device, num_epochs=50, patience=10)\n",
+    "context_model_2.to('cpu')\n",
+    "\n",
+    "# Save the best context weights to disk\n",
+    "context_path = os.path.join(WEIGHTS_DIR, f\"ctrnn_context_best_h{hidden_size}.pth\")\n",
+    "torch.save(context_model_2.state_dict(), context_path)\n",
+    "print(f\"Saved Context Model to {context_path}\\n\")\n",
+    "\n",
+    "# Plot the training curves\n",
+    "plt.figure(figsize=(10, 4))\n",
+    "plt.plot(per_history['train_loss'], label='Perceptual Train Loss', color='blue')\n",
+    "plt.plot(per_history['val_loss'], label='Perceptual Val Loss', color='blue', linestyle='--')\n",
+    "plt.plot(ctx_history['train_loss'], label='Context Train Loss', color='orange')\n",
+    "plt.plot(ctx_history['val_loss'], label='Context Val Loss', color='orange', linestyle='--')\n",
+    "plt.plot(per_history_2['train_loss'], label='Perceptual Train Loss CT', color='red')\n",
+    "plt.plot(per_history_2['val_loss'], label='Perceptual Val Loss CT', color='red', linestyle='--')\n",
+    "plt.plot(ctx_history_2['train_loss'], label='Context Train Loss CT', color='green')\n",
+    "plt.plot(ctx_history_2['val_loss'], label='Context Val Loss CT', color='green', linestyle='--')\n",
+    "plt.title(\"RNN Training Curves (Subsampled 10ms)\")\n",
+    "plt.xlabel(\"Epoch\")\n",
+    "plt.ylabel(\"Cross Entropy Loss\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f01e82ac",
+   "metadata": {},
+   "source": [
+    "### 4. Time-Resolved PID Analysis\n",
+    "We generate a large testing ensemble (200 trials), run them through the trained networks without updating weights, and calculate the Gaussian PID at *every single timestep*. We use the final trial choice as the target variable for the PID."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "1dff232f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_hidden_trajectories(model, dataloader):\n",
+    "    \"\"\"\n",
+    "    Runs the dataset through the model and extracts the hidden state, target, and context \n",
+    "    at every timestep for every trial.\n",
+    "    \"\"\"\n",
+    "    model.eval()\n",
+    "    all_hidden = []\n",
+    "    all_targets = []\n",
+    "    all_periods = []\n",
+    "    \n",
+    "    with torch.no_grad():\n",
+    "        for obs, labels, periods, cohs, ctxs in dataloader:\n",
+    "            # We bypass the final linear layer and get the raw hidden states\n",
+    "            # Standard nn.RNN returns (output, h_n). 'output' contains all timesteps.\n",
+    "            _, hidden_states = model(obs) \n",
+    "            \n",
+    "            all_hidden.append(hidden_states.numpy())\n",
+    "            #all_targets.append(cohs.numpy()) # Using continuous coherence as our target!\n",
+    "            all_targets.append(labels[:, -1].numpy().astype(float)) # Using labels as our target!\n",
+    "            all_periods.append(periods.numpy())\n",
+    "            \n",
+    "    # Concatenate across batches\n",
+    "    H = np.concatenate(all_hidden, axis=0)      # Shape: (Trials, Timesteps, Hidden_Size)\n",
+    "    Y = np.concatenate(all_targets, axis=0)     # Shape: (Trials,)\n",
+    "    P = np.concatenate(all_periods, axis=0)     # Shape: (Trials, Timesteps)\n",
+    "    \n",
+    "    return H, Y, P"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "bf255d84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting hidden state trajectories for the Test Sets...\n",
+      "Perceptual Trajectories Shape: (2000, 75, 100)\n",
+      "Context Trajectories Shape:    (2000, 75, 100)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Extracting hidden state trajectories for the Test Sets...\")\n",
+    "\n",
+    "# Load the dedicated Test Sets (never seen during training or validation!)\n",
+    "per_test_loader = load_mante_data(f'{BASE_PATH}/perceptual/test_uniform.npz', batch_size=BATCH_SIZE, shuffle=False, subsample_step=SUBSAMPLE_STEP)\n",
+    "ctx_test_loader = load_mante_data(f'{BASE_PATH}/context/test_uniform.npz', batch_size=BATCH_SIZE, shuffle=False, subsample_step=SUBSAMPLE_STEP)\n",
+    "\n",
+    "# Load models from savepoints (optional)\n",
+    "perceptual_model_elman = WrappedElman(input_size, hidden_size, output_size)\n",
+    "perceptual_model_elman.load_state_dict(torch.load(f\"{WEIGHTS_DIR}/elman_perceptual_best_h{hidden_size}.pth\", weights_only=True))\n",
+    "context_model_elman = WrappedElman(input_size, hidden_size, output_size)\n",
+    "context_model_elman.load_state_dict(torch.load(f\"{WEIGHTS_DIR}/elman_context_best_h{hidden_size}.pth\", weights_only=True))\n",
+    "\n",
+    "perceptual_model_ctrnn = WrappedCTRNN(input_size, hidden_size, output_size)\n",
+    "perceptual_model_ctrnn.load_state_dict(torch.load(f\"{WEIGHTS_DIR}/ctrnn_perceptual_best_h{hidden_size}.pth\", weights_only=True))\n",
+    "context_model_ctrnn = WrappedCTRNN(input_size, hidden_size, output_size)\n",
+    "context_model_ctrnn.load_state_dict(torch.load(f\"{WEIGHTS_DIR}/ctrnn_context_best_h{hidden_size}.pth\", weights_only=True))\n",
+    "\n",
+    "# Extract Trajectories\n",
+    "H_per_elman, Y_per_elman, P_per_elman = extract_hidden_trajectories(perceptual_model_elman, per_test_loader)\n",
+    "H_ctx_elman, Y_ctx_elman, P_ctx_elman = extract_hidden_trajectories(context_model_elman, ctx_test_loader)\n",
+    "\n",
+    "H_per_ctrnn, Y_per_ctrnn, P_per_ctrnn = extract_hidden_trajectories(perceptual_model_ctrnn, per_test_loader)\n",
+    "H_ctx_ctrnn, Y_ctx_ctrnn, P_ctx_ctrnn = extract_hidden_trajectories(context_model_ctrnn, ctx_test_loader)\n",
+    "\n",
+    "print(f\"Perceptual Trajectories Shape: {H_per_elman.shape}\")\n",
+    "print(f\"Context Trajectories Shape:    {H_ctx_elman.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "cecb901e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Analyzing Perceptual Elman Network...\n",
+      "Analyzing Context Elman Network...\n",
+      "Analyzing Perceptual CTRNN Network...\n",
+      "Analyzing Context CTRNN Network...\n",
+      "Succesfully calculated PID metrics\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from src.analysis.gaussian_pid import gaussian_pid_rnn\n",
+    "\n",
+    "# Run the analysis (Warning: This might take a few minutes!)\n",
+    "print(\"Analyzing Perceptual Elman Network...\")\n",
+    "pid_elman_perceptual = gaussian_pid_rnn(\n",
+    "    activations=H_per_elman,\n",
+    "    target=Y_per_elman, \n",
+    "    timestep=None,           \n",
+    "    bipartitions=\"random\",\n",
+    "    n_bipartitions=50,\n",
+    "    seed=42,\n",
+    "    log_base=2,\n",
+    "    regularization=1e-5\n",
+    ")\n",
+    "\n",
+    "print(\"Analyzing Context Elman Network...\")\n",
+    "pid_elman_context = gaussian_pid_rnn(\n",
+    "    activations=H_ctx_elman,\n",
+    "    target=Y_ctx_elman, \n",
+    "    timestep=None,           \n",
+    "    bipartitions=\"random\",\n",
+    "    n_bipartitions=50,\n",
+    "    seed=42,\n",
+    "    log_base=2,\n",
+    "    regularization=1e-5\n",
+    ")\n",
+    "\n",
+    "print(\"Analyzing Perceptual CTRNN Network...\")\n",
+    "pid_ctrnn_perceptual = gaussian_pid_rnn(\n",
+    "    activations=H_per_ctrnn,\n",
+    "    target=Y_per_ctrnn, \n",
+    "    timestep=None,           \n",
+    "    bipartitions=\"random\",\n",
+    "    n_bipartitions=50,\n",
+    "    seed=42,\n",
+    "    log_base=2,\n",
+    "    regularization=1e-5\n",
+    ")\n",
+    "\n",
+    "print(\"Analyzing Context CTRNN Network...\")\n",
+    "pid_ctrnn_context = gaussian_pid_rnn(\n",
+    "    activations=H_ctx_ctrnn,\n",
+    "    target=Y_ctx_ctrnn, \n",
+    "    timestep=None,           \n",
+    "    bipartitions=\"random\",\n",
+    "    n_bipartitions=50,\n",
+    "    seed=42,\n",
+    "    log_base=2,\n",
+    "    regularization=1e-5\n",
+    ")\n",
+    "\n",
+    "print(\"Succesfully calculated PID metrics\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4518f333",
+   "metadata": {},
+   "source": [
+    "### 5. Evaluating the Hypothesis (Visualization)\n",
+    "We plot the Synergy and Redundancy tracks. According to our hypothesis:\n",
+    "1. Synergy should peak around the dotted vertical line (end of stimulus).\n",
+    "2. The Context task (bottom row) should have significantly higher Synergy than the Perceptual task (top row)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "99055618",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1800x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Dynamically find the End of Stimulus index (same across all models/tasks)\n",
+    "stim_end_idx = np.where(P_ctx_elman[0] == 1)[0][-1] \n",
+    "\n",
+    "# Define the grid\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(18, 12), sharey=True, sharex=True)\n",
+    "\n",
+    "# Add an overarching title to the entire figure specifying the hidden size\n",
+    "fig.suptitle(f\"Information Geometry: Architecture vs. Cognitive Demand (Hidden Size: {hidden_size})\", \n",
+    "             fontsize=20, fontweight='bold', y=0.96)\n",
+    "\n",
+    "def plot_pid_ax(ax, data, title):\n",
+    "    \"\"\"Helper function to plot PID lines on a specific subplot axis.\"\"\"\n",
+    "    time_axis = np.arange(len(data['synergy']))\n",
+    "    \n",
+    "    ax.plot(time_axis, data['synergy'], label='Synergy', color='#54A24B', linewidth=3)\n",
+    "    ax.plot(time_axis, data['redundancy'], label='Redundancy', color='#4C78A8', linewidth=3)\n",
+    "    ax.plot(time_axis, data['unique1'], label='Unique 1', color='#F58518', linewidth=1.5, alpha=0.8)\n",
+    "    ax.plot(time_axis, data['unique2'], label='Unique 2', color='#E45756', linewidth=1.5, alpha=0.8)\n",
+    "    \n",
+    "    # Mark the End of Stimulus\n",
+    "    ax.axvline(stim_end_idx, color='red', linestyle='--', label='End of Stimulus')\n",
+    "    ax.axvspan(0, stim_end_idx, color='gray', alpha=0.1)\n",
+    "    \n",
+    "    ax.set_title(title, fontsize=15, fontweight='bold', pad=10)\n",
+    "    ax.grid(alpha=0.3)\n",
+    "    \n",
+    "    # Only show axis labels on the outer edges to keep it clean\n",
+    "    if ax.get_subplotspec().is_first_col():\n",
+    "        ax.set_ylabel('Information (Bits)', fontsize=13)\n",
+    "    if ax.get_subplotspec().is_last_row():\n",
+    "        ax.set_xlabel('Timesteps (Subsampled)', fontsize=13)\n",
+    "\n",
+    "# --- Row 1: Elman RNN ---\n",
+    "plot_pid_ax(axes[0, 0], pid_elman_perceptual, \"Elman RNN | Perceptual Task\")\n",
+    "plot_pid_ax(axes[0, 1], pid_elman_context, \"Elman RNN | Context Task\")\n",
+    "\n",
+    "# --- Row 2: CTRNN ---\n",
+    "plot_pid_ax(axes[1, 0], pid_ctrnn_perceptual, \"CTRNN | Perceptual Task\")\n",
+    "plot_pid_ax(axes[1, 1], pid_ctrnn_context, \"CTRNN | Context Task\")\n",
+    "\n",
+    "# Add a single master legend to the first plot\n",
+    "axes[0, 0].legend(loc='upper left', fontsize=11, framealpha=0.9)\n",
+    "\n",
+    "# Adjust spacing so the main title doesn't overlap with the subplot titles\n",
+    "plt.tight_layout(rect=[0, 0, 1, 0.93])\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neuroai",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/05_Full_train_pipeline.ipynb b/notebooks/05_Full_train_pipeline.ipynb
new file mode 100644
index 0000000..da9abfe
--- /dev/null
+++ b/notebooks/05_Full_train_pipeline.ipynb
@@ -0,0 +1,668 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a22658ed",
+   "metadata": {},
+   "source": [
+    "# Full CTRNN Training Pipeline\n",
+    "Train an ensemble of CTRNNs on the **Perceptual** and **Context** Mante-style (i.e., Mante et al. 2013) tasks. For each trained network we save its learning curve, scalar metrics, best-checkpoint weights, and test-set hidden activations.\n",
+    "\n",
+    "PID computation and analysis are handled in notebook **06_PID_analysis.ipynb**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c7c51b0",
+   "metadata": {},
+   "source": [
+    "### 0. Imports\n",
+    "Load PyTorch, the project's Mante-task data loader, and the CTRNN model. This notebook trains the 20 (10 perceptual, 10 context) CTRNNs for both tasks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "7205bf02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import torch                         # core PyTorch library\n",
+    "import torch.nn as nn                # base CTRNN model\n",
+    "import torch.nn.functional as F      # masked_cross_entropy loss\n",
+    "import numpy as np                   # stacking hidden activations for saving\n",
+    "import matplotlib.pyplot as plt      # learning curve figures\n",
+    "import copy                          # snapshot best-checkpoint weights during training\n",
+    "import os                            # file/folder management for saving models and metrics\n",
+    "import json                          # scalar metrics (loss/acc) per model\n",
+    "import time                          # wall-clock timing of epochs, training, and per-model runs\n",
+    "\n",
+    "import sys\n",
+    "\n",
+    "# Make the repository root importable when the notebook is launched from notebooks/ or the workspace root.\n",
+    "repo_root = None\n",
+    "for candidate in [Path.cwd(), *Path.cwd().parents]:\n",
+    "    if (candidate / \"src\").exists():\n",
+    "        repo_root = candidate\n",
+    "        break\n",
+    "\n",
+    "if repo_root is not None and str(repo_root) not in sys.path:\n",
+    "    sys.path.append(str(repo_root))\n",
+    "\n",
+    "from src.tasks.data_loader import load_mante_data   # NeuroGym trial data -> DataLoader\n",
+    "from src.models.ctrnn import CTRNN                  # base continuous-time RNN (see README)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c026aa6",
+   "metadata": {},
+   "source": [
+    "### Notebook parameters\n",
+    "Choose which task(s) to train and how many independently initialized CTRNNs (seeds) to run per task. These seeds are the sample the PID notebook later compares across, task by task."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "2e2e49f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ---------------------------------------------------------------------------\n",
+    "# Top-of-notebook parameters\n",
+    "# ---------------------------------------------------------------------------\n",
+    "TASKS = ['perceptual', 'context']  # 'perceptual' = low integration demand, 'context' = high integration demand\n",
+    "N_p = 1  # number of independently seeded CTRNNs to train on the perceptual task\n",
+    "N_c = 1  # number of independently seeded CTRNNs to train on the context task"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "227142c0",
+   "metadata": {},
+   "source": [
+    "### Paths and data settings\n",
+    "Data paths, batch size, and output directories for this run. `SUBSAMPLE_STEP=10` compresses the raw dt=1ms trial data to an effective 10ms timestep (750 steps down to 75), keeping sequences short enough to train efficiently. `TEST_SPLIT_FMT` builds a *per-model* held-out split name: within a task the train and val sets are shared across all seeds, but each seeded CTRNN is tested on its own file (`test_01`, `test_02`, ...), later used to extract hidden activations for PID."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "275176ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configuration (paths are relative to the notebooks/ directory, i.e. repo root via \"..\")\n",
+    "BASE_PATH = r\"E:\\TUM\\3sem\\NeuroAI\\project\\data_generation\\seed42\"\n",
+    "BATCH_SIZE = 1024 * 2\n",
+    "SUBSAMPLE_STEP = 10   # raw trials are dt=1ms; step=10 gives an effective 10ms timestep (750 -> 75 steps)\n",
+    "# Each seeded CTRNN gets its own held-out test set (train/val are shared across seeds of a task).\n",
+    "# Seeds are numbered 1-based, so CTRNN_01 -> test_01, CTRNN_02 -> test_02, etc.\n",
+    "TEST_SPLIT_FMT = \"test_{:02d}\"  # per-model test split, formatted with the 1-based seed number\n",
+    "\n",
+    "# Output directories (created per-task inside the training loop)\n",
+    "FIG_LC_DIR = \"../figures/learning_curves\"\n",
+    "RES_METRICS_DIR = \"../results/accuracies_n_losses\"\n",
+    "RES_WEIGHTS_DIR = \"../results/model_weights\"\n",
+    "RES_ACTS_DIR = \"../results/model_activations\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "545426a1",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70862864",
+   "metadata": {},
+   "source": [
+    "### 1. The Architecture\n",
+    "We wrap the existing `CTRNN` so that the forward pass returns `(outputs, hidden_states)`, with shapes `(Batch, Time, Classes)` and `(Batch, Time, Hidden)` respectively. The CTRNN re-initialises its hidden state to zero at the start of every `forward` call. We need the hidden states because they, not the outputs, are what gets saved and fed into the PID analysis in the companion notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "b489acba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class WrappedCTRNN(nn.Module):\n",
+    "    def __init__(self, input_dim, hidden_size, output_size=3):\n",
+    "        super().__init__()\n",
+    "        # Reuse the shared CTRNN (src/models/ctrnn.py) rather than redefining the dynamics here\n",
+    "        self.model = CTRNN(input_size=input_dim, hidden_size=hidden_size, output_size=output_size)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        # CTRNN expects (Batch, Seq, Dim).\n",
+    "        # return_dynamics=True also returns per-step predictions, used only by the\n",
+    "        # predictive-coding condition (trained elsewhere), so we drop them here.\n",
+    "        outputs, _, hidden_states = self.model(x, return_dynamics=True)\n",
+    "        return outputs, hidden_states   # hidden_states is what later gets saved for PID"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9572bdc5",
+   "metadata": {},
+   "source": [
+    "### 2. Loss & Accuracy\n",
+    "The loss is cross-entropy over the decision-period timesteps only (periods == 2), flattened across batch x time (masked_cross_entropy), so every decision timestep of every trial contributes a term (multiple terms per trial). Accuracy uses the same decision-period mask: the proportion of decision-period timesteps whose argmax(output) matches the label, pooled across all timesteps (correct / total). Restricting to the decision period matches the task itself: the network only has to commit to a choice once fixation and stimulus presentation are done."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "b3a2a686",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def masked_cross_entropy(outputs, labels, mask):\n",
+    "    \"\"\"Cross-entropy over the decision-period timesteps only (mask, e.g. periods == 2),\n",
+    "    flattened across batch x time. Assumes mask is non-empty (guarded by the caller).\"\"\"\n",
+    "    # Only the decision period has a well-defined choice label; fixation and stimulus\n",
+    "    # timesteps are excluded so the network isn't penalized for not answering early.\n",
+    "    masked_outputs = outputs[mask]   # (n_decision_steps, n_classes)\n",
+    "    masked_labels = labels[mask]     # (n_decision_steps,)\n",
+    "    return F.cross_entropy(masked_outputs, masked_labels)\n",
+    "\n",
+    "\n",
+    "def masked_accuracy_counts(outputs, labels, mask):\n",
+    "    \"\"\"Correct predictions and total count over the decision-period timesteps (mask).\n",
+    "    Return counts (not a proportion) so they can be pooled across batches.\"\"\"\n",
+    "    preds = outputs[mask].argmax(dim=1)\n",
+    "    correct = (preds == labels[mask]).sum().item()\n",
+    "    total = int(mask.sum().item())\n",
+    "    return correct, total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a395f752",
+   "metadata": {},
+   "source": [
+    "### 3. Training, Testing & Plotting helpers\n",
+    "`train_ctrnn` trains a single network: it records train and validation loss/accuracy **once per epoch** (train loss = mean over batches, accuracy = pooled correct/total over decision-period timesteps), tracks the best checkpoint (lowest validation loss), and supports a toggleable early-stopping flag (`use_early_stopping`). It uses an Adam optimizer with a **`ReduceLROnPlateau` learning-rate schedule** driven by the validation loss (`scheduler.step(avg_val_loss)` each epoch): the LR is multiplied by `lr_factor` after `lr_patience` epochs without val-loss improvement, down to `lr_min`. The schedule *type* is the same for every network, but its plateau patience is set **per task** by the caller (see the training loop), and `lr_patience` is kept below the early-stopping `patience` so the LR can actually drop before training halts. Batches with no decision-period timesteps are skipped (`mask.sum() > 0` guard). On completion it restores the best weights into the model.\n",
+    "\n",
+    "`test_ctrnn` runs the best checkpoint over the test set **one trial at a time**, zeroing the hidden state at the start of each trial; it computes test loss/accuracy with the same decision-period masking and collects the full hidden-state sequence per trial.\n",
+    "\n",
+    "`plot_learning_curve` draws the two-panel learning-curve figure (loss and accuracy vs. epoch)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "a11be7d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_ctrnn(model, train_loader, val_loader, device,\n",
+    "                num_epochs=50, lr=1e-3, patience=10, use_early_stopping=True,\n",
+    "                lr_factor=0.5, lr_patience=5, lr_min=1e-5):\n",
+    "    \"\"\"\n",
+    "    Trains a single CTRNN:\n",
+    "    - Loss: cross-entropy over the decision-period timesteps only (periods == 2),\n",
+    "      flattened across batch x time (masked_cross_entropy).\n",
+    "    - LR schedule: ReduceLROnPlateau on validation loss (factor=lr_factor,\n",
+    "      patience=lr_patience, min_lr=lr_min). The schedule *type* is identical for every\n",
+    "      network; only its plateau patience varies by task (set by the caller). Keep\n",
+    "      lr_patience < patience (early stopping) so the LR gets a chance to drop before\n",
+    "      training is halted.\n",
+    "    - Logged once per epoch: train loss (mean over batches) and train accuracy\n",
+    "      (pooled correct/total over decision-period timesteps); likewise val loss/acc.\n",
+    "      Each logged epoch also reports its wall-clock duration in seconds and current LR.\n",
+    "    - Batches with no decision-period timesteps are skipped (mask.sum() > 0 guard).\n",
+    "    - Best checkpoint = lowest validation loss; restored into the model at the end.\n",
+    "    Returns (history, best_state).\n",
+    "    \"\"\"\n",
+    "    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
+    "    # Reduce LR when validation loss stops improving. This adapts per-seed to each\n",
+    "    # network's own convergence, so no global epoch-budget tuning is needed.\n",
+    "    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
+    "        optimizer, mode='min', factor=lr_factor, patience=lr_patience, min_lr=lr_min\n",
+    "    )\n",
+    "\n",
+    "    best_val_loss = float('inf')\n",
+    "    best_state = copy.deepcopy(model.state_dict())  # fallback snapshot before any epoch runs\n",
+    "    patience_counter = 0\n",
+    "\n",
+    "    # 'lr' logs the LR in effect during each epoch, for the learning-curve figure.\n",
+    "    history = {'train_loss': [], 'train_acc': [], 'val_loss': [], 'val_acc': [], 'lr': []}\n",
+    "\n",
+    "    train_start = time.time()  # wall-clock start of the whole training run\n",
+    "\n",
+    "    for epoch in range(num_epochs):\n",
+    "        epoch_start = time.time()  # wall-clock start of this epoch\n",
+    "\n",
+    "        # --- TRAINING PHASE ---\n",
+    "        model.train()\n",
+    "        running_train_loss = 0.0\n",
+    "        train_batches = 0\n",
+    "        train_correct, train_total = 0, 0\n",
+    "\n",
+    "        for obs, labels, periods, cohs, ctxs in train_loader:\n",
+    "            obs = obs.to(device)\n",
+    "            labels = labels.to(device)\n",
+    "            periods = periods.to(device)\n",
+    "            # cohs (stimulus coherence) and ctxs (context cue) aren't needed to train the\n",
+    "            # network; they're only used later, alongside the saved activations, for PID.\n",
+    "\n",
+    "            optimizer.zero_grad()\n",
+    "            outputs, _ = model(obs)          # (Batch, Time, Classes); hidden states unused while training\n",
+    "\n",
+    "            mask = (periods == 2)            # decision-period timesteps only (see masked_cross_entropy)\n",
+    "            if mask.sum() > 0:\n",
+    "                loss = masked_cross_entropy(outputs, labels, mask)\n",
+    "                loss.backward()\n",
+    "                optimizer.step()\n",
+    "\n",
+    "                running_train_loss += loss.item()\n",
+    "                train_batches += 1\n",
+    "                c, t = masked_accuracy_counts(outputs, labels, mask)\n",
+    "                train_correct += c\n",
+    "                train_total += t\n",
+    "\n",
+    "        avg_train_loss = running_train_loss / max(1, train_batches)\n",
+    "        train_acc = train_correct / max(1, train_total)\n",
+    "        history['train_loss'].append(avg_train_loss)\n",
+    "        history['train_acc'].append(train_acc)\n",
+    "\n",
+    "        # --- VALIDATION PHASE ---\n",
+    "        model.eval()\n",
+    "        running_val_loss = 0.0\n",
+    "        val_batches = 0\n",
+    "        val_correct, val_total = 0, 0\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            for obs, labels, periods, cohs, ctxs in val_loader:\n",
+    "                obs = obs.to(device)\n",
+    "                labels = labels.to(device)\n",
+    "                periods = periods.to(device)\n",
+    "\n",
+    "                outputs, _ = model(obs)\n",
+    "                mask = (periods == 2)\n",
+    "                if mask.sum() > 0:\n",
+    "                    running_val_loss += masked_cross_entropy(outputs, labels, mask).item()\n",
+    "                    val_batches += 1\n",
+    "                    c, t = masked_accuracy_counts(outputs, labels, mask)\n",
+    "                    val_correct += c\n",
+    "                    val_total += t\n",
+    "\n",
+    "        avg_val_loss = running_val_loss / max(1, val_batches)\n",
+    "        val_acc = val_correct / max(1, val_total)\n",
+    "        history['val_loss'].append(avg_val_loss)\n",
+    "        history['val_acc'].append(val_acc)\n",
+    "\n",
+    "        # --- LR SCHEDULE STEP (reduce on validation-loss plateau) ---\n",
+    "        scheduler.step(avg_val_loss)\n",
+    "        history['lr'].append(optimizer.param_groups[0]['lr'])\n",
+    "\n",
+    "        # --- BEST-CHECKPOINT TRACKING (lowest val loss) ---\n",
+    "        if avg_val_loss < best_val_loss:\n",
+    "            best_val_loss = avg_val_loss\n",
+    "            best_state = copy.deepcopy(model.state_dict())\n",
+    "            patience_counter = 0\n",
+    "        else:\n",
+    "            patience_counter += 1\n",
+    "\n",
+    "        # Print progress every 5 epochs (and on the first epoch)\n",
+    "        if (epoch + 1) % 5 == 0 or epoch == 0:\n",
+    "            epoch_time = time.time() - epoch_start  # this epoch's wall-clock duration\n",
+    "            current_lr = optimizer.param_groups[0]['lr']\n",
+    "            print(f\"Epoch [{epoch+1:03d}/{num_epochs}] | \"\n",
+    "                  f\"{epoch_time:.2f}s | \"\n",
+    "                  f\"Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | \"\n",
+    "                  f\"Train Acc: {train_acc:.4f} | Val Acc: {val_acc:.4f} | LR: {current_lr:.2e}\")\n",
+    "\n",
+    "        if use_early_stopping and patience_counter >= patience:\n",
+    "            print(f\"Early stopping triggered at epoch {epoch+1}! Best Val Loss: {best_val_loss:.4f}\")\n",
+    "            break\n",
+    "\n",
+    "    total_train_time = time.time() - train_start  # wall-clock duration of the whole training run\n",
+    "    print(f\"Training complete ({total_train_time:.2f}s). Restoring best model weights...\")\n",
+    "    model.load_state_dict(best_state)  # use the best checkpoint, not necessarily the last epoch\n",
+    "\n",
+    "    return history, best_state\n",
+    "\n",
+    "\n",
+    "def test_ctrnn(model, test_loader, device):\n",
+    "    \"\"\"\n",
+    "    Runs the model over the test set one trial at a time (hidden state zeroed at the\n",
+    "    start of every trial). Loss/accuracy use the same decision-period masking as\n",
+    "    training (loss = mean over trials; accuracy = pooled correct/total). Returns:\n",
+    "      - hidden_activations: float32 array, shape [n_test_trials, T, n_hidden]\n",
+    "      - test_loss, test_acc\n",
+    "    \"\"\"\n",
+    "    model.eval()\n",
+    "    hidden_list = []\n",
+    "    running_test_loss = 0.0\n",
+    "    test_batches = 0\n",
+    "    test_correct, test_total = 0, 0\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        for obs, labels, periods, cohs, ctxs in test_loader:\n",
+    "            obs = obs.to(device)\n",
+    "            labels = labels.to(device)\n",
+    "            periods = periods.to(device)\n",
+    "\n",
+    "            for i in range(obs.size(0)):\n",
+    "                # Process one trial at a time so each hidden-state trace comes from an\n",
+    "                # independent trial (a fresh forward call resets the hidden state to 0).\n",
+    "                # This matters for PID, which treats trials as i.i.d. samples.\n",
+    "                outputs, hidden = model(obs[i:i+1])   # (1, T, C), (1, T, H)\n",
+    "\n",
+    "                mask = (periods[i:i+1] == 2)          # (1, T) decision-period timesteps\n",
+    "                if mask.sum() > 0:\n",
+    "                    li = labels[i:i+1]\n",
+    "                    running_test_loss += masked_cross_entropy(outputs, li, mask).item()\n",
+    "                    test_batches += 1\n",
+    "                    c, t = masked_accuracy_counts(outputs, li, mask)\n",
+    "                    test_correct += c\n",
+    "                    test_total += t\n",
+    "\n",
+    "                hidden_list.append(hidden[0].cpu().numpy())   # (T, n_hidden), saved for PID\n",
+    "\n",
+    "    hidden_activations = np.stack(hidden_list).astype(np.float32)  # [n_test_trials, T, n_hidden]\n",
+    "    test_loss = running_test_loss / max(1, test_batches)\n",
+    "    test_acc = test_correct / max(1, test_total)\n",
+    "    return hidden_activations, test_loss, test_acc\n",
+    "\n",
+    "\n",
+    "def plot_learning_curve(history, save_path):\n",
+    "    \"\"\"Two stacked subplots: (1) train/val loss, (2) train/val accuracy, vs epoch.\"\"\"\n",
+    "    epochs = range(1, len(history['train_loss']) + 1)\n",
+    "\n",
+    "    fig, (ax_loss, ax_acc) = plt.subplots(2, 1, figsize=(8, 8))\n",
+    "\n",
+    "    ax_loss.plot(epochs, history['train_loss'], label='Train loss')\n",
+    "    ax_loss.plot(epochs, history['val_loss'], label='Val loss')\n",
+    "    ax_loss.set_xlabel('Epoch', fontsize=16)\n",
+    "    ax_loss.set_ylabel('Loss', fontsize=16)\n",
+    "\n",
+    "    ax_acc.plot(epochs, history['train_acc'], label='Train acc')\n",
+    "    ax_acc.plot(epochs, history['val_acc'], label='Val acc')\n",
+    "    ax_acc.set_xlabel('Epoch', fontsize=16)\n",
+    "    ax_acc.set_ylabel('Accuracy', fontsize=16)\n",
+    "\n",
+    "    for ax in (ax_loss, ax_acc):\n",
+    "        ax.spines['top'].set_visible(False)\n",
+    "        ax.spines['right'].set_visible(False)\n",
+    "        ax.tick_params(labelsize=12)\n",
+    "        ax.legend(fontsize=14)\n",
+    "\n",
+    "    fig.tight_layout()\n",
+    "    fig.savefig(save_path, dpi=300)\n",
+    "    plt.show()\n",
+    "    plt.close(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93908708",
+   "metadata": {},
+   "source": [
+    "### 4. Model dimensions and device\n",
+    "Both tasks share the same 7-channel input and 3-way output (fixate, choice 1, choice 2), so one architecture serves both tasks. Only the training data differs between the perceptual and context runs. Falls back to CPU if no GPU is available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "2cdf07a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using compute device: CUDA\n",
+      "   GPU Name: NVIDIA GeForce GTX 1650 with Max-Q Design\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Task-dependent input dimensions: perceptual uses 3 channels, context uses 7.\n",
+    "input_dims = {'perceptual': 3, 'context': 7}\n",
+    "hidden_size = 100\n",
+    "output_size = 3    # Fixate, Choice 1, Choice 2\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(f\"Using compute device: {device.type.upper()}\")\n",
+    "if device.type == \"cuda\":\n",
+    "    print(f\"   GPU Name: {torch.cuda.get_device_name(0)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50912dd9",
+   "metadata": {},
+   "source": [
+    "### 5. Train and save every seed, for every task\n",
+    "For each task, loop over the configured number of freshly initialized CTRNNs. Each one is trained, tested on the held-out split, and has its learning curve, scalar metrics, best checkpoint, and test-set hidden activations written to disk. These saved hidden activations are what the PID notebook consumes next."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "03e2fd19",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "======================================================================\n",
+      "TASK: PERCEPTUAL  (1 CTRNNs)\n",
+      "======================================================================\n",
+      "\n",
+      "--- Training CTRNN on Perceptual Task | CTRNN_01 ---\n",
+      "Epoch [001/50] | 17.75s | Train Loss: 0.4375 | Val Loss: 0.2717 | Train Acc: 0.7809 | Val Acc: 0.8843 | LR: 1.00e-03\n",
+      "Epoch [005/50] | 19.43s | Train Loss: 0.2564 | Val Loss: 0.2504 | Train Acc: 0.8865 | Val Acc: 0.8899 | LR: 1.00e-03\n",
+      "Epoch [010/50] | 17.94s | Train Loss: 0.2549 | Val Loss: 0.2479 | Train Acc: 0.8878 | Val Acc: 0.8894 | LR: 1.00e-03\n",
+      "Epoch [015/50] | 16.27s | Train Loss: 0.2528 | Val Loss: 0.2481 | Train Acc: 0.8876 | Val Acc: 0.8893 | LR: 5.00e-04\n",
+      "Epoch [020/50] | 26.92s | Train Loss: 0.2524 | Val Loss: 0.2486 | Train Acc: 0.8882 | Val Acc: 0.8897 | LR: 2.50e-04\n",
+      "Epoch [025/50] | 24.37s | Train Loss: 0.2523 | Val Loss: 0.2482 | Train Acc: 0.8883 | Val Acc: 0.8893 | LR: 1.25e-04\n",
+      "Epoch [030/50] | 23.86s | Train Loss: 0.2518 | Val Loss: 0.2499 | Train Acc: 0.8884 | Val Acc: 0.8890 | LR: 6.25e-05\n",
+      "Epoch [035/50] | 24.98s | Train Loss: 0.2513 | Val Loss: 0.2515 | Train Acc: 0.8883 | Val Acc: 0.8874 | LR: 3.13e-05\n",
+      "Early stopping triggered at epoch 39! Best Val Loss: 0.2476\n",
+      "Training complete (850.55s). Restoring best model weights...\n",
+      "Using test split: test_uniform\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved learning curve to ../figures/learning_curves\\perceptual\\CTRNN_01.png\n",
+      "Saved metrics to ../results/accuracies_n_losses\\perceptual\\CTRNN_01.json\n",
+      "Saved model weights to ../results/model_weights\\perceptual\\CTRNN_01.pt\n",
+      "Saved hidden activations to ../results/model_activations\\perceptual\\CTRNN_01.npy\n",
+      "Total model time: 1102.41s\n",
+      "  train_loss=0.2519 | val_loss=0.2505 | test_loss=0.2501\n",
+      "  train_acc =0.8884 | val_acc =0.8884 | test_acc =0.8917\n",
+      "\n",
+      "======================================================================\n",
+      "TASK: CONTEXT  (1 CTRNNs)\n",
+      "======================================================================\n",
+      "\n",
+      "--- Training CTRNN on Context Task | CTRNN_01 ---\n",
+      "Epoch [001/50] | 24.88s | Train Loss: 0.5756 | Val Loss: 0.4091 | Train Acc: 0.6951 | Val Acc: 0.8138 | LR: 1.00e-03\n",
+      "Epoch [005/50] | 23.50s | Train Loss: 0.2742 | Val Loss: 0.2886 | Train Acc: 0.8790 | Val Acc: 0.8662 | LR: 1.00e-03\n",
+      "Epoch [010/50] | 22.97s | Train Loss: 0.2674 | Val Loss: 0.2706 | Train Acc: 0.8818 | Val Acc: 0.8798 | LR: 1.00e-03\n",
+      "Epoch [015/50] | 20.41s | Train Loss: 0.2621 | Val Loss: 0.2861 | Train Acc: 0.8841 | Val Acc: 0.8642 | LR: 1.00e-03\n",
+      "Epoch [020/50] | 20.04s | Train Loss: 0.2632 | Val Loss: 0.2787 | Train Acc: 0.8841 | Val Acc: 0.8786 | LR: 1.00e-03\n",
+      "Epoch [025/50] | 19.79s | Train Loss: 0.2555 | Val Loss: 0.2649 | Train Acc: 0.8870 | Val Acc: 0.8819 | LR: 5.00e-04\n",
+      "Epoch [030/50] | 19.93s | Train Loss: 0.2574 | Val Loss: 0.2650 | Train Acc: 0.8861 | Val Acc: 0.8834 | LR: 5.00e-04\n",
+      "Early stopping triggered at epoch 34! Best Val Loss: 0.2606\n",
+      "Training complete (723.06s). Restoring best model weights...\n",
+      "Using test split: test_uniform\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved learning curve to ../figures/learning_curves\\context\\CTRNN_01.png\n",
+      "Saved metrics to ../results/accuracies_n_losses\\context\\CTRNN_01.json\n",
+      "Saved model weights to ../results/model_weights\\context\\CTRNN_01.pt\n",
+      "Saved hidden activations to ../results/model_activations\\context\\CTRNN_01.npy\n",
+      "Total model time: 937.39s\n",
+      "  train_loss=0.2551 | val_loss=0.2655 | test_loss=0.2592\n",
+      "  train_acc =0.8873 | val_acc =0.8821 | test_acc =0.8803\n",
+      "\n",
+      "Pipeline complete.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Number of independently seeded CTRNNs to train per task (perceptual/context) is set at the top of the notebook.\n",
+    "N_MODELS = {'perceptual': N_p, 'context': N_c}\n",
+    "\n",
+    "# Per-task LR schedule: the schedule TYPE (ReduceLROnPlateau on val loss) is identical for\n",
+    "# every RNN; only its plateau patience differs by task. The perceptual task has a low\n",
+    "# integration demand and converges cleanly, so it can cut the LR sooner (lr_patience=3);\n",
+    "# the context task is harder and its val loss is noisier, so it waits longer before cutting\n",
+    "# (lr_patience=6). Both stay below the early-stopping patience (10) so the LR actually gets a\n",
+    "# chance to drop before training halts.\n",
+    "LR_SCHED = {\n",
+    "    'perceptual': {'lr_factor': 0.5, 'lr_patience': 3, 'lr_min': 1e-5},\n",
+    "    'context':    {'lr_factor': 0.5, 'lr_patience': 6, 'lr_min': 1e-5},\n",
+    "}\n",
+    "\n",
+    "# ------------------------------ Main Training Loop --------------------------------\n",
+    "for task in TASKS:\n",
+    "    n_models = N_MODELS[task]  # Number of independently seeded CTRNNs to train on this task\n",
+    "    print(f\"\\n{'='*70}\\nTASK: {task.upper()}  ({n_models} CTRNNs)\\n{'='*70}\")\n",
+    "\n",
+    "    # Create all output directories for this task\n",
+    "    for base in (FIG_LC_DIR, RES_METRICS_DIR, RES_WEIGHTS_DIR, RES_ACTS_DIR):\n",
+    "        os.makedirs(os.path.join(base, task), exist_ok=True)\n",
+    "\n",
+    "    # Train/val loaders for this task are shared across every seed; only the held-out\n",
+    "    # test set differs per model (loaded inside the per-model loop below).\n",
+    "    train_loader = load_mante_data(\n",
+    "        f'{BASE_PATH}/{task}/train.npz',\n",
+    "        batch_size=BATCH_SIZE,\n",
+    "        shuffle=True,\n",
+    "        subsample_step=SUBSAMPLE_STEP,\n",
+    "        input_dim=input_dims[task],\n",
+    "    )\n",
+    "    val_loader = load_mante_data(\n",
+    "        f'{BASE_PATH}/{task}/val.npz',\n",
+    "        batch_size=BATCH_SIZE,\n",
+    "        shuffle=False,\n",
+    "        subsample_step=SUBSAMPLE_STEP,\n",
+    "        input_dim=input_dims[task],\n",
+    "    )\n",
+    "\n",
+    "    # ------------------------------ Train, Evaluate, Test, and Save CTRNNs --------------------------------\n",
+    "    for idx in range(n_models):\n",
+    "        seed_num = idx + 1              # 1-based seed numbering used for names and test splits\n",
+    "        name = f\"CTRNN_{seed_num:02d}\"  # padded, 1-based: CTRNN_01, CTRNN_02, ...\n",
+    "        print(f\"\\n--- Training CTRNN on {task.capitalize()} Task | {name} ---\")\n",
+    "\n",
+    "        model_start = time.time()  # wall-clock start of this model's full train+test+save run\n",
+    "\n",
+    "        # ---- Train/Evaluate ----\n",
+    "        model = WrappedCTRNN(input_dims[task], hidden_size, output_size).to(device)\n",
+    "        history, best_state = train_ctrnn(model, train_loader, val_loader, device,\n",
+    "                                          num_epochs=50, **LR_SCHED[task])\n",
+    "\n",
+    "        # ---- Test ----\n",
+    "        # Use the available shared test split instead of seed-specific filenames.\n",
+    "        test_split = \"test_uniform\"\n",
+    "        test_loader = load_mante_data(\n",
+    "            f'{BASE_PATH}/{task}/{test_split}.npz',\n",
+    "            batch_size=BATCH_SIZE,\n",
+    "            shuffle=False,\n",
+    "            subsample_step=SUBSAMPLE_STEP,\n",
+    "            input_dim=input_dims[task],\n",
+    "        )\n",
+    "        print(f\"Using test split: {test_split}\")\n",
+    "        hidden_activations, test_loss, test_acc = test_ctrnn(model, test_loader, device)\n",
+    "\n",
+    "        # ---- Save ----\n",
+    "        metrics = {\n",
+    "            'train_loss': history['train_loss'][-1],\n",
+    "            'val_loss':   history['val_loss'][-1],\n",
+    "            'test_loss':  test_loss,\n",
+    "            'train_acc':  history['train_acc'][-1],\n",
+    "            'val_acc':    history['val_acc'][-1],\n",
+    "            'test_acc':   test_acc,\n",
+    "        }\n",
+    "\n",
+    "        # Resolve output paths\n",
+    "        fig_path     = os.path.join(FIG_LC_DIR, task, f\"{name}.png\")\n",
+    "        metrics_path = os.path.join(RES_METRICS_DIR, task, f\"{name}.json\")\n",
+    "        weights_path = os.path.join(RES_WEIGHTS_DIR, task, f\"{name}.pt\")\n",
+    "        acts_path    = os.path.join(RES_ACTS_DIR, task, f\"{name}.npy\")\n",
+    "\n",
+    "        plot_learning_curve(history, fig_path)\n",
+    "        print(f\"Saved learning curve to {fig_path}\")\n",
+    "\n",
+    "        with open(metrics_path, 'w') as f:\n",
+    "            json.dump(metrics, f, indent=2)\n",
+    "        print(f\"Saved metrics to {metrics_path}\")\n",
+    "\n",
+    "        torch.save(best_state, weights_path)\n",
+    "        print(f\"Saved model weights to {weights_path}\")\n",
+    "\n",
+    "        np.save(acts_path, hidden_activations)\n",
+    "        print(f\"Saved hidden activations to {acts_path}\")\n",
+    "\n",
+    "        total_model_time = time.time() - model_start\n",
+    "        print(f\"Total model time: {total_model_time:.2f}s\")\n",
+    "        print(f\"  train_loss={metrics['train_loss']:.4f} | val_loss={metrics['val_loss']:.4f} | test_loss={metrics['test_loss']:.4f}\")\n",
+    "        print(f\"  train_acc ={metrics['train_acc']:.4f} | val_acc ={metrics['val_acc']:.4f} | test_acc ={metrics['test_acc']:.4f}\")\n",
+    "\n",
+    "print(\"\\nPipeline complete.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neuroai",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/example_rnn/Exercise Handout.pdf b/notebooks/Jan_example_rnn/Exercise Handout.pdf
similarity index 100%
rename from example_rnn/Exercise Handout.pdf
rename to notebooks/Jan_example_rnn/Exercise Handout.pdf
diff --git a/notebooks/Jan_example_rnn/__init__.py b/notebooks/Jan_example_rnn/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/example_rnn/dyn_rnn/RNN_Lorenz.pt b/notebooks/Jan_example_rnn/dyn_rnn/RNN_Lorenz.pt
similarity index 100%
rename from example_rnn/dyn_rnn/RNN_Lorenz.pt
rename to notebooks/Jan_example_rnn/dyn_rnn/RNN_Lorenz.pt
diff --git a/example_rnn/dyn_rnn/RNN_VanDerPol.pt b/notebooks/Jan_example_rnn/dyn_rnn/RNN_VanDerPol.pt
similarity index 100%
rename from example_rnn/dyn_rnn/RNN_VanDerPol.pt
rename to notebooks/Jan_example_rnn/dyn_rnn/RNN_VanDerPol.pt
diff --git a/notebooks/Jan_example_rnn/dyn_rnn/RNN_VanDerPol_100.pt b/notebooks/Jan_example_rnn/dyn_rnn/RNN_VanDerPol_100.pt
new file mode 100644
index 0000000..c3ea51a
Binary files /dev/null and b/notebooks/Jan_example_rnn/dyn_rnn/RNN_VanDerPol_100.pt differ
diff --git a/notebooks/Jan_example_rnn/dyn_rnn/__init__.py b/notebooks/Jan_example_rnn/dyn_rnn/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/example_rnn/dyn_rnn/dynRNN.py b/notebooks/Jan_example_rnn/dyn_rnn/dynRNN.py
similarity index 100%
rename from example_rnn/dyn_rnn/dynRNN.py
rename to notebooks/Jan_example_rnn/dyn_rnn/dynRNN.py
diff --git a/example_rnn/dyn_rnn/main.py b/notebooks/Jan_example_rnn/dyn_rnn/main.py
similarity index 93%
rename from example_rnn/dyn_rnn/main.py
rename to notebooks/Jan_example_rnn/dyn_rnn/main.py
index 7bcccea..970b877 100644
--- a/example_rnn/dyn_rnn/main.py
+++ b/notebooks/Jan_example_rnn/dyn_rnn/main.py
@@ -2,27 +2,28 @@
 import torch.nn as nn
 import torch.optim as optim
 import matplotlib.pyplot as plt
-from example_rnn.dyn_rnn.dynRNN import DynRNN
+from notebooks.example_rnn.dyn_rnn.dynRNN import DynRNN
 
 # Define task directory
-dir = "dynrnn/"
+dir = "example_rnn/dyn_rnn/"
 
 # hyperparameters
 BATCH_SIZE = 1   # not really used here, since we train on a single trajectory
-EPOCHS = 1000   
+EPOCHS = 100   
 LEARNING_RATE = 1e-3
 
 # Choose dynamical system
-system = 'Lorenz'  # 'VanDerPol' or 'Lorenz'
+system = 'VanDerPol'  # 'VanDerPol' or 'Lorenz'
 
 if __name__ == "__main__":
 
     y_true = torch.load(f'{dir}y_{system}.pt', weights_only=True)  # Size (300, 2) or (300, 3)
-    # print(y_true.shape)
+    print(y_true.shape)
 
     n_timesteps, dim = y_true.shape
 
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
     y_true = y_true.to(device)
 
     # Create RNN
@@ -85,8 +86,8 @@
             )
 
     # Save trained RNN
-    torch.save(model.state_dict(), f"{dir}RNN_{system}.pt")
-    print(f"Trained RNN saved to {dir}RNN_{system}.pt")
+    torch.save(model.state_dict(), f"{dir}RNN_{system}_{EPOCHS}.pt")
+    print(f"Trained RNN saved to {dir}RNN_{system}_{EPOCHS}.pt")
 
 
     # ===== EVALUATION: AUTOREGRESSIVE ROLLOUT =====
diff --git a/example_rnn/dyn_rnn/utils.py b/notebooks/Jan_example_rnn/dyn_rnn/utils.py
similarity index 100%
rename from example_rnn/dyn_rnn/utils.py
rename to notebooks/Jan_example_rnn/dyn_rnn/utils.py
diff --git a/example_rnn/dyn_rnn/y_Lorenz.pt b/notebooks/Jan_example_rnn/dyn_rnn/y_Lorenz.pt
similarity index 100%
rename from example_rnn/dyn_rnn/y_Lorenz.pt
rename to notebooks/Jan_example_rnn/dyn_rnn/y_Lorenz.pt
diff --git a/example_rnn/dyn_rnn/y_VanDerPol.pt b/notebooks/Jan_example_rnn/dyn_rnn/y_VanDerPol.pt
similarity index 100%
rename from example_rnn/dyn_rnn/y_VanDerPol.pt
rename to notebooks/Jan_example_rnn/dyn_rnn/y_VanDerPol.pt
diff --git a/example_rnn/true_rnn/RNN_Lorenz.pt b/notebooks/Jan_example_rnn/true_rnn/RNN_Lorenz.pt
similarity index 100%
rename from example_rnn/true_rnn/RNN_Lorenz.pt
rename to notebooks/Jan_example_rnn/true_rnn/RNN_Lorenz.pt
diff --git a/example_rnn/true_rnn/RNN_VanDerPol.pt b/notebooks/Jan_example_rnn/true_rnn/RNN_VanDerPol.pt
similarity index 100%
rename from example_rnn/true_rnn/RNN_VanDerPol.pt
rename to notebooks/Jan_example_rnn/true_rnn/RNN_VanDerPol.pt
diff --git a/notebooks/Jan_example_rnn/true_rnn/__init__.py b/notebooks/Jan_example_rnn/true_rnn/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/example_rnn/true_rnn/main.py b/notebooks/Jan_example_rnn/true_rnn/main.py
similarity index 98%
rename from example_rnn/true_rnn/main.py
rename to notebooks/Jan_example_rnn/true_rnn/main.py
index c2283a9..97f1d05 100644
--- a/example_rnn/true_rnn/main.py
+++ b/notebooks/Jan_example_rnn/true_rnn/main.py
@@ -2,10 +2,10 @@
 import torch.nn as nn
 import torch.optim as optim
 import matplotlib.pyplot as plt
-from example_rnn.true_rnn.trueRNN import TrueRNN
+from notebooks.example_rnn.true_rnn.trueRNN import TrueRNN
 
 # Define task directory
-dir = "true_rnn/"
+dir = "example_rnn/true_rnn/"
 
 # hyperparameters
 BATCH_SIZE = 1   # not really used here, since we train on a single trajectory
diff --git a/example_rnn/true_rnn/trueRNN.py b/notebooks/Jan_example_rnn/true_rnn/trueRNN.py
similarity index 100%
rename from example_rnn/true_rnn/trueRNN.py
rename to notebooks/Jan_example_rnn/true_rnn/trueRNN.py
diff --git a/example_rnn/true_rnn/utils.py b/notebooks/Jan_example_rnn/true_rnn/utils.py
similarity index 100%
rename from example_rnn/true_rnn/utils.py
rename to notebooks/Jan_example_rnn/true_rnn/utils.py
diff --git a/example_rnn/true_rnn/y_Lorenz.pt b/notebooks/Jan_example_rnn/true_rnn/y_Lorenz.pt
similarity index 100%
rename from example_rnn/true_rnn/y_Lorenz.pt
rename to notebooks/Jan_example_rnn/true_rnn/y_Lorenz.pt
diff --git a/example_rnn/true_rnn/y_VanDerPol.pt b/notebooks/Jan_example_rnn/true_rnn/y_VanDerPol.pt
similarity index 100%
rename from example_rnn/true_rnn/y_VanDerPol.pt
rename to notebooks/Jan_example_rnn/true_rnn/y_VanDerPol.pt
diff --git a/notebooks/poc_hidden_states.npy b/notebooks/poc_hidden_states.npy
new file mode 100644
index 0000000..a2c2a08
Binary files /dev/null and b/notebooks/poc_hidden_states.npy differ
diff --git a/notebooks/recreateRNN.ipynb b/notebooks/recreateRNN.ipynb
new file mode 100644
index 0000000..70af433
--- /dev/null
+++ b/notebooks/recreateRNN.ipynb
@@ -0,0 +1,224 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "04654375",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import math\n",
+    "import torch.nn as nn\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "os.environ[\"KMP_DUPLICATE_LIB_OK\"] = \"TRUE\"\n",
+    "\n",
+    "\n",
+    "def sine_dataset(T=200, batch=32):\n",
+    "    t = torch.linspace(0, 10, T)\n",
+    "\n",
+    "    data = []\n",
+    "    target = []\n",
+    "\n",
+    "    for _ in range(batch):\n",
+    "        phase = torch.rand(1) * 2 * math.pi\n",
+    "\n",
+    "        x = torch.sin(t + phase)\n",
+    "\n",
+    "        data.append(x[:-1].unsqueeze(-1))     # input u(t)\n",
+    "        target.append(x[1:].unsqueeze(-1))    # output z(t)\n",
+    "\n",
+    "    return torch.stack(data), torch.stack(target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bdfd57ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "class PFC_CTRNN(nn.Module):\n",
+    "    def __init__(self, N=100, input_dim=1, dt=0.001, tau=0.01, noise_std=0.1):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        self.N = N\n",
+    "        self.dt = dt\n",
+    "        self.tau = tau\n",
+    "        self.noise_std = noise_std\n",
+    "\n",
+    "        # recurrent connectivity J\n",
+    "        self.J = nn.Parameter(torch.randn(N, N) / (N ** 0.5))\n",
+    "\n",
+    "        # input weights B\n",
+    "        self.B = nn.Parameter(torch.randn(N, input_dim) * 0.5)\n",
+    "\n",
+    "        # bias current c^x\n",
+    "        self.c = nn.Parameter(torch.zeros(N))\n",
+    "\n",
+    "        # readout weights w, c^z\n",
+    "        self.w = nn.Parameter(torch.randn(N, 1) * 0.1)\n",
+    "        self.cz = nn.Parameter(torch.zeros(1))\n",
+    "\n",
+    "    def step(self, x, u):\n",
+    "\n",
+    "        r = torch.tanh(x)\n",
+    "\n",
+    "        # recurrent + input + bias\n",
+    "        dx = (-x + r @ self.J.T + u @ self.B.T + self.c) / self.tau\n",
+    "\n",
+    "        # noise term (important for biological realism)\n",
+    "        noise = self.noise_std * torch.randn_like(x)\n",
+    "\n",
+    "        x = x + self.dt * dx + noise * (self.dt ** 0.5) #euler update, delta t = 1ms\n",
+    "\n",
+    "        return x, r\n",
+    "\n",
+    "    def forward(self, u_seq, x0=None):\n",
+    "\n",
+    "        T, B, _ = u_seq.shape\n",
+    "\n",
+    "        x = torch.zeros(B, self.N, device=u_seq.device) if x0 is None else x0\n",
+    "\n",
+    "        outputs = []\n",
+    "\n",
+    "        for t in range(T):\n",
+    "            x, r = self.step(x, u_seq[t])\n",
+    "\n",
+    "            z = r @ self.w + self.cz\n",
+    "            outputs.append(z)\n",
+    "\n",
+    "        return torch.stack(outputs, dim=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "7d436f0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 0.7361158728599548\n",
+      "20 0.039235346019268036\n",
+      "40 0.0266824159771204\n",
+      "60 0.02103176712989807\n",
+      "80 0.021333839744329453\n",
+      "100 0.017352469265460968\n",
+      "120 0.016687067225575447\n",
+      "140 0.015198515728116035\n",
+      "160 0.01357941422611475\n",
+      "180 0.01372489519417286\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = PFC_CTRNN(N=100, input_dim=1)\n",
+    "opt = torch.optim.Adam(model.parameters(), lr=1e-3) #paper use hessian free optimization\n",
+    "\n",
+    "for epoch in range(200):\n",
+    "\n",
+    "    u, target = sine_dataset()\n",
+    "\n",
+    "    u = u.transpose(0,1)         # (T,B,1)\n",
+    "    target = target.transpose(0,1)\n",
+    "\n",
+    "    pred = model(u)\n",
+    "\n",
+    "    loss = ((pred - target) ** 2).mean()\n",
+    "\n",
+    "    opt.zero_grad()\n",
+    "    loss.backward()\n",
+    "    opt.step()\n",
+    "\n",
+    "    if epoch % 20 == 0:\n",
+    "        print(epoch, loss.item())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "086f6cae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pred shape: torch.Size([199, 32, 1])\n",
+      "target shape: torch.Size([199, 32, 1])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"pred shape:\", pred.shape)\n",
+    "print(\"target shape:\", target.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "3af77df3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = torch.linspace(0, 20, 200)\n",
+    "x_true = torch.sin(t)\n",
+    "x_seq = x_true[:-1].reshape(-1, 1, 1)   # (T, B, 1)\n",
+    "pred = model(x_seq)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "047aff4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with torch.no_grad():\n",
+    "    plt.plot(x_true[1:].cpu())\n",
+    "    plt.plot(pred[:,0,0].cpu())\n",
+    "    plt.legend([\"True\", \"CTRNN\"])\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "i2dl",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/roadmap_generator/SETUP_INSTRUCTIONS.md b/notebooks/roadmap_generator/SETUP_INSTRUCTIONS.md
similarity index 100%
rename from roadmap_generator/SETUP_INSTRUCTIONS.md
rename to notebooks/roadmap_generator/SETUP_INSTRUCTIONS.md
diff --git a/roadmap_generator/setup_roadmap.py b/notebooks/roadmap_generator/setup_roadmap.py
similarity index 100%
rename from roadmap_generator/setup_roadmap.py
rename to notebooks/roadmap_generator/setup_roadmap.py
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..bff5c74
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,21 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "neuroai-project13"
+version = "0.2.0"
+description = "Information Decomposition in Task-Trained RNNs"
+requires-python = ">=3.11"
+dependencies = [
+    "torch==2.4.1+cu121",
+    "numpy",
+    "scipy",
+    "matplotlib",
+    "seaborn",
+    "neurogym==2.3.1"
+]
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["src*"]
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 3724d07..e3d05d2 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,14 @@
-matplotlib==3.9.2; python_version >= '3.12'
-numpy==2.1.3; python_version >= '3.12'
-torch==2.5.1; python_version >= '3.12'
+# Core ML — CUDA 12.1 build, compatible with all team GPUs + Python 3.11
+torch==2.3.1; python_version >= '3.11'
+--extra-index-url https://download.pytorch.org/whl/cu121
+
+# Numerics & stats
+numpy==1.26.4; python_version >= '3.11'
+scipy==1.13.1; python_version >= '3.11'
+
+# Visualization
+matplotlib==3.9.2; python_version >= '3.11'
+seaborn==0.13.2; python_version >= '3.11'
+
+# NeuroGym tasks
+neurogym==2.3.1; python_version >= '3.11'
\ No newline at end of file
diff --git a/results/model_activations/.gitkeep b/results/model_activations/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/results/model_activations/context/CTRNN_01.npy b/results/model_activations/context/CTRNN_01.npy
new file mode 100644
index 0000000..0b176da
Binary files /dev/null and b/results/model_activations/context/CTRNN_01.npy differ
diff --git a/results/model_activations/perceptual/CTRNN_01.npy b/results/model_activations/perceptual/CTRNN_01.npy
new file mode 100644
index 0000000..2a96ce8
Binary files /dev/null and b/results/model_activations/perceptual/CTRNN_01.npy differ
diff --git a/results/model_weights/.gitkeep b/results/model_weights/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/results/model_weights/CTRNN_ContextDecisionMaking-v0_h20.pt b/results/model_weights/CTRNN_ContextDecisionMaking-v0_h20.pt
new file mode 100644
index 0000000..5259d06
Binary files /dev/null and b/results/model_weights/CTRNN_ContextDecisionMaking-v0_h20.pt differ
diff --git a/results/model_weights/CTRNN_ContextDecisionMaking-v0_h80.pt b/results/model_weights/CTRNN_ContextDecisionMaking-v0_h80.pt
new file mode 100644
index 0000000..0e9c83a
Binary files /dev/null and b/results/model_weights/CTRNN_ContextDecisionMaking-v0_h80.pt differ
diff --git a/results/model_weights/CTRNN_PerceptualDecisionMaking-v0_h20.pt b/results/model_weights/CTRNN_PerceptualDecisionMaking-v0_h20.pt
new file mode 100644
index 0000000..2ca5f2f
Binary files /dev/null and b/results/model_weights/CTRNN_PerceptualDecisionMaking-v0_h20.pt differ
diff --git a/results/model_weights/CTRNN_PerceptualDecisionMaking-v0_h80.pt b/results/model_weights/CTRNN_PerceptualDecisionMaking-v0_h80.pt
new file mode 100644
index 0000000..d41b9b4
Binary files /dev/null and b/results/model_weights/CTRNN_PerceptualDecisionMaking-v0_h80.pt differ
diff --git a/results/model_weights/Elman_ContextDecisionMaking-v0_h20.pt b/results/model_weights/Elman_ContextDecisionMaking-v0_h20.pt
new file mode 100644
index 0000000..a1d0302
Binary files /dev/null and b/results/model_weights/Elman_ContextDecisionMaking-v0_h20.pt differ
diff --git a/results/model_weights/Elman_ContextDecisionMaking-v0_h80.pt b/results/model_weights/Elman_ContextDecisionMaking-v0_h80.pt
new file mode 100644
index 0000000..49ed58e
Binary files /dev/null and b/results/model_weights/Elman_ContextDecisionMaking-v0_h80.pt differ
diff --git a/results/model_weights/Elman_PerceptualDecisionMaking-v0_h20.pt b/results/model_weights/Elman_PerceptualDecisionMaking-v0_h20.pt
new file mode 100644
index 0000000..878bca2
Binary files /dev/null and b/results/model_weights/Elman_PerceptualDecisionMaking-v0_h20.pt differ
diff --git a/results/model_weights/Elman_PerceptualDecisionMaking-v0_h80.pt b/results/model_weights/Elman_PerceptualDecisionMaking-v0_h80.pt
new file mode 100644
index 0000000..f835fbf
Binary files /dev/null and b/results/model_weights/Elman_PerceptualDecisionMaking-v0_h80.pt differ
diff --git a/results/model_weights/context/CTRNN_01.pt b/results/model_weights/context/CTRNN_01.pt
new file mode 100644
index 0000000..673c420
Binary files /dev/null and b/results/model_weights/context/CTRNN_01.pt differ
diff --git a/results/model_weights/perceptual/CTRNN_01.pt b/results/model_weights/perceptual/CTRNN_01.pt
new file mode 100644
index 0000000..622bbdd
Binary files /dev/null and b/results/model_weights/perceptual/CTRNN_01.pt differ
diff --git a/results/pid_outputs/.gitkeep b/results/pid_outputs/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/results/pid_outputs/pid_task_rnn_comparison_h100_mante.png b/results/pid_outputs/pid_task_rnn_comparison_h100_mante.png
new file mode 100644
index 0000000..68ef1f0
Binary files /dev/null and b/results/pid_outputs/pid_task_rnn_comparison_h100_mante.png differ
diff --git a/results/pid_outputs/pid_task_rnn_comparison_h100_mante_medium.png b/results/pid_outputs/pid_task_rnn_comparison_h100_mante_medium.png
new file mode 100644
index 0000000..0b64581
Binary files /dev/null and b/results/pid_outputs/pid_task_rnn_comparison_h100_mante_medium.png differ
diff --git a/results/pid_outputs/pid_task_rnn_comparison_h20.png b/results/pid_outputs/pid_task_rnn_comparison_h20.png
new file mode 100644
index 0000000..bcdfef3
Binary files /dev/null and b/results/pid_outputs/pid_task_rnn_comparison_h20.png differ
diff --git a/results/pid_outputs/pid_task_rnn_comparison_h20_mante.png b/results/pid_outputs/pid_task_rnn_comparison_h20_mante.png
new file mode 100644
index 0000000..7816fb1
Binary files /dev/null and b/results/pid_outputs/pid_task_rnn_comparison_h20_mante.png differ
diff --git a/results/pid_outputs/pid_task_rnn_comparison_h20_new.png b/results/pid_outputs/pid_task_rnn_comparison_h20_new.png
new file mode 100644
index 0000000..5fbabfb
Binary files /dev/null and b/results/pid_outputs/pid_task_rnn_comparison_h20_new.png differ
diff --git a/results/pid_outputs/pid_task_rnn_comparison_h80.png b/results/pid_outputs/pid_task_rnn_comparison_h80.png
new file mode 100644
index 0000000..5df9b52
Binary files /dev/null and b/results/pid_outputs/pid_task_rnn_comparison_h80.png differ
diff --git a/size_comparison/CDM/results/ctrnn_pid_sweep_cdm.py b/size_comparison/CDM/results/ctrnn_pid_sweep_cdm.py
new file mode 100644
index 0000000..fa03d4a
--- /dev/null
+++ b/size_comparison/CDM/results/ctrnn_pid_sweep_cdm.py
@@ -0,0 +1,905 @@
+"""
+ctrnn_pid_sweep_cdm.py
+======================
+Train a CTRNN on ContextDecisionMaking-v0 across hidden sizes [20, 16, 12, 8, 4, 2],
+run time-resolved Gaussian PID analysis at each size, save all plots, and produce a
+summary comparison figure.
+
+Key differences from the PDM version
+--------------------------------------
+- ob_size = 7  (fixation + 4 stimulus channels + 2 context channels)
+- Coherences are [5, 15, 50] not Britten levels
+- Stochastic delay period → MUST be fixed for PID (set delay=0)
+- Two PID targets analysed:
+    (a) signed coherence of the RELEVANT modality  (task difficulty + direction)
+    (b) context label (0 or 1)                     (which rule is active)
+- Stim-end detection uses fixation channel (ch0) same as PDM
+
+Observation vector layout (ob_size = 7)
+-----------------------------------------
+  ch 0 : fixation signal
+  ch 1 : stim1_mod1  (relevant or irrelevant depending on context)
+  ch 2 : stim2_mod1
+  ch 3 : stim1_mod2
+  ch 4 : stim2_mod2
+  ch 5 : context1 cue  (=1 when context==0, i.e. attend modality 1)
+  ch 6 : context2 cue  (=1 when context==1, i.e. attend modality 2)
+
+Usage
+-----
+    python ctrnn_pid_sweep_cdm.py
+    python ctrnn_pid_sweep_cdm.py --hidden_sizes 20 10 5 2
+    python ctrnn_pid_sweep_cdm.py --n_train_batches 1000 --n_test_trials 200
+
+Outputs (results/pid_sweep_cdm/)
+---------------------------------
+    pid_timeseries_h{N}.png    — per hidden size: loss + two PID panels
+    comparison_summary.png     — multi-panel sweep comparison
+    sweep_results.npz          — raw arrays for further analysis
+"""
+
+import argparse
+import os
+import sys
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import neurogym as ngym
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Project module imports (with fallback)
+# ──────────────────────────────────────────────────────────────────────────────
+sys.path.append(os.path.join(os.path.dirname(__file__), '../../..'))    
+
+try:
+    from src.models.ctrnn import CTRNN
+    from src.analysis.gaussian_pid import gaussian_pid_rnn
+    from src.tasks.neurogym_wrapper import create_dataset_generator
+    from src.tasks.data_loader import load_mante_data
+    from src.tasks.mante_config import CONFIG, UNIFORM_COHS, MANTE_TEST_COHS
+    _USING_PROJECT_MODULES = True
+except Exception as exc:
+    _USING_PROJECT_MODULES = False
+    print(f"[WARN] src/ modules could not be imported: {exc}")
+    print("[WARN] using built-in fallbacks instead.")
+
+print(f"Using project modules: {_USING_PROJECT_MODULES}")
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Configuration
+# ──────────────────────────────────────────────────────────────────────────────
+
+TASK        = 'ContextDecisionMaking-v0'
+INPUT_DIM   = 7     # fixation + 4 stim + 2 context channels
+OUTPUT_DIM  = 3     # fixate / choice1 / choice2
+DT          = 20    # ms per timestep
+from pathlib import Path
+
+RESULTS_DIR = Path(__file__).resolve().parents[3] / "results" / "size_comparison" / "CDM_results" / "pid_sweep_cdm"
+RESULTS_DIR.mkdir(parents=True, exist_ok=True)
+DEFAULT_HIDDEN_SIZES   = [2, 4, 8, 12, 16, 20, 40, 60, 80, 100, 150, 200]
+DEFAULT_N_Batches      = 157
+DEFAULT_N_TEST         = 2000
+DEFAULT_LR             = 2e-3
+DEFAULT_BATCH_SIZE     = 1024
+DEFAULT_N_BIPARTITIONS = 50
+DEFAULT_SEED           = 42
+Default_N_EPOCHS       = 50
+
+# Fixed timing for PID evaluation
+# CRITICAL: delay must be 0 — default is stochastic TruncExp(600,300,3000)
+# which makes trials different lengths and breaks time-aligned PID.
+EVAL_TIMING = {
+    'fixation': 200,    # 10 steps @ dt=20
+    'stimulus': 1000,    # 50 steps (rounded to 38 at dt=20)
+    'delay':    0,      # 0 steps  — MUST fix stochastic delay
+    'decision': 100,    # 5  steps
+}
+# Total ≈ 58 steps → seq_len buffer = 70
+
+PALETTE = {
+    'synergy':    '#54A24B',
+    'redundancy': '#4C78A8',
+    'unique1':    '#F58518',
+    'unique2':    '#E45756',
+    'mi_joint':   '#1A1A1A',
+    'stim_end':   '#CC0000',
+    'context0':   '#7B2D8B',
+    'context1':   '#2D8B7B',
+}
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Fallback CTRNN
+# ──────────────────────────────────────────────────────────────────────────────
+
+class _FallbackCTRNN(nn.Module):
+    """Euler-discretised CT-RNN: h(t+dt) = h + alpha*(-h + tanh(W_in u + W_rec h))"""
+    def __init__(self, input_size, hidden_size, output_size, dt=20, tau=100):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.alpha  = dt / tau
+        self.W_in   = nn.Linear(input_size,  hidden_size, bias=True)
+        self.W_rec  = nn.Linear(hidden_size, hidden_size, bias=False)
+        self.W_out  = nn.Linear(hidden_size, output_size, bias=True)
+        nn.init.orthogonal_(self.W_rec.weight)
+        nn.init.xavier_uniform_(self.W_in.weight)
+        nn.init.xavier_uniform_(self.W_out.weight)
+        nn.init.zeros_(self.W_in.bias)
+        nn.init.zeros_(self.W_out.bias)
+
+    def forward(self, x):
+        B, T, _ = x.shape
+        h = torch.zeros(B, self.hidden_size, device=x.device)
+        outputs, hiddens = [], []
+        for t in range(T):
+            h = h + self.alpha * (
+                -h + torch.tanh(self.W_in(x[:, t]) + self.W_rec(h))
+            )
+            outputs.append(self.W_out(h))
+            hiddens.append(h)
+        return torch.stack(outputs, 1), torch.stack(hiddens, 1)
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Fallback Gaussian analytic PID  (MMI-PID, Barrett 2015)
+# ──────────────────────────────────────────────────────────────────────────────
+
+def _fallback_gaussian_pid(activations, target, timestep=None,
+                            n_bipartitions=50, seed=DEFAULT_SEED,
+                            regularization=1e-5, **kwargs):
+    """
+    activations : (N, T, H)
+    target      : (N,)  continuous scalar
+    Returns dict with synergy / redundancy / unique1 / unique2 / mi_joint
+    each of shape (T,) if timestep is None, else scalar.
+    """
+    rng = np.random.default_rng(seed)
+    N, T, H = activations.shape
+    y = target.astype(np.float64)
+
+    def _mi(X):
+        """Gaussian MI between multivariate X (N,d) and scalar y (N,) via R²."""
+        X_ = X - X.mean(0)
+        y_ = y - y.mean()
+        Sxx = X_.T @ X_ / N + regularization * np.eye(X_.shape[1])
+        Sxy = X_.T @ y_ / N
+        var_y = np.var(y_) + 1e-12
+        try:
+            r2 = float(Sxy @ np.linalg.solve(Sxx, Sxy)) / var_y
+            r2 = np.clip(r2, 0.0, 1.0 - 1e-9)
+        except np.linalg.LinAlgError:
+            return 0.0
+        return float(-0.5 * np.log(1.0 - r2) / np.log(2))
+
+    def _pid_at_t(h_t):
+        syn_l, red_l, u1_l, u2_l, mi_l = [], [], [], [], []
+        half = max(H // 2, 1)
+        for _ in range(n_bipartitions):
+            idx = rng.permutation(H)
+            i1, i2 = idx[:half], idx[half:half * 2]
+            mi1  = _mi(h_t[:, i1])
+            mi2  = _mi(h_t[:, i2])
+            mi_j = _mi(h_t[:, np.concatenate([i1, i2])])
+            red  = min(mi1, mi2)
+            syn  = max(mi_j - mi1 - mi2 + red, 0.0)
+            u1   = max(mi1 - red, 0.0)
+            u2   = max(mi2 - red, 0.0)
+            syn_l.append(syn); red_l.append(red)
+            u1_l.append(u1);   u2_l.append(u2)
+            mi_l.append(mi_j)
+        return {k: float(np.mean(v))
+                for k, v in zip(['syn','red','u1','u2','mi'],
+                                [syn_l,red_l,u1_l,u2_l,mi_l])}
+
+    def _wrap(r):
+        return {'synergy': r['syn'], 'redundancy': r['red'],
+                'unique1': r['u1'], 'unique2': r['u2'], 'mi_joint': r['mi']}
+
+    if timestep is not None:
+        return _wrap(_pid_at_t(activations[:, timestep].astype(np.float64)))
+
+    results = {k: [] for k in ['syn','red','u1','u2','mi']}
+    for t in range(T):
+        r = _pid_at_t(activations[:, t].astype(np.float64))
+        for k in results:
+            results[k].append(r[k])
+    return {'synergy':    np.array(results['syn']),
+            'redundancy': np.array(results['red']),
+            'unique1':    np.array(results['u1']),
+            'unique2':    np.array(results['u2']),
+            'mi_joint':   np.array(results['mi'])}
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Dataset helpers
+# ──────────────────────────────────────────────────────────────────────────────
+
+def resolve_dataset_paths(data_dir=None, train_path=None, val_path=None, test_path=None):
+    if data_dir is not None:
+        train_path = train_path or os.path.join(data_dir, 'train.npz')
+        val_path   = val_path or os.path.join(data_dir, 'val.npz')
+        test_path  = test_path or os.path.join(data_dir, 'test_uniform.npz')
+
+    resolved = {
+        'train_path': train_path,
+        'val_path': val_path,
+        'test_path': test_path,
+    }
+    for name, path in resolved.items():
+        if path is not None and not os.path.exists(path):
+            raise FileNotFoundError(f"Dataset file not found: {path}")
+    return resolved
+
+
+def build_npz_loader(npz_path, batch_size, shuffle=False):
+    if npz_path is None:
+        return None
+    return load_mante_data(npz_path, batch_size=batch_size, shuffle=shuffle)
+
+
+def load_npz_split_as_tensors(npz_path, device):
+    if npz_path is None:
+        return None, None, None, None
+
+    data = np.load(npz_path)
+    obs = data['observations'].astype(np.float32)
+    labels = data['labels'].astype(np.int64)
+    cohs = np.array(data['coherences'], dtype=np.float32)
+
+    ctx_key = 'contexts' if 'contexts' in data else 'contexts'
+    ctxs = np.array(data[ctx_key], dtype=np.int32)
+
+    return (
+        torch.from_numpy(obs).to(device),
+        torch.from_numpy(labels).to(device),
+        cohs,
+        ctxs,
+    )
+
+
+def _fallback_dataset_generator(task, dt=DT, batch_size=DEFAULT_BATCH_SIZE,
+                                 seq_len=120):
+    
+    env = ngym.make(task, dt=dt, timing={'delay': 0}, use_expl_context=True)
+    env.reset(seed=DEFAULT_SEED)
+    print(type(env))
+    print(env.observation_space.shape)
+
+    def _gen():
+        obs_b, gt_b = [], []
+        for _ in range(batch_size):
+            env.unwrapped.new_trial()
+            ob = env.unwrapped.ob
+            gt = env.unwrapped.gt
+            T  = ob.shape[0]
+            t  = min(T, seq_len)
+            ob_p = np.zeros((seq_len, ob.shape[1]), dtype=np.float32)
+            gt_p = np.zeros((seq_len,), dtype=np.int64)
+            ob_p[:t] = ob[:t]
+            gt_p[:t] = gt[:t]
+            if t < seq_len:
+                ob_p[t:] = ob[-1]
+                gt_p[t:] = gt[-1]
+            obs_b.append(ob_p)
+            gt_b.append(gt_p)
+        return np.stack(obs_b, axis=1), np.stack(gt_b, axis=1)  # (T,B,F),(T,B)
+
+    return _gen
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Select implementations
+# ──────────────────────────────────────────────────────────────────────────────
+
+CTRNNImpl    = CTRNN                    if _USING_PROJECT_MODULES else _FallbackCTRNN
+pid_fn       = gaussian_pid_rnn         if _USING_PROJECT_MODULES else _fallback_gaussian_pid
+make_dataset = create_dataset_generator if _USING_PROJECT_MODULES else _fallback_dataset_generator
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Wrapped CTRNN — uniform (outputs, hidden_states) interface
+# ──────────────────────────────────────────────────────────────────────────────
+
+class WrappedCTRNN(nn.Module):
+    def __init__(self, input_dim, hidden_size):
+        super().__init__()
+        if _USING_PROJECT_MODULES:
+            self.model = CTRNNImpl(input_size=input_dim,
+                                   hidden_size=hidden_size,
+                                   output_size=OUTPUT_DIM)
+        else:
+            self.model = CTRNNImpl(input_size=input_dim,
+                                   hidden_size=hidden_size,
+                                   output_size=OUTPUT_DIM, dt=DT)
+
+    def forward(self, x):
+        if _USING_PROJECT_MODULES:
+            outputs, _, hidden_states = self.model(x, return_dynamics=True)
+        else:
+            outputs, hidden_states = self.model(x)
+        return outputs, hidden_states
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Loss — only penalise decision timesteps
+# ──────────────────────────────────────────────────────────────────────────────
+
+def masked_cross_entropy(outputs, targets):
+    mask = targets != 0
+    if mask.sum() == 0:
+        return torch.tensor(0.0, requires_grad=True, device=outputs.device)
+    return F.cross_entropy(outputs[mask], targets[mask])
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Training
+# ──────────────────────────────────────────────────────────────────────────────
+
+def train_ctrnn(hidden_size, n_batches, device, weights_dir,
+                n_epochs=5, seed=DEFAULT_SEED,
+                train_loader=None, val_loader=None):
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+
+    model     = WrappedCTRNN(INPUT_DIM, hidden_size).to(device)
+    optimizer = optim.Adam(model.parameters(), lr=DEFAULT_LR)
+
+    if train_loader is None:
+        if _USING_PROJECT_MODULES:
+            dataset = make_dataset(CONFIG)
+        else:
+            dataset = make_dataset(TASK, dt=DT)
+
+    print(f"\n  Training CTRNN  hidden={hidden_size}  epochs={n_epochs}")
+    losses = []
+
+    for epoch in range(n_epochs):
+        model.train()
+        epoch_losses = []
+
+        if train_loader is not None:
+            for batch_idx, batch in enumerate(train_loader):
+                inputs, targets, _, _, _ = batch
+                inputs = inputs.to(device)
+                targets = targets.to(device)
+
+                optimizer.zero_grad()
+                outputs, _ = model(inputs)
+                loss = masked_cross_entropy(outputs, targets)
+                loss.backward()
+                torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+                optimizer.step()
+
+                loss_val = loss.item()
+                epoch_losses.append(loss_val)
+                losses.append(loss_val)
+
+                if (batch_idx + 1) % 200 == 0 or (batch_idx + 1) == len(train_loader):
+                    print(f"    epoch {epoch+1}/{n_epochs} batch {batch_idx+1:>4}/{len(train_loader)}  "
+                          f"loss={np.mean(losses[-50:]):.4f}")
+        else:
+            for i in range(n_batches):
+                inputs_np, targets_np = dataset()
+                inputs  = torch.from_numpy(inputs_np).float().transpose(0, 1).to(device)
+                targets = torch.from_numpy(targets_np).long().transpose(0, 1).to(device)
+
+                optimizer.zero_grad()
+                outputs, _ = model(inputs)
+                loss = masked_cross_entropy(outputs, targets)
+                loss.backward()
+                torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+                optimizer.step()
+
+                loss_val = loss.item()
+                epoch_losses.append(loss_val)
+                losses.append(loss_val)
+                batch_num = epoch * n_batches + i + 1
+                if (i + 1) % 200 == 0 or (i + 1) == n_batches:
+                    print(f"    epoch {epoch+1}/{n_epochs} batch {i+1:>4}/{n_batches}  "
+                          f"loss={np.mean(losses[-50:]):.4f}")
+
+        if val_loader is not None:
+            model.eval()
+            val_losses = []
+            with torch.no_grad():
+                for inputs, targets, _, _, _ in val_loader:
+                    inputs = inputs.to(device)
+                    targets = targets.to(device)
+                    outputs, _ = model(inputs)
+                    val_loss = masked_cross_entropy(outputs, targets)
+                    val_losses.append(val_loss.item())
+            print(f"    epoch {epoch+1}/{n_epochs} validation_loss={np.mean(val_losses):.4f}")
+
+    save_path = os.path.join(weights_dir, f'ctrnn_cdm_h{hidden_size}.pt')
+    torch.save(model.state_dict(), save_path)
+    print(f"    Saved → {save_path}")
+    return model, losses
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Test batch — fixed timing, collect rich metadata
+# ──────────────────────────────────────────────────────────────────────────────
+
+def get_test_batch(n_trials, device, config, seed=DEFAULT_SEED):
+    """
+    Returns
+    -------
+    inputs          : (B, T, 7) tensor on device
+    targets         : (B, T) tensor on device
+    signed_coh_rel  : (B,) np array
+    context_labels  : (B,) np array
+    """
+
+    # Create environment using same config as training
+    dataset = create_dataset_generator(config)
+    env = dataset.env
+
+    env.reset(seed=seed)
+
+    obs_l, gt_l, coh_l, ctx_l = [], [], [], []
+
+    for _ in range(n_trials):
+
+        env.unwrapped.new_trial()
+
+        ob = env.unwrapped.ob.astype(np.float32)
+        gt = env.unwrapped.gt.astype(np.int64)
+        trial = env.unwrapped.trial
+
+        context = int(trial["context"])
+        ground_truth = int(trial["ground_truth"])
+
+        # Verify once with:
+        # print(trial.keys())
+
+        coh_0 = float(trial["coh_1"])
+        coh_1 = float(trial["coh_2"])
+
+        # Relevant modality depends on context
+        rel_coh = coh_0 if context == 0 else coh_1
+
+        sign = 1 if ground_truth == 1 else -1
+        signed_coh = sign * rel_coh
+
+        obs_l.append(ob)
+        gt_l.append(gt)
+        coh_l.append(signed_coh)
+        ctx_l.append(context)
+
+    inputs = torch.tensor(
+        np.stack(obs_l),
+        dtype=torch.float32,
+        device=device,
+    )
+
+    targets = torch.tensor(
+        np.stack(gt_l),
+        dtype=torch.long,
+        device=device,
+    )
+
+    cohs = np.array(coh_l, dtype=np.float32)
+    ctxs = np.array(ctx_l, dtype=np.int32)
+    print("Current cohs:", env.unwrapped.cohs)
+    sampled = []
+
+    for _ in range(100):
+        env.unwrapped.new_trial()
+        sampled.append(env.unwrapped.trial["coh_1"])
+
+    print("Unique sampled coherences:")
+    print(sorted(set(sampled))[:10])
+    return inputs, targets, cohs, ctxs
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Stim-end detection (shared helper)
+# ──────────────────────────────────────────────────────────────────────────────
+
+def find_stim_end(inputs_np):
+    """
+    inputs_np : (B, T, 7)
+    Returns median timestep of last stimulus step across trials.
+    Stimulus period = fixation channel (ch0) is 0.
+    """
+    fix_ch    = inputs_np[:, :, 0]  # (B, T)
+    stim_ends = np.array([
+        np.where(fix_ch[b] < 0.5)[0][-1]
+        if (fix_ch[b] < 0.5).any() else 0
+        for b in range(fix_ch.shape[0])
+    ])
+    return int(np.median(stim_ends))
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# PID analysis — run for two targets
+# ──────────────────────────────────────────────────────────────────────────────
+
+def run_pid(model, inputs, targets, cohs, ctxs, n_bipartitions, device):
+    """
+    Runs time-resolved Gaussian PID with two different targets:
+      'coherence' — signed coherence of the relevant modality
+      'context'   — which rule is active (0 or 1)
+
+    Returns dict with keys 'coherence' and 'context', each containing
+    the standard PID atom arrays plus stim_end and seq_len.
+    """
+    model.eval()
+    with torch.no_grad():
+        _, h_seq = model(inputs)               # (B, T, H)
+    acts       = h_seq.cpu().numpy()           # (B, T, H)
+    inputs_np  = inputs.cpu().numpy()
+    stim_end   = find_stim_end(inputs_np)
+
+    results = {}
+
+    for target_name, target_vals in [('coherence', cohs.astype(float)),
+                                      ('context',   ctxs.astype(float))]:
+        print(f"    PID target: {target_name}  "
+              f"| H={acts.shape[2]}  T={acts.shape[1]}")
+
+        pid_out = pid_fn(
+            activations=acts,
+            target=target_vals,
+            timestep=None,
+            n_bipartitions=n_bipartitions,
+            seed=DEFAULT_SEED,
+            regularization=1e-5,
+        )
+
+        results[target_name] = {
+            'synergy':    np.array(pid_out['synergy']),
+            'redundancy': np.array(pid_out['redundancy']),
+            'unique1':    np.array(pid_out['unique1']),
+            'unique2':    np.array(pid_out['unique2']),
+            'mi_joint':   np.array(pid_out['mi_joint']),
+            'stim_end':   stim_end,
+            'seq_len':    acts.shape[1],
+        }
+
+        tend = stim_end
+        print(f"      Stim-end t={tend} | "
+              f"Syn={results[target_name]['synergy'][tend]:.4f}b | "
+              f"Red={results[target_name]['redundancy'][tend]:.4f}b")
+
+    results['activations'] = acts  # (B, T, H)
+    return results
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Plot — per hidden size (loss + two PID panels)
+# ──────────────────────────────────────────────────────────────────────────────
+
+def plot_timeseries(pid_results, hidden_size, losses, save_path):
+    """
+    Three-panel figure:
+      Left  : training loss curve
+      Middle: PID with coherence as target
+      Right : PID with context as target
+    """
+    fig, axes = plt.subplots(1, 3, figsize=(18, 5),
+                             gridspec_kw={'width_ratios': [1, 2, 2]})
+    fig.suptitle(
+        f'CTRNN  hidden={hidden_size}  —  ContextDecisionMaking-v0',
+        fontsize=13, fontweight='bold', y=1.01
+    )
+
+    # ── Loss ──
+    ax = axes[0]
+    smooth = np.convolve(losses, np.ones(20) / 20, mode='valid')
+    ax.plot(losses, alpha=0.2, color='steelblue', lw=0.8)
+    ax.plot(np.arange(19, len(losses)), smooth,
+            color='steelblue', lw=2, label='Loss (20-batch avg)')
+    ax.set_xlabel('Training batch')
+    ax.set_ylabel('Masked CE loss')
+    ax.set_title('Training loss')
+    ax.legend(fontsize=8)
+    ax.grid(alpha=0.3)
+
+    # ── PID panels ──
+    panel_cfg = [
+        ('coherence', 'PID — Target: signed coherence\n(relevant modality)'),
+        ('context',   'PID — Target: context label\n(which rule is active)'),
+    ]
+
+    for ax, (tgt_name, title) in zip(axes[1:], panel_cfg):
+        pid  = pid_results[tgt_name]
+        T    = pid['seq_len']
+        t    = np.arange(T)
+        tend = pid['stim_end']
+
+        ax.plot(t, pid['mi_joint'],   label='Total MI',   color=PALETTE['mi_joint'],   lw=2.5, ls=':')
+        ax.plot(t, pid['synergy'],    label='Synergy',    color=PALETTE['synergy'],    lw=2.5)
+        ax.plot(t, pid['redundancy'], label='Redundancy', color=PALETTE['redundancy'], lw=2.5)
+        ax.plot(t, pid['unique1'],    label='Unique 1',   color=PALETTE['unique1'],    lw=1.5, alpha=0.8)
+        ax.plot(t, pid['unique2'],    label='Unique 2',   color=PALETTE['unique2'],    lw=1.5, alpha=0.8)
+
+        ax.axvline(tend, color=PALETTE['stim_end'], ls='--', lw=1.5,
+                   label=f'Stim end (t={tend})')
+        ax.axvspan(0, tend, color='gray', alpha=0.08)
+
+        ax.set_title(title, fontsize=10)
+        ax.set_xlabel(f'Timestep (dt={DT}ms)')
+        ax.set_ylabel('Information (bits)')
+        ax.legend(fontsize=8, loc='upper left')
+        ax.grid(alpha=0.3)
+
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    plt.close()
+    print(f"    Saved → {save_path}")
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Plot — comparison summary across all hidden sizes
+# ──────────────────────────────────────────────────────────────────────────────
+
+def plot_comparison(all_results, save_path):
+    hidden_sizes = sorted(all_results.keys(), reverse=True)
+    n    = len(hidden_sizes)
+    cmap = plt.cm.viridis(np.linspace(0.15, 0.85, n))
+
+    fig = plt.figure(figsize=(18, 16))
+    gs  = gridspec.GridSpec(4, 3, figure=fig, hspace=0.5, wspace=0.35)
+
+    # Row 0: synergy over time — coherence target (left) and context target (right)
+    ax_syn_coh = fig.add_subplot(gs[0, :2])
+    ax_syn_ctx = fig.add_subplot(gs[0, 2])
+
+    # Row 1: redundancy over time — coherence (left) and context (right)
+    ax_red_coh = fig.add_subplot(gs[1, :2])
+    ax_red_ctx = fig.add_subplot(gs[1, 2])
+
+    # Row 2: bar charts at stim-end
+    ax_bar_coh = fig.add_subplot(gs[2, 0])
+    ax_bar_ctx = fig.add_subplot(gs[2, 1])
+    ax_ratio   = fig.add_subplot(gs[2, 2])
+
+    # Row 3: scalar summaries
+    ax_loss    = fig.add_subplot(gs[3, 0])
+    ax_syn_end = fig.add_subplot(gs[3, 1])
+    ax_red_end = fig.add_subplot(gs[3, 2])
+
+    fig.suptitle(
+        'CTRNN PID Sweep — ContextDecisionMaking-v0\n'
+        'Left/Middle: coherence target  |  Right: context target',
+        fontsize=13, fontweight='bold'
+    )
+
+    stim_end_ref = all_results[hidden_sizes[0]]['coherence']['stim_end']
+
+    # ── Synergy over time ──
+    for panel, tgt in [(ax_syn_coh, 'coherence'), (ax_syn_ctx, 'context')]:
+        for i, h in enumerate(hidden_sizes):
+            pid = all_results[h][tgt]
+            panel.plot(np.arange(pid['seq_len']), pid['synergy'],
+                       color=cmap[i], lw=2, label=f'H={h}')
+        panel.axvline(stim_end_ref, color='red', ls='--', lw=1, alpha=0.6)
+        panel.set_title(f'Synergy over time — target: {tgt}')
+        panel.set_ylabel('Synergy (bits)')
+        panel.set_xlabel(f'Timestep (dt={DT}ms)')
+        panel.legend(ncol=n, fontsize=7, loc='upper left')
+        panel.grid(alpha=0.25)
+
+    # ── Redundancy over time ──
+    for panel, tgt in [(ax_red_coh, 'coherence'), (ax_red_ctx, 'context')]:
+        for i, h in enumerate(hidden_sizes):
+            pid = all_results[h][tgt]
+            panel.plot(np.arange(pid['seq_len']), pid['redundancy'],
+                       color=cmap[i], lw=2, label=f'H={h}')
+        panel.axvline(stim_end_ref, color='red', ls='--', lw=1, alpha=0.6)
+        panel.set_title(f'Redundancy over time — target: {tgt}')
+        panel.set_ylabel('Redundancy (bits)')
+        panel.set_xlabel(f'Timestep (dt={DT}ms)')
+        panel.legend(ncol=n, fontsize=7, loc='upper left')
+        panel.grid(alpha=0.25)
+
+    # ── Bar charts at stim-end ──
+    atoms       = ['synergy', 'redundancy', 'unique1', 'unique2']
+    atom_colors = [PALETTE['synergy'], PALETTE['redundancy'],
+                   PALETTE['unique1'], PALETTE['unique2']]
+    x      = np.arange(len(hidden_sizes))
+    width  = 0.18
+    offs   = np.linspace(-0.27, 0.27, 4)
+
+    for ax_bar, tgt in [(ax_bar_coh, 'coherence'), (ax_bar_ctx, 'context')]:
+        for j, (atom, color) in enumerate(zip(atoms, atom_colors)):
+            vals = [all_results[h][tgt][atom][all_results[h][tgt]['stim_end']]
+                    for h in hidden_sizes]
+            ax_bar.bar(x + offs[j], vals, width,
+                       label=atom.capitalize(), color=color, alpha=0.85)
+        ax_bar.set_xticks(x)
+        ax_bar.set_xticklabels([f'H={h}' for h in hidden_sizes], fontsize=8)
+        ax_bar.set_title(f'PID atoms at stim-end — target: {tgt}')
+        ax_bar.set_ylabel('Information (bits)')
+        ax_bar.legend(fontsize=7)
+        ax_bar.grid(axis='y', alpha=0.3)
+
+    # ── Synergy ratio: Syn/(Syn+Red) at stim-end for both targets ──
+    x2 = np.arange(len(hidden_sizes))
+    w2 = 0.35
+    for j, (tgt, color) in enumerate([('coherence', PALETTE['synergy']),
+                                       ('context',   PALETTE['context0'])]):
+        ratios = []
+        for h in hidden_sizes:
+            pid  = all_results[h][tgt]
+            tend = pid['stim_end']
+            syn  = pid['synergy'][tend]
+            red  = pid['redundancy'][tend]
+            denom = syn + red
+            ratios.append(syn / denom if denom > 1e-10 else 0.0)
+        ax_ratio.bar(x2 + (j - 0.5) * w2, ratios, w2,
+                     label=f'target: {tgt}', color=color, alpha=0.8)
+    ax_ratio.axhline(0.5, color='gray', ls='--', lw=1)
+    ax_ratio.set_xticks(x2)
+    ax_ratio.set_xticklabels([f'H={h}' for h in hidden_sizes], fontsize=8)
+    ax_ratio.set_ylim(0, 1)
+    ax_ratio.set_title('Synergy ratio at stim-end\n[Syn / (Syn+Red)]')
+    ax_ratio.set_ylabel('Synergy ratio')
+    ax_ratio.legend(fontsize=7)
+    ax_ratio.grid(axis='y', alpha=0.3)
+
+    # ── Final loss vs hidden size ──
+    final_losses = [np.mean(all_results[h]['losses'][-50:])
+                    for h in hidden_sizes]
+    ax_loss.plot([str(h) for h in hidden_sizes], final_losses,
+                 'o-', color='steelblue', lw=2, ms=8)
+    ax_loss.set_title('Final training loss vs hidden size')
+    ax_loss.set_xlabel('Hidden size')
+    ax_loss.set_ylabel('Loss (last 50 batches avg)')
+    ax_loss.grid(alpha=0.3)
+
+    # ── Synergy at stim-end vs hidden size (both targets) ──
+    for tgt, color, marker in [('coherence', PALETTE['synergy'],    'o'),
+                                ('context',   PALETTE['context0'],   's')]:
+        vals = [all_results[h][tgt]['synergy'][all_results[h][tgt]['stim_end']]
+                for h in hidden_sizes]
+        ax_syn_end.plot([str(h) for h in hidden_sizes], vals,
+                        f'{marker}-', color=color, lw=2, ms=8, label=f'target: {tgt}')
+    ax_syn_end.set_title('Synergy at stim-end vs hidden size')
+    ax_syn_end.set_xlabel('Hidden size')
+    ax_syn_end.set_ylabel('Synergy (bits)')
+    ax_syn_end.legend(fontsize=8)
+    ax_syn_end.grid(alpha=0.3)
+
+    # ── Redundancy at stim-end vs hidden size (both targets) ──
+    for tgt, color, marker in [('coherence', PALETTE['redundancy'],  'o'),
+                                ('context',   PALETTE['context1'],    's')]:
+        vals = [all_results[h][tgt]['redundancy'][all_results[h][tgt]['stim_end']]
+                for h in hidden_sizes]
+        ax_red_end.plot([str(h) for h in hidden_sizes], vals,
+                        f'{marker}-', color=color, lw=2, ms=8, label=f'target: {tgt}')
+    ax_red_end.set_title('Redundancy at stim-end vs hidden size')
+    ax_red_end.set_xlabel('Hidden size')
+    ax_red_end.set_ylabel('Redundancy (bits)')
+    ax_red_end.legend(fontsize=8)
+    ax_red_end.grid(alpha=0.3)
+
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    plt.close()
+    print(f"\nComparison figure saved → {save_path}")
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Main
+# ──────────────────────────────────────────────────────────────────────────────
+
+def main(hidden_sizes, n_train_batches, n_test_trials, batch_size=DEFAULT_BATCH_SIZE,
+         data_dir=None, train_path=None, val_path=None, test_path=None, n_epochs=1):
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(f"Device      : {device}")
+    print(f"Task        : {TASK}")
+    print(f"Input dim   : {INPUT_DIM}  (fixation + 4 stim channels + 2 context channels)")
+    hidden_sizes = sorted(hidden_sizes)
+    print(f"Hidden sizes: {hidden_sizes}")
+    print(f"Train batches per model: {n_train_batches}")
+    print(f"Test trials for PID    : {n_test_trials}")
+    print(f"Eval timing (fixed)    : {EVAL_TIMING}")
+
+    os.makedirs(RESULTS_DIR, exist_ok=True)
+    weights_dir = os.path.join(RESULTS_DIR, 'weights')
+    os.makedirs(weights_dir, exist_ok=True)
+
+    # One shared test batch for all models
+    if test_path is not None:
+        print("\nLoading fixed test split from NPZ...")
+        inputs, targets, cohs, ctxs = load_npz_split_as_tensors(test_path, device)
+        print(f"  inputs : {tuple(inputs.shape)}")
+        print(f"  cohs   : range [{cohs.min():.1f}, {cohs.max():.1f}]  "
+              f"std={cohs.std():.2f}")
+        print(f"  context: {np.bincount(ctxs)}  (count per context label)")
+    else:
+        print("\nGenerating fixed test batch...")
+        inputs, targets, cohs, ctxs = get_test_batch(
+        n_test_trials,
+        device,
+        CONFIG,
+        seed=DEFAULT_SEED
+        )
+        print(f"  inputs : {tuple(inputs.shape)}")
+        print(f"  cohs   : range [{cohs.min():.1f}, {cohs.max():.1f}]  "
+              f"std={cohs.std():.2f}")
+        print(f"  context: {np.bincount(ctxs)}  (count per context label)")
+
+    all_results = {}
+
+    for h in hidden_sizes:
+        print(f"\n{'='*60}")
+        print(f"  Hidden size = {h}")
+        print(f"{'='*60}")
+
+        train_loader = build_npz_loader(train_path, batch_size=batch_size, shuffle=True) if train_path is not None else None
+        val_loader   = build_npz_loader(val_path, batch_size=batch_size, shuffle=False) if val_path is not None else None
+
+        model, losses = train_ctrnn(
+            h,
+            n_train_batches,
+            device,
+            weights_dir,
+            n_epochs=n_epochs,
+            train_loader=train_loader,
+            val_loader=val_loader,
+        )
+
+        n_bipartitions = 2 * h
+        print(f"  Running PID (n_bipartitions={n_bipartitions})...")
+        pid_data = run_pid(model, inputs, targets, cohs, ctxs,
+                           n_bipartitions, device)
+
+        all_results[h] = {**pid_data, 'losses': losses}
+
+        ts_path = os.path.join(RESULTS_DIR, f'pid_timeseries_h{h}.png')
+        plot_timeseries(pid_data, h, losses, ts_path)
+
+    # Comparison summary
+    comp_path = os.path.join(RESULTS_DIR, 'comparison_summary.png')
+    plot_comparison(all_results, comp_path)
+
+    # Save raw arrays
+    npz_path = os.path.join(RESULTS_DIR, 'sweep_results.npz')
+    save_dict = {}
+    for h, r in all_results.items():
+        for tgt in ('coherence', 'context'):
+            for atom in ('synergy', 'redundancy', 'unique1', 'unique2', 'mi_joint'):
+                save_dict[f'h{h}_{tgt}_{atom}'] = r[tgt][atom]
+            save_dict[f'h{h}_{tgt}_stim_end'] = np.array(r[tgt]['stim_end'])
+        save_dict[f'h{h}_losses'] = np.array(r['losses'])
+        save_dict[f'h{h}_activations'] = r['activations']  # (B, T, H)
+    np.savez_compressed(npz_path, **save_dict)
+    print(f"Raw results → {npz_path}")
+    print("\nDone.")
+
+
+if __name__ == '__main__':
+    p = argparse.ArgumentParser(
+        description='CTRNN PID sweep — ContextDecisionMaking-v0')
+    p.add_argument('--hidden_sizes',    nargs='+', type=int,
+                   default=DEFAULT_HIDDEN_SIZES)
+    p.add_argument('--n_train_batches', type=int, default=DEFAULT_N_Batches)
+    p.add_argument('--n_test_trials',   type=int, default=DEFAULT_N_TEST)
+    p.add_argument('--batch_size',      type=int, default=DEFAULT_BATCH_SIZE)
+    p.add_argument('--n_epochs', type=int, default=Default_N_EPOCHS,
+                   help='Number of training epochs per model')
+    p.add_argument('--train_path', type=str, default=None,
+                   help='Path to NPZ file for training data (optional)')
+    p.add_argument('--val_path', type=str, default=None,
+                   help='Path to NPZ file for validation data (optional)')
+    p.add_argument('--test_path', type=str, default=None,
+                   help='Path to NPZ file for test data (optional)')
+    args = p.parse_args()
+
+    main(
+        hidden_sizes    = args.hidden_sizes,
+        n_train_batches = args.n_train_batches,
+        n_test_trials   = args.n_test_trials,
+        batch_size      = args.batch_size,
+        train_path      = args.train_path,
+        val_path        = args.val_path,
+        test_path       = args.test_path,
+        n_epochs        = args.n_epochs,
+    )
\ No newline at end of file
diff --git a/size_comparison/CDM/results/pid_barplot_hidden_size.png b/size_comparison/CDM/results/pid_barplot_hidden_size.png
new file mode 100644
index 0000000..a5fc0d6
Binary files /dev/null and b/size_comparison/CDM/results/pid_barplot_hidden_size.png differ
diff --git a/size_comparison/CDM/results/pid_sweep_cdm/comparison_summary.png b/size_comparison/CDM/results/pid_sweep_cdm/comparison_summary.png
new file mode 100644
index 0000000..cba8f41
Binary files /dev/null and b/size_comparison/CDM/results/pid_sweep_cdm/comparison_summary.png differ
diff --git a/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h100.png b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h100.png
new file mode 100644
index 0000000..357f2d8
Binary files /dev/null and b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h100.png differ
diff --git a/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h12.png b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h12.png
new file mode 100644
index 0000000..ec633d5
Binary files /dev/null and b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h12.png differ
diff --git a/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h150.png b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h150.png
new file mode 100644
index 0000000..b218bc3
Binary files /dev/null and b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h150.png differ
diff --git a/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h16.png b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h16.png
new file mode 100644
index 0000000..106fb70
Binary files /dev/null and b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h16.png differ
diff --git a/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h2.png b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h2.png
new file mode 100644
index 0000000..189b5e6
Binary files /dev/null and b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h2.png differ
diff --git a/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h20.png b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h20.png
new file mode 100644
index 0000000..3081c54
Binary files /dev/null and b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h20.png differ
diff --git a/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h200.png b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h200.png
new file mode 100644
index 0000000..49e428e
Binary files /dev/null and b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h200.png differ
diff --git a/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h4.png b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h4.png
new file mode 100644
index 0000000..ef5c021
Binary files /dev/null and b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h4.png differ
diff --git a/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h40.png b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h40.png
new file mode 100644
index 0000000..3888cbe
Binary files /dev/null and b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h40.png differ
diff --git a/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h60.png b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h60.png
new file mode 100644
index 0000000..6586066
Binary files /dev/null and b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h60.png differ
diff --git a/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h8.png b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h8.png
new file mode 100644
index 0000000..7808503
Binary files /dev/null and b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h8.png differ
diff --git a/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h80.png b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h80.png
new file mode 100644
index 0000000..fde013e
Binary files /dev/null and b/size_comparison/CDM/results/pid_sweep_cdm/pid_timeseries_h80.png differ
diff --git a/size_comparison/CDM/results/pid_vs_hidden_size.png b/size_comparison/CDM/results/pid_vs_hidden_size.png
new file mode 100644
index 0000000..6ac6e39
Binary files /dev/null and b/size_comparison/CDM/results/pid_vs_hidden_size.png differ
diff --git a/size_comparison/CDM/results/plot_pid.ipynb b/size_comparison/CDM/results/plot_pid.ipynb
new file mode 100644
index 0000000..a623037
--- /dev/null
+++ b/size_comparison/CDM/results/plot_pid.ipynb
@@ -0,0 +1,500 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4a282ee0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Keys in the NPZ file: ['h2_coherence_synergy', 'h2_coherence_redundancy', 'h2_coherence_unique1', 'h2_coherence_unique2', 'h2_coherence_mi_joint', 'h2_coherence_stim_end', 'h2_context_synergy', 'h2_context_redundancy', 'h2_context_unique1', 'h2_context_unique2', 'h2_context_mi_joint', 'h2_context_stim_end', 'h2_losses', 'h2_activations', 'h4_coherence_synergy', 'h4_coherence_redundancy', 'h4_coherence_unique1', 'h4_coherence_unique2', 'h4_coherence_mi_joint', 'h4_coherence_stim_end', 'h4_context_synergy', 'h4_context_redundancy', 'h4_context_unique1', 'h4_context_unique2', 'h4_context_mi_joint', 'h4_context_stim_end', 'h4_losses', 'h4_activations', 'h8_coherence_synergy', 'h8_coherence_redundancy', 'h8_coherence_unique1', 'h8_coherence_unique2', 'h8_coherence_mi_joint', 'h8_coherence_stim_end', 'h8_context_synergy', 'h8_context_redundancy', 'h8_context_unique1', 'h8_context_unique2', 'h8_context_mi_joint', 'h8_context_stim_end', 'h8_losses', 'h8_activations', 'h12_coherence_synergy', 'h12_coherence_redundancy', 'h12_coherence_unique1', 'h12_coherence_unique2', 'h12_coherence_mi_joint', 'h12_coherence_stim_end', 'h12_context_synergy', 'h12_context_redundancy', 'h12_context_unique1', 'h12_context_unique2', 'h12_context_mi_joint', 'h12_context_stim_end', 'h12_losses', 'h12_activations', 'h16_coherence_synergy', 'h16_coherence_redundancy', 'h16_coherence_unique1', 'h16_coherence_unique2', 'h16_coherence_mi_joint', 'h16_coherence_stim_end', 'h16_context_synergy', 'h16_context_redundancy', 'h16_context_unique1', 'h16_context_unique2', 'h16_context_mi_joint', 'h16_context_stim_end', 'h16_losses', 'h16_activations', 'h20_coherence_synergy', 'h20_coherence_redundancy', 'h20_coherence_unique1', 'h20_coherence_unique2', 'h20_coherence_mi_joint', 'h20_coherence_stim_end', 'h20_context_synergy', 'h20_context_redundancy', 'h20_context_unique1', 'h20_context_unique2', 'h20_context_mi_joint', 'h20_context_stim_end', 'h20_losses', 'h20_activations', 'h40_coherence_synergy', 'h40_coherence_redundancy', 'h40_coherence_unique1', 'h40_coherence_unique2', 'h40_coherence_mi_joint', 'h40_coherence_stim_end', 'h40_context_synergy', 'h40_context_redundancy', 'h40_context_unique1', 'h40_context_unique2', 'h40_context_mi_joint', 'h40_context_stim_end', 'h40_losses', 'h40_activations', 'h60_coherence_synergy', 'h60_coherence_redundancy', 'h60_coherence_unique1', 'h60_coherence_unique2', 'h60_coherence_mi_joint', 'h60_coherence_stim_end', 'h60_context_synergy', 'h60_context_redundancy', 'h60_context_unique1', 'h60_context_unique2', 'h60_context_mi_joint', 'h60_context_stim_end', 'h60_losses', 'h60_activations', 'h80_coherence_synergy', 'h80_coherence_redundancy', 'h80_coherence_unique1', 'h80_coherence_unique2', 'h80_coherence_mi_joint', 'h80_coherence_stim_end', 'h80_context_synergy', 'h80_context_redundancy', 'h80_context_unique1', 'h80_context_unique2', 'h80_context_mi_joint', 'h80_context_stim_end', 'h80_losses', 'h80_activations', 'h100_coherence_synergy', 'h100_coherence_redundancy', 'h100_coherence_unique1', 'h100_coherence_unique2', 'h100_coherence_mi_joint', 'h100_coherence_stim_end', 'h100_context_synergy', 'h100_context_redundancy', 'h100_context_unique1', 'h100_context_unique2', 'h100_context_mi_joint', 'h100_context_stim_end', 'h100_losses', 'h100_activations', 'h150_coherence_synergy', 'h150_coherence_redundancy', 'h150_coherence_unique1', 'h150_coherence_unique2', 'h150_coherence_mi_joint', 'h150_coherence_stim_end', 'h150_context_synergy', 'h150_context_redundancy', 'h150_context_unique1', 'h150_context_unique2', 'h150_context_mi_joint', 'h150_context_stim_end', 'h150_losses', 'h150_activations', 'h200_coherence_synergy', 'h200_coherence_redundancy', 'h200_coherence_unique1', 'h200_coherence_unique2', 'h200_coherence_mi_joint', 'h200_coherence_stim_end', 'h200_context_synergy', 'h200_context_redundancy', 'h200_context_unique1', 'h200_context_unique2', 'h200_context_mi_joint', 'h200_context_stim_end', 'h200_losses', 'h200_activations']\n",
+      "h2_coherence_synergy: shape=(115,), dtype=float64\n",
+      "h2_coherence_redundancy: shape=(115,), dtype=float64\n",
+      "h2_coherence_unique1: shape=(115,), dtype=float64\n",
+      "h2_coherence_unique2: shape=(115,), dtype=float64\n",
+      "h2_coherence_mi_joint: shape=(115,), dtype=float64\n",
+      "h2_coherence_stim_end: shape=(), dtype=int64\n",
+      "h2_context_synergy: shape=(115,), dtype=float64\n",
+      "h2_context_redundancy: shape=(115,), dtype=float64\n",
+      "h2_context_unique1: shape=(115,), dtype=float64\n",
+      "h2_context_unique2: shape=(115,), dtype=float64\n",
+      "h2_context_mi_joint: shape=(115,), dtype=float64\n",
+      "h2_context_stim_end: shape=(), dtype=int64\n",
+      "h2_losses: shape=(7850,), dtype=float64\n",
+      "h2_activations: shape=(2000, 115, 2), dtype=float32\n",
+      "h4_coherence_synergy: shape=(115,), dtype=float64\n",
+      "h4_coherence_redundancy: shape=(115,), dtype=float64\n",
+      "h4_coherence_unique1: shape=(115,), dtype=float64\n",
+      "h4_coherence_unique2: shape=(115,), dtype=float64\n",
+      "h4_coherence_mi_joint: shape=(115,), dtype=float64\n",
+      "h4_coherence_stim_end: shape=(), dtype=int64\n",
+      "h4_context_synergy: shape=(115,), dtype=float64\n",
+      "h4_context_redundancy: shape=(115,), dtype=float64\n",
+      "h4_context_unique1: shape=(115,), dtype=float64\n",
+      "h4_context_unique2: shape=(115,), dtype=float64\n",
+      "h4_context_mi_joint: shape=(115,), dtype=float64\n",
+      "h4_context_stim_end: shape=(), dtype=int64\n",
+      "h4_losses: shape=(7850,), dtype=float64\n",
+      "h4_activations: shape=(2000, 115, 4), dtype=float32\n",
+      "h8_coherence_synergy: shape=(115,), dtype=float64\n",
+      "h8_coherence_redundancy: shape=(115,), dtype=float64\n",
+      "h8_coherence_unique1: shape=(115,), dtype=float64\n",
+      "h8_coherence_unique2: shape=(115,), dtype=float64\n",
+      "h8_coherence_mi_joint: shape=(115,), dtype=float64\n",
+      "h8_coherence_stim_end: shape=(), dtype=int64\n",
+      "h8_context_synergy: shape=(115,), dtype=float64\n",
+      "h8_context_redundancy: shape=(115,), dtype=float64\n",
+      "h8_context_unique1: shape=(115,), dtype=float64\n",
+      "h8_context_unique2: shape=(115,), dtype=float64\n",
+      "h8_context_mi_joint: shape=(115,), dtype=float64\n",
+      "h8_context_stim_end: shape=(), dtype=int64\n",
+      "h8_losses: shape=(7850,), dtype=float64\n",
+      "h8_activations: shape=(2000, 115, 8), dtype=float32\n",
+      "h12_coherence_synergy: shape=(115,), dtype=float64\n",
+      "h12_coherence_redundancy: shape=(115,), dtype=float64\n",
+      "h12_coherence_unique1: shape=(115,), dtype=float64\n",
+      "h12_coherence_unique2: shape=(115,), dtype=float64\n",
+      "h12_coherence_mi_joint: shape=(115,), dtype=float64\n",
+      "h12_coherence_stim_end: shape=(), dtype=int64\n",
+      "h12_context_synergy: shape=(115,), dtype=float64\n",
+      "h12_context_redundancy: shape=(115,), dtype=float64\n",
+      "h12_context_unique1: shape=(115,), dtype=float64\n",
+      "h12_context_unique2: shape=(115,), dtype=float64\n",
+      "h12_context_mi_joint: shape=(115,), dtype=float64\n",
+      "h12_context_stim_end: shape=(), dtype=int64\n",
+      "h12_losses: shape=(7850,), dtype=float64\n",
+      "h12_activations: shape=(2000, 115, 12), dtype=float32\n",
+      "h16_coherence_synergy: shape=(115,), dtype=float64\n",
+      "h16_coherence_redundancy: shape=(115,), dtype=float64\n",
+      "h16_coherence_unique1: shape=(115,), dtype=float64\n",
+      "h16_coherence_unique2: shape=(115,), dtype=float64\n",
+      "h16_coherence_mi_joint: shape=(115,), dtype=float64\n",
+      "h16_coherence_stim_end: shape=(), dtype=int64\n",
+      "h16_context_synergy: shape=(115,), dtype=float64\n",
+      "h16_context_redundancy: shape=(115,), dtype=float64\n",
+      "h16_context_unique1: shape=(115,), dtype=float64\n",
+      "h16_context_unique2: shape=(115,), dtype=float64\n",
+      "h16_context_mi_joint: shape=(115,), dtype=float64\n",
+      "h16_context_stim_end: shape=(), dtype=int64\n",
+      "h16_losses: shape=(7850,), dtype=float64\n",
+      "h16_activations: shape=(2000, 115, 16), dtype=float32\n",
+      "h20_coherence_synergy: shape=(115,), dtype=float64\n",
+      "h20_coherence_redundancy: shape=(115,), dtype=float64\n",
+      "h20_coherence_unique1: shape=(115,), dtype=float64\n",
+      "h20_coherence_unique2: shape=(115,), dtype=float64\n",
+      "h20_coherence_mi_joint: shape=(115,), dtype=float64\n",
+      "h20_coherence_stim_end: shape=(), dtype=int64\n",
+      "h20_context_synergy: shape=(115,), dtype=float64\n",
+      "h20_context_redundancy: shape=(115,), dtype=float64\n",
+      "h20_context_unique1: shape=(115,), dtype=float64\n",
+      "h20_context_unique2: shape=(115,), dtype=float64\n",
+      "h20_context_mi_joint: shape=(115,), dtype=float64\n",
+      "h20_context_stim_end: shape=(), dtype=int64\n",
+      "h20_losses: shape=(7850,), dtype=float64\n",
+      "h20_activations: shape=(2000, 115, 20), dtype=float32\n",
+      "h40_coherence_synergy: shape=(115,), dtype=float64\n",
+      "h40_coherence_redundancy: shape=(115,), dtype=float64\n",
+      "h40_coherence_unique1: shape=(115,), dtype=float64\n",
+      "h40_coherence_unique2: shape=(115,), dtype=float64\n",
+      "h40_coherence_mi_joint: shape=(115,), dtype=float64\n",
+      "h40_coherence_stim_end: shape=(), dtype=int64\n",
+      "h40_context_synergy: shape=(115,), dtype=float64\n",
+      "h40_context_redundancy: shape=(115,), dtype=float64\n",
+      "h40_context_unique1: shape=(115,), dtype=float64\n",
+      "h40_context_unique2: shape=(115,), dtype=float64\n",
+      "h40_context_mi_joint: shape=(115,), dtype=float64\n",
+      "h40_context_stim_end: shape=(), dtype=int64\n",
+      "h40_losses: shape=(7850,), dtype=float64\n",
+      "h40_activations: shape=(2000, 115, 40), dtype=float32\n",
+      "h60_coherence_synergy: shape=(115,), dtype=float64\n",
+      "h60_coherence_redundancy: shape=(115,), dtype=float64\n",
+      "h60_coherence_unique1: shape=(115,), dtype=float64\n",
+      "h60_coherence_unique2: shape=(115,), dtype=float64\n",
+      "h60_coherence_mi_joint: shape=(115,), dtype=float64\n",
+      "h60_coherence_stim_end: shape=(), dtype=int64\n",
+      "h60_context_synergy: shape=(115,), dtype=float64\n",
+      "h60_context_redundancy: shape=(115,), dtype=float64\n",
+      "h60_context_unique1: shape=(115,), dtype=float64\n",
+      "h60_context_unique2: shape=(115,), dtype=float64\n",
+      "h60_context_mi_joint: shape=(115,), dtype=float64\n",
+      "h60_context_stim_end: shape=(), dtype=int64\n",
+      "h60_losses: shape=(7850,), dtype=float64\n",
+      "h60_activations: shape=(2000, 115, 60), dtype=float32\n",
+      "h80_coherence_synergy: shape=(115,), dtype=float64\n",
+      "h80_coherence_redundancy: shape=(115,), dtype=float64\n",
+      "h80_coherence_unique1: shape=(115,), dtype=float64\n",
+      "h80_coherence_unique2: shape=(115,), dtype=float64\n",
+      "h80_coherence_mi_joint: shape=(115,), dtype=float64\n",
+      "h80_coherence_stim_end: shape=(), dtype=int64\n",
+      "h80_context_synergy: shape=(115,), dtype=float64\n",
+      "h80_context_redundancy: shape=(115,), dtype=float64\n",
+      "h80_context_unique1: shape=(115,), dtype=float64\n",
+      "h80_context_unique2: shape=(115,), dtype=float64\n",
+      "h80_context_mi_joint: shape=(115,), dtype=float64\n",
+      "h80_context_stim_end: shape=(), dtype=int64\n",
+      "h80_losses: shape=(7850,), dtype=float64\n",
+      "h80_activations: shape=(2000, 115, 80), dtype=float32\n",
+      "h100_coherence_synergy: shape=(115,), dtype=float64\n",
+      "h100_coherence_redundancy: shape=(115,), dtype=float64\n",
+      "h100_coherence_unique1: shape=(115,), dtype=float64\n",
+      "h100_coherence_unique2: shape=(115,), dtype=float64\n",
+      "h100_coherence_mi_joint: shape=(115,), dtype=float64\n",
+      "h100_coherence_stim_end: shape=(), dtype=int64\n",
+      "h100_context_synergy: shape=(115,), dtype=float64\n",
+      "h100_context_redundancy: shape=(115,), dtype=float64\n",
+      "h100_context_unique1: shape=(115,), dtype=float64\n",
+      "h100_context_unique2: shape=(115,), dtype=float64\n",
+      "h100_context_mi_joint: shape=(115,), dtype=float64\n",
+      "h100_context_stim_end: shape=(), dtype=int64\n",
+      "h100_losses: shape=(7850,), dtype=float64\n",
+      "h100_activations: shape=(2000, 115, 100), dtype=float32\n",
+      "h150_coherence_synergy: shape=(115,), dtype=float64\n",
+      "h150_coherence_redundancy: shape=(115,), dtype=float64\n",
+      "h150_coherence_unique1: shape=(115,), dtype=float64\n",
+      "h150_coherence_unique2: shape=(115,), dtype=float64\n",
+      "h150_coherence_mi_joint: shape=(115,), dtype=float64\n",
+      "h150_coherence_stim_end: shape=(), dtype=int64\n",
+      "h150_context_synergy: shape=(115,), dtype=float64\n",
+      "h150_context_redundancy: shape=(115,), dtype=float64\n",
+      "h150_context_unique1: shape=(115,), dtype=float64\n",
+      "h150_context_unique2: shape=(115,), dtype=float64\n",
+      "h150_context_mi_joint: shape=(115,), dtype=float64\n",
+      "h150_context_stim_end: shape=(), dtype=int64\n",
+      "h150_losses: shape=(7850,), dtype=float64\n",
+      "h150_activations: shape=(2000, 115, 150), dtype=float32\n",
+      "h200_coherence_synergy: shape=(115,), dtype=float64\n",
+      "h200_coherence_redundancy: shape=(115,), dtype=float64\n",
+      "h200_coherence_unique1: shape=(115,), dtype=float64\n",
+      "h200_coherence_unique2: shape=(115,), dtype=float64\n",
+      "h200_coherence_mi_joint: shape=(115,), dtype=float64\n",
+      "h200_coherence_stim_end: shape=(), dtype=int64\n",
+      "h200_context_synergy: shape=(115,), dtype=float64\n",
+      "h200_context_redundancy: shape=(115,), dtype=float64\n",
+      "h200_context_unique1: shape=(115,), dtype=float64\n",
+      "h200_context_unique2: shape=(115,), dtype=float64\n",
+      "h200_context_mi_joint: shape=(115,), dtype=float64\n",
+      "h200_context_stim_end: shape=(), dtype=int64\n",
+      "h200_losses: shape=(7850,), dtype=float64\n",
+      "h200_activations: shape=(2000, 115, 200), dtype=float32\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Load results\n",
+    "npz = np.load(\n",
+    "    r\"E:\\TUM\\3sem\\NeuroAI\\project\\github_neuroai\\results\\size_comparison\\CDM_results\\pid_sweep_cdm\\sweep_results.npz\",\n",
+    "    allow_pickle=True\n",
+    ")\n",
+    "\n",
+    "print(\"Keys in the NPZ file:\", npz.files)\n",
+    "\n",
+    "# Inspect contents\n",
+    "for key in npz.files:\n",
+    "    arr = npz[key]\n",
+    "    print(f\"{key}: shape={getattr(arr, 'shape', None)}, dtype={arr.dtype}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0ed36fa8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "npz = np.load(\n",
+    "    r\"E:\\TUM\\3sem\\NeuroAI\\project\\github_neuroai\\results\\size_comparison\\CDM_results\\pid_sweep_cdm\\sweep_results.npz\"\n",
+    ")\n",
+    "\n",
+    "h = 20\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "\n",
+    "plt.plot(npz[f\"h{h}_coherence_synergy\"], label=\"Coherence Synergy\")\n",
+    "plt.plot(npz[f\"h{h}_coherence_redundancy\"], label=\"Coherence Redundancy\")\n",
+    "\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Information\")\n",
+    "plt.title(f\"PID Dynamics (Hidden Size = {h})\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d5e6f7a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "npz = np.load(\n",
+    "    r\"E:\\TUM\\3sem\\NeuroAI\\project\\github_neuroai\\results\\size_comparison\\CDM_results\\pid_sweep_cdm\\sweep_results.npz\"\n",
+    ")\n",
+    "\n",
+    "hidden_sizes = [2,4,8,12,16,20,40,60,80,100,150,200]\n",
+    "\n",
+    "coh_syn = []\n",
+    "ctx_syn = []\n",
+    "\n",
+    "for h in hidden_sizes:\n",
+    "    coh_syn.append(np.mean(npz[f\"h{h}_coherence_synergy\"]))\n",
+    "    ctx_syn.append(np.mean(npz[f\"h{h}_context_synergy\"]))\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "\n",
+    "plt.plot(hidden_sizes, coh_syn, marker='o', label=\"Coherence Synergy\")\n",
+    "plt.plot(hidden_sizes, ctx_syn, marker='s', label=\"Context Synergy\")\n",
+    "\n",
+    "plt.xlabel(\"Hidden Size\")\n",
+    "plt.ylabel(\"Mean Synergy\")\n",
+    "plt.title(\"Synergy vs Hidden Size\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b3cef5ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6EAAAJOCAYAAABGPgHJAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAAbxlJREFUeJzt3QmclfP+wPHvObO2zbRp04quSilb3WQpUuJGsgsJIYXq2kIlW+qSUMpyla6S69quEN2UuCKVCKnQ1dxWSU1NNcs5z//1/bnn/OfU8zvmTGebOZ+312Pm/J7n+T3bmWm+5/tbPI7jOAIAAAAAQBx443EQAAAAAAAUQSgAAAAAIG4IQgEAAAAAcUMQCgAAAACIG4JQAAAAAEDcEIQCAAAAAOKGIBQAAAAAEDcEoQAAAACAuCEIBQAAAADEDUEoAAAI6z//+Y94PB6ZPn06dwoAcNAIQgGkvB9++EGuv/56OeywwyQ7O1tycnKkS5cu8vjjj8vevXtjdn++/fZbuffee80f+LH20EMPyRtvvFHm7X/++We55ZZbpFWrVlKlShWpV6+edOzYUe644w7ZvXt3TM+1oujatasJzHTxer3mfXPkkUfKFVdcIfPmzUv06QEAkLQ8juM4iT4JAEiUt99+Wy688ELJysqSK6+8Utq2bStFRUXy8ccfy6uvvipXXXWVPPPMMzE59j/+8Q9z7AULFpiAJpaqV68uF1xwQZkyWdu3b5djjjlG8vPz5eqrrzaB6C+//CJfffWVzJkzx3xt3ry5pDp9ZvoBxtixY83rgoIC+f777+W1116TH3/8US666CJ58cUXJSMjQyo6/VOhsLDQXEtaWlqiTwcAUMGlJ/oEACBR1q1bJ5dccok0a9ZMPvjgA2nYsGFw3eDBg01AoUFqqvnrX/8q69evl3//+99y4oknhqzTwDQzM1OSUUlJifj9/rieX25urlx++eUhZQ8//LDcfPPN8tRTT5lgfdy4cVLRabZXWwkAABANNMcFkLLGjx9vmpZq0FU6AA044ogjTJPU0kHO/fffL4cffrjJnGqAcdddd5kMUWla/qc//clkU7UJq/7xrk19Z8yYEdxGM5KaBVXdunULNutcuHBhcJt3331XTj75ZKlWrZrUqFFDzj77bPnmm2+C6zVw1mago0aNCjn+rFmzTF1Tpkwxr/V7zdK98MILweNohtdGs3ua7frjH/94wDptchoIRkaPHm0yY9p0d3/XXXed1KxZU/bt21fmexKwY8cOGTp0qDRp0sTcZ30OGshpgLl/H8VHHnlEJk6cGHwm2sRZ6X08/vjjzXF03dNPP22aPus+Aaeeeqq0b9/e9R5os9qePXtKeei9e+KJJ6RNmzYyadIk2blzZ0THK31tmoUPXNsJJ5wgn3/+ech+mpXWZxloSt6gQQOTvdbMdWmBa1+zZo0JmjV4PuSQQ2TkyJEmy5mXlyfnnnuueb5ax6OPPlqmPqHfffedyfhqXdpsW6/j7rvvDq7ftWuXeZb6/PUatFn3GWecIcuXLy/XvQUAVBLaHBcAUtGhhx7qHHbYYWXevn///tp9wbngggucyZMnO1deeaV53adPn5DtmjVr5hx55JFO/fr1nbvuusuZNGmSc+yxxzoej8f5+uuvzTY//PCDc/PNN5v9dZu//e1vZtm8ebNZP2PGDLP9mWee6Tz55JPOuHHjnObNmzs1a9Z01q1bFzzW4MGDnfT0dGfZsmXm9caNG53atWs73bt3d/x+vynTerOyspyTTz45eJxPPvnEep0PPfSQOa/p06eHvR9r16412+n5lVZYWOjUqlXLufrqqyO6J6qgoMA5+uijnTp16pjtpk6dau6zbnfLLbcEt9N7oMdu06aNeYYPP/yw89hjjzk//fSTs3z5cnO9er+0/MEHH3QaNWrktG/f3uwT8Oyzz5rXK1euDDn/JUuWmHJ9BuGceuqpzlFHHWVdf//995t65syZE9HxAtd2zDHHOEcccYR59uPHj3fq1q3rNG7c2CkqKgru+8gjj5jnet999znPPPOMuUdVqlRxOnbsGHz+avTo0abODh06OJdeeqnz1FNPOWeffbYpmzBhgnk2gwYNMuVdunQx5R9++OEB93vatGnBsi+//NLJyckxz2rEiBHO008/7dx+++1Ou3btgttcdtllTmZmpjN8+HDnueeeM9fSu3dv58UXXwx7bwEAlRtBKICUtHPnTvNH9bnnnlum7VesWGG2v/baa0PKb731VlP+wQcfhARcWrZo0aJg2datW01g9Oc//zlY9sorr5jtFixYEFLnrl27TLA5cODAkHINUHNzc0PKNWjTQEWDoX379pnAQgMDDcZKq1atmgmiy0KPc8ghh5hza9WqlXPDDTc4s2bNcnbs2HHAtp07d3Y6deoUUvbaa68dcF1lvScauOm5rlmzJqTOO++800lLS3PWr18fEhTptWo9pWmQU7VqVWfDhg0hAbMG66WDUL2e7Oxs54477gjZXz8c0HPYvXv3QQWhr7/+ujne448/HtHxAtemwd327duD27355pum/K233gqW7dmz54DjvvTSSwfc60AQet111wXLSkpKTFCrAb4G6wG//vqrCWRLv1/cgtBTTjnFqVGjxgHvtdLBr75f9YMSAABKozkugJSkfRuVNnMti3feecd8HT58eEj5n//8Z/N1/76j2hRTm9IGaHNFbaqoA9b8Hh1ZVZukXnrppbJt27bgos08O3XqZAYyCqhatappIrlq1So55ZRTzHk89thj0rRpUymv+vXry5dffik33HCD/PrrrzJ16lS57LLLTFNKbY5cejw7Hczps88+M014A2bOnGma0mrz00jvySuvvGK2qVWrVsi1d+/eXXw+nyxatCikzvPPP9/UE6Db/Otf/5I+ffpIo0aNguXapLdXr14h+2qTVG2C+tJLLwWvSfd/+eWXzf7aDPpgB4MKNEktz/Euvvhicx8CAveu9P3SJrAB2vRZ71WgGbVbk9drr702+L2+n7TJsp7LNddcEyzXZtS/917VJtj6LLTp7/7vtdJNnrUufX9s3Lgx7L0CAKQWglAAKUn7vpUOEH7PTz/9ZPpfajBTmvaf0z+0dX1pbkGgBhQa1P2etWvXmq+nnXaaCbBKL++//75s3bo1ZHudTmbQoEGyZMkS069QA4ODpX1ktU/ppk2bZPXq1aaPox5f+59qH9rSgZL29dPAU2n/Rx1Bt1+/fiHBSFnviV773LlzD7huDULV/tfeokWLkNe6XqfV2f85KbcyDaJ1EKaPPvrIvNYAdsuWLWaalYMVmMqm9AcdkRxv//sVCEhL3y8dyVj7LesHBxqQ6r0K3JNAX9RwdWpgrH1J69ate0B5uPdqIEDV0aR/r9/1119/bT6U0L7A2je1LB/EAAAqN0bHBZCyQahmyvQP5EjsH1jZ2KaxKMusWIEBeP72t7+ZIHd/6emhv7p1YKTAgEaakdyzZ4/JkEaDXu8f/vAHs+jASC1btjQBZyCjpoGRDjikZRqg6rQzej77jxhb1nui164D19x+++2u2+p5lFY6E1geGrRrAKdTqWgmWb/qPQ8EvQcj8N4qHfxGcryy3C8dFOiTTz6R2267TTp06GCyr3oPzzzzzJCBnMLVeTDv1d+j56cZ3Ndff918gPKXv/zFDDKl09jsn5kGAKQOglAAKUuDJx19dPHixdK5c+ew2+o0LvpHvWbqWrduHSzXLJY2ndX1kbIFtDoaqtLmr2UJhnSUWm2Oq6Op3nHHHXLnnXeazGVZjhUJHYFVg07Njpam2T1tZqojt2owqnOMHnXUUeU6hl67ZhDLGwTqPdPMnk6vsz+3Mg3AtKmxNmnW4OiNN96QgQMHHvRcmNrMVkcp1g8DTjrppJgcTzOV8+fPlzFjxoSMkBzIpMeSvhdUWT7E0az6jTfeaBbNVB977LHy4IMPEoQCQAqjOS6AlKXZNu2Hp1k9DSb3p1nFxx9/3Hx/1llnma86HUhpEyZMMF81SxipQB9ADWJL02yZZmofeughKS4uPmC/0lOiaH87DT51Ggztn6oZMZ0W5MMPPzzgWPsfx0br1Cld9qfNfXXqD+0vWJpmtLQ5pwZVely3LGgkmTP9UOC99947YJ2ev06TE44GcxrAanBXuh+iBqA65Y0bbQqrAd31119vAuCDOf9AAKrzhOoHA/o10PQ72scLBK77Zyz3f4/Ggjb71Uzu888/b5oXl1a6v+v+TYL1QwJtgbD/tEYAgNRCJhRAytKsm2artF+jZjc1o6d93IqKikwTRx0kJzCfps7v2L9/f5M51WBIB93RoEzn3tRBZXSuz0hp80kNJDR40z/WtW+l9gPVP9S1P6YGK5o1uuSSS8wf/frHvg48pH1ANdDUgWj0nLSJrGaWlGbF3nrrLRkwYICsXLkyGOged9xxpv+hBs0aBGi/QR3kyI02A9aM5nnnnWf2y8zMNAGVBhyaZdS5UUvTuUL1HPWc9Hp0QKXy0iD6n//8p8lS673X42tArNeiTX11vsr9+y/uT/sdatPPQF9ZDYb03PTZrlix4oDtNXOr6/R56/tA73lZ6XPTJrVKm0FrsKtNTfUDDL0nOpBTNI9Xmga3Gghqv0v9sOLQQw81171u3TqJB822a5ZXz1/nhdX3lD4ffY/qfdb+1o0bN5YLLrjA/PxoU2F9D2rGfP95SAEAKSZkrFwASEE6HYhOe6LzSuqchjrthM6VqPNf6rQnAcXFxc6YMWOcFi1aOBkZGU6TJk3M/IiltwlMR6JTpbhN6aFLaTp3pM5zqdOP7D+tiX7fs2dPM82FTu1x+OGHO1dddZWzdOlSs37YsGFmv88++yykTl2v05HovI8B3333nZlSQ6fe0OOEm67lq6++cm677TYzj6fOOap1NWzY0LnwwgvNHJxuAnNd9ujRw3V9JPdEp6jR+6pTz+jz0PkxTzzxRDMnZmCOzMCUIX/5y19cjzd//nwzz6bur/dN56jUqWD0PrrReTi1Pp0jtaz0vHWfwFK9enWnZcuWzuWXX+68//77YfcNd7xw16blOt1KwH//+1/nvPPOM1P66PtEn5HOFbv/doEpWn7++eeQ+vR9oNPD/N70M25TtCid4zVwfL23Ot/oyJEjg/PF6vtI52fVnyk9jn6vc5ECAFKbR/+X6EAYAFCx6ZQumtmdMWNGVEaWjQXNWH/zzTeufSa12fWwYcNMJu9gprcpq3gfDwCAZEKfUADAQXv22WdNc8u+ffsmxd3UaVpK08BT53rt2rXrAdvqZ7E67Yw2sY5HQBjv4wEAkGzoEwoAKDftf/rtt9+avrJDhgwJ9kFNNB29VfuU6ledw1X72Grf1tJTv2hfU+1/umDBAtPn9M0334zpOcX7eAAAJCua4wIAyq158+ZmZGEd0VcHNKpRo0ZS3E0dmEmDvc2bN5sBn3QKHh1tuPQgQNoUVgfTqVmzppk+JDC4U6zE+3gAACQrglAAAAAAQNzQJxQAAAAAEDcEoQAAAABQASxatEh69+5t5vz2eDzyxhtv/O4+hYWFcvfdd0uzZs1MFxXtSqNzfycSAxOJiN/vl40bN5q+TPowAQAAAERvVPBdu3aZwMnrrVg5sH379klRUVFMj5GZmSnZ2dllHuSuffv2cvXVV5d5RPqLLrrIjN+gI7MfccQRsmnTJhP/JBJBqIgJQJs0aZLQBwEAAABUZnl5edK4cWOpSAFoi2bVZfNWX0yP06BBA1m3bl2ZAtFevXqZpazmzp0rH374ofz4449Su3ZtU6aZ0EQjCBUJjuZ4kpwl6ZKR6GcCxJct+++xf1Lp8Vr2SUtz3z4tzKee5dnHmxbZtViO8dsungjrCnNekbaksN1Hs84b8TGcSK8l3Pna1nmjV5f1fNOiWJeyPTLbLuVoEeNYj12e5xXx4cPcl+jVVS7lSHiEfZaRimJVUT0vmygfolzPv6IJ93s0Gfmtvy3E63Nf5yl2z1h5i+yBkdeyj6eoxH5uJe71eUpKItre2C/LVuIvkoWbpyXNCOplpRlQDUB/WtZccmrEJoObv8svzY77jzlWWbOhkdCpwY4//ngZP368GcVep1I755xz5P7775cqVapIohCElvojVAPQdA9BKFJMeYJQ6z6WgNJSHn6fKAah3igGoWHqSngQGmmAmPJBaDnui+3e23YhCC3HvSQIjRaC0AoWhHosQahjCUL9YYJQyzpPmA9lxbHs47fs4w2XHbQEwRW021v1Gh6zxIL/f58+5efnh5Rr301dDpZmQD/++GMT4L7++uuybds2M03YL7/8ItOmTZNEqViNsgEAAACgkmnSpInk5uYGl7Fjx0alXu37qcH/zJkzpWPHjnLWWWfJhAkT5IUXXpC9e/dKopAJBQAAAAALn+MXS2vpqNQd6C+bk5MjAdHIgqqGDRvKoYceagLbgNatW5vBov773/9Ky5YtJRHIhAIAAABAAuXk5IQs0QpCu3TpYgZh3b17d7BszZo1ZpTiRA4SRRAKAAAAABZ+cWK6REKDyRUrVphF6ai6+v369evN6xEjRsiVV14Z3P6yyy6TOnXqyIABA+Tbb78184zedtttZoqXRA5MRBAKAAAAABXA0qVL5ZhjjjGLGj58uPl+1KhR5rXOARoISFX16tVl3rx5smPHDjNKbr9+/aR3797yxBNPSCLRJxQAAAAALPzmv9jwR1hz165dTX9Om+nTpx9Q1qpVKxOIJhMyoQAAAACAuCETCgAAAAAWPscxSyz4YlRvsiMTCgAAAACIGzKhAAAAAGBRnlFsy8ofo3qTHZlQAAAAAEDckAkFAAAAgDDZSh+Z0KgiEwoAAAAAiBsyoQAAAABgQZ/QSpYJHTt2rJxwwglSo0YNqVevnvTp00dWr14dss2+fftk8ODBUqdOHalevbqcf/75smXLlpBt1q9fL2effbZUrVrV1HPbbbdJSUlJnK8GAAAAAJDUQeiHH35oAsxPP/1U5s2bJ8XFxdKjRw8pKCgIbjNs2DB566235JVXXjHbb9y4Ufr27Rtc7/P5TABaVFQkn3zyibzwwgsyffp0GTVqVIKuCgAAAEBlmyc0Vksq8jhO8lz5zz//bDKZGmyecsopsnPnTjnkkENk1qxZcsEFF5htvvvuO2ndurUsXrxY/vjHP8q7774rf/rTn0xwWr9+fbPN1KlT5Y477jD1ZWZm/u5x8/PzJTc3V7rKuZLuyYj5dQJJxeOxlNs/o/J4LfukpblvbykPv0+Yz8hs9dmuJczxPdbrj7wu6z42tvto1nkjPoZjqy/Sawy3LtJjhFnnWO9xFOtStrdSee6LheOJ/Blbzznyw4e5L9GrK14fdYd9lpGKYlVRPS+bKB+iXM+/ogn3ezQZ+e1/dnt97us8xX737Yt89ros6zxFYVoKllj2KS6JaHvDH3rOJf5C+dfGp83f9jk5OVJRBGKENavqS40ascnd7drllz+03lLh7k2lGphIb76qXbu2+bps2TKTHe3evXtwm1atWknTpk1NEKr0a7t27YIBqOrZs6d503zzzTeuxyksLDTrSy8AAAAAsD9/jJdUlDRBqN/vl6FDh0qXLl2kbdu2pmzz5s0mk1mzZs2QbTXg1HWBbUoHoIH1gXW2vqj6qUZgadKkSYyuCgAAAACQlEGo9g39+uuvZfbs2TE/1ogRI0zWNbDk5eXF/JgAAAAAKh6dIzSWSypKiilahgwZInPmzJFFixZJ48aNg+UNGjQwAw7t2LEjJBuqo+PqusA2S5YsCakvMHpuYJv9ZWVlmQUAAAAAkEKZUB0TSQPQ119/XT744ANp0aJFyPrjjjtOMjIyZP78+cEyncJFp2Tp3Lmzea1fV65cKVu3bg1uoyPtasfeNm3axPFqAAAAAFQ2OmZULJdUlJ7oJrg68u2bb75p5goN9OHUfppVqlQxX6+55hoZPny4GaxIA8ubbrrJBJ46Mq7SKV002Lziiitk/Pjxpo577rnH1E22EwAAAACSS0KD0ClTppivXbt2DSmfNm2aXHXVVeb7xx57TLxer5x//vlmVFsd+fapp54KbpuWlmaa8g4aNMgEp9WqVZP+/fvLfffdF+erAQAAAFDZxHIUW7+kpoQGoWWZojQ7O1smT55sFptmzZrJO++8E+WzAwAAAABUyoGJAAAAACAZ+cUjPvHErO5UlDRTtAAAAAAAKj8yoQAAAABg4Xd+W2LBn6Kj45IJBQAAAADEDZlQAAAAALDwxbBPqI8+oQAAAAAAxBaZUAAAAACwIBMaffQJBQAAAADEDZlQAAAAALDwOx6zxII/RvUmOzKhAAAAAIC4IRMKAAAAABb0CY0+MqEAAAAAgLghEwoAAAAAFj7xmiUWfCl618mEAgAAAADihkwoAFceb5jR2tLS3PexlNu2N/t4LMfxJOlnZI4T+T62awzH74+4Lk+EI+w5aWHuse04tmOEu0bLe8n67MNch2PZJ+zbxfYxs6UuxxumMssq67X47e8X2z7Wyw/3MylO5M/FE726rOfst6yI5o93nAaW9JTjZ9/2fo0Xj+WUozkYZzyOEVaYn7GoCfuzF726rG+xDPcfGH9amLrS3ffxZoT597jY8suy2BIulPjK/G+Y46vYI8A6MRwd12F0XAAAAAAAYotMKAAAAABYMDpu9CVpezcAAAAAQGVEJhQAAAAALHyO1yyx4ItDt+ZkRCYUAAAAABA3ZEIBAAAAwMIvHvHHKHfnt41IXsmRCQUAAAAAxA2ZUAAAAACwYHTc6CMTCgAAAACIGzKhAAAAAJCQ0XGdlLzvZEIBAAAAAHFDJhQAAAAAwo6O64nJ/fHHqN5kRyYUAAAAABA3ZEIBAAAAwELnCPUxT2hUEYQCAAAAgAUDE0UfzXEBAAAAAKkRhC5atEh69+4tjRo1Eo/HI2+88UbI+quuusqUl17OPPPMkG22b98u/fr1k5ycHKlZs6Zcc801snv37jhfCQAAAIDK2hw3lksqSuhVFxQUSPv27WXy5MnWbTTo3LRpU3B56aWXQtZrAPrNN9/IvHnzZM6cOSawve666+Jw9gAAAACAChWE9urVSx544AE577zzrNtkZWVJgwYNgkutWrWC61atWiVz586V5557Tjp16iQnnXSSPPnkkzJ79mzZuHFjnK4CAAAAQGXlczwxXaLZkjScf//735Keni4dOnSQREv6/O/ChQulXr16cuSRR8qgQYPkl19+Ca5bvHixaYJ7/PHHB8u6d+8uXq9XPvvsM2udhYWFkp+fH7IAAAAAQDIrKENLUjc7duyQK6+8Uk4//XRJBkk9Oq42xe3bt6+0aNFCfvjhB7nrrrtM9lSDz7S0NNm8ebMJUEvT6L527dpmnc3YsWNlzJgxcbgCAAAAABWZL4ZTtPjEiWh7jYV0idQNN9wgl112mYmhIsmepmQm9JJLLpFzzjlH2rVrJ3369DF9Pj///HOTHT0YI0aMkJ07dwaXvLy8qJ0zAAAAACSLadOmyY8//iijR4+WZJHUmdD9HXbYYVK3bl35/vvvTSpZ+4hu3bo1ZJuSkhIzYq6uC9fPVBcAAAAACMfveM0SC37nt0zo/t0DoxWvrF27Vu6880756KOPTIvRZJHUmdD9/fe//zV9Qhs2bGhed+7c2bRvXrZsWXCbDz74QPx+vxmoCAAAAACSXZMmTSQ3Nze4aPfBg+Xz+UwTXO2G+Ic//EGSSULDYZ3PU7OaAevWrZMVK1aYPp266A07//zzTVZT+4TefvvtcsQRR0jPnj3N9q1btzb9RgcOHChTp06V4uJiGTJkiGnGqyNGAQAAAECy9wnNy8uTnJycYHk0sqC7du2SpUuXyhdffGFiJKXJOsdxTFb0/fffl9NOO01SLgjVm9KtW7fg6+HDh5uv/fv3lylTpshXX30lL7zwgsl2alDZo0cPuf/++0MeysyZM81N1ea5OiquBq1PPPFEQq4HAAAAACKVk5MTEoRGg9a3cuXKkLKnnnrKtBz9xz/+YQZ/TZSEBqFdu3Y1kbjNe++997t1aMZ01qxZUT4zAAAAABDx/2+u0FjVHa2WpE2bNjUDsG7YsEFmzJhhEnRt27YN2V9nFsnOzj6gPN6Sp3cqAAAAAKBcLUmnT58umzZtkvXr10uyIwgFAAAAAAu/eM0SC/4I6/29lqQaiIZz7733miXRKtTouAAAAACAio1MKAAAAABY+ByvWWLBF6N6k11qXjUAAAAAICHIhAIAAACAhV88ZokFf4zqTXZkQgEAAAAAcUMmFAAAAAAs6BMafWRCAQAAAABxQyYUAAAAACx84jVLLPhSNCeYmlcNAAAAAEgIMqEAAAAAYOF3PGaJBX+M6k12ZEIBAAAAAHFDJhQAAAAALPwx7BPqT9GcIEEokOo8cfjl5/fb16WluZd7wzRP8brv40m31JUe5ledZR8nw30fJzPDWpWTYakry73cb9nerMt0fy6+LPvzsq3zZ7jfS1+mtSrxWfbxWy7fn25/Xk5aZOX+MI8r0rrC1WetK92J/PiWx+KkhanL9igtt9Lx2uuy/Q0Tdh9PhOXlqsuyT7jWZ5bjeCI9RhieclyL7fieMMe3rQt7/AjrKg/btTjlOIT1uURRee5xOE6EzR/DbW+7Z7b74vPZ6/IVW/6t2Of+S8xTaP/3wLvXfV3aPusukr7X/dzSCiXiutKKQm+Mr2ifyA/27ZF6CEIBAAAAwMLveM0SC/4Y1ZvsUvOqAQAAAAAJQSYUAAAAACx84jFLLPhiVG+yIxMKAAAAAIgbMqEAAAAAYEGf0OgjEwoAAAAAiBsyoQAAAABg4Yth301fit51MqEAAAAAgLghEwoAAAAAFvQJjT4yoQAAAACAuCETCgAAAAAWPsdrlljwxajeZJeaVw0AAAAASAgyoQAAAABg4YhH/DEaHdeJUb3JjkwoAAAAACBuyIQCAAAAgAV9QqOPTCgAAAAAIDWC0EWLFknv3r2lUaNG4vF45I033ghZ7ziOjBo1Sho2bChVqlSR7t27y9q1a0O22b59u/Tr109ycnKkZs2acs0118ju3bvjfCUAAAAAKiO/44npkooSGoQWFBRI+/btZfLkya7rx48fL0888YRMnTpVPvvsM6lWrZr07NlT9u3bF9xGA9BvvvlG5s2bJ3PmzDGB7XXXXRfHqwAAAAAAVIg+ob169TKLG82CTpw4Ue655x4599xzTdmMGTOkfv36JmN6ySWXyKpVq2Tu3Lny+eefy/HHH2+2efLJJ+Wss86SRx55xGRYAQAAAKC8fOI1Syz4UrR3ZNJe9bp162Tz5s2mCW5Abm6udOrUSRYvXmxe61dtghsIQJVu7/V6TebUprCwUPLz80MWAAAAAEAKB6EagCrNfJamrwPr9Gu9evVC1qenp0vt2rWD27gZO3asCWgDS5MmTWJyDQAAAAAqNvqEplAQGksjRoyQnTt3Bpe8vLxEnxIAAAAApISknSe0QYMG5uuWLVvM6LgB+rpDhw7BbbZu3RqyX0lJiRkxN7C/m6ysLLMAAAAAQDh+8ZolFvypmRNM3qtu0aKFCSTnz58fLNO+m9rXs3Pnzua1ft2xY4csW7YsuM0HH3wgfr/f9B0FAAAAACSXhGZCdT7P77//PmQwohUrVpg+nU2bNpWhQ4fKAw88IC1btjRB6ciRI82It3369DHbt27dWs4880wZOHCgmcaluLhYhgwZYkbOZWRcAAAAAAfL53jMEgu+FJ0nNKFB6NKlS6Vbt27B18OHDzdf+/fvL9OnT5fbb7/dzCWq835qxvOkk04yU7JkZ2cH95k5c6YJPE8//XQzKu75559v5hYFAAAAACSfhAahXbt2NfOB2ng8HrnvvvvMYqNZ01mzZsXoDAEAAACkssDouLGqOxUlbZ9QAAAAAEDlk7Sj4wIAAABAojmOV/yON2Z1p6LUvGoAAAAAQEKQCQUAAAAAC594zBILvhjVm+zIhAIAAAAA4oZMKAAAAABY+J3YjWLrt08UUqmRCQUAAAAAxA2ZUAAAAACw8MdwdFw/o+MCAAAAABBbZEIBAAAAwMIvHrPEgj9FR8clCAUAAAAAC5/jMUss+GJUb7JjYCIAAAAAQNwQhAIAAADA7wxMFKslEosWLZLevXtLo0aNxOPxyBtvvBF2+9dee03OOOMMOeSQQyQnJ0c6d+4s7733niQaQSgAAAAAVAAFBQXSvn17mTx5cpmDVg1C33nnHVm2bJl069bNBLFffPGFJBJ9QoFU5/gtK9Ksu+gnb5YV7sWZmfa6sizrbOV6ytlZruW+qu7l/qoZ1rpKqrhfpy/bVm7vu1GS5b7OZy23VlXOfdzL/VnuM2H77LdY/FXc3xdOpnu5t0qJta70TJ9reVZWsWt5lQx7XVUz3PepmlFk3Sc7zbJPunt5ltd+/Kw093XpHvdrzLCUm328tp+9yNkmUS927D/HaRLZ8cPdlwxvZNcf7r54Pf6I9sn2uD/H8Me3X0umZR/beaWJfab5NOu1uB8/Q3wR1xVOuHNz4wszQIrPkq2xnZdte+VPcA4k3HVGeh9tddn22eGraq3rF1911/JfS6q5lm8rdt/erCt0X7e90H78/MJs1/Jd+9z/cdlTaP+3tXhfaIjh31soMl0q9sBETnIMTNSrVy+zlNXEiRNDXj/00EPy5ptvyltvvSXHHHOMJApBKAAAAAAkUH5+fsjrrKwss0Sb3++XXbt2Se3atSWRaI4LAAAAABbO/6ZoicXi/C8T2qRJE8nNzQ0uY8eOjcnzeOSRR2T37t1y0UUXJfR5kwkFAAAAgATKy8szAwcFxCILOmvWLBkzZoxpjluvXj1JJIJQAAAAALDQ/qAx6xPq/FavBqClg9Bomz17tlx77bXyyiuvSPfu3SXRaI4LAAAAAJXUSy+9JAMGDDBfzz77bEkGZEIBAAAAwKI883mWlT/CerU/5/fffx98vW7dOlmxYoUZaKhp06YyYsQI2bBhg8yYMSPYBLd///7y+OOPS6dOnWTz5s2mvEqVKqbvaaKQCQUAAACACmDp0qVmapXA9CrDhw83348aNcq83rRpk6xfvz64/TPPPCMlJSUyePBgadiwYXC55ZZbJJHIhAIAAABAAvuEllXXrl3Fcexz106fHjoh68KFCyUZkQkFAAAAAMQNmVAAAAAAsAjM6RkL/hjVm+zIhAIAAAAA4oZMKAAAAABUgD6hlQWZUAAAAABA3JAJBQAAAAALMqHRRyYUAAAAABA3ZEIBAAAAwIJMaIplQn0+n4wcOVJatGghVapUkcMPP1zuv//+kAla9ftRo0ZJw4YNzTbdu3eXtWvXJvS8AQAAAAAVMAgdN26cTJkyRSZNmiSrVq0yr8ePHy9PPvlkcBt9/cQTT8jUqVPls88+k2rVqknPnj1l3759CT13AAAAAJUnExqrJRUldXPcTz75RM4991w5++yzzevmzZvLSy+9JEuWLAlmQSdOnCj33HOP2U7NmDFD6tevL2+88YZccsklCT1/AAAAAEAFyoSeeOKJMn/+fFmzZo15/eWXX8rHH38svXr1Mq/XrVsnmzdvNk1wA3Jzc6VTp06yePFia72FhYWSn58fsgAAAADA/rQjoF88MVmcFL3dSZ0JvfPOO02A2KpVK0lLSzN9RB988EHp16+fWa8BqNLMZ2n6OrDOzdixY2XMmDExPnsAAAAAQIXKhP7973+XmTNnyqxZs2T58uXywgsvyCOPPGK+HowRI0bIzp07g0teXl7UzhkAAABA5UGf0BTLhN52220mGxro29muXTv56aefTCazf//+0qBBA1O+ZcsWMzpugL7u0KGDtd6srCyzAAAAAADiK6kzoXv27BGvN/QUtVmu3+833+vULRqIar/RAG2+q6Pkdu7cOe7nCwAAAKByIROaYpnQ3r17mz6gTZs2laOOOkq++OILmTBhglx99dVmvcfjkaFDh8oDDzwgLVu2NEGpzivaqFEj6dOnT6JPHwAAAABQkYJQnQ9Ug8obb7xRtm7daoLL66+/XkaNGhXc5vbbb5eCggK57rrrZMeOHXLSSSfJ3LlzJTs7O6HnDgAAAKDii+V8nn7mCU0+NWrUMPOA6mKj2dD77rvPLAAAAACA5JbUmVAAAAAASCQyoSk2MBEAAAAAoHIhEwoAAAAAFo7jMUssOCnaJ5RMKAAAAAAgbsiEAgAAAICFXzxmiQV/jOpNdmRCAQAAAABxQyYUAAAAACwYHTf6yIQCAAAAAOKGTCgAAAAAWDA6bvSRCQUAAAAAxA2ZUAAAAACwoE9o9JEJBQAAAADEDZlQAAAAALCgT2j0kQkFAAAAAMQNmdDSPJ7fFiCVeKL4WZTXUldamGOku/8acjLsv55s65wM9+P4M+3H92WnuZYXV3Pfp7ia/XeEbV1xdfftS6o69vOq6o+o3Mj2uRZnZJe4b57lXq6yMopdy6tlupdXSS+215VuOX6a+z7pHvs1ej3u98wbZh+bNEtd4fgc92ecHsV/OjI8vojKw0kLdy/F/fozvO7Py+/Yf46KHfefo33+DNfyX31VrXUVWvaxCffsbffMV47P4Ev87tcYjl88Eb2PMsJcS7X0QtfyGmn7rPtU9Ra5lmd5iyN+j6WJ+7lleOy/R2yKnej9+Wl7ltqHL9Lj297HPstzDMf287LN9g+CiBT4slzLC33u57vXZ/9Z2VOS6VpeXI73cVZGSUS/j1VJZug+Psv7tyJlQsO9pw627lREJhQAAAAAEDdkQgEAAADAQnO+TuSNZ8rESdG7TiYUAAAAABA3ZEIBAAAAIEz/bv0vFvwxqjfZkQkFAAAAAMQNmVAAAAAAsGCe0OgjEwoAAAAAiBsyoQAAAABgoXOEemI0n6efeUIBAAAAAIgtMqEAAAAAYKFzhMZsnlAnNW87fUIBAAAAAHFDJhQAAAAALBgdN/rIhAIAAAAA4oZMKAAAAABYkAmNPjKhAAAAAIC4IRMKAAAAABbME5qCmdANGzbI5ZdfLnXq1JEqVapIu3btZOnSpcH1juPIqFGjpGHDhmZ99+7dZe3atQk9ZwAAAABABQxCf/31V+nSpYtkZGTIu+++K99++608+uijUqtWreA248ePlyeeeEKmTp0qn332mVSrVk169uwp+/btS+i5AwAAAKg884TGaklFSR2Ejhs3Tpo0aSLTpk2Tjh07SosWLaRHjx5y+OGHB7OgEydOlHvuuUfOPfdcOfroo2XGjBmyceNGeeONNxJ9+gAAAAAQNYsWLZLevXtLo0aNxOPxlCnmWbhwoRx77LGSlZUlRxxxhEyfPj3hTySpg9B//vOfcvzxx8uFF14o9erVk2OOOUaeffbZ4Pp169bJ5s2bTRPcgNzcXOnUqZMsXrzYWm9hYaHk5+eHLAAAAACwv98ylp4YLRKRgoICad++vUyePLlM22u8dPbZZ0u3bt1kxYoVMnToULn22mvlvffeS+iDTuqBiX788UeZMmWKDB8+XO666y75/PPP5eabb5bMzEzp37+/CUBV/fr1Q/bT14F1bsaOHStjxoyJ+fkDAAAAqNiSaYqWXr16maWstMuitibVLo2qdevW8vHHH8tjjz1mujAmSlJnQv1+v0kdP/TQQyYLet1118nAgQPNzTwYI0aMkJ07dwaXvLy8qJ0zAAAAAEQif79WmtpyMxq0dWjpVqNKg89wrUYl1YNQHfG2TZs2IWUava9fv95836BBA/N1y5YtIdvo68A6N9oeOicnJ2QBAAAAgP05MV6UjoOj3QoDi7bcjAZtHerWalQD3b1790qiJHVzXB0Zd/Xq1SFla9askWbNmpnvNbWsweb8+fOlQ4cOpkxvqI6SO2jQoIScMwAAAABEIi8vLyQxpkmzyiypg9Bhw4bJiSeeaJrjXnTRRbJkyRJ55plnzKJ0RCjtXPvAAw9Iy5YtTVA6cuRIM1pUnz59En36AAAAACq4ePQJzYlR60xN2Lm1GtVjValSRRIlqYPQE044QV5//XXTh/O+++4zQaZOydKvX7/gNrfffrsZJUr7i+7YsUNOOukkmTt3rmRnZyf03AEAAAAgkTp37izvvPNOSNm8efNMeSIldRCq/vSnP5nFRrOhGqDqAgAAAABRVbrzZrQ5kW2+e/du+f7770OmYNGpV2rXri1NmzY1ybsNGzbIjBkzzPobbrhBJk2aZBJ3V199tXzwwQfy97//Xd5+++0yz1Zy2GGHSUoNTAQAAAAA+M3SpUvNrCG6KJ3KUr8fNWqUeb1p06bgIK5KW5JqwKnZT51fVKdqee6558o8PcsRRxxh5hh98cUXZd++fZIymVAAAAAASJgY9gmVCOvt2rWrOI49fTp9+nTXfb744otynd7y5ctl2rRpJtgdMmSIXHzxxXLNNddIx44d5WCQCQUAAAAAHEBnIHn88cdl48aN8vzzz5tMq47B07ZtW5kwYYL8/PPPUh4EoQAAAABgoYnHWC4VQXp6uvTt21deeeUVGTdunOmXeuutt5r5Ta+88koTnEaCIBQAAAAAELYv6o033igNGzY0GVANQH/44QfT11SzpOeee65Egj6hAAAAAJDAeUKTlQac2id09erVctZZZ5lRd/Wr1+sNDnyk/VCbN28eUb0EoQAAAACAA0yZMsVM7XLVVVeZLKibevXqyV//+leJBEEoAAAAANhotjJJRseNt7Vr1/7uNpmZmdK/f/+I6iUIBQAAAAAc4KuvvjqwUEQ8Ho9kZ2dL06ZNJSsrSyJFEAoAAAAAFrEcxdZJ8tFxdYoWDThtMjIyzNyhTz/9tAlKy4rRcQEAAAAAB3j99delZcuW8swzz8iKFSvMot8feeSRMmvWLNMX9IMPPpB77rlHIkEmFAAAAABsNFsZq4ylk9y3/cEHH5THH39cevbsGSxr166dNG7cWEaOHClLliyRatWqyZ///Gd55JFHylwvmVAAAAAAwAFWrlwpzZo1O6Bcy3RdoMnupk2bJBIEoQAAAADwO/OExmpJZq1atZKHH35YioqKgmXFxcWmTNepDRs2SP369SOql+a4QIrzeN1/+XnCjHTmqV7NfUUN93J/Tcv2IlJU0/04hbXsv5721XL//Kyopvu1FOXa27oU1/S7lntrFrqW5+YUWOtqUM19XcOq+a7l9bJ2Weuqn+G+T/2MndZ9Dklz36dm2h73cu///4Oyv2yP+z3LkNhLCzMAQlVPmmt5FU9mmPoi+7y12PFZ1+3073Mt32MZWSKvpKq1rp99Oa7lv5RUdy3fUpxrrWtjYU3X8vwS+8/xnhL3e7a72H2ffSX2p1/ij+weV8kotq5L81h+Ji3vyfIIV5dtndfSZi4zrcRaV3aa+3VmWK4xXGpge5H779Ftjvv7RfnF/WfJV44/em3nnGW5/vIcw++43wCv7X6FOa/w+7j/jGd5SyJ+v9jqyva6P/vc9L3Wumqlu/+urpHm/nunRpq9rmyP7b1n//0WTfv/HO/Z5ZMr4nJkRNvkyZPlnHPOMc1vjz76aFOmGVCfzydz5swxr3/88Ue58cYbI6qXIBQAAAAAKnDfzVg58cQTZd26dTJz5kxZs2aNKbvwwgvlsssukxo1apjXV1wR+UcMBKEAAAAAgBDa7Fab3GrG84YbbpBoKlef0JKSEvnXv/5l5oPZteu35mQbN26U3bt3R/XkAAAAACCRUrVPaEZGhuzb594cPO5B6E8//WSG5T333HNl8ODB8vPPP5vycePGya233hqLcwQAAAAAxJnGexrnaRIymiJujnvLLbfI8ccfL19++aXUqVMnWH7eeefJwIEDo3pyAAAAAJBQKTxP6Oeffy7z58+X999/3yQidU7Q0l577bX4BKEfffSRfPLJJ5KZGTqqXvPmzc3wvAAAAACAiq9mzZpy/vnnR73eiINQv99vhuTd33//+9/gCEkAAAAAUDlov81Y9d30SDKbNm1acvQJ7dGjh0ycODH42uPxmAGJRo8eLWeddVa0zw8AAAAAkCCxGJQ24kzoo48+Kj179pQ2bdqY0ZJ0jpi1a9dK3bp15aWXXir3iQAAAABA0knhPqE//fSTnHnmmbJ+/XopLCyUM844w7R+1cGK9PXUqVPjE4Q2btzYDEr08ssvm68aAV9zzTXSr18/qVKlSrlOAgAAAACQXGI1KG16uXZKTzdBpy4AAAAAUGmlcCb0oxgNShtxn9C0tDTp1q2bbN++PaR8y5YtZh0AAAAAoOLzx2hQ2oiDUMdxTPtfTct+8803B6wDAAAAgErD8cR2SWKxGpQ24iBUD/zqq69K7969pXPnzvLmm2+GrAMAAAAAVHyPPvqo/Pvf/w4ZlDbQFFcHJ4pbn1DNdmqz28cff1yOOuooufjii+Wee+6Ra6+9ttwnAQAAAADJSBt7xqrBp5PkDUkDg9LOnj1bvvrqq6gNSluugYkCrrvuOmnZsqVceOGFsmjRooOpCgAAAACQZHRQ2ssvvzy6dUa6Q7NmzUIGINJBij799FPTPDfWHn74YRkxYoQZKjjQNlnTwn/+859NdK59VXUO06eeekrq168f8/MBAAAAUMml8Oi4au3atbJgwQLZunWrGaiotFGjRklcgtB169YdUHbEEUfIF198YUbIjZXPP/9cnn76aTn66KNDyocNGyZvv/22vPLKK5KbmytDhgyRvn37mrbLAAAAAIDyefbZZ2XQoEFSt25dadCgQcgYQPp93IJQm+zsbJMljQVte6ztjvUmPPDAA8HynTt3yl//+leZNWuWnHbaaaZs2rRp0rp1a5Od/eMf/xiT8wEAAACQImI5iq2T3AO7auz14IMPyh133BHVess0Om7t2rVl27Zt5vtatWqZ17YlFgYPHixnn322dO/ePaR82bJlUlxcHFLeqlUradq0qSxevDgm5wIAAAAAqeDXX3814/9EW5kyoY899lhwMtLS88TEg/b1XL58uWmOu7/NmzdLZmam1KxZM6Rc+4PqOhvtO6pLQH5+fpTPGgAAAEBl4HF+W2JVdzLTAPT999+XG264If5BaP/+/V2/j7W8vDwzCNG8efNMc99oGTt2rIwZMyZq9QEAAABAZXPEEUfIyJEjTVfHdu3aSUZGRsj6m2++ObZ9QktKSsTn80lWVlawTAcimjp1qhQUFMg555wjJ510kkSTNrfVUZiOPfbYYJmeg04HM2nSJHnvvfekqKhIduzYEZIN1fPSjrM2OsLu8OHDQzKhTZo0ieq5AwAAAKgEUnh03GeeeUaqV68uH374oVlK04GJYh6EDhw40DR91RFq1a5du+SEE04wU6Q0bNjQNNl988035ayzzpJoOf3002XlypUhZQMGDDD9PrVzrAaOGo3Pnz9fzj//fLN+9erVsn79euncubO1Xg2kSwfTAAAAAIDfnxklGsochOqUJ5p9DJgxY4bJSuq8MTo1igaFf/nLX6IahGo/1LZt24aUVatWTerUqRMsv+aaa0xWUwdFysnJkZtuuskEoIyMCwAAAOCgpfDouAsWLJBu3bpFvd4yjY6rNmzYIC1btgy+DmQfNQAN9BX95ptvJN40A/unP/3JnMspp5ximuG+9tprcT8PAAAAAKhMzjzzTDn88MPNVC06Xk/cg1AdGGjv3r3B19o5tVOnTiHrdT7PWFu4cGHICL163MmTJ8v27dtN31QNQMP1BwUAAACAiPuExmpJYpqIHDJkiPzjH/+Qww47THr27Cl///vfzbg8cQlCO3ToIH/729/M9x999JEZ/Oe0004Lrv/hhx+kUaNGB3UyAAAAAIDkULduXRk2bJisWLFCPvvsM/nDH/4gN954o4n7dFCiL7/8MrZB6KhRo+Txxx836ViNgK+66iozIFHA66+/Ll26dCnXSQAAAABAUkrhTGhpOmOJzjKimVFtAfv888/LcccdJyeffHLE3TLLHISeeuqpZsoUjXinTZsmzz777AGZUo2SAQAAAACVQ3FxsWmOqwPQNmvWzEyTqQPWasvY77//3pRdeOGFsRkdV7Vu3dosbq677rqIDgwAAAAASS+F5wm96aab5KWXXhLHceSKK66Q8ePHh8xeojOXPPLIIxF3y4woCAUAAAAApIZvv/1WnnzySenbt69kZWVZ+43qVC6RIAgFAAAAAJsUnid0/vz5v7tNenq66boZkz6hAAAAAIDKb82aNbJkyZIDAtJu3bpJx44d5aGHHjqo+glCAQAAAMDC48R2SUZ33HGHzJkzJ/h63bp10rt3b8nMzJTOnTvL2LFjZeLEieWuv9zNcXWC0q1bt4rf7w8pb9q0ablPBgAAAACQWEuXLpXbb789+HrmzJlmjlAdGVcdffTRpq/o0KFD45MJXbt2rZkLpkqVKmY43hYtWpilefPm5isAAAAAVBopOE/otm3bpHHjxsHXOvCQZkIDunbtKv/5z3/ilwm96qqrTOdTTc82bNhQPJ7k7kwLAAAAACi72rVry6ZNm6RJkyam5atmRocPHx7SKlanbSmviDOhK1askKefflp69eolHTp0kPbt24csAAAAAIDYmTx5smmJmp2dLZ06dTpgEKH9af/NI4880rRm1cBy2LBhsm/fPuv2mum8//77JS8vz+yrgaiWlZ66RY8ft0xomzZtTHoWAAAAABBfL7/8sslKTp061QSgGiT27NlTVq9eLfXq1Ttg+1mzZsmdd94pzz//vJx44olm5Ftt3aotWidMmOB6jAcffFDOOOMM0/0yLS1NnnjiCalWrVpw/d/+9jc57bTT4heEjhs3znRS1WF527VrJxkZGSHrc3Jyyn0yAAAAAJBMtPNhrEax9ZRjHw0cBw4cKAMGDDCvNRh9++23TZCpweb+PvnkE+nSpYtcdtll5rVmMC+99FL57LPPrMfQbVatWiXffPONHHLIIdKoUaOQ9WPGjAnpMxrzILR79+7m6+mnnx5Srm2CNZr2+XzlPhkAAAAAgDvti7ls2TIZMWJEsMzr9ZoYbfHixa77aPbzxRdfNE12dY7PH3/8Ud555x254oorJBwdB8jW3fJgu2FGHITqyEiVlSctTTyetESfBhBXnnTLr4GWzaz77Dqshmv5jsPcf372HBo6lVNpTq1i1/Ls6gXWfdLTLR92Oe6fJ3qK7T/X3mL36/cVuXeZ377F3tpju5PrWr7ad6j7DmlhPla1rfJH/pmpp9iyTzk+1fX43OtywowwYPv02PHaVkR5wDvbcWy3xba9qSvCY2fY3/tiuZfW90WY8/JajuMJt4/XfZ+MjMg/TPb73W+MbcyKtLQwvxMsz9+2jzdMeiLNco3lYTsvfzner+lpvojuo/JYrrMkzD6RPpdwx7e9X6I5PmWG5b4U+9Iifsbh3he2Z2bbI70c7yOf5V42qLHLuk/zGr+4lh+Sudu1fFtJdWtde3yZruV7LeWq2PKL3G8t95S5ruKCIhFZKRWWXmu0/20K+F+9+fn5UlpWVpZZ9qfdIjXpV79+/ZByff3dd9+JG82A6n4nnXSSSRyWlJTIDTfcIHfddZckSsRB6KmnnhqbMwEAAACAZBPLqVSc377oYEGljR49Wu69996oHGLhwoWmK+VTTz1l+pB+//33csstt5iBh0aOHCkVIghVH330kRkhV1O5r7zyihx66KGmc6rOE6oRNgAAAACgbPLy8kLG1nHLgqq6deuagYK2bNkSUq6vGzRo4LqPBpra9Pbaa681r3Vcn4KCArnuuuvk7rvvNs154y3iI7766qtm9CUd3nf58uVSWFhoynfu3GkibAAAAACodJnQWC3y2+CupRdbEJqZmSnHHXeczJ8/P1im06fo686dO7vus2fPngMCTQ1kzaX9zlyf69evd91Gy3Rd3ILQBx54wIzA9Oyzz4aMjKsjLmlQCgAAAACIjeHDh5tY7IUXXjAj2A4aNMhkNgOj5V555ZUhAxf17t1bpkyZIrNnz5Z169bJvHnzTHZUywPBqI22dP35558PKN++fbtZF7fmuDr/zCmnnHJAeW5uruzYsaPcJwIAAAAAyUbHuYrZFC1O5PtcfPHFJjAcNWqUbN68WTp06CBz584NDlakGcrSmc977rnHzGKiXzds2GCmXNEAVOcC/T2BGVD2t3v3bsnOzpa4BaHa1lg7s+rcMaV9/PHHcthhh5X7RAAAAAAAv2/IkCFmsQ1EtP9UKzrQkS6RZFuVBqCaNa1atWpwnY7Oq3OMavAbtyBUJ0bV0ZR0MlQ9qY0bN5o5aW699daEja4EAAAAABV1dNxk88UXXwQzoStXrjR9UQP0e50nVOO/uAWhd955p+n8evrpp5tOrto0VzvO6kncdNNN5T4RAAAAAEDiLViwwHzVfqaPP/54yMi90RBxEKrZTx3K97bbbjPNcrU9cJs2baR6dfuEuQAAAABQIaVgJjRg/Pjx1gBUM6Q63Ut5lHtSGE3DavDZsWNHAlAAAAAAqGTatWsnb7/99gHljzzyiIkDY5oJ7du3b5krfO2118p9MgAAAACQTJJtdNx40gGKzj//fNMsd8KECWZqFp0CRrOgs2bNKne9ZcqE6vQrgUXTsToZ6tKlS4Prly1bZsp0PQAAAACg4rv99tvNILQfffSRHH300WbR8YC++uorOe+882KbCZ02bVrw+zvuuEMuuugimTp1anByUx2m98Ybb4x6h1UAAAAASCjH89sSq7qT3BFHHCFt27aVV199NThPqU7beTAi7hOqU7PoSLiBAFTp95qq1XUAAAAAgIrv3//+t8l+rl271mQ/p0yZYmZE0UD0119/jV8QWlJSIt99990B5VqmU7cAAAAAQKUbHTdWSxI77bTTTMD56aefSuvWreXaa681c4iuX7++3CPjlisI1U6p11xzjemY+vHHH5vl0UcfNSek66Jp7NixcsIJJ0iNGjWkXr160qdPH1m9enXINvv27ZPBgwdLnTp1zCi92nF2y5YtUT0PAAAAAEg177//vjz88MOSkZERLDv88MNNhvT666+P3zyhOhyvtgHWwHPTpk2mrGHDhmbe0D//+c8STR9++KEJMDUQ1QzsXXfdJT169JBvv/1WqlWrZrYZNmyYGTb4lVdeMQMjDRkyxIzmqzcGAAAAAA5GKo+Oe+qpp5qv33//vfzwww9yyimnSJUqVcTj8cjIkSPjF4R6vV4zSpIu+fn5pixWAxLNnTs35PX06dNNRlRH49UbsHPnTvnrX/9qhgfWVHFgECVNFWvK+I9//GNMzgsAAAAAKrtffvnFDEq7YMECE3hq39DDDjvMtIytXbu2SVDGpTluaRp8xnNEXA06lV6w0mC0uLhYunfvHtymVatW0rRpUzOUMAAAAAAclBTuEzps2DDTFFf7gFatWjVYrv1E33333dhmQo855hgT+ZbF8uXLJRZ00KOhQ4dKly5dzBDBavPmzZKZmSk1a9YM2bZ+/fpmnU1hYaFZAgIZXQAAAADA//cJfe+996Rx48ZSWsuWLeWnn36SmAahOiBQ6YGAnnrqKWnTpo107tzZlGnT12+++cbMFRor2jf066+/NgMhRWPAozFjxkTlvAAAAABUYjHsEypJngktKCgIyYAGbN++XbKysmIbhI4ePTr4vY6Ce/PNN8v9999/wDZ5eXkSCzrY0Jw5c2TRokUhUbgOkFRUVCQ7duwIyYbq6LjhJlAdMWKEmde0dCa0SZMmMTl3AAAAAKiITj75ZJkxY0Yw9tPWsdpCdfz48dKtW7f4DUyko9AuXbr0gPLLL79cjj/+eHn++eclWhzHMZOhvv7667Jw4UJp0aJFyPrjjjvOtFGeP3++mZpF6RQu2mY5kKV1o1H7wUTuAAAAAFJELPtuOpLUNNg8/fTTTfynyT8dnFZbwGom9GBmI4k4CNUhefWA2g64NC3Lzs6WaDfB1ZFv33zzTTNXaKCfp07FouehX3VkJs1q6mBFOkiSBq0agDIyLgAAAACUn47Fs2bNGpk0aZKJx3bv3m2mw9Q4TafpjFsQqoMDDRo0yAxA1LFjR1P22WefmQzowcwV42bKlCnma9euXUPKdRqWq666ynz/2GOPmWljNBOqgw317NnT9FkFAAAAgIOWwpnQ9evXm26Ld999t+s6nZUkLkHonXfeaeaGefzxx+XFF180ZTovpwaGOodMNGlz3N+j2dfJkyebBQAAAAAQHdodctOmTVKvXr0D5g/VdT6fLz5BqNJgM9oBJwAAAAAkG08MR8f1JHkmVJOCblN1arPcg+mKWa4gFAAAAABQOQ3/30wiGoBql8vS07Ro9lO7Y3bo0CG2QagO+qMdUuvWrSu1atVyjYYDdKQkAAAAAEDF9MUXXwQzoStXrpTMzMzgOv2+ffv2cuutt8Y2CNXBf3Q0JDVx4sRyHwwAAAAAkNwWLFhgvg4YMMCMBaSzkERTmYLQ/v37u34PAAAAAJVaCo+OO23atJjUW+Y+ofn5+WXaLtpRMgAAAAAg/goKCuThhx+W+fPny9atW8Xv94es//HHH2MbhNasWTNsX9DAyEnlHaYXAAAAAJJNKo+Oe+2118qHH34oV1xxhTRs2DBsPBiTIDTQLjgQcJ511lny3HPPyaGHHhqVEwEAAAAAJI93331X3n77benSpUtU6y1zEHrqqaeGvE5LS5M//vGPcthhh0X1hAAAAAAgqSR5xjJWdGYUnSkl2rxRrxEAAAAAUOHdf//9MmrUKNmzZ09iMqEAAAAAkHJSeHTcRx99VH744QepX7++NG/eXDIyMkLWL1++PP5BaLQ6pgIAAAAAkkufPn1iUm+Zg9C+ffuGvN63b5/ccMMNUq1atZDy1157LXpnBwAAAAAJlMqj444ePTqxQWhubm7I68svvzwW5wMAAAAASCLLli2TVatWme+POuooOeaYY+IThE6bNu2gDgQAAAAAFU4K9wndunWrXHLJJbJw4UKpWbOmKduxY4d069ZNZs+eLYcccki56mVgotI83t8WIJXs18E8YF/9qtZd8puluZbvaex336FuobWuqlWLXMu9XktdIlJS4n78oiL3X2n+IvftleOz9G0vsZQ7noj/IfH4LfsU2evyWtZ5bOdr1rmXp1nq8tofi6QVRlpu/1fUWxJZeTiOZSwCx/6IxZfpvo/fUu7LCleXpTzbUp5lvy/+TPd1jmUfJ8P+M+F43ffxWspVWpr7uvQ09+Okp/nsdVmOk2b5OfaGaX/mD/cz5sLnj/zfbSfCY/xvL4kWj+X607z2e2yTme6L+Di2+++J4tWX5w77LM8lW4olmmzP33a/onmMnwtCu7GVtm2P+7+71TLdr792tn200jpZBa7lVdIiv5d7fe5/J+wutv+y3FsSuk/JnjD/4CCp3XTTTbJr1y755ptvpHXr1qbs22+/lf79+8vNN98sL730UrnqJQgFAAAAAItU7hM6d+5c+de//hUMQFWbNm1k8uTJ0qNHj3LXS9oPAAAAAHAAv99/wLQsSst0XXkRhAIAAADA7/UJjdWSxE477TS55ZZbZOPGjcGyDRs2yLBhw+T0008vd70EoQAAAACAA0yaNEny8/OlefPmcvjhh5ulRYsWpuzJJ5+U8qJPKAAAAADYpPDouE2aNJHly5ebfqHfffedKdP+od27dz+oeglCAQAAAACuPB6PnHHGGWaJFprjAgAAAMDvjI4bqyUZffDBB2YUXG12u7+dO3fKUUcdJR999FG56ycIBQAAAAAETZw4UQYOHCg5OTmyv9zcXLn++utlwoQJUl4EoQAAAABgk4Kj43755Zdy5plnWtfrHKHLli0rd/0EoQAAAACAoC1btrjODxqQnp4uP//8s5QXQSgAAAAA2KRgJvTQQw+Vr7/+2rr+q6++koYNG5a7foJQAAAAAEDQWWedJSNHjpR9+/bJ/vbu3SujR4+WP/3pT1JeTNECAAAAABaxHMXWk6SZ0HvuuUdee+01+cMf/iBDhgyRI4880pTrXKGTJ08Wn88nd999d7nrJwgFAAAAAATVr19fPvnkExk0aJCMGDFCHMcJzhnas2dPE4jqNuVFEAoAAAAANrHsu+kk721v1qyZvPPOO/Lrr7/K999/bwLRli1bSq1atQ667qTvE7po0SLp3bu3NGrUyETeb7zxxgHbrFq1Ss455xwzZ021atXkhBNOkPXr1yfkfAEAAACgsqhVq5aJrzp27BiVALRCBKEFBQXSvn17k/J188MPP8hJJ50krVq1koULF5qRmrQTbXZ2dtzPFQAAAEDl7BMaq6U8NDZq3ry5iXk6deokS5YsCbv9jh07ZPDgwWZE26ysLNPXU7OciZL0zXF79eplFhvtEKujN40fPz5Ydvjhh8fp7AAAAAAgfl5++WUZPny4TJ061QSgEydONP00V69eLfXq1Ttg+6KiIjnjjDPMun/84x9m+pWffvpJatasmbDHlvSZ0HD8fr+8/fbbJpLXG683Vh+EW5NdAAAAAKjo84ROmDBBBg4cKAMGDJA2bdqYYLRq1ary/PPPu26v5du3bzcxUpcuXUwG9dRTTzWtTROlQgehW7duld27d8vDDz8sZ555prz//vty3nnnSd++feXDDz+07ldYWCj5+fkhCwAAAAAks6KiIlm2bJl07949WOb1es3rxYsXu+7zz3/+Uzp37mya4+qItm3btpWHHnrITLOSKEnfHPf3MqHq3HPPlWHDhpnvO3ToYIYT1k8ENMJ3M3bsWBkzZkxczxUAAABABRSH0XHz90uKab9NXfa3bds2EzzuPz2KvtY5PN38+OOP8sEHH0i/fv1MP1Ad6fbGG2+U4uJiGT16tCRChc6E1q1bV9LT000aurTWrVuHHR1X57rZuXNncMnLy4vD2QIAAADAgZo0aWJm+ggsmjSLZuJOuy0+88wzctxxx8nFF19sxtXRpF2iVOhMaGZmphkuWDvhlrZmzRozr42N7ZMFAAAAACjN878lFjz/+6pJsZycnGC5LVbRJFxaWpps2bIlpFxfN2jQwHUfHRE3IyPD7Fc6abd582bTvFdjqnhL+kyo9vlcsWKFWdS6devM94FM52233WZGiHr22WdNannSpEny1ltvmRQzAAAAACS7nJyckMUWhGrAqNnM+fPnh2Q69bX2+3SjgxFpnBToyhhI2mlwmogAtEIEoUuXLpVjjjnGLEqHI9bvR40aZV7rQESaStYpWtq1ayfPPfecvPrqq2buUAAAAACoTKPjDh8+3CTgXnjhBVm1apUMGjRICgoKzGi56sorrzTdDwN0vY6Oe8stt5jgU2cX0YGJdKCiREn65rhdu3YVxwn/dK6++mqzAAAAAEA0eZzflljwlKNe7dP5888/m6ScNqnVgVnnzp0bHKxIW4zqiLml+5u+9957ZiDXo48+2swTqgHpHXfcIYmS9EEoAAAAAOD/DRkyxCxuFi5ceECZNtX99NNPJVkQhAIAAABAAqdoSTVJ3ycUAAAAAFB5kAkFAAAAgHBSNGMZK2RCAQAAAABxQyYUAAAAACrI6LiVAZlQAAAAAEDckAkFAAAAABtGx406MqEAAAAAgLghEwoAAAAAFvQJjT4yoQAAAACAuCETCgAAAAA29AmNOjKhAAAAAIC4IRMKAAAAABb0CY0+MqEAAAAAgLghE1qa1yPi8cTv7gNJwJOZ4Vru6M+DhbfIvTxrm/vnWiV7s6117c3Kcj9+lnbAcOdk+N1XpLvv40mzbC8imdWL3atK97mWe71hzstxv2der/vxszNKrHVVy3S/yTUyC6375GTsi6i8Rrp7uaqfke9aXjt9t3t5mnu5quZ1P2ef4/5+yfHazyvD4/5cqnrs9zJNP8J2KzedfA5UZDmvcHUVh9nHxifu75c9fvefySJJs9blL8fxI/WLr7p1XbHjfm75/iqu5b+WVLPWtcef6Vq+s8S9rl3F9t8vOy3r9pS4H0MV+tz/NNpT7P5cCovtf0oV+9zvS3Gxe3lJif0ZOz7394vfUtdvFVp+j1vq8hSH+71v2cdyDMuPym8s6yw/3uK1XYfuY//1HvHxrecV5hi267T8cyDpe8PUZTnOTsuP944wP/Y/WNZZflQNy4+elFR1v8iSavYb49QI/Z3s32v/vV4h0Cc06siEAgAAAADihkwoAAAAANiQCY06MqEAAAAAgLghEwoAAAAAFoyOG31kQgEAAAAAcUMmFAAAAABs6BMadWRCAQAAAABxQyYUAAAAACw8jmOWWPDEqN5kRyYUAAAAABA3ZEIBAAAAwIY+oVFHJhQAAAAAEDdkQgEAAADAgnlCo49MKAAAAAAgbsiEAgAAAIANfUKjjkwoAAAAACBukj4IXbRokfTu3VsaNWokHo9H3njjjeC64uJiueOOO6Rdu3ZSrVo1s82VV14pGzduTOg5AwAAAKhcfUJjtaSipA9CCwoKpH379jJ58uQD1u3Zs0eWL18uI0eONF9fe+01Wb16tZxzzjkJOVcAAAAAQAXvE9qrVy+zuMnNzZV58+aFlE2aNEk6duwo69evl6ZNm8bpLAEAAABUSvQJTb1MaKR27txpmu3WrFkz0acCAAAAAKhomdBI7Nu3z/QRvfTSSyUnJ8e6XWFhoVkC8vPz43SGAAAAACoS5gmNvkqTCdVBii666CJxHEemTJkSdtuxY8eapryBpUmTJnE7TwAAAABIZd7KFID+9NNPpo9ouCyoGjFihGm2G1jy8vLidq4AAAAAKmCf0FgtKSi9sgSga9eulQULFkidOnV+d5+srCyzAAAAAADiK+mD0N27d8v3338ffL1u3TpZsWKF1K5dWxo2bCgXXHCBmZ5lzpw54vP5ZPPmzWY7XZ+ZmZnAMwcAAABQGaTqfJ4pG4QuXbpUunXrFnw9fPhw87V///5y7733yj//+U/zukOHDiH7aVa0a9eucT5bAAAAAECFDkI1kNTBhmzCrQMAAACAg6LxRqxiDic1Y5lKMTARAAAAAKBiSPpMKAAAAAAkCvOERh+ZUAAAAABA3JAJBQAAAACbWM7n6aTmbScTCgAAAACIGzKhAAAAAGDh8f+2xIInRvUmOzKhAAAAAIC4IRMKAAAAADb0CY06MqEAAAAAgLghEwoAAAAAFswTGn1kQgEAAAAAcUMQCgAAAAA2jhPbpRwmT54szZs3l+zsbOnUqZMsWbKkTPvNnj1bPB6P9OnTRxKJIBQAAAAAKoiXX35Zhg8fLqNHj5bly5dL+/btpWfPnrJ169aw+/3nP/+RW2+9VU4++WRJNIJQAAAAAPidPqGxWiI1YcIEGThwoAwYMEDatGkjU6dOlapVq8rzzz9v3cfn80m/fv1kzJgxcthhh0miEYQCAAAAQALl5+eHLIWFha7bFRUVybJly6R79+7BMq/Xa14vXrzYWv99990n9erVk2uuuUaSAaPjluJJTxOPh1uC1HL7koWu5Tv89r4F3+491LX8l+Jq7nUVV7XW5RePa3mxP826T5Fl3e7irIjr8juW4/vc99lbnGGta1+x+++PvXvcz2tXof2+/FzofnxPof2zQ2+Je7mnxGOf98zCY39g7tuH+yTXss5jqcsJ9/Go7VKsJxx5XXHjRHhe4e6xJ/JrtFbnLcc9trwBnLRyPOMIlSeLUJ5rSdb3iyfcefk9Ef3s2crDnlaG5dmH3cl2fPfz9WeVr8+c9fCeCN9L5fnZs+zjz7A/sPS97uXeYsuhw5yX3/LnbHE1+07FuZY3QE33E6hVa7e1rpa1t4XWXVAk/5UKLA7zhDZp0iSkWJva3nvvvQdsvm3bNpPVrF+/fki5vv7uu+9cD/Hxxx/LX//6V1mxYoUkCyIuAAAAAEigvLw8ycnJCb7OynL/ADtSu3btkiuuuEKeffZZqVu3riQLglAAAAAASOA8oTk5OSFBqI0GkmlpabJly5aQcn3doEGDA7b/4YcfzIBEvXv3Dpb5/b9lvdPT02X16tVy+OGHS7zRJxQAAAAAKoDMzEw57rjjZP78+SFBpb7u3LnzAdu3atVKVq5caZriBpZzzjlHunXrZr7fvxlwvJAJBQAAAACbg5jP83c5kder07P0799fjj/+eOnYsaNMnDhRCgoKzGi56sorr5RDDz1Uxo4da+YRbdu2bcj+NWvWNF/3L48nglAAAAAAqCAuvvhi+fnnn2XUqFGyefNm6dChg8ydOzc4WNH69evNiLnJjCAUAAAAABLYJzRSQ4YMMYubhQvdZz4ImD59uiRacofIAAAAAIBKhUwoAAAAACRwntBUQxAKAAAAABWoOW5FR3NcAAAAAEDckAkFAAAAABu/89sSC/7UTIWSCQUAAAAAxA2ZUAAAAACwYWCiqCMTCgAAAACIGzKhAAAAAGDhieEotp4UvesVPhPq8/lk5MiR0qJFC6lSpYocfvjhcv/994vjpGYnXwAAAABIZhU+Ezpu3DiZMmWKvPDCC3LUUUfJ0qVLZcCAAZKbmys333xzok8PAAAAQEWmya1YJbic1EycVfgg9JNPPpFzzz1Xzj77bPO6efPm8tJLL8mSJUsSfWoAAAAAgMrWHPfEE0+U+fPny5o1a8zrL7/8Uj7++GPp1atXok8NAAAAQAWn/UFjuaSiCp8JvfPOOyU/P19atWolaWlppo/ogw8+KP369bPuU1hYaJYA3R8AAAAAEHsVPhP697//XWbOnCmzZs2S5cuXm76hjzzyiPlqM3bsWNNnNLA0adIkrucMAAAAoILNExqrJQVV+CD0tttuM9nQSy65RNq1aydXXHGFDBs2zASaNiNGjJCdO3cGl7y8vLieMwAAAACkqgrfHHfPnj3i9YbG0tos1+/3W/fJysoyCwAAAACE43Ecs8SCh9FxK6bevXubPqBNmzY1U7R88cUXMmHCBLn66qsTfWoAAAAAgMqWCX3yySdl5MiRcuONN8rWrVulUaNGcv3118uoUaMSfWoAAAAAKjptYOmPYd0pqMIHoTVq1JCJEyeaBQAAAACQ3Cp8EAoAAAAAsUKf0Oir8KPjAgAAAAAqDjKhAAAAAGATy/k8ndS87WRCAQAAAABxQyYUAAAAAGx0Ls9YzefppGYqlEwoAAAAACBuyIQCAAAAgIXH+W2JBU9qJkLJhAIAAAAA4odMKAAAAADY0Cc06ugTCgAAAACIGzKhAAAAAGDh8f+2xIInRvUmOzKhAAAAAIC4IRMKAAAAADb0CY06MqEAAAAAgLghEwoAAAAANjqXZ6zm83RS87YThJbiyc4WjzczcU8DSIBbJt3gWu547PukFVnK97r/Jk3fZ/8Nm1bsXu4tse/jLXJf5/HZju+z12U5TnaJ+0gBVXxhRhDwWm6a3/34Hsd+Xrb77/E74ZsLuR5fYt5+xmO5X9E+L4+trjAcjydqddmuxfF6Iz6Gk2Z7yGF++Gws+1iPUY66wu5ie/7luceRXku491G65Q3rD/dz7I3sd6Lt594cx/K7ynJbwj2v8ryPHcu5WX+PhPv9YqnLSYt9o7pw/x7Z7qU/I8x5Rfj71R/mGm3333bvy/Mzabv+cHX5M9zX+TLt+/iy3NeVZGVZts+21rUqq27otoX7rNsiNRGEAgAAAECYDxvK9cFlGXhiVG+yo08oAAAAACBuyIQCAAAAgA2j40YdmVAAAAAAQNyQCQUAAAAAGyfKg/yVlppdQsmEAgAAAADih0woAAAAAFgwOm700ScUAAAAABA3ZEIBAAAAIFy/zVjN5+mk5m0nEwoAAAAAiBsyoQAAAABgwzyhUUcmFAAAAAAQN2RCAQAAAMBG5wj1xOj2+FPztpMJBQAAAADEDZlQAAAAALBgntDoq3SZ0Icfflg8Ho8MHTo00acCAAAAAKjMQejnn38uTz/9tBx99NGJPhUAAAAAlWl03Fgt5TB58mRp3ry5ZGdnS6dOnWTJkiXWbZ999lk5+eSTpVatWmbp3r172O3jodIEobt375Z+/fqZm6w3FwAAAAAqm5dfflmGDx8uo0ePluXLl0v79u2lZ8+esnXrVtftFy5cKJdeeqksWLBAFi9eLE2aNJEePXrIhg0bJFEqTRA6ePBgOfvss01kDwAAAACVMRM6YcIEGThwoAwYMEDatGkjU6dOlapVq8rzzz/vuv3MmTPlxhtvlA4dOkirVq3kueeeE7/fL/Pnz5dEqRQDE82ePdt8CqDNccuisLDQLAH5+fkxPDsAAAAAsMvfLx7Jysoyy/6Kiopk2bJlMmLEiGCZ1+s1iTjNcpbFnj17pLi4WGrXrp2wR1LhM6F5eXlyyy23mAhf20SXxdixYyU3Nze4aEoaAAAAABKRCW3SpElIfKLxiptt27aJz+eT+vXrh5Tr682bN5fp4d1xxx3SqFGjhLYgrfCZUP0kQNs/H3vsscEyfTCLFi2SSZMmmYxnWlpayD76yYG2oy79yQOBKAAAAIBEJdZycnKCr92yoNGaSURbkWo/0bIm8GKhwgehp59+uqxcuTKkTNtHa3tnjfL3D0DDpbcBAAAAIIRfJwuNYd0iJgAtHYTa1K1b18Q3W7ZsCSnX1w0aNAi77yOPPGKC0H/9618Jn02kwgehNWrUkLZt24aUVatWTerUqXNAOQAAAABUVJmZmXLccceZQYX69OljygKDDA0ZMsS63/jx4+XBBx+U9957T44//nhJtAofhAIAAABArHgcxyyxqjtS2q2wf//+Jpjs2LGjTJw4UQoKCkxrUHXllVfKoYceGuxXOm7cOBk1apTMmjXLzC0a6DtavXp1syRCpQxCtY0zAAAAAFQ2F198sfz8888msNSAUqdemTt3bnCwovXr15sRcwOmTJliRtW94IILQurReUbvvfdeSYRKGYQCAAAAQFSUcz7PMtddDtr01tb8dv+E3H/+8x9JNhV+ihYAAAAAQMVBJhQAAAAAbPyOdt6MXd0piEwoAAAAACBuyIQCAAAAQAXqE1rREYQCAAAAgFUMg1BJzSCU5rgAAAAAgLghEwoAAAAANjTHjToyoQAAAACAuCETCgAAAABhp1FhipZoIhMKAAAAAIgbMqEAAAAAYOP4f1tiwYlRvUmOTCgAAAAAIG7IhAIAAACADaPjRh2ZUAAAAABA3JAJLcVTrYp4vFnxu/tAEqi2yb0vQlpRmFHgLKvSCt3r8hbZ+zt4fe6VecxIdJZ9Cn3u+9jKi93Lf1tX4l5eVOy+Q4m9Lilxr8ux7WPZ/red3O+ZYznf31a63zPHZzl+mHtsPb5lH6c8/V0s51upeDzl2Cd6nw97vJ7oHSdsXZZ1Xm/U7ovHVle4faznVY7nEs3nVZ7je9OidhzrfQn3XNLKcfx4/LyUh+13TzSPb3vGYe6jk2Z5L9ne+7bto30t5fldvd8+Jb5CWSUVGKPjRh2ZUAAAAABA3JAJBQAAAAAb+oRGHZlQAAAAAEDckAkFAAAAABsnhuMYOKl528mEAgAAAADihkwoAAAAANjQJzTqyIQCAAAAAOKGTCgAAAAA2Ph1vmv7nOcHX3fqIRMKAAAAAIgbMqEAAAAAYEOf0KgjEwoAAAAAiBsyoQAAAABgQyY06siEAgAAAADihkwoAAAAANj4HU2HxrDu1EMmFAAAAAAQNxU+CB07dqyccMIJUqNGDalXr5706dNHVq9enejTAgAAAFAJOI4/pksqqvBB6IcffiiDBw+WTz/9VObNmyfFxcXSo0cPKSgoSPSpAQAAAAAqW5/QuXPnhryePn26yYguW7ZMTjnllISdFwAAAIBKMjpurPpuOvQJrRR27txpvtauXTvRpwIAAAAAqGyZ0NL8fr8MHTpUunTpIm3btrVuV1hYaJaA/Pz8OJ0hAAAAgArFZCvJhEZThe8TWpr2Df36669l9uzZvzuYUW5ubnBp0qRJ3M4RAAAAAFJZpQlChwwZInPmzJEFCxZI48aNw247YsQI02w3sOTl5cXtPAEAAABUIH5/bJcUVOGb4zqOIzfddJO8/vrrsnDhQmnRosXv7pOVlWUWAAAAAEB8pVeGJrizZs2SN99808wVunnzZlOuzWyrVKmS6NMDAAAAUJHRJzTqKnxz3ClTppgmtV27dpWGDRsGl5dffjnRpwYAAAAAqIzNcQEAAAAgJvGG3y+OJzZ9Nx0nNfuEVvhMKAAAAACg4qjwmVAAAAAAiBn6hEYdmVAAAAAAQNyQCQUAAAAAG78j4onRODROao5vQyYUAAAAABA3ZEIBAAAAIGy2Mkaj2DpkQgEAAAAAiCkyoQAAAABg4fgdcWLUJ9QhEwoAAAAAQGyRCQUAAAAAG8cfwz6h/pS874yOCwAAAACIGzKhAAAAAGBBn9DoIxMKAAAAAIgbMqEAAAAAYEOf0KgjCC01NHKJvyj6dxhIciXF+1zLneIwQ5FbVjnF7p3rvSX2Tvden3tlHr/9+N4Sn/s+PvfjeHzu2/+2rsRy/GL3Hfz2umzrHNs+jvux/7eTe3HYfdzvmWMb9CDcsPDW45djiPryHL/S8CS0kZLH8UTvOGHqsq5xvNG7L9a67Dy244S9L3F4Xv7yHD+tHPu4H8fjsRzfVm7WxaHxXLjjR5Ptd09Uj2+7x/bn6NjeS9b3fphnEs1rKdfv/dB9SnyF/yuumL/3S6TY+rdPVOpOQQShIrJr1y5zMxb+97lEPw8g/tZz04GYKc8fLdH8Qyc1B10EkMR/c+fm5kpFkZmZKQ0aNJCPN78T0+M0aNDAHCuVeJyK+pFEFPn9ftm4caPUqFHD/HA0adJE8vLyJCcnJ9GnhjjKz8/n2aconn3q4tmnNp5/6uLZx5eGG/o3dqNGjcTrrVhD0uzbt0+KimLbWjIzM1Oys7MllZAJ1cYMXq80btw4pImKBqAEoamJZ5+6ePapi2ef2nj+qYtnHz8VKQNamgaHqRYgxkPF+igCAAAAAFChEYQCAAAAAOKGIHQ/WVlZMnr0aPMVqYVnn7p49qmLZ5/aeP6pi2cPJBYDEwEAAAAA4oZMKAAAAAAgbghCAQAAAABxQxAKAAAAAIgbgtBSJk+eLM2bNzdzAXXq1EmWLFkSvyeBuBg7dqyccMIJUqNGDalXr5706dNHVq9efcCkxIMHD5Y6depI9erV5fzzz5ctW7bwhCqZhx9+2MwLPHTo0GAZz75y27Bhg1x++eXmZ7tKlSrSrl07Wbp0achk6qNGjZKGDRua9d27d5e1a9cm9Jxx8Hw+n4wcOVJatGhhnuvhhx8u999/v3neATz7ymHRokXSu3dvadSokfn9/sYbb4SsL8tz3r59u/Tr18/MH1qzZk255pprZPfu3XG+EqDyIwj9n5dfflmGDx9uRsZdvny5tG/fXnr27Clbt25N7BNCVH344YcmwPz0009l3rx5UlxcLD169JCCgoLgNsOGDZO33npLXnnlFbP9xo0bpW/fvjyJSuTzzz+Xp59+Wo4++uiQcp595fXrr79Kly5dJCMjQ95991359ttv5dFHH5VatWoFtxk/frw88cQTMnXqVPnss8+kWrVq5t8B/XACFde4ceNkypQpMmnSJFm1apV5rc/6ySefDG7Ds68c9N9y/ftNkwpuyvKcNQD95ptvzN8Ic+bMMYHtddddF8erAFKEA6Njx47O4MGDg3fD5/M5jRo1csaOHcsdqsS2bt2qH4U7H374oXm9Y8cOJyMjw3nllVeC26xatcpss3jx4gSeKaJl165dTsuWLZ158+Y5p556qnPLLbeYcp595XbHHXc4J510knW93+93GjRo4PzlL38Jlul7Iisry3nppZfidJaIhbPPPtu5+uqrQ8r69u3r9OvXz3zPs6+c9N/t119/Pfi6LM/522+/Nft9/vnnwW3effddx+PxOBs2bIjzFQCVG5lQESkqKpJly5aZZhkBXq/XvF68eHEiPyNAjO3cudN8rV27tvmq7wPNjpZ+L7Rq1UqaNm3Ke6GS0Ez42WefHfKMFc++cvvnP/8pxx9/vFx44YWmKf4xxxwjzz77bHD9unXrZPPmzSHvi9zcXNM1g38HKrYTTzxR5s+fL2vWrDGvv/zyS/n444+lV69e5jXPPjWU5TnrV22Cq78rAnR7/ZtQM6cAoic9inVVWNu2bTN9RurXrx9Srq+/++67hJ0XYsvv95v+gNpEr23btqZM/4HKzMw0/wjt/17QdajYZs+ebZrba3Pc/fHsK7cff/zRNMnUbhd33XWXeQ/cfPPN5ue9f//+wZ9vt38H+Nmv2O68807Jz883HyimpaWZf+8ffPBB0+xS8exTQ1mes37VD6lKS09PNx9U83sAiC6CUKR0Ruzrr782n4ij8svLy5NbbrnF9PPRwceQeh86aXbjoYceMq81E6o//9o3TINQVF5///vfZebMmTJr1iw56qijZMWKFeYDSB28hmcPAIlBc1wRqVu3rvl0dP8RUPV1gwYNEvRoEEtDhgwxAw4sWLBAGjduHCzX563Ns3fs2BGyPe+Fik+b2+pAY8cee6z5ZFsXHXhKB6nQ7/XTcJ595aWjYbZp0yakrHXr1rJ+/XrzfeB3Pf8OVD633XabyYZecsklZkTkK664wgxCpqOlK559aijLc9av+w9IWVJSYkbM5e9BILoIQkVMc6zjjjvO9Bkp/am5vu7cuXOUbzkSSccq0AD09ddflw8++MAM2V+avg909MzS7wWdwkX/UOW9ULGdfvrpsnLlSpMFCSyaGdMmeYHvefaVlza73386Ju0j2KxZM/O9/i7QPzJL/+xrE07tB8bPfsW2Z88e06evNP3gWf+dVzz71FCW56xf9UNo/dAyQP9W0PeK9h0FEEWJHhkpWcyePduMkDZ9+nQzOtp1113n1KxZ09m8eXOiTw1RNGjQICc3N9dZuHChs2nTpuCyZ8+e4DY33HCD07RpU+eDDz5wli5d6nTu3NksqHxKj46rePaV15IlS5z09HTnwQcfdNauXevMnDnTqVq1qvPiiy8Gt3n44YfN7/0333zT+eqrr5xzzz3XadGihbN3796EnjsOTv/+/Z1DDz3UmTNnjrNu3Trntddec+rWrevcfvvtwW149pVn9PMvvvjCLPon7oQJE8z3P/30U5mf85lnnukcc8wxzmeffeZ8/PHHZjT1Sy+9NIFXBVROBKGlPPnkkyb4yMzMNFO2fPrpp4l7MogJ/UfJbZk2bVpwG/3H6MYbb3Rq1apl/kg977zzTKCKyh+E8uwrt7feestp27at+cCxVatWzjPPPBOyXqdwGDlypFO/fn2zzemnn+6sXr06YeeL6MjPzzc/5/rve3Z2tnPYYYc5d999t1NYWBjchmdfOSxYsMD133j9IKKsz/mXX34xQWf16tWdnJwcZ8CAASa4BRBdHv1fNDOrAAAAAADY0CcUAAAAABA3BKEAAAAAgLghCAUAAAAAxA1BKAAAAAAgbghCAQAAAABxQxAKAAAAAIgbglAAAAAAQNwQhAIAAAAA4oYgFACQdK666irp06dPok8DAADEQHosKgUAwMbj8YS9OaNHj5bHH39cHMfhJgIAUAkRhAIA4mrTpk3B719++WUZNWqUrF69OlhWvXp1swAAgMqJ5rgAgLhq0KBBcMnNzTWZ0dJlGoDu3xy3a9euctNNN8nQoUOlVq1aUr9+fXn22WeloKBABgwYIDVq1JAjjjhC3n333ZBjff3119KrVy9Tp+5zxRVXyLZt23jiAAAkEEEoAKBCeOGFF6Ru3bqyZMkSE5AOGjRILrzwQjnxxBNl+fLl0qNHDxNk7tmzx2y/Y8cOOe200+SYY46RpUuXyty5c2XLli1y0UUXJfpSAABIaQShAIAKoX379nLPPfdIy5YtZcSIEZKdnW2C0oEDB5oybdb7yy+/yFdffWW2nzRpkglAH3roIWnVqpX5/vnnn5cFCxbImjVrEn05AACkLPqEAgAqhKOPPjr4fVpamtSpU0fatWsXLNPmtmrr1q3m65dffmkCTrf+pT/88IP84Q9/iMt5AwCAUAShAIAKISMjI+S19iUtXRYYddfv95uvu3fvlt69e8u4ceMOqKthw4YxP18AAOCOIBQAUCkde+yx8uqrr0rz5s0lPZ1/7gAASBb0CQUAVEqDBw+W7du3y6WXXiqff/65aYL73nvvmdF0fT5fok8PAICURRAKAKiUGjVqJP/+979NwKkj52r/UZ3ipWbNmuL18s8fAACJ4nEcx0nY0QEAAAAAKYWPggEAAAAAcUMQCgAAAACIG4JQAAAAAEDcEIQCAAAAAOKGIBQAAAAAEDcEoQAAAACAuCEIBQAAAADEDUEoAAAAACBuCEIBAAAAAHFDEAoAAAAAiBuCUAAAAABA3BCEAgAAAAAkXv4Py5FiujhouvEAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "npz = np.load(\n",
+    "    r\"E:\\TUM\\3sem\\NeuroAI\\project\\github_neuroai\\results\\size_comparison\\CDM_results\\pid_sweep_cdm\\sweep_results.npz\"\n",
+    ")\n",
+    "\n",
+    "hidden_sizes = [2,4,8,12,16,20,40,60,80,100,150,200]\n",
+    "\n",
+    "heatmap = np.array([\n",
+    "    npz[f\"h{h}_context_synergy\"]\n",
+    "    for h in hidden_sizes\n",
+    "])\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "\n",
+    "plt.imshow(\n",
+    "    heatmap,\n",
+    "    aspect=\"auto\",\n",
+    "    origin=\"lower\"\n",
+    ")\n",
+    "\n",
+    "plt.colorbar(label=\"Context Synergy\")\n",
+    "\n",
+    "plt.yticks(\n",
+    "    np.arange(len(hidden_sizes)),\n",
+    "    hidden_sizes\n",
+    ")\n",
+    "\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Hidden Size\")\n",
+    "plt.title(\"Context Synergy Dynamics\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "913b032f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# -----------------------------\n",
+    "# Load data\n",
+    "# -----------------------------\n",
+    "npz = np.load(\n",
+    "    r\"E:\\TUM\\3sem\\NeuroAI\\project\\github_neuroai\\results\\size_comparison\\CDM_results\\pid_sweep_cdm\\sweep_results.npz\"\n",
+    ")\n",
+    "\n",
+    "hidden_sizes = [2, 4, 8, 12, 16, 20, 40, 60, 80, 100, 150, 200]\n",
+    "\n",
+    "# -----------------------------\n",
+    "# Collect summary statistics\n",
+    "# -----------------------------\n",
+    "mi = []\n",
+    "red = []\n",
+    "syn = []\n",
+    "uniq1 = []\n",
+    "uniq2 = []\n",
+    "\n",
+    "for h in hidden_sizes:\n",
+    "\n",
+    "    # Context PID\n",
+    "    mi.append(\n",
+    "        np.mean(npz[f\"h{h}_coherence_mi_joint\"])\n",
+    "    )\n",
+    "\n",
+    "    red.append(\n",
+    "        np.mean(npz[f\"h{h}_coherence_redundancy\"])\n",
+    "    )\n",
+    "\n",
+    "    syn.append(\n",
+    "        np.mean(npz[f\"h{h}_coherence_synergy\"])\n",
+    "    )\n",
+    "\n",
+    "    uniq1.append(\n",
+    "        np.mean(npz[f\"h{h}_coherence_unique1\"])\n",
+    "    )\n",
+    "\n",
+    "    uniq2.append(\n",
+    "        np.mean(npz[f\"h{h}_coherence_unique2\"])\n",
+    "    )\n",
+    "\n",
+    "mi = np.array(mi)\n",
+    "red = np.array(red)\n",
+    "syn = np.array(syn)\n",
+    "uniq1 = np.array(uniq1)\n",
+    "uniq2 = np.array(uniq2)\n",
+    "\n",
+    "# =====================================================\n",
+    "# Figure 1: Hidden Size vs PID Components (Line Plot)\n",
+    "# =====================================================\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "plt.plot(hidden_sizes, mi, marker='o', label='Total MI')\n",
+    "plt.plot(hidden_sizes, red, marker='s', label='Redundancy')\n",
+    "plt.plot(hidden_sizes, syn, marker='^', label='Synergy')\n",
+    "plt.plot(hidden_sizes, uniq1, marker='d', label='Unique 1')\n",
+    "plt.plot(hidden_sizes, uniq2, marker='v', label='Unique 2')\n",
+    "\n",
+    "plt.xlabel(\"Hidden Size\")\n",
+    "plt.ylabel(\"Mean PID Value\")\n",
+    "plt.title(\"CDM Coherence PID vs Hidden Size\")\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"pid_vs_hidden_size.png\", dpi=300)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# =====================================================\n",
+    "# Figure 2: Grouped Bar Plot\n",
+    "# =====================================================\n",
+    "\n",
+    "x = np.arange(len(hidden_sizes))\n",
+    "width = 0.16\n",
+    "\n",
+    "plt.figure(figsize=(14, 6))\n",
+    "\n",
+    "plt.bar(x - 2*width, mi, width, label='Total MI')\n",
+    "plt.bar(x - width, red, width, label='Redundancy')\n",
+    "plt.bar(x, syn, width, label='Synergy')\n",
+    "plt.bar(x + width, uniq1, width, label='Unique 1')\n",
+    "plt.bar(x + 2*width, uniq2, width, label='Unique 2')\n",
+    "\n",
+    "plt.xticks(x, hidden_sizes)\n",
+    "\n",
+    "plt.xlabel(\"Hidden Size\")\n",
+    "plt.ylabel(\"Mean PID Value\")\n",
+    "plt.title(\"CDM Coherence PID Components by Hidden Size\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.savefig(\"pid_barplot_hidden_size.png\", dpi=300)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neuroai",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/size_comparison/CDM_results/pid_sweep_cdm/comparison_summary.png b/size_comparison/CDM_results/pid_sweep_cdm/comparison_summary.png
new file mode 100644
index 0000000..b290946
Binary files /dev/null and b/size_comparison/CDM_results/pid_sweep_cdm/comparison_summary.png differ
diff --git a/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h100.png b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h100.png
new file mode 100644
index 0000000..501cda5
Binary files /dev/null and b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h100.png differ
diff --git a/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h12.png b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h12.png
new file mode 100644
index 0000000..de4dd9a
Binary files /dev/null and b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h12.png differ
diff --git a/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h150.png b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h150.png
new file mode 100644
index 0000000..0a88806
Binary files /dev/null and b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h150.png differ
diff --git a/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h16.png b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h16.png
new file mode 100644
index 0000000..98bac21
Binary files /dev/null and b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h16.png differ
diff --git a/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h2.png b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h2.png
new file mode 100644
index 0000000..8ee63ef
Binary files /dev/null and b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h2.png differ
diff --git a/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h20.png b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h20.png
new file mode 100644
index 0000000..4a523ff
Binary files /dev/null and b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h20.png differ
diff --git a/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h200.png b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h200.png
new file mode 100644
index 0000000..9fbf932
Binary files /dev/null and b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h200.png differ
diff --git a/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h4.png b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h4.png
new file mode 100644
index 0000000..8234f27
Binary files /dev/null and b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h4.png differ
diff --git a/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h40.png b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h40.png
new file mode 100644
index 0000000..560410f
Binary files /dev/null and b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h40.png differ
diff --git a/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h60.png b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h60.png
new file mode 100644
index 0000000..97eee05
Binary files /dev/null and b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h60.png differ
diff --git a/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h8.png b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h8.png
new file mode 100644
index 0000000..362eae4
Binary files /dev/null and b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h8.png differ
diff --git a/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h80.png b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h80.png
new file mode 100644
index 0000000..053a3ec
Binary files /dev/null and b/size_comparison/CDM_results/pid_sweep_cdm/pid_timeseries_h80.png differ
diff --git a/size_comparison/PDM_results/pid_sweep/comparison_summary.png b/size_comparison/PDM_results/pid_sweep/comparison_summary.png
new file mode 100644
index 0000000..fec11b4
Binary files /dev/null and b/size_comparison/PDM_results/pid_sweep/comparison_summary.png differ
diff --git a/size_comparison/PDM_results/pid_sweep/pid_timeseries_h100.png b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h100.png
new file mode 100644
index 0000000..2dcbe3f
Binary files /dev/null and b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h100.png differ
diff --git a/size_comparison/PDM_results/pid_sweep/pid_timeseries_h12.png b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h12.png
new file mode 100644
index 0000000..ce96f77
Binary files /dev/null and b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h12.png differ
diff --git a/size_comparison/PDM_results/pid_sweep/pid_timeseries_h150.png b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h150.png
new file mode 100644
index 0000000..34f79e5
Binary files /dev/null and b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h150.png differ
diff --git a/size_comparison/PDM_results/pid_sweep/pid_timeseries_h16.png b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h16.png
new file mode 100644
index 0000000..0d810f1
Binary files /dev/null and b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h16.png differ
diff --git a/size_comparison/PDM_results/pid_sweep/pid_timeseries_h2.png b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h2.png
new file mode 100644
index 0000000..5706f3b
Binary files /dev/null and b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h2.png differ
diff --git a/size_comparison/PDM_results/pid_sweep/pid_timeseries_h20.png b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h20.png
new file mode 100644
index 0000000..2d9fb67
Binary files /dev/null and b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h20.png differ
diff --git a/size_comparison/PDM_results/pid_sweep/pid_timeseries_h4.png b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h4.png
new file mode 100644
index 0000000..155c19c
Binary files /dev/null and b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h4.png differ
diff --git a/size_comparison/PDM_results/pid_sweep/pid_timeseries_h40.png b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h40.png
new file mode 100644
index 0000000..764e977
Binary files /dev/null and b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h40.png differ
diff --git a/size_comparison/PDM_results/pid_sweep/pid_timeseries_h60.png b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h60.png
new file mode 100644
index 0000000..18c3600
Binary files /dev/null and b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h60.png differ
diff --git a/size_comparison/PDM_results/pid_sweep/pid_timeseries_h8.png b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h8.png
new file mode 100644
index 0000000..ae93290
Binary files /dev/null and b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h8.png differ
diff --git a/size_comparison/PDM_results/pid_sweep/pid_timeseries_h80.png b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h80.png
new file mode 100644
index 0000000..c243cbd
Binary files /dev/null and b/size_comparison/PDM_results/pid_sweep/pid_timeseries_h80.png differ
diff --git a/size_comparison/data_loading_ver/check_dataset.ipynb b/size_comparison/data_loading_ver/check_dataset.ipynb
new file mode 100644
index 0000000..0254ed1
--- /dev/null
+++ b/size_comparison/data_loading_ver/check_dataset.ipynb
@@ -0,0 +1,351 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e96e2740",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "coherence: 9.948979\n",
+      "context: 1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "CDMdata = np.load(\n",
+    "    r\"E:\\TUM\\3sem\\NeuroAI\\project\\data_generation\\CDM\\seed42\\context\\train.npz\",\n",
+    "    allow_pickle=True\n",
+    ")\n",
+    "\n",
+    "obs = CDMdata[\"observations\"]\n",
+    "labels = CDMdata[\"labels\"]\n",
+    "coh = CDMdata[\"coherences\"]\n",
+    "ctx = CDMdata[\"contexts\"]\n",
+    "periods = CDMdata[\"trial_periods\"]\n",
+    "\n",
+    "i = 0  # pick trial index\n",
+    "\n",
+    "print(\"coherence:\", coh[i])\n",
+    "print(\"context:\", ctx[i])\n",
+    "\n",
+    "T = obs.shape[1]\n",
+    "\n",
+    "plt.figure(figsize=(14, 6))\n",
+    "\n",
+    "# ---- plot a few observation channels ----\n",
+    "for ch in range(min(5, obs.shape[2])):  # first 5 channels\n",
+    "    plt.plot(obs[i, :, ch], label=f\"ch {ch}\")\n",
+    "\n",
+    "# ---- overlay task periods ----\n",
+    "plt.plot(periods[i] * 0.5, label=\"period (scaled)\", alpha=0.6)\n",
+    "\n",
+    "plt.title(f\"Trial {i} | coh={coh[i]:.2f} | ctx={ctx[i]}\")\n",
+    "plt.xlabel(\"time\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b657e36f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "coherence: 18.367348\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1400x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mod1 difference: 0.15746588\n",
+      "mod2 difference: 0.16398159\n"
+     ]
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(4, 1, figsize=(14, 8), sharex=True)\n",
+    "\n",
+    "i = 2\n",
+    "stim_period = periods[i] == 1\n",
+    "print(\"coherence:\", coh[i])\n",
+    "# observations (sum of first stimulus channels for clarity)\n",
+    "ax[0].plot(obs[i, :, 1], label=\"stim1_mod1\")\n",
+    "ax[0].axhline(np.mean(obs[i, stim_period, 1]), linestyle=\"--\",\n",
+    "              label=f\"mean stim1_mod1={np.mean(obs[i, stim_period, 1]):.3f}\")\n",
+    "ax[0].plot(obs[i, :, 3], label=\"stim1_mod2\")\n",
+    "ax[0].axhline(np.mean(obs[i, stim_period, 3]), linestyle=\"--\",\n",
+    "              label=f\"mean stim1_mod2={np.mean(obs[i, stim_period, 3]):.3f}\")\n",
+    "ax[0].set_title(\"Stimulus channels\")\n",
+    "ax[0].legend()\n",
+    "\n",
+    "ax[1].plot(obs[i, :, 2], label=\"stim2_mod1\")\n",
+    "ax[1].axhline(np.mean(obs[i, stim_period, 2]), linestyle=\"--\",\n",
+    "              label=f\"mean stim2_mod1={np.mean(obs[i, stim_period, 2]):.3f}\")\n",
+    "ax[1].plot(obs[i, :, 4], label=\"stim2_mod2\")\n",
+    "ax[1].axhline(np.mean(obs[i, stim_period, 4]), linestyle=\"--\",\n",
+    "              label=f\"mean stim2_mod2={np.mean(obs[i, stim_period, 4]):.3f}\")\n",
+    "ax[1].set_title(\"Stimulus channels\")\n",
+    "ax[1].legend()\n",
+    "\n",
+    "# labels (decision target)\n",
+    "ax[2].plot(labels[i], color=\"black\")\n",
+    "ax[2].set_title(\"Decision label\")\n",
+    "\n",
+    "# task period\n",
+    "ax[3].plot(periods[i], color=\"gray\")\n",
+    "ax[3].set_title(\"Fixation / Stimulus / Decision\")\n",
+    "ax[3].set_yticks([0,1,2])\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "stim_period = periods[i] == 1\n",
+    "\n",
+    "print(\"mod1 difference:\",\n",
+    "      np.mean(obs[i, stim_period, 1]) -\n",
+    "      np.mean(obs[i, stim_period, 2]))\n",
+    "\n",
+    "print(\"mod2 difference:\",\n",
+    "      np.mean(obs[i, stim_period, 3]) -\n",
+    "      np.mean(obs[i, stim_period, 4]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1dff6b2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (160000,)\n",
+      "Min: -18.75\n",
+      "Max: 18.75\n",
+      "Unique values: [-18.75       -18.367348   -17.984694   -17.602041   -17.219387\n",
+      " -16.836735   -16.454082   -16.071428   -15.688775   -15.306123\n",
+      " -14.92347    -14.540816   -14.158163   -13.77551    -13.392858\n",
+      " -13.010204   -12.627551   -12.244898   -11.862245   -11.479591\n",
+      " -11.096939   -10.714286   -10.331633    -9.948979    -9.566326\n",
+      "  -9.183674    -8.801021    -8.418367    -8.035714    -7.6530614\n",
+      "  -7.270408    -6.887755    -6.505102    -6.122449    -5.7397957\n",
+      "  -5.357143    -4.9744897   -4.591837    -4.2091837   -3.8265307\n",
+      "  -3.4438775   -3.0612245   -2.6785715   -2.2959185   -1.9132653\n",
+      "  -1.5306122   -1.1479592   -0.7653061   -0.38265306   0.\n",
+      "   0.38265306   0.7653061    1.1479592    1.5306122    1.9132653\n",
+      "   2.2959185    2.6785715    3.0612245    3.4438775    3.8265307\n",
+      "   4.2091837    4.591837     4.9744897    5.357143     5.7397957\n",
+      "   6.122449     6.505102     6.887755     7.270408     7.6530614\n",
+      "   8.035714     8.418367     8.801021     9.183674     9.566326\n",
+      "   9.948979    10.331633    10.714286    11.096939    11.479591\n",
+      "  11.862245    12.244898    12.627551    13.010204    13.392858\n",
+      "  13.77551     14.158163    14.540816    14.92347     15.306123\n",
+      "  15.688775    16.071428    16.454082    16.836735    17.219387\n",
+      "  17.602041    17.984694    18.367348    18.75      ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "coh = CDMdata[\"coherences\"]\n",
+    "\n",
+    "print(\"Shape:\", coh.shape)\n",
+    "print(\"Min:\", coh.min())\n",
+    "print(\"Max:\", coh.max())\n",
+    "print(\"Unique values:\", np.unique(coh))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "38413106",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['observations', 'labels', 'coherences', 'contexts', 'trial_periods']\n",
+      "obs shape: (2000, 115, 7)\n",
+      "coh range: -18.75 18.75\n",
+      "unique contexts: [1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "PDMdata = np.load(\n",
+    "    r\"E:\\TUM\\3sem\\NeuroAI\\project\\data_generation\\PDM\\seed42\\perceptual\\test_uniform.npz\"\n",
+    ")\n",
+    "\n",
+    "print(PDMdata.files)\n",
+    "\n",
+    "obs = PDMdata[\"observations\"]\n",
+    "labels = PDMdata[\"labels\"]\n",
+    "coh = PDMdata[\"coherences\"]\n",
+    "ctx = PDMdata[\"contexts\"]\n",
+    "periods = PDMdata[\"trial_periods\"]\n",
+    "\n",
+    "print(\"obs shape:\", obs.shape)\n",
+    "print(\"coh range:\", coh.min(), coh.max())\n",
+    "print(\"unique contexts:\", np.unique(ctx))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b40fde2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "coherence: -14.158163\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1400x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "coherence = -14.158163\n",
+      "mean stim1 = 0.375027\n",
+      "mean stim2 = 0.6181635\n",
+      "difference = -0.24313653\n"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
+      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
+      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(4, 1, figsize=(14, 8), sharex=True)\n",
+    "\n",
+    "i = 0\n",
+    "\n",
+    "trial_coh = coh[i]\n",
+    "mean_coh = np.mean(coh)\n",
+    "\n",
+    "stim_period = periods[i] == 1\n",
+    "fig.suptitle(\n",
+    "    f\"Trial {i} | coherence={trial_coh:.3f} | dataset mean coherence={mean_coh:.3f}\",\n",
+    "    fontsize=14\n",
+    ")\n",
+    "print(\"coherence:\", coh[i])\n",
+    "ax[0].plot(obs[i, :, 1], label=\"stim1_mod1\")\n",
+    "ax[0].axhline(np.mean(obs[i, stim_period, 1]), linestyle=\"--\",\n",
+    "              label=f\"mean stim1_mod1={np.mean(obs[i, stim_period, 1]):.3f}\")\n",
+    "ax[0].plot(obs[i, :, 3], label=\"stim1_mod2\")\n",
+    "ax[0].set_title(\"Stimulus channels\")\n",
+    "ax[0].legend()\n",
+    "\n",
+    "ax[1].plot(obs[i, :, 2], label=\"stim2_mod1\")\n",
+    "ax[1].axhline(np.mean(obs[i, stim_period, 2]), linestyle=\"--\",\n",
+    "              label=f\"mean stim2_mod1={np.mean(obs[i, stim_period, 2]):.3f}\")\n",
+    "ax[1].plot(obs[i, :, 4], label=\"stim2_mod2\")\n",
+    "ax[1].set_title(\"Stimulus channels\")\n",
+    "ax[1].legend()\n",
+    "\n",
+    "ax[2].plot(labels[i], color=\"black\")\n",
+    "ax[2].set_title(\"Decision label\")\n",
+    "\n",
+    "ax[3].plot(periods[i], color=\"gray\")\n",
+    "ax[3].set_title(\"Fixation / Stimulus / Decision\")\n",
+    "ax[3].set_yticks([0, 1, 2])\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "m1 = np.mean(obs[i, stim_period, 1])\n",
+    "m2 = np.mean(obs[i, stim_period, 2])\n",
+    "\n",
+    "print(\"coherence =\", coh[i])\n",
+    "print(\"mean stim1 =\", m1)\n",
+    "print(\"mean stim2 =\", m2)\n",
+    "print(\"difference =\", m1 - m2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27f9eb2c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neuroai",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/size_comparison/data_loading_ver/ctrnn_pid_both_tasks.py b/size_comparison/data_loading_ver/ctrnn_pid_both_tasks.py
new file mode 100644
index 0000000..9d167d5
--- /dev/null
+++ b/size_comparison/data_loading_ver/ctrnn_pid_both_tasks.py
@@ -0,0 +1,973 @@
+"""
+ctrnn_pid_both_tasks.py
+=======================
+Train a CTRNN on both PerceptualDecisionMaking and ContextDecisionMaking,
+run time-resolved Gaussian PID analysis on each, and produce comparison plots.
+
+Mirrors your colleague's notebook structure (load NPZ → train with early
+stopping → extract hidden states → PID) but CTRNN only, both tasks, with
+the full PID sweep infrastructure from ctrnn_pid_sweep_cdm.py.
+
+Key design decisions
+--------------------
+- Loads pre-generated NPZ datasets via load_mante_data (same as colleague)
+- Trains with validation loss tracking and early stopping (same as colleague)
+- CTRNN only (no Elman)
+- Both tasks: PDM (input_dim=3) and CDM (input_dim=7)
+- Two PID targets per task:
+    PDM: signed coherence + binary decision label
+    CDM: signed coherence of relevant modality + context label
+- Shared test batch used for all PID analysis (never seen during training)
+- All plots and raw arrays saved to results/
+
+Usage
+-----
+    # With pre-generated NPZ datasets
+    python ctrnn_pid_both_tasks.py \\
+        --pdm_dir data/pdm \\
+        --cdm_dir data/cdm \\
+        --hidden_size 100
+
+    # Without NPZ (falls back to on-the-fly NeuroGym generation)
+    python ctrnn_pid_both_tasks.py --hidden_size 100
+
+    # Quick test run
+    python ctrnn_pid_both_tasks.py --pdm_dir data/pdm --cdm_dir data/cdm \\
+        --hidden_size 20 --num_epochs 5 --n_test_trials 200
+
+Outputs (results/ctrnn_both_tasks/)
+-------------------------------------
+    training_curves.png          — loss curves for both tasks
+    pid_pdm.png                  — time-resolved PID for PDM
+    pid_cdm.png                  — time-resolved PID for CDM
+    comparison_4panel.png        — 2x2 grid: task x PID target
+    results.npz                  — all raw PID arrays + activations
+"""
+
+import argparse
+import copy
+import os
+import sys
+from pathlib import Path
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import neurogym as ngym
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Project module imports
+# ──────────────────────────────────────────────────────────────────────────────
+sys.path.append(str(Path(__file__).resolve().parents[3]))
+
+try:
+    from src.models.ctrnn import CTRNN as _ProjectCTRNN
+    from src.analysis.gaussian_pid import gaussian_pid_rnn
+    from src.tasks.data_loader import load_mante_data
+    _USING_PROJECT_MODULES = True
+except ImportError as e:
+    _USING_PROJECT_MODULES = False
+    print(f"[WARN] src/ modules not found ({e}) — using built-in fallbacks.")
+
+print(f"Using project modules: {_USING_PROJECT_MODULES}")
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Configuration
+# ──────────────────────────────────────────────────────────────────────────────
+
+
+RESULTS_DIR = Path(r"E:\TUM\3sem\NeuroAI\project\github_neuroai\results")
+
+WEIGHTS_DIR  = RESULTS_DIR / 'weights'
+WEIGHTS_DIR.mkdir(parents=True, exist_ok=True)
+
+DT           = 20     # ms per timestep
+OUTPUT_DIM   = 3      # fixate / choice1 / choice2
+DEFAULT_SEED = 42
+
+# Task specs
+TASKS = {
+    'pdm': {
+        'name':      'PerceptualDecisionMaking-v0',
+        'input_dim': 3,
+        'timing': {           # fixed timing for PID eval
+            'fixation': 200,  # 10 steps
+            'stimulus': 2000, # 100 steps
+            'delay':    0,
+            'decision': 100,  # 5 steps
+        },
+    },
+    'cdm': {
+        'name':      'ContextDecisionMaking-v0',
+        'input_dim': 7,
+        'timing': {
+            'fixation': 200,  # 10 steps
+            'stimulus': 1000, # 50 steps
+            'delay':    0,    # MUST be 0 — default is stochastic
+            'decision': 100,  # 5 steps
+        },
+    },
+}
+
+PALETTE = {
+    'synergy':    '#54A24B',
+    'redundancy': '#4C78A8',
+    'unique1':    '#F58518',
+    'unique2':    '#E45756',
+    'mi_joint':   '#020202',
+    'stim_end':   '#CC0000',
+    'train':      '#2196F3',
+    'val':        '#FF9800',
+}
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Fallback CTRNN (used when src/models/ctrnn.py is unavailable)
+# ──────────────────────────────────────────────────────────────────────────────
+
+class _FallbackCTRNN(nn.Module):
+    """Euler CT-RNN: h(t+dt) = h + alpha*(-h + tanh(W_in u + W_rec h))"""
+    def __init__(self, input_size, hidden_size, output_size, dt=DT, tau=100):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.alpha = dt / tau
+        self.W_in  = nn.Linear(input_size,  hidden_size, bias=True)
+        self.W_rec = nn.Linear(hidden_size, hidden_size, bias=False)
+        self.W_out = nn.Linear(hidden_size, output_size, bias=True)
+        nn.init.orthogonal_(self.W_rec.weight)
+        nn.init.xavier_uniform_(self.W_in.weight)
+        nn.init.xavier_uniform_(self.W_out.weight)
+        nn.init.zeros_(self.W_in.bias)
+        nn.init.zeros_(self.W_out.bias)
+
+    def forward(self, x):
+        B, T, _ = x.shape
+        h = torch.zeros(B, self.hidden_size, device=x.device)
+        outputs, hiddens = [], []
+        for t in range(T):
+            h = h + self.alpha * (
+                -h + torch.tanh(self.W_in(x[:, t]) + self.W_rec(h))
+            )
+            outputs.append(self.W_out(h))
+            hiddens.append(h)
+        return torch.stack(outputs, 1), torch.stack(hiddens, 1)
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Fallback Gaussian analytic PID (MMI-PID, Barrett 2015)
+# ──────────────────────────────────────────────────────────────────────────────
+
+def _fallback_gaussian_pid(activations, target, timestep=None,
+                            n_bipartitions=200, seed=DEFAULT_SEED,
+                            regularization=1e-5, **kwargs):
+    rng = np.random.default_rng(seed)
+    N, T, H = activations.shape
+    y = target.astype(np.float64)
+
+    def _mi(X):
+        X_ = X - X.mean(0)
+        y_ = y - y.mean()
+        Sxx = X_.T @ X_ / N + regularization * np.eye(X_.shape[1])
+        Sxy = X_.T @ y_ / N
+        var_y = np.var(y_) + 1e-12
+        try:
+            r2 = float(Sxy @ np.linalg.solve(Sxx, Sxy)) / var_y
+            r2 = np.clip(r2, 0.0, 1 - 1e-9)
+        except np.linalg.LinAlgError:
+            return 0.0
+        return float(-0.5 * np.log(1 - r2) / np.log(2))
+
+    def _pid_at_t(h_t):
+        s, r, u1, u2, m = [], [], [], [], []
+        half = max(H // 2, 1)
+        for _ in range(n_bipartitions):
+            idx = rng.permutation(H)
+            i1, i2 = idx[:half], idx[half:half*2]
+            mi1 = _mi(h_t[:, i1])
+            mi2 = _mi(h_t[:, i2])
+            mij = _mi(h_t[:, np.concatenate([i1, i2])])
+            red = min(mi1, mi2)
+            s.append(max(mij - mi1 - mi2 + red, 0.0))
+            r.append(red)
+            u1.append(max(mi1 - red, 0.0))
+            u2.append(max(mi2 - red, 0.0))
+            m.append(mij)
+        return {k: float(np.mean(v))
+                for k, v in zip(['syn','red','u1','u2','mi'], [s,r,u1,u2,m])}
+
+    def _wrap(d):
+        return {'synergy': d['syn'], 'redundancy': d['red'],
+                'unique1': d['u1'], 'unique2': d['u2'], 'mi_joint': d['mi']}
+
+    if timestep is not None:
+        return _wrap(_pid_at_t(activations[:, timestep].astype(np.float64)))
+
+    out = {k: [] for k in ['syn','red','u1','u2','mi']}
+    for t in range(T):
+        d = _pid_at_t(activations[:, t].astype(np.float64))
+        for k in out:
+            out[k].append(d[k])
+    return {'synergy': np.array(out['syn']), 'redundancy': np.array(out['red']),
+            'unique1': np.array(out['u1']),  'unique2':    np.array(out['u2']),
+            'mi_joint': np.array(out['mi'])}
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Select implementations
+# ──────────────────────────────────────────────────────────────────────────────
+
+_CTRNNBase = _ProjectCTRNN  if _USING_PROJECT_MODULES else _FallbackCTRNN
+_pid_fn    = gaussian_pid_rnn if _USING_PROJECT_MODULES else _fallback_gaussian_pid
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Wrapped CTRNN — uniform (outputs, hidden_states) interface
+# ──────────────────────────────────────────────────────────────────────────────
+
+class WrappedCTRNN(nn.Module):
+    def __init__(self, input_dim, hidden_size):
+        super().__init__()
+        if _USING_PROJECT_MODULES:
+            self.model = _CTRNNBase(input_size=input_dim,
+                                    hidden_size=hidden_size,
+                                    output_size=OUTPUT_DIM)
+        else:
+            self.model = _CTRNNBase(input_size=input_dim,
+                                    hidden_size=hidden_size,
+                                    output_size=OUTPUT_DIM, dt=DT)
+
+    def forward(self, x):
+        """x: (B, T, F) → outputs (B,T,3), hidden_states (B,T,H)"""
+        if _USING_PROJECT_MODULES:
+            outputs, _, hidden_states = self.model(x, return_dynamics=True)
+        else:
+            outputs, hidden_states = self.model(x)
+        return outputs, hidden_states
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Loss
+# ──────────────────────────────────────────────────────────────────────────────
+
+def masked_cross_entropy(outputs, targets, periods=None):
+    """
+    If periods is provided: mask to decision period (periods == 2).
+    Otherwise: mask to any non-zero target (targets != 0).
+    Matches both colleague's approach (periods==2) and fallback (targets!=0).
+    """
+    if periods is not None:
+        mask = (periods == 2)
+    else:
+        mask = (targets != 0)
+
+    if mask.sum() == 0:
+        return torch.tensor(0.0, requires_grad=True, device=outputs.device)
+    return F.cross_entropy(outputs[mask], targets[mask])
+
+
+def masked_accuracy_counts(outputs, targets, periods=None):
+    """Return (correct, total) for decision-period predictions."""
+    if periods is not None:
+        mask = (periods == 2)
+    else:
+        mask = (targets != 0)
+
+    if mask.sum() == 0:
+        return 0, 0
+
+    preds = outputs[mask].argmax(dim=1)
+    correct = (preds == targets[mask]).sum().item()
+    total = int(mask.sum().item())
+    return correct, total
+
+
+def compute_decision_accuracy(model, loader, device):
+    """Evaluate decision-period accuracy on a loader."""
+    model.eval()
+    correct_total = 0
+    total_total = 0
+
+    with torch.no_grad():
+        for batch in loader:
+            obs, labels, periods, cohs, ctxs = batch
+            obs = obs.to(device)
+            labels = labels.to(device)
+            periods = periods.to(device)
+            outputs, _ = model(obs)
+            c, t = masked_accuracy_counts(outputs, labels, periods)
+            correct_total += c
+            total_total += t
+
+    if total_total == 0:
+        return 0.0
+    return correct_total / float(total_total)
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Fallback on-the-fly data generator (used when no NPZ provided)
+# ──────────────────────────────────────────────────────────────────────────────
+
+def _make_neurogym_generator(task_name, input_dim, batch_size=16, seq_len=130):
+    timing = {'delay': 0}
+    env = ngym.make(task_name, dt=DT, timing=timing)
+    env.reset(seed=DEFAULT_SEED)
+
+    def _gen():
+        obs_b, gt_b = [], []
+        for _ in range(batch_size):
+            env.unwrapped.new_trial()
+            ob = env.unwrapped.ob
+            gt = env.unwrapped.gt
+            T  = ob.shape[0]
+            t  = min(T, seq_len)
+            ob_p = np.zeros((seq_len, input_dim), dtype=np.float32)
+            gt_p = np.zeros((seq_len,), dtype=np.int64)
+            ob_p[:t] = ob[:t, :input_dim]
+            gt_p[:t] = gt[:t]
+            if t < seq_len:
+                ob_p[t:] = ob[-1, :input_dim]
+                gt_p[t:] = gt[-1]
+            obs_b.append(ob_p)
+            gt_b.append(gt_p)
+        # return (B, T, F) and (B, T)
+        return (np.stack(obs_b).astype(np.float32),
+                np.stack(gt_b).astype(np.int64))
+
+    return _gen
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Training — mirrors colleague's train_rnn with early stopping
+# ──────────────────────────────────────────────────────────────────────────────
+
+def train_ctrnn(
+    task_key,
+    hidden_size,
+    device,
+    train_loader=None,
+    val_loader=None,
+    num_epochs=50,
+    lr=1e-3,
+    patience=10,
+    seed=DEFAULT_SEED,
+):
+    """
+    Train a CTRNN with early stopping on validation loss.
+    Accepts either a DataLoader (NPZ path provided) or falls back to
+    on-the-fly NeuroGym generation.
+
+    Returns model (best weights restored), loss history dict.
+    """
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+
+    task_cfg  = TASKS[task_key]
+    input_dim = task_cfg['input_dim']
+
+    model     = WrappedCTRNN(input_dim, hidden_size).to(device)
+    optimizer = optim.Adam(model.parameters(), lr=lr)
+
+    # Fallback generator if no DataLoader
+    ng_gen = None
+    if train_loader is None:
+        print(f"  [INFO] No DataLoader provided — using NeuroGym on-the-fly")
+        ng_gen = _make_neurogym_generator(task_cfg['name'], input_dim)
+
+    best_val_loss     = float('inf')
+    patience_counter  = 0
+    best_weights      = copy.deepcopy(model.state_dict())
+    history           = {'train_loss': [], 'train_acc': [], 'val_loss': [], 'val_acc': []}
+
+    print(f"\n  Training CTRNN | task={task_key} | hidden={hidden_size} | "
+          f"epochs={num_epochs} | patience={patience}")
+
+    for epoch in range(num_epochs):
+        # ── Training ──
+        model.train()
+        train_losses = []
+        train_correct, train_total = 0, 0
+
+        if train_loader is not None:
+            for batch in train_loader:
+                obs, labels, periods, cohs, ctxs = batch
+                obs     = obs.to(device)
+                labels  = labels.to(device)
+                periods = periods.to(device)
+
+                optimizer.zero_grad()
+                outputs, _ = model(obs)
+                loss = masked_cross_entropy(outputs, labels, periods)
+                loss.backward()
+                torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+                optimizer.step()
+                train_losses.append(loss.item())
+
+                c, t = masked_accuracy_counts(outputs, labels, periods)
+                train_correct += c
+                train_total += t
+
+        else:
+            # On-the-fly: treat one epoch as n_batches mini-batches
+            n_batches = 157
+            for _ in range(n_batches):
+                obs_np, gt_np = ng_gen()
+                obs    = torch.from_numpy(obs_np).to(device)
+                labels = torch.from_numpy(gt_np).to(device)
+
+                optimizer.zero_grad()
+                outputs, _ = model(obs)
+                loss = masked_cross_entropy(outputs, labels)
+                loss.backward()
+                torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+                optimizer.step()
+                train_losses.append(loss.item())
+
+                c, t = masked_accuracy_counts(outputs, labels)
+                train_correct += c
+                train_total += t
+
+        avg_train = float(np.mean(train_losses))
+        avg_train_acc = train_correct / max(1, train_total)
+        history['train_loss'].append(avg_train)
+        history['train_acc'].append(avg_train_acc)
+
+        # ── Validation ──
+        model.eval()
+        val_losses = []
+        val_correct, val_total = 0, 0
+
+        if val_loader is not None:
+            with torch.no_grad():
+                for batch in val_loader:
+                    obs, labels, periods, cohs, ctxs = batch
+                    obs     = obs.to(device)
+                    labels  = labels.to(device)
+                    periods = periods.to(device)
+                    outputs, _ = model(obs)
+                    val_losses.append(
+                        masked_cross_entropy(outputs, labels, periods).item()
+                    )
+                    c, t = masked_accuracy_counts(outputs, labels, periods)
+                    val_correct += c
+                    val_total += t
+        else:
+            # Validate on a fresh set of on-the-fly trials
+            with torch.no_grad():
+                for _ in range(20):
+                    obs_np, gt_np = ng_gen()
+                    obs    = torch.from_numpy(obs_np).to(device)
+                    labels = torch.from_numpy(gt_np).to(device)
+                    outputs, _ = model(obs)
+                    val_losses.append(
+                        masked_cross_entropy(outputs, labels).item()
+                    )
+                    c, t = masked_accuracy_counts(outputs, labels)
+                    val_correct += c
+                    val_total += t
+
+        avg_val = float(np.mean(val_losses))
+        avg_val_acc = val_correct / max(1, val_total)
+        history['val_loss'].append(avg_val)
+        history['val_acc'].append(avg_val_acc)
+
+        # ── Early stopping ──
+        if avg_val < best_val_loss:
+            best_val_loss    = avg_val
+            patience_counter = 0
+            best_weights     = copy.deepcopy(model.state_dict())
+        else:
+            patience_counter += 1
+
+        if (epoch + 1) % 5 == 0 or epoch == 0:
+            print(f"    Epoch [{epoch+1:03d}/{num_epochs}]  "
+                  f"train_loss={avg_train:.4f}  val_loss={avg_val:.4f}  "
+                  f"train_acc={avg_train_acc:.3f}  val_acc={avg_val_acc:.3f}  "
+                  f"patience={patience_counter}/{patience}")
+
+        if patience_counter >= patience:
+            print(f"    Early stopping at epoch {epoch+1} "
+                  f"(best val={best_val_loss:.4f})")
+            break
+
+    print("    Restoring best weights...")
+    model.load_state_dict(best_weights)
+
+    save_path = WEIGHTS_DIR / f'ctrnn_{task_key}_h{hidden_size}.pth'
+    torch.save(model.state_dict(), save_path)
+    print(f"    Saved → {save_path}")
+
+    return model, history
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Hidden state extraction — mirrors colleague's extract_hidden_trajectories
+# ──────────────────────────────────────────────────────────────────────────────
+
+def extract_hidden_states(model, test_loader, device):
+    """
+    Run the test set through the model and collect:
+        H : (N, T, hidden_size)  hidden states
+        Y : (N,)                 signed coherence (PID target 1)
+        C : (N,)                 context label    (PID target 2, CDM only)
+        P : (N, T)               period labels    (for stim-end detection)
+    """
+    model.eval()
+    H_list, Y_list, C_list, P_list = [], [], [], []
+
+    with torch.no_grad():
+        for batch in test_loader:
+            obs, labels, periods, cohs, ctxs = batch
+            obs = obs.to(device)
+
+            _, hidden = model(obs)           # (B, T, hidden_size)
+            H_list.append(hidden.cpu().numpy())
+
+            # Signed coherence: cohs is (B,) or (B, n_cohs) depending on task
+            if cohs.ndim > 1:
+                Y_list.append(cohs[:, 0].numpy().astype(float))
+            else:
+                Y_list.append(cohs.numpy().astype(float))
+
+            C_list.append(ctxs.numpy())
+            P_list.append(periods.numpy())
+
+    H = np.concatenate(H_list, axis=0)   # (N, T, H)
+    Y = np.concatenate(Y_list, axis=0)   # (N,)
+    C = np.concatenate(C_list, axis=0)   # (N,)
+    P = np.concatenate(P_list, axis=0)   # (N, T)
+    return H, Y, C, P
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Stim-end detection
+# ──────────────────────────────────────────────────────────────────────────────
+
+def find_stim_end(periods):
+    """
+    periods : (N, T) int array with 0=fixation, 1=stimulus, 2=decision
+    Returns the modal last-stimulus-timestep across trials.
+    """
+    stim_ends = []
+    for p in periods:
+        stim_steps = np.where(p == 1)[0]
+        stim_ends.append(stim_steps[-1] if len(stim_steps) > 0 else 0)
+    return int(np.median(stim_ends))
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# PID analysis
+# ──────────────────────────────────────────────────────────────────────────────
+
+def run_pid_analysis(H, Y, C, P, task_key, n_bipartitions=200):
+    """
+    Run time-resolved PID for both targets relevant to the task.
+
+    PDM targets: coherence (Y), binary decision (derived from Y sign)
+    CDM targets: coherence (Y), context label (C)
+
+    Returns dict with keys per target name.
+    """
+    stim_end = find_stim_end(P)
+    results  = {}
+
+    # Define targets depending on task
+    if task_key == 'pdm':
+        targets = [
+            ('coherence', Y.astype(float)),
+            ('decision',  (Y > 0).astype(float)),   # binary: choice1=1, choice2=0
+        ]
+    else:  # cdm
+        targets = [
+            ('coherence', Y.astype(float)),
+            ('context',   C.astype(float)),
+        ]
+
+    for target_name, target_vals in targets:
+        print(f"    PID | target={target_name} | "
+              f"H={H.shape[2]} | T={H.shape[1]} | N={H.shape[0]}")
+
+        pid_out = _pid_fn(
+            activations=H,
+            target=target_vals,
+            timestep=None,
+            n_bipartitions=n_bipartitions,
+            seed=DEFAULT_SEED,
+            log_base=2,
+            regularization=1e-5,
+        )
+
+        results[target_name] = {
+            'synergy':    np.array(pid_out['synergy']),
+            'redundancy': np.array(pid_out['redundancy']),
+            'unique1':    np.array(pid_out['unique1']),
+            'unique2':    np.array(pid_out['unique2']),
+            'mi_joint':   np.array(pid_out['mi_joint']),
+            'stim_end':   stim_end,
+            'seq_len':    H.shape[1],
+        }
+
+        tend = stim_end
+        print(f"      stim_end=t{tend} | "
+              f"Syn={results[target_name]['synergy'][tend]:.4f}b | "
+              f"Red={results[target_name]['redundancy'][tend]:.4f}b")
+
+    return results
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Plotting helpers
+# ──────────────────────────────────────────────────────────────────────────────
+
+def _plot_pid_ax(ax, pid, title, stim_end=None):
+    T    = pid['seq_len']
+    t    = np.arange(T)
+    tend = stim_end if stim_end is not None else pid['stim_end']
+
+    ax.plot(t, pid['mi_joint'],   label='Total MI',   color=PALETTE['mi_joint'],   lw=2,   ls=':')
+    ax.plot(t, pid['synergy'],    label='Synergy',    color=PALETTE['synergy'],    lw=3)
+    ax.plot(t, pid['redundancy'], label='Redundancy', color=PALETTE['redundancy'], lw=3)
+    ax.plot(t, pid['unique1'],    label='Unique 1',   color=PALETTE['unique1'],    lw=1.5, alpha=0.8)
+    ax.plot(t, pid['unique2'],    label='Unique 2',   color=PALETTE['unique2'],    lw=1.5, alpha=0.8)
+
+    ax.axvline(tend, color=PALETTE['stim_end'], ls='--', lw=2,
+               label=f'Stim end (t={tend})')
+    ax.axvspan(0, tend, color='gray', alpha=0.08)
+
+    ax.set_title(title, fontsize=12, fontweight='bold', pad=8)
+    ax.set_xlabel(f'Timestep (dt={DT}ms)', fontsize=10)
+    ax.set_ylabel('Information (bits)', fontsize=10)
+    ax.legend(fontsize=8, loc='upper left')
+    ax.grid(alpha=0.3)
+
+
+def plot_training_curves(histories, save_path):
+    """Plot train/val loss and decision accuracy curves for both tasks."""
+    fig, axes = plt.subplots(2, 2, figsize=(14, 8))
+    fig.suptitle('CTRNN Training Curves', fontsize=14, fontweight='bold')
+
+    for ax_row, (task_key, history) in zip(axes.T, histories.items()):
+        epochs = np.arange(1, len(history['train_loss']) + 1)
+
+        ax_loss, ax_acc = ax_row
+        ax_loss.plot(epochs, history['train_loss'], color=PALETTE['train'], lw=2, label='Train loss')
+        ax_loss.plot(epochs, history['val_loss'], color=PALETTE['val'], lw=2, ls='--', label='Val loss')
+        ax_loss.set_title(f'{task_key.upper()} Task', fontsize=12, fontweight='bold')
+        ax_loss.set_xlabel('Epoch')
+        ax_loss.set_ylabel('Cross-entropy loss')
+        ax_loss.legend(fontsize=9)
+        ax_loss.grid(alpha=0.3)
+
+        ax_acc.plot(epochs, history['train_acc'], color=PALETTE['train'], lw=2, label='Train acc')
+        ax_acc.plot(epochs, history['val_acc'], color=PALETTE['val'], lw=2, ls='--', label='Val acc')
+        ax_acc.set_xlabel('Epoch')
+        ax_acc.set_ylabel('Decision accuracy')
+        ax_acc.set_ylim(0.0, 1.0)
+        ax_acc.grid(alpha=0.3)
+
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    plt.close()
+    print(f"Saved → {save_path}")
+
+
+def plot_pid_single_task(pid_results, task_key, hidden_size, save_path):
+    """Two-panel PID plot for one task (one panel per target)."""
+    target_names = list(pid_results.keys())
+    fig, axes = plt.subplots(1, len(target_names), figsize=(8 * len(target_names), 5))
+    if len(target_names) == 1:
+        axes = [axes]
+
+    fig.suptitle(
+        f'CTRNN | {task_key.upper()} | hidden={hidden_size}',
+        fontsize=13, fontweight='bold', y=1.01
+    )
+
+    for ax, tgt in zip(axes, target_names):
+        _plot_pid_ax(ax, pid_results[tgt],
+                     title=f'PID — target: {tgt}')
+
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    plt.close()
+    print(f"Saved → {save_path}")
+
+
+def plot_comparison_4panel(all_pid, hidden_size, save_path):
+    """
+    2×2 grid matching colleague's notebook layout:
+      Row 0: PDM task  (coherence | decision)
+      Row 1: CDM task  (coherence | context)
+    """
+    fig, axes = plt.subplots(2, 2, figsize=(18, 12), sharey=False)
+    fig.suptitle(
+        f'Information Geometry: Task Comparison  (CTRNN, hidden={hidden_size})',
+        fontsize=16, fontweight='bold', y=0.98
+    )
+
+    panel_map = [
+        (0, 0, 'pdm', 'coherence', 'PDM | Coherence target'),
+        (0, 1, 'pdm', 'decision',  'PDM | Decision target'),
+        (1, 0, 'cdm', 'coherence', 'CDM | Coherence target'),
+        (1, 1, 'cdm', 'context',   'CDM | Context target'),
+    ]
+
+    # Use shared stim_end reference per row
+    stim_end_pdm = all_pid['pdm']['coherence']['stim_end']
+    stim_end_cdm = all_pid['cdm']['coherence']['stim_end']
+    stim_ends    = {0: stim_end_pdm, 1: stim_end_cdm}
+
+    for row, col, task, tgt, title in panel_map:
+        ax  = axes[row, col]
+        pid = all_pid[task][tgt]
+        _plot_pid_ax(ax, pid, title, stim_end=stim_ends[row])
+
+        if col == 0:
+            ax.set_ylabel('Information (bits)', fontsize=11)
+        if row == 1:
+            ax.set_xlabel(f'Timestep (dt={DT}ms)', fontsize=11)
+
+    # Single legend on first panel
+    axes[0, 0].legend(loc='upper left', fontsize=10, framealpha=0.9)
+    for row in range(2):
+        for col in range(1, 2):
+            axes[row, col].get_legend().remove()
+
+    plt.tight_layout(rect=[0, 0, 1, 0.95])
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    plt.close()
+    print(f"Saved → {save_path}")
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Main
+# ──────────────────────────────────────────────────────────────────────────────
+
+def main(
+    hidden_size,
+    pdm_dir,
+    cdm_dir,
+    num_epochs,
+    patience,
+    lr,
+    batch_size,
+    n_test_trials,
+    n_bipartitions,
+):
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(f"Device      : {device}")
+    if device.type == 'cuda':
+        print(f"GPU         : {torch.cuda.get_device_name(0)}")
+    print(f"Hidden size : {hidden_size}")
+
+    # ── Build DataLoaders ──
+    loaders = {}
+    for task_key, data_dir in [('pdm', pdm_dir), ('cdm', cdm_dir)]:
+        if data_dir is not None and _USING_PROJECT_MODULES:
+            train_path = os.path.join(data_dir, 'train.npz')
+            val_path   = os.path.join(data_dir, 'val.npz')
+            test_path  = os.path.join(data_dir, 'test_uniform.npz')
+            # fall back to test.npz if test_uniform.npz doesn't exist
+            if not os.path.exists(test_path):
+                test_path = os.path.join(data_dir, 'test.npz')
+
+            input_dim = TASKS[task_key]['input_dim']
+            loaders[task_key] = {
+                'train': load_mante_data(train_path, batch_size=batch_size, shuffle=True, input_dim=input_dim),
+                'val':   load_mante_data(val_path,   batch_size=batch_size, shuffle=False, input_dim=input_dim),
+                'test':  load_mante_data(test_path,  batch_size=batch_size, shuffle=False, input_dim=input_dim),
+            }
+            print(f"  [{task_key}] Loaded NPZ from {data_dir}")
+        else:
+            loaders[task_key] = {'train': None, 'val': None, 'test': None}
+            print(f"  [{task_key}] No NPZ path — using on-the-fly NeuroGym")
+
+    # ── Train both models ──
+    models   = {}
+    histories = {}
+
+    for task_key in ('pdm', 'cdm'):
+        print(f"\n{'='*60}")
+        print(f"  Task: {task_key.upper()}")
+        print(f"{'='*60}")
+
+        model, history = train_ctrnn(
+            task_key   = task_key,
+            hidden_size= hidden_size,
+            device     = device,
+            train_loader = loaders[task_key]['train'],
+            val_loader   = loaders[task_key]['val'],
+            num_epochs   = num_epochs,
+            lr           = lr,
+            patience     = patience,
+        )
+        test_acc = compute_decision_accuracy(model, loaders[task_key]['test'], device)
+        history['test_acc'] = test_acc
+        print(f"  Test decision accuracy: {test_acc:.3f}")
+        models[task_key]    = model
+        histories[task_key] = history
+
+    # ── Plot training curves ──
+    plot_training_curves(
+        histories,
+        save_path=RESULTS_DIR / 'training_curves.png'
+    )
+
+    # ── Extract hidden states and run PID ──
+    all_pid = {}
+
+    for task_key in ('pdm', 'cdm'):
+        print(f"\n{'='*60}")
+        print(f"  PID Analysis: {task_key.upper()}")
+        print(f"{'='*60}")
+
+        model = models[task_key].to('cpu')
+
+        if loaders[task_key]['test'] is not None:
+            print(f"  Extracting hidden states from NPZ test set...")
+            H, Y, C, P = extract_hidden_states(model, loaders[task_key]['test'], 'cpu')
+        else:
+            print(f"  Generating {n_test_trials} test trials on-the-fly...")
+            task_cfg = TASKS[task_key]
+            env = ngym.make(task_cfg['name'], dt=DT, timing=task_cfg['timing'])
+            env.reset(seed=DEFAULT_SEED + 999)
+
+            obs_l, gt_l, coh_l, ctx_l, per_l = [], [], [], [], []
+            for _ in range(n_test_trials):
+                env.unwrapped.new_trial()
+                ob    = env.unwrapped.ob.astype(np.float32)
+                gt    = env.unwrapped.gt.astype(np.int64)
+                trial = env.unwrapped.trial
+
+                # Build period array from gt
+                period = np.zeros(len(gt), dtype=np.int8)
+                fix_steps  = np.where(ob[:, 0] > 0.5)[0]
+                stim_steps = np.where((ob[:, 0] < 0.5) & (gt == 0))[0]
+                dec_steps  = np.where(gt != 0)[0]
+                period[fix_steps]  = 0
+                period[stim_steps] = 1
+                period[dec_steps]  = 2
+
+                # Coherence
+                if task_key == 'pdm':
+                    coh = float(trial.get('coh', 0.0))
+                    gt_label = int(trial.get('ground_truth', 0))
+                    signed_coh = coh if gt_label == 0 else -coh
+                    ctx = 0
+                else:
+                    context   = int(trial.get('context', 0))
+                    coh_0     = float(trial.get('coh_0', trial.get('coh_1', 0.0)))
+                    coh_1     = float(trial.get('coh_1', trial.get('coh_2', 0.0)))
+                    rel_coh   = coh_0 if context == 0 else coh_1
+                    gt_label  = int(trial.get('ground_truth', 1))
+                    sign      = 1 if gt_label == 1 else -1
+                    signed_coh = sign * rel_coh
+                    ctx = context
+
+                obs_l.append(ob)
+                gt_l.append(gt)
+                coh_l.append(signed_coh)
+                ctx_l.append(ctx)
+                per_l.append(period)
+
+            # Pad all trials to the same length
+            max_T = max(o.shape[0] for o in obs_l)
+            F     = obs_l[0].shape[1]
+            obs_pad = np.zeros((n_test_trials, max_T, F), dtype=np.float32)
+            gt_pad  = np.zeros((n_test_trials, max_T), dtype=np.int64)
+            per_pad = np.zeros((n_test_trials, max_T), dtype=np.int8)
+            for i, (ob, gt, per) in enumerate(zip(obs_l, gt_l, per_l)):
+                T = ob.shape[0]
+                obs_pad[i, :T] = ob
+                gt_pad[i,  :T] = gt
+                per_pad[i, :T] = per
+                if T < max_T:
+                    obs_pad[i, T:] = ob[-1]
+                    gt_pad[i,  T:] = gt[-1]
+                    per_pad[i, T:] = per[-1]
+
+            obs_t = torch.from_numpy(obs_pad)
+            model.eval()
+            with torch.no_grad():
+                _, h_seq = model(obs_t)
+            H = h_seq.numpy()
+            Y = np.array(coh_l, dtype=np.float64)
+            C = np.array(ctx_l, dtype=np.int32)
+            P = per_pad
+
+        print(f"  H shape: {H.shape}  Y range: [{Y.min():.1f}, {Y.max():.1f}]")
+
+        pid_results = run_pid_analysis(H, Y, C, P, task_key, n_bipartitions)
+        all_pid[task_key] = pid_results
+
+        # Per-task plot
+        plot_pid_single_task(
+            pid_results, task_key, hidden_size,
+            save_path=RESULTS_DIR / f'pid_{task_key}.png'
+        )
+
+    # ── 4-panel comparison ──
+    plot_comparison_4panel(
+        all_pid, hidden_size,
+        save_path=RESULTS_DIR / 'comparison_4panel.png'
+    )
+
+    # ── Save raw results ──
+    npz_path = RESULTS_DIR / 'results.npz'
+    save_dict = {}
+    for task_key, pid_results in all_pid.items():
+        for tgt, data in pid_results.items():
+            for atom in ('synergy','redundancy','unique1','unique2','mi_joint'):
+                save_dict[f'{task_key}_{tgt}_{atom}'] = data[atom]
+            save_dict[f'{task_key}_{tgt}_stim_end'] = np.array(data['stim_end'])
+    for task_key, history in histories.items():
+        save_dict[f'{task_key}_train_loss'] = np.array(history['train_loss'])
+        save_dict[f'{task_key}_val_loss']   = np.array(history['val_loss'])
+        save_dict[f'{task_key}_train_acc']  = np.array(history['train_acc'])
+        save_dict[f'{task_key}_val_acc']    = np.array(history['val_acc'])
+        save_dict[f'{task_key}_test_acc']   = np.array(history['test_acc'])
+
+    np.savez_compressed(str(npz_path), **save_dict)
+    print(f"\nRaw results → {npz_path}")
+    print("Done.")
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# CLI
+# ──────────────────────────────────────────────────────────────────────────────
+
+if __name__ == '__main__':
+    p = argparse.ArgumentParser(
+        description='Train CTRNN on PDM + CDM and run Gaussian PID analysis')
+    p.add_argument('--hidden_sizes', nargs='+', type=int, default=[100],
+               help='One or more hidden sizes, e.g. --hidden_sizes 20 50 100')
+    p.add_argument('--pdm_dir',        type=str,   default=None,
+                   help='Directory containing PDM train/val/test NPZ files')
+    p.add_argument('--cdm_dir',        type=str,   default=None,
+                   help='Directory containing CDM train/val/test NPZ files')
+    p.add_argument('--num_epochs',     type=int,   default=50)
+    p.add_argument('--patience',       type=int,   default=10)
+    p.add_argument('--lr',             type=float, default=1e-3)
+    p.add_argument('--batch_size',     type=int,   default=2048)
+    p.add_argument('--n_test_trials',  type=int,   default=2000)
+    p.add_argument('--n_bipartitions', type=int,   default=200)
+    args = p.parse_args()
+
+for h in args.hidden_sizes:
+    print(f"\n{'#'*60}")
+    print(f"  HIDDEN SIZE = {h}")
+    print(f"{'#'*60}")
+    
+    main(
+        hidden_size    = h,
+        pdm_dir        = args.pdm_dir,
+        cdm_dir        = args.cdm_dir,
+        num_epochs     = args.num_epochs,
+        patience       = args.patience,
+        lr             = args.lr,
+        batch_size     = args.batch_size,
+        n_test_trials  = args.n_test_trials,
+        n_bipartitions = args.n_bipartitions,
+    )
\ No newline at end of file
diff --git a/src/analysis/PID_figures.py b/src/analysis/PID_figures.py
new file mode 100644
index 0000000..3cd4671
--- /dev/null
+++ b/src/analysis/PID_figures.py
@@ -0,0 +1,44 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Define input data directory
+dir = "src/analysis/results/"
+
+# Load PID results
+SYSTEM = "Sinusoid"
+pid_path = f"{dir}pid_{SYSTEM}.npz"
+out = np.load(pid_path)
+
+# Plot PID bars with std error bars, and total MI as a title annotation
+unit = 'bits'
+
+labels = ['Redundancy', 'Unique1', 'Unique2', 'Synergy']
+values = [out['redundancy'], out['unique1'], out['unique2'], out['synergy']]
+stds = [out['redundancy_std'], out['unique1_std'], out['unique2_std'], out['synergy_std']]
+total = out['mi_joint']
+
+plt.figure(figsize=(8, 6))
+bars = plt.bar(labels, values, yerr=stds, capsize=5, width=0.6)
+# Add value labels above bars
+for bar, val, std in zip(bars, values, stds):
+    plt.text(bar.get_x() + bar.get_width() / 2, val + std + 0.02, f'{val:.3f}', ha='center', va='bottom', fontsize=12)
+# Give different colors to each bar
+colors = ['#4C78A8', '#F58518', '#E45756', '#54A24B']
+for bar, color in zip(bars, colors):
+    bar.set_color(color)
+
+plt.title(f'PID at last timestep - {SYSTEM} (10 unit) RNN\nTotal I(X1,X2;Y) = {total:.3f} {unit}', fontsize=15, fontweight='bold')
+# Fontsize and limits
+plt.ylabel(f'Information ({unit})', fontsize=15)
+plt.yticks(fontsize=12)
+plt.ylim(0, max(v + s for v, s in zip(values, stds)) * 1.1)
+plt.xticks(fontsize=15)
+
+# Remove top and right edges
+plt.gca().spines['top'].set_visible(False)
+plt.gca().spines['right'].set_visible(False)
+plt.tight_layout()
+plt.savefig(f'{dir}pid_bars_{SYSTEM}.png', dpi=300)
+plt.show()
+
+
diff --git a/src/analysis/RNN.py b/src/analysis/RNN.py
new file mode 100644
index 0000000..07b3b18
--- /dev/null
+++ b/src/analysis/RNN.py
@@ -0,0 +1,58 @@
+import torch
+import torch.nn as nn
+
+
+class ElmanRNN(nn.Module):
+    def __init__(self, dim, system='VanDerPol', hidden_dim=None, num_layers=1):
+        super(ElmanRNN, self).__init__()
+        ##############
+        ##############
+
+        # store basic info
+        self.dim = dim
+        self.system = system
+
+        # choose hidden size (you can tune these)
+        if hidden_dim is None:
+            hidden_dim = 10
+
+        self.hidden_dim = hidden_dim
+        self.num_layers = num_layers
+
+        # classic RNN cell with tanh nonlinearity and a linear readout
+        self.rnn = nn.RNN(
+            input_size=dim,
+            hidden_size=hidden_dim,
+            num_layers=num_layers,
+            nonlinearity="tanh",
+            batch_first=False,
+        )
+        self.readout = nn.Linear(hidden_dim, dim)
+
+        ##############
+        ##############
+
+    def forward(self, x, h0=None):
+        ##############
+        ##############
+
+        # x is expected to have shape (n_timesteps, dim)
+        # or (dim,) for a single state in testing mode
+        squeezed = False
+        if x.dim() == 1:
+            x = x.unsqueeze(0)
+            squeezed = True
+
+        # add batch dimension for the RNN: (seq_len, batch, dim)
+        x = x.unsqueeze(1)
+        out, hn = self.rnn(x, h0)
+        y = self.readout(out).squeeze(1)
+
+        if squeezed:
+            y = y.squeeze(0)
+
+        ##############
+        ##############
+        return y, hn
+
+    
\ No newline at end of file
diff --git a/src/analysis/compute_pid_sinusoid.py b/src/analysis/compute_pid_sinusoid.py
new file mode 100644
index 0000000..11414c2
--- /dev/null
+++ b/src/analysis/compute_pid_sinusoid.py
@@ -0,0 +1,124 @@
+"""
+compute_pid_sinusoid.py
+=======================
+
+Test the Gaussian analytic PID implementation on the sinusoid-trained RNN.
+
+Goal: compute PID at the "last timestep" of the RNN's hidden-state trajectory,
+using the hidden activations as the two source groups and the input sinusoid as
+the target.
+
+Important modelling note
+------------------------
+PID is estimated from a "covariance over an ensemble of observations". A single
+timestep of a single trajectory is one observation, which cannot define a
+covariance. The sinusoid RNN was trained on one trajectory, so to obtain a valid
+"PID at the last timestep" we must build an ensemble. We do this exactly the way
+the our NeuroGym project will: we run the trained RNN over many "trials" and use
+the trial dimension as the sample/ensemble axis. Each trial is the same sinusoid
+with a random phase and a small amount of input noise (mirroring the noisy,
+parametrically-varied stimuli of the decision-making tasks). The PID at the last
+timestep is then computed across trials:
+
+    sources = hidden activations h(T) at the last timestep (n_trials x n_units)
+    target  = (clean) sinusoid value at the last timestep (n_trials,)
+
+Run `main.py` first so that `RNN_Sinusoid.pt` exists.
+"""
+
+# Track run time
+import time
+start_time = time.time()
+
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+from RNN import ElmanRNN
+from gaussian_pid import gaussian_pid_rnn
+# Define input data directory
+dir = "src/analysis/results/"
+
+# --------------------------------------------------------------------------- #
+# Settings (kept explicit so every choice is visible)
+# --------------------------------------------------------------------------- #
+SYSTEM = "Sinusoid"          # which trained model file to load (RNN_<SYSTEM>.pt)
+DIM = 1                       # the sinusoid is a single (1-D) channel
+N_TIMESTEPS = 300             # trajectory length, must match how the RNN was trained
+PERIOD = 50.0                 # sinusoid period in timesteps, must match training
+N_TRIALS = 500                # ensemble size: number of phase-randomised trials
+INPUT_NOISE_STD = 0.05        # std of additive input noise per timestep (keeps MI finite)
+N_BIPARTITIONS = 200          # number of random 5/5 unit splits to average PID over
+SEED = 0                      # reproducibility for phases, noise and bipartitions
+LOG_BASE = 2                  # report information in bits
+
+if __name__ == "__main__":
+
+    # --- reproducibility ---------------------------------------------------- #
+    rng = np.random.default_rng(SEED)          # numpy RNG for phases/noise
+    torch.manual_seed(SEED)                    # torch RNG (defensive; eval is deterministic)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # CPU is fine
+
+    # --- load the trained sinusoid RNN -------------------------------------- #
+    # The constructor's default hidden size is 10, matching the trained model.
+    model = ElmanRNN(dim=DIM, system=SYSTEM).to(device)         # build the architecture
+    model_path = f"{dir}RNN_{SYSTEM}.pt"                            # path to the saved weights
+    state = torch.load(model_path, map_location=device, weights_only=True)  # load weights
+    model.load_state_dict(state)                               # restore the parameters
+    model.eval()                                               # inference mode
+    n_units = model.hidden_dim                                 # number of hidden units (10)
+
+    # --- build the trial ensemble (phase-randomised, slightly noisy sinusoids) #
+    t = np.arange(N_TIMESTEPS)                                 # timestep index 0..T-1
+    phases = rng.uniform(0.0, PERIOD, size=N_TRIALS)           # one random phase per trial
+    # clean target signal per trial and timestep: sin(2*pi*(t + phase)/period)
+    clean = np.sin(2 * np.pi * (t[None, :] + phases[:, None]) / PERIOD)   # (n_trials, T)
+    # the RNN input is the clean signal plus small observation noise
+    noise = INPUT_NOISE_STD * rng.standard_normal((N_TRIALS, N_TIMESTEPS)) # (n_trials, T)
+    inputs_np = clean + noise                                  # noisy input stream (n_trials, T)
+
+    # --- run the RNN over the whole ensemble in one batched pass ------------ #
+    # nn.RNN (batch_first=False) expects input shaped (seq_len, batch, input_size).
+    x = torch.tensor(inputs_np.T[:, :, None], dtype=torch.float32, device=device)  # (T, n_trials, 1)
+    with torch.no_grad():                                      # no gradients needed at eval
+        h_seq, _ = model.rnn(x)                                # hidden states: (T, n_trials, n_units)
+    # reorder to the wrapper's convention (n_samples, n_timesteps, n_units)
+    activations = h_seq.permute(1, 0, 2).cpu().numpy()         # (n_trials, T, n_units)
+
+    # --- target: the clean sinusoid value (the underlying signal, like coherence) #
+    target = clean                                             # (n_trials, T); univariate per step
+
+    # --- compute PID at the LAST timestep, averaged over random 5/5 splits --- #
+    out = gaussian_pid_rnn(
+        activations,                 # sources: (n_trials, T, n_units)
+        target,                      # target : (n_trials, T)
+        timestep=-1,                 # -1 selects the last timestep
+        bipartitions="random",       # average over random balanced unit splits
+        n_bipartitions=N_BIPARTITIONS,
+        balanced=True,               # 5 units vs 5 units for a 10-unit RNN
+        seed=SEED,
+        log_base=LOG_BASE,           # bits
+    )
+
+    # --- report ------------------------------------------------------------- #
+    unit = "bits" if LOG_BASE == 2 else "nats"
+    print(f"Gaussian analytic PID at the last timestep (t = {N_TIMESTEPS - 1})")
+    print(f"  ensemble: {N_TRIALS} phase-randomised trials, "
+          f"{n_units} units split 5/5 over {N_BIPARTITIONS} bipartitions")
+    print(f"  (values in {unit}; '+/-' is the spread across bipartitions)\n")
+    print(f"  Redundancy : {out['redundancy']:.4f} +/- {out['redundancy_std']:.4f}")
+    print(f"  Unique 1   : {out['unique1']:.4f} +/- {out['unique1_std']:.4f}")
+    print(f"  Unique 2   : {out['unique2']:.4f} +/- {out['unique2_std']:.4f}")
+    print(f"  Synergy    : {out['synergy']:.4f} +/- {out['synergy_std']:.4f}")
+    print(f"  -----")
+    print(f"  I(X1;Y)    : {out['mi_1']:.4f}")
+    print(f"  I(X2;Y)    : {out['mi_2']:.4f}")
+    print(f"  I(X1,X2;Y) : {out['mi_joint']:.4f}  (= sum of the four atoms)")
+    
+    # Report run time
+    end_time = time.time()
+    elapsed = end_time - start_time
+    print(f"\nTotal run time: {elapsed:.2f} seconds")
+
+    # Save PID results
+    np.savez(f"{dir}pid_{SYSTEM}.npz", **out)
\ No newline at end of file
diff --git a/src/analysis/gaussian_pid.py b/src/analysis/gaussian_pid.py
new file mode 100644
index 0000000..b6668a2
--- /dev/null
+++ b/src/analysis/gaussian_pid.py
@@ -0,0 +1,398 @@
+"""
+gaussian_pid.py
+===============
+
+Gaussian analytic Partial Information Decomposition (PID), using the
+Minimum-Mutual-Information (MMI) redundancy of Barrett (2015), i.e. the same
+Gaussian machinery that the `phyid` library (Mediano/Rosas/Luppi) uses for its
+Integrated Information Decomposition (PhiID), but specialised to "static" PID:
+two source groups -> one target, instead of past -> future.
+
+Why this is the right reduction
+--------------------------------
+phyid's `calc_PhiID` decomposes I(past ; future) over a 4-variable lattice and
+therefore returns 16 atoms. Static PID with 2 sources X1, X2 and a target Y is
+the much simpler 4-atom Williams & Beer (2010) lattice:
+
+    I(X1, X2 ; Y) = Red + Unq1 + Unq2 + Syn
+
+with the standard consistency equations
+    I(X1 ; Y)      = Red + Unq1
+    I(X2 ; Y)      = Red + Unq2
+    I(X1, X2 ; Y)  = Red + Unq1 + Unq2 + Syn
+
+Fixing the redundancy fixes all four atoms. Following Barrett (2015), for a
+jointly Gaussian system with a univariate target the MMI redundancy is
+
+    Red = min( I(X1 ; Y), I(X2 ; Y) )
+
+from which
+    Unq1 = I(X1 ; Y) - Red
+    Unq2 = I(X2 ; Y) - Red
+    Syn  = I(X1, X2 ; Y) - I(X1 ; Y) - I(X2 ; Y) + Red
+
+Each Gaussian mutual information is computed in closed form from the joint
+covariance (this is exactly the mean of phyid's local Gaussian quantities, so
+the numbers match phyid up to the choice of log base):
+
+    I(A ; B) = 1/2 * log( det(Cov_AA) * det(Cov_BB) / det(Cov_[A,B][A,B]) )
+
+Units: returned in nats by default (log base e, matching phyid's Gaussian path);
+pass log_base=2 for bits.
+
+Known limitations (worth stating in the report)
+------------------------------------------------
+1) MMI forces one of the two unique terms to be exactly zero and forces
+  redundancy to be the smaller of the two source-target MIs, regardless of how
+  the sources actually relate. This is a rigidity of the MMI definition.
+2) Barrett's exactness result is for a "univariate" target. The code accepts a
+  multivariate target mechanically, but the MMI interpretation is cleanest for a
+  univariate target (which is what this project uses: signed coherence /
+  stimulus value).
+3) The Gaussian assumption is only an approximation for bounded tanh activations.
+"""
+
+import numpy as np
+
+
+# ----------------------------------------------------------------------------- #
+# Low-level helpers
+# ----------------------------------------------------------------------------- #
+def _as_2d(a):
+    """
+    Coerce an array to shape (n_samples, n_features).
+
+    A 1-D array of length n is interpreted as n samples of a single variable and
+    becomes shape (n, 1). A 2-D array is returned unchanged.
+    """
+    a = np.asarray(a, dtype=float)          # make sure we work with float ndarray
+    if a.ndim == 1:                         # a single variable given as (n,)
+        a = a[:, None]                      # add a feature axis -> (n, 1)
+    if a.ndim != 2:                         # anything that is not 1-D or 2-D is invalid
+        raise ValueError(f"expected 1-D or 2-D array, got shape {a.shape}")
+    return a                                # shape (n_samples, n_features)
+
+
+def _logdet(mat):
+    """
+    Stable log-determinant of a symmetric positive-definite matrix.
+
+    Uses np.linalg.slogdet so we never overflow/underflow on the raw determinant.
+    Raises if the matrix is not positive-definite (sign <= 0), which signals a
+    rank-deficient covariance (too few samples, dead/constant units, or perfectly
+    collinear units) rather than silently returning nonsense.
+    """
+    sign, logabsdet = np.linalg.slogdet(mat)   # sign of det, log|det|
+    if sign <= 0:                              # non-positive determinant => not PD
+        raise np.linalg.LinAlgError(
+            "covariance sub-matrix is not positive-definite "
+            "(rank-deficient: increase samples, remove constant/collinear units, "
+            "or pass a small `regularization`)."
+        )
+    return logabsdet                           # natural-log determinant (nats-friendly)
+
+
+def _gaussian_mi(cov, idx_a, idx_b):
+    """
+    Closed-form Gaussian mutual information I(A ; B) in nats.
+
+    Parameters
+    ----------
+    cov : (D, D) ndarray
+        Joint covariance over all variables.
+    idx_a, idx_b : list[int]
+        Column indices into `cov` for variable groups A and B (disjoint).
+
+    Returns
+    -------
+    float
+        I(A ; B) = 1/2 * [ logdet(Cov_AA) + logdet(Cov_BB) - logdet(Cov_AB,AB) ]
+    """
+    idx_ab = list(idx_a) + list(idx_b)                 # indices of the union A u B
+    ld_a = _logdet(cov[np.ix_(idx_a, idx_a)])          # logdet of A-block
+    ld_b = _logdet(cov[np.ix_(idx_b, idx_b)])          # logdet of B-block
+    ld_ab = _logdet(cov[np.ix_(idx_ab, idx_ab)])       # logdet of joint AB-block
+    return 0.5 * (ld_a + ld_b - ld_ab)                 # Gaussian MI identity (nats)
+
+
+# ----------------------------------------------------------------------------- #
+# Core analytic PID (the function the whole project is built on)
+# ----------------------------------------------------------------------------- #
+def gaussian_pid(sources_1, sources_2, target,
+                 log_base="e", standardize=True, regularization=0.0):
+    """
+    Gaussian analytic MMI-PID for two source groups and one target.
+
+    This is the atomic primitive. It treats every "row" as one observation
+    (e.g. one trial) and every "column" as one variable (e.g. one RNN unit, or
+    the scalar target). It estimates a single joint covariance over
+    [sources_1 | sources_2 | target] and returns the four PID atoms in closed
+    form.
+
+    Parameters
+    ----------
+    sources_1 : array_like, shape (n_samples,) or (n_samples, d1)
+        First source group X1 (e.g. one subpopulation of RNN units). 1-D input
+        is treated as a single variable.
+    sources_2 : array_like, shape (n_samples,) or (n_samples, d2)
+        Second source group X2 (e.g. the complementary subpopulation).
+    target : array_like, shape (n_samples,) or (n_samples, dt)
+        Target Y (e.g. the stimulus / signed coherence / sinusoid value).
+        Univariate is recommended (Barrett's MMI exactness assumes this).
+    log_base : {"e", 2}, optional
+        Units of the returned information. "e" -> nats (matches phyid's Gaussian
+        path, the default); 2 -> bits.
+    standardize : bool, optional
+        If True (default), each column is divided by its standard deviation
+        before forming the covariance. Mutual information is invariant to such
+        per-variable rescaling, so this only improves numerical conditioning.
+    regularization : float, optional
+        If > 0, adds `regularization` to the diagonal of the joint covariance
+        (ridge) to stabilise near-singular cases. Default 0.0 (off). Note this
+        slightly biases the MI values, so keep it small.
+
+    Returns
+    -------
+    dict with keys:
+        'redundancy', 'unique1', 'unique2', 'synergy' : the four PID atoms
+        'mi_1'     = I(X1 ; Y)
+        'mi_2'     = I(X2 ; Y)
+        'mi_joint' = I(X1, X2 ; Y)
+        'total'    = sum of the four atoms (equals mi_joint up to rounding;
+                     a built-in consistency check)
+    All values are floats in the chosen log base.
+
+    How to call
+    -----------
+    >>> import numpy as np
+    >>> X1 = np.random.randn(2000, 5)          # 2000 trials, 5 units in group 1
+    >>> X2 = np.random.randn(2000, 5)          # 2000 trials, 5 units in group 2
+    >>> Y  = X1[:, 0] + X2[:, 0] + 0.1*np.random.randn(2000)   # univariate target
+    >>> atoms = gaussian_pid(X1, X2, Y, log_base=2)            # bits
+    >>> atoms['synergy'], atoms['redundancy']
+    """
+    # --- 1. shape everything to (n_samples, n_features) ---------------------- #
+    X1 = _as_2d(sources_1)                  # (n, d1)
+    X2 = _as_2d(sources_2)                  # (n, d2)
+    Y = _as_2d(target)                      # (n, dt)
+
+    # all three must share the same number of samples (rows)
+    n = X1.shape[0]                         # number of observations
+    if not (X2.shape[0] == n and Y.shape[0] == n):
+        raise ValueError("sources_1, sources_2 and target must have the same "
+                         f"number of samples; got {X1.shape[0]}, {X2.shape[0]}, "
+                         f"{Y.shape[0]}.")
+
+    # dimensionalities of each block
+    d1, d2, dt = X1.shape[1], X2.shape[1], Y.shape[1]   # feature counts
+    total_dim = d1 + d2 + dt                            # size of the joint system
+
+    # need strictly more samples than variables for a full-rank covariance
+    if n <= total_dim:
+        raise ValueError(f"need n_samples ({n}) > total variables ({total_dim}) "
+                         "for a full-rank covariance estimate.")
+
+    # --- 2. assemble the joint data matrix [X1 | X2 | Y] --------------------- #
+    data = np.hstack([X1, X2, Y])           # (n, d1+d2+dt)
+
+    # optional per-variable rescaling to unit std (MI-invariant, aids stability)
+    if standardize:
+        sd = data.std(axis=0, ddof=1)       # per-column standard deviation
+        sd[sd == 0] = 1.0                   # guard: leave constant columns untouched
+        data = data / sd                    # rescale each variable to unit variance
+
+    # --- 3. single joint covariance over all variables ---------------------- #
+    cov = np.cov(data, rowvar=False)        # (D, D); rows=samples, cols=variables
+    cov = np.atleast_2d(cov)                # keep 2-D even if D happens to be 1
+    if regularization > 0:                  # optional ridge for conditioning
+        cov = cov + regularization * np.eye(cov.shape[0])
+
+    # --- 4. index sets into the joint covariance ---------------------------- #
+    i1 = list(range(0, d1))                 # columns belonging to X1
+    i2 = list(range(d1, d1 + d2))           # columns belonging to X2
+    iy = list(range(d1 + d2, total_dim))    # columns belonging to Y
+
+    # --- 5. the three mutual informations (nats) ---------------------------- #
+    mi_1 = _gaussian_mi(cov, i1, iy)            # I(X1 ; Y)
+    mi_2 = _gaussian_mi(cov, i2, iy)            # I(X2 ; Y)
+    mi_joint = _gaussian_mi(cov, i1 + i2, iy)   # I(X1, X2 ; Y)
+
+    # --- 6. convert to the requested log base ------------------------------- #
+    if log_base == "e":                     # nats: divide by ln(e)=1, i.e. no-op
+        scale = 1.0
+    elif log_base == 2:                     # bits: convert nats -> bits
+        scale = 1.0 / np.log(2.0)
+    else:
+        raise ValueError("log_base must be 'e' (nats) or 2 (bits).")
+    mi_1, mi_2, mi_joint = mi_1 * scale, mi_2 * scale, mi_joint * scale
+
+    # --- 7. MMI atoms (Barrett 2015) ---------------------------------------- #
+    redundancy = min(mi_1, mi_2)            # MMI redundancy = smaller source-target MI
+    unique1 = mi_1 - redundancy             # X1's unique info (>=0; ==0 if X1 is the min)
+    unique2 = mi_2 - redundancy             # X2's unique info (>=0; ==0 if X2 is the min)
+    synergy = mi_joint - mi_1 - mi_2 + redundancy   # leftover that needs both sources
+
+    # --- 8. package, with a self-consistency total -------------------------- #
+    return {
+        "redundancy": redundancy,
+        "unique1": unique1,
+        "unique2": unique2,
+        "synergy": synergy,
+        "mi_1": mi_1,
+        "mi_2": mi_2,
+        "mi_joint": mi_joint,
+        "total": redundancy + unique1 + unique2 + synergy,   # should equal mi_joint
+    }
+
+
+# ----------------------------------------------------------------------------- #
+# Versatile RNN wrapper: one snapshot OR every timestep, with bipartition averaging
+# ----------------------------------------------------------------------------- #
+def gaussian_pid_rnn(activations, target,
+                     timestep=None,
+                     bipartitions="random", n_bipartitions=200, balanced=True,
+                     seed=0, log_base="e", standardize=True, regularization=0.0):
+    """
+    Compute Gaussian analytic PID on RNN hidden activations, at one timestep or
+    across all timesteps, averaging over random subpopulation bipartitions.
+
+    This is the convenience layer the project will actually call. The "sources"
+    are two subpopulations of hidden units obtained by splitting the population
+    in two; the "target" is the stimulus. Because the MMI atoms depend on which
+    units land in which group, the result is averaged over many random balanced
+    bipartitions (this is the "random 40/40 splits averaged over many splits"
+    procedure from the project plan; for a 10-unit test RNN the splits are 5/5).
+
+    Parameters
+    ----------
+    activations : array_like
+        Hidden activations. Either
+          a) (n_samples, n_timesteps, n_units)  -> per-timestep PID is available, or
+          b) (n_samples, n_units)               -> already a single snapshot.
+        `n_samples` is the ensemble dimension over which covariance is estimated
+        (in the real project: trials; for a single-trajectory test RNN: time, if
+        you pass the trajectory in the samples axis).
+    target : array_like
+        The target / stimulus. Either
+          a) (n_samples, n_timesteps)  (a value per trial and timestep),
+          b) (n_samples,)              (one value per trial, shared across time), or
+          c) (n_samples, n_timesteps, dt) / (n_samples, dt) for a multivariate target.
+    timestep : int or None, optional
+        int  -> compute PID only at this timestep (supports negative indexing,
+                  e.g. -1 = last timestep). Returns scalar atoms.
+        None -> compute PID at every timestep. Returns arrays of shape
+                  (n_timesteps,). Requires 3-D `activations`.
+    bipartitions : {"random", "half"} or list of (idx1, idx2), optional
+        How to split the units into two source groups:
+          a) "random" (default): draw `n_bipartitions` random splits.
+          b) "half": a single split into first-half / second-half units.
+          c) explicit list: each element is a pair (idx1, idx2) of index arrays.
+    n_bipartitions : int, optional
+        Number of random splits to average over when bipartitions="random".
+    balanced : bool, optional
+        If True (default), random splits are (near-)equal in size (n//2 vs the
+        rest). If False, the split point is uniformly random.
+    seed : int, optional
+        Seed for the random bipartitions (reproducibility).
+    log_base, standardize, regularization :
+        Passed straight through to `gaussian_pid`.
+
+    Returns
+    -------
+    dict
+        Keys 'redundancy', 'unique1', 'unique2', 'synergy', 'mi_1', 'mi_2',
+        'mi_joint' mapped to the bipartition-averaged value(s). For a single
+        timestep these are floats; for timestep=None they are arrays of shape
+        (n_timesteps,). Each atom also has a companion '<atom>_std' giving the
+        standard deviation across bipartitions (useful for variability checks).
+
+    How to call
+    -----------
+    # Snapshot at the last timestep (trials as samples), univariate target:
+    >>> out = gaussian_pid_rnn(acts, stim, timestep=-1)        # acts: (trials, T, units)
+    >>> out['synergy'], out['synergy_std']
+
+    # Full time-resolved PID profile (one PID per timestep):
+    >>> prof = gaussian_pid_rnn(acts, stim, timestep=None)
+    >>> prof['synergy'].shape                                  # (T,)
+    """
+    # --- normalise activations to a 3-D (samples, timesteps, units) view ----- #
+    acts = np.asarray(activations, dtype=float)     # to ndarray
+    if acts.ndim == 2:                              # (samples, units): a lone snapshot
+        acts = acts[:, None, :]                     # insert a length-1 time axis
+    if acts.ndim != 3:                              # otherwise we cannot interpret it
+        raise ValueError("activations must be (n_samples, n_units) or "
+                         f"(n_samples, n_timesteps, n_units); got {acts.shape}.")
+    n_samples, n_timesteps, n_units = acts.shape    # unpack the three axes
+
+    # --- normalise the target so we can index it by timestep ----------------- #
+    tgt = np.asarray(target, dtype=float)           # to ndarray
+    if tgt.ndim == 1:                               # (samples,): same target every timestep
+        tgt = np.repeat(tgt[:, None], n_timesteps, axis=1)        # -> (samples, T)
+    if tgt.ndim == 2 and tgt.shape == (n_samples, n_timesteps):   # (samples, T): per-step scalar
+        tgt = tgt[:, :, None]                       # add a trailing feature axis -> (samples, T, 1)
+    if tgt.ndim == 2 and tgt.shape[0] == n_samples and tgt.shape[1] != n_timesteps:
+        # (samples, dt): a multivariate target shared across time
+        tgt = np.repeat(tgt[:, None, :], n_timesteps, axis=1)     # -> (samples, T, dt)
+    if not (tgt.ndim == 3 and tgt.shape[0] == n_samples and tgt.shape[1] == n_timesteps):
+        raise ValueError("target shape is incompatible with activations; expected "
+                         "(n_samples,), (n_samples, n_timesteps) or matching 3-D.")
+
+    # --- decide which timesteps to evaluate ---------------------------------- #
+    if timestep is None:                            # all timesteps requested
+        t_indices = list(range(n_timesteps))        # evaluate every step
+        single = False                              # we will return arrays
+    else:                                           # one specific timestep
+        t = timestep if timestep >= 0 else n_timesteps + timestep   # resolve negatives
+        if not (0 <= t < n_timesteps):              # bounds check after resolving
+            raise IndexError(f"timestep {timestep} out of range for "
+                             f"{n_timesteps} timesteps.")
+        t_indices = [t]                             # evaluate just this one
+        single = True                               # we will return scalars
+
+    # --- build the list of bipartitions (index pairs) ------------------------ #
+    rng = np.random.default_rng(seed)               # reproducible random generator
+    if bipartitions == "random":                    # many random splits to average over
+        splits = []                                 # collect (idx1, idx2) pairs here
+        for _ in range(n_bipartitions):             # one split per iteration
+            perm = rng.permutation(n_units)         # random ordering of unit indices
+            cut = n_units // 2 if balanced else int(rng.integers(1, n_units))
+            splits.append((perm[:cut], perm[cut:]))         # left vs right of the cut
+    elif bipartitions == "half":                    # a single deterministic split
+        cut = n_units // 2                          # midpoint
+        splits = [(np.arange(0, cut), np.arange(cut, n_units))]
+    else:                                           # caller supplied explicit splits
+        splits = [(np.asarray(a), np.asarray(b)) for (a, b) in bipartitions]
+
+    # --- accumulate atoms over timesteps and bipartitions -------------------- #
+    keys = ["redundancy", "unique1", "unique2", "synergy", "mi_1", "mi_2", "mi_joint"]
+    mean_out = {k: np.zeros(len(t_indices)) for k in keys}   # mean across splits per t
+    std_out = {k: np.zeros(len(t_indices)) for k in keys}    # std across splits per t
+
+    for ti, t in enumerate(t_indices):              # loop over requested timesteps
+        H = acts[:, t, :]                           # (n_samples, n_units) snapshot at t
+        Y = tgt[:, t, :]                            # (n_samples, dt) target at t
+        per_split = {k: [] for k in keys}           # per-bipartition values at this t
+        for idx1, idx2 in splits:                   # loop over the bipartitions
+            atoms = gaussian_pid(                    # the analytic primitive
+                H[:, idx1], H[:, idx2], Y,
+                log_base=log_base,
+                standardize=standardize,
+                regularization=regularization,
+            )
+            for k in keys:                           # stash each quantity
+                per_split[k].append(atoms[k])
+        for k in keys:                               # reduce across bipartitions
+            mean_out[k][ti] = np.mean(per_split[k])  # bipartition-averaged atom
+            std_out[k][ti] = np.std(per_split[k])    # spread across bipartitions
+
+    # --- shape the return: scalars for one timestep, arrays for all ---------- #
+    result = {}                                      # assemble the output dict
+    for k in keys:
+        if single:                                   # one timestep -> plain floats
+            result[k] = float(mean_out[k][0])
+            result[k + "_std"] = float(std_out[k][0])
+        else:                                        # many timesteps -> arrays of shape (T,)
+            result[k] = mean_out[k]
+            result[k + "_std"] = std_out[k]
+    return result
diff --git a/src/analysis/main.py b/src/analysis/main.py
new file mode 100644
index 0000000..46439a8
--- /dev/null
+++ b/src/analysis/main.py
@@ -0,0 +1,219 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import matplotlib.pyplot as plt
+from RNN import ElmanRNN
+
+# Define output directory
+dir = "src/analysis/results/"
+
+# hyperparameters
+BATCH_SIZE = 1   # not really used here, since we train on a single trajectory
+EPOCHS = 2500   
+LEARNING_RATE = 5e-3
+
+# Choose dynamical system
+system = 'Sinusoid'
+
+
+def r2_score(y_pred, y_true):
+    """
+    Coefficient of determination (R²) between predictions and targets.
+
+    R² measures the fraction of variance in y_true that is explained by
+    y_pred, and is the standard 'accuracy' analog for regression tasks:
+        R² = 1  → perfect prediction
+        R² = 0  → model is no better than predicting the mean of y_true
+        R² < 0  → model is worse than predicting the mean
+
+    We use R² rather than normalised MSE because it is scale-free and has a
+    natural [−∞, 1] range with a meaningful zero point, making curves from
+    different runs directly comparable.
+
+    Parameters
+    ----------
+    y_pred : torch.Tensor  (any shape)
+    y_true : torch.Tensor  (same shape as y_pred)
+
+    Returns
+    -------
+    float
+    """
+    # total variance of the targets (denominator)
+    ss_tot = ((y_true - y_true.mean()) ** 2).sum()
+    # residual variance left unexplained by the predictions (numerator)
+    ss_res = ((y_true - y_pred) ** 2).sum()
+    # R² = 1 − (unexplained / total); clamp denominator to avoid div-by-zero
+    return (1.0 - ss_res / ss_tot.clamp(min=1e-8)).item()
+
+
+if __name__ == "__main__":
+
+    # Generate a simple sinusoid trajectory to test the PID metrics. Size (300, 1)
+    n_timesteps = 300
+    period = 50.0
+    t = torch.linspace(0, n_timesteps - 1, n_timesteps)
+    y_true = torch.sin(2 * torch.pi * t / period).unsqueeze(1)
+    # print(y_true.shape)
+
+    n_timesteps, dim = y_true.shape
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    y_true = y_true.to(device)
+
+    # Create RNN
+    model = ElmanRNN(dim=dim, system=system).to(device)
+
+    # Optimizer and loss
+    optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)  # create the optimizer
+    loss_function = nn.MSELoss()  # loss function (MSE)
+
+    # For tracking over epochs
+    training_losses = []
+    training_r2s = []          # R² at each epoch (teacher-forced predictions vs targets)
+
+    # ===== TRAINING LOOP =====
+    for epoch in range(EPOCHS):
+        model.train()
+        if epoch == 1000:
+            for param_group in optimizer.param_groups:
+                param_group["lr"] = 1e-3
+        if epoch == 2000:
+            for param_group in optimizer.param_groups:
+                param_group["lr"] = 5e-4
+        optimizer.zero_grad()
+
+        # Teacher forcing over the full sequence: predict x_{t+1} from x_t
+        x_in = y_true[:-1]
+        target = y_true[1:]
+        pred, _ = model(x_in)
+        loss = loss_function(pred, target)
+
+        # Backpropagate once after summing over all timesteps
+        loss.backward()
+        optimizer.step()
+
+        # Track loss and R² for this epoch.
+        # R² is computed on the same teacher-forced predictions used for the
+        # loss, so it reflects how well the model fits the one-step-ahead
+        # prediction task it is actually being trained on.
+        training_losses.append(loss.item())
+        training_r2s.append(r2_score(pred.detach(), target))
+
+        if (epoch + 1) % 50 == 0 or epoch == 0:
+            print(
+                f"Epoch {epoch+1}/{EPOCHS}, "
+                f"training loss = {loss.item():.6e}, "
+                f"training R² = {training_r2s[-1]:.4f}"
+            )
+
+    # Save trained RNN
+    torch.save(model.state_dict(), f"{dir}RNN_{system}.pt")
+    print(f"Trained RNN saved to {dir}RNN_{system}.pt")
+
+
+    # ===== EVALUATION: AUTOREGRESSIVE ROLLOUT =====
+    model.eval()
+    with torch.no_grad():
+        y_pred = torch.zeros_like(y_true)
+        # initial condition: first value in dataset
+        y_pred[0] = y_true[0]
+
+        # always feed prediction at n-1 as input to predict state at n
+        h = None
+        for t in range(n_timesteps - 1):
+            step_pred, h = model(y_pred[t], h0=h)
+            y_pred[t+1] = step_pred
+
+        # Evaluation loss and R² on the autoregressive rollout.
+        # This is harder than teacher-forced training: the model must sustain
+        # the oscillation from its own outputs, so errors accumulate over time.
+        mse_eval = loss_function(y_pred, y_true).item()
+        r2_eval  = r2_score(y_pred, y_true)
+        print(f"Evaluation MSE over full trajectory: {mse_eval:.6e}")
+        print(f"Evaluation R²  over full trajectory: {r2_eval:.4f}")
+
+
+    # ===== COLLECT HIDDEN STATES (for testing the PID metrics) =====
+    model.eval()
+    with torch.no_grad():
+        # full hidden-state trajectory under teacher forcing over the sinusoid
+        x_seq = y_true.unsqueeze(1)            # (seq_len, batch=1, dim)
+        h_seq, _ = model.rnn(x_seq)            # (seq_len, batch=1, hidden_dim)
+        h_seq = h_seq.squeeze(1)               # (seq_len, hidden_dim)
+        torch.save(h_seq.cpu(), f"{dir}hidden_{system}.pt")
+        print(f"Hidden states {tuple(h_seq.shape)} saved to {dir}hidden_{system}.pt")
+
+
+    # ===== FIGURE 1: TRAINING CURVES (loss + R²) =====
+    # Both metrics are plotted against epoch on a shared x-axis.
+    # Loss (left y-axis) uses a log scale so the steep early drop and the
+    # slow late convergence are both visible. R² (right y-axis) is linear.
+    epochs_axis = range(1, EPOCHS + 1)
+
+    fig, ax1 = plt.subplots(figsize=(10, 5))
+
+    # Left axis: MSE loss (log scale)
+    color_loss = 'C0'
+    ax1.set_xlabel('Epoch')
+    ax1.set_ylabel('MSE loss (log scale)', color=color_loss)
+    ax1.semilogy(epochs_axis, training_losses, color=color_loss,
+                 linewidth=1.2, label='Training loss')
+    ax1.tick_params(axis='y', labelcolor=color_loss)
+
+    # Right axis: R²
+    ax2 = ax1.twinx()
+    color_r2 = 'C1'
+    ax2.set_ylabel('R²', color=color_r2)
+    ax2.plot(epochs_axis, training_r2s, color=color_r2,
+             linewidth=1.2, label='Training R²')
+    ax2.tick_params(axis='y', labelcolor=color_r2)
+    ax2.set_ylim(-0.05, 1.05)          # R² lives in (−∞, 1]; clip low outliers
+
+    # Combined legend for both axes
+    lines1, labels1 = ax1.get_legend_handles_labels()
+    lines2, labels2 = ax2.get_legend_handles_labels()
+    ax1.legend(lines1 + lines2, labels1 + labels2, loc='center right')
+
+    plt.title(f'Training curves — {system} RNN')
+    plt.tight_layout()
+    plt.savefig(f"{dir}sinusoid_training_curves_{system}.png", dpi=300)
+    plt.show()
+
+
+    # ===== FIGURE 2: TRUE VS PREDICTED TRAJECTORY =====
+    y_true_np = y_true.detach().cpu().numpy()
+    y_pred_np = y_pred.detach().cpu().numpy()
+    time = range(n_timesteps)
+
+    # Plot for Sinusoid system
+    if system == 'Sinusoid':
+        fig, ax = plt.subplots(figsize=(10, 6))
+        ax.plot(time, y_true_np[:, 0], label='y_true', c='C0')
+        ax.plot(time, y_pred_np[:, 0], '--', label='y_predicted', c='C1')
+
+        # Evaluation metrics shown as a text box in the upper-right corner.
+        # Using a semi-transparent box so the label is readable over the curve.
+        metrics_text = (
+            f"Eval MSE = {mse_eval:.4e}\n"
+            f"Eval R²  = {r2_eval:.4f}"
+        )
+        ax.text(
+            0.98, 0.97,                 # upper-right in axes-fraction coordinates
+            metrics_text,
+            transform=ax.transAxes,     # interpret x,y as fractions of the axes
+            fontsize=10,
+            verticalalignment='top',
+            horizontalalignment='right',
+            bbox=dict(boxstyle='round,pad=0.4', facecolor='white',
+                      alpha=0.75, edgecolor='0.7'),
+        )
+
+        ax.set_ylabel('y')
+        ax.set_title(f'Sinusoid trajectory: true vs predicted ({system} RNN)')
+        ax.set_xlabel('Time (s)')
+        ax.legend(loc='upper left')
+
+        plt.tight_layout()
+        plt.savefig(f"{dir}sinusoid_trajectory_{system}.png", dpi=300)
+        plt.show()
diff --git a/src/analysis/results/RNN100_Sinusoid.pt b/src/analysis/results/RNN100_Sinusoid.pt
new file mode 100644
index 0000000..b6d48e0
Binary files /dev/null and b/src/analysis/results/RNN100_Sinusoid.pt differ
diff --git a/src/analysis/results/RNN_Sinusoid.pt b/src/analysis/results/RNN_Sinusoid.pt
new file mode 100644
index 0000000..31c507c
Binary files /dev/null and b/src/analysis/results/RNN_Sinusoid.pt differ
diff --git a/src/analysis/results/hidden100_Sinusoid.pt b/src/analysis/results/hidden100_Sinusoid.pt
new file mode 100644
index 0000000..5e3d791
Binary files /dev/null and b/src/analysis/results/hidden100_Sinusoid.pt differ
diff --git a/src/analysis/results/hidden_Sinusoid.pt b/src/analysis/results/hidden_Sinusoid.pt
new file mode 100644
index 0000000..8d6bcab
Binary files /dev/null and b/src/analysis/results/hidden_Sinusoid.pt differ
diff --git a/src/analysis/results/pid100_Sinusoid.npz b/src/analysis/results/pid100_Sinusoid.npz
new file mode 100644
index 0000000..d3ada93
Binary files /dev/null and b/src/analysis/results/pid100_Sinusoid.npz differ
diff --git a/src/analysis/results/pid100_bars_Sinusoid.png b/src/analysis/results/pid100_bars_Sinusoid.png
new file mode 100644
index 0000000..9274db3
Binary files /dev/null and b/src/analysis/results/pid100_bars_Sinusoid.png differ
diff --git a/src/analysis/results/pid_Sinusoid.npz b/src/analysis/results/pid_Sinusoid.npz
new file mode 100644
index 0000000..ae90ed7
Binary files /dev/null and b/src/analysis/results/pid_Sinusoid.npz differ
diff --git a/src/analysis/results/pid_bars_Sinusoid.png b/src/analysis/results/pid_bars_Sinusoid.png
new file mode 100644
index 0000000..a9cc18e
Binary files /dev/null and b/src/analysis/results/pid_bars_Sinusoid.png differ
diff --git a/src/analysis/results/sinusoid100_training_curves_Sinusoid.png b/src/analysis/results/sinusoid100_training_curves_Sinusoid.png
new file mode 100644
index 0000000..70e9505
Binary files /dev/null and b/src/analysis/results/sinusoid100_training_curves_Sinusoid.png differ
diff --git a/src/analysis/results/sinusoid100_trajectory_Sinusoid.png b/src/analysis/results/sinusoid100_trajectory_Sinusoid.png
new file mode 100644
index 0000000..380a64b
Binary files /dev/null and b/src/analysis/results/sinusoid100_trajectory_Sinusoid.png differ
diff --git a/src/analysis/results/sinusoid_training_curves_Sinusoid.png b/src/analysis/results/sinusoid_training_curves_Sinusoid.png
new file mode 100644
index 0000000..e54f4f9
Binary files /dev/null and b/src/analysis/results/sinusoid_training_curves_Sinusoid.png differ
diff --git a/src/analysis/results/sinusoid_trajectory_Sinusoid.png b/src/analysis/results/sinusoid_trajectory_Sinusoid.png
new file mode 100644
index 0000000..120f580
Binary files /dev/null and b/src/analysis/results/sinusoid_trajectory_Sinusoid.png differ
diff --git a/src/models/.gitkeep b/src/models/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/src/models/ctrnn.py b/src/models/ctrnn.py
new file mode 100644
index 0000000..f2d7718
--- /dev/null
+++ b/src/models/ctrnn.py
@@ -0,0 +1,70 @@
+import torch
+import torch.nn as nn
+
+class CTRNN(nn.Module):
+    def __init__(self, input_size, hidden_size=80, output_size=3, dt=20, tau=100):
+        super().__init__()
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.output_size = output_size
+        
+        # Euler integration constant
+        self.alpha = dt / tau
+        
+        # Network layers
+        self.i2h = nn.Linear(input_size, hidden_size, bias=True)
+        self.h2h = nn.Linear(hidden_size, hidden_size, bias=False)
+        self.h2o = nn.Linear(hidden_size, output_size, bias=True)
+        
+        # Predictive head for the Predictive Coding condition
+        self.h2pred = nn.Linear(hidden_size, input_size, bias=True)
+
+        self.init_weights()
+
+    def init_weights(self):
+        # W_rec orthogonal (gain 1.0)
+        nn.init.orthogonal_(self.h2h.weight, gain=1.0)
+        # W_in, W_out, W_pred Xavier uniform
+        nn.init.xavier_uniform_(self.i2h.weight)
+        nn.init.xavier_uniform_(self.h2o.weight)
+        nn.init.xavier_uniform_(self.h2pred.weight)
+        # Biases to 0
+        nn.init.zeros_(self.i2h.bias)
+        nn.init.zeros_(self.h2o.bias)
+        nn.init.zeros_(self.h2pred.bias)
+
+    def forward(self, x, return_dynamics=False):
+        # x shape: (batch_size, sequence_length, input_size)
+        batch_size, seq_len, _ = x.size()
+        
+        # Initialize hidden state at zero
+        h = torch.zeros(batch_size, self.hidden_size, device=x.device)
+        
+        outputs = []
+        predictions = []
+        hidden_states = []
+        
+        for t in range(seq_len):
+            u_t = x[:, t, :]
+            
+            # Euler integration step
+            dx = -h + self.h2h(torch.tanh(h)) + self.i2h(u_t)
+            h = h + self.alpha * dx
+            
+            # Compute outputs
+            y_t = self.h2o(h)
+            pred_t = self.h2pred(h)
+            
+            outputs.append(y_t)
+            predictions.append(pred_t)
+            if return_dynamics:
+                hidden_states.append(h)
+                
+        outputs = torch.stack(outputs, dim=1)
+        predictions = torch.stack(predictions, dim=1)
+        
+        if return_dynamics:
+            hidden_states = torch.stack(hidden_states, dim=1)
+            return outputs, predictions, hidden_states
+            
+        return outputs, predictions
\ No newline at end of file
diff --git a/src/tasks/README_data.md b/src/tasks/README_data.md
new file mode 100644
index 0000000..efa33d7
--- /dev/null
+++ b/src/tasks/README_data.md
@@ -0,0 +1,60 @@
+# Mante 2013 Dataset Pipeline — Generation & Loading
+
+Dataset pipeline for Project 13: Information Decomposition in Task-Trained RNNs.
+This pipeline replaces the standard PerceptualDecisionMaking-v0 to perfectly replicate the biological constraints and signal-to-noise ratios of Mante et al. (2013).
+
+## Files
+
+- `mante_config.py`: Central configuration (timings, coherence scaling, dataset splits).
+- `data_generator.py`: Generates .npz data splits for both Context and Perceptual modes.
+- `data_loader.py`: PyTorch DataLoader wrapper with built-in temporal subsampling.
+
+## The Two Modes (Isolating Integration Demand)
+
+To prove that Synergy differences are due to cognitive integration demand and not simply network dimensionality, both tasks use the exact same 7-channel `ContextDecisionMaking-v0` environment:
+1. `--mode context` **(High Integration):** Both sensory streams (Modality 1 and 2) are active. The network uses the explicitly signaled context cue to route the correct stream and ignore the noisy distractor.
+2. `--mode perceptual` **(Low Integration - Masked):** The context cue is clamped to Modality 1, and the Modality 2 sensory stream is mathematically zeroed out ("dead wires"). The network simply accumulates the active stream without any cognitive conflict or dynamic routing.
+
+## Biological Constraints
+### 1. Coherence Scaling
+
+Mante et al. define coherences as fractional differences (e.g., c = 0.15). NeuroGym's internal ring-representation math divides inputs by 200. To recreate the exact signal-to-noise ratio from the 2013 paper, we multiply Mante's fractions by 100 in our config (e.g., `0.15` becomes `15.0`).
+### 2. The Time Constant (tau) & Subsampling
+
+Mante et al. used a continuous-time resolution of `dt=1ms` with a total trial duration of 750ms.
+
+- **CTRNNs** handle 750 timesteps natively via their leak parameter.
+- **Elman RNNs** suffer from vanishing gradients over 750 steps.
+
+**The Fix:** `data_loader.py` includes a `subsample_step` parameter. By loading the data with `subsample_step=10`, you compress the 750-step array into 75 steps, perfectly mimicking a 10ms biological integration window for the discrete Elman RNN.
+
+## Quick Start
+### 1. Generate the Data
+
+(Warning: Generating the full 160k trial datasets takes a few minutes and ~3.5GB of disk space. Do not push the .npz files to GitHub).
+```Bash
+
+# Generate the High Integration target
+python data_generator.py --mode context
+
+# Generate the Low Integration control
+python data_generator.py --mode perceptual
+```
+
+### 2. Generate a Tiny Test Set (For Debugging)
+```Bash
+
+python data_generator.py --mode context --dt 1 --n_train 500 --n_val 100 --n_test_uni 100 --n_test_mante 0 --output_dir data/tiny_test
+```
+
+### 3. Load into PyTorch
+```Python
+
+from data_loader import load_mante_data
+
+# Elman RNN loader (subsampled to 75 steps to prevent gradient vanishing)
+elman_loader = load_mante_data('data/mante_style/context/train.npz', batch_size=64, subsample_step=10)
+
+# CTRNN loader (full 750 step biological resolution)
+ctrnn_loader = load_mante_data('data/mante_style/context/train.npz', batch_size=64, subsample_step=1)
+```
\ No newline at end of file
diff --git a/src/tasks/data_generator.py b/src/tasks/data_generator.py
new file mode 100644
index 0000000..5fad982
--- /dev/null
+++ b/src/tasks/data_generator.py
@@ -0,0 +1,213 @@
+"""
+mante_generator.py
+Generates the Full Context and Masked Perceptual datasets.
+"""
+
+import os
+import json
+import argparse
+import numpy as np
+import neurogym as ngym
+import gymnasium as gym
+from typing import Dict
+
+from mante_config import CONFIG, UNIFORM_COHS, MANTE_TEST_COHS
+
+def make_env(config: dict, seed: int, test_mode: str = "uniform") -> gym.Env:
+    """
+    Instantiates the NeuroGym environment with Mante et al. specifications.
+    
+    Args:
+        config: Dictionary containing environment parameters.
+        seed: Random seed for reproducibility.
+        test_mode: "uniform" for dense coherences, "mante" for discrete coherences.
+        
+    Returns:
+        Initialized NeuroGym environment.
+    """
+    cohs = MANTE_TEST_COHS if test_mode == "mante" else UNIFORM_COHS
+    
+    env = ngym.make(
+        config["task"],
+        dt=config["dt"],
+        sigma=config["sigma"],
+        timing=config["timing"],
+        use_expl_context=True,
+    )
+
+    env.unwrapped.cohs = cohs
+
+    env.reset(seed=seed)
+    return env
+
+# In data_generator.py
+
+def extract_period_labels(env: gym.Env, seq_len: int) -> np.ndarray:
+    """
+    Extracts timestep-level period labels using exact mathematical timings.
+    0 = Fixation, 1 = Stimulus, 2 = Decision
+    """
+    dt = env.unwrapped.dt
+    timing = env.unwrapped.timing
+    
+    # Calculate duration boundaries in timesteps
+    fix_steps = int(timing["fixation"] / dt)
+    stim_steps = int(timing["stimulus"] / dt)
+    
+    arr = np.zeros(seq_len, dtype=np.int8)
+    
+    # 0 to fix_steps is implicitly 0 (Fixation)
+    
+    # Stimulus Period
+    stim_start = fix_steps
+    dec_start = fix_steps + stim_steps
+    arr[stim_start:dec_start] = 1
+    
+    # Decision Period
+    arr[dec_start:] = 2
+    
+    return arr
+
+def generate_split(
+    env: gym.Env, 
+    n_trials: int, 
+    seq_len: int, 
+    is_perceptual: bool
+) -> Dict[str, np.ndarray]:
+    """
+    Generates a dataset split. If is_perceptual is True, it masks the distractor stimulus.
+    
+    Args:
+        env: The initialized NeuroGym environment.
+        n_trials: Number of trials to generate.
+        seq_len: Total timesteps per trial.
+        is_perceptual: Boolean flag to apply the distractor mask.
+        
+    Returns:
+        Dictionary of numpy arrays ready to be saved.
+    """
+    ob_size = env.observation_space.shape[0]
+    
+    observations = np.zeros((n_trials, seq_len, ob_size), dtype=np.float32)
+    labels = np.zeros((n_trials, seq_len), dtype=np.int64)
+    coherences = np.zeros((n_trials,), dtype=np.float32)
+    contexts = np.zeros((n_trials,), dtype=np.int8)
+    periods = np.zeros((n_trials, seq_len), dtype=np.int8)
+
+    for i in range(n_trials):
+        env.new_trial()
+        
+        ob = env.unwrapped.ob.copy()
+        gt = env.unwrapped.gt.copy()
+        trial_info = env.unwrapped.trial
+        
+        # In ContextDecisionMaking: Context 1 -> attend Modality 1, Context 2 -> attend Modality 2
+        # (Neurogym internal contexts are usually 1 or -1, or 0/1 depending on the version. We map to 1 and 2)
+        ctx = 1 if trial_info['context'] > 0 else 2
+        
+        if is_perceptual:
+            # TRUE PERCEPTUAL MASKING: Eliminate all dynamic routing.
+            # 1. Force the Context cue to ALWAYS be Context 1.
+            # NeuroGym context channels are at indices 5 and 6.
+            ob[:, 5] = 1.0  # Context 1 ON
+            ob[:, 6] = 0.0  # Context 2 OFF
+            
+            # 2. If the original trial was a "Context 2" trial, 
+            # move its stimulus data from channels 3/4 over to channels 1/2.
+            ng_ctx = trial_info['context']
+            if ng_ctx == 1:
+                ob[:, 1:3] = ob[:, 3:5]
+                
+            # 3. Permanently zero out the distractor channels (3 and 4)
+            ob[:, 3:5] = 0.0
+            
+            # Override the tracked context variable so the labels match
+            ctx = 1
+
+        T = min(ob.shape[0], seq_len)
+        
+        observations[i, :T, :] = ob[:T]
+        labels[i, :T] = gt[:T]
+        
+        if T < seq_len:
+            observations[i, T:, :] = ob[-1]
+            labels[i, T:] = gt[-1]
+            
+        # Target Coherence (signed for Choice 1 / Choice 2)
+        coh = float(trial_info.get("coh_1", 0.0))
+        target_choice = trial_info.get("ground_truth", 1)
+        coherences[i] = coh if target_choice == 1 else -coh
+        
+        contexts[i] = ctx
+        periods[i] = extract_period_labels(env, seq_len)
+
+        if (i + 1) % 5000 == 0:
+            print(f"    Generated {i+1}/{n_trials} trials...")
+
+    return {
+        "observations": observations,
+        "labels": labels,
+        "coherences": coherences,
+        "contexts": contexts,
+        "trial_periods": periods,
+    }
+
+def main(args):
+    # Update the master CONFIG with any CLI overrides
+    CONFIG["dt"] = args.dt
+    CONFIG["sigma"] = args.sigma
+    CONFIG["splits"]["train"] = args.n_train
+    CONFIG["splits"]["val"] = args.n_val
+    CONFIG["splits"]["test_uniform"] = args.n_test_uni
+    CONFIG["splits"]["test_mante"] = args.n_test_mante
+    CONFIG["seed"] = args.seed
+
+    # Allow saving to a custom directory (e.g., for tiny test runs)
+    output_base = args.output_dir if args.output_dir else CONFIG["output_dir"]
+
+    is_perceptual = (args.mode == "perceptual")
+    out_dir = os.path.join(output_base, args.mode)
+    os.makedirs(out_dir, exist_ok=True)
+    
+    print(f"=== Generating {args.mode.upper()} Dataset ===")
+    
+    rng = np.random.RandomState(CONFIG["seed"])
+    
+    for split_name, n_trials in CONFIG["splits"].items():
+        if n_trials <= 0:
+            continue # Skip generation if user sets trials to 0
+
+        print(f"\nProcessing Split: {split_name} ({n_trials} trials)")
+        
+        seed = int(rng.randint(0, 2**31))
+        test_mode = "mante" if "mante" in split_name else "uniform"
+        env = make_env(CONFIG, seed, test_mode)
+        
+        data = generate_split(env, n_trials, CONFIG["seq_len"], is_perceptual)
+        
+        path = os.path.join(out_dir, f"{split_name}.npz")
+        np.savez_compressed(path, **data)
+        print(f"  Saved to {path} ({(os.path.getsize(path)/1e6):.1f} MB)")
+
+    # Save the final configuration as a readable JSON file
+    config_save_path = os.path.join(out_dir, "config.json")
+    with open(config_save_path, "w") as f:
+        json.dump(CONFIG, f, indent=4)
+    print(f"\nConfiguration saved to {config_save_path}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mode", type=str, choices=["context", "perceptual"], required=True)
+
+    # CLI Overrides
+    parser.add_argument("--n_train", type=int, default=CONFIG["splits"]["train"], help="Number of training trials")
+    parser.add_argument("--n_val", type=int, default=CONFIG["splits"]["val"], help="Number of validation trials")
+    parser.add_argument("--n_test_uni", type=int, default=CONFIG["splits"]["test_uniform"], help="Number of uniform test trials")
+    parser.add_argument("--n_test_mante", type=int, default=CONFIG["splits"]["test_mante"], help="Number of Mante discrete test trials")
+    parser.add_argument("--dt", type=int, default=CONFIG["dt"], help="Integration timestep in ms")
+    parser.add_argument("--sigma", type=float, default=CONFIG["sigma"], help="Noise standard deviation")
+    parser.add_argument("--seed", type=int, default=CONFIG["seed"], help="Random seed")
+    parser.add_argument("--output_dir", type=str, default=None, help="Override output directory")
+
+    args = parser.parse_args()
+    main(args)
\ No newline at end of file
diff --git a/src/tasks/data_loader.py b/src/tasks/data_loader.py
new file mode 100644
index 0000000..05880a3
--- /dev/null
+++ b/src/tasks/data_loader.py
@@ -0,0 +1,58 @@
+"""
+mante_loader.py
+PyTorch DataLoader wrapper with built-in temporal subsampling.
+"""
+
+import numpy as np
+import torch
+from torch.utils.data import TensorDataset, DataLoader
+from typing import Tuple
+
+def load_mante_data(
+    npz_path: str, 
+    batch_size: int = 64, 
+    shuffle: bool = True, 
+    subsample_step: int = 1,
+    input_dim: int = None,
+) -> DataLoader:
+    """
+    Loads .npz data, applies temporal subsampling, and returns a PyTorch DataLoader.
+    
+    Args:
+        npz_path: Path to the .npz file (e.g., 'data/mante_style/context/train.npz').
+        batch_size: Training batch size.
+        shuffle: Whether to shuffle the data (True for training, False for validation/testing).
+        subsample_step: Stepsize for temporal subsampling. e.g., if dt=1ms and step=10, 
+                        the returned tensors act as if dt=10ms.
+                        
+    Returns:
+        PyTorch DataLoader yielding (observations, labels, periods, coherences, contexts).
+    """
+    data = np.load(npz_path)
+    
+    # 1. Extract and apply temporal subsampling using NumPy slicing [:, ::step]
+    # Shape goes from (Trials, 750, Features) -> (Trials, 75, Features)
+    obs = data["observations"][:, ::subsample_step, :]
+    labels = data["labels"][:, ::subsample_step]
+    periods = data["trial_periods"][:, ::subsample_step]
+    
+    # Trial-level variables (no temporal dimension)
+    cohs = data["coherences"]
+    ctxs = data["contexts"]
+
+    # Optionally select only the first `input_dim` feature channels.
+    if input_dim is not None:
+        obs = obs[..., :input_dim]
+    
+    # 2. Convert to PyTorch tensors
+    obs_t = torch.tensor(obs, dtype=torch.float32)
+    labels_t = torch.tensor(labels, dtype=torch.long)
+    periods_t = torch.tensor(periods, dtype=torch.long)
+    cohs_t = torch.tensor(cohs, dtype=torch.float32)
+    ctxs_t = torch.tensor(ctxs, dtype=torch.long)
+    
+    # 3. Build Dataset and Loader
+    dataset = TensorDataset(obs_t, labels_t, periods_t, cohs_t, ctxs_t)
+    loader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)
+    
+    return loader
diff --git a/src/tasks/mante_config.py b/src/tasks/mante_config.py
new file mode 100644
index 0000000..71e8087
--- /dev/null
+++ b/src/tasks/mante_config.py
@@ -0,0 +1,93 @@
+"""
+mante_config.py
+Central configuration for Mante et al. (2013) dataset generation.
+"""
+'''
+#the commented code works for src\size_comparison\CDM\ctrnn_pid_sweep_cdm.py
+import numpy as np
+
+# Total trial duration = 750ms at dt=1ms means exactly 750 timesteps.
+TIMING = {
+    "fixation": 300,
+    "stimulus": 750,
+    "delay": 0,
+    "decision": 100,
+}
+DT = 10
+TOTAL_TIMESTEPS = sum(TIMING.values()) // DT
+
+# Coherence distributions
+# NeuroGym expects positive coherences and applies the +/- internally based on ground truth.
+
+# Mante's raw fractions: 0.009, 0.036, 0.15
+# NeuroGym requires them scaled by 100:
+MANTE_TEST_COHS = [0.9, 3.6, 15.0]
+
+# Mante's uniform distribution bound: 0.1875
+# Scaled by 100: 18.75
+UNIFORM_COHS = np.linspace(0.0, 18.75, 200).tolist()
+
+CONFIG = {
+    "task": "ContextDecisionMaking-v0",
+    "dt": DT,
+    "sigma": 1.0,  # Noise standard deviation
+    "seq_len": TOTAL_TIMESTEPS,
+    "timing": TIMING,
+    "coh_levels": UNIFORM_COHS,
+    # Dataset sizes
+    "splits": {
+        "train": 160000,
+        "val": 2000,           # Used for early stopping
+        "test_uniform": 2000,  # Psychometric curve testing
+        "test_mante": 2000     # Specific Mante coherence testing
+    },
+    
+    "seed": 42,
+    "output_dir": "data/"
+}'''
+"""
+mante_config.py
+Central configuration for Mante et al. (2013) dataset generation.
+"""
+
+import numpy as np
+
+# Total trial duration = 750ms at dt=1ms means exactly 750 timesteps.
+TIMING = {
+    "fixation": 300,
+    "stimulus": 750,
+    "delay": 0,
+    "decision": 100,
+}
+DT = 10
+TOTAL_TIMESTEPS = sum(TIMING.values()) // DT
+
+# Coherence distributions
+# NeuroGym expects positive coherences and applies the +/- internally based on ground truth.
+
+# Mante's raw fractions: 0.009, 0.036, 0.15
+# NeuroGym requires them scaled by 100:
+MANTE_TEST_COHS = [0.9, 3.6, 15.0]
+
+# Mante's uniform distribution bound: 0.1875
+# Scaled by 100: 18.75
+UNIFORM_COHS = np.linspace(0.0, 18.75, 200).tolist()
+
+CONFIG = {
+    "task": "ContextDecisionMaking-v0",
+    "dt": DT,
+    "sigma": 1.0,  # Noise standard deviation
+    "seq_len": TOTAL_TIMESTEPS,
+    "timing": TIMING,
+    
+    # Dataset sizes
+    "splits": {
+        "train": 160000,
+        "val": 2000,           # Used for early stopping
+        "test_uniform": 2000,  # Psychometric curve testing
+        "test_mante": 2000     # Specific Mante coherence testing
+    },
+    
+    "seed": 42,
+    "output_dir": "data/"
+}
\ No newline at end of file
diff --git a/src/tasks/neurogym_wrapper.py b/src/tasks/neurogym_wrapper.py
new file mode 100644
index 0000000..db19c0b
--- /dev/null
+++ b/src/tasks/neurogym_wrapper.py
@@ -0,0 +1,123 @@
+import neurogym as ngym
+import torch
+import numpy as np
+
+try:
+    from .mante_config import CONFIG, UNIFORM_COHS, MANTE_TEST_COHS
+except ImportError:  # pragma: no cover - fallback for direct script execution
+    from mante_config import CONFIG, UNIFORM_COHS, MANTE_TEST_COHS
+# Safe observation maps (as you already did)
+OB_DICTS = {
+    'PerceptualDecisionMaking-v0': {
+        'fixation': 0,
+        'stimulus': [1, 2]
+    },
+    'ContextDecisionMaking-v0': {
+        'fixation': 0,
+        'stimulus1': [1, 2],
+        'stimulus2': [3, 4],
+        'context': [5, 6]
+    }
+}
+
+
+# ─────────────────────────────────────────────────────────────
+# CONFIG-DRIVEN DATASET CREATION
+# ─────────────────────────────────────────────────────────────
+
+def create_dataset_generator(config):
+
+    if isinstance(config, str):
+        config = {"task": config}
+
+    task_name = config.get("task_name", "ContextDecisionMaking-v0")
+    batch_size = config.get("batch_size", 64)
+    dt = config.get("dt", 20)
+    seq_len = config.get("seq_len", 150)
+
+    sigma = config.get("sigma", 1.0)
+    timing = config.get("timing", None)
+    use_expl_context = config.get("use_expl_context", True)
+
+    #  FIX: correct coherence handling
+    cohs = config.get("coh_levels", None)
+
+    env_kwargs = {
+        "dt": dt,
+        "sigma": sigma,
+    }
+
+    if timing is not None:
+        env_kwargs["timing"] = timing
+
+    env = ngym.make(
+        task_name,
+        **env_kwargs,
+        use_expl_context=use_expl_context
+    )
+
+    env.reset(seed=42)
+
+    # ⚠️ SAFE ONLY IF TASK SUPPORTS IT
+    if cohs is not None:
+        try:
+            env.unwrapped.cohs = np.array(cohs)
+            print("Using coherence levels:", env.unwrapped.cohs[:5], "...")
+        except AttributeError:
+            print("Warning: task has no 'cohs' attribute")
+
+    dataset = ngym.Dataset(env, batch_size=batch_size, seq_len=seq_len)
+
+    dataset.config = config
+    return dataset
+
+
+# ─────────────────────────────────────────────────────────────
+# SINGLE TRIAL GENERATOR (ALSO CONFIG-DRIVEN)
+# ─────────────────────────────────────────────────────────────
+
+def generate_single_trial(config):
+    task_name = config.get("task_name", "PerceptualDecisionMaking-v0")
+    dt = config.get("dt", 20)
+    sigma = config.get("sigma", 1.0)
+    timing = config.get("timing", None)
+    use_expl_context = config.get("use_expl_context", True)
+
+    env_kwargs = {
+        "dt": dt,
+        "sigma": sigma,
+    }
+
+    if timing is not None:
+        env_kwargs["timing"] = timing
+
+    env = ngym.make(
+        task_name,
+        **env_kwargs,
+        use_expl_context=use_expl_context
+    )
+
+    env.reset()
+
+    ob_dict = OB_DICTS[task_name]
+
+    observations = []
+    actions = []
+
+    while True:
+        action = env.action_space.sample()
+
+        step_returns = env.step(action)
+
+        if len(step_returns) == 5:
+            obs, reward, terminated, truncated, info = step_returns
+        else:
+            obs, reward, done, info = step_returns
+
+        observations.append(obs)
+        actions.append(info['gt'])
+
+        if info.get('new_trial', False):
+            break
+
+    return np.array(observations), np.array(actions), ob_dict
\ No newline at end of file
diff --git a/src/training/.gitkeep b/src/training/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/src/training/loss.py b/src/training/loss.py
new file mode 100644
index 0000000..1c17fe1
--- /dev/null
+++ b/src/training/loss.py
@@ -0,0 +1,35 @@
+import torch
+import torch.nn.functional as F
+
+def compute_loss(outputs, targets, hidden_states=None, predictions=None, inputs=None, 
+                 condition="vanilla", lambda_reg=1e-4, mu_reg=0.1):
+    """
+    Computes the loss based on the experimental condition.
+    - outputs: (batch, seq, output_size)
+    - targets: (batch, seq) - typically only evaluated at the final timestep or decision period
+    - predictions: (batch, seq, input_size) - predicted NEXT input
+    """
+    # 1. Vanilla Supervised (Cross Entropy on the last timestep for PoC)
+    # Note: In real NeuroGym tasks, you mask this to the decision period.
+    ce_loss = F.cross_entropy(outputs[:, -1, :], targets)
+    
+    total_loss = ce_loss
+    
+    # 2. Activity-regularized (Efficient Coding)
+    if condition == "efficient":
+        # lambda * mean(h^2) across all timesteps and batches
+        activity_penalty = lambda_reg * torch.mean(hidden_states ** 2)
+        total_loss += activity_penalty
+        
+    # 3. Predictive auxiliary (Predictive Coding)
+    elif condition == "predictive":
+        # mu * MSE(pred(t), u(t+1))
+        # We predict the input at the next timestep. 
+        # Exclude the last prediction since there is no t+1 input to compare it to.
+        pred_t = predictions[:, :-1, :]
+        actual_next_u = inputs[:, 1:, :]
+        
+        predictive_loss = mu_reg * F.mse_loss(pred_t, actual_next_u)
+        total_loss += predictive_loss
+        
+    return total_loss
\ No newline at end of file