From 64c0a0adbdded3ad069927fbe47ab79a59b1be13 Mon Sep 17 00:00:00 2001
From: kyleyinxu <kyleyinxu@gmail.com>
Date: Mon, 27 Jul 2026 03:02:29 -0400
Subject: [PATCH 1/4] Run experiments with Qwen-3.5 architecture support

---
 demos/Jacobian_Lens_Demo.ipynb | 1973 ++++++++++++++++++--------------
 1 file changed, 1090 insertions(+), 883 deletions(-)

diff --git a/demos/Jacobian_Lens_Demo.ipynb b/demos/Jacobian_Lens_Demo.ipynb
index 4f1a35497..c90725ca5 100644
--- a/demos/Jacobian_Lens_Demo.ipynb
+++ b/demos/Jacobian_Lens_Demo.ipynb
@@ -1,903 +1,1110 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "21aefd10",
-   "metadata": {},
-   "source": [
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/TransformerLensOrg/TransformerLens/blob/main/demos/Jacobian_Lens_Demo.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21a6b3c5",
-   "metadata": {},
-   "source": [
-    "# Jacobian Lens (J-lens) Demo\n",
-    "\n",
-    "The **Jacobian lens** characterizes an intermediate residual-stream activation by its\n",
-    "*first-order causal effect on the model's output*, averaged over a corpus of contexts. For each\n",
-    "layer $\\ell$ it averages over prompts and valid source positions while summing the effects on all\n",
-    "later valid target positions, producing a matrix $J_\\ell$ that maps the output of block $\\ell$ to\n",
-    "the final block's output. Its pre-softmax readout uses the model's own unembedding:\n",
-    "$\\mathrm{lens}(h_\\ell) = W_U\\,\\mathrm{norm}(J_\\ell h_\\ell)$. The logit lens is the special\n",
-    "case $J_\\ell = I$; the J-lens is\n",
-    "its causal, corpus-averaged correction, and it surfaces interpretable content at depths where the\n",
-    "logit lens reads noise.\n",
-    "\n",
-    "Introduced in [*Verbalizable Representations Form a Global Workspace in Language Models*](https://transformer-circuits.pub/2026/workspace/index.html)\n",
-    "(Gurnee et al., Transformer Circuits Thread, 2026). Pre-fitted lenses for 38 open models are\n",
-    "published at [`neuronpedia/jacobian-lens`](https://huggingface.co/neuronpedia/jacobian-lens), and\n",
-    "an interactive version lives at [neuronpedia.org/jlens](https://www.neuronpedia.org/jlens).\n",
-    "\n",
-    "This demo, on `gemma-2-2b`:\n",
-    "\n",
-    "1. loads a published lens from the Hub,\n",
-    "2. reads J-lens vs logit-lens tokens on a two-hop prompt with an *unspoken intermediate*,\n",
-    "3. causally swaps one concept for another (France → China) in lens coordinates, and\n",
-    "4. steers with a J-lens direction.\n",
-    "\n",
-    "**Note**: `google/gemma-2-2b` is a gated Hugging Face model — accept its license and authenticate\n",
-    "(`hf auth login` / `HF_TOKEN`) before running. The demo uses ~6 GB of GPU memory\n",
-    "(executed on an 8 GB card), so a free Colab T4 should work; GPUs without native BF16 use FP16 automatically."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "454cdfb3",
-   "metadata": {},
-   "source": [
-    "## Setup (Ignore)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ae487ba2",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-07-11T09:06:11.958584Z",
-     "iopub.status.busy": "2026-07-11T09:06:11.958352Z",
-     "iopub.status.idle": "2026-07-11T09:06:12.029888Z",
-     "shell.execute_reply": "2026-07-11T09:06:12.029521Z"
-    }
-   },
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running as a Jupyter notebook - intended for development only!\n"
-     ]
-    }
-   ],
-   "source": [
-    "# NBVAL_IGNORE_OUTPUT\n",
-    "# Janky code to do different setup when run in a Colab notebook vs VSCode\n",
-    "import os\n",
-    "\n",
-    "DEVELOPMENT_MODE = False\n",
-    "IN_GITHUB = os.getenv(\"GITHUB_ACTIONS\") == \"true\"\n",
-    "try:\n",
-    "    import google.colab\n",
-    "\n",
-    "    IN_COLAB = True\n",
-    "    print(\"Running as a Colab notebook\")\n",
-    "except ImportError:\n",
-    "    IN_COLAB = False\n",
-    "    print(\"Running as a Jupyter notebook - intended for development only!\")\n",
-    "    DEVELOPMENT_MODE = True\n",
-    "    from IPython import get_ipython\n",
-    "\n",
-    "    ipython = get_ipython()\n",
-    "    ipython.run_line_magic(\"load_ext\", \"autoreload\")\n",
-    "    ipython.run_line_magic(\"autoreload\", \"2\")\n",
-    "\n",
-    "if IN_COLAB or IN_GITHUB:\n",
-    "    %pip install transformer_lens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "fe86001d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-07-11T09:06:12.031375Z",
-     "iopub.status.busy": "2026-07-11T09:06:12.031092Z",
-     "iopub.status.idle": "2026-07-11T09:06:27.807249Z",
-     "shell.execute_reply": "2026-07-11T09:06:27.806799Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "21aefd10",
+      "metadata": {},
+      "source": [
+        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/TransformerLensOrg/TransformerLens/blob/main/demos/Jacobian_Lens_Demo.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8b9855cb054f4b06803233534b78ddf9",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "id": "21a6b3c5",
+      "metadata": {},
+      "source": [
+        "# Jacobian Lens (J-lens) Demo\n",
+        "\n",
+        "The **Jacobian lens** characterizes an intermediate residual-stream activation by its\n",
+        "*first-order causal effect on the model's output*, averaged over a corpus of contexts. For each\n",
+        "layer $\\ell$ it averages over prompts and valid source positions while summing the effects on all\n",
+        "later valid target positions, producing a matrix $J_\\ell$ that maps the output of block $\\ell$ to\n",
+        "the final block's output. Its pre-softmax readout uses the model's own unembedding:\n",
+        "$\\mathrm{lens}(h_\\ell) = W_U\\,\\mathrm{norm}(J_\\ell h_\\ell)$. The logit lens is the special\n",
+        "case $J_\\ell = I$; the J-lens is\n",
+        "its causal, corpus-averaged correction, and it surfaces interpretable content at depths where the\n",
+        "logit lens reads noise.\n",
+        "\n",
+        "Introduced in [*Verbalizable Representations Form a Global Workspace in Language Models*](https://transformer-circuits.pub/2026/workspace/index.html)\n",
+        "(Gurnee et al., Transformer Circuits Thread, 2026). Pre-fitted lenses for 38 open models are\n",
+        "published at [`neuronpedia/jacobian-lens`](https://huggingface.co/neuronpedia/jacobian-lens), and\n",
+        "an interactive version lives at [neuronpedia.org/jlens](https://www.neuronpedia.org/jlens).\n",
+        "\n",
+        "This demo, on `gemma-2-2b`:\n",
+        "\n",
+        "1. loads a published lens from the Hub,\n",
+        "2. reads J-lens vs logit-lens tokens on a two-hop prompt with an *unspoken intermediate*,\n",
+        "3. causally swaps one concept for another (France → China) in lens coordinates, and\n",
+        "4. steers with a J-lens direction.\n",
+        "\n",
+        "**Note**: `google/gemma-2-2b` is a gated Hugging Face model — accept its license and authenticate\n",
+        "(`hf auth login` / `HF_TOKEN`) before running. The demo uses ~6 GB of GPU memory\n",
+        "(executed on an 8 GB card), so a free Colab T4 should work; GPUs without native BF16 use FP16 automatically."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "454cdfb3",
+      "metadata": {},
+      "source": [
+        "## Setup (Ignore)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "ae487ba2",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-07-11T09:06:11.958584Z",
+          "iopub.status.busy": "2026-07-11T09:06:11.958352Z",
+          "iopub.status.idle": "2026-07-11T09:06:12.029888Z",
+          "shell.execute_reply": "2026-07-11T09:06:12.029521Z"
+        }
       },
-      "text/plain": [
-       "Loading weights:   0%|          | 0/288 [00:00<?, ?it/s]"
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Running as a Jupyter notebook - intended for development only!\n"
+          ]
+        }
+      ],
+      "source": [
+        "# NBVAL_IGNORE_OUTPUT\n",
+        "# Janky code to do different setup when run in a Colab notebook vs VSCode\n",
+        "import os\n",
+        "\n",
+        "DEVELOPMENT_MODE = False\n",
+        "IN_GITHUB = os.getenv(\"GITHUB_ACTIONS\") == \"true\"\n",
+        "try:\n",
+        "    import google.colab\n",
+        "\n",
+        "    IN_COLAB = True\n",
+        "    print(\"Running as a Colab notebook\")\n",
+        "except ImportError:\n",
+        "    IN_COLAB = False\n",
+        "    print(\"Running as a Jupyter notebook - intended for development only!\")\n",
+        "    DEVELOPMENT_MODE = True\n",
+        "    from IPython import get_ipython\n",
+        "\n",
+        "    ipython = get_ipython()\n",
+        "    ipython.run_line_magic(\"load_ext\", \"autoreload\")\n",
+        "    ipython.run_line_magic(\"autoreload\", \"2\")\n",
+        "\n",
+        "if IN_COLAB or IN_GITHUB:\n",
+        "    %pip install transformer_lens"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "loaded gemma-2-2b: 26 layers, d_model=2304, device='cuda'\n"
-     ]
-    }
-   ],
-   "source": [
-    "# NBVAL_IGNORE_OUTPUT\n",
-    "import torch\n",
-    "\n",
-    "from transformer_lens.model_bridge import TransformerBridge\n",
-    "from transformer_lens.tools.analysis import JacobianLens\n",
-    "\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "model_dtype = (\n",
-    "    torch.bfloat16\n",
-    "    if device == \"cpu\" or torch.cuda.is_bf16_supported()\n",
-    "    else torch.float16\n",
-    ")\n",
-    "# Raw HF weights (the Bridge default) — the published lenses are fitted on raw\n",
-    "# activations, and JacobianLens refuses compatibility mode or process_weights.\n",
-    "model = TransformerBridge.boot_transformers(\"google/gemma-2-2b\", dtype=model_dtype, device=device)\n",
-    "model.eval()\n",
-    "print(f\"loaded gemma-2-2b: {model.cfg.n_layers} layers, d_model={model.cfg.d_model}, {device=}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad038b6e",
-   "metadata": {},
-   "source": [
-    "## 1. Load a published lens\n",
-    "\n",
-    "One artifact per model: a `[d_model, d_model]` matrix for every layer except the last (the final\n",
-    "layer's transport is the identity by construction). `from_pretrained` validates the lens against\n",
-    "the model and fails loudly on dimension or weight-processing mismatches."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f961d030",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-07-11T09:06:27.824146Z",
-     "iopub.status.busy": "2026-07-11T09:06:27.823519Z",
-     "iopub.status.idle": "2026-07-11T09:06:28.209427Z",
-     "shell.execute_reply": "2026-07-11T09:06:28.208823Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "fe86001d",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-07-11T09:06:12.031375Z",
+          "iopub.status.busy": "2026-07-11T09:06:12.031092Z",
+          "iopub.status.idle": "2026-07-11T09:06:27.807249Z",
+          "shell.execute_reply": "2026-07-11T09:06:27.806799Z"
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8b9855cb054f4b06803233534b78ddf9",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Loading weights:   0%|          | 0/288 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "loaded gemma-2-2b: 26 layers, d_model=2304, device='cuda'\n"
+          ]
+        }
+      ],
+      "source": [
+        "# NBVAL_IGNORE_OUTPUT\n",
+        "import torch\n",
+        "\n",
+        "from transformer_lens.model_bridge import TransformerBridge\n",
+        "from transformer_lens.tools.analysis import JacobianLens\n",
+        "\n",
+        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "model_dtype = (\n",
+        "    torch.bfloat16\n",
+        "    if device == \"cpu\" or torch.cuda.is_bf16_supported()\n",
+        "    else torch.float16\n",
+        ")\n",
+        "# Raw HF weights (the Bridge default) — the published lenses are fitted on raw\n",
+        "# activations, and JacobianLens refuses compatibility mode or process_weights.\n",
+        "model = TransformerBridge.boot_transformers(\"google/gemma-2-2b\", dtype=model_dtype, device=device)\n",
+        "model.eval()\n",
+        "print(f\"loaded gemma-2-2b: {model.cfg.n_layers} layers, d_model={model.cfg.d_model}, {device=}\")"
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "JacobianLens(layers=0..24 (25), d_model=2304, n_prompts=454)"
+      "cell_type": "markdown",
+      "id": "ad038b6e",
+      "metadata": {},
+      "source": [
+        "## 1. Load a published lens\n",
+        "\n",
+        "One artifact per model: a `[d_model, d_model]` matrix for every layer except the last (the final\n",
+        "layer's transport is the identity by construction). `from_pretrained` validates the lens against\n",
+        "the model and fails loudly on dimension or weight-processing mismatches."
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# NBVAL_IGNORE_OUTPUT (download progress)\n",
-    "lens = JacobianLens.from_pretrained(\n",
-    "    \"neuronpedia/jacobian-lens\",\n",
-    "    filename=\"gemma-2-2b/jlens/Salesforce-wikitext/gemma-2-2b_jacobian_lens.pt\",\n",
-    "    revision=\"a4114d7752d11eb546e6cf372213d7e75526d3a1\",\n",
-    "    model=model,\n",
-    ")\n",
-    "lens"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "64f87b93",
-   "metadata": {},
-   "source": [
-    "## 2. Reading: a two-hop prompt with an unspoken intermediate\n",
-    "\n",
-    "*\"The currency used in the country shaped like a boot\"* requires an intermediate step — **Italy** —\n",
-    "that never appears in the prompt. The J-lens surfaces the intermediate and the answer's concept\n",
-    "band in the middle of the model, while the logit lens at the same layers mostly tracks surface\n",
-    "continuations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "1b19ec1a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-07-11T09:06:28.217489Z",
-     "iopub.status.busy": "2026-07-11T09:06:28.217204Z",
-     "iopub.status.idle": "2026-07-11T09:06:29.114425Z",
-     "shell.execute_reply": "2026-07-11T09:06:29.113969Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "layer | J-lens top-1         | logit lens top-1\n",
-      "    4 | ' anything'          | ' is'\n",
-      "    8 | '\\n\\n\\n'             | ' is'\n",
-      "   13 | 'LookAnd'            | ' is'\n",
-      "   17 | 'LookAnd'            | ' called'\n",
-      "   21 | ' euro'              | ' called'\n",
-      "   24 | ' the'               | ' the'\n",
-      "   25 | ' the'               | ' the'\n",
-      "model output:  the\n"
-     ]
-    }
-   ],
-   "source": [
-    "PROMPT = \"Fact: The currency used in the country shaped like a boot is\"\n",
-    "\n",
-    "result_jlens = lens.readout(model, PROMPT, positions=[-1], return_full_logits=True)\n",
-    "result_logitlens = lens.readout(model, PROMPT, positions=[-1], use_jacobian=False)\n",
-    "\n",
-    "top_j = result_jlens.top_tokens(model.tokenizer, k=1)\n",
-    "top_l = result_logitlens.top_tokens(model.tokenizer, k=1)\n",
-    "print(f\"{'layer':>5} | {'J-lens top-1':<20} | logit lens top-1\")\n",
-    "for layer in [4, 8, 13, 17, 21, 24, 25]:\n",
-    "    print(f\"{layer:>5} | {top_j[layer][-1][0]!r:<20} | {top_l[layer][-1][0]!r}\")\n",
-    "print(\"model output:\", top_j[25][-1][0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "08ac5860",
-   "metadata": {},
-   "source": [
-    "The answer concept (` euro`) and the intermediate's semantic neighborhood dominate the J-lens\n",
-    "readout from the middle of the model onward; the logit lens catches up only in the last few\n",
-    "layers, where $J_\\ell \\to I$ and the two lenses converge. (Early layers are noise under both —\n",
-    "the paper's \"sensory\" band. The recurring code-ish tokens in early J-lens readouts are a known\n",
-    "signature of gemma-2-2b's lens.)\n",
-    "\n",
-    "We can watch each concept's *rank trajectory* across depth:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "2e0887dd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-07-11T09:06:29.118703Z",
-     "iopub.status.busy": "2026-07-11T09:06:29.118453Z",
-     "iopub.status.idle": "2026-07-11T09:06:29.588577Z",
-     "shell.execute_reply": "2026-07-11T09:06:29.588189Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "f961d030",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-07-11T09:06:27.824146Z",
+          "iopub.status.busy": "2026-07-11T09:06:27.823519Z",
+          "iopub.status.idle": "2026-07-11T09:06:28.209427Z",
+          "shell.execute_reply": "2026-07-11T09:06:28.208823Z"
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "JacobianLens(layers=0..24 (25), d_model=2304, n_prompts=454)"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# NBVAL_IGNORE_OUTPUT (download progress)\n",
+        "lens = JacobianLens.from_pretrained(\n",
+        "    \"neuronpedia/jacobian-lens\",\n",
+        "    filename=\"gemma-2-2b/jlens/Salesforce-wikitext/gemma-2-2b_jacobian_lens.pt\",\n",
+        "    revision=\"a4114d7752d11eb546e6cf372213d7e75526d3a1\",\n",
+        "    model=model,\n",
+        ")\n",
+        "lens"
+      ]
+    },
     {
-     "data": {
-      "image/png": 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IkSPTPZZy7CmxpLymab393k55r3br1i3duu7u7hIg/fPPPxrLc/u9K0mSVK9ePcnCwkJ6/vx5hsckSZI0ceJEycjISIqIiEhdFhQUJOnr62f4eU0r5TjflvI9lXJO9+zZ897vrfy6FklS5ufrfd97mWnVqlWG7/uU633JkiWlxMTE1OWLFi2SAOnWrVuSJGXvOzoj69atkwBp/vz56R5L2d7evXslQJo5c6bG4506dZJkMlnqd+mHvM+z8rqZmZlleG7e9Vnp1q2b5OLionE9unr1aqbxpWVlZSUNGzbsnet4e3tLgLRp06bUZYmJiZKTk5PUsWPH1GUKhULj/EmS+rvB0dFR49hTXjsTExPp5cuXqcsvXLggAdKYMWNSl2X1ejV69GgJkC5cuJC6LCgoSLKyssr0uplWRp9Rd3d3jXORtlzp7+8vlS5dWipSpIjk4+OTus7mzZsluVwunT59WmNbK1eulADpv//+e2ccb38HSZIkzZ49W5LJZOmuT29Lia9y5cpSUlJS6vJffvlFAqR9+/ZJkvTm2tC0aVON98zSpUslQFq3bp0kServvwIFCkhlypSR4uPjU9c7cOCABEhTpkxJXTZs2LAMr3GZ8fb21vh+bNu2rVS6dOksPz87PummYtevX+fRo0d0796d0NBQQkJCCAkJITY2lkaNGnHq1Kl01Y6DBw/W+L9u3bqEhoYSFRUFvOnPsW/fvg+qsvT29qZUqVLplqe9ixAeHk5kZCR169bNsHq3cePGGndtypUrh6WlJU+fPk237u3bt/H29sbDw4OjR49iY2PzzviUSiWHDx+mXbt2FClSJHW5s7Mz3bt358yZM6mvxYfo2bMnFhYWqf936tQJZ2dnDh48+N7ndu3aVSP+unXrAmR43Bmdx7Tr7dixAysrK5o0aZL6vggJCaFy5cqYm5unVoUfPXqUpKQkRowYoVFVmtPOqP7+/ly/fp3evXtja2uburxcuXI0adIkw9fjXedyx44d1K1bFxsbG43jady4MUqlklOnTmls632vpYmJCYaGhpw4cSLDKmld0rZtW44cOaLx06xZMyBrn6u9e/eiUqmYMmVKuo6bWR3xxMfHJ0+a0L3vPIWFhXHs2DG6dOlCdHR06nkPDQ2lWbNmPHr06J3NSnbt2oVMJmPq1KnpHvuQ0V5SvP35S1G4cOHUc5Mit9+7wcHBnDp1ir59+1KoUKFMj6lnz54kJiZqNB3ctm0bCoUi1/pMpXxfHDhwgOTk5AzX0fa1CN7/vfeh+vTpo9H2/e1z9SHf0Wnt2rULe3t7RowYke6xlNfo4MGD6OnpMXLkSI3Hv/nmGyRJ4u+///7g48uN1y2jz0rPnj159eqVRpOsLVu2YGJiQseOHd+5PWtray5cuMCrV6/euZ65ubnG+9zQ0JBq1appfE/q6emlnj+VSkVYWBgKhYIqVapkWDZp166dRm1QtWrVqF69eur3WXauVwcPHqRGjRoaNW4ODg706NHjncf1IV6+fIm3tzfJycmcOnUqtRk8qD+fJUuWpESJEhqfz4YNGwKkazb3trTfQbGxsYSEhFCrVi0kScqwOWBGBg4cqNGqZMiQIejr66e+rinXhtGjR2t8hw0YMABLS0v++usvQN1sNSgoiKFDh2r0d2vVqhUlSpRIXS83WFtb8/Llywyb8ebUR9lULCAgQON/KyurDKsPHz16BKiH7M1MZGSkxpfg2190KY+Fh4djaWlJ165dWbt2Lf3792fChAk0atSIDh060KlTpyyNVlG4cOEMlx84cICZM2dy/fp1jXaTGRUe3o4xJc6MCpht2rTB0dGRQ4cOZakpV3BwMHFxcRQvXjzdYyVLlkSlUvHixQtKly793m1lpGjRohr/y2QyvLy8slToe9e5SSulnf7b66Zd79GjR0RGRlKgQIEM9xUUFASo+5JkFLeDg8N7k8B3SdluZq/zoUOH0nXUfNe5fPToETdv3kx33G8fT4r3vZZGRkb8/PPPfPPNNzg6OlKjRg1at25Nz5493znUcHx8PJGRkZk+/i4mJiZYWVll+3kFCxakcePGGT6Wlc/VkydPkMvlGd5Q0Lb3nafHjx8jSRKTJ09m8uTJGW4jKCgo06YlT548wcXFRSN5zg2ZXecyWp7b792Ugtf75qkoUaIEVatWZcuWLfTr1w9QFw5r1KiBl5fXO5+bVd7e3nTs2JFp06axYMEC6tevT7t27ejevXtqR2FtX4vg/d97ebFd+LDv6LSePHlC8eLF3zlS2vPnz3FxcdG4YQbq62zK4x8qN163jD4TTZo0wdnZmS1bttCoUSNUKhW///47bdu2TXccb/vll1/o1asXbm5uVK5cmZYtW9KzZ0+NG5Ggvm6+Xb6wsbHh5s2bGss2btzIvHnzuH//vkbynVHcb783QT14yvbt24HsXa+eP39O9erV0z2e0XdmTn399dfo6+tz7969dN9vjx494t69e1m+Pr3N19eXKVOm8Oeff6Yrq2T1u/Lt19Xc3BxnZ+fUclNm5QlDQ0OKFCmS+vi7yh0lSpTgzJkzWYonK7777juOHj1KtWrV8PLyomnTpnTv3p3atWvneNsfZeLi7Oys8f/69esz7HCVcqdmzpw56dpipni7AKinp5fhetLrdtomJiacOnWK48eP89dff/HPP/+wbds2GjZsyOHDhzN9foqMEqzTp0/zxRdfUK9ePZYvX46zszMGBgasX78+w45M74sxrY4dO7Jx40a2bNnCoEGD3hmbrsvqcb/vHID6vVGgQAG2bNmS4eOZXaS06V3nUqVS0aRJE7799tsMn1usWDGN/7PyWo4ePZo2bdqwd+9eDh06xOTJk5k9ezbHjh1L1/Y6xbZt2+jTp092DitVr169cnUOk+x+rnTR+85TyjVu3Lhx6WoyUuS0EJ5Zzcvbg12klVk79IyW58V7N6t69uzJqFGjePnyJYmJiZw/f56lS5e+93lZfU1SJmE8f/48+/fv59ChQ/Tt25d58+Zx/vx5zM3NdeJalJuvaXa2+yHf0XnlQ97nufG6ZfSZ0NPTo3v37qxZs4bly5fz33//8erVqyzVBHbp0oW6deuyZ88eDh8+zJw5c/j555/ZvXt3at+VrMb+22+/0bt3b9q1a8f48eMpUKAAenp6zJ49+4MGI8mP69WH6NChA5s2bWLRokXMnj1b4zGVSkXZsmWZP39+hs91c3PLdLtKpTK1z8x3331HiRIlMDMzw8/Pj969e38y0z5kpGTJkjx48IADBw7wzz//sGvXLpYvX86UKVNSp4j4UB9l4nLkyBGN/zO7+5/SnMrS0jLTO7IfQi6X06hRIxo1asT8+fOZNWsWkyZN4vjx4x+0n127dmFsbMyhQ4c0hutbv359jmOdM2cO+vr6qR3539cpysHBAVNT09TRNNK6f/8+crn8nR/U90m5w5ZCkiQeP36c2mE9v3h6enL06FFq1679zs5+KVXGjx490rhjFRwcnKUmVJl9GaZsN7PX2d7ePt2wmO86l56ensTExOTq+zxlu9988w3ffPMNjx49okKFCsybN4/ffvstw/WbNWuW7vOZVS4uLjkJNZ2sfq5SOmDevXs308IT5KzpVF5JeU8aGBh80Ln39PTk0KFDhIWFZVrrknIX+e3RaXJyp/rtGHLzvZvymmQ0mtHbvvzyS8aOHcvvv/9OfHw8BgYGdO3a9b3PS/uapB0OPrPXpEaNGtSoUYMff/yRrVu30qNHD/744w/69++fb9eivJDTz0ROv6M9PT25cOECycnJmQ6j7+7uztGjR4mOjtaorbh//37q45B37/MPfY169uzJvHnz2L9/P3///TcODg6ZFvbf5uzszNChQxk6dChBQUFUqlSJH3/8USNxyYqdO3dSpEgRdu/erXEcGTUthfTf7wAPHz5MHcwlO9crd3f3DLeX0XdmTo0YMQIvLy+mTJmClZUVEyZMSH3M09OTGzdu0KhRo2yfy1u3bvHw4UM2btyoMddYdr8jHz16RIMGDVL/j4mJwd/fn5YtWwKa5Ym014akpCSePXuW+lqnXS+lqVuKBw8eaDSRy43vOzMzM7p27UrXrl1JSkqiQ4cO/Pjjj0ycODFHQ7N/lH1cGjdurPHzdg1MisqVK+Pp6cncuXM1Jn5LkTIPSXaEhYWlW5ZS2MnK0HgZ0dPTQyaTadzZ8fHxyZXZdmUyGatXr6ZTp0706tXrvUOk6unp0bRpU/bt26fRfCswMJCtW7dSp06dHDUdSBl1JMXOnTvx9/fP9gU1p7p06YJSqWTGjBnpHlMoFKlfXo0bN8bAwIAlS5Zo3InK6mzRpqamQPovQ2dnZypUqMDGjRs1Hrt9+zaHDx9OvSCl9a5z2aVLF86dO8ehQ4fSPS8iIgKFQpGleFPExcWRkJCgsczT0xMLC4t3vs+dnZ3TfT6z+pPbTbWy+rlq164dcrmc6dOnp7sDlvacm5mZZTi0JOTtcMjvUqBAAerXr8+qVavw9/dP9/j7rnEdO3ZEkqQM74ClHLulpSX29vbp+posX748B5G/kdvvXQcHB+rVq8e6devw9fXVeOztO+H29va0aNGC3377jS1bttC8efN3jriYIqXAnfY1SRn2Nq3w8PB0+3z7+yK/rkV5wczM7IObhkLOv6M7duxISEhIhrVkKa9Ry5YtUSqV6dZZsGABMpks9bsnr97n77puvEu5cuUoV64ca9euZdeuXXz55ZfvnTxUqVSmOx8FChTAxcXlg8onKbUyad9vFy5cyHSo8L1792r0qbt48SIXLlxIfY2zc71q2bIl58+f5+LFixqPZ1YzmVOTJ09m3LhxTJw4kRUrVqQu79KlC35+fqxZsybdc+Lj44mNjc10mxm9fpIksWjRomzFtnr1ao1meitWrEChUKS+ro0bN8bQ0JDFixdr7OvXX38lMjIydQTNKlWqUKBAAVauXKnxfvj777+5d++exkibKTdOP+S9CxAaGqrxv6GhIaVKlUKSpEz7+2XVR1njklVyuZy1a9fSokULSpcuTZ8+fXB1dcXPz4/jx49jaWnJ/v37s7XN6dOnc+rUKVq1aoW7uztBQUEsX76cggULps5XkF2tWrVi/vz5NG/enO7duxMUFMSyZcvw8vJK1970Q8jlcn777TfatWtHly5dOHjwYLpsO62ZM2emzlUzdOhQ9PX1WbVqFYmJienGec8uW1tb6tSpQ58+fQgMDGThwoV4eXkxYMCAHG03u7y9vRk0aBCzZ8/m+vXrNG3aFAMDAx49esSOHTtYtGgRnTp1Sp0DZvbs2bRu3ZqWLVty7do1/v777ywVcExMTChVqhTbtm2jWLFi2NraUqZMGcqUKcOcOXNo0aIFNWvWpF+/fqnDIVtZWWU6W21m53L8+PH8+eeftG7dmt69e1O5cmViY2O5desWO3fuxMfHJ0vxpnj48CGNGjWiS5culCpVCn19ffbs2UNgYCBffvlllrejTVn9XHl5eTFp0iRmzJhB3bp16dChA0ZGRly6dAkXF5fUpgOVK1dmxYoVzJw5Ey8vLwoUKJD6Ocqr4ZCzYtmyZdSpU4eyZcsyYMAAihQpQmBgIOfOnePly5fcuHEj0+c2aNCAr7/+msWLF/Po0SOaN2+OSqXi9OnTNGjQgOHDhwPQv39/fvrpJ/r370+VKlU4deoUDx8+zJX4c/u9C7B48WLq1KlDpUqVGDhwIIULF8bHx4e//vqL69eva6zbs2dPOnXqBJBh8pCRpk2bUqhQIfr168f48ePR09Nj3bp1ODg4aCRLGzduZPny5bRv3x5PT0+io6NZs2YNlpaWqTcn8utalBcqV67Mtm3bGDt2LFWrVsXc3Jw2bdpk+fk5/Y7u2bMnmzZtYuzYsVy8eJG6desSGxvL0aNHGTp0KG3btqVNmzY0aNCASZMm4ePjQ/ny5Tl8+DD79u1j9OjRGgPd5MX7vHLlyhw9epT58+fj4uJC4cKFM+y7kdnxjRs3DiBLzcSio6MpWLAgnTp1onz58pibm3P06FEuXbrEvHnzsh1769at2b17N+3bt6dVq1Y8e/aMlStXUqpUqQwTTS8vL+rUqcOQIUNITExk4cKF2NnZaTQDzer16ttvv2Xz5s00b96cUaNGpQ6H7O7univloozMmTOHyMhIhg0bhoWFBV999RVff/0127dvZ/DgwRw/fpzatWujVCq5f/8+27dvT52XKiMlSpTA09OTcePG4efnh6WlJbt27cp2DWlSUlLq9/GDBw9Yvnw5derU4YsvvgDUN2smTpzItGnTaN68OV988UXqelWrVk197xgYGPDzzz/Tp08fvL296datW+pwyB4eHowZMyZ1n5UrVwZg5MiRNGvWDD09vWx99zdt2hQnJydq166No6Mj9+7dY+nSpbRq1eq9/bTeK0/GKssDKcMeXr16NdN13h4OOcW1a9ekDh06SHZ2dpKRkZHk7u4udenSRfr3339T10k75Gxabw9v+e+//0pt27aVXFxcJENDQ8nFxUXq1q1buqE8MwJkOkzhr7/+KhUtWlQyMjKSSpQoIa1fvz7D4fwy28bbw/xldDxxcXGSt7e3ZG5uLp0/f/6dsV69elVq1qyZZG5uLpmamkoNGjSQzp49q7HOhwyH/Pvvv0sTJ06UChQoIJmYmEitWrVKNyRgZsMhZ7Qf3hqmslevXpKZmVm69TIbvnT16tVS5cqVJRMTE8nCwkIqW7as9O2330qvXr1KXUepVErTpk2TnJ2dJRMTE6l+/frS7du3073mmTl79qxUuXJlydDQMF28R48elWrXri2ZmJhIlpaWUps2baS7d+9mGPv7zmV0dLQ0ceJEycvLSzI0NJTs7e2lWrVqSXPnzk0dSjGrr2VISIg0bNgwqUSJEpKZmZlkZWUlVa9eXdq+fft7jzc/veszJUlZ/1xJkvoaU7FiRcnIyEiysbGRvL29pSNHjqQ+HhAQILVq1UqysLCQAI2hH/NqOOSsvOclSZKePHki9ezZU3JycpIMDAwkV1dXqXXr1tLOnTvfG4dCoZDmzJkjlShRQjI0NJQcHBykFi1aSFeuXEldJy4uTurXr59kZWUlWVhYSF26dJGCgoIyHSY2o+Hi3d3dpVatWmUYQ26+d1Pcvn1bat++vWRtbS0ZGxtLxYsXlyZPnpzuuYmJiZKNjY1kZWWlMUTo+1y5ckWqXr26ZGhoKBUqVEiaP39+uu+Lq1evSt26dZMKFSokGRkZSQUKFJBat24tXb58Od328uNalNXz9fZxZCYmJkbq3r27ZG1tLQGpn4GU6/2OHTs01s9syOGsfEdnJi4uTpo0aZJUuHBhycDAQHJycpI6deqkMZx/dHS0NGbMGMnFxUUyMDCQihYtKs2ZM0djCOaUbeXkfZ7R63b//n2pXr16komJicaw1e/6rKTw9/eX9PT0pGLFir33dZAk9Xt5/PjxUvny5SULCwvJzMxMKl++vLR8+XKN9by9vTMcqvbt716VSiXNmjVLcnd3l4yMjKSKFStKBw4ceOd39Lx58yQ3NzfJyMhIqlu3rnTjxo10+8nq9ermzZuSt7e3ZGxsLLm6ukozZsyQfv311zwZDjmFUqmUunXrJunr60t79+6VJEk9jPDPP/8slS5dOvX7oXLlytK0adOkyMjId8Zx9+5dqXHjxpK5ublkb28vDRgwIHUKi/cNbZ0S38mTJ6WBAwdKNjY2krm5udSjRw+NaRRSLF26VCpRooRkYGAgOTo6SkOGDJHCw8PTrbdt27bU7zpbW1upR48eGsNYS5L6e2HEiBGSg4ODJJPJ3js08tvDIa9atUqqV69e6mfa09NTGj9+/Htfr6yQSVIOe9/lk8WLFzNq1CgeP36cbgInQfedOHGCBg0asGPHjtS7m4IgCNqmUChwcXGhTZs2/Prrr9oORxBShYSE4OzszJQpUzIdhUsX+Pj4ULhwYebMmZNaQyQIeeWj6eNy6dIlzMzMNDoPCYIgCEJO7N27l+DgYI3Os4KgCzZs2IBSqeTrr7/WdiiCoDN0vo/Lrl27OHHiBFu2bKF///7v7ZwmCIIgCO9z4cIFbt68yYwZM6hYsSLe3t7aDkkQADh27Bh3797lxx9/pF27dqmjcgmC8BEkLuPGjSM6Opp+/fqxYMECbYcjCIIgfAJWrFjBb7/9RoUKFXJ1/iBByKnp06dz9uxZateuzZIlS7QdjiDolI+mj4sgCIIgCIIgCJ+vj6aPiyAIgiAIgiAIny+RuAiCIAiCIAiCoPN0vo9LXlOpVLx69QoLCwtkMpm2wxEEQRAEQRAEnSFJEtHR0bi4uCCXa7fO47NPXF69eoWbm5u2wxAEQRAEQRAEnfXixQsKFiyo1Rg++8TFwsICUJ8MS0tLLUcjCIIgCIIgCLojKioKNze31DKzNn32iUtK8zBLS0uRuAiCIAiCIAg6IywsjEOHDtGsWTNsbW21GosudKkQnfMFQRAEQRAEQdB5n32NiyAIgiAIgiDoIltbW7p166btMHSGqHERBEEQBEEQBB0kSRIqlQoxX7yaSFwEQRAEQRAEQQcFBAQwY8YMAgICtB2KThCJiyAIgiAIgiDoICsrK9q2bYuVlZW2Q9EJoo+LIAiCIAiCIOggU1NTKlSooO0wdMYnkbh4eHhgaWmJXC7HxsaG48ePazskQRAEQRAEQciR+Ph4nj59SpEiRTAxMdF2OFr3SSQuAGfPnsXc3FzbYQiCIAiCIAhCroiIiGDnzp0MHDhQJC58QonLxyogNgDfKF8KWRbCycxJ2+EIgiAIgiAIOsLR0ZEJEyZgYGCg7VB0gtY75586dYo2bdrg4uKCTCZj79696dZZtmwZHh4eGBsbU716dS5evKjxuEwmw9vbm6pVq7Jly5Z8ijzndj/aTbNdzeh3uB/NdjVj96PdebezSD94dkr9W9A94vwIgiAIgvAWuVyOkZERcrnWi+w6Qes1LrGxsZQvX56+ffvSoUOHdI9v27aNsWPHsnLlSqpXr87ChQtp1qwZDx48oECBAgCcOXMGV1dX/P39ady4MWXLlqVcuXL5fSjZEhAbwLRz01BJKgBUkoofzv6AX7QfxW2L42rhSkHzglgaWiKTyXK2s6ubYP8okFQgk0ObRVCpZy4chRZF+kHYE7D1BCtXbUeTM5/i+REEQRAEIcfCw8M5duwYDRs2xMbGRtvhaJ3WE5cWLVrQokWLTB+fP38+AwYMoE+fPgCsXLmSv/76i3Xr1jFhwgQAXF3VBVdnZ2datmzJ1atXdT5x8Y3yTU1aUkhIrL61WmOZuYE5ruauuJq74mLuQkGLgqn/u5q7YmpgmvEOEmMg8DY8PQknZqXZiUpdSPZs9PEW+D+lgn7kS9g/ElImlpJU8OdI9e9CNcG2COiJ6mFBEARB+BypVCpiY2NRqVTvX/kzoPXE5V2SkpK4cuUKEydOTF0ml8tp3Lgx586dA0g9mRYWFsTExHDs2DG6dOmS6TYTExNJTExM/T8qKirvDuAdClkWQi6TayQvMmR4u3kTlhCGX7QfoQmhxCTH8CD8AQ/CH2S4HVtjW1xMHXHVM8VVocQ1LpKC4X64hD7HRZGM4ev1AvT08DXQp1CyAielErb3hDqjoVjzj6dgnBijTloOvXk/pBb0bQpD4braiy27Iv3gxu9wae2bpCWVpE7MAOT66uTFvtibH4fXv40s8j1sQRAEQRDyj52dHT17fqQ3Z/OATicuISEhKJVKHB0dNZY7Ojpy//59AAIDA2nfvj0ASqWSAQMGULVq1Uy3OXv2bKZNm5Z3QWeRk5kTU2tOTW0uJpfJmVpzKh2KvmkuF6+I51XMK/xi/NQ/0X74RT7DL+IpL+ODiFYlEZYQRlhCGLfTbtwUMHVGJkk4yPQxTk7AV18fZDJkksS4sHB6+l2GbV+BmQNU6A4Ve4K9V36/DO+nUsKzk3DjD7i3H5LjMlhJgo2twaUilOsKZTqBuUO+h/peikR4cBCu/QZPjqmTrgzJwLEUhD+HpBgIeaj+eZuFy5skxr4YOBRX/zZ3hMyaF35KTewEQRAEQfisyCQp3e1erZHJZOzZs4d27doB8OrVK1xdXTl79iw1a9ZMXe/bb7/l5MmTXLhwIdv7yKjGxc3NjcjISCwtLXN8DNkVEHCdF/6XcXOugpNTBc0HY0PB//rrnxvw6jpEPE99OEou45W+Pn76+ry0sMfP3B4/I2NeocQvKYJ4ZUKm+3WQG1MuLpYycdGUSkyidFISVgVrqJtclWoLhpk0QcsvgXfUycqtHRDt/2a5dSGIeAG8/baVA68TAZkeeDaE8l9C8ZbaPxb/m+pk5dZ2iA9/s9y9NlT8CpJi4e/vQFKqY2+zUH0eJAmiXkHIAwh++CaBCXkIMYGZ78/ICuyLvklkUpKaZ6fhrzGfRhM7QRAEQfgM+Pv78+uvv9KvXz+cnZ21EkNUVBRWVlZaKyunpdM1Lvb29ujp6REYqFlICwwMxMnpw4YONjIywsjIiGXLlrFs2TKUSmVuhPphrm7Caf8onFIKktUGgqn9m0Ql8kXGz7PxAOfyWDpXwNK5PCWcK4CZncYqkiQRlhDG4eeHmXVhVrpNBKsS+NdYj3+NrVOXFUx+Run/JlH61GRKu9WhZKWBWLjXyrXDfa/oQHWicvMPCLj1ZrmJDZTpCOW+hIJV4Npm2D9as6BfrAXc2Q03t4HfFXh8RP1jaA4lv4ByXaBwPZDr5c+xxIXBrZ3qWANuvllu4aKu4arQHew83ywv3hLCnqqbhaXUhMhk6r+tXNWJWFrx4RDySJ3EBD94/fcDCPeBxEjwu6z+yYykUr+GH3NfJ0EQBEH4xFlaWtK0aVOtJwy6QqdrXACqV69OtWrVWLJkCaDupFSoUCGGDx+e2jk/J7SWRUb6wcIy72gu9JqdFziXf/1TAZzLqQvyWRQQG0CzXc00+tLIZXJ+qfcLAbEB3Am5w53QO/hG+2b4fA+VnFLWRSlTpCmlnapQwrZE5gMCfIikOHXzqRu/azafkhtAsWZQvhsUbQr6hprPi/RLX9BPEfIIbm5XJzFpaqiwcIayndTNyZzK5t4xpFAp4elxde3K/b9AmaRermeoTkwqfg2eDfI2eVIkQugTdRIT8uh1UvMQgu6DKin9+r0OfFx9gwRBEARByFe6VOOi9cQlJiaGx48fA1CxYkXmz59PgwYNsLW1pVChQmzbto1evXqxatUqqlWrxsKFC9m+fTv3799P1/flQ2jtZDw7BRvbpF9epAEUbaJOUpzKgnHOY9r9aPc7+9IARCZGci/sHneCb3PH9yR3wu7ySkpf0JUjo4h1EUrZlaa0XWlK25emuE1xjPWNgSxOqKlSwfMz6qZgd/+EpOg3jxWsBuW7QukOYGqbswOXJHhxQZ3A3N4NCRFvHitQWl0LU7Zzzmscwp7CtS3q5CsqzTwsTmXVyUrZzjk/lpyKeAGLyqVPlEdcA7si2olJEARBEIR3SkhIwNfXl0KFCmFsbKyVGETiksaJEydo0KBBuuW9evViw4YNACxdupQ5c+YQEBBAhQoVWLx4MdWrV8/RftM2FXv48KFu1LjI9GD0rTxpuhMQG8CL6Be4WbhlnlC8JSz8KXevruHO08PcSY7gjpEhQfrpWxfqyfTwsvbCzMCMa0HXkJAyTpCCH6iTlZvbIerlm+XW7ur+KOW6ajafyk2KRHh0RJ3EPPznTW0IMnWNQ7kvoWSbrCeKSbHqpOvab+okLIWxtfo4KvZQ15Lpkqub3jSxS1GiNXTeCHo63WpUEARBED5L/v7+rF69moEDB4o+LuhA4qJtWj0ZaQuSaTtl6xpJUvcbubqJ4Lu7uStL5o6REXeMjLhtak4YigyfJpfJOdTiD5yenFT3W3l17c2DRlZQup26KVihGpmPgpUX4sPh7j64sQ18z75Zrm+sbtJV/kt1nxI9A81RuCxd4OUldb+V23vS1BTJ1OtX/Er9fAPt3BHJkpQmdjFBsHcIKBPViVa7lSBm5RUEQRAEnaJUKomLi8PU1BQ9vXzqp/sWkbjoEK2fjHf11dBFiTFwZ4866Xp5EQkI1NNjr50Ty8zSf6DWBYRQNf71EMZyffBqom4KVqyFbhTww5+/HhBgm+aQw6b24FgafE6/rhWTgXkBzdG8bAqra1bKdwOrgvkeeo49+Fs9JLZKAVX6Qat5+ZtACoIgCIKg87ReVk7js01ctN5U7FMQdA+uboYbvxOQFEkzNxdUbxV8h4eFM8jMS124L9MRzOy1FOx7SJJ6NLcb2+D2TogNzng9fWN1/5uKX4F7rY+/oH9rJ+zqD0hQezQ00f4cR4IgCIIgqEVERHDq1Cnq1auHtbW1VmIQiYsO0aWT8dFSJMKpuey+uoxp9raoXk90Kb0u1I+vMp6epXWwCVxmlAo4uwT+/SH9Y922Q/Fm+R5SnrqyAfaPUv/daArU/Uar4QiCIAiCoBYSEsK+ffto27Yt9vbaufmrS2Vlkbjo0Mn4qL0ebCBALuOFgT4FkxXssLRkjbUFAKMrjaZf2X5aDjIb8nnwBK07uxQOT1L/3WIOVB+o3XgEQRAEQdAJulRWFr1xhdxh5QptFuGkgqoJiTirYESdaQwtPxSAhVcXsvLGSi0HmQ2vjwfZ6347KYMnfIpJC0Ct4eD9nfrvv8fD9a3ajUcQBEEQBOEtn+0YqGn7uAi5pFJP9UzsrwcbkFm5MgQw0DNg0dVFLLu+jGRVMsMrDEf2MfQNeet4PtmkJUX9iZAYDeeXw75hYGgGpdpqOypBEARB+GwFBASwceNGevXqhZNT1qaz+JSJpmI6VP31Kdt4ZyNzL88FoE+ZPoypNObjSF4+N5IEf45QD/ksN4Duf4BXY21HJQiCIAifpZiYGG7cuEH58uUxNzfXSgy6VFYWTcWEfNGrdC8mVJsAwPrb6/nl0i985jmzbpLJ1E3kSrcHVTL88RU8P/v+5wmCIAiCkOvMzc2pXbu21pIWXSMSFyHf9CjZg8k1JgPw273f+PHCj6jSdn4XdINcD9qvhqJNQREPW7poTh4qCIIgCEK+SExMxMfHh8TERG2HohM+28Rl2bJllCpViqpVq2o1Dv/IeM4+CcE/Ml6rceSXLsW7ML3WdGTI2PZgG9PPTRfJiy7SN4Qum8C9DiRFw+YOEHRf21EJgiAIwmclLCyMjRs3EhYWpu1QdILo46LFdnvbLvkycfctVBLIZTC7Q1m6Vi2UrzFoy/4n+/nff/9DJan4wvMLpteajp5cT9thCW9LjIaNX8Crq2DuBH3/AdvC2o5KEARBED4LCoWCqKgoLC0t0dfXzphaoo+LgH9kfGrSAqCSYOLuW59NzUsbzzb8XPdn9GR6/PnkT74/8z0KlULbYQlvM7KAr3ZBgVIQEwCbvoCoV9qOShAEQRA+C/r6+tja2motadE1InHRkmchsalJSwqVBHuv+mknIC1oXrg5c73noi/T5+Czg3x76luSVcnaDkt4m6ktfL1HPSR0hC9sagexIdqOShAEQRA+eZGRkfz9999ERkZqOxSdIBIXLSlsb4Y8g9GAfz70gP4bL+EbGpf/QWlBY/fGLGiwAAO5AUeeH+GbE9+QpEzSdljC2yycoOc+sHSFkAewuT0kiIuoIAiCIOSlpKQkfHx8SEoSZSMQfVy03sfl+923UUoSchnU8bLn7JNQFCoJQ305g+sVYUh9L0wMP/2+H2f8zjDq2CiSVEnUda3LggYLMNIz0nZYwttCHsG65hAXAm414Ovd6okqBUEQBEH4JOlSH5fPNnFZtmwZy5YtQ6lU8vDhQ62dDP/IeHxC4vCwN8XZyoTHQdH88OddzjxWN8VxtTZhcuuSNCvt9MlP2Hju1TlGHhtJgjKBms41WdRwESb6JtoOS3hbwC3Y0Epd41KkAXTfBvoiyRQEQRCET5FIXHSILp2MFJIk8c/tAGYcuMuryAQA6ha1Z2qb0ngV+LQnILoUcIlh/w4jXhFPNadqLGm4BFMDU22HJbztxUV1X5fkWCjRGjpvBD3RcVAQBEEQclNgYCBbtmyhR48eODo6aiUGXSoriz4uOkgmk9GirDP/flOfEQ29MNSXc/pRCM0XnmL2wXvEJH66o29VdarKqiarMDMw42LARYYcHUJscqy2wxLe5lYNum0FPUO4fwD2DQOVmI9HEARBEHKTqakplSpVwtRU3MQFUeOiU1lkZp6HxjLjwF2O3gsCoICFEZNaleSL8i5Zbj6WHBBAks9zDD3cMXByystwc8XN4JsMPjKY6ORoyjmUY2XjlVgYWmg7LOFt9w/Ctq9AUkLV/tByLnziTRoFQRAE4XOiS2VlUePyEXC3M2Ntr6qs610FdztTgqITGfXHdbquOs89/6j3Pj9i504eN2iIb+/ePG7YiIidO/Mh6pwp51CONc3WYGloyc3gmww4PIDIRDGKlc4p0RLarwJkcGkt/Dtd2xEJgiAIwicjKSkJPz8/MarYayJx+Yg0LOHIodH1GN+sOMYGci76hNFq8Wmm7rtNZFzG859EnziJ//8mQ0rFmkqF/5SpJAcE5GPkH6a0XWnWNVuHjZENd0Lv0P9wf8ITwrUdlvC2cp2h9QL132fmw+l52o1HEARBED4RoaGhrF27ltDQUG2HohM+26ZiujKq2Ifyi4hn1l/3+OuWPwC2ZoZ817w4nSu7IZfLiL95k+Bly4g9eSrD5xfauBGz6tXyM+QP9jj8Mf0P9yc0IRQvay/WNl2LnYmdtsMS3vbfYjgyWf13g/9Boepg6wlWrtqNSxAEQRA+UsnJyYSFhWFra4uBgYFWYtClpmKfbeKSQpdOxoc4+ziEqX/e4VFQDACt9UMY8vwE8kvn1SvIZG9qW9Lw2LcXk+LF8zPUHHka+ZT+h/oTHB9MEasizK4zm5jkGApZFsLJTPf77Hw2jv0Ip355879MDm0WQaWe2otJEARBEIQPpktlZZG46NDJ+FDJShW7Nx1EtXEtFQIeAKCSyzFt1RrX4UOJu3QJ/ylTNUZ9MqtVE7c1a5DpfTyTWz6Pek6/Q/0IjAtMXSaXyZlacyodinbQYmRCqsiXsKAMkOayItOD0bdEzYsgCIIgZFNUVBQXL16kWrVqWiun6lJZWfRx+cjFXb2K/4ABlPt5PBUCHqCSyzlUqBr9G31LW7OG/PFKwrx9Byz3/kX07MWYLVmBzMSE2LPnCF60WNvhZ4u7pTtz6s3RWKaSVEw7N42AWN3vs/NZCHuKRtIC6hHHXl7SSjiCIAiC8DFLSEjg7t27JCQkaDsUnSBmjPtIxV2+TPCyZcSde90kTF8fq3ZtsR80iHilGYf33cHfP4op++6w4sQTAqISkCSQy5JY3mc07stnE7p6NSblymLRuLF2DyYbklXpByFQSSpeRL8QTcZ0ga2nunmY9NacLvtHqud8KdFSO3F9LCL9IOyJ6BskCIIgAFCgQAFGjhyp7TB0hqhx+cjEXrzI8169ef7V1+qkRV8f686d8fznH1xmzsTQzY0qHrbsH1GHGe3KYGGsj39kwptBxSQY5m+PUbceALz6bgKJT59p8Yiyp5BlIeSy9G9bZzNnLUQjpGPlqu7TInvdBFEmB6uCkBAJf3SDv8ZBcrx2Y9RVVzfBwjKwsY3699VN2o5IEARBEHSKSFw+ErEXLvL865749uxF3IULYGCAddeueB36B+cZ0zEsqHl3Vk8u4+sa7sztXD7dtpSSxKsu/TCtUgVVbCwvR45AGfNxzE7vZObE1JpT0yUvq2+u5jPvrpUlAbEBXPS/mLdN6yr1VPdp6XUARt+GEVeh5nD1Y5fWwJpGEHQ/7/b/MXr8L/w54k1NlaSC/aPVNTCCIAjCZysoKIilS5cSFBSk7VB0guicr0Mdjt4mSRJxFy4QsnQZcZcvqxcaGGDdqSP2AwZg4OLy3m34R8ZT+6djqN46y4XtTfmpQUFsx/RHERSERbNmuC5cgOwjmfU8IDaAF9Ev8Iv2Y+q5qagkFUPLD2VIhSHaDk1n7X60m2nnpqGSVNoZ1ODRUdg7GGKDQd8Ems+Gyr3VI999rl5cgtNz4eE/GT/e6wAUrpu/MQmCIAg6IyoqivPnz1OjRg3ROR+RuOjUyUghSRJx584RvGw58VeuACAzMMC6cyfsBgzAwDl7zaK2XfLl+923UUoSMhmYGOgRl6QEYKBdDO03zgSFggLjx2PXr2+uH09e2/5gOzPOzwBgeq3ptC/aXssR6RaVpOL8q/MMPjoYKU3HeblMzqGOh/K3b1B0oDp5eXJM/X/JNtBmMZja5l8M2iZJ8OyUOmF5lvE8S2oyGHNH9HURBEEQtEqXysoicdGhkyFJErH/nSVk2TLir10DQGZoiHXnztgN6I+B04cXMP0j4/EJicPD3hQTAz3mHn7Algu+SBJ08D3HgKu7QC6n0Lp1mNWonluHlG8WXV3E2ltr0Zfps6zRMmq51tJ2SFqjklQ8DH/IpYBLXA64zJWgK0QmRma47rpm66jqVDWfA1TBuaXw73RQJYNlQei4Btw/8XMmSfDwkDphSRllTa4P5b6EOmPA96y6eZikfPOcvoegUA2thCsIgiBoX3JyMuHh4djY2IgJKPmME5dly5axbNkylEolDx8+1NrJSA4IINHHB0VgEBFbtxJ/4wbwOmHp2hW7/v0xcCyQJ/u+9TKSyftuc903nG+u/kHjF1dQWVlTbO/ubNfqaJskSUw8M5G/nv6Fqb4pG1tspIRtCW2HlS+UKiX3w+9zOeByaqISnRStsY6xnjEJSs2hFLVS45KW31XY1U89hLJMDt7fQd1xoPeJDXaoUsLdfXB6PgTeUi/TM1L3Bao9EqwLvVk30k/9elxZD7d3gWMZGHjy03tNBEEQhCzx9/dn9erVDBw4EGctlc1E4qJDtHkywnfsJGDKFI2Z7WVGRth82RXbfv0wKJA3CUtaKpXEjisvmH/gFpP/mY9n5CsCXT0psW0rTva60XQuq5KVyQw+OpiLARdxMHFgS8stOJt/XAlYVihUCu6F3uNy4GUuB17mauBVYpJjNNYx1TelkmMlqjhWoYpTFUrZlWL/k/2pfVwAOnh1YFrtado4hDcSo+HgeLjxu/r/QrWgw2qwdtNuXLlBmQw3t8OZBRD6SL3M0Byq9FUPVmDhmPlzY0NhaWWID4fmP0EN0XdLEAThc5SUlERgYCCOjo4YGhpqJQaRuOgQbZ2M5IAAHjdspDGbPTIZHjt3YFK6dL7FkSIiLomVf5ym4YLxWCTHc7hITUwn/I/etT0w0Pt4Bp+LSoqi19+9eBzxGC9rLza22Iil4ceVgL0tWZXM3dC7XA64zKXAS1wPuk5ssuYocOYG5lRyrERVx6pUcapCCdsS6MvT36UPiA1g+fXl7Hm8h1J2pfij1R+6MSDDze1wYCwkRYOxFXyxBEq11XZUHyY5Aa5thv8WQ6SvepmxNVQfDNUHZb0/z+X1cGA0GFnC8EtgIeYpEgRBEPKfSFx0iLZORuz5C/j27p1ueaGNGzGrXi3f4njb7b2HkE0YgxyJBRW78KxqQ6a1LU0tT3utxZRdAbEB9PirB0HxQVR1qsrKxisx1NPOXYqsCogNwDfKl0KWhbAztuN26G1106/Ay1wLuka8QnPuEwtDCyo7VqaKYxWqOlWluE1x9OR6WdpXWEIYTXc2JVGZyKYWm6hYoGJeHFL2hT2Fnf3g1VX1/5X7QLNZYGiq3biyKjEGLq9T99+JCVQvM3NQ165U7QdGFtnbnkoFvzYGvytQtou6H5AgCILwWYmOjubatWtUrFgRC4tsfo/kEpG46BCdqnGRy/E69m+OOuHnhuDlKwhZvJgkuT7j6g7jkY0bbcq7MKllSZysjLUaW1Y9CHtAr396EZscS4vCLfip7k8ZTlypC3Y/2s0PZ39IHfFLX6aPQlJorGNlZKVu9vW66VdR66JZTlQyMvXsVHY/2k0T9ybMrz8/R/HnKkUSHP8R/luo/t+hBHRaB475XwuZZfHhcGE1XFih/hvUAw7UHgWVvgYDkw/ftt9VWNMQkD7KoZH9I+N5FhJLYXsznK1y8DoIgiB8pgIDA/ntt9/46quvcHR8RxPjPCQSFx2izZMRsXMn/lOmqpMXuRzn6dOw7tQpX2PIiKRS8XLYcGKOHyfW2p7+tUYQYWiGmaEeoxoXpU/twh9F87Gzr84y7OgwFJKCvmX6MqbyGG2HlI5/jD/NdjXTGKYYwNrImqpOVVMTFS9rr1xNvB6GP6Tjnx2Ry+T83eFvXMzfPydQvnpyHPYMUtdc6BlBsx+han/dmvMlJgjOLYNLv6qbuAHYeqpHCCvXFfRzqZbvwFi4/Ks6iRt8BvS0M6pMdm275MvE3bdQSSCXwewOZelatdD7nygIgiDoFJG46BBtn4zkgACSnvti6F5I6zUtaSmjonjWuTPJz32RKlVlcq0BXHkZBYBXAXOmf1GaWl6633xs3+N9/O+//wHwv+r/o2uJrlqO6I2guCBGHhvJndA76R77temvVHPO/SaDae+AT74wggv+F+hTug9jq4zN9X3lWGwI7B0Cjw6r/y/eEtou086cL5F+EPZEnZggqfuvXN0IitcjtRUoDXXHQun2kIOasAzFhcHSKhAXCk1mqEci03EZTXyrJ5NxZkIDUfMiCILwkdF2WTkt3b9t/okzcHLCrHo1nUpaAPQsLSm4ZAkyExNkVy+xPPESczqVw87MkMdBMXRfe4HhW6/iHxn//o1pUVuvtgyrMAyAWRdncdz3uJYjUjvsc5gOf3bIMGmRy+QUssz9O9PbLvlS+6djdF9zgdo/HcNdvxkAOx/tJC45Ltf3l2Nm9tB9u3pULT1DeHAQVtSGZ6fzN46rm2BhGdjYBhaUgoXl4OIqddLiWhm+/F1dE1K2U+4nLaBO1JpMV/994id1EqXjnoXEaiQtAEpJwidEB99ngiAIOiw4OJhVq1YRHBys7VB0gkhchEwZFyuG80z1jPRha9bQLPw+x76pT6+a7shlcOCmP43mnWTlySckKVTv2Zr2DCo3iI5FO6KSVHx76ltuBd/SWizRSdF8f/p7vjn5DZGJkZSyK8XwCsNTm4HJZXKm1pya47lVEhVKHgdFc+RuIGtOPWXMH9f4btet1MKkSoINR0xwMStIdFI0+5/sz+mh5Q2ZTD0UcP9/wa4oRL9SJxDHZoJS8f7nZ5UiCSJ84cVF9ZwrF1bD0WmwrRf8OQKkNO9vSQkFq8HXe9VxlWgJ8jy+lJbvDm7VITkWDk/K233lAv+IhAyX3/PPeCJUQRAEIWOGhoYULFhQa0Mh6xrRVEyHqr90VeDsnwjbuBG5mRkeO7ZjVKQId15FMmXfHa48V3dG9nQwY3rbMhRxMNPJzrjJqmRGHBvBf37/YWtsy28tfsPNMn/nCrkUcIlJZybhH+uPXCanf9n+DC43GAM9AwJiA3gR/QI3C7csJy2JCiUvwuLwCYnDJzSWZyGxPA+N41lILK8i48nKJ7t/Kz+2PV2Ch6UH+9rt09kBDABIioW/v1MPNQzq5KHpTFAmqptwWbmmf44yWd0XJToAYgIg2l/9d+rv13/HhWYvFm10lPe/Cau91UnU13vBs0H+7j+LLvmE8dXaCyQqVMgACVJ/A4xpXIyRjbx0YxhuQRAE4b10qaz8ySQucXFxlCxZks6dOzN37twsP0+XToaukpKT8e3Tl7jLlzH09MRj2zb0zM1QqSR2X/Nj9sF7hMYmaTxHFzvjxibH0uefPtwLu4e7pTubW2zGxtgmz/ebpExiybUlbLyzEQkJNws3ZtWZRYUCFVLXyWz0pSSFCt+wOJ6/Tkx8QtMkJxHx6ZrjpGVupI+HvSnudmY4mBmx8ZwPb6++b3hlBp9qT0xyDCsar6COa53cPfi8cHsX7B8NiVFpFsqgWHMwL6CZmMQGQ7qjzoSeoXquFHMn9W8LZzAwfT3CWZptyPRg9K2ME6W89vd3cGEl2HnBkLOgb5T/MbzD3VdRdF19jugEBQ1LFOCHL0rhF55AIVsTNp/3ZeXJJwB0qlyQWe3LYqivw4myIAiCDlAoFMTExGBubo6+fvr52fKDLpWVP5nEZdKkSTx+/Bg3NzeRuOQBRUgIzzp0RBEUhEWzZrguXJB6xzQyPpmZB+6y48pLjefIZfDfhIY6VfMSHBfMVwe/4lXsK8o7lGdt07UY6+fdEM8Pwh4w8cxEHoWrZ07vVKwT46uMx9TgzdwkaUdfkgHVi9hioCfHJzQWv/B3Jydmhnp42Jupf+xM8bAzo7C9Ge52ZtibG2rc1d52yZfvd99GmeYjX83DlgoVTvL7gy3UdqnNyiYrc/01yBO+F2Bd06ytK9dPk4yk/XFO89sZTGwyHrXs6iZ1oiQp1UlLm4VQqWduHk3WJUTCkioQGwSNpkDdb7QTRwZ8QmLptPIcITGJVPWwYVPf6pgYavb52XLhOVP23UGpkqjtZcfyHpWxMvk4RkkTBEHQBn9/f1avXs3AgQNxdnbWSgy6VFb+JBKXR48eMWHCBNq0acPt27dF4pJH4q5d43nPXpCcTIHx47Hr1zf1sbNPQui+5kK657Qq68QPX5TBwUJ37gw/jXjKV39/RXRSNI0KNWKe97wczYmSEaVKyaa7m1hybQnJqmRsjW2ZXms63m7eGuv5hMTSYO6Jd9YJmBrqpSYkKTUohe3N8MggOXkf/8h4fELiUKhUDP3tKtGJChqUkXNF+R0SEvva7qOIdZEPPOp89OyUuq/L2yp8BW7VNJMSU7uc90GJ9FNPkGlbRDs1LWnd2AZ7BoK+CQy/BNb52+QxI4FRCXRccZaX4fGUdLbkj4E1Mk1Ijj8IYviWq8QmKSnmaM663lUpaPORTDIqfJDkgACSfJ5j6OGucwPRCIKuS0xMxM/PD1dXV4yMtFOW0qWystbr6U+dOkWbNm1wcXFBJpOxd+/edOssW7YMDw8PjI2NqV69OhcvXtR4fNy4ccyePTufIv58mVasiOPECQAEzZtH7Pk3iUphezPkGZSf/7oVQN1fjjHzwF2CojPusJvfilgXYXGDxRjIDfjX91/mXJ5DbubvfjF+9Dvcj/lX5pOsSqa+W312f7FbI2lRqSR2X31JpxVnM0xaBtUrwvZBNbk4qRF3pjXj4Ki6LOtRifHNStClihtVPWxxsDDKdj8BZysTanraUbeoA6t6VsZQT87x2yqcDCoDsOXelpwcev6x9YS3++PI9KDB91C5FxRrCs7lwNwhdzrOW7mq+7RoO2kBKNcF3GuDIh7+maDtaIiIS+LrXy/wMjweDztTNvWt9s5alAbFC7B9cE0cLY14GBhD++Vnue0nOu1/qiJ27uRxw0b49u7N44aNiNi5U9shCcJHxcjIiCJFimgtadE1Wk9cYmNjKV++PMuWLcvw8W3btjF27FimTp3K1atXKV++PM2aNSMoKAiAffv2UaxYMYoVK5afYX+2bLp1w6pdO1Cp8Bs7lmR/f0BdIJ7doSx6rwvSejLoU9uDCm7WJCSrWHvmGfV+Oa4zCUwVpyrMqjMLUBfWN93dlONtSpLEn0/+pOOfHbkSeAVTfVOm15rO4gaLsTOxS13n5MNgWi05w9jtNwh5q28QqOe76F3bg2qFbSlgYZxnnZhredozr0t5AJ48rgjA/qf7iUz8CAqRVq7QZpE6WYE3Tbh0IbHIazIZtJyrPub7B+DREa2FEpekoM+GSzwMjMHR0ojN/apnqXa1tIsVe4bWpoSTBcHRiXRZdY5/7wXmQ8RCfkoOCHgzyTKASoX/lKkkBwRoNzBB+IjExMRw7tw5YmJitB2KTtCppmIymYw9e/bQrl271GXVq1enatWqLF26FACVSoWbmxsjRoxgwoQJTJw4kd9++w09PT1iYmJITk7mm2++YcqUKVnapy5Vf30sVAkJ+HTvTuLdexiXK4f7b5uRvx6mL6Upkoe9Kc5WJkiSxKlHISw48pDrLyIAMNKX81UNdwZ5F6GARd71L8mKDbc3MO/KPADmes+lmUezD9pOeEI4M87P4MhzdSGyYoGK/FjnR9ws3jTjue0Xyey/7/HfY/UIVhbG+gyt74WFsT5T991BKUnoyWTM6lAmXwc1WHv6KTP/uotp4UXoGQcwpvIY+pbp+/4n6gJdasKV3w5NgnNLwaYwDD0PBvn7WUpUKOm/8TKnH4VgZWLAjsE1KeZoka1tRCckM3TLVU4/CkEug2lflObrmh55E7CQ72L++48X/fqnW15o40bMquf+BLuC8CkKCAhg/fr19OnTByctNbXUpbKyTicuSUlJmJqasnPnTo1kplevXkRERLBv3z6N52/YsOG9fVwSExNJTExM/T8qKgo3NzedOBkfk6SXL3nWsROqyEisu3TBefq0d66fksAsPPqQa74RgDqB6VHdncHeRShgqZ0ERpIkZl+cze/3f8dQbsiapmuo5FgpW9s443eGyf9NJiQ+BH2ZPsMqDqNP6T6p/WZehMUx9/AD9l1/BYChnpyeNd0Z1sALG7OME778NvPAXTbc2oGJy05sDAtwrOsh9OXaGb1EyKLEaFhaVT2CWv3vof53+bZrpUpi5O/X+OuWP6aGevzWvzqVCn3YCH3JShX/23ObbZdfADCwXhEmNC+BPKO2p8JHQ1Kp8Bs9hujDhzUfkMvxOvav6OsiCB8RXUpctN5U7F1CQkJQKpU4OjpqLHd0dCTgA6uaZ8+ejZWVVeqPm5v2O7Z+jAwLFsR17lyQyYjYvp2IXbveub5MJsO7mAO7h9RiU99qVCxkTaJCxbr/nlH3l+NM33+XoKj8b0Imk8n4rup3NHRrSJIqiRHHRvA08mmWnhuXHMfM8zMZcnQIIfEheFp5srXVVvqX7Y+eXI/w2CRmHLhLo3knU5OWdhVc+Pcbb/7XulRq0gJv+p5oawS271uWpKl7c1QKM8KTgth4Q0cnpBTeMLKAZj+q/z4zH8Ke5ctuJUnif3tv89ctfwz0ZKz6uvIHJy0ABnpyfupYlvHNigOw+tRThm29SkKyMrdC/ugkBwQQe/7CR92kKmjuPHXSIpNp9DOzbN1aJC2CIHwwnU5csqt3797vHVFs4sSJREZGpv68ePEin6L79JjXrYPDyBEABEybTvyt2+99jkwmo16aBKbSWwnMtP138j2B0ZPr8VO9nyjnUI6opCiGHFEnIu9yK/gWXQ90ZduDbQB8VfIr/mj9ByXtSpKQrGT5icfUm3OcX888I0mpoo6XPQdG1GHhlxVxs9W9EZTkchkLulTBEfWkhosuredFWJyWoxLeq3QHKOwNioR866g/59ADfr/oi0wGC7tWpG5RhxxvUyaTMayBF4u+rIChnpy/bwfQbc15QmMS3//kT8yn0Jk99NdfCVu3DgDnWbPwOvYv1l27AKAIFH2ZBCE7QkJCWLduHSEh7y6XfC50OnGxt7dHT0+PwLcudIGBgR/czs/IyAhLS0s2b95MjRo1aNSoUW6E+tmyGzQI8wYNkJKSeDlqJIrw8Cw9LyWB2TWkFpv7vUlg1v/nQ91fjvPDn3cIzMcExkTfhCUNl1DIohCvYl8x9OhQ4pLTF9yTVcmsuL6Cr//+Gp8oHwqYFmB1k9V8V+07DORGbL/8ggZzT/DLPw+ITlBQ0tmSTX2r8Vv/6pRxtcq34/kQRvp6rO0wAiQ9JKNndN+8k/AMBg8QdEhKR325ATz8B+4fzNPdrTn1lOUn1JNIzmpfllblcndOgbYVXNncTz0q2TXfCNovP8vT4M+nQ2pyQAD+k6d81J3ZI3bvIWiO+gZigfHjsW7fDgMnJ+wGDAQg7tIlFMHB2gxRED4q+vr62Nraam3ySV2j04mLoaEhlStX5t9//01dplKp+Pfff6lZs2aOtj1s2DDu3r3LpUuXchrmZ00ml+Py808YuBdC8cqfl8OHE3P2XJa/aGUyGXWLvklgKrvbkKhQseFs/icwtsa2rGi8AhsjG+6F3WPcyXEoVIrUx30ifej1dy+W31iOUlLSonALdn+xmxrONTh+P4iWi07z7c6b+Ecm4Gptwvwu5flrRB3qFcv5Hen8UsTGhUZu6okdg2VH6bfx0mfdZOej4FAMag1X//33d5CUNzVl2y+/4MeD9wD4tnlxulXLmwEkqhexY/fQWrjZmuAbFkeHFWe5+CwsT/alS5KDgng18Xt4u9upSkXi0/xpBphT0ceO4z95MgC2fftqzPVlWNAV43LlQKUi6oj2RsIThI+NtbU17dq1w9raWtuh6AStJy4xMTFcv36d69evA/Ds2TOuX7+Or68vAGPHjmXNmjVs3LiRe/fuMWTIEGJjY+nTp48WoxbS0rO0pOCSJWBgQPyVq7zo2zfbTRxSEpidg2vyW7/qVHa3IemtBCYgMu8TmEKWhVjSaAnGesac9jvN96e/54L/BdbeXEuXA124FXILC0MLfq77M7/U+wWfIIlua87TZ8MlHgRGY2ViwPctS/DvN950qFTwo+xgPKBCLwAMLG9y7ZUvI36/hlKlM2N4CBmpNx4sC0Kkr7q/Sy7753YAE3bdBNSd54d4e+b6PtLydDBnz9DalHezJiIuma/WXmD/jVd5uk9tkRQKwjZu5GmLlsSdO5fhOqFr16KKj8/nyLIn7soV/MaMAaUSq3btKDDum3TrWLZoAUD0wb/zOzxB+GgplUpiY2NRKsVNRNCBUcVOnDhBgwYN0i3v1asXGzZsAGDp0qXMmTOHgIAAKlSowOLFi6levXqO9rts2TKWLVuGUqnk4cOHOjFSwscsOSCAxw0aat4tzMHoMZIk8d/jUBYefcjl5+rmZ4b6crpXK8Rgb08kJJ6FxFLY3ixPOrT/6/svo4+PTre8unN1ZtaeSWKCBXMOPeDATf/U2PrU8mBofS+sTDOffO9j0fPvnlwLuoYitBHxQU34qkYhZrQtk2dzygi54O6fsP1r0DNUD49slzvJxdnHIfRef4kkpYouVQryc8dy+fY+iE9SMnrbNQ7dUTcX/q55CQZ7F/lk3odxly8TMH0GiQ8fAmBctixmtWsTunq1urlYSsd2pRKTihVxW7EcPR2865rw4AHPv/oaVXQ05vXrU3DJYmQG6a+Dyf7+6u8JmQyvEycwcCyghWgF4ePi7+/P6tWrGThwIM7Ouds8N6t0aVQxrScu2qZLJ+NjFnv+Ar69e6dbntPx+iVJ4uyTUBYceZPA6MllqFQSEiCXwewOZXN93pOA2ACa7myKlGZeexky/mj+J9svxLLlwnOSlRIyGbSv6Mo3TYvjaq2dEcHywmGfw3xz8hvM9K0IvDMOSWXA+GbFGdbAS9uhCZmRJNjSCR4fBc9G8NUudcE3B268iKD7mvPEJilpVtqRZd0roa+XvxX1SpXErIP3+PWMurlUt2qFmNG2dL7HkZsUwcEEzZ1L5L4/AdCzssLhm7FYd+qETC4nOSCApOe+GLoXItnPjxdDhqKKisLQ05NCa1Zj4OKi5SN4I+nlS553644iOBiTSpUo9Ota5CaZXwt9unUn/to1HL//HtueX+djpILwcUpISOD58+e4u7tjbKydqSN0qawsEhcdOhkfs+SAAB43bPSmU+lrhTZtxKxazicaS0lgfvnnPjdepp/Z3c3GBAcLI2zNDLExNcTm9W9bM4PXv9XLbE0NsTIxeG8Trov+F+l3uF/6OF4NJibSA4B6xRyY0LwEpVw+vfeNQqWg5e6W+Mf606zAKHaeVN/lmdOpHJ2riCHEdVboE1heA5RJ0GUTlGr7wZt6HBRN55XnCI9LppanHet6V8XYQC97G4n0g7AnYOuZ4wlCN/z3jOkH7qKSwLuYA8t6VMLc6OPqrCopFIRv/Z3gxYtRxcSATIZ15844jBmNvk3mQ0onPnqE74CBKAIC0C9QALc1azAuXiwfI8+YIjSU5917kPT8OUZFi+L+22b0rN49CEnYpk0EzpqNSaVKeGzdkk+RCoKQE7pUVv5sExfRVCz3Rezcif+UqRrJi0HBgnhs+wN9O7tc2cfZxyF0X3shR9uQy8Da1BAbU4M3ic7rZCcl0ZEbRPLD9e6QpsZFkmTEPp5AaUc3JrYoSW0v+xwejW5bf3s986/Mp5hNMaoazGTlyafoyWWs7VWFBsVFEw+ddexHOPULWLrC8EtgaJbtTfhFxNNpxVn8IxMoX9CKLQNqZD9JuLoJ9o8CSQUyObRZBJV6ZjuWtI7cDWTk79eIT1ZS0tmSdb2raG3uo+yKu3JF3SzswQMAjMuUwWnKZEzKlcvS85P9/fEdMICkx0+QW1hQcNnSXLkp9KGUMTH49uxFwt27GLi44P7771lq+pUcGMjj+g1AkvA6fgwDLTV9EYSPRWxsLPfu3aNkyZKYmWX/ep4bROKiQ3TpZHwKUpo4yK0s8RsxkuQXLzAuVw73jRve2Xwgq/wj46n90zHS9hWXy2BZ90rIZDLC45IIi00iPDaJsDj17/C45NTl0QmKzDf+FgOrSxg570Ymk5AkGYn+Hfifdy++ruHxUXa6z67IxEia7GxCvCKetU3Xsu2UEbuv+WFqqMcfA2tQrqC1tkMUMpIUB8urQ4Qv1B4NTaZl6+khMYl0WXmOpyGxeBUwZ/ugmtimmSw1SyJfwoIypE38kenB6Fs5rnm5+TKCvhsuExKTiJOlMet6V9XpWk9FSAhBc+cRuXcv8LpZ2NixWHfqiEwvezVYyshIXgwdRvyVK8gMDXGZMwfLZk3zIOp3UyUl8WLgIOLOn0fPxgb3rVswKlw4y8/3+eor4i9focCE77DLoImxIAhv+Pv7s2bNGgYMGCD6uCASF506GZ+axGfPeP5lN5SRkVg0aYzrwoXZ/qLOyLZLvny/+zZKSUJPJmNWhzJZ7uOSpFAREZ9EeGyyOsF5K9GJiFMvfxEWx9OQWGT6kcgNQ1Al2SMprPh9QA1qeuZO7dHHYOb5mWx7sI0Gbg2YW28h/TZe4vSjEOzNDdk1pBbudtq5+yO8x4O/4fcvQa4PQ86CQ/EsPS06IZlua85z2y8KV2sTdg6pmb0aDUmCBwfh8GR1E7G39ToAhetmfXuZeBEWR58Nl3gcFIOZoR4z25fF0dIozwbr+BCSQkH4H9sIXrQIVXS0ullYp044jB3zzmZh76NKSMBv3Dhijv4LMhmO/5uEbY8euRj5u0lKJX5jvyH60CHkpqYU2rgRk7JlsrWNsC1bCJwxE+Py5Si8bVseRSoIQm7RpbKySFx06GR8iuKuXMG3dx+k5GRse/XCcWLuzO7tHxmPT0gcHvameVJQyahmR08m48yEBjpTMMoPTyOf0nZvW2TI+Kv9X9gYOdN11TnuvIrCw86UXUNqYWdupO0whYxs/RIe/g2F60HPP9/bUT8hWUmvdRe58CwMOzNDdgyuSREH86ztS6WCe/vg1FwIvJ35en0PQ6GcjQiZIjI+mcGbr3DuaWjqsrwarCO74q5eI2DGDBLvqee9MS5dWt0srHz5XNm+pFQSMH0GEa8L/XaDB+EwalSej7YmSRIB06YR8cc2ZAYGuK1ehdkHzKmmCA7mUT1vkCQ8jx7FsGDOauEEQchbulRW/niHZcmhZcuWUapUKapWrartUD5pppUr4/zTbADCNm4kbPNvubJdZysTanra5VkS4WxlwuwOZdF7XRBIqdn5nJIWgCJWRajtWhsJia33t2JupM/6PlUpaGOCT2gcfTdeJi4p683vhHzU4ifQN4Znp+D2rneuqlCqGL71GheehWFupM/GvtWylrSolHBzh3pAgB291UmLoTnUGQvNZqubh6X15wiID//wY0rDysSAnzuW1QxHgu9338Y/UjtznihCQ3n1/SSed+9O4r17yK2scPphKh7bt+Va0gIg09PD6Yep2I8cAUDoylX4/+9/SIq8/SyGLF1GxB/bQCbDZc4vH5S0AOg7OGD6+rs3+tA/uRmiIHxyQkND+e233wgNDX3/yp+BzzZxGTZsGHfv3uXSpUvaDuWTZ9WqFQ5jxwIQOHs20ceOaTmirOlatRBnJjTg9wE1ODOhgdbv4mrLVyW/AmDP4z3EJMVQwMKYjX2rYW1qwI0XEQzfeg2FUvWerQj5zsYD6r6eBPDQJEiMznA1lUri2103OXovECN9OWt7VaGM67tHhkKZDNe2wNKqsLs/hDwAIyvw/k7dj6XxVKg5VP13rwPQ/xhYuKjX++MrUCTmyiG+jEifoCglCZ+QuFzZflZJSiVhW7fypEVLInfvBsCqU0c8/z6IzZdf5koT2bfJZDIchg7FacZ0kMuJ3LWbl8OGo4rLm2MP27KFkGXLAHCaMhnL5s1ztD3LlurJKKP+FomLILyLXC7HyMgIufyzLbJrEK+CkC/sBvTHuksXUKnwG/sN8bduaTukLMnrmp2PQS2XWhS2Kkxsciz7nuwD1DOb/9qrKkb6co7dD2LSntt85q1OdVOtkWBTGGIC4MRP6R6WJImZf91j91U/9OQylnWvRI0i7+jDpUiCKxtgSWXYN1Tdj8XEBhr+D8bcggbfg6ntm/WtXNV9WgpWhq92gpElPD8De4ekGzr9QxS2N+PtcTJkMvCwN83xtrMq7to1nnXuTOD0GaiiojAqVRKPP37HZeZM9G1t37+BHLLp3JmCS5cgMzIi5uRJnvfpgyI8d2q1UkQdPEjgzB8BsB8+HJtu3XK8TYsmTUAuJ+H2bZJevMjx9gThU2VjY0Pnzp2xyUHfuE+JSFyEfCGTyXCaMhmzunWREhJ4MWQoSS/9tB2WkAVymZweJdSdf7fc24JSpQSgsrsNS7tXQi6DbZdfsPDoI22G+Unwj4zn7JOQ3GvqZGAMLeeq/z6/AgLvauxn9sF7rPtPPbHjnE7laFzKMePtJCfAxTWwuKJ6iOOI52BqD42nqWtV6o0H4/fU0jiWhq6b1QMG3N4F//6Q48N7u0kngLWpAXZmed/vShEWxqtJk3jerTuJd+8ht7TEccpkCu/YgUmFCnm+/7QsGjak0Pr16FlZkXDjJs+7dc+162vMf//h990EkCRsunfDftjQXNmuvp0dZjXU/Z1ErYsgZE6lUpGUlIQqF272fAo+28RF9HHJfzJ9fVwXLMCoRAmUISG8GDQIZWT6ySQF3dPGsw0Whha8iH7Bab/TqcublHJkRjv1iEKL/n3E7xd9tRXiR2/bJV9q/3SM7msuUPunY2y7lEuvZdHGUKI1SEo4OI5tF5+n7mf1aXXSMqV1KTpUKpj+uUlxcG45LCoPB8dB1Eswd1L3Xxl9C+qMBiOLrMdSpD58sVT993+L1MlQDqU06dzYpyq2ZoaExyaz7XLe3MFPDggg5uw5QlatUjcL2/W6WViHDnj+fRDb7t3zpFlYVphWqoj71i3ouziT5OODT7cvSbh/P0fbjL91i5cjRkJyMhYtmuM4aVKuDgBg8bq5WdQ/f+faNgXhUxMYGMjs2bMJDAzUdig6QYwqpkMjJXwukgMD8enSFUVgIKbVqlFo7RpkhtmcJ0LId/Mvz2f9nfVUd6rO2mZrNR87/IDFxx4jl8GanlVoVDKTO/dChjIaxQ6ggps1xgZyZKgLizLZm8HBZMg0BgpLKVDKSLuOermtIpAf/fphJCUwJmkIe1RvhiSWAWcnNtRsDpkYA5fWwrmlEBusXmZZUJ2oVPxaXZOTEyfnwPGZ6okpu26BEi1ztr3XNp3zYcq+OxSwMOLUtw0wNsi9JCJi5078J09RD/n8mlHJkjhNmYxpxYq5tp+cSg4M5MWAgSQ+fIjc3JyCS5em1mxkR+LTZzzv0QNleDhmtWpScOVK5Ll8nVaEh/OoTl1QKvH8528MPTxydfuC8CmIj4/n8ePHeHl5YZIL8+F9CF0qK4vERYdOxuck4cEDnnfvgSo2Fqu2X+D80095PpSnkDP+Mf602N0CpaRk1xe7KGZTLPUxSZL4dudNdlx5ibGBnCXdKmJmpK9T82rost1XXzJ2+4083ccQvT/5zuAPgiVLGiXOI4o3c/Ckzk+UEAkXV8O5ZW9G/7J2h7pjoXx30M+lgqskwf6RcHUT6JtA7wNQsEqON5ukUNFg7gn8IuL5vmUJBtbzzIVgIdH3BU+bNdNIWpDJ8Dx6BENX3RvKVxkVxcthw4m7dAmZgQEuv/yMZYsWWX5+cmAgPt26oXjlj3GZMhTasAE987yZs8m3/wBiz5zBYfQo7AcPzpN9CIKQM7pUVhaJiw6djM9NzOkzvBg8GJRK7IcOxeH10J6C7vrmxDccfn6YDkU7MK2W5ozsyUoVAzZd5sSD4NRlujKvhq5SqSQ2nPXh53/ukajQvBTLZfDDF6WxMTVEAo3BD1L+lJDe/C29madekqQ3c9ZL6vXkqmQan+iATbwP6xXNmKboBaiH+v5vdAWc7m6ACyvVyQuArad6VLJyXUDPIPcPXqlQT5L5+Ii6v0z/I2BbJMeb3XH5BeN33sTa1IDT3zbAwjhnscffvoPfqFEk+6XvM1Jo40bMqlfL0fbziioxkVfffkf0oUPqiSonTsS259fvfZ4yIoLnX39N4qPHGHp4qJuf5eEgAxG7duM/aRJGxYpR5M99ebYfQfhYxcfH8+jRI4oWLSpqXBCJi06djM9R+I4dBEyeAoDzrFlYd2iv5YiEd7kWdI2ef/fEUG7Ikc5HsDXWLNA8CYqh0fyTGss+x4k7s8InJJZvd97kok8YAF4FzHgaHItKejNvUK4nfE+Ow+Z2KCWYlNyPm1JR5pZ6TKkX2yDp9XDJ9sXVne3LdAB5HvfXSIyBDS3B/4Y6Uep3BMzeMapZFiiUKpotPMWT4FhGNy7K6MbF3v+kDEhJSQSvWEHo6jWgVKZfQS7H69i/GDg55SjevCQplQT+OIvwrVsB9eiODmPHZlq7rYqPx7dvP+KvXUO/QAHct27N88khlZGRPKxTF5KTKfLXAYw8c6eWTBA+Ff7+/qxevZqBAwfi7OyslRh0qaz82XbOF3SDTefO2A0aBID/lCnEnjun5YiEd6ngUIHSdqVJUiWx8+HOdI8HRiekW6aNeTV0mUolseG/Z7RYdJqLPmGYGuoxs10Zjozx5r8JDfN23iDPBuBSET0Z/GT4K38ZTaDUk7XqpMWxDHTeCEPPQ7nOeZ+0ABiZQ/cdYFVIPbTy719Ccs5GVNPXkzO2SXEA1p5+RlhsUra3EX/nDs86dSZ0xUpQKrFs2YICEydCyjwKcjnO06fpdNIC6okqHSf/D4fRowEIXbMW/wkTkZKT060rJSfjN3oM8deuIbe0xG3tmnyZ0V7PygqzWuqJLMXoYoKQnpOTE5MmTcJJx683+eWzTVzEqGK6w2HUSCxbtQKFgpcjRpLw8KG2Q8p3yQEBxJ6/QHJAgLZDeSeZTEaPkuqhkbfd30ayUrMAlNG8GgAu1jnszP2J8A2No9ua8/yw/y7xyUpqFrHj0Oh6fFXDHZlMlvfzBkX6qWs3Xks9VV8sgUGnoXS7N4Xz/GLhqJ7jxdgKXl6E3QNAlUENRza0KONEaRdLYhIVrDz5JMvPk5KSCF68BJ+uX5L48CF6tra4LlyI6/z52PXqidexfym0cSNex/7FulOnHMWYX2QyGfaDB+H844+gp0fkvn28GDIUVWxs6jqSSoX///5HzMmTyIyNcVu5AuNiH1ZT9SFS+t+I0cUEIT2ZTIa+vr7oB/zaZ5u4DBs2jLt373Lp0iVth/LZk8nlOM+ehUmVyqhiYngxeDDJQUHaDivfROzcyeOGjfDt3ZvHDRsRsTN9TYYuae7RHHsTe4Ligzj8/LDGYxnNqwEw59ADVG8PmfUZUakkNp/zofmiU1x4FoaJgR7T25ZmS//quNnm32SJhD0BKYO5AGwK53/CkpZDcfjyd9AzhHv74dCkHG1OLpcxrpm61mXjWR8Co9LXBL4t4d49nnXpSsjy5aBQYNG8OUUO7MeyebPUdQycnDCrXk3na1oyYt2xAwWXLUVmbEzsmTM879Wb+Hv3iDl/noCpPxC570/Q08N14QJMK1XK19gsGjVCZmBA0uMnJD4S80EJQlrh4eFs27aN8FyeWPZjJfq46FC7vc+dMiICn27dSXr2DONSpXDfvAm5Wd6MZKMrkgMCeNywkeYs4jIZtn37YODsgp6FOXILy9e/LZCbW6j/Njf/oPkikgMCSPJ5jqGHe44KXytvrGTZ9WWUsSvD1lZb090J8o+MxyckjqDoBL7ZfgOFSqJPbQ+mtC712d01ehEWx3e7bnL2SSgA1QrbMqdTOdzttPDejvSDhWU0kxeZnnpOFqvcbxaU7ffbrZ2wq5/672azoOawD963JEl0XnmOy8/D+apGIWa2K5vxesnJhKxaTcjKlaBQoGdjg9OUydkahetjEn/jBi8GDUYZEZHuMeefZmPdrl2+xwTwYugwYo4dw37oEBxGjtRKDIKgi8LCwvjnn39o3rw5tnk4UMa76FJZWSQuOnQyBEh68QKfrl+iDAvD3NtbfYdQX1/bYeWZmPPnedG7zwc9V25qqk5mLMzRM7dAbpGS1LxeljbRsbAg7soVwtatVw8/9bqN/oc2dwmND6XpzqYkqZLY3GIzFQpUyHTdvdf8GL3tOgATWpRgsPfn0flWkiS2XvRl1l/3iE1SYmwg57vmJehV0wN5Ru3p8svVTbB/tHpCSpketFkIlXrm+m4idu7Ef8pUdVKenffbf4vgyBRABp03qJuvfaALT0Ppuvo8+nIZx76pTyE7zdqthPv3eTXxexLv3QPAomlTnKZOQd8uZwME6LrYi5fw7fnWOZfJ8Dp+TGu1SZH79/Nq/LcYFi5MkYN/fXY3OARBl+lSWfnTLREKHyVDNzfcli/jea/exJw8SeCsWThOnvxJfolJSiVRe/emf0Amw6J5M1CqUMVEo4yOQRUdjTJG/VtKTARAFReHKi4OPmQ2XZUK/ylTMatT54MKKnYmdrQs0pK9j/ey5d6WdyYu7Sq6EhKTyMy/7vHT3/dxMDeiY+UMZmn/hLwMj2PCrluceRwCQFUPG+Z0Ko+HvQ7UIFbqCZ6NIOypevjhPKpp0ZisUaXC/3+TkWRyrNu0fveEs7VGQsQLuLQGdg8Ec0dwr/lBcVQvYke9Yg6cehjMwn8fMr9LBeB1LcuaNYQsX6GuZbG2xnHy/7Bs2fKTvNako8qguaAkkfTcV2uJi3mDhsgMDUl69ozEBw8wLlFCK3EIgqDbROIi6ByTChVwmfMLfqNGE771dwwKumHX98NqJXSVxhwLoJ7qPIs1IVJSUmoSo4yOeZ3cRKN6629lzOtl0dEkBQSQ/PTpW0GoSHz0+IMLKl+V/Iq9j/dy5PkRAmIDcDLLfDv96xYhKDqR1aee8u2um9iaG9KgeIEP2q8ukySJPy694Me/7hGTqMBIX863zUvQu5YHetqsZXmblWueJCwpIv/6S3OyxtcCJk0i+JdfsGjRHKvWrTGpVAnZ231rZDJo8TNEvYIHf8Ef3dTDJNsX/aBYxjUtxqmHwey55scQb0/cIvzxnziRhLt3AbBo0hinqVPRt7f/oO3niUg/dX8kW888OU+GHu7qPk1pExi5HEN37c23pGduhrl3PaKPHCXq739E4iIIr+nCcMi65LNtKrZs2TKWLVuGUqnk4cOHOlH9JWgK3bCBoJ9+BsB14UKNTrIfM2V0NC+HDtOY1dqkYkWSnvti6F4oT+54ZtiXBjAqVgy3lSswcHH5oO32PdSXSwGX6FumL2Mqj3nnuiqVxNjt19l7/RUmBnr8PrAGFdysP2i/uuhVRDwTdt/i1EP1BJyV3W2Y06kcRRzMtRxZ/orYsxf///0v/dwnMhlyG2tUYW86mBq4uGDZqhWWbVqnH8UqKQ42tga/K2DtDv2PgvmHJbuDN1/h8C0/JkVdptZ/eyE5GT0rKxz/9z8sW7fSrVqWq5tg/yh1PySZHNosUteSSZJ6tDVJCSpFmr9TfhSa/0tvL1dprBOxcQX+266CJAOZhHP/Vlh/M0+rhx518CB+Y7/BoFAhPA/9o1vnRRC0JC4ujvv371OiRAlMTfNxMJc0dKmpWLYSl2fPnnH69GmeP39OXFwcDg4OVKxYkZo1a2Js/HEOd6pLJ0PQJEkSgTN/JHzLFmRGRhTasB7TihW1HVaOJAcG8WLgQBIfPEBuZkbBZUsxq1EjX/b9dp8DmZERUnw8ejY2uC5YgFmN6tne5jHfY4w6PgpLQ0uOdj6Kif67h/FNUqjot/ESpx+FYGtmyM7BNT/6gr0kSey4/JIZB+4SnajAUF/O+KbF6VunsG7VsuQxSZIIWbGCkMVLADAuW5aEO3c0+rhYtW9P3IULRO4/QPThwxpD8hoVL45Vm9ZYtmqFQcpdxZhg+LUxhPuAS0Xo/RcYZr+53YNz13kwZjxFI14CYN6oEc4/TEXfwSHHx52rMho8AVAPXJ379xiT4+QkRetjaKHAwEyWZ4M0ZJUqNpaHtesgJSTgsWsnJqVLay0WQRDe0KWycpYSly1btrBo0SIuX76Mo6MjLi4umJiYEBYWxpMnTzA2NqZHjx589913uLu750fcuUaXToaQnqRU8nL4CGKOH0fPxgaPP37H8CN7j6VIfPqMF/37k/zqFXoO9hRavRrjkiXzNYbkgIDUmh2USl6MGEHi3Xugp0eB8eOw7dUrW3c5lSolrfa0wi/Gj8k1JtOleJf3Pic2UUG3Nee5+TKSgjYm7B5SiwKWH+eNj4DIBCbsvsmJB+palgpu1sztXB6vAh93MpZdUnIyAdOnE7FDPZS33YD+OIwZgyIoKNOaRFVCAjEnThC5/wAxp05ByqSIMhmmVapg2aY1ls2aoacIgbWNIT4MijWHrltAL2utnCWFgtC1vxKybBlScjLRBiYcb/o1384drZt382/tgl19P+CJMpDrqycNlem9/luu/i3TUy9PfUwPFIkQ+SL9ZnodgMJ1c3wYOfFy1GiiDx3Crn8/Cowbp9VYBEEXxMfH4+Pjg4eHByYmeTTH13voUln5vYlLxYoVMTQ0pFevXrRp0wY3NzeNxxMTEzl37hx//PEHu3btYvny5XTu3DlPg85NunQyhIyp4uJ4/nVPEu7cwdDdHfc/fkffxkbbYWVL2iFIDd3dcft1LYYFtd9BXZWQQMDUqeo5HADL1q1xnjEdeTYujpvvbuaXS79QxKoIe9vuzVKBMCQmkU4rzuITGkdJZ0u2DaqBpbHBBx9HfpMkiV1X/Zi2/w7RCepalrFNijGgbpHPqpYF1HfJX44eQ+zp0yCX4zT5f9h065atbSgjIog6dJio/fuJu3w5dbnMwAAz73pY1SyJ+ePpyKUEqNIXWs1X94V5h8THj3k1YSIJt28DoFe7Hj0sGxBsZMG2gTWoXkTHRg57dEQ9FHRCpOZymRwGnFDXhMjkGSQoeu99LdLJcFhsGYy+o9UaF4Cofw7hN3o0Bq6ueB49opsJpiDkI13o46JLZeX3Ji6HDh2iWbOs9S0IDQ3Fx8eHypUr50pw+UGXToaQOUVwMD5dvyT51StMKlem0LpfkRsZaTusLIk5eZKXo8cgxcdjXLYsbqtWoq+lsdgzIkkS4b9tIfCnn0CpxKhkSQouWZzlxCo6KZrGOxoTp4hjVeNV1HKtlaXn+YbG0WHFWUJiEqlRxJaNfathpJ/9uWnyW2BUAhN33+LYffUkqeULWjG3c3mKOlpoObL8pwgO5sWgwSTcvYvM2BjX+fOwaNgwR9tMfvWKyL/+Imr/ARIfPkxdLjc1xsIxDCv3OEy7fYfMO+O78ZJCQei69YQsWYKUnIzc0hKnSd9j+cUX/G/vbbZc8KWqhw3bB9XUjUKxSgknZsOpOer/rdwgyu91H5e8G65aY1hsAGNrGPcQ9LV7XVXFx6ubi8XF4bFjOyZlM55/RxA+FyqVisTERIyMjJBraaJgXSorf7ad81Po0skQ3i3x0SN8uvdAFR2NeYP62PTsiVHhwjo9i3XE7j34T54MSiVmdepQcNFCnZ1UM/biRfxGj0EZFoaelRUu8+dhXrt2lp7708Wf2HJvC3Vd67K88fIs7/O2XyRfrj5PTKKCVmWdWdKtonbnOMmEf2Q8z4JjuR8QxcKjj4hKUGCoJ2d0k6IMrFsEfT0tzjqvJYlPnvBiwEB100dbW9xWrsCkXLlc3UfCg4dEHdhP5IG/UPj7py7XN1Zi2bguln3GYlyqFIrAQJJ8ngMSQfMXkHDzJgDm3t44TZ+OgaO6U39AZALec46TqFCxvk9V7Y9sFxOkrmV5dkr9f9X+6ok3Y0PydLjqVJF+EHwf9gyB2EBoOReqDci7/WWR39hviDp4ENs+fXD87ltthyMInz1dKitnK3GJiorKeCMyGUZGRhi+a2x+HaVLJ0N4v9jz5/Ht1//NiEU5nEgxr0iSROiatQTPnw+AVdsvcJ45E5mBbjeHSvb35+XIUSTcugVyOQXGjsG2X7/33pn2jfKl9Z7WSEj82e5PClsVzvI+zzwKoc+GiyQrJXrX8mBqm1K6cSf8tW2XfJm4+xaqNFfKsq5WzOtSnmKfYS0LQNzly7wYNhxVZKS66eOa1RgWyruhdCWVivgrV4jcf4CoA3tQxSWnPqZnb48yNFRj+GW5hQWO33+PVbu26d5Lsw7eY/Wpp5R2sWT/8DraS5Sfn4UdfSAmAAzM4IvFUFZL17GLa+DgOLBwgZHXwEC7fc6ijhzBb8RI9J2d8Tr2r05dDwQhv4WHh3P8+HEaNGiAjZaayetSWTlbtwmtra2xsbFJ92NtbY2JiQnu7u5MnToVVUaTWwlCLjD08NAc0vf1RIpJae7GapukUhE4a3Zq0mLbry/Os2frfNICYODsjPtvm7Hq0AFUKoLmzsNv7FiN0Z8yUsiyEN4FvQHYem9rtvZZp6g9815PDLjhrA8rTj75oNjzwtPgGCbs0kxaZMDyHhU/26Ql6u+/8e3TF1VkJCYVKuD+x+95mrQAyORyTKtWxXn6NIqevUjBbl5YuMWDXEIZEpJuzphC637Fun27DAu8g709MTfS586rKP65E5CncWdIkuDMQtjQWp20OJSAgce1l7SAuimaZUGIfqVuQqZl5vXqITc1ReHvT/z169oORxC0SqVSERUVJcrWr2UrcdmwYQMuLi58//337N27l7179/L999/j6urKihUrGDhwIIsXL+ann37Kq3iFz1ySz/P0E9upVPj26Uv0iRNou+WjKimJV+PGEb55MwAFvvsOx/Hj00+yp8PkRkY4/zgTp6lTwMCA6L//wefLbiQ9f/7O531V6isA9j3ZR1RSxrWzmfmivAtTWpcC4Jd/HrDjcgYjHuUjpUpi+6UXdFh+Nt0gtBLwMjxBG2FplSRJhK5bj9+YsUjJyVg0aUyhDevzfaAMubExFt/voOCXxSlYOyzDdVRx8Zk+39bMkH511DWC8w4/QKnKx2tGfDj80R2OTlX3LSnXFQYcA4fi+RdDRvSNoO5Y9d+n50Fy5q9ffpAbGWHeqBEA0f/8o9VYBEHb7Ozs6N27N3Z2OjagiJZkqzS1ceNG5s2bx4wZM2jTpg1t2rRhxowZzJ07l23btjFp0iQWL17Mpk3av2MjfJpSZ3x+S7KPDy8HD8Gn65fEnDqllQRGGRPDi0GDiDr4NxgY4DJnDnZ9eud7HLlBJpNh060b7hs3oOdgT+KjRzzr3EU9bG0mqjlVw8vai3hFPHse7cn2PvvWKcwg7yIATNh9i2P3Az84/pw49TCYVotP8+2um0TEJ6d7XE8mw8NeO5OAaYukVBL44yyCfvkFAJuvv8Z14ULk2pq/y8AYuv2OcZGCpJvfJAszwPevWxhrUwOeBMey55pf3sWZ1qtrsMobHhwEPUNovRDar/qgeWnyRMWv1QMDxATAlQ3ajgbLFi0A9ShjkrjTLAjCa9lKXM6ePUvFDCYArFixIufOnQOgTp06+Pr65k50eWjZsmWUKlWKqlWrajsUIRsMnJxwnj7tTfIil1Ng4gTs+vdDZmJCws2bvBg4CJ8vvyTm9Jl8S2AUwcE879mTuHPnkZua4rZyBVZtWufLvvOSaaVKFN65C5MKFVBFRfFi0GBCVq7MsCAhk8n4utTXgLq5mEKlyPb+JjQvQYdKrihVEkO3XOWab/j7n5RL7vlH8fWvF+i57iL3A6KxNNbnf61KMrNdGfReNznSk8mY1aEMzlbaGUtfG1Tx8bwcNYrw334DXtcifj8RmZ6WR4AztcVg8G6cayaA7PXnXCbh3K/5ewfssDA2YIi3JwALjjwkUaHMuzglCS79Cr82hYjnYO0O/Y5AlT7ZH8Y4L+kbQt1v1H+fWaD1WhezOrWRm5ujCAwk/to1rcYiCNoUEBDArFmzCAjQQtNWHZStzvnFihWjQ4cO6ZqCTZgwgT179vDgwQMuX75M27Zt8fPLp7tYOaRLHY6ErEs7kWJKIUURGkro2l8J//13pAR1Ux6TihVxGDEc05p5N/Rpko8Pvv0HkPzypXp0pdWrMSnzac34rEpKIvDHWURs2waARZPGOM/+CT1zzbvFCYoEmuxsQkRiBEPLD6V90fY4mWVv1LdkpYr+Gy9z8mEwNqYG7BxSC0+HvJvQMTAqgXmHH7DjykskCQz0ZPSq6cHwhl5Ym6oHHPGPjMcnJA4Pe9PPKmlRhIXxcshQ4m/cQGZggMsvP6feCdcJr+cjSY7lzQzwphJ0Wgel278zMYhPUuI95zhB0YlMb1uanjU9cj++xBg4MAZubVf/X7wltFsOJjo6D5UiCZZUhkhf9ehmNYdpNZxX300gct8+bHr0wGny/7QaiyBoS2xsLLdv36ZMmTKYaWlUUl0qK2crcfnzzz/p3LkzJUqUSK2puHz5Mvfv32fnzp20bt2aFStW8OjRI+a/7pis63TpZAi5QxEcrE5g/vgDKTERAJPKldUJTPXquZrAxN+6zYtBg1CGhWHg5kahtWswdHfPte3rmvAdOwicPgMpORlDT08KLlmCURHNEcSGHh3Kab/TAMhlcqbWnEqHoh2ytZ/YRAXd15znxstIXK1N2D20Fo6WudssKTZRwapTT1lz6inxyeo77q3KOfNts+K42+lI8x0tSnr+HN+BA0l+7ovcygq3ZUsxrVJF22FpenYKNrbJ+DFbT6jcC8p3B3OHDFfZfM6Hyfvu4GBhxKnxDTAxzMVapOAHsO1rCHmgno+l8VSoNVK3alkycmUj7B8JZgVg1A0w1F6zyJiTJ3kxaDB6DvYUPXFC+7V8gvCZ0qWycrbncXn27BmrVq3i4euJwYoXL86gQYPw8PDIi/jynC6dDCF3JQcFEbpmLRHbtiElJQFgWqUK9iNHYFatWo63H3PmP16OHIkUF4dxqVK4rV6Fvr19jrer6+Jv3ODlyFEoAgORm5vj8svPqZMOBsQG0GxnM1S8aUoml8k51PFQtmteQmMS6bTyHM9CYinhZMG2QTWxMsn5yGwKpYodV14y7/BDQmLUiW1ldxu+b1mSyu46eic8n8XfuMGLwUNQhodj4OKC25rVGHl6ajus9DKaAR4ZGJhAcpz6X7kBlGwNlXuDRz2NPnJJChUN553gZXg8E1qUYLB3Lh3jzR2wfxQkx4K5E3ReD+5Zm5hV65TJ6lqXiOfQdCbUGqG1UKSkJB7WrYcqMpJCmzbmynVbED42iYmJvHjxAjc3N4y0NPG2LpWVxQSUOnQyhLyRHBhI6Oo1RGzfjpSs7mxtWr26ugbmA+8gR+7fz6uJ34NCgVmtmrguXpKu2dSnTBESwsvRo4m/fAUA+2HDsB82lEuBl+l3uF+69dc1W0dVp+z3J3sRFkeHFWcJjk6kemFbNvathrHBh911lSSJEw+CmXXwHo+CYgBwtzNlQvMSNC/jJOaKeC3633/x+2YcUkKCOiFftRJ9h4xrLHRC2hngU2aaL90Bbu9SdzJ/dfXNujYeUKkXVOgBFo4A7Lrykm923MDKxIDT3zXA0jgHybEiEf6ZCJd/Vf9fuB50/BXMtTzRZXZd3Qx/DgdTexh9U6sDCLyaNInIXbux7vYlzlOnai0OQdAWf39/Vq9ezcCBA3F2dtZKDLpUVs524hIREcGvv/7KvXv3AChdujR9+/bFysoqTwLMa7p0MoS8lezvT8jq1UTs3AUpCUzNGjiMGIFppUpZ3k7ouvWpoytZtmqFy+xZyD7CyVdzSkpOJvDnX1I7bZvXr4/+D9/Q/HBHVFLOa1xS3HkVSddV54lJVNCijBNLu1dCL5uTBt55Fcmsg/f473EoANamBoxqVJQe1d0x1P94hqrOa2FbthD44yxQqTCrV5eCCxYg11Kb6myJ9Mt8pnn/m3B1I9zcDomvh+mW66v7m1TujbJwfZotOsPjoBhGNirK2CbFPiyGcB/Y3gv8r6v/r/ct1J8A8o+weZMyGZZWUR9Tk+lQe5TWQok5fYYXAwagZ2dH0ZMnkOnray0WQdAGpVJJbGwsZmZm6GmpuaQulZWzlbhcvnyZZs2aYWJiQrXXVbaXLl0iPj6ew4cPUykbhT9doUsnQ8gfya9eEbJqNRG7doFCPfKVWe3aOIwYjkmFCpk+T1KpCPplDmEbNgBg26snBb777qOaoyUvROzZS8DUqUhJSRi6u3Pv23ZM8luRmrx0K96N72t8n6N9nH0SQu91l0hSquhZ051pX5TOUg2Jf2Q8cw89ZPc1dcd7Qz05fWp7MLSBV640O/tUSCoVwfPnE7pWXVNg3bkTTlOnflqFxKRYuLNHXQvz8tKb5dbuPHBtz1dXihJnaM+pbxtgZ57N5hgP/oY9gyAhEkxsocMaKNo4V8PPd9e2wL6hYGoHo26CUd4NkPEuUnIyj+rWQxkRQaH16zCrWVMrcQjC50yXysrZSlzq1q2Ll5cXa9asQf/1F5pCoaB///48ffqUU++Y40FX6dLJEPJX0ks/QletJGLP3jcJTN266gSmXDmNdaWkJF5N+h9R+/cDUGD8OGz79hXNi16Lv32HlyNGoPD3R25qitGYweyM+4/9SZepVKYJCxosyPE+Dtx8xYjfryFJMK5pMYY3LJrputEJyaw6+ZQ1p5+SqFAnUF+Ud2F8s+K42X5ec7C8jyopCf8JE4k6eBAAh1EjsRs8+NN+bwfcVtfC3NgGiZEAKJFzRFmZ8BLd6Natd9ZqSpQKODYd/luk/r9gVei8AawK5lno+UapgGVV1TVZjX+AOmO0For/5ClE7NiBdZcu6uHwBeEzEhkZyalTp6hXr57WWjfpUlk5W4mLiYkJ165do0SJEhrL7969S5UqVYiLi8v1APOaLp0MQTuSXrwgZOVKIvfuA6V6dCkz73o4DB+BvoM9ifcfELJ2LfGXL4O+Pi4/zsSqbVstR617FGFh+I0eQ9zFi6nLVDJY10yfKT+fwcoo5xfcDf8944f9dwH4uWNZulbVnGhQoVTx+6UXLDzykNBY9YAM1Txs+b5VSSq4Wed4/58aZWQkL4ePIO7SJdDXx3nmjP+zd59RUV1dAIbfO0PvIB1BVOy9Yexd7AWNSYw1li9RE40pmmZJYjQxGktINLEbNTbsvfdYY8Euglgo0jsMM/P9GCUiWKh3gPOsNcsw5dw9DIG77zlnb2x69ZI7rKKTngzXNutmYe6fzrw7w9INg4ZDoN4AsHLN+bUJYbDhPbh3Qvd14w90y6oMStCy0YtrYPP7ulmkcZfB2FKWMJJOnSJk6HsobWyodOwokqGYLRVKj8jISDZt2kTv3r2xl6kAkD6dK+cqcXFycmLlypV07Ngxy/179uxh0KBBhIfL0+k6P/TpwxDklX7vHpG/LyBu61bIqVOzoSHuv/lh0aJF0QdXTKQ/eEBgh466pntPaIFUDwdcGrfCpEYNTGrUwLhKFRR53Bf00+4b/HY4EIUEM3xrU9bOFM8yZlx7lMD0XdcJfJwEQAV7cyZ2rkqH6k4le/YgD1RhYSSfO8/jX+ejCr6Hwtwct3lzsWjWTO7QZKMNv8bO5T/SPGkv1tKTi3CSAip30lUk82qvS1aiAyEpEnZNgKQIMLKEnvN1fWNKGnUG+Hnr3nO7Sf81qCxi2owMbrdshTo6GvdFi7BoXnp/TgVBDvp0rpyrxOWjjz5i06ZN/PzzzzRtqivteOLECT777DP69OnDnDlzCivOQqNPH4agH9KDg4mYPZuEvfuyPqBQ4HXwwCu7cpdmSf+cJmTIkFc/0cAA40qVMK1Z479kpnJlFK9R6lGr1fLZhstsOP8gx8ftzI0Y174S73h7YKgs3fuPchK7YQOh30zKTC4VlpaUW7kCk+dm0kujM0HRDFx4hG4GZ5jmcR6TR//NwmBiC6mx6FLxJxxrQL8VYO9V1KEWnUtrYdNIXdPMsZfBRJ6/k6FTphD791qs+/jiOm2aLDEIQmmlT+fKuUpc0tPT+eyzz1iwYAEZT/YEGBoa8sEHHzBjxgxZ6kvHxsbSvn17MjIyyMjIYOzYsYwYMeK1X69PH4agP150Au6xfDnmjUUvgRdRhYVxp227LDNWagkW+UiMcnoTkzsPSb16FXVsbPYXP0lmTGpUx7RmzZcmMyHRSbT86XC2+we+UY7POlXJX0nbEkwVFsadNm2zzIiJhDyrwUvOcOTWY3rXc+OXtqa6howX/9JtvM9CgjHnSnbSAqBRg19jiLoNbb+Glp/JEkbS6TOEDB6MwtqayseOlspKjkLpFB4ezooVKxg0aBBOTk6yxKBP58q5KhljZGTE3LlzmT59OoGBgQBUrFgRMzP5NrtaWlpy9OhRzMzMSEpKombNmvj6+lKmTBnZYhKKPyPPcrpGdc8uGVMoMCrn8eIXCRg6O+Py7VRCJ03Wfe8UCo68U4UDHrcpX8uOsV9MRavVkvHoESkBV0m9+t9NHRtL2vXrpF2/TtyGjboBn01mnllm9iAmBQD7lFhcEyN5ZGFPpKkNXWq5iKTlJZL/vZg1aQHQaEi/FyISlyc+7ViFI7ces/niQ95v1ZIqnX6Aim1hVZ/nnqmFhNCSn7golNBqAvgPh5O/gvdIMCn6DcJmDRugdLBH/TiSpFOnsGjVqshjEAQ5mJub88Ybb2BeHErTF4E81bo0MzOjVq1aBR1LniiVyszEKS0tDa1WSynvqSkUgJxOwF2+nSpO7l6DTd++mDdvTvq9EIzKeRCSegmOfMq2wG2MqTsGpUKJoZsbhm5uWPno9stlJjNXr5J69RqpAQEvTWbsK1RkZmQ61aODUAAaJObXexNP+7byvXE9p46PJ3LevOwPiIQ8i1plrelc05ldAWHM3neThQMbgmM13X6XZ/oTISl1fWNKg5q+cHQmRN6E0wuh1edFHoKkVGLV0YeYVauI37VbJC5CqWFhYUELsbc20yuXivn6+r72YP7+/rkO4OjRo8ycOZPz588TGhrKpk2b6PVcVRs/Pz9mzpxJWFgYderUYf78+Zl9ZEC3XKxVq1bcvn2bmTNnMnr06Nc+vj5Nfwn6RxUWlnkCLpKWvElTp9FmXRsS0hP4s+OfvOHyxmu9Llsy83RmJiYm5+dLCiodEkuecqJJTSVk+HBSzp1HsrBAm5ycJSG36dtX7hD1yu3wBHzmHEWjhS2jm1HH3QYurIBt40Cr1iUt3edA/UGFcvzQuBSCIpMob2+Oi7VpoRwj165sgI3DdLMtYy+DqU2Rh5B87hz3BgxEYWlJpRPH81zgQxCKk/T0dMLCwnB2dsZIpp95fTpXfuXOVWtr69e+5UVSUhJ16tTBz88vx8fXrl3L+PHjmTx5MhcuXKBOnTr4+PgQERGR+RwbGxsuXbpEUFAQq1evLpbVzQT9ZOjsjHljb3EynA/GSmM6e3YGYOudra/9OkmSdLMyHTvi+PE4PBb9SaWTJ/A6eAD7MWOyP1+rIfL3BWhVqgKLvSTQqtU8/PRTUs6dR2FhgedfK/E6eACP5cvxOnhAJC05qORkSa96bgD8vPem7s76g2DcFRi8XfdvISUta8+G0GzGQfr/eZpmMw6y9mxIoRwn12r0Boequr0+pxfIEoJp/foYODqiSUgg6fgJWWIQhKIWFRXF0qVLiYqKkjsUvZCrzfmFTZKkbDMujRs3plGjRvz6668AaDQa3N3d+fDDD5k4cWK2MUaNGkXbtm3p+4I/xmlpaaSlpWV+HR8fj7u7u15kkYJQUl2MuMjAXQMxNTDlUL9DmBvmb61uTkUAnjLyqojzN5NEEQV0s1Zhk6cQu24dkpER7ov+xNxbfF9ex/3oZNrOOoxKrWXNiDdoUrHw902GRCfRaubhLNuQlJLE8Ylt9GPmJcAfNgwFY2tdXxcZZl3CfviBmBUrserRHbeffiry4wtCUcvIyCA2NhYbG5vM5u9FrVjNuMgpPT2d8+fP0759+8z7FAoF7du359SpU4Cu2kJCQgLwX3fRKlWqvHDM6dOnZ5klcnd3L9w3IQgCdRzqUM6qHCkZKey/tz/f4z3dg4Tiya8whQJrX1+Udnak3wkkZPBgHn76GapnZmZLo8j5vxK7bh1IEq4/zxRJSy6425nx9pMGpz/vvVloeycfxaaw5kwI/1t5jo6/HM1WO0Gt1XL9UXyhHDvXqvcCx+qQFgf//CZLCFaddLO3iQcOonnmIqQglFQGBgbY29vLlrTom1cmLp06deKff/555UAJCQn8+OOPL1zylReRkZGo1eps5d+cnJwICwsD4N69e7Ro0YI6derQokULPvzww5cWDvjiiy+Ii4vLvN2/f7/A4hUEIWeSJNGjYg8Atga+/nKxl7Hp2zfLkifXH6ZRcddObPu/A5JE/Pbt3O3chejly9E+Kd9emkSvXk3kb7qTS+fJk7B6rnGw8Gpj2nphbKDg/L0YDt0smCQ4LUPN8duRTNtxjQ6zj9B0xkG+8L/CnqvhpKpyaHwLfL7xMrsDQuUvPKNQ6CqMAfzzO6TkvN+sMJnWrYOBiwuapCSSjh0r8uMLQlGLj49nz549xMfryQUMmb0yfXvzzTfp06cP1tbWdO/enYYNG+Lq6oqJiQkxMTFcu3aN48ePs3PnTrp27crMmTOLIu5M3t7eXLx48bWfb2xsjLGxMX5+fvj5+aFWqwsvOEEQMnWr0I35/87nTNgZHiU+wtXCNd9jGjo7Z9l/pLS2xnnSJKx9+xD23bekXrpM+PQZxG70x3nSN5g1bJjvYxYH8bt3E/7d9wDYjxmD7dtvyxxR8eRkZcKQpp4sPHqXn/fconVlRxQKKdfj3ItK4sitxxy5+ZiTgVGkqP77u6OQoK67Da2rONKqsgPXQuP5elMAaq0WhQS2ZkZEJqbz/l8XaFfVkak9a1DWVr4WBFTrAU41ITwATvnpersUIUmhwMrHh+hly4jfuQvLZ1ZkCEJJlJaWRmBgIPXr15c7FL3wWntc0tLSWL9+PWvXruX48ePExekacUmSRPXq1fHx8WHYsGFUq1Ytf8E8t8clPT0dMzMzNmzYkGXfy+DBg4mNjWXLli35Oh7o17o9QSjphu0ZxpmwM3xY70NG1h5ZqMfSajTEbtzI41mzMxteWvfsieNnn2Jgb1+ox5ZT0j//cH/ESLQqFTbvvI3zpElIUu5PtgWdmKR0Wvx0iMS0DH7tX49utV+dcKekq/nnbpQuWbn1mKDIpCyPO1ga06qyA62rONDcyx4bs6yVgkLjUgiOTMbT3gxbMyP8Dt1hwZFAVGotpoZKPu5QiaHNymOolGm197WtsG4gGFnq9rqY2RXp4VMuXya431tIZmZUPnEchake7P8RhBJMn86V87Q5Py4ujpSUFMqUKYOhYcE1e3vR5nxvb2/mz58P6Dbne3h4MGbMmBw35+eWPn0YglDSbbmzha9PfE05q3Js67WtSE6oM2JiePzLHGLXrwetFoWFBQ5jx2L7zttIJWzNcOq1a9wbOAhNUhKWHTvi9stsJKVS7rCKvTn7bzFn/20q2Juz9+OWGDyXMGi1WgIf62ZVDt+M4HRQNOkZ/y37MlBINChnS6sqDrSu7Eg1F8tc/+zfiUjgy00BnAmKBqCqsyU/+Naivodt/t9gbmk0sLAlhF+B5uOh/eQiPbxWqyWwfQdUDx/iNmcOVp18ivT4glDa6NO5suxVxRITE7lz5w4A9erVY/bs2bRp0wY7Ozs8PDxYu3YtgwcPZuHChXh7ezNnzhzWrVvHjRs3su19yY1nl4rdunVLLz4MQSjpklRJtFnXhpSMFFZ2Xkldx7pFduyUy5cJm/otqVevAmBcrRrO33yDWf16RRZDYUoPCSG4/7uoIyMx8/bG/c8/UBgbyxpTWFIYIfEheFh54GxefEuKJ6SqaPnTIWKSVYxsWYGhzTyxNDHk5J3IzFmVBzEpWV7jam1CqyqOtK7iQNOKZbA0yf9FPq1Wy/rzD/hh53Vik1VIErzb2IPPfKpibVpwFxFfy/XtsPZdMLLQ9XUxL/yqa8+K+PlnohYtxrJTJ8rO+aVIjy0IRSkiIoLVq1fTv39/HB0dZYlBJC7POHz4MG3atMl2/+DBg1m2bBkAv/76a2YDyrp16zJv3jwaN25cIMfXpw9DEEqDL499yba72+hXuR/fNPmmSI+tVauJXb+eiF/moHmy5NXa1xfHT8ZjUKZoT7wKUkZkJMH930UVEoJx1aqUW7kCpaWlrDH53/Zn6qmpaLQaFJKCyU0m41vp9Rsa65vRqy6w40po5tdKCdTP/PU0UipoXMEucwlYRQeLQptRjEpM44edN9h44QGgW3o2qVt1utV2KbplgVqtbtYl7DI0GwcdphbNcZ9ICbhKcN++SCYmVD55AoWZjPt+BKEQJSQkcPbsWRo1aoSlTL/X9elcWfbERW769GEIQmlw6tEpRu4biaWRJYf6HcJYWfSzAhnR0UTMmkXcRn8AFFZWOH48Dpt+/Yrd0ip1YiL3Bg0i7dp1DMuWxXPNagwcHGSNKSwpDJ+NPmi0/y2XUkgK9vTZUyxnXkLjUmg24yCa5/5alrUxoV01J1pVceCNCmUwMyrapYcnAyP5elMAd5/soWlV2YHvetbEo0wRncTf2Al/vwOG5rq9LuZFt3dMq9US6NMJVUgIbrNnYdWlS5EdWxBKG306V9brPi6Fyc/Pj+rVq9OoUSO5QxGEUsXb2RsnMycS0hM4cv+ILDEY2NnhOm0a5dasxrhaNTTx8YRN/Zbgfm+RcvmyLDHlhSY9nQdjPiTt2nWUdnZ4LF4ke9ICEBIfkiVpAdBoNdxPKJ7l54Mik7IlLQAz36zL1J41aVvVqciTFoCmFe3ZNa4F49pXwkip4Mitx3T45Qi/Hb6DSp1zaeUCVaUzuNQFVRKcmFv4x3uGJElYdeoEQPyu3UV6bEEoSiqVitDQUFQqldyh6IUCS1yK28TN6NGjuXbtGmfPnpU7FEEoVZQKJd0rdgcKrqdLXpnVq0f5Detx+vprFJaWpF69SvBbbxP6zSQyYoq+R0VuaNVqHn0+geR//kFhZob7H39gVK6c3GEB4GCWPXmSkHC3LJ4Nf8vbm/N8FWSlJOFpL//yJGMDJePaV2bXuBY0qVCGtAwNP+2+Sbd5xzl/L7pwDy5J0PoL3X+fXQSJjwv3eM+x6qxLXBKPHkWdmPSKZwtC8RQZGckff/xBZGSk3KHohVwlLi/q0aJWq+nfv3+BBCQIQsn3NHE5/vA4kSny/jKWlErsBrxLxV07se7VC7RaYtev526nzsSsW4dWo0EVFkbSP6dRPWl8KzetVkv4tB9I2L0bDA0p++t8TGvWkDssQBeb38XsjYglSSIqJUqGiPLPxdqU6b61UD7ZP6KUJH7wrYmLtf6U4a3oYMHqEY2Z3a8OduZG3AxPoM/vp/jC/wpxyYV4pbayD7jWB1UynJhTeMfJgXHVqhh5eqJNSyPx0KEiPbYgFBV7e3tGjhyJfQku458budrj4ujoyPTp0xk2bFjmfWq1mrfffpuAgACuX79eKEEWJn1atycIpUn/Hf25EnmFzxt9zsDqA+UOJ1PyuXOEffsdabduAWBQtiwZDx/qNiMrFLh8OxWbvn1ljTHy9995PHceSJJufX/nzrLG86wVV1cw89xMDBQGzGo5CwsjC5YELOHEoxO4W7qzrts6LIws5A4zT57tr6JPScvzYpLSmbHrBmvP6Zbm2VsY8U236vSo41o4m/dv7YXVb4KBKYy9BJZ5r/iZWxFz5xL1+wIs2rbF/bfsCbMgCPmnT+fKuZpx2bFjB59++ikbNmwAICMjgzfffJOrV69yqJhd7RB7XARBXj0q9gDkXy72PLOGDSnvvxGnLyYimZqS8eCBLmkB0GgInTRZ1pmXmHXrdEkL4PTll3qVtJwPP8/s87MB+KzhZ7Qt1xZvF29+bPkjruau3E+4z9RTU4vd0uKnXKxNaVKxjF4nLQC25kb82Lc26/7XBC9HCyIT0xn790UGLj5DcGQhLKmq1AHcGkJGSpHvdXn685907BjqhIQiPbYgFIWEhAQOHjxIgvj5BnKZuDRq1IiNGzfy3nvvsXXrVvr06cPNmzc5dOgQzs7Fq1KM2OMiCPLq5NkJA4UBN6JvcDP6ptzhZCEZGGA3eDAu03/I/qBGQ9SSJahjY4s8roT9+wmbois7W+b9/2E3cECRx/Aij5Mf8+mRT1Fr1XQp34V3qr6T+Zi1sTU/tfoJA8mA3cG72Xh7o4yRlh7e5e3Y+VELPvOpgrGBguN3Iuk45yjzD9wmLUNdcAd6dq/LucWQUHSJvXGlShhVrIhWpSLhwIEiO64gFJWUlBQuX75MSkrKq59cCuR6c37btm1ZsWIFffr0ISgoiCNHjuSrEaQgCKWTjYkNrcu2BmBb4DZ5g3kBs7p1QZH912TMipXcbtGSB+M+JvHIEbQZGYUeS/LZszwc/wloNNi82ReHsWML/ZivS6VR8emRT4lMicTLxovJTSZnW5JUx6EOH9b/EIAZZ2ZwO+a2HKGWOkYGCka38WLvxy1pUcme9AwNs/bdosvcY+y8/IiTgZGExhXACZFXOyjbCDJS4fic/I/3miRJypx1SRDVxYQSyNHRkXHjxsnWfFLfvHKPi69vzg3D/vnnH7y8vLJsFvL39y/Y6IqAPq3bE4TS5lDIIT469BFlTMqw/839GCiKvqTsq8Ru2EDopMmg0YBCgVUnH9IC75J2879ZIqWDPdY9emDTqxfGlSoVeAypN29xb8AANAkJWLRrR9m5c5AM9Od79dPZn1h5bSUWhhas6boGT2vPHJ+n0WoYdWAUJx6eoIJ1BdZ0XYOZofyVuUoLrVbL1kuP+G77dSIT0zLvV0gw3bcWbzXyyN8B7hyAv3xBaazb62Llks+IX09aYCB3u3YDQ0MqHz+G0tq6SI4rCEVBFRZGevA9jDzLYSjT6iZ9Old+5YyLtbV1jjcfHx8qVqyY5b7iROxxEQT5NXdrjq2xLVGpUZx8dFLucHJk07cvXgcP4LF8OV4HD+A2ezYVtmym/CZ/bAcNRGlri/pxJNGLl3C3ew+C+r5J9KpVBbaULP3BQ+4PH44mIQHTBg1wm/WzXiUtu4N3s/LaSgC+b/b9C5MW0DWh/KH5DziYOnA37i4zzswooigF0M1O9KzrxurhjbPcr9HCF/5X8j/zUrEtuDcGdRoc/yV/Y+WCccWKGFeuDCoVCfvFcjGh5IjdsIHzvXrzx7q1nO/Vm9gne8xLs1xVFSuJ9CmLFITSaMaZGay6vopOnp2Y2Srnkuv6TJueTuLRo8Ru2kzikSPwZNmYZGiIRdu2WPfuhUXz5nlKNjKio7nX/13Sg4MxrlSJcn+t1KuryXdj7/L2jrdJyUjhvZrv8XGDj1/rdWfDzjJ873A0Wg3TW0ynW4VuhRyp8KyTgZH0//N0tvsnd6/O0Gbl8zd44CFY2evJrMtFsHLN33iv6WmlPZNatSg7f55sV6YFoaCowsK407YdycbG3Kpahco3bmKWlobXwQNF/vOtT+fKBdaAUhAEIS+eVhc7GHKQ+PR4maPJPcnICMv27XH3+5VKR4/g9OUXGFerptssvGcPD97/gNut2xD+40+kPimx/Do0SUnc/9/7pAcHY+jqivuiP/UqaUlSJTHu8DhSMlLwdvbmw3ofvvZrGzk34n+1/wfAd6e+4178vcIKU8hBTg01AabtuMa6s/fzN3iF1uDRRDfrcmx2/sbKBe2TPVWpV65wp207cWVaKPbS7twBjQazlBTq/nsRs5QU0GhIvxcid2iyylXiEh4ezsCBA3F1dcXAwAClUpnlJgiCkFvV7KrhZeNFuiadPcF75A4nXwzs7LAbNIgKm/wpv8kfu8GDUNrZoY6MJHrpUoJ69CSoT1+i/1pFRkzMC8fRpqfz4KOxpF65gtLGBvdFizDUoyIoWq2WSScmERQXhKOZIz+1/CnX+5P+V/t/NHJuRHJGMp8e+ZQ0ddqrXyQUiOcbaiokqFXWmgwNfL7xMt9vv4Zak8fFGM9WGLuwHOIeFFDUL6YKCyNy3vz/7tBoCP1mEun385mECYJMUm/cIPyH6QCoFQriLS1RKxSgUGBULp970Yq5XC0V69y5MyEhIYwZMwYXF5dsVWN69uxZ4AEWNn2a/hKE0mppwFJmn59NPcd6rOi8Qu5wCpRWpSLx2DHiNm0i4dDhzKVkGBpi2aYN1r16YdGiOZKhIaqwMNKCgohZtZrE/fuRTE0pt3wZprVry/oenrf86nJ+PvczBgoDlvospa5j3TyNE5EcQd+tfYlJi+Gdqu/wZeMvCzZQ4aWebajpbGXC3AO3mbNfV+2tTRUH5r1TD0sTw9wPrNXCsq5w7wQ0HAbdCnfmJemf04QMGZLtfkUZOxw+GIVNH18Upvrde0cQALQZGUQtWsxjPz9QqZDMzYk2NmafT0c67N1L9Y8/lqUBsj6dK+cqcbG0tOTYsWPUrVu3EEMqWvr0YQhCaRWRHEGHDR3QaDXs6L0DD6uSeUUpIzqa+O07iN28ibRr1zPvV5Ypg0mVKiSdOvVfs0uFAveFC7Bo0UKmaHN2Luwcw/cOR61V82XjL7P0a8mLYw+OMerAKADmtJ5Du3LtCiJMIY92XA7lk/UXSVVpqOxkwaJBjfAok4fKb0HHYHk3UBjCR/+CjXvBB/vE070AaDQ5Pq58MhNq2/8dlOLvvKCn0u4G8eiLiaReugyARft2uEydSlpyMg+uXqVsjRqYuxfe/0cvo0/nyrlaKubu7l5sOx4/T1QVEwT94WjmSBOXJgBsDdwqczSFR7eUbCAV/P0pv2UzdkOGoCxTBnVUFEknT/6XtABotYVSWjk/Hic/5rOjn6HWqulaoStvV3k732O2KNuCoTWGAvDNyW94mPgw32MKede1tgvr/tcEJytjboUn0tPvOKfvRuV+oPItwLMFaFRwbFbBB/oMQ2dnXL6d+l/PJYUCp8mTcJ48CUM3N9TR0TyeM4c7bdsRMWs2GZGR+Ttg3EMIOqr7VxDySavREL1iBUG9e5N66TIKS0tcf5xB2fnzMShTBnN3d6p06iRb0qJvcjXjsnfvXmbNmsXChQvx9PQsxLCKjj5lkYJQmu28u5MJxybgZuHGTt+dKKTSUTtEq1IRuWgxkXPnZnvMY/lyzBt7yxBVdiqNiuF7hnMh4gJeNl6s6rKqwHqwqDQqhuwawuXIy9R2qM2yTsswVORhiZJQYMLiUhm58hyXH8RhqJT4vlfN3Pd5CT4By7qAZAC+C3Wb9q3dCidgnvS7uBeCUTmPzKpL2owM4nfuJOrPP0m7fQcAydgYmz6+2L03DKOyuYznwgrYNha0GpAU0H0u1B9U0G9FKCVUDx/y6MuvSD6tq/Jn3rQpLj9My1I1LDExkX///Zd69ephYWEhS5z6dK6cq8TF1taW5ORkMjIyMDMzw9Aw6x+W6OjoAg+wsOnThyEIpVlKRgpt1rUhSZXEEp8lNHIuPbOhOS51UShkKXv5Ij+e+ZG/rv+FhaEFf3f7m3JW5Qp0/IeJD3lz65skqBIYWnMo4xuML9DxhdxLSVfz6YZL7LgcCsDw5uX5oks1lDmVJHuRXxtCpG7fjJwn+lqNhsTDh4lcuDBzKQ5KJdbdulJm+PDXm92MewhzauqSlqckJYy7UqgJmVDyaLVa4jZuJHz6DDRJSUimpjh9/hk2b7+dbf94eHg4K1asYNCgQTjJVKRFn86Vc5W4LF++/KWPDx48ON8BFTV9+jAEobSbfHIy/rf96eXVi++afSd3OEUqdsMGQidN1iUvCgUu306VZRNmTnYH7+azI58BMLfNXNp6tC2U4+y/t5+PD+t6wfze/neauzUvlOMIr0+r1eZ9037cQ5hTI+sSSEkB4wJkO9HXarUknz5D1B9/6JZnPmHRrh32I0dgWqdO9helxMD1bXDmTwi7nP3xwdugfMtCjFooSVQREYRNmkzi4cMAmNavj+v0HzAqV7AXgwqSPp0riwaUevRhCEJpdz78PEN2D8HMwIzDbx3G1KB0VQLKaamL3AJjA3lnxzukZKQwrOYwxjUYV6jHm/bPNP6++Td2Jnas774eRzPHQj2e8Hqe3bRfydGCxYNfY9N+0FFY3j37/Q7VoM7bULUr2Mu3jyvlSgBRf/5Jwr59mcmV2RtvYD9yBGb1ayHd2gUBG+HOAd1enRfxbAl9F4OF+FkVXi5+1y7CpkxFHReHZGiIw7ix2A0ZgqTnLUX06Vw5z4lLamoq6enpWe6T+83khT59GIJQ2mm0Grr4d+Fh4kPRUV0PJKYn8s6OdwiOD6axc2MWdFiQ634tuZWmTmPAzgHciL6Bt7M3f3T4A6VCv/+olxZXHsQxfMVZwuPTsDUzZMGABjSuUObFL8hpadXzynhBlS66m7s3yPBZp929S9Sfi4jbti2zXLlJmQzKVIvH0i0VSQKcakLNProXHPwetGpA0i0V02aAmT30+g0q+xR5/IL+y4iJIfy774nfuRMA4+rVcJ0xA5PKlV/52sjISDZt2kTv3r2xt7cv7FBzpE/nyrna/ZqUlMSYMWNwdHTE3NwcW1vbLDdBEIT8UEgKelTsAcDWOyW3ulhxoNVqmXRyEsHxwTiaOfJjyx8LPWkBMFYaM7PlTEwNTDkTdoY/rvxR6McUXk+tstZsHdOc2mWtiUlWMWDxadaefUkXb2s33Z4W6UkyIimh/VTo8jNUaKMrlRx1B07Og6Wd4OdKsHkUXN8O6UlF86bUKozVgbh6R+PVIxbbSolISg2pUQY8PG7H3SPViC0/He3ww9BivO427goM3g4fX4UPjuuSmuRIWN0PdnwC6clFE7tQLCQcPszdHj10SYtSif2oUZT/++/XSloADA0NcXZ2zravvLTK1YzL6NGjOXToEN999x0DBw7Ez8+Phw8fsnDhQmbMmMG7775bmLEWKD8/P/z8/FCr1dy6dUsvskhBEOB+wn26+HdBQmJf3304metPx/jS5Nkmk8s6LaOOQw5r/wvRtsBtfHn8SxSSgkUdF5WqYg36LiVdzWcbLrH9dTftxz2E6LtgVyHr3pbUON0yrJs74fZe3ddPGZhAhdZQpTNU7gSWBbh0UqOGeyd1y8CubYGUZwoLWZUlo1xXoq8pidl6AE1Cgi4cVxfKvDcMm759UMfGkh58DyPPcrolnapUOPAt/OOnG8O+CvRZBC761ThWKFrqxETCZ8wgbsNGAIwqVMD1xxmY1qolc2S5p08zLrlKXDw8PFixYgWtW7fGysqKCxcu4OXlxcqVK1mzZg07n0yBFSf69GEIgqAzeNdgLkRcYFz9cQyrNUzucEqdZ5tMftX4K96umv9+LXnx9fGv2RK4BUdTR9b3WI+diZ0scQjZabVa5h24wy/7bwG53LSfE7VKl0zc3AU3d0DsczM5bg11SUzVruBQFaRcVDbTBQwPz+uSlQB/SAz77zFzB6jRW7cUrKx3Zj8YdWIiMWvWEL18BeonvV8kc3O0ycm68Z4vohF4EDZ9oBtbYQjtJkGTMf/1lxFKjaTTZwj94gtUjx6BJGE3eDAO48aiMDHJ9VhqtZqkpCTMzc1RyrQXRp/OlXOVuFhYWHDt2jU8PDwoW7Ys/v7+eHt7ExQURK1atUhMTCzMWAuFPn0YgiDobLy1kSmnplDBugKbe27OVh5SKDwRyRH029aPqNQoulXoxg/Nf5Dt+5+sSubtHW8TFBdEc7fm+LXzKzX9fYqLPG3afxWtFiKu6WZibuyERxeyPm5b/sm+mM663jDKJ0sY4x5CdCDYVdTN7Gi1EH4VAjboEpZnkyETa6jWQ5eseLb4b4wcaFJTidu0iciFf5ARFpb1wefLlidFwbaP4MZ23dflW0HvBWDlmr/viVAsaFJTiZg9m5gVKwEwdHPDZfoPmHvnvR9XaGgof/zxByNHjsTFxaWgQs0VfTpXzlXiUrt2bebPn0+rVq1o3749devW5eeff2bevHn89NNPPHjwoDBjLRT69GEIgqCTkJ5Am3VtSFOn8XfXv6lhX0PukEoFlUbFsD3D+DfiXyrZVmJVl1WyV3a7FXOL/jv6k6ZO45MGnzCk5hBZ4xGye37T/u8DGvDGyzbt51Z8KNzapZuNuXsE1Gn/PWZio9sQb2QO55f91xiycieICoTIm/8919AcqnbRJSsV24KBca7CSDx5kvvvZZ8BztYoVquFC8th9xegStbF2GMeVO+Zq+MJxUvK5cs8mjCR9KAgAGz69cPx889RWpjna9y0tDTu37+Pu7s7xsa5+5ktKPp0rpyrxOWXX35BqVTy0UcfsX//frp3745Wq0WlUjF79mzGjh1bmLEWCn36MARB+M/nRz5nV/Au3qn6Dl82/lLucEqFwm4ymVfrbq7ju3++w0AyYHnn5dR2EHsH9E14fCojV5zj0oM4DBQS03rX5K1GHgV/oLRE3ZKsmzvh1m5dj5WXURpDpQ66ZOVpgpNHOTaKBTxWLM/5inrkHdg4DEIv6r6uNwA6/QjG8nQ/FwqWKiyM9OB7GLq6ErvJn6g//gS1GgMHB1ymfY9Fy5LT20efzpXz1cfl3r17nD9/Hi8vL2rXLp5/SPTpwxAE4T/HHx7ng/0fYGNsw8E3D2KoFBVVCtOuoF18fvRzoHCbTOaFVqvls6OfsSd4D24Wbqzrvg4rI/H7Wt+kqtR8uv6/TfvDmpfny5dt2s8vdQbcPw1n/oBrm7M/3uxjaPGxbllYAcnSKPYJA1cXPFevzrn3UkY6HJ4Ox38BtLoCBb6LoGyDAotJKHo5/RwAWHXrhvPXX6G0sSmwYyUlJREQEEDNmjUxN8/f7E1e6dO58msvFlapVLRr147bt29n3leuXDl8fX2LbdIiCIL+esPlDRxMHYhNi+Xow6Nyh1Oi3Ym5w+STkwEYXmu4XiUtAJIkMbnJZMpalOVh4kOmnJxCKe+drJdMDJXMf6ce4zvoyrwuPh7EsOVnuRWewMnASELjUgr2gEoD8GwGPj/oloc9S1KC94gCTVoAbPr2xevgATyWL8dzy2aMypcn41Eo90eMQB0bm/0FBkbQfjIM2Q5WZXXV1RZ3gCMzddXNhGJFFR5OzLp1hH4zKVvS4jxlCm4/zyzQpAUgISGBAwcOkPCkwl1pl6sZFwcHB06ePEmlSvJ1ui1o+pRFCoKQ1axzs1h2dRlt3dsyt+1cucMpkbI0mXRpzML2C/W24WNAZAADdw0kQ5Mha7Uz4dV2Xgll/Drdpv2nFBJM961VOEvILqyAbeN0jSElJXSfA/UHFfxxnqN6+JDg/u+SER6Oab16eCxZjML0BfvCUmJg+3i46q/72qMJ9F4ItvqxJFPIKiMmhtSAq6QGXCHlSgCpV66Q8fjxC5+fba9TCaJP58q5Slw+/vhjjI2NmTFjRmHGVKT06cMQBCGr2zG38d3qi4HCgINvHsTWRDS6LUharZbxh8ezP2Q/TmZOrOu+Tu9LDq+4uoKZ52ZipDBiVddVVLWrKndIwgscuhHB0GVns9ynlCSOT2yDi3UhFH14Ub+YQpZ66xb3BgxEEx+PRevWlJ0/D+lFzQK1Wri8FnZ8CukJYGwFXWdD7TeLLF4hO01SEqnXrukSlCeJiur+/exPVCoxKleO9Lt3s97/fHW5EkafzpVz1QY5IyODJUuWsH//fho0aJBtrd3s2bMLNLjC9GwDSkEQ9FMl20pUs6vG9ejr7AraRf9q/eUOqURZfnU5+0P2Y6AwYHbr2XqftAAMrD6QM2FnOPLgCJ8d+Yy13dZiZpjP8rtCoTA2zL4aXa3VEhyZXDiJi7VbkSYsT5lUroz7gt8JGfoeiYcPEzppMi4/TMu5jLgkQZ23weMN8B+p26PjPxxu74Guswp8aZuQnSY9nbSbN0m5coXUJ4lKWuDdbEu/AIzKlcOkVi1Ma9XEpFYtTKpVQ2FqmnWPy5N+PoWVtERFRbFt2za6d+9OmTIFWK2vmMrVjEubNm1ePJAkcfDgwQIJqijpUxYpCEJ2f137ix/P/kiNMjX4u9vfcodTIoQlhbE3eC+zzs1Cg6bYLbuKTY2l77a+hCeH071Cd35o8YPcIQk5CI1LodmMg2ieOctQSHBiYtvCSVxklnDwEA8+/BDUasqMGI7jJ5+8/AXqDDg2C478qFviZu0Bvn9AuSZFE/BTz/e/KeaeVvsy8iyHgYMDaYGBpF4JICVAl6ik3byJVqXK9joDZ2ddglLzSaJSowZK6xcnkqqwMNLvhWBUzqNQZ1piYmI4dOgQbdq0wdZWnlUH+nSunK+qYiWBPn0YgiBkF5USRfv17cnQZrC552Yq2lSUO6Rizf+2P1NPTkWD7upiHfs6rOyystg1+Twffp739ryHRqvh+2bf09NL9MjQR2vPhvClfwDqJ6cazb3s+Wt4Y5mjKjyxG/0J/eorABwnTqDMkCGvftH9s7pZl5hgXZGBFp9AqwlQFJUUL6yAbWP/63/TfW6R7A0qLNmqfRkZQXp6tucpra11Myi1amJaqzYmNWtg6OhYxNEWH/p0riwSFz36MARByNmHBz7k8IPDvFfzPT5u8LHc4RRbYUlhdNzQES3//dpXSAr29NmDs3nxW5v9x+U/mP/vfEyUJnzV+CvecH2jWL6Pki40LoWtFx8xfdcNzIyUnJrYDmuzklvePPLPP3k8S7d03vWnH7Hu0ePVL0pLgF0T4OIq3dduDaDj96DJyP1MSEY6pMVDahykxj759+kt/r//TgiFG9ufe7EEb/2la9BpVLyWYKrCwrjTpq1uH9GzTEwwq1kzy5Ivw7Jli83FGo1GQ1paGsbGxigUr10MuEDp07mySFz06MMQBCFn++7tY/zh8TiaOrK37169rXqlz9LV6Xx9/Gt2Be/K9tgSnyU0cm4kQ1T5o9ao6b21N0Fxuk7VChRMbjoZ30q+MkcmPE+r1dJ57jFuhCUwoVNVPmhdcmdOtVotETN+JHr5cjAwwP03v9dvRhjgD9vH6RKLTBI0GAwudbInHzndMgqg7LSkAPsq4FoXXOrq/nWula8GnoVJk5TEg3Efk3TsWLbH3JcuwaJJES+/K0BX7l7Bf6U/vgN9qVWhliwx6NO5skhc9OjDEAQhZ+nqdNqsa0N8ejwLOyykqWtTuUMqVm7F3OKLY19wK+ZWtseK84xLWFIYPht8Mpe9QfF+PyXdhvMP+HT9JZysjDn2eVuMDOS5elwUtBoNjyZMJH7bNiRTU8otW4ppnTqv9+IH52BRu/wHYWSp2+yf7Wb1XxGAoz8Dz50GmtlDcmT28fQ0mUm5epVHn3xKenBw9geLebUv/9v+TDs2DftUe6JMoviyxZeyXJjRp3PlXFUVEwRBkIOR0ojO5Tuz9uZatgZuFYnLa1Jr1Ky8tpJ5/85DpVFha2yLj6cP626tQ6PVoJAUTG4yudie5IfEh2RJWgA0Wg33E+4X2/dUkvWo48rMPTcIj09j26VH9GlQVu6QCo2kUOA67XvUMTEkHT/O/ZH/o9zqVRhXfI2ZJlVyzve7v6Er9ZxTEvL8zdgKXmdm2sYj5/438aEQehEeXfzv38QweHxdd7u05ukbBfvK/yUyrvVenMwUcBEArUZD9IoVRMyaDSoVBk5OWHXtSvSyZUVS7auwhSWFMfXUVDRKDY/MHwEw9dRUmro2LdW/30TiIghCsdCjYg/W3lzLgXsHSGyciIWRhdwh6bWHiQ/56vhXnA8/D0Drsq2Z3HQy9qb2DKs1jPsJ93G3dC/WfwA9rDxQSAo02v+SFwkJd0t3GaMSXsTIQMHgpp78tPsmfx67i299t2KzzyAvJCMjys6dw72h75F6+TIhw0fguWb1q0+k7SrqEoJnfq6RlNB3ScFX/ao/CCq2y97/xspFd6vS+b/nJoRlTWQe/fskmbmhu11+UvXx+WTGpS6EB8CuzwusCEBGVBSPvviCpKO6pWEW7dvh8t13GNjaYjdoYJFU+ypsIfEhaLQajNRGuCa78sjsEenK9FJ/YSZX87TLly9nx44dmV9//vnn2NjY0LRpU+7du1fgwQmCIDxVy74WnlaepKpT2Xdvn9zh6C2tVsvmO5vps7UP58PPY2pgypQmU5jXdh72pvYAOJs708i5UbH/4+ds7szkJpNRSP/9KXs+kRH0y7ve5TAzUnIjLIHjd3JYjlTCKMzNcV+4AKPy5ckIDSVk+HDUsbEvf5G1m+7EXnoyY/J0JqSwShVbu0H5Fq8e39IZqnSC1hOh/9/w6U345Ca8sxZafwGVO4Oliy45eZrI7J4ISzvBzk//S8S0Gt0sT9zDPIWbeOIEd3v2IunoMSRjY5wnT6Ls/PkYPCkVbOjsjHlj72KdtIDuwoyEhFmGGQ2jGmKWYYZCUpT6CzO52uNSpUoVfv/9d9q2bcupU6do3749v/zyC9u3b8fAwAB/f//CjLVQ6NO6PUEQXu7Py38y7995NHRqyNJOS+UOR+9Ep0Yz9eRUDt7X9dSq51iPac2m4W5Vsv/QhSWFERIfwvx/53Px8UV6VOzBtObT5A5LeIEpW6+y7GQwLSrZs3JYyS2N/CzVo0cEv9OfjPBwTOvVw2PJYhSmr+hlE/cw+0xIYcT2TN+TAjnZTwjPOitz/zSkRGd/3ttroGqX1x5Wm55OxNy5RC9eAoBxJS9cZ83CpHLl/Mesp/rv6M+VyCsAmUt7S/sel1wlLmZmZty4cQMPDw8mTJhAaGgoK1as4OrVq7Ru3ZrHjx8XZqyFQp8+DEEQXu7Zcr67fHdR1rLkrpHPrcP3DzP55GSiU6MxUBgwuu5ohtYYWqoqsAVEBvDOjneQkNjQYwOVbUvuCU1xdj86mVYzD6HRwq6xLajmUjr+9qbdvk3wuwPQxMdj0aoVZX+dj2Qob1nonDrA2/TtW7AHiXsIc2pmXfoGun047SZBg6GgfPnOhfR793j4yaekBgQAYPP2WzhNmPDq5K8YS0xPpM26NqSqU/m64lu08uqGs3NdWWLRp3PlXC0Vs7CwICoqCoC9e/fSoUMHAExMTEhJKYDye3lw//59WrduTfXq1alduzbr16+XJQ5BEAqfs7kz3i7eAGy/+3z/gdIpSZXE5JOT+fDgh0SnRuNl48WarmsYXmt4qUpaAGra16RjOV1iO/fCXLnDEV7A3c6MzrVcAFh0LEjmaIqOcaVKuC/4HcnYmMQjRwj9ZhJyFnZVhYVlbdao0RA6aTKqsLCCPVC2pW8KsHLV9ZrZ+SksbAlB2csYPxW3dStBvX1JDQhAYW2N2/x5uEyZUqKTFtC1AUhVp1I12ZiM/bcxWthL1zC0lMtV4tKhQweGDx/O8OHDuXXrFl266Kb4rl69iqenZ2HE90oGBgbMmTOHa9eusXfvXsaNG0dSUpIssQiCUPh6VNQ1c9sWuE3WP/r64EL4Bfps7YP/bX8kJIbUGMLf3f6mql1VuUOTzYf1PkQpKTn64GhmYQJB/4xoUQGArZceEhaXKnM0Rcesfn3c5vwCSiVxmzfzeNYsWeJIu3uX8Okz/ktantJoiPh5Vs6lhfOj/iAYdwUGb4dxATD2CnSdBaa2EHEVlneDdYMhNiTzJerEJB5NmMCjzyegSU7GrGFDKmzehNWTi+YlWkYaW68sA6BdcjIGqJHyuTeopMhV4uLn50eTJk14/PgxGzdupEyZMgCcP3+ed955p1ACfBUXFxfq1q0LgLOzM/b29kRH57CWUhCEEqG9R3tMDUwJSQjh4uOLcocji3R1OrPPz2bI7iE8THyIq7kri30W80nDTzBWGssdnqw8rT0z14D/cv6XUp/c6qu67jZ4e9qhUmtZdjJY7nCKlGWbNrh89x0AUYsWE7V0WZEcV5OaStyWLQQPGMDdLl1J2LMnx+fFb99OYKfOBA8YQOymzWiSX1CeObeeLQKgNIBGw+HDC7p/JQVc2wy/NoLDM0i5eJ4gX1/itmwFhQL7jz7EY/kyDF1cCiYWfaLVQlQgXF4HOz+HP9vx4KdynEu4i6TV0is5jLfYhi1xurLV0XfljlhWsjegPHr0KDNnzuT8+fOEhoayadMmevXqleU5fn5+zJw5k7CwMOrUqcP8+fPx9vbONtb58+cZPHgwAU/WQL4OfVq3JwjC6/nq+FdsDdxK38p9mdxkstzhFKnnm0n2rNiTid4TRXnoZ0QkR9DVvyup6lTmtplLW4+2cock5GDftXBGrDiHlYkBJ79oh4Vx6erQELVoERE/62ZcXH+cgXXPnoVynNSbt4hdv564rVvRxMfr7lQosGjdGkNXF2JWr8nc42LTrx+q0EckHTueORujMDfHqksXbPr4YlKnTuGUsA4LgF0T0AYfJ/qGORFXrEEDBi4uuP08E7MGDQr+mHJJioSHF+DhOXh4XndLicnylAU2VvjZ2tA4JZU/wyJQo0SJGklS6mauCrFYQ0706Vw5178lYmNjOXPmDBEREWiemWKUJImBAwfmOoCkpCTq1KnDe++9h69v9koJa9euZfz48SxYsIDGjRszZ84cfHx8uHnzJo6OjpnPi46OZtCgQfz555+5jkEQhOKlR8UebA3cyp6gPUxoNAETAxO5Qyp0ao2aFddWMP/f+ZnNJCc3mUy7cgXQYbuEcTRzZED1ASy6soh5F+bRqmyrUrffpzhoV9WRCvbm3I1MYt3Z+7zXvLzcIRUpu2HDyIiMInrZMh599TVKW1ssWrYskLE1ycnE79pF7Lr1pFy6lHm/oasrNm/2xdrXF0MnJwDKDB+ere+JKjycuE2bifX3RxUSQuz69cSuX4+RV0VsfPtg3bMHBk9W3RQI55pkdF3KozHDSLp8BwBL9xRc+hijLFuM97KoUiD08pME5UmiEhOc/XlKY3CpA24N0Lo1YNutPyEplJ6VfAkL38wf9Gckq3Hp/lWRJy36JlczLtu2bePdd98lMTERKyurLFm3JEn5XqIlSVK2GZfGjRvTqFEjfv31VwA0Gg3u7u58+OGHTJw4EYC0tDQ6dOjAiBEjXpk8paWlkZaWlvl1fHw87u7uepFFCoLwejRaDT4bfQhLCuN/tf9H38p9i31Pkpd5kPCAr45/xYWIC0DWZpJCzuLT4+m8sTPx6fF82/RbelfqLXdIQg5Wnb7HV5sCcLMx5chnrTFQ5moFe7Gn1Wh4NHEi8Vu3IZmaUm7pEkyfLH/Pi5SrV4ldv574bdvRPN3va2CAZdu22Lz5JubNmiIpXv97rNVqSTl3jtgNG4nfswdtaup/Y7ZpjbWvLxYtWiAZ5G+2LPHoUR5N/AJ1dDSSiTFOfepho92BpEnTbepvNBzafKHbE6Mv4h5CdKCuYai1m26GKuq2Ljl5cE6XqIRfBU1G9tfaVwa3Brpb2YbgWAMMjAD4N+JfBu0ahKmBKYf7HUaKDuV2wAUq1ayPqVPFIn6TOvo045KrxKVy5cp06dKFH374ATMzs4IP5rnEJT09HTMzMzZs2JAlmRk8eDCxsbFs2bIFrVZL//79qVKlClOmTHnlMaZMmcLUqVOz3a8PH4YgCK9v1P5RHHuoq0QjZ337wvS0meSMMzNIzkjGzMCMzxt9jm8l3xLdcbygLAtYxqzzs3Ayc2J77+2lYmauuElVqWk64yDRSen82r8e3Wq7yh1SkdOqVNwfNZqkY8dQWltTbvUqjCu+/gmqOjGR+O07iF2/ntSrVzPvNyzngU3fvtj07o2Bff4vcqgTE4nfsZNY/42kXrqceb/SwR6bXr2w9vXFuHzuZs006ek8nv0L0cuWAWBcpQpus2fp3n9MMOz9Gq5v0z3Z1A7afQP1B4PcM6gXVsC2sU9KPEtgXwkSwnSV0p5n7qhLTtzqg1tDcK0HpjYvHHrqqalsuLVBr/pRFdvExdzcnCtXrlChQoXCCea5xOXRo0e4ublx8uRJmjRpkvm8zz//nCNHjnD69GmOHz9Oy5YtqV27dubjK1eupFatWjkeQ8y4CELx92w/l6cUkoI9ffaUmJmXqJQopp6ayqH7h4AnzSSbTyv1XZNzI02dRrdN3QhLCuOTBp8wpOYQuUMScvDLvlvMPXCbOmWt2Ty6WalMyjXJydwbOpTUS5cxcHHBc/Wql25E12q1pF6+TMz69cTv3IX2yQZ6ydAQyw4dsOn3Jmbe3rmaXcmNtNu3ifXfRNyWLaifWW1j2qABNn36YOXTEYW5+cvHCAri4SefkHbtOgC2Awbg+NmnKIyfKzBy9zDsmgiPdc/DuRZ0ngnlmlBkNGqIuKZrphl4BG5szfl5hmbgUleXpJRtqEtUrMvCa/5Mp2ak0nZdWxJUCSzuuBhvF29SUlK4c+cOXl5emMpUAlqfEpdcze35+Phw7ty5Qktc8qJ58+ZZ9tq8irGxMcbGxvj5+eHn54darS7E6ARBKAwh8SFZkhbQLR+7n3C/WCcuTzvAP0h4wNx/52Y2kxxTdwxDagwR+zRyyVhpzKg6o5h0chJ/XvkT38q+WBmJC1T6ZmCTciw4EsilB3GcCYqmcYUC3DtRTCjMzHBfsIB77w4g/e5dQkaMwPOvv1Da2GR5njo+nrit24hdt460W7cy7zeqUAGbfm9i3bMnBraFv5zKuFIlnCZ8juPH40g4coS4DRtJPHaMlPPnSTl/nvDvv8eyS2ds+vTBtG5dJElCFRZGevA9DMt5kHzqH8K+/x5tcjJKGxtcfvgBy7Ztcj5Yhdbw/jE4uxgO/wBhV2BpJ6jZFzp8Wzh7PlJidMu97p/RJSsPz0N64stf030e1H33lc00X+bwg8MkqBJwMXehoXNDQLe33N/fn5EjR8qWuOiTXH13u3btymeffca1a9eoVasWhs91fO3Ro0eBBmdvb49SqSQ8PDzL/eHh4Tg75+/kZPTo0YwePTozixQEofjwsPJAISnQPNeJ+VHiI5kiyj//2/5MPTkVDf+9Jy8bL2a0mEEVuyoyRla89ajYg+VXlxMYF8jSgKWMrT9W7pCE59hbGNOnQVlWnw7hz2NBpTJxATCwtcVj0Z8Ev9Of9DuBhLw3DIdxYzGqVImMR4+IXbee+N270T5ZNSIZG2PVqRM2/d7EtH59WWaqJCMjrDp0wKpDB1ThEcRt2ULcxo2k37tH3IaNxG3YiFHFihh7eZGwb1+2vjFmjRvj+tOPmYUCXkhpCG+8D7X6wsHv4PxyCNgAN3dCi/HQ5EMwzONSUI0Gou7oEpT7p+HBWXh8I/vzjCyf7EepCv8sgGcvnklK8Gqfr6QFYOsd3UxOtwrdUEi62TInJye++OILDPK5j6ikyNVSMcVLphwlScr37MWLNud7e3szf/58QLc538PDgzFjxmRuzs8PfZr+EgTh9fnf9mfqqalZkheFpOBL7y95q+pbMkaWezktfZOQ2N57Ox5WHjJGVjIcDDnI2ENjMVGasMN3B45mjq9+kVCkAh8n0m7WEQAOfNKKig6lt7x32u3bBPV7C21KSo6PG1eujE2/flh374ZSDy+8arVaUs6fJ3ajvy7ResH7sBs+HMePxyEp8zCT/Ogi7Ppcl2gA2JQDnx+gatdXL8tKS3yygf7MkxmVM5Aam0OAFcC9Mbh7Q1lvcKz2396aCyt0zSC1al3S0n2OrslmPkSmRNJ+fXvUWjVbe22lvLX+VNnTp3PlXKVvuVmS9boSExO5c+dO5tdBQUFcvHgROzs7PDw8GD9+PIMHD6Zhw4Z4e3szZ84ckpKSGDp0aL6OK5aKCULx5lvJl6auTbmfcB8XMxcWXF7AlsAtfH/6ex4kPuDjBh9nXrHSd3/f+Dvb0jctWsKTw0XiUgDauLehrkNdLj6+yIJLC5jUZJLcIQnPqehgQftqTuy/Hs6iY0FM9815n2ppoLC0/K961zMsu3SmzODBmNSurdf7gCRJwqxhQ8waNsTpqy957OdHTA5NNi1atMhb0gLgWhfe2wNXNsC+byD2Hqx9Fyq00c3AgK7al5Wr7rGnS77un4HwgCeb6p9hYPKkwlcjXbJSthFYOLz4+PUHQcV2umaQdhUKZLnajrs7UGvV1HaonSVpiYmJYf/+/bRv3x7bIlgGqO/y3IAyNTUVE5P8V2g5fPgwbdpkX9c4ePBglj2pMvHrr79mNqCsW7cu8+bNo3Hjxvk+NuhXFikIQt5ptVoWXl6I30U/ADqW68i05tP0upJUmjqNH8/8yPpb67M9VtKKDcjtfPh5huweglJSsqnnJr26minonAmKpt/CUxgbKDgxsS32FsavflEJlPTPaUKGDMl2v8fy5Zg3zt58W9+pwsK407Zd1mViCgVeBw9k9o3Jl7REODYLTv0K6vSsjxlZQnpC9tdYldXNpDy9OdXKLEcslz5b+3Ar5hbfvPEN/ar0y7w/KiqKXbt20blzZ8oUZO+cXNCnc+VcJS5qtZoffviBBQsWEB4ezq1bt6hQoQLffPMNnp6eDBs2rDBjLRT69GEIgpB/2wK3MenkJDI0GdR1qMu8tvOwNdG/q1QPEx8y/vB4rkVdQ0KiVdlWHH14FI1WU2LLO8tt9IHRHH1wlA7lOjC79Wy5wxGeo9Vq6eV3gksP4hjbrhIfd6gsd0iyKPQTfRnEbthA6KTJuvekUODy7VRs+vYt2IPcO6XbtP88yQBc62Rd9qVnTRxvRt+k77a+GCoMOdTvENbG+rUEUJ/OlXO1jmLatGksW7aMn376CSOj/zLTmjVrsmjRogIPrjD5+flRvXp1GjVqJHcogiAUoO4Vu/NHhz+wNLLk4uOLDNg5gHvx9+QOK4ujD47Sb1s/rkVdw9rYmt/a/8b8dvPZ02cPS3yWsKfPHpG0FIKx9cciIbHv3j4CIgPkDkd4jiRJjGipq1q68p97pKpK51JuQ2dnXL6dCk/3FT850S+uSQuATd++eB08gMfy5XgdPFDwSQuARpXz/e+uhREHodN0qNFb75IWgC2BWwBo7d5a75IWfZOrxGXFihX88ccfvPvuuyifWZdYp04dbtzIoQKDHhs9ejTXrl3j7NmzcociCEIBa+TciL86/4WbhRshCSEM2DmAfyP+lTss1Bo1v/77K6MPjCY+PZ6aZWqyrts6mrs1B8DZ3JlGzo3E8rBCUtm2Mt0rdgdgzvk55HGltFCIOtVwpqytKdFJ6Wy88EDucGRTJCf6RczQ2Rnzxt6Fl4DZVYTn9zVKSnCoVjjHKyAZmgx23N0B6KogPi80NJRvv/2W0NDQog5NL+UqcXn48CFeXl7Z7tdoNKhUL8h0BUEQZFDBpgJ/dfmLmmVqEpsWy/A9w9kdtFu2eGJSY/hg/wcsvLwQgLeqvMXyzstxtSh9ncLlNLruaAwVhpwOO82pR6fkDkd4joFSwXvNdPuPFh0LQqMpvclloZ/olzTWbtB9ri5Zgf+qfenhDMuzTj46SXRqNHYmdjRza5btcSsrK7p06SL7Ei19kavEpXr16hw7dizb/Rs2bKBevXoFFpQgCEJBsDe1Z0mnJbRxb0O6Jp3Pjn7G4iuLi/xK+6XHl3hz25ucCj2FidKEH5r/wNdvfI2RUt7NoKWRq4Urb1XRlcuec2FOtl5Agvz6NXLHysSAoMgk9l8Pf/ULBOGp+oNg3BUYvF33bz5LFBeFrYG63i1dynfBUGGY7XFzc3MaNmyIubl5UYeml3KVuEyaNIkxY8bw448/otFo8Pf3Z8SIEUybNo1Jk4pXeUmxx0UQSgdTA1N+af0LA6oNAHQnq9/98x0ZmoxCP7ZWq2X19dUM2T2E8ORwPK08Wd11deZyJUEeI2uPxNzQnOvR19kTvEfucITnWBgb8O4b5QDdrIsg5Iq1G5RvofczLQBxaXEcCjkE5LxMDHRVfG/evElqDiWyS6NcJS49e/Zk27Zt7N+/H3NzcyZNmsT169fZtm0bHTp0KKwYC4XY4yIIpYdSoWSC9wQmNJqAhMT6W+v58OCHJKmSCu2YyapkJhydwPQz08nQZNChXAfWdF1DJdtKhXZM4fXYmtgypMYQAOZdmIdKLZY665shTT0xVEqcCY7m35AYucMRhEKxJ3gP6Zp0KtlWoqpd1RyfExMTw99//01MjPj/AHKZuAC0aNGCffv2ERERQXJyMsePH6djx46FEZsgCEKBGlB9AHPazMFEacLxh8d1MyFJBb8U5W7sXd7Z8Q67gnehlJR81vAzZrWahYVR6e0Grm8GVR9EGZMyPEh8wIbbG+QOR3iOk5UJPerorpiLWRehpNoWuA2AHhV6vLCpqKOjI59++imOjo5FGZreKh5tpQVBEApIW4+2LPFZgp2JHTeib/Duzne5GX2zwMbfHbSbt3e8zd24uziYOrDEZwmDagzS607XpZGZoRnv13kfgAWXFpCsSpY5IuF5I1rqNunvCgjlfrT4fISS5V78PS4+vohCUtC1QtcXPk+pVGJubp6lmm9p9srExdbWFjs7u9e6FSdij4sglF61HGqxqssqyluXJzw5nMG7B3Py4cl8jalSq5hxZgafHf2MlIwUvJ29Wdd9HfWd6hdQ1EJB61O5D+6W7kSnRrPi2gq5w8kUlhTGmdAzhCWFyR2KrKo6W9Gikj0aLSw+LmZdhJLl6WxLE9cmOJg5vPB5sbGxbN68mdjY2CKKTL9J2leU11m+fPlrDzZ48OB8B1TU9KkbqCAIRSsuLY5xh8ZxLvwcSknJpCaT8tT4MSwpjE+PfMqlx5cAGFZzGGPqjcFAYVDQIQsFbFfQLj4/+jnmhubs9N2JnYm8F+H8b/sz9dRUNFoNCknB5CaTS3Uz0mO3HzNw8RnMjJScmtgOa7PsVZcEobjRaDV03tiZR0mP+KnlT3Qu3/mFz42MjGTr1q306NEDe3v7IozyP/p0rvzKxKWk06cPQxCEopeuTmfyyclsv7sdgBG1RvBhvQ9fe2nXqUenmHB0AjFpMVgaWjKt+TTaeLQpzJCFAqTRanh7+9tcj77OgGoDmOA9QbZYwpLC8Nnok6VEswIF23tvx93KXba45KTVauk89xg3whL4vFMVRrXO3ktOEIqbs2FneW/Pe1gYWnCo3yFMDEzkDuml9OlcOc97XLp27Sq6eAqCUOwZKY34ofkP/K/2/wD488qfTDw2kXR1+ktfp9Fq+OPyH/xv3/+ISYuhql1V1nZbK5KWYkYhKRhXfxwAa2+u5WHiQ9liuRVzK1tfGQ0aum3uxtvb3+b7f75ny50t3I29W2r6z0iSxIgWFQBYdiKYtAy1zBEJQv497d3i4+mj90mLvslz4nL06FFSUlIKMhZBEARZSJLEmHpj+LbptxhIBuwM2snIfSOJS4vL8flxaXF8ePBD5v87Hy1afCv5srLzylJ7Vby4a+LahMbOjVFpVPj96ydLDGfDzvLdqe9yfEyj1XA16iprb67l6xNf03NLT5qtacbwPcOZc34O++/tJywprMgbqxaV7nVccbIyJiIhja0XH8kdjiDkS7Iqmb3Be4EX9255VlhYGNOnTycsrHTveXtKLMAWBEF4onel3jibOzP+8HjOh59nwM4B/Nb+N9wt/0tIrkZd5ZPDn/Aw8SHGSmO+avwVvSv1ljFqIb8kSWJcg3G8s+Mdtt/dzuAag6liV6VIjp2akcrcC3P56/pfANgY2xCfFo8G3R6XSW9M4g3XN7gSeYWAxwFcibzC9ejrJKoSOR12mtNhpzPHcjB1oKZ9TWrZ16KmfU1q2NfAyqj4L4E2MlAwtFl5Zuy6waJjQfRtUFZU6ROKrYP3D5KckUxZi7LUc6z3yudbWFjQunVrLCxEOX3Ixx6XmjVrsmvXLtzdi+cVRj8/P/z8/FCr1dy6dUsv1u0JgqAfbsfcZtSBUYQlhWFnYseUJlMwMzTjauRVfr34KyqNirIWZZndejbVylSTO1yhgHxy+BP23ttLy7It8WtX+DMvlx9f5qvjXxEcHwxA38p9+bThpySkJ3A/4T7ulu44mztne12GJoPA2EACIgMIiAogIDKA2zG3UWuzL6PytPKkpn3NzISmil0VjJXGgG5PTUh8CB5WHjkeR5/EpahoOv0ASelqlr/nTavKL67CJAj6bOTekZwKPcWoOqP4oO4HcofzWvRpj8trJS6+vtkrmmi12ixXPAwMDHB2dqZDhw507969YKMsRPr0YQiCoD8ikiMYc2AM16OvZ3usddnWTGsxrURczRb+ExwXTK8tvVBr1SzrtIwGTg0K5Tjp6nQWXFrA4oDFaLQaHE0dmdpsKs3dmud5zJSMFG5E3+DK4ysEROpmZh4kPsj2PAOFAVVsq2BmYMa58HNo0Rab6mXfbrvGkhNBtKhkz8phjeUORxByLTwpnA4bOqBFy07fnVlm818kLS2Nhw8f4ubmhrGxcRFEmZ0+nSu/1h4Xa2vrbDcbG5ssX5uamnL79m3eeustJk2aVNhxC4IgFCpHM0d+bPljtvslJL5s/KVIWkogT2vPzJP3X87/Uih7Rm5G3+SdHe/w55U/0Wg1dKvQDf+e/vlKWgBMDUyp51iPQTUG8VOrn9jVZxdH3zrK7+1/Z1TdUbQs2xI7EzsyNBlcjbrK2fCzaNG9P41Ww9RTU/W+b8zQZp4oJDh2O5Jrj+LlDkcQcm373e1o0VLfsf5rJS0A0dHRrFy5kujo6EKOrnh4rT0uS5cufe0Bt2/fzqhRo/j222/zHJQgCII+eJz8ONt9WrQ8SHyAi4WLDBEJhe39Ou+zLXAblx5f4uD9g7TzaFcg42ZoMlgSsITfL/1OhiYDOxM7vnnjG9qXa18g4+fE1sSW5m7NM5MirVbLo6RHbLq9iYWXF2Z5rkar4X7Cfb1eMuZuZ0aXWi5svxzKomN3mf1WXblDEoTXptVqM6uJ9fTq+dqvc3BwYOzYsWKPyxN5rir2Is2bN6dhw4YFPawgCEKR87DyQCFl/TWpkBSvfaVMKH4czRwZUH0AAPMuzCNDk5HvMe/G3WXgzoHM/3c+GZoM2nu0x7+Hf6EmLTmRJAk3Czf6Vu6b7ecawMzArEjjyYuRLXWlkbdeekRonKhsKhQf16KucTfuLsZKYzqU6/DarzMwMMDGxgYDA1FPCwohcbGxscHf37+ghxUEQShyzubOTG4yOfMk7+leAH2+Ki3k39CaQ7EysuJu3F22BW7L8zgarYYVV1fQb1s/AqICsDSyZHqL6cxuPZsypmUKMOLcef7n+qkfzvzwyv5Fcqtd1gbv8nZkaLQsOxksdziC8Nq2BG4BoK1HWyyNLF/7dXFxcezYsYO4uJzL85c2In17DRqNhvR0/f5lLuSNoaEhSqVS7jAEPeZbyZemrk1fWuVJKFmsjKwYWXskP5/7Gb+LfnQu3znXTeLuJ9znmxPfcD78PADN3JoxtclUnMydCiPkXHv259pAMmD0wdFcfnyZH07/wJSmU+QO76VGtqjAmaBoVp8O4cO2lbAwFqcygn5TqVXsCtoFvF7vlmelp6fz4MEDcR76RJ7LIRd3r1sOOT09naCgIDSa0tGluDSysbHB2dlZ9AUQBCFTmjqNbpu6EZYUxicNPmFIzSGv9TqtVsv6W+v5+dzPpGSkYGZgxmeNPqNPpT56/Tvm+MPjjNo/Ci1avnnjG/pV6Sd3SC+k0Whp/8sR7j5O4ptu1RnWvLzcIQnCSx0IOcC4Q+NwMHVgb9+9GCiKV7KtT1XFSm3i8tTLPgytVktISAgqlQpXV1cUigJfWSfISKvVkpycTEREBDY2Nri4iM3WgiD8Z9PtTUw6OQkrIyt29dn1ykpyYUlhTDk5hROPTgDQ0Kkh3zX7jrKWZYsi3HxbdGURcy/MxUBhwBKfJa/VHE8uq0+H8OWmK7jZmHLks9YYKMXfZ0F/jTs0jgMhBxhSYwifNPxE7nByTZ8Sl+KV8hWxjIwMkpOTcXV1xcxM/zctCrlnamoKQEREBI6OjmLZmCAImXpU7MHyq8sJjAtkyZUljGswLsfnabVatt/dzvTT00lQJWCsNGZc/XH0r9Y/x03w+mpYzWFcj7rO3nt7+fjQx6zttlZvlrY9z7e+G7P23uRhbAo7A8LoUcdV7pAEIUexqbEceXAEyP0yMYDw8HD++usvBgwYgJOTfv7/WJSKz29UGajVui7ERkZGMkciFKanSalKpZI5EkEQ9IlSoeSj+h8BsOr6KiKSI7I9Jyolio8Pf8yXx78kQZVAbfvarO++ngHVBxSrpAV0Vce+a/YdlWwrEZUaxfjD4/V2s76JoZJBTTwB+PPo3ULpuSMIBWFn0E4yNBlUs6tGJdtKuX69mZkZjRo1EhfQnyhev1Vlos/rkoX8E5+vIAgv0sa9DXUd6pKqTuX3S79neWz/vf303tKbAyEHMFAY8FG9j1jeeTnlrYvvngszQzPmtpmLlZEVlyMvM+30NL1NCga84YGxgYIrD+M4HSSa8wn66WllwrzMtgBYWlrSsmVLLC1fvxJZSSYSF0EQBEF4AUmSMpeI+d/yZ8udLdyOuc3EYxP5+PDHxKTFUMW2Cn93/ZsRtUcUu023OXG3dGdmy5koJAX+t/1Zd3Od3CHlqIyFMX0b6PYP/Xn0rszRCEJ2d2PvEhAVgIFkQOfynfM0Rnp6Ovfv3xdVxZ4QiUsJNGTIEKZMmSJ3GDkKDg4WMxyCIBQrDZwaUNm2Mho0fH3ia3y3+rLj7g6UkpKRtUeypusaqthVkTvMAtXUrSlj648FYMaZGVwIvyBzRDkb1rw8kgQHbkRwJyJR7nAEIYutgVsBaO7WPM+9m6KioliyZAlRUVEFGVqxJRIXoUBotVoyMvLfYVoQBEHfhCWFcTvmdrb757Sew4f1PsRQaShDVIVvaI2hdPLsRIY2g/GHxxOWFCZ3SNlUcLCgfTXdhuXFx8Wsi6A/1Bo12+4+WSbmlbdlYgD29vZ88MEH2NvbF1RoxZpIXEqpLVu2UL9+fUxMTKhQoQJTp07NTDyezopcvHgx8/mxsbFIksThw4cBOHz4MJIksWvXLho0aICxsTHHjx8nLS2Njz76CEdHR0xMTGjevDlnz56V4R0KgiAUjJD4ELRk3+dhbmQuQzRFR5IkpjadSmXbypmb9dPUaXKHlc3IlhUA2HjhIY8T9C8+oXQ6HXaaiOQIrIysaFW2VZ7HMTQ0xNHREUPDknmBJLdKbeLi5+dH9erVadSoUZEcLzQuhZOBkYTGpRTJ8V7m2LFjDBo0iLFjx3Lt2jUWLlzIsmXLmDZtWq7HmjhxIjNmzOD69evUrl2bzz//nI0bN7J8+XIuXLiAl5cXPj4+REeLjZOCIBRPHlYe2SqEKSQF7pbuMkVUdMwMzZjTZg5WRlZcibzCtH/0b7N+w3K21HW3IT1Dww87r+vF31lBeLopv3P5zhgp816dNj4+nr179xIfH19QoRVrpTZxGT16NNeuXcvVbIBWqyU5PSPXt5Wngmk24yD9/zxNsxkHWXkqONdj5OYPxbJly166x2Xq1KlMnDiRwYMHU6FCBTp06MB3333HwoULX/sYT3377bd06NCBihUrYmxszO+//87MmTPp3Lkz1atX588//8TU1JTFixcD4OnpqXd/9ARBEF7G2dyZyU0mZyYvCknB5CaTcTZ3ljmyouFu6c7MVrrN+pvubGLtzbVyh5SFJEnUcNU1xdv070OazTjI2rMhMkcllGZJqiQOhBwA8l5N7KnU1FRu3bpFampqQYRW7BX/8idFKEWlpvqkPfkaQ6OFb7Zc5ZstV3P1umvf+mBmVDAf16VLlzhx4kSWGRa1Wk1qairJycm5Gqthw4aZ/x0YGIhKpaJZs2aZ9xkaGuLt7c3169fzH7ggCIJMfCv50tS1KfcT7uNu6V5qkpanmro2ZVz9ccw+P5sfz/xIJdtKNHBqIHdYgG5Fw5oz/yUqGi184X+FlpUdcLE2lTEyobTad28fKRkpeFp5Usu+Vr7GcnR0ZMyYMQUUWfEnEpdSKDExkalTp+Lr65vtMRMTExQK3VXFZ2dGXtSc0dy8ZK/xFgRBeMrZ3LnUJSzPGlJjCNejrrMreBfjD49nbbe1evH9CIpMQvPcRL5GC1cfxovERZDF02piPSr2yHcl1dC4FIIikyhvby5+nhGJS66YGiq59q1Prl4TFpdK+9lHsvxSVUiwf3wrnK1NcnXsglK/fn1u3ryJl5dXjo87ODgAEBoaSr169QCybNR/kYoVK2JkZMSJEycoV64coEt4zp49y7hx4wokdkEQBEEekiQxpekUAuMCuRVzi48PfcyyzsswVhrLGld5e3MUEtmSl++2X6WKsyXudqLjuFB0HiU+4mzYWSQkulXolq+x1p4N4cdNZ2hjeIdDKi8m9PbmrUYeBRRp8VRq97jkhSRJmBkZ5OpWwcGC6b61UD7JuJWSxHTfWlRwsMjVOAXZ+2TSpEmsWLGCqVOncvXqVa5fv87ff//N119/DYCpqSlvvPFG5qb7I0eOZD72Mubm5nzwwQd89tln7N69m2vXrjFixAiSk5MZNmxYgcUvCIIgyMPM0Iy5beZibWxNQFQA3536TvZ9iy7Wpln+zioksDY14F50Cr1/O8HF+7GyxieULk835Xs7e+Ni4ZLncULjUvjC/wqpGiXBaltSNUq+9A8o9cUnxIxLEXirkQctKzsQHJmMp72Z7FN9Pj4+bN++nW+//ZYff/wRQ0NDqlatyvDhwzOfs2TJEoYNG0aDBg2oUqUKP/30Ex07dnzl2DNmzECj0TBw4EASEhJo2LAhe/bswdbWtjDfkiAIglBEylqWZWbLmby//322BG6hepnq9K/WX9aYnv87KyHx3rKzXAuN5+0/TjHnrXp0qin/sjahZNNqtZm9W7pX7J6vsZ4ugUzGiPMZZZ8egODIZNnPI+UkaeW+VCKz+Ph4rK2tiYuLw8rKKstjqampBAUFUb58eUxMXn9Zl1C8iM9ZEAQh95YFLGPW+VkYSAb82fFPGjo3fPWLilBiWgZjVl/g8M3HSBJ83bU6w5qXlzssoQS7GHGRgbsGYmpgyuF+hzEzzPsyxauP4ug67zhKNFhJqcRrTUBScnximyJPXF52rlzUxFIxQRAEQRBybXCNwXQu35kMbQafHPmEsKQwuUPKwsLYgEWDGtK/sQdaLXy3/RpTtl5F/fxmGEEoIE835Xco1yFfSQvA74cDAbCRUuhlcg07RSo/+NYs1bMtIBIXQRAEQRDyQJIkpjadShXbKkSnRjPu0DhSM/Sr14SBUsG0XjWZ2LkqAMtOBvP+X+dJTs+QOTKhpElTp7E7eDeQ/2Vie6+Gsf1yKAoJfny3GY07vcnG8R1L/cZ8EImLIAiCIAh5ZGpgypw2c7A2tuZq1FW++0f+zfrPkySJ91tV5Nf+9TAyULDvWjjv/PEPjxPS5A5NKEEO3z9MQnoCzubOeDt753mcuBQV32wJAGBEywq0r+lGp8bV8bC3LqBIi7cSkbj07t0bW1tb+vbtK3cogiAIglCqPN2sr5AUbA3cyuobq+UOKUfdaruyenhjbM0MufQgjt6/neBORILcYQklxNNqYt0qdEMh5f30evrO64THp1He3pyP21cmISGBw4cPk5AgflahhCQuY8eOZcWKFXKHIQiCIAilUhPXJoxvMB6AmWdncjbsrMwR5ayhpx3+o5pRrowZD2JS8P3tJCcDI+UOSyjmIlMiOf7wOJC/ZWIn7kTy99n7AMzwrYWJoZLk5GQuXLhAcnJygcRa3JWIxKV169ZYWlrKHYYgCIIglFqDqg+ia4WuqLVqPj3yKaGJoXKHlKPy9ub4f9CU+h42xKdmMHjJGfwvPJA7LKEY23l3J2qtmlr2tahgXSFPYySnZ/CF/xUABrzhQeMKZQBwcnJi/PjxODk5FVi8xZnsicvRo0fp3r07rq6uSJLE5s2bsz3Hz88PT09PTExMaNy4MWfOnCn6QAVBEARBeCFJkpjcZDJV7aoSnRrN2ENj9W6z/lNlLIxZPeINutZyQaXWMn7dJeYduK13+3OE4uFp75YeFXvkeYxZe28REp2Mq7UJEzpVLajQShzZE5ekpCTq1KmDn59fjo+vXbuW8ePHM3nyZC5cuECdOnXw8fEhIiKiiCMVBEEQBOFlnm7WtzG24Xr0db499a3eJgMmhkrmv1OP/7XUXSGfve8Wn2+4jEqtkTkyoTi5GX2TG9E3MFAY0Ll85zyNcSEkhiUnggCY1rsWliaGmY89fvyY33//ncePHxdIvMWd7IlL586d+f777+ndu3eOj8+ePZsRI0YwdOhQqlevzoIFCzAzM2PJkiV5Ol5aWhrx8fFZboIgCIIgFAw3Czd+bvUzSknJtrvb+P3S75wJPaN3fV4AFAqJL7pU47teNVFIsP78A4YuPUt8qkru0IRi4umm/NZlW2NtnPvKX2kZaiZsuIxWC73rudGmqmOWx42MjPD09MTIyKhA4i3uZE9cXiY9PZ3z58/Tvn37zPsUCgXt27fn1KlTeRpz+vTpWFtbZ97c3d0LKtxSZ9myZdjY2MgdhiAIgqBnGrs0ztys//ul3xm2dxg+G33wv+0vc2Q5G/hGORYPboSZkZLjdyLp+/tJHsamyB2WoOcyNBlsv7sdyPsyMb9DgdyOSKSMuRGTulXP9ri1tTWdO3fG2lqUQwY9T1wiIyNRq9XZNiQ5OTkRFvbflZv27dvz5ptvsnPnTsqWLfvSpOaLL74gLi4u83b//v1Ci18uQ4YMYcqUKS98/PmEY8qUKdStW7fQ4woODkaSpEI/jiAIgiC/DuU6ZPlao9Uw9dRUvZx5AWhT1ZF1/2uCo6Uxt8IT6eV3goCHcXKHJeixk49OEpUaha2xLc3dmuf69ddD4/nt0B0Apvasga159lmVjIwMoqOjycgQTVNBzxOX17V//34eP35McnIyDx48oEmTJi98rrGxMVZWVqxcuZI33niDdu3aFWGkgiAIglA63E/IfmFQo9XkeL++qOlmzabRzajiZMnjhDT6LTzFgevhcocl5EJYUliRLE0MSwpjWcAyALpU6IKh0vDlL3hOhlrDhI2XydBo6VDdia61XHJ83uPHj5k/f77Y4/KEXicu9vb2KJVKwsOz/tIIDw/H2dk5X2OPHj2aa9eucfZsEdWaj3sIQUd1/+qRZcuWMXXqVC5duoQkSUiSxLJlywDd/qJatWphbm6Ou7s7o0aNIjExMcdxgoODUSgUnDt3Lsv9c+bMoVy5cmg0YrOjIAhCaeJh5ZGtEZ9CUuBuqd9LtN1sTFn/QROae9mTnK5mxIpzrDwVLHdYwmvwv+2Pz0afQl+a6H/bH58NPpwN151Dmhua53qMJSeCuPwgDksTA77vVfOFK1Ls7OwYPHgwdnZ2+Yq5pDCQO4CXMTIyokGDBhw4cIBevXoBoNFoOHDgAGPGjCn6gLRaUOWhAdDF1bDrc9BqQFJA55+gbv/cjWFoBoWwzOqtt94iICCA3bt3s3//foDMdZQKhYJ58+ZRvnx57t69y6hRo/j888/57bffso3j6elJ+/btWbp0KQ0bNsy8f+nSpQwZMgSFQq9zZEEQBKGAOZs7M7nJZKaemopGq7t4VdehLs7m+bvwWBSsTAxZOrQRX/pfYf35B3yz5Sr3Y1KY2KkqCoVY8qyPwpLCmHJyClp0Vew0Wg2TT07mt39/Q6lQFthx1Bo14SlZL6gvurKINyu/+do/28GRSczaewuAr7tWw8nK5IXPNTY2xtPTM8/xljSyJy6JiYncuXMn8+ugoCAuXryInZ0dHh4ejB8/nsGDB9OwYUO8vb2ZM2cOSUlJDB06NF/H9fPzw8/PD7Va/fovUiXDD675Oi5aDez8VHfLjS8fgdHrZfRPZ0xeh6mpKRYWFhgYGGSbxRo3blzmf3t6evL999/z/vvv55i4AAwfPpz333+f2bNnY2xszIULF7hy5QpbtmzJHENfy2IKgiAIBc+3ki9NXZtyMOQg089M5/LjyzxMfIibhZvcob2SoVLBT31r42Fnxqx9t/jj6F0exCTzuU9VHsWlUN7eHBdrU7nDLPW0Wi3/hP7Dr//+mpm0POv5JKMwPF0C+TqJi0ajZcLGy6RlaGjmVYZ+DV8+A5mYmMilS5eoU6cOFhYWBRVysSV74nLu3DnatGmT+fX48boqJIMHD2bZsmW89dZbPH78mEmTJhEWFkbdunXZvXt3vjuIjh49mtGjRxMfHy8qNbzA/v37mT59Ojdu3CA+Pp6MjAxSU1NJTk7GzMws2/N79erF6NGj2bRpE2+//TbLli2jTZs24kqBIAhCKeZs7kz/av05eP8gp0NPs+jKIiY3mSx3WK9FkiQ+bFeJsnamfL7hMjuvhLHzim7vhEKC6b61eKuRh8xRlk4pGSlsC9zG6uurCYwLzPE5CknBvDbzKGNapsCOG5USxYcHP8ySJOVmCeSasyGcDorG1FDJ9N61X1m0KDExkePHj1OxYkWRuKAHiUvr1q1feRV+zJgx8iwNe56hmW7mIzfiH4Gft26m5SlJCaNPg1UuZm8MsycKhSk4OJhu3brxwQcfMG3aNOzs7Dh+/DjDhg0jPT09x8TFyMiIQYMGsXTpUnx9fVm9ejVz584t0rgFQRAE/fRBnQ84HXqazXc2M7LWSFwsct6MrI961yuLkVLB6NX/Zt6n0cKX/gG0rOwgZl6K0KPER/x942823t5IfLquF5+pgSm9vHpRxqQMv136DY1Wg0JSMLnJZFq5tyrwGKY0nZK5BPLpcV5ntiU0LoXpO28A8KlPFTzKvPrcztnZmQkTJuQ75pJC9sRFLnlaKiZJr71cK5N9Jeg+F7aNA61al7R0n6O7X08YGRll+z6cP38ejUbDrFmzMvenrFu37pVjDR8+nJo1a/Lbb7+RkZGBr69vocQsCIIgFC8NnBrg7ezNmbAzLA5YzNdvfC13SLmSU6latVZLcGSySFwKmVar5Xz4eVZdX8XB+wcz90yVtShL/2r96eXVC0sjSwB6evXkfsJ93C3dC20/1dMlkLk5jlar5atNASSmZVDPw4YhTT0LJbaSrtQmLkW6VKz+IKjYDqLvgl0FsNavtb2enp6Ze4vKli2LpaUlXl5eqFQq5s+fT/fu3Tlx4gQLFix45VjVqlXjjTfeYMKECbz33nuYmopf5oIgCILO+3Xe50zYGfxv+zO81vBisVH/qfL25igk3UzLs1xtXryxWsifNHUaO+/uZPWN1dyIvpF5f2OXxgyoNoAWbi2ybbx3Nncukp+r3B5n66VHHLwRgZFSwU99aqN8zSIPkZGRbNmyhZ49e2Jvb5/XcEsMUeqpqFi7QfkWepe0APTp04dOnTrRpk0bHBwcWLNmDXXq1GH27Nn8+OOP1KxZk1WrVjF9+vTXGu/pcrL33nuvkCMXBEEQipNGzo1o4NQAlUbF4iuL5Q4nV1ysTZnuWwvlc3sSftl3SxSeKWARyRHMuzCPDus7MOnkJG5E38BEaULfyn3x7+HPoo6LaO3eukCrhRWmqMQ0pmy9CsCHbb2o5GT52q81MDDAwcEBA4NSO9eQhaQt5f+3PZ1xiYuLw8rKKstjqampBAUFUb58eUxMxBWV1/Xdd9+xfv16Ll++LHcor0V8zoIgCEXndOhphu8djqHCkF2+u3Ayz1+xnaIWGpdCcGQy4fGpfLL+EmqNlg/bevFJxypyh1bsXXp8iVXXV7EveB8ZWl2neGdzZ96p+g6+Xr7YmNjIG2AefbTmX7ZeekRVZ0u2jmmOkUHxmjd42blyUSu16Vue9rgIL5WYmEhwcDC//vor33//vdzhCIIgCHrI29mb+o71uRBxgaVXlzLRe6LcIeWKi7Vp5p6WtAw1EzZeYf7BO7jbmtGvkX4319RHKrWKPff2sPr6aq5EXsm8v75jfd6t9i5tPdpioCi+p6v7r4Wz9dIjFBL81Ld2rpMWtVqdWc1VqSweM0yFqXilfAVo9OjRXLt2jbNnz8odSokxZswYGjRoQOvWrcUyMUEQBCFHkiTxvzr/A2DDrQ08Tn4sc0R591YjD8a08QLgy01XOHa7+L6XwhaWFMaZ0DOEJenKSUelRLHg0gJ8NvrwxbEvuBJ5BUOFIT0r9mRdt3Us77ycjp4di3XSEp+q4qvNumRsRMsK1C5rk+sxIiIimD17NhEREQUcXfFUfH8aBL2zbNmyXDW/FARBEEqnJi5NqONQh0uPL7EkYAkTvItvuddPOlbmfkwyWy4+4oO/LrDhgyZUdZZ3OY2+8b/tn1k+WEKitkNtrkVdQ6VRAeBg6kC/Kv14s/KbBdpzRW7Td94gPD6N8vbmfNy+cp7GsLW15Z133sHW1raAoyueSu2MiyAIgiAI8pAkiQ/qfADA+lvriUyJlDmivJMkiZ/61sa7vB2JaRkMXXqW8PhUucPSG2FJYZlJC4AWLZceX0KlUVHbvjYzWsxgT589vF/n/RKVtJwMjGTNmRAAZvjWwsQwb8u8TExMqFy5stiD+0SpTVz8/PyoXr06jRo1kjsUQRAEQSh1mro2pbZ9bdLUaSwLWCZ3OPlibKDkj4ENqOBgTmhcKkOXniUxLUPusPRCSHxIZtLyrK8af8WqrqvoWqErhkpDGSIrPCnpaiZu1C0RG/CGB40r5D0hS0pK4syZMyQlJRVUeMVaqU1cxB4XQRAEQZCPJEm8X+d9ANbeXEtUSpTMEeWPjZkRy4Z4U8bciGuh8Xy4+gIZ6uwn7KWJVqvl2INj2e5XSApau7cu+oCKyKy9NwmJTsbV2oQJnarma6z4+Hj27t1LfHx8AUVXvJXaxEUQBEEQBHk1d2tOjTI1SFWnsvzqcrnDyTePMmYsHtIIE0MFh24+ZvLWq6W2x0uaOo0vjn/BsmvLAJDQ9b9RSAomN5lcrJqP5sa/ITEsOREEwLTetbA0yd9skouLC19//TUuLi4FEV6xJxIXQRAEQRBk8exel79v/k10arTMEeVfXXcb5rxVD0mCVadD+OPoXblDKnJRKVEM3zOcHXd3YCAZMKnJJPb23csSnyXs6bMH30q+codYKNIzNEzYeBmNFnrXc6NNVUe5QypxROIiCIIgCIJsWpZtSfUy1UnJSGHF1RVyh1MgOtV05uuu1QGYvusG2y8/kjmionM75jbv7nyXi48vYmlkye8dfufNym/ibO5MI+dGJXamBcDv0B1uhSdSxtyIb7pVL5Axo6KiWLFiBVFRxXspZUERiYsgCIIgCLKRJIn3a+v2uqy5sYbY1Fh5Ayogw5qXZ0hTTwDGr7vEueDiP5v0KsceHGPgroE8THyIh6UHq7qs4g2XN+QOq0jcCIvnt8N3AJjaswZ25kYFMq5CocDc3ByFQpyyQylOXEpyVbEhQ4YwZcoUucPQK56enhw+fFjuMARBEIQctHZvTVW7qiRnJLPiWsmYdQH4plt12ldzIj1Dw4gV5wiKLJmVobRaLauur2LMwTEkqZJo5NyIVV1WUd66vNyhFQm1RsuEDZdRqbV0qO5E11oFtx/F1taWPn36iD4uT5TaxEVUFStYKpUq233p6ekyRCIIgiAUN8/Ouqy+sZq4tDiZIyoYSoXEvHfqUrusNTHJKoYuPUN0Usn626jSqPj+n++ZcWYGGq0G30q+LGy/EBsTG7lDKzJLjgdx6UEcliYGfN+rJpIkFdjYGo2GtLQ0NJrSXaHuqVKbuBS1sKQwzoSeISwpTO5QANi2bRuNGjXCxMQEe3t7evfunfmYJEls3rw5y/NtbGxYtmwZAMHBwUiSxNq1a2nVqhUmJiasWrWKIUOG0KtXL6ZNm4arqytVqlQB4P79+/Tr1w8bGxvs7Ozo2bMnwcHBmWM/fd3PP/+Mi4sLZcqUYfTo0VmSobS0NCZMmIC7uzvGxsZ4eXmxePFitFotXl5e/Pzzz1nivXjxIpIkcefOnYL9xgmCIAiFoo1HGyrbViZJlcTKayvlDqfAmBkZsHhwI8ramhIclczw5WdJVanlDqtAxKfHM2r/KNbdWoeExKcNP2VKkyklri/LywRHJjFr300Avu5aDSergm0UGR4ezowZMwgPDy/QcYsrkbjkglarJVmVnOvb3zf+xmeDD8P2DsNngw9/3/g712MUZDnFHTt20Lt3b7p06cK///7LgQMH8Pb2zvU4EydOZOzYsVy/fh0fHx8ADhw4wM2bN9m3bx/bt29HpVLh4+ODpaUlx44d48SJE1hYWNCpU6csMzKHDh0iMDCQQ4cOsXz5cpYtW5aZKAEMGjSINWvWMG/ePK5fv87ChQuxsLBAkiTee+89li5dmiW2pUuX0rJlS7y8vPL2TRIEQRCKlEJSZPZ1WXV9VYmZdQFwsDRm2dBGWJkYcCEklk/WXUKjKd5lkkPiQxiwcwD/hP6DqYEpc9vMZXCNwQU626DvtFotE/0vk6rS0MyrDP0auhf4MWxsbOjbty82NjYFPnZxJGlLa4HxJ+Lj47G2tiYuLg4rK6ssj6WmphIUFET58uUxMTEhWZVM49WNZYnzdP/TmBmaFchYTZs2pUKFCvz11185Pi5JEps2baJXr16Z99nY2DBnzhyGDBlCcHAw5cuXZ86cOYwdOzbzOUOGDGH37t2EhIRgZKTblPbXX3/x/fffc/369cxfZunp6djY2LB582Y6duzIkCFDOHz4MIGBgSiVSgD69euHQqHg77//5tatW1SpUoV9+/bRvn37bPE+evQIDw8PTp48ibe3NyqVCldXV37++WcGDx78yu/H85+zIAiCIA+NVkOfrX24E3uHD+p8wKi6o+QOqUCdDIxk8JIzqNRa/teyAl90qSZ3SHlyNuwsHx/+mLi0OJzMnPi13a9Utctfo8XiJjQuhUXHglh8PAhTQyV7xrXEo0zBnKfpm5edKxc1MeNSCl28eJF27drle5yGDRtmu69WrVqZSQvApUuXuHPnDpaWllhYWGBhYYGdnR2pqakEBgZmPq9GjRqZSQvoGi5FRERkxqtUKmnVqlWOcbi6utK1a1eWLFkC6JbBpaWl8eabb+b7PQqCIAhFRyEp+F+d/wHw17W/iE8vWd3Cm1a056e+tQFYePQuK/+5J3NEubfp9iZG7htJXFocNcvUZE3XNaUuaVl7NoRmMw6y+Liu0WS7ao6FlrQkJydz8eJFkpOTC2X84sZA7gCKE1MDU073P52r14Qnh9Nrcy80/LepSiEp2NxzM05mTrk6dkExNX35WJIkZVualtPme3Nz81fel5iYSIMGDVi1alW25zo4OGT+t6Fh1vWwkiRlbkR7VbwAw4cPZ+DAgfzyyy8sXbqUt956CzOzknnlQxAEoSTrWK4jC6wXEBgXyOrrqzOXj5UUveuV5X50CrP33WLylgDK2pgWi0aFGq2GORfmsDRAtzTbx9OH75t9j4lB6VqpEBqXwhf+V3h2pd/OK6GExqXgYl1w52pPxcXFsWXLFkaOHCnOayjFMy55KYcsSRJmhma5upW3Ls/kppNRSLpvtUJSMLnJZMpbl8/VOAW5ZrR27docOHDghY87ODgQGhqa+fXt27fznOnXr1+f27dv4+joiJeXV5abtbX1a41Rq1YtNBoNR44ceeFzunTpgrm5Ob///ju7d+/mvffey1O8giAIgryenXVZeW0liemJMkdU8D5s60XfBmXRaGH06gsEPNTv/TzJqmQ+PvRxZtLyfp33+anlT6UuaQEIeBDH89uTNFoIjiycGRFnZ2e++eYbnJ1LbuPO3Ci1iUtRlkP2reTLnj57WOKzhD199uBbybfQj/kykydPZs2aNUyePJnr169z5coVfvzxx8zH27Zty6+//sq///7LuXPneP/997PNiLyud999F3t7e3r27MmxY8cICgri8OHDfPTRRzx48OC1xvD09GTw4MG89957bN68OXOMdevWZT5HqVQyZMgQvvjiCypVqkSTJk3yFK8gCIIgv47lOlLeujzx6fGsvrFa7nAKnCRJ/NC7Fs28ypCcrua9ZWd5GJsid1g5CksKY8juIRy8fxAjhRHTW0xndN3RmRdkS5O4ZBWz9t3Kdr9SkvC0L5zZEEmSUCgUparowcuUvp86mTibO9PIuRHO5vJnzK1bt2b9+vVs3bqVunXr0rZtW86cOZP5+KxZs3B3d6dFixb079+fTz/9NM/Tk2ZmZhw9ehQPDw98fX2pVq0aw4YNIzU1NVcbvH7//Xf69u3LqFGjqFq1KiNGjCApKWsjr2HDhpGens7QoUPzFKsgCIKgH5QKJSNrjwRgxbUVJKlKXuNGIwMFvw9oQGUnCyIS0nhv6VniU7Mvy5bT1cir9N/Rn+vR17EzsWOxz2K6Vegmd1iyiEtRMXDJaW6EJWBupETxJI9QShI/+NYslGViANHR0axZs4bo6OhCGb+4EVXFclFVTNBvx44do127dty/fx8np9ffPyQ+Z0EQBP2j1qjptaUXwfHBjK0/luG1hssdUqF4GJtCL78TPE5Io7mXPUuHNsJQKf915b3Be/nq+FekqlPxsvHi13a/4mbhJndYsohPVTFw0WkuPYjDztyI1SMaY21qSHBkMp72ZoWWtIAucdmzZw8+Pj7Y2dkV2nFeRlQVE4QClJaWxoMHD5gyZQpvvvlmrpIWQRAEQT89O+uy/OpyklUls6qSm40pS4c0wsxIyfE7kXzpf6VAe7flloDh6agAAB0xSURBVFar5Y/Lf/DJkU9IVafSwq0FKzuvLLVJS0KqikGLz3DpQRy2Zob8NawxVZ2tcLE2pUnFMoWatADY2dnxzjvvyJa06BuRuAjF3po1ayhXrhyxsbH89NNPcocjCIIgFJDO5TvjYelBbFosf9/8W+5wCk1NN2t+7V8PhQTrzz/g14N3ZIkjXZ3Ol8e/ZP6/8wEYUG0A89vOx8LIQpZ45JaYlsHgJWe4eD8WGzND/hremOquRTvjoNVq0Wg0siaz+kQkLkKxN2TIENRqNefPn8fNrXReERIEQSiJDBQGjKg9AijZsy4Abas6MbVHDQBm7bvF0hN3ORkYSWhc4W7aD0sK40zoGa5HXWfYnmFsv7sdpaTkmze+YYL3BJQK5asHKYES0zIYsuQMF0JisTbVzbTUcH29aqgFKSwsjO+++46wsLAiP7Y+En1cBEEQBEHQW90qdGPhpYU8SHzA+lvrGVxjsNwhFZqBTTy5H5PCH0fvMnXbdQAUEkz3rcVbjTwK/Hj+t/2ZemoqGu1/veYsDS2Z1XoWTVxLb3XOpLQMhi49w7l7MViZGPDXsMbUdCv6pAXA2tqanj17vnYLiZJOzLgIgiAIgqC3DBQGmXtdlgQsISVDP8sGF5TBTcpl+VqjhS/9Awp85iUsKSxb0gIwp+2cUp20JKdnMHTZWc4Gx2BpYsBfwxtTq6x8SYOZmRl169YVzSefKLWJS14aUAqCIAiCUPS6VeyGm4Ub0anRrL+5Xu5wCtW96OzL4dRaLVcfxRfocS5GXMyWtABIlN5+ISlPeuqcCYrG0tiAlcMaU7usjbwxpaRw9epVUlJKdsL+ukpt4lKUDSgFQRAEQcg7Q4VhZjnkpVeXkpqRKnNEhae8vXlmj5BnTdhwmV1XQvO9SVutUbP6+mq+OfFNtscUkgJ3S/d8jV9cpaSrGbb8LP/cjcbC2IAVw7yp624jd1jExsayYcMGYmNj5Q5FL5TaxEUQBEEQhOKjZ8WeuJi7EJkSycbbG+UOp9C4WJsy3bcWyied0hUS2FsYEZWUzgerLjBy5XnC4vKWuN2Ouc2g3YOYfmY6qepU3C3cUUiKJ8dRMLnJZL1olF3UUlVqRqw4x8nAKMyNlCx/z5t6HrZyhwWAk5MTEydOFK0enhCb8wVBEARB0HuGSt2sy3f/fMeSK0voW7kvxkpjucMqFG818qBlZYfMBoe2Zkb4HbrD74cD2XctnFOBUUzoXJV3vT1Q5DQ985w0dRp/XP6DJQFLyNBkYG5ozrj64+hXpR8RyRHcT7iPu6V7qU5ajt+JxOxJ0tKgnH4kLQAKhQJj45L5c54XYsalFJoyZQp169aVOwxBEARByJVeXr1wNncmIiWCjbdK7qwLkKXBoYmhkk86VmHHRy2o52FDYloG32wOoN/CU9wOT3jpOOfCztF3a1/+uPwHGZoM2ri3YXPPzbxd9W0UkgJnc2caOTcqtUnLyJXnOXZbl7QsG+pNQ0/9avQYExPDxo0biYmJkTsUvSASlxJoyJAhTJkyRe4wGDJkCL169cp2vyRJBAcHF3k8giAIQvFmpDRiWM1hACwOWEy6Ol3miIpWFWdLNrzflKk9amBupOTcvRi6zDvGL/tukZahzvLc+PR4ppycwtA9QwmOD8be1J7ZrWczt83cUpmkPC8tQ837f53n6K3HmBoqWTqkEd7l9StpAdBoNCQlJaHRZC+kUBqJxEUQBEEQhGLDt5IvjmaORCRHsOn2JrnDKXJKhcTgpp7sG9+KdlUdUam1zD1wm67zjnM2OBqtVsu+e/voubln5l6gvpX7sqXXFjqU64Akld6qYU+lZaj54K8LHL75GBNDBUuGNKJxhTJyh5WjMmXKMGjQIMqU0c/4ippIXIqIKiyMpH9Oo9KjzqcLFy7E3d0dMzMz+vXrR1xcXOZjGo2Gb7/9lrJly2JsbEzdunXZvXt3ltdfuXKFtm3bYmpqSpkyZRg5ciSJiYmAbjna8uXL2bJlC5IkIUkShw8fLsq3JwiCIJRAz866LApYVOpmXZ5ytTFl0eCG/Nq/HvYWxtyJSKTfot10XD2U8YfHE5kSiaeVJ0t9ljK5yWSsjKzkDlkvpGdoGL3qAgdvRGBsoGDJ4EY0qSiSguJCJC65oNVq0SQn5/oWvXo1d9q2I2TIEO60bUf06tW5HiO/5Q+fd+fOHdatW8e2bdvYvXs3//77L6NGjcp8fO7cucyaNYuff/6Zy5cv4+PjQ48ePbh9+zYASUlJ+Pj4YGtry9mzZ1m/fj379+9nzJgxAHz66af069ePTp06ERoaSmhoKE2bNi3Q9yAIgiCUTn0q98HB1IGwpDA239ksdziykSSJbrVd2fdxC96ocwPzCrMJyzgPWgXtnN9lQ48NNHRuKHeYeiM9Q8Po1RfYf12XtCwe3IimXvZyh/VSoaGhfP/994SGhsodil6QtAV9RlzMxMfHY21tTVxcHFZWWa9GpKamEhQURPny5TExMUGTnMzN+g1kibPKhfMoCqhr6pQpU/j++++5d+8ebm5uAOzevZuuXbvy8OFDnJ2dcXNzY/To0Xz55ZeZr/P29qZRo0b4+fnx559/MmHCBO7fv4+5uTkAO3fupHv37jx69AgnJyeGDBlCbGwsmzdvLpC4C8vzn7MgCIKg//669hc/nv0RF3MXdvTegaHSUO6QZBEYG8iUk1O4+PgiAAYqT+Lu90KT5oxPDSe+7VkTJyvxt02l1jBm9QX2XA3HyEDBokENaVnZQe6wXikpKYmrV69So0aNzPOtovayc+WiJmZcSikPD4/MpAWgSZMmaDQabt68SXx8PI8ePaJZs2ZZXtOsWTOuX78OwPXr16lTp06W/4maNWuWOYYgCIIgFKa+lftib2pPaFIoWwK3yB1OkUtXp+N30Y++2/py8fFFzAzM+ML7C44P3MgHTZthoJDYczWc9rOO8Nc/99BoSu91apVaw0dr/s1MWv4sJkkLgLm5Od7e3rIlLfqmRPRx2b59O5988gkajYYJEyYwfPjwQjmOZGpKlQvnc/UaVXg4d7t2g2erQSgUVNixHcNcNBOSTE1zdVxBEARBKMlMDEwYWmMoM8/NZNGVRfT06omhonTMulwIv8CUU1MIigsCoHXZ1nz1xleZ1cI+86lKt9quTNx4mUsP4vh6cwBbLj5kum9tvBwt5Ay9yGWoNYz7+yK7AsIwUipYOLABrYpJ0gK6VSEhISF4eHiIVSGUgBmXjIwMxo8fz8GDB/n333+ZOXMmUVFRhXIsSZJQmJnl6mZcvjwu304FxZNvtUKBy7dTMS5fPlfjFHQVkJCQEB49epT59T///INCoaBKlSpYWVnh6urKiRMnsrzmxIkTVK9eHYBq1apx6dIlkpKSsjz+dAwAIyMj1Oqs5RkFQRAEoaC8WeVN7EzseJj4kO2B2+UOp9AlpCfw7alvGbx7MEFxQZQxKcPPrX5mXtt52UocV3Oxwn9UMyZ1q46ZkZKzwTF0mXuMuftvk55ROkrrZqg1jFt7kR1XQjFUSiwYWJ82VRzlDitXYmJiWLNmjejj8kSxT1zOnDlDjRo1cHNzw8LCgs6dO7N37165w8rCpm9fvA4ewGP5crwOHsCmb1+5Q8LExITBgwdz6dIljh07xkcffUS/fv1wdn5yteazz/jxxx9Zu3YtN2/eZOLEiVy8eJGxY8cC8O6772aOERAQwKFDh/jwww8ZOHAgTk9mkjw9Pbl8+TI3b94kMjISlUol2/sVBEEQSh5TA1OG1hgKwB+X/0ClKbl/Z/bf20/PzT1Zf2s9AH0q9WFLry34ePq88OKmUiHxXvPy7P24JW2qOJCu1vDL/lt0nXeM8/eiizL8Ipeh1jB+3SW2X9YlLb+/24C2VV9/pYu+cHR0ZPz48Tg6Fq+Eq7DInrgcPXqU7t274+rqiiRJOW7k9vPzw9PTExMTExo3bsyZM2cyH3v06FGWvRpubm48fPiwKELPFUNnZ8wbe2PorB9Nn7y8vPD19aVLly507NiR2rVr89tvv2U+/tFHHzF+/Hg++eQTatWqxe7du9m6dSuVKlUCwMzMjD179hAdHU2jRo3o27cv7dq149dff80cY8SIEVSpUoWGDRvi4OCQbQZHEARBEPKrX5V+2JnY8SDxAfMvzCcsqXDbDoQlhXEm9EyRHedq5FXGHRrHx4c/5nHKY8pZlWOJzxKmNJ2CtbH1a41V1taMJUMaMe+depQxN+J2RCJ9F5zim80B3A5P4GRgJKFxKYX6fkLjUorsOMdvRzJ61QW2XnqEgULCr3992lcvfkkLgFKpxNLSEqVSKXcoekH2qmK7du3ixIn/t3f/QVGV/x7A37v8WOV3/NzdZBHFH2MqjggrOaEGX/HHmIZjmk2thDIVekOuWdQYOnbTxCZG4+a3acIZRx3FRLPJJr8kml/QJhrwOpMkBIHCStgFBJJF97l/EHu/G5R9a3ef4/J+zewMe/awz3tnHx7Px3Oe5/wTcXFxSEtLQ0lJid3d1g8fPoxnnnkGe/fuhdFoREFBAYqLi1FTU4Pw8HAcPXoUZWVltgPm/Px8qFQqbNy48Q+1/++sKkbuid8zEdH9LacsB6d/OA0AUEGFrGlZ+Nvovzm8ndMNp1FYVQgB4bJ2BniqPJE+OR2ZUzMxwvPP/1v1v90W/Nen3+Jo5TW77WoVsCFlPBZM0f3p9/4tp/6nBe/84ztYhevaAfrb+u+n4jB/sjL+0/jPaG9vx7lz55CUlISgoCApGZS0qpj0wuVfqVSqQYWL0WhEfHy8rTCxWq2IjIzE+vXr8corr6C8vBz5+fkoKem/e252djYSEhKwatWqIdvo7e1Fb2+v7XlnZyciIyNZuAxj/J6JiO5f5m4zUo+mwgr3nrfx95S/4+EHHXc/tI+rr+M/DlU57P2USK0C/vnKo9AF3r8LHLW1teHEiRNYsmQJQkPl3HNGSYWLolcVs1gsqKysRG5urm2bWq1GSkoKKioqAPTfW+Ty5cu4fv06AgMDcerUKWzevPk333P79u3YunWr07MTERGR8zV2Ng5ZtPh4+sBT7bjDnDvWO+i50yOtHUffpybUTzPkdl9vD3h6OG4mwZ27VnRbBi/U44p2rAJoaOu5rwuX0NBQZGRkyI6hGIouXNra2nD37l3bZO8BERERuHLlCgDA09MTb7/9NubOnQur1YpNmzYhJCTkN98zNzcXOTk5tucDZ1yIiIjo/mMIMECtUsMq/r94UavUOLH0xKCVtv4Kc7cZqR+lSmsn0t+xxyrRob5Qq4B/vb2Lh0qFf/znbIce6Ld0/IxZO76Q1s7oUMfcvJuUQfrkfEd47LHH8N1336G2thaZmZm/u69Go0FAQAD279+PmTNnIjk52UUpiYiIyNG0vlrkJeZBreo/pFGr1MhLzHNoMeGO7egCR2J72hR4/LIimYdKhTfTJjv87IS7teNqZrMZb731Fsxm5y4Gcb9Q9BwXi8UCHx8fHD161G7ei8lkQnt7O06c+Ot3yuXkfOL3TER0/zN3m9F0qwmR/pEOP8h353ZaOn5GQ1sPRof6OPUg393acZWuri5UV1cjNjYWfn5ybh7KOS5/kLe3N+Li4lBaWmorXKxWK0pLS7Fu3TqX5VBQbUdOwO+XiOj+p/XVOvUA313b0QWOdMkBvru14yp+fn6YNWuW7BiKIb1w6erqQm1tre15fX09qqqqEBwcDIPBgJycHJhMJsyYMQMJCQkoKChAd3c30tPT/1K7hYWFKCws/N07uw+smW2xWDBypPv8EZC9np7+SZBeXo6d+EhERET0V/T29qKlpQU6nQ4azdALKgwn0i8VKysrw9y5cwdtN5lM2LdvHwDg3XffRX5+PsxmM6ZNm4bdu3fDaDQ6pP3fO/0lhEBjYyP6+vqg1+uhVrvFlCD6hRACPT09aG1tRVBQEHQ6x68pT0RERPRntbS04P3330dmZqa04xQlXSomvXCR7V5fhsViQX19PaxW914ffjgLCgqCVquF6pcJfURERERKcOfOHXR2diIgIACennIulFJS4SL9UjFZ/silYkD/PJtx48bBYrG4KBm5kpeXl+2SQCIiIiIl8fT0RHBwsOwYisEzLgqqIomIiIiIBnR0dKC8vBwPP/wwAgMDpWRQ0rEyJ20QERERESmQxWJBQ0MDr/z5Bc+4KKiKJCIiIiJSEiUdKw/bMy6FhYWYNGkS4uPjZUchIiIiIqJ7GPZnXDo6OhAUFISmpibpVSQRERER0YDW1lYcOXIETzzxBMLDw6Vk6OzsRGRkJNrb26XNsxkw7AuXa9euITIyUnYMIiIiIiLFampqwqhRo6RmGPaFi9VqRXNzM/z9/aXcx2OgiuUZn+GN/YAA9gPqx35AAPsB9VNCPxBC4NatW4q4GfuwvY/LALVaLb16BICAgAAOTMR+QADYD6gf+wEB7AfUT3Y/kH2J2IBhOzmfiIiIiIjuHyxciIiIiIhI8Vi4SKbRaJCXlweNRiM7CknEfkAA+wH1Yz8ggP2A+rEf2Bv2k/OJiIiIiEj5eMaFiIiIiIgUj4ULEREREREpHgsXIiIiIiJSPBYukhUWFmL06NEYMWIEjEYjvvrqK9mRyIW2bNkClUpl95g4caLsWORk586dw+LFi6HX66FSqXD8+HG714UQeP3116HT6TBy5EikpKTg6tWrcsKS09yrH6xevXrQ+DB//nw5Yclptm/fjvj4ePj7+yM8PBxLly5FTU2N3T63b99GVlYWQkJC4Ofnh2XLluHGjRuSEpMz/JF+MGfOnEFjwnPPPScpsRwsXCQ6fPgwcnJykJeXh2+++QaxsbFITU1Fa2ur7GjkQg899BBaWlpsj/Pnz8uORE7W3d2N2NhYFBYWDvn6zp07sXv3buzduxcXL16Er68vUlNTcfv2bRcnJWe6Vz8AgPnz59uND4cOHXJhQnKFs2fPIisrCxcuXMDp06fR19eHefPmobu727bPhg0bcPLkSRQXF+Ps2bNobm5GWlqaxNTkaH+kHwDA2rVr7caEnTt3SkosiSBpEhISRFZWlu353bt3hV6vF9u3b5eYilwpLy9PxMbGyo5BEgEQJSUltudWq1VotVqRn59v29be3i40Go04dOiQhITkCr/uB0IIYTKZxJIlS6TkIXlaW1sFAHH27FkhRP/fv5eXlyguLrbt8+233woAoqKiQlZMcrJf9wMhhJg9e7Z48cUX5YVSAJ5xkcRisaCyshIpKSm2bWq1GikpKaioqJCYjFzt6tWr0Ov1GDNmDJ566ik0NjbKjkQS1dfXw2w2240NgYGBMBqNHBuGobKyMoSHh2PChAl4/vnncfPmTdmRyMk6OjoAAMHBwQCAyspK9PX12Y0JEydOhMFg4Jjgxn7dDwYcOHAAoaGhmDx5MnJzc9HT0yMjnjSesgMMV21tbbh79y4iIiLstkdERODKlSuSUpGrGY1G7Nu3DxMmTEBLSwu2bt2KRx55BJcvX4a/v7/seCSB2WwGgCHHhoHXaHiYP38+0tLSEB0djbq6Orz66qtYsGABKioq4OHhITseOYHVakV2djZmzZqFyZMnA+gfE7y9vREUFGS3L8cE9zVUPwCAVatWISoqCnq9HpcuXcLLL7+MmpoaHDt2TGJa12LhQiTRggULbD9PnToVRqMRUVFROHLkCDIyMiQmIyLZVq5caft5ypQpmDp1KsaOHYuysjIkJydLTEbOkpWVhcuXL3Ou4zD3W/0gMzPT9vOUKVOg0+mQnJyMuro6jB071tUxpeClYpKEhobCw8Nj0KogN27cgFarlZSKZAsKCsL48eNRW1srOwpJMvD3z7GBfm3MmDEIDQ3l+OCm1q1bh08++QRnzpzBqFGjbNu1Wi0sFgva29vt9ueY4J5+qx8MxWg0AsCwGhNYuEji7e2NuLg4lJaW2rZZrVaUlpYiMTFRYjKSqaurC3V1ddDpdLKjkCTR0dHQarV2Y0NnZycuXrzIsWGYu3btGm7evMnxwc0IIbBu3TqUlJTgiy++QHR0tN3rcXFx8PLyshsTampq0NjYyDHBjdyrHwylqqoKAIbVmMBLxSTKycmByWTCjBkzkJCQgIKCAnR3dyM9PV12NHKRjRs3YvHixYiKikJzczPy8vLg4eGBJ598UnY0cqKuri67/yGrr69HVVUVgoODYTAYkJ2djTfeeAPjxo1DdHQ0Nm/eDL1ej6VLl8oLTQ73e/0gODgYW7duxbJly6DValFXV4dNmzYhJiYGqampElOTo2VlZeHgwYM4ceIE/P39bfNWAgMDMXLkSAQGBiIjIwM5OTkIDg5GQEAA1q9fj8TERMycOVNyenKUe/WDuro6HDx4EAsXLkRISAguXbqEDRs2ICkpCVOnTpWc3oVkL2s23O3Zs0cYDAbh7e0tEhISxIULF2RHIhdasWKF0Ol0wtvbWzz44INixYoVora2VnYscrIzZ84IAIMeJpNJCNG/JPLmzZtFRESE0Gg0Ijk5WdTU1MgNTQ73e/2gp6dHzJs3T4SFhQkvLy8RFRUl1q5dK8xms+zY5GBD9QEAoqioyLbPzz//LF544QXxwAMPCB8fH/H444+LlpYWeaHJ4e7VDxobG0VSUpIIDg4WGo1GxMTEiJdeekl0dHTIDe5iKiGEcGWhRERERERE9O/iHBciIiIiIlI8Fi5ERERERKR4LFyIiIiIiEjxWLgQEREREZHisXAhIiIiIiLFY+FCRERERESKx8KFiIiIiIgUj4ULEREREREpHgsXIiJyqDlz5iA7O1t2DCIicjMsXIiIiIiISPFYuBARkVuxWCyyIxARkROwcCEiIqfZv38/ZsyYAX9/f2i1WqxatQqtra0AACEEYmJisGvXLrvfqaqqgkqlQm1tLQCgvb0da9asQVhYGAICAvDoo4+iurratv+WLVswbdo0fPDBB4iOjsaIESNc9wGJiMhlWLgQEZHT9PX1Ydu2baiursbx48fR0NCA1atXAwBUKhWeffZZFBUV2f1OUVERkpKSEBMTAwBYvnw5WltbcerUKVRWVmL69OlITk7GTz/9ZPud2tpafPTRRzh27Biqqqpc9fGIiMiFVEIIITsEERG5jzlz5mDatGkoKCgY9NrXX3+N+Ph43Lp1C35+fmhubobBYEB5eTkSEhLQ19cHvV6PXbt2wWQy4fz581i0aBFaW1uh0Whs7xMTE4NNmzYhMzMTW7ZswZtvvonr168jLCzMhZ+UiIhciWdciIjIaSorK7F48WIYDAb4+/tj9uzZAIDGxkYAgF6vx6JFi/Dhhx8CAE6ePIne3l4sX74cAFBdXY2uri6EhITAz8/P9qivr0ddXZ2tnaioKBYtRERuzlN2ACIick/d3d1ITU1FamoqDhw4gLCwMDQ2NiI1NdVuAv2aNWvw9NNP45133kFRURFWrFgBHx8fAEBXVxd0Oh3KysoGvX9QUJDtZ19fX2d/HCIikoyFCxEROcWVK1dw8+ZN7NixA5GRkQD6LxX7tYULF8LX1xfvvfcePvvsM5w7d8722vTp02E2m+Hp6YnRo0e7KjoRESkQLxUjIiKnMBgM8Pb2xp49e/D999/j448/xrZt2wbt5+HhgdWrVyM3Nxfjxo1DYmKi7bWUlBQkJiZi6dKl+Pzzz9HQ0IDy8nK89tprQxZBRETkvli4EBGRU4SFhWHfvn0oLi7GpEmTsGPHjkFLHw/IyMiAxWJBenq63XaVSoVPP/0USUlJSE9Px/jx47Fy5Ur88MMPiIiIcMXHICIiheCqYkREJN2XX36J5ORkNDU1sSAhIqIhsXAhIiJpent78eOPP8JkMkGr1eLAgQOyIxERkULxUjEiIpLm0KFDiIqKQnt7O3bu3Ck7DhERKRjPuBARERERkeLxjAsRERERESkeCxciIiIiIlI8Fi5ERERERKR4LFyIiIiIiEjxWLgQEREREZHisXAhIiIiIiLFY+FCRERERESKx8KFiIiIiIgUj4ULEREREREp3v8B29LpWBjccEoAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "64f87b93",
+      "metadata": {},
+      "source": [
+        "## 2. Reading: a two-hop prompt with an unspoken intermediate\n",
+        "\n",
+        "*\"The currency used in the country shaped like a boot\"* requires an intermediate step — **Italy** —\n",
+        "that never appears in the prompt. The J-lens surfaces the intermediate and the answer's concept\n",
+        "band in the middle of the model, while the logit lens at the same layers mostly tracks surface\n",
+        "continuations."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "PINNED = [\" euro\", \" Italy\", \" currency\", \" boot\"]\n",
-    "ranks = {token: [] for token in PINNED}\n",
-    "layers = list(range(model.cfg.n_layers))\n",
-    "for token in PINNED:\n",
-    "    token_id = model.to_single_token(token)\n",
-    "    for layer in layers:\n",
-    "        logits = result_jlens.lens_logits[layer][-1]\n",
-    "        ranks[token].append(int((logits > logits[token_id]).sum().item()) + 1)\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(8, 4))\n",
-    "for token, values in ranks.items():\n",
-    "    ax.plot(layers, values, marker=\"o\", markersize=3, label=repr(token))\n",
-    "ax.set(yscale=\"log\", xlabel=\"layer\", ylabel=\"J-lens rank (log)\", title=f\"J-lens rank of pinned tokens — {PROMPT!r}\")\n",
-    "ax.axvline(25, color=\"gray\", ls=\":\", lw=1)\n",
-    "ax.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b41698de",
-   "metadata": {},
-   "source": [
-    "## 3. Intervening: swap France for China in lens coordinates\n",
-    "\n",
-    "The paper's *flexible generalization* protocol: pick source and target tokens, form\n",
-    "$V = [v_s, v_t]$ from their J-lens vectors, read the activation's lens coordinates $c = V^+ h$\n",
-    "(pseudoinverse), and write back $h \\leftarrow h + \\alpha\\, V(\\sigma(c) - c)$ where $\\sigma$\n",
-    "exchanges the two coordinates — leaving the orthogonal complement of the activation untouched.\n",
-    "The swap is clamped at every token position across an intermediate-layer band (layers 10–24 ≈\n",
-    "the paper's 38–92% workspace band)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "b2359547",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-07-11T09:06:29.590963Z",
-     "iopub.status.busy": "2026-07-11T09:06:29.590451Z",
-     "iopub.status.idle": "2026-07-11T09:06:30.101969Z",
-     "shell.execute_reply": "2026-07-11T09:06:30.101589Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "'Most people in France speak'\n",
-      "  baseline : [' French', ' English', ' a', ' at', ' the']\n",
-      "  swapped  : [' Chinese', ' English', ' a', ' some', ' the']\n",
-      "'The capital of France is'\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "1b19ec1a",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-07-11T09:06:28.217489Z",
+          "iopub.status.busy": "2026-07-11T09:06:28.217204Z",
+          "iopub.status.idle": "2026-07-11T09:06:29.114425Z",
+          "shell.execute_reply": "2026-07-11T09:06:29.113969Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "layer | J-lens top-1         | logit lens top-1\n",
+            "    4 | ' anything'          | ' is'\n",
+            "    8 | '\\n\\n\\n'             | ' is'\n",
+            "   13 | 'LookAnd'            | ' is'\n",
+            "   17 | 'LookAnd'            | ' called'\n",
+            "   21 | ' euro'              | ' called'\n",
+            "   24 | ' the'               | ' the'\n",
+            "   25 | ' the'               | ' the'\n",
+            "model output:  the\n"
+          ]
+        }
+      ],
+      "source": [
+        "PROMPT = \"Fact: The currency used in the country shaped like a boot is\"\n",
+        "\n",
+        "result_jlens = lens.readout(model, PROMPT, positions=[-1], return_full_logits=True)\n",
+        "result_logitlens = lens.readout(model, PROMPT, positions=[-1], use_jacobian=False)\n",
+        "\n",
+        "top_j = result_jlens.top_tokens(model.tokenizer, k=1)\n",
+        "top_l = result_logitlens.top_tokens(model.tokenizer, k=1)\n",
+        "print(f\"{'layer':>5} | {'J-lens top-1':<20} | logit lens top-1\")\n",
+        "for layer in [4, 8, 13, 17, 21, 24, 25]:\n",
+        "    print(f\"{layer:>5} | {top_j[layer][-1][0]!r:<20} | {top_l[layer][-1][0]!r}\")\n",
+        "print(\"model output:\", top_j[25][-1][0])"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  baseline : [' a', ' the', ' one', ' also', ' home']\n",
-      "  swapped  : [' a', ' the', ' one', ' also', ' home']\n"
-     ]
-    }
-   ],
-   "source": [
-    "def show_next_tokens(prompt, hooks=None, k=5):\n",
-    "    tokens = model.to_tokens(prompt)\n",
-    "    with torch.no_grad():\n",
-    "        if hooks:\n",
-    "            with model.hooks(fwd_hooks=hooks):\n",
-    "                logits = model(tokens)[0, -1].float()\n",
-    "        else:\n",
-    "            logits = model(tokens)[0, -1].float()\n",
-    "    top = logits.topk(k).indices.tolist()\n",
-    "    return [model.tokenizer.decode([t]) for t in top]\n",
-    "\n",
-    "BAND = range(10, 25)\n",
-    "swap = lens.swap_hooks(model, \" France\", \" China\", layers=BAND, alpha=1.0)\n",
-    "\n",
-    "for prompt in [\"Most people in France speak\", \"The capital of France is\"]:\n",
-    "    print(f\"{prompt!r}\")\n",
-    "    print(\"  baseline :\", show_next_tokens(prompt))\n",
-    "    print(\"  swapped  :\", show_next_tokens(prompt, hooks=swap))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "cdc8c44e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-07-11T09:06:30.105260Z",
-     "iopub.status.busy": "2026-07-11T09:06:30.104879Z",
-     "iopub.status.idle": "2026-07-11T09:06:30.422053Z",
-     "shell.execute_reply": "2026-07-11T09:06:30.421595Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "08ac5860",
+      "metadata": {},
+      "source": [
+        "The answer concept (` euro`) and the intermediate's semantic neighborhood dominate the J-lens\n",
+        "readout from the middle of the model onward; the logit lens catches up only in the last few\n",
+        "layers, where $J_\\ell \\to I$ and the two lenses converge. (Early layers are noise under both —\n",
+        "the paper's \"sensory\" band. The recurring code-ish tokens in early J-lens readouts are a known\n",
+        "signature of gemma-2-2b's lens.)\n",
+        "\n",
+        "We can watch each concept's *rank trajectory* across depth:"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "'Most people in France speak': rank of ' Chinese' 348 -> 1\n",
-      "'The capital of France is': rank of ' Beijing' 969 -> 9\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Rank of the China-appropriate answer, before and after the swap\n",
-    "for prompt, answer in [(\"Most people in France speak\", \" Chinese\"), (\"The capital of France is\", \" Beijing\")]:\n",
-    "    answer_id = model.to_single_token(answer)\n",
-    "    tokens = model.to_tokens(prompt)\n",
-    "    with torch.no_grad():\n",
-    "        base = model(tokens)[0, -1].float()\n",
-    "        with model.hooks(fwd_hooks=swap):\n",
-    "            swapped = model(tokens)[0, -1].float()\n",
-    "    base_rank = int((base > base[answer_id]).sum().item()) + 1\n",
-    "    swap_rank = int((swapped > swapped[answer_id]).sum().item()) + 1\n",
-    "    print(f\"{prompt!r}: rank of {answer!r} {base_rank} -> {swap_rank}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6506c6a",
-   "metadata": {},
-   "source": [
-    "At $\\alpha=1$ the language template flips top-1 outright (`French` → `Chinese`), and the\n",
-    "China-appropriate answers jump hundreds of ranks on the others — the same swap redirecting\n",
-    "*different* downstream computations, which is the broadcast/workspace claim. (On this small base\n",
-    "model the capital template's top-1 is a filler word even unperturbed; the paper reports 42/48\n",
-    "top-1 success for country swaps on Claude Sonnet 4.5, and that doubling to $\\alpha=2$ recovers\n",
-    "some $\\alpha=1$ failures. On gemma-2-2b we instead observe $\\alpha=2$ overshooting into\n",
-    "verbalizing the injected concept — the swapped prompt's top-1 becomes ` China` itself — so\n",
-    "$\\alpha=1$ is the better default here.)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7020c7b5",
-   "metadata": {},
-   "source": [
-    "## 4. Steering along a J-lens vector\n",
-    "\n",
-    "`steering_hooks` adds a token's unit-normalized lens direction, scaled by the activation's median\n",
-    "residual norm times `alpha` (the paper's directed-modulation protocol). `ablation_hooks`\n",
-    "(projecting a direction *out*) works the same way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "89692d80",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-07-11T09:06:30.427385Z",
-     "iopub.status.busy": "2026-07-11T09:06:30.427165Z",
-     "iopub.status.idle": "2026-07-11T09:06:30.640333Z",
-     "shell.execute_reply": "2026-07-11T09:06:30.639945Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "2e0887dd",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-07-11T09:06:29.118703Z",
+          "iopub.status.busy": "2026-07-11T09:06:29.118453Z",
+          "iopub.status.idle": "2026-07-11T09:06:29.588577Z",
+          "shell.execute_reply": "2026-07-11T09:06:29.588189Z"
+        }
+      },
+      "outputs": [
+        {
+          "data": {
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",
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "PINNED = [\" euro\", \" Italy\", \" currency\", \" boot\"]\n",
+        "ranks = {token: [] for token in PINNED}\n",
+        "layers = list(range(model.cfg.n_layers))\n",
+        "for token in PINNED:\n",
+        "    token_id = model.to_single_token(token)\n",
+        "    for layer in layers:\n",
+        "        logits = result_jlens.lens_logits[layer][-1]\n",
+        "        ranks[token].append(int((logits > logits[token_id]).sum().item()) + 1)\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(8, 4))\n",
+        "for token, values in ranks.items():\n",
+        "    ax.plot(layers, values, marker=\"o\", markersize=3, label=repr(token))\n",
+        "ax.set(yscale=\"log\", xlabel=\"layer\", ylabel=\"J-lens rank (log)\", title=f\"J-lens rank of pinned tokens — {PROMPT!r}\")\n",
+        "ax.axvline(25, color=\"gray\", ls=\":\", lw=1)\n",
+        "ax.legend()\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b41698de",
+      "metadata": {},
+      "source": [
+        "## 3. Intervening: swap France for China in lens coordinates\n",
+        "\n",
+        "The paper's *flexible generalization* protocol: pick source and target tokens, form\n",
+        "$V = [v_s, v_t]$ from their J-lens vectors, read the activation's lens coordinates $c = V^+ h$\n",
+        "(pseudoinverse), and write back $h \\leftarrow h + \\alpha\\, V(\\sigma(c) - c)$ where $\\sigma$\n",
+        "exchanges the two coordinates — leaving the orthogonal complement of the activation untouched.\n",
+        "The swap is clamped at every token position across an intermediate-layer band (layers 10–24 ≈\n",
+        "the paper's 38–92% workspace band)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "b2359547",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-07-11T09:06:29.590963Z",
+          "iopub.status.busy": "2026-07-11T09:06:29.590451Z",
+          "iopub.status.idle": "2026-07-11T09:06:30.101969Z",
+          "shell.execute_reply": "2026-07-11T09:06:30.101589Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "'Most people in France speak'\n",
+            "  baseline : [' French', ' English', ' a', ' at', ' the']\n",
+            "  swapped  : [' Chinese', ' English', ' a', ' some', ' the']\n",
+            "'The capital of France is'\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "  baseline : [' a', ' the', ' one', ' also', ' home']\n",
+            "  swapped  : [' a', ' the', ' one', ' also', ' home']\n"
+          ]
+        }
+      ],
+      "source": [
+        "def show_next_tokens(prompt, hooks=None, k=5):\n",
+        "    tokens = model.to_tokens(prompt)\n",
+        "    with torch.no_grad():\n",
+        "        if hooks:\n",
+        "            with model.hooks(fwd_hooks=hooks):\n",
+        "                logits = model(tokens)[0, -1].float()\n",
+        "        else:\n",
+        "            logits = model(tokens)[0, -1].float()\n",
+        "    top = logits.topk(k).indices.tolist()\n",
+        "    return [model.tokenizer.decode([t]) for t in top]\n",
+        "\n",
+        "BAND = range(10, 25)\n",
+        "swap = lens.swap_hooks(model, \" France\", \" China\", layers=BAND, alpha=1.0)\n",
+        "\n",
+        "for prompt in [\"Most people in France speak\", \"The capital of France is\"]:\n",
+        "    print(f\"{prompt!r}\")\n",
+        "    print(\"  baseline :\", show_next_tokens(prompt))\n",
+        "    print(\"  swapped  :\", show_next_tokens(prompt, hooks=swap))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "cdc8c44e",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-07-11T09:06:30.105260Z",
+          "iopub.status.busy": "2026-07-11T09:06:30.104879Z",
+          "iopub.status.idle": "2026-07-11T09:06:30.422053Z",
+          "shell.execute_reply": "2026-07-11T09:06:30.421595Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "'Most people in France speak': rank of ' Chinese' 348 -> 1\n",
+            "'The capital of France is': rank of ' Beijing' 969 -> 9\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Rank of the China-appropriate answer, before and after the swap\n",
+        "for prompt, answer in [(\"Most people in France speak\", \" Chinese\"), (\"The capital of France is\", \" Beijing\")]:\n",
+        "    answer_id = model.to_single_token(answer)\n",
+        "    tokens = model.to_tokens(prompt)\n",
+        "    with torch.no_grad():\n",
+        "        base = model(tokens)[0, -1].float()\n",
+        "        with model.hooks(fwd_hooks=swap):\n",
+        "            swapped = model(tokens)[0, -1].float()\n",
+        "    base_rank = int((base > base[answer_id]).sum().item()) + 1\n",
+        "    swap_rank = int((swapped > swapped[answer_id]).sum().item()) + 1\n",
+        "    print(f\"{prompt!r}: rank of {answer!r} {base_rank} -> {swap_rank}\")"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "baseline : [' the', ' a', ' my', ' visit', ' see']\n",
-      "steered  : [' paris', ' Paris', 'Paris', ' París', 'paris']\n"
-     ]
+      "cell_type": "markdown",
+      "id": "f6506c6a",
+      "metadata": {},
+      "source": [
+        "At $\\alpha=1$ the language template flips top-1 outright (`French` → `Chinese`), and the\n",
+        "China-appropriate answers jump hundreds of ranks on the others — the same swap redirecting\n",
+        "*different* downstream computations, which is the broadcast/workspace claim. (On this small base\n",
+        "model the capital template's top-1 is a filler word even unperturbed; the paper reports 42/48\n",
+        "top-1 success for country swaps on Claude Sonnet 4.5, and that doubling to $\\alpha=2$ recovers\n",
+        "some $\\alpha=1$ failures. On gemma-2-2b we instead observe $\\alpha=2$ overshooting into\n",
+        "verbalizing the injected concept — the swapped prompt's top-1 becomes ` China` itself — so\n",
+        "$\\alpha=1$ is the better default here.)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7020c7b5",
+      "metadata": {},
+      "source": [
+        "## 4. Steering along a J-lens vector\n",
+        "\n",
+        "`steering_hooks` adds a token's unit-normalized lens direction, scaled by the activation's median\n",
+        "residual norm times `alpha` (the paper's directed-modulation protocol). `ablation_hooks`\n",
+        "(projecting a direction *out*) works the same way."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "89692d80",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-07-11T09:06:30.427385Z",
+          "iopub.status.busy": "2026-07-11T09:06:30.427165Z",
+          "iopub.status.idle": "2026-07-11T09:06:30.640333Z",
+          "shell.execute_reply": "2026-07-11T09:06:30.639945Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "baseline : [' the', ' a', ' my', ' visit', ' see']\n",
+            "steered  : [' paris', ' Paris', 'Paris', ' París', 'paris']\n"
+          ]
+        }
+      ],
+      "source": [
+        "steer = lens.steering_hooks(model, \" Paris\", layers=range(10, 21), alpha=2.0)\n",
+        "prompt = \"This weekend I am planning a trip to\"\n",
+        "print(\"baseline :\", show_next_tokens(prompt))\n",
+        "print(\"steered  :\", show_next_tokens(prompt, hooks=steer))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "00df913e",
+      "metadata": {},
+      "source": [
+        "## 5. Fitting your own lens\n",
+        "\n",
+        "For models without a published artifact, `JacobianLens.fit` reproduces the reference estimator on\n",
+        "a raw `TransformerBridge` — one forward and `ceil(d_model / dim_batch)` backward passes per\n",
+        "prompt, deterministic. Quality saturates quickly: ~100 prompts of 128 tokens is usable (the\n",
+        "published lenses use up to 1000). Fits parallelize across prompt slices and combine exactly with\n",
+        "`merge`:\n",
+        "\n",
+        "```python\n",
+        "from datasets import load_dataset\n",
+        "\n",
+        "texts = load_dataset(\"Salesforce/wikitext\", \"wikitext-103-raw-v1\", split=\"train\", streaming=True)\n",
+        "prompts = [row[\"text\"] for row in texts.take(500) if len(row[\"text\"]) > 600][:100]\n",
+        "\n",
+        "lens = JacobianLens.fit(\n",
+        "    model, prompts, corpus=\"your-corpus-id\", dim_batch=16, max_seq_len=128\n",
+        ")\n",
+        "lens.save(\"gemma-2-2b_jlens.pt\")           # official artifact format (+ provenance metadata)\n",
+        "# ...or shard, using the same corpus id for every chunk:\n",
+        "# JacobianLens.merge([JacobianLens.fit(model, chunk, corpus=\"your-corpus-id\") for chunk in chunks])\n",
+        "```\n",
+        "\n",
+        "## References\n",
+        "\n",
+        "- Gurnee et al., [*Verbalizable Representations Form a Global Workspace in Language Models*](https://transformer-circuits.pub/2026/workspace/index.html), Transformer Circuits Thread, 2026\n",
+        "- Reference implementation: [`anthropics/jacobian-lens`](https://github.com/anthropics/jacobian-lens) (Apache-2.0)\n",
+        "- Published lenses: [`neuronpedia/jacobian-lens`](https://huggingface.co/neuronpedia/jacobian-lens) · interactive: [neuronpedia.org/jlens](https://www.neuronpedia.org/jlens)\n",
+        "- The logit lens: [nostalgebraist, 2020](https://www.lesswrong.com/posts/AcKRB8wDpdaN6v6ru/interpreting-gpt-the-logit-lens)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c99c3348",
+      "metadata": {},
+      "source": [
+        "## 6. Qwen3.5-4B\n",
+        "\n",
+        "Qwen3.5 is **Bridge-only**: there is no `HookedTransformer.from_pretrained` path, so we load it with:\n",
+        "\n",
+        "```python\n",
+        "TransformerBridge.boot_transformers(\"Qwen/Qwen3.5-4B\")\n",
+        "```\n",
+        "\n",
+        "Use the Bridge default raw HuggingFace activations here. Turning on\n",
+        "`enable_compatibility_mode()` or `process_weights()` changes the residual basis, so the\n",
+        "published lens artifact will no longer match.\n",
+        "\n",
+        "We'll load the published Qwen3.5 lens and compare J-lens vs logit-lens on the same two-hop\n",
+        "prompt from section 2.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "47f23c87",
+      "metadata": {},
+      "source": [
+        "### Free Gemma before booting Qwen\n",
+        "\n",
+        "Qwen3.5-4B is larger than gemma-2-2b, so we drop the Gemma objects before loading it.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b91cb43f",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# NBVAL_IGNORE_OUTPUT\n",
+        "import gc\n",
+        "import time\n",
+        "\n",
+        "lens.clear_device_cache()\n",
+        "del result_jlens, result_logitlens, lens, model, swap, steer\n",
+        "gc.collect()\n",
+        "if torch.cuda.is_available():\n",
+        "    torch.cuda.empty_cache()\n",
+        "    torch.cuda.reset_peak_memory_stats()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d8c80798",
+      "metadata": {},
+      "source": [
+        "### Boot Qwen3.5-4B through TransformerBridge\n",
+        "\n",
+        "Use the same `device` and `model_dtype` as above. Some hybrid layers may emit Hook-alias\n",
+        "warnings for missing `attn.*` targets on linear-attention blocks; that's expected, and\n",
+        "J-lens only needs `blocks.{i}.hook_out`.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "901ed8f5",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# NBVAL_IGNORE_OUTPUT\n",
+        "# Hybrid Qwen3.5 emits expected Hook-alias warnings on linear-attn layers\n",
+        "# (aliases point at attn.* which is absent there). Harmless for J-lens,\n",
+        "# which only needs blocks.{i}.hook_out.\n",
+        "QWEN_MODEL = \"Qwen/Qwen3.5-4B\"\n",
+        "qwen_started = time.perf_counter()\n",
+        "\n",
+        "model = TransformerBridge.boot_transformers(\n",
+        "    QWEN_MODEL, dtype=model_dtype, device=device\n",
+        ")\n",
+        "model.eval()\n",
+        "qwen_load_s = time.perf_counter() - qwen_started\n",
+        "\n",
+        "assert model.cfg.n_layers == 32\n",
+        "assert model.cfg.d_model == 2560\n",
+        "print(\n",
+        "    f\"loaded Qwen3.5-4B: {model.cfg.n_layers} layers, \"\n",
+        "    f\"d_model={model.cfg.d_model}, {device=}\"\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a785c040",
+      "metadata": {},
+      "source": [
+        "### Load the n=1000 lens artifact\n",
+        "\n",
+        "`JacobianLens.from_pretrained` downloads the published artifact, checks that it matches the\n",
+        "live model, and pins the Hub revision for reproducibility.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c2426b93",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# NBVAL_IGNORE_OUTPUT\n",
+        "# Official walkthrough artifact (n=1000), pinned Hub revision.\n",
+        "lens = JacobianLens.from_pretrained(\n",
+        "    \"neuronpedia/jacobian-lens\",\n",
+        "    filename=(\n",
+        "        \"qwen3.5-4b/jlens/Salesforce-wikitext/\"\n",
+        "        \"Qwen3.5-4B_jacobian_lens_n1000.pt\"\n",
+        "    ),\n",
+        "    revision=\"16a01f309fcec900fdcec3f4cd5b64f3d00e4d5a\",\n",
+        "    model=model,\n",
+        ")\n",
+        "assert lens.n_prompts == 1000\n",
+        "lens\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "080b3b6e",
+      "metadata": {},
+      "source": [
+        "### Compare J-lens vs logit lens on the two-hop prompt\n",
+        "\n",
+        "Using the same prompt as section 2, we print the top J-lens token and top logit-lens token\n",
+        "at a few representative layers.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "090e24f5",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "PROMPT = \"Fact: The currency used in the country shaped like a boot is\"\n",
+        "QWEN_LAYERS = [6, 12, 18, 24, 30, 31]\n",
+        "\n",
+        "if device == \"cuda\":\n",
+        "    torch.cuda.synchronize()\n",
+        "readout_started = time.perf_counter()\n",
+        "\n",
+        "result_jlens = lens.readout(model, PROMPT, positions=[-1], return_full_logits=True)\n",
+        "result_logitlens = lens.readout(model, PROMPT, positions=[-1], use_jacobian=False)\n",
+        "\n",
+        "if device == \"cuda\":\n",
+        "    torch.cuda.synchronize()\n",
+        "qwen_readout_s = time.perf_counter() - readout_started\n",
+        "qwen_total_s = time.perf_counter() - qwen_started\n",
+        "\n",
+        "top_j = result_jlens.top_tokens(model.tokenizer, k=1)\n",
+        "top_l = result_logitlens.top_tokens(model.tokenizer, k=1)\n",
+        "\n",
+        "print(f\"{'layer':>5} | {'J-lens top-1':<20} | logit lens top-1\")\n",
+        "for layer in QWEN_LAYERS:\n",
+        "    print(f\"{layer:>5} | {top_j[layer][0]!r:<20} | {top_l[layer][0]!r}\")\n",
+        "\n",
+        "print(\"model output:\", top_j[31][-1][0])\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d9e0825c",
+      "metadata": {},
+      "source": [
+        "### Runtime and peak GPU memory\n",
+        "\n",
+        "We also print load time, readout time, and peak GPU memory when running on CUDA.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9c58cac8",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# NBVAL_IGNORE_OUTPUT\n",
+        "print(f\"Qwen model load:     {qwen_load_s:.2f} s\")\n",
+        "print(f\"two readouts:        {qwen_readout_s:.2f} s\")\n",
+        "print(f\"total since boot:    {qwen_total_s:.2f} s\")\n",
+        "if device == \"cuda\":\n",
+        "    allocated = torch.cuda.max_memory_allocated() / 2**30\n",
+        "    reserved = torch.cuda.max_memory_reserved() / 2**30\n",
+        "    print(f\"peak allocated GPU:  {allocated:.2f} GiB\")\n",
+        "    print(f\"peak reserved GPU:   {reserved:.2f} GiB\")\n",
+        "    print(f\"GPU: {torch.cuda.get_device_name()}\")\n",
+        "else:\n",
+        "    print(\"peak GPU memory: unavailable (this run used CPU)\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "04f72e93",
+      "metadata": {},
+      "source": [
+        "Qwen3.5 shows the same pattern as Gemma: the J-lens surfaces intermediate or answer-like\n",
+        "tokens earlier than the logit lens, and the same `JacobianLens` API works on a Bridge-only\n",
+        "model.\n"
+      ]
     }
-   ],
-   "source": [
-    "steer = lens.steering_hooks(model, \" Paris\", layers=range(10, 21), alpha=2.0)\n",
-    "prompt = \"This weekend I am planning a trip to\"\n",
-    "print(\"baseline :\", show_next_tokens(prompt))\n",
-    "print(\"steered  :\", show_next_tokens(prompt, hooks=steer))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00df913e",
-   "metadata": {},
-   "source": [
-    "## 5. Fitting your own lens\n",
-    "\n",
-    "For models without a published artifact, `JacobianLens.fit` reproduces the reference estimator on\n",
-    "a raw `TransformerBridge` — one forward and `ceil(d_model / dim_batch)` backward passes per\n",
-    "prompt, deterministic. Quality saturates quickly: ~100 prompts of 128 tokens is usable (the\n",
-    "published lenses use up to 1000). Fits parallelize across prompt slices and combine exactly with\n",
-    "`merge`:\n",
-    "\n",
-    "```python\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "texts = load_dataset(\"Salesforce/wikitext\", \"wikitext-103-raw-v1\", split=\"train\", streaming=True)\n",
-    "prompts = [row[\"text\"] for row in texts.take(500) if len(row[\"text\"]) > 600][:100]\n",
-    "\n",
-    "lens = JacobianLens.fit(\n",
-    "    model, prompts, corpus=\"your-corpus-id\", dim_batch=16, max_seq_len=128\n",
-    ")\n",
-    "lens.save(\"gemma-2-2b_jlens.pt\")           # official artifact format (+ provenance metadata)\n",
-    "# ...or shard, using the same corpus id for every chunk:\n",
-    "# JacobianLens.merge([JacobianLens.fit(model, chunk, corpus=\"your-corpus-id\") for chunk in chunks])\n",
-    "```\n",
-    "\n",
-    "## References\n",
-    "\n",
-    "- Gurnee et al., [*Verbalizable Representations Form a Global Workspace in Language Models*](https://transformer-circuits.pub/2026/workspace/index.html), Transformer Circuits Thread, 2026\n",
-    "- Reference implementation: [`anthropics/jacobian-lens`](https://github.com/anthropics/jacobian-lens) (Apache-2.0)\n",
-    "- Published lenses: [`neuronpedia/jacobian-lens`](https://huggingface.co/neuronpedia/jacobian-lens) · interactive: [neuronpedia.org/jlens](https://www.neuronpedia.org/jlens)\n",
-    "- The logit lens: [nostalgebraist, 2020](https://www.lesswrong.com/posts/AcKRB8wDpdaN6v6ru/interpreting-gpt-the-logit-lens)"
-   ]
-  }
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From cf500f3f1708bb3ceec0b55557c5e0011904d11f Mon Sep 17 00:00:00 2001
From: kyleyinxu <kyleyinxu@gmail.com>
Date: Mon, 27 Jul 2026 11:22:45 -0400
Subject: [PATCH 2/4] Remove runtime demo, cleanup documentation

---
 demos/Jacobian_Lens_Demo.ipynb | 486 ++++++++++++++++++++++++---------
 1 file changed, 361 insertions(+), 125 deletions(-)

diff --git a/demos/Jacobian_Lens_Demo.ipynb b/demos/Jacobian_Lens_Demo.ipynb
index c90725ca5..cd5274ab1 100644
--- a/demos/Jacobian_Lens_Demo.ipynb
+++ b/demos/Jacobian_Lens_Demo.ipynb
@@ -37,6 +37,11 @@
         "3. causally swaps one concept for another (France → China) in lens coordinates, and\n",
         "4. steers with a J-lens direction.\n",
         "\n",
+        "On `Qwen/Qwen3.5-4B` (Bridge-only; Anthropic's\n",
+        "[`jacobian-lens` walkthrough](https://github.com/anthropics/jacobian-lens/blob/main/walkthrough.ipynb) model):\n",
+        "\n",
+        "5. loads the published n=1000 lens and repeats the two-hop readout.\n",
+        "\n",
         "**Note**: `google/gemma-2-2b` is a gated Hugging Face model — accept its license and authenticate\n",
         "(`hf auth login` / `HF_TOKEN`) before running. The demo uses ~6 GB of GPU memory\n",
         "(executed on an 8 GB card), so a free Colab T4 should work; GPUs without native BF16 use FP16 automatically."
@@ -67,7 +72,9 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Running as a Jupyter notebook - intended for development only!\n"
+            "Running as a Jupyter notebook - intended for development only!\n",
+            "The autoreload extension is already loaded. To reload it, use:\n",
+            "  %reload_ext autoreload\n"
           ]
         }
       ],
@@ -113,7 +120,7 @@
         {
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             "application/vnd.jupyter.widget-view+json": {
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+              "model_id": "448943416fc149028bdd5e802c48cb1b",
               "version_major": 2,
               "version_minor": 0
             },
@@ -128,7 +135,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "loaded gemma-2-2b: 26 layers, d_model=2304, device='cuda'\n"
+            "loaded gemma-2-2b: 26 layers, d_model=2304, device='cpu'\n"
           ]
         }
       ],
@@ -284,7 +291,7 @@
       "outputs": [
         {
           "data": {
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",
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aMaWVTnm705hFdUDjX1ZmJbTCSddIK91pyUu0l8zuPzm0Oi1vm8Lqu+R7R/9D5likvcnsmmgsIlMPRdNBMmWlFd6C8pmSPy9Iw5eQkJDhMcoei3Ly3MsrtLKraPuetq3y8oxO239J40x1khZ5HdFYSuZVtPqrCJmOkUzy33//KbXeMrpXqG/SireiSRZpW2g8I014dn2OVrXp/7OC+pZiP6d2Is2Q4nOS6k3eftQONN7Q/UGa2IzmJjRvUNQG0fnI8kH+PMvNeEX/06hRo1QaN9JcksauoCHtBz1b6R6lPic3g5ffn6RlqVSpUqr7kzRJRFqzubQoPoOoX9P/kmaf+l5G5oAZQZoeRauS0aNHsyAb8nqVjw00Fig+w0hLRnNDMumUm62SFpisdRT93cj6gK5PflxBQP2Q6jUjM94SaSpGKkRFaNDPSH1IN4E8ZG9m0ERG8SGY9kEn/44mPdQB6KFJqlNS/5L6kgZXUrXSBDwn0SrowZ0R9GAjEwAayBXtJjOaPKQto7ycGU0wXV1dmYqWVOM5MeWihz6pNcmEIy1089LgRT4pZLeYF0glrghdH9mN5mTSl1XbKCK30097rOJx1Deo7UmtnxF0cxOkjs2o3HT+7ITArJCfN7N6pvZK66iZVVvS9ZAaNu11p72enNYlmVmRuQM93Ok36QFCJhn0QM0q1DCZu1G95gW6h+lezi1ki0yT3LzeV2RCRPduRgsKyia7diIzVXoAzpw5k70ya/vMTEvo2skWWlF4LggyG+cy2l/QfVc+8couTwU9qMlcgiaEZEJE0Hvq6zQmFQQ0GaKJJplwkGkJmfnQ5I4m6nJHYWWPRTl57hXGefP6jE7bf2kMzSpSGtUb9XHFBTP5OCv/Xpn1ltE9QUIsLehRf6Q5Bj13KZAHLdKkvY60kDke1SeZQpLwSyaFNG4rLkTKx8208wsqP92LipB/Dplak1+KovCdUbnT9k2CfI/IFCy34xW1Cwk9acnomZlfBgwYwPoQmdOlfb5RH6X9OR2f0kKm1bQoRj48aecqOX1Wpq1XAwMD1j/k86bM5hMkdFK7y7/Pat5B4yEtTBcUZHZIAhUJnjSekkkrjXtkJlgiBRdqMEXIHjYjhyv5Ss3ixYvT2WLKSTsBpBWGjJDbadPkiiRykrJJOiWneFqlI+mbNBWZ/b+cjAQssuMlG0pyBibnKro+kq7pujJyZMqujIrQQ5MGHhoAyW5RlcnpdWfXBvK+QRMFqpeMyGyQUiZZtSVdDz3syCcpI+jhkdu6pNUbEpZII0HCEj1oyCaWVszS2l7LoXuBVlnzAj1sCzKHSW7vKzGSXTvJxzjyIUiryZCT30l4ZpqXtMEuFMnMDj2j/YXRd3MKTejI/ppWBkmwJb8e8i0pqDqRJ2Gk85J/Dd1HpAmiiSDto+ePGMaigqzT3Jw3L8/owiIv/bwg6i2je4LOS5M88hOgsYv8D0iDkhNNIGkzSPNDgSdoTkJ1S4tQ5Fcl913JadkpYAnNrUjYJp8g6qf0f/QcyEswkqIYr/ICLT6Tfw75etC1pS0z+f0sW7Ysw/8lATEzqO/IfWZoIk/CAS1GklaJ6rW4pH3ICFoYID8bWjykeTJpR6kvkxAnTxFRogQXMgVRJLPVf7k5Fa18ZLYimxdodZZWQehFnZmc6shRkYSZvPwONShpCeihphiujyZY+YUGLVpJkDvyZ+cURQ9JigAhj6ahCK240LVndaNmh3yFTXGQpFUYucN6UUF9g1YDSPrPytlPrjKmciuuWJFmKicmVJk9DOXnzayeyfwhbVjMrNqSrodyhBRkP5efl7Qu9KI6oMkFTbrogZYR9DBKe3/mFFoVLUhyel/JHTApwk5mk6f8mk4VFvI+SQJZXtqerp3qhx6smWld5KvIaaPT5GelOm0ZCrLvyusko2hGaSGH7kmTJrHVbNIWUj2SVj07FOtEMRx8ZnVCWhx6UWAAEprJ3IUCRZDmvqjGosIgv/dEfp/R9P9kFkWagMzC6FO9Uf2SaZKitoLGWfn3hdnP81pHJFTTWEsCL5mz0bM5s8l+WmiRhp4T9CKNADnlU99TFFxyAgnd1NdI6FG8joxMSzN6vhPkwC4P5pKb8YraJaPzZfTMzC9kakgCE02qSetP1jSKfezp06dsvpfbtiQnfrp+WnBUzDWW22ck1QMFKpATGRnJzM1Jm5Z2PqE4NpD5GAX6kNe14nFyUzc5tE/RRK4gnnc0h6HxlF5UFhIQqR9S4JX8hGZXSR8XagTFV1oNjBxSk1Kno8hSionf0uYhyQ30gE+LfLKTk9B4GUErGNRJFFd2SAVYENl26bwUiYNM2WhFO7sQqVQWUumRD4+i+RZFnqAHLoVrzI/pgDzqiOLASDdgbgfU/EKrUlTfc+fOTfcd2fDKH17Uv2iQpSggiitROc0WTUJgRg9D6rPUb2hAU/yOJlu0SiYfkHLalnQ9FL6VJqFpofPTNeUGMhekKCCK0L1ED/6s+jldV9r7M6evgjbVyul9RauJJJBTNLG0K2CKbU6DcEahJQs7HHJW0AoomR9R1D+6j3I7xpEWj64xoxUw+bXT/U6CdFpfE1o9KwgKuu/SBI+0bNu2bWNmGlmthNN10dhDgjhpPCjKUlYRF9NOuBXrRB72VhESKNL+ZtrnRVGNRYUB3RN5NQ0tiGc09V/yGchISyavIxpLqX7THkOmezQ+yJ89hdXPsxo3soIW8+hFpum0CENCdnbJQ+k607YHjRG0KJSX+YlcK6PY30hQzCxUOI2tij51FEGLjpfXcW7GK2o30krSORS/z0wzmV/IooA0QTSppmhccuj+pGsi7VdaaLGD7vvc1B+9J81ObqDnvqKZ3vr169nYIK9XGhvILIwizCr+FkWAo/4gj6BJvknUBhs2bEjVH0gwJnM4xUib8oXTvPRdgvyWFKHy0TOeypeZv1+x1rjkFJqM0E1PjUtaGTJhIdtJ6oSkHaGBilYzcgNNbmhgowYm6ZRWM2hgI3tReb6C3ELnIs0NPTRpFZ3OSTG0aQUgrb1pXuuBHsw0QaObkBy60krbipBPgDxXDa3Y0GBJAw119LRx3nMLrerSeaktSBiihy5dJzmRFSVke07mVqQWJv8HEtZoUkArG+SMRwMLCQjyHDB0HPl40GBKDnV0o+dkgkMrqHSzkgkVmbzQ9ZPtPb1Ig0J9k8Kuko29PBwyrfhklq02s7YkNT4JMlRGUkHThIAGVFrxIeGQJuw5Ka8cWiWiFSb6DSo/9QEyPaA2oweoKpDT+4o+k8aUJo5kYkGrQqShIadCeuDLTQeoTumBQfcH/Q89AOT3UWGFQ84JdE10T5E5A91HtOJG7USTCzKBotXCzKBVPLLvpgce9X2qKxLeyMyOvqMwnARpBii0LW3p4UdjIPWRgqCg+y5B10N1QivN5NhK9vh0HjLvpftdEVoJpXudyEh4yAgaL8i/ge5bKj9NUEhQovFCUVgiQYaeDxTilSbotGhDEyB69sgXJ4pqLCoMqK1obCOtFfkLkVkXmZcW1TOa2o4Ww+j3aYJL9y/1HdKw0LOLfEKoPNSX6R6nPlCzZk22OESLc2QOqxjopjD6OdURlYfGIhpPqC9m5LuR2fVRmxM5MROj/kVzEeovdJ3UHvTbNJaR9ia3UD8jbQv1XxpPafWeJr30TMhI0KRxke47ch6n+QI93ykEsKIZaE7HK/ofCsVLYxKZc8rDIdO8qyDmRRlBz2Sa6FM4Z1qkozqn8ZF8dCiIAvVJ0oySgEgaO9ovz0uVEWQaRv2L2pD6NPVnEkJzqyElbYX8efz27Vs2plAdkik0QWMDCVy0AEX1Rfvlx9F9Ke87NK6Q2SDdZzTuUJhreThk0opRegvFfktQUAvS9NEYl5tnP41j5C9E9UV+siQY0eIB9aPs/LSyRaYiyMMeUkjAnIZDlvP48WNZ9+7dWZhCCn9JIfF69+4tu3jxYoYhZ7MKb0n/Q2HeKFQhhZ+jLYUuTBvKMyPoPJmFKdy6dSsLBUjlq1SpEvvdjML5ZXaOtGH+MroeCstH4eoMDAxkd+7cybKsVM8UopKO1dPTk7m4uMhu3bqV6pi8hEP+559/WBhSCslHoRMpnGbakICZhUPO6HfShqmk/6XwkzkNX7pp0yYWapDKQuFTKUzj5MmTWfhnORRecPbs2TIbGxt2XMuWLWUvXrxIV+eZQfVGv0H9JW15L1y4IGvatCk7r5GRkczV1VX26tWrDMueXVtGRESwunV2dma/ZWFhwUJMLlmyJCWUYk7rMjAwkPUz6otUnxSCsmHDhix8tZjI6p7KzX0lH2MobDYda2pqyur3/PnzKd/7+fmx/kr9hP5fMfRjYYVDzkmfJyh0OYXQpnCmmpqaLOx4586dZYcPH862HBTylH6H6of6jaWlpaxjx46yhw8fpupvw4YNY/2Arp/GTwrBmVmY2IzCxVP9UP1lREH2XTl0j1J4WhMTE5mOjo6sYsWKspkzZ6b7Xwr3Su1N16YYIjQ7qH7onqDyOjo6spC8aZ8XNI7S84G+p35F4x61y4MHD5QyFuW0vdJeR2ZERkbK+vbty+qYjpffA/Lx/tChQ6mOzyzkcE6e0ZlBfXPGjBksfDL1fboHKNSxYjh/6l8Ukpee13QMjQnUjxRDMBdEP8+o3t68ecPCc1N7KYatzupekUMheikkeIUKFWQ5gfryb7/9JqtZsyYrP43d9H7dunWpjqOxK6NQtWmfvVQ/8+fPZ/uoXWh8pNC5WT2jly5dKnNwcGDHN2vWjIX4T0tOx6tnz56xstL9S8dQ6HQa0wsjHLLiPUb3LIVFp1DaBI1Bf//9N6sz+fOB7lW6F8PCwrIsBz3P27Rpw57VNK4NHz48JYVFdqGt5eW7evWqbMSIEex3DQwMZP369UuVRkEx/DGN41SnFLJ69OjRLHx1Wg4cOJDyrDMzM2PnUwxjLX8ujB07lj0PKHRzduJC2nDIGzduZP1efk87OTmxvpldfeUENfoDFYBW0EjqJn+ItAmcOOKHwurSqhetIMpXNzkcDkfZkMkFrYTTyjyZVnA4YoHM4Mj8lnwvMovCJQZIk0WaJNJYyDVEHE5hoTI+LqTqJFWhovMQh8PhcDj5gezyyXZe0XmWwxEDFGWRzJLIXInD4aiIjwvZA9JqPTlkkd1pds5pHA6Hw+FkBzkNk608+bVQeG+y+eZwxACFnKcohxSBifwZ5VG5OByOCggupHYkhzNygqQoIBwOh8Ph5BcKtECBLijKV0HmD+Jw8gsFAbp16xZzbKaALRwO5ysq4+PC4XA4HA6Hw+FwSi4q4+PC4XA4HA6Hw+FwSi5ccOFwOBwOh8PhcDiiR/Q+LoUNJVv7/PkzS4hDWXQ5HA6Hw+FwOByOAHmVkL85hY6nxLHKpMQLLiS0ODg4KLUROBwOh8PhcDgcMePt7Q17e3ullqHECy6kaZE3hpGRkVIbg8PhcDgcDofDERPh4eFskV8+Z1YmJV5wkZuHkdDCBRcOh8PhcDgcjlgIDg7G2bNn0b59e5iZmSm1LGJwqeDO+RwOh8PhcDgcDkf0lHiNC4fD4XA4HA6HI0ZIy/LDDz8ouxiigWtcOBwOh8PhcDgckUb0ogi4PF+8ABdcOBwOh8PhcDgcEeLn54e5c+eyLYcLLhwOh8PhcDgcjigxNjZGly5d2JbDfVw4HA6Hw+FwOBxRoqenh1q1aim7GKKhWDjnlylThoUypmyepqamuHz5srKLxOFwOBwOh8Ph5IuYmBi4u7ujXLly0NXVLfG1WSwEF+LWrVswMDBQdjE4HA6Hw+FwOJwCITQ0FIcPH8aIESO44FJcNC6qjF+UH7zCveBo5AhrfWtlF4fD4XA4HA6HIxKsrKwwdepUaGpqKrsookDpUcWuXbsGV1dX2Nrasoyc//vf/9Ids3btWmYOpqOjg4YNG+LevXupvqf/a9GiBerXr4+9e/dCVTj6/ijaH2mPYeeGsS19LjTCfICP14QtR3zw9uFwOBwOh5MGcoPQ1tZmW44INC5RUVGoWbMmhg4diu7du6f7/sCBA5g0aRI2bNjAhJYVK1agffv2ePv2LUqVKsWOuXHjBuzs7ODr64s2bdqgevXqqFGjBsSuaZl9ezakMin7TNs/b/2Jz5GfUdmsMuwN7WFnYAcDrQIwf3u0Czg5HqDfUpMAriuBOgOh8hP94A+AmRNgbAeVpji2D4fD4XA4nHwTEhKCS5cuoVWrVsyPu6SjdMGlY8eO7JUZy5Ytw/DhwzFkyBD2mQSY06dPY9u2bUx1RpDQQtjY2KBTp0549OiR6AUXMg+TCy1yZJBh47ONqfaZaJswAYZecmGGtvYG9rDRt4GmeiaqQ5kMCPMGPlwWJsWQJe+XAicnAE6tVXfCX5wm+iSAya8lpX3GA5aVALt6tNSi7BJyOBwOh8NREpR8khb5acsRgeCSFfHx8Xj48CGmTZuWso9UZaRVuX37Nvssb0xDQ0NERkYyqbR3796ZnjMuLo695ISHh0MZkE+LRE2SSnhRgxpa2LdAUGwQPkV8QkhcCELjQtnrZdDLdOeg/7fSs4I9CTYaBrBPksEuOhz2Yb6wD3gP8+gQqCUf66euDi9NDTgmJMI6KQk4PQloPhmwq0O2dlAJ4qOAR7uBM1O+7qP6OzEOMK8AlG4ElYEGII9rwI3lX4UWOfR5a1tAQwcwLQuYOyW/nAUNE20NSqlOu3E4HA6Hw8kT5ubmGDhQRRdnS5rgEhgYiKSkJOaYpAh9fvPmDXvv7++Pbt26sfd0LGlnyNclMxYsWIDZs2dD2ZAj/qzGs1LMxUgIoc/dy381l4tKiGICjE+kz9dtuAd8Qj3gExOAWFkifKN82eu+4slpPmtlCF2pPmxl6lBPjMM7LU020VWTyTAtKAQ/vDsD0ItW9mv3B2r0AQwsITqkScDHq8Czg8Drk0B8ZAYHyYDt7QGHRkCN3kDVboCeGURJ0AfgyT7g6X4g/FPmx6lpAImxwJfXwistZEJoVk4QYlIJNU5ZX3txMrHjcDgcDodTolCTycimSByQk/2xY8fQtWtX9vnz58/MDIxCHTdu3DjluMmTJ+Pq1au4e/durn8jI42Lg4MDwsLCWC6YosbP7wm8fR/AwaYerK3TJBiKDAB8nwF+8tdzYeLLjMqAIHUJPmlosJePjj4+GZjDR1sHn5AI/4QISOXmYRlgB21Ujw5DpdgYVI5PQKUEKcyc2wlCjHNbQF3JMi1dK03unx8GIv2+7je2Tw4wkEW3VdcCyrcDavYRthraUCqx4cDLY4LA4n3n634dY6BaT0DXNFnzkgSoqQOuK4CafYEwLyDIHQhyE4QN2lL7h3plff10PkVBhl70/tM94L8pxcPEjsPhcDicEgD5b2/duhXDhg1jLhHKgObKxsbGSpsrq4zGxcLCAurq6kyrogh9trbOW+hgisxAL4pURi/S0iiNR7tgfXI8rOUTybqDAF2zZGHleeoJuyKGNlCzrg4L6xqwsK6OWjY1AJMyqfwhEpISmCbmotdFLHu4LN0pfBAHHz0dnNHTSdlnFfUIlS/eQaWLWqhk3xRVag2BtUMTJlAWCeGfgeeHgKcHgAAF0zgdE6Bad6DG94BDQ+DxbsFPR3Gi79xGEHKeHQD8XwBvTgkv+l/SwJAQQ/9bVNfCNEXXBGGFNEWJMcJ+amfyL6rVF6jYCdBMrv96Q4Fgd0GLIteE0Ht6lW+T+tyJcUCIhyDEpAg1ya+Iz0BMCPDpvvDKjOLg68ThcDgcTjGHBIV27dopXWAQC6LWuBAUSaxBgwZYvXo1+0z+LI6Ojvj5559TnPNVUookrcGKaun9G1KhJqyck2BiXR2wpm2NXJl0UfQyCrWs6EtDZmnzm86HX7Qf3gS/YS+PcI8M/99YpoZKBg6o4tAMlSxroJJ5JZQ2LA11iToKhLgI4NUJQeCgib5ck0BakwrtBRO28m3Ta02YyVOaiX7KRb8QzkdCUITv1/0mpQXhh4QY0kIUpSmYRUVBWKHfN7IpXD8gqpcUoSZZYxPwGojLwJ9r0CmgbLPCKw+Hw+FwOByVJlxEGhelCy7kUO/m5sbe165dm0URc3FxgZmZGRNQKBzyoEGDsHHjRibAUDjkgwcPMh+XtL4vKtUYNEnf6Zp+f/n2QIV2goBiVRXQ0s/3T1F+mKx8aeT+NG+D3+J14Au88biE14Ev8UEWg8QMNBS6GrqoaFoRlcwqobJ5ZbZ1NnGGlrpWzhJqJiUCHy4JwsWb01+1EYRjY2FyX7WrYPJUEFoP5h9zIrV/DEXsot+p1gPQN8/f78SGKZiC3U1tCla9lyCw2Co5CEJmgvLoW0I/43A4HA6HIzpiY2Ph5eXF5sQ6Ol+tZIoSLrgocOXKFSaopIWElR07drD3a9asweLFi+Hn54datWph1apVTBOTHxRNxd69eycOjQuZPU14XiimOyRQeEd4w8HQIXOBIg3xoV5we7gJr9+fxuuEELzR0mJO/jEZhOjVkGjAQteC/Q6RTkAi+fjzY0GIeHEYiPry9Z9Jq0SalRq9ANMyBXTFaS8mCnjzryAskdBEZmasoBqCTw859VfsCGjq5i5oQIopWOxXUzAyWyNhpQKdTzmDTOZhpJNN7OSQgDzoRP6FRA6Hw+FwOIXi47Jp0yaMGDGC+7iIQeOibJQqRSpOJOW+GmJ0lqYu4n2P+ZYkvTwGT1ksXmtp4Y22Nl4bW+G1ugzhSQpak2RIeDnbdjus318UBJbAd1+/1DMXHNNJ61HUIZkp6IHcH8b3ydf92kZAlS6CKZljE8FnKG0UrkA34KncFIyCBCRD0dnkpmCGefO/KhLkJnbE4SGCAEnaoIH/EzREHA6Hw+FwRAMtsEdHR0NPT4/5fSsDrnEREUpvjKx8NcRIXCTw6jjweA/gdYvtIsn3PxNLTDFNr63Y6uuPBrHJUdwoLwk5pNPk3rk1kFnyzKIk4M1XfxhK2CnH2EEQRj5cTNaKqQnaoJCPX48hx//qPcVhCpYX/F8BOzsD0UGAfQNgwFFA21DZpeJwOBwOhyMiwpU9V1agxGpclG4qVhwg7cOTvcDTf+AXHYD2DraQppm89w6LwO9GVaFW8wegsqt4V/UpISQJYqRJIcEsI0d2hpoQLECMpmB5gaLX7egMxIYKWqb+hwvEr4rD4XA4HE7+CQ0NxbVr19C8eXOYmJgopUq54CIixNQYKgs5299ahaN3l2C2hRkTXijRpSxZiBlWbRjG1xlfdGGV80tCjJBX5erf6b/rvUswJytOkO/Rzi5AXBhQphnQ9yCgpafsUnE4HA6HU+KhZOzHjx9Hly5dWJqQkj5XLrEaFzE2hkqTHGzAT6IGb00NOCQk4rK+PuabC6sDI2qMwM+1flYd4aWIgyconU8PgF1dgfgIwKkV0Ocf1dcmcTgcDofDKVZz5fThoTicvECTedeVsJYC9WPj2PaHlvMwpf4U9vWmZ5uw/ul6lbseJqwQ8uAJxVFoIezrAf0OAZr6QtS1gwOERJccDofD4XA4IqHEaly4j0vRBRvY+XInljxYwt6PqTUGo2qOgsqgasET8svH68DeXkJunYrfAr13iiOIAofD4XA4JRBKBbJz506WJsTaWjlRS8WkcSmxgosYG6M4s/3Fdix7uIy9J3+XH6v/qOwicTLjw2Vg3/dAUpzgz9NjG6CuweuLw+FwOBwlJGp/+vQpatasCQMDA5T0uTI3FeMUCUOqDWECC7Hy0Upse7GN17xYcXIB+uwD1LWECGvHRgoJNzkcDofD4RQpJKw0bdpUaUKL2OCCC6fIIC0LOegTyx8uZyZkHJFSvo0QQU2iAbw4DBz/WQgZzeFwOBwOp8iIi4uDh4cH23JKsOBCPi5VqlRB/fr1lVoO37AY3PoQyLYlgZE1R+Knmj+x9+T3sufVHmUXiZMZFTsCPbcLgQme7gNOjefCC4fD4XA4RUhwcDDzcaEth/u4KNVu78B9L0w7+hxSGSBRAxZ0r47v6zsW+35JblVrnqxhkcaIaQ2moW/lvsouFiczXhwBjvwohIau/yPQaQmgKmGtORwOh8NRYRITE9lcleaoGhrK8TflPi4cpmGRCy0EbelzSdC8UC4XMhmTO+gvuLcAB94cUHaxOJlRrQfQdQO1HHB/C3BmGkmfvL44HA6HwylkSFgxMzNTmtAiNkqsqZiy+RgYlSK0yKHPF175oyRAwsu42uMwpOoQ9vmvu3/h0LtDyi4WJzNqfg98t1p4f3c9cP4PLrxwOBwOh1PIkEXQf//9x7YcLrgojbIW+sw8LC0zj7/Eb4ee4ktEXIkQXibWnYiBVQayz3Nuz8HR90eVXSxOZtQZAHReLry/tQq4PI/XFYfD4XA4hUh8fDxzzqcth/u4KN3HZfrRF0iSyZgQU9vRFA89Q9h3htoamNi2AgY2Lg0NdUmx93lZdH8R9rzeAzWoYU7TOejq3FXZxeJkxt2NwH+ThfcuM4AWye85HA6Hw+EUO8JFlMelxCagpKhi9EpKSsK7d++U1hjk0+IRGI0yFnqwMdZlgsufJ17iuY+gEqxoZYg/v6uKxk7mKM5QNyRfl3/e/MOEl3nfzIOrk6uyi8XJjFtrgHMzhPetZwHNJvG64nA4HA6nGBLOBRfxIKbGkJMkleHAfW8sPvsGIdEJbF/nGjaY8W1lJtwUZ+Fl3t15OPD2ACRqEsz/Zj6+LfetsovFyYzry4CLs4X37eYBTYQcPRwOh8PhcAoGf39/7N27F/369YOVlRVK+ly5eNsgqSjqEjX0beiIy7+2xIBGpZkZ2alnvmi15CrWXnZDXGJSsfV5md5wOnqU7wGpTIrpN6bjzMczyi4WJzNIy9JyuvCetC93hfDWHA6Hw+FwCgY9PT3UqVOHbTkl2FRMjFJkZrz8HIZZx1/iQbL/SxlzPcxyrQqXSqVyfI4EPz/Ee3hCq0xpaFpbQ8yQ0DL79mzmqK+upo6/m/+N9mXaK7tYnIyg4ePSX8D1JcJnct6vN5TXFYfD4XA4xYRwEc2VucZFBahqa4xDoxpj+fc1YWmoDY+gaAzZcR8/7rwPz6CobP8/9PBhuLVqDa/Bg9mWPosZMhOb1XgWujh1QZIsCVOuTcEFzwvKLhYnIygRZavfgSbjhM+nJgKP9/C64nA4HA6nAKBoYj4+PjyqWDJccFEhM6pute1x6ZcWGNG8HDQkarjwOgBtl1/D0nNvEROfsflY1L178P19JiCVCjukUvj+MYtpYMQuvMxuMhuu5VyZ8PLb1d9wyeuSsovFyUx4aTsHaDha+Hz8Z+DOBuDjNSDMh9cZh8PhcDh5JCgoCFu2bGFbTgk2FRNLVLG84hYQidknX+L6+0D22dZYB793roKO1ayZkBP34QMC129A+KlTGf6/486d0G/YAGInSZrEfF3+/fgvNCQaWN5yOVo6tFR2sTgZQUPJ6V+AB1u/7lOTAK4rgTpCrh4Oh8PhcDg5JyEhAcHBwTAzM4OmpiZKuqlYiRVcxNgYuYWa7uxLf8w99Qo+oTFs33cmsfjJ6xpkl89nmdm87OlT0HFygiqQKE3EtOvTcMbjDDQlmljhsgLN7Zsru1icjAj1BlZUS71PTR2Y8BwwtuN1xuFwOByOihEuorkyNxVTYUiz0qGaNS5MaoEZVbQx/cEejNwxE7JL55jQotOqNcoeOwqbv+YCktRNHbhiBRN8VAHStCxotgBtS7dFgjQBEy9PxKkPp3DP9x78osRt8lbiCPmYfp8sCfB9qozScDgcDoej8kLDhQsX2JYDaPBKUG1i371D0Pr1+ObM2RQNyw2b6thXqS0ibMtiarQhunfvgaga9eDz8j2sNKWImfYbIs5fQNCWLbAYPhyqIrxQdDHpVSkuel3EtBvTUjnydy/fXdlF5BBmToJ5mCzZp0rO6UmAviXgUJ/XE4fD4XA4OSQ2NhavXr1CjRo1lK7tEAPcVExE6q/cEPv2HQLXrUPE2bMp+wzbtYPFmJ9wB6aYc/IV3AOFiGOOZrrwDolhcg3lhNlo4gX77auYFsZx6xboN24MVeFT+Cd0PNYx1T4SXs72OAtrfXGHeS4xPNoFnJwgaFpIiNE1A6IDAYmG4MTf6CfBoZ+THgpmEPxBEAC5aR2Hw+FwREC4iObKXHARUWPkhNi3bxG4dh0izp1L2WfYoQMsRo+GTsUKKfviE6XYdvMjVlx4h9iE1Kvf6pQvMPEa4k+dgLqpKcoePQJNGxuoAmQeNuzcsHT7t7XfhvrWfDVfXBNwd8CsHKBtCJwYC7z6n/BdxW+BrmsBXVNll1KEAt94QVvFgxpwOBwORySEi2iuzH1cVITYN2/waew4fOzSVRBa1NRg2LEDyp44DvsVy1MJLYSWhgSjWjhhaa+a6c5FgZP9hoyDdpXKSAoJwafxEyCNj4cq4GjkyDQsadFQ41aPooK0BWWbCVsdI6DXDqDTEkBdC3h7GtjYHPj0UNmlFAcUqpxy35BwJzexoy1prXg4aQ6HwynRBAQEYM2aNWzL4YKL6Il99QreP/+Mj127IeL8eSawGHXqiHIksCxfDp0KqQWWtNQpbcrMw9Iy+5wbQqfMgcTYGLHPnsF/3nyoAmQORj4taYWX32/+jqAYHuM8OyiYgVKCGpBpWIPhwLBzgGkZINSL1GTAnfVZRr8r1sRHAfc2A2vqAcfHpP+eTO1Ia8XhcDicEouOjg4qVKjAthxuKiYq9ZciMS9fInDdekRevCjsYAJLJ1iMHgVtZ+dcnevAfS9MP/oCSTIZmz9qqUsQlyis7I43DESHPX+zyaPNvHkw6aEaTu408faO8Iaehh4mXZmEz1GfUdmsMjMZM9AyUHbxRMmBNwcw7+48yCBTblCD2DAhSeXrE8Lnyq7Ad2sAXROUCEiLcm8T8HAHEBsq7NMyBOIjSVpROFANmPiS+7pwOBwOR6mEi2iuzH1cRNQYRMyLlwhcuxaRly8LOySSrwJLPvKu+IbFwCMwGmUs9KCpLsHiM29x4IE3+26w20V8/+I/qGlpofQ/+6BbtSpUCY8wDww6MwjBscFoaN0Q69qsgxaZJJVwohKi8DjgMR76P8Ttz7fxMuileIIakJaFJu9nZwDSBMCktGBOZlcHxRafh8DtdYKvjzRR2Ec+QA1HA7X6Ai+Pfg1qIGfAMcCpldKKzOFwOBzlJ6AMCQmBqakpT0BZkgWXtWvXsldSUhLevXunNMElwc8P8R6ekMbGInT/fkReufJVYPn2W0FgKVeuUH77sVcIZp14iefeIZh1Zzsa+r9GkpUNKv3vCDRMVctxmiblQ88MRXRiNMv3srj5YqhLKAxBySEsLowJKfLX6+DXkKYNSyy2oAY0mT80BAj1FPxf2s0TTMqKS9QxaRLw5jRwZx3gdfvr/tLfAI1/Aip0ABT7qTyowdMDwJPdgmAz+jagyU0EOBwOpyTi6+uLTZs2YcSIEbBRUiAlMS3yl1jBRQyNEXr4MHxn/pHaxl8igbFrZ5iPJIGlbKGXQSqV4eADb6w+8Qhz/lsC26ggeDrVQPVdW2FnrlomV3d87+CnCz+xJJW9K/TG741+Z0k6iyuBMYF44P8AD/0e4mHAQ7wPeZ/uGDsDO9SzqgdnE2cse7iMmYmJLox0TKjg4/HmlPC5Shfgu9WAjjFUlrgIweGefHhIKCMoHHS1HkI4aNtaWf9/bDiwpj4Q6Qe4zABaTC6SYnM4HA5HXMTHx8Pf3x9WVlbQ0lKONQkXXESEshqDNC1urVoLEYXkqKnBcedO6Dco+hXw0Oh4bNt1Hi6rp0EnKQEHK7eDxbix+LFZWWhrqI7m4qzHWfx29Tc2QR9dczR+qvUTigufIz+n0qh4hHukO6ascVkmqNS1qsteikLJ0fdHMfv27BQtTC3LWtjdaTdEAQnvdzcA52YKpmOmZQXTsewm+GKDgg7c3SiENo5LznJMYZ/rDQXqDweMcrFa9vwwcGQYoK4NjLkjaF84HA6HwyliuOAiIpTVGFF37sJr8OB0+5ng0rABlMXLnfshWTCbvZ/VaCi+VKuPP1yrwKViKaiSE/pfd/9i72c0nIE+lfpAFYINeIV7sXDPJGyQItQz3DOVoEIBCBRRgxoqmlVMEVLqlKoDc13zbH/nsvdlzL87H5oSTZzreQ4WuhYQDRQi+dBgIMxLMB1rPx+o/6P4Tce87wG31woBB+TmeeblBXOwGn0ALb28CXO7uwLuVwDnNkC/w+KvBw6Hw+EUKBEREXj8+DFq164NQ0NDpdQuF1xEhKg0LhIJnC9dhKa1ck13fOfORejefYjS1MXYFuPha2CBNpWt8EfnKnA0z8METAmsf7Ie656uY5P7RS0WoUOZDhAripoQKm8V8yrwj/ZnpmCKqKupo6p51RRBpVapWjDWzps5Vb/T/fAs8Bl+qvkTRtcaDVEREwL8b4yQ74Wo2g1wXSXkgxETSYmCoEICi8+Dr/vLtQQajRGEDUk+U2UFugHrGwNJ8UDvXYIZnQpBQUE+BkahrIU+bIx1lV0cDofDUTnITGzPnj3o378/MxdTBlxwERFK93H5Y5YgvEgksJkzGyY9e0LZyOLj4TloMGIeP0aYjSOG1R+NKIlmSlLL0S2coKslbvMx0lhQ6N8Dbw9AQ6KBda3XobFtY4gN0oC0O9wule+JHC2JFqpbVv8qqFjWgp5mwQiOZz6ewW/XfoOZjhnO9zwvvihspG0ggeAC3R+JgplUr52ATQ3llIc5zX8AzJwALX3BFIyiooUJkfmYdqhGb8F/xaqAo/JdmgdcWwQY2gI/3wO0lbPillsoDPu0o88hlZE/FbCge3V8X99R2cXicDgcTi7hgouIUHZjsKhinl7QKu2odE2LIgn+AfjYoweSAgOh1rYD5tbojZsfgtl3dia6mNm5CtpXtRK183uSNAmTr03GOc9z0NXQxfb221HVoqqoykcmbYffHU733ZT6U9CrYi9ok39DIayAO5hpY8iFbkyz81fTv9DFWaQr+d73gcNDBAGB6qLjQqDukKI1mSIh5eT4ZBMwNUFISYoTvtOzEEzZ6g8DDArJnDIhBljbUHDybzIWaCeYQYoZ6mdNF15iQoscdTU13JjqwjUvHA6Ho2KEiyiqWD7tGDj5hYQV8mkRk9BCaFqVgv3yZYC6OmTnz2C17ges71cHtsY68AmNwag9DzFw2z18+EJJ88QJhUNe0GwBGto0RExiDEZfGM1yvogB9zB3DDwzMEOhhaJ9tSndpsCFFloBp8lk38130WLRNVQ16Mj27361m2moRIlDfWDkNaBCR0FYODUROPKjELWrsJJj+j4DXp8Ebq0Gjo0CToz96rdCmjEqh5mzEPmMEkS6TCs8oYXQ1AU6LRHeUx4Y/9T5eMQICceKQgtBCXAplxSHw+Fwcs6XL1+wceNGtuXwcMiikiLFSNCOHQhY+DegoYHSu3YB1apj/ZUP2HjVHfFJUmiqq2HoN2UxtlV5GGhrQKyJGIeeHYpXQa9gq2+LXR13wUrfSmlalj2v92DVo1WIl8bDQNOACSknPpxgPi4FmdGehJFPITF44h2KW26B+Od+sllTMurqMTCtvBBxSXHKz+eSHSRYkSBx4U8hQSOZbPXeCeiafTXhMrbL/jwJsULkL9JehHgkbz2/buWZ7LNj0EmgbHMUKQf6CwKVQyNgyH/5958pRI48+oRfDj5Nt39et2ro17C0UsrE4XA4qgjNT2/cuIFvvvmGzVdL+lyZ53ERUWOIEZr8fv7lF4T/+x80LC1R9ugRtvUIjMLcU69w8U0AO87KSBvTO1VG/TKm8AiKFp0zbnBsMAb+N5BF6qKcJjs67MizY3teod/+/cbvePLlCfvc1LYp/mzyJ4siRr4u3hHecDB0yHNeleCoeDz9FIqn3smvT2FsX1Z0drmFq34n4OLgglWtVkH0eN0FDg8Fwj8JeVEowSNpQdQkgOtKoFY/INwntTCiuI3wzf43yPzLtDRgUhrQMwPubxV+Q46aOjDhec4EpYIk7BOwpgGQEAV0WQfU7gcxQoltSasXk5BEhnWpvLfIwu9P16oY1KSMEkvI4XA4HFWdKxcbwSU6OhqVK1dGr169sGRJslmFijWGWJFGReHj998j3u0D9OrVg+P2bVDT1GTfXXztjzmnXsEzKLUJiBidcX0ifTDg3wH4EvMFtUvVxsa2G5nvS2FDmpR9r/dh5aOViE2Khb6mPn6r9xvTquTVRygmPgkvP4cxbQoJKCSoeAWnN8MhjVhlGyM4Wxrg2GOfdCEA1g6yw9R7A1g0s9PdTsPByAGiJzoYODgI8LiW/jsSKkgjkxVaBoJQQsKJaZmv72lr4ghoG2Tg4zJBOC+d33UFUGcglMLNlcD5PwA9c+DnB4JgJSLIdLTn+lsIiU5A8wqW+KtLVfiExsLBTBcbrn7Anjte7LgxLk74tV1FUfvIcTgcjhhITExEZGQkDAwMoKGhHMsWMc2Vi43gMmPGDLi5ucHBwYELLoVAnPtHePTqxYQYs0GDYDVtasp3sQlJWH7+LTZe+5hOeLk5tZWoNC/vQt5h8JnBiIiPQHP75ljhsoLlMyksvMO9MfPWTJaHhSB/mzlN5sDWwDbHYWOTpDK8D4hgwskTb0FIeesfwfanpZyFPmo6mKCmvTHbktCio6me4uMy/egL5msgp3wpA5Sp+g/u+N1E/8r9MaXBFKgEH64Cu7/L+Dtynjd2+CqMpNqWESb7uZ0ws6hi7kJ0s6LWtCiSlABsaAZ8eQ3UHSxomUSCX1gseqy/xXzgqP/tG94I+grmo/SoWXvZDUvOvWOfe9W1x/zu1aGpLl6TN04BBqHx8IRWmdKi8+fkcMSOr68vNm3ahBEjRsDGJhdJjAsQLrgUMO/fv8fUqVPh6uqKFy9ecMGlkAg/fx4+Y8ex93bLlsKoU6eU7259CGTmIWlp6mSOOV2rwckyzSq2Ennk/wgjzo9gvh3fOX3HomoV9MovaVkoFPPyh8tZYADS7Pxa71f0qtAr3W+lDRs7uUMlOJjqMbMv0qi88AlDdHx6LYKFgTZqOZigloMgpNSwM4GxXtZCGAlI5CCtqynByD0P4R8ehypOvvDWWsk0QRd6XoABaSTEDgkSK6opOM3TMowEGHYBsK0tav+PfON5C9guBFZg10sBDJRMWEwCem+4zQRqEr4Pj2oMc4OMg0swAfrYCyZ4u1S0xNp+daCnJU7/OE7xDfvP4agKcXFx8PHxgZ2dHbS1CzZoT07hgosC165dw+LFi/Hw4UMmVR47dgxdu3ZNVWFr165lx/j5+aFmzZpYvXo1GjT4ml2+S5cu7Ptbt25xwaWQCVi6DEGbN0NNTw9lD+yHdvnymYY/lUOT8S617PBzK2fRCDBXvK9gwuUJSJIlYUjVIZhUb1KBmqT9cfMP3PO7xz6T0ztpWewN7dMdm1W9KaKvpY7qyVqUWvYmbGtjrJMvgeu1bzibbEbEJcCq8mpE4zMzYRtYVUlmULlFTCZcRc3/fgKe7AWsqwPDrwDqypv4k8Z14NZ7uOcRjFKG2jgyugkczLLON0QmpmP2PUJsgpT15e2D68NMX2S5hDjFOtEyh8NRTcFF6cuSUVFRTBgh4SQjDhw4gEmTJmHWrFl49OgRO7Z9+/YICBCcwo8fP44KFSqwF6fwsRw/DnqNG0EWHY1PY8chKUIIS0vmTeTTQrkaCNqOa+WMtlWs2KSc/CvaLruKiQeewF0EIZRbOrTE7Caz2fvtL7dj+4vt+T4nmcIcfHsQ3Y93Z0ILaVmmNZiGLe22ZCi0RMQmYPXF9xkKLWUs9NCvoSMW9ayBcxOb49mf7bF/RGNM61gZHavbwNZEN99aIjIj2zigLjPVCfZtyPbte7OPRT5TCUhIISf5QaeEbUkRWoi2cwAdE8DvOXB/i9KKkZgkxbh/HjOhxVBbAzuHNshWaCFaV7bC3h8bwURPk5k+kl+MdwY+WhzVJu7Dh9RCCyGVstxlHA4nZ5B/y+3bt9mWIzIfF5qIpdW4NGzYEPXr18eaNWvYZ6lUyvxYxo4dy8zDpk2bhj179kBdXZ01akJCAn755Rf88ccfKidFqgqJwcH42KMnEn19YdCmNexXr06ZRMtNkWjiLffVIFOnFRfe48Jr/xQNTNdkDUw5JWtgSGBZ9nAZe5+fRIy+kb6YdWsWbvveZp/rlKqDuU3nwtEofXCCLxFx2H7zI3bf8UREbGK674vaN+j4Ex+MP3APBs4LoaYRjRUtV6B16dZF8tucfPBgm5DXRssQ+Pk+YFS0ts/06Jh+7Dn+uecNLQ0Jdg1tgEblzHN1DreASAzado/5xVgaamPnkAaoYsvH4eKATCqFz4QJiDh3PvUXXOPC4eQKsjbavn07hgwZAmslaSrFNFcWteASHx8PPT09HD58OJUwM2jQIISGhjJtiyI7duzI1lSMbAXppdgYJAiJoTFUiZjnz+HZtx9kCQmwnDQJFiOGZ/s/zz+FYeXFd7jwOuCrAFPbjuWAIbt4ZbH0wVLseLkD6mrqWOmyEi0cWuT4f+n2OeZ2DIvuL2L5Yihp5Pg649Gvcj+Wk0URCiG96bo7Dj/8hPhEYRXSyVIftR1NcfTRJ6Z5IU3V/O7VijwaG0V8WvZgBbQtrqCsQXWc6LGvSH+fkwdoJXtrG8DnIVCtB9BzW5FW47Jzb7Hqkhu7j9f1q4MO1fImOPmHxzLh5Y1fBNPabBxYF02cLAq8vJyig8ZFyv8VvHOnEAiDXsmaF+NuXWG7YAFvDg5HhQgXkeCidFOxrAgMDERSUhKsrFInC6TPJIHmhQULFrDKl79IaOHkHt3q1WE183f2/suKFYi6dSvb/yEfjS2D6uPkz9+gdaVSbKJ+9JEP2iy7ypLV0cReGUysO5E56ZO/yy9Xf2HO+zmBcq+MvjiaaVpIaKlpWROHXQ9jQJUBqYQW0jiRPX+rpVew764XE1pqO5owM63zE1tgSa+aTMPyz/BGuDHVRSkhpEc2L4eu5XpBJpPgY+RzHHgmaI44IoYCEHy7TAhK8OII8OFykf307tseTGgh5natlmehhbAy0sGBkY3RsKwZIuISMXjbfZx69rkAS8spaoI2bRaEFgC2CxcwnxbjHkJS3cTAIN4gHA6neGpcPn/+zKIokNN948aNU46bPHkyrl69irt300exyg6ucSk4qOv4/v47wo4chbqpKcoeOQxN29RhfrPi2adQrLzwPiWJpbpEjZmQjW3ljDJFrIFJkCYwZ/1rn67BUMuQJaisYFoh0+umTPd/3/sbEQkR0JJoYWztsUxgUZeopxxz0y2IaTJuuAWm/C9FURrVwgkNypqJLocFRXlqs3sEAnEHiKiLI71Wo4KVobKLxcmOfycD9zYC5s7A6FuARuFGnfn3uS8TxOnJMaFNeUxoU6HAnPwnHXyCf5/7sQX6PzpXwZCmZVESUeXwwSEHDsJv1iz2nsLmU/h8eUh9d4pEqaGBCjeuQ93ERMkl5XBUA1rEP3HiBL777jtYWChHG801LjmEGoh8V/z9Bd8IOfQ5r3Z+FEqO1Fy7d+9Go0aN0Lo1t+XPKzTxtp45EzpVqiApJASfxk+AVMEMLztq2Jtg6+D6OD6mKVpVKsUmzkcefULrZVfx66Gn8AwqOg0M5XJZ0mIJalnWYjleRp8fzaKDpSUgOgBjL43F7zd/Z0JLdYvqOOR6CIOrDWZCC13D6We++G7NTfTfepcJLSSQdatthzMTmmH7kAZoWM5cdEILQeVc0m4Mey8zeIIBO86z3BwckdNqBmBgBQS5ATdXFepPUdjzCfufMKGFgkeMby1EFSwIKN/Q6h/qYGDj0uz8s0++wt9n3rBFgJIWPpgicXkNHsy29FlVCD9zFn6zhaAn5iNHpggthHa5skIUysRERFy8pMRScjiqBSWdNDMzU1rySbEhao2L3DmfQh9TCGS5c76joyN+/vln5pxfnKRIVSX+kw88evRAEtVh584sRn9eVgopZ8nKC+9w+e2XlIl099qCE39p86LRwITFhbEElW6hbihtVBqLmy9mgoyDoQMeBjzEgrsLEB4fzgSdMbXGYFDVQdCQaLDVYhK6Nl9zh0eQEB1JR1OCPvUdMeybsjmKtCQWfjjVHy+CniLuSyuU0+iBg6Maw0in8JJ0cgqA54eBI8MADR3gpzuAWcFrKsjksc+mO4iMS0SHqtYs/wrdowUNPZLWXfmAxWffss/d69jh7x41SkSiynhfX3yg8MGKj2UVcWaPun0b3iNGMr9Hk969YT37z3QLNF/WrkXg6jXQb94Mjps2Ka2sHA5HdefKShdcKBIYZbwnateujWXLlsHFxYVJlySgUDhkcsbfuHEjE2BWrFiBgwcP4s2bN+l8X1S9MVSZyBs34f3jj1935CPRGAkwKy68wxUFAaZHHTv87FIejuaFLwD4R/lj4H8D8TkqYzv7KuZVMK/pPDibOrPEe3vueGL7TQ8ERgraJgrxOqhxGQxqUkYlc1Oc9TiLX6/+CiTpI+L9VDQpZ40dQxqwyFEckULD+K4uwMerQPl2QN+DgkN0AeEVFI3u62+xPk6+KBT2mDQkhcmhB96YevQ502K2qGDJAgDoaxffFcfox4/x+fffkfDBPd13dqtWwahdW4g5WIvXoMGQRkfDsH17lqBYTV09w/DI7t92BjQ1BXMxY2OllJfDUSXI1zs2NhY6OjrMCkkZiGmurHTB5cqVK0xQSQsJKxQljKBQyPIElLVq1cKqVauYJiY/UN4YelGHePfunSgaQ+UTjbm0KtCVwsdeIVh58X2KAKPBBBh7poHRUFfDx8AoFo2sMMIG3/e7j6Fnh6bbT8kqx9YZi+DIJGy78RF773qxFWjC1lgHPzYrhz4NHFQ6E3iiNBGdjnaCb5QvpAG9EBVUF11q2WJ571qQFMIKO6eACHwPrGsMSBOA7/cAlV0L5LQUvrvnhlvwDIpmuX8OjGxUZBq4y28C8NPeR4hJSEJNe2NsG1wf5gbKyRxdWCT4+CBg6VKE//tfpseolyoFh/XroFu1KsRGnLs7izCZFBrKcnw5bNwIiVbmCzburq6Ie+8GmwULYNItdbJpDoeTHkrOvmnTJowYMQI2NkUb9l4OF1xEhJgaQ5WJunOX2WSnxXHnTug3bJCvcz8iAebCe1x9JwgwNHeWJ22k9/O6VccPDQo2Etc933sYdm5Yuv1zG67F7RcmLKFmfJIQ3rOilSFGtigH15q2xcacZceLHVj6cCls9crhw+MRoOjNdI2UAJMjYi7OBa4vAYzsgDH3AO385UkiobzPptt44RMOe1NdHB3dBKWMdHJ+gjAfIPgDYOYEGNvleQFj6I77CIlOYAsVlOulKDSvhU1SZBSCNm1C8I4dkMXHMw0ZRd7SdnJCwOIlQvhgiQTqZmZICgyEmo4ObOfPgxE5uIuEBF9fePTtx3J66VSvDsft26FukLVZ75c1axG4Zg0MWrSAw8YNRVZWDkdVIW2Lp6cnSpcuzbQuJX2urHSNi7IRU2OovMaFbLPTZEkue/oUdJycCuQ3HnqGYNGZN7j7MTjddwba6jDV12Irweylq5G8/frZOOV96u/1tdTT2WJTqOP2R9pDKlO8Hgmi3KZAmiCYN9QvY4rRLZ3gUrGUKJ3t8+vr0/ZwW8QkxmBgmQVY+59wfbO/q8pM4DgiJSEGWNsQCPUEmowD2s3N86kobDcJDBRggkweD49qnLuEsY92ASfHUyZCIWSz60qgzsA8leXDFyFR5aeQGFgYaGPHkPqoZqeaZkaypCSEHj2KLytXMYGE0GvYEFZTp0CncuWvUcU8vaBV2hESPT34/PIroq5fT3F6txw/DmoUDluJJIaEwLNff8S7u0OrbFmU3rcXGqam2f5fnJsb3Du7CuZiN29AnT93ORzREy6iuXKJFVy4qVjBQ9FvfP+YlUp40W/WjJk4qBVQNAyKatR3c+7DYGcF+dAY6igKNoJQ4xZ9Eb5ae6CmJoNMpoY43+5ICKuPNpWtMLplOdQtbYbizLw787D/7X60sG+BSmrjseTcO+Y2sT4fyQY5RcC7s8C+3oBEAxh5HbCqkutTSKUyjD/wBCeffoaeljrLMVTTwSR3mpYV1QShRY6aOjDheZ41LwGUqHL7fbz2DYeBtgY29K+Lb8qrVqJKcmD3X/g34t4KgQe0SpdGqSmTYeDikuXiBwk7AcuWIXirkGSUjrddvAjqBvnTqOUVaVQUPIcMReyzZ9CwtkaZfXtzFQr/Q+fOiHf7AJuFC2CiEIyHw+GkJyoqCq9fv0blypWhr6+cZN1ccBERYmqM4oB8pVAaGwufCRMgi4mBab9+sE5OVplffMNi0HThpRRTMbm52N4fG0FbU4LwmASExyYmbxOY83x4TCJ7L/8uQuG7hKSs5XY1jTBItAIhjbcAEo2x98eGaOKsWpOlvOIR5gHX/wl+Eie7nsSmSxEsgaa2hoTVQ70yxVtwU2n29wPenAIcmwBD/s2Voz6tZVEo4h23PJhfGfmVNK9gmfPfTkoELs8DbixL/92gU0DZZsgrdN+O3PUQt92DoKmuhqW9a+G7mjmfMCuLuI8fEbBoMSIvC0lCJUZGsBzzE0x/+AFqWfiDpCXsxAn4/j6TmZZpOTnBYd1aJvwUJdL4eHwaNZolHaZcLKX37mHmbbnhy+o1CFy7FgYtW8Jhw/pCKyuHU1x8XDZv3ozhw4dzH5eSrHGRwwWXQqzb8+fhM45MRWSwmjEDZgP6F8h5D9z3wvSjL5Akk0FdTQ3zu1fLU7Z56vpxidJk4UYQZuRCzhOvUGy/5ZHuf2jlubGTOUoKYy6OYUk5f6j0AybXm4pRex7iwusApp06MroJnEspZ8WXkw2h3sDaBkBCNNB1PVCrb46rbN0VNyw6I2gEVvaphS61cqghSUoAnh0Ari0BQj5mcIAaMPEFYGyfr+aLS6RElU9ZviRiZucqLOS4GCGH9S/r1iFk3z8sfwnU1ZmwYjHmpxyZVWUWwevTmJ+RGBAAibEx7Jcvg36TJigKSPNDZmsRZ85ATU8PpXdsh26NGrk+T9z793B3/Q5qmpoof+sm1A15olsOR8yEi2iRnwsuImqM4kjQli0IWLKUOZmSyRg5ZBaU5sUjMBplLPQKJapYRpodEpJuTHUplN8TK3d872D4ueHQ1dDFhV4XoAl9/LD5DgtZbWeii2M/5dJZm1N03FgBXJgF6JkDPz8A9LLXkB184I3Jh5/lTiBIjAOe7BM0LKFewj76zdJNgDf/0mz367EdFwENRyK/kCnbnFOCVoigZJidqtmgXKnCiTKYWyiXScg/+1neEmlYGNtHYx+ZhWmXK5fv8ycEBMBn7DjEPH3KxlarKZNhOnBgofra0UKP35+zEXrgAPNPcdy4IV8C04dvOyP+wwfY/r0Qxl26FGhZORxO8Z0rF48QSHn0calSpQrq16+v7KIUa8yGDYNxzx7M78Vn4iTEJtt25xeanJDmo7AmKXTeBd2rM2GFkGt2xDApKkoaWjeEs4kzc9I/9v4YdLXUsXVQPZQx14NPaAyG7LifEg6aIzIajwEsKwHRQcDFOdkefuGVP6Ydfc7ej2rhlL3QkhAL3NsMrKoNnJogCC36lkDbucD4Z0JIZvJpIfOwbyYJ/3NmKvD+Qr4vjcJyz3KtgqkdK7HPFJa839a7bLGBNLLKgib3EZcuM22C//z5TGihbPEOW7ewCFoFIbQQmqVKwXHXThh368bGVv8FC+E7fQYz4yosvqxaJQgtamqwW7wo31oeo/bt2Tb8zNkCKiGHUzwJCgrCnj172JbDNS6ikiKLK2SP7TV8BKLv3oWGjQ3KHjwADctc2MwrkcLW7KgCR98fxaxbs2Crb4vT3U9DQ6IBz6Ao9GAJCePRrLwF84MoLqGgixUeN4EdFD5XDfjxAmBfL8PDHnoGs6AXZDpJuZKW9KqR+ep9fDTwaKeg0Yn0E/YZWAPfTADqDAK0MghVTBbJx38GnuwBtAyBH88DpSoXyP3ZZMElKNo7K0szSosy/gsXIvr2HaEc5uawHDcOJj26F1hwkowEpZBdu+D/9yImwOjWrAm71auYYFOQBO/azQQxwvrPWTDt0yff54x99w4fv+vCzcU4nGwICQnBhQsX0KZNG5jm0cS0OM2V+UyDU+iQ86n9qpXQKlOGxfv3HvMzpDExKlHzha3ZUQU6le0EU21TfI76jEtel9i+0ub62DqoPnQ11XH9fSCmHHnGJlEckVGmKVDzB5riAqcmCo7zaXjvH4GhOx4wocWloiUW9qiesdASHwXcXAWsrCloTkhooXwxnZYA458CjUZnLLQQdL7Oy4HSTYH4CCHqWZQQCjg/UBLatL2OfN9osaGoSAwMhO/MP/CxW3cmtJDfhvnwH+F09gxMv+9daEILQe1kNmgQHDZtYg7/ZDrm0as384MpKMJOnkwRWigMc0EILQRpoiiMMpnVRV65UiDn5HCKIySs9OrVS2lCi9jgggunSFA3NmamErSlEJqfp06DLE3OF4440dHQQa+Kvdj7Pa/3pOyn8Ljr+tVh4aSPPvLB0nPvlFhKTqaQ6ZaOMeD3DHiwNdVXn0NjMHDbPRagorajCdb2q5NecxYXAVxfBqyoDpyfCUQFAMaOQOcVwLjHQIPhgGYO/Jw0tIDeuwHTsoJZ2f6+grlZPqCElBRVUBH6SBrSwkYaF4fATZvxoX0HhB46xDQehh07oNx//6LUL78Uaahig2+aMk02RRpL9PeHZ/8BCDt5Kt/njbx6FZ+nTWfvTQcOgPmoUShIocuwAzcX43CyQyqVIj4+nm05JVhw4T4uRQ+F7bRfs5o5dkacPcsSsHFUgz4V+zATsccBj/Ei8EXKfpdKpTC/WzX2fs1lN+y966nEUqo+ZPpEuYpoW2AYWAKtZwnvL/0FRPix859/6Ye+m+/ANywWTpb62DaoPvS0FLQDsWHA1cWCwHJxtuArQ0JHl7XAuEdAvSGAhnbuyqJvDvQ9CGgbA953gZPjBDOyAvJFI3Q01aGvrVFo4d4j79xByL59cO/YCV+WLWM5TShrPCVgtF++HFr2+YualldIo13mwH4WYlgWF4fPv/2GgCVLWCSwvBD96BE+jZ/AoqEZfecKq6lTC9z536hDB7al5JpJkZEFem4Op7jg7++PBQsWsC2H+7iIym6vpBD6v//Bd+o09t5m/nyYdO+m7CJxcsD069Nx0v0kvi33LRY2W5jqu+Xn32Hlxfds9XvjgHpoW8WK12kuIadyco6nSHY0P/zxm7JoVcmK1SlNGOVb+k5CW5bDSPicsk/hu1THSpNgc+Q7aPs/xvtS7dHee1BKxDwjHQ38N6E5ixLHiA4G7m4A7mwA4oSIWDB3Bpr/BlTrCagXgFDw4TKwp4cQcazVTKD5r/k6HQli7l+iMOPYc3gERWNCm/KY0KYCCjzB7sw/UglalHyx1KSJMOrcWemZ7OWQoEKLQkGbNrHP+i2aw27JklyFHI59+w6eAwZAGh7O/t9hzRpmAlfgZZXJmAAY7+EB28WLYezaucB/g8NRdWJiYuDm5gZnZ2fo6irHbF1Mc2UeDllEjVGSCFixAkEbNgphNbdugX6DBsouEicbXga9RJ9TfaChpoGzPc+ilF6pVBOQqUee48ADb+hoSrDqh9osuzmZ8pRk/6BcOZkvvJQf5UO2VFNzx3GtmVBXk6Fv/HTckgqaMhKIbk5tBRuNaOD2GiFSGPmhEBSVjASWqt0AiXrBFuj+VuB0crSxXjuBqvnPoE65XcbsewRDbQ1cm+wCU/2cJ3fMijhPT7h36JhaO6SmxszCtMuUgRgJO30avjN+hyw2lvmS2K9bC+2y2Ye3jvf2hmfffkj88gW6deqw8VlSiJMl+bPAoE1rJiBxOBzxES6iubI4log4JQ6KtmNIZgIJCSwfAa24ccRNVfOqqFOqDhJlidj/Zn+q72h1/69u1dCyoiViE6QYseshi1Kl7PC0qkBCkhSLzrzJUGixNdFBOUt9lLPQZyGoHc304GCmy7Qj9LIx1oGVkTZKGWrDwoBeWjDT14KJniZLEmqoo8Em8fpa6vigUR57pW3ZeedrbEEzyTNYIwhmsjBIz/4hmIRRLhYSWqyqCcLE6NtA9Z4FL7QQ9YcBDUcL74+NAnwe5fuUHatZo7KNESLiErHpunv+y0gKqIcP4TVwUHqTNpkMif4BECvG337LstqTVij+40d49P4ekdevZ/k/JKx4DfuRbbUrVGC5twpTaEllLnaNzMWiCvW3OBxV1bg8e/aMbTlc4yIqKbKkIY2NheegQYh9+oz5v5B9trqJibKLxcmCC54XMPHKRJhom+B8z/PMcV+RDwGRaL3saqp9JTFxZ07xCorG+AOP8dgrNN13hVFvfgH+0F9bE4ZqwgOQzMUSoQ4ttWQ/CJuaQIspQIWOLLFhoSNNAvZ9D7idF0IqD78EGNvl65TnX/lj+K4HQsS7KS5MoMsLNIkmHxbyZ8kQiQTOly5C09oaYoainn0aNx4xjx6xMpf69VeYDRmczl8lKSICngMGIu7NG2ja26P03r3QtCrYsMqZmot16Ih4T0/YLlkC487fFvpvcjiqhK+vLzZt2oQRI0bAxsYGJX2uzDUuHOV1Ph0dOKxdCw1bG/bQoocr5XwpiZDTb9Sdu2wrZlwcXGBnYIfQuFCcck8ftcg/In2UqKIOT6sqHH/ig06rrjOhhTQjPzRwLPSEp9baiTBQ+9pGZCbGhBbr6oLT/IirQKVvi0ZoYQVQB3puAywrC+GV/+kjhF3OB20ql0JNe2PEJCRh/ZUPeTpH5PUbcP/ONUVooSS6VjNmfK0XiQQ2c2aLXmghNCws4Lhje0oi4IBFi+A7dSqLiqa4iPRp9E9MaFGn47duKRKh5Wt0MUHrEnH2TJH8JoejSlhbW2PGjBlsyynBGheKKkavpKQkvHv3ThRSZEmFOYL27cui8xj36A6bv/4q8Og1YoY5/f4xi00q5BMik549IVZ2vtyJJQ+WwMnYCce6HEvVVuSrQeZhcsdvOdcnt4SDmX7RF1aERMQmYNbxlzj62Id9rlfaFMu/rwUHM73CT3j68Rqw0zX9/kEngbLNoTRCPIHNrYDoQKBSZyFscj6Ep6vvvmDQtnvQ1pDg6m8usDbOQbhmErJDQ+G/8G+E/e9/7DNpHmzmzoF+48bsMy0sxHt6Qau0o0oILemSVe7Zy5JkIikJOjVqwHrmTCRFhCNoy1ZE37oFiYEBSu/eBZ3K+U8OmhtiX79meXDUtLVR4dZNSPT5WMHhiIlwEWlcSqzgIsbGKMlEXrsG71Gj2eTd8pdJsBg+HCUBmgi5tWotCC1y1NRgPftPaDuXh4aFOTTMzUX1II+Ij0CbQ20QnRiNjW03ooltk1Tfk0/L9KMvmKZFzvf1HDJPbFiCeOwVgvH7n8ArOJppO8a1Lo+fXZyhkTZ3SmER5gOsqAbIFPubOjDheb5NtDKCTfQ9PKFVpnT2E32vu8DOzkBSPNB0AtB2dp5/lx5rvTfexn2PEAxoVBpzuwqBCLIi/Nw5+M2Zi6TAQHYPmg7oj1ITJkCiV/g5YYqSqNu34TNhIpLCkiPGydHQQOnt26BXv36Rl4na60OHDkjw9ILdsqUw6tSpyMvA4YiVkJAQnDt3Du3atVNaEkoxzZULL6Uvh5MLDJo3h9X06fD/6y98WbqM+bwYtWtX7OuQJnWphBZCJoMfaWAUUNPVZQIMvciUQ8PMDOpMqLFIEW7Uk99LDA0zFRByNZHMBEMtQ3Qr3w17X+/Fnld70gku39d3RPMKlkxz4B0cjalHn7FoY7TqPbFtwYaoVRWSpDJsuPoBy86/Y+/JsX5ln1qoV8asaAtCwonrSuDkBCEUMQktrisKRWjJtSbRsaGQI+bocODmCsCiAlC7X55+m/r/L+0qos+mO9h/3wsjW5SDvWnGAgg5ovvN/QsR586xz1rlyjGtr16d2iiOkPbIfv06FjksFVIpNB0clFImai+j9h1YCOfwM2e54MLhpBHsyTqohOsZUuAaFxFJkRzA7695CNmzB2o6Oii9ezd0q2e/UqrKfFm7DoGrV6fbT6Ya5CybGBQEWS4jiVC+BSbcyAWd5G2CzyeE/3dGiI6UT5M0r3AvdD7WGTLIcKLrCZQ1zjzMKiWlnHFMSFq5sHt19GngiJIEZaefeOAJ7n4MZp8717DBvG7VWdQvpUGal2B3wKxcoWla0mkSJRI4XTgPLVvbrP+ZkmReWwxINIGBx4EyTfNcjn5b7uCmWxDT+P3ds0aq72gSEHb8OPwXLISUtA/q6jAf/iMsRo+GRDtvDv2qAvnTeQ0enG6/486d0G+onND0MS9fwqNHTzb2M3OxYqbp4nBUmXARzZW54CKixuAAssREeP/0EwuNqW5pgbIHD0JTSVE0CpvAjZvwZfly4QNpSDIRKMj3JzE4mEUHSgoKQmJgEBKDFN8HMfMW2kpzk306n1GRxl4aiyveV/B9xe/xe6Pfszx2ydm3WHPZDeoSNWwZWA8ulYrG8VfZnHnhiylHniMsJgF6WuqY06UaetSxK/Ymc5E3bsD7x/TmnhJTU5j27AHj776DdvnyGf8zCTuHhwCv/gfomgqRxkjAygMPPUPQY/0t1u8uTmqBMhaCyWXC58/wnfUny9hOaFepDNt584rct0NZZCZYKjNKGjMXa9ceCd7esFu+DEYdOyqlHBwOR9xzZS64iKgxOAJJkZHw/KEv4t6/h3alSii9Zw/UDcTj41EQD+gvy5YjaPNm9tlizBgW8SfByzvfTr8UHYgJNGkEnJgXLxF54UK64+3WroFR69Z5+q17vvcw7Nww6GrostDIxtrGWV7zr4ee4cijTyxM7f4RjVDTofiGvo6OT8TcU6/wzz1v9rmGvTFW9amdMnEuzkijo+E1YiRiHjzI8jjtypWZAGP0bSdolkojyMZHAzu+BT4/EkzGhp0HdPPWX4Zsv4fLb7+gW207LOtVAyH79+PLkqWsnGpaWrD4+WeYU3jgQsgML2bEGBQkYOlSBG3eAsP27WG/coVSy8LhiAUeDjk1JVZw4VHFxE2Cjw8+ft+HaRIMWrRgWZ/V1AshCV4RI5NK4T9vPkL27mWfS02eDPOhQ5SzwkpObtbWcNiwHjqVKuX6nDR09DzZE+9C3mFi3YkYWm1o1mVIkmLojvu4/j4Q5vpaOPpTE5Q2L34T+Rc+YRi3/zHcv0QxRdqoFk6Y2KYCtDSKf/R5cvj2HjkKMU+eAFpaQGJiysTYauZM5psVduIEC8ZByWcZEgn0GzWCcZfvYNimzddAFBF+QqSxcB+gnAvQ7zCgnnu3zOefwuC65gbsor5gS8A54Oljtp+ywtv8NRfa5fKmzSl8U74PgJlTwZry0eOewk3HhQOPdiPhv0WID5dAy0gKze+XA3UGQpnQAotHT24uxuEoEh0djTdv3qBSpUrQU5IJpcoKLh8/fsT169fh6enJKtLS0hK1a9dG48aNoaOTs3CTYkNMjcFJTcyzZywhmiwuDmaDBsJq2jSVN4PznfkHwo4dEyKHzZoF0z7fK22FVd3YGEkhIczx33b+vDyZZvzP7X+YeXMmrPSs8F+P/6BJfglZEBmXiO833sbLz+EsE/yR0U1gnscEgWJDKpVh282P+PvMGyQkyVhG++W9a6GJswVKAgkBAcw8LO7dO0iMjeG4cYOQtT2D8MGJISGIOHMGYSdOIuaxIEgQ1BdJeDH+zpU5kat9eQVs6wAkRAH1hgHfLhXMKnN5320eNw8NrhyGtjQRanp6KDVxIkz79YVaUeWryQ2PdgEnxwuR39QkQjAFuUBB9258BBAbBsSGJ28VXiSQsPehCvvTHEdBGTKiEKPL5cpcrG07JHz6BLsVy2GUnN+Fw+Eol3BVE1z27t2LlStX4sGDB7CysoKtrS10dXURHByMDx8+MKGlX79+mDJlCkqXLg1VQkyNwUlP+JkzLHQnYT3rD5j+8INKVhMl1vSZPIVN1sgJ2HbBfGYmU9Qo5qGgBKA+v/yKqJs32XfkmGw5YUKuNFtxSXFod7gdgmODsbjFYnQok/1EIyA8Ft3W3YJPaAxqOZjgn+GNoKul2tq0gIhY/HLwKdMmEe2qWOHvHjVgqq+FkkC8tze8hg5j/gkalpZw2LoFOhVyFkEu3ssLYSdPMk0MhcOVQwEmjL/tBKPqZtC5PwVqajKg4yKg4cgclyv27Vv4zvgdsS+E4BCPLCug1sqFqFpHpL4sGYWrJoxsBU0JCSEoACMJEojS/gYx6BRQthmUScCSJSyvDCWltF+R7API4ZRgYmJi4OHhgTJlyrC5d0mfK2cruJBGRUtLC4MGDYKrqysc0oRLjIuLw+3bt7F//34cOXIE69atQ69evaAqiKkxOBkTuGEjvqxYwSb8Dhs2wKDZNypVVSwr9fjxiLp6jdnR21KegrZtIQZkSUksQABNFAj9b76B3dIlTBuTU9Y9WYf1T9ejpmVN7Om0J0f/4xYQiZ4bbiE0OoFlOt/Qv27R5TIpYC698cdvh54hKCoeOpoSzOxcBX0bOBZ7B3w5se/ewXvYjyysMIXTddy2FVp5CKtLj6LYZ8+YFib833+ZNlCOlo0pjC08YVw2FprD9wPls75/pPHxCNqwEYGbNjFzNYmRES64/IDFcEabKlbYMqjoc5XkiA+Xgd1dsz9OXQvQMc78pW2U/N4kzXfJ+2NCgBXV0+TzkQATXihV40LEPH8Bj169mPaNRRdT0kSNwxEL3Mcll4LL2bNn0b59e+SEoKAgJhXWrVsXqgIXXMQPdVHfadNZNmvK7Fzmn32ZRyQSGUmRUfj000+IvnePhfm0X7MGBt/kPbxrYRF2+jRbmZbFxkLT0REOa9fkuI4DYwKZ1iVBmoC9nfaihmXqsLOZ8cAjGP223EVcohR9GzpiXtdqKjXZj01IwsL/3mDHLQ/2ubKNEVb1qYXyVoYoKZAvi9fIUSycsHaFCnDYsjm9o30ekCUksMhkzB/m4iWmsZSjZ5UIo4FjYdRrMNSTF5sU8xMl+vvj84wZiHf7wL4zbNuG+dd4qemh7bKrkMqA/41pyrR9oiLcF9jfD/j8MPV+Eii+3wuYO38VQDR1CsgkLTmfD6FvAUx6myc/ogI3F2vTlvk52q1YAaMOOZt/cDjFFalUypQE2trakCjJvFVMc+US65wvxsbgZL2C6j10GKIfPICmnR3KHDzAcpOImaTQUBZdiVaRSeBy2LgBeiIW6mNfv8ann8eyCQP5AdguXJDjJKAzbszAiQ8n0LFMRyxqsSjHv3nmhR9G733IfIZ/bVcBP7dSDYH0nX8Exv3zGG/8ItjnoU3LYnKHitDRVG2Tt9wQefMmPo0dB1l0NHRr1WL9OzeaupxC+YwoOSTlXIm+dz9lP0UEM2jZEhqWFgj5Z7/g/yEPK05KCXNzWM/8nUWokgvEvx56isMPP6FZeQvsHtYQosH9KnBkGBD1BVDXBqQJyT4uyQlCC8tpnkzT/F8KST/JL6bbJqBm0fndZYb/4sUI3roNRp06wm7ZMmUXh8Mp8YSrquBCBc/wJGpqTBIkkzJVQ0yNwckacuj16NOH2cHrVKkCy4kToV3eWWl5B7KCcq54DfsRcW/fssmcw9at0K1WFapQxz4TJyH6zh322XzUSFiOG5etE/Ob4DfodbIX1NXUcabHGVjr57xNdt32wB/HX7L3S3rVRM+69hAbvmEx+BgYxQIKXHwdgL9Ov2aaIgsDLSzuVRMuFUtGXho54WfPwefXX1lkMP2mTWG/elWRJAxMcH+NsDm9EP46FnFhmQeCMGzXFtazZ0PD1DTVfu/gaLgsuYJEqQwHRzZGg7JmUCokbF1fClyZLwgqVtWAXjsBTd1CTRCajmtLgEtzhdDTP90BJMoVwGOeP4dHr95sAYWZi6lo8B8OpyAICQnB5cuX4eLiAtM0Y1pJnCvnSudkYmLCKi3ti/aTwxA55s+aNYuptTicgoYmIQ7rNzCTq9hXr+A9fDgL8UvRssREgq8vPPsPEIQWSws47t6lEkKLvI4dt2yGWXJWbfIT+DT6J7bqnRWVzCqhvnV9JMmSsP/N/lz95sDGZVjIYGLqkWe49u4LxMSB+15ouvAS+m6+iyYLL2Pm8ZdMaGlRwRL/jW9e4oSW0CNH4DNxIhNamAP1+nVFluVcs1xlWCw+irJd4lG2fQAMq2Ucsc20X/90QgvhYKaH3vUF/5sl594ysySlERUE7OsFXP5LEFpq9wd+vABYOAvCCjnJF5W/SYMRggla4Dsh8aeS0alWDZq2tkybx0JnczglGJpTk+DA59Z5EFx27NjBIopNnz4d//vf/9iL3tvZ2WH9+vUYMWIEVq1ahYULF+bmtBxOjpHo6bLwyClIpSzEcKybmyhqkSIkefbrj3gPD2jY2qDMnj05jq4kFtQ0NGA1dQpsF/0NNW1tRF69ylY/4z4IPgOZ0b9yf7Y99O4QYhJjcvWbk9tXRNdatmwlfPSehywXilg0LdOOPmd+EYpMaF0e2wfXh6Vh8QjlnFOCtm5jvlB035n06skCOUiKWtNuWQFq3++EjpkMVuVepA+PLJGwqHmZMbaVM8upc+9jMG66BUEpeN8DNjYD3C4AGrpAl3VAl7WCpkUZkNN+o5++al+UvPhIVhxk4kdEnDmr1LJwOMrG3NwcgwcPZltOLgWXnTt3YunSpZg7dy6LMEYver9kyRIcOHAAM2bMYILLrl27eN1yCgVywJXbsKcgk7GJdcCKFczUSVnEvX/PhJaEz5+hVaYMyuzdCy0VCw+uCIVrLr13LzRsbJgg5tH7e0RcupTp8S3sW8DewB7h8eE4+eFkrn5LIlHDop410dTZHFHxSRiy4z4z61EmtBp/9OGndEIL0bCcOStzSYHqImDZcgQsXsw+m/84DNZz5igvKayTC9BpMTT1pLCpFwLI2yI5A3xW5qM2xros6hux9HwRa13ot26vA7Z3FBJrksP98ItA7X5QOhRmmqKRBbwC3pxSdmlSnPIjrlxhkRk5HA4n14LLrVu3WHjktNA+ColMfPPNN/Dy+hqLX6ysXbsWVapUQf36Ig2LyckQihpEk5O0yGJimFkTmY75L/ybJcMr6ozPlCyTQsJSdKXSe3ZD08YGqg6ZuJU9fAh69etDGkUR0sbgy5q1kGWwIqsuUUe/ysIEbM/rPZBmlCciC2gVfH3/uqhkbYgvEXEYtP0eQqK+RpMqKmgie/lNALquvYnF596l+15dTQ1lLJSTvVhZIbP9/pyNIAotTAqPXyah1K+/Kj8CXP1hQMPRMHGKhnNnXzi6BMLZ1Q8m5bIXeH9ycWKhqx97heLy2yIaKyj548GBwNlpgDQRqNoNGH4ZsBKJGamuqWAyRlxblH6BqIjRqVGDaa2Zudj160otC4ejTPz8/DB//ny25eRScKEcLlu3CvkeFKF98vwuFBJZWc5DuWHMmDF49eoV7t//GqWGI35oJZVWVFOEF4kE1nPnwG71KuhUrcoEmOAdO1g4Tb85c1iErMIm+tEjeA0ezKKI0cO29K6d0LAoPtnSKXob5eYw7S+YggWuWcOiSSVFRqY7tqtzV+hr6uNj2Edsf7EdflG5G2iNdDSxc2gD2BrrwP1LFH7c9YCFHS4qgeXquy8sOSZpfJ5+CoOupjpaVLBIWdAnoWV+92ps1b4kQGGIP//2G0IPHGAmWdZzZsNi+HCIhsaCeRNpXvSt4qGpmyiE+KVoWVlQylAHgxqXYe+XnntX+FoX32fAppbA6xOARBPouBjouV0w0RITjccAmvqA33Pg3RmlFoUEY6N23FyMwzE0NETr1q3ZlpPLqGInTpxgySUrVaqUoql48OAB3rx5g8OHD6Nz587M1+X9+/dYpiIhDMUUKYGTtwzwcrMQ6spRN24gcP0GxDx6JByoocFMnigrvHbZsoUTEvbnsUxgIq2E/fr1UDfQL7ZNGXrkKPz+/JPl2dBycoL9mtXp6nX4ueG44ytEJZOoSTCr8Sx0L989V7/z3j8CPdbfQnhsItpXtcK6fnWhXkimWdRvbrgFYvn5d3jkFcr20Wr8gEalMbKFEywMtJmvi0dgNNO0lBShRRoTg0/jxiOKVrs1NWG36G8YdewIUfHxGrDTNf3+mn2BNrMAw8xNxoKj4tHs70vMNHFD/zroUK0QNKT0eKV8Kf/+BiTFAcYOQtQwe/GGRcf5P4CbKwHbOsDwS+l9iIo4T5BHnx9Y8IfyPLoYh6M0VDqPy8ePH7Fx40a8eyeYUFSsWBEjR45EmTLC6pWqIabG4BQM1KWj799H0IYNiLp1O0UzY9ShA8xHjoROxYJxlo+4eBE+EyaySbx+82awX7myRGR5jnn2jGlcKNGfxNAQtosXwbBlS/YdaVgoGaUMX4cVEl7O9jibqxDJxF33IAzYeg/xSVIMalwaf35XtUDNk6if3PoQxASWB56Cb5S2hgT9mcBSjq3Kl1SSwsPhPWo0WwCgDOb2q1bBoNk3EB2kWVlRLXUGeDkSDaCyK1B/OFC6SYYT8GXn3mLVJTdUsDJgEeIKVDiOjwJO/wI8/Uf4XL490G0DoKfkEMzZEfkFWFEdoAAb/Y4A5dsorSh0j5L5b6KvL1skMWyjvLJwOMqCkk96e3szyyZtbeUEhBHTXJknoBRRY3AKnpinTxG4YSMiL19O2WfQujUsRo2EbvXqeT5v2MlT+Dx1KpCUBMN27WC3ZDFLiFdSIF+eT+MnCJotNTVYjh/HhML7fvcx7NywdMdva7+NhUvOLaef+eLnfx6xheupHSulhE3OL7dJYLnwjkWWkvvX9GvoiNEtnFDKqOQKLPK29Ro+AnFv3kBiZASHDRugVye9b6NoUMwATwkbaw8AAt8CXsmLFkSpqkCDH4HqvQFtg5TdYTEJTOtCmr2VfWqhS60CCj/85Z3gz/LltZD5vtVMoOmEDP3zRMmZ6cCdtYB9A2DYOaVqXfwXLETwzp0w6tyZjbMcTknD19cXmzZtYpF7bZTkO6vSgktoaCjzaXn9+jX7XLVqVQwdOpRdkCoipsbgFB6xb94gcONGIbRmcpenxHkWo0dBr169XJ0r5OBB+M36k53HuGtX2Pw1l4UQLmmQ/4Pf/PkI3X+AfSYBTn3mBHT4r2sqx/y8alzkbL3xEXNPvWLv8zu5JC0OCSx33JMFFnUJfmjggNEtnWFtXLIFFiL+kw+8hg1lSV7VLSzguHULdCpWhOghzUvahI3kp3FvM/D8EJCQ7LBPUbNq9QXq/whYlGe71lx6jyXn3qGshT7OT2wODfV8ChfPDwMnxwPxkYCBFdBzG1BGhNqqrIjwA1bUEMzbBh4HygkaVWUQ/fgxPH/oC4m+vmAupqQVZw5HWSQlJSEqKgr6+vpQV1IkR5UVXMifpX379izZZIMGDdg+cm6PiYnBuXPnUKdOHagaYmoMTuET5+6OoE2bEXbyJNOWELr16sJi1GjoN22SrSlS0PYdCPj7b/betO8PsPr992yzyhd3mCA39y+WkFC7fHm8/M0Vv3usSRFeKNLY1AZT8/Ubf516hS03PkJTXQ07hjRAU+fcBT+47xHMTMLINEwusHxf34FFlyopPivZEefmBq+hw5AYEABNOzsWkEGVw3mnEBMKPNkH3N8CBCvkIqLJeP3hiCzTBs0WX0NIdAIW96yBXvWEQDO5JjEOODtd+B2iTDOgx1bA0AoqCfnl3NsElP4GGHJaacWgCIbMXMzPD/br1sKwVSullYXDKamEq6rg0qxZMzg7O2Pz5s3QSF5hTkxMxI8//gh3d3dcU8EMt2JqDE7REf/pE4K2bEHYkaPMR0WerZk0MAYuLumEEbpNAteuYxG1CHL2t5w0SfkhYUVC9KPH+DR+HJK+BDLzIp1fx+BA8AWcTniEutXbYVnL/AXrkEplGLf/MU4984WhtgYOjmqMyjbZ368PPUOw4sI7XH8fyD6T4NO7ngPGuDjD1oQLLIp+S97DRyApLAza5Z3hsGULNK1UdMKdGRTC2/2yIFhQxCy5VtDYAffMu2DUq2rQM7XCpV9aMtPBXBHiCRwaBHx+LHxu9gvQcjqgrsKaWNJiraoFJMUDg/8FyjRVWlH8FyxA8M5dMPrOFXaLFimtHByOMqD5Kc2vmzdvrjTrJpUVXEjT8vjxYxZVTBEKK1yvXj1ERys3YZyqNwan6Enw90fwtm0IOXAQsuQkZ5SHxXzkCObMT/b+cR4eiPj3P4QePMi+t5wwgfnIcNLWZQB8xo1jfkVypGrAlo4amDX/Okx0TPJVZXGJSRi07R4z87Iy0sbRn5rCLhPh47FXCJZfeI9r776wzxoSNbaSPsbFCfamJScHS06IunOH5eeRRkezcN4OGzdAQwVC2ucLEjQebgce7gRiBLPBeGjgVFIj6DUdiQ7tXXPu1/H2P+DYSCFPC+VC6bYJqNAOxQLyHaJ6Iu0UmYwpcWHEs29fSAwMBHOxEuRPyOEEBgbi2LFj6NatGyyUlGpBZQUXKysr7N69G+3apR6Uz549i4EDB8Lf3x+qhpgag6M8EoOC2IpeyN69LNEioW5mhqSQkFSJ2KymT4fZwAFKLKm4ifPyhnua8YFqL7JaGZRt3gk6NaqzoAiUGyYvkDN17w238dY/AuVLGeDwqCYw1tNM+f6pdyjTsFx+KwgsFCWqZx17/NzKGQ5mXGBJFU7cwxPx3l7wnzOXaR31GjeCw5o1zJegxJAQC7w8BtzfDPg8TNktta4JSYPhQPWegGYmmrmkRODSHCF0MGFXD+i1AzDJo6mZWAW81XWEhJnDzgMOgom4UszFXFqxSIb269bBsJWLUsrB4ZRUwlVVcBk3bhyT+pYsWYImTZqwfTdv3sRvv/2GHj16YMWKFVA1xNQYHOVDpjLBe/ciePsOSCMiUn+ppgbny5dS8sZw0hN15y5Lxpkdmra2bHWfhBid6tWgW7VqjifMn0Nj0H3dLfiFx6KWvQkmtC3PZMs9dzxx8U1AisDSvbYdxrYqD0dzLrAoEnr4MHz/mCWYTiVj2LYtbJcuKdEr2fFeD3B+x19ok3QD2mqC+SjToNTuD9QbBpiVTQ4C8AHQMhT8WbxuCcc1HAW0nQtoFMP6Oz4GeLwHcG4L9D+stGL4zZuPkN27YdzlO9gm+xlyOJySN1fOleASHx/PhJQNGzYw3xZCU1MTo0ePxsKFC5USX5qinLVp04aVh17jx4/H8FxkdhZTY3DEQ+SVq/AeNSrdfsedO6HfUDmrjqqykk+OtIqTYjIXO9BMDX0NXKD51hPx7u6ptFgMiQTaTk7QqV4dujVImKkOnQoVoKb5VZuiyBu/cHRZcxNxianzd1Aajm617TG2lTPKWJQgzUE+2ocEcqfz56Blb4+Szr67Xlh87CaG6N3Ez4ZXIQnzSv5GDShVGQigaJoKfZcEmC6rgardUGyhaG2r6wnhpikhpZ1ykmdGP3wIz379ubkYp8RB1ky7du1ilk1WSvI9FNNcOVeeg1paWli5ciUWLFiADx+E6CxOTk7Q01PeiqahoSFzWqIyULi4atWqoXv37jDPoykKh0NoV6oo5FxQnOBJJNAq7cgrKAtIG2UzZ/bXFX2JBNf6VsYxh7cwrloOv9Rbi6TISMS+eImY588Q++w5Yl68YAnm4t6/Z6+wo0fZuSgvjk7lysmamWpMmKEoVxQ4wVhXkyWmtIgJhW1kID4bWCBI1wT/DG+EhuX4vZ8ZZB6Wqk8TMhkSfD5zwQVAr3r22HDVGsuCO0K7+TiMtHEXzMjcLgABQkjuVPQ9oFSn9SKBQkxX7wU82w9cWwL8kJxQs4jRrV0bGqVKsah3UTdvwtCFm4txSgYUBrlRo0Zsy8ml4CKHhITq+UjeV5BQTGu54ETZRUmBlMvUNBxOjibg9JmbiWWPSc+e0P/mG8R7ejFBzy/+NXB5HE5+OInxdcZDw8AA+o0aspechIAAxL54gZjnz1OEGWlYGHP0p5eQ1x6QGBoyISbQ3hmjnnxA54+3IYEMUqhhZa2ekMoa8d6cBYlBQnS1VHCB/Ot9ry7BuNbl8euhp1h/zQN9J7eGYcUOwNP9ggN+WhTyFRVrmv8KPDsAvP0X8H0G2NQo8iLQggXligrZs4fl4+KCC6ekYGBgwKL6cnJoKkbai5xyNHmlNDeQtmTx4sV4+PAhyw5KPjRdu3ZNdczatWvZMX5+fqhZsyZWr16dkkdGbi7WokULvH//nh03ZswYlVR/cUTqxJw8AedCSx7rUJqANofaIDg2GGtarUELhxbZ/g8NSwleXoh59hyxL54L21evIIuLy/R/ktTUYHr8X9hVKJPHkhZvqP48BwxMCT7BSBbISdjkCCQmSdFuxTW4f4nCpLYVmCDDfFtWVEstqKipAxOef014Wdw5PBR4cQSo/B3w/W6lFCH6wQN49h/AFjDK37xRon2yOCUHctOg+a+1tTWzfFIGYporZxusngqa01deIPMuEkZIOMmIAwcOYNKkSZg1axYePXrEjqUkmAEBghMuYWJigqdPn+Ljx4/Yt2+fSkY344gTElbIp4ULLfmoQ4kmvi33LXt//EPOQqpSfhwyCzN27QyradNQ5p99qPjgPsoeOwrr2bOh3zz96pO6TAb9V19DMXO+Eu/tDa8RI5nQotewIcqdO8f8tZwvXeRCSxo01CWY2KYCe7/5ujvCohME4cR1pSCssA6qDriuKDlCC9H8N2H7+gTgn4HZXBGgW6cONCwtWeCU6Nu3lVIGDqeoCQoKwvbt29mWk0vn/MKGJitpNS4NGzZE/fr1sSY58Z9UKoWDgwPGjh2LqVPTZ+P+6aef0KpVK/TMZAWRzMnopShF0vnEIEVyOMWVt8Fv0fNkT2hINHC51+V853TJ0Mmc0NCA+Y/DYDFyJCS6PMGkPNS3R9++SPD0gnalSii9exfUDQ3zVf/FHUp42mnVdbzxi8DPLs74tX1F4QsWVcxd8PsoRKHFNywGHwOjUNZCHzbGIurHBwYIgku1HkDPbUopgt/cv1jYeuNu3WC7YL5SysDhFCUUeIosi2iRXiM5+XtRo1IaF2Wrx8iEjKKGyZFIJOzz7eTVFtKuRCSHrZVnF61YMfkhkwEUWEBRS0RCC4fDKVwqmlVEZbPKSJQm4t+P/xaYDxILoCD306hQnkZ4BG3YCPfOroi4dBklHdKweI8azYQWCkHtsGkjF1pygESiholtBa3LtpsfERSZvNhFwkrZZoUqtOy/54UmCy+h7+a7aLrwEg7cl0c2E5HW5cVR4Ms7pRTBqEN7to24eBGy+HillIHDKUpIWKHEk8oSWsRGtoJLhw4dcOfOnWxPRMLD33//nanJV16zhSYlJaUL/0afyd6P8PT0ZE5LZEJGW9LEZBU4YNq0aUzAkb+8vb0LrLwcDidzujh3yZW5WHaQXwaZOslNnsodPw77NauhYWODBB8ffPrpJ3j/NAbxn3xKZLNQUslP4ycg9vlzqJuYwGHLFmiWKqXsYqkM7apYobqdMaLjk7Dxmnuh/lZkXCLOvvTDuH8eYerR5ynRwqUyYNrR50wDIwrIKb9iJyEk9PWlSjMXU7e0gDQ8HFE5mJtwOKoOaTso0TttOTkQXHr16sWSS1apUgVTpkzBoUOHWNJJ0oRcuHABq1atQu/evWFjY8N8UFxdXYu0XslJ/8mTJ8zH5dmzZxg5MoPILwpQrhlSc+3evZuFl2vdunWRlZXDKcl0KtuJmYq9CnqFdyHvCtwHiUxNDdu0gdPpUzCnXE4aGoi8dAnunTsjcMNGSEvQ6ixZAPv+/juibtyAmq4uHDZugHa5ssoulkpB/WlSO0HrsvOWBwLCYwu0fT58icSW6+7ot+UOas85h5G7H+LEU990x5LwsvayGzNfE5XW5flBIEhIi1CUqKmrw6htO/Y+/MzZIv99DqeoIfcGSkGi6OZQksmRjwtVFgks5Ch/48YNpqlg/6ymxgQacpYfNmwYKleuXKA+LmQqRqGODx8+nMrvZdCgQcze7/jx48XKbo/DKe5MvDwRF7wuYGCVgfitfvIEqJCIc3OD35y5iL53j33WKlsW1n/MhH7jxijuBCxdiqDNWyhePBzWrYVBi+wjuXHSQ4/Hnhtu46FnCAY1Lo3ZXarluZpiE5Jwxz0IV95+waU3AfAKjk71fWlzPTQoY4bDjz6ly89KNCxrhiW9asLBTHl501LY0xNwOw/U7g90KTgri5wSde8evAYOgsTYGBVuXM80US2HwykYxDRXzpNzPhU8JiaGJXnULMABIzPnfNKqUAhkuXO+o6Mjfv755wyd81W5MTic4s4V7ysYe2kszHTMcKHXBRZxrDCh4S381Cn4/70ISYFCDhOjb79FqSmTi63ZVPCuXfCfv4C9t5k/Hybdi3FW9yLgllsg+m65Cy11CS7/1hJ2Jjl3lv8UEo3Lb7/gypsA3PwQiNiEr8Ek6HwNy5mhZcVSaFWpFHPEJ8inZfrRF0iSyaCuBnSpbYczL/yYyZq+ljpmuVZliTLpeak0vO8BW9sCEg1g7CPAtHSR/rwsKQnvW7Rk97TD5k0w4DkuOJxCRUxz5Tx5+uQn/HFaIiMj4ebmlvKZQhqT6ZeZmRkTUCgUMmlY6tWrxwSYFStWsBDKQ4YMydfvki8OvciHhsPhFA1N7ZoyoYVyutz0uYmWDi0L9fdocmfs6so0Dl9WrUbIvn0IP30akVeuwHL8OJj27Qu1YuTwGP7vv/BfsJC9t5w4kQstBUATZws0LmeO2+5BWHPpPRZ0zzz5YkKSFA88QnDlbQDTqrwPiEz1vY2xDhNUXCpaoqmzBfS10/e97+s7onkFS3gERqOMhR6LKjahdTR+OfQE9z1CMPnIM5x75Yf53aujlKEOlIJDA6BcS8D9CnBjuRAauojNxQzbtkHoP/sRfuYMF1w4xRpK/0GpPvr27YtSxXTBTaXCIV+5cgUuLi7p9pOwsmPHDvaeQiHLE1DWqlWL+dWQJqa4SZEcTklg8f3F2PVqF9o4tsFyl+VF+tsxL1/Cb/YcxD57xj5TeGDrWX9Ar3ZtqDrkqOw1fASQkADTfv1g9fsM5a7KFyMeeAQzkzHSgCzpXRONypmnhCkOiIhl5l8krFx/F4iIuMSU/1OXqKGuoylaVrKES8VSqGRtmOc2SZLKmE/M0nPvEJ8khameJuZ3q46O1W2gFDxvAds7AqQ1Hf8EMLYv0p+PunMXXoMHQ93YGOW5uRinGEPBr+7fv89SgxgqKZS9mObKShdclI2YGoPDKWk5XS71ugRTHdMi/X2ZVIrQw4cRsHQZpMn+esY9e6DUL79Aw7Roy1JQxL5+zTKKU/hjww4dYLd0CVuVVjZ+UX7wCveCo5EjrPWtocq0W34V7/wFDQrJHq0qlkJARBye+wh9SI65vhZaVBQEleblLWGsV7DmkG/8wjHxwFO89hUiDHWrbYc/v6sKY10l+Hls/xbwvAE0GAF0Wlz05mLNWyApKIhFzDP4pmmR/j6HU5IIF9FcWdR5XAoTMhOjwAIkwXI4HNXN6ZJb1CQSmPbuDaf//oVxj+5sX9jhI3Dv0BEhhw4xwUaViP/0CV4jRjChRa9BA9j+vVAUQsvR90fR/kh7DDs3jG3ps6pC4YgVzb5oue/im4AUoaWGvTHGty6P/41pivsz2mBZ71pwrWlb4EILUcnaCMfHNMUYFydI1IBjj33QYcU13Hgv+HAVKS0mC9uHO4EIIUVBUZuLERFnzxTpb3M4RUlCQgJ8fX3ZllOAgouqKW7GjBmDV69eMfUbh8MpWro6CwE4jrsVTE6XvKBhZgbbefNQet8+aFesiKSwMPjN/AOeP/RlGgxVIDE4GN7DfkTSl0B2DfZr10CirS0KTcvs27MhlQlCIG3pM+1XRSiLfUaPuFEtnJigcuLnb1jCyloOJix5ZWGjpSHBb+0r4dCoJihjrgffsFj033oXs46/QEx8Efptlm0OODQEkuKAm6tQ1Bh16MC2EecvsLxFHE5xhHIabtq0iW05uRRcyM8kI8jBnZyGOBwOJzc5XV4Hv2amY8pEr05tlD1yGFbTpkKip4eYp0/xsUdP+M2bj6SICIgV0rB4jxyFeE9PaNrawmHTJqgryf45LWQeJhda5NBn7wjVTPhLEb/SyiPqamoY1KQ0LA2VJyjWLW2Kf8c3w4BGQlSvnbc98e2q63jsFVI0BSCbObnW5cE2IPILihK9evWgbmaGpNBQFiKZwymOWFhYYMSIEWzLyYPgsnXr1nRCS58+fVgkMA6Hw8kJJjomcHEQgnIc/6A8rYsciixmNmgQyv33H4w6daK46wjZvRsfOnVC2KnTiPf1Zc7ACX7i0BjQ6vKniRMR+/w51E1MmI2/ppV4os1k5ICuBjU4GDpAFSFH/AXdqzNhhaDt/O7VUhz0lYmelgbmdq2GXUMbwNpIB+6BUeix/haWnnuL+MQiMHt0ag3Y1QUSY4DbQtqCorxvDdu2Ze8jeDJKTjGF0o5QkveCTD9SYgSX06dP49dff2UJIYnExET06tULL1++xOXLl6FKcB8XDke5dHHqwran3U8jQSoOMw+a/NstWwrHbVuhVaYMM8H6/Ouv+ODSikUwcmvVmjn2KxMyy/Wd+Qeirl2Hmo4OHDash3a5shALYXFhzCwsLTLI8DLwJVQVClN8Y6oL/hneiG3ps5igEMpnJzRH11q2kMqA1Zfc0G3dTbz1K2StIQlzzZO1Lve2AFFBKEqMOrRn24gLFyBL/BrRjcMpTlHFLl26xLacXAou5Mh+5MgRDB06FCdOnECPHj3w9u1bJrRYW6tWxBju48LhKJcmdk1grmPOcrrc+HRDVM2h36QJyp44DrNhw1J/IZXC949ZzCFeWXxZthxh//sfoK4Ou+XLoFurFsQCCaC/XPkFnuGesNG3wcHOB7Gt/Tb0LN+TfT/z5kyVNRcjSMPS2OlrKGSxQcEAVvSpjbV967BwyS8/h8N19Q1suvaBhVMuNCq0B6xrAAlRwJ11KEr06teHuqkpkkJCEM3NxTjFEEr4/uzZM7bl5ME5v1WrVti1axcTWihZ5NWrV2FlZcXrksPh5ApNiSY6l+ssGnOxtEi0tDJObCeV4mOv3ghYuRLxn3yKtEzBu3YjaPNm9t5mzhwYZpADS5maoIV3F+Ku313oaehhdavVqGxeGfWt62N6o+moaVkTEQkRTLCJI2duTqHxbQ0bpn1pVakUy/ky/983+GHTHXgHRxe+r8vdjUBMiFLMxcK5uRinGEJJJydMmMCTT+Y0j0v37kK40LTcuXMHzs7OqZyFjh5VvXCXYopNzeGUNN6HvEf3E92hoaaBi70vwkzHDGKCfFrIPIyElQxRU4N+48Yw6d0Lhq1aQU1Lq9DKEv7ff/CZ9AuLxWs5YQIsRo2EmNj3eh8W3FvAfFlWtVqFlg4tU31PEcV6neyF0LhQfF/xe/ze6HellbWkQI/3A/e9MffUK0TFJ0FfSx0zO1fB9/UdCj45Kd0jG74BAl4CLacBLaeiqIi8eZNF1yPNS/nr15gww+FwiudcOVuNCxU0o1f79u3h5OSUap8qwX1cOBzlU960PKqaV0WiLBH/ffwPYkPT2ho2c2YDkuShUiKB1Z+zmImWfpPGTIiIunULPhMm4n2LlvD/exHi3N0LvBwUGODz5Cns90z79oX5yBEQE7d8buHv+3+z95PqTkontBCUgHL+N/PZ+wNvD4iyvYsbJJz0aeCI/8Y3R4MyZkx4mXr0OYbtfICAiNiC/TG6R5r/Krwnc7FYIUFmUaDfsCELUsHMxXiKA04x48uXL1i3bh3bcnKgcSnuiEmK5HBKIv+8+Qfz785HJbNKOOR6CGKENC/xnl7QKu3IhBk55OtCzvphR48hMSAgZb9u3bow6dUTRu3bQ6KbP3+I2Ddv4Nl/AKSRkTBs144JTWJIMCnHPdQd/f/tz8zAKD/PnCZzslzNX/VoFTY/38zMyfZ33o+yxuIJLFCcIR+XrTfcseTsO2Y+Rj4wv7avyEI906tA/HakScC6xkDgW6DVzK+CTBHgO3MmQg8dhr5LS9jMmpXqPuVwVJkgNzfcvnEDjb/5BubOzijpc2UuuIioMTickkhobChaHWrFHLtJcCEBRtWgaEaR164zISby6lWKE8/2SwwNYezaGSY9e0KnSpVcn5d8aDx+6MOim1HOCoetW0SRYFKx7fr+25c53NcpVQeb222GlnrW5nKJ0kQMPzccD/wfMI3bvk77oKOhU2RlLulQlLGJB57gle9XjQjlqKFwzwUSKe3ZQeDocEDXDJjwHNA2QFHgt2AhQnbuFD5IJExTSvcdh6PK0DOFAsIwU0wl9utwEc2Vc+Wc7+/vjwEDBsDW1hYaGhpQV1dP9eJwOJy85HSRmxYddxOfk36OHYRbucBh3Vo4X7oEywnjoWlvD2lEBEL2/YOP3XuwpJYh+/cjKTIyR+dMDAmB948/MqFFu0IF2K9bKyqhJSEpAZOuTmJCi52BHZa7LM9WaCEo8eii5ouYPxP5OJFfDKfoqGhtiA3960BRJ0YBx6YdfQ7fsAKIWlS1O2DmBMQEAw9S530rTI0o5V1KG/1PLHmXOJy8EP/5Mwt9T8tg4YaGbOvL+3XuBJfBgwfj0aNHmDlzJsvlQs74ii8Oh8PJC2RilJLTJUkcOV3ykwvGYtQoOJ07y/LBGHXqCDVNTcS+fAm/P2fjfbPm+DxtOqIfPWbO0xkhjY6G96hRiPfwgIatDRw2b4K6iDTCVO55d+fhvt996GvqswhiuQmsYKlnyYQXcuQ/+v6oygqsqsqn0Bik7XkkvCw79y7/YZPVNYBmvwjvb60G4gspkpkC8R6e6QNoSKX4smYtknjuC44KQmO/96jRzK8x3NgYZzp/y7aQSpnZckkmV6ZihoaGuH79OmqJKG9AcVJ/cTglFTIfanOoDYJig7DSZSVaObZCcYK0J2HHjzMb/PgPH1L2azk7MbW/cZcu0DA1ZSvEcR8+IHDzZsTcuQt1Y2OU3rcX2k5OEBN7Xu1hzvgkeKxpvQbN7Zvn6Twbnm7A2idroaOug33f7mOmY5zChzQrTRdeYsJKWhqUNcOK72vB1iQfPi+0+LC6LhDqCbRfADT+CcqK/icxMGABLcwGDYSGuXmhloPDyS+ypCQE79yFLytXQhYnhI1P0NBAqIkJTEJDoSmVwvnSxSL34RLTXDlXGhcHB4dMVwhVDR5VjMMRD2Q+5Orkyt4Xx9V3EkrMBw9GuVMnmSBi3K0by3of7/YBAQv/hlvzFvjYqxfcXFqxsK4ktEBDA/Yb1otOaLn+6ToWP1jM3v9S75c8Cy3E8OrD0dimMWKTYvHL1V8QnVD4q/McIZEm+bSoJwdRoG3vevYsXPK9j8HouPI6zrzwzXtVqWsCzSYJ72+uBBIKOIJZDqL/GffqCe3yziyoRdCmTXBr3QZ+8+YjwTcf18XhFCJxbm7w6NsXAYsWMaFFr3EjWP4yiQkrloGBbGszZ3aJDzyRK43LuXPnsHTpUmzcuBFlypQpFh1YTFIkh1OScQtxQ7cT3VhOlwu9LsBct3ivjpIJS/jp0wg9eAixr16lP0AiUcrKWlZ8CP3AIohFJkSie/nu+LPxn/nOBxIUE4TeJ3sjICYA35b7Fgu+WVDwOUY4mWpePAKjUcZCjwkzHoFRGL//MZ5+CmPf/9DAEX90rgJdrTz4sCbGA6tqA+GfgE5LgAbDizz6n0wqReTlywjcsBGxz58LB2lowLjLdzD/8Udol81jRLswHyD4g+DLY2xXoNfAKXnIEhIQtHUrAteuY+9JS1hqymSmjaexMMTdHY/v3EXtRg1hWq4cSvpcOVeCi6mpKaKjo5GYmAg9PT1oamqm+j44OBiqhpgag8Mp6fxw6ge8CHqByfUnY0CVASgpBO8/AP8//0y333HnTug3bAAxEBIbgr6n++JT5CfUtaqLzW03Q5NW1guAh/4PMezsMCTJkjCr8Sz0rMCjQSmL+EQplp1/h43XPpB5PZxLGWBVn9qoYpuH5+O9zcC/vwJGdsC4x4CGcoJL0DQn+s4dBG7cxLYMNTUYdmgPixEjoFO5cs5P9mgXcHI8IJMCahLAdSVQZ2ChlZ1TvIl9/Rqfp89A3OvX7LNBixawnv1nqgUrCoy1a9cuDBw4EFZWVijpc+VcCS475aEGM2HQoEFQNcTUGBxOSWf/m/3M6buiaUUc/u4wSgoZ2uiLSONCAROGnx/OBAx7A3vmj2KqY1qgv7HtxTYsf7gcWhIt7P12r0qGxS5O3HQLZGGTAyLioKUuwbROlTC4SZncacPIRGxVLSDCF2g0Bmg8RukaipgnTxC4aTMiL11K2affojksRo6EXp062WtaVlQThBY5aupC2GeueeHkAml8PALXr0fQ5i1AYiLzZ7T6fQaMOncWpcY5XERzZZ7HRUSNweGUdMLiwuBy0EWlc7qoerz+tNDa1p+3/2TRvww0DbCn0x44mRS8341UJsW4S+Nw9dNVOBo6suSUhlqGBf47nJwTHBWPyYef4sJrIblqq0qlsLhnDZgb5EJzcngo8OKI8F5EGorYt+8QtHkzwv/9N2XBgHIlmY8cCf1vmqaePNL3HteBG8sB98vpTzboFFC2WRGWnqPKxDx9is8zZjAfR4ISC1v/MRMaFhYQK+HFQXCJjY1FfHx8qn3KvhhVbwwOhwP8cuUXnPM8h/6V+2NKgyklqkrS2uiLgV0vdzFnfImaBGtarUEz+2aFKrj2OtkLvlG+aFu6LZa2WCrK1ceSBE0Rdt32xLx/XzMzMktDbSzrXRPNyltm/88ZaSgog0y3jUDVboBG9nl/Cpt4T08EbdmK0P/9D0gQQrFTslgSYAzrloPaswPA03+AMO/MT1LJFei2HtDmgjYnc6SxsfiyajWCd+xgwrC6uTmsZ86EUYf2WVZbYGAgjh07hm7dusFCScKNygouUVFRmDJlCg4ePIigoKB03yclZ4tWJcTUGBwOB7j26RrGXBwDU21TXOx1scD8KDh5a4uxl8YybciU+lPQv0r/Qq/GZ1+eYdCZQSxE9tQGU9Gvcr9C/01O9rz2Dce4fx7jfYCQQHVk83L4pV1FaGlkEZz04zVgpxAtMB3axkDFDkBlV8CpNaClp9RmSPD3R/C27Qg5cACyWCEKmpZRAswrR8K4dAzUdI2B6j0AXVPgxgqKWysIYQwZYFYO6LkNsK2t1OvgiJPoBw/gO+N3JigTRq6usJo+jUWczA6an167dg3Nmzdn89WSPlfOVTjkyZMn49KlS1i/fj20tbWxZcsWzJ49G7a2tsxxSJXg4ZA5HHHSxLYJLHUtERIXgms+15RdnBILZbWffG0yE1rIWb6oBIgaljXwS10hgeGSB0vw/EtyNCiOUqlsY4QTP3+Dfg0d2eeN19zRc8MtFoksUyjqFpmHpUINoIiBcWEAaTMO9AcWlRO2zw4BsUJEsyJFKoVm9FtYVfKAs6s/LKpGQKIpRXy4JnzvmsLtUmUE282BtM0CoPUfgk8LmYdNfAkMPQMYOwDB7sCWtsCtNRnmk+GUTKRRUfCb+xc8+w9gQotGqVKwX7cOdosX5UhoIUhgcHV1VZrQIjZypXFxdHRkAkrLli2ZxPXo0SM4Oztj9+7d+Oeff/Av2YqqGGKSIjkcjsCyh8uw/cV2tHRoybKyc4qW4NhgFkHMJ9IH9a3rY2PbjdCUFJ3mix5Lk65MwgWvC7DVt8VB14MwphV6jig488IPU448Q1hMAsv9MqdLNXSvY5exWR+LwjVB0FCQI7vrCqBWP+DTfeD1SeD1CSBUIRM49bNyLQVNTMVOgEEOTNLySvBHwQzsCZmCKZTBvDySKvZE6DstBO0/hqTAQLabTHvMBg+CQatWSPoSCK0ypQWTzpgQ4MRY4XqI8u2ArusBffH6LHAKn6hbt+A78w8k+Piwz8Y9e8Bq8mSo53KuSdZMZPGkr68PdfU8hCYvZnPlXAkuBgYGePXqFRNg7O3tcfToUTRo0AAfP35E9erVERkpqJBVCTE1BofD+ZovpOvxrlBXU2c5XSx0+QSgqIhPisfwc8PxKOARHAwdsK/TPpjomBR514yIj2D5XSj8ckv7lljVahX3dxFZDpgJ+5/g7kchDUKXWraY27UajHQ0M8l74i6YU6WNvkVTEL9nwqT/1Qkg8O3X70hb49hEEGIqdwaM7fNf8LgI4NVx4Mk+wPPm1/0kGFfrLghV9vVYuGS5X0Lo0aMI3rIVCZ8/pz6XYhANuo4H24Az04CkOMDAGui+CSjXIv9l5qhcji5KIhl6SIiMqWlrC+u5c2DQtGmezufr64tNmzZhxIgRsLGxQUmfK+dKcKlRowZWr16NFi1aoE2bNqhVqxaWLFmCVatWYdGiRfj06RNUDTE1BofD+Qqt+D8PfI7f6v2GgVWVH4WoJECPgz9u/YH/uf0PhpqG2PPtHpQzVk7CM+JV0CsM+HcA4qXxzHxscLXBSisLJz1JUhnWXXbDiovv2XsHM12s7FMbdRzzESr7y9tkTcxJwPdJ6u9s6yQLMd8BFs45PyeZbpGQQsIKCS0JcvM2NcDJRRBWKn0LaOpmegpKDBi8bx8CFixM/UXasOX+L4FDQ5IFMDWg2SSg5XRAXSPn5eWoLBGXL8Pvz9lI9Pdnn0379oXlpElQN9DP8znj4uLg7e0NBwcH5qZR0ufKuRJcli9fztRU48aNw4ULF5jNHf17QkICli1bhvHjx0PVEFNjcDicrxx4cwB/3f0L5U3L44jrEb7aXgTseLEDSx8uZZquda3XoYldE6V3yYNvD2LunbmsTNs7bEftUtz5WWw89AzB+P2P8SkkBuoSNUxqWwGjWjix9/mCTMhenxKEGK/bghO8HMvKghBT5TvAqpqgIUmb0Z6Zgu0Hnu5LbY5m7gzU6gvU6JOr/CtRd+7Ca/Dg7BPFxkcDZ6YCj5Jz3zk0BHpsAUwE/yBO8SMxJAT+CxYg/IRgLqhZ2hG2f/0Fvfr1URwILy55XDw9PfHw4UPm50LaGFVETI3B4XAyzulysPNBVDbPRXZrTq654n2F5VGRQSaqaF70iJpyfQr++/gfSumVYvl9zHTMlF0sThrCYxMw49gLnHwqmFM1KmeGFd/XhrWxTsHUVWQA8Oa0IMR8vApIE79+Z1pGMENzv5IcellNEF6C3b4eo22kYApWP8UULN+JYgEWHcpsYAZa4RdHgZPjgbhwQMcY+G41UKVLrn+XI+Lw9R6ezOn+y6pVSKJouxIJzAYPhuXYnyHRzVyDlxvIv+XFixeoVq0a83Mp6XPlHAsupFXp0KEDNmzYgPLly6O4IKbG4HA4qfn16q8463GWTaJpMs0pHN6FvGMmWdGJ0ehdoTd+b/S7qDRcUQlR6HOqDzzCPdDUtinWtVnH8spwxAVNJw4//IRZJ14iOj4JJnqaWNSjBqrbG+NjYBTKWujDxrgAJnPkDP/unODY73YRSIzJ/NhyLkDt/tmaguUpUSzdIzIZ1DQ14bhrJ/RqZ6ANDPEADg8DfB4In+sOATosKJCycJRH8J498J83X/BtSkbL2Qm28+ZBt2bNAv0tPz8/bNu2DUOHDoW1kvJ7qazGxdLSErdu3eKCC4fDKRKuf7qOny7+BBNtE1zqdYnndCkEgmKCmD/R56jPaGjdEOvbri/SCGK5Ea76ne6H2KRYjK09FiNqjFB2kTiZ4P4lEuP3P8Fzn9ShjclybEH36vi+fgGaTMVHAbdWA1cWpP+u53ZBy1JIiWI1HezhP38+Ii9cZBHHyh48AE27DEzPkhKAS38BN1cIn0tVEXK+lOJaZFVAFh+P2LfvEPP8GWKfPkP048dI8FIwPSTU1FDu7FloOzqgOBKuqoLLxIkTmWPQwoVpnNNUGDE1BofDSU2SNAntDrdDQEwAlrdcjjal2/AqKuAIYsPODsOTL09Q2qg09nbaK+qwwxQ0YObNmUzbsrntZjSwUfAr4IiK+EQp/jz5Evvupp7gqaup4cZUl4LRvMgh35YV1ZLNxJKh0MuUbyUXPix5zdPh0a8/4t68gXaFCii9b1/mjtgfLgFHRwJRAYCGrqB5qTs4T2ZrnMKB+W17eiLm2TPEPHvOhJW4V69ZcIbsSOfrVIwIF9FcOVdhLhITE5m6ihzz69atm87Wjhz0VSkBJb0oPjaHwxEn6hJ1dHbqjG0vtuG423EuuBQQflF+8Az3xIG3B5jQYqhlyPLliFloIbo6d8UDvwc4/uE4S455+LvDPFS2SNHSkKBzDZt0gkuSTAaPwOiCFVxIOHFdmT5fTCELLYREXx8O69fhY6/eiHv3Dp9//RX2a9dALaN8G06tgNE3gWMjBSHm1ATBL4fKrlv0Icc5QGJgYIqAEkvbFy8gDUufBFXd2Bg6NWpAt3p1aDjYw2/G76l9nSQSaJUunOALQUFBOHnyJAuIZW5uXuKbLVcaFxcXl0y/I3voS5cuqVyFikmK5HA46XEPdUeX4114TpcC4uj7o5h9ezakyavTalDDhrYb0MRW+RHEckJMYgwzbXMLdWPJMUnzQgIuR5y5XpouvASpLLW52M2prQpWcMlJvphCJubpU3gOGMjMisyGDoXV5N8yP5gmvLdXAxfnCEEGjB2BnlsBh+K5Wq8sp/mUBKEK2rHYV6+SBZXniH32LH1uHhoTtbSgU6UKdGpUh271GtCtUR2ajo6p/P5S+Top5vMpBEJCQnD58mU2Bzc1zUeo8WIyV85XVLHigJgag8PhZAz5NjwLfIZf6/2KQVUH8WrKh6al/ZH2KUKLXHA51/McrPWV4/SZF9zD3JmzPgkx5OtCPi8ccXLgvhemH32OpOSZRo86dljauxaKI2GnT+PzL7+y9zbz/oJJjx5Z/8Onh8CRoYIDP2mJWs0Amk5kE2FOwQRPMPrOFRItLSasxL1/ny4iHB2j5VROEFBq1oBO9erQKV+eCS859XUiTYuigFQcCRfRXJkLLiJqDA6Hk3UuD2cTZxz97qioIl6pEnd872D4ueHp9m9rv41pL1SJ0+6nMfX6VCZ4rW+zHk3t8paVmlM0mpc1l95j711vVLczxsmx3xTbav+yajUC160DNDVRetvW7PN4xIYBpyYCL44In8u2ALpvAgyLcCKcNv+NipJZuGpFNKytmbmXXJuiU60q1A0MIGakUilLQkk+5hIlCbVimitzsZ7D4Yie9mXaQ0uixcyDXge/VnZxVBIKKbzzRXJCPAXI0d3BUPUi4Xxb7lsWupnyzky5NgVnPp5hGiWO+CCzsF/aVWJ+LxRp7Kl3KIorFj+PgWGHDpRDAp/GjkN82uhTaaH8Lj22At+tATT1hBw165sC7y8IAsXHa8K2MKBoZ7fXCYENdroK20e7oKpE372XodBi1Lkz7NeshvPVqyh/5TLsV6+CxfDh0G/UUPRCC+Hv749FixaxLYdrXEQlRXI4nMz57epvOONxBj9U+gHTG07nVZULvMK9WHLJD2EfmK8QmYrRhJ+EllmNZ6F7+YIPGVsUxCXFwfWYK3yjfNlnCSSY1UR1r6e4M/HAExx77INede2xuFfB5roQE9KYGObvEvviBbScnFBm/z9QNzTM/h+/vAUODwX8XyTvIM2yDKCcReTAX0chySVN0OMjBY0NJbiMDVd4H5bmveJ3Cu8TojMuB5mrVeoE2NYG1MUXGj0t5PFAJmJ+lFclNjb1lxIJnC9dVGlTrpiYGHh4eKBMmTLQLaCklqo8V+amYiJqDA6Hkzk3fW5i1IVRLPIV5XTRUs/eBpkD3PC5wSJwRcRHwFLXEstdlsNKzwreEd5M06JKvi1pIQ0LhcsmIUwOCWNne5xV6esqrjz0DEaP9behoynB3WltYKwn/klxXknwD4BH795I9PeH/jffwGHDeqhp5CCQa0KsEG3s6T/pv7OoJAgbccnCiEK/LxRIA2Rf///t3QdUVFcTB/A/vTcpAlIVey9gr9iNiiWa2PsXS9QYNZZYY0nUxBJrEnvvvcbexYYVURQEpSlIl8535m4WQVFByj5253fO+rax+9xdljvv3pkBnBoAjvWBEjUBLV1IScqbNwieMkX00iHazs6ik31hJM0X5vccHXxyMHZQ2PdakS2HzBhjilLHpg6s9KxET5dzL86hhWMLfjM+cxRy9f3VWHJriRjYV7WsKnrhWOpbituVYWBPf8wzBy2EZpMoKFOG/5+yqeFghnLWRngUEoNdt15gYANnKCut4lawW74Mz3v2QtzFiwj9bR6sJ+dgppgCg2o9sg9cXj/68DpqFkvLzXSNZVsd43eXqbz5B7dlOk9B0MoGWfvf0CwPlW0Oug28jZAtXaMT0dAB7GrJghin+oCdG6CtD0WJvXABQZMmIfXVa5FTZDV6FIr164eUV6+UJmmeqkDOvTAX1vHWCDEIwcQGE1V+RjlXgcv69ethYWGBdu3aicvjx4/HX3/9hQoVKmDr1q1wdHQsqPeOMabiqORt+1LtxWCcerpw4PJx8cnxmHp5Ko77HxeXu5bpioluE5VuloqOQNIMy/tV0opizo4qoKIaveo44ud997H52nMMqO+k1IU29CpWhO1vv+HlqFF4s3EjdEqVhNk333z+BylJnpaHZWmoqQ54rATMS2UNTjR189bAMrv+N7QkjWYsXvsA/heB55cA/0uyxpl0nk7n/wuaaDkZBTGODQCH2oBODpbE5VFaQgLC5i/Am82bxWVajldiwXzoli8vLlOwUtQDFvlMC5WuN04xRq3wWjipfVJcptL1qnxgJldLxcqWLYsVK1agWbNmuHLlCpo3b46FCxfi0KFD0NTUxJ49e1DUSGn6izH2+TK4HfdxT5dPodmGUWdG4cmbJ9BU1xQBS7ey3VSmLw3l8BzpfAS2hraK3jWWjdjEFNSefRJxSanYMqg26rlYKP3r9HrlSrxatBjQ0IDDP3/DoG7dz/8QJclnF1AUhJz0v6GhYvhT4PlFWRBDwUv0e0UDKLiyqfrfjAwFMnUAPbN8rV6W4O2Nl2PHIenpU3HZrFcvWI39Eeq60lrClh88gz0x8MRASVSBjJbQWDlXgYu+vj4ePXoEBwcH/PTTTwgODsaGDRvw4MEDNGnSBK9evUJRI6U3gzH2eT2P9MTdV9zTJTtXgq5g3PlxiEqMgrmuuchnqW5VXek/VvI14H/e/hNer7zwVcmvMLfhXEXvFvuIn/fdw6arAWhb2RrLe9ZU+teJhllB439C9MGDUDc2htP2bdBxdpZ0Q83PoqFj5PN3QQzNzNDlLNSA4pVkMzJUwezmWtksUnbFBj73dKmpiFi3DmEUACYnQ8PSArazZ8OwUSModd+tXa2QhjSF5/BFF9VyyIaGhggPDxfnT5w4gRYtZGvMdXV1RdUDRQgMDBRBEy1Xq1KlCnbu3KmQ/WCMFY6OpTqK7T7ffWJAwGQDo3X314niBRS0VLaojO1fbVeJoIXQH3E3GzdMqD1BXD707BC8w7lstlTRcjFy4kEowqLfqwKlhGg5nM2sX6BXrRrSoqPx4ruhSI3MQUloClacGxZ40EL9T+KuXhPbHKPlaWZOQPWegMdyYPRd4IcHQOe/gRp9AXMXWfGA0HvAtZXAjdXvlr7RlmaTcljmmbrbB/QfIJaHUdBi6O6Okvv3K3XQIv9ea2TfCAbJBqgfWh9GKUaiCqQqLxPLdeBCgcqgQYPE6fHjx2jbtq24nmZcqEybItAStUWLFuHhw4cimBo9ejTi4uIUsi+MsYLX2rk1dDR0RE+Xh+EPVf4lp+7x1Ijx95u/i+VSHi4eWNt6LYobFFe516aieUW0dZb9XaLXgwNbaSpnbYxajmZISUvHtuuBUAXqOjqil4imrY2oevVi9A9IT05W9G6JMsLUtDGgXz+xpctfzMQOqNIN6LAE+P4m8ONjoOtaoKzsdzILWgJ3Z5ts5uYTog4fxrOOHoj39ISavr4IAMXrWKwYlB19n9OSX+Kgb4F1DeerfGJ+rgOXZcuWoW7dumJJ2O7du2Fubi6uv3nzJr799lsogo2NDapVqybOW1tbi+IBERERCtkXxljBM9Y2RjOHZhmzLqosKDYIfY72wRG/I9BU0xT9bWbWmykCO1U1ssZIaKlr4VrwNVwKuqTo3WGfmXXZ6hmAlNSPdzpXJpoWFrBfsUIMwOOvXkXIrNkKDa7jPK8j+Ocp75o2pqWJy69XrULK69d5fwKj4kClzkDbBbLlYe87PRNY1w54ceODm1Kjo/Fy3HgE/TgWaTEx0K1SBSX37BaljZW5oENm10Ou42XsS6hrxGBM+FKU2fpVkW4QqpDAxdTUFEuXLsX+/fvRmjrD/mfGjBmYPHnyF+3A+fPn0b59e9ja2ooP4759+7INmGhGh5ak1a5dG56entk+FgVQqampsLfnijKMKTOPUh5iSwP2pNQkqCJK3Pzm0Dd4FPEIxXSL4a+Wf4nmnKryR/1jShiWQI9yPcT5P27+gdS0VEXvEstGm8rWKGagjeCoBJx+FKYyr5Fu2bIosYAG8mqI3L4dbzZuKvR9eOvlhRfff4+APtnnmLxauAhPGjXG83798Wb7DtErJU9oqRvltFCRAUJbF3dZeWXKj/nHHdjRR5b8T1URr1/HMw8PkRNEvVgshg+H0+ZN0FbQyp5CFR0MPDoMnPoFe4+PFFe1jomDZro60nO5xE5Z5boBZWRkpAgcwsLCkCaP0v9bw9m7d+9c78DRo0dx6dIl1KxZE507d8bevXvh4SEblJDt27ejT58+WLlypQhaaFkY5bH4+PjAysoq4340y9KwYUP8/fffqFevXpFMOGKM5QwNRlvubomw+DAMrTIUnct0Vpl1v/SVvdl7MxbcWIDU9FRUMK+ARU0WwcbQRtG7JhmU59NmTxvRdPOX+r+I5XNMeuYe9caqc8/QqIwlNgxwgyoJX70GYfPni4G5/aqVMGzYsECfLz0tDbHnziF89Wq8vXHz43dUU4NO2bJIfJSpZ4yGBgxq14Zx2zYiv0TTLFOlsNx4v9hA1AvgzBzAa4vIh0lP18SrV3URfuaZWEKmZW8P23m/Qb+6kubqxUfI+uUE3QJe/reNCRY3RauroZl9CSSqq2P5i1ScTumOIembYIMwoO8hWe5TIZLSWDlXgcvBgwfRs2dPxMbGih3PfGSPzud1iRY9xvuBCwUrrq6uYqaHULBEMyrff/89JkyQJWImJiaK/JvBgwd/Nnii+9Ip85tBjyeFN4MxlnPDTg7DhZcXMiqtUNJi59KdlfolTEhJwC9Xf8GBpwfEZaqeRf9vXerlwLKgYgWU52Klb4XDnQ7zayRBAeHxaLzgjEhzODeuCRzNDaAqaOgVPPlnRO3ZA3VDQzht2wodF5f8f56kJEQdOozwNauR5Cub0aBmjSbt28N8QH8x+xI8ddoHneaTAgMRfewYYo4eQ8LDTLmEmpqinLNxmzYwcm8GDROTvO9k6AMkbpuIoG0PkPBG1mvKpF5pFF/wNzSKKUmuXlIcEHz3vyDllmxLQdz7aEmdZTnsMLfGLwm+cElKxuaXb+ALZ5SGH/TUkoHR9wq9ylyRDVzKlCkjEvLnzJkjSiPn+868F7gkJSWJ59m1a1eWYKZv375i5oeWrNHu9+jRQ/SYmT59+mefg+5DS9veJ4U3gzGW8zKRLXe1zNI1XVFlIgvz/0z9WaggAfUq+bHWj+hVvpfKLw37mMTURHTY2wFBcUEYVWMUBlUeVKjvF8uZvms8ce7xK/yvUUlMbCtrIKgqKKgIGDAQ8TduQMvODk47d3z5bMZ7UmNjEbljJyLWr0dKaKi4Tt3AAKbfdEexPn2gVfxdQEDVxD7VaZ6KCUQfPSYCmSwzMVpaMKxXD0ZtWsPI3R0aRrlvPkljuDdbtyJs3nykJyRAQ1cN1jXDYWyfABhYAU0myMoma2hBcj7WlyYlCQh78C5AodmUV95ZG4rKUWU22xpAiRqyLfXB0THEt4e+xf3w+xhn0xR9rmwqnH4+yhi4GBgY4N69eyhZsmTB7Mx7gUtQUBBKlCiBy5cvi6IAcuPHj8e5c+dw7do1XLx4EY0aNRKlkOU2btyIypUrZ/scPOPCWNEnpcZcheFGyA38eO5HRCREwFTHFPMbz0cdmzqK3i3Jo7LIEy9MhIGWgWhKSblATFr+fRiKwRtuwExfC1cmukNX6788CBVB+SP+X3dD8osX0KtVE45r1kBNWzbr8CWSw8JE3sybbdtEUjvRtLREsb59YNq9+xcFF5klPvNDzPFjiD5yFIlPZBWviJqWFgwaNoRxm9YwbNoMGoafnz2jAgBBkycj7tx5cdmgXj3YzJ0DrVeXgFMzgTd+sjtSaWX3aUD59rIyzFIgGoSOeteXpmoPQEtPFqiE3AdS363syWBo/S5AKVFdttX/8Dvp8ZvH6HKgiyi4cqrbKehFvYHvg1twqVgDesVLQRGkFLho5ubOrVq1wo0bNwoscPkSDRo0yJJr8zk6OjriRAn/dKJkfsZY0eJg7CBmWOTd0jPnNigTOq60zWcb5nnOQ0p6CsqalcXiZotFAjr7PCqNvOHBBnhHeOOvu39hgptseTGTjmblrGBroougqAQcvR+MTtXtoEpohsV+5Qr4f/OtyD0Jnj4DNrNn5XomlQKKiLVrELVvf0aZZW1nZ5gPHADjDh2gnodgKDOdks7QGToUFkOHItHXF9HHjiP66FHRyT729GlxosDLsHEjGLVuDaMmTcRMT8bMjv9zaDs5IuGhN4J//hmpERHi/lZjx8KsV0+oqasDxTsD5b4Cbq4Dzv0GhPsCO3oDdm5Ai5mA47sD2YUuOQF4fBw4QInz/x33p79DXu8VWdA1yTqTQltj2xw9hbxaZhP7JuJgS/CbROy58BBDyjeAXr7/h4qeXM24rF69GjNnzkT//v3FjIaWVtapuw4dOhT6UjFliiIZYzm358kezLgyI0vwQmWAFzReIL7wlWGp0+yrs7HXd6+43MapDWbUnwE9Tf7TlRtXg69i8InB4ujlfo/9Iuhl0vLnqSf4/d/HqOloht1Dc15cR5nEXriAwP99J3JNrMaNEwFHTsTfvi0S7mNPnc7oiUKNLs0HD4Jh06ayQKCA0TCSZl9ijslmYpL8/TNuU9PVhWHjxtAwMxVL1zJKL/+HCgHYzp8H3TJlsn/whGjg8p/AlaVAcrzsurLtgObTAMuyBfr/kj1/FBDoCTy/DARcAV7eBD5WybK8B1ChPWBbXVaA4Atmh5JTk+G+0x1vEt9gmfsyNLJrJA7Op6SkiL6F6oXwfkp9rJyrwOVTLxgFHXmdvfhYcr6bmxv+/PNPcZneQAcHB4wYMSIjOV9Z3gzGWO7zPgJjAmGpb4kF1xfg3ItzIv+DEtY7le5UJP8/AdEB0NfUx1zPubj7+q6YWfqhxg/oW7Ev57N8oe9OfodLLy+hpWNL/N7k9/x901iehUUnoN6vp0VDyqOjGqK8jWr+LY7YsBGhc+aIAa/dsmUwatb04xXCzv5XIezmuwphhs2awXzQQOjXqFGIe51NEOPjI8uJOXoUyQEBH70vLV0rPnlSzmaDYkKAs3OBWxv/y/dQB6r3BppMBIzzsaJi7Csg4DLw/IqsVHPo/Q9zU/QtgPj3+txQ/kk+JM2ffH4SP5z9AZZ6ljjR9QQ01XO1MKrASGmsnKtXJDdLsnKKKpT5+vpmXPbz84OXlxeKFSsmApQxY8aIGZZatWqJAIbKIcfFxYlZn7zgpWKMFX2UiC9Pxl/UdJGYgaFp9qmXpyI8IRwDKw0sMoP97GaQqNnm/EbzUa+Eah6Fzi9jao7B5ZeXceL5Cdx9dRdVLN/lRDLFszLWRcuKxXHkXgg2XX2O2Z2yz1FVdma9eyHx2VNEbtuOoLFj4bh1i+j78tkKYR2oQtgA6JRSTP5DZvR9q1uunDhZjh6FRG9vEWBFHz7ywX2N27bN+RI2I2tZL5g6w4FTM4BHh4Bb64G7O4C6w4H6owDdLxhQRwbIZlPkp/B3eTtZEugd6wMOdQHHerLZlNsbZT1VMifN50OlL/kMe4dSHTKCljdv3uDkyZNo3rw5zPKpeINK9XGRS0hIEA0h8+rs2bNo2vTDowoUrKxbt06cp1LI8+fPR0hICKpVq4YlS5aImRhliyIZY3lDX2eLby3G6vurxWWqujXOdZyYtZD6TEur3a0+yNnZ0HoDqhdX0h4GhWzKpSkiqK1hVQPrWq8rMgGtqrjs+xo9/rkGA20NXJvcHIY60jjSXNgoPyVg8BDEX70KjeJWsJ48GdqlSiHu7DlEbNjwrkKYoSHMvukOs969s1QIkyLKbfFt5p51mZi6OlxOn8q2ilmOBFwF/p0KBF6TXdY3BxqNB2oNAOJeZV/ti4a7rx/LZlLEjMplIPrFh49tVVGWRyMPVD6Wm/J+X5o8or5kLXa1EH8HDnochJOJrOFmeHi46HnYpk0bmJubQxGkNFbOVeBCS8GoFDI1gwwNDcXjx49Fov6UKVNEZ/uBAz+s8iN1UnozGGP5Y+PDjZh3fZ4438a5DWbXnw0tKZbT/M+RZ0fw04WfVKZKmqKCw6/2fiVyh5Y0XYKmDtkvw2GKQUMR9z/O4dmrOMzyqIRedRxV9q1IjYzE07btROL6+/KzQlhhity1K9t+MXlCw1fqMn9y+ruZEgpgqLEjJc7TAasGY2SVu+Q5KvHhWR+DZjVsqskCFZpVsa+dbaWvwvDPvX/EgTc6uLK+zXpISbSExsq5OqQxe/ZsrF+/HvPmzRPNHuUqVaoklnAVpcCFl4oxprx6V+gtqrH8fPFnHPU7isiESCxsulCUxZXaYG33k934zfO3D26jWSJ7I3uF7JcyoiWF9LmgwcHCWwvR0K6hZNaPM9kSo561HfHLoYdiuVjP2g4qOyuWlpCA1DdvPrjeavw4mPXqlW8VwgoTBSkGDRp8sl9MrtHno/xXQJnWsqVbp2dlzT2hGewLC7L+DBU3saslm0mhk50roG0gib8Fe5/Ilol5uLzL82YfytX6iQ0bNuCvv/5Cz549oaHxrtZ61apV8ShzU6IiYPjw4Xj48CGuX7+u6F1hjBWAdiXbiaosVIXrSvAVDDw+UPRBkdIMwNBTQ0VeS0JqAhyMZCWeCW2pwICyNtNUlAGVBsBMxwx+UX4ip4hJS9cadtDVUsejkBjcCvhw4K4qqGSwvEJYZroVKxXJoEWOghWD2m75E7RkpqEJ1OoPeKzI/vYSrkDzGcDAf4EJAUC/Q0DTSUDJJpIIWsitsFsIiJEVZmnl1CrLbcHBwaKiL21ZLgOXly9fwsXFJduk/eT/6oYzxphUUFL76parxWD1QfgD9DnaBy9jXyr8yNqBpwfQeX9nUelKW10bY2uNxQGPAzje5bhYHkbbzqU7K3Q/lZGRthH+V/V/4vxyr+WIl5dXZZJgoq+F9lVk+QSbrn68GpWyoz4ntJwqC3V1MVPBPqF4RdnysMwocb7beqDBaMDeDdCUZuAnn21p7dwa+lr6WW6jpVlt27ZV+BKtIhm4VKhQARcuXPjgeuqzUr06J5AyxqSnsmVlsV7Y1sAWz6Ofo/eR3vCJ8FHIvrx++xojz4zE5IuTEZMcg0rmlbCz/U5R6lhDXUPMsFBOC8+0FJxuZbqJJXhUdW79A2mtI2fIyG05fDcYEXEf6Zeh5GhGgnJAMoKX/3JC8n2mQtlQgjxVHqNgheRjta+CFJccJyoekk4uH5bxNzAwEJV1actymeMydepUUe2LZl5olmXPnj3w8fERS8gOHTpUpF5PznFhTHU4mzhjY9uN+N+//4NvpC/6H+uPJc2WoJZ1rULbh2N+xzDr2ixEJUaJ3IphVYehf6X+nGdRyKhIw6gaozD23FisfbAWX5f9GhZ6FoW9G+wjqtqbonIJE9x7GYWdNwLxv8aKL/GrNDkhqqBGH6CUe75W+ypox/2P423KWzgZO6GqZdVsq/g+f/4cjo6O+VLNV6VmXDp27IiDBw+KetIU+VEg4+3tLa5r0aIFihLOcWFMtVjpW4kyuFSxhWY7KIg5FXCqwJ/3TcIb/Hj2R4w7P04ELeWKlcO2dtswuMpgDloUhBpRVrGoIgYLtGSMSUuvOrIlUVs8A5CW9kUdG5RCgeWEKDsKVpwbFomgJfMyMWqanF1BCurjsm3bNrFleejjoiykVOKNMVbwElISRBBxNvCsSIKfUmcKupbJY1nOj6DAaOaVmaIogIaahghWhlQeIunSzKriZuhN9DvWT7wvezrsQUnTkoreJfaf+KQU1J5zCjEJKVg/wA2Ny1jya8OU0rOoZ+i4r6P4Hvq367+w1LfMthWJvHeiRqbCWKo6VpZ2VzbGGMtnupq6WNhkoUh+p0ZfVNVr1Z1VImk+v9DMysQLEzH6zGgRtLiYumBzu80YXm04By0SUbN4TTS1b4rU9FRRHplJh762JrrUsBPnqTQyY8qKmuKShiUaZhu0EApWaJWTooIWqfls4GJmZoZixYrl6FTUclyo2ICrKzd3Y0zVUI7J9LrTMbiyrB/VUq+lmHNtDlLTUvP82BdeXBAVww49OyRmdKgE7/avtqOiecV82HOWn0bXHC2OdNLs242QG/ziSnC52CnvUARFvlX07jCW75LTknHA94A471H6471bIiMjsW/fPrFlOUjOp8aSyohyXOgkn/5ijKkWWks8ssZImOuZiwaQ23y24U3iG8xpMAfaGrkvmRmbFIv5N+Zn9AehRMtZDWZlm2zJpKGkSUl0Kd0FOx7vwB83/8DmtptVtumh1LhYGaFOyWK4+iwC2zwDMKZlWUXvEmP5isrhU3VDapbcyK7RR++XkpKCiIgIsWU5CFyoihhjjCmrnuV7ij8cky5OEtVdIhMisajpIhhqG+b4Ma4GX8XUS1MRHBcMNaihV4Ve+L7696L5JZO2odWG4uCzg7j3+p4oSfp+8zem2NLIInC5Hojv3UtDS4NXtzPlIU/Kb1+yPbTUP573aGFhgQEDBhTinknbF38LtGvXjrt4MsaUQhvnNljuvlx0Lb4Wcg0Djg8QPVc+hxoYzro6C4NPDBZBi52hnWggOd51PActRQSVQu5fsb84v/jWYiSnKr6ZckhcCDyDPcVWlbWsYA0LQx2ExSTi34ehit4dxvIN/X05/+K8OO/h8vFlYiwfA5fz58/j7Vted8oYUw51beuKoINmX7wjvNHnaB8ExgR+sipVlwNdsN1nu7jcvWx37O6wu1B7w7D8QQ1AKYCh95uWjSkSLTVstasVBp4YiJa7WmKnz06oKm1NdXR35SR9pnwOPzuMlPQUUZbdxczlk/cNCQnB3LlzxZZxVTHGGMtQ0aIiNrTZgBKGJcQgtveR3ngU8eiDcsrzrs8TTSxfxL6AjYEN/mrxF36u8zP0tfT51SyC6H0bVm2YOL/yzkrEJMUoZD9exrzE9MvTkYY0cTkd6Zh5dSY89ntg0oVJWHt/rVgXHxYflq9V8KTsWzcHUNrR5afhePoqVtG7w1ie0e+ufJnYp5Ly5QwNDdGkSROxZTnIcfkY6uCppVV0exFQVTE6UX1sxhiTczR2xMY2G/Hdye/w+M1j0euDKpDRTExccpxI4vaP9hf3pZLK42qNy1U+DJOmTi6dsPHhRvhF+WHN/TUYVWNUoT4/zeD9fPFnEay872nkU3HKzETHBKVNS6OMWRmUNistO5mWVrrg2c5MH83KWuHUozBsvhqAqe0rKHqXGMsTyqd7GvUUuhq6aO3U+rP3p4Clbt26/KrnpgFl586dP7iOfixz9RVNTU1YW1ujRYsWaN++PYoKKTXVYYxJBx11//7092JA+T5LPUtMrzf9k5VgWNFzJuAMRp4ZCR0NHRzqdAjWBgXfsTw0LlQEw0f8jmR7O5XUnlZnGsLehuHJmyd4EvkEz6Ofix5E2aE8K3kgIw9qHIwcRAnw91EOTUB0AByMHQrl//qlzjwKQ/9112Gsq4lrk5pDT5v7WbCii3qH7Xq8SyTlz2k457P3T0xMxMuXL1GiRAno6OhA1cfKOZpxyUm54LS0NDx58gT//PMPxo4di5kzZ+bH/jHGmEIYaRthZr2ZaLe3XZbrqWrYqharxICQKZcm9k1EY0oKVpfeXirKWRcUKgKw0XujWJr2NuWt+Fx1LdMVzibOWHBjgQhMRNBSd5qY2Xt/uSJ13BaBzJsnYmaQAhpK+KXli3Q6E3gm4/7a6tooZVrqXTBjWlrcnwKmTz2PVDQqYwk7Mz28ePMWB+8GoVste0XvEmNfhAq6HPU7Ks53Kt0pRz9DpZA3btyIIUOGwMbGRuVf+RzNuOTGoUOHMGzYMAQEBBSJF1dKUSRjTFqoshMlSb+Pkvhdrbl5rTK69+oeehzpIQKJne13omyx/O8fQnkqv3r+mrHkkHr9TKw9MaNJKc2EUI6VvZF9rmZC3iS8yRLI0HnfSF8RGH0OBS/HuxyX7MzL8rO+mHfMB1XtTLB/RANF7w5jX+TA0wOYfHGy+N0+3OlwjvpGUf+W2NhYsWRMU/OLMzyUZqyc769AgwYNUKsWV9VhjBV9tISGBnSZl+XQZfqjw5RTZcvKopcL9fRZeGshVjZfmW+P/SLmBeZfn4/TgafFZXNdc4ypNQZflfxKfK7kKHj4kgDCTNcMbjZu4iRHn116XhHQRD4W27uv7iI0Pmt5YbofBUtSDVxolmXhv49x50UU7r2IQmU7bhzNip6MpHwXjxw3u6VgxdTUtID3rOjI925O9OLu2SPrHM0YY0UZDeJoCY18UClfUiPVwR3LH6OqjxI5ITQzciXoSp4fj5Z2LfdaLqqDUdCioaaB3hV642Cng+hQqkOWoCW/0WNTAO7u6I6hVYfijyZ/YFPbTVB/788/zTBJOSCnfi5tKsmWyWy6+lzRu8NYrlE+2Y3QG+J3jX7vc4pmOQ4fPiy2rABmXJQR5e8kJSUpejdYAaDKeBoanOjJPo7W/dezrfdFS3dY0WRvbI9vyn6DTd6bRB7IdpvtXxRc0Ers0wGnMf/GfLyMfSmuq21dGxPcJny2d0OBB+T1pokkYflsIlUzuxZ8DR1dOkKqetVxxIE7QeI0qV15mOgV3cqmTPXs890ntvVK1MvV3xEaf7548YLHoQWV41IUyyE/fvz4o+v26APj5+cnghemnGiWkCri5XTaljGm/ChfpO2etohNjsWcBnPQvlTuqmVSWWXKY7kcdFlcpoEKlc5u4dhCMt818lyas4FnseHhBlFNbXPbzQWS15MfaLjSatF5PA6NxfT2FdCvvrOid4mxHElNS0XL3S1FD6YFjReI5ahFSbSEclxUNnDJyZtBLw0VGUhOToatrS3U1QtuOp8VPnp/4+PjERYWJoIXrtbBGMvsn3v/YPGtxaLJKC3rooH951Cvn1V3VomKYSlpKdBS10K/iv0wqPIgyfZYoVmXYaeGiaVxVDp521fbRFU9KdpwxR9T9z+Ai5Uh/v2hkWSCQMY+5eLLixh6cqjov3T669PQ1tAuUi9YtIQCF14q9plKDjSwpaBFX1+af3BY3ujp6YktBS9WVla8bIwxlqFX+V7Y9mgbguOCscV7C/pX6v/JAyGH/Q7jjxt/4NXbV+K6xnaNMd51vMgxkTJaBvdrg1/x9aGvERATgKmXpopcGCkGBZ2ql8CvRx/BNywW1/wiUKekuaJ3ibEcJ+VTIY7cBi2hoaHYtGkTevXqheLFi6v8q81TCJ9Ay8iItnbRioxZ7siDUppZY4wxOV1NXXxf/Xtx/u+7fyMyITLbF8cnwgf9jvXDxAsTRdBCuVDL3JdhqftSyQctcqa6pvij8R+iKMHJgJPY+HAjpMhIVwsdq5UQ5zlJnxWVZafySoKdXHLWu+X9MYqrqysfQP8PBy45IMWjTiz/8PvLGPsYOkJKTRtjkmPw972/s9wWlRiF2Vdno9uhbrgVdgt6mnoYWX0k9nbci0Z2jYpkKWiaISILby7E7bDbkKJedWTB4PEHIXgVk6jo3WHsk474HRHLRssXK/9F+WNGRkZo1KiR2DIOXBhjjLGP0lDXwJiaY8R5Wi52+NlhBMUEYdfjXWi/tz22+WwTOSKUbHvA4wAGVxmco1wYqaJqam2c2iAlPQVjz45F+NtwSE1FWxNUdzBFcmo6dtwIVPTuMPbJJaR7nshahHQqnfvZFnmRqMDAQK4q9h+ecVFC/fr1w/Tp0yFF/v7+PMPBGCtS6peoj5ImJcVgfsKFCWi1p5UoJfwm8Q1cTF2wuuVqUSlIGUpl0wz09HrTxf837G0Yfrrwk6iIJDW9ajuK7ZZrAUhNU+kaQ0zCvCO88fjNY2ira6Otc9sveozw8HCsWbNGbBkHLiwfjypQMQPGGFM2VDaYyhu/jxo67mi/I0unemVA1c8oOZ+WvlFvl+V3lkNq2lWxEX1cXka+xVmfMEXvDmOfTMp3d3AXFcW+hIWFBYYOHSq2jAMXlbV//37UqFEDurq6KFmyJGbMmJEReMhnRby8vDLuHxkZKa47e/asuExbunz06FHUrFkTOjo6uHjxIhITEzFy5EhRoYseu0GDBrh+/brC/p+MMZYfHa+pQeP7XK1dRbljZVTKtBSm15XN3P919y+cf3EeUqKrpYGva9qJ85ykz6QoMTVRVBokHqU98tQom8ZUtGUqHLhQ88kKFSqISg2FITjqLS4/fS22inbhwgX06dMHo0aNwsOHD7Fq1SqsW7cOs2fPzvVjTZgwAb/++iu8vb1RpUoVjB8/Hrt378b69etx69YtuLi4oFWrVoiIiCiQ/wtjjBU0qgxGJYMzo8tUPUyZtS3ZVuS8EKqYFhQbBCnpWUe2XOzs41cIjIhX9O4wlsXpgNOISYoRfaDq2NTJUw+VEydOiC1T4cBl+PDhYtCem9kA0bAwKSXXp41X/FH/19Po8fc1saXLuX2M3PQJpSDkUzkuNLtCAUffvn3FbEuLFi3wyy+/iAAmt2bOnCl+vlSpUmLWZcWKFZg/fz7atGkjAsO///5b9EpZvXq1uL+Tk1Ou/i+MMaZolLsyre60jOCFtnRZGXJaPmec6zhUtqiM6KRojDk7BkmpSZAKZwsDNHCxAP1J2eIZoOjdYSzbZWIeLh4fHPjIjYSEBDx+/FhsGTegzJW3yamoMPV4nj43lEM4Zf8DccqNhzNbQV87f/qF3rlzB5cuXcoyw0I9a+iXghpu5katWrUyzj99+lT0Qqlfv37GdTS16ebmJmZkGGOsqOpcujPq2dZDYEygmGlRhaCFULM8KjxAJZ8fhD/AvOvz8HOdnyGl0sgXfV9jx/VAjG5eGjqaGoreJcbE7OTV4Kvilejo0jFPrwgtExsxYgS/qqo+46LKYmNjxawL5bDIT/fu3cOTJ09EXoq6uuxjkXlm5GPNGQ0MDAptvxljTJEoWKG8FlUJWuRsDW0xt8FcqEEN232249CzQ5CK5uWLo7ixDsLjknDsfoiid4cxYf/T/SIvrrZNbZQwlDVMVYZUAynIn0P4KkJPS0PMfORGSFQCmv9xTsy0yKmrASfHNIa1iW6unju/UFK+j4+PyD/JjqWlpdgGBwejevXq4nzmRP2PoeVi2traYjbH0dExI+Ch5XijR4/Ot/1njDFWuBraNcSQKkOw6u4qzLwyE+XMysHFLPu/IYVJU0Md37g6YPGpJ9h8NQAdq+VtkMhYXlFfp/2++8X5Ti5f1rtFbvv1APy21xNNtXxxJtkFP3VyQ3dXWQNWVcWBSy5QFa3cLtcqaWmIuZ0rY9Ke+0hNT4eGmhrmdK4krleUqVOn4quvvoKDgwO6du0qZlho+dj9+/cxa9YskZNSp04dkXTv7OyMsLAw/Pzz55cG0OwLlewbN24cihUrJh5/3rx5YvnZwIEDC+X/xhhjrGBQ+ec7r+6IJTA/nP0B277aBgMtxc+6f+vmgKVnfOHpHwGfkBiUteYO40xxPEM88TL2JYy0jEQZ5C9FMywT99yDbroG/FPNkJCmIcaSjcpYwsZED6qKl4oVAoqOL05oiq2D64itoqNlqvJ16NAhUaWCqqpRkLJw4cKMWRJCzY6oPDKVOqbZEgpocoKCnS5duqB3795iZsfX1xfHjx+HmZlZAf6PGGOMFTQNdQ381ug3WOlbwT/aH9MvT5dEsRVavdC8vJU4//sJH15SwySRlE9V+XQ1c76y5n1+r+PEap14aONmip3Ypqanw/+1alfQU0uXwreOAlF5ORMTE0RFRcHY2DjLbZSs7ufnJ2YdKPeDKSd+nxljLOe8wrzQ/1h/pKSnYKLbRPQo30PhL98vhx5i9UW/jOXYtNJB0QcJmeqJSoxCsx3NkJSWhG3ttqGiRcUvfizv4Gi0WXwBGkiDsVoCotN1ATUNcQC8sGdcPjVWLmw848IYY4yxHKtmVQ1jao0R5+ffmC+WjykSLalZe0kWtBA6Sk1LbDiZmRW2Y37HRNBS2qw0KphXyNNjrTz3VGxN1d7CQ/chiqkniFQDVV4mRjhwYYwxxliu9CrfCy0cWyAlLQVjz43Fm4Q3CnsF5UtqMqPLS04+QXJqmqJ2i6mgvb57M5LyKS/6S53yDsV+ryDZ7GGPeqjd+mvsHtOSZxE5cGGMMcZYbtGgbGa9mXAydkJIXAgmXpiI1LRUhTWipAHe+7ZeD0THpZdw70WUInaLqRifCB/R60hTXRPtSrb74seJTkjG5L33xflBDUuiZWU7tK5dAQ4WJvm4t0WXUsy4dOrUSSR/U4UsxhhjjBU8Q21D/N7kd+hq6OJS0CX8dfcvhbzstHSGclqoaifRUAO+rmkHU30tPAyORsdlFzHr0EPEJ6UoZP+Yatjnu09sm9o3RTHdYl/8OL8efYSQ6AQ4mevjh+ZlEBMTg7Nnz4otU5LAZdSoUdiwYYOid4MxxhhTKWXMymBK3Sni/Io7K3D55WUJVO9shvlfVxX90jpUtRXLxv656IeWC8/j3ONXCtk/ptySU5MzGrN6uHh88eNceRqOLdcCxPm5natAT1tDtJS4deuW2DIlCVyaNGkCIyOu284YY4wVtg6lOqBrma6iU/hPF34SS8cUNfNSt5R5RvKyhaEOlnxbHWv7u6KEqR5evHmLvms8MXrbbYTHJipkH5lyOvviLCITI2GlZ4V6tvW+6DHeJqViwp674nyP2g7is0yKFy+OMWPGiC2TQOBy/vx5tG/fHra2tmLN7L59sqm2zJYtWwYnJydRkrh27drw9PRUyL4yxhhj7EMT3CagfLHyYvD247kfxRFoqWha1gonfmiEAfWdRS7MPq8gNP/jHHbffCGJPjRMeXq3dHDpIHJcvsTCk4/xPDweNia6mNimXD7vofJQeOASFxeHqlWriuAkO9u3bxeR5rRp08RUGd2XGihSN3fGGGOMKZ6Ohg7+aPIHjLSNcPfVXfx+83dIiYGOJqa2r4C9w+qjnLUR3sQn48edd9BnjScCwnkJDvtyoXGhIscrL8vE7gRG4p8Lz8T52Z0qwUhXK+O2V69eYcWKFWLLJBC4tGnTRnRlpwT77Pzxxx8YPHgw+vfvjwoVKmDlypXQ19cXnd2/RGJiomikk/nEGGOMsbyxM7LDnAZzxPnN3ptxzP+Y5F7SqvamOPh9A4xvXRY6muq48OQ1Wi46h7/OP0UKl05mX+Dgs4NIS09DDasacDR2zPXPJ6WkYfyuuyIXy6OaLZqVy7okTFtbW6w6oi2TQODyKUlJSbh58yaaN2+ecZ26urq4fOXKlS96zLlz54run/KTvb19Pu6xalm3bh1MTU0VvRuMMcYkool9EwysNFCcn3ZpGp5FyY4iS4mWhjqGNXHBsdGNULekORKS0zDnyCN4LL+E+y+5dDLLOVpqKF8m1rl05y966VacfQqf0BiYG2hjavuKH9xOY1U6yE9bJvHA5fXr10hNTf0gIYkuh4S8S/6jQObrr7/GkSNHYGdn98mgZuLEiYiKiso4BQYGQtn069cP06dPz3HAQfetVq1age+Xv79/nhoyMcYYk74R1UfA1doV8Snx+PHsj4hPluZSLOr/smVwbczrUgUmelq4/5JKJ1/CnCPeIlGasc+5FXYLATEB0NfUFw1Zc8snJAZLzzwR56d3qIhiBh/OqqSkpCAiIkJsmcQDl5w6efKkWPtHpeJevHiBunXrfvS+Ojo6MDY2xsaNG1GnTh24u7sX6r4yxhhjyoySk+c1mgdLPUv4Rvpi0sVJuBZ8TWHVxj6FDqZ1c7UXpZO/qmKD1LR0/HX+mVg+duEJ5xQURfQ58wz2LPDPGz2+vHdRG+c20NfSz9XP02dt/O67SE5NR/PyxcXnLzs0vv3zzz85x6UoBC4WFhbQ0NBAaGholuvpsrW1dZ4ee/jw4Xj48CGuX7+OQhH1EvA7L9tKCM2+zJgxA3fu3BFf4HSi6+T5RZUrV4aBgYFYUjds2DDExsZ+dDaFlvHduHEjy/WLFi2Co6Mj0tLSCuX/wxhjTPEs9Cwwv/F8qEENpwJOYdCJQWi1uxX2PNkDKbI00sHSHjWwpl8t2JroIjDiLXqv9sSY7V6IiEtS9O6xHKLPF33OBp4YWKCfN/E8u1rhcpCsb5GpTu6Xza+95CeS8o10NDHLo9JHV6QUK1YMffv2FVsGfFnNtkJCiUg1a9bEqVOn4OEhq9RAA2C6PGLEiMLfISqb+CVT3l5bgKPjgfQ0QE0daDMPqNYjd49BkXwBLLPq3r077t+/j2PHjomZKyJfR0mByJIlS+Ds7Ixnz56JwGX8+PFYvnz5B49DiWO0ZG/t2rWoVatWxvV0mZau0WMxxhhTHSUMS2S5TAnMM67MEH0urA3ydvCxoFBi9Ikx5lhw3Afrr/hjz+2XOPv4FaZ8VR4e1UrwcmcJoxmQ6Zeni35C8s/btMvTsMJrBTTUNfLteVLTUhESn3U2Z+2Dtfim3Dc5/lw/D4/DghM+4vzkduVhbaL7yZVCNMZiEglc6Ai+r69vxmU/Pz94eXmJyNLBwUGUQqZIkwbDbm5u4gg+lVCmKmN5QeWX6UQ5NDlGQcsc2zw9rwhejoyVnXJjUhCgbZCju8pnTHJCT08PhoaG0NTU/GAWa/To0Rnn6ZeGqr9999132QYuZNCgQeJ2mqmhXzQqX33v3j3s378/4zG4Zj5jjKmGgOiAjEGkHA0mA2MCJRu4EEMdTZFv0LGaLSbsvicSp3/Yfgd7bwdhtkclaGqowe91nMiRkTe7ZIpD4wrPEE/8efvPDz5v5P0goyDk5nNN+0ufKyoKUa+UObq72n92nEyrYqgdiKGhIVSdwgMXWlrUtGnTjMsUqBAKVmgATjMCtL5v6tSpIiGfkshpdiCvHURpqRidqBwyV2rIHs3AUBW2R48eideJEsMSEhJELhGVpH4fzYrRa7p3715888034v2j95aPFDDGmOpxMHaAupq6GNTJqUMd9kZFo5pndQczUTqZSiUvOe2L849fodnvZ5GSKhseUzPLuZ0ro7urg6J3VSW9TXmLw88Oi9LblEuVHfq8LWq6COZ6si70+SH8bThGnRmVJUiiz3lOP9fbrgfiyrNw6Gqp49fOVT47i0eBy8WLF1GqVCkOXKQQuDRp0uSzR+FpWZhCloZlt1yLZj5yIzoIWOYmm2mRU9MAhl8DjHMxe5PLpK+8opyVr776CkOHDsXs2bPFDBj94gwcOFCUqc4ucKGlfX369BHLwzp37owtW7Zg8eLFhbrfjDHGpIGOPk+rO00sD5MHL7R8rLh+3g48FiZtTXWMaFYabSvbYMyOO/AKjMy4jfpuTNpzH43KWPLMSyEvCdv6aCt2P9mNqERZ+Wo9TT10KNVBFIRYfme5+LxRMEGfv6YO7w6O55fp9aZnfK7lz5OT2ZaQqATMOewtzo9tWRYO5p8f29FqmJ9++ilf9lsZKDxwUZQvWipGUXEOl2tlsCgNtF8MHBwNpKfKgpb2i2TXSwQFHO+/DtQ/h/KJfv/994z8lB07dnz2sWi5WKVKlcRyMpqhoQCGMcaYaqLeFpTTcu/VPUy8MBGBsYG48PICGtk1QlFS0tIQ41qWRc/V17Jcn5qeDv/X8Ry4FDA6wH077LaYXaFiD6k0nvovEP623LfoVLoTjLWNxXUdXTqKZVs0A1JQSxLln+vcPA/9H37edw8xiSmoZm+K/vWdC2TflJ3KBi6FulSsRh+glDsQ8QwoVhIwyZqwqGi0lEueW0R9cIyMjODi4oLk5GRRgq99+/a4dOkSVq5c+dnHKl++vCgzTUcHBgwYIHJoGGOMqS4a1NHpXvg9rL2/FktuLUGDEg3EkeqipKSVgVgeRjMtmdmafjyxmuVNUmoSjvodFQGLd4RspoK4WbuhR/keaGLX5IPEe/nnraDl9nkO3g3GSe8waGmoYV7XKtCgD1MOexpSrnDHjh1FtV1VV7S+NYoyClacG0ouaCFdunRB69atRT6KpaUltm7dKpLAKMn+t99+EzMomzdvFvkuOSFfTkaBC2OMMUYGVBwAQy1D+LzxwQn/E0XuRaFEfMpp0XgvJ2HRySdceCafvYp/hWVey9BiVwv8fOlnEbToaOigS+ku2NV+F1a3Wg13B/d8rRZWkMJjEzH9wANxfkTT0ihT3CjHP0vFk2hsRlsGqKWreJkn+YxLVFSUaEyZGSWi00wElQPW1eUjKjn1yy+/YOfOnbh79y6KAn6fGWOscKy8s1IMSJ2MnbC3417RrLKoCY56K5aHhUQnYOzOO6KR4Ej30hjTooyid63IoyWFmx9txnH/40hJk3WKt9K3EsvBKGgx0zVDUTRq223s9wpCOWsjHBjRQOROKctYubAVvW8MRea4sM9WvqCk/qVLl4rSyYwxxlhmvSv0xhbvLfCP9sfBpwdFbkJRnHmRl0FOSE7FxD33sOTUE9iZ6aFbraJRMU1KktOScfL5SWzy3oS7r94d8KxuVV0sB6OZFS11LRRVp7xDRdBCK8N+61Il10ELjVPl1Vw1NIrGDFNBKlohXz6i/JaHDx/i+vXrit4VpUGV36hhKFWK42VijDHG3megZYBBlQeJ81T9iXIYirJv3RwwrEkpcX7Snnu4+OS1ondJ0tXAPIM9xZZEJETgr7t/ofWu1hh/frwIWihAoepg277ahg1tNqC1U+siHbREJyRj8t774vzghiVR1d40148RFhYmlu7TlvFSMV4qxnipGGOMFaLE1ES029MOofGhmOA2AT3L9yzSr39aWjpGb/fCgTtBMNLRxK6h9VDWOuc5DKpgz5M9GeWD1aCGqlZV8fD1QySlyQJXCz0LdCvbDV+X+VqcVxaT9t7DlmsBcDLXx9FRjaCnrfFFy9kDAgJEU3ZdBaUtSGmpmMrOuDDGGGOs8FGS9XdVvxPn6Yh7fHJ8kX4b1NXVMP/rKnBzKiZK3fZf64nQ6ARF75Zk0AxL5l4+1LjRK8xLBC0VzStiToM5ONHlBIZWHapUQcuVp+EiaCG/dqnyRUELoWClTJkynGut6oEL5bdUqFABrq6uit4VxhhjTKVQrw0HIwexXIhK3RZ1OpoaWNW7JkpaGCAoKgED1l1HXKIsuVzVBUQHZAQtmU1ym4St7baifan20NIousvBsvM2KRUT9sjydXrWdkCdkuZf/FhxcXHw9PQUW6bCgQvnuDDGGGOKQXkLw6sNF+ept4u8A3pRZmagjXX93WBuoI0HQdH4futtpKR+OGBXJVS49uLLix9cTz18qKO92nulpZXFwpOP8TycGpPqYkKbcnlepnXixAmxZSocuDDGGGNMcVo7t0Zps9KISY7BugfrlOKtcDDXx999a0FHUx2nH4Vh+sEHKtvjhQovTL44GWsfrBWXKbdFHrRMqzutUJpEKsKdwEj8c+GZOD+7UyUY6eZtNsnGxgY///yz2DIOXBhjjDGmADSA/b7a9+I8LRd7/VY5KnLVcDDD4m+qgSYTNl0NwN//DWJVCS0BHHRiEA4+OwgNNQ1MqTMFJ7qewJpWa3C8y3F0Lt0ZyigpJQ3jd91FWjrgUc0WzcoVV/QuKR2ecWGMMcaYQjSxb4IqFlXwNuUt/r77t9K8C60r2WBy2/Li/Jwjj3D4bjBUhe8bX/Q43AO3w27DSMsIK5qvEBXDaIbF1dpVaWdayPKzvvAJjRHLBae2r5gvjxkeHo4NGzaILePAhTHGGGMKQjkOI2uMFOd3PN6BoNggpXkvBjZwRt+6juL8Dzu8cPN5BJQd5bP0PtobL2Nfwt7IHpvabUJd27pQBT4hMVh2xlecn96hIooZaOfL46qrq8PAwEBsmQoHLspcVaxfv36YPn26ondDUpycnHD27FlF7wZjjLH31LapLU4paSlYcWeFUgVldNS9eXkrsYRo0Pob8H+tvJWhtnhvwfBTwxGbHIuaxWtiS9stKGlSEqogNS0d43ffRXJqOpqXL46vquRfPoqZmRm6dOkitkyFAxeuKpa/kpOTP7guKalod0RmjDFWOEZWl826HHh6AM+ilCcnRENdDUu+rY7KJUzwJj4Z/dZ6IiJOuf42UsA56+oszPWcK8oed3LphL9b/A1T3dx3iS+q1l7yE0n5RrqaIiE/P6ulpaWlITExUWyZCgcuimjA5BnsKbZScPDgQTHbRI2NLCws0KlTp4zb6Bdu3759We5vamqKdetkVV/8/f3FfbZv347GjRuLx9i8ebOY6fHw8MDs2bNha2uLsmXLivsHBgaiW7du4jGKFSuGjh07iseQk//cggULRNUMc3NzEVhmDobol/ann36Cvb09dHR04OLigtWrV4tqLXSefjYzLy8vsY++vrJpW8YYY9JVxbIKmto3FQPfZbeXQZnoa2tidb9aKGGqB//weAzZcAMJyalQBtFJ0WKWZbvPdlE1bEzNMZhRb4bS9WX5lOfhcVhwwkecp7ym4sb5290+NDQUv/76q9gyDlxyhQbJ1OE3t6dtj7ah1a5WGHhioNjS5dw+Rn6WUzx8+LAIVNq2bYvbt2/j1KlTcHNzy/XjTJgwAaNGjYK3tzdatWolrqPH8vHxwb///otDhw6J4INuMzIywoULF3Dp0iUYGhqidevWWWZkzpw5g6dPn4rt+vXrRZAkD5RInz59sHXrVixZskQ836pVq8TjUHAyYMAArF0rK7coR5cbNWokghrGGGPSN6L6CDH4PfH8BB6GP4QysTLSxbr+ruKI/I3nb/DjzjtIo9JTRVhgdCB6HemFy0GXoaeph0VNF6F/pf5K25slOzQ2m7D7HhKS01CvlDm6u9rn+3PQQd+uXbuKLQPU0lW1wPh/qKGPiYkJoqKiYGxsnOW2hIQE+Pn5wdnZWcwqUABRe0tthezntR7XoK+lny+PVa9ePZQsWRKbNm3K9nb60tm7d6+YBZGjX5hFixaJ2RGaLaHXhC5T4CJHtx07dgwBAQHQ1pYlpdFzzJo1SwQb8i8zCljo8WhWp2XLluLnKP+EAhcNDQ1xH5qhoUS0bdu24fHjx2L2hoKh5s2bf7C/QUFBcHBwwOXLl0UARsESzfjQLEzfvn0/+3q8/z4zxhhTjAkXJuDws8NoUKKBqEalbC77vkbftZ4iF+K7xqXy3JxQUW6G3sToM6MRmRiJ4vrF8WezP1HeXFZFTVUER73F6ot++OeCH3S11HFidGPRx0fVxsqFjZeKqSBaRuXu7p7nx6lVq9YH11WuXDkjaCF37twRy7VoxoVmSOhEy8UoWKBARa5ixYoZQQuhJWNhYWEZ+0u30bK07FCQ0q5dO6xZsyZjGRwtLfv666/z/H9kjDFWeIZVHQZNNU1RnYoGx8qmnosFfu1cRZxfee4pNl97jqJmn+8+0aOFgpaK5hWxtd1WlQtatl8PQP1fT4ughbiXK15gQUt8fLwYB9GWAZr8IuQcTYXSzEduhMaHwmOfB9KQlqXp1r6O+8RRitw8d37R0/v0Y9HMyPsTcdkl31N5vs9dFxsbi5o1a4ocmPdZWlpmnNfS0vpgH+SJaJ/bXzJo0CD07t0bCxcuFMvEunfvDn195TzywRhjysrB2AGdSnfCzsc7seTWEqxrvU7plh51qWmHF2/eYuHJx5iy7z5sTfTQtJwVpI7yjxbfWow192UHCVs4tsDsBrPzdXxSVGZaJu65J5pMyh29HyyutzHJ/9eCZjn279+PIUOG8LhGlWdcvqQcMn150nKt3JycTZwxrd40EawQ2k6rO01cn5vHyc8v7ipVqohclI+hgCI4+F2zrCdPnnxxpF+jRg3x81ZWViLfJPOJph1zgmZxKIg5d+7cR+9D+ToUNK1YsUIsV6O8F8YYY0XP/6r8D9rq2rgVdguXgi5BGY10d0GXGnZi8Dt8yy3cfxkFKaOl8j+c+SEjaBlSZQgWNF6gckELuf8iKkvQQuiy/+uCmRGxtrbGlClTxJapcOBSmOWQO5fujONdjmNNqzViS5cVadq0aSLRnbaUe3Lv3j389ttvGbc3a9YMS5cuFYn7N27cwHfffffBjEhO9ezZU1Qto0pilJxPuSSUzzJy5Ei8ePEixz1YKFeFghHKi5E/xo4dOzLuQ0vJKFdm4sSJKF26NOrWVY2GV4wxpmyKGxTHt+W+Fedp1oWO9CsbOhg5t3NlkdAdn5SKAeuuIyjyLaSIqqH2O9YPpwNPQ0tdC3MazMH31b/POCCrSqLik/H7v48/uF5DTQ1OFvoF9lmhnF9lm3n8Uqr3qVMQawNruFq7iq2iNWnSBDt37sSBAwdQrVo1Eah4enpm3P7777+LssMNGzZEjx49MHbs2C+enqSfO3/+vEie79y5M8qXL4+BAweKHJfcJHjRTApV1Rg2bBjKlSuHwYMHIy4uayMvelxK/O/fv/8X7StjjDFpGFh5IPQ19eEd4Y2Tz09CGWlrqmNFr5ooU9wQYTGJ6L/2OqITPlyWrUgPXj9Aj8M9xPtQTLcYVrdajfal2kMVRb1NRp811/AoJAYG2hpQV3sXtMzpXKlAlomRiIgIcbCZtoyriuWqqhiTNprRoaID1DemePGc5w/x+8wYY9Kz3Gs5VtxZIZZW7+2wFxrq7wq4KJMXb+LRafllvIpJRMPSFljTzxVaGoo/rnzC/wQmX5yMhNQEuJi6YKn7UpQwLAFVRAFl79Weosmkmb4Wtg6pAxM9LbE8jGZaCipoIRSwHD9+XLSWKFasGBSBq4oxlo+oghgtO5s+fbqoJJaboIUxxpg09anQByY6JvCL8sOhZ4egrOzM9LGmryv0tDRw4clrTN57L197t+UWPfdfd//Cj+d+FEELlabe2GajygYtMQnJ6LtGFrSY6mth86A6KGdtLIKVuqXMCzRoIRSsfPvttwoLWqRG8SE9Y3lEU6iOjo6IjIzEvHnz+PVkjDElYKhtiEGVBmXMviSlvmtarGwq25lgaY/qYvnRjhsvsOyMr0L2g17jSRcn4c/bf4rLPcv3FD1a6L1QRbGJKei39jpuB0SKGZbNg2qjgq1xoQeSVKBIxdsuZuDAhRV5lJSfmpqKmzdvokQJ1TwixBhjyuibct/ASs8KQXFB2P1kN5SZe/nimNGhoji/4MRjrLvkh8tPX4syuwWdfO8Z7AnvcG8MPD5QzG5pqGng59o/Y4LbBGiqa6pu0LLGEzefv4GxrqYIWira5qwaan4KCQnBL7/8IraM+7gwxhhjTKJ0NXXxv6r/wy9Xf8GqO6vQsVRH0SJAWfWu64SAiHj8fcEP0w8+FNfRLAxVIOvu6pDvz7fnyR7MuDIjS+U2Iy0jLGiyAPVs60FVxSWmYMDa67iREbTUQaUShR+0EMrDpsqsOW0hoex4xoUxxhhjktXJpRPsDO0QnhCOrY+2Qtn1q+f0QY+QSXvu5/vMC820vB+0kEXNFql00BKflIL+667D0z8CRrqa2DiwtljKpyhUnZUqwHJTbRUPXL6kASVjjDHGCpeWhhaGVRsmzlMDxOikaKV+C55HfNjIMDU9HQ+C8vf/7RXmlW2PHDWobr+Qt//11PH0i4CRjiY2DHBDVXtTxe7T27d48OCB2DIVDlwKswElY4wxxr5cW+e2KGVSSgQt6x+sV+qX0tnCIKNHSGY/7bqLY/eD8/z4qWmp2OK9BVMuTfngNmoqaW9kD1UNWgauv46rzyJgqKOJ9QPdUN3BTNG7JQoP7dq1S2yZCgcujDHGGCsaqIcLdWsnGx9uRPjbcCgrKq9LOS3U2JBQEGNhqI3wuCR8t+kWhmy4gZCohC967CdvnqDPsT6Y6zlXlDqmJXgUrMieRx3T6k6TRKPswpaQnIrBG27g8tNw0Vxy/QBX1JBA0EKoxcOECRO41cN/VLNUBGOMMcaKlGYOzVDRvCIehD/AP/f+wU9uP0FZUSJ+ozKWGQ0OzfS1sfS0L1aee4oTD0Nx5Wk4xrcph55uDlDPbnrmPYmpiaI3Cy21S0lLgYGWAUbXGI1uZbshLD4MgTGBYqZFlYOWi76voS+CFjfUdJROzxR1dXXo6Ogoejckg2dcVBA1aqREL8YYY6yoUFNTw8gaI8X57T7bERyb92VTUpa5waGulgbGtiqLQyMboJq9KWISUzBl3310W3UFT0JjPvk4N0JuoOuBriJwoaCliX0T7Ou4T5SaplkWClZcrV1VNmj538abovEnBS3r+ruhlpN0ghby5s0b7N69W2wZBy5K29eEghMp7IeHh0e2f3z8/f0Vsk+MMcaKrro2dcUgOzktGavuroKqoY7tu4fWw7T2FcRAm8r1tl1yAQv/fYzElNQs96V8oOmXp6P/8f7wj/aHhZ4Ffm/8O5Y0XaKSQcr76PUauukmzj1+BT0tDazp5wo3Z2kFLYSaT8bFxYkt48CFMcYYY0Vp1qW6bNZln+8++Eep3kEwDXU19K/vjH/HNEazclZITk3H4lNP0G7JRdzwjxAd1v99/i867uuY0bSzS+kuYpalpVNL8RqqOlnQcgtnfF5BV0tdBC11SppDiszNzdGnTx+xZRy4FJrkkBDEXb0mtlKxatUq2Nvbi9rg3bp1Q1RUVMZtFNnPnDkTdnZ2Ym0lLS07duxYlp+/d+8emjVrBj09PfELNWTIEMTGxorbaMZn/fr12L9/v/iSpNPZs2cL/f/IGGNMuVSzqobGdo2Rmp6K5V7LoapKmOphdd9a+PPb6iJ53zcsFl//cwwtt/THmLNj8PrtazgZO2FNqzWYXm86THS4gSFJSknD8M23cPpRGHQ01bGmr6tYkseKBs5xyQU6ipEWH5/rU8SWLfBt5o6Afv3Eli7n9jHoufOTr68vduzYgYMHD4qA5Pbt2xg2TFYnnyxevBi///47FixYgLt376JVq1bo0KEDnjx5Im6naUu6zszMTJSU3rlzJ06ePIkRI0aI28eOHSuCodatWyM4OFic6tVT3YZWjDHG8o+8wthR/6PwifBR2ZeWDgq2r2qLEz80RO2q3jAo+QdCUm4C6epwt+6JXR12iaV1LFPQsuUWTnrLgpbVfV1Rz8VC0i8PjZ9mzZoltgxQS8/vEXEREx0dDRMTEzHbYGxsnOW2hIQE+Pn5wdnZGbq6uiKA8KlRUyH7WfbWTajr6+fLY9FsCP0SPH/+HCVKlBDXUfDSrl07vHz5EtbW1uJ66nUzadKkjJ9zc3MTDTupeefff/+Nn376CYGBgTAwMBC3HzlyBO3bt0dQUJAo20c5LlR3fN++fZCy999nxhhj0jf+3HgRuNDsy1L3pVBVTyOfilwWr1de4rJmshOiAj2QlmiNVhWLY2bHSihuzH/bklPTMGLLLRx/EAptTXX806eWqNwmdXSgmBpQVqxYMWO8JaWxcmHjGRcV5eDgkBG0kLp164rlYT4+PuIDSsFH/fr1s/wMXfb29hbnaVu1atUsv0R0u/wxGGOMsYI0rNowaKhp4NyLc6ILvKpJSk3CMq9l6Hqwqwha9DX1MdFtIi723o3v6taHprqaGKQ3//0cNl19jrS0dJUOWkZuvZ0RtPxdRIIWQuMsOnCsqKBFapSij8uhQ4fw448/ikEzzQIMGjSoQJ5HTU9PzHzkRnJoKJ61+4qSRt5dqa6OkocPQat48Vw9N2OMMcZknEyc4OHiIRLQF99aLHI5VCXx/FboLUy/Mh1+UX7ichO7JphcZ3JGtbDxrcuJJWQTdt/FnRdR+Hnffez3eom5navAxcoQqiQlNQ2jt3nh6P0QaGuoY1XvmmhcRIIW+aqQgIAAccBZl1eFFP0Zl5SUFIwZMwanT58WeRrz589HeHjBdNSlL0RarpWbk46zM2xmzhDBiqCuLi7T9bl5nPz+MqZfAppVkbt69apoclS2bFkxDWhra4tLly5l+Rm6XKFCBXG+fPnyuHPnjpjCzHy7/DGItrY2UlOzlmdkjDHG8st3Vb+DlroWboTewJXgK0r/wsYkxWDmlZnoe6yvCFrMdc2xoPECLGn2YYnj8jbG2DOsPqZ+JSudfN3/DdouvoDFJ5+IXA+VCVq2e+HwvWBoaahhZe8aaFrWCkUJ9W/ZunUr93FRlsDF09NTrPujZU+GhoZo06YNTpw4ASkx7doVLqdPwWH9erGly4pGUXvfvn1F8HHhwgWMHDlSJNNTfgsZN24cfvvtN2zfvl0s/ZowYQK8vLwwatQocXvPnj0zHuP+/fs4c+YMvv/+e/Tu3VvktxAnJyeR2E8///r1ayQnJyv0/8wYY0y50GC9e9nu4vySW0vyvZCNlJx8flKUON75eKe43Ll0Z+z32I9WTq0+enCTSicPaOCMEz80QtOylkhKTcPCk4/RbskF3HweAWUPWsbsuINDd2VBy4qeNdGsXM5XukiFlZWVOEBPWyaBwOX8+fMioZuO8NMvXnaJ3JQMToNgGijXrl1bBCtyNGuQOVeDzlOCudRoWVvDoLab2EqBi4sLOnfujLZt26Jly5aoUqUKli9/V1aSAhn6RaEleJUrVxbJ+wcOHEDp0qXF7VRC+fjx44iIiBAJ+127doW7uzuWLn2XIDl48GAx+1KrVi1YWlp+MIPDGGOM5dWgyoOgp6mHB+EPsOrOKoTEFWzbAXp8z2DPQnueB68fYPSZ0fjh7A949fYVHI0dxbK4GfVm5LjEsZ2ZvuhVsvibajA30MaTsFh0XXkFU/bdx5PQGFx++hrBUW8L9P9Dj19Yz3PxyWtR8vjAnSCR67OsRw00r1D0ghaioaEBIyMjsWUSqCp29OhRMaCtWbOmGEjv3bs3S7d1OuJPjXdWrlwpgpZFixaJ0rt0FJ+iz127don+IPIBMy0VowCIyvHmd1Uxppz4fWaMsaJt+KnhOP/ivDivBjUMrzYcLZxa5Pvz/Ov/r0iIT0d6oT2PnKaaJvpX6o8hVYZAV/PLxyRv4pIw+4g3dt18keV6dTXgh+Zl0KayDfLb0XvBYqaH6gMU1vMQeq7lPWuidSVpHDT+ElSdlQ7yN2rUCKamplD1qmIKD1wyo4Dj/cCFghU6oi8PTCgBn5om0rIkWr50+fJlEazQz5HRo0eL6gs9evTI9jkSExPFKfObQY/HgYvq4sCFMcaKLpqZaLWrFdKg3Hkbq5qvQr0S+dcP7cCdlxi5VbmrsVHgcmlCM9iYFN0CR7TUnpp5d+zYERYWiuk5I6XARdJVxZKSknDz5k1MnDgx4zpK/m7evDmuXJEl4VGQQjkWtDyMXlSawZkyZcpHH3Pu3LmYMWNGoew/Y4wxxgpWQHRAtkELlQfWVM+/YU5KWgriU+IV9jxaGlrITxaGOtleb6CtAU0N9XzNNYlLSlXI89DMi//r+CIduFCwMnDgQEXvhmRoSj3KpKpU8mRvObr86NEjcV5TU1N0eG/atKmYjRk/fjzMzc0/+pgUBFHuxvszLowxxhgrehyMHaCupo609HfBC12mxPX3K23leWZndyuFPY+9Uf6OVZwtDMSMROb2Lhpqajj5Y+N8HehTzkn9X08r7HmcLPKneTeTBoUn5+eHDh064PHjx/D19cWQIUM+eV8dHR0xzbVx40bUqVNHJJQzxhhjrGiioGFa3WlicE9oS5fzM5hQxuehoGFu58picE9oO6dzpXyfnVC25ylsISEhosorbZnEc1xoqRhVr6IE/Mx5L1SCl5KVaM1fXnFyPuMcF8YYK/popiIwJlDMTOT3IF+Zn4dmKmg5Fc1MFOQgX9mep7DExsaK1hVVq1YVbT8UgXNccogaGFK1sVOnTmUELrQcjC6PGDEChUVCsR0rAPz+MsZY0UeD+4Ic4Cvr89DgvjAG+Mr2PIWFgpX69esrejckQ1MKkSQt8ZKj8sPU6LBYsWJwcHAQ+Sg0w0K9QCgRn8ohU7f2/v375+l5qTcMnT7V2V1eM5tmfvT0lOeXgGUVHy9LgtTSyt/ER8YYY4yxvKBKuMHBwbCxsRHpDqpO4UvFqAcLJda/j4KVdevWifNUCplKHtP6vmrVqmHJkiWiTHJBT3/RSxMQECA6vlODTKpoxpQHvb8UtISFhYna6PSlwBhjjDEmFRS0/PXXXyKH20ZB4xQpLRVTeOCiaJ97M2i2hWaBaIkaU04UtFhbW4scK8YYY4wxqUhJSRFjVRqjamoqZqGUlAIXhS8VU5ScLBWT59mULl1aBDBM+dDyMPmSQMYYY4wxKaFghdInmAzPuEgoimSMMcYYY0yOxqeXL19GvXr1xHhV1cfKnLTBGGOMMcaYBNGKH39/f1758x+ecZFQFMkYY4wxxpiUREtorKyyMy6U31KhQgW4uroqelcYY4wxxhhjn6HyMy4UPVJVqcDAQIVHkYwxxhhjjMlRy4YdO3agW7dusLKyUtiMi729PSIjIxWWZyOn8oHLixcvxJvBGGOMMcYYyx4d5Lezs4MiqXzgQv1ZgoKCYGRkpJA+HvIolmd8VBt/Dhh/Dhh/HzD+u8CkOD5IT09HTEyMJJqxq2wfFzl6AxQdPRL6MPJSNcafA8bfB4z/LjAeHzCpjRNNFLxEDKqenM8YY4wxxhgrOjhwYYwxxhhjjEkeBy4KpqOjg2nTpoktU138OWD8OWD8fcD47wLj8cGnqXxyPmOMMcYYY0z6eMaFMcYYY4wxJnkcuDDGGGOMMcYkjwMXxhhjjDHGmORx4KJgy5Ytg5OTE3R1dVG7dm14enoqepdYIZo+fbpofJr5VK5cOX4PlNz58+fRvn170cyL3vN9+/Z90Oxr6tSpsLGxgZ6eHpo3b44nT54obH+ZYj4H/fr1++D7oXXr1vx2KJm5c+fC1dVVNMK2srKCh4cHfHx8stwnISEBw4cPh7m5OQwNDdGlSxeEhoYqbJ+ZYj4HTZo0+eA74bvvvlOpt4MDFwXavn07xowZI6qK3bp1C1WrVkWrVq0QFhamyN1ihaxixYoIDg7OOF28eJHfAyUXFxcnft/pwEV25s2bhyVLlmDlypW4du0aDAwMxHcDDV6Y6nwOCAUqmb8ftm7dWqj7yAreuXPnRFBy9epV/Pvvv0hOTkbLli3F50Puhx9+wMGDB7Fz505x/6CgIHTu3JnfHhX7HJDBgwdn+U6gvxcqJZ0pjJubW/rw4cMzLqempqbb2tqmz507l98VFTFt2rT0qlWrKno3mALR1/DevXszLqelpaVbW1unz58/P+O6yMjIdB0dnfStW7cqaC9ZYX8OSN++fdM7duzIL76KCQsLE5+Hc+fOZfz+a2lppe/cuTPjPt7e3uI+V65cUeCessL8HJDGjRunjxo1SqVfeJ5xUZCkpCTcvHlTLAGRU1dXF5evXLmiqN1iCkBLgGipSMmSJdGzZ08EBATw+6DC/Pz8EBISkuW7wcTERCwl5e8G1XP27FmxbKRs2bIYOnQowsPDFb1LrIBFRUWJbbFixcSWxgp09D3zdwItKXZwcODvBBX6HMht3rwZFhYWqFSpEiZOnIj4+HioEk1F74Cqev36NVJTU1G8ePEs19PlR48eKWy/WOGiwei6devEoISmfGfMmIGGDRvi/v37Yp0rUz0UtJDsvhvktzHVQMvEaDmQs7Mznj59ikmTJqFNmzZisKqhoaHo3WMFIC0tDaNHj0b9+vXFwJTQ7722tjZMTU2z3Je/E1Trc0B69OgBR0dHcbDz7t27+Omnn0QezJ49e6AqOHBhTIFoECJXpUoVEcjQl9KOHTswcOBAfm8YU2HffPNNxvnKlSuL74hSpUqJWRh3d3eF7hsrGJTjQAeuONdRtX3sczBkyJAs3wlUwIW+C+jABn03qAJeKqYgNM1HR8zerwpCl62trRW1W0zB6IhamTJl4Ovrq+hdYQoi//3n7wb2PlpOSn87+PtBOY0YMQKHDh3CmTNnYGdnl+U7gZaXR0ZGZrk/jxdU63OQndq1a4utKn0ncOCiIDTtW7NmTZw6dSrL1CBdrlu3rqJ2iylYbGysOHJCR1GYaqJlQTRQyfzdEB0dLaqL8XeDanvx4oXIceHvB+VCtRlosLp3716cPn1afAdkRmMFLS2tLN8JtDyI8iH5O0F1PgfZ8fLyEltV+k7gpWIKRKWQ+/bti1q1asHNzQ2LFi0SZe/69++vyN1ihWjs2LGijwMtD6PyllQam2bivv32W34flDxAzXyEjBLy6Q8QJWFSwi2tbZ41axZKly4t/nhNmTJFrGmmuv5MNT4HdKKcN+rXQYEsHdAYP348XFxcRGlsplzLgrZs2YL9+/eL3EZ5LhsV5aA+TrSlpcM0ZqDPhbGxMb7//nsRtNSpU0fRu88K6XNA3wFbtmxB27ZtRT8fynGhMtmNGjUSy0hVhqLLmqm6P//8M93BwSFdW1tblEe+evWqoneJFaLu3bun29jYiPe/RIkS4rKvry+/B0ruzJkzoszl+ycqfysviTxlypT04sWLizLI7u7u6T4+PorebVaIn4P4+Pj0li1bpltaWopSuI6OjumDBw9ODwkJ4fdByWT3GaDT2rVrM+7z9u3b9GHDhqWbmZml6+vrp+fwcMsAAAQUSURBVHfq1Ck9ODhYofvNCvdzEBAQkN6oUaP0YsWKib8LLi4u6ePGjUuPiopSqbdCjf5RdPDEGGOMMcYYY5/COS6MMcYYY4wxyePAhTHGGGOMMSZ5HLgwxhhjjDHGJI8DF8YYY4wxxpjkceDCGGOMMcYYkzwOXBhjjDHGGGOSx4ELY4wxxhhjTPI4cGGMMcYYY4xJHgcujDHG8lWTJk0wevRoflUZY4zlKw5cGGOMMcYYY5LHgQtjjDGlkpSUpOhdYIwxVgA4cGGMMVZgNm7ciFq1asHIyAjW1tbo0aMHwsLCxG3p6elwcXHBggULsvyMl5cX1NTU4OvrKy5HRkZi0KBBsLS0hLGxMZo1a4Y7d+5k3H/69OmoVq0a/vnnHzg7O0NXV5ffUcYYU0IcuDDGGCswycnJ+OWXX0SgsW/fPvj7+6Nfv37iNgpOBgwYgLVr12b5GbrcqFEjEdSQr7/+WgQ7R48exc2bN1GjRg24u7sjIiIi42coyNm9ezf27NkjAh/GGGPKRy2dDnkxxhhj+ZicTzMgixYt+uC2GzduwNXVFTExMTA0NERQUBAcHBxw+fJluLm5iUDH1tZWzML07dsXFy9eRLt27UTgoqOjk/E4FNSMHz8eQ4YMETMuc+bMwcuXL8WsDGOMMeXEMy6MMcYKDM2QtG/fXgQntFyscePG4vqAgACxpSCFApM1a9aIywcPHkRiYqKYZSE0UxMbGwtzc3MR6MhPfn5+ePr0acbzODo6ctDCGGNKTlPRO8AYY0w5xcXFoVWrVuK0efNmEVhQwEKXMyfQU/5K7969sXDhQrFMrHv37tDX1xe3UdBiY2ODs2fPfvD4pqamGecNDAwK6X/FGGNMUThwYYwxViAePXqE8PBw/Prrr7C3t89YKva+tm3bisBjxYoVOHbsGM6fP59xG+WzhISEQFNTE05OTvxOMcaYCuOlYowxxgoELQ/T1tbGn3/+iWfPnuHAgQMiUf99GhoaImF/4sSJKF26NOrWrZtxW/PmzcVlDw8PnDhxQiT3Uz7M5MmTsw2CGGOMKS8OXBhjjBUIWhq2bt067Ny5ExUqVBAzL++XPpYbOHCgWD7Wv3//LNdT5bEjR46IKmN0W5kyZfDNN9/g+fPnKF68OL9zjDGmQriqGGOMMYW7cOGCKHEcGBjIAQljjLFsceDCGGNMYaiC2KtXr0TpY2pQSUn8jDHGWHZ4qRhjjDGF2bp1qyhlHBkZiXnz5vE7wRhj7KN4xoUxxhhjjDEmeTzjwhhjjDHGGJM8DlwYY4wxxhhjkseBC2OMMcYYY0zyOHBhjDHGGGOMSR4HLowxxhhjjDHJ48CFMcYYY4wxJnkcuDDGGGOMMcYkjwMXxhhjjDHGmORx4MIYY4wxxhiD1P0fU5MA/Pd2UV0AAAAASUVORK5CYII=",
             "text/plain": [
               "<Figure size 800x400 with 1 Axes>"
             ]
@@ -349,16 +356,10 @@
           "text": [
             "'Most people in France speak'\n",
             "  baseline : [' French', ' English', ' a', ' at', ' the']\n",
-            "  swapped  : [' Chinese', ' English', ' a', ' some', ' the']\n",
-            "'The capital of France is'\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "  baseline : [' a', ' the', ' one', ' also', ' home']\n",
-            "  swapped  : [' a', ' the', ' one', ' also', ' home']\n"
+            "  swapped  : [' Chinese', ' English', ' a', ' some', ' at']\n",
+            "'The capital of France is'\n",
+            "  baseline : [' a', ' the', ' also', ' one', ' home']\n",
+            "  swapped  : [' a', ' the', ' also', ' one', ' home']\n"
           ]
         }
       ],
@@ -400,8 +401,8 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "'Most people in France speak': rank of ' Chinese' 348 -> 1\n",
-            "'The capital of France is': rank of ' Beijing' 969 -> 9\n"
+            "'Most people in France speak': rank of ' Chinese' 347 -> 1\n",
+            "'The capital of France is': rank of ' Beijing' 990 -> 9\n"
           ]
         }
       ],
@@ -463,8 +464,8 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "baseline : [' the', ' a', ' my', ' visit', ' see']\n",
-            "steered  : [' paris', ' Paris', 'Paris', ' París', 'paris']\n"
+            "baseline : [' the', ' a', ' visit', ' my', ' see']\n",
+            "steered  : [' paris', 'Paris', ' París', 'paris', ' Paris']\n"
           ]
         }
       ],
@@ -475,60 +476,19 @@
         "print(\"steered  :\", show_next_tokens(prompt, hooks=steer))"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "id": "00df913e",
-      "metadata": {},
-      "source": [
-        "## 5. Fitting your own lens\n",
-        "\n",
-        "For models without a published artifact, `JacobianLens.fit` reproduces the reference estimator on\n",
-        "a raw `TransformerBridge` — one forward and `ceil(d_model / dim_batch)` backward passes per\n",
-        "prompt, deterministic. Quality saturates quickly: ~100 prompts of 128 tokens is usable (the\n",
-        "published lenses use up to 1000). Fits parallelize across prompt slices and combine exactly with\n",
-        "`merge`:\n",
-        "\n",
-        "```python\n",
-        "from datasets import load_dataset\n",
-        "\n",
-        "texts = load_dataset(\"Salesforce/wikitext\", \"wikitext-103-raw-v1\", split=\"train\", streaming=True)\n",
-        "prompts = [row[\"text\"] for row in texts.take(500) if len(row[\"text\"]) > 600][:100]\n",
-        "\n",
-        "lens = JacobianLens.fit(\n",
-        "    model, prompts, corpus=\"your-corpus-id\", dim_batch=16, max_seq_len=128\n",
-        ")\n",
-        "lens.save(\"gemma-2-2b_jlens.pt\")           # official artifact format (+ provenance metadata)\n",
-        "# ...or shard, using the same corpus id for every chunk:\n",
-        "# JacobianLens.merge([JacobianLens.fit(model, chunk, corpus=\"your-corpus-id\") for chunk in chunks])\n",
-        "```\n",
-        "\n",
-        "## References\n",
-        "\n",
-        "- Gurnee et al., [*Verbalizable Representations Form a Global Workspace in Language Models*](https://transformer-circuits.pub/2026/workspace/index.html), Transformer Circuits Thread, 2026\n",
-        "- Reference implementation: [`anthropics/jacobian-lens`](https://github.com/anthropics/jacobian-lens) (Apache-2.0)\n",
-        "- Published lenses: [`neuronpedia/jacobian-lens`](https://huggingface.co/neuronpedia/jacobian-lens) · interactive: [neuronpedia.org/jlens](https://www.neuronpedia.org/jlens)\n",
-        "- The logit lens: [nostalgebraist, 2020](https://www.lesswrong.com/posts/AcKRB8wDpdaN6v6ru/interpreting-gpt-the-logit-lens)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "id": "c99c3348",
       "metadata": {},
       "source": [
-        "## 6. Qwen3.5-4B\n",
-        "\n",
-        "Qwen3.5 is **Bridge-only**: there is no `HookedTransformer.from_pretrained` path, so we load it with:\n",
-        "\n",
-        "```python\n",
-        "TransformerBridge.boot_transformers(\"Qwen/Qwen3.5-4B\")\n",
-        "```\n",
-        "\n",
-        "Use the Bridge default raw HuggingFace activations here. Turning on\n",
-        "`enable_compatibility_mode()` or `process_weights()` changes the residual basis, so the\n",
-        "published lens artifact will no longer match.\n",
-        "\n",
-        "We'll load the published Qwen3.5 lens and compare J-lens vs logit-lens on the same two-hop\n",
-        "prompt from section 2.\n"
+        "## 5. Qwen3.5-4B (Bridge-only walkthrough model)\n",
+        "Gemma above shows the full J-lens workflow on a model that also exists on\n",
+        "HookedTransformer. This section checks a second case:\n",
+        "1. **Bridge-only** — Qwen3.5 has no `HookedTransformer.from_pretrained` path; we can load it with `TransformerBridge.boot_transformers(\"Qwen/Qwen3.5-4B\")`.\n",
+        "2. **Official walkthrough parity** — same model, n=1000 Hub artifact, and\n",
+        "   two-hop prompt as Anthropic's [`jacobian-lens walkthrough`](https://github.com/anthropics/jacobian-lens/blob/main/walkthrough.ipynb).\n",
+        "3. **Hybrid architecture** — full attention + GatedDeltaNet; J-lens only needs\n",
+        "   residual `blocks.{i}.hook_out`."
       ]
     },
     {
@@ -536,28 +496,27 @@
       "id": "47f23c87",
       "metadata": {},
       "source": [
-        "### Free Gemma before booting Qwen\n",
+        "### Cleanup\n",
         "\n",
-        "Qwen3.5-4B is larger than gemma-2-2b, so we drop the Gemma objects before loading it.\n"
+        "\n"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 9,
       "id": "b91cb43f",
       "metadata": {},
       "outputs": [],
       "source": [
         "# NBVAL_IGNORE_OUTPUT\n",
         "import gc\n",
-        "import time\n",
         "\n",
+        "# Clear memory from gemma-2-2b experiments before booting Qwen\n",
         "lens.clear_device_cache()\n",
         "del result_jlens, result_logitlens, lens, model, swap, steer\n",
         "gc.collect()\n",
         "if torch.cuda.is_available():\n",
-        "    torch.cuda.empty_cache()\n",
-        "    torch.cuda.reset_peak_memory_stats()\n"
+        "    torch.cuda.empty_cache()\n"
       ]
     },
     {
@@ -574,23 +533,289 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 10,
       "id": "901ed8f5",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6a4af29377f14ad09cd0f3bb8a77b29a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Loading weights:   0%|          | 0/723 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.0') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.0') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.0') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.0') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.0') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.1') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.1') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.1') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.1') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.1') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.2') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.2') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.2') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.2') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.2') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.4') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.4') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.4') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.4') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.4') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.5') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.5') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.5') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.5') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.5') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.6') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.6') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.6') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.6') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.6') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.8') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.8') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.8') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.8') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.8') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.9') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.9') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.9') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.9') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.9') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.10') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.10') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.10') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.10') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.10') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.12') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.12') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.12') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.12') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.12') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.13') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.13') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.13') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.13') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.13') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.14') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.14') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.14') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.14') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.14') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.16') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.16') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.16') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.16') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.16') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.17') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.17') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.17') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.17') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.17') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.18') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.18') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.18') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.18') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.18') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.20') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.20') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.20') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.20') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.20') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.21') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.21') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.21') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.21') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.21') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.22') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.22') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.22') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.22') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.22') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.24') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.24') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.24') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.24') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.24') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.25') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.25') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.25') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.25') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.25') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.26') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.26') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.26') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.26') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.26') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.28') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.28') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.28') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.28') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.28') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.29') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.29') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.29') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.29') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.29') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.30') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_out' -> 'attn.hook_out' on BlockBridge(name='model.language_model.layers.30') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_q_input' -> 'attn.hook_q_input' on BlockBridge(name='model.language_model.layers.30') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_k_input' -> 'attn.hook_k_input' on BlockBridge(name='model.language_model.layers.30') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n",
+            "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_v_input' -> 'attn.hook_v_input' on BlockBridge(name='model.language_model.layers.30') did not resolve; this hook will not be accessible.\n",
+            "  getattr(module, \"_register_aliases\")()\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "loaded Qwen3.5-4B: 32 layers, d_model=2560, device='cpu'\n"
+          ]
+        }
+      ],
       "source": [
         "# NBVAL_IGNORE_OUTPUT\n",
         "# Hybrid Qwen3.5 emits expected Hook-alias warnings on linear-attn layers\n",
         "# (aliases point at attn.* which is absent there). Harmless for J-lens,\n",
         "# which only needs blocks.{i}.hook_out.\n",
         "QWEN_MODEL = \"Qwen/Qwen3.5-4B\"\n",
-        "qwen_started = time.perf_counter()\n",
         "\n",
         "model = TransformerBridge.boot_transformers(\n",
         "    QWEN_MODEL, dtype=model_dtype, device=device\n",
         ")\n",
         "model.eval()\n",
-        "qwen_load_s = time.perf_counter() - qwen_started\n",
         "\n",
         "assert model.cfg.n_layers == 32\n",
         "assert model.cfg.d_model == 2560\n",
@@ -613,10 +838,21 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 11,
       "id": "c2426b93",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "JacobianLens(layers=0..30 (31), d_model=2560, n_prompts=1000)"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
         "# NBVAL_IGNORE_OUTPUT\n",
         "# Official walkthrough artifact (n=1000), pinned Hub revision.\n",
@@ -646,75 +882,75 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 12,
       "id": "090e24f5",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "layer | J-lens top-1         | logit lens top-1\n",
+            "    6 | ' ...'               | ' souhaitent'\n",
+            "   12 | '...'                | ' nemlig'\n",
+            "   18 | '.\\\\'                | '原件及复印件'\n",
+            "   24 | ' currency'          | ' called'\n",
+            "   30 | ' Euro'              | ' called'\n",
+            "   31 | ' the'               | ' the'\n",
+            "model output:  the\n"
+          ]
+        }
+      ],
       "source": [
         "PROMPT = \"Fact: The currency used in the country shaped like a boot is\"\n",
         "QWEN_LAYERS = [6, 12, 18, 24, 30, 31]\n",
         "\n",
-        "if device == \"cuda\":\n",
-        "    torch.cuda.synchronize()\n",
-        "readout_started = time.perf_counter()\n",
-        "\n",
         "result_jlens = lens.readout(model, PROMPT, positions=[-1], return_full_logits=True)\n",
         "result_logitlens = lens.readout(model, PROMPT, positions=[-1], use_jacobian=False)\n",
         "\n",
-        "if device == \"cuda\":\n",
-        "    torch.cuda.synchronize()\n",
-        "qwen_readout_s = time.perf_counter() - readout_started\n",
-        "qwen_total_s = time.perf_counter() - qwen_started\n",
-        "\n",
         "top_j = result_jlens.top_tokens(model.tokenizer, k=1)\n",
         "top_l = result_logitlens.top_tokens(model.tokenizer, k=1)\n",
         "\n",
         "print(f\"{'layer':>5} | {'J-lens top-1':<20} | logit lens top-1\")\n",
         "for layer in QWEN_LAYERS:\n",
-        "    print(f\"{layer:>5} | {top_j[layer][0]!r:<20} | {top_l[layer][0]!r}\")\n",
+        "    print(f\"{layer:>5} | {top_j[layer][-1][0]!r:<20} | {top_l[layer][-1][0]!r}\")\n",
         "\n",
         "print(\"model output:\", top_j[31][-1][0])\n"
       ]
     },
     {
       "cell_type": "markdown",
-      "id": "d9e0825c",
+      "id": "00df913e",
       "metadata": {},
       "source": [
-        "### Runtime and peak GPU memory\n",
+        "## 6. Fitting your own lens\n",
         "\n",
-        "We also print load time, readout time, and peak GPU memory when running on CUDA.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "9c58cac8",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# NBVAL_IGNORE_OUTPUT\n",
-        "print(f\"Qwen model load:     {qwen_load_s:.2f} s\")\n",
-        "print(f\"two readouts:        {qwen_readout_s:.2f} s\")\n",
-        "print(f\"total since boot:    {qwen_total_s:.2f} s\")\n",
-        "if device == \"cuda\":\n",
-        "    allocated = torch.cuda.max_memory_allocated() / 2**30\n",
-        "    reserved = torch.cuda.max_memory_reserved() / 2**30\n",
-        "    print(f\"peak allocated GPU:  {allocated:.2f} GiB\")\n",
-        "    print(f\"peak reserved GPU:   {reserved:.2f} GiB\")\n",
-        "    print(f\"GPU: {torch.cuda.get_device_name()}\")\n",
-        "else:\n",
-        "    print(\"peak GPU memory: unavailable (this run used CPU)\")\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "04f72e93",
-      "metadata": {},
-      "source": [
-        "Qwen3.5 shows the same pattern as Gemma: the J-lens surfaces intermediate or answer-like\n",
-        "tokens earlier than the logit lens, and the same `JacobianLens` API works on a Bridge-only\n",
-        "model.\n"
+        "For models without a published artifact, `JacobianLens.fit` reproduces the reference estimator on\n",
+        "a raw `TransformerBridge` — one forward and `ceil(d_model / dim_batch)` backward passes per\n",
+        "prompt, deterministic. Quality saturates quickly: ~100 prompts of 128 tokens is usable (the\n",
+        "published lenses use up to 1000). Fits parallelize across prompt slices and combine exactly with\n",
+        "`merge`:\n",
+        "\n",
+        "```python\n",
+        "from datasets import load_dataset\n",
+        "\n",
+        "texts = load_dataset(\"Salesforce/wikitext\", \"wikitext-103-raw-v1\", split=\"train\", streaming=True)\n",
+        "prompts = [row[\"text\"] for row in texts.take(500) if len(row[\"text\"]) > 600][:100]\n",
+        "\n",
+        "lens = JacobianLens.fit(\n",
+        "    model, prompts, corpus=\"your-corpus-id\", dim_batch=16, max_seq_len=128\n",
+        ")\n",
+        "lens.save(\"gemma-2-2b_jlens.pt\")           # official artifact format (+ provenance metadata)\n",
+        "# ...or shard, using the same corpus id for every chunk:\n",
+        "# JacobianLens.merge([JacobianLens.fit(model, chunk, corpus=\"your-corpus-id\") for chunk in chunks])\n",
+        "```\n",
+        "\n",
+        "## References\n",
+        "\n",
+        "- Gurnee et al., [*Verbalizable Representations Form a Global Workspace in Language Models*](https://transformer-circuits.pub/2026/workspace/index.html), Transformer Circuits Thread, 2026\n",
+        "- Reference implementation: [`anthropics/jacobian-lens`](https://github.com/anthropics/jacobian-lens) (Apache-2.0)\n",
+        "- Published lenses: [`neuronpedia/jacobian-lens`](https://huggingface.co/neuronpedia/jacobian-lens) · interactive: [neuronpedia.org/jlens](https://www.neuronpedia.org/jlens)\n",
+        "- The logit lens: [nostalgebraist, 2020](https://www.lesswrong.com/posts/AcKRB8wDpdaN6v6ru/interpreting-gpt-the-logit-lens)"
       ]
     }
   ],

From 7c1e1088e4521946eb9e6b6e2ef3bda7ee6ea6e0 Mon Sep 17 00:00:00 2001
From: kyleyinxu <kyleyinxu@gmail.com>
Date: Mon, 27 Jul 2026 13:02:23 -0400
Subject: [PATCH 3/4] Full local run of notebook

---
 demos/Jacobian_Lens_Demo.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/demos/Jacobian_Lens_Demo.ipynb b/demos/Jacobian_Lens_Demo.ipynb
index cd5274ab1..758a84f4c 100644
--- a/demos/Jacobian_Lens_Demo.ipynb
+++ b/demos/Jacobian_Lens_Demo.ipynb
@@ -43,7 +43,7 @@
         "5. loads the published n=1000 lens and repeats the two-hop readout.\n",
         "\n",
         "**Note**: `google/gemma-2-2b` is a gated Hugging Face model — accept its license and authenticate\n",
-        "(`hf auth login` / `HF_TOKEN`) before running. The demo uses ~6 GB of GPU memory\n",
+        "(`hf auth login` / `HF_TOKEN`) before running. The demo uses ~6 GB (8 GB for Qwen3.5-4b) of GPU memory\n",
         "(executed on an 8 GB card), so a free Colab T4 should work; GPUs without native BF16 use FP16 automatically."
       ]
     },
@@ -120,7 +120,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "448943416fc149028bdd5e802c48cb1b",
+              "model_id": "28eb2ae142be471fafb7e5b10a186685",
               "version_major": 2,
               "version_minor": 0
             },
@@ -540,7 +540,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "6a4af29377f14ad09cd0f3bb8a77b29a",
+              "model_id": "7bce27fd736d484981fac5f42c04e83f",
               "version_major": 2,
               "version_minor": 0
             },

From b40ce9b0cd78f9e301ea2ed3ac4a73f03fe8a559 Mon Sep 17 00:00:00 2001
From: kyleyinxu <kyleyinxu@gmail.com>
Date: Mon, 27 Jul 2026 13:08:54 -0400
Subject: [PATCH 4/4] Adjust layers to fit Qwen architecture

---
 demos/Jacobian_Lens_Demo.ipynb | 35 +++++++++++++---------------------
 1 file changed, 13 insertions(+), 22 deletions(-)

diff --git a/demos/Jacobian_Lens_Demo.ipynb b/demos/Jacobian_Lens_Demo.ipynb
index 758a84f4c..de22ab40b 100644
--- a/demos/Jacobian_Lens_Demo.ipynb
+++ b/demos/Jacobian_Lens_Demo.ipynb
@@ -190,7 +190,7 @@
               "JacobianLens(layers=0..24 (25), d_model=2304, n_prompts=454)"
             ]
           },
-          "execution_count": 3,
+          "execution_count": null,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -552,7 +552,7 @@
           "output_type": "display_data"
         },
         {
-          "name": "stderr",
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "/Users/kyleyinxu/Documents/Projects/TransformerLens/transformer_lens/model_bridge/bridge.py:494: UserWarning: Hook alias 'hook_attn_in' -> 'attn.hook_attn_in' on BlockBridge(name='model.language_model.layers.0') did not resolve; this hook will not be accessible.\n",
@@ -848,7 +848,7 @@
               "JacobianLens(layers=0..30 (31), d_model=2560, n_prompts=1000)"
             ]
           },
-          "execution_count": 11,
+          "execution_count": null,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -877,33 +877,24 @@
         "### Compare J-lens vs logit lens on the two-hop prompt\n",
         "\n",
         "Using the same prompt as section 2, we print the top J-lens token and top logit-lens token\n",
-        "at a few representative layers.\n"
+        "at a few full-attention layers (plus the final layer).\n"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": null,
       "id": "090e24f5",
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "layer | J-lens top-1         | logit lens top-1\n",
-            "    6 | ' ...'               | ' souhaitent'\n",
-            "   12 | '...'                | ' nemlig'\n",
-            "   18 | '.\\\\'                | '原件及复印件'\n",
-            "   24 | ' currency'          | ' called'\n",
-            "   30 | ' Euro'              | ' called'\n",
-            "   31 | ' the'               | ' the'\n",
-            "model output:  the\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "PROMPT = \"Fact: The currency used in the country shaped like a boot is\"\n",
-        "QWEN_LAYERS = [6, 12, 18, 24, 30, 31]\n",
+        "QWEN_LAYERS = [    \n",
+        "    model.cfg.n_layers // 4,\n",
+        "    model.cfg.n_layers // 2,\n",
+        "    model.cfg.n_layers // 4 * 3,\n",
+        "    model.cfg.n_layers - 2,\n",
+        "    model.cfg.n_layers - 1,\n",
+        "]\n",
         "\n",
         "result_jlens = lens.readout(model, PROMPT, positions=[-1], return_full_logits=True)\n",
         "result_logitlens = lens.readout(model, PROMPT, positions=[-1], use_jacobian=False)\n",