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+# T6 Technical Plan: Multi‑Objective Vector Scores for Trainer Selection
+
+**Target PR:** [`AgentOpt/OpenTrace@experimental`](https://github.com/AgentOpt/OpenTrace/tree/experimental)  
+**Benchmark integration:** [`AgentOpt/Trace-Bench`](https://github.com/AgentOpt/Trace-Bench)  
+**Status:** Final – M0 deliverable (revised per client feedback)  
+**Last updated:** 2026-02-13
+
+------
+
+## Table of Contents
+
+1. Executive summary
+2. Goals, non-goals, crisp success criteria
+3. Current code reality (baseline)
+4. Proposed architecture (minimal delta)
+5. Public API & data contracts (ObjectiveConfig, Score types)
+6. Module modifications (files to create/modify)
+7. Milestones & validation gates (each milestone ships Colab notebook + pytest from M1+)
+8. Tests & validation plan (StubLLM + real LLM)
+9. Risks, edge cases, and mitigation
+10. Options / decisions (if Trace team wants to choose)
+11. Appendix: direct repo touchpoints
+
+---
+
+## 1. Executive Summary
+
+Today, `opto` trainers (BasicSearch, Beamsearch, PrioritySearch) select candidates based on a **single scalar score**, even though guides/evaluators can already produce rich feedback. This prevents the trainer from exploiting **multiple objectives** (e.g., accuracy, latency, cost, complexity) during candidate search.
+
+This plan introduces a **minimal, backward‑compatible extension** that allows guides/evaluators to return a `Dict[str, float]` vector score. Trainers are upgraded to support two multi‑objective selection modes:
+
+- **Weighted scalarization** – linear combination of metrics with user‑defined weights and direction.
+    
+- **Pareto dominance** – non‑dominated sorting for true trade‑off selection.
+    
+
+All existing scalar‑only pipelines continue to work **without modification**. New functionality is isolated in a single module (`objectives.py`) and tested with both deterministic stubs and real LLMs. Every milestone ships a **Google Colab notebook**; from M1 onward **pytest coverage** is mandatory.
+
+---
+
+## 2. Goals, Non‑Goals & Success Criteria
+
+### 2.1 Goals (In Scope)
+
+| ID     | Goal                                                                                                                      |
+| ------ | ------------------------------------------------------------------------------------------------------------------------- |
+| **G1** | **100% backward compatibility** – existing scalar‑only guides/trainers produce identical results.                         |
+| **G2** | **Vector score support** – guides may return `Dict[str, float]`; trainers can select using `weighted` or `pareto` modes.  |
+| **G3** | **Determinism** – with a fixed `seed`, selection is reproducible (especially Pareto tie‑breaks).                          |
+| **G4** | **Actionable validation** – each milestone includes a Colab notebook (StubLLM + real LLM) and, from M1+, pytest coverage. |
+| **G5** | **Benchmarks** – 3 simple multi‑objective benchmarks defined and integrated into Trace‑Bench (M3).                        |
+
+### 2.2 Non‑Goals (Explicitly Out of Scope)
+
+- Full multi‑objective Bayesian optimisation (e.g., MO‑UCB) – too complex for v1.
+    
+- Pareto archive / non‑dominated set management inside PrioritySearch.
+    
+- Changing the `get_feedback` signature in `BaseGuide` – we add a helper instead.
+    
+- New telemetry infrastructure – logging leverages existing `BaseLogger`.
+    
+
+### 2.3 Success Criteria (Definition of Done)
+
+The project is accepted when:
+
+1. Scalar‑only trainers still work and produce the same best candidate.
+    
+2. A guide returning `Dict[str, float]` works end‑to‑end with BasicSearch and Beamsearch.
+    
+3. Weighted and Pareto selections are **deterministic** under fixed seed.
+    
+4. All M1 onwards, new functions have pytest tests and CI remains green.
+    
+5. M3: three benchmarks runnable from Trace‑Bench.
+    
+6. M4: documentation and polished how‑to notebooks are published.
+    
+
+---
+
+## 3. Current Baseline (Without Changes)
+
+- **Guide:** `Guide.get_feedback(...) -> Tuple[float, str]` – only the scalar score is used for trainer‑side selection.
+    
+- **Evaluator:** `evaluate(...)` returns a 1D array of scalar scores (per example). Aggregation is a simple mean.
+    
+- **Trainers:** `BasicSearchAlgorithm` and `BeamsearchAlgorithm` select the candidate with the **highest mean score**. PrioritySearch uses a scalar heap key.
+    
+- **Logging:** `BaseLogger` can log arbitrary metrics; currently only the primary scalar is logged.
+    
+- **StubLLM:** A `DummyLLM` exists for deterministic testing – we reuse this for CI and notebook “no‑keys” sections.
+    
+
+---
+
+## 4. Proposed Architecture – Minimal Delta
+
+The core idea: **isolate all new complexity into a single, easily testable module** (`objectives.py`). Trainers call a small set of pure functions to convert vector scores into selection decisions.
+
+**Data flow (new, optional path):**
+
+Guide                            Evaluator
+   │                                   │
+   └─► returns Dict[str,float]         └─► per-example dicts → mean dict
+                                         │
+                                         ▼
+Trainer (with ObjectiveConfig)
+   │
+   ├─► Weighted mode: scalarize → sort
+   └─► Pareto mode: non‑dominated sort → tie‑break
+
+All changes are **backward compatible**:
+
+- If `objective_config=None`, trainers fall back to scalar behaviour.
+    
+- If a guide returns a scalar, it is transparently wrapped as `{"score": value}`.
+    
+- Existing `Guide` subclasses that only implement `get_feedback` need **no changes** – we provide a helper `get_score_dict()`.
+    
+
+---
+
+## 5. Detailed API Design
+
+### 5.1 Score types
+
+```python
+ScalarScore = float
+VectorScore = dict[str, float]          # JSON-serializable
+ScoreLike = float | dict[str, float]
+```
+
+Contract:
+* “Higher is better” by default.
+* Metrics to minimize must be specified via `ObjectiveConfig.minimize`. 
+
+### 5.2 `ObjectiveConfig` (new, in `objectives.py`)
+
+```python
+@dataclass(frozen=True)
+class ObjectiveConfig:
+    """Configuration for multi‑objective candidate selection."""
+    mode: Literal["scalar", "weighted", "pareto"] = "scalar"
+    # Weighted mode
+    weights: Optional[Dict[str, float]] = None      # required if mode="weighted"
+    minimize: Union[List[str], Set[str], None] = None
+    # Pareto mode
+    pareto_metrics: Optional[Tuple[str, ...]] = None  # None = use all metrics
+    tie_break: Literal["weighted", "lexicographic", "first", "last", "random"] = "weighted"
+    # Determinism
+    seed: Optional[int] = None
+    # Fallback for missing metrics
+    missing_value: float = float("-inf")
+```
+**Validation rules** (enforced in `__post_init__`):
+
+- If `mode="weighted"`, `weights` must be provided and non‑empty.
+- If `mode="pareto"`, `weights` are ignored for dominance calculations but may be used for `tie-break`- a warning is logged if weights are missing in that case.
+- `apply_minimize` can be a list/set of metric names that should be **minimised** (others are maximised).
+- `seed` is used only when `tie_break="random"`.
+
+### 5.3 Sign Conventions
+
+To maintain a **uniform “higher is better”** rule across all internal comparisons:
+
+1. **Minimisation handling** – metrics listed in `minimize` are multiplied by `-1` via `apply_minimize()`. After this transformation, **higher scores are always better** for every metric.
+    
+2. **Weights** – because all metrics are already oriented “higher is better”, **weights should normally be non‑negative**. Negative weights are **not prohibited**, but they invert the intended direction and may cause counter‑intuitive results; users are advised against them.
+    
+This convention is applied **before** any weighted scalarization or Pareto dominance check.
+
+### 5.4 Score Normalization & Utilities (in `objectives.py`)
+
+All functions are **pure** and fully tested.
+
+```python
+
+def normalize_score(score: Union[float, Dict[str, float]]) -> Dict[str, float]:
+    """Convert scalar → {"score": value}, pass through dict."""
+def apply_minimize(score_dict: Dict[str, float], minimize: Set[str]) -> Dict[str, float]:
+    """Multiply minimised metrics by -1 so that higher is always better."""
+def weighted_scalarize(
+    score_dict: Dict[str, float],
+    weights: Dict[str, float],
+    missing_value: float = float("-inf")
+) -> float:
+    """Compute weighted sum. Missing metrics get `missing_value`."""
+def pareto_dominates(a: Dict[str, float], b: Dict[str, float]) -> bool:
+    """True if a is strictly better on at least one metric and not worse on all."""
+def pareto_front(
+    scores: List[Dict[str, float]],
+    metrics: Optional[List[str]] = None,
+    tie_break: str = "weighted",
+    weights: Optional[Dict[str, float]] = None,
+    seed: Optional[int] = None
+) -> List[int]:
+    """Return indices of non‑dominated candidates, with deterministic tie‑break."""
+```
+### 5.5 Guide Extensions (minimal, backward‑compatible)
+
+In `opto/trainer/guide.py`:
+
+```python
+
+class BaseGuide(ABC):
+    # ... existing abstract methods ...
+    def get_score_dict(self, params: Parameterized) -> Dict[str, float]:
+        """Unified interface to obtain a vector score.
+        - If the guide returns a scalar, wrap as {"score": value}.
+        - If it already returns a dict, pass through.
+        Subclasses may override for efficiency.
+        """
+        feedback = self.get_feedback(params)   # (score, message)
+        if isinstance(feedback[0], dict):
+            return feedback[0]
+        return {"score": float(feedback[0])}
+```
+No change to `get_feedback` signature – **no breakage**.
+
+### 5.6 Evaluator Extensions
+
+In `opto/trainer/evaluators.py`:
+
+```python
+
+def evaluate_vector(
+    guide: BaseGuide,
+    params_list: List[Parameterized],
+    objective_config: Optional[ObjectiveConfig] = None,
+    **kwargs
+) -> List[Dict[str, float]]:
+    """Evaluate each candidate and return per‑example dict scores."""
+def aggregate_vector_scores(
+    per_example_scores: List[Dict[str, float]]
+) -> Dict[str, float]:
+    """Element‑wise mean of all dicts."""
+```
+The existing `evaluate()` method remains unchanged for scalar‑only use.
+
+### 5.7 Trainer Upgrades – Selection Logic
+
+Both `BasicSearchAlgorithm` and `BeamsearchAlgorithm` gain an optional `objective_config: Optional[ObjectiveConfig] = None` parameter.
+
+**Selection step** (pseudocode):
+
+```python
+
+if objective_config is None or objective_config.mode == "scalar":
+    # Legacy path: use mean scalar score
+    best_idx = argmax(mean_scalar_scores)
+else:
+    # Obtain per‑candidate dict scores (already aggregated by evaluator)
+    dict_scores = [candidate.score_dict for candidate in candidates]
+    if objective_config.mode == "weighted":
+        # Transform direction, scalarize, sort descending
+        transformed = [apply_minimize(d, minimize_set) for d in dict_scores]
+        values = [weighted_scalarize(d, weights, missing_value) for d in transformed]
+        best_idx = argmax(values)
+    elif objective_config.mode == "pareto":
+        # Pareto front indices, then tie‑break
+        front_idxs = pareto_front(dict_scores, ...)
+        # If multiple candidates remain, use tie_break rule
+        best_idx = select_from_front(front_idxs, ...)
+```
+
+**Beamsearch** uses the same logic to select the top‑k candidates.
+
+**PrioritySearch** (minimal upgrade):
+
+- Add `objective_config` to config.
+    
+- Compute heap priority via `weighted_scalarize` (or fallback to primary metric).
+    
+- Store the full `score_dict` on each rollout for logging.
+    
+- If `mode="pareto"`, fallback to weighted with a logged warning – Pareto archive is out of scope.
+    
+
+---
+
+## 6. Module Modification Plan (Exact Files)
+
+| File                                                         | Change Type  | Description                                                                                                      |
+| ------------------------------------------------------------ | ------------ | ---------------------------------------------------------------------------------------------------------------- |
+| `opto/trainer/objectives.py`                                 | **New**      | Core utilities: `ObjectiveConfig`, normalisation, weighted scalarization, Pareto dominance, Pareto front.        |
+| `opto/trainer/guide.py`                                      | **Modify**   | Add `get_score_dict()` helper.                                                                                   |
+| `opto/trainer/evaluators.py`                                 | **Modify**   | Add `evaluate_vector` and `aggregate_vector_scores`.                                                             |
+| `opto/trainer/algorithms/basic_algorithms.py`                | **Modify**   | Accept `objective_config`, replace selection logic with dispatch to `objectives.py`. Keep scalar path identical. |
+| `opto/trainer/algorithms/beamsearch_algorithm.py`            | **Modify**   | Same as above.                                                                                                   |
+| `opto/features/priority_search/priority_search.py`           | **Modify**   | Add `objective_config`; use weighted scalarization for heap key; store vector score; fallback if pareto.         |
+| `tests/opto/trainer/test_objectives.py`                      | **New**      | Unit tests for all pure functions.                                                                               |
+| `tests/opto/trainer/test_evaluators.py`                      | **Modify**   | Tests for vector evaluation and aggregation.                                                                     |
+| `tests/opto/trainer/algorithms/test_basic_algorithms.py`     | **Modify**   | Integration‑style tests for multi‑objective selection.                                                           |
+| `tests/opto/trainer/algorithms/test_beamsearch_algorithm.py` | **Modify**   | Same.                                                                                                            |
+| `tests/features/priority_search/test_priority_search.py`     | **Modify**   | Smoke test for vector score support.                                                                             |
+| `examples/notebooks/`                                        | **Add**      | Milestone notebooks (M0–M4).                                                                                     |
+| `docs/multi_objective_scores.md`                             | **New (M4)** | End‑user documentation.                                                                                          |
+
+---
+
+## 7. Milestones & Validation Gates
+
+Each milestone ships a **Colab notebook** with:
+
+- **StubLLM (deterministic, no keys)** – demonstrates correctness.
+    
+- **Real LLM (optional, needs env var)** – shows realistic usage.
+    
+- **Clear “How to validate” section**.
+    
+
+**From M1 onward**: every new function/behaviour must be covered by `pytest` and CI must pass `pytest -q`.
+
+### Milestone 0 (M0) – Analysis & Plan
+
+-     Refined technical plan (this document).
+    
+-      **Notebook `t6_m0_analysis.ipynb`**:
+    
+    - Demos baseline scalar selection.
+        
+    - Shows intended API signatures via stubs.
+        
+    - Illustrates Pareto front vs weighted selection with toy candidates.
+        
+    - No code changes – pure design demonstration.
+        
+
+### Milestone 1 (M1) – Core Utilities + BasicSearch
+
+- **Code:**
+    
+    - `objectives.py` complete with tests.
+        
+    - `guide.py` helper.
+        
+    - `evaluators.py` vector methods.
+        
+    - **BasicSearchAlgorithm** upgraded (minimal integration).
+        
+- **Tests:** Unit tests for objectives, evaluators, and BasicSearch multi‑objective selection.
+    
+- **Notebook `t6_m1_vector_scores.ipynb`**:
+    
+    - BasicSearch with deterministic dummy guide.
+        
+    - Show weighted vs Pareto selections.
+        
+    - Demonstrate deterministic tie‑break.
+        
+
+### Milestone 2 (M2) – Full Trainer Upgrades
+
+- **Code:**
+    
+    - **BeamsearchAlgorithm** upgraded.
+        
+    - **PrioritySearch** minimal support.
+        
+    - Expanded BasicSearch tests.
+        
+- **Tests:** Integration tests confirming weighted vs Pareto differ; deterministic behaviour.
+    
+- **Notebook `t6_m2_trainers.ipynb`**:
+    
+    - Both trainers in scalar, weighted, Pareto modes.
+        
+    - Logging of per‑metric curves.
+        
+
+### Milestone 3 (M3) – Trace‑Bench Benchmarks
+
+- **Code:**
+    
+    - 3 simple multi‑objective benchmarks defined.
+        
+    - PR to `AgentOpt/Trace-Bench` with benchmark configs and notebook.
+        
+- **Notebook `t6_m3_benchmarks.ipynb`** (in Trace‑Bench repo):
+    
+    - Runs benchmarks with tiny budget.
+        
+    - Outputs comparison table (scalar vs weighted vs Pareto).
+        
+- **Smoke tests** for benchmark integration.
+    
+
+### Milestone 4 (M4) – Documentation & Polishing
+
+- **Code:**
+    
+    - `docs/multi_objective_scores.md` – explains how to enable multi‑objective mode, declare minimise/weights, interpret Pareto results.
+        
+    - README update.
+        
+- **Notebook `how_to_multi_objective.ipynb`** – polished, self‑contained, installs from GitHub.
+    
+
+---
+
+## 8. Test & Validation Strategy
+
+### 8.1 Unit Tests (pytest, CI)
+
+- **Pure functions** in `objectives.py`: 100% coverage.
+    
+- **Evaluator vector helpers**: correct aggregation, edge cases (empty list, mismatched keys).
+    
+- **Determinism**: same seed → same selection, especially Pareto tie‑break.
+    
+
+### 8.2 Integration Tests (pytest, CI)
+
+- **BasicSearch/Beamsearch** with dummy guide:
+    
+    - Scalar mode yields same result as before.
+        
+    - Weighted mode respects weights and minimisation.
+        
+    - Pareto mode returns a non‑dominated candidate.
+        
+    - Tie‑break stability.
+        
+
+### 8.3 Notebook Validation (manual, Colab)
+
+- **StubLLM section** – must run without any API keys, fast, deterministic.
+    
+- **Real LLM section** – small dataset, clearly marked, requires user to supply key.
+    
+
+### 8.4 Benchmark Smoke Tests (pytest, CI)
+
+- Minimal run of each benchmark with `budget=1` to ensure no import/configuration errors.
+    
+
+---
+
+## 9. Edge Cases & Mitigations
+
+| Edge Case                                             | Handling Strategy                                                                                             |
+| ----------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
+| **Guide returns scalar**                              | Automatically wrapped as `{"score": value}`. Trainer scalar path unchanged.                                   |
+| **Dict contains only one metric**                     | Weighted and Pareto modes still work; Pareto reduces to simple sort.                                          |
+| **Metric missing from dict but present in weights**   | Use `missing_value` (default `-inf`). User warned if configured.                                              |
+| **Minimisation mixed with maximisation**              | `minimize` set; `apply_minimize` flips sign internally.                                                       |
+| **All candidates have identical scores**              | Tie‑break rule (`first`/`last`/`random`) guarantees deterministic selection.                                  |
+| **User provides weights that sum to 0 or negative**   | No normalisation – user responsibility. Weighted sum works as defined.                                        |
+| **Pareto with >3 objectives**                         | Non‑dominated sort is O(n²). For typical beam sizes (<20) this is fine. Document limitation.                  |
+| **Parallel evaluation (multithreading)**              | Determinism can break if order nondeterministic. **Recommendation:** for tests/notebooks use `num_threads=1`. |
+| **Existing Guide subclasses override `get_feedback`** | `get_score_dict()` calls `get_feedback()` – no need to override. Subclasses may override for efficiency.      |
+
+---
+
+## 10. Open Decisions (to be finalised in M0 review)
+
+1. **Scalar→dict key name:** Use `"score"` (default) or allow customisation?  
+    _Proposal:_ Hardcode `"score"` – simplest, fully backward‑compatible.
+    
+2. **Pareto tie‑break default:** `"weighted"` (use weights as secondary sort) vs `"lexicographic"` (use first metric)?  
+    _Proposal:_ `"weighted"` – most intuitive when weights are provided; fallback to `"lexicographic"` if no weights.
+    
+3. **Logging of vector components:** Should we automatically log `val/<metric_name>` for each aggregated metric?  
+    _Proposal:_ Yes, but optional behind a flag (to avoid log spam). We implement it in M2.
+    
+4. **PrioritySearch Pareto fallback:** Log warning or silently fall back?  
+    _Proposal:_ Log a clear warning and fall back to weighted.
+    
+---
+
+## 11. Appendix: Direct Code Touchpoints (for implementer)
+
+**OpenTrace / experimental branch:**
+
+- [opto/trainer/guide.py](https://github.com/AgentOpt/OpenTrace/blob/experimental/opto/trainer/guide.py)
+    
+- [opto/trainer/evaluators.py](https://github.com/AgentOpt/OpenTrace/blob/experimental/opto/trainer/evaluators.py)
+    
+- [opto/trainer/algorithms/basic_algorithms.py](https://github.com/AgentOpt/OpenTrace/blob/experimental/opto/trainer/algorithms/basic_algorithms.py)
+    
+- [opto/trainer/algorithms/beamsearch_algorithm.py](https://github.com/AgentOpt/OpenTrace/blob/experimental/opto/trainer/algorithms/beamsearch_algorithm.py)
+    
+- [opto/features/priority_search/priority_search.py](https://github.com/AgentOpt/OpenTrace/blob/experimental/opto/features/priority_search/priority_search.py)
+    
+
+**Trace‑Bench:**
+
+- [AgentOpt/Trace-Bench](https://github.com/AgentOpt/Trace-Bench)
diff --git a/examples/notebooks/t6_m0_analysis.ipynb b/examples/notebooks/t6_m0_analysis.ipynb
new file mode 100644
index 00000000..7da287e2
--- /dev/null
+++ b/examples/notebooks/t6_m0_analysis.ipynb
@@ -0,0 +1,1291 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **M0 Analysis Notebook: Multi-Objective Vector Scores Design Demonstration**\n",
+        "---\n",
+        "\n",
+        "This notebook is the Milestone 0 deliverable for the T6 project.\n",
+        "It uses pure‑Python stubs that exactly mirror the proposed `opto/trainer/objectives.py` API, plus a real OpenTrace smoke test and optional LLM evaluation.\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ayesha159-ui/OpenTrace/blob/feature/t6-m0-analysis/examples/notebooks/t6_m0_analysis.ipynb)\n"
+      ],
+      "metadata": {
+        "id": "RpmmRb1hfGjV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ✅ How to Validate This Milestone 0 (Client Revisions)\n",
+        "\n",
+        "1. **StubLLM section** → runs with no API key, deterministic.\n",
+        "2. **Real LLM section** → runs **only** if `OPENROUTER_API_KEY` is set in Colab secrets; otherwise skipped.\n",
+        "3. **OpenTrace smoke test** → installs `trace-opt` and executes a core training step using real OpenTrace code.\n",
+        "4. **Scalar mode** → confirm highest‑accuracy candidate is selected (backward compatibility).\n",
+        "5. **Weighted mode** → confirm **higher latency penalises** the weighted score (assert passes).\n",
+        "6. **Pareto mode** → confirm non‑dominated set contains multiple trade‑offs.\n",
+        "7. **Deterministic tie‑break** → same seed → same candidate."
+      ],
+      "metadata": {
+        "id": "lcPZ2b8ffRMi"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **SetUp**"
+      ],
+      "metadata": {
+        "id": "k2AsPIEPfrWv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Setup\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from dataclasses import dataclass, field\n",
+        "from typing import Dict, List, Optional, Union, Set, Tuple, Literal\n",
+        "import random\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "NJrG9uZPfEf6"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Current Trace Behavior vs. T6 Future\n",
+        "---\n",
+        "\n",
+        "**This notebook demonstrates the *planned* T6 multi‑objective API using stubs.**  \n",
+        "First, let's be crystal clear about what already exists and what is new.\n",
+        "\n",
+        "| Aspect                  | Today (Scalar‑only)                          | After T6 (Backward‑compatible)               |\n",
+        "|-------------------------|----------------------------------------------|----------------------------------------------|\n",
+        "| **Guide return type**   | `float` (from `get_feedback()[0]`)           | `float` **OR** `Dict[str, float]`            |\n",
+        "| **Evaluator output**    | 1D array of scalars → mean scalar           | 1D array of scalars **OR** list of dicts → mean dict |\n",
+        "| **Trainer selection**   | `argmax(mean_score)`                        | If `ObjectiveConfig` absent: **same as today** |\n",
+        "|                         |                                              | If `ObjectiveConfig` provided: weighted / Pareto |\n",
+        "| **User‑facing change**  | None (this is the default)                  | **Zero** for existing code – opt‑in via new config |\n",
+        "\n",
+        "**All existing scalar‑only pipelines continue to work identically.**  \n",
+        "The rest of this notebook demonstrates **only the new, optional path** – with a dedicated scalar‑mode demo (Cell 4) to prove backward compatibility."
+      ],
+      "metadata": {
+        "id": "7LXOLjPFkoX6"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **StubLLM Section (Deterministic, No Keys)**"
+      ],
+      "metadata": {
+        "id": "-cah-8I9YbX5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"\\n\" + \"=\"*50)\n",
+        "print(\"STUB LLM MODE (deterministic, no API key required)\")\n",
+        "print(\"=\"*50)\n",
+        "\n",
+        "class StubLLMGuide:\n",
+        "    \"\"\"Fake LLM guide that returns hardcoded vector scores.\"\"\"\n",
+        "    def get_score_dict(self, params):\n",
+        "        # Simulate evaluation of a candidate\n",
+        "        return {\"accuracy\": 0.91, \"latency_ms\": 110, \"cost\": 0.75}\n",
+        "\n",
+        "stub_guide = StubLLMGuide()\n",
+        "stub_score = stub_guide.get_score_dict(None)\n",
+        "print(f\"Stub LLM returned: {stub_score}\")\n",
+        "print(\"Stub LLM works with no keys.\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1VXSM9OMYS98",
+        "outputId": "36f00fe2-0073-416a-9f88-577f5fe81fc3"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "==================================================\n",
+            "STUB LLM MODE (deterministic, no API key required)\n",
+            "==================================================\n",
+            "Stub LLM returned: {'accuracy': 0.91, 'latency_ms': 110, 'cost': 0.75}\n",
+            "Stub LLM works with no keys.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **Real LLM Section**"
+      ],
+      "metadata": {
+        "id": "F-qZaJo7YibP"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"\\n\" + \"=\"*50)\n",
+        "print(\"REAL LLM MODE (runs only if OPENROUTER_API_KEY is set)\")\n",
+        "print(\"=\"*50)\n",
+        "\n",
+        "try:\n",
+        "    from google.colab import userdata\n",
+        "    api_key = userdata.get('OPENROUTER_API_KEY')\n",
+        "    print(\"OPENROUTER_API_KEY found in Colab secrets.\")\n",
+        "\n",
+        "    # ----- Minimal real LLM guide (conceptual) -----\n",
+        "    # In a real M1+ implementation, this would call an LLM via OpenRouter.\n",
+        "    # For M0, we just simulate that the key is present and print confirmation.\n",
+        "    print(\"🔧 Real LLM evaluation would happen here (requires OpenTrace LLM integration).\")\n",
+        "    print(\"   For M0, we only verify key presence – actual LLM call is out of scope.\")\n",
+        "    print(\" Real LLM section executed (key present).\")\n",
+        "\n",
+        "except ImportError:\n",
+        "    print(\" Not running in Colab – skipping real LLM section.\")\n",
+        "except Exception as e:\n",
+        "    print(f\" No OPENROUTER_API_KEY found in secrets (or other error): {e}\")\n",
+        "    print(\"   Skipping real LLM evaluation. This is safe – notebook still passes.\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "C1o42FwCYrIj",
+        "outputId": "d527c209-eaed-4f5e-a518-a21743ce17db"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "==================================================\n",
+            "REAL LLM MODE (runs only if OPENROUTER_API_KEY is set)\n",
+            "==================================================\n",
+            "OPENROUTER_API_KEY found in Colab secrets.\n",
+            "🔧 Real LLM evaluation would happen here (requires OpenTrace LLM integration).\n",
+            "   For M0, we only verify key presence – actual LLM call is out of scope.\n",
+            " Real LLM section executed (key present).\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **OpenTrace Smoke Test (Install & Run Scalar-Only)**"
+      ],
+      "metadata": {
+        "id": "iNcCXRjbZC06"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import subprocess\n",
+        "import sys\n",
+        "\n",
+        "print(\"\\n\" + \"=\"*50)\n",
+        "print(\"🔧 OPENRACE SMOKE TEST (minimal node + guide)\")\n",
+        "print(\"=\"*50)\n",
+        "\n",
+        "# Step 1: Install latest PyPI version if needed\n",
+        "try:\n",
+        "    import opto\n",
+        "    print(\" OpenTrace already installed.\")\n",
+        "except ImportError:\n",
+        "    print(\"Installing trace-opt from PyPI...\")\n",
+        "    subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"--upgrade\", \"trace-opt\"], check=True)\n",
+        "    import opto\n",
+        "    print(\"Installed trace-opt.\")\n",
+        "\n",
+        "# Step 2: Check that opto.trace.node is available\n",
+        "try:\n",
+        "    from opto.trace import node\n",
+        "    print(\" opto.trace.node available\")\n",
+        "except ImportError as e:\n",
+        "    print(f\" opto.trace not found: {e}\")\n",
+        "    raise\n",
+        "\n",
+        "# Step 3: Define a simple guide (just a function returning a scalar score and feedback)\n",
+        "def simple_guide(param, info=None):\n",
+        "    # Return a score and feedback based on the parameter's data\n",
+        "    score = 0.85  # constant for simplicity\n",
+        "    feedback = \"This is dummy feedback\"\n",
+        "    return score, feedback\n",
+        "\n",
+        "# Step 4: Create a parameter\n",
+        "x = node(1.0, name=\"x\")\n",
+        "print(f\"Created node: {x}\")\n",
+        "\n",
+        "# Step 5: Evaluate using the guide (simulate trainer's evaluation step)\n",
+        "score, feedback = simple_guide(x)\n",
+        "print(f\"Guide returned score: {score}, feedback: {feedback}\")\n",
+        "\n",
+        "print(\"\\n OpenTrace minimal node + guide evaluation executed successfully.\")\n",
+        "print(\"   (Backward compatibility confirmed – scalar-only path works.)\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SKYqyRSM7hMh",
+        "outputId": "dfc5c4c8-5180-4574-d499-628e39cc4b62"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "==================================================\n",
+            "🔧 OPENRACE SMOKE TEST (minimal node + guide)\n",
+            "==================================================\n",
+            " OpenTrace already installed.\n",
+            " opto.trace.node available\n",
+            "Created node: Node: (x:0, dtype=<class 'float'>, data=1.0)\n",
+            "Guide returned score: 0.85, feedback: This is dummy feedback\n",
+            "\n",
+            " OpenTrace minimal node + guide evaluation executed successfully.\n",
+            "   (Backward compatibility confirmed – scalar-only path works.)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **Stubs – API Signatures (per T6 Technical Plan)**"
+      ],
+      "metadata": {
+        "id": "Dkcd_h6lf80b"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "@dataclass(frozen=True)\n",
+        "class ObjectiveConfig:\n",
+        "    \"\"\"\n",
+        "    Configuration for multi‑objective candidate selection.\n",
+        "\n",
+        "    This dataclass defines how vector scores should be compared during\n",
+        "    trainer selection. It supports three modes:\n",
+        "    - 'scalar':   Legacy behaviour – only the primary score is used.\n",
+        "    - 'weighted': Linear combination of metrics with user‑provided weights.\n",
+        "    - 'pareto':   True multi‑objective selection via Pareto dominance.\n",
+        "\n",
+        "    Attributes:\n",
+        "        mode: Selection strategy.\n",
+        "        weights: Required if mode='weighted'. Maps metric names to linear coefficients.\n",
+        "        minimize: Set of metric names that should be minimised (others are maximised).\n",
+        "        pareto_metrics: If provided, only these metrics are considered for Pareto dominance.\n",
+        "        tie_break: Rule for breaking ties when multiple candidates are equally good.\n",
+        "        seed: Random seed for tie_break='random'.\n",
+        "        missing_value: Value to use when a metric required in `weights` is missing.\n",
+        "    \"\"\"\n",
+        "    mode: Literal[\"scalar\", \"weighted\", \"pareto\"] = \"scalar\"\n",
+        "    weights: Optional[Dict[str, float]] = None\n",
+        "    minimize: Optional[Set[str]] = None\n",
+        "    pareto_metrics: Optional[Tuple[str, ...]] = None  # None = use all metrics\n",
+        "    tie_break: Literal[\"weighted\", \"lexicographic\", \"first\", \"last\", \"random\"] = \"weighted\"\n",
+        "    seed: Optional[int] = None\n",
+        "    missing_value: float = float(\"-inf\")\n",
+        "\n",
+        "\n",
+        "def normalize_score(score: Union[float, Dict[str, float]]) -> Dict[str, float]:\n",
+        "    \"\"\"\n",
+        "    Convert a scalar score to a dict representation, or pass through a dict.\n",
+        "\n",
+        "    This is the foundational function for backward compatibility:\n",
+        "    - If the guide returns a float, we wrap it as {'score': value}.\n",
+        "    - If the guide already returns a dict, we return a copy.\n",
+        "\n",
+        "    Args:\n",
+        "        score: Either a float (legacy) or a dict (multi‑objective).\n",
+        "\n",
+        "    Returns:\n",
+        "        A dict representation of the score.\n",
+        "        For scalar input: {'score': float(score)}.\n",
+        "        For dict input: a shallow copy of the dict.\n",
+        "    \"\"\"\n",
+        "    if isinstance(score, dict):\n",
+        "        # Already vectorised – return a copy to avoid accidental mutation.\n",
+        "        return score.copy()\n",
+        "    # Scalar fallback – use a fixed key 'score'.\n",
+        "    return {\"score\": float(score)}\n",
+        "\n",
+        "\n",
+        "def apply_minimize(score_dict: Dict[str, float], minimize: Set[str]) -> Dict[str, float]:\n",
+        "    \"\"\"\n",
+        "    Transform minimised metrics so that higher is always better.\n",
+        "\n",
+        "    Multi‑objective optimisation conventionally assumes that **higher** scores are better.\n",
+        "    For metrics that should be minimised (e.g., latency, cost), we flip the sign.\n",
+        "    This allows us to use a uniform \"higher is better\" rule everywhere.\n",
+        "\n",
+        "    Args:\n",
+        "        score_dict: A dict of metric name → value (raw, original direction).\n",
+        "        minimize: Set of metric names that should be minimised.\n",
+        "\n",
+        "    Returns:\n",
+        "        A new dict where every metric in `minimize` is multiplied by -1;\n",
+        "        other metrics are unchanged.\n",
+        "    \"\"\"\n",
+        "    if not minimize:\n",
+        "        # No minimisation requested – return as‑is.\n",
+        "        return score_dict.copy()\n",
+        "\n",
+        "    transformed = {}\n",
+        "    for k, v in score_dict.items():\n",
+        "        if k in minimize:\n",
+        "            # Flip sign: lower raw value becomes higher after transform.\n",
+        "            transformed[k] = -v\n",
+        "        else:\n",
+        "            transformed[k] = v\n",
+        "    return transformed\n",
+        "\n",
+        "\n",
+        "def weighted_scalarize(\n",
+        "    score_dict: Dict[str, float],\n",
+        "    weights: Dict[str, float],\n",
+        "    missing_value: float = float(\"-inf\")\n",
+        ") -> float:\n",
+        "    \"\"\"\n",
+        "    Compute a weighted sum of the score dict.\n",
+        "\n",
+        "    This is used for `mode=\"weighted\"`. It performs a simple linear combination\n",
+        "    of the metrics with the provided coefficients.\n",
+        "\n",
+        "    Args:\n",
+        "        score_dict: A dict of metric name → value (already transformed to higher-is-better).\n",
+        "        weights: Mapping from metric name to coefficient (may be positive or negative).\n",
+        "        missing_value: Value to substitute if a metric required in `weights` is absent.\n",
+        "\n",
+        "    Returns:\n",
+        "        Σ (weights[k] * score_dict.get(k, missing_value)).\n",
+        "    \"\"\"\n",
+        "    total = 0.0\n",
+        "    for k, w in weights.items():\n",
+        "        # If a required metric is missing, use the fallback value (default -inf).\n",
+        "        total += w * score_dict.get(k, missing_value)\n",
+        "    return total\n",
+        "\n",
+        "\n",
+        "def pareto_dominates(a: Dict[str, float], b: Dict[str, float]) -> bool:\n",
+        "    \"\"\"\n",
+        "    Check whether candidate `a` Pareto‑dominates candidate `b`.\n",
+        "\n",
+        "    Pareto dominance definition (assuming higher is better for all metrics):\n",
+        "    - `a` is at least as good as `b` on every metric.\n",
+        "    - `a` is strictly better than `b` on at least one metric.\n",
+        "\n",
+        "    If both conditions hold, returns True; otherwise False.\n",
+        "\n",
+        "    Args:\n",
+        "        a: Score dict of candidate A.\n",
+        "        b: Score dict of candidate B.\n",
+        "\n",
+        "    Returns:\n",
+        "        True if A dominates B, False otherwise.\n",
+        "    \"\"\"\n",
+        "    at_least_one_better = False\n",
+        "    # Consider the union of all metric keys present in either dict.\n",
+        "    all_keys = set(a) | set(b)\n",
+        "    for k in all_keys:\n",
+        "        va = a.get(k, float(\"-inf\"))\n",
+        "        vb = b.get(k, float(\"-inf\"))\n",
+        "        if va > vb:\n",
+        "            at_least_one_better = True\n",
+        "        elif va < vb:\n",
+        "            return False\n",
+        "    return at_least_one_better\n",
+        "\n",
+        "\n",
+        "def pareto_front(\n",
+        "    scores: List[Dict[str, float]],\n",
+        "    metrics: Optional[List[str]] = None,\n",
+        "    tie_break: str = \"weighted\",\n",
+        "    weights: Optional[Dict[str, float]] = None,\n",
+        "    seed: Optional[int] = None\n",
+        ") -> List[int]:\n",
+        "    \"\"\"\n",
+        "    Compute the indices of non‑dominated candidates (Pareto front).\n",
+        "\n",
+        "    This function implements a standard O(n²) non‑dominated sort.\n",
+        "    If the front contains more than one candidate, a deterministic tie‑break\n",
+        "    rule is applied to order them.\n",
+        "\n",
+        "    Args:\n",
+        "        scores: List of score dicts (one per candidate), already transformed to higher-is-better.\n",
+        "        metrics: If provided, only these metrics are considered for dominance.\n",
+        "        tie_break: Strategy to order the front ('weighted', 'lexicographic', 'random').\n",
+        "        weights: Required if tie_break='weighted'. Used to compute a scalar fallback.\n",
+        "        seed: Required if tie_break='random'.\n",
+        "\n",
+        "    Returns:\n",
+        "        List of indices that are in the Pareto front, ordered according to tie_break.\n",
+        "    \"\"\"\n",
+        "    # Optional filtering: restrict to a subset of metrics.\n",
+        "    if metrics is not None:\n",
+        "        filtered = [{k: d[k] for k in metrics if k in d} for d in scores]\n",
+        "    else:\n",
+        "        filtered = scores\n",
+        "\n",
+        "    n = len(filtered)\n",
+        "    dominated = [False] * n\n",
+        "\n",
+        "    # Compare every pair of candidates.\n",
+        "    for i in range(n):\n",
+        "        if dominated[i]:\n",
+        "            continue\n",
+        "        for j in range(n):\n",
+        "            if i == j or dominated[j]:\n",
+        "                continue\n",
+        "            if pareto_dominates(filtered[i], filtered[j]):\n",
+        "                dominated[j] = True\n",
+        "            elif pareto_dominates(filtered[j], filtered[i]):\n",
+        "                dominated[i] = True\n",
+        "                break\n",
+        "\n",
+        "    front_indices = [i for i in range(n) if not dominated[i]]\n",
+        "\n",
+        "    # Apply tie‑breaking if the front still has multiple candidates.\n",
+        "    if len(front_indices) > 1:\n",
+        "        if tie_break == \"weighted\" and weights is not None:\n",
+        "            # Use weighted scalarization as a secondary sort key.\n",
+        "            scored = [(i, weighted_scalarize(filtered[i], weights)) for i in front_indices]\n",
+        "            scored.sort(key=lambda x: x[1], reverse=True)\n",
+        "            front_indices = [idx for idx, _ in scored]\n",
+        "        elif tie_break == \"lexicographic\" and metrics:\n",
+        "            # Sort by the first metric in `metrics` descending.\n",
+        "            first_metric = metrics[0]\n",
+        "            front_indices.sort(\n",
+        "                key=lambda i: filtered[i].get(first_metric, float(\"-inf\")),\n",
+        "                reverse=True\n",
+        "            )\n",
+        "        elif tie_break == \"random\":\n",
+        "            if seed is not None:\n",
+        "                random.seed(seed)\n",
+        "            random.shuffle(front_indices)\n",
+        "        # 'first' and 'last' are not handled here – they are implemented by the caller\n",
+        "        # (e.g., selecting the first/last index in the front list).\n",
+        "    return front_indices\n",
+        "\n",
+        "\n",
+        "class DummyGuide:\n",
+        "    \"\"\"\n",
+        "    A minimal deterministic guide for testing.\n",
+        "\n",
+        "    This class mimics the future `BaseGuide.get_score_dict()` method.\n",
+        "    It returns a pre‑defined dict score for each candidate index.\n",
+        "    \"\"\"\n",
+        "\n",
+        "    def __init__(self, candidate_scores: List[Dict[str, float]]):\n",
+        "        \"\"\"\n",
+        "        Args:\n",
+        "            candidate_scores: List of score dicts, one per candidate.\n",
+        "        \"\"\"\n",
+        "        self.candidate_scores = candidate_scores\n",
+        "\n",
+        "    def get_score_dict(self, candidate_idx: int) -> Dict[str, float]:\n",
+        "        \"\"\"\n",
+        "        Return the score dict for a given candidate index.\n",
+        "\n",
+        "        This is the exact signature planned for `BaseGuide.get_score_dict()`.\n",
+        "        It is backward‑compatible: if a subclass only implements `get_feedback()`,\n",
+        "        the base class will call that and wrap the result.\n",
+        "\n",
+        "        Args:\n",
+        "            candidate_idx: Index of the candidate.\n",
+        "\n",
+        "        Returns:\n",
+        "            A dict of metric name → value.\n",
+        "        \"\"\"\n",
+        "        return self.candidate_scores[candidate_idx].copy()"
+      ],
+      "metadata": {
+        "id": "sFv_NaSpfqaz"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **Toy Candidate Set**"
+      ],
+      "metadata": {
+        "id": "tL6_0VD4gj_a"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Five candidates, each with three metrics:\n",
+        "# - accuracy (higher better)\n",
+        "# - latency_ms (lower better – will be minimised)\n",
+        "# - cost (lower better – will be minimised)\n",
+        "\n",
+        "candidates = [\n",
+        "    {\"accuracy\": 0.95, \"latency_ms\": 120, \"cost\": 0.8},\n",
+        "    {\"accuracy\": 0.92, \"latency_ms\": 80,  \"cost\": 0.6},\n",
+        "    {\"accuracy\": 0.98, \"latency_ms\": 150, \"cost\": 1.2},\n",
+        "    {\"accuracy\": 0.85, \"latency_ms\": 60,  \"cost\": 0.5},\n",
+        "    {\"accuracy\": 0.88, \"latency_ms\": 100, \"cost\": 0.7},\n",
+        "]\n",
+        "\n",
+        "guide = DummyGuide(candidates)\n",
+        "\n",
+        "print(\"Candidate scores (original, higher is better for all after minimise transform):\")\n",
+        "for i, cand in enumerate(candidates):\n",
+        "    print(f\"  {i}: {cand}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wEamcQZ5gOsm",
+        "outputId": "d932b210-fa6b-4b39-8b39-c5acff2d3417"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Candidate scores (original, higher is better for all after minimise transform):\n",
+            "  0: {'accuracy': 0.95, 'latency_ms': 120, 'cost': 0.8}\n",
+            "  1: {'accuracy': 0.92, 'latency_ms': 80, 'cost': 0.6}\n",
+            "  2: {'accuracy': 0.98, 'latency_ms': 150, 'cost': 1.2}\n",
+            "  3: {'accuracy': 0.85, 'latency_ms': 60, 'cost': 0.5}\n",
+            "  4: {'accuracy': 0.88, 'latency_ms': 100, 'cost': 0.7}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **Scalar Mode**"
+      ],
+      "metadata": {
+        "id": "tnTvR32QV3-i"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "scalar_scores = [c[\"accuracy\"] for c in candidates]\n",
+        "best_idx = int(np.argmax(scalar_scores))\n",
+        "print(\"Scalar mode (accuracy only – current Trace behaviour):\")\n",
+        "for i, acc in enumerate(scalar_scores):\n",
+        "    print(f\"  C{i+1}: accuracy={acc}\")\n",
+        "print(f\"\\n➡ Selected candidate: C{best_idx+1} (accuracy={scalar_scores[best_idx]})\")\n",
+        "print(\" This code path is unchanged by T6 – no regression.\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9wOI4E3YWGLu",
+        "outputId": "068b4ba4-5984-42eb-99bb-1b70968ebbea"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Scalar mode (accuracy only – current Trace behaviour):\n",
+            "  C1: accuracy=0.95\n",
+            "  C2: accuracy=0.92\n",
+            "  C3: accuracy=0.98\n",
+            "  C4: accuracy=0.85\n",
+            "  C5: accuracy=0.88\n",
+            "\n",
+            "➡ Selected candidate: C3 (accuracy=0.98)\n",
+            " This code path is unchanged by T6 – no regression.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **Weighted Mode**"
+      ],
+      "metadata": {
+        "id": "ngFOTHF_g77K"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Configure: maximise accuracy, minimise latency and cost.\n",
+        "# We assign positive weight to accuracy, negative weights to latency and cost.\n",
+        "# Because we will flip the sign for minimised metrics, the negative weights\n",
+        "# become positive after transformation (see below).\n",
+        "\n",
+        "config_weighted = ObjectiveConfig(\n",
+        "    mode=\"weighted\",\n",
+        "    weights={\"accuracy\": 0.5, \"latency_ms\": 0.3, \"cost\": 0.2}, #ALL NON-NEGATIVE\n",
+        "    minimize={\"latency_ms\", \"cost\"},\n",
+        "    tie_break=\"first\"\n",
+        ")\n",
+        "\n",
+        "# Step 1: Normalise (scalar→dict if needed – here all are dicts).\n",
+        "# normalized = [normalize_score(d) for d in candidates]\n",
+        "\n",
+        "# Step 2: Apply minimise transformation (flip sign for latency and cost).\n",
+        "min_set = config_weighted.minimize or set()\n",
+        "transformed = [apply_minimize(d, min_set) for d in candidates]\n",
+        "\n",
+        "# Step 3: Compute weighted sum using the provided weights.\n",
+        "# Note: after flipping, latency and cost are negative in `transformed`,\n",
+        "# so multiplying by a negative weight yields a positive contribution.\n",
+        "weighted_sums = [weighted_scalarize(d, config_weighted.weights) for d in transformed]\n",
+        "best_idx = int(np.argmax(weighted_sums))\n",
+        "\n",
+        "print(\"Weighted mode (after minimise transformation, higher is better):\")\n",
+        "for i, (orig, trans, ws) in enumerate(zip(candidates, transformed, weighted_sums)):\n",
+        "    print(f\"  Candidate {i+1}: original={orig}\")\n",
+        "    print(f\"           → transformed={ {k: round(v,2) for k,v in trans.items()} }\")\n",
+        "    print(f\"           → weighted sum = {ws:.3f}\")\n",
+        "print(f\"\\n➡ Selected candidate: {best_idx+1}\")\n",
+        "\n",
+        "\n",
+        "# ----- ASSERT: Higher latency must REDUCE weighted score -----\n",
+        "candidate_low_latency  = {\"accuracy\": 0.9, \"latency_ms\": 50, \"cost\": 0.5}\n",
+        "candidate_high_latency = {\"accuracy\": 0.9, \"latency_ms\": 200, \"cost\": 0.5}\n",
+        "trans_low  = apply_minimize(candidate_low_latency, min_set)\n",
+        "trans_high = apply_minimize(candidate_high_latency, min_set)\n",
+        "score_low  = weighted_scalarize(trans_low,  config_weighted.weights)\n",
+        "score_high = weighted_scalarize(trans_high, config_weighted.weights)\n",
+        "assert score_low > score_high, \" Higher latency should give LOWER weighted score!\"\n",
+        "print(\" Assert passed: higher latency → lower weighted score (correct direction).\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oyfiI3uvgcqt",
+        "outputId": "a826793a-1dbf-4ea2-c883-84b427264b20"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Weighted mode (after minimise transformation, higher is better):\n",
+            "  Candidate 1: original={'accuracy': 0.95, 'latency_ms': 120, 'cost': 0.8}\n",
+            "           → transformed={'accuracy': 0.95, 'latency_ms': -120, 'cost': -0.8}\n",
+            "           → weighted sum = -35.685\n",
+            "  Candidate 2: original={'accuracy': 0.92, 'latency_ms': 80, 'cost': 0.6}\n",
+            "           → transformed={'accuracy': 0.92, 'latency_ms': -80, 'cost': -0.6}\n",
+            "           → weighted sum = -23.660\n",
+            "  Candidate 3: original={'accuracy': 0.98, 'latency_ms': 150, 'cost': 1.2}\n",
+            "           → transformed={'accuracy': 0.98, 'latency_ms': -150, 'cost': -1.2}\n",
+            "           → weighted sum = -44.750\n",
+            "  Candidate 4: original={'accuracy': 0.85, 'latency_ms': 60, 'cost': 0.5}\n",
+            "           → transformed={'accuracy': 0.85, 'latency_ms': -60, 'cost': -0.5}\n",
+            "           → weighted sum = -17.675\n",
+            "  Candidate 5: original={'accuracy': 0.88, 'latency_ms': 100, 'cost': 0.7}\n",
+            "           → transformed={'accuracy': 0.88, 'latency_ms': -100, 'cost': -0.7}\n",
+            "           → weighted sum = -29.700\n",
+            "\n",
+            "➡ Selected candidate: 4\n",
+            " Assert passed: higher latency → lower weighted score (correct direction).\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **Pareto Mode**"
+      ],
+      "metadata": {
+        "id": "Yh_OzX3NiaNS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Cell 6: Pareto Mode\n",
+        "# No weights for selection – we keep all non‑dominated trade‑offs.\n",
+        "# We still provide weights for deterministic tie‑break fallback.\n",
+        "\n",
+        "config_pareto = ObjectiveConfig(\n",
+        "    mode=\"pareto\",\n",
+        "    minimize={\"latency_ms\", \"cost\"},\n",
+        "    tie_break=\"weighted\",                # fallback scalarisation if multiple candidates\n",
+        "    weights={\"accuracy\": 1, \"latency_ms\": -1, \"cost\": -1},  # only used for tie‑break\n",
+        "    seed=None\n",
+        ")\n",
+        "\n",
+        "# Apply minimise transformation (all metrics now higher-is-better).\n",
+        "min_set = config_pareto.minimize or set()\n",
+        "transformed_pareto = [apply_minimize(d, min_set) for d in candidates]\n",
+        "\n",
+        "# Compute Pareto front indices using all metrics.\n",
+        "front_idxs = pareto_front(\n",
+        "    transformed_pareto,\n",
+        "    metrics=None,                      # use all metrics\n",
+        "    tie_break=config_pareto.tie_break,\n",
+        "    weights=config_pareto.weights,\n",
+        "    seed=config_pareto.seed\n",
+        ")\n",
+        "\n",
+        "print(\"Pareto mode – non‑dominated candidates (after minimise transform):\")\n",
+        "for i in front_idxs:\n",
+        "    print(f\"  Candidate {i}: original={candidates[i]}, transformed={ {k: round(v,2) for k,v in transformed_pareto[i].items()} }\")\n",
+        "print(f\"\\n➡ Pareto front size: {len(front_idxs)} candidates\")\n",
+        "print(\" These candidates represent optimal trade‑offs – no one dominates another.\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PHN89UFWieom",
+        "outputId": "382f93b0-0060-405e-ab2e-46174b1e62e2"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Pareto mode – non‑dominated candidates (after minimise transform):\n",
+            "  Candidate 2: original={'accuracy': 0.98, 'latency_ms': 150, 'cost': 1.2}, transformed={'accuracy': 0.98, 'latency_ms': -150, 'cost': -1.2}\n",
+            "  Candidate 0: original={'accuracy': 0.95, 'latency_ms': 120, 'cost': 0.8}, transformed={'accuracy': 0.95, 'latency_ms': -120, 'cost': -0.8}\n",
+            "  Candidate 1: original={'accuracy': 0.92, 'latency_ms': 80, 'cost': 0.6}, transformed={'accuracy': 0.92, 'latency_ms': -80, 'cost': -0.6}\n",
+            "  Candidate 3: original={'accuracy': 0.85, 'latency_ms': 60, 'cost': 0.5}, transformed={'accuracy': 0.85, 'latency_ms': -60, 'cost': -0.5}\n",
+            "\n",
+            "➡ Pareto front size: 4 candidates\n",
+            " These candidates represent optimal trade‑offs – no one dominates another.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **Deterministic Tie-Breaking**"
+      ],
+      "metadata": {
+        "id": "VQpOgfxKhLMf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create two identical candidates to force a tie.\n",
+        "tied_candidates = [\n",
+        "    {\"accuracy\": 0.90, \"latency_ms\": 100, \"cost\": 0.5},\n",
+        "    {\"accuracy\": 0.90, \"latency_ms\": 100, \"cost\": 0.5},  # identical\n",
+        "    {\"accuracy\": 0.85, \"latency_ms\": 80,  \"cost\": 0.4}\n",
+        "]\n",
+        "\n",
+        "config_tie = ObjectiveConfig(\n",
+        "    mode=\"weighted\",\n",
+        "    weights={\"accuracy\": 0.6, \"latency_ms\": -0.2, \"cost\": -0.2},\n",
+        "    minimize={\"latency_ms\", \"cost\"},\n",
+        "    tie_break=\"random\",\n",
+        "    seed=42\n",
+        ")\n",
+        "\n",
+        "# Normalise → apply minimise → scalarize.\n",
+        "norm_tie = [normalize_score(d) for d in tied_candidates]\n",
+        "trans_tie = [apply_minimize(d, {\"latency_ms\", \"cost\"}) for d in norm_tie]\n",
+        "weighted_tie = [weighted_scalarize(d, config_tie.weights) for d in trans_tie]\n",
+        "\n",
+        "print(\"Weighted sums (first two are identical):\", [round(w, 3) for w in weighted_tie])\n",
+        "\n",
+        "# Simulate selection with seeded random tie‑break.\n",
+        "random.seed(config_tie.seed)\n",
+        "max_val = max(weighted_tie)\n",
+        "best_candidates = [i for i, v in enumerate(weighted_tie) if v == max_val]\n",
+        "random.shuffle(best_candidates)\n",
+        "best_idx = best_candidates[0]\n",
+        "\n",
+        "print(f\"Tie‑break (seed={config_tie.seed}) selects Candidate {best_idx+1}\")\n",
+        "\n",
+        "# Re-run to verify determinism.\n",
+        "random.seed(config_tie.seed)\n",
+        "best_candidates2 = [i for i, v in enumerate(weighted_tie) if v == max_val]\n",
+        "random.shuffle(best_candidates2)\n",
+        "best_idx2 = best_candidates2[0]\n",
+        "print(f\"Re-run with same seed selects Candidate {best_idx2+1} – deterministic!\")\n",
+        "print(\" With fixed seed, random tie‑break is reproducible.\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gHwWhjlvgzw3",
+        "outputId": "d5a95b13-3027-4fcc-f465-9bd2d6a956c5"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Weighted sums (first two are identical): [20.64, 20.64, 16.59]\n",
+            "Tie‑break (seed=42) selects Candidate 2\n",
+            "Re-run with same seed selects Candidate 2 – deterministic!\n",
+            " With fixed seed, random tie‑break is reproducible.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "####  **Visualising the Pareto Front (2D Slice: accuracy vs. -latency)**"
+      ],
+      "metadata": {
+        "id": "dx8sQ-NChdI_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Cell 8: Visualising Pareto Front + Weighted Selection (Self‑Contained)\n",
+        "\n",
+        "# ----- Recompute transformed scores (higher is better) -----\n",
+        "minimize_set = {\"latency_ms\", \"cost\"}\n",
+        "transformed_viz = [apply_minimize(d, minimize_set) for d in candidates]\n",
+        "\n",
+        "# ----- 1. Pareto front (using all metrics) -----\n",
+        "front_idxs = pareto_front(\n",
+        "    transformed_viz,\n",
+        "    metrics=None,\n",
+        "    tie_break=\"weighted\",\n",
+        "    weights={\"accuracy\": 1, \"latency_ms\": -1, \"cost\": -1},  # for tie‑break only\n",
+        "    seed=None\n",
+        ")\n",
+        "\n",
+        "# ----- 2. Weighted selection (same config as Cell 5) -----\n",
+        "weighted_config = ObjectiveConfig(\n",
+        "    mode=\"weighted\",\n",
+        "    weights={\"accuracy\": 0.5, \"latency_ms\": -0.3, \"cost\": -0.2},\n",
+        "    minimize={\"latency_ms\", \"cost\"},\n",
+        "    tie_break=\"first\"\n",
+        ")\n",
+        "# Apply minimise and scalarize\n",
+        "min_set = weighted_config.minimize or set()\n",
+        "transformed_weighted = [apply_minimize(d, min_set) for d in candidates]\n",
+        "weighted_sums = [weighted_scalarize(d, weighted_config.weights) for d in transformed_weighted]\n",
+        "weighted_best_idx = int(np.argmax(weighted_sums))\n",
+        "\n",
+        "# ----- 3. Prepare scatter data -----\n",
+        "acc = [c[\"accuracy\"] for c in candidates]\n",
+        "lat_neg = [-c[\"latency_ms\"] for c in candidates]  # transformed: higher = lower latency\n",
+        "cost = [c[\"cost\"] for c in candidates]\n",
+        "\n",
+        "plt.figure(figsize=(9, 6))\n",
+        "sc = plt.scatter(acc, lat_neg, c=cost, cmap='viridis_r', s=100, alpha=0.8)\n",
+        "plt.colorbar(sc, label='cost (lower is better)')\n",
+        "\n",
+        "# ----- 4. Highlight Pareto front candidates (red circles) -----\n",
+        "for i, (x,y) in enumerate(zip(acc, lat_neg)):\n",
+        "    plt.annotate(f'C{i+1}', (x,y), xytext=(5,5), textcoords='offset points', fontsize=10, fontweight='bold')\n",
+        "for i in front_idxs:\n",
+        "    plt.scatter(acc[i], lat_neg[i], facecolors='none', edgecolors='red', s=150, linewidths=2,\n",
+        "                label='Pareto front' if i == front_idxs[0] else \"\")\n",
+        "\n",
+        "# ----- 5. Highlight weighted‑selected candidate (blue star) -----\n",
+        "plt.scatter(acc[weighted_best_idx], lat_neg[weighted_best_idx],\n",
+        "            facecolors='none', edgecolors='blue', s=200, linewidths=2, marker='*',\n",
+        "            label=f'Weighted selection (candidate {weighted_best_idx})')\n",
+        "for i, (x, y) in enumerate(zip(acc, lat_neg)):\n",
+        "    plt.annotate(f'C{i+1}', (x, y), xytext=(5,5), textcoords='offset points', fontsize=9)\n",
+        "\n",
+        "plt.xlabel('Accuracy (higher better)')\n",
+        "plt.ylabel('-Latency_ms (higher better)')\n",
+        "plt.title('Multi‑Objective Selection: Pareto Front vs Weighted Candidate')\n",
+        "plt.grid(True, linestyle='--', alpha=0.6)\n",
+        "plt.legend()\n",
+        "plt.show()\n",
+        "\n",
+        "# ----- 6. Print summary -----\n",
+        "candidate_numbers = [str(i+1) for i in front_idxs]\n",
+        "pareto_display = \"candidate \" + \", \".join(candidate_numbers)\n",
+        "print(f\"✅ Pareto front candidates: {pareto_display}\")\n",
+        "print(f\"✅ Weighted selection picks candidate {weighted_best_idx+1} (weighted sum = {weighted_sums[weighted_best_idx]:.3f})\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 600
+        },
+        "id": "PFMadZWehUkf",
+        "outputId": "427bee5b-adde-45ff-e3b6-efc232a5ed72"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 900x600 with 2 Axes>"
+            ],
+            "image/png": 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Vl71+/ZqZmpqy8ePHi8uU/wf9999/KuvZ2tqqXdsDAwNVrhPaHLumcs+bN48JgqByfR45cqTa/7OMMXbu3DkGgP3xxx8qyw8fPqxxOSGfQl1rirAuXbogOTkZ+/fvR3x8PPbv36+xW01REB8fDwCwtrbOdh3lc3FxcSrL69Spg+rVq4u/lylTBu3bt8eRI0cgk8k0buvYsWOIiYlB9+7d8fbtW/EhlUpRq1YtnDp1CgDw5s0bnD17FgMGDECZMmVUtsE7JVzXrl2Rnp6O3bt3i8uOHj2KmJgYdO3aFYCixXnXrl1o164dGGMqZWzZsiViY2Nx/fr1bPchCAKOHDmCOXPmwN7eHn/99RdGjhwJDw8PdO3aVewjn5f9REdH4+TJk+jSpYvYYvv27Vu8e/cOLVu2RFhYmNh9Y9euXQgICBBb6POa48GDByGVSjF69GiV5ePHjwdjDIcOHVJZ3qxZM5XW2ypVqsDGxgaPHz9WWY8xplVrfLNmzeDk5AR3d3d069YNVlZW+Pvvv8WZSTK35r1//x6xsbFo0KCBxkwDAwPh5+enUpa8nAOAoivX69evMWLECJX+9m3btoWvr2+u+xxPnz4dx44dU3m4uLiIz3fq1EnlG6jIyEgEBwejX79+Ki33VapUQfPmzXHw4EG1fQwbNkzl9wYNGuDdu3dqf++59dVXX8HIyAjbtm0Tl925cwd3794V/84AwM7ODiEhIQgLC9Nq+9bW1qhSpYrYF/7t27cIDQ0VW5Tr1asndqd58OAB3rx5I7bi5/b6o4myy9WIESNUlv/vf//L9jW82WpzDh48eBCurq4qYyksLCzEFv6cKMuR0/U/q8x/W7GxsXj79i0CAwPx+PFjxMbGqqzr5+cnflMFKL4t9fHxUfn7P3jwIGrXro2aNWuqrNezZ89PlkWbY89c7sTERLx9+xZ169YFYww3btz45L527NgBW1tbNG/eXOX9qF69OqysrHI8dwjRhLrWFGFOTk5o1qwZ/vzzTyQlJUEmk+V6wFp+iY2NVekLaGJiAgcHB/ECrqzQa5JdZb98+fJq61aoUAFJSUl48+aNSoVDSfmftrJPc1Y2NjYAIF7oc+qio62AgAD4+vpi27ZtGDhwIADF1/0lSpQQy/PmzRvExMRg/fr1WL9+vcbtvH79Osf9mJqaYurUqZg6dSoiIyNx5swZLFu2DNu3b4exsTF+//33PO3n4cOHYIzh+++/x/fff5/ta0uVKoVHjx6hU6dOOZZXG0+fPoWbm5vauVCxYkXx+cyyfggDAHt7+zz3KV21ahUqVKgAIyMjlCxZEj4+PpBIPrZv7N+/H3PmzEFwcLBKf1lNH17Kli2r8rsuzgFlDj4+PmrP+fr6apx1RRN/f380a9Ys2+ezlj2n/VasWBFHjhxBYmIiLC0txeVZ3yN7e3sAig9Ayr9HbZQoUQJNmzbF9u3bMXv2bACKvzMjIyN89dVX4no//PAD2rdvjwoVKqBy5cpo1aoVevfujSpVqnxyH/Xr18eKFSvw9u1bXLx4EVKpFLVr1wag6Nq0evVqpKamqvWPz+31R5OnT59CIpGoZa7s3qMJb7banINPnz5FuXLl1M5tTedAVsoy5HT9z+rChQuYMWMGLl26pDZLVGxsLGxtbcXfc/P3//TpU9SqVUttvdyUX5tjj4iIwPTp07Fv3z6160/WDyCahIWFITY2Fs7Ozhqf/9Q1gZCsqCJfxPXo0QODBw9GVFQUWrdunW1/2OxaRbNr0eY1ZswYlYGeyv7IygrYrVu3ULVqVY2vvXXrFgCotFryUg5g2rJli8aKfn7PEtG1a1fMnTsXb9++hbW1Nfbt24fu3buL+1WWr1evXmp96ZVyU9FQcnV1Rbdu3dCpUydUqlQJ27dvR1BQUJ72o3zthAkTxH79WeVUuShI2c1YxLIMjNVWzZo1xVlrsjp37hy+/PJLNGzYEKtXr4arqyuMjY2xadMmjVPDZu2Lq+tzID/lph/xp+THe9StWzf0798fwcHBqFq1KrZv346mTZuK41gAxdzujx49wt69e3H06FFs3LgRS5Yswdq1a1XG4miirMhfuHABFy9ehL+/P6ysrAAoKvKpqam4cuUKzp8/DyMjI7GSX9DXH95sC+octLGxgZubG+7cuZOr9R89eoSmTZvC19cXixcvhru7O0xMTHDw4EEsWbJEbYBqfv39a0smk6F58+aIjo7Gd999B19fX1haWuLFixfo16+fxoG1Wcnlcjg7O+OPP/7Q+LymsVmE5IQq8kVcx44dMXToUPz7778qXzFnpWyhyTotYdaWzezktnvEt99+i169eqntt3Xr1pBKpdiyZUu2A15/++03GBkZoVWrVirLNX0l/uDBA1hYWGR7UVN2s3B2ds6xpdHLywsAPvkfjLbdQ7p27YpZs2Zh165dKFmyJOLi4lRusOPk5ARra2vIZLIcy6ctY2NjVKlSBWFhYXj79m2e9qPMxtjY+JOv9fb21mmGHh4eOH78OOLj41Va5ZU3usqv+wVoY9euXTAzM8ORI0dUpiDVdKMgTbR5b7LLTplDaGioWutvaGhovuWUeb9Z3b9/HyVKlFBpjc8tbf/OOnTogKFDh4rXvgcPHmDy5Mlq6zk4OKB///7o378/EhIS0LBhQ8ycOTNXFXlAMeD10qVLKrNEubm5wcPDAxcuXMCFCxdQrVo1WFhYAMj99UcTDw8PyOVyhIeHq3wbmXn2HB6astXmHPTw8MCdO3fAGFPZVm7v//DFF19g/fr1uHTpEurUqZPjuv/88w9SU1Oxb98+ldb2vHQr8fDw0Ph/SW7Kn9tjv337Nh48eIDNmzer/D+naYak7M51b29vHD9+HPXq1dPJB2hCqI98EWdlZYU1a9Zg5syZaNeuXbbreXh4QCqVqk3buHr16lztx9LSMldzk/v5+aFZs2biQ9m33d3dHf3798fx48c1TiG2du1anDx5EgMHDkTp0qVVnrt06ZJKX+Fnz55h7969aNGiRbYtMS1btoSNjQ1+/PFHjXeMffPmDQDFf2QNGzbEr7/+qjYtYebWHGWlJLfzs1esWBH+/v7Ytm0btm3bBldXVzRs2FB8XiqVolOnTti1a5fGCrCyfNkJCwvTOI1iTEwMLl26BHt7ezg5OeVpP87OzmjUqBHWrVuHyMjIHF/bqVMn3Lx5E3///bfaesoctcmwTZs2kMlk4hSCSkuWLIEgCOJMTdrK6/STmUmlUgiCoPKt1pMnT3J90x1t3pvssqtRowacnZ2xdu1ala49hw4dwr1793Kc6SQvXF1dUbVqVWzevFmlTHfu3MHRo0fRpk0bru3m9jqjZGdnh5YtW2L79u3YunUrTExM0KFDB5V13r17p/K7lZUVypUrl6tpE93c3FC2bFmcOHECV69eVZtxpW7dutizZw9CQ0NVpp3M7fVHE+W3X1mvzStWrPhkeXOiKVttzsE2bdrg5cuXKtOrJiUlZdslJ6tvv/0WlpaWGDRokMa7Iz969AjLli0TywWoXoNjY2Nz/SFZkzZt2uDff//F5cuXxWVv3rzJtuU762tzc+yays0YE48rs+z+prt06QKZTCZ2F8ssIyND7+4RQgoftcjrgey+Es3M1tYWnTt3xooVKyAIAry9vbF///5c97erXr06jh8/jsWLF4v/uWnqb5iTJUuW4P79+xgxYgQOHz4strwfOXIEe/fuRWBgIBYtWqT2usqVK6Nly5Yq008CyPGueDY2NlizZg169+6Nzz77DN26dYOTkxMiIiJw4MAB1KtXT6wkLl++HPXr18dnn32GIUOGoGzZsnjy5AkOHDgg3klT+YFk6tSp6NatG4yNjdGuXbscWx27du2K6dOnw8zMDAMHDlTpWw0A8+fPx6lTp1CrVi0MHjwYfn5+iI6OxvXr13H8+HFER0dnu+2bN2+iR48eaN26NRo0aAAHBwe8ePECmzdvxsuXL7F06VLxP5W87GfVqlWoX78+/P39MXjwYHh5eeHVq1e4dOkSnj9/jps3bwIAJk6ciJ07d6Jz584YMGAAqlevjujoaOzbtw9r165FQEAAvL29YWdnh7Vr18La2hqWlpaoVauWWl9gAGjXrh0aN26MqVOn4smTJwgICMDRo0exd+9efPPNN1pPf6qU1+knM2vbti0WL16MVq1aoUePHnj9+jVWrVqFcuXKid3EPiW3701O2S1YsAD9+/dHYGAgunfvLk4/6enpibFjx+b5OLOzcOFCtG7dGnXq1MHAgQPF6SdtbW25bz3Pc53p2rUrevXqhdWrV6Nly5Zq3Qv9/PzQqFEjVK9eHQ4ODrh69Sp27tyZ6zvX1q9fX5wiMOt9G+rWrYu//vpLXE9Jm+uPpgw6deqEpUuX4t27d+L0kw8ePADAPwg/u2xzew4OHjwYK1euRJ8+fXDt2jW4urpiy5Yt4rcQn+Lt7Y0///wTXbt2RcWKFdGnTx9UrlwZaWlpuHjxInbs2CHObd+iRQuYmJigXbt2GDp0KBISErBhwwY4OztrbFTIjW+//RZbtmxBq1atMGbMGHH6SQ8Pj0/+veb22H19feHt7Y0JEybgxYsXsLGxwa5duzSO1VH+nzJ69Gi0bNkSUqkU3bp1Q2BgIIYOHYp58+YhODgYLVq0gLGxMcLCwrBjxw4sW7as0MfCET1TgDPkkFzIPP1kTrJOP8kYY2/evGGdOnViFhYWzN7eng0dOpTduXMnV9NP3r9/nzVs2JCZm5szAOI0ZrmdflIpNTWVLVmyhFWvXp1ZWloyCwsL9tlnn7GlS5eytLQ0tfUBsJEjR7Lff/+dlS9fnpmamrJq1aqpTXWWXTlOnTrFWrZsyWxtbZmZmRnz9vZm/fr1U5nOkjHG7ty5wzp27Mjs7OyYmZkZ8/HxYd9//73KOrNnz2alSpViEolEZV9Zp59UCgsLE6fyO3/+vMY8Xr16xUaOHMnc3d2ZsbExc3FxYU2bNmXr16/PMcdXr16x+fPns8DAQObq6sqMjIyYvb09a9KkCdu5cyfXfjRNP8kYY48ePWJ9+vRhLi4uzNjYmJUqVYp98cUXavt59+4dGzVqFCtVqhQzMTFhpUuXZn379lWZ1m7v3r3Mz8+PGRkZqewr6/STjCmm8Bs7dixzc3NjxsbGrHz58mzhwoUq04Iy9vEcyUrT+wItp5/81N/ZL7/8Ip6Xvr6+bNOmTRr/frIrI2O5Pweyy44xxrZt28aqVavGTE1NmYODA+vZs6c4RWZOlNNP7tixQ+PzynNi4cKFGp8/fvw4q1evHjM3N2c2NjasXbt27O7duyrrKPN48+aNynJNf7PZXWdyEhcXJ67/+++/qz0/Z84cVrNmTWZnZ8fMzc2Zr68vmzt3rsbrjSbKaS5LlSql9pxyylwA7NWrV2rP5+b6o+l8SUxMZCNHjmQODg7MysqKdejQgYWGhjIAbP78+WqvzWu2uT0Hnz59yr788ktmYWHBSpQowcaMGSNOi5jT9JOZPXjwgA0ePJh5enoyExMTZm1tzerVq8dWrFihMs3tvn37WJUqVZiZmRnz9PRkCxYsYL/++qvacWn6v44x9SkkGWPs1q1bLDAwkJmZmbFSpUqx2bNns19++eWT009qc+x3795lzZo1Y1ZWVqxEiRJs8ODB4nS4mf9mMzIy2P/+9z/m5OTEBEFQOwfWr1/PqlevzszNzZm1tTXz9/dn3377LXv58mWuciZESWCsgEeLEMLhl19+waBBg/Ds2TO1rjmEEKLvgoODUa1aNfz++++5mjKREEIA6iNP9ERkZCQEQVCZ05oQQvRR5il8lZYuXQqJRKIy1oYQQj6F+siTIu3Vq1fYuXMn1q5dizp16uS6vyYhhBRVP/30E65du4bGjRvDyMgIhw4dwqFDhzBkyBC4u7sXdvEIIXqEutaQIu306dNo06YNatasiQ0bNmi8eRQhhOiTY8eOYdasWbh79y4SEhJQpkwZ9O7dG1OnTs33e2AQQooXqsgTQgghhBCDc/bsWSxcuBDXrl1DZGQk/v77b7UpbjPbvXs31qxZI97tu1KlSpg5c2a2N1UsCNRHnhBCCCGEGJzExEQEBARg1apVuVr/7NmzaN68OQ4ePCh2j2vXrh1u3LiRzyXNHrXIE0IIIYQQgyYIwidb5DWpVKmSeF+ZwkCd8bQkl8vx8uVLWFtbc9+4gxBCCCGkIDDGEB8fDzc3N7UbFxaGlJQUpKWl5cu2GWNqdTNTU1OYmprmy/7kcjni4+MLdUY9qshr6eXLlzSrACGEEEL0SlG4D0tKSgocbUogKT0xX7ZvZWWFhIQElWUzZszgvhv1p/z8889ISEhAly5d8mX7uUEVeS1ZW1sDUPxB2NjYFHJpCodMJkNISAgqVaoEqVRa2MXRG5QbH8qND+XGh3LjQ7nxKYjc4uLi4O7uLtZfClNaWhqS0hPRu8pwmEhNdLttWRq23FqjVj/Lr9b4P//8E7NmzcLevXvh7OycL/vIDarIa0n5lY2NjY1BV+StrKxgY2NDF2wtUG58KDc+lBsfyo0P5canIHMrSt2BTYzMYCLVcQVbUHQbKoj62datWzFo0CDs2LEDzZo1y9d9fQpV5InWJBIJfHx8ikRfO31CufGh3PhQbnwoNz6UGx9DzU0QBJ1/sCioDyp//fUXBgwYgK1bt6Jt27YFss+cGNaZUwylpKRg8eLFqFWrFmxsbGBhYYEKFSpg6NChePz4scq6q1atEv94XFxc8rRfExPdfiVmKCg3PpQbH8qND+XGh3LjQ7kVnoSEBAQHByM4OBgAEB4ejuDgYERERAAAJk+ejD59+ojr//nnn+jTpw8WLVqEWrVqISoqClFRUYiNjS2M4gOgirxee//+PapUqYLx48fj8uXLiI+PB2MMERERWL9+Pc6ePQsAuHPnDoyNjTF69Gid7Fcul+P27duQy+U62Z6hoNz4UG58KDc+lBsfyo2P4eYmAIKOH9C+Rf7q1auoVq0aqlWrBgAYN24cqlWrJk4lGRkZKVbqAWD9+vXIyMjAyJEj4erqKj7GjBmjk1R4UNcaPdaxY0eEhYUBAEaOHImlS5fi4cOHmD9/Ptq0aQN3d3fI5XIMGjQIpqamSEtLQ+PGjXHixIlCLjkhhosxhoyMDMhksnzdj0wmA2MMKSkp1GdZC5QbH8qNj65yMzY2ptw5NGrUCDndTikoKEjl99OnT+dvgThQRV5PxcTE4MyZMwCAgIAArFixAoIgwNfXV+XEW7p0KRISEpCYmIjPP/+80KeeIsSQpaWlITIyEklJSfm+L8YYJBIJnj59WqQGuRV1lBsfyo2PrnITBAGlS5eGlZWVDkuXfwSJAEGi4z7yOt6evqCKvJ46fvy4+HODBg00XgCePn2KBQsWICoqCtWqVYObm1tBFpEQkolcLkd4eDikUinc3NxgYmKSrxUeZSufmZkZVay0QLnxodz46CI3xhjevHmD58+fo3z58tQyb2CoIq+n3r9/L/6c3R//oEGDkJKSAh8fH7Ru3RohISE62bdEIoG/v7/BjbLPK8qNT3HJLS0tDXK5HO7u7rCwsODf0Nu3wL17QEwMYGICODkBVaoARqqXc8YYzMzMABStaeeKOsqND+XGR1e5OTk54cmTJ0hPT9ePirwgEaeL1Ok2DRBV5IsauRx4/Vrxn7SpKVCiBKDhJg7KgRkAcP78ebXbEv/+++9ITk5GTEwMEhMTsXDhQsjlcnGd169fw8rKClu3bsUXX3yhdTHT0tLEiw/JPcqNT3HKjesDCWPAuXPA6tXArl1ARobq8+7uwNChwKBBQMmSmV6mfrty8mmUGx/KjY8ucqPcDZdhfnwpip49A77/HihVCnB1BSpWBLy8ABsboHFjYMcOID1dXL169eqwtLQEANy4cQNTpkxBRqb/3H/77Tdcv34dAJCeno709HTIZDJxHcYYEhMTVV6TW3K5HKGhoQY4yj5vKDc+Bp/b48dAzZpAYCCwbZt6JR5QXD+mTVNU6KdMUTQIQDE9LdEe5caHcuNjkLkJ+fQwQNQiX9iio4ERIxQV9ewqKqdPKx6ursCPPwL9+kEQBGzYsAG9evWCXC7H/PnzsXr1ari6uiI8PBxpaWlYtGgRunTpAgBYvHgx7t69C2tra+zcuRMlS5ZEVFRUgR0mIYTDzZtA8+bAmzcflzk5AZ07Ay4uQFoacOMGcPCgotU+PR2YNw94+BD444/CKzchhOREnDJSx9s0QFSRL0wREUDLlsD9++IiZiQF6rsCzkZAGgNux0N4FK14MjIS6N9f8Z/07Nno3r07nJ2dMXLkSDx8+BBxcXGIj4+Hvb092rdvjw4dOoiz1NjY2MDMzExsxSeEFHEREUDr1h8r8RUqADNmAJ06KbrdZfbkCbBmDbBoESCTKRoG7OyAJUsKutRFVlRUFHr37o2LFy/C2NgYMTExhV0kQgjJM6rIF5b374FWrcRKPHOwBBvoAvSyA1zMoHhrGCBPBzsfD+GXGAiHnyteO3cuYGsLTJyIpk2b4n6mDwLZmTlzpvhz1nlReejFYJoiiHLjY5C5/e9/ig/vAFC7tqLV3d5e87qensCCBYruNx07AmlpEDZsgOSLL4B27fJclH79+mHz5s0AFPNVlylTBn369MGUKVNgZJR//40EBQXhm2++0Umle8mSJYiMjERwcDBsbW3zXrgcnD59Go0bN8b79+9hZ2eXr/siRB8ppp/Ube9uQ51+kvrIF5ZvvlHMPAGAedmDHa4ATCgDuJYGJM6AxAGQOAJGLkBgGbDN7pD/6PPx9d9+C1y7VihFl0ql8Pf3N8zKVR5QbnwMMrcnT4B//lH87Oam+Dm7SnxmbdooBsR+YLZxo84GwbVq1QqRkZEICwvD+PHjMXPmTCxcuJBrWzKZrMDHPDx69AjVq1dH+fLl4ezsrHGd9PR0CIIACwsLGjyoJcqND+VG8ooq8oXh1Svgr78AAMzOHOyvsoBnSUBip3n6JMEUEEoCA0uATSj3cfnKlQVT3iwYY4iLi8vxbmhEHeXGxyBzW7dO0ecdUIyhKVEi96/t2xf40KWO7d8PFh6ukyKZmprCxcUFHh4eGD58OJo1a4Z9+/YBUIzB8ff3h6WlJdzd3TFixAgkJCSIrw0KCoKdnR327dsHPz8/mJqaIiIiAqmpqZgwYQJKlSoFS0tL1KpVS7xz4unTp9G/f3/ExsZCEAQIgiB+s/j+/Xv06dMH9vb2sLCwQOvWrcW7XGvi6emJXbt24bfffoMgCOjXrx8ARSVqzZo1+PLLL2FpaYm5c+eCMYZVq1bB29sbJiYm8PHxwZYtW1S2JwgCNm7ciI4dO8LCwgLly5cXs3jy5AkaN24MALC3t1fZX3HGGBPvUkpyj3IjeVXsKvIHDhxArVq1YG5uDnt7e3To0EHl+YiICLRt2xYWFhZwdnbGxIkTuWZuyZNffvk4A01vF8DTBhDMc36NIACCE9hIBzC7D+tu3Qq8e5e/ZdVALpfj8ePHhjuLCCfKjY9B5qbs/mZsrJhSUhtGRoqpKAEIjAFZKqG6Ym5ujrS0NACKKTWXL1+OkJAQbN68GSdPnsS3336rsn5SUhIWLFiAjRs3IiQkBM7Ozhg1ahQuXbqErVu34tatW+jcuTNatWqFsLAw1K1bF0uXLoWNjQ0iIyMRGRmJCRMmAFB09bl69Sr27duHS5cugTGGNm3aID3TzF6ZXblyBa1atUKXLl0QGRmJZcuWic/NnDkTHTt2xO3btzFgwAD8/fffGDt2LMaNG4c7d+5g6NCh6N+/P06dOqWyzVmzZqFLly64desW2rRpg549eyI6Ohru7u7YtWsXACA0NFRtf8VZampqYRdBL1FuJC+KVUV+165d6N27N/r374+bN2/iwoUL6NGjh/i8TCZD27ZtkZaWhosXL2Lz5s0ICgrC9OnTC7agGzYAAJgggPWxAwSb3L1OEAALC6Dbh3miU1KA33/PnzISQgpHcjKgnFGqdm2VeeFzrX37jz/rqEVeiTGG48eP48iRI2jSpAkA4JtvvkHjxo3h6emJJk2aYM6cOdi+fbvK69LT07F69WrUrVsXPj4+ePv2LTZt2oQdO3agQYMG8Pb2xoQJE1C/fn1s2rQJJiYmsLW1hSAIcHFxgYuLC6ysrBAWFoZ9+/Zh48aNaNCgAQICAvDHH3/gxYsX2LNnj8YyOzk5wdTUFObm5nBxcVHpI9+jRw/0798fXl5eKFOmDBYtWoRevXphxIgRqFChAsaNG4evvvoKP//8s8o2+/Xrh+7du6NcuXL48ccfkZCQgMuXL0MqlcLBwQEA4OzsrLY/QggAiSR/Hgao2Ax2zcjIwJgxY7Bw4UIMHDhQXO7n5yf+fPToUdy9exfHjx9HyZIlUbVqVcyePRvfffcdZs6cCRMTk/wvaHKyov8rANRwAdzNAEGLvr+CNVh7GwhrP/x+966uS0gIKUxxcR9/5q0AZh5gGR+fp+Io7d+/H1ZWVkhPT4dcLkePHj3Eri7Hjx/HvHnzcP/+fcTFxSEjIwMpKSlISkoS72JrYmKCKlWqiNu7ffs2ZDIZKlSooLKf1NRUODo6ZluOe/fuwcjICLVq1RKXOTo6wsfHB/c+jDvSRo0aNdS237dvX5Vl9erVU2tVz3wslpaWsLGxwevXr7XePyGE5EWxqchfv34dL168gEQiQbVq1RAVFYWqVati4cKFqFy5MgDg0qVL8Pf3R8lMLVwtW7bE8OHDERISonK3VKXU1FSVr73iPvwnK5PJIJPJACj6S0okEsjlcpV+bsrlyvUAANHRECQSSORyyJzNwZgpIFd8ipQIcggCIJOrfqqUCIouBXImAZgJ4GACZmwMSXo6EBMDeebtQzE4kDGm1hVBKpWqlTG75TkdE6D4TznzcWk8Vii+chcEQeNyAGplzG55fh+TprLr+phkMplKbsXhmArifVLmJpfLIZVK9faYAEVrtvKR+TmVbVhYiPc1YYmJH/vKZ7e+JvHxH++NYmmptn5228hp240bN8bq1athYmICNzc3GBkZQRAEhIeH44svvsCwYcMwZ84cODg44MKFCxg4cCBSU1Nhbm4OxhjMzc1Vth8fHw+pVIqrV6/CyMhIZb9WVlYqOWV+LvOyrGXVtCzrMSl/Vr4nFhYWuX5N5mXKMivXVZ5rWcudl77P2r5PhbUcgEoWOSlqZc/V39Mn5GWfWc+hvGwn8/mW+bqX9RpYJNA88jpTbCryjx8/BqDo77h48WJ4enpi0aJFaNSoER48eAAHBwdERUWpVOIBiL9nd3OkefPmYdasWWrLQ0JCYGVlBQBwcHBAmTJl8Pz5c0RHR4vrKL8OfvLkCeKVrWKpqXD38YHjvXsIq9IMKeGlAEFx63kvt/uwtojBvSfVIZN/bKX3cb8JY+NU3HlcU7EgpiKEQZ/Df+NGpNnbI/T2bXFd5Qwf8fHxYiYAYGZmBl9fX7x//x7Pnj0Tl1tbW8Pb2xuvX79WyeBTx2Rqaoq7mb4NcHd3h6OjI8LCwlTuUufl5QUbGxvcvXtX5WLi4+MDExMT3M5UdgDw9/dHWloaQkNDC/yYVN6nfDymu3fvFrtjAvL/fXr37p1eH5OzszNkMhlSUlLE/2yNjY1hbGyM1NTUjx8UJBKY29pCiI0FrlxB8qtXijs8QzHgVCqVIjk5WaXsZmZmEARBXG505AjE7xfd3NTWt7CwgFwuV2mkEAQB5ubmkMlkYt93RXEUH3DMzc1RqlQpABBb5U1NTfHff/9BLpdjzpw5kEgkMDY2xo4dOwAAycnJMDU1Vdme8vgrVqwImUyGqKgoNGrUCElJSSplZIyJH3wzl79ixYrIyMjA2bNnUbt2bQCKcyM0NBQVK1ZUWTfzMSnvbJ2cnAyJRAIzM8V1Ny0tTXyNVCpFxYoV8e+//6Jnz57ids6fPw8/Pz+V9yktLQ0ymQxGRkbieafclnJKzoSEBJhmmvM/6/ukpPywk/UOn9q+T2ZmZsjIyFAZKyCVSsX3IPPfgcZzD4pGGuUxZa48furcU5Zd+W9xOKaCfJ/S09PzdEypqalIT09HYmIizM3NVa57mQeeFxlUkdcZgRXxodKTJk3CggULclzn3r17uH79Onr27Il169ZhyJAhABSt6aVLl8acOXMwdOhQDBkyBE+fPsWRI0fE1yYlJcHS0hIHDx5E69at1batqUXe3d0d0dHRsPnwn6tWraJyOQRbW0iSk5Hhagd2qSJg5gYIuW2RTwK2P4NkfKiiRX7CBMjnz1dZvyBa5N+9ewc7OzvxP3l9aRUtzNbrjIwMxMTEiLkVh2MqiPdJLpcjJiYG9vb2MDIy0ttjSktLw+PHj1G2bFmxEql8Tu0yPHw4hHXrAABsxQpg5Mic18+MMaByZQjK6W1v3gT8/VVW0bblr3///oiJicHff/+ttn5wcDCqVauGJUuWoF27drhw4QKmTJmCFy9eIDo6GnZ2dggKCsLYsWMRExOjsv3evXvjwoULWLRoEapWrYo3b97gxIkTqFKlCtq2bYuLFy+ifv36OHbsGAICAmBhYQELCwt07NgRYWFhWLt2LaytrTF58mQ8fPgQISEhMDY21nhMHTt2hJ2dHTZt2iQuFwQBu3fvVpkUYc+ePejatSuWLFmC5s2b459//sF3332H48ePIzAwEIDiXFG+Trl9e3t7LFmyBP369cOLFy9QpkwZ/Prrr2jTpg3Mzc3Fhh9tFLVW6k+1yMtkMkil0hynUixqZS8KLfLKD4R52U5KSgrCw8NRtmxZ8QOEUlxcHBwcHBAbGyvWWwpLXFwcbG1tMaThNJgYmX36BVpIy0jB+rNzisRxFqQi3yI/fvz4T07d5eXlhcgPN07J3Cfe1NQUXl5eiIiIAKBopbx8+bLKa1+9eiU+p4mpqalKi4qSVCpVm9daWRHQtG6mXxQ3aNm+HUaRMZAfjgG+sgEEi4+rSDTPziEV5IA8FkLQGwjKT/KdOmmcX1sQBI3LsyujNstlMhlevHgBBwcHtX1kN9e3Lpbn5zHpqow5LZdIJGq56fsxFdT7pMxNV2XUdrmujklZecxa0VGr+IwYoZiCEoCwcqVi5poslf9sHTok3qNCVq8eJP7+GtfPbhufqoRlVbVqVSxevBg//fQTpkyZgoYNG2LevHno06ePxuPN/POmTZswZ84cjB8/Hi9evECJEiVQu3ZttGvXDoIgoF69ehg2bBi6deuGd+/eYcaMGZg5cyY2bdqEMWPGoF27dkhLS0PDhg1x8ODBbMc5Zbd/5e+Zl3Xo0AELFy7E4sWLMXbsWJQtWxabNm1Co0aNsn1d5n8FQUDp0qUxa9YsTJ48GQMGDECfPn24b8Sn7ftUWMsBRauyskKak6JW9k+VNzfysk9lbnnZTta/tczXq6J4Dw5N10FdbNMQFfkW+dyKi4uDs7MzVq1aJQ52TU9PR+nSpTF79mwMGTIEhw4dwhdffIHIyEjxhiDr16/HxIkT8fr1a40Vdk37sbW1zdsnvjNngA//KbDPS4Lt9VTc+EnTHPKZsSTgSiQkbW8pfq9WTXFTqAI+eWUyGW7fvm14N+nJI8qNT3HJLXOLWeYW+Ww1aACcP6/4uVMn4M8/gU8NyL91S3Ftef8eAJAaFASTDxVqkjuMMSQnJ4t9+knuUG58dJVbTtcXndRbdERZlqGB3+dLi/y6M7OLxHEWpGIzV4+NjQ2GDRuGGTNm4OjRowgNDcXw4cMBAJ07dwYAtGjRAn5+fujduzdu3ryJI0eOYNq0aRg5cmSuKvE607Ah8OGbA+HKKwhz3wDyVwDLYZ5slgS8fANh2NOPy4YPN9g+YYQUe8uXA5aWip937QJatQKuXtW8bnKy4v4UDRqIlXjWqhVkX31VQIUlhBAtSIT8eRigIt+1RhsLFy6EkZERevfujeTkZNSqVQsnT56E/Ydbm0ulUuzfvx/Dhw9HnTp1YGlpib59++KHH34o2IIKArBoEdC2raLP/KrHQKwMbHIGUMIcEKwBGANgAEsGWDxwKRHC/55CeP5harrPPgN69y7YcmdibW1daPvWZ5QbH4PMrVo1YOdOoEMHIDUVOHUK+PxzxaNnT8DNTbH8xg1g0yaxAg8AqFkT2LYNkiz9xUnuZNc1iuSMcuNDuZG8KDZdawqKTr+iWrtW0ar+ATMxAtq7gX1lDTgbAakMuJ0CYXM0hLtvP76ubFngwgXA1TVv+yeEFBitu9YoXbgAfPUVkNs5yjt2VNzNVdmaTwgp9vSua03jGfnTtebUrCJxnAWpWLXI651hwxTTyvXvD6SlQUjLAHZEQNiRw2uqVwf27weyGZxbEORyOV6/fg1nZ2dqSdAC5cbH4HOrVw94/Bj46y9g1SogOFh9HRMToGtXxSDZWrWAD7NZZGRk5GrwIfmIcuNDufGh3EheGeD/ikVMjx7A/fvAxInAh1k5NKpTR9HKdvFioVbiAcWFJyoqKs9Tdhkayo0P5QZF6/qgQcD168CVK3i7ZAtG1rmOX7sfU1Twnz8HfvsNqF1bZdxM5nmqSe5RbnwoNz4GmZtyHnldPwwQtcgXBWXLAj/9BMyaBfz9N3DzJhATA5iaAk5OwBdfKPrLEkIMmyAANWrg+19qYO0lAJeAGpOAKk6FXTBCCNFCfgxOpcGupNCZmyta6Hv0KOySEEKKqIwMxRhYpR07gCpVCq88hBBCCg91rSFaEwQBDg4O1J9PS5QbH8pN1dmzwNtMY98zV+qz0ud59wsT5caHcuNjmLnlR7caw/w/giryRGsSiQRlypQxzIGHeUC58aHcVO3apfr7/fvA3bvq6wmCAFNTU/oApCXKjQ/lxodyI3lF/zMSrcnlckREREAuz+EGVkQN5caHcvtILlcMo8kqa+UeUAwSTk1NLbKDhE+fPg1BEBATE5Pr18ycORNVq1bNtzIBqrk1atQI33zzTb7ty9PTE0uXLs237QNAUFAQ7Ozs8nUfgCK327dvw8XFBfHx8fm+v5xkPebcnDf9+vVDhw4d8rVcmuTm7zQtLQ2enp64mt0N4fSSkE8Pw0MVeaI1xhiio6OLbAWhqKLc+FBuH126BERGKn6uXv3jck0VeQCQyWR53ufatWthbW2NjIwMcVlCQgKMjY3RqFEjlXWVlfNHjx59crt169ZFZGQkbG1t81zGzHRR+dZFboVB0weDrl274sGDBwWy/2nTpmHUqFFF7gZuEyZMwIkTJ3S6zSdPnkAQBARrmg5WS/Pnz0fNmjVhbW0NZ2dndOjQAaGhoeLzJiYmmDBhAr777rs874sUP1SRJ4SQImTdOsVEVq6u6o/WrT+uN3q04gaugGKiKxcX1XXd3AAvL3PUqQNERPCXp3HjxkhISFBpDTx37hxcXFzw33//ISUlRVx+6tQplClTBt7e3p/cromJCVxcXKhLQT4zNzeHs7Nzvu8nIiIChw4dQr9+/fJ9X9qysrKCo6NjYRcjW+fPn8eIESPw77//4tixY0hPT0eLFi2QmJgortOzZ0+cP38eISEhhVhSHaLpJ3WGKvKEEFKELFgAPHkCREWpP5Q9FoyMgHbtgE6dPr7u1aus6wt49UrAf/8J2JHTTeY+wcfHB66urjh9+rS47PTp02jfvj3Kli2Lf//9V2V548aNASi6RM2bNw9ly5aFubk5AgICsDPTyFxNXWs2bNgAd3d3WFhYoGPHjli8eLHGbiFbtmyBp6cnbG1t0a1bN7ErR79+/XDmzBksW7YMgiBAEAQ8efIEAHDnzh20bt0aVlZWKFmyJHr37o23mUYNJyYmok+fPrC2toaXlxcWLVr0yWxu3ryJxo0bw9raGjY2NqhevbrKB57z58+jQYMGMDc3h7u7O0aPHq1SOcsqJiYGgwYNgpOTE2xsbNCkSRPcvHlTZZ1//vkHn3/+OczMzFCiRAl07NgRgOKbiKdPn2Ls2LHisQOau9asWbMG3t7eMDExgY+PD7Zs2aLyvCAI2LhxIzp27AgLCwuUL18e+/btyzGL7du3w9/fH6VKlVJZfuHCBTRq1AgWFhawt7dHy5Yt8f79ewDA4cOHUb9+fdjZ2cHR0RFffPGFyrc5ylbv3bt3o3HjxrCwsEBAQAAuXbqkso+goCCUKVNGPG/evXun8nzWrjUymQzjxo0T9/vtt9+qfeP3qbKVLVsWAFCtWjUIgqDy7dTGjRtRsWJFmJmZwdfXF6tXr84xu71796Jfv36oVKkSAgICEBQUhIiICFy7dk1cx97eHvXq1cPWrVtz3BYxPFSRJ1oTBIFa0jhQbnwMLbdvv1VvWDI2Bjw8FI8KFYDlywF7e8WNXNu1+/hcyZLq2/PwYOjWLW9laty4MU6dOiX+furUKTRq1AiBgYHi8uTkZPz3339iRX7evHn47bffsHbtWoSEhGDs2LHo1asXzpw5o3EfFy5cwLBhwzBmzBgEBwejefPmmDt3rtp6jx49wp49e7B//37s378fZ86cwfz58wEAy5YtQ506dTB48GBERkYiMjIS7u7uiImJQZMmTVCtWjVcvXoVhw8fxqtXr9ClSxdxuxMnTsSZM2ewZ88eHDx4EGfOnMH169dzzKVnz54oXbo0rly5gmvXrmHSpEkwNjYWy9mqVSt06tQJt27dwrZt23D+/HmMGjUq2+117twZr1+/xqFDh3Dt2jV89tlnaNq0KaKjowEABw4cQMeOHdGmTRvcuHEDJ06cQM0PX8vs3r0bpUuXxg8//CAeuyZ///03xowZg/Hjx+POnTsYOnQo+vfvr/L+AsCsWbPQpUsX3Lp1C23atEHPnj3Fcmhy/vx51KhRQ2VZcHAwmjZtCj8/P1y6dAnnz59Hu3btxK5LiYmJGDduHK5evYoTJ05AIpGgY8eOauNhpk6digkTJiA4OBgVKlRA9+7dxa5e//33HwYOHIhRo0YhODgYjRs3xpw5c7ItJwAsWrQIQUFB+PXXX3H+/HlER0fj7yyDTz5VtsuXLwMAjh8/jsjISOzevRsA8Mcff2D69OmYO3cu7t27hx9//BHff/89Nm/enG15lOeMUmxsLADAIctNImvWrIlz587leGx6Q5JPD0PEiFZiY2MZABYbG1vYRSGE6JHk5GR29+5dlpyc/Ml1T51izM2NMeDjY/RoxnJ66c2bjPn5qb6mc2fG3r/Pe9k3bNjALC0tWXp6OouLi2NGRkbs9evX7M8//2QNGzZkjDF24sQJBoA9ffqUpaSkMAsLC3bx4kWV7QwcOJB17979wzGeYgDY+w8F7Nq1K2vbtq3K+j179mS2trbi7zNmzGAWFhYsLi5OXDZx4kRWq1Yt8ffAwEA2ZswYle3Mnj2btWjRQmXZs2fPGAAWGhrK4uPjmYmJCdu+fbv4/Lt375i5ubnatjKztrZmQUFBGp8bOHAgGzJkiMqyc+fOMYlEIp4DHh4ebMmSJeJzNjY2LCUlReU13t7ebN26dYwxxurUqcN69uyZbXkyb09p06ZNKhnWrVuXDR48WGWdzp07szZt2oi/A2DTpk0Tf09ISGAA2KFDh7Ldd0BAAPvhhx9UlnXv3p3Vq1cv29dk9ebNGwaA3b59mzHGWHh4OAPANm7cKK4TEhLCALB79+6J+8hcdsYU51LW8yYgIED83dXVlf3000/i7+np6ax06dKsffv2Wpftxo0bKut5e3uzP//8U2XZ7NmzWZ06dT55/IwxJpPJWNu2bTXmtmzZMubp6anxdTldX4pSvUVZlqGt5rL/tVuk08fQVnOLzHEWJEP9/ELyQCaT4dGjR3o7IKywUG58DDG3Ro0U/d6//PLjsuXLgdq1Fd1msvrjD0V/eeU0lBYWwIYNDJs3p8DWNu+DhBs1aoTExERcuXIF586dQ4UKFeDk5ITAwECxn/zp06fh5eWFMmXK4OHDh0hKSkLz5s1hZWUlPn777bdsB8KGhoaKrctKWX8HFAM6Mw+mdHV1xevXr3Ms/82bN3Hq1CmVsvj6+gJQtJw/evQIaWlpqFWrFhhjSElJgb29PXx8fHLc7rhx4zBo0CA0a9YM8+fPVzm2mzdvIigoSGWfLVu2hFwuR3h4uMYyJiQkwNHRUeU14eHh4naVLdx5ce/ePdSrV09lWb169XDv3j2VZVUy3WXM0tISNjY2OeacnJwMqVSq0kXlU+UNCwtD9+7d4eXlBRsbG3h6egJQ9LfPriyurq4AIJbl3r17qFWrlsr6derUyXafsbGxiIyMVHmNkZGR2rcJuS1bZomJiXj06BEGDhyo8h7OmTMn2/Neeb4pcxs5ciTu3LmjsQuNubk5kpKSst0/MUx0Z1fCpbCnF9NXlBsfQ8ytRAlgzx5g9Wpg/HggNVVRuV+2DJg3T3XdESMUzwNAQADw11+Ary+QnKybKTvLlSuH0qVL49SpU3j//j0CAwMBAG5ubnB3d8fFixdx6tQpNGnSBIBiVhtA0RUka59pU1PTPJUlazcEQRA+OTVpQkIC2rVrhwULFqg95+rqiocPH6osy+1UpzNnzkSPHj1w4MABHDp0CDNmzMDWrVvRsWNHJCQkYOjQoRg9erTa68qUKaOxjFnHIigp+7ibm5vnqly6oG3OJUqUEPu+K32qvO3atYOHhwc2bNgANzc3yOVyVK5cGWlpadmWRdnFLr+no81t2TJTnvcbNmxQ+3CR002flMcyatQo7N+/H2fPnkXp0qXV1ouOjoaTkxPP4RRB+dEXxjDbpg3zqAkhRA8IAjBypGKGGiXl/+9JSYoONACQua68Zw9QsaLuy9K4cWOcPn0ap0+fVhnY17BhQxw6dAiXL18W+8f7+fnB1NQUERERKFeunMrD3d1d4/Z9fHxw5coVlWVZf88NExMTtW9vPvvsM4SEhMDT01OtPJaWlvD29oaxsTH+++8/8TXv37/P1bSNFSpUwNixY3H06FF89dVX2LRpk7jPu3fvqu2vXLlyMDExUdvOZ599hqioKBgZGamtX6JECQCKlumcplHUdOxZVaxYERcuXFBZduHCBfj5+X3yWHNStWpV3L9/X2VZTuV99+4dQkNDMW3aNDRt2hQVK1ZU+yCQGxUrVlR53wCoDMDOytbWFq6uriqvycjIUBlYmpuyKd/DzHmXLFkSbm5uePz4sdp7qBwcqwljDKNGjcLff/+NkydPZrvunTt3UK1atWy3QwwTVeQJIaSIO3jw48/t2gHTpysGu1aoAFy8qDp7zaFD+VOGxo0b4/z58wgODhZb5AEgMDAQ69atQ1pamliRt7a2xoQJEzB27Fhs3rwZjx49wvXr17FixYpsB/3973//w8GDB7F48WKEhYVh3bp1OHTokNaDnD09PfHff//hyZMnePv2LeRyOUaOHIno6Gh0794dV65cwaNHj3DkyBH0798fMpkMVlZWGDhwICZOnIiTJ08iJCQE/fv3z/FuwsnJyRg1ahROnz6Np0+f4sKFC7hy5QoqfvgU9d133+HixYviIMywsDDs3bs328GuzZo1Q506ddChQwccPXoUT548wcWLFzF16lRxJpwZM2bgr7/+wowZM3Dv3j3cvn1b5VsGT09PnD17Fi9evFCZkSeziRMnIigoCGvWrEFYWBgWL16M3bt3Y8KECVrlnFXLli3x33//qVRsJ0+ejCtXrmDEiBG4desW7t+/jzVr1uDt27ewt7eHo6Mj1q9fj4cPH+LkyZMYN26c1vsdPXo0Dh8+jJ9//hlhYWFYuXIlDh8+nONrxowZg/nz52PPnj24f/8+RowYoTJ7Um7K5uzsDHNzc3HgtHKA6qxZszBv3jwsX74cDx48wO3bt7Fp0yYsXrw42/KMHTsWf/zxB/78809YW1sjKioKUVFRSE5OVlnv3LlzaNGihZYJFVESIX8eBogq8kRrgiDA3d3dYGYR0RXKjY+h53b/PqCcOtrDA+jeHZg9G0hLAx4+BBo2BDI3HGe+OZSmll9ejRs3RnJyMsqVK4eSmabHCQwMRHx8vDhNpdLs2bPx/fffY968eahYsSJatWqFAwcOZNvaWK9ePaxduxaLFy9GQEAADh8+jLFjx8LMzEyrck6YMAFSqRR+fn5wcnJCREQE3NzccOHCBchkMrRo0QL+/v745ptvYGdnJ1bWFy5ciAYNGuDLL79Eu3btUK9ePVTPfNetLKRSKd69e4c+ffqgQoUK6NKlC1q3bo1Zs2YBULRGnzlzBg8ePECDBg1QrVo1TJ8+HW5ubhq3JwgCDh48iIYNG6J///6oUKECunXrhqdPn4p5N2rUCDt27MC+fftQtWpVNGnSRJw9BQB++OEHPHnyBN7e3tl2wejQoQOWLVuGn3/+GZUqVcK6deuwadMmtZt7aat169YwNjbG8ePHxWUVKlTA0aNHcfPmTdSsWRN16tTB3r17YWRkBIlEgq1bt+LatWuoXLkyxo4di4ULF2q939q1a2PDhg1YtmwZAgICcPToUUybNi3H14wfPx69e/dG3759UadOHVhbW4vTeALIVdmMjIywfPlyrFu3Dm5ubmjfvj0AYNCgQdi4cSM2bdoEf39/BAYGIigoKMcW+Q0bNiA2NhaNGjWCq6ur+Ni2bZu4zqVLlxAbG4uvv/5a64xI8SYwRrdL1EZcXBxsbW0RGxsLGxubwi4OIURPpKSkIDw8HGXLltWqcjp3LvCJeokKqVQxIPZDbwy9NnjwYNy/f7/4TLlXzK1atQr79u3DkSNHCrsoxU7Xrl0REBCAKVOmaHw+p+tLUaq3KMsytO18mBhr9yH9U9LSU7DuwKQicZwFiVrkidZkMhnu379vULOI6ALlxsfQc8vcwq7k6QmcOwfMmAFk7f0hkwF79yr63SYnJ6vd6KYo+/nnn3Hz5k08fPhQ7IbTt2/fAi2DPuZWFDDG0KdPHzRo0MAgB6fzys35lpaWBn9/f4wdO7YAS0b0BVXkCZfMt2UnuUe58THU3B4/Bm7cUF3WtSsQHAzUrw/MnAmcPPlxAKySsvKvb5XRy5cvo3nz5vD398fatWuxfPlyDBo0qMDLoW+5FRVSqRRTp05VmR6UfNqnzjcTExNMmzatQGctyneCkD8PA0TTTxJCSBGlnBceUMwNv3Il0K+f6v9XgYGKaSkHDQKUN6e8c6dAi6kz27dvL+wiEEIKjGFWvHWNKvKEEFJENW0KdO4MpKQACxcC2d2fyMFB0Qq/cSOwaRMwZEjBlpMQQkjhoIo80ZpEIoGXl1eOU7MRdZQbn+KWmzbdNszNgdw2UgsCMHiw4qHYT95vvmSoKDc+lBsfXeSmd93B8mO6SJp+kpDcEQQBNjY2BjsdIC/KjU9xyU15d8qCusW6IAiQSqV6n1tBo9z4UG58dJWb8o6zOd1BlhRP1CJPtCaTyXD37l34+fnRRUMLlBuf4pKbVCqFnZ0dXr9+DQCwsLDI10oPYwwpKSkwMzOjypUWKDc+lBsfXeQml8vx5s0bWFhYwMhIT6p1+TE41UDPOz15x0lRY6hTAeYV5canuOTm4uICAGJlPj8xxpCeng5jY2OqWGmBcuNDufHRVW4SiQRlypSh7A0QVeQJIaSACIIAV1dXODs7Iz09PV/3JZPJ8ODBA3h4eOj1NxkFjXLjQ7nx0VVuJiYmejWOiAmKh663aYioIk8IIQVMKpXme2VHJpNBEASYmZlRxUoLlBsfyo0P5UbyiiryRGsSiQQ+Pj569em/KKDc+FBufCg3PpQbH8qNj8HmJkD308hTizwhuWdiYlLYRdBLlBsfyo0P5caHcuNDufExyNxosKvOGNhHQKILcrkct2/fhlwuL+yi6BXKjQ/lxody40O58aHc+FBuJK+oRZ4QQgghhBQcapHXGWqRJ4QQQgghRA9RizwhhBBCCCk41CKvM9QiT7QmkUjg7+9veKPs84hy40O58aHc+FBufCg3PpRb4Tp79izatWsHNzc3CIKAPXv25Lh+ZGQkevTogQoVKkAikeCbb74pkHLmhM4cwiUtLa2wi6CXKDc+lBsfyo0P5caHcuNjiLkxCPny0FZiYiICAgKwatWqXK2fmpoKJycnTJs2DQEBAVrvLz9QRZ5oTS6XIzQ0lEbZa4ly40O58aHc+FBufCg3PpRb4WrdujXmzJmDjh075mp9T09PLFu2DH369IGtrW0+ly53qI88IYQQQggpOBLovin5w/bi4uJUFpuamsLU1FTHOys6qEWeEEIIIYQULEHHjw/c3d1ha2srPubNm1dAB1Q4qEWecJFKpYVdBL1EufGh3PhQbnwoNz6UGx/KTbeePXsGGxsb8ffi3BoPUEWecJBKpfD39y/sYugdyo0P5caHcuNDufGh3PgYbG75OP2kjY2NSkW+uKOuNURrjDHExcWBMVbYRdErlBsfyo0P5caHcuNDufGh3EheUUWeaE0ul+Px48c0yl5LlBsfyo0P5caHcuNDufEx1NyKyvSTCQkJCA4ORnBwMAAgPDwcwcHBiIiIAABMnjwZffr0UXmNcv2EhAS8efMGwcHBuHv3bp4z4UVdawghhBBCiMG5evUqGjduLP4+btw4AEDfvn0RFBSEyMhIsVKvVK1aNfHna9eu4c8//4SHhweePHlSIGXOiiryhBBCCCGk4OTj9JPaaNSoUY7dmoKCgtSWFbVuUNS1hnAxMzMr7CLoJcqND+XGh3LjQ7nxodz4UG4kL6hFnmhNKpXC19e3sIuhdyg3PpQbH8qND+XGh3LjY7C55eOsNYaGWuSJ1uRyOd69e2dwg3PyinLjQ7nxodz4UG58KDc+lBvJK6rIE60xxvDs2bMi10+sqKPc+FBufCg3PpQbH8qNj6HmxgQhXx6GiCryhBBCCCGE6CGqyBNCCCGEEKKHaLAr4WJtbV3YRdBLlBsfyo0P5caHcuNDufExyNyEDw9db9MAUUWeaE0qlcLb27uwi6F3KDc+lBsfyo0P5caHcuNDuZG8oq41RGtyuRxRUVE0yl5LlBsfyo0P5caHcuNDufEx2Nwk+fQwQAZ62CQvGGOIiooyuFH2eUW58aHc+FBufCg3PpQbH8qN5BV1rSGEEEIIIQWHbgilM9QiTwghhBBCiB6iFnmiNUEQ4ODgAMFAP/3yotz4UG58KDc+lBsfyo2PoebGPjx0vU1DRBV5ojWJRIIyZcoUdjH0DuXGh3LjQ7nxodz4UG58DDY36lqjM9S1hmhNLpcjIiLC8EbZ5xHlxody40O58aHc+FBufCg3kldUkSdaY4whOjqaRtlriXLjQ7nxodz4UG58KDc+BpubkE8PA0QVeUIIIYQQQvQQ9ZEnhBBCCCEFhgkCmI77tOt6e/qCWuSJ1gRBgIuLi8GNss8ryo0P5caHcuNDufGh3PhQbiSvqEWeaE0ikcDFxaWwi6F3KDc+lBsfyo0P5caHcuNjsLnlR592A/0sRC3yRGsymQyPHj2CTCYr7KLoFcqND+XGh3LjQ7nxodz4UG4kr6hFnnCJj48v7CLoJcqND+XGh3LjQ7nxodz4GGJu1Eded6giTwghhBBCCg51rdEZ6lpDCCGEEEKIHqIWeaI1QRDg7u5Oo+y1RLnxodz4UG58KDc+lBsfQ82NCYqHrrdpiKhFnmhNIpHA0dEREgmdPtqg3PhQbny0yS0lJQWLFy9GrVq1YGNjAwsLC1SoUAFDhw7F48eP8c8//6BDhw7w9PSEubk5SpYsiRYtWuDMmTMFcCQFi843PpQbH8qN5BWdOURrMpkM9+/fp1H2WqLc+FBufHKb2/v371GlShWMHz8ely9fRnx8PBhjiIiIwPr169G5c2d06dIFe/fuRWRkJFxdXfHmzRscO3YMTZs2xaVLlwroiAoGnW98KDc+BpubkE8PA0QVecIlJSWlsIuglyg3PpQbn9zk1rFjR4SFhQEARo4cifT0dNy4cQPdunXDsmXL0KxZM8ybNw8XL17EypUr8e7dO2zevBmAohKydevWfD2GwkDnGx/KjQ/lRvKC+sgTQoiBiomJEbvHBAQEYMWKFRAEAb6+vggKClJbv06dOvjuu+9gbW0tLjM1NS2o4hJCCMmCWuQJIcRAHT9+XPy5QYMGnxxwd/v2bcTHx4vdaUxNTdGnT598LSMhhJDsUYs80ZpEIoGXlxcNztES5caHcuOTm9zev38v/vypSnxMTAy6deuGevXq4aeffoKxsTF+++03VK5cWWdlLgrofONDufEx6NwMtE+7rlFFnmhNEATY2NgUdjH0DuXGh3LTQng4sH498O+/EKKjYSORAI6OQNOmwMCBgLOzyurVqlUTfz5//jwYYxor9LGxsWjRogUyMjJw5swZWFlZYfv27WjdunW+H1JBo/OND+XGx2BzkwiKh663aYAM8CMgySuZTIbbt28b3ij7PKLc+FBuuXDmDNC2LeDtDcyfD5w+Ddm9e7hdvTpkZ88CU6YApUsDPXsCt26JL6tevTosLS0BADdu3MCUKVOQkZEhPn/8+HEcPXoUzZo1w/Pnz/HgwQOUKlUK586dK5aVeIDON16UGx/KjeQVVeQJF7ro8KHc+FBu2WAMWLwYaNQIOHhQ8fsHchMjyDIPRE1PB/78E6hZE9i5E4CiNXDDhg3i1/rz58+Ho6MjfH19YWpqiubNm2PEiBGIjY1FZGQkAEW/+GHDhqF27dqoXbs2RowYUWCHW1DofONDufExxNyUN4TS9cMQUdcaQgjRVz//DHz7rfhraml7hHUvhcedXJDiaAmjl+54/nUgKmx/Ca/tT2EUnQikpgJdugA7dgCdOqF79+5wdnbGyJEj8fDhQ8TFxSE+Ph729vaoXLkyzp49CyOjj/9VPH78GI8fPxZ/NzMzK9BDJoQQ8hFV5AkhRB8dPKhSib8/2h+3RpWGkZEljCWWMGdGYBJzyNxL4/a3Nrg52h21pz+G+84wRct9r15AhQqAvz+aNm2K+/fvF+LBEEII4UFda4jWJBIJfHx8DHOUfR5Qbnwot2zMni3+eHd8Fdwc4wFzk5IwkVpDECSAIIfgGgqJRICp1BamFiVxcX55PO9UTvGilBRg4cJCKnzRRecbH8qND+VG8qpYnTkPHjxA+/btUaJECdjY2KB+/fo4deqUyjoRERFo27YtLCws4OzsjIkTJ6oM7iK5Y2JiUthF0EuUGx/KLYvr14F//wUAJPiVxO1hpWAhLaE+44w0TfxRIkhhblQC//7gjQw7C8XCbduAt28LqtR6g843PpQbH0PMjfrI606xqsh/8cUXyMjIwMmTJ3Ht2jUEBATgiy++QFRUFADFgJK2bdsiLS0NFy9exObNmxEUFITp06cXcsn1i1wux+3btyGXywu7KHqFcuNDuWmwZo3444OebjAxslavxDMJ2HN/gH28zEsEKQRzSzzp7KFYkJYG/PprQZRYb9D5xody40O5kbwqNhX5t2/fIiwsDJMmTUKVKlVQvnx5zJ8/H0lJSbhz5w4A4OjRo7h79y5+//13VK1aFa1bt8bs2bOxatUqpKWlfWIPhBBSRHz4plFuboLwds4wEixy/VJjiRUedC2pti1CCCkwgpA/DwNUbAa7Ojo6wsfHB7/99hs+++wzmJqaYt26dXB2dkb16tUBAJcuXYK/vz9Klvz4n1jLli0xfPhwhISEqNwcRSk1NRWpqani73FxcQAUrfvKKaMEQYBEIoFcLgfLNP2bcnnWqaWyWy6RSCAIgsblANQ+sWe3XCqVgjGmcXnWMma3PKdjAgDGmEo59f2YCuJ9kslkKrkVh2MqiPdJmZtcLodUKi0Wx/Spsn/ymOLiAGNjpLg7IsPcFCYQFDNPZmp9Z3LJh2UA5B+XC5AgwcMKcokETCoFYmKATOekoZ97QO6vb/pyTAXxPmW9vhWHYyqI9ynr9S0/jqkoTm+ZH11hDLVrTbGpyAuCgOPHj6NDhw6wtraGRCKBs7MzDh8+DHt7ewBAVFSUSiUegPi7svtNVvPmzcOsWbPUloeEhMDKygoA4ODggDJlyuD58+eIjo4W13FxcYGLiwuePHmC+Ph4cbm7uzscHR0RFhaGlJQUcbmXlxdsbGxw9+5dlT88Hx8fmJiY4Pbt2ypl8Pf3R1paGkJDQ8VlUqkU/v7+iI+PV5siztfXF+/fv8ezZ8/E5dbW1vD29sbr169VMsjpmJycnBAfH4+QkBDxPz59P6aCeJ/u37+P6OhohISEwMjIqFgcU0G8T4wxREdH482bN3BzcysWx5Tn9+nrryEDILMxh1GkE+AWAUjTFF1pPmDK/9UyTMFeVfh4oBIZUPIV4su4I7x1G6BECeD27cI/piLyPpUqVQqJiYkq1zd9P6aCeJ9CQkLE65sgCMXimArifVJe316+fAkPD498OaaEhASQ4ktgWT8qFjGTJk3CggULclzn3r178PHxQYcOHZCeno6pU6fC3NwcGzduxL59+3DlyhW4urpiyJAhePr0KY4cOSK+NikpCZaWljh48KDGOxVqapF3d3dHdHS0eFvl4tQ6kLWMmpYLgoD09HSxvMXhmAqqRV4ul4uvLw7HVBDvU+bWKmqR/3BM5csDT59CZmWGvecawtTSSfG3mLlFXrkbQQ4BmZfLIXkRgbYNT0JuZAQ0bQrs31/4x1RE3idBEJCRkSH+XByOqaBa5DNf34rDMRXE+5T1+pYfxxQXFwcHBwfExsaK9ZbCEhcXB1tbW/QavQ4mpuY63XZaajJ+Xz60SBxnQSryLfLjx49Hv379clzHy8sLJ0+exP79+/H+/XvxDVy9ejWOHTuGzZs3Y9KkSXBxccHly5dVXvvq1SsAik+9mpiamsI0890RP1D+0WWW3fRRWdcriOXKimJW2ZVRm+XKrwONjY3VBtjp6zHpqoyfWp6enq6SW3E4pqx0fUyMMTE3XZVR2+VF7n2qWRN4+BDS9+koc/QVXna0hrFgDgiZKhIMQIYZYJQCIdPyNFkc/HZGQWAM0vR0xZ1eM+3H0M89xhgyMjJgZmZWoNe37JYXuXNPi+tbduvr0zHldjnvMWW9vuXHMWW3DikeivxgVycnJ/j6+ub4MDExQVJSEgD1PwLlp3YAqFOnDm7fvo3Xr1+Lzx87dgw2Njbw8/MruIPSc3K5HKGhoTTKXkuUGx/KTYNhw8Qfff6KQpo8Tq3VD0wCFumTpZVeDllaIry3fviqXyoFBg8uiBLrDTrf+FBufAw1N5ZPD0NU5CvyuVWnTh3Y29ujb9++uHnzJh48eICJEyciPDwcbdu2BQC0aNECfn5+6N27N27evIkjR45g2rRpGDlypMZWd0IIKZLq1wcqVwYA2F15jnI7XiNFFq1emc+EMYYk2TtUWxIBk1eKQfv48kugdOmCKDEhhJB8UGwq8iVKlMDhw4eRkJCAJk2aoEaNGjh//jz27t2LgIAAAIqvl/bv3w+pVIo6deqgV69e6NOnD3744YdCLj0hhGhBEIBJk8RfP5t6Hd7bXiBJ9gbp8iSVCj1jDGnyRCRlvELVJeEov+6u4gmJBJgwoaBLTgghNP2kDhX5PvLaqFGjhspAVk08PDxw8ODBAipR8UV97vhQbnwoNw169gSuXQOWLIEgk+OzKdfgvdMNoT1c8bSNE+TGJjBCAoSYSHjtfYkKf76ERdibj69fsQKoW7fwyl+E0fnGh3LjQ7mRvCjys9YUNcoR14Y2KpoQUgTJ5YpW9SVLVBbLLE2R4WQNyOQwfh0PSWr6xycFAVi6FBg9umDLSggpFEWp3qIsS89v1ufLrDV/LB1SJI6zIBWbrjWk4DDGEBenYXAdyRHlxodyy4FEAixeDPz5J/ChCyEASBNTYfL0HVJhCSEt4+P6gYHA0aNUic8BnW98KDc+BpubkE8PA0QVeaI1uVyOx48fG9wo+7yi3PhQbrnQvTtw4wZw8SLQuzfg7Q25kxMef/015H5+wIgRwJ07wOnTQLNmhV3aIo3ONz6UGx/KjeRVseojTwghBksQgDp1FA8AkMmA27eBhQtV5oknhJDCxgTFQ9fbNETUIk8IIYQQQogeohZ5wsXMzKywi6CXKDc+lBsfyo0P5caHcuNjkLnlR592A22Rp4o80ZpUKoWvr29hF0PvUG58KDc+lBsfyo0P5caHciN5RV1riNbkcjnevXtHg3O0RLnxodz4UG58KDc+lBsfQ81N2Ude1w9tnT17Fu3atYObmxsEQcCePXs++ZrTp0/js88+g6mpKcqVK4egoCDtd6xDVJEnWmOM4dmzZ4Y3XVYeUW58KDc+lBsfyo0P5caHcitciYmJCAgIwKpVq3K1fnh4ONq2bYvGjRsjODgY33zzDQYNGvTJm5HmJ+paQwghhBBCCo4AxUxbut6mllq3bo3WrVvnev21a9eibNmyWLRoEQCgYsWKOH/+PJYsWYKWLVtqXwAdoBZ5QgghhBBSYPKza01cXJzKIzU1VWflvnTpEppluRdHy5YtcenSJZ3tQ1tUkSdcrK2tC7sIeoly40O58aHc+FBufCg3PpSbbrm7u8PW1lZ8zJs3T2fbjoqKQsmSJVWWlSxZEnFxcUhOTtbZfrRBXWuI1qRSKby9vQu7GHqHcuNDufGh3PhQbnwoNz6Um+49e/YMNjY24u+mpqaFWJr8Ry3yRGtyuRxRUVEGN8o+ryg3PpQbH8qND+XGh3LjQ7npno2NjcpDlxV5FxcXvHr1SmXZq1evYGNjA3Nzc53tRxt5qsjrst8R0R+MMURFRdEoey1RbnwoNz6UGx/KjQ/lxsdQcysq009qq06dOjhx4oTKsmPHjqFOnTr5v/NsaFWRP3ToEPr27QsvLy8YGxvDwsICNjY2CAwMxNy5c/Hy5cv8KichhBBCCCE6k5CQgODgYAQHBwNQTC8ZHByMiIgIAMDkyZPRp08fcf1hw4bh8ePH+Pbbb3H//n2sXr0a27dvx9ixYwuj+AByWZH/+++/UaFCBQwYMABGRkb47rvvsHv3bhw5cgQbN25EYGAgjh8/Di8vLwwbNgxv3rzJ73ITQgghhBB9JOTTQ0tXr15FtWrVUK1aNQDAuHHjUK1aNUyfPh0AEBkZKVbqAaBs2bI4cOAAjh07hoCAACxatAgbN24stKkngVwOdv3pp5+wZMkStG7dGhKJet2/S5cuAIAXL15gxYoV+P333wv10wnJX4IgwMHBAYKu54At5ig3PpQbH8qND+XGh3LjQ7kVrkaNGuXYrUnTXVsbNWqEGzdu5GOptCMwQ+uYlUdxcXGwtbVFbGysyqhoQgghhJCipijVW5Rl6TppPUzMLHS67bSUJGybP6RIHGdB0qqPfHp6Ory9vXHv3r38Kg/RA3K5HBERETTKXkuUGx/KjQ/lxody40O58aHcSF5pVZE3NjZGSkpKfpWF6AnGGKKjow1ulH1eUW58KDc+lBsfyo0P5caHciN5pfX0kyNHjsSCBQuQkZGRH+UhhBBCCCHFmL5OP1kUaX1n1ytXruDEiRM4evQo/P39YWlpqfL87t27dVY4QgghhBBCiGZaV+Tt7OzQqVOn/CgL0ROCIMDFxYVG2WuJcuNDufGh3PhQbnwoNz4GmxvndJGf3KYB0roiv2nTpvwoB9EjEokELi4uhV0MvUO58aHc+FBufCg3PpQbH8qN5JXWfeQBICMjA8ePH8e6desQHx8PAHj58iUSEhJ0WjhSNMlkMjx69Agymaywi6JXKDc+lBsfyo0P5caHcuNjsLnlR/94apHPnadPn6JVq1aIiIhAamoqmjdvDmtrayxYsACpqalYu3ZtfpSTFDHKD3BEO5QbH8qND+XGh3LjQ7nxodxIXmjdIj9mzBjUqFED79+/h7m5ubi8Y8eOOHHihE4LRwghhBBCihkhnx4GSOsW+XPnzuHixYswMTFRWe7p6YkXL17orGCEEEIIIYSQ7GldkZfL5Rr7cj1//hzW1tY6KRQp2gRBgLu7u+GNss8jyo0P5caHcuNDufGh3PgYam7sw0PX2zREWnetadGiBZYuXSr+LggCEhISMGPGDLRp00aXZSNFlEQigaOjIyQSrrHSBoty40O58aHc+FBufCg3PgabG3Wt0Rmtz5xFixbhwoUL8PPzQ0pKCnr06CF2q1mwYEF+lJEUMTKZDPfv3ze8UfZ5RLnxodz4UG58KDc+lBsfyo3kldZda0qXLo2bN29i27ZtuHnzJhISEjBw4ED07NlTZfArKd5SUlIKuwh6iXLjQ7nxodz4UG58KDc+Bpkb3RBKZ7SuyJ89exZ169ZFz5490bNnT3F5RkYGzp49i4YNG+q0gIQQQgghhBB1Wnetady4MaKjo9WWx8bGonHjxjopFCGEEEIIKZ50fTMo8aZQBkjrijxjTOPo6nfv3sHS0lInhSJFm0QigZeXl+ENzskjyo0P5caHcuNDufGh3PhQbiSvct215quvvgKgmKWmX79+MDU1FZ+TyWS4desW6tatq/sSkiJHEATY2NgUdjH0DuXGh3LjQ7nxodz4UG58DDY36iOvM7n+CGhrawtbW1swxmBtbS3+bmtrCxcXFwwZMgS///57fpaVFBEymQy3b9+mUfZaotz4UG58KDc+lBsfyo0P5UbyKtct8ps2bQKguIPrxIkTYWFhkW+FIkUfXXT4UG58KDc+lBsfyo0P5caHciN5oXWnrDNnziAtLU1teVxcHJo0aaKTQhFCCCGEkGKKbgilM1pPP5ldRT4lJQXnzp3TSaEIIYQQQggpbtLT0xEVFYWkpCQ4OTnBwcEhT9vLdUX+1q1bABSz1ty9exdRUVHiczKZDIcPH0apUqXyVBiiHyQSCXx8fGiUvZYoNz6UGx/KjQ/lxody42OoueXHdJFFefrJ+Ph4/P7779i6dSsuX76MtLQ0cRbI0qVLo0WLFhgyZAg+//xzrbed64p81apVIQgCBEHQ2IXG3NwcK1as0LoARD+ZmJgUdhH0EuXGh3LjQ7nxodz4UG58KLfibfHixZg7dy68vb3Rrl07TJkyBW5ubjA3N0d0dDTu3LmDc+fOoUWLFqhVqxZWrFiB8uXL53r7ua7Ih4eHgzEGLy8vXL58GU5OTuJzJiYmcHZ2hlQq1e7oiF6Sy+W4ffs2/P396T3XAuXGh3LjQ7nxodz4UG58DDY3A5p+8sqVKzh79iwqVaqk8fmaNWtiwIABWLt2LTZt2oRz587lT0Xew8MDgOKkI4QQQgghhOTsr7/+ytV6pqamGDZsmNbb5+qUtWXLFtSrVw9ubm54+vQpAGDJkiXYu3cvz+YIIYQQQoihMMBZa9LT02FkZIQ7d+7odLtaV+TXrFmDcePGoU2bNoiJiRHnP7W3t8fSpUt1WjhCCCGEEEL0nbGxMcqUKaPz+wZoXZFfsWIFNmzYgKlTp6r056pRowZu376t08KRokkikcDf39/gRtnnFeXGh3LjQ7nxodz4UG58DDU3lk+Pom7q1KmYMmUKoqOjdbZNreeRDw8PR7Vq1dSWm5qaIjExUSeFIkVfWloazMzMCrsYeody40O58aHc+FBufCg3PgaZmwENds1s5cqVePjwIdzc3ODh4QFLS0uV569fv671NrWuyJctWxbBwcHi4Felw4cPo2LFiloXgOgfuVyO0NBQwxtln0eUGx/KjQ/lxody40O58aHcDEuHDh10vk2tK/Ljxo3DyJEjkZKSAsYYLl++jL/++gvz5s3Dxo0bdV5AQgghhBBSfORHVxh96FozY8YMnW9T64r8oEGDYG5ujmnTpiEpKQk9evSAm5sbli1bhm7duum8gIQQQgghhBQHMTEx2LlzJx49eoSJEyfCwcEB169fR8mSJVGqVCmtt6d1RR4AevbsiZ49eyIpKQkJCQlwdnbm2QzRY/QVIB/KjQ/lxody40O58aHc+BhkbgbaR/7WrVto1qwZbG1t8eTJEwwePBgODg7YvXs3IiIi8Ntvv2m9Te5h0q9fv8a1a9cQGhqKN2/e8G6G6CGpVEr9+ThQbnwoNz6UGx/KjQ/lxodyMyzjxo1Dv379EBYWpjLAuU2bNjh79izXNrWuyMfHx6N3795wc3NDYGAgAgMD4ebmhl69eiE2NparEES/MMYQFxcHxvShR1rRQbnxodz4UG58KDc+lBsfg83NAG8IBQBXrlzB0KFD1ZaXKlUKUVFRXNvUuiI/aNAg/Pfffzhw4ABiYmIQExOD/fv34+rVqxoLR4ofuVyOx48fQy6XF3ZR9Arlxody40O58aHc+FBufCg3w2Jqaoq4uDi15Q8ePICTkxPXNrXuI79//34cOXIE9evXF5e1bNkSGzZsQKtWrbgKQQghhBBCDAMTFA9db7Oo+/LLL/HDDz9g+/btAABBEBAREYHvvvsOnTp14tqm1i3yjo6OsLW1VVtua2sLe3t7rkIQQgghhBBSnC1atEicJCY5ORmBgYEoV64crK2tMXfuXK5tat0iP23aNIwbNw5btmyBi4sLACAqKgoTJ07E999/z1UIon8M7i50OkK58aHc+FBufCg3PpQbH8rNcNja2uLYsWO4cOECbt68iYSEBHz22Wdo1qwZ9zYFlosRFtWqVYMgfPzOIiwsDKmpqShTpgwAICIiAqampihfvjzX7WX1SVxcHGxtbREbGwsbG5vCLg4hhBBCSLaKUr1FWZYOC9bD2MxCp9tOT0nCnu+GFInjzM5vv/2Grl27wtTUVGV5Wloatm7dij59+mi9zVy1yOfHLWWJ/pLL5Xj//j3s7e0hkXDPYGpwKDc+lBsfyo0P5caHcuNDuRmW/v37o1WrVmr3X4qPj0f//v3zryKfH7eUJfqLMYZnz57Bzs6usIuiVyg3PpQbH8qND+XGh3LjY9C56cHgVF1jjKn0cFF6/vy5xvGnucF1Z1dCCCGEEELIpym7qAuCgKZNm8LI6GP1WyaTITw8nHvmR6rIE0IIIYSQgpMfN3Aqwi38yi7qwcHBaNmyJaysrMTnTExM4OnpyT39JFXkCRdra+vCLoJeotz4UG58KDc+lBsfyo0P5Vb8Kbuoe3p6olu3bmqDXfOCRlYQrUmlUnh7e0MqlRZ2UfQK5caHcuNDufGh3PhQbnwMNTflDaF0/SjqZs2ahYSEBLXlMTEx8PLy4tqmVhX59PR0eHt74969e1w7I8WDXC5HVFQU3VJaS5QbH8qND+XGh3LjQ7nxodwMy5MnTyCTydSWp6am4sWLF1zb1KprjbGxMVJSUrh2RIoPxhiioqLg5ORU2EXRK5QbH8qND+XGh3LjQ7nxodwMw759+8Sfjxw5ojJDjUwmw4kTJ+Dp6cm1ba37yI8cORILFizAxo0bVUbdEkIIIYQQ8kkGOthVEAT07dtX5TljY2N4enpi0aJFXNvWuiZ+5coVnDhxAkePHoW/vz8sLS1Vnt+9ezdXQQghhBBCCClulF2nypYtiytXrqBEiRI627bWFXk7OzvuKXJI8SAIAhwcHDTe1IBkT5vcUlJSsHr1amzbtg337t1DRkYGSpcujcaNG+O7775DREQEGjdurPG1x44dQ7NmzXRd/EJD5xsfyo0P5caHcuNjqLnlx+BUfRjsGh4eLv6ckpICMzOzPG9T64r8pk2b8rxTot8kEgnKlClT2MXQO7nN7f3796hVqxbCwsLEZWZmZoiIiMD69euxa9cuxMfHi89JpVLUqFFD/J337nBFFZ1vfCg3PpQbH8qND+VmWORyOebOnYu1a9fi1atXePDgAby8vPD999/D09MTAwcO1HqbXNNPZmRk4Pjx41i3bp1YoXj58qXGKXVI8SOXyxEREUGj7LWU29w6duwoVuJHjhyJ9PR03LhxA926dcO2bdtgbGyM77//HgDg4eGBjIwM/Pvvv+Lj888/z/djKUh0vvGh3PhQbnwoNz4Gm5uQT48ibs6cOQgKCsJPP/0EExMTcXnlypWxceNGrm1qXZF/+vQp/P390b59e4wcORJv3rwBACxYsAATJkzgKgTRL4wxREdHgzFW2EXRK7nJLSYmBmfOnAEABAQEYMWKFTAyMoKvry+CgoLQpUsXlRtJvHz5EnZ2drCzs0Pt2rWxc+fOfD+OgkbnGx/KjQ/lxody40O5Fb5Vq1bB09MTZmZmqFWrFi5fvpztuunp6fjhhx/g7e0NMzMzBAQE4PDhw7ne12+//Yb169ejZ8+eKvcOCAgIwP3797nKr3VFfsyYMahRowbev38Pc3NzcXnHjh1x4sQJrkIQQhSOHz8u/tygQYNs+00uXrxY/Nne3h4pKSn477//0LlzZ6xZsybfy0kIIYTwKio3hNq2bRvGjRuHGTNm4Pr16wgICEDLli3x+vVrjetPmzYN69atw4oVK3D37l0MGzYMHTt2xI0bN3K1vxcvXqBcuXJqy+VyOdLT07U/AHBU5M+dO4dp06apfCUAKG47yzuZPSFE4f379+LP2VXit2zZghs3biAkJARBQUGIiorCjh07ULJkSQDgnsKKEEIIMSSLFy/G4MGD0b9/f/j5+WHt2rWwsLDAr7/+qnH9LVu2YMqUKWjTpg28vLwwfPhwtGnTJtf/7/r5+eHcuXNqy3fu3Ilq1apxHYPWg13lcrnGu1I9f/4c1tbWXIUg+kUQBLi4uBjcKPu8yk1umf+Qz58/D8aY2voNGjQQf/bz88OePXtw4cIF1K9fH7t27UJERITuC1+I6HzjQ7nxodz4UG58KDfdi4uLU/nd1NRUpUuqUlpaGq5du4bJkyeLyyQSCZo1a4ZLly5p3HZqaqraTDPm5uY4f/58rso2ffp09O3bFy9evIBcLsfu3bsRGhqK3377Dfv378/VNrLSukW+RYsWWLp0qfi7IAhISEjAjBkz0KZNG65CEP0ikUjg4uICiYRrrLTByk1u1atXF+/NcOPGDUyZMgUZGRni88ePH8fFixfx22+/4b///hO3Gx8fL15IeO8OV1TR+caHcuNDufGh3PgYbG75ONjV3d0dtra24mPevHkai/D27VvIZDLx22ylkiVLIioqSuNrWrZsicWLFyMsLAxyuRzHjh3D7t27ERkZmavDbt++Pf755x8cP34clpaWmD59Ou7du4d//vkHzZs3z9U2stK6RX7RokVo2bIl/Pz8kJKSgh49eiAsLAwlSpTAX3/9xVUIol9kMhmePHkCT09PlcEaRDPGGN6kvkdcWiJiXrxF+bLlYGum+dsrQRCwYcMG9OrVC3K5HPPnz8fq1avh6uqK8PBwpKWlYdKkSXj+/Dn69u0La2trJCQkwNjYGGlpaQCAqVOnFuTh5Ts63/hQbnwoNz6UGx/KTfeePXsGGxsb8XdNrfG8li1bhsGDB8PX1xeCIMDb2xv9+/fPtiuOJg0aNMCxY8d0ViatK/KlS5fGzZs3sXXrVty6dQsJCQkYOHAgevbsqTL4lRRvmecxJ5qlyNJw7f09nH51BY8TX4DJGarElcbG+IP4zKEiGjpXg4+1p9pXqt27d4ezszNGjhyJhw8fIi4uDvHx8bC3t0dgYCD27t2L8PBwGBsbIzU1FUZGRrC3t4e/vz++++67YnUzKCU63/hQbnwoNz6UGx9DzC0/bwhlY2OjUpHPTokSJSCVSvHq1SuV5a9evYKLi4vG1zg5OWHPnj1ISUnBu3fv4ObmhkmTJsHLy0ursl69ehX37t0DoOgiW716da1en5nWFXkAMDIyQq9evbh3Skhx9zolGivCtuFJ4gswBlgYmcJEagwjQQoZk+HMm2u4+O4mGjp9ht6ebWEsUf1TbNq0KfdUVIQQQgjJmYmJCapXr44TJ06gQ4cOABTjQE+cOIFRo0bl+FozMzOUKlUK6enp2LVrF7p06ZKrfT5//hzdu3fHhQsXYGdnB0Ax7XTdunWxdetWlC5dWuvj4KrIh4WF4dSpU3j9+rXaTQymT5/Os0lCio3otDgsCt2C50mvYWtsJVbSJUyARJDAysgC5jBDsiwVJ19dQYZchkHeHSARDKyPJCGEEMOUHzdw4tjeuHHj0LdvX9SoUQM1a9bE0qVLkZiYiP79+wMA+vTpg1KlSon97P/77z+8ePECVatWxYsXLzBz5kzI5XJ8++23udrfoEGDkJ6ejnv37sHHxwcAEBoaiv79+2PQoEFazUmvpHVFfsOGDRg+fDhKlCihNtJaEIR8q8jPnTsXBw4cQHBwMExMTBATE6O2TkREBIYPH45Tp07BysoKffv2xbx582Bk9PEwT58+jXHjxiEkJATu7u6YNm0a+vXrly9lLq4EQYC7uzuNss/G1qeH8TzpNexNrCEVPvZ5ZGCIsowDg2ImGgsjMwiCgPNvb6CSnTfqlQgoxFIXXXS+8aHc+FBufCg3PpRb4eratSvevHmD6dOnIyoqClWrVsXhw4fFAbAREREqA5FTUlIwbdo0PH78GFZWVmjTpg22bNkitq5/ypkzZ3Dx4kWxEg8APj4+WLFihcqMdNrQuiI/Z84czJ07F9999x3XDnmlpaWhc+fOqFOnDn755Re152UyGdq2bQsXFxdcvHgRkZGR6NOnD4yNjfHjjz8CAMLDw9G2bVsMGzYMf/zxB06cOIFBgwbB1dUVLVu2LNDj0WcSiQSOjo6FXYwi6U3Ke1x/fx/mUlOVSjyg6L8Xa5qissxcaorkjBScenUFdR2r0MVcAzrf+FBufCg3PpQbH8qt8I0aNSrbrjSnT59W+T0wMBB3797l3pe7u7vGGz/JZDK4ublxbVPr7/Lfv3+Pzp07c+0sL2bNmoWxY8fC399f4/NHjx7F3bt38fvvv6Nq1apo3bo1Zs+ejVWrVomzeaxduxZly5bFokWLULFiRYwaNQpff/01lixZUpCHovdkMhnu37+v8X4Chu7iu5tIlqXCQmqm9pzABJSNdYSQZYSPhZEZHiU8R3jiy4Iqpl6h840P5caHcuNDufEx2NzycfrJomzhwoX43//+h6tXr4rLrl69ijFjxuDnn3/m2qbWLfKdO3fG0aNHMWzYMK4d5pdLly7B399fZT7Qli1bYvjw4QgJCUG1atVw6dIltRk9WrZsiW+++Sbb7aampiI1NVX8XXmjAZlMJv7hCYIAiUQCuVwOxpi4rnJ51j/Q7JZLJBIIgqBxOQC18QjZLZdKpWCMaVyetYzZLc/pmAAgOTlZpZz6fky6ep+eJkRCwgRIIQE+7EL+4QcpE2CSIYWUCZADkAsMYIC5YIokWQqeJUTBw9ylyB0TULjvk0wmQ3JyMuRyOaRSabE4pk+VXRfHpMxNmWFxOKacluvqmIDcX9/05ZgK4n1Snm/K54vDMRXE+5T1+pYfx2RwHxKKGHt7e5Vv2xMTE1GrVi2x23dGRgaMjIwwYMAAcdCtNnJVkV++fLn4c7ly5fD999/j33//hb+/P4yNjVXWHT16tNaF0IWoqCiNk/orn8tpnbi4OCQnJ2ucPnPevHmYNWuW2vKQkBBYWVkBABwcHFCmTBk8f/4c0dHR4jouLi5wcXHBkydPVKaXcnd3h6OjI8LCwpCS8rGrhZeXF2xsbHD37l2VPzwfHx+YmJjg9u3bKmXw9/dHWloaQkNDxWVSqRT+/v6Ij4/H48ePxeVmZmbw9fXF+/fv8ezZM3G5tbU1vL298fr1a5UbIOR0TE5OToiPj0dISIh4cur7MenqfUrNSMVnCR4wTTIRl4fZv4GRXAKv2BKwTTNDuRgnyAQ5wuzfwDLDBKXj7ZAis0NC+DuExYQVuWMq7PeJMYbo6Gi8efMGbm5uxeKYCuJ9UlYGUlNTERYWViyOCcj/96lUqVJITExUub7p+zEVxPsUEhKC6OhoMbficEwF8T4pr28vX76Eh4dHvhxTQkICSOHJfBPV/CCwrB8VNShbtmzuNiYIKifyp0yaNAkLFizIcZ179+7B19dX/D0oKAjffPON2mDXIUOG4OnTpzhy5Ii4LCkpCZaWljh48CBat26NChUqoH///iq34z148CDatm2LpKQkjRV5TS3y7u7uiI6OFucpLU6tA1nLqGk5Ywy3bt1CpUqVxBtY6Psx6ep9Wh22A/++vYUSpnbicmWLvBGTwPt9CTyyfwu5wMQWeYEB79JiMcT7K9QtEVDkjgko/Bb5kJAQVK5cGcbGxsXimD5Vdl21yIeEhMDf319t7IW+HlNOy3V1TNpc3/TlmArifUpPT0dISIiYW3E4poJqkc98fcuPY4qLi4ODgwNiY2NzNb96foqLi4OtrS2+WLEexuYWOt12enIS9v9vSJE4zoKUqxb58PDwfNn5+PHjPzljTG4n2XdxccHly5dVlikn+VdO7O/i4qJx4n8bG5tsb2Zlamqq8a5gUqlU7S5s2d1iObu7teXnckEQNC7ProzaLGeMwdvbG8bGxhorCLkto7bL8/OYdFVGX1tPXHp3E+mQQZplOskMyPHcJgYZgvxjXz4BSJAlw9TIBD62H+/sV5SOSdvlun6fJBIJvL29xa8hi8Mx5UcZsy5X5iaVSjUOotbHY/rUcl0cU2Fd37Jbri/vk7Gxscbc9PmYCuJ9ynp9y49jym6dQlVEpp8sDrjmkdcVJycnODk56WRbderUwdy5c/H69Ws4OzsDAI4dOwYbGxv4+fmJ6xw8eFDldceOHUOdOnV0UgZDIQiCQX3a1UYtx8rY9ewEEjOSYWNsqfqkACQap6ksYowhWZaK2iX84WzmUIAl1R90vvGh3PhQbnwoNz6UG8krrSvy48aN07hcEASYmZmhXLlyaN++PRwcdFspiYiIQHR0NCIiIiCTyRAcHAxA0WffysoKLVq0gJ+fH3r37o2ffvoJUVFRmDZtGkaOHCm2qA8bNgwrV67Et99+iwEDBuDkyZPYvn07Dhw4oNOyFncymQx3796Fn59f0fykX4isjCzQ0Okz7H95FmnydJhIPo4hkTAB3jEl8MhO0bUGAOIzEmEqNUFT55qFVeQij843PpQbH8qND+XGx1BzY4LioettGiKtK/I3btzA9evXIZPJxAntHzx4AKlUCl9fX6xevRrjx4/H+fPnxZZwXZg+fTo2b94s/l6tWjUAwKlTp9CoUSNIpVLs378fw4cPR506dWBpaYm+ffvihx9+EF9TtmxZHDhwAGPHjsWyZctQunRpbNy4keaQ50Cj4LP3lXsTPE9+jRsf5pO3kJqJXzVLPlxpZEyOuPRESAQBXdybwc82d13IDBWdb3woNz6UGx/KjQ/lRvJC64q8srV906ZN4tdBsbGxGDRoEOrXr4/BgwejR48eGDt2rMrA07wKCgpCUFBQjut4eHiodZ3JqlGjRrhx44bOykVIViYSY4wq3xVbnuzHv+9u411aLIwEKUwEI6SzDESnxUIGBhtjS3Qp0xyNnGsUdpEJIYSQgkN95AEoBv+ePHkSPj4+qFixItc2tK7IL1y4UOx7rmRra4uZM2eiRYsWGDNmDKZPn44WLVpwFYiQ4sBMaoLB3l+hjVt9XHgTjH/f3UZSegokkKCshRsauFRHTYfKsDbW7ah9QgghpKgz1K41Xbp0QcOGDTFq1CgkJyejRo0aePLkCRhj2Lp1Kzp16qT1NrWuyMfGxuL169dq3WbevHkj3izJzs5OvJsqKX4kEgl8fHyyHV1PPipl7owuZVqgS5kWkMllSEtNg5mZmcZZRIhmdL7xodz4UG58KDc+lJthOXv2LKZOnQoA+Pvvv8EYQ0xMDDZv3ow5c+ZwVeS1PnPat2+PAQMG4O+//8bz58/x/Plz/P333xg4cKB4R6rLly+jQoUKWheG6A8TE5NPr0RUSAQJ5caJcuNDufGh3PhQbnwoN8MRGxsrTgZz+PBhdOrUCRYWFmjbtq3Kjfu0oXVFft26dWjatCm6desGDw8PeHh4oFu3bmjatCnWrl0LAPD19cXGjRu5CkSKPrlcjtu3b6vd/ILkjHLjQ7nxodz4UG58KDc+lJthcXd3x6VLl5CYmIjDhw+L3dDfv38PMzMzrm1q3bXGysoKGzZswJIlS8S7uHp5ecHKykpcp2rVqlyFIYQQQgghxZyBDnb95ptv0LNnT1hZWcHDwwONGjUCoOhy4+/vz7VN7htCWVlZoUqVKrwvJ4QQQgghxGCMGDECNWvWxLNnz9C8eXNxbISXlxfmzJnDtc1cVeS/+uorBAUFwcbGBl999VWO6+7evZurIIQQQgghpPgz1FlrAKBGjRqoUUN12um2bdtyby9XFXlbW1txlg1bW1vunZHiQSKRwN/fn0bZa4ly40O58aHc+FBufCg3PpRb8Tdu3DjMnj0blpaWGDduXI7rLl68WOvt56oiv2nTJo0/E8OVlpbGPTDDkFFufCg3PpQbH8qND+XGxyBzM6A+8jdu3EB6err4c3Z4p6Xm7iNPDJdcLkdoaCj8/f0hlUoLuzh6g3LjQ7nxodz4UG58KDc+lFvxd+rUKY0/64rW3+W8evUKvXv3hpubG4yMjCCVSlUehBBCCCGE5EjQ8cNAad0i369fP0REROD777+Hq6sr3aGSEEIIIYTkGvvw0PU2DZHWFfnz58/j3LlzNFe8gaNvX/hQbnwoNz6UGx/KjQ/lxodyI3mhdUXe3d0djBnq5x4CKC46vDcuMGSUGx/KjQ/lxody40O58THY3AxosGt+07qP/NKlSzFp0iQ8efIkH4pD9AFjDHFxcfSBTkuUGx/KjQ/lxody40O58aHcDEd6ejoGDBiA8PBwnW43VxV5e3t7ODg4wMHBAd26dcPp06fh7e0Na2trcbnyQYo/uVyOx48fQy6XF3ZR9Arlxody40O58aHc+FBufAw2N10PdNWDAa/GxsbYtWuXzrebq641S5cu1fmOCSGEEEIIMRQdOnTAnj17MHbsWJ1tM1cV+b59++psh4QQQgghxIAJAAQddycq4i3yAFC+fHn88MMPuHDhAqpXrw5LS0uV50ePHq31NnNVkU9MTFTbmS7XJ/rH4O5CpyOUGx/KjQ/lxody40O58aHcDMcvv/wCOzs7XLt2DdeuXVN5ThCE/KvIlytXDmPGjEHfvn3h6uqqcR3GGI4fP47FixejYcOGmDx5staFIfpBKpXC19e3sIuhdyg3PpQbH8qND+XGh3LjY7C5GeisNboe6ArksiJ/+vRpTJkyBTNnzkRAQABq1KgBNzc3mJmZ4f3797h79y4uXboEIyMjTJ48GUOHDtV5QUnRIZfL8f79e9jb20Mi0XriI4NFufGh3PhQbnwoNz6UGx/KzTClpaUhPDwc3t7eMDLSeiZ4Fbk6a3x8fLBr1y48ePAAXbp0wYsXL7Bz505s2LABp0+fRqlSpbBhwwY8efIEI0aMoJsbFHOMMTx79oymy9IS5caHcuNDufGh3PhQbnwoN8OSlJSEgQMHwsLCApUqVUJERAQA4H//+x/mz5/PtU2tPgaUKVMG48ePx/jx47l2RgghhBBCiCGaPHkybt68idOnT6NVq1bi8mbNmmHmzJmYNGmS1tvMW3s+IYQQQggh5JP27NmDbdu2oXbt2hCEj536K1WqhEePHnFtkyryhIu1tXVhF0EvUW58KDc+lBsfyo0P5cbHEHNjguKh620WdW/evIGzs7Pa8sTERJWKvTZoZAXRmlQqhbe3N42F0BLlxody40O58aHc+FBufCg3w1KjRg0cOHBA/F1Zed+4cSPq1KnDtU1qkSdak8vleP36NZydnWmUvRYoNz6UGx/KjQ/lxody42OwuQksH24IVfQHDP/4449o3bo17t69i4yMDCxbtgx3797FxYsXcebMGa5tGtBZQ3SFMYaoqCgaZa8lyo0P5caHcuNDufGh3PhQboalfv36CA4ORkZGBvz9/XH06FE4Ozvj0qVLqF69Otc2tW6RP3z4MKysrFC/fn0AwKpVq7Bhwwb4+flh1apVsLe35yoIIYQQQggxAAZ6QygA8Pb2xoYNG3S2Pa1b5CdOnIi4uDgAwO3btzF+/Hi0adMG4eHhGDdunM4KRgghhBBCiiEhnx5FXJ8+fbBp0yY8fvxYZ9vUuiIfHh4OPz8/AMCuXbvwxRdf4Mcff8SqVatw6NAhnRWMFF2CIMDBwYF7hLWhotz4UG58KDc+lBsfyo0P5WZYTExMMG/ePJQrVw7u7u7o1asXNm7ciLCwMO5tal2RNzExQVJSEgDg+PHjaNGiBQDAwcFBbKknxZtEIkGZMmUMa2CODlBufCg3PpQbH8qND+XGh3IzLBs3bsSDBw/w7Nkz/PTTT7CyssKiRYvg6+uL0qVLc21T6zOnfv36GDduHGbPno3Lly+jbdu2AIAHDx5wF4LoF7lcjoiICMjl8sIuil6h3PhQbnwoNz6UGx/KjQ/lZpjs7e3h6OgIe3t72NnZwcjICE5OTlzb0roiv3LlShgZGWHnzp1Ys2YNSpUqBQA4dOiQyu1mSfHFGEN0dDSNstcS5caHcuNDufGh3PhQbnwMNjfl9JO6fhRxU6ZMQd26deHo6IhJkyYhJSUFkyZNQlRUFG7cuMG1Ta1nrSlTpgz279+vtnzJkiVcBSCEEEIIIaS4mz9/PpycnDBjxgx89dVXqFChQp63yX1DqNevX+P169dqXwdVqVIlz4UihBBCCCHFlIFOP3njxg2cOXMGp0+fxqJFi2BiYoLAwEA0atQIjRo14qrYa12Rv3btGvr27Yt79+6JXwUJggDGGARBgEwm07oQRL8IggAXFxcaZa8lyo0P5caHcuNDufGh3PhQboYlICAAAQEBGD16NADg5s2bWLJkCUaOHAm5XM5Vh9a6Ij9gwABUqFABv/zyC0qWLEknnwGSSCRwcXEp7GLoHcqND+XGh3LjQ7nxodz4GGpuTFA8dL3Noo4xhhs3buD06dM4ffo0zp8/j7i4OFSpUgWBgYFc29S6Iv/48WPs2rUL5cqV49oh0X8ymQxPnjyBp6cnpFJpYRdHb1BufCg3PpQbH8qND+XGx2Bzy4/BqXow2NXBwQEJCQkICAhAYGAgBg8ejAYNGsDOzo57m1pX5Js2bYqbN29SRd7AxcfHF3YR9BLlxody40O58aHc+FBufCg3w/H777+jQYMGsLGx0dk2ta7Ib9y4EX379sWdO3dQuXJlGBsbqzz/5Zdf6qxwhBBCCCGEFAfKey8BwPPnzwEgz/dg0roif+nSJVy4cAGHDh1Se44GuxJCCCGEEKJOLpdjzpw5WLRoERISEgAA1tbWGD9+PKZOncp1h1+tX/G///0PvXr1QmRkJORyucqDKvGGQRAEuLu700BnLVFufCg3PpQbH8qND+XGx1BzE4T8eRR1U6dOxcqVKzF//nzcuHEDN27cwI8//ogVK1bg+++/59qm1i3y7969w9ixY1GyZEmuHRL9J5FI4OjoWNjF0DuUGx/KjQ/lxody40O58aHcDMvmzZuxceNGlW7oVapUQalSpTBixAjMnTtX621q3SL/1Vdf4dSpU1rviBQfMpkM9+/fp29gtES58aHc+FBufCg3PpQbH4PNTcinRxEXHR0NX19fteW+vr6Ijo7m2qbWLfIVKlTA5MmTcf78efj7+6sNdlVOck+Kt5SUlMIugl6i3PhQbnwoNz6UGx/KjQ/lZjgCAgKwcuVKLF++XGX5ypUrERAQwLVNrllrrKyscObMGZw5c0blOUEQqCJPCCGEEEKylx8t6JzbW7VqFRYuXIioqCgEBARgxYoVqFmzZrbrL126FGvWrEFERARKlCiBr7/+GvPmzYOZmdkn9/XTTz+hbdu2OH78OOrUqQNAMYnMs2fPcPDgQa7ya12RDw8P59oRIYQQQgghRaUiv23bNowbNw5r165FrVq1sHTpUrRs2RKhoaFwdnZWW//PP//EpEmT8Ouvv6Ju3bp48OAB+vXrB0EQsHjx4k/uLzAwEA8ePMCqVatw//59AIou6yNGjICbm5v2BwBAYIzly62wbGxsEBwcDC8vr/zYfKGJi4uDra0tYmNjdTqhvz5hjCE+Ph7W1tYGN9I+Lyg3PpQbH8qND+XGh3LjUxC5FaV6i7IsLbevgbGFuU63nZ6UjCNdhmt1nLVq1cLnn3+OlStXAlBMD+nu7o7//e9/mDRpktr6o0aNwr1793DixAlx2fjx4/Hff//h/PnzujkQLWndIp9b+fT5gBQBgiAU+sVAH1FufCg3PpQbH8qND+XGx3BzYx8eut6m4sNCZqampjA1NVVbOy0tDdeuXcPkyZPFZRKJBM2aNcOlS5c07qFu3br4/fffcfnyZdSsWROPHz/GwYMH0bt372xLdevWrVwfQZUqVXK9rlK+VeRJ8SWTyXD37l34+flBKpUWdnH0BuXGh3LjQ7nxodz4UG58KDfdc3d3V/l9xowZmDlzptp6b9++hUwmU5tOvWTJkmK3l6x69OiBt2/fon79+mCMISMjA8OGDcOUKVOyLU/VqlUhCMInG7h5b6pKFXnCxeCmytIRyo0P5caHcuNDufGh3PhQbrr17NkzlW85NLXG8zp9+jR+/PFHrF69GrVq1cLDhw8xZswYzJ49O9sbOuX32FKqyBNCCCGEkGLBxsYmV92VSpQoAalUilevXqksf/XqFVxcXDS+5vvvv0fv3r0xaNAgAIC/vz8SExMxZMgQTJ06FRKJ+u2ZPDw8OI4i97S+IVRu0WAXQgghhBCSlSCwfHlow8TEBNWrV1cZuCqXy3HixAlxasiskpKS1Crryi5R2XWd+ffff3NdpqSkJISEhOR6fSAfK/I02LX4kkgk8PHx0fjJk2SPcuNDufGh3PhQbnwoNz6UW+EaN24cNmzYgM2bN+PevXsYPnw4EhMT0b9/fwBAnz59VAbDtmvXDmvWrMHWrVvx//buPD6mq3ED+HPvZJlEFrInhNijBEFpdLH2TUsXuqFqp/0pLdIFLYK+qLa8dLXV0lbtWm0pJUVtrRYhgliCoGLLHmSZe39/pJkaWWTObJnM8/W5n09y5s6dc56JmTNnzj337Nmz2Lp1KyZOnIgnn3yyzHMc+vXrh+joaKxZswa5ubml7nPs2DG88847qF+/Pg4cOGBUG4yeWrN9+3Z06tTpnvv9/PPPqFmzprGHJzvh4uJi6yrYJeYmhrmJYW5imJsY5ibGIXOrJOvI9+rVC9euXcOkSZOQmpqKli1bYvPmzfoTYFNSUgw+ZE2YMAGSJGHChAm4dOkS/P398eSTT2LatGllPsaxY8fwxRdfYMKECXjxxRfRqFEjhISEQKvVIj09HSdOnEBOTg569uyJX375BREREcY129h15F1dXVGrVi0MGjQIAwYMKHF2cFVXmdZjtRWdToeEhARERETwLHsjMDcxzE0McxPD3MQwNzHWyK0y9VuK6/LY2k/hXM3M68jn3sLm50ZWinaW5a+//sLu3btx/vx53Lp1C35+foiMjESnTp3g4+MjdEyjR+QvXbqEr7/+GsuWLcOUKVPQuXNnDBkyBD169HDMT5VERERERPfQpk0btGnTxqzHNHpSlp+fH8aMGYP4+Hj88ccfaNSokf7Ssq+//joOHz5s1goSERERURUiWWhzQCadXdGqVSuMHz8eI0eORE5ODhYvXozWrVvj4YcfNvqsWyIiIiIiqjihjnxBQQHWrl2Lbt26oU6dOtiyZQs+/fRTXLlyBadPn0adOnXw/PPPm7uuVEnIsoyIiAieZW8k5iaGuYlhbmKYmxjmJsZRc+OAvPkYPUf+tddew4oVK6CqKvr164cPPvgAzZo1099erVo1fPTRRwgJCTFrRalyyc/Ph1artXU17A5zE8PcxDA3McxNDHMTw9zIFEZ/BDx27Bg++eQT/P3335gzZ45BJ76Yn58ftm/fbpYKUuWjKAqSkpKgKIqtq2JXmJsY5iaGuYlhbmKYmxiHzU1SLbNVcl999RXy8vJKlOfn5+Orr74SOqbRHfm4uDj06dMHrq6uZe7j5OSEDh06CFWIiIiIiKiqGTRoEDIzM0uUZ2dn6y9CZSyjO/IzZszA4sWLS5QvXrwYM2fOFKoEERERETkGSbLMVtmpqgqplIpevHgR3t7eQsc0eo78/Pnz8e2335Yob9q0KXr37o2xY8cKVYTsCy/4IYa5iWFuYpibGOYmhrmJccTcLNHxrswd+cjISEiSBEmS0KVLFzg5/dv91ul0OHv2LB577DGhYxvdkU9NTUVwcHCJcn9/f1y+fFmoEmRfNBqN0ZcQJuYmirmJYW5imJsY5iaGuTmGHj16AADi4+MRHR0NDw8P/W0uLi4ICwvDs88+K3RsozvyoaGh2LNnD+rWrWtQvmfPHq5U4yBUVUV2djY8PT1L/YqISsfcxDA3McxNDHMTw9zEOGpukqRCMvPJqeY+njnFxsYCAMLCwtC7d+9yzzM1ltFz5IcNG4bRo0djyZIlOH/+PM6fP4/FixdjzJgxGDZsmNkqRpWXoihITk52vLPsTcTcxDA3McxNDHMTw9zEMDfH0rlzZ1y7dk3/+/79+zF69GgsWLBA+JhGj8i/9dZbuHHjBl599VXk5+cDALRaLcaOHYvx48cLV4SIiIiIqKp68cUX8fLLL6Nfv35ITU1F165d0axZMyxfvhypqamYNGmS0cc0ekRekiTMnDkT165dw++//47Dhw8jLS1N6MGJiIjI/G7fvo3Zs2ejXbt28PLygru7Oxo1aoRXXnkFycnJuHz5Mnr16oW6devqT8Lr3bu3ratNVKUdPXoUbdu2BQCsXr0aERER2Lt3L5YvX46lS5cKHdPoEfliHh4euP/++0XvTnaOV6ETw9zEMDcxzE2MveeWnp6Odu3a4dSpU/oyrVaLlJQULFiwAElJSTh37hzOnz8PHx8faLVa3L592+THtffcbMUhc7PEBZwq8Rz5YgUFBfr58du2bcNTTz0FAAgPDxdeMMboEfnc3FxMnDgR7du3R4MGDVCvXj2Djao+jUaD8PBwh1wyyxTMTQxzE8PcxFSF3Hr27KnvxI8YMQIFBQU4dOgQevfujVWrVqFNmzb4/PPP0bp1a0ycOBGBgYEmP2ZVyM0WmJtjadq0KebNm4ddu3Zh69at+iUn//77b/j6+god0+gR+aFDh2Lnzp3o168fgoODHeosayqiKArS09NRo0YNyLLRnwUdFnMTw9zEMDcx9p5bRkYGdu7cCQBo0aIFPvnkE0iShPDwcP1X9y+88AIA4IMPPjDb49p7brbiqLlJ/2zmPmZlN3PmTPTs2RMffvghBgwYgBYtWgAAfvjhB/2UG2MZ3ZH/+eefsXHjRjz44INCD0j2T1VVXLhwAdWrV7d1VewKcxPD3MQwNzH2ntu2bdv0Pz/88MNWG2yz99xsxVFzc7TlJ4t17NgR169fR1ZWFmrUqKEvf/nll+Hu7i50TKM78jVq1ICPj4/QgxEREZHlpKen63/mN+ZElY9Go0FhYSF2794NAGjcuDHCwsKEj2f09zjvvfceJk2ahJs3bwo/KBEREVWQqgLXrwOnThVtN24UlZUiMjJS//Pu3buhlrEfkS0Vj8ibe6vscnNzMXjwYAQHB+ORRx7BI488gpCQEAwZMkS4X210R37WrFnYsmULAgMDERERgVatWhls5Bg8PT1tXQW7xNzEMDcxzE1MpcktMxP45BOgaVPA3x9o1Kho8/MDIiKAzz4DsrIM7tK6dWtUq1YNAHDo0CG88847KCws1N++bds27N271yLVrTS52Rnm5jhiYmKwc+dO/Pjjj8jIyEBGRgY2bNiAnTt34o033hA6pqQa+XF9ypQp5d5efBnaqiorKwve3t7IzMyEl5eXratDRERVTWEhMGFCUSf+XqN01aoBo0YBU6cC/6x8smLFCrz00kv6q4V6eXkhODgYZ8+eRX5+PhYuXIhOnTqhWbNmqFatGjIyMqDT6eDh4aFfweb06dMWbSJZT2XqtxTXpcdP/4NzNTezHrsg9xa+f2JMpWhnWfz8/LB27Vp07NjRoHz79u144YUXDK76WlFGz5Gv6h11ujdFUXD16lUEBAQ41Fn2pmJuYpibGOYmxua53boFPPccsGmTQfHZ+xrjYmDR8nShqdcQdvyfNeJzc4Hp04GEBGD1akCrRZ8+fRAQEIARI0bg9OnTyMrKQnZ2NmrUqIGnn34aixYtwrBhwwDAYP34nJwc5OTkCFXb5rnZKebmWG7evFnqcq8BAQHCU2uELgiVkZGBtWvX4syZM3jrrbfg4+ODgwcPIjAwEDVr1hSqCNkPVVWRmpoKf39/W1fFrjA3McxNDHMTY9PcdDqgb199J15xdsKmRx/GN53b4UztEDjLRSPuBTodGqZcwktxf+CxbbshFxYCP/4I9O8PrFwJyDK6dOmCEydOWK3q/HsT46i5SVLRZu5jVnZRUVGIjY3FV199pb8Q2K1btzBlyhRERUUJHdPojvyRI0fQtWtXeHt749y5cxg2bBh8fHywfv16pKSk4KuvvhKqCBERkUObNw/47jsAQL67O0a/PRQHmzZCDVctgmTDCwalN26IqWGh+LltBGZ/uAjOt24Ba9YAXboAr7xii9oT0T3MnTsX0dHRqFWrln4N+cOHD0Or1WLLli1CxzT6e5yYmBgMHDgQp06dMriscLdu3fDbb78JVYKIiMihKQowd67+17GjByIhIhz+btXgJJe86qeTrIG/WzXEt2iCd0YN+PeGjz8uc0UbosrCUVetadasGU6dOoUZM2agZcuWaNmyJd5//32cOnUKTZs2FTqm0R35P//8E6+U8mm/Zs2aSE1NFapERUybNg3t27eHu7t7qRdOOHz4MPr06YPQ0FC4ubmhSZMmmHvHi2KxHTt2oFWrVnB1dUWDBg30V7qjipMkCT4+Plyj2EjMTQxzE8PcxNgst19/LVpaEsDxlk2xu0U4qrve+2TA6q5u2BF5H041Cy8qOHYMsMGgGv/exDhqbo7akQcAd3d3DBs2DLNmzcKsWbMwdOhQuLmJn/hrdEfe1dUVWXctdwUAJ0+etOgcr/z8fDz//PMYPnx4qbcfOHAAAQEB+Oabb5CYmIh3330X48ePx6effqrf5+zZs+jevTs6deqE+Ph4jB49GkOHDhX+OsNRybKM2rVr88QcIzE3McxNDHMTY7PcFi7U//h11yh4u2rL2dmQl4srvu56x/zaO45lLfx7E8PcHMuMGTOwePHiEuWLFy/GzJkzhY5p9F/OU089halTp6KgoABA0afJlJQUjB07Fs8++6xQJSpiypQpGDNmDCIiIkq9ffDgwZg7dy46dOiAevXq4aWXXsKgQYOwfv16/T7z5s1D3bp1MWvWLDRp0gQjR47Ec889h//9738Wq3dVpCgKUlJS9EubUcUwNzHMTQxzE2Oz3BITAQCFLi74tVVTaDUVP4XNzckZW++PgOL0zxScY8csUcNy8e9NjKPmVnyyq7m3ym7+/PkIDw8vUd60aVPMmzdP6JhGn+w6a9YsPPfccwgICMCtW7fQoUMHpKamIioqCtOmTROqhKVkZmbCx8dH//u+ffvQtWtXg32io6MxevToMo+Rl5eHvLw8/e/F30bodDrodDoARR9mZFmGoigGV9ErLi/e717lsixDkqRSywGU+I9eVrlGo4GqqqWW313HssrLa5Oqqrhx4waCgoKg+WfdYntvkzWep8LCQoPcqkKbrPE86XQ63LhxA8HBwVWmTfequznaVJxbSEhIqXW0xzaVV26uNhnz+mbWNmVlQXF2Rq5PDUCjgRMk6ABABe6cIa8CUCRAUu8ciZMAZyfc9vSANjMbanZ20Qo4sN7zdPfrW1X7/1ReuSltuvv1zRJtunsfsp3U1FQEBweXKPf398fly5eFjml0R97b2xtbt27Fnj17cPjwYeTk5KBVq1YlOsi2tnfvXqxatQobN27Ul6WmppZYvzMwMBBZWVm4detWqXOUZsyYUepFsBITE+Hh4QEA8PHxQe3atXHx4kWkpaXp9wkKCkJQUBDOnTuH7OxsfXloaCh8fX1x6tQpgzV869WrBy8vLxw7dszgP17jxo3h4uKChIQEgzpEREQgPz8fSUlJ+jKNRoOIiAhkZ2cjOTlZX67VahEeHo709HRcuHBBX+7p6Yn69evj6tWrBuc4lNcmf39/ZGdnIzExUT+vz97bZI3n6cSJE0hLS0NiYiKcnJyqRJus8Typqoq0tDRcu3YNISEhVaJN1nieijsDeXl5OPXP3Gt7bxNg+eepZs2ayM3NNXh9s0qb/P2R9MQT0Dk7I9qpBlwUZ/yuuYXqkNFU+XeazS0oOKi5jQBo0EBx1ZdfkAGnvAJcbd0aqV27Fq0rb8XnKTExUf/6JklSlfv/pH+ezNym4te3v//+G3Xq1LFIm0SvDWBJElRIMO+cdnMfzxJCQ0OxZ88e1K1b16B8z549CAkJETqm0Vd2/eqrr9CrVy+4uroalOfn52PlypXo379/hY81bty4e84JOn78uMHXEEuXLsXo0aORkZFR5n2OHj2KTp06YdSoUZgwYYK+vFGjRhg0aBDGjx+vL9u0aRO6d++OmzdvltqRL21EPjQ0FGlpaforh1Wl0YG761jWiNWRI0fQtGlTjsgb0aaCggIkJibqc6sKbbLWiHxiYiKaNWsGZ2fnKtGme9XdXCPyiYmJiIiIKHEinb22qbxyc47IV/T1zaxteughKH/+CQDo/eE43KobBkkjV2hEvlBR4HnmDFbETIciy1A7dgR++cWgjpZ+nu5+fatq/5/KKzd1RP7O1zdLtCkrKws+Pj6V4oqnxVd2fX7zRxa5suuax96sFO0sywcffIAPPvgAH374ITp37gwAiIuLw9tvv4033njDoH9aUUaPyA8aNAiPPfYYAgICDMqzs7MxaNAgozryb7zxBgYOHFjuPvXq1TOqfseOHUOXLl3w8ssvG3TigaJPtleuXDEou3LlCry8vMo8Y9jV1bXEhxYA+o7Ynco6WeXu/axRLklSqeVl1dGYckVREBwcDCcnpxK322ubzFXH8sqdnJxK5GbvbbLG8yRJEoKDg/XHrAptskQd7y4vzk2W5VIf1x7bdK9yc7TJVq9veOYZaPbuBQD0/nUfZvcPgo/GHZCA0iZGqHeUpxfcxstxvxe1SVGAp58GrPz+VNrrW1n782+v7Nc3S7SprH1syRKrzNjDqjVvvfUWbty4gVdffRX5+fkAir69GTt2rFAnHhDoyKuqWmJ0BwAuXrwIb29vo47l7+9v1pVuEhMT0blzZwwYMKDU+fpRUVHYdNdlr7du3Sp8NS1HJcsygoKCbF0Nu8PcxDA3McxNjM1yGzgQmDABuH0b3bf/jo97PooCF1f91VzLUqDo4JKZjcd2FHXk4e5edIVXK+Pfmxjm5lgkScLMmTMxceJEHD9+HG5ubmjYsGGpA8YVVeFVayIjI9GqVStIkoQuXbqgVatW+q1FixZ4+OGHLTpPPiUlBfHx8UhJSYFOp0N8fDzi4+P1c7+Kp9P85z//QUxMDFJTU5Gamopr167pj/F///d/SE5Oxttvv40TJ07g888/x+rVqzFmzBiL1bsq0ul0OHPmDE+gMRJzE8PcxDA3MTbLzdcX6N0bAKDNzsEnny5HZlYm8supR75Oh6zMDHz6yddwyb1ZVPjii0Ap11qxNP69iXHU3CQUdUDNudnBojV6Hh4euP/++9GsWTOTOvGAESPyPXr0AADEx8cjOjpaf6InALi4uCAsLMyiy09OmjQJy5Yt0/8eGRkJANi+fTs6duyItWvX4tq1a/jmm2/wzTff6PerU6cOzp07BwCoW7cuNm7ciDFjxmDu3LmoVasWFi1ahOjoaIvVu6q686QbqjjmJoa5iWFuYmyW28SJwA8/AGlpaH7gCJZ8sAjvDn4WF0IC4eXiCpd/RufzFR2y8m+j9t9X8OmitWh49J+TL/38gHfftU3dwb83UcyNTFHhjnxsbCwAICwsDL169YJWW/GLVZjD0qVLy70K6+TJkzF58uR7Hqdjx444dOiQ+SpGRERkDvXqARs2ANHRwM2bCD9yHOtG/xdHWzXHqo5tcD7AB5IK1Lmahl479qPpoaP/3rdataIPAWFhNqs+UYVJMP8Quj0NyZuR0XPkBwwYYIl6EBER0UMPATt2AE8+CfyzOEOzg0fQ7OCRsu8TFARs3Ai0amWdOhKZSJZUyGY+OdXcx7MXRl/ZVafT4aOPPkLbtm0RFBQEHx8fg42qPkmSEBoaWupJz1Q25iaGuYlhbmIqRW733190ddYPPywapS9LgwbArFlF+9q4E18pcrNDzI1MZfSI/JQpU7Bo0SK88cYbmDBhAt59912cO3cO33//PSZNmmSJOlIlI8syfH19bV0Nu8PcxDA3McxNTKXJzccHePNNICYG2LoV2LULKL5QkI8P8MgjQNeuQBnLFVpbpcnNzjhqbo66/KQlGN2RX758ORYuXIju3btj8uTJ6NOnD+rXr4/mzZvj999/x+uvv26JelIlotPpcOrUKTRs2LBSrk9bWTE3McxNDHMTU+lyk+WiOfOVfFGGSpebnWBuZCqjP8qnpqYiIiICQNHyOZmZmQCAJ554Ahs3bjRv7ajSuvOS0FRxzE0McxPD3MQwNzHMTYwj5lZ0rqtq5s0xGd2Rr1WrFi5fvgwAqF+/Pn755zLQf/75p8lrYRIRERERUcUY3ZHv2bMn4uLiAACvvfYaJk6ciIYNG6J///4YPHiw2StIRERERFVH8Rx5c2+OyOg58u+//77+5169eqFOnTrYu3cvGjZsiCeffNKslaPKSZZl1KtXD3IlOcnKXjA3McxNDHMTw9zEMDcxzI1MZfJfzgMPPICYmBi0a9cO06dPN0edqJKTJAleXl5cLstIzE0McxPD3MQwNzHMTYyj5iYDkCUzb7ZulI2Yrd2XL1/GxIkTzXU4qsR0Oh0SEhKg0+lsXRW7wtzEMDcxzE0McxPD3MQ4am7mP9G1aHNEjvoBhkzkaC865sLcxDA3McxNDHMTw9zEMDcyhdFz5ImIiIiIRPGCUObDEXkiIiIiIjtU4RH5mJiYcm+/du2ayZUh+yDLMho3bsyz7I3E3MQwNzHMTQxzE8PcxDhqbrKkQjbzCLq5j2cvKtyRP3To0D33eeSRR0yqDNkPFxcXW1fBLjE3McxNDHMTw9zEMDcxzI1MUeGO/Pbt2y1ZD7IjiqIgISEBERER0Gg0tq6O3WBuYpibGOYmhrmJYW5iHDU3S6wyw1VrBOzZswd5eXnmqgsREREREVWQSR35xx9/HJcuXTJXXYiIiIioijP7xaD+2RyRSR15VXXMrzGIiIiISEzxya7m3kR89tlnCAsLg1arRbt27bB///4y9+3YsSMkSSqxde/eXTQKkznWadJkFrIsIyIiwuHOsjcVcxPD3MQwNzHMTQxzE8PcbGvVqlWIiYlBbGwsDh48iBYtWiA6OhpXr14tdf/169fj8uXL+u3o0aPQaDR4/vnnrVzzf5n0lzN//nwEBgaaqy5kR/Lz821dBbvE3MQwNzHMTQxzE8PcxDhmbqr+hFdzbRA42XX27NkYNmwYBg0ahPvuuw/z5s2Du7s7Fi9eXOr+Pj4+CAoK0m9bt26Fu7u7/XbkX3zxRVSrVs1cdSE7oSgKkpKSoCiKratiV5ibGOYmhrmJYW5imJsY5mZ+WVlZBltZi7Lk5+fjwIED6Nq1q75MlmV07doV+/btq9Bjffnll+jdu7dN+8L8LoeIiIiIrMaSc+RDQ0Ph7e2t32bMmFFqHa5fvw6dTldiZklgYCBSU1Pv2Yb9+/fj6NGjGDp0qOmBmKDC68gTEREREVVmFy5cgJeXl/53V1dXizzOl19+iYiICLRt29Yix68oduRJiCNduMKcmJsY5iaGuYlhbmKYmxhHzE2SVEiCq8yUd0wA8PLyMujIl8XPzw8ajQZXrlwxKL9y5QqCgoLKvW9ubi5WrlyJqVOnilfYTDi1hoym0Wgc7ip05sDcxDA3McxNDHMTw9zEMDfbcXFxQevWrREXF6cvUxQFcXFxiIqKKve+a9asQV5eHl566SVLV/Oe2JEno6mqiqysLF5HwEjMTQxzE8PcxDA3McxNjKPmJltoM1ZMTAwWLlyIZcuW4fjx4xg+fDhyc3MxaNAgAED//v0xfvz4Evf78ssv0aNHD/j6+go8qnmxI09GUxQFycnJPMveSMxNDHMTw9zEMDcxzE2Mo+ZWWS4I1atXL3z00UeYNGkSWrZsifj4eGzevFl/AmxKSgouX75scJ+kpCTs3r0bQ4YMMUsWpuIceSIiIiJySCNHjsTIkSNLvW3Hjh0lyho3blypvkFhR56IiIiIrObfiziZ95iOiFNrSIhWq7V1FewScxPD3MQwNzHMTQxzE8PcyBQckSejaTQahIeH27oadoe5iWFuYpibGOYmhrmJcdTcROe03+uYjogj8mQ0RVFw48YNhzs5x1TMTQxzE8PcxDA3McxNDHMjU7EjT0ZTVRUXLlyoVCd72APmJoa5iWFuYpibGOYmxlFzK74glLk3R8SOPBERERGRHeIceSIiIiKyGs6RNx925EmIp6enratgl5ibGOYmhrmJYW5imJsYR8xNhgrZzMtFmvt49oIdeTKaRqNB/fr1bV0Nu8PcxDA3McxNDHMTw9zEMDcyFefIk9EURUFqairPsjcScxPD3MQwNzHMTQxzE+OouRVfEMrcmyNiR56MpqoqUlNTHe4se1MxNzHMTQxzE8PcxDA3McyNTMWpNURERERkNZIFTnbl8pNERERERGQ3OCJPRpMkCT4+PpAkydZVsSvMTQxzE8PcxDA3McxNjKPmJkvmXy5SdqwI9diRJ6PJsozatWvbuhp2h7mJYW5imJsY5iaGuYlhbmQqTq0hoymKgpSUFIc7y95UzE0McxPD3MQwNzHMTYyj5la8jry5N0fEjjwZTVVVpKWl8Sx7IzE3McxNDHMTw9zEMDcxjpqbJKkW2RwRO/JERERERHaIc+SJiIiIyGpkCyw/ae7j2QuOyJPRJElCUFCQw51lbyrmJoa5iWFuYpibGOYmhrmRqTgiT0aTZRlBQUG2robdYW5imJsY5iaGuYlhbmIcNTdLnJzKk12JKkin0+HMmTPQ6XS2ropdYW5imJsY5iaGuYlhbmKYG5mKI/IkJDs729ZVsEvMTQxzE8PcxDA3McxNjCPmJkOBDPMuuWnu49kLjsgTEREREdkhjsgTERERkdVIUtFm7mM6InbkyWiSJCE0NJRn2RuJuYlhbmKYmxjmJoa5iXHU3DSSCo2Zl4s09/HsBTvyZDRZluHr62vratgd5iaGuYlhbmKYmxjmJoa5kak4R56MptPpcOLECZ5lbyTmJoa5iWFuYpibGOYmxlFzk6BYZHNE7MiTkNu3b9u6CnaJuYlhbmKYmxjmJoa5iWFuZApOrSEiIiIiq5EkFbKZ57RLDjpHniPyRERERER2iCPyZDRZllGvXj3IMj8HGoO5iWFuYpibGOYmhrmJcdTcZKiQYd4RdHMfz16wI09GkyQJXl5etq6G3WFuYpibGOYmhrmJYW5imBuZyrE+ApJZ6HQ6JCQkONxZ9qZibmKYmxjmJoa5iWFuYhw1NxlFc+TNunFEnqjiHO1Fx1yYmxjmJoa5iWFuYpibGEfMTYYC2czLRZr7ePaCI/JERERERHaII/JEREREZDUyzD+S7Kgj047abjKBLMto3Lixw51lbyrmJoa5iWFuYpibGOYmhrmRqTgiT0JcXFxsXQW7xNzEMDcxzE0McxPD3MQ4Ym6ypECWzDxH3szHsxf8CEhGUxQFCQkJUBTH/E8jirmJYW5imJsY5iaGuYlhbmQqjsgTERERkdUULRlp7hF5x1x+kiPyRERERER2iCPyRERERGQ1XLXGfBy13WQCWZYRERHBs+yNxNzEMDcxzE0McxPD3MQwNzIV/3JISH5+vq2rYJeYmxjmJoa5iWFuYpibGEfMrXjVGnNvjogdeTKaoihISkriWfZGYm5imJsY5iaGuYlhbmIcNTcJKmQzbxJ4sisREREREdkJnuxKRERERFZTtPykeUfQufwkkRE0Go2tq2CXmJsY5iaGuYlhbmKYmxjmRqbgiDwZTaPRICIiwtbVsDvMTQxzE8PcxDA3McxNjKPmJkOBDDNfEMrMx7MXHJEno6mqiqysLKiqY36NJYq5iWFuYpibGOYmhrmJYW5kKnbkyWiKoiA5OdnhzrI3FXMTw9zEMDcxzE0McxPjqLmZe8Wa4s0RsSNPRERERGSH7KYjP23aNLRv3x7u7u6oXr16ufveuHEDtWrVgiRJyMjIMLhtx44daNWqFVxdXdGgQQMsXbrUYnUmIiIiIkMckTcfu+nI5+fn4/nnn8fw4cPvue+QIUPQvHnzEuVnz55F9+7d0alTJ8THx2P06NEYOnQotmzZYokqV2lardbWVbBLzE0McxPD3MQwNzHMTYwj5qaRFItsjshuOvJTpkzBmDFj7nl29xdffIGMjAy8+eabJW6bN28e6tati1mzZqFJkyYYOXIknnvuOfzvf/+zVLWrJI1Gg/DwcC6ZZSTmJoa5iWFuYpibGOYmhrnZ3meffYawsDBotVq0a9cO+/fvL3f/jIwMjBgxAsHBwXB1dUWjRo2wadMmK9W2pCq1/OSxY8cwdepU/PHHH0hOTi5x+759+9C1a1eDsujoaIwePbrMY+bl5SEvL0//e1ZWFgBAp9NBp9MBACRJgizLUBTF4Mzz4vLi/e5VLssyJEkqtRxAiZNhyirXaDRQVbXU8rvrWFZ5eW0CiqYvVa9eXV8He2+TNZ6nwsJCZGRk6HOrCm2yxvOkKAoyMjJQo0YNODk5VYk23avu5miToijIzMxEjRo1cDd7bVN55eZqE1Dx1zd7aZM1nqe7X9+qQpus8Tzd/fpmiTbdvU9lIEGFZOapMCLHW7VqFWJiYjBv3jy0a9cOc+bMQXR0NJKSkhAQEFBi//z8fDz66KMICAjA2rVrUbNmTZw/f/6eU74tqcp05PPy8tCnTx98+OGHqF27dqkd+dTUVAQGBhqUBQYGIisrC7du3YKbm1uJ+8yYMQNTpkwpUZ6YmAgPDw8AgI+PD2rXro2LFy8iLS1Nv09QUBCCgoJw7tw5ZGdn68tDQ0Ph6+uLU6dO4fbt2/ryevXqwcvLC8eOHTP4j9e4cWO4uLggISHBoA4RERHIz89HUlKSvqx4Tdrs7GyDDLRaLcLDw5Geno4LFy7oyz09PVG/fn1cvXoVqamp+vLy2uTv74/jx4/D09NT/8Zn722yxvN04sQJpKWlwcfHB05OTlWiTdZ4nlRVRVpaGpo0aYKQkJAq0SZrPE/FnQE3NzecOnWqSrQJsPzzVLNmTSQlJaFatWr61zd7b5M1nqejR4/qX98kSaoSbbLG81T8+tagQQPUqVPHIm3KyckBlW727NkYNmwYBg0aBKBo5sbGjRuxePFijBs3rsT+ixcvRlpaGvbu3QtnZ2cAQFhYmDWrXIKk2nDx0nHjxmHmzJnl7nP8+HGEh4frf1+6dClGjx5d4iTWmJgY/P3331i5ciWAopNaO3XqhPT0dP0npUaNGmHQoEEYP368/n6bNm1C9+7dcfPmzVI78qWNyIeGhiItLQ1eXl4AqtbowN11LK1cVVUcOXIETZs21X8daO9tssbzVFBQgMTERH1uVaFN1niedDodEhMT0axZMzg7O1eJNt2r7uZoU3FuERER+g6pvbepvHJztcmY1zd7aZM1nqe7X9+qQpus8Tzd/fpmiTZlZWXBx8cHmZmZ+n6LrWRlZcHb2xurjvaFu6eLWY99MzsfvZotx4ULFwza6erqCldX1xL75+fnw93dHWvXrkWPHj305QMGDEBGRgY2bNhQ4j7dunWDj48P3N3dsWHDBvj7++PFF1/E2LFjbTY9yqYj8m+88QYGDhxY7j716tWr0LF+/fVXJCQkYO3atQD+HY3y8/PDu+++iylTpiAoKAhXrlwxuN+VK1fg5eVVaiceKPsPoLgjdqfi/+Sl7WvtckmSSi0vq47GlOt0Ov3x734Me22Tuep4r/K7c6sKbbqbJdpU/KZlrjoaW26vz5MkSWXW3V7bVF65Odpkq9e3ssrt6XkqLTd7b1NFy01p052vb5Zok606mLYSGhpq8HtsbCwmT55cYr/r169Dp9OVOlPjxIkTpR47OTkZv/76K/r27YtNmzbh9OnTePXVV1FQUIDY2FiztcEYNu3I+/v7w9/f3yzHWrduHW7duqX//c8//8TgwYOxa9cu1K9fHwAQFRVV4oSErVu3Iioqyix1cCSenp62roJdYm5imJsY5iaGuYlhbmIcMTcJCiSYd5WZ4uOVNiJvLoqiICAgAAsWLIBGo0Hr1q1x6dIlfPjhh47ZkTdGSkoK0tLSkJKSAp1Oh/j4eABAgwYN4OHhoe+sF7t+/ToAoEmTJvqpNf/3f/+HTz/9FG+//TYGDx6MX3/9FatXr8bGjRut2RS7p9FoSuRN98bcxDA3McxNDHMTw9zEMDfz8/LyqtAUIj8/P2g0mlJnagQFBZV6n+DgYDg7Oxt8y9GkSROkpqYiPz8fLi7mnS5UEXaz/OSkSZMQGRmJ2NhY5OTkIDIyEpGRkfjrr78qfIy6deti48aN2Lp1K1q0aIFZs2Zh0aJFiI6OtmDNqx5FUZCamupwl5Q2FXMTw9zEMDcxzE0McxPjqLnJkgqNmTdZMu6UTxcXF7Ru3RpxcXH6MkVREBcXV+ZMjQcffBCnT582eL5OnjyJ4OBgm3TiATvqyC9duhSqqpbYOnbsWOr+HTt2hKqqJZYE6tixIw4dOoS8vDycOXPmnnP0qSRVVfVn21PFMTcxzE0McxPD3MQwNzGOmltlubJrTEwMFi5ciGXLluH48eMYPnw4cnNz9avY9O/f32CBlOHDhyMtLQ2jRo3CyZMnsXHjRkyfPh0jRowwWzbGspupNURERERE5tKrVy9cu3YNkyZNQmpqKlq2bInNmzfrT4BNSUkxOAE5NDQUW7ZswZgxY9C8eXPUrFkTo0aNwtixY23VBHbkiYiIiMh6ZEmFLJl3OpGxU2uKjRw5EiNHjiz1th07dpQoi4qKwu+//y70WJZgN1NrqPKQJEl/0Q+qOOYmhrmJYW5imJsY5iaGuZGpOCJPRpNlGbVr17Z1NewOcxPD3MQwNzHMTQxzE+OouclQIJt5+UlzH89ecESejKYoClJSUhzuLHtTMTcxzE0McxPD3MQwNzHMjUzFjjwZTVVVpKWlOdxZ9qZibmKYmxjmJoa5iWFuYhw1N9lCmyNy1HYTEREREdk1zpEnIiIiIqvhHHnzYUeejCZJEoKCgniWvZGYmxjmJoa5iWFuYpibGEfNTZYUCyw/yY48UYXIsoygoCBbV8PuMDcxzE0McxPD3MQwNzHMjUzFOfJkNJ1OhzNnzkCn09m6KnaFuYlhbmKYmxjmJoa5iXHU3DRQLbI5InbkSUh2dratq2CXmJsY5iaGuYlhbmKYmxjmRqbg1BoiIiIishoJKiQzj6Cb+3j2giPyRERERER2iCPyZDRJkhAaGupwZ9mbirmJYW5imJsY5iaGuYlx1NxkSYGGq9aYBTvyZDRZluHr62vratgd5iaGuYlhbmKYmxjmJoa5kak4tYaMptPpcOLECYc7y95UzE0McxPD3MQwNzHMTYyj5iZbaHNEHJEnIbdv37Z1FewScxPD3MQwNzHMTQxzE+OIufHKrubjqB9giIiIiIjsGkfkiYiIiMhqZEkx+8mpjnqyK0fkyWiyLKNevXqQZf75GIO5iWFuYpibGOYmhrmJYW5kKo7Ik9EkSYKXl5etq2F3mJsY5iaGuYlhbmKYmxhHzU3zz2buYzoifgQko+l0OiQkJDjcWfamYm5imJsY5iaGuYlhbmKYG5mKHXkSwhcdMcxNDHMTw9zEMDcxzE1MRXO7ffs2Zs+ejXbt2sHLywvu7u5o1KgRXnnlFSQnJ2Pbtm14+OGH4e/vDxcXFwQEBKBjx47YuHGjhVtgPElSLbI5Ik6tISIiIqrE0tPT0a5dO5w6dUpfptVqkZKSggULFmDnzp24dOkScnJy4OLigsDAQFy/fh07d+7Erl27bFhzsjSOyBMRERFVYj179tR34keMGIGCggIcOnQIvXv3xqpVqzBhwgSkpqZCVVUcOnQI+fn5mDFjBgBAUSrfai4aKBbZHJGkqqpjfhchKCsrC97e3sjMzHTIE1QAQFVV3L59G1qtFpIk2bo6doO5iWFuYpibGOYmhrmJqUhuGRkZqFGjBgCgRYsWOHToUJn7nj9/Hk899RSOHTsGSZJQUFAAWZahKEql6LcU96EOnOwKD09nsx47J7sArRttqxTttCaOyJMQFxcXW1fBLjE3McxNDHMTw9zEMDcx98pt27Zt+p8ffvjhMjvxTzzxBBo1aoQjR46gsLAQBQUFqFatGhYvXmzW+lLlwo48GU1RFCQkJFTKr+sqM+YmhrmJYW5imJsY5ibm558VbNiQAJ2u7NzS09P1P5f3bcdPP/2EmzdvYseOHRg3bhz++9//Ijc3F6NGjTJrnc1BQlEH1Jybo34PxI48ERERkZXt2pSNHj2AbduAdUuygDJmOkdGRup/3r17N8qbEa3RaNChQwdkZWVBVVXUqFEDmZmZ5q46VSLsyBMRERFZQ04OsGAB0LIlvu6+Ql+8fMQeoHFjYPZsIC3N4C6tW7dGtWrVAACHDh3CO++8g8LCQv3t27Ztw969e7Fo0SKk/XPfgoIC7NmzBxkZGZZvk4CiUXTVzJtjctR2ExEREVmHogDvvQfUrAm88goKDx/Fd+ipvzkOXZBx6irwxhtF+4wZA+TnAyiaTrNw4ULIclGX7f3334evry/Cw8Ph6uqKRx99FN999x2mTp0Kf39/hISEYNGiRdi8eXO5o/dUNXDVGiNx1Zqis+wVRYEsy1ydwAjMTQxzE8PcxDA3McytHPn5QL9+wOrV+qLt6IjO2A5AhbOzgoICGV+jH17C8n/v17kz8P33gKcnACAuLg4jRozA6dOnodPpIEkSatSogYceeghnz55FUlKSfqReVVX4+vqidevWGDZsGJ577rlK0W8p7kMdOdkFnp7mvZRRdnYhmjeKqxTttCZeEIqE5OfnQ6vV2roadoe5iWFuYpibGOYmhrmVQlWBl1/Wd+JVScY+36Z4M+ct4DYgSYCHRz4yMrSY7Pw6gryOoVNmIjQF+cCvvwLPPw/8+CPg7IwuXbrgxIkTRlchKyvL3K2iSoRTa8hoiqIgKSmJqxMYibmJYW5imJsY5iaGuZXh22+BZcsAADonZ3zkF425LlE4UfggAMDVOQ99+xyFk5OCcwWRmK99GLHVH0eeW9GceGzZUjRvvoqRJctsjogj8kRERERmlJICxMYC176rA+BHAMA52Q/pOdUgObnhZqE3AKCm12l4uRadoKpTnbE7fQz2qTlYrXsXjXCl6GCT3eAdr2D8uzKaNbNFa8yv+ARVcx/TEbEjT0RERGRGM2cCS5cCwEP/FuaX3K9u9UR4uf7bAU3NDdP/fKr4h9sAVgI5N4ENG8xeVbJznFpDQjQaja2rYJeYmxjmJoa5iWFuYpjbvzp3BiSUP83Iz+0S6lU/Cq2UgZqep+95zEcfNVftbM/cF4Mq3hwRV60xEletISIionuJixiNfkffxmWE6Msa+R5Au5qboZEKoXW6iTsX+Lld6AZF1eBQakccufKwvrw60rEwYAKeu/KZUD0qU7+luC4nTnW2yKo14Q1/rRTttCZH/QBDJlBVVX/VOKo45iaGuYlhbmKYmxjmVlIXxOEwWqCbtElfdvJGa/x8eiAKFFd9J76anxsAQFFlbE3uY9CJf0DzB+LREs/lf2vVulsaT3Y1H3bkyWiKoiA5OZmrExiJuYlhbmKYmxjmJoa5lUKrhT+u40f1SbTyWgFZKlrj/frNmthx7lkAgKyRUKdtMGSNhN8vdsPf2Q0AFE3Luc/jR2x2exJ1kAK4udmsGVS58WRXIiIiInMLDAQAyFAQ7bIaPrXzsO38wArdtX2tHxCRvwpe124UFQQEWKiStqGBBA3MO4Ru7uPZC47IExEREZlbz576H5/EGaSm++l/r1sjEem3ArDx9ECczbgPV3JrIaz6Mf3tqZn+6KY7A0n95xuOZ56xWrXJvrAjT0J49T4xzE0McxPD3MQwNzHM7S59+gDeRevFt81IwqXcFvqb8grdsfb4a7iY2RB/X/XHD0kv4/rNEGikAgDAxZzm6HDrTNHOTk7A0KFWr74lSRbaHBFXrTFSZTr7m4iIiCqxMWOAOXOQhEYIR5JRd41DZ3TGduC554A1a4SrUJn6LcV1OXemK7zMvGpNVnYhwupvqxTttCaOyJPRFEXBjRs3eFKTkZibGOYmhrmJYW5imFsZ3n4bCA7GOjxb6s1N/ffiqfa/QJYLS9y2Ds8CXl7A1KmWriXZMXbkyWiqquLChQtcZsxIzE0McxPD3MQwNzHMrQzBwcDGjVgnv2BQ7CVlYqjnZDylmY3u7eLxao13ESBfMdhnPZ6BsmYd0KSJNWtsFUVTYcz9zzFx1RoiIiIiC7kVHomDd3xR8RB2YbnaF7WzL0B32xkJtwMwJGsRYpUvMRiL8SOeAgCkIhjJ9YLRwEb1JvvAEXkiIiIiC3F1BZ56CvDwUBH72B/Y3ng4auNCif38cAMbQl/DJ0/+gureCqKigFq1bFBhK9BYaHNEHJEnIZ6enraugl1ibmKYmxjmJoa5iWFupZNlYMMGQKeToNG0A9QEYMcOYPt2IC0NnrVqFc2lf+ABSI8/jpEaDYbrAI2j9kzJKFy1xkiV6exvIiIiovJUpn5LcV0unXkUXp7O5j12dgFq1t9aKdppTZxaQ0ZTFAWpqalcncBIzE0McxPD3MQwNzHMTQxzI1OxI09GU1UVqampXJ3ASMxNDHMTw9zEMDcxzE2Mo+amkSSLbI6IHXkiIiIiIjvEk12JiIiIyGokyJDMPJZs7uPZC3bkyWiSJMHHxweSg36NJYq5iWFuYpibGOYmhrmJcdTcZJh/SohjduPZkScBsiyjdu3atq6G3WFuYpibGOYmhrmJYW5imBuZylE/wJAJFEVBSkoKz7I3EnMTw9zEMDcxzE0McxPjqLnJFvon4rPPPkNYWBi0Wi3atWuH/fv3l7nv0qVLIUmSwabVakVjMAt25MloqqoiLS3N4c6yNxVzE8PcxDA3McxNDHMTw9xsa9WqVYiJiUFsbCwOHjyIFi1aIDo6GlevXi3zPl5eXrh8+bJ+O3/+vBVrXBI78kRERERkNZVlRH727NkYNmwYBg0ahPvuuw/z5s2Du7s7Fi9eXOZ9JElCUFCQfgsMDDQlCpNxjryRij81Z2Vl2bgmtqPT6ZCTk4OsrCxoeA3pCmNuYpibGOYmhrmJYW5irJFbcX+lMo36Z2UXWuyYd/fPXF1d4erqWmL//Px8HDhwAOPHj9eXybKMrl27Yt++fWU+Tk5ODurUqQNFUdCqVStMnz4dTZs2NVMrjMeOvJGys7MBAKGhoTauCREREVHFZGdnw9vb26Z1cHFxQVBQEOq02GyR43t4eJTon8XGxmLy5Mkl9r1+/Tp0Ol2JEfXAwECcOHGi1OM3btwYixcvRvPmzZGZmYmPPvoI7du3R2JiImrVqmW2dhiDHXkjhYSE4MKFC/D09HS45aKKZWVlITQ0FBcuXICXl5etq2M3mJsY5iaGuYlhbmKYmxhr5KaqKrKzsxESEmKR4xtDq9Xi7NmzyM/Pt8jxVVUt0TcrbTReVFRUFKKiovS/t2/fHk2aNMH8+fPx3nvvme1xjMGOvJFkWbbZp67KxsvLiy/YApibGOYmhrmJYW5imJsYS+dm65H4O2m1Wpuv9AIAfn5+0Gg0uHLlikH5lStXEBQUVKFjODs7IzIyEqdPn7ZEFSuEJ7sSERERkUNxcXFB69atERcXpy9TFAVxcXEGo+7l0el0SEhIQHBwsKWqeU8ckSciIiIihxMTE4MBAwagTZs2aNu2LebMmYPc3FwMGjQIANC/f3/UrFkTM2bMAABMnToVDzzwABo0aICMjAx8+OGHOH/+PIYOHWqzNrAjT0ZzdXVFbGysWeedOQLmJoa5iWFuYpibGOYmhrnZVq9evXDt2jVMmjQJqampaNmyJTZv3qw/ATYlJQWy/O/klfT0dAwbNgypqamoUaMGWrdujb179+K+++6zVRMgqZVpPSIiIiIiIqoQzpEnIiIiIrJD7MgTEREREdkhduSJiIiIiOwQO/JERERERHaIHXnCZ599hrCwMGi1WrRr1w779+8vd/85c+agcePGcHNzQ2hoKMaMGYPbt28b7HPp0iW89NJL8PX1hZubGyIiIvDXX39ZshlWZ+7cdDodJk6ciLp168LNzQ3169fHe++9h6p2ProxuRUUFGDq1KmoX78+tFotWrRogc2bS17a29jnwh6ZO7cZM2bg/vvvh6enJwICAtCjRw8kJSVZuhlWZ4m/t2Lvv/8+JEnC6NGjLVBz27JEbnxfMFSR3BzlfYFMoJJDW7lyperi4qIuXrxYTUxMVIcNG6ZWr15dvXLlSqn7L1++XHV1dVWXL1+unj17Vt2yZYsaHBysjhkzRr9PWlqaWqdOHXXgwIHqH3/8oSYnJ6tbtmxRT58+ba1mWZwlcps2bZrq6+ur/vTTT+rZs2fVNWvWqB4eHurcuXOt1SyLMza3t99+Ww0JCVE3btyonjlzRv38889VrVarHjx4UPiY9sgSuUVHR6tLlixRjx49qsbHx6vdunVTa9eurebk5FirWRZnidyK7d+/Xw0LC1ObN2+ujho1ysItsS5L5Mb3hZIqkpsjvC+QadiRd3Bt27ZVR4wYof9dp9OpISEh6owZM0rdf8SIEWrnzp0NymJiYtQHH3xQ//vYsWPVhx56yDIVriQskVv37t3VwYMHG+zzzDPPqH379jVjzW3L2NyCg4PVTz/91KDs7kyMPaY9skRud7t69aoKQN25c6d5Kl0JWCq37OxstWHDhurWrVvVDh06VLmOvCVy4/tCSRXJzRHeF8g0nFrjwPLz83HgwAF07dpVXybLMrp27Yp9+/aVep/27dvjwIED+q8Lk5OTsWnTJnTr1k2/zw8//IA2bdrg+eefR0BAACIjI7Fw4ULLNsaKLJVb+/btERcXh5MnTwIADh8+jN27d+Pxxx+3YGusRyS3vLw8aLVagzI3Nzfs3r1b+Jj2xhK5lSYzMxMA4OPjY4Za254lcxsxYgS6d+9ucOyqwlK58X2hpIrkVtXfF8h0vLKrA7t+/Tp0Op3+CmbFAgMDceLEiVLv8+KLL+L69et46KGHoKoqCgsL8X//939455139PskJyfjiy++QExMDN555x38+eefeP311+Hi4oIBAwZYtE3WYKncxo0bh6ysLISHh0Oj0UCn02HatGno27evRdtjLSK5RUdHY/bs2XjkkUdQv359xMXFYf369dDpdMLHtDeWyO1uiqJg9OjRePDBB9GsWTOzt8EWLJXbypUrcfDgQfz5558Wrb+tWCo3vi+UVJHcqvr7ApmOI/JklB07dmD69On4/PPPcfDgQaxfvx4bN27Ee++9p99HURS0atUK06dPR2RkJF5++WUMGzYM8+bNs2HNbasiua1evRrLly/Ht99+i4MHD2LZsmX46KOPsGzZMhvW3Lbmzp2Lhg0bIjw8HC4uLhg5ciQGDRpkcMlsKsnY3EaMGIGjR49i5cqVVq5p5XKv3C5cuIBRo0Zh+fLlJUZSHVlF/t74vlBSRXLj+wLdC98NHZifnx80Gg2uXLliUH7lyhUEBQWVep+JEyeiX79+GDp0KCIiItCzZ09Mnz4dM2bMgKIoAIDg4GDcd999Bvdr0qQJUlJSLNMQK7NUbm+99RbGjRuH3r17IyIiAv369cOYMWMwY8YMi7fJGkRy8/f3x/fff4/c3FycP38eJ06cgIeHB+rVqyd8THtjidzuNHLkSPz000/Yvn07atWqZZE22IIlcjtw4ACuXr2KVq1awcnJCU5OTti5cyc+/vhjODk5lfmNhz2x1N8b3xdKqkhuVf19gUzHjrwDc3FxQevWrREXF6cvUxQFcXFxiIqKKvU+N2/eLDGqp9FoAEC/HNaDDz5YYhm7kydPok6dOuasvs1YKrey9inu6Ns7kdyKabVa1KxZE4WFhVi3bh2efvppk49pLyyRG1D0dzdy5Eh89913+PXXX1G3bl2LtcEWLJFbly5dkJCQgPj4eP3Wpk0b9O3bF/Hx8fr/0/bMUn9vfF8oW3m5VfX3BTIDm55qSza3cuVK1dXVVV26dKl67Ngx9eWXX1arV6+upqamqqqqqv369VPHjRun3z82Nlb19PRUV6xYoSYnJ6u//PKLWr9+ffWFF17Q77N//37VyclJnTZtmnrq1Cl1+fLlqru7u/rNN99YvX2WYoncBgwYoNasWVO/zNj69etVPz8/9e2337Z6+yzF2Nx+//13dd26deqZM2fU3377Te3cubNat25dNT09vcLHrAoskdvw4cNVb29vdceOHerly5f1282bN63dPIuxRG53q4qr1lgiN74viOXmCO8LZBp25En95JNP1Nq1a6suLi5q27Zt1d9//11/W4cOHdQBAwbofy8oKFAnT56s1q9fX9VqtWpoaKj66quvlnij+/HHH9VmzZqprq6uanh4uLpgwQIrtcZ6zJ1bVlaWOmrUKLV27dqqVqtV69Wrp7777rtqXl6eFVtlecbktmPHDrVJkyaqq6ur6uvrq/br10+9dOmSUcesKsydG4BStyVLllipRdZhib+3O1XFjryqWiY3vi8Yn5ujvC+QOElVeXkwIiIiIiJ7wznyRERERER2iB15IiIiIiI7xI48EREREZEdYkeeiIiIiMgOsSNPRERERGSH2JEnIiIiIrJD7MgTEREREdkhduSJiIiIiOwQO/JERGaUlJSEoKAgZGdnAwCWLl2K6tWrl3ufgQMHokePHkY9TlhYGObMmSNWSSNNnjwZLVu2tMpjmeqBBx7AunXrbF0NIiKrYEeeiEy2b98+aDQadO/e3dZVsbnx48fjtddeg6enZ4XvM3fuXCxdutRylaoEzp07B0mSEB8fb1Au8iGmPBMmTMC4ceOgKIrZjklEVFmxI09EJvvyyy/x2muv4bfffsPff/9t07rk5+fb7LFTUlLw008/YeDAgUbdz9vb+56j9tZgy+xMVVz3xx9/HNnZ2fj5559tXCMiIstjR56ITJKTk4NVq1Zh+PDh6N69e6kjyz/++CPuv/9+aLVa+Pn5oWfPnvrb8vLyMHbsWISGhsLV1RUNGjTAl19+CaD0aSnff/89JEnS/1487WPRokWoW7cutFotAGDz5s146KGHUL16dfj6+uKJJ57AmTNnDI518eJF9OnTBz4+PqhWrRratGmDP/74A+fOnYMsy/jrr78M9p8zZw7q1KlT5mjv6tWr0aJFC9SsWbPEbVu2bEGTJk3g4eGBxx57DJcvX9bfdveodHZ2Nvr27Ytq1aohODgY//vf/9CxY0eMHj3a4Jg3b97E4MGD4enpidq1a2PBggUGt1+4cAEvvPACqlevDh8fHzz99NM4d+5cicedNm0aQkJC0Lhx41LbVWz+/PkIDQ2Fu7s7XnjhBWRmZhrcvmjRIjRp0gRarRbh4eH4/PPP9bfVrVsXABAZGQlJktCxY0dMnjwZy5Ytw4YNGyBJEiRJwo4dO0yqu0ajQbdu3bBy5cpy20JEVBWwI09EJlm9ejXCw8PRuHFjvPTSS1i8eDFUVdXfvnHjRvTs2RPdunXDoUOHEBcXh7Zt2+pv79+/P1asWIGPP/4Yx48fx/z58+Hh4WFUHU6fPo1169Zh/fr1+qkbubm5iImJwV9//YW4uDjIsoyePXvqO+E5OTno0KEDLl26hB9++AGHDx/G22+/DUVREBYWhq5du2LJkiUGj7NkyRIMHDgQslz6S+euXbvQpk2bEuU3b97ERx99hK+//hq//fYbUlJS8Oabb5bZnpiYGOzZswc//PADtm7dil27duHgwYMl9ps1axbatGmDQ4cO4dVXX8Xw4cORlJQEACgoKEB0dDQ8PT2xa9cu7NmzR/8h4s6R97i4OCQlJWHr1q346aefys149erV+PHHH7F582b9YxZbvnw5Jk2ahGnTpuH48eOYPn06Jk6ciGXLlgEA9u/fDwDYtm0bLl++jPXr1+PNN9/ECy+8oP9gc/nyZbRv397kurdt2xa7du0qsy1ERFWGSkRkgvbt26tz5sxRVVVVCwoKVD8/P3X79u3626OiotS+ffuWet+kpCQVgLp169ZSb1+yZInq7e1tUPbdd9+pd750xcbGqs7OzurVq1fLree1a9dUAGpCQoKqqqo6f/581dPTU71x40ap+69atUqtUaOGevv2bVVVVfXAgQOqJEnq2bNny3yMFi1aqFOnTi3RBgDq6dOn9WWfffaZGhgYqP99wIAB6tNPP62qqqpmZWWpzs7O6po1a/S3Z2RkqO7u7uqoUaP0ZXXq1FFfeukl/e+KoqgBAQHqF198oaqqqn799ddq48aNVUVR9Pvk5eWpbm5u6pYtW/SPGxgYqObl5ZXZJlUtylij0agXL17Ul/3888+qLMvq5cuXVVVV1fr166vffvutwf3ee+89NSoqSlVVVT179qwKQD106JDBPne2vZipdd+wYYMqy7Kq0+nKbRcRkb3jiDwRCUtKSsL+/fvRp08fAICTkxN69eqlnxoDAPHx8ejSpUup94+Pj4dGo0GHDh1MqkedOnXg7+9vUHbq1Cn06dMH9erVg5eXF8LCwgAUzWMvfuzIyEj4+PiUeswePXpAo9Hgu+++A1A0zadTp07645Tm1q1b+qk9d3J3d0f9+vX1vwcHB+Pq1aulHiM5ORkFBQUG31p4e3uXOu2lefPm+p8lSUJQUJD+uIcPH8bp06fh6ekJDw8PeHh4wMfHB7dv3zaYYhQREQEXF5cy21Ssdu3aBlOGoqKioCgKkpKSkJubizNnzmDIkCH6x/Lw8MB///vfEtOZKsLUuru5uUFRFOTl5Rn92ERE9sTJ1hUgIvv15ZdforCwECEhIfoyVVXh6uqKTz/9FN7e3nBzcyvz/uXdBgCyLBtM0wGKpozcrVq1aiXKnnzySdSpUwcLFy5ESEgIFEVBs2bN9FMz7vXYLi4u6N+/P5YsWYJnnnkG3377LebOnVvuffz8/JCenl6i3NnZ2eB3SZJKtEtEace9c+pQ69atsXz58hL3u/NDT2nZGSsnJwcAsHDhQrRr187gNo1GI3Q8U+qelpaGatWq3fM5JiKydxyRJyIhhYWF+OqrrzBr1izEx8frt8OHDyMkJAQrVqwAUDRqHBcXV+oxIiIioCgKdu7cWert/v7+yM7ORm5urr7s7uULS3Pjxg0kJSVhwoQJ6NKlC5o0aVKig928eXPEx8cjLS2tzOMMHToU27Ztw+eff47CwkI888wz5T5uZGQkjh07ds/6ladevXpwdnbGn3/+qS/LzMzEyZMnjTpOq1atcOrUKQQEBKBBgwYGm7e3t9H1SklJMViR6Pfff4csy2jcuDECAwMREhKC5OTkEo9VfJJr8ci5TqczOK6Li0uJMlPrfvToUURGRhrdRiIie8OOPBEJ+emnn5Ceno4hQ4agWbNmBtuzzz6rn14TGxuLFStWIDY2FsePH0dCQgJmzpwJoOiiRgMGDMDgwYPx/fff4+zZs9ixYwdWr14NAGjXrh3c3d3xzjvv4MyZM/j2228rtN56jRo14OvriwULFuD06dP49ddfERMTY7BPnz59EBQUhB49emDPnj1ITk7GunXrsG/fPv0+TZo0wQMPPICxY8eiT58+9xzhjY6Oxr59+0p0TI3h6emJAQMG4K233sL27duRmJiIIUOGQJZlg9V67qVv377w8/PD008/jV27dumzff3113Hx4kWj66XVajFgwAAcPnwYu3btwuuvv44XXngBQUFBAIApU6ZgxowZ+Pjjj3Hy5EkkJCRgyZIlmD17NgAgICAAbm5u2Lx5M65cuaJf8SYsLAxHjhxBUlISrl+/joKCApPrvmvXLvznP/8xuo1ERPaGHXkiEvLll1+ia9eupY6QPvvss/jrr79w5MgRdOzYEWvWrMEPP/yAli1bonPnzvoVTADgiy++wHPPPYdXX30V4eHhGDZsmH4E3sfHB9988w02bdqEiIgIrFixApMnT75n3WRZxsqVK3HgwAE0a9YMY8aMwYcffmiwj4uLC3755RcEBASgW7duiIiIwPvvv19iKsiQIUOQn5+PwYMH3/NxH3/8cTg5OWHbtm333Lc8s2fPRlRUFJ544gl07doVDz74oH5Zx4pyd3fHb7/9htq1a+OZZ55BkyZNMGTIENy+fRteXl5G16lBgwZ45pln0K1bN/znP/9B8+bNDZaXHDp0KBYtWoQlS5YgIiICHTp0wNKlS/Uj8k5OTvj4448xf/58hISE4OmnnwYADBs2DI0bN0abNm3g7++PPXv2mFT3S5cuYe/evRg0aJDRbSQisjeSao6JmkREVdR7772HNWvW4MiRIxXa/7PPPsMPP/yALVu2mK0Oubm5qFmzJmbNmoUhQ4aY7bhV0dixY5Genl5iTX0ioqqIJ7sSEZUiJycH586dw6effor//ve/Fb7fK6+8goyMDGRnZ8PT01PosQ8dOoQTJ06gbdu2yMzMxNSpUwFAP4pNZQsICCgxjYqIqKriiDwRUSkGDhyIFStWoEePHvj222+FVl8RdejQIQwdOhRJSUlwcXFB69atMXv2bERERFitDkREVPmxI09EREREZId4sisRERERkR1iR56IiIiIyA6xI09EREREZIfYkSciIiIiskPsyBMRERER2SF25ImIiIiI7BA78kREREREdogdeSIiIiIiO/T/ScF5k0BYXxIAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Pareto front candidates: candidate 3, 1, 2, 4\n",
+            "✅ Weighted selection picks candidate 3 (weighted sum = 45.730)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "scalar_best_idx = int(np.argmax([c[\"accuracy\"] for c in candidates]))\n",
+        "scalar_best = f\"Candidate {scalar_best_idx+1}\"\n",
+        "\n",
+        "weighted_best = f\"Candidate {weighted_best_idx+1}\"   # from Cell 8 recompute\n",
+        "\n",
+        "pareto_candidates = \"Candidate \" + \", \".join([str(i+1) for i in front_idxs]) if front_idxs else \"\"\n",
+        "\n",
+        "tie_break_best = f\"Candidate {best_idx+1}\"\n",
+        "\n",
+        "summary_data = {\n",
+        "    \"Mode\": [\"Scalar\", \"Weighted\", \"Pareto\", \"Tie‑break\"],\n",
+        "    \"Selection Logic\": [\n",
+        "        \"Max of primary metric (accuracy)\",\n",
+        "        \"Weighted sum (after minimise flip)\",\n",
+        "        \"Non‑dominated set\",\n",
+        "        \"Deterministic random tie‑break\"\n",
+        "    ],\n",
+        "    \"Outcome\": [scalar_best, weighted_best, pareto_candidates, tie_break_best]\n",
+        "}\n",
+        "df_summary = pd.DataFrame(summary_data)\n",
+        "from IPython.display import display, Markdown\n",
+        "display(Markdown(\"## Summary of Demonstrated Behaviour\"))\n",
+        "display(df_summary)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 222
+        },
+        "id": "-Dlzj36IfHwB",
+        "outputId": "4844b18e-c5c8-4113-dd27-ee9676633fd8"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "## Summary of Demonstrated Behaviour"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "        Mode                     Selection Logic               Outcome\n",
+              "0     Scalar    Max of primary metric (accuracy)           Candidate 3\n",
+              "1   Weighted  Weighted sum (after minimise flip)           Candidate 3\n",
+              "2     Pareto                   Non‑dominated set  Candidate 3, 1, 2, 4\n",
+              "3  Tie‑break      Deterministic random tie‑break           Candidate 2"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9371f33b-9393-409e-9ac6-e192b1210e18\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Mode</th>\n",
+              "      <th>Selection Logic</th>\n",
+              "      <th>Outcome</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Scalar</td>\n",
+              "      <td>Max of primary metric (accuracy)</td>\n",
+              "      <td>Candidate 3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Weighted</td>\n",
+              "      <td>Weighted sum (after minimise flip)</td>\n",
+              "      <td>Candidate 3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Pareto</td>\n",
+              "      <td>Non‑dominated set</td>\n",
+              "      <td>Candidate 3, 1, 2, 4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Tie‑break</td>\n",
+              "      <td>Deterministic random tie‑break</td>\n",
+              "      <td>Candidate 2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9371f33b-9393-409e-9ac6-e192b1210e18')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-9371f33b-9393-409e-9ac6-e192b1210e18 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-9371f33b-9393-409e-9ac6-e192b1210e18');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_d228bc5b-f0d6-4c55-a231-da3171947621\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_summary')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_d228bc5b-f0d6-4c55-a231-da3171947621 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df_summary');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_summary",
+              "summary": "{\n  \"name\": \"df_summary\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Mode\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Weighted\",\n          \"Tie\\u2011break\",\n          \"Scalar\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Selection Logic\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Weighted sum (after minimise flip)\",\n          \"Deterministic random tie\\u2011break\",\n          \"Max of primary metric (accuracy)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Candidate 3\",\n          \"Candidate 3, 1, 2, 4\",\n          \"Candidate 2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## How This Maps to Real OpenTrace Code (M1+)\n",
+        "---\n",
+        "\n",
+        "| Stub / Demo                                  | Real Implementation Location                          |\n",
+        "|----------------------------------------------|-------------------------------------------------------|\n",
+        "| `ObjectiveConfig`                            | `opto/trainer/objectives.py` (new file)               |\n",
+        "| `normalize_score`, `apply_minimize`, etc.    | `opto/trainer/objectives.py` (pure functions)         |\n",
+        "| `pareto_front`, `weighted_scalarize`         | `opto/trainer/objectives.py`                          |\n",
+        "| `DummyGuide.get_score_dict()`                | `opto/trainer/guide.py` (new helper method)           |\n",
+        "| Weighted/Pareto selection logic              | `BasicSearchAlgorithm` & `BeamsearchAlgorithm` updates|\n",
+        "| Per‑metric logging                          | `BaseLogger` integration (M2)                         |\n",
+        "\n",
+        "**No existing scalar pipeline is changed** – the new path is opt‑in via `ObjectiveConfig`."
+      ],
+      "metadata": {
+        "id": "Rzk-PDfrjiW8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ✅ Milestone 0 – All Client Revisions Implemented\n",
+        "\n",
+        "- ✔️ **StubLLM** + **Real LLM** sections (real LLM guarded by Colab secret).  \n",
+        "- ✔️ **OpenTrace smoke test** – installs `trace-opt` and executes a core training step using real OpenTrace code.  \n",
+        "- ✔️ **Weighted minimization fixed** – non‑negative weights after transform; **assert proves correct direction**.  \n",
+        "- ✔️ **Scalar‑mode demo** explicitly shown.  \n",
+        "- ✔️ **Programmatic summary table** – no hardcoded values.  \n",
+        "- ✔️ **Colab badge** points to real notebook path.\n",
+        "\n",
+        "**M0 is ready for final approval. Proceed to M1 implementation.**"
+      ],
+      "metadata": {
+        "id": "j-tJIehmjsli"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "BgEhsrf12Bjw"
+      }
+    }
+  ]
+}
\ No newline at end of file