diff --git a/.gitattributes b/.gitattributes
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diff --git a/README.md b/README.md
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index 0000000000000000000000000000000000000000..e3db68518943cccf7a67d114b672310107af42fe
--- /dev/null
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@@ -0,0 +1,161 @@
+---
+title: GAIA
+emoji: 🛰️
+colorFrom: blue
+colorTo: green
+# TBD
+# sdk: gradio
+# sdk_version: 3.50.2
+# python_version: 3.8
+# app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+# GAIA: A Foundation Model for Operational Atmospheric Dynamics
+
+We present the GAIA (Geospatial Artificial Intelligence for Atmospheres) Foundation Model, a novel model that combines masked autoencoders (MAE) and Self-Distillation with NO labels (DINO) for analyzing global atmospheric patterns in satellite imagery. By integrating these complementary self-supervised learning approaches, our model simultaneously captures both local features and global dependencies, addressing two critical challenges in satellite data analysis: reconstructing missing regions and estimating precipitation patterns. The model demonstrates superior attention distribution and temporal pattern capture compared to standard MAE approaches, while maintaining robust performance in downstream tasks
+
+## Architecture Overview
+
+GAIA employs a transformer-based architecture specifically designed to handle spatio-temporal satellite data with the following attributes:
+
+- **Backbone**: Masked Autoencoder (MAE) [He et al. 2021](https://arxiv.org/pdf/2111.06377) combined with DINO self-distillation [Caron et al. 2021](https://arxiv.org/pdf/2104.14294) architecture
+
+- **Local-Global Learning**: MAE handles local patterns while DINO captures global atmospheric dynamics
+
+- **Self-Supervised Training**: Self-supervised pre-training on masked satellite imagery
+
+- **Training Strategy**: Our training strategy combines the MAE and DINO loss to learn local and global relationship in the geostationary satellite data
+
+- **Resolution**: Supports processing of medium-resolution geostationary satellite data (0.25 degree)
+
+- **Input Channels**: Single channel (long-wave infrared) from geostationary satellites
+
+![Pretraining architecture](docs/model_architecture.png "GAIA architecture")
+
+## Pre-trained Models
+
+The base GAIA model is pre-trained on a comprehensive dataset of geostationary satellite observations from 2001-2015.
+The pre-trained model weights are available at [this link](https://huggingface.co/bcg-usra-nasa-gaia/GAIA-v1/tree/main/checkpoints).
+
+```python
+model = GAIABase(
+    config_path
+)
+    
+model.configure_model()
+state_dict = torch.load("checkpoints/gaia-v1.pt")
+model.load_state_dict(state_dict)
+```
+
+## Downstream Tasks
+
+The pre-trained model is adapted to the following downstream tasks.
+
+### 1. Gap Filling in Operational Data
+
+GAIA has been fine-tuned to address operational gaps in satellite data:
+
+```python
+model = GAIAGapFill(
+    config_path
+)
+    
+model.configure_model()
+state_dict = torch.load("checkpoints/gaia_gapfill.pt")
+model.load_state_dict(state_dict)
+```
+
+The results showcase several key strengths of our approach:
+ 
+- **Large Gap Reconstruction**: The model successfully reconstructs substantial missing regions while preserving the temperature gradients and atmospheric patterns consistent with surrounding areas.
+- **Pattern Continuity**: The reconstructions maintain smooth transitions between filled regions and original data, avoiding artificial boundaries or discontinuities.
+- **Detail Preservation**: Fine-scale features such as cloud formations and temperature variations are accurately reproduced, suggesting the model has learned meaningful representations of atmospheric physics rather than simple interpolation.
+- **Visible Patch Reconstruction**: The fact that the model's reconstruction closely replicates the fine-grained details in the input image suggests that the encoder is generating rich latent representations that preserve details in the input.
+
+
+### 2. Precipitation Estimation
+
+GAIA was fine-tuned to learn the relationship between Geostationary Operational Environmental Satellite IR and precipitation data:
+
+```python
+model = GAIAPrecip(
+    config_path
+)
+    
+model.configure_model()
+state_dict = torch.load("checkpoints/gaia_precip.pt")
+model.load_state_dict(state_dict)
+```
+
+The model effectively captures the spatial distribution and intensity patterns of precipitation across a variety of atmospheric scenarios. The precipitation predictions obtain an SSIM of 0.881.
+
+
+## Demo and Inference
+
+### Quick Start
+
+1. **Create a Virtual Environment (Recommended)**
+   ```bash
+   conda create -n gaia_env python=3.10 -y
+   conda activate gaia_env
+   ```
+
+2. **Clone the Repository**
+   ```bash
+   git clone https://huggingface.co/bcg-usra-nasa-gaia/GAIA-v1
+   cd GAIA-v1
+   ```
+
+3. **Install Dependencies**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+4. **Run Inference Notebooks**
+   Navigate to the `notebooks` directory to run the demo notebooks:
+   - For gap-filling:
+     ```bash
+     cd notebooks
+     jupyter notebook gap_filling_inference.ipynb
+     ```
+   - For precipitation estimation:
+     ```bash
+     jupyter notebook precipitation_inference.ipynb
+     ```
+
+## Feedback
+
+We welcome feedback and contributions! Please:
+- Open issues for bugs or feature requests
+- Submit pull requests for improvements
+- Share your use cases and results
+
+
+## Citation
+
+If you use GAIA in your research, please cite:
+
+```bibtex
+@article{gaia-fm,
+  title={GAIA: A Foundation Model for Operational Atmospheric Dynamics},
+  author={Ata Akbari Asanjan and Olivia Alexander and Tom Berg and Clara Zhang and Matt Yang and Jad Makki and Disha Shidham and Srija Chakraborty and William Bender and Stephen Peng and Arun Ravindran and Olivier Raiman and David Potere and David Bell},
+  year={2025},
+  eprint={2505.18179},
+  archivePrefix={arXiv},
+  primaryClass={cs.LG},
+  url={https://arxiv.org/abs/2505.18179}, 
+}
+```
+
+### Copyright Notice:
+Copyright © 2025 Boston Consulting Group and Universities Space Research Association. All rights reserved. Unauthorized use, reproduction, or distribution of this software is strictly prohibited unless it is licensed for use under terms in the “Apache 2.0” license.
+
+### Scope and limitations:
+
+GAIA is released strictly as a research prototype meant to showcase how this methodology can learn useful representations from GOES data. It is aimed at research and educational communities to demonstrate the potential of MAEs and DINO in gap filling and precipitation estimation. The model is not a production-ready forecasting tool. It is intended to let researchers experiment in controlled, academic settings.
+
+In offline experiments GAIA’s embeddings have proved valuable for gap filling of infrared imagery and precipitation estimation. This model shows promise and could be extended for further tasks, such as identifying atmospheric rivers or tropical cyclones. However, it is important to note that the model was trained only on infrared channels, covers 60° S–60° N between 2001 and 2015, and has not been tested outside of this data. The method also may lead to issues such as over-smoothed outputs due to the pattern continuity method. Consequently, any quantitative conclusions drawn from GAIA should be treated as diagnostic and must be cross-checked.
+
+Because of these limitations, GAIA must not be used in safety-critical or high-stakes settings such as flight planning, maritime routing, disaster response, or financial and insurance decisions. It should not be repurposed for other applications without extensive additional work. Anyone who wishes to deploy it operationally would need to accomplish task such as retraining or fine-tuning on up-to-date sensor data, conducting rigorous calibration and out-of-distribution testing, applying rigorous human oversight, and obtaining any required regulatory approvals (e.g., from the FAA or national meteorological agencies).
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--- /dev/null
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+version https://git-lfs.github.com/spec/v1
+oid sha256:31a3ea0f8dbb5a472aa6664af01e7feadfe3c2aa5edafbc644ed6752eb0b4fd8
+size 2100463430
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/configs/base_config.yaml b/configs/base_config.yaml
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index 0000000000000000000000000000000000000000..c489bb051aefeabd39bf9531351bb3e9f71bd8f7
--- /dev/null
+++ b/configs/base_config.yaml
@@ -0,0 +1,48 @@
+# Model Configuration
+model:
+  name: "GAIABase"
+  # Image and patch configuration
+  img_size: [1, 480, 1440]    # Hardcoded value: time, height, width
+  patch_size: [1, 30, 30]     # Hardcoded value: time_patch, height_patch, width_patch
+  in_chans: 1               # Hardcoded: Number of input channels
+  
+  # Encoder configuration
+  encoder_embed_dim: 912      # Hardcoded based on original computation
+  encoder_depth: 24
+  encoder_num_heads: 16
+  
+  # Decoder configuration
+  decoder_embed_dim: 512
+  decoder_depth: 8
+  decoder_num_heads: 16
+  
+  # Common configuration
+  mlp_ratio: 4.0
+  norm_pix_loss: false
+  drop_channels_rate: 0
+  
+  # DINO specific configuration
+  adjacent_masking: false
+  norm_last_layer: true
+  dino_head_dim: 1024
+  warmup_teacher_temp: 0.04
+  teacher_temp: 0.04
+  warmup_teacher_temp_epochs: 1
+  student_temp: 0.1
+  center_momentum: 0.9
+  momentum_teacher: 0.996
+
+  # Masking configuration
+  mask_ratio: 0.75
+  
+training:
+  epochs: 100
+  val_step: 0
+  optimizer: 
+    learning_rate: 1.5e-4
+  scheduler: 
+    type: "CosineAnnealingLR"
+    warmup_steps: 500
+    total_steps: 10000
+    eta_min: 1.0e-6
+    last_step: -1
\ No newline at end of file
diff --git a/configs/gap_fill_config.yaml b/configs/gap_fill_config.yaml
new file mode 100644
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--- /dev/null
+++ b/configs/gap_fill_config.yaml
@@ -0,0 +1,35 @@
+# Model Configuration
+model:
+  name: "GapFill"
+  # Image and patch configuration
+  img_size: [1, 480, 1440]   # [time, height, width]
+  patch_size: [1, 30, 30]    # [time_patch, height_patch, width_patch]
+  in_chans: 1                # Number of input channels
+  input_horizon: 1
+  epochs: 100
+  
+  # Encoder configuration
+  encoder_embed_dim: 912
+  encoder_depth: 24
+  encoder_num_heads: 16
+  
+  # Decoder configuration
+  decoder_embed_dim: 512
+  decoder_depth: 16
+  decoder_num_heads: 16
+  
+  # Common configuration
+  mlp_ratio: 4.0
+  norm_pix_loss: false
+  drop_channels_rate: 0.0
+  
+  # Masking configuration
+  adjacent_masking: false
+  mask_ratio: 0.10
+
+  # Pretrained model settings (if applicable)
+  pretrained:
+    enabled: true
+    checkpoint_path: "./checkpoints/foundation_model/last.ckpt"
+    freeze_encoder: false
+      
\ No newline at end of file
diff --git a/configs/precip_config.yaml b/configs/precip_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..effdcbb6ca76aa798b04306009ed78293006184a
--- /dev/null
+++ b/configs/precip_config.yaml
@@ -0,0 +1,32 @@
+# Model Configuration
+model:
+  name: "Precip"
+  # Image and patch configuration
+  img_size: [1, 480, 1440]   # [time, height, width]
+  patch_size: [1, 30, 30]    # [time_patch, height_patch, width_patch]
+  in_chans: 1                # Number of input channels
+  
+  # Encoder configuration
+  encoder_embed_dim: 912
+  encoder_depth: 24
+  encoder_num_heads: 16
+  
+  # Decoder configuration
+  decoder_embed_dim: 512
+  decoder_depth: 8
+  decoder_num_heads: 16
+  
+  # Common configuration
+  mlp_ratio: 4.0
+  norm_pix_loss: false
+  drop_channels_rate: 0.0
+  
+  # Masking configuration
+  adjacent_masking: false
+  mask_ratio: 0
+
+  # Pretrained model settings (if applicable)
+  pretrained:
+    enabled: true
+    checkpoint_path: "./checkpoints/foundation_model/last.ckpt"
+    freeze_encoder: false
\ No newline at end of file
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diff --git a/docs/model_architecture.png b/docs/model_architecture.png
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diff --git a/downstream/gap_fill/gf_dataloader.py b/downstream/gap_fill/gf_dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..a31ce91807a3f8a5925c1ced803c8a95b42b4724
--- /dev/null
+++ b/downstream/gap_fill/gf_dataloader.py
@@ -0,0 +1,167 @@
+import torch
+from torch.utils.data import DataLoader
+import lightning.pytorch as pl
+import xarray as xr
+import numpy as np
+import glob
+import random
+
+
+class GapFillDataset(torch.utils.data.Dataset):
+    def __init__(self, ds):
+        self.ds = ds
+        self.resize_shape = (480, 1440)
+        self.input_horizon = 1
+        self.norm_range = [200, 350]
+
+        if "Tb" not in self.ds.data_vars:
+            raise ValueError("Dataset must contain 'Tb' variable.")
+
+        if len(self.ds.time) <= self.input_horizon:
+            raise ValueError("Dataset too small for input horizon.")
+
+    def __getitem__(self, idx):
+        input_data = self.ds.isel(time=slice(idx, idx + self.input_horizon)).astype(
+            np.float16
+        )
+        # Fill small missing pockets
+        chunks = {"time": 1, "lat": 3298, "lon": 9896}
+        input_data["Tb"] = (
+            input_data["Tb"]
+            .chunk(chunks)
+            .bfill(dim="lat", limit=5)
+            .bfill(dim="lon", limit=5)
+        )
+
+        input_data = self.resize_data(input_data, *self.resize_shape)
+
+        tb_mask = input_data["Tb"].isnull()
+        input_data = xr.where(tb_mask, 0, input_data["Tb"])
+
+        if tb_mask.values.all():
+            return self.__getitem__(random.randint(0, len(self.ds) - 1))
+
+        input_tensor = torch.from_numpy(input_data.values).half().unsqueeze(0)
+        input_mask = torch.from_numpy(
+            np.repeat(
+                tb_mask.values[np.newaxis, :, :, :], input_tensor.shape[0], axis=0
+            )
+        )
+
+        input_tensor = torch.clamp(
+            input_tensor, min=self.norm_range[0], max=self.norm_range[1]
+        )
+        input_tensor = (input_tensor - self.norm_range[0]) / (
+            self.norm_range[1] - self.norm_range[0]
+        )
+
+        timestamps = input_data.time  # an xarray DataArray with datetime64[ns]
+        years = torch.tensor(timestamps.dt.year.values, dtype=torch.int32)
+        doy = torch.tensor(timestamps.dt.dayofyear.values, dtype=torch.int32)
+        hours = torch.tensor(timestamps.dt.hour.values, dtype=torch.int32)
+        minutes = torch.tensor(timestamps.dt.minute.values, dtype=torch.int32)
+
+        return {
+            "x": input_tensor,
+            "x_mask": input_mask,
+            "temporal_pos": [years, doy, hours, minutes],
+        }
+
+    def __len__(self):
+        return len(self.ds.time) - 1 - self.input_horizon
+
+    @staticmethod
+    def resize_data(multi_data, new_lat_size, new_lon_size):
+        new_lat = np.linspace(multi_data.lat.min(), multi_data.lat.max(), new_lat_size)
+        new_lon = np.linspace(multi_data.lon.min(), multi_data.lon.max(), new_lon_size)
+        if "time" in multi_data.dims:
+            return xr.concat(
+                [
+                    multi_data.isel(time=t)
+                    .interp(lat=new_lat, lon=new_lon, method="nearest")
+                    .expand_dims(dim="time")
+                    for t in range(len(multi_data["time"]))
+                ],
+                dim="time",
+            )
+        else:
+            return multi_data.interp(lat=new_lat, lon=new_lon, method="nearest")
+
+
+class TrainingDataModule(pl.LightningDataModule):
+    def __init__(self, data_path, max_files=None):
+        super().__init__()
+        self.data_path = data_path
+
+        chunks = {"time": 1, "lat": 3298, "lon": 9896}
+        file_batch_size = 10
+
+        goes_files = sorted(glob.glob(f"{self.data_path}"))[:max_files]
+        datasets = []
+        for i in range(0, len(goes_files), file_batch_size):
+            batch_files = goes_files[i : i + file_batch_size]
+            batch_ds = xr.open_mfdataset(
+                batch_files,
+                combine="by_coords",
+                parallel=False,
+                chunks=chunks,
+                compat="override",
+                coords="minimal",
+                combine_attrs="override",
+                engine="netcdf4",
+            )
+            datasets.append(batch_ds)
+
+        ds = xr.combine_by_coords(
+            datasets, compat="override", coords="minimal", combine_attrs="override"
+        )
+        ds["time"] = ds["time"].dt.floor("s")
+
+        n = len(ds.time)
+        train_size = int(0.8 * n)
+
+        self.train_dataset = GapFillDataset(ds.isel(time=slice(0, train_size)))
+        self.val_dataset = GapFillDataset(ds.isel(time=slice(train_size, n)))
+
+    def train_dataloader(self):
+        return DataLoader(self.train_dataset, shuffle=True)
+
+    def val_dataloader(self):
+        return DataLoader(self.val_dataset, shuffle=False)
+
+    def on_epoch_end(self):
+        torch.cuda.empty_cache()
+
+
+def create_dataloader(
+    data_path="../data/IR/*/*.nc4",
+    max_files=None,
+):
+    """
+    Create a dataloader for gapfill inference.
+
+    Args:
+        data_path: Full path (including wildcards) to the data files.
+        max_files: Maximum number of files to load. If None, all files are loaded.
+
+    Returns:
+        DataLoader: PyTorch DataLoader object.
+    """
+
+    if torch.cuda.device_count() > 1 and not torch.distributed.is_initialized():
+        torch.distributed.init_process_group(backend="nccl")
+
+    data_module = TrainingDataModule(data_path, max_files=max_files)
+    data_module.setup(stage="test")
+
+    # Use validation dataset if available, else use training dataset.
+    if hasattr(data_module, "val_dataset") and data_module.val_dataset is not None:
+        dataset = data_module.val_dataset
+    else:
+        dataset = data_module.train_dataset
+
+    loader = DataLoader(
+        dataset,
+    )
+
+    return loader
diff --git a/downstream/gap_fill/lightning_presetup.py b/downstream/gap_fill/lightning_presetup.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ffb48791fbc2b97ac53ae64a0eb98e464e59859
--- /dev/null
+++ b/downstream/gap_fill/lightning_presetup.py
@@ -0,0 +1,39 @@
+import torch
+import lightning.pytorch as pl
+import yaml
+from downstream.gap_fill.lightning_wrapper import GapFillLightning
+
+def GAIAGapFill(params = None,
+                config_path = None,
+                img_size=None,
+                patch_size = None,
+                in_chans=None,
+                mask_ratio = None):
+    
+    # Load config if provided
+    config = {}
+    if config_path:
+        with open(config_path, 'r') as f:
+            config = yaml.safe_load(f)['model']
+            
+    model = GapFillLightning(
+        params=params,
+        img_size = img_size or config.get('img_size'),
+        patch_size = patch_size or config.get('patch_size'), 
+        in_chans = in_chans or config.get('in_chans'), 
+        encoder_embed_dim = config.get('encoder_embed_dim'),
+        encoder_depth = config.get('encoder_depth'), 
+        encoder_num_heads = config.get('encoder_num_heads'),  
+        decoder_embed_dim = config.get('decoder_embed_dim'),
+        decoder_depth = config.get('decoder_depth'), 
+        decoder_num_heads = config.get('decoder_num_heads'),
+        mlp_ratio = config.get('mlp_ratio'), 
+        norm_layer = torch.nn.LayerNorm, 
+        norm_pix_loss = config.get('norm_pix_loss'),
+        drop_channels_rate = config.get('drop_channels_rate'),
+        mask_ratio=mask_ratio if mask_ratio is not None else config.get('mask_ratio'),
+        adjacent_masking=config.get('adjacent_masking')
+    )
+    return model
+
+
diff --git a/downstream/gap_fill/lightning_wrapper.py b/downstream/gap_fill/lightning_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..d99ef6ad04133e11322b98893990cba25843c507
--- /dev/null
+++ b/downstream/gap_fill/lightning_wrapper.py
@@ -0,0 +1,188 @@
+from typing import List, Tuple, Union, Dict
+import logging
+import math
+import torch
+import lightning.pytorch as pl
+
+from downstream.gap_fill.models import GapFill
+from deepspeed.ops.adam import FusedAdam
+from torch.optim.lr_scheduler import LambdaLR
+
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+Shape3d = Union[List[int], Tuple[int, int, int]]
+
+
+class CosineAnnealingWarmupLR(torch.optim.lr_scheduler.LambdaLR):
+    def __init__(self, optimizer, warmup_steps, total_steps, eta_min=0.0, last_step=-1):
+        """
+        Cosine annealing scheduler with warm-up period, operating per step.
+
+        Args:
+            optimizer (Optimizer): Wrapped optimizer.
+            warmup_steps (int): Number of warm-up steps.
+            total_steps (int): Total number of training steps.
+            eta_min (float): Minimum learning rate after annealing. Default: 0.0.
+            last_step (int): The index of the last step when resuming training. Default: -1.
+        """
+        self.warmup_steps = warmup_steps
+        self.total_steps = total_steps
+        self.eta_min = eta_min
+
+        super().__init__(optimizer, self.lr_lambda, last_step)
+
+    def lr_lambda(self, current_step):
+        if current_step < self.warmup_steps:
+            # Linear warm-up
+            return float(current_step) / float(max(1, self.warmup_steps))
+        else:
+            # Cosine annealing
+            progress = float(current_step - self.warmup_steps) / float(max(1, self.total_steps - self.warmup_steps))
+            cosine_decay = 0.5 * (1 + math.cos(math.pi * progress))
+            return cosine_decay * (1 - self.eta_min) + self.eta_min
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class GapFillLightning(pl.LightningModule):
+    def __init__(self, 
+                 img_size: Shape3d = [4, 224, 224], 
+                 patch_size: Shape3d = [1, 16, 16], 
+                 in_chans: int = 3, 
+                 encoder_embed_dim: int = 1024, 
+                 encoder_depth: int = 8, 
+                 encoder_num_heads: int = 16, 
+                 decoder_embed_dim: int = 512, 
+                 decoder_depth: int = 8, 
+                 decoder_num_heads: int = 16,
+                 mlp_ratio: float = 4., 
+                 norm_layer: torch.nn.Module = torch.nn.LayerNorm, 
+                 norm_pix_loss: bool = False,
+                 drop_channels_rate: float = 0.0,
+                 adjacent_masking: bool = False,
+                 params: Dict[str, Union[Dict[str, float], int]] = None,
+                 mask_ratio = 0.75):
+        super().__init__()
+        self.params = params or {
+            'optimizer': {
+                'learning_rate': 1e-3,
+            },
+            'scheduler': {
+                'type': 'CosineAnnealingLR',
+                'warmup_steps': 100,
+                'total_steps': 1000,
+                'eta_min': 1e-10,
+                'last_step': -1,
+            },
+        }
+
+        self.model = GapFill(
+            img_size=img_size, 
+            patch_size=patch_size, 
+            in_chans=in_chans, 
+            encoder_embed_dim=encoder_embed_dim, 
+            encoder_depth=encoder_depth, 
+            encoder_num_heads=encoder_num_heads, 
+            decoder_embed_dim=decoder_embed_dim, 
+            decoder_depth=decoder_depth, 
+            decoder_num_heads=decoder_num_heads,
+            mlp_ratio=mlp_ratio, 
+            norm_layer=norm_layer, 
+            norm_pix_loss=norm_pix_loss,
+            drop_channels_rate=drop_channels_rate,
+            adjacent_masking=adjacent_masking
+        )
+        self.mask_ratio = mask_ratio
+
+        self.log_helper(f"Mask Ratio: {self.mask_ratio}")
+
+
+    def log_helper(self, message):
+        logger.info(message)
+
+
+    def forward(self, x, x_mask, temporal_pos=None, mask_ratio=0.75, epoch=None):
+        self.log_helper("forward pass start")
+        if epoch is None:
+            try:
+                epoch = self.current_epoch
+            except:
+                raise ValueError(f"epoch value is invalid")
+        self.log_helper("finished forward")
+        return self.model(x, x_mask, temporal_pos=temporal_pos, mask_ratio=mask_ratio, epoch=epoch)
+
+
+    def training_step(self, batch, batch_idx):
+        x = batch['x'].to(self.device)
+        x_mask = batch['x_mask'].to(self.device)
+        if batch['temporal_pos']:
+            temporal_pos = [time_component.to(self.device) for time_component in batch['temporal_pos']]
+        else:
+            temporal_pos = None
+        
+        (loss), (pred, mask) = self(x, x_mask, temporal_pos=temporal_pos, mask_ratio=self.mask_ratio, epoch=self.current_epoch)
+
+        return loss
+        
+    def on_after_backward(self):
+        # Clip gradients to prevent exploding gradients
+        torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
+
+    def validation_step(self, batch, batch_idx):
+        x = batch['x'].to(self.device)
+        x_mask = batch['x_mask'].to(self.device)
+
+        if batch['temporal_pos']:
+            temporal_pos = [time_component.to(self.device) for time_component in batch['temporal_pos']]
+        else:
+            temporal_pos = None
+        
+        (loss), (pred, mask) = self(x, x_mask, temporal_pos=temporal_pos, mask_ratio=self.mask_ratio, epoch=self.current_epoch)
+
+        return loss
+    
+
+    def configure_optimizers(self):
+        optimizer = FusedAdam(self.parameters(), lr=self.params['optimizer']['learning_rate'])
+
+        # Check if warmup is needed
+        if 'warmup_steps' in self.params['scheduler'] and self.params['scheduler']['warmup_steps'] > 0:
+            warmup_steps = self.params['scheduler']['warmup_steps']
+            
+            # Warmup function: linear increase from 0 to target LR, then constant
+            def warmup_fn(step):
+                if step < warmup_steps:
+                    return step / warmup_steps  # Linear warmup
+                return 1.0  # Constant LR after warmup
+
+            scheduler = {
+                "scheduler": LambdaLR(optimizer, lr_lambda=warmup_fn),
+                "interval": "step",
+                "frequency": 1
+            }
+
+            return [optimizer], [scheduler]
+
+        # No warmup: just return optimizer
+        return [optimizer]
+
+   
+    def save_model(self, path):
+        torch.save(self.state_dict(), path)
+
+    @classmethod
+    def load_from_checkpoint(cls, path, **kwargs):
+        model = cls(**kwargs)
+        model.load_state_dict(torch.load(path)['state_dict'])
+        return model
+    
+    @classmethod
+    def load_from_ds_checkpoint(cls, path, **kwargs):
+        model = cls(**kwargs)
+        model.load_state_dict(torch.load(path)) # NESTED state_dict exists in DS converted checkpoints
+        return model
diff --git a/downstream/gap_fill/models.py b/downstream/gap_fill/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..73f8639a49fd1003d7b623dbb09c981b975bb5bc
--- /dev/null
+++ b/downstream/gap_fill/models.py
@@ -0,0 +1,639 @@
+from typing import List, Dict, Optional, Tuple, Union
+import random
+import torch
+import timm
+import numpy as np
+from einops import rearrange
+import torch.distributed as dist
+from timm.layers import drop_path, DropPath, Mlp, trunc_normal_
+import logging
+from torchvision import models
+from mae_dino.model_layers.layers import PatchEmbed, Attention, Block, PatchEmbed
+from mae_dino.model_layers.pos_embed import get_3d_sincos_pos_embed
+import torch.nn as nn
+
+Shape3d = Union[List[int], Tuple[int, int, int]]
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class TemporalEncoder(torch.nn.Module):
+    def __init__(self, embed_dim, tokens_per_frame):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.tokens_per_frame = tokens_per_frame
+        
+        # Define embedding sizes for each temporal component
+        self.year_embed_dim = embed_dim // 4
+        self.doy_embed_dim = embed_dim // 4
+        self.hour_embed_dim = embed_dim // 4
+        self.minute_embed_dim = embed_dim - (
+            self.year_embed_dim + self.doy_embed_dim + self.hour_embed_dim
+        )
+        
+        # Embedding layers for categorical temporal features
+        self.year_embedding = torch.nn.Embedding(3000, self.year_embed_dim)   # Years from 0000 to 2999
+        self.doy_embedding = torch.nn.Embedding(367, self.doy_embed_dim)   # Day of Year 0-365
+        self.hour_embedding = torch.nn.Embedding(24, self.hour_embed_dim)     # Hours 0-23
+        self.minute_embedding = torch.nn.Embedding(60, self.minute_embed_dim) # Minutes 0-59
+
+        # Initialize embeddings
+        self._init_weights()
+    
+    def _init_weights(self):
+        torch.nn.init.xavier_uniform_(self.year_embedding.weight)
+        torch.nn.init.xavier_uniform_(self.doy_embedding.weight)
+        torch.nn.init.xavier_uniform_(self.hour_embedding.weight)
+        torch.nn.init.xavier_uniform_(self.minute_embedding.weight)
+    
+    def forward(self, year, doy, hour, minute):
+        """
+        Args:
+            year (torch.Tensor): Shape (batch_size, time), integer years
+            doy (torch.Tensor): Shape (batch_size, time), values [1, 366]
+            hour (torch.Tensor): Shape (batch_size, time), values [0, 23]
+            minute (torch.Tensor): Shape (batch_size, time), values [0, 59]
+        
+        Returns:
+            torch.Tensor: Temporal embeddings of shape (batch_size, time * tokens_per_frame, embed_dim)
+        """
+        # Ensure inputs are of type Long
+        year = year.long()
+        doy = doy.long()
+        hour = hour.long()
+        minute = minute.long()
+        
+        # Get embeddings for each temporal component
+        year_emb = self.year_embedding(year)       # (batch_size, time, year_embed_dim)
+        doy_emb = self.doy_embedding(doy)          # (batch_size, time, doy_embed_dim)
+        hour_emb = self.hour_embedding(hour)       # (batch_size, time, hour_embed_dim)
+        minute_emb = self.minute_embedding(minute) # (batch_size, time, minute_embed_dim)
+        
+        # Concatenate embeddings along the last dimension
+        temporal_emb = torch.cat(
+            [year_emb, doy_emb, hour_emb, minute_emb], dim=-1
+        )  # (batch_size, time, embed_dim)
+        
+        # Reshape to (batch_size, time * tokens_per_frame, embed_dim)
+        batch_size, time_steps, _ = temporal_emb.shape
+        temporal_emb = torch.repeat_interleave(temporal_emb, self.tokens_per_frame, dim=1)
+        
+        return temporal_emb
+
+
+class Encoder(torch.nn.Module):
+    def __init__(self, 
+                 img_size: Shape3d = [4, 224, 224], 
+                 patch_size: Shape3d = [1, 16, 16], 
+                 in_chans: int = 3, 
+                 encoder_embed_dim: int = 1024, 
+                 encoder_depth: int = 8, 
+                 encoder_num_heads: int = 16, 
+                 mlp_ratio: float = 4., 
+                 norm_layer: torch.nn.Module = torch.nn.LayerNorm, 
+                 drop_channels_rate: float = 0.0,
+                 adjacent_masking: bool = False,
+                 ):
+        super().__init__()
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.in_chans = in_chans
+        self.encoder_embed_dim = encoder_embed_dim
+        self.encoder_depth = encoder_depth 
+        self.encoder_num_heads = encoder_num_heads 
+        self.mlp_ratio = mlp_ratio
+        self.norm_layer = norm_layer
+        self.drop_channels_rate = drop_channels_rate
+        self.adjacent_masking = adjacent_masking
+
+        # -------------------------------------------------------------------------- #
+        # MAE encoder
+        self.drop_channels = torch.nn.Dropout3d(self.drop_channels_rate) if self.drop_channels_rate > 0 else torch.nn.Identity()
+        
+        self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, encoder_embed_dim)
+        num_patches = self.patch_embed.num_patches
+        tokens_per_frame = num_patches // img_size[0]
+
+        self.cls_token = torch.nn.Parameter(torch.zeros(1, 1, encoder_embed_dim))
+        self.register_buffer("encoder_pos_embed", torch.zeros(1, num_patches + 1, encoder_embed_dim))
+
+        self.encoder_blocks = torch.nn.ModuleList([
+            Block(encoder_embed_dim, encoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=self.norm_layer)
+            for i in range(self.encoder_depth)])
+        self.norm = norm_layer(encoder_embed_dim)
+
+        self.temporal_embed_enc = TemporalEncoder(embed_dim=encoder_embed_dim, tokens_per_frame=tokens_per_frame)
+
+        # Initialize weights
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        encoder_pos_embed = get_3d_sincos_pos_embed(
+            self.encoder_pos_embed.shape[-1], self.patch_embed.grid_size, cls_token=True
+        )
+        self.encoder_pos_embed.data.copy_(torch.from_numpy(encoder_pos_embed).float().unsqueeze(0))
+
+
+        # initialize patch_embed like nn.Linear (instead of nn.Conv2d)
+        w = self.patch_embed.proj.weight.data
+        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+
+        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
+        torch.nn.init.normal_(self.cls_token, std=0.02)
+
+        # initialize nn.Linear and nn.LayerNorm
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, torch.nn.Linear):
+            # we use xavier_uniform following official JAX ViT:
+            torch.nn.init.xavier_uniform_(m.weight)
+            if isinstance(m, torch.nn.Linear) and m.bias is not None:
+                torch.nn.init.constant_(m.bias, 0)
+        elif isinstance(m, torch.nn.LayerNorm):
+            torch.nn.init.constant_(m.bias, 0)
+            torch.nn.init.constant_(m.weight, 1.0)
+
+    def patchify(self, imgs):
+        """
+        imgs: B, C, T, H, W
+        x: B, L, D
+        """
+        s, p, q = self.patch_embed.patch_size
+        x = rearrange(imgs, 'b c (t s) (h p) (w q) -> b (t h w) (s p q c)', s=s, p=p, q=q)
+
+        return x
+
+    def unpatchify(self, x):
+        """
+        x: B, L, D
+        imgs: B, C, T, H, W
+        """
+        s, p, q = self.patch_embed.patch_size
+        gs = self.patch_embed.grid_size
+        imgs = rearrange(x, 'b (t h w) (s p q c) -> b c (t s) (h p) (w q)', h=gs[1], w=gs[2], t=gs[0], s=s, p=p, q=q)
+        return imgs
+    
+    def log_helper(self, message):
+        logger.info(message)
+
+    def hinted_random_masking(self, x: torch.Tensor, x_mask: torch.Tensor, mask_ratio: int):
+        """
+        Perform per-sample random masking by per-sample shuffling.
+        Per-sample shuffling is done by argsort random noise on patches without missing value pixels (indicated by x_mask).
+        x: [N, L, D], sequence
+        """
+        N, L, D = x.shape  # batch, length, dim
+        
+        # Calculate missing data ratio from x_mask
+        x_mask = x_mask.sum(dim=-1) > 0  # [N, L]
+        missing_ratio = x_mask.float().mean(dim=1)  # [N]
+   
+        adjusted_mask_ratio = min(max(missing_ratio).item() + mask_ratio, 1)
+
+        self.log_helper(f"missing_data_ratio: {missing_ratio}")
+        self.log_helper(f"adjusted_mask_ratio: {adjusted_mask_ratio}")
+
+        len_keep = int(L * (1 - adjusted_mask_ratio))
+        
+        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]
+        
+        ids_x_mask = torch.where(x_mask)
+        # ids_x_mask = list(zip(ids_x_mask[0].tolist(), ids_x_mask[1].tolist()))
+        # TODO translate to NP
+        ids_x_mask_dict = {}
+        for bs, p in zip(ids_x_mask[0], ids_x_mask[1]):
+            bs, p = int(bs), int(p)
+            if bs not in ids_x_mask_dict:
+                ids_x_mask_dict[bs] = []
+            ids_x_mask_dict[bs].append(p)
+
+        noise += x_mask
+        
+        # sort noise for each sample
+        ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove
+        ids_restore = torch.argsort(ids_shuffle, dim=1)
+        
+        # keep the first subset
+        ids_keep = ids_shuffle[:, :len_keep]
+        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
+        
+        # generate the binary mask: 0 is keep, 1 is remove
+        mask = torch.ones([N, L], device=x.device)
+        mask[:, :len_keep] = 0
+        # unshuffle to get the binary mask
+        mask = torch.gather(mask, dim=1, index=ids_restore)
+        
+        return x_masked, mask, ids_restore, ids_x_mask_dict
+
+
+
+    def hinted_adjacent_masking(self, x: torch.Tensor, x_mask: torch.Tensor, mask_ratio: float):
+        """
+        Perform masking by keeping contiguous blocks of patches in time and space,
+        excluding patches where x_mask is 1 in each patch, and adjust to keep mask_ratio consistent.
+        x: [N, L, D], sequence
+        x_mask: [N, L, D], mask indicating invalid patches (1 where invalid)
+        patch_embed: object containing grid_size attribute (T, H, W)
+        """
+        N, L, D = x.shape  # batch size, sequence length, feature dimension
+    
+        # Get grid sizes
+        gs = self.patch_embed.grid_size  # (T, H, W)
+        T, H, W = gs
+    
+        # Calculate total number of patches and number to keep
+        total_patches = T * H * W
+        len_keep = int(total_patches * (1 - mask_ratio))
+    
+        # Reshape x to [N, T, H, W, D]
+        x = x.view(N, T, H, W, D)
+    
+        # Process x_mask to identify invalid patches
+        # x_mask: [N, L, D] -> [N, L], True where invalid
+        x_mask = x_mask.sum(dim=-1) > 0
+    
+        ids_x_mask = torch.where(x_mask)
+        ids_x_mask_dict = {}
+        for bs, p in zip(ids_x_mask[0], ids_x_mask[1]):
+            bs, p = int(bs), int(p)
+            if bs not in ids_x_mask_dict:
+                ids_x_mask_dict[bs] = []
+            ids_x_mask_dict[bs].append(p)
+            
+        x_mask = x_mask.view(N, T, H, W)  # Reshape to [N, T, H, W]
+    
+        # Initialize mask of zeros [N, T, H, W]; 0 is remove, 1 is keep
+        mask = torch.zeros((N, T, H, W), device=x.device)
+    
+        ids_keep_list = []
+    
+        # For each sample in the batch
+        for i in range(N):
+            # Valid patches are where x_mask is False (0)
+            valid_patches_mask = ~x_mask[i]  # [T, H, W], True where valid
+            num_valid_patches = valid_patches_mask.sum().item()
+    
+            # Adjust len_keep if not enough valid patches
+            len_keep_i = min(len_keep, num_valid_patches)
+            if len_keep_i == 0:
+                ids_keep_list.append(torch.tensor([], device=x.device, dtype=torch.long))
+                continue  # Skip if no valid patches
+    
+            patches_selected = 0
+            attempts = 0
+            max_attempts = 1000  # Prevent infinite loops
+    
+            while patches_selected < len_keep_i and attempts < max_attempts:
+                attempts += 1
+    
+                # Compute block sizes
+                block_size = int(round((len_keep_i - patches_selected) ** (1/3)))
+                t_size_block = min(T, block_size)
+                h_size_block = min(H, block_size)
+                w_size_block = min(W, block_size)
+    
+                # Randomly select starting positions
+                t0 = random.randint(0, T - t_size_block)
+                h0 = random.randint(0, H - h_size_block)
+                w0 = random.randint(0, W - w_size_block)
+    
+                # Get indices for the block
+                t_indices = slice(t0, t0 + t_size_block)
+                h_indices = slice(h0, h0 + h_size_block)
+                w_indices = slice(w0, w0 + w_size_block)
+    
+                # Extract the block of valid patches
+                block_valid_mask = valid_patches_mask[t_indices, h_indices, w_indices]
+                block_already_selected = mask[i, t_indices, h_indices, w_indices]
+    
+                # Find valid, unselected patches in the block
+                selectable_mask = block_valid_mask & (block_already_selected == 0)
+                num_selectable = selectable_mask.sum().item()
+    
+                if num_selectable == 0:
+                    continue  # Try another block
+    
+                # Determine how many patches to select from this block
+                num_to_select = min(len_keep_i - patches_selected, num_selectable)
+    
+                # Get indices of selectable patches
+                selectable_indices = selectable_mask.nonzero(as_tuple=False)
+    
+                # Randomly select patches from the selectable ones
+                selected_indices = selectable_indices[torch.randperm(num_selectable)[:num_to_select]]
+    
+                # Update the mask to keep the selected patches
+                for idx in selected_indices:
+                    t_idx, h_idx, w_idx = idx
+                    mask[i, t0 + t_idx, h0 + h_idx, w0 + w_idx] = 1
+    
+                patches_selected += num_to_select
+    
+            # If not enough patches were selected, randomly select from remaining valid patches
+            if patches_selected < len_keep_i:
+                remaining_selectable = (valid_patches_mask & (mask[i] == 0)).nonzero(as_tuple=False)
+                num_remaining = remaining_selectable.size(0)
+                num_needed = len_keep_i - patches_selected
+                num_to_select = min(num_remaining, num_needed)
+                if num_to_select > 0:
+                    selected_indices = remaining_selectable[torch.randperm(num_remaining)[:num_to_select]]
+                    mask[i][selected_indices[:, 0], selected_indices[:, 1], selected_indices[:, 2]] = 1
+                    patches_selected += num_to_select
+    
+            # Get the indices of the kept patches
+            mask_i_flat = mask[i].view(-1)
+            ids_keep_i = torch.where(mask_i_flat == 1)[0]
+            ids_keep_list.append(ids_keep_i)
+    
+        # Determine the maximum length of ids_keep across all samples for padding
+        max_len_keep = max(len(ids) for ids in ids_keep_list)
+    
+        # Pad ids_keep to have the same length
+        ids_keep_padded = torch.zeros((N, max_len_keep), dtype=torch.long, device=x.device)
+        for i, ids in enumerate(ids_keep_list):
+            ids_keep_padded[i, :len(ids)] = ids
+    
+        # Flatten x back to [N, L, D]
+        x_flat = x.view(N, L, D)
+    
+        # Gather x_masked with padding
+        x_masked_list = []
+        for i in range(N):
+            ids = ids_keep_list[i]
+            x_masked_i = torch.index_select(x_flat[i], dim=0, index=ids)
+            x_masked_list.append(x_masked_i)
+        x_masked = torch.nn.utils.rnn.pad_sequence(x_masked_list, batch_first=True)
+    
+        # Generate ids_restore (identity mapping)
+        ids_restore = torch.arange(L, device=x.device).unsqueeze(0).repeat(N, 1)
+    
+        # Generate the binary mask: 0 is keep, 1 is remove
+        mask_flat = mask.view(N, L)
+        final_mask = 1 - mask_flat  # Invert mask to match expected output
+    
+        return x_masked, final_mask, ids_restore, ids_x_mask_dict
+
+
+    def forward(self,
+                x: torch.Tensor,
+                x_mask: torch.Tensor,
+                mask_ratio: float,
+                # temporal_pos: Optional[torch.Tensor]):
+                temporal_pos: Optional[List]):
+
+        # Drop input channels
+        x = self.drop_channels(x)
+
+        # embed patches
+        x = self.patch_embed(x)
+
+        # add pos embed w/o cls token
+        x = x + self.encoder_pos_embed[:, 1:, :]
+
+        if temporal_pos:
+            temporal_encoding = self.temporal_embed_enc(*temporal_pos)
+            # temporal_encoding = self.drop_temporal(temporal_encoding, new_mask=True)
+            x = x + temporal_encoding
+        
+        # masking: length -> length * mask_ratio
+        x_mask = self.patchify(x_mask)
+        if self.adjacent_masking:
+            x, mask, ids_restore, ids_x_mask_dict = self.hinted_adjacent_masking(x, x_mask, mask_ratio)
+        else:
+            x, mask, ids_restore, ids_x_mask_dict = self.hinted_random_masking(x, x_mask, mask_ratio)
+
+        # append cls token
+        cls_token = self.cls_token + self.encoder_pos_embed[:, :1, :]
+        cls_tokens = cls_token.expand(x.shape[0], -1, -1)
+        x = torch.cat((cls_tokens, x), dim=1)
+
+        # apply Transformer blocks
+        for blk in self.encoder_blocks:
+            x = blk(x)
+        x = self.norm(x)
+
+        return x, mask, ids_restore, ids_x_mask_dict
+
+
+class Decoder(torch.nn.Module):
+    def __init__(self, 
+                 img_size: Shape3d = [4, 224, 224],
+                 patch_size: Shape3d = [1, 16, 16], 
+                 in_chans: int = 3,
+                 encoder_embed_dim: int = 1024,
+                 decoder_embed_dim: int = 512, 
+                 decoder_depth: int = 8, 
+                 decoder_num_heads: int = 16,
+                 mlp_ratio: float = 4., 
+                 norm_layer: torch.nn.Module = torch.nn.LayerNorm, 
+                 norm_pix_loss: bool = False,
+                 ):
+        super().__init__()
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.encoder_embed_dim = encoder_embed_dim
+        self.decoder_embed_dim = decoder_embed_dim 
+        self.decoder_depth = decoder_depth
+        self.decoder_num_heads = decoder_num_heads
+        self.mlp_ratio = mlp_ratio
+        self.norm_layer = norm_layer
+        self.norm_pix_loss = norm_pix_loss
+
+        # -------------------------------------------------------------------------- #
+        # MAE decoder
+        self.decoder_embed = torch.nn.Linear(encoder_embed_dim, decoder_embed_dim, bias=True)
+        self.mask_token = torch.nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))
+
+        self.grid_size = [d//p for d, p in zip(self.img_size, self.patch_size)]
+        num_patches = np.prod(self.grid_size)
+        self.register_buffer("decoder_pos_embed", torch.zeros(1, num_patches + 1, decoder_embed_dim))
+
+        self.decoder_blocks = torch.nn.ModuleList([
+            Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
+            for i in range(decoder_depth)])
+
+        self.decoder_norm = norm_layer(decoder_embed_dim)
+        self.decoder_pred = torch.nn.Linear(decoder_embed_dim,
+                                      patch_size[0] * patch_size[1] * patch_size[2] * in_chans,
+                                      bias=True)  # decoder to patch
+        
+        tokens_per_frame = num_patches // img_size[0]
+        self.temporal_embed_dec = TemporalEncoder(embed_dim=decoder_embed_dim, tokens_per_frame=tokens_per_frame)
+
+    # Initialize weights
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        decoder_pos_embed = get_3d_sincos_pos_embed(
+            self.decoder_pos_embed.shape[-1], self.grid_size, cls_token=True
+        )
+        self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))
+
+        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
+        torch.nn.init.normal_(self.mask_token, std=0.02)
+
+        # initialize nn.Linear and nn.LayerNorm
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, torch.nn.Linear):
+            # we use xavier_uniform following official JAX ViT:
+            torch.nn.init.xavier_uniform_(m.weight)
+            if isinstance(m, torch.nn.Linear) and m.bias is not None:
+                torch.nn.init.constant_(m.bias, 0)
+        elif isinstance(m, torch.nn.LayerNorm):
+            torch.nn.init.constant_(m.bias, 0)
+            torch.nn.init.constant_(m.weight, 1.0)
+
+    def forward(self, x: torch.Tensor,
+                    ids_restore: torch.Tensor,
+                    temporal_pos: Optional[torch.Tensor]):
+        # embed tokens
+        x = self.decoder_embed(x)
+
+        # append mask tokens to sequence
+        mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
+        x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1)  # no cls token
+        x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]))  # unshuffle
+        x = torch.cat([x[:, :1, :], x_], dim=1)  # append cls token
+
+        # add pos embed
+        x = x + self.decoder_pos_embed
+        # remove cls token
+        x_ = x[:, 1:, :]
+
+        if temporal_pos:
+            temporal_encoding = self.temporal_embed_dec(*temporal_pos)
+            # Add temporal encoding w/o cls token
+            x_ = x_ + temporal_encoding
+
+        x = torch.cat([x[:, :1, :], x_], dim=1)  # append cls token
+
+        # apply Transformer blocks
+        for blk in self.decoder_blocks:
+            x = blk(x)
+        x = self.decoder_norm(x)
+
+        # predictor projection
+        x = self.decoder_pred(x)
+
+        # remove cls token
+        x = x[:, 1:, :]
+
+        return x  
+
+class GapFill(torch.nn.Module):
+    def __init__(self, 
+                 img_size: Shape3d = [4, 224, 224], 
+                 patch_size: Shape3d = [1, 16, 16], 
+                 in_chans: int = 3, 
+                 encoder_embed_dim: int = 1024, 
+                 encoder_depth: int = 8, 
+                 encoder_num_heads: int = 16, 
+                 decoder_embed_dim: int = 512, 
+                 decoder_depth: int = 8, 
+                 decoder_num_heads: int = 16,
+                 mlp_ratio: float = 4., 
+                 norm_layer: torch.nn.Module = torch.nn.LayerNorm, 
+                 norm_pix_loss: bool = False,
+                 drop_channels_rate: float = 0.0,
+                 mask_loss_weight: float = 1,
+                 adjacent_masking: bool = False,
+                 ):
+        super().__init__()
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.in_chans = in_chans
+        self.encoder_embed_dim = encoder_embed_dim
+        self.encoder_depth = encoder_depth 
+        self.encoder_num_heads = encoder_num_heads 
+        self.decoder_embed_dim = decoder_embed_dim 
+        self.decoder_depth = decoder_depth
+        self.decoder_num_heads = decoder_num_heads
+        self.mlp_ratio = mlp_ratio
+        self.norm_layer = norm_layer
+        self.norm_pix_loss = norm_pix_loss
+        self.drop_channels_rate = drop_channels_rate
+        self.mask_loss_weight = mask_loss_weight
+        self.adjacent_masking = adjacent_masking
+
+        self.encoder = Encoder(
+            img_size=img_size, 
+            patch_size=patch_size, 
+            in_chans=in_chans, 
+            encoder_embed_dim=encoder_embed_dim, 
+            encoder_depth=encoder_depth, 
+            encoder_num_heads=encoder_num_heads, 
+            mlp_ratio=mlp_ratio, 
+            norm_layer=norm_layer, 
+            drop_channels_rate=drop_channels_rate,
+            adjacent_masking=adjacent_masking,
+        )
+        
+        self.decoder = Decoder(
+            img_size=img_size, 
+            patch_size=patch_size,
+            in_chans=in_chans, 
+            encoder_embed_dim=encoder_embed_dim,
+            decoder_embed_dim=decoder_embed_dim, 
+            decoder_depth=decoder_depth, 
+            decoder_num_heads=decoder_num_heads,
+            mlp_ratio=mlp_ratio, 
+            norm_layer=norm_layer, 
+            norm_pix_loss=norm_pix_loss,
+        )
+
+
+    def forward_mae(self, imgs: torch.Tensor, img_masks: torch.Tensor, temporal_pos: Optional[torch.Tensor], mask_ratio: float = 0.75):
+
+        latent, mask, ids_restore, ids_x_mask_dict = self.encoder(imgs, img_masks, temporal_pos=None, mask_ratio=mask_ratio)
+        pred = self.decoder(latent, ids_restore, temporal_pos=None)
+        loss = self.forward_mae_loss(imgs, pred, mask, ids_x_mask_dict)
+        
+        return loss, pred, mask
+        
+    def log_helper(self, message):
+        logger.info(message)
+    
+
+    def forward_mae_loss(self, imgs: torch.Tensor, pred: torch.Tensor, mask: torch.Tensor, ids_x_mask_dict: Dict[str, List]):
+        """
+        imgs: B, C, T, H, W
+        target: B, L, D
+        pred: B, L, D
+        mask: B, L. 0 is keep, 1 is remove,
+        """
+
+        target = self.encoder.patchify(imgs)
+
+        if self.decoder.norm_pix_loss:
+            mean = target.mean(dim=-1, keepdim=True)
+            var = target.var(dim=-1, keepdim=True)
+            target = (target - mean) / (var + 1.e-6)**.5
+ 
+        loss = ((pred - target).to(torch.float) ** 2).mean(dim=-1) # MSE
+
+        non_missing_data_mask = torch.ones_like(mask)
+        for index, patch in ids_x_mask_dict.items():
+            non_missing_data_mask[index, patch] = 0
+
+        # Loss over non-missing, non-masked patches
+        loss_unmasked = torch.clamp(loss * non_missing_data_mask * (1-mask), max=1e8).sum() / (non_missing_data_mask * (1-mask)).sum()
+
+        # Loss over non-missing, masked patches
+        loss_masked = torch.clamp(loss * non_missing_data_mask * mask, max=1e8).sum()  / (non_missing_data_mask * mask).sum()
+
+        loss = loss_unmasked + self.mask_loss_weight * loss_masked
+        
+
+        return loss
+
+
+    def forward(self, imgs: torch.Tensor, img_masks: torch.Tensor, temporal_pos: Optional[torch.Tensor], mask_ratio: float = 0.75, epoch=None):
+        if epoch is None:
+            raise ValueError(f"epoch value is invalid")
+        # MAE
+        mae_loss, pred, mask = self.forward_mae(imgs, img_masks, temporal_pos=temporal_pos, mask_ratio=mask_ratio)
+
+        return  (mae_loss), (pred, mask)
diff --git a/downstream/precip/lightning_presetup.py b/downstream/precip/lightning_presetup.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ec19e57aadd29dc56ef84240c0b50180febfa95
--- /dev/null
+++ b/downstream/precip/lightning_presetup.py
@@ -0,0 +1,38 @@
+import torch
+import lightning.pytorch as pl
+import yaml
+from downstream.precip.lightning_wrapper import PrecipLightning
+
+def GAIAPrecip(params = None,
+                config_path = None,
+                img_size=None,
+                patch_size = None,
+                in_chans=None,
+                mask_ratio = None,
+                val_step=0):
+    
+    # Load config if provided
+    config = {}
+    if config_path:
+        with open(config_path, 'r') as f:
+            config = yaml.safe_load(f)['model']
+    
+    model = PrecipLightning(
+        params=params,
+        img_size = img_size or config.get('img_size'),
+        patch_size = patch_size or config.get('patch_size'), 
+        in_chans = in_chans or config.get('in_chans'), 
+        encoder_embed_dim = config.get('encoder_embed_dim'),
+        encoder_depth = config.get('encoder_depth'), 
+        encoder_num_heads = config.get('encoder_num_heads'),  
+        decoder_embed_dim = config.get('decoder_embed_dim'),
+        decoder_depth = config.get('decoder_depth'), 
+        decoder_num_heads = config.get('decoder_num_heads'),
+        mlp_ratio = config.get('mlp_ratio'), 
+        norm_layer = torch.nn.LayerNorm, 
+        norm_pix_loss = config.get('norm_pix_loss'),
+        drop_channels_rate = config.get('drop_channels_rate'),
+        val_step=val_step, 
+        mask_ratio=mask_ratio if mask_ratio is not None else config.get('mask_ratio'),
+    )
+    return model
diff --git a/downstream/precip/lightning_wrapper.py b/downstream/precip/lightning_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6ff918655530b2cd5b661af0c8a5c059df8d8b4
--- /dev/null
+++ b/downstream/precip/lightning_wrapper.py
@@ -0,0 +1,193 @@
+from typing import List, Tuple, Union, Dict
+import logging
+import math
+import torch
+import lightning.pytorch as pl
+
+from downstream.precip.models import Precip
+from deepspeed.ops.adam import FusedAdam
+from torch.optim.lr_scheduler import LambdaLR
+
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+Shape3d = Union[List[int], Tuple[int, int, int]]
+
+
+class CosineAnnealingWarmupLR(torch.optim.lr_scheduler.LambdaLR):
+    def __init__(self, optimizer, warmup_steps, total_steps, eta_min=0.0, last_step=-1):
+        """
+        Cosine annealing scheduler with warm-up period, operating per step.
+
+        Args:
+            optimizer (Optimizer): Wrapped optimizer.
+            warmup_steps (int): Number of warm-up steps.
+            total_steps (int): Total number of training steps.
+            eta_min (float): Minimum learning rate after annealing. Default: 0.0.
+            last_step (int): The index of the last step when resuming training. Default: -1.
+        """
+        self.warmup_steps = warmup_steps
+        self.total_steps = total_steps
+        self.eta_min = eta_min
+
+        super().__init__(optimizer, self.lr_lambda, last_step)
+
+    def lr_lambda(self, current_step):
+        if current_step < self.warmup_steps:
+            # Linear warm-up
+            return float(current_step) / float(max(1, self.warmup_steps))
+        else:
+            # Cosine annealing
+            progress = float(current_step - self.warmup_steps) / float(max(1, self.total_steps - self.warmup_steps))
+            cosine_decay = 0.5 * (1 + math.cos(math.pi * progress))
+            return cosine_decay * (1 - self.eta_min) + self.eta_min
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class PrecipLightning(pl.LightningModule):
+    def __init__(self, 
+                 img_size: Shape3d = [4, 224, 224], 
+                 patch_size: Shape3d = [1, 16, 16], 
+                 in_chans: int = 3, 
+                 encoder_embed_dim: int = 1024, 
+                 encoder_depth: int = 8, 
+                 encoder_num_heads: int = 16, 
+                 decoder_embed_dim: int = 512, 
+                 decoder_depth: int = 8, 
+                 decoder_num_heads: int = 16,
+                 mlp_ratio: float = 4., 
+                 norm_layer: torch.nn.Module = torch.nn.LayerNorm, 
+                 norm_pix_loss: bool = False,
+                 drop_channels_rate: float = 0.0,
+                 params: Dict[str, Union[Dict[str, float], int]] = None,
+                 mask_ratio = 0.75,
+                 val_step=0):
+        super().__init__()
+        self.params = params or {
+            'optimizer': {
+                'learning_rate': 1e-3,
+            },
+            'scheduler': {
+                'type': 'CosineAnnealingLR',
+                'warmup_steps': 100,
+                'total_steps': 1000,
+                'eta_min': 1e-10,
+                'last_step': -1,
+            },
+        }
+
+        self.model = Precip(
+            img_size=img_size, 
+            patch_size=patch_size, 
+            in_chans=in_chans, 
+            encoder_embed_dim=encoder_embed_dim, 
+            encoder_depth=encoder_depth, 
+            encoder_num_heads=encoder_num_heads, 
+            decoder_embed_dim=decoder_embed_dim, 
+            decoder_depth=decoder_depth, 
+            decoder_num_heads=decoder_num_heads,
+            mlp_ratio=mlp_ratio, 
+            norm_layer=norm_layer, 
+            norm_pix_loss=norm_pix_loss,
+            drop_channels_rate=drop_channels_rate,
+        )
+        self.val_step = val_step
+        self.mask_ratio = mask_ratio
+        self.sync_dist = True
+        self.mae_loss_start = None
+        self.mae_loss_min = None
+        self.dino_loss_min = None
+        self.dino_loss_start = None
+
+        self.log_helper(f"Mask Ratio: {self.mask_ratio}")
+
+
+    def log_helper(self, message):
+        logger.info(message)
+
+
+    def forward(self, x, x_mask,precip_target, precip_missing_data_mask, temporal_pos=None, mask_ratio=0.0, epoch=None):
+        self.log_helper("forward pass start")
+        if epoch is None:
+            try:
+                epoch = self.current_epoch
+            except:
+                raise ValueError(f"epoch value is invalid")
+        self.log_helper("finished forward")
+        return self.model(x, x_mask,precip_target, precip_missing_data_mask, temporal_pos=temporal_pos, mask_ratio=mask_ratio, epoch=epoch)
+
+
+    def training_step(self, batch, batch_idx):
+        self.log_helper(f"batch: {len(batch)}{batch}")
+        #x = batch['x'].to(torch.bfloat16).to(self.device)
+        x = batch['x'].to(self.device)
+        x_mask = batch['x_mask'].to(self.device)
+        y = batch['y'].to(self.device)
+        y_mask = batch['y_mask'].to(self.device)
+        temporal_pos = None
+        
+        mse_loss, pred, mask = self(x, x_mask,precip_target=y, precip_missing_data_mask=y_mask, temporal_pos=temporal_pos, mask_ratio=self.mask_ratio, epoch=self.current_epoch)
+
+        return mse_loss
+        
+    def on_after_backward(self):
+        # Clip gradients to prevent exploding gradients
+        torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
+    
+
+    def validation_step(self, batch, batch_idx):
+        self.log_helper(f"batch: {len(batch)}{batch}")
+        x = batch['x'].to(self.device)
+        x_mask = batch['x_mask'].to(self.device)
+        y = batch['y'].to(self.device)
+        y_mask = batch['y_mask'].to(self.device)
+        temporal_pos = None
+        
+        mse_loss, pred, mask = self(x, x_mask,precip_target=y, precip_missing_data_mask=y_mask, temporal_pos=temporal_pos, mask_ratio=self.mask_ratio, epoch=self.current_epoch)
+
+        return mse_loss
+    
+
+    def configure_optimizers(self):
+        optimizer = FusedAdam(self.parameters(), lr=self.params['optimizer']['learning_rate'])
+
+        # Check if warmup is needed
+        if 'warmup_steps' in self.params['scheduler'] and self.params['scheduler']['warmup_steps'] > 0:
+            warmup_steps = self.params['scheduler']['warmup_steps']
+            
+            # Warmup function: linear increase from 0 to target LR, then constant
+            def warmup_fn(step):
+                if step < warmup_steps:
+                    return step / warmup_steps  # Linear warmup
+                return 1.0  # Constant LR after warmup
+
+            scheduler = {
+                "scheduler": LambdaLR(optimizer, lr_lambda=warmup_fn),
+                "interval": "step",
+                "frequency": 1
+            }
+
+            return [optimizer], [scheduler]
+
+        # No warmup: just return optimizer
+        return [optimizer]
+
+   
+    def save_model(self, path):
+        torch.save(self.state_dict(), path)
+
+    @classmethod
+    def load_from_checkpoint(cls, path, **kwargs):
+        model = cls(**kwargs)
+        model.load_state_dict(torch.load(path)['state_dict'])
+        return model
+    
+    @classmethod
+    def load_from_ds_checkpoint(cls, path, **kwargs):
+        model = cls(**kwargs)
+        model.load_state_dict(torch.load(path)) # NESTED state_dict exists in DS converted checkpoints
+        return model
\ No newline at end of file
diff --git a/downstream/precip/models.py b/downstream/precip/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..db9286840ca7881dc355add5bb9e3524d5cd9313
--- /dev/null
+++ b/downstream/precip/models.py
@@ -0,0 +1,648 @@
+from typing import List, Optional, Tuple, Union
+import random
+import torch
+import numpy as np
+from einops import rearrange
+import logging
+from mae_dino.model_layers.layers import PatchEmbed, Block, PatchEmbed
+from mae_dino.model_layers.pos_embed import get_3d_sincos_pos_embed
+
+Shape3d = Union[List[int], Tuple[int, int, int]]
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class TemporalEncoder(torch.nn.Module):
+    def __init__(self, embed_dim, tokens_per_frame):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.tokens_per_frame = tokens_per_frame
+        
+        # Define embedding sizes for each temporal component
+        self.year_embed_dim = embed_dim // 4
+        self.doy_embed_dim = embed_dim // 4
+        self.hour_embed_dim = embed_dim // 4
+        self.minute_embed_dim = embed_dim - (
+            self.year_embed_dim + self.doy_embed_dim + self.hour_embed_dim
+        )
+        
+        # Embedding layers for categorical temporal features
+        self.year_embedding = torch.nn.Embedding(3000, self.year_embed_dim)   # Years from 0000 to 2999
+        self.doy_embedding = torch.nn.Embedding(367, self.doy_embed_dim)   # Day of Year 0-365
+        self.hour_embedding = torch.nn.Embedding(24, self.hour_embed_dim)     # Hours 0-23
+        self.minute_embedding = torch.nn.Embedding(60, self.minute_embed_dim) # Minutes 0-59
+
+        # Initialize embeddings
+        self._init_weights()
+    
+    def _init_weights(self):
+        torch.nn.init.xavier_uniform_(self.year_embedding.weight)
+        torch.nn.init.xavier_uniform_(self.doy_embedding.weight)
+        torch.nn.init.xavier_uniform_(self.hour_embedding.weight)
+        torch.nn.init.xavier_uniform_(self.minute_embedding.weight)
+    
+    def forward(self, year, doy, hour, minute):
+        """
+        Args:
+            year (torch.Tensor): Shape (batch_size, time), integer years
+            doy (torch.Tensor): Shape (batch_size, time), values [1, 366]
+            hour (torch.Tensor): Shape (batch_size, time), values [0, 23]
+            minute (torch.Tensor): Shape (batch_size, time), values [0, 59]
+        
+        Returns:
+            torch.Tensor: Temporal embeddings of shape (batch_size, time * tokens_per_frame, embed_dim)
+        """
+        # Ensure inputs are of type Long
+        year = year.long()
+        doy = doy.long()
+        hour = hour.long()
+        minute = minute.long()
+        
+        # Get embeddings for each temporal component
+        year_emb = self.year_embedding(year)       # (batch_size, time, year_embed_dim)
+        doy_emb = self.doy_embedding(doy)          # (batch_size, time, doy_embed_dim)
+        hour_emb = self.hour_embedding(hour)       # (batch_size, time, hour_embed_dim)
+        minute_emb = self.minute_embedding(minute) # (batch_size, time, minute_embed_dim)
+        
+        # Concatenate embeddings along the last dimension
+        temporal_emb = torch.cat(
+            [year_emb, doy_emb, hour_emb, minute_emb], dim=-1
+        )  # (batch_size, time, embed_dim)
+        
+        # Reshape to (batch_size, time * tokens_per_frame, embed_dim)
+        batch_size, time_steps, _ = temporal_emb.shape
+        temporal_emb = torch.repeat_interleave(temporal_emb, self.tokens_per_frame, dim=1)
+        
+        return temporal_emb
+
+
+class Encoder(torch.nn.Module):
+    def __init__(self, 
+                 img_size: Shape3d = [4, 224, 224], 
+                 patch_size: Shape3d = [1, 16, 16], 
+                 in_chans: int = 3, 
+                 encoder_embed_dim: int = 1024, 
+                 encoder_depth: int = 8, 
+                 encoder_num_heads: int = 16, 
+                 mlp_ratio: float = 4., 
+                 norm_layer: torch.nn.Module = torch.nn.LayerNorm, 
+                 drop_channels_rate: float = 0.0,
+                 adjacent_masking: bool = False,
+                 ):
+        super().__init__()
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.in_chans = in_chans
+        self.encoder_embed_dim = encoder_embed_dim
+        self.encoder_depth = encoder_depth 
+        self.encoder_num_heads = encoder_num_heads 
+        self.mlp_ratio = mlp_ratio
+        self.norm_layer = norm_layer
+        self.drop_channels_rate = drop_channels_rate
+        self.adjacent_masking = adjacent_masking
+
+        # -------------------------------------------------------------------------- #
+        # MAE encoder
+        self.drop_channels = torch.nn.Dropout3d(self.drop_channels_rate) if self.drop_channels_rate > 0 else torch.nn.Identity()
+        
+        self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, encoder_embed_dim)
+        num_patches = self.patch_embed.num_patches
+        tokens_per_frame = num_patches // img_size[0]
+
+        self.cls_token = torch.nn.Parameter(torch.zeros(1, 1, encoder_embed_dim))
+        self.register_buffer("encoder_pos_embed", torch.zeros(1, num_patches + 1, encoder_embed_dim))
+
+        self.encoder_blocks = torch.nn.ModuleList([
+            Block(encoder_embed_dim, encoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=self.norm_layer)
+            for i in range(self.encoder_depth)])
+        self.norm = norm_layer(encoder_embed_dim)
+
+        self.temporal_embed_enc = TemporalEncoder(embed_dim=encoder_embed_dim, tokens_per_frame=tokens_per_frame)
+
+        # Initialize weights
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        encoder_pos_embed = get_3d_sincos_pos_embed(
+            self.encoder_pos_embed.shape[-1], self.patch_embed.grid_size, cls_token=True
+        )
+        self.encoder_pos_embed.data.copy_(torch.from_numpy(encoder_pos_embed).float().unsqueeze(0))
+
+
+        # initialize patch_embed like nn.Linear (instead of nn.Conv2d)
+        w = self.patch_embed.proj.weight.data
+        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+
+        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
+        torch.nn.init.normal_(self.cls_token, std=0.02)
+
+        # initialize nn.Linear and nn.LayerNorm
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, torch.nn.Linear):
+            # we use xavier_uniform following official JAX ViT:
+            torch.nn.init.xavier_uniform_(m.weight)
+            if isinstance(m, torch.nn.Linear) and m.bias is not None:
+                torch.nn.init.constant_(m.bias, 0)
+        elif isinstance(m, torch.nn.LayerNorm):
+            torch.nn.init.constant_(m.bias, 0)
+            torch.nn.init.constant_(m.weight, 1.0)
+
+    def patchify(self, imgs):
+        """
+        imgs: B, C, T, H, W
+        x: B, L, D
+        """
+        s, p, q = self.patch_embed.patch_size
+        x = rearrange(imgs, 'b c (t s) (h p) (w q) -> b (t h w) (s p q c)', s=s, p=p, q=q)
+
+        return x
+
+    def unpatchify(self, x):
+        """
+        x: B, L, D
+        imgs: B, C, T, H, W
+        """
+        s, p, q = self.patch_embed.patch_size
+        gs = self.patch_embed.grid_size
+        imgs = rearrange(x, 'b (t h w) (s p q c) -> b c (t s) (h p) (w q)', h=gs[1], w=gs[2], t=gs[0], s=s, p=p, q=q)
+        return imgs
+    
+    def log_helper(self, message):
+        logger.info(message)
+
+    def hinted_random_masking(self, x: torch.Tensor, x_mask: torch.Tensor, mask_ratio: int):
+        """
+        Perform per-sample random masking by per-sample shuffling.
+        Per-sample shuffling is done by argsort random noise on patches without missing value pixels (indicated by x_mask).
+        x: [N, L, D], sequence
+        """
+        N, L, D = x.shape  # batch, length, dim
+        
+        # Calculate missing data ratio from x_mask
+        x_mask = x_mask.sum(dim=-1) > 0  # [N, L]
+        missing_ratio = x_mask.float().mean(dim=1)  # [N]
+   
+        adjusted_mask_ratio = min(max(missing_ratio).item() + mask_ratio, 1)
+
+        self.log_helper(f"missing_data_ratio: {missing_ratio}")
+        self.log_helper(f"adjusted_mask_ratio: {adjusted_mask_ratio}")
+
+        len_keep = int(L * (1 - adjusted_mask_ratio))
+        
+        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]
+        
+        ids_x_mask = torch.where(x_mask)
+        # ids_x_mask = list(zip(ids_x_mask[0].tolist(), ids_x_mask[1].tolist()))
+        # TODO translate to NP
+        ids_x_mask_dict = {}
+        for bs, p in zip(ids_x_mask[0], ids_x_mask[1]):
+            bs, p = int(bs), int(p)
+            if bs not in ids_x_mask_dict:
+                ids_x_mask_dict[bs] = []
+            ids_x_mask_dict[bs].append(p)
+
+        noise += x_mask
+        
+        # sort noise for each sample
+        ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove
+        ids_restore = torch.argsort(ids_shuffle, dim=1)
+        
+        # keep the first subset
+        ids_keep = ids_shuffle[:, :len_keep]
+        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
+        
+        # generate the binary mask: 0 is keep, 1 is remove
+        mask = torch.ones([N, L], device=x.device)
+        mask[:, :len_keep] = 0
+        # unshuffle to get the binary mask
+        mask = torch.gather(mask, dim=1, index=ids_restore)
+        
+        return x_masked, mask, ids_restore, ids_x_mask_dict
+
+
+
+    def hinted_adjacent_masking(self, x: torch.Tensor, x_mask: torch.Tensor, mask_ratio: float):
+        """
+        Perform masking by keeping contiguous blocks of patches in time and space,
+        excluding patches where x_mask is 1 in each patch, and adjust to keep mask_ratio consistent.
+        x: [N, L, D], sequence
+        x_mask: [N, L, D], mask indicating invalid patches (1 where invalid)
+        patch_embed: object containing grid_size attribute (T, H, W)
+        """
+        N, L, D = x.shape  # batch size, sequence length, feature dimension
+    
+        # Get grid sizes
+        gs = self.patch_embed.grid_size  # (T, H, W)
+        T, H, W = gs
+    
+        # Calculate total number of patches and number to keep
+        total_patches = T * H * W
+        len_keep = int(total_patches * (1 - mask_ratio))
+    
+        # Reshape x to [N, T, H, W, D]
+        x = x.view(N, T, H, W, D)
+    
+        # Process x_mask to identify invalid patches
+        # x_mask: [N, L, D] -> [N, L], True where invalid
+        x_mask = x_mask.sum(dim=-1) > 0
+    
+        ids_x_mask = torch.where(x_mask)
+        ids_x_mask_dict = {}
+        for bs, p in zip(ids_x_mask[0], ids_x_mask[1]):
+            bs, p = int(bs), int(p)
+            if bs not in ids_x_mask_dict:
+                ids_x_mask_dict[bs] = []
+            ids_x_mask_dict[bs].append(p)
+            
+        x_mask = x_mask.view(N, T, H, W)  # Reshape to [N, T, H, W]
+    
+        # Initialize mask of zeros [N, T, H, W]; 0 is remove, 1 is keep
+        mask = torch.zeros((N, T, H, W), device=x.device)
+    
+        ids_keep_list = []
+    
+        # For each sample in the batch
+        for i in range(N):
+            # Valid patches are where x_mask is False (0)
+            valid_patches_mask = ~x_mask[i]  # [T, H, W], True where valid
+            num_valid_patches = valid_patches_mask.sum().item()
+    
+            # Adjust len_keep if not enough valid patches
+            len_keep_i = min(len_keep, num_valid_patches)
+            if len_keep_i == 0:
+                ids_keep_list.append(torch.tensor([], device=x.device, dtype=torch.long))
+                continue  # Skip if no valid patches
+    
+            patches_selected = 0
+            attempts = 0
+            max_attempts = 1000  # Prevent infinite loops
+    
+            while patches_selected < len_keep_i and attempts < max_attempts:
+                attempts += 1
+    
+                # Compute block sizes
+                block_size = int(round((len_keep_i - patches_selected) ** (1/3)))
+                t_size_block = min(T, block_size)
+                h_size_block = min(H, block_size)
+                w_size_block = min(W, block_size)
+    
+                # Randomly select starting positions
+                t0 = random.randint(0, T - t_size_block)
+                h0 = random.randint(0, H - h_size_block)
+                w0 = random.randint(0, W - w_size_block)
+    
+                # Get indices for the block
+                t_indices = slice(t0, t0 + t_size_block)
+                h_indices = slice(h0, h0 + h_size_block)
+                w_indices = slice(w0, w0 + w_size_block)
+    
+                # Extract the block of valid patches
+                block_valid_mask = valid_patches_mask[t_indices, h_indices, w_indices]
+                block_already_selected = mask[i, t_indices, h_indices, w_indices]
+    
+                # Find valid, unselected patches in the block
+                selectable_mask = block_valid_mask & (block_already_selected == 0)
+                num_selectable = selectable_mask.sum().item()
+    
+                if num_selectable == 0:
+                    continue  # Try another block
+    
+                # Determine how many patches to select from this block
+                num_to_select = min(len_keep_i - patches_selected, num_selectable)
+    
+                # Get indices of selectable patches
+                selectable_indices = selectable_mask.nonzero(as_tuple=False)
+    
+                # Randomly select patches from the selectable ones
+                selected_indices = selectable_indices[torch.randperm(num_selectable)[:num_to_select]]
+    
+                # Update the mask to keep the selected patches
+                for idx in selected_indices:
+                    t_idx, h_idx, w_idx = idx
+                    mask[i, t0 + t_idx, h0 + h_idx, w0 + w_idx] = 1
+    
+                patches_selected += num_to_select
+    
+            # If not enough patches were selected, randomly select from remaining valid patches
+            if patches_selected < len_keep_i:
+                remaining_selectable = (valid_patches_mask & (mask[i] == 0)).nonzero(as_tuple=False)
+                num_remaining = remaining_selectable.size(0)
+                num_needed = len_keep_i - patches_selected
+                num_to_select = min(num_remaining, num_needed)
+                if num_to_select > 0:
+                    selected_indices = remaining_selectable[torch.randperm(num_remaining)[:num_to_select]]
+                    mask[i][selected_indices[:, 0], selected_indices[:, 1], selected_indices[:, 2]] = 1
+                    patches_selected += num_to_select
+    
+            # Get the indices of the kept patches
+            mask_i_flat = mask[i].view(-1)
+            ids_keep_i = torch.where(mask_i_flat == 1)[0]
+            ids_keep_list.append(ids_keep_i)
+    
+        # Determine the maximum length of ids_keep across all samples for padding
+        max_len_keep = max(len(ids) for ids in ids_keep_list)
+    
+        # Pad ids_keep to have the same length
+        ids_keep_padded = torch.zeros((N, max_len_keep), dtype=torch.long, device=x.device)
+        for i, ids in enumerate(ids_keep_list):
+            ids_keep_padded[i, :len(ids)] = ids
+    
+        # Flatten x back to [N, L, D]
+        x_flat = x.view(N, L, D)
+    
+        # Gather x_masked with padding
+        x_masked_list = []
+        for i in range(N):
+            ids = ids_keep_list[i]
+            x_masked_i = torch.index_select(x_flat[i], dim=0, index=ids)
+            x_masked_list.append(x_masked_i)
+        x_masked = torch.nn.utils.rnn.pad_sequence(x_masked_list, batch_first=True)
+    
+        # Generate ids_restore (identity mapping)
+        ids_restore = torch.arange(L, device=x.device).unsqueeze(0).repeat(N, 1)
+    
+        # Generate the binary mask: 0 is keep, 1 is remove
+        mask_flat = mask.view(N, L)
+        final_mask = 1 - mask_flat  # Invert mask to match expected output
+    
+        return x_masked, final_mask, ids_restore, ids_x_mask_dict
+
+
+    def forward(self,
+                x: torch.Tensor,
+                x_mask: torch.Tensor,
+                mask_ratio: float,
+                # temporal_pos: Optional[torch.Tensor]):
+                temporal_pos: Optional[List]):
+
+        # Drop input channels
+        x = self.drop_channels(x)
+
+        # embed patches
+        x = self.patch_embed(x)
+
+        # add pos embed w/o cls token
+        x = x + self.encoder_pos_embed[:, 1:, :]
+
+        if temporal_pos:
+            temporal_encoding = self.temporal_embed_enc(*temporal_pos)
+            x = x + temporal_encoding
+        
+        # masking: length -> length * mask_ratio
+        x_mask = self.patchify(x_mask)
+        if self.adjacent_masking:
+            x, mask, ids_restore, ids_x_mask_dict = self.hinted_adjacent_masking(x, x_mask, mask_ratio)
+        else:
+            x, mask, ids_restore, ids_x_mask_dict = self.hinted_random_masking(x, x_mask, mask_ratio)
+
+        # append cls token
+        cls_token = self.cls_token + self.encoder_pos_embed[:, :1, :]
+        cls_tokens = cls_token.expand(x.shape[0], -1, -1)
+        x = torch.cat((cls_tokens, x), dim=1)
+
+        # apply Transformer blocks
+        for blk in self.encoder_blocks:
+            x = blk(x)
+        x = self.norm(x)
+
+        return x, mask, ids_restore, ids_x_mask_dict
+
+
+class Decoder(torch.nn.Module):
+    def __init__(self, 
+                 img_size: Shape3d = [4, 224, 224],
+                 patch_size: Shape3d = [1, 16, 16], 
+                 in_chans: int = 3,
+                 encoder_embed_dim: int = 1024,
+                 decoder_embed_dim: int = 512, 
+                 decoder_depth: int = 8, 
+                 decoder_num_heads: int = 16,
+                 mlp_ratio: float = 4., 
+                 norm_layer: torch.nn.Module = torch.nn.LayerNorm, 
+                 norm_pix_loss: bool = False,
+                 ):
+        super().__init__()
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.encoder_embed_dim = encoder_embed_dim
+        self.decoder_embed_dim = decoder_embed_dim 
+        self.decoder_depth = decoder_depth
+        self.decoder_num_heads = decoder_num_heads
+        self.mlp_ratio = mlp_ratio
+        self.norm_layer = norm_layer
+        self.norm_pix_loss = norm_pix_loss
+
+        # -------------------------------------------------------------------------- #
+        # MAE decoder
+        self.decoder_embed = torch.nn.Linear(encoder_embed_dim, decoder_embed_dim, bias=True)
+        self.mask_token = torch.nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))
+
+        self.grid_size = [d//p for d, p in zip(self.img_size, self.patch_size)]
+        num_patches = np.prod(self.grid_size)
+        self.register_buffer("decoder_pos_embed", torch.zeros(1, num_patches + 1, decoder_embed_dim))
+
+        self.decoder_blocks = torch.nn.ModuleList([
+            Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
+            for i in range(decoder_depth)])
+
+        self.decoder_norm = norm_layer(decoder_embed_dim)
+        self.decoder_pred = torch.nn.Linear(decoder_embed_dim,
+                                      patch_size[0] * patch_size[1] * patch_size[2] * in_chans,
+                                      bias=True)  # decoder to patch
+        
+        tokens_per_frame = num_patches // img_size[0]
+        self.temporal_embed_dec = TemporalEncoder(embed_dim=decoder_embed_dim, tokens_per_frame=tokens_per_frame)
+
+    # Initialize weights
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        decoder_pos_embed = get_3d_sincos_pos_embed(
+            self.decoder_pos_embed.shape[-1], self.grid_size, cls_token=True
+        )
+        self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))
+
+        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
+        torch.nn.init.normal_(self.mask_token, std=0.02)
+
+        # initialize nn.Linear and nn.LayerNorm
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, torch.nn.Linear):
+            # we use xavier_uniform following official JAX ViT:
+            torch.nn.init.xavier_uniform_(m.weight)
+            if isinstance(m, torch.nn.Linear) and m.bias is not None:
+                torch.nn.init.constant_(m.bias, 0)
+        elif isinstance(m, torch.nn.LayerNorm):
+            torch.nn.init.constant_(m.bias, 0)
+            torch.nn.init.constant_(m.weight, 1.0)
+
+    def forward(self, x: torch.Tensor,
+                    ids_restore: torch.Tensor,
+                    temporal_pos: Optional[torch.Tensor]):
+        # embed tokens
+        x = self.decoder_embed(x)
+
+        # append mask tokens to sequence
+        mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
+        x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1)  # no cls token
+        x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]))  # unshuffle
+        x = torch.cat([x[:, :1, :], x_], dim=1)  # append cls token
+
+        # add pos embed
+        x = x + self.decoder_pos_embed
+        # remove cls token
+        x_ = x[:, 1:, :]
+
+        if temporal_pos:
+            temporal_encoding = self.temporal_embed_dec(*temporal_pos)
+            # Reuse drop mask from encoder for consistent dropping
+            # temporal_encoding = self.drop_temporal(temporal_encoding, new_mask=False)
+            # Add temporal encoding w/o cls token
+            x_ = x_ + temporal_encoding
+
+        x = torch.cat([x[:, :1, :], x_], dim=1)  # append cls token
+
+        # apply Transformer blocks
+        for blk in self.decoder_blocks:
+            x = blk(x)
+        x = self.decoder_norm(x)
+
+        # predictor projection
+        x = self.decoder_pred(x)
+
+        # remove cls token
+        x = x[:, 1:, :]
+
+        return x
+
+
+class Precip(torch.nn.Module):
+    def __init__(self, 
+                 img_size: Shape3d = [4, 224, 224], 
+                 patch_size: Shape3d = [1, 16, 16], 
+                 in_chans: int = 3, 
+                 encoder_embed_dim: int = 1024, 
+                 encoder_depth: int = 8, 
+                 encoder_num_heads: int = 16, 
+                 decoder_embed_dim: int = 512, 
+                 decoder_depth: int = 8, 
+                 decoder_num_heads: int = 16,
+                 mlp_ratio: float = 4., 
+                 norm_layer: torch.nn.Module = torch.nn.LayerNorm, 
+                 norm_pix_loss: bool = False,
+                 drop_channels_rate: float = 0.0,
+                 ):
+        super().__init__()
+        self.patch_size = patch_size
+        self.in_chans = in_chans
+        self.encoder_embed_dim = encoder_embed_dim
+        self.encoder_depth = encoder_depth 
+        self.encoder_num_heads = encoder_num_heads 
+        self.decoder_embed_dim = decoder_embed_dim 
+        self.decoder_depth = decoder_depth
+        self.decoder_num_heads = decoder_num_heads
+        self.mlp_ratio = mlp_ratio
+        self.norm_layer = norm_layer
+        self.norm_pix_loss = norm_pix_loss
+        self.drop_channels_rate = drop_channels_rate
+
+        self.encoder = Encoder(
+            img_size=img_size, 
+            patch_size=patch_size, 
+            in_chans=in_chans, 
+            encoder_embed_dim=encoder_embed_dim, 
+            encoder_depth=encoder_depth, 
+            encoder_num_heads=encoder_num_heads, 
+            mlp_ratio=mlp_ratio, 
+            norm_layer=norm_layer, 
+            drop_channels_rate=drop_channels_rate,
+            adjacent_masking=False,
+        )
+        
+        self.decoder = Decoder(
+            img_size=img_size, 
+            patch_size=patch_size,
+            in_chans=in_chans, 
+            encoder_embed_dim=encoder_embed_dim,
+            decoder_embed_dim=decoder_embed_dim, 
+            decoder_depth=decoder_depth, 
+            decoder_num_heads=decoder_num_heads,
+            mlp_ratio=mlp_ratio, 
+            norm_layer=norm_layer, 
+            norm_pix_loss=norm_pix_loss,
+        )
+
+    def log_helper(self, message):
+        logger.info(message)
+    
+    
+    def forward(self, imgs: torch.Tensor, img_masks: torch.Tensor, precip_target: torch.Tensor, 
+            precip_missing_data_mask: torch.Tensor, temporal_pos: Optional[torch.Tensor], 
+            mask_ratio: float = 0.75, epoch=None):
+    
+        if epoch is None:
+            raise ValueError(f"epoch value is invalid")
+
+        # Logging mask ratio
+        self.log_helper(f"Mask ratio: {mask_ratio}")
+
+        # Logging input shapes
+        self.log_helper(f"Input imgs shape: {imgs.shape}")  # Expected (B, C, T, H, W)
+        self.log_helper(f"Input img_masks shape: {img_masks.shape}")  
+        self.log_helper(f"Input precip_target shape: {precip_target.shape}")  # Expected (B, C, H, W) or (B, C, T, H, W)
+        self.log_helper(f"Input precip_missing_data_mask shape: {precip_missing_data_mask.shape}")  # Expected (B, H, W) or (B, C, H, W)
+
+        temporal_pos = None
+
+        #imgs = imgs.to(torch.bfloat16)
+
+
+        # Encoder pass
+        latent, mask, ids_restore, ids_x_mask_dict = self.encoder(imgs, img_masks, temporal_pos=temporal_pos, mask_ratio=mask_ratio)
+        self.log_helper(f"Latent shape: {latent.shape}")  # Expected (B, L, D)
+
+        # Decoder pass
+        pred = self.decoder(latent, ids_restore, temporal_pos)  # Expected (B, L, D)
+        pred = torch.nn.functional.relu(pred)     ### Apply relu to the decoder
+        self.log_helper(f"Pred shape: {pred.shape}")  
+
+        # Unpatchify the prediction
+        pred = self.encoder.unpatchify(pred)  # Expected (B, C, T, H, W)
+        self.log_helper(f"Pred shape after unpatchify: {pred.shape}")  
+
+        # Conditionally unsqueeze precip_target if it has 4 dimensions
+        if precip_target.dim() == 4:  
+            precip_target = precip_target.unsqueeze(2)  # Add temporal dim at index 2
+            self.log_helper(f"Adjusted precip_target shape after unsqueeze: {precip_target.shape}") 
+
+        
+        # Additional logs for pred and target statistics
+        self.log_helper(f"Target min: {precip_target.min().item()}, max: {precip_target.max().item()}, avg: {precip_target.mean().item()}")
+        self.log_helper(f"Pred min: {pred.min().item()}, max: {pred.max().item()}, avg: {pred.mean().item()}")
+
+        # Count zero values and NaNs in target
+        target_zeros = (precip_target == 0).sum().item()
+        target_nans = torch.isnan(precip_target).sum().item()
+        target_total = precip_target.numel()
+        target_zero_ratio = target_zeros / target_total
+        self.log_helper(f"Target zeros: {target_zeros} ({target_zero_ratio:.6f}%), NaNs: {target_nans}")
+
+        # Count zero values and NaNs in pred
+        pred_zeros = (pred == 0).sum().item()
+        pred_nans = torch.isnan(pred).sum().item()
+        pred_total = pred.numel()
+        pred_zero_ratio = pred_zeros / pred_total
+        self.log_helper(f"Pred zeros: {pred_zeros} ({pred_zero_ratio:.6f}%), NaNs: {pred_nans}")
+
+        
+        #MSE LOSS
+        loss = ((pred - precip_target).to(torch.float) ** 2)
+        self.log_helper(f"Loss mse: {loss.shape}")
+        mse_loss = loss.mean() #/ (non_missing_data_mask.sum() + eps)  # mean loss on removed patches
+        self.log_helper(f"Loss mean MSE: {mse_loss}")
+
+        return mse_loss, pred, mask
\ No newline at end of file
diff --git a/downstream/precip/precip_dataloader.py b/downstream/precip/precip_dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..db139b99585989df1c22f8949172bac0505de958
--- /dev/null
+++ b/downstream/precip/precip_dataloader.py
@@ -0,0 +1,263 @@
+import torch
+from torch.utils.data import DataLoader
+import lightning.pytorch as pl
+import xarray as xr
+import numpy as np
+import glob
+import random
+
+
+class PrecipDataset(torch.utils.data.Dataset):
+    def __init__(
+        self, ds, resize_shape=(480, 1440), input_horizon=1, norm_range=(200, 350)
+    ):
+        self.ds = ds
+        self.resize_shape = resize_shape
+        self.input_horizon = input_horizon
+        self.norm_range = norm_range
+
+        if "Tb" not in ds.data_vars or "precipitation" not in ds.data_vars:
+            raise ValueError(
+                "Dataset must contain both 'Tb' and 'precipitation' variables."
+            )
+        if len(ds.time) <= input_horizon:
+            raise ValueError("Dataset too small for input horizon.")
+
+    def __getitem__(self, idx):
+        input_data = self.ds.isel(time=slice(idx, idx + self.input_horizon)).astype(
+            np.float16
+        )
+
+        # Fill small missing pockets
+        chunks = {"time": 1, "lat": 3298, "lon": 9896}
+        input_data["Tb"] = (
+            input_data["Tb"]
+            .chunk(chunks)
+            .bfill(dim="lat", limit=5)
+            .bfill(dim="lon", limit=5)
+        )
+
+        input_data = self.resize_data(input_data, *self.resize_shape)
+
+        tb_mask = input_data["Tb"].isnull()
+        tb_mask_array = tb_mask.values  # Convert the DataArray to a NumPy array
+        precip_mask = ~input_data["precipitation"].isnull()
+
+        input_tb = xr.where(tb_mask, 0, input_data["Tb"])
+        output_precip = xr.where(precip_mask, input_data["precipitation"], 0)
+
+        if tb_mask.values.all() or precip_mask.values.all():
+            return self.__getitem__(random.randint(0, len(self) - 1))
+
+        input_tensor = torch.from_numpy(input_tb.values).half().unsqueeze(0)
+        output_tensor = torch.from_numpy(output_precip.values).half().unsqueeze(0)
+
+        input_mask = torch.from_numpy(
+            np.repeat(tb_mask_array[np.newaxis, :, :, :], 1, axis=0)
+        )
+        precip_mask_array = precip_mask.values
+        if precip_mask_array.ndim == 2:
+            output_mask = torch.from_numpy(
+                precip_mask_array[np.newaxis, np.newaxis, :, :]
+            )
+        elif precip_mask_array.ndim == 3:
+            output_mask = torch.from_numpy(precip_mask_array[np.newaxis, :, :, :])
+        else:
+            raise ValueError(
+                f"Unexpected precip_mask dimensions: {precip_mask_array.shape}"
+            )
+
+        input_tensor = torch.clamp(
+            input_tensor, min=self.norm_range[0], max=self.norm_range[1]
+        )
+        input_tensor = (input_tensor - self.norm_range[0]) / (
+            self.norm_range[1] - self.norm_range[0]
+        )
+
+        timestamps = input_data.time
+        print(timestamps)
+        years = torch.tensor(timestamps.dt.year.values, dtype=torch.int32)
+        doy = torch.tensor(timestamps.dt.dayofyear.values, dtype=torch.int32)
+        hours = torch.tensor(timestamps.dt.hour.values, dtype=torch.int32)
+        minutes = torch.tensor(timestamps.dt.minute.values, dtype=torch.int32)
+
+        return {
+            "x": input_tensor,
+            "x_mask": input_mask,
+            "y": output_tensor,
+            "y_mask": output_mask,
+            "temporal_pos": [years, doy, hours, minutes],
+        }
+
+    def __len__(self):
+        return len(self.ds.time) - self.input_horizon
+
+    @staticmethod
+    def resize_data(multi_data, new_lat_size, new_lon_size):
+        new_lat = np.linspace(multi_data.lat.min(), multi_data.lat.max(), new_lat_size)
+        new_lon = np.linspace(multi_data.lon.min(), multi_data.lon.max(), new_lon_size)
+        if "time" in multi_data.dims:
+            return xr.concat(
+                [
+                    multi_data.isel(time=t)
+                    .interp(lat=new_lat, lon=new_lon, method="nearest")
+                    .expand_dims("time")
+                    for t in range(len(multi_data["time"]))
+                ],
+                dim="time",
+            )
+        else:
+            return multi_data.interp(lat=new_lat, lon=new_lon, method="nearest")
+
+
+class TrainingDataModule(pl.LightningDataModule):
+    def __init__(self, data_path, train_split=0.8, max_files=None):
+        super().__init__()
+        self.data_path = data_path
+        self.train_split = train_split
+        self.max_files = max_files
+        self._prepare_data()
+
+    def _prepare_data(self):
+
+        ir_files = sorted(glob.glob(f"{self.data_path}/IR/*/*.nc4"))[: self.max_files]
+        precip_files = sorted(glob.glob(f"{self.data_path}/Precip/*/*.HDF5"))[
+            : self.max_files
+        ]
+        chunks_ir = {"time": 1, "lat": 3298, "lon": 9896}
+        chunks_precip = {"time": 1, "lat": 1800, "lon": 3600}
+        batch_size = 10
+
+        # Load IR (Tb)
+        ir_batches = []
+        for i in range(0, len(ir_files), batch_size):
+            batch = xr.open_mfdataset(
+                ir_files[i : i + batch_size],
+                combine="by_coords",
+                parallel=False,
+                chunks=chunks_ir,
+                compat="override",
+                coords="minimal",
+                combine_attrs="override",
+                engine="netcdf4",
+            )
+            ir_batches.append(batch)
+        ds_ir = xr.combine_by_coords(
+            ir_batches, compat="override", coords="minimal", combine_attrs="override"
+        )
+        ds_ir["time"] = ds_ir["time"].dt.floor("s")
+
+        # Load Precipitation
+        precip_batches = []
+        for i in range(0, len(precip_files), batch_size):
+            batch = xr.open_mfdataset(
+                precip_files[i : i + batch_size],
+                combine="by_coords",
+                parallel=False,
+                chunks=chunks_precip,
+                compat="override",
+                coords="minimal",
+                combine_attrs="override",
+                engine="h5netcdf",
+                group="Grid",
+            )
+            precip_batches.append(batch)
+        ds_precip = xr.combine_by_coords(
+            precip_batches,
+            compat="override",
+            coords="minimal",
+            combine_attrs="override",
+        )
+        ds_precip["precipitation"] = ds_precip["precipitation"].where(
+            ds_precip["precipitation"] != -9999.9
+        )
+        ds_precip = ds_precip.interp(
+            lat=ds_ir["lat"], lon=ds_ir["lon"], method="nearest"
+        )
+        ds_precip["time"] = ds_precip["time"].astype("datetime64[ns]")
+
+        ds_precip[
+            [
+                "precipitation",
+                "randomError",
+                "probabilityLiquidPrecipitation",
+                "precipitationQualityIndex",
+            ]
+        ] = ds_precip[
+            [
+                "precipitation",
+                "randomError",
+                "probabilityLiquidPrecipitation",
+                "precipitationQualityIndex",
+            ]
+        ].transpose(
+            "time", "lat", "lon"
+        )
+
+        # Merge
+        ds = xr.merge([ds_ir, ds_precip], join="inner")
+
+        n = len(ds.time)
+        train_size = int(self.train_split * n)
+
+        self.train_dataset = PrecipDataset(ds.isel(time=slice(0, train_size)))
+        self.val_dataset = PrecipDataset(ds.isel(time=slice(train_size, n)))
+
+    def train_dataloader(self):
+        return DataLoader(self.train_dataset, shuffle=True)
+
+    def val_dataloader(self):
+        return DataLoader(self.val_dataset, shuffle=False)
+
+    def on_epoch_end(self):
+        torch.cuda.empty_cache()
+
+
+def create_dataloader(
+    data_path="../data",
+    num_workers=1,
+    prefetch_factor=1,
+    micro_batch_size=2,
+    max_files=None,
+):
+    """
+    Create a DataLoader for gapfill inference.
+
+    Args:
+        data_path (str): Base path to the data directory.
+        num_workers (int): Number of worker processes for data loading.
+        prefetch_factor (int): Number of samples to prefetch per worker.
+        micro_batch_size (int): Batch size for each step.
+        max_files (int, optional): Maximum number of files to load.
+
+    Returns:
+        DataLoader: PyTorch DataLoader object.
+    """
+
+    # Initialize distributed training if needed
+    if torch.cuda.device_count() > 1 and not torch.distributed.is_initialized():
+        torch.distributed.init_process_group(backend="nccl")
+
+    # Create the data module and set up for "test"
+    data_module = TrainingDataModule(data_path=data_path, max_files=max_files)
+    data_module.setup(stage="test")
+
+    # Use validation dataset if available, else train
+    if hasattr(data_module, "val_dataset") and data_module.val_dataset is not None:
+        dataset = data_module.val_dataset
+    else:
+        dataset = data_module.train_dataset
+
+    # Create and return a DataLoader
+    loader = DataLoader(
+        dataset,
+        num_workers=num_workers,
+        prefetch_factor=prefetch_factor,
+        pin_memory=True,
+        batch_size=micro_batch_size,
+        persistent_workers=True,
+        drop_last=True,
+        multiprocessing_context="spawn",
+    )
+
+    return loader
diff --git a/mae_dino/lightning_presetup.py b/mae_dino/lightning_presetup.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c623acd3e1f184a963454d00c40beb4396ff57c
--- /dev/null
+++ b/mae_dino/lightning_presetup.py
@@ -0,0 +1,67 @@
+import torch
+import yaml
+import os
+import lightning.pytorch as pl
+from mae_dino.lightning_wrapper import GAIABaseLightning
+
+def GAIABase(config_path=None,
+                img_size=None,
+                patch_size=None,
+                in_chans=None,
+                epochs=None,
+                adjacent_masking=None,
+                mask_ratio=None,
+                val_step=None):
+    
+    # Load configuration file
+    config_path = '../configs/base_config.yaml'
+    
+    # Load config if provided
+    params = {}
+    config = {}
+    training_config = {}
+    if config_path:
+        try:
+            with open(config_path, 'r') as f:
+                params = yaml.safe_load(f)
+                config = params.get('model', {})
+                training_config = params.get('training', {})
+                print(f"Loaded configuration from {config_path}")
+        except Exception as e:
+            print(f"Error loading configuration from {config_path}: {str(e)}")
+
+    model = GAIABaseLightning(
+        params=training_config,
+        img_size=img_size or config.get('img_size'),
+        patch_size=patch_size or config.get('patch_size'), 
+        in_chans=in_chans or config.get('in_chans'), 
+        encoder_embed_dim=config.get('encoder_embed_dim'),
+        encoder_depth=config.get('encoder_depth'), 
+        encoder_num_heads=config.get('encoder_num_heads'), 
+        decoder_embed_dim=config.get('decoder_embed_dim'),
+        decoder_depth=config.get('decoder_depth'), 
+        decoder_num_heads=config.get('decoder_num_heads'),
+        mlp_ratio=config.get('mlp_ratio'), 
+        norm_layer=torch.nn.LayerNorm, 
+        norm_pix_loss=config.get('norm_pix_loss'),
+        drop_channels_rate=config.get('drop_channels_rate'),
+        # DINO Args
+        adjacent_masking=adjacent_masking or config.get('adjacent_masking'),
+        norm_last_layer=config.get('norm_last_layer'),
+        dino_head_dim=config.get('dino_head_dim'),
+        warmup_teacher_temp=config.get('warmup_teacher_temp'), 
+        teacher_temp=config.get('teacher_temp'), 
+        warmup_teacher_temp_epochs=config.get('warmup_teacher_temp_epochs'), 
+        epochs=epochs or training_config.get('epochs'), 
+        student_temp=config.get('student_temp'), 
+        center_momentum=config.get('center_momentum'),
+        momentum_teacher=config.get('momentum_teacher'),
+        mask_ratio=mask_ratio or config.get('mask_ratio'),
+        val_step=val_step or training_config.get('val_step')
+    )
+
+    return model
+
+
+
+
diff --git a/mae_dino/lightning_wrapper.py b/mae_dino/lightning_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..45d7b0e3afa74a1e979bfaec572223a8d28f05e5
--- /dev/null
+++ b/mae_dino/lightning_wrapper.py
@@ -0,0 +1,213 @@
+from typing import List, Optional, Tuple, Union, Dict
+import logging
+import math
+import torch
+import time
+from datetime import datetime
+import lightning.pytorch as pl
+
+from mae_dino.models import GAIABase
+from deepspeed.ops.adam import FusedAdam
+
+import torch.nn.functional as F
+
+Shape3d = Union[List[int], Tuple[int, int, int]]
+
+
+class CosineAnnealingWarmupLR(torch.optim.lr_scheduler.LambdaLR):
+    def __init__(self, optimizer, warmup_steps, total_steps, eta_min=0.0, last_step=-1):
+        """
+        Cosine annealing scheduler with warm-up period, operating per step.
+
+        Args:
+            optimizer (Optimizer): Wrapped optimizer.
+            warmup_steps (int): Number of warm-up steps.
+            total_steps (int): Total number of training steps.
+            eta_min (float): Minimum learning rate after annealing. Default: 0.0.
+            last_step (int): The index of the last step when resuming training. Default: -1.
+        """
+        self.warmup_steps = warmup_steps
+        self.total_steps = total_steps
+        self.eta_min = eta_min
+
+        super().__init__(optimizer, self.lr_lambda, last_step)
+
+    def lr_lambda(self, current_step):
+        if current_step < self.warmup_steps:
+            # Linear warm-up
+            return float(current_step) / float(max(1, self.warmup_steps))
+        else:
+            # Cosine annealing
+            progress = float(current_step - self.warmup_steps) / float(max(1, self.total_steps - self.warmup_steps))
+            cosine_decay = 0.5 * (1 + math.cos(math.pi * progress))
+            return cosine_decay * (1 - self.eta_min) + self.eta_min
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class GAIABaseLightning(pl.LightningModule):
+    def __init__(self, 
+                 img_size: Shape3d = [4, 224, 224], 
+                 patch_size: Shape3d = [1, 16, 16], 
+                 in_chans: int = 3, 
+                 encoder_embed_dim: int = 1024, 
+                 encoder_depth: int = 8, 
+                 encoder_num_heads: int = 16, 
+                 decoder_embed_dim: int = 512, 
+                 decoder_depth: int = 8, 
+                 decoder_num_heads: int = 16,
+                 mlp_ratio: float = 4., 
+                 norm_layer: torch.nn.Module = torch.nn.LayerNorm, 
+                 norm_pix_loss: bool = False,
+                 drop_channels_rate: float = 0.0,
+                 # DINO Args
+                 adjacent_masking: bool = False,
+                 norm_last_layer: bool = True,
+                 dino_head_dim: int = 1024,
+                 warmup_teacher_temp: float = 0.04, 
+                 teacher_temp: float = 0.04, 
+                 warmup_teacher_temp_epochs: int = 5, 
+                 epochs: int = 100, 
+                 student_temp: float = 0.1, 
+                 center_momentum: float = 0.9,
+                 momentum_teacher: float = 0.996,
+                 params: Dict[str, Union[Dict[str, float], int]] = None,
+                 mask_ratio = 0.75,
+                 val_step=0):
+        super().__init__()
+        self.params = params or {
+            'optimizer': {
+                'learning_rate': 1e-3,
+            },
+            'scheduler': {
+                'type': 'CosineAnnealingLR',
+                'warmup_steps': 100,
+                'total_steps': 1000,
+                'eta_min': 1e-10,
+                'last_step': -1,
+            },
+        }
+
+        self.model = GAIABase(
+            img_size=img_size, 
+            patch_size=patch_size, 
+            in_chans=in_chans, 
+            encoder_embed_dim=encoder_embed_dim, 
+            encoder_depth=encoder_depth, 
+            encoder_num_heads=encoder_num_heads, 
+            decoder_embed_dim=decoder_embed_dim, 
+            decoder_depth=decoder_depth, 
+            decoder_num_heads=decoder_num_heads,
+            mlp_ratio=mlp_ratio, 
+            norm_layer=norm_layer, 
+            norm_pix_loss=norm_pix_loss,
+            drop_channels_rate=drop_channels_rate,
+            # DINO Args
+            adjacent_masking=adjacent_masking,
+            norm_last_layer=norm_last_layer,
+            dino_head_dim=dino_head_dim,
+            warmup_teacher_temp=warmup_teacher_temp,
+            teacher_temp=teacher_temp,
+            warmup_teacher_temp_epochs=warmup_teacher_temp_epochs,
+            epochs=epochs,
+            student_temp=student_temp,
+            center_momentum=center_momentum,
+            momentum_teacher=momentum_teacher,
+        )
+        self.val_step = val_step
+        self.mask_ratio = mask_ratio
+        self.momentum_teacher = momentum_teacher
+        self.sync_dist = True
+        self.mae_loss_start = None
+        self.mae_loss_min = None
+        self.dino_loss_min = None
+        self.dino_loss_start = None
+
+
+    def forward(self, x, x_mask, temporal_pos=None, mask_ratio=0.75, epoch=None):
+        if epoch is None:
+            try:
+                epoch = self.current_epoch
+            except:
+                raise ValueError(f"epoch value is invalid")
+        return self.model(x, x_mask, temporal_pos=temporal_pos, mask_ratio=mask_ratio, epoch=epoch)
+
+    def training_step(self, batch, batch_idx):
+        x = batch['x'].to(self.device)
+        x_mask = batch['x_mask'].to(self.device)
+        if batch['temporal_pos']:
+            temporal_pos = [time_component.to(self.device) for time_component in batch['temporal_pos']]
+        else:
+            temporal_pos = None
+        
+        (total_loss, dino_loss, mae_loss), (pred, mask, _, _) = self(x, x_mask, temporal_pos=temporal_pos, mask_ratio=self.mask_ratio, epoch=self.current_epoch)
+
+        if batch_idx % 50 == 0:
+            logger.info(f"Train loss : {total_loss}")
+
+        return total_loss
+        
+    def on_after_backward(self):
+        # Clip gradients to prevent exploding gradients
+        torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
+    
+        # Update EMA for teacher after each backward pass
+        with torch.no_grad():
+            for param_q, param_k in zip(self.model.student.parameters(), self.model.teacher.parameters()):
+                param_k.data.mul_(self.momentum_teacher).add_((1 - self.momentum_teacher) * param_q.detach().data)
+
+
+    def validation_step(self, batch, batch_idx):
+        x = batch['x']
+        x_mask = batch['x_mask']
+        if batch['temporal_pos']:
+            temporal_pos = [time_component for time_component in batch['temporal_pos']]
+        else:
+            temporal_pos = None
+
+        mask_ratio = self.mask_ratio
+        (total_loss, dino_loss, mae_loss), (pred, mask, _, _) = self(x, x_mask, temporal_pos=temporal_pos, mask_ratio=self.mask_ratio, epoch=self.current_epoch)
+        return total_loss
+
+    def configure_optimizers(self):        
+        optimizer = FusedAdam(self.parameters(), lr=self.params['optimizer']['learning_rate'])
+
+        if self.params['scheduler']['type'] == 'CosineAnnealingLR':
+            if 'warmup_steps' not in self.params['scheduler']:
+                warmup_steps = 0
+            else:
+                warmup_steps = self.params['scheduler']['warmup_steps']
+            total_steps = self.params['scheduler']['total_steps']
+            eta_min = self.params['scheduler']['eta_min']
+            last_step = self.params['scheduler']['last_step']
+            scheduler = {
+                "scheduler": CosineAnnealingWarmupLR(
+                    optimizer, 
+                    warmup_steps, 
+                    total_steps, 
+                    eta_min=eta_min, 
+                    last_step=last_step
+                ),
+                "interval": "step",  # Explicitly update learning rate every step
+                "frequency": 1  # Apply the scheduler at every step
+            }
+
+        else:
+            raise NotImplementedError(f"The specified {self.params['scheduler']['type']} scheduler is not implemented.")
+        return [optimizer], [scheduler]
+
+    def save_model(self, path):
+        torch.save(self.state_dict(), path)
+
+    @classmethod
+    def load_from_checkpoint(cls, path, **kwargs):
+        model = cls(**kwargs)
+        model.load_state_dict(torch.load(path)['state_dict'])
+        return model
+    
+    @classmethod
+    def load_from_ds_checkpoint(cls, path, **kwargs):
+        model = cls(**kwargs)
+        model.load_state_dict(torch.load(path)) # NESTED state_dict exists in DS converted checkpoints
+        return model
diff --git a/mae_dino/model_layers/layers.py b/mae_dino/model_layers/layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..94bf4131db3df8bf89f66c26714ac69357a48b92
--- /dev/null
+++ b/mae_dino/model_layers/layers.py
@@ -0,0 +1,132 @@
+from typing import List, Optional, Tuple, Union
+
+import timm
+import torch
+import numpy as np
+from einops import rearrange
+from timm.layers import Mlp
+
+
+Shape3d = Union[List[int], Tuple[int, int, int]]
+
+
+# PatchEmbed
+class PatchEmbed(torch.nn.Module):
+    """ 3D Image to Patch Embedding
+    """
+    def __init__(
+            self,
+            img_size: Shape3d = (4, 224, 224),
+            patch_size: Shape3d = (1, 16, 16),
+            in_chans: int = 3,
+            embed_dim: int = 768,
+            norm_layer: Optional[torch.nn.Module] = None,
+            flatten: bool = True,
+            bias: bool = True,
+    ):
+        super().__init__()
+        self.img_size = img_size
+        self.patch_size = patch_size
+        assert len(self.img_size) == 3 and len(self.patch_size) == 3
+        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1], img_size[2] // patch_size[2])
+        self.num_patches = self.grid_size[0] * self.grid_size[1] * self.grid_size[2]
+        self.flatten = flatten
+
+        self.proj = torch.nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
+        self.norm = norm_layer(embed_dim) if norm_layer else torch.nn.Identity()
+
+    def forward(self, x):
+        B, C, T, H, W = x.shape
+        assert H == self.img_size[1], f"Input data height ({H}) doesn't match model ({self.img_size[1]})."
+        assert W == self.img_size[2], f"Input data width ({W}) doesn't match model ({self.img_size[2]})."
+        assert T == self.img_size[0], f"Input data timesteps ({T}) doesn't match model ({self.img_size[0]})."
+        x = self.proj(x)
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # BCTHW -> BCL -> BLC
+        x = self.norm(x)
+        return x
+
+# DropPath
+def generate_mask(x, drop_prob: float = 0., scale_by_keep: bool = True):
+    """ Create drop mask for x. Adapted from timm.models.layers.drop_path. """
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0 and scale_by_keep:
+        random_tensor.div_(keep_prob)
+    return random_tensor
+
+
+class DropPath(torch.nn.Module):
+    """ Adapted from timm.models.layers.DropPath. In this version, drop mask can be saved and reused.
+        This is useful when applying the same drop mask more than once.
+    """
+    def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
+        super().__init__()
+        self.drop_prob = drop_prob
+        self.scale_by_keep = scale_by_keep
+        self.drop_mask = None
+
+    def generate_mask(self, x: torch.Tensor):
+        self.drop_mask = generate_mask(x, self.drop_prob, self.scale_by_keep)
+
+    def forward(self, x: torch.Tensor, new_mask: bool = True):
+
+        if self.drop_prob == 0. or not self.training:
+            return x
+
+        if self.drop_mask is None or new_mask:
+            self.generate_mask(x)
+
+        return self.drop_mask * x
+
+    def extra_repr(self):
+        return f'drop_prob={round(self.drop_prob, 3):0.3f}'
+
+
+class Attention(torch.nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.qkv = torch.nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = torch.nn.Dropout(attn_drop)
+        self.proj = torch.nn.Linear(dim, dim)
+        self.proj_drop = torch.nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x, attn
+
+
+class Block(torch.nn.Module):
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=torch.nn.GELU, norm_layer=torch.nn.LayerNorm):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else torch.nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+    def forward(self, x, return_attention=False):
+        y, attn = self.attn(self.norm1(x))
+        if return_attention:
+            return attn
+        x = x + self.drop_path(y)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
\ No newline at end of file
diff --git a/mae_dino/model_layers/pos_embed.py b/mae_dino/model_layers/pos_embed.py
new file mode 100644
index 0000000000000000000000000000000000000000..84b3d292cee8fb06eacd0807e65e0249ca00ecd5
--- /dev/null
+++ b/mae_dino/model_layers/pos_embed.py
@@ -0,0 +1,146 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Position embedding utils
+# --------------------------------------------------------
+
+import numpy as np
+import torch
+
+
+def get_3d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
+    """
+    grid_size: 3d tuple of grid size: t, h, w
+    return:
+    pos_embed: L, D
+    """
+
+    assert embed_dim % 16 == 0
+
+    t_size, h_size, w_size = grid_size
+
+    w_embed_dim = embed_dim // 16 * 6
+    h_embed_dim = embed_dim // 16 * 6
+    t_embed_dim = embed_dim // 16 * 4
+
+    w_pos_embed = get_1d_sincos_pos_embed_from_grid(w_embed_dim, np.arange(w_size))
+    h_pos_embed = get_1d_sincos_pos_embed_from_grid(h_embed_dim, np.arange(h_size))
+    t_pos_embed = get_1d_sincos_pos_embed_from_grid(t_embed_dim, np.arange(t_size))
+
+    w_pos_embed = np.tile(w_pos_embed, (t_size * h_size, 1))
+    h_pos_embed = np.tile(np.repeat(h_pos_embed, w_size, axis=0), (t_size, 1))
+    t_pos_embed = np.repeat(t_pos_embed, h_size * w_size, axis=0)
+
+    pos_embed = np.concatenate((w_pos_embed, h_pos_embed, t_pos_embed), axis=1)
+
+    if cls_token:
+        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+
+# --------------------------------------------------------
+# 2D sine-cosine position embedding
+# References:
+# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py
+# MoCo v3: https://github.com/facebookresearch/moco-v3
+# --------------------------------------------------------
+def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
+    """
+    grid_size: int of the grid height and width
+    return:
+    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    grid_h = np.arange(grid_size, dtype=np.float32)
+    grid_w = np.arange(grid_size, dtype=np.float32)
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+
+    grid = grid.reshape([2, 1, grid_size, grid_size])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token:
+        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = torch.arange(embed_dim // 2).to(pos.device).type(torch.float32)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = torch.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
+
+    emb_sin = torch.sin(out)  # (M, D/2)
+    emb_cos = torch.cos(out)  # (M, D/2)
+
+    emb = torch.cat([emb_sin, emb_cos], dim=1)  # (M, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float32)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out) # (M, D/2)
+    emb_cos = np.cos(out) # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+
+# --------------------------------------------------------
+# Interpolate position embeddings for high-resolution
+# References:
+# DeiT: https://github.com/facebookresearch/deit
+# --------------------------------------------------------
+def interpolate_pos_embed(model, checkpoint_model):
+    if 'pos_embed' in checkpoint_model:
+        pos_embed_checkpoint = checkpoint_model['pos_embed']
+        embedding_size = pos_embed_checkpoint.shape[-1]
+        num_patches = model.patch_embed.num_patches
+        num_extra_tokens = model.pos_embed.shape[-2] - num_patches
+        # height (== width) for the checkpoint position embedding
+        orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
+        # height (== width) for the new position embedding
+        new_size = int(num_patches ** 0.5)
+        # class_token and dist_token are kept unchanged
+        if orig_size != new_size:
+            print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
+            extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
+            # only the position tokens are interpolated
+            pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
+            pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
+            pos_tokens = torch.nn.functional.interpolate(
+                pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
+            pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+            new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
+            checkpoint_model['pos_embed'] = new_pos_embed
\ No newline at end of file
diff --git a/mae_dino/models.py b/mae_dino/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bce3a2a10e212ef28359e969fa8843433cac228
--- /dev/null
+++ b/mae_dino/models.py
@@ -0,0 +1,792 @@
+from typing import List, Dict, Optional, Tuple, Union
+import random
+import torch
+import timm
+import numpy as np
+from einops import rearrange
+import torch.distributed as dist
+from timm.layers import drop_path, DropPath, Mlp, trunc_normal_
+import logging
+
+from mae_dino.model_layers.layers import PatchEmbed, Attention, Block, PatchEmbed
+from mae_dino.model_layers.pos_embed import get_3d_sincos_pos_embed
+
+Shape3d = Union[List[int], Tuple[int, int, int]]
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class TemporalEncoder(torch.nn.Module):
+    def __init__(self, embed_dim, tokens_per_frame):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.tokens_per_frame = tokens_per_frame
+        
+        # Define embedding sizes for each temporal component
+        self.year_embed_dim = embed_dim // 4
+        self.doy_embed_dim = embed_dim // 4
+        self.hour_embed_dim = embed_dim // 4
+        self.minute_embed_dim = embed_dim - (
+            self.year_embed_dim + self.doy_embed_dim + self.hour_embed_dim
+        )
+        
+        # Embedding layers for categorical temporal features
+        self.year_embedding = torch.nn.Embedding(3000, self.year_embed_dim)   # Years from 0000 to 2999
+        self.doy_embedding = torch.nn.Embedding(367, self.doy_embed_dim)   # Day of Year 0-365
+        self.hour_embedding = torch.nn.Embedding(24, self.hour_embed_dim)     # Hours 0-23
+        self.minute_embedding = torch.nn.Embedding(60, self.minute_embed_dim) # Minutes 0-59
+
+        # Initialize embeddings
+        self._init_weights()
+    
+    def _init_weights(self):
+        torch.nn.init.xavier_uniform_(self.year_embedding.weight)
+        torch.nn.init.xavier_uniform_(self.doy_embedding.weight)
+        torch.nn.init.xavier_uniform_(self.hour_embedding.weight)
+        torch.nn.init.xavier_uniform_(self.minute_embedding.weight)
+    
+    def forward(self, year, doy, hour, minute):
+        """
+        Args:
+            year (torch.Tensor): Shape (batch_size, time), integer years
+            doy (torch.Tensor): Shape (batch_size, time), values [1, 366]
+            hour (torch.Tensor): Shape (batch_size, time), values [0, 23]
+            minute (torch.Tensor): Shape (batch_size, time), values [0, 59]
+        
+        Returns:
+            torch.Tensor: Temporal embeddings of shape (batch_size, time * tokens_per_frame, embed_dim)
+        """
+        # Ensure inputs are of type Long
+        year = year.long()
+        doy = doy.long()
+        hour = hour.long()
+        minute = minute.long()
+        
+        # Get embeddings for each temporal component
+        year_emb = self.year_embedding(year)       # (batch_size, time, year_embed_dim)
+        doy_emb = self.doy_embedding(doy)          # (batch_size, time, doy_embed_dim)
+        hour_emb = self.hour_embedding(hour)       # (batch_size, time, hour_embed_dim)
+        minute_emb = self.minute_embedding(minute) # (batch_size, time, minute_embed_dim)
+        
+        # Concatenate embeddings along the last dimension
+        temporal_emb = torch.cat(
+            [year_emb, doy_emb, hour_emb, minute_emb], dim=-1
+        )  # (batch_size, time, embed_dim)
+        
+        # Reshape to (batch_size, time * tokens_per_frame, embed_dim)
+        batch_size, time_steps, _ = temporal_emb.shape
+        temporal_emb = torch.repeat_interleave(temporal_emb, self.tokens_per_frame, dim=1)
+        
+        return temporal_emb
+
+
+class Encoder(torch.nn.Module):
+    def __init__(self, 
+                 img_size: Shape3d = [4, 224, 224], 
+                 patch_size: Shape3d = [1, 16, 16], 
+                 in_chans: int = 3, 
+                 encoder_embed_dim: int = 1024, 
+                 encoder_depth: int = 8, 
+                 encoder_num_heads: int = 16, 
+                 mlp_ratio: float = 4., 
+                 norm_layer: torch.nn.Module = torch.nn.LayerNorm, 
+                 drop_channels_rate: float = 0.0,
+                 adjacent_masking: bool = False,
+                 ):
+        super().__init__()
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.in_chans = in_chans
+        self.encoder_embed_dim = encoder_embed_dim
+        self.encoder_depth = encoder_depth 
+        self.encoder_num_heads = encoder_num_heads 
+        self.mlp_ratio = mlp_ratio
+        self.norm_layer = norm_layer
+        self.drop_channels_rate = drop_channels_rate
+        self.adjacent_masking = adjacent_masking
+
+        # -------------------------------------------------------------------------- #
+        # MAE encoder
+        self.drop_channels = torch.nn.Dropout3d(self.drop_channels_rate) if self.drop_channels_rate > 0 else torch.nn.Identity()
+        
+        self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, encoder_embed_dim)
+        num_patches = self.patch_embed.num_patches
+        tokens_per_frame = num_patches // img_size[0]
+
+        self.cls_token = torch.nn.Parameter(torch.zeros(1, 1, encoder_embed_dim))
+        self.register_buffer("encoder_pos_embed", torch.zeros(1, num_patches + 1, encoder_embed_dim))
+
+        self.encoder_blocks = torch.nn.ModuleList([
+            Block(encoder_embed_dim, encoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=self.norm_layer)
+            for i in range(self.encoder_depth)])
+        self.norm = norm_layer(encoder_embed_dim)
+
+        self.temporal_embed_enc = TemporalEncoder(embed_dim=encoder_embed_dim, tokens_per_frame=tokens_per_frame)
+
+        # Initialize weights
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        encoder_pos_embed = get_3d_sincos_pos_embed(
+            self.encoder_pos_embed.shape[-1], self.patch_embed.grid_size, cls_token=True
+        )
+        self.encoder_pos_embed.data.copy_(torch.from_numpy(encoder_pos_embed).float().unsqueeze(0))
+
+
+        # initialize patch_embed like nn.Linear (instead of nn.Conv2d)
+        w = self.patch_embed.proj.weight.data
+        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+
+        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
+        torch.nn.init.normal_(self.cls_token, std=0.02)
+
+        # initialize nn.Linear and nn.LayerNorm
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, torch.nn.Linear):
+            # we use xavier_uniform following official JAX ViT:
+            torch.nn.init.xavier_uniform_(m.weight)
+            if isinstance(m, torch.nn.Linear) and m.bias is not None:
+                torch.nn.init.constant_(m.bias, 0)
+        elif isinstance(m, torch.nn.LayerNorm):
+            torch.nn.init.constant_(m.bias, 0)
+            torch.nn.init.constant_(m.weight, 1.0)
+
+    def patchify(self, imgs):
+        """
+        imgs: B, C, T, H, W
+        x: B, L, D
+        """
+        s, p, q = self.patch_embed.patch_size
+        x = rearrange(imgs, 'b c (t s) (h p) (w q) -> b (t h w) (s p q c)', s=s, p=p, q=q)
+
+        return x
+
+    def unpatchify(self, x):
+        """
+        x: B, L, D
+        imgs: B, C, T, H, W
+        """
+        s, p, q = self.patch_embed.patch_size
+        gs = self.patch_embed.grid_size
+        imgs = rearrange(x, 'b (t h w) (s p q c) -> b c (t s) (h p) (w q)', h=gs[1], w=gs[2], t=gs[0], s=s, p=p, q=q)
+        return imgs
+    
+    def log_helper(self, message):
+            logger.info(message)
+
+    def hinted_random_masking(self, x: torch.Tensor, x_mask: torch.Tensor, mask_ratio: int):
+        """
+        Perform per-sample random masking by per-sample shuffling.
+        Per-sample shuffling is done by argsort random noise on patches without missing value pixels (indicated by x_mask).
+        x: [N, L, D], sequence
+        """
+        N, L, D = x.shape  # batch, length, dim
+        
+        # Calculate missing data ratio from x_mask
+        x_mask = x_mask.sum(dim=-1) > 0  # [N, L]
+        missing_ratio = x_mask.float().mean(dim=1)  # [N]
+   
+        adjusted_mask_ratio = max(max(missing_ratio).item(),mask_ratio)
+
+        len_keep = int(L * (1 - adjusted_mask_ratio))
+        
+        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]
+        
+        # x_mask = x_mask[0]
+        ids_x_mask = torch.where(x_mask)
+        # ids_x_mask = list(zip(ids_x_mask[0].tolist(), ids_x_mask[1].tolist()))
+        # TODO translate to NP
+        ids_x_mask_dict = {}
+        for bs, p in zip(ids_x_mask[0], ids_x_mask[1]):
+            bs, p = int(bs), int(p)
+            if bs not in ids_x_mask_dict:
+                ids_x_mask_dict[bs] = []
+            ids_x_mask_dict[bs].append(p)
+
+        noise += x_mask
+        
+        # sort noise for each sample
+        ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove
+        ids_restore = torch.argsort(ids_shuffle, dim=1)
+        
+        # keep the first subset
+        ids_keep = ids_shuffle[:, :len_keep]
+        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
+        
+        # generate the binary mask: 0 is keep, 1 is remove
+        mask = torch.ones([N, L], device=x.device)
+        mask[:, :len_keep] = 0
+        # unshuffle to get the binary mask
+        mask = torch.gather(mask, dim=1, index=ids_restore)
+        
+        return x_masked, mask, ids_restore, ids_x_mask_dict
+
+
+    def hinted_adjacent_masking(self, x: torch.Tensor, x_mask: torch.Tensor, mask_ratio: float):
+        """
+        Perform masking by keeping contiguous blocks of patches in time and space,
+        excluding patches where x_mask is 1 in each patch, and adjust to keep mask_ratio consistent.
+        x: [N, L, D], sequence
+        x_mask: [N, L, D], mask indicating invalid patches (1 where invalid)
+        patch_embed: object containing grid_size attribute (T, H, W)
+        """
+        N, L, D = x.shape  # batch size, sequence length, feature dimension
+    
+        # Get grid sizes
+        gs = self.patch_embed.grid_size  # (T, H, W)
+        T, H, W = gs
+    
+        # Calculate total number of patches and number to keep
+        total_patches = T * H * W
+        len_keep = int(total_patches * (1 - mask_ratio))
+    
+        # Reshape x to [N, T, H, W, D]
+        x = x.view(N, T, H, W, D)
+    
+        # Process x_mask to identify invalid patches
+        # x_mask: [N, L, D] -> [N, L], True where invalid
+        x_mask = x_mask.sum(dim=-1) > 0
+    
+        ids_x_mask = torch.where(x_mask)
+        ids_x_mask_dict = {}
+        for bs, p in zip(ids_x_mask[0], ids_x_mask[1]):
+            bs, p = int(bs), int(p)
+            if bs not in ids_x_mask_dict:
+                ids_x_mask_dict[bs] = []
+            ids_x_mask_dict[bs].append(p)
+            
+        x_mask = x_mask.view(N, T, H, W)  # Reshape to [N, T, H, W]
+    
+        # Initialize mask of zeros [N, T, H, W]; 0 is remove, 1 is keep
+        mask = torch.zeros((N, T, H, W), device=x.device)
+    
+        ids_keep_list = []
+    
+        # For each sample in the batch
+        for i in range(N):
+            # Valid patches are where x_mask is False (0)
+            valid_patches_mask = ~x_mask[i]  # [T, H, W], True where valid
+            num_valid_patches = valid_patches_mask.sum().item()
+    
+            # Adjust len_keep if not enough valid patches
+            len_keep_i = min(len_keep, num_valid_patches)
+            if len_keep_i == 0:
+                ids_keep_list.append(torch.tensor([], device=x.device, dtype=torch.long))
+                continue  # Skip if no valid patches
+    
+            patches_selected = 0
+            attempts = 0
+            max_attempts = 1000  # Prevent infinite loops
+    
+            while patches_selected < len_keep_i and attempts < max_attempts:
+                attempts += 1
+    
+                # Compute block sizes
+                block_size = int(round((len_keep_i - patches_selected) ** (1/3)))
+                t_size_block = min(T, block_size)
+                h_size_block = min(H, block_size)
+                w_size_block = min(W, block_size)
+    
+                # Randomly select starting positions
+                t0 = random.randint(0, T - t_size_block)
+                h0 = random.randint(0, H - h_size_block)
+                w0 = random.randint(0, W - w_size_block)
+    
+                # Get indices for the block
+                t_indices = slice(t0, t0 + t_size_block)
+                h_indices = slice(h0, h0 + h_size_block)
+                w_indices = slice(w0, w0 + w_size_block)
+    
+                # Extract the block of valid patches
+                block_valid_mask = valid_patches_mask[t_indices, h_indices, w_indices]
+                block_already_selected = mask[i, t_indices, h_indices, w_indices]
+    
+                # Find valid, unselected patches in the block
+                selectable_mask = block_valid_mask & (block_already_selected == 0)
+                num_selectable = selectable_mask.sum().item()
+    
+                if num_selectable == 0:
+                    continue  # Try another block
+    
+                # Determine how many patches to select from this block
+                num_to_select = min(len_keep_i - patches_selected, num_selectable)
+    
+                # Get indices of selectable patches
+                selectable_indices = selectable_mask.nonzero(as_tuple=False)
+    
+                # Randomly select patches from the selectable ones
+                selected_indices = selectable_indices[torch.randperm(num_selectable)[:num_to_select]]
+    
+                # Update the mask to keep the selected patches
+                for idx in selected_indices:
+                    t_idx, h_idx, w_idx = idx
+                    mask[i, t0 + t_idx, h0 + h_idx, w0 + w_idx] = 1
+    
+                patches_selected += num_to_select
+    
+            # If not enough patches were selected, randomly select from remaining valid patches
+            if patches_selected < len_keep_i:
+                remaining_selectable = (valid_patches_mask & (mask[i] == 0)).nonzero(as_tuple=False)
+                num_remaining = remaining_selectable.size(0)
+                num_needed = len_keep_i - patches_selected
+                num_to_select = min(num_remaining, num_needed)
+                if num_to_select > 0:
+                    selected_indices = remaining_selectable[torch.randperm(num_remaining)[:num_to_select]]
+                    mask[i][selected_indices[:, 0], selected_indices[:, 1], selected_indices[:, 2]] = 1
+                    patches_selected += num_to_select
+    
+            # Get the indices of the kept patches
+            mask_i_flat = mask[i].view(-1)
+            ids_keep_i = torch.where(mask_i_flat == 1)[0]
+            ids_keep_list.append(ids_keep_i)
+    
+        # Determine the maximum length of ids_keep across all samples for padding
+        max_len_keep = max(len(ids) for ids in ids_keep_list)
+    
+        # Pad ids_keep to have the same length
+        ids_keep_padded = torch.zeros((N, max_len_keep), dtype=torch.long, device=x.device)
+        for i, ids in enumerate(ids_keep_list):
+            ids_keep_padded[i, :len(ids)] = ids
+    
+        # Flatten x back to [N, L, D]
+        x_flat = x.view(N, L, D)
+    
+        # Gather x_masked with padding
+        x_masked_list = []
+        for i in range(N):
+            ids = ids_keep_list[i]
+            x_masked_i = torch.index_select(x_flat[i], dim=0, index=ids)
+            x_masked_list.append(x_masked_i)
+        x_masked = torch.nn.utils.rnn.pad_sequence(x_masked_list, batch_first=True)
+    
+        # Generate ids_restore (identity mapping)
+        ids_restore = torch.arange(L, device=x.device).unsqueeze(0).repeat(N, 1)
+    
+        # Generate the binary mask: 0 is keep, 1 is remove
+        mask_flat = mask.view(N, L)
+        final_mask = 1 - mask_flat  # Invert mask to match expected output
+    
+        return x_masked, final_mask, ids_restore, ids_x_mask_dict
+
+
+    def forward(self,
+                x: torch.Tensor,
+                x_mask: torch.Tensor,
+                mask_ratio: float,
+                # temporal_pos: Optional[torch.Tensor]):
+                temporal_pos: Optional[List]):
+
+        # Drop input channels
+        x = self.drop_channels(x)
+
+        # embed patches
+        x = self.patch_embed(x)
+
+        # add pos embed w/o cls token
+        x = x + self.encoder_pos_embed[:, 1:, :]
+
+        if temporal_pos:
+            temporal_encoding = self.temporal_embed_enc(*temporal_pos)
+            # temporal_encoding = self.drop_temporal(temporal_encoding, new_mask=True)
+            x = x + temporal_encoding
+        
+        # masking: length -> length * mask_ratio
+        x_mask = self.patchify(x_mask)
+        if self.adjacent_masking:
+            x, mask, ids_restore, ids_x_mask_dict = self.hinted_adjacent_masking(x, x_mask, mask_ratio)
+        else:
+            x, mask, ids_restore, ids_x_mask_dict = self.hinted_random_masking(x, x_mask, mask_ratio)
+
+        # append cls token
+        cls_token = self.cls_token + self.encoder_pos_embed[:, :1, :]
+        cls_tokens = cls_token.expand(x.shape[0], -1, -1)
+        x = torch.cat((cls_tokens, x), dim=1)
+
+        # apply Transformer blocks
+        for blk in self.encoder_blocks:
+            x = blk(x)
+        x = self.norm(x)
+
+        return x, mask, ids_restore, ids_x_mask_dict
+
+
+class Decoder(torch.nn.Module):
+    def __init__(self, 
+                 img_size: Shape3d = [4, 224, 224],
+                 patch_size: Shape3d = [1, 16, 16], 
+                 in_chans: int = 3,
+                 encoder_embed_dim: int = 1024,
+                 decoder_embed_dim: int = 512, 
+                 decoder_depth: int = 8, 
+                 decoder_num_heads: int = 16,
+                 mlp_ratio: float = 4., 
+                 norm_layer: torch.nn.Module = torch.nn.LayerNorm, 
+                 norm_pix_loss: bool = False,
+                 ):
+        super().__init__()
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.encoder_embed_dim = encoder_embed_dim
+        self.decoder_embed_dim = decoder_embed_dim 
+        self.decoder_depth = decoder_depth
+        self.decoder_num_heads = decoder_num_heads
+        self.mlp_ratio = mlp_ratio
+        self.norm_layer = norm_layer
+        self.norm_pix_loss = norm_pix_loss
+
+        # -------------------------------------------------------------------------- #
+        # MAE decoder
+        self.decoder_embed = torch.nn.Linear(encoder_embed_dim, decoder_embed_dim, bias=True)
+        self.mask_token = torch.nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))
+
+        self.grid_size = [d//p for d, p in zip(self.img_size, self.patch_size)]
+        num_patches = np.prod(self.grid_size)
+        self.register_buffer("decoder_pos_embed", torch.zeros(1, num_patches + 1, decoder_embed_dim))
+
+        self.decoder_blocks = torch.nn.ModuleList([
+            Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
+            for i in range(decoder_depth)])
+
+        self.decoder_norm = norm_layer(decoder_embed_dim)
+        self.decoder_pred = torch.nn.Linear(decoder_embed_dim,
+                                      patch_size[0] * patch_size[1] * patch_size[2] * in_chans,
+                                      bias=True)  # decoder to patch
+        
+        tokens_per_frame = num_patches // img_size[0]
+        self.temporal_embed_dec = TemporalEncoder(embed_dim=decoder_embed_dim, tokens_per_frame=tokens_per_frame)
+
+    # Initialize weights
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        decoder_pos_embed = get_3d_sincos_pos_embed(
+            self.decoder_pos_embed.shape[-1], self.grid_size, cls_token=True
+        )
+        self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))
+
+        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
+        torch.nn.init.normal_(self.mask_token, std=0.02)
+
+        # initialize nn.Linear and nn.LayerNorm
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, torch.nn.Linear):
+            # we use xavier_uniform following official JAX ViT:
+            torch.nn.init.xavier_uniform_(m.weight)
+            if isinstance(m, torch.nn.Linear) and m.bias is not None:
+                torch.nn.init.constant_(m.bias, 0)
+        elif isinstance(m, torch.nn.LayerNorm):
+            torch.nn.init.constant_(m.bias, 0)
+            torch.nn.init.constant_(m.weight, 1.0)
+
+    def forward(self, x: torch.Tensor,
+                    ids_restore: torch.Tensor,
+                    temporal_pos: Optional[torch.Tensor]):
+        # embed tokens
+        x = self.decoder_embed(x)
+
+        # append mask tokens to sequence
+        mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
+        x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1)  # no cls token
+        x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]))  # unshuffle
+        x = torch.cat([x[:, :1, :], x_], dim=1)  # append cls token
+
+        # add pos embed
+        x = x + self.decoder_pos_embed
+        # remove cls token
+        x_ = x[:, 1:, :]
+
+        if temporal_pos:
+            temporal_encoding = self.temporal_embed_dec(*temporal_pos)
+            # Reuse drop mask from encoder for consistent dropping
+            # temporal_encoding = self.drop_temporal(temporal_encoding, new_mask=False)
+            # Add temporal encoding w/o cls token
+            x_ = x_ + temporal_encoding
+
+        x = torch.cat([x[:, :1, :], x_], dim=1)  # append cls token
+
+        # apply Transformer blocks
+        for blk in self.decoder_blocks:
+            x = blk(x)
+        x = self.decoder_norm(x)
+
+        # predictor projection
+        x = self.decoder_pred(x)
+
+        # remove cls token
+        x = x[:, 1:, :]
+
+        return x
+
+
+    
+    
+class GAIABase(torch.nn.Module):
+    def __init__(self, 
+                 img_size: Shape3d = [4, 224, 224], 
+                 patch_size: Shape3d = [1, 16, 16], 
+                 in_chans: int = 3, 
+                 encoder_embed_dim: int = 1024, 
+                 encoder_depth: int = 8, 
+                 encoder_num_heads: int = 16, 
+                 decoder_embed_dim: int = 512, 
+                 decoder_depth: int = 8, 
+                 decoder_num_heads: int = 16,
+                 mlp_ratio: float = 4., 
+                 norm_layer: torch.nn.Module = torch.nn.LayerNorm, 
+                 norm_pix_loss: bool = False,
+                 drop_channels_rate: float = 0.0,
+                 # DINO Args
+                 adjacent_masking: bool = False,
+                 norm_last_layer: bool = True,
+                 dino_head_dim: int = 1024,
+                 warmup_teacher_temp: float = 0.04, 
+                 teacher_temp: float = 0.04, 
+                 warmup_teacher_temp_epochs: int = 5, 
+                 epochs: int = 100, 
+                 student_temp: float = 0.1, 
+                 center_momentum: float = 0.9,
+                 momentum_teacher: float = 0.996,
+                 ):
+        super().__init__()
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.in_chans = in_chans
+        self.encoder_embed_dim = encoder_embed_dim
+        self.encoder_depth = encoder_depth 
+        self.encoder_num_heads = encoder_num_heads 
+        self.decoder_embed_dim = decoder_embed_dim 
+        self.decoder_depth = decoder_depth
+        self.decoder_num_heads = decoder_num_heads
+        self.mlp_ratio = mlp_ratio
+        self.norm_layer = norm_layer
+        self.norm_pix_loss = norm_pix_loss
+        self.drop_channels_rate = drop_channels_rate
+        # DINO Args
+        self.adjacent_masking = adjacent_masking
+        self.norm_last_layer = norm_last_layer
+        self.dino_head_dim = dino_head_dim
+        self.warmup_teacher_temp = warmup_teacher_temp
+        self.teacher_temp = teacher_temp
+        self.warmup_teacher_temp_epochs = warmup_teacher_temp_epochs
+        self.epochs = epochs
+        self.student_temp = student_temp
+        self.center_momentum = center_momentum
+        self.momentum_teacher = momentum_teacher
+        self.log_count = 0
+
+
+        self.encoder = Encoder(
+            img_size=img_size, 
+            patch_size=patch_size, 
+            in_chans=in_chans, 
+            encoder_embed_dim=encoder_embed_dim, 
+            encoder_depth=encoder_depth, 
+            encoder_num_heads=encoder_num_heads, 
+            mlp_ratio=mlp_ratio, 
+            norm_layer=norm_layer, 
+            drop_channels_rate=drop_channels_rate,
+            adjacent_masking=False,
+        )
+        
+        self.decoder = Decoder(
+            img_size=img_size, 
+            patch_size=patch_size,
+            in_chans=in_chans, 
+            encoder_embed_dim=encoder_embed_dim,
+            decoder_embed_dim=decoder_embed_dim, 
+            decoder_depth=decoder_depth, 
+            decoder_num_heads=decoder_num_heads,
+            mlp_ratio=mlp_ratio, 
+            norm_layer=norm_layer, 
+            norm_pix_loss=norm_pix_loss,
+        )
+        
+        self.teacher = Encoder(
+            img_size=img_size, 
+            patch_size=patch_size, 
+            in_chans=in_chans, 
+            encoder_embed_dim=encoder_embed_dim, 
+            encoder_depth=encoder_depth, 
+            encoder_num_heads=encoder_num_heads, 
+            mlp_ratio=mlp_ratio, 
+            norm_layer=norm_layer, 
+            drop_channels_rate=drop_channels_rate,
+            adjacent_masking=self.adjacent_masking,
+        )
+
+        # DINO Head wrappers
+        self.student = PassThroughHead(
+            self.encoder, 
+            DINOHead(encoder_embed_dim, dino_head_dim, norm_last_layer=norm_last_layer)
+        )                
+
+        self.teacher = PassThroughHead(
+            self.teacher, 
+            DINOHead(encoder_embed_dim, dino_head_dim, norm_last_layer=norm_last_layer)
+        )
+
+        # teacher and student start with the same weights
+        self.teacher.load_state_dict(self.student.state_dict())
+        
+        # teacher frozen
+        for p in self.teacher.parameters():
+            p.requires_grad = False
+    
+        self.dino_loss = DINOLoss(dino_head_dim, warmup_teacher_temp, teacher_temp, 
+                             warmup_teacher_temp_epochs, epochs, student_temp, center_momentum)
+    
+    
+    def forward_mae(self, imgs: torch.Tensor, img_masks: torch.Tensor, temporal_pos: Optional[torch.Tensor], mask_ratio: float = 0.75):
+
+        latent, mask, ids_restore, ids_x_mask_dict = self.encoder(imgs, img_masks, temporal_pos=temporal_pos, mask_ratio=mask_ratio)
+        pred = self.decoder(latent, ids_restore, temporal_pos)
+        loss = self.forward_mae_loss(imgs, pred, mask, ids_x_mask_dict)
+        return loss, pred, mask
+        
+    def forward_dino(self, imgs: torch.Tensor, img_masks: torch.Tensor, temporal_pos: Optional[torch.Tensor], mask_ratio: float = 0.75):
+        with torch.no_grad():
+            t_pred = self.teacher(imgs, img_masks, temporal_pos=temporal_pos, mask_ratio=mask_ratio)
+        s_pred = self.student(imgs, img_masks, temporal_pos=temporal_pos, mask_ratio=mask_ratio)
+        return s_pred, t_pred
+        
+    def log_helper(self, message):
+        logger.info(message)
+    
+    def forward_mae_loss(self, imgs: torch.Tensor, pred: torch.Tensor, mask: torch.Tensor, ids_x_mask_dict: Dict[str, List]):
+        """
+        imgs: B, C, T, H, W
+        target: B, L, D
+        pred: B, L, D
+        mask: B, L. 0 is keep, 1 is remove,
+        """
+
+        eps = 1e-6
+        target = self.encoder.patchify(imgs)
+
+        if self.decoder.norm_pix_loss:
+            mean = target.mean(dim=-1, keepdim=True)
+            var = target.var(dim=-1, keepdim=True)
+
+        loss = ((pred - target).to(torch.float) ** 2).mean(dim=-1)
+
+        # Process mask and loss to exclude ids_x_mask patches (patches that include missing data) in the calculation
+        for index, patch in ids_x_mask_dict.items():
+            mask[index, patch] = 0
+            loss[index, patch] = 0
+
+        loss_mask = torch.clamp(loss * mask, max=1e8)
+        loss = loss_mask.sum() / (mask.sum() + eps)  ### mean loss on removed patches
+        return loss
+
+    def forward(self, imgs: torch.Tensor, img_masks: torch.Tensor, temporal_pos: Optional[torch.Tensor], mask_ratio: float = 0.75, epoch=None):
+        if epoch is None:
+            raise ValueError(f"epoch value is invalid")
+        # MAE
+        temporal_pos = None
+
+        mae_loss, pred, mask = self.forward_mae(imgs, img_masks, temporal_pos=temporal_pos, mask_ratio=mask_ratio)
+        
+        # DINO
+        student_output, teacher_output = self.forward_dino(imgs, img_masks, temporal_pos=temporal_pos, mask_ratio=mask_ratio)
+        dino_loss = self.dino_loss(student_output, teacher_output, epoch)
+
+        # Total Loss
+        total_loss = mae_loss + dino_loss
+        return (total_loss, dino_loss, mae_loss), (pred, mask, student_output, teacher_output)
+
+
+class DINOHead(torch.nn.Module):
+    def __init__(self, in_dim, dino_head_dim, norm_last_layer=True, nlayers=3, hidden_dim=2048, bottleneck_dim=256):
+        super().__init__()
+        nlayers = max(nlayers, 1)
+        if nlayers == 1:
+            self.mlp = torch.nn.Linear(in_dim, bottleneck_dim)
+        else:
+            layers = [torch.nn.Linear(in_dim, hidden_dim)]
+            
+            layers.append(torch.nn.LayerNorm(hidden_dim))
+            layers.append(torch.nn.GELU())
+            
+            for _ in range(nlayers - 2):
+                layers.append(torch.nn.Linear(hidden_dim, hidden_dim))
+                layers.append(torch.nn.LayerNorm(hidden_dim))
+                layers.append(torch.nn.GELU())
+                
+            layers.append(torch.nn.Linear(hidden_dim, bottleneck_dim))
+            self.mlp = torch.nn.Sequential(*layers)
+        
+        self.apply(self._init_weights)
+        self.last_layer = torch.nn.utils.weight_norm(torch.nn.Linear(bottleneck_dim, dino_head_dim, bias=False))
+        self.last_layer.weight_g.data.fill_(1)
+        if norm_last_layer:
+            self.last_layer.weight_g.requires_grad = False
+
+    def _init_weights(self, m):
+        if isinstance(m, torch.nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, torch.nn.Linear) and m.bias is not None:
+                torch.nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        x = self.mlp(x)
+        x = torch.nn.functional.normalize(x, dim=-1, p=2)
+        x = self.last_layer(x)
+        return x
+
+class PassThroughHead(torch.nn.Module):
+    def __init__(self, backbone, head):
+        super().__init__()
+        self.backbone = backbone
+        self.head = head
+
+    def forward(self, imgs: torch.Tensor, img_masks: torch.Tensor, temporal_pos: Optional[torch.Tensor], mask_ratio: float = 0.75):
+        x, _, _, _ = self.backbone(imgs, img_masks, temporal_pos=temporal_pos, mask_ratio=mask_ratio)
+
+        # Either use cls token or use Global Average Pooling
+        # # CLS Token (default)
+        x = self.head(x[:, 0]) # Use the cls token
+        # # Global Average Pooling
+        # x = self.head(x.mean(dim=1) # Use the cls token
+        return x
+
+class DINOLoss(torch.nn.Module):
+    def __init__(self, out_dim, warmup_teacher_temp, teacher_temp, warmup_teacher_temp_epochs, nepochs,
+                 student_temp, center_momentum):
+        super().__init__()
+        self.student_temp = student_temp
+        self.center_momentum = center_momentum
+        self.register_buffer("center", torch.zeros(1, out_dim))
+        self.teacher_temp_schedule = np.concatenate(
+            (np.linspace(warmup_teacher_temp, teacher_temp, warmup_teacher_temp_epochs),
+             np.ones(nepochs - warmup_teacher_temp_epochs) * teacher_temp))
+        self.log_count = 0
+
+    def forward(self, student_output, teacher_output, epoch):
+        student_out = student_output / self.student_temp
+        temp = self.teacher_temp_schedule[epoch]
+        teacher_out = torch.nn.functional.softmax((teacher_output - self.center) / temp, dim=-1).detach()
+        
+        loss = torch.sum(-teacher_out * torch.nn.functional.log_softmax(student_out, dim=-1), dim=-1) # Changed from student_output
+        total_loss = loss.mean()
+
+        self.update_center(teacher_output)
+        return total_loss
+
+    @torch.no_grad()
+    def update_center(self, teacher_output):
+        """
+        Update center used for teacher output.
+        """
+        batch_center = torch.sum(teacher_output, dim=0, keepdim=True)
+        # TODO: Tom et al.: Will this impact the DeepSpeed and lightning? Should we keep it or remove it?
+        if dist.is_initialized():
+            dist.all_reduce(batch_center)
+            batch_center /= len(teacher_output) * dist.get_world_size()
+        else:
+            batch_center /= len(teacher_output)  # Use only batch size for single-process mode
+    
+        self.center = self.center * self.center_momentum + batch_center * (1 - self.center_momentum)
diff --git a/notebooks/gapfill_inference.ipynb b/notebooks/gapfill_inference.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ef4b78519b7fa28143b1663edde504f6a3a0a2d8
--- /dev/null
+++ b/notebooks/gapfill_inference.ipynb
@@ -0,0 +1,298 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b0ed0610-9643-4e48-a05d-281703e16998",
+   "metadata": {},
+   "source": [
+    "### Import Required Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0c14de93-d962-4225-86c5-c2a44ad716ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
+      "  warnings.warn(\n",
+      "/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2025-04-25 13:20:34,206] [INFO] [real_accelerator.py:239:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpr6vshjte/test.c -o /tmp/tmpr6vshjte/test.o\n",
+      "INFO:root:x86_64-linux-gnu-gcc /tmp/tmpr6vshjte/test.o -laio -o /tmp/tmpr6vshjte/a.out\n",
+      "/usr/bin/ld: cannot find -laio: No such file or directory\n",
+      "collect2: error: ld returned 1 exit status\n",
+      "INFO:root:x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmp6obl3v8t/test.c -o /tmp/tmp6obl3v8t/test.o\n",
+      "INFO:root:x86_64-linux-gnu-gcc /tmp/tmp6obl3v8t/test.o -L/usr/local/cuda -L/usr/local/cuda/lib64 -lcufile -o /tmp/tmp6obl3v8t/a.out\n",
+      "INFO:root:x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpkrdiql1w/test.c -o /tmp/tmpkrdiql1w/test.o\n",
+      "INFO:root:x86_64-linux-gnu-gcc /tmp/tmpkrdiql1w/test.o -laio -o /tmp/tmpkrdiql1w/a.out\n",
+      "/usr/bin/ld: cannot find -laio: No such file or directory\n",
+      "collect2: error: ld returned 1 exit status\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../.')\n",
+    "\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "\n",
+    "from downstream.gap_fill.gf_dataloader import create_dataloader\n",
+    "from downstream.gap_fill.lightning_presetup import GAIAGapFill"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a442874-1f90-41c1-90a1-c7a364bce1a0",
+   "metadata": {},
+   "source": [
+    "# Load model from checkpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2c41511f-d7e5-48c5-9ff3-31c93f5a85b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
+      "  warnings.warn(\n",
+      "INFO:downstream.gap_fill.lightning_wrapper:Mask Ratio: 0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "config_path = \"../configs/gap_fill_config.yaml\"\n",
+    "\n",
+    "# Create model using config file\n",
+    "model = GAIAGapFill(\n",
+    "    config_path=config_path,\n",
+    ")\n",
+    "    \n",
+    "model.configure_model()\n",
+    "\n",
+    "checkpoint_dir = \"../checkpoints/gaia-gapfill-v1.pt\"\n",
+    "state_dict = torch.load(checkpoint_dir)\n",
+    "model.load_state_dict(state_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ebc874a-cad4-4baf-8c9b-b751dc8f7f23",
+   "metadata": {},
+   "source": [
+    "### Run Inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3cb316ff-d7cb-43fc-8f6c-dbe2e5b95508",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_inference(model, num_samples, device='cuda', dtype=torch.float32, mask_ratio=0.5, \n",
+    "                  data_path=\"../data/IR/*/*.nc4\", **dataloader_kwargs):\n",
+    "    \"\"\"\n",
+    "    Run inference and plot ground truth, masked input, and reconstruction.\n",
+    "\n",
+    "    Args:\n",
+    "        model: PyTorch model with .model.encoder and .model.decoder.\n",
+    "        num_samples: Number of samples to visualize.\n",
+    "        device: Device to use ('cuda' or 'cpu').\n",
+    "        dtype: Data type to cast inputs.\n",
+    "        mask_ratio: Ratio of input to mask.\n",
+    "        data_path: Full path to the data files (including wildcards).\n",
+    "        **dataloader_kwargs: Additional keyword arguments for create_dataloader.\n",
+    "    \"\"\"\n",
+    "    model.eval()\n",
+    "    model.to(device)\n",
+    "    \n",
+    "    loader = create_dataloader(\n",
+    "        data_path=data_path, \n",
+    "        **dataloader_kwargs\n",
+    "    )\n",
+    "    samples_collected = 0\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        for batch in tqdm(loader, desc=\"Running Inference\", unit=\"sample\"):\n",
+    "            x = batch['x'][:, :1, :1].to(device=device, dtype=dtype)\n",
+    "            x_mask = batch['x_mask'][:, :1, :1].to(device=device, dtype=dtype)\n",
+    "            _temporal_pos = [t.to(device) for t in batch['temporal_pos']] if 'temporal_pos' in batch else None\n",
+    "            temporal_pos = None  # do not include temporal encodings at inference time\n",
+    "            \n",
+    "            latent, mask, ids_restore, _ = model.model.encoder(\n",
+    "                x, x_mask, mask_ratio=mask_ratio, temporal_pos=temporal_pos\n",
+    "            )\n",
+    "            pred = model.model.decoder(latent, ids_restore, temporal_pos=temporal_pos)\n",
+    "            pred_unpatched = model.model.encoder.unpatchify(pred)\n",
+    "            \n",
+    "            s, p, q = model.model.encoder.patch_embed.patch_size\n",
+    "            mask = model.model.encoder.unpatchify(mask.unsqueeze(-1).repeat(1, 1, s * p * q))\n",
+    "            \n",
+    "            for i in range(min(x.size(0), num_samples - samples_collected)):\n",
+    "                plot_reconstructions(\n",
+    "                    ground_truth=x[i],\n",
+    "                    masked_input=x[i] * (1 - mask[i]),\n",
+    "                    reconstruction=pred_unpatched[i]\n",
+    "                )\n",
+    "                samples_collected += 1\n",
+    "                if samples_collected >= num_samples:\n",
+    "                    return\n",
+    "\n",
+    "\n",
+    "def plot_reconstructions(ground_truth, masked_input, reconstruction):\n",
+    "    \"\"\"\n",
+    "    Display ground truth, masked input, and reconstruction\n",
+    "    as global maps with borders and coastlines, flipped vertically.\n",
+    "    \"\"\"\n",
+    "    projection = ccrs.PlateCarree()\n",
+    "    borders = cfeature.BORDERS\n",
+    "\n",
+    "    fig = plt.figure(figsize=(15, 5))\n",
+    "    images = [ground_truth, masked_input, reconstruction]\n",
+    "    titles = [\"Ground Truth\", \"Masked Input\", \"Reconstruction\"]\n",
+    "\n",
+    "    for idx, (img, title) in enumerate(zip(images, titles), start=1):\n",
+    "        ax = fig.add_subplot(1, 3, idx, projection=projection)\n",
+    "        ax.set_extent([-180, 180, -60, 60], crs=projection)\n",
+    "\n",
+    "        # get the numpy array and flip it vertically\n",
+    "        arr = img.squeeze().cpu().numpy()\n",
+    "        arr = np.flipud(arr)   \n",
+    "\n",
+    "        im = ax.imshow(\n",
+    "            arr,\n",
+    "            transform=projection,\n",
+    "            extent=[-180, 180, -60, 60],\n",
+    "            cmap='RdBu_r',\n",
+    "            vmin=0,\n",
+    "            vmax=1\n",
+    "        )\n",
+    "        ax.add_feature(borders, linewidth=0.5, edgecolor='black')\n",
+    "        ax.coastlines(resolution='50m', linewidth=0.5)\n",
+    "        ax.set_title(title)\n",
+    "        ax.axis('off')\n",
+    "\n",
+    "    # shared colorbar\n",
+    "    cbar_ax = fig.add_axes([0.92, 0.15, 0.02, 0.7])\n",
+    "    plt.colorbar(im, cax=cbar_ax, label='Normalized value')\n",
+    "\n",
+    "    plt.tight_layout(rect=[0, 0, 0.9, 1])\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "56bdc450-135a-4797-b622-9312208fd683",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Running Inference:   0%|                                                                         | 0/6 [00:00<?, ?sample/s]INFO:downstream.gap_fill.models:missing_data_ratio: tensor([0.0716], device='cuda:0')\n",
+      "INFO:downstream.gap_fill.models:adjusted_mask_ratio: 0.0716145858168602\n",
+      "/tmp/ipykernel_2067/2337928963.py:88: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
+      "  plt.tight_layout(rect=[0, 0, 0.9, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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9M27/6KOPWLlyJYFA4F86MjFt2jTmzp3LxRdf/G8fihBC7FRuvvlmWrRoQTQa5csvv2TKlCl8+umnzJ07l2Aw+G8f3j9i0qRJ1K5d+19dsbStYzjwwAOprKzE7/f/OwcmxC5MSpfUDAl0i53KkiVLGDx4MM2aNeO9996jQYMGGb8fN24ckyZN+sOZq/LycrKysnbkoe4whx56aMa/g8Eg9957L4ceeujvBqR35tcshBC7ov79+/PCCy9w7733YhhVXbZp06bRvXt3CgsL/8WjE0IIIf66fv360aNHDwBGjhxJ7dq1GTduHK+++ionnHDCv3x0Na+mx2iapv2/mVAQYmcjge6asWu+arHTGj9+POXl5TzxxBNbBbkBDMPgwgsvpEmTJt5tbj3pJUuW0L9/f7Kzsxk2bBjgdCwuu+wymjRpQiAQoF27dtx5553Ytu09ftmyZSilql36tWWJkBtvvBGlFIsXL2b48OHk5eWRm5vLaaedRkVFRcZjY7EYl1xyCXXq1CE7O5sjjzySlStX/s0zlHkcv/zyC0OHDiU/P9/LCOzdu3e1AfHhw4fTvHlz7zXXqVMHgJtuummb5VBWrVrFoEGDiEQi1KlTh8svvxzT3DV39hVCiH/akCFD2LhxI++88453Wzwe58UXX2To0KHVPubOO++kZ8+e1KpVi1AoRPfu3auts/3OO++w//77k5eXRyQSoV27dlx77bW/ezyxWIyBAweSm5vL559/DoBlWUycOJGOHTsSDAapV68eZ511Fps3b854rG3b3HrrrTRu3JhwOMxBBx3Ezz///FdPicetMfr8889z22230bhxY4LBIIcccgiLFy/OuG/v3r3p1KkTs2fPpmfPnoRCIVq0aMFDDz2UcT93Wf2yZcuqfS63lFfv3r154403WL58udc+uu2nEEKIv+aAAw4AnIQm1/z58znuuOMoKCggGAzSo0cPXn311a0eW1RUxCWXXELz5s0JBAI0btyYU045JWMieP369Zx++unUq1ePYDBIly5dePLJJzP+jjveu/POO3nkkUdo1aoVgUCAPffck2+++SbjvmvXruW0006jcePGBAIBGjRowFFHHeW1Hc2bN+fnn3/mo48+8toId+zltjMfffQR5557LnXr1qVx48ZA5lgsnTuu29IzzzzDXnvtRTgcJj8/nwMPPJBZs2b94TFsq0b3Cy+8QPfu3QmFQtSuXZuTTjppq5Iy7rhaxoBCiP8yyegWO5XXX3+d1q1bs/fee/+lxyWTSfr27cv+++/PnXfeSTgcxrZtjjzySD744ANOP/10unbtyttvv80VV1zBqlWrmDBhwnYf5wknnECLFi0YM2YM3333HY899hh169Zl3Lhx3n1GjhzJM888w9ChQ+nZsyfvv/8+AwYM2O7nrM7xxx9PmzZtuP322zOC93+kTp06PPjgg5xzzjkcffTRHHPMMUBmORTTNOnbty977703d955J++++y533XUXrVq14pxzzvlHX4cQQuyKmjdvzr777suzzz5Lv379AHjrrbcoLi5m8ODB3HvvvVs95p577uHII49k2LBhxONxnnvuOY4//nhef/11r435+eefGThwIJ07d+bmm28mEAiwePFiPvvss20eS2VlJUcddRTffvst7777LnvuuScAZ511FlOmTOG0007jwgsvZOnSpdx///18//33fPbZZ/h8PgBGjx7NrbfeSv/+/enfvz/fffcdhx12GPF4/G+do7Fjx6JpGpdffjnFxcWMHz+eYcOG8dVXX2Xcb/PmzfTv358TTjiBIUOG8Pzzz3POOefg9/sZMWLEX3rO6667juLiYlauXOn1FWSDZiGE2D5ugDg/Px9w2qj99tuPRo0acfXVV5OVlcXzzz/PoEGDeOmllzj66KMBKCsr44ADDmDevHmMGDGCPfbYg8LCQl599VVWrlxJ7dq1qayspHfv3ixevJjzzz+fFi1a8MILLzB8+HCKioq46KKLMo5l2rRplJaWctZZZ6GUYvz48RxzzDH8+uuvXnt27LHH8vPPP3PBBRfQvHlz1q9fzzvvvMOKFSto3rw5EydO5IILLiASiXDdddcBUK9evYznOffcc6lTpw6jR4+mvLz8L5+zm266iRtvvJGePXty88034/f7+eqrr3j//fc57LDD/tQxpHPb8T333JMxY8awbt067rnnHj777DO+//578vLyvPvKGFCI7aeUhqrButlK7Zq5zRLoFjuNkpISVq9ezaBBg7b6XVFREclk0vt3VlYWoVDI+3csFuP4449nzJgx3m2vvPIK77//PrfeeqvXATjvvPM4/vjjueeeezj//PNp1arVdh1rt27dePzxx71/b9y4kccff9wLdP/www8888wznHvuuTzwwAPecw8bNuwf3UyzS5cuTJs27S8/Lisri+OOO45zzjmHzp07c9JJJ211n2g0yoknnsioUaMAOPvss9ljjz14/PHHpZMjhBD/kKFDh3LNNddQWVlJKBRi6tSp9OrVi4YNG1Z7/4ULF2a0f+effz577LEHd999txfofuedd4jH47z11lvUrl37D4+hrKyMgQMH8vPPP/P+++/TtWtXAD799FMee+wxpk6dmpFhftBBB3H44YfzwgsvMHToUDZs2MD48eMZMGAAr732mpeZdt1113H77bdv76kBnLZozpw5Xq3R/Px8LrroIubOnUunTp28+61evZq77rqLSy+9FHAC9HvvvTfXXHMNJ598shfA+DMOPfRQGjVqxObNm6ttH4UQQmxbcXExhYWFRKNRvvrqK2666SYCgYC3ufJFF11E06ZN+eabb7y9KM4991z2339/rrrqKi/QfccddzB37lxmzJjh3QZw/fXXewk+jzzyCPPmzeOZZ57xVvSeffbZ9OrVi+uvv54RI0aQnZ3tPXbFihUsWrTIC7q3a9eOo446irfffpuBAwdSVFTE559/zh133MHll1/uPe6aa67x/n/QoEFcf/31XlZ0dQoKCnjvvffQdf0vn7/Fixdz8803c/TRR/Piiy9mlOx0X/efOQZXIpHgqquuolOnTnz88cdeWZP999+fgQMHMmHCBG/fJpAxoBB/h5QuqRm75qsWO6WSkhKg+qyp3r17U6dOHe/HDR6n27LhffPNN9F1nQsvvDDj9ssuuwzbtnnrrbe2+1jPPvvsjH8fcMABbNy40XsNb775JsBWz/1Pb2q15XH806p7nb/++usOfU4hhNiVnHDCCVRWVvL6669TWlrK66+/vs2yJUBGkHvz5s0UFxdzwAEH8N1333m3u5lZr7zyCpZl/e7zFxcXc9hhhzF//nw+/PBDL8gNzjLn3NxcDj30UAoLC72f7t27E4lE+OCDDwB49913icfjXHDBBRnLr/+JNu+0007L2FDLXQK/ZVtkGAZnnXWW92+/389ZZ53F+vXrmT179t8+DiGEEH9Onz59qFOnDk2aNOG4444jKyuLV199lcaNG7Np0ybef/99TjjhBEpLS712ZePGjfTt25dFixZ55TReeuklunTpkhHkdrltzZtvvkn9+vUZMmSI9zufz8eFF15IWVkZH330UcbjTjzxRC/IDVu3KaFQCL/fz4cffrhVia6/4owzztiuIDfAyy+/jGVZjB49eqt9qaorcfJHvv32W9avX8+5556bUbt7wIABtG/fnjfeeGOrx8gYUIjt4wa6a/JnV7RrvmqxU3Jn28vKyrb63cMPP8w777zDM888U+1jDcPw6p+5li9fTsOGDTNm8QF222037/fbq2nTphn/djtMbodo+fLlaJq2VcZ4u3bttvs5q9OiRYt/9O+lCwaDXh1vV35+/t/q9AkhhMhUp04d+vTpw7Rp05gxYwamaXLcccdt8/6vv/46++yzD8FgkIKCAq8UVXFxsXefE088kf3224+RI0dSr149Bg8ezPPPP19t0Pviiy/mm2++4d1336Vjx44Zv1u0aBHFxcXUrVs3Y7K5Tp06lJWVsX79eqCqPW3Tps1Wry09oLA9/qi9dTVs2HCrzb7atm0LsFVNbiGEEDvOAw88wDvvvMOLL75I//79KSws9DK3Fy9ejG3bjBo1aqt25YYbbgDw2pYlS5ZkrNypzvLly2nTps1WAeFtjff+qE0JBAKMGzeOt956i3r16nHggQcyfvx41q5d+5fOwd8Zoy1ZsgRN0+jQocN2/4107jmobhzavn37rc6RjAGFEP91UrpE7DRyc3Np0KABc+fO3ep3bs3ubQ1WA4HAVh2cP2tbM+O/t+HGtmbo/0qd7H9CemafSylV7XH81Q1EtjcLQQghxF8zdOhQzjjjDNauXUu/fv0yamWm++STTzjyyCM58MADmTRpEg0aNMDn8/HEE09klLEKhUJ8/PHHfPDBB7zxxhv873//Y/r06Rx88MHMmjUr4/p+1FFH8dxzzzF27FieeuqpjLbUsizq1q3L1KlTqz2eLQfCO8I/2d5uT3svhBDir9lrr73o0aMH4JTY2H///Rk6dCgLFizwJlwvv/xy+vbtW+3jW7duvcOO7c+0KRdffDFHHHEEL7/8Mm+//TajRo1izJgxvP/++3Tr1u1PPc+2xmjV+a+1QTIGFGL7abqGVoNZ1jX5XP8lu+arFjutAQMGsHjxYr7++uu//beaNWvG6tWrKS0tzbh9/vz53u+haia/qKgo435/J+O7WbNmWJaVsbs4wIIFC7b7b/5Z+fn5W70W2Pr1bM/SNyGEEP+8o48+Gk3T+PLLL3+3bMlLL71EMBjk7bffZsSIEfTr148+ffpUe19N0zjkkEO4++67+eWXX7jtttt4//33vXIjrkGDBjF58mSmTZvGeeedl/G7Vq1asXHjRvbbbz/69Omz1U+XLl2AqvZ00aJFGY/fsGFDjWWArV69eqsNvxYuXAg4m37CX2vvpY0UQoi/T9d1xowZw+rVq7n//vtp2bIl4JQXqa5d6dOnj7cat1WrVtUmQKVr1qwZixYt2mrF0pbjvb+qVatWXHbZZcyaNYu5c+cSj8e56667vN9vTxvxZ8dorVq1wrIsfvnll9/9e3/2GNxzUN04dMGCBdt9joQQW1OaQmlaDf7smv1VCXSLncqVV15JOBxmxIgRrFu3bqvf/5UMrv79+2OaJvfff3/G7RMmTEApRb9+/QDIycmhdu3afPzxxxn3mzRp0na8Aof7t++9996M2ydOnLjdf/PPatWqFfPnz2fDhg3ebT/88AOfffZZxv3C4TCw9YBfCCFEzYpEIjz44IPceOONHHHEEdu8n67rKKUysr+WLVvGyy+/nHG/TZs2bfVYt/Z2LBbb6nennHIK9957Lw899BBXXXWVd/sJJ5yAaZrccsstWz0mmUx67UefPn3w+Xzcd999Ge10TbR56cfz8MMPe/+Ox+M8/PDD1KlTh+7duwN45cTS23vTNHnkkUe2+ntZWVkZ5WCEEEJsn969e7PXXnsxceJEcnJy6N27Nw8//DBr1qzZ6r7p45djjz2WH374gZkzZ251P7et6d+/P2vXrmX69One75LJJPfddx+RSIRevXr9pWOtqKggGo1m3NaqVSuys7Mz2s+srKy/PIZq1aoVxcXF/Pjjj95ta9as2er1DRo0CE3TuPnmm7cK4Ke3sX/2GHr06EHdunV56KGHMl7DW2+9xbx587yNrIUQf5/U6K4ZUrpE7FTatGnDtGnTGDJkCO3atWPYsGF06dIF27ZZunQp06ZNQ9O0repxV+eII47goIMO4rrrrmPZsmV06dKFWbNm8corr3DxxRdn1M8eOXIkY8eOZeTIkfTo0YOPP/7YywTbHl27dmXIkCFMmjSJ4uJievbsyXvvvcfixYu3+2/+WSNGjODuu++mb9++nH766axfv56HHnqIjh07eptlgrOkrkOHDkyfPp22bdtSUFBAp06d/rAWnhBCiH/eqaee+of3GTBgAHfffTeHH344Q4cOZf369TzwwAO0bt06Y+B888038/HHHzNgwACaNWvG+vXrmTRpEo0bN2b//fev9m+ff/75lJSUcN1115Gbm8u1115Lr169OOussxgzZgxz5szhsMMOw+fzsWjRIl544QXuuecejjvuOOrUqcPll1/OmDFjGDhwIP379+f777/nrbfeonbt2v/YOfo9DRs2ZNy4cSxbtoy2bdsyffp05syZwyOPPILP5wOgY8eO7LPPPlxzzTVs2rSJgoICnnvuOZLJ5FZ/r3v37kyfPp1LL72UPffck0gk8ruTEEIIIbbtiiuu4Pjjj2fKlCk88MAD7L///uy+++6cccYZtGzZknXr1vHFF1+wcuVKfvjhB+8xL774IscffzwjRoyge/fubNq0iVdffZWHHnqILl26cOaZZ/Lwww8zfPhwZs+eTfPmzXnxxRf57LPPmDhx4lZ7Nf2RhQsXcsghh3DCCSfQoUMHDMNg5syZrFu3jsGDB3v36969Ow8++CC33norrVu3pm7duhx88MG/+7cHDx7MVVddxdFHH82FF15IRUUFDz74IG3bts3YULp169Zcd9113HLLLRxwwAEcc8wxBAIBvvnmGxo2bMiYMWP+0jH4fD7GjRvHaaedRq9evRgyZAjr1q3jnnvuoXnz5lxyySV/6RwJIcS/TQLdYqdz1FFH8dNPP3HXXXcxa9YsJk+ejFKKZs2aMWDAAM4++2xvufTv0TSNV199ldGjRzN9+nSeeOIJmjdvzh133MFll12Wcd/Ro0ezYcMGXnzxRZ5//nn69evHW2+9Rd26dbf7dUyePJk6deowdepUXn75ZQ4++GDeeOMNmjRpst1/88/YbbfdeOqppxg9ejSXXnopHTp04Omnn2batGl8+OGHGfd97LHHuOCCC7jkkkuIx+PccMMNEugWQoj/qIMPPpjHH3+csWPHcvHFF9OiRQsvuJse6D7yyCNZtmwZkydPprCwkNq1a9OrVy9uuukmcnNzt/n3r732WoqLi71g93nnncdDDz1E9+7defjhh7n22msxDIPmzZtz0kknsd9++3mPvfXWWwkGgzz00EN88MEH7L333syaNavGMsXy8/N58sknueCCC3j00UepV68e999/P2eccUbG/aZOncpZZ53F2LFjycvL4/TTT+eggw7i0EMPzbjfueeey5w5c3jiiSeYMGECzZo1k0C3EEJsp2OOOYZWrVpx5513csYZZ/Dtt99y0003MWXKFDZu3EjdunXp1q0bo0eP9h4TiUT45JNPuOGGG5g5cyZPPvkkdevW5ZBDDvGSnkKhEB9++CFXX301Tz75JCUlJbRr144nnniC4cOH/+XjbNKkCUOGDOG9997j6aefxjAM2rdvz/PPP8+xxx7r3W/06NEsX76c8ePHU1paSq9evf4w0F2rVi1mzpzJpZdeypVXXkmLFi0YM2YMixYtygh0gzNh3aJFC+677z6uu+46wuEwnTt35uSTT96uYxg+fDjhcJixY8dy1VVXkZWVxdFHH824ceO2uS+IEOKvq+ks6101o1vZNb07nhBCCCGEEDWkd+/eFBYW/mEtVyGEEEIIIf5pJSUl5ObmsnT8BWSHAjX2vKWVMVpceR/FxcXk5OTU2PP+23bN8L4QQgghhBBCCCGEEEKI/zekdIkQQgghhBBCCCGEEELsIErX0XS9Rp9vVySBbiGEEEIIIYQQQgghhNhBpEZ3zZBAtxBCCCGE+H9ry42WhRBCCCGEqGkS6K4Zu+arFkIIIYQQQgghhBBCCPH/hmR0CyGEEEIIIYQQQgghxA6iNA2l1WBGdw0+13+JBLqFEEIIIYQQQgghhBBiB5HSJTXjTwe6o2XFKNsCywLbAqVBanbAVlUnT9kWlhHEtEFXYNs2s7/9li++/IojBg7A79P535tv0LJFC/ofdQyfffIx61avZPcOuzH1uec57LBDmfPjXAYPP51Y0mb6zFe4eMQwrrnqCsaMuwOAjp27csDBhzJg4EB+/uUXevc7kpxIBEPZvPnCVA7ddw9yI1ksX7Gct9/7kJ8XLOKll1+lcaNGHHJYX3748Ufi0SjzfvmZKc+9wCF9DiNgaAQNjWjSYlPUBKA0ZlIcTRI1LXTlvD7ThoRpoSuFpikSpgWArpR3DjRNEdQ1fLoi4jfI8mv4NIVSENQ1LNvGtCFkKGzAskFTEDY0TBuKYyY5fg1dU0STFkkLwj4NTYFp2URNm6Rlc+LRR3LNqJvo3Hl3/H4fmgIF6JrCrynn37ZzfMqMg5kE28IK5lAcs9hQkWTBkqV88c5r3HbpOVw3ajR33/8gv6wppmmOj5/WV7JkcwXta2cR8mkYmmLdknkc2HMf+h5+OE8/cBe5uXlEK8rIbdrOe/2hUJBNq5ajDB8bkz4ifh2/HU+dHAOsJOh+4pbz+QhotvOZArAtVDJe9RlL3W6j+GH+IsbfNYHzL7mCFu07UD/sfHzjFsRNi4huoeIV2IGI81TRUuzU+6LstOdwuc+htK1ut7W03WnT72M7n39l22DGq263klX3tS2UlXS+K1UfClAav8yfz6BTzuLesbfQtEFdOrRt7X2PsCzQNGx/hI+/mcOhh/dzvntFhaBpmMrg6lE3cN/ECTRu3IQxd9zJkUccgS/t8BMWVGxeT4MmzQH4ZMkGyhMmizZWMGvGdBZ/Pot5X3xAwza7EQplkZefj26ZLPz5R8698nqaN27AHj32JJJXi8KKBBUJk+KY89o2VyYI+3Ta186iTthHxK8RMhRavAKspHNOtmArVXWO3GuGew1Jfx/cU536Gxnv2x+wlar+flu+39u6XWnYRoCEL0xl0sKyIWnZuN/opAUWVX/fTH1uVeoYbdvGwqkDZegK03K+z/7U/8dNi7hlY9mQMJ2/E/ZphH0acdOmJOac44qEiWXZaJpiY0WcDeVxKuMmSctmbVElBZEA2UGDWll+dKUIGBphn06DSADTtvlhbSnZAZ3uDXMwLec1AFi2TTRppZ5XJ2A41wcjdVEri1sUR5PoSpGwLKJJi6BR9aGqFfbx05efMPXpJ4mVl+H8SlFRXoZhGPh8Pt56exaNGjZg1eo13uMm3DGOz7/4khdmzOSskSO4546xzrn2BSlNwldfz+b2m0dzyKF9uerCs7nmqqvYVFpOWWWM4RdfzXcLl5HdogNlSY140mJzaQXrV60kWLsRuuYce9ivUz8vRHbQIOTTaVMrTKe6WWSnrjnBSO4ffn62pabavK69D+f4s26EnObO+1W8HHPp+2j1umCt+8F5jlAtVE5jVG5T7MrNaHnNUbrP+extWoTKqkeLA47iyRPq8va77/5um6e1OASV3dj7/O5MkovfRm/YHUIFqC2v23+SFS/D3rwUrW4nrDXfYq2fi6/radu8v125ieSCVyC7MUbzXijdj20lSf74dNWdlI7R+WTvnD71xC0Mau20Q9vd5imdjhdOZelrE9HrdUGFCrY6Ns3w8d1r42mZ47SFu1Kbt6o0QddjbyJethk7GSM5dxoARpfh3vtgblqMXbQUu2Sl85nRDNADYJvYlZvQG3QHI4TKqoMygtV/AP5BXY8ezCcXdvvPtHk3fLqOu6+/888d/C7GKl2NtXERWHFwewNWElJ9GrtkJfiyIFHuPaa6Ns8ORGg1ciobfvkcq6IQa/W3qJzGaHU6YK36BtuMgW2i1d8DkpWocB1U6rto2xbEy1CBnD993PHvJ2/3a46Wl6a+6067Z+vOdUV51ySt6hqlGVXtoGXy1ezv+PyLLzli4EBCAT9vv/Wm0+YdcSSfffwR61b/5rR5z7/EYX0OZs5Pczlt+HCUbfHaa69z/LBTuOaKyxhzx10A7NGtK4cd1peBAwcw7+e5HHPUEYQj2ViWxZNPPU2v/fcjJzeHFYsX8Pa7H/DzoiW8NPMVGjduzEGHHMpPc38iGo2y4Jefuf/J6Rx8qDPOC+gKXVPoyhnPJS0bXSkifg1Dpd5j9zrpcl+3GUfZVtXrrqYNUkl3rJP5O1tpoBmYOGO5dO4YMNVlw8bpSygzzoAjjuLW0deye6dO+AzdOxbbF3T+3z3e9OPRDEwbKlL92Q2rV/LyzBlccOFFjLr+Ou67ZyIrN5USN20MTeHXnR+fplDA3J9+Yp999qbv4Yfz8GOTyc/Lw4xHKSioaoNCoRAb1632PqvpnwvnRTjnMGY6492ArtDtpPN723LGpG7fKnVfS/cxZ+587rzrTq656io6dWif8XlMv47Zhh+UhkrGUWYidY6rafuU5t2e/n4qy6xq89xjT33eMZPea1CWmfYmbjFucY/NTDrtWOq1/7JgIUefdDr3jb2JJg0b0KFdG+85bM3wrr+ffDWbQ/sNAKCytAiUhonGtdeP4t6JE2jUuAnj77iTo448whnPW1XHVbxxI/Wbt3Ieu3GN1+ZNnf4CL738Km++/Q5dOnUkKxwmPz+PeCLOnB/nctOo66hfrx57du9G3YL8jPfKG6+l+rq20jLPfer8uK+h6hwlM/vJUDXWS///bYz7Mvoa7nuReo/S39f0/pH3UPf9SXvcVs/hXq98AZK601fXFF5cx2Vt0aS6MRWApI03NjRTba+uFIamvDG4aUPCsjEt23sOv+bcx/3OW7bz/XbvoxT4UtejLXvkZuq+Kv1YrORW1yYbZ+zvjku3vL6kvz7bdr7zOtZW5/KDDz5kylNPUVpa6nyebJuKykpnnGcYvPX2LBo2bMjq1au9x9x95x18/sUXvPjSDKfNu3O889nR/dhK45vZsxl9/SgOP/xwzjvvXK6++ho2b95MPB7nxhtGs3b1KvbssQdBvw9baSSTSZav+I1WrdtU/z7qftLfJsuGrHCo+hf8H6c0VbOB7m19MP6fU7b9J3rVOB0gbzCWPlhKXQwzLkCaQVkyFSCybfS0L7nhBmFtk3Akm9ycHE44ZhDzFy7kk8+/9J6vsqyE0rJyLrzsSl6d+RIVFRUcf9wx3Dr+bjYQIWho+HSNhGmhKUXdLINaAYWKlaFsG1vTsQ0/puZnQ2WSyniSjz54n3tuv4n5P//kPY/h89O2Q0eemDqdlk0a4tMUZQkL04KimMma0iimDQVBHwFDI2FZJEwbn67QlROItmybhOlc/BKWja6cgJJPc+7v0zRM23lMyNApj5uE/TpZPo2cgIZfUxlBMzcoluPP/AJkfERtixdfeomTTzmVDh068uRTT9Fpt3ZOQ5l6R70Lo/ue2RYqEQXDTzkBCiuTLN5USe2wj6Z6OT323IubbrmVISeegBErZTXZvLlwI/s2yaN9rQAA5WVl1Klbl7ZtWrFw0RI6tm3FitVrKS0rp2jBt0x/fRZnXXYt/foczIzpU7FDuWjJqNfJU2achO4MKDdVmgQMRY5f8xoYXYFKRqteZ2ogXGr72VBhoikIGIrcgI6uFDHTQgERg6oG16zq+KUPBr3BYXqjXvVFyAzK6kZmI59+HsHpWFlVnTbvuZVW1UkCHnnqWR6b+jwD+/Xltf+9w4JFi/H7faye+zV+n+F1jKo6ryFsw0/C1thrrz2ZP28e06ZNIxgMMWbsGIqLi7n/wYc57ODebCwqIUtLeufJ1g1C2XkAjB07lt3264OVXZf6uVnMnz+f0/rt5x1X30vG0rX34dTNj/D51Ado1aYdJ5xwAi3yAoRVAmUmsf1hVpQmWF4UpTiWpEVeiHa1gvgSFah4+dadCvd1/M7/e+d4y07ONgbof2bQv03VBXm2pBlY/hCV+LyOh2071y23gwF4E1Iut6Pj05TX+dGVM5ll2jZJ0/aCsZYNuoYXfDY0ha5BacxibVmMsrhJWTyJadlUJJzryZriKMUVceJJi4q4iZl6ctOyiQQN9miWT4c6ERKWRX7IRyzpBKsrEiaNcoIkLJuwTyNoOB35hGlRkTC965Rl2/hSAzH3GlU/20/IcILvqT4OlZvWc9voq6mVl8voa66kdl6udy5Xrl1Pg6bNMWNR4vE4WaEAc378iVnvvs8PP82l1wH70WX33dF0nZYtW1KroADb8LN46XKmv/gSn3/+ORMeeowmdWtxy6hrWb+pmKlPTWHvfkdz+MW3s7ncef2l0SSVqXNQGTe982paNrlhH+0b5pAX9rGxLE6r2lns1TiXgqBO0NAoyA5v98enpts8X9fTsM0E5qqvsIuWOteXvBYYjff5U0G4lgccxZyxfTDRfrfNQ2kQzMdo2Qfl2/7z82+wNi/FXP4hBPMwmh+ECuZt99+ykzGSC15Gb9Adld9qm4F/20yQ/OkZCORArASCeRAvByuBsftJ2EVLMX/7DJXTGL1FH5RSvPDMbfRtFv5bbV6J7afnNbNY/vlr23wNmuHjm1fG0zYc3+XavMKkj3ZH3kD5p3c756LhntjZTdCD2SilYUeLSM6fWXWumvVCy22G0nTMNd+jgrlo+S23eW53BC/QDf+JNm/U24t45NZ7tv/5/h+yExWYq75GGQG0+t0yrr12vBx8YcAGy8QIZTN9zFB++ODNbbZ5CV+YVoMnUPjtDOzydehND0QZAczV32An49ibFqLyW6E3PeAfmXz8W4HusmKUmfQSF2xN32qC19T8XqDGTbrRlfL6RG4g2acpdGzCWVmpNu8o5i9YyCdffFX1fCWbKCkr5/Irr+KlGS9Tnhrn3TV+PHXq1q261kC1/U0TN8hjUxqziCaSfPzh+9x1641bjPN8tGrfkXufeJYGDRqga067rCvlBH50RUHQyEgaSecGpwwyA8peHzH92MxUoFv3px1o1W0x00mMMC07LYilUsfjBLdM28anKQwFL74wnZNPPY2OHXbjmcmPsVsqaGr7gs712qq69qYnlZi2M4atTFgEDA1dQVFxEYf03JsrRt3AEceciK0UWT6NkKF5z68rKEuN89q0acOiRYvo0KEDv/32G6WlpRSuXcULL77IOedfRL++hzLjuWkoXcfW/SRx2jW/BlhJYrZOWcLC0KraPDPt907A2zl+2xf0+lC65gQI3QkJlTaGTed+VjNvS7VTRjAzwF3N5C6asdWkqzt+9NrN9L/vtbFWxr8feeo5HnvmOQYe3pfX/jcr1eb5WfXLbAKGlhkYBucaHMjKaPOemTqNcCjE2LFjKCouYcL9DzLg0IMoKiklkOrHu2P4YF5tAMbddgv9+x5G82ZN8fl8LFiwgC57V43znnpkEkcP7Ic/EOTmcXfSvl07jj/2mKrAs9t+p09QuP3b1DlzP19enzf9XLr9gLQxd7WB5urGfFu+F5AZ2K5uXFhdoDt9UmMbY1Jb07F9QeIYXgKQrpykwC2vuO5Yb8vQmJkaF7pjRHCC1E7AXHljRiv1e9O28eupIDegUtcaAIuq+xjutTIt0O39HZzrhHuc6XFKlbqfG++Jp8Z27jgUyHhelXaNdq8t7vu3evUqrrrmOmoVFDBq9A0U1KrlfedWrV5No3p1SMbjzjgvHGLODz9kjvM6d0bTDVq2aO6M83xB5i36lRkzZ/DlF1/w4KOPk5uXxy2jr6eoqIinn5zCiUOGMPmxx5wrePoYy32ft3zPt/x8pV2DQ6GdK9BdUlJCbm4uqx65hpzwjk+w8J63IkqjM50+dU7On58839n9pUA3SkOLlnq3Zcyy4cyw2rqf5avXkF+rDklbURa3iJvOF94JojiNbshQaCo1ixwvw7ZtFvy6nK499uKxRx5i9jdf8+NPc1n86zLWrV8PwCnDhvLwww+xtNTm5w1l5AYMcoMGYZ9O6xwNlYg6s7S+ACW2n02VphdUsm3nglGZtHju6Se5/eqLM15f8/a7M3Hqy7RtWJuCkE4s6QStY6aTUa2ADeVxIgEngO3TFflBHct2MiJN2/YuQmVxk1jSeZ2Al32ZMG0vqJTl19CAOmGDAInMk637KY5bRFJZ3ArAjFNh+7zZQ5W6KGzaWEhx0Wauv+467r79Zn5btYoOXbvjj+Q5ATaFM3OXyijDSqKsJLbupygOq0rj/Dj7a47o0Q7NMLj7kaco3rieu66/FGUlWWXUoV7YwChehUrEcDLCsznt7At48Y23ufX6q7no1BPBSqLpBpZlcfwZF/LqOx9ySK/9ef3110lqfuKmcz5z/U4Ggq0ZLCtOUCfsBPzdjAaVypjwPmOawdoobKxIEvbp1AnrmLaTbe90FqF5rg8tXoHlDztZAkpDi5XjZqnZ/jC2ZjiZx1teMNNngd0ZeTfDzv2SWGbm47z7RJ3nMILO86YHCIDZP8xl3/7HYug6SdNk6jNPM+ykk+nRowc+Q2f64w9Sr169rTtwSsPWfVi+EE8+PZWPP/2MDp060efgg/nqm2+54tKLGX7aaWT5dcaMGYOt+1G2k81+3kWX0qZ9B9YWlzNx3G2Mf+YVuu+1L7PffY2br76E/Y4cjC+vHp/PfJqChk2pXb8BeryCXgcdzLlnjqQgqHvnEM1gcZFzHspiJq0LAuREN2S8P7/H6yy5nbwtZ+2rfcyfz2rb+sFp2QDuv9N/l35XIwCG33u/0QxiliJq2l6Hwa8pL0tXU851JGHaaEoRMFRaxrTTcXACyBBL2l5gO2HZhAznNZXGrYxguY3N+vIEv26q4LeiSsqiCYorEl4wHJyOmNvRKYgE0DXF2qJKeraqRfeGudhAaSxJ0NAojZsURxPoSpEbNMgN+gjozrHqSlEcMymsSHirUHy6M/jJDfqoFXKuZZVJZ7VKns9mxvRpTJkyhfvuGEOnDrtt/T6ldzjcjH43oOZmpBl+7/tvmiYNaufT/8hBHHXs8Rx02OH4dB2fpjjphGN4751ZPPPGB+gN27BkYznrS6KsL4mhayojwA1QGTfJDftokBekdd0I+UEfm6MJmuSG6Fovi4KAc99gOOuvf45SarLN87Xqg1m8CqtyoxNMTQU+VUFrjKYH/KnjbdSjH2/e0he//ufaPIK1MNr0Q+m+7T5H/wY7GQMzjrn6W/TGe2PHy1GhfCdb90+yytejgk42k7VxASQq0Rvt+buPSS7/GLtoKVqDHmh1OgBVgwhz6fvYJStQkYYYrfvyxOSb6de64G+1eevLE5xw0yxWfzdrm8ekGT6+fXU8rXN2vTZvdVmSLsfcSMW811H+XLCTWGu/R2/dHy1SD3Pzr1i/fY5Wqy34I1iF81D+bPBnYZtxtEhD9NpVK9FqQtejB/PJBV22moyAf6fNu+rtJTx488S//rz/D9m2hb1pCdamReiN96l2BcWWdH+QX94YRyOt/HfbPF/DriSMPFROEy+YnVwyC7t0FUbbI1Dh2v/Y6/hbge6Kcu9akJElW80k76p1hejZtShNOAEeQ3NWqoUMjYqE5a1gdbOFDTsJiRgLFy6ky9778diDDzD7+zn88NNPLFmyhHXrNwBwyrAhPPzww9iakdnXTGUuugGghGVTmbCoTDrB9oRlY9vOCrqSWJK3X5zKuGsuyXh9bTt25t5prxCORPBpGprmjM+yAzr1s3wZGZzpAS/3KPxpfZD0DGz3/oDzOjUjI1HCe28siJmW1zd0g2RbBt7TMz0NLDYVbnDGeaNv5K6xt/Hb6jV07NyVcFakKsvcC1QaXgBsQ0WS0riFT1P8MPtrWrfbDUspnn9yMhs3rOeKG24hL6CTE9Azs9lT53vEyDN5acYMbrnlFs4/9xywkui2hWUmOfHk4bz25v845KBevPHKTGzdT6XlBPUCygTNYGPUpDxhkRvQyfE7wXcFGGbUazds3SCpOeP14piJP9VnDRnOZ0dXKtUHTwswu33OeEXVa05leIPzOrzga3obtkWSgjup6wXHqwuupl1b1ZbtlmUx+8ef2bffMVVt3tNPMezkU+jRozs+XWf65Ieo26DhVoFz25/lTFagmPLkk3z86We069CJQw4+iO9mz+aKSy/m1OGnkRUKMO7Wm6omoc0k511yBW3btqa8rJybbx/L+2+/Sc999+GFl2Zw/sWXMuLUU2jauDGTHnmMls2b0ahRQ0qKSzn4kN6cNny4NwGf8TLTx2juOa4uyJjef3CT56oLRG4rIJ0+4b7FfasbQ7iTDu51yOubpP7O7wW6bU2vavM0g2SqTwx4mc/pAeT0a4s7EZUeuPZWXKT+696Wfi3QVCoATuq65w6DU8FwqLpWWLaz8jdjBX7adc6d9HID1ltmoTvBd+c+cdO5/umpa1p6sDv9/m6A38AimUzy7LPPMuXJp7j37jvo2KkTtu6v6ptC5oSUOwGqlPM+KA3bF8AygsRTE3jJpNPm9TviKAYefTwH9+2HoWsoBSMGH8cH785i1oef0rlrV0KpCTjdDbp7b1zVZ8lWWtVkYjW/h783zvs3uIHu1Y9dV+OB7oYjb9vlAt1/emSoElHK9SyMQC6BZAXKTFQtucTpNKtkHJWM88LUZ0iG82l98CB8moahK+pl+WmSG8BMBYZtW6MskSRkaKxfsYqnHn+MSZMeICc7mwfuf4ARJw9h1OUX0bBdFwAO2L8nt93hZO6EfE7jt7kyQbbfIJj2zbd1n7M0JW55pQGcLEmIJZ1l+SNPH8GG1b/x6L13OcFZM8mKhT8z6syh3P3gY/ibNyMnoGElbKwkBA3FhvIEuUGDgKERS1rkBw2y/Tox00JLfRG11IxZQHeeN5q0SFgW/tTShKCh4bOrMkCTFqwqS5Ab0J2l9soJVpKMk6c0bBV2sstt0JRB3LQoT1hsqkiSGzRoGFHOUjKlMePV1wmFs2jZpAF3TLyPWMKk98EHc8bIkWRlZXnLPUxlYOgaFaaiMmmiKcVr05/h89d8jLv7Hi66+CJOOv4Y7GA2qmIzjaKrUOUJVDLqBFStJHrZBp6ceDuTn3wKFa90sugtDcw4GjDjkbsxWuzBex99SqyokGB2HobhJ6k7S7jjGBRVmNjY+HVnxthInzVPzaxbRpDVZUnWlsXJDxnUCTtZmpVJi4hfR9cg4tNSS+CSXrBbS3V+3EbOVAYbKpLEkj50BbXCBiESVdkAhp+EHkwFLN2JBed4MjpA4HWwSHUsvcG+ufWsv9+nc8qJx/LMC05W2eG9erJx5VImTHqY2d9+S35eLrbuw/Y7WZUmGslUloxp2cSiNiecdCqDhpzC1Vddyah99maP7j2IRLLZrVNnPvngPe6eMJHLLjzfOW3RMl58+RXy8z+mToNGnHfFdfTsuR8bVq/ixWlPMfmVd3l95ot89PE79Bh8Pi33PoTYxtXom3/jp9kfYdkjSVigpQZpWDYRv8b68gQJyylnoap5nzKWq6d1NLyND1Lnz+tIpXeUtrzOuD2ALTIG2DKItWV2QHUD/fT3LCOQlMpSSsZRpIL6uoU/VX6iMmmRTHUaAPTUQE0pJ3i9KZpMTSI53/eIX6MSJ8Ad9mlkp1Zp6FYcW/eztjzJypIYpXGThGnROCdIXtBwgucKlm2qYOWmCmpF/ESCBiG/waaymNch21gWp26OU7Yk5NepnxOke8NcIn6NTZVJEpZNjq5RN8t5vW4md8K0CBo6Gk4HKcev49eVd+3xac5yXV2Ruh1CQZ0Nvy3l+PPO5cgBA3jz5ZcIBvxgmZnZMZqBrRtV2TiBCAk0LyhvWjbFMYviCpOSWAUqGeeac0/jmrETOHHYKQR1RZZfR1fOdXHms0+RU7s+zWtHqN0gm0Y5AdaUxlhTFkNXih9/K6I0miQ7aFARN4kEDW8yYM6KIgZ0rE9xLMnGijh+PYKJ5iyT/Rtqqs3Lzs6hbO0PqPzWaPW6Yv38rPP8WfXRG+39l4455HPK5fxem+flgkQ3kVzyNkbz3ih/5G+dq5qkjAA2YBcvw9R0VCAba/2PYJmo7IZotdr9YfDeLFwASkNvvA963U4kl2w7mOwymh0IzQ7c+niUwmh5CIk5T2CXrca2kkT8OmHN/FttXm7QwBcM/OFxVSRMVDL1PdyF2jy/HnCCk5uXYusBlD8LrX43tEg97HgZ9saF6G2PwN78K3bxCvQG3dHymmPHSrAqN2OXrABqNtCtaSpzKTz/dpu3jRTWXYwdK3VWZeQ2RW912F+aNNtQkaBO7dxttnmX3XY3T3yRpGj53IzH6S0OIfnjU1u/z/8m5ZROMA0Dnx+0ZNT5zFgmYGLbOkp3VmU89fTTBHJq0fGwY1hf7gTN6mb5aZEf9gK4lQnnmmdoisLffmXa5Ed5YNIkcnKyeeChhxkx/FSuv+ZKGrVwlqkfsH9P7hw3BkithEsL+pAK/Ni2m4RkE02mZWfqKtV/c66xpw4fQdHalTx8z13ouoFpJlk8by6XnXYi1931II2bNgULfJrmBbLc0LQb3HZX93lZnEZV4Cg9+z49GO5MCm4dQHOvc5VJJ9ANTh8MzQYr1Sa7XWbbibjpSoGmUVCQj7ItZrzyKqFQkBYtWnDHxHuJx2Ic1OsAzhhxGuFItve9t9BIWM75qYib+HTFtKeewPD5ufzmcQw54zwuOvUEklYqmxwLFY9mlqMCJj/6EJMnT3aC9lYyFRhOoil44ZkpBAvq894HH1FZWUkw4s84L3ELokknwzSoK5RtoW+xYsj2BYlZisLyJBsrEyRMm9phnzdJbzovf+vSI97xOEE3WzeI2TolMYuyuLvKO0RImU4gVmlOIDwV7LRs8Ok4ATzi2JbK/M677V/aNTkjyJ1WhsPvMzLavL6HHUrhhg1MnDiR2d9+Q16t2mD4sbzJIoN4qo9mxmwMDY4fdgpHDT6Z66+5igN77kM3t83bvTMfv/8ed9wzicsuu9SZ4Fc6L8yYSUFBPg0bNOSGUdez7377s3zFcp546hk+/fgjnn/hBV598y1uvukGBh11FL/++is//fwzb7zxFsNPP9P5bun+6tsO9zqQ+s6Bhu6u6PA++873UlU3MeCdLzvj3+7fdQPYTsDa3KoM15YBbi8Ijga2ue2JCPc1uEkoSnnXVpWMg1WBTzMwfMGM8kHpnyxNOSVATCsVOE7d7gS+U09n2yQs5/scNy0vSxqqkhqdUrU24CY0uPnXznc7I+idOpBtreUxLRsTp5+GUhnljdKD3LGknQpuK5Rme2+WnXouN8itlELZ8Ovy5Zx//vkcMaA/b7w6k2Aw6JTXS6WIu6sp0AxnxJCaILc1g0rT+V7HTYvSmEVZaZzKpEksGuXac0dw5e0TGHD8UKcNMMGwnQSqB5+cRvtGtdF8AWJJG59mo21ZP2aLySQr9V5t9VnbcmJlJ6Q0HZVeMrAGnm9X9Kd7WLbhJ6xM1lSCZfuplxXGl0jLFrItrECEsoTF5eedwZx5C2nQLI/fimP8tK6MxYXl1Mry06NhLpZts3TdJu4fcyNvTX8SgAYNGgBw3pkjGHXlZZRVVPL5F19xz7jbGHzc0eTl5WNrGuWWTu2QonleiBXFlU5NWdOm1NTJ9gWdLyKpWS1NEUta5AV0imLO4CKRynC74cYb0eu15LUn7mfV4nlYlsVPs7+i7z5daNGqDVNfe5vGdWs5dXNtneyAQb0sJ6hengo+u99PXSnK4iZR06YiYbK+LE5xLElZ3Ok0hH06mnLqeTtL+jRCPqcmuE/TKIvrTh1rTVErnOXM4ltxVKyMHMukzJdDZdIioCuiSUUkoLOuPE5FwqRNQZDaQUVs3VLvvVK2hWmazJj1MYOOOZbOnbtw4AH702/AQHRdx8QpqVKZqts787lnADj8qGOoHdTQbRO9eC1mpDZacqO3PNmbAU79v7FxmVeSxAtcphrAFyc/wHEjzmPfgw5jxgvP0rjt7t4FOmZaGJqTze63nUCRnXrvlF014F9aHKckapIdcIJaNs5F39AUYSNVYwqnEXPqhybQKzZXfWiVs5yuNG5RkbBYVhSlIOjDtCEvaJDrM1AJJ4DvHlfMdGdZQ8774MeZxUzGUclY5uBSM8BMVl2AvSwB54V2bt+Wu0ddybMzXsWyLGo1aUV2djalpaX0O/xwylSIvEDEqxMYt5xOmptVWydsEDKcpU/rV63gljHjsC2L0dddw7PPPss3X3zGzJkzab/XgYwcchzdu3WmT+8DmDTuZvJq16PYn09hhcnC9atZv2Y1Ux+5H79h0LxZM+Z9Oosf/vc8Ddp3xWfGSKz9jeGDj2WP7t3p3q0rhxzWD7+hUxA02FxpUjfXh79snfN63Rnd1DmvOt9uZyh1jkyravkrVlVDnqrdulWdw/RGbMtOWHrm2u91dCCzRmA1nTCn85TeYTIhGUOPVxIBsjQdOxTBzWxy6lQ614+1UScgXDdskO3XWFGScLKXUq/OtJ2JtWLLJj/gI2Bb1AoZTmelNM6qygRLiyppkRdiXXmc+RvK2FASpVPjXDrWjVAcTVIcS7KhxMnobVM/m0jQoFmtLDrUidA4N4BpQZZPozhmEvHrqaC5s3QtqDtB7oCuCOi6l+Gk287kYCS1Njd9+R0418poaRGPTn6cjz54j4fuvZumLduAZpBA8671ti/oTXYo2ymXY2p+lhTFKY0lU3sXOJ14n6aoSDjn7YM3XqXbgYdyyNFD0BTkplbDxE2bYLKUfgOPBGDzqmU0b9Mey/ZTO+xnz0Y5xE2biF9n0YZydE2xoSRKyK/jN3TKogma1c5iyeYKyqJJOtXPpjJpAxZ+3eDvzJXXVJs3/MyzefTTMpIVxVhl61CN9kHLb4lm/HGQM53hNwjqGmWmuc02b4FVm48fGw/RzYANFRtI/vICBHLR2/RHq4E6xf8EZQS2qqtt2xZ28XLMX99xappH6qNym1Rfy3vzYucxec2xdB9UM5D+q/QWB2MufZ/kgldZ89t5JFt0/lttnk9T/JmaerkBA5Usc/6xC7V5I4YcS9TORmU3Qm/SE5X2fbHj5diJcqwNc51rsz+CVbQcq3ABZNVFWQnsaAnJX99BhWqjwgWpbNsdO2hSSlW7FB74V9q8WpG/do35/8ZOxrA2LsAuW4PedP+/POFnWyY/rHVW/Gyrzet11IlM/uypzMfZFuaSt53/j5f/rRJM/ygzjq35vRJqWb4AAb8Bbgkv3SCpB7GBKy+/jK+/m0OTOllk+w2WFVWyrKiSkliSlvlhwj6d9ZuKmHj7jfxvizbv3DPPZNQ1VzrjvC+/ZOKd4xh8wgnk5jsBXRsn6GTaVUvz02lKobC9vY/ippOgkDBtYsmqz/+FV48i3Lg1Lz16HysW/oJlWfz83dcMO2RPmrdszRMz/0dWQQFGanLeTrvcuqUI3Oxr07Yx7ap6uwonYOWWeXMzypOa7WUhA16ZkqQF5QnnumzjZMDbtgLTuS74NIVfd8ausEXgS2nk16rl7F3gZn3qBqZp8sorr3DUcSfSpUtnDjjgQAYOPAI0vCz30riz4u+15509DA7ufyS5kQi6rmNoTilIFS/PnOB0k1UsE9tnpcZ8qYzOtIm6F56azPGnjGDfAw5i5ovTadzKmTi0lUEytbIxqGvOnhRWVbth606GbaWl2FRpsqE8gWnbFIR8hH1O8oUvVQJHKad37WV1um2mt6+F0+aVVJqsL09QWBHHpzkBxvygTp4/7AS7gUrTTiUdQNinCBoahhHMqPurElGUe4lO6+u570N6aUyA3Xdrx12jr/LavNr1GmS0eeWmhpaqCx03LeJx01uFoBSEUqv9/LpizcoV3Hz7OCzL4sbrr2H6s8/y9Ref8crLM2nXvSdnDjuWPfbYg4P79OG+e+8jPz/f+7CsXLWGlatWMWHCPeiGQZMmTZn5yms8+vgT9OzZk5KSEr76+msGDRpE9x496NalM/379UPX04JfGW2H5p0Tt960m+nrTBBk3j8jCST9d24Wu/eZykyAymhzleYEtNPPfVqWvvceANus273lY6yqTHE7NWmvu2WFtmgvFaArp759LO3CY1pOcNt5fc53eVNlgs2VzvjXndgL+3RygwYYTnA2oar2b/KewzlxXrDbLX9SHS2Vfe2O1bzzT1UGuZ0ay5l2VXDbXTnhXJ9s7/rk1xUlmzbz1BOP88nHH/Pggw/SrGlT773IuN5aNlbaChNnvKYoSyTYUF417q9ImCRSB/Ph66/S+YA+9Bx4PGXxpFceM2Bo2GaCM090xnm/rVxJs9ZtsWyFZStsxbYD/XZVyU/N/QzBTh3gFjXrTwe6k5ofQ0G23/KyHnV/2JvlNJVBRcJibVmSNjlhunXfi7gF9YPQLDfIW4sLWbm5gk9feY7Zb77Az3NmVx2Ez0etWrVYvGAemuZctAwTeuzZnX59D606CNsirBLELB8Ns50GcVlRZeqCAZUBnbphPzrOMrBYambf3VwyO6AT8mlEfDpryxK0OaAv1/Y6jPMPaFtVO8myWLJoAbddfxX3PPgouQGDulkGIWW6LS2BoE5F0mZjpfN3o6aTZf3DuhLWlsSojDu3x5Mm8aQT3A75DW/pvd/QyAv7CPt0rFQGeCTglGBpknQ6kQnTwq/7qRM2yE+WEjETqFiUHM3ADmVRJxwm1wdaxUavNIQXiLYsdN3guCMHcPTxJ7Jw4ULuu/9+Bg8ZwuGHH45lWYSy87hu/L2Ymo8Pv/qO77/8lGeenEJeOMCTk+7G9gWcv5laJp4xU5reSKT/Ny1gOeigfalc+BWhtnuzW7e9adGiOTdfcyW9D+2LlV3XWZ6maygr1YmKV1QFyg0/6yqSJC3ICeokLaezGUiVWtCU02HM8mlo8YqtMqTSGzoTjTVlMdaXx5y66bqiImE5db79blZqnOKkj/XlzuREXtBHXlAn4tecQIMy0H2pAX283Cn9ohlVy+ZSAwA3CAww/ZU3Oen8ywFo1KA+q9asJRwOc8bI06lfvx4dOnTihhtGM2HiPcRTpy9saGTrlnNOAKwEtmngVxqNGzbgnrvupLDQWd75zRefAdCsVRtuuXE0bTt15txLrqDf3p2Z+9MPvPbOh7Ro1oRg7SaMOMbZ4Gvvnvsx+LTTad2+I5O/XsGvP33PazeN5NQ7n+aMI/uQZVcwb853vPDsMwQMnX6HH87GmIVPVzS0i50OYNoSMhvL65TYRiqD0kx65YMALM3A9mehRYtRyQS24XPOXTLuZHxvOZB3Mz3cz9OWnZzU+Va2RWFxOQsWLWbRkl/5+rs5/LpsOQG/H13XmfbYJAJ+X+bnYcuAeloHyQrlklSGV7eswlQopYMeImzHUNFSzEgdvl1dSGnMZI8GObStFSQ/qJNI1dYOGE4gWdcUc9dX8ENlgja1wjTO9pOwoCDkI2FarCqJsmRTBfPWl7G8sJy8sI+CkI8VxVGa5jqBxoPb1aVj3Yh3Laib5fNq9rsZ0xG/Rlh3Pm9FcahMJklYFj+tq6B5Xoj6Eb/XOXU6YU6Q3g18gjMI8RmKCXfdycyXXiIajbJ40ULWlcaobfvAdGp46yqI39Dwk/TeY8sfYn1M57eSCpYVVXpvT4NIAJ/uDCLc/Qt+W7yA3ocPJOzTyAvoTk1ILEx0Vixdj+EPsr64jJDhrI5ZvWQBe+3Zg0/em0VxNEm//Q6kUU7QKQtVL5t15TE2lsVp3yCHull+1pTFSFo2mysTrCmLUxA0CBg2OX9jRVtNtXkrKxWPfXYdKA0tqw4qt8l2Ha+dGoz/XpuX37k3Rrv5JH94MvPBsWKsVV+jVZOxvLNQSkPltUDlNndez4afMZe9j8pp7IwEjAB6k/1QmoHe/hjssjXYmxZhawZ6s15/+/m13GaoLqeS/OFJzum3D+P/bptn2+hbZrtU+7rZJdu8Ju06sbC4FpY/D6KbsUpWgD8bpfkwF7/pnJtIfbTa7b0yFFb5BszFb6K17o/RcE8nsFdRiLVpERradn/3/pLqsgP5d9q89SVpiQG7GHPdj1iblzoTALFip1zUdqxsWbaxglb54d9p86rJ2oqXg1IZk3V25WZnz4A2A8FKoGU3/Lsv8S+zdT9mao8OL4NaN8BveAGH8phTRizXr7Fvt06YmnPtqJ/t5+uVxSzeUMZHM5/lk1eeY+FP33t/2/D5KKhVi8WLFqGnSnMZfj89euxJv76HpQ4gFYxKfd5NW/MyJp2ERjfwaRM0nN+5e5/YtjPuyw/50JVT4rE4ZtHt4AF07XU4w/du6Y3zbMti6eKFTLz5Wsbd/7BTas52glhu7qVbsiBqWsSSVeXs9FSJEaVAV7YXiIq6JeFs5y+klzxw65kXR5PEkk6fWkubXPXpiohfJ2Rr3uaQ7sbWpq3QNcMZh6UH5cwkum5wzDHHcMygo1iweCn3PfAAg4cMpe/hh5NIWviycjh99HhqZUeY9u6X/PjNZ7wx/RmyI1nc98hjTl9MmahEpZPAsMUkma0Zzu26b+ua1bbFkX0PomzlQiKN27Jb5260aN6Mm0ZfT+8+fQnmFhDUnU3XlVuqJD1Ij0ZFwiltEjWt1EpE57V7JV3SyzWkym4CXhlN5+/5nGS3uMm61MqCXJ9OcTSJX1fORHAqE7W4IumsUDVtcgIGEb9GxK95Y0udVBmT1JjXW8Hqrl5KBe7ccdD0V9/i5HMvBaBRw/qsWu22eSOp36AB7dvvxqgbb2LcXRMA5/qQ5YMC0l+PW5dao0nDBtw74U4KNzht3tdpbd5tN42iTccunHbeJRxwwAEsnP8Lv7z2Oi1atCAvO8IhhzrfoQP2P4DTRp7plKGwbb7+5hsGHn4Yb779Njff5tTn/e7bb5j6zFMYhlH13YOMetx6avNsTVWVsXDfBy/LepuTqtXcvuUEr2U5JdZSE9RbbfqZ+tm4uYgFi5awaPHiqjYvEEDXdKY9/mBVm5c2+V7dpqS2pjt9li1XQ7ntcaod1g1nJYm3L1IqSGzZVcFYZz8lk82VCS/w7NOd61EgtX8cuBtXOlMG7koHX1p9JE2pjCB4+ib36WO3qvtvUS4pFSh3f9xrocspU1J13PdPuItXZjrjvEULF7KhsJBmTRpnvE/uBJ+7uaV7jY2bFhVJm8LyOOvL40T8OpGAgU/XCBrOa1n960IO6DuQ/JDPC367f3P1ypXohsEva4qxcSYO5s37hQP32YuPP/6Y8tISevfunfaBcZIoY6nVqbpVNbGoq+oz8nc6mu781OTz7YL+dKB7c9REV84sV9inETbcelvOUrdYajlWnbBBqQUllUlM26YommTRN5/y/NPPsLkiwffvvEzDlm3YrfveHHf+NQw+vDfxjWuIBP1ekBsglJNHKDsHi9QMq5lwZv4SUQJ+DUvXyQ3o7F43i01Rk7KYiaFBZYlFyNDIDTg/lamlbQnLmV3O8buBKZMW+WFM2+bhl99jwrUXUSs3m1BuPu+89QYD+g9g8by5tOuwO1DV4KtElIQ/QmFlkuVFUWJJizVlMX7bXMmGkiilUedi7jc0wn7nQxVPWphWAl1TxFMBpuKKOJGgz7tPJG6SGzRYWxpzdgY3NBpEAoR9Ggk9i0hQI5woRYuVQrycfFUJbomttA4A4G0qBaBsk3ZtWnHfvfcy8b4HMJSzpKxf/wHEolHsoM7cuXO55KILmTN/Ce3rOpEh0x9GL16DipaBLy3TKNX4V/19y6s/6knd12foVC78ipmzPuSpl17n5DPPBeC3tRvIC/ic5dZux8UysTWw/WGSaOjKeT9jSadDWxDSqUxa5PrARvM2nwRnZtetJ5ix1My0MBIV1A4HqEg4y/cChkZuQHfq5VYWYwWyWFIGCwpLWVFcSdDQiPidEjW1w37qZvmcLDxN4Q9E0GwLEpWZQV/LbSxtJk2ZxkWjbgWg1757oek6xWXlrFqzltzcHF599TUaNmrEmedeyDezv6N7jx689vqbNCnIcl5D+mw0OBMNSqP/4Yfx6KOP0rVrV+bMmUMgGKRRk2aUFBexZuUKhp5zCU9Nf4nbbruNfffai3AowMgLL6dL587sscce5OTksOiXudxx/VWMOPtcNi9Zw+fTnuTMR/9Hm1bN+K0kyl6N8ziy32GEdYuHH3mMHnvtgy+SR1t9M1q00jvX6cuj0xZseoFsK9V50VIBEhUtxvaFsUIBLH+YuGkTcJcUup9XPa2+nhknrvwkLZuArrCSCWbOeImVK5axbv0G1q1dS1FxMZFIhK6dOtC6ZQsuOudM2rZqAcCESQ9z7Mmn4/f72Lv7Hlx2wTkUbtrEtBdncs0NN/PrT9/RqGGDjOubFi3F3TrI1nQibgcprcPmlsQBaJLrJ8ssJwuwA0HKUpvFxEyb3zbFiCYtfiuOUpmw8GvZrCh2JuTcuv8bUh3ytvWzaZQbJOTTKQj6qJ3lp1W+wq9rGBpePfCgXrVpig0YupsZ6JyzkKHICzjXxJb5QTZHnSyZgOEE3rN8GrHUXF1xzMSynU2jEpaNGbfJK6jFbh068ML059B1ncuvuIr6DRux7wG9OHHYySQry2nbtAGF69YQzs5lQ4XJ6sI4y4rKvAy2nKBBQdCZwNM0iCYtr8Zci7YdWPDTD3TcvStJy6lj+dvKlVxw4cWgG3TrsSchQ3NqV/uC7N6xA8898QjRshJOPvV0Viyaz/5NclHAytIECSsbTTn7IWhK0Tw/RCyV0bW5MkHYp6Nr1aSC/QU11ea5eTMqVUdwe2mpa5u79LC6Nq9l3SyU0tDbHom54hPQfKAHoPQ3tNwm2JWb/lRt2v8ypRQE89Aa90RvUrUxU3LJ22A5tUOJbsZa+QVGx8Eo3z+3kY1SGkaXU7npzAP55MUpf6vNyw/qmNWlM27BW5mxi7V5/YZfwLyX38YqW4/KqgNKx1rxKQQLIFTb2fCrYhPWyi/R63QEK4FVOB+jw/FVm7AaAVROIwCnhndW3YzM8H+aaVpObdb/SJu3K2d0KyOICuVjb14CKMxV34A/jBZpgFarrbcJrbH7Sb9bCqlBbvB327wW7Xf37mvHyzB/+9zpR4frZB5PKB+9+UFgJTGXfYDqNGSHrzDY0rqKZFpw0V2l5vQb3Gy/mGmTSDgrMZKWIm7GCRmKLz/+gCnPTKWwNMq8D1+nUau2tN9jb4ZddC1H9umFudlp8+zUxJ6ykoTDYcLhsFMGwQ1yQ9UEoFVVn9blBo7dS6ObFVsVfMbra2mpMhaBoJ9JM99lwrUXkZOTQ15+Pu++9QaH9+vP4nlz6dBpd6dMCWSUK3FLX26sTGBZNrmpknPu3k/uRnMWzn01hVe60i1tEE1abK5MsDmaoDRmEkyN79wVvm6iUyzpbCyelQr4uoHwjBbADdK54z3drcut0659e+67917uu/8Bp3SmaXPkwIFEK6OQHWHZwnmMveYyvv5lMU0b1ieYKsmnVRY7SVJpk5ZVE2XJqueAqmSqtH6x39Ap/20+L7/5Nk9Pn8EpI85wPksbCsnLCjv7Omy5WaFmEE+VgzA0qB32edn5UBVUzahJ7D7eLb/ljkHNBKGAU+c47NNT+84YBA1FbkBHWUkS/gi/FSf4eUMZmysTWLZTWjQ3YNAoJ0iD7IAT9PZpRAJZTq86LaCv3NJ9lvPvjDav595omkZxaTmrVq8lNzeXV157jUYNG3Lueefz7ezZ9Nx7T155/Q2a18mrCtKnsTUdpRn069ePRx99lC5du/JDqs1r2KQZpak2b9i5l/L6qy9zz/gxdO/Rg1AwwAXnnMXunTvTbY89yM3J4Ycff+SySy/hzHPPp6S0lCcnP8ans+fSqFEDEpZNdm4ufQ49FE3BIw8/xN49unulcdxgr8JKTbz6nQ1YvbJmVeck4zOZdq62CnIrDTuQjZVq8xIWXl3mZOo7ZmjOGH7mjBdZuWI569atZ+26dRQXFxPJyqJL591p07IFF517Nm1btwJgwgMPcezJI/D7DKfNu+gCCjduYtoLL3HNDTezZO73Tpun9KrPtZl0JrK2KKGTPqmskuA3wvh1O1WyxAm0umUZXVYqscQNtVreHk0WPt0kaOhelrWhOcs+3GuFjfJKmEBV7ez0Ot+u9H0D3H+7JUvc66Bbnxsyy6zomo1upa4jtk1BLWec9/xzzjjvmquvpnHjxvQ+8EBOPfUUysrKqF+vLvNXrMUIhkmmAvxuLfKKVBJnrbCPbL+Bpjmv123zWrbrwMKf5tCtW1eyAwYJ02L1qpWMvfZSdN2gY9fuRJOW99rbtOvAk89MpaKigqEnnczyXxfjM3RvYsoGKhM2FnaqPbIzzonbBmSF2TlpWtVmuDX1fLugP70Z5fcri7wveV5AJ+RTlMQsGkWcLB/LH6Y8YRFL1e6pSCQZMfg4fv7pRwo3OJtJ3jb9XZq0bouZ+vI0yQ3SvnaYgqDuBG8UzsYaqSVJyraxAlmgGWgVm3GXzrrZokk9SNxyni9mOkGO0riFX1ep5e5VO0lryrlNKade6urSGJsqE7TID1O4fBGH9HQ2onrqmWc55aQh3uu+494HOPXUU8k2nEa+nAA/rq/gt+JKSmMmJdEEFXEnc9vN1PanZvaChkbYpxPxOx0FTUFJLMmPq4rZWBbHtJzZv4KIU38XID/sx6c7DXbCtGiaG6JFfpBAKlskoCt8lZud2Um3/lS8PJV97QQOvR2elYYdcALXttK8TGzThrMuuATN8HHYsUPIsioYOmggy1evI09LZQ0YfozCXyFa7swCGUbVLuKpi1DVsuW0jVDc2dGUM6+5lcnPzQDgyquuYvy4cfTo2plhJx7LOWedlfEZs/1h0KoyRwKpmns6FvFUZkdQV87mXcm401l2N81xZ5m3DLjbFvFIPcpTHfOIXyOPqLdxyaaYzawlm1i+udLLvl+UWooa8uvUivjZrX4OjXICNMwOOgEAXRFJlKDiZWBZGTPRJ19wBc+9/AZPP3Anhx64H1//8DODz7yARd9/xciLriSclcVLL73EhqJSkskkDWrn07x5c778+ANn2WYyjh2I8NKMGVxwwQVkhcOsXLUq4zy9+fVcXnvjTdrt3Yum9WpxTPfWzu2fzaZj21agdMoTFi1yfZmdTMPPex99xt0TJ3DAgOOYn7Ub2bm5HNahHo2zg3StF/LO44UXXMCjU57m048+YK9W9TMvBmkz4l7gw5WaMbf8zudOi5c7WUJZtYiaqYwYO60UTjLuzNoaYS/bJqDZxO2qTSqmPjedp554nBuvuox6detQv159IlmhzABJ2jFFo1FKSsvIy83hvMuu4rfVa6hTqxZDTzgOv8/H+In3Mfrqy9lvn7T6x9tcZufWvjW871Nh0kduwAls6wrKEhbzCyvxaRrFsQRLNlVSEPJRK+zDp2tk+51SQ2GfTnE0waqSGD5d0SgnSG7AoHbYR7ZfoyhmEvHpBAyVsQkPpDaUTX3PFHgbEFXG4jz59DP8+ONcCjdtxLIs2rZtxx577cPBfQ6l3Kxq2NwOkVJOTW6XBk7pJJLs3rEDq1evBuC7eYt5bsqjvDJzJq1at+bNN99k2osz6bjvQawriznlmWJJb7le2Ods1OtuwpuwLCzLWba2uaiIW668mHEPTaZFrp+vvv6Gm0ZfT9+Bgzh55Fko5ZRjyQ3o+BIVVVk16e9N6j0ww/kUViRYumQxTVu0Ak1Do2oSoSRqkrAsIn6Dbo3z2F411eatKU+y14iHMOOVWJaJpunef9NZqeuMe7v7b9syUZpOw07d+d91B6c2H62+zXv157U8et8LmGUbKPviQQBCXU+kcs5073mCuw3A37Crl4VoWyZWIo6ddp1zn9N5W/z8HqXrWIk4Vtpmh1vdp5padZrPj274MZNx9LTnqO7cbOs8WWnHXP7LGyhNx1evE2BS8d0z5Bx0Nbrh9/7mP1HDrvynmcRWOtn7f6fNi1mKfa5/hyUfvbLN59IMHz++cQfNtZLUH9y12rzcAy/G9ke8aL9ezWcxvmER0WWf46vfERVphDICaIYfpeloPr/3WSr/5TXiv31L9j5nYuQ28h6vND3js7/lv7eU/r1J/zzZlknnw3rx9mnt/zNt3mML41x/U2ZZjW0xk3Fs00Tz+b1rgubzY5tV16E/ojQdpevVntM/yzaTxFd/j1m6DjtR4QQQsmqh5zTCqNWq6vybaX9f171/Kz3zuUo/vRc75nz+sw+8lPhv35BYPw8tXEByw0JCu5+Ant8s4/30rn0+P3ffdjrdGuQA1bd5N9z3GMPHvM+GH94ituhd9NrtCDTZ07seup/D9HNQ3fXNNJOYJWudjXfd/ole9Vrd81n60fg/fS639NnSjYTdPojmTLi7E+3unhhuSYyk5WyEffqQY5k39yevzbv9hfdo0Ly1F+jJD/loWxCibpYPn1YV1HEziL3xQmqfArccoomWthk4XqZ13LSpTFZtGg5VQXm3K2raTs3awgqnlEVByMfmlb9yVC/n8z/12ecYNmSw97rvvPcBhp18qvfveKqWdmFFgvXlcS9z06c79Xdzgwb5IR/BVIJDIhXQ1pUi7NepiDt9kIqEyZrSGPPXlbFyk5Mo0bggTHbQIGnZ1MpySrRF/LozFgz5qBU2nH5Qauzqrv5Izy72Ngp1N9tLSzxyx3rlCYvzL76UODpHDz4JFStn5PFHsGLNOnICRlUCWaLCCb5a2whiKs0bc3uBwS3uc/YVo5g87XkArr7ycsaOv5Mee3Rj2PGDOPeMkdj+UMbm9CZaKtBdlaXpBgHd99PbpC59k0jb2RRPmYmqrN3UxGgiXIuimFNGwac5iR+6FcfU/KwqS/D5iiJ+XlPiBActm41lceJJi7ywj/p5IernBGmUE6Blfpi8oLN5ZoAEWsVm59yn7Ul0ynmXOm3egxM4tPeBfP3dHAafcT4Lf/yOM86/hFA4ixkznDavNBqnZf1aNGvenC8//Zi83BwnjqE0ZvxOm/fKFz/x0quvs8cBB9OhST0O2705AK99MpuWLVsQCfgI+ZS3oZ+7kaFpwxuz3uP+eyfQb9Dx7Hvw4RQU5BM2FLlBnYCukapgyEUXXsCjjz3OZx++R4/dO2S8r7YRwPaHnVVcbsma9MkKN3NapTaIdL/PaUFjW2lOWRgj4NVjd98ft2/tluN4Yfp0pjwxmRuuv4b6dWpTr3YtIpGsjOS1dNFolNKSUvJyczj30itZuXoNtWvXYtgJx+Pz+7hjwr2MuvoK9tt3n8zHuyudUjXeM8Z8moGt6ViBCCWp8m9uTX23pJCzwtx2Vs2VxQDnuqArhaE7JYjCPp1sv+HsQ6RVfZ6VcibINJR3u9Nfd85F+uaYVqoSiWVDIhblmaee5Icff6RwozPOa9m6DXvsuTf79D4US9PRU5UNvBUwCpy1L07g2wZqh3R0O8keu3f0xnlLlyzh4UceYcbLL9OqVSveevNNHn/2JXoceAjRpJk24Wl7X393E9+EaZOwLG9j342bixh/zaWMmTSZ/JDO97O/ZdxNo+jd70hOHH6mFxiPJi18ukar/CBZPmfTWUNRFa8Ap0yW5uezH+bTuHlLp51MXd/dPQ8qkyYJ02a/FrXYmbibUa6denuNb0ZZf9i1u9xmlH860D1z7hoifp0G2U6mUMA2CQV8vPzcVNo1rU/9ghwCfj/RaJRvvvueDRsKmTjpYZ596gna7LEvcX+EJZsqiCUtNkcTBHSNprkhGmQHCBqKQKoeV8SnOcuHUjv52oYfdL/zBXAHymlZDrbhd+6bCnBW6CFs2+nwxJK2V/vRtJ0guLv3TknUpE6Wj9yAkx18wfnn8dgTT3LuGacz9PhjOHLwSWzaVLWsc83mMvy6YnPU5Pu1pZSkMrfdrKlG2UEiAYPcgOHUPEPx84ayVGfFufBFAk493U0VSQor4kRNi7WlMYorE4RSmd2l0STN8kO0KgiTF/SxNrUZW50sPy3z/Jg2BKObUbEy0DTM7HreBlHezLzbYTSCWEFngxIVr/A6RLbu59pbxvLAXWOp27AJU2e8ylsvTqNOQT7njxzuLFVORNGixZCIO4ME3ZfZ4KRnmmwR+Pb+qxlsLi2nIp6gYf163gXs6tvv4u5JjxHdvH6LT2NqZtjdQdsNoqeeK4YPCwiblU7nJpXJ7gbZvUxzt9alW3cqUscrdaJsC62ymBJfbirrAhZvcmoLzl1dzJqiKGXRpDdx0SAvmCo9o5Mb9tO6dhaNcoK0yg9Szy5BJSqqZrttmzc/+JSjTjkTgMrlc/lx3kKGnnUxn73zOvXbdmavHt0ZM3Ys3ffuScKyWbV8GX0P60OL1m258PzzOKbvQViWRZe99+e444/npNNO54Jzz+H5l2Zi2zZJ26kRv7HSqQ0d1DW+fu9NPn7vbe594EFyAk6Q27Rs8oM6evnGqgkiI8CM19/iuenPc/+UaUz4ZClFFQmO6tyAbvWzqRtwsqxtFJ167MMxg45iz84dqJ+fwz49unmTTxmbiZDqbLt1t42g87642druUjLD7+0YDnifT+891/3OJmW6U9bE/fvxeJwuPXux1x5dmTJpojMYTF+ipvuqsvm3/CxVs3wOoKysnBOGj+Su229mt7ZtMn+Zni3D1oFu92eFGSFu2bTIdTbv2BQ1ifg1SmKWlwFUkTDJDRjO+1wSdQLCAcMLCmf5NG93eXeizp+6DvrSNijR3QnA9O9Z6rj6DTyK+vXrc+utt1C3bj0Afpk3n1dmvc/H777NY1OfJxAMUpaqFRAylJe1pNKytrJ8zhJTv+ZkbOma0/GIx6Lk5hew//77cfWoG5k283WGXHA1ZXGTkM9Zolcvy49Pd0qOVCZNLAvCfqdT4tOUlw1w5uBBPP7Us4R9iuaN6jP2/kep1XI3dmvfHp/udppscgMG+SEnOz2WTE1YWnGUmfSCdcuXL6d9h44cdcIQbr/nQS+Twq9rrC0uJ5aaGDuqY2YW419RU21eKLXBrjuwdSck/GnBBWeJovI24nI3OLZtZwM0K1WvVFfqd9u8grCBrhQ5AY0rLrmIJyc/ztlnn8OJgwdz7DFHs2nTJu/1z19bjKEpNlYm+H5NCeWpbI7SaJKAodEkL0TE73ToneWaih/WlnqfKWeSV6duxO9ktFUmiSZNVhVHKaqI408FCdw66y3yw9TN8rMqVZ8+P+SjY50wpu3U2vSZUWylUWoZ3vnJ9mlVEyJKw1QGlUknGylkaF52ezRpcdOYcTxxz3hqNWjMI8+9zPszn6N2QR7nnnceIV2hklFUtDSzHUmX3v6519TUXgXefzWdotJyKosLaVQ77++1eZpBj9Hvs/C9mdv8jGqGj7lv3EFTvWyXa/M+eu9tbp/wABG/RnnCqbuaG9DJDWhetk/cspk5cyYvPT+dOx59mjs/WEJxRYIBnRuwT5Nc6oYN/LqGZVl079qFIwYNYt+99qRe7QL22XsvL+vLTmU1qvT2K3UtTqJ5SRrOOal6zFb3x8mk/q+0ebY/hGUEqzLG0jPyUhlV4Awsfy6sJJa0aFMQImHZFMdMsnwaZXGLkliS9eVxyuJJcgIGlg2rSqIEDI38kM9r99y9aNxN3U0bb6PnjDYvLUHFHbi7x3jMEQOoW78+1994C7Vq10EpmDd/Pm/Mep8vP5jFxCeeIxAMevVTc4OGN0jXNLy6oeFUm+fTFFHTSpW7UJjJOI3rFLDvfvtz8dWjeXbmqxx1zhWUxZ09MXy6lspA1dA0RTB1Lf69Ns+0LXZv0Yhr73qQvBa7sVu7dgQMp3RDZcIiJ2jQIBIgJ7UiJ8unEfI513N3CfjKFcvp0bkjhx9zIleNv995X2POtbiwrIKY5dQlHrFn02o/A3/GOwvXO+chlWxDMoHhM3jzhWm0adqIJnVyCRg6lbEY337/A+s3rOeeBx5i2jNTadl5D0oIs7I0SmXCCUC4K1PrRvxk+ZzEo2AqiG5ozobdXr8mVZ7D/S6kZxKnszWDOAYx0/LqZ7vL9d0ASMJyyse473Ne0OlvXXzB+Tw+eTLnnnkGQ44/mqNOPCmjzVuz2dnrYHPUZGlRJRsr4l4wByA34HM2Gk5N7vt1jQ2ppfyawisV5e7LVFjh/K6o0kmIMjRFJGgQT1pk+Q3a1ApTLxKgIm5SHEvQIDtIyzy/txeISlSmklSCTtKXbaHFyqsCQrqB7Qtj+wJVWaqp5JESFeT2seN54M6x1G/clCdeeJn/vTiN+rULOP/0U5xyJYmo899k1EskcE6y5Z3r3xv3AVXjvMooDRvU9963q28Zx90PPEJ001rczcsBrx+XVIYT/Nuyb+tlk2veXhEeNziZXq85dX8rlJsqOYiXEGVqzn4V5QmLtWVxvl1dzDe/bmJDSYyE6QTbIqlN3gHywj5a1o1QLxJwNlbNC9I4ojvB7niF91xvffAxR53kZK5XrpzPjz/PZ+hZF/L5WzOot1sPp80bN56ue+5D3LRZuvRXju5/GK3atOPsc87h+KMGYieidNlzXwYdeyzDTj2dS84/hxdmvOx8d2xFWbxqJUHYp/PFu2/w2fuzmHD/g9610X2tQTuGildiazq2P8zMl1/hueefZ/wjz/C/xYX8ur6MfVvU4sBmuRQEUlfSZIyOXbtz7KCj6NGtC/Vr5bFv146p91TLGFd7ZWdSG9GS9l55m1Ontz9uDCJ1X1v3OY9Jazexkk77aFvE43G67nMAe3bvxpMP3Y/Crgqkpx6HZWVOcKSFrjI2tEz1gUoropx4ygjuGnML7du1Tb2WLYL1W4z3SBvnWcFsNsbsjL64aTmb4Fq2Uz51TWnUS+6JJS0KK+IkLGdyr16Wn+yATlDXvDbOXS3itm9uGaSM0jDpx5fSr39/Z5x3yy3UrVsX04affpnH+x98yKy3/8dDT08HI+BdB3161d91KeX0j4K6ylghoysw4+44b38uu3Y0b7z1PwaedTnry+PoSlE/O0C9LL9Ta9t2EjYt2/lchnwaCkUydR0+b9jRTHnmWYKGolnD+oye8BB1WnWgZZu23njYHRs3yvHj15z2xmsTUodmA4uXLqdzx90YdMIQbr/nIUzbyVyvTJoUl0dJKJ1Y0uLoTts/zvs3SKC7Zv3pddIRv87a9es5rN3uZGXn4PcH6LrP/nzwhjMIO+/M0xl56sl03b0jXTp1QPP5uXb0DZSaGhsqkhSWJ1Kd4ySmBZGw4QQ3LJvSiiTZfsObZTQtm9xAGMOMes9v6/5UJmPS2RDN3SgJwPB7O4NnJWOp2WODsJ4kz2eA5mTPNciq2mCgZa6z+YSWjKIqK3ngzrGccOzRPP3MNPbq3J55X33EnocMZMWKFQCsK0+QtJw6YLkBgxapQb6eWs1UGjNZtLGCRRvLAbyM7J/XlLCmKEqtiJ+iigR1cwK0qB0hO6CTEzDYq3EuFQmLgpCPirhJaTzpBFUMjcY5PhpEDDZUmOQEnAXuodJV2L5wqhOUQC9a5dS58lcVo1WWWbXcTTkNi9IMr+EqT1gMOu1c5v/yM4sXL+bT92Zxx9gx3HjDaFSi0hmIpR6rdL1qF+StNoPQqhq7tCxYdxkiZpz8SIh8QpgVJSxYupJ5S5byyZezyc3JZlNxKfm1amFqfu+CXJm02VSZpI6uE0xlqCsA2yKoucunqjK47S06Zsq2sNNqiisriV5ZTMQfQivd7M3aRrQkyopjhSPoWsgLZi0vrKCoJEoyblKvboTOTfKoHfazujRKZdxkzsoiFvgNfskJ0qdVAc2yQlWrDZIJb8B/zqlDCTXrxFGH9+GUE4/mzon3Ot+jcJA7x4/jsiuvZp99e9KoWXP6HXkMUx6ZxMR77mH+slV07daNnvvuwxED+nHKkBO57Ior0TSnvrKVKkfQIOLHpylChqLrkKN56clHyPZr+MwouYazA3nUtAmFcp3303Y6CaaZJB6PMeLEo1kftSksjbG+c0f2aNOURrXzaN28KWPuuJOOu7XjtxUraFy3FuMm3Mf1l5zHwEMOTL3XeJ0NWynn8+aefzOt05A26HY7q+s3beaByU+zbt0GDMPgoAP347CDexHJynICTUrzarZhW4yfeB8A11x+aUbj7H0O0+7vXQ+qWz6XJpIVYvL9EzjlrPN54sF7sG1o3KBeZgc79Xe8Tllqpt/NAGiU7XMyEeIVBHxO1iM4S9Sy/Yp7br6WX37+iW5du9GqXXua7daFZm13IyegUxAyCKTqEFqp5cCGpjJn/dMyebwarumDDN2PZdt07tKFxk2aUq9hY++70nH33Wm1W0daNmvC3eNuZ8Rlo0hYFnlBZ/m1u2GkW4POl6oJmbScjXp8uiKQqgunlKJ5s6bMX7CQ5es2Yxt+1qfKrlQkTBrlBAj7dPy6SmUn6sRNyxvwO5npGlHTovdBB/HTN5/x2muvMv6BR+h8yBFEkxZl8SR1swKAs6QtJ6BTFrdYW+oEKrL8OhsrEyRMi4RVQdPcIFp+I2YvXc8P331DWSoAmzBtpjx4D5WVlfzw7ddkR7I46q3Xt/k5+CM13eZFfBpGKpgbDqSCnmZsqzYvqPtAGV6bF8IJWDnltRJ/rs2LV/LIHbcy7JgjePqZaezXvjHzvvowo82LJi2SqfrzLfPDFIR81bZ568udTDUno1/9bpuXH/LRoW6k2javQXaANgVB2tUKem1eTkAnULIK2w6joiUoyyQvNZiztSxUWXlqhU8CW9PR/GH0UC62ZqDFKzBSGb6WrjH0jPNZsXAeixcv5sdP3+PeO8dy4w2jyYpuQqssdr4/SSfLzU4PLkJaBtM22jw3yADU0gzICfz9Nk+lXUt/h1IqYx+NXanNaxgx8FsxavmdNs/CGZhr0VI028KnGfjtBFYixkUnH+e1ebHOHVm2RZvXabc2rFuxlHV187hjzPMZbZ476M6Y1Ei1R3p1wZ+02/7LbZ6qLEZXpdUff1qbp9sWu9fJx7LBSFRgB4PkB53vVl7QJmHqPDFu9FZtXovfafNMvzNodts8n5bW5tlp2eHuXh7KqX/dravT5rVuVtXm7btHF7p13p2XWzdj8j3jGHHZKHy6Ii/oI2Q4A3tNOddZd+O/SGrPi2jSmRAK6M5EbyLV5i1auIDCzUWEwiEsm9SeOk6Zhaa5Ia/Nc7KL/2ybd2Qqm01RO+wnYVnEkhYNswNUJi1vk6+kpfNrkdvm2TTNDRKqU9XmhVMNh0/z8dyj92W0eSP+RpuXsGw2FxVy4r4dCafavB77Hci7r74EwHlnncHIU4bRbfdOdO3UEc3n57pRN1KahA0VSTYVRUmaTiAjkSrlAaSCE86m3bGkU3pNQ5EX9ONXVRuh6ZrhXPdSmd1uIAzdqCqXlIwTUBoB3QAdbJ+BqaomZUyrqqCeT0vLFIzFeeCusZx49ECenvYce3XpyLxvPmXPgw732rzVpXHKEyalcZOEadGmIIvaYWfFzeZKJ0GpLG6ypiyGT3MyKJcUlrNyUwXZQYOCSICyqPMe1ooEaJATpGlukD0a5GCmarcnUnuJOGO8ILXDOpZtUBwznJrelo0vFeTWYuVOP9RKOteC9Mzm1DjP9itvYtQGlOUkiEVjNoPPOJ8f585l9bJf+fz9Wdw9fiw3jbrOCW7HnZVz6e0YVI3rvEz79OvKFtcYlTq2/Ows8nOyMRNxFsxfwLzFv/LJF9+Qk5PDxook+QU53nfV1gzithMYDOgaAXeTS3dRsLsq1w1yu6/XDXBXd52zneS4kD/sJH1VOONwZSTJ9ocJGYqIP0hOwCBoaLw+Zw3rVhZj+HRyW+TTs3Vtwj6NVcVRfl1fxtyVxdTNCXJgq1rkBSNk+0Jp5z3pBbnPGT6MUOP2HNXvUE454WjuuP9hACJZYe4cN5bLLr+cffY7gHatW3lt3ti7JrBg4SKvzRt0xBGcftJgLr/ySqd/pmkY3qSts2Fo0NDoMOQ4Zj79GLWCGppSXtkPC5ykoWTcOUeJKFY8SqKygnOGHcummM3G8jjzdmvP/N1a0DA3SKtWrRlzx13s1rY1hRs2sHbtOsbfPZHrLz6XAX16gWl5Y7ktV+0qK1m1n0T6GCWtGVy/aTMPPP4U6zZsRDd8HNzrAA47qBeRSJbTt9GMjJWzd9w1AbC59vJLnCB3avJf2Tjl5nASqjIC2mmfT+UeT5rsrDCT77+bU846jycevBcbnUaNG6HMtM9P6nvj/T03ecFykr5yA2EnAG0nwTax/U5iU8KyCRuKiTffxLy5c+nStSst2+5Gq45daNe+Q2qiUvNW4rsT727Nbnes5wV2vXYPr5/pnnHLttl99840adqU2vUbYivnmtZ5991p26ETdRo0ZuL4MQy/9DpM29mcPGRo3oRrepunKbyVFO7KXl0plKbTrFkz5i9YwMLVhWxKKJYVOauUa2f7qZ3aU86vO38zbOne5J+uqto8gN4HHcQ3X3zKm6+9xoRJj3LAwGNZVx4nqGvOShIFtu3D0BSrSuLe30jNZZCwnKQvn64IZ9fjqyXr+Pjzz5lfWO4l1kx95F4qKir5cfbXBIIhjn7nza2vCTsDqdFdI/50RvdnSzdSJ8vHD/MWsqk8Srd2LWlZJxefppx6u8l41YVLad6y7qKoSWncCWZUJMzUzLeT0VEQ9BEwNK9m2ebKBLXCPpIW1ArpFARTS7RTS1uUGXcyTn3ODIiXNeouV03LinEvwlYwG3e5U/qyOFJZTl5GlbuELnXxtALZTJ4+k3PPOQeA3Lx8+hw7jEGnnEHXNs0oSGUIKKVYVZpgfmE5v6xzBgx+QyPk01NLAJ1abEHDyW6bt6GMH1YU0SAvSNt62QQNjbpZfnIDPioSZioTxKZRTpDWORoqEaXQDmFaUDeso2/+rWp2MxlLZYL5nIaO1EA9mcDyh7BTA37n9rgTLDHjFFl+lm52aoEP7NmF1b85nbyV8+ZQP2hh+bNQyZhzvtNmUL2GJK08iL1F4+J8qNxZXaexWr5uExeOHkPjRo34ZeFiZn8/h4qKCtasLySSFXY2Ek3V31Zm3JnUMOOoRMxZMue+VsjI3tsqEJGaXba1qs1H3IxvK5zvdOxsuyoooBlYoVyv9uCasgRPfLuSLxc4G4Hs2aYWQ7s1ZllRJd/+VkRZNEEk6KND/Wy61M+mfpZBTrIUklEnCGWZFBWX8PDUF+i1Tw96dOviLc/aXFTMfkcOZvGvywC4+OzTeXbGqww76RTuvvsuAHrutx9rVq1k6bLlDD3xeNasXUc4GOCl6c9i+cNePSpDgYqVYRkBvvjofR59ahq//Pwznbp0ZeDRx7Lf3ntRL+LDDuZ4kw4qVoayTErKKzls0HF88sF7zC9RTPlqGbWTG2miV1C5YSVF69eQFQ5x7vCh3jl9/8OPuPXOexl46EFcOnKYsxmQGwhKz8AgLbsbnIwTpZFMJrHMJLdOfJAff1nA5eeOpF2LplTEE8z64GPe/vBTDMPHfvvuwydffE0iESOZNGnfvh352RE++vxLKioq+fh/r6Y+dFZmUNrrJGxjoO9+Rrc41vmLFnP00OF03b0DiUSSUDBAcXEJhxzcm4vOPiMziy7t9ZYUbebt9z6gsHADB+6/PwV16lC/QUOSmh/LhuemP8fPP/3E6Jtu4cef5rJkySJemzmDIwcdzYnHH4ehnEzcWDyO4U9t2mk7t1ngbaSktnxNbqAt5dbbx5Cdk8M55zh1gN3BvPu3fitNcM3F53PcsFPYb5+9nc1DUoP7srjlbUgZ9mlk+TSnprNyAt5utsGjTzxJUWk5X8+eTa/jT+PJ8TdwxYPPkRM0vCy9gqBB1HQGce4GUatKojTMDhI3LXIDOrNem8mbr7zMN199wf2PPMYBvQ6iMll1DNl+HduGopjJpkpnuXFuwKja1VxTZPt1AobO2tIYEb9O1LRYUVSJX3dKRIV9OkZq86GPXnuRmGlxx5XnV/+Z+BOkzZM2719t88wEPcbNZv6sl7b5GdUMH/PeupNG1kZp86TNS/tASpsnbd5f98mvheQFfSxcsoiyygStmjWhbn4OuQGnlIO3GWBq1YubMVscM1OlzJyJy1jSqWvrHptTO1llLHMPGTp1wga5Aef9MW0wsKpWpTonoapkBGRsaOgGe4GMjGZvMlJp3jU1Y9xgJb3rtRXI5pFpL3HheU6bl52bR9/jT2LwiLNo06wJ+cGqz09xzGRBYQXfrixmyfoy6uYEaVoQTq0McCZBNlUm0DVYWxJjU1kMv6HRum6E+pGAsy+KoZEbMLxzkRf00SjbR1i3qbQUsaSzH0gwVuxM7KbtYWD7At4qSHeFs5vkZPur6py77cmGqM368gTRpMWJvfZgVVqbVzcn5P1tJ2M3uXWiR3rw291PYMugd1pW97LCEi6+ZnRVm/fd91u1eW55Lvf99ms4fZL0SWN30i+9tER60N22nNVWqVUx7n3d1S5uUo93my/krQSstHUWFEZ5/odVfPL9GpIJk47t63Bk5wb4dI3FG8tZU1SJrila1I7QvnYWrQtC1DNiaOUbvZdevGkjj0x9gQN77kOPrrs7bZ7SnDbviBOq2rxzzuTZl2Zy0smncNddbpu3P2tWr2Tp0mUMHXwCa1avJhwM8tLzz2H7gs73yqYqwBor/z/23jrMjipr+/6VHm/XdNxDQhIgWCA4hAR3CO4SYHB3h+AMDO7B3WVwD5aEJMS9k0579/HS749dVed0mOd955l5r3lmvqf3dXGFPn26Tp2qXftee6173TfffvMNjz71LPMXLWbTTcdy8MEHseUWW1BVVtJTd9fDn+5snslTpvLZl1+zvMvijXnrSG9YxSZRk3zLWtra24nrKmccd3jwLHzy5TfccPs97L3bTpx3yrGB8WvxvCjuXPO9wHzGt+U4OI7DDXf+mbnzF3LBGScxfNgQsjmDjz77gg8/+9LDvG356rsfME0Ty7YZOXwYZWWlfPn1N2TSGb746N1Ccr14PsqykO3xuxw2IrkhyaJLqjgp77osXLiAA488nnFjRmNaFpFIhM6uLnbbeUf+dPqphXnns8e9Y3Yn03z4ySe0trSyw/bbUV5dTV1DfxE7OS4vv/wSc+bM5fJrrmPR7/NZsmQJb73+Gvt5mOebyebyBnoo1KMAB6IuoMoFNrf/nTa2Zbn5ZoF5p51+RvA7vzicMcXae8k5Z3LQkcey1VZbBlIgjlcIsRw3MLiNeFhl2ALvhC6+xBNPPU1bV4qff/mFPaadyEM3Xcm59z9HQlcpj6jEdVEg8tnbsiSKoqm8WH9jukJUk/nwrdcCzLvnwUfYcvudgg7zqNfRkzYdNnhKBY3JXCDrCGJ91GQp6JSKh1RSeYtlHRmyhk1dIkRpWMO0Bb58//5raIr0T2He/8QIGN0vzqAk+v/OI+j/+rmZLHWHXfi/jtH9dye6v1reSr/SELos0Zq1yVmija8iLKQ6/OG3H5qOS2dOOCqn8jbtOZPunCUcfD32YmlILCqaIhX0zTSF6phGRaSgUQZiIQi0ov5GRc9nn7iqLjS8rHyP1wEBdoBsZAUIekDYw23aEewBO1bJ82+8ywlHHyk+32sbr6quYZc99uTGO+6hJh4i5ORwZZWMo7C4LceSdlFJHlwepV9JiJqQTRaNn9al2KwuRt52eXNhK2nDojKq0+lV/3VFZmBZhGGVUXRFYkVHjuqYzvCYFWyeAeRsB3IuKQKPog2Jq4VFBdQ3FXFsnEgpTijWo51NsvK0yiWsT5lEPE3chAq5rjYqnG6kbDdurAJsA9nMCl1DVQ2ueQ8wAH5duJzLb7sXSZIwDJPFy1ZQXVlOdWUlluMQjkTRQyGuvfwSkqkUJ07/E4sXLyEcDvP97HlsaGpi2OixgNh8+BXGkJkqBCzefcQ2AvZBoEm3sbSENx9cLUJOSxC2s0LD3A+IrFwPuRNXjxXYIo5FWk3w/tJ2Plqwgf3H9WFCnwQLWtKs7soxr7GLzowwFa2M6+wxooZNa2KU6SJYk/IpkSQp0qOTHItcLk/JoDEATNxqC04/8Thmz51Hc2srZ04/k6+//pqnZj7HUw/eR1tLM9ffdT/bbD6OkkSC1956l2+++BRTjwvGTNbyKsUyF51xIvGwxpWXXUL/6nIWLl/N0Sedhq6pVFdWcPl5Z7LF5psj6REBoPksZ190KZbt8pcH/oythHhlQQurOrLsNrSKMdURDMcloTgFGRwAy8DK57juzvuZPf930pkcY0YOZczI4Wy92VjGbjKywDyT5B6BRt6y2feok+no7GLyzjtw3cXnIOH2bDuTFTqSab6c9QsTdpxMeUUFuiLxxfc/kWxvZsSwYVRVVlIZLcy7pBInZTrICGZWzBH3OdCHK05ESDKo4eA58e+/5FgYuRwh2aWrqxMJl5J4jJvuvJcff53Lyccdg2la7Dl5d/RwlPkLF3Lf/X+hra2NqXvuSWV5KT/8+DNLli2npKSEuj4NDBsxku+//54hQ4dyxllnextoiVRHK5dechHNzS2oqoaiKGi6TiqVYvSmY7n04osoKSkJAlxZAtMweOH553nl1Vepra1lu4kT6UqlOGD//Zk1axaPPvoY77z3XsDMBrFRlxDEgI6czYbGtVxz+cXcfscdVNfW4yDMPXyWiCpLAavYN5Y1JB3bFezuc/90FpOmHsi7r7/EXsedyccvPMGkPaay+VbbUBsPBXIlEU+GwvXOvSNr0pUT+t1DSxUG1FYyZswYXnnvE2wtRFNSSDeFFdH+DfDLui6au/OCAVwepX+pSGS2Z00aSkJIiLbntClYcLIkkTIsNqQNamM6siSxoCVFczJPfYkwCDt8fEFr9787ejGvF/PgfxDzFP3vSnTPe3cG/WnvxbxezOvFvF7M+6cwb9bqdsrCKrYjmO7CLFEKCgP+NPflqbKWQzJvk7FcknmLrrwVmP3JPnM+pHrsZxdNloNiaEwXhrtRtZDoViREJ2+RTGWx0XxQfCkuLEEPxneQgPMwIFhP/aRukeSHEy1n5iuvc9IxPTGvsrqGnXafzE133EtFVCOuiQRUY8rk61WdrO/O0b88Qr/SCH0TOnVxje68zbKOHH0SIfK2w6/rk8xa2Q5ATYm4r7oqM6o6TkNJ2JPucWko0amOqMKHBSFtJOW6RUKvKNHny1IEyXp/TVfDnjSEXJB3UDTanBCtWWFwnNAVSjXIt6ymXLUL65yfvC6KMTZOMLqyyuyFy7j8lruQAMO0PMyroLqyAsu2BeaFI1x75eV0Z7KceOrpLFm8mHA4zA9z5rG2cT1DPcwrDyvENTlI7klmrhC7FCcw/VGcyPTXOg/3nWg5phJGda2A/V6IayxkIyskX/wEqKpjKWHaczYLWzMsak0zuCJKv5IwTak8v21IsrY9g+246KpMTUmYoZUxJvRJUKOZSJ5sjGSZwfH8z8vlTUoGbgJ4mHfC0cz+7XeaW9s5c/oZfP3d9zz1zDM8df9dtLW1ccOd97H1FuMpS8T55uc5PP/KG3TkbLKWQ84S3RBxXeb8004kEg5z5ZVX0r9fX+YsWMhJxx1LKKRTW1XB5eefy+YTJiCrKtgWlmnwp4suw7JsHrz3TjJqnE9XdvLr2i52GlLJ2NqYOLYmo2RFQcW/7lY+x3V33Mvs+QtJZ/OMGTmMMcOHsM34MWw6ehSBj4D/LCqCQJHP5wuYt8uOXHfJuYIV7SfFvee1PZXjy5/mMnHSjpSWV2A7Lj//8gttLRsYOWwYVaVxKkqF3Kqr6uRdhawltPgjqkxUtgOcDggc/hzxJVL8n1UdUwmLYqZtErbSdLe3gKpTkkhw0x138+Mvczj5hGMxTIspkyejh8PM+30hf77/Adra2pk6eXcqK8r54cefWbxsOaWlZdTV1zNs+Ai+//57Bg0Zwulnng2IfUBHWyuXX3Ypzc3NqIqCoiiouk4qmWLM2LFccOFFQYLRT3BbpsELL7zAq6+8Qm1dHdtuO5FUOs2+++7LrFmzeOyxR3njnfeKCfNB7GHYwiNoxarV3HTlpdw643b69OmD662nvoGm7mmHqx573JWEzFXeFhJk559zNttOOYAPX3+Z/U88i3dmPsoWO+/JDttvR1XU20NIgp0OYq/TlRN4J8sStTGdiGQzul81m4wewzsff4qkh2lOmzSnjEIHk+2yoCXFvHWiY2JoVYxSr8tCk2ViuoIqi67XjGF7XgtO0EVcGdUwHZemZJ62jEHEk5A6evN+/CcNP9G94eU7/uWJ7tpDzu9NdP9XY976LjRZojVjUhXVyJhOYPLhG5b4GndZr5rv4pI1hSHHsvYMqzqyVMdD9C8NUxPXSehCO0+W8Db8MhFVCqpMPXSLPGbbHyq8G4Nijyq0B/5+Sw+A1xqMohcSpxAEP0hC9zrviHNo7Uyy/dZbsL5xrXd8IbQUTyS4bcbtqLZgNZlahLXdBq7rUhIWbZqmAyW6YI18vaqTUTVxSkMKS9qyNHblMGybeEgjEVYDg4w+JWFc1w2uQU1ME1qultfG5lXzHSTOvfY2fvvkDfr26SNYYJJnOOl4oA9i0+8HRB4QLMuHsR2h11sT09BdA6VrPXKqDdfIIemeZpAjDG4kVccNxwXAuy6OFmHR6nUcefr5zJn/O8cfsi/dqTSSJLNoxSp++30xALffcA1nThfMm7bmDTSM2JSnZz7PLTfdwIL584P7dNuzbzFx4kTKIypZ0yGqKYFjeUNCp9xNiVY7tyBVEjDoigJdV4sKzUs9SnPGpjNvUxkR7TYJRQS5kpEt6L17LtABs9EPrlW9wGAAOtRSLMelOW2xpC1NR85k9upO2lIG4/qXsd3ACtHOWqITcnLI3RtEW6FccHtO53JEdJ2u7iRLVq3mvKtv4oef5zB+zCbMnreAxd9/wqCGelxZIZM3+W3JKh5/4VV+W7iI0gEjUav6sekeBxEPa9SVhGhIhFn8y/e8+NDdnHHxVWRt2LBmFSM32xJsixf+fCtLfp9PIh4jEQ0j4fLJF19z/NHT+PPddwVMz8Xd8OGSFrboU8r4uhhNaZOolwyKp5vEtbHMHowP13WZPe93Vjeu48Ib7uDNJ+5n1PChwfPnt34hydz10BOs29DMRdNPRlUUysrLCsGPH9R6AboTKQ2umxkqoSNnUx1y8dtVJccG2xAMDT1K1pHo9LSxI2qRjrPmz98iExu/VQ5oMRSWdeTImDa1MZ3+pTolTqbHhqhxfRMvvfEOra1tzPplNsgSI4YN48xTT2ZEsa636+BKCmuaNtDRnWbRokWUl1eww867iIo+nsmudy7dptA1UymsXx9/8im33HIzZaWllJdX4DgO7e1taKrKlCl7cuS0aaxctZo777qLcDhMPptm03GbccwxxxCNxYOW3WCtdCwsrw9UAl544QW++upLVixfwYEHH8zxxx+PUsQCCRgF3vUxUMmYDo1Jg7NPPpbTLr6ax+64kfGTduOdpx9i2YK5zFrRQm0ijK5IrO7OE9UUqiIq61MmXXmLyohGXVwlkmnj9HPOZ/+9JrPPlD3xzYS7XZ0VnXnCqkKVx2Ren7ZYlzToyluEFZlt+8bJWi4dOZuGuErGcmlMmsH3zVk2y9uzZEyb8ohG3nJIGhbLWtLYjsOgqjhnbDuQf3T0Yl4v5v2PYp6VZ8Ltv/1fE90/vXUbQ8v0/1WYd9qFV5B35V7M68W8Xsz7f4h5ny9rpSKiCS1aj3meMW1kWaIyogVatT6jz/D0anOWTSpv05jM0diVI6Ir1MdD1CdClEc0TzZGMBgjmuRJEBVMyIL76ViiC8XvUIIe3SiBjOLGw/0b/jG+djAU/GFcR7Bm9RhuKB7IP7R1Jdlxmwl/E/NuuW0Gup1Dch1sLUZH3sayXfGYuRBVZRKqQ8ZRWNiaoTom2vzXJfPMX98NQN/yKLLkdfGEhJGlf23jmkJlVBOMR9tANjOAC2YeHJtzrrud3z55g4a+/cQ67XVnBUl7KKzrfldXpJTGrExL2qQ8olIdVYk5WeRUi/BeMvKijV31npm/leSWZBatWse06RcyZ/7vHHfoASRTSYF5y1f2xLwzzwRZpnXDevoOG81TM5/nphtvYNGCAuZd+8QbbDphKxpKQsR1BcsRJp6JkHiOYuSRsl3B3q5H51QRkcmXaQNoykFbxiKuC5mPiCoRwgzWTjndJpjwXpEPjxAgWTns0gaMaCVaXjDn7VgljUmT1ozF2mSO+U1JmjqzJHMWmw8sZ3SNKFD4XU2SIfTcfdk0gGw6RTgUojOZYunKVQXM23Q0s3+bz5JvP2ZgQy2uGiJtuvy2eDmPPf8y8xYupm7oJujV/Rm120EAVMZ1qmM6jfN+5NWH7+Xcy64ipGs0rVnFuAlb41gm9956PYsWzCcWi1ESiwrM+/xLjj/qCP58371C9i4UZ35rju/WdDKmNsGoqgiW4yIBJSEFPdsR+G4Ud3+5ksLseQtYvbaRC6+fwZtPPhBoXYsvXrg/dz34GOs3NHPh9JNRVY3ystLg2Qu6ER0LV9FxtUiAgY4aDhLZuiyJIr0nHedqYfKuQsoU8k1hRcTKuqe/XiyT6SoFbW0bWcgwmg5tWdEtUxHRqI2pooPOL6w4Fo1NG3jp9bdpbW1n1i+/giQxYsRwpp9+OiOHDw0Y8v6zsbaxkY7uDAsWLaasooJdd9klYCMrkjARdyXZM4EvFFQdF/766afcevPNlJaWUlFRAa5DW1sbqqoydcoUjjzySFasXCkwLxTCyOfZdNMxHHPscaiRWKC57XgFwR7LH/Dc88/zzddfsWL5Cg446GCOPvY4NEUUdv3zclxxjjgWlqzTkrFY1ZXniukncKqHeSO33ZkPnn2EVQt/Y97aNkoiOlnLwbDdgIiTNITMpE8ciUo2Z592MvvvtQf77LmHMB8Nl9Ces2nPeaxvTRh2duZt1idzyJJE35IwpSHh7wMEBevuvENnzgokpJrThvd5Co7j0pw2WNuRxbBsakrCXLLzRl5b/+ajN9H9rx1/d6L7+5XtaIrEhrTB0IoIiiSxpjsvzBhlqUdrWqCvownX7daMSUfWZFFzioEVUSY0lBLTZOK6cAvOmC4p06YmqgYVIyDQqQ0WJigwvfyNv2ey57cbu6G40CS2coVkKAStwa6seuaVRS1x3r9OKIEVEgyTkJMLDKmytsvKJYvYfbfdsB2Hzs5OAO674zZqKsoQeoKS9zkarhrCQiZjOoQ8faYHvlnF0No4E/uX05jM05I2MGyHoZVRKiMaWcthdWcO03EYXina0HKmQ21cBJcKIOe6sMOlKLlu5Fw3yWyeNz74BNOyiEVjbLfNlmRyOUYOG0pIhtnzFrDP1D0ZNWIYTiiBZOWx49Us6TDQPFOY+qiM0tmInGwRetymUQiAJBlMbyOs6eQyaeKb7kxlRTlt7R1UV1ZwzglH0NTcyugRw3j61bdp7ejipw9eYeGKtdQ09KeuoS9OPkO0biAAi5s6mdOcprujnTZT4qLdBOvrrBdnMbC+guqYTlNS3OvKqM64ugQDSnUSdqrAcPCYasWmFK6s0C1H2ZC2KNEV5rWkSeUtHBe68iZxzzBti/oE9TE10Bj1mQ1+GzxQYI0oKlI+jROrZG3SJBFSSOZtVnflmd3UzZKmJBnDJqorVJeE2WlwJcMqI1RHFJT2VWIT6Wl+YRnoA8cxYdwYaqsqmPXrbxw4dTf+fMPl4kNlFSecwIxV056zWZ8ymLchxcL1nSz/6SvmfPgKp9z2CH1KwpRHNDSv6nnPRadjmwaJsnJGjRnLz99/TXtLM4cfcwKV9X2Z99P3/PLdV4R1lYULF3H+2dPZauJ29G9ooDuZ5KlX3uC1dz5iwtZbc+JhB7DF5ptjOiJZV+V0B4w9v93TLWI2Oo7Dzoccz+evPFlo697ovux//BlcPP0ktt1i/B/YOK4W7cEMcGWhtai4gq3S7epkTZeQKlFuJ0XAqugYroxhe0ZrAK6DjRyYY4U8YzlLCWM4LmGvjWttUmxINVlGkSVa0wbxkDCzGFIepjysIPtMGuC0cy9m3YZmTjz6SB55eibXX3EpY8dvFjAnJMcONgauquO7ydteG7Wvte0HJb5GW0iyC0GZJyEAIOPS5jlq19XW9Fh/Fy9axJVXXckLzz1Ha0sLhu1Q16chkLhQ/I07hc8CjyXniH9xbG6fMYN4IsFpZ0ynuWk9r776KnPmzmWrbSZyygnHAZAyHdalLD769kceufEy7nn+Lc4/5mDMfI4bHnuRP1/+J26/fQYVtQ2sTQrttfKIKjaxqkSFVmBd/PTzr6ST3ey8zeY9uy4g2ID4c983cbO0KADtOZuM6fRwK6/2EgtZy2V5R4asKdb4kNeSl7cclramMSyH+tIwZ04cxD86ejGvF/P+RzHPybDlzbP+j4luSZZ59JGr2XFA+f8azLv34jOwLZN4Sek/hHmPPf8yb334WS/m9WJeL+ZtNL5a3kpIlYNOI1mC9SmD1oxBQleI6+JZ8NnIYVXugXlrurK0pgz6l0cYURWjLqYTUoUkR84WhssJXUh3+cbLwX30NJl93HMVrSBNsrGUFATJ7R66zT4Rxuvy+QPzW9VxQgmcULyQ9PHmpOW4LF28iMm7b4R5d91OTWlcfKSqe+atXjHPEvM+qsm0ZW3e+72ZsK6wWUMptuOyypOZGVIRIaTKdOdtMoaNLEFIkbFcl5imUBZRKdVlFEP4XVh6FMXMIme7SKdSvP7hp5iWTSwaFZiXNxk5dBAhWRKYN2V3Ro0aFci2mLFqVnUbtGVM+iRCNMRklO71QiYk5+lXa3oPzWP/GmfzBonROxQwr6qSc044kqbmZkYPH8rTr75Da0cnP33wGgtXN1Ldpz/1ffth57PEqkU3wcqWbha2ZZi9Yh3dpsRN+24GwI0f/kZ1WQKAtrRB3nKojofYpCbO8IowVW6qUKDwE+5esdo3sOzKCwPXjOWysCWF6bjEdYWUYZMyLBoSYYZVRqmLqUSyreK7enPECSVEh4vrYClhUZRDxFuWFqUrbwdzYVVXngXNKX5a2S4kZ6I6NSUhRtfEGVUVoyYiIWc6Ak8rV5LAcdD7j2bCuE09zJsrMO+mqwtYoYWwE7W05FzWpwx+auzmuyUbWDP7G5Z/8SbH3fQQw6pjlIc1rxMQbjv/dCzTIOZh3q8/fENLSzP7HXEsgwYMYPHsWfz4zZeENZWFixd7mLcD/fvU0t3ZxsMvvMlHn37O5ltuxeEH7s+48eJ+6IpEDCHLIpn5wjNUxIp3kNn54GP5/NWnA2JE8TMIsP9xp3PJWaewzRabFfDO67x2tUghflVUHDUcJIctxw0S3VFNJmoLHHI92TKXgiyVIvtyHOIzfWKEn8Lyf9+WtejMibmQs0QnjKZI1CfCVHom95otfGpwrCLMm8YjTz3LdVdexrhx45GLJWH8r6uooOhYLsF3gML5+UU7H3+KcdCXLpFcl86OdlzHobqmJsAwF1i0aBHXXn0VLzz/PC2trRimSX2fhoCZDUXFXS8GcL3X/LXUdWxmzJhBIpHg9DOm07R+Pa8EmLctJxx/PJoM3YbDqq48n//wC4/fdBkznn2Ty044hHwux02Pv8QDV/yJK2+4har6vrSkBSu7Pi4MNmVJmL1rZgYpn+TnWd97mLdFgXziOiLh7XehFO1fnFAMQ9LpzNkoMuQsl6zlePsqGVkSr+Vth46sxfpUXmj6e9ImWdNhXTJHZ8akMq7zp+0G/+Fe/TuPINH9yt2UxP6Fie50ltqDz/m3TnQvW7aMJ554gmXLlnHPPfdQU1PD+++/T//+/Rk9evQ/dMy/24xyeKVoASkNKVRGVQxbLEx5SwQieUuw2Hz3cjFZRbtad87CtB3G9ilhs7oEJSE5cLDtzNm0ZiwGloVEEO+30HgLieIKYw0cSzwwfjU771VTHTvQbBIJgqz3ekHiwtctwzKQ8Jhutil+D7ihOE44gSurKK6Dlm4RGxwtCm6amGMxuqGc7z54lR33PZwhm43j51/nkNBcDpiyq6ia+pqpshpognWaBdbfuj5rmbe2iylTRvB7a4aqqGBjjK6Osrgtx6DyECW6zJIOg/XJHPUJsQkdUqajeG1ZrhZiWVpmcImK2r4aHItjDtwLgK7uJF/9PJeSeIz5i5eRNwy22GJz/vzETNaubeTQA/fjoL0mkw9VCracBAldRk63iQo/iJZtPYQTKUfOJ3u4EUuOTaikkhlXXcSF193GcdMOY/Kkrdlvxy054pwrue+pFwF4/ckHCCdKyTqNDB2/FamWdSxZuABFURg3ZjRnH38E3/8wi872gt4ZQNfsv9J/1LGk8hadGRPDm1c/rO1iRafOxL4lVMSi2JJKS9aiOW1SFlapi2mETG9jKkMqb4uWHI+N0lASYvbvXRiWTWU8JAwaqmP0jSWQHIvV2RLWJg06PLM7WZbQZAdFculfplMTLUcyxLFaMhZhRaImrtO/rLA4dWZMbMflnQVNbDWggrF1caLhBuqVHHKuC1fRkV2X0cOHEAnpfDXrF7bfcjPuvOqigKnlRMtJWtCdtljVKeQAVrRm6Fi7gu9e+AuSa/PEudOor++Dbduouk5XRzvzf/2Rd35dQc4Wz9/MJx+jpqE/L774IiOHDeaw/fbhktOPpyudZfc9p/DrnLmsWtvI74uWUFNTzYRttyckOUTqBnL2mWdy9523s8kW26CaGZK2TcLX/sunPOObKGa6mxvuuIc3P/iUvXadVNjwb9R26EoS11x0LlfcfAeKqvLo3bdSVVmBaZqk8yYllaWF4E/RydgSKcOmJSNa1S3HQpMlaqIabl7BlWQytoQqizaqQNrBdVAlmdJQFMNxSZoOhq0RwSUqmWCDgte+GQqxsjNPTFeoiulCOyyioMgSXXmbzz7+ioENtdSXJWhqaeOtF54BSWbM6E145MmnufjKa7jg3D+x2+Q9xff1Kv6S6wQtzMUaa4bXxq3JeJsbKVh2/bf57DJZkqiprgrMukAER7YLOdMmlUpz8CGHoodCaHqIquoqrrv+RiJhoWnoBzo4FnlHIWc5xHURlKgSILlccP55HHfCiXz62WdEozGm7H8Qy994kxNOOwvbFefblXd4/qOvef2BW7jwrkdwkJAVlWwqSTwc4vZHnqYsrLKiM4emyKL1NuSKlttsCrLQ2dnJkiWLGVhfQ+2ogT3aUoO2WcdC9hJJkpEJEq+yBOtSFnnbIaErZEwHVRKMAC3XRd+E0BgennBwFR0bmfacSPisTZpM7FfKuqRBSejvhre/OXoxrxfz/icxb9u+JVimzf9ttKQNOvN2L+b9nZi3zaSdCEmf9GJeL+b1Yt5Go08ihCKDjOTpvLrkI5pX3JUDI0Xb68DRFNkzWBQJpbiuEq9Q2aQ6TlVUJaIKQ1nbT0Z5HVAqDi6yMEWTJaFFjGB/BrgCSF6XjuTYheQbFJ49XzO8WPbC68gQiVvv2VR1IVUSTngmlwaad2xXVlABzTbZtG85333wOjvuewhDxo/l59lzSegKB0zd3XuvWuiUkGTsWCU5W7BRmzMWq6tXkcpZ7DqhHxURjbXdORzXZUh5hKgmYzjC3DlrOuiKHBjClYcVSlxR3M6Fy1mfMqmJqcS7ViObWY46eF8k1wkwL1FSyoLFSwXmbTae+594jtXr1nPYfntx4IEHYXiyaFVRnYqIgpxuFXPV9roD9FAhGVWEeUgy4VgJM66+mAuvvZVjjzyCyTtuw/47bs0RZ13KfU++AMDrTz9MqLScrNXIsLFb9MS8Tcdw8lGHMWvWLLo6emLejx+8zp6HHY0sSUR0BV2VMR2HRa0pMqbNJtUxyiMlhGS3h263f262I4wAhYGzkLSJ6wqm47KgKcmSpiSj+5YSUmVCaoRQrBIbmZasRVPSZPWGLBmzi5p4iNoYRDzKbVwLgSVkQxRJaMvXxDQ6EiHG9C0N8K4zYzK3KUlXzmJkdZyqSBkVlZWigGDlkI1sEeb9zPZbjufOay4TBS4tiqtHyEkh1nVbLGvPsLIzy5INSfItq1n7xasouRSvXnYsffr0wbIsJFWju6ODBbN/5OPfVpExbTKmzWszn6Sh/wA+f+c1Vg8exIH77csFZ55Gd2cHu+85lV/n/MaqxiZ+X7CAmupKJkzaBde2qRkwjAvOOYs777iTrbfZhlw6ScrMkgiFexTlJNvAMAxuuOMB3vzgr+y1644iye09i64keT/LYFtce9GfuOLmu1A0jUfuuZ2qijIsIy8wryISsLotZNKeV4Jlu5iO6IIMKTIJ3SNO6FG6vPgjrHgMbsdGMnIgyeh6FMMRyeXiJLfoGHKI62KehFUdRSaQYQrmjmnz9oefMLChnrryOE0trQLzgDGjRweYd+E5Z7P7Hnv0MBr34+cgOe15TLiuMM12Nir0QiEhLkkSmtfJWVtdhe329KlwXTAti1QqxcEHH4wWCqHpOpVV1dxww42EQqEexWQkmawppEtCioSuiHunKgoXepj3yaefEYnF2PeAg1jxxhucMv0sQBigb0hbfPj1Dzx/z02cPeMh0qYjuutSScK6xvUPPCkY2DmT0rBKZVSlLiykBPEMvzu7ulm8YjUDGuqpG+2xqn0pJK+4JBueoa4aLngpWAZaSNwf0xEJbdN20RUhyxaRbNAcLFmnPq4xqiqC6Qi9cdvDk7QZpytnBWzw/8jRa0bZY3zxxRdMmTKF7bbbji+//JIbb7yRmpoa5syZw2OPPcYrr7zyDx3372Z0N3WmKQvJSEYGV9XJOApNaZOOrDDc8kfGtEnoajD5MqaNLElURDRkSaJfiRZU4kBU58KqHLQ6SmZOPBSheOA825K1vZYvoTMm55Ki1QZ6Vu2dokDIqygGVUroEQj5rTFuKCGSCQC2gdK9oUfrjO/6LDk2OBbawPFsNmYU8xYuoXX+D0Ri0aD1FEkWpiBqSCzsXtIhbTosbc+zoCXFmNo4YysUVqUl5jQl2aZvKTnbIaKKFpCs5wCv4pCyCJgPSroNM1rJii6DhoRGLNNc0JTcqJXdlVVcL4kh59MkDYsX3vyAhx5/kqNOmc6h044hrsvEzW4kI4VkilYuMSNENU7OJ3FtG0lRyNtwyqU38Ozr7wKwxfixTDtwH5YuXca240dzxIH7MH/xUsbvcTAA11x6EV2pNH3r6zj/siv5/J1X2XqC0M10kTj1nAt5+umn2WXvA/j0ndcBuGLmRwwZPgIQC195RGN1p0jgtKUMxvUpYdt+wgiuy2vP9iucUTsbGIU40XIsPc6qboOQIlMdVflkRSdxXWFYZYSIKpP0DBR+aOxm1sp2IrrK8Jo4Y2rjDC4LEVcJgmo5lxR6mlLcMy4SulYpw+HbNR10ZExWtaYZUBULtEw3rS+hLhGiwmOhDQ7nUTvXBvdnzYZWBm2zBx++/QYTd9kDwxYV7azp0pQ2WNSapiVtkOzq5NbDd+byex/jwKmTaWtt4ZhDD0TCQZIlQlqIQcNHsMXmE3CBhXN/YfYvP/HAk8+z5ehhhM2kMONKpjnujLO56tILGbfppvj6Za6skncVvv19BeefcQqnnnAs55xxKi+/+S6H7CeSSWuXzKe8fgBNLW088JcHWPj77yxetIglixcBMH/2z/QdOkoEh0Yq0NoLDIW8Z/Otj7/ghVffoF9dNb/O/Y3SRIL27hSVlZW8+MRDwoDGYxRmpFDAAIprMglJsHNyakywN7SCRqHk6/IVJR1cRSuwQYo3R34rnR5FsgwySoT2rI3uBVOzZ33HQw89xMihg1m2ZDF/Ou1kJmw+vsezlctm2OOAw/ji4w8CRpvfZu5619SvrvvMMt/oSvXmq4sIhHxmGhSZD3q/9//1Hb51RUbC5f133+Gdd96hta2Nzz79lCeffpa99ppKd94moslEZDcI7G0H+sTVghaix+7NukILekPa4utPPmD1kkVccMH5mA7kbYdXf1rGjWefwMk3P8DmQ/rTlbe48KBdaFu3hgFDhvLmJ99QEpKp1MVGX852ifZ6NYSVz2Hkc5Rtsi1rf/yE2urKHptQ19dULro3rmeQh6KLf/175QpDRVePirUu111gI3styEF3hx7BUsIoElhuoa26tjTGPzp6Ma8X8/4nMW/T+hIuvf9bGn96/7+co7Kq8eObtzG4TO/FvF7M68W8Xsz7pzCvO51FkwvJmZTp0Jy2aEkbQbJadAiIYqbPOAVRtNAUYbbYJ6ER8TqVLEc86xKeRrormNs+OxLXwfK6EwzbJa7LJFREV5OZ7WEe2WNuQ88OpUCmpIih7M1P37DRchF6zuk2ZCNb8LhwHYGb3j3T+m3C+DGjmO9hXjQaCZJ7rqILzWcthKWEA1PFlOkwa12KVN5ifH2CmCbTnLZoTucpj2hUe/IkvqeH47peJwRCR1sykPJpsuEKNmQs+sQ1Qh2rAo3yoFgCwbwKnntZJpVM8uIb7/Lgsy9x9Emncci0Y0iEZKJWGjnTEZgtA4HMiWTlCvI9ps3Jl9/MzNfeAQTmHXHQ/ixbuoRtNhvDtAP2Yf7i5YzffX8Arr78Erq7kzQ0NHDBJZfxxdsvsdWWWyJrYWwtzEnT/8RzzzzNznvtz2fvvgHADS9+zMBhIzFth5QhMMn3K8kaNg2lYbboU0JNVBVrnm0UJJy8zjZXC2EqYbKWQ9rrbrBsN5BTSIQUZCDj6Vwvacvww6oODMtmcHWcYZVRBpZFKA0Jia+oKonnTJLJa3GyloPrQtp0WJcUMczq9myg2207LmVRjUEVUerjAvMqIgqliiXk0PJJkGTWNLcLzHvrVXbYbQp5WyTou/I2jd15GpM52rMm7W3t3DZtF6649zF2321X1q7bwAUnHI7riiJ0SNcZNnwkY8ZvDpLE/Nm/MH/2zzz8zAtsMnwIpYqFnO2iO5XmuDPO5arLLmLcmNFi7ntxUrup8MW8Zdx64XSOPeYYzpt+Km+8/R777zMVgLXLFlFeVU17V4r77n+AeQsWsHjRIpZ6mDdv3jwGDx4cFFr9eBEQsqLeOvv2h5/w4iuv0be+jl9/m0dJIkFHV1Jg3pMPe/GiWLcylkPWFHJtqiJRFVHR7FwgZyJJItEteTFy0OHod0QVy/h4skQ9zGnVMJYrktwZ73OimszsWd/x6CMPM2LYUJYtWcQ5p53yR8zL5dhjv4P54tOPe3R0BZ/vyYBAT9Y5UEhEbzR8jCvGP/9123ExvCSuJsMnH7zbA/Mef+pZdttzT2wHz1RSwnaFQa4fHwTdXt6wXSGVlLddPv3wA5Yv/p3zL7gAgKaUybtzljPj3JM45eYHGNlfdEqdf+DOtKxbzYDBw3jr02+oiAg9fdVICT8QMweKimnZmMkOyjbZlsZZH1NbXVn0RYv3HGqQ8HYVHTecwNFj4t5598fXDfeNg30ZHSAwkZUcC1cNB+QcvzBueRexIhH94wX/Nx4+o7v5jfv/5Yzumv2n/9syurfddlsOOeQQzjvvPBKJBHPmzGHw4MHMmjWLAw88kLVr1/7fD/I3xt+d6M53tvTYXKblCCnDYV3SIGmISalIEiFVDvSUTMehKqpTGpLJWi7lYaGh5bepFc5CLrDVrDzIKmakHMtxac/ZzN2QQpEktutfQszoRjIzPdx+gR4tlcGCJ8kFcPTdglVNVFZlRQQQ/ubAq1AVm10EjAyPOQew2dTD+e33xXQt/IFo1AsoixyfA61Ery3YiZRieMF0d96hNqaiZztI6WUsbsvRv1SnPKyIh9x1RDu1mcVVQ9il9SjdG0RFzHvQrcqBSEYGJd1e0Fs1cqIFeyN9NRxLtGGrKq4aZl6ryfGnnEZVZQUzH7mf8pAkAh48lpDXliT5rVjJNk6/9i4ee+UdYtEo3745kzlLV9O4oZULTjyC1999n6bWTk45dhrZZBflm2zzX8+fDSuCxcpVtEBuJItGS3sH3VoZC1vTpAyxWXFcoes4oaGUhS0puvIWm1THmVABUj4ZaI/20OCDoHXPVbQACP1NhqvodOnlfL26m7nru2npzhEPawyvibPjwHL66gZKstmbh14SxBV6d+u1OhRZbNxShkPeC9SWtGUwbSdwAs6YNsMqY4RVmbKwSthjN1bmN3iVTgcnUc3cFevYatwYflu0lIY+9cEmbGl7BtN26cpbtLS3M/OWy0k1raa8rIyuVIbx2+3MtpMmse3E7elct5L3X3+Z555+isOOPIpvvvyCXXfakcVLFqPpIdauWkUkrLN8xUpuu/5K9pk61QNpf2eiYmlRVnebHLnP7qxvXEu//v0IhyO0trbwu6cpO2LYUBYtWUqfvv04/85H6chb3HD0XtzzxufstvV4qqPCRV5XJLR8d4GF6uktvvrSCzz74qt8/8scnrzjOqbuMomc6TB38XKuvv0+3pv5SOFvJJmsK/PrvN8ZMGAgkbIqHK9F1vFYRA4C4OKajJJq6fF8+sMtfg68dqkeyTHHwlU0nLBY7JN5izNPPJbrr7+WgQ19CtqlG41kspvjTv8Tr858SnyOouEqaqHFXVZ76E0WB0V+u9nGLMAeP2/0OxuZvGnx8TezePX5Z+lfV8UZp59OdU0tpsf48LXTomqhfd3y2AsBY7iIkWB5LKfmtMmfTjiKO++8gz4NfQGYv2YDxxx7HFsdeho7TZqIpkhkM1muPXJPjjv3El5//AF++eE7lI41wTplJmpZ1tjCrbfdxswnH6N90U+U6EWGsRuxrfzzCO5Rkakiahi/5U3oLicLbcfe3/VoZ/aDef/aF5mvYRmEymv/cA//3tGLeb2Y9z+JeR05kzuenU3bsvn/5WcoeoT5T59KeefyXszrxbxezOvFvH8K81KZrGBGepiQR6M1a9PmGW36XSN+gluTZeIhhYQunnvVMz4TeqyFZI7PuveLqOJFFUsJ05W3ac3arO7MElJlRlZFqVHySF7CMJjr3lryh7Ex09s7tquGg5974KSZFebMgKtq4DgFxrj33+ZTDi1gXsRLRHhJVlfxij1QKPYqatCKD1AWVtBl6DKc4LXSkEKZVmR66xdCPNwU5r7dOJFyrPJ+yEZGSErYpsBKK18wRi5+ZoqeI1eLMq85w/Enn0pFRQXPP/YQ5SE/8S/30CqXrDxSLgWyzKlX3spjL75JLBrhm/dfZ+6C31m3fgPnnX4ir7/9Hhta2jjlmCPIZLNUDN/8v5w/ufYmcUoe1vmG0elcnnUt7WTDZbRmTJqSedZ354iHVZF0DmsBeWBEZZSGuIqcagmSqhuvd36i3tEjwb0IroEeo80J8dGydpa0pJm/tgtFlthrbD27DC6nXjXEHtI3KvXiJSdaTkovo9uwkZFwEIXY9ck87TmTrGkHJrhRTaHGS3KrspChqYooRHLtghihhXEipSxetY5xYzZh9u9L6ddQT85ySBoO61MGjd05WjMGHe2dvH//dbjZFLJtkMpkGTtxJyZMnMRuO+1I8+rlvPv6y7zwzJMcfuTRfPvlF+y0804sWrwEXdNYt2YVkXBIYN4N17LPlMmFeeJ7QehRWjI2++25q8C8fv0IhSN0trcyf948cd2HD2XR4qX06duf0299kLasxd0n7cuMVz5lhy3HURZWKdEVSkMyslWQuQMRS73y8ovMfOFlvv/5V56883qm7DyJvI95M+7h3ZdnFuaqrJJz4Nf5i6jvP4CS8mocT/7DdlxcCFjKuuSIeVAs5efP+6JnwH8W/5bckROKk7NdcBxOPekErr/2agb27VN4Fly3h/xYsrub404/i1eefzYwty3+zGCdh8D40V/r/itZLT/R7SfBi5nhhgNt6TyzfvqZ1194lrrqKs4643Tq6mrJmxYowoQRhGSIjvCgSZnCG8gniiDJhc4oV8TghuVwyrHTuPOOO+nfry9Zy2X2yiamn3ICo/c9ia0nbkNdIoSRy3H54Xtw3DmX8PZTf+Gn779D7V4PliGKhKEEy9a1cMeMGTz1+KO0rVhIQnPFOuI6hfXF/09WC0UC/2d//fTmgKtHRNepX4RzHBGTezJpuI4wk/Ux0y/sQUGX3XUIx0v/1nL0bzt6E91/e8TjcX777TcGDRrUI9G9cuVKRo4cSS6X+4eO+3cnurvTWSzHJapKKN3rcSWZ7nA1yzvzmLZbcNTWFRK6LP5zMoUD+IF3wEBRCxtO34DENsUGKwuSBMvbc3y1sh1VlthzeDWDy0NErbSoIPpGHEXVfH+B6zH8B0ySAuaZZOULABosXAUzoWD47Rc+G0OSueOx59ls9Eh23m7rnp/jJxa8c3DCiWDjLTlWYJQYBBgeGEquEzB0fAd4N5tCkhUIRSCfxXVs8bOs4MYqcPSICIocCztWidLRiGsZwXskRcG1bVxD6LJK4Sh2aQPpcAWO4/Lpu6/z2ONP8PaT9+NGykTywDZE27pjoXatY+mSJZx4+a18/et8hvTvy46TJjL/98VsNnY0t914PVq2nc7OTnY/6gzmLVzc41LEYzEGDujPvAW/s9Ok7Zj58J+pqigLAhUnWh4YZPkVWQAnUcvqpEnecunOWzSU6DTk14vNRSiGq8eFG7m/KAYJGTdg4kEh+AwSH65om0HRaY714/7v17C2PcPgmjijauJoiszQiij9SzTCZhIl2SzumQfkdqKGTsKkDAdFFgyMnOXSlbNIGoLdqSkyqbxFSJWpT4QC9kapIpy/sXI48WrkbBdOpJSsI1GRiDF48GDm/zaXLhNShkNElWjN2vyyrouM6WBuWMlL997EidfdS01FGRP7lRBSJPK2S9/aarbfaRc+fl8wMI486ig++etfGT9mE2zbpqOzk/2nTmbrbbfjqutu4C933MSgmjKi0VgA6m6kjA3Eeeulmdxw3XUAfDxrLkMro3z00lPsd9zpPP3AnXw3ez5/efix4B6fc+ejTN1rH35u7KK5O8cW/cvZpm8ppSGZEispTGAUjbVtXWy3215cNv04Tj1kH2566Bnakhne+uhzamurSacz/PLFhyCrfPjJpzzw6JNomsamo0Ywb+ES6vo0cMqZ51DTb2DAKCrRBRNDz7T12DCIe19k1FP8mj+Kn29J5uGnn+Ojz77EMAx233UXzjrhqJ4b8OLhWDhmnjMuvIKmllY0TadPfT0nHHU4YzfbIvibgPW28fDn4381vESJbdtcdOnl2LZNTW0tn3/+BVtusy177TmZidtsHVSyhdapMIMKK8KExw8IfJaUItHjWgDcNuN2Pv/yK3Bd9t57L0469XQAViycx8lnn8eep1xMzbDRRD2TpBumH8Ov33zOtDPO44GbribctkwEj7FK3vr8Ow456EAA7n7oMfaZsid9owiGjKKK59xnIhVv/r0gyFVD3ncX67irR0UCWNVxwqWiTc4zyfE3TnI+XUji+q1Ysrdh9A2rvHU/VFrENPhvjl7M68W8/2nMs0sbCqakRcaBUCju+trbvZjXi3m9mNeLef8M5rV0CzmPuCajZLtwJYmkEmd5Z57G7rzQKg6pRDWBeRURlahsFxjafjLZtgK880i3aDJBQsTwsjEtGYvfmtMs2JAkoiuMrythdE2UMnKCQejPHT9BC3/ELB9ffd37IlNGycoVDOUUtcdx/OEnVPzPciWZOx9+ms3GjGLniVv2KKT6n9GDcVg07/1iI7ZVSKL6OOcZIQbsRcfuuW/xvSFCEZxQonBu/nlauUBfO/CUsCxcSySIpGgcu6wf2VAZedvhsw/e5YlHH+btZx4KEuqurAgZiHwKOZ9k+ZIlnHD5LXz90xyG9O/HjhO3Yv6SZYwfuykzrr0c1THp7OhijyNO+D9j3vbbMvOxh6mqKCtIjviFNsfqkUx2JZnWrM2yjhyO47JJdZQKsw3JzAUdC5KZKZgob7R+BIz9Yo8EnznqWNilDSy1Etz31QrylkNNSZjKuM6Asgib1MRpSGgBy92XVXD0GHZZA22GKOr6OVLXFR0mLWmD9pyJZQuWuEh060HyN67LQvfZzAdxlyUJubvKkhiDBg/m1zm/kfakJgyvsLuyI8va7hz5phW885fbOOyKO9liUD1DK8JoskRHzmbMgDq223EXPvlgI8zbdAy2ZdLR1cV+e+/FNttuy5XXXMeDd9/OwOoSop68k6tHcSKlZFyNx598mttvvh5cly9/mUe/sggfvfoc+x99kod5C/jLw48G1/qUWx5m+933pLErR1sqz+j6EiY0lFIRVqgISSjJDbiySuO6dWw3eV8um34ipx22Dzc+8CRt3Wne+viLAuZ99QlIMh9+8hkPPPI4mqay6SajmPf7Iur6NHDqWedQ228gOctFkUXyNqE4wmTclyjyivF+ESsw2fbNtKFnsdebdw8/+yIffvIZhmGwx647c9aJx/ZIbAfriDdsM8/08y+hqaUNTdPo06ee4485SnSH+Ynv4v/+1tio2LrxsB2Xiy6+GMu2qamp5bPPP2fLbSay2+67M2nitoGHAQjsy1mOkPZQQbINHDUcmF4qEkL+SVYxHJFsv33GbXz55Vc4rsOUqXtx5hlnoDgGC5as4LQzz2LKqRdTMWQTKiIaEU3h9rOP46evP+OUP13E5ZddQk1OxKFW5UBeeu8Tjj1MmKTe+eCjHLDXVOoTulijbQPJMgsdp/4z7z+balgYyHr3zMeqQHKxqPjWQwN+40Kx170TYF5RISNUVv2378G/6QgS3W89+K9PdO972r9tortv37689NJLTJw4sUei+/XXX+eCCy5g2bJl/9Bx/25BN9cVOkC2CyRqkXNJNFliUFmItd3Ctb5/iSaMbVwXKdUtAuBwSWFy+0wzLzgQG16hr+yGYrRLcZq7LGQJlrVn+HRxC4Nr4oysiokWRckW2prehJdsoUUYPFQB6HmBoCSLB8P/LMcCs8jN3n8P3mK38YbfyhVV+8R7VzWuZ9/JuwYOwv6D6AehfoArZ7sCFpc4limYdYoukhxWXjA3wHvAdZFcUHQkVQefYRCKQLIDdAX0sLdA5JDNLE66G0WSxYbf/xvHxvECn+DeqWHsWCWaC5qbZ7/dduD552aSNFziulFg5LgOci7JE6+8w2sff8FtN1xLQ98GFCsPqo4eL6UkkeDWu+7j6ptnAHDaMUdQWV7KF9/9yNXnTefaO+9nx0nbcd7pJ7LtuE2wtBhvvvUmh+07NQAI31nb14hDDePoEZSONQzy50xYBjst2AxWTizstiWCSM8xWSqq0fiJDkkRgZyryCJB4L/HsXC0CN+u7aY9lWdM31L6lQqDGN/YwAWiaoR+JXUieSDLOOFS8moU1xS6YgAVYYW06RDTFUKqzItz1gGw7cBywqpMRBWbUjmfxtRkdFlB8jZiZkTo7yVCCtFYjNk//SDmjysc6A3HJWfZxEMqjmtR26eeZHsL7z3/BGtmf8ewwQM58uTpDBk5ip12n8y+++7DBeedywH77cPMZ59l1113ZebD9xALadx03yN0pLJsv8vuXB8tZdLUfTjqoH047chDqK2pJpXL886nL7G4sZWtdtiZpvXreP/L79A0DWyDqTtvx/fvvMipl1zLScdMY/mcH6gOwcdzlvPAE8+w5qcvGLjf6cxusli4LskXFREaKqLUlYQ4ctPBaPlu3vviQ1Rd57jDDkbGZEjfOq658Ho2GT6Er995uUd1/POvv+OWay5n1PBhwaZj9rwFzLj6EkZvMpILvbarfGcSwzLQ46INyme8BKwoQPICWByvra2oKl8sfbB8+QpOPeUUdttuy0D3uMAMoEewhKwi6zIP3j0DJBnDgRUrV3HP/X9hzfU3c+etNzF06FDBWsLosb4gywUmkFvoPvAr4JJj4XrAPue3+TQ3b+C6a69j5cqVXHjBBWiqWKp9ZoCuCJZw1ob2rE277VIb04gW9cxJ3n/BJs37+fffF/D0o3+hqq6vV/V3WTD7Jy689AquuushWuU4eduhJCw2tHfdcx9/vu0GxvSt5tB9p7DHzjvw4NMvsGjJUqpq63j06Znsu99+nlGMS0qTCUXKUfPdqB5Ttvh5dSXEWqeGccMlZPUScrZodVdkiagW8dbQjmCT6ugiuBGtrDnxt77eWzGLwLG8TaRVCHz/wdGLefRi3v8w5uFhnpRPi83/RryEHomSXszrxbxezOvFvH9ixDXRiZSzXSKhmFgfXYmKsEpZWMhJlIcVZCODZOcgKwyQi41tcbw4XdEDVr/iWkiWRUYK0Zw2ac2YpAyLlZ1ZmrrzJMIqQyqiDCoPi0JZqgPJEpIdQaKjqLhTPGddNfLHRJJ/TawCY9ctPlaQkDeDBKm4ASLRsmpdE/tO3qVQoPBNG11HMBB9lnGRhAOApKi4lh7cNzmfLhSm/OEVqF3TEIaQiiZeNw2vwOt1GZnZHvJawTH8wq6f5LZMJD2MEy7FiZQiuRIlmsQBu03ixWefJpk1icdjBekjR7Aln3j1PV5//2NmXHkhDQ19kfQQkiSjR+OUlJZz6+13cPUtdwBw2nFH/hHztp/IedNPYdvNx2LbNm+89yGH7bNnUFRwi+4Vio6rR5BsAyXdRr1lUlsqCuNSek2QcCafFMXn4ILKPb67T2pyjRxyJIbrE239eyFJOJFSlqxMURrVKYtqVMZ0TNulOW2gtWVw3Sg1sShlUYLCvxspI+MopE0xD3RvTclbLlnLpjVjsD4lGMVhVUZTZMKqkHWQjTRYDihSwFi1kMlZDpYD0ViMWT/94nkVyGiKS1fOxrRdQqpM35Iw4egAnm1rZt77L/Lst58zeOBATpp+Nv2GjGDn3Sczde99OP+8czh4/30F5u2yMzMfeYCYLnHTvQ/S0Z1i2x124aZb40zaYy+OOmhfTjvqUGqryknnDN7+YhaL16xni+12ZsP6dbz3xXdEQjqylWOvnSYWMO/Yo1g+dxYVVTV89O1PPPLo47TN/Yp++5zGN4tT/LqqkyVDKonqCkMqY0waUENlWOG9L99A03SOO/wQJNlkSL8+XHPBtQHm+clJXIfPv/qaW667ilFDBwVr1pzf5nPbNZcxetRILjrvXFxJIpc2yJgm8VikkNj01zz/Z0lGdl1cuyBvFOjo+3PPsVi+dAmnnXAsu+20Q884WJIFBrkOkh9auQ6KogjMk1UM22HFqjUC89au5c5bbmTo0KGFY4DAOr+4s9HctVyBQYprFZ27zOw5v9Dc3MzV11zLypUrOf+CC1BVtYeBJYj100JII7kG2GGFmBYGV3Tb+V1TfnFbk8XnLfp9AU89+hDVdX0wHOFFMffXuVx22eVcfueDNEvCYDeiKdTGdO669z7+fOsNDKgp56QjDmTydlvy4LMvs2jJUqpr63nk6ZlM2XtfsqZD0nLRHYWKWNiT8er2Ooos0PQe653oIivF0oSfiOuK50uTVdG1A4ExN5Jc8Bby5RGDDqbCpQ0IOfC3C+z/KUOW+ddqdP8XRZl/k3H44Ydz8cUX8/LLLyNJEo7j8M0333DBBRdwzDHH/MPH/btnSKx9mWAYldQjGTlcSSQAJGBUmSQ2+10ClAOtM8D12pMlvCpcsAnLBIGDEy1nbmueX9e3kDZssobNnNWd7Dmmju0HlFEbVZEkCTnXUQhcoLBohGKCKaFFgmq/ZGYKFWyvir6xqYA4D6tnEFLEBOih9eaxMR548jlmXHzmH4IuyXZ6HEfyGGP+cAHJ77jx2uZ6MLCCimUe17EFk02P42ohZNMQG3sIvo+TF8dwOkSrmGvkAnYbji2OASIAilfjuKDnhK6gZGSoqa5mzdq1jBo5AhyLbXbbi/raGmTHZMzYcbz85nuo+aQIzv2WEVnliiuv4vb7HuCDF55g0uS9UVybxQsXcOC048lms2SXz0bRQ8F3sc0MTc2twfeWJBlXC3kmaeDoMTKaYC/EFVXMHY8Zg/deV1aQXFckSQy50JLoei1Ktkh2iFvhgZhtB9cdVQDsWr2exu4Wdh5e7RlwSTR250kbFhEtRr8SjbAiQT7vLbIylh6nPVuQKYhpMoos4bqwtF1UMPuWRygNq3RkTTKmQ3drM6/dfwvLFi+krLQEWQvjuA6yrJDO5dEUMQ/33nsfVFyUrvVUKiqpWB3vfv49t91wNWaqiwlbbc2I4cOIhkOMjJo8/vLT/PjrHK697Fw+/OivPPXQn7n3zjv4+evP+PjVZ7lpxt18/t231A0fx5evPEHj2tUsWLyCZx5/mHFjN+XWG6/jtdde48lX36GltQ1Zlthvrz3ZesfdOeq447noymvYdMwY0SLlVUs3nzCBL7//ifUpk6TtUl+us1d1f/baZXt+WrSKw088hWQ6S6RhOAsrGxg0YRIVA0YEzueHHnMCZ551NotXrmHcsP4csddu1NXWcOvDz5JOdhMrKQs24Yos4zpuj0r4+NEjeeb+25lx30Psd8BB6LpOJBwmlc0ycuRIrrriCsIl5YIN4rXty0YGW4uimBma2jt47JnnGDl8GKNHjyGXN9BUhZKSBBW1fTjptDM4ffp0dtvx9R5MSzH3vBYqSQYr11Mex3XQJZkRQ4dw/733sGTRQq687kZmPvkYslLcrlVIavRgugBdeQfbdUnoKrr3lkw2x1PPzsRxYeCgQQwcNCiYx4634TcdF9VjssmIBJOvVVa8BimIpIevaSY7Js3NzRj5HAccdAg33XwLm225Nc88+TivvvUuJ974AJlQKZi2aMWM6nz+1it8+/5rnHr4fuw7eVeOnDKJB196j0VLljJ03Jbsu/9B7LTnPoGkR3PaYERVlCGhXCA/sTHrWBSiNLEOOBaRXDthTVT8pXxSbPC8pKAE2PFqoTGZ6w42mK4kBYFlj9Rf0aZY9hMO/+DoxbxezOvFvF7M68W8Xsz734J5asca4uESb70ShsMhRaIsrIgEn5lBSgmt1h4SAZIc6KIDOFpY+D8EF0AmTYjFrTm+WtVOc3cORZYxLJtR9SVM6FNKfVyjTAc5Iwq7ruolgH0mn+xpevuJT39ueoniQMfZdf6Ib35ixJ+XkluQwSpmDHt/52Oe5Fhe0dgr3nrv8QsNLojEcYD9KihWgfXts8S9wrM/N3z2tisrQYJK1nSwbS9R552nkcfJpQXOIdZ6SdVx/d9bpngtFMGNlOFKMrpjIGdE91NNdRVr1m9g1Ijh4Dpss+sU6mtrkCTYdJORvDrzSfHs+gUS18FVNK649gZuv/tePnjxSXbYaRcUVWPR4sUcMO1YMnmDzMrfUL2EluSY2JZNU1NT4fraVqCbjKLiqELPXJZA0qJIVldQGEfVcVRdYKWZEczQjWKVIC7xMA5ZFtdQD4nkdz6L49i4lf1oykFH1mR4TZyoJq5be9ZEliTKIxqlIZGglnI2TqRU+IuE4mRzNrYDih/ieHIUzSmDjpwpjFQViZAiI0uwYf06brj9elYuWURZWRmqpmM5AvNyeQMkCcl12G/vvYibXaipJHIohhmu5vNvf+C+W64jl+xiy60F5sUjYapVk49fe4GffvmFay8+lw/fe4en77+Tu/98P3O+/YyPXpvJzTPu5vPvvqd26Ci+evlxGlevZsGylTz37DOM2XQst95yM6+9/BJPvPo2rS2tyBLsu/dUNt9+J44/7njOu/xqRo8eIzpj1DBStJzx2+3MZ9/tTXvWxgGUmMp+O27Nfjtty9fzV3D8qafS2pmhpN8I1tb3IzxgcwaMGUtIHciIqihTjzieM8/+E4vWNjG+fyWH7zGJ2sfv4rbHXySd7CZaWsAZRVEprtdLtsH4UUN49t5bue3PD7PvQYegaxrhSIRUNseoESO46vJLiUQiwshaC2NLIiHsCxI0Nzfz6DPPM3LEcEZvMroI80opr+vDcadO5+wzz2TX3XYDM1eIQ2UZPENNJPePsbFjoSsqw4cO5v6772DJkiVcef1NzHzycWS5EAvD32Z2Gw6kDNuTYpGJeInsVDrDM8/OxHZcGgYMZNCgQQK7PBa3n+TWZYQ0lxRCkSRc3ODaqUXaKDaiSyaiSGBmWd+eJGdYHHDIYdx87VVstdWWPPb087z+1tuce/vDdChRcFzPD0fii3de5Yt3XuW0Iw9i/0mbc9TkbfnzKx+yaMlSBozZnMn7HMhOk/cmazrkbJemVJ6QGqYirHnrqC2eQwikA8Vz6nWbmVlUWUbxCqJSPiXWQS0CVj7Q8fbltwJfAgccP37YuEjpekxw+f/QMdY7/qPGTTfdxPTp0+nXrx+2bbPJJqJTc9q0aVxxxRX/8HH/bukSa9Uc7w+cggmGqgf6PZKREcwjo1CFDjZnHmsrMPPwWprlfJL1ag3fruliwYZkYHDU0p1jZJ8Stm4oozSs4uJSEVYpJ4MbElUovA11xtVQZeHwLnnGFX6lHSjowkGhKuwFRH4bXXH1z2/b3lizyVVDZC2JA084nQ8ev0sEnVo0eECLjx+4cvuJAQ+oHY+1EbBrigI2ybGxu9qQwlH8Fm5X8UxSPG0qO1oBgJJpx052gM94+FsVIS8IlStqsSoHF+Q+bIsfZv/GPQ8+xi1XX8ZX337Hl9/+QGNTM688+wRaaRWGpAsjIdNrNffaKjszBnV1daxvXEuV0x1Ur91QTJh5pdtEe7zrBZGuE7CP/A08iE2T7DHc7Gh5YEgj5VO4Whgl01HQflS1gnGY64hF1LGRNM+Qp2jDL26BCASBoLUPxybVbwKvL2ylPKIxsipGa8ZkUWuaNZ1ZJg2sYGxtlIRkoHSJJEquYiCqa5F1FdqzNhnTIaRKxDWZkpBCY1KY0pVHChXYyqjK8o48a+f9xP5T92D2vN8ZVRPFDSdQOtbihBMY8VpwbBp//CuPz3yJeUtWkMnlSFTUUDd6Au+/8xb3P/gIu44ZwM/ffEFjSxt77bEbSqaNETsfgG27VFWWoUoSHz11LxXVNZx0yQ3ss+NW7DNpS65/4Cmu/8vTAGy3xViWrGokHo+TSmeYsNmmTNp6S84787SgFd6SVB596W2eeeUNXnn3I1E9doQRiyyBLrnkHZf1nRnmt+UYU1fCtlUSSrIZO1bBzEVJnn7iaRZ88hr7nHEJzXO+ZsHPP1BdVckOO+3M9DPP5tmnnqBx5XLuvvYS5OQGXNflmVff5sZ7H+b8008ESWb/vadw36NPcfB++9C3Is5lt9xNU0sr0w7cj0P3m9pTSy+cAEXnyptuY+cJY9l15x2wS+r5vS0Pjs0rT/yFzz98j0Q0zOq1jVx+4Xl0JVPMX7yU0tIyzFyGZDrD4iVLyWRzHHPccZxw9LSCPEDwDFmFdn5/7dtovQhYAYrGMy++wsuvvIqqquC6aJqK7bjYtk1JSQmHHHQAUybvgext4Jaua+Xjz75g3pxfySW7SSa7yeeyHHfy6Wy30644uJSHCqarQKAJp7pF64t/PsXnK8mBhlvWdFizZAEvPf8cz86cSWdHB5OnTKW5cQ01lWVMPvhI6raejOGKID5j2lRFdVb//CXTjzmMRDzGnjtswzHHHM219z9Nq14DkUq23u9wjtl+GIPKIoG5WcpwCKsS9TEVKdctDOmMIl0tL6EpKYrY+Hv6xSgqUj4d6DQnbZlSKylYpo4lNj+WJf5O9dYdzU8umj2kNnw2F4AyeMs/ro1/5+jFvF7M68W8/zzMW7p6HX361NO4rqkX83oxrxfz/hvDXv5jDw1qwZjWgk6UgI3vF1+L5kPwfi0k1kArD5KQe2lMWfy8rpsvl7ayvjNHTUmI6pIwUV1hhGcQWKIrRDVRWJO8YmNwTAgMyBRJFDR6zF3/+xcbNfs6r8UJqKKkdpDkLp7Tskoub3LgSWfx/lN/Dl7z534P7Xfv3Hok1iW5p9GjV7xA9nwT8pmCvJaPeT7b1T8XX97MzOBmunEzXvHC0xMHkLznybVMkGXkshrsknrRUWVmkcycwLyHnuCWKy/iq+9/5MvvZrF2/QZeef5Z9ER5j2Syn2BCVmnPWvSpr6Np9QoqNCsoqPtG1cUa8kFxwVsPfDmeoNDjXVsnFPPY5g5SLinMtTcyxgyS2sG/dpDY9jt2/WdJfHfR1eWz2imrpbV0KHM2pMiYDjUxPZAH6cgYDKuKsW2/UmrkrOiSAxGX6RHycpiWjEXadAgpEnFddBvZDjQmDWRJIqHLqIqEZQsJpTk//sBh++zBwvlzGVRXXSjeK7r4vpbJqvm/8OSTTzNv4WIyeYN4TQO1I8bz2QfvcNf9D7HLZiP45au/0ti4jr132wnZSDJil4OwbYeqijIUReGjZ+6nsryMky+5nr13nsi+O2zN9Q88xXX3PwEIzPvpt4VUVVVimhYTNhvH9ttsyflnnhbcV9txefSVd3nmpdd47q0PAzkyXZFwXLAsm7TlsKCpi6aMzdj6UrbuE0PtXo9VUs+ny1q59d6Hmf/J60w59SI2zPmKVb/9THVVJVtvtyMnnnEm77z4DGtXLGPGzTeiJDcgpVp55rV3ueHPj3P+9JMB2H/KHtz3+LMcvP9+9Kmt4orrb6apuZVpB+7LofvuWeiaACF3oUW56oab2XH7bdl1p51I6yWsT5mkcyYfvPgUH7z1msC8NWu4/Pxz6MrkmL9oCaWlpRj5PKlUikVLltHe0cExxx7L9FNODMzfe3THeUWoHvGs/7ovDeUVoJ557gVefvU1VK+TTtM0bNvGth1KSks4+KCDmDJ5dxRVw5ZUlq1Zx6eff8Hc2b+SSXaRTSXJZbMcd8rpbDFpZ0B00kQ0rzvTJfDmCMki+e4qOobtCga356HgF0bzjkTOdrFsh1VLfueFmTN59rmZdHV0MGXqVJoa11JdXcM+Bx3CqJ32ZlW3Qd52KA9rxEMq87/+hItOPlJg3o4TOeb4E7nh3odpC9WSV8oYu9ehHDNpGKOq42Qtm4iqIEkQ12VqoypKcoN4nj28ci0DOeSb93oxqySLmFmLBgazbiiOHS1HyXYVMM82gs4MFCFH44QTBU82v9PMX3e99VwdMI7/pOFLl7S8/xglsX+dkWZ3OkP1lBP/baVL/LF69WrmzZtHKpVis802Y9iwYf/U8f5uRreonhgBYAvQTnptyR5bQVaFSYEW7aGp6XobfjcUxw3Fggr9r/kyZn67nM6MSVfGpCKus1W/MkzHpTSksrJTMLgGV0RI6Aob3AhKzkYCYprY7EddoXUKiHMrMhXxAx+/Gt+jEi8X9FL9RaOYiVGs/+N6DJVwSCWbN0h2J0mUJILEQ+EYXqKg+PVixlsx28BbQP0H1smmRStbONrj71xZxQ3p4FjBZt+2ijYmPuvB/wyvnds1bFA1nHh14IoszsHm/b9+TuP6Jq688Vb22nUnLj73TAaO2JRuKUxH3kWSxDWoikQx8zkyhsTSRUs49MADAKhv6Iu5ZkGw0EhGFmQvaNGjuLIaaHdJZlYsWKomgno9IvQ7/ZZMD3TsWCWyI9yVsQ1kz3hFMjwGSxD8GYL5F7T5KQVGW3AvPHZgLgOOg913DJ+u7CKqKVRFRfCTtxwWN6cYVBUlHvLco/Np4eqraKgSyKk2Iola2rImYVWhPCzep7oWA8MmA0qiPVoeXWBAqc6wHbYH4JQTj0fH4vB9p3DaYXuhWDnCXivjkKoEN5x+JK5poNT25/POEl588TkUM0e91Uy4xWC7kf1gZD8ko4MMEo1NzUzbe3ee/PPtfPjtzxxy9pWce9T+3HD28Tz4/BtcePO9jB0+GGPOxximyXm33E8iHqMzneO4ww+iNBrmtfc+YpNB/Wjc0MK4EYM55IwL2X2H7ehsb+W3ObMZMnQYmq6zatFC7rz7bppbWtF1jZa1qwjHS9n9wMPoc9RBDCirQrJN6soSjN51X5Z8+wEbfvwrRx12KJPvu501K5Zx+dXXMH5Yf+66/0HefeNVTrvqVm664QaqupZy7P57cOsDj3H+NbcwoKGe5197i0ED+vPWe+/z8WdfcvNl5zNukxEcfPLZjB8zitJYhLoqEZz7G5RtNh/Lrfc+wE477cD6tMV7777Dy489wMolC7n56ss4cNftiJWUoEcTuGqIvF5C1nJQJfjrh++xfOUq+laUc+R+e4r2q42Td8VrH/Tc8PubO6lggnLUkdM4etrh3hx0MCwLVRbsj5a2dma++BIHHXYktm2BJFFaVs7Ou+3O1IOPpL6mmiG1pVhqmLaszaquHJosYzsioAgrUiHI2XhN8RkKFE7NBRo3tHL++ecRi4Rpbm5mm+0msen4zbn2hpuYMHY0qpmhWwrz9epu4rpCR9ZEU2QU22FAWYS5jSsASKbSXHvB2Zz/zF9JDtiRRJ8tiJeF2WeLgewyoIT1aQvDdqmKKIGjeHvOpiIUxy5tQOlcI5J1Rc+nC4LxYuWEeWG0EjUhkiop06EjZ2NoceJlJYRkF6VDtLe6/n3wNqCyJ+nQI0D11/jidup/YPRiXi/m9WLefy7mnXni0b2Y14t5vZj33xmyWvBo8C6s5CuIeAlbX6ojkKkKCpce7vn4qIZIKnGWbsjyybJWvlzYgu26TBhUwaZ9SnBcF9nDnGXtGcKqTH0iTGVEIaSo4AhZFxk3aNFXKMI4P8nqy3FYnoeBTc/ks4fFgUyJn9guTmwVJevDkQjZvEEq2U08pCOp6h+SKwHuFXU4BcNP1vodS/7n2Ianp+2zkpXCdQsYigZYOdxcBieXEaxly0QKhQvdSyAw0MNEOewlkDzmpI8v7//1MxrXr+fKm29nr1125OKzT2XgyHEkpTBtGQfbsVEVv7AgYxoSS5Yv5TBPf76u/yCBeZJXTPJxTZKRvA4O13UCo21Jkr3vUVwwx5NwIZgfwivECIrrgcmmIrpTfDPl4sS2a9vi2gGSXzQyHFE0AKR4KUbNcH5a0cWyjgzDKmNENYXG7hwtqTxlUY2B5RFhgp3OiuKFGhYxmqKTNR0MT387osqUhhQ0OwcKhMpDRGQXOZcUDPywYBRX7DARgONOPAUVm8P3mcxph+8LkozsFYaGlWlcf/z+ICvIDcOZ1RXiwSefwc1nqHfaCa+dzcQBFUhDasFKkbMtgXn77cmT993BR59/zaFnXMy5xx7MddOP5aEX3uTCm+5h7IghGL99gpHPc96MB0nE43SlMkzZfRdK4lFef+dDRg9soHFDM2NHDefQ085j950m0dnextL5c+g3eCjhUIgf5sznz/feQ2d7O6GQTuOqFYTjpex76DT6TTuQfvEwpgOqplO31Z4s+voDWn7+hFOOPIzd/nIXy5ct5dprr2W7TQZx/4MP88YrL3P6hZdz3fU3UFuqc8xBe3PLg09z/tU3MaChD8+/6mHeu+8GmDd29EgOOakI86orC50OjsU2m23KbXfczS7bTsBSE7zy+pu8/PhfWL1kITddcwUH7bY9iUgYraRcrKuSTtZykHD59MP3Wb7yEWqrKjl8nz0E5m2UJC1mCLtF60CPf4OikMPR0w7nqCOnBX9n5nOoqoqkqLS0tDLz+ec5+LHHsb3nvKSsnIk77cY+hx1FeUUldWUJYvEYnTmbpe1ZKsIaclRFkcWzqDqeDBUquHKw/wibouPSN4P2z3VdSwuXXngBIV2ntaWFrT3Mu+6Gm9h63GhsF5rSFi1pk6RRKPRFNYX+pWFmNa0CPMy76FyufO4jWusnoffZnAnj6jlyy36MrY0LmTlbIaSKAm/WdOg2HEpjwhNClmRkN4lreMU41xUyswBqCEcVBq2+R0NX3sHJWISVBKGyEvGMpduE2XYujaS5AnMUHV9WMUiCF6+7f4NJ/x8zZL+r4F/4ef8Bo3///vTv3///2fH+bka3vegbwRbydDUDMPM3ZHLBhdqJliPnk0i5ZDAJnUipaFNQVNKEuO/7NXy7uBVFlojoArx33aSWSQPKaUrlSegqrRmD+kSInOVQFdWojCgkJMPbTOYKG3x/Q+s6BaORYoabW2jLFotEQcdOOL162kIgvqMkF4JUn0XlVZ2HbTeZ75+9l4qoeF2urBfn4P99MXvDM/5wZQXyWaHHJitCi83Iiaq8Zyrib1AlVStsYi0zqNgH+qO+gYmmCyacqotzlGQwcri5NK5lis1kTV/sRK2olvmVNS9JkE52E4vFcbVwoXXLkchbLjnboTvvoEguY/qUB9dq6v4HUxaPUFsW57aLzyq0eMsKTqxStK8YqYKuaC4pWA2SHJh+iXMIFe6drArmgqL3uBeB0RiAHio4k5tZseH3rkvwHr993U+I+NewvJ7PcrU4rsv85hSDyqMMLIswrzlJSJHZsqGE6qhKqLsRyczjxCqFdmr3BuzSerKuEizATrRcJDTSbeKaaUKfSrJyOOFSMZ+MNEa8lj2nTOWTl5/k5hl30lAR54RD9w3usZtLI+nehkTVuOv9X7n1tls5/sQTueiQXShXioJm73udf/tDvP3FdziuxJKv3gU9SmfjCv7y1PM8/dbHbD1+NDOuOI97H3uOp157l+vPPoGtRg/nnuffoitrkMnmSKZS9K2uYG1TM8tWN6KqCqZlM2RAP775aXbwkXvutTdlZWWcdf5FhKr6kjFtSsMac5et5ru3XmD14vkY2TSWK9OeNUgbNiO23okT992FT998ma+//4GGhr4s/n0+jmOz9647MWLYEH5Zsoa6/oM495DdKJVNXvnoKw6bfhG11VWM32QEY0YOJ6RrTN55EhMnbAaSzI+z5/DEC6/R2NTM8YcdxH5Tdw/YtJLr8sCTz/HXL7+hLSc22aFogm8++YCzTjuFDz/+mEvPO4sjjjyGZY0bWL5yNW+9/TYrVyxn1KhRXHfRn4hoRczQjdg6xeydP+i+FW/6i4F3Y9At3gD5OmRAR0cH5WWlwiwuY9OUNqgIq4RUmaRh052z6cqbNJSIeVIdVSnVZWF4VfQZZibJlVdfwzc/zeblF56jrq4ex7Z4aubzPPfK61x02RUY6RSDhg7jyquu4ohpR7H/3lNQzQxtTohv13YT1RRM22srlCSGV0UZbDTS3tVN7ZhtAHjh8Yf4qmRrvp/TRFlNjEO27Muho2uQEZtR1TFQO9aCJJOvGEhb1qYyomC7EDJTqO0rwZOeCIwGQ5EAP5Llg2jLWGxImziuS1VUJ2ParOgUgWDf0hB1MQ3d9dgAepSMLRGz02itwqCiBxvZ09SUh27DPzp6Ma8X83oxrxfzejGPXsz734J5K37+w5zw77Gr6EIPXpIL3gOuI6SX/M6DUAxXj+GqOsuTDp8ub+eXVR2sbstgGTYDauPsNqqGgWURcpYTSEt05S0UCfqVhqmLaURUwTQV2t6GYIcXyXcFcly+6WMRE9ifK8VJKylIiNuFuVuclA2wXMz5YRP34PsX7qcirCGpGnKsRDCWi3ETCti5Mcsbgk4cH+8CfWmPiSwpSmFdh7+5lkvhmGBshyIFbPUKnm4ug6RqSCWVgpm8sVmp65DOG8RioiPM1aLYkVK68sJ7wfSkfyTXZXhdWXD6U/Y/mLJYhLqyGLdedm7BxFOScfSY+BvfI8DT0g3Y2ZJfpC0yofbkX/A6EiQjKzDTyAe4L8lKIVYI7rEbfFf/PcUmnE53m3g9msCt7MdatZa/Lm+nOZlnTF2CqKawvCNDVFPYqqGUAQkFOd0mzk+PiJjKM8jukTgr7hbxk+teXOV/L2QVU4uy115T+fDNV7n1uqvpU1XGiQfvJSTWPEkZvxihlNdwxxvfcuttt3LM8SdyyVF7U24IT4yAlQ5cePfjvPXZNzgOLP7+E2THon3dGh589qUA826/7BzufepFnnzlHa4/8zi22nRkAfNyeZLJFH1rK1m7rsnDPBXTshg8oB/fFmHeXnvvTaK0jOPOuoBQVQOaLKRZ1q1fz19feY5Vi+djZDNYrkRn1qAzZ7HlTrtx8O478MU7r/Hdd9/R0NCXhQsX4Ng2e++2M8NGj+Hn335n6NChnHbwnpS4GV798MsA88aNHsmmI4ej6zp77jyJiVuM8zDvN5548VUa12/g+MMOYL+9pgTzTjKyPPDUc3z89Sy68zbdOZNovIRvP/mAs844jQ8/+phLzzmDaUdMY9n6VpatWssbb73NyhXL2GTUKK678jKiki1it42wLlgj/kbBKxh+cc9/vUiaS/Jj76IEYvG609HRQXl5OQYq7TmLjOl4RTtJSL7lLTSvPU6RJEpDCtVRlbCbxzecx3Uw011cef0tfPPjL7z83DPU1ffBsQyeeuFlnnvpVS69/Ery6ST9Bw/lyquv5pDDj+TgfaciAY0pk99b0qQMOyjuxkMqW/UpoVbJ0tncSN2IzQB47tlnmFexBW9+spTy2jjH7DiYbfqWURFRSOgKOpaQx7INzHgtSUN0/UUUCcnIoCSboUP4t/hdJ5Km44QS2CV1dEthWjM265N5koZF/9IIzek8tgv18RDlEYWYJhNR5cCMU5EQrO/kBhEL+4Sc4EbK/1QX0//ECBjdHz/9r2d0737Mvy2j+4QTTvg//v7xxx//h47796u4+3pcVl6AtbpRAGwbIghyHbHhN7JBe64/JCtPVo1xwyfL+GlJG6WJELoqk8pZjOyToH9pmEVtaVJ5i4HlUfqXRcgYNo3dOcojKhFVhryBhCEq145VYK/5bDZ/2AYY+WDThB4KQLRH0ONrfoEIPKBngFK88LkOVRUVrGvtoLxWbPLcZAdSXPx/j3ZiSSocvyiYcS0zeJ9rgaSLlhgpEgfTEOeQz4pKtWX2MJtC1VBKK7ETNdheFU3tXIec68JJdwTVbdc0vMBMR2ldKYJrx8KOVQYbmVgsXnC4lX3jEM/tWJJ486UXadzQAsDtDz3J6K0nUVtZwajKEHJyA44XzOS0BB15m7qQimsbyFZOtGFbogXdb8mTUm0QEbqkrqJC53oRvKmizdENxVFSLbiZVOF+hCI4+SySkUfyEgGO4xRdP+G2LKla4DxefA+kRDktJYPJp5LIsoQuC422kCoR0RQ2q0vQ4HYgdWRAUbGqBpMyHWKKjKvqKKkWSlItIsCLlou5beXo0Mopk40gEQCJgMmJ6+C0N6JZGZzODXz91Zc8fekp2BvWiGSMl+xR9DBSvJTPlrTx2V8/4IOvfqChaxEnnnsF1RVlHLDrdkzeboL4Po7DyAEN6LvtwI8LlmJ3taLiUKY6XHr8Iew1aUseePEdjjzzUnbadgKD+9Vz8lW3c+Up03jl/U8ZOWQAT959M3MXLOTwMy8B4PgDJvPJd7/Q1NJGU0sbZSUlhCNhnnrqaXaYtD02MrZLoIOZsxy232Qgu4+9jFINcCw6LQHezWmTIeVhajSTzYYN5LtDDmGTwQN45603WTJvDv3jEqlkN5sNX8k2+x3FJrFrOWTyTuCxUmdc+iem7LYzZSXxwvPrzdMtNx3FlpteTjqTZe9jz2DYoL6MGrdF0BZ7+vFHcvzR05j5+WzunHEzajpNaXkFcxctZf+DD2W7yfsx9cDD6Nu3gZEjRzJ0+Aiuu/k2oppMON8VtF32+Fz4Y3W/mNHjB/JFm3hktYd5SJCItIs2RY6D5Bi4ikpFaUIkx2yLmkiY0lCYjrzt6ZcqNMQ1DCfEhrTFS3PXM7AiSnlEY6uGBFWuMKx77+23uO+Rx9l75+34xDCZ89MPfNi4loeffYntJm7Ney/PRNND3Hf7Ldxxyw0cO/08pkyZjIJDsx3i42XtDKuMEtUUunLiPOsTIYakl+CmOvll7jIm77gd91x/GX+67g4OunEy3/yyjsalbczMWcxe3cmWAyuYPLQCx1WoSdSgtq9ET64nFK7FclxMx6XTjlBdPwZ93Vww8gVGarobKVGBXVJHWJHIWkIzrj1r0p416cgKXcTWjEF5ROOnrhStGQNNkTHtTqYOq0DKZYNEarBmK4pIHPJPjl7M68W8XszrxbxezOvFvP8tmOezlYvXYAiMiYP5YhmC1esnY6BHAmJVymXmr+v4Yu56JFkiGtWpKI9QGddJ5m1WdmYJKUIfVpYl8pZDSJU9HVvhf6F4hUE/oeqPoGAIhVZ373yLCzCSP5c37lryf78xx6vomaiqKGddUwvl9eXg2DiqhhSWCjq0FLCzWI/dZyG7jl1gG6s6EmFcTQdJGKoCOEWYB4hisaoh6WHcWLnwmfCkSMh2IeeTPZOojg1y2OswSgmJH0UHVfh3SI5FLCye32LZG1+WI2XYvPzic7S0CD+JK+95lDHbTGJk31oGlGhE8p24joWrhjBDJeQsR8gpSDZyqkWwY73PdCUhNyO5TkHv22NhAoI9rWoidvEY676JJrKCaxm4+WyQzHaNHG5xUde7PkCgW+7atsD8qgG0hWr5vSlF3nKoimmEVRnHdamJhRhdE2NAyETubMLVBKvUcGV0CgbZkuGZ8moRnEQ1phJGknVUL+7ypVkkx/YkGgxc00Y2c7jrF/PV19/w7PXnYTWtDnDZdWzkeBlK3UA+nb+GDz/+iK9n/URJxwpOmn4O1eWlHLDbJCZvMz4o5I/o38BBu+/Ij/MW4XY1g2NRrlhceuyBAvNeepdp51zJjlttxuB+fTj5mju58uQjeOX9TxkxuD9P3X0zc39f9F9jXmkJ4XCYx598mp123CGQlMs4SmA8XRcbwOaXXEpUE540WcuhKWmSMW0GV4SpC8NWQ/tw0PffM3LoIN56602Wzv2JfiU66c5WNh9Uz7b7HM6giMkhu20fmAbedvn5TN15O8rKygrriPccbzl2FFuOvYJ0JsPex05n6JDBjBk+NIi1Tzv1VI497Wxe/OBzZtxyM9lMhrLyCub+vpj9DzqY7ffcnz0POYo+DQ0MGz6CUSNHcNuMGWItMTJC+14N9eh0dDeKc8UcU4sKZVKhqOcVwHz9bFnyMM9jGwfFN8cRkOkdu7yiElwH3TWoiuh0y3bw92FVoSKiYNguG1ImSzrSmLbDwPIog8vC1EQV5OQG3nvnXe577Gn23m0HPsnlmPPzD3zYtIGHn3qO7bbdivdeeR5ND3Hvnbdz2623cPpZ57DPXnviuNCes1jVKZ7B/qVhunIWpWGNsbVx6q1m5Oa1zP5hNpN32IZ7r7uUs2+6n8Nv3AEtpNK0spPntFU0b5pnRE2c4ZUx+sRVErKC2t0GkkxpvBrTgS7DIW+FqKgYSNg2oFusKU4mKZ7xUAJcB1WRkCWQZYnaWIiwV+TOWw4JXSFvOaxP5clbDpVRjbp4iL4lGtHidcCT5guKXr3j/zejo6Ojx8+maTJv3jw6OzvZZZdd/uHj/v2zxHVEBfZv/aljifZsSRZMKs9J1WeEiQBZxwiXcvc3q/l+QTOO7dDhMSr0iIbtuLz123pKoxo1JWG68hbrO3OMbyhlTG2cwQlZgHlR9d7XRg2q9P6iYxqFTSCeqH3Q8qUGlWSXnpXkHhv+ou8GiOBEVvnz9ZfyzBvvcMsph4oAKN2NrGpiAxv8jY2byYmfZVkEMt7nO+lu72cZOSGCKCRZaLF55lpBYkDVkKMJpEgcV4/j6BGMeLWoYHWsQe5qwskkRYtMUVubFIqI1rbODeA5t9vRAksN/7y8IVl5ZNchEoqzuKWDcYP7Br8bO34zDj34IJrTJlFNOAir0XLRxqUJ5k1cE8GkLesofgXcq+K74YQw1dH0wN1Y7liH6zieG28iqKyTTQp9p9IqnGg5lqKL6mGmM2C7uY5T+J7gbfg9pltUbBqdWCV2opZ3lidZ3diEYTk0d+cILfuWHfebiuNqbFGfoELOs2btBkoGbkJUlWnP2sgSlFhdYmPnOjhecqVJr6NaUcDIUJlcHYCbnEsGLemuFkHOJ7n+lrtpWr+elfN+ZeGyVZSr4KQ6A4aiFE1ARQMdpQO5/U9HcOvNt/Dh8w9RZqd5+8sfePGWixk3pH+PADgW1nnytfepr6lAsfIFMxpg7NCBPHj5mbz/7c+cN+NhyhJRFr35KLWVZbz31SwWr1zDyuXL2Gb8aAC222wMe2yzOVVlpcx44iXOPXEat115UcByJJdEkhUUXbBETMclosmUeD26XaZDWxbimsuQmEPfRIyQlUHOdFGfCDNxwma8+/77JOIx9tn/APrW17F4+Qr6N/ThrzP/wo7jRuAaOfaeuDkAcxcsZNp+kwO2pFg8CoGI5FjEohGeuet6Lr7lHkYOGcSFpx1HKFGOXd6Pk44/kZ9/+YXTz7+U77/4KwMa+mBZJr/OnsuxxxzNFhO25IxLr6MkJBPXZMJWGixZ6J56nyXYcoXqvm++VXheClV9Vymsfbakis2tKd4rS54LNhq6IgXJAz8I6tE65B/fdQgpKtURFcNxCSsSpgMJTUKJaxy7eQMlIZmUIdor//zoY/z07ZekU0ne/MttnHrtXfy2YCGPPvkMe+88kQMn78S19z7CRef8CcuBC2+8k3Wzv0YbNJ54voNsuIJf1ncHG37fpCekSgwO5yEr1rCY6jK4Xx/6VCTo7uxgu/5l3NaZo2ned6Q7xpBNGSxan2Teui5O3WYAneEolYqOEy0nrsgoOOiKjCyJNrWyPmPRmhYEmzA7VolkGazMaaiGRV1cZUVnnkFlYeK6wkpJIqTKpAwhu9CeM9m8PkE/PS/mqesETFknl/bWBVOseX678T8zejGvF/N6Ma8X8/jvYd5+BxxEn7qaXszrxbz/PMzzxsZJ42IGdND27+OSP3cUFVcNkVMivDB3DR/9sIZsKk+0JITruBimwJjOjHjGyzzcq4zplIZUhlREKQ8ryEamwBj2GNs9CrrFBVvHxvEKY6ga6KECG7j4bzb+Pn5y3rZ7sLKFZADcd+MVPPvGW9ziSS2Ry4i12DMdLjY/dooZxxCwrV2vo0kKxyDkSYs4No6fCPXxWtWQVB05EsPRIkLfOZTA1aPI2S7BQu5uxUp3E3g1FJsw5zJBAdXVIkEhSnSdFSXwHK9oIOusb+tkzKCG4PJsOm48xx5xKGlTJLNtV7CXJcDVwqQMB8cVRRnf7NWf08Hx/a43TxZNss3AMJJgXSsYa0slldjxaiTHQk634WZTEBZJeizRAYbfBQSFjqdQBDmSwAknSMf7sLQjz08rW1janCKiqyTnfMZm+02lorwcVZEoV2zWrlxG38HDSBMim3eIqC5hMx3IvLj+Nff8UDQjI4gFRiYorPgG0UJjOMd1t9xL07pGVvz8HQuXr6TMzuCkMkHRRS6pQO4zlGzVUO4482ruuOUm3nryL5RLBm9/9SMvzriccUMH4OZzSCHxPWMhlSdfe4/66grkXIpiibKxQwfy0FXn8P43P3HuLfdTloiz6O3HqS2N895Xs1iyai0rVyxnm3GjAIF5kydOKGDeCUcw48oLcbQIbrgEBwTLHlH0iaiiAKJIkLXcgPUf02QGlYdQJEhIBnKqg7qyBBMnbM67775LIh5n7wMPo2+fOhYvWy4w7/mH2XHToTi5NHtvI/ST5/72G0fus2vgLeAWP4veOhKLhHn67hu55Ka7GDl0IBecdjzhRDmuHuOUU8/ix59/5pyLL+erTz8OMG/OnLkcc8zRjN18AhdedQOKDCW6EuiPS1q4gF+uA5ZUWMc26koq1uIOCrreuZoOGLaD5YAkgebJKamyKjTPHQsk8T2CLgZ8komDKsmUhNSgk8LvDAJI6DLVMY287YhinyZx/71389N3X5NOdvPmAzdz2jW389vvi3j0qZk9Me/887GMPBdddwtrF86lsqE/KDJtOZs1XXlShk1NTKc0rCJLEhURjb5hC7lZkDoi0SiD+zXQp6KE7o5WhlXGMPMWq759m45Vw0l1bk51bZwBVVH2GFXLTgNLSSg6rhbBdIRhpq5BSpLIWg5aRX/kUAzXtrx1KYxdUkdOiZA1xZxK6Ao1XudO30SY9pyJ5sUJVVGNqqhORURBkyUisotkCPk7t1gO7P8HQ5KVnsW8f8Hn/TuP119//Q+vOY7D6aefzpAhQ/7h4/79UVFRu5akFRZHF8H2kDMdhfZoSQ50tux4NU1KFWu68tCV4fP5G7BMGyNrASZaSMUybWZnTVRNIRrR6Kow2XJQBXsMr2arhjh6agNSskhPyTJQsp1FQbhoq5OsHK4XDBQPyQ8CjTxupsgkxnuwgnYkr81Q/E60YvsWtz5bYdzIoZz320IG7HMS+0/agjvOPh61owWpYYioGubSXrXdCTbuoj0bXMOr8js2UjiG02eUp1+W9l53cPKF85M8HTY32QF0oOhhlLbVQVLA2fh7FhuUeBvG4F75uqKSMAbxmYeuGvI0RYV7bvGGH+Ctj7/AsF0SIYWWtInjQkVEpTQqtCNDICr8mQ6SoUraQn0g1IeoJlGa3SCYGJKMq6q4XS1IpdXinDwzG58VQC4duIs70XKcSHmQ4AFwK/thldSjr/tNJE5kBbyWdylWgl3WD0ePkEXDsF3Wtht0ZE1q4yHWdefYrF8ZAzc9iGE1GpJlcOqpp/LMy29w3GH788B1F9OmlFFS049Irl0YjDkWdkk9Vrwaw3apzbUjdeWQ8umgNT0ASu9fyWuNWrN2PQtXrKHCTjOoplywEzx2h5wowx01CcfM0pnOsXTxQpb+8CltK5ew7967YPz0Ts/nzmtV3GPr8dx89nEcvMcO3usOG9o6eO6DLxg+sB9tnd3c/cyrLFvTyMu3XsKg+moAvnnqTqLb7MfUk84PDvnNr/MoiUXYfnORBLjrseeYcdk5wkjNf5MWRnJswopGSAsj2XkwLNxQnGTeZlCpjtq9HimdJqJHUFKtIokTSjB0QF8mTD+J+QsWYKFw3vRTOOvSa/ll9lx2mLgNpEXVTpNcHrz0dD6Y9Ruukef3ZStZuraJsopKtpswPjDd8J/7fg19mHn3Dbz6/ifsf9I5jNtkBEccfhi3XnsFx5xyOtMPncIhO27BCWeew9HTjuCgI46iubWNqZN3x1U0rr7mGhwgr8UFa8+mEKiESyCfAlcSm3/A1SPBRt03Yyre2Emug+IYKIqOIovkiOuC5K0VtguoYRyvgi9buaDiH8gy+PPHsZBllQgm2KB7chlRCeJyGtdWKFHFe197/TV22WYLTjh4KpGSUnbdbivGjhjCeaedgGJmmLV0PZff/gC/zV9AW3sHU3fflT2POYMfXnscp3Yoq7pNErpKaVgjY9jEQzLlYYUqOY/atpJkexuX3vEg61o7mHH5eZxw/lUce/A+9C/RaRhSQeeaamRVJ5cxCMc0fl3RwTd9Spk2Rsy5NVmFuO5QYbSgdjehhUuxS+uw3DCN8eHInpEJiHY9NWdTEpJZ3JYjEVKpi2tE182Bqk1pzVr0SYSIaTLjaiMoyQ0onR24koxdUofatU7oPOthsa54G1exISnSdf5HRi/mAb2Y14t5vZj338G8jOlwyTnTezGvF/P+8zBPknEpMmoret4l1/GSOUVyIIArq9iJGprdGKs786xPdfLhT2vpasugKBJm3sYyHZSshW05qLpCWFOCJPemNQmGVIQoNTqQOzoKps+2FUhfbDxcu8CY7qFz6rH9eiSfZaWn9IAvFYWf0C9KdnlJ2nGbjOT8a2+l/57HcMDO23D7mcegqxpSKIKkKCJZXSSzJckKruwERV43n8W1TKRIDCIJYc5pZoPirjgPJ5CuQJZFVxIInWsji2yksTuacXzZLk9SLPgbv9iLSNAJVrUZMMwdPSKk0/yEm29uG9J7JLkB/vrFV6RNl4xhkzFFl0V1NEpJVFybUgXkfArX0emyVTJSOZGSSspCotAfyHX57FZfYxuPOW9mxHscS3QwyTJOKCEMKi1DSH6Fo6LTKVKGrOjI6fYCK14P40ZKsBI1WKESDMdlfcpk0couGrtztKUN4mGNPqVhNj/icIZVhAlZGU45/XSefeEVjj9sf+67/RaylkZFVTVKvhs51SJk3cIl2NFycrZLBFC61yOnWsRcKmKMSsXzRFZZs66JhSvWUBVRGVRTiZNNBzIzclk1cp+hWNVDae7KsGTxQpbM+oK2xpXst/cuGL+8J+5bvtDFhm0LzPvT8Ry8i9D/RtVo7uhm5jt/ZVj/Btq7Ux7mreOVO69kUEMdWCZfP3YrsUmH/BHz4lEmbTEWgLsef54ZV5wv5keuGyXXLRLekVIUSUaRQXFEIlGVdRRJIhqSiRndSLluMbe8++yEHYYM7MuEM45n/u+LsFyJc88+k7MvuZqfZ89mx602g3SXl+CFBy8/iw++/xXXsfl9+UqWrlxNWVk52221BbIse+sOAeY9e98tvPbuhxxw4tmMGz2SIw4+KMC8Uw7Zm/0mbcGJZ5zN0Ucfw8GHHc6GljamTN4dWdG49KprUCSEyabjelgk/G10BSTHW8fkjTqU/qvhCh16TZZxXQk8eoffwFJgeYuEt49txeuWqwqpQxVQfSdv1wmOFZctEnpPY9zXXn+dnSduxYkHTSWSSLDLpImMGT2a8085Fsm1+XneIi6//QHmLvid9pYWpu6+C3sdMo2vv/wcSRbmlabtElJF50zecuhfGqJGM1HaV5Ps6ODSux5hXUs7My45k+Mvvp5jD9qbPgmduoHlbOg3Cj1Rjpm3aG1JkcmajO9fhowgVqSVGIbpEHJTqJ1rKVPDWOV96XZ0nEg9WctBiUgIWHJxDBvDdknlbUJeUaXE6GBUVQUbMiqGLQre1VGVaPdalJZ2UfiLlCLn0z2K8IHM0cbF+f+0IRVh17/q8/7DhizLnHfeeey0005cdNFF/9Ax/nsa3V4VWw5FgKLWjj+wQESl14mUslyq4ad13Tiuy5KWNJ//1kRnS5pkewbXsSmvKyUUUdFCKpIkMXpAGfuPrWenPkLvUs6nxWepGpJlIuW7RXt2KIKrxwvtUX4bWHAeyh82/z1+Bz20zwJGHIWqRxBYeMOv9q1pXMeg7fcGoPPDxwirKlI0gVLdR7Tr+u3U/sbbq9i7ltHzNT2MnChHjsSwO5p7GIz0OM/ic/BbvW07YMmBYLT5WqduNi02F5EYeO1Tkib09ZxwQmiH+kGd1xbo6lHccII7/vIol18sJtM1V1/NxRddSN6R+HVDhm9WtjOkMkZ5RGVUVYyaqILSvV7Mj1gllqzTkbPJmI7Q29OyBaOuVAty8zJxXjFPGyhgUdiFSl0ogl1Sj2TlkLNduJlu5FAEO1EjjtO9XgRKfiAbL8WqHEyrGyFnuXTnbRa1plnZkSGZs6gvDRPVFEZUCXOS0XpnYObjqmFIttLU1snQ3Q/n1/dfZPjgAUHF2U7UimRJPi1MgYraEiVZEUwFL7CWNL3QumnkGbXnNG47+UBueOZt3rrkGEo0mfBmO2D32QTJyjN/8XKmnXgax+27K+ceuJtIMMkKPbRpgc6ODizTpDQSZl1bBw+99j7H7rcnA/rU8voXP3DsFbczZfstqass55ffl3DdaUey59ZFDsSywo4nX8KsBUu58bxTufT2vwBQprsvWQABAABJREFUlogxfuQQVq9vZvnaJtZ88Rr1DX1wwqXCkVzRsePVwbPta946oZi4ful2cXxVpXHNWlo2bGDB8tU0tnfTmcpgOBIzLjkTVw3jaiFsx+XQE8+gtqKM8044jGFeMiSbSrHPBTdTV1tNRVkZo4YNpr0ryVc//sqooYM598RpDGyoK3oAxMb7x7kLmH7Vbbiuy9GHHshz73zCDhO35vNPP+H8s6dz3R338frb7zDj9juQJYk5P//IJx9/DKqOv+TZrghW/KVfV7w2N0+DzZbF8x+YYfmbPb9V02+r9e9XkXabz5SQ8ylcPVoAGN+Mzm+bK9Z781qCA1aW/2wbWbH580bL+kYefOwJFi1ezLyFS6mqLKeurp5BQ4Zy3bXXMGfuPLaZOJGykhISiThffvAm3/y6gL9+M4szL7maxXN/ZvSEbZElMG2X8ohC38xq3I71HHvpzeyw5XiuuOcx7rzmEqbtvQv7nHgO7zxxH06klNVKLfd+vYJflrZhGTZaSJzrpNG1XL1lAjndRqZqOJoMSqoFJdUitKo9dm67qbCwNUs8JCr6JbpCynBImw7vLGrmgFE19Jc6C+tKaYOQuTBSglHbJVgIUml1wGQuZiwHxoZGHknT/3mN7l7M68W8Xszrxby/A/McSeXmC0//l2DeiUcexlNvfMikbbfqxbxezAuewX8a85b9EBR4fc8F8UOBfRmY5HqyFFbVUBYkFeY1J2lK5lnenGL+qk7Wr+gg2dxIKF5OZd9KYiUhcmkTLaSww9h69hxZw6iqCOVmR6H456+PnlmhKBiGxWu+5MVGck1AwaDYKcZlOUh0+54OAcvYG8EathGzE0lmzbomBm03FYDOj54krCko5TWiYJvqFPhVlGyWVE2wt32tbT8JHo4hx0pEsraYzW39jaLERhgeGDJ6zG85EiswuY2cYA+HwoH/gaTquOG4YHYXaU/7zGQnFMOJV3PnfX8OMO+qq67m/IsuJmXYrE0azNuQImlYDCyLMLo6RkNCQ/XnZryaLksOZC50WSKiyUFHRjj7/7H31uFxXFm3969OVVdzt8Aiy5aZ2QFDmBOHOZmAQxNmZuZMOA5zJhNOHGbOhMnhmNmSxWquLvj+OFXVLSfvvZl57537zjeq58kTSW4oOOesffZee6121PZlslMrEitJuXmSOO69dyJJ7LCUP/PNVcsNmC2py27nMhJrkjUU60azMlXEsB168hbzWnpY3pFDFQoRXaU6qjOiOsqIqhD9tCJqd7OfXFdsi9Wt7QzffA++fv1pRg5pkqxbLVTS6y7mEUZGStV5Y8STR1BESf7GMyEv5hi91V5cd8x+XPbgs7xw1kHEBegDh6GOWA8rXsuPv87ngIMP5ZA9d+K0fbfHam9GCUVQ6wbK+9m6GsfIk7LANC0SAZVVLS3c/eK7zN5lWwYN6M9z735cwrx+lXz90wIuO/Ygtps2uTRubIvNjr6Az3+czxUnH8E5N9wNQEUixuRRw1m+poXFK9dIzBvYhKOF5D1XhL9W+QQERZGeELYpMRDANlm5bBntnd38tHg5K9s66erJYANXnXm8X7SwbIf9DjyYusoEpx68JyMGykR8vmix03HnUVdTTVVFBaNHDqezu4cPP/uaMSOGcvIRBzGksa7UCeRh3rwfOO7CawGFQw45hEefepaNNpjKex98yGknHMtlf7mZZ158mRuuvwFFgW++/II33nwTLaBjOQ6Wx55WFJ+FrbmGtuVyJA6yAKxAKbZ3k++eaXu5dru/VrjvV91yuYV8j4YtMc9dh5wyGTCP4e3joEuQWRdTW9es4q677nYxbz7V/Wqob2hg6JAhXHrxhXz3zTdM23QLKpIJ4rEYH7w6lw/m/crbH37CMWdeyILvv2byhjPpctnSA5M6tdmVKKk2Dj79YjbdYArn33QPN15wCvvvugM7H34KL91/E2b1UBbnAjz01Ure/3Y1ubSB0ASxZIiDNh3C/uNrCXatwKpoxFGEjHNdbyI7GCUfqWFlymBpV56iZTO+Nkb/mEZL1uTL1SlURWFyfYz+QROtdaF8X6QSO5REKeakqXu2pxRvCtcjwN2vKJreuwvnv+lL8f/i8DS62997kkTsX6jRnc5Svfk+/2M1uv+r45VXXmH27Nm0trb+U+//x/rcPIB1AwPZ0mOV2GHuAqGYeRwtRCran1+Wd9MQC9IQDxLTNX5Z3YNZtBBCobIuxiZjagH4ZXUPW42tZfaEGlkhzmZkm3a6vRSMuIGcE46XgnAzL1uePN0y3E17eSC07ubZDaZ7bfjLEgZ+5t9aJ5iyLAjAwIZa3nnsTk65+C8EhSKDlnwGu0dWoK3OVhl4uAGPiFfI93ttvB7LwCxipzr9YMlnBXjnuE5A53jnqemlliZvA2pb2DkDx21n9P7msT5sPSoDC8f222UVMy8XKNx2u2KezScM97/vrDNOQ+RThC2DmRUa06dLo6q0IdvbytlpTiCCFtKoDRSxwyGWdBv8kNaJ6Q6qUmRgICgDOy3gsx8BlHwaO59BuIkAKyKdk9VuuSlXQtKpW02tLd2DMtOuYv1YlqQcPlreLluJhMJ3q+Q5qUIQUBVGVEfoH9fpHzRRUm4AU5R6cg7Qv38DXz53P11r18CgBvdm25I5427kHUXp1a7o2BZKmZkKZayafCHPlDHDee6zn5g8bjSNh13Kp3dczANX3c6KNS0ULZvuri7emnMRNdEQi5at4Pl3P2Ztzubn1gyWWURJtWGZJgjB8jUtRPQA/asrGN7Unwn7Hs8xe89ihxlTWfHy/dz/4ttc/cBTnLD/Luyw8Qa9gzTgg4dv4q4nX+KEq28H4J27ruTMm+9n/pKVTB07nMUrmxm42R5sO20yN19wCidcdiMBoTB7jx3ZY4eteOXt97nlkWdY1tzKpPFjWdW8lkQ0jCYERdNkQE0Vq1vbcGyHsw7bly9/XcKLH3zOklUtDB7YiCOiqJg8+eA9LPz5R868+ibCoRCa4rBo6TI+//YHfnh7LqOHDJBBhKpTtOHnRUs5+tzL2WT9SRyy18401su1YvmaNs657nZuueYynEQ9P/78C+MntTNpSH8ufe0FRLyaG+9+kB223or+DfUIYPCA/hxz7LHMmTOHgNt+qiKr/t5hO2AFItgOBIRMBsj6vZBsDsfGVoRkClgOuipAC7ktm+7cdvVJHVV3mQTCD2I82QNpgFT6XsUyeyd7/LVUK61z3jhzbGpr+nHq8cdy5/0PcfBBB3Hjnffz0WdfkC9apC3BlJGDMJoXse8Rx/PcS6/SMGgYE6tGcM3t91FEZczEqSiAYdoY3e0McArQsYrvFy3nsVfeobkrzZN3/IXNNpzEZ7+uZOCQEb4+cmPY5rRNh/J6Y5KX560hXTCpiuqMqInKTUW6A73GJrDmJ3m6Wgg7KPV8hW2SiFTTlAxSFdaIUkApZmg1I3y2souZTZUM1AtYwRoUy0BNt8nNvzueFVXFti1Esp/83bawjTyY7mZOcx26cyk5L9ddv/+Zow/z+jCvD/P8+9WHef815r3x6Tf/Msz7+dcFjBg95n+JecMHD+K444/n1ltv7cO8Psz7Y0d5EaucMefYfsJBMfM4gYh8plqQHiXE0q4eaqM6k+sTfJcI8cuqHipqolQ3jKKqOsqI+hi6JrBshzENCbYYXMnAsIXavVwWcj32bLlECvSSK8Io+F1AQCmRrQVKsiH+wPZe45oklxUXe7Gwi4Ys7jrefSthvYd5J198HeFwGDufwWpvRsQrsDM9sssG8PTXFU2XBSRXn9lvEzeLEqPK1lHK5Kg8s+VeDG2vC0rTS+8xLOyyQq/3Pd7nikgCJ1Ihu5e8wysaeMlmI4tItfTCvPOOPwzynQSFSnVCMKGmhnTRJm86BIRS6oxCFv8q9CjJcIjuIqQKFt0Fm5AmjfR8WRI9JAsUbtcZpoltGohgWOJytFpKo7iSBKVkuMtotQypRy4ESryKYt1ovmrO8tnKLnRVkAxpLFybpj1tENFV1ESIpmSIMf3CVNhpRDbTK8ntFA0aKuJ8+dz9pDpaUQbWyjFm5P2Er6f17pRhngI46TLigVBBkfckn+5h6tgRPPvxPCaPGUHjYZfy+aO3cO8DL7Gy+UGKhTzdPd28fefl9AsHWPDrfJ5/5yPWWgHmr+6gWMih5HowC3kcobKipY2IrtG/upLhA+sl5u25AztstB4rX3uI+55/i6vvf4IT9t+llOT24qiAzgcP/IW7nnmVE66aA8A791zDmTfey/wly5k6dgSLV66RmDdzPW4+90SJeapg9u6z2H27zXn1nQ+55a/PsqylnYnjx7K6eS2JsI6mqhLzaqtZvbYN27Y56+A9+XrJKl77dB5LmjsZ0iBk90Wkkif++hALfv2ZMy+9inAoiCZg0dIVfP7Nd/z41rOMGjLAL3oWj4OfFy3nmHMuZZMNJnHInjuVYV4r5/zlTm6+7mryaCxdvZYxY8cxcdxYLj33DESskpvufoBZ22xF//p6hAIjBg/gpOOP44ZbbkMN6AhFcc0fXea17WArAqGGUJB456qH+Nr5XnHIdKBoOwSEQFvXlNmbXu77FbeApbrFJcUsM760bIl3HnvX8zWwJFMcxfT9NxyhgmUjbIu66ipOPfEE7rzvAQ4+eDY33nEPf//kUzJ5gwIBJq4/jULrCvabfTjPvfIG/YaNZ1zFcK6dcw9rcjYjxk/BARJBDSfVTk1ERfSsZd5Pv/LYy2/T3JniybtuZNNpU/n8+18ZOGQ4ZtVgRK6TIbEadhtXj64JvlveRU+uSGVUpzaqEyhKnxGlkEYtpBDdLTjhhJS8c2xC2VaGJmuIBASOAw1hEJl2ClaCtWmDwZVhqkIqSq4TRwshjAxqai1qam0p3gcIeUWvor8+Kt56Dr7k4n/bl6Lv+B9znHrqqb1+dxyHNWvW8PLLLzN79ux/+nP/cKLbc4iWG2UZ8HitSU4gXNJQK6t2qQpMrItRH9VQUy2EG6p5yLSproow56D1iOuClGET1wXBDQb4LRWeaYUopCSwA4SioINjFGS7dKRCnoMWQtEMGfy5wY2nQ+of5QG11/aluSw8q+i+1/ptcsALtL3J5ZltKQqbbjiVSeNG8vQ3i9hryjAcs4jV3iyZZUKUMdwM2Z7kfb5ZxNFAIeBX4e1cRgaWZUGLv3FzgyL/37yfvcMsYtt5yGdKlxuSrXBoAZR4JVakUra5W4YMwsNJUpYgpzjkRS0JXaVSyYMQjB5cauNe9unrDBs5BicQRLQtRZhFYrVDiJnSAKaXO7WRRrjmbHklSDKoMiCuEzR65Ia9tc1lNMj2OSWfkUZV3q12W1TQQnKDUEiVxlLOHQflCRAjDwPH8vHqPMu7c8xb0YWuCXRNPsNYKEA6XyRjWAxMBulvd6K1rMaK9UNkU9iuW7h3/0c31sCg/pI56R0eu9ENwkuJltI489sP3ZZ/x7IIBkM8es258llEE+SMItOPuZi/v/I0G04YS2b5L0SCOm+//xH3zH0TK5zgyKOPoXHICNZoVWwxIIxY9Dn57i6CoSCOWeTGJ19lyqghbD5pND8sXsl+22zC9DFDAdhr82m0tHVw6ZH7yznpSRB4Y1eoHLXPTmw7YyoX3/UoX/yymJvPOYH2zi7GDW0iHgnz2Gvvc86he3PtnQ+x39YbsfdWM7nywae554nnScYiHL/X9jz54TesTed5/IG7aIoJaaaTz2IbBbY8+lzeuPVi9HCEzTeZyYZ7HsEhZ53GXddcxMhhcRQjh6oGGDViGM/dcxNdmTymolJdkZCt8cWsHMPu5iogNCaMGs5z983hjgcf5rYHH+Pc4w7j9CtvYeXadq6+7FLGzdicou0wfPwUDtl7V0S+G0cNsGb5ElTb5J25jzGgrh/CMrH1MA/87UnOPedsRo0dz177/YlIOISgxHIDGRAFhLfZd8e2Nx4UaVam2Qaq0HBQ/Eq/Uxb8eGPDAakNV85qKeZ7u0abrq6ho5Zt7ksJPU8X1w+aXIZdpK6JrfY+mOkTx3DdZRfy2lN/BTOP6FosGRpCY79dZzF/yXJ+TQnaMgZrli2mu6eHmoZqCpbNV59+wtJvP+HXGCxZvpKn3/iAVZ++TF1dAzg2C+Yv4NzLbmSzbbanGKvDsB1SBZtl3XlURWGLsbU0JcNEAoLNBsYg1UJhyHS0YlbKaQDmkJGsygkG2R0o+RShXDeDhIoVGwhFGyOU5PGvlvPejy0ctNFgJtfVoCKTBVasH0rLYvey3SBHl4Gkk+mS63y5XrGqglkobQb/m0YlfZjXh3l9mNeHef9TMe/gPXfqw7w+zPs/inne/PHuu5d0/g2zG7cQ5tioisLwqgiN8QBRCmSMCDXJEFOGVLLhoCpGVEf89vmQplAf1dA6V0BPGVPSLoBh9cKdcmM4TwbA6xLysa1cuqqcwV2WrJcyVpZMnELpdd51eMlu9/DlyYBNp63P5HGjePKjb9h75kScfAaru93FN3dN9BjauGt0OaZ62GVZYOR9Wa9eXUq9MM7F/l5J8dK/+wlYTUcJRaTJb7Jaalu7nSTefbODUQoEyBRtcqbUWq4ICkS6ldGD+vunuOSn7xg6fLhM/BVSUDSo0kPYkUoZa2TTPuPaZ6kCARF2k84KsYCQBqLZkkG0Av41+rGBGpAkAbezShQyJW1vUxZ0hWvI7GRTiFgFxZrhfNuS4/X5razsyBLRVRfrTKpjOtWxII3JEKP6Rag02mUh2tUMxyiUNNyFYPSg/vLZGwU5pm2rZIjtGZq6594r5vAK6d57zSK6qvLoDZdI1qlRIG85bHjAifz9hceYNmEU6VWLCVkG7837kXtefIeiFuWoY48l3K+RyrpGhtcliayeR2b5IoK67Ey78fGXmDJqGFusN54flqxkv203YvrYESAEe20xnZa2di49cn8f6zz5Hc/I9Oh9d2G7jTfk4jkP8cVPC7j5nONp7+xi/MihxKNhHnv1Pc49ZC+uvfsR9ttqJntvOYMrH5nLPU++SDIakpj30bes7c7wxF03MyAR8GXIbKPAlseczxu3XCgxb6dd2WJfg0NOOYXb59zG6AE1KPkeArbF2AE1PHvfrXTlDGzTpCoRk4nwYrZ0v4WKpupMGD2c5+69hTseepTbHnqSc487lNOvvJmVLW1cdcXljN9wYzJFmwnAgfvuhdq1CsXMsnpJC8I2effph2lsqJProqpz/xPPctH55zJqzFj23O9PBINB1DL8UXAQ5Xj0e4dtogGqqsnCsJf8xpOfkkxvr2isqLqsIrtdg755rmdM7jg4ig2Ot55ZvfYNipmXsTkluTBHCxLpV88eB8xmzKiRXHfFpbz2+P3y33NtbtdDkf322IX5y1axtNvgp9Y0zcsWk8CgMl5JwXL4/otPWPjlh8xLqCxZtoKn3/yAVZ+9Sl2N7F5bsHAh5151A5tvtTVOMIalR+guguWYDK6KUBsPkgxpDEyGGdMvjMitkYx/qygJOqqKmexPRoSJ2jm0zhZEtpPGUFx2aBQ02tQK3pzfyi9remhMBCWLXo9iuwUPJd2OnU31Kl76a7Fl/bbztNwUeJ0u1H+rQ4jf7sH+b3/f/+Djm2++6fW7EIKamhquv/56DjvssH/6c/9wVORtJuQvbgBUttj6QWmZI3vY6KF/IIDauhQcm5dWObR35Zg+uoagpqAoCkMrdEJmBrWnucQUKBZQLENWzLWA1LG0vbZlFRHWIdvVOxgo3ZlScChU7GwPiq2WNvsAeqjUau62Rq0bnPgtlB6zznGkrptdCsbuuPxcZh12EiMHHMKkhgrZVuex1LxNbdHARla4RTAs2wxtW16LHkIJhmTQBDJhUH7Ytg8yvt5ZIFBaRN12Nu/9iqrK4CcYQlQ34Ki6NORyq5BmspEl3QZmtw3YLOrIsqQzS0UowJSGBGNjRU467xIA+lVXkkmnJbswGMbqbpef37xQXmOsAlxTNEC2XKs67cmhOKZDdUiVbU+OLXXYtCBKIYWSz/gtlyIcLbEizDxqZ7pkwGXk/UDfZ0CAz35QAjortTpe/WUZlu3w06oeHNshFg4QC2lsMzbB2Joa6uMBasIa6po1OEJF5Lr977TNLL4BWigqn1eZiVo5W1JxXFOwdZ6Pn7gp5OTv8SqpM6fpiEIGu5Cio6ubJ684jUsvuxIrn2Xe4pX0i0fYfINJXH7/k8xdbvJuxuC0wUNYsVqyDe3h0wnlu3EWfAlmkaJhuI7RFgfusBkPv/QW00YdDsDwxjquPe7AEkvKY08WDcrZJkMHDeTS4w5hxI6lytiA2mrOPmxfHrr0VBzLYuaEkf6/LVjZwttffg/A0fvsxMihg9h5xHAGxlUc0/RZJyub1zJiYH8CQsj5BMyYNIVVsRHsc9sbrL/jPlywzQgGF1b4z7oy5IJ7Tpq7YJruvbfkJluTm9uwaoNl0ZPOccQ5V7Hf7ruwy647Y8XrpKaeYhHQNTBAMYuI7haKS+YTUx2alBRKTuOTHxZhB4Jsu9UWJONxbr7nAV564Xke+OtjxKMRaaDlLR9e+5p3lMlUKMU8mqr5WpWKu4ZIIya91MLmsp88Mz8HsBEyURAI+e3anslNqZ3N9JkO/t8sU+rIuS1udjCKKXRUYP9dtueb915h9IabQ6oFNdUimTIhmWS57a/PkDMdtlpvLGbRYOJ605gwsB+ZQpGH7r6Tn7/+nA2H1fPsh/NIJOJcetIRjN5qb247/yROuvJWulJpAB665lz0rhWoyQYimkEmIiv7VWGVouVQFbDQ2pdiR6sJrf0Fu7MZx7YRFZKF1hgV2EQR+ZTcSAGimMOK1bAqH2Dr4f3YbkQN64W7sBwTkW5HzbTLeeDrcMoNiBpMYKe7/MDHMytSQhFfQ7tcn/q/c/RhXh/m9WFeH+b1YV4f5v2nYJ6Pb56chyKkoW7ZmHBUXbJxLZlQjVJgRIWO1rYIK17Ll6uzNLdnGVobY0R1hKakTkQTaFYeJdeNWNvtd0Aptil1q/NZ6begBXC8/I+my/WXQomJ3WuYqr6kmLcWlaQmdGmYqEhjV6eYK62nrFMYFmVGlR6b3DTdZBzcccX5zDrkBEYNHcSk/lXSBNS2eiXcnaL82QZ5Tl6iRg+hhKN+ccIp86PwE8DliW5Xr1kmL2Wxupzt7SfHhYoI6IhYBXaiVs4x08BRdfLhalqzJj1dFmszKVb1FOjKFxmQCDGpPs6gWCUnXnQS4GJeqgtRzKGYBazu9hI+uEU4x8jjCIESjsmiN+DoEYqGTUwXxIwuRFe7m8TWUeJVpfXNO3dkgUaxiqi5Lsh1+Qksx5ZdRt69cLzieNHAiSRZnnF4fUErC5pT5AyLDsAwM9QkgkwbUsWI6ih10QD1QRu1rb0keZPPlrrevOJsWQeZUpb98CVVAF96xh0bSigqz9NL4IeiKPG4LKArCqKYw3HytLW28dg5R3DJJZdh2w7zFi+nXyLGFjPW56arLqen/2QWdGQpONAvGKZoO5hVgwkH49CzFqt1FcWi9OhwTIMDt57JQy+8xbTRQ8G2GFZXybXHHuB30vzuIVSGDBrIJSccxogdDvT/7GPeZaeDbTFz8lj/mhcsX8XbX8wDJOaNGDqYncaOpbGuGsXIyGdTyLFyzVpGDKiXWtWFPCLXw/DRU0jVjmXPSx9it9mHcsSGTQwJZVG7V6FYBpXBEOgKFHMl6SEAW3UTlAbYJuGAjDd7MhmOOPsK9tt9F3beax+KwQSGJTsLNKGgFF2j2lwPxVWLiakOA9QMIt/FJz8tw1Y1tttsIyoiYW6+615efOF57n/kb8SiUTSXui2UkkyJA36y2vfHWAePVJDjR9WxbAdVKH7ngSf860mW2DYEhAaedJdjoxRtcKxSQbe8iOutsW4iXRrMB31NbyyDWdtvx+dffMmE0cNRMu0ohYxfyDKqGrn1kacpWLDF1LEUiwZT1p/G0PoqckaRv95zFz99/RnTRw/m2Vc/Ix6PcekJhzN6yz259exjOPnaO+lKyfjlgb9cgtqxDKtyIKoCjQmdqnCAgIBkSCUuTNT0Gtl1WiwgCimszrWIfo04egTNRpI5PImzTDt2pBIj0Ug6XWT9/km2GlpN/5hGqJhCKeYQhhwXdjaFnemRGBCOothl646ml2Jib60Gt5uwTF7r3/Eo95f4V33f/+Dj3Xff/b/yuf9Y+d+2cLyWJNuUgFLIue08ZRpnLttJ9cwnUp0URmzCQw98iCJA11Q0odAYFVKTp3s1Sj7tvl2TbXierqeQmy3HyPubQau8ou+Csl/pBxnshOOyUizKNvzlVXvblGyasnZo//Cqt3qwtABRFiSZBqATDOqcd8KR7HHqBdRUVXLQbttx1HYzEKlOd1IKRDSB1dOFk04jAhpqshqt/xC5IQuGEMl+WM3LcHIZ+bnl7Wyuzpsf5NkWdtbyr1XRJTunV1CUz+KEonLxDMXl5qOQBlW2yQRVQVVI8PaSTlZ05aiMBAhqghUtrZx24OG8+/6HbL3lFjw95yoiiUosTUe0LumtMQc4Wblx9jbCSiSGks9QY3yPWdmE0pP3jcAcRcEJxnCsIkrYXfyNvDQF8zTtXFdzEQxLV+jK/rDqV/n5rlGL/+yCIezh03n/107W9hRQhcLgmig5w8IwbYbWxthyaCX9wnLjFci2lwLXYs59hmVBs237pmDlY12OBSGZFL1M28p0bh3bdz03K5twtCBqai2ifTlmTwdzHnmKDRor2HnDcey2w1ao8UqMpT9z6f1Ps/12WzMikOK0idV81lPF92szFG0Hke1E5LuxEg3Y6S6+W7yCj7/9kRNnbcSnPy7gjmde54R9d+qV9NK8zf3vzFnPsA2hMnjQQIrfvk5nexvCgWg4iBoI+IG6vxAKlVP33ZEpwwex77abMHhwEzc+/Sa3Pfw0e2+9ketOr/Dyh58z5/EXOeeQPf1A3epux1o9H2vNT+x6/L1sMKiKoo10LTcLJb0yDQn4pumzORQ91NsgyLF56++f8/xdfyEQicmEirvZjliSZakFpdEPwBOvvcdTL7zCyXtug9XdzuIlyzj/qjvYZfuteWjxChoaB/DEQ/fz1bwfuPX6a7jkwvNlld0LbkCua6reSysOwNZli3fadaYPoqC678uaDrqq+Ak2uWbg/2w5YDoKNgFypk00ECTkyE2pUiyUmE3ee7VgqWJtS6mMrBrGtB3iwkakWjnrzLM46uzLeObZqdQGNHddtimIEB+1GrRliyz+9SfGTJzCAYcfxZ1/uZLvf/qZ7TfeEICqZJIP3jHJ5QoYpsmwAQ2kMlm++PZ7zj98H+pra9hp602JRsKIbIev8TgkJs1BbC2JMNOoa1dIZle6vbROI7VGV2YVBupZrFiNvKZAEKWQwY7X8UV3gPVqHIYV16IUerDNJCLTjmhZVGpZ1gIuY1hFCYZk8lErMYO9+YhRcNfr/w1T4x89+jBP/tyHeX2Y14d5fZjXh3n/v8c8L+nrqJ6utVFiH4KUtCkzzRU9Lai5LqzudlqiQ3jwtW+xLBtdE1SFNeLCRGRTiHxKypR4n5Pt9uePbyps5Eva1XpIJtnWSVR61y/lMYJybRWGTF4H9JJBtFc4cXpLfJQX9Xy91zLjVa/A6n1GMBTivBOPZPcTL6CmuoIDd96Go7adJln4XnEyIM0hHSOPbRblswpHEcEQIhKX62mmx2d8Ky6zu5SItXAKJQNfn23snq+iBUodK97ftYD8Xi+x5LJEAwKiAeGb0QVUhYpQgLRhsXj1Wo469Ujef+9dttl8E566/RpiooTzSiiCEo5Jdrh3D70CSiEHuTRaMIxdyFARrUKxHMmgdmxZnFJdPevyAWW6SWXdnd8uLsnr0eU9NA3/uux8RiZS45UUa0fy8Y+tfLm4A1UoJCMBcoaFrglG1seZ2j/JwESAsHCkgaRt+oxrXw/dkwFyTao9CQTHdMeRNxbce+4Xe/WQHz/J10ivDTtaLT0/zDxqph2zbTW3P/wk04b1Z5cNxrL79lv6Be1LH3iGHXbcgdrBI1jWY7I2YzCiOsqARIBooQvFzEuCQzbFvJ8X8PF3v3DyXtvz6U+LuOOFdzhhn538QghCoAm1dE3rJPj8DhBNZ/DA/hLzOjtRcYiGQwghSpjusdOBU/fbmSkjh7LfrC0YPKiJG598lTkPPck+m66HbZvYZpGXP/1WYt6he8uxnE1hp7vQtRis/pmDL72LETVxKV9jmvL+eGPK09svM3cHl0hSpo//1kefMfeu69DDUexQHFsRaI5JwJaY53UXogieeOkNnnrpDU7ecxvs7nYWLlnO+dfcyS7bbcNDjz1F//6NPPHow3z5zTzm3HAtl1x0YQmnvAqHW7j1C72uBBdCI2s6FG1BQCjoqoImXKNlpPyJpgqf+S9c40sFmUAv2lCwpNxXLKChqZpbLFRlEc1Lkrumu44iJE/cLRYXAxEyRRtddYhYJmeffDwnnXQiTz79LLV6CKWQwdHDdIdq+HZlivZskYW//MjICZM54NCjuPvGq1gw/xe228jFvIokH71jkstLzBs+sJFUJsuXP87ngiP/RF2/KnbafCbRuI6S74YuQVLVSKg6TjQqz6/QLXHKkElxu5DD7G5HJKsp1gynI2/JZ68I7Jj0klAsAzPZSK5oMzAeYIjThsi0Q05ii1LM4mTTEve8+Qf48oUee1tVS/Oz7PDY3v+Hd3z/0qO0Dv7rvu8/8fjjiW5Nw8kbKEYexcj3bpEr5PAMPxxVl6CQ6SoFLUJlUZdBqiPHuEn1HDClP0MLK6AAipGWRktaQFZrulv9NjQRScgg0jOc8lp5yzfoXsBQzkSKxPyqrg8GQXfhdTf7/mf9zqFoAdkKJrSSEYEi/AoSiiKNdxTBZjOnccYxh1GRjHPVzXfz6vuf8vBpB5EU0rjESnWiqAKtX7100M6mMFcvkRX5eAV2qsvVb7Ml86088CnKgK9XkOYmQHq1pmuB0sbOthHhqGyzdk1+hJHBDoQJLP+awbaFE0lSGa5nYVuGSEDl5yUruWLPGVxw+VU8dtu1VFVWyAUt340TiNA1cEPWpE1M2yGkSbf6uC4IpFsQrUuwU12Q6fFbSFRF8c2/nEAQLBM13Voy+RCqa8ZSxr4Kx+Tp59KQy6CGc1i2q7eqh7A9pkNAxxi1OU/82MpHC9tY3pphypBKRtXHiekayaBGQFVI6PK5B7LtMumhhyQrxmMNWL2TPUpAB70skBSlREppIyzI5vJEKiqxXekCzyTMjlVJzaquFdg9HTzy9As8/PwbbDdtMudeeTXNFSMJCIWi7VBZP5GNuyN8/PmHbLTJxrzcrDK4QmOm8yvF1d9TXJTx5w1Aa1sHA2sq0VTB6PETKJgmf75iDk39zmFQbTVVFXFCul6WMLL9pJLHFOks2HSlMwzpl0QBKior5Ws9x+/ywx1XG4wdzgbjR4JQ+fS7n1m8eAm3XXwaWEXmvvUhdz3xAlusN57HrjzDv9+OG7ArC75gfCjHnu2vMbH/WER3Qv57pELeb8vADkkjJ1+PUA+hCFXKGrhBpiV03vjoczI9XVQoDqpZwHJsAm5Q6wQiEjwVQS7Tw033PsJBW01jqymjwbYYFA+imAZHz9qIYE1/mUgJxZmoaLz5zru+vqi/uV9nw66YpWSTJXRShi0ZBrpSMipxbDQhXaNVTa4ZhiWTAKYjgx9dQMGCVEHe26CqoOQK8ju9Vm1NbmSdgJRC0FUFNdftrmUKYc0ihWBVxqYxWs2he+9MMhzg4Ttv5fTjj8SqGECbHeTzJV20ZQ023mJr2pYv4sW33scpZHlwzg1sv/GGTJ4ylW+/+ZqO7m5CQR3LsTl+7x1YuaYZY96bUJTztK0nTXdXN9GITHApRhrh2JK1qggcPYzaugg72yPZoS4TFy0AtUNYbidQFAWta6Vsy47XygRbvA7FSLNhRECPKY3gwGf7mKlOfyyLcLRXxV5U1ZcMpUJRnHSXNPorGmVsEVuyHb018Z89+jCvD/P6MO8/GvMW9WFeH+b9B2FeeQLNYy0qXkeJKynieMlvL4GY7cHsbEUdMILPV/eQTRfYcqNB7DOxgUa6UdtaZcLUKspx7uKenc+4iWZXzqO8mLtuV0uZLIlfyA3oMoHkdR8ItcQqtgzwEqjQ2/jRK8i6hbteCX2lJP/iuCbTCI3NNt6IM447gopEjKtuuYvXPviUR846nKQeKslcuJhcXlC0bMvv+PAZwRoyyWqXDCVxP8Pv1PKwWgvIzzflei27tkTJ7NktPGIaku1a6EHvWkONbVFdNYDuSAWrUnnypk3zmhYO33smV1x1DY/ffTNVlZWSZVvIYCXqyQbidOQscqZNQlepjago+R45RlMtOD1y3TK721ECa9FiFb5kih2Ra6uaaYdC9jdFBU+eCfAl1HBk54pXtECXa7fVvgatroncoA14ZX4Hc79ZTSpjMGlIJWP7JyhaDjFdpSkZpjaiEXEKUDD8dckx8nKcrZvUcZ977+Ku3Vvj1x1f2VyesBaQxT9LSm2gh/wiisi04zQv4sG/PcVf3/qEbSeP5JxTTsAasznzuwyCmkJTTGWTVVn+/uV3zNhgPSbUjmVwRZB+ZBC/vuvHRNgWdj7D2lUrGVidRHVMRgxsoGAUfcxr6ldBdUWCUCTau7jrJaxdYkN7Jk93a5fEPKFSkUyU5pD3+vIxJoTEvLHDQQvwyTc/8M0Pv3DbhadAscBzb33A3U+/wpYbTuKxK88gGdJ9QoPEvM9oVNLM6niHif2GoKzQUWIVslDirSO4ScmyhL33PBRHFnhty+SNDz8ll+pBFwrCNcTu1UmiBaUfSKqDmx58nIO22YitpozGMYsMSoZRTJOjd9oEva4JO1qNHYozaaLDW2++WZLcUQRglMU5JZNJxbGlIbMDluP4bG13cqIqAkco2A4Y3rJUpv0NoCgKBcsmbdiSQR6QY8zzHCgl22W3ksd+dhQFhRK717IdCg6EgxFmz55NPJHg4Ttv4fTjj8KqHMAaQ+Ob5T20ZQ1mupg39433UYwcD99xI9tttCGTpkxl3jdf09HVTUgPSMzbZ0dWtrRifP82FAtgFlnb1kFXTw+xZIW8pryU/nG0EI4pJZqUYg562jBTnTJuFCoimsCuG0GroRIQiow7jSxOICRNTbUAopAmYVuI7nZEISXXOl3qcptrV8rPCbueAuUyHu7fHTUgO8ncOFsa0rueIN56af9nJm///3JMmTIF5Q8W67/++ut/6jv+uHSJ3wbkzvAyMxRFVWVlPZfC6W77TburUtPE09+vYbftRnDu5kOkHmCLUdLmEkJWYI1U7+/0AhW3Qq5EEiiqipXqLH22psvqaSEHhtw4OFnJlJMBowts3gbBLGCty2TyDqH6gOt4AbhrFORvBspbTpAL3bGz96e9o4Nf5y9kaGMd2595PY0VUb5auIItpozmxB03ZkpNf2nQ5bW6mQZ2d3upeqzpfjDm6Zv20nvz/2/5AAVy4gs9RGDAcLko9LTLBSgYRxRz0LNW3ttUJ6ZrEmY1TSHSXKAmFuTITcZQyGV58ZVX2X5sAyiCFcFGuhyLYZVBskWbRe150oZJZ85k55FVBNsW+ht6JRxDyaZwCnle/OAztp0xFd3dcCpV/eV9VAyc1qW+9qyXxJEMQve6CrkSm8G2sLpd5kOyRo6TUBSMPNboTXng22ae+XwF3e1ZEpVhhtbEGF8bpzKsURFUEYpCRCkiMt2IbKfUb2xbXnIpV3u3ACqq6icX1k0GlW8cHLPI+J1n88HTDzCwKu6zBhShQleL1I4Vglfe+4T3P/6CFy86Cn2LPzF3lcKnPywlGdE5cHJ/ij+9jzHvHR594kVO3HAws6rrcVZmyac6XaadvA9ClxuCzccO5stfl3DKnEe54fDd+PCak9ny7Jt44YMv6Eilae/JoCgKR+66NdtMm+ybwmHbrE3lOP6KW1CFoF9Fgm9+XsD1ZxzN6MEDScbCJbaI5jIryliEgD9er7j3cYqmyXNvvMfDz7zEjImjURTYZbNpJIKuMV8ZU1QEQ8w56UAuvPdJHKFy741XEYjGes0dL5CR40H4CSCP3aEAQrXYdP2JxHV3Y2TkUQo50CSDRtiWv1n5y823c9xuW7PPplNRFAXHyCNsmxN325J9T72E+y48ierhNtDIvXffxcuvvMqN113tnjP+xl+2rhqgar3+pqoGcV1Hs/Ky+m/hV+GDVhpd0wGdrCVZAIpl0FFQCQdk213asDEsG1UoGDboQclCVQppeU9UDSwTpVgg7ORKrujBOFktiq4oRDQIa8KjmDBzylhOPOMcNthqFoHGkaxNd5MtWkyqjzPlsEM46KDZBITCmWeeRsBt891hxx1pXrEUs1jk/buvoKEySTQe9xmsdlGaPkUVm0Ixi53qQnUlMBQ3wWvHqlGXfo2V6ZGGWdmUv06p8UpMPcKAiEKgdSFOdytKvBLl518gGEK4Y81rVbbyWZ+pa7tMNpAbO2+TiMv8ctJdEKvACSXluhCrkGt7KCZbSIuGZEmYRmnT+E8efZjXh3l9mPefjXnmOpgnhNKHeX2Y9/9fzCtLojl5w2eqowf95KRiGdjdbbLAGorKwqVpYFYPxu7OcsTu49h9TC2NSkrq6boa37bHaA5Fwc6XirXl5yyEZBULtcTKRbJrfWNGr8OmaPiGuL1luGTnVS+zZW/tLUsKOlpQJnM8HwqPCQ6lpKz7syIUjp29Px2tLfw6fwFDG2rY7vRrGFCV4Mv5S9li0mhO2m0LJg0d4CfiJYs7BblMKUmvllrwsV3dcLO8uFuSQrGLJmpE9XXZlUgcrXGo/PdsWl6HbcnElGnKomYug2NbqMlq7GCc7q4ilg1nbzuRQi7LMy++wk4bjkGxTbr1SlLYNCQ1ug2bha05VnTnCQcEUxtkQc6fp7aUWXIsi5f+/jXbTp+MbrUjjDxKohqI+6+zsz29Epq+Yaht9b5OrygghCwaueugWtNIz8D1edTFvHRXnmgiSG0ixNDKCMmQRk0kQFBVqAgKlHRKmmwaGZx0d2n+eI9PXTeeUEvJVtw4pGwdd2yLcTv8iQ+evIeBjQ04ubS8dtuCbI8sTGZ6ePntD/jo5yW8dv+NKIMn8W5HkCee/5lpQ6vYdVQN6QVfUlyzhL8++SwnTBtMqHE5YVWluHoJ2eZm7KJJIBpCEQKhB9h87GC+WryKk296iBuO2osP/3I6W555PS+89ykd6SztqSwKCkfutjXbzlgPX6rHtmhp7+SEa+9C1TRqKpN8/eOvXH/msYwe1EhFIlaaA0G3K8S7nvL5YcIVdz9KJBTkubc/5OG5rzBjwmhMy2TnjdYjEQyUilHClR4pGtx6+C5cfM/DOCjcc/UFBBJu4rgMp3DsXsxbX6bHyKOEoggh2HT9ScR1KYHne1d4saxrpqzYJtfNuY/j99yevTeahGJJeRyhqpy4x1bse/KF3HfJaVQPHwOOzb333sv8hYt8KSzFcuWyHK+TyUbxOyVNFEWgBgTxgKutbbuY5645AaHhaDqmoslONDdBZ9iOb+hsWI5r5Opg2AJVj+BYRqnALMqMyV09b8VxpESXIjvykkEXD8w8ODYzpm3ISWecw5QtZpEYNJpf2zqxHIdhlREmHnoIJxxxKNGA4IxTTyMYlgSXWTvuSMvKZZiGITGvupJYIoGIVUg8KxZwCnliqo1hm9jZFIrXXROSyWelKHXVnVSnXL/Noiy4uV44CI2aoIPWuQJnzQLQAqiRBI6731DdOQVge/PPlP5CEg91f10pX/uk3npAyjqa+ZJUpKL4hsn+2vLvfJR1VvzLvu9/2LHbbrv9X/8OxXF61a3+y8Na9Jnc/LouxnYuIzfVmlyAnEwXdqrLrfaUAE4JRelsmMwD36zhyPX6E861SQH7vGQFeQyF8qDEY7qUV8ZFJC6DQD2KMDKSNSZUGUza0izLSXfLtj93AimRmARf18lViSbK2sfWYS6Brylq61G/ytfrKNcWhN+Y8Si2iZnP0W/9bZn/8FUc/Zf7+G7pGkb070exaDJmQC1bztwQHQu7kOeteQtYtKYVy3E4btbGbDNphAwcsz2yjc1yWwP1gF/1N/PughuPoYQiiGBYLlxjNkbJ98Dq+ZINUcj5beDeoQ0aRWv1eOa1pBGKwkfPPcKlZ59OZsFX6LlOGfDHqvmqWEtLpsAG/eMIRcF2ZAXdsGwSRqes7qe7So/L3azOW7CUicMHyeqMW51Tk9UUm6YQaP4Jq3W1vE/BkL9A9QpGLVfD1NOQRQb+Pptk8GTuXWjz/couFrSkaV+TYsTwak7YbCh1MZ1+YY2IpqCYeURPiwx8etrlZ7vt8L/XuiH1Dd3NvRsAKJoOiX5+e6UXePvjxdM2XeewFcGs487nwUcfoztcR7Zo8dGyTtrTBpeOSnPLpRcz9/OfmDV5JJtPHMH4QfW932/IxJrizSFV4Fg2juNw/pNvMXXEIP720bccuM3G7LP1TP9cunN59rxoDjeeeDBDBzURj4bBtrnsgaeZMXkc28xcH4Cvf17Iix9+zjufz2NAbTXDBw1g8qihbLfxNCLCpqW9i83/fDYjBjUyrH8NVx21P7+uWMM2J15Kv8oKXr73eq6980FWrG6hMpkgGtJZsnINe20xnQO3mUlQU0vJlYDU0r3/9b8TqBvI4Qftj2IWsANhOb88XU8j3Ys1ZHWuldflJiSOueRGpo4dweG7beuPLW/T05k1eO2Tr3n+9XdYsnwFL193NhXC9Oe3Y1k8//G3PPXhV9xz1pHEKqtQ6wby0EvvYQUiHLz/Pn77mGJJlpnngI0jDUEU09W9LGO/lf/fb+cPSMZHd8GmEsm0ympRdKFQsBzShk1rtkhQE+iqgmE5hFSF6oiGqihkizZF2yGsSXMfxZB6dHY4iemAnm3HM/dCEViJOgYOHUFbaysAr/3STGMihFCkXIMqQKCQM23WpA1W/TKPA3famkgkwl9OOIgj9pyFEoljd7ai9h+CHUqiZjuwUp2ydboseauEo3KzGa/CTPaXG6zVP0tdZndtUpPVckOsx3zHeyfd7c89r8XbWxv8oNvd9HiSDvJWWyWm4zpzVq2s9du1nUAEJdcjk2+uhqSPTUJFHbrBb+boHz36MI8+zOvDvD7MK8O8ZCJGNBRk2armPszrw7z//2Her3+XzFpPCgPks/YSM2ZB3qtMCpGsRtFDmC3L0RqH8nNkNL+0Zdh8UJJkrgWRbpVJblUWhTwc8mVK3GQXWqBMkqTE5PMY4F7yG89I2ZVf8N8TDPuFGZ8JXzRkwqzs6GXu6T4zRw14/9jrtb7EwDpFXi/5VTQMaqZuxYK/Xc9RV9/Bd4tXMWJALabtMGZgA5tNGkUoKOfrG1/8wKLVa7Fsi+N23JRtpoyW48FN9DmW5eOeosrvsYsmtmUTiIblGAxF0eqboH4YqBqiu6WEmW4XmZ3pQQlHCQybSLpmDD+35UkZJh8/9wgXn306mYXfEDTkM3BqBrM80MDCjiwjqyNoQprS6qqCKhSiRg9q5zKcbFrGJUa+hHkLlzFxWJOUr3Gfl0hUY8drELlO7K42P0HpF1HLkpa/6z/hGSqGIphDp/PC/A7+9vkK2rtyOLZDNBZk1uQGthxSTTKoUhu0UIp5RLYTNdflz7Xywm65JJovF+OPsxJpQXatOSVmvdm78Cl9SErdZIoWwHJg1nHn8eCDD2FVDWRxR563F7axYVMFs/rlmHPBmTz7/hdsN24om4xqYvLIJhQhsIomdtGUkm4BzX/mXhHaNi2JecMH8rePvuPArWeyz5bT/PWjO5Nlzwtu4caTD2XowEbi8RjYFpfd96TEPFei6eufFvDiB5/xzmffMqCuhuFN/Zk8ehjbbbQB0XCI5tY2Nj/0dEY09WfYgHquPv5gflm8nG1OuNjHvOvuepjlq5upSsSIBHWWrG5h7y1mcMDW09FZh50tVO5/9UO0in4cdshsf57YehQ03U+Y+hJIRgE7m/KNYBU9xDGX3MDUUUM5fM8dEKrmfy5C0JUzePWjLyXmLVvOS9eeSVUkKIt7RTnGXvj8R558/wvuOetI4rX1iKo6HnrpfRwtyIEHHihldeC3HU3enLctH5+9Ncv/G7gyVNIY2HSQuOVKd+VsBdN2UBWFTNGmLWtStG3iuvxOXVWI6VIKxXJkUlxXFUKqgihIkoodjFG0QXcMKfNkGTiKwNGjDBg5rhfm9YvoRAIqIU1BUcCyoWDZmDa8//dPOHGfHYhEIlx/5tEcutkUuYYEw4iqeokfRhq7u83HPBn/SSkkJRSFRD/pM2MaEh+72/1n5UkxKaGI3B9ZBnZXm29K77OvPT8CLeB32pXLLgF+F99v4lShyhg/GCk9J48J75KFAB8T1WHT+Hc6enp6SCaTdHzxKolY9F/3vekMVRvsQHd3N4lE4g+/b86cOVx33XU0NzczadIkbr31VjbccMP/8vVdXV2cd955PPvss3R0dDBo0CBuuukmZs2a9X/iMv7h449LlxgFnFhM6hSWbc4U3W2/crU/fUdwQIkkKDZN5YUfWmlPG6hCQeteLYN6PYSIJmSwHYlJt+VIAjRNTpp1Kv2expliWTia5leJUQROMScnnpsoEMl+vwlcEG4LjN82s86//5HD2wj4mmgl11zHnYRaMMhTt1/N+Y++wnrDBrDZpDG89Nl3zN5mBhMaq/l44SpsoYGiMHb4IK46ZBfy2TTnPfgib302j6tm7ygZTm6gowY0PxBSVIFQhb8h9DY1dj6D1vwrmEXMdDc9eZN0OsV3C5bS2ZOmM51ll512oLpuAmu6DCIBlZiucc8N1/Dpmy8S1BRpsFLZH8UsMKVaoasygVAgrtoopoFIdxMKRLCi1bLFr2ttqQXYDR4mjXLZBmZRVm9tG6u9GTX7AVaZTpr/TN0gF9tG0QII10xNMfKguiwAPYQIRbCSjTQrFWSMZpIRnYkDk+TqYtQkQkQCKgldJVbsQW1d4S+KTqbHD6rKndXLF1XPwA34TZVbMb1WQ1cL1wt2POZhr/ElP6O5rZ2a6mrqkjGqwjoqNlPUImZPO7dccDmPfvI971x4OEJTS7p+ZYGuf2/cjb9dLOma7brBWK5+/gMO2Go6+2y+gRtIyvuajEa4/tg/8dS7n7Fw9YtYKFx8+N7Ytk1Xd8qfS1NHDWHqmOEsWr6ag3faiuqKBJ9+9zOzz76CVCZLKBhk+qQxrDduFKdeewe/rmgmHomw+5YbUXQUBtVU0pNKs+/WG3HgDpuBUDFyOR597X12Ped6rj/+AMYNavTZSACD6mu4+vk3GDy4ic3XmygdsBXh6x7L1lETz3xJxCqw2pvlRUfjXHHSoex16mXstNk06vtVuWPfomg57HPS+Wy/+UxuOvNoKoOCgG1hlyWkHMfh2Y++5tw/7UgkIA1qrNbV7L3xJG7424uce8GFHHLQgcQTSRr6N8r3eLqPjmTMlCcA7VBctjIWZSLAUQMUtAiLOgtEAgoNMRnAFEQM03b8lracaWM5DkFN0JkrUrQckiENxxHkijYxXSUSEKiKZMzKz9YQ+W6EY6N7eqaAlWwgbWsUig6PvPkZjz9wN68+9Vcm1EYJBwRpw0YokDZsdBXWZgw+Xd7JyKYxbL/jLrz28gvst83GbmLRkozXWA1oIezOVf7a4psGluln2pFKhJFBaVtW2vCXM3XyWfmfy2CzU2UJXSHKNBJtcJkBfkU/n5HGP95m2GWNiIhkktouk85L+DqeFmYk5s9tu5CTOoq5tAzE/jtHH+b1YV4f5vVhXh/m9WHefwrmeZ09jg1mXsqTWBaKakmWsSe/EY7KhLRlodU1kamfyHvzmgmoCiFNSL1ko+DK4ciH63WQePfVY4v7BUBTGrM6llyfHEXprUtumyW9ZberCkrFVwe7N2vYZbv6bPl12WzlCRS1bCts2/KznHVe5+rJO0JDC+s8dce1nHf/s6w/aiibTRjJS59/z+ytZzBhcAN//2UZtlBRFMHYYU1cffie5NPdnPvg87z7zc9cvv82LpbZOLaNbdkItXcRWahuK78WkOzv7naEW/Ayu9ro6kmRyRf47tdFdKYydKYy7Lr7bjTWjWZ5V5Fs0WJQRYiDb/oLn70+l6DqQDCMEgzjODaNMY1EMIYChDSBnm1HKeSxg3F52XoMxShIc2t3XDqWxeRxo2Ri2e1osXMZnKKBpkqmsycRIpPL+Oai2LaMgYTXOeaul96YTtZix2r4sS3PvNU9hHWVQfVxVKHQUBFmdL8otVGNpGajda4Gy5AGr263m8/Yt6ySdq/HpC9PckOJRQ++KaF3lCfj/U6bsvc7tkVzSys1iTh1VRW0CYWZDTqbxAPkv3+d2658nL9+/B1vnXsIiqKgBjREJEKxuwfHstFCOiKgYRdNzLyBbZhuslsQiIR8zDtw65nss/WMXuecDAe54fgDefrdz1iwugXLdrj4iH1LmOde19SxI5g6dgSLVjZz8M5bUx2P8um8H5l97tWkMjlCQZ3pU8az3tiRnHrNHH5dvpp4JCwxz3IYVFNJdyrNvtttxkGzNsexLIxCnr+99kFvzAO3MGczqF8FVz35so95SiEjmfrRSn/eKG7cqLiSSFKuTsYZV57yZ/Y86UJ23nKjEuYBxaLJ3sefyw6bzeCms4+lAoOAU5LGUVQb23F45oMvOffAnYmGQ7JA1tHCPpuvz/UPP8N5557DwQcfTKKikob6+tK8Lj10HFX46wcgDSFdBrgTjNFdhHTGRhU2YU0gFCgANgq5okwyK4pDwbQp2jbZok3BNLAcqAoHCGkysa0JgeU4lM92xbYQRpagED7O2pFKUo5OV97ikTc/45H77uLtZx5lbE2UmC41+NOGjePi7YruPIs6cgSbRrPVDjvz9qsvst9WM1GwUIKyYGYlGmQx1DblOPfmB2XmuXqoFO8Xc1jd7RKHhArBUGlu5LNgp91ig9tlk8+WmNq23DN5pIry7jnhES1c6bbfk35y8hlf6s+TifQlX36vCNl3/F85nnjiCU499VTuvPNOpk2bxk033cR2223Hr7/+Sm1t7W9ebxgG22yzDbW1tTz99NM0NjaybNkyKioq/vUn7x5/XLrELKIYad/V2wEQArun3a+EKuGobF2IJnAsi2LjeO79RlafLt6skcDaX/zKkFpZ67e1KAXZIu4zCfSQBExKbUc+Y01VcVyzCQXk690WGg9YPTYb4LdhKJrmTsx1WqrcjZNn4iDb2Ex/E4/7Hf7h/t1xW/j8llQvGSA0ttlkBlfNeYDNt53OKx98xuWH7MY1j7/C3ifvx+iB9X7ApTUMxsln0QIBbjhkRy594k1e+vt3bD9lJO/8tITVHT3stfFE4npEBrt5ee+FLgOfVz7/nr/PX0HBhq3XH88vy9cwY9xwtjzpCgACmkrRlM/mtDmPMmWaZEOdeuZZ7LvReGbvtyd2PivZO0JFSbVip7sQgTCVoQRqzxqplWgUIBjG7v4VzSzKip9QIRJC8Vhe7j31dAvtnKe5WWaA4VYOESp4bdTu7yISh1AUO5yEnnaUSFxW96sGk9Hi5E2b95d08vr3zUwbVs3Wo2oZkAihCRgQ1xEKOAVFbvS9BNK6h13SKfUZc25w7bGlerUSp6WOYrmuWUln7HcYAmaRkKbRvGwJ1sfPUli5gl8Wr+antR089eXPbD1uKHNP2hczJ5+jFjJRYzG0ylrMtjU+c1Gen+2zO+QpCKYOH0ChaLH9jKnycjI97sZInsek4U1MGt6EoodozltceuejDK7vx57bbPwbfda2zi42mTgSXRVMGljDUTtuKgEwGveTMFXxCOOGDGTMyGFM3esorjjxUP7+2Ve0tHWQzZWMyvRwmEN33ortp0/mpBvvZ7NJozl6ly1QhCBrOTz01md8Oe9Htjv4BBpqqnj4ohPZdNIY1Or6EoB5VfNgAvQYwgU7EY5y3yPPk4hH6Vdbj4VDPpuhK5Xmnc++ZeSQJk6eNRM71YZworIlyrJkYkwILv3by2w8YSTjhg7E133MpghG4LyDd+WrBcv50+zDGDl8KIWixRUXXcCY0aNcLUpFVo6tMlM4I0tGjfJjl8UGDVFwbLpyFtmiRUMsgK5IE6bF3Qa/tmURitxE/NKWYXS/KE3JMJGASlE4WI5ksoU0geM4WA4YjnQYBwiZBka8gYKrfZo2bCIhQcGysWxoz1lcdtLhfPy+dCp+9P67OeKoozEsh668SSSgsqQzz+cru3j+42WMHFzJPnvswphB9UTDkqUjonHsAePpCSRZ3FVgUv+xqCt/cNd3qamMFkAJRbCj1QgjB+0res1vf8y6zCKnaKCEozKp4JsllrGIvc2b9x6hljYltiU3+fEa7GAUNd3qtjG6rJdCHuEynDCNEqsApIawqkO2W67l3vz9J48+zPMfWB/m9WHe/zPMy/8HYt7jz7zRh3l9mPcvxzyMvCxmetrc7vk6ZQkINVmNoyhYXW2ISJzOxvV4a0EHA5MhNhqYINSxFLunA8yiZOV5Ro92we8cUaIJFFvKNfldIn7C20B4DNdsj3w9Icm89XDPPTdsC9yCns94LzMyXvc5+N/jFnCluZ3zG3mudRndimNLORShyY4H25KYd+u9bDFlDK988BmXHbgj1z79FnvPOJjRDdXyfW5xG0BYBjccPIvLn3mHlz75nu0njeDN7xbS3JVizxkTiAdCvdY/73jls+/5+6/LKDiKxLylq5g+ahBbnX4d8PuYZ1gOp5xxFjN33JJD9tsDy5Dmr45rGi0SNnSuoELVZELSNZlzitKTxDGL8v56Jqhl67/sHIvLceFKjgFY3e0lhjaycIn3jJD4IyJuEt2TSIhFIBDBjtXQo8Vpz1k89d1KFjSnmDq4kvUHJKmNBglqCrGASkIXiFQrSqZDYi6lomw5m9tZJ9ntkQhK8jVuDGSWnad32GWGlEJFcZmPfsJfDxEKhWhpXkPxnUdImkW+n7+MeYtX8+TnP7HlqEHMPWEf7EJRFjJCOsFEBZHx62O1N2O2NWOksqXh6LJ+hcvw9jBv2/XG+p4lvvk2MHHoACYNH4Sih1jTk+HSe59kcEMte26zsX+fPZmQ9vYONp04kgAOkwfVc/Ru28hioh70C2dVsRDjhg1i9OCBrLf/cVxx4mES81rbyWUyvm6/HgxxyM5bsf30KZx4w71sNmk0x+y6FUowRCZX4ME3PubLHxdIzOtXxSOXn8am609CsS0ULzZxbFB17GAcJRBBhCL+mvX46x+QjCeoqqqiaBjk83m6Mjne/fpHRjT15+SdN8XO95TN/dJz8zFvcKNfnLBtC72Q59w/zeLrJWs4YPahPuZdfvGFjBk9Ssa1AVl4UYysXEPc4mqBAGlbJRIIozoKBdPCsBzCQiasbaAjb9GRM1mTKtBdkBWdfpEAyWCAuK6SN22CmkJIU1AVRRpaOhIHFcAGAnoM29UFB3ACQSzHwTAdckWLnOlwxcmH89F7EvOeevBuDvnz0XTl5fkALO7M8tTXq/hlQTvjRvVjp112YXJjkohwwJZ7Aqd2CB1qEsNyqK1qQku1yTkRjoJty2J7vyE4ehitbTHO6pU4tt3blNxbF7y/e2t2PuPvV+Rjtvz1xBs/5caLjm0hRAgnVi0LgmYB0h0+ucKXevJM6N2v9yVfzHyvLox/10MRZSSef9H3gWSUlx/BYJBgMPi777nhhhv485//zKGHHgrAnXfeycsvv8z999/P2Wef/ZvX33///XR0dPDxxx8TCMiYZ/DgwX/o/CzL4sYbb+TJJ59k+fLlGEbv7pqOjo4/9DnrHn9YuqT4zasycElU+y1TfkuQq/foV85jFVjRauZblSzqyDKrOoOabitptIHUZQpHsfUoSqYTO5tCVNZC0fA/X1FlG7ivCemCLGUMFiUUlcYsji0dXE3D1/vyTZ+8ZIGi+EZK3vl6eqjlWqU+Ww1KzJbyBIC34fcOlxUw5+Enmb3P7sSCKr8uWMSJl91APKAy971PGT2wnqO3n8FhG01A6K7eWl2TXCDMIlbrKjo7ezjm3hek029AY5v1xvDwu1/y+Kl/Ih4s6WZ25E06CyaTj7+akQPrOWP/nfni18W89eUPLF7V4r9u5fN3Uj9iDOmB6/PcR99w08Vncc8DD3P5GcdzyJ/2oXHEeC495zTmXn+erNgFdLkoeeZfUGJ0eT9DKeAJhksMizI2mh8AlQ8097PLDVVkEsYqLYTe9wOicSRmv6F825KjMqzx3tIOvl/ZzYj6OOv3T1K0HarCAQYndcKpVbIKaps4HatLDAFKrIFeLtXeuC1j2vW61t/5v6LpzF+4iCED+qMJxdcR3Ov4c7ji9OMZ2VTPmtWrOercqzhpowlYlsUt73xJXTLGlIF1bDaqiQGVpUSEx1yMDh9ObtPZxLPNGO/8DaMn4zPbyoNeNaChqILD73yO6w7bhep4tDQnyq/FawPVAtLkwf3db41378FpN97HEbtvx5gBdT77ordWosq+51zL6598Tdd7jxOcsQdbrjeOeCTMnNOPoF8iKudVMOS/V+oKGtzx9Ks898EX1FUmSGVyBJMVvPD2R3S9dh+FgsFWJ13G69eeTr+GRgmy4I8JRdP9JIBXHX7103mce+M9NNVVI2yTbN5g8IAG1h87gr332JVEqsVPEnqMJ0VV+XLBMu588T3uPV0u0F6Vufy+/eVvLzBs6CD22nE7VnZlueiW+xBqgEsvOJeaujp3rhdLDtlaCCXfgx1KIIwsi1s6ueTyK+jp7qZl1XIO//NRHHjggQgFMkWbVMEibzkULYdEUEUVYFoONpIFFxAKlg2qgFzRJmtKRpwmFHKmRUxXCWuCvOmQDAoSQZW0YRPWZIupArz34UfstP22vP3xl4wYNYqnfmzh04XtNFSE+ODLVaQ6cgSCKnvvMIrTNmoikmnGWfS13AxO2Z4WI8Cv7VnShsWsQSHUpV/7TDIZ/Azmo64wq1J59hkaQiz4pPf89uaJyzwCEHFpTmS1r3EHvF3G8gn4Ywy7xDbwWo0VPQQVdVjJ/tLpO9+N3dEiA/hgyN94KZEYXjuz47m3u8xV7/f/Tht3H+b1YV4f5vVh3v8LzHvlk28576Z7+zCvD/P+tZj31culX9aZG96haAGZdNc0zKrBzM+FaM8VmdY/SmD+h/I+BsPyOiNxabrr2FLSxbYQoaiU5CrkcLKp3y2g+cU3VzaiXKO713lBSZLETXYrjiPlTcrWMS956b9ODfgsU7+QW2aI5UsWOLZMcssvAkVw28NPcuheOxMNB/ll4WJOuvAa4kGVue9/zuiBdRy17XT+vM00nKKBCEdRaxoR4ShW51qs9mY6O3s4+u656JqKHtDYauJw/vrRvF6Y51g2nUWbTsNm8nFXMnJgA2ccuCtf/LyQtz7/jsWr1/rnuurFu6kbM5FM/USe/uArbrjoTO554BGuPusEDjlgfwY1DeTCiy9h7k0XYne2uskk9557hVi/2CB+M27LjeD8uMd7r9vh4GGhL0/gxS2eREu5hIn7DJVQFCrqKVQPZVGXwU9r02SL8tkWbZugKmhMhBhSEaI+FkDPtEqzS8uQhRS3OOmNAa/byveigN7nVTa2/PXbLVyVs/3nL17KkPoaNF2ev9XezP5X3M0V557CqGFDWL1oPkedeyUnbzKJYqHIre99RU0swnpNdWw8fCCN8ah7TrL7LFgRJzp0CGLzA1C7VlP89l0yi5e459cb87SQjiIER9w9l+sO35V+yUQpnvM6PtyktzcnlHC0FKesk5g87eYHJeY1NfxmHntFu33Pu57XP/mazjceJLTZnyTmRSPcfsaRVMdCv4t5jmVx+1Mv89wHX1BfmaQnl0cPBnnxg8/pfu9x8t2dbHXS5bx+3RlU19b6uCzXD1noQhFSisjFsRc//JILbr6HpvpahGORzeUY3FDH+lMnsedWG1Hh5LEzKX/8ePP5q8WrufOl97j3jMNK1yZU/5kqeoi/PPYiw4Y0SczrTHPRrQ8iVI1LLr6QmsZBsjvA9c9wFAU0nZSjS+8TTWHRilVcccWVpLq7Wb1yGccedRT7HXAgKcMmU5RGzEXboWjZhAOCsCawbIeii3O6KhPdpu2QKdpkixa2DRFd9Q27AWnqKEBXBTFdoAn5Httx+OCjj9hzx+354POvqB44jL8v7+LnNT2s7SmwcFkn2ZSB4zhsvMEALth6OHXdC7BbV8g5MWQSS5R+/Lg2Q0AobDooSfiXd3ByGdSaRghGMKsHsyAtSBcsJtcG0RZ92stTx5fW89ZhIXzMs7vbpTZ/2Rjz13TvfXrI7woBJK71a3IlUvKl/VLZWuRJQcqbo1MuoVWe5FZHbcS/0+FJl3R+/RaJ+L9QuiSVoXLq1r/5+0UXXcTFF1/8m78bhkEkEuHpp5/upaU9e/Zsurq6eP7553/znlmzZlFVVUUkEuH555+npqaGP/3pT5x11lmovyOjWH5ceOGF3HvvvZx22mmcf/75nHfeeSxdupS5c+dy4YUXcuKJJ/7D1wz/iHSJu3Fau3g+FZGw3IQEw70DOCQbxVZ1nECY+pDGaLMDpbMdK93lB22eo7RTNGRbtutALwP9Mp08D2ijCd+4yWv9FbEKPxBStADoQZRYhdteXeZKXBYEOloIRKmSKtsIXX0uRQFV9ytGQGmjv86GH8fubSbgHlttPI1IMAAKDBrYnwkjhjL3zff5846b0tmd4rlPf6AmGWPHScMImEWcbEpWxtNdIFRieoBHT9ibpWs7Gdi/H8FYkgH1NZz/+JvcctjOIFQ++HExsy67F4Dv7ruMEU2NKELloO1kNde0pHbrkh6Dmo12IhOqZGFHHrW2icvvf5rxg5NcffxBHHfNXbzxyB28/PYH/Lz4IEY1VMsWtLL2ZGloVlbt9iv0Mjj6jXGYVQo2yg9fnwlKQWpZ4CN1VbMlBoEWYG1sEO2dBm1Zg7Of/Z6pw6rYalQtacP0DVMGVwTlhr91WelzPUMDLzgAHGH3du0V62gleufpBWZQYkICwjVXuPq+J4iFQ9zx9CvMOetoECpz3/mY846dzS33vskLb77PhdtNZ8KAWg6893lu3m9bKsOySmYbJmbOwLFtLKOIUAVOKIid7iLesxylY5Vs73HbGRUh/LZGed42qq6RzeWJYLvPwAsqdXyWG5Qq2sj56FCmW+s+r34VCTLZXEkrq+wZy3sHN595FINmHcqJV94KwF0nHcTAxobSa91g2CkaJZ1Xx+aYXbfkqJ03J5fPk8obvLO0i5UrV/HW5/PYaaP1OGOPrbjhiZe54ODdCUYyvczP/KSRJwUhVLafMJTuI/bnwWdfYc4psxlcLQ2kZOtsFtu2SgAaEPz9h4X0ZDLMefF9Hjnv6FLQI0Tpu1xWwMiBDdz55Ev8unApx+2/G/ee/Wdeev9TTj7lZOJVNWyx9TY0DRnGj78uJBqL8f577zF81Bg23WRjVi38mZvuuo89jjuH2TNG8O2Xn7H9Hvvx/ttvcuQxxzJt2jQSuhxPhiXZa7bjYKr4FX3TBsOyUWyFtqyJ5TiENIGiKMR1zWcCJIOSBacqSC1US+AoOpmiw0UXXcjg4SOpGTScBe05VnZkWbKsi7+/tZxc+2pqR05io/VquWz7Mez9zVeMqQpAQEfEK1mQUWnL5unMFRlbG0Nk2yFZC5keyUAuGqDqNCaCRAIqipX3Ww39+VM2t5VgWAbayVoZwHS3lyQQ3KOUUJTPxTMf8RKvjmmg9LSh5dM4kQrZQhvJosSq5PpuFmSw7bXJeZvggI6d7pIJOU3/jUbnP3z0YV4f5vVhXh/m/T/AvB0mDqPn/zHmff/zfGKxOB988H4f5v2nYJ6b6Gzv6qEiGkJ1pYY8E01f0kgPSYkLTWdohc4oPYVYOA+rux0Rr3CTTG5BqqyrxLsXvYwm3cS5oqolY1j3PikRtZT4sC3XlK6EV4o7phwXA2Siit6yKJoGii4T1orAVvUSy1xOOD/J7Wm/eslvbFN+Tlmye+uZ6xPWJSt80OBhTBg3mrmvvlXCvM9+oCYRZaepI+W15zM4nmyLFiCmB3jkmD1Z0d5NY10VoUScpsb6Xpj30aLl7HDxnQB8/+DVDG+sBdviwC03RDnhIEzHgWCEJR1p6sZPplgznC9Xp2mP1HPtw88xsSnBVWccx/EXXMXrTz3My2+9x08/78Po/jWlhKVh9b63XhHGS1R5OFWGL2iBUmLbewaeHna5Bq93eLhZvl66RTc1HMWoHMDXzRme+34Ny9qy1CaCTB1USdJN+Md1jYqQSrBnFaJztTTnLZOz8YsXQpWY512Dd37la/06RWsI0KubwE2elmPe7eedgF00mPvep5z752ZumjuXF975mHM3m8q4ZIxDH3+TG/feiooynW0jVZqDiiooZvLY6S60nhboWIXV3Y6ZN1BUgYrWK9FtuTIm2XyBsBcnUuom9PX+y+aRgF6JaP96cDEv09vs1k9Wu/P6ptOOYPDOR3DSX+4B4K5TDmZgQ32piOd9X9kzBDh2j205aqfNyBWKZITOm1/+wKq17bz14afMWn8MZ+69DTc+8Qrnz96NkFuMdUyjxOb3ztctGO40bTypQ/fmobmvM+eU2QxpqPUT+U4hh53O+2PVcRw+/nkJPXmDOS+8xyPnHtXrunvdJ9tiZGMtdz7xIr8uWMxx++3KvWcfwUsffcEpJ51AvKKaLbfaisahI1mytgstoPPuq88zduw4tt5sE7778SfuuudejjrrIqaNHcayH75ih5134403X+eYY45l2vQZKIDlSLkuKWEi8U91TSpNy8HAkXIjBYuUIWNoy3FIBCXmhV0fC11V0IUiDaBNG13VKDiCqy+7hKEjRpFsHMaC9ixfLOng6x/X0r6mh9SaRVQNGc2UidXcuc8Ujp03j9qkkMW2eCVtwTre+Gkt3bkiI/pF0b0hJwR2tgfhPtNs0SKiq4hMO3aqU+KSP9/K5osWkIa3lf2lgaWRlzJcHkZ4hSeve6ksrhbhaOk5pdtRvU4ZNYCSqC7J29im7GzynnkuXZr363o7/LseXrH6X/l9wIoVK3ppdP9XbO62tjYsy6Kurq7X3+vq6vjll19+9z2LFy/mnXfe4YADDuCVV15h4cKFHHvssRSLRS666KL/5ek9+uij3HPPPey4445cfPHF7L///gwbNoyJEyfy6aef/t9PdCuROIpZpFIPYakBAlpZsOG2ZqEIbKFhJfujdq8m2fIZjqdHVzRkkkCoKFVVUr/NLOKYGVfEXvUZDCDB02tzQhGlDWa5MYlt4mRTEvy8xRNwXIdelJIwPqaJcHKy1cllvziKwFH13zLZfk/753/1N/fn0UMHyR9VHVFVxbcLl7N0VTPXPngtu5x8CZcftien3/0UzV1pjtp6A+xMD6oeQsQq5M/5PI5tM7Aijq5pOKbBJuOG8+H387n/7S84YKvpzLrsXrZebywvXXtWifUH/qQPJqtRtACjx/fHCsXJmTbxoMqk+gRBVUHrXEF7Vw/vvvc+1pqFDGusY6+TLuT7x2+RWn9+xbgMhMDf4KnJapRQFHPNklLA77Gj3LYWEY76VVqE2qu9TYnKDbTHjvEXRBd0FaGi1g3EciCoKby/qA1NV0lGdD5e0s6QfjG2GpqkNqoRza5FZDtd/adSuxmAr6+GV1kM9TLC8V7jH27brxKKoDgOlmciBGBbWNk0uUKBfbbdmB022ZBHXn6Hkw/dl7fuvpozL7mOw2dM4O6dN8HMFVjZ0snCtZ1kDIPqWBgzZ/jBj/ANSORnp1c0k/z+A+xcBiudlsmBvIEWKjHvvEDIMkzyhomuKCXNVSF6B5f+XAvh6ef5LM/yQMhjrPwOa8fTmq2PR3jqkhPY+6JbuWT2rhx5/f3MveQEgvFEKfj9HQM4eTsF0UiEaCTCwaPGseOoWiYecg4NMZ09Z0xgSXMbO559PRceuiebThkLtsWKZSv4ZvFKtp+5HsF4wmdbOKbBvpuuR7ajjafe/ZyzDt7dP9fy1uFiNsUp971AIh6jKhnn4XP+TFVinWqpN6bdIGjnjaay0/SJvPLJNxx01hU01lQxckgT58zeg2htI5/8tIhnn3mGx5+ZS0+2wK0PP0GqeTmXnnsGg4YM5ehr72FsQyUVCYemkeM4/M9HUqErXHLuWdxz150MGjoUVJ285UjNUhuEArbjJQKkOZfjOAgBUU0jrCmEA8I3CAprAs1IAxpKriCTjmoAS9F47ZW5KJbJ82+8R8aw6MgXpS50QGDmMsQbhlEzMMFQp4WLr76epuGjcXpWlmQSbIeOXJEfmlOMrY1h61G0LslI82QC7FAczbKZUumgLJ5XMnzyg2rhrwVOIYda3YCZqEcUUjje2uCNZXculj8Lx8j7iRu/iq8FcPIZ2cKsBSEUwVGEbNXWQihaHrpbS99rGuAZLppF7PL5+08efZjXh3l9mNeHef+pmPfk3JfoSme49aE+zPtPwTyvM6iyUmBaFpoe8ouqnlwKYddnwiqidixHy6elnBfIJLfLoMbDQS+p7RYEHSgZOHsJ9LLEqeMVbfUgiBBKPiP9FLyESRkOe8ltKenlIPsG3M/32L1Ck5r4ntGcUKUmt11mNqeU3bdyA1ahgW2WZIaExqgRw6Vsl9BQYxG+/WURS1e3cO0j17PLKZdxxRF7c9rtj9GSynHUttN8w0hF0xGRBGo2i4VJU20lqiYTQJuMG8aHPy7k/ne+4k+br8cOF9/J1lNG8+LVp/UqrIpIHISK7jKVRw2dRDFRR9ZWqY3pzGyqpCkZJJBppb2jk3c//DvO2iUMa6xj79Mu5/tHb8RxHHAcFE8D3e3uKg0CIQsPAb23kbWfzHYLg+VMb/f8/M8xi9iFssSkV+S1bRwNhBrCDoRJ2xoduSyxUIBYSKMrW2Teii6mNlWy4YAkA+MBwvkOlEIG9CBCqC5JoLwA7TH3PZwrsbx7GWLiFlVclrqjKDJB5yW5TdklkTOKPub99dX3OPnPB/HmzbWcdeVNHDx5FHduPxOraLI2Z7C4o4tsoUhlXGrjm3kDK2/43UmKKrCLRdKrWkl8/Zac964HhZnJYQJqKIiqayhC+DIm+UIRzbaxjaKUbXPvqZw77vUVi0CxtDb9ZjKXxTNlhQzv3xR3GtXHgjx10bHsfcntXDJ7V466/kHmXn4ieijir3O/kQNy770QgmhQIx5LcNA2M9lp8jAmHXouz118HHvMnMTi1WvZ8azrufCQ3dl0ylicXIaVa9v5dukatps+mWA4UhovuQz7bb4h2e4unnznM8458gC/qOzks3Ie2RaWZXPynU8Sj4aprkjy8LlHUhULy+dbdi8cs+j/vvPMKew0bSKvfP49B51/HY3VFYwcWM+5h+9LuF9/Pp33E089/TSvvP0+XR3tXHz7w4RSazjrzNNobBrKJXc8QkNFjKYKHQb058gj/0wsFOSC88/j3rvuZNDgwSB0DMshZ0opLlVIzMsWbUy3Y8kBAqpCv4hOSBMEBKhCIRlUiagO2CaKZaIYsqPK0SOYDrzy4lzMYpHHXnmHoiVNLusrwthuYS7ROILq+hhj1Fauvv5GRgwbgtO9Rs7RZC0deZOgJhhVG2NiXQyRaZcSVJ7kYShKQYQIaQYjKzTEogXYtu2u5WXEiLICAkJ1PUfc8WVbcu55c1ELgFH2WtOQ3adCRdHlWLTTGRS3cIu7tjuBiDT+dGwESFKMN+7ceNnxty1Wr/PrO/7YkUgk/iEzyn/ksG2b2tpa7r77blRVZb311mPVqlVcd911/9tEd3NzMxMmTAAgFovR3S1ljXbaaScuuOCCf/qc/niiOxwDRRAw8mjewNRccw5Vl23Njg3pDtSultIm3V0kRbyyxGYKRMB1KXc6mv3v8PQrRVSyV3ygyvaUNi0ApomilvR5FE33N/Lg6vi4wUmvFjY1XNIQsixZ3VD137SuKQ7y/eK/uD3r6ph6gZGnbWcZBMw8rz12P3+Zcw+V2xzMKYftz2anXcuPD1/LBfc+yWfzl/PAqQfh2BZqtFoySdz2Z9uSAKfqcuN2+t7bs+vFt7Pj9EkAXHvcgTLxYQZctoWOSFZjN4zCtk3U9uXY0WrW5qXNRjKo0hQx0ZZ9hRKtYMaU8YRDQTY68CQ+vO1CDrz8Do6+7h4emPsGAF89cA3jhw8u6Sy5gadvMKCqqDWNWK2regXdvju3p2NYvkCGo6jxSszm5X7rHFAKQITqV2+teB1pw2Z+ewZdUzENize/XoVZtBi57UiSQZVorg3FNGTgGkn646SXRpzHbERWk0WswmV+2W71viwAAPl30+w11hzbkixEReFvV5/jv36HLTZm6bLlHP+XOVyw40xqFMFeD7zIoLoqIhVJAgGV8176iE0G9+eAcUPBco1nLNsPZjxNUqtzLQiV0KhJGF98gl00KbqvE2WtbY7jYJgySCqXOCi1lOEHbLJyX8bE8Db+3jU5Tm+A+B2wsPMZZk0dzeYTR3HiHtswpKaCSx+Zy2WH7ikByzRKbUVQYgC4/ymajtYwGDubIhmN8OENZzHhyIuYe95hnLLLJhy2zTQuf/Itrvrr8wSEwpvfzmfMwDoWNHdw+uw95We6gW3n0oW0Na/hyQ++5uzD98Up5LG6WuVmygXrRS2dfLlwBW9ed0avdiCl/Pn2ukC35TgQZKdNp7PT5jNZuGwlK9o6ufKev2EHQowaPYaBtdUMHTaMn1enePCGy6kI6zjFAkcefjgNI+oICIXrbr+Vzz//gi233JJtN5xAv4b+rFjTwqChQ0kXbQqmg+NATUTzTbMCQra7eUFRY0BHVeTPAKqiEFEdRLZdOnC7a5WjBshrUc4+80zunHMbn/66jEA4SsxxiBdUwrpGpqdAuLKOoZMa+eWWI/lpxvrcf/N12MKhUDGQQLQatXs1y7pyjKiOsN2QhHT6zvVIzWI3katEYsyd30VVx0IitUGqC3mfoezYls8S9ZN4ZhGrfQ1qSLZYEwz3aluVG1zVTwI7OVcb0pCbQdwklc+cNErGYIqWRrit297YUHS3ldY1KlEcWyZd86Xq/z979GFe+c3ow7w+zOvDvP8kzBs8eFAf5v2HYR56EMU0UQEVEMGwTLiUdfMorkmlZJQaMqFpS2a7d10iVoETqZCJm7xrWFZm4ulLaZWNVccqJUQdjVKHknd4RSmvG6msSFtiqJZMKsvlXH7zX/nxX/zu69WX/V1xbIl5yPPTHItXn3uK62+ZQ+VWB3Ly7L3Z9OSr+eH+K7jw/mf4fMEKHnAlFZRwFGFbqEYeerqwiqbfvYJQOX3Pbdn10juZtcFYwMU8r+NGyGeu9WuQsiu2CUYBM1pNq6ES0hwGJ3W0hEDtXo3obmbm2CGEQ0Fm7nOki3m3c/TVd/LAy1Lr18c8jVIS2pZa/yKaQIlVSBmfzrW9/l3eClF6nt77PDke08DJZX4f81xWuxKKYsdqWJM2WdVTIGeYNHfl6WjNsDIaYNqQKuqjGuFcm5yjnhmoBk62zNehzPC5xOB3mZ5+cr2sOIWLDV6HQVmS2xtbf7vidP9cZ22zBUsWL+bi+x7jrE2n0lgRZ7+HXmZAZZxwJISmaZz38kdsMqSRgyYNx84bPitbU3U34a1iG6bsdghHUWMxApl8L9NlAOEmux3HoWDKzxB64Pc7kMrGO1DCeG+seK+zf+c6ywtx7t9mrT+OzSeN4oRdt2RoXRWX3v8slx2xl1t0LfZan8qT3Z60hNfVkoxG+OCmc5hw+AU8f8HhnLLzxhy29YZc/sSbXPXIXAKqcDGvnvmrWzn9gF3l9bns367WtXS0tvHUe19w9uw93Q7GTOlabJuFK9fw5cIVvHHVSSSTyd/EXf4z9p55WTF7x43WY6ctNmLRymaWrVzFlXc9gqkGGT18KEMbB1JZWYmpRXjg+svon4xgGwZH/fkIklUxwprglltu4csvv2Czzbdg65nrU9OvmhWr1tA0bCSG5WC6p6EKCKquZ4CjAAqaquA4ULSlfFdQVQi4kiYRpYjIpno/ZzWA6cCZZ57JHXNu45uFK1Bd2bOAqtCRLpBLFQhFdcZOqOfzqw/hlw3X5+FbrsFWBFaiDlXVyMcb6GzJsn7/JNVhjTq9iOhol+fmecs0jGR5j8H3X39JzdAkNbblS6x5TP5e49AsSsyMVOCoAUSiuqwYYssxGI6Wus5sCycvC552pgcyPVIWMlntdvVJ8gd5C8fuQISiEoscp/QsXd8Mj/CDUehN9Ph3PH4Pj/5vf98/cPTr1w9VVWlpaen195aWFuo9U9d1joaGBgKBQC+ZkjFjxtDc3IxhGOi6/rvvAxgwYABr1qyhqamJYcOG8cYbbzB16lS++OKL/5J1/keOP3zVTqoTu20Vdk87nkkKIFum82ns7jbs7jbJNvOYTUj2i9/urQVwTAMrXktPxRCpVRqKSA3LgC4dWgM6jlmU+qVuwKhous+eUdxA0C7k5ALo6TK6TB5HDcgqExK0hBt8Ae5ElC2uXpAogzjpPqs4jvybUHslA/7LQGld8y6jII1S8hmUTAdaMcOZJxxFZuE35I0im08ezVHX3sMjl53Okx98xSe/LpcLfF4GuooeQgmGCMRjEuCEiogmCCWSXHrAjpz18EvUJON8+v2vPvhIvda4BND2ZSgrfpSBttBIBgU1EZUKO01g1Q9ycepshoDOa3ddQ9Yw+OTHBRRNk0sP2pnlz97G4IYaXvv0W2zLdCtsAZeRIkqsDEXBrh+FWt2AqKwp6bGB/9ryQwnoKJqOuXZlqRrvV9hLzA6RrEZE4ijFPIs6sqzqKfDL6h4yPQXWruyhpyNHtmiRM23sWA2KbUm2S7ZbjktvjJQHuq4ZgmPkSxIB/8Xi6L3GM9/xxpvfCteLzWNxwmU3cf62GzC+topd7plLpLoKJxQmkzcICMHT5x9FOKRz6BNvsaCjG6to+sGNGtLRExHC1YlSG1BPB3oiQqg6QayxH4FoCC0SIhANEYiE6DEtsobpMzTLz89jzfitRbibew94vA2Ep8/r9N7we235jt9u5D7DYoHDt53GTU+/zp4bT2Hu3+fxt7c+lm/LpErssrL3e2NZaxwm57LrxN7UUMNRszZhtyvuZ/1TbqAqrHPD7Fm8cPZsbjhsV3adPpHFze08+f4XXH3fE7zw7id89c13rFi5mkv+9gp11RW8cc2pLPj5Z9rWrOL1T7/lonufcp+JyughTRyw1XTe+OpHPxnlMWJ7HetIT3jPVAlHGdZYxxYbTOaJmy/jkbtuZfd9/sTgEaPpbFvL0AHVxAYM569PPs3Lzz/H0LET+GRFNy//0sLc51/k9vse5JiD9mHQ4CF89NlXVNTUgdCIBuQ8DGuCtGFhOxDXBfGgoCaiUR1WpQYuMkAqWrL6H9EU1FSL3FhacoPpCBUnGENXFe6ccxsnnHE2y/MqnyxrZ1VPnsEVYeIhDU1XOe7g9bn/gMlMGjmUm887Ccw8aqoFzTFpMYPMUwaQt2yGJOVal9ETWJUDoXGUZKEa0vBjaFWYyVOn0q+qCiWgo8YrEYnq39w/fyNhFnHWLsPsNxRR3R9FCyDilZKxVpBjS8QrUPQQauOwUgu3p0PofoYIRxFVdXLNz2Wwu9uxWlfj9LTL//IZfz13cmnsrrXY3W3Q1YKd7vpvt3H3YV4f5vVhXh/m9WFeH+b9p2AeptlrrZBJCvkclHwaJ92F3b4Gq32NHONmsdQN484rkOuQVdGfQs1I7GSdn4BWgmFXU1j1u08c25LY5sl5lZ9LPiMTeh6z3DsnKOlrO25ipZzN6ZkReqaSLsY568wN/29lye3ywylvLfeS3EVD/lfIomY70LLtnHn0bNKL51GwbLZYbzxH3/AgD517JE+8/yUf/7pUvt3zYghHEZEIWkhHBDTJMNZDEvMO2pmzH3mVmmScz35eLL+3PGlbft16UBoLKhALCIJdKwisnY/obvbXxtfuuIpsvsAnPy2iaFpceuS+LJ97h495juP430/ZOMS2ZOdXtAq1frAs2nts6N9jEPusc28+lNbl3+ive54VivALXMvasqxa0kHLkma627Ks6s7TkbfIR2qwo9XymvMZaVxYztD2DO7cRJnXaSMLncLH817z1o0/vLnkv3bd63HZ4MdfdB3nbDONiQPq2OXeuYSjIQjpZE0TXRX89fBdCQVUZv/1NX5pbvc/QlEFgWiYcE0FoeoEjuXODaGixyMEK2IEK2LoiQhaJIQW0lF1jR7TIle05MLoxhnlhUu7kOuV4OvFai0r9PqmuOXj2cNH71555+rYHLHDJtz83FvsMXMSz338DY++/qGML92iRTmj2/GKfqEIanV9LzmZptoqjtppU3a97L4S5h2yIy+cdRA3HLozu06fwOLmNp589zOuvv9JXnj7Q77+aT4rVq7m4geepSYR4Y1rTmXh4iW0tzTz+sdfc+EDz/qfP2pQIwdsuSFvfvNrr2dcboTodcjIe1JGDnEL08P617DFxJE8/pcLeeS+e9h9/4MYPGwEmY5WqioTDB41lr8+8RSvPvsEEyZOpCdv8emyDv72zFyunnMfRx5yIE0DGvnkq29JVNdiOxBSFddTQhAJCKmzHRBUhFQqQirJoEpMl/rdqqIglFKSW820y4KOkfW9cBxVw3bgjjm3cfzpZ5MTYVZ1Z+kpmBQth2REZ/iYWmbvOpZLZo1m/Iih3H7e8Yh0K4plkCNAd7iO5T0GRduhNqoR1BQKIoRZM5xizQiUWFLeHy2E7cDG0zegJh72k8oiEpeF3V5dge4YKOQQhZQ0ktRDiHhlySPFNVH2pBtFstqNdRP+HFCr690EeNZf+3tJnpimLDaWewJk0zg97dg9Hf4+qXzs/bsdjivT8q/87x85dF1nvfXW4+233/b/Zts2b7/9NjNmzPjd92y00UYsXLgQu6z4NH/+fBoaGv6XSW6A3Xff3f+uE044gQsuuIARI0Zw8MEHc9hhh/1D515+/HFGt1eJdVkyVqpzHXaMVaquepV9z+SpaGB1t/sAt9qK0JEuMCg5kCRu5d5rnxKqTBQEdClMb5d0Mcs3I4qn0ShUyWBzF20n24MStqX2KEiTAa8y5elp2rZk+USSsoXNDZb82r2r8+Yfjl0KmNx/99lsluFX/W2z6LcDeW2FSroLXQja1q5lZVsXuir44odfuPrYA9j6rBu4/+w/86dtNpaLSjThDw5fc7KQQ01WM23KeKatN5HdL7yNfXbcFq2mUd6TUAzFMvxEixKKYCUbcYRGqJAGE7QuudlWEtXQ0w6FHNOHN3Lg9puy5/k3cclhe1IdjxDZ9nB22Wg9zrvrMd747Ftev/1ytIp+WJ1rfZCzMz1yQ+c4OO4GX8QrfJZKaTyUmApOUbKglHC0l7mMcDcWfsBrGtjd7SjpLrYPRTEnTQfg2VyRYsEkVhGiIRYkbzp0FWyq9DBq+3I5tv43Ivfe5l8JR2W10H+0pRZUbFe3zqv2u3pm61axAda0tpMIKIwb1MB8vRI1FmfuxcdS2a8fAMayBeSWr2SPEU288dUvHPTYG+w1bignb7U+WihIuDqB3jAQO5fBaG/DzmYRegCtogprrTSa0eMRlw0ndfFqqpNsMXkUL3+3kFnrjyUYS/SuZnubjHUA3mc6lLPdbBvHKmMUeH/zghmjtCDuPm08e17zIK09Mzh2l8245fl32X/TqfJtmR6Ey+RQInF/oyWS1aCHMFfM98cBQuWs/Xfk1hfeQ1EUeb8VQc60uPyZd6msrmLyiMHMveBIvpq/lJ9XtPDZL0v4aUUzi9e08t3ilTz4xsc01fUjlc1RGYvwyJufsM3649h4/Agcy+LQ7TZi94vmsNP0SYQikRII/s4z9O+Py2JxchmZeKwdBKpOd3Qg+sAGRjaNIZS4jbrJm7H0+y/ZZbc90Owim2+9LXseejQPPfYKbdWTuO2TFVyycR3nXHUDW+64G9VNw7EQ4IBQIKwpGDboig2OTaCMPRvWBLqAdNEmGVQJZNtRjOxv1xxFsHDlGi66+FIAasduyI1nHMvSBT9z4n0vkzdt+jlp/jyym33HxonnV/HiHVeiODaWFkJ0SxO7umQ9ECAZVOku2HTkTXShUBFSSQfqGBAooGgB7FCS2nCAvKUgcp3YQvhJtJKRX4khquiqP9+UL+ZiuZsMNVmN2bxcJg8zqVKCLhKTm18XG5y6obSHGqjOr8FMNIBjo3avgf4hFCOLHavB1HTUdCtKsSB1qB235c+xZYLEZV71YmP9E0cf5vVhXh/mlY5/BvNmjWzi3O2n92FeH+b1Yd6/Aeb5WuLu9TlGHvKlMeNhG7bdi6nuFX+8pIijKHRaOss7czQlGqiuMqUmf9n89LtivMKfm4QrJf49GaaSCaWXEJGa3t56q0FASBmS8nEuXJ8Kocl1e91Nfpk2Mu668Lv3r7yLqUz6yB8LuQyKEIQ0nbbWdla0tKOrCl8tXCUx77RreeCco9h/2419ZraIJKTIisd+dKVIpk0Zz7RJY9n9otvYa8vpsjskWS/xo5DByadxUqvltdUNRTENaqwCSmcWxSqCbZZYu8CMCSMl5p13A5ccvif94hHCWx78G8wTmubHFo6Rx+pcizCLiFiFawInSvrO6xg+lrTrXVZm2ZoswtFS11OuVHS1sz2oLfMZkaynfvwAqsIB1nbm0MMBKmqiDKwIExBuQGKbCCOD2d0un7077uR3l9Y433zZZXH7Zr9C9eOc8uRuqYjbm3XuWJafEF21bBlxAZNHD+LH9hSqrvP8jRdSmUxgZ3uwutuxs1kOmDmBt75fxKEvfMDeY4Zw/MwJBJDmknqFxCyjowtISWyz7TJJLze+c+9jTXWSzccP5eVv5zNr+kSCbtzns9M9rW7vMIvSk8IrQnjXZdvgOBLzvOR/WaIavHkoE4y7TRvHnpd/TOs2Mzhuly1KmCdUnGJRzje3UOQVL0S8EvQgdudaaZbuJp3P2ncHbp37jn9NjuOQs+GyZ96lsiLJ5BGDmHvRsXz18wJ+Wr6GT39cKDFv9Vq+W7qah975gqbaalLZLBXRMH9961O2nTKajSaMQlFVDttxc3a78FZ2mjmFUCjY2xjeFr0KXz7Ro+yeqZW1EI5jxetoJ054UAWjmsYRTt7C8Blb8+tXn7LdzrsRUSy22W57pu56MHc9/DjBUTP5qS1LXayCC6+8nq133I2GoSMxbIewqqAJBcsBy3ZQVcnaVoWC6zWJqiroQv6iCQXVNqQUnWn0kqCxw0nmLW3hyisuB6Bu3IYcc/hsViz8hUsef5P6eJDh4QL9AkvZf/xIqu12XrjneunloAVRChmiooAdjJIOCOK6Sq5oUxFSKVg2zXmLgNBpTDYismlsIKIpVIQ0lJTrF+LNG480UF4McueH1d2OomdKRX+Pve2t5a43gaLpoLudroEoNIygJz6AWG4taCGZ0C7mUIoSf2w9Iok3Zh5h5EpFRleCSmS7/ELVv7VO9/9wRjfAqaeeyuzZs1l//fXZcMMNuemmm8hkMhx6qDQ8P/jgg2lsbOSqq64C4JhjjuG2227jpJNO4oQTTmDBggVceeWVf0hf++qrr/Z/3nfffRk0aBAff/wxI0aMYOedd/6Hz907/nCi285nJDvKKoE8QpScyD0AcVkO624ClaDr3hurIGXYtGQMskWL9RsGoRsZ7I5mEJavoeVVlBTTLG0MbelKLyJxCOg4uTSKUBHBsNwsC9dh2wtIQG7gXfMU1IBfSbYD4dKG39v8CNH79/KjrGXN06rzTLl6uVF714sLIu4m6uSD92a/0y9l4cpmtj7xMr544Fp233w6o/Y5gX7JBNtOnyw3o55GY7GMqWXbiEQVuZ4ugsEgydo6lvabQkAoLOzIsWm4DVKdUFGHHalkuRXjgx9a2WdcDRo2+bqxmDWOXHQ73pHamEaBto4uHj7vGPbefANZ2VcUXvrka354/Ga2P/FSLr7ncQ7fcxZN1UkUYfvXY3WulQvcuoYS3rW7bMNe5hW2hZ3qwtMl1WoHSNaF997yTam7QddXf88+4yYQD6osbs8yvamCjQcmCLYthJY1MiDz2ud6sUrKAl4P/LykgNty47Oeylu53RYqP0j23KuFWtK9BeYvX80ex53LnYfuyFvtJve+9yZjp2yAGDCSzLjNCWsKwYndhLOdRD5/lcLfXuO9I3blnWXNHPb0O5y740ZsUBFD7VyLY1nSaVtVKWZy2F0p2d4ddY2nbAunkPcDy6O3ncaFj7/BPW99ye0nH8yQ2spS8FbGtvBb24TqG5X8bpuPp/VY9n9wK/bu3FZUjfP23papx13Osocup6WzhyNvfZxbjt2XSNjVRnOTOLZtoVY34IRiOF0tv0maVMWjPHfBkex+2d3sf9PjfLVwJShw3K5bccMzb7DjptN58JvlzBozkM03mISqUKralwem7s+XHrI72599A+MGN7KqtYN+iRihYID2njQDYvHSMIgm5CbWM58rA0fPhV1RVdTKWtYmh7O0q8BPCzuYvzZNVdBmzepVNNWPQIjxZHQD5f2bGDBkOFdcfS1vvP46Ew8+nxmDK3E0nR9++BHTUchvvTk5vVa2qSkWQVWTuohWHsXIoYPPJtJUDccRJIwUSjaPUiygFLMuA1fH0cPYwThrsxbTp22IJYKc/fw3/O3Bu+my+pMWa3jrxxa+e+MZFn37BReeejzBYhphZGT7t22CbWIH44hUK6ptEqkYSrro0Jm3GJrUEZl20koVBdNl+doWTjBKfURDKaTBKMgNWrrLXetKDA/h6hB7bf2Op6mpqti5DMXl8/3n6JtxBXTsng6UUATHbdtzhEZ3wWJpvoq2jhSV4QATageSMixqVF0GQ2ZBMnw02bJthZNYQufr5gzTQ20oHavkszbyf7xl6XeOPszrw7w+zJPHP4t5by9b04d5fZjXh3n/JphXzgL1JH+A3nr03tpQxvIrlzZQNB07lKTHsFjaKdeTZM0gAmYBulpKyWwXO7wOJLvsOxVTfo9XsAUXW8Q687lMT9sRlBIHWlCy11QdJxDE8drdoRerzTeZVDWJgY7V6zP97yjvBnGfsaMoJZ1qlyV88oG7s98Zl7NwZStbn3IlXzx8vY951ZUVbDdjKuiqNBAsN9/EYz6HyOdzBENhKsZPp6dqGGvSRSIBQX89g0h3oOghzCEb8umaHFV2gKEVMexwNSnDoiqkorf8gpPqAMAq5Gnr7OHhC45lny1n9Ma8J25j+xMu4uK7H+Pw3bdjUEOtLBAiCx52d7ss8rqa1lDGpl9H+qPEoi5hiFcI9HWy3fHlSf9Y7c04LctJDkoxpWEE00fV0J012GxEDdsNryTesQiltVNKA3mdCuVJLQ/L3HPwZRKglJyD38eA8iFU9gzA7cYSKr8sWMiep17GnbN34M12i3vf+I5JG87AHjUTqqoQtkkg3Yq59EdCuR8wbJu3D5zFeytbOOKljzh3h5lMa6zB6OqRGtxuQruYyWFm8ghdkzgoBMWslDIJRCRuHbP9DC58/C3ueedr5hy/H0Nqq/xrUSjNO8m0lvNPAV+Gplfyz13XvHFa+rPqej2UZFvO2297ph53OcsfuYrmMswLh8tYvtGEO88DEIriZHuwe+R4U9x4rioe5bnzj2D3y+9l/xse5atFKwE4bretueHpN9hpy0148IuFzFpvLFtsPAPhWD6hxDs3X84CuOzP+7L96dcwbsgAVrV2UJOMEwoEaE9nJeZ5BaSoLCrYqa5SQcoz0PWerR7CrhpAd7iOxV0FPlq2lsVr06h2gRUrVlAVHkh45iQ0kaPz5WsZOGQYzz9wOwveeZ0xB5xLc7rAqlSReT/8SM6CGZtsSrh/HY4q/SV0VcF2FHRMlHxWPi2h4ahuYcMyUUyJdYqRk11Lqi59GQBHC7GgtYdtNp2BKXT+dP9HPPXyY3Togynqa3ll3hqCC9+l9ddvOPqoo4kGFJSejCSuhCT+i0y7XO8SDYTUJEFNkDMdBqhSAqgjZ7nJd7fYmO+hKi49bdbtfOslVePFhF7cCCU2titd0mvs2VIGz+ukUVQ5bmw9QnOmiGVXsqIjTySgMriimqqoJo0ybROlmHfHtJTOsiOVdJkatuNQnahHa1soGd6/M6f7jv9zx7777ktraysXXnghzc3NTJ48mddee803qFy+fDmirAgycOBAXn/9dU455RQmTpxIY2MjJ510Emedddb/9rvy+TyhUGlPOX36dKZPn/7fvoY/nOh2MimpuQOlNgLPcMlnMgkX6NxWXSFki3EkIavCgJVooH9AIxqI0BiWbAS7a60fBDrgs49KLQ12r82Dne6S1XvbxslnsMsWRzRdtjyYWb9dzjbyKMGIbO/2ghh3w+8oQlYdvRY47zq8a/JYEuu2bJcHP54+GMhWN1c7yHOCdkyDDUYPYdFLD/Da37/kgHOuYc4zrzHn7GNIffAE+55zHf0aGlh//GjZAu4Gi+WHEtDpzJv0axyAXT+CkCYNDDaqAaWlAyWaoFjVxPnvreKpuR8TjoepP3o6nbki9324hNf2G4ia7cQwLT765ke++O5ntECAtV0pvlq0iqmjh7Ni7h18umAl9WMmo+sBrn7gKY7bbxf5XGzPIKZkBtJL/8u7/1B6VmUbeau7vfQalyXnt/h677Flm4tnOmFneljRU+SzJR3MGldPQzxEqPkHnFSnD9giXlHasEKv5EO5O/i6TuH+8Tsta0owXGIHeJ/lap+KRDVn/+Vi/rTJZMYNHcD+97/FtE234KXn5+IMWY+olUFkurG+fYf8mhVYhomqCJJ11exalWCXPbbmgqfe44XvF3HOzhuTTMZQYzGpV6UKQEOEBIH+QxCVNRjzv8Uumv7mpS4R4abDduGKp9/hxNseZXhDP/bfZDJ1yRjXvfh3AkLh5F02Y2BVTL5BD6H8nlmDovj3rRfLAUqaaabh35/1Rg1mUF0/FKFy8UG78OKn33HxX1/imj/vjYjG0eqbsPUoarxKgpRlYKW6Sps8nz1nsO2U0Zy/77bYjsJVh+1OdTLO8o4UC1s6uPPqC3juw6+5873PWbtiGY+c8+fSItoryJU/19f24y/H/omdz7mBE/fclp032YB4JMSApibZGu+xPvsNkO23Ze135QGySFT746TKaOXrfBjLcTBMiw9XpqmctBUJMlg/zuWbN/4GwHcbb8LiL//OVVdfhdJ/JEMqI7QbCqeffzFffPIRf7n2Oq684nJi0TBKPisfh6KyZuVyrr/5FqZOHM/222xFZdNIeY5GFsXIIowMWNKA5fa/PccxRx1JTzbPT/NXs+lmm9Gw0R5MPfA0WjI2a3/6jIqpu2MZeWJBjdmHHMovbfvQomuoyUpoboNABDKdaNZKrHAFBCPY0WpShk1jTAbNlgOtooK/vLWQP08fhBXrh5ZqhXQraroVs7LJZzB5plkes8uTeFD0kPy7O9dEvMI3w/ODEbfa760NaqwCu2oASrGAHYqjWAYNsQDVYZX+cR3LcQgaPUQyHZK5ZptSLiSXwQHU/sNQe9agOw4bVg9Bbe/CdFuqfzO3/8GjD/P6MK8P8/57mLdbVYJd99imD/P6MK8P8/4NMK8c1/y1zrZ76Wv7jNpyOaawK8kVkkw8K1pN2BZMqItTFVZla34xh1VeoHJNcP3P9pKlZYfPEPTeV15EKmTdz9J83W5H1fENJxWBo+l+kdYp7xRwbJnk9mRNFIGiajiOjeKuPJ5RpZ8M93XS3dfZVm9Ws22x4aRxLHnn6f+PvbeOsrO6/39fjx63OeMzSSYTJQkxYgQL7hJKcSlSpFCkOBQopUCR0kIpTnF3Ce6QkARCIO4yybget0fuH/s5Z2agv7v6/fZ7v7e9N3utrJVMzhx5zn72e+/P5y288/FnnHTVH7nv1ff525XnCsy75k7Ka+uYPmFMSSU0sNEmqTqSy01vLE1FbR2FipEkMyYBXaFSziA3r0Z2eego35kb563ny0XbCEa97De1jrqwm4hbY7ehIaqBfG8X89ds5tuV69B0nY6+BN+u2ciUUcPZ/uZDfL12K1V19egunT8+/jLnn3Bk/0V3bLUG2YH8eAxoptoDfla6P4rzJpvGBkECcL67khWBM18tV4B0wSLgVqkJu5lWG8CfasPubMLKZUTDzhtAsqx+e5v/wzz/SajpQGbvj3/P2fNIxXlW/H3nMVff/RgnzZkuMO+Rd9l1r/148+15qJpookm5JFZ3C4XtG7ENE0WW8Yd8/GzYJI44fDeue/kz3tm4jcvmTMOjKviqy0qZFZIiowe8qOU1IMvIvZ0UEsnSHK8KB/jLWUdy80sfc+F9LzCqtoIT9pxCVcDHHfMWoCkKl8zdhyHlIfEdyVap+DzYKmbA/uQfYOKg+02WmTZmOEMroyAr/O7UI3l78XJ+9/Q8bj/3OFFIDkUhUO4Uz4WipOTfXJwvTvbAAZPH8NvjD8Ky4bZzTyDidbO1vZMNrV3cf/1FvDF/KQ/O+5z21jaeuuZsJxNlQPMMUbiXZIWacvdgzNtrJsFgiCGNjYO+e8kXFI1NEMXu4v+5fcJCwyuak3Imhu6vQZEkMnmT7T0Z4pkCkcn7kYt307L4VVrnvwzA0iXf8v38rzjnyuuoGb0zIyIiQPPy637H4vlf8cCf7+Smm/+ApGhIeVHYtm2btuZt/OmevzF18kQOPOhgymqGlkglkpFFziYgI6zQ7n/1fc4960ziBizbsIF95uzF8H2OpWz3M2hP2WxduoDAhMMJDp9ERdBF40HH4D3iOALlPtKSC7eiiQavIdSWktPgLXgiWDnRAJMliaQJbfE8K9qTVAdcVNdU4A6WQTZGQHYIL7Y1YD9U6G8EFfe3zr1duraqJlSCpinIIQPmVHHtUCvrMUK12JoHKZ8Cy6DGr4n73uUl4lLw5PqQe2NCMWpksdNJSsoMQEl2UhaoAklGjndj9XWVzlD/sUOSxJ//zdf7b4wLLriACy644B/+32efffaTn+26664sXLjwv/w6lZWVzJ07l5NPPpl99913UAH9XxmSbf9zWrfCN2+KDnu0Fqu3rZQkXXqiIjsI+iV6LrdYYFQN2xvCdvmw3CGwLdJagEDfVqRCmq71K/ly+TqOOGBvZEcCAZQ2NHJ5HRSTt4vPL4sE6h//DFkewLQT/1YCEcFmGxi0JcnYqiZYJGZedPkd/9KiZLsoJSnJ2YohJJYhNlmWKTqaxaCWgR5uzubLKjKkBmwYnn33U3abPJ5hVQJQYgXY96JbePLZ5xjX8z1WJlXqoiuRSrGBy6b5Yc1GbnpjIXfeeRdD9JyQZ8c6BQulfjx7PbSO5nUtVA6r4u7Tp1Hu1VBliZZEjhkVCl0/fMn4Q04iVgRU4Mwj9+fz71aiyBKJdIZX77yWqTvvhB2s4J6/P8Plt/yFq848jv12mcDsiWNR7GIoxI+As8gsLBZMHOAryoIlVTBASp6hzu/0B6A4fq2+AHYqgW3kUUbuws2rZaYPCTOpykdlrg2ahS+XJCtI/hC26hbfXyou2DVOoJv4vqyfMtgGvt9BC3e/9OYfFwcUJLeXra0dnHbJdbxx3lG8991atspuPl+9hbf/cj3muH3QWldgxbro++wD0h29yJrKWa99ystXnIJtmGSOvopobBNv3HETf/lgEVfM3Yu9J4wQGyDLwnJ83FS38PA102ks00JWZGwkXljwA09/9QNn7zuDI2bsxObOXm566RO2dPZyzXEHUVUW5i9vfopXkbnk5wcwus7xR9T6Nz+SrHDrU6+z//SJTBs3sv8zDvgu7Hy2xJAr3ltPLFjJe18v5cGLTyGoK9SdeCVNz96OHo6iDBuHGaxGibdhbVsrni4d77++DmAW50M8W+CJTxazels7bb1xehIpDp4+gatPOrzE0nv03S/56oc1PHbNOYPnzI++F4wCyVSa8iPOA+DFp5/ksKnDOfLXN/DEDRdQ7laQozWlBPmSd3I2LaRUxRAqfxg73o0UiJCtnkBH2mBDT4brn/yQDR++TqZjM6ktS0ovPX7yVMaMHceNd93LD20JYlmDfRvL0BUJ2zQ5+WeH8+QDf2VobTVX/+E2vvjsUyoryomURTn950ewYsUKXnrnYz5+/90S00aJtYjDrW2xbVszw/f+GQBjR49it913R5pyKCsTYRrrQ6zb3MMPf/4FZjbO2Q/NY0zjEMaW+5hU5efBb7Yzd3w1O9mtoohgGVh9XUKK6vKSqRxLUzyPhETErbCoOc6KtgTT6kP4dZVdKlSUVDe5QA2e9lUifLGQxk7GkPwhrJ72UnFuIJtL9vgciWq/xNTOZfoLLC4PJVa0UUCpGYYZrKHgq6Ana1IpZ8S6JqvY3dtL81WwLTODio1FH9SBa77tsGiL66c2/Yif3sv/5NiBeezAvB2YtwPzdmBe6aV3YN7/tzHP+OGD0npSDAr+sd2B5AQGl9h7jkWF7Atiqy4szSN8lXUvkplHTnQix9robtnGF9+v5vA5u4oD7EC2YNEnunjfDLAwGWRP8SPGZ2k9k+VSkd2WVVGMdsIkS4HLZkHgnawMxrsfy7mdIrZk5ks2XbYkiaax81jJzJewsehrXrIMcZqZz326mN1nTGGIT9x3saoxHH725Tz97LM0pjeJRqhllkKLi37gSze38dfn53H+dbcwqsyNL9eHtGUpZmcz9l6ncNTfv2PNt1uYOKuRk2YOZVylH9OycasywwMS3Us+YvxhpxBL9CvMzvrZIXy2+DsUVSORyvDqHVezy6QJWL4w9zz5EpffdCdXnf5z9ttlPLvtMhEZBu11Bo6BxciBa534t1xan/r3NHJp/kiqhlJRB5aF0boZ2RskOekwXl/ThaZI7FofYlihBamnWaxbxeZJ0WotmxZKiOK6PmAulopeRbsS5/sYZH3zDxompfmeE9ZESijKpk2b+cWlN/De1afw3vcbWNed5PNVm3nn2YexPCGkfAaraTXxJYvI9SVQfR5OevA1nvzFYUT32AN5zEzkdC/zHv4bt7/wAb85eDYHzBpfwrxiYKWsCNJBEQdt00JSFV5ctJKnv1zKL/eZxhHTdmJLT4zfv/gJWzt6uPr4A6muiHL3W1/g0VQuOWIvRlWFkd2+Ug6A5ahF/vjs2+w/YxLTxo3q/86KDVgH56xMahDGPP7RIj74YT0PXnYGAdmm7vjLaHr2dlxllajVQzH9wqpM7m3B6GwepGCzC4VBzZt4MsUTny1h9fYO2npiAvNmTuTqU44qrR2PvvVJP+b96H4f+G87mybe2U7VqdcD8PSzz3PUbjtz9Amn8vgtV1Me9Aq1oy/Yrzxw8kckb8BR0ST752X1CPKRYbSlDJa2Jbj/tS/4/s2XMGLbiW9YXHr5nSZNZaedxnHlbXezvjuNZduMKfehyTK6ZHHuCUfx2P33MqS+jhtvvZ2PPv6YirIIZeEgvzh2LitWruSldz7m/U8+x7Bs3IUEcrpXsLrTMZq2N9N4wImAwLwps3YnOvsIlqcipJM5jILFqvvOxEjHOPVvb7H35JHUBdzIskQsW2BoyMN4Vxwl2SmK1M7+2wpWkgwNpSdjiga2abOiPUkibxDQVUZFvQwPu/BnReCrWPfEPr9krZfPOh7t/YGkJTVP8Z5zrPGKIebFTArJ4yudmZRQFKt2LBlPOZIk4c50C8sWQM6nBtlC/gTznByfQQ2rYgHe2Xfrs+b+w7Xq33XE43FCoRA9KxcQDPj/9143kaRs/GxisRjBYPB/7XX/2fHaa6/x7LPPMm/ePEKhEMcddxwnn3wy06ZN+5ee95/36PYGsLNprM5tgzbvwE/BDvoBD7CyKaR8VjBILAs50UlIUTATvViZFPc+/xbfrtlEhc/NYb+5mU8eupXJE3aCQk6AkKwi6S4oHhidG85K9olNbHFhdHkEIOazgw7dtqwgmXmkgvh7sYsvJTL979cXwVY0lExf6eBn6b5+VttAOYdtiYOhovTL4CTZ8YUU8kIUHdsySoweSdWR3GLDc+LBe5eukaTqvLN4OeWT9uSV555h3KGT++V6soKtujD9FRCWqdfLeeOYX3LtDTfRLutM90WQY51I5UMxfVE0l8rU3UdzxkiLlS/9la8Wf8eC71dx3qknsOtpx3DZrXcTSyTZacxotHAlx849gkUfvUOkqha/S6G7q5NZp13Kxacew9UnHsYFl17JtInj+PqLL3jlw6947v3Puf+6iyEjPJlKyfDFQ6LDcCmyG0r/Z5nIoSiS14+xbX3//LB+3P3Li669JTz42n1DGBbpYf9gDLm3SYTuOM8p+4JYuh96mkuMglJYlVN8kFS9JCe1rQFyxeJ8LbI0i4Fj5gBwLc1jcdiXXR5MfzkX/eZm/C6N0x98jdWtXVx6+B6k0hkRQLRpIXawEmP7Rsy8gerWUX0eysIBOlu7Ka8IE41tIr/0E+aMGca0hhpue+9rXpq/jD+ccABhj6t/42NaWIU0Zl5spr/b2spNr33OARNH8tIFx6KrIsm7sTzC4xf8nIc+WEQunWKXfXbn+UOPZFVLL3feez/5XI7fnHw0O1cOTrh36xoffrucW599G4pFLdty9KE2pTR4ywJJbG4lb4DmngS/vvdZnrz0VHabMApz9EzkQACMHJJZwNy8HDubHuSZ9uMDP0DQ6+bXR8wpvZ+sYXH8LQ8PksPqmsrwmvL++eJ8H4O+R4d94lc1Tjh4DtU7z2b6QUex9J1H+fDjTzg+1s3Hj9wu5o5lCcDVPJDsKbEpJd2N5QQ9yf4wtupGwcKlSGxavYK1j11Lb8vW0uvWzjyIrvXLWbtuA1NOv5Z313XSly4Q9es0xbIMC7vx6yqnnHIKv/z1b9i4ZQtBv5/aijLu/es91NQPQypk2WPGVDZua2XqzFmsWruBSDjE8YfuR1U0wivvfczydZu57KyT2G2PPTjo56ci5ZLcML8TlrUC0Lq+GXXsXEJqCtkbpsrnYlZdAF/vZsJeD019GYJVddTRDG0bxdcZ70Yud1OwREL419t68OsqiiQxrkrI3iZUeIACciaGN9GBmegVDGXn8CvbgwFSsCAVJN0p3OWzgsVjmsg1o5ALacz2Jmc+mSWmqBQIYwaqMP0VKGaeKqMXpbtJrOvBKJZTGMDlLrEKSodbHBmi14/V11V67p/c3//C2IF5OzBvB+btwLwdmPdfx7xVq9dQWVlBTXlkB+b9B2EeUAqmLRY4Br7OoLlYXAsd9YuZ6BWsT3ceudDvq2onY5ipOPc8/SpL1m6mMhzi0Auv55MHb2XS6OFCXeANgssrHp+Kg5XFNhBrmvO6kqoha/qAZqxQHpUYh0WmtpkHQ8jekZzftwyn0etGMgSjEsMQ710tetqr/WGUA23AEHL/Qbgoyf1NZBXID2g0Ou/3xEP2FvdZrBslWs2rC1cxatd9ePLJp/jdMbuV8FTS3aUGga25qRtbw5OPHMHJF1+DHXELjI5UooSirInlKZgWs+eM5tgheTo/fpw/ffUVC75fzTlnnc7FJx/FZX/8K7FEip3GjiFaVcPBhxzC1598QFm0Ap/XS3dvj8C8037Oby8+l4vOPIVp40ezcMECXv5oPs99+BX3X/trYS1TLFgPXIuKzVRZ+YlXt+TxIcnCuocBBU9xKZ3mjVOExbJQotXEciaNZV5m1vrQNi3sD/jV3f1FbieAsdSYdXCsmH1BSd1UbIhY/Y3doqLCeY/9jHPTURb4Sr+vBCJY7hCX3HYfAY+LU+95gZVN7Vz2s31IpR1LFqOAnE9RiHVjWxaqz4OvuoyysgBZTewLlEQHhZbN7FYb5ckzDufOz5bw+g/ruOm4/SgL+JAUgXem0+S1LVHk/qG5gz+8+SX7j2/kxV8dgyZJmNk8w8tCPP6rn/Hwx9+Qz2SYOqqBZ/9yAGs6E9z5wOPkMml+c+LhTAwpIhPEGR6XzoffruDWp98ccIPbgA2WLa5L0bZOUQU2uLw09yS54M+P8/ivjmb3KeMp7Lw3WkU9RroXJdmJFeui0NmMlegrNU1L13RAkz0YDHDhUfuW1o2caXHcTfczcOiayvDaSn4yZKUf7xBhk+FgGSceeTANE6Zy+BFH8MMnb/DBgiUcf+UtfPL8I9iJnn61hC8i5kayTxRg0wmxPkUqnTXGFGpKCWJb1vDdg1fRNwDzKnc5gL7NK1m3fgMzzvotH67vImdYuFQZTZYZU+6jMuDitFNO4awLLmL1mrVUVFRQU1XJ3+66g5q6OpBkdt99NzZua2XG1EmsWrueSCjI8YcfSGVFOa+98wHL1m7k0l+ewh67zuCQAw+gzV3LA4u2kV2ynXQ8R09zC96pp6NmtpNR/DSEPcyoUJDyGdblfXy2uYfAyCgNci92T4vIg5BlZH+URM5iRUeSTMEiXTDpyxaoD7ppiHio8Wv4C06BPJ8tkR2sgSGmA9WCRbIHIOtubNkSRW5vUIRRmgXMdELsfV1u7FRCKADqx2L6yrDcQXQEM1uNtZSUZ0ZR7eG83qBsBmfdKe6pi5Z2xfkB/GczuneMQWPu3LnMnTuXRCLByy+/zHPPPcesWbNobGzk5JNP5vrrr/9vPe8/z+heMq/kPwkDNl8DgWOAr6BUCuvxC086J/F2IFNADoT7O0CqRsxSuPLOB3j27Y/QNJWeRW8j+wUzTbJt5GxMdH6KTLbih3ALKYnkDfZ3522rP7Fb1UFVsdPJ/m60R2xwMfMlFoCcT/V32Z0OoGTb/cy4AaEkJSabs9n5ie+bZQhGSDZZ8uwDx1PL6Rwjy8huH22RsTz4wht8+fyDfPT3uwRjzwELORjFjA7D1oSP3a5z9uPmP97O3rvPQu3ahO0OkPZV89CSFrqTeabl1vDCk49x7H6zmTZrNx55/QM+ff8dvnzpUexYJwCb2rrxB4NUDB+DlM9g97SweUsTp9/4FyrqhjFx5BBuuvshrv7VL9hr9iwOOvlcOt95lIDPU+rCFsNn+n3SBm6EHLmJ1c+Ek31B8Xu5wb8neXylVOdS8caykLwBenc6AJ8m49n6jTi4Owd4ZBn8ZaVra6subNWN5XLk/6pLyHcyfci5BHZGdHF/wgIZ2OEvjmKnfwCDTrJtUQACtGETmTZyCAdOHUt0aCMBK4dnxiEcu9cULE8EaeWnpJYvEdK0SAQlWs2Vf7iHoyaNYtb+e2GbJpmmLSUJG8CS7e3c+u7XXH7UXswZ34htmhjZPLZpkcvmuOv9hWzujnPHKQdT5nP3d/4VGUUT3m/t8TQn/Pk53r7pAsL7Hk/cFcWvyWzZsoU/33ErXds28+vD92TXCWOwLZM/PjeP/adPZPqEsf3d+Hy21JEf6JEm6W6U2UfTo4RQMKkqC5N66288/O1mvop5OP70czhS3YCV7CvdP0VWR8l7dwDTsQRQAxg7875dSVNnH+cfeyi2bXPNA8/y5fer+fju3+LSB6emlw59Aw6DxXvw5sdfYcnKdWQyGWwkTjvxGE485TTkzs1IHj/YFka0ESXeBrEOR0apUqiZgJzsRG5dh1U3DiQZyx3gtLN/xa5zTyFXNoxl22IEvToLv29lw/vPk1z6LFVH3EL9zhNpHB5h//FVYjORLnDWtDoyBYv7/3Yv3yxcwOhxO3Ps4Qdx/VWXMXrEcG67+feUuWXKRk4i4Pez5qOXuOPhZ7jl3kc49shDuf+aX+GxC2huD7IvSKFiFBszKo99s433vxKbsZaVy8mnYkRHTqXc+J6zzj6T00fIKKluFtjDaIplmVoTZIQaR4k1i8R6wBgxk60piflNffh1hVjOYEt3min1IfYfHkZXJJREO2q8TdgtVI9EKuSge5u4h9OJ/tBFZ+6UCm3OQUfS3SKkxl+BkugQElSXp+QXK1UMZb0+DLcqUeO20Do3YHU19xeKi/J7R4ZZCkIaIB2XXB6UUJTC9g0/2hQ788TlQdvlUP67Ywfm7cC8HZi3A/N2YN5/HfO+XbSQMeN35phDD9iBef9pmFdU5BQZdgMacyUVkzOni3YApWKiZQnMcwqk/cGSBWFfIcv05Uyuuucxnn3nEzRVpfebd5E8fmzNK3CmkBZNmqIlwoDXltw+0N3CosTBJMnIDQ4iLA7dJbDKMgTr2mkgy4VMPwPf7RMWY5JcYoKXitmONcNAnPtJA1iSBQvSzJfsuwaxmR3moxKppC0wgpfffJtXn3qUjx+8RbBuZVnYKfjKsNwBUfB2+Zm562xuve129p82HjkTE4qwskbeWd9DLFfAt3UJbzz9KMceuAfTp8/g0dc/5KOPPuar5+/DziSRXR7Wt3ThjVRQXREtYd6mDRs44+b7qayqYuexIwXmnXMKe+2xGwedfC5d7z2OT7FKIXKDCt0D1qPSx/9RY6XI9hyEeQMwbqDnu23k0RsnkKieSN6yifRuwGrdVMppkINl2LpYvyQji626sV0+bM0rGvlGTnxHhmjoS+kYVjpRCvYuMrUH2kgNsr6RhUKuFCoqK1iBCmx3EFe0humjGzhw2ngioQChsijREeM45IijUPq2UVj/A/nWbeTjaWRdRQ94uf6Z9zlywgimjGtADZeR7+4i1yeUZLIi811LJ7fOm8/lR+/NnqOGYBaMEsM7l3EwryfObSceSDTgxSr056aobh1ZUwXm/eU53n/4DryT9yMuuVEkiY7tW/nz7bfQtX0rFx4xh5k7DRcqpmfeZP8Zk5m204hBn72YRzEwq0L2BlEn702zu46gCuWREJmPHueBBevpwM+JZ57DKFcWafMS7EyqlGEzMJy05Ndc3HMUMzScpsjbi5bR1NnHBccfITDv/mf44vtVfHLvjaWQxoEqrEHzrjgXNRe3vPA+S5avJp2MC8w7/mhOPvZnYp5o3tI8kQpp5FQ3diqO5A+TqRpHxrAIJ7YhmQUsfwVZV4izzz6bOT//BespZ21rkrDfxdqNPax+/e+klr9C+cHX0zBtJiOHRxhW7qMy6GJydZDhETdgc89df+KH75ey88RJHHHgflx7xaWMGjWK2+64kzLNJNowRmDehy9wx8NPc8vfHuPYIw7m/t9fjjvRhR4IQaQGM1jNioTKbR+tZ/XqDnIZg461S8kneqieOAdXfBE3XXUhR9SB0r2VbeUTueerLRw4tpI5laAku5DySZBkjGgjqxIKX2/rI5U3yBsWhmUza2iYMeVeqj0yavcWiHcILHOJM4Wd6is1S+xc5h/uYeRApB8DdHdpDZXMQv95Qndj1U+gDdEIqXSZqD1NkOgSCiQno6DY1CqSXUp46cwf2RdE9vgESSjRN3heOOeufwXz/t8YRUZ396qF/+uM7ui4Wf+2jO5/NFatWsVJJ53EsmXLRD7Rf2P88x7dpZAi57A2EPQGHPgklzjsFSeunc+VHlYEIdsBfsnjd55bgGLYo/Pgtedz95XncdKVNzPlmHN44/7baBg+HGwLS/chB6VBbIZB3nEOy2oQs6bo65V3Hm8g3ldGyJ9sI4/sspHIiY1msSPs3OiSy4NkGf2bHWeTI3xO1dLPi35vdvFnji8cJRAdUBxx3juyjJVNUZ3ZRmWhixmTJwh2g1uw6izdh+kOlgJ6kGTeefFJTjj9bCY9+xwVmoe0r5p43uKjFe08cdJk7rn2Ts49/gjmzJpGLjyEvz9yLFddfD5kRBfdGLUbjeENSJbBSquC5d0JfjZqKCM8fp5/+G8Mnb43bTNnsvfs6dx63+OsaepgxqxdOfrqOxg3tAaXrnPYXjPZa8p44V06oBPX/9msnzBB7FwWHJkLilKSt/T7lVql78y2TGRFobxnjTgIVAzBTvWVQn8ALNWFHRJglnGFaUsZxDIGvZkCmiwzpcaHV9Eww3WoPU3Iye7S5nOQDL3Y9YdBBQDZGwRV3B6WO4Sleyj4q4i3NTFs9Diuv/9JEfhQSGNEG5H6tiO3rqdv0XwKqSx6wIuZbSe/djNVXjetScH4sp3Pa5sWRjaPoqtMb6zjpYuO57i7n2fR6i1cdOBMMEyWbGrh1nlfceKMCVx1+B64wqJQJeULmBiCCacIKVHtkCiTRw+nqWYKaTmCXLDpyRYorxvK3ffeT9uHz/Dnp17hzufeQVEUNrd2cORuUweFD8n+sLhHjXz/xrB4vSQZRQaXonHgjIlILjfnHrU/2x57k5euO5tDb7sa2fGHs4v7s59IrvtZj0UPyEQ2zztLVvPA21/w3E2XIKkaHyz6gfcW/cBRe87A7Q+UNsilNccBOdscAL6ajmSZXHvSYcT6YkQaR3PLi59QO2InlEQ7dqga0xvBcvlImDKe8pG4dA+mL4qc7uWaDzcBcPvUOpTurTz94dfcce+DJFIZHr7/b3zVkqE9kac7Ka6XHB4Clkn761fgcd+OJzCN77b2Ul/mZWjYzdNPP8tbb7xK2Odhzu6zOf+CCwi5FD548Qlu+NO9PPPCK6xbtZxbrrqY839xInnNx6Lla5AkiR/WbCBQ1whtG0XwULCaXtvN/KYulmzuxSiYFHImRj6Dt7yOiiEhXOkG2uMZJMuFZBYYVe7hhaXN7DksjOWOiKCTMvF9bk1JdKTydCRztFo23ckciiyRMyySBYsoJkqyEzPWjewP0+epImS3OsyoAazHIttqwGHHzmWRK+vFPDcK4Ilg+svJ19ZhA56ONUhmntXaUCo8CtFEE6Ty2Om4+D4HrOeyx4dcVoUZrMG2LeT2jeJ7L4Y+yjJGsBpJ3y7W/YHBVabZj1n/zbED83Zg3g7M24F5OzDvv455e+02awfm/Sdi3kCPZWcMDHctsmAH+kqLNUNFyucEM7Xo05vLlAq5GPlSg6asLMjDt1zDX644l5OvvYMpR53Baw//iYaG4U54pFcUOZOxH92TFnY6jjSQYayKrIh/uL4ZBpIiimc2jnVJPouVTfXbjGQF81h2lFGDfLwV3RF7OA3e4t/7L0ypQC7ZFugg2zYW9Mv6i02MTIoKby9KbzMzJ4wG3YUsK6C7sVwBbN0rLMTyKdRsjA9efIITzjyPKc8+S1lZPTlbYf62OBu7U8wdX8XTT7/Heccdxp6770YuMoyH/34S11x0ngirdnuxNC8j6xXMUB1rcj4M2WbciHpGBst45sV9GD56HFu7k+w9ewa3PvgUa7a1M3uXyRx97Z8YN6QKXVU5bK9Z7DV1/KDA6UHXuXgPDLDLsk0n1wJ+UuQWjx1gQSMrGG1NBABL9yFlk8J6w2kYSraNbVvYugczVEtK9hDPm2TyNumCiUvVKXMrRAIKSiaGpLqQdZcIRhxgm2Y7c9guvr6zhypa/ZSsbRQFW/diuXwkt65k6MRdue4v96H0bAPdLVQY8VZyKxaS2ba933orb5DrS1Ltc9PSl2AXTQXLHFSoNvMGu9RX8fyvj+XEv73EgsZ6LtpvOrIkCcx7Zz4nTB/HVYfvgR7w/sN7U1IUaqoqmDxmBBvzHsoNnXTBwKPK+KqG8Ke/PUTvss/505/u5o5n3yph3hG7Ti41b4WdREYoFdPxfqVYRth8SLZNPGfhVlQOnDUZKxXnvEN345qnP+T3F5/DE7dehTJg74m7P5QQ03S+cxnJGtwUSWTzzPtmJQ+8+QnP3XQJqBofLv6Bdxcu5ag9Z+DyegfPExBF8oGKgaL/uFHgqgOnEttzAuUTZ3Dzs+9RM3aKsHPLprF9UcxgDYYNmqyI/WnFSHpNnW+3xAi5VWaGgsixZl544hFue+gpEskU9957L6t7C3zi62ZZUx+ZZA5X7c6kfniRrnnXo3Ajbt8eZPImrX0aXk3mnVdf4NuP5xEK+Jiz266cfs6vcOsq777zNjf+4RYef/oZNq9ezq1XXsivzvwFOV85X6/eiiRJLFu7gWBZJVBACkQwIvW05zVWdfbS1p2mkDWxLRuXvwxfxRCqG8Jo8dFk8wZYYDRvpHzIVCqDbryaguHygaqLppbqIi656c2kaYtn6Yxn0VUZRZYomDaaLCHlkkj5pMjz8QaxPCEwDeRcGlvO9tsxFkkSRXZ10R7I6+9vJMoqRrSBLkMjXKvg6t6ErbmwPCFcBQipFlrHBux4t9jXFIN4LUtgXjCK5Y+CJKOmezB7OwZ4g8v960xxHjhnLvEc/73i57/FGJiD9L/1ev8BI5vN8uabb/Lss8+Wgi8vv/zy//bz/dOF7kEMJvq7/LLLjRyMCuYRiE2Zc7gyE70Oq00vBbaITbpIqJaKkhcHMGVvAFwe3L0dVJeFWb5+K20tLQwNe5C9QWSlIECp6E/nyNkGMa5kRXTKiwwr52A/cANk5TJCjuj2lfy7SqycAaOYFluUwNny4Ms1sNNvS3IptKTIApALYtNpq2opybq5pY266krxWs5nsJN9nHXYnpxx7ddcePM93PDrM4iUi0RTKRtHzvSCrGKrbsrCIc6++HImztydA69/lD2n6hRMm5N2cvHAH3/Hq+9/xg1XX4Ftmyz/diFt7e1cMHcfrHQcu348bRmLOneAr1NBzr3sFo7fYzyZUceg6W6qqyIsW72OXsnPhAjI6T5SPR387IxfYZgWSzdt5+5LzmDXs64GYPXL99FYFUUqehQWx4CObCmJe0BwVpHhODBlGRBJ6/QvrGbrZiSPDzNQhZzoKX2PktcvNiS+KOv7DDp7UlT4dMJulXTBotyrsbYrS8itMTwARnkjsjeCnOxESvb0h72V3o/Vv/kpBnkZ4iAiwt107EAlPVkTSwnRG0/SZvvwhgLoiowrH8dq3YTZ3UZw0hTBWvn2G+yshayr1AR9dCFhZQR7sshqU3QVy7SwUllkReb5M4/kwhc+4Kg/PYsswaShNdx32qFU+r1IjlwZy0JSTFSnqFRMMZZcbranCkyYsgvdpkTWEGCWLtiYtsSQibtw21nQlcryyzse45tHbyl9fjkQRqocBvEuzO620rzUp+6D1boJq68TuXU1DJmBLov53dfVRdgo8IejZ3PXouHc+8EPXHzILkiqjtnd6jx38fsVgJkyJdZtaWNzezcrtrayYmsriqpw2O7TeOO2yymLRtne1cujb37EtWccy8/32700n6SBhQSHHWAX8qUQNTlSKQqDzZsoG9bIy8YY7nn5Wu57+mWals5H6dvO5oKXXNZiTWeccq/OtNo6tsTyjHW5uWW/CtGR7ljHlo0buOiaG7ntd9dw8ulnoSgSu9UHSOYMHvpyM5pLoWLUVKzC5STn/4Utz1+BqT9B57Y4ukdlYlkXWxZ8yimX/56jR3o58vhT2WPnEQzd9WCkvMpf7r2fPS++E60rwd9uOx1bUbns1vtonD6H8y6/lnnP/l14REYqsdwhUt5KCnmLvRoiKJJEdvoQmnrSfPyNj0LOoG/pGxSyKQKH7o1kFLByGaLZVhRZoiNVoEKX6bA8+HUZX7aHYQE/Ad3ND20q7y9v47DJNXQl84yKeonqNkqyGzvRK+4Jt59wbAtSPimCXnIZUDW640nI54gGfAPCqcS9bIbqkB1mr+mLIuVTXPnuOnRV5rf7jKEnYyBbNpF8N5Y3gpSNIzkbHkl3Y3a3ibXBNLGTMWTdD5YhMCTnFO6K8tZ1i0XX3/EtlUPlmG398sN/aezAvB2YtwPzdmDe/xDmbWzt4pt1W1E0dQfm7cC8f0/M+9EQ6iCtn+HrzE3byPdbNzmN3eL7Kw3LcgqiKrLiWCoMYAZ6dI2a8gjL122io7WNhvKQWAd1l2BgF8cA+XrxPRRZhvbAAurARq9lAQVR5NTdQmHkYOA/WgdxCfVQqcFbtCYZEFhZHKXHOP8nmfkBzWBRNG3u6KKuXHw/diGPHetGUTXOOOYwzrzsei66+V6uv/gcynwOiz2XRLZtwUiVZKKhAGdfdCkTZ+zG1Y+8ysRRDfRmCjQqSV78y8O8+vZ73Hjx2QCs+GY+7e3t/Oqw3cEoYFSOpjWvEnIpdKQMTrv6VsY0DuGxy0/DTvRSU1vNgmVrCIbLqNXzSPkMiUSCn590KrZl8N3azfzlotOYfa6Qia9+8V4ah9SKz25QsvAq+WAXmZ0O098qBUaKfU/Rw7nIxCzuWYqM60LLZpRIJfiC/UVyxD2NO0TCV8vqrgyxXIIyt0bWtIhlC1T5XGiyhA0EXCF0dwA7G0bWvNDbOqDxLBrKkqxgq86/TaHIsE2zf6/k9mG5A3TnJWxfPT2xOGvyQUYPnQpmXjTCmjdi57N4hw3DNgqkm7ZTSGVRPTo1QR/tubz4LM66YOUNVI+OBZgFA8WyeO70I7jwhQ85+t6XUGSJifVV3H/6YVRHgoK57RaqHdlyQsOL887tQwlF2R5LM2HaLNos0GVxxfKmjWnZ1FZGue28E+iKJTjrtkf45uE/iF9WtZKVmhXvgaRgxirRatSqodj5rPDb7m1h9MgGsT4BXU1biRoFbvv1ydz74ns8+dbHnH7MYchdTRS2b0TSNCdUtb8omsgWWL+9lc2tnQLz1jehahqH7jqFN269lLJolOaeOI+8/gHXnnEsxx64V2ktLTF8B5JKiux7jw/K6pBzCYzuViqnzubl1BDufuljHn7uFTYsXYja10Knq5ov1vYAMLHaT0D3YOdBlW0mVfvRFQk50cnWTRv59Q23cdv1V3HKySchu1TqgzIjy318u6kHSZbwVzVQ2PsqEl/8ifZ5N6BHn6AzFMIfdpPc9D3t387n0utvZfbwMubOncusqZOYtvsc2nvi3H3PX7n1wafY1PY5f7vjZux8mhtuuYNddt2Diy+7gtefeUKsK+EqTE8IW9EJu2VmDwmj7TeKrzd3s7UrTUuVj3hXhqaPn8e2MvQdtg+SDZaRR5VAkSXSBbNkZ2d5I6QNm4Jh4XcpmJbN9p4MFUEXo6sDVPp1Qi4FuS8mFE9GQaxfpiGagaoq7g3LoiueQrItopHwTyxDSk2+fBYkmT5L57XVHSRzBvuMqCGASihniyDMZLewjQpVIOXSwjat5PvtMLMNgSNWph/zkBXsdAIzmxZKDWffJ6l6SWH5D8Ny/1PGjkL3oPH+++/z7LPP8vrrr6OqKscccwwffPABe+6557/0vP98oftHcm2puPl2+7D8UWRVFaFbHZvpl4QOkFg6bKZ+w/ksdiYpDh0+IX0zeztEh9U0uePKCzA8QcIaWMk+rHi389r9QS/FA39RfiZZpjik+8OCLZFOCDl2UVLspAEXN25AP/usePgc4P2FLIPLIzZLRg7UwQf9Hw97gKRbsq1SsJHk8YsbWdVYvmEL9UOH/iToQy6r47FHH2XB1wuZe+6VHH/I3syesy87jx2FGRkGgOUOkLMk9tn/QGRVZekj1xOvryGTSeN3abw5711efeQetmxr5qv58znr8hs4bJ89SmFYli+KnbbocVdRK1v89cbLmF3vR2tbhdXVzKTjL2TjthZCAR/fz/+URdvjHL3/nNJ7HFVfw/UPPss+03Zm8cr13PXUaxQMg8fnfYrHrTNhxFCuOOVnbNzSxJmH7oXfrTseaf0sqYGL0iArABgElkUWpATYuqdfAmlZDuvDw5vr+7j/0434PRqjqv1UBt00RLyEXCouVcajSSRMGdW2MbQQwZCGmk0O3nAXixLF8ArTRHa84SRVB02np2w0rUkDWTLRZImTzjybTZs2MmvcCExnoTLam7CzabSJe2I73pC2aeEK+xkzaTRvrmjC6GqjkHYKYgO6/VbeAF1F0VTuOm5/vt7czLrOPi44aFcUf6C/eJVNibk4wJNKcjzmlGE7IdsW6rYfqApVY0TqAVASbciJBFZfBwDloQCKIg347KKYYPe0YLZvE8zCQBjZG8SoGCkWCMskv+57Qok+pOGTuOyUuex3zT08fuWZTJoxk/N+Ppkjjz2R8/YZj8sXRIp1D2LiIMvc+NRbLN/awuSRw2isjrLnlHF8vWYL7991tZD3mgZL1m/lmr8+wRWnHs1uU8YLxpqqiYJSUS7rHJ6KxaKSj2WxCCIr2FWNvPpuM9GpRyD1rOGLdpM9hozmL2+vpSLopi7sZpfaIJ6+rVT6hmCpPiHhSnSxZPkqLr/jARY+9WdGj59AAYscKl0ZA79LpTrkpjrk5vP1zXijtdgT5pJa/rK4v2WIVPp4988XcPJtTxCqqiOi99FYV0Vf6zZaXnuCN96axx+v+Q2vfPUOSzY2s3BTB7NrXcxsrOCRl97i2w/f5OWnHsMMVGLrXtKmRHO8QNitEHIprG6L8+3GHsoCLjLJHH3rFtK58B0KvS1s3HsW9oh9BFMol0KRZRJ5EzkdoyabQDLzNAVGE7VlQi4YFfWSH1fJkJCH2UMiNIZ15EwvUjaBlUmJtHvbwtJczkYoD4igEE2G7zZsYc4uOzuTuMhaVEShspDB1t2oPVvoiY5l53qLeNbg+g/Wk86bXLLXCLKBCtpSBYb5NJRuMfesWHf/3NZ0gS8dW0oBJUA/s6NYPATBmM2kMHPiEFUKyPlXxg7M24F5OzBvB+b9D2He9PGj+HjZej645dIdmLcD8/4tMa+4Rg9syBXfE7oTxmjmBe6AWGOLDd8iww7RjCoVeZMx7IEM+FIhVOb2S8/llstVQl5Xf6G8VLAecH2LxXbHo1tyiuG2IVicP/ZjLtkmFC28fvxBnceXbL6AUvByscE7kMVdbPj+yMpEsgzHQsVhjpvifl2+bhN1lTNKr2EbooCn+TWeuP8vzP/2B47+5cUce/hB7DFjChMmTRHqC9VNxrCRJJi8+36gKDz42wsYXldNIZvF7/Pw9ltv8erDf2Zjb54v53/G2RdfwWH77I7Z2yGCnFU3ZtbAtqHMo/DErVcxPKyjbfsOyygwZdbubNzWSijg5/sFn7G6PcF+e+1e+kyjhtbyuyfeYJ/pE1m8Yh1/fv5t8qbF42984GBeA1ee9jM2NG3nzEPmEHA5ewCDn7Jyi9dJHWDFZFmimTpArSL5gljeiAjFywiFjRwsIx9tYN7qLh79bBOqrjB7VDleXaHS78KtyhQsi5ypYVrg0SRUPYg3qKAaWXCCeMV76w/LLNnRDfT/DVWTLWugKV6gK50l7Nb4xS/PJd62lURgJF5VR8vEyPd2oISiqHUjMDubKazeQCGdwV9XTmNDLSu+WyMa7LksZjZPMXhSzA0LC5AUmbuO249FTW2sbu3m/ANmiiBmp4BXZJ0rDhYUhxKpQKociqxqqIl2aj0hoWhQNNHwirdidbdiG3kH8+TB+09nL2jFurFzWZRIJWrNcCx/FMkyUSwTs7sVxfgMOVTOlRecxcHX3MzjV/+Syak+zjh0dw47+wqOPuVMQlUqbN8oPLpDUfEGVZXfP/E2yzZtZ/KoBoF5E8YIzLvtSuGvb9ss2dDENfc+yZWnH8vsnUf3s4VLDZMf36v9qjfJMrALeZRAhELFKF6bvwl16F4E1FaWdBoMCQ6hJZZnWyzDkJCHMrdK2Lm95UxMKJ0kme++/orL73iAxa88SuPoMVgyWLaFV5OpC7pprPTRF89i5E0Suhu1YU+MTZ+iqqB7VFRd5tP7b+KMu55GjlQRCnkZXlVGbNMPvLVxFW988Ck3/P4PPPjggyT6ennn+80cOr6GCcNrefS5V/h43hu89NTfMf3lFHQ/nWmDZJ+YJ6m8SSJv0BHP0dWTJpsq0LtmAb3ff0Shr4X1X++JNGwPMXezcUZGRTCmahvIqW4sdwiv5sZvJSgrD6FMqGZUhY9yr05DxMOQgIae6hQWd4gGCraFnEtQsiZ0zgK62813K9ey94zooMYulgW5DJJHBZcXW5LRFdF0+WB5Gyu2x5g9spw9GyL4NBndX4GtulDibdjpeMk6qkj4sbIpcMg6gwKfEYXv/rVaKxW3S9Y7A5siO8Z/9Jg7dy6HHXYYTz75JIcccgia9q9njsB/pdANg9htyIoIuQlWCs8zXxQpl+qXkhVN44vpqFDq4BSDJKxY90+YESA2Vl5ZRrKzWNn+nxdlUcXCgJ1JgaaLG0HThW+pURBSHCe0gmJIUPG5dTcEy8UGpRhQ4gRKSW5fvyG/4xNU9DuzTRMU6//YESky20pdfqcAgWWCc/jHKHDgLuPFIdzxMZM0HSlQhpxNICU7mT17Nm/Me5cPP/2MS/54HzOmTGLY3nOprqlhYpWJDHg0mc+/WUZQszG6m6kcMhyAL957k5PPOo/jjvkZS5Z8y5hRI5k5ZWIJ5OVUN0NyKaR4knLbosEyoE343Vmjd+OFpx7lxptuxjAM8rFOpk3dlZULP8XbtRnLtrjyvue47qrLGBP1YBXyyJbBig1bmD51EpVlYd77+jv22XtPkvPe44VPv0GT4bSD9/yHHn8Dw0QGBhCIWTlYGimnewXjwpFC2pJMl+3hvVXbaNvSiyRLNG+PYRkW/rCbGWMraKzwUx1wcfCICLIEkiIRL3gJ1E9Ea1/T7wHofEer22O4FRgxbAiSPyyko7qHLkNjfWcGTZaoC+pUeVUCuV5q2r5DHlGN3rOF7MpFZLZtRw94sXUPPZ9/jJnN460uwzdlNsNaW9n62qfIB+1CYNwuZFctKcm4i8NyNkO6pqJaYOQKwrPUiqGGFWxDFG5kf7h/M6/qSIqCWjeCloJOdXWVCBDr3ILa24IV6xbsikAYK9HXf/1tu/+wASLNvdDvxaeM3x1722r46jkKhQJavfB3MzubsUOVzGuxiVRWcd3fX+PV+irc7du48qTDmXnO75k9fRe6l80Hy6S+oozKsB9JklBVhZd/ew6yL1A6uCzZ0s57P2xg0rjRzPt8IR8v/I6rTz+GObvs3M8yLMqciszYIkvV42Og557dtb0EnHKqm8v2HcXKsREeuOYCbnlnDUdNr+eePYPYkozS2ww9zVjxbqLqZjH/ZJkHnnudd7/6ludvuZzKMTtj59KoPVuQIsOo83gI1vqJ71zDS0u24/KHyKfiVM78OVZdhNG1SfY5aHcevOMOKnaazg8Lv6K+vpbtQ4ZSXVPLN0t/YHhtJbddfSG/feQ1dj1oLtv+/gD77rcfHUs/56Sjj+CYQw5AjlRjesJ0ZE2aO3J4dYXNvWk+39CFadks3dhDb0eSbkkiFcuSa1vNXhf8gUiqiQoyyLE2x9vT4Hf7jeOrbXHB2svEkRSFIZkmpM5ubG+IOdX17FJTjQ0EzSTksihdmwUDyxcU96aZR84m+llOzggFQ0ydMFbYXBSZxcV7vKcZ2x8WXnmSRFDKM602RHsqT2tfhuOm1DEyopMxbBqUJFIyXgptKuFMcZOTjv9UTl0s2Dl/irYDpU2Qc1D4H9kA7cC8HZi3A/N2YN4OzNuBef9/wDzH4qBYeJJkpV9WrupCtZPPIElJEcBZZG1bAyy1LEsUlp11xkrF+zGvVIwWhWafW/lpgOYApUkR/2yHSSwNYANLilIqrhYZ5yLc18Fbxw6LYjFa7W8MlVjjxUJoIT/IxqI0BmIc/YXt0vduOqGzP7qMB83eZYD0XthDSF5/6XG77r4nr8/bg7fe/4gr73mSIaO+4ZBjT6ahvg4Ar6YQcSssXbYSl2SR6W6hqk40fz9/fx4nn3WOwLzvljJm1AhmTpkg7MmMPGrHOoYpOqRFFkdZPom9OYlZMZxU3RReeOklbrz6cgr5PLlEHxMn78LKJQvxtq7CzOe46uGXueE35zGqphwzFReYt7WF6RPHURkt472vFjNnj11JvPcxL3y0oIR5pbUKxHql6oOuycAwO1HIkvub8ak4kuoGM19SnpnBGrbECry/sp3Ny7eTS/ayZWUtmkuhprGMycPC7FQTZFyFnyqfSkARNmoF2Q8VI1HlLRDr7G8mqxorm9rw6BqNtZXgF77olu6jw/axpU00bKr8OnV+DVe2l6ru1ZRFLcgkKGxdQ7alGT0cFPZKrVvIdsfwVEbQh4+lIVJL82dLsdJpFH8Ab72bXFcP2e44Zr6AomslhrZqg2LaWIZZyq6QHYywDfqbNEXbPN2NWjWUFsNFVVUl9LYitW8Wj3PuPysVLzV9nCve3yDNm5iOOsTOZ5HcPuRIhfg+2jZiJnqxs2kRwpvow6gYydur2olU13D9E2/ycnUEKdHH1WefyM+PO4HxEyfRvmYJVjLGkMpyKiMBJElCUVVe+cPFpest6W6WbGrhvSWrmTR2JPO+XMzHi77nmrOOZ69JY/tZwgNY96U54jTiSzYVmRRSXzuW490vJzu57sDRHDzmLG6/7Fzu/mwj+4+v4uSdK5kSjSInOpGa14k1zR8SLxPv4cHn3+SdLxfx/C2XU1EdFQ1K1YWcSxHyhKjy6Qwr97F8W4xUb4x8KkbZxMNwj2pkWKSXstFjWPHW49TuPJPFX3xOpLySseU+qoc1snjZKhprKrntygu59fFX2eeoY3j/6Yc5/vCDaF25kFMO2YtjDt4PJVxFUvHSXrAppAz6sgbb41m60wW29KT5am0nLRt6yKYy6F43dmwDO5/6WyrNFsJmCluSUKI1yLEW5jSMoS1pIOXTyL0tKFqXsO1ysgNq/OUEXGEqvCq+XB9yX8zJFMiWlBmSbWNbhshJkFVA2M2FQkGmThg7QMFB/5ruciO7faUsF18+zr6NUUzbpjuZZ1yln7qAhkuR+okzfe0lzCvZLZasScx+oozcjwvSgL0xllUqhP9HW5YUxw5G96DR3t5OIBD4H3/e/1qhG0rMAQA7UEEmPAx3LoatuZB0P/iiKIl2zPYmsUkZyMSRB3u92aZZCoFQKmpLcjUrHafkeVmUODlMp+J7kBDeW8UNSnHYRYabc9CXw5UiIMtwmAo+sfG303GH6aaXusfIKpbjt2bnu0vsCdt5jIRzczqTxS4GkViGCCJxFg6MgliMQSwKppN079y8xQ5VsWtmeiPk/VXomV56bTfLe1N8rY1FPvhy/O5tzLv3D5hGgcOPmsuI3Q5iXHUQnyZx9+03M7y+hmNO+yXJvIXs8WPZNr+58HzOueQKNJebi39+AA+/PI+Qz8PxPwtgZVMOO8OFFa7B1rw0Wz6SKQu7YRo3P/46w7U0SqwZYmshKGPJUexCnmdvvlSwTGJJVEfqt/PQaiYOr8M2TY6YMR5JMjhi79k8+MJbfLN2MycdPAdVHcCMHCB5K/rLDtzQl4pCgOTI3KSureD2YqUTyOFyTF+UTMGiMugmk8jQveE7FJcHd6gCb1kNn8ZzjDxsJwD6ciYVRg9SIUsgMoTtCRNvZCfK/V0ovc1Y6ThKKMoH7y1hybKV7LJTI6u2NHPP3x4gbkj0Zk3KPBpuVaLMrWK3b2DBwm/wm2m2/+l+zvnZQUyr8qH53MheL4WFb+GrLsMqGOh1w7AbplARraOzYKPWNGCbJvpRFyK/cz/xza1Iiozq0Z2pIqSUi5vaGFlXLmRsurgWSqSyFPxRYikAkuaDcDULPllIuEow2oqBDZKz8S20bBaFq6Jvrs0gkCgGZ5Wuf1cTRndb6XXyW1Y7UtkwT3/+A8s6szzy6vv84axj6EhmqJu+J8tWJFgx/xM2r1/P+lf+iicbZ3NzG1u2t7Jyy3auOuNY0Y13NlnIChXlZVx735NMGtnAkMoo9111HtGQWORKXn/FA1Tp3u/365L94VKhouQv7EhkJ9a4CLureFKFzuYYa+qCyDUF7FS8JK20C3nsTIqeeJIXPprP2/OX8NatlyKrMlZvG3KkGiNcD4qKreh0JQts6U0TyxToXP8dme4WsvFO7DUfsdWl8dcFn9Py1SsAZHvb+aJ+NC99HqR26i/Ze1wlZ44RAUr7HKGw/KuPeOgPV3D3fQ8x98wLePDmaxjRMBTTNonlRWjIVF8SbIudyi32bhhJW6rAhp2qWNme4IemPkJKng+/bWaPnRuozfv48pMPkbRdaY1OoCq1DW/3BqZWN5LXg3h6tjlWFR1I5XV0BEfQ1JunLmhT2/kDVrJPXFd/GDubFv5r+Zz4u5wDXwTTE0J2wv1yhikOveHyn659qTiyZYLchxSuRu1Yx/iaCXg1hUv3HE65lIFsnKK7qGQW+rv7RsHxEXTmuONlPciv9kcMx+K8HeQD6RwO/kfGDszbgXk7MG8H5u3AvB2Y9/91zJPl/sBTy0QKhDEjdVj+CmHRYVmYmgdZklAss8TMsw0cj15RzJaUfmuKUgPPKaAXm1WW4xVMsYGl6k4R38E9p/BlF/IlHCz5LEN/IcQbEGGWii7eYyFfyhoohkSW5PFO6JqtupGUrPAOHxCgB6LRKOEoJop5FEXMK+QHBdfaP5LOl6xVTNHkLb1flxvbFRSqBUWlz1BZ1B7ne+84Gk/9HWrz99z/h2uRbZM9DzqCsXsezOTaEFU+lZt+fxPV9UM5+sRTcSsizNmyLC694DzOveRyNFXhN2eezMNPP0/Y7+XYQ/YV187rx9I82N4IdngIPXKApp4slI/kzmffptplobetxoo3URYCI+lG9kR57o5rnQJ0GlmysS2TicNqmdQ4BICj5swEWeGIfXbjwedeZ/GqDZx08Bzhh6wyAOd/pDIozgVZFkVQJ5QZy8JKxVE9PtFoKHpnax4KBWHJUcgmiW9fS6JlA4ruIdW7E+l4DYosURd0I0ugJMV9agZqaMmpRCvG4nEHkHtbxBociPDBd5/x3bKVTJ04gVUbt3LnPfdhywqJtIEsSZR7NYYGNOy29Xz99SIeyvSyrb2TXx4wi+n15VgFAzOdhs5mcn0J9KCX4MhhqPUjqao1aU9k0KrqUOtGCO/htUsppNYI1ZSmouiqwHjLYtHmFhorI2K+OPd2cb9VDDEv7jtlXwDbF2H+p4uJlFcKYkOpkZTASvaV9pUluxPbHrRe9Dd2FdF8tSyMzmaM9iZRSK+oKxXWX3z7QzqyNk+98QHX/fJ42vsSDNntQN57+jM+++h9Vq1dK7ypY1vZtG4dm7c2sWLjVq488TBxHxTXAU2noiLKtfc9xaTRwxlSVc79115ANNifLTBoDFzfnDWkmFNiORkXxfkj5xI0VtURGF3NXZJNU3OMLdUB1N5t0Lq+NAftfBarPU5PX5znP/yKt+cv4e07r0bxBYR6SvOUPPKxLWQJ8oZFT2uCnk0/kE/24o3W0bn0IzS3xpqvv6BtwasARMY2kQsPY/66RpSdf8F+E6qYO76KMivBMcfpfPz+e9x32++55+6/cvQpZ/PgjZcyYtQoTNtpuNs25R6FerfB+PIwyYLFtniQqF9nYciNrsrU+mye+Hgz+0wfSV0hzJpFn2KGapDdQaxsHH+qjSpfNbbsRdJ0oRJ1+yBYjuWvwMwK+xBfukM0vIuNcie4nqINiKph+6LYio7q8mBm0+QME1SXyNCRFXBUAXYmJZoU2RSy8zySZVJXFuKw0RXIEtT4VJR4qwiRlRXkdG9pL1Ty6Ib+s5X5D9QgAxpj/c1euXQWKs21/9BhS9L/rWL2/4nX+3ce/08UueG/EkZZ7KTLzqZF02n1DkErWLgUDUNxo+UzKN1bxYbC5RYbtiK4Fbv2skg6RndhdraUNkXkc0IyrbqRAbNXyE5lbwDJF0Y2ciVPzOJ7KW6sJK9zcYoSHSf8SA6Wifdq5gXDwxdEskwwslhO59POZ1FCUWzDAE3G1jwiBMYbEJvkQl5IA8H5XCL13FY0wWizjFKye7FzKuRH2UGhYcCgznKpg+kkiGcNi14pyNdb+/h0XSemZXP+PiNZ2VbF/Y8dQUtPghvvvIdFdz7AqIYa8qk4675bTDab4Y0Fy7jgV+dx1VXXMWnKVGKZHG2traxYsYJDzr+ORCrN2i3buOmR51m7tYUFX37GqIkTaEkaRHWFnkyBRN7kd2+sxOVSOXpaHaftPBZl5cf9By6nk1b6u5Ogbhc3c86QNB2/rnL6IXty36vvs62zl8a6qtKmvSjjw7LEpqi4yBZDfopSK8eLSQlEML1lmN4IStqRYIdlsqaNIkukOrdRSIvrr+hu0j2tpHvgyY9Vzj1oDD0Zk0rFQM4lsHJJKn1+muJ5bHc5lSGR2G0mevnNsQeQPf4Ikuksdz7yFG0dHWjhSsrcCu0pg7wJhmWzoSvL8OooR0wdQ80Bs/jzqx/xtipzzTH7smxLOyOqIixat43eVBZpUw/yD03YsrjGtlFAMgoo25ehzjoY23wb27QwCwa9hsm2rj5ae2LM37CNK4+eg+IPiMO2y9MfJgIY6ThmMokky8i+IH95+nXWr1nFnddejC0j2CMVQzHWLMIuFARDMFiGbZrc/tRrtPXG+g8RRl4cOow8kqqh1g4XnqWAEq1GHjGFRHAooc6VWLEuvl74AZeedykFSWXW4ccy+dxr2GmfI2kO7k/53hdxwEiDiiENSOkYE0eMY3x7E4dkUw7TSCw3ku5mVXMnT771MYseuwNdkUtrQ2keDfQDK/wDeZIlvDSFRYMoFhYLG7Yl0p07C5VU1Q3FlVjAQeMuxFa7IKhDT0tJFvzu/CWccesDXP+LuTx73fnIxU2iUYBMAsmbwtY8yKaBT/M4098mVDea8JCdqBtdzTeL7mfda39FD9cw9txHaXn7j4RHT6Vr82rUYbNZtayNLZt6OHTU7jRkt3LqpApOfGgJ4TnTOOHYY/lqwXx+ec2tvP7oPQTkbsLRACGXjp3zIOVTmIEqYlmTVN7i3k830ro9xhPnz+ar+fO57/vFNHhs/nzT73nhpZcwIj4Klg2xNmSXh2iuHVOvEoe73g5RdJVkKjItVCoFaO0orYXiWpr93mdFdpYsIwUqsD0h8RhVI93RTjKeoKzGEN/NAFaW7PEhhSrAMrBkBdsTYUNMSGkrCl0oqW7MUB1SIS0KepaFZThWGrJcwo0fB2sNXENK86S4phTZbQOKi/+qd9sOzGMH5u3AvB2YtwPzdmDe/08wD+j3yVV15HAlWX8VibyFV3OjK5YIpDVypWJ40adZ2JU4hYgi6xpE+KOzjopMCi+WJCNJUum9yx5fqdAkFb20S/jpFJsHqpScQprsWJmUitKSLIooxQcaRqmACAjbLsVhbsvCi7ZUaCk205xmsKS7++1aBhS5B2UcGIVBeFeyX9CEBVTxMZLuxgxVsz2rYlg2G3uTzFvRxob2JJOHhdnv4EM49/ij2d4d46577uPuB39OWVkEt5VhzXeLyGezfLZ4Kcefdia3Xn0tEydPpTtn09LWzooVKzjo1AtIxGOs3drMTQ8/x5rN21j40TuMmrEzbakC8bhJ1szQHM/y1YYu1rcnOXqXes7YeSfUzd+KbBHn+lqxboE5RbszyyopykpqNVnBL9ucccS+AvO6+2gcUodk9TflbKPQbxFSXMtUTRRzi9fd2cuo5TWY3jJsVUeROkUoqG1RMC06EznS3S0YjkWO6vGTS/QS6wqxrClG1O+iIewmpHtR4q3oio5fj7CmO0ONv5qqch011gKWySW/OI5cwSKRyXHnI8/Q2dlJTU0Nfk0hZ9jkTJtEwaK5qZnG+mqOmD2ZmoCbu177hHddGlcfvhs/tHQxotpm/spN9OUKaIaG3JTFNgt0x5Klho7tNPu9FWHsqMhm6U5n2daboLWrj/mbm7nq5/ugeBybMqf4X7ynzVg3RjyGrKmoupu7H3uWtVtauON3V2ORE/dAIQ+OzZXkNJFsReWO5+bR3hsvFQGLKiF0N7IviOwLCs9jWcE1dhpWzRgK3ihash02LWXhl89x/K+vpTlts9uBhzH14huYtP8mmisOp+7Qy5hZkaQ6oCNldSbOmMmE8TtxaCY1qEEnudys2LSNJ9/+mMXP3oOu9NumDbQlKo2B6smB/+/sF2XdLdQUuYwIfHXshGQJhjcMw2r/jCn152N5NdRiM9IS1lDvfP41Z97xd64/4xie+/0lSNjCKs6xQaMYOCvJKJJJ1KcTrvBRM3F3Ctkcbp+H3o962fj639BC1ex84RNseeUmhk6ZhdS1mRarjNa162lv34m9h0eJ2n3MGVXJQ3/6jqP2mMrxxx7N/PkL+OX1d/L63+/F5zcIaDK6LOFSJMhbGDbEchZb+jJs70nj0RWmNZTB9lW0rl7K2KDEPb+9mddefBYUHcsto2ZiyIkOAoARrMH0lyMVbUEAOROjSpKR+zqxu7djOc1QSS3eixpFVQWWiWQUsHU/tupC0t1kMgnSBYNI8czirMmKqiEFIqXmou0KkAgOZXNPFl2RqQ9qyIl2MPJIpoGST2H1ij2W5HKLeZJNI3kD/QXvHzVy+RHm9VvcDCaJ/EePHYzu/5Xx32B0yyiRSsyq0QR1GZdsQwG0VCdqrAXLV1bycCtOQrliiNiI2BbEOrDSCSRng6BW1mNpHscLDxHG4QCa7AuCv0z4pZn5EgOiCMglhoCsYMa6SyEQssfngLKBnY2JxzlsrGIyuRKKYuUyWIk+zFj3oLAV2+XHKtOR0gmx6XNCpIryYzPRi+wNisNSJjUg9MJh7BXl2bLw3ZRcntJGVnLeB4Dsd9gS3giyYbO+O81r37ewZXsMo2CxdHUnY0aU8c66bhQZjjnzPBIN+7PluxVk6GbPO2/msv1H8fyLL3LOL07hyLMu5BcnHU9NUCcc8PHGY/dy6B7TsCUFOx3ngRfe4r5XP2L2HnNY0tTDHZ9sYFi5j/qIeE97jq+iOuimK5lH7dqENYBVVCziDOrOD1xknJ9bqTjICpGAjwNnTmLegiVc8LMDxbWBQR1+yUCkNhc3tAMWMDufRfb4sDQPZrCKmKkSjdRiROpREu0E9SCLNnbTMHUSucw40vEcbSvm46sYiu4LIkkSW3szzKwXkmw51Y0Sa8HtizIyHKU7a5Lw1eJzh1B7mwB46Y0PuOfRpzls/71p8JiQbcbWvURDAboMldbm7ewyfQbLHv49I2vKkTSNu35zFr998DnOeuA1CoaJIsuMGVrD+IZaFJcbUjGwDO696BT0hp0wA5WQz7Bk0QL+8uRHbG/vIBoJk0xnmb3TcMo9GvecfwJqJIpaNRRJEXPbSsXR60Yg+YJi/mnOptzl5Z2PPuXF197A17EcSw9hN9Qhd20Wc8zjE/dstIYX33iHbL7AkzdcOKjwhGWhRmtQaoZhdjrsB0VBHj2dhekwM8wUZngItG2laf0ahleGOf83l7LXzF2IJxIs+/YHfBNHokoZvvp6Cd3h24j4hTxKLS+IMLJCnvUtnTzx+vssXbeJhtpqHvvDZbjc7sHsNfoP/LZp9rOEij8vyll1N2a4HjkbAyMPngiWkcMK1SLl06DqlGsac8+8gF8ethe3XnMZ8cgIQl2rxeEFsFMJHpv3KeufuRN/wD/oPRQPe0pvc+nxtZqXs6cNY0ptkE9HRDEti+09GfQbX6W3PY4n4MYbdGMta2DVm3/nulfms7wlzW4TqhhdFaDeayMlsxgmnHn66SxYvo6jDjuIF155lQvO/xX3PP0aV//m18iZGLY7gO3y0256WNGUZH13iq1dKc7ZczjlXp2NPWnKR4zD4/Hy3hP30trWTsDrBcugzmgXqeqWhaSnkLUEluYUK70hTH8FtqIhb/52gJWBuPfMopS62HF3Dm125xa0bAzLObR9vnQlrR09nD9pGnaip7/g2jCZVrmMKr0ggkqMPCviMpe/tgyfS+WVo+uwk32oZgFb0UoHxn7Jq9J/OLJEaFxJ0p3o7WeHDTzUOwUsoHSQGmQL8a+OHZi3A/Ocz7oD83Zg3g7M24F5/1/FPKH2KYiw1mg9pr8CxTYIuYTqR051I7eth2A5tjckCsiqJhjV6oCisBOAWywEyx4fkj8kmNSWgewEj5mpuGCnukNYLp/waE70YmVSKJX12JoXKd5B0de5xLyWi4HLTgEkkxTFNcd+pCjFRxPsbCveXWqg2bKCrblL/saypmMnevutx6C/eF+8N4qsxQGFw9I8Kip2igx3+os5AGr1UIxQrWiMyyZL2xI8vbCJ7dtiGAWTb3LCj3dCfYiIV2P63NNIDt+f9V99jeoLM+fEmzlmej1LP57Hb87+BXPPuojzfnESUbdMOODnjUfv5pD99oZCBnIZ7nvjUx545hVm7XcIC7d08d66Lj7+oRW/T2e/CVWMrQ2iyBIFy8KQddSBjeliI93t7S+AART9cIvz1QnbjQT8HDRrMm9/sZhf/+yA/gb/j+a3JPc3H4q2acWhhKKY/nLMQBUZW8EnySjxViTLJF0w0RWZhl1mYhSmkexN0bPpBwCyqTy93Sm6kzlyho3tc2HLKkqinbCeZFTZUGwb8u4KkAW7VMomefmtD7n78Rc47MD9GOnOQs9GvLqHyrIa+vLQ197ClDmHsOzRmxhVW4mVy/Cnc4/jhpc+5exnPiWfSSMXcowZWsvO48ciKypSLokV7+Hei04WKiZ/FCmX4PumDu559gO2t3dTFvKTyhnM3nk05V6dv55/Anp1PWpNA6gaRvNGwWyvGooSqURye8l3dwEGhCt558tvee2NN/G3/iDWEdXdbx9XJBS4vby2cAU5FJ648kzn+sui4Ki7kcMVJSuQQvNGUeSu3Ym4FiKTNakIVKGFomzdvIkJdWVc+4frmTJpEvFEgm8Xf49rVDVu4ixd8h2tWjXV1TrkUkiKhqK7sbIp1q1dz5PvfcHStZtpqK3isRsvRZco7ZfsQr6/qev8u7T+FJtlzryRPT5Mb0RYahhZ5HwGVDcEK7EAOd2LokU58vQLuGDuPtx+zWXkNDdSsEbszR3yyGPvf8WG5/6MV5OQVLmktrAyKWRAsQxQdIi1MEzz8LOdhlLu1fl4bQfbezLkDYuKG14h0ZNC1TUqqwOkhzSw5q2/c+y972HJGqNHl7PnmArqAxpyZwIrb3DWmWew4IdVzD38MF589Q3Ov/g33P3Ma1xz4TnIqW7cqgtbctNtuVi+Lc7Cpj5My2Z8TZCIRyNnWMgjBea98+QDdHa0o3pDIhw13g6xNsxsGkn3oSQ7BY4Ckj+E5Q4gFdIoya4SiUY0Q52G3QAsKe47pN4WlHwSO5PENvJ8sXwtzZ0xzj/uUPEcsrBvzNVNwrRByyeRjBwZdxmfbO7jxW+3M2lomLOn16Ek2sU67DRkJMfisajSk3Sn4J3PioyUYBQsU+CxURBrvi+AnUqIOQul3CPJ5RGNx+I5ZcfYMf5vxj9d6C766UguN1awEltz4Ul3CC87RYOOzZiWhVm7M0gymrUKI1xPFz4yBYuGzGbsnpZ+g/lEr7jBJEnIwgZ2y4uBKI70Ss6nsNNOp9QTwAxUiQNFXHSIigtn6WZ2RnHTQSYlDj9FXz3LwnIkZ8XwHyxTLKa2hZxNYLkDyBVDobNJbNgc5lpRnm0WN0WyCIQBBnUkS6EsjjyrJFsuMjZkBdtXRsZbiWRBxrBI5A1My8a2bYyCiVEw2dIS54flbUwYX8WcsRXIsgSqm0ImRWtTH23JHN9lRqBNmssbzzxOonUrW1YuJW/avP3BJwypKmdLey+PPfUMqsfPlTf8nj/89ipqAzqNlX7eXbCVY/cZwS51Ic4ZlkPOd9MUGI3UubTkAVtiOHgDotvvsD3+b32SZIW9Jo3l06WrS/8eKBUeKMctsayKUkdnvtnlQ7G8QtqVyJmEyobSl4ekXElAldAVmQNmDuGgMRVc+vR3xJqrcYfKBVNLlnCpzmuYeWzdOdTZonMqAT4zRUrx4YsMRYm1cMQhB/Lzo4/EnemFWJtgW/a1Irm9VLmCeJUsI+qr+eCHdaxt7aa8upqaijL+8KtTOfCi3/PefX9A0XRio+bgVywky0Dt3ICdipcKWVLHZv7+wuu88cVi7rnkdOqqKjjwwhv54Im/IlWPwl7+CfmCwfvfLmd7m4+j99yFKrcPs7MZY/g05HSvODSoOnY2RUciTS6XxasgimexNqTOLRCpRg5GS8UT2xPk/rc+529338WYMg071YedFgFMwkahDquvS3T60wnkMTMwQnW48hnWpRU+3NjFRbVDKNhwxmmnstve+5FNxKjd83g6Viyi9+tHqZxzDj2rP+SddV0cPKqcMmKYmpfFKxbwxGvvsaG5jZvPOYEbfjEXze0ECxUGbp775Wq2UUDW3eLe+1FRQPb4kAIRCpoXRfciG1mxwU12Cqm4O4itexiqamysqOasCy9lw8KPGHv4EVi6D6N6HHrLMiyjQMG06M7k8YeUn85lWXgZ246thOT24veEGBryM6LCR0ciR65g0pPMM2ZsFaZls++4Kj7Yvj+bvl/EBy8/R2DqoezeKGwHfnnzfbR+/Q5Bv4/X5r3PrOm78OknH+MNhjn4yJ9xxdVj+e3ZxwubCTOPLatUaR70aj/zN/dw4NhKNFkm4FIpWDbeoIt9DziAZ59/AYCa2lpmzZzJzJkzuOMXB4v1VBcHO9k5IMqFDFLXZmwjj+UUJ4H+0BvLsd5wCpxKICIkxk7QouQLYmVSeDWNX51yDOTS4t51e7EqR7Ao4QWySGE3yztSvP5DC93JPPuOr2JMpR/LH0H1tGCrLmzHP3HgHBC+m4X+IkJFAxvkanyaTDVx5JbVwuNNVpB0ZXBB1uVGKquFeNeggK//7tiBeTswbwfm/fthXns8tQPz/gMxb1tbJ6os7cC8f2fM8wWEV3AggqV7wMiiFNKCaW0UkLMxbK9f2NsAsurCdvlJuYWSyJfpEnJ1KH3OgUxAuZAR13ZAg0/2+LAsAzXWgtHVKhqyo3ajPWtTqRVQzTzk0qCqSLbAHjsr8MWW5f7CSTGzosSWH8D8c1j5kmUKX23N3V+YLwh8KqmmBmJake1dzNzgHzSlAIqFu+Ljnb9LHh+mJ0zBV0HGsJAkCOgKiiyRTefJ50xM06KrOc6a9d2MHRWlJiyKOoVCgXxPG51tCT5f20mzZwLuyUfz2tOPkevcxsZlS8iZFm99uoC6hka2bdrA3599CdUT4Nrf3cR1V19J3hTl5boKH3uMruDwMeVUkcQeHwBFRW1dhuU030E0bYvro/g8/1hZUro+wJ5TJ/DZt8sp5U+Uvlj5p787YJ5L3gCyqmN7w1iuALasksyYZNQy/NUV9GQNejJJjpxai3fmEHoyBV5esJVsvA7N7UOSZSzDRpFl3KqMVBBWTrbqxvJXYFg2pgXJvIVXC+GvDCAZeQ45aRhHn34euiJjZ2PImRh0dqAnu4gGKnHZcUbU1/DBkjWs7UxQMWQY1V6VG086iIMvv4N5d1yJPmE32jz15AyLIa4CavemUtA4Lg9YBo88+wpvfPApd194CnVlIQ664k4+eO5BVN2N1ddBtqOV91ZvpW9zlv3225eqiixyLotSUYvliaAqCnrrFsxkgvbOTnKZFFo+Ka5jPgseXdhC5LMYXa1otcNhzGzuvfIe/vrXvzI6qkJPs1B/DGzAINY+vXECdnQIljtAImOxNZajN6sxqmE6edXNCSedzD77HUB3Tw+TjzydZZ9+SGbR49Ts/2s6V37Ity1x5jSU40f4Ly9e/G0/5v3qFH531vGo9M+JQZjmNIcGzakB+yXbFGoVyxXACNaQNW28siJ8pNUAlieEVMiWfn3cyAZ+fcllLPvyQ4Yefji25sH0lyMrGvS0YRgmnX19NNTVlOZpKbzWKEA2DaRF3o3uxueLMrLMS0d9iJBXpy+Vo8erQaWfyqALv1sjNnkvti5bzOpP3+TY089EkSVkSeLu+x9m/gdvEfR52dbcgqqqfPrV13h9fg455FCuuPxyrjvjaLAtsQbJKtFwLUNDHlojogE4NOTB71LIGRYu1cPe+x/Aqy8+B8Cw+lpmzZrFrKmT+dMlp2Fm0yCr2LoPW0sh6VnI55AzMSQjh9HVip1NOfZQ/TZ6xRyEol1V6Z4FJI8fyzDw2Ca/mrsvdjaFHK3Bcocwga6MyfruDFv6MmQNC+hgQ0cCgBFRn8i/UHShzMhnSwGtg77nov+824s9bCKdegUAZeXdyC1rxNkmlRg0f2zLRHa5hYWbkS/lsPzHDkkSf/43X+//h+OfLnTLviCU1YEkY3nCSNkEkmWQLR9JwbLxd2xGKq8naYCuANXjSBs2ybSBV5Mxy4aipmMlyW4JAPM50BEepQO8eiRFwQzVCUZcNobkD5OvHschD35DX2cbV/58IkeNGo8SaxW/U96AkuzEbN1cYmgMTHUusq4Ah5WW7/cQlZ0QCDOPWTkaLAO1axPIKrI3IDZWP5IJljZFzigegAd56uFslIqLajFQCIQ8WdXImjbpgljcyzwak4aGGVbuZXtPhqkNESp8Oi9+s50jJ9XSWOZh84Rq3D6disBUwl6Nmx5bQqavl6oJcxhx0IHMimRI9nazdMHnPLx4MY88/xplkQjP3Xcne+02i/VxmL3HXizvSHH6LnVcvNtQIrEtIHWDDXayj6HSBrGglFWTrRqHLIG65rMBn63fz27wJBmw2ZNlCobBftN35sWPvmJ84zDGOx5vQH/RwBlKpFJc00IeORRFcnsxdS/d+IiaObyai6dWdKFIEhu7UiSzBS7YewQjyzys60rjC7oZMWMysiLT1ZLAKJj0pQsULBupkMX0l2O7/KzrzfPOou2k8yZeXeHNhcuoqfTTWOnDo7tojHrZfWg9kiRR55ORcknREQ1VEzS2cNz+e3DZvU+X3vd+u81g3p1XMWXcaB5fm8EYNZm/3/ElMydWc8lejTT4KyBUx9qcjxVfvse9d9/NHjN34dUnHkTFBNvi1CP255b7HuO3l/4aG7jr2TcxFI3GqvGc8usrGNtQT2vzdk48ooO+nm5efXMelqQwvGEoK1v6SOUtfnPFVdxz45WohQy2xy8OJpqOUlGLUT6SAx5Zzndbe1kl1zJWTyOb/d6EyDJG6+aSjFepGsIHiQiBXIpRZR6a4jleXbCVLVUFzKqRPPHgA3g0mXjOonXkBr78dDXb592KteRRSHQypwI2LfqIc+/6C/lMmuHlIS44cg6Nw4agqeqgza6FhGRbJeZMKVQonxUBRD9mvuluiA7BkhUh0XeZJGydfN6mz46KlG2XjGmDDIwu89Bw0rH8+ffXctRukwDQ21Yh2TaS28eiVRv4fOkqTjt0n0HySiUUFUUJ1QUBFcnIYrn6/aMiHo1Hb72OrvY2jrn2L5w8tY7WRI7meJbe8gkY6TgbF31MZOkn/OauHl798HNu++wd3pr3Dj5d4SlZ4rvV6xk6bDinn3AMq3oLpLJ5KGTFpstoQVZ1rKoRoPs5ZucaKn0qm3qzFEyLdMFkSNAFtjjIRMIhevtiLFy0iHgigX3dFSjxVkzNC4oqJL3eIJaiY3c2UZQay2XVoqhhFEqH6eI9LJfXYXpCSKlN4n71RbAVjavveIDdp09GdnnIV+9ER05hdVeKIaab8RUaBcvmse9aeODpJZj5DC5/iPXruqgZGmZ2fZAKbxlS55b+QEVH4guUPHVLIY5mAY9LwqtJkDOwK4aBY9NRPECXwsEsUxxcw9VI+aw49P4LYwfm7cC8HZj374d5q9riJLIG5198CQ/eev0OzPsPwTx9B+b9+2NeuFIUCjWXCNbUPViqsAyQU91IuTiSbWNoXnKmjc9fQVbxsLZLFJ0ml4eQ29aLZoljlYPisM/zuVIgXrHho5bXkK6dhAS4t32HHAjTVDGFm95YS08yz1m7NTC+ciQ1FaDEWlHSPQOYxU4BJJctsYjtdKL/vvEG+jMRENho5TJIqgvbFcDW3MipbqGcKuJhPtuvcsr1F9IGvd4AO45iYGYpPLRYKHK5kUNRcHkwPWESeYtk3qRgiaDJqQ0RYpkCjRU+xteHaO3LkswW2Lk+RMStYVo2umsfsuk8mktl0/YYHU0xrMBOjD19Do1VObq7Oti6YDOPLF7Mo8+8SCRSxnMP/pm99tyTNR0p9txrLzRZZvawCGfsUkeFWxIqn2QnOE1I2zKRymoxAlUi4M7dg9zbgpXoLfmPS7KFLQ+wkih+fgDLpJDLsu8u43jx828YP3wIE0Y2iKb2gCZv8e9KtHrQzy1/FMtfga0KC4SMIbFoe4yeTIGtXSlMy2basAjjKvxs7ssQDXtIjh6BaVgUcsK+ybSEv7/lDoBlYPqirOrK8uGGFrb3pGnty6LIEn63ildXaKz0M3uIl+ERF7LXi+KrxRdOYOdT2IpOwOPmuIP34rL7nil99fvtugvv/PVGpsycxXMdHppWwBcrl3DE9HpOmVRNKFhDbzRCMm/x9UfvcP89f2aPmbvwyovPoeWT2LFOTj3qQG65/0l+e+HZ2Lksf3rqFUxPkCEz9+P4sy9i/JhRbGtu5tRjjiSxbR2vzvsQy7IYPnIEK7d9RqoAZ15yDY/fdRPS1qXifvcFUSKVqEPHskQexpVPr2BDZwJXVQOmJ4+aT2L3dfXvzZwmhhQIY9eOJeutoCme59vmGMuaYyiyRK3VS6BuBJf8/k6Ghlw0xXKsrtlEtn4/mt++Feu7v2MnOpnoz7Nm4Wfc/qc/kc9mGB4NcsHhe9I4rB5NVbGziZI1iGVZYNtIkiQauwOVEcV55agDJVUTeyNfGWaomoxhoysStstPm+Uhmbfw5yQqNRWpkEPXJCq8Gicd93P+cuvvmbvreGHdV1wHdDeLVm3ki+9W0lBT1d/0UhThF120KHLscooNMK8m49dV3rvvZnq7Ojjh+rsZVx2g3KvRFMviHTkNIx2n+btPeXLlZ6T6enjuvc+48M1XeObVtwm7FXRFZvX6DVTWD+O0446hOW2TzmSxJUeFohrYniBYFhG3zuyhYfKmjW1DxhDXxqMqSI4eIhwO09fXx8KFC4nH4xg3XImsebBcPmzNjeUJoRhZYS2VS2PGBTtaiVSWAjntdBKsIrEmgOQVtjrkhWrDVt1Y7gBX/+kh9pgyHskfplA5miUdeTp68syoC1DuVjDL3Hzd1Mvz768j1t6Dkc+g6h50VWbfxp2IyCokurB6O0VOju4urceCOFIMXFVKyoSsaZHxlOOtaEAe2Ch1VCGSpotGqCuAZGSRnJyS/9ixw7rkf2X804VuY9RuSLkkkiWCr/qUKDnDJpMo0KBnsIfsTMYTZcn2OE2xLEeNLUeVJTpSeVoTOTb1pLmsMdTP8tFEorCVTUFWBFXZsiU2R7Lw7fr7RoMlW3q554gJfLktwUtvrqGQM3B7NdpTOX732Tau3qsBf/tyrOZ2TGezIanaoAP+T3xDh03EdgVQujYIL7JgJXaf4x+UjZNQ/JQVMqUDhxwIi87Rj72BfsTQGtTN/tGQXG7hR1Q+jHygBrWQRipkCGnQnrJwqxLlXp3dGspQJCiYNuMrvYTJcvCoSfg0md6sSU3YzXEzhmDZNs/N34rXrxOM1BKt8jNzp2pmNZQxbPwUMobJA28v4vObz6Js319QvttcNmsyjz5+C0/9/WH2P/86yr06jfFVwhBf82JUjET2hJDb1mKnE9jZNG7AVnS+bU3xp/se4ppjD+CuZ16nJhph1JBqTj1wj598VtsyMQyDodUV7DphFN2xJAG/Ez5RlLENYArYRY9EVUONVGKbJi2R8STyJrptobndfNeawLJt6oJuutN5epI5LMsmZ9jEcgbH7zqUkEvj1aXNWKbFlDEVjK8JCP8rJ/27NWVw+8frWbRwG5pLwShYVA8L09qRpCeRY2xdkHNm1BPVTExZwwRUWcVWNJRYm0jeLojC1O5TxxMsqyCZSLDnVX8jmy/w4G9v45ALr+W40HbG08Nwy08uWEdbssDfv17PN/c9yANPPY/kDRH3qbgUGU+imeOOOYob7rqfKQcewyXHHcrH36/h/Qf+yFeRWcS31PDW6tXUzTySLelN+CSVF645G90f4NHkUL75Ns2GJ69i1fOvc+S5l7FfWRha1qFEKpEjlTzVU8UXi7diZpOkt69mtrwNOadj9XUNYloWNx6S24dVsxP7KiqvLFjBkTc+zvm/u4LHTxrPOWedzg3XXsdrqzto7cuw35hKJtSH6Jk+gqPm3Msnd11O65YNHLLvXuw2aSx/PPd4hkeD2PmsCDoJVyHZFi1LF1BR5UORFa786yPc8/xbfHDfTcyZNhFkpT8kqrghHiBzk9w+UpEGXEYa1YK2nIyMzZdNfby9rJW5k2vZc1iYgC5j2aAqEka4mk0tnaS8VXjIi02A5kWSFQ6cvQt7TZs4wEpAMINKm3zdBE/Q6Zh7iWshtnen6E7nWfT6U4zZZy6jKwVzsj7oZn13GrO3FUV3E5BNyv1+qkZO5RdzD6atvZ3erIllS0Q9CqNGjOCzBYuwAL+u0NnVRdYdweXyY5YNJW1rKBLoNowLmICJXu4ha9g0Rlxs3t7GmEnTeOftt9BdbqZMG8PSbxezbdt2Zh5wBG88+SDV/jwYimC72JbYAJbXQ7wL2eOjEK4nEfUTyrSjJjrEWuAwiY1wvQgQsUwUXxBT92C5Atx63glI4SrIp1BirXzU4iXq1Wj0g9a1lmzlWGYNjdB39ES+WtHG9nUdhMojjK0N4NNk5LY2zOKBX9Wwy4diu/xi49K8VqyjDiPY3LyM+lAzxqjdaMqGGWa2iO8nlxGHNAdHUDWkYB3x8HB85FDzSSx/xf9xPf5nxg7M69uBeTsw798S8zY9eRVrX3uXYy+8dgfm7cC8HZj3P4V5ZQ3OnDCwdS99BZls3sCjyoR8UafpG2JRc5KCZbFzpQ/ZtmlN5ljVnsC0o0yvG4/cvBJcResKuT+UsuhtXchjdjYjDdmJ9zb2srknzeyhY4nZBnc8vZRta7sorwswb2UbXekyDhoZJSrLpTAtq7dTBCo74aySLPf7cltmyQKspI5SdEx3ADkTw1I0bM2NiYyST4NhIHn9yJbpeP8OaNwWG9T/wFO4ZLNQVDL5Ao7/rY4ZqaNQDPAEPJJE3pQxbYuoV2NafYjKgIuZ9WFGhHVMWyicfJosQpZlifoyL1u7Uny3oZtEbwbNpVDTOIRjdx/OHsMi7LX7bL5vjfPUO4v57u4LqNjvNDI77UeL7Ofppx7giUcf5tc33EZ90E1t32qs7laoG4tR3oiUSwk1SCaBVMhAuhdb1fj2hxX86d77uebkI7jrqVepKQsxamgNpx6yd+naljzPZZl8NsOQ8jCzJ0+gJ5km4PX8pCFcyqNwfM+LWGh7Q/QFhrC8PU25z2JIUCNrFNBkmRq/i7xh0Z3MizBK06bMrXHwxGpSYytY35ZgTXMcRZUZXu7Hq8nYkozljbAtUeDJb7fx8fytyIqMosooioxl2+guhbxhMXtIRBRPbVBlSeSYOMo2Kd1HISuYtbtPHU8oFCGeM9j78r/Q1JPiiS/+zM7HXcSIwhYaeyYS6Utj+StYsC3Okq2dvHvvfTz61Av4gxGaJYmKkIpH93Hswftyw4PPMPXgY7n45wfzybL1fPjSYyzWxqAmGtmebmW3w0fTtOYLvD2dvHjDeehTD+D5b7eyYZ3Jt7dfwOotH3DOVdezW/lQ5FwCI1iDVT2OzqzNVyvb8ZKjY90PeLM9KGYWK96D2d2KXSiIQMtSuGVQWF34Knj+w6/4Yv4iTjz9dHYKq9x40RWcd+nVxHIGr62OYVo29WUe8uOGsu8ud/HVPVfSumUDh+05k92nTebWM+bSWB3Fcqz05CFjAGhbtpioR0LVdC6/6xHuefZ1gXkzpoh5McjeRnbsbpyf6W5MfzndhkYibxDQZdIFixeWt7G9J82+YyvZfUgIv9tNPm+hyhJVtXVsbm4j6avGo4Cc7EQyckiBMg7aazZ7zZqG5PYK24sis1iW+4NrZRVbVrG8ERKSm23xDGs7knz39jOM2Wcuoyp87FwVQAK29GUwe9tQdDceCvg9AcaNnMq5xxxCa1s7qYKFaUOFV6JmyHAWLBKYV7BsgXmaH3fIg+kOiNczwKNIRCShNunO2cRyAiNTvd1MnDqdd99+C5fLxYwZM1m8eJHAvIOO5o2n/061Q1CQ072QSWA511HS3VA7mlRoKBbgy/agGFuxYt3CD94tgnmxDHHOyaaRfQaSZXDrb69ANvJYioYhqQRcFl5Nwa/JyLkktRQ4enw123vSfLNcIxXPIskSAbeKZYNk5rGcxqM2fByF6nFkbQVPtgeleRVWJoWV7MNKxVHyWSrqGjEjwyjIIWzdKzznnYZoySJLVoRCLVIPsorijYgGxX/osCX5fzmM8t+v0H300Uf/04999dVX/1uv8U8XurcmTFyql2qXha3oKAWLMo+CnupB6eukPTyGRZv7WNWeYM/hZfg1cUFjWYPJNQHKvTqW34fU0wIILymMgpBWZ9P9C3BAyHZll4empgxrtsX4cluCSx5ahGlYzNltGNMbyhgScvPdll7a0waeyDDyNRPxtK3AionupRKKYqXiSG6f6CAVvcZkGTnRLrqNup+OsvG8t6GHE8cNRe3egmQWCBZEerPtCSJZBhLi4F+UMvZvhIqL5U89gooebZI3gFU5Akv3ocTbwMiTt2wU3YtkFlAS7YzxBojLXpJ5i/qAjqpI+DSZUK4bbIsICaSsRaU7xNCQB02W+GR9J401ATZYNoWcwbTGMnYfFqE+qFPmUVnZkcJ0Ryk/5Pd0rHiJ39/1V6477zRULG6+9gr2byyjOZHHymWQXWLBTRs2fsvAqh4D6xeRjMX45OOvWLaxiYdf/5BYPI7fyvH0B/PZZ8YkKsM/8ng0TbpiCSrKwhiZLM2dPXy1YiN7TR4rOnYDg0lK8hWHSZHLIrt9mIFKul1VdCULnPf4N2gulftPnkprMkdjxEtjxENzPMvY2iCPfb2VAyZUMaEyQJlHY9H2Po6eUsfa+hB5w0KTZaIehRQqXoeR0BbLUjM8gm3bqJpCoieDN+hibF2QUdUBYjmTctXC1bVBeOdmkqUOpGWa3PirU3nji8V89d1K9pk+EROVOZfexcGNQRZ9+gGrP3+K8gmjuO2PjzNm5EMMU9384rF1bP/kcU479lRGVYVI2xpdGYPhhW0kkin8Hj/DG0cwsqmZrlSWsnAYVdfYpcZPKp7n9LMOYdGGbo45bl/q5BSdkp831nTx/HebSfQV8I07CuObp3j36+/Z79AxSNUjuG6FzGm71HGyto0TxgxHylfzQO5iXnj/K8r3Po4TG6pQurdidrf233dOUJ3as4VE9UROOmQOsjfKPiNv58G7b+f8M07BN2QM6fWddMRFIS+TN2ms9LH23VfEYZJveetvN9M4coSQt9oWW5vbWdfWx3uvvsvG9ev4/sM3OOfnh3Lt6cfwx4vP4vLTjqEqGhZFtmyqVGwrev4OLNhZyT48sW10e+tIF0wkScKwLTRZYmpDhLqgm7xpszmWJ2Sl8MkWHQUvp/7qIo4+5xIa5l7Inw8fizfWRJ+ps3JLC40jR/aHPuWzyOFybFdQMEQGjII3Sixl0BzPsqFDSAj33ndfZg0JEXYpNMXyWLbN1DovC4w82Vg35WNHcdPdDyBnYuw+awbXXX8DJ190JXsMDfHLU46nZsgwHvjbvSS3CrsDze3FVF3ETBVNhrU9WSZGNZS+NsxgNcm8RTJvoUgw7+23ee7vD/Dt98s55eQTWfrtYoY3NtLR0cH3S5fy1cJv+PlhByBlYo53qGCP2uEaktFRfLy5D7abTKm2cAdq0NwhwdrSvJj+CiSzgGTkRBiMaSKne7FcAWR/CNMXpVAxEn3LN5zqLiC5/ZjpKh5pj1CXjtEQ8XDQ2Eoqgy4eak6Qiuf41ewGfN3rBfvY+U7Nxul81pLHTMDwcJCR1YY4mOpxrEQf+XQSBdBaVzA0UCWk01WN0Lq+5CcJiLXFttAViZztZntwHH1Zg13+KXT7x2MH5u3AvB2Y9++LebEdmLcD83Zg3v8s5uU0fJpM2OUlb9nkTQu/JhMwk9iWmzYpzNb2LF9u6WGnSj8+TcZdSDCjLoBLlSlYNnl3CLcT9Fmy5nLCP8WNJ+zA1PoR2O4gmYLJ6pY4mzqSLN/UQ1dzgtoRZRyySx1Bt4ZXUyg5MoXqIDIMLbAFs7cDOVojLCN+FMIp6e7Sod5yB0j4aunJGNRHIyixVuRMDFmSRXHHLcLXFNsueXOL6yyXbG5sx4YExyql+DlAWHXJIeExjWUgpXtBVkUhXdGR8mlcmkrIpaArEnnTpjbgJuLRKPcoqPkkmlnAbRlIyTRK2TAUSUKRJWrCHsJBN4WcgeZS2W1cFZOqA4TdCh5VoifswRWuomz/a2lb/jqPPPwQV539CyTb5ObrrqbCq2HatvBal1VMzbkujsczVh+xpk18unQ1y7a08NALbxJLJAjoCk+//yV7Tx1PVUW01NS2s4Jl2ZUpUFEWxrQlWnrifLVyPXOmTgDTGIxxzn6naLVjpRPCkzs6ii4lzHtru3nss41Ewx7O3mM4BdNCVSTqA27SBZMfmvpo7cuQzJtUB1xE3BrVfhcVPh1FlsgbFnVBFz5NBDvbkkwqX2Dpxh5MwyJS5UeSJNLxHC63SkN9iOnDy4h4VDyKhBIXc8FO9ZWyNGyjwO/OOo43Pl/Uj3mql/Nvf5g1Ld14t37LmgVvMnt4lDt/+yQT/3Y7ldNGsmDzRt595G5OPOVMqsvLsWwbjyaj23nieQhGq2hsbGTE9ja6cxZl0XJUVWVYyM2wyiCr1hf4rs3k/CNOYWadn7RhMW9TH4+vgw1LtqAN2538+vf5bMlyZh69N3Kyk5geIZWx8Kgyp0yq5ldTK3kwdhWvvPAcJ593Eb6yBjze7zDam0Tzp2gXobuxk310BgrcfNphaKEqzj7nl7z56D2c+8szmDR5Egua+nhtwVb2nlzLuNog69uSvPL4U9SWDwG+5e0XnmLYsKHC717R2b5pA2t7crz+4DwSrVv44p3XOPuwvfntBadz22/O5vLTfkZl0Cfur/xPLbPEzWqJrIh0HNnViz/kJ2faJAsWedOmocxLyKNhWTadaYO8qZBO9AkGucfP3F+cx2G/vIRzrr2ZQ0cNJRjbSl9fL8s3bKVx9GhhD+YNOGpFHdvtF2xoWSiAbM1DTvPTFs/THBdKgCLmjav041IkYjmDrGFR7ZewnGDvoRPGcvd9D9Hd083es2fyu9/dwK8uv5ZKn5/zTjuBYQ0NPHL/30hsXweAS1MxXRF68ZJJi0JttU8VGTmKimkpghSgSrzz4Ts89tD9fLzoey448xQWL17EsOHD6ersFJj39UJ+fuh+wk5RkoX9nDuE5YtS0Lw0xfNsaoozoszL8FAULAPZ7QdZxdI92JIsshGK66bqLmVN2I6yx5VoZXyuF0v3kZGG8fLmHJU+HUXKMao6QEc8R28qT1XIzdGTailLNWP1dWCl4siBCGZkGFuTNk2xJCPLQtTVT0DOxFCzMQrbN5BPJ5E6W5BVN6okiQawLBj3VjaFnUoImyiXG0mSyaERy1igVpI1LUb/C5i3Y/y/O0KhUOnvtm3z2muvEQqFmDZtGgBLliyhr6/vv1QQ//H4pwvdzfEcXk3BtjVSTgfbp8nUWwYtoTE8vHg7QyIeTppcg0eVyZo2ga2LODBcgy0bDKv2Ifd1lA73ktuHjUheLaZ3Sx4f5pCJ9OKlK2Ow2/AsO9cEeWJRE2ccOY5EVvh5Dgm5SeZNzp7dgE+TsVUXedPmjVgNezfsRGVyK92hBgqmjU+T8SZbRBCYbQkpnmkIqZSRp0y3+dlO5Uj5hEiadTrwtqJhBqv/L/beOkyu69r2/W2sXVzV1UxqMaNlWzIzM0PsmB1DzMwx2zHEMVOcxBQzM1tmEFpgsVrNWIwb3h+rqrrl5N2Xk3PvOTn3aX2fPrXU3buq9l5rjbnmHHMMJNtCyQxtAsONwUrBm1TVgu0JI2Xj0LmqfM8kw4sTaQJFR8olkHMJHFkh0LNUONvKCj3uejyqjF+yyMoSWdOhO5WnKeAioLpEu2khjaMaJCSDnJmlMuDiql1GC73D9UEe76sAAQAASURBVFEmVnlZ3pvCoykoxX7YSo/OrNEVqPoMJh65DfcdvgWpZV/x8/JlHHngvlQmWwn7KlihzkKRJEZ6HEJdi0VAXjuaD39uY58TzsFjuPj9BacyetQovvzmW1oaavnsnssxXC5Ou+MJLEWnp7ePyS2NZLJpzr7nKVrqqljf2UvI52HvuTPZcdrYcrvpJgxBVR8KJlUNyReiW6/mp+4kBcuhuSFIW1eCp+a3M7clzLM/ttHan+bwLRtpG0jT3hnHM7OeBZ1x1vYmGVPtZ1Z9kM9W9dJY4REBYWw9ZrgRR9aJuMG2HVRdYVJDgBWdCVZ/8xX1U7agdnodbQNpfupO0jimAlegViRpKGqiFYM1FTjlyAN5/Pk3+Pj7xRR+/pK2yEhqPSrbZBuQ974Ks3IMz334FU+9+QHHHn8S67/4gMy6xey7z520Z2ROOWR3fC4Vx7aI5mx6urupqavjg7deR+5Zzfu/Oh2AYNsPLLxuG95uM1ncGuXeL9eztidFJm9hOQ4L33wHTyhM4qc3qNtyF6b7hCGe07WGG8ZWY3duZFXNHD5fNsBks4MX35+Ho3vIJCbxan2Ai0b0MSsko+kuEWgEa0knYuiR0Xy04GeCo6ZjOi6+fu91vvxxMZF9TmO6V2NOc4jxVT4aAi4AFnUleHKNRP8331AxfU/2uvphttj9ANp/+IR4MoU/FKZ51Fi+++htTvzdPezZ4uXDL78Xt1WGmqrIkAZiyQxKEYFySbu0LP8A0L2GSr2dKrePfN0UYjmbfcZWULAdelImuiLREtTp70/iGF7G+FysHbc1fV0PsUsQOpImYxSVXLofRdOQAhGkUqVT0YW+rSSJCr/mwlQM0qbNQKIgWtY0hW1awszecTcOOvQw1kezKBLl7+217WyejUTIJONcc8Fv8ekylhrmgb+9zmE7b0Vs/XIexSKWzpDPZrj5+mvp6Rtg8U9LkfJppEIW2efDchymhyXRjhmqx1ENrFyBWp+KJzPANRefyy233MKkljo8bg+qqtLR3o5ZTJr0DcSwfFV0ZmUs1UFXZFyqRG/aZNX6GHMbA/SkTBp8KmrfWiQzi1nRQkp2M5i12BjPUecbyYiqMSjRduRcQtwTRQfbJGM6qIFqpIF2rEAd296/gp4Nvfz6qC2o87v43RvLWP7lYsZuNZn1i9fzyrJuLhqrl9lpkuFBiXawi08BVcfy+7BogkQ3dqgRsgnWLFvGFMOLk4whRftwdANUA6l6hJAiKLZ0O/ksUqIPLVBLVvViqBKxrPnPwttmzNuMeZsx778Q8wb6+9l6663524uvbMa8zZi3GfP+TTBvaU+K0RUeFEkimbdI5G08moZUyJDW/PzYGaMh4OKQybXU+VTcuShSIU2VnGHXWi/goPSvHZIQGV4QHcYmJVTLYLCFvrRFujBAY4WHkEejNuRGnS0xttJLtc+FocisGkjRkTRxfHX0pS0GMgXGVE0lWGfTm5cwg1DtUVAGNgi8UXRs1YWje4QMjySTNQXDEscW+4WVL891EGvaLnZ7SLqBXPTnACExJpdwTZaRU/046WR5fcqBCixfFbY7KBJG2RRKfoPQfpZkHFXDBnSXjxIS+HQhjaApEo6sC+aplcfRDOJ5W6yD2gCjwwbHTKujN12gLZ7FUGUqPVoZ8zyawsTmEI49nWlH78CDR84ms/xrVq9YzhEH7U+NkqGg++jNVSIFK6nQLNRoh4gLZIUPv1/CPieei8dwcecV5zFmVAtffPsDI+pq+OyPV+I2DE79/eOYpknPQIzJI+rJZLOcfc9fh2Gem723nsGOk0Zt0olS7voqdY4UE8m2y087QVb2CPyurvCwsT3GiwvaGV3tozOaocInnolHVzBth7W9SZI5E59LJezW8OkqiiwR9GiMCnsIuQSjVQIibjcj6/yousLUpiDzV/fTs6EbXySEVR+gP5VnVX+aoMtPRZHFC8NkNFQNPWhwymH789hLAvNy6xeTr6rhkIkRtLYs9jaNyB4/z3+5hKc/W8qc0HReeep5epd8z5zbbwTg6IP2w++SwXGIpnMMDAwwZ9Z0nv/zIziZBO8dczJm+1rqs2lu328rnl7cxaLWKPPWD7CwSxR+vl3Szbof5hNfvwCpbzEj5+7KSDUtCAq5OGEtRtAdxNKrSBVsFiyYz0vvfEhWNvjYvx0Bt8ppo01mZ5IYbi9ypB5HkknmTIxwhK8Wr8TTNAXV8PHFO6+xYuESDjrjUr5qjbK2P81usxo4dka98D9Z2EnG8rHgu4+omL4H25x2LYcedTQ/fvIOmXSG5rpqmkeN4d3XX+W2+x9n7ugaPnj3HQAUbGoi4aEiEsNkmEpzRBUxkZPLYieiyGYBby6B2xPGNoLkVA8twQh5y2EwayFLEDIUtLQFhodowWbSnB3puO8BPluyni3qA/g1FznTRtVdOFUtQvpIkpAUoW9u626hbe3ykbGK3XLJAp2JHLbjUOvXmbbNzux14CHEsiaDmQKJvIVHU9hj+y15PxIhnUxwyUUXEc9b5PUADz/3OofstBWD65bhkiCZSmHmc9x0w/X09Pay7KclZXkURQJdkXApEnIuCbJKDg0bC58u48Li4nN/y40338KciS34vV5UVaWrowOreB97kzlygQZiOQt30I9HlchYDsm8TTpboD2ew6XK1PpU5EQ3KDpWRTM52aA3bdKfKVDliVAfrBdFVa0of2WbOLqPpBbAq2aQo53YoUZeXNbLH15cwoQJVRw2qwFdkensSbLm+59YG65hy1EV7FjhEZ4O3oAoLCS6GekJ09hYJfaujIxZ0UzGlnD7a1m7ZBFTqmqxezYgD3QgG14IRIRfjWOjxLpF15tZQEr249HduPw19GQcVg9kGFcd+Ffg7r9/SPL/siv2/8jr/ZuNJ554ovz1pZdeyhFHHMFDDz2EUsQSy7I488wzCQT+9Wf8Tye6E3mTESGDKo+KlSzQGsuxXcSCXJ5qV4Zfzayn0q3iT3dBNo9cyOAAcj4F+RROd7xcrQPKrZqlTU+paSJTNw3Ldnjgy1b+9uYK4l1t2IU8tePHc8o2LcyNODguHz1pC12R+POCDs54dyUn7DeBnniO6oCLgEvByWisi+b4bN0AO46sYIbHwDaCyLkEMXcNsZxNvU+0VyjY2A7YRkC0cJU0c7IplFiHcA8vOknb1aNxNLd436qOOtiG7QmTc4exHXAX0si+EKiqSAj4KrCNIGq0DcnMCbadboDuEq652Qy1+ZSooGkualSDareL0WoGRykahLnDOJIXqZDB0WDrxgD1ahYltgGpkGbfoIe4O8j2zUE8skVn0VSiPZ4l4nOx88RqxlZ6+WjKVD54712+/vprbvrdtTzxxJ846ZjDGFM7idb2Tr77fiEzqgw8Xi84DvuccA7tn7xApQ5XPfY8++6yHV9+8y2Txo9hzuyZ2PEB7jnv12RyefyTR/PQqx/QMxDjqN23Zadp4/jNnU8QTabZbvY0pOKBv8wgGtbCXdYarGzmm3wVP63s4/2fuplQH6CzL8X6xauJ96dZPDrC5MYAoWJg0+A3mNEYYllXglVdCXYYX8V+4ypZ3J1ky5YKXKrMyKAG/TJa72ps3YsUauD4bUawtj/Nr2bW8/7qftYsmoLh1Zn3cy9VfhFcpQs2LsfGUYSb/ZDurYLsDXDBmady/rEH8chzr1M5e08+f/05zv79/QwmUlxy3Y3sWe/hissu4fzf/Z6n3/iIWHcfwcZRHHPMMVSGfOQTSd57+Q1sT5hkOsshBx+ELEnYA+10dPVgGO6yrp3Wt4a7381SG/Gw98Qa/NNVJlYafLw+xqofp5NY/Cq1k6cT6FnMw7d9w/ET7xSbw2APkuGlNZbh57Y+br3hAo6//kFaf/iUD75+nZ9WLqRnahNTfSY7bzGFKWNGEHdaOeu631Nd38RAPEH9XudieN3ceu153PvS++TdOic/OZ/jtx/JvuMihOwk3baXh79cT9OcXTBCtcQWvoS51ZGoco6b7rqfhOxiz2AMOZ/iwNULuGSX8Rzx0Hy2mjRmaHMptayVtINdBoo/LA7ekgzeMLKZhaJBVGkNOaqBGmsn7A7jSB70TB+WE8StSrg7F1NvBBlQQ3jtDNs2B5m95VbcfcpB9N7/JgdNreOC35zIQF7iwR96+M3cFpFcUzQc3Y2UzyClBzErmonlLGRJQpUlcqZNzrLpTBY47NyrWR/NEPHoBF0qtuMwpzHEus5e+np7qa2tobWjg9FTZhDPWew4axKv/u1JLr/2BiTHZtnK1QA88NBDHHrUsbgUGbsgDtZ+xcaUVAoYaLm4aKfWPTT6q3DyaarHTAJg6vQZ9Jsal159DYfttzemOXTQveaOP+LZ8WjGV/mYWOnGp9jIqT7CmkpFQwjThh86YtT5KqlK9mM2TqUjpyIle3h6SQ/vL0uy+6wGDp9ay8hwE0qiWxzyXF6URDc+ReebXCVzqnUczc3vjprOG0s6OXhyLZM9WV4+ZTavbNvCnz9bC8CYSi+DgSBhKw+qC1v3IBWy5ENNJPI2fmT0eDtWsIEbPl3P0TPq+UtyJEe4GphtbESKdgv2oWNjBuvZUHAzxl4mnlm0RzAzB1txBxuQXEEGspuyvP6jYzPmbca8zZj3H8O886677R9iHrk877387CaYt27dus2YtxnzNmPevxHmBV0qfl0wCfOWjEt1ikZ/WdyyQ3PQYFTYhTfThxwrmjLLghEoJXsFez6bKmtWSy6RMC7Jcym1LZiRFqK2zjsr+3l7SRfffbGWXHKAMbMnctbu49h1VAi/JpM2HeJ5i3kbciztbKcuZBBNFxgV8TAuYiDZJsmCwvyOBFNqfIypGIGTieFoLmzVIGs5yICLAhWSkL6Qk70iuS2rSPm0KGRIskj82raQQvFWYHmEGadUyCLnU1hGkCgGmizhU3QUOstyHI5qIDm2kALJpYomzlmxjyqKuEeWiWTm8CgabpePkEsF2wTHRM6lkPJCZsdx+TFth/ERL2PCOnrHYjBNaiuaGBOuJG87uBSZvGXTnbLpTxeoDbnRx8jMag4zYfJUPnr/Pb758nNuvP56nnzwD5x41CFQM4lV6zawav0yZkZUPF4vUrCafU48l455rxJRba565Fn22XEOX3z7A5NaGthq4mgkWeGe804gW7DwuVQeevl9emJJjtptG3ac2MIZ9z5DNJlh+7lbDunOl/SWi6zZ0n4nh6uwGqexIa/zlx/b+XZ1P7Uhg97BDH3tCX5IFVBn1dMTF7IhO4ypZNuWMK2xLPM3DLKhL8WE+gCVHg3LgeqAweiIl8aAhhJrF/PNtqj1hDlmdiProxnGVnhx6yqD3UkkWSKTt1jZlUBXZSZWeQmVEmmZ+CZa7pJucMFpx3H+8YfwyPNvUD11Gz5/83muuPWPDCYz/PrCaxgzaSKnXnINd99yA+9++jndq9vxVI/gwjNOpirgYyAa44MPXkEy8wyoYQ479BDWtW7EzGXp7O7DcHuERFysn3BVL5OrfeiKzKgKD3U+Fzu2VDCi0suTmQKF1q+pnrU1Wvsi7rv5a4568HrsdAInl0WbtgMf9rv4/VuLWfLoZdz6yF+Z/+n7vPzCk2R6W4lNrKBRTrHz1NFM32ILBqMxzr75PuqaWljXG2XaiVeh6wZP330a5zz8Kou6EmiKRG3A4NBJVYRzvayUwrh1hVE77kdi9HhaP3yC6u2Ox7YtbvvDAzRUhWlR4iipfjb+9D1HTI5w2M0fsvWkMWW5or/rAiwa/zqKJua+oiM7tpDeyKaKhrhepHwG2bFxfG4UWcKjSiRlCUUGIxfDQxLTCOEUZIIuN3vsuB0vXnEs203+goGKAGf/5ngGczZ/+XYdx+22FcpAqzBKVDWkQhbJsbEVFSSX6EgpeshYtoPlSOx/zlV0JXPIkoQiSQQMlbERL3IW+nt7qa2tpb1tI3PHjKPKozJpxihefe4pLr/meiRg2c+Cxf3QQw9x4JHH4lJlHDOFVMgQ0CCne5AlkLJCPsdFgRqPjlPIUtPQDMCMGTMAOOuSqzju4H02wbwbbrmN0LYHMbHaT61Xx6MqeLMDeCWZrC9MLGeSLtjIgNy5EkI1mJEW1rZ18vrKPl79boDxYyJct9d4RviqkNODYt/LCJ1+KRQgIRn4a8bRbbmZ39rFnFn1nL39KMaFVJKmkFi5P2eRimfpT+bJGHX4qpqEFIwiCl22r4qelIlPlwkU50JnskCmEGRRcCb9tpsdan3kf3gfJVKLMmKSKFp6I2JtF7tUHbMg/JKMIDY6tl0WrfqfNzZrdG8y/vSnP/HFF1+Uk9wAiqJwwQUXsM022/D73//+X7ruP53o3qvZoN+UcHcvo7FqDM1SBjkZA8dGHVhPi68KWwrjyCpqRrQsSP4wtlvQ0qV4vwjYXW7RmlJkscjeAOboOTyxpJeff1rN9qMrOWp6HZm8yTN/6yNQ24iiykTcGkq8FSk1SL1tYbbMZqvGENNOnE1PMsfbizq597Bp6NkYve56pukOYyvq0WSJlTEFxZQYZUAg20tANZAGooKRZubw5dNCo6s4CaRCWuioJqNCU8wXFPqGhazQwLNN8bU3gqOIltygS8FRdNH6bWaRXW5sRUfOxgBEFdEotu1k00hyDkdWkMiJTV5zoSS6xcJ2iwSFo3lQB9aDbWJWjmFlf5Yar4bavRI73g+GF6tlFLGcRa1XRe1vw+NvZkTQxeiwwfRaP4Yi0Z0q8Ob7H/Puqy9yww03kCtYXHDbA9z8yLPsu9fuPPX8S8TiCSaNHsEFvz2T3t53OWS37alQbUDm6wVLOabCRyToR9Fd2PEBkGXmTJ1Qbsd87LLTBZsvm8LJptlt1kTOeeZzbvzzqxyx67YEXNomDt4lhqDs8ZNv2ZJF3Rm+bRvkhc/XseGnNfzoCaIZLlTdjayI55I3bSbVB6hwa/h0hSd+aGPB+kEm1PsZX+kl6JKp9OjIssSYCjdysldUBG0/cnoQJdHNgRPq+a49gaHI1PpdHHnQZJa2xXDrChGfjl3Uygt5A5AX2rrDAdrOpCDgRvIFOf3UE4nmLfY++iSeeuUtegoK1/3uapZdcSdHz9qOGt89fP3WOxz/tyWsXNlHgA5O2H0WL999HR0ZmYZ8O35vBI8/yMyR1Xz15Rfc/vBfuf+a88U9UnWSddO544gMzQEXIR3xObpXsW/9CMZdvwenHfMIv95rX774MsbdZx4ljH4UBTkQYc/rnmDR8nVEN66jYddTWd5vs0VdBUtccXoyMRoPvRd//wK69QKfvvgujmXxzmN3sExt5rwnF5JNu+j+aQU5JcCtr6zjD2fM4b6jZ/Bl66DQt1MMChmHUdU+Gis8XHHZjhx3XZ6FHz7PnrP3Ylrv1/gCAcjJfPXjIqp0yC7/moFYnAuPPXBT063h7drl1tCh7ckKNSEne5G1FD/7JmHaDg4QSxdo7cwwq06jYAeRgFTBxifJSNFOghUjkOMxalSDc088hhceu49xNX5OOPFkWr/5AoCLTjySvZcuo0XKlvW+LF8lkm0hWXkqXDqOJDOQFYwSTZFoCbmZtyGFr5DEo1UxmCmwTVOYCrcCdZWMmzyN0WPGcP3Nt7PP9BY8lvhM+2w3m4ZnnuOAffaktqqSmy49h+P23xnHSmIrbiTHQS7qhg54GsmYDk2yheQ4xFwR9FyW5pbRJJNJfmrtIeB189aqfkaNmYGvaSLZgU4OP/Rg3nnrTaK9XQz0dlM/KoJXk0XL7mAHGF4iFW6WpRQqPTohVQRNcrwbj6+Jn7t62NDZT3zAZlVXAqbWovWvx1FFQIqk4qgGcqKbcZExkI3z2oY8LWE3d+83XpjZda/Bn89yfF09xx8R4dldRjOh0ksw14/tDjLoqiJkJyl4q9j7gW9Jx3Pc8est2M5VQG9fxDXbjGVVGuYt6OTVt1ey7M7dUPracAp57P4OVGCUJONYFnZikLJ7t+ahw/GSTxWYXb9pG/5/dGzGvM2Ytxnz/mOYV+u/h1f/Aeb97bYr/lsx7+dgYTPmbca8zZj3/zFm1nnxODmkbAxDc1PtUlGiG5GinWj5FJOrxiA5gn0sWXmkdFRgnL8C2xtByiZwklEh81GUrcC2kf1hzNFzeHlllIVLO5lc56dgOySzJrnkAIUiS31UhZtwbD1O1xqClQ14aiYwpynEqv40/ek8vfEs46t8+FWQB7upDY1g2+YgigSJgo2qBzAUCdnM4rFMweA2cyKxXUiLwrNmIFl5lPQA1mCPYJWqOpLHh6N5BMtT0UQyTBL64o6iIttCzxnHFtdz+wT+aW7xOrmUKHbLCuhDRpUU8si2CcXuAFv3CuwFkUy3TSSrgG34MV0ByFk0+DW03tVCO95fge0Jk7ccPKosDAZVL5oiMb3Wz8w6PznLQZbglXc/4r3XXuSGm24lly9w/k33cOODf2H/PXfjyedfFpg3diQXnXIsPQNRDtlrFyrDQZzBHr76cQnH7V9NJBQQZrlFDJgzaXS5y+SxSWPLxrl2Isre++3Lb//4NDc8/jxHHbAXXqto9msrZakXJ5NCCVcTb5rNs0t66E3m+HZ1P+tW9LIiKQoDhWwSqGFtb4rJDQFmNoVoCbtJF2zW9KZYvjHGiBofzUE3o8NuBrImU2r9jIt48VgZ0XGhqZBLImXjjK+sI1uUNdl5TISeeJauaJa6kIHPUPG7VAxVQsqnkLOJos67Up6vjllAduvgD3PaUQcRLcBeR53MI8+9xqq4xQN33cTtDzzGdlvO4jFD52+vvsIV76zk28VdBK129t1hGm/fdz39sSSRUICAz4ff72f6qHq++vQjbn/sGR743cXFToFGurVKFnd1sUV9kLmNPrTe1UhWnsmzx7HXyCCnfXkbJ+yzK1991MPdF52K7A8g+8M4kSYOv+IuPv5mOanu9YRmH8mnGzKMCvqocGdoTfeT2PoydGs5nW6Lz/72JnY2xdtPPcKCfJgb31nJ6gXryQx2kZMCfLIyza8bJWp9LrqSOWHy6asi3Zdjh/FVHLtVE359Fn+oSLLmy1eYs9ORzEotwZMFK9bPNys3UGXIZDYsYzCW4MLjDxWdLMPMd4cPx7KgKLnhuLwiflQNnEgzg0YVHUmTAjaxhElP1yAtITdBQ6VgO0TcClI2jpRPoqT6iRgi7v7VUYfz1EN/ZGVPkhsu/C1rv/0SgN8edzi7LV9OoyRj626BtbIJqpDrwHIwVFHcFZ0TOgXbIW7ncOWSVFZW4lLlciyW0hXGTJrKuLFjufmmG5m31SSkfAYcm3223YJRf36EPQ86QmDeZedy3IG7YisFHGSkIjZKhQym7CZr2lQY/rKESCqbZ/TIkSSTSZa39aLpLr7vzvFVvhpv0wTseA877bkPX330Hv3dnaxtbWOHlmmisyXWjhzrBk1HN/wYqkLBclBkCckXpFA5ioG8RG9vH+RzKIqM3xDSSnK6HxRdSJgpKnImhjcXpeAO4+ChApkLdxyFX5cJJtuROwcJA8c2Rjj4ou3oTQtclSSJQvU4HElGsk06M3Dv+6tZuCHKVXtPYPtAAtKD1PobWdqb5tH3RTHgqdO2pqmuRfgaxftQCxmsfBo5M4hdNBKXdAPLV0VKdqNINtNqfGwe/3cM0zRZsWIF48eP3+T/V6xYIQxt/8XxTye6lVgX1Va+fPiVc6ki7V7FAbLeGhJ5C7+nCm+qH7tunND40X2syXsINtdStfYzYQZQ3YJt+FHinRRqJ7HPY/NZ9f1qbNsic9BsdtxrHJfuOJITt2xilF+mPQ3NhU4Y7MQuHgi0npVsUzcGJd5NsrGR5qAbQ5XIG0GcnIWcS+HLRLG9EWq8HjyqjImGZuaK1V8TKdopAhxFwZGV4mdLY8f6hH6mPyQO76o4oCLLKLF2LH8NkpkTAClJNJo5SAlWQCnQcRRN6AwV0uK1HAdHVUHSkQp5YaSi6SIoymeRtJxgBhTSyCkT2x0k7w6jKRo5zccbK/tpDhqMMLtEcAbIqoatGlQroA5swHYHCeoyhiIRz9s0+DXckoVLdTG/M0HLzgdzaMsUTt5newy3G391HZak4vb6aJm+NYvmfcgp510KQHbtAlQ7hz3Qzcv3XMexF99I66sPo9gF4RxfakcbdlgbrgnbPHY8C77/PdV19Uw6/CxuOP90TthxBrLbu2nrku7i89Y4N72xnD8dP4szZ9fzVdt03l/Zy3YjK/jbj20ksyZuXWGrlgrGRjxoikwsZ6LIErNHVbD/xBpqfBruXJRxkRA/dpqM0HPIsUHM6nFg5YX2qO4jkROGCqmC+PuYmQ10j63Eoyn0pPJYjkO9kkbt7oBcWrBRfBUisE32i2qimcUKNpB1R9jmyNO58s6HuHtBmlg6R9+aZTxz/hF4rryZjV29KLF2JjcGWb8xyjZbbMOOU5tYN3k8S1esoGFKMzg2Aa9B2K3T1tZOTSTMqIZaHG8Yy/Bz0OM/YJs2L54yGyXajmRbxQNFnBsvvIIrf30gu+93AHpmkEdffZ8zDtsbCiYPv/wc2WScPz78II9928mpO4xhTlMAMzeSZ596kkBtLWtWd9ITmUBX2MvdD55I0EwQV/20rx4gFnXoWvwOZtt3yJJe3ge+a4vx+Hsr2W1UhEYtT7MV48qtwqDqKANrmbLLgZx67BF0PPd7jrv6VSr8PsY01vDx/KU8f+tl3PXs65x62L7oLtff7TEOlI2KzL6ijqo3gOQLIWWiQoJBVgm6ZOZ3JulL51mwMcranhTpmfXsMCLMYMZkTFgHSSanuvn24/fYbu4csE3Gjx7J6eecz/PXnsEOk8Yw4rwvuOmw7Xjy6We449pL+OPN1+HonnLyz9HdmIpBf8bCpdqEXAq2A4ai49ZkMuNH8pcH/8A1199EQTLQFQkJUCVYuWwJl195NReccTLtK5dRX1+PpGrIcoI63WHn7eby8K3XMX/5SpE81D1gmxSMIIo7KJI7soTl2NieMBlXiHTO5t4HH8Tt9bNg2c9Uh3wMZC3mbxjk+2Q/quHFTEV59q9PcMBld+F0rYLWn2jYaYLQIZRVkSj0RnBUncl6P5NrVVj3A3b1SJ7eqDK3yWLriaOpapmAg0OVRyVYiEHeJOppJlNwqHZZaNFOnHyWSjOLk4xxqOEF2wOdJhTy2PF+5EAEuyhrcbRuIOWqWKOMJmwofN+WYOtGP20DORRV5tyDJlHv15EScazEILKnn7GeMM+fPZdUwUYqaT8CTi6D1bGm3O4Pov1fMjw4kkRDoRs6V4n2t8gO/yzEbca8zZi3GfP+T2HezKn/rZj33vsfbsa8zZi3GfP+P4Yv1iq+kGShb27bIinjr8RWXUhmnpTiRfU34HKnBdtdMxjUI/SmTapCNVRo7nIiF8CRFeL+Rm75aC1/feIjZE3joMO24ZKdR7Pn6AjZQ6cgI2QIAtle6GwX8gXRHjRgjifI1gGDrLuB1nge24GkCUZ4BDhQm+3ACjUwkHOwHfAUEijxrrIkV8lkTs6nhPFoQSS67UyqLMslqTqO5sHRRfeSZOaKb95GKqRRMzGChl/o25biAECyTSh2RUm2KbDN7ReFXMcWJmqKUpbHwDbLiXdbd+O4/DiOjePTiZoqvVHxnseGFZz+NtGq769AMvP4dA9yNo5UyKDqPnRFwqfJBFxib+9Nm3RkoHnHg9i1YSIXHLwzhuEmXF1PWnLh9vrYcu52fPjeO5x0qZDXyP74NrKq4fhDvHzHlfzq6jtofeUhZDMrupCgqLetF6dFMYEPOGaeuoib73/4gdrqKsbvdSzXn3U8J++/y9CzT4kksuOLsGogS28yx/YtFRw9vY6ORJ61A2mi2QI98RxL20SBfHxtgGqfi4LlEMsWiPh0dp5ay4z6ADNr/UTcCqoiESlqnFPI4Bh+TMVAcXlBkrHSkMxbJPMW9X6DI2c1CFZrUfKlzu/Co8nIsUGkQhrJ7RUyNwC5tDDLlBVsI4hVO4npB1bQ+fv7uG9hGlly2LB8MWcevBu33fVHNnR04pYdJtQHWLiyj7FjZrHztFH0TZvMymVLmDt3LnI+jc9tUOH30NbZRU1lmJEjGoU8W8VonlvQyVs/trP3uCrkbBwcG1v3IudS3HzR2Vx15q/Za4sJuGJtPPL8G/xm/51B1Xj4xQ/JJWMceeXNqC43kxqFZ8MIbyM/fPEZmZowAx39zK8ez6xtxnLlcSdS5VbI27B2URcdrXmiG37Cav8eyZGRizeoP12gPZolVbCpJMMMr8X0icJjJWMEOe/0k3Gfegw/vf5njj3/YcKhIGOb6/n4hyU8f+OF3PXEc5x6+H7oLpeIfcrGx2JNOIW8WOMlDXfdQFJ0cAmpH0dzky44rOxPsbgjzoqOOLoqs82YSiZV+6jz6YRcClLKImNKfPfdArbbdluwTWZPHMO5F1zAl3dfwMzRIzn5wm+48sA5/PHxp7j1d1dx39XnicS6ooHmxtY9ZC0Hq8gM1hWJOr+BLIEsQWTSaB6+9y7OveJ6CpKC5QjWt+M4rFn+E5dccTWXnn0qXcvmUxv2o/jD2JJMhUdn5+3m8tAdNzF/6QpsIyg6GQHbrZb3l2zeJl0QBAtDC5A3He6+5248Xj9fLlxOpd9NV8pkWW+SjW1dGN4A/RtX8M7zT/Hr6++jb81P+LpXUOedJcwoEV47yCpyNsG4UBA5k0Jat4RCyxZ83y3Y6dOmTmbCJIddZ+Wo8mqEnSRSIUuPWkF/wmJEoBZfqh9p/QKMilpslx/VtmgupMv+F5LjYA32YG9chVvVGVk/EivUhC352ZCwsBwTRZJY1JVgRUeCsFdnRMhAjouOp2CilzmhOh45cUu6kjkavEUPhKJ3iJXPQmIQq0hmoLjvSNk4PscmkE0g5eIQ2fqfA7h/t7GZ0b3JOPHEEzn55JNZs2YNW221FQDffvstt956KyeeeOK/fN1/OtFNrAtJVrCqRtHjeKnxONguL3rnUgo140nkLX7uyzAiZKBXjmJlHDoTWUhALJtgUrWPyOitaCsYrB5Is6I1xVFTphBNWqz4ehmDaxfhqxnJqGofXjuDEu8inE9CzKTF5YFcWmyKxYqOHe1BTceRZIVAPsUOho5junBSOlWKjhJrx0nHUcwcQV8VSn+nMP3JZ4UjLYjKrZwtM61IxQWrjaIpkNsnKlLFZEfJhVtJ9iIVK2+ybUI2VQ5AHUka0oFTNLEhIA7EJaMvx0yKZIIRBCsPiUEkVYV8TgTlgVqymp++lInjuChkCzQHDUaHDZT+ASxFKbey6t0rBDB7K2hz/HyxvI83FnUyttbP2CovYyNe1kczPD5vHb1tMRRFZu6Nb3PwjAg3nXQwS+b/wIQ5OxM44ApqO9rxqzaP334dUrAWu5DG6e0g5PcxkMrx8ZffYiIxc1QDNVWiql/SlJTcXuGI2zAJeeMSnEyK9S/dxwGX3MZj917Hsx9/x9O5JMfusV05YeAA9kA3uxpJdj5xIpLZh7R6HdubeXaY3oKtZxm35zjmdyQYG/FQ59PKzup5S+b4WQ2MCGiofWshIxhJBdthVNiNEl+P7QkLXT5ZR3KHcVSdBesTrBlIM6M2gG07tEYzjKpw49cV1g6maQm5URKdQ/NeVpALGWyXHztYizTYDsW2oYGsyaJ8gEhdEx/97jhCo6ZywsXX0bPqJ675zXHceONNYJn8Zqsmvl3dz9wWwcr46rsfOO2Cy7D8HtS+tWRj/bhd9chWAUXTsapGIeWSWP4amiL9LP25j+eX9nLylCpy6xexeE0boebxuNweuqYfxgY5xNytt2Sno07jqbc+ZmRDDQfsOIfP7jqfjkg9zdUR2uNZznx+CZMbAxx/62Pc94dH6Zv3ZyKHn89337WxYtsWwm43g9EMiZxFoCpIn+HDUjRkO0MgrDOrzsdv7/ua5vGVgt2m6tjuMD2Wi/qepZiVY7h264KY0wfuwQn77sz8ZT9z6m2P8vSNF5FFYf7KDVx55omQETqwmzDcSsO2y7q2dmIQ2baQ/TZmZBRSPoVPV5hZ52N9NEfBFpXqSo9OlUelwa+hJHuxXX5cSo7t5myFEt0oNnjb5NKLLyLWuorKEWPpGYzz9uwtOXCr8fzpD63Eo1H8VYYIeBWVrOSiYNoUbAfbdDAUkVBL5G0KNmSMILZl8eFn89BHz0STJXZsCVPh0bjiogs47zcnEYsn+NU1f+Cdh27G7Rbs1lq3xJ/vvwtH87DVziNJORouB1TbRMWkLauiKwEs0yZnOrywvI9an4tKj0577yAnXXETkWAAxc4TNnR2HlfFDWecR3TVDwA0bL0nofqRrP7xIzoiPuR8RjCHFI24pxq3IhHN2VRmE8j5FMdedjPXXXcdQfcYKtwKjiIxxu4UB8O8OCDmG6bzh0/Xc8iUWuqTreVDkJOMDbXqlsy2Mkle/PhrDjvxdJyaccjrfhA/E+tldC5NtqIFK59nRZ9K1rS5dv9JTK/x4FYkpLgjDusJ4ZjeqLtpVWv45KMP2LXJg20WeOPz79hu5mQifi8OxeShrOCkE8iGBycZw85lsTIp/t4y8T8wNmPeZszbjHn/tpi37dytOfT0i/4pzNvvghtYvxnzNmPeZsz7Xw453iP242ADGT2AJEm4CkmkfArbX8O6WIHWWIIp1V4czU2XUsuq3jTJfJSAoQIGeBvoSBRY1Z9iYpWPWp/KZ+ujvPj6cgbXLSI0Ygr9yTwRt4qnf7W456oLUrbAK0DyCma6E+9HzmdBVfFF25ige7F9VdiKVxhKJnuRCxkkq0ClyysK0sl+0WXzCwkNR9VEkTI7INiBiC4byfCIop5t4limYHpaeeRcUiT5bVMwwHPx4k0Sx+YSKxMQjNDS14oukuzFxLejiCKUZOVFYTifEkk2d5C0IljLmbRNdyqLIkmMixho3StEcsdlgG2iRNvQChkc1UVfeByv/9TDZz8LX4Y9xlXhUmV+6knw2vwO1v3cSy5jMvfGt9l9UpAHzjqChT9+z/TtdqXhuOupWrmWgAZ/uvtmlMpGsXeZeSrqG+iPp/j4+4WYuSwzx4+ktqpSFHNVDVkxynITsi8vpKayKTa8eC/7X3QLj95/Pc++9RFPvv4Bv9p7R/F7JePDvlZm+xPMmF4LpJCzXYw24+wQVihERrI+X8X6MREqPTp1Pg1NkcgUHCrcGpOrvFR7NYxUN1Iuga2E8WkeDFVGzwyKIqwmEoiOopMxHRZ3x+hO5gi7NTRZaNeH3Rp1fheyBLos4ZYdITEmq0W9dVN0Q7mFQS2OjWME2JCwSPmq8VU1MO+mXxMePY29fnM5cs9qTj32cC655nqURDf7jq1kQ1+KiM+FX5f56utvuPCii3EUUGIdZGN9uKwqZDmA4vLg6D6wTQYyFp8s6yEZzfL84k7O26YZNbmKxauXEhw5hYLqRp+9P+0RDzvuluOQE0/nqXc+ZWRdFfvNmcFrd19Nj6+Z9dEcP/elWNwZJxnxsPVpV5N/82XWzH+aqgPP4a3FnaQLFjPrRIeYpkj4wt7inJZRVY0ZI4PIksQr89tJZgqcM7cJLETRS9FxFA2jkGC7CgupkGfrg/fg+K3HsmDFak697VGeueVSsoaP+as3cuU5p0IuI2LB4YlKyyqz5pEVyGVxUglkswCVzThGAMkRxcWtGwIokoRlO+hFffpqr0ajX0NL9eJIEobHyw4zJkCiuyxDdM2Fv2XN0UcTrq6jPxbn5S234ri9t+Plh+8QmKcJbW5b95C3SnIlAvcUSWhm96UL2I5DuKoG27L4+qsvaZi+NZosMS7iZUyVmysuOp+LzzyZWDzBsVf8nnf/dBdSJoVUyFMTCvPEg3/E0b3M3nkUOQcUBywH0gXIWsJQumA7DGRN1gwWyvIoXX2DXHPrndRUBPBqMjVeFZcis+yZW+lf8R0A9VvtyXozTKFjIz3rAmDlRTepq/RMVbFvDW5Ejvfwq0tv5Nrb76Gmskl0hzlQ48SppxNSiOJysIk7P1zN+Fo/4yf4sbo2iHOBVUBO9IqYvrhv2oUcL3z2A4cd9SskQIl3Yve1IycGUYJVNLmCdDl+Fg2YFGyHOw+ewki/hJyJYXsjgr1umsjRTsaHZcY21fDpu6+z25hKCr3tvP7eJ2y3xTQiXkN0hpTOMNk00kA7kizjZNPYuQzK6M2J7n/69f6Nxx133EFtbS133nknnZ3iTFJXV8fFF1/MhRde+C9f959OdDvZNFK4GiQZWYJvB1X60km2aZrK0q40lV6ZsFsc5nJoLOrqJ+BSmVnrJ5qzCOgKUclDVzLLmoEMYyNeKrLdnPduP4GqWgJVtRy051iqvDpteZ0R8SETL8x8ubVTMoqL2CyI4CWfhcSAaGeonUBaceNLduCk40U2Ql5o/SWjoh2ieA3hBm6VP5sSLJoD6YYIjEqJgGwKdJdgneWKP28JfUVJ03GsYiAFwjQmny0HWbLbWw5ynGxKtO/kMmAWsH0VSKkB7HTRWCbaJ0T7ZVVoJIUC+HSZtngen65Q59OpyhWZbbKC5BOuupJZAEUnHW7hrUVdfLK8h6BHY8H6QeaOCPP8wna+WtSFZdkYHh2zYNFYH2FcSxP3vf4ZwdwAo0eNoj8PVxduZdmjl1K51T7YuoOa6i9rjD59z428+Lfn8fn93P3ie1x29L5oikRk4iymNzYKQy9VF5sdQof07S++py8aJ+zzElIcTr7pARKpNKcftDuSJOE4RYafZcHPX4mgEERw2roUSdMZXdnAyLowUm4AKZrHCjYQU/0MZi3GaQkc0wvxHjALqOkoFaE6KixTPC9ZRS26KMdsF9UDG2gONrCwI06tX6cnVUCRhOnc1g1+jmxyQDEhYYv2xKIDMWZWBLuSjOQJgCTTJYV4eVk30XSB4x54E9fAWp65+TK+XtPHLedcxJL2QZbFHJBkYjmLZCrPxliWga4OBvRKvuzIMqteY7SVp6kiwOr1G+no7WfF+nZcVU08/9h9vGnMYcmKXlyGyss/trGtO8rZ51yLXjuS5cvv49H772HGmApUWeLyR5/h5ftvYcsJo4Smoy/Ee9YorrvvK7abXseh0+q5fM9xjK1wYzsOa485mtCaz/j4s79QCO/Fpc8tYta4SibUBZhe62f61FoK2Z2xZ+2EEluE8vVDdJ4yl0itD7em4NVkTFnBFeuk2hPG6u3AWbNEaPvKMpLhJZpIceUjz/Pk785n4oSx/Pa2h7jujOPKB/7yGN7SZttDura2hSTLQg7CE0YqZJCsPC4zTaJoIDSpysfUaj9NQZ0P1w5S6dHZpi6IjDiIyJmYSMAVMij5LKGgSbC+lo68jal78Hh9rM/qmI6EHetB8WjY7iCWN0LedkgVbFIFG0OVkHFwqTJ526EtnsV2YPLsuWS61nPYPrtTke3GsftRe7v43bF7UBn0c91td5J3ZPY762oeu+VyWmqrcQxfUdJARSpIeFwqcnqAlFHBst4MAZdNzoKeVIG1A2ne/amLEZUeoht+5vE/3sHWO+7CVb/aFyUbQ81nmBoKsXHDBm658x4qR03k2jseYNlHL/PDB29x7llnkNEDxPM2QZeOW7KRM3EimahgjeQy/OW2q5D9AUb6HCjEsLQwjmZgd68Rz8MXIm+D31B5en4bk3cfg+LqxNE8KNGNyI6DFesX0h2ajgMcvMu2rLErSCcsprTMQulYhuxy40gS3733KsGKRp5ZNYIJ9QEOmViNN9NH1lNFtHIqkVw3WOLQIWdj1PtUvNOnw8AqsEwW/LyWWeNHEQn6xV5aTLo6RWYkFI26VJ3/zNiMeZsxbzPm/Rtj3sNP8fCNl27GvM2Ytxnz/jdhnp1OIMsKODYDWYt1g1l8LoWIO4KTKBDLmlR7hRHy2lSeZb1JPJrClBofQZdSZhl3JnPMb4tR6dGJuBV6knmsfIbaaTux1z5TOXRGPZbjINkmdslc07bKbL6SQddQQUEDTR9iUpuiY0Wy8jiWJfY+20TKJoUXhj2MRWoWxB4ry8iaDpR0o4fkOXAEc10uMbRts2haaW2aoNN0sU8W5xG2PYR5IBJ7RT1ywXwWHU92JjWUXDc8SB4h15AzHbKmgw1UeTUqDIVAbAN2fwey24vsDWDrPmEQ6w6SdFfz6rIeXvi+DZ9LJZbO8+naflZ0xFm/MUo2VUBRZQIVbhrrI1TVVHHT8x8TKQwyevQovu9K03v2Xcx/4CIqZ+1KLqBj9KwQ5qGqxlM3X8Ir732KV1O4+6UPuPzXh6JKDuGqNNPGFyVMVBcomihGZFK88/1P9CfSVAQDhNwGJ994H4l0htMP2EXU2iwTOZXATCWQipI7TvHZOpaFkkkxOljNqKAbLBNyKrY3gqXouFSFkJxH6d8AsR4cM4/mDxP0VSKZhaLHgI5UknaRLbyY1PlcLOtOCP1eWcLnUhkRMmjwaSjYSLkkWsc6UZhXDbEvOTZSQcjyUMhjhRtYk/fw5s89rO1Jcsojb+N0r+aF2y8n4j2QsUecTl88Q6+pkPHVkRjMEfToBN0a3y5bgxOu5y/LY+w9tpIx+RRNkSBr1m+kc+FyVrR2ojVP4fknHmLjaB+Lv1lL86R6Qh6N979bzM2XX0q4eQyxjoe59qbbaAwYhAyF8+/6Iw9cexGzxzRix/pRRk/no3SEm577AVVX2Ht6HROrfUyv9TO2wsv2Z5/Cn/8s8+lbj9Az/Wg2bIjyyYgQu06qodKjUdsSxtrxQFT9EFyxxXzzyHVMufGPZAoCn6JZC8fw4fcaqNEOnM5VWP1d2MV40NJ0ErqPKx99gSdvuJCJ48dy9i33c93ZJ0Aht8neUooh+aUEQXGtC7N2ROFFkgE3igQtITeVHo2goWHZDj/3pelK5JlaEyHo2ENFiUwSbBvFNrGtPKMaqhkoAC4vhsdLb/8gpmliWQUh0VXIIGkGliMRzVqkCzYeTcbnEvG+5Tis6k+zPpqhZfpWDLSt5bgD98QjW4L40Zvi2nNPpTLo49rb7qJgWux7yoU8dv2FtIxsKSeaKcgoiooiySCpJIvdF9FsAZcqo0gS37XF+GR5D4osoQ2u45n772Ltsl05Yb+dUdMxgrJKSDKJ9XRw/g13MGnKNO579E/0fPsGyz94i/PPPoO0o5HN2RiqhpccUiYmOlAKGRzb4i+3Xonss7GlKHYgIva8VBwnLfTpFauAS/cysS6A7TgMOgaR0bMELtkWUqJX7NW2hewN4CBx8IEH0CsHyFsODQGQ+toBsPw1fPPpRxQC9RSqJ5AzbTQZLEklY1RgKBJabiWOvwrH5SNrhLEshxmTxuMk25Ekifkr1jBr4hiR6C7u12WN7kxqCB/+gSTO/5ThSBLOf2Hy2ZGk/7LX+leGLMtccsklXHLJJcTjIi74z5hQlobkOM4/peRe+PEtZF8ISVHINUynqygq71FltGQ3ju4tM0LeWT1IXzrPXmMijLB7hEaeW1ToM9UTeHfNIHMbA1g2rI1mmVLlJmM61GY7UNID2Job4n2iRUFWxOHftoYO/LJcBmY5WIntCePoPmKqn4CdLmrqycjpQaH7KavietmUCFyGm2YUN1nZ8IrDveMIILfNcnWQYuCCy7PpTSkGPJJtiQNrYrDccjH8+rLhFYdiBBND9oUEy64YJEOxDdHtRQ5WUqiZQFvKwXLAr8tUOknBMOhZI5IdxYSEHawFRWgH9sliMsiSMGnImg59aZNFXXG6EjnW9iQZVe2jIWgwKuxBUyRChkp3Mo9PV1nQGefuFxaz9K5D+GBpKzt6+stty7JbvH9rsAfZ8LKytYMXP/oCyfDw3hc/MO/lJ0QLfNE8yYr1I7u9nHXjPcwcO4LjDtqLfCqF5NiEd/0Vk0c1s3RtK3vPnck7Xy/g9rN+xakH7YHHGNbaK8sCtOpGYPlrUNKDmJEWnv05wVaNQUYGdbTe1RDrgUAlUjZZbiMXCR0bJRgRDELHxnZ5cXQvX/bCst4kh0+uZiAjnI2rpZRItJR1s9KitdDlx3GJqjuODYoOVp4PB9w88/1GeuM5GircjK72MbMhCLkMN19/DRPrK5m4+yE8f9d13HbTDWwxcwaFbIZli34kb8EF19zA/tc+wrlb1fLSvTfxzBvv4TgSx+yzE6Obm1FkeObzhTz2l6eYeP5rPHDWNsyp93LQEUdh7HA8ZxywHbsEE6Iy6vLz1w0SL1x3Nm89cL1IMCUGcWrHsO3Dq+lrT1DdFEBWZO46eiYNAR3Hgbxlc//HP/Gn669BcocZu/+pbDO9lm1HRVjVl6IzmmFSfZCpNT4SeYs7br8dS1KYc/CvyRYs9pxYA8AuTR601gUUNgqNLbVuJNZgD+mCxdHX/IFrTj6C2ZPG0jkQ5cxbHuTVu64aYrT94rAvlpT4P9ntFfNcVpD8FcIh2+UVAY3uJaf5WDWQZURQx6fCurjFcQ98Tc+Gbo45YjbHzmygzqcSiq0XSSHbLu8nhOto87aQLti88uyTLJr3EWccujvbz5goDoqaMBgS5kBezGADlgNaSZKhkKFdjvDVxhhbNgiQf/XRP5Dra+ekQ/cmGArhaxhLV0HjzBOOwUShrbWVgM/Dg3fewoRps4qtwSIQtD1hojkbyxFO2SASUaVA682FHWixVhb85WbefvMNpo5pIRIO88rDtzGhpZldT7oQbYcTeO6ak/FoChddeQ3PPnQPZ19yFWeedyHNQRfRnEVEs4Rbu5UXe1ppv7Os8p7lVDaTDDbji7ch9a4X7b2IdnrC9ZiheixZ5/21UdIFi0PHh1H71uL0t+Hkspvscd21s6jQHSE/YBWw3UG+T3qZUuVGlQVL8PMNUWI5k0lVPm59Yz7n7DWdHcNZJNsk46ujK1Wg2eOgtf/E5bfczbXHH4QqM8Q0DldjDQ4liKVhezqygmuHo/8ZePuHYzPmbca8zZi3GfM2Y95mzPv/C+aZSz5C8gawvRHy/jr6MxaqDBW6I9ae4QdJZiAv8W276PDZrjlAON4q9kIxoemrmMD37Qnq/C6agzrxnE00a9Ic1AnHW8WBW1GRejdsIv9U+oySqgvDXrMgumF0F44rgOWtwDYCFGzQJWFeLBXSwki1kMZJxcvF3U3M72S5bDIoleSzSmy6okY9dtHgrfT/pQREMQleSno7hTyOWSjvuZKqDWk7D/sskqqXGeVDGvyKSHR7Q5iRFrrzGjYOblUmoMsoiW6UwfYhA1pNFzrgLi+O7qNgBOnPFPW9cbBs4a+wqj9NayxT/riW7TAyLEwNY7kClR6BuW+u6OGdb1v5/nf78tXKdmYGLZTeNUP3jGICOpNi5fo2Xvrka5AV3vthKZ+/8pQoLBSTwk5iADsV57d3PMoW06fwq713JDcoko4Ve57I5FFNLF27kb3nTOedbxZx22+O4tQDd8Pj8QzN2WJBQA5EhAmot4KU6qc9IYoTo0M6rr7VQ3MrlxFGhcV4oFyoKMoToRrYhp/vuzK0x3PMqvfj1WR0RcJfNLvGyqOkB7EHu8Tzq2zE9teIpGA2gfAHGcVX7Wn+9M0G2vvT6JrCNuMq2bIpBLkM9952PY5msM/hx/LC3b/jxut/x9Rp08lkMqxbvpis6XDJ1ddxyp1/Ze+Rfj54+BaBebbDMfvtyqj6WmTH4pkf1/Onxx9n/G9f5fqTZzOrzs8ZJx7HrKPPYsTocRw1pZpAbAPZihZ+6ohxyenH8+Hzf0aJbsSOD5AfvxO/fW057705n4bxzZy7/yQmVfmIuFUsR8hxfLx0A1dedDE5y8OY/U9jxqRqth5VQbXXxTtLu3DrClMaghiqzGt/eoC8pFC//aH43Rp7T6gh7NYYHXbhbZtPfvViZLcXpaYJJ50g3r6BY3//l/8l5pW16otJ7lJRt7xOXG6xLv0VIg4urkHbX8WgpRPPWyiShFeT+bY9znXPLCQxkGGXnUZy0tYjGBcx8OWjqIOtWLF+cS2PD9tXxYBRw+qBLJ+88iyLv/yI3xy6BzvOnDxUiFI0HM2D5a3AdAVENxygFPOBvRmT/rSJLEnU+TQe++MdZOKDnHTYfoS8BoGqWmJ5iRNOPwvLLLCxrQN/wM+D99zFpDEtYr2ohpAgUw2SBZt4ziZvOQL7chadyRzvL+/m584E9XY33z52I0++8CpzJ40kEg7x8mN/ZMKIWnb89QXsdcqFHH7APjQFXFz3u+v50313cdL5l3PRxZcQNISkYoWhoPWvR87GxF6Vzw51tZh5ZMOLWTsexwigDG6E7jXYiSiSrKDWNpOvm0x/XqI/Y9EWz1JhaMwMO8irvyknuiVVx7Et1MYxpGqmMJA1qZPTKB3LIFjLRqORCkMhYzp0JgUz3q3JLOhM8PRny9h55ihOn92A7uQxZbE36ZlBlL51XHbD7Vz364NQFWVoH0XEuI5ZGJo7w/ZZfbsj/hGc/duOeDxOMBikZ+O6/y2J3P/I61Y3jSQWi/2Xvu5/ZJimyaeffsqaNWs45phj8Pv9dHR0EAgE8Pn+NT32f166xLZAVcnXTEDOp2nM9WJ6R5RdodF0HN3HgNHI2IiHWfV+WnIbId6DY9s4/R04hTwuzc0WdQ0MZi2a/BrbuXqR+pKEiwBmDa/2DW9XGL4xykJfVDa8omqWS2H5qigUHAYlDynTxqfLVMgxwWrLZ8ush/JnGf43gskg5bMQiIjWbcBJxwWbyfCKr0u/U0xglCphpYVnZ1KbVP8lWQGzsMmBH8BORsvGZGUAQCQerGAD/XmJkCHjt5IieZEaRMonwfAIRlt6EMm2kDODSI6DrbmpsTuHGH3FTb7OSjC1RiU7aRSKBMmCjS5LbEwUGBMQwWqLP81XcQ9//mwthkdn9LRZbFMlYcnVKNEuEfRmkjiZFGpVA45tMbaxhit/czxvz/sOo3aU0GlyB8HxI3ltZLOAk89y8bEHcNglt/L+j8t57tZLeP/zb5g7ZRyfPHQjjlng9c++IeL3UBNwE979eObdfw1bTpkg7pUsgyxj9XYgGYJt1G9qHD3Wi9I2H3ptEZypGlJyAMe2y8yQUlunnYojA7bmJuOpZnFPms5kllq/i56USTxnMsork8LPS+1ZDprQREX/CsjnsANBBrwNvLOqH02RqfS40GSZZb053liwmgqfzthaH3Uh4QTdHs/SEvJwyHnX0j/vVT54+FYmNVUz753XuO+mq3nxnY82WU5Nd57JsZkUB+22PW898ntcelEDT9Vxsinm7Lw7j/3lKS7dyc2Dl5/B44pM29pVvPTY7oxyesQBAZAUnbaYxPcbBvheGUtnMsfe46bQmTJJ/HQfoY4FTGk6hOk77EtYSnP61XcxuGohUrSLnBFCkbKY0TXI3T+wZOk42lbk2HWnbdFVmdU9CVJ5k2Yty27TRvPB6y8yIbEVk7falqqgh5ChgDRUJ9NGT+Wa1xYw/81nyET7uOqUY5g9YRTIMufc/jA3n3WcmPelNV46nA0bJVabY1lC39PlxvSEcTS3OGyoLqR8Cs3lozmo45fytKVUulN5PAEXNSNrifh0nl7QzuHT6ghqLiS7OFdkBce2kWI9eEOjyFsORx57PBeefiLquu/KbCLJtgSjEZCyNorcjewO0me7sGwXPrefOlVij9FhdFmiK2XSmSiw7Oc2Nj75NmuWL+XGc09kuy1nMXPSBEKhECe++ArfrNjI+Vecy7vP/QVHskWrma+KaM6mM1mgYNs0BwRbyqMp/NAR48/vLMXb/hmfP3EHp557MWMrfbStXk7jmInc/8zr/LhwEcu++RTLGyFmqQQVk/t/fzMXXnghvabGytVrCE+dIFrzVI2AoiMVMtilgEEpBny2hRSIEPU3YVmOSJYWGVGSomAnBpHyWVTb5E/tfv725XoOndvMyqjJeH81cve6YputjRypx/JGsBy4+uMNfL+qnz8eNYMaXaUvHac1rrK8N8kBY0McEh7A9oS55ttuerq6SOUnI+dSSGYWt5lndDaG3T6ADdxw0qHiGdmUD8XWYE95Pg0Fz8qQsdB/ZmzGvM2YtxnzNmPeZszbjHn/f8E8WS4XPLR0P9XuIEgySrwLebADVdOx3CFkbxMuVWZkyE0o0w2DHdim0N3FLFDhDjKtppa8ZeNRZUKZblqsGM46wQqUvQEx53/B4Ab+jvFZSm44bgXbCDCQtcgUbHRFJmQEceeTSOkodjq+SeFheOG1vMayKWQQ+KboQmPbGfZ6+SILVXeV5UhkM1cufjhQLh6XXsfJC9Z56TkgC53ZcnfWL4ak6li+KuKOjuXYVBgKRiGBPDBYNnKWvCIJ4WSSYBYE7rsSKPFOGjIJ7GQUJVyNrbmRHJtx1WEKo0RhImPaKBJEPCpu2SFjG6QLNj/1pPhqZR/pRJ6mSTMZE3GjdC4qJ7nL77eIzWMba7jsuIN494eleKobRLdBJlZO3kn+CqR8losO250jrr2Pd+d9y3O3XbYJ5gG89tE8Kjwuav0eKvY+hXkPXMeWk8eJhJksJCycbAo8IRzNQJclRoZ0XNGNOAsXY2XTyOEqoX2cz+JkUkIKQ9ORgxHsXAYn2oPk8hMzqtg4UGBJd5KgoZLIWVg2KDIMAorkp9anIuczyKFqbJefDlc9n60fJGvaNAcrsBxY/G0Hn6/oJW/ZzBwZxq2rhDwag5kCLlXntCtu4rvXnubdB29hbF2EeW+/zH03X8OLb3+4ybN+53en8HoqwQHbzuKtB2/B8AXA5RYJ+3SCOQcezZ8ef5zf7uDipZvO4w1ZonP9Ks7ddxsqNAt54ZuY2RTa1rUs7kmyri/OV1GdSs8E3AGZVDRPYcFbqMveJVx9ANHMWH5c286rz/+N/pULyEf7UD1+JDOPOdDJ4LJ5rNVnoPavZdqsLUhkTXRVxqMpVJNm9tgm3njpBfadM52tt9wexaXjd8kYuRhWbzuSy0AevxWPvvM1rz33FLnBHq467VhmTxqL4zicc9tDAvOGxXjDmZT/KMkt+0I4nhCWNyLkppwhGa+QS8atCnkPWRJ/Gx4NSZZI5y2W9CQIu1V8ij20Rs08TrQPxXHw+usAOOSY47jkhMNQuleWpXhQBYlDKqRR4nlkI4XljZC0VfKWg65IVBRNGpN5m76MycZYjpU/raR98DnWLl/C9ReexbZztmTmxDGEIlWcfM5FrNjQyfnnn8U7r78qfCI0F7Zq0Jex6EkVSBcsqr06hirTmcixoD1GZ3+c1Pev8vRTd3PORZcydURNGfMeePJ5flywkKXff0mfWgFAwKVw843Xc+zpv8XU3Kxft45Zk8ehSCDnRXG63HmpG0i6C/I5HDOPHaimDy/ZpEmTEUDWjXJR0UoMogx+QlXLDJYlDW54aQmFnMV1R01nz8aJKH0bhgg3kUbMYD09qQKfrB9kfX+asVWjaTE8pJNpRobcBF0yYytcqI5JwhLsdaMQpyXsxsjFkDJRVMdBysWxB7oxMyluPOXwIRwoFkbK+1NpTwfxHkrkgP+pY7N0ySZjw4YN7LXXXrS2tpLL5dh9993x+/3cdttt5HI5HnrooX/puv8xRnekvtzSFaudxvponnEVLozuZdjRHuRABanaaZi2g0dx0DuWCMfwZLRcDZd9IXC5RZWraLaBrPDXNz/iqD13QNdKpgXWJgf9koO3qAQr5Yq3Y+ahZjS97noypnDN1RVJOHf3rB/6AMWDRPmDK/9Aya7IlsOxcdLJoY16eEsdiHbrUkXfLPy/aC5aSC53uYWuXJUqMd+Gt9UVf1/2BnCqRmD5qlBS/WCZyJlBME0R2AQjgong2DjZ9CbsByebKrZx2EP3BZFIMOsm0mkZOA7Ua3lh1JJPgmniuAMUqsbwyPxO2gbS3HHAVNb88DkjIj6sjjWo1Y0U2laL969q5eciaTqffL+IL9b1M/bI8ziqPseJF13HggULOXDv3Vn47Vf4dIVT99mB3z35Os/ceys33f8nzjrmQCaPHweazjuvv8G3C5dwxaG7cscL73HNk2+y9Kk7GDOiqVztL7XJwLBAbFjgWpoPdio+1Orv9godVX9YsCFUF9/kKulO5TEth0IRRFsHMxw5tZb5nQm2bAhQ55FRB1qxfJXEZQ+frIvy+BfrsRyHhrCbHYtaeLbj0BJyU+93YagSP/WkCLo06v06eUsYonWtmM9td9xFbVWE0w/dm6pQYKhFS9XpjcaoCgU3rXbLimjxjfcB8PGStex57GkAXHDBhdx1151iyqybD5nE0MYfquHwS26ntX4HkmYN2e5VaLpFbexHxh1+Pq89+DD57mXY2SiTdz+Iymk78thJO1FZ6GNJLshlLy/i+8duxSqYuJQUZxx/BPN6FWI5iVnBNIs+ep0j99uLkAtWbujg+yXLOf/889ly2iTef+ZRHn3hTRqqKuiJp9l65z247tDtkDOJ4mfVuOmJlwgFvJx1+L7/sKr/d1+rmmB6hqqxdS+2NyKuVcgIDTJAjXaAY5MIjySZtzFth3dWCfO6So9O0FAZ6ZdYM+8tmsNeDFXeJMnntMxgI2EUGRrMXmhbPrRWdQPJXawclky6VAPLV0k/XqI5C7cqE3GrJPIWqwayDMST3H3Z2Wy/yx6cc8Se7LHPATz14F0Eq+o5/rQzePbNDzFUmWMPPYDX/vYUUjaO7Q7iGAFe/fBzrrvych546T3GVLhRZXhhWQ9Pf7Sazs+fJb96Hn6vF8Uu0Fxfy4cff8LuO+/A20/8cYgFrBsMNgnzhqzp0NnRgWYYzBo7glzvxiF9yMEOHF8EBtqR/GHBnNC9yMl+bH8VP9nV+HUZtyYT0Sy03tU48f4hRm02hTV2Gw78y0/EoxnmTqnh0p1GEWn7rhwAZcbtxPyuFLNqvRzyxI/88PoHVI2dQWW9n+fPmEOVSzDe+r1NhFwyvRmLGpI4siLahzuWiyRDcf+0U/HyXlDa88SbGTqgDu0FYt9QwtUQrEZtnvr3e/M/OTZj3mbM24x5/7Mwry4+n7GHnbcZ8zZj3mbM+xdG4ce3kMPVIIuuGTPSQm/GosJQcPWuRErHcPxVFCItJAs2QTOB0r0SO52gZKqKrCD7QxCsFTIE0W7BtJQVnvzgS47ae+chzCuNEvYV56ukCz3oUsJY9vixqkYxoIYZzFpoMlS4VbxmArXr578rrG4yhuEOJVa3xyfmeS5TZo07donlbxeTQwJTnHy2jMUg9IaHY0Jp/B2zeziel4YsI3sC2MEabHdYGDwXdcbtosSXZHhxDJ/QDC8WcsuYlx8yiRyOr5Lhwa4aSd5Xg+2Aq5BESfYJlrusgiSTqBjNK8v72BjNcN0eE1i94GtG+sAqyYnkiozwYc8BWeHTRSv4Ym0/p1xyHaoMl/z2NyxcsIAD9t6DhT98i09xOP3Qfbj2iZd4+u4buOmPj3DWkfsxecI40QH1/kd89cVXXHHE7tz18sdc/dfXWfqXWxnd0iyec2mOqxpKVb1Yn/2dovtL1cVckhXxLFKJ8trHtpHDVUJjHTCrx7EqKbNmIE17PEc8WyDi1WkIGFQYGrIMhqrQ6Nfw5aM4qosey8XXG2N8uKKHnGnTWOFBV2Us26Ep7GZqtZ+wW8V2IGvaFGwbt6qIriiPSs+KH7jtzj9QWxXhtAN2obqqShSLsmkkVaNnIEqVz0Dy+Ifmn8cn1lda4PfHS9ez57GnA3Dh+edx591/ACDfuQqpf6PQ4Q9EyDbNYt+jfk2sZWdMo4lk2wo8Xon8ys+YcezFzHvmCeJrFyKZCWpm78XJJx/L2btOwyU7LO3P87cfN/CXm64klyqgkmD3/fYj74lQQMMZbKfj2/c4er/dCSsFVrb18P3iZVxw3rnMnjqR9998hUf+/AwNVRF6MiZzd9iJa089AtYtRvYIEsQmmFdcb+U9qhRHDS/42jaS11/GvJIpsqNooOpCRiafxJGFlE3SklkbzdEay1Lp0VEk0GSZKdVu1n34HM1eFU9FRPimpOJILjfOyC1Yb3owFJm6bBtSX2t5TZY6RcrJP0kus7t7HC/RrEXEreDWZKJZi+V9aTb2xfjL9Rewx557cuKRh7L/Hrvw9L23Eays4ldnX8Kb735AxrT51RGH8sbLLyDnhKSf7fLx+ofzuPbKy3j8tQ9oCohOvs82DPLIR6tZ/eYTJFfOoyrsR3VMmhvq+eijj9h9p+156+nHkDODYk55Q5jhZhKSQcZ0WL+xDZ/Xw6yxI8ikkmXZQjk9KCQFzTySN4Rt+EXXi5mlUDuJ1TFhEtkU0NDT/SjxTqHH7faCWUAJV5OoncadX7by/vcbOXT7kZy5ZYOQOUpFkdw+CrWTiBWgO2Xyu3dW8PYjf8EVrOI35x7JSVs2UulW8UiFsl9GDo2M6eBWJTzxNiHVV4wFy3ssQ7gmvBOGukWdYcXE0vdlfxhJN1DGb/v3+/6/8Sgzuttb/+sZ3Q3N/7aM7oMOOgi/38/jjz9OJBJh0aJFjBo1ik8//ZRTTz2VVatW/UvX/acT3bnPnwWK7XyNE3E0DwOyn5Bmo8Q6cVqXigqzLyKq0qaJHe8vA7/s8ZcP6nY6sQmoSorCgp/XMnX0CFS9pJ9WPAQV8kPBSzHwKQNxcbLbNWPJuiNoMsjZOOrAeuxo3yZgXb4mDLWg/TIJUAz4h79G+WA1/KaV9U6LrIpSRanUpgblzX0Tzbxh1xNMBtHiLYwwigkFtxc7lxliIKQT5eBMdos2dqeQF5uB4SmbRJXvV6n1vXhNSdVwPEHBXABR6cumyz8j+YQTsFk9jm/XdHPEbnP46ZvPCUsZoXcX78cp5EUQbXjBNsW9lWUGM3m2+9U5/OYvH1Ph1rBX/0DrsgXMmT2LnSc1oioy5LPs+ZvLefOJ+/lhyXJuf/Bxnnvyz6S0AEr/Bi4852weOOsojrnxIXacPgHNMDjl0H2GntuwZ1h6RqX7W0rGOJY1BKrFA6MIZgOQTZFv2ZL5XWkMVeaHjhgRj86ESi9jAsJkzfaEUQZaMStH0ZkRrssFW7QTliq7/RkBDEGXeE4eTcbI9IuARSm+JzMnWuzMPHI+VW4Lt1PxTVqHSi2UpTba0vcVfxhb9yKlBsvtlYW6KTy5pAdjsI3TDtqVbDbHh98tpqaxmXFqDMtXRd5y+HDRak467njqxk+lr7sfM9HL3N33pG77Q5jdUsGbCztoXdvPuYdPY05jiAa/RiC6DjPcyNJBm4/X9nPHPW8RqGtkWu8LBCuracsqjJswgYQWRF38Dg+fdyySL0giU+CeR55g+aq1tNTXcMZRB3D4Bdfz7uN3EXC7hhIwssy9L7xDZ98AN591/DBW6VCVtrymhh36gaGDv+HFzqbKTB25bhS2N4KS7MUMNZJWvWQKNt0pE1WWGBvWWTWYx++Sach18vO3nzF25AgUTewdpaq/Ut3IQOUk/IqNuvrrIRAtzbtiYqm0hkoMICtYz8acRoVbRZEgbzt0JU38LpnWn37k1dffxOX1sfdBh/P7665g2932YvH3X7PTrImceMTB7HvC2Tz+zAt4VYlnX3iJ6VvMJuDz8eQzf2OHPfZlztSxdAwkeem9j3nwiVfIDPYx46QrqeiYx6yGECP8CiedeY4IgvMZwUZKDiC5fcSqJjOQMdEVmU/ff4tgppv6sJ8ZW83F0QyUVD+OojPQ00lFOIQdasQqmtfpmUFsl5d1SYmRPoe8pNOfMWnKtEK0CwKVIuFSlMewPWFWFEQCZlxYR1v/vTjsIpKiVmULcs8asiPncM5ry5neHOL0GVXiXpcCGX+YbO0UTNsRjKb183Eyqb87zA/ffyXdQK1uxHKHePrBezlqp9mbzqHi/iB7A2BbaFsewL86NmPe0NiMeZsx798d8wqxLnY59EjCM3ffjHmbMW8z5v0LI//NK2WZK9kXwmycSk9BaEcb3ctwktGiJrWGZBWEhEoxuQ0I4zJDmBjbiUHs+ID4f8ODpBssXLmOKS2NqLomkvql8cuk8C8Y3rLHjxVppuCrwaHY5p7oxklFiwVQq4y7f6fZOnxPLeJTKbE9PBm3Ce4NZ5mXMK+U6C4VV0vmlsOKeTAkI/VLqbCyfEnx+pKqCa31YdJc4l55hRZ4IT+U1C7tFcU9prSvlt+/boCvAsflFdrVuTjkc+VEvJUYhLqxrJOr6ewZ4Mjd5vDT5+8Q1sGJ9ZYLqrLHL7q3HBsnLjToB9M5tjvjOq57/hOSeRNl7Xw2rljE3C1msOOM8WiO8MzY+9hTeePPwzDv6afAHSDb18FF557N/acdPIR5bjenHLSneB4lKbNcRnytaji5rGBrFmMFOxUvJ7yGF6DlYEQ8T0UhXzuR5VGHNYNp4lkTn64wqdpHk1/DnR1AMrM4uk/IVegeYnkbxwHbcciYDhlT/FuVJTQZ/C6FoGqLpKEkg6yKvafYBSDnM+I+myaoKna0r/wcHTOP7AmAqpa/XyYSqJooKhbyWIM9qJV1dEam8PG6Qfzxdo7ee2ey2SyLflpGuK4JQ5UImmKf+WztAL8+/njUunGkB6Lko53MPfBw9jn8KD5Z1kPbxhj+iJvtJ1Szy5hKplV78MgWeVRWDWS5+s3lLP1uHS2T6/B+9RC1tbWkZIN2qZK508cx8NVrPHzh8UjeEPF0jnvuu5/la1ppaajhNwfuypGX38Hb911P0F+UprMtZE+Ae196bwjzSut5GOb9Um6ivKZcBpIhivRl/WXDA/5KEWNIMo4RIK76iWYtYjkLXZHLMYkqQ6U5wKqvPmBMUz2qe2i+AMhNE0lXjELHRGtdgNXfJZK5w0ggkssodkoW9wmXm0LtJHoyDm5NwqXIZEybdMHGrcqsXPwjL7/6GqrHzyGHHcFNV1/Gbnvtww/ffsX2W87k5EP3Zd/jTufFF57HQeKlV19n0szZpHDx8ovPs9/+BzBl3Gj6YikeffVdnn/2TXLxQWaefAUNvV8ysdpPY8jHyaeeSmHjMrEWFa1o2msQD46gPVHAo8nM//hNtHyS2soIs2bPRsqny/JgAwMDVAS8OL5KzEAdkm0ip/qxfVUkTZAliXjeIpm3afJr+HqWFqXLhLGlZOVJVk/mvTWDBA2Vnep1lLXfl4sI1I4W988qEA+P5snFXeRNm9O2qMfXvqA8DxxfBNsbEZ8hG0PqWYfV3zkUZ5T2tWFzRglGRPwN/PXPT3L0LltvOpdK8lYuIfWmbbEv/5NGOdHd0fZfn+iub/y3TXRHIhG++uorxo8fj9/vLye6169fz6RJk0in0//Sdf95M8p8VkxK20JJ9FCoHE1v2qQqu1LoI5oF0To22INVAmOXe6gSbnjBG4bUoDjwD2MpOJbFzIljQZZZ29aJoanUV0XENYpBVxlA+MUh3Cwgd6/CK68tM87KIWMpSfCLIKqURCgnE0oLTlaGAs7/lwM/FFtjiq1TkqoNLda8VQ6iyqZdpaqabojv6UpZJ6ns4l5iz9kWlpkXQF4KHIufW3Z7cSRJuESX3kfx8C5pOo6qgqyK++XYmwCLlI6VX0cqVcM0XbRQOTa2N8KXi1fw68MO5J4//IFApAqiHdjJKHYmhRKppdA8E8nMk5AMAsFBlHgn0Vg7OBKjvBKSS6Nu8mTOPeU4rj3r1+y61QVYqoHtCWM5MoPBFurmtDB63gLO/93tfPjeuzTU1yPlklzx7PvUVldx1N47stu5N3HYbtsTCoeGbrg87DkVD2KAqFiWmWGyMHHSdHGvZUWYqgEZ02FcxGBhV4qwoTGpystYuxu5rVcAdbQTbAt1oJUGdxA5lcDW3SixXvJ1U+hKmdT5NNxS0fQml8DJy8jda8Szd7kFIBe168T8McsHzPIoBWmleZ+MlU1ZJFkRyR5FVJntdBKnagTrYgUUSaJl7Hi+W9PN2b86lJtuuYX+HHQP5FEUi0O3HEWvHuE3JxzN9x+/zWn3/In377yMe47dgZYJtcjpQU6aPp22pIVHkwkbCp9siLNFXQv5vINbg29W96O4DPbcZQyx13R2334uc/fYj9P/tpjB/hTBjW2siMyiJuAm9PNHXHbYbuU18qd3P+L8I/fFZ6axE2IjiqUz3PTEiyiyzM1n/mpTZtnwQ1w5gM9vskYdswCpBFYqUXz2RT2/dAy1kMHyVYKiksgJyYZxFS56MyZSLsk4t9AQlhO9TJw+A8m2sGL9xRbyYuIvMYi/2hTSDaX3UjTlG2qZspHDQguZWJf4GcNPtbcG3RGGKS4FgloMB42RO+3Mheefx3MvvIRcNYJr73uCa357Cp1tG1j0/TdE/D6222YbrrrsEh646SqafAojmkeQtmWO/vUJrFq+nPa+Ks4++yx62jawyzHnszJZyVFz6nn22s+56MbXMKKtXHtjHV9+/hnbbrc9VriJjzNxxvjceE1bVMw1cOfjzN5hN9SqEbz4xkucc94FbFixGC0XR3P7QBVmgxkzRMa0qdTdKKl+Rssy5A10l0xvusDKTCW7+fPCMEPRSblCRKUwPl3mk5XdHDKxWpjBgNAHTsVxfCIpY2fT6Avf4uExOtCGsyAr9mZZxrFtyKTQujagFYMb+x8xoIpYUlr7St1IsWY2LqelKvQLluvQgdIuspT+M2Mz5g27F5sxbzPm/Tdj3uo168h7Gv6XmHfbgTM3Y95mzNuMef/qsK2hJFGkDkdWCehFT4p4P04+i1WUxdok8chQkrtcjJQVJI+/XMDBtpk5dRJOIc+67gEMV5r6mqoiLipDCbB/xF7PZ1EG24UJnG0Jc8eS/0RRtqmUXB4uK1Iewws5DOuUYVgB8e+KxEOvPzypLVEs8qnDCtTFvyVNR1LcRR3voeQ4gCMPsVlLyVl5WHG5rKcPIuFUSpgP01+XPC6RcLVNZEXBUXUhOyQrovuj2O2AbpSNUJFV5EAFlqLTs2Etxxy8P3fd/Qf8Nc3Y2RhSKlpOttsVjWR8dcgS6N5ulPQggx194DjU6Tb4vFQFp3L+qcdz7UVns+sWE7GNKvLuMHlJo1A1hjE7jGHkvIWcd+2tfPz+u9TV1yMVMgLzaqo5ap9d2O3cGzhs9x0IBTURG5UwrvgcJVexmyIZFVIl+ay4b0Umd/leFwvdoCMVsnh1N5UeHb+uMKPWR0WqHXljlygo2DaKL4RdTLwFVR05E0POJrC9EeIuPw7gl03kfAbHlpCTKaSedcUOg2HpEkUXBfR00WTYLOJbqfhQyGMXiyHlBN0wiQgKeVBVQZQwgmQKNrGsiSvcxLKNPZxwxMGcfeX1rOnP4TgKEZ/M6IYqmppHcNxRh/PFh+/wxHuvc9GZp3LbaQcxzujn7AYXgxXbsaArRY3PRdhQyJo2GSQUyUaWJCzbQVZ1aiI+LJeLPbafw9a778+9X66nJ5ln9dpWMtUTcCkSAaedyw7Yvjz//vTeV5x/9H74VYSms26QsODG+59EUVRuPuu4crLyl2trE8mS4UXf3KYSeyWjQRmQPQHhU6EIJrAkCY8VXZEIGwqqLdjLaryL8S1NlNi/5WvbNlI+iauQFPGhVoxDZFnsU0W5ufL+oxuiiFHIoyS6qQ4I2ROsPLqmEtRkpEQPO+64IxdccCGvvfISrpoRXHDnY9x+0W/oad/Awm+/psqQ2W77HfjtRZfzh7vvoqKigrqGJtK2wqknn0Tr6p8ZiFVx4W/PZu3aNTTvcTqB5knMGR3gw9c+4o9vvk4o18d19bXM++5Htp89HStYR1sGetMF+toTVBga1V4Vchm2mjOXisaRvPTyS5xz3vlsXPyNMLU0ckiKhqV76ctYyJJElWogZxMEbBPJyqP66kjk8sTzNp6it0vM30SyYBN2KWQthzq/YJ/nZAOvPyzuWy6DkuzHMfNYve341OWcXTcCZBXrpyWY6aHOCynWj6QoQhoxl8UqdqnIvtDf45UsCykV3RB7Vz7HyPrqoX12+P5d9CDZPP7vGbZtY/2DbuG2tjb8fv+/fN1/OtFdDhAKeWzNTZ/jZrwexe4WLaclrbpyhRkEU6EYXNgDXaKCPdx4pHRtZWgDfPSld1i2ZgOvP3DL0MRWPZtWw0vDtnBMyomFXzJmhhuAbHLwL31dChCG69qVgyxrU1bB8ODLLAzpkhYZaJIuKpPlCi6lyn4xEeD2ic3WcUATLaJSEThlvRi8SDKWJ0zaU407F0VJdIsK8rCDiSNJYsMuHnYlTS+zq4a34JSfbIn9NuwZIgu9VwAUHSsV5YxfHc6RB+zFwXvvjiPJFCpHoTk2sr1OtI+l+gWzxXBz8vmX093VRTRns7Grh42fvcK+Rx7HvDc+ZMbUKRyw377CPVtX6c06bLPz7lxw/nl09vRRKJh43AbnXfk7qiZtgda9isLG5ey3xVhcus7UsS2cdedjPH3jRZualrmMTRhtmwS0ZUZAtvzcHZVyK26w/2dsd5Ad6sJgSSjxVpz+NuzSPSkxC3MJpHRUBKBFrTwl1U+d4UdJ9YrKflYEXRLD2BWIqqaovOpDbYilOVB6dsVgWujtDhlRbRIApOMChP0VOMBYs52eriVU+yZTX1FHd2cH4RkH0vX2n9ny15ew6uV7SaTrCIV0li76nguP2Y/d6wf5aKCVUSENp3URDqBKMn1mDVe8vpRXT57NblUWWAmUWDvrfeNYvrSb2jEjCCs5Pv1pBZP2OopD5DRX7T2e2z9YRUx1k47HsP1ucXjJZYcCC8vEhV1uAZ23cBk3Pv48Fx93MLttNZ1fjvJzHH5fys9C3hT8hgX5sjcgdIV1AymTQo11UxtuICVVY9oOlW4Vpa9LVL4VTbBri/NneOKI4rOTcyny7jBKRQNKLoHlqcAxAsKYrePn4vsZ2iIlTwA5m8ClC4MwANsI4Bh+UrjIda5m3s8dfPb9Qr7++D38Ph/3PfAQN15yNn975U3OvOJ3+ANh1qxZza3XXM7eu2yHI6cpeMO0ZhyOO3gfahsa2bh+HQ8/9zoP/fVZKuub+OLPr7L3gYfQnYVmReO3Jx7NnX96lpnTp2GoLv727Cto1S3Ut4xmZHWQwvKvePDu+3lox0PxRZMcc9yvcblcFGK96LqK3+fFrBxFa8pBy1lE3CoUMkKT0jZRYu0owCzVAK8MqTR4K9ho+/nTvA2cNaeJlf1Z9h5bybur+9FkiWPra1HSA+L+RruGGImqVk7M/V1QMvzfmzzzYnuxomySOHPyWaz2NcjBCE42xR9fep9xzfVUeF04gFxqNf4FI+5fHZsxbzPmbca8fx/Myy5dzlZHHb4Z8zZj3mbM+z+EeSVWpVrTjBWoI4+Ki7xI4pe6cIav6eG/XJpn2bT4U/JykJWydnepkPvoK++ybG0rr99/s5hnJSz7ZfKiiFcCc4ZkPEpSF9JwFnfxtTZhj8ImCe6SFNiQD4Y8lDgtFX3Kr/33uvrD8aSc+DQZ6ioqJpaRFVH0HqaJKpXusaxiG36cYrJUzqWQ8kmkkj64qoKiI7nlIfws/W7JPFOScWQZisx6R5LEsygl0Q2PkCwpvh6qgVnIc9LRh3HkwQdx6F47i71bNVBcbqxS55miY9oOBdvhNxdeTVdXF6YNbR0d/PDeyxxyxNHM++ALpk+byn777Q+Kiu3y0hrPM22bnTn5zHMYHOinYJrououTLr6aLeZuR2L9Mgqda9h/5ii0XIqpY0Zw9p2P8/Qtl246b0sSXWZBFIZKDOjicyqb0ZWLI0rZR0RJ9NDijTCi0oWj+VCi7dC5CtsumnrbQqNdKWKdk02LoowkgxPGq8komRhKdGPZjNvJpoSmvD+MpAXK936TUVrndrHjTDfK8UsZu0uSNsVYEUByVeBoHrDyjLB7mJBpxeOpJaD66ehoZ8+DT2LZfXew/SmXs+qle/E0hKnxGyz49isuO+tkRqTXY0W7GefOUFjzE3asn+BcL+mCn2cWtLPr2Eq2qPPhyw2AJNMre0mn8xhenZCS5cOflnH48SeTMW1GVHpJZk3yiouFXSlmjW5AGdxQloUpeb64pOLntS2+WLCSm558nYuPP5jdZk8t48PfdUL8I4zbZH0OkTBEMlo8XycZBVlGNbPUBOqwPf4hJn08KaQ4MvFy4tQpiDhFdnuHJIVME8nMYRt+zEAtslYs0Lu8QiYvG8MpFvbKQ1XBMkWnmm2LApLhJ4eGy1dJtqeVz5eu49NvfuCHzz6gtiLAPfc9yG1XXchzL7zImVfdhM8fZO2a1dx69SXsu9UkbN3E1A36TIkt99uLmvpG2jes49rHX+DL99+iWu5k4Xcb2eegQ/FrEvJAP7898RjuevgJtphwA2ZY5taHHifuqWerWVPYdlQ1L7/2Bvf+/h5e/OBzBnqHMC/jqPjzSQJuDStQR4/jJWfaVHs1yBafiZVHTvXjyWcYF6oXDG4zB7JKznL4uS9NwXKQZYn+dB5Zkgi7NcbUTkLVPMjJftB0UWwLRnAyKazODZR8ECRVL3Y0COKNFe8vd2KUu1Gg3JkhMRQTOaVia7GL457n32bsucdT4R8ysZXK+/Z/srD73zwcSRZ7+n/h6/07jz322IM//OEPPPLIIwBIkkQymeTaa69ln332+Zev+8+bURaH5PbiaG4qSYk2iExqiMk1PEArTlrZZQg2QyY1xGor6TX98tqqxq3nnzpU3S/pOBXZXmUTgeHtacMCobKW2S8DsF8GNf8g6Bl+8CpX/UzK1940OBoWSBUXmmDniLblcmufRxMVwmLLk6P7cBxbmAttElCZQo9NkkVVOroBKRcX7vCl4FGSyj/uSFIxyFLLwQylAMg2y0AsOQ4oivhT+l1FE79TCoIch632PIRtZ02lvW0jZ595BsfstwsTR7cQaRwl3LANH5KZR070EnDH+OTD95kwqpl5b7/Lq18s4OHfX88jTz3P0ccey21PvICvpoJvBrOs6UyTLvSxtH5Xjpg1m5qaOuKeKny6wqdr+vE4GmMnzmbWzjvi7liEnUnRH0uiuz0owYj4DJouAn9ZHqq+luaLNuTMPjxJ49gWsuot32Mr1ocU60UuBbNF/bThLJJyBVHTy2wV2e1FiXcKA5ZCuthKuKnJjFRkVJQO+ZKsCNMjVRPGWWWWgrXJRl5OBpUODuW5LJfb6+T0IG29g4wd0Ui112bdws/5ecVyTtr+J/b99eFccspcencNcd41t3LMbnPY5biLsAd76Fq9gtqa6qKOrWjrdCSJy1/9iZ+/Xc7sVf08e8mOTLdbcRSdeRsGOf3QKRiqzCM3Xwtb/opHP8tQMzZH2K2x/KO3mVI/gub6WtyqLJIpiaiQFVA1JEnCwUGSFV755Bue/+hLXr79MrzuYSYRww8NxURJ6d8SlIP0ctDjciNX1Arzu2waJ5cpG7GVgi4pm0LOpggY7eKgYXiEqZpt4WT7hcnJcFO+UpKvuB7krp8xAtWYkRbUgVYczQArT8JTi69JR4m24WQSyFoOs2pkec1IhQySlcd2+clbDrG8RixXYLzuYtLY0fzwxadcdPsDfPH2y5x/8E48etPlHHvA7ux21Gn0D0Txeb18+dE7HLjX7jiKRmeywMsff82Y7ffF7TaIS36efOxBLr7wchIDvTwwbwPNo3ekSYoh2Ravf/IVX3z1DR9/9CGHnHUFiqLgOA62baMoCpZlcew5lwHQuuALAHK5HG1KJfXhMMm8zWuLevnbvHXcceQMar0qjqpju3zCyIResE2sQC1SPgWGCDCbC51cs10tBUXh574UiuRju+YwluOQC1ahBmpQ13wr5vew4FXsj8OStsNZSn83N0QSVvaHcMINKL3ry+vbLjJ7rN52AGoqgngMvXy4Hd7mu8me/p8cmzGPzZi3GfOGzdn/Jsw74cjNmLcZ8zZj3v9BzCtpU0u6gWTlMTL9AvMGe8rJ+JJ5Yel1S8PJZYYKvZpelp0C0cFUajMHuPX8U8vJR3EflPKaGM6UdkzK920TjBuevJYV8XPDxy+T08Pknzb5vMVrlrqIJIaw9u9M9AD4B2afqioKuEV5ixLWlHCsVBDcJEFqmaJYWBDST5Lj4OiCOSk5Dk7p54uyBZtgXbFALO7RsA8uyUMsbkUvJzUc1YWDxNa77ce2s6bSuWEVvz3zDI4+eD8mjBlJTcBdltmQU/0EUv1IZpZPP3iPCePG8Na7H/H1N99y/fW/44Xnn+PgI47m3r8+jxEO0YlER1+eT9f1M/PgE8isW0xTQyO+6gZ6Unn60nnWJW2mTNqSqTvtiNEj5G/6E2l0lyFYnaVRwoqSJm+Rrb+JDnppvpQk10rfK+SRUoPIib6hIrxuYBULxkPFw1+swXwOdJfQNU71I+dTIr5TNex0HDtW1OqXZSTbGupk+AW+SbKCow5d18llxXvXDTD0YcQCrYyDTkkOxTbpXPszU/wKVaEcaxd9wuqfV7DDks84/4QjuPikbendMcB5193O1nttw8WnPoDatoT2pT9QG/KJQl24GiVSSybYxGffr+bBmx/ko70P4t4TZjMz5MJRdfqjBSIhN5GQm+VvPoI0/Whe6/ES2zAIQO+izwjUNqH5K1CwizHDsHsvSaIDBXj1y4U8/9FXvHTT+fgrIpt4bvx97DmEceL/FSQ0UcgvdqI5RXZ1+T7ZxS6HQl7gbjKK4q8Qa8uxhSxdPjtMt3lojxieTHWyKZRkr1g7siqkqFRD/O3YOEkdpVhMshUdx+UHRRVxab4kNaiSQWMgY+HTZUKGjynjxjD/q8+4+I4H+eDVlzj2gL155PfX86t9d2a3o06hv38An9fLVx+9x/4HHkha9bJ+IMfzb37OpJ32J2VK1HrCvPHXRznxvEtZ29pOoWIElVvvRE9Wos4I8saHnzHv2x/58OuDOXTytiiKgu04/M22kRUF27LY77QLaY/nGFz2HSAwb9B2QUUNnckCby3pZVVXJwdNr6fR42C7hByZnE+DmUeyLaRMTOwXLi+OJFMp55hS7aUnZZIuWChDYTgZW8Jr+FFzCSFNVewwLHvz5LLlfbNU/Bku4VQuaBb/rYSrQdOHunRSccoSJsXnWxMO4PEY/7CAWV7H/1PHZjPKTcadd97JnnvuyaRJk8hmsxxzzDGsWrWKyspKnn322X/5uv/hRLeTy6LkEsiJXqz+zjIDANiUQcZQ25aTSmzaavePDs8Ma+8s/+zQz0uaLl6nBDIloLEspOEXse1ND/zDX+cfLIhNK/j/mBHxS32pTRIAwxINZZadVbwPxc3VkVUkW1QIhdO3OIRJVmET12+p2E5SrthTDHaKLTXSP5BTH/4/43c+iOqKME/98RaaG+rEd0tJAEm4qTuyIkwfHBupkOXTb35k+rRpPHLPrQC89fprfPDeB9y0fC3eYIiHLj+D6tpayHcLRkc6Tm3Qy7M3nI+0/HMObQ5w6FMPY4YbUaLtWKEwywdNgobGLiMrmNcaZVpziK1mTqLBr7FqMM9989YCMLMxyIRKN4qdx1E0Tr7qdj78bhGO47DbaZdz1ZnHseX0aXj9FaIqaw9r5yt9/mEMxvJmW9wkJbdXHA5LCSNZEQft8BDT0Nnk2apQNJSS3d6yo7mUTwrH4mEasKW/hVnNUBLKoTCUhCrNvRIID5tv5cP+LwMAVcdxbNEqnorz9rsfMGnsSKqnjmfJ0uUAXHn8AbTURrA7VlBlp8lm0ozyWMJYp2kyn3auJdIyATtUB/F+HMtC7mvl4r2mc87PffSvWcoPHTOQG0bw9s89vPjxUrwBF1uMq2Tj6p+Zutsp3LT/ZLbQenDcIc5b+SHVl/6RgaxJMi/RFBmF1NOGnYgi+0NIigjq3/l6AS998jVPXnceqvqLtlEoJwc3+bqY6JBkGSVShxUZIYy4rDzSYBuSYZYP81asv3jvBUDaoTpsSQZVJGzSmh/HAV8+ihJrx471bZLkk1xuoUHocuNkkiKQM9tRFA3JzKLk4jiqgccTFktbc4MRpBBuFExT2yTtq8cwU2KtFjJ4cgkMb4SQYUDaJqLDxvYO5m6/E7vvuQfX3nkfMw49jUN23ZujDtqHv736NnU11Vx3290cuO/erMrofL1xkM+jATau+plzHn6JrZpDbN0QYH5XkrXq/8PeW4dJVl7t3r9t5dKu0z1ujDDMIDPIYIO7OwQnITjBnRDcQgjuJGFwgjsEdwYYtx7pae+uLpct3x/P3ruqmsl7cnLOyffmTT/XNVdPV1ftveuxtZ617nXf9UiVfo6Y2YKcXsdgZCSRSJR850rueeMrAB7+03ze//RzJk6eyupF37PdVlvA5O3Y9+xH+eUc8d1bx03ikgvO59OPP6K3o53drniIUXovLd5NUBJxct4KPJleLH8ESwtgyQo5NYBH82EgI0ugJAXCM+uvpzqgkS4YtEQ9tMVy/JQz2KxaEuKCiibKnMEdbzcou7HmrA/V4/LnmYN9yEA+XE+qZhymBRVkkZd9gpmIAXDHmYIPMJlKEwoGygOC/5eC3DBs84Zt3rDNG7Z5wzZv2Ob9z7d5ruBxNoXcv95G6KVF0tY0kOzErkO7MxStKWyeXJznpddG2Ee3UsoWE3aDU6VB81JUt73fmUbWpSkpowTZaOXTkMSC7uxRZpkttkrfD+WfsVv5cxXce7t7aUmwwpJkN4GLZdNuOYE59yKmSCI6QQfLLNuTLUzX9lmSVGbPkFUm7ngAddWVPHnHb2ltqhdr3bm/IuikTH8UyxMUCapClg8//oxNN5nI/b+/FfQ8rzw7n7fffJPrft9GMODn3svPEuLBJAUVWiZFQ2WYP1/+KzwL32an1jp2ePKPmB4/lj9KzPTQnzHQTZOAR2FcTRDdsJg9dzuqfCpL+jJ8uLyHWLrA3tMbGVvpw2tmQZI56eo7eOfz74TNO/F8LjvjZLaYOoGQ12N/32LFgPN/yzBc/m6XZskOcha50QtFoUrbX1JrGov9aqPtLRD9a1NwOch6SbeTibatk33BskCv6YAWxGQU73E5uQtlQVqnGquMasGZT6omkiOWCXoWjDyvvv0Bm4wfTU3Qy48/LQLgkuP2Z1RDFdaGxdRJObKpJGOjKmrvKqxQNSvCMp4Rk9BrxiH1bEBSFHzZAfbepIEnG8eR6M/w9IJ2BifVsXYwxsNvLyfWk6KmOcLShQuZ8csTOGfHsWwaLtBHkNtPn8+Uk2+kK5UjlvcSHTMbdckHWKmEQOj6AqBqvPHlDzz/0dc8+bsLUeWS6qESmze0uZVnsowSrcaoGYUeqkWyTJSBdcjZQeF/pOIudZLkD4rkvy3kbvjCguvZ9i0d2hk50YMx0I1DeSN5fEjBCqRMAjMZg8EeFNvntFQfKBmhueALi+HyV2J5Q0Kw0dSR8hn3/3JCUCUFAX9I6DOQN6kKelm9rp15O+/ElnPncflvr+eiCy9k2z0P5PADXuSpF16hsa6WK2++k70POpSOZIF3VvbyQVeAFYsWceztf+GwmSMIaDJfrh+kOxEhVCszrjZMlV+hkMgRDofJJOPc98xrADz45Hw+++JLxk2eQtvC75g6YxbpsbO5+8NVtG4QftKESZtwznnnuTZv2i/vZMvaPJtscxRSIYueTaPKdmLGF8FUNHt/MjC9YSzNh5wZpJYMUrCCtYMmdUEvlX4FTZbI6Rb+YDVWqq/kjGG4+kNOENuxA24Vj7MO8lmbzlCs0cK6ZSjRagoVTSgt04WuRqJLVGLYCdXbf3kYksdLKlcg6LX3XmeNO/Qzw+1/RBsxYgQLFixg/vz5LFiwgGQyyYknnshRRx2F3+//X1/g77T/7UA3poHZ3+k6Pk4TB6ASp0DVhPhVroQzqbR8pQThZoFbfuQe5u3Dk6RqWCou/5xlGGUHJkkW4l04XG1/rzmH9RIRrLLD45Dv+DOhoFJHZIgD6/zNEVtCksXh3jLtw6V9eDd1t8QUxSMO+EMz806WtzTzUhoEcN5T2uzfJ4wZxevvf8wN9zxKJBzkxovOcg/7WCaW5hWqwoFKsZkA+x19Ii89dKeLkttr1iT2nD6Glz/6ks9+Ws5RF17HI7+9kKbqKJZhoIQqOHyXbXnroy85eMetMPUCiqqh2siDPCpgUONXUGSJHUZVMLYywGDOYFl/hqhXRZEFV9he4yrR+tqQcnHmv/IOj//1LXw+H8sWLuCKyy5l/5PPQ1EUZk2ZwAkH7sncLWbQELEne1nmeIiAG7hcgnIgjBSKIlsRYUTT8aJjVHLYwBsoH1t/WBz49ZxA15lGUSTGKjm8lAiMlFINuPOnfKR+fhgZsoYAgfCwESUnH7g7yAKpcdCvL+G1x//IyKY6TNWHpGgoHh/nHrkfj73+EVec2kqsq4PH7rqZkXv+gk+SEbb1+LAyAqWwa2U3D54/l3WDW3DgpBqW9ed4e0EH/Rv6yaZCrAhr+ChgfvwId32SgmycTC7PQDzNwh9+Ysx+m6AjY6EgKwqGXVoWDYf5fNFKFq1ex/zrzhcH/qHIGc0j+s/msEPVikEQB/Fqo2K60wYbEgb1oVaiFQqpgkm93oviC7octEZVC2agUhwg9DwpNUxetwh5ZCyPn1T9VPzBDqyV34JpIkerMRvGY5o6SqLbPXQByIkeLF+IgdqpAIQlMILVGMFqOlM6qbhJUKunIagykNYJagEqc53IehZL9SHJg2hhgYCS8hnqQrVsM2c240eP4uNPP2P6dqsZHLMZj913N9ncKazd0MHt1/+WdqmSlf1pHv9wFeuWDpLt7+Lbp//I0ddfxwdtMe7/2yqu2Gsyv7xoBJGBlViSwmmnnMyanJe3B4Lce+89ACz69ks+fuU5Hv39LVRVVnDXFeex2ohw7cUHsuS5+wBoqqng3nvv5RfHHstbr3ey+s0nOejkoznskIPYfNpkFq9ax1tvvcX6pT+yRqpmSmOUQGwNkmnQHxqJT5UZoJLOdJ53fljPuJogMxvD1JCiui6AIan0rllCbSCIHO8ull+XIl7kIUHVEkfJXXs2IgxZwUon2LBmHaOmAJJMhxWm3hDloa7AoWFwxp2Pc+/5J+L12AeN/6pM8p9pwzZv2OYN27xi3w/bvGGbN2zz/sfaPAdd6SIqS+gWSgP0pb87za0a2dj3NQ1xrRKuaidJK+kiYeZSPQz9vKwUk7umgZXfeOK4DOXnBN9LPleGfN9YkrckEP5fJg5Ukcwt1SiwSgLXkp4VPM727+7+aZXYs1LardK/2c3Zq0uD6AATxowUNu/ex4s2z/2MLRTsCCcCKCr7HXcaLz38exHUlVX22Wlb9tx0LK9+u4zPFi7nqPOv5eFrzqG5rkYEDDUPh+86l7c//ZqDd9wKy+hADmeQwzWYigef14tfk/CrCiFNJqjJfLouRjxr4FVkBjIF/B6FmSMrmTemkurBVUipAea//TGPPfcKPp+P5d99wWW/vYH9TzwTRZaYucl4TthvF+ZuOZOmmspixYtpuuNVWnkEiPlkj5XLge0kfvU8mCVjaPspWGY5vZmTeM/n3LkhKQqWqiI7lXv/BRew5SYubfsnl+iFlKzzUq0KB7UvGUIs2LF5mKaweU/cy8iRo8R7JIGEPf/U43j01fe54pRGelWFy666lhkHn8yqjMaEuhEYG1ajxNaxdfNUHr3hKAKaQpVfYzBb4K/fbWDxhx8RaRzLlps3stoqwCePcMenWaRkLynJhyZbNFk9TKmdQYXHmXoKpp3UikSjfL5oOYtWrWX+785H83rKRM5dcXNVaMyUJaBs31gg3INYqpf+rEFnsoAsNVBdMQK/JhFQhR6AkuoDW0vFCNeTNiQUCUwLYikdSYKQp5KQL4wKyHpBUGb4ghCpwwhWI2l+UQFgmm4lgKV40CtHkEfFsCz8igSWSSwPyZRBQNMIer1kdYug5sNnGtC3DiWfBCMvKhONApKepTFcyeyttmLC2FF89vnnfPT192w9a1MefehBspmjWLu+k9tvvJb2vIcv1w+yYG2MvO6hMNjN6tceonHudawcyLB+IMP2E2vZa3w19YPLMQZiHHjutfSolTz2Ux8PPXAvAD9++yXvvPwc999xE1UVUTouO4ePY34kSWLDCoHKr4qGeOSB+9n3kCPo6+ok880LbHHyMRxz0N7M2nIrflqygnffeZuVq9cQCvuJeH3IyR6QZPosP8s6swxkFHrTeWKZTsZVB9myOUy1T0G3wDAtettXUy/rIpCtai6lYGkrVtvIZUkQh+bH1XWwhWfbl/3EaK8XM1RLT28fdXblnyXbgXNJ5oxbH+aPZx+DVy4mZN2g+b9psySprHL1X3G//+5NVVWOOuoojjrqqP971/zfendJFvVnnHCyguRxMrCmEEexS5CKZXZi43MEvtyDm2OslCGHN9uhsexDF+AKdQkOS7/7mlXIF5EzFFEVTpM0T1nZm/N9Sr9bec/YgmDOpl16OHOQACUoBiG0VcDh6ZIMW3Qrk0QKVrjCNshmGc+ai2xzsvcOEk2Siw5UqeCT3Zy/WyUO07MP/5E1a9bg1VSBbrPRdW4zdOTCgMs9CrDljGlUarjvs3IZevtj3P30qzx09XkMDA5y7s338PBVZ9O2eg3Vlf001VSSymbLsnQOIsEjmYyp8JAqmGR0k7ZYDp8iUx1QyeompmXhUWVOnTMSJTOIlE/SnTY56uzL2GzaFBYuXcaYiZug6waXnXkyV557Ou1r2zjjyps5+jfX0v7m41SHg0ilC7Y04DPEgbX0ApI/LBBjQXHwLyu9DIQFL6Iku2MmeXyQS9vch7a6ul1y5jqo2VRx8x5y4C8ruSydV6W8uHa/OfMJVxjOEEIliuIqUWMaWL4A4VCQPY/9Ffn1i5ELGVaubae9L47WMApjZR9SuIrTTjyd3158KTPmzEVZ9jGmXUJnGUJUbDtpNZY3ibRmNWNbt2D3zZp4tC1GuMqPb/1XKNE6kuuWceHtN9Ic1ghJeT5MVfPEXTdx2slvcvZl1zK5IVK2pisqIsx/+xM+uPfa4sGrdL24vGsl45LLluwBphiDeD9ycpARwQiemmks6cvQncyxPp5lWn2YmkAF4+u9eGPryPhrWNGfI10wSBegNarjUSTyhkXY6yXSs0QEsmzEowgeeumyqjAqawlpMhUDK5CySdE/wGDWIKNbJD0yHsUib1iYFrTHs0yrC5I3LNIFk760TjRYjZzswQxWY/qjyNk4lieEPLABJRPnqF3ncPSvL+DxRx/hqUfuZuLkPzK1Isqzd1/PmdfcxmW/u5mPPt6fP777PeGQF03vZNNDf8nyr15Fk+H79kFG1QYZyBQIGikkI89jL77FCy88z+hTniLiUajzmLy6uo3fnH8uiXSG6ZMn8MPiZWy/98F8/sUXAHz38hPccTNcef4ZLF64kLde/Su+lll0ti1nxpSJqMefwL2PPMq0ubvDW28xZ6fdqK5vJFpdw/EnnMi+e+zGyr4s5zz6Df1dSUy9wOhpTZxw7EyMjhXseeovufyXR7PdzGk88/jT7L3jNoysjthrsaRksZQ+w5kfpejOUnSYrLgUAaMn1mOqXuTsIE35AaymcUiFNFZyUCA/8lkuPvYANH8QSVNdZM3fE1X8327DNk9ca9jmDdu8YZs3bPOGbd7/bJvnBBRzRZRcOZ/8kIqkIZVNUul1Sr+33R8O+lNSSqqTFMVFa5faMOc9Q1sp/7zLe+3sh6U29mfPUax8cr+T854Sru7SvigNcDqBSpcnWhVBUseOSbIqKohsVHfRzv08yO3YtzLOVMf+lfC2DrV3SDLPPXgXbe2deD0arU0N4Nhd+32SZUEuiVxSDbXl9MlUB71l6PK+ZJY//PkFHrrqHAZ2247zbrmfh68+lzUbuqgOB2iurSIZj4t93ha3NDzC//AqEookkcgZ5A2LWFbHq8h4VZm+TIHuVI7JjRG2ba2kwkojmTq9AzGOOutSNps+lYVLljJ62kxh8846jSvP+zXr16zmjMt+yzGX3ET7u3+hJuQX1CKAQ4fj/rPnu5XPioq3QFgEpTUPslmk/LGc6jPVg+TxiuS6ZbqBbCxTVAU5AVFHVNvWALEs0wYkKGXry9VkceLl9rxwaapcH0qgWjeaNDF1uxKuaDOlQEjYvGNOo7D2JwBWrO+io7MLNRDBVLxIssLpZ57NbZddLGzewDpRySPLmLFeNH5id9XCQoK8B6OikX03a+Lr9+qRZIWln7yNHK2nd/USbrnrFlrUFFrzJN5fPch9t93AlV+8xLWXXUhT65hiENE0qAh4mf/2x3zwx6vxerSyIDd2ktpNSLhr3v5ZSn8W70PNpqiL1OGvaGXNYJ6lfWna41miPo0pdVW01lUj51IktQjrYwW6kjmaI17CHoWCaRHxKIStLEqsGykXd4PxRGowIg1sKHjIKgFaRo/E17cKKx0Tvk2gio4M9KQz1AU18qpMImewPpFHkaAm4CGWM2iP5wh7VKbVjcIjSZieAGaoFkwdKdWHnE2AaXLI7jtw7Gln8fBjj/PY/Xex7X0PAfDMvbdy1lU3c9n1t/LRxwdy8bOf0d6XJ75uGaP3OpkVX79J3rD4fM0A732zHs9WrVR5LIz1y3nimzbe+OsL7HX3B0Q8KmHF5Mdlq7jswvNJp1JMmzqFH39ayPb7HOravG9feYJbb4Krzjudbxf8yCdvv0pozFZ0r13J1KlTOOGEE7jn8aeYM28v3n3nbbbbdhtq6uqorqnjlJNPYu5OO/NFe4LXF3by3eIeEv0ZJk2rZ699ajFWfsNeZ5zHFWeexDZTx/PIY/PZe972jKytcBOh7hp1/DpnXy09M5XYPRA+klJZh5XPMioYRsrEkYH6gIyVMJCj1Ui+AGYihpVOcNHR++KtaUALV2BmU5iDfUVqx3/TZlni37/yfv+dm6IozJ07l+eee46qqir39a6uLpqamjYqVPmPtH8C0W2WI8RKDn5fLlqBJMtsucl4sXmXlLdJ/qCLXJC8fvsgX3rotjnRnJ/2/8ua7Wg5ZRI/f7YSlMTPDhmZskOVexApvU8JF6rLffd3uqGUL640eyvKestV7e03CYSZ48w4To9dllZWulZy8Hfe5zpPIDKKgCUrSLbThCTjkUzGjx5ZfEhXHKKE05Rin5h6noHBOFscehqr13cQHlzLY8+/xUvvfcLtF53OiMY6mqsi7Lbjdhxz5Z2MaKxn7fp2zHyOi4/cGzObstWoNSTLIlvZQk9a54euJMm8gSJJtA2kaY76aIlWUhPQ+LJ9kE1HVBDSFCzNS6FuAjWpPvTV32IEq9nQF2evAw7hhKMP56zjDkXSs7Q01vPczRfj2Xxvmnc7ltlTxrFJayObjBtFKhZjwsgmDth9ZyFwVoqQNAxxODdtnlJvSemD/R5JtbPSRlJwRbniJHYW2EGuOCVqshCKQfEI1IaeR/b6Be7Q3vDloDj0mLlMsZxNVlwEHYDpCYpS7bQQXJB0ivcqdRTcwJhO9/cf4h+3OXfe/wj11RXc8+ifAYlw0M9dV1/Aj99+Q2NNNTNDeVjwpnA8tOI1ja51yOFKrNqR0LOGQO8y9p08iltufY6+NoVV694k07MBq8JH07jJhBHzuDYTYL/zr6MlsZJLzj+bmdOmcOGh8/B6fCxYsYZfXX8P1//6WFrqa4YuE9FnNiK16AgqJcg2gQyUfAKhIfuDGIEqqgp9zG6sJmMGaIvliXgVOpN5ulI6NZERLO7N0hbLMKbKT6VfI5HXqfCpVHkVwgOrsQZ73Llp1YwlUzmSZF4om9/83gqu3n0CUdULer9AfXjE/eM5nQkBE0v18Gmv4BjcojlCsmDy7uoBvlsXoy7io2JqA821UaRcEoc3UY53CtQIsNuee/Pm/FqOO/sSWsdNJLb8e+RJzQDstP123Pf4UwD8aucZjJw6i5Yt53HiL3/FvWe+zwsP/p4udQRfd/gw4j3scexckGS+XvATe+65J+eeuz09Cz5m8oTxfPvtt2hYNDY188gf76S6sYWeDevIKEGuufhcvNUNfPDxpzz3yuuYhR+ZcsBpNO94OPl372HLOdu6Y3TuZdfQ9tUubDt7C84/7XjW5b08+egjHP7oQ3y/bDVbH3wCRxy+L6rHy65ja6hTcqy3LEaPbOHHxcvZdup4Dt5+S8bsdSwFXeeO807iuN3nEvB5RTlhMCIcE714SHVa6QES00D2e1znxVJ9GNFG9KqReDp+gkQvZmUz+KLIFTmQZKaMGIcebcLS/FiqByW2AWPV9z8P5P6zbdjmideHbd6wzfsfavPiZnLY5g3bvGGbZ5agYZ2AhGPvTAPJlPly4XIAtpw8duOBdRsRbEFRqNkJJm+kOXzyGz2DbyxA6OwPQ4Pu9nXK6IKg/L5DKEpc7m1XH8MoPi+4AcvSz2AKwTXXD8CpbFKLSVunqmljrRShPQTVbZVUOUmmLgJrzt9slLamKowf1WJ/6WIg3FI0sZ9aphBGtoPmhmUxkEgxa7/j6O7qgmQ3Tzz+NC+9+wm3X/hLmqsijGisZ7e5czjm8tsYUV/D2g2dmIbOxUft6/aL81ymN0hPxuCzdYP81JmgIeIj6lMZXx2gMST49w0TKgMqHkVC94SQQrVUj1AptH2PpflpTxTY64CDOfGwAzj7qH2xCmlaRjTzwl3XoU2fR/PORzB7yng2GdnI5NZG0ukME1ob2X/eXJRIpdhPVTAzdsWBYQeqSwVFS9fCxuaeYveVByRdF0FbRcMs0fMQvopeRsEluqGEzkuxRUgdlLjmEch516bqQjC0IERjS6nHytaPKcar+8fP8I+ezh33P0JDTRV3Pz4fCQgHA/z+uiv4sb2fhuoKNlP74bNnMG1+fzkYwcpnMWO9wqZH6pBzCeT2hWzeNAtvqIpsvJdVCz4k09eOGvVD3RiCgRxgMnvyKKbe/UdWLf6JX597ATOnT+XCo/fG6wuyYPFyfnXjfcLmNTeWJ3UVpcx/LeXVd5vjE6iaqzkh9bcTsSzGV40ioMkokkTBNEnkDHpUDUUK0RETSV9NkYlnDQYyOhGvij8gIcf7hIirZQnQRbASI9pESvazsDvON+2D7Da+ls2iNajpmEg4FDIUVAvThIhHwbAsetM6yZzOqEo/QU1mVSzLp2sGGFnppzXqocZfgenxg2Uip/pQkj1YqThyUGefLSfz1hN3c+wFFzJq3EQWffcljVNbAdhxh7nc97jgFb7+4DmMmr45k7bdndMvOJvrTvqIFx69l/5gE4WUj8GeDnJWK6Gm0Xz9wIvsusceXL/fVJZ//j6bjm1hxY/f4dU0mpubuf/++6msrqGnu5sABc6/4AL8oSiffCpsXl/iR7Y9+gzCsw8m8/Yf2XbObHcYjj7nUnbaeRfmbrkZZ1xwCet7B3n6yUe55777WbKqjeYdDmPO7vsyqr6CncZUMzZo0qHnGD16FD8sXcmclioO2mFLxu7zCwq6zu1nHsNxu21HIBQqVvOUVj8MneNOctdpXj+y5sHU/BjBajB1lIG42IPrRpGtHoMnM4DSuZRJ4QpomkChsgVMHTW2AaunDaOv8+dr+9+kmZaF+S+MPv8r7/XPNMuyyOVybL755rz88stMmTKl7G//bPvHA91lSIVilqZUEGXahDEsX7MBKRi2PyNU452/y+EKzERMlGTbf4diOWXRKTLdLyZJUtmm6Rz4HZRb+TOWLKqN/c35qReEWIQtjlLmFDnN4XRzft+Ig1RaGmdlU0XnSFaKpUrgHsqLX8J0DwquQXV4kjRPEUFVcuh3SsNdnikQZaN6ThhR1VvOD+fcSvFg+sXhxFI9gqtUVpEyMWTVw87z5qEGItRXRjj2tCs4aMetePWua/CNGAfxXiRZ4cTDD+DEww/AHOzl2Mtvpa0/xqQxrW4/mYkYiupBqR7F4t40K/vTLO9McPq2o9l5TAUhTUbOJVmVkvhuXYztx9XwXWeC3UZHUOIdYJokayZiWBBpruHJtz9jqtYPhZzgm0t0YuUyHLLzHM46ZE9mjWth6dJlLOtNEAlKfL5wCfu+8j63nn8qEyeME4ElW/naKuTFT72AVFbCbxRLh3VdlGdLskCWeQOirzWb19UJvCgeEWixLDEeWkCIOOiCdwqf6FvTGW9PUDjBzpi4KuiKGAszAGEPciEtyubsAAJQ5IMrmceqqhL76WOOPPMSIpEwpxx5EEcfdghSsg8zm+LlH35kdFOdeG/dCHGNUBVW73oRaMtlQJb5Ll/F9LGNqMs/IVPViqx6mD5vS5Rv1tGTncr6r97hiQWd/HKrFpTBDqb4TOpGVACb8dCT85l/x9X84S8v8ptjD+LeFx/k6et/w4wJo3++Bkuai1odekCxS0zNxEARQTPYh1LdhOUJEit4qPApZHSTiFdlQyJHqqAhy6AqEnVBDRmBLol6BY+YVEhjtEwXPLNALtLMQNYg6lVQshJ7TW2gLZZjhNdCCkSEWJCp0xDSaPEbSJkEBU+IjmQCRZJo8ur06hqGafHtyn4mt0T5oSvJhoAGKGzW4EErZJH0LKa/Usw/I8/Mzbdg2mabE9p0B668+hpuvfRsZm0ygf3mbU+6ex0zt5vH1jvsxMq17ewzcxRHjPXSevmFPP7Ekzz1/HU0jh7P43f8yOOnwevPP80DT87nrGMPYltPF3/qaOOrr79hsK+Hl996j9t/fxd3Pz6fy6+8mqljmpEsk76+fqZssyvhiiriA31ul8+uMTnirF+y5PMP6OzqYt4ee3Pxr0/mqquu4dCDDgBTp7Igc8XZp7Iqdza777UXkpHnvZvOQfMHmXT0wYybUM9oX4D7rjjLDXLWV1fQ/+HTXHznQ5x964N89uNSHrvsdOGEZlNFdFSZc2+U7ZUOmkwKhCFSB7FOFF8YI1yPlUmCXkBO9PBgrJn2GFy6VTUke0FW6bP8+CyZUKASafI2SG3f/2we/m+1YZtX7AeGbd6wzRu2ecM2b9jm/U+2eZLHV57UdZojpmqaTB09guUdPSKYb9sTR/wP0xDcqnrBRnGWBPldfmOzmJjVC0WbN6S5CNGh/Vfaj/D36bucv7sCzOZG16LYr4rBfPe1jQUinffY1y8V3hWJWmmjuhJ/t7kJXvtzNt1JaaUTCGR3WbK4BPXtBrmV4nHe8gSEDTZ1JMtk3k474glGwBvgl6dczMHbbMprD9yMr7JGVGD5Apxw7FGceORBkM9x9PlX07aunUkjm8T1smn0zrXImp9csIG2WJpV/WnCPpWZjRFqgxp+VUKVIZnXGUjnyeoGrVE/DUEVOZvA9IWJ+2qxANlr8dYnX1HlUzD61yDFhPYJeoFDdt6asw7chZljm1nR0cuS1euokA0+X7iC/V55n1vO+gWTpk0XVWReXxGln02VCxqW0IlItg2z7GlmyYIOAVVogaCW6Jw4Y+KOrbBt7hx1+l1WhX10fBZVL3ld6CogyViGLkT/1KyooLID4pKilAXPAchlUAMeBpZ8xVGnn08kHOSUow/j2P12wcokkb1+Xnz/c0aPaBTJZVlGCVeKJHI+hZ5JiaB8RSPpaCte2ULtX0vBNDH1PIGqRkYHJpIyp7D4b2/yt7Z+WmaNJGBkkADTgglTpvHcM89w7y3XcdejT3HB0ftx70vv8PQNFzJj4hj7O/6dNee0Uoo8Y8gacqocPT5MWUGVoMqvEPb4SeRNBnM67fE8hmUxmBPaJ3VBDd2ERE4n7JXxWTmwTMErbft3pj9KLA8eYFSln6xhUhNQkQpxV/AQy6TSp1DjVwlle8kGaknkdQqmhUeRKJgWi7qTfLCwi9qoD9OC5ogXTc4zvlqhXlYxA5VY4XqkfBqAsTseSP2EvzJ+xz249pprqbzkDDafOJL9d9rGtXnb7bgTbes3sO34RuaOjBK8+Gwenf8i85+9iobR4/j9TT/xe+CNpx/jgT/N5+zjj2C82cWX3Rv4csEiBjIFXn31VW698y4eePgxrrrmajaN+pGzg/TE02yy4354I5VkB/vdrj7Om+X4s0/l8M/fp7Orm3l77M3tF/6aq6++msP32BFLMZnQXMtlvzmH9tPPYfe99ybqsVj60OV0B73MOGw/1JmTaK2JcN91F2Ol45gDPTQ0NtL/0bNcfPsDnPP7J/h8SRuPXXW2DaSxk4V2FYOrL1N6nnHmfCGPZQtaSnUjBd2MZaLGOzEHelBqR/FdZ4r2uMXOY7akIt0haAiRQfYghetQLBPZoQwbbv/2TZIknnvuOW644QbmzJnDE088wX777ef+7Z9t/3CgW6ludMtfLcNwD+hdiQx1QQ+GYdLX18cmUyYiVzZgpWLCkYvWgTeIlE1gda8pv6iLRBpSomwvhPaefmRFoanGhrCXcJ3+rEx7SNmb+35ngTkciTbSoFTUxLLRblYhjxyuKCIaHG4wWSki1oaWI5bcx8nUWkPuK5l6WYbXkmQkh78tl7a/kxA3koy8ONS7WXz7Xu5BvgQFp2hItqGWCmmR+bfRc6Y3jBGoRM4lkHJJm1cqh+UNYioaZkUzLzz1JD/88AN93Z08/afHaait4eCDDhTIrUQPVqQGM1CJnB4g0b6aJ196g5Wr1pDXdSLeknIRu4Qsb1hEvSojIj6OnNZAhVemP2uQwiRj+gloBaIBD4u7k2w5ooKvOjNsFVL5Nhvh/Ls/p74myLk7jaM2qJEMNKDJEp5kF4oviJHL8sQVZ7jO84SWRiZOGEd6+RI2b65jn94Yp193F9eecTxzZm+BU7pbllm2N17HKTfTCcyYKPmSAxEsX0g4605QxkEiGnmsdBIpEHLLE+2BLh7+VbMssCOZuhvsKR17x4mS9LxwZlWv4N008sjpASzLFFy3siJK6nTKgmzBSAWP33UzOx9yLDtvM5tn//oKa9e3M6G1mXvnv8xjV50FegFjsA85VMFg1XhCHatQapsw4/1Y+Sx1Qc127jSq/CqWabBhVT9bjJvMoifv4YCTzmR8QxUPLuhlk9pKsnGTnJ5gj1EB5MwgJ+6xLRfc/hAnXn4zsf5Y8cAPfzfY5qw3q5AXgRVn3dgtnc6wZO0GZk2bjFzdiF7ZyqqMRqVPxrQs1scLyJKELElIQE430WQJw4REQahhKxJ4kl1YnhCmx09ai5DVTYKmRcQjky6YZHUTTZGZXOMnp40ib1qEB1aDqZPVTbyJLlBUtHyS6fVhOpM57v1hAE2W6YxnyabzfL2wmzffWYmRz9I4to4Td53AqAo/fekGetN5TtysAQl44sduFjYcijeusfc5N3Heb8/numuuYs4mo8HUaaqv4Q/XXiKEgyQZC9hm9lbM3WQkV932RxSPjxq/wosvvsgDjz/JL/bfjdP23gkp2cfhB+3Hj0uW88mX33DcCSdx9x/uZsF335BMJHj0zhvAsjjomvvpuv9ZasZNoFLNcvCBe7ODt4venh5mbzqRX510LPc/8gQLv/2S2oZG+vr7SXatJapZVNnifWOVHCMqgwy0bM8tZ/+aYNci7rv3Xu69exVnHrEf2246qQxN6otWcPg+u/LIK+8x/62PmLf9Nhy7wywkr18gSYcc+F1UVOmhUZaF6JteQPIF6fbUYWQtGgEpXIkRrifemefJ5xeyvn80R2/ewrRIgNpkO2awGmWwXThNDWP5P2nDNm/Y5g3bvGGbN2zzhm3ef4rNU+tGYAz2iTnroE9lmc6BOA2VEYyCTn88ySYTxwkKJOf5NY/gbtezSPksZiFVDGaXflfn/yVrYUPvAJJE0eaVNqdiyUnMKkoRQWrvEaX3KV17pUHwoQHFn+kJ/J1AetlnnUTvULSqKSgqJKcixk7wWaZeLkLpNJsOY2gy2CotwbfXhovONgruXurSokgyluoF1eOivSU9L55P8WB6g0h6nuefe5YFP/5Ib1cHrz/1CI01lRxy0H72PX0QEohKqZAh3tUhbN6adRQKhaLNk2URRDZFUBBgan2YMVV+mkPiPRndIpY16Erlyekmfckci3waY6u8qMEqfkp6uPy5BVSFPJy6zSim1gVIFkyMYAtVvgjS2gVYuSxP/vZ8zHgfVqHA+IZqxkZ96LF+NqsIsfe0sfz65ge45oSDmbPNHORgpFg5YxpFcMKQ8XRR1B5vkTsdcKlM3J9CNFv4I97iWDj2zP7d/Vn6/9IkhWWCCSiySPACkuYFPS+SjFYxmSI5a62kqikYjvD472+ybd4cnnnjQ9auW8uElkbueWw+j132K2HDfUHXX7LkIv2R8/3zloxqmVT5VCK1lRi6ydjRU3nj8bvZ96RfM76hir60ztK0xJLePpojPmY0BFEH1nLy7nP4ze0PcuIVtxKLJ5gxfuTPEkVFceXS5JFR/jc7sSXJCqlkksWr1zJr8njUYATTHyVRMMnqFpYF6YJJMmegKRKaLBPQiiKIiizRGvVS6zGQ40Ik0gxWE1fDGBYYOYvBnElQE2PiU2QyukUqUkfAG0bODGBpAbyKjD/ZAXqevNckp5ukCwY9qQKDOZ1PV/TS15GgY9UAixZ2kcvohCp87DN3NNuMqqQ7ZeFVZLYf1YJhWry/coCqfc9jed5g74tv44xrz+J3117D3Gm1YORpqqvmjt9eTs4jqt68+TjbztqUbbbaglvuvAtT9RJVTV568QXuf+Jpjj9wT07ddTZS3zqO2mMHfvppIZ989S3HnnASd//hD/z4/bekU3EeuOU6TF+Ua+99gt8+/AJaTStWaoBrTj+S2vQG+tcuY/aEZk4/4Wjue+wvLPz2C+obGhmMDRDTFSI+FTnVh5RLUlsxkqqAj7PP+BVzR0bpWvYj991zN3ff9wBnHbIH287evJi4zMl4ZIXD99+zaPPmzuG43efae4WCJJtuMneo5k/pXmzZSV6lIMA8aUNC9UWRAmH0iibmv9nGm++t4uOdxnDKnFGM8GgEc3FRGaj6MEI1yKOm8+/aLP5OJdP/w/v9d26WZaEoCnfeeSdTpkzhsMMO47LLLuOkk076P7ruPxzoNlqmo3YsxkzGxAHZdjKWrmqjbtoEZFnCQCZXMPD0rEMKhAVH1vqlSJoHs4RX7WclZ0MWgiQrpLM5mmqrkd2SMrP4cyMOh2UaSP6g4JYrLZdz7ucsMgdl4/yuF1yknKQIMRghICSCBBsTLnF5uYY6sFAsBYdiKbiRF5ljh7PNKWWSJJsrzOseMq0SI2yVZpAts5zTVHxp8buigRwRSt6mLnjivCFxyJdk8ASEUc2nMcL1wlnKxolbXnbYfDq3/WIvbnz5Na6/6BwszY+cS2DUjkFJCFGILz54l0v/8DinHLIXr919DRGtJFOnaqgNregVI/AoErVBjVEVXryqRHfaoGBaGBbIEoS9Cidt3kw8Z7I+nmVWXRDSSa55fQntK/robU+warMmtghZJK0a3l4VY7vWWir0lYJrFDCzKQod6wBQKgwsw0TP5mny+7jviN245vFnuPa+PxGuqOShGy4l7Pe6wR1J82AMpLBUytSAJV9QqGAbeXHgNnXkQkaUmukF4Tx4fKL/oYhMtMfAKTHERlAIZ9QAI43lC5WjFe3PCPSFzdVn5EV22h9F0gvuuEtyHkkFZJ8oI7fvWeGTeO7Oq9nhmDOZMnE8O8+ZydNvvM/8Wy8n5BfOmaR6MHraCfV1iFumk4KnLlxFUJN5ZlmMw1rHo8qSu/66jDCbbjaLs39xOGPlfnoba1k7mOf291ZwzV6TkfQ8SrwDv9/PXVecw0FnXs5fP/qKjTZnzZQeFtzXStauvaZ1w2BtZy+zNlUwB7qhspVR0QCmBf1ZA68qE/Io6CbUBlTiOZ3GsJc1sSxRn0ptwIO/a5FYV4qGVMiyOKbz2pJu6iJe/rakh7mTaklmdd78bgOvt0Q5bstW2mIZDh5Vg2Qa6CZIRgEjWM2ipEaFT2ZclZ/NGgRK8bg/LSCfM0gP5iik42QHe1mbTXL9ujhbbd2KIkuMqAqwIamjyLD/pBp2unQnNFni+8XLeWX6gdx0171sN3EEe++yg32IKe4RcnpABJlklUafhZTtwvQ3cfBOW3HozFYkRcEY7BMBnWyKlUt+ore3h+MO2Z+XX3wBgO71qzE9QZR0P7/ZLMy5f74YU/UhZ+MoqX4gwvj6KEp/GztOaGT/P1xLIpUmWlPHd6s7OOi4U2ioinD3tZcQbh6DpOeo8Co8f+I0pHwGRo3kpmsu5+mn53P9w09z0j4701xbRX8yTSyT5aZHniUQDJJKZzjplFM57OADOe6Mc7n45COZ1FSL7PX9jIfTWWduib8tamNmUkj5LJWtCgUTZK+fdMNUejM6yztXUFUfQpUlEnmdTMEi6gmgDG4ASSYbrGdJNsOWG5+d/1AbtnnDNm/Y5g3bvGGbN2zz/lNsXqF2PKqsCM7fkqTM8vWdNFRXIqsWumWRzeTwen1FAUl7/VkUg4pOlY/THBEyybEXskw6m6OxukLYPHcdldi4IUJnTvC6LNjtfk4pfgbKbF5ZM4v0Cn9XcNk0RfK29HmGcnhTDBxIpuIGTUtR2Dj/SvdCpw393blHiYikiy5WBFWWGyC1TFHRpPnK72df1wm2Wp4AGTxsP3tL7jjneG54+Gmuu/IyjEgjUj6NnB109/MvPvucS2+7l1MO3YfX/vg7IrIpqLoAOVwh+tUSybaQRyWgKQQ0mbxpYZgQ0GRUGWY1hpnVGCajm2QK4pmyWphXlqzjm7e/whet4ZCZzQTzcbLeKEtiGfLBCE1VTaCvKY5jNoVpCoBBPp4mn0jT6PFw/5G7c/Wzr/PbJ/9KOBrlwd9eQCRagcuNTUm12caC3k7i1ciLMdP14p7trEXLdFH6OAmLUvHs0v9LNpXa0Cq20nGWZExFQ5JVKGSLYwwi6eNwgdsocSyTCr/C83f/ju2P+KWwedtsydNvfsD8Wy8n7NVcijgrMSD4yu2KLTkgRIWTBZP+jEF1aCQruwVgIz2YZLC5gknTNuPik4+iMaSRNyy+aI+zpCvJJnUhovkB5OwgXk3hrot+xYFnX8XLH3xeDs6w+0lwjBerLjaaOHKqQQDdsljXE2PWNJvjW8+hamEsyyKjmxRMk0q/RkCTMS2Q8zqDWZ3BnM6IiI8qn4Kc6hcJCVkh44nw+do4P3Ul8HsUNFnGp8os606ypjdFdcjDpi0VbDWigjEVzagSKPZDGtFGMnmLUZV+fIqEX5Ppz6hMHRFlTWeCvo4Eg129xDtWEgtEeUWW+HhpD+l4lunja9iiOUJAk9mqOcImtZPoSOb4btFSdjj6V9x59z18OX0i++wwGywLySjg1dNinI0Clip8rihxTMmHZErsv+duHLjjbOTBTsxkDDMVR9LzrFy6kN7uLo474hBeeUnYvPVr1ohKGD3HTlNHM/naMwloMn5VxpfpQ5ELsMloyAyw47Qx7PfH35JIZYjUNfD98rUceuD+1NdVc/e1l1IZ9ODLDhD2KkypDeKRJcaMbOHmKy5k/lNPcf0TL3JKAZob6unt3ECsf4Cb/vIqgWCIVDrDaafaNu/sC7nw2AOYPLrV3lvtfclJ4JUgvUvFSSWPD0uSUDKDBHxhTI8fuXkC6zIy3y7rpZDN4fcodsWBhWQaKMleTE+QVNVYOvN+Jv581f1bNNMS//6V9/t3aaeccgrjx4/nkEMO4W9/+9v/0bUk6x8kPmnrS9CcbUfqbxeCKCCQW8GwW/IMuOTxSm2x5MlMxJDDFeIg0tdRLGvYWBYdsTC6+wYIejXBd+ce3oul5I6z6H7G63dRCKUbsuQPFrnbFEUEBUoWnrhuyaZdkp100QCljpP9/jLna6iAil3K5zyzZJf3uhn8EkEQ8llxoLOdfddgQnkZnJFH0rNYWkAECdybFxWFxYHfQUsVRLbOMotK3JLILkt6HhSV9xeu47STjmfh/N/jiVRi1Iwm460g1PUjZn+X3Z8mx197F1eceBhjRrciV9Wjt68qlojJCkplHVIghJVNs6FhC2I5g0mehHuQAVy0B4CleDCiTciZAeTMIJ0VEzn8wa/Q8wZNDWHuO2QqsiTx6vI+Dh0bgAVvodY2Y6YTZH74HAA1EsVIJtCzecyCjp7No2gqlmkSaKrjL18sQams5bgjDnb7EsDs7xIlojafm1vG7YxXNgX+sEC0ZdPFQJTmscuvi/yzkp51udckzeOOlyuW4/G5TpDrqDrPIolglqX6sDQ/Uj7llkK54+kIq1lmsXzcyCNlk1h6Hm3qTkiSxJ47bSfQdLqO1x/gz1edgTxyqiiXS/RgJmPkp+yCluziN5/E2W1yPVc+tQBJhjnTGvhiYRf9XSlG1eeoafuAI8+9kgk1AUYXNmBEG7n1iy5+MyOE0rvaLmuVsbJpRu1xLLvP2Yx7LjytOB9LDg5D16jbSg/9Q4TLlMo6zGQMORDGjDbQ6WuiyqcwmDOo8krkTEkEKoCCadGd0ol6FaqzHWDoxMItrI7l6E7mWdyT5InXlpJJ5qkdEaGxLgTAhs4EiirTVBfi3B3HsWm9HyXZI4I+9tob9NUSzfagpAewJBkzUMn3mRDvrezjje820N+VZLA7Rm1LDaEKH+GghxFVfo7fqpWZUod7LUvRMAOV9MbTrF65gq1aQuz+i7NZ1d6Fx+ujoamRiWPHcth+e7LNTruwPmUhD25gRGWIv/zlLxxxxBHI6QHkfEqsYVPH7O8CVeOZdz/j1kfnc9rRBzNx3Fju/dNz7LvHrkSratlliylI6UEhTGaZmL4oUu9aTKfMyzSonncM911+FkddciP33/Y7TtxnR0zNz+U33MZjz79G26IFYOrseuQp/OHx+Yz3ZZEz4mCULMA9993Pi2+8jZnP8c0iwdtZEY2SzeW44uzTOP+w3TFHb84pxx3J4y+9xeq/PkhTdbQ4PzQPki8ogsOlHNSy4goOOWg32evHkiT0ugkMGB5Of+4nVq7o471LdqCi6wdMTxC9qhV1YD2Wx08m1Eh3qsC4usjP598/2IZt3rDNG7Z5wzZv2OYN27z/FJu3ti9JY6ETqWeNSOzZ/Mdu0NgODjvJUDfY5iJqhZ0wM6kysdoyfuOSQHf3wCAhr4eAz1v+IBsRGCulKCkLgDvBSQdl76w/5/X/VXOqnOygqvOa+LmRNWt/T3esnO/k8RYrk5wE78boRoYggd3kLiLp5FTGOPbLfQ7TdCmHLNWL5YuAkRfCeK4tVUTw205AWt4wn3z3Eyf94lgWvfAAWrQSvWoUKCpqXxvGhpXudzv+mt9zxWlHM3biRFEhM9Atkp6+IGpDq0jCqBp642QGCCBJEFV0d33I+YyLSpfTA1ieEEawCiXRjZxPsaZiCnd90sa3K/uZObaKfac2Uu3XkCQYHfXgW/etQHLnshQ6VlMYjCMrImBWSGfJx9NYhomkyKg+D77qCPO/b0OOVnPswfuWjbeZiotgmh3sdv0aWSn2vxN0HDJfLEX7ud9iB8RdPm4oT8I7NCbO9UvmjqNxYSmaGN9C2u1zyTTcRLFb0SZJwpfJpzBzGdvmwZ47bivK9yUVjwJP33UdaLb4aTaJmY4jByIUWmfyxI/dvL2wixWr+qmoDSLLEmsW91DI5pgxSSP//etc+tsbmVEfQMsO0kWIWNZgXKUHrWeF/Ww61mAPI3c5otzmOb5gab9uzOaVrqOSIKfk9bljI/mCGJXNFEL1pHUTwwRVBo8iY1mWCxowTAufKhPKCWoO0x8lbSqsTxS4/7M2PvhsLXrepLI+iNevkcsUkGQJRZWprvBz9OxW9h5XiZzqw/IGwdDRPcI2qjknIQp6RRPL4/D4N+v44JsNJGMZchkdRZWpaQrT1BBmRJWfA6Y3sXWljmzbUOeZuvMqq9rWsMXEkex10BGsWr8Bzeun0bZ5hx64H7Pm7kwyb5Lq62JMpZe/PPMcRx64D1I+I6oU7UohK5/F0vM8+/4X3PLoM5x25AFMHDOSe//yIvvutScV0QjzZs9EziXc84nk8WHpBczBPvfsUL3Tkdx38WkcdcUd3H/T1fziqMORChkuu/EOHn/uVdYs+BQj0sg+Bx7Mw39+hoZCN3KqDyNcTzKnc+8f7uLF19/EMi2+/mkJABUVFWSzWS4/93TO//WpyPk0J551gbB5L9xDU11NcW90KE1cLv3y5IhjE6VASPjMdoVfd2gkt3/cRl8yz9W7TaAxvQ7TF8YIVqOkB5CycRKRVt5ZNcChmzZvfP79N23xeJxoNMqa9g4ikX/eXv8z9x3Z3Mjg4OC/9L7/aBs9ejRff/011dXV7msrVqxgn332YdmyZf/vxSgVScIMVKJmB8FZRCAO/h4fUigqyqU0P3KkTpQlShKEalH8AuFjxrrtA3ER+bLRcga9QF1l9OdBgVLeJ9NAsrkdJVvcwH1PCSLAdcQKefswJrvINcAV6JI8PrHBlKiIW4YhSqPAvb7kE+JKstcv+CB1WySrxNGTA2G3LLy0yYWMe3C0bLSb5KFoVEvRT5Jcdkh0XCHJyLsODQhj6xymnUO95Q2JsnlVCFW5pU0e8eySmQHTYO64Gs475TgOuuB6nr3nFhTViz/bj75uudvXXy1ewZqObkZWh7EKeYyuda4zqNa3UmjZFGv5p2ArwteNsmgeXIa+ZLFAd/gCKA0jsTx+TF9YPL+hI+VT0LEcI5OiLtbN27/cmo6szLZnzWfT937khvN2Y/dxVSjrvoTqBvSutegdbaS7YwD4DPF8ikdFC/owe2IYBR1vhRBF2GXrWVz00PMcs/8e5c65L1guMCPJtkMv0CaOsFL5wNkBIUUTfe1w39lzxDnwA6LPZaUMpehk+i1KHF61hJfW1MXB39SFkMNQJ1jWXGSIZOoCiad5ePimK1m9fgNXnn0qVrwPY9QsrvjN2Xz67Q9sPWkbXl+bZ8mHH/Le3z7mN79pYUk+xF8ee4e3Ro0l3tNDfP0y1nzfTOPE8RSyBbpTQX764lv2WLeSYOOmoMuosQ2cukUrSv8yzGg9nZ4GGrIboGslm00YQ0Uo9PP+ctARdt9tTDhoo82mA5ADYeFgZwep94aQe/uo8YVR+rtQVZ/g6TUNTG+YaC6BlVJI1k0ho1tkCyZRr4oiSbzx2XeMGFVJVcjD0pX9rFzRhyRLVDeGyWUKJLM6Ly/uYpPaUaim7pZ/a72rqOxaKQ5spoFU1QSWSWvEy/TGCLF0gf7mCFNGTKY+6GVUhZ9EXmd6fZCookO/Cbm0MOCmjhTPUR/vpa5SRzZN3njuT+xw6Il8+tHfuPa66/BGqrjtjpu45Z6HWJUwybcvZWTLCOoqwpxzxXVcdvoJaIrE3M1nMHXcSNdJPHSPHYlWV6EU8mw9eTRb//Z8Mc8dRKXHi5WKY+l5li9cyLimOjAN4qk0r33yDX3v/Znvlq0WYyUrmN4wUj7JNaceyQ33Ps6n77/D7N33I6TCBHUQKZdHr2zhiw0pnv2+nVsuvZLlq9pYuHo9mWVf8OGPK+lO6ew9qYYQBTB0lEQ3d19yBi+8/TGHXXIj3y9dzYa/3k84Ehb3tcfblGWsVMKeP6LszcqkkIJhpNpR6DbnstrXRmVlK9fuNYmP1gzweXuCeVUNJP113PrRGrYZXcO2VRGW9GVY2pv6Pzr0D9u8YZs3bPOGbd6wzRu2ef8pNi+jCwoo1bEh2IhT217I/iCGv0LYNs2HpXrFfpwV5eSSYQfGTQNKkqaSTkmlUNGm1VUWkwBDNSGAorCss585Nsd+JkBwhbvVSn/ni5UEPSm1eU6TFUED4VQkDa3EMo0i4rs00F3yebCR1uICxcC2g/SGMjs2tP2M21sqVnZJekEk/JwkrjckqmHyaUGR4lTF2Dzdggs6j6TnmTOplfNOO4EDz72GZx64C62QQU6mMDrbMAZ6kDSNr5evZU1nD6NqKzETMWH7TRM5VIFS24QRrEZWvSKwnh6gymcg5ZPIg10YPe0CrV/fCqFqYfcClSDJgmKgZy3ZtctoGRPnd7tux+srY9z2+hK+XNLDgVuP5MhpDfg6f0JvX4lD4ZPr7aeQyqJoKp5IAC3gQ5Jl9HQW0zCRPcIezJu9GRc9/CLHHLiXPS88bnWEMy5OcFqyLHAEuh3BSFN3xUvLg9glCQonQa+q5bzozs9SyjWlGCAvtWmUvsehXjN1LMsU1Wn256RCzk36WqoXSdF4+OarWL2unSvPPFG87o1w6VXX8sl3C9ly7yNYOZDlLy8/xfdff8H+x/+KeOda7n12Mf2rl5DoWEG0ZRNC9SNJ93dgFvKsG5xEz08L+fTbBUzcZSs8+ST1mkltZSVSPo0ZrBbPq2eRE/1sNmkcFeFwuT0bMofL+tAJRjn+ruYRe4Hjp8pFQUIrm0JJ9QEQdfrdpndx6WTsiiUzUInpizJg+Vi0IU1vOi9odGIbaBhVyWCvSJonY1lUj4xlWKTjOXKZAmtjGdJ6BWEjD3kxPpqeQ84MIiX7RBA+GEHOpaj2R5jVUkkyq+NRZUZUBagNemgMe6kLeqnwKTRpeZTeNQIwYOubyNkE9akYdcE0Uu9q3pj/CDsecRqffPQh511+DRNb6rn5xhvJ3HkPpuph1cIFjBnZQl1VlHMuvpLLfi1s3vZbzWTK6Bb3rHDoHjsRqaxEVWS2nTmNbWdOE/Q6SCIJrPmRgyK5s6xtHWOrQ1j5LImCyWufL6DvnSf4ZuFS0d8eH5YvglTIcO2vjuHGex7l48++ZOuddsOjyNT4FSzLR6ZhKl93pBjIFDjmgmtZvradRYsXk2r7iY+++YHOpM4+M8cQirUjd69GClfyx+svFzbv0lv5fnkbHW88RjgcwtWPUT0C5FKSjDSTMeRQBXKkWlD/yCqmrIDqIeRR2H9qI1+sj4n3+qP0yhG+WhXDpypsWt+CYlk0hjfivw63f8u2evXqn702btw4vvvuO7q6uv7p6/7Dge6oVyFthQkFq5GC/WLTap6IlEtBsl8ICwUDdPpH8G1Hgql1IUIemYxu0iINQDaFHIhApAoz3i8c6ViXK3xipeJliDcXMeCU/bmHcbFZug6QaYAaEM5YPisElkxb3CUYRvYHsTw+kSlGOEVOoALA4bB0nB85VGHzsmruopRCUXEdRzU5m8a0S4VkX1CgoVIxrLRwWs10ouigeHzINkeqqfnd7J/Tyri//k6TLEs4SFrAPjTnkfAUS6oAt4xYFyXBeV8Uj2VCIY2l+pCzCaRsonzwZZlTjz4Uj6rwwJ+e4Zdnno3lCyOHKzEH+zAzKf74wtv87oQDwShgZdOuEyoHwmRHbkF/1qBBL2CpIAcjKF1L0NevQKltFo6LKlAJUi4FioYZqUfKJpA7lwsnKxDGSiWQf3qPlqZxLLrvSBRZIpk38asyRusM5BWfY+kF8vEUnkgASZaRFRlZU90ybkmWkQFFU5E8PhobR9EfTxbRhZKMJAtHxezfYB/8swIBZjvAlu6g1GRX6dwJ3limgawoRSdVVkmmM8iShd9GseEIcGmB4pgoxYBA6ThLet7lg5MkGRSBCJAsu2TSLtsXpeMmkp4T88VGByDJHH3ogex+5IkU+rtRVQVLUTnj0D047oLfclo8xYsLO/lgTZaBDR18dNAvqN9yX7TgSDKJDMGqGlI96zDyGTpXrMIXqQFkDr3oJv542w1s9cSfiIWaiVhZfKqMXj2K5UmZVq+C3NODkUowb6tNefyV90R/lZTilZVwO4cLZ60OfY+9Tpz1bqbigjMYxJ4w2AuqB3mgQwSR8l2Ci66iRlwyk2Jg5Gz+vKCT+qCXdYMZRlb4kWWJvz3wO0657XEMy+KgzZqp9Gvc9NYyVi/tIVoTIAHkdYNE3sATridtSASwDxSqhplMCR5KQwRiqoxBtmyKMqsxhCZLBFRJIM8yXZihMOg6phYV46zaW2suDdmUcKRUzUVRvvHaa3z27QJuvPpyZAmaaqtIpzMcOHsTfn3a7UR9KvsfeRx11VW89uGnXHLGKYyZMBHLyLC2s5fqcAD0AluPG8Gq/hSSL0DXunVU+jX629tRwxVM3OUQtt1sCvNmTWXq6BHEujoJeDWOuOJOTMvk8HnbsNm4VpKfPIs6eVtiSgifKnPfk9fi9WgccurZ/PURBSyDrORBwyAQCFBZWcURJ57KghH7s9mMTYnUj8AydHYaXVEUs3LEnbpW4ZVMfnzzL/z1r6+yen0Xf37/S8686V5euPNq9tpmltgv/06A10olsHJLUGsaBYpJVsmpAUwrzzatlVzzxlIKW7bQHu/mh7UxXvu4ja03a+SYsSr7ThjzX+6r/6s2bPOGbd6wzRu2ecM2b9jm/afYvNqAiqVGMEPVyPFuYTM0WxA4m7bFXmvo9zfSnzHAgKAmi4D1YLtA9yoaslqkcZBMAzOXcW2FM4eLmgIlQWW9gKMJ4exDjt2znLUDZSLKLhUTiOoUsG2kVhT8LEVmO4lnuzm2UFIU0ELu84qgtrDVsj8oApI2yhK9gJnNFxHDzvVMXSSBSzUKxEPiVC2VVbmU/C6eU3WD41IhI/a9UuoMJyFsUz9Zmk/YZyVvU3GYSEa2bN/V9CynHnkgXtnk/ief4tennYyleVHqRggBx2yaPz7/Fr876SAhhuzYPUBtHkuhYRORvCrkkHJpaF+CBEi+IGY2JcRHsRPjpo7pi2KpHpRYO3SuxBzsQ4lWYwz2oa76nL1rR7Lzr2aTyBsYJvhVCcsTEpVxyVgxgQ9Iih0gNk3Mgo4ky2CYyJqKHKqgsb6F/kRRFBvNg2nrPViJAZtKw/aPnDlmizBvjD9dsiwsB2Ftj1cqk0OWJfy+YrWSm8iVpGI1kju/Si5omSX+S0mCvzh53c9JZqGYwHfeK6scfcgB7H7kSejpFIosgerjrOOP4NhzL+fUtpW89MN6vu41aVu0jLfe/wWNWx9MMudHC0ap22QbMgNdtH/9OorHT8WoqUiywnanXcWL99/OvK2ewIo0ErGySPk0CclHwvJQ7VMJxjswEgPM23wqj7/2QZlobKkvWgpu+Bl1iZMwt9ej8FMF6EAEPzWsdBw5LoLdDiLZFbGVFSxZhpCfRKCBt1YO0JUaFELMPhWvIvP+fb/llNufQJEkWqI+CqbFF239vPP5OmLdcUZMqKM26GEwZ+AJN6JIAq2uxNoh1inoeQJhkFWkQppqv5edx1SwTWsUjyIR8cgomUHQM6AYmP4oGDJmsBoplxJJ6Fza3Rckjw8pEMH0BHj9tVf5dsEPXH35ZSiYNNXXkM7mGTdhPE/efRsVHtj/8GOoq7Ft3pmnMXrMOFBM1rStoTocQJISbDttIss3dGPJCl3rhc0byORRvAEm7nIo286axi5bbMqU1noG1ntKbJ7F4TvPYda0TUh+9gLahNkktQhqTZT7n7wJr0fj0JN+zV+fuB/JyFNIDYJpEA4GqKis5JiTfknFfvsyY9YWVEQiUMgxb3KzWKNGCiso6BpJxfFGavjhvRd55ZVXWf2HR3jytfc569YHhc3bbouifTOKc0MOVYBpYgx028juAIrXj+mLokhQE9CYVBvi3i/WUR3yUOHTeXtRF8tX9rPdzEb2bYbZU8f/bB3/u7Rh6pJ/rPl8PkaOHPlPf/4fDnTHsgaGZaFFmvHpovzGyqUgLoSNUtk8emEAQqMZWxUgb1hsSBToTedpqqrHrA0j51IC4VSVRipkhGKqnhPOjC+AOdgnDJmjyo3Y+CQolo47mXMn++6830GsOSVB/iCYJmYmxaKVbVRV11Af8BRRA07G11YHNwvCWJsItJyzKZuZFNilj26m2DRcwSQzGUOyOS1NR9ALBF+dbbAdMSIXebKxVuoUQXlG2DaWllTCb2lznkKxLFgcCIUh1YysKJGyP++8RzIKRQNtO1jHHLwfh5x8Frvv8CUjZ++GXNUg+KEUhSN22oqnPvyKLadOwBWRkWXkaDWJvEljag1SbQvmQCeFtctAVvCMm44x0I0cqRIOUzaF3jIOXfZQMC0iiRXoPe2IcuBUkdOvbz2BvvUU2pZQGa12kYSMmYFWUYcSFeUMRl8n+Y51mAXhqOipLEZBF4EAj4YcjLA2nqO1uUk4QE6Ztqkj5TI4wmmOk23ls0jhMPgjkIkXkYT+kDjwBwTqxDKESrCDDmlvW01jfS3IkkASaXa5vuP8OAf+vxPUKS1ZxNCLB2dZBhMsSRwS5FxCIBE8Aj3jHH4kWWHi6JG899UCdtlqU5SlH1MX8nPFcfvx47oeDpq3NSvWjsZcuAqPMUCmYxFNO2yNqinoBYNgbQt6JkXdmNG0jvCz8i/X8fnnCht6Bjj4lHM469Tj2XLTaUwIZ+nTNZpCMqGOBRi2UxLwethyynhkVXVL1yWvT1AlOEiKUoSnc3ixHZ3i60UON0kR4yH7gj8LHpgN41GSPWCZ6JWtYj77wiTzJjUBD3VBDy1RH5Nr/HSmCjz/11dZn8hTF9RoCGoMZA1O3m40t8azZFMFGur9HDurRXAQZhP4/VHkdAoj2oyS6LLLCk3IZVDoAlkl6ksjFXKiXCyTEo6ZL4Sc6hOiULF2IQKkeQWPWD5b5OJUbUE008SbG2SnMZXs9OjvyecLdPf14fd6ePdvn3Du+eeTig+iShAO+nn34y949+MvqK2pRpWgo0f0//jWJpav3fCzeaWpCvXVlYT8fl7925dMHjWC7WZOBcC0TDp6B1jy9O/FHNQLaLKCtOprwuNmM5D38ejzrxGOVKCoClvvdzQ7b7Mlr//lYQ4/81IA3p9/P13JApdfcC63XnMJ2Xf/xm0PPM7ZR+6HJ59l99Mv5417b6Cru5umUWPING1KhWkxrXMA/59f5syb7uXa04/lgLOu5IAdZhMJB3ng8jPdvdyl63B4B/MGevd6dw4FBjqYWNNKX7CF6pCHBz9uY+6kWvwehYraIIODcaS8j741y4hOmbHRtfePtGGbN2zzhm3esM0btnnDNu8/xeYN5gw2JE2ao62ETQMp2SeSU6oXohGS2Rz5vEI/BoNZHcOy6M9A3tCoCzfjCdWKOW2LKMrZQVGSr2i2mJm977nJAdNdQ5LqEXtjrhjsk/1B9/8/q3YqRZWaBotWrKbC76GhuqL8faZRUv1kV6TY9ytFihsD3T9Dqjpr1tDzxfVp31sqvb4uApmoIjg5FNntfocSG+RQX1iygkvH5VSxOIjiodzQJUhxSbc5pu2gd1lzfnfsoOrlqMMO4dCTzmT33Xdj7LgJgibAptE5Yt42zP/ga7aavknRn7CTHJasIhWySPkkxkA3ZqxHBKR8wWLyXC8gRaoxQrXoWgDF0pHzKXS7ugKAfBY9m0Ie7MMnK3hiPUIMOxjBilaj1Lcgt0yGWCdKbTfmYJ8b9Mx29ZKPp5EUWVQ0hUPIwTBtvYO0NtaXfXXJ1MFJ1MqCqkuyNkJdAsJ2WWIOFvnW7fFUxFzZ0L6BxtpqJI+G5QylU3kkyeI1B30PxfVsrwF37GTZnRfSEL9HsqzysUYkcyxJRsJi4ugW3v3sa3adMwu5kKE2oHL5obvyw8qV7L/vQWzoqGd96mU8ik62ezXVMw/BMi0KWZEsDta2UjV6Ko2jAmx4/ga+kSX6B2Kcfv5FHHzY4ew6ezNq/CqqJJJd3oE1GJ1tQNHmSba+itucpNOQSj6XP7+0uXbNHPoXsf5k4UfKmgcrWAneIKYkCzocSUKvGkn7QJ6sblAf9DKmyk+1X0OTJV545XW6knkMC8Iehf5MgQn1YRaOED7MqKYIE2uCRL0KKiaGJaPYVRFyuMrdAyQ9hzyYRs4mqND8yIWMm2x35o6lBWw/NC+qKFSvTaGWL+rkOGNq6njTPWw9upo3n7yHfC5LT28fPr+Pdz/8mPPPOp1kMoUqQzgYGGLzJDp6egEYP7KZ5Wvaf9ZvmqpSX11ByO/j1Q8+Y3JrE9tOnyDOABKuzXPsiSYrKF3LCDZNoSvv5ZH5zxOKRNFUla33OoSdt96c1596hMPPuASAD555lO6+Aa6/5Fxuv+I8Xhvo5LZ7H+Scw/bEGwiy6ykX8vajd7Jh+RKaxlajV4ygoXYcU9d34g88xVm3Psh1553KAWddyYHztiHs9fLAlWcV10hJs/K2zlAuI2oAzbV4BzYwpqoZb20z7y/vYcHaGCNrghimRajCx+QqFb+ZpGPVUsb8H9i8/7/bv2ns+f9aq6qqYtmyZdTU1FBZWekm0zbW+vv7/6l7/MMc3Wv7kuQNkyq/SjjdiZzqc41pb43gRgxoMgtXraWuqYUV/RnaYhkq/Rrb2ZmxvrROyKNQoZki849wtqV8UvBb9a53yxwcgSwpGBHGKBUTvKf+YBERV8iL3x0HyDSLG65dMiN5fFx4yz3sMWcmU8ePpqauTjjr9uHERbuVlq2VoACsdKKMwwvYOO+bU8pmBx6sfFY4ATY6rhTd4Bza/leoNvfvNlepm2UuETmRLEtwiqm+ohFXBO+pk7F20EduwKD0voZACXzz/Y/86YnHueOGa7AS/ZiJARfNNuHwc7jq+AM4fKfZvPTxt+wyZxbRxmaoasZSNLLBOtrXr6N+4Rt4vR68kzbHjNYjda0SYxmqRq9qFd2USyFlYlirf8AY7LO59WShvFtZh5XPioCAaZCLJSiksoTHjkKtbUZtaKWwYTVGXye+aXMEcqBjOVYui2UaFNYuE6WVlY2ceOP9nHvsgcwc24rsD7o0A1YqZpeORtzyNceptGQVKZt0eUadvidvI8tMoSKM5ilDfbi8p7IqxkHRikGboQ5oyTwA2/FyHKTScbHMosOp50QAR5JdRBy6DpoH38Q5tL0zn7qA5qJAJX8QuaoBI9rMuHPeopBNUl+TJ71+CU3bH4GsymiKzKpF3WTjMULVVXjzKyh0LMMcuScTNqljrL6GFX97jY8+/YzLzjmdk085FX9sDXSuFIGQfJaO/kHOvO1hnr3hAvGcXn8xcw9YDjdmydpx28bQTE5Zm9fnjo+DGkTxUKgdJy6VGXR5CVOVo1kTLzAhKhAaysB6jEiD6CfNyyedYg08/PkaflrUzS5btzK5IcK3awcY3xDmpM0ayRkWoUKclCdCMBdjQI1SHVsheGH9QvDOyqYFZ6aNxHV4W2Vf0OWydcbbGVuH5sEpeZU8PojUgOJByifdMTf9UYGWsdEcklFwAwqmafLVj4tZumw5Xy9cyoaefgqGRTTo5+uFS6mpjDJ3xmSi0UpmjG0hZ5rc+OhzfPXDIvaYsxnH7LUT++++UxFN5aCmQKy7UvqJcAX6pB3oTOkUTIuoV2Fw6ZccdsIvaaqM0BtPccJBe3PSMYeCJLNu3Tqm7n44C958hot+dztrNnQymEiy38GHceHxhzB+q51Y+/4zeEZOQxlsxxzoZs2GLp5/9xOmbropjz79Eh9//R0dvQN89shNzJw0zn2WMv7oElHGcsFFGWnUdGKhZjqSOv2ZAjUBjdFRD2qiE8sTxFdZxz/bhm0ewzZv2OYN27xhmzds8/5DbF5vPEV/1qDSp1CR7hA2D1xe/YLswfJHWLSmi9rmEazqz/Jjd4LGkJeZjWGiPgVFksgbFh5FIqgn3OSkkujC7O8UXyNQpHFBVoo2L9Ff5Jt1k6zl1FxOILyUNxzgotsfYs/ttmDqhLFURYJFegQngexUT5lmGRe6E4B3OdPlEs5vKK5bO9nn7olOksIZF9Xj7onYPLM4dE723liaBHT3Nbu5dA3g0mE4yG3HDlrOT4/fDboxBJksDd1/ndcLOb796nOefP5Vbr/qN6Drgoc7ncAyDSYefQFXn3gwh8/bmhc/+IJ5MycRHTEamidiRJvJ5At0r1xEU3It6mA3SmUdStNo9/pGRQumNygC/bkk6sBajI7VmOmEsFe2mLMcrgC9QHb9GvSUGEvF58FTEUGpbhAoT3tuyMEIcmUdViaJ0dOOMdAj6C4q6yj4wpx4/b2ce+yBzNpkApKsiKC5bSssPS/2r1LObSgTlnSEmF3/wv7cxpp7LZti4WcJ3dJ+V1TKhEUlWyuk5D1WKarcELRgbkLfsZX2/PBN2oY17z1LXUUYRy/CTAygVDeyuHpzjr37UwY6Bwkr7aQ7ltGy45HkMgWy6QKZRJ58TvBMF7p+pNC/ik0POYWIXyW35gc6vnmH7mU/cvwJJ3LBqcehxDuhvx0zEQNVY0NXD2fe9hDP3nhRcS45egKw8bVT0sqoTEo/7xGJFof6Q6wZDcsXEQFko4CUiYHqIRNqJJE3qPAqqLm4qHYAkFUyviq+60rTmciRyOu8uqCDqpCHsE/gODcfWckOoyqp9StkDAvdtAjLOrLNiS1nBovVY472iCKQ5o4/KPuCguLN5mJ316Vd6VJWhaB6kIIRsT4dRD/FKg8H4V/K+W4ZBl988z3LVq7mm0UraO/po1AoEA0FXZu3/eYziAR8zJgwmrxpcsODf+Grn5axxzabc8xeO7LfTtsIn7dUv8BBUdvJc0sv4Bk1mXzzpnSU2LzYigUcfsxxNFVXFm3e0QcLm9fRzdRdDmLBG/O56Po7WLN+A4OpLAccfAjn//JkJszYnHWfvYpW0yoSAF2rWL1iBS98/C1TN9mER196g4+//JaOnj4+f+wWZk4eX9QQcpIkQ+xcUeBYCKqbE7ahz/RiAcm8Sc6wqA+qVBRiIMl4qxt/tl7/OzeHo3vlug7C/0Ku7EQ8ztiW/14c3Y899hiHH344Xq+Xxx577L9873HHHfdP3eMfDnTr6xejVzQh6XnUgbXuIgaEyIJpkKsahZruJ+ev4t3VMVYPpGkK+9hqRISsbjHGl2Nd3ktGtxgZ9dCZKjBKiqEkbO6VXAYzUkcy2kqkeyEARqhWHBTTMZHttw/TZiKGZRoo1Q1ikThIAFvNFXA5o1KpNHc99RKGrHH5GSeJa4SiWMlBHO45J+vvODFWNlUUWHI6a4hQV+lrQAnyLuA6RELAykYO+AJIloWp+YsHQvm/ANWXotvsQ5+bzXfKwW0xL1cIyBvcaDDBGvqa6gM9axt9cVDZ5+gTefQ3J1A7carIoq4TYjsbege4/vEXaOvoYdm6Dl677izGTZ5MbPQc7rvnHt752yf0DMS447eXs300h1JZR27s1mR0k5Csk0PDKxmo/WuFaFghjTzYRWHNYhF4scugTNtY5jo7UX0ecrEkqc4+JEVGT2XRwgG0oOCo9VVHCM2Zh9E8FTk9IJyEfBI9OciFN9yFrHm56fxTBUrP60OqaXWzrNhlRrLXLwIwpaJhdh87gjDuQS4TLxozh/PWCfT4RJ87hmwoB5+L1ECsFWdMgSKnm/Paxhwne3wkPYtko0HFnw3uf/oVvMEgv9hvN6FSnogJR23WXrzcluGsG15j3/03Z9fxlVxx6lGcevsT9KXyhH0q3fEcb3zchqzKdP64gPiyD2jc8RQ233IEI2uCTKgL8eKd1xDK9HDGEfuyxcRR7rpwypKnHH4mlxx/CEfvvbM7/+VAWDi3QwVK7ECcE7ArFS9yvieyjOT1i7XiC6DXjCMh+QgbSXRvBFkCJTOIEu/A1PxY/gqUwXZRKm0aEK4R45UeRG+YyL2LUmiyzBer+vjii/UEol6OmDeOWU1RNqkNUDAtNFni6reXc+SsEWwVTtOlVFFPEiW2TqzPXNGRkUIVYowT/WLsS4WvjIKYJ3Z236WIcJez7YjLCqlUipVt65g+ZVIRYZJLC8RsNuU6VpJlYSQGBHen3S8AiVSa3939MLc8/jy/PHA39tluc2ZPncjkQ8+gtirK1Scewl5zZrilgE4fy8GIGxwqBkuLpWTqiHEY4XrMUC2GBYqZR+1r47GHHmLC2NHMmTndRVlZisaKpUs59fIbCQcDvPLh5+53TQ4O4I13FFFJyX4bhWwfdipqePuTr7n5wT9TyKbZfOIobjjjFzgcmJYkk8pkCfk8Zc6zIyzmCuZ5fULMzRsW4+AXwT05PYBUSKOMnsU/24Zt3rDNG7Z5wzZv2OZVDNu8/xCbl+9uw/IEkQoZkVgRN8YV/Q1WY3oCGKZF3oTP2xO8v7yHCfVhZo+ooMKn4FclkgWTvGFR6VPwKjIeyUTta4N4N5I/hBmsFrRU2bhIcnhF/yqpPqx03E2clgZEHMFIK5ctSw6AsE/pbI67nn4V3bK4/FfHl30vJ2DirGM3SG2W8HU7iYUSIeeyVppskItB9lI+b5fn2dkbSwKsZYFRB8VrmW7CyNnvnGC2u3c7QfISu2kpmhCdpBjYdoUu7eu7SWLnfnoWOZtg71+czhPXX0zUDgSiFzBTcdo7u7nhTy/T1t3PsrUbeP2m8xk3dTqD0VbuefI53njnPRKJOLdedQk7tAaRKxtI1U5CNy0CUgHJMpEKORyKFTkzCIMCmW2m41gFgc6WZAUznSC9oRs9k0fP5igk0hRSWbSgj3BrPaZhYmTzhFvqCMzeFaNmNEiyEHoe2ICey3DRnY8gS3DjGSL4IXl8SJHqYoDRFvYDG41bQicjWWaxn8FF6eLs0UObrIgkhuL5u6KTokLJKuNKdxMhilqkuxpq89xBFQhwSbcTgLruPst9z7yC1+fjF/vMc2keUDWsEVP4w5ICdz78OY1j6zhl3mhuOeM4dr/sXgD8HpVktsCC1QO0Lexgw+fPYg6sYvyhV7LlFiNQZImpI6J8+9jNmIlefn3oHmwxqqFc5FyWXZt31B47uOKCRV58sdf+PXBEqe9YJiBrU1UQqsIM1ZL3V2KYgh7FgxA5lfIi0OzouyjxDpGE9/pdkexC7TieXdzL12sGSGR1Pvp4DZX1QQ7ZfgxbjKhgZFTwaifzJj92p/ApMtPqAwRUGTWfFGKpuUSRrs3rF5R7pl6kQvL63bni+kuFtKAvLNUNsL+b7A9iqV6SmRwr2tYyY/L44jo0CkXAgC1iC2AlB0WyV7H1czw+YfPueZSbH57Prw7Zi723mcnsyeOYfPhZ1FZFuPrkw9l7m1nFOVgaMC5JxLk22U40ydWNGNFm8v5K8oZYv2pfG489+hgTxoxizsxpYgxtAMyKNes59fIbCPv9vPLhZ+5XTXetRcvFXV9c0rOYg73IgQiSomDmMrzz7RJuvvdR8rksW0weww1nnVjcsyyLZDxOyOd1k/6lvpFlGoL6qLoBpb7F1vrxYmlCtFLKJpAsE2XUZj9fT/+N23Cg+1/b/uFAt7H0EyxfyEX/GMFqV2U5E24mljVQZUgXTFRFYlFPms/XDLDNqEoCmkLIo7JJqECv5ac7pdMWy7BJbZBRAQM50YNUyGD5wpieoFAcTg+ArJKtGoW/axGW6kM3TNR0PxRy7qFfkhWU6gbBFZaIiQO8Ux7nlJQClqLSlczREPa5iDZUzUXRmIO97qK2sik3KyYHI8Kg5LMCrTMkCFAWANA8ZQ6Us1k5h38AOVpTVOZ2OEYBR3lZ0rPFv0MxC+0c+qE8S1zC4WYpHlsYSt6oIS09+LuILi1gG9U8H37yOa/99SVuuepC9/vrHW1FBzCf5YKHX2Lh8lVYEmTUIOeedgL77bojNz35Mo2jx7HrLruS0U18ikzIIxPK9gqxiUwCfEHxjF2rXefdcjn1TMxUHD0+SLo7Zh/0M+jZPEY2j2mYaEEflmGK7H8kSHTqFLRJWwhqAL0AyT7G7HQwvzn+UH55+H4uOhFZQa5qEA6Q09cDHWJM7DkgKYrNLacJZ40haAqHtw1cwR1MoyjkpetYgYqiA1RaxmYjnsqCAcqQYE8pEm7oOIOb8QeQ8knhUBXyZHM59vv1Zbz5wM1CbTkVx8qmkKbuwJ5PtZFN5YlEfBw1u5Vf7zyd8dM2I53JMnWzWey6575sscUWPPNjB0888AQYWa689CySeYP3F3dzxg5jmX/9Bcwe38TChYs4db95NNdU0Bcb5NUPP+fzZWvpT2V5+W9fkPv2DZAVVq1dT0tNJYpVcqAsLVtzjJn9N8kfLJaqOocXJ9vv8WFWNNLjqXMPTi1hDZ8hSsosjx+1rw0r0S+uVYJQNWtHUwjV8/G6OB2JHJNqQlzz+hJ6upLU1oc4YqsW6oIeJtYEWN6X4S/frOfozVtoDHupCSiEZR1P52JX/RrTEE606nP7HxAOUakwnifkOvpSelAcrJ2SNo8QTZI0DwNZkzVr17HpJhOKyIBM0g1gCgE5ESAykzGBNCgRIgSBeBns68Xn0XjijY9Ytb6T5es6ePDCkwgHxHuHoo4effMT8pZEd08v73zxPW/fcQmaqoKs0NE3wO1PvcZtN1wNkoxe2Yratwosk1POu5w3P/2W1a8+7Aa7JI/PFaFJZ7Isa1vHb+96kKP32on9dt6Wfl1icVs7tRVhxs7cFiXVLw4cvgiWolFQ/RgdS1nw9Zdsf8RpfPvUH5g6pgXLMPh+yQru/MvLPHzZ6aKUyQ7mDg1YOMis0kOpo+BNPocyeS7/bBu2ecM2b9jmDdu8YZs3bPP+Y2ze6m/sPTnnjrWDIE4GG2hPFPAoEpYFOcPis3UDrO5NM60pwtjKAEGPwoiIRk9aZ1lvmpBXpSnsoSmk4R1Yg1zIoEebhCBaNi6C85aF6RMIbzk9gJFNoigKpBOYiQExlqrmVjJZmVQxQWQnkZxAmyXJdA4maW5sKH4pVRMJPsMQdm4j1VBuIF0vlAW6f2brStuQIHfp2LhihyVczlCeABQvyCIRKMuC6sVGqrqI7o0EZAERcNW8ZVoBfzfQXSKGKZk6H374Ea+98SY3nnmc0A2RZKy+9Rh9nS7H8Pl3P8mKdZ0kCiaG6uW8k49h3z1348YnXqZ13Hj22X03MrpJMm9SF1QJpTrF8+cSQucDYeeMwb4ye4eqYQ72ke3uIdsXx8gXMAs6RjaPkddRPCqSLGOZJkZex1cdoWarWXgmb160ZfkUY7bblwtOPorTDt4TRyjUQdKagUrxXfMZkYTLporJfY8PSwtg2ZUCLjVFSVKhjHMbXGoZMc4C0WspWtHmOX2u54uBbse2OdVmJcnkoeM79HcpnxH7q6m7we6sBfudcgFvPnSroEgLRkSCsHECj6yGW5/8nmkzGvntXpPZepNRjJ+2GYVcjs0234Jtd9mLyJhN+G79IC898yyZ2CAz9jqEkTUB/B6V3SfW8ucbLmKrUbUsXLiQU/fbmRHVFfQl07z66Xd8vnAF/ckUL3/0FdmvX0WSFVat76ClphK1NEdtI3PdteGsTft39IK7npyKCUnVkMKVGOF6Uv4aEjkTE4tKryJsnlEAPYuSHrDFWIUPgEck65FV9MoRfNVdYElvCk2WePiDVcR6UkyfWs++0xuZVBvCMC2+bI/xwZIeZo+rZo/xNdQGVAJGRoBYUrHiWgaRyAcxl1VRteYIQjuofgd9XxrsdvYWp+oplsrR1t7JppPHFddlLiN8azvhJgdEwNG1eaWVLHa/JhJJfJrC4699ULR5F59C2Oct9m9Je/TNT8jrBt39Md75cgFv334xmk0l1tE3wO3z3+DWy8+BUJUQBddzSKkBTrn4t8LmvflEMRBfUjWTyRVYvr6Ta+64j2MO2J0D9tiF3licJW3tVFdXM2l0C5biwYg0kFX8eBE0idlknO+/+IgdD/4F3z17H1NaG4TNW7qKO//8Eg9ddDKy5hXnlCFzyT3TlIj/yv6ge87ANFGn7cy/U3MC3SvWbviXB7rHtTb9twp0x+Pxf/i9/+wz/8Mc3WYyhlXZjNS/Adk0UO2slBysYK2V5y/fb2Cb0VX80BHntW/aKeR0amqCtFT6qQl46E0XqAuGyeoWry3tZu7oKiQJewMpoNslmpg66uAG1zj7+tvEwTTVz4Y1axjRWI9c6lTYh1GnTFJSBN+a5PXZB7qwKAlXNZoCIfE3WUEKBd1MvtnXIZwkfl52aqbigvvURqtAuQPkOpvOJum8T1ZsfrFCefYzkwSS4nev3xbIEJmw0uz/zw7tpp0tlhXccuFShWgH8SbJWHK5Cq019JqS7PICmYFK4WwaOnNnb8Ed9z3Mgm++Yer2u2FpARS7j/SutQDccOphtK9fzym3P847N5+HahlYq75nWqjAM88+wcG77kC9khWZV9vBcTZwM51AkhX0rrWY+QJKpEIgZWQZOVQhDnl2MMDIOs6mjBoUm5+iqZiG2PwCtRUotc1Ieg4lKZyVL77/Eb9XEwd+s8hziWlANuVy6llawOWmcw9ZwQhu6bRdbmSpPlHCm44LR1myS9u8ASRyWB6fe2ATFzZBKm7GjhK6y8lnl3CKz1jC2XIHqfyAP9RwDW2WjZ70qAqaqoo+ttGcUjACepaXT5zFFe+spDrkZY9xVaz/9m+kK0dx4P77sd9O2/Gnh//Ai/fn6OzpY9tpm+JRVRpz3bSMHc8x0+sJxNdz0+qVHD13Gs++vIbfPfosbR3dRAN+9tl6M84/bE9GtLZyxOW3ie/n8XH67/7AvtvMZJtpE5g6dmTZgf+LhcsYP6qVmoYGgS5NJ5ECIcz+riLfqVLM6EqKghzroM6XoCZcD1IeeSBRLP0C28mwjaJ9iFHClRiaH9XIMrkmiFdVmFjjY97Uep7uS1EV8mCYFiv7xaHitUUCXduRzLFlc0iIleR1TG8YKdkn5qc/iGWZWL1rsRCODL6geJZ8zg0gSTYSwsymitxbzp6gF2wuugJRWWbGhJFYpQJDcpHbzspny8vh7IOYmYrjCA9apkHYJw5fx+86R7yvlBbALFIBWKYBlsVpNwikx6RRzSxpa2eP829k88njiAR8XPPws5y4947o65aJPXL9cgxZxjIM3vvqB8aNbObHFW1MtRXBrXwWyRdE9gcJRCLMmLEpz95/K1Y2jaR6WLN4ObsecQr3X3UO4yt8SIEweutmfNOdZ0v/AOs7VzJ5s62IL3iPwg/viICVPYdnTBjNbWf/gvuef5NTD9gFSafYnyX8tmXNOfBmU+6c2Ajhxj/chm3esM2DYZs3bPOGbd6wzfvPsHlWMiYCvk7/OVQ8tSMZzBmsHcyS000+Xd3Hqu4UXb0pPH4NRZboSuZorfDTGIq6enyaLKFIEqYFlr8CPViN6Q0hZ+PC5tl7jtjrxb6yobuXlrrqsiQQULQrdqLB+f4iGCNsn6QXaKqvLUcGOs1Gb7vBJPs1tw/z5Shx2Iits8fCmWc/E99zntk0kMi4QW+HwsRNJCoeF6EtmQaWg/AG0Rc2gttFD0O5aCWIPZvyZK5440aSviUUKnNnb84d9z3E4o5+JrVuCpKMbJkopok5KKhqbjjhQNrXr+OUu5/hzTsuRSlk0b95i+m1Kk8/P5/9d96OGtmiVs6gdHeJpLnHh5GIuQKXpkt3pCAHwqLKyBYgLH6Fog2QFBnJEX7WVNSgj0BdJXK4QvSnjbr98pvv8Hs0N8jtXstJrA/pA8u1izKSqro2z6UKKW0OPYzdV2X9OfR3N6Fm208boS8hg530tCj5fqX823+HXgZDd5P5jjg0OvhkGU2RhN32+pBUDWMwjjLQzjatU/li61bmjK2mJaKxdvEC8v4qDtx/X7abM5sXHrkby9Dp6upi1rQZ5CskDh8nMWXySKI+hYpMF9cv/oEjZ+7Nc2s2cP3jL9K2oYtoKMA+W8/k/CP3YURjPUdceYcQGvf6+dV1l7Df3C3YZso4po4fU0b188VPy5gwZiTV9ZXFPnKSvflsydqV3bUipwcI5dMEIvVYkoyS6hFi3na/WYl+u1JQK1LkVfhEJYgkM6bSR2PIg9emKLv3neWs6UzwbZWoYEoXDFb3ptm0tYLNm6LU+FU8soQuB5A1PxIx8Xw257yDupZkRfC761lBUWIHfyWP7ZsaRnmQ27E9dqVk1Odh04mjikmPoeNumphpO9BnGkUUvFO1YqOaQ5oEssQJe851P1fa3KoUAEUt2ryRTSxZs4Hdz7uRLTYZRzQU5OoH53PivvPEWk/E7KluYFkW7325QNi85auZNn60vefaSSRVIxjxs+mUKM/dfb34XCFPW9sadjnydB64/hImNVUjeURF04Bu0OBXWbvseyZutQODP7xP/tvXXFAMwIyxLdx65jHc/9K7nHrgbqKiwqmWcfqyNPFun3/MVFwAPZz9+9+02bvHv/R+/91aRUXFf8nLDbhCuIZh/Jfv+3vtHw50Sx4frPkBOVSBFYhSaF+BGgxDLs1kcxlXbtXC0qyPP3+1jmjYS0+2wLq2AR6PZxlRF6Kxwse4qgAhj8yc1ko2bwwSz5tg5DHCdWRMiVCqyINqZdNIfiFiZWleJKC1udHlU1OqG4RowUCHezB3MmGlZRtSsEJcI5sWm2rNCEj2F0slNQ9ypFpsTE7m2XF8nE52SigdA22aYgO0EQCuoIlZ3MAl1VM8jNgOkSQr4jnsa8uAJOex/BH74DDo8mr+rCke0HOib0qcH6cU3HJKaTZWElU2kOV/dzj4kGUkReHmqy7mvEuv4qXZ22F5w5j1Y5GzCbRwFdZgD3pPO80VQaaNauKLjz9iq3EtFBJJdp86GckocNie8/B6NC47/XhmTBqH2d8pUFd2ebEkK5j5Ano2j6TEy1BJlmmQiyVFhj9vIxTt7L5kP5+qaUiKTCGdJRCqEGi+TIIDTr+EhctW8sGjt5UdllwxmcE+pHQClw/XRje65faGQFrI+ZLAj+OYRBvEBmHkkfQkZBJFBW/HcHkDWB67PN8QCuVOiVyxxE0tGa+ShV1qAB0DZujlJd6SDBjCCbIRibLXz2mXXMcB287ETMVRqhttLtYwZAbp0uo5fEYz46t8sPgD7n/pHYxAFUt++oE52+/E7nvthZnLsOqrD7jj4T+zrGeAU486mFXvPIW+Yi353g2k+ntZunwV+8zdil/su4trcNygmGWxYq0QyrD8ER6+7iJuf+Bx1nb2sLchs+12W2PGeskkksw742oO3GUuT95ypegLWRaIBVn5ubGyM+eOsIecHkDODhZLTx0Uh6y4InhO2ZcbDMsMUu8Nkw97yBsWe02oZX1/mo5YliVdSRRZojXqY7txNUyvDzHSb0AuTs4TYU1KZXz1KLR0DFnzCNRtIV1EwJoGkoMOcjLPsuLO8bL17+wnZvlhtTTQYWG6SLjSQ1pZKbyz35Q6OUMdHtupdxFwpYc9y+TUA3fjvuffZM+tZ3HRMQdw+B478uWSVXz2w2IAXvnkW+4+r7j3SUBnbz9tG7q4/+rzefDFt7nz/JNxS/GzKQzb8XRKix16hFlTJ5Fe+pmLEDNTcdQVnzE7WovZvoFxqkbh+zexcmI9lYnayAqV0QinHbR72ZyQNE+ZeEcpogtZ5p0vf2DeltPBNF2ux3+2Ddu8YZs3bPOGbd6wzRu2ef8pNs/hx1ai1Uj+EEYqDoN9SHqephEeIs01/NCdpi+ZZ0NngsRABn/Iy3dtAwCMrAkwusJPQFOoCXioCahU+hQ0GZfCSMkMutQ7TnWIpOcFDY6Rp7W2CqsgUJJKtFqgN7OCM96lT5JLaENsxLaZs4NpquZSZjl0BGVc2CXI7LK+BMoEe925/HPk9kbpLexm6fniZxF+hBQIFfdCXS+irV0kr40EdnQmnD3TQXubphs8BYrfxw52i9dKUMSlNk9WseyAovgKKjdfeRHnXXUDLz06Tezj3iBSzQi0aLUI8CVitGAxrbWeLz54n9lTJ6DHB5kX9ML0Zg7baxe8iswlvziYGZPHib1bLwjKIVtYzswXXMFkZBlZVpA89v7lCBnKIrhtGSagIynidbOgI3tUPOEASnUDhl8Euw889TQWLl3B+w/fUtwvnIBgiVYJqib2LbOE3940IZ9DcihMHLqFoXQipYlct8PtoJuD5laKuiFOgLvsvRtJ3pdRl5Tyzpeg8pFlkfSQbLo2WQV0fnnp7zhgu82FTVc1TL0g5q7HR31Q5fRtRzO6wou5dgEP/ekZ9FANC3/8ga132InDD9yXXFcby5Yv446H/0xndy9nHXcIK19/Ar2nHT0VJ5VIsHx9F/vM3ZLjdt/ODbI6fp1hmKxYI0SALVnh0Zuv5Pb7H2dtRzd76xbbzJwukLvpDPN+fSUH7ro9T/5eBENd/YYh+7bzfQFBUZZNiooGUy/ThLCcsUMEdJ0KF9lOUkiFLFFvCAnQFInp9WFmT6plVXeKZLZAVzJH2KsyoznK9IYQDUGNgFQgZ2oM5AwaKlvQcgmhQWKvPxdR7GjX6ILuzhWDt4PeznxzA83unJSRTKVcpNaeK+bQvaM0SF3y2s/Ed0sqUcQ9Nr4HSZbFqQfsyn0vvCVs3rEHCpu3aAWfLRTUfK98/BV3n3cC4PicCl0Dg7R1dHP/1efx4LOvcedvTnGTEg7dnuTxibVsa3cAzNpsOunFHxX323gfKtAUqkVaupBR+QT5r18BPVMGnnFaVWWlCHLb4yvJovJDoNqNYhViMCJAPKbBWx9+yrwZE7H0gqhuGG7/tu3999//f36PfzjQbVU1g6JimiZKup/XP/2WvXfcGisdR/L4OPe6C+jLwS/Ouoh2Qry9qIueRA7TtOhP5ulP5vlrqIstWypYN5ilPZFlYk2QMRVeKvuXEbFMjHC9KIPsW4ccjGB4w8i5BFKiRzhE9qG8eNhWoaIeySmxNk0o5MtLQzOJYlm3g0byBTAH+zBKRE0cB9zl4tPFfRxFbkd0S/IFsNJJmztVHFgdYyCVZvVMUbbpiBkUxb6KmX9HoVcydaxAVBhkXQeH0tJxZiQZS/OLTLXLUyq7B0wso+gYyUrxdVkuCnQZuuB0GxoUKDXyisq4MaOZMH4cr9x1I7tvPwdtzBTxfexMpzZ2Omasmw2xJDMmjkNSJGQljd61jl0nNrHT6fvR1jvIH//0NIvXdTFjVCPXHrsvZqyfTF8cRVNRbESOWdDRswNoYTE+VjaFrzpqv55znaFAbSVGQUdPZZBkGbOgowWEarswIhovv/8JHz56q1Bct7OuP+NjdIxQPoskK8g2wk04y3aJoerD0ryiDM0oiPJDQMrF3bliOgJujgCHx+u+D0MXKLaSw72LSlTUInLNdmScJtnq3G65t/Nep9xblsEscZ5klb+++xG1kQDHzt0UK5vG6GlHrqwVqElZpcav0mQNwoJ3Wdy2ns9+WMKhJ/6Km+57jAwaSt6kQR9k5sRRPHj377ng0svZduIIcku/w9LzLFzfQ2NVhO2mT2Df39zA3M2mMKaxVjgeTtAEk7BfBFHkQprG2ipuPP802vpTVNfUYiUHScYGmH3SxZx7ynGs6+imPVmg2WvaiMGSA4iNCpBDFZjpOLJREE6lkS86H876z9tjaWec3XVsGFjJQRStG8nUMWSVxlCAJX1ZqgMqs0dVcf9HqxlMF/B7FAIehcaIj1hWJ6srhD0BArrJiIiGMrC2mKlPxcE52OazAu1m2ArbsoKVF4g29MLGFcedw1np0tMLSKEgZjr+s7+ByGSXiXXkioiGoc7CUBEPSy/QHUvy2aIVbL/pJCqCPhav7eDj7xeT//Il99qSqjF7+mRmT53AzY88w/5zNy8GEuyA2NJ1HVx6ypHsuMV0bn3kKeH8OA6Q3QdWNiUoEGQF0zSR1DymaSD5JIHYUr1Q2ShKSQd7yhFUvqDrLEqap+wQ2RtPks7maG2odZ9JcgIOpuGKxwBgmuyy9azi+zYmoPi/0YZt3rDNg2Gb959m8xoqw8M2b9jm/WfavMlzURLdSHaC5fWPv2LPzTcRic/1C7nyjkeJWV72PPk8Rta08PWqfgYTObIFAz1vsDCV5+2aIJPrQrTHc0R9KtPrw4yu8KBmBtyKD0FhpYhkpqyKIHchLcr6HQ5tG/FpeSOg+pD0AmYq484DJwEg6SVINTvhIhJpNoLb5o8vFSF1g4ylVSR2QFsEc+y5pedLrquVBMVLkioOQtF5LidI7lzfNMR+CEVkt2mIgJ5jv+z3WuKiwn6V0kEhbKNUqm8xJIgqriuC5GXCz/b1XJuoaowbN44JrY288efH2G3HbVEq60BVXe5gORhB9gfpyBjMmDYFrAJGXsfI68wbV8cuFxzL6o5u7v7rW1x09+NsOrKBaw6bh5lKl1UmAZgFgGSxn+y/WYbhBgsVnwdvRRjV78HI6xRSws7o2bwQ5A5VgVHg5fc+5sOHb6axtqrY3yASw0pxjbjVF6qnjAZC0jyYNu0LsiLsXWlg2tTF/qaXCHyqjj1S7fkqEgqS7WNZkoREkZ6mjKKrxOaVBbmhGOwutY+y6tpFJ5n/yofvUxsJcNxOWwhaMl8ASfMgVzdhBKsJKybTgjnUrqUsXPwTny9YxOEn/5or73qIgYJCnWERDPjZbNoUHrrzFi64/GpmT2yhsGYJxmAfP63rorEyzHbTxrPvBTez3eSRjGlpduee8Nkswo7/oXporIpy4wW/YtX6TmqqxVikMgm2OuZszjvxSNZ299PeF6e5tkqsUQdVT9Hmle3/sorls3mX7USFe/8hSUXJ47Np9ZIosoqVzyD5cmieSpJ5k7xhMq4uRHc8x6L2OB2xLFNGRGmp9NOdKiAhUeVXAQvF2YssUySN7cSY40+V69HYCS9n3O0kSlnAuWQPKG1ONYY1FJHqAA6GvlbSX0X6l5JrlgTZuwaTfL54NdtvtgmVQT+L1nXx8YIlrs0DMe9nz5jC7BlTuPmR+ey/3eZlt7RMgyWr13HJiYey45YzuPWxZ8QYlTxzMSErY2TT5VReioap+sCLSI4bBZTeNnSbWtFNQqvlz+60vmRG2Ly66qLmkLNubcojZy5YpsEuW0zDKuQF+KdmJP+uzbLEv3/l/f67te233/7/+T3+4UD3nzoCzK2TWf3xK8wd38TeO25dNlHP+8VBXHvfX/jwjb8SL1hM2u5AKmIZFFkili6QzOpE/RqLepLM/9tqGutC7LNpI9Nq/ViekMtrJWcHMSsbMTx+jHA9Ul+b4CnLpstJ/2UFuZDGUoroNKfEEbuM2xEMkYOCH08yChh9G0pQasXSEOd3BxnlLm7sw4QpI+kIIQQoO+zjvAeQPF5hNB2Da4t+gROsKDpOVjaNFKqwuReF+jC5tCghlmSQlLLMsKV4hBEekrEXfGMKGEWUVKnCs2M85VxK8Et5AsWBdcq7EA4DisrkSZP44utP2G7VaqJNoymsXyGy9ckE3rFTkKPVDGQLBCqr3ZIDPdYvvla4grGVddwysgWAs+97lp+WLGVCNISsyBgFHcs0kTVb2ME0MbNZzGwnlmmSiyXJDiQwMnkUv+hbf10lqBpGMulymSoVVRh9HQLRVdWMpqrMmT656Dw4hwPngO6MqV0maqkg+cO2A5xHklVMb1DwVRUyyKYQhjGD1eI5pagwwHoWKegVYk2+kChBHCIyg6mXvW5JlDvUpY5PqXCTHSyQTLlsXBzHyeGKE06VzKvvfsQFB88rirwU8kiBMOZAN1YmhWfdUnc+GwWdMSMa2Hq77enPGmgy1PoV5FifWHceH7ceuzu5WB8vf/IN7/6wnBXrO7GQOOWG+7jiuAM599YHeOGG84vfwzb03yxZSWowRsgpK/MFGTNCcMDq3T088Pwb+Dwa1557GvfOf4XvFq+kaaftIGerWlN0fqB4uDAzKXG4ttduGd+rqiEhUBuucnU2jWTTOsh61kUBapkBagMRsobJ5NoQ202s5YsVfXhUGd20aOtP05fOM7ulAlXWaAwIp9XyBIQISa54uLJKDzSKjYx0su8lSDYo7iHFsEaR78wNAoIbSHSvX9Isw+Z0zaSKyELH+DuHwVJePHuenXbTAzz6+t/Yc5vN+eML73DJsftx/h+e5LbzThH3tLmVnVLeD7/9idGNtdz30rvceeaxZU5VIp2lOioy6QG/jx1OvZT7Lvs1E1saxfPYwRYJMI2sGEs7SOgEqCwnCBaoQFYUDFOUokvgliQ6SBwAclk2DKb48JufGIgn+dXBe4j+KORRaxoxM6liSbtz8HfQIZFquiom8n1Xij3559uwzRu2ecM27z/P5nk0ddjmDdu8/0ib93Z7gdG+IJ1ff8Lc8Y3sueU0kMVakTw+zjvxCK554BkW/e11utIGU7Y+gA7b5jktnTdY1pvi/UXdGKbFgZs3UxesxacXkPJJ0AIieO0F0xcWc13PoRg2+tqpTjEFJQuBCpH0jFQjy0rJnBCIdicZCxRRhZkkZj5b1Kso+Y6Sfe2hdCRFgUmlWCmlam4gB0mmVLcAhzqrRDSwDB1uB6wse7yQFWGXZBXL1H9OkYFIvAoEqwiAuwFrWQSr3c/Icjknt/NsNpWGBEXxRfvvliWqhBxajE3Gj+XThQuZN3U0ZqwHyzAwkzFx+VAFSnUDA8kMgcoqzHg/WtBXpFbIphgZ8XHzMbsjyQpn/nE+P61Yy+S6KsyCjmmYyDYViaTIFFJZjLyOVshj2UKTRkHHyIh9Tw368FaG8FaEycUSACgeFU9EJIQcijJNU9l65vSySjInmeBQP5Qm48r2HTuhaimaSEQpJlJm0PYztJKkrBgjSnwJB13v+hh6iZaFomLZCdoyMWeHF90JcNtilWXNpvayLEWgh53rOUkOQ+fV/4+9/wyzrKrW/uHfnCvuVDl0dQ50Q5OT5CxIEMWICRWOiDmhInoMBHPOYsac8CgmRBQJKohEyXRDN51T5R1Xmu+HOVeoanyOx/O8/o/PqXlddXX1rr3XXmumMeYY97jv6//ARWefqinOkhhRrukkQWMC2ZhAmuqxMAqI4pilS5dy2DHHMzTRocu3KBEi25r+ygU+8eqzaW/fxM9uvo3f/eWvrN26i9ic8951zplc+Jlv85P3vkGvA7OnScvmjocepVmvU06R3q7PHiuWaYq2doMv/egX+J7HZW99HVf86Jfc9fBjzB8e0r6s4Z7PxqSAsk86LYQvNFVLEmUJSUzCPEmDzo6b+TJJiipOIi30rRK6vAqx7VJ2LJb3ltk1L2CyGeLakiBK2DLZZroTsbinxD6DVeZVbbodkI1JLeZbqqCEQKY84JBTo1kWKkqXa57cUhSqkVIbWeC1TgEFKp1PT0C9MMMPSJHkRZT3rKqSYuXRKz/xDa781Y3a5v30t/z7uc/mzZ/+Bh9/88uzQHWW2EsSbvzL3SybP8wXr/4dn37LBXlAXVpMtzoM9PaAtCj7Hsf/21tzm5fdS2FvS/sjBcVUXJRb0tWoQQsRx8haT/aeGXukzJ95864Jbrr7AcYmp3hNWs1UmHuZXk4YoDotnawu16B/EaOlER6f7HDYbr36r9ESpX/+md/3r9CazSYbNmwgCGZSS+2///7/0PX+7kD3tukOw3svZPEBe+aIIHQ5QdSzkKFl/VzxiYO4t13j0bEma3Y12Gt+F0GUMBIlTDQDtk62Gen2SeKExx+f4Pe+zd5DVfYeWIotwJraSmfBAcigiQhbIG3i3oVYYxsQPruXkE7sQHb1acGwJEYOjCAsh3jrOggD5PAiffjqNFGthkafAEmroA5sEG3FckzQm0h6sJ9RZuRXtBORljyZMhtcX5eApc5HoRQ7e1+rkNXONnqTveu0dKbWsrRDEgcoWRDXAc1DOrukKi2hsmxUejhMD4xFnjK3rJFxSQHNNot3LD34n/uC5/L6v97Pl9bs4tVdtyIBd9FyLSDyl1uo9/TiV6rYQwszRdwMNdGcJg5CpO+TtNvs2jXG/eu2sOrAVQgpsaQkMhl/y3ezkmxvoA8hLVqjGkVmlVySIMLyNR9f1GgRNdqoOMEuuUivlJemxSGnHHu4ya6mJU65ynA61jOyrEmCmtyZZxtVgtUYJa4OotwKCSDcUv5+IUk8H+F4OttruVrtPokQYVsLyzkl7cgUyhETv5oJn2G7mcODlIZjyoizzS6XMyXkwoyLnlMm+59oo7H+8Q0scBNAYnX3Y/UOIfwK0fYNmbhTmnWOohCrNc1gcxNxaSFKocXvVKIDGyoB1+MbN9zO7/50O+899xksG+5DBW1+9+Ambn3kcRzHIUrA8U0ZvCkj22vJApSQOtsax0jbQflVVGOCz191DW/7/Hf42YffBkGbHTt2sG7DRs548nH62Yol91JmPKAYw5gd/FPDmPIrJpq7UQmRi9OZw7OwHYM0jQAPWd/JvFLALmcA11I8c595rB6q8vhEizhRLOopMVRxzY9NjMA2CvXK9lHlXtPvsVbnFgIMP1t630VBQJXIDBEwo8w6c4TyQEUqdpKKtcx4rxF/S8vGdOncLAeo0HfpPvWer17FldfcxK8+/15OPuJg/v0TX+G0C3UZYWN6Wl+vVMnWi7As1m7axrIF8/j4619CsQlpUa2UaG3eCcC3PvTvbN2+kxe//YPc9LWPzEAkFP9VrZRfTyMwis5aYkrR08BsehgEcsEaKfnhtTfwts98kxMP3Y8DVy7h6EMPRIUB8fR4zk1dvNkkQZS7WFteyQ2P7GLbVIcz9hrmH21zNm/O5s3ZvDmbN2fz5mze/xabt7Xe4YiFA+x12AEk4zvyca4NEPYvpbpA8vkvnMjmluTG9ePcs2mCkZ4SIz0+caJ4bEedx3bU6Sk7tMOYdiPghod2MlTxOH7JEqrBBAhJ4HcjAEtFRMIGD1whsTpNFKYiyC/r52tOZAh2UemCUgVhhCWTlqHjslyIg4wXOpunSZzRkwiTgN3N7tmOtnvp/y0TDHVKOkgHeq3FGo2ubF8H55XS+5id5MHNONB2DRMQM6X6KmgjHRdUO6MpESpBxYHeE2yPFP0rhNR7uQmWKsipLdIgaNpm0WSkgVhVCNRmf0/5p83rL33pS3j92y/hk9ffw+tOPBDR0kmUuN0hbjbZ2YrwS2Wsrj6EZSMrXSSTowZxr4NocV0nq8brLR7eNcV+i+dpIeVWgLJ0kFsmMqtokiVdURS1A5IgyqhNVJKQGCR3EmgKE6dW1lQ80tJ9WqpxyrFHmqqxxoygW5ZwS/fT4toqBEoVOrmqkkjbvIItEqFJirglElkrIOBNcDuOsvdkAWuD7E6M3RRFm1ZEb8+mNimOi7leavOU7RsxSh1QX//4BhaW9F4pShUdPIxC4tFtM5JCwnaJpiexWxMMdbZRGphPPUhACj2fPS2MbUUBV/7mj/zu1ju5/JwzWT7cB1Ly23vWcuuajbh+icSvYZEj8BOl2GvpApRSpDRRKV2TCjp87tv/wds+fSU/+/g7tc3btp11j2/kqScco/mtC1QcwlS0pAh8FbQz7QosF5wCetiy9OsG7Y6MtK9ou1myL3E0rZ2c2k5fqQerXAN8rMW9jHT57Kh3cG1JX8mht+SwsMuny5O4UmifWyVadNev5RRpzfFcVDUV4Ybcp0rizOalr+t/ZeZ/ZImzKEJYOY1SEdG8W2K4kMBJ7V6xcin9boD3fP0/uPJXN3LNF97PyUcewjs+8SVOfcPlADSarVyLoFC9smbDFpaNDPHx17849xdNq1YqtDZugyTmWx+4mK07x7TNu/JjmY0qAieyCiU0IEFEoU6IplVmRb0N2wGcmRUwpv3od3/kbZ++khMP3Z+DVi3jqAP2Nn2RZMFuFbRn0HypnhHWySF++8BOGkHEYYv7+FdsSim9pv6J3/c/ue3cuZPzzjuPa6655gn//v93ju4D4k24EwohLWStN1u0cbmPht/HjumQhbVBnDCgHsS6RNKx6Cs5bJ5ss2ygyu3rx7hr/TjLFvVQb0esmlfjxnVj3L7Z5pglvWyaqjC1dQzbEuw31EUyGdIMYZ+hVbib70F29c0wDsnkLs25FXS4//4H+PKPr+HD734rwnb56wMPszyCnt5eXWLYac9AGIlSJeMUlJUukk4r4xItZptSrjcgL5EGjXyaGpuxaQkjOpKq8Srbn8G3Jio9+mBSn8zK81QSI8pd0JzSm32pkpX5yDjQyD/p5Yd3r7L7wX9WSfATNqF5Lp/I+WGWcZZS8ukPXcaXvvl9nv2VX/Kqpx7Dqe5G4naAXfZJohDPkhm/q5AWlpkT9rzFRNs2kNQniMOIp+y3B7es304jUSywLY5eOj9zYizHJg714Tau1wmmmlk2P20qSahv3oXbVdZOk2vj9VT1uHhaddcKGsRp5kc+QT8UDpOqOZUfrqQ5ONpGvTwt7wsaIG0Sy9Eq4nGEDBr1C8g/AAEAAElEQVQk1HTwREhUyg9mHOC0H1WaRTUOqGhPsbMe8Nd77uLJxx2tX0tizT2bBWcEQpEjgVIUW1oOniQYctvskf70lzvZc8EghB2SVp2kOUXS0H2n2g3NaSWtHMmpEianphBjmxmeV2aX3cf6sEqlbx+CWLFrtM6nPvl9ap7FhtEp3vTFH3HBaUdz2qF70wojEstm8+gEkQI7FTSLAj7wg2s557TjqFYr2TrYtnOUy774aW6+9S9MTE7x1ys/yF57r0YJweWf/AJf/PCl+tnCICsHnzFVDZIt5Tcr8hGCXoeq3K0dnyTKBX6CTm4YwyBDXiq/hogC+soxk7FNM0xY0OVT9WzWj7foRAn1IGZHI6CvZOFbQpdh9cxHCclYaNEME3p8i672TqzmOKpjeIdNKZWw3QJKNj/gasSrn6Eos5YiFCyLpN1kBsrWHOBT9Fx2ODPoJWZ9T7pX7Zpq8Otb7+ZD376ay191DicfcTBCWrzrghfwthc/g1pN8z/PEFsyDsj5zz6DSsnnjLd+kHe89Dm88TlP4aHHt7BqyULufOgxDtl7FQCe77N0ySKGhwdphjFlZ2Z5227iTUmSczSb98hSJadZkDmdQnogTD//wtNO4AtX/Zp716znzoce5aj99jSOY4joGUL4ZZKJXTOdw3aD5exCDQje96lL4JTv8Y+2OZs3Z/PmbN6czZuzeXM273+LzRsYX0t3vQOdFlZ3fxawUG6JiVAyFcTEns3ORodYKRb2lXFtiW9LptoRlhTEiWK0HuBYkt6hKgv7SmyeavObRxOkgE4cIsUoI1WP3pKDbyu6PMmAV0FV+hB+NUfDRh3TpxoV/cAja/nyD3/OB9/6Kqwk5p6HH2P50pCevtDQGTRmzA/huBlfcxroZjYNju1owUgTBNZ0Iib4GXVIWo28Ssb1Z6B908BmKiyJtMErkWlmZOhFmaFDMzoiR5kEsqX3QKNPoCuq7N1tXPr/3ZDg+d+zSoLZFVBCIIqxVkC6Pp/+4GV86Rvf4RkfupJXPes0Tt9nEdG6R+mM1wkTC89zNa3S9LjmRe7uz9Z2PD2OjGOC6SZPXrmIP9y3hqnJaRbXyhyzfIG5JRPsdmwsxyYYHydqtIkaLZRBfactDiOEQXoD2L6rhSg9QxMVdYijMBNULqLnM5q1NKFmUMaprkhKtZUJgJqANUb7I+WRzvwClWj7aGk0t6aSirMkra60sfLPCMmuXbv463338+SjD9ffUQjm7ZaQECJDkIskzpPBKVhASFARt9z2F/acP5gh7mVaxQdZIFU4ho/c+FaTY2Ow8QEqe/UzqWzGOuDWFjHaipmabPDJSz5Ht5OwcXSaN3/1J7z8tKM5/bD9aSWKGNi8a5wwUdi2lQWoP/DdX3LOacdTdh3tB8cx2yamufyKb3PzbXdqm/etj7LX6r1ym/ehS7J+y2g+0qla0JWY0dI5bWuamGQWOCGjlUmviVmDKeVXElF1JLEPlvSQUuBYgulOTJgoWmFCGCviBKJEYblllK3pkyaDhHqQ0FvuoSoklhjX69/Q+WQ2qbjkCujt2QHc1I5lfNZmH08pq7J+iHM7oZ/dyaqUZnyX2bNG621+fetdmc178pN0hcO7LngRF5/7HKq+l1070+8x7fznnqlt3ps/oG3e2afz0LqNrFo0wp0PPMLBq1cC4JUrLFvRy/DQAM1IUS4kofW6sZjdUs2fNDEgvFLmEwBZckyZtSvQ+9ALTz2OL1z1K+5ds447H36Mow7YewZl24x5Y2i+xMRWlg5XOWEw4T3veg8c/53d59Jc+5drb3zjG5mYmODPf/4zJ5xwAj/5yU/Yvn07733ve/nYxz72D1/3Pzkp5u2G31/PA/fcCaVa7uCVqsjONOWoQY9vcf+uFlJA2bGoeTY7pjt4lmT/kS5Gah79VZfBLo9WEGcllJYUPLxtip/cv41t0x2kgCRR3LV1moojqbqWFu+qDnDvuOCzV/02M+Ky3KXLt9sNFvR2cdGLn4E1uZPXffiLHLByKV02M7P75pBXRJYJ29UbGcaJKdcQflmrRNuONoRxnAmF6M9FqPrk7htbq45o17WycLMOU7s0x2qnpR229PDf1a/FXmxXb3zGIEMBIRJqZXsRB9oAFppKM/NxlBllEQWa36rw88Qjbgz37LK3IiLOspFS8oqXnct3rvwKjwRlnv75X3FnK6G1c4IyMeNtjVoJNz9KsP5BhF/GWbo6yzILv4I3spDnn3w4T9t/OZU45os33onXU6XU3wVAc+cEtu/iz5sHaLRb1GgTTDWzkrZUqEQ6NsKS2L6LMzCcITCS6Ql2TkzT11WdkQHNfvwysntAI6aU5sgU1W6dUa71EZd68n51ylkfyfa0zuqm/WJrrlvRqWvjbcYy8bvz+RjqcY4rfSjH0+X1wGB3WR/4E80/NsPASzufGyrZXbALZjq0xgA22h22j47zus9/n+/97s+s27iVNY88wkNrH+WhTdt58PEtRM2U+iBh/xWLGZ9u8Iubb2Pdpm0EEzuZX3Xo8y3czhQXv/IlPPm0p/LRz32ZP912O1d+5N3ctXmcD1z9R04/6VjWbtrORy48n3JXF0pI/njvI5z/sW+wbstO3vzCp5uyIu2A/uhn17Bq/gD3/OSrPH7tt9lr330R5Rqjk9NUSj4vPvFQ1OimrEyyKHqiklgL2piDhYpCvdbS/nD9fA6bMUDaGaeeDnjYerzNmChpk5R7sRqjdNsJfb5NX8lhpOqxoMtjaU+JFX1lHCnZ3ojYPB2SKHSJp0oYiCdYbNXpbm5FtiY1ki8KsgN/GgDR2faZ6076FaRXygMbxlFPDBoJyBStZ5R2G57IonBSikrSTq1leOy0I795bIoDXvJWzv/AFRy57yqef/JRGRrO99zdD/xZlt/KrvuCM05i541X4VUqnHLhhzjiFe9idHKa391+H4ftu4q0fFxIi2OedBCf/f7Ps8PcbtygBtUmyzVdBts7lHFB64NjSEqzkNQn8oOkX9GlaY7L8PAQn7v4lSwZGaJcynkzkRbJxI6s9D3rC+C2ex/i4XtuZ/Uey/n2977Pf6fN2bw5mzdn8+Zs3pzNm7N5/1ts3t03a5unkZKlDHkvwg49nqTXt5hox2yY1MmSLt+m7Oh7HKy4nLBykJNWDxFECUEY0wpi6u2I0UbA9kaHHY2AZhgTxgk7GgFrRhs8OtZkqpMQWj5JuZcHtkzw2e/9LBMgTgNBKgpZ0F3mrS94Kk4c8tr3fZoDV+9Bl+eYxG5L36vjZnQDeq64M8ZeGBqWVPBMmCQbKtGJqDhAmB8d5CpQ5yQJtBt632vXodPUtiwV0kvXp+PqxKRfyflpI329jFM+DXCpRFceFegw0qDsDLumkjyJDLkNm53wNYFYJYT+Se2sCainn1FCo/VfccHL+fY3ruThKcVTL/syt402sEsuFQsmxscNbUOoKSYKYsbCdrF6h/DnzeP5Jx7C0w7ak5pjc8Xv7zC3ZxLklsRybGRZ25o41EjuqB0QB5rmJAkijQQPI9xamVJ/l/YX/AokCUljiu0b1tNXqxQ43AtUM35FVwAYdC+uhyx3IWq9qEovyilnQW5lApMiDhCdOrI1iWxN6kQk6DE2FWrKKZskhLt7YNaysx8RBwz2dmmbZ+k1k/Z/Nlamym2GbRPSUNSk/lnu3yAkjVab7WMTvP5LP+W7N9zJo+s2sPax9Ty8fiMPPvY4Dzy6nqjdycRP91++kPF6k1/88Q7Wr3mYxvhOSo6kZEtEa5KLXvESnnLa6Xz481/lT3+4iW98+oPctWmU9//495x+4rE8unWXtnmVMkoI/nj/Gs7/8Fd5bMt23vyCp5l5rCubrvrFdey5dGFm81YfcCCyXGN0bFzbvKccpUXQzd6XzXtTtaSa9dyegAlgR3m/pIkby8m4/HOwgpUHkNP+sl1D+RVQdSXdnsX8msvKvgoLujy6PZsuX8+Bnc2Qnc2IemjWaxLR5UrmV22qwQSy04Ak0us1aBuBWf3cFHyujN4oTahIkxwI2rkoa6elq3ys/D25/k1FV/kUdBqEZRktCTd7VmG7CM9n09gk+59zIS973+c5cr89jc3Tn/NLPrVKOfv87CB3ep0XPv1Udv7+h3gln5Pf8D6OeMW7GGt0+O1f/srh+++VI+8tY/O+97N8Xy2eJdJnMDZP+GVEuaaTuqUUBKCT2ZruLDD7hpMlsIXjMjzYz+fepm1epVzKvnvGGofcvlZ7uO2eB3jkzj+xz8oVXPX97/Kv2pL/D37+J7frr7+ej3/84xx66KFIKVmyZAnnnHMOH/7wh/nABz7wD19XqL8Ty775oXu4689/4IC992RkaDAXvbB9Er+GbE3S6F/JL9aMsasZ4EhJI4jYNNbk1NXDDFdcPFtyzSM7WbNtmul2RH/VZaSnxNFL+zhouMxUkLC1HuDbFpum2hl6YvVAmbIj6Y2nUWGL22/9Eyc+91w2/vEXDAwMaOe8Ma4PEdJClmskzWlUGPD+r/6AZ554BHuvXJ4fFDsthBC5uIHt5mUzcaDLbzutGWWVWflkilpJCsJbaWemnELmfZkol3ldlKrmjblzkiIGsmvYjj6wABkfnLRJSt25ajMgws4MnszsMJkKYczO/j/BAf9vNmOQ09KtpNzL2MQk559/Pi88fE9uvunP9FbLXPbVK5FBg+ixe7GGFwEQ79yshU1Mtj1Y/yDJ9ARBvcWbvvkrDl4ywgsP25s40AatPNSD3TdAPDGGPTCPqQcforVjHBUnmZhJbfEwTsU3B38La3ABVnc/yfQESMmvbn+QP9//CJe/9lxSJJuKQt3vprRetOu5qIVlkXg1ncmPQ0RrSr/mlDKjpwVyDErR0gg4bFcf/oVENseJK7pcRrYmUY6PCJp67pgyNhm0ciezyBGbRIiCU5uiVoQRp8nGKDX6KYJA5tntq376c266/ne88fh9+N4Nt7N9uollWdlPpOCBDVvZe/liuiolEgV/um8N67ftYtVeq9m8fSfd3d0cd/RR3HHHHVz+nndy6PJ5bK0sIUoUC5JRtjxwJx/82o/4/LvfoG+702J862YueO9nWDF/mBeccjT7rlg805gCp7/5/fzqyx/FKVc1clDaiKjNdTffwi1/vJV3vuJFGuVZRJMW1s0M2gRMECctdUuzxYY3MUNoRG3dN+YgIUrVDImhnDJxbQjllomFnZXLNsKEHY2IqU7E4m6Pki14bKLDUEUfTOZ5Bk1nuViNUWRjFNGcnFF+lSEV0vmWcscVBKPSclfRrud0B2kZGoUDfqGUD8gEq5L6RI4GSrPqxoEQtoPwSnzue1fT01Xls9//Gb+/4v14tkHIeX52IC5yFRa5YbMSu9Qh6bTpJPC8i97P1HSdF515Muc/67QsMCNLFRLL5SkvegXXfu4yc80CyrdQbpY5daBRxK2GDsykz56KLGZrIz8Epv3xlZ/9jh9f/yeu+fQlu6Hn0s+nDuR7PvM1fnDtjfQNzuOFr3w9bzz/Jfyjbc7mzdm8OZs3Z/PmbN6czfvfYvM2rn+Me268lgOXz2dk4cJ8TUqbxK2gbJddzgB/2DDJml0NLCnoRAmtIGZxX5mV/WUSpXhoV4OtE20e39WgFcQsH6pwxNI+Vg9WqbqSMFG6qqIZsnmqTdW1WNlfpr9k0+1KxPQObr/1Fk58zou1zRscQpkkTjw9PjO5ksTa5p10FKtXLs/onRJTup8GurUwYSHpmWg0bcqfXdSreCKUZjZ/C0jiLJlj7ienHMopRtIKmLQiTBSSOZk9Myhy5ZZykd80sF3cG9LnTilMZtu2ohhweu0nSAALpWagl9MA/djoLl72qtfywqP25c93/pXenm7e8cqXZKJzKSo+aUzlXLlRSLR9A/HoNoJ6kzd+7eccMDLICw/fBxXr7/Z6q3gDfRCFBFMNmttGae6Y0I/i2kjHxuupURrqwfZ1UM/q7teJfZMQuubOh/nzA2u57FXnZEJ4oFGeqc0RpopCWDp4rGzznjjIAtnK9rIkbpY0T5H8KRe7pYPbxf7UNsygyVUyU3TSjFHK551+Z5aYn93nT+CTpPZOWW6GOL/qZ7/kpt9fzxtP2JfvXHcrO6ab2K6DZTtYAsJE8eCm7ey9dAFdtSoJ0ti8ney5cg827Rynu7ubY449lrvuuIPL3/k2Djz0MLZ19HePyCZb7/8LH/rC1/nse9+BiEOSVoOJsVFe/p6Ps2LBMC98yrHssyxF6FvZHD79ze/nmq9+AssklhJD9XPd9Tdyy+138s6Xv2A3u5bNv1kB2NTHLCY5M72PAtd8Oo7ZGKdzXEgSr6ap1YzPEikIYkUrSpjsxEy1Y6qehWsQ3mXHote3KDsSV4IImsj2JKI9bdZdpO11IUif3fvsILLraQAAaM2dqbF8ryg+d4oAh8wuprYwaU5piq2UBz+lbzFzXPgVPvvtq+iplLTN++yluLbM7GHx2ru1os9RQJS3Wy2ed9EHmGq2eNEZJ3L+c8/U3+f6SK9EIiRPefFr+M0XP5jZSn2NZMZ1d+Mlh4w2cTbdWCbgWaRwCQO+cvV1/Pj3t+Y2T1qZf5GKCaff8+5PfZkfXvdHeobm8dyXvZa3vfKlT/zc/0Pb1NQU3d3d3LtuM7Va1z/te6enp9hv2QImJyfp6vrnfe/f27q6uvjrX//K0qVLWbJkCd/97nc5+uijWbduHfvssw/NZvM/v8gTtL+buqQhfGS5Rx+SzGFfBHWaU+Pccfs9HHfQakrNHXR7PvV2wP0btrHP0nlsnWhz5S2Ps8/Cbp61zzxecuAIcTKPR8baXPeI5sC7c4tGr5QdyZJuj6ojCeOEdRMtLAFbpgPKjsWkW2a40sWhJ5zG2vvupqdsEZe6NGpJSIRxMtPDv3Bc2mFEd18fsn8EOi3iVp3Pff9nrFq2mFOPO1JvNJUubVzSQxZ6M0sSbWykOYSnSCFlSk81iqucoVuyBZnESL+CqPZopIFRqFWtOqJU1UY2LYGyXHBKGoWTbmztBriedlZS42laWm6sxZ+MM59m4FKer2JLjeoTlbn9rZZms6UNIkA2x+nv6uL73/gq57/uQizH44E1G9l15acYfNqzUIc/E1XfiWhPI4M29C1A7dqAajX0ASho41kWF55xFB//9S360N/uIB2HYKqJkGN0JupMb/xrhgKIwwi3VMYpl/CHB/J7i0JUp20Uu/Wh52u/uJ6vXvqmbNyE42pBJfPMMmyRFPiiVBiAE2nxpuaUzsKmjqe0dL+qBBEluVNsysys5laScm/OyRa2NN+nlAb91kAGLY3QEDJ3hFWig0qG3xQhteMlciEUJSQiMKIdhbmYKoFnJYmWzTHHHMOXv/0DBpcs463P6dEGp4iQkpJ6Itk53eKHN9zGL/9wB2ccexjnLV3AY5u28aJ3v5Hz3vVx9j70KJ77wnPYZ9UKIiGRHYUQkNSGeWhng6XLlxGX+xBJzPaxSZ731g/wnpedzfEHrt6tjCv9f1+twlSY0DM5iizXUN3zULbH/Q8/yiGrV2jD1jTl+sZIFg9+Wem5+TsmGyysEjNawfkB4+S6nhYTSee7pTliZdAgkTaWawMSJ2xSccos73GZDmxcS1BJWuzVX8aLmjoYJ2qGi7ZNiqJTld4sGERkyg5dz5TQ5asvRbDJ3nkEw3sB4Iyu1/NC2sg0SKESVH0C4RpUR6mG8EqG2zhGVfuhOY10dfmmFs9LBXe0OnXiV/nJH+7k8le9iFu+9al8jmf9685wQGZwy6X3GwU6+AnguHhhwNWfuSznSk3RObZjqAoU9z60ls27Jlg4b2hGifpMxI0upyQKiYM2Vu8QSWHeJKbcXri+QaXsrkJ+3lNPoFLy+e1td3PyYQfqF1PBGtvRfWPEvU464mAe2bCVJz35DLpHFvPfaXM2b87mzdm8OZs3Z/PmbN7/Fps33Q51BYilkZJKagqBZqPBnX++g2OfdAD9/TWW9pbYMN5g885xQukxWg+ot0PCJKGv5HDckj78FZLtjYA7t0yydaLFeDtktBUSJTaOJSgZ3txdTU39tW06IFHQDCWu08+eJzyNNQ89QK8dkHgljcptT2FBRt+g+x5a7Q5dXTVtt6RF0qrzuR/8glVLF2qbl9LiWG5uT8KAxCBhIczHEYAwX5NpACd54uAcQKqToFHPrubtdso6MYWnfQjLQZogHRQSG7qef7exEEmEiEKDCp5V6RJHiFS4shg4VYmmhAKEApXmemdRkem9Qo+xCFtZYLWvr4/vf+c7vPyCVyCmGtz98AYuetqj+Aceh6r0Zt9jdbezhLUWSUwQfgUnjnnDyYfxid/8mRdZ+6HihCQMSYJIo1uDkGC6SdQOsqSukIaeq7eK2z+gk0KZ32HGx3b4yk+v5WuXvrnQ/1bOp26StBnNhOuDEIhQJ2JVHGci23kHz+pTaed0ImEn5+dO90GnpBHDpr+ysUkD4yZhkSWOi9+TjlUh8K3HOM6TvwnapgmZJfGPOfY4vvyt7zO0ZDlve64BAxSCrML1mY4UOyYb/OiGP/PLP92lbd7iEdbtmOD5L34xF1x0GQcfdgTPPfv57L9ioaYlwyNMFMov89CWUZbusYq4OojsNNixc5Sz33Q5l1zwfI7bf888wTgrOdvX3cXE2Ci9lRKy2oOqap2J+x5ew6F771GgS5NZYjT9/26BY4wtNUknYVkmMRADQZY8AB0Anx3kBg2gEHFI4ugkhg3YMqHs25QdSZ9v49mCkiWIq47hII8ASaxsTWMSh7qyIgIBhofczehXSF9L/dDYILZNBZXyqqhWF9KtapuRJjcM3ViRzkZYBUqTtB9Su5Py4KdClVISS5uf/P5WLn/dedz6vc9r1HghSYf0ZwJPYFZwOs59M6kDzX6lwtWfuSwPWKfryjaUPtLm3ofXsnnXOAsGesk4wrNgPYUknKEck0lerZXodaeU0jYvDXIXqPTSf897+ilUyuXc5hX1QAooeICTDj+INVt2ccgJpzG8eCn/qi1RiuSfyJv9z/yuf6TtueeePPzwwyxdupQDDjiAL37xiyxdupQrrriCkZGR//wCf6P93YHuqU6E3z9C7577kXQmzaElwBNtDlg+gohDrMYoxy5ezQ1/3cm9O9eztlzlgMU9dKKEbRMtvv6XDZy85xBHL6xxxLBDzZ3HQ7sahHHCD+7cRNW3Kbk2xy7rY6TmMdWOdNatZNPn29z0+AT3bZtm5WCFfYcWUOtxiROFFIKKV8EOm4gkJjYCXiqOueyVL9IID4M2EU3Fa593pj60R4FePGGQiQGo+mTulKbOS5qZwhxI7LLOdqVGVeqScjzDB1qf0IfOdNP2tUBPyi0maiZ7X1jkiZAIy9El4FGgDy7Z3wvoAJVoI6w0n6UyB9Rssw9a+rVCSeiMUrfZGeUnOPzn5VYWQvj6u6M2nmPx4uc+g7u/93WW7rWEH20a5dzbbyD6w28oL1uBs+ogRPcAwV9vYmrrJr520908sHkXoxNTVF2bwUqJJ++5BLvsY5d9omYbu+IjfR+3K6G5c5y0bFvfrtRlWZ02slQhrk/rYIDbIJnYqTf/BOr1Or0pX6a0SFoNveG7fp5NNwcDwJQJaYdDlLSoW2J4ZpWjeaUQdlbmkR3m0mx70EJ5FX1ot32UMayiUOavHSNvJkesGQuNtkCLg6QBoPTPvhFBiQqccELOcKgAhocGeeXLzuVzv7uTC49aodFUZk5LX5cOdQHd3d3M7+/hHS99Jmcdf4Tmu4tjhCX57Dtew6Wf/AzP/eh7sCY2g2Uz4HfTcHVfbto1xciCRSjH5xe/vpZPf+HLvPrFZ3PC0YfpYBnoAEyKUjFG7PgDV3Ptz3/J2Scfjejup9OzCH9sPb/+/c288kMX5X1RQKICGvFW5E9MNEenigJEpJ30lOMTtGODzPn1MiQhuo+T4hyPI/N+SUf6uLaLE9SRnWn6nLIW8us0KJtDeYreyNZdikZMS0ttHzyNhpVhC2V7xKVuw7NnQ7kPqzlGMrkTZ3oM1b9IC6YEAUpIwsE9EHHEaOLRN6SwxzchAk0RoNwqQtqo6THUrg0m6IFBGMX5HPZK7Ng1xoc/8xlefNapHHXgPlnXpofo3ZATkDkrKUIX0Ic4KXNnIj3kG/GsNDij0TN6Dn79o5dwzts/yDlPO4WXPfM0Q22QIz6wXUStL1trqt3M1mbSbnDPY5t4cM06nn/K0WbcTZAsVTr3K8i+YWR9knKlwnevvZlTjjxkJtWFlBrBkcRIx+WEo4/gaz+5llULBjnuuP+eFveczTNzac7mzdm8OZs3Z/PmbN7/8zYvSRLcwcV07/ck4rCBbI4jojae67D/6lWIJMaa3MKynuU8JpuMTWxgYPn+xIkiiBJ2THWIE8WuZsAefSVW9PpYAu6Ugg1jLTaM6aTWUJfHyv4KlgBLCKquhWMJ6p2YNaNNNky28G3J3oO9qIqHIwWeJahVHBPQinOh5iTm8tedZ/Y/nSwVQvDa5z9tRgIp3eNTwcgibUIWgEnngMwDTDNQ3NIC286Cp1nFAWEe6DFJYWHWobIcjSK2tGCsiNoos/+pOIZZqO7Uz9BisJHRP1D5/ZsAYMrlrZ7IxumHmJGI0l+4e/JXuRWUU0IGDUQS4VZ7Oef5z+HB3/yERa7DV353O69atIpk4yN6/6r2ICyLeHwHU1s385Vr/8SDW0cZnaxT9WwGSj5P2W+FpiuxJIlrI10bFceETc3PnQTp8ycoK8H2PWzfIPGDEGHJbK4L2yVOoNHq0NtVnRFY0wKOIvNlsrGy7ZzLOf2/0PznStoknt7vRdDUVUWz7I1K9zTTl0pauc1LYgjb+nektmsp6nh2NZmpSJrB4Z7+LYkgbOfc6RnC3MnQ3cPDw7zy/PP47K9v5c1P3p9keqLwjHpv6q742uYN9PKOl5zFWScfr2/BspBVh0+9/dVc+qnP8t3PfxxrcgvKKTHYu4ggViAUm0brzFu8HOWU+Nmvf8NnvvAlXv2Sszn+8IPzgGQxiWiShicctDe/ueGPPPeEwxCDi4m7RpD1nVx706286kNvzen+MrRv+n831yvIUL1JIUiKTmwWA9lp36Rz1tg7ZfyTtP9E0ETYHsq189c6dSpxSCXRHPAi7GAX6ICQNpZb0smSJMqTQlZezaZ9zwAlbWLbywTRRdBChAGqOYUlt2kB29gIbjseibRRXi0TRrfqOzNBbOVWUSVLiz03pmZW+Uije2I7YDls37aNj3z7q7z4Gadx1MH7ZYmlFA2eC5HHM6m/LCsXeE7yxO2MChHztxSpns4dvSYkX//opbzwrZfz4qefysueddqMYLdwcxsJs5PNAA53P/AwD65ZxwtOPyFbo0oWqqHKNaTtIoO2sXk3ccrRh2VIb6JQCxC7fmbbTzzmSL7+yxvYa2E/xx/3JOba/xvtDW94A1u3bgXgPe95D6eddhrf+c53cF2XK6+88h++7t8d6N5v8RB/rfTxmbsnOGWPAfbtyS/QrRISU6pZau7gKQftwf6rlnLj+nHu2jjBoUt6WdpXphnG3Pr4OGtGG6zsrzBQdgjjhN6Sw5OW9rFxokXZtbh3+zSOJSk7Fvdvn6YT6fccsbAbKQV3bZpksh1x0+OKvQerHDSvotNvQgsmpWIzGN5MUdU3myHQ0jK2tJks6gwnIFV8Tcsys81CZ6OSJNFlaunCtk1XGpSFzuLFeam4OXSqKIDGBFT7kIEOJmRlwgZVJpJIO0KGy1IVdYuyMqg0G2leTrOH6CxxilYpotsyYzvbMUpfywILZqNP+d2Elx1Aly5exK/qTZa7LmWR4K/cj8m/3ML2G/9E+cEHKc8fImm3+cTVNzHcU+Vdpx1Of8lj8/gUh1zyZU571XNIjNhIUhAjEVJSHuyluXOcOIxIjBq326XLd9MDv+27BOPj+BVddvGtX9/I6UcfuntpTrthRGekLuuxLChXtXEREtmeRFkOcdeIFlnzalhT21C2i3IrumQ3iUn8mlHq1mi1xK+hSt1ZGX3u/ETZe/ShsIjQKiwzIVGONwM1gJSZkjtgsslBPi7pmFpyRvDnGaefwtO/+R3+7bgD6O60NRLJ8/ODmu3kc5WZGXOkxX7zezn3KUdx9qvfyif+/Y0sW6U5KWtqFOV3oaTNrkaHpDrIs15yAY/d9nsWD3ZrJEeKqJGWvmYhaxyGEY6t7zNxSuxqRTQfup9lI0M4dt4XqZHOuskroSo9HPGMF/OBt7yaEw/ZF1wvE+9SSeFQUOxPoZ0ehMxQZ8r1Z67pdL7HEV5c15l3r4qY3KL5+aJOJkqTH2CCmQ5s5vQWeIv9GkmRv89KeVQlyvYy0T+xc712SKRE2AH243cibJfB2iCxt4CoZz7W1HZkaxwlLaL+VYjuOtb0DhjfarjiYtZs3saO0Um+fe3NrN24mcXzBnnuaSfx1DOfqp2ssIVoTuoDX9DJD2izmnDcmeIiBWSBNKWgRQRGtgeigy1KCE4/4ShOOfpQ3v/5K1l6yvP5wrvfxBknHKUDCObQl4xvy+ai9Eoa/eN6SGlx0N57ctDee85Q5/7FjbfyhR/+gu9c/iYee2wLTafKMXsv5czjj6DZarNxdIrFI0M5D3W6Tyex7me/zDc/9T49PFMboLbXbs/+97Y5mzdn8/77Nu8rnPaqZ8/ZvDmbp59hzubN2bz/wTZvweIljFk1frJmgpV9FQ4cXoq9cy22Dd1dNZRKEGGT7mCcUw9exf6rlnHfds0b3okS+qsujpTcvG6MjZNlFnR5hLGi27OJq1qkMohiJpohj4kmZcciVoowVnQiLZja7dkMlF2aYcw926bZOt1hpOaxqNuj5hrUrGNQlhFZQkKWKtqupEKDKcI+bYbTXihFUqTSMUFFKWehIQvzRs/1nPZEJREIYRDvDsIEVlUU6Gom0DoVgIjSgLerE4HS0EdZ0Qx7NjtQnQWpVaLpxYpBUpUg4kTv07OpIURBxLK4jtPAtzJJk3T92hrlTtAwaNaAZQvmce22UZaWfbw4pr3mXsYf2QhAdcEgXk+VYLrJR35yI/P6urjkGcfR6zps3D7OqZ/4Li85fF9A27us+9tBRlEShxEqSbLEqnRskjghGNcUXpZrI5rTCEPt8M2b7uKM4w7LA3SQI0Ojgm9jULHK9hFWlO1xSaUf5ZQRYVPbn9RmOX6WIM9o0uII5fgok/wEMh5uHRg0QXpp6aD4bP2P9P7S6xlbWUR8qzQ4LyQQ5+OsEv0d6Z6aRJz11NM56xvf5t+efCjdsyen8e1IEhIF0rKzYGcqJLvvQIlzjz+I5730fD7x9texbM89sVWCbSg+lFJMN9sox+fZL3k5j971Jxb3VWFyh06cG/HAzO8zwemg08GpeDpw6ZaIkax95BGWjQxio7IkkJBWLgQLWUL+yGe8lA+87XWceMTB2u/IqiFmJW9mr4+UnitNEqX83SYJIaJOXi1mwBGyNanHvrBGin0ujD6KytZJklcKinz9aj/HnfGaVImm6KqPIQraFdKv6GdpTxN3zSNyq4hKPyLsIMKmtjdeldjxkU4Z2ZzQtExxzMOPrmfH2ATfue5PrN20jcXD/Tz3qadw5lNOzCh1RBxAa1onPLv6dODfCIWmvrlQehyKAW+khSwEplWkg8/FgLUeZ03ld/rxR3LK0U/i/Z/7GkuffDZfeM+bOePoQ8x7YlQQz9xLzevpejxo7z05cK89ZtCX/fKPd/CFH/yM77z/ItZt3EUzSjjmoH1ym7dznMUjQxllSTYFgnZWxffNj12Csn3icBoo0OT9CzVlfv6Z3/c/uZ1zzjnZ74cccgiPP/44Dz30EIsXL2ZgYOD/8Mn/c/u7A92PtT2+eetDrK/bjNY7vPTQRbz8Gc/jU5dczMH77pWJAFjT20FIFgDP3GuAsmOZA7zeWAbKLralDexkWxuMsDFFNQrpr1To9m3CWLF5qk1vyWGJKDHe0hO9UXHZb6jKofNrtEKVCaIAuvw1aOsF3rcAUR/VC7pUIbF9RNTm59f/gTOPOGCGI6wSbQxEofRQZ9JycQAVBvrwYZU18qjTyhe0nxtDJW2kZRFX+1FuGRE0NY9lEiGa47qkNUVYRUa8S0pdCi4K5bwGiSOSSBuKsIlwvMwIagEG25TCREa0ROVGIYm0gU0Vy41xEOSlVsrOs6AzNv1ZjpZKXRHzupSCRClW1Cpcfdt9nGT41VSc0Ng6iuW7JEHEX9Zs5NvnnokUgqgdMFzyefUJh1AqewhLErcDVJwQtQOidoDtu7hdZToT01hGoTtV37YGF5BsXp99BiAe38H6XZP84Ld/5Kcfe2c+UQulOhliMQzycRIyE7jB9hizusHqph0nDAzsgdOZQjk+cfcIIo5Qlo2II0Qr0Kg2v6bHwPSjbE9naDYg54wt9mkczFTTziZMgkjQhwdhDG0a/HkCB0gIqR1RKbUDmES85qUv4HPX3sq7n3Wc5m+1HS2QJS3tuPsOyhy4UmGk7OuDNk894SiklHzoS9/iUxe/BrdSR0iLztQo3/3m1/nJt74CccDLzjuPbZHHQr+GbAO9I9pYT43l2dxOi007x7n65r/wi49erB83iVjY2sQHf/5rnnvy0cxu6Xq4+e4HOf+Sj/PQtd/nfRe9gUMO3I+kqwcZtpCVLuJSj3ZazdohPdA62ugnKRJGGUfDK2cOUTaf40CjEs0hXwRNPS5RO+MUxSAcE9ubwSM7c23lQTEZaJVpkXI/qmQG/1/iVsDv1k5Wu0GqxJ0+u5zeqd9X7s3K+mV7ErGzQ1LqJu6ej7Q9vv3tb/GDq3/N0vlD9HXVuOhlz2fFgYdpPrhKPwrY6c1jaxyycL5L/8RapFKEw3tCFGjBnc50jpaEDBk4WzBH8wxqZyhDkklNb6CSCNWsa2fVcbFtm3e/4QImphs8tGErZ8weYOMkAxoNLGVBUMsEEwqlbMcevB/PeuMlLDvzZXzmHa/lvHd+hPe+6QLect7zeOHTTtEiZ2hnOeWkVqZfpZ2v8cSt8Jg9nz13m3F/f5uzeXM2779v8w6es3lzNm+3e4A5mzdn8/7n2bw1Y21+dPs67tkas8+SHuThi3nD2S/nU5e8jYP2W60fIdBI70oSsbx7GFvWKDsW4+0QxwQi21HMaDMgTBJ6fYeBsosTNOgqBfQPDOFIkb2/GcY0k1iLVCaKwYrLYMWlFcWMt0KkEFRdm6ojzTw2aFrH1QmEUkXPZbPf/Pz6P3DmMYciEitHjhqBOJHkaNk0OJqh9otzAvKqiQKyNBWIRNo6SOh4BWqtBBF1kHI8D84UEmLScUnAiPa6eg+MQv08qQBm2M7oM9KWalRk1ESQ74/F19J7LAZRUxqwWQHv7PlMQE/YRnjR9lFuGSkgaAcscV1+ds8azlq5WItFxgnt0cnMjt25fgvfPPZAxHSLjuwwr1rieYeuZrpj9vY40fQljn4ep+wTG3HKWEqIE4SUOBUft7eXaGqSyAhVJnGCU4lZv32UH/72T1z9mcszSocMPW8CZ+kzYBvhSIOil0ZvIu4aYXM9wnWqDHRZyKBpql1sEqekfZY4gNZk3ndpEhF0ktYkvkWqIwJZ0HFGIr3Y33G+dyItsBITTLWy8SFmRkBXRJ2MskuGGhjwmvNexOd+cTPvPPPwnNYiFeg1P8qykelcTiseKl0Iv8IZJx6NVa7woa//kE+//XU47aa2eVh897vf4cc//AEALzvvPHY0QhYu7MUywXkRtfOAuglWbty6g6tv+gu//OQ79Z7WnsZlMz/98Y95zvFPIqNWSfmmjZjjTXfdz8sv/SQP/e4q3ve213PIgftnFV5CGk71QnWRnqNxzntvgs0zxqgw/7OKwLRC0KxpzbNu1m0hcZ6NX0otktoCg+B+ov1ABPU88J0mLvxqZjOlQSKrSCO9ibZjt6aQ3cMadGA5iBCN5I5DkuogcakbaTl8++pfa5s3Mkhfdxdvu+Ac9thzT+O/+sSlbsbtbkZbEUPdNj1TGxCdKeKuEQ0YCeqITiOjJMyojdL5l24fab8V6JRSXvvsWVN/w7axHZd3v/GVTEzVeXjjFs6wj9CfTytYUtCF2Vt1t+VnDj22ZAnhYw89QNu8M87lM+9+M+dd/F7e+6YLePOLn80LzzpNB+Klpav8UjqWdB811DFK2iSVfkapMJ9/zZYo/fPP/L5/pVYulzn44IP/29f5uwPdNz0+xoPjgk6rTTBUYf14i3ddcjld/SUSr5I7xlGEVd9J4lbwqoIjFnbTihIe2Nmg7Ej2Ha6ydbrDcNVjuGKzrMcnDCtMTNfZu7uH4YrN9kbEo2NNLAEr+srESrFmtMkfNozzyPZphrp8Vg1USBQs7vZpRYpqqRs1vApreocWsaoOIMKWzu5bNiJs8rTjDs8PhGmGMS0bLaBzsnJH14dSTR8cTFO2j/JqmgPTbHJaoEJvlIlb0ZuE+YzsNMjKVCr9WdlKVgoUhaj6JMLtQLUfZVSclWVDp6FLz+MQ0Wk8waZuDv4wE+lmjG6aTc6cfNAHR3JesOzvRbRf8fPS1gfR9E9Ae6LBXksX8NnJOknJ5TN/uIc1W3fx5P1X0l6/hUe37jL3pEgCnbmXjp5qt6zdxIPbRjl68TwW9NR0H5m/ad42C2HJTKnb66lijywlaUzhuw7t0Uks3yUMAr58/Z2cc+aTKZVL2ZhlXImQb+yuN0NxOxMlaUzQVxllXdJDECvqQcCKni6s6e3IoEVcHSCWLrFw8fwE4cbGobaxGhoBlvjmGYKWdk48I2AiLZTtau5SITOeStDOkQA9xmnJURQYQ9XQn3d8M2875iBWQKaYkkZUwuknHcs3r7qa+0Zb7F3WHLqyuz8r2fzST3/LN375ez76lgs02i0tFUtRntLi9BOO4r5HH+e4cy/k6ScexR5LFvCj627mza88j0rZR3UanP3007nkPe/hRz+5mnL4mHHWPYTtI5qTJG09Vz/xg19w+atfjNfdhzSoUuqj3HDrHbzxWU/Ox6XgqJLEHLrXcj524flIFE8+4kD9vvak6S9HO4sp953lIhxTOprEGQ8hQFLuxUpiiDoZv2Rq6JXQyAeRRCgg7l2ErO/EaoxphywVRkM7mwhTQpoGE9OMfqFcTqkESQsVRaioaZaPCQq6vj7sWy6JV4NStw4uhk2kEX1R7QYiaGPFIcqrQBIhwxa061gq4d777uXUs8/lkP334UMXvZp991ialxNObIVKj0ag+F2UHH1oBHIO19YkU/4gTleNUmcCWd85Y52nhydR7KPs71G+hooHKikzkbs0wPHRf38TBz/1BZRch/OecSq+65CW4RcdrDTrn5YtqjDInSBp0V2DLdf/gOe8+XLOe+dH+MCFF3DHfQ8zMTVFf0935sRj20ipBVw27Rhj4bxBcFwSvxvldzFu1QibBbTUP9DmbJ4Z+jmb95/avLVbdhHHyZzNm7N5+rvmbN6czcu+8l/H5v3Hvdu4e0tEazpgohmydbrDRe98N7XhQZTfpfvPBPxkexoRBSzuWYAjK2yvh4y19TjtOdCfjUuXZ2NLQbtqMzVVp6+vjCVh85TEtyWeLQljxWQnYv14k79sCgiihKGax+Jun3oQk6gOXZ5FtTY8Y35n68OI3Yqow9OffKxO9NlaJA/Dz5xxDRfGNNU3SCtLMjFBYVDb5jsyQcIC0lNJywQyU2omgXJLebIp1ZUIYlPZFCKBREitd2D7KCtCQqZBIcIWxGYvFQKR2jxja4vaFbvN0XReZwFtzdsviu9PA7SFOZ9Ro0gLLA+RRMhOgziK2Wegh8/e9yitZotP3Xofj41NctzyBbSkYN3YNFGSkBh0tu17WVL21vVbeWjHGEcvnc9IuYRKdEA7DiNUnCAdB6vkapvnu7i1MlbvkKbuGd+RCTNHQvKla2/hRacei+9YM9aVfhwr12lI96u0j1RKM1JHtKeAMhPtGKWgv1TGTiJk0EY5JSIkWD6Ol2itjgIHs7ZtEuXYkBixShnliXphtCdSv8IkRoQyNs0kQdLqGmRUoLUxPkgaEE/nXDo+ZtxPP+EovvnDn/BAHfauOmbeOpnN+/LPrucbv7qBj154vumPvLpKixX6nHbUIdz36AaOffHrePoJR7LH0kVcdd1NvPn8F1GjjWpN8txnPo33XPpefvbjHxCbexFRCWH7Wt+jru3TJ374K9772pfi1ro1clkliOYEN9zyF974rIt390cM3d1h+63m4xe/BhmFnHzY/rqvUl9T2hnwQUSYvizQlaTrMgU9zFoHKcIeIbMkjkj70fS3iDpgezoZUkzkpiKXJklU3AcynzENvKfJq2RKzz+vlAfhpU0iLRMoD3WfNaZI6hNYlkXih3ptW662ea0phOXwwH0bOPXZL+SQ/fbmQ//+RvZdMj9P6rQbGWhB2R6TnZjRZkiPZ77HVHaNWzXcahdlbxqrMTZzLhV93+JekL5umX2tmNyRVk5hYvriI++5mENOeza+bfNvzzoDz50lylnQLZjRpKF5M75fd63Klhuu4jkXXsp5F7+XD771Ndx+7wNMdSL6e7szqiHhmL0+6LBx004WL1tqRJE9lFtm0u3lkZ0t5vdWd//Of4Wm4J9Km/0/PNCtlOKqq67i97//PTt27CCZpavyH//xH//Qdf/uQPfaHXWiIEYIQcnVpWUHHn+sdiItF9Gezt4r4hCrNQFTLtXyPKquZL+hCvfuaCCFRrj1eBb1IKHsSPxSiWrJoxUltEJFl2tx8EiNbk8vGEvAvIrL2rEWS3tK3LF5ku/fvgnXluy7sBtLCnzbYsNYk31GujhmcTdlKemuVrVzYPtGvMNk8Nt1zS0KMxZfdv/GSKhyt3lP3k0yaKAsRwcWnDKiU9cbruFXE60pvTk0J/JspkHOSFM6I5IYZdsI/IxnTiVxnim0dbYKr4KyHM2dFrVzo5hu3imaxqB69JeZw3phg08zlMWggTaqYfofjQ5MHX9HI6xE2EGoFsqNMq6qu27/C8u7yoStiENHBnjVz25ir/4e3n/G0XzhnjWMTzc59+j9ufQXf+DODVs5eLEmkFdxwsuPOZCb1m8BIXjd93/D1a97ns74y4QojAgbbeIgxPJdpCURloVV1RuYs3APbvrFT/nwT25kuh1QLpU49vBDePYJRzzhfM0yyraDajeRnjKl7rocLB1za2ory3psNsoaFUfy6ETAHrVezVsqJE59O67hqkwq/WDZBMLFK3VnTpCIA8O/Z0q54wCcskafpM6p7efOMuRjYhBVIgpIRbuU4xsjL3PuuSJ/bWGcFfD+t1/IGy5+Fz++6CX6oOlXsiDWfY8+zg8/8u8sHBkyc6aQbTVBgKmpaR5+fDN7L1/M/qtXsWNsgul6k9MOXoWY3I6q9LHvXiv5zXXX4bdGNb+dkIikre+h0ovluMSTozTaIYODA1lGW3hlHn50HXstXYDr5ZlZVSwhth3Krs/TTjoGYdkzy9hcX/eVCZoIlWRlxCRxzl0aB6Y0saM/W9LzRtlO7ty7mmNWSVujvVqTMLVLO7ERCHMAzTLbKWeiX80dHjOO6dpKuW4zhE2SB2QwgRCiDtLwkCq3lKGApNdANCf0XBzfivQrJNV+HUyc2oEArv7VbwjCiJ985nJs38+RZem+1WkipE1Q6iWKEhZ2ebiWIPG7sZpjWPWddAmpxXOSOEcszDowzbB/BtkwA9Viyn6x7UxkBJWQlLq1kygkZz3lRF7/3k8y3Wxx0b89TyM97LR8z5QNujqAKCwXOs18DkAWkB0YHOCGKz/BdbfcwS133csbzz2b/p5ufVBN542l92/pV1i8rJeoa56+rlmr3a6kHswsJf2vtjmbp9uczZtp817785tZ0ds1w+a94PC9effPtszZvDmbN2fz5mzev6zNA4ijBGkLFvaVGCi77HHs8XQ7QHNc7/1hMw+YqATZHGew3I8Q4FiC7Y2AyU5EzbUoOxYVR+JaAvwSPWWPIFZYAhZ2+fi2oObqgHcrStjVLLFhssWGyTYTrZD7Nk9S9R1Gun3WT7SYakdYUrCir8zK/jJVx8K1BWVH4gVTOlAsbYSs63lq6Ccyyqw4zvbBDBmo8mBalugxSGDlaToUEbYh7mTPLJIop0JIA8yQz7GC3SkK76kwQIgprKitKaGcMolX0cHuoK4TyJYLws+vk9q79CLFpCx50HrG/4so1CSecT/6D4UqnKitBQqTWNt4r8Id9z7Aiu4KYSvi8MXDvPInN7BqoIePPu8UPv67vzDZDnj+oat5989v5t6dExy4YJCUQuWC4w/ixjWbUFLwuh9cx9WvPVtzbicJcTv3OyzHxq1V8Pu7sHr6EK6P0z+PW+57hA/94Brq7YCS73PsIfvx3NNPyoOmBTFDjRo1FS1JjHBUHuCMA70nJzH25BYWDCxnq5DUTQKm5tUouWVE2MZtT+pApkoMUt+fIdStx8BGGAFDYSiedP92tA2zXaN3UUgwC1/PrwJCOE06pvNNWa5OaEQBFBLsKTo8Hc8PXPx6Xv+Oy/nxxeeaILdLSttz37pN/PDj72bRyHDeN56f9ZWwXaaabR56fDP77LGM/VevYvvoOFPTDU4/8iBoT6LikP32WMJvrrtOJ5szn0tXx2G5iGo3NC0a7YCB3q68ekUlPPzwGvZauhDXKfLOkSV2hbQo+RZnHncEQoicFsoydl4Yap0ozBNF2R6s15dK15lK8soly9U2Ju1PM09k0M7RzXEAUUQSBYg4Rlqd7DtTfzGj7DJjk1Wqpc/hlmYi++Mg3wdTpH/q9zgeiVsGVdO0JGET1WlBZxuW2XcSp4SwImRnmqt/+lOCIOAnX/oYdhyYueBkSVIRhyhbf1e3Z+FIj5Jj+P+FQDbH6S0DHZ3w0x1g53Yba8a/WTWkSrJ+0+K3nQJ9n0BFJvFruSRuCREFnHXak3n9ez9Jvd3hovNflNuzLKkR50HyQsIo5+/WfTzQ38sNX/841/3pdm7564O88SXPob+7mq1lXFPpF+h9d9GKFcR+t+Y7V4kGHQBDlZnzba7967Y3vvGNfPGLX+TEE09keHgYIcR//qG/o/3dge6eskt/t0+9HXHcin4GyjaPTERAL0vKDpXGqJ6Urk/s1fQG65QpW4rpCDxbsqKvTD2Iqbp6QQyWbSNGAkJIqq7ENyXeQazwwrpZ4B5DJZ/+BVUsElb2lzhjzyE8SzDeitje6HDL+nFcW7JhvMU1nZgNY036qy4V1+bYJZJ5tUV4lqQ0vRmRRMTlXmTQ0os74xrt5HxdUWAOcaHJIOqskvIrusQr0Vx1IupkG640ZaZa/MJDxUG2SEUUZSWrqsipVvw96CDVJIlTAqeE8qp6g7FsjYpJW6GkRtm+QRaE5qDeLGSWzbULWbzsEJpmo9O/WXaedU4SsHTwQUShEfvS2cqfX3cDzx7ux+vyecXy/bns5rt51sF7MrxgiMuWzaexa4JST43vvurZvPLKX/DU/euctMdiFiwcZF474Pnz+pCOzbX3ruW3j2zgyNVLqBn0m1PxtSAJ4FR8nKH5WP3zuPuBR7jsS9/ltjv/yrIFw7z4WWey1+IRjtxvT6yiirbpT1HYcFP+qCQr9zKbYpopiiKsiY3I7r15dFyPzx1tWNxdoSQEyvepyUiL8sQhhC08N8nLmDp1zXHnljUyzThK2mkNjdMVanRIkZNUSMDKAgdIY6DsnH9LO1xa8AKYodANuUPb3dVFEEYaMVKpFcqIJEIaAZvMScydRSEtWp2Asy96P5e+/mUcsf9q7fyHAX+863427hhjyaIFiPYkVeDQA/bTxt3cpzKCSKIzReJWsLrhRU8/he/88nre9W/P1VyFtsvvbrmDkw6fWX6i0WRJNv9T50w1pzLjDuiDs1/J56yQqFZj98xxKq4XBgivlM/9JEG5vqGY2KG/uzmR95Hhvc3nj50LeqWOQjHzbfjwMpGZlKMvDcIUSk7xDWdcUIcoQlDPsuWq0k9cHdCcbUEd2RxHterIqR1QqpH0LkA2x9i2dQvX/+hrWNUefSApoMVEqUri6+CT056k7HfTFdfZ3qng9izCN+hAqzGaldfm/VgQejOcuUAWHFVp4CINKKbj4/aAPbN8M3UWL7noQl7wzKdx6osu4IIXPZveStnwJtuISk/mWBEGOoDmlHQ5fdTWZYZxbIIZuhTulCMP4dSTjtOOl6G8SPkHE9vX9+XpOZFUB/UzxgFN5bB9MuSebdOsGKzxj7Y5mzdn8/6WzXvOIXvtZvN++obnz9m8OZs3Z/PmbN6/rM2r+TaWLenpKfGkxb0MVhwmOzGTHZhX6acUtXOaGrOfKsvBFtDlWsQJxErRDBMcSwe4q66kZGukZMkWxApcSxAnCksKbBUhOnUcaVOruSyrlWFxFxOdhK31kPFWyHQQMdWJmGpHbJ1osWmsyd1bXAarHlPtkC7fYb/hKou6+xnss3Gao5piqRBgVGnQyIhJ6heTGQjgLKicJTwSndwKW7keQTqmaTOih8IxcyjozAhup+h9QI9zFGpOcaU5qpVbJa54CK+iqYXSz6XBKLMuk4IYZUrfkiVmmJW4mYXcTOlaMns3uwmpA7ZxgKLCz393M8+uVoiDmFccvT+XXH8Hzzl0NX29NS59xvHEUYwlJVe94pm89vvXcca+yzl5r2UM99WY31PlnEXDCCn5zX2P8psH13HkqiX0dpURUmK5Nm76e1cP9shSrMH53PXwei679L3a5i2cz4uf/TRWL13IUQfti2Xl/PnCdmcm6tNKGcIZHL5AVkmjGhNYzla88kIeGW0TK1jU5dPjWziyjOsIyl5FV5+Zdd6RPm7FN2hwsqBz6v+kAevUZxJJjFJJrj9iu1pwO/UvTPBdFALlWaA1TWQUki7FYCTSpqu7hyAMMzBBUSg1tXnYTq4XULAV7TDiuW95L5e+/mUcefABGX/2n+6+nw1btrF48UJE1KZqKQ49cD+9f8MM2pt0XQjb4UVPP5Xv/Pom3n3+83SlC/C7P97GSYcdmN/frJatiWxtpJVork68pMFrMDY3ynRXhFIQhxnqOl0HmV+ZVi6EbZ3MTdd9GORaMo6rk7BCZpWJM9aH6dPd1gWF5JHl6vkRR7mIt6EsShNkojOFCHXgPKn0E/d0ayqVoIFoT6PSasj0miph285dXH/VN7AcF0ShCsMgmxMTzBdRhx5X4ngeSqF1N0JdQSfb01nCNuODT2nhZg6ErrCIC0CQWXuCcsqZfof275ws4X7pGy7gBU8/g1NfdAEvf+Gz6S3rSlGRCYi6uU+RAVUMtVxK65Lk1F2nHHMYTznhmHxuRCFKJtqnsf0Mla+cMkm5l8itIgU0woQt9YgNEy32nrcbe/2/REtQJP9EmPU/87v+kfatb32L//iP/+CMM3YjhPtvtb870G1JQRAlhHHC4m4fz5bcv3Ma35YMlCxKpW5E6OnDvjkgNtwuXASeBa5UlGxBJ9YbShArHElmBEokiDjSFca2i2vZKKH5wjqJwELgmEP2fCEZqTi0rRIlWzJYcVg9WOHurdNMdiLKjsWqoSpX3b6JhX0lyo7ksdEmhy3qYdXAEL39I7hSUFbbtFBFqRukjT36mF5YpjT63vsfZMWyJZRqFe1YlGrGERIZnyidplngZFkwVKKRG0BazqmkjfDK+YapEi2kE3U0Ys5sKioMDMqmTqISLZBk+zmyDVC2FvUSnUbOcaoSw7Ea5cIXKeomLUFKXzcGRcTkm3WaqRRa0biI2EiNhpI2lZLPkN3BGezhwmv+RJ/rsN/yBcSdkCSKKZnS7K6Sx5WveCbfu/U+3vKzGzlir6W8/PiDqPku0rF505nH8P0//pW/btzGxc88AbunL0P6Cb+Cu8f+NGsjvO4DnyUO25xy0ol84aILGJ4/H9W/CLau0UJUs41qMnMpi7RkxpQsqkCLpGVK0Ea4pNuz+P26BrFS+LbEsQTzKi6jrYjhik2/W9G0AH4NZfu0rRJCCPzOJHU84jChWh1EBk2QkrjSr0sAG6P6MC8ksjluUJGlPCvp+DmyLTEH6hSNEbUz7sgUSZVxaKXjCFQrZWzP2825QVrEcUwn0IZFzDrwb9uxk3d/5mu87LlP58gjDtdj32mCV+IZp57Iz2+4hdec+wKCIORn117PwuEBrOkd+nCTxOCWSNwSVtRGNLSg1PwFi9i064aMGzWeHueXv/8DP/zw2wsOWoE2oTBOmSOUOqepyF27YdAylp4jqYPnejrI1qprR9K8PytPDdpIy4LmmDbA5kCScYl5Zb2uUkROolGcRRRidjAuokMlM4Nq6ftUku9/2XyMDJez5mJMD12yMZqhhZRbJSr1Ius7EfVRVGMCmUQkboWH121kv5XLMioF6SlzuNT3rfwuolI3QiXYYZNHWj5CxAyVBIlXy3hQZWN0d9Gc1KmzCrx1oPmS0/tPD/4pJ61KaPSvpNQe0wIvQTNHXlo2e+6xgsvf9iae/Zp38LlL3kzNtVkwYMSzbBvlVpFJjGxPZ4jZ/OAcQATaAc7niHKriPTwKC3dFwZZp5xyNhaTnYRGKJlod7h9yyS76jNRy//VNmfz5mzenM2bs3lzNo85m/e/xOY1g5g4Sqj6uoJJKXhoV4Nt9Q5HLeplVbmbdAakgZzJEFQnoWQLBkoWvi1oRwpLgiuFTuqaeeFJqdd9EGC7ZWIEsbCxvCoxkiBROFLgRG16ieitJIR9XYy1YhphwnQQsXmqw+apNpOtkJ31Dg9tmaLejvhz1WX5UJU9Biqs6C0zUO5iXtXBbezUwoLpvh51NO1KUIdOi78+toEVK/ag4jna5qXJVNdwo4edvHrI8NVnSRMwgbSUG9rVlS1pcietkDAc3Om6UfUJmBrVwe4kQZW6daWAW0F0phEq0UHSFFVcTJAZMUUBM9djcf4WAuTZa+TvFUqhTHI3vbZQCbSnEZZL2XNYtmiAchjzpl/+kcGuCnsvGNSXCmMsQ7XV11Pj6+c/nR/c8RBv+ekNPGnFAl75lMPpki7SdXjjU4/m+3/8Kw9sG+Wis45D+i5JGGH5Hlb/POyRZTR6FvPW93+CTrvNKSecwBXveiNDS1dqWzA9jkpm8QDPqohI9/js9xThbehMUqFQYs33vmGyzfrRJisHKyzuLlF2LMquRY9nMVTuRTbHNee0V9Hj6mkx5yBWxMrCK/VieYYb3yRoZToWwszvMIFEVwRE0kVZ4EDG5Q4gRDQDfU/Bvum5YxVQ+oJqtYbtFvbZdCyt3OYBuTC5adt3jeU27/An6YA9gA3POO3J/PzGW3nNS84mCGN+ds1vWTg8lCeapa2r7Wwnp+qTFvPnz2fzrus1lVMUoOIos3kz7fHfoLIotiQ2e2BhDzZjLb2STvpBFvjW92VlvqcI6notp3t9HORVZKmORGq7isHtdA3LQrLoidZTOkapD1vkZTfjrWzXIKLDvDoIchCGtEm8GsJy873HVI0IIXn4sfXsu2oFmEpIIaPsu5TtkVQHmRY+jhQkwLZ6yFQ7ZnF3FwOVRO9bFPzdQvA+s4EF/1YQQbJ7kFsZcc/Eq5BU+hFBUyd/4kj7BCbZs+eqVbz3bW/i2a++mM9d8maqtmThUH9u89Ikc9bXxjeXScYLnoAOdidJjv5OExbSyrn2U5/Bq9DAo9GOCWLFlukO92ybZrQRcObe8/7Pc+x/aFP/ZOqSfypNyj/Quru7Wb58+f/16/7dgW7Xlri2pBUK1ow2WdztM9WJqAcCKYTe8JPYCBkFjDr9PLqzxYIul8GSjSUUnkgo2Tax0ln/WIFlFqVIjAhSwQEXYRvllnEtwVSQECce3aUydqBRb5XpHVh9S1k3ETDaChmpeTTDmPVjTRZ0+zzzkAWsH21y7f3bWb9pkpvu347n2ZRci3OOWMwhI4M8ON6EKdiz36VrYG+62zt15kpOs+/+B+h7mVVCrRyfpNyLPb5BL+qiM2E2lGIpaOJWcrEL0AfslDPTcohLPcTdI8ighTX+eFauI9uTJLarOVCLwlugnXnLRbYmdVbTshFBKyunEWYDzu7LtJTzLb1PGTTyTQjyTKNSuUJ4Qmb4JIqOlKiKj+c5fPxlT0daksZ0i5SX1HJtLN/FCyPOO/5gzjvxEH5x9yOc85kf8fqnHsVTDlnNwUtHuOvxrUyHCVbZlNS7PsL1secv5Sd3PsbZ557MJZ/6EkeddhYnlnYimhPEgyuMkUhmKgRD5ggWHZ/suQvGNnOIHDc7PJUtvQPc+fg4+y3s4RcP7uCgBd04UgA+Qcmnr1zFb41CkuA5PiQhyvGo0NFGS5URnWl9wHd0EEW5FZJyL/XEptIlscce1wGUqENS7iVQEse1EY6vAwRhGxEFmbKzSLQCuBbOKJRRQTaHXNelUqlSTyy6TD+m6Id6q8M7Pv11fvCxdxHECZd/7pv0dnfx1JOO4X2fv5JnnHoizz7jZD2vLRfKLjGCRzduob+3m3d95DPccd/DPOXYw/nmJy7P5pOoj0K5G/waSakbaebaXiuXIS2L17z/syyfP8Tvbr2TM447nLLvZ+XbxbGY8XuKwihk/lVScDwMF21aol0U+hG2k5UIE0VZua9CO4IIqakbzMEezB4T1PODYxoAsL0ZDs8MZxRyJ0c/QPZvVpoPmeNBkmgUoO0i3FIe1FG65Dc7+LoljRis9GnBM8tl/bpH6aqWufbGP3La8UfpQ5M58Iok0odey2GykzDZiam5HndvG+eHf9nE0asGOGRBH3t4JUYqdnaAy0SOUnReijjJByQ/IKXleCrRTlocEPUvxRJCl92m95IGQM3nz3nuM1m9x3JOfMEFHLR6D371pQ+bcbJ1f1tOjnxID40pwsJz9cEk6KASqcvdPCgKhaVBAtFpEHQtoBMr2u2YsXbMPdumGaq43L1hIhNt/EfbnM2bs3kwZ/PmbB5zNm/O5v2vsHmWFHglHSSb7ET0hg6OJZlX9ai6JtCTUjM4JSYC2NWKcKTAtWzKMsbzJJFnESfa3gFECoSwkUIHz7D0fVpCr2uFpBMpWlGCJQRV18dBUw84nSn6Sl3ESjHWUjTDGMcSjHTpOR0nip1TbR7b0eCR9eMAVLp95nX7nLTXEEcv7mHrrgDfsugteXR7Vbqrg1jNcUR7iv3270e5JZJY0wgAmgPWJIAzWp6UNquAus2CX+k8NfuVFpANcxohIVBehbg6CJaLPboeMb1TVzSkwcxCoilxSmYtxQYFHMzgcc58hkJSN7snyMcpTfim1yqsaWH2Cp0YRn+P0OKDQiW0FJQrPq7n8pFzTkdYknC6SRKGSBws10ZYEt9yd7N5rznlME47bG+etGoxd6/fylQnwCqXkdUepAlC2wtW8JO/rOHs807mXZ/8Eqc87Zkc1i+wprbp/bUxSsprr8VDnRmB7jRRmNq2GVUxkFMgeCVwtchhyRYMlF0e3DrFmp0N6kFMt2/TX3Zplx3CxKHXH8CzBClIWqgEGbbxQe8hQZTpGWjRS5ek3Eti+8QK7CTQOiWGN166NkGsEE5ZB1xUktNLpE3aKKeQ2EifwYgwAzh+Sdu8SFErF0AAcUy90eYdn/oaP/zkZXTCkMu/8C1t8048mvd94Rva5j311IzCSQlBkiSs3bSdge4K7/zo57jzvod5ynFH8s2PX5rv5QCqrPcvQ18C2uYJy+K1H/wCy+cP8ds/3c4ZRx9C2f0bNBJFWou0HyFL0gtm+i6A3vtMZaEWCc/tZZZATqtiAGz0PmnELTP7NLulQeCCPZhh68x7Zvw+OxmRXjdF9qfJMctFJJ5ORhkwheyMmvE13OC2A0leFbNu01a6qlV+c8NNnHb80RkndkqlpRwfZbt0AkWUQDtO+Ou2On94dBdLBiocsaiH/nKNJV0u7vTWGUK2+Rjubu903wqEknl/OdovVU6Jeixx3S78lDteSJTUtlyEbc55zlnstccyTnzBKzho9Upt8yDnmk9R9bt9r6E8lJYWuZ/V55kwsLS1f2DonVrKYrIdUw8SdjYC7t0xzT0bJii7/0kiZa79y7RLLrmESy+9lK997WuUSqX/a9f9uwPdAFXfZmFfia3THRZ0eZy0rJd6kOBagrbTi5ciraRNTUhW9ftaKdts+EiJJRJz0AeEbQ7+OusUK7D1jgeQIYAQkqojsQR6AVk2hC0St4Q3/jh7lnp4VJZ5aFeDsmMxUHXpxAmOlOw9r0acKMbqAUsGyjyyeYpGvcP3btvIjr2HSZSi27fZ6FjUXIuFXYNsb0SsGhxmOoJ6kDDiJxqN4ZR06YzjIRuj+YaXbn7KqM+nqvZGBVcGoMKWCWy4GVIsK3cB6jg8MBkxWFvN/KqD2xzFmtqqDwZCakSUV8vRJkmUIV5E2MkddHjCA0nWzIEhu+f0b0WF4XRTjCO90ZjfRdjh0cfWseKEA8B3UY6L5dg4wwsIpppE7QDL1WJa0rFJLEkSJ1iOzVmHrubux7dpjkxASMltj25m61SbMw7bn31Xr0SWu1BRQLzXCXzywqdx6mmnsfr4M3Assyn7VZJSN872R3SWORXhSkuyjUOUcZTZu2e/0/epJEG4nkZOOCWsqa08fc8RHEvw2GiTejtk7WiDLt+mGcb0lhy6fRtHVklair36JY4RXUvL0GRzPHeADGcbboXpxMaVgkShxby8CrKjles9p0TilomRCL+Wj0eKCEnL2wqOh5I2wsakA/W8W7JoAY9NBxw0WHA0kpiuSpnJRouN23by3i9/n8MO2JvB/j6+9h/XcMbJJ/CsZzxDi2dAxkv3ha98g2/+5FcsXTDMbXfdx+abf5obH6U092ESI5qTGSIMoQVZVBzzxcvfxj1r1rFxwybe9JLnIAp4Q5E6GLB7mdjs/6djav6WleB3WjkCrugcRZEOtqEdIWVKVLUzag76RYOfRIUDv7l2MbAy2wF6ot8zJIAAYQFWdmhWQuSl92bcUkdRxAGxM5iJ54lOI0dtlXshCvjxr2/g6utu5OrrbiRad2cutAfZZ6ywQ7/j0etWEFGHY5f0cvvj48SJ4uZ1Yzw27nPWnoP0erUZ/LqZg5deq7gXQI7UNX9PvApCacOzeTrEEoJ51SrtKKEmbehM60CEW0IkEYccegj77LmS1597NsIvo9xqvlYKvLOJWzEcz/rwn5R6EZGt+UylDWFTrwWvrA+YUpfHqXI/ojWBU99Oyx+kGSZMtvWBe/NUm06U0F/9G+v/v9DmbN6czZuzeXM2b87msdvamrN5/+/ZvLJrsaC/jGtLNk+1qboWi7p8qq6m2QrtLmzH14KzKqHsSvqUhWMJTU9iEpa25WJLPVcUkJjloP+VYPka6Z0mIywXzxLY0tLUXomhG7E9ZGcaN4npL/USJopdzYDxVsiuehtLChb0+CzqLdFX9fjzo6M0Jtu0GwGPTXcYqwdMtENaQcxA1WVRd4mFXT5TgcC1apSq3biWwBICl4gk0JVOytUJItkYzQPRYiZP9oxkL5h5r3JEp9KoXr1PBCRWjUC47KxHWKWF9PYswY8ayPakXvNJRFLu1Qh5BT2ehPYUItGoUxHFOTJ8VvJWFNcbJqGlkqyaKft91voWCTltg6UrfUgS1m3cytKDV9D2bHAcbF8jtKNGmyROkA5Ix85ElQEs2+LM/Vdy56ObkZ4WQCRJuG3dFrZO1DnzuDr7jixFBW3s4cVEy4/gM697H8c++Skce/pZSCEyrYbsntOLp3QGTxDUmhHgLlb4JAkqCvXfTcKv1B7jsAU9hHHCml0N1myfpuTaLBso04kS6p2YjaYKocd36CtZ9LsYHyKGGD0GQdMkdUsk1FCOT5hAECe4lqvphcN2tv5dy6UdK5RTxpESglaeQIFs7ySxd/dLhEkgW5IlSxbz6GSHg3p6Z9C3dNUqTO4YZeO2Hbz3i9/hSfvvzeBAH1/7j19x+skn8KyzztI2r1B984UvfZVv/fhnLJ0/zG1338/mP/4sp59Ig8txrOkmTEVNugZUHPOl972dux9ay8Yt23jjS56T+btp38+wbWmlShIXkru5D5ONc4p2tx0dwDbJ2yRNZhQS/WkVUnbPxWRssaV9bLkzAtxAvoaK7yuukTTYnM4plYDMNTBS3vDid2t9EvPdtqk6jAINVBAG0V9Yiz++5nqu/s3vufo3vyd67Ha9X5gElkDPI2tyK4OeTnYnXoW9h6o8vLPOprEmtwIV1+aMVf2MlHszgAazbacqaNsUnlUJDKjA1ULjKkF5VSbqMQkx3V4N3xV4hFiTU/ozxkfWNm8P3vBvL0SUqjrYn/WTSRyn89lUw2A5OnidCnvHgab8Sfsk0VznytK6PSJsIqSFtCsEsWJbvcO2eofReoAtBaV/4UB3onLb+M/6vv/J7eyzz+Z73/seQ0NDLF26FGcW3/+dd975D1337w50T7cjar5++5/W7qLbt6m5NpaEu7c1GKy4lGyPkaqDHTaxrISaXdi05e5OplIJlkGUCCGxVaQFH0xWOZ34MmrroquUX0wZIvok1gs3qLNHPMGy5Yu4dWuLTVP6gGTJhP5yiRWDFR7bUeeva3YRdGJsRzI23eHeTRO4tiSIEga7fA5b1ENfyWaorBeoJRxsKQiEiyz3s60eUnY8+uOW5kNKS9XSA7e5XxE29QItZrOkrcUugDiO+cVvb+bppz45c74r7TEO7Ssxriy21EPaUY09+0taXAWIvC4enwrYNh2wrNdnqOxj0zbOuDk0FMtpzOacirOkm0cRDZBt1gVHLmvGKVfKlBpbNknYQQRtvGqJR6wKwrLoTNTpTDyM5bvEYZQ5PsKysBwXaQQIHtoxxo0PPc5rnnESyna55i8PMNUKWDzcx9h0A9VpYy/fl3h8B2snQ+TQUk46+mAWd5eYX3NRSRkRdXC23oeKQn3Ye4LD4wxk225lzTONoObTjJDGcewJWjx39R7csa3Jxsk2D++sM7/LZ7/hGvOrNi2DOKm5kihR2G4JEbRI3IoWekr5Si3D5WmEoLrDUT1fTbmrDFoZEgDjOHRiRSA8Sr40Zd8lhBGB09ygnkEIdPR3GJSIMGWMpxx3FNfdfgeH7Lca1TEBAmmhVMKT9l7Jyy/9JK9/8XM486RjdJn2U0/NSpbTQF06H+578GGed+Zp/PJ3N7JyyULdV0W0mOMivRJJqwH1CUSlS6+DdgOkhRCCA/fZiwMOPFiv4+ZUriCdjk1RlTtDs+ly37RUWzv/abmiyfyn5d4FFMdsJECGOBUyQ98Iw4tGFCEtzfOmbF+XRc06sGRroxgAm92eyKEqBAKUJU0AyhzqU1V2MGghvX8lxllTbhXRmtBomrCJsn3e/PJzuPiDn+LNr/y3LFg4456U5kwmDpBxhPK78KXkhJWDTLZDDlvYzY5GqM/uChK/liPqIN+3ssBKcXEkWTBExKFG05q9d81oi25fB2mrrqTil3Gmd6Bsh6Q6mH3+xKMP564HHuG0059qAiDmuqniuTIldwUUoojDHD0CKK9Lz1HDx5wiC0R7ihRZUCsFxJ7LWDsiUbB5sk3ZtbCeKIj0X2hzNm/O5s3ZvDmbN2fzCn//W6/N2bz/J2zeaL2TUXY9PtpgqOIyUvMIE8Ujo22aYcwefSVqbo2qI3HjgD5X6s4uIgnTZzUBDyulS0AfttPfc175CAuwVJIFy5Vl58K0YZNS2GSptFm0fJhHJwJu2TjBlsk2482QoarHgp4SfRWX+kSLOEpQSjE+1uTO9ePUfJvReodGoNfNsp4S0vCEtxKlK7Qcm9ip6X0uVpRVlHO8Z4nQnGYpS8CaoBRKo6jTioA4CnObB4ighd+ZpNfvYlcrYuN0SMXxGekqY01t1YkgJNsbAWOtkPk1l4FSjYr5LuW5iNiBQOaBJDAl/2qmjkXKJTzbzhXtYBag10HDxK2g/C6Y3omIA5yyxyOdGGnbdCbqmaCk7XvYJRfLd7F9N+N7VnHC2qk6Nz+6mdc9/wwUgmvueJDpdsTikSHG6i1Uc1oL59b6eGQ8wB1ZwYqVq+grOfSVbB1ADjTKXUQdvYfaTp7wg91Q3fq1gl0sJApT/m7Nq93EDhoMlZucsmIhqwer3LFlks2TbeIEBsoOS7p9LAlhonCloMuzNDVGkicZUh2KzJdIYkRrEl9IDXxIMH6ayOaHiNr4tg6GR5aPVTI+YBTkvNMpB3hhzeu9w9bPkMScfNzRXHv7Xzh475XQ0NUHunpH8KR9V3H+uz7C6196Nk875QSUtHjGGadq22zQxFkyMg6578GHOfvpZ/Cr665n5dKFWfemAW4FOsgcBUArE2/FvA5w4L57c8ABB+p9Omjr4HsWwJ5lo9LxMnQm+vqFhH1hHDNfptBUEuuKpnTMbR0MnZG4SXmy0+oIaai5bE8nrwyN0G427j9L7qbjndpNc5+JVUIoU1VVpMIr6EEoKhAZOpU40P5LwX++8IKXcPEHPpHZvBlrNImQYcsIq2oubhG1Ga4M86RFPUy1I+bVPGquRdUpzP1iMLv4mEUqpMJzkGjfWfmahm86grG22YcV2FLQV3Kogk7WVvp1xVYUcMJxx3DnA2s49eSTtO2cpS2Q3Ysywsop1aFJGGQ0XtLOKkKL9ybCDkJIPMen7Ehqrs24FRp7J2gFu8+zf5X2r0Jd8rnPfY6PfOQjbNu2jQMOOIDPfOYzHHbYYf/p577//e/zghe8gLPOOouf/vSn/+n7X/rSl3LHHXdwzjnn/H8jRjnZDJg2E7/sWlhCUHG0gvFYW4uFrOwvs9UcjPscYbI5ujx7RrlgmrWToNLDKBhhIvOFcaTV6AO9yLMyY9OUEIiwSeJ3g0EW2TvXclRXL/sOzuO2LXXWj7e4bcMEQ10e20abNKcD/IqDlIKwE3HP2lEAenpKxInikbLDI6MNTl4xwJp2yJJui7IjiBLFZCem7EiqroVoNHIxntS5SJ2elCspzbrbfsZxKRxPqwALi6H+nuzAj5CaC6k1Sb/lUO1ZxM5mxI7YY5gWolPHdkp0ex7XP1ZnzViDRd0ljl5Yw6v0a8RKp44IGtqoBi2dBTPfn/FeFg/8zDrkz25ZVj0vH7/voUdY2l2h47lc+q2f84WXnUljwxaCqSY9eyxAxQnCkli+hyjXsAcXkNQniCdHWTg8xLknH84l37+OXVN1hnpqNMKY5x12AMcd+STtGE6OIrv6eWhXg7UbdvD1zT/lzLNfRNkREOqJEYzsiz25GbauyTL5otanDUF9snD7Mx2gjLO0cEgUqcMUpCJbLZzWnRxe6eOQlQt5ZKjKvdunCU0abGDqsQyhqNxSFtixjPgF0s4OVDJo6ax90eCkG7mlUU4iDg1a06VqAllTwqfsulhKrxvZniZxNO+VSGQ+jilqKokhiTj+sIP58Oe/youedgpqdBuLRuZl2e+XPv0U3vma8xC2o1FqBZ6yDBlirquE5BOX/Ts//OnPeeEzTsdHqy4LywJTLqaMsyFLFZJWA9WY0gf/aj+yM52X2oIu/5U2ojlJWoYoigd9yB0XKQ23nrP7QX6GAzQLxTiLAy4tv0qfMRVWQdqokkFJCamz1+k6TrnNiqVuszP7xdchE0VJr5062Vm5vZD5QToVvoPMidWHW314UJYNbgnVaWje0kTznD529y28870fMmVmdr7PmJK5bE1bLgR1erq7WdDlsbRHczmv6vPpa2zOSv6BfA6mz1bcn+NckCt/BhsRBSReVdNpKMX8mocUMNqKaYUJi71Kvh9GAcqrsnz1vnz4Ix/lHa9/RXZgTR25VHhOxAH4lVwA0aAY0oCAslzwKrp/ksSgAXyUU0I2Rol7FhBg0y0CVvb5+LaFZ0uqvs3juxr8d9qczZuzeXM2b87mwZzNm7N5/1tsXshkMySIEuJEsWpYBz6U0lQmzTBmoh0Tm+Bw2XZnCKAV99dchM/YvnRapHoISZL3f/r8KbeuEFmSQRlBWxGHOrm26zFW9synuqKP9RMd/rh+jK1TbZpBzI7xFlNjLaQU2I6F7VhsHm0ipMB3LB7b0eCxHXUOXdLLfsO1TCRaCE0JAGAJQa8vEc2GrhxKA8LF9ZCOe7qfOGW9P6ZJ4LCNkG0GBwby/QaQ9Z10yXFqtkPUs4BdrZjJELprw9oeCvAswbbpDvdsm2KvgQoHzqvSXc6RzkWeeBE2ySgl0uTurPtVxXld3JvT140+h3JKkETcf8+dLO/roqkUl/7wOm3zto4iLIkWk3QQZm+SpQqy0qX3v3aDhU6Jc087lku+cw2j0w2G+nqox3D2EYdw7GEHYfUMYAFRz0Ie2dhg/ZadbFr/GM970UuoeRZNu0KlawTlVWBiC7ADYbvIUiXfe6JCQuWJgqmFoLiSpg/iWHNJBwmi06KrM82e1UGWrB5k03TA+ok2Vdem25OU2mN5pUHH7AtRMKMaTSNOXUMnU8+p2dL5IATCsvW8MNzDMo5wHR+TvSQWNtLViZEsoSJtVBzk1CbFeZZEnHDYQXzkc1/mnDNOgvo4Cwd6s2d96dOfwjtfda62JUUO+Vnjnvo3mc076zRKrjND+yNNXuB6mai4CgO9UITUlFlmXmdJTGkjwxZJp2Xs1SyUfSEJkVegObl9I/dZ9OfyYGxRWDqlyJvBlZ0ih1OfJL03s3emaO5UUJtZ/sBu+3+a9Cz2Y9pmgweK10r3vPR66d5mu0YAs1sndtvTeh2HTaTlapv3vo9owcnUhyokAbN7Ntf0LUHN7F1Luj3mVeyMiim7l7QVK7ZSShRZ6F/IEeuxpo1rtRX1jqb18WxBFCvGWjGlngVY9Z2ZvVeWzR57rOJDH/sk73jtyzQ3fbrvpEH7wn6TVp+lNjwTZcX4FlKipKd9CpOgSKSd6SX0+haWcOnyLRxLMtEM2TFVoNn5F2v/CmKUP/jBD7jwwgu54oorOPzww/nkJz/JqaeeysMPP8zQ0NDf/Nz69et5y1vewrHHHvt3f9cvf/lLrr32Wo455pj/8n3+n9rfHehePljFkg2dAQ9iJjshD+5q0O05HLmwi5KtF9ZkJ6bkSEhSPk69OIuZcZEusijQggyucdKNEQf0ZpVmyiHPVpsNQIZtzdEZdVC2R1wd0HyPcUB3cysnD1VZ29PLtukKzTBm/2W93KMUrWkdWIijhPnzaizsK2lut81TrNk6zbzeEq0gprfs8sDOOlXXZqDssKjbp+zYWAJN0p9Embpvdr9pNh9Qtp9z+klbI5nKvViTmxFhhyMOOzx/NoN+UZaDSGL8sfXM757H1sBmbVSjv9rLaCsCYvYdrnHf9mmufXA7NdfiSSMVWrFC2hU8r6oNtFtBdqYRYUejeErdmcOmZnGAZa24yRdeS8uYAH7805/xlFVL+MTta3nXS5/JYFAn6qlh+y5xEKHihLgdZKXa1vAirP4R5PgOeusTnP+sEc6XFr+980E+/7Pf8/0rv0j/yELi6iCPNwTNMCFRigfvWweTW3jh5R/j63/ZyFHL+un2HBZ2L2Pt41M8af4Iff6W3OihD3rCN2IVUTjr0UxwAPRBOM6Rb6nRTEXRSGJEYwynM8XqSj/L91zAeDtmVyvGG1iuM6xtk0nv1I1Ylp0bTpUbOVFwOhHScIwGEIKwNK+bckqEbhVbRVj1nXSLaZLqIJHQyNEkLe1ODxGWLJRCRWBpkRDXdfjwOy7ktPPfQpfn8JYXP4O+gQHuXvM41VrVOIRONp6ZcZaSJEn4xne+DUjOe95Z+J7DaScey3mvfytffe9FuoTWBBpS5wdLK8xnaLRQO8uJU0LEdV2qWe413GUuUto6IJAqLoMZg8J8yxwbK1dWfyIO2idAM6Zl5qmARerMZerhTlkL8M12WoolngVO4idcHwXnI3O6U0cpDUoYzuWiAF8WULD14VlzF6f7mt7/IiRjrZiEGvNr0lACtFnQ3810o8najVtYuXAkP8gUgnIKjXITge6/ZtjD0h6HqiPpiqaz50zvVSM2ovxZUsGWItKhyKEnLV3C2xzH71rIeCtEKUXJsRgs20x1Yur2ANVgQqPwogDZGOW5Tz2FK6/8RhZ4zA77qWNqAmiz95yUrzUp9yKiUCNI/Ro42nEKy/2MtmL6epfQjhJcC5rKQQhY2euypMvhjq22LhH9b7Q5mzdn8+Zs3pzNm7N5czbvf4vNO3BxLxvGmqzZNk29HbF+tMmK3jILuz1WD5SxhEAITb1VbOoJ9lJtGzSCMSvvV4nh5O/MfH9hD0EZQbJ0/oGu1JCWruYJ28jGKPMtl3nzeukrDbNuXFekjPSXmRpvEXbyPT8KY8pll5XzdOn/mm11Htg8xd4LuthrpIuhistQ1aPbs+nyLEqufjjllFBRG9kO8ntL10saCDPl/spoUuhkhQ78iyTmyEMPyp8zDYAmsabgqe+kuzzIZCdmWwRQJq5HdGKFFPD4rgaj9YCqa7NHn49rCSQWXnVQz1tpI+s7NcWYtMDytH9hKimeaEz+ls3TfSwRnTo/+vmvedphe/PRG+/i3ec9h8G4yWSjjdtVRloS6WhubmlJhF/BGlyAsB2SVoO+oM35Txvg/LNO5ncPPM7nrvo1P7zio/Qu3oNWbQFrxtpMBzH1LRF3rd3E1NbHecrrLuXR8SbTQcQW32GoUmPbeMB+w0vp70wTT4+jrB69TqKOmUtyxt6pHyMPhmqBUP1aSm0h06RlHEO7gRW0qTRGWdGzkJ7hiua9jxVeuXemn6MScGROaQZZP6fVZxmqP/WJVKIrMuII3LKhhbGz+QFg2W6Osja2KVbo6gc7n/9KKf19gOtYfOjdF3H6BW+iVvJ5y0ueRW93F3c9/BjVSnlGf2SBaGmhpE2i4Bvf+g4A577gOfjlEqcdfxTnvfFivvbBf8+DuulcN/zY2DYCrTeh9QUKU0clKGkSPY4PbVujMFMh3bQVUdyzWvp65rOkFF3FNxXGUUl7ps1LE6BCZj4olpvZq4zXPsyTFFk1RGbTrBzgkCWIxO62bnYwfMY92oZvPIA4nzu6z7QfUw8TpgMbS/YxNNCPPbYBETZZMNTPdL3Bmse3sHLhUP7ZdP5FEcLSdkSGEX44zfxamVaU0ONbWNPbM174NFELJoCd2t1UTHIW+MMcThBJjGxPosIWrtNPbJKOFUciHF2BFwlLC+YWNHee8/Qz+Po3v5UlHLLLFmgF06RyJgBdOOdkVQLpe40QcEf6NMMELAhDLVJcshVV18K3JWWnSm/JYf148wlm1Vz7v9U+/vGP8/KXv5zzzjsPgCuuuIJf/vKXfO1rX+Piiy9+ws/EccyLXvQiLr30Um6++WYmJib+ru9atGgRXV1d/7duPWt/d6D7zD0HGG/1ctfWKbZOtdk80WZe1aO3pMt8kjDBkYKqK7GEAGYa0mKJsVZiNc627WYTPkVnYQyBNBtPigIRoXaO0gwpseZBFUET5VXNYTzWWbLmOHu4bUZGhrhne5PBLp/l87u4/+FdTO1q4ZZsdrgWIz0+l5yxF4Nlh9u2TPHwjjpV3+aYJb2MVHXpXMmW2J0pzZEWRLo819xL+nykC9k4Qolf06g2IRFxlIsupVlfyMs6QJcWOWXNzwfIqe0ssGySUi8T2LTChA2TbcqOxLEEVd/h3u11BsouYaJoRzGOlISJYsNkzKr+QVYOuFpIoz29e5aw2GZn+Au/ZwcE4OH1m/i3sw7jy9+9iSPn12hsmDKINoskjHAqPnbZ1xxcSaLLpCs9yPl76ENfe5JrfncjX//tn/nRlV/C6erl4biXy370MA8+uAO/7NLV57HuW+/i0Be+gdWrVrJ2V4PJdshQxeXxiTZ/2TjB0h6f7v7FyPY0Ue9ChOH+BPRhEWZkK2c8qrQ0MiZsaaNpjILmknTMfNMls7I5Tqk9jW/7enzqElXqJq70044VpbLOprcSQckSiE5dK3YbQZvMaBYDAkUDaZA8NgnTscQqz6OStJDNcRyvQmL7BYNsStpSlItlg6uVv1OU3EH77sWXP3IZ3/nhj5loh6x9YC0//uz76e3rMfMtd4D0mrRIpMNrLnwb++yzD3+49Tb23HMV+61cwjmvuZAPXXgB8xctyvvO9lCubcr3dEAJr4y0LFRohF/COri+vqcoQJk+Ssx8kkxDGMxwYrIDfTpusx3XJ3hv+jwpCiFxSjMMvIja0DEG0NGKzTOQa2npmMlgz0bPPmErjp0wvKSzsuUImZUoZ45vupYKh5BsfSUxhG1st0xgnKONosKiUoI11UTGAe95+9v4+Be+yuc/dCkiIndmkihDGqpSmaQ6iAjqLO/xGfJirMbODImS32SKrpKZw6qF4ArP+LeaSig1d9Bb8mhFipKjEEA/DZTdBW29zym3grJh7cMPsWLhvFx5vBgEsdysrDkNCKCSDJkkVEJc6kWELc0NbQRmptxexuohcQLbGtpO9JccXEswWNIOsx+3OGrE5ciF1f98TP8Pbc7mzdm8OZs3Z/PmbN6czfvfYvNOXNbH9qEqywbKbBjTQbw1Yxolvrjbo+ZLLaycKCOuLLT+RLEJSWxsoeXaMxK2wiSPUvRlHhQ0yYx0bqV7mVWoPkgRr36XnstBE3tsA3tV+hla0MVIzSOIE7aNt9i5aZJGo4PjWbglm30WdnHynkOMVD221juMNgO6PJuRmkfNtfFtQdmR9MhAJ0uVWZ8pnUQ6HbLAl601E9LAWlp1EgU6+BlHmQ1PRST1PuwbrmOBCFuUpjfjWy5JqZvJ2GZnM2J7vYNnS/YYqrF5osWa0Ya2f65FJ1LUg4gwVoRJm/m1Xhb0DlGWMbLT0EHUdN4VEk/F+Vwcp/S1NNgkog4PP7aB8497Gp/63V0cc/C+1P96J25XGadcQro20pI6wRtEOIDwy4hSFTGwGKkUIqhz7Q1/4GvX3MxVX/4U1vBybhuXfPba+3jwoZ3EccLQwhprr3wnJ5//FhYuXc5EK6QTJWye6rCj7DDeDhmsuHT3LkY6JeJyLwiTDGtNZTzPRZ7q7LEsa6bvlSa30jcY4V1haLHs0ccYtlwSp4SiCpF5v1PSFUYdPf+TtI/CFjJu6DU+qy9nJ/vSPSuxPULLJ0wUjutiY5DaKVpb5tQ+SkjN15+OjbTAScWEYw7eZzVf+silfOeqq5nsRKx98FGu+vTl9Pb1zbgfJUwFjWWjlOI1b3k7++yzN3+45VZWrVrF/isXc87rLuJDb3sNIyPz8vmSPgfkyHYnRefm1W7Zfp/o+aO8iq40FBIh6ogwyBK3uwWxTaVZZvdghm1LBSopvJ6huFOubYColVUUpgFSZXtZYjyrIDNJqlTjJXutsC5yzQly6hMgoyAs9E825gX/JPspILqV4+dzLWhScqtMBwlTQYwlLQYqfVhT2o94z9sv4hNXfIXPfegyffkC9ReGslD5Nd03SjGvovdKpzmK1RyfSeMHMwAbu9k4E3if8YwASYKMGlQrg9RcmzBWxInWC6jaCSJu6+S/7aISTYGzdu1ali9e9ITfVew3la4J87pIx8zv0u/JxtGjgceuRkgjSLCkoBnElF2LmtGKqDmSChHz5lfYq///nmjhP7v9f0VdMjU1NeN1z/PwPG+39wdBwB133MHb3/727DUpJSeffDK33HLL3/yeyy67jKGhIV72spdx8803/93397GPfYyLLrqIK664gqVLl/7dn/vP2t8d6B4u21QcyWSnjGMJPEvSChPu2jrFvkM1ap5NO4qpuhYLqo4pW8zRW8UDv4CM61Pz83X0JuZWIGyBozkbFYnOtBkEmxaLkXlJogr15qoMb5+oatGjqI3yKog4oNLcwdHVhNWHLeSK2zaxbus00hYkkSIKYu58ZBfNIOYlhy3mzOU1nrbYYwIfx0gul2yBbE9pZI7lElYGMzTDboq+Qur79Kr6PsO81EkYBeg0C6mkpTmOzMEj7afEq+WbbBIhOtP0yhY9VYfBco37djaYV/VoRwnbptpcs2YnE82QsXqH4/YYYFcz5IEtk9zfNc1JK/o5YLhCqTVRQEbkgYbife/2HIZHEfJNf+eOHTh+GSEFnV2jxIG+b8vXRtku+1iujYoCrFov8c7N2JWerOT5lk11XvDW9/OMF7yYu9R8BlWJK/60jq27GoTtmEe++jKCsU0sOf0CpsuL+fJN6xjs8rh6wwRdPSVefcJy5nV53Le9zorl3STSZmPLoq/URy1qI4OGPnymyChmHhKF45KYw4YqHoBnlSBmc9b8iLCZH0ya4yjLxU3V2IUky0cWN/rUAKbGB2aKqxiUXOLXsKa3U66NsLUeUrc8+spl3NY4Mgq0ETCZ3UxoBnTpohFa0nNK3+vRhx7A+z51BR+/+LX4BnGaORR2oXS3YKAfWvMopUoVz7Y44kmH8taLL+btr3ophxy0f86flf6bGnDDn0ocmPKwkukPg56zPY3Y9KpZv0T9/VjjG5FiUvMwpsJaqSNTVGB2fQhMSdJs3tl0PFOnKOW3M/tDNg6mjF2XQOWln1mZm5Aov2tmOXZ67aL1KQbLzNpIy7aLju2MsU/LD0WkDzupArxKdHApvbQQdOwyLjBU0a9LAQG9eFEb2ZpkrxVL2LZ9Ry5yEhvuvySaoVCdcuGOxJtQsZ8H+grohvwBC0gq0MI5M14rIGYK80VEAXsN9NGJdf9YGD7jTj07jKYl2Tf/6c+cdOTBM78v7Zs0UJquq9mOtdAoirDcj53ylzoelhBsrwd4tmRln874C8CSgomORsd2e2WCRNHpJPx3hJvnbN6czfvv2rx7xhJe95GvcMyTT52zeXM2b87mMWfz/ifbvMGyhWt5JIaaZLQR0Ahi1k+0mOxEGdXHwi6fwbJl7EIhmZsNsh5TjdTMg8B5krNs9piZiRA1a5zSCh0sO0PGa15eoYN/lhYr66dJb63C0N4aDfmjRkC7MUXYiQlaEbfct50gSjjvsMUct7gLW0A9TIgSHe9xpcCPW4i2FkhNvKqea0bvQRXsR64hkFKqRGASuiJqI6IQZRvuYGPnMo7kNDhoOdr2x6Hmm29N0mN7VLq78C3B1npAy1T7rNnZ4PbHx3FtyUhPiZJjMdoIaAUxIz0+B490saq/RK8J3Kb3mCJZd5uLhX+VEHp8Cm3X5DR+/xBCWsTjO0jCCLdW0RRdjo3lu1iujeV7qCggGd+JVaqC7RKXe7n7kcd5/Qev4MmnPIVtPavYPBbzk3u3sH7TJM16wOPfeR33jW9iwcnns64zyI51Y7iWpG6SOKceNJ+hmsdUJ2Kq2o0/qAXALQndgJXStaSUP5BTmLhelnjPByyZuc5Tu1dE56tEcyGHrRnJDCAPpBZsGpDbucI+KFReQZahf2M9j9w4RJT7GWvHWmzU9/E7upov8SoIITWti5IkClxJTpWScq6btXL0kw7hfZ/6Ip/49zfgCQWp6KahFEmbUMoI2jo8tGYNpXIJ13U58tCDeOvb3sbbX30ehxyw/wxh0xn9VvxXyJyiKg4ydLdIYrBsIulilwxFSKjFp0VjDBW0EV4pryIrCEvmdDTs1orCk/mLUq8t0Fzp5rUsWWC5et6ndqCAcM6Qw7N9wdn/hxlo7hlB7ll+o4jaeo2bvslEJJVCJRFC6j5VRuhVAoNlm1qU7oNdJFEH2Z5m9aJBtm3bpueVZWd7oYgD8Er62mEH5Wg/xJ3emgXWU9q0GTbP2JNsXKPUPsuZVWVZ/5lnB5ywyfyax0QnphUldHsS2aln/SXa01nl5s1/+BNPPvLgGWju9H0y5apPgRWpf5l+rxmfuNSNoFsDeByfsXrE4xNthqseI1WbdmQRGPsbxoo6CZb0iMMkQ57/K7ZEKZJ/4v2n37WoAOYAeM973sMll1yy2/t37dpFHMcMDw/PeH14eJiHHnroCb/jD3/4A1/96le5++67/8v3d84559BsNlmxYgXlcnk3McqxsbH/8jXhvxDo3tWK2VoP2FHXhrQZxkx2IvpKDp0oIUxCwjhhYU2rZ4ugqZ3iYpkIkBgHKC3JUEJqXqIwzRR5ubF2fETYJvFrM7ncYCbCLYl1iaZKNLrCKZkMaUVz/ynFQOsR3n5AL6eu3J/LrnmIqak2jmfjOhZl1yJJFGOBIEhcgjim6mjkgi0tPNslEd1gaTEaS0V60043PVNaqSwnL0k3yJYM0ZY610pqp8dsLnFtmJayKBHqQIdb1n1inrMRJkx2EoY9mz5gv6EKa0Zb7D1YZUc54Lb1Y4zWA4a6PO7ZMsVDW6ao+jZVP2bDpDbQhw0OY01uy0tAZx1inmjjF8zc0AEq5RLX3b+OM455ErbvorrKJEGEsCRJGGG5NkmcEI9NYNXrSMdGBW3c5fsSl/u46ec/5Korr2DnZIO3nvccDjryWPpPfAEnHzif7z/6KMHYJgB69z+DScOrN23GaXK8yad+t5Y9R2os7CszvqSHnrLL/4+9Nw/X7Kjq/T9Vtcd3PHPPnaQzh5AQCIMRZJJZBnFARUZBURlEcOKCIgoqchUEFa6IXBAEIoKowFUjYwABgUASMnXS3elOd5/uM7znnfZY9fujau/3PaeDF/F3uXI5+3n66TO8Zw+1q9ZatdZ3fb+3HBlw7mxM0NlDKKRFwuQZImpA5lA9YWwTKr4To8hHNVeXkUw2HGVet5VOizVNK5qLis/OVSTPqAzfXUAlNrft1L93VAA6bOJlAzQhK+OCXBtmoxmaRd+uo+oaYWuyrrzABuHCfV1tKI3mZ572FF791r/it37uaTXyQShVq2ZPO30hFe9+2//g81/8Ik961EN5z/veQ5GMeNj3Pciuu2xsE2jpcNIe5pJ1TM3/GtkpJxsS4bgKN0REO+pAmaE7OyzXnbCbYDuWAlMUTARKysk9l5MNv5hqPZzmk6vXW/VMVauYF20SzKs3/LqokwDmbpIJdXXfnWv6/dYB0N1t+KfmgyjdpmP6M1WCaUpkQ4dNAgrkeEgoFUPVxJMCYwxFZxd+NiYsRsS+4o4jRzmwb7cNglxS1XiyRobItG/HX5d2w78lKOsPhqz1evQHQw6ccw5xHAFySkRl4nCN3hLwTp3rHLHKiXDBcsRpxyVdZDZhpou6Pfcr19/Arzz3J85Ad07TXdSJMWcvJwiGApH0oRmhow5Zaci14cQgZ5SXXLQQ0zApmQwotGGQaYa5RgroZ5pRrjk9ytg50zzz/X6Tx7bP2/Z5/1mf99H3vp03v/ZV2z6v+v22z9v2eds+77+sz1tPSk4Oc06PMstV3fCRQiAFlMaghKAVKpq+tEWlqYLZtMjkGWWUKRtRF2dogMxq1POkUFYVTgrQOUJrMMaOMUk9frYQ3LQ2JRvirR1hn/T4uftcwH33dPmTT93BbXesEbesPTi6OuZrJ/t0Q0WgJGOXbGoHkrASUvVCTNShNOC7tVzf/3Syu7KfuoRy7OZjMeEA1i6Drjx00CQPO5Ta2DjB6UdU9jZzlBmBEgTATKQoTUA5lf8otaE3yrj1RJ9CG05tWDu/vJHgS8lM5NOdmbUUUy4JWR/Sq+fn1nk9Xdit/qIZR3z0c1/hMQ+8LyYZMV7p4UUhfuCSadqiuXWpMRtDdH8dLxni7Tob1R7yz397NW/6ozdwYvk0P/lDT+DyBzyI837g6exYanHq4EGyNevzZi59NGlSYMyYItOMBimer7jmhpNIKXjs5bvY2VqiNHBqVJCVmv3dWbqzHqp3DDMaIIKoRhtXNrCyw5v8fpVgq56z6g5wc9KOgfu/SlRPrUuMnsQ+W2zMpGgw9fNpXzpVyFGNWbLSsDLOyUqPXa0uXukojrIRCIlf6QekU7RKVWJeTYQWf+bpP85v/+k7+O0X/pR7JjWxJ7UfdgVC5fPut/6p9XmPeQTvef9fkxeahz3oqpreBECUUzZQyM1UPdPzXrjumCpZ6eZoCYRBw4lxjqyIs0Nj23GqOLKniu6wqYArjKlpSqY5++tjazFymqZjOhap7EQlWDmtGbH1Hd7N+9yU2K+OrYWAaW72eh5ZW4UX1HFQfWqj8YQkUJKs1AxyTasxi0yHhCYh8hV3HDrEgb07a3AIYWezCG7p+K23iKJW194YjqzPG46tz4um6MmqcaluSZ4ZC4syRw5OsdTdRcMJrWsDImjUa2ji21O+cv31/MrPPP1u5p7EKOq1WHOow+Z5pa2gcloalIrJCkM/m9jmhicBm9DOSuf3shIprZ6CFII9s3xHHqWesNx8u64HcOedd26iCLk7NPe3cvT7fZ72tKfxZ3/2ZywsLPyH//71r3/9/y/3sfX4phPdAA1fsdQKGeUlCw2/5oMLPYESgj1tHz8fWQGHLar2lROV0xGQM6QiHUyUx73ABjUwMRyuPWaSRLAoJFEktuXbaESyUQu2AOigYTcU8QxicAoANVrj3i2PN/7wPfGlYCMrWR5k5FrjK0Fa2kBkNlQIIWxFNdmYOCodEOmhDSaqILV6Dves9YbHaMhGdWBXbfYtks8Zj9Yix4cFgyzn/K4kD1oMMs24KAjd/ZTaoLE8sAIYuQrW2jgn9CTfc848B1eGHD495OjqmHFWkhWa0JP0koKyAyMR0mzM2lbn6WC7+v/uNi9bDKgdQI/jG2Pu+7jHofI7SNcHGK0xWqNL+y9dt1W/qvIvh32y26/HP+tiPvf5L/Kin3oaYRjwI498EO+75nP88e++iJ9/9Ru46S3PBeCZb/oAt406DNaTmmcvT8coJRlupKwe73PxRYvcuH+Wne2AQVrw5eN9loch3WieCxdn8U7dZt9HKK1Ks5CYoFU7cxNYIaBNGw+jreq48muHWL/36Sqk+3z9dTV2U+e5Oye6idtreqynxrwbKr5+ashCI8AYnzJq0fZlLV5hVY5TRDrAhC2M8u1Gr2qVchvOJz76+3nf332Uw6sDzl6as3yjW+6nUgUXumD3fJsnff+DeN0b/oRjJ5d53e/9DkYJi84skvq8ttU9cJvNyCJJk/6mdarDSRsnRiPHa4TtPaynmpnAImNM2ESMC5twqcbL86CwSQi7OIrJxlspm1Cr3pcfbLIPm96BdC1SUAeyNskmEAZEntTq22eg4abfqTv/Gaiwas1XGxnYzE86jVxySQXhxNOmueG0F5HLiEBoCiQq7qLGPRpezqD0CJTkrkHBvuY8auM4L/m55/Lmt/1Pfu83XzE1ZgUmbNl2Tl1sQk5qJB/79GfQWnP9LQf5509+hplOi/mZGcIw5Cs33szunTt48Pc+gB978hOJA/dctQDWJGip7Vn1nouMnZwmFzsw0iJ0jR/VPIJIj/7aKW657Xb27Vy0SaJ8NPEFVeA5hbaog1X3O0vHkaB0xrGxREkwBo70EvZ3I7SBuzKP9SRHG4OvpBXIUgJtrE+6z67/XBs3bPu8bZ+37fOqz2/7vG2ft+3z/t/2eYGyCexW6NEKrT6DFILIs8nUhi+ZjRQNWSLKpJ4D02tNG8vhLbA+sKIgMkpPxlR5UEyE9kxo77tGsAJIifbbyHRo5/N08cBojN8kEwFKgCetiLMYreMv38L95vez9OgLObZhaUBGeemQ7z5KCkJPECiFJ6HlgUgnPk+MewRgW+nNhNZjkqSZQr+6IlItslfRlUzZjsRvc3KQo4RgLlYgQtLcABaxPS5MXbDoBPaetLGF9aQoCT3JrpkIJQVHV0esDjLGeUnsW7uRFCUr45z5WDHfnEMNVyd0MWAT37UY39SLmlqv9t4DhEwBwclxwZX3fyDezoDg0F32dbiklyk1ptTokU1267xA+CcQQYQqcj73mU/xC0//YYJ7ncePP+7hvPfD1/CWV/4cP/HfXsff/vTzAHjgy99J2ZhhtJGy3kvJ0gLlSYLQ49TRHmWh+WTosbsdMRt7HNtIybXm9Cjm7JkG53R2oaAeZ1Fm1s46lL0R8WbqB6MnaOsqye35oNWE3mJLfHAGj/G0j5vyeZWtOKOwCxNUeIV+LjOk8FgeZJwe5QghmI1CQinwRCU6qZFlupnyxyVV7byzBYsnPurhvO9DH+HQqXXO3rUwuUcxKUgKY6zYMbB7cZYnPeYR/P4b/5S7Tizz33/nVRhhNicv6zGt5oQrpFZjWdusYFI8cQW6oBXRSzWJgG7YRI3X7Wcr/1bFhXeD1J7Wf6g5wIXc1Elxhs8DanFMsIllqSZFnsrfuYRzdQq2uL7JL7bc07TA8jdKjFdjVZ2/LiYpKg0Mg+VeNwY85wMCIPA9BrlmOREsze7FO307L/2ZZ/Hmt7/L+jxhi2xaSESh6oKgna/2PLos+NhnPo/Wmhtuvo1/+tRnmem0mZ/pEkYxX7nxJuvzrro/P/ZDPzhJetdj/Q2KN2WBGq7QrrUHZJ3InubU7vc3uOX2w+zbvevMc1Tjb4KJ3zMTO1lrihQJXpmwkvmsjAu0MfSzkoavCJXg1Lign2oKbdHPo7xECkHTs0KZ28d//Oh0Ot8UF/bCwgJKKU6ePLnp5ydPnmTnzp1nfP7gwYMcOnSIxz/+8fXPtPONnudx8803c+65597ttfI85xOf+ASveMUrOOecc/4jj/O/Pf5Die5BaqtxkZKuiqzohjbosRWbpA5iNgUlMDEE1c+EpHD2yPMqVfIcHMcXUIvWVJx2VUup8SJ0ECPLDOIZy51ltBWjGvcwfkRWGpQUeFEbygw1XAFAjnvsWbCB9I6oyQV+bkWEgiYiX0EOe/X9VptCHVTq6r3NgatnVZeRNlDVQdNuyioUgPLc7yeBoPFD93/MqDA1qqBfSk6PcgIlUBLGbnBGheHkICXXhh3NgLw0rIxy+q6FOvIks42Afst+v9QJ6ScFaaFZG2XcumL5Pu+9q83ibBO1fmyCOpg+6uSE2fTeplE+/cGQhz/qEbz2g9fw8Ff+LOnpd6PzAul7Vpwrt+JcnjOmOi8wyRATRIxu/jJ//88fJzZjNAFCKX700Q9mx85d/O7v/ToH7nE5b776I4w0vOeLR7lpPaEsNOP+iCKzYk5hq0lnNubo8oBX/u0N7Fls0o48RlnJYy7dyUJeMhM2aSxcQuxJlM7wVg65AEQ4IazICo2UGca1lG0ylZVTrPzpdCV1eoM41RJYt1dXG+/pOV8FKdMbyWqT4wJ2mQ4xuqDbjlBCsDzMSAvNQhEwCCTdMKTRDBiVgrgxi5cObDJKWA4wWQzdu3PvTJd87kvXkZZAuLktXUhvIsDkRZgiY31jwC//5u9wzrnn8brXvQ7l+H7rOe1aII0f14itqmW4Dvik4+WsNiFeCKV1zGExYqUIuHU1RQrBlQvzGBXUm9U6sHRotlqZ2bWhGeVPkHNTwWS1KarVt6ffoVt7doM1qWhbjkR/s1O+u43/9HGGA68mxyTos1x1TuRDStBMWtndu6la37UXIbMRYyLWC0PoadeW1WIpH9LyI8bGZyMtGc90aMpTGC9A+VYQTUdt5GgNE8+QhF2Olx1mIsVs0cOM+7zxbX/JP/zzJ3jIA+5Nt93i0vPO4md//InEUVQ/T54XLK+s8p6/+0ce/ISncO3/+ls8z6MoCnZeeG8OXvd5uu3WZDPk/k4YjfEtAtQfnpoEM1AjEIb9Ho94wg/zKz//HITbwFbzo+JFrBBZdYBdBVGVcE+ZIdMhqnecuLGX91x/kqdetpML5mIybe3memLtmBQCT9qWNikE57QFqfkPubZveGz7vG2ft+3ztn3ets/b9nnfDT4v1zbJoLVhNvbphB4NX9INpRUlA1sIyota3KxEsrWqNF3cLY1N3PoqsKh/XVoNBF1MkneuaFolhysbXFFiISbUQaKw9Fgy6eM358m1E/bzQ4js2MvBKc6JZzh7QSDKdOLDPA+R9xDD0SQppYv6mtYXWE7mijJlOqE5bdPqJL2zbdP8s9VnAJLS0HdxRKEtSl8b8KUgmkrUHO+nHHIFW4BjGwnr45zAsxogStp/caDqf6EnWR/l3Hx6wNo457y5BufMnU3YP247U6RCZsPJfJNb5ojRNvnt1rAoEvrDIQ967JN529V/x4N/8yX4X/0S+TCxvNx5gXIJbyElEuvz8v4A2Vqn1PD313yKqByBCwme8gOPYGFxiTe86bc579J78cq/+CAnxwVfOrTGjbecZri2Sp4MkZ5POogJGi2C2GNlfczV/3aUUtvJddZCAymsgDOLLebmL6LlS9RozdpVJ9Bpoo61F0nfCdaOJza06l5yooZCaFsknPZXW23f1HvfitDdWuSt/dGWn1eflaM1ltq7uCv2uXXFzrO9HUsbNxs1aPsShZ5Q1VXXdrRdm5LGlc8rdI2Q3US7YnRNmWSEZG2U8iuvfA3n7N/Lf3/1K5FlvqkrauLTJknn+pgq5FbjZ+/NOJufIYuEjUxxeD1hLvY5MLuf1vAEIulvEqn+hpRSlb2cLqJW7wYmIJKq48MVXa2Au57oRaBrmqjpc1bAkU1Cpnf3vrfSekx1Z9YdbUZPxJXdWG/qmPAsl3ha2kJWVhoqEjeJ1fVpOLN5YpghRMBOPwZjubtN4PQ3nL8vZ/ZwYqyJIsmsGcBghTf9+Tv5h3/+OA95wH3otjvc44Jzed5Tn0wcBvW8yfOck2sbvPdDH+XBj/8Rrv3Hv7M+L8/YeeHlHPzqF+h2ZzbNARsL2mcTZT5Bllcxgxuv0UaPRzzpKfzyzz0HUdE2TttKowFVj5Hx/IleQdCoYwiRDmwnk5xjeZjSCjyWmj6lxnUpFYxyW/DzlaDhKxq+ohNKAiXIyy3O5zvo+L9FXfLNHkEQcJ/73IdrrrmGJz3pSfYcWnPNNdfw/Oc//4zPX3TRRXzta1/b9LOXv/zl9Pt93vCGN5xBmTJ9+L7P+9//fl7xilf8h+7xmzm+6cjo1hXL0XbeXINdLctdKtMBIrMGuQ6kp5189b37WdXCrZEYV/UvLYmb20DnVn3cibDUHFjaKrjruMtKamgHikBC0txBIOx1RoWhpVKr8B21ifI+I7+NFpIwaGLSYR2ciGTDbuSF5XsT6QCi7hkK9LWIRdRBJBuT6h3UG37jTRTUK5EVoctaOMGE7c0VY1d9JhvSaDXohoq1pORIL+N4P6XhK3a2AxvYCOiEklHmcevqkBP9FF8JVsc5kSc5tp7QjjzH3WYD+qwoefAFiySFZpgV9Sby724+zeU72+xo7mYu9mgoYw2MU43+yD/+E/e8+CL27XJcPGazM9roDzBCcNmjnsK/vfyN3B79LjsDz1bzlaRIrEHuXHAApEQ2O7atDND9dd537VcAKE/dhWwOkHETHTRZvfM2jt9+M7/zujdy4VKbw72EH7zXbq7thFx3xxrrp4akY0WelmiXIEnHOaONlCIvSUY5M4tNPn3bae65d4avnuiTFZrvPXuO3e2As1uL9t0pD5GP0WHLciCWFjEmytxxmLnAoHBciq6lR0ftuopa8zFOO4VqjjrnXItnVcdWJwoTJNJ00JyneGtHOTC3m2uPrNNLcnppwVIzICt9lpoerXwD0gLdmKUvGygpaGYbGDGarDOjEVJx//vcixsPHub8iy6uHfyRI0dYHyYk4xEzjYh/+tTnuOmOO7nl9jv4b7/8SzzovpejesfsLVVINtiMfKiewbVrmbCJkR5y3LPIUqPRcRc1OFU/nxyu0G3v5WMrI3Kt+eJdmocfmOe8pUW807fbE+oCyEAIiwLyqg2qcEHElk3EtG2pNkZyyvZMUT7Y8bYIN9w9bg1QN31d/f6MFzf1Ga2tD59GaOliItini81oiGp+ebYldSAixrnGGDg1Kmn6kmGuKYMGu7MhKgwY5SUnRwVna83jf+wZ/P7vvgYjBCupYcGPWUs1n/rYh/ng529iZZjxA5efxe1f+ASdZsTf//kfElab/Ls5fN9jz84lXvxTP8HK+gav+r0/4Bdf8LP8t1e+mv5giEZuft4q2DOToBpdbGqDt5zTDZ734p/l1172Mp7wqIdj0j7C2V7LaTtBzYkyt63CXlwjVkwtNpdhPB+Rp8yFbtNrrL9o+JJ+qrl+uY8vJZ3IYy6yYz0XS5YTwTAvKI2h+58AuG37vG2ft+3ztn1e/QzbPm/b5/0/7vM20pK81OzpROxoenRChacdRUxqCz/TyV+MRk0lhqqcQ+33XJIbmBR8dVGj4DfRCWALJWnQoZeWKAEtIwn9GBM0SEuD8ARBhLXj2RCZbKDCDggPE7ScWHNqx7lIEG5NncGLXvmn6roV1UDFKy/kZtHVqWT8JhS32pKkrxLnU+PT9CXzDY+0MIxyzfFBSjtQzES+TdJXRQttONZPkMImc/ppwSgrGWU2Sa6k4IKdbUpt6CcFseNLDz1JUmiObST0kpxh1mJ3ewkvAF8J2rKwtgn4h2s+yWUXX8C+3Ts32wsDlAX99XWMkCxdfCX/9PGX0gtfQzzTcgLMEnIo84JwpkW4YxciboIuMXmO8H3+6qMfs+9/tIEoMqTjH+/feTNHbrmJ177+jVy0d5ZeYingAk9y+2xMkZdsrI45eettFNkY6e1gsJ6wdnJAMsyJmj4b4xkCT7LeibhtZUgn8tjZCrl4scW+hVnUaK1+z0Z6JNEsQWMWtXEcMe5tSloCE4onV+g1xp8glLf4s+kiR02NUlE2VX5wawJ8SzwojIF0iK9WmIs7DLKS3sqQUV7SDT1KE6KkR8ufJH513CXVgjDu2mJ1lWTN7Xq6/33uxY2Hjlmf597nkWN30ettMB6Pmem0+adrv8BNdxzhltsO8vKXvpgH3u8KW3ybEmqcdBY6SowpSqI6se2FLvHs6G6qrh3l2y4nIA52cmwj4R9vXuas+Sbfd/Y85y/tQvVPIse9yZhXw1R1Bbq1KO7OB1Wfq5LwWtdFebSuO7Wmi+2m+r2cSnJPHxVd0vQ7qq8t63uxtGJTnYyVX64KdtN0X9W1lU+hIorSdiaWxpCUVQEEhDCEWoAuCaQi8pQV9lUBP/CMn+d1r34VhYoopbVd4+GAD3/ww/zjV27Bl/DA83dz0+c/QacR8g//43UEcVwXtsUWP+97ir2Ls7z42T/Oyto6r/q91/GLP/88/turXkN/MEBrXXd+Vfdfzb3J3HfP6FD91n8F/Mwv/jIv+5Vf5gmPfgQmH0140beOp3EUh9JDaI1BW1FNL0IkG0hdItIhqjlHaaATesxFHhuZpdE6tD5GCUEn8oiUpBUqlLR+JXNimd+phzbm28ox/q0k1X/xF3+RZzzjGVx55ZXc73734/Wvfz3D4ZBnPetZADz96U9nz549/M7v/A5RFHHppZdu+vuZmRmAM35+d8eTnvQkPvjBD/LiF7/4P3yf/97xTSe6D8zZRbej4SGLBNKi3gQYL7RE/C7ImDaaWzc9VQBUGmuoPfREkKRCyLhgwv7BxDBmRrKR5vTSkgMdjzAfcTAJmI89Vsclx3SbAzPz+KMVq3AbthgUhsALHPfiyAXmY0zcnRirsIUJGohsuAl1VG1qKLMJD5Nw/JBVQFRklldVF7ZtWzm0R60WPtXC6f6vW9rHPRZ1wUKrTd8ErI4tak0OBd3QY5SXrI1zDq6NbDth4BF6kq42rI5zltoWKbenE3HJYpNeatW4l4cZo7zEV4J93YgdrZBQCQIl6GearxxaRwnBhQsNDrQblMLjYU94Cr406DzZLGLiEiUf+Og/86yn/hiHV0fEjQZzkSK68HKCjVVka4bk1q/ZCn/cxD//cnRz3jpMXaB6y1x2kXWEj3np7/LcpzyRbrtFK/B469vfyZ+97lXc9wH3II8UQkTkpeHyvTM84sIllloBh9bGvP7DN3Hq6AaD9YRKWO3U0Q1aMxHN0GN9lPOvt6/QjjyOryf0k4JzF5vc0Y1Yau7krGZAK1tHpgNuGwUUWnD+3CK+HyMTh2h07asiG4OWaFdVrVowRRXgTm/4YdOmbxMX490cZ7S2bfn+QDfgy76ilxS0QkPkWe7D1XEJcYdmsoooc6LAbhIz1aY7254IdbkA5aoHPYQfec4LeM6zn81Gb53+6ilajYhz9u8lDEPuOHaSy6+4Ny964fM5sHc3KtlADFecaEZYJ64Auxnzo82oSOfQjRdRNuctEq1IJpVcMWkpRHqESrCzHXLTqQHv/qfb+ML5qzz7AWdxzx1n0y36k8pukU4q+H48SWS4n4lp51ltRKc5R6v2++mk4daxnl7jLnCbRkrY65jJuaskUNUuX+paHMkKgbggY0psCenZALosqNrrdNgiKw0e0HDvNS0s7+VcpNjV9Kzg1TgjEJpzZkJuWU04K44556x93HXkMMNcc3iQ8NcfuZp3vv0veOQTnozsLHDhAkSUPODyS3jyYx7OtCDdGSI6U4dUkle/9Gfxz70v7/vA3/G8p/0oAK/9wzfwO7/5G+7vxSZetur7TShBd+7jy6f55Kc+zV+8/X9iTAFpHx13kUnfItfcGqlQnXaeWZsoymLSLuhQIghJL4es0Jwe23WVpoaDKwOefp/9vP4fPkd81n5aoWI2UsxFitLAUliSTiTzvqVj2+dt+7xtn7ft86bve9vnbfu8/5d9XjuQzMYxc5FHmG0gxuXkWR0atk6Qlrrm7kdspi8xQO6GXgkr4LlprtQJtqAG/YFNnknhEhjGEPuSRMWcHhSkpSb2LIovVA1ajQCZjVECxqUhjrvIckoAVXq2aAfWDwI6bCGzESYd1IJotUiy0c6Hmc2o07JAKA9T2UL3s7s9tiRTKTOC0Qq7lA+Rx1oYcXyQcnKYkZeG0JPk2pAWJRvJpGPJl4J26KGkYGVgC6oH5hucP98k8iSnRxmlgbzUpA4FPhdbarVca75+ekheWlT+vk7IUncPlBkPf/wP4yuBnqIaquIDgL+55tP81E/8MEma0Wg0GOeGuft+P/7JI5hRHz3cwGQJsjOPv/8CmN1tkfRFhhqvc1nfAH/Go5//Sp77lMfTbcS0GjFv/Z9/yVv/4Le5zwPvRyE8OqEEWigpeOiFi8w3Au7sjfmra1scufk0eZI7XD1IT+L5iiIrOXx6xNgl/0ttmGn4nBikHJhtsKPVZCHuMO/lyCLhVOIxyjXz8RLzYdv6vArVCy5J6oRDXeximEoWVvN+C3gBN+enRTynk91n+Lvpw2hk0mdHd44Dcw2WB7bQb9+dYJwbAmmIpALhM9aCfqpp+JJW1Ladc2Ve25OrrnogP/Ksn+U5z34W/fU1NnprtOKYc/btJohiDh07zuX3uoIXvfCFHNi9ZDsAU9sNVWmuVJ0LlQaHEaLmIxdFYv1ja5E86qJMYTuf3FjJbFzHTiJPme0ozp1r8JmDK7zrqwe5du9pnv09Z3HZjkW6jkruDAHaqjtmKv46o1he3ecWv1TTXyAn1F7TnZXT3P9g/Vj1feUHmXrH1dxw67sCABjjOsPcOeuCSGU/KnvhRGoxWN0Jd/uFNnjSIrkDJWj5EjnuE8Zdmr6k1KCb85xz1j6OHTtGfzgiwefd73on7/vLt/O4J/4Q0ewiusjI85wHXH4PnvyoByPqRLvT/tkyT6viohTw6pf+HP6B+/C+D/wdP/P0Hwfgta9/I6/5rVdhJJZqUAirVVMXdUQ9NkY4HRspOba8OvF52omUq6nCwTRqXhiHpPdAue5UL7J0P5Wtxhb6lgcpSkDsxQwzzZ3rI37y3vt5+dWf5N4XnceuVsh87NEObZdLVmqKbfaS/6PHU57yFE6dOsWv//qvc+LECe51r3vx0Y9+tBaoPHLkCFL+OzbvP3Ccf/75vOpVr+Laa6/lPve5D83mZr2RF77whd/SeYUx31yKf2M4tvydRWIn8FQba1X5Fpl1llXFp14gsEmoRBswxi58pbM6AKrFkpRft6dtUkZ2Fc6V1CCAo33Lc6W14b572jbgKQxLKkUUKboxy0pS0g4U0fAkskJZBDHL4U4WvNxu2qMOmYZodMp+ZrrKqbwasaKDuK7mAZM27a1G2bXWVG3dm4zvFCKgrsKVGUZ63Jx3uXllSKQkZ83EFNrQS23bRqRs28Z8w0cb2+a2MrKiMaEn+b6zZmm5jWDTVYQT1wre9K1xHeaaflYSSMHOlo+fDSbOxrdCRSIbYYIGJRJlrBhLqQ0/8IM/zLv+5HVc/cmvMExzXvDTz6aXlsyElfK6nRtytIbqL1N0d4MKrJquLjC65L1/8yF+9zWv5llP+wmM1mysr/KUJz6WS84/14my+ZStRU5lqm5XWWz67G8Kbulp/vvHbuNrN50iGWZ4vkKXmrgV0l1ooISg2/CtaMs45/wdLRY7EftmY+7qJRyYb/ADF8wTk3P7AK7+2gnOW2hy+c42580EyP5JOw+V59SJXaupmlT56w1hhbTauqmcfr93h6BisimYrj5v+l56FHP7Ody3iBYhBLEn6KXWWXVDVfMDAwTGrh/jRzaAcG2KJu6S5gV//7cf5L6Xns+Bxa7dXCnfvmv3tcyGdWuZ8Rv1HK8Urac3w/XGz81n44csB0uWMkEIduUnKNs7EHlig0ohEUkf3ZynF8zytZMjvrbc513X3IbnK57xsHO5354Zzp0NCYsRIu3XtkC6wNvUm6tp7sapNqlqncpJEqZOyFXJA+XX/K6T9+M29RXP3PSzVq+y2tjWL28zSsMob1NQVSdeqvfqKtmWO86jFB5ebjdcxo84Mii5Y21MXhoOzMWcG2V1QhEh2RAR771+mede3EAkG7zzve/nw//yKdaSkgc/5KFc9YNPZ64V8ekja5Ta8NTLdjK/ftumzf3dBt2b0Av2+MCH/4mbbj/Em995NXedXAbgBc97Lr//O6/eNFfrsZoKgKqWShM0+MWX/jJX3vsKfvJJj6439oBDSGpLO+A4N2tRxSAGL4Ii4aS3wGJoagQSRcZqax/v+MpxLtvZRgrBiUHKKC950cMvIx32uephj+SDH/gbWr5NpJQOOZ2Whrl248zn/yaPbZ+37fO2fd62z9v2eds+rxq37waf50tsYXc6mTuV3K+RjO4dV4XM6j6MMQhhxUWBu/d50++j8qsVlYvySfw2p8c26V1qw5FeSmkM+7sRDWfbu4G0wrZBTK/0CJSgma5b5KuxXRY67oKQtuigfAq/gZduoAanbQHCCycI9a3Jazn1s7ubU1uEp+vPTSdJq7VjbKK/7O7iCydTDq2P2deN6YYeaaEZ5SW51nUbvq+syFpaaJaHGf2s4OyZmIsXmzR9SVIYK3AtbOGoSqI1fUlWGjaykqI0NHzJXKRsh48XWNsyZc8q/yfSPqU2PO5Hn8ZfvvXNvO+fr2WUFvzsc59FTG4pm1wBDyEtJUieYqRCx11ri8sC0gHv+9BHefXr/pBnPuMZkI3Z6K3ylCc8lksuuoCqGFi2FrlrULCeFLQCxXxsqZsOrqa86VO3c8NNp+ivjimyjLgd056LaXYiAl8x1wqs8KE2dBs+5y61iANFPyk4azbmQWfNsrvlcdNKypePb+ArwRW7OpzTDfCHpybo4TzdZFPr+T1td2Fz8nAqSVq/8srmCrnp68k88Tb9rSgzdNxl1NzJyVFBWhiEsGCIca6ZiTxmI4VwBR8loOV0KwDrq52tN8onxePv/+5DXHnJ+RxY6tZFKR026wKuKBJXzC5qP7Z1zk9Tb0wjlXXYpBfOc2pUMB97zBY9F+s5QfV8jEz6lO0lTtPk0HrKpw6t8qHPHUGXmh960Dk86vxF9rZ9muXQUqeV2WT8t66f6Th7KkaowBS1zyuzTYKMxvMxfmMTCGU6xrbv5W5oWaavXSVoq3lRXa+iJ3Gis/brvI4pantQ+W73DKlR3DUoOLg6ouErzpqJWIgVUTlGZEN03OXISLCeFJwzE9Id3mV93sc/yyDJuN8DH8wPPeO5eJ7HR245RTPw+PF7LtFeufXu0fFbj6mCOcDffOQabrr9MG9+5/smPu9nnsPv/97vbrZ3U3+/aXxcDPCSX3kZ97nXZfzkDz7Ozsta3Nm9BxXU1xbZeKIDYzS6OU/fBLSURo571jZ7PoflEtfcsYovJXs6IWvjgrv6CS97zBWkwz7f+7BH8ldXv5/ZSOEZ++yZkRhj6La+dZ/3f+PY2Nig2+3yj1+9g2a7/W277rDf55GXnUOv1/umOLq/3ce/x80thOD222//ls77TSe6k2F/k+jG1g2NyK3iuQniTW1e0xt+PRUE+dJ+7+WjTcGTCVsTR1PY/w/nsVUAjxQNkXMilYxzYx1+qfn6qSE7miHnzYUYYJwblkK76I6NoBVIZouebTV11fuyvcNW9v2IQYFtF/ME3soha1Cnlb+NroO6Shl5uioMOFX4uxFZcJvGmgu02jA6Mv4KPTNSMdcvj1hqBszHitiTjArNyWFBK5Ac28isAI2UNZIDYJhr0sIwFysCJRDYFrdYGusIKufm3okRwrYkl5OK/rQhN8q3SL9q41tmfPSaj/P5L32VX3v5y3n6T/88L3zGU3jgRXsoFs/DP3kzZXuJ1WgHd/WtOvzudoAnBb4UZKWhHUqaySoA3/eEH+Xlr/xtvv+B96+Fwqpr1ZszP0YHDeQWNOJdqccNy0P+4rOHuf32VTcNDUIKhBQ0WgFRMyD2FUoKWpHHYy7dyclBynVH1nncpTu5754Og0zzhWM9vnRkjVbk85P33sNC7DETWMShHPcmL3CLM9kUvFbf14tgs/M8o/X47o6t53GOUzdmOZpb3t2k0BxcG3HubANPClcpNnRDxbybB6JIKFSEl4+QSW/CV+bHyPFaLaBVdvfUAnFyuFIrwFeB0aQdOXHPMNk8T88PEzQxymOgPZZHli7grLZirAW91CaWYt++99Nqhn5mA/ZD62P+4tOHaIYeF+1u8+BzF7j/3jbddGWCHNS6RpggpEWm6sLOZTEldgWb0Gc1Mq/M6+RkjcANYkvdMI3CmBarceeaRrptQvhNJQuqaxo/ItUWNSqqebwVqSCkRXVViI8yY4zPONf0Us3RjYRd7RAlBOd0faTjVUZ63JzEfOjrJ/mlKzqIfIQO28i0T7+xEyXg344PCT3Jpw6tcuFii0efO0Nw8qYz59zWJNP0XJ76zOGjd3He9z4agOc+9Uf4s3ddzeGbr2fH0tIZY2Cv4VBuQmCCBlmW8eQnP5kP/9XbUMnGpk1gvel33HWARTNWm36Hvvq3UwWXLMa0No5am+o3+ELP57oTfRq+ZJBZrra7eglmvMFvPvF+APzYTz6d33/Dm2j4iqTQtgUukP+pAGjb5237vG2fx7bP2/Z52z7vu8jn1cUtNy+nx64uhvmh7Wxw3Upgk9yVkLCE+n9PCltALLJJos0VTESeIIqUsjHLXQNrOxYbHlLAiUHO6VHBbOyRlYas1DR8RdOXNHxhOcNdYfiuQUHsCeYZooar9v04rmbcHByJkFGumYsUXu+Y9XMVmr56zuniUWX3plDAdaFv2rdUPq5ap9OaD0I6YViJKDLyoMWRjawWpgykYJBrhg7+roSgNKYupAXK+pN+VhB5lpc2UlaoNJCCUDpEqy4mCdBq3haJfQZXzNzE6z+1VtEFMu3zkY99mn/98tf4lVf8Js/96efys8/8SR540V5M2ERHXYwXMNAeK+OStLSxSDe045K6BP28XyKKjIc+9kn8xm/9Ng/+nvvVwts1YEBKTNBCB5Zywdq5UZ1UvaWn+eeDp/n7L9/F6vF+LVSplERIgR8qGq0QrQ3ddshl+2dY6oQcX0/IipIHnD3HpTtarIxyPnfnOrcvDziw1OJBZ81x7lxIW7nOivH6Zls/7Ze2Fmy/UZG2Whfm30FyT5+3SpjqAh21Kds76BWSUa5JCsMtK0O6kU839PCcaOpcpGhJl9hzTfgBRU0XYhyliEx6NfBCN2YnfiHZqHUmkJ71eSqwXXuu0wGmfMNUcbjyeacyxXpS0g4lC249DnNNoITtsjAZvdLjcC9lbZxzbCPlo9ef4FQv4UEXL/Ggc+a4bEeTGT2o4+Y6VgNqDmeoeZynj+n4dNM8LpK6yFsV33TYnnQcGT1JTE93amwpbmx6V9OFbS+gEB6ldvo3ZnOcYOnDvIkvducUbk0Mcs3quOTw+pjQkxyYjdnZ9PDHa4h0gG7M8uV1xfFBylX7Osz3j2CEoOzuYoxPLy050c/ppTlfvmuD8xaaPO68WfzlW84sqnyDRPfWAu/hu5Y576pHAvCcp/4ob33X+zjy9a+ytGv3GZ89Y95LSZYk/OCP/hgffvdbkW6/UhVH6jGeFgCt4kmn3TNo7eZYP2cuUiyU66ALdHsHXz015rN3ruNLK4i8PEjpJwV+Pqh93o/8xNP4gzf+MZ6UNQ1H5Mn/VHH3/8ZRJbo/8pVvf6L7Mff6r5vo/j91fNPUJWe0ptaLSzo0UDLhGPwGG34loHQySNqA0pPARwe2daxAgoootSH0JacyxacPr3Llni5ZaTjQ1LR8j9tXR1y1OwJj2HdghptWUo4PCs7vQBwHlChGhSYtC8ig22hbgn9XeSoNEDTYyDTjQtftQfuCePKI1SJ3Vb1acKuq+ikPJJPnnhqX2snUVVKfSnW5EhxCFzZJogJ8A/dYbNAQOSIfgJaMS5vsaPmSe+1ooJxcsMgTMNbALMRV67tB6Ny19EjItUULVQ7YOW5hcJu3LQmK+vfGCgM4Q396bYNXvfYPef973sWHr/kk5569n6vudQn65EHEicOUZ10KR65ncXaZ+ajLoeYe7lhL6GcFvrJB2UIzYHdrlpYs+K1XvIx3vvNtPOJ7rpgaq8Ap++qaY81LLCrDeJHlfM3G7IqadPd1uO+eS/nCsT6fvmOVrCj5/E2nWD05YLCe0GgFeL5CSEEYe7xnlHPZ/hlWBxkfu+UUg6xkvuFTGkMr8hlnBTcuD9jfjZlreOxuzRIYXQcRdUAIm3i4zlwgWyr+Qm4OfLY6kLv7e2MryQJQg9Ps9y1KNO/Mszy0AUTDV8zGHq1A0Q5U3Vq2nPscXB4yF/vsaO6gqwrG+DRMaqvcRW6Df+VjhEQ6ZKNxQk+1SItUk4SQF0AVOFeO3wnlmaCBHK3RbMyyvxMwyDRfPW3b8CJlBWPW05KWP8OJfobW0M9KfCm511kzDJKCpU7Evx3rsdQKOH9ugeb4tL0P55yrtVM7ygoRUeUgpgUyXBtttfbqn7uNJUWGkB7GnwQ4d4dcq1FbU++i/pywiuA2GQAFtk00UE7YRhYYGZ4pfuLWYFYaktJjXJS2E1zCjlZo0RtYEb7W1Fy5YXlA4FUBhGbFxMwHmqTQfPJwjz2dkIavuGJ3h/vtaeOvHJqgT6pnmG7j+9/Mv7P27SU/cn39oyvueQ8+9olP8WM/8kOTc1St6TWKwqvn+Y1f/iJX3vMSZJ6g/bhGUdr2/HwSdFXJMD+CPKn5DuVYM8hCTo0KmlEHdMEx3WR5OKAdKk4NMwIlafiKxVZINLOD33rH3/KKpz+R9/zlO7j8Pvfl6c94JrEviZWYIEO+xWPb5237vG2ft+3ztn3ets/7bvF5dYLv7saueg5pfUM5Xcx1/1eHhlpzYRNtibCIwFIGjAtN7DeQQYM7+zlfuqtP7EvuvavNXKRo+JJOpNjV8lECVpOSgfNdSkqkMHgyYJRq1pOCzFe02h1CrN3SQVzf51rus5bkJEUJBCw05xHj3sTfyakkdrX+pgu603NsKkm2yT66fxOph2lOFlv8SQrNTGjpZigzMhGQa0OhDbFnxa4DKRgVdm23lEVE0nRis5V9rVHJWY2QBSZJwOoZwAp6VmtiC1q2Kmyd6o141Wv/kL9+//v5yD/9M/v37ed773k++tBXEXETvz1L2VthtjVDe+dF3Jn63NVPuXmlJC1s582udojXDekGHr/5yt/gnW9/Gw+59yWTwpv0JpzmurBi2dOFZyGRozUubMTsu2IXjzl/kYOrI75+asD1R3vcftcGvdMjirxESkGWlgx6CcO04OzFJqU2DJKCQVJwfJBy9kxcF4CPr4+5sTHAV4LFhs9M1CUUwvFGb+5u2SQqOL0mqnGb/r9eGmciw7euoZpeQvmgLV2Yt3aUOT9kJmyTRE1ODq3d7aUFvhQs1GhhOwfHmeboRuboTizthSeofSKeKyRM+wOwmg4V4rnq2tNThR4A171Ud0A5sUCZj1mM2gTKUsEc2cjYSEqkhNhTFNrQDCRZWTDKbVeCrwRnLTSIA8UgyfnK8Q3agceFC20aVXdS3Y2UT/xaPTe3dNFMU4pUYyg9KoosppHWlXh01XHifJPZUrTb8gLrhDXY+N52tHlQ6epog6e8OoleoZ+NZ9ec7Waxxb5KgFYDgRLs6UT4jkqvekYTNDB+RC8dkRbaFgmjNkb5jPHpp5pBpjk5TGkHHg85Z5793QBv43gdH0wX3raKfJ4Ru7n5sH//PrJjN1Ml/+992aV87BOf5Ck/8RP22atjK8DHvYPrb7iBKy+7h7UdfjgpDOoCChf/5yk4ZHelPyTLHGPsGksLTS8VzDeb5Cri0HrGdSf6LG+kzDR8wOrwzLcClJzn5W/7AL/97B/k6ne/k3tfeT+e/sxnoqQdz1htfu7vpOO/uhjl/82jwmAL8Z9/v990orvmYZzm3pGyFiM6o4ULam7SXFuOIuQE1abMxOHW/IJeUHNDBkpgCDi2MUYKGGWWf9NIj07S46rd7RohIIqM0AtYGeUc80P2xBmFDDg5LDjeT7looUGGFfOqjIMn4NS45NaVMQfXRqyPc+61q8Pinh0EWCMiB6dgyuBX9zv97HiR3SgVKZVwE4CoCOpcFXWaS7JuSRXStQ4H+NnAsusZjY46jAuDMnZDsZqU7GxKRqVVWW8FDQIJhbF8r6kWhMK4+7HvqQ4u3PVx5zbKn3DTufbXum15OohyRvHzX/4qF110MTt37CC+4xjtZgOZWY4v4iacOEi5cgLdX0ft2Mf+xZh4YYFj/YwjvYQ1x8HaTwNkNuQVr34tv/fGN7NWBrTiyAbCDk1m/BDyFJENpsYnqZ2SHPdoOaP7yB0eV+07m8O9jKfeey9HNxLe8sk7uPOOVZJhjvQEeeoxcvx2Qyfs8rnbVziw1KIT+cSB4vj6mOODlIVGQKNQbKQl3cY8vpBWsM1MHK2BM6vNZtKqS13dVJv/B4TZHBjdLWKg2oxWqJDCtnsG2YiHLnZYUw0O91JuXRmxp2ORb7EXMCM1C7EVM7n+5JCDq4azZ2IWGhoZhIShRZ1WvGYyG1ukkdsITLcjmrBt57SQmKCJkMMaLYJXCXWFFhmXDlBGI5XP6STi0NqY793fRQBfOtJjVzvkUJaQO7WIXBs8JYgDj7Hj2LvhaI8v3r7Kax5/Ce1wFoVgKXDoj9wFLCrARG1kkdUb/k1JR83EudYUCy6JEbbqjb9Ih2gxFQTKSWLm7tBLk8r+xObVVXwh8XRG7AX1Bk+ryLboGsulKpji/AU8KRmMS4yx6IDSUIsNnTMbUrq28SrQXh27lnDpgdYoKUhUmywt6UQercCjlxZcvqNlEyZVYqaaP9PzS0wq7vXzbbHXmxIeQvL9D34gz33JfyMOfK763qtYWFhEAH/0J28hDAN++rnPYTwY8IY/egNfvu5rnFw+xa+/9AUYP0SHbZAecry2WdBNQ8WBKWDCjWmsyEnoKYtsKWNuXB6Q6x6hJ+klBf2koB15HFodcfZcg9CThJfem/0HzuWpP/k0fuNXXsKdB2/lA++/mhe/6AW88Oeex3/m2PZ52z5v2+ex7fPY9nnbPu+7w+fVXO7V+EwlV0XBRJTTaJScJLi1sf5Ou/fsS5vkFjCZn9X4C4kUEChLFVVghRpLY2j4yv6tzpjzYS4KbBcM0AxmWEtKekmONgEN37atL48K+llJw7eibjrquHEvKA3cNbBJ9JPDlFIbLlpscc+lJovNeUszU/mm6tnNxNdXa7GeN9VnzZSvq/zKVKGqnlNG28KZb5PQLQ+QJSJLMcrDl5aWwnew+Ky0SW8lBKFL4NS0IGKKZqK6fl18iSZrQQhrh4PWJPlVda1MrY/pdWF93oUs7txDIz5Itxkhkx46tDY0v+sO0CUmHaOKjP2L59CaW+TGUyPu7I0ptUteJQUNnfDrr/pt/vufvIUVmsSRJBalpSiYTmhKD5GPrM33wPgWlSmyIc1syLmex4F9Ta7a12H14iVODnOuO9HnmhtPcvjwOoP1BF1oRr2U9VNDmp2QMLZUXhztMUgKWpHHYifixPqYW5YHtAJF6LXs3PXbxFWMMnWY6XgPJkXGylbUa2Xq63JLMkYoqkLHRPugSgg6e1olsYsMWazQ8Po8YMc8vdLjrn7OsY2EQVaQ64jFhkc3gK4qSBs+Xz895F+Ppuxqh1ww32CxEeE1pyjvKp9ntE2o1vdVxTt6kix2f2Oks+OuS8j6xzGiSFDDjE5nF6dGBcuDjKVWgC+t6HdpYG1sBRdHeck4LxnlJTONgKzQHF9P+MrhdY6vj3nURUucN9egFUR0Q1COjqVKrBshkNlwUmyunqfmjbYFHJH26/us77V0xcXSFhHR8aRIrQtbZKnWTvV6q46lSsxyS/5bGG01dZSk0Ia0tMU1ZYpJwtuNqcIVdw2MC02ubUxfGMMoL1GFRb8bcGOvQHr0kgIpbFci2p6r0JZ+KC10zb2/vxswz9DphzQ2+S6hS8tTPoVYt9zjassDbSm+Cfj+hz6I5/7CrxFFkfV5SzsQxvBHb/pjwjjmp5/zU4wHfd7wxjc5n7fMr7/k+ZNiiBvHrYKflprGdjTVifmggQRageL4IGN1LBjlfXppgTaGwJMsbyQoKZlp+Oxsh+TacOX97s9ZB87jR378qfzGr76Eo3fcyt/89dW8+Bd+gRf83M8AMdvH/xvHO97xDn7/93+fW2+9FYALLriAX/qlX+JpT3vat3zObzrRXXMETh2b1ODd5nca0VOpbtvWbft/aUSNQpkECZMA3CuTesEYP0JKWGqFJKXm4oUGIrf8d3K4AkLWXF//dte6dXDAntgiAA6ujlgeZly04NoajGagPVZHJVDypbv6XP1vR2lFHu3IY5RZIaz77e2wK3DOTwUuEC/qwGd6TEwQ13ymxm/UAZCRquaQrBxGdT4qrq3akaR1kGWkQmYjmkbT9DxMWCkhjxxSIcdktl3G1xapF4NtdS0nm/2aTw02OzSA6jO6gGozOoUqMn5cV/suvuQeBB/9R/KgxXA05oavfZXiyAUIqZDtGfKjB+vTFscOorKExT0X8/4P/T2iNcdonHL9zbdyx03XkyUJz3zRr2I6S3z80Dqzsce+bsRio01HZBgvYkiICDq2Db1CExpdcyJOV6mb5ZCL5xogJHvaAVf86GX8xZeO8Q+fPUx/dcxokBE1fHr9lG475NRGylwr4PblAXvmGvXG86a7NtjRtEihMlL0M00czhBV77bMahGmTYgN4wQq3Ndbq/x2jtg5o+VEyMkN+JYPbv7ebI2bkg3mzDqzjRZr3Vnu6ufcsGzbeM+aidjXtkjHXe2QG08NOD5I7aZD+IggIJAa40lkkWCMhqAxoRJwrf3Gi9wz2CBDpH07F0onUiI9dGuxFrjDC2zrqB+7dlKFNhB5VjX+ptNDZmOfSFnBncK1NzYCxSrwD188Wiuq/8sdK1y5u0s79FiMI8sjLBW6vWj5vDw5EQqq2timHKvxHBdpaVvLqmSXUT5UG39jbCJHF5ig6WBHLtEipP26ml/Km/CzVu9XOVE9hxYQuqiTmr60mzpRczq6jX+RYTzPiYJA7AlGuXFoX0GuNcc2Us6dC+mUg3qzrsMmS60+a2OH0pKSueExspl9LA9z9nYsAnih4TPLiLy5SOC4lE3QsDbmbgSTJsG62rxZq8cxqDdRZ+/fxzve9Dre9f6/5a+uvpr1Xp8dO3fy3r/+GwBe9NJf4dyz9/P7v/Gr/OpzfhwvtMFXvWFJLUJUx92a225T+yBTGzasvZ+PISs1b/3cYUptuGzfDKUxjF379o5WyJ3r1paO85I43WDPrp08//nP5yUveQmf+NSnOXTwVi6+8ILNqONv5dj2eds+b9vnbfu8bZ+37fO+W3xeNS6uCCLkxOcZx7FKxdNudJ04rfxdlVGrupk2HRVliZAIXRC6d6f9FpkrDDV8y8c/zQltlM9QNbltLeHG5QHdyGehYXmaU219Xi/J2dUKiEVJaTxODC3/8+nRiEPrY752tMfty0PGacHRc2bx5Q78pSYzYROZukeWalOBoioQ177EaITx6gJKPV7CotSF0bbwtBUNrAtEzsRnVsLNgCgzWp5H7Fndh1rcVnqg883FqC3vhqniblVcnvYXhjPpGirXV9tAt/YuvvhCgo9GpKVhNB5zw1evQ6/cB91ft3FKVumUaLTWKC9gdkeTz//9X+G15+iPUj5/6HZuueFrJOMxT3rOSzhqOqweH9CNPBYaAZ2wQzuwNr/QBhSooEMoje0mqrtqosm9FRmNwCNu++xtCu65tIOHnDPLWz57mH+94SQrxwcUeUmRlZSFRmtDVmhGWUk/KWzyfZTRTwqUFAyykl5SEDh0bdict7QpRTpByetiE7CVungxRe82bUem3/d08ruK6ZTc9H19VGjqymYVWY3w7s7tYqlpaWKWBxnLg4xFR/EG0Ao8upFFT58a2TnQDiQtj0kMMY0yn75n97tKh8MmihWCwCaEpWfvbVqXpsgs3Ypb391QIQUcWi85tpHiK2HR80IQKkmpDaOs5PblIWvrY3orIz7bTwk8RXK25vz5Bi0/wHM83SZoMpQxoRL4pdWROSO2qAqwbiyFKEC7OS89W+Ct+KJzS9tjHB1OVWDDaIzRtX8SRYKprjW9jjUTTnshkYAQwiakBWeuyamvhQCJQAqDkhYNro0hd4PnCWxsLD02ckvltbcTWYHKjTV02CSnySgv68s0AkvZRM6kcGF0rZ9jHKBl0/xD1oX+M+bc1H2fvW8v73jT7/Ouv/k7/urqv2a912fnziXe89cfAOBFL/llzj17P6/79V/hV3/6J/H8YPO+p6Jfc6KdVTJdaIeur96dOyqR+EFa8G/HegySnFbk0wgUjUAx0/BrCqdcu+LnqMeOHTt4wQuez6/+8kv5wmc/wx233crFF5zLd/JRGlvg/XZe77/y8Qd/8Ae84hWv4PnPfz7f+73fC8CnP/1pnve853H69Gle/OIXf0vn/eapSypeQ0DobKIUDnaCazER9RCTFu5xofGlIHAVa4Nr366MjnKBsYookXjZoF6wJRKtqZVWQzmF2PAiTpgWH/nqSb52tMeumZgn32MnvoTlVPLVkz2+ctcGi62QXlrSDRVHR4IP3XyCv//yXUS+Ig5sW1PoSW49MaDbCOyGX46gwCJ9qs1+VTkXskavGD+efB803Wf9esMsErdpqpABvq0wChdQGS/c1NIotgbpReYq+ZOqXS0SUTlm904q1ENt0KcrbTDVPqbqCqgJbHCelpZDNhAueDLatQGV7Nq7l4N3HGGYa97yJ2/ig2/6bThxG0aXFKeOYYocpERICVIhGx1K6fGiF7yAxz72sfzgE5/IMx/7fZzz0heQ+03S0tBPCw7M2Rb1608OXFU6RuaarDQc62dcf3LAgbkG585GLIZmglZwbUKVuFm1kZ5VEh1GPOjsOW490edoa8jqyQFjh24LIo+5ZsBSJyQOPE5tJPSTgqzQrAwyvnKsR641p0cZ58410KEkVwGtKLCBQy4nY1MdxrVDVcGPkjUP1dbAom5FVluCpOpU0+0/U+ffpBKOFY6ZKxJmo4hzZ+c5Mcy55fSIYRYxH3ssNnzut6fLUtOjl5Zk2jDISqv0LLN6nhjftk1V1e9KlKcSjhClm0uulbtCjFhRk6HjmOtaLlnlccuJEa3QozSGtUSTa/ueu6FHKQ15qUmKksiza259lJOOczxfIoTg2ltO40vJJUstypkA5TbWuYpY3sig4dEJWqA3qEU/pJ4EdS4yrTet1dqo0GzSwzjOwupvTNCc0BxUG4XqfQE6wG3aI4QeOYSvtVljo4g8265WIXAMIPyGpaswrsLvR3Vrr8BW7TXgC8tdOd/wuXVlxMHVlEuXOvYzAv7l8AZv+/QhHnv5LrtO3QZ6kGtuXx0T+5K00Fy02GJZx8SFpmztItTWTsuRRlRBztYAaGuQNjVfK8FAkScgJTv37OUlL3oBQhcUecYHP/yP/Pxznsl9L78Hv/W7r+PJj3sUg9GIcQltsPOlzOz8mUIaWbSf2wdX96W1fW0ONTXG58CM4G9uOs3yRsqumYhxbhNzgSfZ24lYaoX4UrCW5Fy+o8OvPvtZLCzuoNSGhih40U8/m+9/8PfyqPvdEzNag/hbr/Rv+7xtn7ft87Z93rbP2/Z53y0+bzJ2U3QdU4m82ha57wtDncBRQuB7op5LYmrNb0U/WlTmEOPHpKUm9iWX77SUJbHjna4Q74cSn8/eeZrbV0bMNHx2tUMMhhOue+nGk31WBilLrZDYa3JiOObvv77MV4+sW5QkkBWaJC+JQ4+zFprs7UTMhBI57ltbUIm2VodLcqOCWrR4E1J7SxKu5suVyiI1p5OY0wm06b+pxDmNQUwL4AoJopgkI6ufT3UgbUJq1356S2VBemgvoBQVrYL1eRWdDC5JhpTs3rufg3ccYVxo3vzHb+Jv3/xazEnr8ygyTJrY59Kl9Xueh8jH/NovvojHPOaxPOFJT+L+j34o5//C85Fxi7ET2BzltkujlxS0Qo99ndC+j9KiXE+PMqQQ7O822NnsEJl0gk6viqTGigCKsiAkY3+nwcMuWGRlkJEMc4YbKXlaMB5kGG2I5hq0I49Sa1YHOcsbaV3gPXh6SK41+7oxZ89EDDxJN4xpOJ8nyhxjJpRQNomozqzCTq8XqN/3pm6ZKvlY/bxKeE/7SaNt8bSa70Iiihy1didLymOhOctSo8Gh9ZQTg5RCB7QDyVzss9Dwa10QIaydTEtBy28S5RMqtlqccYo+COXV3UOT+Y7rVHRzUdqYSvsxQpcUwqOfjhjllkJICisQvp7ktEOPUEk0NjmZO7HQcW4LEH5ox2d1YJ9jRzMkaxn8IAYVMFIxvaSkFUg6flzHHTUl1t0JxVbrwrh7Vj7Tou7VHBK5mtgt96xVwl550aafA/b9e6A920FWLSublAQtBEoGtd9UDtwC1tZvan4z1d/C6VGGErbwO9ecZz3VfPpwj2tvOc3jLttl16grVGXacGhtzCgvaYUeUghOjyWdsEUYSFtkLjNk0kc6UdhpXvJ6Hoopv1NRME09K26u79q9m5c+/2cAKIqCD3zkn/i5Zz2N+11xT171+6/nyY95BIPhkHFe0ArCyf5gquuz6pSrbWElXF/93hXVRZEQKp9eWnD78oB25NGKbHdDJ/KYjXykFGiX5F5qBbzkGc9g966dtH1JJDXP+aln8YiHfB+PfOD9La3Yd+ihmcydb9f1/isfb3zjG/nTP/1Tnv70p9c/e8ITnsA97nEPXvnKV/6fT3QbISmEh18mNfdRJaRTt3Jv2fAbwHdCQsoZ4qY/CXQKFVkhE7folJnmwYu44ZRd6BcvRLVYR7V5PaabvOVfj3D4tEW77Z1rkGvDwdUxn75jldVBymIn4q+/cCc3n9jg6Vfu462fO8wNh9dJRhljJUnaIZ3YJy0033/pDr7/3AV2xhIxTGuEV0WqL5R7ZldxN35cj4MOqZFlxgsnqJhqc5rntUFhSvwBKUEGtjpZTlphhGsHtucpMcLeh54WWMjTWlHZbuCb9pz15rJ6tZY/0cCmDc7YKJSetBtKAVLZ+/FMYTc3vsc73v6XPOrxT+TO4yeZX1iiKUrKqOnuM8ektiIqO/McKmLOmtsPfswDHvAA3v32txI2O8jRGkZJhBlhQkUx08IASaE5ZyZkkGnuWEsJPcl87HH+XMRM5PGV431O9FNageL8+YZVWnctqkp4KGUDOKmcCE5p2NMJue+BObtJWGxy5OSAteUBg/Uxi+2QwFO0I49BkhN6khPrSc3hVmrNTCOglxaOD9TjksUG7aCBUpav1kBdCbdBqd7sPKokkJBMh0YVT9XdouCq790c2xQESb1lY+qUjMuMeHCcs6XH/nMW6WWaU6OC06OcpaYVsTAGBplGCFgb5+xoBczGO+iYZDLPlFfPwSooBzBR2yH7pugLhLR8gdJDu8SRjrsYFbDQsPccKcnKKGPgxHOWhxlLTdvqnJaaXlpQasNMw6fRCglDj50zERfv7tDwJUrAkY2cVrSbyBOMxyX7O5aSwYRtur4TUKmSWlX7fHXvXjAJlI2masM3UoEf28ijQhqWeU19UK0d4Z4z0xBIhy4FdNBwwaushbWUq1wrYddR7gRL0qJaU4JuKGvetlybiXCTtgFUN1Ts60aUxnDXIGc2UvSSks8dtsmZ0JOTQExKWr6sW3D3z8QcWR9zXAmWmiH7Oj6HB5JWALudmNmman6VtBRnujsznSgUE/ReLchkNJ4fMBqP+fBH/xfkCa/8pReCkOy/4kFcfN4B/tfV/9MmKiv7MzWPbZK04VCRslbqnkYYNsoxRjU4NczYNRNxYKlFM1CcHmS0Q4+01Ny6MkQbw0K+zptf81r+6E/fwkc/9AFe84pf5VEPewiPeMiDeMvv/5bjmN6KIP2PHds+b9vnbfu8bZ+37fO2fd53i8+r16obt9KAknJT0nrrWq6TORiMsY8XSDHxa9LaLLDzpuaO9kISFXNqkLORlCw0POsrHT8v0uNkEfKerx7j4zecpBX73GNvl7nYJy8Nx/oJN961QT8p6I1ybpobkJeafzva4yOfPUJ/dUxrJiJuB/ihx45uxOX7Z3jg/ln2dXwrwGumOImr5BDUyFbjh5vQoBgNxdQcq8Zjmp4FNqNhp5Od7rlq36i1Q0FuKcwAMJWYmnovtgAQ1HHHpsSpnKxrpEemqbtK7OU0UggCJfGqIpf0+It3W593+NhJduxYoukLSkAEESZLEHET4fnIZodDQ83+mf3k8Qz3v/8DePc7305DlBZNK0tMaAtHJQGFtsnBSmCz6nJp+pKmL8lLw5HemF5asNwMmI19umHpkrdBDRgQQGp8a08KzblzDR584SIAR08NGawnJCMbQ6gFwWInIvQkpU5RUpAVmpO9hNVBxtFVu86WBzHdyGd/N2Kx4dMOm8RejkyZeidTKG+3pusYp0roTSe3p+LBer5MH1uSqtW7rTm+Pc9e2xXg5OAU817AzMI8I2MTr2uJpQYpjSHyFKGyie5+WtLPSpaaPvs7HXwvqbsI6vjIxVlGeuADxZTHFq5zy+gaUW2Ub+daabUbupFHWmgMhrS0MW+pDWmhST1b3Fgb5aSFJitKYl9RNgPC2CcMPeZaIVIIVpOcEwOPhcYipYbBqMBTAm0g9xuW0i7tT/nrLTaneg8VVU+9ZIJJR0Q5AZeIsrAxYQWSwN57YQDhbe4+cZ9LahohEM7fgbVzVbFXAL6y8b7E2r6q8CdcAjxSkoWG7wo/JQMX158cFnz5aA+A2dhHClwH1pCoNc9akrM2yjnbU6yN7ZjubFvKpnFuMMDO5hztqluymoNM4t46JpNs1hmo5me1h6m+BzzPYzwc8uF/+mfQJb/xqy8F4Kx73p+Lzz+X//XX77Dxu6M73KpfMFk7jpqo0reo2iTKAikCeklB6EnmWiHtyL6XUsMgK0kKja8EurfMB/7orbzhT6zP+7Vf+1Ue/ZAH8sgHP5C3vPY3IRtt1h35DjtKVxD6dl7vv/Jx/PhxrrrqqjN+ftVVV3H8+PFv+bzfPHWJkPjpBkiPUnkohy4C6vbW6nPVUSnzGmNb80JPEnugpIcWth3EOkFFKIxrGbLtbaWBUa656fSQ/d2QVqCcIJUNTL5+YuQ2aoZTvYRT/ZTrjqwz37ItbRUX5e7ZmDjwGOUl5+9sE3iS25eHlsjek+yaifjeA/Pcd0+HmUgx1tAImhMEggtK8qiLl244ZzRpDxTV5r4sap622oD44eaNlFSWoygdWkeZDieGWsjJ8wUNizKprqFLjNaIChXozm2UbTWaoAk8jGyQlDbgtAZa1JV8hG03BZClwZcuIHXVWgF4+cjeg2+f8V8+/nF+6nk/z6f/5Rr2n3U2BBGy2bGtbH6AF0bg+dxx7AQXPv7JPPQhD0YGEbv27iNozWxG1wmJ0FgEI+Ab257dAnZ0fUzYYlBArAT7m4I9589hsBvXjaxkXGhOjXK0hrmGRzdU9TzLSsNcbAVsHn5gnnNnG3zi4GlObaQE+2cY9VPuPNFnqROy5FAFSgqUFIzGOUfcZvTw6RErg5RuI2C+GXCsn3CvnR32tH0aPrXDrFs29Zagl4lDMdM/1wW6MWvRZd8IGbIV2bY1QHbn2mqq1MZx5oxmNmxxdrfLHesZ/3L7GrOxDeJODjPagaKfliSFoWxEzATuvhxSorqeDqYVre1802HTvjvHa1vPc10gixTEmEtnQjZMQNuXBCqklxQcF1ZMTEpBXmh8KRnqkvVRRrcRsHexSSOwKNNW6DEb+xzvpySFZpCVnD/f4PQoZyPzWYg9TgwKet4M+1rNGt2ihUBUyLuKa8+J320NJusvHa9btbmp5r7BtpWNq/XjSCgnnMuC0gR4ws6dKgjKXTAPUDiuSe2C+41M03KLLnMiLb4T0ah8zoHZmKw09XxeTQoecmCey3Z3eOC+rt2QuWeRAnwpWR+lnDvbQElB7rjxlAwYZiUnBindXS1aW3mWq03S1LgIXZwxv+ogfjqIceP39B/7EZ73Sy/n+S/7LRYW5rjuuq+xtLTI/e9zLzcZN9MaTAdBNVcy2ASA4wSs3slIxawOCo6vj9k712B9lBMoyWwjoB14rI5z1kYZeal526tfwuc/+S/8zz/7UwDuc+V9ue3WW3n/O/7M2m4v2swr/K0c2z5v2+dt+7xtn7ft87Z93neJzxO6qNewkB5KVCKjE4Tl5uSdtDQr+aQ1P1CCbqhQbk2VDvWthMABvl2xxQoxpoXhaD9hlPs0fEkkMygzTNjm5pMjbjjas4hdT/LlQ2vcvjxg71zMyiDjzpURs03LBTzOSsa5Zqbhs2dPh/VOiJCCOPS4aHeb++yf5Z472uxqeXimsGKVKmAqD4znu7kAk6KBe26NtOMBE0qQTfNq0n1Ucf9idF1wnRS0OZOSxGib5anmihdsRgBPJ6ioYgisYJcBITxLk1bY9Rkor16PntMJAci19Y01/YI778c+9nGe9tM/yyc//s+cdfY51t4328iqu0WXCKk4tLzGBY/8MR764O9DhTG79u5DhE1MOa47eEQ2RjBGColX0cEYSw+W+DG5NrR8iUg2WFqIuHhhlqQ0jAvNODeMXCJQGzsETV/iuWJaaaxoZyuQXLTQRAnBoaURXz2yztE7e5SlJis0gySnDLw6sZOXmmSYkUnBqieBASfWx7QijyNzDfZ3Y/Z3I3a1fObibt3dURcsCupYre6aqZLTLqk63SVTzYHJ5NicTKwKkZsAAlt/7zesuKQuUBsnaUv7s1Znlo1UcnpccnDVChm2AoWUgkhJSg1HNjLagc9sFNVrru6kc0LjtU7F1oR9hfx2c1pkY9AFni44u9thZ9Mj8iRJoTkw12A9yc+gRAg9yXwrZDxXsrxhzzvfst1lkWcLw6O8ZKFhixmRJ5mLPfppyclhQah89rR3EAxP2RNWxXAzEVmt19nUGjFGTeKIilrQobmrwp0tuAq3jiZxY6CshkRW2qJtoW2yWlQFPOcXDZMib30b7nfV4UmIsAh3T8JspFAitsUJt66VFOyfi2lHHufONfAElo7M2QUpBK3QoxN5rmNqcr2k0KyNc7sW/IYtolaJ/C1FOOPiBLHFD24q3k39zAjJ037ix/jZl/waz/+1V7KwuMh1132VpcUF7n/lFe59bCnATcd1bg3UMX6FJHcdoTpoMOhbvxYHitLRDXUin8iTjPKS472ExXbA2379l/jcJ67hbW/5EwCuvPK+HLz1Ft7/9rc4GxnUcfv28Z1/nHfeebzvfe/jZS972aafv/e97+X888//ls/7TSe6K9EOEwVWfCZPJsYZJq2AuCqfgbWkZJhrctea1lYuUKZAqKAWlrJcpY7H1Fj0jhRwj8WYhi/5h1tP89Bz5pmLrMMsgha3r55wDq0gcCIkWaFZH+X1pn+QFMw0fPbOxty6MmIu9lk8a5ZW5HP2vG1hPn++yYGZiG4oUVLg6Yy+iFgdlkRKEnoWkacENbJOBzGiLJDpEB02KYWH8iPb+lSkrm1UbgokjFQWFVWJNznhhRo9V4s6yYngmAum6uRBpSI81a427WRt9VMTuLaaskLTuHY1bXDiMBnhVICnhFVuxmiL4tEFpfCQAj5z7bV84G/+BoBnPPun7PU7C5jm/KaW733Nec47ax+xL/ntP3g9Mzv2cHJUsDOinidbA8Mi7CAFDHNNy4NUC1q+VcI2ysfP7WZnTkhmQ4u6yExAVlpUQlJouqFiLDRKQFoYfCnI3YM96dJdxIHHp76+TKMdkgwzvnZnj+PrCa3IIyt0zQ1YukA58CRHV20yaZyV9CKbMFpqhtxrZ4vYi1ASAs9Wcb0ysYHAtKF3zqMWyShSUIFdQ+4d1/9vTZZtSSBsciJbHFK9iXNoLZENCPMRF0YNdl44zycO9/jS8Q3On7d8X63A4/QwQ+ASbUrT8hxPrtegcE4+FsLymk5RC1gV+mIi8FVf292L1nTMCDHKaQnJWTNNbjw1oOErNpICX1m6hHvuaDPOLGJn10xk30GhGeclvpKcGKQM3O9Pj3Ly0iYNT48LvnCsh68Eya4O53UCuxn2Y8ej6E34et3GDOlZgY6qYUd6aD8ic0SFpTEobCV+XGhKY9G42rWoVWi0QAqLRNFuI59a+xR7suYRC6Rt41bOjvlSkJV2E5cWZb1n0YYaxQcwLkzNF2gMJKXh2EbKbStDi9jUhnvtanNWqBD5mF6qaYeKpVYbgJODlG7kMRvbNXbFrGG5bBB7AvIJeqQSYbNt6TYhUtFmiHw8hURywfDWwNvZHCMkL3jOM7niwY9ibb2H7/u86Pk/z6f+9Yt2vuhiogQ+hW7D6No2Gj9CZCNMOGvfoTv/emJRKkudiE7ksT7KWRlm7JuJ6UTWHm0kOZ972+/x6Ec9kt/5kz/nxT/9LPbtWOR7rriU5/7kU4h9SdmcByEZlYI5vvVj2+dt+7xtn7ft87Z93rbP+27xeRVys1p/Yoquaxr5Vwj786TQrCUl64kdw3bgufdqMEy0KSSbE0G2AFjQ8mCh4XFwDQ6tj9nbDVlQKcJoxirm2MZpSm3odCJL/7ORsLE+roUGO7EV2M0KzdHVEd3YZ0cr5Eeu3Is2hn5q19UlSy0OzETMRIpAaIzw6KWa4bjAl4KGb1HOOF7r6h6piq1KI5VLrID1afU8kaDBCL0pAW7QdqVNJ6qNRZduEsHbemz5fDXm07ZQOpR8lbCrfJ5wCTdBVSByoqDu7wPp2feypSD5mc9cywc/YH3e05/9U7ZAKRwFkpmMye7WEuedvZ84Cnnt6/+I2R27ybUh9KNN9zo9hjqyIq1pOVn3lJn9TJ4Q6yFxmTGrAnS7zUgrp28yQcdKl4hU0gr6kVme6k7ksWcmAmYotWFldcRwkPKVw+t0Yov0LLXBVChbNwnHWckgKRyicsggsTQ4+7sR5841mIsjIiFQvivOZCOr82G0i9smCW1LVxNv8ll1B1QVr2xNKhq9WWS4WlvTwphTdsj4dr2IIiFaPUQoFXPdXURek6+fGrKW5MS+ouErCm04NcxJYx9joBXYQoHvRWgVWWRyaSwtnpwgyOu4LfAmSXpHeSWk1W/w85Et1iclvh9yVjfk1hWPlWFG4itagYfvkqC5tlzppev6aUUeTcfR3ksKTvRTBmnBno5NVA4yG5sc3Ugc/UmTc7qLeOmGXSuO1qMuxFVFAjFF2+OKBtq3tIBVN4FtErFzQBsD2O4j7ToNADJtC7FZWXWm2OLcRHfHSVpgv68EZEtji73YJYgSFuWtJKQFdZE+9gVKSAIlGBea00P7ruNAMcos0rsTtOz8LDTd0KPRUuxoBuSuYBwq2+HU9CULDY9WICFlimYpnCS7haxjWpkOENmWWGxLx0n9tRvfF/zMc7jigQ9jbb2H5/v8wvN/jk997gsTXzf9N9OHkJMuGTe/RD52FFHW7o4KQyvyKHTI2Ilugl3noSc5e77Bh97wSh7y8O/nZX/4P3jlC5/DzsUFvufel/PTP/YEolYXHbXruOY7NdVtjHHz8dt3vf/Kx2/+5m/ylKc8hU9+8pM1R/e1117LNddcw/ve975v+bzfPHWJM+a2WsukXUDKTZXM5VFJrg3rScHJYcZe10I0Eyoi56iMsBvOpNBEnhXtQQqknrRkAYRKcv3JAaU2VoHaGeReahW225GHkoKlTsjeuQbLGymrg5Ruw2fXTMzudkQ/K+o25Sv3dImU4P57u7R8y4NUamj4VuF6VBjGxmN1XHKkl7C/G9nWG23wxJSatvSsAygShFR4QqKDhkWuVWinsvqshJJa9KMWmIKaM0kYvZmH02iMiiZtabCpxbI0IKtK7FSVzijPCWxZY+tJa9Ar46WMBuHVwY2q3lu1sTOO41BKlNCkWc7Jkyc597zz2OgPuN+DHka6eIENvjNN19Oo3nGQklBqbjt8J7cdvpNnfu1LPP7AWa6FPamN73Sl2N5PQS+XriXI4ouiYmidlpSYsFlvqEU+xngBUdqvjZoXdumlpRPCsMrepfBYc3NqeZjxiPMXGCQ5X7jlNADZOOc0MEgK4kBZAY3QJgACT7J3LiYrNN1GwE7ngBu+4qvHN1geply5u0vDt+8v9gUtPyCOI5QpHO+uHV+Z9DFRh/VUE4RNGrJ0HHDU77FCOYgynzidrZv+avNVZpt/Ph00VZXcam7qgrYsePg5M9y5kfPZO9esQIizcXduJCgZ2eCn4aFFSJnbTW+kBDIbYYKGbWX2bULBuCC/mjsTw6A3c6i5+9vTgEsWW9y6ap1rK5i0F12+q8Pn71x3yTmPbkMyE/sc20jIS8NGknHWTMwot+v8xCDFV5JLd7Tohh7HBxlNP2QmmiN2QTMqIBUBA11SGAgDG4TYSr3lNBsXhiwzdRtz1XpWzT6gbsMVLomUuKRQoAQSgcbUAU6ubUBkhUeEQ7XZgESb6hzuHZX2PgT292lp6mCpNNYOZaVhZVxw28qQ4+tjds3EXLLUYn/DYFQLhGRtXBIqyWU7WnQCxSgv2dMOUELQTzWNQLIkcyhyG5C7eVaoiOVRQcOPKDJrFwKlafuRXVt+PLE/VeJk63xz319yj0sB+NQnP8G1136GP/+Lv+DNf/ymGjVR8eTV89f9rY7aZCJglGs81UIaQdOP6yAo11bEbbEZoKSgN8ootcfyQLKe5Oxs+LziUZdS5BnP/upRXvOnb2XnjiWufsefoUar9jZVwEhbYadGDWX51o5tn7ft87Z93rbP2/Z52z7vu8XnVahkYYwtlkxxTFf2uHAFXcuzrDk1zCiNpVCajayYpJKiXntlhf7XBiFtsscWmiIoM2LPQwnB8iAlLYzVDFUBG5nldwZoRR5752KCvV2yoiTwFEudkJnIZzb2GWQFUgiWmgELjQDPzVclbSFmJlJ0fQBNgaSXlJwYWO7oPZ2Q0LMJwqDitJ1OTE8lLY30ENIDsYUiR0owUwUm97tKePLMQsikiDKd2K7SEKVDkCrhCkQVbVRV5GNzQrs6pikYxPT3U8momt7HXTPNcpZPnuTAuefRH1iftxHOEzQWSUuNJwUNkyJHawS64LZDR7jt0BGe+ZUv8tgn/KC9RpltRlZW/N/u2qmjLanfiwrQQfWwVfHY6ik0/bheSyZsUqiIzNGNVYnIyuZEShIqyXwr4PydLbJCs7YyJBnmjAJJEPkEjhZBeRI/9GhFHrETvlvsRLQjj3booY3hlpUhxwcpZ8/E7GyFBI6WoulHzMQWOWtUgBaTTgdUQKYnnWMCINmwfm9roXbrUSXD6zURTooErtBa/a1x4tkmsD5Pjnvsb3Zp+m1ODHKO9BJuXx0hhaXCAKuJoLEo+KIuXtrv6/NOP0t1L26tG2cfq7kqyineeK2ZiRR7OhF39RLu6iWcNRsz78QE15LcFiacLsw4KzmxkTLT8Mmcj6g49C3vd0E39NnRDBnlVjT0hCeZi9vW57l7yEpD4jracIll5TqGJNZHWQyFRhszoRWsQA71e7K/qyh1yG1BrronbQBpXOeE9W3CiEkBSULu6E2qZLkQlX+r7steq/ab0vrgO3spH799haOrI3bNxBztJOxs+7QDS7uXDQ2hJ1lqBbQCVXdmVOdr+ZK2b+eIkcr6dCHIVcRaUlIaSSAFStoCdStsoaqOk2nqnekiB5vn6yUXXQhM+7y38+Y3vYFaxH3rfK5snvLRQYPVpARCYk/SNBqZDRFG13uT/V2rWXPTxgbLGwmDJGe+ZX3I8x98MUWe8czrjvLOd72DuYVF3v4Xf043X7dr0F2r1hv4Dj2qzpxv5/X+Kx8/9EM/xL/+67/yh3/4h3zwgx8E4OKLL+bzn/88V1xxxbd83m860V1tVo3y+cM/ehO/9huvIlm3G6mqYplp6KUl60lOUlhUW15qdrX8WqW3dlTuvHU1rTR4VVXYbXSyUtdCB1lpMKEPUtWtTXGgOGuhyZ5uROQp9nYiLllqsdjwrIKtq2TLpO+cSMFnTxtuXB7w2AvmWYg9/HyEKHK0bzeQp0bWYXRDj4ZrmdJgORKre5sOiCvkQ4Xg0CUIM2l1xbVzwyYl4VpAaKqlxo7l5lY6KRyvjsGNmakFoe0mgtpwGenVPLHWB7hrGdBGIoWkLA3CoTEC4aqR+JTGbmxQri26MFz93vdwj3tcStBo8MKXvZL3vvMveNTjHk9aav7trg3mGwH32rGbxdFRPv3F63js9z+U17/2NZy1fz+MexNkQ4XIw21czUS4bEb5ILBJDulhTMM51M2iZEb5NoEghA3GpSIsRsiwwbF+zulRzmzssbcNiw3F6YbPF471ON5XzLdCLj8wx3W3r1JkJYP1MaYTkZVWpRvAd0rRK4OMXTNxveFfaPicPRsT+4obTvT5dLHGXOyTFBolBJfvbLPU9PGlZJBZntB2KOiGXfzC0A5sUuPYCJSM6ERNmqQIJyBRHUb5TgTLIQzBimDlqUNBbgmAtjoatwarlmuMJlKCfR2fmfMW+NzRHp87vMZ8K6DhKv9zsc8g1wwyTTuQSGGDOqMaDHJdB0N1MF4bg6nvHd1Atbm0wi0akY04f77JsX7CKC8JPUk38tHaMBv7XLqzzWmnFr42yrjlZJ/5Vli31kspSAvNRlJYFJoQ7OmEHEtTDq2PyUtNN7JCZKFSqFLXG6/To5zIk6yOc9sap2xwYgNWWYsEVranCkI8KSiNRrq1te7QQKOspBt5xL4NYgRQ6K0VflMHxUDdZqmZBEkag5KSpKiuZ4fQk4LYE6wlJYfWxxxfH9v7KzTXneizuzVHMx3U1BJfXx6Qa8PDz5llsWm5Uxu+RQoY6dEv7TNGwqpxC20DgqQwDLKCuVixIK3oT6oVXnOxFicSUlrkyjc6pIcOm9z29a9x3sX3BOAHfuAHeOpP/BgU44kNNNrSXVRr1xiE0QRSc8rZ73GhOa/bqDc1sWdtdjfyuW1lyMogY5SVrPaGnPzMB/nke97KJZddzq/84Vt58mV7AXjPZ7/O2Hg0wzYi7UOZ0Rov1xtC4v3f+Fn+N8e2z9v2eds+b9vn1fN+2+dt+7z/x32etfc+Rkr+8E1/zK+9/DcmPg9qnYFeWrI8yFxRFfZ0QuYij3ZgX3DFYTvdbl86ex5IiZI2+alkQJqX5KW2BWFwlA62c2Am8usuiAOLLc6eiWn4lqZhoWF9bNOXdYLJLxO0F3JHL+POXsLZMzF72j6ezhClZiRCVscFG6lNpEmHeJYuYVYVJm2RW9obNrntkqgKXEbXiW1gQvmgvAkdidGbE9xVEltYITwjJoWmemyrgtOWn5fGIrgF1OJl01QK9dhOfa9doq1Go2JpS0qtUVI5ZLdN1P3lX72Xi+9xKVGjwQte9kre9fa38eBHPQ5jYGWc40vBOTMh82Gbz372czz24Q+2Pu+ss2G0UidoTdiaPLs7RJEiipTICwmVB2LK10/FFUb5toAwnXjTBRQZyossvYGjN8lKW3gzxqI/qyRqVbj1fEWuS4pMA3mNYjQO2T1ICgJP0op82pHHTOSzsx0SKcnxQcrpUcbtayNODFLy0gri7WyH7OuEBMpnNCrJdUHsSWJPkaU2GV1RUlh6lTaNsGVjqOm1JZRDaE844S3KP7OxkvQwFfXZdIfJ1LhUAt8YjSgy5sOIUAXEvuSrJwYcXBlyapjRcZ1ZezoRZQiRJ1zxw3J6Z0ZiHI2HLyfUZPV1wNL1uLky/V6NsInnmJyLFxqc6KccWh2RlhbI0Qo8q7UA9NOIXNuOi9VByiDJ60KVjSEsd7Wlo9J1kn5tnDPICna1Ixqe7Y6SwlKRbaQlvbRA64oaSxJ6ktB1nUVK4slq3puJjoA2CD0ptIK1U+OiJC8N7dCj4eE47KtY1C5K+72oUdvKddJVFEEAnkOWGwOZ8+FK2MJwNbzDXHNymJEVuqZzafgKJYS1H0HE8nDIsY2UyJPMxz7CTBDkdv67opHwUFEHMe4htEXPDzIrjD0bKVrepJNKR223KGUNIhBb/V619hw6/NZbbuH8Cy4AnM976lMxpkCUm0EHmzpQlMe4sF1guTY0fUOj1cGUOWhdFxKkgN445/blIUlekqUp1//DX/KZq/+cCy69jJ945Zv44cutz7v6c1+3YIvCiT2DFeKs7rnR5Dvx0N9mRPe381rf6nGf+9yHv/zLv/z/9Zz/IY5uE1qExVUPfhiPf9wXAbZwtkmUtHx6YSRZaFiF4Hkvx+iS0m1oM4cqqRauxgbJ1rhICue0Ab7/3AWuO7HhNqQehYrYE8EDz5rh+EbC+ijjwqUWO5oBM5FPJ1C0fIkyBUZ4lFiFe4EVmxnlIzaSnJVRQTdUeIHl97ztdMLpUUbo2VYRKWx7SaAEnUAikrxezLWYStC0DqviJiuzCe9Wxb1aodeErB1XhYAyrg3OFSZdGyh1hdwaDiYcW1WgVY27nograOE5hIzeJIZWTWu7UbHVyVhNgp9MG9YTywWqhG0La/iCQmtGWnDDDdcjpeLzn7mWOw8f5mu3HOSCc8/he/Z2OTnM+eBNp9kYFHzk3dfwIz/+LNrn3JOBEjREXrcAW4c8EUyo1MCreZUZSSAs91YhPDzHJ4WUGDmVMHFiGlU7nQ4ajHPNnrYNeL+2bJ3Dnk7IjlbAI89b4G9vPMm1t5zmHns7XH5gjltPDFg5OaC/OkZIgedLhBSMtWFjfYzn2v8DTzLT8FlLcrpjj3ag2DcTuzmXc2J9TFpoDp4esqsbsacTkTvPudQK6aeKflYyF/vMx8oKLqUlvbTkZKFYaLQRWP45UWbIbIyWHquFYDwoSEvNIDPMRA12t3w8U0Ce2ISH8sFtpDYhBrCBesV3K4qEyItQkeLBZ88QepJ/uP4E3YblwsKtQSmqSp9xiE9XxXbzTFTISjdHNZZruOZKc4FQPc8d/+miX3L/vV3+9WiPjbRgfze211SCubhNLyloBIoblwfcWGgnYKLZ1Y3wpeDOXuICEjuLR3lJJ/JYatr7UULQzzSrpbYbbA1JqS1PnTHMuYCpauvXCiSaoavclwZCT9AJlEtEGk71C1aTnIavGKQFSakZpAVKCs6fa7LY9G3AKCokALXqeVkYAmU3r7nWm0SArHW0PIPCBU1NX6KZiHQpCefNNQi9Jbqhx0LDRwm7OW4EEWOj+NLxVUJP0goUG1lZt7i3QsViw6MQHnFl1d3Gy7hNw86WxzjXzEYKo20bfBh36ReGti+nEEvpJLCuX/zmr3fuP5fxYKMWaJRJf4IUKJJNbeBCUwf1/cIhkQTsafuI0m74RZkjlUWKhZ5kecNSLawcuom/e/nTeN6vvJJ/+ORnGfttHnPPs1h8wI8yf+EVnCoDi3ZuRFbEprSIG4SctIh/q8e2z9v2eds+b9vnse3ztn3ed4fPq2hWCiT3f+CDefzjH29tdzUeU6hjKQXtwKuLL+3A+SDpkWlTJ5gqVHVFE1AVWKAS44KFRoCUlsvdRB2M0cix4cBco+ZslULUKMemb0V8A5MxMnZtejpDZGMSEXL76pjrT/bxpWSp6aFUwKgwnBzYrqvcJeRmY78ungaWfN6+G11iKjS7o8DZVPCdmhdVgcGKm8rNyVpnJyrfA5uT0MCEKuaMQ9s/kpu36fX5XDWpSnJXR2XLCux1qlhjkJVkDlVddWsUWjPWkq87n/elz32G40ePsHb8Tnbs3Y8S0EsLPnZozFp/wPve9gEe8sSnk+66B2uex4y0Y74p0QWTBH/Fz+vGou6yqihd7EQCPIycKhyUBRi/PmegJKXRpIW1bWmhLbDA2e7KjsaBYmYmZpwWDDcSstQmvIW0icZ0XNBfHXM6VPSWckrdYtwokcL6sNCTRJ5kmJUcW09YHaSMspL5VsB5Sy1agRVj9JVgZyukHXiUxrDQ8B1hlKl1WiwNXKPWJ/Bzm/TWQYONTDPICsYO2Rx7Ht0woKU0ogohpmIfqq4RY5DZEO1i00IGKAEtVRB3AkLVwVOCa25aptsI6jXjyRBjBJ6yfO2FtolY6eyyMmITp/6mQkr1noTll69iuSrRvrPZ4vKdbfe3pqbZ2BGGLDQC0sLqKczFPkfWxqwM0gl3utaMxjbRnZW2O2SxGdCN/DpW7qcFAo+ktMWp3NFbSSlo+Apf2cT1ICsY5fZnpWeXYqkntDc2PhQkpWY9KVgb55auq9R1sr0deHUsFbsuuVLbtVZapU6UcPQ7bkX/e3QQFsUtiH1rA0tjSArBrlbIQ8+bt/dqYC62z2uCBuupTYQfWx/TDhX7u5bXvl9aP9H0Jb4ylYVACUHoh4hsTCgNndDytHdDRZgPQEhyv0FSQORJFK5ILz33oieaOtM+0EiPXXv2MhiObFG4+l2+pZtlej9UgTw0dEJb1Is8MQEq6AKkpa5ZGeWMspI4UOhTt/PuFz+Dp7/kFbzro58i7s7x6HueRfdeT2bX5fdlnYhT44LZcAYhBIGcute7tZ3bx/YxOb7pRHeFmhmXht0XX8E733M1BlcNEpKxFnXLYiOwSsBndXy72XNVKqULSrxJ20x1bmf0ksJuWG3V36JcGp7gwGyDQVZyp1CcHo3Z0wkoNdxv3wyfumOVlVHGlbvbdAKFJwWrSUmgpDN8Gk/GRJ4g6Ru6oUcr9Di6kbCz5VNoiyqZb3jMNzxO9HNrTALJQuxZNd1qw+qct0iHdUA41oJABqipNlvtBxP122rj6002/HX7NXaj5Lm/w/GuGUA4zlFwSAP3dS0EJT3bFiytsI5Ulkc2NAVGBPQLSdtzGzNlnSFSoLWtQKbOGQ8yXVcjlbDVxl5qyIuCG66/wb2fkqse/ij+7Ytf4Lw9O5gJFccHOb204ODHPshH//b9zOw+m5V99+O9N5zkksUW58/HdMOYWFmURloalG/bW/tZicgmG00oKVyrfiuAkfFpyEnb/Ngo4sC1gXtBjRIUZUZb2f9nPMn9drc4PS45vJ7U7bdX7OmyqxMx3/BZG+cEnuR4K+DwXRvkaUmaFHWlX2uDFyiOpAXLazYBsGc25uZWQCvy67ldzfPMKUvffmrA4ZUhygVqFyy1uHJPl91txdFeyh1rRa0UX1WrTw5tkNHyFbHv0Y67iGxEK2jQz3K0gdhTrCcFTd8iBxpBA/JkwvcoAOHXbaUi2ajHJVcRHjaRooRHpASXLrU4tn+WT91yilZkEXkNX7LYsPc0Lmxlu+lLtLHzYzqBVPGdQcWFqyatslNV/6qFUZQZ+9sR/v4Z7upndYDYS0pbvcaKdFy+s11zc1VV7EFWMkgLrj/aoxEoLtrdoZ/ZtaRiH6kNvaRgoWnRekmhQUIrVPUmHGyiqwpwqpbpqm1OIJz4TYFAOKRSTqQka+OctbFF6Q6zgr7jlJuJfZaaVijKV4JRXtZ0FN3QJ9e6vh8lRM0jCtT3BBYNkDjOwqYvybVFhBSeYV/H8uj10oJF1wY4NhJfClqO4+7EIK0FYSJPct3JDfZ1YxYafp3k2NcJmYsVfpkgshENIYkD2y6M9Cz3rNF40gZbfjq0Ak1S2Yq9e5/TaEaLcLb2V7kNv8hGNVJTwKYNv1G+m5PWVzQ8Sdu3aK4o7zs0sD1/y5cc62dESjLTCDh16FY+9LKn8tzX/CmXP+zRjPyYaz79WZoLu7jkR36OZJRxdD3hqycHHI19Tg4zLphvMhMaemnG+bNd/jPHts9j2+dt+7xtn7ft87Z93neJz8v9hutA0lx6xZW8/V3vwShRCw2OC5vIkwK6ocdMpOqEM7qsk7JKCCI1ScRIYddaWhhKaRwK23J5B0o4+hDbkVBo4xJJsLcd0EsiVoYZ2hi6kVd3So1yTSo9VseFS8oJStNkbWNMLy1YaoUkpWZ5WDhxTMtXnGufk4MUOVXk9OUW3upKvM1RZ5QGpJlsmGtxXmf/aoHTOnnrkuKl1QdQ1XmFpHSfyV0CTQiBnOoesahbJrQVlZ+VHkJIq/GBRU6WLhnpGVeIFh4lk3WXa8MoN2w4HlxfWTuXFIa81KR5zpe/ej1gfd59H/IIvvTFL5CHHU4N8xrR/PEPvpePfvCv6ew+i/CSB/L5oz0uXGi69WYFD60fl5TGxjOFljUthE2cFihpi2KBsvY1kLb4XSo5eRbBRH/DJccCIQk86AQ+w1xzajRBATd8xYH5BvtmrNhfPy04fHrI7ctDev2UZJSRDHOKvERKgZCCZJQx3EhZ7SXEsc/Omaimhav+VcnYSizvtuVBjRzvNnz6Cy3On2+wpxNyaH1MP7MaJ93Io+ErVssCKaEVKGJPstRoWH0Lo2txwbTQdXI4Kw2moYj9hn2flV8xltfdKA+jggmKFWvrjRR42LHrhJJ7LrU4tpFw3ZF1BkmOErYgFXsVr76pO2JKbDK4lJZHW0LdkVOhP6uuJyVsZ8H0PDXKxn1ndQPa4Qyro4JRXtq1WVhdEd+t713tkKVmwE2nh4wzS9GVl4ZDqyMOn7Z0X3vnGjQDD8jrRPtSC9qhRyAF7dAjKWzXUcNXTl/CjkPDt+OsJIxzO8cFtqiWa/t9hd7uJQV9528HWck4L+knBVmh+dpxKxK5px3RCj20NnXnSujJ+rrKPRvY7rgaxGJAiAmaW7hY1pMCD0HsGTqOKseXkm6gbJFQWlHoXNuYvR153L4ycrzngp7rHLCdLBNatB0tn6VGhAptHDTv2zWlDRaoY7S1Ke4dVsKUm44tiWujfErh1bFElVCuBJtrOzldBKn0aIwmVB6xpyZdemk6Kfy5IH++4XNIClTvTv7XbzyNn/u9N3PVIx7H6VJz46c+Q3tpN+c+8WeIWwFfu2uD0FfsbIX1bc7FPnOxRzvwvmM5uks9ATV8u673X/GQUtZ6E9/oEEJQFMW/+5lvdHzTie6vnMrpRJr9naBuGRQueDZGkxa2+tUOFOtJyWLDmygROwOL9AiwHG+bEFtSYpTd9BusQTBYBMp8w6IDvny8z9kzMded6BN6Myw1vVqoouErjIF/PLgGwM52yI5mUKNnTjuF335mNxtLzQBfSVbGJe3ABpu27bMg15qOZ9FInnSiKhUvm7tfGzDb1iHlyP5FWUyCngrlM13Zr5IA07+bRso5obK6Uj99uNZRAwjh1TxNVVtnXfGfav1qeB5oF6y5aysgtl4S4aqL7vSbjDIIvnLdV3jnm/8IgAsuvZzLrriS1Gvywg/dxiV7Opy/s82+SPOpD72PD/3dR1hPbRDV9CXd0Br90DmBE8PC8apNNo3GwLiw7yMvLR/VICtsq2LL446+YU87INSJddBG1qJhAhsAFdIa81Bn4HmEwrAn1izETXqp5tTIcvBVbUH33zvDuXMNrj28xsMuXuLQyojP3HyK8cAibKoqsdaG0SBFSsHt45zxQpNxNiArNHOtgL1zcc07Nt8K+dIhO+8OLLUYJDnXHV1nNva5YlcLXwnWxjnLw8wqhK+PKY1hfzfi2EbKJYstLl6I6vdZaENaGnpJjhRWjbqXlnUbUCcM6CrBQMd0nMiIdMmalh8hjCaVEatjG9xHynOoGkEnlCw0fOZbATfdtWHbh11CoRsqNtKSTqg2tbckDuFZJeMqVFjVwpW6DaaSkk30kA69CbArhrkoZi0tWRvbgPuO226hMztL6O3m7JmAyOu4ltKCVuBxbMMm2tqRRz+xm+4LF1t4SnB6ZBMIs7GPrwQzkV9vwJuBR8dxmg0yzTivFMntbVXzsOVabDdSzclBysoopxUoemlRB5+3nOxvCnYHSU7fiS4dXB2RFboWAWwGivlGSVEaVsc5SsJ8IyDXljNSCVyrpajbxy3Xqd3wKIesSErDiuOlnI/9GgUwyi2v3kIjYKkVsjLKuGF5QDNQSLeGqqA/FZrTo4xx4TMuBJkIUX5IKA2pFmjcDlSElKXBYAPAdkUBICo+ygnNQI2kVF6dSJoEP6bm9bRtmaFN9LrPGxXYZJUK8Bz6LBTaojAdxQbC+pS00Cw0A2YbAV/68HsAuOWz/8IYnxtCwYf/7A/4+d/5Y+6UTY6cNHz+4ArtyPIujrMSrQ3HBykXLTSZixV7Yr7lY9vnbfu8bZ+37fO2fd62z/tu8XmHepkTZ5TEnpgUHF2RskI4CgSRJ2gF0vFau/kv5ASx7JJQVet8qCSltms6KQ2RspvX2LM0U2npcWR9zLGNxKHFLf9vWjieaF/hS0k/Kzm2kZIWmj2dyCVudY30HOU2EVYaw0ZSsDzMGOU2KR8qQTuQCMe/3AqkE6EEp1g3ERd0SRyFfVdSMBFQhs0aE0VmXd7doQun/aiQGGPXVpVQrDI/Qti1UQtMqsCO/VbapsrH6sKicI3e1GlSFQkAEkDjhGQdLZkSwnH1S77yb9fxnv9hfd65l1yG2HMhedji6htXKfUKO2di9saaf3z/u/nLD36EUW79VidUbuxsQnjoKJSqxLY2lv4ocd1WDV/VyeM1SsexbXVMZiJFILGaD7kd37qLoEqOubhBFgktPyLuSBYbin4WMsg0JwYpvdT6vW7ocY+lFgd3jjk1SDmxPubm4302To/IUktXI0OJ50tGGymDnhU4jZoBcWj5u1su6V1qU1Nw9BwCNfCqQoWu51roKU6PcpYHGfQm3MwNX9EJPbqRT+xJZoLAFTA80sJwepTZBG1kizcnBrYYECpJoGxCNfQUUeA6AwtDU/ngqH+S0uAbEM7nCazw71zsM0gKTm2k7JqJ2deN6ERqE61U5fGq96Wm1ur0URqDdnQxwsVMFtCs67nXUh6Ro+sbZJphbsUWv37TjSwtLnDp2Xtp+pJzZ2O6oc/xQcogKzg1zOiNslpkfabho2RMN/Lr9Xtnb8wgLVhoBG6s7fPZ7iDDODe1wHZFaSMFdSxWGsPqqOT0KCMpbTHWAkJKhplNcPdGWS0YHQeKY+sJa6O8FrodZSWeFMw27F7AjpkdwW7os6cTMt/wnXaF7WSyXRubB1SKSRyRl4ZektHVHqEKGOfGaczAjlbIUivkluUBN58asNQKWU9ya+9NTOipOrGeFIZeWhIor9bmKM2EsqUqKObaodFdV44N0Z1gsPN9tc+TlvJEKvt7UTrO/cr3+bHtngIoi4nOUB13a0s4WHdjuvMUKUEQW7FOIdgzE/G3/3Q1AF/6xD/hRTFJkvGRt/4BP/Pbb+RLgyZrK0Ouu2ONcVZy/s42842A0JOu4B4wF/ssdM6ct98JxzZ1iT0+8IEPfMPfffazn+WP/uiP0PpufOs3eXzTie5RbgWQBDAXmLrabJwjij3DYGRbo9qhdOgkQAV1K8w0SqsW1ygylBcQe4GtBLv3UG3owRr9Xa2QtLBVyA/ccILLdnf48tEepTYcWRvD/hmu3N22WX/XFrm/G9AJFVnps5HZDUeuNVrDapJzy8qQhUbADtcWGvuSs7qRE1yy6IXSGDIk7bCJTId15UpkI4QuCaK2fU6jXSBtWzcyPAKyCe+V9CgcYs0TDqVWqbVj/94TEgQURtSBENjNaDV+SlpRH+twvE0cbapqRxESpSdVO5EndVBmwAkCwWyoiL0Jsq06T6kNL3v+T9Xj/4zX/A/+5N1/zcX3vYpCSa67fZUb7uxx3s4Wu+5xH578Qz/Egx75WH7qWc8G7EawQhFUYktgHVQ7sM/bDOxGfC62QjTraclCI+BrJ/t87qhVQp+Nfc6da7DYsNyZYT5iQ0S0/QhUgF+1i7p5VI1jrMdESjE/36LQhtPjyFbLseiyJ168AyngoWfPcu5Ck0/fdprbjm1QuKBFCENZatvyJkqW12zL985Zu4PojSzq5I6TA249MQAgy0secN48V+6foR1Y9elxbq939myD06OMlVHGHadHBJ6sg/QbTw3opTnnzMbsaHiMC81Np+w593QiYt+2IY2LwokvOb4vh5BpBxbNmRSGVsNuNML/r71/D7csK+t78e8Y87rWXvtetauqq/oKjU0DzaWBFk44akLEgxfwFkST5qhHc04eOWonjxB/HlrNiSBHEzxCNGpUYkTRxHCOxh9R+ImKkiBNc20bumn6UtV139e112XOOcb4/fGOMeZc1dXQdPXedNHfz/PUU1Vrr73mXHOOOd4x3sv3tRMcSRo45DAux8bEIPEL3QP9HFVjMV+mGFUG50c1jsyLEd+aNjg057MwAd8wTTbKob9Unih89I478Jm778K3f9c/RGXkeU8QnFK6zUTplEAVAA71JSpvnW+0lxQYVg0e2pYMr7W5DP1sESd3pqjncpzfrXB4qYdke4JTm2MUqcbXXruKa5d6+OTpHa+H7PDQ9gS1sbh+VcoENyYG6+M6lswVqcbhQYFEA0Of2bIxloWG8QuA1D8ARwbSCGVjUmNtoYwLHADYHFXo56L1utzLcH5UYcNrrhoLnBlWuPfMDs5sTzFfprjhigVkWmG1n8FYhQksLCSLKFEAlMgozGfK6wVrLBYJzgwrnNiZYNoYACUSLY7QcW1xeCClgJu+0Yv2WXsj53BqOMV1y30cnc9x9aKU944bF8u9V3viMBxOxdmW6lkpjUalSBTQZH1kegI0bSaRsw2gEmkcZC2SxDstTROdAw4WSHPZlPjNoUtyjIxCkfXF8eGzL0PGjtNJ7D6vbAPjpFTxhgNz+Edv/Jc4/h2vw//9v3wjXto0yNauwk+/8z04uLSAX/7Lz0NrhXra4K8+ew7/wzMO4BuecRBPXy6wW1ssFAlODb+A7upjgDaPNo82jzaPNo8276li8x7cmuDIoMBqL8FAh4xiH6yEzJObU4NUS2VGkfgcUB+0VJ19dMxM9k7ZwlkkeYlhbaXpJGROVkqkfYJj6tyohnUO21mCM7sV7jm7i+PrIvtw/WofZSrODeOkH0DI+K9tq+M8bSy2fIAmOJuUtyUFgKSQpplzvoPgpLEwOkEvK9tMYmeBppJ7n+ZwalaWy/lqpFSncEn73Af1nkTr2czJbta2UkgAGEhGn4NcO3EIydxjnWTZZjoVmSD/UYlKoXUbgICzrU3tXHvjg0pBo9+h1Wd23hH2kz/8A/F3vu8tv4oPvu+/YuUZL8CnH9jEZFThiiMLGFy/iuue+2J8//f8A/wPf/+V+L7v/T4kOmgbi67yxriJAbPg2C5SjcUi9RrKrS7yqDY4s1t56QjJXj0yX+DqxRKrPXlmhrUFVBplrqKzzTZQpkFmDTIAg94cmn6O1V6CramN1QAH+hmumJdg6uakxt3ndvE3n1/H3Q9tYWdjDBcCvHBoKgNnHRLvnCiG1QAAcbtJREFUwJ7UBsNJg8V+5hvEioOxNhaHFks84/A8ji6WyLSOclirvmopUVM8vDOJPQfWFkqvmywBqasWCyyWYidPDac4s1thtZ9juZd5aSgLGJ9l7RRc4jN9/f2aNg79nkjHFYlIaIR7HbSZM61w5WIPq4McD5wb4bwPamaJVDP1s8RXMPnMbTg4p3xfiNYR+/GP3oHP3P23+M7X/kO5dw7Ik1A5AWilRcM7lfVICotFH/xINLAzlR4gtc7x4NYYy70M83mKg3MZFsoE929OsDM1OLbSx6DMcN8ZCaxvTxqs9nPM5wnKRByaWivvpAaWswyZFsmu9VGD4zuT2Adj0ogkjHFoAyxKYcc7tqdGsswL38R0kItzOFQ8yXOr0M8TrA0KaAUMlYnB30ljsD2pccbLuaVa4YYrFrBQphjkKVQq6RlJErK7fWa3D84Dsp4o/c/F4W+QaQ1TJrHqSwFYm8txfpDjzPYEiVYY+WqC2ohE2YFegl6mkSiFyliMaslWD4EuY13M4g7/H1YGZZogUQmSRCFVOvaXiHJbXjpI2ZEk5IRAExADvFGSCACyEo1KvRxZKrIi3WaX1sZgFZyNlQwAcHS+xLfd9tN4wTd9N37zh14N0zRYu+bpuP03/jPmBwOM7juPO4ZTVNMGJzcneNaxRVy/2o8NT8lXBq961ase8dpnPvMZvPGNb8Qf/uEf4nu+53vw0z/904/78x+zo3uxTKG9PuByIpvVoAs3NQ6ndhsvPK9x2Ge2AQBMFaPOYcDL5ta/phro6S5cUqHIBpga+4iymUGukSUK95wfxVKRT5/awepANjAPnNvFvetjXL/aw0IuEcVBrtFPQ6d3h1wrzOUalZEJ5+hCjp5f6OhqBJekGNoUU9+heNxIlkDlMxjSXoK+kqZQspiT8kM1HcLlfSmtbSrJcHNW+vmG7rSAZMJp3ZZpdyP0QNQ3lbLKFEqL8yJkFFklC/NxZTExFgt5gqmTspKQBSATmy8dDBqTzkpam+o0/0Kr6dadFOP/E4V/9sYfx4/9yOtRVRXu+fM/wic/+AF8++3vwMeO72KxP4denmB1vsSzv+9H8dn3/QHe8aZ/ip94/T8GIAZoXMtkVqQJDvQTZEn7XecyjYP9FFNjJTtEAcsqQS9VeNHRBRzfnmJUG/zZvefx3rtO46VPW8Xfv24FWdHHqY0KZzVwbB4o6ykUfOOqQKf8MIFFooFDc2nUQpv4BXYvlYXfS65cwpWLJT52xTb+7K4zGI2lxE1pBV0or+0mf05vTdBUBkkqGqe5z1IYlCmuP7yCQ4MCi74k6cyuZGAdnMtxbF6iwNY6nCvTVu4g0xjkJTbGNf727C7cgTkMctmYf/j4Jka1wQuuWMR8Ltk148b5RnUac6mOE32eyEYSrm71bH32pVaikXVq2OCgkhKylU75z/lRhTPDCvfXYxxdKOOmwDgXF1D+wsZyw+e+4AV4wc03w/rFT3AOWB85DmOwdgq5Tx0IzqaFQjYl+sYbUVuL4dRga1Jj0kg5+aE5KZc6N6pwcFBg0hgcHCyithYnNyd4z6dP4SXXLOPFxxYBAPetj7FYpjjQ76GXKeRassc2xjXOjSrZ2CRSFl4bG3Ugj8wXWCwT3wBFStgmkDLzxVKkHnpZgnvP7UpH71TjwCCP+oDDqon/PrM9gbEO48rg/LDC0JfAbY5q1AslHtqaYJCnWO6l0ACGts0YLPwcFUoD5zKNqxYLbE0bfPrMEBvjJjbKyRLR2zsyX+DBrQxTn51wdlfm2kxr3Ls+wmpvHoNUFqNKSYfuUe1wfKeCdcBKKZu8RClMjcXWxMBCnDtS7g6s9gv0lXRYd51u7C7NYxmz0ymQdrKcvD6q8gsdVy5galxb6u8zg2NZcN6XBVE1ls1/PcHTlnvYmBgsFMAzDsyhh2vxpt98D65+5nPx0NYUp6YKz+zn+Hs3HsJffvYs1ocVVgY5diYN/vTec7h7STp5f9WBudj05PFCm0ebR5tHm0ebR5v3VLF5AGIwIXzf2kKCkY1UXtTGSpZqoZGjERvnpaLC0R3ajNE20GuR2hHmsj4qY+LxQpJZqlXMnh1OG2yN6ygjsdTPkKcatZe+WSgypH5c91IJFg9ri53KYMNXByz3RJd/LtMY5Do60keNt1VoM6tHtUNlDJbKFPOqkWahOpV71Hj99mLgS+99Jqt/vhtA7Lt/TaF99pML7R0ABcm6VJAAL+AlKCBrAOdCQApIlEPTSbMNGdnBliXBMeVta7C1QJBQkABCotrMQQnOyWfd9oYfxxt+9PWoqwp3f+CPcNdffQC3/sy/xQc/t4UztZFGj7XFK37gNvzNH/0efuEnbsM/+YHvh4bC9tRifVyjtjImMq2w6GWVylSjl6nYoBH+u1WZw1KZ4OBchs1Jg7vODPFX951HohVe/lVreN6hOfQyjZ3KwvpAWBH6NIRqC2djNYWyDTI0WMlT9LIUwypU3AH9TOzEUilBuiODAoeXevjre85h6B3RWZHAGIskNJJsLOppgzHEHiz2sujkzhKNtYUCRxdLrM3lvqLE+cqYFItFGiVJdiaNb7ooDYm1Au7fHIt0iirifH12t0KR6BgYzxKNUWVQG4NUp0iytvLMKr9W8WNKNZMomzQ2UgUlgYMEq70MVx+Yw85Eei1sTSWoUKQaVy2WWMh9o3QAiX9CnQMMWu34577gZjz/5ptjw8VQiRDWXqH/Cfz/EyXrskTJGvPwIMfLbn6u2JqqkQCWFamNftYGQQAgTzWOrfSQaI2dSYMT2xMc6OdYLFJfxSRriUGRYMHLEG1PLe7fHOP+jRF6nWBXbaSqyTjnm1BL0+l+lsA6ycTPEvn+tZctqbwcymK/7c1RW1mrnd+tsD6cRmf4qDJYH1bYnTZY6GU4uFDhVDlFbSzmcznffoZYeZbYtoJQnllgvkiwNshxYmeC07uy9luby32vAjn+oEjx9NU5LPWyGem4M8MpPnt+F+lBeVYKO0GSlpgaEwN9IUmkl+nodG+sVMGFxvYAMMhTLPWXoSbb4sROsrZRapA46VRVxEQV1+rHh4bwMpY6hPd0KjyVqTFfahwZSH8hANiY1Eifdi1+7Nf+AIef8RxsjBqMVY5FrXD1gTmc2Z7i5PkRtnam+PiDmyhTjbW5wieRSILF5YpI4+3v8Z7sPPzww7j99tvxzne+E694xSvwsY99DM9+9rMv6TMf8whZ94uHA/0ULpMu5+PaAZAmAsPKyMPb7yzqfPaDNFOSCUSZCjYtvf6SBvI+XJJCV2MUzQgm6aHxJRYAvI6RwnXLUvb6ufO7WB3k0mRkkMNah2euDaQcyzisjxusTxpcmxWA5FigMg6f35wiUQqDQnScDvQS6GoEVIgaopVxOLFTYbdOo/ZTnihUzuLETo2nLw6gRhsz5dLKWqAaycbff44yDYoEcBCnSJwsbNNqXF3gEIj4CUEy1VRctISVTaqB3EmGkFx/N6N71iVmGzn7iAdKKwBKoW4sGiubYAugTGRz+XNv/peoKlnMP+OqK3D/oMQPfvW1+O1PnEKiFQ750r//8qv/Gsfv+yz+ywc/IiVOTvSxjHMY+EygRMNrbmqfmeC8PqlGBd9oxBqMaoe1foqVUqLzJ3emuPOhTQDAdmXi4sw4h1xDykWBNrLov2toeKYAVLZtkCHXwIkupJXPPDxIsdKbw+FBgWccmMOf33sOJzcnGE4aDMc1nL/uzjo0tUE9bZAVKfr9HEdXeujnCVZ8E5NpY7E1bXBmWGG3anBwLsf6pEbjNQ2Xexm+yuunAfB6oVI6vFhKRP/EToXaWqwNCiRa4eTOBA/6G3dmtxJNzUmDp630MchFJ7BIfPmo7VwLABVSrI8anNiuUPrGGkWqcGy5hzM7UxSpxlIvw7lRhbO7FW5cG8A6JxF9hEwYFzct006WG7Q4w6xrG18FgxoWm9Y5VFqyDvJORHvgS+Ab61DPzTYoksWJZBuc9R26e3mKK5d7uPZAH9uTBvdvjnFie4p+pn3UXeHBM+v4vle+DD/3tl/Ac256Lo7Mr6Dw37k2Flct9jCXa0wbh7O7Fc6NavQzX+5cG5zYmsQIeC9PxLhujrE1qmOpNoDY2X1cGWyPayz0ZAM2nDSY1O3mbThpcM+pHWyNKqwOCjxtdQ79bM7PuA5TI+W/Kz1pJBg2/qG0/nmHB9gY1/jkyW1sTXs4PCh84xXtM0N6WJ/UsvjLe+LUsBZ3nd7BZ84O8exD83jW2px3YEq5uXXyfI9qyWqYz0WvNU1k0xKatG1NxQmQ6TJmjKyPJUOuMg1W+ykOlzKXuySPVsSoVLLenAXKEo2fPxsXNrt6prmNgUbqrGxgfElcmSocm8+wMTG4drkvpajzL4BWCs86LPP9md0prl/tY+W5V2BrWuPB9THWh5JR+MF7zwEA7nhoA888vICbrljE44U2jzaPNo82jzaPNu+pYvPKRGPSGAyrBDbTMI2NY3ViLHYri6UylV4OtkKsgTcVdMfxoYDYf0Er35TYZ0hn9QgLuTRYC9nFwTl2dEECHhujCpujCmsLJa5e7sEu9VBb65snayz3pFKiSGU+SJSKDnNxbokU13zuK32aMVRt4LICCjl2a5/96x3pQXJjc2JQDvrIm2l7UZyFqscxi1H5ALYDkHktXMBnvdtmRo+7dXj7z7LNTEA2URrWa5aEWcw4wCiZhbRqz01jtqmd82Mt9Y3inNIxUBx00a0DpsbGgE6eqCiRYgH8q7f8S9Te5j3z6itw/50Fvu76wzi0NMCpnSlW+1Il8Z9++edx9oF78Sd/fQcW8lbqKQTJykQaHfZSGe8hmBu+t2SW6uhkW0kVDvZFX7e2Ipl0YjtUE6QxODs1Fnnel/VU0nFX+IoL56vn5Hq6tieBddGBm2mF5TLB3Nocrlrq4aYrFvCJh7fxwLldPHR+BGNsuyxxLv5/Oq6xBWCxJ9JXImmSxUoBYx2mxuLkUDSwC//dEyWBGWNd7AnRz5JYsTf1a8epT9GvfZAz/Pz8SGzekUGB61Z6UBCHZZnqNovfywRBp2gcMDUGw0p6J+Ra7vHqXI5jK33Ml6mXyWhEdsO4qOEe7JM4Kh206zi9XbsmDecJKDhI5rd1Lq45NJRUJzgZW0WisdKThI3KpDAuj1UcoTGkaE2neHhrEm0eAIyrBsfXG5HpWCyxUKRIGgnYuu0RvvWlfwdv+Vdvw9NvvAk3rq3hysUyBqATBSyWkkUvFQaSLS3PlcPWtMbGqA2gbY5qPHBuF+vDCj3flBuAD1Q0MNZhUhtYvwjQqUaWaNTGwjYW2+Ma950ZYmtU4eBCietW+76aQcF605hph1TLExeCFnOZBvopnn9kAXee3MZ950c4M5zi6GKJ1X4eNcD7viJtWDVYLFMslCnODSvceXwLD26O8ay1AZ623EOeSJCjMvJHKSD1iQFhDpkYi52qQZn6oICxmBiHyiTIkwFq5zCdWkxGoke+XCZYKXORbgrPXGjC3elJYCCNsCXIEUxC2jrF/Xyn6glgaqjpECu9OYwbCWbdeHCAhSLF+PCqz97XWJvLcWy+RD9LcGhQ4NyownEvnfbJ41vYHNWoGotjKz0888gCnn9sCZcjlC5p2draws/8zM/gF3/xF/G85z0P73//+/Gyl73sCfnsLymju5cmaKzDyTFgYwmCw24lD9m1SwUSJ42gYgMpoNXy8ShnZfEDeSgqZCjKFGo6RC9XGDnEEpxAkSi87OpFFKnGg1tjZInCcNogSzS2divcvznGmd0Kh+cLXLMkEdOzjcFKmaCPXawcGsQSQsBCTYfx3JRfgK2mwLiX4TPnR7hqsURtpDROQ0r6DvQGWNGpF933ekZKS2R5uiuL107Z9rhxUCpDL1WS7dc1ypDIfuIu6HjbcSh0G5QkiRJdQw3k/rqlWrIYXAPfKKfV0Oul0sQn00BlFYa1QW0dVkopLTEO2Jwa3L85xRXzOZa8fqOD33j0+vHY7/y3b8ev/9a70Fjgm75qDaPa4GMf+TA+d/+9GG1v4t/8xn/wpakKK6VkUJwbNzAWODyQRdDEL5Yb2zorilRKeEJ5ulby84mflAeFjJtPn9zBfedHuPnoIo4ulDg5lAjokUEuOoE+CyRk3QTdVynDEqOdJQpjX9pWJv46JgqbEyn1WZtLkeo59H255AfvPYfPnGyQZ6J9aRoLpRTSTIyEcQ5boxr9PMFwUsPkKT53bhdrC7I5A4BTO1OYrQnOez3U1UGBPJWIfT9PsFM1cTGRKGB91OCus0MYJ2VYZaKxNWlwstOlumosHoTc52uWejBOtEYRi/49tkGu044Wb+0zLRSuXJQstuBQG1YGvSyZcbSF7JL4cQ7xZ/L929JUB/hmKnIti9RH9rVus3nCOPZZP6FkVDIgZj+rSDRuObaA1X6G+zZGOLU9xadObMWF0NpCgflcNvTnRxXu3xjj4bs/jodPHMd3f+e34+9/47fgbb/27/H0Xg/GOuxUFmd2p9g638TvUluLDZ+FcGpnOrPRXx+KXltVG5jGIkk1tnYBpRVsY2Gtw2RUwVmg6tUwodGXVkhSjTRLYKzD+rCCsW4mSyAc3znAD2+M/WZykCdwvsR9Pk/wyutX8Rd5go8/vI3tSYOrl3pYLGXzWBvp6n5yPMViIZ3CR7XFIJfsys9tjLAxqXHNUs/rWjY4tlBikCeYGBsdhIkGqtprrCqJ8GeJ9ht82WA9vFNFLcKwcTnUL6GbicxnKsWosRhkkunmlEZlZdMw8fN4nogTddw4FH4ebIyDTkuoxIozWKcYTxxqa6VTN0SLb7FMcc/5kTRoyVMMCmmOd2S+kGy8a4FRI461LZ8NcnI4xeH5ApcCbR5tHm0ebR5tHm0e8NSweSGjurYSmAhyCaNaAlrWOhwZZOJc7WQTB+dvqO4JUieh0sFAw+gSeSmNQUODuK5DrZ9pyWJdG6BMNE7sTLBbmagtWxupGtHeCTSXJ6itZC/OF9InYbEQx3MCub6YdqRolD9HBUx9pcggT9GPz6VUbxUTg7W0EOe2UnBJJuX9TRX1bAEvpRHuq0uQeJsfn/+O3QsOweAMj5/hLHIFOK07DvPOMwPAGDlf4yxqq5FpB+s/vEy0r1RIUdU29g/oZ9IzwliH7an1z4lGPxNb6CBjvWvzfvOX345f+6134fAgwyBfxLAyuOvOj+Bv7/ksMN7BL//mb4smt79fvUwh8ZrgQR4l8fc1BHadUlGv2ninjgMwblp7dGRQ4KGNMT51YkskfA4OYK3zQbsCu4XGXFaiF3oiOIsGGom/9uG6SWBL1gK1dTC+64nyNrGfSVZ/sTbASpnhxME53PnQJu4pUmxuT2KAN/cNCE1jMfF9JlYHMp+tD6cYVw1WfVB2Z9Jg7BsGh4Dgqq8eMtZJU9+qDQZuTWqRn/LHmvfVUCe2J7HZ4PmhaFZvjmuMaoNrlntY9VIVBToVTN6BmKq2Sm1r2ohjWEuz2CNLZbz2ALx8SRIDArYzRkV8ySEk/BrnYP1gdt4R7gyQOsDq1kme+Oexm3OgFZBBgr0u89ULNsifyHvLRCE5Mo9BkeL+DdFTD835qsbGNcTZVCRQ8lTj7Gc/iYdPHMetr/kOvPyV34Jf+Hf/XpoR21DRJH/CtR7V1jvBHc5sT3F8fYThpMHGbgXbWC/XZtDUFmmmcdx/qSTV8XVnna9wk74DaZYgzRIo/96zO9MYHL56uRczskOVhlbKB8udT3mWCFSZSLDnlmOLWChSfOrUDu45u4t6xfmeOtIs2jiptOxnicjclBmGlcGZ4RSfOLWDc6MaB/pZbPpdpgl6iVS6hPVxZfxzYRxq06D0DnsAGNYG07HzUnNy7dJE4VkHB1guE3FYJ3nss5FoaX4aApmVv2fQKiaXSJNhqQbSIeCVpAB6qLM+hmPjVbwV5osEh+YKrE9q9DKN1X6GA/0cgyKJFQhaAc21q1G67fSuyPGI3exUzVxmWCt9JfbzeE9G3vrWt+Jnf/ZncfjwYfzO7/zORaVMLoUvKee/l4rhzBPR25vWFtvTBsOqwXJPSsnC4E+UbzrldTyltK+TEZaJzhSSHJV1eHC7wZHBAHNANKaywOkeX+PvXLWAjckc7l0f+1KWNGp9HRkUODQnD/tnzo2xNsixkGskWQk1HcZML5eVUo5oGrjUn0eRY6cB7tvYjVke/Uzj5LDCoUGOlTLDuXGDpfleLFt0YVFnjTgCnI2fq0yFXlqitsDm1KIyCRJtYrmmA2Rh0HWIeC27ELENi6pQKhualISmEAoWmZHXlAo6pFKG2VjERmTWl+SW/r7BWVRWYWtq4iQRNZgA5Bp4/s03457P3i0/g8Y9n38AV15zHQ72E5wdAf/Lt30DAOC/3XEnDs9lsSR7uxL9vYU8QZh/gpFLtULVyCKtl0r2wa5ty58GucZ2JVk2g0zj5iPzKBONDz+0iSLV+Oz5XZwcTrE2l6NME8nacZJFoABULonlU42feIHWEbJYSBfwjYlBmSqfcSALs+2pGJTDgwLzeYqjLziGu84M8cnjm5Lx4cty13crTHYrTMc1zlcmZj6tLRS+eUkeu34b62In50GZYtMbw6V+BgwKbE8bbIxrnNqZ4sE8wXyexo3KfesjDKdNXEjlaYKtURWbpJzZhS/RnoOxBkcHaasT2iFPNFZ6KXZ8tD/TUkp3ZrdCpqW8GRCnXlh86ZAxAjxi0x54tPmy9gYVABItGTOhhKuLZM+1m33rpGEf/P/X+imWygGevtKL3bLP7lbYmnqtNa9VNm0aFKnGdTfehHf8Px/AcLiDleuehb96UMrgT25OYKxFnspGfGtUxfsQqBr7iE7rearltZ6U6YeFVp5qFKn2nyvZcNvjGraxcdPfy5IZzTdjJYMs1Spem7DR2JoazPkmQUFnGUDUn3zF05axWKa48+FtnNltGyGNfFnptLE4WYvOW1gU9DNpRigb4CmuXCxxoC8OMtHyFIeJNCJE1Ek8N6oASEMfrWTj7/z9kA2VOPaOLeQxsy1RwE5tMchknlJeqzk0PwmyANJ8KmTbINoR3UwkOyrNsdVIGfpimSJXDv/Pb/86vu11PxCbpj20NRH937Q301hpLtMoUhflIaQM3sQF76VAm0ebR5tHmwfQ5tHmfeXbvPkijU560VOW7NOQibvcy7x8gmolM5yVRmVq1uGgfE8GAHBJia2plNaLRqxILFnvbLNOxoGUpSv0swGuXurhgc1xK42RKGRaelccnMuQaDm/3dqinyloNwWaConzOrI6bcvwOxmG04nBuDHeedQ2DLYOvkLFYHGhj9L4a5nmcN1GbArtdzUVEp1jaoG6sXGMyzOtotZ8psUpHIO6QHvtvNbx2P9+0NHOlFQcWScNXZ1r5xXlNZUnxgLQSHQ7B4UxEqiMOPXzkEluGyidQimFF9z8wtbmKY17P/8Ajl19LVItQeNbX/X1AIAP/PePYrWXoPFO5ET5iiot4z1o+odAUdtvxMEakc+aNjaOYWMdjBFn93Ur0pzuc+tjbE9q3Lc+wiBPcGS+8P0C5JqkWhyODtqPGXGoKZ+9nmiFDECWSFXXuBHJM+cDwGmiEMKQi2UKoERxzQqOrfRx98PbcX6T72FRTRrU0wZbGyPcPa6Re3mn4DwOc2PVKJzcnMQg36BMsdjPYtDoRDpBpjW2fY+BJS+pY6xUXW2OpzizPcHYN7rM0yQ2vNyaNji5I5V5ZaqQFrk07WwqhAa6cBa9NI8ObLFhItUxn6e+UWs7N4cs0jBCop3y4wpArI4IQQ2l0GZ7Q+ayIJ8mwWP5oGDfwlKkawNT7yAN7ykShSODDItlgmuXerHHxE7VYOznG63g5x+x2fNX34B/+pt/DDcd49gNz8F77zmHMpWA15ltqYbaHNVRSivIrPVzCRRKEFbWy+PKRImuxX6GeW/zdiZiXxvrcHZ7il3fwDRRKgaCg90L+tuL/QwHF0oslhkyH7RqrPPXXYLyRSrvTbWE6I0PiF41n2EhX0CiFO44sYUzwymyRKGXJd7m29h7I1HSx+WapR6uW+lHmSZA+lD0s8Rrt0s1ycT33AFCgEH0ykViT8cmsda52EsmHGO5TJC6JgZWwgjKFQBIckUITMgrcr9qH+SaGPnZIE+QGel/4fI+1scGD2xNpMIqA/7Tb/06vv11P4jTu1OMa4s0UTHoEzTWpUGrQl6K3N9XHSgx9b04dqsLEh7IZccb3/hG9Ho9PP3pT8c73/lOvPOd77zo+/7gD/7gcX3+Y3Z0r81lMeK3XRmc3JniQF+agly1WEiGkt9QJkGIvjO5Op0CGq2mJyAbW9vAWI3VXoL1icGZUYODfcnKOTsyvrFBOymnWqJgi8UAO1OD2gLzvsRytS/NK2rjcP1qD6WfWFRVzXwX5SxcWsLpFJtTyQhJFHB6V7I4+lmC86MaxgHnRhUGeYq5XBrmzGUljswNWv0iaCmpMl5/LWj3GQuNCZKkxHwuC+JRbTH2zToAMQYT49rsLsgkFTdbzmJYWzywVfnGHpLJ5HyEMtMKK2USr0twkDjMZgzBf1YopTNONv+LRauo1NkjQwH4ju96LX7/d98F5xye+exn400/9iN4yV/dgcok2Dp3BgDw4PkdlKk0QjBWShs1pFxJ1sIKb3nzm3HXpz4J4xzuv/9+/Pvf+884dmQNqVbYmRqMGsnmGeRi+Htpa3WzROH5Rwa4frWPjbFs9gaFlCWe3JniiNdxtA6xDAze4QG0UWzZ0MlrGWTz31jJKkqU8w2RFCrrcGJbdEbXBjledvUyblwb4KGtMc7uSsbTtLE4uz3BfWd2MRpV2NqtMJxKGe/BhQK9vML1B+dw8NoVnN2t8IDXuzRWIspboxpboxrH18c4tTnGyG+6l/oZ1hZKnB9V2Jk0OLs9wc6kiWXiS/0MvTzB4YUCi0UWS+RO71Y+K0kj0zJ+kk6mW65bg7pQSHaqtW2DDEAWdUWi8bn1EVaPzscxEJ8XIJZBhr+BR/47jL2QAWeNOAFC5mKI+idaxcVQ+N3w+11nQq4V1vpSKlVbhyODtmkYIJ+9U1nvCLA4178e5aTB5qT22VFyXUd+Q7zYy3BsuRe12w70cxzoZ9BKoYxGXzJFJ43Fg1tjXLXYQ5GquCk/tVPFxedu1ZYwntmW0v+wmArOoKetDXDjoXkcXSge4USxThYimZZnIDQuaawsShzkGXruoQH6WYK/emBDxnAi2U1FqrFYprjvvDRqOj+SqP+RgY7fr+dL4kPzkzxRODtqkGvJlpXSQnGejGoTO9f3swQrvQwKiBqDgLy/l2pA5zFjVxxnfqwhnXF4hI1s0ICujMNuLeWVgEWepHCpRP7/9twu1icSpcdkhL/48z/HP/jeH8SDW5PYDO3E9gSZVnjaSh8awOakgXFSCt9uMEWTdSGfUYz7kqHNo82jzaPNo82jzXuq2LzCZ1uOG5EpGVbi9Dk0l6OXeQd7ouLcCmCm90IoZw9ySq0slZd0sQ7jWhxQ/UzHubqxPrtby7Oz4DX6V3oDjGvpG9H4YMFcLhU0wQkXtN+dSqG0FQ1tIDqUo844JOt5WFlsjOvovN+aNNEGLvcykRAaNTjQW5jJoBW5iM7F0iJTlgDoJykaaEz8OLDeCaSsNNxsdOswzEJmMhClJ7rauUUizyaUOI/k+yX+d5U0dgZQ29bRm3TmEwCxcgz+9xfLrJX5Um1lyWu/+7vwe7/723DO4YYbn43bf+xH8Mcf/IgE08+eBQA8cG4HvUyucWVaKZsilWuvlcLPv/Ut+OQnPglrLe5/4AG86z/+Z6wcOAgg6NBKpnBupSorzI95orCQJ1gpU1y12MO5UYWTO1MMvDZzcNhrJc7XqZF1QHSWO9ExD/YmzreZig03Kxsyl9ugWshsvmqxh+WeNK4MshY7kwbrwylObk6wPa5RTxtYYzH1TsWdiQRj81Rj3jtRE62x6nXz14cV7ju5A6UVDi6WOLk5jvNDP09wcKFEP08wqkx0zI4rg9VBjkEpDtc5X2kQpJukWbCOVX/zeT/qvMM2Xt5E4UA/R6rleoe1QKDyvRm2Jg1WeimKRM3YuDisVavd3f2IcO2CvYo69waolPPBnNbmhSSF+Jn+OCHIH9Zq85lGb0HjqoUMlZ8fwmGDw3i3bhvM7hxa8D0jDHaqBrtVg5EPwK8tlDi20o+2aD5Po5xcP0tiv4oylWd1w2vMB4mQIMd2fHuCM7tVlFXJU3EId4PGY3/Mxb7ooV9/cE7GrL+YqVawTmHSGBirYZ1UY3Tl9mTdIHPhMw5IdcWJnYnIJo7rGFjOtEZlLI5vjnAmT2PPjSO+eiAEVUov51dbh3HjfCII4nM4MW1lzqCQQH+ZaN9wWmHBN8TsZzquS51fQ8ucoaJEY+Wd6HGNhFaj21iZ02T+scjTHEhLbFUWZ/0zrrXChpngr//yL/Dtr/tBABLMrqdSeTbqZTg2X8YgX+McnHNobNuEdS5r+7ZcjkiQdX+P92Tk1ltvnZkvnmiUc49NtOWzZ7bR+MXGzlT0gvJEtK/mfTfy0BG52xwJWrqQO9WWZk2Nw7lxg5UywSCVsjZAHhZ5OGUjBgCbE4NepuLDlGvVNqvqnJ9z3fJTnw0GMaSDpO1KrULDMB/lN/5zlW0wdgnOjxoc92WLYfFTJhqDIoG1wE7V4Mh8gQO9FEs5oKrRbCaD1jOakd3jhJLVMLBD9EsDbVRatYuUiX9j+G6hG3IX5xCztMJiJzgM4mK0M1EZ1zZgaazD+thgqUywmMv9Cwuvh44fx41f9YyZY73oq1+KI0euwGhnC+9735/iZ972Dnz7a/8hykQm4co6LBVJXPx88C//HK/6xlfOfMbffOIuXHHsSjQW6PvF08TrSoXGMrWV+7hb21hOKiVJFR7amqAyNpa3PfPgnN/wBkeKi1kVoXTU2PbahU1nY32H9trCoi39kuwH0bEKi/5R4zCqDI7vTHBie4LzwwpbI3EC7EwanN2Zes1V53XcUhxb6ePIUolDc9JcCgA+fXIbd3x+A9W4llIopdDrZVjxZXG9PMHqII+ZWOOqwaDMcNVKD8tlhkGRxgZNk8Z6g5RISbjveP70lR7Wet0Vr8bmVBYFeaJxarfCie2plNBZh1Etxvra5T62JjW+9tpl70hx6E4Mkj2BR4y/i43FMLbjKSBon/r/d8apOLswk/UZjheembBICiVSiV+shIWuda2OauGNs/H3NxA+C/5cglZh0n3u/Ly1VQM7U4MHt6aorcWBfh47lZ8d1fjk6R2c2pl6nTC5t8OJLLYSrWLH9iNLPVx/cA6LZYajvrxfNvUuZkUEB0iqVWyaFBaofb94mfosmVFt8ef3b8QNkfXl/plW2JjUsbwtNHGprfWludKQpfYL/lD+VVsrpfLrY5wbSdOua3ypeJ7omGVcJO05LZcJauOkSVgiWQ/blZFGREUSs6PCJiPTraMnzDvDShat84Vs1j+/VcE54IHN8czCaaWX4Yr5AufHNe74/ClkaYqFwZwv5+xjbS7DwMss7FQW53YrZIlkIyx6B9e1B+YffcB+Ee54aCPagVFto7TCSj+Vkj4A84kVR+9lYvMalWJrauL7diqLs6Ma92+M43wQutUPfAnvqDY4ulDi4JxkeYTzic9WZ/UU5CEa29os48uJu5mbIbOqOyeHuT5sc+T7ITpsw2uBMGeEuSW8L8xV8u/wrEnpbWMcTu/WODgnYyTMLY11OHH8BG656YaZMfBE2LwPfOSTWDt6FRrv1M4TjYmxMeO7TLQv43cY+RTfSWNwZlhhYixO7Uwf1eZZ5zCsxfkQNGet/75xXvMZMY2RLJuQHdXzWo3jRjJBD83loiV9iTavTNvmWn/z+XXc8+Am6qlBkorNK3oZjqzKpm6vbV6iFf723Ejm64lsHMNG9RlrA2xOanzD9QewUqaXlc2bNBY7VdhIqvj7weY5uCjto9A6lZSSEvVEt+cCiEb1ztTgc+sjWAes9kU+o58lOLE9wT3nRzi+IbrQoRFnyJoDJDtuUEq24/WH52OmfsgynDYWgyJBpvUl27zQwOzcsMKoMljqZ1jty9itbauTL/q/ra2rvZPs0FyBe9Z3cXZXMjCDPEqmdZQICXIoxon0UbgvIdNvWFkvD5BE6Yv9sHlhL1IbKTXfGNcoUh0DsI11eM4laHTfe2Ybtd/njGqLfqax2suwWIizP4FU4MR+FEGvOklnZKqMg6+oEA3yxSJBqqSCYeJla7pj13Tm7K5N0EDcD4QxHp6jqXGYNM5nt6t43ZWvElKmEVuclnGfV/uqn42J8c5ug41xI/u6QSFZxLVBbRzWBjkOzaWYz/RFs7C7TSABcQiFYGtY3wW7DYjtDtnF3U19qASYdq5J13kTgmgXzhsXPZfOeYS5IuiR91ItmuVo1yoPHD+OG54xu8974S0vwdGjR7G9vYk/e9/78Ja3/Rt8x3f/QwQNYOuAvpcJSzXwV3/5F3j1N83avA9//C5cedVVcQ2QaAnSNXb2/hrviG6Mi07O49sTL5kj53l0ocTVi4VfX7r2Xhr5d6YlSzbY+e41a6w4eMP1A2SOHHnH6aAT+K6N8/0mpjg5FJmLrZFkGW+N6ljFAgBFkeLwUokjSz2sLRSYy2VumzQWdz28hU89sInJqEJWpLHR5dJ8Ic7XMo0Z22FuH5QZjiyVOLpQ+n2CnFOmxfYFW5L5sbHaT7FYiEQFfKXJ1tRiu5K+AlvTBvdvjLExqWXt4+cyce6XuGaph/m81ZBvxyIuSliTdddtXcLvhQqGbhA3BHzD72V+/neYfZ6Bdt4P4yP8nnOydpl652qwY4FQDWl8wDTRrZ0LFRXhvML3HTcW58cNHtoSOaqVMsNyLySA1Ljn/C4e8DYvBHJHlYkSNWE8rQ5yPH1tgKPzZXSWp97pb/z4mzS+6XORYpDLz1vfTVuxOKwMTg5rfG59hDO7FYZTaSZ6xUKJRCk8vDNB1Vis+saVRaqjTSjTdj078v0rwvgJAY5RLdrwRaJ9c+osBt2tX2+XqYqSSGUi5xUCXFoFSZRO7wDVrneUavfelZV1fpjjx43sNU74xuvBOX9kUHhZRoNT65vYqS1MUqCfaRxdKHFkvoj9g2p/D7SGt/Pi6L5yZXDxgfskZXt7G4uLi/ilD3wavcHj36N+qYyHO/jfvvZZ2NrawsLCwr4d98vNY3Z0f+Bz55BphUNzEnVbLBLM54l03QakrMu0JcjQvkO5b5oQtLSmxmJ9bJAo4PAgi1NVdMRa50uBLVZ6KZRCdILOnLhCdBB36Ub/LMIEImWIS4XG+sTg3Njg+uW8XawBsjCyDWxaojIOn1mf4OSONIDZnhhsTWscmS+jk6ifSxOy6OwGZhcbaS5OY19m5XwUOrGVlI+H8m3fzTYspJUvrxt3omXOO4/CxiQYgS7dBZRzrQOwd4F+UViAwV+7EO0LznFpLCCZKm/6yZ/E//XWt878fp7nqKoKK6urUDrBnXd/DtPGYtxIg7B+Jg3b8kTh0FyK++67D2dPn8LC/DyufNozgCSL5d3BKTKsQ3lZOyn2UoVhHUrDDe7fGGNiLIbTBgf6Oa5e6vnGVIiNG0Kk3znEYwQHSnczMWnczOQbnJ3j2kqWBCTCGxwQodFUYx3O+7Lrh7bGvvvyFGe2RUusamzc+A3KFPNlisNLPYwraWqxOijkWlWyQdyZNEi1ihlueSqR1a4RTbSWzLdBgUGeYLFM43UDgBsPzgGQjVRsvqM699rJz3qpwsbU4MR2hTO7FbYmddsMxThct9LH+VGFl165FDMNu2Om6zy6kC/kCLhgmM5kd+nO4udCJ0Db1bvdsIdzSLTcw/DvYWWhlER3+5mOzwIA3H777XjVt3wzXvLiF8XrASDqQwZnd8yOcBY7jTSi+tzGGKPa4M7jW8hTjZc//QASpfDfT2zi7oe3YfxivOr8ESNvcd3Buej8ueHgAFculP5+tgsKoF3odp0ZC4WOC7ZUy3WSDYYsHj5+ahi11ADg6ELhN/OiUzfIExz1xytT6Tg+rJpYGjgoUsznCU7vVlj0GoGZ1jPljVqpuNEIGqmH5nIsFnrmvgXn5HwhGo1VJ8BQJirOV8pZTK1ouAUNx5VC4dTY4r510Sc0zuFAP8c1SwVO7dQ4OZxKad9SD3d/7CPYbSwOPeMmWOt86amUUq+UCSrrsDGWzevJ4RQDn83xd68/+OiD84uw8HX/fHbsJu2mSOkES1c+E5/6xVchq4aXjc37+OkxXvm/vh3WtrqRzho4Y2CbCkrPZgQqncD59+osh9IJtH+PtQZaJzOfdamEY114Hk8kzppH/fyvvuI8/us73zHz2uOxebf+2w/g3r/5BPTcQegkecT3Cte8O6YAxNfC3z/4w9+FZx1ZeFSbVxmHV775z/DQx/57/IzuPeu+Fo574fEAIOsN8J63/2Ncv9K7JJsHAH/y7j/BdGc9noepxrPXc34FX/eab8LAb/j30uZ94swIr/on/wb1eHjRa+KswbHn/494/0/9vRnb9GS3ef/2w8fxC//Xf7joOJ7c836kazcgXTz66Cd5AWE8xn/7Z0RneZwbHjF2HmWMhb91OqvTfesPfRd+8CXXXLLNe+tv3YkTH/+rmXNQOpkZ993r0n3ew8+7537heV7Ihc9ol6wc4L/+0j/GsQX5jL20ecY5/OD/8XuYbJ9tv5sxM/MzAGy97//8gt/nC/HZM9vRuZRosSE9n7kbHchNNZPc45IURuexESIga+kdvy5b9M79sPfoJidUxiHV4sC7WGAp2DJg1mFceaeXVNTIa71UMr2tk2xG5yQbX2RY2gfZ+DXLbm2xOWlwereCtW0ARY4rlQMhkBEcjGGP1XXOXejULjqfc6HzGXikAzzYc3nuXZzrHC4+d8jvXLBuRWc94dr1bZAQCc60EGQK73PO4ad/6qfwr37u4vu85dVVaJ3gv3/63uhsDPdFsvTFIXbywftx+vQp9OcGuO76ZyDN8hlnZ5hHQyC7DUSLjZkaFwOXZ3ar6JRd9Jndc/4+djO8u85ToA00hfkzOMC7163bsA9oGyM6J9mu00bW7+dGFR7cmuDhrQlSrXB+OMX50Megs9YflCnWFgpctzZAzyddnB9WOLU5xvlhheGkQe33WQfmiygXBci838+T2PR3UGY4ulTGAG9tpFnfgX6GI4M8OhdFa91rovuvVlmpnpsYi8ZIwPxcR/aqNlJFsVCkOLZQxozuMG7CPbI+QBrcQuFcY5LCo/xM45FJeO24RZS2CWNR+2cpBH/C/Nil8ckJrhO0KVLR/b7wefyp29+E/+mbvhnPfcHNMwFcjdZnAsizUnnnc6hwG1Zt0kFwhp8b1bh/c4ytSYOzw2l0dofvPSgzn8mvsNzPsFy2geEgd5X5RICwbgnB/yLV8XvkiZZm6z6YIXrcIu/14JZPMtitsDonyUtBVzuMf+2/Wz9LsDVpfPNtHa9xCAxNvdZ3cJwvl6LpPV+kM45u52Q8ZFrFKo7wnAZC5n547cIggnGSdNHVbd+uLO7fnMRA+pWLJY4tlBjV8j1rI83Ij9/1MUyNw8rTny3VjLnod0uQ3NsItL6cXEsT3GtW989Z/EQQHN1v/7NP7buj+4e+7tlPOUf3Y5YumTYG/V6G2rYLFwCYOpmkS+ej56p14LZlw61zNddSFhGMTR4XOPLzMPGdGzU4vVvj0FwWHQaLhY4OypDtpZXzJYEy4YUIWZ60pW1KyWQ89M06Ds35c1SzjYOc0tDVCCWAm1ZSHJ6bx8M7Ffp5goUywSBLUFvR/po2Fg9uT7Fdplgu+5LZV08QO3ObRjqMA14nDggdyoNunGomcM4iTMkT46CRIHFBz0o2PtZJB+nwEVPjoHx5LODLgWxbtqwgRk+cHuLI7WXtxjjViJqsNjgFw4IMQK4s4IB/+oYfx7333Y///B9/L16jypfEf8M3vxr/7YN/gV9429tw6/f/IHReoEjkvhon57ZbW6wduwZrx66J0U0pq5Jz3q19SaMOC71W4w1oy7QGucYNB/q+0RliVuzZUY1emvjNv8N/eve7sLW+jm/+1u/ENVcekayPxsXGR8EQt1m8otm2kGsoJeMyaP4ZC+x6x9+oNtipjC/3SXB0oYidnrcmDTYmsgGrfbbcli9xy1ONuTzFYpnG6KVkFEpZXC9PvRaqibpswaCKdphE/wc+O/fksMHx7YlfXGrccmwJvVTKsEKGqVbtgheQ+7s1NTBOx41jpkUDS5yc8gyViY6ZF/I5wcC1DUuUasdYQF9Q3hbe1z3+oxHzdLrvMeGZbV9UnfFdGXHChGaAALAxrqG1dPEe5KIjGHR7X3DzzZibX0DjEPV4Q0aa9Va9cg5bRrII5jJphrYxlgZPYTF9anOMP777DP7H61Zww4E5PHBuF/ed2UUvF23ScWUwqQ2ayiD1enA3HRzgxoMDDIoEp4bTmDUWslziVw6LIRsWdJKlNDEOiVUoU98IbmqgoPDstQE2Jw3uOT9CbS0+tz7GAd/AY7mX4dTOFGd2KxzoZ7ERTpA7ACSj+9zIxsXYqZ1pXFQAiHp0W5M6LtwODQpknWc3BIi2K4NFvwhJvIM2XN8JgBKAgTiv8kQ2/41fAFZOYWNcY31S48yw8lqrGof7KQ72Uiz3Uty/Ocbx7Qme9pwXYG0uQ2XE8QbAn6NsjpbK1DdrS3DAZtiaNBhW00cffI+ByebpL/jzvL8otis4Si4Dm7dTNRievv+SrstXMjf/8I+iv3v2km2eXnoaqukdwPThSzqfq1fn8MKji49q8/7j77wLD//lBzHaraGy/hf/wEehmQwxaWwsN328Nm9UGVS7W6iGG49+rPEQK4McR5Z6e27ztiYNxhunYKrJo57PeGfoP+fysXmfPr6F8fmLjy2LAmZ7A9WTTLtyZ9LEOfFSbJ4xFtPtc1/mbyNUeYmdyuyLzTs3qjEdrj/qfX8iiFrAnSzIUGUacGneZhF7e+dvZawyypI2aSXoBgcHr/Ibme3K4OxujSxRmM/TVmpJq+iAtK6t/rE+0KsRMoKBzGuJhEBAcPAGh3l00HWet+B4kr1FAq2KWLkVfqa1VJCI80kcUXmiotxD2Id0k4q0kgsVjiXva9cDYa+llIKzbaM/OBevW5duFZRWrdNaKyD1x67MrOM9OK+BzncPXiF//S+cu/7ZG/857n/gfvzB7z/S5r3yW74Vf/2Xf45fecf/jdf+zz+AvCzimriyDhYWidY4dvU1OHr1NbGCVpKLQibx7HcIFX8hUSIkcc0XKeYLSV4IlZnB+RakbRLl8J7f+x1sbazjVd/+nTh0+PCMQz18dnjN+fsdnctQyJPW8RokPirjsDOVrFetFQZ5ihsOzMUG3RNjMfaZ/sHe7UwkyW/eB3hD5ng/T3B4qYdenmDcmRuCjSs6yWd559/GOpzanmJjVEfnt2jW55gvpKI07GPLjpPaOC81VIesWxkLRapRGO2vu1SArfQyzBe+Wtk7+GXszo7bMHhis2a4R/ysi4WbsWkhmzow9Xt3rWRu7L75QnNpXRuI2pmGZsryrsUyQ5kq5FbFzPBEK9z0gpsxmBeHYeOT1RQApxSM39uF58S4tgI41QrzhUZjWq1368RxLNVt4lRfncthfEKQNHJU8Z4FWZRMt19Yq25AyXmntKyXhlWD2mr00gQWFiVCRdNsc95Dcznm8wRbgyJqVgOtTElsZKhVHKNB0zu+x3Xk7Px+L5zTqDYxez7TGtrLzhjngDSBbgDXifHG227b6rPgJFeuDV4kyjch9ddnt5a+B1sTcbKv9XPJJi80VnpJ7K8BAM96/gsl8ITZiuc2sCZyMOE5J+Sx8Jgd3YtF5hcFbfdiyd4G4KR/alz8APHvkL0YjF7I4Ej8YiFs+I0Li3uJqGoNrI8arzmkcWo4xXEAVy+V6KU6RpXCRFdbB+1Hfp4oZAlQmXazYKxMeouFlAWFzsKZlixq6FYLURprNTiUW6iFPC7CE6W8Bpv8e31c4xOnhzg2X+LQIMNaf4DUyIbKpTlC93HVVJLB3b0+Pns86NslEANgnDQ1ybRCoR1ybSXrG60TuBs9cw5xFRoc4Q5t4w2lpBR+u0Is1R5OLAZ5IotwFa6DRAEntZVGBs4hzzO869//Jt5641fhrs98Fu9+97vjeHjXb/473HjT8/Ff3vMf8evveBsWlpbx6u/6HnzqY3fip9/2y6h7PUyztgQpZCnOZRoT3y0+1So6t9syHwcHh6mxohHpS2ASpVCkkvk08e9fKtOYCT5uLO7//AN4+8+9Gf/i//hx/KtffSe+5VXfCmC2hLuxDmNj0djgKAJU1S6uFVpHeDASoeRnVBucG7UbWXkWEiyXWWyYs1BKyW6ZtE2vlnspijSJTSW0kuZf50cV7jm1g8ZKs64jiwMcHBQ+e6vdpFbGYly7WKa7Npd7p6mKBtWEBWMn+AHIa+dGNYo0R6ZVjKCOK5HlCQv0EPW/cBN/oS0JmsWA39R4p1uZ6vicRemCR/z2LMa1i+6wFpd74mYyBMLCTTL+Jdu/l/rGGVrh8CDHuLFxbgoZAADwim/85plFYbhWwemvvbM2OJKO71TReRqafwwnNc5sS0bH0w5IGfGZbXHOnPWNYmojHdrlujic2Bjj2MoEVy6WmBiL7Yk0YVvuZTEbWb5biLq7uMCZNi6ORyjJTrROMpY2JwaNlcXy84/M457zo3j/Jsbi2FwpGn5FivVxjY1xjRPbovcZxkNpNBZ8kOPE9gSnhyLDErQBZSGsMfCNqUK5mLFe29HP/8NarnmoAJhYF/UbwyI36DEDQOWAItFIYfGv3/FLeOHXfj3c4iEc6MuC7uhCiYP9FOsTg+UywTV9g6sWFrA1tXERt9JLcWgu9Qs0WfCcGjbS0KvIoixDyGjfS5z1jifdBk6BJ7fNa5tEkYvRL4snxOY11ROT5S7SaerRbd4D92Pjw/8BAJBc83XQS9c87mOFTe2l2LyTw+ljysbfGtX7YvO6ToUvRAz44/KwedetzT3qMfTS1Y/pO+83wbl9qTYvzR7bPd0vpo2JGfjA3tm89XGO5Itkn18q0b50nMRAu3YKFahAu9+wrh2rlZXAauGdwsHRWJnWWaWVio6m2opcxHAq86VxDlkicinS90CaEAYbirifUNDKIVEypkJWble+K2h3h0BKcMLKcYJDSbJErZMEBGnc5zBtDGqlfGNF7dfKeiYgpf3fYW4MmYbBYVZ34kzB+Wyg4rhXHd9g2MsphSg7FGQButcaCPP0I5sJXug41ipcE/lMp1oHUTcpSicZfv3XfwM3PetG3HXX3+L3fq+1eb/9G7+GG296Pv7fP/h9/Mov/mssdmze7f/6lzDo92dkssJeT7LsZ7ODAXGOB0IBYbgvQe4h0xqZbjXI4/v9P+9/4AG84+fejP/zTT+Ot/+738Irv+VV7ee4VgpUKQdYP6faVkat4/ePUhiNDXJQDbanjVRte2dn7R2KmdboZSKNd3i+iBWPgCTbVD5rupeLDFdwYkfJFC95sdjPsdTPfOKZjVJa82WKkZcGM9bhwCDH4UER5TQSJdUCIu/RXnOglWNx8Dr7vkqinyWwnQSIfpbESoLg5A7P8MVsV/e1buZud40Z5okwrqwDGmeDR9t/zuz6IozjC7ODg+M08cl4C0Uafz6cGowqA4UExnrde62grMPf/YZvEv+Fr+RIXZDSaJ34CjIGQrAlkCgF3anCUMpLyvnM7CJtpbUyLc7iIMc1MVY0rn1CUGiYm/rnwDppOGoVoiMZ8H08lIWSjvXR5wC0zRyzRGPZN4LWWsFaFzXGrZOglfERxlrZ+F3CZ1jrZq6rhZv5+dQnbMpnWmS+cbtxPvs8JsK11aSJVjBog1fOKdR+3gtzb6gcMdbhF9/xb/Cir/t66KXDGNUGy2WGKxdKrJQSuClQY3Exx4F+gmElVRJFGqo3EKt+5L60SZ3azxl7qeu8H8RgxT4e76nIY3Z0B03WXqpiU4jEO0cVEMu1g+M2TJ4ObST3/LjBak8mrhSqdSjCN9hwDg2A+SKJD9u5UYUj86L7uFMZ3Ls+xrGF0j8QKkpz9FIdjVcD+Cg8YJRs/qWUzT6iVARQyHSKiRG96oP9HHkmHbrHyNBYM9NRWoxlq7l0aC7HxFicHtaYNA4LRSayF1pJd3WgdXID4vz2zbuUm8i1SnMoncauxPCainAOjUoBv5BUSkXdXJl35QfdbG6gdWaETL9+JrqCW1PjnZ2iLVokrfOkiNdDssGjMSgSvOGf/SiszvB3vuZr8fp/8r/F49z1iTsBAM95wQtx0wu/GiuHjuCGmxyyPJfML79gACRaXyQK21MbI9xhYRay1kO5WqKULMwcMPK/3/iFXXeyky72KmZs/+CPvgE/9M/eiM9+4k70BnNx3HWj6GFxUFsJcuSdjDxAorUOFttTi9M+K7ZIddz0DysDrVTUZQyR1m6JT21t3PgUicbJHbH4YcFTG1lQ70waDMoUvVxKuIfTBr08wW4lC6aQDRCacaWJaGct9zIc6GczGp/aBxKmDm3GKCSzLdzTg/006lUa67A5rmXhVaS++ZHuLATbUvFA+G/Y+LcLF3k9nE+I9FrXKSFTwLh2F3W0uWi0Z16dybyX8SKLlrDwqHyajFIqbopCFlFjHTYmBqfXJ7hqsfABFhuzInZrG7XcBpk0jRpWBsOpQZmKftk1yz18+PgWzg8rVI2N3br7WRLLFgdlinFlgApwvQzOOhgjC5dx1eBz6yPM5bIwK1ONqxZ76KVJHPNybeScQ7R90hip2ojXUM4x0wqTZvb6Hejn/h5IhP64b1w1rBrRHM0TrM3lOOOd8Pec3sHmqMbqIMfaQomFMkPum5FUjcWommK3SjA3k2Glo5N+fqWHoHEPAEuFXIsGQTPRL2LRZoskiegG1/47Nxb4ru/8dmTzqzgxrDGsmjhmMu0rLPz309MhltIcJs/joh6mQur1QHPlcMUghXHi2F4s07jh7G5E9orGOq8ncnnYvP24Jpcz54dTjI27JJs3bSzqTgbOpSABLPeoNu/7f/gN+H/PPBMP/7ffh9LZJR1rp2rw0Pb0kmze9qR+1M8PKJ1gbaHAYj/fc5sX5scvRut8vjxs3uXOpdi8JxvGO4n22ub1sse8Xbvk72OcOBMcHIw/s6Rj48J51VakuYJjderXVqmWsv7w/QA8wikxl2mszWUYVjomFogWu8G0MbE3SnB6FZ0s8US3/zYO0N4xHHVjIc9MbdumhK7jzAvJEF1rGJ7F4EiSeyeSY8Epmyed7Gn/ew7t/6UqcjbbcMbiOufXfrMSR+H9zl/79nzbipX2sXcIyjvheMqfSHifOPecd9T776nlPaFfRTieUg69NMEb/umPwKkEL33Z1+BHXv9P4rl1bd7zXvTVWD54BE9/toXVqc8KFYdZkUqWqiQkudgrIjYttNIPQua41hnWfqv2/V1t7SLVCPmxxsk+7x//6Bvw6Y9/FIO5QbxW8N9d7kObcOFHX1wXhuvqXOvsnvoAByA299xQ1svL/TwmYXVtg3EOVWPRyxJkidcwbmz8TmGd1dXiNlZ+Z+htZJCtHE5qDMoMC2WGo4uyjl0oUp/1mqKXSiPpia/8CfNJsD1Bamy70zvNWMQs467MaarlOTSuU12EMF4QgxYqOBTROrK7T69SksU7E5hVssYN80fXGR72Es7/P2QwN955Hgja2hpS1WNdt8p4NjhsXOu4S9Ts8wTrnwvXPqfB8d3YWQd9+D7wYyLMxCHgEq9RTFgJ654axikvRSK+A/kOISFFnOLBB2JnJoJOUMA6ONXKsoSkwJDQ0EsTzOc2fo4ce3Yshi9pfAJNEtdk4jAPSTfhOmgLTPzMlDmFRGlJxPO/XyYaZWqh/ZyvnHi6ldy2eK/CdTPWxfkPaKtcXvWt34ZiYQWblY1JSA5oK36shppsYwHAfH+AyjeND30EDDRGjZ1Z8ig4CWDtnbrhvhGSW/fzeE9FHvPK6Vmr3qECjakRI1YoaS4Cr0OdKaCBxvbESMl0I5GxhULHxaqxDpuVxWov8SVYgPOOR6VEU6uxzgvR5zizK8ZgkKfY8qVCO9MGhwd5lJnYqSwWCo1BJg1TlFLINQAEHUAgNcCodticGuxMJWuul0nkbaWU0vu5TOPksEaqRcPIOFm0aQXcc75CkWos91KfsScR1MxPfrW1OLkzxfFthzLVWO1nGGQZlspEpECs3/h6xzYgmeQubTMPJdNUliCJd8IbLymiINEt40R327o2op9ocSyNa5HpiNG1jhGaNi5uUGtjfdfpNmKZJwq7lcVCkcyUHAEi6QKd4NZbX4fXfM8/wvakwenzG/izP/2vuO+zf4sf+fGfgvOfK8cT49VY6WwvBkPFKGJodCfZ2zLp1UaczrIQcTHTcdKYGWMtpTmhNFJO9ELH/nNe8IIoE5B4Z/rU2KiLp5QY0eDwBGShXJnQiE37DtZBAqHBmm8YmSVNzEoKWbIhG3pqpLy4aix6eSIZlJ1ZLGqSe50sADi20vcORpmCxpXB0cUSRaJjE7ijC0WUKuj5MZ4oyY6QUl8Xy0PbkjxgWBncvznFYpFiXMtmRHQHU2yOajS+WVeZJji7WyHRkilbJBd3cncbklyoyTa8oEw66vD5xVkvU1gs5NmcGtFFC9I0aaLjZiRIX2xP5bpftdhDol2MJofFXaY1cu2iY8PUs93pAWBr0uDgXIZEAefGjTw38FlDupXaCBk2osco47+xDnee3MHEy5IcWSrxvKuWxcnt2uyLvi8xTLRClWr0vN7sfJliUGYIHbOXS2kSmSVy3YKURXAYBz03QL6bZPkiaus5J47NUW3Q889tt/ysNjbq+g2n0tDn/LCSzBGtMJdLFiYOzeOjD2xgXBncd2YYtem6i+GwWO95DbrQzXyxSGEhjTrDOYVGOaG7fXfYhIykyjhov7BMtGw4rjh82C/wFI5vS7bMtJFNWe4UUvj+Cb7JVWMcKoSsglwqYJzYoyIBrpyX3hGDTOaQyjpp1LMPXE427+TOpcm5PFW4JJsH+4U//EvkC9m86FTtP34t+sB8nl6yzZs29hHazRdjUKZxvttLm5cl+jHrvV9ONq+6DANW877J8aXavCcbtmP39tLmWWP3tHcBgLhmjt9NBSeVmsn3DA7trWk7pkvfSF7snhF970yyr0NwSkGe00Q7GCP3V0EhUSGY5qLDe31c+/ECVMqhtgql1ehlQKE6EiJQSFMgscCkkazO9UmD2orzNWRYdpvkzTi5oKIkRbcvhjjPvOPOWBQmZHaqRzjdWke0ONG6XreQqR3+bb2TPbwnjJ0Zp1EnYSA4Z0OSD9B1jjgoN1vhGz4nOtH8OXcd3OG44fmTdb/Mlf/oda/Dt333P8KkNljf2MCfvPe9uO+zd+NHf+KnYKyLvVyCDF841+DI84ebWQsaK+dSGwck/rp2vFexcasLzStbx3bYEwYm3ll+43NfIFKYnakwXIf40dbFLFTnWkm3QJ4oNFZhozbYqWSttTZXYLnMYg+jTIsjszYWW9MGO9MGO74R745uUKQ69psI9jnRCvNlkKaUpKZeLu6W3DcrXOxlOOKbTw5y2R8s+GbfYU9+YTDEIWRgBw12hWElmuKTxkqQOlFoJI19RuNeHKfyPId7JG7J2Yxqyd5VgG6rjJxfU8POVh6FLO4gl5f4wFbu31AZ8Ul077M4cr2M3tRAa2nSWKYa2oo8TZq0GtyAjFtxAodnpuP0RVshGY7hb/3M9QtSleE50C4E4DBDGM8hQzueu2qlP2ojskZZon0mtF+HhXnJB1a0Eimb8NxYi5j1HWQ5GgtMrYV1Gsg68wG62e0asyEz+X/o+GMc0FQu2s6QSa4v+G7hOlgnD0qQqQyyK9bvKa2VzOrwLKZaBkz3ujvnJUbi9fEBEtdewyOHDwEAslTOfctXK0iwA4BKkaW5TB4qrAHkmY3B/vCN45wr52Nc23/tckXuxf6d/34e68nEY3Z0b9WSYWHc7MYiTJTOOQxrh3EtmcOVEfF7a4GdShx+i2WK07sNlsoEQc8nNK/UCjESX/toVqYVDvYzDCvJvjzQz7E1rX0E1c1ouhkrm3qt2k6w4TwTWQ2hD2m4JllK4oA1zmE9T3HFfI5+JmXeD25Pcd/GGFki2a5FqmI39vOjGpPG+rKW4KR1USNJMlAsHtqaYLmXYbtKvPM0idqSIQve6jSWZ4Umkm15VdusJSxwQrZC6C4cMn8sWo2xncrEyFmigiaUQeqz+ULjOeOkO30ow1ksU5wb1RjVKVZ6GTItXW01ACR51DaujAOUxvLyCr71H3xXvMbOAUWSRG090S0UTa35XMdNYdABBuC1mGQhE0p9qkY2p+Hv+VwyNIMDqZdpFKl8r8pLkISFl0S7NZRvEpZrWaQGKz5ppBQ6LCZSLZlxPb+ocA7Y9Nlgg1wckWWqMW3EkD370Bw0FLamxpcWqfh71onhlk7uFkO/aDI+e2NUG+xWDfJU47Dvdhw6IR+cy3HNUg+r/QzW+QY6us2wD+M8LCYkiCEVFqFBTIaOvpZfiGdJ0FmW6wgAa3M5zo1qGDuK2nLGyXXJU9l4LeStozsYWxmwKo7F7sIovtFjnc/oDf+Hw7gGKi1O9FxLBvawkqxEY504MzKJfm9OTXSoREkb5xBDQjrMOiHi3Y4v41y8t1fM50iUwvGdCv0sgXOSeZL4YEbI+gaA9XGN0pf49bMEn9sY4ZMPb+P4ujSanTYWu1WDRAN3PbyNnUmDcWUwrgwW+1ksVwyO5WMrfZRpgvlCMqq1ksYhXcImOLysk7DoQMziDIu2XV9lUaQiiXNhpka3i3VtHeaLFCc3x7jn1AQDv+Du5SnW5gs84/A8Nkc1qsZg6GVaQrZJKK8EZEG+1M9wcKHE2qDAgX7uy9PkuQnP/qSRcuXu9wrSIu2zLvPX0IRn0aBMFeZzjSsXi9h8cdo4aCXPZJLkcJBrcWrYxAWNViLDtFjIGKm8wzhRksUsizWHaaJwuIc9ZVjZy8rmPejHM7k4gzJ7QmxeVjwx2ZdafWGbZy0ek2P5sbDcy5An6pJsXqLUF3XGKZ3g6tU5PPfw/J7bvMljdAjPOr6e/DZv8TFmqj+ZCE6OS7V56gI7+mRgP2yegnrCnvVHQ5y0LmZkBrkLrdrEGge5N8PKYjhtdeO70gS10UAB9LIkyi6ETE+gfXREIgAAkrh+EadLHZMOTCdhREPK/Y218WchszvVCnNZqLBIcG4kyUfWAYuFNIrr5wlSnxgEtM5jrX0ZuQrOqvY+1r5CLzgXg9NUvuvs9XMdZzXQqYzsvC/4GkLjye7rKhzfX0eDVqM8ZCLrC+Yj52bPI7wvnEPMXlazJxuexXBMp9NZCQmtsbS8gm//rte2rycK/SxkDcs8bB3QS+VedZ2T3az/cH9E5s/BWjsj5RB8eOF6JV7ixVrp+1Db4FhUsBYI/WHF8SVr0jindK7NzDVR4tyM8zmAMlUwTsVGgnN5yEiXZ3A4NbF5YNjHTRvbkbhpJQ7lmnkna7j/WuQ3J43BYinyr0HTuR8CuDrYHMkE7q4vwj4sNCx+LNYsZC4n/kIE34JVj6yq6Dq5pZK68zkXBHYDQY87VDkp1b7mnGRSp0rF517GMgC/PpWgmEOiU5Sp3MhuzzBAMpKTpL0u9oLxrTrX6IJhDcA736FmXcNqVqLF+mer6/sTuR15QbS49cy81yWRiICXM1EXvLfT5wPwzmeFzPc/CBnf4Vwn1qKuLBqbtD15MNvzKPhtgr2sfaPj6Oe5YO650EnfnZ9lrSS+nyCNkihA6wQ7lYljO0sUEp344JzMbzH44OQsFWQsODUrhxMy+UOl9Wovw2KRRl9P2PskPtlzWFtsTAx2K9lb9lJZ+4Zv0WaMd+7nxW4MIRfwmHdjn9+YxsjU2lyK+TyRjrU+qzn3Dl8AfkEvm5ORMdiZiCE4N6qxNpehl0pDx36awEB3SlwkBJcn4gANhjEaw16KLFHRWRpKVJe8w672jqvgAE60m9FdTvyDkyc93Ls+ihHcMtX4zPkR+lmCw4McVy8WGM9lGNcOp3crTHcNlnsZDg1yTBuHs7ui43v1Ug+9VLULdD+5V1ac1OvjGluTBlmiYa04PeaLBAte9ys4OsJizTjnuz67mYkQQCyhCZnK1gcZKiOZD+dHdcxUHVYmOuPluvjmilXbDCiUmg2KNJYlJ1rh/KjCsMpxZL7E2pw45WEbQGnvTJINV6aBIk2ijlKiETtBD2uZqJIs6Gq3BjxE4Bp78QkZkIVNkeqY5Rze09hwrHbCHtXyXRfLtiNxcDzEwEDHATXykfvQlGO5TLC7O0ZVlNichA7VCaxL0ViH+SLBdctF1DWTQA9iJpmxkslaGYeVMsVqL+tkV7SLjKBN1y3fmzQWK70MC4WOCwAAsQmOUpKJH7JX5L7J2Jg2DiFmkPsy2XAZnV8x51rhaSt9WYhmYhSLVMV7H0iUwsFBEUvwgHbB2ZUw6Wb6h/MJWattaRp8w9O2BDssMpVTGDcOI+ceoZNmLJBmMuKPDES/rrFto5jwnnAMp9rxVCQKdapap1T8TIe/9Y69Z6ykODmsoJU4wHemkiVWJJKFNJ+LI/Kus0P/zCpce6CPcdVgUGY4slgiSxTOeRkTANG5HXTFjq30cXihxHyReD0/cRad2a2wXGawTo4TMvnCdZzJ+IHDxLQb21CC6Zxc6zIVqaVhZVB7fbXCL8gAyWgcViIZlKcJRtUk6gae2hzj+PoIS/0MvTzFYj/HoMxiKWW39DJwcKHEwUGBNd+MRUr+ZHGb6W7z2FnC8iQ82aIT2Y4b60IzWglU9NJ2/GqlZprHhvnw3FA2vyu9FMYCp4Y1Ei0VOZUVx0BlLHqZOEasBQ4vPbqW7aXirG/mkqjLxuZVzVO1eO2xIY4Qdck2L82fmMzLSWP8nP9oNu+LO5YvhrMNlJ5d/m2MRdf6Umze9tQgLb5wdCkpevh7161ipZfsuc1bH3/x8e6s8c6jy8fmdYOclwvBbl6qzXuystc2b/wYqyUuBZEOEIx3ZGgvRxCeCVm7yzO4UCaYNs4HuwyyRDSM+1kS7ZB8is867GbxqlCpEDIGQ9aprGmCVJL1TjvduTbddV5tWzmRPBF7d0C16+mNcS1JSIlCbZTUZuh2T2UVUKZJrE67UFKui3Xecee9K923iNMt6JK38iHdT3EX/B3mmwuDaUr5DGkHODWbMRt+3zkJhgV5iZCB3j2n4KDScG2gSXUy0jWinrrqnJd8H/lfkeiZAHt3f6UqG/edSs1mV4fKXJGCaV8PusCJbZvgdREpBsl+DfOl+BYcEpXE93ez4rWSLNWus9v4zNXg2KzGYwwGfWinovN/3FhfTZeg11dRLslC7E2QRAx79SALEmQ/rGsl8rqZ7VrLmOree3Fsi1O0+3pjvdyRDpXbzssAyvcoEhWdxda1wTQLIIH0b1ibk2o/pdps7wvvYwieONMGORIVco9nA1XdgEdtHRpfxR/us/zf20HVZvpaOGhfnZ3E/UH3POQYUjWfREe2cVIJEvYYcn+D1IrMraE6uxuwCeM+BN7DPZGftQ55hOuH2WtjlZu1Rc4HhvBIdOc9gAQxdJQjVPF84rMV/ABofR3hGQTaMRS006eNRWUkkJdpFf0ktQ8KyT1sR04/afsuZdo7rq2Xge04x0N2fHfJEJpshmqF0CzT2lZqKvhMwvu1asdN/K5o565ukDf83HbsxlzWBnXgr0niZVWMH/PBUS7rqQTK93MLxwLaxM/ucS5XQjX5fh7vqchjz+ie1tipGsznKTYmssEfZBpzmZYojJcO0FBREkMyMixKF6Q8EvQy5UtSZUFQeUeR9tHEsIC5UItt0th4k/pZiESJXIexgNadDYTzCxEDTBo1u5CCGIanLffwuY0xjBOH9Eovw9a0wblRheuW+1jpJShLjYWihPUGUfvf7WcJDszl6KUK80UCTA0qbwStA/qZRHHTuQx3nd3FtLE4ulB6J6vBwwhdZCUzy7hWTqRMNOZyWTyO6gb9LIlNGwHg3KhGbWwsTQkZ5UXayoMcXRDn9ag22J42GNUOxzfGWB9K2XqeapwfVhhOmtjUaFwZrAwK5KmORvaKQYqBbgCfZZJpwCWAcXL/cj+BhsVMyFTPE1kwBGdA2BiG8VFbh1w7jLyai3WyoR/VNmZPhAh+m+XkJ18/XqQUUfS8B7ls+EN2V2VczDCdNOLwKlJZwde2LffRkHP7jV/9Jbz+R27DfT5gsNrPoJWML2P9xjttZ1MJSsi/e5lGbh3GSkq0GmPRWFmcyIDzDW+84S8TjYnR0TCv9JK4iAlZL6Fsd9oEY9U6A8aNb+jpDWLiV6ejWhZl3dKURCscmksxrKxoj6mgGSpZu0G2BJAgCODlPvoZ0pkNvpqRygn/r6zDwzvT2IgDaMvsAbnmYZx2y0hXexJEEA1EoJ+2umRbUwPnJGN3PvdjyB/PeImH7mLa+6CQKIWpd74ov/D5/OYE08Z67XHg2ELhr6PD8e0JbjjQx5KXLQoyRdev9vHg1gQntifxmQAkoHRmp47a3F0nt0h8yPWcLxIcHkizUa0RS/MyrTCft8EYuY4yBrqGO2jHBYey9U6mENgKUiN553OmTVtyGypKzg8r5Klob58fVujlks00baT5zbgyUR8+0QpD3xE70QqDMkM/T9DLE8wXInUz8BlRDrLIlUZssnHvlkcCeMSiKmy+UtVuSAHJBah9dmNoHAWE8u/2A8pEockkC07me8lOkeslwSRjHR7emfhxJ/d06wnSSX40nJUsVyS4bGxenn4FCNvtIXmqkdjqkm2eu0jw53Gdj3/OH83mPV7s2buQHLop/t9Zg3OjCofn80uyedY52C/ijNNaGhGF67eXNu/CzdfFcNZg0jj0OqvhJ7vN6+qaXk48UTbvyUQo/95rm7cfPv5gg7TPkA3fQ9aPwcHh0EtlPpwaB+ssBkWCxTL1yQPi0BPJFkTtdZHkmD1ecPSWqTj/psbFgG/ukxDCdQ3uwdapJucVxof2c0nmnWN9rwEOtBm23Y2+hewBnOs4orpBZiBmOobfDf2RQmZsd0RaFyqAvEQL2uvXHetBOzscx2E2+7TbqFVUY9rzCWffDZQH6cowyLpZlQGlWid3+D6hP1Pum9DJNZZ9iIwD+XbdAF7ScXQ5J+uKxnbWf52AhYWCdj5LvnPtY/aod64BbcZ+mDMlextxrxauVeH3LWEPHHTxxYZ4SQZ/TRuL6MRVUPjNX/tl/K+v/1Fx2vt7PmrkGvYzhdjoWMl554mK9iaMWw2FCha1X4DmCVB6h2MYjwDidUo73zU02JPKhtkmxkDIfg0yL95p3HFqyxhspUdCf5hMSyV6m1SmZgJK8Xf9mJHx6wBolOlMYdLMGAgO6FAtLrKp8l3HjWS2Z7qV9wg9ekITzBAUaJN6VAwKGx8EiRntru2bFn5HHL2qM+ofKb0DtEllsVrEj9VQ9W7Q7gm7STxKKVxoUeSz2kxyhVlbpaDglEMOjUz7CvlExUBcN9DSJdEqVpGE8dqeh1/zJNqP62D7JOibaanK7Grbh/sE+MCIl6m5sPl2aPjZncei5IpW0W8S35+0fTNKL90Sfhqua/h318l9sfcEwvrFOocUKgaSgDbpMVzHQa69v0uuydRfj5DE03W4h4qCy9l3a62bGZP7cbynIo/Z0X33uV0cXx95x06Kb37mIZzeneLE9hRfd+0KrhiIwyTsjVwOLNkEjZUmhImS6aq2knU4bhyWy8SXuTm/AG4nduMXw6GYxjjgxLZsMEa1lH/2M40yVT4S2Cmp8OcsixiJKHaNUIhKXbPU806tCp86tYOrlnu4crGHs7sVNieSmTDny4vyQpzPD2xNUKQaC4W8vjM1GPhyp8a6WMo+qg3e+9mz+PiDmzi3M0WZJdG5OCjTqN9lOgM90Sq+57xvhlGkGnkq2kXHVvp4/hUL2JpI04m/PTvEy65eiaVVgC95NVJa1c8SlKnGfedH+PDnzmPj9BD1tIFONNI8wWS3wufvOQelFZJEo5zLsLjcx/yzU6z2M3HAZDngZINp/TXu+cZIYbMVptYQvQyEjVzQ6NIQRw0aKVP5/MYIdxzfwtdetyrZaVphPhdt9D+95xyuPzCHZx4cAJDJMkzwwQHtgJnNj4ObMa7r4xp3nRnilmOLODmc4sT2BLccW4Lyn2EBGOPwD37gf8eotji2UGLaWNy/Kc3fEi2bT61CpgkA5RvwNA69zBtKrTDIpdTGod24hYYSW1ODvzmxib973XI0BOd8k7rKtAv5cP2ck/MLWXSyKEY0qIkCekpHp4tksao4+XcpEg2VI26eJ43FcpnhDz9+csa5Oew4BQdFgqUyndEIlWfQxY1ZKK8Mpedh4RWbXTrEv8Pkmmlg2DTeqOmov66UPMfWSSCgtjJmwmIw6NL3UoVKARWC/nq7wA7nNawMjJOM2fs2RqiNw31nh1js5zg4KPC11y7jjoe3cd1yPy58wsYgUSIb8anTQxxfH+Gl167i5M4UH7l/HQBwYn2M7c0xnHNIswTlXI6ef66NdVjsZ1gfTmNZ+bhqfBPLHH/36Qdw/+YYy70UR+ZLWWj7Oa678An3P5adOofudmrqMx+DdAEgTWc+fWaITx3fgrEOZ7enGPv7aRqLyaiCbRyUVsi83Iez8v80SzCYy6VjfGeB8nU3riH3jVSCw3hrUqNMNQZ5ig+f2MSLjy5htZfGEsCL4fx3CIuirp3VfkyH17pZiuHbhc1VP9P+usiCe2fS4P9333ncdHgef/K5dTxtRebyT53YinrBvTzFK284dNHzeiLQaY6VMsVSIffncrB5z1gb7Nn1+EqgaiyQXLrNa+onJvOy1Yt8FJv3ONetXSc3IHIiIah+KTZPsgS/cDDF+kqIqL+KvbN5F278LoZpKpwaVjg4l11WNu9yo9sE7FJt3pOJC+NNe2Xz1sf1Fw0iXSoxK92vU+Pj03GS5ImK2dqNdZjPNYA0OkxD5nbQn1dw8Tt3nd3BPkaHJYBxbbExlucl1TmyKG/R2sQu4TqGJmxBYzxkq+ZaYaWXYVS1EisuvN9XERifNRk+J7sgoKI7zu/oVHePPB/b+X/reJeGeGF1bTtOzqCBbP1ZWdvO5yGwpTrPu3NA7VznPS7eK9W5liY46+LaOGTqt2MtnLZCcBK2mejh/KPvWgVn8+w1D2uKblZiOK7MU17ixbqo45t5B7rWQaJGztnCdeZOxMc/OPa0cjPPSvhZcES236vNRjZ+zxhs+A/80I+Kk7lz08pUGvGFLPWQTRpsWmgsq5U4YlUiiRRhbxccfWHO7j7rwUZon6DgXEhyCMFKQCsdr12qle8X6pA6uTuhyiBcp3CNY3UlHDJfHaJU2/PA+usQ1oBtEKYNyk4bG/uUBUdi9/mXDHo5p+D0dv66BsmWTDugDt+9bWQYkstC0CbTGkmqfDKgPHuNcRg5i8q0DmC5Jt4569qxFo5/IeF5ct33QpzbEixu709oAB3Hj3MSjFHtNQpBtBBMvtBxG8JHSoWxLE7j4OSOwbgLcsK7Ph53wWvBiR0yqGtjOr2PbNvEUruZY4XxFWRRtFbIXOvEjp8ZAkx+HssSFRvHyrnKucQqNtXq0ods726lj7ynY7eiHZe57sIKFYVO9Vvn9+W+yNwQpHmkwsBiaiQhLVT+Oz8uHLrBUvcIKR5CLoZy7iKzByGEEEIIIYQQQgghhJDHzfb2NhYXF/Ev/793opyb37fjTnZ38P/5n56Pra0tLCws7Ntxv9w8MR2TCCGEEEIIIYQQQgghhDyCrqLDfh3vqQgd3YQQQgghhBBCCCGEELJHGLu/zmfz5FR/23OenN1dCCGEEEIIIYQQQgghhJDHCDO6CSGEEEIIIYQQQgghZI+gdMn+QEc3IYQQQgghhBBCCCGE7BF0dO8PdHQTQgghhBBCCCGEEELIHmH32dFtn6KObmp0E0IIIYQQQgghhBBCCLmsYUY3IYQQQgghhBBCCCGE7BHG7bN0iXtqZnTT0U0IIYQQQgghhBBCCCF7BDW69wdKlxBCCCGEEEIIIYQQQgi5rGFGNyGEEEIIIYQQQgghhOwRzOjeH+joJoQQQgghhBBCCCGEkD2isQ7JPjqfGzq6CSGEEEIIIYQQQgghhDyRMKN7f6BGNyGEEEIIIYQQQgghhJDLGmZ0E0IIIYQQQgghhBBCyB5h9zmj2z5FM7rp6CaEEEIIIYQQQgghhJA9wjgH4/ZRumQfj/Vkgo5uQgghhBBCCCGEEEII2SOo0b0/UKObEEIIIYQQQgghhBBCyGUNM7oJIYQQQgghhBBCCCFkj2BG9/5ARzchhBBCCCGEEEIIIYTsEXR07w90dBNCCCGEEEIIIYQQQsgeYZyFsXZfj/dUhBrdhBBCCCGEEEIIIYQQQi5rmNFNCCGEEEIIIYQQQgghe4TdZ+kSS+kSQgghhBBCCCGEEEIIIU8kxjpoanTvOZQuIYQQQgghhBBCCCGEEHJZw4xuQgghhBBCCCGEEEII2SMaC6h9zLJunpq9KOnoJoQQQgghhBBCCCGEkL2C0iX7Ax3dhBBCCCGEEEIIIYQQskfQ0b0/UKObEEIIIYQQQgghhBBCyGUNHd2EEEIIIYQQQgghhBCyRxjr9v3P4+Ed73gHrrnmGpRliVtuuQUf/vCHH/W9v/qrv4qXvexlWF5exvLyMl7+8pd/wffvB3R0E0IIIYQQQgghhBBCyB5h99nJbR+Ho/vd7343brvtNtx+++346Ec/iuc+97l4xStegTNnzlz0/R/4wAfw2te+Fn/2Z3+GD33oQ7jyyivx9V//9Thx4sSlXq7HjXLOPTVFWwghhBBCCCGEEEIIIWSP2N7exuLiIr7mre9F2pvbt+M24138+Y99A7a2trCwsPCYfueWW27Bi170Irz97W8HAFhrceWVV+L1r3893vjGN37R3zfGYHl5GW9/+9tx6623XtL5P16Y0U0IIYQQQgghhBBCCCFfYWxvb8/8mU6nF31fVVW444478PKXvzy+prXGy1/+cnzoQx96TMcajUao6xorKytPyLk/HujoJoQQQgghhBBCCCGEkD3COQdn9/GPF/C48sorsbi4GP+8+c1vvuj5nTt3DsYYHDp0aOb1Q4cO4dSpU4/pO77hDW/AFVdcMeMs32/SL9uRCSGEEEIIIYQQQggh5Csc+zh1sy/leADw0EMPzUiXFEWxJ8d7y1vegt/93d/FBz7wAZRluSfHeCzQ0U0IIYQQQgghhBBCCCFfYSwsLDwmje4DBw4gSRKcPn165vXTp0/j8OHDX/B3f+7nfg5vectb8L73vQ833XTTJZ3vpULpEkIIIYQQQgghhBBCCNkjnHP7/udLIc9z3HzzzXj/+98fX7PW4v3vfz9e8pKXPOrvvfWtb8W/+Bf/Au9973vxwhe+8HFfnycKZnQTQgghhBBCCCGEEELIHhG0s/fzeF8qt912G173utfhhS98IV784hfjbW97G3Z3d/G93/u9AIBbb70VR48ejTrfP/uzP4s3velNeNe73oVrrrkmankPBgMMBoMn7st8CdDRTQghhBBCCCGEEEIIIXvEl0uj+0vhNa95Dc6ePYs3velNOHXqFJ73vOfhve99b2xQ+eCDD0LrVhzkl37pl1BVFb7jO75j5nNuv/12/ORP/uQlnf/jRbkvNZedEEIIIYQQQgghhBBCyBdke3sbi4uL+Oqf/kOk5dy+HbeZ7OK/vembsbW19Zg0ur9SYEY3IYQQQgghhBBCCCGE7BHOyp/9PN5TETq6CSGEEEIIIYQQQgghZI94PA0iL/V4T0Xo6CaEEEIIIYQQQgghhJA94nLQ6P5KQH/xtxBCCCGEEEIIIYQQQgghT16Y0U0IIYQQQgghhBBCCCF7hLMObh+zrPfzWE8m6OgmhBBCCCGEEEIIIYSQvWKfHd2go5sQQgghhBBCCCGEEELIE4l1DmofG0Tap2gzSmp0E0IIIYQQQgghhBBCCLmsYUY3IYQQQgghhBBCCCGE7BHO7bNG91M0o5uObkIIIYQQQgghhBBCCNkj2Ixyf6B0CSGEEEIIIYQQQgghhJDLGmZ0E0IIIYQQQgghhBBCyB5hLaD2Mcva2n071JMKOroJIYQQQgghhBBCCCFkj3DO7atuNjW6CSGEEEIIIYQQQgghhDyhOCt/9vN4T0Wo0U0IIYQQQgghhBBCCCHksoYZ3YQQQgghhBBCCCGEELJHWOv2WaOb0iWEEEIIIYQQQgghhBBCnkCcdXD76Hzez2M9maCjmxBCCCGEEEIIIYQQQvYIOrr3B2p0E0IIIYQQQgghhBBCCLmsYUY3IYQQQgghhBBCCCGE7BHWOSi3jxrd+3isJxN0dBNCCCGEEEIIIYQQQsgeQemS/YGObkIIIYQQQgghhBBCCNkjnNtnR/dTNKObGt2EEEIIIYQQQgghhBBCLmuY0U0IIYQQQgghhBBCCCF7hLMOltIlew4d3YQQQgghhBBCCCGEELJHOOf2VU6E0iWEEEIIIYQQQgghhBBCyGUIM7oJIYQQQgghhBBCCCFkj3B2n5tRUrqEEEIIIYQQQgghhBBCyBOJtQ7YR+fzfuqBP5mgo5sQQgghhBBCCCGEEEL2CGcNnDX7erynItToJoQQQgghhBBCCCGEEHJZw4xuQgghhBBCCCGEEEII2SOY0b0/0NFNCCGEEEIIIYQQQgghe4Szdp8d3XbfjvVkgo5uQgghhBBCCCGEEEII2SOcMXBmHx3d+3isJxPU6CaEEEIIIYQQQgghhBByWcOMbkIIIYQQQgghhBBCCNkjnNtnjW731MzopqObEEIIIYQQQgghhBBC9gg2o9wfKF1CCCGEEEIIIYQQQggh5LKGGd2EEEIIIYQQQgghhBCyRzCje3+go5sQQgghhBBCCCGEEEL2CDq69wc6ugkhhBBCCCGEEEIIIWSPcNbus6Pb7tuxnkxQo5sQQgghhBBCCCGEEELIZQ0zugkhhBBCCCGEEEIIIWSPsNYA+5jRbSldQgghhBBCCCGEEEIIIeSJhBrd+wMd3YQQQgghhBBCCCGEELJH0NG9P1CjmxBCCCGEEEIIIYQQQshlDTO6CSGEEEIIIYQQQgghZK8wBk7vY5a1eWpmdNPRTQghhBBCCCGEEEIIIXuEc/vbjNI5OroJIYQQQgghhBBCCCGEPIE4a/fX0W3tvh3ryQQ1ugkhhBBCCCGEEEIIIYRc1jCjmxBCCCGEEEIIIYQQQvYIZ/dZumQfj/Vkgo5uQgghhBBCCCGEEEII2SNEumT/5EQoXUIIIYQQQgghhBBCCCGEXIYwo5sQQgghhBBCCCGEEEL2CEqX7A90dBNCCCGEEEIIIYQQQsgeQUf3/kBHNyGEEEIIIYQQQgghhOwR1hooOrr3HGp0E0IIIYQQQgghhBBCCLmsYUY3IYQQQgghhBBCCCGE7BHOWEDtY0a3sft2rCcTdHQTQgghhBBCCCGEEELIHuHcPmt0u6emdAkd3YQQQgghhBBCCCGEELJHOGv2N6ObGt2EEEIIIYQQQgghhBBCnoq84x3vwDXXXIOyLHHLLbfgwx/+8Bd8/+///u/jhhtuQFmWeM5znoM//uM/3qczvTh0dBNCCCGEEEIIIYQQQsge4azZ9z9fKu9+97tx22234fbbb8dHP/pRPPe5z8UrXvEKnDlz5qLv/+u//mu89rWvxfd///fjzjvvxKtf/Wq8+tWvxqc+9alLvVyPG+Wcc1+2oxNCCCGEEEIIIYQQQshXINvb21hcXET6nO+BSvJ9O64zFZpP/ja2trawsLDwmH7nlltuwYte9CK8/e1vBwBYa3HllVfi9a9/Pd74xjc+4v2vec1rsLu7iz/6oz+Kr331V381nve85+GXf/mXn5gv8iVCjW5CCCGEEEIIIYQQQgjZK0yNfc00NjUAcbR3KYoCRVE84u1VVeGOO+7AP//n/zy+prXGy1/+cnzoQx+66CE+9KEP4bbbbpt57RWveAXe8573XOLJP37o6CaEEEIIIYQQQgghhJAnmDzPcfjwYZy66/f2/diDwQBXXnnlzGu33347fvInf/IR7z137hyMMTh06NDM64cOHcLdd9990c8/derURd9/6tSpSzvxS4CObkIIIYQQQgghhBBCCHmCKcsSn//851FV1b4f2zkHpdTMaxfL5v5Kgo5uQgghhBBCCCGEEEII2QPKskRZll/u0/iCHDhwAEmS4PTp0zOvnz59GocPH77o7xw+fPhLev9+oL9sRyaEEEIIIYQQQgghhBDyZSXPc9x88814//vfH1+z1uL9738/XvKSl1z0d17ykpfMvB8A/vRP//RR378fMKObEEIIIYQQQgghhBBCnsLcdttteN3rXocXvvCFePGLX4y3ve1t2N3dxfd+7/cCAG699VYcPXoUb37zmwEAP/zDP4yv+Zqvwc///M/jG7/xG/G7v/u7+MhHPoJf+ZVf+bJ9Bzq6CSGEEEIIIYQQQggh5CnMa17zGpw9exZvetObcOrUKTzvec/De9/73thw8sEHH4TWrTjIS1/6UrzrXe/CT/zET+DHf/zHcf311+M973kPnv3sZ3+5vgKUc8592Y5OCCGEEEIIIYQQQgghhFwi1OgmhBBCCCGEEEIIIYQQcllDRzchhBBCCCGEEEIIIYSQyxo6ugkhhBBCCCGEEEIIIYRc1tDRTQghhBBCCCGEEEIIIeSyho5uQgghhBBCCCGEEEIIIZc1dHQTQgghhBBCCCGEEEIIuayho5sQQgghhBBCCCGEEELIZQ0d3YQQQgghhBBCCCGEEEIua+joJoQQQgghhBBCCCGEEHJZQ0c3IYQQQgghhBBCCCGEkMsaOroJIYQQQgghhBBCCCGEXNb8/wFdghC150pxHwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1500x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Running Inference:   0%|                                                                         | 0/6 [00:07<?, ?sample/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example call using the cleaned-up dataloader and inference functions\n",
+    "run_inference(\n",
+    "    model=model,\n",
+    "    num_samples=1,\n",
+    "    device='cuda',\n",
+    "    dtype=torch.float32,\n",
+    "    mask_ratio=0,\n",
+    "    data_path=\"../data/IR/*/*.nc4\",\n",
+    "    max_files=20,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d12b9761",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/precip_inference.ipynb b/notebooks/precip_inference.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6f125dac9ad1cf54dd951de011d2ed0fb52e8ec1
--- /dev/null
+++ b/notebooks/precip_inference.ipynb
@@ -0,0 +1,307 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b0ed0610-9643-4e48-a05d-281703e16998",
+   "metadata": {},
+   "source": [
+    "### Import Required Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0c14de93-d962-4225-86c5-c2a44ad716ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
+      "  warnings.warn(\n",
+      "/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2025-04-24 12:51:36,085] [INFO] [real_accelerator.py:239:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpvzykj5sk/test.c -o /tmp/tmpvzykj5sk/test.o\n",
+      "INFO:root:x86_64-linux-gnu-gcc /tmp/tmpvzykj5sk/test.o -laio -o /tmp/tmpvzykj5sk/a.out\n",
+      "/usr/bin/ld: cannot find -laio: No such file or directory\n",
+      "collect2: error: ld returned 1 exit status\n",
+      "INFO:root:x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpzxqor2qm/test.c -o /tmp/tmpzxqor2qm/test.o\n",
+      "INFO:root:x86_64-linux-gnu-gcc /tmp/tmpzxqor2qm/test.o -L/usr/local/cuda -L/usr/local/cuda/lib64 -lcufile -o /tmp/tmpzxqor2qm/a.out\n",
+      "INFO:root:x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpizocejf0/test.c -o /tmp/tmpizocejf0/test.o\n",
+      "INFO:root:x86_64-linux-gnu-gcc /tmp/tmpizocejf0/test.o -laio -o /tmp/tmpizocejf0/a.out\n",
+      "/usr/bin/ld: cannot find -laio: No such file or directory\n",
+      "collect2: error: ld returned 1 exit status\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../.')\n",
+    "\n",
+    "import torch\n",
+    "import torch.distributed\n",
+    "from torch.utils.data import DataLoader\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "import time\n",
+    "\n",
+    "from downstream.precip.precip_dataloader import TrainingDataModule, PrecipDataset, create_dataloader\n",
+    "from downstream.precip.lightning_presetup import GAIAPrecip"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a442874-1f90-41c1-90a1-c7a364bce1a0",
+   "metadata": {},
+   "source": [
+    "# Load model from checkpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2c41511f-d7e5-48c5-9ff3-31c93f5a85b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:downstream.precip.lightning_wrapper:Mask Ratio: 0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define path to config file\n",
+    "config_path = \"../configs/precip_config.yaml\"\n",
+    "\n",
+    "# Create model using config file\n",
+    "model = GAIAPrecip(\n",
+    "    config_path=config_path\n",
+    ")\n",
+    "\n",
+    "model.configure_model()\n",
+    "\n",
+    "checkpoint_dir = '../checkpoints/gaia-precip-v1.pt'\n",
+    "state_dict = torch.load(checkpoint_dir)\n",
+    "model.load_state_dict(state_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ebc874a-cad4-4baf-8c9b-b751dc8f7f23",
+   "metadata": {},
+   "source": [
+    "### Run Inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3cb316ff-d7cb-43fc-8f6c-dbe2e5b95508",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_inference(model, num_samples, device='cuda', dtype=torch.float32, mask_ratio=0.5, \n",
+    "                  data_path=\"../data/IR\", **dataloader_kwargs):\n",
+    "    model.eval()\n",
+    "    model.to(device)\n",
+    "    model.eval()  \n",
+    "\n",
+    "    loader = create_dataloader(\n",
+    "        data_path=data_path,\n",
+    "        **dataloader_kwargs\n",
+    "    )\n",
+    "    samples_collected = 0\n",
+    "\n",
+    "    for batch in tqdm(loader, desc=\"Running Inference\", unit=\"sample\"):\n",
+    "        # Extract inputs and metadata\n",
+    "        x = batch.get(\"x\")  # Input images\n",
+    "        x_mask = batch.get(\"x_mask\")  # GOES masks\n",
+    "        precip_missing_data_mask = batch.get(\"y_mask\")  # Precip masks\n",
+    "        precip_target = batch.get(\"y\")  # Target images\n",
+    "        temporal_pos = batch.get(\"temporal_pos\")  # Temporal position metadata\n",
+    "\n",
+    "        x = x.float().to(device)\n",
+    "        x_mask = x_mask.to(device)\n",
+    "        precip_target = precip_target.to(device)\n",
+    "        precip_missing_data_mask = precip_missing_data_mask.to(device)\n",
+    "        _temporal_pos = [temporal_component.to(device) for temporal_component in temporal_pos]\n",
+    "        temporal_pos = None\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            mse_loss, pred,mask = model(x, x_mask, precip_target, precip_missing_data_mask, temporal_pos, mask_ratio)\n",
+    "\n",
+    "        unpatched = pred  # Shape: [batch, channels, temporal, height, width\\\n",
+    "        \n",
+    "        # Convert tensors back to CPU for saving\n",
+    "        imgs = x\n",
+    "        imgs = imgs.float().cpu()\n",
+    "        precip_target = precip_target.float().cpu()\n",
+    "        unpatched = unpatched.float().cpu()\n",
+    "        mask = mask.detach()\n",
+    "        mask = mask.float().cpu()\n",
+    "\n",
+    "        s, p, q = model.model.encoder.patch_embed.patch_size\n",
+    "        mask = model.model.encoder.unpatchify(mask.unsqueeze(-1).repeat(1, 1, s * p * q))\n",
+    "\n",
+    "        for i in range(min(imgs.size(0), num_samples - samples_collected)):\n",
+    "                plot_reconstructions(\n",
+    "                    input=imgs[i,0,0,:,:],\n",
+    "                    masked_input = imgs[i,0,0,:,:] * (1 - mask[i,0,0,:,:]),\n",
+    "                    ground_truth=precip_target[i,0,0,:,:],\n",
+    "                    reconstruction=unpatched[i,0,0,:,:]\n",
+    "                )\n",
+    "                samples_collected += 1\n",
+    "                if samples_collected >= num_samples:\n",
+    "                    return\n",
+    "                        \n",
+    "def plot_reconstructions(input, masked_input, ground_truth, reconstruction):\n",
+    "    \"\"\"\n",
+    "    Show ground truth, masked input, and reconstruction.\n",
+    "    \"\"\"\n",
+    "    fig, axes = plt.subplots(1, 4, figsize=(12, 4))\n",
+    "    titles = [\"Input\", \"Masked Input\", \"Ground Truth\", \"Reconstruction\"]\n",
+    "    images = [input, masked_input, ground_truth, reconstruction]\n",
+    "\n",
+    "    for ax, img, title in zip(axes, images, titles):\n",
+    "        if title == \"Input\":\n",
+    "            ax.imshow(img, cmap='jet', vmin=0, vmax=1)\n",
+    "            ax.set_title(title)\n",
+    "            ax.axis(\"off\")\n",
+    "        if title == \"Masked Input\":\n",
+    "            ax.imshow(img, cmap='jet', vmin=0, vmax=1)\n",
+    "            ax.set_title(title)\n",
+    "            ax.axis(\"off\")\n",
+    "        if title == \"Ground Truth\":\n",
+    "            img_log = np.log1p(img)  \n",
+    "            ax.imshow(img_log, cmap='jet', vmin=0, vmax=1)\n",
+    "            ax.set_title(title)\n",
+    "            ax.axis(\"off\")\n",
+    "        if title == \"Reconstruction\":\n",
+    "            img_log = np.log1p(img)\n",
+    "            ax.imshow(img_log, cmap='jet', vmin=0, vmax=1)\n",
+    "            ax.set_title(title)\n",
+    "            ax.axis(\"off\")\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "31a296ef-f9a2-4c34-944d-9a8b093082be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Running Inference:   0%|                                                                         | 0/4 [00:00<?, ?sample/s]/usr/local/lib/python3.10/dist-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
+      "  warnings.warn(\n",
+      "INFO:downstream.precip.lightning_wrapper:forward pass start\n",
+      "INFO:downstream.precip.lightning_wrapper:finished forward\n",
+      "INFO:downstream.precip.models:Mask ratio: 0\n",
+      "INFO:downstream.precip.models:Input imgs shape: torch.Size([2, 1, 1, 480, 1440])\n",
+      "INFO:downstream.precip.models:Input img_masks shape: torch.Size([2, 1, 1, 480, 1440])\n",
+      "INFO:downstream.precip.models:Input precip_target shape: torch.Size([2, 1, 1, 480, 1440])\n",
+      "INFO:downstream.precip.models:Input precip_missing_data_mask shape: torch.Size([2, 1, 1, 480, 1440])\n",
+      "INFO:downstream.precip.models:Latent shape: torch.Size([2, 715, 912])\n",
+      "INFO:downstream.precip.models:Pred shape: torch.Size([2, 768, 900])\n",
+      "INFO:downstream.precip.models:Pred shape after unpatchify: torch.Size([2, 1, 1, 480, 1440])\n",
+      "INFO:downstream.precip.models:Target min: 0.0, max: 63.0625, avg: 0.1356201171875\n",
+      "INFO:downstream.precip.models:Pred min: 0.0, max: 20.54570770263672, avg: 0.07404941320419312\n",
+      "INFO:downstream.precip.models:Target zeros: 1211042 (0.876043%), NaNs: 0\n",
+      "INFO:downstream.precip.models:Pred zeros: 1242850 (0.899052%), NaNs: 0\n",
+      "INFO:downstream.precip.models:Loss mse: torch.Size([2, 1, 1, 480, 1440])\n",
+      "INFO:downstream.precip.models:Loss mean MSE: 0.7870287299156189\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Running Inference:   0%|                                                                         | 0/4 [00:31<?, ?sample/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example call using your custom dataloader\n",
+    "run_inference(\n",
+    "    model=model,\n",
+    "    data_path=\"../data\",\n",
+    "    num_samples=1,\n",
+    "    device='cuda',\n",
+    "    mask_ratio=0,\n",
+    "    max_files=20,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4e594bb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bf39a4ee0a7931f7a85a14e1a06074287e8cc81e
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,20 @@
+netCDF4~=1.7.2
+deepspeed~=0.16.7
+einops~=0.7.0
+lightning~=2.5.1
+matplotlib~=3.8.4
+numpy~=1.24.4
+pandas~=2.2.3
+s3fs~=2025.3.2
+scipy~=1.12.0
+timm~=1.0.15
+torch~=2.6.0
+torchmetrics~=1.7.1
+torchvision~=0.21.0
+tqdm~=4.66.2
+xarray~=2025.3.1
+zarr~=2.18.3
+dask~=2024.1.1
+bottleneck~=1.4.2
+h5netcdf~=1.6.1
+cartopy~=0.24.1
\ No newline at end of file