Repository: pangeo-forge/pangeo-forge-recipes (language: python)

Issue:
Handle inputs with variable items per input
This problem was originally noted by @naomi-henderson in #47.

Our current recipe assumes that the number of "items" per input is constant:

https://github.com/pangeo-forge/pangeo-forge/blob/f9b698a35a62ab169bce546b15fef9722c70cd90/pangeo_forge/recipe.py#L149

where "items" refers to the length along the `sequence_dim` dimension (e.g. time). This assumptions buys us something big: we don't have to examine each input file, and we can determine which region of the target to write each chunk to using simple math:

https://github.com/pangeo-forge/pangeo-forge/blob/f9b698a35a62ab169bce546b15fef9722c70cd90/pangeo_forge/recipe.py#L304-L310

This makes sense for 1-file-per-day netCDFs, but many datasets have variable sizes, e.g. one file per month (31 days, 30, days, 28 days) or just whatever random stuff CMIP models do. For those cases, we will inevitably have to peek into each input file to determine its dimensions / shape and pass this information on to `prepare_target` and `store_chunk` somehow.

Here are some of the issues raised by this scenario

### How to determine the total size of the target dataset?

In the current recipe class, we calculate the length along `sequence_dim` deterministically as 

https://github.com/pangeo-forge/pangeo-forge/blob/f9b698a35a62ab169bce546b15fef9722c70cd90/pangeo_forge/recipe.py#L312-L314

and then use it to resize the target zarr.

https://github.com/pangeo-forge/pangeo-forge/blob/f9b698a35a62ab169bce546b15fef9722c70cd90/pangeo_forge/recipe.py#L183

A key point is that the size of the dataset is known from the beginning, without reading any files.

For these uneven datasets, we can't do that. We need to look at each file. Fortunately, we already almost do that, when we cache the inputs.

**I propose we write a .json "sidecar" file for each input that contains the dataset shape, variables, etc.**. A very easy way to do this would using xarray's [to_dict](http://xarray.pydata.org/en/stable/generated/xarray.DataArray.to_dict.html) function (with `data=False`) from `cache_input`. Reading these files should be cheap enough that we can read them all in `prepare_target`. Then we can figure out how big the target dataset will be.

### How to handle chunking and chunk overlap?

Currently we assume a fixed number of `inputs_per_chunk`, which implies the pangeo forge "chunks" will vary in size and not line up with the target chunks (zarr chunks have to be constant shape). Our whole lexicon breaks down a bit, since pangeo-forge's "chunks" no longer map directly to target zarr chunks. We possibly need a new parameter, `target_chunks`, to specify the target chunking explicitly.

I see two different directions we could go to handle the problem of overlap:
- Keep `inputs_per_chunk` fixed, accept that pangeo-forge chunks will not line up with zarr chunks, and either
  - switch to append-style writes (as in the original recipe). We lose the ability for concurrency in the `store_chunk` stage, as the appends have to be sequential.
  - manually calculate the overlap between the pangeo-forge chunks (irregular) and the zarr chunks (regular). Then use distributed locks (also raised in #45) to prevent concurrent writes to a the same zarr chunk by different processes in `store_chunk`. 
- Keep the 1:1 mapping between between pangeo-forge chunks and zarr chunks, but break the requirement that `inputs_per_chunk` be fixed. Instead, allow multiple different chunks to draw from the same input. `open_chunk` possibly needs to figure out how to subset inputs. This is maybe easier because we don't need locks for concurrent reads. However, _it makes it impossible to precalculate the number of `store_chunk` tasks_, since we don't know the total size of the dataset until we start running the recipe! 🤯 This would violate our current Pipeline execution model and require a deeper refactoring of execution.

In any case, the `open_chunk` and `store_chunk` functions will need to have more context about the dataset they are writing to and their position within it. This information could possibly be calculated and stored with the sidecar file mentioned above.

---

In general, I think we should not hesitate to use the storage layer to communicate. The alternative is having tasks pass information to each other directly within the pipeline. Currently all of the pipeline function calls and arguments are determined at the outset, when the recipe is initialized, and none of the functions return anything. Changing that would be a pretty deep change at this point and would likely force us to drop this whole Pipeline structure I've cooked up and just start writing prefect task / flows directly again.



Discussion / hints:
This connects somewhat to #49 as well (multiple chunks per input). In both cases, there is going to be a more complex relationship between inputs and chunks.
I am hitting this issue when accumulating over a `sequence_dim` that changes from file to file.  This is satellite data with a variable number of data points per orbit.

```python
ValueError: variable 'Latitude' already exists with different dimension sizes: {'pixel': 20} != {'pixel': 373721}. to_zarr() only supports changing dimension sizes when explicitly appending, but append_dim=None.
```

It is interesting that I can see the `xarray` chunks just fine, but when I go to write the chunks to `zarr` it fails.

I tried modifying the `zarr` write to include the `self.sequence_dim` in the write and append, but then it complained that appending and region are incompatible (which makes sense).

Not quite sure how to proceed - thinking that a rechunk is probably necessary when writing in these cases?
Hi @swnesbitt -- thanks for your comment! TBH, I had no idea that anyone was using pangeo-forge "in the wild"! 😆  I'm not surprised you hit some errors, as pangeo-forge is still very raw and rough.

We have some ideas about how to fix the specific issue I described above. To understand if this is related to your problem, we would need a bit more detail from you. Could you post your full recipe code and share the details of the input files (e.g. output from `ncdump`)?

Thanks! Yes, the recipe is here: [https://gist.github.com/swnesbitt/0f506fbfea004eb8c1bd721d69a525bb](https://gist.github.com/swnesbitt/0f506fbfea004eb8c1bd721d69a525bb).

Below is the ncdump output for one of the files. I am trying to concatenate the zarr along dim `pixel`:

```
(base) snesbitt@keeling-g18 /data/accp/a/snesbitt/reduced-trmm/1998/01$ ncdump -h L2RADAR_v4.19980131-S185411-E202529.001024.V06A.HDF5.nc
netcdf L2RADAR_v4.19980131-S185411-E202529.001024.V06A.HDF5 {
dimensions:
        pixel = 21761 ;
        range = 176 ;
        dim2 = 2 ;
        ray = 9139 ;
        scan = 49 ;
        dim3 = 3 ;
variables:
        float Latitude(pixel) ;
                Latitude:_FillValue = NaNf ;
        float Longitude(pixel) ;
                Longitude:_FillValue = NaNf ;
        short binBBBottom(pixel) ;
        short binBBTop(pixel) ;
        short binRealSurface(pixel) ;
        int64 dim2(dim2) ;
        int64 dim3(dim3) ;
        int flagPrecipKu(ray, scan) ;
        int64 indices_0(pixel) ;
        int64 indices_1(pixel) ;
        int landSurfaceType(pixel) ;
        int orbit(pixel) ;
        float paramDSD(pixel, range, dim2) ;
                paramDSD:_FillValue = NaNf ;
        float piaFinal(pixel) ;
                piaFinal:_FillValue = NaNf ;
        int64 pixel(pixel) ;
        short precipRate(pixel, range) ;
                precipRate:_FillValue = -9999s ;
                precipRate:scale_factor = 0.01 ;
        int64 range(range) ;
        int64 ray(ray) ;
        int64 scan(scan) ;
        double timeMidScan(pixel) ;
                timeMidScan:_FillValue = NaN ;
        int typePrecip(pixel) ;
        short zFactorCorrected(pixel, range) ;
                zFactorCorrected:_FillValue = -9999s ;
                zFactorCorrected:scale_factor = 0.01 ;
        float zFactorCorrectedNearSurface(pixel) ;
                zFactorCorrectedNearSurface:_FillValue = NaNf ;
        short zFactorMeasured(pixel, range) ;
                zFactorMeasured:_FillValue = -9999s ;
                zFactorMeasured:scale_factor = 0.01 ;
}
```
For reference, the recipe creation is here

```python
recipe = NetCDFtoZarrSequentialRecipe(
    input_urls=input_urls,
    sequence_dim="pixel",
    inputs_per_chunk=20,
    xarray_open_kwargs={'drop_variables':['flagPrecipKu']},   
)
```

I notice you are not specifying the `nitems_per_input` parameter. Its default is 1. If all files were the same as this example one, you would use `nitems_per_input=21761`. However, I think that this varies from file to file. This is the core problem.

We plan to support this soon, i.e. within weeks.

Thanks for the useful example. Are these data public? If so, could you open an issue here - https://github.com/pangeo-forge/staged-recipes/issues - to keep track of this potential use case?
Yes, it is public NASA level 2 data.  I will certainly post a recipe once I get it working.  Cheers!

Fix patch:
diff --git a/ci/py3.7.yml b/ci/py3.7.yml
index facb70f0..91ea7bba 100644
--- a/ci/py3.7.yml
+++ b/ci/py3.7.yml
@@ -23,6 +23,7 @@ dependencies:
   - pynio
   - pytest
   - pytest-cov
+  - pytest-lazy-fixture
   - rasterio
   - scipy
   - setuptools
diff --git a/ci/py3.8.yml b/ci/py3.8.yml
index c707d0f2..672441d2 100644
--- a/ci/py3.8.yml
+++ b/ci/py3.8.yml
@@ -23,6 +23,7 @@ dependencies:
   - pynio
   - pytest
   - pytest-cov
+  - pytest-lazy-fixture
   - rasterio
   - scipy
   - setuptools
diff --git a/dev-requirements.txt b/dev-requirements.txt
index 5ae2269b..b4014950 100644
--- a/dev-requirements.txt
+++ b/dev-requirements.txt
@@ -1,6 +1,7 @@
 pytest
 pytest-sugar
 pytest-cov
+pytest-lazy-fixture
 scipy
 netcdf4
 h5netcdf
diff --git a/docs/api.md b/docs/api.md
index d45ceb91..5d6ffd9d 100644
--- a/docs/api.md
+++ b/docs/api.md
@@ -27,6 +27,11 @@
     :members:
 ```
 
+```{eval-rst}
+.. autoclass:: pangeo_forge.recipe.NetCDFtoZarrRecipe
+    :members:
+```
+
 ```{eval-rst}
 .. autoclass:: pangeo_forge.recipe.NetCDFtoZarrSequentialRecipe
     :show-inheritance:
diff --git a/docs/recipes.md b/docs/recipes.md
index d04c2606..8e6a104e 100644
--- a/docs/recipes.md
+++ b/docs/recipes.md
@@ -30,7 +30,9 @@ target_path = "pangeo-forge-bucket/my-dataset-v1.zarr"
 target = FSSpecTarget(fs=fs, root_path=target_path)
 ```
 
-Temporary data is recommended to use a {class}`pangeo_forge.storage.CacheFSSpecTarget` object.
+Temporary data is can be cached via a {class}`pangeo_forge.storage.CacheFSSpecTarget` object.
+Some recipes require separate caching of metadata, which is provided by a third {class}`pangeo_forge.storage.FSSpecTarget`.
+
 
 ## The Recipe Object
 
diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
index 8c0294e5..c3df2dd7 100644
--- a/docs/tutorials/index.md
+++ b/docs/tutorials/index.md
@@ -7,4 +7,5 @@ These tutorials are deep dives into how to develop and debug Pangeo Forge recipe
 
 netcdf_zarr_sequential
 multi_variable_recipe
+terraclimate
 ```
diff --git a/docs/tutorials/multi_variable_recipe.ipynb b/docs/tutorials/multi_variable_recipe.ipynb
index acef1ae8..924663e1 100644
--- a/docs/tutorials/multi_variable_recipe.ipynb
+++ b/docs/tutorials/multi_variable_recipe.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# NetCDF Zarr Multi-Variable Sequential Recipe\n",
+    "# NetCDF Zarr Multi-Variable Sequential Recipe: NOAA World Ocean Atlas\n",
     "\n",
     "This recipe is a little bit more complicated than the {doc}`netcdf_zarr_sequential`.\n",
     "You shold probably review that one first; here we will skip the basics.\n",
diff --git a/docs/tutorials/netcdf_zarr_sequential.ipynb b/docs/tutorials/netcdf_zarr_sequential.ipynb
index 767e17b5..6d7dce82 100644
--- a/docs/tutorials/netcdf_zarr_sequential.ipynb
+++ b/docs/tutorials/netcdf_zarr_sequential.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# NetCDF Zarr Sequential Recipe\n",
+    "# NetCDF Zarr Sequential Recipe: NOAA OISST\n",
     "\n",
     "This tutorial describes how to create a recipe from scratch.\n",
     "The source data is a sequence of NetCDF files accessed via HTTP.\n",
diff --git a/docs/tutorials/terraclimate.ipynb b/docs/tutorials/terraclimate.ipynb
new file mode 100644
index 00000000..05bd1428
--- /dev/null
+++ b/docs/tutorials/terraclimate.ipynb
@@ -0,0 +1,4423 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Complex NetCDF to Zarr Recipe: TerraClimate \n",
+    "\n",
+    "## About the Dataset\n",
+    "\n",
+    "From http://www.climatologylab.org/terraclimate.html:\n",
+    "\n",
+    "> TerraClimate is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2019. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. The data cover the period from 1958-2019. We plan to update these data periodically (annually).\n",
+    "\n",
+    "## What makes it tricky\n",
+    "\n",
+    "This is an advanced example that illustrates the following concepts\n",
+    "- _MultiVariable recipe_: There is one file per year for a dozen different variables.\n",
+    "- _Complex Preprocessing_: We want to apply different preprocessing depending on the variable. This example shows how.\n",
+    "- _Inconsistent size of data in input files_: This means we have to scan each input file and cache its metadata before we can start writing the target.\n",
+    "\n",
+    "This recipe requires a new storage target, a `metadata_cache`. In this example, this is just another directory. You could hypothetically use a database or other key/value store for this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pangeo_forge.recipe import NetCDFtoZarrMultiVarSequentialRecipe\n",
+    "from pangeo_forge.patterns import VariableSequencePattern\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define Filename Pattern \n",
+    "\n",
+    "To keep this example smaller, we just use two years instead of the whole record."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VariableSequencePattern(fmt_string='https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_{variable}_{year}.nc', keys={'variable': ['aet', 'def', 'pet', 'ppt', 'q', 'soil', 'srad', 'swe', 'tmax', 'tmin', 'vap', 'ws', 'vpd', 'PDSI'], 'year': [1958, 1959]})"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target_chunks = {\"lat\": 1024, \"lon\": 1024, \"time\": 12}\n",
+    "# only do two years to keep the example small; it's still big!\n",
+    "years = list(range(1958, 1960))\n",
+    "variables = [\n",
+    "    \"aet\",\n",
+    "    \"def\",\n",
+    "    \"pet\",\n",
+    "    \"ppt\",\n",
+    "    \"q\",\n",
+    "    \"soil\",\n",
+    "    \"srad\",\n",
+    "    \"swe\",\n",
+    "    \"tmax\",\n",
+    "    \"tmin\",\n",
+    "    \"vap\",\n",
+    "    \"ws\",\n",
+    "    \"vpd\",\n",
+    "    \"PDSI\",\n",
+    "]\n",
+    "\n",
+    "pattern = VariableSequencePattern(\n",
+    "    fmt_string=\"https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_{variable}_{year}.nc\",\n",
+    "    keys={'variable': variables, 'year': years}\n",
+    ")\n",
+    "pattern"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VariableSequencePattern(fmt_string='https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_{variable}_{year}.nc', keys={'variable': ['aet', 'def', 'pet', 'ppt', 'q', 'soil', 'srad', 'swe', 'tmax', 'tmin', 'vap', 'ws', 'vpd', 'PDSI'], 'year': [1958, 1959]})"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define Preprocessing Functions\n",
+    "\n",
+    "These functions apply masks for each variable to remove invalid data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rename_vars = {'PDSI': 'pdsi'}\n",
+    "\n",
+    "mask_opts = {\n",
+    "    \"PDSI\": (\"lt\", 10),\n",
+    "    \"aet\": (\"lt\", 32767),\n",
+    "    \"def\": (\"lt\", 32767),\n",
+    "    \"pet\": (\"lt\", 32767),\n",
+    "    \"ppt\": (\"lt\", 32767),\n",
+    "    \"ppt_station_influence\": None,\n",
+    "    \"q\": (\"lt\", 2147483647),\n",
+    "    \"soil\": (\"lt\", 32767),\n",
+    "    \"srad\": (\"lt\", 32767),\n",
+    "    \"swe\": (\"lt\", 10000),\n",
+    "    \"tmax\": (\"lt\", 200),\n",
+    "    \"tmax_station_influence\": None,\n",
+    "    \"tmin\": (\"lt\", 200),\n",
+    "    \"tmin_station_influence\": None,\n",
+    "    \"vap\": (\"lt\", 300),\n",
+    "    \"vap_station_influence\": None,\n",
+    "    \"vpd\": (\"lt\", 300),\n",
+    "    \"ws\": (\"lt\", 200),\n",
+    "}\n",
+    "\n",
+    "def apply_mask(key, da):\n",
+    "    \"\"\"helper function to mask DataArrays based on a threshold value\"\"\"\n",
+    "    if mask_opts.get(key, None):\n",
+    "        op, val = mask_opts[key]\n",
+    "        if op == \"lt\":\n",
+    "            da = da.where(da < val)\n",
+    "        elif op == \"neq\":\n",
+    "            da = da.where(da != val)\n",
+    "    return da\n",
+    "\n",
+    "\n",
+    "def preproc(ds):\n",
+    "    \"\"\"custom preprocessing function for terraclimate data\"\"\"\n",
+    "    \n",
+    "    rename = {}\n",
+    "\n",
+    "    station_influence = ds.get(\"station_influence\", None)\n",
+    "\n",
+    "    if station_influence is not None:\n",
+    "        ds = ds.drop_vars(\"station_influence\")\n",
+    "\n",
+    "    var = list(ds.data_vars)[0]\n",
+    "\n",
+    "    if var in rename_vars:\n",
+    "        rename[var] = rename_vars[var]\n",
+    "\n",
+    "    if \"day\" in ds.coords:\n",
+    "        rename[\"day\"] = \"time\"\n",
+    "\n",
+    "    if station_influence is not None:\n",
+    "        ds[f\"{var}_station_influence\"] = station_influence\n",
+    "\n",
+    "    with xr.set_options(keep_attrs=True):\n",
+    "        ds[var] = apply_mask(var, ds[var])\n",
+    "\n",
+    "    if rename:\n",
+    "        ds = ds.rename(rename)\n",
+    "                \n",
+    "    return ds\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define Recipe\n",
+    "\n",
+    "We are now ready to define the recipe.\n",
+    "We also specify the desired chunks of the target dataset.\n",
+    "\n",
+    "A key property of this recipe is `nitems_per_input=None`, which triggers caching of input metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NetCDFtoZarrMultiVarSequentialRecipe(sequence_dim='time', inputs_per_chunk=1, nitems_per_input=None, target=<pangeo_forge.storage.UninitializedTarget object at 0x7fbb09e49340>, input_cache=<pangeo_forge.storage.UninitializedTarget object at 0x7fbb09e49370>, metadata_cache=<pangeo_forge.storage.UninitializedTarget object at 0x7fbb09e49520>, require_cache=True, consolidate_zarr=True, xarray_open_kwargs={}, xarray_concat_kwargs={}, delete_input_encoding=True, fsspec_open_kwargs={}, process_input=None, process_chunk=<function preproc at 0x7fbb09e530d0>, target_chunks={'lat': 1024, 'lon': 1024, 'time': 12}, input_pattern=VariableSequencePattern(fmt_string='https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_{variable}_{year}.nc', keys={'variable': ['aet', 'def', 'pet', 'ppt', 'q', 'soil', 'srad', 'swe', 'tmax', 'tmin', 'vap', 'ws', 'vpd', 'PDSI'], 'year': [1958, 1959]}))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chunks = {\"lat\": 1024, \"lon\": 1024, \"time\": 12}\n",
+    "\n",
+    "recipe = NetCDFtoZarrMultiVarSequentialRecipe(\n",
+    "    input_pattern=pattern,\n",
+    "    sequence_dim=\"time\",  # TODO: raise error if this is not specified\n",
+    "    target_chunks=target_chunks,\n",
+    "    nitems_per_input=None, # don't know how many timesteps in each file\n",
+    "    process_chunk=preproc\n",
+    ")\n",
+    "recipe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define Storage Targets\n",
+    "\n",
+    "Since our recipe needs to cache input metadata, we need to suply a `metadata_cache` target."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NetCDFtoZarrMultiVarSequentialRecipe(sequence_dim='time', inputs_per_chunk=1, nitems_per_input=None, target=FSSpecTarget(fs=<fsspec.implementations.local.LocalFileSystem object at 0x7fbae8454970>, root_path='/var/folders/n8/63q49ms55wxcj_gfbtykwp5r0000gn/T/tmpkbnbkvox'), input_cache=CacheFSSpecTarget(fs=<fsspec.implementations.local.LocalFileSystem object at 0x7fbae8454970>, root_path='/var/folders/n8/63q49ms55wxcj_gfbtykwp5r0000gn/T/tmp509h8xjw'), metadata_cache=FSSpecTarget(fs=<fsspec.implementations.local.LocalFileSystem object at 0x7fbae8454970>, root_path='/var/folders/n8/63q49ms55wxcj_gfbtykwp5r0000gn/T/tmp_nymgtio'), require_cache=True, consolidate_zarr=True, xarray_open_kwargs={}, xarray_concat_kwargs={}, delete_input_encoding=True, fsspec_open_kwargs={}, process_input=None, process_chunk=<function preproc at 0x7fbb09e530d0>, target_chunks={'lat': 1024, 'lon': 1024, 'time': 12}, input_pattern=VariableSequencePattern(fmt_string='https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_{variable}_{year}.nc', keys={'variable': ['aet', 'def', 'pet', 'ppt', 'q', 'soil', 'srad', 'swe', 'tmax', 'tmin', 'vap', 'ws', 'vpd', 'PDSI'], 'year': [1958, 1959]}))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import tempfile\n",
+    "from fsspec.implementations.local import LocalFileSystem\n",
+    "from pangeo_forge.storage import FSSpecTarget, CacheFSSpecTarget\n",
+    "\n",
+    "fs_local = LocalFileSystem()\n",
+    "\n",
+    "target_dir = tempfile.TemporaryDirectory()\n",
+    "target = FSSpecTarget(fs_local, target_dir.name)\n",
+    "\n",
+    "cache_dir = tempfile.TemporaryDirectory()\n",
+    "cache_target = CacheFSSpecTarget(fs_local, cache_dir.name)\n",
+    "\n",
+    "meta_dir = tempfile.TemporaryDirectory()\n",
+    "meta_store = FSSpecTarget(fs_local, meta_dir.name)\n",
+    "\n",
+    "recipe.target = target\n",
+    "recipe.input_cache = cache_target\n",
+    "recipe.metadata_cache = meta_store\n",
+    "recipe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Execute with Prefect\n",
+    "\n",
+    "This produces A LOT of output because we turn on logging."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# logging will display some interesting information about our recipe during execution\n",
+    "import logging\n",
+    "import sys\n",
+    "\n",
+    "logging.basicConfig(\n",
+    "    format='%(asctime)s [%(levelname)s] %(name)s - %(message)s',\n",
+    "    level=logging.INFO,\n",
+    "    datefmt='%Y-%m-%d %H:%M:%S',\n",
+    "    stream=sys.stdout,\n",
+    ")\n",
+    "logger = logging.getLogger(\"pangeo_forge.recipe\")\n",
+    "logger.setLevel(logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2021-03-20 21:30:59-0400] INFO - prefect.FlowRunner | Beginning Flow run for 'Rechunker'\n",
+      "2021-03-20 21:30:59 [INFO] prefect.FlowRunner - Beginning Flow run for 'Rechunker'\n",
+      "[2021-03-20 21:30:59-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper': Starting task run...\n",
+      "2021-03-20 21:30:59 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper': Starting task run...\n",
+      "[2021-03-20 21:30:59-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper': Finished task run for task with final state: 'Mapped'\n",
+      "2021-03-20 21:30:59 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper': Finished task run for task with final state: 'Mapped'\n",
+      "[2021-03-20 21:30:59-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[0]': Starting task run...\n",
+      "2021-03-20 21:30:59 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[0]': Starting task run...\n",
+      "2021-03-20 21:30:59 [INFO] pangeo_forge.recipe - Caching input ('aet', 1958)\n",
+      "2021-03-20 21:30:59 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_aet_1958.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/miniconda3/envs/pangeo2020/lib/python3.8/contextlib.py:120: UserWarning: Tasks were created but not added to the flow: {<Task: Task>}. This can occur when `Task` classes, including `Parameters`, are instantiated inside a `with flow:` block but not added to the flow either explicitly or as the input to another task. For more information, see https://docs.prefect.io/core/advanced_tutorials/task-guide.html#adding-tasks-to-flows.\n",
+      "  next(self.gen)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:31:20 [INFO] pangeo_forge.recipe - Caching metadata for input '('aet', 1958)'\n",
+      "2021-03-20 21:31:20 [INFO] pangeo_forge.recipe - Opening input with Xarray ('aet', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_aet_1958.nc'\n",
+      "2021-03-20 21:31:20 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_aet_1958.nc' from cache\n",
+      "[2021-03-20 21:31:20-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[0]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:31:20 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[0]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:31:20-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[1]': Starting task run...\n",
+      "2021-03-20 21:31:20 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[1]': Starting task run...\n",
+      "2021-03-20 21:31:20 [INFO] pangeo_forge.recipe - Caching input ('aet', 1959)\n",
+      "2021-03-20 21:31:20 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_aet_1959.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'aet' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:31:38 [INFO] pangeo_forge.recipe - Caching metadata for input '('aet', 1959)'\n",
+      "2021-03-20 21:31:38 [INFO] pangeo_forge.recipe - Opening input with Xarray ('aet', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_aet_1959.nc'\n",
+      "2021-03-20 21:31:38 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_aet_1959.nc' from cache\n",
+      "[2021-03-20 21:31:38-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[1]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:31:38 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[1]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:31:38-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[2]': Starting task run...\n",
+      "2021-03-20 21:31:38 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[2]': Starting task run...\n",
+      "2021-03-20 21:31:38 [INFO] pangeo_forge.recipe - Caching input ('def', 1958)\n",
+      "2021-03-20 21:31:38 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_def_1958.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'aet' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:31:55 [INFO] pangeo_forge.recipe - Caching metadata for input '('def', 1958)'\n",
+      "2021-03-20 21:31:55 [INFO] pangeo_forge.recipe - Opening input with Xarray ('def', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_def_1958.nc'\n",
+      "2021-03-20 21:31:55 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_def_1958.nc' from cache\n",
+      "[2021-03-20 21:31:55-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[2]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:31:55 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[2]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:31:55-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[3]': Starting task run...\n",
+      "2021-03-20 21:31:55 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[3]': Starting task run...\n",
+      "2021-03-20 21:31:55 [INFO] pangeo_forge.recipe - Caching input ('def', 1959)\n",
+      "2021-03-20 21:31:55 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_def_1959.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'def' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:32:14 [INFO] pangeo_forge.recipe - Caching metadata for input '('def', 1959)'\n",
+      "2021-03-20 21:32:14 [INFO] pangeo_forge.recipe - Opening input with Xarray ('def', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_def_1959.nc'\n",
+      "2021-03-20 21:32:14 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_def_1959.nc' from cache\n",
+      "[2021-03-20 21:32:14-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[3]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:32:14 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[3]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:32:14-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[4]': Starting task run...\n",
+      "2021-03-20 21:32:14 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[4]': Starting task run...\n",
+      "2021-03-20 21:32:14 [INFO] pangeo_forge.recipe - Caching input ('pet', 1958)\n",
+      "2021-03-20 21:32:14 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_pet_1958.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'def' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:32:35 [INFO] pangeo_forge.recipe - Caching metadata for input '('pet', 1958)'\n",
+      "2021-03-20 21:32:35 [INFO] pangeo_forge.recipe - Opening input with Xarray ('pet', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_pet_1958.nc'\n",
+      "2021-03-20 21:32:35 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_pet_1958.nc' from cache\n",
+      "[2021-03-20 21:32:35-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[4]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:32:35 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[4]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:32:35-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[5]': Starting task run...\n",
+      "2021-03-20 21:32:35 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[5]': Starting task run...\n",
+      "2021-03-20 21:32:35 [INFO] pangeo_forge.recipe - Caching input ('pet', 1959)\n",
+      "2021-03-20 21:32:35 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_pet_1959.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'pet' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:32:56 [INFO] pangeo_forge.recipe - Caching metadata for input '('pet', 1959)'\n",
+      "2021-03-20 21:32:56 [INFO] pangeo_forge.recipe - Opening input with Xarray ('pet', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_pet_1959.nc'\n",
+      "2021-03-20 21:32:56 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_pet_1959.nc' from cache\n",
+      "[2021-03-20 21:32:56-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[5]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:32:56 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[5]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:32:56-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[6]': Starting task run...\n",
+      "2021-03-20 21:32:56 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[6]': Starting task run...\n",
+      "2021-03-20 21:32:56 [INFO] pangeo_forge.recipe - Caching input ('ppt', 1958)\n",
+      "2021-03-20 21:32:56 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ppt_1958.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'pet' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:33:35 [INFO] pangeo_forge.recipe - Caching metadata for input '('ppt', 1958)'\n",
+      "2021-03-20 21:33:35 [INFO] pangeo_forge.recipe - Opening input with Xarray ('ppt', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ppt_1958.nc'\n",
+      "2021-03-20 21:33:35 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ppt_1958.nc' from cache\n",
+      "[2021-03-20 21:33:35-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[6]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:33:35 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[6]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:33:35-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[7]': Starting task run...\n",
+      "2021-03-20 21:33:35 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[7]': Starting task run...\n",
+      "2021-03-20 21:33:35 [INFO] pangeo_forge.recipe - Caching input ('ppt', 1959)\n",
+      "2021-03-20 21:33:35 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ppt_1959.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'ppt' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:34:09 [INFO] pangeo_forge.recipe - Caching metadata for input '('ppt', 1959)'\n",
+      "2021-03-20 21:34:09 [INFO] pangeo_forge.recipe - Opening input with Xarray ('ppt', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ppt_1959.nc'\n",
+      "2021-03-20 21:34:09 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ppt_1959.nc' from cache\n",
+      "[2021-03-20 21:34:09-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[7]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:34:09 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[7]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:34:09-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[8]': Starting task run...\n",
+      "2021-03-20 21:34:09 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[8]': Starting task run...\n",
+      "2021-03-20 21:34:09 [INFO] pangeo_forge.recipe - Caching input ('q', 1958)\n",
+      "2021-03-20 21:34:09 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_q_1958.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'ppt' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:34:23 [INFO] pangeo_forge.recipe - Caching metadata for input '('q', 1958)'\n",
+      "2021-03-20 21:34:23 [INFO] pangeo_forge.recipe - Opening input with Xarray ('q', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_q_1958.nc'\n",
+      "2021-03-20 21:34:23 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_q_1958.nc' from cache\n",
+      "[2021-03-20 21:34:23-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[8]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:34:23 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[8]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:34:23-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[9]': Starting task run...\n",
+      "2021-03-20 21:34:23 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[9]': Starting task run...\n",
+      "2021-03-20 21:34:23 [INFO] pangeo_forge.recipe - Caching input ('q', 1959)\n",
+      "2021-03-20 21:34:23 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_q_1959.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'q' has multiple fill values {-2147483648, 2147483648}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:34:35 [INFO] pangeo_forge.recipe - Caching metadata for input '('q', 1959)'\n",
+      "2021-03-20 21:34:35 [INFO] pangeo_forge.recipe - Opening input with Xarray ('q', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_q_1959.nc'\n",
+      "2021-03-20 21:34:35 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_q_1959.nc' from cache\n",
+      "[2021-03-20 21:34:35-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[9]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:34:35 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[9]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:34:35-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[10]': Starting task run...\n",
+      "2021-03-20 21:34:35 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[10]': Starting task run...\n",
+      "2021-03-20 21:34:35 [INFO] pangeo_forge.recipe - Caching input ('soil', 1958)\n",
+      "2021-03-20 21:34:35 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_soil_1958.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'q' has multiple fill values {-2147483648, 2147483648}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:35:26 [INFO] pangeo_forge.recipe - Caching metadata for input '('soil', 1958)'\n",
+      "2021-03-20 21:35:26 [INFO] pangeo_forge.recipe - Opening input with Xarray ('soil', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_soil_1958.nc'\n",
+      "2021-03-20 21:35:26 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_soil_1958.nc' from cache\n",
+      "[2021-03-20 21:35:26-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[10]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:35:26 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[10]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:35:26-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[11]': Starting task run...\n",
+      "2021-03-20 21:35:26 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[11]': Starting task run...\n",
+      "2021-03-20 21:35:26 [INFO] pangeo_forge.recipe - Caching input ('soil', 1959)\n",
+      "2021-03-20 21:35:26 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_soil_1959.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'soil' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:35:48 [INFO] pangeo_forge.recipe - Caching metadata for input '('soil', 1959)'\n",
+      "2021-03-20 21:35:48 [INFO] pangeo_forge.recipe - Opening input with Xarray ('soil', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_soil_1959.nc'\n",
+      "2021-03-20 21:35:48 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_soil_1959.nc' from cache\n",
+      "[2021-03-20 21:35:48-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[11]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:35:48 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[11]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:35:48-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[12]': Starting task run...\n",
+      "2021-03-20 21:35:48 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[12]': Starting task run...\n",
+      "2021-03-20 21:35:48 [INFO] pangeo_forge.recipe - Caching input ('srad', 1958)\n",
+      "2021-03-20 21:35:48 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_srad_1958.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'soil' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:36:09 [INFO] pangeo_forge.recipe - Caching metadata for input '('srad', 1958)'\n",
+      "2021-03-20 21:36:09 [INFO] pangeo_forge.recipe - Opening input with Xarray ('srad', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_srad_1958.nc'\n",
+      "2021-03-20 21:36:09 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_srad_1958.nc' from cache\n",
+      "[2021-03-20 21:36:09-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[12]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:36:09 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[12]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:36:09-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[13]': Starting task run...\n",
+      "2021-03-20 21:36:09 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[13]': Starting task run...\n",
+      "2021-03-20 21:36:09 [INFO] pangeo_forge.recipe - Caching input ('srad', 1959)\n",
+      "2021-03-20 21:36:09 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_srad_1959.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'srad' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:36:28 [INFO] pangeo_forge.recipe - Caching metadata for input '('srad', 1959)'\n",
+      "2021-03-20 21:36:28 [INFO] pangeo_forge.recipe - Opening input with Xarray ('srad', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_srad_1959.nc'\n",
+      "2021-03-20 21:36:28 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_srad_1959.nc' from cache\n",
+      "[2021-03-20 21:36:28-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[13]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:36:28 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[13]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:36:28-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[14]': Starting task run...\n",
+      "2021-03-20 21:36:28 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[14]': Starting task run...\n",
+      "2021-03-20 21:36:28 [INFO] pangeo_forge.recipe - Caching input ('swe', 1958)\n",
+      "2021-03-20 21:36:28 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_swe_1958.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'srad' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:36:55 [INFO] pangeo_forge.recipe - Caching metadata for input '('swe', 1958)'\n",
+      "2021-03-20 21:36:55 [INFO] pangeo_forge.recipe - Opening input with Xarray ('swe', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_swe_1958.nc'\n",
+      "2021-03-20 21:36:55 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_swe_1958.nc' from cache\n",
+      "[2021-03-20 21:36:55-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[14]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:36:55 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[14]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:36:55-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[15]': Starting task run...\n",
+      "2021-03-20 21:36:55 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[15]': Starting task run...\n",
+      "2021-03-20 21:36:55 [INFO] pangeo_forge.recipe - Caching input ('swe', 1959)\n",
+      "2021-03-20 21:36:55 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_swe_1959.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'swe' has multiple fill values {-2147483648, 2147483648}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:37:22 [INFO] pangeo_forge.recipe - Caching metadata for input '('swe', 1959)'\n",
+      "2021-03-20 21:37:22 [INFO] pangeo_forge.recipe - Opening input with Xarray ('swe', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_swe_1959.nc'\n",
+      "2021-03-20 21:37:22 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_swe_1959.nc' from cache\n",
+      "[2021-03-20 21:37:22-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[15]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:37:22 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[15]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:37:22-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[16]': Starting task run...\n",
+      "2021-03-20 21:37:22 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[16]': Starting task run...\n",
+      "2021-03-20 21:37:22 [INFO] pangeo_forge.recipe - Caching input ('tmax', 1958)\n",
+      "2021-03-20 21:37:22 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmax_1958.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'swe' has multiple fill values {-2147483648, 2147483648}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:37:59 [INFO] pangeo_forge.recipe - Caching metadata for input '('tmax', 1958)'\n",
+      "2021-03-20 21:37:59 [INFO] pangeo_forge.recipe - Opening input with Xarray ('tmax', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmax_1958.nc'\n",
+      "2021-03-20 21:37:59 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmax_1958.nc' from cache\n",
+      "[2021-03-20 21:38:00-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[16]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:38:00 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[16]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:38:00-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[17]': Starting task run...\n",
+      "2021-03-20 21:38:00 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[17]': Starting task run...\n",
+      "2021-03-20 21:38:00 [INFO] pangeo_forge.recipe - Caching input ('tmax', 1959)\n",
+      "2021-03-20 21:38:00 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmax_1959.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'tmax' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:38:40 [INFO] pangeo_forge.recipe - Caching metadata for input '('tmax', 1959)'\n",
+      "2021-03-20 21:38:40 [INFO] pangeo_forge.recipe - Opening input with Xarray ('tmax', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmax_1959.nc'\n",
+      "2021-03-20 21:38:40 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmax_1959.nc' from cache\n",
+      "[2021-03-20 21:38:40-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[17]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:38:40 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[17]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:38:40-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[18]': Starting task run...\n",
+      "2021-03-20 21:38:40 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[18]': Starting task run...\n",
+      "2021-03-20 21:38:40 [INFO] pangeo_forge.recipe - Caching input ('tmin', 1958)\n",
+      "2021-03-20 21:38:40 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmin_1958.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'tmax' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:39:21 [INFO] pangeo_forge.recipe - Caching metadata for input '('tmin', 1958)'\n",
+      "2021-03-20 21:39:21 [INFO] pangeo_forge.recipe - Opening input with Xarray ('tmin', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmin_1958.nc'\n",
+      "2021-03-20 21:39:21 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmin_1958.nc' from cache\n",
+      "[2021-03-20 21:39:21-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[18]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:39:21 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[18]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:39:21-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[19]': Starting task run...\n",
+      "2021-03-20 21:39:21 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[19]': Starting task run...\n",
+      "2021-03-20 21:39:21 [INFO] pangeo_forge.recipe - Caching input ('tmin', 1959)\n",
+      "2021-03-20 21:39:21 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmin_1959.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'tmin' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:40:04 [INFO] pangeo_forge.recipe - Caching metadata for input '('tmin', 1959)'\n",
+      "2021-03-20 21:40:04 [INFO] pangeo_forge.recipe - Opening input with Xarray ('tmin', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmin_1959.nc'\n",
+      "2021-03-20 21:40:04 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmin_1959.nc' from cache\n",
+      "[2021-03-20 21:40:04-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[19]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:40:04 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[19]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:40:04-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[20]': Starting task run...\n",
+      "2021-03-20 21:40:04 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[20]': Starting task run...\n",
+      "2021-03-20 21:40:04 [INFO] pangeo_forge.recipe - Caching input ('vap', 1958)\n",
+      "2021-03-20 21:40:04 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vap_1958.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'tmin' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:40:28 [INFO] pangeo_forge.recipe - Caching metadata for input '('vap', 1958)'\n",
+      "2021-03-20 21:40:28 [INFO] pangeo_forge.recipe - Opening input with Xarray ('vap', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vap_1958.nc'\n",
+      "2021-03-20 21:40:28 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vap_1958.nc' from cache\n",
+      "[2021-03-20 21:40:28-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[20]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:40:28 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[20]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:40:28-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[21]': Starting task run...\n",
+      "2021-03-20 21:40:28 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[21]': Starting task run...\n",
+      "2021-03-20 21:40:28 [INFO] pangeo_forge.recipe - Caching input ('vap', 1959)\n",
+      "2021-03-20 21:40:28 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vap_1959.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'vap' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:40:54 [INFO] pangeo_forge.recipe - Caching metadata for input '('vap', 1959)'\n",
+      "2021-03-20 21:40:54 [INFO] pangeo_forge.recipe - Opening input with Xarray ('vap', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vap_1959.nc'\n",
+      "2021-03-20 21:40:54 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vap_1959.nc' from cache\n",
+      "[2021-03-20 21:40:54-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[21]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:40:54 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[21]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:40:54-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[22]': Starting task run...\n",
+      "2021-03-20 21:40:54 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[22]': Starting task run...\n",
+      "2021-03-20 21:40:54 [INFO] pangeo_forge.recipe - Caching input ('ws', 1958)\n",
+      "2021-03-20 21:40:54 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ws_1958.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'vap' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:41:13 [INFO] pangeo_forge.recipe - Caching metadata for input '('ws', 1958)'\n",
+      "2021-03-20 21:41:13 [INFO] pangeo_forge.recipe - Opening input with Xarray ('ws', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ws_1958.nc'\n",
+      "2021-03-20 21:41:13 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ws_1958.nc' from cache\n",
+      "[2021-03-20 21:41:13-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[22]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:41:13 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[22]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:41:13-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[23]': Starting task run...\n",
+      "2021-03-20 21:41:13 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[23]': Starting task run...\n",
+      "2021-03-20 21:41:13 [INFO] pangeo_forge.recipe - Caching input ('ws', 1959)\n",
+      "2021-03-20 21:41:13 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ws_1959.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'ws' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:41:31 [INFO] pangeo_forge.recipe - Caching metadata for input '('ws', 1959)'\n",
+      "2021-03-20 21:41:31 [INFO] pangeo_forge.recipe - Opening input with Xarray ('ws', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ws_1959.nc'\n",
+      "2021-03-20 21:41:31 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ws_1959.nc' from cache\n",
+      "[2021-03-20 21:41:31-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[23]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:41:31 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[23]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:41:31-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[24]': Starting task run...\n",
+      "2021-03-20 21:41:31 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[24]': Starting task run...\n",
+      "2021-03-20 21:41:31 [INFO] pangeo_forge.recipe - Caching input ('vpd', 1958)\n",
+      "2021-03-20 21:41:31 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vpd_1958.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'ws' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:41:58 [INFO] pangeo_forge.recipe - Caching metadata for input '('vpd', 1958)'\n",
+      "2021-03-20 21:41:58 [INFO] pangeo_forge.recipe - Opening input with Xarray ('vpd', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vpd_1958.nc'\n",
+      "2021-03-20 21:41:58 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vpd_1958.nc' from cache\n",
+      "[2021-03-20 21:41:58-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[24]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:41:58 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[24]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:41:58-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[25]': Starting task run...\n",
+      "2021-03-20 21:41:58 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[25]': Starting task run...\n",
+      "2021-03-20 21:41:58 [INFO] pangeo_forge.recipe - Caching input ('vpd', 1959)\n",
+      "2021-03-20 21:41:58 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vpd_1959.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'vpd' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:42:27 [INFO] pangeo_forge.recipe - Caching metadata for input '('vpd', 1959)'\n",
+      "2021-03-20 21:42:27 [INFO] pangeo_forge.recipe - Opening input with Xarray ('vpd', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vpd_1959.nc'\n",
+      "2021-03-20 21:42:27 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vpd_1959.nc' from cache\n",
+      "[2021-03-20 21:42:27-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[25]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:42:27 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[25]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:42:27-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[26]': Starting task run...\n",
+      "2021-03-20 21:42:27 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[26]': Starting task run...\n",
+      "2021-03-20 21:42:27 [INFO] pangeo_forge.recipe - Caching input ('PDSI', 1958)\n",
+      "2021-03-20 21:42:27 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_PDSI_1958.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'vpd' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:42:47 [INFO] pangeo_forge.recipe - Caching metadata for input '('PDSI', 1958)'\n",
+      "2021-03-20 21:42:47 [INFO] pangeo_forge.recipe - Opening input with Xarray ('PDSI', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_PDSI_1958.nc'\n",
+      "2021-03-20 21:42:47 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_PDSI_1958.nc' from cache\n",
+      "[2021-03-20 21:42:47-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[26]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:42:47 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[26]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:42:47-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[27]': Starting task run...\n",
+      "2021-03-20 21:42:47 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[27]': Starting task run...\n",
+      "2021-03-20 21:42:47 [INFO] pangeo_forge.recipe - Caching input ('PDSI', 1959)\n",
+      "2021-03-20 21:42:47 [INFO] pangeo_forge.recipe - Opening input 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_PDSI_1959.nc'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'PDSI' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:43:14 [INFO] pangeo_forge.recipe - Caching metadata for input '('PDSI', 1959)'\n",
+      "2021-03-20 21:43:14 [INFO] pangeo_forge.recipe - Opening input with Xarray ('PDSI', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_PDSI_1959.nc'\n",
+      "2021-03-20 21:43:14 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_PDSI_1959.nc' from cache\n",
+      "[2021-03-20 21:43:15-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[27]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:43:15 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[27]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:43:15-0400] INFO - prefect.TaskRunner | Task 'SingleTaskWrapper': Starting task run...\n",
+      "2021-03-20 21:43:15 [INFO] prefect.TaskRunner - Task 'SingleTaskWrapper': Starting task run...\n",
+      "2021-03-20 21:43:15 [INFO] pangeo_forge.recipe - Creating a new dataset in target\n",
+      "2021-03-20 21:43:15 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('aet', 0)'\n",
+      "2021-03-20 21:43:15 [INFO] pangeo_forge.recipe - Opening input with Xarray ('aet', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_aet_1958.nc'\n",
+      "2021-03-20 21:43:15 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_aet_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'PDSI' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'aet' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:43:23 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('def', 0)'\n",
+      "2021-03-20 21:43:23 [INFO] pangeo_forge.recipe - Opening input with Xarray ('def', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_def_1958.nc'\n",
+      "2021-03-20 21:43:23 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_def_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'def' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:43:32 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('pet', 0)'\n",
+      "2021-03-20 21:43:32 [INFO] pangeo_forge.recipe - Opening input with Xarray ('pet', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_pet_1958.nc'\n",
+      "2021-03-20 21:43:32 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_pet_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'pet' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:43:41 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('ppt', 0)'\n",
+      "2021-03-20 21:43:41 [INFO] pangeo_forge.recipe - Opening input with Xarray ('ppt', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ppt_1958.nc'\n",
+      "2021-03-20 21:43:41 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ppt_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'ppt' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:43:56 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('q', 0)'\n",
+      "2021-03-20 21:43:56 [INFO] pangeo_forge.recipe - Opening input with Xarray ('q', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_q_1958.nc'\n",
+      "2021-03-20 21:43:56 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_q_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'q' has multiple fill values {-2147483648, 2147483648}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:44:12 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('soil', 0)'\n",
+      "2021-03-20 21:44:12 [INFO] pangeo_forge.recipe - Opening input with Xarray ('soil', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_soil_1958.nc'\n",
+      "2021-03-20 21:44:12 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_soil_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'soil' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:44:21 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('srad', 0)'\n",
+      "2021-03-20 21:44:21 [INFO] pangeo_forge.recipe - Opening input with Xarray ('srad', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_srad_1958.nc'\n",
+      "2021-03-20 21:44:21 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_srad_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'srad' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:44:30 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('swe', 0)'\n",
+      "2021-03-20 21:44:30 [INFO] pangeo_forge.recipe - Opening input with Xarray ('swe', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_swe_1958.nc'\n",
+      "2021-03-20 21:44:30 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_swe_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'swe' has multiple fill values {-2147483648, 2147483648}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:44:47 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('tmax', 0)'\n",
+      "2021-03-20 21:44:47 [INFO] pangeo_forge.recipe - Opening input with Xarray ('tmax', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmax_1958.nc'\n",
+      "2021-03-20 21:44:47 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmax_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'tmax' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:45:02 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('tmin', 0)'\n",
+      "2021-03-20 21:45:02 [INFO] pangeo_forge.recipe - Opening input with Xarray ('tmin', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmin_1958.nc'\n",
+      "2021-03-20 21:45:02 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmin_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'tmin' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:45:16 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('vap', 0)'\n",
+      "2021-03-20 21:45:16 [INFO] pangeo_forge.recipe - Opening input with Xarray ('vap', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vap_1958.nc'\n",
+      "2021-03-20 21:45:16 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vap_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'vap' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:45:31 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('ws', 0)'\n",
+      "2021-03-20 21:45:31 [INFO] pangeo_forge.recipe - Opening input with Xarray ('ws', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ws_1958.nc'\n",
+      "2021-03-20 21:45:31 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ws_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'ws' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:45:40 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('vpd', 0)'\n",
+      "2021-03-20 21:45:40 [INFO] pangeo_forge.recipe - Opening input with Xarray ('vpd', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vpd_1958.nc'\n",
+      "2021-03-20 21:45:40 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vpd_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'vpd' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:45:50 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('PDSI', 0)'\n",
+      "2021-03-20 21:45:50 [INFO] pangeo_forge.recipe - Opening input with Xarray ('PDSI', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_PDSI_1958.nc'\n",
+      "2021-03-20 21:45:50 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_PDSI_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'PDSI' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2021-03-20 21:45:59-0400] INFO - prefect.TaskRunner | Task 'SingleTaskWrapper': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:45:59 [INFO] prefect.TaskRunner - Task 'SingleTaskWrapper': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:45:59-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper': Starting task run...\n",
+      "2021-03-20 21:45:59 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper': Starting task run...\n",
+      "[2021-03-20 21:45:59-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper': Finished task run for task with final state: 'Mapped'\n",
+      "2021-03-20 21:45:59 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper': Finished task run for task with final state: 'Mapped'\n",
+      "[2021-03-20 21:45:59-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[0]': Starting task run...\n",
+      "2021-03-20 21:45:59 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[0]': Starting task run...\n",
+      "2021-03-20 21:45:59 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('aet', 0)'\n",
+      "2021-03-20 21:45:59 [INFO] pangeo_forge.recipe - Opening input with Xarray ('aet', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_aet_1958.nc'\n",
+      "2021-03-20 21:45:59 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_aet_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'aet' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:46:07 [INFO] pangeo_forge.recipe - Storing chunk '('aet', 0)' to Zarr region {'time': slice(0, 12, None)}\n",
+      "[2021-03-20 21:46:09-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[0]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:46:09 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[0]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:46:09-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[1]': Starting task run...\n",
+      "2021-03-20 21:46:09 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[1]': Starting task run...\n",
+      "2021-03-20 21:46:09 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('aet', 1)'\n",
+      "2021-03-20 21:46:09 [INFO] pangeo_forge.recipe - Opening input with Xarray ('aet', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_aet_1959.nc'\n",
+      "2021-03-20 21:46:09 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_aet_1959.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'aet' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:46:16 [INFO] pangeo_forge.recipe - Storing chunk '('aet', 1)' to Zarr region {'time': slice(12, 24, None)}\n",
+      "[2021-03-20 21:46:18-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[1]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:46:18 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[1]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:46:18-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[2]': Starting task run...\n",
+      "2021-03-20 21:46:18 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[2]': Starting task run...\n",
+      "2021-03-20 21:46:18 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('def', 0)'\n",
+      "2021-03-20 21:46:18 [INFO] pangeo_forge.recipe - Opening input with Xarray ('def', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_def_1958.nc'\n",
+      "2021-03-20 21:46:18 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_def_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'def' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:46:26 [INFO] pangeo_forge.recipe - Storing chunk '('def', 0)' to Zarr region {'time': slice(0, 12, None)}\n",
+      "[2021-03-20 21:46:27-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[2]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:46:27 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[2]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:46:27-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[3]': Starting task run...\n",
+      "2021-03-20 21:46:27 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[3]': Starting task run...\n",
+      "2021-03-20 21:46:27 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('def', 1)'\n",
+      "2021-03-20 21:46:27 [INFO] pangeo_forge.recipe - Opening input with Xarray ('def', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_def_1959.nc'\n",
+      "2021-03-20 21:46:27 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_def_1959.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'def' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:46:35 [INFO] pangeo_forge.recipe - Storing chunk '('def', 1)' to Zarr region {'time': slice(12, 24, None)}\n",
+      "[2021-03-20 21:46:36-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[3]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:46:36 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[3]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:46:36-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[4]': Starting task run...\n",
+      "2021-03-20 21:46:36 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[4]': Starting task run...\n",
+      "2021-03-20 21:46:36 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('pet', 0)'\n",
+      "2021-03-20 21:46:36 [INFO] pangeo_forge.recipe - Opening input with Xarray ('pet', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_pet_1958.nc'\n",
+      "2021-03-20 21:46:36 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_pet_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'pet' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:46:44 [INFO] pangeo_forge.recipe - Storing chunk '('pet', 0)' to Zarr region {'time': slice(0, 12, None)}\n",
+      "[2021-03-20 21:46:45-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[4]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:46:45 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[4]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:46:45-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[5]': Starting task run...\n",
+      "2021-03-20 21:46:45 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[5]': Starting task run...\n",
+      "2021-03-20 21:46:45 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('pet', 1)'\n",
+      "2021-03-20 21:46:45 [INFO] pangeo_forge.recipe - Opening input with Xarray ('pet', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_pet_1959.nc'\n",
+      "2021-03-20 21:46:45 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_pet_1959.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'pet' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:46:53 [INFO] pangeo_forge.recipe - Storing chunk '('pet', 1)' to Zarr region {'time': slice(12, 24, None)}\n",
+      "[2021-03-20 21:46:54-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[5]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:46:54 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[5]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:46:54-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[6]': Starting task run...\n",
+      "2021-03-20 21:46:54 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[6]': Starting task run...\n",
+      "2021-03-20 21:46:54 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('ppt', 0)'\n",
+      "2021-03-20 21:46:54 [INFO] pangeo_forge.recipe - Opening input with Xarray ('ppt', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ppt_1958.nc'\n",
+      "2021-03-20 21:46:54 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ppt_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'ppt' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:47:07 [INFO] pangeo_forge.recipe - Storing chunk '('ppt', 0)' to Zarr region {'time': slice(0, 12, None)}\n",
+      "[2021-03-20 21:47:09-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[6]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:47:09 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[6]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:47:09-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[7]': Starting task run...\n",
+      "2021-03-20 21:47:09 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[7]': Starting task run...\n",
+      "2021-03-20 21:47:09 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('ppt', 1)'\n",
+      "2021-03-20 21:47:09 [INFO] pangeo_forge.recipe - Opening input with Xarray ('ppt', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ppt_1959.nc'\n",
+      "2021-03-20 21:47:09 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ppt_1959.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'ppt' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:47:21 [INFO] pangeo_forge.recipe - Storing chunk '('ppt', 1)' to Zarr region {'time': slice(12, 24, None)}\n",
+      "[2021-03-20 21:47:23-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[7]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:47:23 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[7]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:47:23-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[8]': Starting task run...\n",
+      "2021-03-20 21:47:23 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[8]': Starting task run...\n",
+      "2021-03-20 21:47:23 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('q', 0)'\n",
+      "2021-03-20 21:47:23 [INFO] pangeo_forge.recipe - Opening input with Xarray ('q', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_q_1958.nc'\n",
+      "2021-03-20 21:47:23 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_q_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'q' has multiple fill values {-2147483648, 2147483648}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:47:38 [INFO] pangeo_forge.recipe - Storing chunk '('q', 0)' to Zarr region {'time': slice(0, 12, None)}\n",
+      "[2021-03-20 21:47:40-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[8]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:47:40 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[8]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:47:40-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[9]': Starting task run...\n",
+      "2021-03-20 21:47:40 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[9]': Starting task run...\n",
+      "2021-03-20 21:47:40 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('q', 1)'\n",
+      "2021-03-20 21:47:40 [INFO] pangeo_forge.recipe - Opening input with Xarray ('q', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_q_1959.nc'\n",
+      "2021-03-20 21:47:40 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_q_1959.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'q' has multiple fill values {-2147483648, 2147483648}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:47:55 [INFO] pangeo_forge.recipe - Storing chunk '('q', 1)' to Zarr region {'time': slice(12, 24, None)}\n",
+      "[2021-03-20 21:47:57-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[9]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:47:57 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[9]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:47:57-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[10]': Starting task run...\n",
+      "2021-03-20 21:47:57 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[10]': Starting task run...\n",
+      "2021-03-20 21:47:57 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('soil', 0)'\n",
+      "2021-03-20 21:47:57 [INFO] pangeo_forge.recipe - Opening input with Xarray ('soil', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_soil_1958.nc'\n",
+      "2021-03-20 21:47:57 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_soil_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'soil' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:48:05 [INFO] pangeo_forge.recipe - Storing chunk '('soil', 0)' to Zarr region {'time': slice(0, 12, None)}\n",
+      "[2021-03-20 21:48:06-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[10]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:48:06 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[10]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:48:06-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[11]': Starting task run...\n",
+      "2021-03-20 21:48:06 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[11]': Starting task run...\n",
+      "2021-03-20 21:48:06 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('soil', 1)'\n",
+      "2021-03-20 21:48:06 [INFO] pangeo_forge.recipe - Opening input with Xarray ('soil', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_soil_1959.nc'\n",
+      "2021-03-20 21:48:06 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_soil_1959.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'soil' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:48:14 [INFO] pangeo_forge.recipe - Storing chunk '('soil', 1)' to Zarr region {'time': slice(12, 24, None)}\n",
+      "[2021-03-20 21:48:15-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[11]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:48:15 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[11]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:48:16-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[12]': Starting task run...\n",
+      "2021-03-20 21:48:16 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[12]': Starting task run...\n",
+      "2021-03-20 21:48:16 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('srad', 0)'\n",
+      "2021-03-20 21:48:16 [INFO] pangeo_forge.recipe - Opening input with Xarray ('srad', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_srad_1958.nc'\n",
+      "2021-03-20 21:48:16 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_srad_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'srad' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:48:24 [INFO] pangeo_forge.recipe - Storing chunk '('srad', 0)' to Zarr region {'time': slice(0, 12, None)}\n",
+      "[2021-03-20 21:48:25-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[12]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:48:25 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[12]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:48:25-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[13]': Starting task run...\n",
+      "2021-03-20 21:48:25 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[13]': Starting task run...\n",
+      "2021-03-20 21:48:25 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('srad', 1)'\n",
+      "2021-03-20 21:48:25 [INFO] pangeo_forge.recipe - Opening input with Xarray ('srad', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_srad_1959.nc'\n",
+      "2021-03-20 21:48:25 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_srad_1959.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'srad' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:48:33 [INFO] pangeo_forge.recipe - Storing chunk '('srad', 1)' to Zarr region {'time': slice(12, 24, None)}\n",
+      "[2021-03-20 21:48:34-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[13]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:48:34 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[13]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:48:34-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[14]': Starting task run...\n",
+      "2021-03-20 21:48:34 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[14]': Starting task run...\n",
+      "2021-03-20 21:48:34 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('swe', 0)'\n",
+      "2021-03-20 21:48:34 [INFO] pangeo_forge.recipe - Opening input with Xarray ('swe', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_swe_1958.nc'\n",
+      "2021-03-20 21:48:34 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_swe_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'swe' has multiple fill values {-2147483648, 2147483648}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:48:49 [INFO] pangeo_forge.recipe - Storing chunk '('swe', 0)' to Zarr region {'time': slice(0, 12, None)}\n",
+      "[2021-03-20 21:48:51-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[14]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:48:51 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[14]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:48:51-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[15]': Starting task run...\n",
+      "2021-03-20 21:48:51 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[15]': Starting task run...\n",
+      "2021-03-20 21:48:51 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('swe', 1)'\n",
+      "2021-03-20 21:48:51 [INFO] pangeo_forge.recipe - Opening input with Xarray ('swe', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_swe_1959.nc'\n",
+      "2021-03-20 21:48:51 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_swe_1959.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'swe' has multiple fill values {-2147483648, 2147483648}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:49:06 [INFO] pangeo_forge.recipe - Storing chunk '('swe', 1)' to Zarr region {'time': slice(12, 24, None)}\n",
+      "[2021-03-20 21:49:08-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[15]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:49:08 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[15]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:49:08-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[16]': Starting task run...\n",
+      "2021-03-20 21:49:08 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[16]': Starting task run...\n",
+      "2021-03-20 21:49:08 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('tmax', 0)'\n",
+      "2021-03-20 21:49:08 [INFO] pangeo_forge.recipe - Opening input with Xarray ('tmax', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmax_1958.nc'\n",
+      "2021-03-20 21:49:08 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmax_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'tmax' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:49:21 [INFO] pangeo_forge.recipe - Storing chunk '('tmax', 0)' to Zarr region {'time': slice(0, 12, None)}\n",
+      "[2021-03-20 21:49:23-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[16]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:49:23 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[16]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:49:23-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[17]': Starting task run...\n",
+      "2021-03-20 21:49:23 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[17]': Starting task run...\n",
+      "2021-03-20 21:49:23 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('tmax', 1)'\n",
+      "2021-03-20 21:49:23 [INFO] pangeo_forge.recipe - Opening input with Xarray ('tmax', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmax_1959.nc'\n",
+      "2021-03-20 21:49:23 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmax_1959.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'tmax' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:49:36 [INFO] pangeo_forge.recipe - Storing chunk '('tmax', 1)' to Zarr region {'time': slice(12, 24, None)}\n",
+      "[2021-03-20 21:49:38-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[17]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:49:38 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[17]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:49:38-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[18]': Starting task run...\n",
+      "2021-03-20 21:49:38 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[18]': Starting task run...\n",
+      "2021-03-20 21:49:38 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('tmin', 0)'\n",
+      "2021-03-20 21:49:38 [INFO] pangeo_forge.recipe - Opening input with Xarray ('tmin', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmin_1958.nc'\n",
+      "2021-03-20 21:49:38 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmin_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'tmin' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:49:51 [INFO] pangeo_forge.recipe - Storing chunk '('tmin', 0)' to Zarr region {'time': slice(0, 12, None)}\n",
+      "[2021-03-20 21:49:53-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[18]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:49:53 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[18]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:49:53-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[19]': Starting task run...\n",
+      "2021-03-20 21:49:53 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[19]': Starting task run...\n",
+      "2021-03-20 21:49:53 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('tmin', 1)'\n",
+      "2021-03-20 21:49:53 [INFO] pangeo_forge.recipe - Opening input with Xarray ('tmin', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmin_1959.nc'\n",
+      "2021-03-20 21:49:53 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_tmin_1959.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'tmin' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:50:06 [INFO] pangeo_forge.recipe - Storing chunk '('tmin', 1)' to Zarr region {'time': slice(12, 24, None)}\n",
+      "[2021-03-20 21:50:08-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[19]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:50:08 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[19]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:50:08-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[20]': Starting task run...\n",
+      "2021-03-20 21:50:08 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[20]': Starting task run...\n",
+      "2021-03-20 21:50:08 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('vap', 0)'\n",
+      "2021-03-20 21:50:08 [INFO] pangeo_forge.recipe - Opening input with Xarray ('vap', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vap_1958.nc'\n",
+      "2021-03-20 21:50:08 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vap_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'vap' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:50:20 [INFO] pangeo_forge.recipe - Storing chunk '('vap', 0)' to Zarr region {'time': slice(0, 12, None)}\n",
+      "[2021-03-20 21:50:23-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[20]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:50:23 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[20]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:50:23-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[21]': Starting task run...\n",
+      "2021-03-20 21:50:23 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[21]': Starting task run...\n",
+      "2021-03-20 21:50:23 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('vap', 1)'\n",
+      "2021-03-20 21:50:23 [INFO] pangeo_forge.recipe - Opening input with Xarray ('vap', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vap_1959.nc'\n",
+      "2021-03-20 21:50:23 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vap_1959.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'vap' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n",
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'station_influence' has multiple fill values {-8, 248}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:50:35 [INFO] pangeo_forge.recipe - Storing chunk '('vap', 1)' to Zarr region {'time': slice(12, 24, None)}\n",
+      "[2021-03-20 21:50:37-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[21]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:50:37 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[21]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:50:37-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[22]': Starting task run...\n",
+      "2021-03-20 21:50:37 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[22]': Starting task run...\n",
+      "2021-03-20 21:50:37 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('ws', 0)'\n",
+      "2021-03-20 21:50:37 [INFO] pangeo_forge.recipe - Opening input with Xarray ('ws', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ws_1958.nc'\n",
+      "2021-03-20 21:50:37 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ws_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'ws' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:50:45 [INFO] pangeo_forge.recipe - Storing chunk '('ws', 0)' to Zarr region {'time': slice(0, 12, None)}\n",
+      "[2021-03-20 21:50:47-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[22]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:50:47 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[22]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:50:47-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[23]': Starting task run...\n",
+      "2021-03-20 21:50:47 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[23]': Starting task run...\n",
+      "2021-03-20 21:50:47 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('ws', 1)'\n",
+      "2021-03-20 21:50:47 [INFO] pangeo_forge.recipe - Opening input with Xarray ('ws', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ws_1959.nc'\n",
+      "2021-03-20 21:50:47 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ws_1959.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'ws' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:50:54 [INFO] pangeo_forge.recipe - Storing chunk '('ws', 1)' to Zarr region {'time': slice(12, 24, None)}\n",
+      "[2021-03-20 21:50:56-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[23]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:50:56 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[23]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:50:56-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[24]': Starting task run...\n",
+      "2021-03-20 21:50:56 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[24]': Starting task run...\n",
+      "2021-03-20 21:50:56 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('vpd', 0)'\n",
+      "2021-03-20 21:50:56 [INFO] pangeo_forge.recipe - Opening input with Xarray ('vpd', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vpd_1958.nc'\n",
+      "2021-03-20 21:50:56 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vpd_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'vpd' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:51:04 [INFO] pangeo_forge.recipe - Storing chunk '('vpd', 0)' to Zarr region {'time': slice(0, 12, None)}\n",
+      "[2021-03-20 21:51:05-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[24]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:51:05 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[24]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:51:05-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[25]': Starting task run...\n",
+      "2021-03-20 21:51:05 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[25]': Starting task run...\n",
+      "2021-03-20 21:51:05 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('vpd', 1)'\n",
+      "2021-03-20 21:51:05 [INFO] pangeo_forge.recipe - Opening input with Xarray ('vpd', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vpd_1959.nc'\n",
+      "2021-03-20 21:51:05 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_vpd_1959.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'vpd' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:51:13 [INFO] pangeo_forge.recipe - Storing chunk '('vpd', 1)' to Zarr region {'time': slice(12, 24, None)}\n",
+      "[2021-03-20 21:51:15-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[25]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:51:15 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[25]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:51:15-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[26]': Starting task run...\n",
+      "2021-03-20 21:51:15 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[26]': Starting task run...\n",
+      "2021-03-20 21:51:15 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('PDSI', 0)'\n",
+      "2021-03-20 21:51:15 [INFO] pangeo_forge.recipe - Opening input with Xarray ('PDSI', 1958): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_PDSI_1958.nc'\n",
+      "2021-03-20 21:51:15 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_PDSI_1958.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'PDSI' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:51:22 [INFO] pangeo_forge.recipe - Storing chunk '('PDSI', 0)' to Zarr region {'time': slice(0, 12, None)}\n",
+      "[2021-03-20 21:51:24-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[26]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:51:24 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[26]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:51:24-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[27]': Starting task run...\n",
+      "2021-03-20 21:51:24 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[27]': Starting task run...\n",
+      "2021-03-20 21:51:24 [INFO] pangeo_forge.recipe - Concatenating inputs for chunk '('PDSI', 1)'\n",
+      "2021-03-20 21:51:24 [INFO] pangeo_forge.recipe - Opening input with Xarray ('PDSI', 1959): 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_PDSI_1959.nc'\n",
+      "2021-03-20 21:51:24 [INFO] pangeo_forge.recipe - Opening 'https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_PDSI_1959.nc' from cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rpa/Code/xarray/xarray/conventions.py:492: SerializationWarning: variable 'PDSI' has multiple fill values {-32768, 32768}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-20 21:51:32 [INFO] pangeo_forge.recipe - Storing chunk '('PDSI', 1)' to Zarr region {'time': slice(12, 24, None)}\n",
+      "[2021-03-20 21:51:33-0400] INFO - prefect.TaskRunner | Task 'MappedTaskWrapper[27]': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:51:33 [INFO] prefect.TaskRunner - Task 'MappedTaskWrapper[27]': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:51:33-0400] INFO - prefect.TaskRunner | Task 'SingleTaskWrapper': Starting task run...\n",
+      "2021-03-20 21:51:33 [INFO] prefect.TaskRunner - Task 'SingleTaskWrapper': Starting task run...\n",
+      "2021-03-20 21:51:33 [INFO] pangeo_forge.recipe - Consolidating Zarr metadata\n",
+      "[2021-03-20 21:51:33-0400] INFO - prefect.TaskRunner | Task 'SingleTaskWrapper': Finished task run for task with final state: 'Success'\n",
+      "2021-03-20 21:51:33 [INFO] prefect.TaskRunner - Task 'SingleTaskWrapper': Finished task run for task with final state: 'Success'\n",
+      "[2021-03-20 21:51:33-0400] INFO - prefect.FlowRunner | Flow run SUCCESS: all reference tasks succeeded\n",
+      "2021-03-20 21:51:33 [INFO] prefect.FlowRunner - Flow run SUCCESS: all reference tasks succeeded\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Success: \"All reference tasks succeeded.\">"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pangeo_forge.executors import PrefectPipelineExecutor\n",
+    "pipelines = recipe.to_pipelines()\n",
+    "executor = PrefectPipelineExecutor()\n",
+    "plan = executor.pipelines_to_plan(pipelines)\n",
+    "executor.execute_plan(plan)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check and Plot Target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
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+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
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+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
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+       ".xr-sections {\n",
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+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
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+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
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+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
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+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
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+       "\n",
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+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
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+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
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+       "  display: inline-block;\n",
+       "}\n",
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+       "  display: inline-block !important;\n",
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+       "  padding: 0 !important;\n",
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+       "}\n",
+       "\n",
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+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
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+       "\n",
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+       ".xr-var-dims,\n",
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+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
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+       "\n",
+       ".xr-var-attrs,\n",
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+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
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+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                 (crs: 1, lat: 4320, lon: 8640, time: 24)\n",
+       "Coordinates:\n",
+       "  * crs                     (crs) int16 3\n",
+       "  * lat                     (lat) float64 89.98 89.94 89.9 ... -89.94 -89.98\n",
+       "  * lon                     (lon) float64 -180.0 -179.9 -179.9 ... 179.9 180.0\n",
+       "  * time                    (time) datetime64[ns] 1958-01-01 ... 1959-12-01\n",
+       "Data variables:\n",
+       "    aet                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    def                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    pdsi                    (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    pet                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    ppt                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    ppt_station_influence   (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    q                       (time, lat, lon) float64 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    soil                    (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    srad                    (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    swe                     (time, lat, lon) float64 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    tmax                    (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    tmax_station_influence  (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    tmin                    (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    tmin_station_influence  (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    vap                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    vap_station_influence   (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    vpd                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "    ws                      (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    method:   These layers from TerraClimate were creating using climatically...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d53a9d93-9caa-4ada-824b-a4b96b9a301c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d53a9d93-9caa-4ada-824b-a4b96b9a301c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>crs</span>: 1</li><li><span class='xr-has-index'>lat</span>: 4320</li><li><span class='xr-has-index'>lon</span>: 8640</li><li><span class='xr-has-index'>time</span>: 24</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a7faee93-8396-4cfc-8588-412d95882216' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a7faee93-8396-4cfc-8588-412d95882216' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>crs</span></div><div class='xr-var-dims'>(crs)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>3</div><input id='attrs-e2deacab-93a6-4f77-a830-be176e3b29cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2deacab-93a6-4f77-a830-be176e3b29cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c98cf8a-f92d-47fb-b269-09eb2c80ca68' class='xr-var-data-in' type='checkbox'><label for='data-9c98cf8a-f92d-47fb-b269-09eb2c80ca68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>long_name :</span></dt><dd>crs</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd></dl></div><div class='xr-var-data'><pre>array([3], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.98 89.94 89.9 ... -89.94 -89.98</div><input id='attrs-c2afd3c2-3328-45fb-8e9f-5f10f7d348f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2afd3c2-3328-45fb-8e9f-5f10f7d348f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-735eb053-6407-49a3-9e35-e972d69f56a1' class='xr-var-data-in' type='checkbox'><label for='data-735eb053-6407-49a3-9e35-e972d69f56a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>description :</span></dt><dd>Latitude of the center of the grid cell</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 89.979167,  89.9375  ,  89.895833, ..., -89.895833, -89.9375  ,\n",
+       "       -89.979167])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -179.9 ... 179.9 180.0</div><input id='attrs-1b29068f-c729-4a11-9d8d-d03a409d88d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b29068f-c729-4a11-9d8d-d03a409d88d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5efb84f9-3fe5-492c-9b65-e19d36094cce' class='xr-var-data-in' type='checkbox'><label for='data-5efb84f9-3fe5-492c-9b65-e19d36094cce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>description :</span></dt><dd>Longitude of the center of the grid cell</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-179.979167, -179.9375  , -179.895833, ...,  179.895833,  179.9375  ,\n",
+       "        179.979167])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1958-01-01 ... 1959-12-01</div><input id='attrs-8fe6da3c-7b16-4b4c-bfb0-592a8afaf62a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fe6da3c-7b16-4b4c-bfb0-592a8afaf62a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38af13dc-867b-462c-ad16-5d34387c3e1b' class='xr-var-data-in' type='checkbox'><label for='data-38af13dc-867b-462c-ad16-5d34387c3e1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>description :</span></dt><dd>days since 1900-01-01</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1958-01-01T00:00:00.000000000&#x27;, &#x27;1958-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1958-03-01T00:00:00.000000000&#x27;, &#x27;1958-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1958-05-01T00:00:00.000000000&#x27;, &#x27;1958-06-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1958-07-01T00:00:00.000000000&#x27;, &#x27;1958-08-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1958-09-01T00:00:00.000000000&#x27;, &#x27;1958-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1958-11-01T00:00:00.000000000&#x27;, &#x27;1958-12-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1959-01-01T00:00:00.000000000&#x27;, &#x27;1959-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1959-03-01T00:00:00.000000000&#x27;, &#x27;1959-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1959-05-01T00:00:00.000000000&#x27;, &#x27;1959-06-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1959-07-01T00:00:00.000000000&#x27;, &#x27;1959-08-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1959-09-01T00:00:00.000000000&#x27;, &#x27;1959-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1959-11-01T00:00:00.000000000&#x27;, &#x27;1959-12-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d13cd931-3e5b-45e7-9751-f5e9c407c599' class='xr-section-summary-in' type='checkbox'  ><label for='section-d13cd931-3e5b-45e7-9751-f5e9c407c599' class='xr-section-summary' >Data variables: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>aet</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;</div><input id='attrs-2ff96c4c-5a95-4ae9-bdd9-749b0003575a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ff96c4c-5a95-4ae9-bdd9-749b0003575a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00ede10c-90cf-4194-815e-410404a10968' class='xr-var-data-in' type='checkbox'><label for='data-00ede10c-90cf-4194-815e-410404a10968' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Actual Evapotranspiration</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>water_evaporation_amount</dd><dt><span>standard_name :</span></dt><dd>water_evaporation_amount</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 3.58 GB </td> <td> 50.33 MB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (24, 4320, 8640) </td> <td> (12, 1024, 1024) </td></tr>\n",
+       "    <tr><th> Count </th><td> 91 Tasks </td><td> 90 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</td>\n",
+       "<td>\n",
+       "<svg width=\"194\" height=\"124\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>def</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;</div><input id='attrs-3bc730b5-2e35-41da-850e-db9b5a5eb254' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bc730b5-2e35-41da-850e-db9b5a5eb254' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39ea9692-9b01-47fc-8cae-aac16bfce146' class='xr-var-data-in' type='checkbox'><label for='data-39ea9692-9b01-47fc-8cae-aac16bfce146' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Climatic Water Deficit</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>water_potential_evaporation_amount_minus_water_evaporation_amount</dd><dt><span>standard_name :</span></dt><dd>water_potential_evaporation_amount_minus_water_evaporation_amount</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 3.58 GB </td> <td> 50.33 MB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (24, 4320, 8640) </td> <td> (12, 1024, 1024) </td></tr>\n",
+       "    <tr><th> Count </th><td> 91 Tasks </td><td> 90 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</td>\n",
+       "<td>\n",
+       "<svg width=\"194\" height=\"124\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ppt</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;</div><input id='attrs-45c11944-c930-4276-a862-89efd8722feb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45c11944-c930-4276-a862-89efd8722feb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8efa1232-efc0-4e14-9aa1-e96bcb8c2e4a' class='xr-var-data-in' type='checkbox'><label for='data-8efa1232-efc0-4e14-9aa1-e96bcb8c2e4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Accumulated Precipitation</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>precipitation_amount</dd><dt><span>standard_name :</span></dt><dd>precipitation_amount</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;</div><input id='attrs-38b76aba-be3d-484c-a09e-3839997fe236' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38b76aba-be3d-484c-a09e-3839997fe236' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee78ccb4-fbb0-43d8-a5c7-5076e1a42683' class='xr-var-data-in' type='checkbox'><label for='data-ee78ccb4-fbb0-43d8-a5c7-5076e1a42683' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Runoff</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>runoff_amount</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tmin</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;</div><input id='attrs-f393019c-58e1-4835-8632-03ed4a33c691' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f393019c-58e1-4835-8632-03ed4a33c691' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-989f477c-bf1b-4650-856b-7ba0a2234842' class='xr-var-data-in' type='checkbox'><label for='data-989f477c-bf1b-4650-856b-7ba0a2234842' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Minimum 2-m Temperature</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>air_temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>deg C</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vap</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 1024, 1024), meta=np.ndarray&gt;</div><input id='attrs-9b4de3b2-9617-4e54-9672-33bbd7ebebd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b4de3b2-9617-4e54-9672-33bbd7ebebd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71f5c274-8e88-4363-aca3-a53b43f0aef7' class='xr-var-data-in' type='checkbox'><label for='data-71f5c274-8e88-4363-aca3-a53b43f0aef7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>2-m Vapor Pressure</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>water_vapor_partial_pressure_in_air</dd><dt><span>standard_name :</span></dt><dd>water_vapor_partial_pressure_in_air</dd><dt><span>units :</span></dt><dd>kPa</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "  <text x=\"7.474299\" y=\"87.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,87.474299)\">24</text>\n",
+       "</svg>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-fb729e3a-a67c-4d34-ac30-41739d6a7937' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fb729e3a-a67c-4d34-ac30-41739d6a7937' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>method :</span></dt><dd>These layers from TerraClimate were creating using climatically aided interpolation of monthly anomalies from the JRA-55/ERA-20C/ERA-INTERIM reanalyses and WorldClim v2.0 climatologies. We do not provide an additional layer of station density since JRA-55/ERA-20C/ERA-INTERIM is used across the entire globe.</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                 (crs: 1, lat: 4320, lon: 8640, time: 24)\n",
+       "Coordinates:\n",
+       "  * crs                     (crs) int16 3\n",
+       "  * lat                     (lat) float64 89.98 89.94 89.9 ... -89.94 -89.98\n",
+       "  * lon                     (lon) float64 -180.0 -179.9 -179.9 ... 179.9 180.0\n",
+       "  * time                    (time) datetime64[ns] 1958-01-01 ... 1959-12-01\n",
+       "Data variables:\n",
+       "    aet                     (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    def                     (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    pdsi                    (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    pet                     (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    ppt                     (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    ppt_station_influence   (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    q                       (time, lat, lon) float64 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    soil                    (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    srad                    (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    swe                     (time, lat, lon) float64 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    tmax                    (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    tmax_station_influence  (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    tmin                    (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    tmin_station_influence  (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    vap                     (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    vap_station_influence   (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    vpd                     (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "    ws                      (time, lat, lon) float32 dask.array<chunksize=(12, 1024, 1024), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    method:   These layers from TerraClimate were creating using climatically..."
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_target = xr.open_zarr(target.get_mapper(), consolidated=True)\n",
+    "ds_target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an example calculation, we compute and plot the seasonal climatology of soil moisture."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (season: 4, lat: 360, lon: 720)&gt;\n",
+       "dask.array&lt;mean_agg-aggregate, shape=(4, 360, 720), dtype=float32, chunksize=(1, 86, 86), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float64 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n",
+       "  * lon      (lon) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
+       "  * season   (season) object &#x27;DJF&#x27; &#x27;JJA&#x27; &#x27;MAM&#x27; &#x27;SON&#x27;\n",
+       "Attributes:\n",
+       "    coordinate_system:  WGS84,EPSG:4326\n",
+       "    description:        Soil Moisture at End of Month\n",
+       "    dimensions:         lon lat time\n",
+       "    grid_mapping:       crs\n",
+       "    long_name:          soil_moisture_content\n",
+       "    standard_name:      soil_moisture_content\n",
+       "    units:              mm</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>season</span>: 4</li><li><span class='xr-has-index'>lat</span>: 360</li><li><span class='xr-has-index'>lon</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-20c539ba-1521-41e6-a69f-bfa33bfaa28a' class='xr-array-in' type='checkbox' checked><label for='section-20c539ba-1521-41e6-a69f-bfa33bfaa28a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 85, 85), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 4.15 MB </td> <td> 29.58 kB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (4, 360, 720) </td> <td> (1, 86, 86) </td></tr>\n",
+       "    <tr><th> Count </th><td> 2347 Tasks </td><td> 180 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</td>\n",
+       "<td>\n",
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+       "  <text x=\"7.474299\" y=\"87.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,87.474299)\">4</text>\n",
+       "</svg>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-7a214818-f0d5-4aef-af7d-01c342493614' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7a214818-f0d5-4aef-af7d-01c342493614' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.75 89.25 88.75 ... -89.25 -89.75</div><input id='attrs-6e8e5f6d-478b-4e3d-b302-ff2338ffbccc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e8e5f6d-478b-4e3d-b302-ff2338ffbccc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-533b77af-4286-4061-9c54-e9c030612a05' class='xr-var-data-in' type='checkbox'><label for='data-533b77af-4286-4061-9c54-e9c030612a05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>description :</span></dt><dd>Latitude of the center of the grid cell</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 89.75,  89.25,  88.75, ..., -88.75, -89.25, -89.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-c008d676-ca01-4691-9dae-58644addcc84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c008d676-ca01-4691-9dae-58644addcc84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e55de4ba-b7e4-443b-a1ca-db5bfbe87e39' class='xr-var-data-in' type='checkbox'><label for='data-e55de4ba-b7e4-443b-a1ca-db5bfbe87e39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>description :</span></dt><dd>Longitude of the center of the grid cell</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>season</span></div><div class='xr-var-dims'>(season)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;DJF&#x27; &#x27;JJA&#x27; &#x27;MAM&#x27; &#x27;SON&#x27;</div><input id='attrs-b9f52dad-5264-424b-974d-83ef8267606a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9f52dad-5264-424b-974d-83ef8267606a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a046e9bd-0e0d-4014-9c76-91db876bf78c' class='xr-var-data-in' type='checkbox'><label for='data-a046e9bd-0e0d-4014-9c76-91db876bf78c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;DJF&#x27;, &#x27;JJA&#x27;, &#x27;MAM&#x27;, &#x27;SON&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa6ed84f-7203-40d4-b847-cc371ea64d0a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aa6ed84f-7203-40d4-b847-cc371ea64d0a' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Soil Moisture at End of Month</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>soil_moisture_content</dd><dt><span>standard_name :</span></dt><dd>soil_moisture_content</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (season: 4, lat: 360, lon: 720)>\n",
+       "dask.array<mean_agg-aggregate, shape=(4, 360, 720), dtype=float32, chunksize=(1, 86, 86), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float64 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n",
+       "  * lon      (lon) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
+       "  * season   (season) object 'DJF' 'JJA' 'MAM' 'SON'\n",
+       "Attributes:\n",
+       "    coordinate_system:  WGS84,EPSG:4326\n",
+       "    description:        Soil Moisture at End of Month\n",
+       "    dimensions:         lon lat time\n",
+       "    grid_mapping:       crs\n",
+       "    long_name:          soil_moisture_content\n",
+       "    standard_name:      soil_moisture_content\n",
+       "    units:              mm"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with xr.set_options(keep_attrs=True):\n",
+    "    soil_clim = ds_target.soil.groupby('time.season').mean('time').coarsen(lon=12, lat=12).mean()\n",
+    "soil_clim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/miniconda3/envs/pangeo2020/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  x = np.divide(x1, x2, out)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.plot.facetgrid.FacetGrid at 0x7fbaf8beba00>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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cnFj7drAWKUtkUCLFqpISu3vQNMhwSNjdQ9Ys4cxZjeNVRTAGueUY9vSOxv/JVIsV1mrsrmpV0wxKwtFN/LgkZKbdhKcYXm2UsFFiXNA1xGscB5DGkz+qrx/KjHp9VZ87LnClvYCQ30/OpE17+/dqvvR3tVEuHYvP4/3XsAYdDkgVJCi5lI5nP0wdIjkG2V6N3Z3DZ+8nOAd5fuBznopIpMRBcRye/Fi+eBziNTYmlZkEvS5edZc+xlSOEAnSCyDQDO3in/jMtH8EAZ8Jdubaa+2JIJFZpvZ85KNPrGjzquPva6/3INCMMsSDaTyv/dqfAODDv/qWJ3rIPXpcN7z1rx86d+vNj61CSrjR1UjX1RMpkki/DnwZsA38O+BvAX8beGUI4UERuQX4WAjhhY/1Wr2XwlMTd+X/GwAfqv95d9tL34kb5RSfPwtA85nPY9dW1TdgPIKVMW5zhWa1aDfFANnUkT+wBVvbscc/boSNAafVRRmUqmKaTJHRkHDTYfwgw41yfKYb6pAJUnv1NJrG1/GAQU0sZ9p+5jZX9OWnc2RWU9+6gS8MdtIQrByoFLoRIQGt8uZaxc0/rW155BlhZxcRg59O28dfr8rl/kTSVl6/80ZVYcEIQWgJlivFfgJq8fUWk9ZXnnjfJSWxiUS6lONaJPQSgfNYn8vEzVOInlthgUC6EoXP/nOcEmW4MhLpVcffd0XfxfVWUfW4dPSeSE9/HBTHAV79mvdSnJkgu1M1hD5zluAcZnWFMJvjn38HITP40mpxwuiaMfjcOdg6rwql1PplrRaLopJVVtQHiRBgbUUVSkYVvwi6/rmAabTd2k4bZF7jx6W2nk01jktUQbmNMWY6pz48xhdWlR5WdHO+sOm9kSFelagIZOdmmEfPKWHXNITJlNA0SurBdR22sUTGHLBFuRZx/KD70v2XGm/2H/fFjjOpgoO5DO/Kfa9nZw5gSdl0qdj/mU0qll4hiXQxEm8/9p+LPo4/fdB7IikuxQtpP25kb6TrqkQKIdwvIh8APg9MgQ+FED4kIjeFEB6Mj3lQRI4d9HwR+S7guwDuuOPxZWU9njycWP8O9fwpdELY8eG3qveBc5qvfc0XtY+VuMF1exPYm8AjpxEjZKLJZL4yVl+BogBrlbAIARkO8Nva5y9FrsnmNCDra4T5nDCfI5MpBhDn9P7YHpcQALGxkvOi5y19hg/95ls5vvI6QtPgfcB87j6sEa2yWktmn3gF6OmAUFVqymk00acsoSyect5Ig9/44wNvl/VVwvZu14J3mZh9zfOBy08sLzVBuhiZc1Bi9tF7Hvs1F59zrRK0xUTwox+6Ou9xse/uYjix+Z0X3NZPGHx6oo/jT12c2PxOcA4zHBKca9XCrf/PS1/UPtbdex8AUpY0j5zWf//27yOAFaPx0kY1xXAIRU7Ym2BWV/DntghiNI6n4lCew94efm+C7OxiHozt6U43zyH+f3HKaAD40ucvfYZTf/Q+Th79bsxDjxCcI/s0rV8RIi1p9YxAPLcyHOj5FeHUvX/zOh/UhTgoHkhZaPHQXbmKdn8svxRvxMuJoxd77P5Yfjlx/HKP4UrwRFTEi7gacRz6WN7jqYvLUSEl3MhqpOutRNoE/jXwzcAW8C+BfwX8nRDCxsLjzoUQNh/rtfoK5rVFmj5iD21oe5hzaiDpg04kAU49/Pfbx59Y+3atMlqd7hXi40JVtSSOWIsYIewjdhKp05pPx/8D6o2UFEcihOlMk0BrkJUxYXu3TXDlIFm216pl8L5LRoLX4zPSVuEAKHJ9De8JVX1VzuPTCs6BtYS6VnPOPGvNtp+K010uSEhEkCInHN3USvlkqt/jwqajfWiWqc/GbH7ga6fkcxHP1Craonroap2DiyWTV4L5S55/1Y+vx5WjVyI9dXB85XXgPGZjDRrXxdemQUqNface+nvt409sfiehrtUg2jmNCVmm62gaLy+mjdEpTksqOMBybI8FIxkOwXtkc13vmM50Xc4yGI80tzitNphhYTroEhLZdFAOK12eLqOhtsTV9TMzjhOLbMZofLOmndQmwwGn7v/b1/nolnFi/Tu6P5IqLctgZaS3TWfL3+O+718GpV7bB1w3B8VxeGbGiWtRcLqacXzxu3omfj9PNfRKpCtTISXcqGqk622s/Q3AZ0IIjwKIyL8BXg48LCK3LLSzPXI9D/KZiEQaAZ2ptRH81nklXKzVxCTPaE6fXXruiS9+M2RZl0Q2jf7buZhwoglo8ICqepY0y8Gr/1FQsiolngB+NscMB4SqRhaSWSkKTUShVRxhbVdldI5QN60aanHc7T2zf8pdxV/WZLduNIEFjB0ogRLl8El2/4yoXHqPrK3ibzmM/NHn9FzmGbIypn72Ub7h69/NL/6ny5dRX0vcfe5nl5OY1CYxrfT4oSWWAMJcycbwBc9CTp/X++dVm5T21bCD8VhV1CuZevfKE++Dr3k+H7v7jZzY/M6LnvcLElTvu9+iNe0EpB49enQ4PvgWAI2RQRWlYW/SqYlGI7AGd3Zr6XknvvjNyM1HkUTmTCZK8jivMVlyQqPxNzQas00swqTCkFiL5IKP5LzYeP9sruvwbA55rnHceyUAnAMvepz1hURAqDsCoVUThcDd2/9Y/Zigi9Uh6BrRNB3xsLhuPBMgQv3CZ5E/vIM0Tkmk9TWq29aR5qm3Xt59/ueWiSToFGxiVEEVv0sp8i6OP+82zJnt+IRK/THTa/ax/AI8Voy+EoJpfxyHg8/74xFNUhbgPMNPfIbpVz33kt67R48nA1eiQkq4UdVI15tE+jzwUhEZoe1srwE+DuwBrwPeG///C9ftCJ8BuKv4y11VMSG2kokRbSVLRI4YJJpNhqaBpsEUBVLknFj/Du4+/3P6sNFQe+wnk1iJ1IqntB1gC61gwXctZbDUNG2iykmsbY8xEUeBmBhLVDPt7rVEVTo+icqZpIoKCyQRwD3Tn9fXf7FWPcLv/jES39/vTfSYjx5Wv4c4reSZAFlbZfrFt7Jze8Gx7WNwdguObEKjvlT1rRvX+xAPxGLScmLzO1VFdiYOdoybijCvkLJoH3vXS97ZbTgiZl/z/CsiRJ4OODCJi5X7J0Nh9qrj0Vh0HttRFoxBZ1/zfE7e9te6v7/kWe1yMAD1TFtbgZ09wt5EVQYihI0VmFaEM+cof/OPlVQaDjn5nNfrpiPP8GUOmcGXuiZ8+FfefM0/67XAa1/+E9RrSoTeaNdmjyvH8cG3LKl+YKFVW8xS+1fwvlWV4hwEbSUTYzhx6K+264BfGWDOSts2rmOiDGmymNiDjZvFLpM0ZjhYOqZWtTSZEvyeHmdSyiRV0r4JoBijRJO1F6hP0iTQ+iufDyLk//WPWyIpzCOBdeyIxoN9Ra8bGXJ4k+r2Q5z5kgGDc0M2f60hDEvCMCfbnuNGT00T7ZRHJpw8+t2qQIqEUUKYVwfHcbscUy7H1+jpgouSMSJPShx/LB+k+Uuez+C/38fJZ30/hMD0xc9q68RpxZCbj6piLOZnMo5Ks7LQ27xn8PE/6eK4sTAoCLnt/rPmaRvH4cmxH+hxdXA5E9kuhhtxUtv19kT6DRH5V8B/BRrgt4F/CKwA/0JE/gpKNP2l63eUNy6SWeZiexlEyXkKwsl/ICWnIVbzxPCh6p9xfPitenNVq2dCrHoGMchw0LaJtUgqo/1YUB+1/7/Y/Qv3tdXUeEztY9Oxp88RPMSq2z17//TxTw5a5QxNA4+eiYqohUQc2uRWjNGkPP6bG8ErSYTBAzsMPzVn+vyj8NxNJOhIY+App0I6CCm5TMmWjIYAhL3JUsvGh37zrRc891KNIp+KuCJJearcP0FcSiK0X8l08rk/pP9/zusZuqgSyHPwjsEfPay/60FBeNZN6nkiAmsr6s0yLMEKMm9U1WAN1E2rNqModOOYL/8mfWF49Wvfe9W8IK4UJ2//gbZiHpIK7goJvRuV9Ozx2Dg++JaluNQSR1mm3oFZqfcn4iW1l5uFYlAqtjgHddPGdayBZHANXQFmP8GT4HynGrkIFo81eK8xM6mHD3jN1HK3hAX10UG4+/zPqSopBGQwgHlFeOR0p0LyByhwFm+/UZRKIojz3PzLZ6k3h2x/1S34HIrzeq7/0394w3U+wEvDqUf/AbAQywclYTZfKhodFMfh6RvLr7g17CpYlFxKDNn/mMU4PvBRAZgp4Tv8/Qd1XRBDuPUmPUwD5BZZX9XbVwZ6vZ45r0Xo3T0k5fQpjmcLv8sQNI6/5r0A13V668kXvBF2lGhvLRGeQGHuRiQ9n+54IiqkhBtRjXS9lUiEEN4GvG3fzXNUldTjWqJVGyUCJCaVPoCrDiZzEoJvSahF+HrB/8C56Ddk9LXTa6U2tYUqabp98T0vIKAe63MYUdXRvuQv+fk85mcB7vnEO5b+TmRYi6a5cIR9tu/n47wm5PG9khH40xLes/PCTapVoVoTBmcCw0drfGnJdiqOf/U7uOe39v9sn5pYTDRPPuv7byhpeyv7vwqbnidavdw/aS0lQce/Il4nBk2sRJCqgcYjVb1AKscNqPPgXXxs/N3WDcKCcgJgUICVtiopwwKZzPFrQ4JbWIdS8ilCsEIzykBoJ9Y9mXjV8fcx+Ow5VcSVmRKbzsHuXpv8S57BcMDJW74PBmW3zsT7M2PITkMYlT1x1ANgmWQR0/2Oot9fag0TI21BaEm1VNXL8T89D5ZJJ2+633G8D2u7vw8ikA4gliSuV8H7jkhK2LeWyb6Clt4oBxJIi5OqltrboPVD7ApjvmuHu+CY9xWLnq6k0nSGma1w+qs3qUeCcZDvBtzAYKvwlCDSLwcpdp+85fv6OH4NcTGFzPGvfof+hpJVQNVoS+RiHA/xtxOSPYTm58kWolUppoJJnivZZAxBBDZXkb05HN3U4my8XbwnGKMx3xh8Ydupu9cDJ1/4JtibEDZX9TONR3oeUuvu/ji+uE7GWD6Mfq7TZ2/0sfwpjKuhQkq40dRI151E6vEUQFz875l1Cp39BNF+mXz31KjGsVYXyagM2v/4xdc+PvzWhba2CGtapVBLbiXvpP3HumjomWfaouaDytYXCaU8Q5whRJ+UxWN4PFzOY/ejreJKbAU8gNx6qiPsTTC1x84N5RbkE0+wgist+KemBP5ScOq+vwU8voz4aRXIF68ta1Shc4BB+LVIui+W+Hzs7je29xWDqIioF9QQ3qs/R/IwarpR3+1/EJUPVpNMIBRZl8Qao0lkHpOzxhNWBwRjENGRxRgI0pEwweiY8ZA/BX6P9z1M80XPwU4qZG/S3V6WWnk1BxDQPoCVpZbchKfVNdvjquGe2T9dKnqkFm24sBhysTie7hNrMYMMP5sjdlk1dMFrr7zugiLJoiq3hTXLJNG+xwNQdD43LYFjra4TwwGMRoTTZ9rn7W93uhguplS6VJxY+/au4OXc0zOO783JpqomEwfiwVvBeq9r5NMQpx78uwC8+rXvbW+7GBn2tFkX95OnySA8qe0X7r/asfxi+VCK4xKgODsjiGDqSMIK4CJRlPynnO9ifIrn3gOmHYCzv90wZDZ1x2psc0IYl52fGfr0sOB/Js4hjYEcxAdCdh2v4/sehiOHCCtD5PzCevl4cdyIWmwcsKY8ba7ZZwiuhgop4UZTI/Uk0jMEJw79VUDNMfcnhqAJ5F35/7YvyexMrRcrmPv/Tp5Hi2bViyqjD1X/DIDj4/+XPiaSQBJl8mF3TyPSY7Wz6Ru1r5na2kKliilJ5t9Fru+RWa3gH9pE9ib4M2eV3AmXTyhdLu6Z/jzHB9+CrK3CbhxRnCa9PcUgmVZLsIbm1kOI81SbA+qxoV4xNAMh3wvUI0M91mC39ukafz2Ddg9O3vy96u00KNVkPipW/E2byM4MWVvV9g2uXsJ5sURz+OmznHz+GxiAJnnWcOKL38wgt4TM4ItMiV0X2gQzFBkhyzA7jlAWyBRVHw0HUTkYjXVNVD4Y07akhdyCC0hQokhcgKQTNAY/UFIpCGCEai3D56ZNat3CAMaXf9NPk+85xAWMC+BBIrG1qGoA+Iavf7cmsB7c0OIzg8+738Ev/z8/8rjnMAhMn6uDRgePdpti1lZ1PQyB2RccgQCmchSfP8upP/6p9mEnn/fDj/sePW5cpBaXRBKHpl5uTYtxfKllfKEFvbvNLz2vHWQRCzntZFQT4zLdBNXjK6+LrxGVc1mGrK4QtneWfJcWcQGxFKd9tmqgODUztRyzugJN3KyuqJ+S+A3C9i40TasweqIk0aVAzcGzbjrdU1BdLFmmRFyRE9bG+GGOLyzTYyW+EKo1QxCwVYAG3EDI9673UT9xBJGn9Tbsgjge8zF/0yYBMLszeOBh4OoSR8mXcH9r+fBPznDyuT8U28qFk8/7YYbWQJ6pmje3GnObBvEgIUCe6UCSIgcTYD7XfyekdSeLcTmzsRASFUZpzfAeqVKOL5ojpEKQMTQrOT5XNbE0ShypEgnszPOq4+/Dzpwqo0I8thD40K9f2OZ410vfibhAs1LgSoME8PH1LiWOv/o178U/dxPYZPAbf7x0n2xuQNMw+1O3ICEgtdc4/qn3t4/p4/jTB1dThZRwI6mRehLpGYzFJHJ/29iStH3htvT3h+p/rqQQmtCKtXrfAgEkRvB1w2vtN7cT3pbIoSRz3++R9BhtZxcjmFpTvswqiTQcEIY6jUaqSkcKEz2UfOD48FuXqqpXG/fM/iknb/8BTbDzTCs1TyUSyRr1isgz3NENQmGpNgvm6xZxQdUaAqYJ+LhK/NY//qHre8xXEU/nSs+JF79ZSaM0oWY0JGystfef+sP3Xuypj/+6qQ0sbhBP/cF7OPmiHyOIMAyB6R3r7eNPvujHGC76L1zEi0FCICD4MlOCJkSyxsQE1Iq2pqXnL7a+LLTA4YL+HY8xRFk7ISCNVtX9MCMYQXzAl5Zgotx9wWsoWMHlYBwoW6NJKV7vM7USRa/92p/oPlqUzQtCMEpeBUHb4uLS+cqTP4WdakV2PwF1EJbaLF+wfD0Gqy9a3XFo6fZTn/7A475uj2cA9k0ua30NW8+iBe/BfeQRojFXLHyo+mfaShN/e346vTC+GiHMda05sf4dGuubpq2mh6bRDRtoCzcsTVBbRKs8Wqi+t36C1hKObrb3y7wmGINbHSC1x+5EtV4icby/5mRSet0TR74LKQr8ZKKcxVOFSLIGKUslj1aGuPURrrRMbs7bdTAYwVRBN98efuPnb5w4/tEPPY3j+Bc/dhwHjb+X/bpf+pZIpHTx+NQfvIeTX/Sj3YPu2ACUTProPW9k+LktvT0pgw9qS41xGwMhy7S9bIFIxhiQoAqc/VjYX0gI0ARCRtdOuq/NKxijpJULsW1NFcQENEeJD3dRGWIyIbgQC1VoXEYws6Zrp1+AGMDH4R5BcwWJn+3Vr30vpvJI7fnwr73lwHP8Sx95U1tUuyCOR+WYhBBzhj6OP51xNVVICTeSGknCRRL/pxvuvPPO8PGPf/xJe7+Tz1dDwsUq8VMd+6ewhQOUQ2agJpyhqi5MTBcgZanS9f2PW1ATSZyslrwJQpysEubzJT+hNJkF55DVFfzW+di2YZbGtC4lbknRlGXIoFT2P88Ij5zWgLYyxq+OMLtTqGutlAxKnfAxncWqokfGSi7dffofPrGTexGcPPrdOm0itquEqupa/64XkiR4ZQyrY3yZ49ZKpPFUh0pmGxZXgomnPm2SbyQS6emI17zqPRAC+dkJUjt8nFQCYPZUmXD37/3kFb32yRf9WEwSO7VQ20KWDOQXYkUQaSt9STHQksJ5piRITBBTNbFNFF3oqo+xDU28X1IyYqM/QvIyyi6UfcuimbCL7Wt24bFG8LmhGcXPYQWfCcYFTBWwcwcB7KxBZk2UyEd/B+d0jYoS/CBCGOYtGeVLVTf50hIEZoczyi1Hvqs/mssxnj/5vB9e8oAKKwNCkVGvD6jXMkwTKM5VnP/CIXYecKWQTQPj+6YAfPhXD050nyoQkU+EEO68lMc+6XE8msGe+szPPGnv+URxfPithEY3nvvb00xRKCka/TfSwIuEVAhqY7URbRerm3aK2ZJ3B7QqX3f2HHZjQ2NHlnVtNnnWbj6lyFXBtLkBsznh/Hb7OkvDJ/bDWmRljLvlMKGw2O0pMq1AhOrWdezcYXZn6pVSajEmPPRo63Mka6vgXNvmdLVx4sh36WeaTNUjajbXz/lUKAyJ6KQroD66gpk2zI8OcAPDbFPPtanBlYJpAr/1c30cv544fufb8YMce36CNB4/6KSxUmn8uOI4/kU/uhyPQX+fCWktSPGtvd13LeWLbeVl0aqLl2I5aCEoxW0jXStb9GJryWu7QByb2H4elPAJVlpiqoVHC0up6BNVyCE3bTEntaNrgQhM7TGVw8xqxAU9j87r51zofiAE/dzp82WqrAqDDDy41YKQGbJzM8Ig40P/5ccv/dw/6/uXPeKGWqRtNkdU6wX1qmV835T5kRJXGHyu7aWD0zV22lyUsHqq4HLi+I2CqEI69+v/8Y6rSiIBVFXgpd/4eT75e9Xm01mNdMOQSOujW8PXfOl34wvLL/7y03fk47XEXcVf1n8sGFcntJPZrL2gyrn4nKQ42q9UEiPtNJg2qIi0r3v3+Z/j+Mrr9PlVvdyaZgQpilaSHaaztnKxSO4k5dP+zaYMSmRjnTAsCJ/6vN5mLXL4kErhXUDO7+jCHo221T9J4I5bkN2pjhUGQlVf9fGoJ2/+3viZjE6YmM81ab5OZFI7uWo8JoxL5PyeSqjXhtRrJXu3Fvgc8j09z7/2f//1J/0Yeyzjrpe8k1BYgoH83jOQZYRBToiJ4D2//Y7HeYWDceKL39yRQdASjIm4aZO9mDxK49tkUhq3PM0o/uYTmZPUBkskUvRIEKcGt0vKp/S7XnydSCL5gSZ+SXGUWtnS6wYbE0zRYmh6XZ8JbpgRDFppBOabFp8J4/srxAfsXq0EUjIJjR5rZFYJpCJrDT3bamz0YvKFJsTzQwXZ1CNNaFvcPnb3G1uSAsA/ekbXqvibD84ha7HyPJm0HjDkub7v2hg3Ltl99pDhIzX1qkUCTA9ZyvOe8ed29HN6z93/rVNNPZVw4svfyj2ffNclJ5/r41vDS77se9tNwtNhCuSTjeMrr1uOodBu2FplcVlqu0hdd7/tfXGz/c2lf6eXKop2yqE+1rbX7KlH/wEnNr+zJZFCVXcqAxHMeKS/8ZWxkrEL758mYi6ZXSdkGbIyxh9dx62U5A+eJ5w9B0C4/Rb8KNff6tZEj8s5NbBtnCqhDm/qZjn5s8R2v8UpnE8UJ5//hs4APwT87l5L1l0XryQRLdSJEA5vIpNp9GARqlvWqFcypocttgrYeeBX/1XfQnO9cfyr3gYe/CgnO6fXchjkLWHyRNbxk18Y27BS/p1+2+l3EVvUtHDTdISKcx3xFNvIWxI5ES6JCFqM7zE30EEYC2vRYiyHpfUBY7SdjajUsdLFPRcgi4SRMYj3bbwnM/hS4ziiRJLPDc1YlfP5ToOZO8x8H4lUN7pOps+ekI6vyDsSXIRQZqp89IFqXcm9j939Rk4+5/Wdt9HOrp6PLNMcIZmHWwN705ZwI8s0jq+O8CsDpjcPyfZc68nYlIIEGN070YJYPOf3fPztV3wNXCscv/PtfOgT73gmkkg/+LffffSD3/vtG9fk9f/uz23x/W9+9IdCCB+8Jm/wJODGIZGGt4SXPe87cOtD6jXdJF+LlpV200VXoQe6DZbzurCkKsB8zqnPflAX+JhonPqjqzdy9K6XvhPgwL7f44Nv6drPqn/WkkjtJmahmrlEIu3zHgrOIVmuj08Ko0V2HzQJTIRIMs5LG1Jr2kX77rP/qE0iF4kqszLGT6eY1ZXW70HKbrrZqfv/tqqZUlCIx2c21tQ/YXunldiH+Rxz7Ei70RYX4OxWdzwpyLVjwHP8oVXMpNLgNZ234zoT0ojZK8GJI9+lqqeyqziFeaXVzKZGslyJnScpEW3fa3O9S2KsRfZmVLdtMD1W0gyFjT/cxQ31HH3koz/6OK96MPaP132qtpGdfP4blmXV+5Em+0CXpMFSn/u1xF0veeeSGbTdniKNx60PcaM8Eih6TJci8T/+FW/TJK2OCWSqPmY68WTpXDR+mQQCQmZUvZOeGwKhyAmFytwJoTO8TqRTOodeK66tsmk/gYRWHzVxzLUCmUb7puPcF7ekCfp5XGgN+sX7loAKmW19jNrnRFm8nUVPpL2qO0aJU98iiQXouYpIvkwh1/ulcoTcYHeUjJZHzuo5SBvemKg+bpvPIqkcE3cl1wv8sQ1dn0LA55bs9A5+PCAMMnxhqdZzpAmUp2eIc5ipvpcvc8zeTGX2kaif3a5tiWaun+lS2u8uFye+WIs59/z+uy+dRBreEl72hd+Bjz441XpxTdaMVv2V/LfS9RQJPER0XUyI99/9uz/JXS+JMfciI8WvBCde/Ob29S841qPfjdva0ves//kSiWRGI0LTtLF00eMoEQzp+EPdIEWOn84QIx3RlEz4gwdrMasrLdnb/uYX2kFBjY1PHv1uQvBaIIn3mfU1wnSGHNrQa35rWxXAMR869fm/cSGJJIIcO4LfGGPOT1riKezuIcMh7rbDet03Hntud3l6Y93o+8TNrh8PkKYhlLmqM9OxRZLtibaRnLztr+kUprTp3tsjTGf42Vxb9p/MopCI5hRlodftbE44vI5sT2hu2cCNMibHcoanG7JdXQueiGrx8YZRPFVw4tBf1Vw0khVJ5d5NELTL13TTXDMF234c/6q3qfqozMGAPT+FxuHXRxrHjbQx8CO/dGk518kXRkPxqlou4i4qbkCvWbMvtqcpjtHmoT0ng7IjktM1HRW5ktRGi++Zzue+OA7otRkJmpZwjUMu2hzAL6zD0e+wJX6S92pmtaATVUk+N+39weprmblDnMfMIknWdHsU8V7J5RCUEPIHXBPOE1aH+rxURNuZKFk9nep15QMh+I5Adp4QfDcYQCIxlgj9PObb1sJ4iFsf0owL9XQStH0+BOqVDHGBat0iTofZ2InDNEqm+dyQ7cwxu3Mt9GeG+a1riA9tHL8WAooTX/bjEAL3/O5PPhNJpHf8Pz9/61v/p28YX5PX/w+/uMef/dYH3hVCuHrJxJOMG9IT6WoHuLbVA7R5MS2UwyJOGfIwa7rFOISuDcsYlTkOB3HBcZx8zuvVTBo04SpLVaf4gOTZZfX3L5JHLUl0gEro+OBbtEoYg+ljTTc5PviWTooa0U4okbipsgKOC6cEpc1QqmggXcARUULlObcBcM9C1eX4yuu0urezqyqi1ZXuteZzNRS1tquOpuOazXUjaw1hb4oMByrPB/VEQtuxZGUMexMNaHWNn05gd0+l+rt7mOlMjQ1vOoJfH2OaRoPw3h74oG1pXBmZdPfpf8jJF7yRkGfI9i5UlRI5IjBJBuH1k0YkBecQkTYRlq1dwnQKZUl+tqDayDFOsGf3sEB128ZVed+nUuJ54sVvXpZjJywkLS0WCQvnut8ASj49GS2tixvWRa8egHolw0RDymZkecWfez/ZRL/bxxzfLAu/zYiWGBdp28fExiRuaYz4wb978V7b0xqnJMsg0/XRCsFaJXoWyeeUeKZKZuydFIA6YEQITp/bTmgx8XmBmGyGzpOlarq1ujUJVvNtcR43TCbbYBqv3gdNTCbT68dkNmSGYLu/ScbeIvgySvK9+idJ7TCzSo/FexiNNBbUjSo7Mgs5mphX9ZIPDHHyU3Cdsmup5Uc81DXm4XPtsZhcK6hmXsPeDOs9+SOxoto4JQSiWbFJZuVpQz0aUT6wzfzWZf+Ng3DyuT/UXiNhdYxUNWGk7U13f/Jdj/v8J4qruWacfP4btK25JTPj/4cD3TTN5moKGzdZMpt3mzFjCHmmxNi4RELQgtL2rqpLd3aRtVX8uS19bmwBu9Q26f3k0QXm2BHJy+/Eob/aKkrvvojaZomsib9XP521bebt7Uk1bLophsCyh19Q5WDaMJ685fuUcL71Ju75RKeCbAtEsSVdjhxq8x6c4+TN36veLxNtxUwt7kymmGjOG85u6WTVlbGSp6X6nNnGK9H00Fl9s0x/g+GMKpawFnMuaydU+psPYeqGMCy0Nc651tD2ismk9VX8qMBM0oZ9oK39Z84qSReP40nBYusR6DV830MEH7ArA4Idkk8y7LQhe2Sb5tjj/96fbjh5xw/qOUiKN2M0/1tcP1Mul67reaXXfJ636+3JW77vSSGSFn8rqQCcUK8XbRyvx5ZXnohG0T48NjmQchYxEBYURWmS5yJhBAfnmItt7NAVlqpafQ1KnRQn8Ry3bWIJ+70RF9OKplFSO91d6jqTYneCNF3sbteMOharxSjxPa8JVUMY5gSb03okpX1B0OcmPyWxoTseH4vbLdG1QDQuKjEnc13bPPq8TOOqJFKuyJSormt9rdQiGIf6hKpaWitTm79YC0WO2ZlTntnVISCjUotlhSUPkO3MKc7F8xP3ljKvwRhMZloCjnmDSEHxyIT6yPCgq+ICJLIxFKre8iPdHz0VlU89nj64YZRI18JL4eQL3rgs00zqm+EAX+ZxA+qXWyAiieS3zutzUhV7oYUhJCO9xRawtNjEqSmL0I1GHJN7+62EP/ns0v2pvexiHkZLU1iyTEeA17X29jvXKpGWnrc4KS3d7roJLantbenzLRJIB6CtDm2sEXKLPHha1UOpGhLfQ8Yjfa1U8YuqoDCfa8XVSLuZl0GpCfDRw7B1vntv52E8xB/RirtEY1CM0U3u1rZu5ppGNwGRJEuv52+/SSuZsypuBvX5px7++wd+tsfDyRe8UaukUf3hYqXdfOpeDTop0CYD8usBEWR9FYDpC2/CuED5uXNXVTl3PXDiy5WAWVQOStMoiZRashYTrUXlzLyOk0kWJNgiSFV3qsOkPEy/44V2rCv1Nnjt1/6EqousAXnsquQr/uz7l0gk8YF8x2Fq1xo9+8K2lTtTRR+RWLELmWkJl/YcZaY1dxYXWiVQut/Ma91ILRDEpJayImsTuZaE8aEjfJL3UNwESeUWksB9ii/Qqtt4BLnFrZT4wuAzo8SQ189hJ/USgaTfpW29mUIWPZaM4EolpMzcKQG0MJY4+TQkT6Vg0Oow6GcxtBPhxAeynXlHHKVYkF5vMtU1M3mwEdfAXNfg1j8iYmlq5kIy3W76i2KpPag9R0YiAbVvI5C+l+SRMxq2ldjQNN05j8SZWKuqhrrWY06Ts+K5RKQlv/2tRzDbU0KR68Se2NpXrxVUa5Z8xzG5KcM4+M2f/+vX3RPp5C3ftxTL23N9y1E1iY3X5H4fLrMzg+Trs0gONA3unMb4pN5tlT1xE9HG5RirTJygKEWuJOPenr7GY00kPcAUW4wgwyGyMtaJaFGVFGIRKuUSUuQtQbk0Uns+78hw6B5nBBHTtWPs32wm0i0Rn1WFu+Nm7Ocf0payNNHN2k5NfPRw997pemsaQmoJibfLcACJ+Dl9Xq/TtVVVyq7p0Aw3LpB6QVngHObcHkynhLpGxHQKgTyHotBR4SLIrNYiTt2AyBUPHzj5oh+jOTRGmoU1DJBP3auFoEX11/VocYtIcXz+BTdh4tp+I7SGnnzhm/Q6jNYAzKuFVqvlXLW9rWk0h4uKGBJZn67LVPBdJJoWr3URTt33t67oeJPCMBRZW4S5mILx6//M+6OiNlCPLXau39vwvp1OUWPQNSa2e3XK+oWCmF+IyUlZzHIcT/fLNOa2STGbYCJhkUiWdO4WXzup8RYLb2Eh/iWk53iv5zjP9bhG5bLfYsoLkloo4SAPoyLDj/KuwBPS54+H55yek3R+9rfsp0LBYtsbdO1/0U8pDAtVIjnfFv7Teiploeu4XyDurNVizeJ3k97PWo3hqX14f7wvy+62xZbEdFxoYTwUGamVUKpG84x51V3Pg7L7fUxn3fUt0vlgFQWUOW592CmWR4W2CRaWZiWjHhmK8w2zIzlB4Df/r0uP4zcKROQdv/DzN7/1G6+REuk//uIef+5bH+qVSE8F/MFnH+Yl3/YzBHliBsB/+n/5AAiUZyqK/b29oEFpVmEWN402MvNVTZjPNRHbjxC0+tw0StYstn7BRQkkiMqR4Ali4LP3IkWhrxWfc7EJaykx7cb0Ltw1KAl7HiQA7kL1kiUulGWbHIbpDEK9L7FNG+eFto9EmAW/JJFNI3KlbqBYuPR8V02TsmwrSuzsdoEsz/XciZqFpsXw7nM/qyqhtOCW+ZJ82ZzZxh9e042qEcDrImwtDG37HiaNFjYmVhS8El0UuCNawZP9G7TLwKk/ep+2EM1qmM+xqY/cWq78Va8yFj7fU0k19ERw4svfukwehQB1vAah+8yZti4lo2qIRa0yjyPqQebazy616zYIixLvNgFFk73cqgeCMdzzW2+74NheeeJ9iKdN8oMIv/SRqBxalJyH7vGqEIoS6KjAyUHHzWfSKWP2tdy2LWqLBUNraNZKTOXa9qz2eVmUi8fHId1xmDoma4k4aicusUQgB6vG04ASV43X148bdJ2kEgjFQrKXkuX9RLr3eFEVkZJr0r5niKODGeWYyrU+QW1FMn7PEgIyd4hTZYMacAfE0Saj7esKkfiKlU4RvNEpc+06IETSyHVJfPpdN41OiHz0TGvi325w6jipMcWASEa2ldrF1ovFc5GMjNP1AVpphuhV13RJdfslKHEXfFDPlEWSPYRu0x83ZmkzL8bAvNJBAIvtIGuHoK6R2tEcW6MZ50yPasJp0tQ8Az7PsFVoCbhLxf/49MN8zbf+zBOeCPl1f+EDiA8MHpxiT293cSrGdEkbxp2JKoEHOW4lkvxVg8xq/Z7bVq/QnoMwmSphEFvCgnMQ0mv7pWJLO7EUtADivZ7T+F2Y8agzaU4TTg/C/oEYdY3M5sjqCtI0quCV0E5JCtTdJsbE1raWuHQdiWbNhcqZFM/Nvjif/o6E6aJSSWSBiARkNOweP50pOWmEsDJEtidaLGmc5gh5xqmH/h4n7/hBXa9C0Fb0MoMyW7YNsNIS3n6QI2sjxBpkOoN5pRNHYywPg1xbX4oMKXPqNS3QmIUWl8vFqd9/NwB3vexdehyxKBSsBbvwu72OBNIi2njyNMfJL/pR/e0VFrDIvIlKtHyJEG2VGlmmBLdVAlHJC1WpMYvr6P521jYXXyBdrYUsa9vdL1ZQO/Glb9G8IPkQEa+VGIuDB/EaZ1/78p8AoY2vANlezSCEmGsIptaCRFsciW2bYf9vMh2zgYCBDCSFCRuLJ1HF661dbiGPMbF9DWuXSY82xlhCnKIqtesGaCySJCn2B+mI60jY6rn23ftaVXW23kqgAqoyJxQBKbLlgkzMzTBmIT6LEsqxjV/2q5/ipLhAWpcX76MrJkPcO6QizAJxFgvjYW2MbO/FdT0W4OI+RJjA5rruFbb3uv1DCErsxGNtibDFeJja4dJ/RoBYnE/FpaS6judO5o3G80LPn+xOYB7jR3rcDL3WByUw0FxkOqPtkLFWh3fkqkiqD41AYHJzodPuAnTzwjJMpXlQjx4H4YYhkYA28fzq7/gZ3ezFPOlSE9Gv+/Pvjwt4oBll+OceJt+pMNNalSzzaGZZN7C3h9/e7RYFEZ1W5r3utWJgagkYvy8J9Ps8DCJaQiiNyl30Yll6XFxUW2l+WDqW9jELf5NnWq2LC7wMB0hKKFMlIFUkMyVa5MghXVQfOd1VxEO3IUomnBJJnRBZ77YiaPX9Q1XruQgednaR3T0o8qUq6GJSGk6fXfiw0ibzoWnUBynJkG//Acgs/rP3gTWYItcFOyqKQOWaJ1+kVbhQZLqBXRnpOWBFP/to2G08RyXyuQdVTm8srA0we3NCkfHKkz9FMzKIB5cLv/YvLt10+p7ffgfHv+JtGOfaRV2r1r7baMzm11WN9HSaUnQxLHo3lDFRUbWgW1IehTLDrQ3bxETHyGpC1IwsUgfs3GNnjU79CAGaRIJYJKiCTvAdmZKZjizxHqkDIVdPo9ktw0j0wH/+t9pWUa1nFNvxN2OEr//Gn1LiYpwhTdB++Npz10vfSW4MvlB1Tzapu+QJsPH/bpjjcxNH14IbZe1nAxCva4XdmxNyS705wJe2VSf53BIyTSR0JHTAWyGLsvpghGacYXJDtk03YWUh6dSRvErSuMLgS4NOUFHiy84dZtoQYq6WiCXZv7lLG9fMxiqZoRla6lVLNvWYEEjtby6SVb60sXUtdCRVPD71oshwA4upfWvM7YaZnheUdDONkt+mWSCVQuc7ANCs5F1iKoKkamSzUF1cmAJJ8OB17ZVUJRyUMFhRL5PkwZbUJIveEmYhYV80Qk5VxUhutOrQg4iIqIhpFaP7Ngpp1HJIZsEba8igZPsltyEuMDhbY+YOuz0ljAe4tCGvPeWWUzPxxMdO1Vw83wtqhHoZUBI0YBo1Hf3qb/+ZpQT2N//JpcXy5DVRHSoJR48xvG9Hq+6TqcazeaXqmXmlZqjB6z4oJfsLhvGhqrXqHNumyTPMoMSf38bHgQzAksdfG9etxYxGev4Xp/6BvqYxSrgkVew+z6wUwxfzALEWGQ702ppMkSJvW7hT0UbyvGuZzjIYDalvXiM7P1MV5uKGMSnVYsxvr69WlZh1G+3pTP+OSmZ7/6PtRrt9fHzNsDqCzz/YfZbRENmbwMqYMJkiK2P97ESvySLH/OFnte11bYVgLaG07et++Fffot5wsT3VjTJ8ackyg4y0+BTKvCWOfW7IH9hqv4csMxrHreWVJ38Kn0trgg/wK//60o2nXamKiOzR7e63uV99cR2JpFOf/eB1e++riVfdFWP57RsM7t1CYpwitZqC5mhl3nrstAMXrNWBDLFtx8xUbSN51hL/eodZVthEpUyrqPHxd59Zjn/F2/DRJzIpOO757Xfg1oY6bS0OofCrJXe99J2YzBBAW50B5jFGljnGCjJd+L0bsPMaEkdT5u20tjAs2vW6LXQ5ukEXRUYYWC3CRAVuiIXaNMEMAWrfTTU1Br8yxMxsR5a3ZJy0LbxYIZS5DpEAjeXzSG55YlFWWmWeVNFoOinDUgdHNH1PBEmzMQDRApM4333WzACGUBDbyUxXDIvEW1JWm9prXpI+J7R5CqCv60L3+QOdF2Rm8LZAaoc0eRdnYyEolIXmcnsTwu7ekh9rENGXawQyg0wrwnik61tqBRwOdZ1MJtyRBGyLTVm3Brd5hqe151hCMv7OoiXAvI6m5EaLPouPXVDWhbIgbIypDt3M5GZt/xs+WmPnOs0uFfnwgeK8ox5bXKmvZquAK4Rix112MehGgg8Bd5BK+Cq99tMdNxSJJI52LLnPgUiM3/mdP6OLR/y+Ljbi9D//O51u8Io/q2a5u7flGJczON1QbNfYnZkudLN5lzC2csMuGU9tAPsJIDFaFZDFrHj/xZkW8QVVT1s1bDcRdH3fsVrcjsiMZNYFcnSJUvXMItZAiAGGWClZlFa2lUjRpLsoEGvxft49JlV/0vNK3ZxIfO5+Ait5f3TMf1DGfp+Hkh+Xeo6fdRNyektJleGg9VCQolCZe1XB7oRwaA3Z3sOsrWiQClqlJeuM7aCrIB7/qrcpIVjVUY00gMk0tivZ1hhYNtY1sGZgz01gOkO8x64P2HlWjnGB0UMNr3n1e9qWoUseB9qqndK1Y7rKw/7gcRVx97mfvWav/VTE4IEdrcIlgtQr+RnidRdy245p94VRhc/cE3LDbMNimkC5BZDpDyVet2ZeY2JFzq8OkNqpUSZoW4wIfqHKliBN0L1YvP1jd7+Rl3/zT+sI+phMmLknZILMtU1Ln0jXTucDpnFaxRTBx2vc1D5OLqGbeLbw9uJDTKK0+hhyq2PqE4FW2vbx3gohE5qhXovF+UaPOXT3k+vUMi+iz4sKKURwA4u3ghukzZy+cIYSP0kFlNrFxAX1Laodsii3X6iQulGOzy1uaPC5UBuDzQVbBT2vUUWlKqSg3FI8ZiW6oF7V78gNLWYu+NIQBNxAkAayeZyutniuXcBOGqT2sQqum1MlKeKkOGNoR79BJNkt+AXlUEv4xCJD3SiZHhNOSf4cEo+/adQXLipb9ICEEBwSFr/YWPn1XteURAwtthAkn6Xo7RBc3XkxJQ+/hAVj1zAaMr5/hjSOaqOkXs2QjYJsryHkhmy3phnluIGeD4wS6+LBp/3GZeae2kYKpomJf9DP6K0WiV7ybT/TElOPNar8V/6NEgKJUN55/jrGwfgz23ouzm7r97bQ0gzoBjKpcJpGfUCKzjtFq9BGyb6i6G53vmtZjMpfSYbPaU23Znl9t5ZwWKvY5uwu0hKJCwTEoncWqII4Ktkky9oWEUnHEJpONZY8UVLLd6M+GDY+vm0Nh04NnIo5jYNB8hSMa1mswOsiFkmryZR2MtHidTQa6nveehPMa8IZLQqFyVSfk5BnsLOHP7KKPR+Qo4d142NFyS5DuxmG5Vag17zy3ZjK4wc5DHLstr7uosLDr48wj2yBNdgzSoJJCNjNAbu3liBQng8MH5lz18veBS5ckmF6UoqEIr+wJfoa45kSx191fKEY9OC2Fv/msWU5jZ6PpGvINf/0uVWCoHaEQnCDjJAbLZKIIE1UjDmHzARMJPgXCXrQ3NbS3T6bw7zCzDIkjHAr5VJ8D5H0oVL1jZnWSja1wx50bWiHmDQNVCx7FkFUp8ecORFEaT1aVBEtFAFCkamCKb5WWh/TxFI30LUj21kwfm5jq3TE2QET2PyoUEVxzDeCEW2Dr6OCUbz+RmNLMyEgxihRFbKllrKQWyWGc6vEVCaaS/iAIe4ZonK2VTZl0haBWvVwjOsp3wlWWiV2OndaFINQ6mcysahkd2vSZNV0fuw0qtXS53dx/Y2Pkz0W8nMbJ0xXmhIOSji3o3sk7wnrK8j5Xb1mYlEgHD7SrePxO8V7mDklP0V0yq7pinIh3t62/qfvPQR8UWBmFTJ3+r3lOeyFpcLHolLZjXU9HZxu8IVQr1iakaXY0fNvZ9ql0QwNzUivHx8LiKmd/3KLQT2eObhhSKS0GWCu7Qni4sKeiVa8A60y6WV/+acRB+WWbv4/eo+27rz8m34aBPxqXHgafY3psYzJTRkwJJsFVj87wuzOMDuTJbm7393TUfVFDlXdkjmyUPXTil9X3Q77jKJbP5Xk9J9UQSG2w6VN32yu75U2A3ABISOLVQXolD55t6GRZLq6N2k320C7+Qh+2lYuzHCo0tK6bicGtYqf1D9cua7tIrHvC74kGBPHYquRYLWqzLf4gK1g+PCM/KGzhL0J3HxUjUubBtncgMmEMK900tqhDfyz19j5glVMs055Vs9XtjVrCbULlA0HXTdlBuVqF4zjGNH6tlUk6PUz27Tt6PB6pBX2wdlGF1mRtvJy/KvfcWDb0tLXuzPRc5xHqfXmOjKv8Ftxc7NoZnqJkEGsws6riz7mchLPVP3zuQZwV+g12frATLwqZFzAzLvr96AJgdcDgwd22kREqkaDe25pDo2jITItQeOGako9u8nSDDRYugKaMdiZJi7FjiotUiLiS4s/MsJboRmZLnELkE99e62kDbQ0AVt7TCQ7ZkdyXvotPx2TJCBoQqUHb2PANgSTt6SQBLCzgK01+Zuv65rSDPUYs1nAW31f00C+21XwCTri2RdCPTZKEhndqJs6bthJpElcN0Wng4AmEc4I1qvCSmICp95COmrXW6EZ67E3Q8Hlght050BJfYOpId8NDM/qeQpW9DgqH/2JFlrqIkGVCCmfC81QSYZsokohaZYrOeqXEFVXkbBKVcgQyR9CwJeG8myl35UPzI6UTI5YTAMr91WtAizkptuc2gGutJi5I9ut8IXtPLJA2yQSIRF9CZKqUJIfgvcLI5d14gugbbzWwqAknN3SwsDWeU1a11dJ02SkiVV0o4oYzII3XTImLeIGK6kc0xTKOI5dYjsUgGSqwGyneyWlkvNKmu+ov8Pw9I7+Xo6uqaKr0CS82siYber5nq8JIQM3FHy8hpdLpI+PYGG+YcBDsafrjbZAxE2ED2RTjfFf9xc+gJ06fGnId5ol37Cv+/Pv1w3PoNv8OAvnv2gdWwXMs1eROjC8f0c3PpMYx30gbJ1XlVBsoW43EFkk65KqbzTsyJumUVXvoodiliF5hj+/owrnjfWO4ImtBXJ2W6/38ShWprvX1+8nFnuC78ir4UC/+xC6YxwOVBE8nXVquLRRjL4xdsdqfB6PCGWmyixr8aNSFYiFVfP5gar1ytPTVh2gVXCjG+WFv2U8AAPT21bweYoRMHxwgtnaIzxyuh12ESZTZHNdn3t2K05fG+CPbLB7xwjCiGJHjz0ZCgNLPkiPBbcy0AlNlWs3iNXhEXJoiM8M842OKK/GhmBh9fM1tvZKWsXpi8fvfPvjms3a3aiMLjLCMEeGz8LsTgmPnrmkYz0IUuRQlguqxAs/92MNRNmP41/9Do3fw5z83LR9zRBb+yQEivvP67WQyBmu7vTgJ4Lyge1W2ZOIujBQklf2ZrGtykYPmwDBEYqM+vAIcZ56PcdnsdDgghYp6tiSmmUwXCBi8mXvoFZRE1uh2mlhziG7Ux34kFtM1XDiS9+CjcRQyLotlcwdYVDgM6OxUrRNzedGvw8LbqTkQ1L5iAv43CBB42u2W7cxlhDXw6hc8bkqp3WNpFPliMZ0n+lnS0VOYmElYHQgg+i5DSIxlkDIdPqZG+ukuHrV4jPBlYKpdY+l7fQl2VSV2hjNEVM8TvmKNKGLjz50pFFa/0TI9mptQW989GYM7XnQ3CG0BGAQwQ9zpI6fRWhb/kycijp79iZ7t+bYuaHY9WS70RMyN4RcyGpHsBnNqvqrmSbQrBbkVdO10KUYLUKzPqDYnSMb0Qczz2A40HY2KxDXUKpKCeq5Fqb9uMQ8sqUWIPc9pG22wwF+Y6zno85bH6hQZLhxoWuwjUrouPfwmcE0XuNcymtcwK2V+tl3Znrtb6x1ZHYyjs9iYd8pGVScrzVfj4S3G+VYD9VajhsaZpsWN4BmoPldM47v580zmkTyBPw1Mh25Vq/7ZOKGIZEw0AzBZ4JpVMoOXYuboBuZbBaQRjdb842Mcqvha//iB3QDkyrXAaQBJMQNtDLZtgJCYH5kgFktGEyrGIi0qm6MaSXvIbr2J6PmAzf4ByQJiWgKKXFNahVZaJdLz0tkzX7yaLHXFjqpefI/iJuJlgAKoZs2AMueCc4hZdG1QgzKOFa21Mp86i02mkB2kwqKdgpDYtCD1d7mZiWPgUmP21ZBW128BiD3rCPY7Rmc39HE4ZajuFGB3Rvq6OxkzN34uGk12JUMM/c0m1ohzc7PuPuT71qqZsHydAyIvewxgNF4jK+08l1a8OCM0QDt9VqqVqHcTkFbg6QfqBfLJaGqNeFPffdl2fVOXwHk8CZ+Y6wJ0gOPtu2ECYvk0StPvO8xvY6+4RU/qS1IK2l6BrqJL3QUqa2krf4QNIC3RJ3IJZFo1xrH73x7VLuhlf4smiPnavrrCyE/XxOsiUoSmN6UY+rAYBqox6LVGFEyKdi4pkQiJU0qU3PnuLG1KElhBNfoby6RPYvYvTXXcxoieVIDItQrdOuOj8SLqKojGBCvbWsu1/dKAV2c0AyiEghp1zrTgKlM6+djPFSrlmo1kjABjOaQ5NtOSa7UqrWQvHQtXnqcqijq/q/ksH5eb/V8+UyoVpRA8hktgeOG+hg719cyjWk/r8/AZBZbecTruU+qJh+/v3qsCaiJ0nTTBOwsVnlNlMTH33FwtKpTn5lWZZYqbPWKwc50TVJiz1Ocb7BzTbjKe8/pOu4djIad2TI6PaU6PALJKR/c0d+diJJCJq6xdU0oC13PUotZ8t4Q0dd2Lipg4mOqWAndVYNihoN2amc4rwSO+t1I522T2oZGZednVdVaEFiEFB0Rkgik5EeT4svihBljlMyYzfWzRSIqWMGe3gFjcDevaWvj0FCPRL9XA77ors9mFAm8y0CIcVyPW2O5rfT7tLWSRz4XrO82F3qNCt/w9e+m2oitJk2sZkfT82CUPM4iyVts1/jcUB0bk+1Uel2ltkcjUYUa41tVaQwdDnSt3q/apSFMqqUYLdZ27WQJqZUtLFSNUwyP31lruA5LPlpCjOnZQkxtOg+WduJRUQBxXHcyEHYOqgCro3YyTyhz/MqAIIIbpmlAoRU022nTXmPJeF9iqw1ZLAgFoTqqptem1o1KvtcVofzaEBMO69+7e8jRw7hDq4TMkE10Gqj+TvRc+FzXGuMW2lF84MO/9haOf9XbOP5Vb1uK3x/52LJR9PGvfofmMrXDjUvstNFNeWE1041xK1ihWoN8V18/kVQ+Kp5Mta+wdwBkey+qALLY5uyeUBzHGsKRQ9RHx+T/bW/ZDJdl8ug1r3z3BZ99P068+M1Q5kij5v8hKnTcmv640truV8o4PVO/+ZAZTnzpW7h7YXLu9cDJF74JCtu1pkH0NEqKlxLZndFOCVsd4lYHuKGNMdnicl2f7CyeSxeg8Z2yLbWK5nZ5cISkGJ9hd6bsh18dtVNHk8rD7lT4Yam/lRA6k34rbbGFQCR2oFmL6kAXCKQBFGjBVmJuHMAPLK5QsigVoZqxbf0PIRaXKm27T4MrQAsCITctEd8qlKElZfYr/dJv2+datJlt6nuB/i7rkeAKzQXzIpDvScx9onqlgGzmCS5osdxIm88E08V20yjR5BuLmS0obY1BgiP4TlWEp12LTO1ioUjPi00KyqiEzHZrxg8CAYpzM8zurFWMB2u1hS03ZDsVs2NDEBg8PNUOhCpuDBdUoNJ43OYIqcuWXCR+v+KCXpOzeecT1WjLm2l0rZfN9W4gxrzCPBoHsqyOVeVVZPihFtp9YXClYOeqDsrPzQjG6HdYq4rbx/WbEDCTWhVUKVdJfoyxSB0yA95j5g3lWa+m2Xn0FAsBU3tcGdXdVmjGet58GXO5gliwu8xKUI9nFG4cEimAqWISa4GQFju92zTq7REEqlVh/EiXCGZzXVxdXOiSkVgQKHa12i/Jh9cIe8cM9UrGzY+OdHM3LpgdU/Ii327I9ipNtqczwsaammg2jSb4QNuCEAJL7Wxioix+YUGCbvLJAumUpJ8hqgOwVuXzG+ttkhrGA5JKSCrXGYNurGjiMJ1FQslAWSjhVTedgXVda1K7Mqa5ZV09Wqa1JuWLRncezN5cmfAUmMtuGkXbw20FH1tdMHEzu9Cv7DNhfrgkmzqtFNy02hEWzuNHBXLzEeY3jdpF11a6Kzdzj527KHkVqiOjCwik137tT2DmDc1KoYmlgN0YqnnhvMacOU/YXEXO71J+7iF90pFNyjNDqo2Sai1j81O+HZ+eKjwhk8sjT9KmJMpzpSyWW9ouFbGi629ax9bLJNbd5352yRvoUpF6z5uhwc492dST7zRtRURcIL//HGE80O8jmUIvGFJfL3zDK34SEw2iMegkr8y01TTxgMDebSWu1LVg7zahGcEoft2Hf3fO7FCJeCh2I6ljtSKXIHGTkza23ivRRghMjlp8DvWqPtbMIZ8QTYb1p2miD2+9InFTE1/Xdxtwb2mT50TiEEkX4muA5jzeKrlJgMG5wPCsw840aZgeyfR4xtK2HAWvJFJTCvM1Qz7pvJcQNay06d8+4DOVWSU10OyQVQVTPA8+I5JughvGY4m/W1BSSxq0K9CCy6EZiJICHojEkw4C0NdNiWs65vQettLbK2cZVJ7iTBwVHqeDqXJOk+7ZkaxVWaXXyyaealV0qN72vF0ry3MT2okqTdONgp/N4fAm9eYQCTC9bUg28Qw/d14rk7M4En5QRoWRwFqha93mWNfGWaXJcVV3JERsiwqh6YjfBtJ4cslKqKpYENCpU1j152E60+9mfU0T1DQdTkTNvJPKJL3X1rY+P12/ZQm4jmhYQGiabozzSP3CBNOazjebo6hUMVHVptfk3m1xI1EGivOCK2mVv5cLO1ciyQ3AIbgK8kmI15L6LfkM5huWtU8r+dKMlDzKdh3NSqzo1qGdWjh6YIorbacQDAE7azj/hWPKkWXgwY1zpkcLJECx3ZDtNdrCvqM+SmFY6LnZrToSL04tkrLUiXZxymYIgbC7hxmPulYv50EcYV51rW7Jj6Wuu7wgFmtanx1r8SslZnfeEYRJbVTkSvad3e68BRcKT22eERUo1eEh1Zr+LsyCik/JalUAZbt123bhY3t3SCpqs7AhFU+2V8dNr0W8/sbcwCLrA+y0wW+u6HM3V2KLmsdMGsLNRwhFxuzoEF/od2Ur1Kuj1qmJrtQiwKvueh/FwvXx2pf/BHZS0awP2ngvLujaD2Snz6tq8vwepq4JdU1x9DDFuWHblrnxaa8kdFBTY5935+6SR143Tj1Q0tSppEq/HCxNrrqw3H/3+Z9TU+nLhOxMkHmhBHNc1xbJAnGe7Lz6MvpRof5yLv73VMBkCtkKUjeEMlO13ChX0q9qVA15eFU9gAzsPWeVemzUx066vF+CFnzCzMd2dpbNpEGLh1WjazQdqVSvF2y9cMRgS3O+fLvS966dtqLF/NeXFjcYtTmsaTRWJpWQFoO0AGRizMv3nA6oAPX5CYFgDPVavNJDIA13KE9Pu9b73MaCyWJBxS951qScTBoP6ZJcLFgvqO6JKq+QaY6UFHzzzawrOsW8JDSJ0FaSQUIssKe9Udx7zTay2Haf3o+uM8R2hSONyRkrD1qKcxV2qpNi3SBOCSuVEKzH6kGaSLNmaCjPNcwPZZg6YzzrvJTszhR7Pqp7Q2h9bM1ZHTQwe8HN2Llj+wvGZLPA6P6JDtuoGj0n60X7XSSFbzLkTyRcyoPt9lTXwiwDuxBLnQcaVRUXQ43FRdFNgMtzmM4wO3tqL3JoldlNYz1PMZcMxjI9thqLW56scpi9Crvl2il8fqXUiZMhwHxOO3EwtT7PVDnlR933GqzQDGJxNe6HZxtR5V1CswLNOOAGnuJc7BgZxy/9GYpAwC85s1/d136644YhkV50x018/O+8fum2L/v+D+o/hNZrIVWo/9P/70fax339n3n/0vPSYyToxqh9DXQhc6VBHGx9yTrzdYnJT4iLpaVZGTJ0AWMt577yMMWOY/zp8/DAI7FibbokNI32Ta1nKdl3kKYctJNkYEmN1PoOgS4gwwF4T3N0TV8qE5pxjp07su05Mq+RuS6WuLoln5b8DIzRsZVV1a0bO7vY9RF+kBMG6oPSGhjCUjVDElEG3bSAuLi6YRkNd+MClhQVMTi4YUwCYtUm5BIX1tBuSJp13eCbWpPAZkUXzLSwBwF8rLDERbk9Hh8we3PMIMeXtMy8yRxSG8J6nMJgLKyvasI+m2PPbjFcXWEwHuCHajC42Ebzi7/8Zi4Vpz7/NwA4+ZzXK1k3KMFYJM8J7jKT0HgNffhX33JJD3+8iWu/+Mtv5tWv0dHHxbbDzlxb3bILBsNpxGwrhV44luuBu172rmi0qFUa9d2x1OMMXxomRzUY5tM4PndkcKVWvqc3eULpGTyq1++ZF2vCkKoydt4Raz6nVUYQ0N8odAbMNiWtdI+PP4fUQkbcM4bo8SIeQiRIWpKnK4bSjqxNm18bEzLpCKSWhEJfzxWmbcFMJolePRWjGjO+v02JoCDeYiqjiqT4vaaWv+SnZBqVpOtapy1rPicmn/p6iVRIv9cQ1UhJdSTps+e0LXxBIvnfEk+xPSx05yEpiWythFQw4Aam2+yWKoWvVywhF+br2p6YT6A87zRZ2rTq8xSPpTo8Ij877dapVonZLRqhaZC9CbkhmnWP9Vp/4OGuTSx5wjROx5HHlomkjPKjAhOVKtRxataiH9qCiXbb+pyIZueAqFaKY9tDUrJMZ0o6GZ10hYgSHfNIViXyIA1OSCRZIqXSZmpJLaMCa7FGE9NspL/zUj2pfGlbIrEeCc0Aqk2o12N7YhbI9vSCnB+G8jK7e150x018/O8ux/Gv+F6N481gQdkUN037fehe/Zr38su/8CO84s+9PxK7+rj5oRKfC9nUqwdEfJ1sqj5YZ758VTcGqRN7T6g2C3IrZCEwu3WVvVtybBXY/C8Pdq2D1oA3+r3M5yBZ9/0GnWgqg1JH3peFxvOkUEp+VRKJH0OnHrIGv7FKiEbx9UaJu2nM6H80WnVuFnyPFlvQF9uh05ShqIIzD55hsD2ifvHReD6TSiH9zoKazs+dWpLE67glKReul5DagBLxsch/eG3JwVuyuMn31saWFo0dfpjhM0M2czCDOqlfoxq5NZpFf+vV4VH7t4SATCtkpdS1w0YvxkKJMb+5pp4zWUZYHSNnzsF0dkEcdwNtZfaRfPrwr1xGHI8DKE7e/L16fvM8qk3i7+lSTLUXJ1aJcPcn33VJ7/14KqRFyG43kVFmNWZSRZNk9dNK7VfJFBlYngD2JOPk89+gCjZATp+Naq9VzbtKVZ8bZwnSaHvTmpKtezcpQVyoaJNf/Zc/xJ/+Xz+AqWnjrk4gNer/52nHwCckFRBOH08IlNu+LUBAfA1PWzQLmcHlplXNNIPu3LlC8wfNaQEXFTieWNSK5Gdsp0yqW4JGQPGBZjVfamELmRZPfS7InsNWHjfQWG/nBiQQQqa/3XQt1guTu/SoCVZzIX1Nixva2HaXiAZVHJtGj8dn+nmSF1EiglyhxfrWnyjGcnHSxvt2fYl/u7EWkrKY7tYjg6ny9ry05NGKpRlqwSDfC5Rb2uLqc0MztrhCz9/s2JDRtG6N1fFumSgU08bd4pwSp8NH9fuz9z6q3QDBI1VGCDq1rBlnhNgtYee+PVfJd9HsVdoCCnTteUZ9tJJadF4j05kabjcuqoizqOwtIPPQNJjzE7K1AdVGjp06JEC1prYa+U6NOL0+aAQzr9oWa5Pa7tIxJKRiQsrjahdJMQiibX2u6NrcfKaF1GYFqg1PyAKh8CBaaJ8eg+Gjl/c77vHMwQ1DIh2ET/4tTUa/8ns+uHT7yv01L//mn44LXKCofOuL9LV/6acBZbtT1SD1VLtSq6/NUBfDnTsEX0A9DvgykO8YVj+Tsfa5CjfMmDzrCDu3C+Iztp53mKO/u0LxyJ5WDdNI5UGpiqW9SddGFif0hFjhWjTWXjTATv9uDT33prAeK4exF7Y4N6PaHDC/Y4XhI3Ps9hRz/6M6OcBYmE9V6g7dQuScLqxl3sk00eqVz7Uq4I2047S1rQXIbduqIfNGzYCtEAqtzNcrGb5IJocAnRkvIVDsxvaKtHGMU1hAvWvcsN0TIA6aFYt4Ddx+I8c0gdHnd5g8W2UgEoP3a179Ht3QDTLC4RUAsr2mNdoFTZxERE3T04S04VCN71aGWtk7u40tC2R9BMYwPzx4XGLmMTGMYzbTOb8ESFmoGiEGyLvP/qOLPvZKji2NBH7V8fdh5tqrrqNrfRyVayGz2pNde/ww76aU/NbbWvXTEzovl4jjX/0OAJUDG+39N/M6Jsdx9PhYWrKmWhN8oX83Y3BlgJEjP52x92yP1EK+raRwtqfvMd+Q1huNAFkUApTnPa7QSl1TqiJOyc1AvitK9NhE6nTqo0ReaZUxfpCotEnwMVmzlV7nptbHJpIoETZA294WIoHVjIT5hrTvKU1sIUOVHeq10B1bM5SYHBpsFYkdl5SYqcVBugQxj9XXjDbBS55zwepj8p3uc6lXXUcQtX9nkWiKiWU+0fvT+dF2Yv2cdgb5TBWhs83491RVRW5FiWlTOdwgo9iuyR44x9hGpWRh2/Vl9Ce6AT3zkiMEA+deWLL+aWH4hw/r7ynLOrVonusY9fhR5PwelAXZ5x/Vjektx2B7V5PTpFgyBjlTdwRBiJvvZNKcjFvzHLKY5M7nXcubMdFHLodp3PxlGQwsbRtaCMh4BE2j68DObkycg067nFZKQKWWtarq1C4RoWmQLCO4ulVEtlXSRGpEEkoapxXVYc7sWEm1ZnG5Xls+h8mtof3ek/rM55EktIHpzVf66+7w238vxvHv7opCACv3zXnFn31/O30n362XuAyAejXD1J5iuyHMhGxPvaz8wFKvZOp3FonRak2YbypRnE0No4dUXTt57jqTI5b5puAGwvDRo4gLZDsVdnemrRC7U+TYIf3tbG0roVTV7dQ0GY8IszjJLZGGiYRK15g3BBfbIm863LYgIULx6ISQW6YvPKaboE+d1iJHGX0Jdyfd95eQvLESmRTJyeFDM9woo1rLcIWQT5RIS8ScG+qP08TWGEkkJBCyjPrIEFcYinNV1wruwThVLNmZj74eoSWZxGsLSGqda0apMNYRwM3I4rMcO7eM/ug0ey9Sskvq5Sl/bpDhbl3HVA5Th9Y4OCEMMmQaYDpF9vbU5LsoCKujpThuxgNCkTE/PLik6/CiGJR67sry0tVIduE3KfKYXken/uA9l31IaTrbyRe8MRIJ8fc9r5C9KTaZ5x/dwMy0sChzXfvu/r2fBJSQTfnAtcSJL32L5sQx/zTb2jKbrl9pYh7S6MbYjTKa9WLJX2f8sGN6WNemYsfzdX/+/QzO1Ups53p9NqtR3RcVZ3YCpq5am4VQ6DVpprUqrrcNtvKtCluLh54wNDSrOd5qPuFK08bvpHA2TVoHo4ffVMkiM+9+n94KpM18VBoToNqwmCrQjAzFtmtb79PEURfzjWCFal1HsCP6m0r+QpqLx/caZJpSL5CDPtdWt2aoQ0WqVYMrOiIrFcxSB0c26dZ102jsdrHtCSCpaHTt1JuaId2woxgXXKHxvxmB39NYrqpDwQ1KJc4GWkxe+cwOwRjMdL5UeC8e1rVo+ux1dm7N8McMwa4z/qNzHVmWlJ+LPnbOY6K/YTmt8asl7o5jqlyaTDXen60QI5QPL6zNi6+VWsZCaH3sQmZ0nZ7rsJ4wyPX31Hjk3E6rpgvlgKTilHmtqr84hbu4f4v8kQy3PtSJkxMlncw8GrVv7el7z+edFUZSQdd1Z00SlbCLMFMlj/2owBSqIpYsUI9TfgfVOjSjgDRKboZGu29SXjk7/IR+4k9r6HS2a6MY6qezPU3wX//+cmUztTklBcwifvVf/nXu/Cs/g3HaHtf2wYq2hFQrunGyqTOtiax7lGs2Q9h+dkG1WpDNYPSwPndyTNh5VsFaE8hFkPsfUb+Ls04Nssu4GqcxwHmmSoAFYmlpksoBIxf9dKpWMD4sjTzNjSFIQTOO5uCPnNWFp+7IiCU41044C8Mi+h8EcIFmPcO4QPHwLsFaNZMc6qjixSlCWNNVJw2d3DaE6FOzfPwSABda82JAk0uvrWqpMrlonpqQiD9QWbN6acRz5AIf+aUf1VanyumiHD/zx37lzZx81ve3C3lYG7WqglDXuoFqBsjKiLCx2lXzPdoC8/DkgmO5VKQk78SL30zbdnApRNLKGNlVhuPUQ3/vit//8ZBIVYATX/7WC+7/xf906dXQq432nKWkb+YIeUr+ug1PFomH1MbmSm0zq9e8tmUkBMGNG+xu1raJBbucKKVEiKiIqUfdNZwSL1tr0hgkJkeRWKmHtESRK8CXtCbY0tC1sSWiJf5uXKl/25igSRIfpMmTpiOcICVjXcUQNKFTeb2Q70UZeR5VUE7zDRHiBA415yb+3rvjWpj4kj5HUiGl/Vu839YdYZaIL1coERaiL0mw4OL5TecpDUMQp59J5e76mj4H5wWXw+iRQDZTpZzPlTwKWTTrN0J2/+lu8qIRxKtvSRAhjArqcR4nuumx1CuWYXp8amFL1b00sCB6ylXP2sROa+yZ3U6VsR+JhElJb/Io8EHNkW3erY919D9KcWiofiVhdQgrkXzYnXVm3WnqSjRKliL658zmsDLW462bbsQwdBuHxbamRHzFVmc1qUiy0KiGapwmzfFcNGslduYpSEo30yrgTIOqUz0ESV5f4K5ydvFf/8FyHH/Nq7rNtS8N7Hb3/fIvqNL45d/805FM9R0h6HSDlqqwrW+aWd7U7N06UJPvoIWkwRn9HU2PZuQTbVkpAfvAaVWVVjX+pk2kHiFrK0oyFrEQU8eJoKnlIF0b8ZwDhNB0BZutHczqSEnBVNwpckyjLbrV7YcoPhc675z2RCxck4lAJF8irOzunGq9wJXC4IwOiLATbSVphlZNraP6SBrftgeHQREr2x7rUUPgMg79sILLDPmuJkZtgUlEN/GxIp5aeD526g0XfL9LcfxFR7UdyIV2XUxI6tjkXfThX30LJ778rd0Y8EG88IbDTrUV5ogdLsdxEWg0js9v6pROl4MUg0/e/gOX/iQfFYXp7/3DVa4i9ptkn7zjB5f+vt4ehidf8EYkeY8lhV5a7xptCUoTyzQeBUJmma9ny8Mrgubz+V4gm4f2Gg4LJE2r2EkG1JnBD4q2FTjEdTlkUUkaAnZSYz1ghWYlV7+0oIpXHfggbSzvpklC6n7wWSyKBiELgonDHcRpHuwWcgg714Bpqk6N70oTcxdLNqkhCPl2HacSWhpULRIyUR8iI/p5ksdbRFKipLVEFaWdoqdaNW1sx8TCVVCiK51bCdH7Lh6jaYTZIX2cODq1UaOPz3z0cFwoeAkxZxHiUA/NpSASY0ZVUMVOUMInEuAB3xKGuMD02evMNi22UvVotWIYDTJkZ9aqgpc7PmTJO64+PCbbjqR+ipU+rclJLh66Qk567wNifjuVrk77M/3f7JYV5CZt4y225toKGfc2oYwG8VGhFIyNBusGV5rWMgMXkCbJ2V33WdJnq+tO+ZTaq+O/00RfIE61U4WXjUNMCOj1m8kSSU88BSlv9hnL9z/D0BtrPzauO4kkIhvAzwJfgi633wH8IfB/A88BPgt8Uwjh3NV6z7RB/tP/6weW/gZtbRtG07rUglKv2NbcNWQqmQ0WJMa8bApMlK1f+3yDqQLbz8nZ/oLAc77yPgrr+Nw9z2HlgRq7V+NGBfWdz2N2yLL5a/dDUeAOjXGlZXa0pFoVyi3P8JE52eceIZzb6qTu0cR7KVlMJpRpWlpMLCXXKnN232myz9RqtrY+Jtx8RD0+fCD/k2gGk+T5aZFqGmXn8wyKjGZFe4WHn9sCdBEU55jfcQhTO+xepQx2YREXVxwPEDCzBj/IsJVXn6R9/Jdp1NyyNcONU8Hy7brt430s/Oq//Ovtv7/uz2trYpLcDu89z/E73976JZizu4RxiZzfUyl6UUR5qEMe3OvOq/Nq7DiZxAl2ru03Tkags1tWHvO4LhlBRxvLbN5NZjkIIhAJpCcTd//OOy/5sRdTIO33Z7oSpdKJL15oNzBx2sqsxo903K5bLXQiiFePgOGjFfkkY+dZGc0Aprd4wnpM2GPLzWe//Q184U99EKkNZrbgoxYJoER6VOOO7DFNGnWrL6XTyjTZSiSNJlqa5Lq0P4n31+vaQmd3LHYWX0e6oN2N6I0b2jgtw0TlDsT3MLRKntTi5pMcPimYYkJbbkUifCTqwRgl6C5WFufrggQh35NYaUzMFO06OF+X9vMFq8mgqWLLn1GCR8k29TSaHYpS96xLRHwGIQvgpU1Q2gScmFg6XVOTybgEKLc82dxjKk3g1Rw8GU3qhBZfZtS3H1ZF2pkdVfM0DpqMsLHK9JYRPhdGD2gFrzxvqMc6Zl3ORfmUSNv+03rSjQc06wPqtQwzdzTH1tQrxweaZ99Edm4Pzp3XNSIpN1MLcvrPiJojG6PKnjzTBHJ7J15AqpzAWuRM1alHosIoVTjbNoW9qSa6o6G2tUUllJo/z3WtEjWJDlUdK5QxTrQ+PoUqWQB/ZF2rndNoVhsfgxjIM7LtOZkLNBsDmqFVhV8uZFOhXg24tYbsbKZy/nitX+v86CMfXZjG9hc+sNRW/PXf+FOID4ymLpIcZsFYFDXDnen0PW91glN5nla1WJ7TH3c28zz4soz6WRXjtRm7p0c871/oJjVYYXbLkO2veS7jhzwrn96GxlPduk4zypgeO0pTCuufqSgf3kUePqPtgvN5RwwlctAaxEbVsdNR0W6lJNuK8SCzyHRGeX5Hf1+3HMZvjpkfGyMuMPjkZy88QWmjkd4v14VIJnNGf7DHKM9xh8YEY6g2CtzQkE2i4qGK7ZG5bVtDpHa6nNSu9e9JptR27jC1YCd1PN+6sfLDDJ9b8vOzx/0+F+P41/+Z93ctMpnwy7/wI5x48Zu56yXvRLzHPHxOr92m4eRN34OUpaYVZaFk4qK/YFIQhLAUx0GJsNktK1dHOSuCrK6oSnAaP+9BrWHGqNo8tpVezsS1J4rUTn8puJgKaTGWX+l5a32eMquqjSyLm/hIHE2mep5q9RK1WxY/KGjWS7VpGHbnNZt6fuVf/zB3fufP0IyEYlu/e58vqvy6ddhnBomeO0BLbCYCqBmPdEjGwOgaIYAVqpWo3DCxjatUEsVWkO+pwbVxIMr1YKSbZIpod4MboL+lWEBK7d6moZtU2mirWbVqyfdC21qeiKXk82bmjjx5UkYCOFihWVVFj2k9O3XP4Au1IGjGlukRnYpcD2Mcr2Ohx6raenhWfy8+V1KnWpWuwCbgVjR+Z5MuhvsiFr70jGPnupbauRbzdBgNjB7W12hG+ngb8640ndU0qpLZ/tKjjO+bYM/uIUF/M82RFeZHSqpVy+CcDgXR1zLMbl1h9Ht7sdAbujUgtW4D9aER1UZOcb6hPjTEThrwUN2+QXFmgnlkqyuuJwLJO5Z95vRY/KrmBXbSecjRNMh0jkznDCMJ1rbZAX6Yq+eWUXLQbk11P5EZqDz52Qm5MbhxoedrXi9MzzNQ73VFiKJoC/1upWyL/cEIdtJg9uaxeJkrKVQ7TNVoR0lhCSbHDWx77t2aI1tXJWVzZsDsJkd52mq+1qPHRXDdSSTgbwJ3hxD+oogUwAj4MeAjIYT3isibgDcBV70/5lf+9Q9feKMLmKj0MQR8MkMKuhiqpFYXfRcVBfmOtmOokaeO83YluJHn5tEOjbf4QqfBmVoXOHHa4zv5opvJ9xrs9pys8YxqzyA3NKOM3dsHDEa3Mvg9T9jZ7XLyAwgkADlyCL8+as3/UmVOjLSbGtmdEkZD7LRmdmxIvjqGnbgwLUrhre2qAJnB7kWVR+OgaXCHV3TkpCFOlpC2bSIIKqdd2ESZWYMM87YCA7RtCNJEBVKsEAE045xmlLVTow5SIB2E//zvfmTp7xMv3udzkFlkb66bJSBMJvp9rK/q5y8y9VNYG8aNH5z6Q/UJ2q/IuRqJ592/q9LxE1/8Zhjk7WQNQM9lksdbEw1x4dT9f/sJv++TjXSuHm9C3GNBK2y6SfexRVKqBj/M299ByKSVxyTjwFQlC1lArF7jn/me7hg+9YbX84J3f5Bsr0umfL5QfYkKGjcINCPi9Aw6ua/TRNIv7Be8BWJRKLV8mUqTxtY8u4iKqYm0hEkiWLvqXVAiIFVcY+taWpNSy1p6nolCORMrTRAJVbXWwc4DppZ2QmVSLqlRpvrOaFteVJREogmjBovqRaRJRzbpiCrTQLWSiDStQLqy++wp0bQzSMomV9Im03befeZmoH/ne4FmJJhZaL0k7Fy9unyhiZnPDcQJNOK1wmrqWMVOI2/HI6Z3rBCMMDhd6XPT2uMDOy/YYOVzOeb0eT3BadJJbEvzZc7klgHNUJgeHqm8fzZi5d6ZJqHzuiMFUlUdutugUyJlVgmkpFAalMvvWRa6vi+0Eqex6r7IkDySQ7nVsfT1grqljhPe1laUZEob5TwnbO+0hyJlQZhXavBsDP7WI5pw+qCkVPSTYV4pATGfY5yHqiIzUK+utJujwaOdifyf/PAPHfSzRX74rx94+9XEf/63y7E8kYyQyN/Q+YyFrk1DmoBYACGbhNasNpuoQqfaKGhWPcWwpswbdoFmZGN7ViDbdax/Vq95P8ixexX51ozsvJDvlfjCMD2S4wZrjBqPnN/pFKetstXH9kKHiIHNNSbPWtVqdNq0LAytgK4YUmzN2b1jxODwJpzdWjgB0v0/KV1T8cl5fb2qRsYDdp/fmbpqa6zopNoqesakKnxUbCwKg9oR3q1iWx9rKvWsUbK9yyv2e1hdDIuelQdiNIxtm40Sb5OJKvtGw5YYbQlYAGvbtrA2jidF8lWI46fu/ZsAnHzuD6mK3NqOhE7q8vR7TKbpwN0P/YMn/N5PNj529xuvaGjHEurY3jsokL1Z226ESDs4oF3XIumW4Eqj/mio2vhX/5X+9j/+s+qDlBQxgBZucm1HS2iGprNKEAAbC5nxbyOqfjeR+Im+gq5UQsU0AVeqEld9U1MRRyh2Q1v4SL5BOpWsU/ZKiOrogkgS0XqBASQ/vXwasHOvOY0x+vNqfPTIWSYngwjVuiWYjPK8kium0DZRtaGw1NFnJ7XctX6KoYvj2UQHDdXjaAYuevzJJNunn1NUJLVejiyoir0SSo1NxJASUM7G7yU+ZvSITnuToC3RSjRJq+BORvy653C4I2ucf/6IeiSsf6ZWNVmcWmfrQD00+MOrmHO7C8MMkqLHQ54zO1JQjw2zQzq9T0LJyoM6REGqZrn1N3kdSqxIJxVSVPqGQj3VpHZKrBvTTU4F/c4y3Rtp565ODSSqOMU5tfvAaCwPoV0rrXOtX1kYFYQQW+AOrbUFrzAukZlOaDNFxt5NA8QHBo/MMfMaP9biqpnWqnSaVUomzQXjob5lQD3U69js68L97PdcuDeWN1z7OP5UhAfcNVMiPf1xXUkkEVkDXgF8G0AIoQIqEflzwCvjw/5P4GNcBRLplSfe1yaSi+qjhG94xU+SJ9mrgVDogpbFcdC21rHSPhPqsdVJQQGdMtSkBTRopTYm1r/6R18A2znHPpPIkQypPdnUke12Y6mb9VI3SNtTLJCfgdFvb0Ge4289Qr12G8WjuxpgtvcIqyP43AP6JsYgmxs0x9ZwpcUXcYyr09HdWRz1qASOYfqsNUwTGDw6oz62irtjg8Gnz2j1J3mCxEVUqhp7uu4SSWjlkzqRJSiRlBnaMdNRPRRsNG2Mxn521pDtzNXcUQSptToeSu0fVrl/AGuwG2PmR4aXTB5dDImkuesl79Te6r2JLvaHNlQ1tRvVCpOpbvRm87adTw5tttcNwMeiIufEl/04UjtOvujHOPX7735Cx7cf4fC6nmfbjZ3VO9TH3+xewQSYy0Bq9Tzo93Et8Q1f/+42GbmYcWiIZqBSOcxkjh+VuI0RbpzhBpb5uiYAxY5O6fNWVW2u1DbTkAfKYc1N6zu87ENv4tGtFW7/P3Luf0VBvqvkyPywJjT5tpIoEiuL9ZonmABBMEm2HEmfYJVEMVXahBEnonXEiM9ia0emMnDNsAK+CDSANMLgdCRyhvq6qlAS6pWuSkjqPopkV4KJhtPitFNv/KDHuNAaZLbtcugGOpla+jTsaSRUq11l1JXxsyWJu0ST0BoGZ/S9Utta8mgKUXzTDLXKmEg5F4/XzrTdzc6jL1PajZooa49tg3kUX9RjiRVcwe9q5Xf0P87ptTAqu4qeFeabJbMjGdlUx/xihJ1XfKGuzzOPnfq24px83CS17Fnh0a9c48gndcRwdma323BPptQbJdPDplWGSVRuVWtDmhIwqwSB4ZnAkV95WDe3SfmQfOxSJXw2103AxgpkBqmcSuzTpj+uuyGqQDQJjYSoNe1mQzITvZRQT7KqhvEQfGD+rHXMfAVTOZ0SCtx9399qr5WTt3yfVv7XV8E5zKNbYKxWbJOZch6Pqa5bP4gwGEevkBoE5mu6vn/6Bw8mj64VFqduHrROveaV7yZvlGCUNOkvmYx6TdSLs+r5VK8PMJVOS1oclpDt1VSHBswOWbIdIXtwBbezwuEKwFGvZJRn5pgqkJ+faRzPLW5c6DjmeU12do+QZZT//V6N4zdtMrvjWbqBKw35doMrDaNPnSE8egaAcPsxprevkU219cvdeki/70mlQyuc12uzyHFrQ2ZHS4aPVuy+8BDVyhE2/seObqJ2dpdJzKaBnYXpsIns2Zlgq5U40RDcwJDv0nrhpVHlWknX8xMKNe71w5xmpIM7iCQujW89GbFCtlvRrBSXPvHsMdAWXL78rZ1fyXikG6u9OUwmSoymyXWgJOiqKobb2LYYx2c1J1/4prZQ9ERx6jM/o0TScKAkrAhyaEPVZXkGuVWycFpdU1Xxa1/+EwB8+NcubejG1cLJL/pRFj2YTv3xTx38QBNltDEHbJWfyZsuWgyQWS0arQzwhaUZZTQDw3wD6jUA4Uve8EFu//lPcfrEF1AIWtwsVC0kHszcI0FoRpZmZLQoAhQ7ofU7JWgBShpd34IR7Exaw+vUut22ssdhGOI0rKYYWXuJ/kcBQ/JjDNEbUdrpccZ1xaR8L7WPahtosGqcXez41nhbldGx+BO9yhKp6wc5pvEMzjT4TNi7OcMX2hKmuUJoySMXh9WAFr50b0NcIzWWN6WQzQKm1hhsmkCxq5/ReZ2kqARXoFo1ZDP9bIsWAEldBTDf1JjphhrbPbB6X8Povj1CbqnXCm3jiufnzJ/KyKao0qhqePi1tyFBJ3Tmu15V1SG1/CnxJ00g956tL1ol3xszuneCOIfZnhLGA2RrB3L1A3KlKk/zacBnMDli8Tdn8AUD6hUYPRoY3z+nuPesfoClqWvxb2Owp3cIiaQGLfiIaHEzrpuAEkdZnHCaCvveQ62/A6l1itqS95Kosr46OmoJ0Oy8TiY8FWP5iS/7cUKR49cGSOMZf25HlaNzbfEX7wkYmvWBKkbXh+otO9K2zmzqqVYto0cCk2PCZ//q4xD3PXocgOutRHoe8Cjwj0Xky4BPAD8A3BRCeBAghPCgiBw76Mki8l3AdwHccccdT/hgpAlq+NoEQpEUSKE1mLMTh507nWSQC2AwTgORqVRumqoNdgYrn7FMbzKsflYodhyLZGYztORxU+MHFlcY3MAy2NUqjB+XWLcOuxPM+Qk50GyOsJOarZffxuyQ4aZfs8isQRqHXxvGDQjMN3VcZzYNZDODmTZaGbQqpwRNYuWcx1YN2ZbXRSxtHIzRatCiZ1JSG4UAxup0gnGGt9KZmVohmAy8Jpqt75HRSok0HrOjCiAJoRtVXZlW4aTsv+We37pI8nGZaDccmyUf/c23qidAWWi/cOohTn3EIWiVdUH1sx8nX/gmjcGpZ/oqIZlZLuL4nW/XKSqAqTWQXa1k93rhYlXf/R5ZF0M7ptijgVkElxuaUloPnWpFkzAbDeNVfg4yN9SzjNtv2+KR6QqP+LWFBE8fl29L69UTMiU/ivMwP2xwQ68KnapT9pi5JoRKsGhi4wotXqXqW/IZ8HlURtRo62dMPH0RtFt1qMSVqSJhFAkcVe9oBxh0hIypOlVQUiWB/l2ebzTxFDWfr0ddMiwBVV00MdGLKgRbgUxjVTWDpgQfE0IJYON93urnI41eT+qjLCqwgrajJdVV24ZnYiIeDfK1TS3EaSRarRxshXbCW4PEaXJaGR0/2BDORBJp7VbMmW01FDbCaG2VYmuN018+ZnS/VrMHpyuqdf1tJ0NSQMeIz9WIO5s6moG2GTz4tauMHgkc2pqA8+x8yRGmhy0+V3VUeT5E76j4meI59wJ7z/bsvsDj8psptz0r986UjDp3XskeUSUSK2NCkeNWBthJhR9lLHpV6MFKu5ZjFv2opPt/bjBGWkNYM82oN4cEI0xuKtRsFfi1X19WTwKcevDvcvJ5P6yb3OEAPx5gdiaE8VgrorDk03Lq83+Dky/6Ma1scnnToS4FVzuOm1h9l9QKHdtAJFZ6xYW2LdDO9f9S65hvQlCPi0g4FTuetU9bXKnXpomFpHjgqrgb5WTbc3yZtSO/zVzNebEB1lZhdw+ztUcZN37VoSHVRsa5L7SMbr6Jw58o2raHwUN7hCLDDTLItJBlY6yReaWboZUB9WrOfENbWEwdGJxpaFYLsjDWVkWDFklSa1WWtcqndPzsTbDzoH5ocTz5UlwLQYs9aQOVWR0JPirV/6rx6jMYp7BJo+PXyQwhppdX43p51V3va5Uav/Q77+Tki35M47Y17UhzskyHY6TP1049vLCl7ORzXt911fvAyTt+8LJavR4LaXJb+15f9KPqG5l+49Zy6lPvP+CZTx88rnorzx7T6ymUWecPI9HHJxFHuW8Ni5e+u4BOXBSNL804KVYAY/CZtqBpTAzYmV7TaSKVJCVupa+jBYGkRopxofa6FqQCgDU0A22FZUFhFIgxcdIVjlJ7djOIRE2Mm6k13kSiKKH1GnIBW3ltU/MB8Y0SZuNc15tJTcjUw8gXlno1b1/XVE7VMCGoEigzMaYryZMKc4kMSyqtpA5Obfjllo9m4NLG5mDUEDwNuIBU2IrtfIXQRJVWsePbicvB6KACHVVPu++xM33N4ZnA+I+3kLNbsLGG2Z0hp7c6BWG4ld1bMqQOyLkdRo9uUK1pQcBEVbkvjXZ9zDTpMbVo8X6kBX5xQ0b3a4UsZIadr36WFhQsDE/r0IB0TUlQj8dg1VT67J8S9o4N2FzVbef4vz+87D1nrbakFznN4RH5aTW9DmXWXq8h14KUNE1bVAcljxZVZH6Y65oVcwCZqoKoOlRGGxV93vB0xT2feMfSb+juT75L23sr3Te5lVLb+sdFq2hKhZRF+IVOkI//7JNbAHo6IlxDT6TQeyJdlff/SuCvhRB+Q0T+Jtq6dkkIIfxD4B8C3HnnnY/7bVyObPljp97AK/6sBvpsTxeqkAlVbC3QyW06Zt7UHjutkbnDrem0oJUHAvVQsHOVyTcDJXUWUa/pa9mqiyx3/+5PcuJL36LGeIA/ut4mvdJY6rWScstR7Hq2vnhdN78x4V29r9ZJNFu6SC5tzGunXgLOUUSzS19m2PMTTcJ29lrG3W+dB1DD7xC0qrY76Sb9WN3AlE2jrRYL8s1mpWiTdlO5zrw8mm/6lVIXNR+NThf7j5P81Bg1qvT+ibduJfHUpNHK43hIO8LWhba1JKyt6Gd1SWZq28rC4N5tpG44+cI36fHFCiNGdDMGnPr0B57YcR6Aez7+9rai+GThWiuQFtsLQ5bhR7lOyYujVB8LLm5ibePBCr7UaX/zTTWHTCSKThrRtrNkph0ywAR8Y/mdh25jXFYc3tglOz8ASm1hQ0eZzjdhdixgKvU5q1dgfD/MN03bGpbaz7K5Ekh2FqJqpqtKpsf5MuALD5mO1jUTZVrCWkM2rKl3C0A/A0H9FahUzZQSL1eCjUojNZZWEihNSGuG8RxFU+35Rhbb0tRE0eeCcQEXzS6boUrvTa2Jtp0Fim01qWwGet5MJYS9zucgkSjzdU0qbayupoRWHFjXTWdJrS7JE2m+CURiLqmN3EDfe+1z+uW5XChnHgSqVUux4xg8OMV86t5unRFBPvNAu76JNzz8mluoVvXcmdrB4U3Of8EAO1ciwCUD60DbDlCcneomuQmU5w2Tm3LqFeFzf+GotvkN9fuyM2H4iJ5zJOgUmzx9Zj2GtU8ZfG7Y+lOBkIOZD5FmxMp9x8imQRPXKpBNO9NrCbkWAVKLr8TKdzT/1Kq0tl1oMtgZwgYrNEWum4RRRjgy6DYMRjcs5bma17z6PUtqzrte8k7s+YkmxatjLVI0Dn9oRVugxwNtL4lIG+JTv/9u7nrZux7z93mluNw4/kTWqOSZ5kYa28y0AqLCs6pADBI8/vAaUnvyHUcQqMdGNyCPxhalxTZyD25luehw9yffpURH+oxHNqDxmN05zeEVsklDcW7G6F6hWS3Y+pINBmca8u0auzPFTCosaKzKF1SpPmjbVq1tnYScemwYnGkwLpCfnSJVnLZ1/yP6nDjBzR1ZReYO88AjXcwF8vMV+Y7odQGqgrUxHvug8Q707yqqa2Zzsu2MMC47jw4XWlWJLExuTV52BxVKLvl7iwRgsIYTL36zqo18bHUJviO5FvyOWgVBJNEGf3IailyHaSwoOZL3ycnbf6BtS7uaOPUH7+HEl11aG9/VwpOhQDp5xw8umWGH0bBVr5NnF1chAbIziZMotXCp17momr3WPCyMy5ZYbca5GrSjuXc+CfjzQr0aW6ziJj8NjDCR7IBI1tTatpoMi72V1lbBNBoD7FQJ0NSKaSOJVJ5XD6a9UYaLfoAuj+pfFSczP6TrbnlO37chtVl1hRVtiRdtZwohtpZrC63djXYRMTe100YLFrmhWS3ayWw+rv0BVVz5NcvwkbkOF8kMbmQpdjy2DjSlLE1W1emqXcErtemZitbPUVzQ1DaKFk30Kk2+iM3I6IChNWJ+oZ97elgVSdlMzc2DjQRU0ByhWoObfqtmeP+OEkYJW9v6NRktJDd3HOPhOzPyXbCf8rjbDnP+OZZsTtvubyrt/gAl4UzlMLMGW2SUZ2bUayXN2HL+hatUq2u4XNvrfA4bf+ywM082c/jM4IYGF8+RuMDgXKCIqufdWzUOn3/ebQAMH/VUK4bVe+vYSVJhZjGem6g+skHXz9ziRjlQxO/GtqQkAMEQRkYNzleLNmb7o+rbKAHmG4bBWSXnFv29AO566Ts1RjReC6uzimxWEQaFqpuz5ccvrgdf9xd0r/LL/0+vPOrxxHG9SaT7gPtCCL8R//5XKIn0sIjcElVItwCPPBkHsz/wph/Zq+5a7v82UfZuJrGK6QMyV5JGao+dNuR7BnHC6r3NZffc3/3fLiQOTnz5WyFocMm3akJusdOy3aj50jA9klFue00uPQip8qq9tzo5wZOd28OvDLR6mPbtRQHzuZp+AmKMkkZOW9HS7UD0VGiQymrfbqqk+1hNEd0AgVYBdBMUEzsL4pQ5D4PIwleVJoKpilh3HiMnb/k+Tj34dy/r/C0hVoRAWX8TK6Zt8giEhtY8NLWStXL5tBYnX4rF8cx+4f5rhCdbjn6tcPwr3qbVxlQE9x417oF8p6ZezZeMcg/Ch3/tLbz25T9ByC2+zGjGSiLZuW7soUsis5kmDSoRj8qXRvC1sHt6xPi2ikceXueR74PPvu71fMmPfDAelyaC1QakCSJtK1hMwohqnvR+ZmEqW/ICSi1tPgc/8DByiPVYG3A2EJxgc0eWeRobR1gbWpLKVtDQtbEl8+rUxpbG7Krviyx5OCVjbBPvD1ZJLjcQij0to9ZjWTLPzmZBX69RIilVFX2mx5ImWbmCVrbuC9SPKVkARdJMj0Ffx+UBb6TzXojqp1TR1YQ6Vo6nnqJyENU0roDiXKUEEnSV/LQBTr9FqyTaygOefBLYffaYYFbY+KMp1XquhqJAcV43CXpN6BrTTp4MkE089cgyekjPVbWqBFg2gfJcIIutzOIh29Kx6PU4th7s6ufJdw31ilCt6+edHYLRIxJNU6O/hRfdsFTRhyFeXtq61i0ol+of88qTy5u13/j/XFhdfPVr30vx8C52Fkn7lZGuZ6AbwCi3N7Pmopu/Sz2e640P/8qbL3rfflI+nX/a+OZV0TKvyasGtzbEDQyD00pEXI6q5qBW55MvfBNmroy1VA1ihczA+MHut4iNbZqxdZfGIckzJh6nESEPgexY2W6ApAn4QYZxTts3EtE1rwizmU5tXayoR9WvPT8lDLLO12iqXlmtOljfEEYjVf6lOOnTBLYFZVPySIqeX7JQjHkiSAbev/SRN3H8zrd3XilGYKF7bUlFlaYZpmNrGiXE0vGnx7fm9+aqKpIWcfcnrw0Bez1w8oVvYnHaH3GDLNM5YVg+Jnm0hKaJnkdEFdtCESl60yRDYgkgc0cm0nYBlOcD9aqw9tnA2ePP5xP/6PW87H//6TYO2pmPbctEY229XcmZ6NfVBMxcW3glhG5EPKj6pzC4gRYIgu2GXPg4MEKg9f0L0sW/xdZxgrSDJUwVutgnKa4aJcgMreEyEM2P0UlzMdaJ89p67ALZnqMZGXxutXsierzZqK62URWEAOkYY0HKFxrLs5n+fJpmUQkLNno4ETpFdzLZdoWSaK1pOLRtbEGEcsdj9pSkcqXgBnreRv/tvuXf536IMD9SsnIfrNxXs/2cAp+XbP5Jw2zDtlNhpdGc0ZUWM3OIc9rG5T3NuIxrEtreZzMKB/O1aE/gIZs6xHkMECrBek8zNC1Zlu847FxoBhnVWqcEFw/jR1w7gTJ5v7HQPivBavsaYCc1bpTzod+8UA18MSzG8oNUQq951XvItmbY3anG7dVR3INqbt2M8nbvBSwNm0jY7x/Y47HhCbir2HWy/7Wf7riuJFII4SERuVdEXhhC+EPgNcDvx/9eB7w3/v8XrsX7X6rJ70c/dOFjjt/59o5IsFGO6MFOKuwkbopGV+/03v077+S1L/8J7O5Mxz5nFrMzb983DDKkGevkolhVsfNo+Ab4QYHd3SYMS6Sq1UQ2jZVPI6FF1CsjliEkTncJszmyttrJjYOHQYlfGUSzNtNW9UwkZ1JvsM8N9ZEB+Fh1aTxM47jfSbMcVBqnLR+xqiilToQ7efP36v1xcb5cdZKEgC8M2bwhjAo1kU3Tilq/Et9t4Ix0ia9H5fxhIQHPUuXVg3dQFJx8gV4j+0fqPlPQ+kYd8Ht65Yn3ker0UjdQVYTxiGC0qlav5IRceMWfe387mvti+PCvvYXjd7499tELe8cy5hto4lMpqZHNVLKezTrJNgHybUPxeYM4eMRv6gvGHrFqXR8/39CEqNjS5Ct5m4mHejXg80C2Z1QSHyebpakiRCPvZHpdrwT8IMBASV1Temzm8Y2q2PxOwTwITC3lWdNOOrMV5LuB4aP6O2hGRqe2VCH282tyauaeZmR0Uo3vfBrsXPvdTRPIz82REKjXSk2GCxMfE1qJtZ1rtdE0UXkQtDI8PSRxYACaTFXdf4lI0jGxMZmMZJdOmOnOW7GtBpqDCh3jK0Kx6ym2nU5sNIJPcu8AZremGFrs3LB7x5Bx9jyKP3xAiexFEjeRwMFz2z/7I61sL7Q/uGcdaSdrjh6ucIWhiJNUmtUSu1frdDcfyHcqijMN4/tsO7ZcfSr0eE2ViJa4Yap1sle+E8msmIMPTme40lCvastzPVQFWD1Sz4dsFrBTR362bqeyJP+YYAy4wC/+p8tr/zloXPoiXvPKd6vaNQRtq2m8qgWmMxiP8KsD6rWSfHt+WZMYnyp49Wu0xfdi06QWsZ+UP3n0u/UfRQ5lqXGgrnWzZg02BPKoWLgabVmpHfnkC95ImiJk5w12W2OtHxQEK1SHx6qUS/FnMo3msHGK1WSKNA0rn83wQ/VZNLWLbWUGv7mGmXaKMrEW9vYgz3HPvaXzPJpWuM0RZq+Kqje6mNjGu0zbi6zA+lj9fOLwERrdkIXC6u3J18bEotF8qkqoYaEq1IXizWUTK0b4hlf8JBbw41LJuEVSaHEoBdC25qc1IbVXpccteEPp5FuBPOfk8374miiLnw54rDj+6te8FwmBwqhiqM2Joi9MGJeELOPEl/344363pz77QU4+5/WEQltHQ6FT/Nwg09x5UkOja6x4j92dR1PpEjfIWP/UlOz8jHJrHaBV1WRTTzbxmMZj42bfDyy+VCPlUGh7ms8h3/Xke030Noq/h9gaJCG0rViuMNQjoRnE4kmuhIlp4mXj1f/PZ1GF62PxymnszKa+bSUHzVGaUYxdVggmaOuqUbLUl10bkxJPPqrkO/WNGyiDlU20MNGs5mR7jZIq8bF+YJGVnPNfkJPvaZGoOqLvb2edYji1BAYjrYraxMKqevF15zc9NhlxSwPFrhaMfCqcZRrPB2capscysj3NC7Ze8WxWP72HvffR7kJoC0Ka64x/+z7GeQ6ZZfQn+p57f+ow8w1hcE6HEflMMJUn21G/NfWuMkjjyLZnyKwh2ytBhPKhPfUeTFNN09sava4yYyAz5GmymTWYyuFzS3G+aaf0VWuWfOKj5UlizmQpDyFTBVK7HokcSOI8Fh4rlh//irdRzKIqNM86orXSjpAggpk2iPd86ONvv6z37dHjSnG9lUgAfw34p3Ey26eBb0epkX8hIn8F+Dzwl67Vmz9W0LwYjn/F24CophAhxJzk7k++6//P3n+H23ad9b34Z5Q55+q7na4uWe694Aq27MiSQhJuIAkJ4YbLTSCEG37EtFByMc1gG9uCh+RC+uWXQkghIQHkXnHDHRdZVjs6kk7fddVZxhj3j3fMudY+RZJlybak/T7PefY+e6+96pxzvOP7fktkDAXMuKB6BEEkmDfAN135OvBKKN06NkKlI9kuMDODmVXNBTGkFh2lbKHfljSrQVvMWGfnTOWUmidkLFYdFVwU0JdI4NCyArLYevGb30+oJ/yK6BGj8EaTjBzBGJn8JPGCGycIF5xQLMrcgDoO+auRkL3vHf+48UUqlwXKyLYn8+lBWJh+1UBSnWBjzZy1VDOU6gZUR08lNZ925pcuP+jzebzWA50/dZpL6+Q8flVNpiif4fqpNEGZxj6InK2uRZPW5//QzeKf053LrvRpkXfWJtSuBbN9wiipusI0WvqCZfuZFUf//k9w1T99K4kScKV7KjT066CBrG7k5L7NTDV+RLWZtiTAqMZEu/ZCCgZCEgheoSqFw+IUqB2L9gJseRL0TDeMKVNAa8Njct94r9TTRDv12Fw1AIZryd/ZmRhE6qmYYtax2FQyWVV5iZkawDaebXbqxGPBCNhrJq7x3QJItSIZJ2JKPasoe8ncRwCYrVpcFlNmlMjfhAEWsBMf/RAUmPizWfR+yAXcsqOy8acJRhGsQTmPa1vZqGwWuK5Fu/imDnqoGLseprP5NQkEyF8dUB0YYKLxvMpLzPoIc6iNnYhBsK3jkBODHRUiL4qvR4Ugk3AtTaSqJAbXZfF66gK6LAnWCX3cyHVXZDXIJDlOs3WUHhgPyTh6E8S0GV0FklEVNykCHtX11UwsH0o1ZvVaZA1u0I5R0Rq76QkrfUKW4KM/yTsv4J/0WKlgFNe99k0XHPhcrG665EfmbAo/Z/nccv9vcdOTfrIBEtPTI4oDvUf0+dbDhpuu/om5h08Q41SMItmKaT21bEsbWWuLUjbtM2Hi6Fw2yjov5W+jIbveGsbXtbCuellPzdkhIUsJ7YRgW/LQSTR/9R5dVnMAyWhZF4OOwQagairhQqnpguQN5uuqOWfD5QJouU0tba4Nsx+o3vP+n+XV17+RoAyhL/eZbsX15FzwqK4QmCd8CQPwvJ5iMUkxruOza/dz3Q1v+rqHS3wz1AOt4zVIe+MzajlhDd75eQS51YSHSM++5ejNzfev/ZZfEv+hthGPmpYRUCSv8KmVflUpkb5HLy6A3u2bjK5d4aO/9+O89HveSlqIz5mZlDLgrKWNSF9KFaA1f36qirYKAbE3iCbIIFIuFxPKvJVQiGDAV9JLyLAmsolioquq28MqiCdplM+Jf6Gkymkf5O8Lj8s0vjf3NfMdGQIvpi5LQLRCl1UTXANV9B5y6Emxi1WrJ4UkKo8CdlORjDqYcYGqPLMj/ch2FqbR+HDCbG3+WD6VtSoZyaDJZaoxKAfxUlSVrGPaiWTNFB5VBkKicKnGTmJ6qg/07i9QPhVWr4LJkQ79U4l8LqPx7r1HrPGzDqLKgJ3J55idycmWjRhtV6FJ3QSww1wkkDWjOA719TSCkO2ERf/SOrVaJXKdJPYfRLPyeT8iPaRPZdCZ7rgoRZ/7DKmZvKcYfd5afq5/0ddaNzzv9eiJhP6EXrsJ3dDjHN+VkJF67QgLx/xefe0VCfOP2n0/1usbDiKFED4LvPACv3rN1/mpXLRueMHrhWlQOlTlGkPTBkHPEvJ9bWFc1Ch1CI9IdOxFy7n5VBIxhDaTAl2Kb1KxZKnaqonzTKayuTN5IDs7ZXbZkiTD3LODGk6jqbWCVjanzmslfkgduWjhvcjXrBj81Uy8OsUFgLxEGYNLxRsqJJH5sOD7VPXEy8MmEkGqrRZqvzaSVDDLhZEUXxu9zi6WQWglBK2FDbaw+LzjE68/72267rVvAgXpKTFhUbNIRfVuDgJptRC7HJ+n9yivIrUjLlD1VLMBvBbo8kaT3bclmw94zBtnPlrllzpyrIQgBoLLqZg0ZgqXffWLXzIJKKciRVv+jS+BZKSEcq4ERKr6jmxd7v9zv/k6nvOjN8/TKCK93Kdi8JzuBNKdKBGLYJAKkZ20SEVXu1PMgoo090htzzYVbBp0bnBtyPd5QupRlSIZKcwUQIBKM5WGNNsJJBORhabr4tljCmHy2ZFs1ILW+JbBGk05sBRtkV5pF82wU9CVoXsiCOvOeXQqm8L0zAw1LQgdmdQ1Ucf18e9BTWUznUZWhBpNSYpSjv1ZDtbQilIYeh2q1S6qcJx9Xp/ZsmJpO5DuiJ/PdL+l6EsqTPe0/Mxul9hT23MWwaAn14rUkm6LEbDqtNG5JT0j111mecMsUP2eSHTyHLW8JCBJu2ZJBly/hc0l7r51Jsfed5bQ7czTrbIE8pIw6OBTkSz4RAtryznULDKEIlBULmXiezcpm2udTwT08qkRsN7G99FLmk4yFsmaHZfzlJZEo3OR1NUBBzXL6ULpVQ+VKXuxMuN4vESZGtBECU+vWaPqmIaNVm92Hov13vf89HmS84vVjc/5vwWcGc3mm4saaOmm+H1L3PDCX0AvMNoe6QTOXRU8JGmTTKoAKiVeF4nBrXZlo9BKcd2E5OxIpOD9DiBTaNrQyDt9ZCKkqQxKah9Da2Ud68Tk1hBgWop3Ui1/8H6+jtc+R5WbT7xnlbCiai8hvQDALZYPka0b13AfxAPHGOlZqjB/7xMrnkmJac6Td3zmwhswXYg8KTkdDdWmM9lUFWUjm1tkNTRgkfdyQU5slMDVEtYwB5jq2wOt289AYh/R1LbHXdUhLF4MhX0nFb+Xh6nOUM6hxg7Vky2Ja2nKbkbrLJJWlkavoJnDZwblggxtn/Vz/OkfRHlOZNNWHSsfdy1VNXNWj6Qte0IBqhJGj4qDU58ZuY6XNQNY2D7ZVkUy1YwPGlymyHIJWtBlBFUWpGm6FMCpve7mcjoXSHZk/SoHqdhflNJfVy3TPL+qLZ6L2chhxwX5aouQyBqk8xI9jgoEwGxXhNNnUXX/mqaoomjUAqrTho0tlFYkw4mY0E9ntE+eFcZlWYIxtL5iQBvCoEOx1sHkjtm+lLPPsqRDQ/e4p0lItUifUYEeCwuqd+cQPZqC1pSHBvI+G0WYebJjG/LciyWCUeL75OOgdjqD1WU5B89uQr+HX+mC1rhUk01K8uWEdgzAaG067KjCRDCxZozpsbyvVbcrn28V0KVu4uzrxOiQWrHWiGwzNS0gswRjBFhKLT5Nm/WwGb4oSLcKYcbVQ3Jjokdb/H+WNOykR2MdB9DjmaQ7tlNJVB3PIC+YPv0wVceQbZXQtjKg36u9+jrWNxxE+kbWg53YtZGoUlGjohH6rZ4DRfUUrWY0PRJRtg9W9QTnpqed4yHjRB9sEoNNNfmSFRpuG/xOnZgAxapMH8uuZnTNEumwS3p8KJvHKppKJwlkKb7faeIpidP62ti1Mfxe9BfwCpQXw1KtUc7Sisaw3gi6X1N1q46N6QoJWseUlcSKnCDEyaC1DYATbFxwfaT3hmgyaITNdMML43sf5XT1ZD/dmBGyRBacmoJdA1SGuafC4oYilFHypufNabzv5muddKP13Ngzsrn2wKR53fCiXyS1kh7hEy1mjdEkVdg2mnxJGDcv+IGb+dS/fN1Dv/MayIwgkE+CJBpF2rkKQt1uHzd88U3z+/3cb86/T4a6MZcMRqjmIfoTNV5EMoTCR7+jGjyqJ481DbxmQ6FAlUh6WynxtiGLxotKpG7JUJoUOxIGksskRW3pyxL9HLRGF1UDXjQeXEaJoaN2jI+kTA5opgfnJtw+hdZ69I4oojS1qIRUlwtLUU0LOc9iMmFoJaidsTAe8hqwUSJFqRwUpQC7ZQUFcm5bC85hdyROfKWbxNdmpaHzEslrcs9s1bJzmQBm6Zph38kt2YCMxqi8IFx5OAIyBh0Ba/EMcrJRHEYQOMsIs5m8rpiSUrMI7Ia8b2Z7IqBTKiaT+Di9dV7kvMOxAFWtFDMMlId7VF2Dy9rCvMoF7KpapvksgxKDKG1NExkPMjH3yTx9ReceU3rspGrA0npK3JhiW02+msnno9RFPQq+5iFE3ESr2Fj6doJKDD4xmJlr4o51BR/44zmN/uGwc7/R9WAMpNe+5JdEUgrzIUArk+t2lH3dcvTmZg35esmSb7n7bbsMuBuAxsnASiWmScVzmUYPIghUM8omOXqcE1opwWSSfuriurSYPGqNbByLUszTZyWqjMmu0RS88URqoqzj1zSl9rzB6N3AC4Cxc0l8vCbIY1pZR+uBTVGvqQvDm8qh4s1U3Ijd+OwoOawHNwjLu2EZDoWB1MhbQyA4JwBcwyyr1/eFoc+ij0/NorF12md8/jWtPJ4zj2ZwxmOtbnzWz8nnkRh8LRGqGTAIQ8NnwiC6/hVveEBvsvNKCWtVDJ6jX5EPc4C+DDK4ZLex/iKLzc5ipH0lbNc63S3UZu/xkFCRoRt0DDJIpcds0lW7ckwEK0OjEP3G7DRQtRVFJCXqEsqeaiTcwsqJTOa+prddNqEItaGymVbNddkbjY3pYpODKUVPQCRTBJRP8Jmm6GmyMwE1LQUQNq3IvhOANmgPjuiJE8Hb0URuY2tqdARv8/kQNJSlsMnK+N6UJdnGDnhPd2eAdkvowjPdn2CnHuUUOtAYiU/2Sxpa7255iHBmnWQ8wR9cYXaoK2moKxJWU7OgCEGGuJOJXFN2RnKtSBP8Wp+qm+AzMQonBBKl0HmF61hUFUg2JlRLLekbQdhG4yl025hRTrncgkRRZQYTj8+gdXPcBKNQ0XIjGDOXkGtwmZGBUp1cFiSMJxlVAjJG0EjWcS9ywU5KudQSlm92cfbdI7GOhiSyn0MQ8+xZSVjpY2bzXsXOHO/68Fyuff0r3tDsZx4v3qrfiPIE3KPkXbTnifQ4q+tfERekEKIR33wDEKxGVXJhmh2WVWTx4vCNaLhvufXXuPHZ/6SJJJaLdIUZiw+KyE2MJEIg0ee6CiRKfFaKnqadO7xRjJ+8AiHQPjlF5U42HCGgd2ZQIBT3EPCDtgBHSmGGU4kQrpOPANVKhXo7LWX6MJOUmZpmGhKDb6fUZra6dJI+EQLFwb4sxtHEOnSyZnqw2AiqqpbcyVdlXPP6hY6sQMfNkJbFo34+qlJRlubnk9Z6wxzvX0rNm0rvZFrTjs12PcUM883koiRBJrCyqNx0lZjjnRv7+0So61/xBnnvrUTkurYhX7JCgx47dOWx4xIzU4wOd8RjqITn//2b+fQ/f3Ag6QU/eDM6WYi592Cm4g1UDmRCGAzYIKaKz/qxm7EzadKKvqLqCOBiKujfGxpT68ZHCeaG1wpQUCTyNcQrZ9mX/9eP1RhqZwE7FulbOhIqvC5sAxZVXXmtVScCVFrkceKnFEHTROOSFD0pG38CoGHC6GlJ0ZdI32wDps+fYm9v0zkZJ6G5b/zK5Jxx4q1itGwuy0rAGR9QdUpOMZ+4MZ7Iz+o3vJ7E5blsAhNhOqhOG4whOTtCFY7qyWtsXyMTUTsJJBPF5KCA2XYcWVtZiprlhKIkOEkT8ZnBdxNMbufG/EphJoWwiMqSMBpTGwIra2FzG9IEtbChVBHYdfs7mPvP4A+tojfHwtLY2pHX0spkUmsM4dI+QSvKrjSDLotMyVmQ5B4foi+C+HU0XkyaSPeXjY9ykI4UppDrbO3zVMvY6kAGlygxRLUiB3jZd7+1MYb/+L/7MZ77D29uPKU+/TtfBaB6TgmTSqNChU8T8dFo1jQZKpjciafWBaoGk+CxBSjB3EC78f4BOY+tBF5gDXiF37e0i8H69RgCnVu3fOlXuenan4I6fAIErPUOlZeovKRa7WIKT75P1iDxHClwSx2Uc+gdMTjx3ShPa1n0OBfQeDyBSTQxdjPUaCzfpyL10HVan65lX7LeNYBWLQGrJ+6RhRSUktAOaNgUahbB3Ua+FtfZekPrFtbuGtSpKmFU1MOa+vzK5JrkuinXv/xXMJWX5MCY6KqUjpvoCCRVFQSNyrL5cGfh/prHTpNdMieco4k2rJ8vzI2f4Qk9FLrp6T8rvVPhwMjm2adW4uRnlQznZjlqtU+lWri2Ac95yZAPVD6rU9ritWkmSZSTQ2kMbBAmPUbxym9/8y7mZNUxVG1NMvMkw1LAxrofTOZ9nZk58f1RMtD0mSYkAZcIi9e1QgNiyd/ENDW0eP8MPbqE2apcu9NRIF+W24pnkiLbCY2Jdtmz2ImLa4oYN+tCXpdPZKDitSbZzik7GUtHc7Jbj3Pq26/CX2FZurNk+d6RXMOKUnrV8RTVbgnI3O3IeVU5wngia9n2UH5eVfOedDKB6XT+ZpelnDtFIeCr1qjMy3nRbhGMITs1xXUTtq5VgKF9Sl5v0Zeep3t/ZF1lCToppSeoKrQ1qP0dAeQSDaVv+n41qyCzKGul7zBiHaG6HfTWmHRHC/NyIlLzZCqs5+LyHr07tgipxW5NhdWYpnJd01oS3rQmHbcpLl3BtQxVR5QSVUdjyhAl7QHftTgElPKpjl5VIsNzLWFb6Sp6RmoBB6UXzKRvibLDoARk9FbWcoD2mZJX/uVfxxv40P/4SV70/W/D5nIt+eh//PGHe/rJ+dHNGmsNlxnoLgNgcmHmgXh3nVdaUXUt193wJoJ67K3je/XNX3sgEtIsm5nDOL8ruhckkea1L/kl4BvTYD5Yvf3Pf4UbXvSL0ty5gA4lwUvjnAwFIBldYrAWsm0nOueWbOrLHiQTYYO0T0waaYYOVeP31Ew46rSVIsagbozmjIWijLK+FJywTJhMhcFlIpVeKSgCkKJV9HGYiJxFp0YYKoV4vdReSaGmuOuwi+0ULIIfNYwk1VDhlfJ4FdNt6lIycVBKgQ1A9DY6V4+96MtUG4fqSMsPXnylohk0XsnGpH5vGoPAepLrF9JLLDdd+1MPPbXkcVLKeVwm6VjeCIhUb1jtOLIjEmFopONA2RYJmvLw/H9wM5/+7QtvoF/wA8LECxbKTLyI6tQvkwuQo6qFGHYfqeczyLaFYm4KAYvsRIyfB3dNKXsJ+YowS2wuIJRLaRLaqvbcG6BOIiuWpPnUpSLYII2oV+iZondMGk87C+RLms6pgE8Vo8OK0Id0GNCVwk4Dk/2SYpLuIOddXqLrTVz9fkapiY80+9CytDY9VUvRXg8E3aZ/XyAZOdKtUuRvRjeTSwXSdNbsi7yQc3cRKFpMb7rgh1qDsE4a0DRtZJ5qJlLU9r1DqmzA1jWGsqPYXlb4NFAdLLAnMnSlqNY6JJNpwyDwqaHqWuyoQk8rzNZYNotpKptJowmra3DPcZmiGiObRhD5bS11jc00aYK+/dj82lOWApxVs3lAQJJAryPTaCuNI0DVksbSJ+BNZBDFtLraNFxXNOl1wrAEHf0UfDQpdZl8r7xEw2sXKAYWb5Sk9Hia5D9vwSeKF/zgzVgnbLU6ZvnhVn0NVEpFTyePi5KNxWrkILEey41mLXGuWS0Xqm9G8/Bbbn+zpF6BrKtx3VFR1qZcaNKkTOHRcfOgqnjct1PUtBAvpDQlFFF6ViedRnZTw7Zt/IKqhe+jhC1NohwtkY3rLJe1sIlwrwgJkNbSNEQWt3D/oSux5OTRK0mphg3YsH9AAKsqXky9mgNZAEHWfz0p8Z1ENqyVR83yhkWkKkk2CFGGrmoAS5/z+Z+7xi9+rZlRDRtp4V8du56L78xNT/rJJx6Q5ITVFqKXkM9sjByfy2RB+rnEBWGEACjFa677tQdMXX3tS3+ZYLQA3AiLyCcGkwv1N90R388qGjur0qNdmA8s1nqoTNM+I5IjvTmiDkrxy30Bj0YzfCdtmCQoS1C6CZIwkWEzWzZyDa/J5kHW+WQ4T0ezCtrrkeVcwfiQorURMHnsB/qK6T5ItyHb1iKFmpa4doKOMjkBIsS82ScKnxr695akx4dgDWtfGAuJLjLnVZ2ouSgfNSb2226XnFSliQxbzlnH1ULqZwgeZQwqTQk1uFQzDBM5j1Uh4GC20WK2D6YHYXSZrHOu7SkGGlPA7EBGb2tCiKb1Shvp4YMX6fa0RE3kMUKvDR784TX0dptw8rQ8ZiGsaDodubZYjd4ZN5K3/p8dg26H/HCf7MSQ6vAq9u4T8p4kERDudqjWevhUyzFkNT5Vkp5aBFz0aJLEuXnfL6bl8X1RNGbq3oqkMpSBqqWbQaEuxbTdRa8kXQXMzGPzIMnUAcoVy8u++620Z75RX3ytJSQGD8jgx7VtfI1zMPzcdfurYgLu1UXLh8bT/lG578d6PSFBpMUJa12uZTCTShaaRO+KM/5mNxx1nUToxc7jOj3stpjNJTs5uvIs35GJ18tWid3O0cMJoZ0y6LWYHhJ6fLAxLjOa6+phLhf/EKSxm0xkI1ovCM7tnh6CNFo1+FLXgvF0PY2UdLTYnGmF2Zk2jW5IrfiFKBU19sIsCI6GzomKF87KzwGcWCEyOILVIinxwmYQwMc3YJtSmoa6vkjh38Us0ghXuG4yA0pFo+QaXKv/ZpdXUrzPuEHf1TQ/waoc2CaZxBtF1Y6Tu57BzBTjI/L7evKjKmEK+UTx3B8RsOizvyVg0vP//s1NipgK4JSSzQygC+jdKxt8b5HktiOQnp2zfJKJUM6TcUXvXtH6KxfQU2lcTOkpuwlVB9ITHl0GOqcqgpKp5WxZzI58jLgNVlhEVQLlQJqo3h2WdAi6CKx9dhu9OSa0M7qdVAyMC0e6I7Ip1xYA12Ual1h6J4Q+rYfi16LOjOeSG6Ob780kJ6QJJIbuvVNCotm5qsXgHk//7jHm1Fb0LorHXX1cRtbAIggTvJcUxvqcrQEi7+dyzrpq43ktx7RKkjmIbOaJR2pa0Dk+Y7q/izdwyV+6B60Ct91/kOpgwfZhGBxLQe1DHV4lX0kpe0Y2DEBIDdW+PrqohIlROmHUTHLCZYcI3Qx1+71xdXeEvBCGVQS4/Ehkbardgl4nGk5qSaeKoG8YjZm97KnM1iydUwXFwFAMFGVn7qU1OahAgZkobJQlemICH5FJWcn39TFmp/OGXheQTX00iHX41DBbEXPVdCTyCReN0IueMOhUBZ0zoQGpHonyqSXYmGATAi4CYw+W6vZAdeNzfx7Xyyijh8k3Cni64QWvp3GnrNeHENCTCt9JH3Gz8ke7/KANoSWvYVOOV5VXkk7VTrA7YirvMyvm9FX0XSmq5rzEOWEbJVbW7nYEg6JxeC1xpZJzRy2ycnT0KxlNoNteWA+FtasAt9RuUrTErF4itjFKvKbKEjWNwFUtp9NxwFI/VH29sZGVXC4gpjUYpVtyn9MSU1S4pbawtWpJ3MLzVkrHIAw9B4bq+1p8fSHI66s9lM4dHNX9TB28URt3V1U0Nn+4n+xjuLynXGnv2mzXCWLvef8vccMLXk91aNBI0HTuhE00Kaj6Ga95lXiKLaYb3vS0nxGwwMRhoSb62TjS05Vc853HrHRwLYsujLBDp078hOI5b09vY8/UrDc3/+ytpVxtRy8ij9mZyWdaluheG9tKqXqpMJMiwFC1rAwIA7Q2Pb17JzJUHRco5yQ50Up/qUpPsZxy6M+KCFCAmVWceX6H1gZk28KcCkZhz07QY0OwFp2X0aBe+taQWgl62J6htoegFPauE9BuCxg0noh3UH1shjD3EvS7+98QvMjrsxSlFKEUn7VQ+6ItAKlBxRS6Vhb9xCLzMJfAC7kOBQbHHLqS82fn6sDh55zk7E6PSbfF5ApF948gv2RAuOw5lF1RPWQbFbrwDVtNtRMZYswKKOfgdvn8a0nWx3B6Q3qc8Rg9mTTndpjO4P6TqEGfal+PZFsS+uzdJ+SzzFIZGu1bZfzUNVqnZ5FFpsiXTfRxEoZx1ZJerbUZU1cjQKicSCVt8KigZVAYZKBUM4frfjPdLOR1ZYbQUcxWNHYmkn3x15LjeLYs7Kf2qVLkf+cQEx5uiSKmTqLzkr7nvzbv3Ruf+/NzRqlSX31i5l494etxu7u9EFD0YPVYazjrevcHfpZXv+aN0cRaUezvkJ6ZCABTekzhaW0okq0ZamsIeYGa5WgP7RDIV1uUPSvJU7mS1KI8mtAW0Zx1IS64ngIGaplABFGsldu3sjl9HSJgJAu9WryPEAChSQdrZKoIhE4qky4lF3hcaFhANEwgwATxToKGbURsAoJWcXobn1+d3uAWPJwa480w/36xgkxZZfNc+0Qo2XQvTjYbnwUi80PNNzaxqZlevfqIGOw9Vuq6G96E6iQNW8MltbRHfi8U4wqfJNFfSOESYRKhhAEUzDl3qtgFNsnirijbYkrtE4lTdy1hgOhSpGPFsqSN2JlCeS0bfYUcJ4pGThm0orXp8SNIRk5YJ4Wnahuma6ZhjRDA5mKlpUuwI0XehWTDkm0J20mX0Xgxk02UymUjpEpH56icU/V0VE0K2vdaabLq43CWS2NXx1LXhpQgDAXnodCYmLxojmQkEy8AUs3wac4xdk3mVZ1AqJQ0kIvshAXm0+LjNRU8YHYBUyEECLrxV0KLof5sDcoe3PGpy+UlZAG9kuPHCT7R7FzZIhl7tIPOybwxr/SZpeonpKdjw61cZB9o1Po2ZtYhHNwvHi3RKylESWqYxY2y0ajEEraHsD2cs6aytNmwJtsFdiLv79KXd9h85oB8eS4r9FbYa+kIklGIXhWRldZSaA9mFtC2lrOJH4YpBCDSZWgAJOWDNOdmgSEXROborVxenAWiFEJMO7+25vNdH/knXP+yX5HjoKKRYH7gTx4+eLRYVXee9rfoo/Rw1t4Hqle/5o3QvCcIeFf6KK8xKL/bTPSRTsb5etUtt72RG170i801wK90BVCuPZKK6EO4M0a108bfrJawNeuYUmKe652crz4eR92OSEBi1YyEEMFltWAwHZy74AQ9KFnbZJMYvRFDlJzr5o7l+pXYORMwBFmbSyfDG5PMn2udPleXipP/aaSLQox5n52/RtfytF3XLXX+1xAECGoYwx6hCapGVtMk3cJuaV29YVeK2dMOP+GS24pLluWYUAjTIzIuQJhEelah25HlqCWhMqBERupkg71YNz3tZwSsQMAFvIdOQtWz6EJj42esFj5rbxSzfZbWusJOHbpIYIpsfp2XYx3mx5tS2KHIolRRzWWKSguLfVag2laGOEHAI5sHqpZcv7OtCjWr0PVnD83xrUqPnpa06oFrYhqz75Uvz9fwoBV2ayoDoSIez1qAfDmn62tYvc7Gn2ep9M9FMQfHqoVrXJLIeb1ANlIp8zCcCJyqykmPXoOoIchwKH7f/FzpOQNZx8FwNKT2VlF2IF8DkytOfuYQdqTQ184I44R8WTM6Ysl2BDRrnypJNqYo73G9THxMowIg+GhiHj+n5MxY/FYP75f3Js8FIOt2mNtjZJClAqyFIAMjEMle3dMUBdmZnKAU7ftHhMQwvHQg6520JphCest0xwmAVL8NOq7DY5G7KVdf0+RvlQuYKGk3uYuesKpZ95QTZllwIfafcn8uU7iWIVufiVTzESpVBYIWRt0jtY6rqor7FXnPX/2aN/Le9/x0wxKER26g9VisAI9aitrjgIj0+AGRbrv9ZKP7fKj1eNrQv/c9P91E2ddHpuskVB2hHhd9xewFy7hshdUvz9CFx4xy9DCnszlhcs0q04OZRFmOSwgZejydJ59oLQwGLTHA5DFu0loBVYwBLykNqmbf1N4HOoIq9QLSbgn9vhDjO+WdTCdA2EUuMn6sNI91KSeLYB0rjtYi7QkSl11f3JXz6FKaXJfJhlfPZLPdmIdaQzMWrZuVOLlpZGx1Q1mbhtYLbZ10k9gG0FIhSJNhLCEVPylmBVhDaCWPzof+TVxBK7QTSWWCRMkulksVfiVFF4Lk5CsC+LgWc136DLJNeM7/72aSSSCbBFRMl/aJ3Afx+zyDZAdAjoFqIEyQ2i9IF5KoErSi6CfYiY2x8/J80qFDF3LchMjMCSpQdi2zNctsVaEXptC9467xbQCwoxJUMd9YuSATnshe88bKsVxJY40XRlHtRaKKUibk9XG2yOirz68QaeyOZoMopo+e5ffdOZ/6J4lMIhcp7fVGcXF6b4wAQJF1FLzf/fs0kXOrBpJ9kI2mE2ZQKKRhVvX9dzrkTznE2WdluAxm+wM+81z93yp2Lpf0nt79Yqafnt0h3HlMri9aoddWoarwozGqKMg6MX2q3WqMff3JM+jVZfy9x9G9riRH9ntzs+wQUJ0OfmNTnncE4lRiodedN+XWonoWe+y0XAfGU8KBNZa+MmFwl+Lsczrz5nIG3ZOumUoGJT8vu4bxPkUykTS/3n05VddSZfLZp9tlM7H3icZMK3xiokeCaoxYCT5OK42kQSIAlVDnAy//a2/hw//1wsbbD6VCoiXq2KgmFOGRqLd/9pea9aZeRx8p8KhZx0Aa+frbOkmwxu9TgyrcRdO8Hov1jk+8fheQVFfIBChxbYtODFUvFWlFLue4S1roKAXBBVRVocbRCHsyhcTiBz3Cahd927FmYxxCEClK8MJ+qAGVmh1kbQOgUEXAOsgmxmiRnof6mld5Qq+FKpPGrHw3S9nPgZm6F6gHUEk8Lt1Cuz7LJXEqpsOqyguLqt5sa1mDGw+m+vGK4vx1vN6M11UzOKxt5FpN1WDSwma/eZ6Ph67/qyw7KsTYOTGUK+0oZZMKWtjjQYMdFqgQqPoyBMxXB2gXwfr1iaQeDmdzRtxQPlu30sG1jDBy25pykNA6MyN0M1zXokq5RtaJn1XbgE/RqcVuehm0RKkY+aTpPc0WcgzXx1SWNglgeI/dkR5WjwBjsNvF3NurHqRWUS6mNVopXDfDZwYzmhGQvk97jxnnwn63wlKym2P523oNrgcyiZxfITNxSBp/H4KAJzE1LSQWlaYCotTyTZizj+pBZgMEKcTEPhBcNM0G0AqVZAIU1wzEmrnkxNs0FIWsk2nSDI03X3aI7ScpyoEgLq7r6N6Z0FpXjC+FS/4gwcw82eYUfdsxGTJrhV5ekvsejVHTmcjmYviFWl4Co/EnTslAJwT5XR2e0+008nXfztBb23K/zon/IaAO7ZdrS2Qhq4H409qzQ2FuDbpU/ZR9nxtSLGfkK5aqJVL01qZvpIkyZPaUSwnT/RYzC6Q7Fe1TM2Gl9RKqjsEUHlVKCEzZT9GFE7P3yDxWXqwZVAjYsZfrcyVDm6pjSIaGfC3l277j1/ngH/7kwz4HQzJnTSofHrHL0Ns/+0timH+R8pH5tFd7dbF63IBIi/V4Aoe+mlqcjr3qxjfF9B1N2RF/jtmqsDsmh9JoFJjIAr81oXVmimt1ma1ZWsSp78EV1GSyu6mqq5Z4xemGgoYSG/JCNsxJIk3eYkV/ktovScAXJYvmVAx0tdVQKKpBhk8N2iPT1BCNszUNaOQzMXFcnBIprXFt27CRaoAnZAnBiLeRKl3jowTIoqvlvhvqfV1mgaGhYuOqFT5LhOFUy+oq35jfBRR0M/GLWDRMfYJULZWpo7f/7Hd/bNfvX/Y33opS0ZhSibdQMOIpBHKcJiMBiWyU7XujME4YPt5oyp7CZTT+RMFC0Y/+SCUQhIGUTMXnwKXyWL5WeUW9unLiE1Z1RGoEgBPvkdlqwmxNTKGzmTwWCpKdCjspJfbaOUnLaCXQazXgjyqqaJCr0U6ikBsGXr2pgjgWSyR1rEkHDNI4xQSjUJYo1Z43pnUTmabShCVJnCD68/1A6k0hEKI0TinxLWho9c7tOsfVufcRK9QTwpgKI081ND5E48Mp6XbAzgLLd8REqcKz+qUxZT8lOznE9VtMrhjQnRyEssSf3YhGm2KWrddWqS7bx2x/RufdX0CZGergfnkvYjKU295BTSboQR8Vaf+hKCIYtjBlraq40Yyvp560xolmmEzlNqfOYKtVgjUcfueQ4vCA+69rU8dGexOTfCJ46Y1MHWerAjQlZ8eYaUY40BJgc8HksvZiEHPYQCODC0F86zTYrp4PQNR8Uvq11KuvfyMhM6hUN9G/D9Xo9qHUuWyMet19JEGlB0tdezzWotH3jc/4OZFxaUkq8qnGp2nD+jDRUJvUyvXGa3w3Q2+XczZDrwvTmaQCrrVoN8yKGrhxoOJF0Xkxvg1+fq1a9BhykuiknJak2uhbWMvg3aCNzisJ4qircnMpTrHAtrQGTCpshSSyGysva3HN8DXRF9HNr2F0RILfDKqsOX8tV35X4Af4uedT7SlTvy6jG8boLhBL1X1J3lxva1+bJ1K945O/wI3PkI3muf5GN7zg9YTMSDBCW9hKLjNUHY1LFckkgjVWi/m7NcKSM/NhCwjDIp0WjUlwOUhlM98xVFlCulNhp2GB/SYDRt9rNUEvTSohyNfp3IQdpQgrfap+hh3m4u1ZH1OlyCPNVI4F12sJGFsDSF64CKp0mOEUVHvOrCscqgwN61cXTgykR5HxF4IcO7V3zXgqQwxozPPFpTuRwWpMR1aLDPdFAKn+6pys+TWrLnoUNmmFiy2nDwIu1dJ1aDyMQpQAqtgDqJYY928/SZFfmbP8ZxnZNlSthHTo6d4/o73RonPfhKqfMj3Upre+KkqBM+tzxUJiMSsHIcsYPnMf3WNjuONeuPQgSmumL38y7Q/eKutv3COoKBlV2yW6rBpgSvkI9C71xRuo3YK8FLltXgoAPp5AIeBZOi0ImaW9OSE722J0ZY+qredScyXgn0+NSNkm4vGUjBV6VqKnIhP2ti3XlUQ3g2IJ1pC0PrHKEHNrEECVEJpQDIg9Zvm1QT6vuvFNqMw0zDydu13S0K+1FhMOQQgJQGPp8urXvPERe6zHYjlUrbl5VO77sV4qnEsPfozWC1/4wvDJT37yG/00vmnqVTe+SYyMW6ILzpcV2y+ZkbQq3NEuyVASqyZPyQmF4Yr/JX3X1tUWXcomPB15lj92v0z8J1OZ2AcPzoveGhoJS9N4xiYsFAXKWlmUkgW6+aJHUJY0LJ/QbYk5Z1E1RoIhs1QrHWne8qoxX8xX0yZqFIimx6JrrxOVaiqqHVcie6qfV5xALEamz2USC9TlGpjySLOgVNPgutWuGAF3bMNESTdm0sTH1zg73CMZSiJcUDRSw7qeiJsjkI0twPaVGdmOZ+tJmmI5UC3Np8R6YtCFonVG5D4SFy8m2KYIMUULVCUA03Q/ZNs0ZpfZTkzVipI3OxUKsjc0KRytzWjsbWTaUvakuUpGEg9sJx5vFZNDluk+hapET68rSMae7rGRNK/DmHYWfXiaY7ueiC/6YdWbp7KcN3xEdpCOyWj17RarZhGlKbWBbD1p3+UFEmUvWAuzXDyPFproWrrWSLvq24cwB4AbacvCtLme4EfD0hC/xhvGLwrVbqNWlqkOL2NGOUFrdp46oH/nuDHq19sT2BlSXX0E9ckvwfOfir71KG40pvFcqt8TwFxyCCqHO3kKlG7eKxafQ2QTSEO8cB+JlaSmyw/jU4vZHInUr054q19v7f206JGiFOqKSxk9ZYWiL5PHsqtob3g6JwqqtmG2T46NldtKOredAaUojyzL9amW0sYyM98AS0HL/+2kwgwLQmKYXNZpUoFMIWkyZc80zIdzza8fqBbBGx2vbe99108/5L9/pEsp9akQwgsfym331vHdVYNItYzItS35isXmgbKjxUcsBKYHElwK3eMV2WaOy4zIjrRCTwuqQUsYyQPD4NMnYTiae6XAfJ1umJBxvay9D63ZzfipAWsQ+TqA1pQH+xTLKdl6LmDScBZBKiX3W3u1JQnV4WWZ8icaPSll0JNaYRzFQU8tKRdJ5oLxtZbnFFI7D9VYlO8uruMQY7kjMOZ8YzgeVvriwbiADZmRbISD1ZT7uphJicpd8/yCVg0b55HczD3W6qZrf4rycGSeaDGILntGfN6A1roANXZSYrbFXzMkBt9K4+fpCS2La1nSO0/hDq1izmzJtXxZGCbN8bktsmVaGb6V4rsp+VqKncgw0Ntocpw7ktNDAWjq9RTAWorLVwFIzsT1aJbP03m9l4FMvbZokUkGo1Db4/k6XjPSlJLHqM+VmmGXJvKzct4LNq+j9hlc7Alq43at5fyrB7bOxwRJL71FzazVet5rl5IeqLI0MoZjT15LVOv7XrReWDivd7HzotdhPQyqnn4Fo8taJJNA945N1l+0RvdERbolvkR6NIOtHaprjqD+7IsXX8e1DJt1t4NaHuDuP9n8XiW2WeeD9+heV9Jap7N5zwHyt90OtDKKS1fQhcee2dk1jAuj8TxoI8vmrxfZm7gnXYJPjSgTgHRzhh7NCFrj+xmzg23ax0bozZ2GmegODAjWMNuXzp9LGVNy6+2Mj15JuXhnVv2UfEU+33ToSNen5PvawiQCPvC/vjo2Ur2W1+s4fOPW8q9mHX+8lFLqF3/n3678/Kte03pU7v/975nxQ9+/+cshhJ9/VB7g61CPSybSXu1mY73w772NqgX7Voe88sid/LF+Ovk9ffxSxYEDOwB4s0oydQyOOfIlMZgrepry8n2iAe+ksDZA3XNCNlhpKk1onOqFqlpYkGRzGgBm8XobDT+BuYfB4tQvBAGQiqqRnagQsFtT3KAdQR1hprgsytbidL9OV9KVxG0HLcCNdgFdSjySqhlCKpKFnEwUAEmliylhehoBLKvn4FGUyynrCDrFtUQiGKwieAEqitXWbibYTW+OSQ8B35JEptpPR5/jEfBErY/8vsSePunNN6O6FcEpGFuCDaippK5VGlCRjRSIaVhxI+4khj3dUWRbnqKnQYtPjSl883n7ROjM+ZIhGGht1Pr3gO/K1LToSUqabBAU3sqksU588yZ6LkWTRZ9atFKoutFb0Oc3DWW9cVoAJmjkI6HZXAWtoFiYooaFqeFCA9g0fs3tNAGRs6lalrFw27BI5a9Nh+vn4b1MAM8Ru5/LQAqLBvJagdeI2fy5twviT1FV2LMjKArCviUGd4zQ43zOxoubOP3pWwnBo289im+kL/PGs24i3X3Hd/2O4AmlyOoaeV1kH+3yboqvU3XauHaCTw3m+EymlWkq709VzaU8OJROm88s+IC69zh97zn56gMi15gEsk2HGZfYnRnadcgHKVVb45e7MU6+Bqvl8au2sJZMFifzY5FmJKNKDFu9l89xwXwz6Ah2PwKkh28keLRXX3u9/YvzKfH1L/sVqq4hX9asX6ZIhrDv8xVBSSpUlUG2pbFTS9UxhEENrqRULU26U0ngwDMP0r0jhZNn5I5rgKbe0C34ilFWwjQkm7OSvJfzJgLHVJUMmsoKXXqSUSWM4CrKwGpPpiQOjVoZoZVgRjnFga6EG+RVvF7LdVNp3YQmACLRg3kgR2SyCKNJrg1eCyNGhSAAf+kgMghx0l8EpYTtkSWRPSPXhoYR4wO3fOlXm4d9zXW/RtAa7UtwAoLUJsrBaK5/+a/wrg//k0f8c3+s1Xve/7O86sY3CahjLMlQNvRmJilfIOxxNS0w0wLfa8f1QjdroRnNza/VVFilfm1ANciwVtbCEL0tvVXoXPyWlAq4tmW6Ysg2FbOnrwHQuXcizKFcWHk+M8Ik00iqYA0qZul8KFH3Zh4BbJyKbODaMyhKJLUSFlEN1kAzvGlM4n2IbN8gQA/Ex9z9+yZRMAQBsqoyDnfkBJDe2jdrf3BuDmbVpVQzVGoGTfXva2CsBnO13g0e1eWDeCg5gy7ktq0zM0IrpX9vgd0WuaJYOETm1Se/9MDruA+Aw49GMBo1v1dayTUkSuNVYvHDUcOoatZ2IsDdaZNfsULVMWRnFt7jqiLkhTx2HHBp51DtNqEomvXd3Jeh9i0TljIxpo5SRRUqNJC0ot1AzXLSwq70qXw23qooMw+NgXwyEc/ZZh1Po71F7NmCEksRFfia5Wd76/hefbPWHoj0BKpBa8aHT13F//2sP+HPr7mMRDmOTVf5ytZ+zMyDEoBjtqrYebIjWZsxurzL4O5AuuNJdxytzQH+9Jn5JjhOL5uo0LqieW8oJG60mTg4B6Px3EcpLn5shGaCotqthnqupgV2Zyy+MaHP6Moe0zVJw9CVmBibPG7EIqvEJ1GCgsK1ddz8R6bJTtFMOmUDL8CDT818yhnZR+VAFn5d+Ng0WoLRosk3F6chXv/yXyEJYLanAoQZkeG4TipsBK14zat/7RGVljxWql4Mn/9DN/P8H7qZqg2tBEYHNJhAdlbYFyYXU2MBBQGFULS9Ih2GeKwKW661Lhvz1rpsRNLNmQBE0fCy6llcZHm4TDG6xFK1I9tuRxpSO4tUYSeRrcWSwc7E66Z3LFCszNklZiZ+CXpazuny0T+IxM5p9bVsojb/bMzK5ikyzcQwhN1MH4hTST2f7C1KzhY8k5Qxc2AqNomhKJpGMdQpZgsTQlLmFHZjBCw6l5V6rrFubO7OrXraGMqKcGZdDK3LCk6flde4kDAXjhxAVR2Uc2hjqDY254a+sZoGctFI3BhCVRK8bn6njPwuLDTEdeOq2y1UjCQ3x05j4pQ2BI/fGZ0HOAUfms2zihNZdfggZ1+6n3QY6J4oyG4/JZNPYyBNaQ1nrPoVNp6SsfnkJVrrkI7lGlJ2DMnEU/S1GHIHAZGrjhYgqXBC39cS02tn8nzKKAOp9bkqHo8PtR5pU+u9+uapd33kn/Btf+XXyQfizza60pPupJGdCckY+nfsoEZTknbK6Mkr7Fxu8AlUXVi6M0F5GB22eLtK99j9qCQhTKcEY4R4O53GDetupmFjyIsAzTWTMozG8/PUaPSpM+javyRNCAdW5wbdRSGJSmmCW+pQLCUML0vQVaBz2tI5uo2OABCVb8ChWnou0hIr7OHKCzvIRF+muI4Hq/DRv0bFoA0X/Wd07sS7xsg10rcTXDs+10W/pFivfckvYSsvMeVR/moqL8+l/cTzOTy3brn9zbz6+jdiJhWvvv6N2EK81yoXMDOH3c5F6j0tJE00AuvNwDCuObbyVFcdwp7cihLGGMxiDap0JOsTfCdt4szNTo5bzsjOTKiWRD7cuXubjgviLTgS5lB59UFmh1bJ1nPsmSGtLx2fP3mjowdmZKU1gIuer9nVAptnMc2vHpjW67o1kSVXChvI+/lQtT5/aj8+aEBSiOdV/djGwGwmzD+t5lLzGtSthyXM11yVCDMOV/ezczVAM9w6lxW12G/UtdBL4Bz6tmOs3J00j53WrKF4brtL9mO8R1WVrONnN847Pprn2hh7e3S7jZ/l8zW+3heU1ZxJDOjECoNpbYWwOiBkCWZSkd293jAa/WhMqP21FsqXFZTD5n5U2qZ40iHy1YTeVzYlkCAGcmAMKi9Ivac4NGC6P6V9ppAQjMRQdY0ASG2RXupSfDuDkYmwnck6rioxEgewM0/V0ri2ibcjDqHOv8ZcrPbW8W+e8o+inM0/DuRseyDSE6A++a9+rPn+GX/4C/zpzrVct3Qrp8olVu2YqzpnuWXtlSzfukOx2sZO4fJrT3HT4S/yb9VLKc708NaQbc7FvipNpYFbTG1jYeFIFuRrNbOiXkycF2+WuCluAKZFI+E0ETpxYghpl2KlxfozUlwmF2RdRu+bSuHtnLHhY+y6jjKoom/QZSAZOWGypAalJFGmec61AXIpjanvJJSDlGBVY7bsE03ZS6g6AmDVcbcf+u8/ySu+8y3YiWvS1/SkFFPGBXox2ovXk7K7jCmfyDVnk4HeShqK8GJymopJWfj5z5WH1mY0wnbiV2MK3/i+1FNok5foqUH5DHpWJkJGM1tRlF1hNwVDM11Kpp6yo/FGWCDTNU37/hKMItmpxLA9SiPrjY1KkkiFT5ro3nqKSllJg9ntNBNWvBcDS2MI2zsL0/3zmTT1FA9oPIgWJ3TCCoLAXBrXmHHXHgm77ivejxIpSqib0hqQOlemtvB8Lsj0aX6nUdrvApgEHIvfR/NsAO48RlAK3e9BK0Nt7wiQ1Uwtd5duZagsw0+nKBJCVSLpcP785xST42omU+2tEqJRecjzBebRue/z7lJpih+0CQp69+WkR9cl6a2WzgCQkmzl9E5YttrCUCh6iu5Jx9KXd9DTKGeNcdPTgwl2KlI1FQLBWnwnwXVsI+v5yO//OC/+O29rnsdHf+/hydieqL6Aj/f64P8UOcQz/vHNJFuarddMSG7tNCmUajQlbGyBVvS0RvklTj/f4FK5bvbunTE9kDX3F2Y5qtORqX2+YOC7WEY3gRoNO1LVF+OFtaxe171vjHNrWbpI1S2u12J2IGN0xOITolRZMVsxKDcg3SwgtZjJwqY7spKpnDBK0hS0pG4FrZtU1prFpAsB+X2W4DODaxl05VFTT0gtrpPga8ZSZCDVbKIbXvgL3PDCX+Adn/wFke0MZ3PZktHiB6XETFoFN9+YP4GrZq2owhESTbYRMJNKPjMXohRSiTm8B6xGTXIBifIKdXoDdXANfMAdWhEj3yBDDj2WY7JOSstXW6jK075DBhSqm4rcsF5zI4hDIRKjdBu2n9Rh2XnMfdM5OFQDQjUbqFk//ZytUw+IYD7sdOesgcsDkafPBLRo5OONt2G8fbXwd0rPQy+UJiww/JSLtge1fxc0LKiwOBQKCmXiz2YCHKnEzo/HRVln7L/nPwu7egMgDjl0XFqjVK4GwwCKUkZgcR3Xk6kYi0eZmdrcPu8+5eWpZghmDh6ANEWdPjN/XfVziet53ScEH9AtUS+oGB5gN0fy+RYFbv3ioNXiY6s0RS0vEayifWKG2tiRPUstdWtl8lnPcuzOjDSLrMbotZqt5yK1NTJwVs4xunrQgEK68ARrwQRcRwzmVen58P/6SV7+199KPQz60H9/eOEYe+v4Xn2z1x6I9ASsQ+kO39o6wUZ6kv+8/ULee+rJLH95SLCa7P4dBmaJxFR8R/9z/KUX/znf+6Ef58A7jwm1fTRu6LdKaaHfKoWvPWFAFoSioPEwAVRiGvNelZiGhdEAS8agOh2wltBv47oZxXLK1pOSJqpzfFkgW1diZDuCYgCzVYVyqjFL1jG9i6AaBku6A7qUi3nVSWPqkscUvjHMTUYVulzQYVcejwAKri3GncFKk1p25b59IlLBzChma5Z05Pm27/h12ue836GdNuajpqig/+joax9L9enfeR0v+MGbqTqSytY+qTCS4o5rg8+gSAQQLPoRODoDNof+fRXJsMRujKXZcdI0SqNv5nKvLEVlGdVyC5dphpcZqrh/6p4QtloylmjWfAlKpemdqLBD8cvo3ts4RxOswrUsrdPTeYJgCIRWBDoTkUT4NCWsdCQZsGUYXiJpHekw0LtnjJ4U0kDHCGyFgCyL4E3DsFloRIOjAXZCc5jGSafjPOZAw14K54A7NTC1CBDFBncXQMVCw8p88rkLgFn8v7XomqEQQaHGq6gq8bW5ZFmhtMLXkjGt0L1BvH7Mz7+alehn8b2qf24TYSpWFQrk9/XrZd5o4xyuSaN5eLowvT1h//uGhJ0d/Hg30zIUBbrXRQO9WYGZLlH2DEufOQWjMWGWy6Z7ZYmwMqDqGnQJrdMzdB4nl52UYnnut1D7JegyoMtAa73kNdf92nlmtufW3tTyiVnBgJtaVAcIsHR7wJ88LeyiWYE6doLeeMbZZx4hv7xgcjJl5dMjkjtOyGa63Rbwe3UgQMBkSjhzdoF9FK8VVQl2wa8lBAGToh9iKMvGb61JjLrsEEEpypU2ygc2nt5mtgL9+wKjSxTtM4F0KP5ykwNGBj5LBp9ktM4WuE6KjkC9Vioa1EqAgR7FtLlOGg2SHa6VUnUs2gXS7RmUsbeI3lBNgEInEUl7NU9qVT7wF175q5hRTn6gi64817/iDehz3+9Ou94Toic5IRVPpidyvfddPy1pgrF04bBnhuJXZTWhneBaCapwlCsZapBhhwWmqKIB/AQ6bQGBrMHce2aeyJWmjd+f6nYIIUO7gGtZwpFl7PaM5K5Tc2AIGuNud2QVc/cJDLCyc0Dk1ErFAAojwE8h63xjvWAMSlsxe4a556AGuh3cUmTQDmfiEWqMMFrq476RnUUGMuZ8dnFYmEBGGXXNoAXwi+vtRaoBWmo2VH2+XgDEaSqugdK7zxm7818HCCWq20ZbS8iL+ZA3SeQ9KgpCEaVmdX8fQbN6HQ/TGb4omvW7CbkIHnfqdP0C5PaHDkh/MJ4QJhMW13+CF1bkLEdtbkWwzZ/3Gi80ADqvWhnZrcdFHrm1vZvp7Jx4KCmF3hjSmlXMLhtgck965yn5+zwXj6Qj+8j3d7Bjhyk9ycZUDLg7KWUvwaVz9jAg6ZkOzMzx6uvf+KCStL11/Juz9phID1xP7BXwCVhf/I5f4PfveBEeKIPmeZ17+H/vfxlPG2/jltqElmW633L8I5fzF4/9CHe99t9Q9uVvawQ/LKRDhFm+YMardl/k44Z3l9EfEKxtoj9rDfci5VVNcsJyGztztDcsG09V2CmoUr7qQtZhO6MBj6qeMFeCFuDBZcT4TTC5ouromNAltzeFJpnMN5d2PGcpNT41AArxM4LGPsYnIpsTzyOYrYgcyhvxzJle1msmxgA3Pvcx65n2qNan/sXrmu+f+w9vbszSg4HpQY+ZKexEkW0IqFT15N/SnQE9LSQBJaaXNRr/dqth1oReG99OmK0lFH1NviKfYbaJTDqdfJWNhIIAOhfmGWHBYDUx2FGJ8uK9oAvXePwo72NSoCHEFL+gwLViykwHQI6nZH8bO0pItICrqpaHaUlVknPHxYmileblHO8hlN51vjXgSd0Y1aaVjXz0wu/9xZqvi8nVlE12y8YSG9lOC01tUUSgOD7nPH/Qx/FlhRoOhbqeWKGi1z9feH27PJEmk3m08SNci6+FU2fm8sT6sZv3WREmUwkP8I7O3V7SHze35H5amUylrcV3U0K8Nigv03nfstGjQ0DMD/yxpBm+/K+/Faz4b6kHaZAv1HTuTS6fGPXFN72OK3/nLQzWxmx7hdm2dE/ETawWBp5qZYROxtqXHKfbqQRRTGdyTHc7MJ4QpjPUlngMuVPCEFCG3Zve+mt9jVpgUahWJv8WPdnSBKbil6i7KWU/obXuMbkMYMRjTuE7IsEbHC2a88BESVRI9K5Ya0Dkw3XvAajaJye1kpSklcS0h9CAO7qUgATlgzA8QkA7UIUT5oAxmJmk3oWlFsrLOq4SjU8y3vPJX2gev04o26vdtZgmeMOLflFAf6Nh0GV2sEPZNbTPFqQbM0nbLSp8ryXfhyDskmi4Hqazht2uWlkjHXZrvSZ2Xec+prRp0u1RlJzFfm3QJ1iD2ZxIH2AN+vSGHO9ZKpKwSZSMRRa8eBJGL8zo9dVUZCyFVKR1qpaUJ3YuE4PzGHyhFAsHldgo/ZyfM/JFEeL5pLTfvU4rfb4UDNgFsux6sIcwJInAjUqlAQ5FIf/PhInjx5P53RWl2FPUa24CbuH3uyqCZvU6HnzYtY5f8PkFT/BaAO92m1CWFweDmgHaxZnQD1ShKODk6bl1hhJPxxogV4kVu41U/NrUeELrflDDiVwr2y1JEwwBH6WvJvcycPaAErlsUApdhiYw52V/461oo3AGzEXeusXaW8v36rFaeyDSE7BWzYifP/FaXtS/m47OGXx2Tj0/9dJlAC5794yzz27xsnf+EFd8+gyhpqvGJCO9KrdT1uInE2qdd738+sVFaMEMt9ngLpgCKmuh0xGjwrKCyZRkOIJeFzPpkw/6uBZkWwIC9e+r0A7sqGK2L43AkKZ7yqNLGB/SKAezNSj7ULVBOYlpnx4AU0DrLJQ9Q/9YiR1HU0+jIrAgm72yZxpgaDEdoWqLgXe2HbBTifr0FlxLocfnL4Zv/+wvPdIf4eOyhlcIsNN/zjrfcemX+Nj6ldz92UtZPi5pbMkkMDmkSIYlelJQPOkgVdfixTs9brzBTB3JqOLuv9LBTqBYDvilCnKNLhRVR2HHAlC11hWmCAImKSiWLD7TtE5O5DioPGomka/BaoxYY8gmJU6ydVFBUVGuthupoh1XnHhJi6oD3eNyLLrU0l7X9KclunSoJJFzJjaaajGdLQSZji4yhmqGT/3/RfPJBvRcmEoqvYvZtFgPaYIX77eZKjYAsWueizIGnvEk9KQgnFkXFs4i7f4C97fwJBaYVY6wMCFtXlea7pKgNc32xaauC+wr9TBwpvqxax8p1UgB/Pz519c1pQizmbwHlUNtSvJOKEpCXognTDRUlaRIJ8bt/bSJskbRAEgv+dtvxfXkddtZYHwko3v8fM+HvYZzrwD+xks/zrt/+2WsWNi5NtC57UwTMqZ7XVhZEnNYI8mUBz8lCVmMJWnVT6foI4eo9vXxmSEdT/GTCbrTERC+rBoJaPBBJOo1gGSMHPdGNya7mMhOsgZ2hpAkmNuOYY2hdWAVt9Rh/VkdWusBUwSSsSc7m4NWFEsJZibT/TptbXxVXxJXJ17Af+9lyKPBdzPxKNmZYmYCLtnbR5Bl+APLMX1VQQEa8TUJ0UDbdayEZWUGn2p8qjEzOTfzJUvdxGRb1a73e9HkfK8uXO/4xOsFSAqBMy9YYvspATtSHPhMQnr/NiqzzVoKCHijtRxXVSWJXUlvnmaWF1RXHmR6IKPoayYHFNoZWuuBZKzR5T7MzhS1E1nJZYVf6VAOMpLVroQ6xIGNWt8m5Pkc8KwlTUrLWuucgD/tVmSxpaA17pI1VOnQW2O5nms1Z+HNZiymFTfrRGLlceA8qXotR1v8/6KMq5ac1z6AyiZzZnGscwe2u1nEF6l6HV9cTyeTZiikLzmMH7TRGzG5MSaehtoQe7HOZT8tMKR3AUjn/s0CKO3LAOXwAZ/3ruHYBe9S7V6zF37ePKZSc5nhIiMrsqVl/a7icYj4n7Xb8vuikCCAfld6Mq0wsxJVenxHzPk55/m/9Hveis8UPihMHshXE7KNknPruhvetHgYNLW3ln9zlTg+PEpMpIfWhn9T1x6I9ASsa5Itzsx6/MClJ3jaR76XK/5UpGxVP6V7ytM+NWN6sIUuINsW1kWjjSZOiOoJfZrARKYXqtdtDASlwaw3Sgq1aNa5GDWqAqo2OWy3wOcyiVLiPWASy/IdGcWSRZdioJxuFsIGMZrpqsan0SR526E8ZFuK8SGFa8nPXQZlD6oOlAOPqwREMAX4VFPaBDtyKC+6Z9eyTPcnDdvIxkmCtwrXkrj3pIBsSzxy6qaz7Go++IdfXYTnXkl99p++jiv/xa+DU0zzhMPpFk9ZOs09l6zCbV2qLlQdhSrBjAuqlS7TfSlVR5EvKVqbsinRhWdyMEEdSKgOFPgdi08DplPiSPBawM4qyORaF5CMRQ4pgKCYguo6KaYGOyshnvpOKsBS5aVhfoFMYU+/eIViAKtfkfNEl57pYY/vOop9GroV3S9lJBOFnlUCGNXRwNZGBpyeGz9zYRDm3J/tah4XpWyc22DpXY3oeZK5hduf93hxCrjY6AKY5WXCkf3kax1ak6J53uc9rws974uAQDX41Tzvoth1H7qe7F5Aqie1INWLPg8PqcGu3wNjUKmAew1wtvgYtenpoheKnst6a+mdardkwjtoUfasyGdzF5Mdo3dL5XclOtZliiCpgIXn3R+YR4i/+jVvFFPhmD4Jew3nE7muaZ3mTzc9G0/VrH0W/MnTzcaILIOdEZQl2dYyg6Mpw8tbrJ5uiRm2D+h9q8yuXJV1zipSrfCzHH35JXLMnzwtx2lL5B5oPd+MEUFtv7BRXkyABDHsnuUCOG2NsJWntd7CFJ5k6Ei2puJrlCWkIWBGufjCAChFtlXiEx1ZoLVsVjyQXFs89GwIYnwdAiwNBLyKAzFVeUJLi5ddqJO9oOwabJ3UWkjCV53IanMxxQUueG7u1YNXsSJy/WQqwxldRuZ2ZqlWOuI1lVeY4UyAmrKcg49JIoOIWiKZJti7T9Kp9pP2U7aenMV0PEXVNgTTIutashBQ4xx3cAk9mtE6udkwlFUdYtEAPQJ4qoXtz9t3/i03Dr4ffXAfft8S6tRm06NODrcxM4/tZ7hMk63PUJMCZTRqOiO4Yvdx7wPKqnn6Jw++Hta1OKTYlVIWfQd3/e1i8MSDrW/BX3TwYlaWqJ50CfkgJTs5Jqxv7E48vZDEngv8f/Gx4nNbZCWd+/wfyvux+24vvo6f54dkjPghDfqE8aTxJ20etx6Axe/rPgylZE+Tx5TdTicmOVq5Psyc+Bda2SfUxtk1A2mxdCESWjv1vPc9cylbAx4tvJS9dXyvHqu1ByI9AetK2+e/P+mdAIQv9wnpBHV8SLY1Iut1wHmSW+9lsDzg2Hcd5shmC9vpEPJcFoSiEFps5Qg7O5E5EWUmLCwSuN2yk3MlKGrhAu49xIu9ardk85al8lyGBcoFki3Rqhf7OpS9hHzVsvX0gCrhwKcDVVtjpwI0mRx6x2RDZkoYHxBZlB1pyj6oSpqboq8p24psKA1m1VLky9Jg2pkwXBqqfR6wk8C5seh1ZRtVwxLYWxR21+/e/jL6ekpLl8x8wthn/O/Xfozv+7O/y+9+y7/mmre+DZVqVKUY/EGf/zT5i9z3HRWvfOrtfPAVT0KfyujfrdAVHL9uhfFlgWd+y128au0rAPyzz38bH33Fb/PbGy/g3/zpt6FzjT0lLJDuPaBdB5cIkFh1oVqucH1FcSCQnbL0jwa6Jyvax7ZRhWuo6qGVyOZJazFBblmSnTk75B2fEj+IK//ZW6FfMnoWXP7fDO95789w1W+9FYCXP+82zs663Da5lLJv6J7okZ1U6Kra3XyurUBiMPefxm3v7AJ1LmY8DYtNWWz46sml/JIm0Ywa1BUZSu0HQWIJ46k0Vhcy0LYWP52hIxVet1uglTAQv3KU5FaHX2juzMEDuMv2Y87siCHnaCyeR8Gjez0wGr+9I0+v7msXZXvnso4WytegkpKGsnlfFie159xX7clw8fctmoV2OrKZPMeUnPq9q5Px6nQdraNnhoZpZHlYg7IGOm38oE3ZT+M1xFMMElw7NtdV4P237PZI+Nh/+HFe9t1vJduq0IUHBa959a9JKmCU+MgTh/fvbXCf8PVHp5/N/dd7XvKM2znz81eie13xeXEOv7GJ0ho/y8k+cTv7di5n82k9Jlev0NkZ4bd3CGc3aGnD5BkHsWMnvmZpij92fzSzn0UWYjTt9QtTfJiv51oJC6mW+aSybqvahD7L8PuWqVZaMgTarkjvOAlAWO6Jd45R+HYiRsoeVFmS3r8t7KHt8Vy23Mrw+/qgoGoJiFBc0SMoGHxpA9IUPZwREku51kYXjmI1Y7oqz7W15dCFpGyGZBFg1o189CO//+O85rpfe0h+ZE+0uvK330Kyb4Y2AvZVMfW22hazwaM/8JO4tiYfGFY/epLVd45wlx9iclkH382wZ8eoyVTWV63x+5fw7SW2rxEnyWzHc/r5hqU7AqYM9O8YRx+inGxzwjX/fAbdNqOnrpEPNMPLNKNLUuzV++iccrRPzcSIOS8ENFpgyTXHrQ+S/BdT1W458zuAAEk3Xfk61p8zAAbsf/vd3HLsN3jFd74FgLPPajG+BIJJ6B5XZJuB5a90MaMcdfd9DegD0KQPN+vIfA26GJv4vHW8CaI5J/L+HCClZuvOJebnM5SCDwRf7f7byPx1m9uoT2yTBg9p2jABdSvDPfMa0Ap731koK9yZdfldr4tKLG5jUwY15xiH10wpvzgEOley5+Z+UBdcxxdew4Ve94VKGYMe9FCDgfzAO1SnjbJ27sGaxmTFOiSgZpbpKJ+d5dBpE2xPQOvEzJlzAYq1FlVb4zJFMvbnpad+9D/+OK/4rreQrde9iuI11/2aPJ1Un8c+2tsrfHPXnifSA9cDgkhKqf/5EO5jI4TwfzwyT2evvp71oRnoHHReEXZi8tD2TtMMqp0hhz62LCwia6CI0dftVpNwIZTbeXrEordR8FqApAX5x65qpgF6vqGNoJJqtZqo9BrxF+Nii7finWAnns59BpNDulVhcg8a0qGj6FrScWgAINcSX53pwYDOFZ3TgBd5nE9htqzxVhhLPibZ2Bg8V/Q1n/6d1/Hyv/YW8WKaeqq25k//YHfiwp4x3vn1h3c9l2U94dq0Yk3LG5qowIenV/Af73gx8Ey+40//Ic98MWgV+NyxSzj7vBatUwaGisvbG+hTGa3TitZWQBeB499esbxvxD3bK7AGv/nx19A6mvKq7Ad4/qH7WLtik/XTA5Jjc9PiA5+ccPJbOoAAjGpDkjTKtZJ8n2PpDi2sMq3BBOpLY7CWkBiqfopPxNvovQseEHUd/b9+fP6fvyNf7v6R+c9u+MA/Il032DFMDlhc1qPVTWXSvjOTiXxq8VmC7XXQzuGHw4UG8IEndU2DWXshLXgqCJtooTEzWqj2Ok7d0hS1f00a++1hTDPzsiEFVJJgu50G+FGH9sPOCL8zQqVJAyqFmolUVZiTm/PHWupjOm3c6bO4HUmxMb2ugFALVYNUviguSl+PL3bXa6x/djFq+wNR4Rf/Fq0bM9H5bebvWx1z3mwQVNw81yyJJBFafJLg1noEo9GV+GsFqxrPLxU4j7H4yr/86+ADaQSczLTEZ5agwFst7CXkby809dyrJ16dmfRorcpgRecysAnTmBJVmwUHT5jl6C/dzdrkEtRwIuex87KOWxN96JQAS5UwQEI0x6W+j0WvmDq9igVZSDwfQq8dmceJMCx7XfkbqyXhtKNoKebnmEe8ZrRGT0vUrJybEyuNKmNUeilmxSFLxYskCBBrR6UY2frordPKcMtd8ZuzCqctZuIggkgf/MOf5OV/7S3YPF4ftOL9f7zHHn4odc3vvwGzCiv9CYUzHO7vcM/GCrOJ+G2ZseHKf/4WkpdaYfhODtA92m4klWpWCaOsZuEOevh2uusx2vdPODLJmK1ZkrGnWM2E+RMCt9z2Rm46+A/AOTpHU1rdlKrVwSdQdmFy0NC91+GXOvh9fezJrZjYVsyP2brPjFKzW078s12Pf8vRm8973Yt93pPedDP9e5SwlgOU/QSfaJLqCHpnFGV5rkk7Dc26ez57tgZPYPdatijFXlzLdhlCM2f2sMjQMUbkeVXZ/H3ztV7napbQUh+WBoSTp4W51xEgL+QFwXmC99jT2/G9M9BNMVrAt+rY/YiZdm+XnxJKCxAdjbcvuo4vgEUPto4/GHi0+B6apcECSKTkuqEUtFuieKjX7ZqV5jygGjApaCPbeqMJnZSqm6B8QBeSxOYyg09VY52w6H1a1/WveAPtyGzU0wrftg1wtAgg7YFHe/V4qAdjIj0N+HsP8HsF/LMH+P1efZOVP/nk5vt3D5/BoY8X+MRg11YIWzsiH4l+IAGwH78VgNBtE5wTL6ROW9IpnJMphjHsSlZakM8Er8VLwYfGeDM0t9OymCdWFimQRTgESBL8SldMjbdmTYSvnhW0jrsYz9ume1IMspWXBK16mtja8pQdxc6VGpdBugkoGNyhQNEYcM9WFcHIkwo1UUrJxf4z/8/rLvo+fvzf/dh5P9tbFC5cGs/nZ5fx3o2n8dl3PYVsC2wMQNmKngkrtwWm+xT9AFUGIQG9kuODwrU94yugf3/0GzqVMjq1Su8ZG7zuae9kn30Jv3z/d3Pot9t85tnP4NDHpyQ/u8XZUwcwOVz6/78NgEvvNNzxo09CeSjXKsg87btSyr6YrYqhdsBn0oT4TkKxmjVstWJw8Wbmqv/wa9z9t3+Gq/6psI/u/oc/vuv373jlb/DkD9+MmULZU5RdS9XRtNYrppf0KHua3rEZZmdKfvUB7GQF9fk7JHb7AnVeg1U3bHXTWEu56qlkDc7WMtOaORCb6fzSJTaeknHoP90qvzMadeUllCttQqKxwwL9FfGt8Pfch7IWc+SgTO0GfQgB0+lANEZVLsYDOzc35VxoKN1wuPsFBY/PZxd9f8+97QV/7HZ/bZhXF34DF76NksLDBySh6uxG/Pk5AFIIYqS9NIAsJbRTfJbMpY/RS8GvdHGdBDOtsNszQmoJSmGAstviT//bbvD5ld/+ZvF3cQFVBQHtlULnFT41oMVDxqV6T2KzVwD86Gf+FifvfgFmJefPb3kKVx4/DlmG7nXxo/FuIDV4STH88l27NmSq3YKypHv7Bv7ofTLUyTKRpjvfDImEkSSMooZZUcvRnUNhoN0i9NqUy2JOrXotMVBOLa5rsaOSdH1KenqCHs92+YjMjvRIN3JUUc3X/wUpcVgdgDGMr+yTjB0u1SgP6eZMZG0+UKfClQf7jC9p4a2s8z5eAj7xb+fr9Yf/608IaMs8EfHc2mMgnV/BKXxhmH58P0v3OPizHa4IG03SXxh0UGe3hInmBYwPnTY7z1xl61pN/06Rg+ud+X2aUY7rZfzZ7/4Yz//7N4NRtG49QXJoFVU6hk8eMD7cZ+1TJTdd/o/mVgejMeaKI7TXM3yiaJ8tcS3D7FAHOyxJj28zu3Y/raOb0G3LOtVu4bME35H13R47c95rfN4PC4jUPitWBecOCu/4x6/juT9yM50z0UNrxdI97lB5gT+wzPTSHt4ouvcMBSC953g8ps+Xjl+oav/BWnKFm6/1wTkIqgGY5ud3BERiEprevwatTHp0axi+4BIm+w2mCLQ2Pd27d1D3n8ZtbKGnM1S/J7cHAWCUQq9vCnt4LN6QIS/wo9Gua0pw4La3zzlIvo7r+EKqHYDKrKgXWhl+uSspekkCWTJnpIUQfV0FWKKswBp8V8yz0ZpQWZT3uE6Ci2batfzVJ2q+gTmnrrvhTQI4RW9VVcpQW08rfMsQjMYUHpfqvb3CY6iEiXRh9ckjcd+P9XowEOnnQggfeKAbKKV+8Wt9EkopA3wSuD+E8JeUUqvA7wNXAkeBvxFC2PxaH2evQB/6SgMk/eG/+TYOn91GTctzZGe6AXxqk0E/nQo9fntHmAdFiZ9Oz5d+qN0b212LZZzOzKOAoxGnlk1taCWowjb042qphUs16VaOnkVPphDQ4xlutUvZsxR9ha4URT/B5OKL461ifFAzujKgKvE0qhPbfBxSBAMqFePtOqnLtSQoKxg48sGCV934pl0X+w//190NxV49tDrplviNL72a4u4+3S1Y+bIknflMM1sVI9OVT6+z6j2zy5Yp+2Jofnqpxf/oPpuQyqr9sX9/PnAHcHd+gO/9jvfx3g+9HID9bzrKD+z7DL84/XbGZ7tMX3g17TvOEk6f5faffl0jM1PaU3UDruNJdwJ2J59vXFqWcpCSDzTT/ZrRZeGCaVnXvffHuefkGvZYmyv/5a+jK4NfLrnm99/And+9O83nKz8roOQzf+pmVAWd4zkqQHbfDtVah/ElLdKeJRnVHgI6mk2f44VwzvdyWyVNlw9zX5QQUJi5r4+RjZZKkwjweFRHQ2Ipu4ZiKd6XtYSDaxAC6amhGPFGULmmhlMKYyDkBWpzm1AUuNF44RmNedTqgSac9e8XvjYGwCDXlnO8IVSvi2q3paFMEkm1WkidlBtFFuagjz+4QlBi1OvaBpNZ8XNpJahxjt4Ykm4MueXutz201xPk/oOoasTcHwhG8+4P7iVC7dX59ZvP+z3++F03U4WM/Z+tZLNXOcLCeVFfE5qYbWgYAE1IRrtFOLNOqM5PSFJGC6BUnz+19LY2PY4Vgies9CgHGa5j8AbsxGNy3SRVqtJjQkBPc0JmCV3xzcEoqpbGtCwmjd5wZTWPbE8T1l+4Juu1E8ZUPc03UytG9YkA4m6lg3KB0SUaE/exJod9n9467/27GHi0Vw9Qp1t0zigOfXyMGeXCXgeR9RqDKlLCZEpY3xQAMk1he8hAQ/tMr2Gf3XL/b13w7tsbnlMv7nHkD0QyVRzoUPTlb8rVDulGlEBHhuw7/vxXePlfewumCJQ9Sz7QrL3vmBybrYx0Y0ZxeEByZizsHKsp9ncaN4J33fubzWNf87a3oQtFsiL94nS/pnPa89K/Jb3CR39vPhT67G/Nh4uv+M63UHYttpOhSkf72AhVVPhuxvTyDt171fkMfHZLtM4Dl0IcEKlwjiRuN0tp188WWE102/hWimqnuH6L7j0j0p0WZurQsxJKRzi0hlnqx1Q8IwxkJ6mOYTrF1Smp04cICD2ceqjreP3fc31W03QOCIUgAPhKXxQLLWn09bZcF5s9R+x7Qr+L72eo3IFR+FaCcuJ1qWK6oxkXmHFBvr+zy5vwok/Xi/F2UMReS6FyD0btreN79bitBwSRQgj/+cHu4KHc5iHUjwK3AlHIyk8D7wkhvFEp9dPx/3vQ7SNcn/3Hv81f/a7Xcuq3r2LlnbfL4usDwc0nJzpS1UOe433AdDuyIe22UcGjQ2jMb3W7LX5G01mzCQsOwXBrjbeRqb4yRiRrNtLhE8v0siW0C5hpJbGrPmAnFa5lcZ1EPBCsUEkn+wzBgqrApRKvWbUlXaVuErINhR1LGlvVEj+cdAeSkTCNyj60T4ukrY5/dyk8DsDhb5r6jqs/y5u/dBPqUwMO3+qYrhmqjiYdOmbLht79km6H1TCtKHuGncsNs30iNatO9FBAsBce//zwp7+Xf3rky7zmS38Z9VOn+cKr38rf+tgPckd+kL941a1c8pRNfjO/gQOfOERrcz8v+MGbWco0BM3wKgNXTGgnju2rBySjjGRjSrWUMbxC/Du2rpXJU+8pgmFfvrTFS97xM5ze6KOOt8g2L2XteCDbcugKXBYouwlVO+Xq428DDStfELbbxrM8S7dpDn9qjL3nNGE4kgZ8bYXk6JjBl8QTbPayp2JmDtNpC/0/yjtCHRO8WAsU9SaFzWtpYIwhKI8yuvE3Ua1MDD8vO4zeHjN69kFQCjN1rN7q4MgBGE5Q04LZlasEo2jfViebBNzG1gXS2mJdqCF8sEbx4dRDbDybBLkkkwlrnRpjbSPBVdaiDuyTRrP+PFaW4eyGmAJDI7tVq8v45T4+NbJx1UoYWjNhXIXU4voiCbAnNr66lyQXSVQI+MTw7g/+HNe//Fe+qvvYqydW/dJ3/id+5oPfxb2vNawduIb9HzhJOH6yOT+alEQnZvPinRLECBggTfH9DqqdYlaWcffcB8GjlwZwyUHUybNgLdXJ04Cs48qYRgobnJPv2y1cNjeZLfqmkZ+DmFcDcbKfUPUsLpVUTFUF0u0KFQKu3xL2XdLBp8uEREeGHlBB55SAB5NDCcpDsjERv5LU4jKDWR8Rui1aGyJbnq2I7EhP5r4se/Xwa99nRMpv77hfQO/EilSyIxJG1jfnTFitUVkqzJDhBFsnlZkLN1ev+K63MFs1DI5WjJ5/CR/6Hz/JdTe8id79FUHB6PIWdv8VDD56DwxHqE6bmy77UQZAWFti89nLJJNAcc1Bks/eSdjaRucFyUpf0vyWuujxjOzEDtMrl5nsNzznR2/GpdA+G+isKg5/ZIQe56hpAWVJdWSNYiXlvussz37dzRz+0x22n9Ln7HOgd0zRPeEYfPK++dp4ZB96mqPObqDuGtH5kkYdPijs3KKMwyADKlw0wXRX2lk190Zq/jb4uW1E8Oh2W6SmRqO7HQHYNrbQPuBHYzSgD+1HDTJm+zM695bCmjpxigCSvHixdXxx7f5GruMLjGu9vCTHWl4Ik2g6k7W9qvBHDlAtZagg7J9irU1rUsCsiAbacTjeygjthGA0ofYoHBfSa3mRzaoiJjZbzftv+amH9no8Il/XChWvee/82M/z2pf+8sN7f/bqm6IC6lFLZwuPg83mQzLWVko9GfhJ4IrFvwkhvPprfQJKqUuBbwfeANRUg+8AXhW//13g/eyBSI94Pe+T383/cc3H+effdYCVd8vBXBvh7dJrLxgGhrJClaVI2nYkCrROTVJZ1kxdVGJ3U3gX/BQamciuxSuRiYACU5vd+YCeOfKDbVyqKLuaKlNUbSiWgSCyKNdSmJls1NtnPbMVjW8BXqZKySQAimwr4K0iHYrULRkrXArJWP7tXCXP5I6feh08xHVjrx68/s7S5/jnq9czOmKYvGrE+iRh//tS2mcdygW2npSw9cxlevflzFY03ZOe7kn46M/NP4TrX/YrvPZf/zLv/Oj/veu+1xJhvKRmfiz93kv+BW/44l9q/v8XXvx5PnT4alr/b1v8E3qQrwZCEqh2MkK/oFVCsZSQnthBlynZjmfncsPKt5zmmuWzzFxC5TWf+8KV9G83LE8kzc1OPemOI9kuCFZRdRMBKa2ic0phJ472PVugFAc+rMS0e2NLzqkayPBB6ORLXdTpDdpfPikGl0XZ+O+EqkJdqPEMsbmMVSeh6FYmYK3WuzaTYTxFddv4boryYkKvZ47WyTFqZ0LY2pbmTGlaRzdko9DNULkhbGwKXb24cMrLBevhNp6PQNPaXL/Kau69ZubXHpUkqK4ECVBL7rKska0FXeySyuA8epKjqoqw2gUXoj9LEO8sIyC3qsJDZyEBH/iTn+JVN75JwFQPNXP6XR/+J1/T69+rx3cdL5dRNrD655p8AGe+7RCr/+4+gPM3hnB+nrD3qKJCnV7HD4dz0LXfo+q3sKc1pMmC6e3CWq6jEXVkAehxQeI8VUiwMwF/lA/o0uMw6MITEk3ZteBDk4BGKt4iutKo1GNSjbfiJeatphpoXCoMYV04fGawE4/LlLAG8go/0OjInlY7E2AZgM+/9eJy9L366qvsRel/mkpwwEoHszkhnFmH8QQ16Is0ynlUvydBK6Wk9r7jMyJYuKH3fdzQ+z7eMfrd3XceZB1UC4fo+97xj3nVTW9u/m9nnsmzL6XzpRMCIgRZN4NRdE8UlD1LevfpGMgiTDs1LQjdFtVyi/yqvjyUUSTjwPJXpgQNydkxITGo42ekn9XiGWhPbmEmHZZvW8GU8sSybcfqFwxLt4+xWxN5jc4TigLjHOW1R8ivWab7gdvkvs5uCDBRewDVSWpht9z0XNb+osn2uQET8rvosRQZ/cqYZq3yo3H09tMNK3m2mpCMHFUvJT27fWHw6NxaXH8faQDpweoCBtsYI++ltSJRTBJhRBtD2LdMtdxqpODlSkbVMWLW77y8Hwuvt/ZhC6kRBtKuxOg5e+wdn/yFh/yU3/uen+YvfNsbZAAOzTD73J51r/bq8VQPNZ3tvwC/A/xLFs1vHpn6DWTL3l/42cEQwgmAEMIJpdSBC/2hUuoHgR8EuPzyyx/hp/X4L6MDv/mh67nrr/xLXvid/4CDf3QUhsNdcpDaYHMxbUGNJzAa40cj+Z1B5CD1ImjtfOoPczmNc3NjOyAMRzKxio1HdqaFa1ns1hRVOrmYG4UqMwya7auUMFSWnUhAAiTbBlOALuSiXbU1xTKUSx4z0ZEdouieEo172dFsPUno7lUXZtcI22D1wxl3/OPHd9N53WvftMuU97obxAj80fRZ+bb3/CSa76F9QmFmYD/Tg+eOOPMiy+EPapY/cZLsTB/fttjNCfs/JBr78vAS175R/AmufMkx+FUIP7t23v0fnazx9I98L5f8VtQpvk++/Nwz/qi5zXuPPoVxlbLyCxM+t34JHWDzA4eYPX3G9z/741zXu5Uf6fxNTnxphbXefvrHhMLdv8+x1B5z4+rn+cD2U7lzZw1VybG7/2Nb6PXtaMAo7BNVVpiljiQMFSVhaxuVZYy+5QrSzYKqn9A6PkLnbVReRIP6Cn/6jMTRLvUlFaTfER+d5cviNN5jdmaE+05E1p9rppEqi14GdSRvLD+beyktpp/4fIaqhFm08bwVVj+xLmk5IRAGXcJaH4oK30k59toea1/y9N93G+Gc+wQeXmP5UMChevqaZCKntRacx0Xj3wvex7nU90UD0lYmG+Y0bRgUemkgXmw+wFY0D+33ZGo8m8lGKE0JszxOfYM0rGWJarexWuPbCa5j8S2DTwTI06XjXR/56sEfM3PRkwt8y/CaV/8a73nvE8OXZW8df3jV0QV6PaF3X0m+Yjh1U8Hg6LNJP31HY2APzKVs8fvadNdv76CrCre1tVvy5jz29A70ewIcKw042RSXFaqKoKxSUGmY5ejxFLptdL+DHWZNzHswCl06fKrxqYCsZVdT9DTeSkqaaynSYUCXGgJoF/DWki9pqha0NgVcKJYTXCa3yXY8O09fRQVZ8yf7FS7rcPl/vp9P/JsLy54fL3Xjvh/k7Wf/xfz/K2Jb+vbNf/WoPeYr/+Kb2bdTimy3EKaO6rcoD/ZJNrbwkwlhc0uu1YmVYQRIv6cNNz77n+BbKTz9GtSX7jzv/rONAjOzdL58etfPF1kgr/zLv05IFMe/43IOfUSkbfrUpoAGXcN0TbPzVy7nyLvPwKF9sDOBWY5KRPZY9gTc7N2Xo6cV5vQmYWckz92HmEAYh6XkaGtgZ4cDJ8/C6jLVcgddeJZuz7HH10UKVpQyGG23COMJ9vN3kxw+IB56zjUsfRVDI+pwBhYYRxcDkObfy23Pv40j5Dn6yGHx9Rm0IQTOvGhZPMNGnqDg1Eugf5dm3xcqki8clXX0XED5kQaJavPuxEpv04mgjzX4k2fmqW0XYy8336rmuqSMkWFbmjaWG2HfMr6d4VsGnTt8y1AMLHbqaZ2egQffyeSatT2OaosInicGPZRrYUhtBOSUAIqzqgE+v6qX7WTwrbzHdVP+wit/9SFJ4fbqm7f20tkeuB6qW1QVQvjtEMKfhRA+Vf/7Wh9cKfWXgNMP975CCP8ihPDCEMIL9+/f/7U+nSdc7Xxxle5dlqvf/nfZeGnRMBCAZmGrDf12abiLucleiMBQmOUy3aiiOeZC1dGdoQaSIv23STkCoePOKjGy856gFH7QYnrZgJ0rU8qO+CsEBbpQ6JmGjqM8XJCvemb7AvlqYHi1Jz/gwIm5XbYlKWvBwNbVhtERRb4vUPVgtt/THQhg8OnfeXwDSOdWDSARBFx6tOq+0yvc96lL0BWoEEi3wB3t0jk8YnJAE7KUcjkjObGN2hzCzhDGE5I7TpCMRHpY14U250/tnWS62eKO7z3ffPH6V7yBK//ZW/nd06/gP1z5fv7h/vfx5OXTPHn5NF/6lddx1/fMF/ffeNbv8+2v/TOGlytOvKyNHTuSoaMKmk+Pr+TdX3wa937yUtINMXUNWYyKneWoyqGKEsYT9PpQDC2H8sTD8oCqpTj7nDYb11o2nrNEfs1BiqdcQnnNoYb5EooCRhPC1g5qZ4zemTI73CZfy6j6KfnhAeqSQ/O0I6VRNhGwI0lQWYZKbAP41nUhf7LgA2ZnSjIOhE6K37fE+DlH2HjeKiovKVc7bD25y74vOAYfOwZlJYami/VwAaSHUnFaq4yR1Lh+D/avYi49Iq+5vq/630Ueq2FTei8spCyFtRX5fa8rm4DpbM4KW7xumQUA6hwfpVvueguum+Ezi88MrmXjJvnhexgFI+lVIdHnM0Ye57W3jj+8+uGnvA8OziiWDIM7xww+0WLnyhS1PNh1u3oAFKryHHZBEOn5YlpUmhAmE0Jm5VpczP+mWcfLSiQ09fTex/TWvEBVHjwkOwW69PhU41qW2f6M0aWJMJAUDbPDlFC1YbpPUXYlvGC2rJnu08xWwFtAISmrVpgww0s1o0OW2YphumYYXqooBuJ5eMtdb/m6vPffLFUDSCjFjas/8Kg+lj29gxpNZbPtPHpniqoii1bViZ9aBig+pmJ5LzHrO3OPvPNYSEBINNoFyktXzvvdDb3v47Xf8kvYYcmxGxWjywKubXFtyy3HfqNhiySTgJ3C5vP3Mbx2CXdgAP0uVA5dOtpnKvr3zEju38Sc2Zr7B9a1yNh1Dj8cSV87ncGZDex9Z2kd28KMZtIrtzJZd1eWGhZQmE4JJ8/M0wvbLRn01ECI1vLvnHV6MUF1V6La4nt0gcTRWqrqVrvCrnGB1oYnGXt2LtdsPF2z+ueawx/aJv3MXSI/7Pd338+jwTKK91szo0NVyWdhLepJl2MPHyS+yN1fY9Xvj0pTVLst/wb9xqvV9Vr4ToZb6pDvb2GmFbqoSDamtM4W0a4iIbSEJ6FGU1nT7bxPfMcnf4EQE/uCUYSWJPEGrR4WgAQQrG7+qfLrzN7aq736BtQDMpGiwTXA/1JK/TDw34FmHB1C+OqMH86vlwN/RSn1F4EWMFBK/XvglFLqcGQhHQZOP+C97NVXVfrQV/j4PVfx0m/7Ene95Wkc/FTgxA/n3PoT+3jqT5xeMMyW29dytYYeu8B60J2OAEIr/Zh2UOJPnubcWHLRNce0NtwcPHJOGlVjUOMZrfscO09fZbas2fzWGWwZwiDHtiqqmUVbjy8NFBrTcridBDPVZOtijJ3vC6ipJttQJGORHJU9xenvmPGkQ2eovGZr1mZrp4N2iuk45ej//tNf74/gG1IPFA3+qhsFSHqkUyP8zOIHjuxWzZkXefRygU0dk5M99JWBzrNX2blCc9npjHDv8aah0WurzX2861U3P+BjvP5b/ye/+KG/0iTqXPVbb0V5BT+sWF3e5Fn9+9jxU66yKf/6sg/zd+99efO3V2VnWNZTWsrxF5c/x3vzb+HgJ6YkX7kfvOcrJy/lOw99hmddvZ/Pq0vofyzD5IFjN/Wxoz6HPyImo2o8IywP2H72KmefrWmdgf2fz3GJpuwqVm4ryFctVaa4+39LCEngKf9qJHKRLEP1uvj1jTlYoRTjg4fQZaB/nyMYRX5kiaSTYo4n+M0tAV57XTHH7XcInRRzeoewvtFMVxsvMgCMTER9INx9H4PtMWE0RqUJeu0KqpZFbQ5Jt0as3R8ZTklCKMoHp75fqC7UGCZWGE0L9P6LlZ/lmLObAiJR4Ze6qLWnoEJAr+8Qtoe47Z3dzffiVHfx8Y1m9vRLsJMKc/IMdQIkqiIMp83NwiyXDdKgJ6yLNBXAKTaftUzNpxoVwFuZgttcXse3/tW38KH//hPnmfKfW/X5poIA4yTSxCovHjLvfc8T45q0V19bKRMoKg/SHwABAABJREFUOwpzcpMjfzzk2Hcd4dRrL2P5jgPYD/05oSrnGzkfCL5CR1YEPuDLBXahMRKicfWlEk99+9FdA6T6PhqJTRBpidKaEOWgqnKYzW3C/hVcv83OFSnjwwpdCSDkU2Gk6kJCLpQXb8LucUh3PHbiKXuGWarItiCZxj6jCrhMAKbxFYGJAzNRmPj0lZ/LRx7vtchCagDyaBx848rfa5jeb9/4l4/YY37gT36KG5/xc6j1TcL+FXw3Q7kggEqnLam97RYkVpL+gqRzNkxZ4J1/9vMP+Bhl11B2De/+4BwIvPG5P094+jX4CBqtfN6gAmw8o8PqF+fR8nLdDCQ6ULYV2bbDrI9gOsOfXUftexpVW6MLg02syOGKAtVqifRueyhgUfCoxKL7PdxVh1GzCnXijLwW7wmnzsoDZil0O1TXXoouHboS+VxtWB+8F+DDGEh0MzhtfPasFa/H2hTbJk2Cqo6+o344bPyT6rVt/n3SDGb96bOo6HGEtXS71xKspntCUfYsJhdWTLK6gj9670UTXx+wzmUH2UTYRA+yjiut8LMcba34DibiGxhWOugj+9CzAu47JXJaf85jdPpzkM9HJnC3i+9llMsZraMbVEsZugyovESd3sCPxhit0d4TilISZEHCe5L5dveWW38tfg5aWNapDINUCKjS85pX/ar8WSXXn3f96YWHQ69+zRtFhuk8wQhA7rtWZOmwt44/DsoHhXuUFpdHy2vp61kPJmf7FCIcql/pYqRFAK7+Wh48hPAzwM8AKKVeBfxECOF7lVK/Dnwf8Mb49Q+/lsfZqwvXJ//4GaSXQDLSLP3XHvmLQXU7qKIQ882FxUHovgrd68piWzemMSlDVRWMxjKVslZ8Qy6g5W6SJXwg4KgTE5RzQpFOLFVbMdsP+9aGsAaHekN8UHz5+CHSrOTSI2c4trGCqzR4RdX16EJT9QO6VCRDMdROdwLDyxTjJ5U8+8hJltMpt22KMtLN5NDXySOtznxs1LkStlfd+Ca0e+QZEEe//6e45j/9Kj5pY2YaVxiqUy1WbleUXRgfUmTboDZ28FFm8c7yPz3gfX78nquYhYSrkyF/Zxl+b/u5PPcpx7jmrW9DeTAofArmnjajo23+6elX8095Nbfe8DvoQ1/h3x6a39f3XfsRPnfsMgo0n5xcTTIEVXnCZArGUI0T/nTrWr738Mf4wvKl/Je7v43WGcWLbvwioypj48tXkvmAVgrfsuz8zSF+mjK8VNM9ldC7L6d/b8AOS3ymSYcwuNMyPgK3f++A7rElLv3POXiPXlnGr29GeaeLse6K8aEUXQWSiccnHdrHnDSo3U7U+nvxJZmWuAMDTBUlJ5ElKIa68t7qfk+a2ckUv7HZNJTtu/qosEoY9FDbQ9mcFCXukjXUWh+dl/hb7zifBv9AtUDVDz6gogEmMAd+okl306TG60298XVbW7C1he73xbA0S2FlCdotVCvDDnr4M+vC5DoHQNK9rsjXamCocNiNsWwAQpCvRqN9Z544laaRGVbKhLnTptzfA8BuzcEmQLwPwhwIClpJtPhDKOXB5FGm4AJVx4rfCHuN51599RX6Eq++/3MFW9em2GExP7eaG/mGvacSKwDRgr+Zn+XoNGCOnZSYbGuFcXTuY9Xr+C7zW988hjKJmBP3xcvQpyId9zbgs0CyrTEGikHA5Ko57quO3F++pCk70DvuJSrbB7xRFF0tQJEK2JGm6gRMoWidhdkafPkXn1hsYoC3b/+bXf+/ceXvnZc++UhV6KSoDfH1U5kX2fZwPGd3RDZuLZdUxpz3/M6tl3zv21A+UF2W0j1eYqeOG573enQeY+tTixnNMFuOxBpaxw2nXyYDpkXW5wf/50/y8r/2FpQP9E468atzvllv7Mktkt5+fKKoVruYLEGdWies9Cn39Ujv0bIWeo0+sA+3f5mql0AnITuBsHHdRN5bawlliRqOsGlCtdzBXX0QM61QX7lnzrJ3DkgagI8QZC0OImIJIaCcEwApph/WQx6MFgBugR3bBFpEKZtOLI3MtGYT+oC99yyh38Utt1Ftgx1XzPZnBLVE0skIX77zoiEdDzTU0Wl6vgzt3HX8nJK1PeC2d1DDIbrTwa5noDVhdZnQTtCH9mPWVvD3ncAXBbqV7ZbgtlpzP0Mthv3JMHpiaSW7U61haSAy9TyHnZGs4zsj1NpK8/e1+f+Nz/o53v75N8hztMIub56z0XONTvXA55IKATMVSSOReVS0bRMqsFd79XivB0tnuwpAKdUKIezSMyilWo/i83oj8J+VUn8XOAb89UfxsZ6Q9eIr7iZ/6q/S/kAbM3OsvPtulv8op3j+taR3WPz9wgrRaYqPJnwqsfidUWPkV9PkAdy6kNL00kAW73N8kSAyBYKwkQgxOtha0UunCaHTxg9abDxNUR3J6Sl4/v572Z+O+A+ffxHtz7eZHs64D5httmjflxC6gZAGioMVZJ5QaFxLEZRhfAWwf0qWOY5urjLcamNPZJgC1LJc8O/6kT2/dhAG0quvf+Ojdv9VC3pHFcNDjh+76R38+6e/mAw488X9LH9ZFnZz+BDuxEledeObMLlndFnG+KAiX4Pq0hnJ0RbBQLn2w6RnLf3nnWW5NYVf3Icd5qw8Vx6r7Mn9TQ5BugnpZsrk0kD78N0XfG7PufxePn/vpfz5zqVsPyWw/ZQO134BuPQgZsfyxfVD/ODB9/PK1fvZ+ssdPrd5hIOtIQcZ8tnvvpTgU9IOfNuVd3I5cHlrg3/z0W9l5SP3Q1lhZzPwge6xNn5rmyOfWyEsdZletkS+ajnxv13F4FhF9xNHxVthNEYllrX33CORtac3BKy99CDlaodwYI1w9N75VDN4WN9AGYMZ9OdNa2QWEA22G2NKpeScK8oohUvx3RbZ8R3UNJdpcr+DPnkWc+y0NHzLPczVV4oXWu13oRQqSfDjydyU15hmYrqYQKO0koQ4rdBpKjIxQHXahOkMHxlRIc93S2frv33K5aiiInzpLvFwuuyIPP50Jo23EoCbpQEhs6jKU610KAcprfu2UZWP5r6SLhVaCerslkhxD++PiTwVqpXiO6nIckZTKEqSMyOq5c6uWPP3vP9nedVNb8ZnsrEtuxpvFSrAi//O22jH29WMI5Bz7GV/460kY0c2LMCFmL4XoGt3NbJ7tVcPpe787p+D74abrv4JAFofvY2Df+rQqytwaD/+2P27wCNAZCbT6S6WSF2+KPBn1oWtFM/VZuMYq1nHKw9VnDJmWSPTQWt8v8PONR22nu7xLS/HdlAc+qBi+QsbTC/ps311QjGApaPiBRY07FxhQAnImi/Fyb6JjKUu+BTaJzQmh969AkCVHbj1l594ANKF6u2b/4obl/7PR/dBRmP0dMb0GUfQxRJ2XKCHOWo4FulYv48fDlFpyg297xNJUpZCu03ot/FZgtkaU+3rs7IlCXuzI33ad5wlnD4rx2W/J4DklHkKF6B6HT792xf+rD/8X3+Cl/6tt6ILT7Kd41d7cOIUGIM/dRp7eJmyn5Dvy2BfRjt64OjC4Ve6qO5lqLyiOLIEwHR/wuDWHfy2+IvV65kyVePNp0ZjzIm4nmWprD/DkVgyOEcYT5pzQrUygisjSGRkMh8lon4az7FZPmfUpkm0f/Bz6wjmMrf585HEt+Z31uJWOri2wRQenVe0TgXs1gTXa6GvuRK7tUMYjmTdbrcEJMvzeTBHZO0sWlH4omhYSKEqZR1Pk/i5dJt1XLcykQMuvJYa/ApFiX/alehbj8JkirryEmq/QT3oCajWpDZLAh1piu9lBGNQpcMNhAVXHl5C5w6di39qGLRjKmRbEgOtRuXzPYjvxp6jmP/sHZ94Pde/4g2StqrAJXOgCcBM5yDSX3ilsJNqj6NX3fRmTJChI4CaloROKgMlqxoW0149tsuj8Q/Z+eerv+/Hej1UY+2PAM9/CD972BVCeD+SwkYIYR14zSN133t14brre36W53/gZqYHUtL7B4TRCdL7txo6NMwZAUCUd8iiIQtYnBYseiaUlSw69STpQlVPMYyRzW0dEWs1+WqGuXbIsw6e4i/v/xyXJBu87nN/g/QrbVQFeqqZDluoUuMNBBvlIIXGDA3ZhkJVMD0Y8B0H2ylFt6Ja75KNFHYiHgzKK674k5Lr/uhNj6qx9GOp3vuuR4cBceff/Fn4m/L9z/35d7LfDnn22nH+fP0Id/7Y3AT1+pf9CjZNaN0rpplL05LOyYzWV05CXojJcZoIQKkUDHowdOBP4Le22X9/TyRKC3Xse65EBbjrRx/YbPVZl90H9/0Q7ROKYhnu/olncdV/Weea/zIhmDbv+3+eXnt2X7Q+fO9VrPYmjPspyboVj6OqiiwZ8RJTWhMmE9R0RufMJp00ZedbLmXnCkvr9EFpruqklapClQ6WBnD6LOGue7F3gY/sv8ZnLHgBdTsdoXzXbKE6VcwsSNp8EAp3HptW54R1E4KAKlkqPkv1VLES/xPlvUxqD+5Hd9r44yflPp1Dt2VSGMoS1coEaD5nolkb94aYkqJik60KK15OaSKbjWhi7We5TCQHffz+FfSpbSbPOEjrNvEwmF2+RLqVY8IKoZ2gKo9rJ8wOtGmdmaFKx2x/xviQxcx6KOdJTg0JrQSShHJ/j3Q0BWspl1vYxKB3pmLm70LDxPL7ZUNxMbPsRTayt2AjrldL2fIVAYdMEfjWv/oWrAtCvy8qVOXx7QTlHFVb74FIe/W1VbsdZTrx+hDlOeeWL6vobVjOk9fOqWaTnCbn/24B5AUBkBrgut2CNGV6eY/1ZypWrt5gqTVlfdxldPcSS1/eQU1yss2UzhlD2dO4VBG0nEvJWEyz876me9qRL2mKvsJbqHoig8s2IRkHSdpsKY68b4sbnvf6h+1j8nirB2P/PNx6xydev+v/r7rpzZKil1ne+bG5VO2G3vcJ0BDBxxBkLfPrm81tXPCooxqHgCDZbV6CLp0TIGUaWZ/RA0fvi/L20VzCdqH66O/9eOP3qPISrhaj/nD0XvTnbicDqhc9lXKwe+vzjk/9Ijc+S5hNydaMkBi694MeTvDnSLZCtXBOOYcfT+T5GoNeW0V1OyI3r4co9bmx0AMQ18ILMnsi2Fuz/OXH5zBca5l6PdCt3zvnoSVspmQrR2+OcPv6IjucTDHTGVSOsDqAfctwxz1R0p6i9+9D7Qwb2VcYjec9BgtMpJoN5RyKRNbxSvYGKrGQJmgTAzHieq6sxV9xCH1yg9GhNp38MtSdx/BZgkoMdFL0TiIpaVoTEis+k/G9DqlFTwqC1WLuHt/PcpBJ7+I8PpUex0xKWce1FlPtohJvpCj9fPvnfvmCx07QimAVLhVWmCnDLhlbsZygysB1N7yJEBlRZlYJKBU9sOowIID3/clexPPjoTw8isbaj/16ME+kQ8AlQFsp9TzmsrYB0HmUn9tefR3q0//8dbzqpjdTHhqg13pw31n8xnyxX5SkNGa3rQzd7eBH4yaxqf69G43RaXpBuuy5zaqqwaqyhDRldrjHmeckvOTS23hu/16e1bqPD4yfQq9VcPrqgqddeZx92ZiPHL2KsJGQjMEninJfhSo1ZqLxCYQM1OEZqXUkn+jR2jAEC7MVKAbg0/D4OHsfg/X7t3wr//XaIeFLfQZ3B276P39E5JGXHMC6IHHBddM1nWGdw0fWSu2l0wAip8QqrT6u/CxvJmh63/lJbg/63F76OzzzD26mfQa2nlnx5f9rmWv/XY7y8KEfejHf+jsf57ee/x92/c0fvPdtpENFaz1l+ymO++nytG85hU+C+BKdE6WrtEI5J+GC8Xf9908YdDpsfutlDLhSvBzKUjaBZzflPKtTXhbYf4sVnMMNhzAaN4aUQMP4CXkhTMLhcO5HBtGTrEBvj6DfY/NFBxgf0pQDqDqrHPhEoHdsjLnnlDCf7joGrQy9fw13/NTu1xYbxsWfmW5HpC5VtWtT2oBr3Q5uc1tYSAvm3ebAPtmQbu2gjt4P3Q6dr6wzeeUzmBy0FANFUAm66pGMA+mOsB1mK5rJ/i6DowVBKzEZ3ZhACMyuWEFXnnQ4JT26jl8b4FMrcpnUoKyRhjSP7315cSD8Ndf9Gm5JpLe6lI2vrjTBSDP6kr/9VjHrzHbH/aajGHvey9B5RdVNCUuKqi03SMZ7F6a9+urrlrvewk2X/yPU/rUmbbFhDMKcjcQC+BO9VcI5TCO5eUx/ml5cztGASXGjH/IcNZ2BNpx8sSVcMeWq5Q32Z0Pefs+zSMaanacM8HaJrScpkhG0zwayLUfRN0z2K7SDoqvRVaCIANPOtZ50Q3PgUxW68IyPJORLqpGRnvzWZQ59aOuReSP36iHVTYd+mNaBVWGDHj/DjYPv33Uc7fLSioPIxWAHqbmR9e5yED3mmiCFr6LqoeBN1/5UHKRo1CWH8cdPAmA/8WXecwFzb7U9Fp8kZKNTrKySRNknuPP71+gNJiyh+DpPn0H1usJ2BbFtqAertbfgA/kI1Qw/eYDzgjGaFMV6ja976BBQIRBCwB+7H3M6Qy0viSS/m2K0xsTb+naCvvWopJQe2EcYjnCb21D7mtXehQsDZNPtgLXo+JxVEvsKJwlx9Lq4jS10u0UYTwnRL0132jDoS49xz0lCUdC7dR230mHju55FviznsZ3A8h0t9MxhJgU+tdDNMCfWwTjM9lSYSwDaUB1eRs9Kko0pWE3VExm68oGgNbooIS/wqyLFV9MS307OA0Kv+//YO+8oua4q6//ufaly56BWKyfLsiznnI0SJsyAwYQhYw+Gj2CysQkGE2wzGJgZDBiGzJAGMEmy5ZxxzpaVs1qdq7viS/f741Z1UktqSS3JsmqvpaWu9N6tV1XvnHfOPnsvvg5pCtxqCz+qx9NDSyBCQVCatTn9raVYntJ6XHa//r5afQGBY4CyIQSvKY6b1LmVWaywkCo4MrAnJtJi4N1AK/AfDBaR+oCKb+ErBHcv+zSLTv8KypCoVAIZiyJLAcXftn1gBE2FPkZMBxNKNFjDMLQQ91DxTX83LCSGJBOuZi5gWSjH1O4cKVhc+xwzrHZmmQF/8WMUPBMyBv/S9BQP901n9oR2VosGCmEMKy0IcsaA9XpogZ8IUYEg7IiR6NNOMP0TBPkZLkaHpfn3HFhr+wpGx8R7PYrPJghsRXybR9jdqx1M/BCKHuFQF7BSwUiN6NYNSzp34c61bMt392l9z91wBQs+trOQ966EFUfD7U/MY8LjaqDAOhRltt7Q96DyetzNLChUyapX5k3I6m7swO9JhTvpjI3EALPAdQet6U19mg+zJStjBjWIBkbOSpT9mgdcqmpSBEmb9MwohVpJEEkQHp2g7i8rS05teVQ+j0zE9RjakIsD5fmDRT4V7qzJogQylSRsbcRo6ybY0VGi8+tRN6O2Rq/XNPS/+hqEHwwUdHpnWHgJ7dongGI1uCmBFzMwXMhMAqMAsQ6DxJpelGGQb01iuCEIsHZkIJvV4wVuBBW3B9hEoix+akqoSuBPrtef52gWvarEKArRotpDzn/SU7qYBASWQAYKPyoQIVjbPM1AsoboyUiB3Rdwz98rncsK9h3LNn2bRad/BZFwkD1ZZF0t0vcJsznCXG7g4rU8IgrokfURF4x7C+WXRtNL4+mYBrIIDXVpzqpZQ4BABXp/gS3onS3w40qPnRsCo2hgZ0K8WOkiLRMSWoJMq8CtAlkUWP36tcow6JuqBbXtITWyCgvp4EIVXdjWPhCfh5ol7MSaKb9mFHexAYyI4+Xv6K35X4zrukdzhtsVnLZ+6O0bXQewLMsw8u4yk0rKQfe3YPA1Q1+/W5Tjc/m3qkIQ1nBN0XKMFyX3xbKTopCaQVxah5WK4sctzN7S2Fd5tC6TJdy+AxGLDTaeymNnJbZimfGkPB9ZV6OdG0OFEApZnSKY1ISxvYtgR4dmVuVKhSjH0SO1UqBMAyFLWqmlOO6lHLITBUYOZMmhsfuoCMmtPnaPQEmBW2MTL7iI9m79HhtrEQUPZRqYnVo0nXgUZRmQ0OO3SgowQs3ICgOk64PnE6ai3Pr46OcIt9rS4+gGyIDS6K3C6Qk4+19uQMUlgaUfCyw9uu50+3oM0tLuk0bOR0mBmdefayWWv3IQKnkAhbVf4eNsSqmfCSF+AbxVKfWr3T23gsMcSmG8oDVZiJZK8Mm4DiClkRlgQCDPqK/TFNXSRX/ZvS0cIvI3GobZj5dHk9BzxX7UoHqV4sp738js6dv55JRbOTa2iV90nQbA4/1TeWJHK8lIkVnNHXSl4riBwbRkH6sfnqoFPBMBGJB8PIKdgf7J4E52MZwASyrcWoHZbbLuit2PN1WwZyyd/skxWSqfe9H1GIUQoxgQKfqYWQvzsZf0fHwQIFWIWr9p2Nw/UPrO7aFqMiIZC0sCy/uDp789RHPh33f/3JHfo7lfuJHGxz1iT6xjp8uyEXR4zdjTyaDyfJL/3ASWiTe5nvzEBPGCC9nssNGAPWGgU4k+Fqq7F2EYWi+oZFcvYzGwLe0Gh+72CsPQo2xoCr/s6KG+I47blMStsSmmJCtvmEnrXyWJe1dp6+NSwjhS+2iYi0xZG6lMvy/rOKzfCtEoRmuLFsIEwnQfxLUYNralE8F8gbCpBpkp0Ht8PXmti0+sXeH0hvQkDQqNChEKsimdADq9UKwyKC6oxXAVTo+PHzUw8wH5qVXESgVylMJq74cg1GNuQYiyDQoTU4SOxOr3MXIei0778rBxjaGIdOtPuWw/bhQVRiFEGVpkW3oCK6v1mABkSaPBLPoD1tTnLb2+IsRZwbhASYFcv03//mqrwfW0GcYQrbFyMXrAGaqsnaZCzfwYYs29qwLTqIUCoX+3KmJRsyqkPdrMTemzOWfqWk6Yu4FnEhMpdERxawPMPgOjCNlJAcVaSegIwmqX1FM2xWpJsUYXiRqeCEmuz9B1bJKti8u/EZ/YBhO3Cl74akULaX+xOPEuYM8FliX1l+nCSOmcLkKDsFAc1vgRMiT0RpzLxmIhP8pzVChZaFzCiuC3e379KFi2+vq9e/6mbw+7vaT+MsJMducnltdaZuWUfjPluIfrDbiKiWgcYYe68VJi9+1JwHpg22X2Udl4ojxiWv7tBQw+NkLoGiEhCAldD7l2C3ZTPUF1jMAxcKstVPMMou1FjJUb9diZkIOMqSFF5rK2oTAMgi3bBvdV6rzINZshFsWYPBGKLsGEOi3Mn0oOssDCEPyAoD4JgaJvVpyeOVqDiBCq1wf4UUH7CYJirYmd1tcEke6QQksS1ZpChAo77aISDggdW41iXI+42SZmf1GPoUuQ/aXvo6lH2FTURngBS469muXPXDvq4Y526OJWvsHC7g9QUmAUQ4yiLh65SYnTEyBdXeC2Mj4iCDGLPn7C5rZHvlAa76zE8QqOLOxRE0kpFQoh/h2oFJFewbjt4S+wpPZSLQpYFjFUO58Qy0ln2J8Z6EKV3VqGCv8NFJ5GWIMO21ZZmM/zwDTwIxI/InCqikihiAifP/UejZ10MasD8oFFwnHpy0ewZIAfSoquiRsYhJPyqE4HlMBpl4QOeEoLcRq2Dnqr3zR2NkkFu8arzvmq7tZMqGbhmdey4oHR9WIAls69Us+99mV08tlYi3RMfVFTdiZhuPbWmJLO3UDaNkubLodYFNXVQ5DN7TYRXXzyNYS2gdneh79uI8A+J67SA7vH1a4eIxO7EQg9v3SRpkB52oo4EcfsjuIlqnCn1GFvFshQEXaPrYg0wFYawtpSQQD9oe44Oo4uEhsSEY0iHVsnW7YNnkfYpkcEMU1kEGAHIUGkFhkTYCh6Zxq4yaOofaILtW4zMmqj8nmdhA4UkAeLSoNjrYPFwKCrBxlxtNBqXx+iplq7p1VXldZt6ES86IJlEdoGYU2cYpVEBgyMoubrjBL9PCQ7N0CkTWqek1h57WRnZQKsPpcgYiL9EKsrp3URiq5mIhU8zdQKAs12skyUbZbo9QFmv6tFyQs7X0TfcbdmJ5X1N+75+6c5/W3/gShrHhVC/LgWCUZA6EhkcfB7XS4ggWaCDhXgrqCCfcWKB67WcTwIoMQuFIYcvRA9wKYIBhhK+CN0WAxjJ4OMnTDg+FYqxBoGblLix0Oqoi6TI9080d5K0OWQnaggEuDZIWHGgGiIyktkQRKi6JsTYPYaIBWx0qmoe35Sj6/KsgOVqBSPxglLUu/RrJkxQBWKmqlZZqkYRskZdDDO7ZZtNBYMLYiUipqLo+8YZtSwq9i8pOb92sHL9QhKjYJ9jeOLj//ioArKHuK48r3BsbvyOFrptgiVZtsLAaWcZ58w5LcqpDnKY4PHXUgxbBpAFYuIbB53ag1uyqCYkhiewvBsnOmtGBu3a3mKkgi4MNipcDy47mAgvx+I40rpZlVTPUZvFhLx0ouUjuOh0rHVkAildc5EifFjuHoE3YsJwkhI8ZgC7qYoThf69y8EZs7HzHpa80gIZMFDCqELRJZBaBmIMNTsqN6s/k6Wi3heoFlbjglSsuS4L7D8qS8PvK/yNEI5jj/8y49z9r/qxqj0wxLbSGDmBYEjCBwDoxCy4v6rWHTal3eK4+XtVPDKQQiEFU2kXWKswtorhBCfBH4LDJTmlVLdB2RVFRwSLO++mSWp9wDoC8Kh3csyhoj+KTX4+FAHlwGa7y4oysK09Ey3ZeoLWMsijDsU6iT5Brjs6PsxUEy3+ukqJvj7qTcxw0xwc3oCIYKHu6eS6Y+glMDcFGGDlSTaIYh0a1osErItEG2H1FoItkXJnDbcnnt/UL7gK4vnHmm4/d6rWHjWVwe0MHaHYM36ge+IkUoiCh5mVxq/zF7xPca7dxO6LmFn97Dkb6FxCbBzUrk4+g79R7EwjDm0UL5pp+/v0NcusrRSuArVwP1Lp32cSckYbNmx01jeqBjS8S+vmx4fMxIh5gaEyYgWxfR9jCAgSPftfVe39HfoulBiaRm+j6hK6eKNFFq0vDetXdpK3Ug8X4+YdXYR7UkTbailb1oD/mn9WMksK09rYPofkzhr2rXTm5Soth0Aw6yIhwn3Dvk8wkIR4XYiolHIZAmmNpOeFaf6+TRh1Kbz2BhOOqTmznWYa3O4cyehJFStVQQWmuljQxAB5YQ0r7CIb3O10GVJZFMZkmKNQ2jrsRk/kiTSntNJfcmhTZmmFt0EPc4rwUoXkQUfkdMMKWWZLJl3Fcuf/+pOh7qchJ635DocoFhrIgOQxRCjECJdXUCCI/d8UcHBxfLumwFYUnspACqXH7ioGnZuGMJaKDMHh7KOVSgHHSBHwxB7b4ySBXc8SqElTqEGjLxkQrKP1dlGenrimBnJX972TW7pX8Dftx3DtvZqCCRBg0fyaRvaHKo2BDi9BQLHYMdJFsUGhZkROJ2Qet6ib57Hhks/NW7Haqxs2iMd5fgJmp0mHGeYmxew382fnbYx1OVzSENyNHbSQDweUaTZUxwvb2/oY0tqL0U6NmEmOwozehfLLo90DrktbVsLirsuMhpFxGKDbJ+x1JJGPm9oLC8zkobolQ46qY0YVS8UUdvbcDq6ME8+ChE4bF0IREPM7UlqX0hQ92AbRnUKPJ9gR/uwhu/IOK5vl9aSz0OhqM01+jK4s1voOSqCkw6JbStSrLPxYpKaZ3owN7brhvGJSVIbFaFRGkWPC3LNoFI+QY9N03MKo6Bw0r4uwikIHRMlwY+ZqBobK+1i5EtsSS8ApXTBSMqBZhBQcmJTiKKvC0m7wFBpC7vXw622CG19HI1CgOGGuCUx9nIcH42dLHYzhVFBBa9EjLWIVPYM/dCQ+xQwfXyXU8GhxvK+nwz8vTj+zsEHSh0inWyapQtFGDZuNEB1DUft2pSD3gCkhIiDijmEtkn/FPBSAc/2tzI/uQVPQSEwmWEm6Aqz3N0zh8e3ToKtUc2EjYdIH5weQbRTYeYVkV7I10msWX14+RReElZ+cXy7lpWLwbFrBJXHq4y6GjAMgg2b96iZtd8YmewNub1QvgmAFeHvgVJCtpsizwCGFKJGOhotNC4ZHAvpdhDRyMCo116vFQh7ehHZHLLbJGxt0oXWcqIYMPgb2tukvfT7DLI5zGhkUJSz5LhSHi0d2PZAwVi7y1WtC9nRkGBbve6ypqc6RJMtJFb3QAiG76OyueECq0P1V2BgPLZsRSxME/yAbGuMQq2g+9gqrKxCBNA/WVJTU4XbnMRMFzBzDl5UYGeVvkgtgB9TxNda1D7WrjuOURtlGYhAIYIAK1SovChpF0iUZaCSMZRhECRtbR2e0A5q0gsHRYINoWn4jM7IHInyOeH0t/4HgS2gZN16z1/HdsFbOadUMN4oF5MWx985MEY6EqLczGHEBfhuRoJ3iuOGREajepRNSpQpUCY43ZD1bEIloMcmdnQPHUGcYmixoydF2GdjZCRBLMSPQXy7wul28aosQkML27bOa6P9/ha8KrDSsPF946s1cqQXkIbme2NBmYGkisVhjcMDgt3F8RGFn6HFzz1tZ2gcH4mFxiU6jrtuiWk1dubQyN+PKplWlFm6IhrR8c8fUcjdx+JbuZgz0LQtj9eV84QREJaJ2VfEjpjaFbVgYeagWC3IzGvAygaYGQ9a6pEr1w/qXJX2Vd427BzHsUxwPaz2DN6JEby4RMkI0lP0TxFY2Sqs5gTOjixmXlGsFjg9Cj+qx8/9mMLaZpNaC7EdLkowUCQSgTZaESGY+YBAQRAxEb7SDKRAi2krR2qntxJLiRAwBEIJ8HxEEZRp7HxgRuCOu67k7H+5gcCWhIbWNByrc/GBcjiu4NAhRBJwgDSRDtB2DybGVERSSk070Aup4GWOIYGuHLzKFFhgcIRmiIvEzptQSEsnraK6iq0XTyE3QZHcoDsR73jNXVxc9TiPFybxfL6VC5Z9HDzBhd5rWb+1AXO7A0IR6RLYfWAUJb2zoTjTR57dT0dXnMQLNrnWkLpIEXFqx8E6OhXsCiV6d9jTO4ySfqhRLiaNGUO//6MUclRZn8DzoWSNu9co/XbCQhEKRc3c2rwdDIOwL4NwtJ0tQmjhzL09nkPGBMK+DCLqI6qSmlXo+9DXP2wdA7BMRHsX1ff0Uf1olFX/3sz0kzZzypkb+fW9Z9IUrSXbJIl11JHYXMB8dKXuwgqhtzuElYRpaqvg2ZNIz0pQ/VyazIwkncdKzJy275aBIohAsVbh18QQfkihKY70tTNKpNujvzWCH4HJKzwim3qht093I3MWYWs9IlT4MQs/rgtKytA6BgD5KVX4EYnd52Nmfey0i/BDinURlGVq/SIFTqDAC1BRfXG89OjPseyFr+32EDs9g515P77nhLWCCg4GdimcLSQymdA6I339Wvskk9llIwjKjYFSASoWQyTiuDOaMLIeQczEdyS5OUWEociuadIF5oIgZntc9sS/UeyMQiiQRUmkSyDbDDJTQgpzXGa9fwPdxRgbumoJPIOtndWIkkTjeDeDKtgLDGWn5ceP1b3PGKUxtLevHSi0jIzlfqgbXftR4CnnwCr0B8bLRMmdTdj24IhomTm0t/sZ8h6EKRGWOUxaYuj7KsfysFBErN6As95kUnQu6Skm3gVpotEi7f9spPEJ6Jjv4NZA48R5JJ7cOiBtoVxvUNep7LxmWaiZk0jPTlD9fJreuVX0zpKgoHZliB8RJDZm2XFqgkKtxOn1CG0T6YPwtaNpYBv0TwU7LWh5oIjwwoFmjgiUFgK3DZRREuZ3DPyoxMoGBHGTwJIYXojZr2O48AIQgiBu64aQKRHZEpvSkCjbZOlM3dhZtuaGXR5eO73vRgMVVHAkYUxFJCGEBVwOnFO6627gB0qpA0wpqOBQ4tbszwc7PiMTypIeUpnCa9RWE/amB4T+pCV2SlyFYeik0zBQNUn6ZgfIlEtPlQWB4I8bF/BiXTNre+vpSsdJrDIJIrCeCZgZAzOrGQJyRAOsblIvvX0xZJ9JaIKVlmQKDi3VaSo4dBjaLTzgXctDgV1Y9A7D3iSiZfp66bcW9PVj1FQBIBPxUqLo6bGR0EYEAUHJHW2finNKEWzVtsflbY/szqpQ6ZEW20aEBmSytN4TsKa5kVonj9Un8aOK/tkBiTahGVPRCKpQRFaldBIaBFAqJgnb1ppqtkHV6gyyvZuEKeman8KPQqFWkG0xyE8ICeLa/UR6IXafR6bVQvqCYo2FMnQH0+4uaA2E0vshFkV4AUHcHtQkktpGOLAlyrDwI5LAEYSGZiiZRR9V6nQKN0RJgZIQxGxEGOoxN0Mgs3s3F3///31y7z+TCioYZ+wujgvDQBVdzQgMFaKhGum6u3VrG8peEoYBiRhulYlMaHaA3R+QelJbgItQ23cX6qBtWw24EuEJlAAlFbkWRWKjXtMxU7aR8Rza+pO4OQvZ5hDEQki8EpQjDl8sNC4ZzjJ/pWGMTOS9ee8D8XioTlTZbAIdb3VzRZb0RPewlj3tz/OHjZsNE8cu3S+tQbHs+LM7MDP1bDnX4OSGTdwmGumZZZKZGkLCx3/RxJ3eiP3sRh0/q5KIRJywu0efJyxLF55tg6pVGURnL8m1JrnGJPkm6J4rUQakZyYJajy8REkjygvwYtqEwosJwpKUlNUPRn4whxGB0ppHjh4rU0KALBlYSEEQMTTz2BD4lkFoOJg5H1mQBDEToxggvABlmwSpCKJoEUYtECAze47jd9x1Ja8692va7a2CIxoh4sC5sx0graWDibGOs90EWMD3SrffUbrv/QdiURUcWoyqHzN0vGXIjLpRlcI7ejLFqEH02S1aR6lQHLggFYZBWLL9lqmETjqlRHSlaV1RRdupUY4/azXdhSjH1myjrVDFtKpuutLxgV3bvQbC1wlpaEGxFp69erAreeKywdEqLwlKQrEtztreKHZyN5oOFew1ltRfhrAslm3/b5Y2Xc6yHTcBO9PL97o7eJhBRiMD4uC7xb4khUNes7zzh1owNNDCz6I8cmagHURqqiAICcosorHuTwrduSyNFqohzi8jRfFVUQuqCsuE6iqcjjzTf2zx2L9PZvoZm2i+sJ8Xupuwe6sw+4rkzpiFUQixu/OIoodo69LnA9sGpVDFIubTaxGppE6s126h+eFZ7DjZom92gJGXOB2SvAE7TrHwozD51n7qn0jTtSCFHxGg9Pmg48QU9U9LjHwBLEt3G02JW22BgsASeoxN6IOmDDBcBUrhVhsU6k1S60KdkEoIbYlRDAktiZey8GMGViZA+iFeU2K3h/T8RdcNpAR33VYZT6vg0GJoHF8R/FbfHsowUiGh6yKCAGFbeGfMo3uuw4TbbGR/hrA/M6CLWD4vDFiBl0ZhVbEI29uJVkXJN0eR6As9UToNpTYF5Op1o0n2miizdEEtFcoAEQie/aaO5a+978NU23k2UQ1odkKYN/ASCisjmPOVG3np8xU20nhgcfQdgzpZJQwrNjIkju8HI+flDmnbY2P07uv7HyHKrQIt9DwSA3pKau8bQgP5+MDt8q6Hx/HQK2kMWSZ+czW9sx2sRxz+0ruAN73uYaLSpcrM8fM1p1JM1gA2fnwGwldEtvYhckWdf5TGu5XrYjy7FpFMQBAg122hpSPB+ndPInpaF4taV7I5X8MT21pJz5EUayIkNzjUvFSgf4pDvl6Sb9RxvFgD2YkRoh0ushhoMw3Hxo8YhI4ksAShLQjL6U9RG1iEtiCwBfk6Eytrk9jmE1q60GRlAkJbn4sK1QbSVyQ25fEbU5jtfbs8notO/4rehxcME86uoIIKdsZYi0gnK6UWDLl9pxDi6QOxoPHAkgWfB2D50185xCt5BWEUkWxp6YBXqLfpOM5gSncDct2W0tMFUEoeSwLawnE0IwHANEisSZNsquWpya0kk3nuL05nZnUXAGsvuQouYSfMvvbGne57fOlwwdvp3/0PkmsNQtMg31IZKRlvKM9j6YQPgRAsjr5jOMtIha/4AhKgXYh2lewdiKTbMDQLCbSzWvn3GCowJEY8RpjPj02wEwa1Hka4J46mLSEcRye5yQR+QxJChdWTJ+hNsWn1ZIpnbMMQisxECzttYmV8QlNSrI8igwiyNo7Zk0P09GuNCDNKMLmR3tlxql/MIIKATIuJ3Qv5FhCtOfLxCEZGkp0UogxFtjWGlQ0JbYH0FNEOqFmVp3dWBD9mYZha3DeMWgQx3d5UhsCPSgq1AgT4DigT4tu0ba9vQaxDs50QaFe2bIBbbSJLNtVWNsBLGICB0zt24u35i66rFJL2E2O1Hq9gLzA0jg/9OwjxkgZ9MxWNj8UxhghqD7AnyrpvhoEKyxfH+jFjR5qYFxA6JghBpAPaT4xx359GZ+NN/Z7WIlr/4U8M3PfXs/9z2HPmXXkj0tPi+SPZxxWMA0I1UEhSnj+sgDQsjr9CC0hlHJQ4rkJuc3+rBcBHE7cvF3uE1IFoX/QOR9nnsKcYBjIeQyQT9E2J4kXBSUMG+MuaY2ipTnNawwZePeUF/jTxTNx+iR+TIKH+mWoA4htiyP4C9PUjIg7BpCbScxKk1uYQYYjsLxI4MKe2nXOSK+mNxeksxFmVtQnqC3TVRJG+Q7ZZIAKthRTbrr+D2WaJ4VrYPfp7GTgGyhL4Ee2Mlq8VyBBQgBBYGaXZxLZ2xTWKCjepj4OVDQkcQaHWQHqUik+CbGuU+JY8y1Zfv/vjGej4v/ikL1UKSUc4FPKAaRepI0UTCQiEEDOUUmsBhBDTGaaoXMErCaPaoqpQJ5CmpR21QqWDb75A8pl2RNBAGDEwHEdfYCtVor2ahPm87tC7HkqFg51Mpah/KovTFwMitJ2uePxDOzsgDcWqq/fciUyukxgFMPMKlGTeZ27k+euuYOncKwFY9uLX9/6gVKAxooumgmDAjeuVnmwOQ+mCatQEdGj3cTfHpCxOubvnlBN5o0qPtREEmuYt5cAFnDAMlA1SCIL+/j2vvZysGhJRGj/dVTdWGAZCSp0wNqQILYnVkye0Tebc3M+WV1VjXVtLZnaEnmNDeo+KYffqoo2Zg3ibIrkuh1cXJ2hJ4scNzFyIH5MIBek5CdyUoPtED2GF1DzkYBZidJzjYdTncYyARMSlu62B2hcFDY+mkekc6eOb8BIm9Q91aa2JqoQuIEVMQlN3LvsmG3hVOlGVriDaASILXkJg5hTS16yJTKsevQlNsPtDIt0eIlDIvA+GoFAXwygo7rjzyl0e0vMX7b+17+KTrwHg1ke/uN/bqqCCMdubl3TrEs/sYEquAWUaqGQcurqH6aroEbdgSAFax3CCANWbRvZnkKaBqq0mqInx9Hd2Has3fHDPo57ChyAKsigwCrrwC7Dk2KsH2BDLn919vlDBHlAuJKlwl02EVzx2FafHGMfHioXyTcMc3HbaR3k5ZdOasexTSN2kZbAYtjsmk6hK4k5toOsYiQh1U6X2cYue0xQb+hvY+OIEWua003r2ZrKuQ282SipWYPOkasweE3lSNQBefT1mj4lf4yMTBboMiEYCjm1q5xONf2RF3zH8v4fexoSGNN+Y9X+sntDM6xMb+HHvfH7avgi7F1KbfcAkNykkvkFiZRSFagnYhAb4MYkXFRQawE2BnwwwMxKjIDDy2tlNuno6AQGZKWD1a21FEUjM0jVAaOkiU2BDYAsefODqXX9GZ1yrdScl6JnbvXdbW3y8jt+3PnnNXr+2ggoON4y1iPQp4C4hxDq0ysQU4D0HbFX7iQoDafxQTkQXGpeURPskQtp6ZC1UCBVCf4b4Opv8pBRWIo4IApTngWTA6luFPuTzOojG9MlZeAFC6TESgA0f+sRuVrL3yNcJ7Iz+e+ncK8HzUY7N0qM/B7BHkdwKdkbZ8Wckjgj20QgIx9m9C9seksCyfthYsLznRwAsSb1Hi1WLIQnOyNvDFjmKk9uAi4tEjXx8yHpkRDvNCSH1CGrBx8p7KFNSbIoS3dRH9boQe/V2alQz2cU+3qYEsTad9OUmKEJLYGUjepxMgJuQFFOSXJPWHRIBmAUw0ibSE1St9+g4zmLe9K2k7AIfm7CCJqPARfd8Wo+V9WYBiHRpVpBo70L5PsJsQMVsQktSrDYpVklCRyeYIhAYRc1msLJKO7H5Wk9BGdpi2MopAkdQlBIrIzFzPsqUhBGDWJu72wLSBRd+A4zB0ZD9ZSAtPrGUhD5+ZCehFQbS+GJgpG0EBvRT8nmMgk92YgQmRkjt6IAwPxDDB55bvkg1zYFC9tDRqOXPj19hx0sqvDofoSwinaU7S+c6UfBYOuvTe2YVVLATbs3/YtT7j7Q4Hno+MuLo0e5dFV/GsaimgoAV4e9ZZL1luHPp/uzDNEs5QDl2D2cZS8tERKOIZAJVlUAZutAiXchODhEKnI02AEYeulri2JZPuiOB2W3Snori1BZQSRe3z0ZGAqY2d5G0iyxtfA5b+Gws1rMpX0utneXJ/FR+98yJLJi2hc9N+jsnOxZnRtqBGB+vXc3P/EUYrq7ROGnIzNDnlsRWj9CReHHdBCrUCvwoeHEIoiHCF0hPaNaRC2F5bM/Q/4JEiHQN/CgoU2H1CSLd+vjWvKSpjP2TrF0exoVnXgsClGkg/GC/mzlL5mmZjfE8H1Zw8BEqCNSB0S4aZbL1sMNY3dnuEELMAuagi0grlVKHldjMwjOuBWDFg1ezdPZnWLZq/zvHRyRKYzzSMgfYC0F3L0bEoeP4Ovwzmqh9oYHqZSu1g5TnDwTIMF9ASEHuVUeDguSz7QBEesaX1Pb0t69g8T0fY0tvNfJvmsGhLEPPg+9GMLSCfcORlngCml1nWhiJBMp19104vExj30MSOewYD33+SB2jIc8pj5+oUA3YCpc7oWXmmDD0GNjgywbFN2VDHdh2yaZYIHv6IFSEE+swMz7F5gSx7QWyJ04itqGPaZ8P8etNNnwoxE87ROryFCYZbJkYweqVxNo0Uz80wMpCoR6sPkodUUXYVGDDGyyi9X28s+UhTohsZYaZACySmxVOexakJExGcNa1Q0nPgjCETA7DsQiiJm5KC3YGdtkFBqwMOOkQo6gQoUIG4CYlXkxoxiK6wCR9RRA1kJ5CipBck06uz1syPF7cvVwXis5ffJ3O1QWgiY/Dnl9+3lhQTlrLRaQKdsbi+DsBrd+yOP5Obs3+/BCv6PDCaIWk8m8+6OrBzOVpf818Wk7cRrY4i8TD67VhxhB9lbLothGLIWJRVKHk4BQE4yoT+twNVzD159fx8VNWcOPDCwFbs5BAOy5ls+O4twqOxDiOCrVBjOMgfP+gGIAMHOcSA7j899A1DcPIx4QsMYRL+mSmCREHaVkQj0G6H6W0zl+YySLralF1VYSGQXZakkyLQaRTa4sChNEQo10SOGD3Qew3cXpnScTkgKDOB0/ASwlkAGKySyxRpMopUG3neSnXzJxYG6syTfR5Dr1ulHdMfpDTz17NmRGJltIdxBY/i5UBOxNSqNHOqTXPGKVxtBDpKfJ1BoVqQb4ZlKEIksFAfBW9JW2pUswNoppBXGzWTGY/KVCmQhYkbq1CmQIzA+3H2zh9ULOywIXnfx3p6uO+osRKWnT6V1AlEw9lCBCCRad/hdse+jwLz7iWFQ/umr00EmUGUrmIVEEFr2SMlYkEcCIwtfSaBUIIlFKHTQa3NyeBCnbGUGr8Iustw7RXZDQCnk/zQ0VOufEx/njUAmrui0O+gCqJaqtQaWaDYSBC6Jtk4PTWYHVkiBQOTGEnDHUnA0AZBgJfXzB7PrgVY8EK9g96rHPnztaK8Pd7l5DvTRdyJHOp7Og26jaNYSwCGMp+CkAJhG3orn5Z98RxELatk1DH0QWaWHTAYrdslxtEDIxigNneR/+URgrH1eCmNOOnNtVBVyhxiyahJ5G1RYJChGyrFrkMbIUyITWrB/+eWqQL9sQsphGSdQ0umv4Cr4t3sj1QbPD7WZ49imh7SQA8pkfPvMn1CD/ETPdBUBoRCAJ65ji6EynRTEgDpA+x9gDDUwhfIV2dKMqI1DpJBlg5hZXRSaxRCBChQgl46H8/wYKP3EiuUReTyh3NMu66dbCYNB4aSEuO+wK3PvXl/d7OKxWVotH+Yyi7eCiEZaJ8n+aHA3rnRvHfncXubcV6soDKF4YxGwDNfLRthGmg+jL69fviErkbbHjnZ/jScw+SqsvSnzE1IzLvDtPyWVJ76S4ZshVUsCeoIIBicedRswOJUeL4LjGy0VQaP8UwCDMZjGQSkUgNPj8e1TWXZAyj4FGcWkuxyqRQY5BvAD8GsR06RqpIiJH0iHSa+DHomx3SP1XHabs+TyJaJN0fRXWV3Bj7TYKURApFyiywuPpZlkRdnsm0siOX4PSm9RxnR7i7UKr6jMB/dZ1DYEO2SWIUdQEotMEoQrHGQpRe4laD3+SiPImwtRCS7LERgW46ibAU40Mdv4WlsOMebsZEeAIjLwhtpZdQPtQhdM+NAFD/9PAC9G0PaR3dRad/hRX373/xZ+nRn0NQmXZ4JSBEEhwg7aIDpbV0MDGmIpIQ4hfADOApBrWQFHBYZnQVFtL+YSgNV0ajA2M0zkvbODa2mctOfYC3nP8p6h7agbQtVH9GX8x6PoSK6N+fwDn9GIychzJN/CoHMz1+xLaFZ1zLujdMxuoX+HEwsyB7+yHiQL8OHiqXZ0n9ZTCxCaiMQO4rjsju5RAo19sp+Tygx2RXyeZoY2tjQFk0W/m+7sgm4toFThr0HdtAMSVIbXAx+12M0iiZ0Z3FDhT9M+L0zGqmUAfFY/MkHopiZ0J2rKojNBWiykNIRcNyPc7WebwiSPoYWYNIu8RtryXSD14CTCMk1+8wa2obsyJtPOcpIsKgP7T50dozMSZaRKMJemaYZKaHpNZIYjtCqrdFQEj8CdVkpkTxo9A/U3cu7U6D6A5IbfIJLYkKFGFEahc2S4t1ClWSdbEElgBZZmwFCjOdY9EpX0aenASgau1gAWkk06hcTCrDKOzjxfQ4XIQvNC4Zux7OYYxKQWn8IAxjQB8ncccLJFZP4Gt//ylveO8HmfvVRmRXNyqbH9ROE5Kwrx8z4ui4CloDcYzjuWPBkvlX0XZuPaF9Hn3HupDwdVHbdSHQRW5hWVB0Wdr8QVBqwC20gr3DER/HS9qOB82JbrR9lPct5N453BoGRCN4TUlkIcDo7IOITaG1iiAi2fTaECeVx98Sx8gLrH5AaZ0gq9PECwW95+QJA8mr5z1HPrC485m5RIWiJppjYjLNpng1ve1JRFEihCJlFcgENjdvO5fHqjYDcF7Tas5PvMBKL49FFEa5QD4jsYo/HH88puNz5tT1tBcSvPjcJCLt2gjHj0NhXp45E3cwv2obvV6MuzfMxN0Rw8gLvIRC+oJCYwihIIyG4ASAwsubRJpzFLI2gW9jZvXIfBCBxGY9xu6bgtoXCgQxE0I9ii79kNvv0RIX5WLSfiNf2O9NLLLfBsBt7q/3e1sVVHCgMFYm0knA0Urtg8pYBa84rAhKLhNCIhrrUVFbGyb09HNaZBNTzSQdJymqVic1U8AwdPIHhK6LtG3MJ1ejZk2m2BynUGvw8N2fG7/1DWGdzf/LF2n5eE6vASAZB0B4nrb1rnyl9xlHeuI5gJehEOmK8Pdjet6S+su0SLhtaR0zy4RoBGUZtJ0mMHOCeLuB3VW6gJOCMOEgXZ9Il4+dFuSbLFQIxXP7ybgmAmisztDekcLYYRNYWsBaNRQQgUAJA6Ogx816ZyvqjuqkOppnXaGeo6vaAHj3k+/mkhlP8K9VT1ATzbF5Zh25jElhQZ7jJm3l2dxMnF6hLyZVgAj0Z5CdEiILOnk1isM1kPyIBAWhaRCa4MZ1Idws6jE2WQx1Z1MKzC2dUHQxaqqQbnKn47Y3Y2oVVPByxMBom5BgGIio7tLj+fTPqeHWzDG8ecHjPHjMqSTu6R72WhUEWmy7tw8RcRARR4/SjCNGimYvnfhh/YdtDRgMIEvjLUVX249XsNeoxPEhOJSxvMw4YpAlONY4vuiUL6OkIIhbyGIUvypCrsnEdwTVjWmak/281BPB6Taxspq948c1S9fqNgjyEYgFNNt9VJk55p2xjTa3mpSRJxfa7MgmMSI+QWghpWYh9XpRCoFJ0ijwhebHaDYSgOR5TzDDygGJndZ5cqSNc+esxpIB/z3xXh4umnyw5214vVVYWT3u7lsBR6V24EgfT0m8otZLVIbCr/GxUy4ikIS+wLBCIhEPpcB1TUIlICwxnqOKqtXa5MNw0bSHyM7HrlxAqqCC0RAqQagOEBPpAGktHUyMNeo+BzQD2w/gWio4jHCb9xtdKbdMhB8SJKPc9uxXuWfDcqaaWsy287g41cmpOI+sJixV5gc0WKa10nFKNW4SzPz4rOn9j72b2589GooSZYdIJ0Clk7xwZZz4emuAXTvlj+2Q0heGau0mbVUfeTu3Fn41Pgup4JWPIa5FahfqeOUE8KAk6SOEsfeKjWLqMRFZV4PbnES6IaElMaZmKfREsLs0S9BtrUG6IT1zY/gxQWJbgPQUiS0Kuy9K+sSi1i3os+hsixDpFbhVimyLoFinOHbKVnZkk2x3aylWS0JHEdqK+niWhOkSFE1ueeY47n7yFOI9isVffpY/pU+gPpJjxftu4ortJ9Jo93N6fDWfW5CifVIS6U8mvq1Ioc4mNASNj0CuQWiKe6iTUuHrz8co6v+lq4tGRtEgiGhNJKMQoqQgtAXSDUEaEI+Rm1FD71H6dY/fPPbC0R37WBQfD7epI4GFVMH4YEXwWxbZb9PaKqFCGAbL0v/DmRd/k7ZiFZMi3bRdUsCeO5dJ334KVdKMEVJoZ6jJE9n+LQvv/tpxW9NLm1v4R+YYbtl2LLOrOmiw+8kEDusvPQWjAHbJgLL5j2u0U6XroopFgiE29ZXfQAVjxlBdwV1gr0fU9xVDiljStvfq++ylLEJL0HlsLW5KM/CFgrdOf5xWq5uHkjO5a9OJpOcEGAVJUOsxd/o2tvWlSG+qQsR8fr3qJOY3b+fk6g2kjDzP9k1kXbqWlkQffigRNYqpVT00O2ka7H7Or+nmzOi6UgFJY7ppcE+hmg/e/i6m/SHkjp/9eOCxiUaSn0y+r3TL5OwI/PnEH/LdSedz67KTECGYZsDtm+ZQn8iglKChrp++qEchHUHaAXWpLG5gEISC/nXVZGsMZk3aQagEa9Y1IyxF0OBhdFgU6nQekJkoMHPgJ+DJ7+3aJGM07IsUyrL139rr14xEhYH08kBlnG33GOs7qAdeEELcKoT4S/nfgVzYyxWLE+9i4ZnXaiX/IxxDT3K3PfIFAM6dugqA77zup5z6vifYfroNQgyM/AgpkPEYyjIoVuluiFu1/2sJ22YTlR5mp66LpuqzJJIFnC4DUTAo1Gurz2KdYtmLX0d19aC6evS6lEI4Dkuq3rv/CzlCcMR3L1WItExCz0f5o+trHZJjtJed1OWdP2TZ1v+EWBS3MUFgl3RPvBC3K4LVbaJMgRICoxgQRE0KtYLcBJCewurz6DglJL3A49XznuO1xzxL06xOwnhA5ORuUrN7Bgo6U+Nd9GSjCCvEnVwkNEH6Aj+UzE7swEkUia6xab4/Te3Tvbz9dx/m16tOYnVPPQCfbLyb0+OrOcHO8aHpd3HilM2EBmQn2HTOl6RnCjqO04mz1a+L03ZfqIW0Xa13JMq1tkAXkkSgbX/LuHvZp7nzjs+ybN03UVGbbKOBmJRDTNqNC18Fhx0W2W9j6YQPsXT6nq3mX+kYGseXdXwfgAf+oI/LxclnWHXOz3n1mx5GRqODo7tCIiwTikXOnrAOJcoW2vu3lrBtNpNNh5gsMruqA0f63L1jFo90TMGdnSe/II8fgcCBrW+dybLt/41yvQGx7wr2Dkd8HKfkXmYYu4zjhwp7852+7ZEvcO9fPkWhWpI+KqAwpwBSM43Sfoz+MMo/HjoeBFhpgyAR0NCU5tTaDRxV14HZb9DU0MfS6S/w5qZH+GTtWq6uX8m0eCdH1+7gX5ueJGa7WDKkNdrDBKuXudGtvDvVTqs5XBcyKhzOj2RpXS6IPL2Rox54x27XPsNMcH7VC7gTXcxj07xj9iO8a9bDSKHwQoO6WBbb8hFWgJAKNzAQQmEaIaGlQAlMGWIbw0fB137i47zw1St4/utXYB6ThlPShDMrYvwVVDCeGCsT6UsHYudCiEloXaVmIAR+qJT6jhCiFvgtWsh7A/BmpVTPgVjD3uDC87+OZRjIoj65Lz7+i8iCS1AdG79Z2sMMy178+rDb73/s3ZyQnMgHqrby9x49dx3MnYK5vVePyigFhSL9M5Lkm5Smr1b7TPmf69n43k/vdl9Ljr0ahOCqW/6XOzLz+EL9iwDIZl24Oiu1CmeRT1TqbumvXzgJaQFWSHKVSb4JQlsx58s34l81F4BZ1zyLrKkGTycQS1s/ot/Xlu+O0xF65aGSeDLoUrQ3rmoHC/tAyVeOtuuWgUJmXVTEpOV2iRdXmOkCouDhRy2kF5DaGJK8vY+uE6vofFPAxcc+yj/Wz2X5vSdAY4E/nPV9jlsQ4brumdz89FmY8zOoLXFueewETpy3HpphQ7qG7nQtKulzdFUbX2l8juPiG/nuL96K3NqBqq2marWg9uQeIoY+3040kkw0FBAlHcQoBCaxHR5OZ57ATuFWCSKduiBUrAYrB9kJBs339AIQxrV2S7HGIXAk0lPYXoCbMrh72ejnnlyzoPV/Sknym/f6sFbwMoWQgrA/g/QDljZ/UGublca5lm39z0O8uoOP5T0/2um++75/CvdxCo9dcxNdboyt/zaHmjU+0Y1p/Ooo1vp2wpoUt/19MioGflKNpqW7E5ak3oNwHHoWzaJ7nmTle2/iN5ka3jbznwA4wiJAUm3pwm3HI80AiBl5wkAw/V/Xsr6nFnF7DWe98ZuY5x6DteKJAcFvFSoWR98BKqywiyvYI8biyHZI4/je6ozFA6yID+Fgg/a57ERkbZHIC1HcJAhl0lMd43e/OQ8vAV+9+Ne8MdHD826R/26/gMnmvcSkxzeangbgiu0ncnztVp7snshDO6bxENN4+Lg/AGCysyC5Iyzi6/pR+QLmkwk4c/dLnmfvQJghfiB5Kj0ZNzQ4o349c6Pb6PCT0Ag3PvoqVLdNV9bCTLok4gWs+gLSCNnWl6Il1cfG940ex09u0bpNWz47sxLHK9grhAqCAzR2thvy42GDMRWRlFL37O5xIcRDSqnT92H/PvAJpdQTQogk8LgQYgXwbuAOpdQ3hBCfBT4LHHIRijvuGk6DXHz8FyFfQERszl+khVbHw6HncMaPTvop33vpfAA6iknMUgNfJaOIggeui8pkqXpwE72zplJoUIjiroPkUDqvVx9HuiFPFKYOFJDKkM2ruAR49OlBx5m1l2iXhak/vAGjCNE2SG6QxHf4tJ9gEjmpm4YVBp1vUBCNjIsYXgVHCPaiUHPQqPD7geXPXMuF538dQhBKgeujTAgiApHOgOOgTIH0QpLrMwjfJ7HVp3+9QzBfYFsBM05cz9R4N/+540IubbyH9bkGojGXwpoUYaNLLFng/8743k77/uiTb+Xhos9xTmlaWkqUbWL3h6x+aSKvP+XxnV4jhaIp2s+2SZr6EGv3ifRKpBvipgyigJkP6Z+kx22VKREFHxUxuecfoyeaS2reP+xiujxaVhbRPlRYOuezLHvpG4d0Da80DC0ulEWZyxo7Syd++IgsJI2Gx67RQtXPdLbgVkH3HJMJnQ5BxERMbsDcsINpv/N58WNVRLZa+LHRs+KhcVw4DjTUYhYUb3qNHm05ztkCDDaEPtgMn35anzNXXXUFADN+8zXCosGzj0/HmpQhkQcU9My2ySw6lZmf0+eJsulHBRWMJw5VHN+b0czHfvxxpv78OhqqMvREE5g5+FjdgwC8cNWxoFw6FthYaZCPxDUb2IXNXi2SNK2m4sNNdzDPirE1GMyHpVA8tGMKUijyrm6qlH+rJVl9wrbZw9YSRi3MWBQRwAa/n6nmztqCZcyyEkxt6aQtnWJ9upb29hT2rABPGWzNV3NS1QZU0UDEfYShCFyDpy8Z3QxnpOkFwM9O0SN1F/L1UV9zMLA4+g5uzf/ikO2/ggoOBMZrIG8UubI9Qym1XSn1ROnvfuBFYCLweuBnpaf9DPiXcVjjuOPWJ68Z+DvyzEacHRmWHPeFQ7iilwc+OOcuAD7WchvJzQHpmTE6T65hy0VNpE9txT1+BqomyZQfvETzg4rkWgPiAdO/s+s54jMu+Q/8iEGx1qbHj+/yed9c8Fu+Mv9PfGX+nwBY2nQ5cz+/jqY/6YBXqIPOeSaJzYrab8dZ9d9Hs/4/G0if0krbG2dTmDeRLd+rYf5fvsis31dGFkfDWMUeKxiOg2YhLOQ+fUZ33HUlhfpBarrdH1L/VAYKRcjmsNa3Y2zpRORcwqhDodbAjyn+o/kJnjjxt0xPdPJY5yTOq36JYyyfabEOXjvtOZwZfcSSBXL9o4eJ7xz/v7z3px/mBbcR8f92oOqq2H5+Nf/6+duZNWcrN04YLCJt8rUgSrWRo97OkJ4JXfNs3JSB1efjdBSIby1gpwNiW3NM+PtWwpiNVxfHr4kQRPddfPeQFZOCgKUzP3Vo9n0EYFnbYGFT5fK6kDT7yG4GATzxgysG/s48Vo8yIDM1ZO1HDda/3mTdv8bovnAaSEnNkxaRLojNTvPhJ97Oh594+6jbPPPib+LPmQR+QHxdP19pfI6/5mKs9hp2eu71C37P9QsGz2Ozvppl7o1pAKyHkvRPhUKtpHqNR8MTkL74RMSMqXhnz6d47jFs/+BJLPjojcz/+I3je2AqqOBgYB/j+IZ3Dj939St4z9o3seMki8i2fqrXBsQ6FNKDfJOiOMVlkqWF82tkjHlWjL4wzzpPF3085XNyYj2vn/QMJzRspiY++mh3uahUxrqLY/SdNZ1nP3oTtXLPcfftrY9wbPN22jfWYm9yePSfs/nN0yfx4H1H88vrX01kmwlKYEc9ool9c3M2ch6LTvvyPr12PLAk9Z5Dtu8K9g0h8oD+O9wxXu9gv0lZQoipwPHAP4EmpdR20IUmoHEXr7lMCPGYEOKxjo6O/V3CPmHZhht1MUkphHtkzeUvNC4Z6DCOhu+1XUD/JIOqtXmsnGLmG9cQvreTbWc5tJ9RRzCjhVhbESsLoDj79Od51yPvG7aNFcFvuXXrk9T9vw0IpXVYZkfaBh4fGbiGYunMT0FyUPDv6e9eMerzVr7hC1Tdt47GR/vonGdTe3OC/q44XsZm1tdvZNbXK0loBfsGYVosst7CivD3uxXu3P8dyYF/+yMq++BvP8Hyp77MrY9fo+1xHQOEGLSeFwIVtVC2QWgJIh2DXf/b/ngKfXc2scWtZY0Pf9x4HF9rfIbnTtOaKxve8dnd7vvp3GT+Mvc3dJxaR9PrNvHJ2rXcetTfAZj+x8uYdfe7eesL72JF3sRTBlVmDqEgiIII0A5thl7PPf/4NLc++kUwJMX6KG6ViZfcfSI72kjPkYo9ndvHEy+HOL6850cs2/7f2uHLkPo7f4Rgd5+1bF6lNU0U2L1Q9ZLkI8fdydcW/453LbmbHWeErL+4FiWh/uk8Z7eu5RsT7ufGln8O286K4LesCH7LtvOE5vD3Z5C9/SzP2zyWncZj2Wm7XN+iU77MolMGL/zWffTju30/XsKkf7KNnVYgoNAAJ1x+IydcXonjFew7Fso3HfgG2jjF8QcWXscLX72CZ76lc96ZiU5a78giMgWcHh8lwYtDkPJpbErTFQx3Uvvw5sV0BbpZ+/dcFVvcWiyhc4B7L7yBZ193DaNhaD7+6YtuYfannwMgJaMArPYyfHDrqbxp3at2eu3RzlZOq15HbJNB86M+rXcFbHjHZ1n7yY9juFCY6IFUBIHEdXcdy+9e/plR3VMXnfZlZN5D5g+N9tWhYiEdrDhewZGJl4UnqhAiAfwf8DGlVJ8YYwKnlPoh8EOAk0466ZBOFyrXg7YOmNJyKJdxwDDS7WmhcQky4uzy+bJ5FWy4jPjSHWw6O0btH2DDr2bS/JYNtM0okp9kkNgWJb6mh3ibzbSZm7im5R/8uOc0vrPyVWwt1nBN42M4QjMjbpl1K9Pf/H5k2uQtib2Tx1q2Q1Pyj/v75zHOEBQ9ExkrYF1p0XOew/yP38jE2mqUIZjwUAYjnWfyn2uIv9iJskxELs8pL32LvqkCL6GY9Y2V2lp8yPd0effNe7WmCo4MKN8DIYcHciHH30pYhePOdMq0mNgJifWcB66HiMfAMJD9BcKIhfShZnXA+zafyY8nPUCwIIO7Kc5Pnj+Nn4rTsCyfuTdfzouX3sQL//Kl3e5r5Rev4LLHniIlo3zpyp9wUWyQSv+tnmnMvX4rSIOus1v41MUX89TJv+FX/SHG7H7i0QLF9gasrEn3vCj5IS0Hv6kKL2li9+kC/x13jt2Z5Y9rj+df4llu/lGGh/OTS/cefIbKsjU3sHT6J1k6/ZMsW/fNg77/A42XVRxXIWSyeuTqFYjR3J7GMv4V3w79F2Qxn4nzy+teTdeiApbjc82Ff+RvHQvo+uIUzP4i06MdRIXDZ3cs4Ki+s/jV1lNY99xEnn7jdzCEYO3FDkdNeAexu2Zy0WX38f/+qjvzoa34yvzdr6E8Xjrr99eSPL8Ad9XRcxQ0LNtMDGi/aDqZo2pQAuxMCAq8pKDhyRBlgNPjM+Nb38LIC9zagLnfaoc+zWxUBc1sWJ7+n709pBUcKSjF8YGxtsMkjs8wE1zecDevf91JTP/KKuxEBDW9jmgn5I8OkELx43Vn0DB7OW+M699D1PC4ees5fOKZyTidmmvw3jffxvdO+OWY93tp1XYurRpu6P3Wr32KxFafzASTi95u8ffZywYeO80xOc3ZwE9O66EvXUO0a/DYWpmA6mcssq0KsPBqhoto7wlh22yW/xmOun/3It8HGqK2mqVTr2DZhkpB+3BBiCBQB8qd7fBvVo1XEWmfj4QQwkIXkH6llPpj6e4dQogJSqntQogJQPt4LPJA4kjrYo+lS/K/p/0QgDl/1F1ENwXvaHmY7+YvIH1/I5kWsNMJuucYvKFmDX/LzOWJnknUR7I8tHkqjvQ5K/ESC6P6AnDd0r07xsvW3LDTfeF9NZCA/vmKGnZOAPyYhR+zsHuGiy3W3bqWupoqNr2uQXdSS45zqsTQWFJ76RFVSDocdH5eFhgiirki+O34H7Mh27/N+834bhvoPlpSc5tE2RbK8xBhCEGA8ANQCjMf8mR7K99JTAXgqJM28PxLkzB7TFxgzeevAEZnAI7ED0/6GWHbbM6N9DJ0QvrjNeu5I5wLIiTbIsh2x1m88iLWPj2JCfcr+iYlyc5V2BmDwAYzA8d86kaeu2Fs+90Tbmy/gKmRrnHZ1uGCI9UifXnnDw/1Eg469nTeWPXGz8MbGRjvFgqqq3I8dsLvmHPfO2mq6aP7ZAe73+GyqheBCKcl1hAg6cnHqJnRw3pfYYmAoyyHlWf+gr8eH+O1sRx/maorR7tiNsCg8+tQ9D1bR90OhQwEWy+ZzsTfrhv2uJ0O6J1h7fS62d/dTNBYw6p3x3jxikaO+kIvwjAQttZWq8TxCkbFaOLW41lAOsBx/CgrztS/ZpGtLQQRm3i7j5uQqIxFh5nEz1h8P3Iub5z7Nx4ohLyl/mG+sOb1NP5TAIr0DMGnj162x/2AbiCP1EcqI7ewn6ar8qSnNnJ67XoeKIQs7zuWUxLreG1Mj8ldNXcZn+m5mAlf7mTpzE8N5PFDm0Mb379v491heIgv2g+BkvKRGssrODgYUxFJCBEH8kqpUAgxGzgKWKaUKvMC96m8KzTl6MfAi0qpoYI4fwHeBXyj9P8t+7L9PWHptI9DLg8MslUqGB37cyJ66Q1fgDfAvM/eSLcf58EF/wcLBh+fe/Pl/OZrSyhc0kN9PMs9K2cTSRQJleBou4ef9s3gjp65vK7uKY52thMv0Wov/9cPcOujsOh0LbB320OfZ+nkj7Fs07dHXcdTF32FY+4Z7AAM1bQqY+msT1OcXMvt9141/P4JH9rpuUqFCNuCVBLSfSxtuvyw/h4NZZeFpc7s7j536URAhahQDRTTBjDeHbrDFSocSBAXWW/Rhcf9SSSGJJtlBsGBGpN74qYrWPC3z0NVEtGf0QmQlGCZKCmpebIL+rNs2DCZJ+on43ZEWRk0YaRcPKmwuna+iNsTQkLet+EiHn1mBrIoWXOJthzffMkUEttCgggkn7ZJ3zmJ6pigUCPIT1CYeUGmWZBoCylWSbpP8Zj2y6+z/v6rdtrHktpLgT2zBx/PTmOb18uKW08keoxmP3766L1+SxUcBCyOvxOAW7M/P8QreXljf+L46jddDW+Cc15/A21P1bHAfysvnV063sfo/6b97cM0tfZwQcsqvtb4DDfIkK/M+TPzbT3O8om2E2i0+/hM7RoAnj7lf7norNez9OOfQkV1IWf5s19l8Ylf5NbHRy8srX7T1cy8bngnf6QQ+pkXf5PEtoAH/vDJYfcvnXoFcvUmdApbeu2Om1h02pdRUmJ2ZQ57IfvFkbeDqVP7sGQWsrvP3UjoUSYVBCjXHYgxAzG9EsuHxfGFxiWDDcRyPN7bY3QQ43i5qPO3P/wPCz9wOSKE6KYM8XSWjuNbMDuizP7xJra/djK/+kgtnjJZ0X00kxK9bAiaCA3Byi/tXUNmV4WkF874JdM/dhmxjfDLl07mly+dT2qd4s/1Z/M/r1nLe1vu59TIFl519Is8dPECJt7Zz5LjvsB9T+2sY7R01qcJ6pNjdsae/sfLIKo/p/mfuJFn/2N8mkx7hbDyWzrcoBAHjDGkxrDdfXGxF0JcCbwPCICPKKVuPRDrh7Ezke4FzhZC1AB3AI8BlwBvB1BKPbeP+z8TXYB6VgjxVOm+z6GLR78TQrwP2ARUWiWvADz/Dc1KGBlcnnr/dznq7x9kxo1x2o+rpa4PCq/J85sVZ/GrxlNYNPdFHts6iQcfPYrIxAwfPvpuAG599Iuapvqn8pY+v8sCUhnPXb/7wLFs9fWj37/9vwdvjMgvl869EkqdzMMeY0xkltRfhog4KNdFCP0aIcWQRMgYSKxUqHadZB0ISvjLCUOSxWFJ4r68711Y/R7ITtOpzZtYO+ko7C0Cevu0LbppIiM2eD5hRycT7p/MfdYcZv8kh1+lfwd3rhjdAW1PWPDPfyO/PoURgtMtOPOZN/DAsX/k/iu+xYLlH6ZlBVS9mKbQHCc93S5pIinsXkFya4jdFyBdRXS9RX4aTP3J9Wx4z4i1jNG96VdPn4LRbmME0LcltU/vZ1wReYWcYyo4rHHvLYMsgLDtf4c99odF/80XNryeJ3tauVa6AzbgAEXlcdfWWaTXVXPvglksaXyeNySfZ9maGzjr9k9T9AbPj7sqIJWx5jO7j+Mji0dl7DRG8kH9320Pf4GFZ1yLiux94ftliWBssWXp9E8iUklULj8wRqUbHSECq+RaqAaaRahw54YRvDLj+NAC0YjYq0KlWVzjqDdzoBkjx//znTDPJLFNEX1oO8r1aHpkAn1TBWFbO42P1fL155eQiLj0/VML3a/89fgXWq44dzk/+/ZFuA8nSG0M8WMCBDy7qYXephjXt19Ijxsjfl4Hq+cnmf3NgMXHf3Hnpm8QIvw9f+fCttm8Y+O5OF0GXkp/joekgARgGqh4jCXzrmL58189NGuo4HDDXrnYCyGOBt4CzANagNuFELOVUns3AzpGjLWIJJRSuVJR5z+VUtcLIZ7c350rpe5n16NwF+7v9veEZet37QZWwYFDWXyvXEx6pGghnABnSy+t2/TX4bNX/R9nnwoz7ngPL11zDKl6AxFCdmuKn0ZPpysd54drPs/vF2SYYSb4czbOtf+4Gs/XidDu6PHjDj/An7Szu8zhhhXBb1mceBfK9YaN6o3E0kkf1X+EISIa0fohtgWFonY3MgydfAZBKRktQSmU56OCAGmZICThkM4nDOnKDd33gaaS7y9GrK/8HoQ5eEESusPHIwdet4/v42BRlD/QeBctv1zBxR/5OIm7ekApgrYdsKMDmhsJTp1H32RJfK3khB89wyOfOGm/9pfdEccsCvxkQK4Fqkr3p2SUi457hqeWH0/vMdX4DpgFhRcXWGlJ4ED3XIkyJMkNivg2RWhaVJ3Swevv/3/cctZ/saT+sr1ay5WnLON73/sX0nMDRpl8PbioFJB2iwoD6dBgZCyfY4Z8dvIy3vuHD7CGyVz9bysHnjv3rx/E7DGJZGB9Zx3Pxlr50aozyd3/VY6blOHNjY9ytLOdpfdm6XcdquwCfz/nuwftvax48Gpede7XDtr+DhRuLfxq4FwnVUjo7cbwpRR/RDwGsSjexCrMfhfZmQbbRpkGIl+EMBhsMLkuyvdRJZaTjMU0iylfGFZkEqaF8j1UqBBSDNf6ERKkGMgHxn00bB+2JwwDhNTNsDLLSIUYzSWdU9Mg2LJtoBm0zzqHI0bcDxaeO+3XLK6+iPRPJiEsizBfIPHnx0naFrKhHtVXYMpnAlZdk4I5eayXogdkHRHhkdrooUzBlvMNJt/qERoW2aLJTevO1ZMKwP9lk+RmONx45ptoeCLL4pOv0WYZlJqYqbE3dtb21lOY6GF1HtoicVivM5pbH/vSIV1HBWNHqA6gJpLac0OzZC5WNhrrF0IMdbE/r/S0nwF3o0U7Xw/8RilVBNYLIdYApwAPjfPygb0oIgkhTkczj8r2WS8LUe4KDl+UE9BzgQ1TGTEU+XXCttksnLOSxyYeR9U6F7s7T3pGFXNq2nkwPY2+VTWEx+pnP56dRvfGauyG/MF9E2j20uLjv4jYRdHlcMHixLsAEJb+ad/m/nr0J0YjCNMA14NEHOWYhDEbmfcQbZ0DVHoAiiUr1lJnVDgOqlgcEK6VMJyxVIIwGEjkBoRfywleqCgznXZV6NprjAMtHUBGIzoZtUz9HsNQd3lLyfTAPsqv28uE92DPt58weZO+QLy8HfFEEtVe+jxLa+6dFcE/VYtxfuPYP8CK/dvf3KvWsqzj+8PuC9u0VN5L6Ub8qKBYI/DiIHytz6IkeFUhVnMOx/bJetU4PaAMiFgeZ9etBvZe7+bSqu18fY4uIEn/8BdA3FcsnfwxgD2yPCs4clGO5Sl0PF87QAT6+ECB6ReLfsA77roMGVi8a84jvL3qcb5nncXvnz2BlkiaNyXS/CYzkVXbmgBQdemD/j6Ep89rC8+4lhUPXn3Q9z+uCBXCtllR+NWoDy8866uIpmpkziWMO7jVDv2tFjJwSK23MfIeSkrChjhm1kMUXAhB+BFdFOrtA8cGy0LkC7pxZEiE6+nmkSyNxbmubqgY+rYwDERUa96pfAEskzDdt2vG8tAYuxtGs5BCF66CAJRgp/Gw3Wx7gIVVyn1kIg4Tm0AIeo6pRgSK5NossrePoCTGPrC9wyCWl8fLbj3q78xa8AHqbgPl+Xq9vg8RhzDpIPyQtW/53H7v77wl1wHvG+aSVj4P3NY1D6czT+fxKVJroVBnogyBlXCxzcGC5xvj/TxQSDPn7S+xuX0W0c7BRtzexvJPz1zOFVveit23f+/rcMbSmZ/SnzWjMDIrOJQ4QQjx2JDbPyyZjeyE3bnYCyHKqmETgYeHvGxL6b4DgrEWgj4GXAn8SSn1vBBiOnDXgVpUBRWUcfeGmbAwQ8/2GCoVJZZK88jWyYQdEZStmGXpeX5PGUyY0UlnWt9eeOa1rHjgahadpmepb3t4Z3HO8Yayj4y6qopY5GfWYRRD3CqT0BTk6yXSg9oXHEQQEtoG0g2QOVdTjstJpjQQ+TwkE5DNlTao9Kx4EOhikwoBYzDBKxeRDENbcPt+qStq6OS0rMvklyTahEQYBjLiEJT3AQPbGtYRNbSFvZASFYaahSXF6MyhUSBtW49HCYGwbYRjQ8QB19PFsmQCISVGLIq/Y4g/wCgU+d3hgFsL7wH3HvNnLnIXIxxH/zMMiEbINQqunv+P0rP2/Te2tOly/cduRs223jsJNRNCC+qOb8c2fTr6Ejxz2k8HXBwBfjG3nq/8+WJS6wR9hQgtVu8+r+v8k5/nrifmIQqCSIdg7hduBAVNj7qj2ggfCCw59mowBCI4pMZlFVSwTyhfwJ4ZkSPMMZJ8o+lp3l37IEdZ2k68wejjuMlbWNNdR8E3ueDCb3DnHZ8FYOnsz7Bs1XUHdq1eAGMYkXmlYPOiJEYeEOAloNjsg4D0zAjRHRFEoIv0hhtFBGAWFZEufSFq5lL4UQMr42FtU7ow5Pt6zEgKKLoIQBSK2owhFtUNJiFQtZpFIrr7wPMwmhrB91FFF+W6hHndDDSSSUQyQdjVMzhSBwjbQpTdgUOl465jQyyq84m+jG7geB5BT6kYuYt4K6RARhxEKgmmQViTwquO0HVMhEh3SKTLp2+KBTPi2PWzia3txl+9dtTtjGVg5FALHa9+2/e56LoLdcFMCIRl6fftmAhz/2OMLiCxy/j4+LPTmW3m8WOQPqHIuUet5qlfHcP501bz2trhAy5nRiSXbm/BaZYkNu+GUbcbyOZVeGtORriSSCcgtD5rmSL//NcPzmjb4pOv0Y7OqhLHDyeESAIOlDubBHhCKbXH5HkvXOxHe+CAfenGdNWrlLoHuKcksI1Sah3wkQO1qAoqAH3yX3UxzP4/LZz9wZPu5oHuGUiheDI9BaPL5LItZ/DD1geZGunk2UgL29uqAVjxwGdZUnspkj0L6I4LDP27XTr1CvD8nYQ+DwfcmvnZmJ63/JlrOXHZVXR1JjHaJXZaUGgIEaHALMQwigorHyJ8hRExMXI+UgiEUuD5YFngunocriqpC0dFnWgylHofaAewcpGn3L0UOLrbWOp4osSALtMAPd8yYepEzFyRsERBF4ahO6VySEAQQotFl9cXhKWi0G66iuXHhNTC6qXxPWGZOpG1bX3bcVCJCKKjR+9jTxhln4e6eARDRDKrkohYlDARReSKeE1JivWKs6Mb938npeRKBQFL6i8btdM48ZzNdP1xEijoFI0YLhSnFvnA5gv4yeT7ADjtqYvpfrqBhqdDiinBe2Y8xKtj2/ZpSZt83XFuntrFjo4UcksEQs1waj/hII6XScnyUYRFDxYqDKQK9hcjx96GYppp852eqXy0ZgPNZj8NTj9bLT32cecdn9UGKBwk+YFSAUkWPZYerRkZy144/EbcxsLUWHH/VXz5udexNtfA0x0teEWLWbU9SKHY3pyitymBzBkYOYlR6qlYfYLQMDELChGCsgTKkLiTakGAdEOMdE6P/6biCD9E9Gd1ccmyIB4ljDv4SUe7e1oGwvV1oykIEcUiougOjM6JRJywoQaaapCZImr7DjBNRCKuR+jLY2dSoGIOQSKCdH1kyT1UuB6izHIqYWDcvGRyIQwDbIugpR7p+oRxG7faolgDIpDkGiXZiYoJDymKtSbRjcbwA1nKBXYnjP2yiuNA2NqI2LgdEYlAEBAko3gpCyM3fmz685ZcN2ohacK0TkTBofYFFytjc8nZ/+SeeUdx923Hcfu0OVx0/k8HnnvZljMo5ixqOkKCyL43aVvMHmbN3crWzZNJbFP40UPDLC6P4x0KjOZWXcHhgb10sd8CTBry8lZg35LgMWCs7myno13UEsBkIcQC4N+VUh88UAuroIIyVr1Ruy9c/8LtADQ4GQCCRMg962cy/ZFjsBoKuGkHo99gzs2dLPnYpXu9n0X22wYSjJE6ArvrHi2ZdxUyX9zr/R3OePT44UnRFzvm8ef1x9JupLD6JHavxMyBlTOx+0Ki7VrTSgQhouAzUNgXQieRBQfheWBZBHUJlBAY6bxmYORd3aVMxlCWgezJIkoaDUIpXWiKONCfQRWKqCDQCaJhQMxB1lTr50QjusATBPqfHwwyoEAnsSlLs5E8HxiuCzXAYhJSU/OVQvm+7rZGIxCL4TdX46Us3CqT5LoMYs1m3X2NjjKLP7JINeT2aEnnrkQ8D1Zn8/PLfsPV770MZ30nKhllx0kxpp+wiSYjMj47EGK3Wly/m/M7Xt2rLyj7zihgRnxMX/Lwlimc0dPI9+b+is/OWsb/1ZzEg63TmdjUw3efPp/vrbqIl8Zm4DIMP+89mWc6W5AoYskiuRYHMy+w+sHuh+M/qCnhT37vEIl0VlDBYYbRikmOsJgT2cZfczH+0HEOT7e3kMvbtP6PxXk3XofdXIPR1jPmfSyy3wbo8/XexHEA4e4b2+FwxdX1K4GVMHnnx/6ei7DZq+OOrrms762lPxsh0xnBSxmYGYFTLfGiEDomRgHsPoVZhGi7iQgUXcdoplCsvQ4RQqTbQwQKt8oi06ILMVZWs52MosIshuTq9f31j/VoJpFh4DbGCE2B3WthRG286gjZ1gjS1UWbSJenGWRAEDGRroSkg9lfRHamkckkynVRrgcqRDgOMhrV+YBp6qZRoYgIAsKISf+UCL0zJfMXr+JVdS/w9btfy6xfFHFrbPJ1gjDu7HywdtFw2lXxaLRYfjAZSmsvqWbWTf26aRZxKNZHIFQEzoFhXAzFA8f+kXOmXUZidQ/bT69nSdRl3etu5u0bzuOhJ4YXmX/Y+iAr6h7hM/+8lKrnMvu0v7BtNqc4Id+b+Vsu6ryczsY49U8qvIRAenDiZTqOP/7DShyvYGeEamzaRfu67T1hH1zs/wL8WgjxLbSw9izgkfFb9XCMtbT7bWAxenEopZ4WQpxzoBZVQQWj4dNHLxu8cRJM/f43OWnSJp56Zi72yjgRS7MEhmKsLKSlDR9AltzGAC38XELo+bu9gBe5kg6TkEfsrPHc6DYiMzx+2H4uHmBmS3oIAkJLEERNpBcifJDKQJWYOcIPwTZRhoEsGoSOReAYCAVIUI5FGHcQXoCfdPCSJhHLGByTK7OHyi4yUiIsE2GaiPZuSCUgldSFIsPQCWNAqbATGXyt6+qROyiNplkDtPwBCr0UiGhUF54MQwuHu94gPVmFhBEDt8rEyoW4tREitoXyfMSuhLXZffK4J/eXA5l4Lql6LwDL0/8DwGmOyZbzHWas9hHpDHamlnVtDThHWRSVt387K9O8hRjWRV9Seyn/93yRqHCokTEyF/eR+EOKdW/7HHP++GXmT9zOyvZGup5s5G3Z93PV/H/w2ZZlJCcGvPqxf6fqrhix9n3rruZCm4UTXyIiPRzp8YP28zHzJkpoPSaMPW5iXHAgWUhLZ2qnrXKXcvHxg53SndxwKqhgnFAuJoG+yDva6uKfhVbufXYOVpdZIuQPnjPHykJaevTnhsVxYRjDGkNDz6crgt8O3C6fR4VSKCEOS/bReGOG1UVEeDzjtBKp81hn1rHDl4j2KDIAPwpBBIwiiFDHec8Ao0oXkbwESBcQ4EcF/a02ZlHHykwrhI7C6RIYRQCBUZRIF6LdIWGp6VKsjRDaAi8uydfHiPQ4eHGJGxc4gcKPCIpVDlYuRChdjHJ6tGuXMiUqFddNnqKLKhQ1+zkWhWQCFXNQJQayKLq6uYVeqx+Hs2pXszI/gerWNG5VErPfI2oJRBjuPBsi5Ms2jo82XvaTN/83n3n8clIrViLqapC+wo/tfwGpvK89YdvZEs6uY90VVxC23QTAr6bezawHjmLm7/6dpy7+Dgmpm1NXPP1mJjybRxT3rcB7wbvfx50//TGhgtfNeo4/egvwoyXR8JKm4sHAgWQhLT75mp32ceF5+hx2x937r3FVwSHFXrnYlySHfge8gHZ2+9CBcmaDvRDHVkptHjGDd3irCFdw2GPDBz7JWbd/mnxrgFsjaXgCMi2Cza9p4Lnrd58ELp3zWZCCHec08tg1N1E87r04z27SxQWldMIhBTKZ0AnIjNbBLuXmNj23v2A257zuBoIfWtw3/0/0hDmWzjFZ9tI3DsK7P7QYSo0GeEuiBxI9/GKLNlXMzfQopE3qngY/IvBjBtKX+I7ALKoBAVNnRw5MiV9lI10LhBZLDiIGvafWYfcpEptz5CfGKaYM3KSg/fgk0Q6oeamICEIt+plzEbXVWtzcMrU+VRgSJiJ4SRtV0pQxioGm2IcKJQWyoIsfwrO0NoDS9HYRhuB5kM0PMmNKwqEYUo/dGQYyVepKRiOoRJSeORGK1VC7UhFb2aW/T/kC2NYuWTYLjUuGdzJ3Yy18MLuVwtYjW0ubLmfZDv15r3z/TVz0izdAXz+Nd7fhJZr5/NHH8NVj/7iHre0BZX2LciFvCLpDlwmGhUTyxCk/x3n9uoHHVi6fSbQD+qbroe9rnnwNqUSBCYk+Jnzbxurq3OUuRxbJhqJv22Sefs0bADjqL2082TULnBAUSE9fQBljk816ecPYRSXMlCw+6UuAdpJZMv8qAJY/W7ElrmB8IZtXUdzcwpsSaa6Me8SeNxEKOo63x6RXcva/fhMvJnjwP37Aa/80kVvefi6yv6AbBv1ZVE2SQmsVkX+upnfpUXQfLWm539ONo2gEOamFeZ+5key8Ijf89VYez07js89crI0CXuEYGceHoiuIcrKTYXLjHfyh7wRqrRzbElU8lp1GMS+RRYH0BWY7JDcHWJmAfIOJmxAoQ2CWemtd8yRGEZwe6JsutBFCTUBVa5pkpMi29mrCoglCkXzOJrQkxVQCw4V8gyBwtA6ekYfMJBM3BQiFUZAYBUCBn5UDxYBIQiJC8GKC0AYrU0ukN8TucZG+0mxoX+s2AoSWRNXFkF5AEDHJtgiMmf0sib/AN/oX4z5US6FOUbM1gx8zceuio144vVzjeBl6vGxQn+ymG77NZ455FaqzG7MxhTLGd0R7d5qBs07YtNN9PWGOU85cyaofz+VY5yP849U3cpQVZ+I3LcwNbbvd1+LI27l1FAH5sG02d/50sJD0wbp7uT01m1iHTWALCjUS+QogH6pd6OOEjsGFF3x94PYdd17J4pO+VHGH2wscBE2k3WJfXOyVUl8FDkqyNtYi0mYhxBmAEkLYaD2kFw/csio4nLC0VctjLdty8Ox4y7j/Vdczs/taav4ZI9rhka8fWyDMzK0DIXD6Q065+gP0nyOYvjGJ6EmDIVH5PMJ0yJw2DYB8vYGbEjQ/lMGIxxANdXTNjVG9Os/0mjZCQnYE6ogoIJUxWgL6/kuWsybXyDvrH+Drmy5iy6rpKKmLQk46xMxrxpAfM1AS7G4DEQRIt+TQZghCU+IlDD23rqBYHyGwJV5CUKiDYn1IaEsivRZWJkS6ITJqgW+AF4BlENrmQFfZj0ldmLINzILEyAV6DC5UiMBAeAHKLhWeyqwiKUsjdY5mERWKWteoxEDCMPRoXMTRo3aJKF59gtDQna1iSpKwTP08w0D5fimRHKX2vpuxtkMKKUbl2678fBVTf16DlzRwkzDZ6Rq3XS5r+96w28u7b6Zv22TagxwdgcEqr1G3W4CX3vAFjr3vRqyswsoIQiD0Jd07UqRfrIXXwuwf7r0dS9g2m4SM8N0Hf8dHzngzbYUUWddGpM3BrmUppKc2jE8ladFpXz4o4v8jMfJ8deuT13Dh+TrpvOOuKwF41blfQyR157ZsWFDBKw97EsQ9kJgzaRth22zCfgsrqxklXmJsIwSbFwNCMfPOd0NHhImtAfH1AaFjYRSKuM1J+GQ7K9fOpnFyF7VC0dXTSKx+NqEJ3cegx7G2OMywOlhlTDig7/Xlhl0Vks6MSCBKSsJZ8VU8ZUxmTmw7L9Y3ktuewOqUyADMHASOwO5XmAVFtMMndCQi1E2fIKLwY5rhEzqK0FZEm7JMruolYRWZENPac1syVaTXNKGkdsNUBvgxKDSGGFmpmU0eKFPhV/sE2UG9JhHox6QPXlwgFBSrNVMq0yow8waxtihWVmG4itASOD0+0lcUak28uMBJa2ZTviXg+KYdOEIxLdrF/Qn0+4jZSF8hvXB0zcSXaxwfgfLnPd+OIpoawDQIIibKHL+RnT2dQzL/VZJsOXdwPXfmm7BlQGZpP7URl9c9dDnLzvge3/vNf/ORU9+4X+u586c/BiApJdWRPNlUDVZW63qhILVxfOL4q87Vzevb7zm47J/bHtk5dwgiukga2JK7l3+G85Zcp8/x9dFDeq4/3KAQhAeIrqZ2WRs6fDDWItIHgO+gbeK2ALcBHzpQi6qggr1BbSpH3+Q4gWOjJPTNCThluT6J/3jez7l+2xI2Z6r5zqzfMsWE6pbNbD3PQATg9Aicbr2d3Jx6Yi+GdJ8xgY7jofHoDoKwh47OFPQLpt0SYGzt0sWEdB/1D5uoqMXseBuPFwPuyx3D0YfwOBwKjExAP16zHmrWAybXTvkz/zrnCiIdAjcFTo+kam2ICAQiVNj9AUHcQpk2Rs4ndAzyDTa5BklmMky82yP2Yhv+xDpiHf1Y02tJbBNsfrMPE3y6wwTJjQKr3yB0DIq1lmaK+EoXqPp8QlNiFEKMYkhQZ5GvNTBjkkinhxAQ2gYqYiLdULOPhNC09jBE+j4qahNWxRBKEcQsjJynGWl+iIpamjJvSPyERXaCRaRXEThCU+JrYpigtZ4ojcPFYpqRVCyOzQFuL13cxhvLOr4/4Jwmm1fx7OZWVMbC6ssRmhFqXxRc9/hiHky/D4CfnfLjcV9Dd+gx2Uzy54x2Kf3eS+fzwTnaHHTir1ex+V2zqX82gGdjFKol2QmCQnPIuo98gqU//Oxe7evDT7ydf63VgX2SCf7W7XSeCb3XziWSY8AiON4WIkJwq/bPkfHC877GHXd/DmUaLDpdGwjc9tA+CDjtJUaOsY2GxSdfo4uqcVsXXeEAenxUUAH856t+wX/NuYCXXpoIkYB5t3yJYxrb+N9pd476/EeLHv+5KMlFsQKXrLuQJ82J9E2JE98gWPX+KDdceAtvjJcs2efp//KqyHm/+SiFGkFya0Dr3Yot5xmcdtaLHGdHIPnMQXq3Lx/sjpEEcHYEsqqNlwothKFEuhIrqws3KCjUSPqmOMS3K6JtBVRRkptn4p/Sj/FiksbHA3pnGkS6oG+6JG/GyVWnmZ7oZFKkG08Z1NgN3JXQRaRijS5OhTZYvRI/oQhqfBAKaQdMauwlCCVtqxoAiYiCnxiUMwgtpdlLBYEyFcoQ9MwLMfKSMKIwsoJYm4WZ06xSLwXZCYLQglMXrCFieESE4PT4av664Bi6nHrs/hjSVyTXZDHmzNBj9mu3EKT79lwwOsRxvFxE2AmZLJgmzjqt6Rk017Do9K/scwza5X6GYNrfLqU5KnB6BxtqG/x+dnitnJxaz5aaKmYku6AR3vLse/jN/J/s9TrOu+OTvLn1cQwmcGnVdhZPPB6AGY/YbE+naN2o369Q2gSlWLN/cfz8Rfo9S0MQWpLzF1/HXbce+ALNWItBYx0zrKCCvcVY3dk6gbcf4LVUcJjiUDCQhuKRJV+DJXDC5VqPqOZpA2t6wFUz/848K0bU8Jhfs52CMklJi++9dD6I12JMzeJPBXdtAhGCH5H4jVX4jiA+o5eqSJ5z6tewqq6JXjfKs9UtTPx9K4knt2rtnfYuhBT8+jtL+K/5i3j9GY8x9b//A4ANH/rEITwiBxe7SkBnWhKrT5DaoMg2aVq6MiGwBck1fYh0BqIRQqekf1DvkGmRuNW6oxjZkQXPx9zWA6ZBZEua3PRqVCCpTebYPtGhWC+RnoMyINcocNKQ2OajpCBwSq5pCqQb4KQFhmtg5jUTSZmCIGIRWgKrzwNhoEyBYWhqvJGXiIKLdMGvilCstYnlPK3nFDMJYhbKEPhxEy8qydcKghjkm0KS6ySy6OuLcNME19UFJNsizGT1WNvuHOBGwcGmwC/bcdOw/8sw+yVGX4FYT470ggZSD0WZcEyarzXux8VXKcFeOvWKnXTFHi228PPeidyyeT4zqzXraaSjgwgURiEkkQ1wkzbWGr293TEDR46xLbz7CqYkLH6y42ze03Qfs1q3AVrLYvWVVzDzuhvxklD3nMIo6iJSYI9PJ2nF/Vft8rFd0c/LjCHQrKELz//6AHtoT9gbp5bb7/ncQIEriJh7tZ8KDh8c6q60bF7Fa4H/2vwx3n7aQ/zykdMp5G2+2PpXID7qa052LECPwF4x8VZuts7lsUeOpXdeFVUtvYMFpCGICof0RVkiDyR0U6GgSGwUPNAwgz/XP8Z8J8MMM0HYNnuYdtMrHXsqJN205Xy6CzHyPRGsgo7nTo8itclFeCG9s6I4fSGFxgheTJBvVlhA1RqF0+1Ss8rCi0uSGwU9Mcnm7hpMGWLJkBozS9qL4tbrQpGdcgmNEK9gEhRMhCeJ1+bI9kYJchbtvUlMM9Cai46iGFMoU0EoIOHjJIt6RM4MUb7E7XQQgdDFpWofJUy8hB5lCm2tcVesV4SOYlKsh6sbHiQlE8RlkYJngaHwHUk0EyK8gNzUaqLbMoSZ7F4f54Mdx8u/69F+31onSs8dioY6Co0Rsk0mJ152434JTe+uuHHW/FU8mD+KycsHOxLTW7fDygsA2NhRy8aOWs6bvobjG7Yyq3Uby7bufn8jR9l8JbnpJS3b+++vu4ZyHP/+ib9gzsYvs3GpQ3yrILE1xHAVwThN8t1x567j4q7c6sqPwfAi3FjOx3t7zh5Lka+C4QgRBAeIMRQeKUwkIcRs4CagSSl1jBDiWOB1SqlrD+jqKqhgL/DETVdw1BdvJJFW9Bcc/tx1IivMImv76wA4ucXiix3zsESANSlD4EvMF+KkNutEqGOBw9bXWpiRLGc2b+GjzSt0Z7KulEjOhG8eN4O/fP5Cko9sRjXVIba2U/tinliHzV+Dk6heJale63He367n7mWfPoRH4+BitAQ0Khze/C/38oc1x+G/lNICmgoS6/th4zY9x53uRzo2RKM4XTYJR9J5Zo5zpq5lyw0m2BYb3zWNE1/3PD+bci+gneCe62vh9BPW83D7VPKTLWwj4A0TX+SZ9ES2/2g6SoCbkqAgtsPDj2vHNOmFuEkTMxeA0IKdmRYDP2oiA6h9UbOGlBR4CQvDc7Rtcc4jni5QbIpjdxe0QGegneG8kiBlvF3x4De/zwOFkHfcfSkT7mNAuDuYNgFj4w5UseQSU8Zo3clyYekQdi7LWFKrXQ7LAvXzJ22h8bH/AM9H2RYP/u4Twz73/bnwUomIFtgegXQQ4x9b59G1I0VXRxKAdxnv42en/HigwHXO6/bfvrYjm6Ajm2B+/XYumPoSMJjwz/38jSS7IT1T4SYEic0+oSUJLYOz3vhN7v+/T+5x+wvP1OFy6DhYWfTyVefo8fXb7x29mPSqc74KQnD7PZ/j/EXXcddtn0EEIcrQ35FXnfu1gXRkZHFpXxFauq2/8IxrQQhWPHj1sG1XUMGBwK3nfpv3P/ZujLjHt075HUdZoxeQysiEBbpDj69vuphn17bScGEHXzjqLyyJ7prpufKsX8BZcPT3L8c4vo/YLSmSzzj8YsYZLKjawvN9LbzU1YBpXM0Trz5y0tzdFZK+M+0P/G/6RH7UfibKNIl0CarWFxGBQglIbdLHu7/Von+q4MOLl/HRmg0sueEd+DGLls+s3YlRdt7zr2d+bDO3dc/j2NQWPrfo78y3owOPr/W1I9cGr4ofbj+P58JmquN5lBK4gUEuFhACGIrGxjRzatpJWgW256tY212HY/ocXbcDa1bAYzsmke6NEVkdIbT02LmV1edz6UMQVRD3+cf6uVzf9CSgzSRyOQenQ5Lc4tE11yK0UqRWrEQVi4Mah7uK1YeYgVTGyBHl8ucsIg6qqD+31V+rYtU5N1NUHmc99bYDtpaTUhu5PzobPyo54fIbeeImXaz6f0fp78a3n9m/39ucr9xIaE0gcnTvwH3lOH7mis+gVAJzRoZckCS+DRBgeIpzXn8D997yqT1uf7Qiz123fWbY43sqFu1p26PdHo8if3ltlUJSBeOFsXL4bgY+BfwAQCn1jBDi18CRE10rOCyw8hodkF5////jw0138D9dZw08duqTF1O4owElIJqHfBPUvhhiFkK8uMHk161HCkW1neeMqjWs9RqYY3USFYOWrp+sXUviawVuuO01RHdIJv9VId2AwBbMvrkbESjyk6sw3JALL/j6bjsTRwJmRnbwobn3cEvNAlZvbiJcY+txQACpLd2REkwDc0eaVE+OHUGMo+Lb2cIkVBDwr5fcx1canxvY5tzoNt5X8zDbgigJo8h97TM4r2k1H699km+JkDXNMzBzEO3S42lB1EAEmt4emhIzHxKWrGzNoiLSq+hPCQILvIQBpea1sgS+aWCGICwDIQRGIUCZEqMrByEox0B6isSaXkR3H5dtOYPX1z7BxIndiKKpxbkjDkZXBkxTX+iXupfCMIYxkcoucKMlnYdCiHPp0Z9DNDfs5FT04G/Hn2W3bN03WXLs1QOstKF4/+z7+MHaz+GkihS6oxhxj/vWzoBTBp9z71/2nPztCf39+uKl82O1LOXTLFt9PQDzrrwRIXW32spoW+DAkVgZHz8+Nou2hWdeO2qBrIzRikeLTv8KSgpwBsP0BQu/Max3NVS3aOR9Qws+Iy8idoehz1l4xvAQL4sBiIo2UgUHFj866adwEmze+p9ActhjmbDApRtfzSOPzUIWBbXzOnEDg77eGPY2m38uGbsg9gsf0EXomRv/nZrnBOfUruZn606l8EgtXpWCiXlm/ParrL1k10zBIwVTzSRX1q3izpbZrM20EFrGgAuaHzEIojpuxToCYp2w4TV1wAa8pE1oilFHEm+e/StmWQnekbyndE902OMzzAQA2/yAk6s3kPFt+l2HIJR4voGwAqSp8HMmvdkoYY0gZRbI2xbVsRgShRQhUekRtTzCVJ6+Jm3gkVxtoASYec2Qlq4gNA3it6Q4Ovw3XjjjlwCEnQ52H/gxSWJ7SHRHEWGZqFwOYMD9rww1VEfwZSKojSN3KkLI5lUs2z5422+bCUgcYbF8wU8Zb13ecuHqNcnneP64Ftb/eg6R7p1dXde8ef/iStVahRcXNF2/DZUvQH7wsfbeJLbtUxPPsc1J4CUEZlG7CI8XRh7n3RVsRjKPRhaoRhZ89qaotLt13L38M5y/+LqDNnZ3OCNUB04TKRzPL94hwliLSDGl1CMj3NleAZr2Y0NFhOyVg9a/7kCZBqIvA7Eofn0CI1Ok2Jxg5WNTOen0VXQXY/zvlpMpBiZ/q+7gkxNuZZ4VY4Pfzx/7j+U9Vc/zgTf9QG/w/8GCb16O3aft6FWgiK7rxm9MkZkc4bR/09bED//y44fwXR8cjOxiPlz0kYREpIctA1LVOXJNDslNFmbpXLLjDbPJtkCxIWDOzf0gJbOv7OH2vilg6EJLh5ukL8yTkA4SyTx7G48WWyiEFh+q/SfXNDxPX5jnNS++hU2rmpAtIUZOElpaUDvSJTE8hRuXICAzUWKnwcorzHyIlQ2x0waBBdJTGF5IocZCmWDmtQin2R8ilMLIuijLIIxHtE5MMSC2LUduWhViShV3PCy5J7OA5n8GiEI7Kt1P92vnEmv3ce56ejCp3MtRtkOCkvj40jmf3e1Y2DEPv41sd4zqp3UB6Onv7Nvulj9z7cDY1GjwNiYwA03uMquLTP9fXThZ99b9E7E8+T3fQvpgzo4SOLDmfVFm/ryDpZM/BkoxWakBwfV1H5hOvgGiXQa55pKw+1h8ShV6G3Lfkobb771qWKEIGGAhgU6CRah41Tlf3SWbqVx92hum0ooHrx4oJA0UlA5jXaTddYkrOHzw3J/nMGd5NwiBn4qTb4oQF9A7C77TM5WP1mzYq+2tefMP4M3w+fZj6O2K4xhgZgSFjMXiBc8PxLUjYbxtNDbSBr8fS0BvaPC6Cc/wVKKHB9uP1S5oUYOt55nUzu/kx/N+zgc+fgXSC3n6yuN4VbAAP2Hs0kZ9lpXY43oyYYEGI2BOZDsPGdPpCWJ0Z2JYpi5oBwUDYYa4GZtHN09mwqw+GuwM93bOJBZ1qXIKbPGr6c7E8AomRELIGYQmuFWCSLfWUJSeoFBr0HWcgqLJG9cu5Mlnp9HwhKDusS7WfcGh/ncxrGfWE+YLqFDtVEACXVRSoxhSHCqI0lL2VDCYdcf7UIFk9n8VcWsd7l6+b/srFyh2hSZp8rnmW3n4hy9yS8fxzLxB58hrPrX/OfLRV90IrQI/ApsvG1QoXbLg8wRxh8m2BGx6P+MRVPkUqyyK1SZmHuLbD8wl7a4KRbvDWOLUvrKUhm1bMcBsrqCCfcFYi0idQogZlNJHIcTFwPbdv6SCCg4sdndxe8tZ/wX8FzdOGvGad34AEY1oBzbbIIiZKEOQb7AQAfxzzVQMOyBwDUzHJ2J6/K3/WNqi6/nC6nexbVMdDx09g6MSbZyWWMPt6XkoCbF2H1H0EG4AhkTmXbLNMXLNkNgMCz5yI+n5PjLhoQLB+n97ZTKUykl22DabriDB8/lWWu1uFjW+wBORKdwzP44fjTKpOInOBUnmvOtFPtuyjHc9+26CeARrbRuqWNSVgjAcZkH+eDHg+WIzz+ZaqTFznBRfR6Ohk9A/ZqaQdW3effZ95AKbYmhy259OGRBCFgGYBUVo6mRRBrqA5PToTlhoCvyIHm9Thi6GhIagWCUwIgK7uyTE6IcI1yeM2ChDIPMeeAGR7XnCiEHN8zHqnskicx6qpxfh2CQ3FbG3pVHRKGF2sHupxlJ84NB0L/cGuY44zg5TF1c69m9bZUHPW9YdB8Drpz8FwKNLv8b0ld+iZiWElknXiYBUiEAw9eYb2HDpvjORot0B0lUU6ixCQ4vFbntVAy1/3jj4JCFAKV76/BUc/bkbcXo9vLhD6uGdrYpHw4oH9767OlLcdKTjy9ACUHks7sLzvz6gWVR+/PxF14ElBxLFsn7SWHDBwm9A3MTI+gPvYyQ7qYIK9gdlUdrRLmQmTdw5zUwBk396OTgOxKMgIpiFkFy9QWyH4pjI5n1aR14V+dWTp2K2WzpuKHBnBCysGWTBjjbq9UosLA19Txu2TOAfmbmszjdRZ2VpstK8uvYZ3EUmT/cdzYSLNvGXGeWxwxiBo8WFZQCBVJi5QDN8x4i1fmaAgQSQkBEez9bS6adY01WPH0jcfhvPDFF5E5mXhI5CuAIjVeDJ7olUOwXisSJFzyRdjBC3XKpiBXqCGEEBhKXZyEGox9rMnK60iEBguILihJAXb5tJVQ/UPd4NUhK/K0G0PY9obkBt3AKBbnINjLSVsKsC0qGK4yJUu7SAHwkr6rH+U5KJ++mPUS5Wvf2fl/JU20Sef/2XgNL3attkAL701GvwiiZhTYAIBLO/eiOrrtp3HSYAO62LZqYDTq9CBLB09mcQQiAtAzAJbcnjS7/KlP+5nki3olgtaPrT6jHvY1+aEENfM/L1oz024KS2h+dXxtIOPCqaSLvHWItIHwJ+CBwlhNgKrOcIFdo+mA46FYw/lO9rF65ojPzkKkJL0H6OQ2CDHw+QnTZmRmACbrXF+rTDj56YyC+6BfkJIfHtkuc3z+Lp2Cx+lTwDMyuZ+s88VkcG+kpC0ckY+ZY4makhxoQ8vY0WRpdF8+Ruzpuwms83PErHtv9CIqhr2XKoD8kBgWxeBeuO5U9rjqUqVuCzs5axZMILXFDzIv+YfSy8Gvr7q1nTU8/WpiqWTHoR/gueuqB22HaGiTpvnMatXcfwQkcTQSh4sbmZh+IdvCb1FM1WL6+f9Cz/Vv0oE40ofarIPT2nUP9MHj+m3dfsziwIgTJ1W1TkPVTUQvghgWOQabHomx0Q22yQ2KrtX0UA/a0SP5IgsdXTAs7FAKMvD4ECQyA8hezrRWZzND7l66QyVISl5NJ62tVObEGJeaTCgQKSCtVOY21DcUgLSEKAsXdB7unv7F8SWMZfu4/jDXWPMfN31+L3RIhsN1j3pY9zwcJvkJ1gU/e4iRcHtwpCC2Z940ZWf3b4vvd0rj731dcTOhK7z8co+DQ9XNDfhZhFsbaktFkqHgEs2/qfALzwtStYaFxCCvBPnQ9ols5ohaLdMoMOIYYWoPbEtFVC4CesgYv8V0Ih6cLzNKurXHyr4PCCCrQ5An5Aod4mcATuG3v44Kx7OcruJSTO826RAKF1DdGMlr/lJjDf2coLxQn8ufN44qZLa6QHR3os2z6P2Es2Tq8edS/Uh/zgjF+wMLp7hsKRwFL60/bjWL+9npqaLB+acQ91RoZfTLkHPlEeQxvUrXrwm9/n1Cs/gJIGTrc+dlozblA37tdrTmVVoRmAyXYX7061Dzw2tIBUxpdueTPK0OPERh7smHZiM4pgZbTDmvQg6E7RvGQL/1L/BFe3vZ5iV5QNGRtyJqcuWMNJjZu4c8NsPaJSr8i3xYj0SAxXj+VFuwJif92C+kEOpEDlCzqeGwZNYYjozQyO4QfBQAFpV6yklwPMjKdNRvaA9W+/klm/1+f18WJrPtU2ke8v+CXvf2wDK56ah8xL4KOs//AnqLnlW/hRQaEO/JguAM3/5I08+83hcXxPo9invf0/EKX0KVnQrDLDVUg3pFhtoiwT4QfI7gxhc/XAOX/jez/Nwku14HZ+4QmIcNesnAsW6mb1nSv2zu31QGNXBaXdfX7lx+669TMDBfwKKthX7LGIJIQwgMuVUq8SQsQBqZTa2fLiFYwK/f3lid2N2Owq8AjTJGipB0MQ2hLpK6LtkGkF6f9/9t47Tq6rvP9/n3PL9O1VzeqSLcvdBhuMbdlqEH4QwDHgQAIEfw0JEAPBdAMGgyk2hIROgBAgdEKCVVywMeCObbmrd612tXXqbef8/jgzs7va1Wola1Ws/bxe89qdmXvv3Llz733O+Tyf5/MIUrsEKChMLWeTQoGTFcR7IdYvsYsmc+EOAB0Wfi1kZ8Sp35dHBAG6NkOpNUmpwUJ64GddhKOIaiIsqdhVrGNv5DPTNj4PL+QOMK+cvY73P/YpAOqsAiVtcVZsB7dxBgD3LTO/321bfltdZ/W+bx1wey86ZQvsvhY/tNBKsHWgga0DDRSVS6OTJdAW3+h+KXPinWwoteHVQX5qjERXQJi2sLMOVraIchNY2SJ4AUIpVCpGsckmSAOpEK9B4tcKkh3Q9KSH0C7FBoFXbxs/hEihLQssKLWncHIBTtFD+75RUe2PMERYFsoPhptma2UGnsdrWZsfgDvSo2h/PB8V0IFw57ML+HN6JtHeBOntkkS35vy33kIGkKHGzZoJhHLMwN2vHbmNMD32vluewi5FSC9CC4H0Q4QfIuIO8X2DpryjdZ8U55t+4QcrWhDh0S1rGG2gfTCp+ljx7a7bPzjqQPNwlFXHAyZj+fGHg52fo5Z2RIpoWhMiiEBD5Aj699Rwf+tslqbW86gfsmrgbBbE91And+II+MXAYlZ3nkadW+LZ7mYG+pO4iYCYE7JixjO8ftrDfKH9r6jdCoUr8nxg0e8PSiCdDJg5bQ93ToPZPzbEqyNCdgX1EO8BhquPK3joewcvTXqkdwa+stgUb2a738g19Q/RZo0kkB7yAqLaCLvXJkhr/DpNeoskqAV3wChOlC3wM1BYXMKPbNaX2qlPFcnbESXPwdoS4+FtMzh92m6m1vfRMVBDseBCJqTQGkPZNsUWSO8QJHJ5ogEzxRFSgJAIoRG5EsRjkC+YWD9EgXQwAulYJoNkwUdbcZR9cJJrw5VH/r7+rqfegBSaxE4bGYBlmimS2BcSJiRg4fuGFBSjDIXEQUoDnYJGegoZarQlUI4gjAmIWQSpwe+8avMXR6yrLjmr+r+Wo38+gNNTHP2NCcKB4tR4yKFDwWQZ28GhJ9ATSZ8Mnkha60gIcW75/0PvZ/kCw9r7Psbys29g+dk3sObRTx7r3ZnEISL30rlkp1n4GRPMGp8JcHIayxfYBUkUN6/XbBJYHggtsEsK6ZvyJ6TAyZuuXzLS5FstehcKhG5ERA0MzJQU2xVRMjK6Wg01dQUakgX+ZtojxEXAE34rM+3CsT4URwXPvebjANy19ZfV13564TeGLfPyWU/y8nFu76cXfoMFv/pU9bklFQ/vm053PkUYSSIlScZ9/MCiOM+nOM3C3RfDLkJsagY3l0ZEmppNIIs22pbkT0mTbxPGXLPHRcU0MoBSk6B3fgy7qNE2FJsk7oBNUOtglRRocPsM2aATMWRzI9HO3eZ5eeAjpEAHo0xEhnZtKZNI+3spHOsytqgpg5YS6Y00v5xoONvi+Hac2p2Q6lAmu+gptBDYBYU7EFJqdCnVmSBcUSFdcclNiFBVTZ+VJUZMQi+/9CaE0khAliKjTBQClXAJWkxG3ckGrNr+5QPun3KtqlG2CNRxR6osP/cGANY88vxj1GgDzSPdNeZoo5KNXvbiT7H2/o8f472ZxCFjRjt98821GrmCMAHYGl/Z3Nq5hAjBpmwTv8yeSf++NPgSu99C+gLpgwzBjUNQ6+LVB5x76lauTPdz7ZXfJPfaEmkZP7bf7zjE5jeaa+aHG+4ERiqvDiUZ9sa5D/CTPe+qPs9Fcb7Q9TIsocmFLqG2aHJzNDlZPOXwugseZk+phh25Ooq+Q1dtLToUJDrLiQINxRYQQrOuo51n97XQlM6Rcj26RBpvsSLal+CZva001+QoFV1z+y6H20KrOSf8jGBg2anU/nErUWdXOR5HCMeGQgEsC10ojixZGxLLj7c4rl0b4UfICZoIj4XgyVoCwG+ImLYuQiiNciSXvOLzICHzbA/2KXX4GQs/I3j4u4Z8vHzJZ0GXkyJagxBccclNw0q6r7jkJkSkiVnC2AwECu1IhNIU2wYN2lc/NYZBuNJoSyIDjbbFcUeqHEk/3gNto+JfNWmwPYnDwXjL2R4VQvwW+DlQJZK01r+akL06zjFJHh3/2D8jf+nKzxPbV6S0OEN+qjGijWnTcSNMCmK9oKxyZiurcPsiZKSH1ZPLQKFtQeQK8m0WsT4FAtS8PB3TbShZuF0SVR8QSwbUpYpIoamNF1FasMevI2l5ZKzhWY0XshqpgkrL9COBCjEF8NI7PgBA8GgdWoCKQV+tyRCKQCBCYcrSQiNxBiN7L7UkEGEc7QiCpMTNmdI1GYjq761cKDUACKK4yVKVGo1fRvOjRaQfGeWKHyKC0LS9329wqZWG/TwThGWN3sFliCLpWA88AXS5i552xu9ncaTgZMHyoX69j7IE2haIUKNixmvD7i1hx01nP2XBaR++ladvMkRSpVwRBVLpqlrp8iWfRUQaGRnJO0KANMuZFXU163n7H8cuQXN29wEQTK3n9j9/1Jhel8vehpavHSty6UiQRycDJgmk4x/7T35WnGV+Mz8j8GuNIjFywa3x2FdK0VlMEyrJlg3tuN0WtZ0gfXN/R5i/YcLcz7WQ+AmLpBxUkB4ugXQyxHGAN827/4hs538v/mr1//c/bsqKfn37ixAaooRGNHi0N/fjyIhQS4qBQ29vCuVbiJyNjAReHfhpgbYwY7GcQ6ngUJKaXDaOkBrbjYwJd1ORpkyeuliRuilFdmVr6dtaR+PTEb3zLIKMUaM0rfNQ7Y3ojsESO1XyTAkboJUariim8q9Aq8rzcRoeHg0ohRDimOzSlHt9ehe6JLosnJyPtgR2Iaw2eVAxpzoGD9KCc6+5FYC6ocOjSIMAPaS0ftmFNyLL5BJIRKAQUYR2pFEVlxXAD/3H2Io4d2MHAN6CKVUSZbQqhmMVT0/EBM0LDQpJNFHd2Tj6xO6RxnhJpAagG1gy5DUNTBiJJIRYAXwFsIDvaK0PXLt0jLBy1uANatWWW47hnrxwscx9IwBr/R8/r+2ISINS9J4KVhFifVD/XIByBLE+ZSSxvsLJhWZZQJaCckclibYl2pL4aZfcFIsoBrF+gYhA70iiayNSLXkK8Xi1X2h7eoDXtT7M2p7T+dCUVThC46BRwNYQmqTDV3vPoMnO8mzH67n1rP9+Xt/xZMOSpZ/jj7ebNuxn/vZWvAZAatxuCxliyhxiRiatXEMeiUijHAiFKWW0SorYQISTF2iJMQWtyL6FmagESfBrNVFGcdk5T7E12wB/aUD4IUhpHvkCKIVVk6lK4WU8hip5JjtpO6Yzl9JgSTOGCkNELGbWLxNNUf44UqhJgYgUsnRsSjpECH6NCVFhXKBscLMKoaDnbFO/VvHDQBg/AzvSwwabAzNd0rtD855vFE2GLBJmcB0Njla9xhherUXkwrILPsXaBw9MMGx/7RQAnrr5Ol72qi9gu3KQjDoJ4OTCKmn2QsCKxYb4W/3EkW1rPQmDIxXHz3/LLTQqRe+ZDURxiOIQ6zWkc09nguc6pxHfa+FkoW2vxslHxDtLSN+UrGIJtBSUWuKU6iX5KYKgJOmLkkDpeX/Pk4VIOpJYOeOfgXZWbf8yT7z7M0Rxm755LmEiQVYl0AL8OhMP4hFIzxCH2jaqcRkYVXi8VxDFbDMmExDFHRPT4xDY4LWF7A0lTVPynFG7i2UtT/PlzStRlvHVswtg5yGodfDrXRKPG0WRdF2U76N9H2zbkEmx2CCpFEXIpgaCGU1YBaPa1Y8+AxwfyaAqxmmufaQhA9PlNoqbCXOUtpChKUErnJLGq5HockMLVRaWKWf45Do3I05ib1BVGguty/FWI3SEiCJQRhWs4hZ+RhIkBOe97Zaqumk07PybWQA8cct1vPjqL6GlQLa7E3EYjlu43Ue3VG8SLyyMi0TSWr9londkKMo+TP8OLAV2Ag8JIX6rtX76aO7HJI4PPF/Dwgvf+CWSgaIwI4Pba8rUmp7wcPfmCOsS2EUbGSikFyHK5sfCM4FJhAq0RkuJSsWwShFO3kJbgoGZgmSHJrNF4NfaFPw0IhLomMLKKH49dy0AV2f+wFDjyaFodfpxRET9SVLeNhGY+e0vIOdIVG2IdCLCrIv0BLJULjv0TPbZ8jWWbwyzpaewPIUMIrQlieKWkVoXjepF2WayoWzjuyMiAVJz9/r5qJLF/Min67w6Wu7vwW9KEcsWUbUphNZY23aj8gVkSxNq+67BHa0okrQAIRCxGCIeMy8VzXvH06Dz9j9+pGpOfbTx1OeMquiiv/kSVqAJkpL8VKjZKon3Kpy8JnKFUZcJQc0WHy0HFUSXrrgZHEF6V2BMU4XpGGQIJPMZQpnXRajQcQuvzqJUb8jEjpdkWPy+WwnSYBcNEQnw1Gev48x33wpJ83zl3H9Bn96CsuUw74ZlF5iSy7GIqInEytOM7H/V0zcdk8+fxCT2h7AslifexJriDw9r/XPecStOBH5rGrukkaGg/llFYl+IX2OT3GEhNCS6NE5eE+sNsUoRVrY0Io47uRBlO+SVGVs8XjiFhbEOznVPrgnk8Qa/3sVPy6o/jQzMwx2g7HljFKoy0EQxgZPXyEAZ9ZIriWICoUxXLpMQgiAlUA6omEVAjN7GBL/afCa2FdF2H3S8WNL4pCY7w4wNu86w0TbMeuoUdFc3IpNG7dpjzNxt25DnYYgGhJQIKfFnNhMmbYK0Q7yzcFzF8TWPfJLl533imHz2MOPnlZ9H+oqehWba2fSEwiopYw+BMdeOXEHdhlJVBXTFyz4DliC52zO/f1lBrKVEoAxxFJb/avN+mLQp1UmUa5RlAAs/cavxYipPJZ767HWcc+2taGNNasipqTHjYbjfdGNUT7ajhCWXlw297zy+DL1PJijEhHVRO2m6swkhRjqLQj/wsNb6f47sLgFwAbBRa725/Pn/DbwKGDeJdCRrSQ+ESfXRxGOt/2OWWlex1LqqSiatDcav2DntI7cSLJY4s+JMvadAs+/g5ELs/hJojZX1sfK+yWpYAuGZDlz4ARTLmclYDBFzEMoQTW5WG6ULgtx0o0ZCgzNgEdRF/NPFd/De+i3j2r83ZXbhCJubgzo++/TL+dBptx3S8TkZUfVjKStONr/y2xS1xz0lMyJ4MD+HDq+Ghzpn0NOTRnfEsDyBOxBVCQXLi7AKATJvyhi0Y5XVZlbZdN1CuRItnTJZAY33WlieJN8uQQS03r6L7bemKW52mf+9OLLgEbTVYJ8yBWtXJ97cFrpeO532rzyADsu+QkIiMN1edBiicyHCdVk98L2jfhwPhhVnfsyIbY9RBhPACoxCsGZbRP0GRanBQYYaEWlEJAgTErGfUKpyfuSnOKT2hIRxC4SZhKBBqNBMPDxD3IV1cYotLn3zRNUjI9YL6T2KfKvEyUNiX4SWcPY/3oqKDzHhLHn4NRaxvgihBUSaS1d+Hhpio+7TC0WebgbnE39eHKwzz5HCpAJpYlGJ40A1lh9KHAfon6dJbxWk9kD3IsmMNTlkMSBKuaCh4VkLGWmcbIj0Iqy+IsLzR8Rx6YdIz8LOS5J7BfFeyS+c87mt7VT+fv4DXFP3JDUyMfbOjIFJNdL4saLpGmCwocbuqz2CrItwAvAsrKyFVRJktmCIwb4QJ+sjSiE6bqNca7CsvKwq1Y5J8inXMP9BxiGKS2RgUfBsvHvaad4XMjDTAa2Y/409lGY30Xm+jfQljU9H7DvDovPSNuo21BGmbTh9Cu6aR8DzTIgQEvwAkYgjWptxt/fgZBIUp2ZY8/AnjsGRPDBWnPVxk7Q6kGv0UYIMzee3/7lAmLRNDNeaWH+IsgXKschsGywrvXzJZxFCkJseJ7WzhHIk2jaNb2SoIBLIoo8IImOwLwR+e5rsNIewnORBw7nX3IpdW45ZwnSBW3jDregp5rkzYBb1MhLbKycaQ7j41V8w3pf74ViSShMBUQwQWrPytA9PJp0mccgYb0FeHDgL2FB+nIEpcXubEOLLE7BfU4EdQ57vLL82DEKIa4QQDwshHu7q6pqA3RiJKy65yfhfTOKoQTo20rFHmhmOA3YRnDx49ZowZSMDjQjKgcGy0DGrmsUQXmCyGiUfcnkIQ/OwLTMwkRIRaZxchJNTVflzsS3CO7WI3xwwb+GucRNIAI4wPG5vkGJbsZH3PPqGQ/6Ok4CEiLEi4XNZPM+FqQ2cnd7OaQ17mT2lCzG9QOGUkOwMB6/WRoaqOvCk5EOhiCh4yJyHLHrIgo814GHnApyCItaviPVCqUEQJgTtd/eaAU13D7l9SWg1kxTd0UmQtilOz0AqwZZXORRbNTJZHtEIWe32MpSYWd37nWNxyMaPY1i2dO+v3889v/sACEEUMzL4ICkJE5IoLhGaEVzG/gM8FZMoV5bVZWXyR2tT2hKzKLS5lOqkkd0XzGPdrUYJldqrKDWYDjE164bHmKc+dx2rdv4rQUqUy2W1IbiURgbmsfzcG1h+7g3Eugrcvfr6YYbUE4lVT980oQPCob5PzxdjxfGJIo8uW3bzZHvjowxhWUjHxLvDieWZzYJiC+SmuKR36rICwTQ4EJHGLkTYuQgrFyALAaLoQckbEceVayawdskkhOyiJr7Horgjw217FvGd/tOe93cd2qlsEuPHK+Y9xayZnbS19hNrLBKlI4KMotQIxWZJkLYIMi5Ca2QxwN6Xx+rOIXuyiIECIldE9hew+osmQZj1cft83IGQxD6FO2BK3PtnObSu3kGpQaJ7+ii0OsgGj2K7JvPAdqbdVSQ7EwptLtteIehd6FTP3aEQmTSEJhmx+vEbuee2DxzlIzZO2OOd6k0c7rrdqGmUK7FzgVHuatP8YmhZuS4ni++8a/i9P4pbKNsQSdWS9XIs17ZEJV38jGUST6ExSn/iS9cRJiCz04z5i4uLDMzWBJnBz3vyC9dxxz0fJkpAGBP4aTOuMGMLYeL4eZ+oxvJKHD8asfyuOz84qUI6xlBaEGk5IQ91MnRnK2MusERrbfQXQnwdWIspN3tiAvZrtCM7YtShtf4W8C2A8847b9j7LySm+GSHVhohBVbalIStaHg7q3u+fdD1lsevpuF37VhS4V7rEjZnQGms/iKUlSciMN2ZRMX/qOQNlh0JAZYFYYQIJMKJTLByzGTUdIwqK5BSFtdeeDfvrn8KiI25X/8x0MrVmZ3ExGAL8l8+exY6ErznnLv46cbzOSu2i3lOmhc9+joAHlo5SVxWEN81QGlqzajvxYTD0kSIF9/OHd2nsXlXM20t/cQa+9jTVEN3bxxnX5ymdYrULgs3X4Kw/LsLgfAlWJYxv44idEucyBUMLAwhFKS2WWRfWc8pv9pHcM48ZDxCRRLRm4V0CrfXx8p7oDQL/30f3pRaxNQ2xLad5ryqGHGWyyYPt7TjaGD14zey4syPHevdAMDyIrRliCTLU5QaLJQlCJPGEL/rbJcgBfM/fSvrP3rdsPv/i6/+EkKDjfHBCjIOQtmESYsgKSg2SYKU8UoDU7o2+8u34M6XxHpMKUX3aTY7Xt1gHHqBrW8eEl+UGRgnOopEMVMWaQ94CC8AL4CYg3bt6qDz0hU3E98xUF39QN1jLl1xMzLQ1VSPCPUwUmUi1U1XvMzsU4UsunxJWRE0ZGB/xx8+whUv+0x12aGvHwrGiuMw8SqkSRw9VGK5iMVYnnoza/L/edB1lsevpv+15zD92q10/9tMio2SpnUF5IC5YGWsHMNDZeJ50TcTe68cy/eL41IERHas2kABzDVuFyVbki38xD+Pa2qfOSyD7a/0zmRrqZFb2x8ZlUiaVCgNoqJCqkB1zOeqhpC0dRaBtlBIfp+YS29firp7XJQj8DOybMScMV06e4qIoofuz5YTAxIhJEiBdMz4Sjo2VjJBkK4HwKsTyBAKp7XR8kA/asEMvFqBuz5Bcg/gukgvZMoffKxSxKm3DoDvo10XXfY4lOkUoq4W3d0DgGhsOKrH7lCw+rFPVc3ojzWENuSvtgWynMxVdU61TK13YZxCqyk/m3pPiTuHdGN70ZtvwcmpqqJYC1E2UXIIal2imKDQIlGOIZAiB+Z99lZkE1ieJN4DyfocP7j435llm7LVRPtgwjeMgZLQ+LRffc0uRtg9eTM/sC10zBlGHq1YNBjrxorj+2P/mD2R6qYVTddUlX4AK+d9gFUbPl99XtnvFQ1vZ0XD2wdfH8ccaxKTGC+JNBVj6tJffp4CpmitIyGEd+DVDhs7gelDnk8Ddk/A5xwyhraYnMSxwaHc3HY+MpWmMztx83loSgPl2ulyMAPK2UxtBp6qYphSHnjKwQyOyWIaNZIVgF0qm/hW2sRGMUo6InEAcjmnSny3fyE/2XYee6ev46Wp9Tgi5C/FmUSeaS/yo60XsGTKeq5Mm0utq7MWfMnMb3+BrW//l0M6Ti9kHCzgxoSDQiC6YuwpNBJrKnJG2x7CVsmT986l0CKRYQyn2zUZsbBcE1X+zXXMJqpJkG+18epgzrw9lEKHvdk2LB/CphTFZhfRDVZJQsyFbA6hFEFDEmfLToTrENvWbSYyZWJSuA7aDyb46Bw5rH782Hgi7Q9dNsS1iwoZKPKtJnQFaWOs7TdHOL0WYUox66tfYsu73gfA6f9yK1aTxCqBFgqhBFFMEMaNqkE5pnTN8gcHnk997jrmfW7QQ8HyNK2PBGxrs5l35g6+OeenXHXfFro+OhMZKpq8sKoss7zIlFEEkbmfSGH+ljPBie39hkBUqnqeVYi6oce6MvBUjkCWs7RRTI5KHE3EAHR/Imj/rPDQ5fYnkU4EHG+tnE8GrPV/XDXYBsZFIFVQf8820u/yCJ7pRS2uH1QiSAGhMdsXQdnDMIxA7ZcIGhLHVcIxykRHYJcUQkuCpEkMyaJFthBnZxSx8DDEG7/efSY799azMdvM/85fPeL9yVK3kRg6wX1xzKYjtYVHCzNxRMS0TD+RksR7jHddqSlGFBNkp9mkd0c4AxLirjkPgrJnUQVRZMZ1WkMygZ82PjmlZk1ms+ngFdTHsfIhyU4FQuJmFfg+MutRWJCm4XebIJmEIDBd2QCEJDhrNl69Q+YPObTvs2rzF4/yUTs0rH7sU8d6FwATRy6/1CRErbxHlI6hbEHkQqHZdMFFmMTQ1lcMJmMXv/dWrBqBsiV20XgfCW265Wobig0SN6exfDMuB3jsq+/l1I/dil00HlqWD3u7agm0ZG9UQgHvuOefCT/ZihaCFlswTBgihBnvh8rcR4LQ+G3pFFpAYkuvObeEAEuOGceHYqgieaLjOAy/voBhBNIkDg6txYQphvRJpET6PPCYEOJujEroZcBNQogUcMcE7NdDwDwhxCxgF/B64I1jrzJ+LE//HWtyPzhSm5vEBONwO7roKCJKKjq7M8TOqMcqm2erhFPtpCQqAwOrXGJU6aAFYFtgSbRro5Iu+WmmLMnylGknb5tHmFbEMh5/VfMY9TI5yp4YrCq0UtI2rh1yx94F9DammOr2srnYzGWnPocjFEvrn+S1qWx1nc3LvwvAm7Zdwvxf3oh7f5q6zRGJjpLpHsfJ1857vP4lH5x2G//Q8yb6d9QigJc2bOCufQvJnNFNfzZBcXMSaCDWG+IMeKAgStpoR+LVORRaJNlL8rxk5ha+Nv0Ortt1KbsyzYQCsqfESe/0mPefHjLvoXv6jMfWQAmrv4h2nXIZhcmEa6WR6ZTpeObY6HzxuFYhHa+IYpIoJokNKMK4oDAzwq0vsailC9eKKIQOmzubmPl1M6iXU8xM0O0XsFcSxmHgtJBEU4EVs54hJkN+es+FpvW3gs3vfh+LPngrqbz5vCAN/ef6dFsa8KlzS8ywM/xk1l0sC/++KsMvTElg56OyYa/Eb8vg9BRN1xitEX5EYnMPBGXFY2WyE2mwBdqxTLZYKbRlEQPWPPrJA0rm93996POjrcI9VkTSitq3srr/P476507i8HE4sVyXr6FQWWx6QyOZ7cY810rHqtefiPRg2a1jG0mBLLvh7xfH/boYYUIaclabUlXL16aBQiDQStAgD73cbnuY5cz63ZyS7mVGoueAy1UUSt8faOHmJ5fh3Ge8/J78wnWH/JknMvaf3Mq29aiO+bw6leec2P08HTSyIL6HvuYk35z3ahLdJpYWmySJbkWYlJRaE8SFwArqzL1VWqAVBOGgCi0RJ6pLsPclilnz97DzwWnYBSi0GH+9TC6g9rFOaktelXQSnk/jH3Ya5XB5jCjr69D5AiLm0nuKITgKr1nAX755cv1uRwRC4Dcl0WWvwoHpFtm5ETquaJ3Sh2NF7N5bx+xbbwFLY7WYOO7kBG6fAIx6OL8wwK3xmN7Qy6YdLbjbY6bzahnpnebaLjZLek7RXLJwPbd0LOM9bbdzlhtn1YLbuEK8tbp83zyX1J4Iu2h+88iVqHTceG7lSxBFxLeUr22/rFiKDaoatWuz/OwbEFFUjeNe6/A5wdBYvT9xdCzj+OqebxslkhQI22Zl2ztZ1fG1o7oPxyMUgmjczj+Hvu0THeM6Mlrr7wIXAb8pP16qtf6O1jqvtT7i8ohy2dw/AWuAZ4Cfaa2fOlLbl9OnsPLUSYn8Cx1rg/9mxqqIxBMJ0869WB5USImOWei4XTZUttExBx130AkXnYqha5KoTByVihHWxvFrXcJERcUg8WssvFqBXwMqGeHaEXk9dmeXdYXprBuYRkdPDf2lBPV2niWpZ/jrhof559Y7+MyUO4YRSEPxgfbVvHHBw4RJ8DPSdJwJIlTMYelFn56Iw3fC4ECZ3XNdl7+Z9Sg6pqhPF7BQxK2QhBPQUp8lmOqTmyLJTXUotSTwmuJ4DS6FFodSvcSrhWVzn+WzU1eREDFe3fgIyZ0WibY8vQsEXr1jSIBs3gw8Yy5BU4qwvtyJL5kwXVzyBYRjG4KyjEkC6dBw1+0f5Pdrrq+WkboDxugaDbYdMT3Vyxvb7ift+CQTnolsyQjVMKj6UjZ45aqDZNwnG8Z5ZqANXRvgtBWwp5gOiVEcvHoYmKfIz4yY2t7LwukdTG3t5acXfgPZth7Ztp695yfYcUWSXZdljDrRV2hpSttKjeVSVSnNo5IRj1S1lLHiyzQUIlSma1zM4opLbkI7pnNgxU+igrEGmBW/hqPlv3SsIDJpVk5797HejUlMMCoG3I/+cT7xfRDrN0mcajOEmDUYxx3LxHHXQSdi6HR8WBwPaly8egvlCJRlvFW0ML4syjLqhqBkc3thxrj2LadK1f9n2Bmua7mLT079HTe2PHnQdducPgDSewxhteDGWw/xyLxw4WnBg/k5JKXHpckNDMzV+DWSICWIHNNt1S4qwpjEq4+hU3ETbx0bXBcScUinIJUE18FviOE2F7moaQtBY8jAHMz4LS2J4jYqE4coQufyRrnmOpCIm5L2+gzEYxCPIVyH6JRWlG3u25ME0qHjzrtNNYeWAmcgMNcdQDJC2IpXTV/HOY07ceIhSFCuJqiLqutrCaVGCDJgp3yScZ/dfbWQt/GmBJSmDcZ8PyMYmCnpPy1ENfm8rukh3th8H11RuhrHd14WY/vyGLsucVFOuYtv2VA7TFrkZpoKhqqaUSlDWA6J46JCYIeqWrInyupzGZiKBW2JEXH7eIvjkyVskzhUjLc7mwAuB2ZrrT8lhJghhLhAa/3gRO2Y1vo24Ii0qhrq77By/vXouBngrzjr40dc5rmi5i3HZaelkxHL039HbPFcZOCQm+aQ3CtwsgEqZuPXmHPACtQQVZJG24IobhHGBMopZxcs0yq20CKIYhAmBcrVqNYSdizitNYuLmtaz6XxsbOXO0v1PNPdirQ0pzbu5fTETmplyBQ7R6NMjbnuYjfB4qZneHzZNLb911xEZw/a8/FetoBYtz/mubw89Wbg0MoHTjRUspj3eyF9KsmTxemcGt/Fm+se4a5Z8+kupHgyP5XNfQ3s29iIaPRoaMrSe16KbK9LzUYbJUHbEOsz5puFqYq05dFmmUHEb7rPpdiuECUHe2GOnmKazDMBJOJmEJSIU2yJEesNsWtqoKcP0in0nr2IIa2j98/APh+80Lp+jRdByow8U1tsCn6Gx5NTOTW1h6tb7+OTPX81bNnN170XgLPedSthUmMNWNX3rmp7iJc3P8GT+WmD285olKtpntcNwLR0P1e1PEBcBty25TZePmvIBFEY0kk5EjRs/FvzOye3SfZeUMv0O8yANrGtzww6PQ8dhMa4H6oeHsKS4DjGlysRR7g2ffPihHFBqiPiJa/7In86wAB0xaKPmBLdsvn//pnPo4EjabR9IKycZX7HVVtuMf87NoQRK6e/h1U7vnJEP2tF/T8c/4b3JwlWNF2DaKgn0QmF84skVsexi1HZi8wmStlI3wTxSom6tgRhyil3czIqozAp0QJjzJ+irEKCyIVim0Y5MOX0Dl7cvI2XJLahSCHLedYNQY5ngxam2z1sDRrZ5jeTlB5vr90zbF9n2plxf6/NXgv2AxnqH9qLPKuZztcWWfw+QyQ98aWR5MSyC02ZzNr7jg+fuolAJY6/c+PrSdoBv9x8FtmeJHJqib4wQdM6RbLLEEjxjiJhxsWrtymcksHJhthZv6pIkwUfcgVUbRKUxu+J87sdi7D6LfyWgKZ1Fn5a0rswTv1z4PbGjDK9TPj7rRncXAHyHromhejsIX/BrGop9UPfe+8R+94rm68FYFXXN47YNo9n3Pn7D3Hpys9TmJogignsAsg+B5WK2BekmRLv5S2n3ce3d11RXWfDB801cea7b0XtZzt6xSnruXDxRr674yVm+281nbOjONRf2sHLGnYzJdbPK5IlRoO2NKFjiC1tCYIam12XShofg4HZgn1nNDD9jiLaErg7+6rJwar6UQiEZZnGKY4DUiJiLjqdIMhYhpxOSV72qi/wh1HGapVYvnLGPwOgGmuPSRyHIzs+fSFATWA528lkrP01zFR7CfApIAv8Ejh/gvbriGH5eZ/AGtJdqEIgwfFTJzyJCUIYYm3vpCVuEyYsdLmNqJlwCSJHECljtqslyMh0ZwgTkiA9WB+tLaNiKDVBmI6Q9T6xeMDsxm6SdsBF9Zs4L7GZgwn76pwC7ZkBtof1lCKH7jDNjGRhXF/l0/sWsqnQzKPrZjFrk48OjQ9LcnOfCVidPSxP/x3AiFLNFzJ5NBSybT1sm80//e9bEK0lzpq+i1c3/4Vrpt/Lp59+Oc/2tWJJjfQF0YBLT2hR25Aj70Tk/CQiFEQJRZiSOFlwWor8qXM2n7Y8rqp9mF4/idNewC86+IGEJOQWNOAMhLhdOUTRx8krvHobpz+NLBTQXd2IWKwqEZ6UBz8/3PsbI3y96G++BEBqt8YuSvak6/m5PIeXtmzm8b8a3cfpsa+OnJj9eOOLOD+xhfMTW/jevosB2PS+9/KX7TP4Wd8FALQ6A8xx9uFjDVv3iS+O3N5g1tAf8V4FwnWq5Wy6VDLlOlEEYYiwLFAKKQXoDE7ByPERcMkrv8A9/zso/K18VqJQNNlzWw5mRPfbp8og9YQmHd3RlZ5HmkCaxPEFnS8iLItpv4pQd6fpOzVuOjS5lvFTiVuECatMJFkIZUikICXRUqDKk8MoZhRHpUZQMeOHphyNimlUbYh0ImZleqi38zgC+lWJ3RH0REme8hawy6vnObudHaUGYlZI2irRqzaNWcI+Fr7681dSv8uYQdc810/h3npqtoUk7nqS5d8cabnwQiaPhkK2rYdn3svTD81CBAKmeKhIwswi4rEYyc4A5Uq0LbGKITGg1OSghT1oRSAEthDIbB4Vd7CLEXWPx8nva8DSYO12kH6EXdRELvi1DlZLDVavDb6PiruESRunPoMoeIiCRzhnCn5Gom34y9cnFUjPF3ev+gAvudLE8WRXhLItClNs1mxdyLymfQBsfs9Iou7xfx157N/1l6sBOK12L0/3t1Zfr13awQXN21ic3Mkr01swdr7Dsf6jo/+Wl19mmknUP2v8MpU7PP6LRHxQZRyGxusy1BBFg3FcCPyaWmSosQsKEWgueeUXAKqxfPl5n0D4IavXfdrYatg2QmviHXlKbSlkpFmy9HPVz73r9g+y/OwbAFPyPolJHEuMl0R6kdb6HCHEowBa614hxNi1O8cJ1jxsWjPuj9XrjmwJ0Mp5prXnpArp+IEKQujrx90RhxkNDMyMsW+pJNZt2nmLEOI9hiTy6kCWVbBRzGQwgjpTnqJdBXHFzKldtCZznFu7jXanl5nOPuqsEoucJOOpDP2ruse4TZzJhQ1b+Nu6h8bMWp75xXdUM6WWB/FeRbFRsuD+fmRfHh0po2Lo7IFUCpRCuC7CdVh2gSFH1z54fHTkOJq46JTNzP3xp+g5Pc0j583m8YYpzGrpZmZ9D1c0P8NX112KkxPUbrQAi57T62iZ003m7B529NQTDsTwGnzsx+IEXXHySY+1HQt5Lt9K2inRXJtj50Ajf3ve/fx37FwKW5KkQ40IIiiWSDy2jUQmbQYD8Rj4ASLmQiZ9xL7j0os+jVCKtfdP3O97xSXG/PJ4bSTw55+9r0qICO2Q7XXw22w25Zr5yrNXcE58Ky2WIWgXTN89jDwZLg9/DQC3fOvfeeLDZ3Ap5r27vh/nlKb7h0wQEwBc8aa3oqxDM8Xc9bYAsb6l3NitjVnf3ICuzSCC0JQ5RsbMtZrV1BoGcjQ9ZKFdm1JLklKjTWJfyNKXfgbphYggIlHwjKw+jExHwYKHTpv9jHWPnnEdz/4eLo4FQTURCiRgUoV0HEH5PrqnFythvEm8OkH/7BhWyRjiiwjskikb8TOi2t5b2YYoCtIYxWAMorgmimtkS4l0avAaSboBMStkTqqLdqePZ/06LKHoCmvY6LXSG6YoRA47S/UUQpe4FdAtk9xRaCer4ry1Zu+o+/7DbBM/fuNylGOhYhZBxmbHMoksCeZ/dzsUS5BJI7Sm+dGC6e5oWahiia89dxkA71zw+6NxmI8r3HnZLaz8m2sJTpvBjisS+HUKt1sSxjX5hS5NT5SwciVE0ccGZFBLoS1Oz6IENVsD7HyIci1kMoGVLSE9m/r1ktrNgtxUh2KraVoQxQCNSSgmHRAprI19iL4BknvKU5x0Cp1wkUWfyE1glQ7dL2s0rGy+Fq3VhCo/jvf72Z9+/j6WXG4IkoQrcHOC3GmwN5+hNl7k/Y9fxRvr7wPgnBnbWfrSQe+92//4EZaf/0nWPHQDG940kw3OHOb9xyZz7kx/D/g+Xf8+hbDR4u9rOhlKIL388isR2oxtVj092Pn48iWfRVuCu27/IFbRTAhkwUeEig3vaAQgvbUNgGn/vdmslEyYzo9uWE0GASaO5ws0PLzPlLPFHLQtCTMuViFk2YU3IouBaQiQL5h9jrmmPK7ooxMu8Y58tYlA0JAYPHAS5EBxRKe1I4HROrGezNAI1AR5IukXgCfSeEmkQAhhUe5DJYRoploEdPxjqPHwkSaPJnF8Q/s+ursXJ+bS2F2gf24D3twSOrCwum1jemyDX6sRkUCosll2c4CMhzhORDrp0ZjMs6TlOaY4fZwT306bpamXCSTjz0IqBGnLY2XNOhrk2JfeeVetI1QWD9y+CCSkOjQ120NUzEZGEdr3kZk0JOKE7XXYPQ6ibO6sXWuwe81JCG1JGp7K07swhdMaobTgosZN9IdJosCi9YkIu6Dway2crKQ1laUhVmDLnibsToewXRvvnIS5xfXmk3QNpLEtxcXTNpF2PdrdPjLpEjJMIAIFJc/4bWXSxlfLlshImWxVVO7Ulc8fke8ng6j6+07EhP3yyz47zL/peMcD//leZt96C62pLPPTe5ni9PKSuAQMcWeMbN/Gx7/xH9y19T+Atw5b3xzD67m73EypQoYcrsJgf0z9rsPWVypEee4xVI224qyPm8xl0TFZ9MpxjyJEmSyK4mlKDYKaTT7SD9G2RHhqsJOk1ub8i8eq6toVZ30cbUuWn/cJ7n74E8O+60R7LExUl5lVzw1mY1dtueWIb38Sxy+00uj+LMK2af/dLrJnt7FzhaZxWh/5B5pQWUALovIlK8rzOOWCV6/L3ioKkhHJ2iK1yRJT0gP0lBJoLUg4Aa6MmBPbS6Odo6BjdAdpeqIU/WECiSIbxClFpuvnQBAH4NNPv5xUzOetZ/4SgMf8Eme58ep+vymzjxveFafuIZeGZz2skmLGbbrcETRClzxEMoFOJ+g5LUmxCWY8bNZttgd4bSp70nZ0W9X1DVa2/yOneK1sfLeN7kuQnSFwcmXvuKIPA1mwbaxiiigmsEoauxjh9BRQcdfEYiFQMRs7Z0iBxl6PgUISr1ZSbIF4N+hi2WS9ZE4cEY+ZsiTXGbwX5z3Su5NE8SM0oZQCUVa3TkQZ28rma00y6wTBfT95Hxf8/S3Up4osaVtP0vK4vmEjEGdAFenbPR24ljP/fR319six1NDKklU7vsLK1ncc8LNuu/PnvGLJ69D2yONT8R+0BgzJrG2jNlr4tX08+84mSk3lz9j11eo6K878mEnk+pExda806FFm7CcigbZtBhakiPVFOL0mjqOMhxJCmEc5jldi3YozPorQpvHGUF/ENY98sipcONKojC2XLP3cCC/GSUxif4yXRPpX4NdAixDiM8DrgI9O2F6dgJhsm3j8QuVylOYsIHIFrQ9F7Gi3eOeLfs+vdpxF90AKFUksO0IpgQYS8YCWTJaVbU8x3emhwcox1R5goVPJZBze5HJpImRp4ingwCK+rWGWBmnzz6138JQ/hXubFhLfY5HcWcDqL5jMZRQhW5rQNSmC+oQZkOaLqHwBMXu6IZJOoMHDEYcGqyfPjLUu/bNrOP3/Pcv1DRv5YbaJz7/4F3yuYQX+7U2EaYgSmg1dzZT64yA1UmrEgI3fEiBsxYKGLmwZkbFL1Ngl2t1+3tZ8L6//8zWEBRvrDOhd6DLVacft8bB37APHJmxMQk0c6Wew9/SZwYR8/r9JRVW59qFBYnzlaSMzaicDhhIVm697L1c/8HZmxLr5rz0XMnX6bbw4ZtOrCtTLJD/87pd51q/j8kTEpd//bnW9JX//tiGKJtDlLm2Xv/ltxrh7/8+8/foR3VUST+7CmdnKFS/7DDamc9Rd+32G21YgCkdusPPCekoNkN6lsTyNXdIkt2erkxksgV1UpPYIY7g9yqC3glUbvzDitSjljiCNjpZSaKLIpInE8Zqxn4SJ41bMpXjWKWSe7kZe3Mw1s//Izb3LKZZshC+NQ28ozLXsKkhENDZlsYTGjyzidkhtvMj8TBcp2yMfN+YqNXaRFneAFnuABqtAo/SQ7l5+1H8uTU6OzqCGUEu2DdQzr76LhniBh7pm4IcW4LL4gTeS749z8YKN2DLiqe52ciWXBc1duEmfQptLao9NeksW2VNunhGGYFnodIL8zAypvSHN9/WhowiZSrLNb4IDNNo4mWDt6CL27GzjU3hKiCxJ3IEEtbbE3a5NKW+oSO320bbAygVoy0IWPVPqX1Z4qpixNAhSFrG+CL9G0vRERJAUSF+TnxrDqXNIl3wzzkrE0VJSmFtH6ukuCALiO/rxW8fve3UgrGi6BsCUPZVxxH2RXMfoHGqf//5OJO66c5CoePD772XlH96DJRS/2306s90urkz381xgUyc9PvpfP+An3S8iJgI+9sRfc8MvHqd797cR+s2snH+9Saz4gUnc2TZz3rGLjW4Dr6h9nfmAMAJL8rvf/3LYPiy78EasfVlcyokdACG47c6fV5d5xZLXseVd72PmD0YmYQYW1JFvl6Q6FJmNA6abW6hAGBITS6BjFpanjX1GOY6rpFslqyrYP44LPySsS3DFJTchiyFWroS2BKsneM6pymTSiRjHjyQUgmiiPJFOFiWS1vpHQohHMObaAni11vqZCd2zkxxLrasAuD366THekxMXlWO31LqK1DNdbH5zO41PKm66+JfEhc+cun00JvL0+3GypRi2pUi5Pu3JLG9svZ/F7t4hJWdjG18fKWSVzY4wQZ9KEmgL7SjcAQurs7ec4ZDoQhE8D7I53J6EyWAo0/VJDOTRdYmDf9ARxjLn9dUuOscat//5o6yc9m7iOwR2IU1HqZYn/CKfWP1akjOyKCXwZiqUrYm1FYgiCYHArvMJW8obCSTxlM8pyW4CbVFvF0haHjEZsDusQ2tYfsZTrHl8EanmPNtaUrTcnSSVaqfUZNO7QOL0Q8tfivgzG/FrHQZmWGPu9/GCO39vZMwVT4ATBT960beZ/4sbObVtL1/YuZJ3TrmLFktQ70KTjHN5Ihqxzl3f/y5L/v5tw17T0hBJ+6t27l59PUuWfg6JyVaunPZuEp4PiThWb8EQh0IMI5Aq683/5UifpgU33grtENRqLE+gpcDywSomie0roqVExc3ER0aDJRQqZh+0lHH1Y59i+XmfGPW9iWojPNR36YXeGe5QcDzdG09EDI3jKpdn+3KHuvUtXH35H3h77R6+3zTAQDFOMe+i8g6IcgfHxhJxN2B2bQ+1bpFdhVpcGXF6zW5iMiRpeVgouvwMabtEXAT42ibQklbL5XHfIm2V8JTD9Fg3kRbssOtIWAHNbpbWVJZZtd3MTXXx0+fOgbzNHzfMQfe72AWBimke3ZOGVEgsAtvTyIEi+AHa8yAI0WGI7OwhXSx7qIXmNa01FyU3EJQv+9gBjs2Rxspp72bVzn89Sp82DoQRSMW0O/N0L05heTapXZq6jSXjc5kyRI9KuVilEFkITLe+mPG+JIrQro1ypPFRkgLlSLTUxPoVItL0nGZRu1HjZhV+ptzSXUqC1jQd58cpLC4x+7v1KFcyMMOl+9yRceRwsWrv14/YtkZsu6yUqSSZThSsetlX+MSTrwLgy5svp++U+2i2Bzg/lWdT6FLnFHlbw31klc1iNwHYfPf27/O2y95k1t/+ZVa2vXNwe7u+OuwYrHrms6yclgdyrN75r6yc/h6jB0vEwbYQfoh27WEEEhw4SXfxX3+RKCMptMHAXNCyhnh3SKwjiygFqLqUieOWrKqRwcRxgLVjJP8qlTNXXHITypbG31drsGxWnPVxwhpzZziSjS0q6qPJOD6J8UDoUYw4q28K0TDWylrrniO+R4eJ8847Tz/88MMHXe5I+jYM7fp2pLE8fjVy9ikQKXTCnTQBfx5Yal2FdF1EKoGe3s7Gv63ly3/9fbIqQV+U5N7eeTzX00LS9Zme7uOC2q28oeZJWqwj52MzFjaFOTrCJI1WkVm2y5OBZrYdUi+TLH7gjQRP1NL+QEhiWz/q2c2DK+pyRxrLMmZ8UlK8+FRkaK7p3685etmD422itHLauyGVJKpPsuGfHJIZj3x/AicR8NKZm9hXSpP1Y+zsrqOhpsCcun20xLLct3cWfmTRnMpxWm0Hr6l/hF1hPa9P9/JskCcjFXcVZpGUHg/lZvOzx8+jsXmAILQIHqrnklf9hQtrNnHDfa9C9NtYBUl6ByAgsU9x30/ed6wPzUmBv/7TP5ZJpFx5oPn8sfSNb+H2H3+P125aSi5wWbPwd8z+1TXUP2nIwaZ1haoUfO1Pv19d70ClKDN/cDOx7aatcDDFw+pycbKCWC84eU2QMtuq2xyiLNh1mcWM1SGFVpsgKQgT0PiUXx2YVpJl+8e2/QeCKiaRniJKmP2+99fvf17HZTQM/cx71nzwEa31eeNZ71jE8ZXN14IUEzahWzn/ejPwty1WPXNiEbPHE4bGce+cOXS8yOWpf/w6X+mdyeZiM//71GKsLheU8Tm0Z+RZ1NbBraf8ijpp87ifoKRtZjt97A7T5LXLw/nZ7PZqWZDcy4P9M3lN8yMscjuY7yRY5/v0qQRZleDh/CwAns22cU7dNqY4fTyWn8H6gRb6vAS71reQ2imxPNPtTUuqNomFKRGpHRYtf/GJ7+iH/iyqu8cY6g9BJY7LdIrO/28eX/2IIQFm2yXapu4+Ksf4eCORKtcmUpK/YKbpniUh1u0TJm0sTyEihVUIq0brKmZM1u2cIeaCmhj9c2LIUCMUFBsF2oJElyY3TZDao8lsD1C2oNBqU/9MDmugxO/u+gW/yae46aY3oaXA9jTKhkR3xB/+518OsueTOBK4cO0H+fsyibQy2ccdxUy1w9rX+qZxVc1zAAftbjwULz/tEm57+h7euetFPNvXyl2LfgvArsio/q65+GpTBigEv/vDr6vrHSiOn3PtrQQpGFgUMH/2Hm6e/Uve9dwb2NubwXkyhZ0zy7U8WsKvtXGyEU5fkbA2jrYEfsYmtSOP8AK0ayNChRZihGn2kss/h9Aaqxhi9RfxW9JEceu4jOMvFAghPvnKr7zs47MunjIh299y727+9z1/uFFrfcIa2B5MifQIxgdJADOA3vL/dcB2YNZE7txE4niX6K0p/cgMPgH19EaWuW9krf/jY7xXJyZuj37K8sSb0PkiKunQ8ohm6pV9POGluadnAXsKGYLQwhM2obKIyeCoEUgAGQFPqzR1VomCDsiqFPXSlLy9ce7D3FUzn4FN04jtiyEsyww+9aAlmY4irEwG3d6EcgRW6ejblR1PBBKA6utH1GYQoUJ2xsj5FtaAjcy6zD2tiyV1z7C253Q6+mvIey71boEau8RfTXuSuAw4Nb6LRitHoyzxH50X8/r0vSx0UigUy5NbabHSLHL/zG9SZ6C1oFB0aXpJJ1+b+gAAUy7+Pm9f8zbT+ccR+HUgg7HL2Va2/yOkTKnkUEnz8vM/WTVdXjPE32YSB8avX/LvY75vfJIOHUuf+StaEjke3Tydv7aXEeseVJfd/sehyYSDZ3+3/t1g/Jn5n59DS2P+a3mDUnIY7OIy/xc30j8rjXLBrwHpw74zXFofLqGcwXNrrNhWarSxPI0IBGF84qTUR8N7yfIVly/57PFvAFrxrhrIsXLqu4Z5aUxi/KgokpYn3kSsM8+UPynOu/BveP3MR3h433Rkr4OdE4gIkOCVbPzIIiUlaRnjFNtMEmfYGebYMKD66Q47GYgMydwWH6BOFogQ7IsK7AqN+YmvLYrKJdKCereAhabN7mNGLM26aAp7ezOmI5w016SyAAFWvvyaJ7HzkG93iHVYCEAkEuhcrvrdtNKAieOb3zmPZ675Oo/45rw5WgQScFwRSBXoKEIIQXxvkaAmhtAap6eADOLkpyWI9YVYpRARRuiYGTdpW+A3xEGDV28TJqFpnYe2BD2nxpBB2QvJMg1VtCUoNdlYvqb31DT5dqNAvzjRibYEItI4OUVuikW89+D3zRW1xndvdf9/DH/9ODe6Pt5w37LPDXv+yiH//1MbdO2eeljbfdWG5axseZK1fzoLFg1/b9XmLw55dvAY9pdvDO/ult/zK952yp+Izwr43xln8uCfFpLcJaoK7yWXf86YaUeaUoNDFJf0nJ6h8dE+ZMFHOxYiiqpldRUBwV13frBqeh3WJbG8iChhn/BxfBInNsac1WitZ2mtZwNrgFdqrZu01o3AXwG/Oho7eKTxfIij5yPvW/bi56EkOmsh1tyZh7/+JAy0IqhxifVGXP2d65AoduVrKPoOnm+T91xCLSkol+3h0fMi2B3Z7ArqCbTkp9l5fGrTK/lY5+ncW4JtxUY2bWslO1PQsygFi+YgU8lqLb2wHRAS7fsQabQUBJkTo2xqoiGKHsq1TXmS1MgpBWrP7+KOvQt4TXoPn5z6O1pqs5zXvoP/13QPZyS389GmZ3l/wyZekSzx4pjNV7qWVE1UASSySjCeYtu8e9HveXHbVhZN6aD3wRbeuetFALTZWf7+pfcyY/Fu8lM1/vwiXj2c/9ZbOP+tBzAFdoyJZzClrqpyrEA7Fto58O+6YvGRV0OeiBjv/Vm2rR/x2B+f2ncqix94I8tf+2aWv+bN3P7j73H7qf/Hh6fexo0v/g2F602Xlke/dh2Pfu35tXze+uZBX4jr3/9jLnnHA5z/psdpfSA7LHYIBcm9itrNmigBTh60ECbzXhy9zOLu1ddXH2YjAq/eItYbEusNRxyzSpx7vnL2iUzS3L36+sNuHrDswhtZetHhNdhYUfMWVtS85bDW1XU1UHP0khMvVOgogp0duFu6aH1nnsez09jTUY/0yudDuZxNR4LOfJp7iy38vuhwT3EWM4Z0RK2RCa7O9LAwsZv/2no+M2I9XJ6IWOQk2RtZPOe184t95/N/3Wfxq6fP5H/uvoA77zmT769/Ec96U4gQbNzUDtuS2HmJXQC7pKveas2Pl2j5S4nGJ4zBd8NfekwXz0QckU4h3DLhoTRCCoTtEPUPEGRMwuBc94RogDzhEEKaDnZBhF0IkV5E0JhCuRZ9cyR9c120JYniNkHGQgsI0jY9C112LnHpWSiJd2tEpAmTFnYJEFBoFUY5FhMEaYtio6DzrzxK9YLCAqNiur/USPzKDva9LKDrbIvsbI3bF3DFyz4zIkaPhgqZVIUa0hBhFKxs/8fnc6heMLj8ss+Oq5y+ecoumqfsGjOOz/uva1n53MtZ+dzLefmCl0IU8T/z1vDGzEY2/s03uexthtibPnUP06fuOWLf4fXpXn448y6+/dpvMvX7T7Oi4e2AIYNEqHA6swgF2akC5Qh2LK8HQBR9hBeOus077/oQa+/7GABh2qnG8XjPyDh+2fKbq4/jNY6fCNBaoLSckIeeIK+lo4nxGmufr7W+tvJEa71KCDHS5OEEwcEuisuW3czv1x78wpmIMrahWLXeXPjLLvjUSWeae6SxpvhDlsevJrGtj64Lm5AB/KrzXF7asplHe6eR91wiJRnwY+Si+LDB5vPFU0GBRc5wM+7H/BK7wjra7AF2hw0oLdgRNjDH3UtvMUF3kKLZyjM/1cFacRpagJYC2ZdHBQEICZhJo4zHEDUZUAq7EJ30N32ANbkfsKLpGqI5TWhbk6ovEoQWmViJ20/9PyBGVyQ5u2EnL61Zz2I3wSl2J5VW7hXMTnRRY4/eLj0hYryhZj2fLrWwM1uL1xQhhebZII+LJil9mhN59szOMbupmx2PzkSOPjYATC0/MGJwuuahG1h24Ql7uz1hMGIAus/4MlAuF3vMN+eBU477t//p4L0llp//SWOsCax9cGzF8ubr3guA6vg6V6b/Yvbp/u9X31//uo/B6+DCN3wJgObHIrJTLaxSiLZMGYflj61CvP9Hg+WUBxtYPt/7yERnMA/WOWb5+aYcYM1DN4y53ER0Rhq2/bKqcMWij0yWsx0hqIEc6rRTcLZ08qeHF9I+r4uO/mYArJIwBvlSIwSsK86g3s5zcXI9EB+xrbfW7OVLJZe7u+fznvqtAMyyBYG2cGREyvKY1tLLzo52Yr2CSElmuvu4o28R9Y/bRjnoGEWLiMAulpWCXoQIFEEqjl8z5APDEIIAlC4ng8pxfGobwZQ6VJPPV/tmAPCehXdM4FE8/rGq6xusbL4WHXdQCRdlC6K4hQw0uWkufp3pXKtcizBhUWy0iPUL421kgbI1yV5jtB6kbfy0xMmCXQA/A9ox6s/cFImb1TTcEaf3NE18q8t1e86l0ckTRBYITZjUJDoEbscAUd3YzVUqCqQRJNIkJhwjiaThSbsd/zmDr/TO5C21xtb3nt99ADh4p7PxqshS7dt4a7tRO0skl8Y1S3q+PWyZVU/fxMpTBxW0foZyU4ByIsj3Eb7Pqq23jvoZt/95cOxx+WWfRTnD/ZYARDj4wu/vPPwua5NKpEmMhfGSSPuEEB8F/gszpP5boHvC9mqCcTA/BS1HLwk4nEH1FS/7DHf84SMHNUIdCwebfExifFhT+hFLrato2rkH78ULeXTabH752tuhZR3X7LyI+3bNZNO2VnqLSU6N7+bKdP8R+dz9CSSABhnw1e5zsYTmysYH6Y+SNKg8r0wX+M4ZP+QDG19La7vgvfVbuHfODjavm4NT0KZtqNbG32nABBw9bwa7LqkDoPFp/4js8wsCQUipwUElIvK70ixevI2z6nbym3yKV6fynB9zWJ/ewg1PvJIPllxeOnsT35tx77BNvLd+y5gf0a8iHBHxvnl3ULcwzwavjUdK06mzCrwouQlHRMxK7eM3G88gFkEYG8w8XLriZkR5zj+UtLb7S6x+/Ebm3mwGEBuvv66afToQVj9x8KzoiYiV09/Dqh1fOSaf/YnT/4dPnA5Lbzaqles3v5abZ/+SxdN3sniM9b7y7BVMcXoBeMn3pvKTDefi/r6GC/7+FjovUGQ2W0z7Xee4CIUrLrmJO+4ZXhpX8dW6+K+/OOx1Kx+ibYG2JEsu/9ywrjdDcemKmxHajFnHSoQd7yXflUx1pUxgNFTMS4fiYNfSaFg59V0ArB743iGvW8Hqp16Y1+jRxlr/x8Yf6f6n0KkkbfcJYgtDNl35TR7yAq5a9Y/Y/RY6sGhPD/DehkdJyzijEUgVfGLx/3H9fa/lmvhFfGvan0nLOHER8mxfCzkvhiUVWJrUS/ZxWuNeLov30JXezt3OedRsCxEa8q0WTt5cWHZe0HNairpNJaySURB67Rnim7tNG+9yebJsbiLa2wVaseXqKdz05v/k1al8lUSahCGSViz+CMqVON0FHCEoTkkjNChX49cYYimxc4DksyWi1lrUzCQyEqR3CkrNUGoWaOkQ6zEEnzsAxSma+F5B3+kRdn2JqT9w8eosMgv7CP7QwG/+cjZOxkdaGmd3DKsIzY/7EEbI3PDE0opFJqE89BofSiAtP/sG1jz6yRHlbSO+656xy7BPVFxxiUmC7x/LjgY2vf+91f9X8FaKBZevPnYp171h7ZjrveT265lZ00OoLLZn69j7tdnYWxJcdNWXKDRLsrM182/eABzcIH1l6ztGLDM0/i9+3xCyyPfBtkFKVs6/viom2B/LLvgUQmtsqJZu7g/liAMqlMeLk72kTSGIJqiL2knTnQ14A3AD8GvMqfqH8msnJI7mwHii1UqTOHSoYhG3u8Cs3yb5j6WtvDy1iW9N+zP/JEPWbDiHHq+OK885MgQSwCO+P0KantUWrbEse0q1PFyYTX+YIJMo4umA0xzNlNQAnlY84vus2zaVmgicXGS6sgmBLnkI1yG48FS6T40xsCig5innuJ70HW2Imgx9cyVOpkioYjzx1Ckkzwz4ZPNT1WWuzvTwrzEfb1MNj6amGue3Q8DdhTnMiHXz+nQvgQ6BDha73Uy1MuRUCV/v4CsPXE5is4tQ4BT2U4oIiO3Ns+Ksj48wz1cJhXI1M7/xRbZee+RNE08IBMEhLT4R538l67di8Uf4MG9i9RMHXycbJbh/YA4X1z5HQ7qAV8jgDijQgigGO1/ecvCNMPagezQjzYqK7UAE0v4Y7XjtP2hcdoE5L8eTzLhsmVnv92sHy+eEOPws6FgYizwCo0Ba+tIjQ9xM+hgdX7g9+ilLravQJY/6Nespds/mPZ85n69MeYhUew6/rxar12ZntrZMIBl4OiDQETujiIXOoBnvlel+fjNvM63uQPW1a+qe4y8DMzhv2lZufegK6jZI8jNczsrsIKdDslECGUEUk9j5iFi/ad/tC4FQECah1Ohi+RptCdzOAkSmi6rWCrRC9fZh1deiprXwtbd8g0vjZiZ4siuQhmJF/T+g5s0wCTQhEPkSTj5OmLJwshIZQG6qTazTQmiFzHtEborsLI0zIBAh5bI1sz1vlodQMRJ7zAQu0ZJHPVGDtkL65kq8zXXUFCHZUOTF07by5L52uuoSnLLKx9nVa/Zj/zDu+ZDLs6LpGlbv+9bw/V/0EXQqxrILPnXSJoStnHdIyx/s3n64qJB4y9N/N64ZbClyeKazhZdN38SazlqimCbZ4VFoTiAi6Pth7bg+92Ak0xNfKpfDly2gVs49NOP20ZroVMYAFQLvUIi8/RM0d6++fsLi+CRObIyLRCp3YXvPBO/LEcXSl5js8dCSg/G2Nh763pHsAjOJ4wNaaaJMjO1LHf77H1bwo4TNtpc7RKmIZL/A7rCY/5/v4Cev/wp10qcrivPz3gv49UPnYtX4/N2iB/ho07Pj/rzvdl3MuWXDZYB7S/CT7svYW6ohafvctnsR5zTuRCF52LNQOOwrpXjIa2Gm3c2/nLeW//qfv8LyNNGMFqz+DGr7LvTiuWx+nU3TQ5rm+2we/s7z82V5IWGZ+0Y491SsIggBuIpXLl7H2elt/KmkaLMLzLGNP8kXT/0F77znHRRnHNiDQqGQo1jIBdriDTXrubeU5OK4zYqED5hSyLSMU9AxkBo7b0w8tRzMPNy9+nouW16+JynFijM+yup1n2b14zeiOuZz8ysyfHnzFeza3HzEjsskDh+rn/jMQb2nfrDhIgKd4bO/ei1awt3hWcR6YMrjA4iSz4ywDm1ppDc+83vVMf+AXWFGw3iSFuOJZc83e3kkcfllnx02qVix6CNEdabs9GAlhRWz88svNQPoO+8+sdpdT2JsKN+n8JIzSP1xAxtfXssr9OXEXl1LfVdEvt0iW2rih3Ob2Ok38LMtZ5PdXEfrfcZIudgocFd08f55t3Nlup8PTlnFa+67lp2ler4ybS3L1r2Jzq4aCnNcZLdLqkPRtzfN2oZTuWvfAp54+hRmbA2xCxFaCpK7i/gNMWQEiW6NFmB5isiV1GyGnrNqafpjsdwYQyNcF+375C6aw8wPPsvPui/gZ8Ar6h8fZiB8MmNFzVsQiThCa0RZhR01ZRiYFcdPCxJ7was3igsZ1NL4UABCUGgRNC3soi+fwL0vjV8LXoMiSkgyj8bILg5IbHaMd9aTNbT/2afQ6tD8eMjOyyVeIzx94Y8A+E1Dig8V/5owEcNRhuTT7gGmTkHIitq3srr/P1jd/x/MueUWMpsFjU+ZzlwXv/oL3Pubyc5uJwI6umvpu6sNGcE9sXNo2qmJ90aGEM5qMlsFhZ2tZuFlY2/rUOP40MYqB8J4CUnpHz+x/ESEAtQEeRcd/RZIRx5jkkhCiE9orT/xfJc5nlAxPTzepfqTmBhUO7yk/47Z/jxkEGEVQ2b/KmJgVpzOS30oWsRbC9yRO42LkhtpsApszDaT3mBTaLf4VfpMHBlyfcPGUT/jg3vPRArNTS3rAMiGce73Qs52Nd/tn8Wtj1+O40Q01eSYmorww0HDZIWkO0rhRxaPF2bw4+yLeXj7dBrSphVo085+CEJEPEaQdki05Mm/AlK/O3IeTic6ljmvByHx62Jk5yjqkh6ntHfQFusnYxWJy7BKIAU6pM0qEaaAjSm4ePRtRlrhEZAQseprTwUF6qwCt+Wn8WB2DgN1T1Tbz3ZGOVqsNL/pPpuXLNjEg4lTYGMKr2F4MKpkkFacMTgZrnQOu/HpV9CYypNqy3GyYlXH1471LgzD0LLBuT8ziQqtDLm46fUfJhvF+fXOs3Cypiw6s0OT2eYhd+wFy8JpSlNscbFGt9k67K5xEwFlS0PgHEL2fDxegs8XQXMKpEALMWbZ3iRe2KiokdJ/2oQOQnQQgpS0rt1B2N5A74IUDU9r7huYQ51TZEZtH08H9dSs7ydoSGCVbLoebeaG4iu58qL/osEKufHc33JubAcf3HM52T8109AJ6x+fh5odUWywyDwHG3tm0vCMZvYuH6toSkhVzEJEQ6YEGqwA7Hxl0qmQviqXoifRuTxaKWOqrTR/eGIBS896CltEvHL2umN3UI83CAFlv0fpRwSNSbw6Bz8jCNIQuSBDsAoQpAUUigigZlsdu3bX4exzsAvl5XyBMwBuFuoecQjKHvc1G8zvlugKcft9krtSlJoMWfSmbZfww1Pu4X27UnSdLWmyWkAK3N7htgFV37P9PJBefukj3Pf0ufQuiFMcn/j0BYk1j3zy4AsdRazJ/aD6/+L330pqj8Iq/6R/+nnZN3BXnPQujZNXRK6gZmMOUfBBQtKV9DS4VTuC/XE8xXEwzSQk4y/lnigl2CReeDiYEukfhBADY7wvgNcDnzhie/Q8sfSiT3P7nz86LtPT8SiTjjTRtH9tqdBgZ03JxlCztEoNNQyWKEyWxh05qEIBHngCsXAu2BLbD4mcBA33mcDg1Wf45r7LOGfp1qqnkXd+HlW0uWzqhmEE0tyfXosIYMPfGoPWXz13Jq31Wa4uNBAqi4c2nsKfHl1AZr1FsQ2YUWRqfR+2VGzobcKWiof3TaeoHIqRwxOd7fiBzSPuDBpiediVINavKDZaCC9E1aWQUrL5r21WzHiKHfl6fvedsc1jTyZopZGOoG+uQ2on9Os6Hrdq2Ts7Q2tqJknbp8nNA5CwfBSS1Lnd9Oyo4+e5Wq5M99MZ5XjAa+GSeDc1MsGzQcDdhQW8q247AF/tm8HaztP43/mr+VNJsTpYzP/2nM1Gbw9J6THF6eWO/kWsqH+S7+++iHee/gfmntvBLVuXjrrPq9cNdou6s2jxjT2XUfIctuUa0dGJXzd9IqOiPhpKIM38z88hLIv4c3G0gFJ7xPxf3EjoX4HcFadxu8bNKZI788iBovE4ANwdPbg7ML4oB8F1e85lXmIv72Q+D3kmRrzolLE9uo4UtrwqjggFM39XHNWXaaIxVDk01oBWW4LLlt2MLosEDxSvj7QCacXij0ClQ2Z3ufTZNs9XbRk0cl3Z+g7z2t6vs3KeMW9dteHzR3RfTnaEPb1IxzbdzpSiNK+VfLvDtDsG6Ds1w+9vO4eLVqzj4saNPBmbSdf5dViexvI1K1c+yK3tjwBwxXc/wKxfdPPRtzWgGz0a9kKsTxHvhUSXxM1FpB7JIfPlknLXxm9Mgqz4kVk4/T5gDKDjnSWkFyIil0J7jGSu3FUhDEEIZCKBlpLuhTb/tsR4bU0SSPvBsiAIkP0anXDxmo0CMdmpYK8hjrSA2IDG8hSFxVNIPtNJZtMATffVoRxoeKaEiBSlphhaQrLDI0zZZKc72EVNzfoshelpOl5kkdjrkNyrKUzTzP7t22mc3sfcP1+LnFKEXUm2vdziB6/4Ju/54jtG3d2hnkcvfe0XSew9jYYwx/q/S0JqUhFyrLGi5i3DfO1O/5dbsXyo2VwArdGW5CWv+yIIwbR8hJMLsLI+wg8R2TxEEShFoj/P1E0uutt4HzKKbeP2MEtGWnxo9+VcXPscVzNILB2KKun5YvNrTNnunJ8dvY7TLyTocie1idr2iY6DkUjfplKbMfYyxw2GEjH743hVHoUZp0okVVAhkCYxgdAK9nYhGhtMZ7OS8TSwPE2ySyOUzb+0v47Hzv9v/mfeGr7d0s5v9p5FPhxUo1y351ykb7JcAPd7Idec/icKyuV3OxbRtaMe4UtEJMxg04NQaLzIxrU8LKGYmunnmb2tbLSaaEwUiJSkva6fC+q3clfnfGo2CqyiIkxYZE9vBgGZdXmaHpH8vm3esTp6xzW00tRt8PHqbZrWRchQs/fcFvbVthC2BMbXVAkuPeNZpFD09qbItGfZETTwo6zFBfEcO/wGnpA5doe1PFM6ddj29/h1/GTub9gVBXx621Vs7W7gnCk7+XPvHPYUMtTHivzD1HuJC59z6nYwxenlA4+/Fq/ojLnf//bsEr7yxNUk7ksRTdWommhM8+NJTDz2Ny1f9KFbka0uiQ5BerdGS4gSFlGdRWJdArsEsYEQq6RM17RMHBGzQQiidAwRKLBqRv2sz3bPZ1nmSZ712ljXO4Un+9p5Z91Ozo85VSLpqECBtvUxO/fGIn2Ol5I0bZmDo5vqkN1GIYozfEh1MC+MSTw/3B79lKXySlQQIsuknldnMzBb0PBYSMMfd2OV2rln7jy+e9mfqF1a4LO1K3G3xXD7BQnLXFOn/+s7mHVbLyKIiHcKdHecQhuEcUlsoKwmDIySCCHM33KJlbIk2pEEKZtYr4edDwnS5jzQUuLXuxRaJJlNAXg+xFzwfOOLZFnUb4y4J3sqEjVZxrY/wtD4SCUdtG2T2NaPirto10LZgtQuIwXxG2JIXxHf3oeOuWjXpuGZAlHCQnqGvEl0lBBKoaWk0OqghaB2fZbOC2rpPSukbh2k9ioKLZK2P2vyrTb5PU20vLiT1lSWXIvLtFQ/t+xcxsH8cC9+9RdwsxH2pt0gJehT0OFkID/WGEognf+WW0j7mvSOElaHIYN0MoFsjuFkQ5z+ErLgm9JFW0I8ZrqnSQGuY0inWVNG/ZxTv/UO2l+yC0sqdvTUc58zk6sv+MlR+Y4joAWzf5VF2yc+YTGJ4w9jkkha6xOGyVj/XMcI74Sh5mCVFtmH05nlULDsxZ+qdmJb9uKyIekYndkqg/SgxhnWrUmGquq2f+ekAumI43b1c5bKK4l6+6G3H7SiZvM27CltRNOaCNMO8R6LuptjzLnq/7Hpym/y9to9OCJiT1DHb/IpUtLjrv+6gEwRLE/zpm2X0OcleOqJU5ANHk4shLgi3lzA25HGrzU/djweUAwcugbSvHjaNhwZMbW+j129dWzvbMCJhTTFC9y2exHZUozSkhz59WnsHKS2ZBHKDGZjA5rcujLH+5pjeDCPN2iFjiB2z5Mk0ilIJUBaTO9OE9TH8TM2MjST/ycfOB2/FsQ0TbbP5dkp7fxf/2K2tK7HkSH/77G/5fVz/8L1jeuICYf3dZyDp2xuW7eY2x5/Cf2LTHa57kmbR2tPxclB/Iou/uf0NXxw75koJL94/Gx+kT4L908ZbCBKDN/divT5letXsKHrMpxHUzQ8ExA5DkHxwLfoldPeDcCqnf86IYdxEsOxov4fEDGXGbU1DCxuAsDNRoRJi2SHRdibIN4LdkmT2tCLijlENTFwNVFjAi1ARsaUVVuCi19tSiDST3aClKxafzM/fO4CHmubzp5Chj3dtejdCSjzl+fHRhKQ421jf6iQgSCoj8qGYoeHeZ+9dUTb4YNhw9O7R5jMD/UlPFreRisWf6RKHi4/+wZEuZtWZb9WP/EZlp99A0jMxLShxpBKQrD8vE9U1Sqrnr5psHPTpAJp4qAVquSBVqR++SDpXwvENDPBy2waoPb9Pcx9/7W8Z+Vt3HzhL7m15Qp68wlW7ziVN9ffxym/3GtUBp5P+5+K9M+Nk9oTGqPsGhvlSIrNLuntEUqBLPnm2hBg5wMK7XGUI0DEsPMh8Y4i2pYEdTFDduxR9C9Mk07aWIUQK19EhyEi5hLGBWu2LWR2wwnb8HhCsFReCUIifR8ZhkjHASmw+nJmIi+NSgmtsbdhfg9LIoRA2hJChZWVRKkYVt5DxRyCuhgDp7hkdgY4Az5aCNp+tYG2/3PpWjaD+D4fd0AS25Nl3+JGrn/tr/j+9gs5o3YX93fPpM9PsO6xWcx7KItfFxu2vzO/93mSGxzSuzT123KIKEKXPLAkbq8FWPjN4YjvWen8OGnef3SwouYtiEyaJssyCuEwNA1rbBshJOn1vWghzDg7CBFBiEonEGFk1Kbl1wFEKSSqTbD0ok8j122ofkbr5aeT3TkVNCRSJnn8kvhr+NMZvxp1n1bO+GcAVm3/8hH/vlGyPKnUhxiMyzjvH27Bz5y8BKhCTFgXtZOpO9sJiaGE0kSTR0NRIY8AKPs2uN0FAO4eZXA8FBV1W+RKrHGar07i8HC7+jlLrasGX9CKqGMvllKoue3UbPEQYcTCr3fz0nv/H//2xa/w9zWd/Dznsc1vZo9fS5iC1F5zc35101/4770vwvIESM3FMzYzI97DL7acRTRgflihIN+fgHJThy3ZBiSaQmBMnVUg0a6gz4/jWiGXT9/GOamt9J+e5It3vxwRRYggYuCsVrpPl3gNilj3ZIZhNOgwQPu+aWXu+0gpcIMIESaRgUL4EU7exau18Wstojjc/tgihIafDGRw7IjSthr+KzqfVZnTSLseXmST82KgBQOnhQhXISyFV2eT3AsyGAzUd+xaQGMyjxhwCDtdgmma1E7B058eaYC+Pcyyc6CWYHeS9scDnH4foRwsf/CesD8ORB6tnH892Barnr7pSBzGSQDL41cjEgm050NXN+mtccK0i/QVyhbE+iSJEITSSN+oFLBMqYWK22gpjImrBiSUGm1kAIVmyZ6XtBM0hsz6ty+ha2I81dlKYSCBs8sluRe+0T+Va2t3jdinZS/+1PMiecbChg9dV+3mAhxSSdtpH7mVMAHicG5LghEZ06EK4qOmQhKCFWd9vPo/gBbCEEcYpXBFLbzizJFjiyhjmOJlF3xqFDv+SRxJ3K5+DjAYy8tJBN3Th2ioQ/bmQUoW/PsefveTl7HjQ5o3zHuE+3pmsbW7gb994i00py3k3l7QmvzUGMVmQbJTEFmCYqOkZ6GLDKDmucBMIgFUWWkYs7B8Ux4nIm38uhyJtsw1j9ZIBfk6iVBxZKCp7YhDLo9urmffGRJXCTbsazo2B/A4hwpCRL6AiMXQngeWhXAdcF1zQ62olaSAZMIoxPpzZVLJwgoVwvOxij4y79HYFzOksFLm96qvNeWFIXgNDok9JRAC5Wo2llrZ/XQrA4078CKbjTtb0K75/fevbLD32Th5iA0oQzLmCqggQNY2ICLTWMPtGjnlOhB5tKL+H8z3ZFLReKSwPH61KZEsFAEQMXMO6ShCSAkqgmweYUlwHHNuOY45X3wfZMysH0UQhgjHRgYRUdIh+4oz0BLsokIGmvSeEBEataKdC+gvtsAZI/dp5YIPErU1TNh3nv99U8amHYtlF9447rnweW+7BaHBaxAntRpeaYgmylj78Hi94wovHBJJgLYFS5Z+jrtuPz6MNtfe/3GjgJJmAIocnlXdv5XyUAgNypVV491JX6SJQcVoe6m8EgAdRYR7OhAdndi1NaYbSC5HZttOPvzMmxB5j41vn0q8C9PhQ4OfFvQt1CxJ7OWM6b/hc85ypsX7+GTzU7xhyxKiP9aT7jO/aRiHMOmQ9yRo6Ho0g7ag7eJdvGjmNv7UMYt0zMOVEX3FBL+593z+p/EMNi75Pu989be44KFrsXyId4doWZbvN06SjSOgFQgHVSyhc3mEFMhOY2rqZouohjREGndvDncvJDsSBBmH3FRzSyy01uDFIWoPcYSm975W+gNj0Bnrg2QSiq2Kmqds+s/xSV7QTf8zDSQ6BNzVzILH34GYm6O3JwV1PtZW02a62soVc02v/dkP+Hmulj8OnE92cx3JXRInWyJK2cT6ICoeg2M3iZGwbXRQLifTGp7ahC0Fcmo7iAyWr7CKEVHMws77aMdC+CFupw9BiI47YFmouI3wIpxeC+1IEp2SRHcMbVmEcUGpwcUquSRLhohyc5pb113OtRf/57H9/uPEOe+4lXAqKIeDlnyMBi0EIohYcebHWP34jUd8/w4Hax79JCvO+jg6Xh4uac2yF38K4UWsefSTrH78RpafW1aCCQGhQmhTQqMtC510q0qxoSTUJI4sKqVtFUTZLOTyyIS592rPQ+xxmflPtfw514RoidE+M4YIHGQxB7ZF8dR2Os8TNJ3axfZpjbj9kuSZPch7G2i/r2jIiTCCRBwRqeokMfWcURHlTm2k0OaAhnhfRORKnFyEVYpwshHdi1ze/Y5f8+XvvIapvzcdG8O0oinh0ZjIH5PjdlxDKxDSqMyK5WAoJJKkUYlUyoq0ggjI5hAVhYlShgBQGlSEaqpFFAOsrj5zD6/M4OIxCALqnsuz94I0qc0BYW2cxnWwestL0BeWWLPl1PLuSIirYdUFK2rewrZ/PoPUgKDhWR+n14O+AXQQGGLCdYmXRWYns7n2cQHLMn5mvg9KG6UYIFIJc76EhhxC26b0VCmIFCII0KUSoqx2QwijRurtRxZcpLRIBXUgBSJSKNfCynmGpFQKkSvR2DN4fR9qt7YjgSg+/un+BX93C0GtwK8DMVI8N4lJVHHMSCQhxBeAVwI+sAl4i9a6r/zeh4C3YcLCu7XWaw66QaUR4ei03vMZuA0tiRsP9i9dW3vfx1iy9HMHXH5oNmMYobTfV7njDx9h6UWfZulFxoB3LO+nSRw6qtnM8iBUWBaEIdr30UpDECL7zEBz1g0PYdXX0rNsHvG+kMgRICwuffht/L/591LnFKm1C1y351weuXcB9Z0aoSFyDZGY3COIuu1y1gLsEvQWEwxk4hQ9By+wkWmNQqAtTWxDApaY/QwyAjGg6Z/j4E0zk1qRs0b9TpMwpCAYjyRV8hCBkcXLgcqA1Mx07V3d2EBsXy1hykHZCeN149r4pRQ1vZiOWsKULib2gfQlSkJ7Wy97OuogobAu6Se7uQ4igeqNIxImAq//6Ej10R1/+Ajvf3wdS2qe4oLMJla1nUqQTRHUupTqLGSokSEEqUObja9aPzoxPYnDwzL3jeZ+ACbbXfmrNLqjE1spZF0KWfCxXNskDCKNyBfMQFNKk8mMIiwvZl6rZM9jLqmwFhWzCNI2sX6Bk4uw8yF+jQMChBw9ro1VJn0kcMc9Hx6mRhoP/vL161hw460AnLLKtKDbdAjrz1/YDr5GOxbLz71hWFef55NIOdQxwFCjezBlbJWS+NEwdD8rn1WBHqIWW/PoJ1l24Y1Hrbz+ZEMljsNgLCcM0UqbOO774AeG0N25B2fPXmRdrVEWaI3bXWL6HXH65lkIBUFG0b+ljjn3FXE6Bsw1K4TxvooUzr6c6dPs+xAppK8REcZbsWhUilqCciSxXQNwehN9UdKM77RGeCGywaOzJ0Pj1EkS6UCoxHEAIRW65CEcG11WFKE1WimEZZW79AWGYFLKlLwphSwYQp9iyZBMQpj7sFIQhuSnJYn1a1TCodgaJ0gJlANyTwxmm7HW1jeP9FVdPfA9Xvb/fQG/xqLUYKZUVodjyqGaaigsaMLNavyMYP1HRo4DDoTVvd95nkdtEiMQRUYiu38cL5YMMVyVhpTjM0DJQ2uFELJ6rlSggwCCACEkdtxUE2hLIIVAlAJDSsUcsCSq/P7+WPXcgeeHRwJrHv4EMDiXHQ8e/MF7WXS9iePT15rGEc8c8T07MTBprD02xkUiCSHmA18HWrXWpwshzgD+P631pw+y6li4HfiQ1joUQtwMfAi4XghxGqbj2yJgCnCHEGK+1vqgrQ3cvdnBC7+MFYs+gijL4/fPbh4qQVTBkss/V/VIuPOug687XmXUgZRJlf088U+34x8VryQdRUT5wuAbWhHu7kA6NtbcmUS1CRp+vxU9kEUHISmtkKta+G37ZXhNce6aY5N7cZGmJ0yXpiApcAcUTs60/PVrLMKEIHIFYRKC+xp4SDSgYhDEoG9RxJz6brpTNZTKP/yAKhK5gAZ3QFP3FyN1fuyr4x+YnEwYOvAcCtXVXZYvC9PRBwzB5NhIP8BNJmgqhWhL4uaSRK757bQNyd0efp1DqcEi3m2UInuea8btlwgNX3/Fj/jp1BcBsPr/LuC5j/3LmPv48L7pPNHXTlsiSzLhU/OiAbbVt5DcKXByZpnH/3Xy9z2WEFJUzyWxn6xZlTzYvguxy0LBYOZ82KRHVDt5AWYga94wHh9d3di1NbhBZdDpQjKBvd34NMz5mMvyujfTdMt2Nj32YfbuqmPb2z5wWN9l+fmfPCT/pKFE0nhL2qbdWax6IUnv0NKYG57ZTcOi0Qfb0jfHbajvYAVXvOwzh0wurTjr4wR1RqUynjK58RI+FaLq0hU34/aVs9EwjDgai5CaxJGFCsLqdQkQ9fUZNUtN2pAOJaNWBaCnj7gUTHm6lSlBP7o2Q8dlTTjbuowyIZEwvij9WUMOF0V1IoolSW7YR3KLhbYlOu4iQ5fs9BjxvggcCzsP//bwZbTvNAbPQXstDXeac/B335n0thsVQ367YS9XEnyU77GA8gOEZRmCqRiZLpjl5wzkBu+9FSJAawgjdBiS3pIlSjpIL2Dn8hg1LaYxdfquOh5//9iEvdsf4Pb5hEkbEWqiqY0ENS7ZaQ5+jUBoePzLk3H8WGJ5/Ory+RJBVD5nIpNgJFToIDTJIj04h9RKV58Lyxo8D6UwZCXl8YGQCM9DpJJl0lIbotm20b19CNtG5vK8fMXr2fFJSSrms3en8UTc9g9jjxFH/S5DunePF9KLUK5k6Us/w+1/PHisnHbXwKCP0mH6KU3ihY/x8hLfxpA8AYDWeh2G6DlsaK3Xaq0rI8z7gWnl/18F/LfW2tNabwE2Ahcc7ucIrRGB8ZE5HHn8nb//0AiS6a47DSmkLTGmymi8uHTFzSPK3ABEpFly+eD2b//zR6uPSUwcblc/N4Fj/2BS+esHhGkHpJFZ6zAwpNPuvchntxHbV0IGEPW7+DWCMG4GOFagjRdPZPwTnLwhmNwBjfQBBfEuSO2GbC7O+n3NzJreyaLTTFv5HhVSOLtIbrogTAgszxj2TeIA0Grkc0uiw6Dsm2GIQlUsmveiyEwmsjlkwUcWAurX9dF0XzeJLp/ULg/tSGQEmW0+dRuK1P5lLwiI9YKdgzuyp9PomAnJcx87+KDx7su/yOb7TuHBXTMollwGinHQAqFAHmJH4OXnfYLl531i1PdWzv0XVs499MHKyYjliTeZR/xqlrlvNC+W7wc6iqqqhqEkpY6i6vlW/b+yjjIqpMpj//V1FKG6e1ADWVSxiOrrR+3uIOroRO3ZC1092J39PP7bU+nLJ3jF2etQHfOrhuzj/l7nH3jQuezFnxru5TcEFeJIhIqlL3k+eaPxQ0QRYsjxveKSm6px/HAUWEM9jCqomGRrSx6wrPxQsPzsG4apkIaqjPc/9mvv+9ikCmmCMSyOlyd/VUWSVuhiqToR1FFUvW5VEKI696H6+tFxG7uk0XUZQyAN7b5XmVgpbSaOQWgUMbZERBo5UER6EW5WoSxBbk4NQQq0L+leJMnNTLFvcQIZgDyKjRdPFAxVlY2AFOY3Ld+TVRAOu++a+3S5m14Uof0AnS8Y5UhUed0QB9r30SUP7VhYxRDZX6D5fov6lEkijof8ueOeD+Ns2I0zEODuy4PWBCnTQc7yMOO7cWLlrPeyctZ7D/x+2YR5EgdHJZYvc8rT1fK5UT1HhsRttDJjQ4bfJyoYtnwlNlXie+U8HMia+4rnofIFE8vzBVQ2i8rmED0DTPm8TWdHLSIQPLDyVjp2jd7h7YDf6bxPgHVgdfpoHn1Qrl4RAuXIQ1IlnexQgNJiYh7H+ssdAYy3nC2ptX5QDDfxPJKVkm8Fflr+fyqGVKpgZ/m1ERBCXANcAzBjxgzCuuSIZbRrI/zRd/VQFUjD1r3rQ1xxyU0oW7Lk8s9ViaXng6FE0u/XXj+MQJrEMcKQAaiQwjxX2rD6Pb1VRUtlWV3ykAMlaje5WCUHGWrcAdNi3s4FaEsQ1dhEjkAoTbzbZAe0NJ4oTl4TxQTOM0kK6QRbGlPEakssV6+gv5RgWksvu7cnSD6Pu8/y+NUArCn96PkeneMSFTUZMCKLqT2vSghWX6sShKGZCOTy0NVdXVe6Lo6cgUrFcDqLIATFGbVEcQdEHS0PCrQwxOB///gyAJ6+aXxZx5e+5ou4CyycO2twXBhYECfeKUl0abQlePTfx95OZUAZtTWYVsKTOGSsqH0rqlgazEJWzhchTelEZUBJ5eUyWVTJalJRKzHi3Kosr5Ws3ieEFIPLKY1wLDM4LRNM1eXK6qWofwAZRUy7IwN3AV8zqy59w1uAz46IY2N1bDuQCmn2v2/gg613oDr+C4CVK98wuM6jnzSTY0tUVTVjYX9VjxBjK5/2j+NWv5m8qfRg9yMRaVTcQZZGn20frlfgnXd/uJoIunTFzSPMcg8Hly03cfz3a65n7f0fH5O8m8RRwv5xoOyHYsVMeemI6zaKwLaRXf00r+3Hn9OK5dpYfXlTDqWUud+WjXkpl7UACN90DCOZwMqWiLkWypEEaQu7BE0POOSnQKFVUmiFZKcmTByeceuKpmtYve9bh31YThhUJvPl31CVRmbPRsTxoesKWVUokYiDXyYChEAk4uDYyH1Zo1DKFWhe7cEfEvxh/fiu3ZXt/wipJPbOfRBFyLoavNo0TlFXk4hjrt/6DvNPYz06mRh74UmMipVt7yTq6Rv2WiWGCssaFseHxtmRMTsaNY5DJfaXtymF8cjS5fhuC4SUwxRy1X3QyhCZ2Rz2LsGpN8cJWjK0/HXa7Pvcf2HVxi+M/E4LPnjI5W63rfkpqsNMp1++/CpUuavr2gcPLfmy/1hBiNGTTJM4uTFeEmmfEGIOZaceIcTrgD0HW0kIcQfQNspbH9Fa/095mY9gCKnKjHa0O+6oV7TW+lvAtwDOO+88PdqaFQLpSHYqqpBG4+1WczBUBq77Z0MrxNRQ1nj5uTeY71Qm9Pb3bpjEBKA8CKkGoIEBgtpWrGjoZHJIwCoUsUop4r2W6cxQiKqTMOUYrwUZaqySMeaUKGJ9AjtmWkQHadPFzR0QiD6HIOWwaaaN7UbYjzbT/mSIVVJkZ7g88q1JifRoOBCRdKDBwaiorBMGiO17jGwzmYBMmviuAbRtgyVIdlhEcUmQkohDIPdWzr8ee04jIrJI74kIE5KBxYpEl0W8d5wbiscOvgyMOkCZBCxPvRlh2wjXQfsVgmI/Cdh+irahA9Chr42GMZfTCnS5VK7yermzlFl5yEDX85BFD+FH3PPTc+G6B7n9J99j6RvfwuVLPjuusuqxyti+ce4P2bqzvfr8Vf/9B/7n9S9DKMWKMz/GHRNocr1/HNe+GZYM9RkCQIiRrz0PVDyW7jpCzSoqSqcKiVR9vXzch5aw7d9Zb1KVdBQwSlmUcB2wTKe0EQhD43Vkl4fJljClbGAIpMrvF0XmeYVMKpP52rVQcRfpR0gvwhnwkGECZQucvCC9c5AIOVJjyRcahsXxMg4phsNgHB+qOhGiOl7TfmB+wyCAcrMSbMsYKI8XjoNOJxB7jdpJpWJoCxIdZv6Rne6MvX6dade76umbWLFo7PvRRLSBP9GxouYthvRNxI1iyDPX1jATlFHi+Gg47FgeKbQeTcFk1hXSGHqLMIKBHE6xNLi+57Ny+ntYteMrwz90lHOw4nE0Hty25qes+P/+FjDx587JOHPI0AjU4XQJGee2T3SMl0T6R8wgb6EQYhewBfjbg62ktb5irPeFEH8H/BVwudbVosudwPQhi00Ddo9zP6uo3Ign8icaqhyqYP8B/WjLHAj7bweM4klEo464TEMAAQAASURBVEwoJ6i18yRGwX4DTzV3GltfrUmdcQ4z/reb6KkN1SCBVuAH2Dm/2mHPKoWIUBGmjdeH0+8TK5nSDCRoxwIdMwacMYmIJHZBoFyBV2MIJRVK6upzeANp4vt8rIEid68+PALxhapAOhjGzj4dGMKyUAWjjtC5PLK3D9neWh2ExvsLhK21aCtGercm0RVy6crPc/eq4b41lRbUlY6AhBHxnf00uHUktucQfoifbiLeq4jGkb0cikPNMk2CQYl7FBnPosoksyxRH/VcqZasjbHh0XrbDyWHhmxzaNZ8qEKpci+pKpiiCNHZA7bNjP/cyMq1b2D3kjryr9SoBkN+VWOeLdHuoffMmDltD3dtXcBUK8vi2A7WfWczAFvfOrO6zFAi5GgRH8suvPHQJnSHgf3j9mjE3OWXjVR9jYZKR9XqepfexJ13fxi933cQoWkRP4mjhFGuy2DBNMKERTyXJyoTSRXVgpn0BQiMn0i1VC2TMp5IWkM2N9IvRGuwbUSuiBQCHViopEOYcolikt2XQHKXJLULwuTz621zMqiQRiOSDiuOV3yTKh25pEDYDsIF4broftMKXbQ1G/KvL8vKWe9l1ZZbRmxrqXXVYBwHdDaH0Brt+Wjfx+ovkN4VJ4qZzzyUZN/qpz4z7mUnMRjHhWWZ39EPhhOHo2F/q4PRMOR+MZRAGlbGPuT8075fXXZ/5bKOzPYEEaq3z3hxKsUrXvbXbL2qjeKHIpzmEvN+/mk2XPnRQSLxMOZ5sm39sFL31b816uLlr/u7Yctd8bLPVJtRTVqkTOJwMa4IprXeDFwhhEgBUmudfb4fLIRYAVwPXKK1HuJgzG+BHwshbsEYa88DHhzPNm//08gL4UgqkCqoKIRGI4YqA89qKZpzeIPEA2WW1zzySVYs/shx0/74ZITXnMBOBXiNNl57BvsphimVUKaDi7YEkSOxhEBojfQjorgxXkSCjtuIsDwptQTaFjj9AdK3UY4NaBACGUBsa4z8U80kCprehQmaHpzs/X4wjDb4hFFIgdEm/WUMNVUGkK6LbGsmmN6Is30fOpdHOA62EKSyLlZ7Grd/nAYIUhA0p7Fzxv9FaE3tFp8oZvYnSB984jzZie3wMZTIqfjvVCTqlZK1/VY4KvtT+X9/IkmXuwoJy0Lu66NmW4bCVAnF4d0Zn09sWDLzOQD+fu0HCSNJ4weNSfD+ndImEqOpa8djBHqoGFoCt3+8Hfr8cEy7h21rFNPuSpnbRHfYm8TYiGIWYcpMPoVVGuaPgnAQMRdqMmhboLGQlZGqYxsT/HLLcILA/LVto0SKoqpHUpRxEaHGr7Up1Uu0FeHVQ+e5CRqemTRDOhwcshppCCqTfOG6iETc+CEVCtUJu8gXkdn8IZWHi3TKqIJzeZNcyheI7UugHItSy8HVwhPdoeuFDlOqNhjHTSm6AH3gRNCR+MxDed18rmVUx5XEVf8AM38mePZdzQSl4dPx50Mmyrb1AKiO+Xxg79k89YY52N07Wdn6Dlbt/fphb/dkhC77F03Utk90jEkiCSFGdXereCNprUdS9OPHvwEx4Pby9u7XWl+rtX5KCPEz4GlMmds/jqcz2/443tj8oaVqlmduYuP1ZKoMYFfON6TV6vU3s/Sln6lmMYdKoVecZQalFdPQSRw6RiMeKhBS4NVaOM/YtN/nE9/YRQhVbySZTILWyI4ewuapaFsglEIUA6xQYe/LEdUm0a5FFLNQrpHBi0ijJWjHlEVZnsbNamQoKbQKGp4LST2+m+y5xh5ssozxEDGWgmQMIqkymbemtqMzSUQpoPdFbdTfvbU8WVCoXB+6uxe0ItHZiPZGJ5GGZi4BUBpnXx6RLZgOMfUZ8m3OYWUvJ3FoqBpml1E1xxyWQTzMAL9f+aQYagI72mC2/N6QDx7cJ0CUOSIdhMY3SQgoFqn5Y0DNI3FUQ4YX/fkW9r2tkU3vPbAh65HAsSi7Ot5KvSrl5XbWKBrGW15QIcIq3e7ueOiGquJYW2JYF9ehndwmcXg4WBwXtoPbUyT+VDe6Eh/KPmd2Qz0k4qiWOnS5u6+INKLom/t+Im5Ih0ihbauaEBIFz7zv+0TNdURlpZHX4iKURoaa9GaLIAN+Ldxz2+F1WTxpcYgKkhFv2abduogUMpM2JtuWBZaFKivRoj0dICRWa/MBtzMilvu+aQVfVqNQnyFKOoRJm3t/M9nQYqKwzH3jiOSLib962Fhvf3XQuDAkPu/fBfCw4vhQ79QoAqVMeVvJY+FXFDoVp/PCemZmvwj/WH9o+3oAyLb1sPeqUd97PomRSUyigoMpkTLlvwuA8zEqIYBXAn94Ph+stZ47xnufAY4vFugQMZbR9uE64w9VHYwnMztJKE0AhKTuyT7i3WliD29EeZ4ZkFoWsqnRLFORtwvz8BriuLaFzPsIS6IdiXIkypWUGixiA4rIMhMJv8ZCOQItwc+YoPPk5yfJhAnHfsadFVR+W12XMaUMeY+6R7shlQQpEP8/e28eb8ld1vm/n29VneVu3beX7AkJkIBhh4BsShaS7h5ndJwZlxERRcTtJ4oKYZNFlhAQGMcZdVBBRRn3ZWac7iSEBEZRIewQhCBLyNJJ732Xs1V9n98f36o6dW7fe/vc23fv5/169avvPbeqzvfUOaeepz7P1mqHqT2tVmiy6FyIblbr3Rdg/9d+jX2P/GV0ajo8l/eMHhzn8ONq1KZX/BUaVarOXlVEHJjI6JYvJDHocBYNupcbBS2OgXdolvYzk9IU2TZG1FG2/4vjaS99z4qIj/94Qx4Z33fGh9r0FM52EfQp/l9oEuJpj1cJ+gzTywrOPBvKmIM43Lm74MEjYZpSfr2WKMKNjcK2cXQsNDiWTENJeS3Cbx9FOnnj7CgKgq6Dzjmj1I60QylpFIEbobu9DhJsOoTg4Sfet7oir7EI4kIPLFUU8NMzoawIQiCvlkCahh42UQRJUjZLPx1lX6UsC88z00F3jRG1lxz/NpbCQvZ0HjFn0e0XYo4/eNqMpqqQNE9wshSURNEsL2OPIjg5haiSzGxn8osRx565cmOXf+1JfxpSMoxlESaprU45/WplOK0li4pIqvomABG5FXhqUcYmIm8EFpm/aSzGmUyFOx0HPvOrXP/shbNU9jwtNPlcq9KETcnciEIx3SPveySzHRqfOFhGrqLt29HzdjL9qO1EbU/9oVn8SJjMlsWCSxVfc6QjI4gq6UhE1Pb4mjBysIdPhNnzarhMSWaUqDPcRA9j9Ski0/7zocxHnRCds5tD+y5j8l9axO0Ofvd2ohOz6MGH0ekw3vfAyfcP9wRpis600LSHeqU+Nc2F/8jw+xtLpuyFVGUB57J0Gk/nfM4Voeb2QFroXqLqZM697tDvjRQO6ZAIop2ToZHr7CwiDropPhGOPd7TuGSKK//mjdz979+4+HqNM2YpDU6Xyq3/+CtcvffmBSfGVXsmGkMyd0Lb0eMggq8I/tGO7ejOSbLROtLL+xaqku5o4GsuLzVuhP3zTPCs7mgc6ZKNxMxcOErcUqKe4rq+zCoFLCNlrVggs7houOx7ab8fUj5dT9MU1xiDOEZbeauAqWn2H/rt4Z7Te7SXhufwKa7VovYv95FdOt9cIWNVWMRGl3Z8Hhs7wFzBaaEso4WeX1x/2/l6KhXHcDGuOYps39bf1jmyGqR7jvM/nvDn3PL1P2TPZab+GBubYbv6XQJUazS6wKUrvhpjRZkvA8nEoyGYaywG0lIztJEgIyPI9AzqFT87i7jdRJ3Qwygbr5E1Ynws+FiIBFSEzvaYZDoLPXC8ktUdLvWICs0jGWkzZCCJh4//gUUsV4KF+iIthcIBcbUaOIFaQjKrtHfVGHsoxnV6aKM/fWUpAtD+e/8Le5ovPKMeD8bSkDjJS8vS0288LAuVRJ7O+ZybAZeL1fN9HsTJ/A2m04zaVMbEo07S6Z5Zo15jYzI3C8nEo2VQFZCyLJSSjI0inf73Tbs9ZLaFi/Npa5mitZjueIRGQtyCzraI+skM6WkeIArBBQSSmdAs3UfggI/+rQlHK8GS7fgC12PfS8uBGBQ39rUaUmSPp32bMLSABBw48b4BO54+fJj4nF1WjrrKSL1++qzvBTKNT7vtGS1sYTsOoZG71JJyW+oJeKV5OGNsdIbHJMfOfA3GiqCsYk+ks2g62weAj4vIXwMKfC/wh6u2KuOM2Aid9vddGkoq9n/jPeu6jmIiFsxTxz4sFcMi9Tp+pIb71nRZnuLGRmG2Q+14jXQsobOjhniIZzI0FqYvTEhmlKmLHWP3w8iDKTghaucZBiLEsxlxK1xQ5k71MtaQRZwI3+3img3IPKP3d3CtNPS/8OF9PHDifct6yltaHyg/p5aBtEbEMU5cuJlMF0hLH5ZCKKpMdytZSgbTPI7nwJS4LEMg9NzK+26p83D0GCPfqJHVukw2Z+c+g7FJGWai61ow7FS61eaU6ZZLpWrHowh30QXow4cHy0zTFKZnkSRB6zF+NDRFFg+z54bJqTMXwsTXIkYeTkHAdT3qwmAM11WQ8P2dO6XP2BgUgzIkidHZFrgOeI80wnu93Il3VTsOsP/B/37mizUWxY2N4sZG8cdP4HvpggHgoVhMaBo2I2kROx52z215txt6c6XhXoDUQadL4+EOD3zoQv76gicAYI0sjI3OsNPZ3ioi+4HvyB/6MVX99Ooty9hM7HvkLwOhx0vBeotHBct2OHMkTvpOhxPc9m1wdAbfmjMdrdUGHQNCVFIj8qba0J0QZs8V6sch6iqaOFzXE7d7YRtVXAYqof+Csc4MEY1KR2Jq3Sw01GyMnvFTnunn1BieW1ofYM/Yi4IT5wRN5/S9mCPslI/N50jO/ftyJ7/MacJZZeAxUeh0kDgO1yXNJzg+dISHPnU5vqH4kQyuXd4yjLOXYqLs3H6OG0FAgjO7RlbtOFA2TdY5N57qfT82LIJvxqChPC1qK8evECTXiXwiuJ4Sz2YgIapcPO7jzR9h3misVDbSAMUENufC1KwzxOz4OjA2inR7yNRUKBufW042145XH1ugF+aCDFkKd7rJbVJMb8wyyAj3D16Jj0yz40t1/uK+p/CknffzM5/6YX7zqX803NqMVcEj+FXKGFqt464lQ4lIInIJcBj46+pjqnrvai3sbOP5z3vbQMNNY304xUkZcDCV7KGHQwpt7pCq13Jkp6SeaCal0clIR2KyukMUJr6ZkUynJIdz4cmRT3bR8LOHbKKOzJcRYawPcxxQl4RLpeS9aJpfuB+dGEPHmuX0HmPzcMv0H7Dv3J9GeymST9TRvCnqAFUnc27/orkMmzK/0PEZYoqMht4bVHo1aZbhM0/jMJz/Xd/ivmPbh39+Y0UpprBulCwiI1AVkACygw8hUVT2x1Gv/TKnOAplo6r42NEbj0hHwvfz3Ls8tRMp8WyKZPl3N7fjEdDZ2UBdCB4Z68giGSOuaKZNyAgpm2vnmUjG5mH/wd/kuqvfhjtvO/G/PoA/djxkJC1mx+f7v8py7Hix/RIyn7QTJjlKHONnZ4Mv0moxMtHkSed8jbFo5ZprG8ZqMWw5298RytgAmsBlwJeBx63Gos5GNrOAVM1A2jIUI7nn3Fiq1/4kl3xyF7UkZAPMdJBehjoHIrRHIsQrzftnQ+QhDqVPmkRoHCFp3rgTiI+3KKe6GSvKsvsizXUK4hgZH8MfOYpEETLbQsdHwnueRCu3YGNt8Arav3lc075U8zTiro4Snn+XftSqmh0J4M7bzexFyvFWkyReuYlANzzrzdbXYwlsZvFosYmym41qWRFwalNtr2VPtKJkVKIoCApRBFkeI3bgE0eWCLVppXG4GzKNHUgGWSPG9TzSzUJGcapoZHZ83aleVwuK998JbmIcf/xk+HvkkGYTxkdhambt12qcERo5vFMYHcF5DzOzA83yh2qKvVwWsONDEUVou4O6bggqq8ftmOT45WNc0TxIV1euv2ExEXyjZJVuJlRXb4raVrjlG7ac7QnV30XkqcBPrsqKzpB9l4d+Mvvvecc6r8TYlCxUtrLQ5lEESRymeuT9cdxsBzfbwcfbcfmEF61H+HqMjx1Tl9TI6oJkMP6tLi5T4qku0ZFptFlb9PmM5XGb//P+jcUyMkWkVkO7XXR8lKlnXsz416ZwR6eR6TY61rCG9ZsUqdfQdoj4uUYdsiyfsKOnj0guNOJ3WBYaQ7zg5oXHkQ0IX9FFF3DkOy8iG085dGhi+Off4Fyz52brK2Msi/i8c0gfPDhUVoFL4tDHpFFHxkaDLR+pgyrJ4RZZzeF6QjzryRoRGgvdWkx3zNHZHrKOxh7IEA+u46md6NHdliz4fMbyKQNCZyAMSBThjx3H7dwBSYxOjKKAdFL2f+vXV3bBxppw4rI6o83djHwZ3OgocuQomqb43LYval/nZikN08fwDBtw+16KFNmPIiGYFSe0H3s+Uxc59h8Ot9zX7fzSsp+jirrNXza1XqiuYmPtVTruWrIsqVNVPyUiT1/pxawIrTb77/uv670KY5MiUYRmEI2Nkk1NLbod5BkB7Q7EGVI0We50IY5IjreQNGQe4UFSj1Nl5KGUzmREVhfiVsga8PWYW0343JiIQ72HKEI6XRpHeqFfxs5x3JEpDnzuLeu9QmMZ7D/02+w796dD6UrmQ2ZSFCHiQuPLguUKQ1Xmc1CX6YSKkwFxk1ab2pTnGY/9Ol2/shlx65WFdPXem9kC7QKMdUJbbVy9kTfO7827TTWzTxr1MO49fywbqeFaPYgElypRN3zfvAjS85BEJLNKb1TwCWT1/FjNiI/8XxuMsZqUAaFlCElhwmUUBPkkBpEyk3j/l9++Sis2VpMP3/4qrvrxd9OZjBkB6HaRyW1Ip9sXkYZhoaDQfDZ7ru1eRglcWWKb9nBjYyFIGQmz5ytxXg/7M4+5Y+jjLcZWyjI1NhbD9kSqzht3wFOBQ6uyIsNYRySKiCa3oeftgi98pf/4nF4lUkvCJJfSqEj/n/fQ6uHub0OScPLZlzByMEzz0igCgbFvtXHtDKLgfEp7BceNG/NyW/anfeezYMjpWRJFIUo9PUNyYjRknbFFSznPMtzoCNrt4WdmQ5NL+j2wNM9MApbmKC4UrVxIODpNZLN6/Sn7t6jHtzu43TuZvjDiwdlxTsw2F13Wvm8L6ez7v3TT6V+DYWxSpJYgOydBhPRfv9F/fM73yNVqoD4ISONjob6gluA6feGp9vAsvpkw9cgR0gYks0p70pE2wKUQdSGrBTteP2HNkNaCsn8VeTBvAaGweh0u2w9kWRCTOt1QuthLOWCB501Pa6dQu/IcRu9+GD16HID4nF3gPdmxE/kgCj+/HZ9PkDxdD8QzQKIofBbzYT3pwYeRKOLBZyU86in3ctno4UX3t957a4dn9TKRtkJj7WG/GeOVf3VCj6TvWa1FnQmWhWScES4IQTLbqTgqp/ZL0U4H30uD8yIC9VoQj9J84oIqmqbgPVHH41op0XQQjhoPzJAcPEl0+CTRkWkAbvm0lUOtBcudnqLdHv7ESbJLzgsZZd2UWz/++hVenbHW7H/ot2BiHBlpBseuyE4oxMM4ObWp9nxUt1lquvsQolR1RHB1jS6Jmb3yfNo7Yapdx7nFj6Wx2xSN4O88cKM5yMbyiSJwjt752wZ7ic2142kvCMWtNvR6UEvQWox0U8T78F2JBF9zdCbC1LUsCdlHGlE20E4b4Tn+31//8pq9xLOZW7sfBECSeMBPm0v53qtHveZNlyX0QKrXQhN1Y9Nz1+/9IjMXKccfHaNjTaReQ/I2E9TruGYjfBYWCgStQIbwUrKQJInDv1oNnZrGNepEu3fSubTD9lqbKxoHl7cGw1hjhi1nu1tV/7z6gIh8H/DnC2xvGJsSiWPIPIefex47HzpENj2d/2HQsJQ3dXHe+0DyyFavV3ZLE3HQ7TH2lWNo7PCjdUQV6eZZR7kDY41r15bS6VSP+gWcisr7XTYvvvB80tjRmRyl/pA14NwqdC7ZQe3ILBw/AVlR8uDKrKRCXPK9/Hu70OSXBRhwXjl1UtRSkSgKTd4bdSSOGfncfZznL+TBaAcawVde8/IzOr5hbHamn/EIskTojQk7PllHZ2fDH+a5QZQ4QbZNQLNBNtYgG0uIj7XBQW+iTtZ0SKq4FLI6dOpC2ggiUpZBVoNkFj7++794yrGN1SPavRO6vVB63O3Oex0uszbFIUmM274NJsbCcBNATs5Y4HmL4DrC7DNn6H2mSe14DXopjI+iIjA9g9TrSObz7OJ5DjBENjDkLS+8Dthxl8T4agn8adBeGvyK3TvJto8i3uMBOZnwlov/FoAXX/H3Qx/PWD2ssfbiDCsivZpTBaP5HjOMzY1zEDlmLhB2+IVvDsvU6HwUcBHFpFGHPHVWarnANNtCmg3S7WEcfJx6tBbjZtrs/8rNa/TCjALJS5XwOtj7ptygkvouruyT4e97AHZeYc1+txgnL60xASRfrlj0Yny3+n7aOZziPELo0bEYNyQ/WDqg6jU4nPlocYmi4HwOEf0so+pRhBsdgdFROo/aRdaI6DUdOz/v+dif/dKixzjw+bee9nkMY7OTNoXWTkfaZPFMlShCkhjdNorWYnwjIm1GdLeNk5xMkUyRnqKJEHUVCFlIvYkwnU08uC586rdMuF1zJsYAkF4KU1Pz9q8RJ7hmM2QfjY2iu7bjR2q4mdArZ/833rMeKzdWga+87uW8/DM/yKdrTwkCkvfB9/YaBMNa6HkmvXSw6XbOMHYcyAVJCdeNwn+M4yBkLgH1nt5F2+lMJrR2RNRPehoXTvPv7/pJ/uaq/7Hovpala2wUFhWRRGQf8G+AC0WkKtdPANbExdjw7N32Yg6ceB97J18CwIFjv7vgts/7d+9k5PxzIM3Y/eleKE0rqEQpBgQGCDecUtzgOfS8ncjRkyGlvpCavRIfmw2PASpiAtI6IXGMNBtomoZ6Xg3RKcSFm3PvweXNlUXKCXzaanPbx1633ss3VpBn/8C72PZwl9rBk/g8+0gadWSk2e9xFoXvuZ6YQvLvuXY63DLzh0M9x629P2HvthcDeRSz04FeGrIYIxcc3tNQ9ECSOEHqdfCK3zFGOhIxdVEw481Da9ePZe+TXx8ivFgprrH6VHuAXL335kVvov7fNx7NeT+7h8/eeyGNzzdDA+WCudkGRVZf6tG8OXbcymiNRRx9Rp1zP94hnk3JGhGNo9DaGSEZNB8K27Z3wZdvMgFpvTj8rN3UTnomuj30xElQxbc7RI+4CL9tBF+LkQePQi1Mve2cG4SnOyz7e0vhD17BPb1p/vrjL2fHZTHNf22CKtmucSRTZp92EQCzuyO2f7VNfKKNA+SbD3Lg8HuHeo5be38ChAng2kjgvof6U3s7HSROTptlXAjartlAxkZJR2Pa2x1HntulOdFm+2iL4zNNHnPxA8s/GUuguK6CCVML4ZFV6120FXoinS4T6QHgLuC7gU9WHp8CzHIaG54DJ94X/l9EPCrwEWQTDST1jNxzmGwhgyCuFINQDZkqhVjUS9HGCLJtHE5Oh3x3SaDTQbQGpOhI3W681pP8vSvEIQBttRbcvPgMGVuT2tcfhmYDNz7Wjyg2G+GPnfmji8MKSAM4V5aixTtGQs+0vMnr3F4txWODfZqSspcCXnHHZzh6zTai5x3F7d9x2iykleRLLxsDBYm3QD62seGp3uCc7mbnOQ3HhY/4K144+yN0H2z0swrnQWq1MjNVehlIDR87kumMsfuEdCwiaoeeSD4WNAIfSd5MG778K+YGrxf7v3QTT3/xu8kaDn/uJJw7CV/46qL73HGr3ShvZVwrYvZcePia82ke6fvvssAlYFgBqYo2EvxYg2ikiW6fwB05hp+egbxUrsopdrzwPUea6I4JfCw0jnle8JR/5j9uvwuAp15y75LXdCbM/sIJVOHp+1/DJ/a9bU2f29j8LCoiqepngc+KyB+rqmUeGVuW665+G6NHZ+mcN07tWBtmc1FhvqkNlfHa2uuh7Q7iXHnj6U7MhG0WuBE98JlfXdXXYizOgaO/AxDGu+cUUSbj7OJjf/pL7Lv05eFGc/sEfqyBihAdnQJg/7d+fUWep8iGVA1ZbjhB4jgve60h80yAk6IBduQQ50IG0miT48+6iImvnMQdneK8f56Ff27woY+u/c2s1DzilMv+6CYu/++p9XYzNgSP/fsXsnvbNPcfnOTCKQ8+ZPAVE7yqjbYljsNUtswj3ZRoNmyjUUzzcErUycjqETpPtelnf90EpPXmE+/7RZ75w+8uf7+l9YF1XI2xXrjzvsJjgNrx9xDPwpGnZiTHg2Bz7iey1Wl4PzpC79wx3LYm4j3Rtw7hsqyc0KzdfGJg1A8eEUVQrzH1xHN4+CkRONj5Bc+f3PZc/oTncs8Lfnvl17kIdx64kSf/XbDbo7Uu1173dgA+fPur1nQdGxnV1ZvOpqt03LXkdOVsf6aq3w98WkROCTmq6hNXbWWGsYak4wm1hz2dyZj6/V388RN5A73FtVNxLkQfZlshMbFeC8bCu3xSWxCcrPZ+47H/od9a7yUYG4C1+m5WsyH37nopIkEY0tm8zFUqDkVGZTR1nvk4NkJ6/g6iruIePg7NRiiRXQe++eJXcukH3s7YeJtON5QD7Xn6m7jlE29Yl/UYRkF3qs73XPkx3vuZ64k7GdKoo9Mz4buUj9Yux33HUehlCJBmSC8jPpGhUZOsHpE2IrKGozcaVKS7fteaZ280/umP7D0xAv/yhtUXdqt9Ba9/7ltBQCMXriNRI9hxVaTVLic14/K/Nxtk25o8+GzH+GOPMvr+bQDUjofrizvvK6u+/vmYbM4SO8/Bn2+x8zdH1mUNxubkdOVsP5///29XeyGGsV686nP/iV7zWbQv2sbot1rIw0cgisiKxnvzTGaTalNmr2jm0TQN/XYa9fB4kpx1I2T3PCXcRFq5nmEMj9senEm8hyhC2+2QJRHH0KiT7Z7goWeMUT+u7PjMsXI8tWTrV072jRe+in0f/XkeODnBN757B64H3/b69/ClX7UMDWPt8QevAOBHn/5t/NT2L/LXn34+9cNtsqPHgH4Gkqa9fChGLdzctdpBpG13wo1fHOFGamgkqBPoeGbPPbvseLUHlWEYwzH7mN1oJEQdT1Z31A938I2I2XNrJLOeg0+PGX/aYTgRobfuIB1ReqNC7eT6rfkz3/VmrvnwL7G91uZrs7uZ/rcxl/23d/H1/2/tyuM3MpaJtDinK2d7MP/xZ1R1wJqIyM2AWRhj0/O2cz7Hc+JnMXVxws7PtdFOh2xm4Qi/OAlRTH9qjrtmWYg+ELKUyLJww2cYW4g9Tw8ioWWeLI+5vRj2PvF1YWLjbBemZxFxQUACZh53Dg88J+aCf0gZ+drxIDKNNsp9pbd4M8/Vpt1NSEL1H3ELHvvG96ARTHxN+cT7LUvAWFtu3Pk56tJg5MEu0cFjpJV+Y0XPEonynnhZBj2Bbi/8qyWgDtdN8TWH1iLEw+jBjJnzzi4hydj6XLMniIU2cXZ53Pb3rx34/Xnf9Q6OPC6mcQSahzMQ6I3FNA/3qN93ghe97fN8vbWLr/7XK5Gsy+En1EKprEDUmf851oI7rn0X13z4l/CpEM06xMMj/+u78NtS4maPr36/DZQx5ud0mUgF13OqYLRvnseWjIj8MvBOYLeqHs4fezXw44Sk/pep6i1n+jyGsRDvPnYZE18+ydf/w3Z2fFEGU94XwPdSXHXqS9nLREOPpChCeyky0jyrStksA+nsQLrWIm+lyUYSpNMLvZLO34XMdmB6Bukp538sZfSue9FWO2x88XnlfvvvXr9mmPf9zaUwCfEsJLOKCrgJgS7MnL/5o2zG5uITnR6PTnrUJSGrO/To8fJvRfN6cYJmWbDTvTQMxhAJfZEadaReQ1o9YlXSbQ1IQbzyqd9ahb4qGxTLQDo72AKJEBuKj/zdK7n8pvcgKcQtx9HnjdI4CpNfSemeP8HvfPw7aX4t4ZKHjwNwwZ2z3H/tdmD9e6zd/08XkXioHwNR2PFdD/KEyQdPv+MWx8OqZSKt3Tzd1eN0PZF+GvgZ4JEi8rnKn8aBfzjTJxeRiwkC1b2Vx64EfhB4HHAB8CERuUJV1zfcamxpph41QTruQ3mIr3y1i6ba4spJC3MnKc0td6uy/8H/vgqrNQxjK3Hgc28Z+H3vE+eJ/G0bR0ZDv4L9n33zWizr9AgkU9A45vGJEKVBUAJIm+u7NOPs4/+cfDK/sPMTQJjIJI06TM8M2HEk72NIFhrbRxGuUUcB7fWQeTKHLVPDMIxhuOfVfTHoytecGkCO2nDyigkA/vF/boySsW/7lfeQpCEbSjJo74TJRov7Wtu5qHl8vZdnbGBOl4n0QWA/cBNQbdc+papHV+D53wO8EvjbymPfA/yJqnaAr4vIV4FnAP+4As9nGKfwex/cy8g2iGbBPXCYbHY2z0TKN8hFornike+ledZS+H1ZY7+3MNZXYetyYKOIGFuUA597C3t3vRSA5j/dw4Gjv8O+K1+DHjoCwN7Jlww06l4vRKEzAcebjs4uTzLlGPsmpCNw99usN5KxdvzSwaeWEePfPH4Rjc9+A0YqSmZhx8ty8zwolGX4dgfJG2zvP/iba7rujU5hx8Fs+VbD3s/V5e63vZxrr387PnaIKt/4sVfy+Fe8h4mvTuNrEddd/TZuv/M1671Mog7MXuQ5/9seRkS5eue3ALh3dge/8dQ/XufVrS/WE2lxTtcT6QRwAvjPACJyDtAAxkRkTFXvXWz/xRCR7wbuV9XPigycyAuBf6r8fl/+2HzHeCnwUoBLLrlkuUsxznJGH1TiluK6eUaR5NOQennJTj4laT6KSKaNljUMYzWYuvYxPPv738VEEiHnnwPp+iflPvMF7yJuKZMdT2t3zPHLheZBRzIDn/ofSxOPzI4bK8Gt33wscZTx/G1f5AszF0Ico+OVSUNz7LjO6ZXkogaGYRirwdd+WLjiL98Mz4QHs4mQ9bPO9UxP/7F309ottM5RXNvxwMPbefKl93H3iVAuf9vVZ08rDmN5DNUTSUT+HfBuQnnZw8AjgC8RSs4W2+9DwHnz/Om1wGuAG+bbbZ7H5h1Bo6rvBd4LcNVVV63fmBpj0/LUn3oPTmHqEhca29XruGZwJl1N0bTXHwe8gJDkiyltxgAW5TKM5VM0337sG96DT6BxbJT6N4+hjWTd1nTdNTehToi3xWR1YfxTDzICtCeD+FM/tnQzbHbcWAk6/7KNmcmUjz/iUQC0nngxycku8a4d+BMng51exI5n0zNEY6NrueRNgdlxw1g+H74tFPFcf+fL+crXzqc+0aF2UumOCyOH1s/cXXv922k2I7rbYupHhfoJePzz/pWXnnsnf3fyyXzy6MXrtraNhKqsWsbQls9EqvAW4JnAh1T1KSJyDXl20mKo6vPne1xEngBcBhRZSBcBnxKRZxAyj6qf3ouAB4Zcp2EsiWRWQSGeyacjdDrgHH56eqj9b8v+dHUXaBjGWc25d/XIGo76vcdBdV2nsbleCJ02H86QzEM2GEr9+B/YNDZjfUhmAIn5zPGLuH9qG2M1wbV7oRn9In0LC8yWG4axmuz8eIK6hNpJT+2kUju5fgNKXNeT9DzJdERWE3xFDXj7E/9i3da10VDAz5vbsjLH3uwMKyL1VPWIiDgRcap6h4jcfPrd5kdVPw+cU/wuIt8ArlLVwyLyv4APikiR+XQ58PHlPpdhLEYy41EnjB5UkpkMbbVDc804wXfySUhDOKCGYRirQTKdEs/A8aftZvxrM2i09tejvU94LRrHRGkKHuTwUYhz98E5Pvce639krC/j9yq9UeFudxnxrHDON44iDxwinZldNAPJMAxjtbnt6vfwrPe+i9ldjs52R9xWpsfXPqt4z9PeAEDnknFmzo0Yv7fH6ANw5PEJH/j29e+zaGwuhhWRjovIGPBR4I9F5GFgVSRUVf2iiPwZcHf+HD+7nMls+3b/FP4RoZLulrveuKJrNLYOUcuDQNZwuJ5CloGqlagZhrEh+NBHXsN1V78tRAvrwWRLunbNFPZ926sRr0iri9ZrQUCSfCw6sP9bv75qz21NfY1h+PYfeTe1aU/UFdKmI2qDTLXQTqc/mW0RLAvJMIy1IKsDDtIRIZlau1yUfd/2agCcCN3zxkGVnV+Y5eGrQt+4L7zDAkHz4Vm9xtqrleG0lgwrIn0P0AZeDrwA2Ab86kotQlUvnfP7W4G3ntFBI4ebNSHAWJjrrr2JWjtDVOmN13A9H/ofZVmY3DKfdJlHNM3pNAxjrbj9ztfwjB99d/n7bf/wulV/zn1X3Ai9HrQ76GwLmdwGR46VKdgSD+s+GMbqcfXemxntKS4NWcXq6riuQq+HLhQMqmQmmS03DGMt+McP/hKPf0W/WfVdv7f65d/7rrgRVCFNIfO0HncBAGOfewi/c3zVn9/Y2gzlBarqTOXXP1iltawoNqbVGIZoqgUiJKqoc4hzKIOTW0qGiGgahmGsBh///bXrN7Tv8lfmjmcG3qNpinT6N+QHjq1N2rtlHxnDkBxvgQciIerUSBshwqteg1hUtd1mxw3DWCe+8M41zvjxHor7mUaddMTROBxs+S2feMParmUTYo21F2dREUlEppi/95MAqqoTq7Iqw1gDavefCDdKkUN6GS4LFZq+l87vaM51Rg1jDbh6783ceeDGsrTHbqyNVafTBSdoq4WIw02E9Hep19COZfgaG4fGt04iqmjs0DihtdPRPOLJDj4UMovFDfZDqthxy0Iy1opqaS6YHTdWn30X/zyMj9LbPYavOVq7E3ojQnuyQXzxBeu9PGMLsKiIpKqW62ZsSfY+8XVlNarWE9yJWXSs2U9/L5zOOaLRbf7P126RxqbHhB9jM+J3bgPAuQimpmEy/M7R42uWhWQYQ9PtoaNj+CSiNq2Mf/kYWZGFBAPla+KEW3tmx43hMTtubFaybU16ozFRz9MbEaYvFsa+pfzTH9kk1WFQXb2eSFs+E8kwtjQakuw0dkgUIYeOkfVO7Rcvrp8af0PygwDc2vuTtVuncVZTOK7mwBprSTZeQ7IMxhplI28rEzc2GtJLQQSfRGSjMZP/fBD/wMFTtnNJcHc1y9jTfCEAt7Q+sKZrNc5ezH4ba83+b/06z/uudwCQNqPy8bt+1wQkY2UwEck4K5GTM2G6UBwjqUdr0fx98iup8OKslM1YGuY4GpuR3o4mAAc+95Z1XolhnIZeD8Thehnt0Trx5Bjy4BxrLg6iCES2wDwcY60xO25sRq7eezOCfX7PBNXVyxjStRvOt2qYiGScnXgFB37bCCceO8HkP9zXn8qWN6ETJ4N9kKwnkrFF2XfRy8LNGECzwf5vvGfxHQzDMDYCqhA7jj92nLQp1I7H1HbvRO97APWKq9WQJA79kVSReh3t9tZ71Yax4uy7IogFWo/RRs0aJxuGsaqYiGScncQxRI7eZIPWDmGy2wuOpTikn/WZi0ohE+nW7gfXabGGsXrse8yrwvhXsRi9Efjw7a8642Ps3fETHDj6OyuwGsNYhCRBmzXak4J4yBoRNOq4kZEwVTCOQxaSDwGgAyfet84LNoyVZ89T3oDrdkN2fbuHtE0oPdtZiQykYrDL2Yqyij2RtkBerIlIxlmJjjfD6Gon+ASo19Gjx+ds5NGMwckuhrHFsSyks5trr3s7osrtH371ei/FME6LNpK8JxK0zoHz//o+AGRsFMkytNWGPAvJMAzjbKA6DfBsFoGM+RGR/zrEZidV9XWLbWAiknHW4scaSKpEHaBRSXmHgWkucPospD2NF5RlcK5RR+p1qCXsv/83Vmv5hrEi7P/y29d7CcYWxLKQjLXAjzUgy+24U7JLziF66AT4DJxDspCBJLUa+w/99qLHKsqBSLPgAyQJxCE1ef/db1vNl2EYZ8Qtn37Tei/B2IKc7QJU6Im0esdeR74HeP1ptnkVYCKSYczlwGffzHXX3AQOxu9LOfztu5g4b5zkrq/g252BccBLQj3a7QYxanYVFm4YhrGKzFfKdt21N1lmkrEhyRoxSLDjUTfmweeMse1rTcY//QBEDuq1kInkh+hnGLnQL7HbDR5+Ma11Ymx1X4RhGMYKMp/4U2Qnne3CkAHAe1T1DxbbQEQmT3cQE5GMs56o7UlHYg49qcGFX2yEkcHVBtpLLWeL43WXmA1jJbh6782Iwh23mNNxVmOXM2MDoyJEbU/jmOf4FcLs+Y6xe5pIptBqh2wiv8QPcbMR/m93LAvJ2NSYeGAYy0MR/Cr1LlrPnkiq+l9WYhsTkYyzltvveDXXXv92oq5n/L6UzraIg//xCrZ9rUs8m5J85X40DZHIvbteCsCBw++d91i3tP+4/HnP2Iu4ZeYPV2XN8zkDVvtsrAbF50qWevNlGIaxRtx+x6u59rpgx+VEys7PJ0xfKNx/wy6yJuz8Qkr9WI/4RJs9V70RgFvueuO8x9r/pZvKn/c9+hXhsfuGaR2xdOY2rDU7bqwG1c+VYRhGFRG5DPg54FIqmpCqfvcw+5uIZJzVfPi2V3HdtTfhukrjWIaPImYuSBh5SJBHXkA020UOHw/p8GnGvt0/ddq+CrdML5ohuKpsxUkKFkVbH+x8by2e/51vBeBDH33tkve9/Q4rZTM2Lh++PdhxgObRjLjtaO1yiIeTj4hpTESMPujwkaCR8Lzvegcf+btXLnrM/V9951osfV62os3biq9pM2Dne+txzZ6bl5Udbp+FpRN6Iq1SJtLGiM/+DfB7wP8Ghqj5HsREJOOs5/YPv5qr972DqJ0x8fUeoiCZ4hsRsxdMEF84Rv1Qm+jYNJA338x7J6x1U+JhjIA5a4ZhzMUn0XovwTBWjds//GquueFmonaG9DzNh8P01XQsYnZXxNHH1KmfVOKW4nqea264mWo1wVqX7JotNwzD2Ngogl8tEWkdy9kqtFV12em2JiIZBnDn/hCVvO7q0PsgfvgkOlJHReiNR7TPa1JrRCQPnmD/VzZWevBiDfS2AuZAG8aZM1/DbMPYStxxa99WXHdNyEyqH+7gowYdDb0Ni/uB6rYbAbPjhmEMg/WoNFaQXxeRNwC3Ap3iQVX91DA7m4hkGBVuv/M15c97n3zq9MP997xjLZezbJYS5Rx2e8MwDMPYDBQlmNc/5y2n/O3//c0r1no5y2JYu2wZS4ZhGCtPKGdbvWNvAJ4AvBC4ln45m+a/nxYTkQxjAQ585lfXewmryp0HbtxSkU7DMAzDqHLbP7xuvZewZmzFnoiGYRjGqvG9wCNVtbucnZc4u3xlEZGfE5Evi8gXReQdlcdfLSJfzf+2Zz3XaBhbmcLhNDHJMAzDMDYfJhwZhmGsPKqyqv82AJ8Fti9353XLRBKRa4DvAZ6oqh0ROSd//ErgB4HHARcAHxKRK1Q1W6+1GoZhGIZhGMZGxDKLDcMwjCVyLvAvIvIJBnsiffcwO69nOdtPA29X1Q6Aqj6cP/49wJ/kj39dRL4KPAP4x/VZpmFsXayXgmEYhmFsXkw8MgzDWB3WM2NIRN4H/FvgYVV9fP7YG4GfAA7lm71GVf9v/rdXAz8OZMDLVPWW0zzFG85kfespIl0BfIeIvBVoA7+sqp8ALgT+qbLdffljpyAiLwVeCnDJJZes7moNYwti4pFhGOuJ2XHDODPMjhuGYWxJfh/4b8Afznn8Par6a9UHllPJpaofyfedYBma0KqKSCLyIeC8ef702vy5J4FnAk8H/kxEHgnMJ/nN28NcVd8LvBfgqquu2hh9zg1jHpY6CW2+yKI5ioZhbDXMjhubheVMNJ1ry82OG4ZhbA68Cn6VMpGGOa6qflRELh3ykEuu5MqDeG8GWoTpbELQXB45zBOuqoikqs9f6G8i8tPAX6mqAh8XEQ/sImQeXVzZ9CLggdVcp2GsJMtxNOdijqZhGIZhrB9myw3DMIxV5Kkiclfl9/fmgbXT8f+JyI8AdwG/pKrHWEIlV4VXAI9T1cNLWXTBepaz/Q1wLXCniFwB1IDDwP8CPigi7yakY10OfHy9FmkYizFMLwIbu2sYhmEYG5Nh7TiYKGQYhnG2oBr+rdaxgU+p6uuXuOtvEbKHNP//XcCLWUIlV4V/BWaX+Pwl6ykivQ94n4h8AegCL8qzkr4oIn8G3A2kwM/aZDZjs1I4nuaAGoZhGMbmxoJChmEYxnqhqg8VP4vI7wD/J/91OZVcrwY+JiL/zOB0tpcNs5Z1E5FUtQv88AJ/eyvw1rVdkWEsDZuIYhiGYRibF7PjhmEYxryorN50tmUeV0TOV9UH81+/F/hC/vNyKrn+B/Bh4POEnkhLYj0zkQxj07JUx9Mil4ZhGIaxcTA7bhiGYWxUROR/AlcDu0TkPuANwNUi8mRCqdo3gJ8EUNXlVHKlqvqLy12fiUiGMSTLjVia42msBAt9/rbC52vYcs/FvoPL2XcrnDvDMJbGUmz5nQdutBI2Y0XZqrZoKW0bzsSf2arnz9h4KKuXiaTztjCas43qf57n4d9bZPulVnLdkU9o+98MlrMdHWZnE5EMYwiGdTrNkBkrzek+eysxQWi1uO7qtyEKH/rIa4beZ6HXKx5wS9uvOB8LHXO5vcoWGtu9HKF5Pd+zjfzZMYyVZqni0Xw/G8Zy2Mx2/Pnf2b8n/dBHXzvUPovZcV1hO17921LO3UJ2/HTPtRDr9b5t5M+OseH5ofz/V1ceU+CRw+wsulptx9eYq666Su+6667Tb2gYQ7KZjIix+Vmt3hxr/ZncKj1GTufsriWr9R7OdT5XI+NCRD6pqlcNs63ZcWOlsQxiY63ZirZ8I9nD+SjWJ7rsVjOrzlrY8dV6vqXY8a2CiLzpgte84PVjT7tiVY4//cmv8MDb/vjNy5jOtmGwTCTDWCbmZBpnyloILqvd92OriEZz2SgOs8uUa/bcXDrGK3ndsWuYcbZj3wHjTFkrG7jcTLqVOP5GsYcLUaxvowpIwJrZ8a3qkxkrh4g8VVU/dabbmIhkGDlLvfBaXbaxlM/AVjLsW+m1bHR8NOgVWzNgw1iYlbDjYN+bs43FSpsW226jMkxW6WZ5LVuFqsBldnyToKzidLbVOeyQvF9EroZFGzP9HvCUxQ5iIpJhYMbUWBqnq83figbfviObk6UInVvxc2sYhjEfZ6MdB7PlmxGz4+uEsnpiz/qKSNuAT7K4iHTodAcxEck4KzEjuj7M14OlytwGivNts5Is1rDxTJ57s3++rtlzc4jAbPAUdmN5nMmUO8PYSKxGU1n7DpyexWz0Wtvx1XqOzWzH7zxwo9nxLU7x+Yw6Hl9zA++1XcOMxVDVS1fiOCYiGWcdi6Wvn6nTsJWjV0tlJRzI1Xbihpn2cTZRfHbF6yllVMbZwbBlHYaxnsx3fS7sr9nxlWMl7OBG7P23lTE7fnaR1XPlKH+rRa3dxkqhyKqVs+miSUCbAxORjDVjGCO/UcZdG8tjIcf+TLc1Vh/xlfNvjqeRY6KSUWXYa/RGGHdtLI+lnEOz4xsLs+PGQpqH2XJjpTERydhQrEZq+lwWK2EyhqeaaVT93dicWMq7YRgrxVz7sNKY3VkZzI5vLcyOG8bKoRr+rdaxNzsmIp0FrIUws9hzrtUx1mvMpWhQ/ov/t3oq/Nzzak6nYRjG6rJeUeSVKA1bCus9rtrsuGEYhnE2ICK3q+p1p3tsIUxEOstYjqMwt8fARnWs1ssJKlJHV2sK5Hoy3/u+Fk0yDcPYeGzUa//ZyLDl4ZtthP162xaz44ZhbGU26rV/Q6Kr1xNpPY2NiDSAEWCXiEzSn9I2AVww7HFMRNrAXP+ctwBw2z+8rnxsPYz+6SJW812Qqo9tdUfFZf3mhVvp4my9Dgzj7KDIvpgPu9k8M6675iYAbr/j1eVja3U+lzo4YDFbfrZ9BraKLTc7bhiG2XFjDj8J/AJBMPokfRHpJPDfhz2I6FYoygOuuuoqveuuu9Z7GWfE9c9+C72JZL2XYQyBVL421QkYW8XxrGKGxzDOLlbyOiYin1TVq4bZdivY8ec/7234mrOpSJuAuaXoBWbHDcPY7KyXHd8qiMibznvFC18/8tTHrsrxZz/1Lxx85wferKqvX5UnGAIR+TlV/Y3l7m+ZSGvEXAM+b+TXBKQNydz3qvq7KHz4tletz8KWiTmThmHMZSveOK8Gp71+NqO1WYixJObzuaql6Jvt82923DCMuWy265ixvqjqb4jIs4FLqWhCqvqHw+xvItIacPXem8vMFdfJwAk+cbie4hOLVm427rhl812kzeE0DMNYPsU1tLDl4hV1AmoTkTYqpyvT3GyYHTcMw1g7lFWczrY6h10SIvIB4FHAZ4Asf1gBE5FWkz1PfxPpeI2sNqT3qCCqZI1+lFJNQNowuF74Oheinvj+jcFaiEbXP/etxPcdpvXY8+b9+1IcXnM0DcNYKtVgx3w33pvxpvt0XP/st5A14yUHc1RAi3I1M+MbimqPwipmxw3D2OoMO3DBMHKuAq7UZfY2WjcRSUSeDPw20ABS4GdU9eP5314N/DhBFXuZqt6yXutcDK04KuJBMkUjmTcqqQ7UvM0NiWgQj1xP5724XrPn5iACZsqHb1/50rWr994MYzG9BRxPgGtuuJk7bj39hd8cT8MwlstWnEx1OqoCkvgQ7FGZ347D2XmONgNF1pGPZHE7rroqJejD2nHgtLbc7LhhGGfKYpmYxpAoq5cytBFSkeALwHnAg8vZeT0zkd4BvElV94vIv8l/v1pErgR+EHgcoWv4h0TkClXNFjnWqnDNDTcvnKa+s1H+WH9oFj9aI5rukI3Uyshm1M4GMo+MjUP90CwAnd0j5WMfvv1VA85btQTRpYqkK/8R3Hf+zyJPvGSoC/2ep78JgFs+8YZ5/37Nnn4mgWheaoEZEcMwFmcrZyBdd+1NC2cMV/oQ1k50yeoRUTsFIB2voU5wPY+PnZWsbUBcpiTHO3Qq/hgwrx0Xr0iquJ5f8XUMa8fVQf1wiz1XvZFb7nrjvNtcs+fmEG4sbjAqfZsMwzAWoyoczb1mbHZbbqwKu4C7ReTjQKd4UFW/e5id11NEUmAi/3kb8ED+8/cAf6KqHeDrIvJV4BnAP679EiHqeLL6wt5j8aXc++TQXD2a6RDNdssiyuz8MSBEOBFzBDYCouBOBBHpzk++ad5tXBbeP3VC1MmQbopr9dj3mFdBlrH/q+8st923+6foPPFSRJXawSkA9t/9ttOu4+q9N8OTLhly0YOZb0uhee8JaHchcpDEtC7ZtqzjGIaxNUimevTG+wLKZuzzthSi7uJCUGHHb3jmr5aPxSe65c9+e7382aK7G4MyyHNsmjvnBFaq/auK3lWuk+HaGa7VYd+Vr4FeEAv33/OOcr9rr3s7PhEa3zoJwIEvvvW061iSHQfULU+NbH7zOLQ7EEWQxGTbmnQrn0vDMM4u5tpxE4lWHlVBV8ngr9Zxl8gbz2Tn9RSRfgG4RUR+DXDAs/PHLwT+qbLdffljpyAiLwVeCnDJJcMb8WG549Ybue6am5a+Y6W0sPHAFFpPTomUGeuHCrQftQvX9aWzGXU9rpNR76Th/XOOzs4Gzc/eC6r4C3cPiC8DWWpPuwwAUYFub8HnvWbPzcu++agdDqKXJiGzbd9FL6P1+AvLMsraoWn0gnHIxxX7PAtJFBONDMMYIB1LaDw4DcCBz755Xdey2nb89g+/muuuXYYdL67vPvgC1z/nLWSNpfdPMlaHoi9V9+IdA7Y1mepR63nEe9Q5ujvqpR3PLjmP3s7RMkNc/JzSsUSCTe0EAXHv5Es4cOx3T3nuMyk3k15W2vHqscpBK5VWW9UpsK1HbF/2cxqGsfVIxxLqD4V7g1s+PX9A3DAWQ1U/IiKPAC5X1Q+JyAgwdAnVqopIIvIhQq3dXF4LXAe8XFX/UkS+H/g94PnM36Zy3spBVX0v8F6Aq666asWrC6/eezPMyUKqHe0g3oMq0su49vq3hyaO540ueBzZGHWPRgUVBjLMspoLJQ8VVR+g/YRL1r2MoRpduHrvzVy992bcYy4A8l5bTmhfMF5uUxWqNobQbRjGRkIF2nmW7Hqz2nYcOKWcLZnOgwV5j5wyKLBAZsfVe29GxpJ5/2asL3MzxXvjySnZYgvZ8dPZ9vkEpOUyN0ugbGSfr3OuOGl23DCMxVCBzrkjp9/QODO28D28iPwEIYi3gzCl7UJCv+rrhtl/VUUkVX3+Qn8TkT8Efj7/9c+BwlrfB1xc2fQi+qVu6053Rz0ISapo3ZEc7yDddFGH3ByAzctcJ7OIFs7nfNY/8VUUOHD0dwYeL6OWS/gciELjywdpPfY8bnjWm+luqw383aLhhmGsBAtlVWzl1PjeWHB94tkUFUfUTpFMy9KAqOtPEZ7Mjm8e5r5X89lxjWXe91Qd6NHjp9hxWF4GUvNfFrbj9pkyDGMlOBvt+FpwFpSz/SyhZdA/A6jqPSJyzrA7r2c52wPA84A7gWuBe/LH/xfwQRF5N6Gx9uXAx8/kieZ+uapRqsW+YPNlEFV7RxSNjotyAJuosfVZTLxpP+PRND7+1fL3M/k8qFCOCZ7reBqGYaw21evXRnBET3c9Xcoaq9s+/3mhf91t//C68nkWbMRtbAlOF4RpP+PR5c9n6teZHTcMY73YaHbc2HB0VLUrkrdAEYlZQu7VeopIPwH8er7gNnlPBFX9ooj8GXA3kAI/u9KT2ari3zV78i9Y9ZTN09m+EJQGHIq8z5GJR0ZB+xmPts+DYRhbiqVc09bLUV3KGge2bUZL3t/Y+tjnwTCMrcRSr2kmOhG0gdUqZ9sYZXIfEZHXAE0RuR74GeB/D7vzuolIqvr3wNMW+NtbgdOPxajw5XsOLtnRnW97dblgpP3qI7WpaoZhGIZxWq7eezPiT99vZj6WY8eL5zQMwzAMY2UYGCBkbFVeBfw48HngJ4H/q6qn1nIvwHpmIq0rhdNZikNzmxiaaGQYhmEYS2atHE8TjwzDMAxj5TEBCYIYsFqCwIYQGn5OVX8dKIUjEfn5/LHTYh8RwzAMwzAMwzAMwzCMs4MXzfPYjw6781mbiWQYhmEYhmEYhmEYhjHAFu2JJCL/Gfgh4DIR+V+VP40DR4Y9jolIhmEYhmEYhmEYhmEYW5uPAQ8Cu4B3VR6fAj437EFMRDIMwzAMwzAMwzAMwyjYGFPUVhRV/SbwTeBZZ3Ic64lkGIZhGIZhGIZhGIZxFiAi/0FE7hGREyJyUkSmROTksPtbJpJhGIZhGIZhGIZhGAbkPZFWaYraxshwegfw71T1S8vZecuISNMn7z/8kVte9c31Xsdp2AUcXu9FbBDsXAxi52MQOx997FwMYuejz2Y4F48YdkOz45sSOx997FwMYudjEDsffexcDLLRz8fQdtzYVDy0XAEJtpCIpKq713sNp0NE7lLVq9Z7HRsBOxeD2PkYxM5HHzsXg9j56LPVzoXZ8c2HnY8+di4GsfMxiJ2PPnYuBrHzsUFR0C04na3CXSLyp8DfAJ3iQVX9q2F23jIikmEYhmEYhmEYhmEYhrEoE8AscEPlMQVMRDIMwzAMwzAMwzAMw1gSGyNjaFVQ1R87k/1tOtva8t71XsAGws7FIHY+BrHz0cfOxSB2PvrYuVh77JwPYuejj52LQex8DGLno4+di0HsfBhrjohcJCJ/LSIPi8hDIvKXInLR0PvrqhX7GYZhGIZhGIZhGIZhbA5E5E27f+7HXt984retyvFbn/sSh37j/W9W1devyhMMgYjcBnwQ+ED+0A8DL1DV64fZ3zKRDMMwDMMwDMMwDMMwABRklf5tkDK53ar6flVN83+/Dww94MREJMMwDMMwDMMwDMMwjLODwyLywyIS5f9+GDgy7M4mIhmGYRiGYRiGYRiGYRToKv3bGLwY+H7gIPAg8J/yx4bCprMZhmEYhmEYhmEYhmGcBajqvcB3L3d/y0QyDMMwDMMwDMMwDMMAUFndf+uMiPyBiGyv/D4pIu8bdn8TkQzDMAzDMAzDMAzDMM4Onqiqx4tfVPUY8JRhd7ZyNsMwDMMwDMMwDMMwDFjd/kUboy+SE5HJXDxCRHawBG3IRCTDMAzDMAzDMAzDMIyzg3cBHxORvyDIWt8PvHXYnU1EMgzDMAzDMAzDMAzDgC2fiaSqfygidwHXAgL8B1W9u/h7NUtpPkxEMgzDMAzDMAzDMAzDOEvIRaO7F/jz7cBTF9rXGmsbhrElEJGrRURF5Mcrjz0lf+yXK4/FInJYRG6as/+dInKviEjlsb8Rkem1eQWGYRiGcXYjIiMi8sci8nkR+YKI/L2IjOV/u0hE/lZE7hGRfxWRXxeRWv63wgf4d5Vj/R8RuXp9XolhGJseXaV/m4NFR8iZiGQYxlbi88APVH7/QeCzc7a5Afgy8P1VwSjnOPAcgHzs5fmrskrDMAzDMObj54GHVPUJqvp44MeBXm6v/wr4G1W9HLgCGGOwh8d9wGvXesGGYRhbkEXlLhORDOMsR0RGReTvROSzedTvB/LHnyYiHxGRT4rILSJyfv74T4jIJ/Lt/1JERvLHvy/f/7Mi8tH8sYaIvD+PKH5aRK7JH/9REfkrETmQRxTfsUIv516gISLn5g7nXmD/nG3+M/Dr+bbPnPO3PyEITwD/geCwGoZhGMaGZgvZ8vOB+4tfVPXLqtoh9O1oq+r788cz4OXAi4u1E4JGJ0Tk+hVYh2EYZzMKqKzSv/V+cWeOiUiGYewFHlDVJ+VRvwMikgC/AfwnVX0a8D760b6/UtWnq+qTgC8RooQArwf25I9/d/7YzwKo6hMI4s0fiEgj/9uTCVlDTwB+QEQunrswEXmPiHxmnn+vWuT1/AXwfcCzgU8BncrxmsB1wP8B/me+piq3A98pIhFBTPrTRZ7HMAzDMDYKW8WWvw+4UUT+UUTeIiKX548/DvhkdUNVPUkICD268vBbgNed7mQZhmEYi7JoOZs11jYM4/PAr4nIzcD/UdX/JyKPBx4P3JZXfEXAg/n2jxeRtwDbCankt+SP/wPw+yLyZ/QzeJ5LcGBR1X8RkW8SUtABblfVEwAicjfwCOBb1YWp6suX8Xr+jCD+PJYgFD278rd/C9yhqrMi8pfAr4jIy/OIJkAG/D3BIW6q6jdOrXgzDMMwjA3HlrDlqvoZEXkkofT8+cAnRORZhBua+eL3A4/nrxsR+Y5hn9MwDGMuouHfah17IyAizwUuV9X3i8huYExVv57/+brF9jURyTDOclT1KyLyNODfADeJyK3AXwNfVNVnzbPL7wP/XlU/KyI/ClydH+enROTbge8CPiMiT2ZxFbtT+TljnuuRiLwHuGaeff9EVd++wOs5KCI94HpCb4WqiPSfgeeIyDfy33fmx/9Q9diE1//GRdZuGIZhGBuGrWTLVXWaIGD9lYj4/DV9FviPc447AVwM/CvBnhe8ldAbKV1k3YZhGGctIvIG4CrgMcD7gQT4I/LesKp6dLH9rZzNMM5yROQCYFZV/wj4NcI4xy8Du/PoHyKSiMjj8l3GgQfzNPkXVI7zKFX9Z1V9PXCY4Nh9tNhGRK4ALsmPPRSq+nJVffI8/+YVkCq8HrixkmFUOJvPBS5R1UtV9VJCiv7ckrb/B9xEyGIyDMMwjA3PVrHlIvIcEZnMf64BVwLfJJSbj4jIj+R/i4B3Ab+vqrNznu9WYBJ40rBrNAzDOIWtPZ3tewklyzMAqvoAwS4MhWUiGYbxBOCdebSvB/y0qnZF5D8B/1VEthGuFf8F+CLwK8A/E5y6z9O/4Lwz710gBGfvs8C/AL8tIp8nRAR/VFU7q10ipqofm+fh/wB8OG/QWfC3wDtEpF7ZVwkOuGEYhmFsFraKLX8U8FsSDu6AvwP+UlVVRL4X+E0R+ZX8b/8XeM0Cx3krwcYbhmEYp9LNr6sKYTjDUnaWcL9kGIZhGIZhGIZhGIZx9iIibzrnJ1/8+pHHXbkqx5/94t08/D/e9+Y843NdEJFfBi4ntP+4CXgx8EFV/Y1h9rdMJMMwDMMwDMMwDMMwDLZ2Y+0807MYQnSS0Bfp9ap627DHMBHJMAzDMAzDMAzDMAxji5OXsf2Nqj4NGFo4qmKNtQ3DMAzDMAzDMAzDMABUVvff+vNPIvL05e5smUiGYRiGYRiGYRiGYRhnB9cAPyki3yRMaBNCktITh9l5y4hIu3bt0ksvvXS9l2EYhmEYRs4nP/nJw6q6e5htzY4bhmEYxsZiKXZ8S6H5v9U69vqz70x23jIi0qWXXspdd9213sswDMMwDCMnj3ANhdlxwzAMw9hYLMWOG5uKM5KytoyIZBiGYRiGYRiGYRiGccZsjIyh1eLvCK9QgAZwGfBl4HHD7GwikmEYhmEYhmEYhmEYxlmAqj6h+ruIPBX4yWH3NxHJMAzDMAzDMAzDMAwDEA3/VuvYGw1V/dRSprWZiGQYhmEYhmEYhmEYhnEWICK/WPnVAU8FDg27v4lIhmEYhmEYhmEYhmEYcDZMZxuv/JwSeiT95bA7m4hkGJuYvdtejFxwLhw/CeNjIAKRwzcTpJsimeJHamjsiP71Afz0DJplpxzn1u4H12H1hmEYhmHsffLrobDNUYSKoI0Y6WVILyMbrYMT4oPHod0J2+qpdyH7D/7m2i7cMAzD2Kzcrap/Xn1ARL4P+PMFth/ARCTjrGLfhT8H3R7a6UAUIaMjkKZ0HncJ9QdPQruLnxzF3fdwEGWcA2D/V25eszXuGXtR+bNrNoPok2VoliFJgsQxxFFY/+Q2tBbjH3Euvhbh68H5RAaPqQLy1MtAQVSRnic53kZm2uix49xQ+yEk6V8Obpn5w7V6uYZhGIaxJPbu+Am020WcC7awXkPP3QmAnJgmO2cS34hIHjzRt+NffvuarW/P2Isg8+AEiSKIY6Rew09NBzter4WgT70GtRp++yjZSAOc4GMHEux2gWj4vffoXaGXhoJkiutlxCfaSKsLM7Psu+hl5T777/uva/Z6DcMwtiQbI2NotXg1pwpG8z02LyYiGZuKfRf+HNrpoK02EkXoYy7FJxEISM/jGxHRbA9JPSpCur2BOsEnuTf2hIuAvkNWpXXJtvJnt/0RSOaJOhnSTbn+2W8hHUv64oyCSz0aCdFsSnz/EbLzdxA9eBRqNYgc2bYmbraLtHthl4MPh/+zLKzde6RWQ7vdMqIoUVT+r1kGkUOaDdKLduHrEa6TgRPwStaM+6/rNLhMwYNPBEUgimCyGf5dNNnfUJV4pse+S36hjIrq9ExYU6MOwP6Hfmuo59zIXHfNTQDcfser13klhmEYZxf7zv1p8IqfnQ1Cyrc9MthxJ0iaoZFDeh7pZeBAazHpWIKPKvbuGY/G9XR+G3j+GBDsXveiSVwnw3VTrn/OW8hGYtRV9slvEESV+r3HAfAjdXq7msQnuvhGhOtmSCdD0hTppOihI8E+xnHwRRp1tN0ZXIMLWcGoBpHr3F1k40162y/BpR68Igq+5gZf1wKogPiwTh+FQJE6ARfR3TUKjAKTSO5LqAh7n/Da4H/EUcheEhfWJLKmgbHV4trrgyj44dtetc4rMQzD2DyIyD7g3wAXikg12jBBKGsbinUXkUTk5cBLCKb888CPASPAnwKXAt8Avl9Vj63TEo11Zt/lr4ReD2o1dOc2/EgNyEtVI0d0vEW2vUlvexA5fBKhsfSzbjJFVFAB11OiTkY6Gj764iGe6RFNd1DncDNtaLXRmVkkidF2B9m5A+cE1xnBN2tI5vGJQyMXnDiF3iN2Ex+dwe/eDt6H9cUR2VgDnRxBBaKdY0QHjyFJAu0OMtpEDx0JUclaAvU6dDqIcyFa6RzaasPOSXoTCQBZ3Q11zqKOR2NBRagdmcU3EnrjycA2Wd0hHhpfegDqNVqP3hXOXy2icSxBxybQJILPH8VddgkcPU7vsRdz/XPfiubLkEzRuDgPSvxPXwyOKnBL6wPLfs+NpXHNnptxvfC5dN3w+bv9jldz7XVvRyNBXX8SxB233LiOKzUM42xk34U/F4IXnQ56zg6k1UUfdQEwx45vC3ZcUi3tOISgjYiUtieZTumNVez4bAo+ZOZoJEQHj0GrDc6hnS5y7i6k00N8g6wRI5lHY1dm/STHOnQu2l4+l+t6iATxkDUTdDz4HbVDs8g5u4KIFEXQrCEPHQnrGBuFsZHwvCIwPRuCXt0uOlKjt72OOshqw9lxqfTjSKa6+FpE2owGtvGRIBJ8neRwi96uZl+Ucg7iiHTnGDCGa/eQXkZ31yjXXXvTwHFUJLRV9YCD+ldD0Gv/19891FqNM6ew41nNEbVDEO/2O18DBMHMR2J23DDWklWczrbOGU4PAHcB3w18svL4FPDyYQ8iOk9N9VohIhcCfw9cqaotEfkz4P8CVwJHVfXtIvIqYFJVF71iXnXVVXrXXXet/qKNVWPvxI+FlO9aEDt05yTy8BH89AyoB3HIaBPZvRN94CFIYuil4Bxy/jnh5zQN/3e60GyEqFvm0ekZpJag5+3CN2KyRoyvRaVDWiDad17FK5IqUSs4Xu7QCXBCesFO0vGE2uFZNIroba/jUj+0Y7hRqV4o56bRhweDc10IFdFMj2i6jb/3/nDeZfD1bwYR6eq9p0Zj7zywts7Z85/3toEbA5dp6Icxz2fzjltuDMJQLKdk0hXbqAz+L17LyLtKnpXGmUdvr9kTzp345R3v6r0347Ig8i703amWbXz4dos2b0ZE5JOqetUw25od3/zsGXsREkXItgnodELwp9VCZ1qgHqnXQ2bOtnH0yDGoJSFYE0XIhecF+y0CM7MhIBPHkCSQpuiJk0izge6exI818LWIrBGunXOvhy4L1z3xIQs36oQeQq6b4Y7PoLWEzkUTAEStdKAUfL5r62aisNnz2nH6NkG8hiytdkZ0bDrY8TiPLachGL0ZRKSNYMfnrmPue1C1yXCqXV5ovwKXaSkiqUCUB4uymjvj13rNDWHdLvXc/uGlZWdfvfdmoq4PWfJAOpacsnbxeellGtb8oY+85ozWa6w9S7HjWwURedN5P/qS149+25WrcvyZL93Nwd//3Ter6utX5QmGQEQSVe3lP08CF6vq54bdf90zkQhraIpIj5CB9AChHu/q/O9/ANwJmOy+Cbkh+UEAbu39SfnY9c9+C/HB4+iRowAcOPG+kN4eRaEvz7YJsskRutvryIUT1B88iZyYQU+cDCnj9x8MB8p82etA7z8Yyq3iOJRfOYd4j24fw4/USMfPWzRlXBRqR1pIq4eo0j13nORYK/yt04NOj96lu/FJhGQ+RIvG6kjqQ3p57EqndbM6oKdbt0s9knqS6VxUe/goOts6ZbuNJB4VTt2dB24sS9hOl801n0NapXDYTue4Xr335iU5d4VzWP2cDpRdahBuNC/fmM/ZLH4eeMxtzA/knQduDOfwdKUcEsowq++lYRhrx3x2HGDfZb8IrTZ+eoZbpv+AvbteihsdCaXY9RrZBTtCZpGGjN/43kP4Y8dDj79DR5BaDTIfyridw997P67ZhO0T+BMnQ5buWAyNGtnEBNkjzylFo4WoH2lD6pFcBCl8BF9PcDMdertH8ZM7Q4ayzwX72IUMnI3gEa8A89nyalBBstAXMepkuJNtJM0belcCQRtJPKra2qgb2gicrgRwvex4lapoNPexql2u2vLyfdLh7Ph628M7D9zINTfcvGgQtciEzuohcHsm59QwjBXlNhH5boL1+wxwSEQ+oqq/OMzO62oyVfV+Efk14F6gBdyqqreKyLmq+mC+zYMics58+4vIS4GXAlxyySVrtWxjCPZuezGaprmTmLGn+cJQHtZLcUD3mVdSy43iDckP4sbGyE5OhZ0PH0WcUCscmrFRfLeL1Gq4Wg3faoEqUkvwJ6cBQpbR9EwZAfWHDqOtFjI1jQOSYpJJlqF+0KpLvg557KPCA90etW8dRcea6Fe/iaYp6hV33wNELu8nJBLK0oDFXdrNj7Y7IROs6NdUrwexL49Y3jL9B+u5vJK9ky855bEGINvG2bvjJ0jyrMvklK0Wp/3tl5/y2Okc1KVQROWKzJ4qg07k/M7lMKxU6vvA65aVc2Ab/3zP0NvO9z4DHDj2uyuyFmNtMTu+cdk7+RLIsnLAw57RH+n38QN6z76S5HALd2KE66MfAMA16vh2B46dgK8FJ1OcIHGCz3sEuZERqCX4o8dxY6Nkx4+HTONaEvokzc7idu5Ap2fwhw4jJ07iRHBAXIhDFVsuTkoBRJ9wOQJIN+/p13Dov96LpCneK9E9EEdR6DeYi1dnBd6Xk1mlUS/9F92xHYD997xjvVZ2CnOv8Q0IjdPTNAQPl8lcW76SdhwWFqaGtdeFbV9o+5Wyt3PXd+etZ3jcfL0rYcfBbLmxwdjajbW3qepJEXkJ8H5VfYOIDJ2JtN7lbJPAXwI/ABwndAP/C+C/qer2ynbHVHVyvmMUWBr82rHvvJ8pjXk5KQzY/+B/L7fZ03gB6hVxUjp6rlEPvQGKhs1lg+mKIwhhvygKDl7kcJdcCFMzoIrOzA6Owe2FptVSC/0KSNO+oxRFUEv6TkfRwyD/Wb0vU7aL5y0QJ0EkKfbV0AQbkbPH6YRws1A534UoeODk+9d3YfMwr1MiApecjxw+Hj47y2Q+IelsjaRVHdCVOgeLOZRLxRzQjYWVs21M9p33MyASggSRQxoNYHCi197Jl4S/58Mc0Ly8LA+soEGYkDgp7W4ViaKyhM3t3glpBs1GvzF1bk9LO54Pb4AieJH7As0G2g3b4PsZyHgf/IJeWpa8V9dR2vHCz82yYMOirR76mUPh7+S4ZrP8ef+h316PFS3IQnZctm+DbnfF7Ticnbbc7LixFM7qcrbHrlI5279siHK2zwM3EKq+XquqnxCRz6nqE4fZf72Td58PfF1VDwGIyF8BzwYeEpHz8yyk84GH13ORZyNFVBHANRv4VrsUeYByZC1AdnSw5/nex702ZKsUjlsetfTF9BJxwbGMIhCHS2SOgNMXbnyrC1/75sDfXbMRnMjIQdsH59E5pF4PgnGWlZNTChFIe73wc5b1neHKWkohK4pKh7lMy+/lQtM6Cq5rjvfI2Cj6yAuRe+4Nk+QadWRkhPSiXVx37U1Lrp1fD6Reg+kWNOpIrxduNsgz1zrhc6mPugh3+ET4+eR0+T4fOPa7K+oUbRUWcziXU3J29d6b4dsv584DN7J38iXLO++RC4J25lfFOTaMzcqe0R8Jk8AadbTXQ5wLAZjcHstoE4ki/OEjA/vtfdxrkfPPQY4E++5PTg8ISBJFaOr7QlGRqerm2PMoQjsdsvseGCiXkloSytXiuD/ZrNtDRpp9cQvCz4WABCGAlT9e9uJzAhlhHUVWUpH1NDYa9ulV+v1UhaitTC6y+cc9kujwVOhB5QTGx0gvmETOIKtnLZF6HiRs1ENz9VxIOsWOn2wFf7PTHZiWZ7b8VIax46fb7pR9KnYchjzv1eAu+Xudefbt/ilaT7tsSWswjJWmyP5frWNvAH4VuAX4+1xAeiQwdErheotI9wLPFJERQjnbdYRu4TPAi4C35///7bqt8Czhevd9pzRGLvCtdvlzkTmkXpE0ZaFMNhkbRadnggiTCzXzcWv3g+xpvGDQ8RQ3IFiVz1tkMHV74aYx6x9Xe2k/0pYfRzudsvSscDbD+vvZR0X/nhtqPxSeUz14lzvcKdrt4nbtgDQje/gwksT9rKetjHO0H3cRJy+tcc5Dx9Bjx4MI5z3xvQ/TveL89V7hKcyNXO2dfElwoqemT7kRqW5/wzN+NXxG5vksWzRs5VmshKA439WIceFA7p18CTI6AhB6cVXeLxkbDe/x8ZM0Pv5VpJbQevIjrPeCcVaxp/GCYPOqJdtFhk4ezCmQRj1cH72iZKXNrpKNNXAHU3BSilALcWv3g+wZe1E4dp4RXASbpJrVW6wp8+DyDJnCjmcZFH32Mt8P8EQuTCvNbbzkYlAhKN0y84f9c9B8YRBK8iwm7faCHc8zobKHDgUB62wQkZyDLOPwk8eY+EadxpFj4bORxMSHpsh2jK73Ck9hPpu779yfhtkgHGmaltd+7XQH7Xjx+Zwn08xs+cpSlN8XU+SoDAcpzvXVe29GnnF5WZ5XFZhkdCQI0rOtvqjrPSQJ2poC52h++hu0nnKp2XHDWCVU9c8JVWDF718D/uOw+693T6R/FpG/AD4FpMCngfcCY8CficiPE4Sm71u/VW5dqtlGhXBTlKCVv88RgXwvrewSehwVEcii+Waxb+idkKepO5kjDIV9irK3Ypu5uCT/iBYRxvxnUj9Ypqq+jET2H1IkluB85ttot8st7T8+5XnkCeGmVT9/D+I82mqVafB67ASapqEcL8v6zrjkvZSqzm0e5d3syNgojftO0PhKi/bjLkR8GMUcT4XXvhmykArKz2AhQMzMsv/gby66T/vbL9+0jZyXHXEV4cDR31nZxZyGOw/cGJrzAvsufXl589usJXm5i2PfI38Zbdbh4vPy6UkCug2Z7aAjddQ5pN2F6dmBG+HmvxyEZoN9V9yIH21A7NDYheaeiUNFuONMe0GcIfsu/vmypLaaeQEs+F7sfdKv0D5/DDi1Aezcx4ytz57mC4Odzm2f5Jk5EsdByKk1wndJtbRfRSZRoBd63AFEUchcynFRhIyP4Y8ezx8I5WFFBks1oLJn9EfKzy5RFNYBuVgk/ZI4J+BdaY8HKPoc5cJX6Xtk4ZioBtErv6bPteU3PPNXkcc/Gu7+13CsLAvtWlTRo8fz85H3Aur1wvFEBgQl9f2sqs2erSST28nO3ca5HzlEun2E2e+4grThaBwO52+zTbzUTje8X6Mj85a1+Z0TZVZxQWHLN9t18Uwyp9ZDMGt++SEgt+NZyIBrxhVB2wn7LvtF5Lzd/esAlL25GGmG7MFjJ0L56mwL7QnNz94LzSb7LnoZjOfBokjQOCYbr5UNx2+/c/0mvu274sYQrCT/jObXFVjYjsPCGV+b8fO6ZcknBK/asdcJEXmlqr5DRH5jvpWo6suGOc56ZyKhqm8A3jDn4Q4hK8lYY8q+RFlWGfWURwnnNKT2FQFJs+yUfbXV6je7LMSpRZ6zKlaVUcylUvRSgpC+nvl++vsi3PLJNw38vqfxgsENcoe0XCecOlUqTcM3MU+lL43jZkQE30zoXLKNE4+sUT+pjH99tn+TsAmY60jtu+hl8z5+68dPLUde6Yaba8GZpuufqYBUPWfzOUF7nhIu83VCpoJkyr7LX9kvR/EKZH2ns8giVAUHGkWI9/0+KI0k76lC+H9sBGZayPYJyG9uNZ+mGH7Jo9eJC1MU12Fq3bXXvZ36fcdJd43hE0fSaoWb7WjwZlVqCft2/xQ0G1AfzHw8sIEa4Bobg2DzcrtXZO0UmZdZNtB3qKRSxg30haQi49cr2u0i3W7f5hVBpYKq4FP0Syoez4UsyLOCYTBLqtinOF4RAHKCRLXS1gTfwoVjVPaZbwrorf80eC0/xY5XfYEo6pe9z6FcbxxvbjuepkRHZ2hdNklrd0JWg/oJH66Zm4j9D/3WwO/7LnrZUHYcNp8t3wxld3ue8gZqgHTT0LaiuNaIA836dru05S70Qyv8R1VI8+97koS/Rw6NJGQVq4bvXd4jTWsJEjk0CT6BOofWc/9gnabP7nvMq8B7uhdupxZH4fXVBq8VA3Y8jgfs/P573mFCkbGefCn//4yaUK67iGSsL9Vm1vOx0OPl3ytNrEOPhN5ARlMpLi1QKleS91oo15X3S8oXcfr9CufSu5CaH8fIaBO6/T44qJ83C2k+5m53ijPKqVPJym3yTCmiKEyoi+NNF9HUqWlmn3ohvVFHbVpJpjPSsWAgk5Ndrrv6besa+VkO1Yaxp8va2CzG/RSHMxcvq/0gFuNMI5aLncfib7UTITJXXgJEIBPK4IeTMlMCkSDOluJR0UAXJMuCEwlIpmieWaSRIxut4XoetjXRyIVDS3gu3R56rEimaCSkzQifrI/jqXF43vjQFHroCNm3XUo020Ue6vejkbHR4DwXY7fLnYNTvu8xrwInaD2hfV4oR7nzwI2b5jNrrAIDNtINPK7dXvi8FM2niz9VPl6a+jJwI8UEVHF5xhLBrs8N6kTuFFGmzMot/Io0HdyvEKGK73yRNVWv94Ubr2jWLQNCrtmERh09dLhc/3wC0nwsx44PbJd5lErG0iYTlHRqGsZHae+M0QiSlqJO6I1GJFMZ11739k2XjQR9Wz5M757NcF3cM/aiU7LYlzKNbqXsOJx6vq654WbUQf1IO4g8nfzC4egLSGmaC8pZCPIU+EoApxB8emmw60kcbFwjD/jEDo0iqIF4j1aDJ5GgzVrpF4j3kAoSK5IpvrZO/nW7Q/LZr8GuHeiF5yAnpsty3FPseBksF0gz9j36FZDEqHO0L54oD2m2fGOxVXsiqer/zv//AwARGQ+/6vRSjmMi0lnGDckPluJM0RAT5heTTicgLcZt2Z8u+LfSQatMSmO+bKG54lF1+xw3OoI/cfKUbbXTgU5nIN3+lvYfL9tpGkZ8qm6zp/GC0HOiF6Gt9sDUmY2KjOX9Eeo1dLRBVnd0xxy1GaU3FtGdCOd/rJMtcpTNxWZOG9533s+ExqJFuV49OFnFyGYeCOnla5XafueBGwec0fpDsxAJvhYHPSfTUI5GEIc0BtE8M6HbCw7nSHNAPNJaHnmMorIcTZ30s4vy8jZ1EsQjr/haFAQkDaKNj8OxsoZDhVMEpLVsxK3CoJNdMDaKNJv9XjEVqqO39z3yl1d1fcbmYE/jBf1S84LCBnpFB+zhnAzacvNKSdsCgZrF7HixDggRdzRkLoWBFn0bccrUNieDGa1Zhow00UrvxXLfbhfthVLyKtddexOw9LLqpQaR9jRfGKbQNuohszqKNnxASGpJyMQUQSfH6e0coTvu0PytjlOlOxFEJGP92Tv5knLqLVCWW9JsBDN2/CSwjj2dBOqHWyCCa6dIN+2XqFZtlZOwcZqG7KKiZ2EhHiUhc1iTuB8oil1/O3JRKgqZRqW/rxp+j/sTGX2zFkra8stIcR14/vPeBsCHPrK6Ac5r9txMw/sgEs3pIyeT2+e9nu7/6jvLn82OGxsFEXk88AFgR/hVDgE/oqpfHGZ/E5HOEvZue3H4oVoytkiJ2TAUjp1vd07pn1SIVUUEU+LcMFYurhLHIVun1R4cITwf4gabheb4EydBHG57ruQXhqfZCJELwB2fRqem2Tv5EpJut3R8h3Uol8Mt7T9m3yW/EG6OXSjtk8pY3XWnGOmcxGS7t6O1iO5kjc62KNzsO8hqgsuULH/rXO5zbrYMpMXYjALSnqveiDtyssxyk9ERdNd2FPCNGDcVIvpLdTr3Pvn1ZakYHg588a0A7LvyNagIokrrkm1A3svoytfQVA2TVC5/ZeiBIAJxFJy+JEIREPCxI2s0kcwjmZZJSNGJFtQjqOXf2+Lr7/KfnetnJ6kiqUeTKAhGECKjnpCFRChV8zWHeCVtRmgk9EYqpWsCaUPIauHzfNWPv5vGaETUVSRVnvdd7yBuhZI6UQYcYS2a9SpkjQgfCxr3Hdmoo0StIIYv9h1pPWpnOIdVcegxr8qjrI72FTtRJ+Wxquz/2q8N+W4aW5F95/9s+CEvxZor0BTDIea17XMmkhZ/lyTuD6Mo+gjlAzRuSH4w720Yehe5kZG+WFR93loNPzvbLw0rso3gVLteFb+KXouHjwQ7fu6u8D0obkJF8NtG0E4PF7lyembyyXvQNC0bec+XTbQS3NL6QBCSIoeMjJS+yoYZrhGFqbTUEnSsSbZthKweMXt+DRUQHzKPXE/RrH+tgs3XD2mrse/yV0IhnBYlnE5gpImOBF9RZltLtuP7rnwNtLsDYm0RiNj7uNcO2HEItrz5zePhlzRj3yN/mWbkBgQfjSJwRfZvDUlzPyH/Jy3yUtW8pLXSBym8EAm2vPg9U0hc6GdY2FjVvvjsQsAIVbSWoBJsrU+iMtFSnZSBoav33kzU9bieR7zn+me/BVFFelnZQyns48oMZbziGxFZLfgM6sIx45bHdT2S+aXb8StuLDOP2pefi7pK4/EKZsc3GRtjitpq8V7gF1X1DgARuRr4HeDZw+wsC03X2mxcddVVetddZ1TatyT2XfhzAOy//zfW7DnPlLKJ9XxCzRyn7nRZSEXj7Gr/hOqEtcJB9d0ut/k/D85e5vsp8NW+QUWPk15a6Y3CwHoG+qZU+yhAGFE8uT38kje10x0T9HaMEM30iO4/FCbF5VPdyCfHFNza+5NhT+GSKcuNvO9PldsIKfGRC+dMhGzHKL4e05lMmN0dk7SUtN4XjQrH8xPv/8V1W64R0spd6kmOtqCXId0eJDG+Hj5PosqBz/zqso6990m/En5QhUwRLbKGTkXm2gzVkKJeNMtM4tzZjEP2UN63yNcixBMEmko2jmun0OunyA8QRaUDGJzaCJ84xIMWvZAIn1GXKulIuImVTMnqDl8TsrrLU94FHwUBSZ0Q9YKYNfJwStTJkEyRnsd1U9xUO/RcyqfGaBKRjSRBAAPS0QQfC74WxCWfCMlURjIThJ+lRkL3PaZ/Q6eNhHRbg6wZh7WniuTXxM0oeorIJ1X1qmG2XWs7vueqNwJwy11vXLPnXAluqP3QKT2NoDKIIheKyr9X+xbNQWq10q7OKz7lwSAANzYW+iRVhCUk9wUq1wVtd/Lx6535n3fuY+KQZgMuPq9/zE4PrSd0d4/gOhnJ1x4K5Vku75/S7lBMYAM4cPL9SziDS2PvrpeWZcJFQ+4NISRFDhkfCxOtRuqk2xr4muPEpXVcfq1SB5KBjwWXqtnxDcDeJ/0KeI+0e2GiXOYh7pey7f/6u5d13FJAqpL7BwP9iERoXbKN5r0n+o9B3kc0KzPuNImDTS6COUVZmmpYf55dLKpBPMmDSsBgI/38O4tI369I+q9XRfo+QWFz48Ju5/5E4vrCcuzAQVYLvoB4xXU98VR+Tchycaubln4DEAJQcX8dmrgQ4Kq7PGtZiGczkukUjWTpdvyKG8vXoPUafrxOOl4ja0SoA9cJr3Gr2/Gtgoi86YIXvuT1o4+5clWOP/Plu3ngA7/7ZlWdv6HbGiAin1XVJ53usYXYMplI99z9ADc841fRWsRtf//a9V7OhmSugDTQVNP1xZrgRGan7FvtbSR5T4MB0SiOgzGpNNOLqo5W0fCzMnFFs04prLh6PTiHldK2YtLSLa0PBCHKAUQDz4HXYIibDfy3DoU1HjtO7ej20Gx3bBTJU+ZJ8+htt4s4h1x4Hvsu/LngnBIisdVxwWfKgWO/y77zfiaUtDXDlJxi8tt6TXIr06Uzj47WiR44StRsEM02SabqzFxQQxTiVjjHH/vTX1rzNRqDXP/ctyL1KDhgeU8AP9YMzSUB6WTLEpD2PvF1Azd/BVqkm0OIGuaCjeTNMFUESSv9D6IIEulH/Yp9vUdEQAXXzULk0ftwnEyRQszNM43Ci5F+mVoUHFBfj8PNUBocRPEKXkC03Car51FND5J54lmPTyNEwUdCMhPW2toZoQKjD/ZAIZnqIr0sNAntpMGRVh/W5hzaSJDUE832yiytWicNwlI9HL8zmeAyxccOn0hZJllNW9djx/up/RDKZEZHws1Dq12WychsTHJyhnhiFD9SY/qSEZqHuvTGYr7zu99Je0dE/YRn5BtTZaPxA59/65Lf+7WgFCeH5J67H+CGZ70Zn4Rz8aGPmi2fy95tL84DNm7Abg/8DyHzteyNFuUiUf4ZK7KP1A9kCpV23LnBfofNRjhMcRNZ/CsmCxbT2hr1kB25azTY5GKiGgS7B32b5yuKceTCmnoZvp7gjpwINtl7ar3z0ZEa/rwduNGRsvxdkiQIOplHdu0In7XUh+tSLnAt92Z8LgcOvzf0MOl0+8NE8vI2iaL1mcgqEp671UbHmsiJaZJWyEDZ5reRjkW0dkZEHYi6ysf+1MSj9Wbft4WyK23UcDPtEPSs1/p3Ylm2fAHp8lfmU9EqQVYnpRhTNrqOghjT/Obx0h6XfXuiKPQqqgaQPLmwQz9TGMJ+mc//LyKOFf+/nKZczUaiLx4VmUy5CKXO5YJVXIpKGtH3EQiZxgAu9fg4NOJ2qSee6uG6afArvA8ZSD4/fuRCj9I8s6nIohIPdAUXO1wal72VsprDx0LWiBa04xLH4X1L4lBtkMTgIuh0ynMnaUrU6eJaDbLROq3zGkQaxNzv+N5fI0tCpv/It2bDa8zP7S2fftOy3v/VZM/T5s6+OotQVi8TaWPk8HxNRH6FUNIG8MPA14fdecuISGQe1+rQHR9fk1HHRQbS3ie8tkzVLBT2tbwI7H1CcLKXfBMhjrlCUcEpPQxg3v5J5c9Fc8yK41kIRoiwd9uLB9LN9zRfOHjwzCNjNbTTRWrNUAeepog4ZHyc/Yd+O3+uvGlntYY6SYIokmaQZqFBZ9HMs9frGxII0QHNx/fWQuqwJhHsnkQmxqCXog8+VI44XikxqZySk6Z941yZ5Lbm/ZJqtVBy1Kz3HYReStYMIqB4RbwQdVfuCreZxo/vu+LGwahd8X/+c7W2fS0pMoB8PcF5j+v0yBoxvhahI0vPbtvzlDfkAo/2+xVFAvEcEUg1lKWRi0uesF3KoLMJ+TGKKVHSjzwWWUMCGrkg2uQRx+J5ymMV/ZDyXkghWihBDKrlx8wbVEtRxhaHdUnPl2Vo4iGEKYF6EJlQxfUAlHQkwnU9PnG4LI9eOskjplE/lb8WU/ZlAER8GeWMCFlNtRPhZjqa6eE64fu+d9dLyzHnoUFqPp0mrWRsnJwKI8WLSK9zIOFGULo9ImC8vQMcuE5C1ohpFG2ljpyAyOF3jHPd1W9DIzdQonLDM361zCgr1nTgs29e8udkuex75C/3J34NS5riZjroeIPeeLLqPcuKDKTCjhJF5ed17sTO1aRw0pf6nG5kpPxsqdd+gMC5PKPADdzYlT3U5ikp6weIXP+7WIhFuc0/cHhwguPcRtWyfQI9ejyIThPjcPRY+F5GEQeKPkOjP3JKtrEbHQv90DIfrhuNehiZ7RzSS1ENr0vjCOml4YatUQ9CWZrit4+SNeNQztLLkG6MzHbY9+hXrNj1ev9X38neXS8NqT0+H5zRS9F8Su2a2/G89CmUDxHEb3FQT4Kt0HD5c6mumC3fdHYc+pkzsCHsOD4PntSSICJ1ujA2gtbiMmNmKex9wmvDdwzy72nUtyVl9k8IrOBye1xkD0UhwDNwjmDQZhdICNqUojAuCLblThUBSZV8RX2clDYU6GcExS6sX7X0L2Sun69a3m+HXkgS/FQNWUi+HqGxEM10EZW+IAX9crxKVjS+Ysd7GS4vV49aKWjcz2oi9J5EJAjWRdlvtxsCTZ3cvnc6/awr8vulLL8n8Uo81WL8eI10xyg+FnpRQqQhW1oTRzQdmpZ3Lpjged8VyuM+8nevBOC6a24KZakC8XQQx9cyc3bfo1/Rz3A1tiIvBt4E/BXhS/tR4MeG3XnrfDLyL2/USvErXC6078rXzB+tryfhQp16pNMNhqHdCaVu3V7Z3FKiKIx4FAlOUpaheQf/MjpHP9tnub16bkh+MPywkHMYRbnBcOXUtLnp8Ld2P9g/TrFv9XUU46gLg1GteYZ+iveciPveyZeU27jLLgbAf/1b5dP46RnKmmiRkKKdG5y9ky/pjyeu1k1LcJS120Myjz98BKnnkdDxMRgd6a91ZARmZ0P/gG4v9DZQj3zrYBCXkhhtd3Dnn4uO1JHj0+y7+OfREycHzs9yU+bl3N0h42B6Fsmn0GgvRVvt8jMgtdqaRDS11QrnqNJ3Qk+eJPnXDrpzG8lEjI+FsS8eAoIRu/2OpTUwLdio43VLxyuPppXf7zg61XGqfvez0DNg7qjW1ea2v39t2UxWE4f2YiBFI0dnMiHqKFfvewe9UUfUU+LZ8B358G3z970oblql3Ssda3EO1PWdu0gGPo9a/Fw4l5kGJ7KaQRRJSCFPs1DW1ohx+VQ0rcVB5MnyrJ+s4nCWE0zy8xq54OBCKWyqSLhmReBald5qmYd2Wjqi0g2CjvSyIESN5E0444h0NCZu+XBz1fW4XobrZuDoi0WNGqL9NHqKXkj5tdLHMb4elY+Jh/h4O4/oVjIhGg203Q5ieKMejt3pluUwkGd5JEm4js0dLtDthWthpxOmVCUJMVAvyg3icD12h05Qe8hDErPv0pfnpYUe1+sFEat47tlWGDc82kRHR+heME48Ha6rt/7j/BlD+y75hX622cRYuHaOhqyU0wpSjWWU+0RRyHxTSE726E2snC3f+4TX5pFq7d9MqoabOZEgTvRaZWnGvgt/rt/jI3Jls2I/FsQC1+rB9Gx476ZnkHN2oUeOlY2iAfY/+N+XtdYy0DK3DCyOuWX6D9h37k+HvkHkGW5zOHDsd/OMpX4Zim/nwyaK/oSRQ1zcn146p3+J1OuDj3sNdjy/9kkcw+Mvhy/cUz6vf/hwaOx99FgQbHfuKP+27+Kfh24XNzaKPzk96J/4vCQ283DfA2gch3NYr5HtGEWdI5rphGyCVqs/3andQmdmkeMniKMorCkfp52dM0F0ZJq9T/oVpNXtZ0ZxBgLCubshdsjJmfA5aXfQNEVnWv3MpLWaxpr5kME4MhJKm5uN8rOQJBGSNvFJnfrRHrX7j5+RHYeNacsH7Hg1YBhVhNC5AlI+CXTfZb84YN/WQlTa/6WbytJlreU+mM/QWkz7gnFQ5Zo9N9Mdj4g7oURLUj1t/6qBEvOqeJRn6eIoewyVAW/tZ/FKNijakPsA0gk2yBfCZH5M6WXgNHwHNf/fa7+nIgxmLEZuUDSqiEel3e5lSI9+eVyenVOUuTnJAzqAbyaoE5KTQVwpeiL6eoLU+hlORRa1r8dlsEy8hmKGOA79E6tBJ5TkcKhKKLO3ajXo5dMpYyknQmv+ryi/k7wfnKZp3pqjOId5f6pWi3i2BbUatfvykr2ROho7sokGvhZRe3gWTSJElT1PewOSemq5H0PxXp2cDdfSZgNtJLQvGM/XDnfcurC4u+/Rrwjrq9eQXorP7fjpkh50pD7v/e9Zg7J6U9Q2wGlV1WPAy5a7v/VEOg1F+mnVQGmzFi6AEJzS1AcjnjsVpCk62xqMMCfxQFS6EA6GmYDmRkeAfqPcG2o/FBpfZtmpTiZQTWnPn2TgsWjnJP7YcXxv8KalOollrpBUTF8Jv/dvrEMDzsrrnCuClH2MdMCoy/nnoA8+3D9WxfiEGv9c33RRcIzyvx84+f5+9FNCCrx2u7gLzsc/fAi3bSIX9lLYPjEnIhEhx6dgppVf6HvllAmp1cKNXpqFm7OJ8RD1PH6yLHWD5YtI+664EW3Wwg1up1eKFXrw4RAZ1nCzsib9kiIXPot540XtVHpbnLuL9sXbQ/12ptS/eYz9X9l4zuNy2fvkvPS4EE562aCI5Pp196W4Mef7U342i+yZ/KZq/5duWsNX0uc7/v07iTph/Z3tEXFbSU6mpUAiWd4QM3IDdf57rnpjcBI9SJqFSWhFqYZIOQ1Fumk5fleK16yKm+32xZ8yi0jKa2OZoZlE/RK3LHcW03Qwxb46frza/6xoDpqEKG02Hnp++DiUrrmuDyJQXhon7VxAKno6OcreTFkziD8+Ds0441aG62S4biVDsBBMJEyVC5lT1cbcMhBgdZ0QaSxFsV6WO40u3OC1O+G7rtrPAIHyPEstKXtIDIhI1Qluhe0oemak2ZzpW4RzVWlKWl5P82uy1EPPGs3L5oqMlaKvDLUkXHfz868nT4bMzEpJU1luV6/B+BicnCqzMKjX8OdO0r5gjM62sM5/+qNfzE/Z+vdE2vfoVww2eC5s8WgulHhyESkPVuTvlRbjq6MoXDejKIg3hY3vDfbXGxgckQdUJIqCIDOaC0vfeE9/XRf+XDhWq90Xc06D1BI0y0q/wE/PDExDO2Ws/diLBrMEov41rsjularoUWbE9b/T4YkqNr/yOg8cfm9/cEfhI+XrcTt3hP3jOHwfKnbeHz+RP59DRpvhs3j5pbj7Hgr7n38O0umS7RrP359w3dA4xs124OjxcL0peilmeSnL5LY8U6kGaYbfNoKbbufPn2eh3PtfhjrXc9n7uNfiJxpIO0XSvnDt772/70uslYhELuRVGigXtlzO201v9xjpSFz2RloooLAZmdeOFyIGDAaCVPtlXtXf67WyQT3Qt/2sTWBoLlfve0d4LQrd8Yiop0RtT9RKkVRxRVCk1u8T+KGPvCaci4rgEiab9QWcIrvpFDueI70siKy5wFTa8Mo5DI21w/9BZPflMaU4n96Ha2X1PgH69jx/rBBOfD74puiRKPnU36JEu/QPijWUASbBN2r5EI0widW1s/5rz32HUCIXzoGv9FPqv/D+j6Ud72UDPhEQrhutdgg2wOAk6SJongd0SOIwrU0kf0/84HNG0YCwhuTXxuKxuZOqC9+06AO3bSx8Ro5Nhb+PhhYdtNph3ziG8dFQwpiGCgPyAL12ukgch2t35oNIPzoCM7P9Plgi+HMn6ZwzSntn8HOXY8e3CiLypgte8JLXj61ST6TpL9/NA3+87j2RrgJeA1xKJbFIVZ841P5bRUQa3XWxPu7fvhyVM2sA/Jzvexcq0DjSo/7gyX6TuOp5KpR36P+91YZeLxhxJ4PjanNnDQgjaytNqKulYQsKSpWb2GpfovnKzk5HiHzHpfH0rdZgv6M5WUdSSyrbtgf/HhbUbxZdZCMVTmY1MlS5kMrEGDo+ghw8cup0mSLCnqv9pcGvJf2+RYXTWGwfRcjOyZBGn8T9m6giUrtzWzCShaPVSJAT0yFimvddCI0E82M2GkHlryV9xT5PgT2TUsU9T3tDiF730nBsVTh2Ap2ZLZuUrvcEN8mn3LUecy4u003veO598usHGkRL4XS2cuGs+HzmIkUpgBR4ghNWZNiQO12dPAKVTyKTXtp3UvIsPC0zWRy3fOINp6zt6r035+UGuYggUkYcr3/uWwfEi9vveHWIBOelXK4TnKyyxC3vxeNrof9BMp2GKGY3fL98oz/RzPX6N3IaSRnFo3I8jV3Zl0YjFxyuQmfredxMN5RIVcq8igwlTYLgUIg2QC7WZH3HULUiIvlwPlX73/nqtTAv2/D1hHSygU9c2Sy7nPLWCWV3kmkpKgUBaVDUlm4WMrkKJ1vz7fObwYIyapunuWsUJsNoNTsq09DkvBoxTbN+o1QR9MTJPFoZBLHiuinNRrj+ZFnfgavamMq1tBSPqqV/1UaneWS9vJZWRfs5x5TiOleMge/2goiQi0QyNhreg3wQwEDzZOeQ888N4spog2y8QToa09qd0BsJTXtVCBNu2uFmSAU+/ke/NLTzWbXjsHxb/p3f/U58LDQPtokfPhEc5eo5LK4JSRxsjXNoLQqZeZ1uyCTLhb8iC7YIlJSliYAbHw/nPReaJI7xU1ODtjy331IVoopsAQi96QrxCuaP+hY+REXgKW1dmuGnZ0J2cS0ZOE5pp2tJGSn3+ZAJcdLv6VOck8q6RNygWDmn14kkCf7C3cg3Hig/7wOZIF7D50m137uw2YDZVpnBU970JzHSaODPncQ9fDycz1zU1HpYdzo5QnyyMyDEymwnCLXdbihjr9dDiZcqWq+FEpYkQlJPuq0Rzkmq3Pax1833sRmKG5715iAi5T3TyDLk0LFSVBx4j9eJuXYcNreItPfJr++XUjv6jZPn2nGCf1dO96psH4YyRLhWbzBrtPAni+9BmeleiDD9CWUL+X/XXXNTyOYp9iMIPNc/5y2g4GuFjZSyl898dhyFdCQiq7tQhtjxRO0M1wmCpcYulG45IepkA69bej74KWn/O1u142VmUrGLQHysHQI6aW6viutiUYqei0m+keQZSKEHkmR9wW7Ajqe+32C7sHsFTkJQeGKEbKTW9w16vi9QV/sl5oGmMgun8p4UNtPXwnvjk+APlEM4APH5ec8zjUr/Px+EAZRl7KUdh8FzkYR7H5maRY+d6H/Waklum2MYHUGTGJmZDeK1zwXNbnewyXix9vJ8DAp1iPSvh5VzV70GS1EJUtzb5AEE7aUhCF5kRTXqIQM5z4ouW2oUfkXRi7FWI905BgIzFzXJaiHTO5wniNu5fxnJkuz4VuEsEZG+DLwC+Dz0u56p6jeH2X/rlLMBPn81T/+xdwNLd0Cf+x9+DSIhzjvo+2YSou3QrzmHEKWt3hTkooX20gGnG+hH+ESCsymhGWbhZFaFpIGpKLkqLM71I5SFmJQ7KaLxoFNbaabXz/CpRF/juLzZEHIxJl/PKdlLuXMpY6PhqaemcY162Ed96D3U6eDGRsPz5zfORbZVeX7qjfKQpbDWaqMHH4ax0VOinDTq4SYsv/BJIzyP5MKSdjr5eYj7fQjiGKamw3bNRnlRB5ATUxz47JsrKcR5JKBeD821J7fDzEx/DaPN4OimGemuUXzsSMciopYnOd7h2uveTjoWrrIf/dtXLPp5msstn3wTe5/4OiTNSyeK81JtHNjurH1vhQor1Yh0vSlS7+t5NgoE50B6oU69nADSzEtU6glEQlaP8jGxRSZL/t3s9hsfhvr60DcK6GfnVB0CX5RVxfiRmOuf+1bS0Zg7bpkn3VgBoWzIDFDcCqoLtf/P/863kvgg+vQmElw364+1j4S4neEbEakL2T54DSNrG3lmUS4EFQ6f64aopq8lpEkUHFJRfNFryFOKTmXETjV3giU0+S6yCoprXJnuHuVOtyvFLRcJUf5aShHL5RHk/HySFtcw388erGQ3ivdkNUfWCI6xS0MmEhD6HQFRz4dzlhWOrpbvj+YR1mrjb9fzpTBH6oPAVDTgzAoRKaxD8htQCKJdeV4KAcoXkdyodCLL73aRWRXnrycXkPQRFyDfuB9pNvISW+1nCkJe1ldpRAp957MU74qs2LQvoBdCUzX6WnVa0xTJCGVD1Ruw6ZmQabR9G3JyKi8LbsLhY/lryIWn7aNk9Qhfc8Qtj0ooCwiToDxpQ0hayty2GMPgo/C5d6ku25aLQm0qDdHpeg2pBnWKZtDtTl7WHSaDiUg5Wl5b7b4NbtTD5ygvSS9KLbTVDoJMWjjo84sGEid9OwelvRQfhWyluJ85pkXfLDhFNAL6PoevfO9qSVhjVYjKJ6Cq+NDsul4PUedWO9jsIsOqlgS/Jon7k1LLNyK/sRMXPgNFxlxRgu897uDRYJfJb3qKz27xuW21wzluNsLzNRvo5Djcl0e/awkyMhLOT72OOzoVPoO1hGx7HlH3HjfT4UMffW0oXyr8rdih4w1cr4eetws5fDysK46CgF+LcdNtuueM4iMhHY2IZzJquR0P/UhkyXb81n/8FfZc9UY0inFTeQZA5sN7l7/PxedqvdiSdlwklFB5gm0tsl8q4md1lLtGEdSKHkD5AQeyQgQlKgNL8wU8Q5mTz3vzRdzwrDeTNeJFywJVwNcirrnh5oEbLFEgU6Isy8fTB1/D9Xx/0qgqUVdKn6DA13I77sJrEV9kYOXfycSRjteQzOPy16uRzG/HKz8XgzmQrO/DnJJJFMQnjQTJBM0jqkJ+vit2XEUGxbTqwIw4LoMloVw8t1te+y6GhIxp8Qq9YJOZ+/5QsVsutxU933+dBPEsS8Jk03C+8tfsg12P8ijFgnYchVp4PW6mE5IEoBSN8QpjjVLMkXI6bf46K9fx/jmXvn0ujlXNVCqSEzrdAfGtFPPzMjxUw7W+qOCAPCOzF6453oesoySB8VFoJ0g9RXdMhCyrk9Nh3T74JulYgs9Fy6wuuJ7mwUWlNyIks7p65VzGRuCQqv6v5e68pUQkycDl9+Y+gae/+N3h4gxU6xo/8b7FHVIfCelYxIlHbUe80jziqZ3oEc32QhQjTaHTCVHK4oI5N+2/Go1SDVGyom44TZFCVKqmLhYNqotpKMXNQcWx0/wCJtvGg9jRavcd5DydOrwI7Uec59TtlumMRfPpwjnMsn5mTzFxpEiRzAWc4qZIW+2QTZVPI5CRZv8CKxJuRKoX0TQtBSkihysaQhbpp8W5H60HB0EVOXw8iCqjIyHVHULZ2a4dIetregY9ZwdyYiYIWhPj4fmnpkNGT+TCORr4kOR13t1ueH4I/xdiWS0qbx6Trz4ISUy92YCZFnhPdsX5zJwTUsSf92/eQdzOQjM+Fu4rUkWTPNpdZKvk014GbjbXkKJEcitROJ6NB6bKPj5luZFq6H/ViPH1hN5EMKBpw6ERRJ0gpHS2BaNaO+n7PYY8RJ0M1+5HdfxoHellISJG/l0tev0UWSu9kBnjJ2t8x/f+Gj6Cf/iLX+bOAzfy7B94V2h6mt/su06IJqYjcRCK0nyMrgs36KiSnMwzgGJXZioVEbx4Jg2OT9EjIBm8kVEBlzugWotK8UWjULqFgo+DqJI2w99qJ9IQsct9q3Q0ZBglRbTOSb6+4ISmzQhNJJxTRx71i6hNRUStjKjjcZ0MjQVthqko0ojznghzGnUCRBHpRB2fRPTGY3qjkmdwQUyIQEoa1pcljgiPugiJQ6aS+FC+lo7kGQ0jjrijZElYd9YITmvU1vA6PXkj0tzh7PjQcy3PCPJxcI6j2SDESRJBN2QgMNvqZ2UUr6Eccyz4VjsviQoBAPfwUajXBjNXM4/6tOyrU1KU6ebnpLxRLxzOem1Q0Cueu4iMRq5/4xXHEIP0eqVNgdx+ZT5cHyfGyXaMIr2M7sWX4evBKU+mevjEEc90gRo+jtEo3PdkNSFuBac96ig+Wtr1LDRKpWwE7GNA4KqX5DfFS7DjAOloDd1Wp/fYScTD6NdOoM7hjpwImTmzLbTXQ0/mfTAa9f55zbK+GCBSTgotG7/n74XUaqdOKs3fo4HJXYXtrAo1+YQfmQ3Zy3OzgiTv13SKrSw+G2lWBpuk2w3CWC4gFVnEkiR9wXykDvc+mK/X96e3Ff5LkoQ1dbqQRHmz3oqg2avYcQgCVBwHIQgoxnn7iWY4z60Oknr04CFkpIk+fLj/Xo+GyalMTeMvOgd3Yha6PWTnJDhHdGwaknCd1njQVT3w+bey56o34lo9dHwkfKYb9VAqU0/KjFLfqFG/56FwDpMkiFqtFvLEy5g5P0YUnv3976J5uBsyNzM/nB0vykWi4PNop3PafVabrWbL5+vBJOUErsEMS61F4TPiCN+bXhauR42YrBGCJNF0L2SPRRK2TT2SpUF0rGa6FOJ7VbSHsK8G0aFofgyhAXIxnVN6lZt/r/haFISdqj3ORZ6oHTJ2wmP5Th6k54lzUUSyYNc1L8HuHzwXX7IM30jIRmJ8FO4dQr++U+14MhuymiQNdtEnDh2tEUUp6Jw2ChLOha+5MiNaBdJGVDazdt1T7TgKLs/8Q/NSLw3CH0nIHErHa2T1EAyC3NZqfj6QfkZR7uPMFbayesiq9rVgt4vX6SMpfZioHQZsaC3s63phzdFsGt5/J+V5Fx+eT8hbc0Du17jgw0Vz1pAL7zo9E6oW4twG12uhwmBqpl/dkGawY3suNGehTBqC4JNm/WzUPMBPJy8rLO6fitI+lwx+PovKhdwOSJr3VKy2qsgDPn5yFK2FASy90THSkZ3h8zCdhQDkTErUTule2CCrFUIeJDPhnjrY8VO+imcXW1tEe4OI/C5wO1AaMlX9q2F23lIiElB27EekjGgCFCmNKsK3v/DdZZPUf/rjXyr3/fu/6o9xfMaL+tt0Jhy9kRrJTEzteEycRLij00jcqYhASV/Jd24weqgaHNbi796js61wsSouTL5/kUTycrgiClo4UMW47CxcoLJtzXDBLrJainezGknQvHSuOjUNF7Ytb9J82Wi7XEdxUcujrvNN2QlTzvpp8tQSyKLyecrIbL2YrNI/J5J6/GiddDQp65olDRfzxpceQLeP4S/cjXvwSNmEvEQVv3s77N5Od0eDaMcorpWGKQvTrX6kudE3jPu//HYgH2k+B909iUyHpph+tF4abz1ngqwe0RuPiDpK4/6T+MTha5AcV6LcMZBecOT3PP1N85YtVXGz3X70oedDTycnpZEpbyJWmJVyMK+9/u2oCFG730em6LGzUWg8OF1+dqX4fuap4FqLQ2q2I49SBeeoNypktTyC5yCrQ9QW4nYQLHzscGmIGPp6jI6GZs3paC4e5RkqhfAQHDzfb+SYb+O8Qs3xrB96V7ghzteshRDTdCFq5gV1UZl+rrEEpy0fIx8EJeiNBacu6vrS0IWG1UVPMcpGlcE5c6RjDh/Vwjjbuisnf4inLH2AUCJWrk1B8icQT98JjVz+f0gRVxdEGh8LvWY+US0uXqMjaghxyxO1c8dcw3kRr9BMQm+C/DodMj5ysasRB7GvGd6PcJ7z40aSO8aKkDuTcX+Cm8tfB1F4r30izE44Rg6Gz0bc8qQjEd3x0JS815QgULXCupLpIGqnIw6XEUT8ng/Ocz2CXpaXO+ZZboXwDmUzTs0yJEnCNK0iMzTL7UC9hmzfFjJckZDpk+XT68ZDM+sy4lj0VChS2qsOrtd+XyvVfoPzaklfMwo3/p1uv2wpL4kqM1gLISl/b12mobQ7ivrXrrE6GodMJJcqnQkhboNGkDYEjSHryOCNzxIJwqWQJfSzCebY8UJs+tif/tLAvh/53yG7pChP1yh8TloXjYfTkJdwSJ4VVIh42u6UgZxC7BnoGVSc+8wHu6catosiRJSBnkvFufQhG0gzV2YzD5YZ5sGOoiQWBm8unOv33ACKRu+a5J8FB9KNwhctyb8/5CUl+bZl6U8cBeGm0ykj6kWZHSMjYf3NRig3n8n9m9yWa7MeysOKxrhJRPTQCYgc3Qu3lz3Xivep/uBJAPxYAzfSPMWOa7uDXnQOOjlGZ1eDaFuDqJ2Gm+52Wn6vqJQYLzaFNts9EXqi5eUo2WgCjRi21dHE0RvN7fh9J3I7LtROemon03At7oZzN5Qdn+mE62w3L3+Mon5gcBVZKTv+/O8M5zFqpblI4M6oxG+1aDyQ935RhUyCHXcOoSLUF4FLVdLcf3M9T1YPQZJgO3O/M9P8mqTlZ0shF0ziMnOmDDhVe2oWX+3UE7WDfelOxDz7+98FccicLGwMAorkgkuwSaIhY1YdxLO5qJQHcdJcUJlrx10vZPQWdhyhFKTSsQSVWsjMjSvXR/r3QSp9O06W9wiKc5urWmYYaZQHpIoYRJ6B62t5ICifjprVcvvrwzpOseMaJqUFkan/XZDU4+thYEs2EpHmgRuXhjUVvj/VrKQ4Cj5Rpf+sOkGTkGWkiYTvdDsE+RwhIJSOOMT3WwFEvVC6LV3FddP8fXZlxrlPorxvVBYmu+WfpXD9z6+xtVo/S6lRh3NGyyFJpaiTJwfQbITr60wL32rhsnF0rEk2Vic+NAWzrX4j+PyzNzeruCg1C5OTe+WgkjJRYHwsPF408Pa+XzKcyOB3wznauxvErYy4nRHPhvJNyX1JnJA2IqKu0h13pE1ycS2Utp2pHd/sSCVwtRrH3gD8GPBYQgFEEeFSwrS207JlRCR10JkUXC/cALg8IlA4kKLgRYg64cJVXAivu/Ym2juSPO0zfFOyGkQeXCb4OL+prIWbuu5EhGR1XG+c+vGU2qHZfvRjthtU6Go5mQjVXkNAiNwVKfFT05Rpn4RIoNRrob6+2wslXc1GiP4VF4ZIodUmuq9SD15ElwsnF/pO50izn8ka5zcRRWnd9nxqwdR0v2Fr0VPDe/zMLG50pOxD5Ca3h33rtb6DWx3zWXwGkySIOPl50ChCG3G4iR3JjUmjiHKE90u80jjUIbtwJ9HJNvLgkfC6HnEB2UiNaLaLHDoWhKtOD00i2jtjfJxQP56Fm+wdQZlPjrbCDcIcUebA594y8Pu+K1+DzHZCBDMK9fLqHH60FpyQhqM7HiFjMHvuDmZ3C+P3e2on+uVNvh4PGMxFyUsH6PTC/3nZSBnlXqKIJDsn8dtDtoA8cOjUCU8VTjcuu0jVTsfifvYElBkl4kHGY6Ke4roh28V10jDpKzd+p3O+V5s9T3tDPz096o/r1iSifU6z7DXgumFCV3tnTHu7kDYh6oYMxu62cD8WdcCljhoel4aeODTD+5PVHT7Jo32F4+XIx8dDbSqP7OfOVdT1TF9YZCvlGUH5W9Udl5BNnmf6hI1cmSlTpGJL7gQWzyce0pHwt6jj8FH+HmXQOBYicZKF7Jq07uiOSxBhNKwzbiuNYyEyGXUKRSYIVj52pfhV9LkpspZ8IpBImIKZf0Z8LHTHwjnpbBOyerjuFq8nawbHNOoItSlH/VgxnjdfYxocaPFx/nwExz4/v73R3BHPp6E1T3iili+bZxZZBEXqfSk25BFLdZQCUtoQ4pYSz6blOQrbBwFv4itTZfTU52Jh6WR3PZ3J0JNi7FttoqMz4Xrb6Wenaacbsiwkb7KbZf1eU2ka0tOL7NC87w7dE/1ecpU+NXr0eL9HR1Gi2wjZlZrkDcPH6uHGu9UL0fWZWcoeHxJ6egxM7ykyjQrhKRewNctC6VNht6ZmSKqZtsW1PonR7U18zdGZjGjtcMQt6I0H8VWy4jPjlhzB81Gw4wBxK/9eFKWZFTset5Soo0Q9JasJ1+wJWQu90RCQ0DyS7tJg73sjec+MOBeNd4+FjL1zxolOdnAn8vfRuVxg8cjYSBD6jh7r99rpdNHZ2VIgKkY5h4blQqg3CM9d9AnULDQ+lXo9vBeVkgadmYWZWaSWhEBNYVMLcbBWC2U6ZTlb/jWtxXkmTP492T4eshFPTEOnG0pwur0Qic6zjVwchRKRLIOJ8RD0iYR0oln2O3F5813JNPQpi6Ngx1XD83U9SAj8+MTRfvy5uX8UvvvJbHjDGw+38aP1YMenQ0Nt2b2TdNc4GgvJV+4PN2OzXcTB/8/en0fbdl3nfeBvrrX23qe77XsPDwABEOxEkeooiWrYWCQBkQSsSjzKjVJ2KrbjOKqR8kiqqMaiRPUNRVJs4lRijziuVJKR1k6sslMOqIYURUu2RatvaFEkhYboXn+b0+1mrVV/zLX2PvfhAXjoRBLAHAMD7957zj77nLP3mnN985vf102nNDuW8tAo829c6CZzpcK9fuz49m/7aX7pk+/pr5Wrba7v+rof1s1gAp7tqsWnmsOXm3n8FMszQrGA6orXjbugo6xwXblc6gSuFS6J0k/haE7MYuFPMcQ5wktvpD49Zvyb9z6jPA5a14bC9GsmG6CA+Ai7Ze/8JTFiVq3m8dTE/KLn8df/GFXW2klNWXXatGp40nld75wBb4lVQTfTxohJuazZcdQ7hvI4INFiWgtGR8OkU0Ah6xZGI4TS9kCK3gt6XYRqqCOki9R7qQkVFTBXVlGknQ3MYNPqmtVsWYJLjZSYcr7o7/MxxOv6hKB1hk0OpG3EttrMiILKbEQ4vqXAVwNoVM4jbhUp5j7l0vCEeVwZS6mWcFfl8dR4D4Xufepdva9zHdKkNd7WXDOPSwC3DgkUKvo8noGoUAjtRAEM2wrlccC1Qy1tuuT4KknnKKAsbmeIlcWPrJ6jVYaVXQfsOlAcq0OzHxfYWpln7soac3Cs+W5U9c0UsYI5auj2JkQrlBcXum4EBp0sI0jtCeMKf2qGdJNBTDzpaVFaxJskap3eQNMO6/d4hCnVeVXOXcI9pMeN3itzc1QpEzTr2OVmQXZ3zQLd2QEPBiHtHCJpdDmNOVv0+NmlUgRZN0we8HqdF0kbMwYdhRw72pnDjwyHL7WYAO0MupHWiDqi/tTz+IvxZRVfF2P8mqf75OcNiCQRirneALp5SRswT79Bi0XqWvvIzm+fA6C5dU+fH2JffJo2Jd42YqwyFPJir4uX0O0J04c0iYXSMr9tnITI9ph8YYl99DJZeFfbJzF1e03fWYt59jVmIT+j6FW86o7NM7D53zmxptnXLDInPQMqLWK5A51HGRI9VHDDOER2pEmUd0nz/fpSATObwnSCrOtU4MrGiJsdgK0Qhs1OD57lwlc7KaFQsV1fbXQsSYkjAiI0uyW2CXRbJcVYF1c/q7STMXKY/W1Wt+2kJJe6DREGlEwjCyD2Lh7A29/4U/jMHEmCfXZ3glm1mMMFslrRfsVLcFeWuIcuYWdjuv0p3dikuWsAoVjo2FCfnO1TLLp8gEy5txZZbtyGWcvkOqM7uwOAbU8Wvk+nY5ltX/GRmIQHe1HCKEm8GNxxq99ZolrraNT1n/NzFe/8+h/lhCtJ6ppnSntIG8hmyyhgEBXwWJ0F6aA60OM0O+BWaSOagJ1Q6HWWqc+5eOwfIzoG6ystGNd7rmfgAPz+h9/FN37XR4BhHQlO1xxf6WY5A996XOmLN0hrmrY3lQ1Dus08RKdJXwtNKFZRQawY6SaWrjLUu0apym54nha3ouyj1BUMTpA2Yv3ABsrOJqFUoKqdGkxHbyubN/jdWPAldFMF4sXruQY7gPmh1NftRqIWxkmzwJe6PsjGOLKymKR/fLQgXu9D02in0647Lf4TcJ5d21Rjyvbn50eGbmxSYSwsS2H2Bf28pfNUFz0V9Jv03Im2x2sOX7ubgLrI8a0F7Ra4JbTbBeXDqM076HW3rhUMWOuYr4pbJr2EZBO8qXsTQ+ip6b1g8mw6jB9D2uzbHmiOrAcgKVsxZ6HZGNVxTKQXOZVVvZELgo63ZZviZhhR7tk2uQOa1wNnCbMKs2x6k4Gs6QFQLGIC+QYgNZT6nT7lEL0XQ6mbFUAB6w7IedzlEUml2csGey7fn/kGMXXAdFFbbDYBuuk+Ex9Yny6xM8fkeAmFY/mKPbqRXifTh9YUjx4pGJRH08cjpK6Jvk1jb2bIe/24WjzJ3N3I5yKGKPGxzYLclc66Vq7SRoyzw5qWmjH9YVNejdZi6jbZblvN5Vn30JteoyTsbZOFd/2kVGH8jZEVsQJGsEerYVRLVONMvO9dnpTRmXK/DJ+7adM1LdDslpg2Jp2WiBuVdDuVdv99JJ7ZI5aO+oZpzxhEEpBu6EeCf+FTmr8zcwbgHd/8E4SRiiT3rMUQsVUBBuxD5+CWG5C6xZ47ID5OHq8OA26pBgTRSV8bXncuT3VVtNIzkU5okD2VMKI25FfFM2EeqTaPIFEwzQZDdlMnxohunGMEr0Dal0rEQl3ENje/0RnsUpmZsXT9d9bsj1ieLZg82uIWLdEKk4eX1Dsz3DLg1mrskNdK2XCv1LpH1wMJOi4VRpb6VEk0mutzHv7Uf/vdvPE7P6Sfa4jaZLbo88rN5pDmLV9o3sq1gI5VgTG5cZFYPmnd8oX0uc8uoTzy2LUycLpJ0gMs9dg2y7VaXWtdWiZME/SafJw8jtCzl/Lnl6/ZPD7cVQr4NErc7JvuoSABcdfO4zHVBzmPR0tfJ/kqvb9S65u40r1WcaQyAaFQ5qskdhToWto3M8fKLBsa+7A6ZZk9LFQXA+I9xXFNmfdandc8HKM2BiZj/JldTNPRnJnR7DrcwmOrAnuw0KZMzzrTvGfqljApabcrZaILuHmjy16MnCAHZC3CfP3uztKXrpp7rNV0SbqOeHis95yz6h55tBr2Y7leqLJzaNo3Jp2+OC6RYxXxjmutCTZNEPoGVFkMDnMhEJ2j3SqTIDv9iLASI7RWy59rtEA3XFsv6Nhs7j4Xx36SEJH/Gvg/AedjjF+dfrcP/C+oo9p9wHfGGK+kv/0A8B8AHvhPYow//yQv8a9E5LUxxk8/nbfwvAGRrhXBgfXowtCBaRSwaKdDkv/Yx3+gF10+/urTTB5Zs7pxQww6DiMeebFvtg2+gouvm1Lv6nFdYmpPzgcWL50ycQZ7uOLCm05TLCNb9yowoQdN3eakpt8r54PqDtU1UlWY7RlhsVQq/IZ2UdbT6U8wO/sUDoqC7oZt/ZMztFPV7ykO615zpDs1QxqPPUwd1SQkvblpoG6RrKWxWuNvOY2vrCZokUGnKUagSK5joh90jCdckXqHh8L2XY7cKZYkdAfQjc2gcRKhOT3p56WV+SK0Z2ba7Uh04Dbo393S98kzFEbtUgE5u6HfAFQPHtKdmtFNXaJoOi1UZiNkVFLcf0EX59kYuXhAMV9R/NEKtmbE6YjZ/dqF7UWNZRibuZ6453M/y91f8f26ufNBQcOiQMZjFSx9imKc9t/cj3/NS/no7/7kid+/7R3amf/lXxg6lk/WvQR6YKw4bJVp1PgkOGx7qrdZ1ESnukJ5g5TtY78Y8c5v/FFCVRB2R9iVbqaUeTTBjwyLmyxRdLObx6uabR2Tmb+qQyqPuVTS7IFbJubLVsTNhW6ysVlL3ThlzW38O2qxaLpIWwndSJk3oVCwoboC3/hdH+l1jfLYVz+qlm6VTZZRDvEMiUb0v8BQjOYiNp9bsVBtn8VNyvbrJvr8TFEOLq1VUYu6YC3FSqnhpomDBpMkceoIJoZ+sxhHAxspOGVo9ueQiuUoCYQz+nPuaPkyAQxOP5vWCe00jZiVG4BcequbCTaDUC5pT89vdlSHhtl9Dbb2+K2yd1GrdwplXSXGUXWondluNDQURpeh2SspL9U6vipJHDRurKvJhWbrviXNTonxynqpjmD3N88TR4UWntWs1znLhaPUna6BBpBK2RigYv7WpnEjMxSjwKB3kPOBH0bfcodTRNfkWlmvEtS1LopAVdCeKjG1x87XQyFd17DoeoOG3vEtNSMk55Cs49d1w6hctoM3hm5vip86fGlYnXG0E73Wmz1odxJQYWF03hAdtNtQXXqqd/NjIzgGYdROryHjI+1MKI/0s8uC9W/+Cx8cPkvAjw0eBXijU8aAhEBA33c71Xxz7ttvVv2HNuk/CCxvHFGNHeXFEctbZ8xf4nCryOlPdDrKnps3RQRG/UicFJLYSauBDdaqiHWvVwEn8nkWZRZJJVlR4PenvaBus6s6IpOHVwokN512nQurwq9pNEWqQsfHU8NKYkzW9gF59CKMKnXsaiPdTB1CQwapnDKKxKsuoYJShl6AODPnss6jVY0sMRngjT3YLEHXNlvreTVnJmltUStvvzUmFqbXEzSN1gbFcTswEUrLnW9TRo0UG6CbFYpzx3RntnTjWen5mFb1Z+LZU8i6QdaqlyQXrpzM41XJ7P4KP3K9ML90ylL7xV+7vpGue/74/Vo3JlkCxiqyK2U5CNw+hcj35rVy9NXMo+vJ45m5luuk4rghO1bmOi/n8TAuBqbb0wXBnqV4+5t+ilAYrDOYtdYUflYpgDAySbMwaeGU2hhAhMtfYZVZvFsye8Qx/cKC5c0TXB1Zn3JURwoWmS7Qja2yfJK7mQRtnElAWU4xElFdxHaS1o1ar+Vv/usfRqrEFOoS8FIITbpt+w23YWAGp+U9j7kRwTPo+sHQiAluyPlupetTKEyfS/PfTWLvmy7l4NT0sw3JlOCxedx61cCJIoSKoRbPOTzVJDqSDH6kgH4Gj6LVesaPhtrj6jyu9Q0ncrdsNnmjPj//flUZxBdUB4Jbdv14na8s7dTQbBs1aVhqHjedjlyZVt/36HJyZK0VWJRVPYhO5wkN0v11eIxNwElhheIA5E8e6vVbpSqJ0wlhVqnO1Mj1I2w2XYsmyRb0jrIhnNyXZb1D51Qio/NDI6nzmldF1D2x88SDI+TgCE7tEasE+lhL3Jro2n7hsGcsI0I8OESOrbqxjUc9eNTnD2t1PymCXD5QMwVT6D3udN/Sbhc9SBgNrE4Z/AjqPWj3PZgIRSCe04t5eROMLjzze/vFeNrx3wD/OfDfbfzu3cDHYozvE5F3p5+/X0ReC/xfgK8CbgZ+SUS+IsbMVLlmvBn4ayJyL6qJJECMMX7t9Zzc8wZEes1Lz/Ibf/9d1/zbN/xHHznx8/SRloPXn+01NbJeDmgSuzpM0l2gTAtv6qw3M11M2y1Yn4FQaWIbX4y4g2Uat4KDV0NwE049fDnNtELWKpAyZZ0kSp3dz2ISYTPjMVSldkNh6HpuMlY22UPjkman1MsgnWtbagIeP7wECTqXm7USYFgEYdikFA5MceJv3ZYugOrsZFVDII05xCzC6QMQkuV5ogEnUbfNLn8PvMQBAbd1HIqYGHugiaCPb8bF0Pn00G7ZntXRTi2mF8rTxC5plOGOO9+n1ulGQShQLYAstAvazZVVk+xJO3WDSxbDlB4Oj+HSFezOFtywB0CzX11XQXd13PPH7+fuV/3twUHOmsHV7ynGRy//V9f8/SZ4dL2xqYnw7W95r9q5tj5tpr3OpIvgd3Rk0LRexyoarYR+/jd//Gm8g6cfd33tD/XMPt3gmA1HLZ2Nr3cN7VSviWiyLoH+141BSo+YiN/qEC/4YJFWcPPsbILqu6SmkE2yc6bLIIkMtPQMLHkIXQKwE2hl8r+LVGTliAMAkwGkUCSwI5FBMutnkxnVPycVbvm5XaXMyfUeqdDV5/pxKmojhAadfu4LOsE0BmsistzQI4qqhdQzo5IgpRauuVuZXtsM3dYsrBwFZBPkiul8XfrsN5edBBKEQv8uXsH5aKFYQjvV7yAzXSZz3fC3OyPsqlPNpKSRUB57xBsmbcQuPe44O2ZOEqBmaWdw5VWOXYHqcgMM+hfS6b9VM0KfWh422IvHFAdTFrdOaV6yizuudSNmzDC6m6ylZdM+2tmBCZT1aUTU0QqGtVySbXtmcXb+hFV4TKKbsskInS/7JB5GJabx6bWDFtPrWovVLOwZ0/halwGpjS+hvyZT8W1EWVbWIKWD0tLOHPW2MtJ8BfU++FEklJHoImIjfmz6DdMf/Oy7kA9+9/Xe0rz21rP8xn92fXl86/6Gel9vpjf9pQ/xa//oe/jV/+17+5+vDtMOIK6JkW6sn1y9o5vKdiqszmr3fXIOiMpOjIVuaA6+MhC3Ok7/qgUqZZOWDjmcI6whpmZPjJwYYU/aRHFdK2kxf58bWkv9vxPo5Hen+HFBN9NmR3DKgF6dHVEsPOVlr0BPMqHolQw22TCZ8VYUei5eNyPdxOpIch1UEyOPWjr90nxlsTGDqmmMLaRGglEWUtZBkwA+rSVZh6VnVPaOThHjfXKhMoQtmxwQ9WFZIBeg3SpUZyWfkxlYCblxAxC2RmQnwH6EMD2e1mv9UTc60r6d6BRdyuNdh93ZQs7sEkVo9nVxf6q5/J7PvI+7b38XTMa6Zm6y0p5iPF4efzrnBWi9gzaT1HgggWVJzJkYCeOKMHZIG9SJeKXg9RdjlK0fpSt1bRts43XMMk5d0mcLeKOOn9GmEercqEDzXTMT/CtnaaRMr512qqNRPmRBZgVUTBNSjTqM7AN9g0cimHqjmZPyoS+kZ2z4kh4c6sGglGtz/pBOawDTpsclxk+fuxNbJwM2poP1bjL7aBQsEq8Adyjom6tAytUZFIoqE1GDkXjNPJ6FsrMDrb6HxJgqNvJ1l9hO6bXyuYpPx7pGHs8sacxGzbQeztWXOoLfjSPi1YTBtlFNG44DoHIX7dRQHnkdR/Zg6oCbt0iMFPOCbmyxjWG9b1jcaBldHFGcO9Z17upJjg2TIZYr4nKFmVfE07sq4r+ue4MhWdXJhVXflGk8sm50mKRMDedCxyfFR3IK79ddUBCoLBBnByOdoiBOxvp95HE1Z3XsPZsBzRe9s5uyGodCsTfeiFGXzWxq1D9Av+DMchZrFbxKLsF2pSxiSSOBUkgvERCtssdDpdcRRUgM18GV7Q9+9rufUh5/XsUXmYkUY/ykiNx+1a//HPDW9O//FvgE8P3p9/9zjLEG7hWRzwHfDPzLJ3iJu57o9UVkL7OcrhXPGxDpieK3/t7JojS7Ppgu8uY//0GKue833b/4az/E6/+DD2M8uGXWT4pp9MXQbimKLy2JVg/dJOLHic4dleL5he84Q7GE8fnI+ALMbxbCHbcwfbSjmHcUD10B2+nCtb2lVoxNco/IiDVA8DrG4IOOOWQ9BeeGETIR3aSs1oixTD7bqG5F2gx2uxOavZLlLVPsOjD+7AViGoeQo6TFs9khTXO4qujver2m4qhVF4rGMjq36DWEwrjQDZhLNP7cBdikC8MwUpQ2lLl7GlIBatcqHNzbaE+sjiMk225bh97R4Zd/4fv7rrMvBdMpOyp3ZEyrwJMXnZv+9re8F4nJnSEt9r/0yfdw9y3/iY5vAHF7gixXKmy7uqzvpapga0o8tUMWUc0OUtW55dO+Ju/57Ae466veo8Ba4bSDua6f9Hlyak9npYF7Hv27T/v1n1LE2IuIijG4i8d0p7Uwv7rgzPfW0yl8rzfufu0P6jXVjysZojG4wzo5qESwBaEUfCU97bvZUcA3jMIgFLi2xFFAXERWluJYFLxIhV07Ux2ArFeUN6FyFPtj+JGOo9kmJlac/pdBqHai9UsW7PYjer2gPG6bmU7EofjKwJRphsdtFnAxgS7DSAn9mJpPDCTTAUELQpcKQj+B2Opjc+FoG0nFo03C2akzmzrXPcCVuo6+kB6Y0hfX/5nEFMmfja31Mzct+ATIdSVI0s6xa5KIdTqfDap8T60f62OCQzUilrp2+9JQxA4/db2wd3XQMbr/ysACskmcXITqUkOzW2hhXmsxO7/ZMbn/aGDRteh6Z3NbVdceRLjyLTfiVoHqUjMIIuaxMdAv2XstzseqwSCZyZOaA72DmgiyWBLXaqAAINszPcamo+QyGQXEOLhieQV2sTaNpXnC9pgwLjHrVpkpi1Wve5QB6k33zd4qvhzAL30PKbdkTR6nj29PTfClvhe3iqz3FLD3owQcrE0PHNp1GkcbRV7x4Q9f51395HF1Hr/j7e87sWHYXHt+7R99DwDf/Nc/rNdXYmQYH/GVYXmD7cdIo4FmW+910GuxG4FdCxe+rsStSopl5NTvQrNdcuHbzjK6EqgutxTn5+rs5r06h954RoG3oiAeHamWUtMSm6bXudkUX77633GxRIzBnr+C2d2iuJhGDX0klo7VrVu0M4sfTRg/MO+NA6TZYCrnjVR2CaxKEEuc6LjG9IE56xsmNDuO0cVGR9FqHQv1I6cMjbwxDIFY6a41JKC03SoIhebj3PzxidmgmizJ0TAouBUKQZxVEd0nYO12YxWu78ZqFhAKOTHimy3Bowg4g13odf1Ln3wPd7/mB/r7KEw0n8tiqSy8JukPpjwek45INsSozi2pz06e6NJ73Ljnvo9w92t/sM/jODeYOTxByKgagLL66TWQnlYk91CJETtfE8az/k9ZAiDH9WgvPdO46+t+WGtHQILHiGAXtW68Y1Q3QdAxTB+1SVIZVqcUPBqAFCiPFQiODZilboiDSw0fgfVuMqFYB7U0b7xqZiVZBD9SDal+rDsxe3PTJps65PVCRZ9JEhhDk4QEwGyCS8YCDb0pheSG0nioCzLIndckn8CkYqH1cbEIuLWovluICqBNBuAmioI+polQGULFyTzOAI6FYtAn0vdGn8OjaJ7t87jo+8kMI9PSN3za7ZN5nADlQoGtbix9Ho/Qj7aBrq221ud1Y6GYR9rtEglqM19d6ajuv9Tnyli5vlldXF7Bvupbji4Fxhci9X5JcQ6917NOUM6PISAdRJMmQLZmxN2Z7n0y667I+q1p4esC9mhFPyreeV1POj+YMezvEncmmAzYZ2e1dD3nqYm4O8Yd1eoKnaVFnBsYx3lP5wNMSmWSrmvkuB20CEH3I0mXMMagNUWeQoFhvQcVEl/X6e9BawpD0kPSke7yKNKNk95kHresDawM2IhbapMwVM9uHn8xHhPfICK/sfHz348x/v0nec7ZGOMjADHGR0TkhvT7lwD/auNxD6bfPW7EGO9/ktf6GPANj/fHFwSIdHXkxPjmP68gxNWsDbfWmzJ3xzKdtJ3pCIxbpEW+7/TrgmiaYS7a1spUal8itNuB8SOCW0fteG4XmGYbaaeYoxV+f0q7XeHmLc2e0tbHj65xl+YQDFkvIc4X2oUuiwFA8mHocqeRBhXODr2Yql3UVMkm1E8d/sw2fuwoH7j8WG2G3AWPUd1RRi69f6WXlwctGFGXMR/UNjg7WMSoHfkQ1KkIVOzTJ1E3o59NzzDKLxnUGrVPwklsmySo6ivTF5O/8s/+dv+83HXejDf/+Q/qJjsVHZPPXSaOlM6JCGZRE6ZawN392h+kn5f2XtlH00kvcouI/m086kcXsEp3Xt84fcxrP9X46B/+NHd9zXs00Sch3s2kcc04PB6E05/DeLqOa49XdF5t1/t0itOeebSpwwXKBpmUOl7XmZ4ZVV3Ra7De0fHTUCqAFIqIJCr6fd/1fdz+370fGoNdSQ9i9GNluZsY6fV9QHV1dDw2gTkjLcx80gyQ1HWMmYkT0+sX2oULoyRO3gygVQ9MmNgndQk6imNSUSeZgbQxPiZ+gxm1oaPUdwhFgZesPST5sVY1+mOgF+q0TXJqQ4gmbnTYJGmK0DOPfAKw7Frfp1smIC0XwZUWJ9KhZIE4nHNmDGZmUR5/kziwp/oRpkBfcGbQq53qKFOm56tznjLRwqxSgXwS00YsmEi7U3B0m1PAKiro327pdSRt7Iu5yMCaxBgtJgO4s5Wuy1eWrG/ZwdSGbn+KnddKXU+6R4COFSU2UT9ynI5H0vaQ2iodnbQLWNfKfpwvFLxO4FF/zWfAJ8aNAlR0rW89jCKhdJhLR31ntR+HM2lULYNHQM9S2XQCSwVyz6RJoqTSBZxXcKAbW5ptXcjzZijadE/ldPJcdu9SfPwX393/+01/8YOPWVPe9Jc+hCmH+yBaoUsbp3Yq/e9z5z//G4HiWO9Xt1YW3PqMsD4dwAVu/KSCG93E0r5il2kIcHBE3N/l+DV7TB8c40eOZvcs0Qpbv/6Afpa9U+RGZ9zIiXPuGxXpfE0dyEYXEiPVuRWxtNT7Jd3eCFN7zLJFaIfvMYpeH5lRVjf63W5sxNzKY3zEtAF3/og4qeiyiLwIJgZl52bgy9pB4Dyz9TZOPWtGnsjjJgPT6uvYbBcUCz3exz7+A0/43b7tne/vBdoBPvHz389dX/fDvONbfwIJYI9XmsdFuOtr3qPMozymdTUTQUQ/g/FI7/XE+IoiPXj0TMCSez793iGPZxe6q8/h6shaJdY8a45r14qnw0jOcT25/Ol+br1WZQi9NELWV8THXjybqALYdu0ASztRFpIfDazgaOD3PvIuXv83P9znjgxA2ibSjbTJ2E4E20RsnZAdEbqZS6Y5mVmUXauU+ZdBUjWCSa8niRXrEojeaX6Wjk3ySN+kyY2VXlMoNY96Jl6Rag43AEkxpryZcvLmWurWeh+azLrOyzm5BhjcWDODaJMpE5w21wBlECf2kU11iPEKyJmkiZMdN/v7WgbQyZf0r2FacFFZ2CAn8nhmZhGgTM2sYLWxV6yk/1wRNT/qppaqLHodIKlRIKnT93Z4e0Wxiqp3tfQ0Ow6/P8UeHg3vdXNaI7tSr9awNUMO5sTlEtnf0zV3a6J7ltTElyQZckKiIQnU56aLxEiM8aQuXow6si46ctvvh3zs92qq9RX1w4pBc/C61tpjXCnjqXTIstbXLAska+JmhnCT3lvaL8h0oq/ddbrOZQ3bUdWv+QT9fN3Kw8qDd7quo9dvuxMJE489UvZrsxcojp/7vcaXeggbtflzcGzgt2KMP/LsHvJEPNOzf/zOD18CIJKI7AL/APhq9M3+DeAzPI5o1LMZv/qPHwtCvOXPfoAqF0BGN3BZ1NUtdSPTj0yttSMxPgfFQjsY00c6bBO4+NUVi9s9b/j6P6YJjj/8+Vf1DgbuuKXdKnWW/6Yp7rhVlzdnKA8aQmFodkvmLz1DeeSZ/InV7mYuQnshz7SgpIWyu/0sfuKwyw6zbvvNtKwa7FIzrjtwtKendFPH5XfezKnfX+IuLYdNU14kc3c8uRCYLmLaVgtW71m+fE8fn5yR3HGrh4gRvFqpi9eFXNYddt1RjCztliOkEbUsxmfqxEBqQz9z3M70+aNHlXXz0d/5iaf8/X7yn34fd33NewYrZIDOYy4ewmI1bLBCVBbA0bGykspCN3GbonmbESOjR+asb5pd++9PIT76+z/N3a/5AeLOFuxvI5cOhz+GMLCTrNGuNnDPQ/+vZ/y6X44hddpUi6gejTOYZUO3N+3vVzGim6WomgfVFfoisd0JhG29J+7960Pxe99f/X5u/3sfRHxuxQ2bl1xk+XEgughBHcbcQnq9AJP01nD63OxMlsdlu0kqJpvE0JgGYpUEQ522LjODqAexbMyngjgIFZg6F36xp7PblfQbYNBNb6a8g/5fbKLkx0Qth15nxo+gHetrSiesTmuxYTao7BkYa6cJHEqaCHathWM3TgBbMi7IGklZZPxq4CgDXtENRfCmeGMuvO1aWVy2U8q71PkDSY9z0GxrmzMXsu3M0uw6Jg8tsYcqVOe3Rhy8eoIE2HrQKwMlFfQS4fybT7PzJw3l+aUWrKlbTxJgXd2+x8ErC7oxXHnVlFBMMS1sPVhQHntGjcdm+93k8NXbEnsZBDOblrg7U9eqywstHmeyMQac/l9VidVg+2tdNWKUTo+IjjItkwV70yJNgzt/WY9ZpQ+zKvXYE6MFc3bvHFVaTCeWit/fUk2JLijA1HbpnFNRfTRXl7a2Jexv0W7N0ngFTL8gHL88EqYeU2s58Uc/9i6+4qc/8pTu7Wcav/a/nszlb/mzH8ABbq25xlc6YteN9OLZzOOg98zosoKZpolUR5HRpYZ2y/HAXcJNr7jIbVsH/M4jNyNhqjlv5YkCy5ftEd0+5WHL7H7NV6YJlActsTAc/JmXUh56Jr//EPHoeHDeygDSJpgkgplOWH3ljUiAEhKTsFMwNWlr2XmtWjHTguUrJuz+IZgri0EPJDOaQedq2xamVV8L2LrR/LK3xfHXnFG2XdRNWXRCIAlFOzNYXidh/2LeKSsoQFeZZGyoz7dd2rwmHZZu4k4U4E+nOfEr/4c2jrK2Yq8FdbTSzVIa64htp5upZqODP50+FqhLISE8d3l81SiLEOgNULJ+4GTcP+8Fm8uXScdrOsJPStyVhQpll2lLEiMqhw89GwQFSLpK6KY6guMW8AcfeBcAv/EPvptv+asfVje2EJWREyNupE3IYmHwpYJJzcxit03KX0I3Ec1vAhITW8eh7A1JY/CjpOuXWDmhSOPOLib3VmUNETjBSgp5vM0MDSmgZ8TmhtMJowrRkty0JMZkRBK6lLWO3Bp8OofghPUpzb/FItXuPgNYqnsW0vhdVynY5Sv6OsLN02iaV5nObnxSq7BLRD1f6XsyOgGOjeDHKY87HZvP72FgHaWaQ3QpKo+10VQd+mHMsBSCU3DFJLZ1fdM21f0tLJYKlCwNx996G82WsPu5teqfpbE8ibA+M2Ky2EcuH6UR3tg3GmPXIUXB+hte1utoBauM5vG5dVq7VtDGDcQgsYy9jvP22kSdJ8YWil1lwVtRYDoziHPjCNLzSJqFjjgqe/3Cvs6Ans0knY7ixsIRZmNMmghhXGr9C8SLl3VfNR71bq+0Lc1rb4MYKS4sdLJhUmmzKk1OmGWNWTc6jldY1qfKvilQzGF9Q8SUnnjaw5UXupr2l3ycE5GbEgvpJuB8+v2DwK0bj7sFePgZvtYTglBfdBAJ+DvAR2OMf1FESmAC/CDXEI16pi+UbYCzCOfjhQQUYZbk+lHHQV/EZxcFXWhNrY5I5XxIctFaXXRd5N9cOkvbWWydOwAG0wl23eHmySUiQKx082YXDcYKxYUFk6alO7tDc9M2xTnTa9G4B86nYioMdM/dGX6iI2z16QppS8qjFmk8JlMeuwCFpd0pabaUVTC/dcRo4hg/oOBF7mpsjqHZVTc43iRGUVb0t01QW4ikXSBZt8CYNJ5jkDRr7w7XmHVBGFkFkdJjJXXSZa12vqEqqBYN9dnJUwaPrgYGP/r76uryzm/6cR3zODrWP5SFdm5yAZpGxKTfzA1jBqtXnwWGrttdX/tDJ35+pnHPv/kZHW2DfmwuMxfkwpVh43c1a+zLKPJn9XgU+be/8afI1O9NbabHxInkbAijEoyKqYdSN4pu2akm0sgSi41RLAum8uztLvjGe97Db96t18btf/9nMWvbi1f6WUyi2fpcP1YtFGwkNobYuR7M6Au+NGoWijTOk7p7PeCcQBQ9EVHWRoTgIuIiBulBpl5cOo0HRRORIP2MegaoMpsKNjqbCUzJLB4JaFGbHpNH5PSzBLsESV3QDDxlEWvbDP/ORaFpteAwCWAiJBYWIIV+xl0Cm4pjMKnZlgtvghaSwQF16uCmYtfWw7l3Iy1csxtXT8dPb7nZTq5DiXJv69SxBaojLbRXt2uXsZ1ZupEwvqQfRD8SmDYI7RSObis58+CRHjx3bp0hdlCdX2Bv2aXZpu8idxNY7wnrPUdXTSmPR1QXVwrE5JHKQM8UyhtbWTWYqBbk4o2ODScNpR4gTaC3sjeHTmBmlmadOf0hFZer9VDEhghVqd3Vta69H33gP+2Pc/fLv3dgIBVODRby+LEP+oW1XjuZ+XhBO6axdBRHKkBSb+vf/+T/+d2PuU3/+D3vGs77e7/nMX9/JvGURmZ9TPeBbk5yt92t9R5vJ8qosunaLufJgXKtjKt2YohFYFGXfHp9lvWVkebxsU2AaKA8aIY8nnKmaTpMrd9ndd9lzeOvvJHywUrHDADWNWG5PAkgjcewv4tNWi1hrIW+WSfB3+SkFQs7uCsFWN62xbh02PseHcYd8uefbhq5fNS/DtBvSFV/Jj84swcSg9gm/aIN7SzpAjaNcIZC3R3VFEDFcQmxZ5cUPtJuFXzinoFBfD1xrRota+698+t/VK/5zmvDpyyUaeST1kg/mhJhseifv/r624GNPP51PwwxPid5PMzGyCgtqNYqU3Fdw+Zmc754nCN9accnPvr9j2EWb8bb3/hT/Vr1C//yhx//QGmTb+p2MF8p7QDAtyG58lp8Zftc60f0uZrhsuQNf/lDuDb2OaTetb1Yftb9aSfSs26z4HM3EW2kpHxtkrSs8RDM0KCQboMkHof/Z0Zt1i+0Sb5GjSe0TM/M38xOgo33wJDrJejrZBBqU2sx1z7Z6Md41RVqJ8q6cuvkZJpG5LL2knRyUndRNH/bNb2LGtBrLEICmpIWZB5Vi2ZgNPdjbYAEGUCwxBrO7E6V+xhqlpDqit6cJCqrWMEYfX51HBTIr7UGDy85AyJ0W6Wyyeq40XwLg+ugjzRnZlR1p8BTdpEui/56lBBptmw/bdJsGYwf0VVCuV0RrTB6+FjXxtZr7us8BH9SUxCQowVWDIxKbfSANp7ySFmZm0BWL5wk34GImr8YNfSgC4mxZHVdS4zSfp0fF0hriFWBOVr1+ml33/S3kkmGI+5vU5yf07sZpjFQabqBhZTOMZYOP3bYOhAKy9YXAse3Gu77vz+WVLEZ8j3Pbh7/solUVz9nx3568U+Bvwa8L/3/n2z8/n8UkQ+jwtqvAj71zE7yieOLCiKJyDbwbcBfB4gxNkAjIn+Oa4tGPaPIi/Lb3vn+axYpb3/jT1E4dRELpem/X9tGzEpFWm3t8WMVGHVr3WS6VcStMiAS6SYG08Dsjx3Hh3ts3S/MHvZ6wVgBH/FjhztuIYCfFao3AYweqpEghO0R9jDiHrgAo4qwM6HbKjC159Kdt7M8K+x+3muHZd6xOlNSHnlCIcxvdLRTsG2BW0VmD7YUc80W9X7J8S0uORSFnjq6eNUebumxKxUcdIfrXguJLo2EoN3AKJZiEVidtoTa4NaRrnU6mmNNor1LX8hJcmWTEHEXj4dFOXfDk8BrbDuiEYwYwi1nn5Xi7o4736cJZKukXNRaYJalLvzO9Jus3go0F/gxDrpUG3H3q9+tWEAI3P3K7+Oez/3sMz5H0NE2GAAq0M/av1QBLJO7CUerZ+X1Hi+ea02jxzvu9TjcRWehKJMmDOAMobB0E9dbuAN0U91gmTRy4SsVy81C1TdM55TG8433vIfd90/h3y2ILuKLVGh40Q2hSUXhgaFOidg2huJIki4Pgw7APPaFVhacbrcGEe9QRcJYj29rUS2mIp3fLOC9jrb1Y3Ah0dRlEDeMSR9Xi8Ck9RTotZJsMxSco0u5QEvnNElFsknne5zOPerzVOxSn1Okcd0sch1K0nsfCmZfabHoaihWA+3dkMCgjWI/Zip7ZBDeTA2/6kALWT/W1y/mOjpgW+gSm0kCtDcIo8tw9uOPAjB/7WlG52tdTwB/asb6hhGLG5yyJi8d07xsRrOtTK3RQUgsFOmFSotFoDyKFEvD8qzhvu88zfgc3PCpQ4iRS6/bodnRjmWooLqsn30U4IhepP3odsP8lQEzK5j89phiAbNHPONHV9iD5ICZdIbCbEwYF4TKYtddL1bfA1cMQARGCBv3RbSDADGgjlbOKIOzrui2R0QnLG4s1aQA2Pntc9e8l8INu5jjNYRImFSYxVo7n8uN9Y98Tkqpz02MHL/7OALYf1rxeID0t7/lvRQxEpJmmp/kXZuCRPhIeaSMnWa3hAhurYLkiApLA4RKmQvbn3asLu4x+wLcdJjYOgmE9KXBl4bq0ppuS8fBglWdHfFeQe7CDXl8d0q3tQchUp8qOXi5ZfuBwM7vqP1NHJeEkaPZdnQTdS6yTcSuvI6grTpt4uyNqHct9bZhdCVgV6rvIi85gzlaqX6HtdA06tZjpN9I9eEcHM1xq500qrohaAzkMfXoRDc4jUeICpKKbvzKw0YFmRNDjiwobxIYCvzir77nGX/XuQEIys6KpU16Xdq0k3GJzCZaW2Qn2y7t+vd2HnO8u1/9bmWtdp67X6UA1z2f/cAzPs+cx9/59T/a/y46Q9jfIrqdBM5FzNHqOWUhXcud9dmMx8vj7/jWn9AGojxxPg9bI73fuoDgiaOSUFja7TJpFKEgiM2sGs0L7WxgEvpt6GaRr33XR3jJ/+8R5OvP4laBbmJVdmKtbHe1lReqA49tDPObbWLkCFaijmSR8puHIusppQZU72xqNTdnYWu7kl4fKIMzXcIH7SqxjMKQS23KzSE9VnxufqjuYm6U2GYAkibnPaMLNX7saGeWYIXVqcG4QIFvvV+LpYIUixt1NM/WEDv6Ubk8ir9pYpENNqpDkOUANEUtgbAJaMvM5b4pFYbfm0brCaAHrmyd8n4HJFdW2+jvyyPP9A/OKcA7naoEx3zZy3Ksv+Isi5sKuonDAc3+mHZLpyFmDzc9K018wKw9Kh+g7/3KV1bwlWeZPdQxeWiJOVzid6ccv2KKaSOrU5ZiFZOoNESj+6VQwPxmbf6v7t6lumjY/7THeJh95rLuVXKTxQgyGvUOmvZorXk8aSlGYzfAeV0XY6kjZVFEm+jZ2VhENV+z8/HaE0tLs1PQbNv++p8+XOOOau7546vA2/FIQfv5Cn/jXu9MnHXPQlHqmpNAq1hY/ET3NcW8o53pZ/77H/zi5vIX4/FDRP4nFA85LSIPAj+Kgkf/UET+A+AB4C8BxBj/UET+IfBpVPb9bz2JM9t1ncIT/fGLzUR6OXAB+P+IyNcBvwn8P3h80agTISLfBXwXwG233fakL/ZUNsafuOdv823/tgIEdq1dYonqvuZHFp8SjG0UZLKrDtMGQmWRYJieiyzOCtVlwS1VLM6uTxZx3UxvZpOFWVHmzCaQEM7sJK0BtbD3I4svhWIOl1+tu7bxeZ1hHV1ssevIaGxUKDclJZPo5b7SRSmPv+TNqenUqSUDWXbRIXVLdEnU0KQueBeUk5rYSOJhfLHTInzR4ieOrkwLaFTKbQY/sjhv2B4r66hR9xSgX0xz9z6a8BiRx6cbmVlUPnJEHJfqhpdGTwQGHYmkl4A12n1wrrdKHn/+Evd89gMqwj1SoVQVnjXc/fLv5Z4/+eCzcq4AH/29nzrx89vfePLnTSfB5yKeayHNO97+vhPdwxw2FQFPFGFaqUtci16DdhBT95UhOwR1Y6PCuE1m+wzdP99YXjI55MJ6hg8nN+UqkCnqMtUNrcooUF6yvUhnHuUhajGrOgsK9nRJa6DvKBYQTdT/XExAio4whSLqXHwZiJ0QxCBeMLUkVlPsTyyaVDiJjrVlBk8eqctAUj8SV5w8zwzmZFCm88pCcuvhb3YN5Zx+g9yLgCamUNYt6kapKE7jZdLpyJmvABko+rkjKon63s6Gbqfp9HndWI9bJBDO1vpZNVtGNSqcFt1nfqejupgAjn7klh54CYXBF0K9B1sPJsaO0yIx2AhFcpIToTxOa8Jxh3QRtzS4leXSV1lCCRe+cQfbRo5eLjQ3t9hLjvF5wa3iBogmvQ6EBCgvONrGsPz6FaGxLL9QMnlkxu7nVeMuOKE81pGk6LRw7F2wfCRkDbb0XiSJZEZrVO/JJqvmEBXIt0JXFrqZGFugIBaGdmJSFz1SXWlpbt17jP5Mc9s+xaUFWTxbmg6/P1W9uFmFmQ8C//fc++H+3+94w08C8LFPPD29tMeL5zKPP+a12qAbudKkXKhGDnbZIrWKplrnCNMSHy1uZdh6UB3n9PuP+NFjc3l9aqSs3BQ//9s//rh5XF0RdUNYHsPiJsN67yzFIlLOdQyuPOww3unozkiwK72W9WDDplVCuhYLA03QvD0uFcipax13yE49SSQ9HhzqZwFgjLoedSq6XRzU6thU6r2gm0WBmBh2IXfIFWBSF0DfM4+ApNX4JJXnMwlnelekzODLrpx5BLTP5RvA7OgLR3z0D3+au1/9bv17kw9iHueFnn5cXb+845tPMqqf6zz+XIFHm3E1G8muPRaUDWHMEzOKE1NdHLq5tkIotRGUxa/9yNBOjQLiVkEdwsCGzc2U8igSrxwAZ1XwvdExp24sBJvWwy7SRT2+SfrGCoJoPRz7JkzUvCZ6nSvbUHT0Kxli5GZRzrtxQzya0UbuTwxcn0wksqaipEaEdCrcne3qN/WSMvvJV9rUzgBvrKR3a8zs4ay1mkEstxRtBAm9vqMImJAY090GSyg1jjIwZNqBQaXj8vTspKxX2JuD9KxF+r1QNGAXg26TBDjaFdxCwaatB1omv/eQMv9FdEzXWb0fO8BZ6h1HO00vWrhU3+nP3diq67KPmBBpd6ukWxUojlq2HoTlact6z4JMMWfGdBPD5dcapIPJ+fQdZEAvaUhmnUcJYJeGej9y/httcprdozjqcMc1flriDlbQdCrZUXvCpFRH1KbTdVGEKJFeswh6kCiWNjE1db0KhdCVrv8su6nm9GbH0kwN1VHQ+k2En/+NHztxC8Wzp3RSoaqIW2PMstHRN6/rc4Se6bLpnHznHT8DPH3d0xdcxGGv/Fwc+0kfEuNffpw/3fk4j/9p4Kef7Lgisv8kr3v5iV6nP058MgHA5zBE5PWokvibYoy/LiJ/BzgC/uMY4+7G467EGPee6Fivf/3r42/8xm880UOeduSujnYjZGDooIlTfFT1/S7gJyWhNDR7Jd1IXQbmLykoVpF/+T9+zzWP+0QJ/53f+KM98u6T64hZtcTK0m5XPVgTKsNqX3fK00fbfnOsGkwNUrd917u5eYv1fkGzJb1uS7GIVIcd7rjFHq/6jX6clHTTguLKagB7gPrsTDWdoGdoiQ9Em4rzlY6mRWd0USttP+5mNqyDpelg3WjXcMOGmjDQRp/I/vbJ4p2v/zGAXk/ELLPd90aJm0YETjjaJHCp74R1aeedtEH6Y6SRj2cTSHo+xl1f855BXBCUeVYWKo4KdFtlb0v8ePGOb/4J7ap0arPazUodY6t08yw+qutIYm8oC0hoJ1r0rG72yLZ+dy+/+QL3nT/Fy/4Ldfe5/e9+ECy4I4NphFAm4ev0VecOWyg02bu5/ro8VhdHm8aqemp8mTQUKmjOdJhxhys9xgaaVUEMQjVtmI1rjuZj2pUjNlZFOldGi7VRAkNs7Cs540Fa1WWSbgBysiMMpE7gMhV9SaiznSnbJxe3vlLGkUssIslgUeqs+ooT4FMuEn2RjhO0KMzHhwF02tQ+yvR6dXJJFPZOetvfDH6VR2nEKI0E1ttCN4Ot+yP7v325B5gfw6ZIa9K9/84Zxudg68GO5RktuqfnO9Z7CrpLiFSHEbcOuEWHaZL4vwE/LnTN3nYsbzCpqFe9O4x+luNLkdEV348QuXVAukg7Nb0jpARY7xuaGdT79GOM1QGMLioLxjYq9locNpi1x6xqLTor1fjS61nXcdOoBXsGkXLxmUGkaKQXI41O+mK/3lWmaHVFL96PffwHuOPt78M0QUXAfdTRuo37sTutujBm2T5rAH4OEfnNGOPrr+exz2UehyHn5mu2uqAOm9IowCd1Q6+5VhU0p8as9x3Vlczk1fV+M5dfD3vzmnm86ZRtOin6PJMdTIt51z8+prF6CRF3eaGbSadd5eNXbBMdTB5psOsOM18jC9XKiodHJzYyMhmr2Hp2dHOquxVP7xJmI0KRxiiSVmF0uqaauiMUdmgIbTS9wrgAI5hVo/k85XbqtHBuADmP6aI/hdj8jN/xhp/U0fc8cp5B5XR+OloYh9fnqjzuUy7PwvWgrOME0j1b7OLna9z1dT9M1nqRttNRnKQD56clpu74hX/1I094jHw/hHGBryyhtEPeHpseRCIqQJA3cu3UnHBcPb7VsPs5vZ4/+U+/j7d8xwfUlbcQikWg3rU0W6afFpCgI0ztVPpGSza26O3kW21otePMCNV1vN4bLO19pSPmKlMB3ZbeE+NHDaaGYpkYOougAFFivfRmGDEBFrXmPF8pUNWN6cfjFRyB6aOaN0yjeaDeUe2gYqkObeWxx64D3XRgXErQiYhmapRlPNLXzE6nOcf3LOakEZefb+vcOIoKCCUJj3YqvTaUJHZyzsh5JH1yQc9HX8OwuMnSTuGW//rfkE0CTkTWX52MWX3NLRzfWjB7qGVxkyM4Yfv+lmZbWZflIuBWkfKgpd1yVBdrTKvTE9EaBWEMLG7WKYx61+h5z/T9Tx+OjA58sra/Rh5P4F4oheUNQr1LDypu3xsoFoHRhTpdj/pezLE2XSRrCUI/khmd7jvCWAkDocqAUQKRStPncQnKfs0g1+Ks60Gk0aWWj3/s3dxx5/soLy2Rg2Ndx2YT1TBMUx3Nq27CrocZxSccJ32K8VTy+PMlROTHb/nOv/kjW6967XNy/OPPfpoH/+E/+MlnUVj7ukNE7qWv2B8TMcb48us5zhebifQg8GCM8dfTz/8rqn/0eKJRX5S4GuR5y7+lRUZ5OVP/GcTLmg4JhmJuMJ0lFobf+H8/VjPiWse9VmwiyG9/409h52ulJnpL2Q6dwTAuWJ2a0mwJ5dxi14Fi3mHqrtfQkPUKcZbi4ip1NZ12MUU3it3E4ubZ9Uf/J62nOPD4aZVYViqA3QNCPmK6YUOm4NBQ/EYjuinKbjROiNEMYxxW1AY7CHi0gM/OQwBGuOv0d/HRi0/mePj40c1KyocO9JymiXGUCuEePMoipBsWnZmS3W+2sl5IZk3FRFd1jm9/y3uBFy66/0QbqTvufB+9TJ9PxXtZ6ncfAmGkM+t33vEzT+ja8wuf0nX2rtf9CO121Vs+A6x3Ta8VkOnoKpCpopLRaIeJZUW4sebeR08TWsPn/qY+3x1bstMY0I+W5eW112EoI9KBSxuOEwBK+ncGXfwI/CQglcfYyKhqKZyndJ7OG4yJxChYG2gFMFFH3FohZh0TL0hIJxEVUBJRlpL10ldwwQKJbr852uZWMWkvSK95lPUe/IhBfDM5zfXnD3QzBcMyWNSN6YVBc/duUzDbV0PHxjT0Ogwk6n3WcOoZku1A+Venl2SvbKX/LFdnhObsjPLRzJcfIor02e/073rG5+sEelUKGl2oWZ2aaAHeDoWrdAE/thSrhpBGVt2iw649pi2UDSVa0GWLZLsOafOun3lx3CRXOJfc87KQu8OPDcszuimqd4YOrS/VoZMQMY3XXJG7hm4YJ9p0HXs24s47fgbTZeq/6d2/8EHB/aroS4lnG0D6Uourc+47v/FHgQQu5EjAWkwsnbI0xELHBq9uBMH1MaOumceTY6rUA/gRJiXNTsnRS0vKo0h51Om1tWrT9eeQpu7B1PGFRnUW68TutZa4PUEevTgIuGQ2TgLIZGcLjFWmrQ8KYFq9/kwT+sZUFsiOifXnJwXShZ45LakRlLvvA5s45cXOQ4sCNIXj7le/+xkzcN561/spNzehPpIXQck22Tk3Zx2SbJGd83jn9fyM9MK2OidsWb3y9J+Ktf2XajxRHs9/SxNbyKrWz21cap4dFcoAmj65KO/P/+aPc9frfoQuAag5J6xPOSQoM7OYe/zY4CvTb7ptG3G11qDtlmP2YDixttsm0CXtSNMFmq0irb2AmNTciFob5LRqh6aH8YmlEmMvsO0LfX5m8Wb2UezVs7VeUAZz+nuf88D5mFjMg+19vqd6Bk8bkwFIyquiOVQSqGW6mIT8pRejBqiOlNkYCwXe1AAnpFvCJXauNmQgASROzz+/98yozecL+TNIwFAT6UbKoDbt8JxcC9g2jwIOOkdZniDaodkle7vES1eGdSJHbk57z+ihI8pLJWbd0E32IAaKw4bFTZPeObbZMkhwCjSOnYJI6R53yw7pPH6kjLby0BPKTKUS3MLrxEhprpnHEaE4bgmFoTx2NFuWZqqi5KTP3jQlxaLT8d40ddHrGWan6nydP4v59M47fobiqEbqVtdv0PWsbnS6YjyiuKxNkY/+7k8+a6/7YnBdjKEvt4gxvuzZOM4XFUSKMT4qIl8QkVfHGD+D0qY+nf77azxWNOpZjadrVTp6UGkIJxaPwuqYVurGFa0nnJk8q+f7i//ih5Qiv1ojrestmxHBhMDOH0uvtdEXTyJIEtnszmxhli1Yoby8xjSl0kPzop8FNvMin2nhUTdPEiLNbqHd+ISel0cdfuZUWzvT+VNSy0j7er8gFNLrUbhlhxQGu2yJxikAVejoGMYg5SBIiTHQddx9098aiuLsUnYd7J+f/40f423veD/tTTs9uOXOHw6dyqsd2MSoXlMIg8tRLkxNArrSKJsCSgaMoXjkkPamnaf71T7voz09o7g41+5x5yHoRiiMSvzY0k0U+Lye+Ojv/AR33KmbkeUNSn1W3QItFmyTuopNxKE27vV+0iICeLRi9oBw+A0N9/217+f2v/tBRkub5vUhz/pHo7pGGXzppgHTqPtJcayPKxZaOPmCxIJKXUSnAJKfetWhjCASqVvHcl4pyGUibWVp145YKwtJOqE8MMneVwvNel8/F1MrD940gh9H/ChQWNMXribR4NkoAIuFsm+KlaGZpxExozoKWjTpe6sOQt+Vi05p1vW20efXgXo7iVEmUGV12vZFdTMb3FuK48RYKvUzDFbFOCXC+Lx2Xk2nrBzTxp49po4pyvQcLTzdwtBM1YXn8GUVW0lnhhjV2ScDSAnMG12sWd1QYetAdalOa4uef3HssXVy7GkD4gN2lZgnbcC2unGWptPnJaHrUFpCYVS7qPaDaGUWtTaCO250vCitd6YLsIbq0ACR6TldZzKIJ0E17KT2yjwqs2/04zWEnn58+1veO1itG8FPi368V7pig8WqGoBPZjjxpRpPN4/f9TXvwazbx3bGQQ0ppCF6i7TFUz72E0WfxwFZ1skFTedczKKm9JGdWr8nu2oTwKPCsyZGujNb2KNVGkVrsGuDWbXJrU/1iJgklzLQDQaoi09ZqBDruMRvjcEI9X7Z50ZTqXssoGxga1SLzWkej1YoD9MYvld2hGnSJk6iXsH587RmaLykWunuV37fADglltI9n37vk35mm2LOza7m//G9VzRPZ3Zrzsf5/0+Wx0WSi9OQx8f3XmH1sr0XLJD0ZKLZQKoL6Zlcsm6JldNxtNH1m39s5vF63xGNikRL0GZCeUwvOG1aXbubbUt12FEc1IzvXYER5l95CrsOfMtf/TDd7ZWyVZYqzzC+5GknRt3UnOZDX+n5V1cixSL0I3SmycxfFGBYhF6HKWsA5gaKW6gRxqAfmHWX9JyLhbJWimOf2KOCHyn6kh2IbRtpJ6Z3C3UrdXnr2Ui5HBIgRIrLOg1g2hHRKkBqa9/r3ZB1dZYKFheXIpPCMn10gl173JUli5fvpNEqzcmLGyzNziCYnZtC48uB8ljfv75Ozl3KmsomQxm4ynk8Fga36FLe1P2QbSraqWH5Fadxqz3KT39Bwe5NYXljiG2LnL9M/U0vQ7oRxXGndvRGG3qjA4+ksT1t+iS2Zgh9o018ROqWyUPxBHuymxY6nndUq2FAYa+Zx1UDzup7XgcKAVubXrhbHScNflQyeXipI2xWFNRJQFh/ff/+k04UXXfc+db3KpCaamZJDmvmygImY/39pCRUj9VxfTFejGuFiHxljPGPROQbrvX3GONvXc9xvthMJID/GPgfkjPbnwD/PorlPkY06osdd7/63bqgZavZ7JbjdA71o7/7k/341LPp+rEZH/29n+Lu29+li5XV+VoMZDtq0wp+7JR+mtB23VB7/MhiJg676FSob1tFaGMefQvaFQ+jUp3MEvwaptVQKKJJsp2qS4IfGZ31LhWMkjb0XQgJ2okIhSbJ7JTjx05p8m0CqEaFdlELlwCGMIBYkPQc3NDlTAK173z9jz1mTvjquOPO94HVz6S8vMZcPDxpcQ1DwbkhaJtFRTFG1QV7UC0OjyGBXEaLkuKhK8/Kd/zlGE90rX/8Y+/mzrfpHDbODcW8qNirXXbYZccv/toTaChc45gA3/rvfohiqWNkWThyvS9a5HktGNenwE8D9opev7MH9Hu+76/pOYsfBDKVmRMJW9J3KLMDi60Fu9LOWv5bKASSK1lm5mSNBGnSWlEGQmeYz0cYG1Q0uzVIJzTeQJS+syidimIaA9WlBEiJoZtGHWHzadQsaGcwM5BsQ68Tgqi7StGBqxUYMq2+r2IJbhkYXaz7sahmyzHKekOgjBUrjC6qvplZt0zu80OXTQS3HPeaFe3E4Jb0Ytr6Wgq+haSTFA3sPhIITsfWyoMOt9QNfKgcbmn7jrRptYCztcUvBgtfrOhedGuk4yxNS277tVsFi5ss2w9E6lMVFWBXHePzTf+ZuHmDLBuk80RnVXy4LPG7UwWOGrQgbdKoxoZjkAFkqQV776AWcgNBndQGFkZaczsdW4tGi2tfGIpFh53XyWY3gfyNv+b4xzPdyEqrjKNQqSipWdWESsGj9ZlRD9pqV/nZBbD+NOO6Nr4bcdfrfkTHu/N6H6Ku/6WCDfd87v06spOiurA8wSR6NiJr3939su/WNTEkRk1y7jGtMtma3UpZw+n7ccdGHazsBHdlSTcd6TjZfL0BkChQL1WpG7Os+TOq9D9jCOMKP1ZDguBEU10zgMhA6qyrFldwOu4hmS0sEGRoJpk26AbM2wQkpXxNNzCDQHN8HmnpfJ/HgSfN5Zsxevg46RdmdvBGYydH1pB6vDye2Ug5j4uAEcb3HzzFb/P5FY+35vSudq/7kfSZijK6k4uUdIFiHmhn17+RzXn8jd/5IUyMMFWtHzpUv6iLdFN1NPZWEtijMgj3fOZ93P2aH+Cf/5y6Sn3T3/iwAuNG82l50GgjwJbYGvxoABpcrYxl06VxLWFwRAsMZhDQu5SGgn5E3GcB7aT/F8qk455YUW4d++asWQfsYYtdWaJRIf9sdBOswzbSi38vtxMLiNS8Mul8F10/rmnaQOwi7mA51Msiem/FCG1HrErkaI6IMLqSwJrgmf3WXO+b5GBYHu6w3td/1zvSvydfCt3IUBx3/V5CHWe17nfrgLSB8qDWcS5ndDRx5HCHNWFaYhYNZr4ilHvYlUlMp6DrbF0j21sqa5DBpFRbH7zMqcNqgPEFwa46JueH0VPbBNxxrRpAzqRmkJoYhMJiW/1MTIten6lmiQZ1wa553DyeH5eNQLJTp1t0g/HCSCcyzEJHw8Ok7PdGv/Cpn3jMNf5sANKm0YsiFlZBQmMwRyu6m/ewRyti6QiVI5TXD+K+GNcZA9n1uTn2Fy++G9Wi/NA1/haBO67nIF90ECnG+DvAteYsn1DM6dmIJ7ux3/GtP9EXdbr42V4raLBw1h/rsxPeetf7SVLUz3rRuRn33PcRLT7ziFiIiMQE/IDMRrTbJet9RzcSmu0kXruMqYNQUCz0jeT5areKGC+0Mwczh9mreqaCaWMvXlwedomBpZeOaWJf4EYjukjH1MF06lSTKaXdNI3Dpe6SH+tGxoyc6nUsNAnK0SKBYwnhN9qpjRvgndpoG975jT+q7iybwp4iRBF+4VM/gl13uPNH+vvCacG+roei3ebOZQIF/TBPLG2rhWnWddhM1JmllV9XBKxVpxcfeFFfYYi7XvcjOKcbl7g1xjSd6mRVFj9y+JGl2Xa8+S8os+xX/7cnthrdDJtstE2b3NBKqBP7xbSp4+ahumh6ce3f/TvvOnGM6rLq4EjMhZuCgiYJQps0Ghas6gGEIunlAC7XP5UCSbnwi5IFLi21F9hvOL03J0a41M2wo472wgiOHcWh2oU3+0pN3/uMUtu7sb5+eQTlYb7H8sidglzL21vcrKXpDHKhwq6UVl8cq8NZlcEiJxRimDy6xh2u1V42RvykZHpQ081KpULHqDazRpBJSRgVWrSs1ro5bFW7bPwQMJ0QxyU7n1ZR3oPX7qh2xG5ymWt0aXBraCdw5dUKapeHcMOja3Uuu3gFOx4RXnWjrjWN0sNtHSkPGkzScpNLh/pBT8aD+5JzdGf0iygPW274V4NejFk2hKrArjvswVI3OeskiD9f6H8vuZHoDN1OSTtzmG6EqQO2Vo2FvqsuFvEOO1LQPTjpAWc/stqBTWugqZXZVF1WjRhTd8kVxWAjyr6rCurTVf+cvAl6tiNrz5lVm0whXD8CWhy1vW4fqGNOjufaofG5iCfN5W/4SfBR156s1ScCRaX5Jn2f65ft87Z3vp9REk59LvN4H8noAUgGFh32sMNai79pRje1LM8Y/AiKuVNNrYWDMyVuGQiVYX1qH7f0jB5dqFtajLCukfFYnUhLp66AaXTS9CN0JOONmGy3tbmjG3EdZYtOGXbj822yqKYX/g1WCFPXM0Wi1bE/8TpeJ/XJkTrV7xpYEzmPRyu95l0fKY+DjjJXl3TW2Cxq/byaZmAhmZSTc0NoM497fzKP58/8hKFHqgFITSEfNJenjeuLeTyNfooQS0d0JWZU9pIEoTDqkjq2mCbwlu/4AL/yz/72dR/bNqpXVKyk1+jrJobJI01icQrFsac86IbvELjn3/xM/29fqvNmsNCNDHamY7puHQYR/RBxaWQ95LpV0A06pBFs6Vm0oJqHVUo/zZY6vfpSwaXgwDXKahp7cKug94QTxo+sVb+0sLruNB3jpC+Wx0BHF/Xcjl42YnVKWdPRau5styPRwt7n1LhGVjVUhYIXXYCjuYKfbat17XKlLP3lElmtFUxOwChFodf/wRFYQzw8BiOUlw9UaqAshs91OqG9cRvTeLpJgU36bBKh3lUJjMWNjmIRlQ24XBHnC+yowpzeZX3LDsRI2XjCzgQ3b7Q50wXNwXWt59s0qntaODh7hrA9opsUnP6Ddfo+DcVRqyNrXVDDnxhVuy1PfyxrFd+ejGj2q7THGOEWvs/jvrL9d+xL04+eXzOPJ8CJGCkWnuK401HhNq8zqN6r98TS0eyP8aW5LnfhZxImmQBIp+PHZr4mTkfaaLh1m+gEt/RIF/nYL6skRGb5AU+qN/pivPAixvhd6f9veybH+aKDSF+qkZ1oTjBirAU8zY1bAM+6dsVTieyac9fX/tBgLwmKyjcdbmEwnUujKUpSiFad4owXgrNUh57q0KcNq2BNcvdBx00kxH5T51Zp9txAN3J9Ui4WnYJHLgFOaUwj24SqzoyibcaHNPM9JG9fZbtSAakwdacdYoMmPmcHqi7QO7EQVR8mD9On975plQ3wi7/2Q7zzm35cOw+tH0YQc2c0ayHJcHwggUsRYtJPSCKkPXiYY1MfSR6bSO5+5fe9WIB6ZbZgBT/Va9KudKTHee0GLs8Wg/bPU4hoBh2B7PoVo7J1QppiyOLYo0sKKL3uP/6I0rbRMTeMgqx5/j9vjmAoFPOYmB9Bb4+bmv5ZQDqzkKKNhCoSWlFNo04InWFa1szrSu8JG2ijEEdeQdAIsYiEWYN47VYq0AquZniPZnhPZg1u1uJrB2tDceuC5vIIOqE41sLJHq3o9qfaQfVqIUsIdHtjBViS0Lyb6/9l1fQgqFnqWkLb6SYsid/H5KooTvUrmAIijC+0mDay3q9Yn4Lqsrq9EcCfVqeXbPVrlq2yAa0h7s7wE2VeSFCQRnXahO1/oxpwbM/g0hVYRF2HvQfnsIcrcEbPu2mgKBLY1SqTqEHfT1UQL1xCtmbE1RrZ2yE++AjRe8zuq+nOqG+zLYVmO7Eq88Y6OT+F5FiV2ZW9xXty0JIQqY6E2Igm1lKFsjHayc2dbu3sSnJci48L2jxTEEdzgiDREwpHu1MhXdZwSIBXYqlca8P3dMfEvpTiHW/4yWFtNvSCp9Ki3fcbpvzyF1E/4kQer9NohEjS6vEUx2265/W8owFfCKZUYd9mV3e6xTzgK8P6xinlQYM9XiMy0nHFLW1thSJpwixbzWHJYck0UZ21Vi3RWR2VQTdWm2FXXRprJwnZKsiTr2NQRjKF6bXlpNbNT3R2MNqAPo9KDNfO4wAqU9eHCowrsPWYPJ61jTKlM/++z88beTwDSJsNwU0wKf9+oxn0Yh4n2aijbPU0AqsMx6b/vkOVHNYCvPnPf5Bf/cfXD5BLHBi9xVzX3iaBFsEpiBStod4vkL2CN/3FD/ajYYsbHdHC6LJPI9MhCVkP7CPJLKNc6mWGStLtk6SHlEe+dZ1OLF+0AUtAdY5SLsvaQHlczjTKgF7eYKkOCsqmU6dDq/nDLlNTJEZIwISJkW48hph0B1+6wh8V0Amjc1ZzUBa190Hr49TMyY6LrGv992ql13i3wQCsm0FIPte13hMbr+oV1g6j4SLQNBQPHxBHBfUtE9zSUF1aI7VncdOO5o4ORd1Wa31ta2B7i25vogwyH1neOkkOcYJbBsYPr5DSIRcuqbGDGGVJjlJDxSeQ72itI+L7Y62xRjoiZxc1YVLiDpV1Ka3Hn97CLBtk3VCdC6y+eqcHuLtJdvBN7LWg37cyqtw187g6uKrOUx5xPLG+OW2+EVBRbEmMOQ9u4XnbO97/GN29ZyN3ZsZVHBn8uEAmqj+W5QViVOe6T/7T7zv5vMTu/nJsDH2phMSBlfhcHPuLHSJSAP8R8G3pV58A/ssYY/u4T9qIF0Ek4G3vfD+mTsh1KhpObGdT8XNCfPNLKMK0SgJ2yX2s6TAijM9b3NpR79p+pjsUA5tiecax9WCgOuyYPFQTSksxcpjG9xseu+zorXwTe6S8kFDxuk1JWFKns8RPlFXkDpWGEI3QzQoQcMfaVQiFzoO7pSa6buJ6l4J2q6BKBV0/PpfBH9Dfk8ZNugAEpB06ljgtcKIzyiRDO/JRBCksxEK7Lqv1yUIzHbsvRjeZUDFoAs6AlkNfP2QmWNgoZtNjoXdquPuVurC/0IrQu173I/hpSdybJMFNRztVO/LpQyskj26EhupINzq+EN70l5Rd+Wv/6LEitgBv+Mv691AIYapFoFsOo1TZFa0bR6KD0Xl1AyvmkfJY3bGWpy3dVHV6TAfjCx7TRXxpaGfC6EALGgUHtLj1lQosxmzx66HZi/hRxLTKAAqlbtClE8qjPCYn+HnJAxdeAgbC6YZqqyVeNrRbglvB+kavgNDKUe9IYg0qfV861OLY6nibtxvU+nsnbD0Es4c9hy+bsXWYivCl6j3UN21j2oCpg47KTRyxtNhlh1k3yHFyp8qjWLkjn+b7JVvP59GQqtTH+g0WnlGdIElMm9HlyPq0cPy1DTQGOgEXMVst5sER5kGhPjuhMsAffg7mC8LLd/CFMLrS4Y4b7OWFdljLklg55GhBvPE0fOFRPa9S5wdksVB6/KhSDZdLV4hNg4wquHKIGY/oXn6z6qBtzQiXLus5X9aOttnbpRm5JEiu93CzbZJVdOy74qoDRz/uaNMGIji9ZnOhoUYFZihK09jj+II6ZrbbbuiAp673+pRulL7h//aR9F3oKdom8qn/5ruf9v3Xr5+i62Vx1NBNigQmaGfZrVQTZDOe6njYl1Lc+db3YtddD4T0ebwfPf8SzuOTijjS0Xg7X4MHs24pY2RWGpanrbKR6qTDlsBLX8HYCNXljurymlBa2tNTbAaLALPu+vELBYUFO19r3ZDWgF6AezYmjCvEigrAJ42VWFhodPMWJiW+spgQkBqk8zpOkRzd2u1K83sOEWJqAGUwCIlE/zh53EAoqh50eutd76c0QBvBGaItEGeTsGx9soGzmcdzbOZxIJYFFGnjGs0wYp3XtBCHPJ4213e//HvBGu757AeepW/8yyPe+fofIxaWsKMzXFqvKct8dCkxPo9XmLVV16yZMtPy5vWJNq6ZfRyd5vHqMNBNDMXc9wx4u065vjLUe8rEmzykejTN6SnNrmPrgZbq8lrzBoCz+J1xf61101K1QkXrkOAE41OjqNZzXe/boYlh6J1Yq8Ogzm4+YhvL5ELs6+PD2y3lcRq3jpH1vmF9WhtWPhkzmHVLGBX9GiT5OrOWMNKR8fIo4JbC5ALML48xHZz+gwa7bhQ08UnrbNMApirJbsGx6waQqG31Ht8Ms7HGhwDGqFZj0xJjhMIh4vqRXlZrZLWmPNpmeUPB4saZOs9OoNnTemt8QahfdSPV588RHnoEWawomjPU+zchASYPLZUFe6TfSdxS4cTw6tuwlxfER86BqLGOXDrEzqZIW4EVTN1QPbAizMZM7jsH4xHd2R3MsiWMlY0VJqWuK1VBNytpdgttfCeAq51Z6u1cg6lbSB5V03HzBCpu5HEMvbFGVxmMi3Rj1+dxt45UBy3d1NIl8e5Q6HLhCwcC3/zXPzxcV8l041/8L9euZa83ojHqTOzVZMGPi8R+SgzodK9sxsc/9u7BWfzFeGbxJQD2PIfx94AC+Lvp538v/e5vXs+TX9AgUi6UhVS4J1p3LMyzao34XMZHf++negFV06moHGhhZtfaOayuWOpdXQDzwtlOhLKJSawtPb72vR5KNFm4lv54eAVz+q4/ECfjRLFU7QTTKJhF6lJu3nxxw0LY1sP4RKbRxywAOymUEt9vUiEGZbLosS2YqFNoVxeL6TnRCe2kojjSpCNNB55eu0REZ7R7PYVNu1/ghNCtbCTg/Hlc/di88d4QEe3/5l64t1korbLNCkM30o11/n2YFomplsQnMyPtcRbsN/yVD6kTVsb8hH5EzfhIdVn/UCyyFW/W2KG31AUtFvK9YDypOEx07bHgK7V+VSq8Oq7EJDjpVrppi3boaIaJJ7aGWATM0mJqwTbC+KICV82WEFZQHijYcLhtOXxoh1GA0QWh2YEbb7/Esik47saEQrtMWw96lmdsuv4hu8eZZLHrR7B1H0zPeYKF3c972qkW4HYdejaNjqQGJAln53FC6a4CPzcjjXT0Oj8wbM5s6n6WxdDVj8pmyAKboYiYwwJp4dRXX+R4VRGiUO941mtH+5Bl9LCHyQQZj6h3bdp4iLIhRolRFAKyaohbE72PX3EL5sHzxHUS1y4LvXdrgdWaWNeIc8RE54/eYxc1lAXh0cHkM7Yd5sYztLedRmLsRx1U+0K/2HpHevHwfjPeqTaWhNi71CD0Whq9OKvkjmbELQNuqXbu7cT0dtIS9VqMVtfjaJLTzUptn5+VLpWQxhltzx4JVhCBT9zz+OMmT9axfNs7NXd+qYhx51zuYiq42/Bll8ff+fU/ih85zaMr6Rslhg639FSHwtGuYXQFRpd0tGO9X9DMVKdEgnbI7aJJ7EbdrEaUuTe4k6Vc15Ceo5tKYkiaYCXG6AikJH0RjBmE5pddP1JiGhWoz5thMWlDLeDHFunsABzFSAwhjbLlpo88Th6n17cJhcEmnTKcgdr3zSvx0DsVgb6HzbXsWnnc2cfP46Cfhc/i4hvUFfv83kk8bqTPJTPT/Mj0zZVf+pUf5G3vfD/NbomtQ/+5uoU6XD1evPHf+ZCud9A7m5ouIm2kPO76JqSyP1UTSTrNMab2mMMlhEDZBYrLDum6NCrVaL2V9LpMrbnOhUbHIA0ENwUs0Q01RwYDosnuaVCstIFTHnZUF5aEUYFtHL4wuLWn3XKMrkSKpbL0TRM4elmpgIPXUXJp0nn5MTiDWdQq6ByHvYYfOUaXul5jcOc+/bzL80tMnUZTN8Xjk3FOL6eweQ9DaoCafpwtdt3AtEtgq+R/W4MYM+imea//NWn0O0KxDLgV1LuWxUs9sQq0tQEs2/cZ1V8rS2Q8IuxuqRZrEzTnFAEpij6PI0KcjVi9fJ9xjLBYDmLUTYPJ78N7KAvMhSvE5QoJgfUNZ3HLguJINYHER2g6wrhk8ZKqZwxHGwlR6EZq+CEB2hnJZRVdI1vB1vSNomgSU9XQO/gBveYbQHnsccvk0mp1eiPn8Wjoa9tupMCR2zjOsxWhsP15hcSyeqI8fj0O4Hd97Q/1Gn0vxgsyvinG+HUbP39cRH73ep/8vN3dXm8nVTeXURPVSn/3VAR+v1QiWoMvoJskjZALS0zd4SLMHjZMz6l9b7a1nbaBZjtrJllgjGm8Oh4kRpPUDWFnoiyDpkvjJzYpCFoVx+s8WIMJkegstm6I41ILvuQgYFeCcUY7QKneC51BJPbCgdrxbHGFdmdUu8MkGmpyRjJGBWltKrC974UdtWjQsTcVDDa4ZYdpPX7sEiWZPnnrv9Nz/cmOqZ5gKkazCGhOvq3Qi3Cm42X3G0I8KQCek6MIq5fvv6AcX+64833I/ph2S5kX3cQkgEH/Hp1QHDdcec0UKCjm+je3VkCn2TK84a8o42jTVrtYKk3dtAFrhWbLUO8om8etYyoYpLfZlQ7qU+oaJkEFLMt50OMIfUcxj1UGR+pYJYvrjh4kyAKbudPlRxDmArHA37xGrpRsf9Ykx7ZIOU+FojGwTADEOjK+qF2xZluPN7oEi/M36L/HA5MIYJIYUtEKzcxgm9iP3OWipbzSsD5T4QvYum+lzk2gmgFNZsVZBXUNw+8ywOmTOyLoBisXpj4xGTKY26aNVSrSsRbarJEWaW+YECqDW0WmDwlHr2tAIhc/czoJgkfMfs3aRer7HYu3nGJ0ZR+3Cmzfu1ZnNKsg0vrmLUaPLnTz2XXIfIVdt6r7sDWD/V2k7RTMFlFKvRGkqojLJYhBqkrf470PEtoOKZx+Duua2LXE2Rg7r7EilI+2HL1mn/ktRjemoMD7Sq+dchF7gAhUpNWudMw3a3ZIIHVCY+paqoCqqRUkDShY1E1gdFkBJuMhpIaiHyn4WR6pRt0zFbv+xX/xQ7z9jT9FFhLO2jbBPXPxTbv2dGP7mDz7XLCYnsrxurGlSGMiX255/Od/+8e54873EY2lvmnI40R1TNNry6n20WfPQdtRlSVhb0q7P8aPLO1upY5NXl2zxEfM8ULvGyCu12mDaHs2hFjbs2b7jeXhXDXPRo5YVsTCKEM5gXOhsmrU0XiwJAe5mFiPHWbV9eBlqESbQq3mcaKymjLr6Jp5HPq/21Xbj6pHZACXfcrNOY+HqzZsWbfw6jwePGLsSa3DmDbefYciqTun18rPXX3lmRdUHgfotiqtLyuTRr3pmXBv+Y4PUB01+LHj+NYKt1bR5Wa3pLq0pt2pHgM4v+kvfYhimVy3krOXHysT3bUeN2+Vide0mNmYMHL4ccH6VEF12Cl7pHDIqu7Fo082ALXhadbtoMOTwIlYWm2ohEgUZUz5SjVt3EobPhJhfClSzEOyiU+gRRuwK5/ArA676hg9EhUUTXprt/3v86HZYkAOF3oubdtfg1IUxHGFhIBtPdYKfjaiPAi4bdX1mTy4wDx6kbiukcl4yMGQhPhDAnwCMSbgJYNK+X4OQTWHYLg/ov4cQUHVLC2RmsIUyfU5OSSXl1d00xnHt1qtz+aW4hHL6lUNq7PacDv4tpdi17dhfMQtPVv3zpG6xW+P8ZOCUGl9bBcNsmpwF49xF7Rujqd3ARLQ5oexvKisYqlK5MYzsFoz+91HtQaJkXh6lzBy2HMHsLtFdUX1r6rLa0LleOgtY6JV0CiDgraG8YXYs5CiSYBPl3WzlHGc9wcmSS3YWhvf5WGjebwwiXUm2FoYHYS+4ROc6j9KFKoj/TyeDb2kzJ5Vh1irebx45nn8rq/7YRDpjSQ++rs/2a9vd9z5vv7c4zN/C1++EXnumEhfGn0JLyKviDF+HkBEXg74J3lOH88bEOkzn330KRevz5dC4Jd+5QeBoTucL8zoNpKE0RleXyYnqLX07lLZnrRYZKtTr3oiwWEuH+PP7OoYAOgiHyOxTrRhH5DEtBHvoGk1CaVZbCkLLSYDSBmwneondVNLMe96oTtJo3KkueVMD5aQGEkhIF6IISCYvmiIiT6vXXZdVKULfS3Y7laYJmhn1ndD4ZldWbpumAXf7GDm4hIGZlF+bC5AzdVFqAAm2QZrpxfQpPhCCwNm6bGFgZGOz2zOiIYkrOrWqdtcCr7I40AJxImRYhnVuaWLuERDDla7PyGxP7pRGvUaaeLOrJEMBpg2sZE8ZFFY7eLE3olFjwvFEkj6B6aLvdaSn2x0VSO9m5vNY2X3jygP1UK4Zzl1eeOiboaTcwHbRmwTVIg0dbKCFYqFvge3UrCqWOgIGqjORBTthGWqfT4HWwfcvGFkhG5sMasmsYUksQjSOYtqKURMf71GZ5Bgh+sXNjaTEWzV38eD8CzDNZ+1y0QLL+Mj3sDBq4T2VStecvqQ3WrF/H++heNbCxa3CHJxzPi8jlGNL3qlvC9qZRXUHRwdQ4yYW8/SnJpQXpgPjKm1Moo4Oka8Fshy9rSeU9OqE9XpfeSKJcwXxGxxDnpv2uE+lKqCRy5gRiMYj4jTEW4dqA6Exc0Dgw1UH648PsnU6saWZsvo9+TBtspc86V+Rm6hrM2YtJRMGpVWKn06pSbdE5FEXYJYaGFr62epusjHN8PN9yv/x/UL3j5e/NKv/OAJnYVnEzh6oeZx0BGEO+583/C9pYgu6Q+Z1IU+vaNsjKbBXFlQGINMC9rtQi3Il52On4FusgsH8yUiRg+bWHp03QDmFIXm8iuHmm/HI/yoUKCoDdCp25+pE6CV1jHTxZ51ovpbNjkZkXSTUIZB/k8EOkE2QNLH5HGDgk7pPgiF7RtJ+gs21qykkWbQ3LzJKr5WHo8p13eiG/IMJm3cIxiT/pbyfm4QvQBDWeMeP7GP2fPk65IQsW0asyyEMDGEctIzYe068JZ/62eRLlKgILS6upneTa3eNviyQHYcs6ZTDc1CJQR8ZXTMbWEQFzATHbuS3lzGDzVcqtXM4fJEXRdHBXFU4kepXg1gutDrJ/lKDTbcKvTMZACfJB5CYXqRZ3xUlhDQj35npl9mCaXfE4LmUGNU+Nrkcc7herPLBlpPEaC85DHHKjIlucmzyXbfNHMxgmBBjDKOgE29LzFG2X/QA0hXRwxBa3ZjtNmAV/bQdMIDd+/QboF5+Zzy12fsfzqyvEGY/mHJ9Jw2QcbnWqoHD5RVlGvdtsM+fB6ZTVl/xY0qjH3gh9HT8QiO5sNoX3aOFIG6IZzdwzhHPDiEC5f0c8gM6PGoXy8AzHzF+AvqzgbQbheUh7A+Bc2ONhJtoyzf6ij0Uw+gTZ12JgMjPbnqZc1M02QRbmViig/9xESe6jBNTC66JMalMsW7kTLSnxUAJjXbIxl4f3bcvz/6uz95won06nhBg0cvnPg+4JdF5E/QyuOlwL9/vU9+QWTF51OReT3xy7+gKLKfWE3ohdDMhNUNWoTu3BvoMIrQGyiPAoubLIcvN+x83lBd6SiMQKxwV9D5460KezBPTIWoHf/5QrskkIT+UmJsO6jUDSIWVovBpqMgaYsUBkm2ocP4i8NZZUsNHUaIxqgtdeoG5gIzGqui4Rl4ynvf1GXyY9ezSUJpMDV991NC2lCL6Fw5KKMqz5HnwiRHLlBAuzeFAyOatJzR4jpokY0DJN1WiZEVRy+I2+yaYWqln7dT1Tz61/+1ary85Ts+QH1qRL2tzI/1PmCUfpxZP9OH9Bi2jcO4ZqfFaigN632rwpcZKzCw3pPefaU8gtGV2I9J9axDKxTrgK1DPybRTa3O4id2SHlFGS7tlmN+s6OdwPhypFhFfJdYRYeRyTktnG0d1C7bCrZRYMHUoRdnjk5Y71nsQaCdGtw6Mrngk6ijMqpOfWYNickCqj2hIqPKZummBTadt3jtRLVblsVLZ1SXGrUBRq83WXd6TefisyMBoEY1RQodM+lHNNpuuO6zgHy+l2FgLoQ0DiMCy9T1HFtCVbA8W3DuWyKj87D7K2O65YhzJcxix+7naiYXHeNH1oSRpdkpmH36Yt9xzCBP9B7ZmuEnBc2Oo/j1Lyhb4obThHOPYPZ38ZcPIAbMZKIONYkJKGKIFy4R5nNlIiU7cSkcMh4RmxYpC7U+b9T6PC5XClyVBdP5iqm1nPae7oYdHnrbFLdSsdbohg18BuKjgSuvdEzPBXZ/7wphXNHsZ39OemcrpSDpBqA6CHTjPE4ZKY6T5lwsaMd5jaEX83zTX/wgv/a/Pj33tjve/j7itNBxvcSG+qVP/ODTOta1YjOv/mnl2BdCLv/4x97N296pAq1vvev9KlacRoi6ieALi7yioFhsMTmnzAy37CguL7HLkuUtE+p9HZGxq6T9daHWXCeiTL0cIul+NkQSEJubQBcu4Q6OYHebMB2ua1m3yqAA2tNT2q1CWc5AO1Wx/fKwIY/JCzq+LMZgkmh4LHIeN4/J45r/wVgVxs1/653jqiKtQxmYOvn5yQplo1j72Dy++b7zxtmaXl9FkqOhrn9pXVmJaihlUfAXYEgXlPF6WuulrPHy5r/wQZq9MuluCt2ejgD7gp4Jsv0F3Xi7pU+5PTOBwC46mr2SdmwYXe76HLp46Ra2neFLFdWuDjpmX6jT2miU3VI5xI+QphscCXPOg8H1sGnAWZobtljdUFAe+b5xaReNgk0+UN6XAKDxiDAbJY0vURDNR+2XhFavRyCWDnOwSIYTieXvtJHa32MhDkw/7+E41dAjbdAIKPBZabPVHq8G3aPxWK/ZVSpoMhjmG32uc33OjmkUlTQyKrlhIqL6gV5FtHtNsBABjySUIGZ3yMTWPXzTSzm+1XD69zvcMtBNx7hlQ/XIMbOzM6oHDgizivrMhOrei9A0hCsHGxqKBplNwTn82FA+uISHz8FNNxAfepTVm76S8acuan0RoxphJMfVuFwif3xE6DoV/Z5O+lE9f+M+pukwRytiOle6Dr5wGWlbpCiYPDJl8rkJtB1+f4vLXzOj3hHGF4dxdRXRlsQihsOXCZNzwvb9DXbV9TqtGdhWU4A0PiuCrQPlkZ6T6SJ2HSjmkW5sQfSzD4UKdkuIvPkvfPApuQ5vxh1vfx8yHjS11HXu2VuLPnqVscQLIcc+lXi+C2vHGD8mIq8CXo2CSH8UY6yv9/nPm93tq1914wv+4r9anyI7AXVjXTC7CfhRZH6zuqdEo0i9PW1VmLiE1WlDKIqeXRDdDLtoaPZGjK+MNGmt1aJTNlk4ZZncIda6qKdkJWHUd09MaJCRg7XGWzRrAAEAAElEQVTaXje7JcbH5KZlaHYK3MLraFsHxEioLMYIMaiGUahsP+ahjhe6mKpQYsQ0pu80SIg9tR8YbJ3DVfRnoLfZ6IU941BM2g37UxFiqR2RMC60E+Ryl7brH0MIOvsOhFFBcdzSbhXP8jf+pRtP5Fz49jf+FOHsKHWyUUm3a0S/wEYFjXyVxI6dXtPNlmoaDawcfXi5CHSVdorKeeitT0MhtDOLDcltsAm92KavjO4n1gosuXlDLCyLl6jTmFuqdpdbR9XE8XoMdVBT7QAJYOIAGuUCxDaB2AqjmI6/Dr3DUSikB41Mk8YuTBZx1q5YcXGlltnpcVlzbLiuswD3hi6CQe/VvNFKmzfpOmXKTSrVjkmfL1XRizdm4d3+v6LQTZkpoe2IdT0UxyVQ1xgjFIuAm1sF1ZpIV0kvoG+XLdGpO19wyogMWyPMQxcIx8epqNXOsswXFE1LqF5C+4bXUN17ifAFRRTD4RFiLbELhOUSYy3xsNGOa9sRmka705Do+0HZS84h06mK41+4REyi3b14rg/aSQXicoVbrTn1h7f1myNEaCcKHpWHnkIgGhXELhYBaTxGGiQkJ6xs+5tcgUzSTipWAQnSO78UR2poYNqCYqPP79ZBhT6fZrz1rvfrpietc7/0yfc87WM91bg6D4s8NRfTF3oeh2vk8nT9BJvGHifaRSc6HYl0jtmDlur8Qsc0KqHedRSl5rzwklO4hy/3bDxJgK3mt7ThjOmezpvvtlP3pvkSRqWuKUEZHRlkziBVr5szsbQTg1spEJXdAENpNb3GCCbiJ06BpRhP5PHeir2LvU4J6PNMq+C56hpKAn3QRJFB75hAcPJmdiOPm42x8wSox8qRjQH0hTR3K5NK7z9/egtplNn1QsvjwOPqin37W96Lv7lSUwkrj7teBZdHuT3t1KnwdqOfb7NnsCvP+HyNXXbMb58yvtBQ7xV0I9X3A6gePNKmCBDHJe2eNi9NiGlUu0sgRLqWfRjy29ZU9ei2LL6U3uJd2qCj3et6GAMLARYrTIiIs5ikpxlLB602htRZtlXAcZVFr40OgGTDif4a22DBhaimD5T6PEmgU8597UbzMl6FjKbn5mPFrF1kbT8JkEPyaPqmTlLWGcr3P54YoubdGDLuoU0cYHyuphuNcEtlFJo6UF5aIcdLisLi9yZphFWI4wqOjgjrk/tOWSyRsqC4dZ8rX73DqeM18ZHzRO+Z/Os/UeC6SPqF61R3GKN6h1mrNJOHmxbGI+zlYzCGMB3pGOzhseb0PGrvPXG1QtY1seuw8wWnV3tced0pfAGSwKOuEopVpAhaF5oqSSmsOszaY53pnc18mfTefNTaQrQ2Kxbqim2aSDFXMCw4bRDmsM0zy+Nve8f7BwJ/Wt/+tHT+Pv6xk3n7qebxF+PLI0TkLwEfjTH+noj8EPCjIvJTMcbfup7nP29ApBfjZHz8Y+/m2/7twQ2sm8Do6y/zTTd+gY995tXYh0bqGvRVV9ga1Zz7nbPYlXD4at0Yu6XDrgSkxLQTpg9FDl92lhv+5QFiDBQWqRVcCVUSwDtaaxHqHHG+SPPVBqlrMBZpW3WSOLWnU19OaaG2DURT4tOiHSodus9FYyjVUaedqSNBdhOSQM9kaifSjxCZTmtMkzuabVBNTUnU+jZoRzSPxBkVfpYQh46WyAA6RdW1kVQsdLOSUBhCZU4wR6pH5/3nvX7JNm6hic2PkyOTwFv+7AcwXbwuwbvnY7zjDT+JoFTfMtVEzQ4sb1JgpbqsdqnlIT013u9a3ErBmeD0u26nwuhyZPZwy+pMQXWloxsbQimMLg4aV6bV7pw6ZzncMiSwUbuj9amKbmTUMbBRUCoEZa2FwrA8o26GbpWAyk43GOpyqAWwW2hxa9fKPPKjjQ1R0hkDcIsMYibQqG7BGNrtiupCnRyZYs8gipUjOtUjk7bDrZqT4GfhsGkDFCuL1L4fL1OGgVctlBAUCHJOu55ti6wNcZLcj2zqHLYnO/XZljuL6OtJm0SxT3oLdVBttHXN9P6KszKjmwwjqsUqYBfK6irPLzHrBi4fUB0eaRE7neiGNmx0SCXSPXoe++h5xFp8LigB6boecAII8+T8ok9ORV4qnGPA7GwTb9gnlA7zwDktxI3R16xrYtv1BbtkfaWuI6zWTH7piNlNZ7n4Z25UodRDz/jhlXaLfaC6MOXKV830upxU6RrQznp2gvETSzSoNlihY4q2jVSXGsyiQULI++Rh/LJT/aVMuf8z/+cP8s9/7vq7mHkcLFgF1l+ML9/IgNob/vKHKBaR5RlhcVvg9CsucfHyFv6PR2CgedWKi0F4yc9tM7t/gR87Fi8ZMb/RIWd1JzZJa93WZyfIw+eJCTTtwaPEbIhpMyhpQxdXa+ToGNma6RhK3rAVheb+VdePn4XCgk/6MWk9w0CUcWoAGcLIaP4shHbmei2xvLbrmIg6YeW12zYRF7shjxvpx+6zXoiymgwyU0Fwaf2Qx0EBgzwOlB7rpyV+rOwpAAIUB6v+81/fNEtjLqqz2I3Vweutd72/v7eeqGHyfI9ooNlKLJERkEaBqiNwq4itI+PzLW7Zgo+MjmqwQnNq3LMju4ll9Mic+sYZ4wsNdtUyCmr8IJndXugonUQ1j3EHojkvjTr7vQndxCEx4kdWGcurTjU+k4mLOq35HhAwtZ4To0rBn9WKXsR6tdbaNTVRJDM/eoFrzYux7QYQKTcasw5Rp6ygyAawEzfAoHycDOzk0cqsX1Qq4y6u1pprEzAlSccsZpmHshhG2mIYgKgMIOWRMRjALbRho/nWDDk15eHy8+fYv7ylzmohEndnqlvknLo1LlbE+YJqviDEgBmPB2C6f2+BsFpjPvk77BqhC7HPzyHlcR2jE11r8nht1+rfjBC7FltVsL9Lfcsu7bZjdH6Nu3/DJGOx1Fyewbi1VUaV94TFEi4fsF93zL/qDMe3qFP17uc95ZVGNa/2S45utZgu0m4VuDTB0E30ezGd6mapmLxOOBivuoVm6Rk9stBR21LXkWcrj+ubSSNyqcH0sWeRSfxiXGfE9N9zdewvfvxwjPEficibgXcCH0Td2b7lep78Ioj0PI5P/lO1ls+Wk3vjFVtuzZtf+Xn+hX05xnmcDVyeT3BzwTYgNy942elLHNUjrizGTKuGCw/uYdeuZ3rI5QPY3YbLBwAYEeJyRcyU2ZyQ6lqFAVOSzDPb0qiIXnF52oM0bjzqk3B92z7dzOHmnQI+q66fNe5KdTPKmjZFYojYdoOZkXOn0Q5ZjIJWuaRxuUSFNyQ6u+qWSIhaaAYtUGLSSvCVxS0adXcTdYsJiWWwqfOTaaF33Pk+BaTWXoEnEcSZpB/AY51oXkCRuyjf/Nc+zOqMbiDcAjCRWEYIOt5mWgWaiEmAOAFI2TFNdYyg2XZUlztMF6majmiE8orSv7upMsUyC6ebGPxIdWyY2GHczEdAxboJCgLZ2iYdokgxF9oZyY1LwCYGVAjp5zTi5KMyojKYVBmidQnIoqfwm6bri99YCMVRrZ3N+Vrp63Wiq5eluo/lDt9mt90Ytbg3Fuk8MRS99TY+6n9t24tv0nliTPplIlCWJ52JsgDtxmtkTR9lHJqT4yDOamGdu79JUN42kdFF/fxXZyu6SljeOmP8yJIwqbSre3ikrmmnT+EvXRmo9ymi9wObKAFI/c85Nh0TN7u2G7+X8RgmY2S+0jUHepZF9P5EMR9DRLw/AVCZ6YTlV55RwHMemd57pILDaWNjjw2Ti2OObylY3LhDOY+UR8oY6ybKYPIlJ/SVpDKqHdLoiG/PtPBafIZCXVdCkZhsiR13vXG1ntALeYP7fIp/+T/p+NBXf99HiNstr9y7yCv3LvJ7f/AadaK0kdBYbVYEZUtUVzqKpeHyq5OwbWOxdeT4K7bZOV6qJknhiG2nIG7KVf3IS3rtmE0irNUx8PkiAdMOWze9lkl0luoLV3rRY6whTkfI0RKZVti1MoIM0G47ZXGWmsfdOo2FPCaP69odLEnkVfM4EcSHQS9NdB2OhWo+GRgArKQNFSqHWba9M1UsLNEZHZHb0Gf6+d/8cUCZNnmEzi6apM+joy2hMI8Zo3uhRdbi/Ka/8eE09sXAwu2SPuDCK3On9UMjzisryc7VqSzYiuVLt7F1yqcimFWHWdWJDW90hNEZ5HAJdUN30zZ23vbHtIdLzLpAVi1xXNDujfAjSygto3svgbOMzq/wG2NBJ7SygF6cGgamD4k9lw1Tcj4l1bMZNNlwFYtZiD0zYdnYK+ZrdfPnHuDReyozcohBR64zgBSD1qC9YYtR0Nd7fVzcALhybIy49SN3VwvQb0SvMdW2yIUrOt66NdNrv7CYS0fEcYkY0QZOatT0DOETGNLALt78f/6bFFqLiDG9SccmgIQYrX+MEGYVbtmqRuK67T/rsFop8JRBp5BYWjEMxwFWL99nedriCyjmMDq3TsxtKI5Ua2t+s4GbDeMLjmKpzeFurIYmeexNvDIlox8cMaXpehBJm9bx2cvjokTLj/3yD1z3c1+MF+MpRr5rvwP4ezHGfyIiP3a9T34RRHoBxKf+G9WheevHvpdfuP8recutn+O9r/85mmj54/VNhCj8wj99M/u/cwi/APffdTs//jf+e/7C9Jj/6vAm/tP1HTQXtnFzaM5MqO6rkdUaplPi0ZFaaueRmZTAZFQNFOHUqZGq0gV/tdbkd3CoJygCB4fIeISIoSwL4sgRRgV+5IjW0U0tRy/VJKAW2WA6IRzoiFEWcMzCwzr2BH7fYhuLW3qIUHgFKhhtJDRJIsut6iuEib6un1hME3CLjlBa2v3RMLqU8vA//7nv5c1//oO4pe9dDYrLK+1ypY2rmatzVJhWOmdfGO5863tf0F2FzBgDkE4YPapjUKNLgICt9XvNmgjNloq+21oBpa0Hup7h0x9znYq+poMuUCzVJthvj+kmjsnDa/xULXoPX17gVobJRa8jZoBbqnhmDLA8W9FsC1sPtCosm865F4pN7DM/tsRONzftxBBcxEVllxivXajV2VEaYVPAqtkr8SPD9L5jzNFKmT7tMAIam1bviQS29mMnMHQjnVOmXxa1n6u+h1k3WmwuV8oiyEVvLmadU0tfa5HFUl8zi1bmyCMhq7WOv6XfxSywvVmEW4s4HR0xD11gcv8jyO4OzBeUf6SFdrYPNlHtfmVrS/VZRhVcutKPoF1ddOYiUkaVrhkhErt2AKU3ivQc+Ti5QGW+1GI/BGLXnWAe9W83vW5I+k9iBDMeE26+geVpy+nfWeAeOE+4dKUv1GU8RpqGyWcCbr7Lpa8eYRs4vqVgcqFjdu+xgnpjHRMMI8t6v1CB+FVQgK50utZM1HErFoZf/cffy7f81Q/3mgdu9fQBpBfHwp5/8Qc/+y5u/+/ex6/fezu7O0u+/Ts/xf/xua/idS95mBCF1Wf24aFziBjG8z2aG7eJ4li8oqM8dEwfamh2HGF3Cg8GBVLzBhmG8U7oN2GSNMdiXZ8YqaFpFVDK+mMymFvk7nvcuZlwdod2u8S0gWanoN4xrPcGgwCJQnUYKeYKMmVAR93eSPpjQju1vUOi6SKmNlCczONAP6YUJgWhsmo40EbcotW6whQ0O6rtk9lEeXzwzjt+hjvv+Bk+9vEfwDSe4sJcGZ0JJLCHK2UwbY+f0ML+hRLf+B9+hKKBKNrQiwLb93mqgxY71/pHQiCUTl1yS4sfK1OoOT3B1J7xFw7xW2PdiKd1T/Lo1XqNNOo0GAtHd2YbO19T3ntBr5XpRJm7q3UPDsqBpzpn6G47Qygth99wlq3PH2PPHWCzTlYGS7pucPzKTU5rEWz/c6wbzcWbAEwCf2QyBq+j1UAP9jwmN20AN9fMdaC6RPl18u9A77kMJm2EJFZSXKwI3vesns3zQMzJ8/DXzptXN2X85YOkjRYIR3N46FE9rBF4FDWqSblZqjTCnfUTN5jDm2FP7YNzhCsHsAFM5zG4/P56AKlwyuASwTx6WdeX9ZroA2E+PwESbX6megrp860qzP4e9b5jcqHjhl+9nAw6St2DVAVFF9h6UAhFyfImsGvVTCqW2hSaPNxqIxAFro9fNgWgmHvcIo06xki7WyVTA88nPvr9fMu/9/Ty+NVx9Wjzi/GnG8Kw33sujv0lEA+JyH8JfDvwfhGp6NubTx4vgkgvoPjEnR/kq/7Jj3H/fJ/bT13kfQ/+WV4xu8iN5QGzh1rMhStgDGd+Z8rHDl7LX5j+Om+dfJb/3L6VG3+lUSHEdYfceIYwrWh3RlR/uITJBA6Ph2LTN7r5bTuwPnUaEmshWwpv2I9mHQbZmhEnY+K4ICSNgnbbMb/Z0k6g3VY2iF1BKEkghNB2ksAFNlhKSqv2hYrYFnMFn7qJU3p6PWzM1WlB6c3SdMhIRfU0EaPaNU5HpZRxoi5i4wtaJEWrAsxu6XnLd3yA0VWfeyx1k62ucb6n17+Q41/9D9/DG/+dD9FOheC0O2QbHenpJtrxOb5Beu0u0wnVgdoHq12rxx7rhiaMC+yi1g4VKCATfM+SsSZ1kkYWXxpWZxzjSwG31rGiaIX1qbIHhlTM2g9uf+ka6KaW8vJanYKisn1s0vtotywudZrcSq+tbqvAF8LyBku0lvHFoJbwaeOTxzDYpMRn7QIfTnY1YSgMgzqxSEyaBkaUvWSNjmTBSQBpM7pO31ey/FZRy24oQDcdYELQAjrdpyfcznJYn8Q4DeGmMwqeJoYik4mSBq4cEBP1XzLw1TSwXGHGI0Km66foC0CbzneeivuqwpzeJ1y83LOVrjac6QvLpCERoQfRri7CnzDShnTrgUYBpIPD4TVDhHWt72O5ojjvGF0uaSeG6fmO6acvwMERGMFuzYizMX46AaBYeOxSQS2cwU8cPunB/Mr/rsxR8bHXhPvn/9/vu/5z3ogXAaTnb9z3V9/Ny/+n93J4NKE+6yiKDkNk7R1yOCc2rY7QHC8oCgtmRLm/xlczyvPHlI/q30IClCkLZQEkJt5jGAP4flMohe3dnqRwPUsiJmH+LMote3s6qjsrlUE8NixuL2l2wFeap4tjcLVqMTYzHVF2SzUq0KJdmztdqSYgtomUaT3zToCiHy8GehaLrVs1FihdD0hJcqMMVl0sQ6E1QjsyVJcb7nzrewF1dspj54OUeApn+9ydGwsv9Fz+m//Vu/jW/6sy3cVHXBMp5n7Q7Ws97c6IaIVm2wIKNo4u1AQruE7BdLts1LL9eK0unZl906QRbqvufNJVxKpA1sXAeOs6fVzW7wMIAffABSgK/OgGZaPlXDiqFEjI4GDWMAJteJwQYx90hTa1hmLT9r/v88oGgCTJ/bf/WwZt8uhYBpqS6I8kRhEAnh7AyXGt3JXFsfMONyaW1wnWbm4eXSO/nozE/pVCc/RVx5PJ5CSI7NOa0HU6bVCWOt6XxuOuFRmYkvEo6RoO3/MJACmdt6QGdFyuFEBK9Uy4mkF8FYD0mKhKJg/XlI8cEc9fVCe96SRdUxPirMLNW8aXHN3YqFlLDVv3rbBHKx2L7Txhe5wMSZQd7+apwegMvhryeO8a9zTz+GYz6MU8/mL8KcR3AncBH4wxHojITahj23XFiyDSCyz+8M/9GP/gj/8Mu6bm+265hz+qb+bHP/nneM1vfRZmU1itsU3gkz/3DXz161/BH3zr/8i4bCkvr6ELmMMFGMFcXFPEqPPaB4e9/W1Y10p1Xa20m4B2U6Rp+4JTRpWO6mRLz9QNjdvTlOwj9emKaIXFjZajV0TcQugmkeJIk4xNhhXdWF0Q8sYcAenoN4FuoZ3OdqbJuhvpWJxbWmwb+zGk8cMrpZ2O0ljOqlOdpOQUpyLdEYmBZssSCji+reQb/8OPwJ5JbCi1O17dOutHCQHuet2PDK5wdYuUJ8d3XqjxL/6X7+Hr/9ZHANj7nAIFzcwg0XB8OxDA1FAsFRBsp0K947jht5aa3K8ca5cqO4Zl2nmp3au4t0WsClY3TWinhsWNymYiqGOMXatFNQKLGy0SLbufqykPO5rtgsm5RplJRjC1T+LQZrCadoI9WGFKh6l931YIhaGbqrPLhddZ/Dhi10I0BrcSpo902HXoO1iyTLpbyYVFxiMohHA87wWjB6FJ23fxYoiDqxroe97QNxFrteiLiX0H/WPza/U/b34xmwWZNXqvTicqVpnYRGxvwWKpuiplSdyaYB69qAKXSVw0JuBls8ATo9P9+dzCat13VMXICTaQAj/9E1Wbpa6Hv6ff6xu4uuD24B/7tyeL3AWNTYv8yRco77d98bpZrEbv8YdHmOkEFkv2zl+CssBfuIQH7P6ufu5lwfrGKd3Uqq7LXDe4sbL4SdGzJ3K84a98SN2sChhf6Ljj7e973JG0q5lHOV4sPJ//8Sd/+Qd5+yfexc3VIa8+c4EvHO/S/aMznDr32z1jQrzqAt3wmw3nZKYg/XxFPDgaRtW8R0ISl16t071yUtskekDioHmS72nvdZ0pE5MxCViLc/0Irl1PaPZGLG50rE+pbo4fRcoDwY91zDO7aQYHzXYCfQJJTHywyK4OIVi9j0IhdGc0j44OQj/uXMy91hDpvsqucVHSaLuIjpVEHW3uJoZurLWGaWMvlOtiYH1mxCc3RGzv+rof1nUfkLrFxEh4sYTmX/33ynR/w1/+EHYdKC+vdPzJGlY3jlmesYwveqrDjnaqsgGHrxxRHUbGDx4hx0toWqwxkASRZVTpNeUsVBV+f0o3K1jeUOBWkXFpsfMEIGUWvEl6QmNt48WDQ6RpGP/2fVoXONUNYl0nV6/EdhIhHqueZdwYaSOPWQGyPdPXSM0dAR0DzXk0RQ8QeRKTthzYxPoA/b8YxAyNjcc0Q/JItxh9rH0SIOnEc6/9PYkRxBV67l1iiZXKyIsJABJrhlHvDBxbiz88usbxghreJD2inhXUi/bnvJtVuwOxC8Tjtn+cVFUPRl89sh5WgzbZJqPq8VhH14rYtIRHzlFcvKysrpXKbpD2KaZtMcsSbthl+oXA6IJjfaZidKHGXZrrGKVXplp9w1QZSiudTsijse3+mOBMP8b2iY9+P2/4Kx/S91DA6KLnbe94/xPqoF4rl7+Yx79EIsLzWRMpxrgE/vHGz48Aj1zv81/MgC/A2LFLPt/t87bRguNwmdEjiUE0X9B+zct45Fsrpg9Hlv96h7/z6ts5/Ndn2G+vIKtGhbEXS+TMKUJVYJOAnZQlYi2mLFRzZD7vuysSAzEngWx3amRw0phNiVWpBeiqRTrP5AHtHFaXSy5/TYldWcaPitq5R+1kEqGrtDC1a2UmrU9tbFiDdhu7BvxS8AX4kRaLXWUo55Hi2FMeaTKIdoOJYVS8MxpBrKgrTcpx7Vjno6NV8UiTGf6Ga/ITP/o7P/GcfZdf7vHb/8W7APi2P/ezaRRROHwFjF9zwPxohMQRe5/rWNzocKvIel83A1J3MKp6cWjpFJQJpVUhZ2D50i2iwPKMS86E9LbDy9OW0RX9TiVEupF+db4wWuhGCJ2hGxuKEDEHrTqXpTEknx1mAHPpEE7tEKYlfjwAhP/8576Xl3/kw5SHkvSUsjh3xGTXtKs0EqQslWoNCiCFoejMEbs2/ZwKL8+wiYMBQNocg9sAmvRnToJFm/oQG8WcFNqZJIShqPMBGY9VB63Ve0fWLVSqsxTpEOfwSfT6Wp3CTc2JvhCOginLodO4ScXvO6onP4trAkSP0wm97kjFrr7/xylWE6gXV6sTGg6QiujlSjcuou583URHagDtXI6LfhzmV/7Z337Kp/ji6NqLAfDPL76Czz5wI6bwvOKPVnrveo/MpsQb9pGmozo3pzrYZ/ZwN7AerR3GXGMc3I3yNeyUddFvBtPGWKKc1CwxG+B0QDVMsq6Sc9grS8ZHa4g71Hulsk6PhWIBqneH5laX8ul6+NknKpBtdO3sRtqSikbzr+n0d/WOoVhExhcaTJ20d5Jjm6SR4miEICYJeCcdxGTIEY3gVrFnL+sH8NjP+mor7BfjZGTdrne84SchRtZnKi59ldXcW1pu/OUrtK/axS08wTnaKZo32g62VXdH2sRezWBd2xG2RixumdCNs0NmoNkpKUVwy3oYwRSB8YhYOR1/u/Um5HCh42pdp3pDObdkVq21A6Oorhl0eK7KIc4Nz0k1Ys+mgatyadLw6wWwHwv0PF7049j9/SXXHj97itHnp8SA6tm+iVHU5zYftHFkk1C39z3g1MfmuNpGfs7gVfT5fWzoE26IbvfsIe+JeQzwyeI63r8p3DBSt/G8WNe6Tl3VsMkAIoBZtcQkxj8C3Lzp/xarJBmQtTObxH5MzMZ+rDU+9Vz+Inj0Yny5x4sg0gswXlFc4N/77b/Bv/WyP+Af/uE38Or/8l4YjQhn91jdUHLjpxqObylY3ub5z37pnZz5fFSRvbrW0Zczpzh83RncKlDJbbgvXCRcuAQvvw1ZrvQxbYsZjxUo2tuBS1dOdm2y5gtAZZLVr8PMF4TzF/rNdTGd8NJ/9jKOb1XNHIDxRU1G3Vg4vtVQXdaCsjqMjM/D+pRQ7ynLNxpYnwYQujGsb4iYWpg9CI1AdRDxVeo2dV41kUqLryztVFkorg5Ip4LLvlJhTbeG8SU/dEyBdqqiuS/GU49P/pPv45U/+2HEC93NNe986R/xmaMb+L31baxOW+odHVcQD+7Kku7UjOZUpeNiZyzjy4HqcoutPcvbtogGHnmDo5hDfToS9hqYO0wnEIVuovT66sBQrCKhhMn5iB9rR6m6VCNtoLys7LHs5gPahS7qlo/+/k9z92t+AKqSc2/aZX0azn5Kr2nbBL72f/8R4g1jGlviJ4HiWB26lmcLJufAXVTXtUynl1HSFrhwaaC7X6N40iIsgSohFXwh9qwhOrW6N7NpEp0Mw2avLIaCN/2t1zHLxXTYeE3vQcqTWiltCxcvsX7jV6pt807B6JEV9v4jHaVpW6IPmFHVsxbEhL4wxfuT+gn9+zLEet0Xtfk9bha/Jo/QbPzuZAzFes8qumpE54kiO8bkx59wb7sWGJZ1Y6oq0fN1421mUxj//9l77zi7rurs/7v3PuX26aNRb7bcwcbGxFQbNwlCSSFOQkh7CSGV3ltIaDYYJyEJvJDwvkBIQsgbkvBL5IILNsaAAWPc5aIujabP3Hra3r8/9rllRiNpJEs2tu/z+eijmXvPPffcM/ectfZaz3qeDOFwgTjv4M1aDa8451rWpLBOf6bjuJ73uk+jfYEKLesx8eS8pPSiy65sL25/Robpu3jy8L9P+hd+4ZPvRG4wFHZ4qPvvt4zXXM6ygUYn7FjrhtUU9id4M2kTKElsjM74iJ4iAEII9NgEuhEgCyXbRKk3rOuT7EgTO6/LJpqOVs1FmuPY96nXoV5HZDIY2UvvI5pGr0QmBq0Ebk3jlTVxRhL0Srw5K2abOxAiEkN9yCPOSXRqZNA2zLCxX6ciuc28QDuWOSxiK5RtHEWcs6PpJmUoCynQniAoqdR63rosuVVtGcoNG8cjJzVg6OKoMXlmASewToK1k0Jy2zyyk5ry6f3UByV6tZO6XWp0PoPpz5NkHOKc/Z55MyHOZMUKs+d8RKxRgSYsKKbOTiARFLe7qIZLftQnOx7ijs61Ra4BjEFOzNi4FSe2yFmp2Qam63aMjdsx8mtnv8jl2dfZ+3ZPEcoV2wBVCr1i2DJre/NEfRlEbPB2TkIQ2PjW0gnrYKumMVlHwRHjVCfmNSSMRkdpzNOWjdR8n84Gy2FHuebtVx/EUtJRjMrncNasJOnJISfm0PsPtNi8rfjWjIfpuNphR8g6mVhp8cjo+KAx2SYOua9m3O3QazxcLO8sIMlUq0n0FDGz5dbaoxXfPc/ep1wXfM+Ouub9+Tpnqe6VyaTPO1Y2w6lYzcpgMNv+DLrtuHxQHM/IeZMJnXFc0G5OQ7eA9DOJpzkT6fGiW0R6BiIvYl6x/l4+NvxTvvEfL8DU6tR/7mTcaowKNBgYunkfXmU5jd+aprF9AHzf3vC0gUqVykpJYQ/kKgFEkV207tyDKBbQ0zPIYtEG7t4SNNIg26QGN7ugHZ0AGqFV8gpDhOtahkNTVC82+HMab9YmA041JuxxbReyz9qv5/bbLqJ1CLGPyciOs8k47WoKUHUBBsIiuKlYJwK8GUu61Z4kKjqEBdu5sAmsAKfpvtXuWhoxfy13xz+97ZAjJl0cGSKxzLHi3T7/Pvk8ChtncAoRieugfRCztlBoPIdg0GfPJXZxYNwEcgnDw3PM3jlE78M2gc2O2b+9VxaEkz5hyWAcQEDQb503hLbC3Ylv/55NsURZi6xbV+rsI6LEdjf9+bfMrQ98HIBnffODbOyZgZdC5ROruOmGd/Osb34QgDPO2UEtdtkxuwq3IuAANngoYcfMtE7HxDJWgN5tWP2gJl28iY5iRvrDvMeh7Q7TvMZM03GpmYjptEPvuSmFXLYZS53C+J0FpVQ/aR4zKr12VZDg/WjUsqdyWYTrkqxbhrNnAjM1007sUlo+UmCWWGhtFYtoU+NNR1Fq4XmZd05oJ85LLSAdLNI5X6ehleguIkoqhgYwe/bN255sxhaJEoNINMaRxHnVWgzfsnX+mNr3v/JWnn/F1S0RTtXQXPzSj7fGcDrRTTa7iIy9b6kVNQb+28XUasieki3kxrG9/oMAuW8Mv2jZjeTzUK3Ze09TM81JR3uaAvOVKjKbwYRhWvjtYDbSoXkiRVsfqfMeoySExhacXAc91EeSkWg31TWqaFRd49QT4oKDURAV7Eh6aUdM4klU2NaPa7py5vdHJBlJ0KOQoY31iW/ZSFqBjGwsb466RwWHJGPjuHWBS0+c6XDkjE3q9ikQqSbeHf/0Ni66vBvHjxVREaKCoLBP49Y8nHrqThXajltm2jLEvJmIuC9DfdCjPmTZZJVVgtpawbLbchT2BJY1BnizEd5MSOLlCEugFegCzK1V1AezDMQaNVNHlzKomdQwommoYDSiGUeNacknNLF1918BcF39K2wZeiPRyj6gD+e+HWw98FkuP+dDAEyc08Pk8yNoSJbftpziY1Xktt322gCbQDRjjuelo91HocPXiYUxpmMMbuHzC+PTQfGqtYv2852MJ6M1Yq6M2LXXGg4qiVC+HdUGdLVmG8hLPdaOx5uMXvtrx7EvxhY+xH4W0z06XBFLug6ylJp3xLYQ2GzWCOXa3MZxWlqOLcaRkiQZx7rlpsx24zlo32qrCWOLRcaRxFnrvgrW7KVT/Pr7X3krP/cbn8ar2LWUamheeukn7Hu0GN/ND9KN5V08tXHYIpIQ4r+WsI8pY8xvH5/D6eKJwMlugY8N/xSAsE8TPPcksndsAyVRDUv9NI5Dzy1VVLCBRp9B9xeQkxrKFegp4k8bonyq5VKrt7sFsaXS64rVTqJas8WgJLFaL9B2g1ISPI+kL4f2HdtxUgL6S63iTJLxqA86aFfgVqzuTdDvkbi2owi2WJSdst0qYUDGkmRK4FWsVkKckUyeZV3V4oLVY8hOGPt7TpK4EJQ8EFZ3R7uASQsWAhq9ksyMZQ2oUFv7+dx8MVw4tEbJMxl/fu8rWe+PMezMMaQqZESCKwzfrm0kMZJbpk9hKrDJyoYL4KGdywGPnocF9Zk+Vr9oL5P5lfQ9mFB8eBYRxux61TC1lTbhOP+8h7nvG6eQG5PMvCLLuZc+wA9OXkcy6VN6VGIU5Ec1I7fNUdlQorpMkmQg6Lcd7blNCSIWlB6WyHSNhDaWWm8McdEHrHaX0Pb7cCRXvW//j2WP/PQVdozxFbf9CUHi4NTaQuFxTsJIEWEMaqCACK07lwwTpBRQqVrBSmNa+gSta2yh40oTcWwp9GkRliBou7el/xujrTW1ENZZJk4Qvmep/nHSsu9ui+faQq7w7HiLUIrg1JV4Y2Uy39uGKFinEnSCmatAkuDstGMFYniQVg8xjNDjk1YzTSlUTwldqbbHxprdQW3sONuCMbYmTAf9fp6L24JC0dGwj+wu2uNoC5PVeY8tSIBl1hb+zPikdakJAkQhT3zyKiusHSSoICHJKuKMIvGlXVAF8z/XhVuustpssWWlqVqMzqR6HUJgUk3XE+US0sVTD5ff/Kdk8hDVXPz9c5DLkUzP2ic7WApmehb1oypyoD9lT9jissj4dtQnl7U0nZRJ0RwBaY25BAGm8xpr3YdEe0zEdawosefRFDgWrose6SfJe7iVhCTjEOUFbk3gTwdoV1r304YgM2VdN1VDI6MEkRjcxODUExJfoVLnzSgtwqrQEPQK3Aq4ddPSM4oKika/gwoNjb6OgnK6eMvM6JbLp8KkI3IWnbG8i4Nx8c1vpeQFnN+3ne21IQ40iuyY6QPAGEGlkkEqTXSmh4gkKrBmEgCzGyUj3wsZGA/s3zdKaIzkqQ/bG1vQB0GfYPV1czSGs2g3IexxafRJcgdi3LJ1Tl327w9BsUC4doCo6DB9sh2LG31ensx0jtKOACUEpphDaMskEdAaQWt9d1Mpha07rpn3GbeOf45LXmJF1q+d+DwA1931YQAufcFHiF8VMLe9l9n1ktpgkdLIJtxygjvbQE7MtrTAcF0bu+fKls2ajoQtnTWUitSLtuh0CwvGuZsjczLrtps/UWxlJDpc2pojdiZt7KhCBpHLoufK6Lmyzc/TfMNEMTqpIAf6kMNDNreQoqUnFY+OATb+mSBoH6OQ88fKOuP3wp8XxtvmKO0iBaPO/xc+3rmt099nhdNd15qrxDEoiezvS2U0UgORMEoLSKmZiRDIRoRXD60rdMFHJFYzsymXoOoxRknbwM7aY1WB5pat80fYXvLyq8jo1PylFtkilMDGck/MY590ndeeGugSvw+NIzGRTgNef5jnBfC3x+9wujjR0KObWj/fF9VQdUllhUvGczGNAFnIW0FfxwHfJ3ftT8j7PkiJjiJkT4lw7SAqsraVYnIW+nuRk9MtQT6Ry4KXzqADQkhrddl0kmoJIdqfk6xLVHKs4KV9ASLRJL5CewqnbtCxbfTIBGRo0C6WEj0FXsXaasrY0uRlbFlJWlm9g0afRAV2e5EI/FlLgRfaOsMYYd3WjLK/N+8Yd37xrfPO3Qt/8VOtn+/4p7cddG67HYWDkVMBGRnhioSyzvDRfZdQiTweGxskqruIOQcjDUPrp1jTMwMawuURYY8is7xKX6ZOZdYQ9EiK6T57tmt6tsP+F8LXLvgcrzF/wI/vOJn87QV+NHAafg3qpzeIsxmcOlRHJD33acKCQCYQ5OyaKckZ/HFF4kN9GahQkGQkUTGDjFONDGUXHM2ioT8ZLvo5mwWjxfDNF30GgFPuuAZvzn6Hhbadd+NYDaawpHCrGrec4IcFy/pLE9IWJbzp2gKLdiSBNtsvFQU1HXoQopOl0xLWltZVZcNqkqKHu2sC0Qis0H1PEZQgKWURcYKarLSSZOM4tlOZy1pmQ6IRvT3gOiQ9OdSUFT0nijBTM9YNpbmwjSOSmZl5h220aX3eI6JTH6n10IIuZyoQvBQ0k1GZz1n2YaW6eAGq41zL1BSgVRhPtO18gqXRa412FLJhi4MAqpEQZyTf/drB9w4ZazuKk47jiKaei8IWGkM73tZNOrsAeN33Xw/mZFQden7sWW0Zz20zDDo0SIQU1kZ7bHz+iKznpqNoCWZuDpHNpsLG0bwirNGyXWRNRbuRbdYiYAvvUiNSS+umLpIs2/uMDKyQtRFYF0tjELGxhgVS4BtbQHJqESTNgi1ox2kVkIJ+l6AkCYv2Pu7NmRY7QDvgNIx1V3IhKMlWHJex4UdfeEvrUF/wy59qFWOdhuG2b7z9oPPbvc4Oxpm9+9FG8mBlOd++fxO5bR75UdMax+mTohVLRQLVFVBfJomKhiSb0HjYBVwKO6utfXplTViU3P+Rt3DmO6+huiZP8adjJAMFZD2C9SWmT3FZ9v2w5XpFuYJTLoAAp26LSP6sHY+TkSYpZVDTNfRAD0nRw5muIcLEFlCbo2yHYdZ869uLN4luuP39AKz/zNWoumXCGSkwjqC+Mo8czBLnrGFLdtccohEhoghozNcVOhQTZwEWjrO3GLgLXtu83ptsYlEqIqLI5vHCgHKtZmnaCDJBaItr/b02ngthGYxBAGFsXVB7e6zRzlzZNo49F+qRZXU1PwOgF2oaGb14AWnxD3iIhxc2yQ4fx1tFJsdtaa2avG/viU1Hvmym7RDdOa7vKIzTnogQUYJwFbIckPRmUxajxEnsvUanAvykk2wLXdde/KpPoiKDjDQiacd0pG0eyki32HWHE9ruoounCo5URHqfMebbh9tACPHhx3sQQggF/BDYa4z5eSFEP/A1YB2wA/gVY8z0432fLubj1+/6X6y9toFTDjHLhxCjE1YHwXFsN2IqRA4OpBTgyD5eb6BqMT33zSLHp1sdiSZUyVJIAUvt9T3I+Fb7pZi3z6XJpylkaYzkqY24xFk7JuZWbBDWjiBO9WmMtIvusM9DBhpvztLabeIIbsUQ9ihEDE6grXPaWkHQC25F4FQhMw7aAz8tRkV5W2wKetPikraUehnbwoGM4eKLPs6NN7+n9dm+8+8HJ5tdHBmzSY533/lLOA9nGbhHU3pgipOCMsZ1mDlngMQTDH5oimocc9rgJCZjk53Rlwxy90kFMsttgG8Kmz7/Cut8sf1P7GK84DZ48YvvYe+frGfinCK5X9rPS4Yf4Wvuc2jszbHp/04jJmbo//ouxl93NkkGwt4E8gnigNUrWvONCG+yRmMkT+Ipgh6FalhXvvJqh+mzI0QsAW/eZ7tpxykkRvJ/x16IJ2P21XqohD6eirn5pVfP2/ahD7yFc/7oGhp9gkafQ35U45Zj3HKEDD10ylKpnNRD/jGJmC1bTZNUh6CtUxAjPW9+otW0w22OoCjVFu4GTKWKcRzLLNK6Za8rPA+RzRCXfPZfkMU7cw19DwdMneqTuPaa6X8wtpo+iYaMj//oAQhCqyUwOWUXrI2gJaQNsEDe0haaDpdUHo1w6GJMrEVGzDqFrpGiNa43z+2Otn4CTbZVGFoL5wX7FEpZvZmhAXTOT8V5FSKMrQ1wYmwBvVZH7ZlAb1h20KHf/vWDC0gXXXalHa9pjhqmnVUZJiTZdojuFqm76ETpbo/8qKbn3mlrXd10pwyjeaMrTW0XYH6RNdG2WDw2QVKdvxi046ILFm+dBSSwhSNh9dZEIW+LqM0FWdK+92jPobHMFuazE7EdGxMCGUSIWJFkHbQrcasJccFF1RNkkF5TwP4X5NGujfXenEGF1vWoWYiPctawIOi1zYHaSHukTUYw/OP5Dlq3/1s3jh8L7jiwnrH9vZTucVn9WEzh7t1Qs25XIptFL+tDTs6hxycty23ZEDiSief2UV2umE77l7f/24da+3zRL3yK73/ZNuu8MkyfpCh+t4rM+STFDJWVDokHYa9Hdodl2el6HR7cDuefCtj8TcaGygqFN6vwd46Bo5BaI6uNNOfUmHyOpCebfn+L3PCd97WO4+w/uYaeHTHuXGRlCgyISKNTvczOPHD7n7yNs95qGUxzaxX5UUFhZx0ZJXgz1oU16ssS54vkflq39/NGcPBI2iLobF7Yn9W8YpItGKUbLBC1FsoWyPRgL3J6DmmMHV/V1kXRZD3ExIxtEEth/3ZRjMznLAM5ha5UMU03tiUc80F4nCLgh3vPVqFIiJb+Y9OZT/ie1V8NInQhgwREvWHvQ+VKy+gCQA/0EA7lcGox2hHWkKURo4RA1kJMzkMGMQQQlfJQn88GW6wR9JKXXWVNeEiLi1Iho+Z9sP137RaPnoLoMsAPicMWkYwx/3qkHSxlmyXgTcADQCn9/d3AjcaYTwgh3p3+3r3yjgPkyLYWG+k1G+7i/776JWz4fwZnJsbEcVsfJbXvJYqsxkIYWtFNz0NNVzD5DERRW7w2TVRFXy9IgShX7AI4CG1CkXbuSbQNAkoR9WbShZ1lAMnIMoIsA0RY7ZBUHFu7VtdAuQKZWOq6W7WCmE7dzh5rTxAWFboj9/Vmwa1aZlKcBW8OW6Ry2zo4CNAZq6/QHGVbeWtjkbPXxdHi7addx0U3vY3C97KUdsS41cR2BesNhPExAko7grZbV6UK+QHingxRAfK7bfBN2hqG8wL4Ge++hiTzLPxp4Hlw91+9hRd+650UnAbnrt7DXWol4WAex3cRI/0M3FNj4B644TvvY8NffhrWVfEcTWMgj1N2yOydI9nYS9AjUBkrpJgb04QDNU4bHuNXhu/kph1fYG/Ux2uLU2REzH/Mnst3f3AqpifGy4Vk/IihQsTfPPhSbpvexPfvOgnZF+L6MeGpebxpSc+jhtz+BkYJnP0zOI/WMMP9AFQ39hIO5cjst7oKzc6fSdrUbd0sFqVoJZaJxtAsnKSjJkJi4qg10mY1kTxMvY4YGcakM/8qhP776yRZh9wBzeSZkmAoYdUNFWtLPzdn2UzZDDgOenRsvhvKAsFPe2BHkXwuFYdLUjso8S0af6fbjpIIJ25rPDgOoliAdBQNrREZ3xaRFu7a8xD5HEmPHb9EYl1aNIhKA1PMYpQLGRcxNce3bn3fQftY9JCbOlTasjOuT23FL3/uh5e8jy6eWfjK8/6etfddRc9jgqSQwenvxUxO29h9CKZD2068OXoi7OIyvYY7dVJkNmtHWlNBfaONZTUsQKtg3WQtuQrjuyQ5FxFpjCOIc47Vn4sF6YQOKkj12BwJxuDUEkSkUbU41Q5zMUqiPUlcsNqG/rRBxrbZYwS4NUNYtCLbRtkCkiCN4VhXt+745/HDzA+HyFeg7+Em08QW5IVSmFIBWU2NV1wXopikN0tjOEPQK3Caxp7zezAtFtiLX/lJ/IJk4J4G8cYV3PCd93HpCz9qczIPassc3EoeV2tkXMLkM7hTde76u/dwwa9fzdwaac1V9s1axkkjgLxqFzWVRNQDZMYhGMphXMk5f3gNMrKjdKUxjTcVoKqhNdIAjOcgI4fRC0qc9sFrrHZX3mA8gz4rgZpizXWa7L6qjQNJgti5DzwXJ5vFWdYcoRJWf6dDrPqQ6DSMEG1Ra8u0PYwYt2qzlEUcp8VeCfU6lIoYT8HOfeimRmL695OlIqZam19EPlLh6PEWiY4RMpttF498z46RV6o2n8n4mOF+kryPrEctTSPRXMuA/VvECbgOOu9ZvTVPYlKTCxkmiEZoz5sxLWbXUY+5Cqz7LXD9HR/g0hd8pBvHn8IQ5sTFkadDfFqSsLYQYhPwDmBt52uMMS99vAcghFgFvBz4KNCcH3oVcGH685eAW+gWkY47zszu5ie//Fd86MXP54GXFq2rUzrmItKuo3GtE0HTgUKktuqzp/fQO1uBKTvGpgp5kjM3UC95ZPbMIgIPPT1rnzMGkc9hsh4Us4h6iMm4JBmFcQRuVaMiwcxGiT8tyKBtYihFi+4cFa27WuILvDkPQ2rxm1OIRKGClNIeWKFNGYJbtWwjsIllVLSJZmHUaihoFxJPtKy3p09JZ7Ql7HthhhXf6RaSjgdeu+oHfKpnFUYKdl3qI/Qy1m6t447O0f+DMRpr+wjP2Yi/a4poRS/OTB1npk6LMwys/fJOtnz5zWzd9Zfz9r3pFY/wiTXf4GXf/aPWY9+55CrOv/a9FP0GL123jYfeN8y2PUOc+qkKpiDQjuR5v/lpnE2CcCpDUogwDuisg9q5n0Ks8WaKlNdlUK8dY33PJGHiMJKZ46qHL6P840GEhk9NQGbaUNxR59QD4xhHEa4oEhazzPb18qkLh8FA8VFF0JdF1aFvAoa/P20T1Zk50JrGs9fZbpgvKdy5i8LUHLgOJo5bY2kthkqaYHYyDFrubNjiEoCMY5sEpkms0QZTbyCNsYzCjauQ5QaNtX3ISOONlln5tX2YegNPKbK+hxOsZvR5DvVVBXI75jCNAB0EmKnDRL4jMYSWikPpPh0J6QK6NY5jNCaMbCE7SdpU9rS4JLK2kG2mZ+zDjh37kxnfdrybh+O4lqmkJGq2RtKTs2NnYWwXEK6DiBKMIzFKce2evz66j5uYdMRWc9kFf8H1d3yA6+780JFf2MUzFiKShEVJZgyi1QOogRLm7gftkwc5QqVspM5rqtXRd62JQJIgpED19kKxAJNTkPExs3Pt60mpecVoYN79J+7NEvR7VJc7ODXrtmaUwJtLbHc+FbAOBjxk6KZup7ZzHykX7VlxbBEb4rwiKkhyo/bt8qMxUUHRsz0hcQX5XTWMIwkGfRJf4lbaY29dHH/0bTM0+gSZPRVwJNGaQZy5Iuax3bB7H/T2YPpLCM8jXt6bihTH+NOSoM8WLlbdXOHy5374oHvb9CaHoA9U4Lceu+E77+P5V1yduvAKwlIWfzpD70MVRJCAtDqUZtDFSNsEnHzeEAO3j0K1DvUGaN/Gv4yLLpVACJyaTQqX7Zixo0azdvS6qV9kUj0ikc0gshmWfS9lzU2XIZ9F531kLYQohrmyHTWvVBGFvG3KZFzMzn02N/bt5xFK2YJtvZEaZnRcm536QOk12PzZJB3jWs043rGN8Lw2x8UYO77+8A50BxNRxglisI/4rI04Y3NQrpBMTmG0sWOuh8Kh9IyeYLQaZ/U6Mpdrj9RJYdlHUmIGewmWFfDHLKNKO1aWIFNuWGOQKLLfB9/H5LKgDd5sROIrRGKL1zKM7b5c1TJUaTZ0lgrVSKz+mqZVSGqOQXbRxdMRS3Vn+zrwOeALHKocfuz4S+Cd0JI8AVhmjNkPYIzZL4QYXuyFQog3AG8AWLNmzXE+rKc/Pvi53+TVb/ssV4/8mMvO/C2c+3dCRbdGQYTntu1LhbUY1ZUKanKW3P6cvSlj3RBEsYCsRXjaumcJvz1uY8LQdoKmjO34G6v5kRkV6IyDk3MxQlAb8i0ryBXICGRiF87CWBOrKA9x0SASgU47WtkxSPIgtEjH2Yydyx8xqIZlG4nEvhasHo12BGBn+Wc3WipS34Oahz74Fp7O2Hz2B7n2J4fW7jkReO33f49zSjlyB8CtJqhAEfZZC2ZmZkEIvHGfaDCL8VycmTqiar9X933iLZz+vmsIew9dtPjJjlX8Z9+zyf0gZYdcYf/7weaPtbZ5309/kTBx2HvpCAD9D0ZkJmPikqK4osyq3hn2rFiHP6Vwey0dWmiDWzV4TsTVq/6bP9r5ah6rDDA9kyc/DcM/rOOUA2QlsIuxmVmE6yKG8qjIFgTy9/sEfdAYBKcGvY9oMlORLSDFidVnADIP7qd+xgpmNihyu/uRu9NxMW3smFWzYBTF6LDNJmh2HptJZWdiqiNrqStw5wlzWpFugSw3CJeXrDNd1sE4JeRQgSRjRWxFrElcwcC9mvwPdmBqdSugeRgr3uOGJpvIcefR+Jf0XouIbAvXaeuwNbUjjE5HcHxLdW9qtIEd9WseR/P9pLCLhjTploFLS6w1iluMDYq5lhDrUnHTDe/msgv+whYKE4N2JZc+/yPc8N1nRvLZjePHCGHHw5zJCsGaXsprs/Q+6KfubNF8Vh4ctCg09YYV4U9HN4VS7W3DsKVp2Lz2mmwkE0btApSUdoQWbIyPDU5N45Xt/8KAEYbsaB2RGBrDWYyyrGIjsMK1wo4jJRk7zisTQNmR9kavRHsgQ4gKVlTbLce4YG23MwrtCOoDkuoySe+jEfd9/Okdxy99wUeelIWpW9H4MwY5MWPFirMZaAToMLTCzpWqZX44ClUJEJH9vv3wH97NaR+8htqamOXfWTyWZ8cNblmQPTC/qNHJOn72m6+hPiDg1AJOzVB6aAa3EjNzkkdjmTVKWXZnZJkkhTQ/rdUgl0PUQ6KRAqqRoKqhHT0u16z9exi2iqAmSRBG2OmVKEbQQI1OWVa+ZxlWcjJI3Qw7HNJ8315PtTrxSAlnn2N1DQOrOSRc24AQwQJW0SLx7FCuaq3tO5okJoqtPh9YVi1YbbNiwRZOhLDMYUA9sNM2meL4iYnjQNMQpJWDLMZWPuzL27FcFos2lkvZZpgZA/kcOuehGjYG64yDEQK3HLbjvufZ8+FYdpIMLeNRaQPC3kuMtgLax4pv/887eenFn0ClzE7jSC5+6ce58ab3HOGVXfxMw3DixtmeKUwkIDbGfPZ4v7kQ4ueBMWPMj4QQFx7t640xnwc+D3Deeec9Df4cTyx++bduYdOX/4C/fc0XePXnb+Rf37kF/79/2N4g0fPGZpoLV9NooG6/h7gp3Okp9PQM0hj08gHkgWkIwtQ1IraOTB1aLbJYsB3NOEaFGWQjJsl79D1khY0TT5L4Au3AzEaH6iqDPwVRrx1bi/N2BC7qSwgGwJ1TaNcWl2bP0OAnqEkXkdhxNu1CXICw3xYxGoMCb1bQGIRoZQA1h+994Omvk6Cz7Q7t5md/wHbfUpG/6374ZyfkPe+88XQe2H06hX0xMtKsurHOzKYsO17us8Zdh/+Dh5GzVfx9Y+1xxxQXXX4ly4C9L/Z49PfXsvF/7zxo/7lCwN//+2XQB+v/c27ec1uG3si+XzuF6gufw7YXf5k39l3A9T88i/LJkrXftO9z6uAYz+ndSfH193DH7Ebu/erpCA1Dd1XJ7auzfaKPSyu/B7f0kp00DBnryOHtmoJyBRMEVgcH0DOzeA8lePkcOSEwnks8kKMx5DO7VjG9SeJWfQZELzLWBL0u+Z1l5OgUme8/zMq9yxD10FrDl2zV0yQGEYSIJLGCzbU6pl63Y2TNRaLnWmHrDh0Tk6S0+VTQs8lCMEliGUlhyNz6DIlnXQx3bREMr5sCIPqvIcIi9OzQ9H5vvx1LTZKDk9mjxaHEwBduYzTS82yXddVyW6RxHWiE6H3754/QHfTy9khO61Cb96lsBlEqYSYmkYMDmGrNjk82GRXZjN02TTyF57bG3prFN1OptpN20muq47q6/nsfPOrTArQo9ZZxpq3Y5zME3Th+7Gj0K/wxH3/7JCLph1PXIx/biy6X57ketdgLQoJJRzWTBD0z12IvCpVuG4b2npKyFzsZEJ2jLkIpTPPayWWtbmKsUY2Y3AE7mulOVOzoel+WsMfBm7UOq6bPI/EkKjLUByTaSfUPlZMaXdBqFLlVUA3LNDYSpk/OkB9LaKz10a6gPghx2kP4yd/Md0p6OsKo9r3t0hd8BKClo3a0zIml4pIXf5Ti6JxdiKfxQGgD2QyyWLTftyCw90ttEJPTqDQuvvhVn2QI2NOneOSKAif/U/mg/ccZgdMwRAWH7EMH5j23ZdWf0jhjJaWsZNfPQ9nVDN/kMnV2Hz2PWraodgzetOTA+R6F5cN4FU1+ZwU5W7OSDL6Pv79sxZbn0vdvisp3NEqB9mh9ktg1npC2eOFZx0Fc1xYxlHUTFXGMqdUQxpCMTaBmZjHpqJ8a6CM4fTWqHqMqDeRcFYIQPWeLVyZpv+9ibmMtNIsvHTG0WYwSfioXkbqzmsFeAKLeDNVVGdxKQuFHe+w0QBDYIlNUbu/3BEN4LlL6iOFB24TxPMz0DMlcxzF0FpY6GkHC99tj+4llMwrHQfeVkI2QZKiXsN8n+8gEKozAGFQxj877HQcgWnqH1Ou2YO5IjGMdmY2yjTSh06K5EMesW2SUwCSWXTlPP66LLp6mOGwRKRW4BvimEOIPgW8ArVaBMWbqcb7/C4BXCiFeBmSAkhDiH4EDQojlKQtpOTB22L10cUz4P997IUPbDG+661e5//n/yGfOc1h3U8aOtBmNiXVrbEZm/Bad3YpmdnRhUq0Q8jlEGIOj0NO1eYu91ow3iXWHUBLR1F9y8shGhAs4NUFtJENYEtRGIM4bWFmnlvNx5hQYyyxKMgZ8DXWJjMBGA+yQaSRRgWUmCWMIS4KopBlYO82k7EVVFEnG6iTJCY/H3vLWhafmaYl5CWY6732ioeoQZ0A1NEGfi1tJUIEt+FVWuGSKBaZesIL+m7ajZ+daRSTZtI4Htr0v7SwfYay8SZE//b1Wv6D/JSeRmTYMDtnb1JBX5rXP/y5f/e7z+fZ/28XGa7//e4wGPfza0F2c7I/yxytOp3ebaQlqJmMe9WFwitD7WII3a+nuJusjfBf2HbCLoiSx7l7puKfxXeRMFWcuQAz4DN0dUFnlEWcFE8/yiArQ/4C24rHZDEJrKNesKG6aBCbFjKVaT6bXkdIIz0VXKoBNdlU+ZxdwaVdOz5ZT5yQ9bxFpF5Bum1VQrlDYF+FU7aJu7jUxL12xjf/+0gtQCvw5KD1gvQxMGLYT7aPBwqJR+n/TknjRBDZ9TPb12ntNIwSj0T05yPtIR9li9e69ixaTFhaQ5h1OJmMZFirtYqbC47oR2Nc0DQXSwtH10b9wmfurrddfV/lS6+fLz/szW1SSsuW+JmJ91CyBiy/8GEZJVHPUJ7IFrE7R1y66WAw73vAOnn/j1SQFH7lvAncyQ9ybtWzKcrnNWmhqIS3i7tTpGtVkDAiVabMsFjAc542JJiBo6yEhJUJrhFY4s/Z+EfflCAZ8gh5J4gpUQ6NCTViQhCVBZlqT+NZUQ4W2GCBjSLlNyAScmnUnBKubWF4PteWK7JjVOdSufVw/Q6bYOvVVhDYYJe29IzlxBYFv3fo+tpz8TlvQVxLhqFYRBs9t54BKkYxN2O9cI0D197b20cqzjkAU27r9062fL7rsSsQpK1CNhMaAg/BiMDC3TlDaYVr3yWe99RriDORHDTKx45EiLcoj0/t9qhFo4thavDsORmurD9hptiAWuBE2Rz21g6nVQQZ2PNpx0CP9VkenyWaF1nlpGk6oRkzY65EJYxurpbLbN9FkATY1jRQHN206rtm2RlL68jhGNGg1tLTnIIMI70DZ2swfsIxvPT5ptRG9BcJUS8FiDaAlMIqk6yB7Si3XWPI5dCmLyPs45SKmUiWZnj3oMxptUOn3qQUl25qMYAXMe30rRxHHEAToag1RryNdF1Ot2dHy6b9n8+Ab7Fu47rzv1yUv/mjr58STdn1h4MItV3HL1qUXpC+67Eq0LxFKoH1lzQOAm25895L30cXPKLpMpMPiSEykH5EuzdPfOxXGDLDh8by5MeY9wHsAUibS240xvyGE+CTwW8An0v//8/G8Txfz0RTXzux3aPTBir/1OGnnG7nytf/I56/9BdS9j5JUKvOChIliZDaDGB7ETM/Mc39J5so4I0MtUU1Rrduuf0pzbsJ2Q9PRkqbdZiNASIXIeOiMi3YV5VWS6jrNaWfvpBp5+E5MaXXAnY+uxc+H9BdqTFVy5N2Yub0lEs92K4M+gyornJodh4uzMHNZDWMEZ6/ahycTKnUf3SdJ9trWpc4+ebPePwtoMpA2P+v9iFiz9f6PHf4Fx4DaCAz/KKa8xmV2ncIrw9r/qRP2ecxesJrMpB0Hai7eheOydfxzh9zfVfdv4a651WzITfDq9fCDnrVsHxvgrLdeg9TgAokLcVbiNAyzX13F+byR8/7gLgbcKjveOJ91NuBW+VbtJLZOnMXQXZriI3PW/QUonXUOc2QRZ1bZvSLD6mtdvJmIh95bAOCUqxyYLiOUZO7cFex9ZQIGpJuw/vO9eLumKNwXQL2BO9sHiaF8conKKsnUFVVGGzmGru2n/87xNgtLa5CSOGep19otIWKDmq0jPBflupb5V8hbmrbWmMFejOcQn7oKb98s5sC4TSp9H+F56HRRqUpFu32linvz3a0F5tq3rOTbF1xAvBH6t8W4s6EdAVjVhxwsIYMIff/DB3dJD5dINoWt06KRzGbs37kl7Js+1zF609xXPDaRbpIuJmfLNjn3XOjtQa5djVQS5iroyen5FsrpvpuMIdMUEy3moVxtnWOR8UEbZKrz0hoR9H0whs19r0f190HaUd+y/I/Yuv9v7T6lxHiqNQ6oGgk647R0EC6+8GPceMviVtFgnR9VPbK8MSEwShIXPVQ9OaGLwS6eXggLgkLdMhDYtR+H5YRrB3BLOfT9jxx0bRktka7TdnPsHIENQzua7rnW3rsRLKqZ0iwktdgCTSfI2OqDyUaEzriEfR5TJzvUVtrRchVAfdjDLVt79PqgHWdrJulxxuocRjk7hp6ZNsjEoBr2GMurFLLZQEIwdV6EM+VQ3CEI+mDb+5/eY2xHwvV3fIDLfu7PUQdmgPnFmOMC14FybGOOnzpjVWs0XQFNylLqZJRsHf27w+7ypCuvQQWQnGQoPSaoLXN5ycuvQsbNoqFGBgkIyEzErPpPxdi5Cu3DD740v/nnz2BH0GdjZGQQM2VMpYqeq6CGBzEDPdb5dfkQohFhJqZss6KYh6kZ9MysjVOlgh11zmVtg2jfAWs6E4Y2Ritpi2dCWAe0Yh6zegRRDxF79lsGSkdBRN2/g/jiU6mtLuBWYmSocbI+4sC4bZylcRCjLUNfKUSz2JUWsMBeZzpqj6I1i766Umk/1gisi6pnx61VEEEck4z0owBTzGMe3bH4H+MIRSHpeR2sxXRErYNF1Tk234QOQ3SHg7P0PBt3lYT+PliWw+ntsazIsQlr3pPNWC3ItCgnCnn7HYtjO1boOqAEYraM76o2M89xbE4kBKZ531KSLevfCqtG0J6Dzigufb5l793w3fe3ij1CG7SnbFHWWMbji179ScucZL47XycuuvxKOyIJVmvLGMJeD9Tijawuuni64UjubOsBhBAZY8w8lWEhROYEHtcngH8VQvwvYBfwmhP4Xs9IyJFtnHrRH7P3/2wACad8boy/vvXX2PsrklN2FqBSmZcMNAVqze69yN6e1n6awSve9hhCKeTJ6zErhhC1ANPsMNDcTXPURFqqfJI6t0UROMpaqhYFcR6MgKlGjt5MnVNLB3hwbhnuHp9gFfQPTjBbyzA3lQcNxjE0hgymJ52JnnPwx1P7bmVwnZi50Ge6niMYyyEigQrtTf6xNz/9x9gWw7U//chBv285/dCL3mPFA3/xFk668hqMFPQ80kBow/QpOcbPyWKEtfXNHrC6AWqgn2RyiuvqXwGshlPUl+WxX/J4znMeBSBMFFuGGzwyPcjrln2XF/pznPfNF1Hca5llKoTiXvs9aPQqcvvaAt2fO/crBx3fV5/3BQD+ftuLWJaZY9tqSena3SmNXeDPGtRwnZeddB87qgPs/vFGjISRoQlybkh1/XIyeZ+5jTnGn2sgkBBZ8W7v4V2YcqWlayQPTGPqdXomZyms6Ofh9XlMb0R9UBAuK+LtmcYUspZy7zr4o2WMq9C+izBWawxpE0VZKrbdE+PYWvfGMe7yIZsoQ6vj2HIig5ZTichmEUlixaPTRDC3PyTK+chA26LGUAntKqbOyDH0vRlkTwnTFN53HJKmLXBnAagzec7nbEc1TTytPoRMu6602FstWnsnFmo/hGG7w6uN/czFgtWOaiK9Twnft/cYx7HjYWCdpqr1NgNPCCu47ThWKFilCbR07OhCFNvCUhjZfcnU2SXF9T/4IJe8+KPWuSMxaK8pyn/k5PFFr/4kmTDBSImqWd0GnfcRiXWjuvGmLgupi6Xhh/9gF9JbRv7QPrB7FG+2ALmsHceMo/kLwyZDMQmt5tgC6CjGTE4hyhVkqYio1hZdHHbChKEVqHdddNa6qoV9HvUByyJw5wRJBsJegzcjKO2K0a4k7JH4MwanbjDSjjRFeYF2IegFFQrcKkR9gsQXxDlwGuBUrRubM+0gI8tYfqYWkBaOr13/vQ9axtCJRBDYOJLNQKkIQWDvoUGI0TEyl0PXalwf/hNgR+ej/hxTp2WY22DQviG3R1LdkFAcUzgNqK60982+e2cxShH1+niT1jFM+y6yFiFdiVuBbe89OGf76aft3/8lL7sKYcCpRZZ5k7JV9cQkYqAnjac+MuMhpmfAdUlKGVRUQNTqtrlQKoHvtu73TS3ATjfUTmauAMvCAoSbuh02jWkiGz+KNz0EUpCcutaOT/VkceJ+a0EPrYKuIGnpGdIUsG/m3wuvw3kuamlcl8I2jwB8D1NMC2GOZGzzWoa+P4UsFq1WFGlhql6fx1pu7bsZT5VCOG6rUdNkIrbOh9HIXI6kXJ53vIvBxBFE0mo1Tkwh+nttDPc9G4tVnBbUTMraajOPCEOIbdwUsbZFQSEsm7upS+ja4pnIYAvlrmNfr7Vle6NabqhgC0mXvuAjiNjYvMe1jGCzhBrQC3/xU7jY+I82qFqIzjiIJvG6W0d6WkDAiXNnOzG7fUKxVE2k7wLPWcJjxwxjzC1YFzaMMZPAxcdr310sjv984d/wvP/9aarLPbwxl/yN97G2eip6+QDsTy1RmnPi2thkUSk7MrMALbeomTn07Bx6EYvsdEd2HCdlCpgwQuSbqtcQFR1WvHQ35/Tv5fTsXjIy4gM/fBX+PVm8GLTrsbe/xzYpQglGoD2DUQY55eJP2bG2sNeQFDQcyBLmEnbd34NTg56yZamEfbD6hgYXb/34IbsMXRwfPPKut3DxVstwevT3JSuGR5nc348z6uF1fpUSWwDYXPod+7vn4k1m2fTuGSodVq3/KVbRL/fwmdwL+esgYIO6z9rV+35LDJYoJgskZ20kt69ObUWWI+GGR09l2faE6VediQoMpesfoP+/HqD//xP858ftrU6dbKiNKMIHrNZ/zwrB9MkOxoH8TkFUlAQnNRi80bddRkDPzs0TvhbVOmJyik3vsYKb+tS1VFbnkHEvzr5pzPSMdUxyXYQQlnGTOouQahrpNGHr7FIK37cWwzpNOI1pz+WnCWlLPyF1JZOOQtQD4hV9qHrE0G2zJH05wl4fd7qOM1Nj2R6r0SMcB9atRoQRev8BVHPkUKb3iMi6oJlKFZMk822DoWO0RllWUpIgPBdnaABdqdqiV/p5dL1hO5LFAnqoDzlbpXbGMjI33wtBQHTuJtyJCiIIkIP9aTc8ZRFVa3Y/fT1EQwXc3VM2WNdq4Hm2i9pfQkSpCGsqQtpK6MF21oMQMTJkf5+rsBBCQ+JLtG8FgRNP4tQSXvLyq/h2ykJ6wS9/ylqap+M4aIMbWraGiBKMUsh6SLysYF1duujiWGCMHXWt1jCjY23mXxOdrOKmEH8cHSTID2ljKAxJJibtqM8iRSSjDUan4v2O2xIzTjIOqhGjFVRHBNVTImQmJpMNqe0tsupbNVQ1SEd1fcKS1UIKS9JabivIjWncssCrGIIeiXZBK1pspey4FdpOMoKwCKturnHJ9R/t2mifYDRZyluW/QEA8bIeK1Kd9RATM+0NXQdZLLJl6I0ACEfhTc2x7Mdlhpvxqsk0aY6YNRsNof1O+rlU5EoKFKA3rMKphHDwbXgevv0/7+SyC/4CWQ2sBk8a9/S+Ucy2HZbJu2a5ZcwXLZtYTdlmiCgVrcSCTqxoh6Ogw5lQLxjpFlLYpkpHQUYU8kjftxpRSQIxGCERMkR4HvJHqXuikuhUc9R+8PTapMnyoRULF16frXFtIZGumMdkMtreB2xTxMDeAwjXRe2LGLovtvE5SRDrVoMj7TlRCuE7yL5ezFzZFl+kQM9VMEk795ee1/47paOxMpu1+/M8nKFB9FzFFpSlsCODTSgFa1cg9o9TO38DuUenMXv2o3vzGFkEJZCFLBJIch5x0cUpR8goIS56BH0u3kyMO1W3jqjVhh3R683bxprvY3pLtkBUq1u9KtfFeC5IKw0AIMPkYBkHITBOcxTRFrPBxuzOtcHzr7gakdhxSYzBrWnccoishXYKQwi0p9Cuff2t//UOuuji6Y4jaSKNACuBrBDiHNqFsxKQO8HH1sUTgO9/5a286NWfJClmcEaGydy/F7AOlcBB7AKTJKi+HszU9LzgJnwfgoB4dGy+G0MHOkfghEzF7sLIdhjwCHt9asOSYS/AEQkbvTHmdIYksBpGScYQ9ybMzWbRsUI00sWrryEWOFWBNweJB0lWgzDIWCLHPPoesm5sYU/b2Q1zgsrLT1GciFG2JpqjPSdddQ2jqofeH3gM/7CM2j1uvwMqLTDEMbrJPmkEkBZiOsVhZdZqGCRpcaapjSNVR5dpQaJw+9ffxuHw+k238fpN8MJ/+RS1IduNalxxBsP/9gAAp77nYR78+Mk8+vb5FPoXvOZqMg+EJL6kusIlMw0DL5hhdmjEdsY6irD2Y9guXtOcRYcR6pG9lOrDVNeXKOyzx26iuJWENYWtm50/HYYHdfpagtmpuGZrPMvt0BTwXBjsw/iuFVRP3cAwHmq6BhKmnztMdURQGzGUtvcxcG8dZ7ZBkvdwZsuwZz96w2pkPtcqkpkkQS0fsee+EZB0aBWJ1C4ZsMflWuaPThNvo1TK8oktfT/9O8tU5FrkshhfQbVKZk/FsqGG+pjd4CPX+hR2F/F3TlpBUd+hsaJI9jGbSMb9eaKSi1tPGUgZH9NTQESpXXMua8cTYkvLN3FsNRfSz9TpvrN172cO+s5oT2IcgZHtrmOclTh13bGNHa01Mv37J1gKvbTdY5Nx0DJL0GNDsTd3vM1Pu3gmYOuBz7J58A0tsWuh7IK8hXmsBXs/kp6HyGZImvfYTqvxpnba4SzAO/aH1hDFOJUQ7TtEeYmMQc446IzEK9VphIJg0Mf1JI1BjzhrndmaotD1QfBnodErUaEhLApUYIizAhXaETh/TiNjCEqCoN+ylp4Ibb+nErY+fNWJ3f8B67Fz+Xl/ZvV3Htlt4086fiVcywDVlWpLtsDEUZtVmv69Zca3f7uO+76JI4yQqKbbpXcwW+5IaLKzLj/nQ0T9WZCghkuo+3fY7/Mju1ps505sGXqjjTmOY4+5kEHU3NYY6KLo0B40OkY2AtukgXbhNtUPM/V6+3WJnC9U37nLBWNii2r8pe7JQkrLsgUwworjh5EtRrmuHQUDKxvhee1G1I7dyOFBRCFvr/8wIhk9gFo+AoUcZnT8oBy+mX81mcQ4TovFJFKdQZHN2IZQc+pAKatl5LnojIPq7SG7c9YybzesZvaUIolnhahL2xsYJQj6XBp9ksJegT9Rb428+rum7HelEZCsG0HWI1SlYZ3Vsn5qSCERIi1gSWXZR0bgTNeI+3KLGl9oR6CVAGFjsxX4F6mLs8X5v/3p9mI5ZTPLWFvmsTGIWKMzDlHBJcpL3Gp3JP1pg64m0mFxJCbS5cBvA6uAq2kXkeaA4z/70sWThn0vyqGCHKWdCd5MjJ/NQBQR79ozL7kUUqArVWShkFJwNSYIrBh3iiPR35vbmLmKDUhJAq5DVHSYfm7Iny+/jQFVYUTVuerA88iWGgRFlzdedgN3za2mEvk8OLqMZNpF1QRR0SYh2oXZc0JGRmY4q2eS+8ZH4OY+/BlDZaWgMWzwJ+37i+5a7UnByZ/fh8n5IBuI2QrJ2LjtqKVFg2ZByLRkc5pJZ9JKxpJKux1pOgqcuhG0ChbNLvzRChR/5/+9nZde/Alqy73W4qaJHb+3eGdJuxJ/skFUsO89860RRu5sLC78nMDC7n8yO4col8lmTwUpED1FS9uenWsn1tocrPtz0M5T97UoBuy5EPmcTYqbbKTmNZfxLWMp1YEAQAp694/Tl7WsrfCkEcJel8oqnzgjGJrKI6II88AjaEDkcjZhVAo9PtlKqGXTGjztULYWCikrShTycNo61L4pktGxdgdVCtRAT0v3QGQzYIztFOfzyEbIvl/eQFQEb8ZSjCee7SNPW0FhX0L2QAOnnjD93GF6tlVwHt6DIwR61TCyGlim29hUKo4q27oX0roCWm0XY5PebMbaRLN4cfWSF3+UuOgS56yOgtDpWJsriLOSC379aquXlBOW3m4UMobirgYi1sR5F6UEcc5F9/o4dU2clcfsCtNFFwBioB9hbGHUVGuo1GFwIYsC0hicXr+dC9ejgWmKDtcbdvFWyKKigPpQjuq5dV6w4TFC7fD9u0/CqwqCXsX42Q5Bv8GpCNwyyEThzyQYmRpnGMs8qqwShAMa2RAUd0DPzhBhYMfPO8gAMmks33VpljU31A97nF0cX2wefAMi0fZvHlsH3uaIk2kEVvrgMI1EjLaj1E00v3vp/0m1ZuUR0iLS9T84etdLOT6NPz1HvGIAZ8/EotfAwQdoWs0DMVOx4szN/GP+B5nnrNZ6OLBxpjli1ry2WgW01jk5QgLaMc4NtIW+OxHH7fynY9zNgC0aVaq22HTyWqJ1Q3g7J2zBI5uFKEaPjttRtEIeXS5b7cSxccyoNb1ovmfzfzk0QJKyHIUUtpF08jrU2BTJ2Ph8UwvlofrSkftUU1BNVe1xJS7hUIGxc31UYHXQEg+qy7M4NXDqYCSMP8tl6G5DducMGSGsIHesoaeAGp+D2bIVdVeKZIX1gBJJ2hiLE8umXNaDrDSIBguLThxcdv6fo/t8koy0MdxYMX+jDTI2vOgXPkWcFZiMICxYIw0VGfyZBBkkaFei+3OoWkzYlwFhG0FN45Yuuni640iaSF8SQnwF+DVjzFefoGPq4gnGbf/xDs54zzWIxIpX9lcSdCmLLHd0QJp0Wy1T5hCQJMhiEZMK9y5qTXokGG1pp1nrXGUSwQ+qG9lSupseqbig9CjX108DA65I2Fvt4fzBXThS81i2n9mJAqWBKtVtvagAhpZPc8nyh/jKrS8kv0ciBVSXW/aRdjVRXuFW4aEPvAVOjBtuF4tg81nvs8F9tmzHhpKkvXBnQdecdqHliAXJBW4hze2vnf3iMR/r0ThqLGQ4nf7+a+h5VONtH+OgElJrnMT+Kj3PFpqMndVXeybsteV5qYWwsno+NMdFl3ZMrSKV0S1reuFnW/R4hLDjcc2OHdiiiRBW42J6FpMkeA/sxisW8Jf1MHtSlvHnD9F/fw5x15xl/tRSvRQjaGkyOK6lzDcCIGm73gDCSxlJWtvvQhiiekroWs0W7FSqf5CKhbeYgkqCkoTLS9SWg4gh1zAU9saMPcdFeDbpDAZ84oykOiLRbhF3bR63qnHnrJ5SkvdgpIRz7472frW2tMvmaAUacjlMIYPxHESUsPnZH+Dau/9i0XNtUotgpQ3f+fe388Jf/BQyMa3nRGLHcppQVXvvlEFb1P7CLVchEsPt//bM1Gfr4vjg2onPs+Wkd4C0NuQijm0xKUk1Q0yHq2oHsxPSUZwOVudS0NY4TF1bHWsEYByJWzb4D2S5Q61j47IJvME6SSWP0NbwIjNudZIay2xXv4Aizgry++19pNEnaQxrVCAYvFujAsPcOo8kA6Y3IKk5VIuw4/fTa2bphohdPE5s7nu9FdHuNADoGJVksbh9JCv5ha5f6b6unfj8MR/n1j1/fXTbLzDz2LLsD+Yz8RZa0aePCced/3kXfhaRuiI2fz/SuVgEltWz6BOt51v7p2PkzWjEgUm8IG20DtgRPr1mCPXATjuOlxa5Oj+rdJ0Fha+EpClxASBc0MYyfcII1dtr2cVp48hEsc0ltO5wepSQ8YkHi8yc5BPnbTO3sE9TH5QE/ZBkIAqw0hYFqKx0UWEJGXawe10JfVncnG8ZxZ6LiBKrk5SOlQlHgZDEORclBNqVXHTZlYds0hhpGcUqMtz2DRvHVUOjHSsnYLICfy51lBUCfypEBjEygLjgcf0PPtiK4108zWBOnCbSM4GJhDFGCyF+H+gWkZ7mWPEv2zCNwC4olYKe4qJ23CZJ7HZSkKRsCek6IExbb2GRgtJCWm6ru6Js4Gv0Crwxl7k4wz2N1Zzv7yUyiuGBMs7wDDsaA2gjuG92hH6/Rk+2QW5FxKbecSZ6ZhivFSh4AV++8wJULAhLEBc0Op8ucNPxtwf+/Jkpwnk8sWVdeg6DsOVYtRia1qqmEWCMsW4onkeye0+r+22/Lx2Ll2aX72jQkZwJx+Xy7OussGwYocOQG5KvHfqzrPpTq6VUbxCP2/b24bY/HPL7DL0/GkNPTB00QrIQOoqRrmPFbJMQM3qgVYQRPUVYPQLGIPccWFx8ehG0GAWdFPq0KylLBXvdep7VTQDbKUyvSxOE6B27W/sSnosII5wgZGDMZ/erR5g5JU/8rPMYvmMS89huVDbbSj5NbM81naKjsiNhTpNVPT1jRUx7SphaDblixC5ImgLgzaSzUgPXJV7eC8Dk6RlkZItIXtlQWWHDV3W1Js4pCnugsD+mZ4elpKuGxp0LMI7CeA7OZA0ciVg2iHEkSd63ncvI6kcJVbLCq3k7jiDrEcZz7OjfAjT1Vy667EoAbr7+XVzw61fjaIOqa5yKIS4oEle0hDadWtLal4jb5+WWre/kws1XLunv20UXR4I5MG7vZ2lBSLguMo47CtbMbwphr1Mdmda4SlPcVxYKJLMHW3C3ik9SWKHe5cP2ejIG7TtkJxOCXodgXw49LAjrLl5DUF5lC761NQnOrCLpj6jmFPXlEp1JaAwosuOWibTs+9YsISxIkkEoPy9lrsSLraa7OBZcnn1d6+fFRrzmbVv4rdZoWlvTMmwzfFrfj8XHtJaMjtde5v5qKwdtFmoOFZsvL/xWa7yryYw/1ji+ZcPbMfXGfGH6Q8RxE0ctkXoTR/NGz1GuZVmHYdooS5ZeSFpQtDJx2+20hWbRSKXPp2gWg4UUJNOzyCiG5cMEQzlm17vEOehZdgrFB6cwo+OoQh5db7Ry94WM8E409dSS2TlEvY4o2rxCrlxuGz/1hh1tb4pdJwl4LvHyfmQYM31ajqDfNlKEgbBoCzhhn8ZdXsN1Y8qjBZbdpsiOR9bxMbDHk+RcVD1CNmJrQJKaZMhyavgxlzYjsxl0zkNG9gPIyJ6biy+ar4HaZLhdfNHHAevC9sJf+pQtKDVi3DDBOFn8Wd0Rx9Nz41iB76ZUQzeOd/FMxFKFtW8QQrwd+BpQbT5ojJk6IUfVxROO+z7+Fvg4bO75XQArjptqJRzEgmgFLtVKIDo7mJ0U3EOyk4RsF5CMsaKyErRn+MW+H1GUDSQZbp89iV9c/RPOyz3Gj+vrmCnlGK0XqcUe2gj27xigMuITxYpoewHOmED6CYkB3Wdw93lQlYTDETv+8Ph0+jstQp+J2LI+1QXSxrphHA5hhIlim2g19W863MI6O2aPCwuKnACmFrW+f5eqKxZNKC/Pvg6Rz2KmGy1KPkJyqboCOPoktLAnSoWdl5YktouubRaVwLcJWNrVEo7TttddavJ50PtAkrolSmNaI1woiRDSJsypRkPrmg20ZYu5DmjNwL0RjQGH6Rc3SPwBVtRDmJnDmPohr/N5ummqfSwEAWZ8Epm6s5j+Ekm+F2dsjmhFL9qTeKNlRDXAGZujvnEAYcCfgsSHxLdOTk4NjK/JTEnyB2K8yYAk65DkFAiIix5xTmGkwMs4qHqEcCTGtRoO2pPgqZZGlJHCjqclxnY1HYn2FJe85GN869sHT3A3O5sXbr4SH2sMYJRBhgkylMjQEGft/fKWa7ujal2cWGx95JMAXJ7/TTvGCvPZFIsxKToaRUZLW9BPf07mygczLTogs1mrg5K1DpJJ1iHJOIQlRW25QfdHRIlCTnnkRiG8ZBalJScPTvLw6BDUXGRdUnpUEPQpCnsMhb0RCAiLisQXzK2313nuriy1c+rs+M3jdx1tftb7D3Iq7WJxtFzKUp0e02E3397o+GrBNEfJjsRyv8z79TQfjdpaQ48jjlM7sivhggNt57Qd49lAi51jveP1ET/LQftd8Hs7F1eIVMSZjjHB+Zun569WQyiBW47IzChGT4PK2RG93x1i5GaDOTCB6u8lmZxa+vGljs16esY6NjcCyGaI1w6j5qwmYdjrkt82BVMzOKMzUK9jntODN2dF8p0aRHlBVABTiFndP82ju5eR2+WQmYmRkUFGVuso8W3MTrIushy03VKjqOXKhufZplHOQ7sK2UhSrSSBUZJD2WF1Fpb8qYjGgHWZFEmMDDUyhDhni3fdOP4MxNOAMXSisNQi0u+m//9Rx2MG2HB8D6eLJxvNMaDNPb+LkBLdSZPtCFAtmmunJWhzO5oPLXLldQoG5rOIbBaTz6FzLrObEshq7mms4mWFB3g0jhmtl/iH1bczqav86+QwPxlbwdyOXuuMlNOouqJxXy+9D1tdksrUEKoEr7zsB/x/285Er4+R23Ps/N3ujPLxwtbtnwZgy5o3s3XXXx5226Ras1o3vb329/2j85htR5WoLRVGz+vKNdFMKKGdVOowhKAxf8OO7/ml8jX2hwVd+MUcU4RS+NmsTVYWLNQOeaidnz/VQLCaEiFMzSB7ipDxEUmCiKN2Anm0tPiObXUjQIIdc0skOgxpuboICSYV9NYS4ghdrSGLBbI7ZxGmh75bM2gXxl8yQmZqmPz2MkKCHJ3ClCu269rROW4m01a0tGNBqqTVkADK56+islLhzeVwq8aOszzXZ+03xghW9JAZrVJd7qIC8PYaGgOS7JRh+hTB4A8chv/rYZuk+z5yWR8ycjBKYqTAqSUkrqSy2sepubgV+/5JVqWuagIZGYSxmgoytHoIXhAjwhiTWWqYtMyJOCcxaWdaxHbMrYsunkhcV/1y6+fLs6+bp0XTQsoCFb6PaDKVOp5r6bJ0LMrtU213KMAuktOOv/AdqitcKisEuVGorY6ZaWQRMdRfWuYjZ32T/5o4h9seOhk55eJVBEnWoD3ofVRT2pa6TrqK8qo84y+IcccliWc1kB779a4U5/HEdfWvzGMjHQrN2NmMc1aj79Bx87hhEVZyKyanuEF/3TKAFmoGLhbH4aDv8aKx3FmkOXbEOL6IBlQYYYisoURzzOtIekhHQudY/CJi+HY0ta2RhJCWWfjAIwRbziUsSOhvoNyE6WclNAaHGbh3ALea4O/vx7gK8fCulmbVvEZQx/hrq6DoeVZUPbbubwfOzwN5Cns1MjbseuUwAw/245Zj3PEKKjTEOUF20mqVZqagPmxwxl2m7lzNuscijIisNponiXExqfC1ESATgy5lkMqykAB01kV7qhXzRaJbWoUYgTPbQDcNRZYAYWxDyGoeAqYbx7voYjEsKTs2xqw/0QfSxc8Wrp39IptLvzOP3g7M77gArYDY4URxWIHOtPOJkFaYd1kfwUCG6nKX4Q0TPG94JxkZ8WA0wO3lTeyd7aGiG3zkwIu5accmglkfryzxZgAkURGMA+Pna0QkrAtSKaKa+Az32YT0jiuOr/XvM5WBtBBHKiA1IZRKxdfrJ6ZotFQcKqk8mtd2iF12Pt7q4EdlZDZzVNoiTTTHO5vJsFAKU28g0p8tlZ2DxkuP4g3ar9Om5dzW+jyJXlRgV0hpx+CmZsgaQ5zrRzuCfRcacnsURpVwqgkZKZEzWcTMXMs5r/U5Ov7uQilk6hqTnLSSoM9nbo2iOfkW5QWZGU3iSxqre5GRprKxRFgU9D0cUR1xcCuG2rCg51HDwPfH7bEbjVASEURIJUgyAqSwDirKFopkAnFeWdFr346aGWn/IazTinJBxgYVeFbjQECcd7j4wo+1qOsL0exOPv+KqzFSkKQOkN/55tJsfrvdzS5OKA5zv5D5nC3wNt0WF7u/HGqUJwwR/b0EywpoX6IVRDlB/JwKhUKdkkrYUJrktmKJn1u5m7/dcRE79gyhJlycmiC3HxCCKA/VEUl1WQ9OA9yqobZcWE3+HnvvuPddx38R12UhHXmMrYmmjfy8scifARxrLD/Ica2jONN0FjvWfGVe4yRlbSGEZfXSMSb2OMf+jJYthm/n4+lBzNtcrVxOdn8NEWepPZihfnIAuYTk2XXGMgX67xNop4Q/2UAODqDiGD02jlAsms8Ipaz0BZBsWkMw6FNd5iAM5MY0UUEgEuusOLPBYeju2LKTigIZ2jjvzUFtGSBg4KdQ2NtABom9JxTsudJuGsOx22mAxGA8B+07rbjdNLYAMK6E2BAXFTLUKK0tU9KTXPZzfw6wqFMbWFbSxS/9OGHJNqIAbvvG0u493Tj+9IM4gZpIJ0xr6QnEkopIQggX+APgxelDtwD/2xhzBLugLp7KuHbu/3CpuqKtV7NIItkU4JPZDLpWa9N4O0SO7UvtzV1mszaY9vWw8zfW0Bg06IxGxAazvZ9v7u7jurHzUAHk9xroETxrz58iEoGIBMIzxAWNPy1RDagvN5x8zi6kMOyd7aH2YC9UfG7NbaS/UHsiTlMXh8Cl6gqbdC7QyXlKozNBOwTbqGlfv+h2h9t1s5vZ6izGSGw3EyVt4SWOMVq3xuVaehRHM+bWdHIx87uVC5lDdtNU54x0LGb/GMU4oXzmEL33K/q2hXhTDUZfUKLHFai+DJlHNCrj21HVat12KaPIdiqzGYRSRKetRoYJascomWiI6PyetNNobbsnV0JSjBi6O0EkhuyBgHp/jjgryY/GTJzpMnB/RHbHrKW1KwmxtnoMQiKBxrKsFcwUdvzNKEGchaAkiXO0Cj1uBYy07+vPgFEAAhna8BjlFUaBDI+sxeLNtpPtON/Vbuniycd19a+0WZgLC8ROKpDrugjfRw4PocfG0fUG0vNa7mvzXyRRw4NQzBOOlJjd4DNzisCfBreMvY6NYKaaJd6bY3bPCKUI7qiehlsRZJpExADCHsjvN9SHBeJZcwR1F8YyZMYFqgZq0sGsWMB46eIJRfO7c0R30KcaDhczD8FmXvKuO+Nq090taGsatc0vFmFDHS3D2Bw8o9U5Ttfcd7x7H44QZKOEVbMe0Z0umz5yP59bdQfrp3+P6cQhXB/i+JLl/7iM7J6KLSwrhWruq2rZSSLj29Gy09YiwwRn+35kMEhlZS+NAYhyEm2lBamviuj9qYtIDM50DRlmSXzIThmCkmUYFR8TlB6tIoxB1kKM56A8O8IWZ6Vt8pAu6DXojMJIJx0507jTDUQ9xDEGk3FJitZ1NSh5SCUQiYd2JcYRsMRlgTcXWxHvLrro4pBYKk//s4AL/F36++vSx15/Ig6qiycXS54jNxqUh1AgioX2grdzcdoJIW3XQCmoNyjuNKiaoHyWxuQ1zqiHWxa4FevM0IQzlzIm3NT1SMK9V7XFsS//9pvnvY0KoK9QY67uU5vLHPXn7+LosXBU7FL5msMz0p7ikK6z+AJrIY6l09jB5ruu8VWr9wB2XEuplmBu6zi0aVPbj+ptOkbO1OKJJ6Tjb55n7ZylxExNU7zHEBVGqKxwcXsc/BlD5kCdqOSj+/LWLaURQTaDmJqxotpN7SUpcLftte8ThMj9k/Q/UKS8ShL2YJ0U+0OU0syty1DYF+KNVenbJiivzViqeSkVqs66iGaX13GsULiSJKUsiWvZRkYKdBrptCPRrtVVqg8bjGPwJyUyss4wGIFbA6dhSDKSxLP7wEB15Aj6Xx3odiS7eLLRGcdvSL427x7dhA5DqIDIZ5HZLCbvI1eMIKam7QaNAFNvm2XY8RUX01+yYvXlgOIeSZT30C40huy1pROJe38Wr+O9vBlhO6/GXuNxzmqi/OD/Wo29077x4XnHJgDjgnQ0ccNl3ZevPK56SF0cjMVGvp/OaLnDPlEM6XQ8bzGTmWNyN+Zg5mAnw2rhPs3MHCLRSLcPmTh8e+vZvPdVVb5x2Weoao9P7HoZ9+9eTuxLGisKZBvLEWOT4ChMENLSdEpdX51H0jgeRsh9Eyz7dsSelw1RX2bQqxoM9lUIpwsEvS5hjwvkKO6OqC53iLLW/EYk1rFR+wrViDFSYpQg8SRRQZF4tskjY4NIQGJSDUNQ9RidUSQFHyUEolJHJAZVDYgGcojYjqnHeQet7D6iXh93JuBwkEGCdiU3X9e93zzjkcasE7bvpziWWkR6rjHm2R2/3ySEuPtEHNDxwJZVfwocvcVnF0tHy4FCG8SmddRXFMnetw9mZlPq7nxLUqEUIp+1DApjQBv6v72L3KnL0X5q3Ssh6DMEffDYW97aeu1JV17TsaODr7rrXvKX7W0f/DRJBiZ/PEzUmyAbT99CxpOBpvC68DyM0Zhqvc2GATC6TS//GaK8H3cIiUkOwa46RhvfeVigP4bR1mmt8z3S/4Wy2xzVmEEzGezQU2h3R+fT4aVrHdxExre2vYll/GQmYpxaTHVVxmoKRQmZbaMgFbgOupjBuIp4VQ/uVB05OQf1hnVcWzPM7KYcvfdXEFoTFgSF/ZoDqwSlTdPEs1l02WPs+ZoDrmTk5j4yMwlRVuDNGdZcW8XZO0m4bgg3iBC1VLTdc9GlLNqTaE9QH5TUl9lmrZGANCQZg8loZD7CxJJQe4gYZCRIcqRjdcIms4IWlV4ewb73osuvTIt9HNZOuIuloanVstRxmy6WjnmLVqMxjYC9bzibwuYDOH83SOEBAbU6JBrVsdCW2SzJSSsRdeuapD0Hdy4kP+pQWSmJ8gbjAgcyDL5gPxO3L281fE790DUHHcd9H283gx74hQ/Ne+70915DfrcgnMtjsk+DbPtnCJd5vw7M1wVaaFN/1GNiT0V0jI8v9tzxjOOgWo8dyvXMPn0M77tIw66TDdXSPhvoI17Ww+5L8zSGE0oPw52Ta6jGPq/s+zHf3HQtYxsrXPjwO3DLkug5gyAGKe0wuBVNcdsMslyzzqlSkKxdzuwpBXoeqiDDGDFXo7rKcO75D3PRwINUkgxfN+cwttpl96YYeSDDwN1QHxYYBY1BQ+kR6842t86ntAOkECR5hySr0iYQxL6wLKKUiRQWJYW9IaJhx9hkEEOUgOtYkx6tEbEmybgQmpbwuPYlItItV7bFcMlLPmZdXA2HNNTo4hmGbvg5JJZaREqEEBuNMY8CCCE28LjV4br4WcVSO1AmSaxg4HQZ1Z9rWYUvhHXlarpIaMumAJCCzCPjrKj2Mn1aDoAf/v1bD3r9I+96y0GPHQqPvOOtnPHuawj6wJuyonin/MU1PPSBpe+ji8PALOhspcXElvhqF20m0XE4H81EXnreoTeSArREus78gt4RME8gt1lUWuyYm7bCjmP//rUaJgjxpwOikkdxew0MhAM5PJmOlSmBrAZQqSFG+imfVMJsKmGUoOehMqpcJ3fAQxhD1JNBaKgPSEqbpqgHLo6fkMiQZM5FRJLZjQK9UzFwTxWhtdUqSjTObJAWjzw70uZajYTEkzR6JY1BCJdHEAnQAuNqhKcRwqBcjTEC3RdBRSEjK7SNBhk1DQVApO6Rd37x4HvTvNOUJqpGHMICposunkAcNo4LSTOF01GM0IbMtGHfnn5GSgpdyiJrdXtNtV4iwffs97szDiSGwq4q+T2S0Qus1ll1jeE7l1wFl7Q3e/DDRxeDvbIVqpcheKFAH+YW2MWxodU8WFBAeqZASHHo9eHxdpprNl6XckxLLSQtFLtncU0zmc0i+nvRxRzBoE/Ya5CxYPCnARN6Naf94V3UjM/v7DqXz6++mTMue5h9lRLVwCOIHMpJEbeqqKwYwK31Y4SguCeiPuhQWSGoDRfxZw3VFX2cc/7DPK/vMX5aWcX9MyOcPnCARuSS90Om/BzVsQJxFrwZcKpWM0mFhtgXhL1uqncKGIOMjGUDZyzDUYb2npA/kMba9P6kPQfhSOuq6kiqK7NkpiJUaBBxe4IBbY7MEjapIDdYQe8uuujikFhqEekdwM1CiMewLOO1wO+csKN6nOgykI4fDkWDb3VNwhBTruDeU4PeHkQuC0Fo7T7NfN0Wqa21eGtBHCfgCmQtYuBHs0ye23NcjllGUNwFYQHcqn1s89kfRAQRVOu2kEX3e/J4sHX8c0C7o/lMSjwB+z3O5ebrH3XiBJ4PK37d4ZiyiCZC+8kF7omLPdfc54LHpOdZIdB0jA2l7LWd8RH9vajd49Y9bv8YoreH2rnLQWTwxmoYKQlW9KDqeZzJCj0/tbom8UCByWcXmT1JkPgGlrnoGQfj2ffvvaGfaL3h3Oc+zEhmjmtWfB+J5PTP/gEqNDi7xy2VPp8H10U8ttseW18vJuNiPIfKugKVlYrKGkPcGyPdBBO4iMgW25rs5DBUeMWQXLbGnMoRFRLCUOJUHeJcKgqenpa7/u7QC+CLL/xYK5kFuPnGY2MgXX6uZWLISStwvHXHwcyNZxK6DKQTh4PGd4xm8Obd5CZWMHG6YPKMEhu+IVBTVZgrQ6JJZmagUsFJEshmMaUcKAGJZSEmRYfBe0MmzvR47E2HL7guBYkLtRWGJKfxJxROGssvff58Meyu0cXjw3WNr84bfXxGMJBS6Ci2Y5xh+LhG2pY6FtdZSFrMFe6oYbTVNetsXC1gEqtSETHQh8l6RP1ZgpIiMy6QoWD77xgQDa76weVgBHLS5dFfvpaPrf4Prhm7mN3VPi4aeojvj2zg3gMj1Ms+Jpbk+urEbszZw/vIqpADjRJjtQIrs1WG/DJ/c/OljGyc5E833sSvFqZhjT2kh6MKv/y9d5DfB17FYBzB3Abovw+GfjRLkveorvDRjiAqCKKcLRolGatVmB2Hwqgmty9Ix9ssG1J7DuXVHkJDnLU6iNoTuBUNklYT+3Cp0uZnfwAjJdJVVphbiGNiIW0+27KcRGhlB7be/7Gj3kcXPzvoCmsfHkt1Z7tRCHEycAq2iPSgMebwQ6U/Y9jc/3sAXDv1hSf5SJ56OFQhqQnTCJClAtGKXtxRiajWrAgv84OqiaM2K0kISBJw2p2ZxVhIx4J7rn4Lmz52Ddo1ZCeab27dHETlaXDVPom4du7/zPv9+vCfgGNwSHmKw4QhMptF+hlbJD3BRbRmIfYG/fX0WkyvK6OhaUe8iKPSkbQWWsXgdPt5z6Ui2Ca2yZCp1RDCjoxRq0OpABJEPoeZmyMzMWDdU7IuxhHEeYVRAhFblqFshKhKQP6AR6PfsQ5p5SxuBWQMteWGyhqD7ovwVMwpuf1YiWyrneJPJ5YBISVUq5aiLqUtCscxaJ+4J5NqGWFZRA2FaShUQ2Ca0S4REAsoxDhOQr3hYbSAxLq1BX22EO3PHj7pBLjkxR8FJRHatKyFjxXX/chqwmxZ12VNHgpbTnsPAFsf+PiTfCRPPTSZSYvF8uujf+Hy7OvIlvLI31OsL81RvnsVPY0YUa6gy+XWfURPzyCqNYSS6J4MzkwdESXIjLXZvufq4/P9vefTb2Ht33+SDRtG2XH/ClQgbaHVX7o2WReLoxm3m2h9N55hcdwWX+yol6k3jjmOL5Vl1Ny2Hcfbx3GQW9zhkBaMhFKpa16qNdo5ot7MvdcsJxjO41QikJCdiFGBIskIwl0ZzMlVpNIs6ykTXTvCy7Jv4QuX/wNrs5MMuFVO8fdzl1rDmv5pSssC9teKxInCUQkzYZZHGwOUGz71hsfePQMMnXU/Q+uneOdJ1/LqfHXeYS9XDt4c+DOaxoBEO9C7zeDWNKIeIaXEqAxRXtAYSPXVHLvY9iesdlK9X5LbB4mvcKoJc2t9VGRdIb00v9eeNdAQCajAOr0CfPdrb1v0dF76/I9YTSWtEYEmzmaX/rdYgGt/Yt3ftpzeHYPr4umPpTKRAM4F1qWvebYQAmPMl0/IUZ0gNAtIm/tez7XTf/8kH81TCwsT0E4RXpGKDLujczz2GyMU9sDwNx7CBIG1Bpd2VEaki10zMkhSzOCMzUG1hpyYOe7HG64Icfd7+HM2KRCTs/Mp+PoZxpw5gXjGJZ7YBC2pVJC53EFjZDfor5+wc9JKPDuFNA/jmnOoAlLTMUZIGwI6E2CZJlDC9yy127OOioB1WfN9u5CLYsRUBTPcDwM9qFQnZW5TEdVIk7miIs5lccsJIm+vfxHZY2+cVcfPRFTmMrz2nO8zHefYevs5lPqqTDTy/FvlOfz1fRchhWH1d6s44+W2MH/Gt8dWb9hCErTG3IIe0RpLw9XIusIt2wKREYIkIzGOIc4JHKWR0hDWPOSMgwytS5swEJYs5V5F8Jw3WlZQ34NWl+rGW+YniEIbaw18oe06XnyRLXLcePN7Dvm3WQxbVr+Jrbv/6qhe80zFlk3vYuu2K5/sw3jKYWEsF1JwmfuryGIRMV0mvHsdv/Art3L7myo8+A+nM3R7gmgESOp2Yeo4VnuskGXsOVm8uQyDP5xGzdStNslxxguHHuPAqiJ1U0T7rm1jQqqPorns5/78kHbdXSwdz8Q4DtZNVSh11OPgC3E0wtiLxfGjdmVLHVWFUlbvKONb44soQhTy6KFeiDUHnt+LSEA7PmGPLcz0PGaoDwjCVQGnLRtn9F/WMdlTpO9395Or5PjY9pfxD5v+iXVOEY2mNvATRpxZGsZhdzQAQE7ac/WrhWm2PPQydkV9vOSMh3jvyHVcWzz1oAISwP/a8XKiPDT6JEZBnIe59YKeRyXRcAEEqMAQjQhqqxJElMZyYY0uVtymSVxhR8eFIcm5ZGY01WFlx10NyNDgzxiMEqjQ/i9jQ21I8ew32Tje/4A99qZu4Q3ffT+bz/4gOnfwzOxFl1951ALb3WbH0whdYe3DYklFJCHEV4CNwE9oayEZ4ClTROpkIHULSMeOw7KSpmdxqiNUlwPFAgJSx7YQ4cg25Xb3KOKUNZish6jWbEFHHj8B7C2nvYdl5w3hNDRxRuA0jGU9db6H1mxZ9gdsPfDZ4/a+z0Q8UxPPFhZhnlwqX3NiCkmHG03jGItXStlrI4XM5drXSca3TnAZ33Y4Y+uCRr1uGYSOwgz0kRQyxAUX7UtUI0EFBiOtzoF2BN5sjKpHxEWPJLXM9afB/DRLbaUP0pBTIfeXl6NzCXPTeU4emKDkBuy8fznejMSZHrf3iVwW4ynbPQ5iOxLreVbIO+cxvSlLdSVEJY2IBSKQyEZ7NC3JYwtIeY1wNXEiyfoRUS4kmnOQNWHHZtKFqj9riDNN7ShbPLr4wo+1i0S3vm/e6WwWl462eNSEqRyceHfRRmdS3i0gHR80G0K6XEblc6y+ocY1ziv5z9/4FFf8/Aoau0pklECOTUGi0fU6hBFix16cWi9RXqB9F+Ed34x4y+nvhbf18pUf/RzC1dCfLvCbBe20KSSCrjzn48UzPY4bPd8IpomjiqlHy2I6TByHo/ibCGmvBdcF17ENIdcl7M+AgfI6gzctqK1OELGwBhKewCsDNYdq5DH7wjqlnjpXn/x1nuu73NaA5co6G0skr8xP4ArFew+cQZ9b5ZdLP+HhqJ/d4QC3NAS7p3upz2XYlh+CFVA7hIDZVJClPgzRYGzJU7kIxjKEBUHQ5xJnBVFeEvRB/5oZag2Pxv487pyisNuOqmUmYqIeDxlowlIqvq3S+O4LVGCsFpI2yMCeY6eR0OizbqsIWzy6+KUfbzV9brzlvS0G0UIck0NbuRvHu3hmYKlMpPOA040xT4O6WRfHA0IK270pFsG3AcM0GlTXJ5BJaJw0ROb+EILAdi4TbS3RdYw0GnHPI4jennRR6HLtfR89IcdZXi1Y+6XtBxephMDU6mwZ+UMAto7+3Ql5/6cznumJJ6Q09kW0DE7IuTlckiqkLe6mhaaliONfqq5oCWYLwAR2BFXksiAl9VOWISODv3/OJkWprS+uazXPMr4d43IlcV6RuILYF8gEkqztAnqzMe50HSME8bCyM+CJwQkMKhA4ZcnAc8b4zuRGtv1wrWXke5qH/+1k6kNg+hP8Kaht7EOGmvIql0a/oGd7QnY8xN1lF5a6lKW+PNdyeTQCjK/xphWqbjuejWUJxtPITIKXiSnmGkSxotbwbAe0Klu6K2v+e5ok5xEM+jT67OqitKPBJS/5WOrAZlqubYue2xdY3ZYbbj86vRbhHA05+BDvra54Rthzd3F8IJRCnbSOudP7AcjvqZE5Y4ZT3TzXnfsFfn7dO/APKEQhD5UqJozQKQNi+F/uITljA5X1eWpDJ9YJdcPfWxtwkejU8huIQSQJl5/7IXTGPerrrYtuHAfaxhILcMLOzaFieTOOp883C0qHw+XZ1yF6SujhXuJSBnesQrSswNRpHlEBtr3us+xNyrx/78u4/eYzyUxafaHaMvDHFbujEegLCUKH56ajoi/KALTHRr/TyHDD3Jnsrvdxdn4XQ1IxKhrcXV3NnrCfK5/177w8Z3UPH40Fv1j8KVA86Fj/4eR/5m3+L1By6/zD6tt5NK6w+fY/ItmdQ2iDMIKpszRrT9+PqxIaoYM/KcnvA39O0+iVzK2x2kciAYyN7QAqsONuXlng1AUo8GaS1qh5ZkqDkZR2hFx02ZV2UF6KlnPboXDxRR8/+qbQ4YxQloim3ujC0dMunlgIYxAnqPRxovb7RGKpGeu9wAiw/wQeSxdPETQXKJdnXmv1UdICzbUTn2fDX32a/IoybtmxDAfPRYQROoxaduQ0F0pStsRwjxdOuvIa5GuHiXo03ozEn4bRV20ArDjulpV/YjdsFrVmy+gw7C68ujg6NDUJDlNIOOqO4nHEUr7PNyRfY3PpdywbyRhELofI51qdfu1LZJRYtlWz0wmQtR1KohiTt2NlTi3B5JsWxobYlxSaBZ6Mg5GSOCsRGpyGbtF4o56EvBewb66EUxGoBjgNRd/DMfVhh/xIBbOth4nTHfw5qKyyLKPcuMRIn2KlaAUsjUE1bOLtliHJCHTGkHgGpyZswilBOAYTS+JA4RUTil7Azv0DiEkfJ4G4AE4lPUFSkPiy5dRyNCKbx7qYbQrWPx5072NdLAXN78ll3q+TlDLEORvHr//eBznrv6zI+4gqoAJDY0We3ANV9GzZiu0DMuMDEJVcyqsl9eHje3wrv7yPEbOf55Z2MBEV+dqbnwPAfa/6sxaDQDZS045aiNw7zpaRP+w2hLo4JhzOqe1Ejqgf4mCApcXx6+pfYcvqNxH1Z0k8SbKmh0a/otFPi1G7XOW5c99qAKqrNdGMJBhO8AbqmPEsuULAucv3cE9Y5yRXkhU+c7pOSdrR9oeC5WzMjLHSn+bS3B5KMs+IUyHWiuXeTKuABDCjPSIjWefAax67hK9v+FbruTVOkb9d+x/sSxwgQ7+ULB+YZd/aDMU9EgxkxiQ7nBGcgQY6ljgK4hw4dUHip5qFMTZXqNqCWJy14tvZcdDKjstlpjVhr2sbPkrY/MPATTe++6j+FMfCKt762KeO+jUL0S0edfFUwFJbR4PA/UKI64QQ/9X8dyIP7GcVl2dfx+Yz3sfmM9535I2f5riu8VU7YhLN12SplDOMXlBg/OUbSU5d23q8KfQnlEKODBOvGSIcLqKzj79qf8Udb2Tj1z5K7gC4FXCqEiPBmzVW4ySB83/704xvXs/WvZ+xlHwh5s3AH048vIv5eMZ3L41Geh46ig+po/CEnqOmK8tRUuqbQukin0MM9mN6C5ZZ6HsYKTCOaI+Cug7kssSDBcKVPZY6Xw+J8y7aETT6JPVBRZyV5A7EVFdkqK7IoH2HJOsQ9FgBaq0gLFqNosJ2xZ7bVlO9v5/8Xlh5S5WVX99B7id7GLjX4F3fg9Dw6tfeRvmlVZKTavSfNMXUy2rsu9hQPqnE2AsH2f4LPYz+nEdtmXVkzO0V+OPWhQZtu5TulEKNu8gpF+exLAceGGbnqBUDz44K3Fl7Tu7/mBUGltWQoCQ4cGnIgUuPXSuji589XOb+KlvWv5UtpxzdguLpiOvDfwIhyI639YzueeWHWz9//+OfY9UHH4YgwCQJuhFYBqPnok9bx89fcxP1YdC+QfuPr7OqRzcxuncFV92/hX63yu8O3cbFuYfocWqcPnSAF616lPWfuZrtv+jz6K/YYrY4MAlzldY+muziLo6MZ3wcB4TjIhz3cekhHTcsEseXkpc2dfRUqNnxakntV2cJlscEI/aalkj+6tlfwzgGowycUeYXnvdD7n/hl/jKls9RG8uzMT/OGZ5PVtjicLOANKmr/GHvHjZ4Y/xc9lEGpKX+bHQKfG7VHbws/yBzut46lnM8h9VOnTP/+g+Yef9qPjOzZt6xDqsCZ3v22u2TOW498z/4lQvvYPQCwb6LNQMv2c+qk8cwRqDLHmF/QqMfKqsEcRZUaAtIAJkZQ3YMZII10oitEHeUh+/+a1tEe8+rEkYvi5i8tEEXXRw1zAn69zTAUikgf3Yi3lwIsRqrqzSClUH9vDHmr4QQ/cDXsELeO4BfMcZMn4hjOBpsWfYHIAUinXfdfJYtJF17z4kZxXoqYOv+v533e3ZUwGgGAwS9grDkkuspIUW5rWGQzRCu6ccph2hHMH1qlvNe/+klu7Nd8hLbhdx1aRb3rFnue9Wfsb9WxPcj6sN2nMWftg5LUd6yEGRsaPRbvZMXv/KT8NIzyd50b2ufzfG8yzOvBdICWRddHApCWvr7CXZlOyY0tceWiGunvsCWNW+2bKQwsdpHiSZzoGE7mUnS/qcUMkxQlQCUJFpWZG6di1M3ZKc0Qhvm1jiERYl2Ib8/oT7kkZkIwUDQY4u72rOFXZ1S0Y1jqeyqGmAqVYTvUXq0yu5LCgD8Su+d3Na/kT6/Tqwl1bpP5GichqC4M2FuvUuSMRgFyawgydr9Z6ZARQYjIOgTxDlBnAPtglMWUM4QrW9w38fnO0pd+5M/5/JzPkR2MmHjF9O/8W8eh79NFz8zMFMziGLBxvX+3me0COr1d3zgoMfeO/YsPjb8UwDesOzbvOPSNzLw3RzUG7bIPDbBnkuK7KwPogJgXQ2plp4ZX3b+nyOM4dFfKbLiOfu59eJPAnaRucyd5Td67uZHwSBX7d4CwEnFcQLtcN65j7Cn0sPknct47IoBNv71ZHunuRyEIVtG/hATRl033i6WhE4X4Z8ZHGUcv+mGd/PSSz8BGiqVDKpsXUm/F8QMyAa/d8ubyU0LnD2KWT9Dn1Pjt3ZexO5KL5+8+F/4pXyZxXgFzaLRc/0KXytv4Ln+/IGUvJStghPYgtVKVWTwvhh3oso1t2zmT179+cMee1E1wEC2v8764hR5J2B8roAaqBNOZ2wTqGpz+qRuWUcYaPRaPSSnasfafvI38+P4bd94OwBbbn0TAMGHl8OvLfmUdtFFF0fAkopIxphvH+55IcQdxpgLjuH9Y+BtxpgfCyGKwI+EEDcAvw3caIz5hBDi3cC7gWNQNzuxEEEMtTpbTnqH7dLTVeMH6/7wO795Lf98zWYyYzVwUj0FrTFRhKnV8e7ZSXT6GsafkyU7cejEs9mFuSH5mj3PYcQj71nFX13+j7wiV0u3+rP2e+c0aEF9GeT3pGM5LnhjBn9W45UT3LmQqOQx9ZpnU14nWHvVXXaEJ47taE8Xh0W3e4nVUIiPXEB60s7VsRS3pETnfGS9AVIggwijFFTsNUw+B9ogx2esoHUxj1ESEYNTN7Zw5IBbtQXbzJQBAZmpiDjvkJ1MCHoPTlKFAacqqKyG4W9HtkHjeZDYLuPcRjjLy/K29TfwilyN/65leNPeX0VEkjgjyJZD1tygibMKtxxTWeUjE4Nb1ex9sWLj1ysYR5FkFZUVHjOnzB9B3P5aS1fffMb75mmzXXfXh3nRqz+JU3lyO9Sbe36Xa2e/+KQew9ManofetZctK/8EM9ADwLU//ciTfFBPPt4x+D0gB8BbH3gN1bWCODuCahhyB2L8oRJr/3EX999+Jo3fiHB35AkGF3eKnBfHh95Isn4F+y4scvObP9VapMIn571mRBV4ea5Bc/m5q9bPXTtXoSsuvSvmyExCaWfCI2/ZROnZEwy/oWJZ0ULMd2LtoovD4HDupk08qXH8KAtJG/75Y7z8lPu4/pHz0C7cWd/Aqf4+3EJIYbfDxDkgQskXv/NiMvsV2odzT9nHYhpGnfhu0MMrCg8DhSUdi6pr6qtLyJ6Qa+sem7OHjqNv6LuLL/S9iGBfntsmTsE/4BCsDXCzMbia+FlVvNvzGGELSULbEbZObHuvLSBtPts6NS4mlK3dQ8sPnGhsOekdbH3kk0fesIufLVgjwBO276c6jpcYTeZYXmSM2U+qs2SMKQshHgBWAq8CLkw3+xJwCz8DRaTFnLy2rPpT+0OcgKPYctp7ntGFpGZH/57dV/O5kzbTsz2DF0SIuoIgRGgNOQcTRrg7x1mWDLLrshxR0bDhmk/z2FsOw0YSAtNbwJ1VHQUki2YX81B4/q9cDYBWgqDfxygbTLIHYOy3zmHoLkuHX6wj20UXjwdPqJ7CUQhrd2Lrrr8EmD/eo0EmsXVXxINK1S7OhAClMFJipCDsASMlYQm8MmSmNEbYa00khsRXIAUqMtz5xcWv7+e97tPMbhAk/Xnk+KRlOriSzIxm2q5jWe1M8fXKMh4Lh9CRAgO1IYlIXPzpGBnawll+1Car/q4ZHv3PT7D5S+/HuAqROPzgS0c3vnTbf7yDzWd/kCTncenzP8IN331yhHu7haTji+ujf5n3++V5SzMT02VwXbZsePtx0bV4KmNgxR706CYAnrdsJ/+zshdwUIEAHFsgbpRwfvAA/sXnEPVoeoYqPOubH+Snr1jc6ehSdQXBy88jLCm8Mh0FJAs5sg2A3xppP/bNjp837LQs5Mq9/bh5mDhLEQ5FvGr1PXzljy7ipL/dQe2slXz7f955vE5DF10AT44u0rHo2z32a+/lT3782tbv2khuLZ/KhqsSgoGI3D4Pb06BgKAHjAMrVfYwe7Q41Z0kJ5yWVlLzWh3q2KZ5vwAI+hxUoHnVaT/l/sZKNme3H3LfAzLPK571U753YC3ju6zAvxr30Hj4NcFDH3gXZ91xDTpvdQsBHvrAWw65v4XY+mI77nfhx67kws1Xcsu1T85ycsumd3VdRbt4WuF4FZEedz1NCLEOOAf4PrAsLTBhjNkvhFhUslEI8QbgDQBr1qxZbJMTjq17/hqALcv/CHpL6Nzj1/d5qqCzu7gQ79/5akrbwZ0JELNV9EAPMxcsR4WGwvYqsh7ATAVn7ySrbpaU1/gknuDc37uGH32hHRxuSL6GHt3Es956DSsBUQuIC5ppXWNKazY6h+6KtES0fZ8erNjdBb92NYln9R/67y0TFz2mTs2gtu/HDPXx3N/5NLmJhPqAorpM0LNdz5ut7uJJSKae4rhUvubEn7Oj6FYeDlsf+kTr581nvA8RWjaSaY6zSYHI5cBzMVkXGWuSF87iejHLP5YFbZg8K0/YA6UdmrFzbYgZuitBRYcOE4XdDYLeLI/+cp6T/nklE2eX2Pi/trFz70rOXDbGz73rjcQZW7Aqb4oRkcB4Gqcu0a7VbsqMVhHVBslAgbho78NbRv6QZMMK68KCHYU9lED2oRwiRT3EGX/Sp6mfUBzu3n688bMQx6+rfhlI43gx343jC3D7vvVk9ziEvYb7f92Kv98ZRPzaf/4x/vg5ODVYcXvCqR/Zzt+svAM9+i+tRWZz35eqKxj+bon7v+Qwcss49TU9Szq+l15q70neeI1NwLU/eS+bPnIN9VUJuZ2Kdf8h+P9uvZA+DHqkn5lNLus/czXLb4OZkyTBs+r0fNsukn/82aUvPLvoohNPSByH4xLLP/Ocr4LVoecNP/wJt+7aiNhcZN2/HqBUGiLJCCorBJWTYtauH2NbHHKG214O/r9qMR1va2OdY5lKc7o+79ruhBzZ1iokPeudP+GU3AHe1LdjSce8pe9u8k7Avz74fEQM7pwgKtmc4Tm/fw3BaghWRAhHI+bcQ+5nMQYSwIWbn9zizdZHPsmW05duznG80DUNepw4kfpFXSbS8YEQogD8P+DNxpg5IZZGOTTGfB4s2/m88857Uv8cphHA6DhsWPlkHsYThpY9+KGeH7yfL7KRqTOL+KvzqIYmOx4R9DkEQxlU6OFXA8zcHJhBxs+zV6oMBRv++moe+9O3seGvr8b0hzx6CRS3jMK/R6ANhR2SN+x4JdoIvvGCvz3kMTTR2VFu9Evu+tt2InnBr11N38OB1VIQgv57y0S9PplpyI4ZUIJLXvIxJp5lk9CffMa+dsvyP7L73n/k9+/iGY6mbe8JfYv2PXMhy+KYoYQV1E4Sy0ByHERngmsMqhZRm84RZGP6FaggJjNttYpyow1yo7D70hzf+fe3H/atbrzlvZz19mvwNpSZOqvE9Onwz+tvYnJtlRf+w9vZ8O1d6MEeaqsLaM8h/4JxHKUZPXsIU4rZ8GXrwJIMl+Y5o21Z82Zb+IoTRGK47s4PHdUp+Pj9L0N+4SQemRg68sYnCNfOfpHNPb/7tGUj/czF8f1jsHHVk3kYTyike+g0sLko/OxZXyU60+HRcJiz7/xVPnrGf/DyHPzeJTfy3cmNTP3VWty5kPOL25GprsruvcsBWL1yP3p0E9fttfs8xzsdnbPC/V8t91PTVsj390cWPYQWOheHcUkjA9ESwQd49puuQVYDvDlDZkxSXgW19TG5e7JkJjXFx8qs/YerANj5vyxTaXPpd+y+U4OBLro4HE54IUnIViw/XnF8bXaSwdIyil+ZBqnw5mLqvmt1zISh6Ab868xz+fDQfa3XPBIs479F1HJd+6vpdbypbwdyZBu9S3zfv1v5/YMee/m2LTzw2ArWrR7npjPmezNtzoac7H6XrzkXEAzaMLD9T2wD99QPXYM5pYoMLIOKvqMbMdejm7jp/9rimMWTw0QynsPmsz94yEJXF1081XC8ikjHPGgqhHCxBaSvGmP+PX34gBBiecpCWg6MHY+DfCJw3Y8+fOSNnoI42mr2H596E3/8WXju73wa7QoKD80SD+SYPNMj6wt6ttXQ/QVkGjD9SYlqQGmXpt4vOe/1n8Y8G/xcxOdmV3LbWd/gnlvrvP6+1zGS3cVP96444jFs3fuZeb+f9oFrGBhNeMFrrqbRJxn+78eItqxHRg5e1kdnPGQjxDsQoQoZhNYYJVEzdZZ/YxSyGZ77u59mcOujrX12OsE8k6yFu2ykJaKj6NI6Z0IeP0HudP9Gm3mFpOMBnfNIBvK4+w/YB+IYkyQIJcFJbb6rAcuvLxD0uDQGEuLVGTITEdlxe+2IRB8V7fy+C77KSY3fxiSCP953Pn+z4gf89W98gb/84mbkbI1kYxFvBqq3DVHcY1gzGVMfdDlwnmDgAYVbibnocttxvPm6d4HnoioBIowP/8aL4Mx3XsM//8lP+acvXkr2oqmjfv1TGc/0zuV1P/yzJ/sQTgiOhWEmR7bBzg2scCr89u2/Q8/3Mrz8uXZxeX95Ba9feRtvumQt+e15frtkU7W9SZn7wz4+8NCrkfe+hy+cXmO1EpRklrve3ZYFeNO+5wJwZn7PId//phvmj6BeeOPb8VaVED8psu4rlqV08mdjaq/Is3ezLfauuK3OxLOzlO53GPleBRElyIlZTv/wDKZSZZ2y94jTfB8Tx7bYDBBFz6jGUDeOLxHHien7ZOz/N3t/xHsGtvGcv72C5W8J8ScbOPWE/KikvNFlz1wPj00OEGiHTyy7G4B39T9C3QS888DPAfCHA7ciR/Yf7m0WhUa3isoAm4pjzP33GrZvHoYz2tu9bfQ5vKj4EK/Ow6Zn7eahB1YhihHrv/pxtr/2PQRDmoyTENVt3rHjN4/NVfOB+jOjyd+JZ3osf7wQnDhNpCdPoev4YUlFJCFEHqgbY7QQYhNwKrDVGNNUpHvdsby5sJSjfwAeMMZ8uuOp/wJ+C/hE+v9/Hsv+n0hcO/33T/YhnFAsvBEt9cZ05/9pa6Bc8uKPWnvvgmD3pXlr8b2nRP+9ZbLjWabPjpGxQ+GyAxx4YJi+jdOcMTDKV3edz47hx3jr4O1ctOJhvrX3FIQ0bPzjUViQ/xyuW//AX7yFF/5Sm5W0sMgEVuPK9JcO0j5paV91ImVnEB/9AvVnEUfdlT2exZBnAC5VV9ik/XiwkhZJOo8bA6m5v+99kJe84pM4WiNk+n5NTSSjkZXAspECg9BQH1DEWchMCpKMQjUSvnXr+5b8fvd86i3s22u1zcS4z9YHzoAVP+DSbMxfZnyM51i3RQNDd0d4sxEYg4wNiecR9Ciy+2toVxEXXS66/EpufhxCltqFm6qn4k8bpuayiL0ZePkx766LpwCeaXEcluZE+vy1jwGw/bVw4UibVfiltbcC8IpXf54/nziNL84t43dLB1ipiqzMxhRP+xeq2meDoziQhJQW3LZ+of+HfOLXfoOHOJXfv6P9+MUXfZwbb37Posdyy8Wf4tR/n9/JXxivL77o4wz+tHHQPraseTPxWevnPXbt9N+3xHhF/WfA5v1xYsvQG0HJpTe1unH8qNCK48ej+PYExPEmE3DZ+yUmZ/VA3fEKwcoSMhDMThTIbPc49YyDi0Qj3gyjYS8bVi29gNQcddOjm+YVkACuWf4jzl51Ppc++57WY28bfQ7fG1tHToY8N3MH7137P/xp+QrKc1kG+yucf+172f7HHzvGT9/Gw1GFe+ZW8uDEMF9/7EPc88qnZ8O/iy6eSCyViXQr8CIhRB9wI/BD4ArgtQDGmHsP89rD4QXYAtQ9QoifpI+9F1s8+lchxP8CdnFQqeD4YHP/72GCAGhrInRx4vCtW9/HGe+6hpnTE1ZtGuNdG67lsXCYv/mPl7P8jphlt84QrCjh3prDnGzI/VeBHaVTmfz/2TvrKLmuK+v/7n1U3ExiliwzxcwgKZlMaDJhhs8OTOIwj+MkjlnxZMI8E4edZEKy7Jji2HGMsmVZlizGltRc/ODe749b1SC1pBa0wO69Vq/uqnr13q3qqnfO22effU6y+D/Zyu2JsxATC6itif59vuPRd7Css42Gt3SxaMe397qGv9++57YaGH4yT9X7ajD6VUiJBAQBCyZ+iEUbb93r/o9URPkC0rGZF3sTKjDE2O7Iwvk170TGY/0Emo6iYZNRrXT1j9Fb+JGGamI4zGvuJ5D2J3HfKeGsKo/63+NRQK7NJtHWAtkculA0JtvxmFlLbx+qp5dU3MUupIg/uxVc41VQnNm024vAPeH8n32MWLfAKoPeEmOm9XauPPF+ur8G5f+rp3FJHrungLYlYV0CEUG5xiaxI8TtDYniNloKgpSFiDTnvfJGHvj9x4ccY17qbQAszv1kj2upfy7iV59fgD9BGAJpDEcs5sVNHWtx8X8P80pe+LjvkptQ7X/Y5f4vNC4np0q84vlXcFLtJt5a90/O9FJABHi0WHDK46+jd00tc07cwPzmZbw6bVpofnP79+jZYuaw1Y7buNc1PPfqL8Cr933t1QEC/Xj7wJ/aNhfZ807+TxY/efReYKpsFiyLecm3okomv91dHF8w/oPY9XXochmtlCkSDI4nlRillR74O4p23dGLgIgSVW9AhikIHUAsPxRxHGDRop9zxqevILU5wNrUgdfdS830GfSFLpNveIKfPPqvvOkH32ZzVOTiuz8MwAdecg83nXhwlSxLPvktbs+nOeZ7V1KzSvPxL9xGWdl8qfkZIM1N209lRn0na2U9U2u6sGXEm/75Hm4743tD9jPvtKvRjrXXYTiqfRZ/LsT44J3v7fegSY/P7vE5o4WgLk7kjbKqbQwHF2OeSHvESEkkobUuVEidr2utbxBCPHmgB9da/53dK7ouOdD97w13dH1v7xuN4aBi2fUDrS1/XHMHAHa+Msmpo4dYVx8A+fPSFJs8Wv/eS3hmDWFzQHytgyokqYoinrtpAsuX2Lh1JTr/eyJzP7uQ5GuO4/R33rLbKVB7w3Bk0W63rVT6Fky5CuJH/0XmXdEvmRd70943ZKdE0rKMP5bSoMXQhLJfB2qBVkOftwey5ahF9bULOaoS9UMlUS41QDCuFmd1qZ8o1KUylMrg2IhUEpEtYjUk6LhkEo0PmIrlfYv2bzqS2yuQIQSDpg2fnXieaTO286nxb6XhGUGUiiGCCKsQoBwLJ28+U9oW2LmQMOWCBicbESYtzv/XG/nbHz7eTx6NFH5KUvdML92za/F6BOXaF0DEf4FijDw6tNjZWLdqppuSMeY1LeOHX3s5f+Z8HrvatK7d1DWdu3fMRimB9AXL1xu/pO1+hvf89Pdc+vSb+eysv/DyRIE/rjmBM2+dwX8/dzEfmHPPfq1vXwnsqkfJxZdct5ctj3wsLt3WP21wr/ArzQRCIFwXkUpCEKALReOHJwRobZL0KDJEkx8MiePSsU1cCIMhZJPZr9z19nA4gnOAqvenjHlE+YGpwAdDUXwoW42eD3L4NQKxQUMmhd60lZbfraRhxnhkbQaA/9xxCtc2P82aeT9g2uJ38bFjFo/KWk5wt3LSpSt48pjxrC03k7CC/sdmxreRjzw6vQQ9foyYFTIt1cFVS17PwpOMSuvyM6/Zp1agX+w4A5EK0H0OCA67CulwTogbwxgOJkZMIgkhzsIoj961j88dwxiGxcunPQ3At1k47ON9p5Uo1ddQ95ym4WdZRHeWcFITnccn8HoV1uMOW86TlKVHYr1DzVpFED9MXaZam5+jGPPib+lP8qTrovzhpf0LplyFTCbQQYioyYBlGQWKH6B7+8ztCkRVqVSpcgohjHqp4qmj/QCobD8okexPRneu2A1KSndJWA8E+1tJHUYdJNy4STxtG7RG+35/RRjYlWTax+Me6h73D/3oF1z91XfQ/PsVqGwWVTCJtFVbSzR1HIUJA8rARQfQPgYw+Web2fhvA74Fq1//GVT7j7lblbCLIEsR2rWIEjYy1GgJItIEKYvcOIfsVI+a5zWxHkV+vGvaZzNDzwl7UyBV0XEKJLcmqF155F7gHAqMVLk1hhcvBk9lGg4fq1/NyfF1vOev7yTWJ3jn/L/z6YaV/LkQ45oV/8JPjvsJc5wkt+fTfOrR1wAwvqmbD8w5VK/AQDnmXHG0X+TJ2hp0NoeVTAwhPgbjrDfcjHXeDJx8RO8Ul1ITBCmwStD4dIRdVGgBdjHCygfIfBkZhFAsgedCbxaScXQmiejqQ/cZBZT2fVNQkgKiCBWE/URMNYaLipm7KUwIVNF4bA0bC4cMc9jNubhqSF2J48Oqevaw735z+cpt4diI2hp0Ook/Lo0sR4hiiHx2tYnlOxNj+xLLK9se6jg+00nx5Ke+xZwfXsn0b2RRpTI6X0D25ej6t5PZ/hLNhqWncu0lJi9f97b9//xfcuG1wNu56xc/BnY9Pzztt7Fw0h9onZoip0p0qQAwlaP31W6C2k0wAf5YSJCWRZLC5099Jw05xr741v3v5PuZvWEyAc5hVX8oVyJ0xa/xEGPwdLqj+dx2qCH0KHoiHd2XjMDIiaAPA58Gfqe1XiaEmAbcO2qrGsOLCsu/NLzx7tSffpWmpyI651og6mh4QlBs8ShnwM0KgpRA1QeMa+ume2MLQcKoGBruXt/fWja//j3A6KrOFq1byIKJHxq1/R8u7DbJURoScUQQQswDx0a7NnguIqhUlBwHlEKXTHIookpyZUnwJSKdMq2kkRpECg0QOUIKc7uCquRbSIGOBt9nHRiZNCT5FPvdfiek6CeOhOOYCi4gbBuiCBmP9ZMv/fvfB6XSXerX+7Seg4VlX70K1f4tPnhyRNPvlCHGomhgMqMlCBPmdQz2P9sfLJhmWk2fuWHX88F/b76EhmURUdIhTNl0HmNjFyC1JSLfZhEkodii0Rb0zhSwSuL1KIKURFY+kvtKgkTpkHybQ2qLDwp6ZnmccuVC6p8t8tf7D92o3vl17zaqAPXiJrPGcGSjqk76QCt8oL+z3HyXVfssLolHLJ6/kE+sfzWfbjDbNltZptV2McdJAuCKiJpMgWw+hryxkQtvHCBzLj/rS3ttXRnDrtgTWbH9VAuvyyI7PcJtLnJcWzub+mpojzeRWWehLIg8h2S7i5uLI8sKr7NEmHZxdrgETSlEqHD68oiEmWBLFBnhcRQZlfLgoQ/VmFcpIglMgUnGzIQ+7ftD4rmwHYRlDRS0hiOUhES6rpnC6bpGMRWE/WqpftXUnuJthdjSSiFiHsLzCCc0ojwT5/w6l3jeR04aj9jcTpTLDbOLgdxkTzgcRseDSZxwatHcqRXSdZGZFJEH1PuMa+7ZRWW4r7jkoq/2K9h2t6+uMEWrlQKMejElh1fyvzxRoD3K0Wql+IMeNIH24S+MeD3V1y4thdNnYZVh9pcW0vag+UwdSkJFixcGcfCiwlg72x4xIhJJa30/cH/FYBut9RpgGKfhMYzh4GHtmz/Nub+7iZq1Gq83YvMldcgQkFBoFthFqHnCZcsJ9aQLpiUuuS2kcNIEEks2cbn7RgBkKnnI1ryg7f0AR+WEl5G2hCza8DXOfNPN2EWNXVRIXxElTLLlpT1EqBBBBEoh/AQEIQTGBJmqd5IfgNLITAqUUesIrYfI5MVgckhIRMVvR4BR+EQRKI2oVh4HVSClYyPbWqBYIuo0k7WEZQ1NJAdPM6skPQQhQgrjCbU3IklIk/xWJf+2bZLmeMy8PinA8yAMzWOF4SvCg/c3+PUCoNVhI5AGY82rv8vLvnSZeR31deb/WZshyLj4qQNX/1UJJLRiwdSPsGjtLUMeT9g+G9osGreXcDsj6p6v+EhIECHkpkQcc9xGVmxuoe4vMeIdIU6fT6EpSbifnabp5Q6RC9kJLrEeRfPfO9h2XiPbT4lz3McX8syNI586dzCwu4EBo40xBdIYDhZmOil+N+PO/tunew4/nzrQsjbR7uKU5k0809lG6SqILawd8B6c2nbI1ulkAy4/60sARyVxNdzQkJ3xj59/dLfqseCkkP/ccQqPdk4iUBaduSSFDWm8Dhuv28UKIF5v4nGsK6Awtw3lCpxshNNVROZLiKo6u+yD7xsi3HPR6QQqaUgjWQqR+RIUimb6Z2Dipa6QRrK2Bn/OBKKYhddRhOfWIqQ0hFWloIGU5se2+lXQIpszeUUUEfX2Dfsah0wztSSiuRFRKqPrM/j1CbrnxElsj4higr5JknhTDbXPl3D6cjCYRKoUhPbmaXS443iVTFl10Y+5+Ph34WRbKDZ5JDYXKLQIZk3YxvmNqw74OFXPHycb7HabX2w+jcuSP2OKnd7tNgCf2HYy5cjm1nGP8uHGfx7Qut406zF+Lk6lvC5NvF2w/RT3gPa3r6gqgQ6HCgnG1EdjGB2MdDrbWZgpailgkhDiROD/aa3ft+dnjmEMB4bBRtjHf3Sg7c2qdAfVrAtRtoNQIBRErkkMVEdXvxHi/qiQ9nkUcjDgLfBiQM8siSyDXbRwcuBUCKUw7SICQywJrRGlEBFaiDyGVAlDsCJDuijLyOIjZSqSFYWLcJ3+qXc6CE2iqLWpMlago8i0xlXIJOnZ6DBEVEkqQNWmEekEslA029s2wnXR1Wl61Url4OljezO3rCiX9KDqav9zHRsRi4HrQiJufIPKZaPc8bxd97UzSTXo9p4+dzt7MYx2ZbOafBZOm0Lima397YvatQlSFso5iAcbpq1we5RjWqKDh49RND2m0LYkN87CT0Hj0oDkdugtSTJOmXG/cZFBROQJSDtoC+Kd+1fuiXVr3KzGKZi2Dr81jZvT+AicPJz63oU8/t1DQyS90KeGjeGFjd15KFVRVRtsDOuJtMCSimzJ4+E7PmkI5kJhv8icfY3jVmkEUpIXARxhc23z09yRfo6ScnimOJE/usexw6nHr5egIUhaaAnlGkm5ViAjsAs2GUsgazzy4z1KdRKvVxPrDPG6zTTPclOc0BNEMYnXG2EVEjh9ZWRvARIeUcrDXrHR5AtaE2QccuNs0lKQ2F4HlqQ4pxWrFGEVA6y+Ur8huI67lVzBgUIRUSrvYlzdrxquFmoqt3XCQzg2YV2CQptH31ToOhESGyWpzZr6Jd2U2lI4VcXVYOym4LQn4uhQx/HByLc51OZC/LRFQikKEyNiVkiNtZdC10HCumXjeHjSJKakune7TafK0+knuLB2BRvCLB/d8CpuH7f/x/y3msd5snkia5yQoKMeocAuwokfXshTXzs0cXyMyDk6MaYe2z1G2s72NWAe8AcArfVTQojzR2tRYxjDcFh688CJ/uT3L8TJafLNNg3PBsS2ZAlrYoQJm9gjq9CVoH6n/7MR7Xt+43uNp09lKlm1R35e/C279QYCE/gXtL3fkEdKjXys7lEOf2aR/zz9T/z3qgvZsaaBhqckoScRkYX0JbaIkH4EMRsCAQpTmZQSbDVAtmkNUoNtmfY4xzYm5VpDroBIJk0LXKQg5qI8B5ktIPygv71HVNVL+QJaCEPqWBas32IqlukUQql+o1BhVQxDLWvANNQPjBrKdQwp5bpG6bRTclglsoQQECnzeVEmaRWWBVKi6pIIP0QIge7tM35QVeJqyM529VHYOencm3nnaCaeC6aY79uidYa8tT+yleINrcSfXAeRQjWm0QLCGNilAzyY50Bo/o+Lnr9pYA1TP8I197uM97qZcuwWdpw2gaYnTRU4Pz0kP1mSXmMx5U8lNj00g/w4i/x4cOb0kvl1msan8sjS7iuie4JTMO1xHcfZlBo0rQ8LEu0BdsbCz0giR3DK/1vIE98Z3QR0NAmk+TXvNMeoqJwWTP1IfzX/QP2txjCG3WHwGHCgv7XlQ3e9GbvHAgET7vG58BvXE1fRiOPq/BM+h16zsf98W227HTxxtIq7ol/uQjJZ5QgtBHc99LkDfIUvDMyP+yz1exlv92BNUjyUnMaK9mbCsk1eOmgBuZkKNFjZSutXysUuQ/6iHFFoESyJ03Wsg3JtnKyJ++e+7Cme6Wxjx/3N2EUboT2scgarrIl3RlitDSAEQX0Cq6RAQMfxDun6SYQehAmB12ujpUcYT2OVNVS8S2pWFpCBKTIJIZDptClIVZXLjm3a6WszYAmjmu7qgWwBYh75cR49MyX/+epfckfX8TzaPInUPx20lIZkjPaiUGbf4/hoYsFc03q96Nlr++/75hf/i/d/4T+of7yT/Iw68BQpp8w/eqfzgQM41mDfnWor4HBY/drv7HVfDTLJDyY+CMAx3/sETUsiOHv/1nTPj2GOk+S1LY+xyD6eh+rq8LoFdt6omQ9FHB9tAmlnH7fq/2KMuBrDaGLE5tha641iqMpirGQzhsOGJ79hTvhnvPUWynUW3naJlffRltwn5dEp/28hfdNgZnMjdPWge3rNA7ZtElDLQkox1BgZ+tUol1mvw25u7H/OgvEfHJGU/GjHmjd+hk89/QSz6nfQ9Wwj5VqIYgKvTyIDoxzSlkAEFcLIqvgFOLYhdMCofqKoIkN3Bgy5tUbbEmpThnzyA/AclOeg4g46lkHmSoh85X8SBOanMiVO2LbZZxgZzyYphxqfl4UhphzHPK+f0FKmnc1xwFIQyV3Jn8HkFwMXKcazRqPr0mjHQpRCo0QCk3Rae0g8hRxCBg0ZG7yTj8Mh91MQotJetpJ/772Ar77qX5jzUAldLmN1ZLEbYghlseTrB5aAaVuCLRHDED5Z5bHVryXl+GyaBE9+0ygSpnz3RuqXWsQ7FCLSOHlFrFsCgp5xMSYt64UgMuTlMKhOMFqc/59dH0u9jUzl79JbT8IuCpQj8Lbl0DJFuUa+MKpTcujnq+pVolJx5p38nwAsfvKLxpeJMUXUGA4udiaThC9IbIPI27eLn0su+ionLHyK1W84gxk/KKO3d/THbBmPgZRIyx82jgvLMiPbK+fWL/78h1x+lj4q29j2FXsyRM+pEikZIyEj2oM0ngwYn+ilPZ0miFv4SZsokgRFG+1bRPUhXk2JnJvEzklUaNFc30d0YY4gF4f1SUqTAqxEwGdaF9M4zuGl1uvZsqwVEYGdF2TWmGMHjSm0LchOdJEB9M7URE0+pRYX5RiTktgOibagXK+Ib5UoDxJboNzo4eRCSLsoJ0NsnduvStJaGVVwJoV2K7lIGJnznm0b4ioh8DOa8+Lr+X10CtH6JMoJkLkiIuOhM5VBEsOoZgeTR0dSHNe2ZP4Jn+OOp83/+1TXxX1zO+KuIsnVgtiGBtZPrDuox7z7nr1PSNz587fEL3GSO7T//Lj/vpKJfytgZXdfzF3Q+r5hieYqkXLx29/FPT/+AafHNrKI49EOKLvSDv8CiOODybs9PXbfHZ/kosuv5947x4ilEWM0hyYd5cOYYOQk0kYhxNmAFkK4GD+k5aO3rDEcTTjcjHcYE4SZGFY+oGdGjFPfs5DHv7fni9rzXmmq7DFX4i4VbDuviZY7S4gwRMQrrUjJOMVj2rBKEX6NjYwgsbITenrBcfBntOJ0FdiwoIH8nADZazP7xrWH4iUfMXBlSGp2N71NKYgEmycKZM4GaZNeI2h5JIdGoG3PqHOCCC0l2rbBEsh82SRx0YBSCCHQrk2UMv5KVk6iHYso4RDFbcKEhfQ9vK4yIlTIviL4FiLmVRLCymQ0ZZRPKh0DhSEpgsgYgFcDQ9WnyTNmmkM8mKQwk2jC0EyRExXZe1hpsfNsI5uPlCGr4jEzaca1EK5tTMZdZ6Adr9JeuQu04jL52kP6fxsR5NBK4jR3O2856yF++77zmfTbbeZ9laaN4WBh0fM3DL299hbuX/cn/rD+OPLP1hPfPvDYuvd+nLNfdzPlGkFis0K5ArugqO1UJLY5hkAqFPd5Def+9RNs+tYxZJ70aPvuEuKdCruo8DrK+I1J3F4fJpgqfGb97hPbfcH8k77QP2b8cCNoq0FLgd1rLrgvPf8riLlTADN15+77Dp2p+BgOHQ53HAfQnsLJSWKdesS+Y9O+dgtyfozNa+aiPE3UmMYqlrBamtDdPYh0iuWfGoe7wyKaWUBHgua/xLj8JdfgX3oK7Wc61K1QnHLlQh753U+4LdvKm/5nEfDCJ5H2hKrZ8XQ7hUM3PVGSUxrXYcvTWZ1tJOd7ZLwSk5Nd3LNuFuLZFH7eQsUVYZ1PU22OBeOe5TONz/KPEtzUOp/jMlt4a93D/V44fzvu93AcXLHpLB7bPpGuRD0isolvs9ES8pMUKq4QGk6cuon6OXlmJHawpHciK7sa0Vqgyg4FO46dteiZo8lOsRHKRluGJIAWvC6oWaNwsyYWB2kbJxviZAOK02sotFi4fQoZQscFPu859e9MstOcVbea5V0zKbQ6ONkM0o/QjrUrgVS5vds4vrPX4SGE35pG+oogZS73qsTN3477PRed+G6CtIVVhK5cYi97Gjn2dg55PjBq4tmD1vPVzlmUlMNJTcuGbPvMB77Fy37xij0fcDcX4/f8+AdDbk+3U/QFlXxQGLLaLkLNmvKwz99XHK5z6H13fHKPRFIVF86/HuSRca4fwwsDIz2jXQG8HxgPbAJOqtwewxgOK/75Px8hjAvClE2UcgjjkJ8As7484J80/ZZbmPXlhUPuy0602Xq2DQK87tCMC08nELEYui5N+YQp9J02np7pDj0zYxQbLURojJcBdKmEnfOJMnGUC/G1DrEd4kWhQqriuhN+w49O/xEA6doCTsrn/+b9F3//95s444zn6DkhoNgSw6/1CFMOKlYxz5HSnHmEQCU9dNxFO6YKqG3LKI48ByvvY/UU0Y6F7C0gFKArpGFcEiYclGOhvUoVUVrGjygeM+SNYxRJIlQIpVCOhYo7RhVlS3RVhWFZRplkyaFJntLGGDuZNBNbEnGIef1/i3hs4HgxD+3Z6IpSScUds21FHdWP6v6HqWLuFlqNfNuDjEVrBlrLLpu6nHfUPUy53iRsWkqcXh+rbPyBTn3vwt3tZq+4Y+lXuGPpV4Z9zNcWdfEiMoRx313SP3IewO0NqXuuiFXwiW0v4XUHWOWIeIdP37H1+7WWTSubIWsS7sW5n5D+89PEthfJT4xjlSP8GhcZghVoCq0HZgg1/9jPAqCF4PIzr+HyMw8NkTTvtKuZd9rV3NH9/SHqosEk3uInv8jiJ78IgN2Rw+7YdSLRGMZwMFBVJP3v5d+h+NJe7KImP1kx9bavMvW2rwJGrVT9GYxVr/82H37FH1HPmfba/MQk/uxx+BPqiGZPYuKvO1nziu9y21tv5aMn/RUVSSIXiuPiuN0lWh4JyI2T9MzRXNsxlyX5SSzJTzrk78HhgmxdudeJXJPsNK9N9XJhTDOv9hmUFjgyIlSSrcUammuyBBmNSkSIskRrwVkta3lD7WPc2DWLDz37emamd3DbkjNosXZtc/JkSK7oEWUigsaA3FRF/qQSddO7STQUiDUUyQUuHaUU7eUa/mPcXZzSsonxNb3UpopgaaKEIsqE+C0BflNoCCQNUVxRatZsP12w4wSHjhNctp0m6Z3u0jU3Se8Ui9xE6J0myY6XTJ+4nSW9EwF4dfppii2K0BOISKEtiSgHAzFdyH2P5YcBd/39s8Peb5UVTi4isV1T2JymkI0x5X+v2+/j3HfHJ0dETDztt/HB11zRf7s9MrEl0BZFfXAInZ3PEwumn8l/7jiWXOCS3GQmOTt5jV3WFNoOzGT7wvnXDyFxRkLoHAwMPu6e3vcxsmj/IfTo/hztGOl0tg7gTaO8ljEcpThSTlBh3MIugrte03k8nPinz9OazuL0Tkac1Mec5u08v2kcn9rwSvzMTGQIWy5RpFY5uFkot6ZxXZvCxBTbTzaGwVFM42QFVlkQuQ5uZwarWDRtT2s2YwFN9bMoNFt0z4UT/8NcSD/1X4d2ctPhxFP/8iU+9OQbWNj2OGBMJ2+bch9MuY8Tl11J01NlZKhQroV0LESkkdt7+tVeSMuofWqShHUJlGehbEl8eSe6L2f8qWwbe4tC1iUpNWYo10rARtTaxNuBTJwobiF9hdVXNqqjcqU1SilEOcQKFTgW2rKQJd8oouIuyEoblRQDbXBgWtnCyKijMmlUwkP2ZM1t2zY+PkKgPMcopZIOuYkeQkFyizERxbYNoRWECM9DVpRMZpTxCJLPQVL4Qy2BrxJIg4mkx8vjsHMCenqxunqQzQ24fTE6TpS0nNy+38daMOPj5g+lhxwP4MH8LNataaF1maLnVScOeSy+ZD19504jtS5A5krIHAQNZhrjQ7/66B6PuXMb2ylXLiSIQ+t2SblG8OQ3rupvSRBArCMgNzFGzbJuLD9JGLMIE6NfWb7k4q8O2xqws0x9Z0+E/YW2zGtaMOPjUCrz103/xfyTvgBKEcWsg3acMRxZOFL+pxPrejjtc0/z00fOwrY0fzznG6j2Hw3ZZucLxPfVbuIVb7uBP+bm8F+9/0pyc4ziy3p55swBT8RTXZdT3c1ccdkPmZN4C+KPSeLtkFjbQ3yDoOOMBh44cTpTU13sKKd41YPv53fnHH1TVvcXe2ptA5j33Muo94pcM+EPvHvC3/n+pnN5fn0rbYttYh0h7jmCxHIH+5U7OKFxSyUfSPHj31xG6z8D7ms9g9qE4LynP0Lrv2xg8Zw/9+/71nGPwrhHeT7IMdMx/lg3dU2npBwKyuXxronMrWnnD8+ewDLZRt/0GHHLZ3NvDQk34Ng5G2lL9LE2W8+WnhpqEiXaYzWgBaLPxu0WBCkojle0zNmB35ckp1Jk1kCpCbSt8ScEgOabU//AeDsPpHmgOBWAIAXZqUkSW8vYhTLR2cfhdBVQy1ftdSpbPw5THK/GjuG+396WXhCCxFoQUSPbLxHMnNTOZfddxV0X7l9R6OJLBkioe+7+1LDbnO5t4Te3bhly3wTXTNE99i4zr+n/nXY/AJ+e+xcW7WVo3KJt3xpyu2/LpH4lnWxdWYnjhpzqKcXpmxsQ3+Dg9WmssuofyDOa2FPcHEwEjYZK6FCRWmN48WFEGbAQYpYQ4m4hxDOV2ycIIcacB8dwRODJb1zF/X/8OPf/+RNEMbDKmppVgvLj9Wz462T8Wk0QWLyx9WHqpeRdbX+j2KYotwQIX+LkIL0hwM4FFCamKGck5daQaEoRe1KecktEYUJE9xk+2RlJ45eUiBszZc8l+ex2Gp7KUn9MB72z1QuCXd5X3Hryz4e9P396kWKLaf1BgIg0ouijuntQuTy6LweFgjHJFgLlWIQJizAu0b1ZQ8TUZPBntrHjonFsuaCWvomSyBWECUnkCQptMYotHqU6h2KTa85qWqNiLtqt8OSVC2OCCBGGRoVkCbRtEyU9VCaOSlV68asjg2Oe+bEkKIUs+YYUsqQhnIRAWwLtWAS1Hn6NzbbzIna8vEjn3Filpa5CEjm28VrSut+8HRioZIqdVFBHCBZM/YgxW67gtdMfp3551edKcseyr3DSR5aw8q3f4oHjf3fgB5S7JnNrC40k1tq4OYVQmsiD0959CwCL2r/Jg7/52C7P2R8oC6xKd1rVc+2u6JfcFf2SKGaTm+hSaJEUptSYpYZ6n1r5LrnwWi658Noh992xzKivgsY4Ydrlzoe/sNf9DJcQ7lwF3VvSuPixq1n82NV73EbVpQgnNXPZ2V/mjiXXcMfTX97r2sYwhv2FbF3JeVNWsfiCr7HNz2AlA1KpInOc5Iief19hMr/behJeF3Qfp3nN9Kd2u+1z5/wvQkGYds24eSGofzpH9vsT8WTIG1sf5snVE5nyP/uvyjgasSdF0sl1m1BasD1KkFUxnl/bRtN9DpmVWUSkaX04IL0xoOg7NLoDqsXxD5SxixE9swUnvuUZPn3FL4YQSINRJZAA3lH7NM/nm6mxC1zY9Dwpq0w8Waa5PsvKnia6/SS1iSLH1LdzQePzfK51Mf8x+R4+f/xfOLd1DfWNOZpbevHG5ynO9AnrQlRdwNb2Wso9MZSnEVqb36HATficOG0T58Rkf7vdxYm1qGSEDMHNKcKETe8prbjbsrBu8wCBNJI4fhgVxTurXPv/z0KgKz9hQrDm8h/wzRm/5JszDoDokpi8yBqemOlWBf5ZmkDaKfHux94OwLjxW3jbzId428yH9v+4Fbzpn+/hyzvOGHJfNY4/0zeO3mwcOxkSxU27o7b2rSV/uPhaVWBViZ+REEDD7Wdvt3fGSJVf+7v9GCrQo/TzAsBIPZG+B3wc+A6A1vppIcTPgLGscgxHJLpPiMBV2NtNu4nanODT699E7XNQrhPUlCBIS9IbTSVChprVH5S4Ky0iF0RZmukSbsTUmVs5oW4L92+ZQeGNsP2UydQ8L/CyisyKPhCCUnOczA0utUGJYrPHua82aoq///bgXOAerVh64Xf4zJyz+b+lJxJf5THubxoHkDUZQ9Qk4ui4S1AfR5YinN4S697l8qOzf8x1584HKdlwY5xPzf0Dr05tZVvkc8l9/0FRarQS2JtieJ1QatZE48pYm2IUG2qRkUbZgliXIv18H4jIEEpaG7PuMDQ+RX6IlBBmYmhbYLkWIlBo1yJIO4hI43YVkdmS8T6yKzL2bM60q0kLaVnElndCEDC9dyJ3/fwnvHf62Wz+cz2EkTH6BkjHDDFWqYgJyxqSVGqld5uAHnJDbSpKFMsynk57wDfH/7P/b9U+a6+tEcNh0aobDVkVDn8sNwvFeovE9pD4jl2j794IkZEgqnCItX98hvk17+yfWHbpBdeCFCS2hZTqHMo1FrEdJZxigG5LceGCG7hv0Sf2uO+dyaORoD+BdOVuk8lqQrg7Ymnnx0eSQN59r6lczzv9iyAFyha7eCGNqZHGcCjwp5O/D6SH3JdTJd617mU89vgMrJKg7rgOyqFNtjeObPd4/jPfGn5nO+EfN3y7/+/TP38lTQ910H65xz2/Oo1nnjiBllYHq6w5+88389Av96xqfDHgupYqKSd5tiyJr3GoX9pLmPb6CYMgaRG/vYa7rbP58afaeXtmO3avD5Zg5VtH9n+pokEm+dL4P7NDeTxanErKKnH+xNVMjHVzVvJ5Ngd1LC1MJNAWT2Un0OZ084rkFlIyxvHeZp7sGk8pdBhf14PbGLGhuw6todCeQiQivA4bEWni7QLlQDm02NhXy9kfu4LC63pYcvovyCpJfKNDrEtj50LKdQ5OUZmCU2Vqr6gUPrTS/QNX9kgkHWJcdPn1kPFw+vbcJvbYf5r/z3Q7xR1Fl5n7eTy1G/KoijqZ4IzYJr7dN/yA73VvrcaV/YsvD66YjuUqllxxAtbmThZtGHisJdbH9roUQmi2bI0RJIXJ/yO44OU3cv8fP75fx9wTLpp3vSmisucC0O5USMM9vvM2w2G43KAat6v7GovjYzhQjLT0ndBaP7LTfcPMrB7DGA4vlnz9Kh6+bfcJX3pjQNNTZVr+0UPjUwFWWWMXItzOIt6zcUrNEVFCoy0NBQt/R5y1m5t4qms8xzduJe6EHHfmGuL/1s6WSyNW/3st2pYoR4AwVQ0nby6Eg+SRpywZTQxHHsSFx7/VPcqk8Z0UZ5YpN7gUx6WMX5HnUpheT256hs3nxwjTDmHGI/l4nPd950p0Qw2qoYaXT32GN6Q7iAuPKXaaBXOXMa65hynjOrBn92Ff2MXMM9bjxEJkYCqGaHCzRrkS1sYGFElCgF019rbM/Qq0LQiSllE1jU/g17loWxgDbiHQroWOVUy0hTC/K6bbwg/RDTUQ81jzGpfru2Zwz8pZxidJK6jJoFsboVRGl8u7GnIeoSokqlXWnTwsHvrVR1nU/s3+aShvWnchAEVdPiA/g0Vrbxn2/p+8xJhjFloFHcc7dB7rUq61Ofvfb97vY+2Ms//9ZkSE8d3aCSLSCKWxCyFCQ5ASBGnjoRDb1HtQjn/PXZ/inruGl/7DyKXtg5PEPW0zEnn74kf/c7deGmMYw2ji+6f9mDVv2L2B+7P/N4vJf4mY+rsC4Z+aCB6tw9kQI7VR8IltJ+/z8R790rdY+f/q0YEgikMUl7hZhZZQzkhOfc/++70dbRhJEaAjTCNDoHJuFJEmTEi2XGDUHTKAL9/xKmb/6EpEFCFzpf1aS1kLNgZ1uCKk3spxWnodx8U3USuLXJhYz+mpNUyJdRC3Ap7IT8ETJs4HWpL3PfzQohQ6FAOHILTwfRukkQG4WTNRNtmukT6wJUax7JCdIHD+WMcvcnXc1n0Gbg/EOkO2nBuj/SxJ12wblq/pJ42AgUEcDJBKw2Hw9LZDhep00iDjDblftq5k0bPXcseyr3DHsq9w3tJXAfDVzlm8/0/v2O/j7S72DP5cTbLTXDf9t1zQvIrXPPS+/T7Wzpjy7ZuwdrhEPS5aCFRzbf9jZ935KZ7YMZEgsigGDrq5TLFRIDREzsFrZ9sXb6KRtLftDiON42PqowODUKP7c7RjpEqkDiHEdCoCLCHEvwFbR21VRxjGnOyPTCw45tMsWv7VYR9b9/+GVwAtuPl9xgzZc4kJQak5gV2MKLUksAvgdllEcY3tS6ySqVCJLosN3W1sLozHycGOuiaimMaqKxOmJT2zU8R6IpQjscIQWY4gZbHtXMWpiz6LH9hMq+/kh9N+T500EzD2R61xNGCwr8LzQY5f9p7GZK+DG2f8hscnTOFGcTlyu0fduIl0nK5Y/NJbmOmk+MCWl/DIxlOoe65A20M5tKx6DUlq7AJlHRAXHtujHJfWLGNB7dNcGs/iiQFj4zk73oJ3Qg87ZrqoUFL39xjS18Q6QXk2shxWzLulSaxCjd+UQFumNa7QaBEmwCobw0U3p5C+IEo4WFqjpTR+4EUf0inT1haGUPIRWqNrUtQtlfzpvouZtsOHcoBoamD7uc3EOyNSQYTIF9CVhHPYSW3D4HCokPYF/1g+g2PuOgaAuhWah3964Pusmnuue4shVpZ8/SqO+4S5kAvS0HGiaUOd/SVz34rP778H2dmvuxmhIdaljVn2hea1zG98L8K2B4KkEHDyDOyixs4FlNpSxJesJ97RM+Jj7ctks5EmnDtjZyJpd4nmSKqQF86/HmLWkO2PZn+FscrrkYc95VcTx++aZmaAST++0gw9SCWoXybx61zKGUm8I+SC9P4NDv7Rv36Hd/7mCuLbwS5EWMWIQluMntmaV132T17x9w/w9LrxjG/tpjufoJg3RPKaN77wphUOjuMbwiyL87OIyYCZbjsT7SKT3A6cc7vo6Kyn64ISrzv+Ma5tfhqA056+EqegmP21rRAEJkbW1Yz42IN9kWY6KWY6eX6fh7zyeCw7hQYnT088wdnxdaz3G7l3x2yWb2pF+Ra/t0/kpKmbcGVEtuQRBBbj63uo9UqU0zbbuzIQCUSfQ7HZTOeKdQijNCoKWJtB2dBxZsBNN72eWJdi3LIOsCXurHraHvJxt2fNhFYYMNXeWVE8DA5XHBehRoygd2bzpgZmdLwdgEl3RPDBAz/2N1dcxDP58XzzFJMUyNaV9G2ZRIcKeNvjVxKGEhVKpqy6DmFp1r5pV++/fUHNcguhIHIttpyXRFtw2dlfBimIe7VoW6AFZD/Uhw4lDcsCSg0mwidvXzLi4+xLDLl38f7H8ZFsv68xbeftx2LiGA4EIyWR3g98F5gjhNgMrGXMaHsMRyG0HyAAYh5hxiOKSZQr6JlhU2wCuwSyT6BcU02TAVgl8LokyjEEg4gEyhGUI494hyS+I8AKFFoIRKTx61wKTRZnnLCCD427k3VBE2lZ7CeQYMAY9IVIJvW/po3j+MvmY0m6ZT4x5Q7Oia/iV22n4k0Med6axPvOu5sfdJ3DdS1PMTexhaVdJ2F3FwAI68x7ddeDA9Zrqn0WT/j1rCi3ERMhL0sMrW4e29rOa1seIynLjLN7ePcjH8LJg5YCEZgkT1sCERjyRjsVY2Rhpr1FcchN0th5Qf1yU11VrjQ/tjR+PCkHq+hhdVT8miJlKpv5ENEX0fJAZO4rl1Ht2xGpJI1PeijXNsRVfS1s3mpMtUeAw0og7aGiujs8/NOP7H2jPWDRxlsBuGjOCo5NbeG0d7u4fSYBfuZXH+WEqxYSxUBE4GcwjtcHgDPecgu4AqegcHMay9dYpQgrH5p2w6hycWBJFm0dMNm9/Kwv4fbsXXV10eXXc++dn9wn8mikGJz47Y0wGrzNzgnjvk53OdqJpDEc/dBRhPB9CFyCGocgIeiaK7Bnlphkd1Md8LA7LPWL3FeYzWXJ5cxxktyeT3P1My8ntVEgqty+1uTGS+xpWT7Z9HeWZDLcEM5nXKKP2oaNtJdq+OfTM5jynZt2W7Q6mtEfxze18fttJ+FHFifXb+ayzFKyKs4tx/6KT8rX8OxJP+eZQAOGVEtuj3C7/SHEyqJnrwUGWnp3bBlPXikm2UNbFQHaoxQzdxp6ecvay0i7ZYqBQ3cxjiXn8tNEnp5inB2b6pAFCzcv8BsiCqHDJS3L6fFjrFjfyvqggaCph5MbN7Haa+S5wji0EoRJQygoW+DkNWiwS5qalTlaH5ZYW7eC4xCt34SMeYzry4NlodIJhOuiSwOq4iGqpCMMWgrECMy/173jE0z7ufkf3f/nPbdn7w3VeHH1Mw/xqrrHOfevnyBXNkqoJS/bQAbgBwuxBKiMBleh9f6/f6dcsRDlgJvXCAUyBC0gN06gbYH0I+wwIqjxhsSyi396HcmtPs5Dz+5x//NP/DwAdzz1pYNOuOxvHN8ZI2l1G4vj+4kXiH/RaGCvJJIQwgKu1FpfKoRIAlJrnR39pR052F1/6hgOL3anQoIBD5KdL96EYxNNaUVLiV/j4GZDumd5ZKdHoMHrNlPZRATKAWWDCMHJgfTBLoKdx5BMviRMQs9Mh9pVAfFNWfJTM2w/1aY0LuAttWs407M50+ve7Tr310PmaMDsiVvofvQalBbEREinSvCF6X/kmtUvZ9JxA5M5NkdZAm3z6mvv5M7XnAYMJY+qkK0reebZBSzaeiwA24IMAJ9q/CcZGef26XfxQAkiBI8Wp5KdqkELnLxNmLQQofG/Uo7AKpmpa1ZJoTxJsVFQbAZnco5yZ5xtNZLEZpu65yPKNRI/Y2PnjZmyXXZJuhZW3kx4E1GE7OxDdfait+8YWLCQCD9AxRyUI6Evh+qsfBYOk8HmaGDdOw4s4RwO962YxRO1E+g7UVOzQuL1as54yy3ELIh3gLYgSApDJAFzvriQ5/5zqBrpkovM+aHq8bMzzn/FjcQUSF+hbEMAe9sKaGdQ+54UZmJcpXWvChGa/1/peDMKfGfPoCq8bbld7jvUOJBYtTvvhKM1/lXXvWDih4AB0nIMhw/7VWmPFOHscSjPothgESQEUUJx+rhNOELxcDnkr9njmB3byvHeFhyh+XnPadzZPgdbKNp7M5TyLl/3LiQKLKa0dTClrovNUQ3ND3fTfl4dvbMcGmfs4Lj6HdxTGAfAhGQPuSDG9lKKjnySY47ZSHcxwSv+/gH+79z/Hq236LBiyoStsOZDZMsxdpRTJITP8d5GFJKbjvnNLtv/8Ou3cMWbPwDs/vt1V2ECP9/6Enxl0RzLMT25g/fWPUqrleK82K7bd+USbN7UgtVcJPItUk/E2JhuwM1CU48mckwsiGKSWrdEVsXoLcXJ1BUoBzYdD7axeHYtJ0zczISJnWzvSeNLFxEJypHFtlYATeMTArFiHSpfIGSQ35HvQ1cPIh5DdPWgBhFIDN5upJPaDiGsUkiUsNEjILn21EK6P7hr6xx+75+Aa0UUnqxHhjD3oYU8+5WrqH1eg9YUGyRhwkK5MP2WW1j9kaGFqL1dc531hptJlI2KGEw7ZbU9LblNIhQmT1NqF1WQ01EEIDh7Llrs6v3XD31k/F9HEnd3t43lq10mvR6tcXwMRw72SiJprSMhxKmVv/Ojv6QjG2OGZEc3oqlt5Ccm0cJ4m7i9GlW1y4kEfq2pYIR1IcKXiEDgZAXaol/1ICMNgalYmotZQENYG8cuKoKMpq41y+zYlt2s4sWDFa8206buXzeQbN53yU27bHfV+MrvPReE+MTcRfxp61Dzw4fKNTTIPDEREpEg0BYWitmnrOf5cU2Un03i9lUUZRVVS3wHyHKE0FCqtSg2Q5jUpN0Aq0lR6IsT9riU6iTKEvhpCD1IbIcwJshNjJHYKrHKEUHCI5YrI2trCNu3D5hrEqGV8dXQtjDS/j1g50T0SG9jG23knqvDDgV2UaMc02IIYJWNL5VQkiBh3rOdCSRgjyqliy+7DksIZKgQoUJKQZh2yE03lfHkhsIuY4MHI0wOLZWPhtJof7A/RtojNeh8IaF6cTu//j3c0fW9w7yaMewP+qYlkJGmVC+M+sAXxKyAe/Kz2erX0hcaNdLj+SnkIo9Ht01ix44Mos/BKgpsIEzayLIkaLG4tGkp32mcSnZGhu4TQy47aRkX1DyHQtITJUjIMsemtlBWDr1hnGW0Ue8VOL5mC+sLDYf3zRhlLDp/gAx6YN0MAC6YMlD8On3QtjOBu+/b8/7eOOOf/Hzr0H6p73efxrvrHqPVSg25v6jLfGzuX/l57ek0x3NINA9tn4tyNfEdEruoscqawjhJcmYPm/MZpJjACQ1byIYej2+eSJjUsCnGk8FE2lp6CHIuoiQhEaHaQnTepm1KJ+GTzag5U+HxZcBALBYWiJiZ4kella3/sb2QM0dCHNdCHBY1xYOXXc/0m24hSivqN4K2AQ0nv28h6WyEkw3RlkcJQajZhUDaGy542Q04VsUc21eG7BECf5zbv82efP1UwumfUAdHXhy/5KKvcslFX91tIWy45+wOg8mjMewDNKM3cfvI4CYPCCNtZ3tSCPEH4NdAP5Gktf7tqKxqDGM4QOwcDC5ccANeR5GeY9P0ThdYZfC6oG+yRxgDr8NMZUOD1wOpTTZWyRBGunIGkaFRJ4VxQeQZNZKWUD6hyPpJLm5HHK8LREuRSyesZH7cH9FaX8hqpCoGJ5wHir9dcmP/3+974s0s6j6RPz14Cjod4iQDGjJ5LGmqhPWZAjtmWJR7XGLbJH5aIEMoZ1y0hDAOUdwQTChBbkWdkbUrgV+n6KgHLAXJEB1IChMs3G5J20M+TleR/NQ0Tq7S/yAEdn0dYdeA8kwVSzgrNoHSqFweHQYI2xnih9RPOml1ZFUy5eEz+048E8PyoWGZb6a9WKYl0SqZ9y1M2ISNVn9wP+7jC3nmxqu45OKK+uieTxO5EqEHlAyXXPxVQxgFCksKlGeZxFNphDDTd3TFmPXOR76wx/U53ZUKZkMCZUtz3MpaBid8dzz1pYP5thwx2JcpMWMYw4Fg58/XvNO/CNMnkJsgiGLCKIXzoNtKPN/bxNo+Q+isW9WK22GZgoFv1MX1wvwO4xAmwS5YBClNUzzPe2tX8KEr1u1xLYoOIq143IdjE61EWvLy1Fre/PxrmXf/h1l8wddG6V04cnDelFUHZT9/PO/r/X9/7KnXEWnBOb/9KNrWNEzpZlbdDiYnulBaUIxcWuNZnu9poiuXIEpHEAgKbVBskiAhSGtKmzP0Oik2ikakFyFtheuGeLN68UOL1nSeWq9I7fQim3tryK6rId4uKbUo+u5tQTRCQ76MGN9GuHnAj0sFIfT2DQzA0MqQR4NuIySiImIdqd/hoYAsh7jlEH0Y4vmF868nOdcFLNJbwn7Fr6p4E0lfoQVYgZmOeuKHjMdh/XKTO9975ydN0VaZKWdVJdGlF1wL2kz61bZE+goRRmhL9g9VAfjbH/Y8bU0+9TwA9okz+wtDC44x8Xtwp8MdT79wB5HPO/2LgBmkMYYx7CtGSiLVA53AxYPu08CLkkQanNRc/pJrgL1fdIzh8MLp9REln74pJniJCMIEKEegHbAL5sftA6uscfIap6CQFdWDtgWRK/BTkqgWUIZQ0pZGRQK3sUh8gk9uhRkjmwvdvS1pCF4MRNJooGrYeN/PFlKud/HrLNqLNtJWWE7FryAwZIMMKtMQdOV/bxklGdr837VlCEJdybXsosSv1ShXIS2FHQ84de5GHt80kdKKBN42s52yBTrmIHpDVG6oWFO6LrpYQiuFcMzpVsQ8hCVRfTmEFIhEAgFEvX39zzsSqpeIw+vxIELwMzZWWREkJW6vJkzaxo+sxkIojZbm+9z0lG+IDVdi+QNtBtkJDrFuxbmvvol4OeonerRlklkRDVQvlSOJPInbG/Z7Ge0O5RZTLa9uc85rbybWMTLS+FBgtImdF4qfQlWFND9jphHd0fejw7mcMYwAouSTm1kHgJ/RWCVhSCIJO/pSlHtjULBIr5W4fZDcFu4ax9OSqlQx8gShkmwKA2Y63h6ODBKJFJIzPTjT6+CBErRHgg1ddSgtmP3ba/rVt2MYOW460cS7v/50IWjojNXwRNFjudeCFBpLKAJlkc3GCXMOdtYylgO2+b/LEKyCwCpaCGWZlqaYjbahaGtwFG59ib5ijPpYgWMy25jfvIxbsxcTXxpHudIolXs1QinwhuZvQ9RGWhm/PCHNcA2t0UGITBofR10q95NIR0IcF8UAbIn0939y6gEdPzReo1qY/MrtUdiBJopJMw23QWAFpiCrHGh6qowIhxbT+qa4/XH877/9GCJQCK2NTYCqxHEFQmi0ZeK4nxJcftaXuPMfn9/t2vTc6cCAfcJl534F+zDnPYMxEgXSgUK2d436MY5qaEavnfEIqhnvL0ZEImmt93/e435CCDEfuBWwgO9rra871GvYGy4/8xq0a+19wzHsNy5334iwLBYX/3e/9zHt1luYGrMotdRhlcAJoHZ1iNsTUGryCJICu2B6qmWo+xUPTlfBeJ9UJnOppIdb4xK5HsUmKLVEyJJE7PDwPQfRorGm5Dm+bStvaPjnQXwXxjAcqgqTKd+7ETlJompCnESAEJqgZBO2x5G+wC0JZABet5GlamEMNGWojQGjpGLGqPurZFFMoiWU6gXlWpvSZA1exMOrp0KXS3KLT/v5taQ3RXTPcYhiNVjj01hlhff0OuOhMHUC0dIVxjMpmTBGnIAOAoSykDEPkUyAZaELRayaDFFv3xGReA7BYfADWHbdVZz4HwvRwhA+5Yyk6xhJZp0m1q2wS5owJojv0ESxoUnf3fd8up/gSG0OsHxjeq9cC3TlO65AhKFJPIMIHbPJtzkUmgVhwlxAnPSBhfgZcAqGcARYerNZV/ASs838Yz9LcXINliPwawbC6YK5RglpDGUPPUa7uvhCIJCGwBqL46OJBS1X9v+9pzbRPe5jtpnUWJpWT5CU2EVofhzi232ClEU3cYSGTIfGLmq8bn/YOI5j4TclcPIOPdMlUUyzJZfhC5v+lc+M/zPHu3s25R4M49+T4KS2zSgt2VZI7e0pY9gJF80z55J7F3+SYos2k2/jIVoJenakoGhh5yQiFNgBxKq+lI7xqrRLZiCClsZEWka6ojYTKBuChEQ5kkJbkr66kFKmj79umkVbuo/aB+L0XprHXZKk0ApuFraf24yfgQm/LKE6u5GpJGFXN1qFSNc154ooMn55lQKENWl8/+sRuTxqR+cRFcdFvgSus/cNDzLuu+OTnPgfRl2kLYHbE7HtDLOOpiURlq+IdwoQpogbeQIG2UVecuG1CEwcFwpEpI3q1zWDTkwcVxAZ8k9bFkHKxk8J3Kyi2Gq+y8d/dCGZ9RFO3uz8vkWf4PxX3Ej2NNO+vqD1ffjHTERYgigz1JRr/gmfO2xKpMvONse966FdPULHcGggRrGdbdTa5A4hRkQiCSH+a5i7e4HHtNb/d3CX1G/m/Q3gMmAT8KgQ4g9a6704pgzgUHgW3fnwWMVptHGn/zPmxd+y35Xitz3yLtLTx1GurSHfZpHaovF6ImLbiyAETi7C8gXS12hb4PQFyHKI1V0wiUIUGZmybSF9C+nbWIHGLglQAuUpsDXEIxrTeVqTWX497a+j8VaMYSdUv99fuvB2/tJ5AqXIIeOU6PHjdJYSbIoaoNPBKhulUbzTmGlraZIRK9BI35g7GhJJgTb+RVFRohyBsi0iR6AV+Nvi1C+1KNdCdrJHrFvTPdMiTIK3JKBc5xDf2Au1GYTWBBkPy3XRYYAuFk11UkiEVmhAOE4/sXRH9/cP3xu5OxRLe99mFPHUf13Fea+6yUjVtUWswxCAQmmcgkYoU20cnHQOp5DRQqAtQzQJDVpqM/a4OqXPs8hPSJCbIAgTpqoNpl3VyVcmNGqQvuaUKxei4iDLoDwQ+QLaqWXnwTJ+y9CpQy+0gQyHKvlZMMO0IyxadeNetjwwHJHfvxcQFm37FgtariTq7uVy943c6f9sn/dRnlSL013CKoaU6hzql/u4XSWiuI3yJIntGhmB1xthlRRObxlZDBCF8pA4rm2JLEdYvoVVlnhdgmw+xnNRE99wLuZl9U/x8kRhn9Y2OdFFWdn0BXtWMo1hVww2O248YQfFwKY+UUBrQaebJJ/zED1x7KIpBDl544EUxgR2SZufguovdggNaHAKphDkpy1CD8KkaWF+fmMLiRUey6enSaUh7IjhaYiSCidvWh2LLQLSSWQQQCKOVS4T5Qsmhlc71ZSZ4Clr01CuqFA9d5chDIcbIowM8XWYzKGf+i/jV3jBy24AAV6naV0TSiPLCjcniDyBildNR+Huuz/dPxynGmtEpNGWQGO8DDWGQBKhQkQRKMhPiZNvtSr5gTnGxZddhzjOQ7kCXQQ7H3Lh/OsJMzaxbkWprqImq0yxC5NDVWh+y1BieHBb3QsCR4hp+BiOToy0nS0GzMF4IgG8BlgGvEsIcZHW+sMHeV0vAVZprdcACCF+AbwCGDGJBKNDJL3QLgaOCmiF9n1UEHK583ruDH4x4qeeXbOK+5+bhfueLqzbG0m0B9i5ABEqlGcbZUJZ4+RChB9h5UpQ8sH3B4yQazLomGPGtIeK+I4Qu2AhQkm5DsqzSkxp6+DmGb/mJHeY0SIjhGqfBTDW1rYXVKdugZH7vindxZvS9/Xf92BJsSZo5q/puWwpZFi/o55CnwfaJtalcfIKr8vHKoTIfNkkWZUpXNq2wJamvSlmI4MYMrAR2jFeSQVNy93b8CfU4i7bQMetE3HcEPV3j/TD6wmnttJ9RpyGJX2seqOH/S+nMP2LTxplkpADsvgwRPmBaWeLj7zy/WJFeqP5LkaeIIyZfkPlGBm82k2BNT/OIbklIMiYMCd9hYg0jq9My4IQaFuSnZqkVCcJ0kaRphxDHNnG9oie2Zr4VkHrI2U6j4uBNATSs1++igU/vYrcOBu3zzBZXk/EBS+9AbuSmM071SiBPKDclDhkAxkOhb+BUEa1OdoIxtUe9H0uGG9MfRdt/vpethzDwULU3QsYv5jLrNfts1LD3ZKlNKkGLSC1VeF2lRDlAOFYWIWI5FaFDDR21kcEEbI3v0scxzYX01bWxwXSmyRhXJBVKXJ1mn8oyfN9jaxofZZ5qWdGrEq6tvlpAM7e9hpm//YapNAsf9WYx8jecOkFhij46/1GufmzY3/Mg8XJ9EYJVpea2ZKoxW+weDbWQjHnEW52cXsFmQ0ar0/j9Zh8TvaVTBwHsKQhGzwTGLyYYwzUQwctJPZGjygGs79ZZPW/Ocz53AoK584iN0NTaHZou205jf9oYOulzTQ/GmftK9MkN0+k7Y6tROs2oJU27WyAzKTMRXgQgOOMOtl9QDgS/BY11D0fYJUiSo2uaUeXAqukCRKCmjUVD8QKZKjpmRUnuTUkTMl+okeXQQYaoUKE1mjHIorZyAiS7RFhXBIkJV5fhAg1bQ/00nNMhkKzTWqL7leXiEDz5DevYsHtVxKmLKySMSAPUhYXX2aaX0SgmH/SFxDlynlkUi0XXV5Rz+2h5f1g4JAokCq5yoLxHxyLh7vDEfDVOVIxUhJpBnCx1joEEEJ8C7gToxRaOgrrGg9sHHR7E3DGzhsJId4LvBdg0qRJQx4bI3leeLCbGsC2WTDhP1i0aThx3K54T81Wnj1+CX+873QS9YKaVRFWwUcLgYgUdiE0KpSiSTzxA5MQVBNPUZEsa22qHZYwhIMABFhloNtlS7yGvNo3H6TdoUomwRihNBzuvvfTQ4iknXGaF5GRG3nMmcoWMoxr6CWfdukq1aPWSdIbBVHMwiqFiOr/u/J/FpENgSGRZBQhazzskkYoQakRGpYF9J7UTGZ5F2piC1EgEdJIq3VDDWtfYVro0hvj1Cy3SGxXyEQCpfQuZptCChaXbhvtt2u/sWjD11gw6cOHdQ0P/O5jAP1JW5iwCGMDkxEBwhjsONF894Yjac557c2Amb4olJHVoyQ6ZhHFLQrNkvw4sHz6/bBQpiLtp41/WqlJs/ZVLqBY8x8f7d/3onULOfFDC1G2IL2xTOSaHVjlCOGHiFKIdm0Y5Gm6P4WIy879CrDnSTMHE5edU5HRV7wiLj/LGIRX/SWqldjLz/rSXn0n9oY9xfEj+qJsDPsN6djMi71pxOe/eaddTdiSQgvITnaoW1FClHyjQlAKGURYZaO6kKVKLA+C/klag+N4v2Ilqt4WWAVwpSDblaAc2KzKNDPZbeV4t3efXlf71lpEzqZ+ehe3Pncp3WGSRjvLB+bcs0/7eTFCtc9iip1mndPNLHcbLU4vDzGTsrJ5JmrDciOCmogoLnFyEqegcXJGRTIkjguJsGS/QlVGGhtwCjZ2QeDXgV+jyE1JUbMKhGPjZENim2Nk1oeIeJzyhBqUA8qzGfdAgHKEURsJSVWKJCqFJ9Bgyf6Jj0caFq24rr8V9HDi/j9/ggsX3GC+d1CxkagWbQyZ1Hmci7Lg0vO/gmBovBuI4yCFAEx7qpYQJW2CpEW+2cLyjSIRjApNWabVvGZVns7jU2w73cXPwKpPDEx0XbTtW5z3qpsIkhaJrUWcLJTrPSxfYWfLoJSJ41obQkEYVfS+FoUOhwhhQcuVQ1qIF7S9n0Vbv9F/u0ocLWh93yFb0xheOBgpiTQeSGJa2Kj8PU5rHQkhRsOtbThns124QK31d4HvApx22mmHhCscI6cOPbTSJmDbI/24GsyLv4VFa2Fh2+M8vegkSg22USP4ITgWlCsXe1GEKAUQRqZyqbWZTGVZ5ndkJLMqaRF5FiiNXVK4fQIRCeycpJz1+HthFufE9m9yyfs2n8HzfU3cdcyfhtw/RijtivnHf5a7l35lt497wuF412FLqYa1SyagUhHxxgInnbwGToYl6yaQfjxGcqtNTb6M0NokiFVZr7Qg5hLVJOid6lKugXELNlAIXDbZrYgIhK6ne5bEieWx7QgRGkaj5RFN+1ngPb6atm2tiKKPjiJ0FCEzKdTMichlawBQpcNjdHk0QlTk5rHOEBkoumcaxV+psUL2NEU43RabLnaZ+vWbWftBQ/Qc9/GFWOOMaaqbM0qjQotNGBMUm00C63VDfId5LEgaqX0Uh6hsJjUKJSjO9HnPqQ/w/rqn6NtyK+9Z/1K2fXEqAHWVNUauRAamLVYWfHOeCQxRqWOmKh7f0AuRAqVYMPtTaM/uNzAfPMmtmmxa5cqVUMX0e3ASeuH86/ul/odVXq91P8kE7DOhdDji+FjF9TBDSDPRagSYF3sTIh6n6w3H0nr7SuzCRONrVg7BthDlCCuokLZ+BEEIKoKK2hDbNnG8WhhybVTcIYrZ2GXjpeIkzPCFaItLmHR4rraZCbFutsc302wN73O0xC/xaHEqMRmwvDiOh3dMoeVuBxlqsusbufPlc/nxjNtpkElU+yx+2NcCwLtnPXBQ3sKjHRdfch3YknvuHkpwnOjmKeiIifZWYiJgeWk8zpIkIoJii0Z5iuCyHD3bk4RLHJLtklQ1jochRMboUEiJti1U3MGvc+mdKslNVmQm9RKsM4q2pke70a2NBGmblsdCYtuL6GIR79nNjNtRB2s24rQ09SvYqlPZrOZGcGx0Vw8Ad/T+8DC8g0cpBChXkt5Qplzn4GckKiMo1YG2TRzecn6i34cQTByXEyV2HuyyNt5JWGjpoKUwht05RbwzGkQgSYK4QChBclMZa0cf6Q0u3cc40FrixD99nuyqOmbfuglsm2TCQyVc451IZQiLAlnwTcwOI/M5qBBI8bXdoLUh6CrqN4A7BuWmu/MO3JlMOiI6XLQ+KN51LzSMeSLtGSO9Kr8BWCKEuA9D8JwPXCuESAKjYQCzCZg46PYEYMsoHGcMRwH2xz8BQIdB/98bL3VJbYTkZtPCYnxwKknsoOokUg6QSNUE1LbQjkWQ8UxFqiKDtXwTzJStsbyIae6O/X6NxyS3UlZ7/jqOtbsZ3LEHAmkw5qa38nhiGna3TdHzmDSli1XZJlTRptgE0pck6xLYUiKEMImCY4M05oqlRpf8eCg3RkxI9tBezLDV02ipybdKUps0YmOCyAV0CZEtkJ3YQPOjChGLQb4ExRIiHkNqjZ7cRqkphjznGGIPLt/vz/WhxKINXzvcSxgCZRvpO4AVaMK4wG8JSTbnKaQ8dGQkP1O+fZPZps3cjm8VyNAoCPumCPw6RdO0TubUb+eRxceCgMgbqE6e9MGFWCXwa6A4KYSy5NLUMrpUyBQ7zc+n3sPFvGuX9WnLTIvzG5N4W3rN50mYSXDx9T1mI79a0QYiDRZoz9knM+xq0rmzF9PBQlWBVMXuiKHq/TsrlcYwhp2x33E8ihBKEXlAOkVQ4+B1lNGOUfgJpYb4ovVDyqFxHMCxCRoSKEeaceOhxsKcSyItcPqMYtGzQvqiOOtDj/VhwNLyBCItmZdcyYqgjplONw8X53DbhpcA0FOIk9+WpNkCWdTEujShljTIZP9y3pnZxoYwywefeBMAXz/lyFWhHgrsTB7J1pWo9lnUyQRK5ckrRYOVY6q3HWWDpcDOC/y6ihIoHlKqt7HKkngmjiUloljxJrItlOeAJQhTDuWMpDBeI2p9tBZIX5DcZHwvRaRxe43FQZUw0MUSorPXVK77smDb6GwOYTtGiR6PmfN6SxOLVh75QwYWrTgy5hLdt+gTXDTv+v44riyjDo4cKDdqIk9j5yXa0mhHM+c/jSm3JSGSoDJAzhimmymtJm4HcwvorXGaHhf9CuVHfvwRLpx/PU42QEQKHXexShHgoHIOXl2O5pM2YoL/MCeQyjVBeVwNbnvWKBvCiNi6bvO4X/2s2WaBCrRrM+9kE78XP/nFvb4fOyuZRqPdfWcyaLAKaeftFrRcifaDMY/AMYwYI53O9gMhxF8wXkUC+IzWukrqfHwU1vUoMFMIMRXYDLweeOPB2nlV2nmknFjHMHqY/aMr+dCr/ogMwevRaFuiKtOXEMLI3iMJyUqlqUosCYF2LBCCKOEQJmyKzY5pffPNRKhCkzHiDetCMokyc9x2YP/8bT5YuwFqN4xo23Wb2pj//U+YkfSzjAHo6tcdmjaXowlfbFqGc07ED584h7bWHpSWdJUSJBvz0Ag9jUm0TOD1xol3hMhgYDJbkJQUmgSnXf4sXxj3Z2Y6KR4tB7xu6Qc49vj1bJ5WQ/HuemrXhCRX94CUFOe0mmmglkDXpSlOrMHt9QnSDvH1PZSak+YYjhgbJb6PuOcuc84+75U3Uq5zSG8KyLfa+M0hiaY8501YzSW1z7K8OJ7b151Ib7EGYhFRPEJ2OYRJCEtGfeS3hDRM6OHEpi3sKKUoTynjJoL+jrM5X1yIqINyo0LVhLz8+Kd5Q8M/iIlwyJo2XuoglBkvXrfSGPbb+YgoZlGulzh9MXN+0dpc3AxWvHnm3IIl+pVIhEYlOf/Ez+PGHaK4Q5gyIfr+P358jxPRdvfYoa5sHmhr275gXuxNYNsszv3kkBxvDIcRlkX9ch9/Yj1uT2DOsTHz3dAVr0JCZdTDMac/jldbZ7QtUTGXKOng1zhEjhiYxhpqLN9Mh/K6zZSv5ye2MjOzg6XlCWwLaqiz87TaPXx608sJlUWPH2P11ias9XGiyUUcLwRP0XmCZQYyJEPe1LyrheckO82t4x4FYMpPrqfpfofGB9rRyRgq7oyRsBXUyTjf75nFcbFNnBPbSmmaT2ydS5TQICG/JYUIBcUJEUHaQsskTj6OVTYm29oW+BkLLY2vTrlW8u8XPcj7Gx7kvD9+FDmuhF/n4kqBLAXY3QVEVx86mwPHRjgOlMqGwBQCXS4jPM/kjPGYOYdHyihTxrBPuHexUdGW6xxinQFB0iVIQ1gbIgLJGRc8S0cpyYpnJiIjiFwIk2YiX5gAu2Qm72kJ5TqIEgrd7RHrEfRUhPvp9eZ772ds8uMcrLJG2Sl6Zgu8bqDb7pcp5E5so3eqjVU2XojJbSF23hCVUaLy/7UlBBj1pFJG1RgpQx5V47clzIQ4KQfiuOcQ1rhEMbOfvcVxGD6WH6o4vmjbt5hf924WNF1hbu/49iE57hGNwSKD0dj3UY6RTmcTwCXANK31NUKISUKIl2itHxmNRWmtQyHEB4DFgAX8UGu9bH/3t7O/A4DKxJl3+hcPugHpzv2nYzh8uDP4BXM/s5ATYxtQM/KEa5PGPFlKVOWkLoVAaI0Sor+iqR1JmHQMXapBeZLQEwRxAdJMe4pippUmSmi8mjITansYZ4++ySyYRLRpSUi5xqLlwnYKocMl936Euy+6Zdjt559oEtPB7TIvJFSVWVWl1t1Fi5lOD5PsNK/IPMmP7TMphzatXi99xZmU1mewxhWINRTJT0gTJgSWbxn5MkbGrCUg4ZWNTzDTMe0MJ7gwfe5mSpHNlTP+xlc7XkrTU0a1phIuTjagaalC+oooEye2vYCKOdjFCO3aZrKYhr/94eDx7lVvqLvv/fRB2+dRgYovmZWz8GttWr0+zottZYrTye2cOGTTNVd9BDDqojChsfoGEv+LG59jY18tdfGBaUxBSqNczYzjN7F2ewNvbniI0z2HargcUALegpaaKAFhTOBagiBjE8YFuXGS3LgMLY8UEKHGLgUDBqzVJFQIRGhIZ2FblXKrQluOMXZ3JeWM7Jc87y6RrCad3rYC2rPwaw/9hKhDcfG7y4TOirpkXuptB51I2he/njGMLu4MfsGCiR9CKFj/Uo8Jdwc42aAyaVMSxW1kKULYsj+Oi8gUjLQQyHKAdizCpEuUsCjXSDOF04LEVh/pKwrNNmEM8m1GGYEWLO9p4f813k/ec3i2PJ5nihMphC7re+vo2ViD020RJRUNtTkAAi+glHaY3NBFwg64OLmcPRWVxo/rIvJboC+LTsXxaz0uXHAD9y36xLDbz69/DwB3dH3vYL/FRwyqaqQf9I7niZ7J/KLrVN4z40GStUUC28UqCKJI4PRauN1GvRImFYVWiZ2TJHZowoTxyanG29AThDHYWqphcX4GJEJU1kEoDUqjYg6WH6JLlWmkQWgUSEGIsG102Ue4jvE0tCxIJABYtOamg/a6Fxxj4vei5bv3enwh4b47PlkpCNloC6wSCF+ibc1x6c1sceqYddYO7v7taf3PWXb9gEpYwxD1oUgHlGIR9g7TNv7Y903MtwJNyZFYPhQbBP6EMpQsZEmyfL4xdT/vazfi15j9eD2mCBhkbKxihPQVuXEO8Q0M+KpFynhhVZVIZR/sCvEICM8ZiOMSRKhRFv1+icPF8cFtbd6OAto5PHEcxiaVjmHfMNJ2tm9ivrIXA9cAWeB24PRRWhda678AfzkY+6r2uAKmZSATRwSRUZocbHhjI16PFMzPvIOGi+fy4WvfT6wSJJRrgRQEaZvIEQhlPAy0BBlVEo64JIyJ/qlP2gJlQ6ENwlSErPPxYgGzGjpJ2AFn163mtPiaIdL10cT0u9/BzG0lnKzDqjunUWpWzPhFkflLd7rIehFic5Tl3Xd/hIlTdnB201ouySzjk6cu5uZnLuUXq08liswFhNqQIEgp4jP7KBYd/No4MoAwoYl1CLwu6JsT8v1N5xGM/wevT3WjULx1wsP8aMPZNNl9nDFrLY8tmIXX7dH0dIhViuia49F7RonMo0kaniljFwLK9TFyE2MUGwRP3XrV3l/EPsDuKx3U/R3peOD3hoA7+9+NyWbDU1DekOTHhbP564TZnNu8hqf+ZXiydMnXr2L6zYZofXyBaYn82aoz+P5x/wPAjzrO69/29lfcSo+KwxQqBNKuWP2xjzD9pqHErVWMsIrgdZnbYcKEWKvXQoSYJNS2zcWIUui+voGpOVIgLAtRcLCCJF1zkwgN8Y6I819xI0KZSmYVVfIovmKbuSMeQ4chVJLPKvl08SXX9beO7EvL3BEHMdC7Nz/zDuN5okaJuK+2G47hsKPqieQ9sZqZT9uUTpwMgIo5hmitc9DSxSqr/jgOmPiuAe1VpkCZ+4WCco1AC0HPzDjKMWa9ygF/YhnLVUxp6iLl+Hxj+8UEWtJRSuEri5zv0bMlQ2ybhVWCQgq6+pI01eZIukXqart4WdPTNNnZ3U52uy1bz0mxTchvNJLa1Gf88jZtw7NakZ19zEu+FYDF+f8Z7bf2iIRsXckvnv0oG54Yj5MV3Ni+ALvXQmUUqbUSr8shioNTAGuLwM8Iim0RaCiMl8ak2dI4fYLa1YpcqyTWrXnil8fxj/rjaFqtERHY+WJ/MZF0DGt8i/GxyxfNOdpzIFdAF4tGkeR5po3Nsg5+F0NP38Hd31GAB37/8f44nt6osMoWfg18v+9SoqQ5r6/78q750pKvX8XxHzFtbqs+aR6f8t0bSbbkidYNHW7TPcOi2KqxC4LIAyceMmHCDrb1pYesY87VC3c5Tvy5dgBE2ILfmMTtyBsPtkLRFIKUBtcUfAgCdC5vnmjJoXH82BRCg13Uw8bxiy+7DlmO+OvfPkt8VYchx5MxYu0hpVZzTTFYnVT1Q6z+PYbRhWAUPZFGZ7eHFCMlkc7QWp8ihHgSQGvdLYQ4OKOoRhmXXHjtkOSzijuWXDMqxzvSPERezFClMomNOfxMDd3HaXLHhThFI20NEoLIFbh5hYoEflpg+YYsCuMCP2P+BmP2p2wIW33i6RIzGjtojOU4JtlOyipxZnw102zN/rayjQRzH3ozz579U77cMYfUEzGsDWtQ01tpe7hMsdHBXrXZTJxLxDn7dSYwP/RLYy78QlUg7QzZupKJwOzvXsPzb2lhfbKPuzieZsckaKe1beDB9dNw+oyKxS5YlNMOthcRTi7iF2xEJCiHNrFOsAqSHfkUizqP5/WpvxEXHjERYEnFinIbDV6O2Mw+gmU1FBot6pYVyWywcPs8SvVQanQIki4yNG0Vyjk4IeOSi7466sqjBVM/ArbNoudvGNXj7C8e+tVHOfl9C4lciHVpegsWfmiztVRjDFvZ1XMDYPVHP9L/t0nEXg3ALd/9Bs98+gQuDq/j+dt+CMQYbq7rxW9/FzDgW7D6Yx/p39fg5G4w1r5WkH6uERkCtDD+V2vQNUlEOTRJRBShy75RSarQ+HPli9StKKIcSRi3zESibMTFl1yH01tChIp41f+jKqsPI7DMSUvshlvRFeXlaKhwDwdGq5VtcfF/R2W/Y9h36ChC5wtmnLpt0z3TxetziHWGhHFJucao9RwpCZKi/3yrK2pFZdGvKgYotApKTZqwJsROBQihibbF0bbGchVezCfuBLhWRLefoBA6+Moi5fiESiKTAZFnI0JzcRGVbPzIoimRoxTZbPLrmex09K9/wYqX8pnJf+Gq665EW+CnoHRykVkru6EvB5ZliKStnf3T5HQQcuECc+6tKpNeyAqknXHvxTez4O0fpvv8SRQnaxJbBaVGowT3a8xUXKHMb7cP/DqJSkREDQFRyTID04oWkSOIdWvcnJmq5RQEkSdws+bzoS2B9BXaEijPQTgWsrvXKEXLZZCGEMBxIJ2EcgAq3PsLGAGq07AWtX/zoOxvOBzphGQ1jhcbBfEuhVCScj24HRbTbn6W+Z9+z7Cf+6W3DCWXjrnJfN/WvrGG566+igXTzFTX2pNa8WstgoxGlgUNmTy2VEx62wYW1JvYvWjtLTx3tdnfYE+iBVPMfVYpRJbDfhsMS2uT6u+oVIoScSgZY3cdRbuN4wDlOgenFHHZuV/B6itCpHFtiejqM5NwLQmuawayxN1+8mJ33oej4aE0hjHsC0ZKIgVCCItKGBZCNDG8leERh7vv+8yQ26OVOC+YYZjlsZHERw50FMHTK6jvm4QMWrCLkg3/ElHb2kfvjjRWp02sQ6Jt8Gt0/whwZUMUV4jGMrYTkUqUaUjkubh5BeOcHk6JbaDV0tTJOBKJueAcHcz4+RVEmRBRlsy8/23EH0/Q9o+86d0XglKdQ/vZoOyZ1D+4BXyfvsmSwoSj4us5KtCOZMYvS2w+JsNlDct4uG86Hz3urzzcNw2/O0b9VqhdVSZI2WzXcdrO2UyNV2LJyknENtuUmxR+WiJ8Qdf2NA/sSDPtyTloR3HDBb/m4uaVfLJ+FY/7Pn/550nEfMisLyOCCG9HGVl2iHVL48kgINYd0TvFxjpIoiEtRT+RtPjxvZs37ivmn/QFRMwzpMQRjJo1hkQpNjvIsqQlmeXsmlVM+f4/uCweotoHJuZc8tZ38fHvGGLghivfssu+Tpm0ATAE0GVveAd3/XzPar59Sd6m/lqz7pVRf8R8evPXmX+88TDTjbXG3LVoSCRkJVuMIuwdWZCC8jENZMdbtDxaNApa23guEEXmR1V8GoTkjorJ6/yTvoC2JfNOu5p7Hrt6yHr0MEWVg4GLL6tU59XwBN6BYvAEpNFSW85LvQ0YPXJqDPsOrTQQoUtlhGXRtngr3S9pYd1roGVcF9lHm7DzgDAehUKZkeEoQNB/n5ZGaWpPzHH+5DXMSm5ju5+hJ4jTMz7B9kKKK6bcD8APN57Dlr4M9YkCWgvqYkX6fA9bKmw3InLMUA23W6KyLuedsJp85PHgxqnYQpGLYiiW8vkVryT4QxNffP6dtKzbOjC043egu7rNa4p5CMdBN9ai4g5iqZnwuvVshyCjmXbrLaz50Ed2+/68kFF3/3pqltey/uW1pDZCqd7cb5Ug1qVIbSwRpB20cMjOgYaWPnqfbiS+A+wiuFmF1xOgHEms05BFItSEKZsw7RC5ArugsPMBIoiQhTK6WDLkUcUwWUeRIfs7uiEZH7YovT/QhWL/37szOz4QzEu97aCtdTRRjePlehsZgDU5z8njN/PUMRM5pmUbU350A1aXuVSddesGVr1vMjKAFZ+/igWt7zMknNYgRD8ZVIVdiGh8SrB5fkRscoG+oseOpU3w+YnM+q8NA/F2EKqxPZzQCGDInvVbEEBw8gxImXz/zmeNX9CCWZ80CjXPRUhppkMOE8dV0qNrboxkO7idRVTCNVNcg8rUt0iZ51oWWBZ3LLmG+Sd9AbRGOxaLd4rjo4WdVU8vemiGqycevH0f5RgpifRfwO+AZiHEV4B/Az6356e8uDBGHh25iNZvgtNbQAiaH5J0nljHWWc8x6MbfmXU4QAAj/xJREFUJlF0EiA0UUIZ1j8SaEdR25KlOZUj45YZF+tlcryDl6aeocUS1MnEAa+pWxVGtJ8Lzl3KA/cdT3yrwO9MULNWYeVKiJoM2UkxCk0C6WuS7T4EAaqtwSiqYi9eEgkhULZg08pm/Ik2U+MdvKdmK++p2cq7gGcfOJ4gaeGnpBnrriy6y3FEJHCzmGpYg2ljlF6ECiVuY5EwsPC1xcNdU7lV+nzzmfOx6nz8wDPjXSWEaQflSbpm29Q/F+JIQGkyG0IKTQevfXY0lUhhxgM8nB25UTvGwcDOCc6b/vkefrP1FObWtHNZ/PH++7tVgb/85Nv8Lt/MhfGN3PPjHwBVVZFRE1x6wbVYwF/v/wzzTrua+f/6Zvz6XVuTd1YbXXR5RVZ+59C1iMoFLJgqottUHJIwVCcMXjj/eiN1z5rpQEJrRMk3o8r796WN6WfBN1J3e/hWq108OiJNWOMNkb7f+cgXhn3uGAzGyKMjF7pcRgGyoY702iK55Um60wmC1pCw10IogfKM4kSEZsoawgy+0AJEIJA1Pv868xnmxjczxd3BRqeBknLojRIkGspcltiAAn7rnUqgLHpLcV4zeQkr8y10l+L0lWLUJIt0VlrXnT4TJ57tbWXdjgaCos1q0cCyTW38Vp/IMRPaWXZSHUHKY+LairF+1Yy30sYqPA+kREuJ1ZlDV7y+wqRGuS/eOL5ow9dY0Po+5PYeRFQLGsrHFKmry9P7TANejyBM2oQJadRnZUnnxlrcAFCQbI9wKgbJypXIQBmPrCjCzoegzQWQVQzQlkSEFWK+anbrOuanp6/iwaMR+SLEjg7LCiFNnBi1lt+DhJ3j+CX3fgQpFOPrerh9+l281zubu1fMJpUusfqWBsJ2RfMjgjPecgvFN8xkfs07DTGr1C6+eQta30ccyNxliCrRUAfkWXlF25BjLpj4IbOWjbea201XYNsW4fRxsH5gMPjd932m32O3ikUrr2fB7E8RNmWQQYQoB2jLQoThkDhuprSa3ECEqn/Ij7WTJcHO15LCDwnq4kPi+Bi5M4YjBSOdznabEOJxjLm2AF6ptV4+qit7keNy5/WAMZUcw/7hruiXXO68Hh1FpNYVmXDrapb8+AR++apbaQ8zxKyA7a1pev0Y2ZKHbSmSrk9bIsux6S28ufZRptjpQXs8eJ5HIyWievwEVlHQ9vcsMl8Gx0Ls6IZMmvp/bqPOsdExG9mVRXV1Iy2JVcqAOrQVqMus13FX9MtDesw9QfqKmT8r850V/8pl73yY1WGO+b/4GEwoMf5tW2l/eBzK1YQtZTZvqcfqdCAdkZ+gEYHAyUMYhwnN3TgyojmeY1qigzu7jmN8ooefrTuNFef9D1dsOgs1Q/DEjAnknmwguQW6Twp551n38rnG55j22/fiddhEc/KcM2XtAb+uSy766i591JdcXDHXvufgEkt+S3rvGx1hWN9RT8IOeM3qy3jfuHtotnIc7yYo64DXp7qBVP+2VTLpkre9iyhm4XaVuPzMa8CWpjpYwWBJ+eVnfQkXYyQ9/4TP4VoWQaNJ8KyywtKau37x4yFrkq0rec1D78OPhpKIs7+0kGC+w8vOf5yV2SaeWzWO2GaH1n8GxLYX0UKgYhYygkRHhIobbybl2dz58J7JoDuWXMO8065G6IH1X3buV/p9YWRgLiwOpiF2dYLeRfOu7yfX7r1zLNk90s6NRxvuin7JZdbr0EojtGbz/CZinZp/f5s5x1687F/pK8fI5mOogoMOJCISOPUlEvEyJzRtJWmXWdo1jmPqtnFeegXnxjqokwnanXVsjlwywidC0GyZ88Pt0+/inKdfTU9Pgp8UziAMLFSPS2KzRZiA8y59hvsfnYvfpJFpn3X/mISTBUeCVfSozRulTJc1mWbPqF3Clhrs9duNd0oQGqV0pNCFIiKZQLZ3GNVBuQwYcorEoVWDzku+9YhqfTJqnSJTbttM/rgWugoO+ZWNNKzR5MYJ/LRrij0B1C6X2CVjoq0llGolYVwS6wjMsIuKsXGYdEBropjE6w4oNcfxOkzrmqpJIKM6owhJJgib0pROGkfqqXajJqlJENYcuPJ8OMXjqKkgj5l2cPd3CNAa6+PJLROYce/b+dypf+Hs01dxcWI1n970ch5Vk+h6OQRdMZond5F9aRPbH2tl+nVL+59ffS+F6yJcx6jKwLSL2jYzrn4KhXmv+7e1rAFCCsj9PMNvjvkGX+s8G4Al52UAY7w9GAuO+bTxQdKaMOng9hYQpZwhIxNxVMpDxV2ULUjsiLALESpmozyzpjufvXa378MdS65hwTGf7o/j0lf9w1QAIm/3Zt37i8Em32PeS5iixJgSabfYI4kkhKgfdHM78PPBj2mtu0ZrYS92CGtsdOjBwJ3BL7jMeh3WM6tZ+p3jcQLN834Lc9ytnJlZAxn4W88snguaiTsBLfEcM5Lb+Uzjs0gOzUV0p8ojEWwKxS5mnM9tbzaTK4II0dmNyuZQQQid3eYzYklkKtlfPdPpBEEK7N4X7+fnrr9/lsvO/jLKFtQv9+n0EyzOzUG1ldGBpM4rsmFSGe1LhBYgNbq1hBcL8ctJpIYgJVC1IZ+atogmK8eWsJaeKMH56ed4qjiZB/zprA5z/HXlbOPBKzR2BK+48j6+0Gj49eeDHBectpxS5DA50YlieAXJvmA4BdLBJo/++rfPHtT9HSrcdsb34Azz96sefP+Qxxyx++/D3T/5wYj2v2DBG0DCoj//nKufeYw1r7uQi+c/wbuaHuBTb31vf6Jx+evezp2//PGQ595+9q6+F2pOnpPHb+G6tr8TH+fBbHjdmkt4IjWThqfT/QlGvCsyo8oVxhOk1uH8V9zI3/7v4/1KJjCjkwejKn+vJoLaEmgBUdzGKo/exem9iz/JRZdfj5bDjyt+sUEM0zIxhn1DlUhCaZofL7LmVTF+ufoUPtf4HOc1r2aHn+KhLVPxs8a3RFualro+6rwiCxqWUmvleU39Y2RkiZNcgSNMEadOerRHES2WxQ419DtRDByEhDCwEFIjMgFRh0WY1MxKbuP+xGwIBCrv4BXBqfjqyhCssjYXfoH5clq+wsqWzEWsHxifp+p0p6JC+36lfcq0sgBEyQiCMYN3AN3Xh5NrwOmMoVzz/tolQanetK05eWOOrqX5keGAL5yotKOEKYsgIXFzCrRGBprIs7DKyqhAC2WIOZUJXBGbXtaCDKHxmRI6HafcmgIN0j/wc+dwRNHBJo+O1gEr1SnDC0+CaT8fIFiaLJeucoJ3HfcQjoj470cv5vy21QC8/A2/47x3wLowyxe3vJQWL8d4r5tvLLuA8XU93D33j2wIs1z4p48y+6NPA9DzmpM44623kDhnLttOdwnjMO36p40aDUi9poPmFSmubX6az2w/ob+deuf8SHsORBFB2qVcZxMmG4htySOzBejtw59iBgHIcoRVUjh9PiijjgsT5rteLULBrjledWJftV1cBsp4JSasXQitg4nBquuxOD6G3WFvSqTHMamsACYB3ZW/a4ENwNTRXNxo4KJ515vRngxUTvcX84/9LEJrFu2BST4QyHSaBU1XEPUaY+A7/Z+NynFe6Lgr+iXz4m+h4bYnKcw7gWt++nrue8+NxGTAXzpOYGshjR/YyMpV23ivm0BHeIdwQs/fS43MdbcT6JBlQchJrql2vWvOQ9yePJnSknoSJR96s6BNsNGhghCichmZSCDHt7HlkkYiz5hNHkocaZV2ESrwLKQf8eCGaTzmTEL3uLi9kmeccbz+xMf4+/ZpbFzfCAjaJnUTswOCmiyRkoxL9jErtY2Jdg+ve/zd3P+S79Egk6wOc3RGaZae8TO+3jOX5oYsLx2/jF+sOoWJF6wnIf3+Nfym7xQmxbtYvPkYEnaZe585hptO3P2aF8wd8G8bfE6Zf8Ln0JULi8VPHnwPpBcifnfOUI8J1T7joOx30Z9NHaXTT/Hcu74FwKNlwbU/+R6ffet7dtletq7cp/3/ctrdMO1ueCN8fvtx/Oxv57D2Ax/nvFfeSMfVPtEdDSDAz8Cxn14IJ7s0P+4jIt2vSNNCDOtJpGyJX2MjQ40MBeVah3NeezMP/vqj+/o2jBijVcG7+JLr+o3qD7RKumDmJ0DrUWtJt2prWdB0BaoyvWfMtHv/UI3j1j+XMbt9IioTZ/qH3sGnTr+Dhzqm0rc5g9dpISKIPOhriXFy/eaKAhEUikgLHDGQ9nrCIS3LZGQKR5T771/ilyiWHVQkkJYm6nUhEpQbIuzGEgvST/On8cex7dlm3G6BnTM+PU5Ro4UZLe7kDPnr19hGiVQbx+nzEJGCcrm/CK2C0IyTlz4ykaDrdSdxyYcfYvVzZj1B36FrnzqSVEgw0IollMYqBDQtcYk8gVNQxDsE5RphfHRKut/3yi5qZKVtCK1Rruz3PnKyEfk2B6HAzUaU6m0S24wfksrETZtRbZqgNsaEV67j+Yen0PpgCS0lsZXbCMc3IPP+nhcNzIsb372dv+tHutH1kYY1b/gMMMjXduWHhzz+hz+eiT8+YO45mzkvth2AH016oP/xX9dkWbelkePuvpJnPvgtps7aCsC2d5yMn4GGZ0PWvz0iyofYPRbPf+F4Zl6zlJ1x3Qm/GfGa289wiO+oRctavF5NzcoCyrO4+77PcMHLbiBMOFjlCD9tk2+zOOX/LSSa5dK41LS1VZVGw6qMtJkkjdZ4XWX8Wo9yrcVZb7iZf/x89OL4ixpaD0zQHY19H+XYI4mktZ4KIIT4NvAHrfVfKrcXAJeO/vIOPu5d/MkBA9B9xOVnXjOkjUDswwdgQdv798k8b3HpNhY0XQFgppKM4cChFdteYlP3nObMP1/FDy//PlsLafK+ix9aSKlQCAJt4Ynhx3qPBkpa4WuLgrL4c7mGv/Yey5sbHiKrYmz3M2zdWseMXABCIOtribbvMNL+QRVuXS4jlMI3ilvEke2JfEggAzPyudzrESUs3JYCqpDkspnLuaL+QdJWie8vv6h/+2w5xrzxyzk5sY5XJvP0qSIf3DifmBsSVb7r0+0UTYktLPWh3soRd3wuSD3H5858jsd9n1PdgaGV7697it/mJjO9tomX1y+hdMzIPlPatZl/0hcGJkhKI5UeLUPkFwPs1lV73Ua1z9r1voobtt26isVPgmo3JNI9vzqN4zmNpR/+FmdMNm2Kf71/8DM/w97w/GuH2grufPx31f+DR352Cpf97Ms88NDnOOUvnzMXRyHYDmRnhyTW2SBA2wLh62FnxlaT0GpyqhxBMekQxgSpzcFu13cgUvZqG9tF80angnnP3Z/a7+po9X3YH1+xqnfGoop3xr5ANtTt83PGMBSLi//L5e4bQWuEHzL9uzaxMwI2dNRj5Yw3jrJAO+Z8nYtcNkdZACwEjhA0iKFpb60UbI9y/a1sAMvK4whKNqpoozVYfRZWSRDFNFpJmqyAm2f/irc9/kHjV6bA8jVWyXwHRaRxsgHKFri95rbdUzTta1qZEfJUTcMH1GqqUMAuavrCGCdP2MyTm8az/t0f50UNrUFrrL4SMkgYRaUEu6iwfDO8wskP5OJCGZNlPy3AFvhpaYgl33jO6cq0vsiVyLDyPAnKcwiaXZw+n+2nxXln03JWeJMJMzGsYkDUVEsUt5DlkRcX56XeNuazdhCx+IKv9f/9sWMGP2ImGE5jaBx1ZIRs90hu1ZzxmSt49IcfgTxM/uENnDZnHYWfecxeAs9+blz/pFU+tm9r2nnS9+xrFgJUcnHTkm7/cznzkm/l/gp5ePlLriG5IUehpYZCGyQ3w/ZTYjQ/sfvpK4NFD5dceC1R3MbPWIQxQXLr6MTxwa1tYxjDcBjp2fD0KoEEoLVeBFwwOksafdxz16f2qEKaf8JuPMOV+fJXsejZa0euQtoPxnHRjm+zaMe3x1rbDgKqFaFxDwQUGgV2t81Ptp/L2U1raUzksaSi5Dv0lGN0h0nWhdmDduxf52pYHQ41Kf5jIcFt2XruKwnuK0xmVbmF5/xWsirOX545no+u+Hc2Bg080jGZ5LMuuYkxyuMz4AcI10XGjVJJ2A4ykUDMnkbYUkPjUsMePfuVq3ZZx4sJdz5iplp0nJBAlCSWE6G1IHNsF+dknmeSnWZWbCvpKb2ccupqvjTz91w27jneUPsIF8R3APCUH6cviJFwfbLatB2uDnN4wuZ4N84cr52vzfgVm8M6Hi0HlJTNHcUBEikj47w9s53bptxHrVXgkYfmMOUbNzPlGzfvce3CD830rcEIol3vG4SdzR5frDiQZEe2rtxFOSSRvPzif2fB3M8MSU6Xfvhb/c/ZE+af8Ln+n309/lA/NnjipeZ/bPkauwDND1rIAESosUrGEFaWIqzi8J+Tu+/9NH//7UCGnNoSDNtyNtgL4UBw4fzrdzua+GBgbwaj8067mnmnXb3X/Sx6/oaRq5DCsH8M+0hRjePYY3H8YCFat5HcjBqClMMXH/sX5k9fTlgbEaQ0fp0iSiocO2Jdtp4PrH0Nn9n0Mh4stdEgd/U1bJBJPrThX/l1rqb/voxV4tiJW5k8dTvHz9lAak43QY0CAYmEUQg977dS95ymdlVEemNEvDPC7QtxCmYKmMz7OF0FoxystRHZgiG/LKv/9+DcTqbTWM1NKEfw5ydO5JFnp7Hq31/c82sW535i3qeaNIXpdQgFTl6Z9rSYwMlCepMi1hngFBVCaeyiQtngpwSFJkHomUEb2hIoW5JoD0lsC7F8hV1USD8iSroIpXA7S0Qxm5ZHCvzP1xeQWSUotLkUW+NoW2IVI+Sm7SyY+hEWTB3BxLxo30ytq6PpX+xY0HIlC1qu3K/nVuNoNZZO/UOxv81xXvwtnPqehSRXOSy9dybLPz4BYERE7bzkW/uVZHvCii9cxZKvX0VhckjfS0qsedfw24lQkdoS4vSBciGKg91XwunI4a7cQnzppt3Gt8FTx1NbDIE0mnH8RQ09yj9HOUY6na1DCPE54KeYl/1moHPUVjXKqFZHd/aQ2Bv2Z7LN/GM/yx3LvmLGUO4nFm371n4/dwwDWFy6jXmxN5FOn0JyG5z3qpVMc7cz1dvBD4rn0FeIsT2bZn2qnimNB88P6bWpXgYb+gI8WZjM8mwbx6S3cn7qOZYWJrJKS66oW8J/N/XSV4zRZGd5SeN6ftswntZHfOxs2YwRrRpvghk/a8XYuMDYl9WviFh+zYubQKpC+hEyAhlI/K4YJ89dRymyeVPaWLlNsrsQQvPk+glcU3w5s2p20GIJ1oQ2M+0iCWnxuUl/wiViup2irAM2qgjPNoqiquroui0n0uPHuXryH5gfG17q/ny5Fa9LUB7kMnfp+WZCV7XHfneE9M6VruFw14Mv7ouNg4ldiaHPDPv4st0IWgeTTX+5E9qjKoFsSKAFcz+DtkT/hLbd4fLXvR0cMzGuiie/MfDdfsnbbxmyfbV1THkWl1z01d0qbdy+0KjaRkDwHOmGmvNOu3qfRx/vlwJpwn8ABzaKe9G6hfv93DEM4E7/Z1xmvY5Yh48WoJXg1nGP0hfG2JCrZe3GZlCCpOtzesMGtpUzZOwSl8W3A/Fh9zk12cFfuk7gtSnTCnNRrItva0lPIc6GDU2kG/LomCKqiYg5AT3K4sHemXi9EXZRoUVlAlikjdIoIdExG1FQA0Sq40CpjPYD47siJEIqdEU1LFyHwimT6JsypjgdjDv6fsSCSR9GS3BykWnnEQKnoAmShlSy8z52HhzPJoxbCFXxRdIQuQI/DVqYVl67EGGVIpTjGMPtmIXyJIn1WYQf4jfEEErj9Wki15h2O9mIIOPgdZYqnklDyaH5de/mju7v99/eXcvq3trYdpmwOYYDwr0X3wwXD9yed9tbkKEmuRUe/VGFBHz/8M+d+9mFuH0Q69ZEDrgvHepFMC/5VjNlkeH9rN5/9j38W2YJV865nP/f3nmHSXJVd/s9FTpN3LyrVdhVWIVFOSCCxCpswgZMMhgMGGz0EYwxwUhCIIGEkJYg2cYmCAzGGCzAGBBhkxIiSCgHlFfSStocJ3asqvv9cat7emZ74k5PPO/zzDPdVdXVt6qr6tx77jm/g0ivbfqOJU/9YI9tkP2dkEwSzWxkxcuu6rf4hXGFRPvQJjQmuh2fyFQXJ6nHvoe0nchmoBMIgcAYc0asWf1DYBGwGfhLY8z+erRzIIbqRPor4Argp9ix6x3xsqmJCKtO/jTrHrpqvFuijDJRKaDxp/fgHnMkP/yblbz7v37Bqxuf4r2nbOfTu17CjRteyW3bl/K6fCM/P2b9qHzn9zpn846mPb2Wndv4BE93z+XmHcfyXPNsOkopXjFzE9tC+OrSH/CW313Ere3HU4g8kvsgTLn4ewNMNocpBbYaRCJB55+dzLZXB3zk7F/x7396Fb+9dvSqLU12oqRH+1GGKBnh7/V4Yv3RhCd3cUnLyVw77yFmugWuWfpT/uFHf0t23SHcceRCZvy/Ozk9DiY6PdF7f2X9jJwpkBarU/HmZy8kMsKvlqwFfB4s5it6VmWO3PC3ND+QwHUgua/3AMHNlipaSGUn0uoTPsnaxz7PCZddT/YQ21nd/MHpme9+4blXD0voux6dpfLvEu34X/7srFez9oXhfX6+28hRt7yb9GPXc+4b7if62iyWzx28uOmGH/6ndST1w93/2Xsm/MJXfR6kR3izP4xIpUpbrfNVLagJwwuHr1WGWOTgtAdHyvp7hxaJpExO5HcP4TU2sOS6wzjr1vfxoUv+l3ccvocT2v6a/I4ML2yaR5Nf4BdL1lEwJV4IQj676zQea59PEDmsnv8oH53xHCUT8Fetf+TKLa/hw9vO5BPzbmWuk+Z9h/6G//ZexlFHPMb/PXUyyR0eLM1x3Mxd3Np9HE+1z8EpGpxShJMPiRIOOFJJk8rPSeF3eXi5eF2xGOtrxJqGYYi4Lm5zEzKjle1fSXP5cVb38g1HPTCep3ZCsbLhnYRnHEeQdEhtz+F25TEJjyjtI1EKvysgzPj4O7twd7bhtTRi3Ea6DnHwcuCEVi8JAbcQUZjhkWgX/C47AM/OS5DaG0AUUVjQbCM6CyFO0VBscIg8q6vktxdxXthh9VHCPnoBcXl5E4YVR1FZF0mOXVzJRJiuY4pVsy8CYN2eG4a0fb0mztfnvseZ776OmTc+AIPojqf2Qpiw6bGlRuu09PIRq4+9hOLCFvKrT6LQ2r+d/dbjLye5tMSqe7az7swFA35XZXLoc/GERSqBkx1YeytyBbcYi3HXKK5StuNlR8WqU6zjaiiTkuetWIMTRNxy66XjbseVCucZY6oHkpcAtxhjrhX741wCjLm3cEhOpLgK24fr3JYxY7AIpNF80K97dODZZmXsMZGh7ZRZdBzu8I2PvIl/nuly15qvc9XcP/HT/ecQdbm8+8LfHfC5B4t59oYNXJAeuuDQM0EXD3afdoAT6dftp7CvkCHpBmxqn03GtwbjntwifAlw/Yh9xQaOadhF97ElFt6eR/IlDFYjK9y3H0kmkffuYoERvvbYuTz1JnUglVl96D+Qe+nhmIQBY0VPS02GxbP28WctD7Er7OIor5GjvCLR4Tm8+5PkZw8+LXCU18ivsin+LGNz1zuKSc6Z/UxlfV8HEkDzAwk7I+rbDkmZm++4rNcgt+w8Kjstms/dRfdW1VGZKPzZWa8e8rbX7F3CR2c+SlJ87ioEONuTuDnY8OTxhHmPTdvn9tF0qI2t8ja4vhL0jlgaiLJe0UBMlpnLwaKQhhul1B9rt/zrqOxHGR3K1dqibJbdL21l1p+yfOsTb+CKvwhZungrzzyyGAw8mj4ElsDdBZ9/27aKux87kubHPbwc/Mfsw3jkz//EOa1PcUJyKxfMepync/N4ttTEwhSERpif6uCkzIvcuPNltOyFkhPR5OfpClPsy2Zo9QWnzfYHnFJElHDBESSyOkhgI2K9nINpaUQCm3ZqogjxPVuVLfJ54iMLOKX1WT750OsBeMNR43ZqJxQrG94JkcHtKuDMimd1Yn1A4zr4nQHGFcKUi5fykKyN0AiTDmESjAup/fa3MI6NFnMLhlKTS6K9iPGsEyCxO0uUSuAEEWHKI0q57PnLLL4f4v1fM8Vml/T9W+33O1KJQOmLk8mwes77WLv765VopFe8+csk2/rXrFHGlnu+81FW3viOQbdzSgY/G//vAi8fIYHBpHwi38EJIdnWf7piaXuGTYvn8bLmTbzrwfYht2+otmYoNvpg7LhqcFYxMQWwXwcsi19/F7idieZEEpHPGGM+c7DbjAe1xDPPW9lbo2EoN9iKs63XtlpQW5m8lDufLb/8E03HLcYplEg/b3j1qrfS9pJW5r1nCzs7mvjE/76TJ1+zkZdnNjHf7eJTL76OJ36yhOxCw/wTd/KPR97MGxtq6ya96/lziRC+d8RvOMpr5L69h/HtzFZOSG7lGzvP47d3nUDkG0hFJJoLFHenWXDkHnYUW9kvDewuNlFsT/LIngXc+fwivF0+hTmCn/Tw2zswYYiTThOedDSLmjcD8IddLTXbMh1Z1fIepLmJfce7OHkIG0tEJ+Z40zEPsTS9hflubxHV9ef8G++86WMccgfwRrvsnkKJbpNgWcoaj19lUzxbnMsh/n6anRw/627g53tPY18uww+fOY2fbD6Zr534fc5OHvhIvepD3+UTD74B/tREYU5vB2R5kFtdma2cDrX70ffhLsgTlaavlspwopDqzdoX/rnX+6U//wzdHSlMXIb7+fd8gmjHEn7c1cL3fnwB35p1Hi1POrQ8V2LJUzuIGtPsKrTSdnyE7Kktsl5L3Hs8qI5EGk5HtG8U02hw4as+38tBdv4FcR5hPAk8lCqrK8+0VQ3X33PFqLZNGV9MZJj/002QSuI/XeD4e1zCfJ7FTS/SfuZC/Lsd/v4lZ9EdJLnv3qNZ8EdoebKd/IIGMrsdHn9+KffNfQknvvFxtna34GBYkGjnq9uP4J4/LsHvFH6dOR2aQ4pNDuFjLax98VQaXnRoejHCby9YsWZHkFIECfuslshQbHZJtBVxugskuuPKb2GIJJOYbNa+F4fiGUvwuh22dzVXSiErPTgtTeRnZXBLBjGGqCFJlPIJky6Rbx12bj4kaE6RyBZxtu6mwXGI/Ea8XITfGRA0egQpq2mUaC9iXMdGa4rQvKkLCQIkCHD3d2EWthKmPcIwInN7I7vPjDh8bUjp2EPxd3ZaJ1KpdxpRufx7uRhOmaNu/DyzMikwHsUmhzPffV1PGtU0YqgRSGNFdbrhha/6PBJYbSzosRHJ9ojMjgJOIbB6WG1ZKJTAERL7PMJkBq9Y27lwyq8+jUSt/PrW0/lF46kAvG10CsQOmwtf9Xl7bGm/39S4vty24eIeO6tMBAywQUQM8A1jzA3APGPMdgBjzHYRmTseDRssEunvRGSgYuECvBX4zKi16CC54PxruOXWS4ekezCUjnI9nUerj/w4uE4lv7o6H7o8m6GMPmVHkvPwk3DMInBdiCL2LxH27p3BqqMe5+jjdnKIv59XpCJKJsm+QprIB6coLGnd1a8DCeDOO04gaIj4dHov5zY9QUc+xXWPXUDuxSb8TodEEfLzQ3ANQcEFAx25FM91z6IYujy7bxYSCm17G3ETIW4AUooI0i6+COL74LrsOyHDCQmrt/LcXw9f52NK4zpIAG4BGud1ctSMvbyscRMnJHax2MtUNvtZdwNPFo6mfbHDjKdCtoadLHSbeKo4j9+0H0dp1t0c5rVza8dp3LH9KA5tauct8+/hmsdW0fliMyef/Bwvad7GD+54BZuPm8PZyQNTkjvCFDed+XX+ZdH53L/7sJrNramHdEgezw8JdVQxrqw+/B+B3k6kZbd8nIZkgq5iBgkFt6PH0XfjzrNI74IZT0Lzpg6ctm5o68AJI1qebaDtZSFHf6OrXFBmUKIdSwYV8B5tqp1By1atGbYj6WCp1gsbKMLKiHDeijUDpudBfZ1Hq4/5hI1MiIWzq+/lvqmqyuhRtuPhnr1WfLmxAbI5pLkJPI9is9CwpcjaP5yKvyBL5Bu8PBDaSl2OJ2TnOnQfHnF6y/Pcv+0VhE838cszhBeenEfLs4KXh1KDkJ3vIQbSOyGz3SG9N8LvjjC+Q+Da6pmOiE2FilNM3HyEky8hhQAcIcokkFTSOiByOcSJU9IPT5Beuh/PDXnyDTpZeQDG4HWXoBsIIvAcjANOKSRK+EgY4WVLSBBBt62A57RnkbAB4wpeZwG3u4TbkrDC2N0FCA1mdgNOvoTTkYMoIpjTjBfaym7dCzzcZ3y8nKH1MQecCAkNhcNbyc5L0PrwvppN7dtnl+fTJDpDsnNdsvPG4FwpA7Iy/Y5eDqQLzrsGAZyCdQpKyTqSTvzo9czuCJEgvl5KAWRztgpyKoXkExgnQ/qWP/X7XVE6omG7h+xyiTw47jPX88Rnxl6vVIII44iNkhwGt96i6WswJppIp4nIvVWLb4idRNW8whizLXYUbRSRJ+rTouEzmBPpm8BgCsPfHKW21J1yGtt4qs2vPv5SjBP3eNNJJJuzr73+f4rVCz8EwNqtX6l386YPJiIqFnGf34Ykk9CYwc3PInFnA/f+32ncK7B9eciiC77K6YkEG4//Jd87dDb/vfWlvXbzsR2nsfanL8XLwsMf+xo3ds3g/PMeojNIcuNjZ3Dj1lfilASnBDNfgGITdB9hIBPg+BFh1iM5N0fu+Sbua0+RaChR3JWGdMiC+fvJ+EW6f3koifYiYdKFpkYbWlko0rAz4KnOcXE+T2jCrm5c36NxqyE3W5BfzWRrxwwuPuoYijMMMj9PmPNADEuP3MrubCN+FtqOcjnnFx/D+BFeQ4DZmeJm/yV4nS5eFoIM/Oyvv80nXnwNXS8085fn3MUHZv2OS7e8BlqL/MeLr+BTO2az9NDtHNu8iy/Ms7oWVsi7kVzo05Tsv4QrwJv/8H4evuX9LPh9Ef/sBPkFIcYbXoUXpb4s+dz1FGfNp/FZl6PvtUKr+5Z6LPr3LzP7/vfhFWDm1rwtNW0MpiFlU1dcIbE/z5LrQ0pzGgf/oirKEUpj5Uxa9NUvYd4sHPmjiRfGXe7cnr98/GZKVx/zCXCsBg6+ZwcZxRIkakeYQU/lJRXPHV1MGGIig9PVDUC0YBHZQzPMWfscFEsc+6257Dp7Bpk3bWeLO4emp1oxYoWXvWX7eNcRD/PzTy5n8XMdGL+DjofmMyclhAkwYoV1m5+3lb9KDQ5e3hCkhCDl4JRMnOogBA0efnsRr6sEIrj7bbRRcUEzAIlt7ZDLg+/HE0EORIb0vpCiGPZ0DO+ZMC2IDEQG74Xd9nwBeB5Ou33tx2kmJp2A0ECyJ+Wt+eHdGN9DijaVzG2TSlpK1NqAUwxxd3UQLGglPzdFcl+RKO3jFENaNuVo3OIhoaHziATd81x2n9RAepfVWIoakgM2e+XpV0AEx7Tba7LjdQs1xGyCsfLMz+IFEU533t6XpRIkEqw8/QoWFgMkX7LLkkn7rPc9xLHRa5Ir0vjwTjj0kJr7br6hGW+2i3HByxmcwLDj5bD+uRP4wB//mmfeOrTU89Fg+ysaWHhbh42UVCYi9xtjBpw9MMZsi//vEpGfAmcBO0VkQRyFtADYNQZtPYABnUjGmM+OVUMOlief3mFnTKsExqpFPsuhebfeckld9R7K1dj6o+JA8hxr0OKKHYRWE2f1MfH09OwZPRVh1Hk06myMfsxy581E+QIShkgQcMSPttFx6nzCpNC1QHDahLf/94d54j1W5O+01Av8MnEyxcgjZwokxefW77yUhAOJTsM7nn8VT+2bw559TfipgLDkkOoU8vNC/DaXIANOBJFnMCWHKO/iNZdIJUp0pCLo8ig5BiKheVY3R7bs5c5nF9M0W/C705QaHBLPFKwwZxjS8PB2nnhh/jifyYlJ1N5B83M5IE1mZwkJDUEyQT4ryJY0frct+frM84sJEzB7Z0R2rgPJCLfNI0qGRI0B6Rd8Ss2GUhPI4Vn+8fnXc//9R5He5vB/617OTcecyNyWThofSLF/ZgZ/U5pHiocSLRboM/N4569OIixLJi3rve7pLYew4jcfwpQW0dgBXjYgtTdBqckhzFCTWhEyyuhTPr+rZl+EzJzBrNPn0j3fZcamAK8jj4SGREeKhhcdMnsCvK4QryNPlPQxjoNJuziA8V2ipIcDePvzrDz1CtY/0NvEvvSBN/HHU/+XvVH3AeXIxzQaSUAS0ZjrIjz1xHYuWPZ5bqlKY6y24+etiCurbrh4SGlsI2X1MZ9g7dNf6Hf92qe/YKOMjCFK+kjiwK7UytOvYP1jn2fFWVey4qwr2aDOo1FnY/hDljtvBhNhYrFjeewZmtrmQxhiTIR05Zh3R0DuzRHfW/EN/nbmuwg3N2BcaAgdFvhtNP5pFybh4xRKND7vUmryCRpcnMAQuYKXDQnTblwmHptCVbJRRxIZgrQblxEXJDRIGNl0S8eKbGMMJpNEsA4PKRYxuTzS1Eh6W5YXtjXDMLQWpwPL3bfYF50RTjrVI2gdGetQiifTAKRQtMuyOdunBogipBTYCMEwAhFM0gPHQUohki1CsYi3r5uGzjzBjAxOe5awcQb+i/swR8yi+5AE7UcJfhck26DYAjOeCnHbc7Xb/MqrbXRTV5y+WCxCGOKU7DVTi1XN7wZsFTqlvpSjkFYv/iiSSVsHY1c3BPG9F4Y4uVLPtVUWwnec+Loq2etPBDB28sAc+MP67SVSrrWdxhXcQoTf4THf7RxTBxLYaPwdL29m3h/7z55QBsDEf/Xa9yCISAPgGGM649crgCuBm4B3AdfG/39ep1YOyFCrs00JxiI8L2pIVlTwKYVx6K2DBDVKMTqOnUHJJJFiwOpjPoFJ+ZikNYJObuCoBeXg2Bj9mOXuW2znM5eD9nYann8Rd8F8EqcupGWzQ6K9xMpfvpM9pzTw7xd/hf9evJ4fdc3ly3tPJhslQGDGJvvbvnbWg9yQPYeuJ+eQXVTilUufZsFJHfzkT6eQ2Oz2dD7zQpAUiKyR6c4mIRVB1sXdmiJsiCiWPO7fdiiHzt1Pyxu3A/DkHUcy4zeACLlTjqDrUJ/040JpsFjB6YaJMJGD/PERZjzRjKTTmK5u5jzdCA1pglmNhGnXhrYXrDBn92ENNG0Jadzm4gQR2dkpjAsznsqTnZ+g2OiQ393A03ceQyYBfjek98DeBUl2i6E03+CuncWM/YZjVz7Fdw7/La/ftIIvHPF/fHPfOTzePo/jl2/iqZuOpthHK7scZSJ7EzQ/67Dgtn1ItoA5NYPfKXjZ/sOQazmQVp5+hS0pO0qiwgqsTL3dRovu2UfL+n20zop/xEIR0ilaH+9gxiMRUcoDEZtO0ZCys5bFgLDZeg/dDquh0r24GYkMx19+PYXjcyRTJYInmzHubI7cfBFuzqlESzz1Dpsi0Tet7bwVayrivfWwbYu/J5XZ8/NWrhm0IEWZI6+/zoZpj6TjNUFm683e/aya+d74jR00SNr+hiabq5TzXvv4Naw4+0o7jogHDSvOuhIJQ4hg5alXQHL6apqNBeUJIRMZ++zPZok2PYuTTOE0NkBnFyYMSf+1z+dTr+XI5iJnf/9e7tt/OI++uIA161/LsfkXYO9+cF2cxhTGT5LZlgNjCNM++dkJwqSQ6AiJPEFCcAKD8QQTC226ucg6lYIICUNM2bEY2e32n9RKotM6nRo6um3kWjrFC6uaIVWo30BlshIPzqNSAHv3IYkEplSy0SCui3ie7bsZA91ZO7B3XRsBWrLRYDbN1LPvXdd+NpW0nwlD62Dq6EIcB6chSdTagL+jA0xEfpbPzrPhiF+X2H+MT6kBWjeFNnU2m4NMuldzX/WaL+I7Nn2Irm6IItvexgYQyGwf3uEPVkJeGT4rEm9DfA/xPNjXhvG9Huek79nn9t791mnkeRAEUBBIp6Ctw0a6ea6NXApD+z6dYtXJnyZsTNrfKzJ4pRCvswARSBQhuSJH7G3iU+f+BTz/IX5xTk9gwOoTPmmvJ2Dt5utH/ZgP+fV28FyM57DqxMtY98jQij2d9r7riXzIz6xfOpcyJOYBPxX7LPCAHxhj1onIPcCPRORvgReAN49H46aOE8nYAfp5K9ZUqs9URxyNR37nukevZtXJsZBZbAzWPfq5Xtssf7l973iOLd/kCpIrIVHU64FSruKkA8PRZWP4QwA7m4kV6gy37yA9pwWnPQvdWaKOTubvWcA/dv89O88Jee7Pv8nnCnO58aEzmLfXkNydY8fLmnhzYzu3Ne/mhfRhOMmQ7x3xGwB+8tuz8LttWCtAIStESQcMeE+nCdIG5hVJzM5RCjJEmZCEH9DVlWLLCwvYtiDLl8/4X1Jvu4Xrf/RGiCKipIOXt86Mx64e+zzrCU/sSDK5PCY2+BKnmjipBBK41sCXQjAOybZSJerCzQe4OY8o4RB5Dl3zHeb/sZsw5RFmXCJPaF9kI8ta70nQvsRj9kv2kN07BycQfvvMUSzvbuHsWZtxBF7MzqC9aAegj67p+a1WL/ggv3pgIxtzHl/Y/FZaH3NoeqGEdOfBcXCL4AxQ5VUjkMYQ10Vc10Y3hCHS1mFTl8qROsYgxQAninoGKEE8Q1kKkIKdUJAoQvIlUrsLBBmP1F5wHk6Tm5eiYTeEaSHR5uJ3YktSj9M8wub3f5zzftq7EMVQOPJfrkNCbBRGPFk7LAy42cBG7txtJ2Oq7fhQKsuNNuv2f6vHoQSV37ysc7ThrstZ8TJb0dWIII7BuJ4dTBqDBFHFbpft/cY/fGrsDmAasDH6MdBjxwGiYhGyUnE2RB1dOOkUYgzrrn0Vp330fh7Zv4iFd8QXaRhCJk1+fobOQz0SewWnGFA4JEPnoQ7NL4R4OesEisqR5LESdqLDpkyJMZWKX/YeEHvPF8HLRoQJYedLHQ4N5pN5OCRqytgoFVdHaQNhSoF1xoWhfRaLVCLPeuHED6yoyott4pTDhG//d1odybLTqayJ6XTlbZpaZJ/bmW15Ft/kUWjxwMGmNBr7uV598/Q7KLzqJbihwdufRXJF6yBMJqzzyvPwu+I0Ou/AB6pGII0d4lqHftn5KOWqW9URt5GBKHZOhpG9PopxR6x6+yAEU7DOJsfB7S5i3NhxaYztW4bGVmTM5vC2Fvn5MevtV4yDzqEE/YTC1eCsv7mOqFEoZWJNoBoxENOHquukDvsedAtjngVOrrF8L3BBHRo1LMbNiSQiXwReAxSBZ4B3G2Pa4nWXAn8LhMA/GGPWD7q/yIrvGffAh3R1OPxwGe5nyx3fMuseuopVJ8ah+Y4zYCfSJO3A1jQkcduzrD72EtY+WaX7IMLKMz+L8Zwhq+wrQ6M8m2nD4sE8+ISdfQitdpLZ/CKzikVm/dZl1QfOwGlsYNFLHVJb9hI2p8nsinjzsxfywyM38P03PcNh/l4+t+c4vv/T8zj0/hAjIUFaKGUcmjYbwu1WHNLLW7HPXa+OOGRGO8+1JxGB7u4UJhIy24TMQxlec67VV7jiJa0k20JKDQ47X2o7Sov+61o2v1NF8Pri+B5RvlB5b/a3WwHWzi6cTNoOGgAJI5Jb4o5/Y4OdhZrfjLunRNCYoPXZALczj7cvJGxJI0FEw7Oh1U/IlehcPIPu387BiWDfSyIouTzz/Dy27m9lxvHdnNn6HFu7D7ABAKx84s9YPvcJTmjZya0zDiO138WkE4TNafxug98NhebhjeTX3zdpspAnBSsSb0NcF1MVTRpls9Bl7KymMZWoUfE82wGNjHU0xaHwThDYDmg8u+k9140XRcx/OkmwcBZOISBo8DGug9ddxMkHBM12ZvPpUhfH+AfqpdTboXLbhos5b+UI9APFpooevs4OrJ8d5sdNXDVp+SuvZuPvetLaqlPZhsvKU62wdt/0wf5Yt++bB7xfvdhWVZKEz+rFH8Vk0rVT1+NBhPEcpHhg79t49tgwho2/V2fSaFKx42B1D/MFxA3iSQVDlM0iQUDrTW1s+lHEse6DSEPaRjG5LmTSpDe30XHYbHLzU0iURAws/MU2iAzRTKtJaHy3EmVgHAcnV0AKAeHMBnv9Oi5EhrDBwymEuNkSzY/tY8vqOVxw7oP8cfMpJNrn4uRLpF61hxQwO9M9ruduImNi5719E9m/2LFfWR4ZCAI7LIsLkIjnQWhTHSW0MgCmaCtsiTiYQtE6nsIIM2eGjew0BtPSiBiD2x3gu0J6j0PkC24/1bhSO7qJEh5ROol4Hk7Ch1we8RsJ5raQ6IwI0g73fGfoE37ru//r4E6aciAmgsihPHg3UU86mgAmLnKE60Astk0QVJxMEoh9H8WfLxR7Uipbm62zwbPRyBSKPalwAJ7HxpzHP7+4nJ8d8wvKqlr1LrpQTsteddLwbE1hBoRJOOxmGyX1+Ki3TJkKDMmJJCJLgK9hS8q9REROAl5rjPncIB8diI3ApcaYQETWAJcCF4vICdiKb0uBQ4CbRWSJMWbQhHFTnh3qh75VZkbaKe1bcngwBgof7OtMWnHWlRRnJmFmkvRzttJTuQNMPIsx1joV04W+s5mmWOrVcTH72pCGDG5LMyQTpB/YjCmW8PalmLGngd1rFnPy8R9EIog8OP7Pn8bvBKcQESWE1N6QRKeDhIbIFyLfodhkNRb8Z9K8sGcBXkkwvoFMiXRDEYlSuIWea7rxxbw1UI7Q+Jy9fR+5Th1ItTDRgc8C25kMMYWC7QiUnxfFuFPanbUzh9siG5YsNldQCgG4Dk5nARz73sFqnKV2lXPf4eev+Wc++NRfsautieKWBj7+Buv//vjxB7Zv7fZ/Z9F313Dpol+zq9RMfrYB4+DlWq32RpwF8+C/aaTZeCKO2I5mCEg5ysD+5lG+YCMNUkkolmzKk4iNgIsFXXEdOwNa7kxWDYikO4uXLxAeNg+vq4iTLcaznw7e/hBE+Itv/hOPfsDqso31DOZt6y/m/OXXYpyh2ZxnP/xRFn/ly5iEYfNfuCz+6fB1XqQ6iqAKN2879ReeezU3V2kmgY3wGW50z+rjrX7i2sevGdL2a5+7rt91tSZ1LjjvGkh5eB35Sht7ocEndaGXHY8nhTBRz70bGQwh4seDvWLJRroAsr8dcYS5f/RwOvOQL2BmNlcmHJzuQs8AMunZiAOwzocosvdvZMWdjedgRIiSLk7J6otFCXiqfQ7Gte2IUj5tj1nh7fs+NrQ0k2lH9W9H2a6H9jdJJGI7Hg/+o1jsPo4GNXFUEY5jo4KCHoeTccpRo/Fv15WrVFjsPLoZt2hwSoZEW2HASeT1ue+x+thLEM+J+2ZW/w4vQ9SQpH1JBrdg+OP3PlqPs6MMl2oNo/i6MpGx9jvh28kiEZv6CJhiERPZCSNTLPYsL+tzlWnvRBoyEMWC3NBz3TkOhCEf+OnfETaGHPvk+9lcFdw6FkgQEaV9q9U3yETj3f/5UU7+x+vpajG8eGG64kialkTxX732PckZaiTSN4F/Ar4BYIx5WER+AIzYiWSM2VD19i7gTfHr1wE3GmMKwHMisgmrRH7noPsUkD6iCstWrankcx6g6TCEfnF/xqO6wtvBCnWXq8zcuvESNtx9OctWrSG1rROT8ll18qcrzVx3j73xD+iMKqNKRWMhiHoZmbA7i+QLuIfMwzRkkF17iTo7obMTdu0m9dwLHHZbEpk3h92vms8D9xzNnB324nNKBi8b4O4pYXyXIOMT+RHJNivKGSY8gr0OXrcV59yXTpD1fOSUPF0nCXujbn6bm8umd/s0PuaT6ASvMNBRKCYo9ep8YiKcTMaK4bZ3xIuMdRIQhzoHAXgeUixabZtikYQxtgKT49p70bGzTNKdRUTw8q2k9hoSnSGv/9k/8jfn/4bZh3XypW1/PqR2fuSf30f70oDMPqtvtf8Yj2S7wSnZ8tJDZaAox3K6jUYwDs7K9Dvi2emwcn30pE6E9poqD0wBUyhgiiW7Xdzh7NVRDR0rbhRTdm6KI1azsbMLebQTRHr1Kcr7XPSNvbz6J2/m2StT/Mfp3+UVsYYWDE9s+/zl1yKB4ZbbLj1g3YXn2gFsX+dMpS0y9EmXI34V9GgojMBR4nQcmMN3/gXX4gbx4L1GSshg9BeBFKUSNlU8NEOOUuqPcsRx9aSRmw8wCY8VL7uqYsc33mHvT7Xj9aU6uhjoSXEuFhFHEM+v6FiZoGQjlfbZqAKno4sIcObNJkp4uOXognzB6pc4jh1MJnz7PCgUIAiQMMQJbfqDFdJ3CVMupaYE2WMakACef2wB/hzoODrD/uNkmqeK9E+vqLIqKr9dHGl2wPpyhFIUn1jHphiZUqm3M79kU5ZMaB1SEgTWgZ/N0vBCll1nNeEWYFbb0Dpa8swWZPZMCAJKh80iTHuESQcvO7yH4KoZfwdQ0V2rtb6/dUpvVqbfQUV0X5yeyZvKpEjPJIcJol59RgO97LgpFu26+LMVOy+OteWFQs/kZNWkS8VB7Xss+WIBZrTQccJMzvnVF9m71A7BhyNJUSm4tOVfa65fNfO9B0TSVohAwpBVJ32KdQ8PbH9mP5hj9oP2tVOcAt4OpS4M1YmUMcbcLb0jYEbT9L0H+GH8eiHWqVRmS7xsQJYct4Bbbv8kF76qd2hgpSJCjX7nUIVC+1KOQio7kvpGOI2EyJXKfsr7KusprXvoql7bqp7C+FCJSAhC20ksFHo5IUwYEuULuJ5L0wslug9JkJ8ByXaxFZv2Z8F1iRIeThAhRnC7SjhFjyDtgHFIdEUYB5I7PIKMgYUBc+e08+POJdzfeQSvP/V+MqcX+fn3zgHAzw7/OFYvsgarHiJ+E4G+KQ1lIw8Q5fIHaCn0RCyFmFzOdjQTCTuLGQRWbLW5yVbnKGveOA40ZDC+h5sHt2BI7S5w5E8cOB8+cOxtfODYwdu6+V0X89JbriN63COz21BosWLGXjyOfuCrQ+tcXPiqz2NSKuA7EsqDf/P0Zrsg1k2wnUnrSKp2/NSqxlIeoA60ruKsjJ1SJnIQF8SJO7flUHrHinOWUzWirm6chgZafzGLd2/6IE+962sjPtZaDiSAX/3wP/DFI9rxXZz5T7Hi7CsBq/dz68ZLWLZqDcbpv8JQNX1tocjQIyWXHLeAdfdezaqltZ1ZUNvRNRKbWI5AWnm6jfStVS1vJFxw3jXcctullXPdnwNX7fgYUz2hgHUciZvERNEBUasmKCHJJJQC3F37KR0+Gycf4rZ1Q5S1KatJN45sCXscE7k8Io5Nh/GcOKoOcnM88q1CboEhucchvzAg8zc7uHD2c/z4l+dUok6Hy8qGd06PtKe+0Uix06+/bU2s/yKuiyn1DPbx7XOVsqaSiZ+zYJ+/hRymFOA8u4X5z8K6PTcMqXlrn7zWOn/iUvFutmQj0IoRzjCd3lIu2qAMm1Uz30vUZdNCxRH7m4tzgDJ0r3571et4QWV5NWXbbyKnemHPPtyqz5ejlk1k7X1k7ARTqYR0ZWl8PklhVorc/AjjGRZ97Utsfv/HR+UcPP7FY3jLne8DoP019ljW7bmBtY99npWnXoFxXSsgPgi33N4720Zkek5AiqmfJlL9tJbGjqE6kfaIyFFQTjeWNwGD1hoQkZuBWjXILzPG/Dze5jKsQ+r75Y/V2L7mmRaRi4CLAA4//HBWnH0lTp9tnNiY3HJr7c7zSBgNp1E15U56332XnUfVzqQVZ12JRD09eRXarjPlQaE4FaMS7W+DeTMqYnuVAWZZfHd/O0nfZeZjNi0psb+IU4pzo+MHkjEGr72AFAMkishsN6T2uRSbPPIzHBLtkNwvuM+k6Uql+dIRryHyDMm9DjOfiJiRDcjO9bjvhhGkOpVL4E5h+jqSyoP4mmKclDerypMv9fjIJQyJOrt6qsIkfBtGn8sjJZfZ93fiduZAhNKcRm659Bwu/8XgbVy96COUjphD8SVp5t2Xw7hC16EpZj0a4HcOzUdfHqCScAeM+tAIpNqsmn0RMqMVOruIyp3GqM+5r56NrI4iOmAQc+C1JX3SwCqfiTuYhKH92cqVpaqvz/IMZykg2rWbGX9qZOZ9JY7K/D+eefM3AFh5pnV6rL/nisrHzr8gjmztU0zi1o1Dc+ZEO5aw7mew/K1/wwXnx6leCWtZ66HD1NeOL3/556Al3cvJ4uYDEEZVQ2j1CXEnOT06z8NyBNIF51/DBedfU+lzlO+9amfSqhMvQ8JYW4cDJ4qUOlDliCjfh47vW4dPn0q44ro96aiJhE1vCUO7bWPGOprzhZ4qYNjIQcraO+kU4ZxmSg0euEKiM4r1D13coqHpBRf/Wy3ca05mMR297l+lh1oTQv06jyhvZqpeB1W/u00jdhI23RDX6RFUFrH9uTjtSNyEjTIbIqvnvR9pacK0d9o0OxFyc3wathbwciGl5oH3tXpJ/FwtV5kbAI1COpCVDe+Mo8+iWL+wZNNYpU9UcBW9r5OhD+YPsP2VVMpy/7H8nX1suetiuu0kpTy3lfSLHqd8Wnjg4cUc+0+PsPKf3nVAFb7VCz7I2u3/3mtZfxFIZZrnWCfay2ds4qs3nMuRH9rN6vkfAGNYv/NrrDz1ikGjkBRlqAzVifRB4AbgOBHZCjwH/PVgHzLGXDjQehF5F/DnwAXGVFxyW4DDqjY7FNjWz/5viNvFGWecMeYuvVppcuVZyOHS1ym14qx4JvjuywlaUiNvpDLqiOtSnJkmecRh8PyLtgRtGRNhggApBKT2lghTdtaLMBZaDWwVELcQ9JSdLQRWnLMUEXkOCV8I0oITQLIjIjvbQUrgGCG1135NbpY7MgcSPUJ7042+M05D7TiYyNh0hTgyRDwfp6XJCij6Pm5njiiToDC3ge4Fw6hVUCjidhVo3JbA29MNruB1p4bsQKpmOBptimVF4m0AOIfMhdQM2LvvwFlJqNlhrCj0lZ1B1f9r0XcGs7KfWIS7llOqOoIpCHDauiEMOeZ73Zx7/F9wx0t+BsYQNCV7f85h2Ln2yQVW/jqqSpXbeON/csE7/xY4+JTtgRhvO77+vs+yaullvUSyDyYqqe+E1fJXfI6Nv/8UYbJGuMkUmImc1LgOkkz2uv963YuR1SdzCkGse2QgncL4cYWwXXt6VW4EMMZqpDm5Ek6TbwNXQ4NxHLy8wTjgdxucQomwIdlfy4bEdIhCGiitbUg2vG8EWhjGDvwerTrxYy2cyCCNDZX09aFiSgHi27Q4UwpsHy/fUKmI9Ztf/NOQ9rN261cG30jpxQr/rXG0kcS/Y67qXj5wAih+M/iO+7Hl/U1EDjRBWb2NiVMwTSlg1z8vxT/Nfo/4fq9UxdULP9QT4TgMHnntZ3nLne/jyex8/ua4uzj09n384PyXVmzNaETbTjvUTPfLkEY8cYm5C0WkAXCMMZ0H+8Uisgq4GHiVMaY6Kecm4Acich1WWPsY4O6h7HPDXT2V0SqzsaMYgVSm3KG+bX3/1WvKGhNl+tOaGC4b7r6cVadc3lNGVqk/fYyJzJ7FjrOSNB4yl1nGED2zuad0aGRwXFthSyJTKZEtxiB5G3VEd86mrCQSlfQo4zkY3yG5O4vX7RP5acIEdC1wCDIQzAg4cvFOnksuoP04mHOPiquPlOHOOlU+F+fVSyoJqSSmq9vqYwBOcyOpfEBqixVZXX3CJw+ourHcfQsAG8M4c9dxiNIJ0tuzSMFqMM18slgREC7MHHyAoRFGI6fc6QsffbryOzu+d6Cm0YA7iQ78P4Ajqedl9TUYUqtsRHX4vLguZvde8D3cbI6mv0tx1gXvY851z3P+rGcAKrZIODAKabjsCLv4TttppF5sgzBi9ZKLWfvUmp7IN+p37fVN8yo7YUaboVbFKTuUhltQoy/VGRXrHrnaRhirE2nsqHFfytzZRA1JnK5uW3GxvJ2JbDpbKkmUShAl7GSQmy1gPIcok8ABZM4sO0mUy9vPZDI2gqVUwskK/n6X4qwUxhXChC2q4WUNEkH+kCb8ztLYnoPJTo1osr7L+6Niy+NKfY4vViy51CfyOOFbbRtspGqtlLbl7lt67DhUxLxNFNnXbR2kdzXaqpt9nfw1WPvUCKpgKhVMGGJyIRL/bpX+eBj2Hzk06E4HvqaGEokMVXY8rIpwdO1+m25+nOY7M2TPXVrRZwNY1fIe68wcIT982df5wP1/zU1bTmLHk3M4tutxxPNYPed9rN39dfsdVeNIzWhRRsqATiQRqVlOoKyNZIzpv1zJ4PwbkAQ2xvu7yxjzPmPMoyLyI+AxbJrbB4dSma0vB9uJHip9dZX6i0Ja/vLPVRL1htIh3nB3j0Os7ICqlCl+8EqrgeLIiCOflP6pNetVRhwhWNBKZoehcVuxJ/S4Ku0NEYLmJMYVcAWnGCKFknUgFUrgeZjmDGEmgVMoYVyXMO0RJRyrm5TxcYoGv9uKbkceuJ0ez26ehzTYzs4939aKbIPRbyh8307EIB2Fcm6909hgQ91bm6Gc6lCuAtPeidmzD2fhgn4Hhr06nQAiFJt9Mps67CCkuRE3F1QqYf3mV58Y0XErw6daF2uAjYa6s95aCUOJfqt+flRt22vgUyzZilKRwXR1M/ve/ez0F/FjfxE/Wf0p5r0YHXS54IpQ99ZDaq4fD6dlPRxIfamOQoKBZ2vLuotDdSiV21/evpLW9tBVtoIbdtJrrPos04WB7HiFji6cfW0Y1+2JJjRRT2q67/fWD4kiG10SGggiTEMK4znWoZRK2ggWsPbBGHAguSdHqTVJkPFoer6Avy9L/pAmjAMbfzc6k4vTlupn8hAcSYAV5Q5DJE5TLAtvE9i+VdTeCY7YSpv90NeWS1kvsaxrl7KOoyjhaf+8ztROS7POo146hMOYPKzaYU1bXitquHLt9bHltTSXxLHPCVMsQhiS3tZFlPYxIqxqeQ9w8FFpXz3tvwFY/OSXa65f9+CVB7X/aYMx/fbpR2Xfk5zBIpGa4v/HAmdio4QAXgPccTBfbIw5eoB1VwOTut5prRLEldevtIdWjw7E8lfEFZp+/6kBqzUpI0Sso2fmIxHOk5uJ4gpKiIM7bw64DqalEbezSNiUwO8oEfkOpBI4ToCUAvA9wozNy+84tplEexwRkXRoXzqDyBeMA0VPcAsGGjXqaFTozwlQYwBfpvLbzmylUsmlK2s7mZ6HiawmhskXrAjyC1txmhsrFTkGI/PIVkxcKU58jzDVSm6Oh1scyQEqQ2WF/9YDF5bLgceUZzNHSl/hzn4jnPpUEDzAkeSW9xFhunOYeB+yeStzt+4kXHI4W5tn8fjFRRZ9dw2b33XwqWeHLNzGZQuBxw96V5OeskOpr9OoMqlTh/SAshZVOTXuwnOvHrVoZsXem+7MGZhsDoKg1/NakkmchoxNV84XkFIJJ5UgzHjIjEaIItz2LMZzidI+UdIjf0gjif1FpBja2f2ZDXQvTIP0VNh0C5N/wDAhGMiZP4AtB6zjCBDcOAXNVt0Tx8HETkQruB4/+8OhTRxEnV09/YP4c6XGYaS2KyOilx2vTiunT8p59TYHwbAimmo4oCrXR7mKa7mJL2zH9T2Y2YpZfCgdx7YcVDuree5DH4MPjdruph1iDtBlH9V9T3YGfMoZYz4LICIbgNPKaWwi8hngx3Vv3RRiNBw51Z3V/mZBB5u11VLfI6CqwpeTShI1JHF37COKQ2eddBppaiR70kIS7SWc7iK4ggQG40GQ8XA9B7IgXgNhQ4Io4RDFoaSRL+Rm2U6LW8KmwFU9XB5dMzLtI2WY1JjJNJHBSbhEXd2I4xB2Z3HSIflzl5J5eg/S2QX4SGODrdaXLxB1Zw8QSKzF2i3/yuoFH7Qh8MYQbdtJsqUR4zZx+1qNQhozqgce/aacDULfWcg+n+9XL2EoKRiVajEO4kRIOm2d1fkCArjtOYzTwPGLtxFcMofl3/icThzUkYPVH6u2vf1FKQxWCETt+AiosuPixULHZQdSfI85mYxNIylHphRL4LkYzyFMexjfwSnaaKQo6ROmPIKMR+gLQYNHMDt2UkQQJXpP/tz1/Y+N6eEqfRCnp4BB+dnsxrpIJi7LLh7iebaIRrHEuo7vDG3fcUSyfW1T2hJthfoch1KhZkRQfb9w6NsOFuFeLtISF9wBML5L9+Jmmjc+waqZ72Xdvm8ebIsVpa4M1VV+OFA9N14EFo16a5RRoZzipgOJUaJqNiHK5WzucnVYaylARKzOwZwkftolcoUo6RCkHLx8hPEdOg9PktofIiWDExqCtEN6ZwkcCBMOUUKQ0OolBCnh3m/VzCZVhkF/opzDIYqr8Dm+h+N7iO/jFiKCeS14+QIk42ovXd2VsuzDwhiiWGxRtu0mvW33QbVXGZiV6Xcgnm9DyasZygz3YNQKaR/mzHl/offiCHieTZvxYtMdGaQ7y+xHAn71wbV0/W+5ypQ++yc71RHLGoU0ClTZcVMsYva346RTPULLYAdzxRI0pK1mYSpBMCNDx5EZjAOJrohio0Nml2edScZgfCG5z6a2J0oR2fkJJAQ3HxGmHO75ttrxg2XYdrzWhFAYVjRyEAcnlYSEj4hji6HE1dhMvoCJIutIGgYm6olECl7chpcvEC6qVZxaGS2cRKLSP+uXIaY5Vuhrw/tEOA34PdWf77XL3lHJkvAR37fXXGR6VQpsX+Sy910Lh95epf5MgbSzejFUJ9L3gLtF5KfYGInXA1O/LMQkZaAc7LGauVx1sv2e8S5dXBYzhhqaNEOljxEqzEmT2dXRs94RKJXiCCKDEcEJDRQiXEfIzXRJdBmyc4REh5DsDGylnrLodmhwixHgYFz4ww91xnLcGKDDUZ49QgS/o0DQlLDGP9ZHWtf+7WF/Xbl8a/k6LYseKvVl2I6+Ie20z7VTfj2UDmjlI/2nrprI2EdGPHMp4oAvUCiS3mYF3ruMTctpHkn7lQnFQOnuYxmBtHrRR1i7+fox+77+OKAwwXDpc386qaS9j3yv4lCuOBl8HzyXoDWDcQUxUGgSvLyQnymk9gteNiJKlO9xK6wcZDzcgsE4wu//9+MHdbxKnYmdRriOVV+NU9eGEkXcl3KVvOr+5todXz3oJiqDUI/Io/7s+DCpngzqq6fkOLHTSOK0gzh9UkohQRrSSRXcVyYHQ63OdrWIrAXOiRe92xjzQP2apUwmVp5p0+zW33PFOLdk9BHPrww4xRGcJUeReWY/0a7dPRonIhAZvGxAyfMpttjbKtlWwgSG7FyhbYmQaINCi4MYHy8bkmgLbBSS5+KEYAJDRP+DSGWM6MeRVBlgAM7eThLbrShiWUTzYBjxwEgZNutz32Nl47uQ0AUT9cxklp09tTqN1bOT1U6hvtv2FXodjBrpk/2/D6EIUVu71eOKS1LjCM6mIot/9Xe85JgtnNi6jWt1IlMZJqsX2bTpieAwGm0qdjy+f92GjNWyKxQq9t1EBseLhesTPibtE6Y9xBi8bISbdtl9ioOEUNztIga8roDE/iLGEYzv4OZtKlOkcjjjT392PNaXE3HAi7WPomhE5dT7onZ8bJF0GjcjmGKRKK6QWJPytTDQhE61jR+uHa/5lf3b8ihfQPywIuZucjkwBicIOPzXPk/PnoW3qItj/+9KnnzD5Sjjh0T2r177nuwM6akpIocDe4Cfxn9742XKKFEW0pwqrHvoqnGPQgJr1Mt/Q6FXyLQ4VngxxkQGae+EfW22TGe1/omJiBIuElol/8iDYrNH5AnGBzcPrc+EZHYF+B0BEhgkjJBShJsPMYKt5qZMWMR17YxReTCSStqOZ7HUU61NmRSs7/qunYHuK549mAhndXRR3wHKSGYs+3RYB4pEqnykFFgHUvX7YpEl3ynxxLb5FHQEO26sPOMzrDzjM+PdjFFlojiVhmPHe1Ftx8spbaWglxByrwGf64ADEth0tch3kMjglgx+N7Q8a0jvKuF1lqwNDwxOKcLNhZWiGJr+MPpsjEYgw1rjGV6JCCkWiTo6bYpwYwOmVMKU1I5PJtbt/xZgBdOdqr56Tfvcq4Jyjb/qz46U+LOD2XFxxFb1jXpfnyZfQLrzeF2Cieyfokxkhtrb/BU9Ur9pYDHwJLC0Ho1SJhdTMQIJsFEK+d7iiMH2nb3ei+vipNOQSpLYncWkPLy0R7HZo9DqIhHMv7OAU7JVWyQ0SBDZSi8BOAU7GJQwIkzp4G/C0CfSxEkkkIY0MqMVs3M3BCH4PtGCWXb9nvbxaqkyQsr6VWWR3ZopboNU+zlwp0NMXashxG3f9j/47CWyXV39z0Q4jQ08+fYkrz/uXnwZvVS91Yd9mLUv/suo7U+ZuEwUZ9FoUJ1WhImslmHVPRwVi1ClpVLWycH3rBZSBBCCsVVTw4RDal9I65N5jGsFmiWIiFIuYgwS23E/MpSa/TE6SmVI9HmGi+vizGgl3LvfLs8XwHVw5szCtHeOUyOVkSIzW21EbhjixJN8Udk+9rXHQ9VHGkYKeq9t4/0PVpTDhKHtfxRLGOzzJyoFeLMbKB42gyOXbcYbxTCVqZwtUneMqd+kwBSYbBhqOtuJ1e9F5DTg/9WlRQfJZK1aMlg1FmWMiA3CUAWSxXWhtRkcBwkCyINrDAkD+RkubmBwgojId22kkQfdi1NEHnh5aNiSt4KcW9vwgfyimfU/xmlGeQbzYAS2xfeQZJy2dsQhBC0ZpBgiYQgiU2oANp0Q38cUS/HModu7cs9wOpLD+tKRz3SW2yYulbY6iQS0NJPY67LuueMB+MLJo9FQIJMepR0Nn+Uvn3xV5tbf+5nxboJSzUApqgdsKtaee56N0RfB6yoSJh0cT/C7AiLfwbg2Milo8snN8XELhszWHIidDEq0FSi2HnyKs3Igo1EoA3GIOrtwZ7baQVy5SEGxpNWwJikmnUTmzILde8F1kSqR8wHTzys7GOZkUZkR2PKyVpKJgkr0lAlD+zqZpGthkj3b5gFwwsLtw95/TaaAs0KZmIyoN2uMuR84c5TbMio43VpWUxk5lRnJMv0MIsV18WbOwGlpsgscIWpMETUkcDoLeJ0FmrYUadiSt84jBxCIkg7p3QEYKGUEtxDiFEKC2XY/t224uM5HOI2pFbo8GOXIjzDEZHMgQmFBM2HSJWhKEDYkdOA4SVnX/m1bztn3qpwzrnXKlBnoeimnovX963ud9Rc2P9j+D/i6uI1VEUlOwoq7l+Y3s/ic5zlp/nZOmj9KHc9xZMXLrkK046uMEMf3EM/vSXEZIK1JXLeSqmxKAQQBJpWwE0OFEn5Hicz2HE7eThhIGBFkPBuh5ENupkN+Xor83BS5BRkAbl+ndrxebIx+fFBixwCmULBalsmkTWkWTRuarKzd9EVMwsM0pCrLnIYMbnNTz0b92dla+kd9l/Wl7/hgmA6o6iilcsVAJ50G1yV//AI6FglhySEsOfzinK8MaZ9KnTF1+psCDCkSSUSqa5Q6wGnAhK1DPdmikJQJhDh2lh+AsF/DIL5nq7JVNo0w5fcOSK6ED2AMxdkZjCO4uQCJDLk5Pl7e4GcjJB/gxDMma59aU6+jUrCaGiv8t1aqrFWE0QfAVmNzkHTcQdnfjrugxVbXA26+RUtvT3YkmbRiukEJEzlWdLU6EnG4JYKrtx/q54bxHb0qvQQlHD+Ntz/Lsc076QhSg3x6eKx98tpR3Z+ijBVOKmk1R4rFAQSWY4es+NaR4Ai4DkYEXEFwcAoljOsSZVyCBg8nMAQZx+oduuCWDBIanKK1JRvuUiHcemOjOWqnBPfzgZ5Is74ThQmftZu+WJ+GKmNGaUaaxLYEUS5ny9OI2EkWE9n0ttGMNjoY3aQqys+gcrptclsHUWI2yYbi4B8eBjrRqdSLod4JTVV/SaxG0uvq1aiDYd3DnxvvJiiTGHFdnMYGzJkn2A5HrW0cWw0i3N9O1NWNaUgRNaVwsiXczrwV5CwFOHs7cDrzBBkXv7OE113CiCABNGwrktqVxyRdokyi5vco9UFcF0kmbYnnfjeKBVjjsGiTy0OxRLDkUJxCgN+hEY9TgbW7v460NOHMntnzm5erMbqujWSoJdBZqwxw9d9gs5kjxI1nWJ10OnZ4uxRfeixP/81Mkk7AnEQXSSfo9/OrD/swqw/78Ki3a7TZcOendTJIGTmui7S2UDhnqdU860e83qaV9EwmSDpN1NqE25nDyVmRZcnb+2nf0jRdC31ysz26Frh0LXTAgERQbHYpNamm4VhSdgr110+LN7L/49TfslaOJHwbgRRFUBjdAbsy9qx/4LN0HZ6i+JIjcNJp22crlsB1rOh2KlmZEASGZp8Hq+bWl2HYe8f3cMrtSiSQRAL3kHk88b6ZsLSTJXN2k072L/J+wfnXTLliTBMWY6zuXR3+pkKa4VCt3mPGmF6lEUTkzcAIyiUoysRFUkkk1gGpdC77mcV0fKl0YEzCw23P2ZVhZCu8GA+MIbG/CJEhSnoYV3CLEUHaJVHSmcuxZkPpRlvevRxpUquTUCXAWZ3mJC3N5ObbSI/M1qymHk4VfDugKGsVlJ0z1ZpolXWj7Rga5v6iXA6npRnxPMjb8uT5mT7hjIA7dhwNwF0rtXOpTHNEIJWk2OyS6JOuUolg6fsRx4nT1UJbOMFxML4LnoNEEcm2iFLGodjoYDzAQOQLYgxhAu7674+NzbEpbCjdCMDK1Nvj37Mf/cpy/62c/ut7dtDe2GCrq+byWjhgiuAEhmKLR8KL0xMdkETCRp7lI+tMDqxjxlRfLn37gPXSQqzCRsNTSaM0xRLRnn34c2dx5Oy9LG3ZrqlsyqRgqE6kSznQYVRrmaJMaiRjK61lD0nTmEraMNM+jqSymK3T3AgNDUS+S+Q7hPOacHMl3D2dEEWYZAIc8LqKGMchPz9NodUl8sAtQpQQfvuzfxrHo52eOK0tdnYhDIly+Zqdhp6ZK7GVNGbbKmyp3QXyc5Lq+JtCZI+dTWJvAWfrDsqDEROGFceRxA4mcXsi0yr0uXb6liBf7r7lQJ21qv0jPiYoDU9PITLQksH1XLJnLGL/sQ7eHmHPnjnMPmngLHMdMCnTAWf+XEwmSedhDs2NjYTt7TUnhMr3ttPU1JPKlvQJZmTw2vNIKcT4LlHCJbnfaiIFKeieJbETCbyc4HdP/hnlycaq2RchjQ1IZAjb2mo7A4h14xxBkkkbgeR5dvBeKLJ2y7+OT+OVurDj7QWOfjABHfH9GAQ9eleO2CilKELCyFZoLNPXjke9h7YVOw6Vaq64DqZQwEQGt7GBsHP4lf1k8aGEzWm6F6ZJ7S3BM0k2PdPAua99esDPaSGmMcRQx+ps9dntWDKgE0lEVgOvBhaKSPXTthnoP2ZeUSYIK86+kg13Xc6qpVa7Zt2jVw/8gWSSqCmDE9iSobWQuLOJ71d0kSSCMOVQakqTLoU4nXmbLOq6EBpwwC1EOIFT0U5SB9I4kUljfA8pBbaaB1Q6nG5DBjzPRqLEVfpE4t87jLj5DtVAmkosW7UGXMHtLtjS3WXnTuxAFHoiFEwQIGKdSlGxeIDDqBblbVbN+DtMsVh5ppRnQsX3rBNpCFTSNsIQ09kFC+ex+2SP/Bzb+XXzolFIypRk9fF20LT28WtYddKnBpUtMJkkxVkZnCI2ZamyorcjqaKRA+A49r4PIvKzE5g5CZqebocgAt/Fy4UEBReJQEIBgaAB/vSFj4z24SpDRJqbraOgvSNe0OM4Et+Lo40F8X2ra9jYAKjW21TjC4+t5h+v3MOnb3wbOG2V5ZJO2d+/sQHCkGDBTLy2LOzdj+StJMG69m8Puv9qW796zvvsdRUEGMryFrENH2oVyFjwX0JDqcmn/UgHt+SR2A/FGXDpCb8e+sEryjgyWCTSNuBe4LXAfVXLOwG1nMqU4oRPXU/6woUkuiLSO0u9Iw76iuUa05NLH2UwruB1lfA7IvLzG0ikfLw9XeBEkPAgEvz2An57gTDtc8ttOpMwXkTNaaKUD8bgsQiAcPOLADbiKOlDKUA6u3pVbVn77JfGo7lKHTGOkH6hHZNK4MycAaUSpjtbEVLv9QwIAutM5MCIo6HgZDLgCCaXx2tprnQ8pRT0/h560ueqo5gqzi2AMEL2dZA7PsN/veI/uOiBd/D4668YdptGytE/+hyOaxAxPPkGjcpTJg5v/+N76fzyTHb/MYnfFVfi6g+3J3VVACKDRBHp7XmKMxKUZqQrgtkSGSQygND0vKHUKORnj8URKbVYt+cGVrzsKggNXsk+S6P9bQA48+dAIoFEEWbvfussLA7NWa9MPhyJuK39OPwu2HXhQpJtC/AKEblZ1l6m9ln7WmpwaNoMbiIe+j63Zfhf1piBpgyyfQ9uawvk8kTZLE4iUdHcKtM3mjleiKRTSHMzYUuaxL4CqVd1cPybbFtuOOO7IzoHI2H5K6/m+Y/0ONmfeqPqEB6AAeqV3TjVI5GMMQ8BD4nI940xGnmkTDrKaUeDRSC97K++TGKWQ7EZ3IKQeOhZIukn195EtppTsQhBgBSbkJJPlHTBOCTaikgYYdKxYHafUEh1II0v5UoVy1/RM5u9ofiDcWqNMp785tefYNWJl4ExmCY7Sy0NGejsAuxAZTRYt/9blRlMSSbB9xHHwRSKEEe89cXx+5SdjiPkOHwBJpVAOnKIGF6RcsbUgVRpjhviOoaTf2k7ng/9+VVj3gZlerD28Z4Iu8GikO65+QSCjCGaF9C4pYYwfhXiOOB71p6HIVIsIjkXSfsVO44IUeLA+/PBr+g86niz4c5Ps+KsKyvv1+e/P46tUcaLjx+/HoCT/+t6JDLsfKlgYps66+E6Rf0nfEinIIpwGtKEW7f3FOKoFvKPnx0VGy+CtLaQP3I2+45LMP/ODjq7Uyw75onRb+MgbPzdZSz9+WcIQwffCznxpit45LWfHfN2KJOXwdLZfmSM+UvgARE5wGdmjDmpbi1TlDFk74kOLU8bOo6BphcMHDIXyeVthEAtkT0T2XXFEk57N14hwKR8wqYUYcqzon5pH+JxoOYwTzw2/v5T490EZQKw7pGrWXnGZ3otW7vza6P+PWt3f51Vsy+yb0QgmUCSCZxySqw4tqpModA7ldazYrAsnEtpTiPdCxLM+M3zFJcs4JCf+Jz7k4v43U9GvbkDsukvP8Vx/3clC1rayJYStHWnueD8a/Q5p4w7yX3Qctoesr+ZQ+vTBSSVhGKxEk1cXZ2tnOpEZCeGCALozuICJp0gSvuESZcwbbvKv/vJx8fnoJR+2XC3RkIq/XPPtz86qvtb+9x1AKxe+CGiTBLxrHPIbW21GzhWR5NSYCenwrBSEZh0CtPayOMfauG8Ux4j+PxSijNStPza5/pfv4WPfHL4Ec4HSxg6pJIlfCekrSvDBcs+zy23f3LM2zFRqVRSq9O+JzuDpbOVawH/eb0boijjxQk/+wzFWY1Ez7lIYPBzEebZFzGl2sF3lcoKoXUumc4upFBASmlcEcKUh4SGKOFgpOYuFEWZQJSj0+pNObJp9WEf7rVcvFgjwXWsRks59cJ1rRhsUyOdS2bQudBl7v1Zov1t+HtbKDU1j0m7a/HEGy7n7X98L9u6m8l1tvLs630Wf+XLPPchrVKljD3RjiUAhKn385rD/sSvti7DLYRE3Vm7Qd+IJHGsDlIUWbHlKMKUSlAs2QFfEOHkrTBv0OSP8dEoijJcHvqXsYsOXLv1K6w8tU8EcFMjABLFFR7LkcbpNLgOnactZM+JLqtOu58n2uaR2dJNfn4GEz+a3nb0H8es/WWeeMPlnPKrT9OQKLIvauC5v0hx5D9fx7P/OLrON2VqMlg62/b45QeMMb3qWYvIGkBrXCuTnj+d/QMW//K97D8/R+vtaTJP7sG4LlGYtxvUCIe3go2xFzmXwxSLSBgipQAv6RNmPDB2k6ngbR4qy19p0wY3/k4FqKcyF5xv00s08mRk9K2StvLMz2JcwcmVkGwBcvnKALd01Hx2vDTNIbd30nTfftsxbW6Cve007G23240TsxNdPL53HuRdEm0OThGOXnM9YUOElIRnP6IdUWVsufRvfshfNe3hznuXYnbsJuyjbWjCnqpsplSyUYHlCSNH7F+hYAeCySROFJEqheTnZsbngMaB81asAeC2DdrFn8qUnSDrH9AUppHQ97wtf/nn2PGyBtJ7DOk9Aalt3eQOb0QCQ/rFDt5z7c/YEzTxk2uW4xYMbWfbiebIA28A6bZ68+CfXcVZ6z5J2JEg2SE4JTj6i9cRNEWYRMTz7/nE+DVu3DH1q842BUSRhtr7XF5j2erRaICIfFxEjIjMrlp2qYhsEpEnRWTlaHyPogxEarvHgtntlJqAICTs6u69QZzSJo70DocvExlMLo/J5nA7c7jZALdgO6+3brykzq2fOGz83WXqQFKUEWA8F+PFGi3NjbbjEgQYV8jsMrht3ZAvWB2lKta+8M/j02DgX079H/ZtaSG1zcPNg1ME4xuidEjYrDKKytjzivTzOMQVFeMy3r1T2JyevzDqqZpojE1pA/u+FEAUIUGEky3ym19Pn4HUbRsuVgeSogyTjX/4FMVWKMwQgrRDx/HNeF0hqW3ddB47g8P8vfx860m0PtFJ81PtzP/t/spnH/j38dVY27m1Fck7uAVwCxA0RCTnZkm15se1XeOOMfX9m+QMpon0fuADwJEi8nDVqibg9wf75SJyGNZB9ULVshOAtwJLgUOAm0VkiTGmdr11RRkFCoeWWNK6i4e650OpVKnYUtFDirUUTNRz00usk2eXhQiu1VRo68ANQtz9cPPTXxj7g1GUOiPh5Dd+E4n191xRieIDiBqSiCtIZxYJDY0vFjEpH/wZAKwdpFDAWHHix66nSTy8HEgIxoGgUfC6fUqNeo0oY8sbn1nORxeuZ5EHxu1xHFVXP5RYs6S8TFwXafBtRJIxSNmwhyGUSvYvkx7T41CUsaA0W6/retJxmEOYSNAcRKR3FfiHB9+K+4dmCidZ2zj7vjYe+tfxF+g/4bLraQx9vCy4RSg1wJzF+wA4pKljnFunTGQG00T6AbAWuAaoDqfoNMbsG4Xvvx74BPDzqmWvA240xhSA50RkE3AWcOcofJ+iHMAndp7KwkP30uzlSe8OMblc3NHsvV3ZgVQ9q1kuwW3CkPW5741lsxVl3Lj5Nyq8ONr0jeBbPe/9IIL/5BbW7vgqq076FLJnX2VdPcS/h0vkQXEGtD5laD9KaNoMDdug+xA0lU0ZU7607yheMXMTs9wct+SakV37DkwWEKe3Ha9OVXcEENbu/vpYNVlRxpXpFCU/Vjz56d5OoZVnfhYjdszw+Ouv4CV/uJ7Z97VZLbYJgpeD7AJD0CAEGUPQGtD99CxmHbOXn7/y38a7eeNLFP/Va9+TnME0kdqBduCvAERkLpACGkWk0RjzwkCfHwgReS2w1RjzkEiv9KCFwF1V77fEy2rt4yLgIoDDDz98pE1Rpjn/+8BpuKmQPc2NZLbloRTYakgDCGtbQkwIju9pifgaLFtldRVuX6dh8YoybGK72PWyI3nlG79EJpVADpmLFMc/TezUD15P5IMfQqILdp8RkdzrUmwGP3tgR3og1I4ro8HXHzqHKHBwzzD8ZOspNOZ2I+kU5GuLjZQjiAHCjk7chumjeTQcynYc1JYrynAxIuAKT70/wdE/+hycCTvyM/Cy4x+p+9J3XkcpIxTnQXKP0H1UQHpOFpP3IRNwz+rPj3cTlQnOYJFIAIjIa4DrsOllu4AjgMexKWcDfe5mYH6NVZcBnwRW1PpYjWU17zZjzA3ADQBnnHHG+N+RyqTE3ePjHVkgMkKY9vCaGon2t8Xpa45NaYvLA9ci6sfZNN3RDqeijJy1O74KwOKvfBmvy+GwtgSJvVlwa5nIseNVr/kicogHLgQpSO0zZLa4+F12/f1fG154vtpxZVTYnYSkYXN+FkHoIi3NkE7hdOeIisVB7XjYnVVHUg3UjivKyNlw9+UAvGKjvY9KoUspbCBMCV5u/MzdslVr8Jo8io2C3w1+N+RyLk3pAk3pAnv2N45b2yYSQv2KI02F4t1DciIBnwPOBm42xpwqIucRRycNhDHmwlrLReREYDFQjkI6FLhfRM7CRh4dVrX5ocC2IbZTUYbFiR+7nrQrdM/3uW/rYRxmDCQSVhNpvBunKMq0Z8ZjDkTgdxSgZCtAjhcXnns1vgjNgaHQ6tL4fA6AziMaCFOQ2jNuTVOmOemtDmEG7t59BNtfnMmMdASdXbbEtutigsFzB9Z1fGcMWqooynSjFLoEP5uDRNC8pUSUEBLt42fLvWyAlw2QKEkp45DeE5BdnueU2Vt5cM9Cnv0rlSxQBmeoTqSSMWaviDgi4hhjbhORNYN/rDbGmEeAueX3IrIZOMMYs0dEbgJ+ICLlyKdjgLtH+l2KMhDJdkPkCbmdSYoNPv6+DigUiHI5G+5eJaxdi43hD8ewtYqiTDdS+yIkNBRnpkh15DD+UM326OPkQ0zCxc0FJDwhPy9FameeJy8ff3FQZXqTbINiBNu3ziT9ogdRhCmVekcK92PHQW25oij14+5Vn+fl3/kykQeJjpItQpEZH1u+8vQrkJRPsTWB1x3ilAyRLzzxhsvHpT0TmnpWUZvq1dmqaBORRuAO4PsisguoiwvVGPOoiPwIeCz+jg+OpDLbirOuJGzwAbjltktHt5HKlCG9N8IIeHmHIO1CGBK1d9jOpinZjQboeCqKoowF+45PMCffDIDXPnZld1cff6ktfe4IhcUzSG3vxvGcimbTxj98qm7fvfzln8N49ntuvuOyQbZWpitn//V1ZLIR6X2C3+2T2h9h2toxXd2DTgQpiqKMCQZysx0QWxXPy46dsvLq4+NxcBhhZjXituXIbN1L6fDZkIVia2LM2qJMHYbqRHodkAc+ArwdaAGuHK1GGGMW9Xl/NXBwNYy1v6AMAadoH+JeVpAIJF/CACYoDfg5nbVUFGUs+MMPPwbAyf94fWXZugdHzfwOyOoTPmkdSGGI8RKkt3QgXbEDq3FW/RvgTAXVAKXepPYGGAeMJ3g5IdkWQimoKoLRP2rLFUUZC/7wo49x8od77Pjv/u/jY/K9q4+Nq/Dl8uAIke/gtnX02uaOm/5pTNoy6dBIpAEZkhPJGNNd9fa7dWqLoowpy1atIbUzC0Ai41OcmYSs1fgQ1+2dzgY6o6koyrjx0D+PXcrY6qP/yTqPTARhBAkfk/ZxtuyyG2QyGuGrTAiWrVpDcnfWlkt2BWhAIoMJwwPttzhqxxVFGTce+pexTf1effQ/QbFkHRalEtHCOfi7uyAIwPM0wlc5KAZ0IolIJ7UrowlgjDHNdWnVKLDhLs3tVAYmuSeHBDYrUyKPyI1XGFN7BrO6A6ooY8SyVWu0Qo4ythSK4LmYxjSIEKUSRGkPc/h83Bd2sPbZL41JMzb+Tju4ysAk9+SQ0FgNJFwiVyBpHUQmMj12u+w0UjuujAPLVlkZWbXlylix+sg40imdgjCEZIKwIYFxXWhKVyrHKYMwBSKG6sWATiRjTNNYNURRxpKVp16BFAPEGKJMgjDtkdpVINq7r7cQZxXiCBtKPx7jliqTmdHsOGonVBkrormtAIQNCdxcQMcSW+638YUcG3Z8dRxbpii9kVwJKRSJZjRiPIfOw1xm/alAlMv36yzaGKkdV4aO2nFlUlIqES6cRak5SWJ3lu7FTeRnOsx4LIsMoVqlogzG+JV5UZQJgHGFKOnhtedxduwjDA/UcBdHKrOYKxJvA2BD8Qdj2k5l+lLubGqnUxlLirMzSGSIkm5lmYa+KxMS1yXM+EgQMfvhPIknttjKL/1EHa3w3wrAhtKNY9tOZdqi9lsZa9a++C+ct3INRqA0O11ZXs9iGFOOKP6r174nOepEUqYlzv4OW13I83Bdl+KsDMktNYQ4xUESCbttOAXueGVM0Y6jMhkpzMkANm8d4K7//uj4NUZRBkCyORAHt6tAaWYaIog6Ovts5CBu7AzVVDZlmKgdVyYjy1at6THijJ2QtzJ9UCeSMj0J4oijdIrSzDR+W67mZpWOJyC+h+kn1U1RJjOrTv40RHZwte6RgyuMqUwdbt14yXg3QVEGphSA71GclSE/2yPRFpJsaiTKF+x6cWw0cYy4rhXdVpQpxsrTr0Dyto+67lG144pFnaAHgTGIVmfrFy1PoUxPEglIpygsbGbv0hROexYThojrIo4gjuD4HuJ7cYlrO8Ben/se63PfG+fGK8roserkTyP7O5GuHJIrsvKMz4x3k5Rx5vZ1Fx90x3PF2VeOUmsUZQDSKUxThv3HJig2OmSe2QfG4CQSiOtaO+75vT6yoXSjprIpU4pVp1yOs7cDyRWQQtFW5VKmNaNhx1ee+dlRao0yFVEnkjI9SSXAcwnSLmES6ygqlnqFupvI2MgjE4GJWNfxnfFrr6KMEevv/cx4N0EZZ8rir4oy4fFcjGvteJgEsrlKpFGvCKR4cmh9/vvj1FBFUZSxY9mqNWrLDxpjI4bq8cfkj0TSdDZlWmJ8lyiTwOsOcfMeUUsDsmdvbyHOikPJ7Xc/ZVYk3majmFJJJOHbSCfPZe1z19XvIBRlFFj30FXj3QRlAjFanc4Nd2n5YKX+hK0ZCA1uHnLzsOWsczlwHTBiJ4igX5HtalbNfK/t3IchiCCppLXlWJFaRZmorHtQIz+VHkbLjq+/54pR2c+kJaLHhtRj35McdSIp05J1D13FhedejXGFZLshbE7iJXwo6yhUYcJw0NB3cV3baQX7wIkiEH/AzyiKokw0VD9BmUxEvoNJOSTbDUFGiFoyyL79PXoTVdFI67uHFoVkoih2JnmIyOAfUBRFmUCoHVfGAk1nU6YtxnUQY0jvLrHnpDTPv/943BktiOdbp5A4vau6DID4HuLEt1PCt6lxnV11PgJFqS8aDq2AprcpExfjOhhHSO8u0fJcxLNvbOa5fzgep7EBiXWRxHURb4hzpiJWTDWdAiDat7+OrVeU+qN2XAG9DkaGprMNhEYiKdOWW267lPOXX4sTGpJthjAhtJ1/NK337sTs3G2rvpgIhuBEqtZLWr3wQ6zd/fV6Nl1R6k65syFTIORWUZSpyS23Xcr5F1g77nWHJPc65OYZosPnEyU9vGe22Wqs7uBzpuv2fbPyetXsiwBY3/XdurVdUeqNOg0URakX6kRSpj0SGtJ7AlJtQvc8lx3LF2DcBTRtCUntzONt2cPqxR+1WgvA2sc+P+D+1m79St3aWu4QVIeq9u0kTLUw1lrHrCjK8Fm2as2I7iO995SJjoQGRwyznggoveCw+4xmIhe8445CImh6scgFyz6PWwiRYsD6+wauOrRuzw11bW/fe7HWYH8q3XdqxxVldFhx1pVsuHv4moN6742AStRQnfY9yVEnkjKtuXXjJQAsW/0F3HxIZhdEvpCd7dJxmEt2dgMNc5K4+QinECIGzl9+LSbWSbhtw9g+lIdiBEY6UFSUavQamlrojLQyVbn1FmvHz1uxBjcfIqUIp+QSpoTsbBuB1HlYgmRbRHpnHhFh5alXYJKuTV8DNtz56TFtsz5flbFAr7Oph/FViUaZGOiVqCjA7Ws/AYDfGZDcV6LluSJz7+smvTckN8slO88nNzdJkB48tW2sqdVJmEoDxtvXXawdIUU5SPQeUqY61ZM6yX1FMtsKzLurk9ZnSnh5Q+QJpeYEYWbiFb2oZeemkoaJ2nFFUSYd9dJDqmeE0xiikUiKElOOSgJYddKnCJtSB2wzUTtBI2mXhpcryvRC73VlqlNtx8GmfvTl5jsuG6vmDJuR2nK9txVlerDx958a7yYoCqBOJEWpybqHPzfeTRgztAOqKIqiTEVGoh0yGdFJIUVRlFHGAFG9NJHqs9uxRNPZFGWaUh1ePlVC5hVFURRlOqGOI0VRFGWsGVcnkoh8SESeFJFHReQLVcsvFZFN8bqV49lGRVEURVEURVEURVGmCSaq798kZ9zS2UTkPOB1wEnGmIKIzI2XnwC8FVgKHALcLCJLjDHheLVVUaYqGgKvKIqiKJMXjSRWFEVRxprxjER6P3CtMaYAYIzZFS9/HXCjMaZgjHkO2AScNU5tVBRFURRFURRFURRlumCoY3W28T64g2c8hbWXAOeIyNVAHvi4MeYeYCFwV9V2W+JlByAiFwEXARx++OH1ba2ijCG1ZhbrES2kEUiKoownaseVqUxfW652XFEURZkK1NWJJCI3A/NrrLos/u4ZwNnAmcCPRORIQGpsX9NfZ4y5AbgB4IwzzpgCPj1lKlDdaawlXK0dPkVRFIvacWWi0tduqx1XFEWZRhhTx+psk7+7U1cnkjHmwv7Wicj7gf8zxhjgbhGJgNnYyKPDqjY9FNhWz3YqymgwkC7BcDuc2kFVFEVRlLFlNO34SD+jKIqiKBOd8Uxn+xlwPnC7iCwBEsAe4CbgByJyHVZY+xjg7vFqpKIMxGCClmMRyq4oiqIoysgYijC1RiEpiqJMM8r6RfXa9yRnPJ1I3wa+LSJ/AorAu+KopEdF5EfAY0AAfFArsymKoiiKoiiKoiiKUn/q6ESaAsra4+ZEMsYUgb/uZ93VwNVj2yJFGTojKamrs5eKoiiKMjFQO64oiqIoI2M8I5EUZdKhnU5FGT/6E7rVe0xRlKEyHDtefs7oM0ZRRo+xqkCsKAeFoY7pbPXZ7ViiTiRFGQLDdR6pMVRGi+EOeCYS5y+/FoBbN14y4HbVzqChHm/1doN1SAfb53DPW39aZ5PNyaw6L8p0YiT3J+i9oRw8k9mOL39lT2LIxt9d1u92A9lxMWAE3GJEmHAq72t9vi/Dsa/DOXcDaZZOVls+0a4dZWojZgoIO4EtDXzvvfeOdzOUKYY6j5TxYKSDHZhY1+CyVWvwsgFBxs5XSATGGedGDcDBdCKH4wAbCk5obfNgDriDYSycSCJynzHmjKFsq3ZcqQeTbUCoTA2mkh2vppYDaKIx0omd0bbjEtn/t22o3+85FgV8hmPHpwoi8tnTWl99+dzUorrsf1d+M/e3/foqY8zldfmCMUCdSIrSD5N55kiZHIxmZ+VgqO4UHmxUjjK6OKHBODLi32cgaqUHjvazTJ1IyniiE0FKvZmINnAk1/FEPI6pxMH0swajVhTYaH6HOpFGn6ngRNJ0NkVRlDFkInbUyh2b8qzZUJiIxzEViVz745Qjk8opguXlZWrNvA7WidQBs6L0oPeDMlQmuv0b6oTARD+OqUR1P2vZqjU1I7NHYseHuo0yQqZIsE09UCeSonDwhlTzkacXI9HYmQydtYFSzSZD+6cyfZ1Gfan1+wymMTHU5YoyWTiY55RqhE0vpqodH4ypcAyTmXI/q1Z/S+24MplQJ5IyLVEjOr042MHBcK+XyXZ9STzRUq1zsHrJxeSOnNlrm4mug6AMDa02pUwV6uH40XtjYjIav/VwbPNks+PVlM/P8ldeTalRh3pTEbXjY4AxdazONvkjnFQTSZl21LtjoA91y2AVt2otP5gqV8rBUz7/F5x/DWFiAitgK2PGwT7PVBNJqQdqx8eGoUTrqB2fWJTP/3kr1kzoQhZKfehvwu9gnmnTVhOpZfXlc5NH1GX/uwrPc3/7WtVEUhRFgeGF4g5nW2XsWLZqDagDSYkZi8oviqJMLIZqi9WOT0yWrVoDasanJf1FjGu67ggwBiKNROoPdSIpY4ZWSZla9A2l1Y7j5Ed/Q0VRBkLt+NSir56j2oDJj/6GymBoKpwyGqgTSakLo2HERiJ62N8+qrftL71qtJjqD+fqc1c+Vu20KIqiTD1Gq+hEfxyMHR/K/kfKdLLjAy1TFEWZthiDMcMoWzzMfU921Ik0DTjYjkFfJ8FE6VhNF4HE8WQov7ueW0VRlPoykufsQA5+tePTB7XjiqJUM1Ge/8rkRp1IU4ChihaP5v4H65RMtzQniWy5zqn0YJ4Ov5uiKD3UqtJXbUum0vNtIjLaAsUDfa6/fkN/y6aDPSjbcZg61/p0+N0URelNX3FtteMjRDWRBkSdSBOI0TT29U7XGqvvmqhI1b0vkSFyp37t8+n4OyvKdKLc6ezbydRO59BZ/vLPEaY9Iv/gbcJYP3On2zO+PNAq/58Olaym22+sKNMRI2rHlfqjTqRxomzI+yvFqExcqn8zMXDrxkvGt0EHiXYqFUXRDubwWXXK5eTnN/Re2OyPT2OUYVO247UGXJMRteWKMr2ZCs+xCYUx9YsY0kgkZShUO4wkMBhXEAHM9Jj5moxUh7UP5Oi7bf3keGBr51JRFGXkVNtxiG35vAaccHpEok5G+k74TPYJO7XjiqIoY0hkIKqTsHa90uTGEHUiHQTnrVxzQKekuoMZ+WLflzsxkcF4AmXdCXUgTViqf5vJ4ijqi3Y4FUUZKtXVqIaqlzMVuOC8awiTvY1xJV25z0SPRCChnQgyrtgUKHUgTQrUjiuKMtUZzI7D1LXlytgzbk4kETkF+DqQAgLgA8aYu+N1lwJ/C4TAPxhj1o9XO/vjgvOuwVR1PMVgnUMS59bHegjVTqbKbKX2OScU5RlKp2S49ZYDU9POW7kGDIgxdUldG0onUSK4bcPQSyEriqIMl+n2/Fh56hWE8zKV92UnkYQRYco9wFYbB4yjBnwiUh1pVGuQNFHsOAxuy6fbfagoyuihcimjSR3T2dBIpIPhC8BnjTFrReTV8ftlInIC8FZgKXAIcLOILDHGhGPdwBUvu4oo4RIlHIwjvfLmb7nt0kokkpcLiTzrUBoNMU2lftSqPlTm1lsuqdl5E+zAoh6sTL8D92UnECYGDkszTu8ZBkVRlNFioOfiZH/mXHju1Rg3tuGu9Kq+tf6Bz1ae+antXYSNSRCh1KhB2hOZga7XMn1t+Xja8erBnEagK4pSL3o9a/o8Hye7LVcmHuPZUzJAc/y6BdgWv34dcKMxpgA8JyKbgLOAO8e6gaWmBE4Q9dI76NUxiZ1KF77q80gYgQhu3v4vUw6TH0qnR6k/5Uos6U17AMgdPbtmmLuYOP3QEdxcgJMPcfJFVi29DCkFrH2q5zpYPe/9FF5yBGIM/s4uANY9evWgbVm2ag286iVDarcY8NsLrDj7SjbcdXnNbc5buca2O/aal2fMUy+0Qb4Argu+R+6I1iF9p6IoUxO/o0RpughAi9iZREd6DeCrbfnt6y5m1cmfxu3IQQTufpAqHYTCoS2VfoDO7o4/ZTvuhIbkc3sPsOO9dChr2PHVx18KpYC1m77Ya7/nX3AtkS+kX2gHYO1jnx+0LUOx49UOpMS+AivOupINdw9gx6Fnkjr+bMWOlwJoaiBsSVNsTQ7aPkVRpiZ+Z4lS09DsuDqQRoaJDKZOmkhGNZEOin8E1ovIlwAHeHm8fCFwV9V2W+JlY85tGy7mgvOvGXS7m3/zSS5YZjsbYsCUrb8IXi7EOHKA5oIyfhiBwhGzSG5rJ/18W81tJL65b1t/MReeax1CpZkZJDIEGa/XAMQ/amEcgSZ4aR95ZkvNffbt2DIMLY3Enqxtu+/2u01/mg/VbXXqNBOrKMrkodqBNNU7l9X2eSDWPXQVq068DIgd8eXJoDj9aeWpV1Ccm1EH0gTBCOAIuC7p5/YPuG21HQ9mZJDQUGr0Dow8jiPJo0wCeep5VjW/m3Ud3zlgf8tWrcEtRlYPa5iaWFIKR8WOq5i7okxvqh1IU92OKxOTujqRRORmYH6NVZcBFwAfMcb8RET+EvgP4EJqKwbVHPmKyEXARQCHH374qLS5mmWr1kCf8GQvF1byIyUwnLdijZ3dTPXfKRAdt084Il8qETm1UtjKHbTzVq7BZDzI9L5Vqqu3lQdkYsBp64ZZM2p+Z3kG+2DEWIutSZatWoNTMiNKndSOp6IoE4l623HA6htVkdybhyiy6U3GcP7ya+2zcWFzzc8vW7UGmZupuU4ZP4xAbrG1t9V2vK+tPW/F4Ha8epmzrwtmz8Ls2dvvdw+Wgj4QG+6+3F5TI4xqUzuuKIoyFqgm0kDU1YlkjLmwv3Ui8l/Ah+O3Pwa+Fb/eAhxWtemh9KS69d3/DcANAGecccaY/BpB2sXvLNlIktDgtxUAKM60YcVeNrCzXFUeYp25nLzU+u3ST+22ncuqtMV17d8edF+1ZhgHE9Dsr8qCam8pijIalJ8t4zWTOR52vDArBUCivQiAvy9nl8+xjiIvFyKhIUy7lQG72vHJQ9/fqpYOkZcNcO9/6oDl67u+O+j++94rQ7Xj5W3L2+s1pSjKaNA3PVtRxoLxTGfbBrwKuB04H3g6Xn4T8AMRuQ4rrH0McPfBfNEBAouDVPEo45TiiKMqL2T17JOJxbQliLh93cUsW7WGIKOCnFOd3JI5sGROr2UVcdZ7n2Hdnht6LTsYtEqLoihjwUDPmonQKa1n+0zCQ4pBjx1P9x9ZrEwNgoxH8MoTDlje16mqdlxRlMlEf8+biWDHJx2RqV86kWoiHRTvBf5FRDwgTxzObox5VER+BDwGBMAH61WZTUxP2deeZQP/qE6fKKMy2klQAPJnHKXXgqIoUwK3GBF5Ts1n2kTqkI70mVtsSVS9S+izW6mg14KiKFMFiWo/0yaSHVcmH+PmRDLG/A44vZ91VwODl7eq4smndwzZ6FeXPxQDSI+QcuRKT0UPV0AgEo05VhRFUaYXA+m+DGRva2nNDIXh2HFFURRFUQanP3s8mB1HpnnarTFg6lOdrX5aS2PHtM+9qjiUqoQKD0b4WFEURVGmMyNxICmKoiiKMjFQO64MxrR3IimKoiiKoiiKoiiKogAYYzB10i4yUyASSf2MiqIoiqIoiqIoiqIoyqBoJJKiKIqiKIqiKIqiKAqoJtIgaCSSoiiKoiiKoiiKoiiKMijqRFIURVEURVEURVEURQGINZHq8TfUSCQRWSUiT4rIJhG5pM5HPCzUiaQoiqIoiqIoiqIoijIBEBEX+HdgNXAC8FcicsL4tqoH1URSFEVRFEVRFEVRFEUBs908T7vZW5edZ+kCGCwc6SxgkzHmWQARuRF4HfBYXRo1TGQqlJgDEJHdwPPj3Y5BmA3sGe9GTBD0XPRGz0dv9Hz0oOeiN3o+epgM5+IIY8ycoWyodnxSouejBz0XvdHz0Rs9Hz3ouejNRD8fQ7bjUwURmQWcXeevORZ4W9X7G4wxN1S14U3AKmPM38Xv3wG81Bjz93Vu15CYMpFIk+HiFpF7jTFnjHc7JgJ6Lnqj56M3ej560HPRGz0fPUy1c6F2fPKh56MHPRe90fPRGz0fPei56I2ej4mHMWYv8Ks6f82vgOsGWC81lk2Y6B/VRFIURVEURVEURVEURZkYbAEOq3p/KLBtnNpyAOpEUhRFURRFURRFURRFmRjcAxwjIotFJAG8FbhpnNtUYcqks00Sbhh8k2mDnove6PnojZ6PHvRc9EbPRw96LsYePee90fPRg56L3uj56I2ejx70XPRGz4dyAMaYQET+HlgPuMC3jTGPjnOzKkwZYW1FURRFURRFURRFURSlfmg6m6IoiqIoiqIoiqIoijIo6kRSFEVRFEVRFEVRFEVRBkWdSHVARN4sIo+KSCQiZ1QtXyQiORF5MP77etW600XkERHZJCL/KiK1yvpNSvo7H/G6S+NjflJEVlYtn7LnoxoR+YyIbK26Jl5dta7muZnKiMiq+Hg3icgl492e8UBENsfX/oMicm+8bKaIbBSRp+P/M8a7nfVARL4tIrtE5E9Vy/o99ql+j/RzPvSZMQaoHe+N2vH+0XuyN2rHp7cdB7Xl1agdV6Yq6kSqD38C3gDcUWPdM8aYU+K/91Ut/xpwEXBM/Leq/s0cM2qeDxE5Aas0vxR7vF8VETdePZXPR1+ur7omfg2DnpspSXx8/w6sBk4A/io+D9OR8+LroTxYuwS4xRhzDHBL/H4q8p8ceK/XPPZpco/8J7WfffrMqD9qx3ujdnxg9J5E7XgfpqsdB7Xl1fwnaseVKYg6keqAMeZxY8yTQ91eRBYAzcaYO41VOv8v4C/q1b6xZoDz8TrgRmNMwRjzHLAJOGuqn48hUvPcjHOb6s1ZwCZjzLPGmCJwI/Y8KPY8fDd+/V2m6P1gjLkD2NdncX/HPuXvkX7OR39M+fMxlqgd743a8RExHe9JteP9My3sOKgtr0btuDJVUSfS2LNYRB4Qkd+IyDnxsoXAlqpttsTLpjoLgRer3pePe7qdj78XkYfjkNdyeG9/52YqMx2PuRYG2CAi94nIRfGyecaY7QDx/7nj1rqxp79jn87Xiz4zxhe14z2oHbfoPWmZjsdcC7XjB6K2vDf6zFAmNd54N2CyIiI3A/NrrLrMGPPzfj62HTjcGLNXRE4HfiYiS4FaOgFmlJo6JozwfPR33JP+fFQz0LnBhvtfhT2+q4AvA+9hip2DITIdj7kWrzDGbBORucBGEXlivBs0QZmu14s+M0YJteO9UTveP2rHh8x0POZaqB0fOtPxmtFnhjLpUSfSCDHGXDiCzxSAQvz6PhF5BliC9TQfWrXpocC20WjnWDGS84E97sOq3pePe9Kfj2qGem5E5JvAL+O3/Z2bqcx0POYDMMZsi//vEpGfYkOZd4rIAmPM9jhNZNe4NnJs6e/Yp+X1YozZWX6tz4yDQ+14b9SO94/a8SEzHY/5ANSO10RteYzacWUqoOlsY4iIzCkLpInIkVihyWfjsM5OETlbRAR4J9DfrN9U4ibgrSKSFJHF2PNx93Q6H7EhLfN6rHgp9HNuxrp9Y8w9wDEislhEElhxwZvGuU1jiog0iEhT+TWwAntN3AS8K97sXUzR+6Ef+jv26XiP6DNjnFE7fgBqx/WerEbtuNrx/lBbHqPPDGUqoJFIdUBEXg98BZgD/EpEHjTGrATOBa4UkQAIgfcZY8pia+/HKvingbXx35Sgv/NhjHlURH4EPAYEwAeNMWH8sSl7PvrwBRE5BRuuuhn4fwCDnJspiTEmEJG/B9YDLvBtY8yj49yssWYe8FM75sIDfmCMWSci9wA/EpG/BV4A3jyObawbIvI/wDJgtohsAa4ArqXGsU+He6Sf87FMnxn1R+14b9SOD4ja8Ri148A0t+OgtrwatePKVEVs0QxFURRFURRFURRFURRF6R9NZ1MURVEURVEURVEURVEGRZ1IiqIoiqIoiqIoiqIoyqCoE0lRFEVRFEVRFEVRFEUZFHUiKYqiKIqiKIqiKIqiKIOiTiRFURRFURRFURRFURRlUNSJpCiKoiiKoiiKoiiKogyKOpEUZQIjIl112OdrReSS+PVfiMgJI9jH7SJyxjC3f1JEXltj3SIR+dNw2zBZEZFPVr1Oi8iDIlIUkdnj2S5FURRl9FE7PvVQO64oynRHnUiKMs0wxtxkjLk2fvsXwLA7nyPk7caYm+r5BSLi1nP/o0Sl82mMyRljTgG2jV9zFEVRlMmE2vFxR+24oijTGnUiKcokQCxfFJE/icgjIvKWePmyeHbwf0XkCRH5vohIvO7V8bLfici/isgv4+V/IyL/JiIvB14LfDGeRTuqemZSRGaLyOb4dVpEbhSRh0Xkh0C6qm0rROROEblfRH4sIo1DOJ7TReQhEbkT+GDVcjc+znvi7/p/8XJHRL4qIo+KyC9F5Nci8qZ43WYRuVxEfge8ub/2xN/5GxG5T0TWi8iCePk/iMhj8ffdOECbG0Tk23HbHhCR18XLF4nIb+Pvuz8+r4jIAhG5Iz63fxKRc0TkWqA8a/n9If34iqIoyqRH7bjacUVRlKmCN94NUBRlSLwBOAU4GZgN3CMid8TrTgWWYmfBfg+8QkTuBb4BnGuMeU5E/qfvDo0xfxCRm4BfGmP+FyDut9bi/UDWGHOSiJwE3B9vPxv4FHChMaZbRC4GPgpcOcjxfAf4kDHmNyLyxarlfwu0G2POFJEk8HsR2QCcDiwCTgTmAo8D3676XN4Y88q4Pf/Xtz0icg3wFeB1xpjdcef9auA9wCXAYmNMQURaB2jzZcCtxpj3xNvdLSI3A7uA5caYvIgcA/wPcAbwNmC9MeZqsTOrGWPMb0Xk7+NZS0VRFGX6oHZc7biiKMqUQJ1IijI5eCXwP8aYENgpIr8BzgQ6gLuNMVsARORBbCetC3jWGPNc/Pn/AS46iO8/F/hXAGPMwyLycLz8bGwY/e/jjmsCuHOgHYlIC9BqjPlNvOh7wOr49QrgpPLsJNACHIM9/h8bYyJgh4jc1me3PxykPccCLwE2xstdYHv8mYeB74vIz4CfDdD0FcBrReTj8fsUcDi20/9vInIKEAJL4vX3AN8WER/4mTHmwQH2rSiKokxt1I6rHVcURZkSqBNJUSYH/U4tAoWq1yH2vh5o+4EI6ElzTfVZZ/pp10ZjzF8N4zukn32V133IGLO+10KRPxtkn90DtUdETgQeNca8rMZn/wzbuX4t8GkRWWqMCfpp2xuNMU/22fdngJ3Y2WUHyAMYY+4QkXPj/X9PRL5ojPmvQY5DURRFmZqoHR8YteOKoiiTBNVEUpTJwR3AW8RqDczBdpbuHmD7J4AjRWRR/P4t/WzXCTRVvd+MDTkHeFPV8juAtwOIyEuAk+Lld2HD7o+O12VEZAkDYIxpA9pF5JXxordXrV4PvD+e9UNElohIA/A74I1iNRXmAcv62X1/7XkSmCMiL4uX+yKyVEQc4DBjzG3AJ4BWoD8tiPXAh0QqWhWnxstbgO3x7Oo7sLOjiMgRwC5jzDeB/wBOi7cvlY9PURRFmTaoHVc7riiKMiVQJ5KiTA5+ig3Xfgi4FfiEMWZHfxsbY3LAB4B1YoUqdwLtNTa9EfgnsQKTRwFfwnb+/oDVbCjzNaAxDn//BHHH1xizG/gb4H/idXcBxw3heN4N/LtYQc5c1fJvAY8B94stF/wN7IzsT4AtQHnZH2sdT3/tMcYUsZ3pNSLyEPAg8HJsR/G/ReQR4AHg+rhzXIurAB94OG7bVfHyrwLvEpG7sCHw5dnUZcCDIvIA8EbgX+LlN8T7UEFORVGU6YPacbXjiqIoUwIxpr9oVEVRJjMi0miM6Ypn3P4deNoYc/04teV24OPGmHsPYh/l45mF7fy+YqAO+GRCbPWcM4wxe8a7LYqiKMrEQO345EHtuKIo0wmNRFKUqct7Y4HOR7Gh2t8Yx7bsA/5TRF57EPv4ZXw8vwWumgodT7Ellx/EzoxG49wcRVEUZWKhdnyCo3ZcUZTpiEYiKYqiVCEi7wY+3Gfx740xHxyP9iiKoiiKMnTUjiuKotQXdSIpiqIoiqIoiqIoiqIog6LpbIqiKIqiKIqiKIqiKMqgqBNJURRFURRFURRFURRFGRR1IimKoiiKoiiKoiiKoiiDok4kRVEURVEURVEURVEUZVD+P7rLFsuSYttFAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1296x576 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "soil_clim.plot(col='season', col_wrap=2, robust=True, figsize=(18, 8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pangeo_forge/patterns.py b/pangeo_forge/patterns.py
index 111efcfa..b9718ae0 100644
--- a/pangeo_forge/patterns.py
+++ b/pangeo_forge/patterns.py
@@ -73,4 +73,4 @@ class ExplicitURLSequence(BasePattern):
 
     def __iter__(self):
         for n, item in enumerate(self.urls):
-            yield n, item
+            yield (n,), item
diff --git a/pangeo_forge/recipe.py b/pangeo_forge/recipe.py
index ad74e622..29744d1d 100644
--- a/pangeo_forge/recipe.py
+++ b/pangeo_forge/recipe.py
@@ -2,11 +2,12 @@
 A Pangeo Forge Recipe
 """
 
+import json
 import logging
 from abc import ABC, abstractmethod
-from contextlib import contextmanager
+from contextlib import ExitStack, contextmanager
 from dataclasses import dataclass, field
-from typing import Callable, Dict, Hashable, Iterable, NoReturn, Optional
+from typing import Callable, Dict, Hashable, Iterable, List, Optional
 
 import fsspec
 import xarray as xr
@@ -15,7 +16,16 @@
 
 from .patterns import ExplicitURLSequence, VariableSequencePattern
 from .storage import AbstractTarget, UninitializedTarget
-from .utils import chunked_iterable, fix_scalar_attr_encoding
+from .utils import (
+    calc_chunk_conflicts,
+    chunked_iterable,
+    fix_scalar_attr_encoding,
+    lock_for_conflicts,
+)
+
+# use this filename to store global recipe metadata in the metadata_cache
+# it will be written once (by prepare_target) and read many times (by store_chunk)
+_GLOBAL_METADATA_KEY = "pangeo-forge-recipe-metadata.json"
 
 logger = logging.getLogger(__name__)
 
@@ -57,7 +67,7 @@ class BaseRecipe(ABC):
 
     @property
     @abstractmethod
-    def prepare_target(self) -> Callable[[], NoReturn]:
+    def prepare_target(self) -> Callable[[], None]:
         """Prepare the recipe for execution by initializing the target.
         Attribute that returns a callable function.
         """
@@ -70,7 +80,7 @@ def iter_inputs(self) -> Iterable[Hashable]:
 
     @property
     @abstractmethod
-    def cache_input(self) -> Callable[[Hashable], NoReturn]:
+    def cache_input(self) -> Callable[[Hashable], None]:
         """Copy an input from its source location to the cache.
         Attribute that returns a callable function.
         """
@@ -83,7 +93,7 @@ def iter_chunks(self) -> Iterable[Hashable]:
 
     @property
     @abstractmethod
-    def store_chunk(self) -> Callable[[Hashable], NoReturn]:
+    def store_chunk(self) -> Callable[[Hashable], None]:
         """Store a chunk of data in the target.
         Attribute that returns a callable function.
         """
@@ -91,7 +101,7 @@ def store_chunk(self) -> Callable[[Hashable], NoReturn]:
 
     @property
     @abstractmethod
-    def finalize_target(self) -> Callable[[], NoReturn]:
+    def finalize_target(self) -> Callable[[], None]:
         """Final step to finish the recipe after data has been written.
         Attribute that returns a callable function.
         """
@@ -117,9 +127,25 @@ def __post_init__(self):
         pass
 
 
+def _encode_key(key) -> str:
+    return "-".join([str(k) for k in key])
+
+
+def _input_metadata_fname(input_key):
+    return "input-meta-" + _encode_key(input_key) + ".json"
+
+
+def _chunk_metadata_fname(chunk_key) -> str:
+    return "chunk-meta-" + _encode_key(chunk_key) + ".json"
+
+
 # Notes about dataclasses:
 # - https://www.python.org/dev/peps/pep-0557/#inheritance
 # - https://stackoverflow.com/questions/51575931/class-inheritance-in-python-3-7-dataclasses
+# The main awkward thing here is that, because we are using multiple inheritance
+# with dataclasses, _ALL_ fields must have default values. This makes it impossible
+# to have "required" keyword arguments--everything needs some default.
+# That's whay we end up with `UninitializedTarget` and `_variable_sequence_pattern_default_factory`
 
 
 @dataclass
@@ -129,14 +155,20 @@ class NetCDFtoZarrRecipe(BaseRecipe):
     The files are assumed to be arranged in a sequence along dimension ``sequence_dim``
     (commonly "time".)
 
+    This class should not be used on its own but rather via one of its sub-classes.
+
     :param sequence_dim: The dimension name along which the inputs will be concatenated.
     :param inputs_per_chunk: The number of inputs to use in each chunk.
-    :param nitems_per_input: The length of each input along the `sequence_dim` dimension.
+    :param nitems_per_input: The length of each input along the ``sequence_dim`` dimension.
+      If ``None``, each input will be scanned and its metadata cached in order to
+      determine the size of the target dataset.
     :param target: A location in which to put the dataset. Can also be assigned at run time.
     :param input_cache: A location in which to cache temporary data.
+    :param metadata_cache: A location in which to cache metadata for inputs and chunks.
+      Required if ``nitems_per_input=None``.
     :param require_cache: Whether to allow opening inputs directly which have not
-      yet been cached. This could lead to very slow behavior if the inputs
-      live on a slow network.
+      yet been cached. This could lead to very unstanble behavior if the inputs
+      live behind a slow network connection.
     :param consolidate_zarr: Whether to consolidate the resulting Zarr dataset.
     :param xarray_open_kwargs: Extra options for opening the inputs with Xarray.
     :param xarray_concat_kwargs: Extra options to pass to Xarray when concatenating
@@ -153,9 +185,10 @@ class NetCDFtoZarrRecipe(BaseRecipe):
 
     sequence_dim: Optional[str] = None
     inputs_per_chunk: int = 1
-    nitems_per_input: int = 1
-    target: Optional[AbstractTarget] = field(default_factory=UninitializedTarget)
-    input_cache: Optional[AbstractTarget] = field(default_factory=UninitializedTarget)
+    nitems_per_input: Optional[int] = 1
+    target: AbstractTarget = field(default_factory=UninitializedTarget)
+    input_cache: AbstractTarget = field(default_factory=UninitializedTarget)
+    metadata_cache: AbstractTarget = field(default_factory=UninitializedTarget)
     require_cache: bool = True
     consolidate_zarr: bool = True
     xarray_open_kwargs: dict = field(default_factory=dict)
@@ -164,22 +197,25 @@ class NetCDFtoZarrRecipe(BaseRecipe):
     fsspec_open_kwargs: dict = field(default_factory=dict)
     process_input: Optional[Callable[[xr.Dataset, str], xr.Dataset]] = None
     process_chunk: Optional[Callable[[xr.Dataset], xr.Dataset]] = None
-    target_chunks: Optional[Dict[str, int]] = None
+    target_chunks: Dict[str, int] = field(default_factory=dict)
+
+    # private attributes, needed for type hints, not meant to be seen or accessed by user
+    _sequence_dim_chunks: List[int] = field(default_factory=list, repr=False, init=False)
+    _inputs: Dict[Hashable, str] = field(default_factory=dict, repr=False, init=False)
 
     def __post_init__(self):
-        if self.target_chunks:
-            sequence_dim_chunks = self.target_chunks.get(self.sequence_dim)
-            if sequence_dim_chunks:
-                if sequence_dim_chunks > self.nitems_per_input:
-                    raise ValueError(
-                        f"`sequence_dim_chunks` ({sequence_dim_chunks}) must be ",
-                        f"<= `nitems_per_input` ({self.nitems_per_input})",
-                    )
-                if self.nitems_per_input % sequence_dim_chunks > 0:
-                    raise ValueError(
-                        "`sequence_dim_chunks` ({sequence_dim_chunks}) must "
-                        f"evenly divide `nitems_per_input` ({self.nitems_per_input})"
-                    )
+        self._cache_metadata = self.nitems_per_input is None
+        target_sequence_dim_chunks = self.target_chunks.get(self.sequence_dim)
+        if (self.nitems_per_input is None) and (target_sequence_dim_chunks is None):
+            raise ValueError(
+                "Unable to determine target chunks. Please specify either "
+                "`target_chunks` or `nitems_per_input`"
+            )
+        elif target_sequence_dim_chunks:
+            self._sequence_dim_chunks = target_sequence_dim_chunks
+        else:
+            self._sequence_dim_chunks = self.nitems_per_input * self.inputs_per_chunk
+
         # TODO: more input validation
 
     @property
@@ -197,37 +233,45 @@ def _prepare_target():
                     pass
 
             except (FileNotFoundError, IOError, zarr.errors.GroupNotFoundError):
-
-                init_dsets = []
+                logger.info("Creating a new dataset in target")
+                # need to rewrite this as an append loop
                 for chunk_key in self._init_chunks:
-                    chunk_ds = self.open_chunk(
-                        chunk_key
-                    ).chunk()  # make sure data are not in memory
-                    init_dsets.append(chunk_ds)
-                # TODO: create csutomizable option for this step
-                # How to combine attrs is particularly important. It seems like
-                # xarray is missing a "minimal" option to only keep the attrs
-                # that are the same among all input variables.
-                ds = xr.merge(
-                    init_dsets, compat="identical", join="exact", combine_attrs="override"
-                )
-
-                # make sure the concat dim has a valid fill_value to avoid
-                # overruns when writing chunk
-                # this is problematic because this value will always come out as NaN
-                # when we open up the target
-                # ds[self.sequence_dim].encoding = {"_FillValue": -1}
-                # actually not necessary if we use decode_times=False
-
-                if self.target_chunks:
-                    # target_chunks has undergone some validation in __post_init__
-                    ds = ds.chunk(self.target_chunks)
-
-                self.initialize_target(ds)
+                    with self.open_chunk(chunk_key) as ds:
+                        # need to have the data in memory to avoid weird chunk problems
+                        ds.load()
+
+                        # https://github.com/pydata/xarray/blob/5287c7b2546fc8848f539bb5ee66bb8d91d8496f/xarray/core/variable.py#L1069
+                        for v in ds.variables:
+                            if self.target_chunks:
+                                this_var = ds[v]
+                                chunks = {
+                                    this_var.get_axis_num(dim): chunk
+                                    for dim, chunk in self.target_chunks.items()
+                                    if dim in this_var.dims
+                                }
+
+                                chunks = tuple(
+                                    chunks.get(n, s) for n, s in enumerate(this_var.shape)
+                                )
+                                ds[v].encoding["chunks"] = chunks
+                            else:
+                                ds[v].encoding["chunks"] = ds[v].shape
+
+                        target_mapper = self.target.get_mapper()
+                        ds.to_zarr(target_mapper, mode="a", compute=False)
 
             # Regardless of whether there is an existing dataset or we are creating a new one,
             # we need to expand the sequence_dim to hold the entire expected size of the data
-            self.expand_target_dim(self.sequence_dim, self.sequence_len())
+            input_sequence_lens = self.sequence_lens()
+            n_sequence = sum(input_sequence_lens)
+            self.expand_target_dim(self.sequence_dim, n_sequence)
+
+            if not self.nitems_per_input:
+                # if nitems_per_input is not constant, we need to cache this info
+                recipe_meta = {"input_sequence_lens": input_sequence_lens}
+                meta_mapper = self.metadata_cache.get_mapper()
+                # we are saving a dictionary with one key (input_sequence_lens)
+                meta_mapper[_GLOBAL_METADATA_KEY] = json.dumps(recipe_meta).encode("utf-8")
 
         return _prepare_target
 
@@ -236,7 +280,7 @@ def cache_input(self) -> Callable:
         def cache_func(input_key: Hashable) -> None:
             logger.info(f"Caching input {input_key}")
             fname = self._inputs[input_key]
-            # TODO: add a check for whether the input is already cached?
+            # TODO: check and see if the file already exists in the cache
             with input_opener(fname, mode="rb", **self.fsspec_open_kwargs) as source:
                 with self.input_cache.open(fname, mode="wb") as target:
                     # TODO: make this configurable? Would we ever want to change it?
@@ -247,23 +291,25 @@ def cache_func(input_key: Hashable) -> None:
                             break
                         target.write(data)
 
+            if self._cache_metadata:
+                self.cache_input_metadata(input_key)
+
         return cache_func
 
     @property
     def store_chunk(self) -> Callable:
         def _store_chunk(chunk_key):
-            ds_chunk = self.open_chunk(chunk_key)
-
-            def drop_vars(ds):
+            with self.open_chunk(chunk_key) as ds_chunk:
                 # writing a region means that all the variables MUST have sequence_dim
-                to_drop = [v for v in ds.variables if self.sequence_dim not in ds[v].dims]
-                return ds.drop_vars(to_drop)
-
-            ds_chunk = drop_vars(ds_chunk)
-            target_mapper = self.target.get_mapper()
-            write_region = self.region_for_chunk(chunk_key)
-            logger.info(f"Storing chunk '{chunk_key}' to Zarr region {write_region}")
-            ds_chunk.to_zarr(target_mapper, region=write_region)
+                to_drop = [
+                    v for v in ds_chunk.variables if self.sequence_dim not in ds_chunk[v].dims
+                ]
+                ds_chunk = ds_chunk.drop_vars(to_drop)
+                target_mapper = self.target.get_mapper()
+                write_region, conflicts = self.region_and_conflicts_for_chunk(chunk_key)
+                with lock_for_conflicts(conflicts):
+                    logger.info(f"Storing chunk '{chunk_key}' to Zarr region {write_region}")
+                    ds_chunk.to_zarr(target_mapper, region=write_region)
 
         return _store_chunk
 
@@ -293,54 +339,68 @@ def input_opener(self, fname: str):
             else:
                 logger.info(f"No cache found. Opening input `{fname}` directly.")
                 # This will bypass the cache. May be slow.
-                with input_opener(fname, mode="rb", **self.file_system_kwargs) as f:
+                with input_opener(fname, mode="rb", **self.fsspec_open_kwargs) as f:
                     yield f
 
+    @contextmanager
     def open_input(self, input_key: Hashable):
         fname = self._inputs[input_key]
+        logger.info(f"Opening input with Xarray {input_key}: '{fname}'")
         with self.input_opener(fname) as f:
-            logger.info(f"Opening input with Xarray {input_key}: '{fname}'")
             ds = xr.open_dataset(f, **self.xarray_open_kwargs)
-            # explicitly load into memory
-            ds = ds.load()
-        ds = fix_scalar_attr_encoding(ds)
+            # Explicitly load into memory;
+            # if we don't do this, we get a ValueError: seek of closed file.
+            # But there will be some cases where we really don't want to load.
+            # how to keep around the open file object?
+            # ds = ds.load()
 
-        if self.delete_input_encoding:
-            for var in ds.variables:
-                ds[var].encoding = {}
+            ds = fix_scalar_attr_encoding(ds)
 
-        if self.process_input is not None:
-            ds = self.process_input(ds, str(fname))
+            if self.delete_input_encoding:
+                for var in ds.variables:
+                    ds[var].encoding = {}
 
-        logger.debug(f"{ds}")
+            if self.process_input is not None:
+                ds = self.process_input(ds, str(fname))
 
-        return ds
+            logger.debug(f"{ds}")
+            yield ds
 
+    def cache_input_metadata(self, input_key: Hashable):
+        logger.info(f"Caching metadata for input '{input_key}'")
+        with self.open_input(input_key) as ds:
+            metadata = ds.to_dict(data=False)
+            mapper = self.metadata_cache.get_mapper()
+            mapper[_input_metadata_fname(input_key)] = json.dumps(metadata).encode("utf-8")
+
+    @contextmanager
     def open_chunk(self, chunk_key):
         logger.info(f"Concatenating inputs for chunk '{chunk_key}'")
         inputs = self.inputs_for_chunk(chunk_key)
-        dsets = [self.open_input(i) for i in inputs]
-        # CONCAT DELETES ENCODING!!!
-        # OR NO IT DOESN'T! Not in the latest version of xarray?
-        ds = xr.concat(dsets, self.sequence_dim, **self.xarray_concat_kwargs)
 
-        if self.process_chunk is not None:
-            ds = self.process_chunk(ds)
+        # need to open an unknown number of contexts at the same time
+        with ExitStack() as stack:
+            dsets = [stack.enter_context(self.open_input(i)) for i in inputs]
+            # explicitly chunking prevents eager evaluation during concat
+            # dsets = [ds.chunk() for ds in dsets]
+            # but that leads to corrupted data!
 
-        logger.debug(f"{ds}")
+            # CONCAT DELETES ENCODING!!!
+            # OR NO IT DOESN'T! Not in the latest version of xarray?
+            ds = xr.concat(dsets, self.sequence_dim, **self.xarray_concat_kwargs)
 
-        # TODO: maybe do some chunking here?
-        return ds
+            if self.process_chunk is not None:
+                ds = self.process_chunk(ds)
+
+            logger.debug(f"{ds}")
+
+            # TODO: maybe do some chunking here?
+            yield ds
 
     def open_target(self):
         target_mapper = self.target.get_mapper()
         return xr.open_zarr(target_mapper)
 
-    def initialize_target(self, ds, **expand_dims):
-        logger.info("Creating a new dataset in target")
-        target_mapper = self.target.get_mapper()
-        ds.to_zarr(target_mapper, mode="w", compute=False)
-
     def expand_target_dim(self, dim, dimsize):
         target_mapper = self.target.get_mapper()
         zgroup = zarr.open_group(target_mapper)
@@ -367,25 +427,48 @@ def iter_inputs(self):
             for input in self.inputs_for_chunk(chunk_key):
                 yield input
 
-    def nitems_for_chunk(self, chunk_key):
-        return self.nitems_per_input * len(self.inputs_for_chunk(chunk_key))
-
-    def region_for_chunk(self, chunk_key):
+    def region_and_conflicts_for_chunk(self, chunk_key):
         # return a dict suitable to pass to xr.to_zarr(region=...)
         # specifies where in the overall array to put this chunk's data
-        stride = self.nitems_per_input * self.inputs_per_chunk
-        start = self.chunk_position(chunk_key) * stride
-        region_slice = slice(start, start + self.nitems_for_chunk(chunk_key))
-        return {self.sequence_dim: region_slice}
-
-    def sequence_chunks(self):
-        # chunking
-        return {self.sequence_dim: self.inputs_per_chunk * self.nitems_per_input}
+        # also return the conflicts with other chunks
+
+        input_keys = self.inputs_for_chunk(chunk_key)
+        if self.nitems_per_input:
+            stride = self.nitems_per_input * self.inputs_per_chunk
+            start = self.chunk_position(chunk_key) * stride
+            stop = start + stride
+            input_sequence_lens = (self.nitems_per_input,) * self._n_inputs_along_sequence
+        else:
+            input_sequence_lens = json.loads(
+                self.metadata_cache.get_mapper()[_GLOBAL_METADATA_KEY]
+            )["input_sequence_lens"]
+            start = sum(input_sequence_lens[: self.input_position(input_keys[0])])
+            chunk_len = sum([input_sequence_lens[self.input_position(k)] for k in input_keys])
+            stop = start + chunk_len
+
+        all_chunk_conflicts = calc_chunk_conflicts(input_sequence_lens, self._sequence_dim_chunks)
+        this_chunk_conflicts = [all_chunk_conflicts[self.input_position(k)] for k in input_keys]
+
+        region_slice = slice(start, stop)
+        return {self.sequence_dim: region_slice}, this_chunk_conflicts
 
     def iter_chunks(self):
         for k in self._chunks_inputs:
             yield k
 
+    def get_input_meta(self, *input_keys) -> Dict:
+        meta_mapper = self.metadata_cache.get_mapper()
+        # getitems should be async; much faster than serial calls
+        all_meta_raw = meta_mapper.getitems([_input_metadata_fname(k) for k in input_keys])
+        return {k: json.loads(raw_bytes) for k, raw_bytes in all_meta_raw.items()}
+
+    def input_sequence_lens(self, *input_keys) -> List[int]:
+        if self.nitems_per_input:
+            return list((self.nitems_per_input,) * len(input_keys))  # struggling to please flake8
+        # read per-input metadata; this is distinct from global metadata
+        input_meta = self.get_input_meta(*input_keys)
+        return [m["dims"][self.sequence_dim] for m in input_meta.values()]
+
 
 @dataclass
 class NetCDFtoZarrSequentialRecipe(NetCDFtoZarrRecipe):
@@ -402,17 +485,25 @@ def __post_init__(self):
         input_pattern = ExplicitURLSequence(self.input_urls)
         self._inputs = {k: v for k, v in input_pattern}
         self._chunks_inputs = {
-            k: v for k, v in enumerate(chunked_iterable(self._inputs, self.inputs_per_chunk))
+            (k,): v for k, v in enumerate(chunked_iterable(self._inputs, self.inputs_per_chunk))
         }
+        self._n_inputs_along_sequence = len(self._inputs)
+
         # just the first chunk is needed to initialize the recipe
         self._init_chunks = [next(iter(self._chunks_inputs))]
 
+    def input_position(self, input_key):
+        return input_key[0]
+
     def chunk_position(self, chunk_key):
-        return chunk_key
+        return chunk_key[0]
 
-    def sequence_len(self):
-        # tells the total size of dataset along the sequence dimension
-        return sum([self.nitems_for_chunk(k) for k in self.iter_chunks()])
+    def sequence_lens(self):
+        return self.input_sequence_lens(*self.iter_inputs())
+
+
+def _variable_sequence_pattern_default_factory() -> VariableSequencePattern:
+    return VariableSequencePattern(fmt_string="", keys={"variable": [], "_null": []})
 
 
 @dataclass
@@ -423,11 +514,14 @@ class NetCDFtoZarrMultiVarSequentialRecipe(NetCDFtoZarrRecipe):
     :param input_pattern: An pattern used to generate the input file names.
     """
 
-    input_pattern: VariableSequencePattern = field(default_factory=VariableSequencePattern)
+    input_pattern: VariableSequencePattern = field(
+        default_factory=_variable_sequence_pattern_default_factory
+    )
 
     def __post_init__(self):
         super().__post_init__()
         self._variables = self.input_pattern.keys["variable"]
+        self._other_key = [k for k in self.input_pattern.keys if k != "variable"][0]
         self._inputs = {k: v for k, v in self.input_pattern}
         # input keys are tuples like
         #  ("temp", 0)
@@ -448,13 +542,24 @@ def __post_init__(self):
                     (vname, input_sequence_key) for input_sequence_key in chunk_inputs
                 ]
         self._chunks_inputs = chunks_inputs
+        self._n_inputs_along_sequence = len(self.input_pattern.keys[self._other_key])
 
         # should be the first chunk from each variable
         self._init_chunks = [chunk_key for chunk_key in self._chunks_inputs if chunk_key[1] == 0]
 
+    def input_position(self, input_key):
+        return self.input_pattern.keys[self._other_key].index(input_key[1])
+
     def chunk_position(self, chunk_key):
         return chunk_key[1]
 
-    def sequence_len(self):
-        n_sequence = len(self.input_pattern.keys[self.input_pattern._sequence_key])
-        return self.nitems_per_input * n_sequence
+    def sequence_lens(self):
+        variables = self.input_pattern.keys["variable"]
+        lens_by_variable = {}
+        for v in variables:
+            variable_input_keys = [k for k in self.iter_inputs() if k[0] == v]
+            lens_by_variable[v] = self.input_sequence_lens(*variable_input_keys)
+        if not all([lens_by_variable[v] == lens_by_variable[variables[0]]]):
+            raise ValueError(f"Inconsistent sequence_len found for variables. f{lens_by_variable}")
+
+        return lens_by_variable[variables[0]]
diff --git a/pangeo_forge/storage.py b/pangeo_forge/storage.py
index 43b11a2d..f55f5093 100644
--- a/pangeo_forge/storage.py
+++ b/pangeo_forge/storage.py
@@ -1,10 +1,11 @@
 import os
 import re
 import unicodedata
+import zlib
 from abc import ABC, abstractmethod
 from contextlib import contextmanager
 from dataclasses import dataclass
-from typing import BinaryIO, NoReturn
+from typing import Iterator
 
 import fsspec
 
@@ -15,17 +16,17 @@ def get_mapper(self):
         pass
 
     @abstractmethod
-    def exists(self, path) -> bool:
+    def exists(self, path: str) -> bool:
         """Check that the file exists."""
         pass
 
     @abstractmethod
-    def rm(self, path) -> NoReturn:
+    def rm(self, path: str) -> None:
         """Remove file."""
         pass
 
     @contextmanager
-    def open(self, path, **kwargs) -> BinaryIO:
+    def open(self, path: str, **kwargs):  # don't know how to type hint this
         """Open file with a context manager."""
         pass
 
@@ -49,19 +50,19 @@ def get_mapper(self) -> fsspec.mapping.FSMap:
         """Get a mutable mapping object suitable for storing Zarr data."""
         return self.fs.get_mapper(self.root_path)
 
-    def _full_path(self, path):
+    def _full_path(self, path: str):
         return os.path.join(self.root_path, path)
 
-    def exists(self, path) -> bool:
+    def exists(self, path: str) -> bool:
         """Check that the file is in the cache."""
         return self.fs.exists(self._full_path(path))
 
-    def rm(self, path) -> NoReturn:
+    def rm(self, path: str) -> None:
         """Remove file from the cache."""
         self.fs.rm(self._full_path(path))
 
     @contextmanager
-    def open(self, path, **kwargs) -> BinaryIO:
+    def open(self, path: str, **kwargs) -> Iterator[None]:
         """Open file with a context manager."""
         with self.fs.open(self._full_path(path), **kwargs) as f:
             yield f
@@ -78,9 +79,10 @@ class FlatFSSpecTarget(FSSpecTarget):
     Designed to be used as a cache for inputs.
     """
 
-    def _full_path(self, path):
+    def _full_path(self, path: str) -> str:
+        # this is just in case _slugify(path) is non-unique
+        prefix = hex(zlib.adler32(str(path).encode("utf8")))[2:10]
         slug = _slugify(path)
-        prefix = prefix = hex(hash(path))[2:10]
         new_path = "-".join([prefix, slug])
         return os.path.join(self.root_path, new_path)
 
@@ -91,13 +93,13 @@ class CacheFSSpecTarget(FlatFSSpecTarget):
     pass
 
 
-def _slugify(value):
+def _slugify(value: str) -> str:
     # Adopted from
     # https://github.com/django/django/blob/master/django/utils/text.py
     # https://stackoverflow.com/questions/295135/turn-a-string-into-a-valid-filename
     value = str(value)
     value = unicodedata.normalize("NFKD", value).encode("ascii", "ignore").decode("ascii")
-    value = re.sub(r"[^\w\s-]+", "_", value.lower())
+    value = re.sub(r"[^.\w\s-]+", "_", value.lower())
     return re.sub(r"[-\s]+", "-", value).strip("-_")
 
 
@@ -105,13 +107,13 @@ class UninitializedTarget(AbstractTarget):
     def get_mapper(self):
         raise UninitializedTargetError
 
-    def exists(self, path) -> bool:
+    def exists(self, path: str) -> bool:
         raise UninitializedTargetError
 
-    def rm(self, path) -> NoReturn:
+    def rm(self, path: str) -> None:
         raise UninitializedTargetError
 
-    def open(self, path, **kwargs) -> BinaryIO:
+    def open(self, path: str, **kwargs):  # don't know how to type hint this
         raise UninitializedTargetError
 
 
diff --git a/pangeo_forge/utils.py b/pangeo_forge/utils.py
index 1ac84c7b..0b04e5f3 100644
--- a/pangeo_forge/utils.py
+++ b/pangeo_forge/utils.py
@@ -1,6 +1,9 @@
 import itertools
+from contextlib import contextmanager
+from typing import List, Sequence, Tuple
 
 import numpy as np
+from dask.distributed import Lock, client
 
 
 # https://alexwlchan.net/2018/12/iterating-in-fixed-size-chunks/
@@ -30,3 +33,50 @@ def _fixed_attrs(d):
         ds[v].attrs.update(_fixed_attrs(ds[v].attrs))
         ds[v].encoding.update(_fixed_attrs(ds[v].encoding))
     return ds
+
+
+def calc_chunk_conflicts(chunks: Sequence[int], zchunks: int) -> List[Tuple[int, ...]]:
+    n_chunks = len(chunks)
+
+    # coerce numpy array to list for mypy
+    chunk_bounds = list([int(item) for item in np.hstack([0, np.cumsum(chunks)])])
+    chunk_overlap = []
+    for start, stop in zip(chunk_bounds[:-1], chunk_bounds[1:]):
+        chunk_start = start // zchunks
+        chunk_stop = (stop - 1) // zchunks
+        chunk_overlap.append((chunk_start, chunk_stop))
+
+    chunk_conflicts = []
+    for n, chunk_pair in enumerate(chunk_overlap):
+        conflicts = set()
+        if n > 0:
+            prev_pair = chunk_overlap[n - 1]
+            if prev_pair[1] == chunk_pair[0]:
+                conflicts.add(chunk_pair[0])
+        if n < (n_chunks - 1):
+            next_pair = chunk_overlap[n + 1]
+            if next_pair[0] == chunk_pair[1]:
+                conflicts.add(chunk_pair[1])
+        chunk_conflicts.append(tuple(conflicts))
+
+    return chunk_conflicts
+
+
+@contextmanager
+def lock_for_conflicts(conflicts, base_name="pangeo-forge"):
+
+    # https://stackoverflow.com/questions/59070260/dask-client-detect-local-default-cluster-already-running
+    is_distributed = client._get_global_client() is not None
+    # Don't bother with locks if we are not in a distributed context
+    # NOTE! This means we HAVE to use dask.distributed as our parallel execution enviroment
+    # That is compatible with Prefect.
+    if is_distributed:
+        locks = [Lock(f"{base_name}-{c}") for c in conflicts]
+        for lock in locks:
+            lock.acquire()
+    try:
+        yield
+    finally:
+        if is_distributed:
+            for lock in locks:
+                lock.release()
diff --git a/setup.cfg b/setup.cfg
index d57b0808..d3d9c961 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -3,7 +3,7 @@ max-line-length = 100
 
 [isort]
 known_first_party=pangeo_forge
-known_third_party=aiohttp,fsspec,numpy,pandas,pkg_resources,prefect,pytest,rechunker,setuptools,sphinx_book_theme,xarray,zarr
+known_third_party=dask,fsspec,numpy,pandas,pkg_resources,prefect,pytest,pytest_lazyfixture,rechunker,setuptools,sphinx_book_theme,xarray,zarr
 multi_line_output=3
 include_trailing_comma=True
 force_grid_wrap=0
@@ -12,3 +12,20 @@ line_length=100
 
 [tool:pytest]
 log_cli = False
+
+# remove this once rechunker executors are factored into a standalone package
+# that exports type hints (https://mypy.readthedocs.io/en/latest/installed_packages.html#installed-packages)
+[mypy-rechunker.*]
+ignore_missing_imports = True
+
+[mypy-numpy.*]
+ignore_missing_imports = True
+
+[mypy-dask.distributed.*]
+ignore_missing_imports = True
+
+[mypy-fsspec.*]
+ignore_missing_imports = True
+
+[mypy-zarr.*]
+ignore_missing_imports = True


Test patch:
diff --git a/tests/conftest.py b/tests/conftest.py
index d3005652..c1b57b61 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -8,8 +8,14 @@
 import pandas as pd
 import pytest
 import xarray as xr
+from dask.distributed import Client, LocalCluster
 
 from pangeo_forge import recipe
+from pangeo_forge.executors import (
+    DaskPipelineExecutor,
+    PrefectPipelineExecutor,
+    PythonPipelineExecutor,
+)
 from pangeo_forge.patterns import VariableSequencePattern
 from pangeo_forge.storage import CacheFSSpecTarget, FSSpecTarget, UninitializedTarget
 
@@ -97,7 +103,6 @@ def netcdf_local_paths_by_variable(daily_xarray_dataset, tmpdir_factory, request
     xr.save_mfdataset(datasets, [str(path) for path in full_paths])
     items_per_file = {"D": 1, "2D": 2}[request.param]
     path_format = str(tmp_path) + "/{variable}_{n:03d}.nc"
-    print(path_format)
     return full_paths, items_per_file, fnames_by_variable, path_format
 
 
@@ -140,8 +145,6 @@ def teardown():
 
 @pytest.fixture()
 def tmp_target(tmpdir_factory):
-    import fsspec
-
     fs = fsspec.get_filesystem_class("file")()
     path = str(tmpdir_factory.mktemp("target"))
     return FSSpecTarget(fs, path)
@@ -163,7 +166,7 @@ def uninitialized_target():
 @pytest.fixture
 def netCDFtoZarr_sequential_recipe(daily_xarray_dataset, netcdf_local_paths, tmp_target, tmp_cache):
     paths, items_per_file = netcdf_local_paths
-    r = recipe.NetCDFtoZarrSequentialRecipe(
+    kwargs = dict(
         input_urls=paths,
         sequence_dim="time",
         inputs_per_chunk=1,
@@ -171,7 +174,7 @@ def netCDFtoZarr_sequential_recipe(daily_xarray_dataset, netcdf_local_paths, tmp
         target=tmp_target,
         input_cache=tmp_cache,
     )
-    return r, daily_xarray_dataset, tmp_target
+    return recipe.NetCDFtoZarrSequentialRecipe, kwargs, daily_xarray_dataset, tmp_target
 
 
 @pytest.fixture
@@ -179,15 +182,80 @@ def netCDFtoZarr_sequential_multi_variable_recipe(
     daily_xarray_dataset, netcdf_local_paths_by_variable, tmp_target, tmp_cache
 ):
     paths, items_per_file, fnames_by_variable, path_format = netcdf_local_paths_by_variable
+    nitems_per_input = items_per_file
+    time_index = list(range(len(paths) // 2))
     pattern = VariableSequencePattern(
-        path_format, keys={"variable": ["foo", "bar"], "n": list(range(len(paths) / 2))}
+        path_format, keys={"variable": ["foo", "bar"], "n": time_index}
     )
-    r = recipe.NetCDFtoZarrMultiVarSequentialRecipe(
+    kwargs = dict(
         input_pattern=pattern,
         sequence_dim="time",
         inputs_per_chunk=1,
-        nitems_per_input=items_per_file,
+        nitems_per_input=nitems_per_input,
         target=tmp_target,
         input_cache=tmp_cache,
     )
-    return r, daily_xarray_dataset, tmp_target
+    return recipe.NetCDFtoZarrMultiVarSequentialRecipe, kwargs, daily_xarray_dataset, tmp_target
+
+
+@pytest.fixture(scope="session")
+def dask_cluster():
+    cluster = LocalCluster(n_workers=2, threads_per_worker=1, silence_logs=False)
+
+    yield cluster
+    cluster.close()
+
+
+_executors = {
+    "python": PythonPipelineExecutor,
+    "dask": DaskPipelineExecutor,
+    "prefect": PrefectPipelineExecutor,
+    "prefect-dask": PrefectPipelineExecutor,
+}
+
+
+@pytest.fixture(params=["manual", "python", "dask", "prefect", "prefect-dask"])
+def execute_recipe(request, dask_cluster):
+    if request.param == "prefect-dask":
+        # TODO: turn this off and fix this case!
+        pytest.skip("Prefect with dask LocalCluster hangs inexplicably.")
+
+    if request.param == "manual":
+
+        def execute(r):
+            for input_key in r.iter_inputs():
+                r.cache_input(input_key)
+            r.prepare_target()
+            for chunk_key in r.iter_chunks():
+                r.store_chunk(chunk_key)
+            r.finalize_target()
+
+    else:
+        ExecutorClass = _executors[request.param]
+
+        def execute(rec):
+            ex = ExecutorClass()
+            pipeline = rec.to_pipelines()
+            plan = ex.pipelines_to_plan(pipeline)
+
+            if request.param == "dask":
+                client = Client(dask_cluster)
+
+                def set_blosc_threads():
+                    from numcodecs import blosc
+
+                    blosc.use_threads = False
+
+                client.run(set_blosc_threads)
+            if request.param == "prefect-dask":
+                from prefect.executors import DaskExecutor
+
+                prefect_executor = DaskExecutor(address=dask_cluster.scheduler_address)
+                plan.run(executor=prefect_executor)
+            else:
+                ex.execute_plan(plan)
+            if request.param == "dask":
+                client.close()
+                del client
+
+    return execute
diff --git a/tests/test_executors.py b/tests/test_executors.py
deleted file mode 100644
index 6294809a..00000000
--- a/tests/test_executors.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import pytest
-import xarray as xr
-
-from pangeo_forge.executors import (
-    DaskPipelineExecutor,
-    PrefectPipelineExecutor,
-    PythonPipelineExecutor,
-)
-
-
-@pytest.mark.parametrize(
-    "Executor", [PythonPipelineExecutor, DaskPipelineExecutor, PrefectPipelineExecutor]
-)
-def test_recipe_w_executor(Executor, netCDFtoZarr_sequential_recipe):
-    rec, ds_expected, target = netCDFtoZarr_sequential_recipe
-    pipeline = rec.to_pipelines()
-    ex = Executor()
-    plan = ex.pipelines_to_plan(pipeline)
-    ex.execute_plan(plan)
-    ds_actual = xr.open_zarr(target.get_mapper()).load()
-    assert ds_actual.identical(ds_expected)
diff --git a/tests/test_patterns.py b/tests/test_patterns.py
index b058d5f5..f1647754 100644
--- a/tests/test_patterns.py
+++ b/tests/test_patterns.py
@@ -41,4 +41,4 @@ def test_variable_sequence_pattern():
 
 def test_explicit_url_sequence():
     seq = ExplicitURLSequence(["a", "b", "c"])
-    assert list(seq) == [(0, "a"), (1, "b"), (2, "c")]
+    assert list(seq) == [((0,), "a"), ((1,), "b"), ((2,), "c")]
diff --git a/tests/test_recipe.py b/tests/test_recipe.py
deleted file mode 100644
index 761c5a40..00000000
--- a/tests/test_recipe.py
+++ /dev/null
@@ -1,169 +0,0 @@
-from contextlib import nullcontext as does_not_raise
-
-import aiohttp
-import pytest
-import xarray as xr
-
-from pangeo_forge import recipe
-from pangeo_forge.patterns import VariableSequencePattern
-from pangeo_forge.storage import UninitializedTargetError
-
-dummy_fnames = ["a.nc", "b.nc", "c.nc"]
-
-
-def incr_date(ds, filename=""):
-    # add one day
-    t = [d + int(24 * 3600e9) for d in ds.time.values]
-    ds = ds.assign_coords(time=t)
-    return ds
-
-
-def _manually_execute_recipe(r):
-    for input_key in r.iter_inputs():
-        r.cache_input(input_key)
-    r.prepare_target()
-    for chunk_key in r.iter_chunks():
-        r.store_chunk(chunk_key)
-    r.finalize_target()
-
-
-@pytest.mark.parametrize(
-    "username, password", [("foo", "bar"), ("foo", "wrong"),],  # noqa: E231
-)
-def test_NetCDFtoZarrSequentialRecipeHttpAuth(
-    daily_xarray_dataset, netcdf_http_server, tmp_target, tmp_cache, username, password
-):
-
-    url, fnames, items_per_file = netcdf_http_server("foo", "bar")
-    urls = [f"{url}/{fname}" for fname in fnames]
-    r = recipe.NetCDFtoZarrSequentialRecipe(
-        input_urls=urls,
-        sequence_dim="time",
-        inputs_per_chunk=1,
-        nitems_per_input=items_per_file,
-        target=tmp_target,
-        input_cache=tmp_cache,
-        fsspec_open_kwargs={"client_kwargs": {"auth": aiohttp.BasicAuth(username, password)}},
-    )
-
-    if password == "wrong":
-        with pytest.raises(aiohttp.client_exceptions.ClientResponseError):
-            r.cache_input(next(r.iter_inputs()))
-    else:
-        _manually_execute_recipe(r)
-
-        ds_target = xr.open_zarr(tmp_target.get_mapper(), consolidated=True).load()
-        ds_expected = daily_xarray_dataset.compute()
-        assert ds_target.identical(ds_expected)
-
-
-@pytest.mark.parametrize(
-    "process_input, process_chunk",
-    [(None, None), (incr_date, None), (None, incr_date), (incr_date, incr_date)],
-)
-@pytest.mark.parametrize("inputs_per_chunk", [1, 2])
-@pytest.mark.parametrize(
-    "target_chunks,chunk_expectation",
-    [
-        (None, does_not_raise()),
-        ({"lon": 12}, does_not_raise()),
-        ({"lon": 12, "time": 1}, does_not_raise()),
-        ({"lon": 12, "time": 3}, pytest.raises(ValueError)),
-    ],
-)
-def test_NetCDFtoZarrSequentialRecipe(
-    daily_xarray_dataset,
-    netcdf_local_paths,
-    tmp_target,
-    tmp_cache,
-    process_input,
-    process_chunk,
-    inputs_per_chunk,
-    target_chunks,
-    chunk_expectation,
-):
-
-    # the same recipe is created as a fixture in conftest.py
-    # I left it here explicitly because it makes the test easier to read.
-    paths, items_per_file = netcdf_local_paths
-    with chunk_expectation as excinfo:
-        r = recipe.NetCDFtoZarrSequentialRecipe(
-            input_urls=paths,
-            sequence_dim="time",
-            inputs_per_chunk=inputs_per_chunk,
-            nitems_per_input=items_per_file,
-            target=tmp_target,
-            input_cache=tmp_cache,
-            process_input=process_input,
-            process_chunk=process_chunk,
-            target_chunks=target_chunks,
-        )
-    if excinfo:
-        # don't continue if we got an exception
-        return
-
-    _manually_execute_recipe(r)
-
-    ds_target = xr.open_zarr(tmp_target.get_mapper(), consolidated=True)
-
-    # chunk validation
-    sequence_chunks = ds_target.chunks["time"]
-    if target_chunks is None:
-        target_chunks = {}
-    seq_chunk_len = target_chunks.pop("time", None) or (items_per_file * inputs_per_chunk)
-    # we expect all chunks but the last to have the expected size
-    assert all([item == seq_chunk_len for item in sequence_chunks[:-1]])
-    for other_dim, chunk_len in target_chunks.items():
-        all([item == chunk_len for item in ds_target.chunks[other_dim][:-1]])
-
-    ds_target.load()
-    ds_expected = daily_xarray_dataset.compute()
-
-    if process_input is not None:
-        # check that the process_input hook made some changes
-        assert not ds_target.identical(ds_expected)
-        # apply these changes to the expected dataset
-        ds_expected = process_input(ds_expected)
-    if process_chunk is not None:
-        # check that the process_chunk hook made some changes
-        assert not ds_target.identical(ds_expected)
-        # apply these changes to the expected dataset
-        ds_expected = process_chunk(ds_expected)
-
-    assert ds_target.identical(ds_expected)
-
-
-def test_NetCDFtoZarrSequentialRecipeNoTarget(
-    daily_xarray_dataset, netcdf_local_paths, tmp_target, tmp_cache
-):
-
-    paths, items_per_file = netcdf_local_paths
-    r = recipe.NetCDFtoZarrSequentialRecipe(
-        input_urls=paths, sequence_dim="time", inputs_per_chunk=1, nitems_per_input=items_per_file,
-    )
-
-    with pytest.raises(UninitializedTargetError):
-        r.cache_input(next(r.iter_inputs()))
-
-
-def test_NetCDFtoZarrMultiVarSequentialRecipe(
-    daily_xarray_dataset, netcdf_local_paths_by_variable, tmp_target, tmp_cache
-):
-    paths, items_per_file, fnames_by_variable, path_format = netcdf_local_paths_by_variable
-    pattern = VariableSequencePattern(
-        path_format, keys={"variable": ["foo", "bar"], "n": list(range(len(paths) // 2))}
-    )
-    r = recipe.NetCDFtoZarrMultiVarSequentialRecipe(
-        input_pattern=pattern,
-        sequence_dim="time",
-        inputs_per_chunk=1,
-        nitems_per_input=items_per_file,
-        target=tmp_target,
-        input_cache=tmp_cache,
-    )
-    _manually_execute_recipe(r)
-
-    ds_target = xr.open_zarr(tmp_target.get_mapper(), consolidated=True).compute()
-    print(ds_target)
-    print(daily_xarray_dataset)
-    assert ds_target.identical(daily_xarray_dataset)
diff --git a/tests/test_recipes.py b/tests/test_recipes.py
new file mode 100644
index 00000000..d8cd83ed
--- /dev/null
+++ b/tests/test_recipes.py
@@ -0,0 +1,119 @@
+from contextlib import nullcontext as does_not_raise
+
+import pytest
+import xarray as xr
+
+# need to import this way (rather than use pytest.lazy_fixture) to make it work with dask
+from pytest_lazyfixture import lazy_fixture
+
+all_recipes = [
+    lazy_fixture("netCDFtoZarr_sequential_recipe"),
+    lazy_fixture("netCDFtoZarr_sequential_multi_variable_recipe"),
+]
+
+
+@pytest.mark.parametrize("recipe_fixture", all_recipes)
+def test_recipe(recipe_fixture, execute_recipe):
+    """The basic recipe test. Use this as a template for other tests."""
+
+    RecipeClass, kwargs, ds_expected, target = recipe_fixture
+    rec = RecipeClass(**kwargs)
+    execute_recipe(rec)
+    ds_actual = xr.open_zarr(target.get_mapper()).load()
+    xr.testing.assert_identical(ds_actual, ds_expected)
+
+
+# function passed to preprocessing
+def incr_date(ds, filename=""):
+    # add one day
+    t = [d + int(24 * 3600e9) for d in ds.time.values]
+    ds = ds.assign_coords(time=t)
+    return ds
+
+
+@pytest.mark.parametrize(
+    "process_input, process_chunk",
+    [(None, None), (incr_date, None), (None, incr_date), (incr_date, incr_date)],
+)
+@pytest.mark.parametrize("recipe_fixture", all_recipes)
+def test_process(recipe_fixture, execute_recipe, process_input, process_chunk):
+    """Check that the process_chunk and process_input arguments work as expected."""
+
+    RecipeClass, kwargs, ds_expected, target = recipe_fixture
+    kwargs["process_input"] = process_input
+    kwargs["process_chunk"] = process_chunk
+    rec = RecipeClass(**kwargs)
+    execute_recipe(rec)
+    ds_actual = xr.open_zarr(target.get_mapper()).load()
+
+    if process_input and process_chunk:
+        assert not ds_actual.identical(ds_expected)
+        ds_expected = incr_date(incr_date(ds_expected))
+    elif process_input or process_chunk:
+        assert not ds_actual.identical(ds_expected)
+        ds_expected = incr_date(ds_expected)
+
+    xr.testing.assert_identical(ds_actual, ds_expected)
+
+
+@pytest.mark.parametrize("inputs_per_chunk", [1, 2])
+@pytest.mark.parametrize(
+    "target_chunks,specify_nitems_per_input,chunk_expectation",
+    [
+        ({}, True, does_not_raise()),
+        ({"lon": 12}, True, does_not_raise()),
+        ({"lon": 12, "time": 1}, True, does_not_raise()),
+        ({"lon": 12, "time": 3}, True, does_not_raise()),
+        ({"lon": 12, "time": 1}, False, does_not_raise()),
+        ({"lon": 12, "time": 3}, False, does_not_raise()),
+        # can't determine target chunks for the next two because # 'time' missing from target_chunks
+        ({}, False, pytest.raises(ValueError)),
+        ({"lon": 12}, False, pytest.raises(ValueError)),
+    ],
+)
+@pytest.mark.parametrize("recipe_fixture", all_recipes)
+def test_chunks(
+    recipe_fixture,
+    execute_recipe,
+    inputs_per_chunk,
+    target_chunks,
+    chunk_expectation,
+    specify_nitems_per_input,
+):
+    """Check that chunking of datasets works as expected."""
+
+    RecipeClass, kwargs, ds_expected, target = recipe_fixture
+
+    kwargs["target_chunks"] = target_chunks
+    kwargs["inputs_per_chunk"] = inputs_per_chunk
+    if specify_nitems_per_input:
+        kwargs["nitems_per_input"] = kwargs["nitems_per_input"]  # it's already there in kwargs
+        kwargs["metadata_cache"] = None
+    else:
+        # file will be scanned and metadata cached
+        kwargs["nitems_per_input"] = None
+        kwargs["metadata_cache"] = kwargs["input_cache"]
+
+    with chunk_expectation as excinfo:
+        rec = RecipeClass(**kwargs)
+    if excinfo:
+        # don't continue if we got an exception
+        return
+
+    execute_recipe(rec)
+
+    # chunk validation
+    ds_actual = xr.open_zarr(target.get_mapper(), consolidated=True)
+    sequence_chunks = ds_actual.chunks["time"]
+    seq_chunk_len = target_chunks.get("time", None) or (
+        kwargs["nitems_per_input"] * inputs_per_chunk
+    )
+    # we expect all chunks but the last to have the expected size
+    assert all([item == seq_chunk_len for item in sequence_chunks[:-1]])
+    for other_dim, chunk_len in target_chunks.items():
+        if other_dim == "time":
+            continue
+        assert all([item == chunk_len for item in ds_actual.chunks[other_dim][:-1]])
+
+    ds_actual.load()
+    xr.testing.assert_identical(ds_actual, ds_expected)
diff --git a/tests/test_utils.py b/tests/test_utils.py
index d486b7af..5bd65355 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,6 +1,9 @@
+import dask
 import pytest
+from dask import delayed
+from dask.distributed import Client, LocalCluster
 
-import pangeo_forge.utils
+from pangeo_forge import utils
 
 
 @pytest.mark.parametrize(
@@ -13,5 +16,35 @@
     ],
 )
 def test_chunked_iterable(iterable, size, expected):
-    actual = list(pangeo_forge.utils.chunked_iterable(iterable, size))
+    actual = list(utils.chunked_iterable(iterable, size))
     assert actual == expected
+
+
+def test_chunk_conflicts():
+    zchunks = 10
+    assert utils.calc_chunk_conflicts([10, 10], zchunks) == [(), ()]
+    assert utils.calc_chunk_conflicts([9, 10], zchunks) == [(0,), (0,)]
+    assert utils.calc_chunk_conflicts([10, 9, 11, 10], zchunks) == [(), (1,), (1,), ()]
+    assert utils.calc_chunk_conflicts([9, 12, 5], zchunks) == [(0,), (0, 2), (2,)]
+
+
+@pytest.mark.parametrize("conflicts", [{}, {0}, {0, 1}])
+def test_locks(conflicts):
+
+    # first make sure the locks work without a cluster
+    with utils.lock_for_conflicts(conflicts):
+        # todo; how to actually test for concurrency! hard!
+        pass
+
+    # TOOD: move this into a fixture
+    with LocalCluster(n_workers=1, processes=False, threads_per_worker=1,) as cluster, Client(
+        cluster
+    ):
+
+        @delayed
+        def do_stuff():
+            with utils.lock_for_conflicts(conflicts):
+                # todo; how to actually test for concurrency! hard!
+                pass
+
+        dask.compute([do_stuff() for n in range(3)])