diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
deleted file mode 100644
index 4c592c6a9..000000000
--- a/.github/pull_request_template.md
+++ /dev/null
@@ -1,31 +0,0 @@
-
-## Purpose
-
-Describe the purpose of this PR and the motivation if relevant to understanding. Include links to related issues, bugs or features.
-
-Remove the sections below which do not apply.
-
-## Code changes:
-
-- Provide a list of relevant code changes and the side effects they have on other code.
-
-## Requirements changes:
-
-- Provide a list of any changes made to requirements, e.g. changes to files requirements*.txt, constraints.txt, setup.py, pyproject.toml, pre-commit-config.yaml and a reason if not included in the Purpose section (e.g. incompatibility, updates, etc)
-
-## Infrastructure changes:
-
-- Provide a list of changes that impact the infrastructure around running the code -- that is, changes to Makefiles, docker files, git submodules, or .jenkins (testing infrastructure changes). If Jenkins plans are also being manually changed, indicate that as well.
-
-## Checklist
-Before submitting this PR, please make sure:
-
-- [ ] You have followed the coding standards guidelines established at [Code Review Checklist](https://drive.google.com/file/d/1R0nqOxfYnzaSdoYdt8yjx5J482ETI2Ft/view?usp=sharing).
-- [ ] Docstrings and type hints are added to new and updated routines, as appropriate
-- [ ] All relevant documentation has been updated or added (e.g. README, CONTRIBUTING docs)
-- [ ] For each public change and fix in `pace`, HISTORY has been updated
-- [ ] Unit tests are added or updated for non-stencil code changes
-
-Additionally, if this PR contains code authored by new contributors:
-
-- [ ] The names of all the new contributors have been added to CONTRIBUTORS.md
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yaml
similarity index 80%
rename from .github/workflows/lint.yml
rename to .github/workflows/lint.yaml
index 0cc08080b..0d15d5926 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yaml
@@ -1,7 +1,6 @@
 name: "Lint"
 on:
   pull_request:
-      types: [opened, synchronize, reopened, ready_for_review, labeled, unlabeled]
 
 jobs:
   lint:
@@ -11,10 +10,10 @@ jobs:
           uses: actions/checkout@v3.5.2
           with:
             submodules: 'recursive'
-        - name: Step Python 3.8.12
+        - name: Step Python 3.11.7
           uses: actions/setup-python@v4.6.0
           with:
-            python-version: '3.8.12'
+            python-version: '3.11.7'
         - name: Install OpenMPI for gt4py
           run: |
             sudo apt-get install libopenmpi-dev
diff --git a/.github/workflows/main_unit_tests.yaml b/.github/workflows/main_unit_tests.yaml
new file mode 100644
index 000000000..f5f6739b7
--- /dev/null
+++ b/.github/workflows/main_unit_tests.yaml
@@ -0,0 +1,61 @@
+name: "pace main unit tests"
+on:
+  workflow_call:
+    inputs:
+      component_trigger:
+        type: boolean
+        default: false
+        required: false
+      component_name:
+        type: string
+        default: ''
+        required: false
+  pull_request:
+
+# cancel running jobs if theres a newer push
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  pace_main_unit_tests:
+    runs-on: ubuntu-latest
+    container:
+      image: ghcr.io/noaa-gfdl/miniforge:mpich
+    steps:
+        - name: external trigger Checkout pace/develop
+          if:  ${{ inputs.component_trigger }}
+          uses: actions/checkout@v4
+          with:
+            submodules: 'recursive'
+            repository: NOAA-GFDL/pace
+            path: pace
+            ref: develop
+
+        - name: external trigger Remove existing component in pace/develop
+          if:  ${{ inputs.component_trigger }}
+          run: rm -rf ${GITHUB_WORKSPACE}/pace/${{inputs.component_name}}
+
+        - name: Checkout out hash that triggered CI
+          uses: actions/checkout@v4
+          with:
+            submodules: 'recursive'
+            path: pace/${{inputs.component_name}}
+
+        - name: install packages
+          run: |
+            cd ${GITHUB_WORKSPACE}/pace
+            pip3 install --upgrade pip setuptools wheel
+            pip3 install -r requirements_dev.txt -c constraints.txt
+
+        - name: prepare input files
+          run: |
+            cd ${GITHUB_WORKSPACE}/pace
+            mkdir tests/main/input
+            python3 examples/generate_eta_files.py
+            mv *eta*.nc tests/main/input
+
+        - name: run tests
+          run: |
+            cd ${GITHUB_WORKSPACE}/pace
+            pytest -x tests/main
diff --git a/.github/workflows/main_unit_tests.yml b/.github/workflows/main_unit_tests.yml
deleted file mode 100644
index 79e745d62..000000000
--- a/.github/workflows/main_unit_tests.yml
+++ /dev/null
@@ -1,31 +0,0 @@
-name: "Main unit tests"
-on:
-  pull_request:
-      types: [opened, synchronize, reopened, ready_for_review, labeled, unlabeled]
-
-jobs:
-  main_unit_tests:
-    runs-on: ubuntu-latest
-    steps:
-        - name: Checkout Pace repository
-          uses: actions/checkout@v3.5.2
-          with:
-            submodules: 'recursive'
-        - name: Step Python 3.8.12
-          uses: actions/setup-python@v4.6.0
-          with:
-            python-version: '3.8.12'
-        - name: Install OpenMPI & Boost for gt4py
-          run: |
-            sudo apt-get install libopenmpi-dev libboost1.74-dev
-        - name: Install Python packages
-          run: |
-            python -m pip install --upgrade pip setuptools wheel
-            pip install -r requirements_dev.txt -c constraints.txt
-        - name: Clone datafiles
-          run: |
-            mkdir -p tests/main/input && cd tests/main/input
-            git clone -b store_files https://github.com/mlee03/pace.git tmp && mv tmp/*.nc . && rm -rf tmp
-        - name: Run all main tests
-          run: |
-            python -m pytest -x tests/main
diff --git a/.github/workflows/main_unit_tests_mpich.yaml b/.github/workflows/main_unit_tests_mpich.yaml
deleted file mode 100644
index ee469b7a6..000000000
--- a/.github/workflows/main_unit_tests_mpich.yaml
+++ /dev/null
@@ -1,26 +0,0 @@
-name: "Main unit tests with mpich"
-on:
-  pull_request:
-      types: [opened, synchronize, reopened, ready_for_review, labeled, unlabeled]
-
-# cancel running jobs if theres a newer push
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  main_unit_tests:
-    runs-on: ubuntu-latest
-    container:
-      image: ghcr.io/noaa-gfdl/pace_mpich:3.8
-    steps:
-        - name: Checkout Pace repository
-          uses: actions/checkout@v4
-          with:
-            submodules: 'recursive'
-        - name: setup env and run tests (test)
-          run: |
-            cp /home/scripts/setup_env.sh . && chmod +x setup_env.sh
-            cp /home/scripts/run_tests.sh . && chmod +x run_tests.sh
-            ./setup_env.sh
-            ./run_tests.sh
diff --git a/.github/workflows/main_unit_tests_openmpi.yaml b/.github/workflows/main_unit_tests_openmpi.yaml
deleted file mode 100644
index edb9b0f05..000000000
--- a/.github/workflows/main_unit_tests_openmpi.yaml
+++ /dev/null
@@ -1,26 +0,0 @@
-name: "Main unit tests with openmpi"
-on:
-  pull_request:
-      types: [opened, synchronize, reopened, ready_for_review, labeled, unlabeled]
-
-# cancel running jobs if theres a newer push
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  main_unit_tests:
-    runs-on: ubuntu-latest
-    container:
-      image: ghcr.io/noaa-gfdl/pace_openmpi:3.8
-    steps:
-        - name: Checkout Pace repository
-          uses: actions/checkout@v4
-          with:
-            submodules: 'recursive'
-        - name: setup env and run tests
-          run: |
-            cp /home/scripts/setup_env.sh . && chmod +x setup_env.sh
-            cp /home/scripts/run_tests.sh . && chmod +x run_tests.sh
-            ./setup_env.sh
-            ./run_tests.sh
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index a6f865f7b..3994470d5 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -15,9 +15,10 @@ repos:
       args: ["--profile", "black"]
 
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v0.812
+    rev: v1.4.1
     hooks:
     - id: mypy
+      additional_dependencies: [types-pyyaml]
       name: mypy-pace
       files: pace
       args: [--config-file, setup.cfg]
diff --git a/Dockerfile b/Dockerfile
index dca0e1924..311ebcb95 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,4 +1,4 @@
-FROM python:3.8.13-bullseye@sha256:2a01d88a1684e6d7f08030cf5ae73b536926c64076cab197e9e3d9f699255283
+FROM python:3.11-slim-bookworm@sha256:7cd3fa11d619688317226bc93dc59bc8966e9aec6bc2a6abb847e8ab7d656706
 
 RUN apt-get update && apt-get install -y make \
     software-properties-common \
@@ -13,14 +13,11 @@ RUN apt-get update && apt-get install -y make \
     proj-bin \
     proj-data \
     python3 \
-    python3-pip
+    python3-pip \
+    git
 
 RUN pip3 install --upgrade setuptools wheel
 
-COPY constraints.txt /pace/constraints.txt
-
-RUN pip3 install -r /pace/constraints.txt
-
 COPY . /pace
 
 RUN cd /pace && \
@@ -32,14 +29,11 @@ RUN cd / && \
 ENV CFLAGS="-I/usr/include -DACCEPT_USE_OF_DEPRECATED_PROJ_API_H=1"
 
 RUN python3 -m pip install \
-    numpy==1.21.2 \
-    netCDF4==1.5.7 \
-    mpi4py==3.1.1 \
     matplotlib==3.5.2 \
     ipyparallel==8.4.1 \
     jupyterlab==3.4.4 \
     shapely==1.8.5 \
-    cartopy==0.18.0 \
+    cartopy==0.23.0 \
     jupyterlab_code_formatter==1.5.2 \
     isort==5.10.1 \
     black==22.3.0 \
diff --git a/NDSL b/NDSL
index 61c4cbe51..a667d2908 160000
--- a/NDSL
+++ b/NDSL
@@ -1 +1 @@
-Subproject commit 61c4cbe51dd20e7d9eba50ea9f9b2427a20e0a80
+Subproject commit a667d29089ab286d2970ae9fab31bca44b7baf00
diff --git a/README.md b/README.md
index 5188c318b..0086cf109 100644
--- a/README.md
+++ b/README.md
@@ -116,7 +116,7 @@ make build
 
 ```shell
 make dev
-mpirun --mca btl_vader_single_copy_mechanism none -n 6 python3 -m pace.run /examples/configs/baroclinic_c12.yaml
+mpirun --mca btl_vader_single_copy_mechanism none -n 6 python -m pace.run /examples/configs/baroclinic_c12.yaml
 ```
 
 ## History
diff --git a/constraints.txt b/constraints.txt
index 110d3ebf1..3f6a9431a 100644
--- a/constraints.txt
+++ b/constraints.txt
@@ -1,114 +1,116 @@
 #
-# This file is autogenerated by pip-compile with Python 3.8
+# This file is autogenerated by pip-compile with Python 3.11
 # by the following command:
 #
 #    pip-compile --output-file=constraints.txt requirements_dev.txt requirements_docs.txt requirements_lint.txt
 #
-aenum==3.1.11
+aenum==3.1.15
     # via dace
-aiohttp==3.7.4.post0
+aiohttp==3.9.5
     # via gcsfs
+aiosignal==1.3.1
+    # via aiohttp
 alabaster==0.7.12
     # via sphinx
 appdirs==1.4.4
     # via fv3config
 asciitree==0.3.3
     # via zarr
-asttokens==2.0.5
+asttokens==2.4.1
     # via
     #   devtools
     #   stack-data
 astunparse==1.6.3
-    # via
-    #   dace
-    #   gt4py
-async-timeout==3.0.1
-    # via aiohttp
-attrs==22.1.0
+    # via dace
+attrs==23.2.0
     # via
     #   aiohttp
     #   gt4py
     #   jsonschema
-    #   pytest
+    #   pytest-subtests
+    #   referencing
 babel==2.9.1
     # via sphinx
-backcall==0.2.0
-    # via ipython
 backports-entry-points-selectable==1.1.1
     # via virtualenv
-black==22.3.0
+black==24.4.0
     # via gt4py
-boltons==21.0.0
+boltons==24.0.0
     # via gt4py
 cached-property==1.5.2
     # via gt4py
-cachetools==4.2.2
+cachetools==5.3.3
     # via google-auth
-certifi==2021.5.30
+certifi==2024.2.2
     # via
     #   netcdf4
     #   requests
 cfgv==3.3.1
     # via pre-commit
-cftime==1.5.0
+cftime==1.6.3
     # via
     #   -r requirements_dev.txt
     #   ndsl
     #   netcdf4
-chardet==4.0.0
-    # via aiohttp
-charset-normalizer==2.0.4
+charset-normalizer==3.3.2
     # via requests
-click==8.0.1
+click==8.1.7
     # via
     #   black
+    #   dask
     #   gt4py
-cloudpickle==2.0.0
+cloudpickle==3.0.0
     # via dask
-cmake==3.26.4
-    # via gt4py
+cmake==3.29.2
+    # via
+    #   dace
+    #   gt4py
+comm==0.2.2
+    # via ipykernel
 commonmark==0.9.1
     # via recommonmark
-coverage==5.5
+contourpy==1.3.0
+    # via matplotlib
+coverage==7.5.0
     # via pytest-cov
-cytoolz==0.12.1
+cycler==0.12.1
+    # via matplotlib
+cytoolz==0.12.3
     # via gt4py
-dace
-    # via ndsl
-dacite==1.6.0
+dacite==1.8.1
     # via
     #   fv3config
     #   pace
-dask==2021.12.0
+dask==2024.4.2
     # via
     #   -r requirements_dev.txt
     #   ndsl
-debugpy==1.6.3
+debugpy==1.8.1
     # via ipykernel
-decorator==5.0.9
+decorator==5.1.1
     # via
     #   gcsfs
     #   ipython
-deepdiff==6.2.1
+deepdiff==7.0.1
     # via gt4py
-devtools==0.8.0
+devtools==0.12.2
     # via gt4py
-dill==0.3.5.1
+dill==0.3.8
     # via dace
 distlib==0.3.2
     # via virtualenv
+distro==1.9.0
+    # via scikit-build
 docutils==0.16
     # via
     #   recommonmark
     #   sphinx
     #   sphinx-rtd-theme
-entrypoints==0.4
-    # via jupyter-client
-executing==0.8.2
+executing==2.0.1
     # via
     #   devtools
     #   stack-data
-f90nml==1.3.1
+f90nml==1.4.4
     # via
     #   -r requirements_dev.txt
     #   fv3config
@@ -117,19 +119,25 @@ f90nml==1.3.1
     #   pyshield
 factory-boy==3.3.0
     # via gt4py
-faker==24.3.0
+faker==24.11.0
     # via factory-boy
-fasteners==0.16.3
+fasteners==0.19
     # via zarr
-fastjsonschema==2.16.2
+fastjsonschema==2.19.1
     # via nbformat
 filelock==3.0.12
     # via virtualenv
+fonttools==4.53.1
+    # via matplotlib
 fparser==0.1.4
     # via dace
-frozendict==2.3.4
+frozendict==2.4.2
     # via gt4py
-fsspec==2021.7.0
+frozenlist==1.4.1
+    # via
+    #   aiohttp
+    #   aiosignal
+fsspec==2024.3.1
     # via
     #   dask
     #   fv3config
@@ -137,70 +145,92 @@ fsspec==2021.7.0
     #   ndsl
 fv3config==0.9.0
     # via -r requirements_dev.txt
-gcsfs==2021.7.0
+gcsfs==2024.3.1
     # via fv3config
-google-auth==2.0.1
+google-api-core==2.18.0
+    # via
+    #   google-cloud-core
+    #   google-cloud-storage
+google-auth==2.29.0
     # via
     #   gcsfs
+    #   google-api-core
     #   google-auth-oauthlib
-google-auth-oauthlib==0.4.5
+    #   google-cloud-core
+    #   google-cloud-storage
+google-auth-oauthlib==1.2.0
+    # via gcsfs
+google-cloud-core==2.4.1
+    # via google-cloud-storage
+google-cloud-storage==2.16.0
     # via gcsfs
-gprof2dot==2021.2.21
+google-crc32c==1.5.0
+    # via
+    #   google-cloud-storage
+    #   google-resumable-media
+google-resumable-media==2.7.0
+    # via google-cloud-storage
+googleapis-common-protos==1.63.0
+    # via google-api-core
+gprof2dot==2022.7.29
     # via pytest-profiling
-gridtools-cpp==2.3.2
+gridtools-cpp==2.3.4
     # via gt4py
-gt4py
+h5netcdf==1.3.0
     # via ndsl
-h5netcdf==1.1.0
-    # via ndsl
-h5py==3.10.0
+h5py==3.11.0
     # via h5netcdf
 identify==2.2.13
     # via pre-commit
-idna==3.2
+idna==3.7
     # via
     #   requests
     #   yarl
 imagesize==1.2.0
     # via sphinx
-importlib-resources==5.10.0
-    # via
-    #   gt4py
-    #   jsonschema
-iniconfig==1.1.1
+importlib-metadata==7.1.0
+    # via dask
+iniconfig==2.0.0
     # via pytest
-ipykernel==6.16.2
+ipykernel==6.29.4
     # via nbmake
-ipython==8.5.0
+ipython==8.23.0
     # via ipykernel
-jedi==0.18.1
+jedi==0.19.1
     # via ipython
-jinja2==3.0.1
+jinja2==3.1.3
     # via
     #   dace
     #   gt4py
     #   sphinx
-jsonschema==4.16.0
+jsonschema==4.21.1
     # via nbformat
-jupyter-client==7.4.4
+jsonschema-specifications==2023.12.1
+    # via jsonschema
+jupyter-client==8.6.1
     # via
     #   ipykernel
     #   nbclient
-jupyter-core==4.11.2
+jupyter-core==5.7.2
     # via
+    #   ipykernel
     #   jupyter-client
     #   nbformat
-lark==1.1.5
+kiwisolver==1.4.7
+    # via matplotlib
+lark==1.1.9
     # via gt4py
-locket==0.2.1
+locket==1.0.0
     # via partd
-mako==1.1.6
+mako==1.3.3
     # via gt4py
-markupsafe==2.0.1
+markupsafe==2.1.5
     # via
     #   jinja2
     #   mako
-matplotlib-inline==0.1.6
+matplotlib==3.9.2
+    # via ndsl
+matplotlib-inline==0.1.7
     # via
     #   ipykernel
     #   ipython
@@ -209,49 +239,51 @@ mpi4py==3.1.5
     #   -r requirements_dev.txt
     #   ndsl
     #   pace
-mpmath==1.2.1
+mpmath==1.3.0
     # via sympy
-multidict==5.1.0
+multidict==6.0.5
     # via
     #   aiohttp
     #   yarl
-mypy-extensions==0.4.3
+mypy-extensions==1.0.0
     # via black
-nanobind==1.8.0
+nanobind==1.9.2
     # via gt4py
 nbclient==0.6.8
     # via nbmake
-nbformat==5.7.0
+nbformat==5.10.4
     # via
     #   nbclient
     #   nbmake
-nbmake==1.3.4
+nbmake==1.5.3
     # via -r requirements_dev.txt
-nest-asyncio==1.5.6
+nest-asyncio==1.6.0
     # via
     #   ipykernel
-    #   jupyter-client
     #   nbclient
-netcdf4==1.6.4
+netcdf4==1.7.0
     # via
     #   -r requirements_dev.txt
     #   ndsl
     #   pace
-networkx==2.6.3
+networkx==3.3
     # via dace
-ninja==1.11.1
+ninja==1.11.1.1
     # via gt4py
 nodeenv==1.6.0
     # via pre-commit
-numcodecs==0.7.2
+numcodecs==0.12.1
     # via zarr
-numpy==1.23.3
+numpy==1.26.4
     # via
     #   -r requirements_dev.txt
     #   cftime
+    #   contourpy
     #   dace
     #   gt4py
     #   h5py
+    #   matplotlib
+    #   ndsl
     #   netcdf4
     #   numcodecs
     #   pace
@@ -261,73 +293,79 @@ numpy==1.23.3
     #   scipy
     #   xarray
     #   zarr
-oauthlib==3.1.1
+oauthlib==3.2.2
     # via requests-oauthlib
 ordered-set==4.1.0
     # via deepdiff
-packaging==21.0
+packaging==24.0
     # via
+    #   black
+    #   dace
     #   dask
     #   gt4py
     #   h5netcdf
     #   ipykernel
+    #   matplotlib
     #   pytest
+    #   scikit-build
     #   setuptools-scm
     #   sphinx
-pandas==1.3.2
+    #   xarray
+pandas==2.2.2
     # via xarray
-parso==0.8.3
+parso==0.8.4
     # via jedi
-partd==1.2.0
+partd==1.4.1
     # via dask
-pathspec==0.9.0
+pathspec==0.12.1
     # via black
-pexpect==4.8.0
+pexpect==4.9.0
     # via ipython
-pickleshare==0.7.5
-    # via ipython
-pkgutil-resolve-name==1.3.10
-    # via jsonschema
-platformdirs==3.9.1
+pillow==10.4.0
+    # via matplotlib
+platformdirs==2.6.2
     # via
     #   black
+    #   jupyter-core
     #   virtualenv
-pluggy==0.13.1
+pluggy==1.5.0
     # via pytest
 ply==3.11
     # via dace
 pre-commit==2.14.0
     # via -r requirements_lint.txt
-prompt-toolkit==3.0.31
+prompt-toolkit==3.0.43
     # via ipython
-psutil==5.9.3
+proto-plus==1.23.0
+    # via google-api-core
+protobuf==4.25.3
+    # via
+    #   google-api-core
+    #   googleapis-common-protos
+    #   proto-plus
+psutil==5.9.8
     # via ipykernel
 ptyprocess==0.7.0
     # via pexpect
 pure-eval==0.2.2
     # via stack-data
-py==1.10.0
-    # via pytest
-pyasn1==0.4.8
+pyasn1==0.6.0
     # via
     #   pyasn1-modules
     #   rsa
-pyasn1-modules==0.2.8
+pyasn1-modules==0.4.0
     # via google-auth
-pybind11==2.11.1
+pybind11==2.12.0
     # via gt4py
-pydantic==1.7.4
-    # via nbmake
-pygments==2.10.0
+pygments==2.17.2
     # via
+    #   devtools
     #   ipython
     #   nbmake
     #   sphinx
-pyparsing==2.4.7
-    # via packaging
-pyrsistent==0.18.1
-    # via jsonschema
-pytest==6.2.4
+pyparsing==3.1.4
+    # via matplotlib
+pytest==8.1.1
     # via
     #   -r requirements_dev.txt
     #   nbmake
@@ -336,26 +374,27 @@ pytest==6.2.4
     #   pytest-profiling
     #   pytest-regressions
     #   pytest-subtests
-pytest-cov==2.12.1
+pytest-cov==5.0.0
     # via -r requirements_dev.txt
-pytest-datadir==1.3.1
+pytest-datadir==1.5.0
     # via pytest-regressions
 pytest-profiling==1.7.0
     # via -r requirements_dev.txt
-pytest-regressions==2.2.0
+pytest-regressions==2.5.0
     # via -r requirements_dev.txt
-pytest-subtests==0.5.0
+pytest-subtests==0.12.1
     # via -r requirements_dev.txt
-python-dateutil==2.8.2
+python-dateutil==2.9.0.post0
     # via
     #   faker
     #   jupyter-client
+    #   matplotlib
     #   pandas
-pytz==2021.1
+pytz==2024.1
     # via
     #   babel
     #   pandas
-pyyaml==5.4.1
+pyyaml==6.0.1
     # via
     #   dace
     #   dask
@@ -363,22 +402,34 @@ pyyaml==5.4.1
     #   pace
     #   pre-commit
     #   pytest-regressions
-pyzmq==24.0.1
+pyzmq==26.0.2
     # via
     #   ipykernel
     #   jupyter-client
 recommonmark==0.7.1
     # via -r requirements_docs.txt
-requests==2.26.0
+referencing==0.35.0
+    # via
+    #   jsonschema
+    #   jsonschema-specifications
+requests==2.31.0
     # via
     #   gcsfs
+    #   google-api-core
+    #   google-cloud-storage
     #   requests-oauthlib
     #   sphinx
-requests-oauthlib==1.3.0
+requests-oauthlib==2.0.0
     # via google-auth-oauthlib
-rsa==4.7.2
+rpds-py==0.18.0
+    # via
+    #   jsonschema
+    #   referencing
+rsa==4.9
     # via google-auth
-scipy==1.10.1
+scikit-build==0.18.1
+    # via dace
+scipy==1.13.0
     # via
     #   -r requirements_dev.txt
     #   ndsl
@@ -388,7 +439,6 @@ six==1.16.0
     # via
     #   asttokens
     #   astunparse
-    #   fasteners
     #   pytest-profiling
     #   python-dateutil
     #   virtualenv
@@ -419,32 +469,26 @@ sphinxcontrib-qthelp==1.0.3
     # via sphinx
 sphinxcontrib-serializinghtml==1.1.5
     # via sphinx
-stack-data==0.5.1
+stack-data==0.6.3
     # via ipython
-sympy==1.9
+sympy==1.12
     # via dace
 tabulate==0.9.0
     # via gt4py
 toml==0.10.2
-    # via
-    #   pre-commit
-    #   pytest
-    #   pytest-cov
-tomli==1.2.1
-    # via
-    #   black
-    #   setuptools-scm
-toolz==0.11.1
+    # via pre-commit
+toolz==0.12.1
     # via
     #   cytoolz
     #   dask
     #   partd
-tornado==6.2
+tornado==6.4
     # via
     #   ipykernel
     #   jupyter-client
-traitlets==5.5.0
+traitlets==5.14.3
     # via
+    #   comm
     #   ipykernel
     #   ipython
     #   jupyter-client
@@ -452,40 +496,42 @@ traitlets==5.5.0
     #   matplotlib-inline
     #   nbclient
     #   nbformat
-typing-extensions==4.3.0
+typing-extensions==4.12.2
     # via
-    #   aiohttp
-    #   black
-    #   faker
     #   gt4py
+    #   ipython
     #   setuptools-scm
-urllib3==1.26.6
+tzdata==2024.1
+    # via pandas
+urllib3==2.2.1
     # via requests
 virtualenv==20.7.2
     # via pre-commit
-wcwidth==0.2.5
+wcwidth==0.2.13
     # via prompt-toolkit
-websockets==10.3
+websockets==12.0
     # via dace
-wheel==0.37.0
-    # via astunparse
-xarray==0.19.0
+wheel==0.43.0
+    # via
+    #   astunparse
+    #   scikit-build
+xarray==2024.3.0
     # via
     #   -r requirements_dev.txt
     #   ndsl
     #   pace
     #   pyfv3
     #   pyshield
-xxhash==2.0.2
+xxhash==3.0.0
     # via gt4py
-yarl==1.6.3
+yarl==1.9.4
     # via aiohttp
-zarr==2.9.2
+zarr==2.18.2
     # via
     #   -r requirements_dev.txt
     #   pace
-zipp==3.8.0
-    # via importlib-resources
+zipp==3.18.1
+    # via importlib-metadata
 
 # The following packages are considered to be unsafe in a requirements file:
 # setuptools
diff --git a/examples/build_scripts/build_gaea_c5_gcc.sh b/examples/build_scripts/build_gaea_c5_gcc.sh
new file mode 100644
index 000000000..87bac83a9
--- /dev/null
+++ b/examples/build_scripts/build_gaea_c5_gcc.sh
@@ -0,0 +1,36 @@
+#!/usr/bin/env bash
+
+# Example bash script to install Pace to run bare-metal on Gaea's c4 cluster
+
+set -e -x
+
+# module load necessary system software
+module rm PrgEnv-intel
+module load PrgEnv-gnu
+module rm gcc
+module load gcc/12.2.0
+module load boost/1.79.0
+module load python/3.11.7
+
+export CC=`which gcc`
+export CXX=`which g++`
+export MPICC=`which mpicc`
+export MPICXX=`which mpicxx`
+export DACE_compiler_cpu_executable=${CXX}
+export GT4PY_EXTRA_COMPILE_ARGS="-O3 -ffast-math -fprefetch-loop-arrays -funroll-all-loops"
+export OPENMP_CPPFLAGS="-fopenmp -fopenmp-simd"
+export OPENMP_LDFLAGS="-fopenmp -fopenmp-simd"
+
+# clone Pace and update submodules
+git clone --recursive https://github.com/NOAA-GFDL/pace
+cd pace
+
+# create a conda environment for pace
+conda create -y --name my_name python=3.11.7
+
+# enter the environment and update it
+conda activate my_name
+pip3 install --upgrade pip setuptools wheel
+
+# install the Pace dependencies, GT4Py, and Pace
+pip3 install -r requirements_dev.txt -c constraints.txt
diff --git a/examples/build_scripts/build_ppan_intel.sh b/examples/build_scripts/build_ppan_intel.sh
new file mode 100644
index 000000000..c14dad781
--- /dev/null
+++ b/examples/build_scripts/build_ppan_intel.sh
@@ -0,0 +1,53 @@
+#!/bin/bash
+
+set -e
+
+INSTALL_PREFIX=$1
+ENVIRONMENT_NAME=$2
+
+PACE_DIR=$(pwd)/../..
+FV3NET_DIR=$INSTALL_PREFIX/fv3net
+
+# module load necessary system software
+module load conda
+module load intel_compilers/2021.3.0
+
+export CC=`which icpx`
+export CXX=`which icpx`
+export MPICC=`which mpiicx`
+export MPICXX=`which mpiicpx`
+export DACE_compiler_cpu_executable=${CXX}
+export GT4PY_EXTRA_COMPILE_ARGS="-O3 -funroll-loops -qopt-prefetch=2 -align"
+export OPENMP_CPPFLAGS="-qopenmp -qopenmp-simd"
+export OPENMP_LDFLAGS="-qopenmp -qopenmp-simd"
+
+CONDA_PREFIX=$INSTALL_PREFIX/conda
+conda config --add pkgs_dirs $CONDA_PREFIX/pkgs
+conda config --add envs_dirs $CONDA_PREFIX/envs
+
+# enter the pace directory
+cd $PACE_DIR
+
+# create a conda environment with cartopy and its dependencies installed
+conda create -c conda-forge -y --name $ENVIRONMENT_NAME python=3.11.7 matplotlib==3.5.2 cartopy==0.18.0
+
+# enter the environment and update it
+conda activate $ENVIRONMENT_NAME
+pip3 install --upgrade --no-cache-dir pip setuptools wheel
+
+# install the Pace dependencies, GT4Py, and Pace
+pip3 install --no-cache-dir -r requirements_dev.txt -c constraints.txt
+
+# clone fv3net
+git clone https://github.com/ai2cm/fv3net.git $FV3NET_DIR
+
+# install jupyter and ipyparallel
+pip3 install --no-cache-dir \
+       ipyparallel==8.4.1 \
+       jupyterlab==3.4.4 \
+       jupyterlab_code_formatter==1.5.2 \
+       isort==5.10.1 \
+       black==22.3.0
+
+# install vcm
+python3 -m pip install $FV3NET_DIR/external/vcm
diff --git a/examples/configs/baroclinic_c12_orch_cpu.yaml b/examples/configs/baroclinic_c12_orch_cpu.yaml
index d70699a1d..6e29da06d 100644
--- a/examples/configs/baroclinic_c12_orch_cpu.yaml
+++ b/examples/configs/baroclinic_c12_orch_cpu.yaml
@@ -14,7 +14,7 @@ performance_config:
 nx_tile: 12
 nz: 79
 dt_atmos: 225
-minutes: 5
+seconds: 225
 layout:
   - 1
   - 1
@@ -73,3 +73,8 @@ physics_config:
   hydrostatic: false
   nwat: 6
   do_qa: true
+
+grid_config:
+  type:  generated
+  config:
+    eta_file: 'tests/main/input/eta79.nc'
diff --git a/examples/generate_eta_files.py b/examples/generate_eta_files.py
new file mode 100755
index 000000000..1fb4d5ee2
--- /dev/null
+++ b/examples/generate_eta_files.py
@@ -0,0 +1,399 @@
+import numpy as np
+import xarray as xr
+
+
+"""
+This notebook uses the python xarray module
+to create an eta_file containing ak and bk coefficients
+for km=79 and km=91.  The coefficients are written out to
+eta79.nc and eta91.nc netcdf files respectively
+
+To run this script:  `python3 ./generate_eta_files.py`
+"""
+
+# km = 79
+ak = xr.DataArray(
+    dims=["km1"],
+    attrs=dict(units="Pa", _FillValue=False),
+    data=np.array(
+        [
+            3.000000e02,
+            6.467159e02,
+            1.045222e03,
+            1.469188e03,
+            1.897829e03,
+            2.325385e03,
+            2.754396e03,
+            3.191294e03,
+            3.648332e03,
+            4.135675e03,
+            4.668282e03,
+            5.247940e03,
+            5.876271e03,
+            6.554716e03,
+            7.284521e03,
+            8.066738e03,
+            8.902188e03,
+            9.791482e03,
+            1.073499e04,
+            1.162625e04,
+            1.237212e04,
+            1.299041e04,
+            1.349629e04,
+            1.390277e04,
+            1.422098e04,
+            1.446058e04,
+            1.462993e04,
+            1.473633e04,
+            1.478617e04,
+            1.478511e04,
+            1.473812e04,
+            1.464966e04,
+            1.452370e04,
+            1.436382e04,
+            1.417324e04,
+            1.395491e04,
+            1.371148e04,
+            1.344540e04,
+            1.315890e04,
+            1.285407e04,
+            1.253280e04,
+            1.219685e04,
+            1.184788e04,
+            1.148739e04,
+            1.111682e04,
+            1.073748e04,
+            1.035062e04,
+            9.957395e03,
+            9.558875e03,
+            9.156069e03,
+            8.749922e03,
+            8.341315e03,
+            7.931065e03,
+            7.519942e03,
+            7.108648e03,
+            6.698281e03,
+            6.290007e03,
+            5.884984e03,
+            5.484372e03,
+            5.089319e03,
+            4.700960e03,
+            4.320421e03,
+            3.948807e03,
+            3.587201e03,
+            3.236666e03,
+            2.898237e03,
+            2.572912e03,
+            2.261667e03,
+            1.965424e03,
+            1.685079e03,
+            1.421479e03,
+            1.175419e03,
+            9.476516e02,
+            7.388688e02,
+            5.497130e02,
+            3.807626e02,
+            2.325417e02,
+            1.054810e02,
+            -8.381903e-04,
+            0.000000e00,
+        ]
+    ),
+)
+bk = xr.DataArray(
+    dims=["km1"],
+    attrs=dict(units="None", _FillValue=False),
+    data=np.array(
+        [
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.00106595,
+            0.00412866,
+            0.00900663,
+            0.01554263,
+            0.02359921,
+            0.03305481,
+            0.0438012,
+            0.05574095,
+            0.06878554,
+            0.08285347,
+            0.09786981,
+            0.1137643,
+            0.130471,
+            0.1479275,
+            0.1660746,
+            0.1848558,
+            0.2042166,
+            0.2241053,
+            0.2444716,
+            0.2652672,
+            0.286445,
+            0.3079604,
+            0.3297701,
+            0.351832,
+            0.3741062,
+            0.3965532,
+            0.4191364,
+            0.4418194,
+            0.4645682,
+            0.48735,
+            0.5101338,
+            0.5328897,
+            0.5555894,
+            0.5782067,
+            0.6007158,
+            0.6230936,
+            0.6452944,
+            0.6672683,
+            0.6889648,
+            0.7103333,
+            0.7313231,
+            0.7518838,
+            0.7719651,
+            0.7915173,
+            0.8104913,
+            0.828839,
+            0.846513,
+            0.8634676,
+            0.8796583,
+            0.8950421,
+            0.9095779,
+            0.9232264,
+            0.9359506,
+            0.9477157,
+            0.9584892,
+            0.9682413,
+            0.9769447,
+            0.9845753,
+            0.9911126,
+            0.9965372,
+            1.0,
+        ]
+    ),
+)
+coefficients = xr.Dataset(data_vars={"ak": ak, "bk": bk})
+coefficients.to_netcdf("eta79.nc")
+
+
+# km = 91
+ak = xr.DataArray(
+    dims=["km1"],
+    attrs=dict(units="Pa", _FillValue=False),
+    data=np.array(
+        [
+            1.00000000e00,
+            1.75000000e00,
+            2.75000000e00,
+            4.09999990e00,
+            5.98951054e00,
+            8.62932968e00,
+            1.22572632e01,
+            1.71510906e01,
+            2.36545467e01,
+            3.21627693e01,
+            4.31310921e01,
+            5.71100426e01,
+            7.46595764e01,
+            9.64470978e01,
+            1.23169769e02,
+            1.55601318e02,
+            1.94594009e02,
+            2.41047531e02,
+            2.95873840e02,
+            3.60046967e02,
+            4.34604828e02,
+            5.20628723e02,
+            6.19154846e02,
+            7.31296021e02,
+            8.58240906e02,
+            1.00106561e03,
+            1.16092859e03,
+            1.33903992e03,
+            1.53650012e03,
+            1.75448938e03,
+            1.99417834e03,
+            2.25667407e03,
+            2.54317139e03,
+            2.85476392e03,
+            3.19258569e03,
+            3.55775366e03,
+            3.95135107e03,
+            4.37428662e03,
+            4.82711084e03,
+            5.31022168e03,
+            5.82387793e03,
+            6.36904248e03,
+            6.94875244e03,
+            7.56691992e03,
+            8.22634277e03,
+            8.93120996e03,
+            9.68446191e03,
+            1.04822725e04,
+            1.13182793e04,
+            1.21840771e04,
+            1.30655674e04,
+            1.39532207e04,
+            1.48307285e04,
+            1.56872617e04,
+            1.65080645e04,
+            1.72810996e04,
+            1.79942988e04,
+            1.86363223e04,
+            1.91961797e04,
+            1.96640723e04,
+            2.00301914e04,
+            2.02853691e04,
+            2.04215254e04,
+            2.04300684e04,
+            2.03028730e04,
+            2.00323711e04,
+            1.96110664e04,
+            1.90313848e04,
+            1.82866426e04,
+            1.73777930e04,
+            1.63224639e04,
+            1.51444033e04,
+            1.38725674e04,
+            1.25404785e04,
+            1.11834170e04,
+            9.83532715e03,
+            8.52630664e03,
+            7.28224512e03,
+            6.12326074e03,
+            5.06350684e03,
+            4.11124902e03,
+            3.27000122e03,
+            2.53922729e03,
+            1.91530762e03,
+            1.39244995e03,
+            9.63134766e02,
+            6.20599365e02,
+            3.57989502e02,
+            1.69421387e02,
+            5.10314941e01,
+            2.48413086e00,
+            0.00000000e00,
+        ]
+    ),
+)
+bk = xr.DataArray(
+    dims=["km1"],
+    attrs=dict(units="None", _FillValue=False),
+    data=np.array(
+        [
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            0.00000000e00,
+            3.50123992e-06,
+            2.81484008e-05,
+            9.38666999e-05,
+            2.28561999e-04,
+            5.12343016e-04,
+            1.04712998e-03,
+            1.95625005e-03,
+            3.42317997e-03,
+            5.58632007e-03,
+            8.65428988e-03,
+            1.27844000e-02,
+            1.81719996e-02,
+            2.49934997e-02,
+            3.34198996e-02,
+            4.36249003e-02,
+            5.57769015e-02,
+            7.00351968e-02,
+            8.65636021e-02,
+            1.05520003e-01,
+            1.27051994e-01,
+            1.51319996e-01,
+            1.78477004e-01,
+            2.08675995e-01,
+            2.42069006e-01,
+            2.78813988e-01,
+            3.19043010e-01,
+            3.62558991e-01,
+            4.08596009e-01,
+            4.56384987e-01,
+            5.05111992e-01,
+            5.53902984e-01,
+            6.01903021e-01,
+            6.48333013e-01,
+            6.92534983e-01,
+            7.33981013e-01,
+            7.72292018e-01,
+            8.07236016e-01,
+            8.38724971e-01,
+            8.66774976e-01,
+            8.91497016e-01,
+            9.13065016e-01,
+            9.31702971e-01,
+            9.47658002e-01,
+            9.61175978e-01,
+            9.72495019e-01,
+            9.81844008e-01,
+            9.89410996e-01,
+            9.95342016e-01,
+            1.00000000e00,
+        ]
+    ),
+)
+coefficients = xr.Dataset(data_vars={"ak": ak, "bk": bk})
+coefficients.to_netcdf("eta91.nc")
+
+# km =
diff --git a/examples/notebooks/baroclinic_c20.yaml b/examples/notebooks/baroclinic_c20.yaml
new file mode 100644
index 000000000..46db5fd04
--- /dev/null
+++ b/examples/notebooks/baroclinic_c20.yaml
@@ -0,0 +1,100 @@
+stencil_config:
+  compilation_config:
+    backend: numpy
+    rebuild: true
+    validate_args: true
+    format_source: false
+    device_sync: false
+initialization:
+  type: analytic
+  config:
+    case: baroclinic
+grid_config:
+  type: generated
+  config:
+    eta_file: 'eta79.nc'
+performance_config:
+  collect_performance: false
+  experiment_name: c20_baroclinic
+nx_tile: 20
+nz: 79
+dt_atmos: 225
+minutes: 15
+layout:
+  - 1
+  - 1
+diagnostics_config:
+  path: output.zarr
+  names:
+    - u
+    - v
+    - ua
+    - va
+    - pt
+    - delp
+    - qvapor
+    - qliquid
+    - qice
+    - qrain
+    - qsnow
+    - qgraupel
+    - qcld
+  z_select:
+    - level: 30
+      names:
+        - pt
+dycore_config:
+  a_imp: 1.0
+  beta: 0.
+  consv_te: 0.
+  d2_bg: 0.
+  d2_bg_k1: 0.2
+  d2_bg_k2: 0.1
+  d4_bg: 0.15
+  d_con: 1.0
+  d_ext: 0.0
+  dddmp: 0.5
+  delt_max: 0.002
+  do_sat_adj: true
+  do_vort_damp: true
+  fill: true
+  hord_dp: 6
+  hord_mt: 6
+  hord_tm: 6
+  hord_tr: 8
+  hord_vt: 6
+  hydrostatic: false
+  k_split: 1
+  ke_bg: 0.
+  kord_mt: 9
+  kord_tm: -9
+  kord_tr: 9
+  kord_wz: 9
+  n_split: 1
+  nord: 3
+  nwat: 6
+  p_fac: 0.05
+  rf_cutoff: 3000.
+  rf_fast: true
+  tau: 10.
+  vtdm4: 0.06
+  z_tracer: true
+  do_qa: true
+  tau_i2s: 1000.
+  tau_g2v: 1200.
+  ql_gen: 0.001
+  ql_mlt: 0.002
+  qs_mlt: 0.000001
+  qi_lim: 1.0
+  dw_ocean: 0.1
+  dw_land: 0.15
+  icloud_f: 0
+  tau_l2v: 300.
+  tau_v2l: 90.
+  fv_sg_adj: 0
+  n_sponge: 48
+
+physics_config:
+  hydrostatic: false
+  nwat: 6
+  do_qa: true
diff --git a/examples/notebooks/baroclinic_example.ipynb b/examples/notebooks/baroclinic_example.ipynb
new file mode 100644
index 000000000..bdd0fbb23
--- /dev/null
+++ b/examples/notebooks/baroclinic_example.ipynb
@@ -0,0 +1,545 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "88a7130d",
+   "metadata": {},
+   "source": [
+    "# Running a baroclinic instability test case\n",
+    "\n",
+    "First we set up the mpi environment:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c712d4f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting 6 engines with <class 'ipyparallel.cluster.launcher.MPIEngineSetLauncher'>\n",
+      "100%|███████████████████████████████████████████████████████| 6/6 [00:05<00:00,  1.08engine/s]\n",
+      "%autopx enabled\n"
+     ]
+    }
+   ],
+   "source": [
+    "import ipyparallel as ipp\n",
+    "ntiles = 6\n",
+    "layout = (1, 1)\n",
+    "\n",
+    "num_ranks = ntiles * layout[0] * layout[1]\n",
+    "cluster = ipp.Cluster(engines=\"mpi\", n=num_ranks).start_and_connect_sync()\n",
+    "\n",
+    "# start executing cells on the workers in parallel from here on\n",
+    "%autopx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c115161",
+   "metadata": {},
+   "source": [
+    "Double-check the MPI setup:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3942150f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[stdout:1] I am MPI-rank 1 of a total of 6\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[stdout:5] I am MPI-rank 5 of a total of 6\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[stdout:3] I am MPI-rank 3 of a total of 6\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[stdout:2] I am MPI-rank 2 of a total of 6\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[stdout:0] I am MPI-rank 0 of a total of 6\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[stdout:4] I am MPI-rank 4 of a total of 6\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mpi4py import MPI\n",
+    "\n",
+    "mpi_comm = MPI.COMM_WORLD\n",
+    "mpi_size = mpi_comm.Get_size()\n",
+    "mpi_rank = mpi_comm.Get_rank()\n",
+    "\n",
+    "print(f\"I am MPI-rank {mpi_rank} of a total of {mpi_size}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "793a5c25",
+   "metadata": {},
+   "source": [
+    "Next we set up the model driver using the yaml config file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cb705ca6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "%px: 100%|███████████████████████████████████████████████████| 6/6 [03:10<00:00, 31.74s/tasks]\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%capture\n",
+    "\n",
+    "from pace import Driver, DriverConfig\n",
+    "import yaml\n",
+    "\n",
+    "config_file = \"baroclinic_c20.yaml\"\n",
+    "\n",
+    "with open(config_file, \"r\") as f:\n",
+    "    driver_config = DriverConfig.from_dict(yaml.safe_load(f))\n",
+    "driver = Driver(config=driver_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "474be0bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[stdout:0] (27, 27)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if mpi_rank == 0:\n",
+    "    print(driver.state.grid_data.lon_agrid.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87ac7255",
+   "metadata": {},
+   "source": [
+    "We can inspect the initial model state:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4337eed3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from cartopy import crs as ccrs\n",
+    "from fv3viz import pcolormesh_cube\n",
+    "from ndsl import CubedSphereCommunicator\n",
+    "\n",
+    "communicator = CubedSphereCommunicator.from_layout(\n",
+    "    comm=driver.comm,\n",
+    "    layout=driver.config.layout,\n",
+    ")\n",
+    "u_wind = communicator.gather(driver.state.dycore_state.u)\n",
+    "temperature = communicator.gather(driver.state.dycore_state.pt)\n",
+    "surface_pressure = communicator.gather(driver.state.dycore_state.ps)\n",
+    "grid_lon = communicator.gather(driver.state.grid_data.lon)\n",
+    "grid_lat = communicator.gather(driver.state.grid_data.lat)\n",
+    "a_lon = communicator.gather(driver.state.grid_data.lon_agrid)\n",
+    "a_lat = communicator.gather(driver.state.grid_data.lat_agrid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "60b1e63b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[output:0]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "engine": 0
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if mpi_rank == 0:\n",
+    "    u_mean = 0.5 * (u_wind.data[:,:,:-1, :] + u_wind.data[:,:, 1:, :])\n",
+    "    \n",
+    "    nx = temperature.shape[1]\n",
+    "    fig = plt.figure(figsize=(12, 6))\n",
+    "    ax = fig.add_subplot(111, projection=ccrs.Robinson())\n",
+    "    f1 = pcolormesh_cube(\n",
+    "        grid_lat.data * 180./np.pi,\n",
+    "        grid_lon.data * 180./np.pi,\n",
+    "        temperature.data[:, :, :, -2],\n",
+    "        cmap=\"viridis\",\n",
+    "    )\n",
+    "    plt.colorbar(f1, label=\"Temperature (K)\")\n",
+    "    ax.set_title(f\"850 hPa Temperature ({nx} x {nx} points per tile)\")\n",
+    "    \n",
+    "#     fig = plt.figure(figsize=(12, 6))\n",
+    "#     ax = fig.add_subplot(111, projection=ccrs.Robinson())\n",
+    "#     f1 = pcolormesh_cube(\n",
+    "#         grid_lat.data * 180./np.pi,\n",
+    "#         grid_lon.data * 180./np.pi,\n",
+    "#         surface_pressure.data[:, :, :]/100.,\n",
+    "#         cmap=\"viridis\",\n",
+    "#     )\n",
+    "#     plt.colorbar(f1, label=\"Surface Pressure (hPa)\")\n",
+    "#     ax.set_title(f\"Surface Pressure ({nx} x {nx} points per tile)\")\n",
+    "    \n",
+    "    \n",
+    "#     fig = plt.figure(figsize=(12, 6))\n",
+    "#     ax = fig.add_subplot(111, projection=ccrs.Robinson())\n",
+    "#     f1 = pcolormesh_cube(\n",
+    "#         grid_lat.data * 180./np.pi,\n",
+    "#         grid_lon.data * 180./np.pi,\n",
+    "#         u_mean[:,:,:,65],\n",
+    "#         cmap=\"bwr\",\n",
+    "#     )\n",
+    "#     plt.colorbar(f1, label=\"U wind (m/s)\")\n",
+    "#     ax.set_title(f\"U Wind ({nx} x {nx} points per tile)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2abf1e02",
+   "metadata": {},
+   "source": [
+    "And then we can run the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c962da72",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "%px: 100%|███████████████████████████████████████████████████| 6/6 [00:10<00:00,  1.77s/tasks]\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    driver.step_all()\n",
+    "finally:\n",
+    "    driver.cleanup()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30a7102a",
+   "metadata": {},
+   "source": [
+    "Now let's investigate the model state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "65213533",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "u_wind = communicator.gather(driver.state.dycore_state.u)\n",
+    "temperature = communicator.gather(driver.state.dycore_state.pt)\n",
+    "surface_pressure = communicator.gather(driver.state.dycore_state.ps)\n",
+    "grid_lon = communicator.gather(driver.state.grid_data.lon)\n",
+    "grid_lat = communicator.gather(driver.state.grid_data.lat)\n",
+    "a_lon = communicator.gather(driver.state.grid_data.lon_agrid)\n",
+    "a_lat = communicator.gather(driver.state.grid_data.lat_agrid)\n",
+    "qvap = communicator.gather(driver.state.dycore_state.qvapor)\n",
+    "qcld = communicator.gather(driver.state.dycore_state.qcld)\n",
+    "qrain = communicator.gather(driver.state.dycore_state.qrain)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "09062a68",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[output:0]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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++UfxsykpKfj9999x4MABVd9H6tjCXt37778fERER/h7Z1atXIzs7G7feemuA3jweDzp27Fjpt8OXYTa//vorDhw4gNtuuw3nzp3z16ugoAC9evXCxo0bKw1fTk1NRVFREQoLC02vH0EQ4YGPM+cFVCzuInzJPeZl9+7dSEhIQHR0NO677z589tlnaNmyJTIzMxEVFYWUlJSA/WvWrOkf4ZKZmVlpNBH/N7+PXdBwTYIIMQ4cOICcnBykp6dLvn/69OmAv4WJHAAkJycDAOrXry+5XTxfyO1246KLLgrY1rx5cwDwz+/RWqfGjRtL7vfjjz/iqaeewubNmysZxZycHH8dWSFVjwMHDmDXrl2yw9rE30UKTmHR4szMTAwaNAjJyclYvnw5PB6P/z0+0ZW6GRUXFwfsE4zJkyfjo48+wtatWzFjxgy0bNlS8TNiZs2a5R/mOXDgwID3YmNjDddRjbb++ecf1KlTB4mJiQH78cNE1SRq4t8An4RLzY0T88wzz2Dw4MFo3rw5Wrdujf79++OOO+5AmzZtFD8LVMy7FJKQkIDatWsH/HYA+IcCiRGvDBcREYF69eqpOrYR+HoJGyDE5OTkBDRo8Lqn1TUJgnACYp/z1FNPYdq0aZX2u/jii/Hrr78iJycHy5cvx5133okNGzZYVEvzoCSPIEIMn8+H9PR0fPDBB5Lvi5MTYQKhZrtSgsKiTlIJwMGDB9GrVy9ccskleOmll1C/fn1ERUXh66+/xpw5c1QteiJnLqWe8SZXD5/Phz59+uCRRx6R/AyfhMhRvXr1oMlDTk4OBgwYgOzsbGzatAl16tQJeL927doAgJMnT1b67MmTJ1GtWjXJHjQxhw4d8hv13bt3K+4vZtGiRfjPf/6D++67D48//nil92vXro1169aB47iA887XW/y97MSI1rt164aDBw9ixYoV+O677/D2229jzpw5mDdvHu655x7DdeN1/d5776FWrVqV3hf3vkZHRwfMmTULvl6zZs1Cu3btJPdJSEgI+Pv8+fOIi4tTleATBEEAgNeEOXl8eUePHg1oKJO7d0ZFRaFp06YAKhaR2rZtG1555RXccsstKC0tRXZ2dkBv3qlTp/zxulatWti6dWtAeadOnfK/ZyeU5BFEiNGkSROsWbMGXbt2tcRM+Xw+HDp0KCC54R/gzS8IwaJOX375JUpKSvDFF18E9LxIDVeTS+b4XgVxQFbT28PTpEkT5Ofno3fv3qo/I+SSSy7B4cOHJd8rLi7Gddddh/3792PNmjWSvWt169ZFWloafvnll0rvbd26VdZwC/H5fBg1ahSSkpIwceJEzJgxAzfffHPAimDBWLFiBe655x7cdNNNeOONNyT3adeuHd5++23s3bs34Hts2bLF/76aeippq2HDhlizZg3y8vICevP27dvnf58FwXqfqlWrhtGjR2P06NHIz89Ht27dMG3aNFVJ3oEDB9CzZ0//3/n5+Th58qS/Z7RJkyYAgPT0dN2ak8NIjxpfr6SkJNX1Onz4cKWFeAiCIOwiKSlJ13PyfD4fSkpK0L59e0RGRmLt2rUYMmQIgIoF244cOeKfBtO5c2c899xzOH36tH800+rVq5GUlKRrBA1LaE4eQYQYw4YNg9frxfTp0yu9V15ejuzsbObHfP311/3/5jgOr7/+OiIjI9GrVy9mdeJ7W4S9Kzk5OVi4cGGlfePj4yXL5I2pcN5cQUFBpYd4B2PYsGHYvHkzvv3220rvZWdno7y8POjnO3fujD179lQayuj1enHLLbdg8+bNWLZsWcA8STFDhgzBypUrcfToUf+2tWvXYv/+/Rg6dKjid3jppZfw008/Yf78+Zg+fTq6dOmC+++/H2fPnlX87MaNGzF8+HB069YNH3zwgWyv0eDBgxEZGYk333zTv43jOMybNw9169ZFly5dFI8FKGtr4MCB8Hq9AfsBwJw5c+ByuTBgwABVx1FCTlPnzp0L+DshIQFNmzaVndshZv78+SgrK/P/PXfuXJSXl/vr3a9fPyQlJWHGjBkB+/EIH/Wglfj4eADQFRPat2+PJk2aYPbs2cjPz1dVrx07dqi+7gRBEMCFnjzWL7VMmTIFGzduxN9//43du3djypQpWL9+PUaMGIHk5GTcfffdePjhh7Fu3Tps374do0ePRufOndGpUycAQN++fdGyZUvccccd+O233/Dtt9/i8ccfx7hx41SNujET6skjiBCje/fuGDt2LGbOnIlff/0Vffv2RWRkJA4cOIBly5bhlVdewc0338zseDExMVi1ahXuvPNOdOzYEd988w2++uorPPbYY/5hmCzq1LdvX0RFReG6667D2LFjkZ+fj7feegvp6emVhi62b98ec+fOxbPPPoumTZsiPT0d11xzDfr27YsGDRrg7rvvxuTJk+HxePDOO+8gLS0NR44cUfV9J0+ejC+++ALXXnstRo0ahfbt26OgoAC7d+/G8uXL8ffffwc8SkDM4MGDMX36dGzYsAF9+/b1b/+///s/fPHFF7juuuuQlZWF999/P+Bzt99+u//fjz32GJYtW4aePXtiwoQJyM/Px6xZs3DppZdi9OjRQeu/d+9ePPHEExg1apR/aehFixahXbt2eOCBB4I+Z++ff/7B9ddfD5fLhZtvvhnLli0LeL9Nmzb+uWj16tXDxIkTMWvWLJSVlaFDhw74/PPPsWnTJnzwwQeyQySFqNHWddddh549e2Lq1Kn4+++/0bZtW3z33XdYsWIFJk6c6E/sjSKnqZYtW6JHjx7+58D98ssvWL58ecCCMcEoLS1Fr169/I8nefPNN3HVVVfh+uuvB1DR0jx37lzccccduPzyyzF8+HC/Xr/66it07dq1UoKr5TsBwNSpUzF8+HBERkbiuuuu8yd/wXC73Xj77bcxYMAAtGrVCqNHj0bdunVx/PhxrFu3DklJSfjyyy/9+2/fvh1ZWVkYPHiwrroSBFE18XEu+DjGj1DQUN7p06cxcuRInDx5EsnJyWjTpg2+/fZb/yNy5syZA7fbjSFDhqCkpAT9+vULaNz0eDxYuXIl7r//fnTu3Bnx8fG48847Ax4xZRt2LetJEIQ6xI9Q4Jk/fz7Xvn17LjY2lktMTOQuvfRS7pFHHuFOnDjh36dhw4aSy78D4MaNGxew7fDhwxwAbtasWf5td955JxcfH88dPHiQ69u3LxcXF8fVrFmTe+qppziv18u0ThzHcV988QXXpk0bLiYmhmvUqBH3wgsvcO+8806lZeAzMzO5QYMGcYmJiRyAgKXvt2/fznXs2JGLioryL3Ms9wgFuXrk5eVxU6ZM4Zo2bcpFRUVxNWrU4Lp06cLNnj07YDl8Odq0acPdfffdAdu6d+/OAZB9idmzZ4//nKekpHAjRozgMjMzgx63vLyc69ChA1evXr2AxwNwHMe98sorHABu6dKlsp/nl9GXez311FMB+3u9Xm7GjBlcw4YNuaioKK5Vq1bc+++/r3B2KtCirby8PO6hhx7i6tSpw0VGRnLNmjXjZs2aFbC0P8fJP0Jh27Ztkt9T+LgQOU09++yz3JVXXsmlpKRwsbGx3CWXXMI999xzijrgj71hwwZuzJgxXGpqKpeQkMCNGDGCO3fuXKX9161bx/Xr149LTk7mYmJiuCZNmnCjRo3ifvnll0rnTAvTp0/n6taty7nd7oDfgNIjFHh27tzJ3XTTTVz16tW56OhormHDhtywYcO4tWvXBuz3n//8h2vQoEGla0IQBCEF/wiFH/bU4X79px7T1w976uh6hEK44eI4HassEARRJRg1ahSWL18uOVyLkOe9997DuHHjcOTIkUpLLxMVhLu2Fi1ahNGjR2Pbtm2SD7YPJ0pKStCoUSM8+uijmDBhgt3VIQgiBMjNzUVycjI27KmLhES2s8fy83zo3vo4cnJydM3JCxdoTh5BEARjRowYgQYNGsguWkIQ4cTChQsRGRmJ++67z+6qEARBEP9Cc/IIgiAY43a7sWfPHrurQRCWcN9991GCRxCELrxww8u4z0n6oUlVD+rJIwiCIAiCIAiCCCNoTh5BEARBEARBEJbBz8lbu7sB4hnPySvI86HXpUdoTp7dFSAIgiAIgiAIgiDYQXPyCIIgCIIgCIKwHK0PL1dbJkFJHhHGHD16VPUDsAmCIIiqTYMGDVC/fn27q0EQBMEESvKIsCE/Px/r16/Hd999h9WrV+PAgQOoXbs2XK7watHJOZsHACgvLdNdRkRUpKE6JNdINPR5giAIJ8FxHE6ePIlmzZqhb9++6NOnD3r06IGEhAS7q0YQYY2Xc8PLMV5dk1YbAUBJHhHiHDp0CCtWrMCKFSvw008/oWHDhujTpw9mzpyJnj17Ijk52e4qMqOPeyi7wooNfv4Yk1pYymrfMrurQBCEg8nOzsa6deuwevVqTJw4EUeOHEGXLl0wePBgDB48GBdddJHdVSSIsMMHF3yMlwjxgbI8gFbXJEIMjuPw66+/4vPPP8fnn3+OvXv3omfPnhg8eDD69+8fdjdhpokdEXJQYkoQ9nHo0CGsWrUKK1aswLp169CiRQvccMMNuOGGG9CuXbuwGyVCEFbCr6751a6LEJ/oYVp2QZ4Xg9ocqvKra1KSRzgejuOwY8cOfPTRR1i2bBnOnTuHgQMH4oYbbsCAAQOQkpJidxWZQ8kdEapQYkqEI9nZ2fjmm2/w+eef4+uvv0b16tUxdOhQ3Hrrrbjsssso4SMIjfBJ3he7mpiS5F3f5iAleZTkEU7ljz/+wEcffYSPPvoIJ0+exI033ohbbrkFvXv3RnR0tN3VM0wfzy2VN3I+6ytCEOGES3nYz2rvUgsqQighjoGhcl1KSkqwZs0aLF26FJ999hlq166N4cOHY/jw4WjZsqXd1SOIkICSPPOhJI9wFMeOHcP777+PJUuW4MCBA7j22msxfPhwDBw4ELGxsZbXRzIRsxJK+pxDsOSBrpM+VCRkoUSoJClGsCImhtJ5LCoqwtdff42PPvoIK1euRPPmzXHrrbfi9ttvR7169eyuHkE4Fj7J++y3ZqYkeTe2PUBJHiV5hN0UFhbi008/xeLFi7Fhwwb06tULI0aMwODBg5GYqLyKo+2JWKhQVRKRcEkcnHi9wuXchhhGkp5Qjo+hlOwBFab1iy++wPvvv4+1a9eiR48eGDVqFG688UbExcXZXT2CcBSU5JkPJXmELXAch02bNmHx4sVYtmwZ6tevjzvvvBO333476tSp498vlA1KWMMiAaGEwXokrps7Ksr/b19ZuZW1IQzijpReIDvcrmOoJXsAcOLECbz//vtYvHgxjh49iqFDh2LUqFG46qqraP4eQeBCkvfJb81NSfKGtN1PSR4leYSVnDx5EosWLcLbb7+N7Oxs3HrrrRg1ahTat28ve+OjRC80EBvOcDOa4YJcYiCErp1zUXP9pAiHaxqKyR7Hcfjll1+wePFiLFmyBKmpqbjnnnswevRo1KpVy+7qEYRtUJJnPpTkEabj9XqxevVqzJ8/HytXrkTPnj1x77334rrrrlO9gAoles5Cq9EMB4MZquhNCoTQ9bMfFtdRTChf11BM+EpKSvDll1/irbfewrp163DttddizJgx6NOnDzwetiaXIJwOn+Qt++0SxDFO8grzvBjadh8leZTkEWZx/PhxvPPOO1iwYAFKSkpw99134+6770bjxo11l0nJnvWwNpehbCxDBTMSAiF0Da3B7OsoRyhd31BM9oCKZ/AtWLAA77zzDqKjo/29e3Xr1rW7agRhCXyS99GvLU1J8oa3+4OSPEryCJZwHIe1a9fi9ddfx9dff43evXtjzJgxGDRoECIjI5kcgxI987DaVIaSmXQydiUDPE68ju4Y6VECvuIS2feE+9iJ3ddTCruvsdI1k+PbgncZ14QtZWVl+OqrrzB//nysWbMGAwcOREZGBnr16kVz94iwhpI886Ekj2BCXl4e3n33Xbz++us4e/YsxowZgzFjxqBhw4amHdMJyZ4e42G3geRxkpG020DyhMr1dNK1k8LI9dRr5p2KWn04/ZpKofY6h9s1NStx/OeffzB//nzMnz8fNWrUQEZGBkaOHKlqlWmCCDX4JG/Jr61NSfJua7eHkjxK8ggj7Nu3D2+88QYWL16MFi1aICMjA0OHDkVMTIypx+2fek/A397cPFWfCxezoTWxCCUDqTVBqCrX1JOUCK6oyKLasIW/puFyrVjiEqxuCiCkrrFL9OxSrrTUppo4E73JYHFxMZYtW4bXXnsN+/btw5133omMjAxcfPHFjGtIEPZBSZ75UJJHaMbn8+Gbb77BK6+8go0bN+KWW27BuHHjcOWVVzI7hjiJU4LMRSC8cQwlwwgEmka6phWIkwDA2ddVbPzF0HUNROr6ymH3dVe6tkLoOldgtNdv69ateP311/Hxxx+jW7dumDBhAgYMGAC3mx5BQ4Q2fJL33s5LTUny7rhsd5VP8kKneZ+wneLiYnzwwQf473//i/PnzyMjIwPvv/8+0tPTdZWnNZGTg8xEBVJmsVJLu4OSAyXD6IqKqvLXVi4BkDp3dlxbLabf/xm+AaKKX1tAW4IHyJ9vM6+9nmsMBH43utb6ufLKK/Huu+9i9uzZeOutt3DPPfcgNTUV//d//4fbb79d9QrVBEFUPagnj1AkKysLc+fOxWuvvYbq1atj0qRJuO2221TdXFglcsGoygZCq0kUY0VioNckAlX32hq9rjwsr6+R6xiMqnqNAXbXWQ4919+s68xTFa83yzl8JSUlWLJkCWbPno1z587hwQcfxH333Ydq1aoxOwZBWAHfk7doZ1tTevJGXfZble/JoySPkOXQoUN4+eWXsWDBAnTq1AmTJk1C//79K634ZUUiJ0dVNAyAeebQSFJASQAbnGD8zTb6YqraNQbMv87BEGrA6mvtr0MVuuZmLNTi8/mwatUqzJ49G1u3bsVdd92Fhx56yNAjigjCSijJMx8a1E1U4s8//8Ttt9+OFi1a4OzZs9i4cSPWrl2LAQMG+BO8/qn3+F+ENbiiovwv044RGxvwUrNPsH2Z1MlGM2wlZl9b/3GCXDuzr6VsnSz67kQFrthYuFNT4E5NgcumxXCsiGdOoV/8SOZlut1uDBw4EN9//z02bNiAM2fO4JJLLsEdd9yBP//8k/nxCMIsfJzblBdBSR4hYN++fbj99tvRtm1bxMbGYt++fViyZAnat29vd9WqLHYbIb8RtCCZq6rYbXRdMdEBht8u0w/Yfy6swu7rLb7G/Da7rr/dcc4KzEj0eNq3b48PP/wQ+/btQ0xMDNq2bYvbb7+dkj0iJPDCbcqLoIVXCFQkd9OnT8cnn3yCkSNHYt++fWjUqFHQz6w6/7btvXjhPNzHbhMot42z6Rl/4boIi13XWcnIO+F6A+p+4+6UZLOrowtfdo7kdqf9tpX2s1oDahdsceJ1l7vmVtG4cWO89dZbmDp1KmbMmIG2bdvi5ptvxuOPP45LLrnE1roRBGE9NCevCvPnn3/imWeewSeffII777wTU6ZMUUzuxDh5Pp6TTICam79TDb8Yu4w/EFrXHJC/7u7q1cDl5VtcG+3XWojlZj8xAQDgO5fluOtqCE/luSdWaIFVD50VOuCvvR+v1/RjWobo+n9z5GXTDvX3339jxowZePfddzFkyBA8+eST9Kw9wjHwc/L+t6M9YhPY9jkV5Zdj7OXbq/ycPOrJq4KcOnUKTz/9NBYuXIg77rhDVc+d3Ujd9F1xoTN0MKhJFdz0rTL+Rgyf1b08wmvvCjGzJ3nd/73ewu9l5nVnZe7Nuu6Vftsi3NX/XTUwxK69FuTOAQtdsB5+ybqXT+n6A7gQI8NBA15vQMwf0GCi7K5GE8BGjRph/vz5eOyxxzBjxgy0a9cOo0ePxrRp03Q/+oggiNCBevKqEAUFBZgzZw5efPFFXHPNNXj++eeZDOFg1Zun6mYPhMeNHpBs0RfD0vybMdeGpeFXdf1D/dqruOZCjFx/K+ZWab3+qn/jcoT69Qc0a0AONdqwen6dGj0Y1gAQ+jpgoAE9CeC+ffvw6KOP4vvvv8cjjzyChx9+GHFxcYbrQhB64Hvy5u7oYEpP3v2Xb6OePLsrQJiP1+vFokWL8OSTT6J+/fr46quvcPXVV1teDyY393BAww1e6pypNf5WGDytvTtVWgM6jJ2e62+lsZe7/qZdZ48n9A0+I4L1/tm2Yqaol89UHQChqwVRb54egvUAAtJJ4CWXXILPP/8cGzduxOTJkzF37lxMnz4dd955JzyMGh8IgnAO1JMX5qxevRoPP/wwCgsLMXPmTAwdOrTSc+70MKDFlErbuMwz5pv4UL2pA8xa8KXgjb+dKyMKjb6pOghFDVhgoOw09wCAlH9bSwvYPYBdllDUAI/ZWoiXGMaenWvuMYWkSLSam62JUNWDBXFBrseP4zgsW7YMU6ZMQVxcHObMmYPevXubXh+C4OF78l7f3tGUnryM9luoJ8/uChDmcOTIETz88MNYvXo1pk2bhgceeADR0foNoFRSJ4YSPBksuJG7aqVV/MNKMyckJQkuwBqDT1QmPhYusbm3QgtShp6vh5laoB49bYivkxnakNICj9maCNWePQY9enpxuVwYNmwYBg8ejDfffBNDhgxBnz598NJLL6FBgwa21IkgCLZQkhdmlJSUYPbs2ZgxYwaGDh2K/fv3o2bNmprKUJPQESqwo8WeN1pmG3w5QxcfS4meGDN1IKUBHuE1YqmHYGZeiNlaoESvMsH0IISlNtTqAQisnxnaCNVkz0aio6Px0EMP4dZbb8Wjjz6Kli1bYsqUKZg0aZKhhmGCUIsPLvhgfISZuEyCkryw4uuvv8aECROQkJCA1atXo0uXLqo+FxJJXajdtO0y9jyskz2tRo4SvQrs1gGPUVOv5foLsaIHJ5Rig1P0IERvL59eTfCYqY1QSvZs7M0TUqtWLSxatAj33nsvMjIysGjRIrz66qsYMGCA3VUjwhwv54aXY/vwctblhSqU5IUBR48exfjx47FhwwY899xzGDt2rOwkatMSOjNNfSjcqHmcZuL0mnujBo4wTwt6zTyPWk2w1ICZib8g0eOqO/t5eq5s65+NqBmlpI91bLAg2eNSnL3gk+tcjiMSPQDo2rUrfvnlF8ybNw+33XYbunfvjtdeew3169e3u2oEQWiEkrwQxuv14o033sDUqVNx0003Yf/+/UhLSwvYJyR66VTgVPPmOvfvw66dauiFKPXuhZipd6omAICL9MB9RvpB6IZgqQdAOuEzK8FnaOZ9aZWvvavM+Y1BUvUWolszrHUh5F89eBMrjuHJM+l3zWAop9L5dapG+FjGRUrfR4zEkgENJmp+3ILH48G4ceMwbNgwTJo0Ca1atcJzzz2HBx54gFbhJJjjhRteMO7JY1xeqEJJXoiya9cu3HvvvTh37hw+//xz9OrVy+4q6SbYjdmpN2UeX61qktsNG3wLTJvpMEj05LThZF3wRk2q7o408f/irV8xd9c0E8+jQRdKpl0IF+lxtC7UEOz7mtJooBI+wRP/2/SEL4hOtGiDx+kacZV5JRM90xoHFEhLS8PixYtxxx134L777sP777+Pt956C23atDHleARBsIWSvBCjqKgIzzzzDF555RVMnDgRTzzxBGJj5Q3gN3tnOqI3T88N2ck3Y0C+1RUwYPBNNvNCgwY4w9Dr0YZTCaYJQP67BtWGxZowvccGqGTiWWnA6SbeCLLaKSw19bhifUi9Z3ay54uLYlYk/xsNJ52YHUN79+6N3bt345lnnkGnTp0wYcIEPPnkk0G9B0Goxce54OMYL7zCuLxQhZ6TF0KsX78e99xzD6pXr66pNc2KJI/lTRhw9g1YychrwW/uLTbyQkxP9AqKmJsQp+qDpTYAwBcbhYhz5szjCqYJIWboo7z6hTlS7iL2SYpT9QGYoxExrDSjViNCWOlFqBEe1lpxqk6s0MjqH6bqKks4imjBggXo3r270eoRVRT+OXkvbrvalOfkPdJhU5V/Th4leSFAfn4+pkyZgoULF+K5555DRkaG5nHxLBM9uvmyQ3jzZW3mtRg01kZerJGqoI9QMe96jDtgXCNScUMIS404UR88LHUipREp9OhGr06E6NGMkk4Aiid6UKMVtYmf1+vFa6+9hscffxyjR4/G888/j/j4eKNVJKoYfJL3/LbuiGGc5BXnl+PRDhuqfJJHwzUdzvr163HXXXehXr16+PXXX9G0aVNLj0833AqsuOEKz7VeM6/XmBkddqVGJ+GMVYbMiEaMmnY9GtGiC19sFLNYEs7DNvUgvg5K2mGR4AnLUdKM1vjB/z7CWS9y8/PMpM9VzwX8LZf0eTweTJw4Eddeey3uuusutGnTBgsWLECPHj0sqCVBEGqhJM+h5Ofn49FHH8WiRYswc+ZMjBs3Dm63+asF2W3WnXajBexpeeevg1ojz9KUqTHxduskXFGrD0C9cWelDXF5UjoxqgtK9KwhmHZY60VcplA3LPQCsEn2nDhXj2Wi5y4q1RRfgMpJHxCY+DVt2hTr16/H66+/jmuvvRajR4/GzJkzkZBA9wdCPT7ODR/j59qxLi9UoSTPgWzcuBGjRo1C/fr18dtvv6FJkyaGyyxqlAoAiP37vH8bGfXg2JHciQnWc2OGGePLFRt4J5l3p8FKJ3o1wiOlFbM0Iizbk1fENJZQoqcOo3oRwl+/ssRIAEDUuWJmZYvh70UAEJlXxqRM1smekzRjR49eMKR6+x588EEMHDgQd999N9q0aYPFixfj6quvtqmGRKjhhQtesF0ohXV5oQoleQ6ipKQETzzxBN544w3MmDED48eP191712PAi5LbWSd24TqPxgkJnhj+2rlKzT9PRY1SmRmwcMYpCZ4YoZE2y7CXVo+p+Me//2epF7MSvZLaiUzK1Ev0yTxbjx8MPsEDBNcWbPUjLFd4TKcle8JevXDSjJ7ePLU0bdoU69atw2uvvYb+/fsjIyMDzzzzDKKjo005HkEQylCS5xB27dqF22+/HVFRUfjll1/QokULTZ+XS+pCBbtNmPBG6lTjfsGEmdfaLjRhZYmRjjXuPHYaMFbmyzydVMBfU1Z6ERt14XHt1ktxLenFH2KP5tpu1oEKvfoi5FuYYzILLKzNBcSaEcIi4ZPTjPj4diV7croBAHe5vWvT8bp1om7EuN1uTJgwAX379sXtt9+OVatW4b333qPn6hFBoeGa5kFJns14vV7897//xbRp0zBp0iQ88cQTiIyUv+EKCaXEzsk3USC4+dJ6AzXbtPOUVo9hYtyVDJidyOnGCZopqi+/YpdazVilFcC4WVejEzPMupRRDxZPpCiqn+QIzSih9L14XZnVG6OEHg1piS9m60erboALyZXd+nGXc7L3KLW6sYoWLVpg8+bNmD59Ojp16oSnn34aDz/8sOZVwQnCCmbOnIlPP/0U+/btQ2xsLLp06YIXXngBF198sX+fgwcPYtKkSfjhhx9QUlKC/v3747XXXkPNmjX9+2RlZWH8+PH48ssv4Xa7MWTIELzyyiu2zlGlRyjYyD///IM77rgDJ0+exHvvvYdOnToF3Z9FUseypd3ozdNfjgPMV7BWUjnEN04rDbsYraZdi/EySzM8erRjt2b06IVHqBtWmtGiFSFqdKO3EYCVbopqRiP+7zxDMYbHbt0AxrTD440JbKWOPVWiuyy92hEipSMWjUdGNVRU88JQQU+xz2h1wk4/RnTDo/TYhc2bN2PkyJGoXbs23nvvPTRs2NDwMYnwgH+EwpNbeiMmwXgcElKcX4ZnOq5R9QiF/v37Y/jw4ejQoQPKy8vx2GOPYc+ePfjjjz8QHx+PgoICtGnTBm3btsXTTz8NAHjiiSdw4sQJ/Pzzz/5pVQMGDMDJkyfxv//9D2VlZRg9ejQ6dOiAJUuWMP1uWqAkzyaWLl2KsWPHYtiwYZgzZ47kM2bM6Kmjm2ZlWN00WdwwAWOmK5hpN2K6WBh2XjvhoBvWRt1pJp1l765W7QhjjJBw0A1gTpInRoueWOhHSNS5YuajA7RoSE4/PEZ1FA4aYqkfNc/Wy8/Px8MPP4yPP/4Y8+fPx7Bhw1SXT4QvTknyxJw5cwbp6enYsGEDunXrhu+++w4DBgzA+fPn/WXl5OQgNTUV3333HXr37o29e/eiZcuW2LZtG6644goAwKpVqzBw4EAcO3YMderUYfr91ELDNS0mPz8f48ePx4oVK/DOO+/gpptuktzPCUMxg90sw8FwsTZbwvOlx7SzMFviIZx2DsVUMluhihkmXXyu1OqHlUHndVKS6kH0ebYL+yjN1VOrE2+M23Dc8UW4bI87ViB1TqU0xTrBA4C8phcaLFlpSWkYp5ZYY1RHThi+GWzopho8xb6giZ5a/aglISEB8+fPR79+/XDvvfdi1apVePXVV+lRCwQA583Jy8nJAQBUq1YNQMWiiC6XK2ARoZiYGLjdbvzwww/o3bs3Nm/ejJSUFH+CBwC9e/eG2+3Gli1bcOONN+qujxEoybOQX375Bbfddhvq1q2LXbt2oV69enZXyY/Vhtxuo2V2a7rahM8sk+VUo84Cu7VjFKVWdEA56TNDNyWpnoD/s9SQWD969RIOiZ5Rg64X8TmPKDTeUCeG1474b9bJHgCUx+k3cfxv0GiyF8qxSCnRE8Mi8RsyZAiuvPJKjBw5Epdffjk+/PBDtG/fXlMZBKGF3NzcgL+jo6ODrvjq8/kwceJEdO3aFa1btwYAdOrUCfHx8fjPf/6DGTNmgOM4PProo/B6vTh58iQAIDMzE+np6QFlRUREoFq1asjMzGT8rdRDy89YgM/nw6xZs9CtWzfcddddWLNmjWKCt/6bR0yrT1HN6EovLbDoxbMTFkNdtN4cxee6LDGSuVEvSfVUMuosEdbXiH5CGbOHSclh5rkW6ka8nSW5F8Ui96JYw99B7zkkAilKiwh4GSWYXuQ0pge+vmXxxnVgVEt2JOs8Tkgwi2pGo+PIl/wvNdSvXx9r1qzB6NGj0a1bN8yaNQs+X2h7CsIYXs5tyguo0FtycrL/NXPmzKB1GTduHPbs2YOPPvrIvy0tLQ3Lli3Dl19+iYSEBCQnJyM7OxuXX3657secWQX15JnMmTNnMHLkSPz5559Yv349rrzySsvrILyBG2n9ZIWdNye7TLqQ3IsqHiIde6bccFmAvLliOfSO15AT9BOqsNBOWbwbZRddeMC5UQ0pGW+jPTFSyUNZvBuRBcZMHYshd04wyXYhlSAJr5VWXalN4IT7adWUnJYAGNKT0V49O4dvmj1sUw2RBT7/dRAnelvefVj6uB4PpkyZgmuuuQa33nor1q1bh8WLFyMtLc1QXYjQhIMLPsYPL+f+Le/o0aMBc/KC9eJlZGRg5cqV2LhxY6WOmL59++LgwYM4e/YsIiIikJKSglq1auGiiy4CANSqVQunT58O+Ex5eTmysrJQq1YtVl9LM5TkmcimTZswfPhwdOnSBTt37kRycrIlx2XRKitHqE5ad0JyJzZW/HXSY9S1mKpwM+h2YWerPcDWmGvtVdGa7CnFICfoKFiil1+HXS97wgl2q9MC1vRkatGV3h46tZpScz9zSrJnpp7kdGTX8F81KCV9HTt2xI4dO3DPPffgsssuw4cffoirr77ayioSYU5SUpLiwiscx2H8+PH47LPPsH79ejRu3Fh23xo1agAAvv/+e5w+fRrXX389AKBz587Izs7G9u3b/UOQv//+e/h8PnTs2JHRt9EOra5pAj6fDzNnzsSMGTPw4osv4oEHHoDLpS8Iq1mARW1SZ9RUAcaSPDtugID9CZ7aYUVqTLpeQ8XKnAPO1RFgrpaM6MgqDfHIaYnFkDk5LelpXLJSSwW1K9fPxXbqqmq4IJch/mTwOGBGg5MWhNpiOaRXrCsjjZVGdaVGU1J6ApylKSUt8Vgdn3j4pI/jOLz55pt45JFHMHXqVDz66KOOHwZHGIdfXXPyT4MQzXh1zZL8Mszq8pWq1TUfeOABLFmyBCtWrAh4Nl5ycjJiYytGzixcuBAtWrRAWloaNm/ejAkTJmDUqFH473//699/wIABOHXqFObNm+d/hMIVV1xBj1AIJ06fPo077rgDBw8exMcff4zLL7/cUHlSSZ7em58VNz7AWTc/O298gL6bn5RBDzdzHmrGXM6Uq9GSXQYKMM+QAxf0ZHTkAMtETy72BMNJelJDzHlj54vFXDbAvFEZLHuljGqrOFX/ubJaV3o1FX+y3NYYJeTNCd1xyy23oEmTJnjvvfcqLWRBhBdOSfLkOmEWLlyIUaNGAQAeffRRLFq0CFlZWWjUqBHuu+8+PPTQQwGfzcrKQkZGRsDD0O1eRZaSPIb89NNPGDp0KLp27Yq3335b87M5eC4bNyfg76g8NpeIhTHXY6KA0DBSQsPuhJuek00UyyRPq6ZCQUs8Rg0UCx2VJlZcb6PJgRjeALOIT3r0VFAr8KJ4SvTVI5T0BADe6Mq/3/hM9V+CpaZ4WGlLnFRZrS2xpgD9ugJCJ9EDKutKi6YANrra8u7DyMnJwb333osff/wRy5YtQ5cuXQyXSzgTPsn7vx+vNSXJ+2/XlbqekxdO0Jw8BnAch9dffx3/+c9/8Pzzz2P8+PGah2eKEzu7kTZQ+oJ4qJiogtoRATc6rTc5gF2LZoWJcjE15ixNuZ45VUJNeUpCZyiOEeOU2yjwxmWnGeevv1FNiY14aaLLsKbUzKmSMuAs4Dz2DbFjhdS50RO/9CLUhF59SfWa8fo1oi8lbSnpir8n6En2rNaWy6s/XnlKuID7n9x5MVtXycnJWLp0KV599VX06dMHL7zwAsaNG6d7ygtBVGUoyTNIQUEB7r33Xqxfvx6rV69G165dVX/WKYmdWeYJCJ0ED6jckik+L8FubmyTuwsUp7oNmXI548Sqd1gONcbJSAt5KCDV46LWjJvR28KjN9kLNnSNlaaEjQda45IRTYWSGVeL1PljcY3kdMWjNeFTMySSZbKnVH859Oor3LRlRYOCy+XChAkT0L59ewwbNgybN2/G/PnzER8fz/Q4hDPwwg0v4ye6sS4vVKEkzwD79+/HTTfdhBo1amDHjh2qlkm1K7GLLPDpTuZCxYyzHgIlRi7pM9OQA/oSPSXjxKr3xYiuQgFWDQbB0NKYoBY1RlZtsqd2XhILI16Y5gLSPIgo1vf5qtB4YITCtEBdxJ0x91wpaUzrnDcjGhN+d6v1FSqJnrg3Ty18DCuPubCNhbauuuoq7NixA7fccgs6deqETz75BM2bNzdcLkFUFSjJ08mXX36J22+/HWPGjMHMmTMRESF9KlkkdXoNecBNLdF6I27lTc0KMy4mp1HFQY3czNS2Kqsx5EYWCdAKry07dBXOlMdc0BWgXVt6einktKVXT1rjlTjxACrOQ7gbcScgPPdqtKa3F0yqd89IvFKb7ElpC7iQjOjRmN7hm/w9qqpojFWDQq1atbBmzRo89thj6NChA95//31cd911LKpIOAQf54KPYzscl3V5oQoleRrhOA4zZszA888/j3feeQdDhw6V3M+OHju5G5oRQmExAzsSPGGLpVajBBgzS3aYcTO05XSs1pVQUzxatKVXUzzCZM9og4GSttToyUiiR1RGSl9ClEy5UX3xFKe6UZIMROcYL0su2VMbr4wme05uTLC6N08JI0lfZGQkZs2ahQ4dOuC2227DlClTMGXKFJqnFyb44IaP8fBK1uWFKpTkaaCgoACjR4/G1q1b8cMPP6Bt27a21UWL6bbaKIVzgqfWKMndwFgYJZY9dsHMOBlx7ZilKyB4wsfKgJckAyXJblMMuJ6GAr36stKAF9QWrUp4MjyGi4qvF6vfeUly4P8B4wkfrzU1vyMpnK4zoca06MuKuZ9CIorVXwM9Sd+wYcPQvHlz3HDDDfj111+xcOFCmqdHEEGgRyio5O+//8bgwYORmpqKZcuWIS0tTfEzLHvz9N68AP03Zyt68fiblx5jpOfmxar3Ti38jYulCedhYcQBYyYcsFZfhekuzVrRozGnNR4EI+4MZ1hfQl0JYaGx4upARIGxMszUmDhJSzjGVdpmBnGnpY+jpFM7NFYu8tEx5/SVI6czHr16K64e+LcRvZmlNalrbVWDqDieqYmFduts7zMPye535swZDB06FNnZ2fj888/RqFEj/QclbIN/hML9m24y5REKc6/+lB6hYHcFQoENGzZgyJAhGD58OObMmYPISLZilEN44zJqksyA5U1Lq6kKlZtWbqN/n1Gm0xTxSJkjFkOeiqsDxdVdjtQXj1AbLq92rUiVo4RUkudU850bb0xjwYw3/55WnYlNd3m8PTHMG+1CcTVtn8mv57LEfBemS+tFSacxWWbURhvC66tWd0oJnnAftXoT64zHiN6M9Opp1ZpdQzfVxkI7tdbiyQuN5OKELy0tDatXr8bEiRPRoUMHLF++HN27d7e6igTheCjJU2DhwoXIyMjAnDlzMGbMGFOPJXfDsoOKm5U202pXqyRgnjFi1SrJX1utRlzJGOlJ9KR0ZocJ12OKrECuFy+YxnyRQNwp7cdi2bui1XirMd3CfdXoLFgMs8J4F9asvM1dpu+YTkXqOwLB9cdSZ2LUxDYtWhPuH0xzSvdLvt56NKdmrp7cddCqNycv+KNHa1qGbFb6bIG03oQJH1CR9EVGRuKNN95A27ZtMXDgQLzxxhsYNWqUvgMTtkILr5gHJXky+Hw+PP7445g7dy6++OIL9OrVS3MZO994SHHIptmJnZIxYnWjsgo9w+h8kYHfU40ZN8sUFVd3pgE3gl4D7lSN6UXqt+RU463VdAs/I6U1tdoyarzFOpOLX0J8keFlvOXQY8hZItfIoEdr4s8KNac1jrHSnBqtARV6A7Rpzgq96Zmf5y678H2E2K01cS9fkyZNcPPNN+PAgQOYPn063G5adIMgAEryJCkqKsLIkSOxY8cObN68GZdccgnT8rXepFj0sKi9QQH6zLcVhkhvgidGyYzrnryvcv53MAOu1xDJJXpatMaqN0+L1sIBKY3xaE381KBWZ0BlrRkx3DxCreltONCrtbwGQOIR7RrTk+iFC4U1AW/0hb9jT6v/rBatCeF14SrX93kxvG45A45Fj+aK0i/821Oi7bNaNefURE8Lwt8lrzktetMDn/ClDh+LV996GwcOHMDixYsRGxtr7oEJZnCcGz6ObWLOMS4vVKEkT0RmZiYGDx6MyMhIbNmyBTVq1NBVTtNZL1X8o1HF/yLzrOs6NnJj0oMTE7xgxlsKoRHSc1PSY4aEBpmFGWJhhPRQlK5PZ0403lasRJfXIPBvtXrTa7iBCq2VJVbMKYw6bzwW5TWqKMtIXFNjuoWxzH/sBtbEtVDszVOD+JyaacJLUy/MYzWiO2E5gH7dqenVk9IcUHF/qIqJnlxvnlqs0lt0Wk3UuiMDJ7Z8i549e2LFihWoWbOKtTgShAhK8gT8+eef6N+/P7p27YoFCxYgOjpa+UMC/ImdxcjdlPQYIaeZbsD8BA8IbOkWnk+lG5IR4w2wNd68EbLDeOsxQOGAEfPDo0ZvrHQGVOhEr97EZrsskWOuN7l4JsQKw11VkDPhLDUH6E/4xJoTls0q2VOjOSB8Ej2tGE30hEida1aJX0R8AtasWYO7774bnTp1wqpVq3DxxRezKZwwDS9c8GpcA0JNmQQleX62bNmCQYMGYezYsXj22WdVP2TT6sRO7c3IKsy+GVmd4IkJZsCNmCApA6THeMsZIJY9x07TnFnYrTVAWwODWsRaAy7oxqjZFpZvxHBHFGjXmRUNC2YbbiueY6akOTH8dfDF+gAA0We1D3uS0pwQtfqT05z4OHq1V9DQB3eRtu/Hn08t2nNaomeF7jwl6rUn/O37Yn26NMcTExOD999/H1OnTkXXrl3x1VdfoWPHjrrLI8zHx7FfKMVHD4cDQEkeAOCrr77C8OHDMXPmTGRkZCjub2ViV1LDh7J4a8YWa23lDvcETwx/I4o0MG8tmPnRYryVzI8RyuOBMnq+LHOsNttKRhtQpzm1WtPauFBSwyf4NzSbbUB7oke9edoRXic1GlSjO55gvXtaYpzWZE/4nfjfl55kT6v2APX64+9/Zt1nrR62qQXh9QHUxz7+UQsulwszZsxA7dq10bt3b3z00UcYNGgQ83oShNOp8kneO++8gwcffBCLFi3CzTffLLufVYmdOLjpJdSHzpmd4Gk13P7jxPpQ8u98bi2mW6vxkTPdVhhuQJ/hrkpYZXZ4AwpcuEasjTYgnezpaUhQozu5GOeL1d6rogc1iV5RrcA6utiEZUmk1geIzbT39yfUnRCtCZ8WhBrU24illOwFu7/q0Z8ZvXpi7QHW6s9u7bmL3JX0pzfpGz9+PGrVqoVhw4bh9ddfx+jRo5nVk2CHz4SFV1iXF6pU6SRv5syZeOGFF7By5Ur06NGj0vuN/jfb/+8IGBNMMPPDKrEzglNvOmpuOFYkeFKmR63p1mq4gcBEz4jhCWa4WevOjOFzQu2FstFmpTlA2Wjr0RtPaSqH8iQfInL1f3cp3anVml6jrUd3UnHNKQStW0TF9Y0+ZcEqQUGQ0qER7fEUNqzoujKqQeBCsqdFf4C5vXolNf/tmitX3wjHuc2Nf0IUfxcRnGrtaRmyqQUtjQ1Dhw5FWloabrjhBmRmZmLKlCnsK0QQDqVKJnkcx2Hq1Kl45513sHHjRrRp0ybgfWFyZwZOSOp49NxwrDTbSjec6HP6Wl61IGe2eeSSPaOGp7Ch15DR4esgNNxmmm2tlNT0atKd1chqT6PJNkNzPGLtGdVceZLP/39WJltrvGOZ6PnjmxQONdlqkftuRpM/tdoLqEsNH7xxFZ+LPK//+Lz+hP82osPCBhXLFnsKtZXBSoNB9RfBOSL+uXzSjVzBCPa9rG58UBNfevTogY0bN6JPnz7Iz8/XtO4CYT4+uOBjvFAK6/JClSqX5HEch4ceegjLly/Hhg0b/CsvKSV2hk1Pqhe+aGu6j800O2aiuXc9gpP8XlI3GTONNg9/s3GXGDufYqNjNNGzu1EhqPYcgN5RHeFksoWaE2/To7+yVO+//9dusAF9Jru4XhlcZRo+4xCTzRqxLrnIf3Vxxpoxxvy1B7RpUUqDwu1adCisAwB443y6Ej1Afa9eWVoZ+AExqnWoQYOOaWgod/kbuqSQiotcpM8y/cnRpk0bbNy4Eb169UJhYSFeeuklSvSIsKdKJXk+nw/3338/vvvuO2zatAmNGzc2tddOfKOxgrK0MpRrMToaMesmoyfBk0N8k4nI1m569Rht3mR74yr+1mq2g5kcI0YbsM5oOz2hM0wQ3fEIz4FWg61Hd8AF7QEXrrta/cnpTvi+Wv1JxTw9BhtQ1l9ZWnisoKKnJ0UtvP6AyueLpekW6k+IWi0qaVC4j5IW5e67fB1Z9urJaZCL9IVUomemBnmkzpXVid/FF1/sT/SKiorw5ptvwu2muVt24+Vc8DJeXZN1eaFKlUnyysvLMXr0aGzduhVlY0eg53efaC9DwexYndQxMTkOaMlmmeBVKjvSh7K0wLtisBsLC5PNU5bqVWW01RocNUY7mMHRY7QVjyehQU29KWGMnMGW05+RhgUp1BhsNdoT7ienQaXYxyrRU4p5msw14AiDbRdSSZ/e+Kd4rCC9e2o1KN5frEW191+jvXpq77thnegp9OapxY7E76KLLvIneqNGjcI777yDiIgqY4WJKkaVULbX68XIkSPx5Q8bkZJxFyKSk5iVbXZixxscJ7RaO8LgaEzwpJAz3KxNNhDcaOsxN3pNtl6EJtsJGmSNpgYGBtoD2PWoKGnPfzwJDWrVnvBzQg1q0Z0ec81VL4UXgK9Efa+45kSPAFChS3f0hevpOhel6nNqdeg/jkAzLq/+RkZhsqc1/mnt1eOql1Z8DgDM0qIDEj2zcJW5g8ZEIXx85LWoVodaqF+/PjZu3IjevXvjzjvvxLvvvguPx96FjKoytLqmeYR9kufz+XDvvffiy43rkTJhDDxJCYbKK0/ygfOY/5RF/qZSpuGGApjXi+KIYZqMTLYQ4Q1F681Ei7kR9urpNdj8Z3mTrcfY6DHZWjVIaEOPudZqrAHtwzjlKE/ywZNcivJ87cmpGnPNxz4h7miveYleGJtrI4ivA2uzHZFwobHDm6OvbE9yKbjkCiNjhh7ltAiob3gIJS3a0ZunhFk6rFWrFtauXYsePXpgzJgxeOutt2jopk344GL/MHRaeAUADD4XwOFwHIeMjAxs3LgRf279xXCC50kuhSe5ctBnAVe9NOBlCTYP1bQ7wQMqbtj8TVvtuffG+XSbbK5+sebPieHqF+vuuVOqty06DAVM0h+AgAQPgOL516M9noiEMnD1i3XHMWEMFJp0rYi/gxrNic8TwRal8yt1jfRqUawdPfdW8f4RCWW6NSn8HmrjnxY9aooJWmKNCQ7O6Y0YLO9RNWvWxJo1a7B+/XqMHz8eHGd+0koQVhK2PXkcx2HSpEn46quvsHHjRtSpU0dXOVI3noiEMt2thnyLYSgZaDOCvlMSPMkyBNdG3GrIwtR4kkt1tVwLtahXg1KoMTNaelFCBTMMktEEr1J5QbSoBSnzy+tJrRblYiGgvxfFU+g2LRay6EGJTC+qtK08MxaempW3G8Htlo9xpafimB6LBVz1UkTGlCMCQMn5GE2fDZaIqdVksIRQryYj6xUAAEqL1H9OS2zkY4MqTfL3PIEmpbToL5txbiKnx0pa1NCbp2XIphaMxo+6devi+++/x9VXX43Y2FjMmjWLVt20GM6ERyhw1JMHIIyTvKeeegoffvghNm7ciIYNGwIA/h47SdVqmmb01sVUqwjQpbHmTSrWNFRTZGisvIFIxc+y07HSO6u8gbA21/5y/72B+Ir0/VTkDI2WRE9Oj0YaG9yx5Zo/pxYWc6Kk9GibkTFpSJLWnqmIuoUAAC9DUw0oa1FNPNSiRT4WAgCqmWeqAWUtRqcV+v9dcjI+aBzkiahVxFyLwYiqWRjwt0uk25Iz0kmgGYZaiujUC6MTtCZ8cgg1J9am2vuz2mQvQI8AomLLbNOkUI88Pq9yLHW52MZHn88lGR/FWgTU69FsImPK0fyT6QHb9g95QtVnGzZsiO+//x7dunVDfHw8nn76aTOqSBCWE5ZJ3quvvoo333wTmzZtQtOmTQPeC5bosU7uxDcPOxHfPNTcOABrEjxA2tS7PRdMSrAbh1kJHk9UbBkQW2EYirNkklERaoYNBWu11mJk9JhrLSaGNUItqtWhlVhhqrVqMDLmQlKuxVSrHb4mpUWt8VBJi3Lx0GxTDUib50r71C6wTY9yploNct+tOFt9wmVEjwF1UdCmnuGURu/LcroMdn+O+jfeq9Wl1nl6avSoFasSPTGczxUQI4N9Ny2aZIEw6VNK+Jo2bYq1a9fi6quvRo0aNTB+/Hizq0f8i48zYU4ePUIBQBgmeUuXLsXUqVOxZs0atGjRQnKfpDq5AIDcE0m6byB6bhxmo/bG4URjLUaY4AH2GBn+Ri+Ev77Bkj2tRkbYk2JVQ4MVrdUxKcbnH5qFWkMtTvAANlrUgpyhBi6YarGh1js3SesQTjFScVFNTGStx6QaBQF/l5ar067b47Ol58QMxOeAJ/dsvCXHFyd8RuZwxsdfKKugQPvvTNirp+UezUqXctdCjS7VajIUiEkpRlRE5fuwlCZ9JR6m83DVJHwtWrTAV199hd69e6NmzZoYNmwYs+MThB2EVZK3Zs0a3HXXXVi+fDk6duwY8F6bL5+stH9SnVxdNwwhdiR1Rm4YWrCqF0+MOMGTIyrCiyjBuQhmXlgkeEKkkj0jJsaIFsXG2gmaZG2qnW6oxVrkkdKkFi0GS/CEsBoy5zfT//7fiKGOiNI2JFivoZaLhwFlR3iZx8dQRep85eepv85qNSmkep0c/79zcrUN5xMmeMK/9WiTr0dBsfpGDD29egmJ6hq41OrSrsYHvb15WpHSpJmNEXzCJ5XsdezYER9//DFuvvlmVK9eHb169TKtHkQF9AgF8wibJG/Hjh0YMmQI5s6diwEDBvi3SyV3QuLjizXfLJKTKlryC6LYP79FjBoDA1TNBE+M+FzxNwnWCZ6QmGpFiI8p1WxceIQGRo8WeSISyhxlqsMBtaZFSos8cpo0i4TYEiTElgAwbqb5bXrjI6DNTAPqNVmtWr7/36VlYXMbsw3h+QSArCxjK1EHg9eHGn1KaVL8nhp9CjUJAPExFT3WWpO9YNoUn0NAnTadnujZRVKNAkRFXrinmalJMQMGDMDcuXNx0003Yd26dbj88sstOzZBsCQs7o6HDx/GgAED8Pjjj2PkyJEAlJM7PUjdKLSaGCXMNi92DPuwIsGTQniTUHOD0JLc8fBmITmpUJOpljMvWk21WJOs9Qjo06STe070zn0ySlRkOWrUzgnYFkyXWntM+OSOR62ZDmakhe8r6VKsRUBfjJQy01IGWitkpqURGmkelkmfWJc8Qr1IaVRJl+L95PQppUv/ZzXqU9yrp6TLqMjykE/0WPfmlZZ7VN/DhUidazMTv5EjR+LUqVMYMGAAfv75ZzRu3Ni0Y1V1aE6eeYR8knf+/HkMHDgQt9xyC969OBfv6UjughnrYDcIo7AwLmbA0rjYleABgeZFeK6lbgxaEzw+uROixlSrMS5qEj05XeoxLWYY6lCHRS+eGuR0aTTBExJMl2qNNL+vlC6VYqSRRE+NFtUaacDZjQ9Ohr8O8VEVcS8zO0nV54LpUohYo1p0ySNO9tTeu7X26tVMzgOSgYJSdfuHQ6LnVITxIT6qVLUu1TJp0iQcOXIEgwYNwo8//ojU1FSm5ROE2bi4EH76Y0lJCfr374/dxadQ+z83weUx1kul9eYAaOs5qZmcp/rGAGjryVNzc7BjRU01SR7rBE+qZVqO/KJo1fvySCV4UohNtVbjIjbUanWppzdPrRkD1OtSrZm2ek6emlZp1kmeFk1qNStarh1gzEjzFBTE6GoAU9JmzeS8wP1NiJcsdQnIa7Ne9eygnzt2LiXgbyOLAUlhljbFBNOqVm0CQHJMxZzicwX6hjZXj68YIp1TrG4lZCHB9CnWJhCa+lTSJVBZm0BoxE0xwbSp9vEKXq8XN954I/Ly8vDtt98iyoJpOlWF3NxcJCcn47rv7kZkPNvzWlZQii/7LkBOTg6Sktgm/6FEyPbkcRyHe+65B/n5+aj1yPWGE7zq8QWIUJlsaEHqxsAali3TUoZFq1nhcXqCFx9V6r8xnMpJVN5fZXLHw5vgcp1DZHkTrlWXantOhNrUYlachF5tssBMo1IrJde/jbWJbpB+DoAxE109vkCXiZbTplycjI8qta3H5KL0syj3Kf92vSr2kUKsXZdLpUH2ePH3uWrB97HIRAPyWjWS4AEXkjUtOuU/IyxLi06levWC3cP5c6JGoyz12ah6lmO1CUBRn2opLYvQpFExauNoMDweDz788EN0794d99xzDxYvXkwPS2cMDdc0j5BN8qZNm4ZNmzbh559/Rq1atXTNwRPeEICKm4JW4yI2LVYkdVqpl5qtuI/emwGg74agxqj497XAsAivm1TCpzXBA4DU2Iok70y+vnkDRlqkpcy0E7UJAA1qZCnu40QjzRqnm2ipeAlo1yevTbV61JLoaaVRdWXtWQXHuVTrU6neJ3KSWVRJM7xW80u0j5AQalOIWp2K9SksV49GE6LV/77UapS/Rykle1ERXtRJzgm6j5Vo0SagrM+/z1UzPNRdK8I4qpX4+HisXLkSnTp1wtNPP41p06axqxhBmEhIDtdcsmQJHnjgAfz0009o2bKlf7vaRE/uZgBoNyy1E3KRV6b+hmbG8I5gNwM1rX2AsSRPiNoETwneYFvZIi2FHrPCJ3di1CZ7UvrUk+gB0GRU1GpT67CjYDf8UNanGm2q1aUWTQJAYmQJTuarb5mWM9A8wQx0sHjJo0WftRMqzJbVcbNRaqAOS33KvXlm9pZIwUqfUW7pff4+H9hwYaY+hajRqpJGeaS0qkajgDqd8vrksUqnTten1t68oPuo1CdgjUa/6vaqqs/8/vvv6Nq1K958803cdtttmo5HVIYfrjlg1b2mDNf8pv9bVX64ZsgleVu2bEGvXr2wfPly9O/fv9L7come2psAoHwjoJuAPGaaFKkbAGD+TUCtmZZL7oTIJXrhaKSruj6dYqDVmmegsoFmFTfFMZPHLH3WkTmeEFb6BJzXCAHIG2khcRFl+Cu7uuJ+WjQq1qcYKb1q0SjPuYJ4TfoUIqVVOY0C2nQKKGu1aco5FJYHf1RIOOtTjTaBCn0CYK5RQF6nSgnfqlWrcPPNN2Pt2rWVnsdMaIOSPPMJqSTv6NGj6NChA6ZMmYIJEybI7nfL5vsAAHvPpuu6CZh5A1BrVJRMSlUz0GJiPBUG+lCOuiF1LExKMDOtJsHjESZ6WvXpFCNtpYkOVY1abaBZmWeAnYEOFjd5WOizacq5gL+VDDSgTp9A+PWWCOFNtBixblkmeUJO5ifp1mh6bIU+Txfpf/ZkTnGsKo3yGNGqWRoN9xgqhxUalUv4Xn75ZbzwwgvYunUr6tevr/q4RCB8ktfvmzGmJHnfDphPSV6oJHkFBQW46qqr0KFDB/zvf/+rNPGVT+zE6LkB1InPxd5zNU0J/lImRRj8rQz8QOiaEz7JEyKX8LEO/kIzrSW5E1IjplC3OQknE13VDEoomWdAv4E22zyL9SgFadSYiRZyslB5YSpAm0YBICW6QqMnCrSZMKFGebRqtU58hT6zS7QNhVer1dpxweec0r2enUbjIkpwMLeGqnoB2nUqTvY4jsOYMWOwfft2bNq0CfHx+hsaqjKU5JlPSCR5HMdh2LBhOHPmDL777ruAJWzlkjseCvzyhKoxkUrwxMRFlOJwnvqFM/SYk0yVxkdIjZjApFBvI4RZ+uQJptNQNSeA8wy0GeaZN86ANvMsZZwBbRrl4yfAPoY2STrr/3dhubKeKZayM9BipAy13gRPjJJm5XTKo0avQp3ysNArafQCrIcVB39fnUZ5jCZ5AFBaWoq+ffsiLS0NH3/8Ma24qQM+yevz9VhTkrzVA/9X5ZO8kFhdc/bs2di6dSu2b9/uT/CUkjue9NgCxaAvFfBZwQd9pwV8lmhZdctKGidWzA9TSvb0mpNacXmqEz1xcsejRp88Qp2mRBdpMiWJkSVBTbQWnYYqTtSp0BTKmRK9CR5wQTNGjLOaYXFSMZSFRoXnh9BGqc+jaKALyyNV9+YJUaPbYMgleEBwzSoleMJ9pPQa7F7P10mtZoV6ldJpXESJZfE0wu1TvO973D5miZ6a1TZLvR7ViZ5RCsujKyV6UtdEj1bliIqKwvLly9G+fXv897//xaRJk5iVTRCscHxP3vfff4/rrrsO69evx+zyBbrL0RrwAW0te8GCPaBsnkN1DgngzF48OcQJnxEDLSRYsieX4AnRaqB59GgU0K/Tin3Ct/UZsL+XBLhgRljpk0dsnNWYZiFinaptINOi04sSziHfG7xV12k6BYCLk0+r2k8NHnDYk10r6D5O0ClPgudCzD2UrzwPVY1WeXjNatUqz+mieM0NuWr0Gs46/eN8LbRMzQy6z97smmidkhlSOgUuaFWNTgHlhVi2bduGHj16YOXKlejZs6eqMokK+J683l+PRUQ82waR8oISrKGePGf35B07dgzDhw/Hyy+/bCjBA+APRqx77dJj8wL+L4WVvSNqAr3VZsRJNE7MQlJExUPG1QZ5QNmU1Pp3eKMw2VOT3PGIe/TM6F1m0SuixpCwRMloaIGVcbYC/loleEqZmmZhL4ke0yzsJdGiUaVevYsSlOfY2cElSacAAD5ORYME2A7Xap0SXPtul8//7325NZkeWyv53ii/eRZeSyntaknwgArNVousiKWnS7QPkefPY1ZZnOrPBOvV06JVNb15cRFlinE1yu0Nmuix1qmauNsipeKYSjrdn5fmrx8grVUze53lEF9HLX5ASIcOHTBnzhzccsst2LFjB+rVq8eielUKehi6eTi2J6+0tBTdu3dHy5Yt8fbbb2P4z/frLis9usKAawnyPEpB3ikteZcknVIV4AG2ZsQDZfmoMSNW9OLx8EkeT7DgrtWQAEC5yh5XKSJULi0tRK15dpJWAeuNMyutAubNIxEj7CEB2Jhm3jAD2k0zH0sB4/E0mFm2SqstkjJR4lNu63S6VoMR7S7H3tzgjRtmaFXMofzquuKpUK88anQr1KoQrbrNLol1hFZLfZ6AZEmKUPAAchzKr4EWSZlBtcq6N0++LhfirJpn6HEch7vvvht79+7Fhg0bAtaNIOThe/Ku+eo+U3ryvh80r8r35Dk2yXv44Yexfv16/Pjjj4iNrTAGaufh8UgFeT0BHpA2JErBHWAzTPOiBHU9ME40IoC6AK/GiLBI8sQJnhhhcDdingFtBtoK82yVGVGj11A2I2q0CphvnPWYZinDDChrVc4wA9r1elHcWWQrfMaM2NoiSbrHIVSTPEC9XuXgdcx6yKYcSREVev27SF2viZxeeeR0G0yvPGp0a5VWK/ZR1itp1XqtAsBzbT5V3L+oqAhdu3ZFz5498d///ldxf+JCktdj5f2mJHnrr51LSZ4Tk7zvvvsOQ4YMwfbt29G8efNK7wdL9pSCu1pDonRzCcXADjjXNMshNNNKSZ6eXjwxKf9e90OF6idoB9OKVeaZ9KqM2XoVarV96hHszasdtBwWLc5JEUXMDDMgrVeWhpnHbOPcIvFkxT4qyqnKxpknTqAzOd2y0qsYOf2q0SuPULdq9Mojp1stWgWMN6C1SDxpqVYBZ8ZWwDq96tEqEDzh+/PPP3HFFVfgk08+Qd++fYOWT1CSZwWOS/JOnz6NNm3aYMaMGbjrrrtk95u2Z3DAj5tlYLciqANAwzjlsf1qgjpQdUzI77l15PdhmOTxKCV7ZhpnILh5ttI0A+r0CpBpBi7oNViiZ4ZpljLMWswycEGvWmIqj1ivQo0KYa1XPqmrtA8ZZ82mWQyvYbOSPB6hdrVqFgCqR+XjXGmC5s8BF3RblfQaDvFVjr15tU3XKyCd8C1YsACPP/44du3ahbS0NMU6VGX4JK/blw+YkuRtvO5NSvKclORxHIfrr78eMTExss8dmbZncMDfZgR1MwyzVFAnA1KBkaAuTPqMDtUEKid5POJkT48J0YuScTajUcJMvQJV14QIkz4zTQhvmPWaZcBYbJUzyzxGY2yL+EwU+lTEYYqxANgYZwCIc//beFEgPVzZqGkGgNTIAvxTpG2pe16zPFq12zD2LHLK5TXJKsbKJXf+fUivAJyjV0CdZoXJHsdxGDp0KEpKSvDFF1/Q8/OCQEme+Thqdc25c+fi119/xa5duyr9MMTJHY+e1ruUyEJZM88iwWsYm6W4D0vDrAY7DLMajAbzVkknKvb5N5j/ni/f06eEnCaAisQqOaJQs/ngMWKcqwXRqxrUaFbJfBBsEJ7nOHeprPkwSqPYil7X3HJtD3gWmmU9mm0YexYNYxHUMOulRTy7lVYJYwivhVDDwlU2pcgtj1VlmhvGVjQSqIm34gRPuE2NdvljBSMlslBVoidGrFmlhok4T6miN4h2lyt6A7fLpzrRY4UXLkVv4OPcqhM9JQq9UYqJXqEvCnHu0krXgdeskl7VMnXXTf5Ez+VyYf78+Wjbti3mzp2LBx54wHD54Y7dq2vOnDkTn376Kfbt24fY2Fh06dIFL7zwAi6++GL/PpmZmZg8eTJWr16NvLw8XHzxxZg6dSqGDBni3ycrKwvjx4/Hl19+CbfbjSFDhuCVV15BQoK+BlMWOCbJO3jwIB555BF8+eWXSE1N9W+XS+6EqEn0+EBuhvkAAoO5mhZmVlgdyNXCKpBrpVXCCf+/hQmfml48NTSMPasp0RMbEK2NEkotzEYgzdqPnPlghdDABtOtlFEWvqc2vrLGKYmdHaZZDSxNs17E1+hocTWZPbWjpN9guhW+L6Vf0mxFgy3rR38ooUazJb4IVQ3AetGiWbUNE8JEr1q1anj33Xdx3XXXoX///rjooouMVZgwlQ0bNmDcuHHo0KEDysvL8dhjj6Fv3774448/EB9f8WirkSNHIjs7G1988QVq1KiBJUuWYNiwYfjll19w2WWXAQBGjBiBkydPYvXq1SgrK8Po0aMxZswYLFmyxLbv5ojhmj6fD71790aLFi3wxhtvqErspFAK5EpmWamlrn5Mlt+EyQVyK4cQAVV32FvAPm7lfZSMh1JPWXJE5ff1mmYeOeMsNh9Gdcv35AUzHzT0zb6hRHLsLahlePhbamTlZ+GJdatGqzxizcoZZSOabRFf0VCT740JWgbF2gpYxVpAWbdqYm2C50KD2t6CyiMr9GhWyD9FNTRpVsi50oSgyZ1e3arVLKCsWzWaBSjW8rCItUeLq/mvIaBPt0DlOXoPPPAA9u3bh7Vr19KwTQn44ZpdV2SYMlzzx8Gv6xqueebMGaSnp2PDhg3o1q0bACAhIQFz587FHXfc4d+vevXqeOGFF3DPPfdg7969aNmyJbZt24YrrrgCALBq1SoMHDgQx44dQ506+keZGcERPXlvvfUWDh06hBUrVuDt/VejXhRwrFR7ayDf4iwVxI0a5RbxJ5DvjbHEKKuFZctyy7gT+KOQjQjtblmWgg/gUsFbL1JDirSYD3EPiZm6DYbVumUJS906jRbxmX7DbJZutZplfv96UVnI9rLrYRYaLKeipo6shxy5XeoSvT8Lg/cAs+oZ4Ye/qUV8zvYW1FHsGTlfFh800WsYexYpnopGN60+gXXvnRm6VTNkUy1ulw8Xxyn3LLLUrVrNRrkq9Li7wP6Hh9ePCZxiI6VbNQh78wDghRdeQOvWrfHWW29hzJgxxisapvjggo9xj7KR8nJycgBU9MjydOnSBUuXLsWgQYOQkpKCjz/+GMXFxejRowcAYPPmzUhJSfEneADQu3dvuN1ubNmyBTfeeKPu+hjB9iTvyJEjmDx5Mj755BMsPTnQv71eVJamAM6boYRYNsPyxD9yNS10rFDTQqcmcAPagnfLuOA3LC3BWylwmzkUQ4ywZVmptU4PbROOANDfMCGsHwuE39FpugXYm2Ul3QLqtKtGt3Zhhm55s6xFt/WilOcbq8UpiR0/x0lJR14oN6q5XRzzRE+JKFc5Lo0/FnQfK3Wd742RjWnCa368JFVyHy3welTSsFC3Rhsnguk2wVNsWcx1qm59nEu1VwCgqN39RcEbMLTMzQuGGt2q0aww0UtMTMRbb72FoUOHon///mjQoIHi5wm25ObmBvwdHR2N6Gj5XkOfz4eJEyeia9euaN26tX/7xx9/jFtuuQXVq1dHREQE4uLi8Nlnn6Fp06YAKubspaenB5QVERGBatWqITPTviHctiZ5HMdh7NixGDZsGP5p+Eyl99UkempalJV6Q2pHZSO7LM6Q6TCjNyRY8FYTuO0kWOBWazhYDdWUg7/e8e4S/FVcU3Y/qaGaUmhpmOBNB4veEKeYZR4WpsMunGSW5bDDKLNM7prGnEJBFNuhOVpoFXe80rZiX6TEnqFBKRfh7xWR49L4Y4h0eSXf+72wrhnVCkq8uwTNYy8YHyUjr0QwDWvRbnJEoaxfaBpzCogBCnzGtBvnLtW9AItYu+GuWx6p3yxwQbusEj0l6kafR7z7wqrIanTbt29f3Hzzzbjvvvvw1Vdf0bBNCcxceKV+/foB25966ilMmzZN9nPjxo3Dnj178MMPPwRsf+KJJ5CdnY01a9agRo0a+PzzzzFs2DBs2rQJl156KdO6s8TWJO+TTz7Bjh07sGTJEnxy5nrJfaSMM3PD4YsOapTNbpkTBjCWQdvqFmUAqoK2nOGww2zwNI05FTTRU4uSYdaiXaXGCaeZZTXatSPB09KqrISUdu3ULW+UjZpkwNxY2zTmFJNyhKgxy+3ij6CM8zA/djgh/h1HurzYXyyvJxZmucAXHWCWxTpWGrIphzD+Olm7apFLbrTggc/yuKu1Ny8YwsYAMcLzc7gkDc1j/tVREP0GI1hvnlLdhDFYPGxz9uzZuPjii/Hpp58GrMZImM/Ro0cD5uQF68XLyMjAypUrsXHjRtSrd6FB9+DBg3j99dexZ88etGrVCgDQtm1bbNq0CW+88QbmzZuHWrVq4fTp0wHllZeXIysrC7VqmbOKthpsS/Ly8vIwceJEzJ49WzbB4+HNh1TQ1tOLJwzaRlvlAHW9eI2jzxg+Dk+oG2W5FmVhwOb3CRastS4CoIRQF0YTPrFh1qNdKezQLsDGbIQDUtqVOjdKRlkNLMyGVlibZDmDHO8uYaJfMbzJIypTxnlkY68YqfOoRc9azbL/uAIdnylPlN0v2xvnn5cnRTDtpngKVcVevcmdmiGbUg0UUudcqYEixl3GrGHYjqHGWnrzlBB6LDn9mtFA4T+mKBkVJnqpqamYPXs2JkyYgL59+yIxUV7bVRGOc4FjrD2+vKSkJMWFVziOw/jx4/HZZ59h/fr1aNy4ccD7hYUVscbtDvTeHo8HPl/FGhSdO3dGdnY2tm/fjvbt2wMAvv/+e/h8PnTs2JHJd9KDbatrTpo0Cdu2bcP69eux4EA33eUEC9ZpEXl+sy4XsJWMht7VssRBRk1rstpgrSbJs2MBALXBWo3RkNtHaDRYJHlSwVpIorsIh0rTZd8PZjSUUNtAoVe7gL4VCvVoF2Dbk2fnQgDBUGuSpfYTm2Ql/aoxycH0u7+olmJviBn6vSjqNPJ8ys/nMxp7L445qeo3QLH3AkZirxBed38W15bdh0Xs5ZGKwWbFXjX6ZeEb1DSikHYvwEq7x0qr4eKYk6Zql+fOZj/5/81xHLp3746OHTti1qxZqj4f7vCra1752QRTVtfceuMrqlbXfOCBB7BkyRKsWLEi4Nl4ycnJiI2NRVlZGVq2bInatWtj9uzZqF69Oj7//HNMnjwZK1euxMCBFeuJDBgwAKdOncK8efP8j1C44oorqt4jFHbv3o2OHTti27Zt2Bx5n+5ylExyWkRe0PedFKgBMsla94t3lwQN1AA7oyGX6JHRqICl0QCqhn6V5m8aTfIAaxspLooKHKpihn4vjjkZ8DcleRXYleQJEcdiVvoVItQy69gr1K9R7QLK+mWlXYC8g9b95HQn1LAZid6ePXtw5ZVXYuvWrQGLelRV+CTvik8nmpLk/XLTy6qSPLl5kgsXLsSoUaMAAAcOHMCjjz6KH374Afn5+WjatCkmTZoU8EiFrKwsZGRkBDwM/dVXX7X1YeiWJ3kcx6Fnz5648sor0fyezbrLCdaKDJgXpAFzAnWomwyAXaAOFZMBmGc0zOwJIf1KY7fRCFX9ipM7HhZJnjip01oGQPoVwir+AsraO16mvCiQVv0KOVSabpp+WcVf0m/oNFKIYaFfIDDJA4DJkyfjl19+wffff1/lF2FxSpIXzlg+J2/FihXYu3cvvvjiC2w51xH/lMk/UFotcibDCHUjz+NPb4XpkgvULFvi1ODkuXisxtUbQXidlHr4APUtcUJ4rbEyGOJy1aJlPpOS0SDYotYki9GqXz0I9cuqLNY0iaoYnlzMOfPZjErYMbeJJVrm7gWjbuR5xLguDEk+WGp8YSshvP6yvPpayYPpN9FdpCrRk6NJ1Cn8VtRQ9+e1QnPzLqBGv3Lz6oSw0u/iA10CEr0nnngCTZs2xRdffIHBgwfrKjPcMHNOXlXH0iSvtLQUkydPxvTp07HlXMVExIaRZzUnetU8+ajmkX+Ir9EW5CZRp1DMRVlmjkN9RU21sDAOark45qQ/QOsNzsFakS+KOo1Ed7HuRgqzDDKg3iSbsfBFMJz86ASrGymUDIZQv4CzDHLDyIoHSuf52K06zGvWLliaZAC4OJr9veNAqfEV2lglcHoQXmNWek50FyPRfaHHW208bhh5lql+gcoabhv7j2IMVrMAUaTLy6yxWMtKm07UsFP0C1RoWE2yKCYpKQnTp0/HpEmTMGDAAERFhWZjFks4Ex6hQEleBZYO15wzZw7eeecd7Ny5E+uOVn6uhJoArSY4a03yxD9eNS3ITh1P3yyK/epySoHZiUMthAZZiNBcGBkqVPF+hbnQmuiRhoPDWsNqjAVpWF3sFWJUw7UisoO+7yQNN4uu0KSXc25DhcflC/hbrrfWCRoWk1meIvueWg0LkdOzFg2r6clzkobV6BcIfQ1bMTdPiJKGg2lXiLA3r7y8HO3atcM999yDiRMnqvp8OMIP17x8+cPwMB6u6S0owY6bX6rywzUtS/LOnTuHpk2bYunSpXA1myC7n1JwZmEsDpbWDNpy7JTADISeuQg2DMxJ5iLGVY6/FYytVnOhZJSFBsOouSjwRTtGwwAZZK37sDLISgaDhYbFxphHbyxu9G95xVzwgSRWaviiyNMog/J+oaRhOSJRoc9DZcZiNSsNxwgaWKRisp4kj4fXM2n4AuGkYUBex3ZpGJDWMRCY6H377bcYPnw4/vrrL1SvXl3xGOEIn+RdtvxheOIYJ3mFJdhJSZ51wzVffPFFdOzYEX379sXqw/L7CYdvygVmvTSKPItiLsLw0CCW5ljYwhYOyA1DVGMsrIa/UbMyFrxehUZZSsNGhgnxGnYS9SPP2V0F5gTTcahoWA9CDbOMv40Yx3K9XBRp3jBpu/FybtUmGQh+Lg6VpTOZ11TMRanuzQMCdcJC03ZrOMZVynzIplENe1w+xyZ6WjUMyJ8PuzQMqNNxv379cOWVV2LWrFl4/vnnNZVPEGqxpCcvMzMTTZo0wfr165FdY6TucrS2uomDshpzbEbLm1wQUtPqBoRfy5sUh8rScUnUSRxUYaCVgrKeljcgMBgbaT1WgnTsPFjomE/+7GxBBpyj4+qe4M/oY6HjYBpuItBrqYpGt6qiY6VYzBMl0rFUbGY1qkKJc9542ffMjsVKyZ2ZvXlaNQyo03GoaxhQp+Oj5dUDziFQWcdmxmIxf5fVCOjN27p1K3r27IlDhw6hZk22865DAb4nr+3y/zOlJ++3m/9LPXlWHOT5559Hnz590KFDh6C9eCwwu8VYcdGWfwOK2oBMVMAnEMKArNdU6EWonWCmwgrs1LEeY0Fc0LDQHKtptGCN3TquH3keAFDos25BAbGRI+Qpg0d1oidE6hxnepMN16eYi1A0yLymAOCoaGn7PF+M7kQv0V0sm+hVHPM86dhi9PTmyVE/ovIoEz061tObJ4X4vn7llVeid+/eeP755zFnzhzD5ROEGNOTvGPHjmH+/Pn4+eefsfpwC93lBGtxqx9ZEYiVWo6V0LJkd7BgzNIYO7nFTS16DAVQ+RyzMsxqWtx4UyE2FGZToWVn6JgwjtT5tTLxs1LHQiNu+rEisvz/ZvmYGkIbwusAAEfLq5l/zCAJH+vyWRBsyKYWHUe5vMy8hZOHbKpFb2OFHEa1rKaxAgA2/N0c3Rvt9//9zDPPoHPnzpg0aRLq1q2r6ZjhAj1CwTxMT/JmzpyJa6+9Fm3atGHei6clGBudx8QHAFaGQu3QICfDqrUNqDw0SIomkacDhsKZZSji3Bda7KRMMuvhQUIdm91qrEXH1FhhDlbpWIhZxtiqxE5swAhnIF7S3uqkj9dftlf/M+3EZZmJFTqOhDcs/AUrSjmPor+o5cmptE1Kyyx688S0bdsWgwYNwowZM/DGG28wL5+o2pia5J0+fRrvvPMOtmzZgt1H66FWBJBZnqi5nBRPkd8cSwViFsZY2NImF4itNsZEIOK5TlYFYYB9j4heQ6G1sYLMMTtYthoLscsYG9FyrYg8pHDB5/sZoU5ELgCgjBoHbEONOVaC13bkvw2CJ8r1zY0p9EUFNL6JSfEU+XtRtHqMZlFnDDUCx7jKg36+TkQuDpXVcFwsdnJvntohm6x784JRPyILZ7wJ/tgkpWW1vXlinnjiCXTq1AnTpk1DWlqa4bqGGj7OBRfjnjcnPzPaSkxN8t544w10794dbdq0we6jFdtqReQB0B6IjbSyKQXwOhG5jjQTTg3AWrAqAAMVQdiomVA8RuR5xLjKdTVW8J83E6dqmVAPK2OseBwdWubjtxnw5onQD8v5TGYgvsZmaFutx1Cr5Th3qeaGZPH3vCjyrGJcVvPQc5ZDNolA1DxoPc2T7/+3ES2Lh2y2adMGV199Nd544w1MmzZNdTnhAsdVvFiXSZiY5BUWFuKNN97A0qVLsftovUrvKwViYQA2Y8l44Q9UjSlmOe8jHIZSONlIAIHXVxh89bSySaG1sYLf32wtW004aNnpyGmZFWq0bFZy56TEzophbg0iKg8LC8aRcuMLmzgZ4fXP8sYxLVtO16Rl42jVMY8WPbNssGDRK62EUS1PnjwZw4cPxyOPPIK4OLa/BaLqYlqSt3DhQjRo0ADXXHMN9hyT369WRF5AEBYHYCVTrNTCVs1TiBPlSZYEYJYtbHU9eaoCYoOInEr7hZqRcFLwDTYkCKicJAYzyFXBTJiNHjNht57NxMyET0rLZmi4TkRFa3hxiPU41//3XPgsbCHWo3+3YJTSUZnE3cphbmrhdcFzojyBSbnm9j5fqHMo6bm+4Jyo0fOR8mTdiZ0QrWW4RSPupPTMUstqevPUoEbL4t68Xr16oX79+li0aBEeeOABw3UIJWjhFfMwJcnz+Xx4+eWXMW3aNOw5Vl9xf/PMcD6KObejTLFS65rQSKgNiEaDr1lGolFELo6Xs1nCXU3gjVTR6icMvqxMBKBOw0YaLNQaY6t7pZUIBWOsRstOpE5ELmL8Qzqt1bJWxKbHadSNqFjR1ovwMgb1g1xLDyp+lKxiNGvMitVSKM2rEw/ZtFPPaoZs1o0oYKplFgmeHnxcYHyW03NmeZz/NyynZ1a9eWWcW9FrFHNuf2wG1CV9LpcLkyZNwtNPP437778fLld4xSLCHkxJ8tavX4+cnBwMHToUf576j+5ytJpiPYHXSlPMOvA6hWBGom5EAZkIndhpJNT0SoejnkPVFDtRy05M7HgjaAS3y9pGC7MJdk7OeIM/LBxQ1/uhxhgHQ6ilbJnGML2LXuipgx4iXT4m8/J4lLTsARd28VmOWhGF/n8b1bMV8Fo6cKwOmtU74d9+8803Y+LEiVi/fj169uxpV/Ush3ryzMOUJG/+/PkYNWoUoqLMXQ5eKehaOXyCN8UsTITTEQ+hUCLYObHSMMcEMRm8luQMhBU4Sc88VUHPWnGKnuVQY4jNJM1dAgAos9Fg1vQEPubE2TOInUuapxhSUeeUTWY5EpxfXwBwxhdt+jFJz+GDGj1b0WghR3R0NEaNGoX58+dXqSSPMA/mSd6ZM2fw2WefYffu3ZILrrDACUEXCAy8LH/u4dRKrER9CcNsl4EAYLmBEB4zXPVclagbUVDJRFQFPQuPYzViA0yYj/ice1zAGW+koTLFQ9zUwOuOtbb16DnG5WPWEKekaTfYxehw65nWg/h8Z/kikeYpM6RpPXoGgHvvvRdt2rTBmTNnqszjFOgRCubBPMlbvHgxunbtiubNm/sfm6AH8VBNcdBlYYjVDNWs5i4DUGb4WED4zfcwCz0GgkWrWiQC73RiA8FyKFBVMsVkICqf7yyfMUOsB9aG2KiG9ZriNE9gPPZWcX2JEc9jsgrxdQGMJ36qjy3QYp6B6RV2xOXK562MNC1ArZ69cPmH1BulwvdZo2nxkM3mzZujc+fOWLx4MSZNmsT0WETVw5Qk79FHH8WJ43VQXXD/PufTdjM3Gmz1tqjpMRDU61EZVsGWR3xdrDIPwAUtGjEPwnKshkyxdaiNOqFsiKu7KyJeqQU6kjpPemDZ80GoR3z9sn3mLvoU5XKhuqDBT63vqNB0aVBNR4Iz3Lgc6fIhxW3dqqZVaV6eVQg1HQkXzmj0tmq4++678eKLL1aZJI+ek2ceLo5jdyp+//13dOjQAadPn0ZuTnPJfZSC7gUDEbxaSsFWTZKnJtiyTPLUBFun93qobSFWm+SpCY8eFcdUYx6Uhk6Ie/LERAlWu9LSaFHd7TOsZ0BZ02rNAytNqzUPVUnTalXBQtNqhgKp1bQaPfOxmccsTacJjlOm4pyrbbQgTVeGlabV6BmoMMVCxAaZpaaFSOlbrGfAHN+RJjoOK02z9B2AszXtVN8h1jOgT9MAAnrz8vLykJ6eju3bt6Nly5aqPh+K5ObmIjk5Gc3efxSeOLbTGryFxThw+/PIyclBUhL758uGCkx78pYuXYprr70WCQkJyJVZcZcPrOKgKwy4LMyDHHzAZWke1ECtaZVh2f4lvpHqDbRqkdOx+H2zCTTEylAvXuigpGmWCPUq1LQVOhZ/TyL08HLqEz0h4mufZ1J8EsZr0nToYdcQZCXKwFVK9FhoOjExEddeey2WLl2Kp59+2kgViSoOM9fAcRw++ugj3HLLLao/U93t879YImxNS3P7Al6soVDuTMy85kLE+jVDzwCQKLiPWPXdCGdhl6bNIMXtQorbRRomAkh0XdBGigmu3kxdp7hdqOXhLNN06Dx63TpYNqazahDVq+lbbrkFH330ERgOtnMs/CMUWL8Ihj15v/76K06ePImBAwfixPE6ivubFWxT3C54OTIOdsJyPp7eIRNC0tw+eARDeLJNGJditnnwctaZB8L5hJqmzTDsTqGGx1l2+6yXbZzwwb6EQqgbMzRuFD26joRLcSSRx8UuydAzLy+Yps96fao0z3K/cNU0cEHX4gVYBg4ciNGjR+O3335Du3btrKwiEUYwS/K++OILDBgwALGxsThvoBytQzVDwTyoDbDV3G5/MHOicajhcSPLF/qJhpRx0DPPw0zs1LWZV9hJumZtHOzEiWY4FGKzFNXcztGoHrT8xnJ81i0CYhReT5GomKua7WP/4PMolyuoB4kEh/gQ1QcrXavVF8v9wlXTgHycjIuLQ79+/bBy5cqwT/K4f1+syyQYJnkrV67Egw8+yKo4WZSMg1dF17bV8/GquSuSIzVB1kkmWAhfLzXfIZSCrNA4mGEatNYj1AhlQ6zltxZKjRtCLRVYnPCluC/cUsosGsyutdejmjv4bc9bheyB0rnIsjEmKiHUmtmx2w5dayXYtaxKmk52e+BRaFhXq2s1c02l5uWxYsCAAXjnnXfw+OOPm1I+4SzKysqQmZmJwsJCpKWloVq1aobLZJLknTx5Ejt37kT//v1VDdXUAx9knRRgU9wRAcFCLsh6wYW0GdaKGuNQzR1ha1IlhZWmQXw8J+laCBmHCqq53UGNg1PNsBWaTlH4vRtBzdA2OcysV1Uh2O/frvhdBh8iRYPtxDpnMfLCqfpxar1CCat17eW4gOH1aunfvz/Gjh2L8+fPIzU1lXm9nIIZc+hCZU5eXl4e3n//fXz00UfYunUrSktLwXEcXC4X6tWrh759+2LMmDHo0KGDrvKZRIuvv/4aV155JdLS0pB7oqLIfE77DyXO5UHpv59zaiAT10spoSEqw58z8bk0yzToCa583VjUSTj8R0rXTkjwnPp7CxWcaIbFsEz4ElwR8LmckeQnuyIdU5eqhFTMcMOFHI7Nsw31kuKOgPvfBhmtPoSFrj0ul6oRRWqguGw94nPOa8kKXZ85URdpdY77/65bty5atmyJ1atXY9iwYaYfn7CWl156Cc899xyaNGmC6667Do899hjq1KmD2NhYZGVlYc+ePdi0aRP69u2Ljh074rXXXkOzZs00HYNJBPnqq68waNCggG0JLn3JXrCgxsIMq20VpuCqD6VhEsGwM7jKwdfJDZeuhgthGU7ASXWpKjjRDOvVNR/X7SLZZc0D46sKXnCGYrYUUteI17qZQ9ukEOo1mM6t1rW4h1pS1y7Ax2i0hAeuKjXygiU+cHDDJRt7crgy04dsrlq1KryTvCo6KW/btm3YuHEjWrVqJfn+lVdeibvuugtz587FokWLsGnTJuuTvPLycqxduxaPPfYYck80qPS+UpAVvm80oOlpPZP64bIKrEDVGtJmFuJrJDTHTjQM4n1Z6kkOqcYLs7VdlWBthIHgZthKtMRoK0l0V5wfVr0ihH0Itc6PrMjzSWtd79A2JcQNz3p1HQm3oQZn0rX5mNF4IQevbaFmpbRtZMjmiBEj/EP4whIzHnkQAsM1P/zwQ1X7lZeX47777tN1DMMTxbZt24aIiAhcdtllivvyQTXBFeF/WUmiOxLJrsAXEXoIr1+iO9J/09SLeH6HGuT0a4euAVTSNTVehCZibVuNWL9Wapn/LbP4TRPOR3y9rbrmSjHazTg5IF1XPYxc8zMn6gb83bVrV+Tl5eG3335jWUXCIcyZMyfo+3l5eejXr5/u8g3fwVevXo3evXvD4/Go2l8uuCoZUD0tZxRQqw7iay3XSqyHYDd9K0ywuMVXStfUKhw6aDGRUteapbblsELXca4IWNgRT4QAYr0X6hwibyeka0KMVBxXq+2oqCj07t0bq1atCttHKXBcxYt1maHAY489hurVq2PkyJGV3isoKED//v1x7tw53eUb7sn77rvv0KdPH6PFGCbOFaHYekJGuOog1EGczfOIjEItwYQQoa5DSdt8fUOt3oR9hIpmQqGOhLlo9ZdatN2/f3988803RqpHOJT33nsPY8eOxRdffBGwvaCgAP369cOZM2ewbt063eUbikj5+fnYsmUL3n//fcn5eGZiVjClIW3hiVgvTm4hJqNAaMGp2k5wRcHngJVjifBBqHU33CiR0Tq/WIYV9TATN1y0+IpOWM7LM1tPQHBN9e/fHxkZGcjPz0dCQoKp9bCDqvwIhZtvvhnZ2dm49dZb8dVXX6FHjx7+HrxTp05hw4YNqF27tu7yDUWqH374AfXr10ejRo2Qe8JIScqQ8WVPnCsShSoWelC7n1UTnVkg1FMZZ68RJW0TLLFT29FVQMtxITiXW038DkWEepNL+Mw4FovGC5aPWmAFadsZFJxsiPja//j/btiwIerXr48ff/zR0Pwswpncc889yMrKwuDBg7FixQo8+eSTOHHiBDZs2IA6dYw9e9zQHXndunXo2bOnoQoA8r1nrIOqE7E7qKo9Pr+fVQFVTasZqxXYxMaUpVkgbYcG4WgUAHO1LXeMUCRWoNVwXIE22G9RHGuLQvS3INZhGedlWl6oQtoOJFT13bNnT6xbty48kzzOxX41zBDpyeN55JFHkJWVhV69eqFRo0ZYv3496tWrZ7hcw0negw8+CIBNi5pcUA0lE0wB9QKhGkzNaB0mbTsbpYTUam2bsYQ8wDZOh4oGYkOoscEJqDlfRvRv1iMSxOjRemDjm7y+3XA7Inbz16qIKyOdq8RMfZup7Z49e+K1114zpWzCPm666aaAvyMjI1GjRg1MmDAhYPunn36qq3zdSV5OTg62b9+Onj17ouTkRQHvaQmu0a4IS8yCmmERaupBgVQ9SucqFJLAaFeE3+BrNcVWaZsVpG31VDVtO71Xg7RrLcHOt9nDJvUQzJOEg7ZJ/9LonUvnRH336NEDo0aNQk5ODpKTk22pg1lU5dU1xdfy1ltvZVq+7ui2ceNGNGnSBHXr1kXJSfn9+ADK/zCcHFBZBspQMvd24YRA6tawwKyaxouqom8iOErn2mlGWE7bTtOz0+pDVCbYNXKC7p2qIafWiwhE7jqx1rZ4Xl69evXQuHFjbNq0Cddeey3TY9kO9++LdZkhwMKFC00tX3dU0TofT364mj1XggKqs7EqkCoh1wroJP2Ie6mdVDdCGqfoWwqn6Cf630TZ64BhceGMFSsH8vDa8rguNK6VhECvN2vs0jbL1TqJQKS0DbDVNz8vL+ySPMI0dD8nj9WiK0oYHfce7Yr89xUR8CLYYqVJuPCK9L+qKqTt8CKUtM3qNy/8rqHwvQm2hPv1D/bdxAkBEfp4RSsas9Q2n+SFG/wjFFi/nM59992HY8eOqdp36dKl+OCDDzQfQ5cjzMrKwm+//YYePXpUmo9nJ3I/HvGPjgg/5K59uLQSh5PpIbQRbtomLRNqkNKJFs1b2UPJQ9om1KA3pvfo0QO33347srKyUK1aNTOqRlhIWloaWrVqha5du+K6667DFVdcgTp16iAmJgbnz5/HH3/8gR9++AEfffQR6tSpg/nz52s+hq4kb9OmTbj44otRq1atoPPxzISCKaEGoU5CxRSTtgk1iHXiRH2TlgmWiPVk9DEJRiF9Ox87kn29iPVUntkUEbX+8v9du3ZtNG/eHD/88AOuv/56q6tnLlVwFPH06dORkZGBt99+G2+++Sb++OOPgPcTExPRu3dvzJ8/H/3799d1DF1J3s8//4wuXbroOqBWQjGI0ph389EzzEVKS2QStEP6diZO0Heky2Pp8YiqjZTezNR84PFI64T1dO7cGT///HP4JXlVlJo1a2Lq1KmYOnUqzp8/jyNHjqCoqAg1atRAkyZN4DL4SA5dSd6WLVswfPhwABVmW+9wSCmzSCaBsBKx3lgaBNI3YTdm6luqfIKwG7Em9foTN1w0Z45wHB07dsTHH39sdzWYYsYculCYkycmNTUVqampTMvUnOR5vV5s27YNc+bMQXlmUwCBvSpaAioZBMJpsDIIUmURhN0Y1XdVML1qViDU2gPP+sHZWh79wuOk4bxezmeZlsTHCaZ50ncFpG9jmKnvjh07YtKkSfB6vfB4yGMQwdGc5P3xxx/w+Xxo1aoVcLby+0oBtSoEUTVoCaIUQO1Di0GQ2r+qYqe+Ae0aJ31XUNXidWQVWo1W79DwMgc81oMl4a5pIaRvdYSSxlu3bg2fz4e9e/eidevWdleHDVX4OXlmozkCbN++HZdffjkiIiKg5mdhd0A1c2VNCqDKhFLwVIPderaaqqJx0ncF4aLvqqJbK1B7LsPtt+BkSN9scbLGxYuvRERE4LLLLsMvv/wSPkkeYRqaI8WuXbvQtm1bM+riGCiAssPJwbMqQxpng5bzSBpnA2nXmai5LvQbqIzUugZVXeM++HSNODIbp2i8bdu22L17t+nHsQ7Xvy/WZRKaf0W7du1CmzZtzKiLZUS6IoK+COuha8IOpXNJ59Me6Jqoxy34j85T+CC8jm7Rf1UJ8XcnjYcPcvpmqfE2bdpg165dzMqzHc6kl0pmzpyJDh06IDExEenp6bjhhhvw559/+t//+++/4XK5JF/Lli3z73fkyBEMGjQIcXFxSE9Px+TJk1Ferj7pLy8vx5o1a/C///0PeXl5AIATJ04gPz9f/ZcRoVl1u3fvdnySFyx4UgANTcTXUClwhrtpIIMQflQ1AyxlgsL5+xLBCUc9hNv3IYwhpQM9mmjTpk2Y9eTZy4YNGzBu3Dj8/PPPWL16NcrKytC3b18UFBQAAOrXr4+TJ08GvJ5++mkkJCRgwIABACoWpRw0aBBKS0vx008/YfHixVi0aBGefPJJVXX4559/cOmll2Lw4MEYN24czpw5AwB44YUXMGnSJN3fzcVxnOp899SpU6hVqxby8vIQk99O90FZUFUCpRMWpQhlzDh/VlFVrhNp3BhO13hVuhZyOGHxrHDEKdqn60EaNwsffAFz8gAgLy8PSUlJyMzMRM2aNW2qmXFyc3ORnJyM+m9Ogzs2hmnZvqJiHH1gGnJycpCUlKTps2fOnEF6ejo2bNiAbt26Se5z2WWX4fLLL8eCBQsAAN988w2uvfZanDhxwn9N5s2bh//85z84c+YMoqKigh7zhhtuQGJiIhYsWIDq1avjt99+w0UXXYT169fj3nvvxYEDBzR9Bx5Nv6K9e/eiQYMGSEhI0HUwPVBLGGGEUNBPKNSRcC5O0o9T6kFUDazWvpN+a0TVQEpfiYmJaNCgAfbt22dDjcKfnJwcAEC1atUk39++fTt+/fVX3H333f5tmzdvxqWXXhqQdPfr1w+5ubn4/fffFY+5adMmPP7445WSwUaNGuH48eN6vgYAjQuvHDhwAM2bNwcQKDwWLTgUKAkrkdKb2a3CpHHCSszUOGmZcDIstE8aJ5yCL7M53LX2B2xr1qwZDhw4gO7du9tUK4ZwrooX6zJR0VsoJDo6GtHR0bIf8/l8mDhxIrp27Sq7eumCBQvQokULdOnSxb9NqleV/zszM1Oxuj6fD16vt9L2Y8eOITExUfHzcmiKYvv37/cneYGFaGvVopYwwomwbKUljRNORI/GSctEOBBMx6RxItRo3rw59u/fr7xjFad+/fpITk72v2bOnBl0/3HjxmHPnj346KOPJN8vKirCkiVLAnrxWNC3b1+8/PLL/r9dLhfy8/Px1FNPYeDAgbrL1dSTt3//flxzzTWK+/EB0qnL4FZl6HpoR6lVmM6ps6DroR05jdO5dCZ0bdhA59C5kMaD07x5c6xfv97uajCB4yperMsEgKNHjwbMyQvWi5eRkYGVK1di48aNqFevnuQ+y5cvR2FhIUaOHBmwvVatWti6dWvAtlOnTvnfU2L27Nno378/WrZsieLiYtx22204cOAAatSogQ8//FDx83Jo+gX99ddfaNasmYbC6QdKhCfU4kuEO6Rtdsj1oBr5jyCchBbdksaNww/XJIKTlJQU8JJK8jiOQ0ZGBj777DN8//33aNy4sWx5CxYswPXXX4+0tLSA7Z07d8bu3btx+vRp/7bVq1cjKSkJLVu2VKxn/fr18dtvv2Hq1Kl46KGHcNlll+H555/Hzp07kZ6eruEbB6KpJ+/o0aNo0KCB7oMR2qFAR4Q7pHGCIIiqAcV7NjRo0ADHjh2zuxps0PhcO9VlqmTcuHFYsmQJVqxYgcTERP8cuuTkZMTGxvr3++uvv7Bx40Z8/fXXlcro27cvWrZsiTvuuAMvvvgiMjMz8fjjj2PcuHFBew8BoKysDJdccglWrlyJESNGYMSIEeorr4DqX1tubi7y8vJQt25d+DIrz8sjCIIgCIIgCMJc6tat6/flIQ+/8Arrl0rmzp2LnJwc9OjRA7Vr1/a/li5dGrDfO++8g3r16qFv376VyvB4PFi5ciU8Hg86d+6M22+/HSNHjsQzzzyjePzIyEgUFxerrq8WVD8nb+/evbjiiiuQn58P7tTFplSGIAiCIAiCIIgLiFfX5DgO8fHx2LFjBy655BKbamUM/jl59V59xpTn5B178Eldz8mzgxkzZmD//v14++23ERGhaZBlUFSXdPz4cdStWxcul4t5rypBEARBEARBEJURP0bB5XKhbt26OH78eMgmeTwuruLFusxQYtu2bVi7di2+++47XHrppYiPjw94/9NPP9VVruok79ixY6hbt66ugxAEQRAEQRAEwYa6deuGz7y8Kk5KSgqGDBnCvFxNPXlyS4oSBEEQBEEQBGEN9erVw/Hjx+2uhnFsXnjFCSxcuNCUclUvvEI9eQRBEARBEARhP9STRyihqSevVatWZtaFIAiCIAiCIAgF6tWrhzVr1thdDeNoXA1TdZkhROPGjeFyydf50KFDusrVvPAKQRAEQRAEQRD2QT154cPEiRMD/i4rK8POnTuxatUqTJ48WXe5qpO806dPG3rqOkEQBEEQBEEQxklPT8eZM2fsroZxaE4eJkyYILn9jTfewC+//KK7XNVz8rKzs5Gamqr7QARBEARBEARBGCc1NRXZ2dl2V8M4nEmvMGDAgAH45JNPdH9eVZJXVlaG/Px8pKamwpfZXPfBCIIgCIIgCIIwRmpqKvLy8lBeXm53VQiTWL58OapVq6b786qGa/ItBSkpKUCO7mMRBEEQBEEQBGGQlJQUABUevUaNGvZWxgg0XBOXXXZZwMIrHMchMzMTZ86cwZtvvqm7XFVJ3vnz5xEdHY3Y2Fj4KMkjCIIgCIIgCNuIjY1FVFQUzp8/H9pJHoHBgwcHJHlutxtpaWno0aMHLrnkEt3lqu7Jo/l4BEEQBEEQBGE/LpcrPObl0SMUMG3aNFPKVTUn7/z585TkEQRBEARBEIRDSE1Nxfnz5+2uBmEQj8eD06dPV9p+7tw5eDwe3eWqHq7Jj/0lCIIgCIIgCMJeUlJSQj7Jc3EVL9ZlhhIcJ13hkpISREVF6S5XdZJHPXkEQRAEQRAE4QyoJy+0efXVVwFUDL19++23kZCQ4H/P6/Vi48aN5s/Jy8vLQ1JSku6DEARBEARBEAShD19mc7hr7Q/YlpSUhNzcXJtqxIgqvLrmnDlzAFT05M2bNy9gaGZUVBQaNWqEefPm6S5fVZJXWlpqqLuQIAiCIAiCIAh2REVFoayszO5qEDo5fPgwAKBnz5749NNPmY+apCSPIAiCIAiCIEKMqKgolJaW2l0NwiDr1q0zpVxVSV5ZWRkiIyNNqQBBEARBEARBENqIjIwM+Z48F0xYeIVtcZZw7NgxfPHFFzhy5EilxP2ll17SVSYleQRBEARBEAQRYtBwzfBg7dq1uP7663HRRRdh3759aN26Nf7++29wHIfLL79cd7mqnpNHEARBEARBEATBFP5h6KxfIcSUKVMwadIk7N69GzExMfjkk09w9OhRdO/eHUOHDtVdrqokLxy6gwmCIAiCIAgiXCgtLaWRdmHA3r17MXLkSABAREQEioqKkJCQgGeeeQYvvPCC7nIpySMIgiAIgiCIECMsplNxJr1CiPj4eP88vNq1a+PgwYP+986ePau7XFVz8mj1HoIgCIIgCIJwDrT6fXjQqVMn/PDDD2jRogUGDhyI//u//8Pu3bvx6aefolOnTrrLpSSPIAiCIAiCIEKMsBiuWYUfhs7z0ksvIT8/HwDw9NNPIz8/H0uXLkWzZs10r6wJqEzyEhMTkZubq/sgBEEQBEEQBEHow11rf6Vtubm5SEpKsqE27HBxJjxCIYSSPK/Xi2PHjqFNmzYAKoZuzps3j0nZqubkpaam4vz580wOSBAEQRAEQRCEMc6fP4/U1FS7q0EYwOPxoG/fvqbkWaqTvOzsbOYHJwiCIAiCIAhCO9nZ2aGf5NHCK2jdujUOHTrEvFzqySMIgiAIgiCIEIN68sKDZ599FpMmTcLKlStx8uRJ5ObmBrz0ompOXkpKCiV5BEEQBEEQBOEAOI7D+fPnkZKSYndVjEELr2DgwIEAgOuvvx4u14UHuXMcB5fLBa/Xq6tcVUleamoqSkpKUFRUhGhdhyEIgiAIgiAIggVFRUUoLS2lnrwwYN26daaUq7onD6gY+1vTFXxfgiAIgiAIgiDMg18rI9R78qr66poA0L17d1PKVTUnLzIyEgkJCTh//rzkEq4EQRAEQRAEQVjD+fPnkZiYiIgIVf01hMPZtGkTbr/9dnTp0gXHjx8HALz33nv44YcfdJepKskDaF4eQRAEQRAEQTiBsJiPBwCcy5xXCPHJJ5+gX79+iI2NxY4dO1BSUgIAyMnJwYwZM3SXqzrJS09Px+nTp3UfiCAIgiAIgiAI45w+fRppaWl2V8M49AgFPPvss5g3bx7eeustREZG+rd37doVO3bs0F2u6iSvbt26/u5DgiAIgiAIgiDs4fjx46hXr57d1SAY8Oeff6Jbt26VticnJxt6TrmmJO/YsWO6D0QQBEEQBEEQhHGOHTuGunXr2l0Nw/ALr7B+hRK1atXCX3/9VWn7Dz/8gIsuukh3uaqTvHr16lFPHkEQBEEQBEHYDPXkhQ/33nsvJkyYgC1btsDlcuHEiRP44IMPMGnSJNx///26y1W9JE/dunWxZs0a3QciCIIgCIIgCMI4x44dQ58+feyuhnHoYeh49NFH4fP50KtXLxQWFqJbt26Ijo7GpEmTMH78eN3lqk7yqCePIAiCIAiCIOyHevLCB5fLhalTp2Ly5Mn466+/kJ+fj5YtWyIhIcFQuZp68o4fPw6OC7H0mCAIgiAIgiBCFPEzqjmOw/Hjx8NiTh7MmEMXoqlKVFQUEhMTkZiYaDjBAzQuvFJYWGholReCIAiCIAiCIPRz/vx5FBUVhUeSR6C8vBxPPPEEkpOT0ahRIzRq1AjJycl4/PHHUVZWprtc1T15SUlJSExMxPHjx5Haej98mc11H5QgCIIgCIIgCO0cP37c78tDHpqTh/Hjx+PTTz/Fiy++iM6dOwMANm/ejGnTpuHcuXOYO3eurnJVJ3kAUL9+fRw5cgStW7fWdTCCIAiCqGr44GNeplv9QByCMB3SuLUcOXIkfObjUZKHJUuW4KOPPsKAAQP829q0aYP69evj1ltvtSbJa9q0KQ4cOBB0H+EPnX6gxmEdOOmaEE6DNO5c+GtD55QIR8ivEKHKgQMH0KxZM7urQTAiOjoajRo1qrS9cePGiIqK0l2upqjWvHlz7N8fOPnTJ/qPcDZ0jdhAencudF3YIDyP4jhP59heKCHRjpKGSePOgjQenP3796N58/CYNkUPQwcyMjIwffp0lJSU+LeVlJTgueeeQ0ZGhu5yNfXkNW/eHB9//DEAdUZKah/64RKhRjCtk8aJcECPoSXtE06EZXJGGiecyv79+zF8+HC7q0EwYufOnVi7di3q1auHtm3bAgB+++03lJaWolevXrjpppv8+3766aeqy9WU5DVr1qxST55W5AIwBU7CCbAwCKRxwsmY2UNB2iesxI7eNtI4YRW81iJq/VXpPRquGV6kpKRgyJAhAdvq169vuFxNSV6LFi1w5MgR5OfnI8bwoQOhwElYiVPMAembMAsnDTej+E7oxUk6DgZpnNCLVo3n5eXhyJEjuOSSS0yqUdVi5syZ+PTTT7Fv3z7ExsaiS5cueOGFF3DxxRcH7Ld582ZMnToVW7ZsgcfjQbt27fDtt98iNjYWAJCVlYXx48fjyy+/hNvtxpAhQ/DKK6+oet7dwoULTflumqJPzZo1kZ6ejj179ki2LJiB1Dj5UAn6LKAbhHGcrB85fTupjoSzCWX9hHLdCbaEqxbC9XsJCbfvYwYs7/V79uxBzZo1UbNmTRNqagOcSS+VbNiwAePGjcPPP/+M1atXo6ysDH379kVBQYF/n82bN6N///7o27cvtm7dim3btiEjIwNu9wWPPmLECPz+++9YvXo1Vq5ciY0bN2LMmDE6TwobNPXkAcCll16KXbt2oVOnTmbURzXBfhiUGFU9wvEmQy3DhJBw1Lgcwu/q5Sp/70iX5lsXYTNlXHnA3x5X1Y5jpPHwQ6hxM/W9a9cuXHrppaaVX9VYtWpVwN+LFi1Ceno6tm/fjm7dugEAHnroITz44IN49NFH/fsJe/r27t2LVatWYdu2bbjiiisAAK+99hoGDhyI2bNno06dOkHrcO7cOTz55JNYt24dTp8+DZ8vMCZkZWXp+m6ao0ibNm2wa9cuXQezimDBkwJn6GJVAHU6YrNPGg8PSN/qEScMYug3YD1K14TQBmncWThJ37t27UKbNm3srgYzzFgN00h5OTk5AIBq1aoBAE6fPo0tW7ZgxIgR6NKlCw4ePIhLLrkEzz33HK666ioAFT19KSkp/gQPAHr37g23240tW7bgxhtvDHrMO+64A3/99Rfuvvtu1KxZEy6XS/8XEKAryVuwYAGTg9uBmh8qBU/rcVIADXXIHDgT0rh1qD3X9FtQhnRrLlK9eGoIZ41bOVolFPX922+/4Z577rG7Gmwx6ZEHubm5AX9HR0cjOjpadn+fz4eJEyeia9euaN26NQDg0KFDAIBp06Zh9uzZaNeuHd5991306tULe/bsQbNmzZCZmYn09PSAsiIiIlCtWjVkZmYq1nPTpk344Ycf/CtrskLzr799+/YYN24cyssr/zCkgpXdLdIel1tzEA3n4Gk1oRhA1eLlfLbrWw+kb7aEu8aFhKLegyF37XwKjiPaFWlGdUylhCuTfc8NNq3GTkeNF6gKGlfSNxBaGg+mbSC09S1e/6K8vBw7d+4M6DEi5BGvUPnUU09h2rRpsvuPGzcOe/bswQ8//ODfxg+dHDt2LEaPHg0AuOyyy7B27Vq88847mDlzpuF6XnLJJSgqKjJcjhjNLq5ly5Zwu934/fff0aqmctAUvl8VgifgfIPgg09zS1k4B1EtiPUeziZYr74B+zWuBzLBFQSL6eGsdy0oxUPz8dp8/NBATy9ZOHsWLdircdK3HHv27IHb7UaLFi3srgo7NC6UorpMAEePHkVSUpJ/c7BevIyMDP+CKfXq1fNvr127NoCK/EcI/8QBAKhVqxZOnz4d8H55eTmysrJQq1Ytxeq++eabePTRR/Hkk0+idevWiIwM9FDC76AFzUmex+NBhw4dsGXLFrQd8xe8Jy9S/VkyCBXYbxAACqLq0GoSyCBUYL/GSd9q0DtUjKeMCzzPkS6PofKcgJpGDMIYZsVGsR6NNs6IywNCX+Okb/Mx896/ZcsWXHnllfB4QluHVpGUlKSYIHEch/Hjx+Ozzz7D+vXr0bhx44D3GzVqhDp16uDPP/8M2L5//34MGDAAANC5c2dkZ2dj+/btaN++PQDg+++/h8/nQ8eOHRXrmZKSgtzcXFxzzTWV6uZyueD16vM0usZjdezYEVu2bDG8NKiX81UKOKEeQInQR3hjN2ISSN+EE2Glb6WyeUjzhFlI6Y0VcsmQ8Jg++EJy1AIRGpRwZZVM+pYtW1QlDaGE3QuvjBs3DkuWLMGKFSuQmJjon0OXnJyM2NhYuFwuTJ48GU899RTatm2Ldu3aYfHixdi3bx+WL18OoKJXr3///rj33nsxb948lJWVISMjA8OHD1dcWROoePxCZGQklixZYu/CKwDQqVOngGVEWRIOLcNuuKi1zGRYzYcz0ySoOR7pmzATq/UtxgcfSkS9hWSKCT1IjQ6w83Ey/CrH4nqRvgk9qB39snnzZrzwwgsm16ZqMXfuXABAjx49ArYvXLgQo0aNAgBMnDgRxcXFeOihh5CVlYW2bdti9erVaNKkiX//Dz74ABkZGejVq5f/Yeivvvqqqjrs2bMHO3furPQAdqO4OI7T7NaysrJQo0YNnDhxAqlcF0MVUDKLUs+G0hpEjQ5J0kNVM8F2zFnSk+SJA6mSSTD6vUjf4YHV+tbbgBEK+pYimOat1ndV0zbgPH2rMbxqEjwj34uVtgHSt5040ZuoTejia/8T8PfJkydRr149nD17Fqmpqbrr5xRyc3ORnJyMZpNnwBMdw7Rsb0kxDsx6DDk5Obrns1lJt27d8OSTT6J3795My9XVk1etWjW0bdsW69evx/Dhh1CiYV4eC6R+IMGCqJ4VNonQx/55YfrQqm+iahKq+paCNF91MUvHTlosifRdNWGt7fXr16Nt27ZhkeARgYwfPx4TJkzA5MmTcemll1ZaeEXvcxF1r5Hes2dPrFu3DsOHD9dbhCrccKtqMaMgai8+cLbeVMPJ8EpB+q7aOFHfZvcI8N/ZKxpsEk2P9ghJSgSr9XoclIDZRQlXRtoOEwK07XKh3KRh8uvWrUPPnj1NKdtO7J6T5wRuueUWAMBdd93l3+ZyuexZeAWoSPL+7//+L+g+QuHLBS8z5/cIjZEwmFIgDW14XVlhFOxOXoNB+g4/rNS2GtTEcKspkXm0h1PqF2qwjm9y18cpSNXPKdoxu25Vbagma+zW9rp16zBnzhxb60CYw+HDh00pV3f06NatGw4ePIjjx4+jhmD4sdyPgN/uhGAqV0cfOMQy6h2hxSmMYVUw1fPMQB5xHZ2gbYBMsNOx2yioQaqOTkz4eHzgUBSkt5NVXCcqCHaundgopvSbc7K2g51rgLTNGqdoWzwf79ixYzh8+DC6detmWR0sw8Tn5IUKDRs2NKVc3dEsOTkZ7du3x/r16zFixCHknmig6nN2BVOPy1VpaIQYN1xkFCxG7nw70SgAwc2Ck40CEPzmRdpmTzhpW2pfvhHLiVoXQ0ZZPUrnKhhO0XYJV667LvzvwAcuLLQNVF19izUQytpet24d2rdvHxKLiGiGkjwAwHvvvYd58+bh8OHD2Lx5Mxo2bIiXX34ZjRs3xuDBg3WVaSiC8fPyRowYoevzaoOp2nl5ZkPBVBtGAqqT0NPzQtoOb8zWtpfj4GH0nJxgsOhVdGqPthaE19Orwh3EhdBvoVBBq1YPDTZL1yx7yPkGDDltOyFma4HXN2nbXMyM2eE6H4+oYO7cuXjyyScxceJEPPfcc/45eCkpKXj55ZftS/LGjRtnpAgAFS0k4WAUgPA2C0DwoMoyoKqZC2eWERZr0UgLHmk7dLBK23bCUttqjhHJ4FmWTkTJXBLmY8ewZ/6Y4aprgLTtVNatW4d58+bZXQ1ToIVXgNdeew1vvfUWbrjhBjz//PP+7VdccQUmTZqku1xDbvOqq67CkSNH8M8//yCVsZ8TB/BwDarhFFC94ELWDFtpGEjbhJXYOQewUHTsuBBt4CDsR6ilSBsfgg5U1jWgXdtK00eIqoNYT2l1jgf8/ffff+PYsWPo2rWrldUiLOTw4cO47LLLKm2Pjo5GQUGB7nINRcqEhAR07twZ3377LZLqHDFSlCKFXHnAyyxYtmyHasJTVeC1ZPdCGFbomqg6CPVkp7bLJIa0WRXHidDHiFbsmD9lhq5ZLt6mZvRFOMHSf7HWk1Ztr169Gp06dUJCQgLTejgGzqRXCNG4cWP8+uuvlbavWrUKLVq00F2u4WbVAQMGYNWqVRgzZozRojShp4VYzeIrRPgSCqaSej4IrYSCruXI8wX29ia6Q2uIL8EOoRasmI9qJqTrqoecZlnoevXq1ejbt6+uzxLO5plnnsGkSZPw8MMPY9y4cSguLgbHcdi6dSs+/PBDzJw5E2+//bbu8l0cZyzr2blzJ7p3746zZ8+i+GxT2f3y/zUiCUFMq1KrlVTLsBBhAicXVNUkedR6ph+rW8+CBU0+uCoFVjVDf4y05BnVNaCsbdK1ubDulTeibbW6BpS1rVSPYHGbta6lSHRHkrZNxq6YLU6EpPaRw4iu8wWNInJ+hKUXkYJ0bT6hqGse4XBNr9eLtLQ0fP311+jUqZOqz4cKubm5SE5OxsUTZsATHcO0bG9JMf585THk5OQ4ekVSj8eDkydPIj09HR988AGmTZuGgwcPAgDq1KmDp59+Gnfffbfu8g13E7Rr1w7x8fH46aefcHnzwPfyJVqY87nyoIkeK6R+aNSaFt4EC67BKIPP0BwPNQ0YLNHbSszy2Y0euKqcabALvbo2ijB+W61xnjxfGcpEOksOsQV9CCBHMD83krO+l07Ki4i3J/hXzjQ/rpGuwwMzdC2ej7djxw74fD5cccUVTMonnIWwn23EiBEYMWIECgsLkZ+fj/T0dMPlG75ju1wu9O/fH6tWrUKPHkeQe6KBbEDlUdOSpgel4ZhqAyuZYefjD64cEGnD/Au5BgweK80wnwQItU2GgS1WLSrkVDMs9X6cy2N2dSTJkVjQJ5GGNTsGqeujBVZDNZW0LLe/kq7V9FDrgXTNDtax2qimAf26/vbbb9GrVy9ERISvFqr66poukTbi4uIQFxfHpGwmqunfvz9mzJiB559/XndgVWOKI+E2HGAj4Qoww1I/XjLI+mFphvnHKLAIsKxQq2+7ej54SNfOJxR1LSTbd+EzKW57DYiwLkLsrpfTMRKrxefco6Iosxvk+DpFGUgUrdC1uLFZCi8HZMv8LrXWixqa1SEVR+zW9VdffYV7773XtPIdQRV/GHrz5s0rJXpisrKydJXNJIL16dMHI0aMwIkTJ3SXwZuM0n974uy8OedwZfBKCIQMg7noDbBKGH2eXravXLdpEOpaTj8sGi/UQLq2B7N0bRQjupYqi8dJehLWS/gLq+agOjqZrH/Pn5Mf8iKX4LMuW62urVjcjXStnyzBuXOarsVDNc+cOYNt27bhs88+s6lGhBU8/fTTSE5ONqVsJhGhWrVquPLKK7Fq1SrcddcJnDhex3CZTjQNUjcTPsBScFVPVTUO4jKdqmsyDfrI8pU7WtOAuYZY6hjxbmNnRE2Phx6ygpwHL1yoZrDeoUKw8+BUIuEOqmNWDRdi+GMa1TRLxE2DctfT+29PU1XQdZbPB4+KuGHlmdDbyPzNN9/g8ssvR61atRjXyFlU9eGaw4cPZzL/TgpmDm7AgAH45ptvcNddd7Eq0o8W06DmMQniIZvS5UCy10MOMg0XCEXjAFhjgOWO5yTjIIR0fQHStTbK4EK270IQTXGbY761xGnFsv41w1m+4D3rgq+FGh7n/AbOeivqrfZU2zOrUjtCHUXC3t+hsC6Aebo2g1DUNa9pQJ2u1SR4ajF7qKZYS2mi91euXIlrr71Wd/mE81EapmkUZknetddeixdffBFFRUWGyolyufxDNqUo8PlQJvhRhUKA9YBTDK48/G/eKQGWR4t5CBXjAAiDrBfBZqyVcpxpLcSB9bBf025UbiGWQ42uw0HTQGjq2m5DLMRJGmeJ0IQS7BAbYCsI5j0ABHgPnnDRtfh0O03XIXxq/WjRdGFhIb755htMmTLFxBo5hCo8J8/gU+wUYZbktWvXDrVr18bXX3+NIUOCD9k853OjuptNAAmXACvGaQFWC164mLWmeTnl1rQycJpa0+wwD1ogTRN6MEPXLGO1EL6uxVxF4p9mwjEINljVNHPGV3GkGJd5Wjj37zHM0DRQoWslTatddIUVXhtWn3Y6TtR0s3qBa1p8/fXXqFOnDtq1a2dG1QiH4FPZAaQXZkmey+XC8OHDsXTpUgwZMkRyHz7A8v9mHWiFJqeYc1tiHLT0ehD24fTETg47NE2EBlZp2mxjDFwwQwAlfFUJ4XUHzEvwzomOY5am+QQPMF/T9CupjNVDNcWI9Qzo1/TSpUsxfPhw04fzOYIq3JNnNkxXVbjlllvQoUMH5OXlBWwXB1jxdnGgVRqyGQlOctiEGPEPThhozZiXF7QscNSiJsIH81rUWAZbJaT0bZYhDqZpNbDUNGEtdhliqe1WJXwAkOL2mnYsgg1qDbFUXDYLOR3L7WOVpknP2nHqgJZsnzmD+PPy8rBy5Uo8/fTTppRPVB2YJnmtWrVCkyZN8MUXX6BnD3VBFiDzIMTtqjw23kn4OGcGXCvNA6CsbXEDBquGCzF2apoaLsxFSdPFnJtpoqc2Xgv3TXSZq7cyzo0zXlHDhsc5zxcMZVj2ekhxxhs4yzmSgVYjFeoc5XJp0rGQcz43irkIpLlLdH1eLeLzAtivaSd7DtYYcQpaNa02PouHaq5YsQLNmjVDy5YttVUwRHH9+2JdJsE4yQOAO++8EwsWLMCIESdw7mg9zZ9nZR5iXL6AoRNSsAq2NGSzMizn5ckhvH4sDIQSesyD8DNmG2KgsqbtNg9VDSO902aYYiX0GmIAKOYiUMxduIWYbY55pOJ2NXdwnVN8Nh+p62LZsX3RAIAYl/6Fhngt82WJ9aynEU5MmYwnEZ87JT2rhRrh9KOkZzPj84IFCzBy5EjTynccNFzTNExJ8qZOnYr9+/cDsfrLyeM8/qArZR709nwElOHyVQq6UiaZhmzaC7/4SrCgW8a5DQfdMrhkW4pZmAi+DMDZhpg1Tu+dtotQN8VyZarVtlIjnFrKOA9OeSsPm6rpKWZSfqhh9kiLU96YStsiLWjAEiOMp2aXb1W8BkjPYtSvfqzvJmO3nsW9ePv378fmzZuxbNkyy+pAhC/Mk7y0tDTceOONePvtt/Hii8ewW0dvnhi7gi1QYcTKOGsDblUyxXI9H+LAG24mQqshZonUTa2qGgirCHc9yx0nxV1qyTGlEJ7z0n9jeN2IAruqE5IcL49HlEVaVTO0zSoNSx1XScusGi3kMEvPVcVrHC+PBwBH6VmKt956CzfeeCNq1KjBuEbOpao/DN1MmCd5ADBmzBjccsstmD59OvOytRgINUM29SJllEs5T5UwESzn5VlpItTMYbLaRDhVz2SK9WGlntVglykGgEJfFAp9Uf6/60Tk21YXHt7oSVFVtR7snDhByyfKE/z/jrOx0UBYD4D07ESCnQ9AnZ7VNMAZGTUk1lEzwb9LSkqwaNEifPzxx7rLJwghpiR5PXr0QGpqKpYvX4423fSXE+MqD5jzISabgYmQGrJZeR+vZG+eFBR0pVEKvnbDB16nmAi7DUSUy4tSzkN6loH0rB0n6VuK4+XxKFN45H39iLyQ6vk4Wp4o+14k2CRwZphisREGlLXMcuixGvg6qtWyks+o2Ifdao1/lycFfb9+RF7Q952GFVo2Aykt83RvtD/g7+XLlyM1NRU9evQwuVYOg+bkmYYpSZ7b7caECRPw3//+F9tvO4o9x+pL7pcp+NHWYhBwQsFEAAhqJEIt8ALWBN8yzqNoJvTMywsWgFmj1GgR5y71N1ro1TLrRotgqDXFTkfcMy2nZycbCYC9llnHZyEnyhP8v4U6EblMyzaTo+WJ8OrsTW8QkaP5M0fKkzV/xmPBgj1mYFYszpT4PYv1HCwuAwhoTBbD17uYi7BEy6WMEkE+zlmlZ7u1zKpXWo3HyPLGaS6X4zjMnj0bEydOrBrPxiMswZQkDwBGjx6Np556Ct9//z3Smwe+JxV0M8sTJY2EFmMsRHjDqOYp1FBz/fC9H0YQGsyqEnxLOY9lw4KUjEShLypoi3ExFyHbYmyWKRbW2SojwYpQMBIAGWMx4hhtbsJ3occhlLStFb3aNAMnNFicEPU0seyJk/IYUu+z0jLvUUJNy3rjMuAcPbPUst650nq0LO7FW7t2LY4dO4ZRo0bpqkPIQz1vpmBakhcXF4dx48Zh1qxZWLWqYgEWtYEXYG2SA3+A4uBrZe8HUBGUlHpAjGBF8PVy7pAyxkINsB7WI9dowRPOpjgctBxqWK1luX1SPEVMjw3Yq20zdUxUIL4X87DSsRr9Su1PWnYeTuzF45HTMaBfy7NmzcK4ceMQF6e9F5Ag5DAtyQOAcePG4cUXX8SuXbtwKlFf8GVhkMW9gUpJn1PwuHyGWtqcQBk8trUaBwvELFBrKFi3GgsJFS0T+jFbx4B2cwwAR8tSAQD1I8+zrg6Aiu9dzF0YpVE/IsuU44QTTmt4O1peDQAQ4zJ3bmi218DzmhBcy8GGaqrlUNmFlRLldKymEZnVUE2iMsF68VjrWNyL99tvv2HTpk14//33mZQfatDqmuZhapKXnp6Ou+66C88++yw+/ngvVh9uobkM3nzk+WJkzYTckE218CaKNxRyQVhNbx6LIZuENErz8qwyFHoMMf+5PF/FKpZ6jbHS8GWhMSZTzBYrGyyOllczXceAfi0LMTvZ8x/n3983D+nbXPT0foivEcAmHksNoed1BwCJbv2PgOFjsrBMLVpWms8nxik6dnIDstrGCrPisZSO1aC3F+/ZZ5/F6NGjkZaWpuvzIQ8tvGIapiZ5ADBlyhQ0b94cv/32G2CwQVoY1LUaCiVzHHAcC4Kw2cPcrIDlkE298/L0BuNgyM3LY2EqEt3FyPPFyJoJow0WQqTOjRYts2ywCIdeaTMxQ8eyx2JkjoOVa3bCB1w4ZwX/PiaiSeRp049JXOBgWTriLX7Op1BjVhwnFHUc6r6CNUq+Qq2OzWp0+/XXX/HVV1/hwIEDppRPVG1MT/Lq1auHMWPG4KmnnsLnn+vrzQMumGMesaFgYY5jXKUBw4P8xxIYMDWBmMxxICx7QFiY4WCLp0ge00RjYYYxltMxQMbYKViZ1PmPaZFB5o+V54tFo8izph6nQPAcwINl6QHvkbbZIT63VmOldsXHNapjuVgsRI2OWY4QCnVPAfx/e2ceHkWVdvHT3dlXFgnBQREXNhX3D9RhkU0hKDjKpmyi4EAAcRsQATcWcR8BZUREBgYFHYEZARVUcAQXZBRElhlkRBQCkSUkIWt3fX801amu1HKr6tbSnff3PHkgSXdVdd2Te8+5760qc1U8O3Ws5yl+OrNkd9hFW6J+/thjj+Hee+/F7373O9uOzevQck37sD3kAcCkSZNwwQUXYOvWrcBZ+q83ijgAOGEq0v0VKA0lK3YWZCr4otYhOzF77IapEPfZMGD/8+dIx86hdI6dqoC4ZY5FfpJci2R33yxHet7FZfbnk7Z1zfH+M+fN7J0GjaJmjqXayfSr3xiF11JN5d+nRh2L0xoGanQsvVQknnXMY3WQEQ3bWcWTaliJr7/+Ghs2bMD+/ftNbZ8g9HAk5OXm5mLs2LGYOnUqPvhAvZp3QPIH0UyhM5VX82r/vkzTVLAs2dSqguihZCoA9Q6Zdcmml6t5vJZs8jQW5UKS5U5Zy1TYjZvGGCBzbIX9VTmOmWMteOhYrz82e0ysmjbbD8uR9sX7VSaOWPXt1X7YDGrnwk2UTLFZDR+QbYunhoGwjo1ej8cLtbY7J/GYw0fiDuJEhVc0LJ2o0Ap28ire1KlTMW7cODRu3Ni2Y4sJ6Jo823Csh/rTn/6ECy+8EB999BF8F9X+vbxD5oFdZlmsgmghvUmLVWMRD0iXbHqlY5Yi75iLQ6ma5qI4lKI6g2x1skLtuEQNG7m+lBdaGo6H60uNEgsa5omoaZ5GuTSUjAuSjljeHg/2V+WgPJSo+vuLkgscPBpzyCfb9lcqa5RlEsLJCghv3ap5CZ4aBsLHradho0s11WB9fNN/K3JVfydq2MsTFVINq+kXiA8Nf/jhh9i6dSuWLVtm+L0EwYpjTrFhw4aYNm0aHnjgAXz33ff49OClAPQ7ZCC6U2ap5olLLKRI/whzE04aPXxbEI1irJsLOVbMhVOUCwkoqK7HfbtyPZsxFqRh94kFDQP2BDutCTfeRvnHypoZbK8EPiVE8xwEm0G+KImv5v9bqW7eRZI4PzPRLqRtDvBbusw6UcyqYaU+WAmvaFirDwbiR8Ne6IOlbc6qX2kVr7q6Gg8++CCmTZuGhg0bcj++WIOuybMPR8sB+fn5eOWVV/DGG2+geXf+nTIr8kFG3jGzLNk0Ws0zi3RmjqVzDgk+tEw+bGmftY7BQYOh95gEIDzzqdexai3ZFNuf53VRelp22hxbWXYshaeG3TIXgL6GY8UgA+YMBgtGVlOIr20QKOG2f6+YZR7srWjCbVv+OHAr8jFXRE+/eks1M/3lplcBsa6mUENp/I8XDYcEn+Ma5tkH867iqemXFfkyzYULF6KyshJjxoyxtF2C0MPRkJeUlIRnn30W9957L/478L84UJBn6P3SzlzLXKhVQkTkAc3tjjnFX6U7C8eK3yd41mCwBDi7MNNJ6y3ZNGMu7DbHALuGWSYqeBJAiCno8dQvwKbhSiHB0aDHMlEhxarJUMPKMvmTwTScDKYBAM5P4rv0/MfKxigJhk14yxS+k1ZK8Op/vY6T/e/e8ibICPB7RIcUsdJeL3Da8jYA/voFgO1lzQBo65fnUk0WWCfaeMLTQ3hNv2Ym2U6dOoWpU6diwYIFSEric91xzEPX5NmG41cN9+nTBy+99BKmT5+OFveY345dHbTUTJUEUzQ7aKeqeSKsJpknIcHnSZPMgp4xNmq0pVg1GceDGZYMspb2xM+tp1/CHnhNZtgV7ET2V+ZYMsnybQH2mOW95dGh3+uaDgk+tw/BEma1K28nOwKe1nVaPLYr6pd1qSYLTuiX5ySFG/r1QhXPLv3Kq3hPPfUULr74Ytxyyy1cth8XUMizDcdDns/nw5w5c9CuXTtsHbYVXyT+0dR26gVOR0yyksEwWs1TwymDwbuaF8tGw8qSTbuNhh0mw4x+WYk1gwzEvn6tIG2vWDLJ8u3bEfZEpOeoadJx2/YTSzg9wZbur6jVt9iFmmatTFCI3kFtX40SijXfb2UFhHjeWLRLVbwaeFbxeGjXzATxzp07MXfuXGzduhU+X90c4whnceX+v5deeinGjBmDMWPGYOPGz7Dwvx1NbUca9ADj1T29oJcRKI8sGRJRMs1UzWOH95JNp4wG4JxBBviYZBb98jLJPCcp6hJuG2W7+Pb0eQCAC1PsXf7+n/Loay1bpMTeTX5iAfl5TvObe0SNFC2T/J+yXNRPLEUhMi3vxwz7ysNVdLP6lfe7SsjPKWBOv1TFq0HNW0jPNYt2rS7T/E9ZeH8z2r4X+ZkgCBgzZgzGjh2LSy65RPcY6hJ04xX7cOchLwAee+wxtG7dGkuXLgXaqb/ul8oGkf8bMaSiqSmqTrNkNJSMspS95U1wOhS9rtqs0Yj1ap4TSzaNdtZ66C3ZLA6lorBa3WicDKapziib1S4Q1q9V7bLAYpJ5TlLUdXjrVw+z4c6KdqWIZhmwP/ABysa5eXIhl23HeiXECErn0ZH9llnfr1XtFlXXTBwrhT07r2OWn3fSbhgjE8NWtWtmBQWLbpcsWYL9+/dj7dq1Zg6LIEzhWsjLzMzESy+9hDFjxmDv3r34e2Ht9cnSzlr+vdh5y6t5crITTmsaDR43n0jzV0YFPbUZOqrmhWGp5omv0eqwT4eSdI1ySTDFcqddP7HU8PvVtCs1HXraBbRNspPaJcIYqURbNRtuaFeuW+nPrFZ9pYbZyRv+/HD6d7V+dnHar47t3yso6Vbp3KQFrE8+GNGtmWCnNLHGql29PlcJI5U9liqefGJY8TXBJF3t1uUqnvzcsOiWZwVaT7fSKt6JEyfw0EMP4dVXX0VGRoblY4g76Jo82/AJguDaqRAEAXl5eTj77LPR/k97o36n1GEr0TTpuG6nLZ2ZkyJ22CyGQ6/jZu205SiZDZaOmzXk8ey4WUOe2Wfd2NVpG1l6odRx6xllqeEwoltA33BY1S5PwyFHrl1Ww8H6KBCe8LyFN2lXX7tquhU5WRX+fev0Q6qvsavPVaIiFJ7vbJOmfjyx3Od+X9oUyX59fTul218r6mv+3ot9rsjJqjRLugX4aZd0WwMP7erpdnfp2chK0H6sBxAd8ADg7rvvxuHDh7FmzRq6Fk/CqVOnkJ2djcuGzkQgydojTeQEK8ux/a+TUVRUhKysLK7bjiVcq+QB4ZuwzJ8/H5dccgkGDPg7DjR7EgB7py1/rdofaHbCacWOW1olaZJ0knmfSsgrIoqvCVTW6riVZupYOu5YR+lzyzkdTNLtuFmqeXqUhpI1TceJqnSmighv3WohalfPcNiJvA3rgm6NmA4n4L28zcjredwYZnfp2ZH/u6VjKbtOn635+5Zp3qtqJ/mq8X1pU7cPQxdpW7MYZT3s0C3r5IQV3fKcnBDR0q0XNQvw1a2dVTyruv3oo4/w7rvvYufOnRTwVPAJAnyc6028txeruBryAODcc8/Fs88+i3vuuQc7d+7E8wcGm9rOscoMHEO4DN4s1fgDp6V/yEDtjlvv2jyeJPurURFK4GI4eF6bx7pkk7Xjdsosqy1742U6tpeci4ZJ5p97VxJMwbHKDFO6BbQNB+mWnVgwygA/3R4oOwsHzvzfjH6t6laO+LmcDnvi5IQeIcFfa5yQEwRfzQYY1hz5fSGu+7SKtK/VO19GOVAWfq7jARjX7LHKsD84Bn6aBaJ161RfC7DrlqUNeOqWRbMAm27dqD6rnS/WvlZaxSsuLsbIkSPx7LPP4pxzzmF6P0HwxNXlmiKhUAjdunVD69atMW/ePDy+s4+h94udtxLSzpx1lk6OaDrcWIahR0jQX4pBHbi+8dDrwJUqeaLhEDEb9OT6lRuQeNMti2YB53XLapbjTbciZk2zCI++VkpJMAmt09UnBKivDcOrrwX0daun2d2lucjQ2QZPzQL6umX1B4B13Zac0ZsV3RpdYqwF9bVnXqOj24Pl+lVhM8s0xXtObNiwgap4CojLNS8fPMOW5ZrfLX2UabnmrFmz8N5772HPnj1ITU3Fddddh9mzZ6Nly5aR13Tu3BmbNm2Ket+9996L+fPnR77/+eefMXr0aHz66afIyMjAsGHDMGvWLCQkuFdPc72SBwB+vx8LFizAZZddhttvvx2P37AaAJjCnlYHDkQPDs1Sf9PsxOslnlbsxKXmSs94mF22KUesivAgAIFbJx6Ej9l86FERStDtxHkt2dSbzTxVnarZiUuXbKoZDlGLRsyykn7lmjWL+Jn1NEuECQl+z1VFeFZCtIwywK5ftT5X3D6PKololneXRi9HJR3bh9kbrsjbiCd6mgXCelTTLKs/0PMGAHvAA6LPiVSzLBMTLPDyBoA7ExMs2BXwlPRqdWJCiU8//RR//etfsWPHDgp4Orj9CIVNmzYhPz8f11xzDaqrqzF58mT06NEDu3btQnp6euR1I0eOxJNPPhn5Pi2tpk8IBoPIy8tDbm4utmzZgsOHD2Po0KFITEzEzJkzuXwmM3gi5AHABRdcgGeeeQZDhw7F9u3b0aBBAzx+yerI75UCn14HLkfs0Oslmn+IKuAt4+H3hZhn65zEC4ZZ3k56HTkLrKYD0DbLrNolzdYteGuWRa9y1Eyz05pVQnp+mqU69yB0r+rVjT7WzlAH8NGsGW9gh14Bb2uW9/JiFnhWnlnQ0yuvgCet4h0/fhxDhgzBM888g/PPP5/p/YR7fPDBB1Hfv/nmm8jJycG2bdvQsWPNc7zT0tKQm6usp48++gi7du3Chg0b0LhxY1x++eV46qmnMHHiRDz++ONISuIzwWMUTyzXFBEEAbfccgtSUlKwYsUKxdkPadgz2pEDwHHZbNz5abVnnY3M2CnROr2Alr/BnSUZWh06i2FW69B/KmuIBiZNgNwwW9WtXLOsS9+0YDEftPQtDO8ZZiua1dKrFKvataJZO/pYADhdHX1n2daZh2u/hvpYANY0u7u4Sc1rErQfAs1Lr4B5zRqFhycA9DVLeo09vQLGl2kKgoB+/fqhsrISq1evpiqeBuJyzSvusGe55rfLHsXBgwejlmsmJycjOVn7ruT79u3DRRddhO+//z7y4PrOnTvjhx9+gCAIyM3Nxc0334ypU6dGqnnTpk3DP/7xD3z33XeR7fzvf//D+eefj3//+9+44ooruH4+VjxTyQPCd9t844030LZtWyxatAgjRoyo9Rqxujfu33dy2ef+0zWzhkqduxIZgUrNDn13aS5Th84Lr1ZGnKrmiZ26XodeEkwyXBmRmg/RDBg1H9LKnhmzLEfUrKhXtWXGRjhQ1iBKs2b1ynOZcbzCqlczKJllwLx2xWPNSS429D6pcZbr1S6k5g6wt8+NJYwYZvk5jHqNg3oFwhpi0evRisxaP2PRqzzciZjRK8ukhBy39RoPyzTt1qtewJNfgwcAb7zxBjZv3kzLND2C/IY3jz32GB5//HHV14dCIUyYMAHXX399JOABwB133IFmzZrh7LPPxo4dOzBx4kTs3bsX770X1kBBQQEaN24ctS3x+4IC91ZOec6NNWrUCIsXL8Ztt92G3//+92jRokXU7wd88ccz/6vp2FkNiFqnLiINfFZnEdMSKqJMs1KHTtfmsSO9Nk+rY+eFnvkAjGnkaEVmxIxYMcxSeOpVjlm98iSW9SrFbr1qaVWOWeN8tCLTknEG7NWrEruLm+B0dfSzG1tn2TfYetU0q7H7VE0FOS2hyvb9ifDSq1K4k/5OS696XgCo0SsPrconfZXYduLcWj+T65VnFc9prEz4SrUK8NGr2aqzUrAT2bt3LyZMmID33nsPjRo1snR8dQk7r8lTquRpkZ+fj507d+Lzzz+P+vmoUaMi/7/00kvRpEkTdO3aFT/++CMuuOACfgfOGc+FPADo0aMHRo4ciYEDB2Lz5s1ITU0FIA140bCYZ5ZOXf76kxWpke/PzzgW9Xu9ah5QO+hJEY0fiwlhCXqxXM3TugGLESNyujrZ8uzddyebol4y2xp8K4YZ0A97RjQr1atcqwCbXrVQ0qudhjlWcdI47y+pMcusmgX0datmnvV0a0avSlrlhbxvBWqbRQA4P8PeCqPbKH1mO9AyzWa1CtTWq1a4k6IU9Iz6gJMVqZr9KmCuiseKvO14adWrExL7S/Svx2TpV60s05Rq9fJ6vwDQDnYiZWVlGDhwIEaNGoXu3bvrvp5whqysLOaHoY8dOxbvv/8+PvvsMzRtqv1IpXbt2gEIL+284IILkJubi6+//jrqNUeOHAEA1ev4nMCTIQ8Ann76aXTq1An5+flYuHAhBn45Wvc90s7faLVEjjTgAdF/+GJnb9U4A+EOS2pG1AZkXp27l6sjTpgRpSWb0rYFwm1vJOgB6rO9erPNgLJWjZoRKUpatQMrWvXqpAQL4qSEU+ZZilyrIkY0Cyjr1oh5BqJ1a8Y8A+a1ylIZYaEyFMCeU41Vf98q6wiX/diBdPJM6zMk+YOW92Vm8oyXVoGwvqpDAcPHIGo1wcQ5YPEALLBoVWlCQg6LVr3ap4patapTHgFPCTWtfneyKdZ0fFn3/YIgYMyYMUhNTcWsWbMM77/OI5z54r1N1pcKAsaNG4eVK1di48aNaN68ue57xGvvmjQJT4Bfe+21mDFjBo4ePYqcnBwAwPr165GVlYU2bdoYPnxeeDbkJSUl4Z133sGVV16J119/HbhE/z1SpEbEimFWQt4h5KSqB0qtal7Na6p0O/n9JWehUmOQa5V1xLPGWVrNs9LJn65O5FrNU+vYAeummdUwS18rmmYzepUbEhHpZ9TSKcCmVRZYtBoL2G2cWbWqpVMpZszzzpO5yEmt/VwyFo5WZOJoWTrOTj9l6H08tOoUe041RnWIrU9tmX2U2353ntSfQEgK6GuQl3FmxQ6tHiqtmYU3qtWjZTW3P2fVqZo+pYif0ys6Bdi1ylOnu0+q95EiLDrlBUt/mhGoZNYpKwsWLMDatWuxbds21+6iSJgnPz8fy5Ytw+rVq5GZmRm5hi47Oxupqan48ccfsWzZMvTq1QsNGzbEjh07cP/996Njx45o27YtgPAKxDZt2kTuqlpQUIApU6YgPz9fd4monXjq7ppKfPrpp+jduzc2btyI56oXGn6/XZ28lOKqmga8IEu5iqFnnlln8lhwuqMPQNA1JU4aEq2O/sdT4eUgmYnss31GjfOh0izzxpmjaVajuCrZtE7Dr+GjVS/qFPCGVkWditihV6l5BqwZaICtfzWiVbFfVdKq13QKAEHG1+nhY7w4JZ51KtemFBadyrUpRUunZvQpQjpVxgs6BcyN/QB0K3lbt25F586dsWbNGnTu3NnQtus64t01rxpgz901ty1nexi62g1yFi1ahOHDh+PgwYMYPHgwdu7cidLSUpxzzjm49dZbMWXKlKhtHzhwAKNHj8bGjRuRnp6OYcOG4emnn3b1YeieD3kA8Oyzz2Lu3LnYtm0bzjor/Ieqdn2eFCc6eqB2Zy9F7Pips3evs5cbERHexlnJmBgxzkp6tcs4yyGd1uA1nYrEgoEG1DUbzzrlpVGATaes1REeOpUbZy2dGjXQcp1qaVOKmk71tCki16gdYz7gvcldL+rU6b7UqEYB9aD322+/4corr8T48ePx0EMPGd5uXScS8vrbFPJWsIW8eCYmQp4gCOjfvz8KCwvx0UcfRZXDtcIeS4evZEh4dvhymqRpL++o68aEZ4evZ5oB66YE0DcmXjHOALtWtXQaqyEP8J5OD59mX9YLWNMrq4EGrJtooEazbmgUoL40VjTK0k5KSDVqRJdSjC5VNTLWA6RRFnjo1IhGeYW8yspKdO/eHTk5OarPdSa0oZBnPzER8gCgtLQUHTp0wNVXX42//OUvtf6g5GHPTKd/dvopWwNeaaXyWu0L69VcxB2rBtoLVZJ9J6PX2acnsT0TL5aMM2C/eQZqa1WqUYDMCS+NirBqFTCm18MlWchOMbbcWISHiS4qT0WTDPblx6RRb2rUDn0eLqnpO81q1ApF5TX9J4tGaawPE8tjPcAn5AmCgFGjRmHbtm3417/+hfR0c/1jXUca8hIS+Ya86ioKeYCHb7wiJz09HatXr8Y111yDiy++GPfdd1/U75dfOx9AOOzt/i0HDdONXxN1qDQr0vEbMSZWURpUz9bYf5I/qNv5J/hDzJ0/DwTBx9z580TNNAPhgZal8xcHb9bO/2RFqinzLBplq+ZZ1KlT5hmofZ619EnUoKVPsxRXJTNpVTTRReWppkz00bJ0HCtNN9WXivuVHoeeXp3WKOsyOCdxug9l1SdrXwpo61Ma7KQY1eix0pp+06g+peFOflxqGuUR8IDo8+1kH8pzEoIFngHPjj7UCEpVvD//+c94//33sXXrVgp4hKeJmZAHhJ9cv3r1anTt2hUtW7bETTfdVOs1u38L37rUjDmRdv7SwcjJwCfy04kGij8/r/5xAPyCXsAf4jYAsAS9ymBAdwCoDAUUBwD5OUlKVH62nhRe5kREqgtRL2bCHk/zDDiv0UMlWaisqt19iPqsi6j9zfLWKaCuVV4mGqgx0lb7UqVjk+uVl4mWotQeRvXp5EQZK2bvVmhFn0aR6lNNk2aQhjv5z/Q0qqRJOfKwZ4cuAaCyKkFXnzyreLyws8qsdD7s0KYeejdaWbduHaZMmYKPP/5Y91lqBCOCEP7ivU0idpZrSlm2bBnGjBmDLVu2RD1/ou0/pym+ntWgsAwCQLRBsXMQYHpddXggOK+hsnnx4lIOlkHgp2MNkJTAMFgwDgJGDDRQu6qnZ1TMzkIDxmaiWTTqhIEG2DRaWR1Q1SYQ2/oEwKRRwH6dGjHSelpVMtIiPIy0lCYZp2Jen15bqmmXNgFj+iypMH7bcDVtamlSipI+jWpSJCOZfVmfXWO7ljYBZ/XJa5km69gOODe+szwLDwB++OEHXH/99Xj11VcxaNAgQ/skahNZrtlvuj3LNd+ZQss13T4AM9xxxx3Yu3cvevXqhS+//BK5ubmqAQ9gW9phZCCQGiojA4GdiAO7Gk3rn3TkOIxU8/SOmYXKqgTmSgnAPhiIM9Gs5pm1qqc1Ew1om2hWjUpno43eKIAVVqMCeEebrFQGAzh0MtuVfRut6P23sBEyUo31QWpVPRYjrVU1MWOm9/1Wc3OkxtnaN6mww0gD2vr0mjZFePSdvCk4WbuvtKpN1nAnfb1Um2Y0WVqeFPUvoK1NVl0a0aQ4eWtVm24s04wFbRYA+M9tU41to6AAvXr1woQJEyjgccYnhL94b5OI0UoeEL7wddiwYdi9ezfK/tQV/hTjD6DkNRiIuGFSxMFAj1BQv7PXUkLThidVf/fLsXq1fub368vKx/Aat2ajpYOCUaMC1A57Rs2K3ESbnY0WNaqnTSA29WlUlyK89Ak4r1EeZhqAJTMt0jC91LI25ahplbeZ9qI2WXQJ8O07AfPaVNKiHDParGY451okBEKG36OmRylSbXq9vwTUx3Q7xnPAW2O6njaNhLzS0lJ06tQJbdq0weLFi+lOmpwQK3lX325PJe+bd6mSF7MhDwjfwvbGG2/E92VH0GTSH+ALmBsYSktTkJ112vj7dAYFuVnx+qDAUwmxaKK1BgUzRgUIG2mzBhqwx0QDykaa9BkNb30C3jPTRafSkJ5ebvh9IqWl4YHZaP/JYqiBGp26pU2rZtooPM00YE/fWXzamBlj1WXRqbSo783oUtSjCKsuWfUoYuRvzWsBzwyxMJ6z9JFyWIJeMBjErbfeipKSEnzwwQdRj/AirBEJebfZFPL+TiEvJpdriiQlJeG9997Dddddh84fF2Fj93qGZ1jEQUEcYMyEPTWOFNU8u4WlkmKGpIQg8+Cgh8/Hb2AIhXy6A4MQ8jENDJXVAaaBgXXpJhC9LI5lcCgpCy97NGqkfz4avjOYWSMtvp+3iZZr04iJrivw1qdRDhY0QGIKu9EpKUs2ZahLS1MsG2oj/acRQ32kKBOVZeFbzTdoUKL7et6GmgCOH8+I+t6IJgF9XcrDnYgRXcrDnXTbWpo0Gu4AoLIsEcfLah5/oKXLuhLw3ECqy2KDmmRFEATcd9992LdvHzZv3kwBzyZ8ofAX720SMR7yAKB+/fpYu3Yt2rdvj4fOfQgPP/wwAPWbsOghHXB4Dg5SsyLCYlp44Q+EmAcIJ7Ej6AH6s4DHj2dAvKTdDiMtNy5mjLSSiQb4TkQA9hlpL8MyCWEHehMRckNdVZ7AVZ9ahhpgm4xQM9TS7Stp1KyhFpGfG7lWeevSDUPtBeTnWY5RTQK1dammQzl6/aaWFuX7kmvSqB7l47eI9HxJNVmXAp4dqx7k6OnSDp577jm8++67+PLLL1G/fn3H908QVol9twagefPmWLduHW644QY0btwYQ4cOxY6bnwSgHfb0Bgg7jTWgb1rcwOlqHmBPxUTJTKsNEmaMNKBc1dMyL04YacCaeeGpyXioljihTd7mRSnoOWmqpfsTNWo14CkhPW8ZmewTKPGgS56c+i28nNyfzL8aLYdVh1KUNMmqQ/m+s7NO26JFEVGToYoAss7Sv2uylwOem5z6Ld0RPWqxePFiTJ8+HRs3bsR5553n6rHEPcKZL97bJOIj5AHAlVdeiffeew+33HILGjVqhJ49ewIAU9hjwe7AB9Q2e0aMCwus1bx4CnolxWxmoKo8/KdgpWpixEgD6mHPqJEGrJlpLZQCCG9dssJTl3bAqk2jptpK5cSsqQai9WnGVAPAsUPhu5SmNDD2jD5WUy0inlMpSia7rptqpfNkBiOarDiREvk3IaPK1P7EoGdWhwBQXZKIYyXG9GhUh6GKGn3Jz7Vcj17XIu8qnhq8NMmTtWvXYsyYMfjnP/+JK664wu3DIQjTxPSNV5RYvnw57rnnHmzYsAHt2rVTfE3bf06zPFiIGDEvVgYMEbXZwbo2YKiZaaUBw+iMoFFDbcW8AHzMNBDWpd1m2g5NAvGtSzUTY6cuRWMNwLI2efWVAFt/yUOTSmSdVVrn+snyk+xtx1OPUv3JMaPHYFF48iqQbex5aCJyHYpo6dEuHUb2XY9tsoy0qI/RMRtQv+nKl19+ie7du2PhwoXo37+/4e0S7Ig3Xvm/PvbceOXr1XTjlbip5IkMGDAAR48eRV5eHv71r3+hdevWtV5z6lC4wc0OGFLKj9fc+dCowTaDmmFkHTBY4V01sauixzILGKoIGBo0WGeppUZGNBFmDIwVEy3fv9N6BNirKGp48VpRsxQfzoSQyHbFN29dqhnr6pJE08b6lElzrWaqRX2qadNOY118OFPx58mNoldmxJoeKwo1qrWMWgSs61Er2Ekxokcx3Mm/Z9Wjmg5FlPRoVIOAMR36qsL6Ums3qR5jLeDx0qIdsNxJc/fu3ejduzdmzpxJAY+IC+Iu5AHAuHHjUFhYiK5du+Kzzz7DhRdeGPndeX95LvJ/6QASi4FPilbnKh00vHoDFilKQU/p81UAzAOHOAgbWSIHKM8QapkZK4ZahIcWAX092mmoT/2WHjEzUuSGOlaoOJyO5CY1wVXTzBiAR9BjMddWjLX0Zyza1DPWgLI27dSjkhZFarVldfQdmhNzlPtyJ4x15REVnSUwmvAqP/OkgxlYg51RlDQo/72WFlk0KEXUoz/VWEXIaPVOS4ciET1W175TuJIWnQp4ilpk1KFbGHkW3r59+9C1a1fk5+dj3LhxNh4VUQtB4C/k+FqkaJq4DHkA8MQTT+D06dORoNesWbOogCeH1cSwDh5SEwM4H/qk6JkYQHnwcLqaV2sQYRhAjJoYK6bayEw1wFbV0zLUgL2m2g0UwxGjqbaDqsNpSGxSO3gqGRrWYOeEJkNlxrtuPV3qGWvxNTzNNRDWZuC0H2jIPrlh1Fwzo9A3Vh1Vfk4l71t0V3t77i2C75ikfwIQTDN+IpQmHVj0J3+9XItm9AcgrD8AOB0+BoFBi3YEvAgKOgRqa9GO28Qz69BAwLNrokHUYjXC/yb87rShYCdy4MABdOnSBXfeeScef/xxnodIEK4StyHP5/Ph2WefRVlZGbp27YpNmzYxvc+OigoAVP2SDrHbZhlAjCIkhtgHkQSh1iDilJEJAhBYB5FqH3PQA9gHEqOmuvrXM+beoJnhZaoBdS1aMdU+2KNFwKChkaGmRcAeU6NaLZHDqEfAvgqKaGrMmmvAusFW06RVgy0ND1q6tNVcs24zTp/BpNY3StuGF6IOjYY7KWLQM6s9QBLwJOhpkVcVuRYq4U5xuzZokHlsdgk9HZoJeL/++iu6dOmCW265Bc8884zhZy0T1vEJ4S/e2yTiOOQB4aA3Z84cjBw5Eq3aXYN6941CIIv9duV2BT55R2WX0eaB4LfnIZW8gx5gzFjrBT2lwSRw2m961hqIDntmZq2BaB1aMTYidhlrwhhaelQzNmb1CPAz2Amn/Kiqb/5W50oGG2DXpR52Gex4x0ioM6vDxBMB4EQAyLI2wPgOpiARMKxDNe3V2v6xmuqeG9U7xe26Pcng0DJNOyYXpBw5cgRdu3bFDTfcgJdffpkCHhF3xHXIAwC/34/XXnsN5UOH4h9zXke9sSMQyDZ+px3R0JgZTPRwJfQpVPOcxCtBD4i+Tk9vUBGNgdmw5wtaO+eiKRcC5gdZFmMNeHvygSde0CIQHfRYzY1Vg50AoNqiyU48Ef47sttk+8vCrw81Mn+3UEKdxMLwpFEo1fiEDqsORa1ISTjlN6XBhFPR+kk8EWDWIKv2ot7zSwrEo69i0GCsBTy7qnhG+sDEwkSEUkNAif2TiocPH0b37t1x1VVX4S9/+Qv8fo+XMeMZek6ebcR9yAOAQCCAxYsXY8SIEfjy9bdx+q7+SGhQz/T2pAMV78AH1JiaxDOmhmVAAQwu2TSAHdU8wDvmOvBLSnhgMYAZcy3VjRVjLTc3RjRoxNxEBY0Mdx9MG6uwalE02ECiKS0CbBMPvEy2XIPSbevp0YzBjtpPYU0FW6tvjDWT7STScyjHX+Y3rEHd/SnoTgqrBpV0p7QfNQ2a0Z44uRC1Hx0Nxpr2DAU8TlU8LQ3azcGDB9G1a1e0b98eb7zxBgIBWqniJrRc0z7qRMgDgISEBLz55psYPXo0Pnrtb/jkk0/QvHlzzZuxRL1fZXCxM/CFUkPwl/kVO0PW4McLu4KeITgGPfk5NWNsWM21mrEGjIc9K+baLP4yv6LRcVqDnsGADtWww2RrTTzwMtnia7XQ0qPZgKekP6D2eRQ1acdkVyzjhKGW609Pc3K0NKinOTnyqh5v3UXtSxb4Yi3gOYGbgU7O/v370bVrV9x444145ZVXqIJHxDV1JuQB4aWb8+fPx/33348OHTrg448/xk/3PgQAzGFPC7srfFH74hH8XF6yCRis5gGmgx7LIBNZDsYx7LGYa4At7LGaa4CvwVbdXxwZbMM6NLLtMzo0YnR4BT0jRltPi2aMNlCjRTuNdmSfhYkIVET/rKKxTl/sAaPNS3vJR5TaO4BgMp/t62E02MmRBz2jmlM6llCy8QkZI5oTCVQAgV9q/42r6s8j14DyrOLJ9RfWnXeqZHv27EG3bt3Qr18/vPDCC3QNnlegRyjYRp0KeUD4Ziwvvvgi0tPT0bFjR6xfvx5t27aNhD2gduAzM9A4Gfgi+1QwOACDyWHEE8s2AaagVzPYGDc4PAy2mVlsIPYNNoAoDfLSnufQ0WBts+2cDtN+TkBVpvkBTqmiYtVsJxb7UHGW8c9ixmzLUQ4+sa1Ntc/ECyPaS/5NtnzcgvYAa1qTklh8xsAb1J7ZgKeGUltVNDT2t+ClyQW7tWcEufa02LFjB7p3746RI0fiqaeeooBH1AnqXMgDwkFvxowZyMjIQMeOHbFq1Sp07tw58nue1T0gbLpEzBgdIwSTaw84mibHA9U8wFrQ0xt0xPNhxGSbreqJbW3W6CiFPR6z2WZNtlV4G2xPLBuWYMTwBCqMaRAwb7YTi32Wg17SCR8q61ufDRXNtvT4WLTI22zLST4SgF9j8UNZrjtCSy3Q/9whl1e/aZlrK9pLOlEzFpnVXiTcSRCPV093dmsOAPxV2m0s152T/Z3WcbmtOREjwU5k48aN6Nu3LyZNmoRJkybZcFSEFeiaPPuokyFP5JFHHkFubi569+6NRYsWoV+/flG/l1b3Lnz2BS77lHdQbhhvEdGgahkdIHrQsdNkqwU9XgOPnSZb3q6i0bAa9njgpMlmRRqO1PTntMlW0p+e4XbC+OhpUM30WA16QI3pNmO4lcy2iJ7pdspsayFve5+BeYmyJiGkHmb/DIJ3iiOqmDHXrEjDnfRnRnSnpTcRNd05oTdAX3NAtO6MaM4odmvOzBJhpX7Oqu5WrFiBu+66C/PmzcPw4cMtbYsgYo06HfIA4K677kJOTg4GDhyIgoICjBs3TvF1+x5+IPJ/XoEPMG687SCUqD34KBldrww+/ipngh5gbgDiNasN8DXadunOjPFRQy1geUV7ZjCjPyVYjY+ZyQY1ww2wa5DFcAPKOnTKcNuJkYDnVfxlfiSWmnsvS7+npDOl1+hpjlVrUqRhz0sBT4qX+jknq3g8JxPmzJmDyZMn45133kGvXr24bZfgDD1CwTZ8gkBXJwLAV199hby8PIwaNQozZsxgXq/NEvjMDEKA+QEI4D+rLcfOAQhwZhCyMtNoZiCyarRFeBttOU6bbdIeG2YNtwgP0w3o68+s7gAg4cxnLMsx9j7q7/Rh0Vzq0ejvq9ON70eKkuZYdSZFSXNWdAbUaE2EVXN2aw3wVsADjOvNSP8m1ZxVvQHA7ifvhyAImDx5MhYsWIA1a9agXbt21jdMcOfUqVPIzs7GtTc9iYTEFK7brq4qxxcfTENRURGysow/GzteqPOVPJF27dph8+bNuOmmm3DgwAEsXLgQKSn6orOrwgeEg4R0IDJqfIygV82TIwTsHYh8QXsreoDxqkp4MDpzzZyJwYilqsI6uw2Yv2ZFD6nu7NRcvGBGe6zwNN1q1RWjpltLfzwCHhD9ufU06ITpjlfk+pKTUMrHeAPmwp30vaLeeIc7ET3NxcMElhl49216mrNKeXk57r77bmzZsgVbtmxBixYt7N0hYRm6Js8+qJIno6CgAH369EFiYiJWrlyJRo0aGd6GNOzZNSCJqBkgJwYkoG7McKsNSrxnua2YILnhtkt3vA034E0j5KbuWEyQFe2JurOiNymV9QXb+zkpUg3Gg96cqKwAYb2ZMdhW+zlftbX3yxFMTk0b0ZiIqLV40Bng3DJNsW8zqjerWqsuLUHOlx8gGAxi9erVaNy4sbUNErYiVvKu6/6ELZW8Lesfq/OVPAp5CpSVlWHYsGHYtm0b1qxZg1atWpneVutpL1o6FqMDU7wZIMAdw21kcOIR9uqq4QbiQ3OANd05ab5TjoX/rcg29345yUXhf8sbmnu/GfMNAAnl4X9PG/Bx8TCRBbBrLe1Izf+rLXgoI1oT9SXFitZEfUkxqjWrGhNh1Vpd7NNErTmlMzkVhUdQ+O4i9OrSGW+++SZSU1PNb4xwBAp59kPLNRVITU3F22+/jalTp+Laa6/Fu+++i65du5ralnzAM2uEWJGaRTMmyCh2L9sEzC3dBIzfkCXtiLkBysqSppRjQMqxcCjjYbozf6oJeHZrDaitN6Naq6tL53iYb6O6k/dFyUV8zbe4fSO642G+pecS4NvfeXH5nBLyc+AGSuFOxIzWlMKdfF96WjOrL6B2wAP0tebFSQSAb8DzgtbkzOl4Kfr1exL35efjySefhN8f+zc+qlPQjVdsg0KeCn6/HzNmzMBFF12EW265BS+++CJGjRplaBtX5Neu4kkHQhYzVJ1ubaAC1DtlJTNk9No8wJtBD9C+VkrtnCSUmw96ALvpVjJEoqkxa7zVTDfAbryd1JqX4ak3PVNkVnMAW9DTM99AbBlwJfMtRel8n24cXxMKZoy2HTrT0pYc1qCnpS2l/SvpjHe4U0PaDuUNjO/Ly5MIXgxzIrufvD/y/9deew19+vTBK6+8gmHDhrl4VAThPSjk6TB8+HA0b94ct99+O7Zv346XXnoJiYl8rkS2u8pXnaI9YKl14mYGKyewYryNDFjiObMr7OkZIzPGW88YmamyGMGs8Y43Mn8WEEw2vlw2Xg24iFR3ThlwKZk/a0/rljap3WZuGPD0w2zTz2Y0xgsjupKjpTMj2lI6HlFjbugrUCEg/bD2a+Qac0pfSmOmns7M6svOpZrSYAcAlZWVmDBhAt555x2sW7cOHTt2NL9zwlV8sOHGK3w3F7NQyGOgU6dO2Lp1K/r27Ytu3brh3XffNXVDFj3MVF7sIOV4eNDSQj5gOVHNA7SDntbA5QXjbdQcsYY9HsYbsF7FM0raEeM6A5zTWsYvAkqaRu+f1YQ7jag3qwYc4G/CgfBxJRULON3Iwt03TRpwFpTa1YjGRJ0a1UdpE59nNSUlrVAACoHKTOvWSR70rOhKStZP4fNoRmNWwh0r0nY203+Z0Yrdz/3kSdZPAk6dV9N28lAnpbCwELfffjuKiorwzTffoFmzZk4cIkHEHBTyGDnvvPOwefNm3HXXXbjmmmuwatUqXH755aqvV1qqaQSpWbMyO2YXaoON0cFLauKNDGBGB69AhfkKC2CtqmfVnGqZb6vGW8SLGgOs60weEo2aJDMG3IrWzLZDLJhw4MxxnsFK4DOCESMuYrQfMxvU0o56M+BJ28kOeGoqqTj6WMVjZ9WXEwFPitkJKqcCnhNVPDV9aQU7ke+++w59+vRB+/btsXbtWqSnc3rGB+EeghD+4r1NgkKeEdLT07F8+XLMmjULHTp0wMKFC9G/f3/b96vUIdptkILJPlODmNEqi1lzZGbppvh5nDLg8nazasDlYc8u8w04Z8DtxmqVxIzOAGeCnlK/kFQscAt6KSdCKK/P5wYGciMOGAt8ThvxeEVLXyzBzqq+Uk6Eor63oi8lTUnRC3tWJt+cDnhOYdcyYF6TBitWrMDdd9+NyZMnY9KkSfD54mOcIgi7oJBnEJ/Ph8mTJ+PSSy/F4MGD8fXXX+Ppp59GQkL0qfx2XnhGympFT20gYzXletflxTJuGHBA34SrDWhiW/IIe6JZ4mHC3TLghDI8jDhgTWeivqzqTM+Ii2jpLVYqLbGG3dU6EXmwk//OqLZYNSWiFPbiXVNuL9Pkra2qqio88sgjWLBgAZYtW4abb76Z6/YJd6GHodsHhTyT3Hzzzdi6dStuu+02bN26FcuXL0dubm6t14lhD7Ae+LSwoxLjVDXPCk4HPUDZhBsZ1KyacKlpcsKEx2uVjxW3NWbWMJmpuqgZcjM6M2rGRcTPm1gaQmluDF1U5HHSC8KdclW6tYkhVl1phTv561h0ZVZPImmFAhJLw8dkRlfxHvDM9lXpBUFUpfuRVGxuv3oUFBSgf//+OHHiBL755htcdNFF9uyIcA96hIJtUMizQIsWLfDll19i1KhRuPLKK7FixQr8/ve/V329NPABzoU+N8xSrAQ9wPzyTcCa8TAa9vRmxEV4LbFTg/cy1HjGbNDzsiEH9DVm1ZCLiOdBhLUfixVDbhfy88YbNV2x6kjpfWqa4qElMdyJGNVVrOjJ7gqe3bqS8/nnn6Nfv37o2rUr1q1bR9ffEYRBKORZJD09HUuXLsW8efPQo0cPzJo1C+PHj2daK+5Ula8q3a/aOesNbmareU4jDqZ2V1yUziMPIw5ohyUj5slJI55YGkKi7K6c8Vh9MTuRwIra32diacjWoGfFlAO1NcYr3MlNuQiLOY+F/oonRo03D03JMasjpW2ImrIj3KkhPYdSTVnRUiwFPPn453SYkyMIAv785z/j0UcfxTPPPIMxY8bQ9XdxjE8Q4ON8oxTe24tVKORxwOfzYezYsbjyyivRr18/bN68GQsWLEB2NvuDzpwKfHKUOnO5cYqFZZsivJfWsQx2opGwK+xZNeKAfWZcCRZNAXXHjMu15YYpl2uLhymXbqe8vt/2gKeE/FwGykMobWJuWIsFU55+uBoAEEyxt1rPQlKxAH81/7/hlBMhhBKsG3ojOpIiaipQfmZppwk9xUpFOP1wtWUt8Z4wKCoqwsiRI7F582Zs2LAB1157LdftE0RdgkIeR6677jp8++23GDJkCK666iosX74cV111leHtSANfu6Ev8DxEJpRMqFnzFGtBz8qgxzvs8TRQUlPPw0AB1sw4YM2Qu4FZXfEw5ryqL9n7q1BRn39JMnt/FQCgrJH59jRryuWI51sJNb15xZRrHTtPzOoptbD28fHSU/KJ2o1gRk9WdSSGOxEjenJLR+rPjnVGT7zYtm0b+vfvjwsvvBDffvstcnJy3D4kwglCZ754b5OgkMebnJwcrFu3Dk8//TQ6duyI2bNnIz8/39RSg849n0Gq7GdWTJQVgil+pgFDyUR5MehpfZZAeciyIQeshT2pmeJpyuVGyi09iZgx5F6D1Ujx0BUPYy5qgIeu5HqS7suItngEPLk5V0KprdQmU0rOTrR8TBmHqlR/x2uyxU6UQp2c5BNB01pSCnZK+2fRklMakiLVk9aknJ1asqIjtyvCX/31AQDh5Znz5s1Dx44dMWXKFEycOBF+v/vVaoKIdWLDRcUYfr8fkydPRocOHTBo0CBs3LgRCxcuNLR8Uw35oOu2SZejZnjtHgAB5UHQ7AAoDvZOm3IlU8XLlCsZKjdNuR5a4UlJT7x0BHjXnLNqitWcA+Z0pWfOpcegpys3zDkLWhpwG6sTBlqwaEeO0aDHoh8pWlrygn70Vl3YpaVYmCiQIoY6KSdPnsTdd9+Nr776Ch9++KHmzeuI+ISuybMPbyWEOKNDhw749ttvMXToUFxxxRV466230K5dO677YA19Vel+R4y5GqEEn+pAaKeZ8lcLlgZCp6p6XjLlgPcmD1jwsikX4WHOtYKelw06UFtXXjDodRWpjszoRo6ejozqRonUwuooDXlBP3Zcl8iC1YDHY5JAb1xTCnVRv//qKwwaNAitWrXCd999h7POOsvyMREEUUPsObkYo1GjRlizZg2ef/55dOnSBVOmTMGf/vQnBALapqpzz2dM7c/OSl8wxW9pQNQKenYi7tPtqh5Qe1A0a8pFWMy5GXNFxlwdtycOAHsMOqCtJ14mHQASi6tQ1jjZ8vZi1aC7TeqRCgBAQia/6jegHPR46EZKamE1EovPXANqQUOxrB0vVPBSj1QgFcCp82suKtELdSLBYBCzZ8/GjBkz8MQTT+CBBx6g5Zl1GXpOnm1QyHMAv9+Phx9+GDfccAPuuOMOfPjhh1iyZAnOOecc2/ctNYC8jJUV3Ap6gHfMOQAknOYTcvTMOU9jLlKd5v5g7KaOeGBVS6LBAoAqjkbdCZMuIgYNwJxZj2WT7jTScy0lsbiKq36AGg3ZoRsx3ImY0RCPCaZY147RvkdNPwB7sBM5ePAghgwZgkOHDuGzzz4zdXM6Is4QhPAX720SFPKc5Oqrr8a///1vjB8/Hpdddhlef/11/OEPf3D0GJQ6ayMGy2o1z214BD2AzyDJ25wD0WHPLpOVWBz9M7cnDtzAqo4A40HPKaOefCKIpGPlKL4wnbuG5CZdRP7Z9DQVy30QYG8lRsuQ203SsfIz/wKVDVO4bFNNM3JYAl88BDy7q3h26ufvf/87Ro4ciVtvvRXvv/8+MjIybNsXQRCA+1PydYyMjAy88cYbePXVVzFixAiMGjUKJSUltV63cd2fHDum1CMVtb7sxO2lJjwGaRazoHc+w4GpitnEsJB8IojkE0Fk7ivVf7FBtAy6k/qpKxg5pzw1JJK5rxRJx8ojxt0qRo5R67PHg1HnidW/PavaETUi14lV3VjpG+XnI1Ae4lL5dVs3PMZO6cSSU313SUkJRo0ahXvuuQfz58/HwoULKeAREXyCPV+szJo1C9dccw0yMzORk5ODvn37Yu/evYqvFQQBPXv2hM/nw6pVq6J+9/PPPyMvLw9paWnIycnBww8/jOpqdx9jQpU8lxgwYADat2+PIUOG4IorrsCSJUvQvn37qNfIg57Z6/TMoFXx41HNc3u5ndXr9ADlqp7ZQVI0MzwqMjWz6TUmi9esOitGKzOxCO9qnlWDZVVDWqZc/J1ZHVkNEuK58ZdVRn5WnptualtuG3WzpBRET9yEUpO4bdtoNZg1wJnRDe8Ji/SfopcemNGNFzRjtq+R6iaUmgQU8ToiNr744gsMGTIEZ599Nr777js0a9bM2QMgCB02bdqE/Px8XHPNNaiursbkyZPRo0cP7Nq1C+np0f3FSy+9pPhItGAwiLy8POTm5mLLli04fPgwhg4disTERMycOdOpj1ILnyDQwlU3CQaDeOGFF/DYY4/hwQcfxLRp05CYyDbYGg19PAdPqdkCYttwWTHq4gDK03AB5ow6q/EyY9Tt0o5Z3QDe0A5gTj92GnbAmH7MVFxYNWRnn6OGnqa8oBs1zch1obsdjrrR04zVypyeZniHO1a9iCjpxgtaAbT7GCOa4d3PAMD6zx9V/HllZSWefPJJvPDCC3jyySdx//33695wjqhbnDp1CtnZ2eh07RQkJPCdiK6uLsemL6ajqKgIWVlZht5bWFiInJwcbNq0CR07doz8/LvvvkPv3r3xzTffoEmTJli5ciX69u0LAFi3bh169+6NQ4cOoXHjxgCA+fPnY+LEiSgsLERSEv+/PRaokucygUAADz/8MG688UYMHjwYa9euxdKlS9G6dWvd90orfU5W+YDwYCEdRPUGGitm3m5YKjJ6n088F7wGUSNVGaPmy2iFz46lgCJa59XLmmHBiPnyl1U6XpmxYtpZKjRuBDxA+7z7qoKoaJLJ45AMk3y4pqIkJHrP7CpphtdSXem25JpxO9yJyHXjq5LcxdhFzXhRK3rs2rULQ4YMQXV1Nb788ku0bdvW7UMi6iinTp2K+j45ORnJydori4qKwuXuBg0aRH52+vRp3HHHHZg3bx5yc3NrveeLL77ApZdeGgl4AHDjjTdi9OjR+OGHH3DFFVdY+RimoZDnEdq2bYutW7di2rRpuPrqqzFz5kyMGzeO+bbCbi7tZEHPeAHuDaRAOOjxGFCdDntWTZibSzr10AtJlWelOXQk6ojGnYcRsyPoAdHa4WnapduTascrpl2O2M9Iw5ZRKppkWnq/10ksroKv0p67qYokHStHZcMUz+oEiA54gDXNeAU7qni19hEKYc6cOZg8eTLGjRuHJ554QtdQE4QvFP7ivU0Ate5i/9hjj+Hxxx9XfV8oFMKECRNw/fXX45JLLon8/P7778d1112HPn36KL6voKAgKuABiHxfUFBg4hPwgUKeh0hOTsbs2bPRu3dvDBs2DO+99x4WLlyICy+80PC2vB76pAiJAfiqgp4YSH1VQW6GHbAv7PE269JtxoJhB7xlvHjqhrcZS/3pBIKZqfovtEDSsXIEissAANUN+d1QgadeeOAlzQF89JJwrPaNv+zSi6iRVI5asTPcuU0sVfH27duHESNG4JdffsEHH3yADh06uH1IBIGDBw9GLdfUm3TIz8/Hzp078fnnn0d+9o9//AOffPIJvv32W9uO0y7o7poepEOHDtixYwfatm2Lyy+/HC+//DJCIWvTHKk/nUDCsZLIFw94mlEvDWY8B3reJjX1pxNI/ekE123KEe+UJ2qGcBZ/WSUX3Uj/1gPFZRGDbQfSbUv7GSv6iWfz7iYs7cNbK2r6s6ITXn8nQFgfXtMIzzGR98TR+s8fjXyFQiH8+c9/xuWXX47LLrsM33//PQU8whjic/J4fwHIysqK+tIKeWPHjsX777+PTz/9FE2bNo38/JNPPsGPP/6IevXqISEhAQkJ4frYbbfdhs6dOwMAcnNzceTIkajtid8rLe90CqrkeZSMjAzMmTMHt912G+6++268++67eOONN0xV9ZRQGlR5zsDHOuKA74WqnlJbSQ2THbPuctMuxaxOvFaV4Q2vap6ImSqNnlkW25WXZljCgPSYWLVDAY8fZoN2oLjMkk6MBkVWnfDuR7yoDy9NeqrdWAUIV+/uuusuHDp0CGvWrEGnTp0cPDIibhDOfPHeJutLBQHjxo3DypUrsXHjRjRv3jzq95MmTcI999wT9bNLL70UL774Im6++WYAwLXXXosZM2bg6NGjyMnJAQCsX78eWVlZaNOmjbXPYgEKeR6nc+fO2LFjByZNmoTLL78c06dPx7hx4wzdpapn60eYXud28BOXbXoJnsbdaNhjNWdOG3deoS8ecTroWTHwgHnNmK30sBh5CnjGkGuEZ/XdaNDjVQEUP4NUI3Uh3AH8A56RiSKtQCclGAxizpw5mDJlCkaMGIEPPvig1q3mCSJWyM/Px7Jly7B69WpkZmZGrqHLzs5GamoqcnNzFatx5557biQQ9ujRA23atMGQIUPwzDPPoKCgAFOmTEF+fr6r16XSIxRiiE2bNuHuu+9Gw4YNsWDBAuY7VrGGPBaUjBkNvuZQGnx5GTQz5p2XQXPCvAN1RycAbDPxIqx6sW3JZ2kZqs9tVGcCHteJgMIiIN3eay619GHnMuAIpWUINcrmtrm6og0grI/qcxvV+jlroJOzfft2jBw5EsePH8fChQupekeYRnyEwg1XT7blEQqffjOT6REKSs+9A4BFixZh+PDhqu+RPkIBAA4cOIDRo0dj48aNSE9Px7Bhw/D0009Hlne6AVXyYohOnTrh+++/x1NPPYX27dvjvvvuw7Rp05Caau8AL0XJYIbS+K7392JFD7CnSgMA/tP8lzEaXc7J06g5oREvw10nhUXhi6dtNPJ6lT0njHzCz4WKP+dp7r2AGW34Cx1+grUMpYqe7Zoojd6+0jkwqg0vjitS7NKG2UAnpaysDE888QRefvllTJgwAVOnTnXUexCEXZipdSm9p1mzZli7di2PQ+IGhbwYIzU1FTNnzsTAgQMxcuRIvPPOO5g/fz66deum+HqeVTw19AaZeDJpVq/V0zxXNpl4Lxh4tc8dT9qQYibo6Zo10fQ6GPacqtJoYUY7XjfzSlgKcqVltlfzHNECoKsHKUaCn9c1odZfuB3wAWDDhg344x//iLPOOouee0fwR3KjFK7bJCjkxSpt27bFli1b8Morr+APf/gDbr31Vjz33HNo1Kj2sgy3MRMCvVrNE2Ex8YYHZ5tNvFJ1z20Tr3WOYj0AqmnEsmlzwtAfPHOXsHray1wsYcDMK6F4HoNBCA29qRvfsfDx+gxcT20IO3Rx8pTyz3nrwqIWpNTSRfDMxJyXdREIQHnBmLsUFhbiwQcfxKpVqzBz5kyMHj3a0P0ACIJwFwp5MUwgEMC4cePQt29fjB8/Hi1atMD06dPxxz/+MdIRr9s9K+o9TlT2jKJper1s2s6EUN9JztdIOVGxEU28iF1m3oJ50wtDQj1v3/DFd6wobNzsMEV2aURu6qXf89QIR1MvRwxTPBAaZnPdnudRC3Vqr7OqCRt1IIY7Ec+2o82had3PLxl+T3V1NebPn4+pU6eic+fO+OGHH2o9VJoguCEA4PwwdO5364xR6MYrccS6deswfvx4ZGRkYO7cubj++ut138Ml9Dk4UHsaOwdrXmaexcTxMvOkixq8rA1WYy/FrEZIE2G8pAcz7S/HqB5IBzV4LORt3rwZ+fn5KC0txZw5c3DTTTfZc2BEnSdy45UrH0FCgPONV4Ll+PTfs5huvBLPUCUvjujZsyd27tyJ559/HjfeeCNuu+02zJ49W/NBjPJKH2Ai+KWn2jdoBwKxM2iLx+m1yo1REyd/vZ1L9uoKwaB9Zs6MNqwaezMVPjL2zqC3bJNHqFPbppYW7Gx/IDY14KGljwUFBZg4cSL+/ve/Y/LkyXjwwQddvfU7UXfwCQJ8nOtNvLcXq1DIizOSk5MxefJkDB48GA8++CBatmyJxx57zNCzOrgEP57EUtADvGPoeZk5M6HPbkNH1MYNcy/frpI2yNy7i13trrUvqQ6o/ZXxSMCrqKjAvHnz8MQTT6BHjx7YtWsXzj33XLcPi6hLCLDhxit8Nxer0HLNOGf9+vV44IEHUFpailmzZqF///6qzwQxQq3QRwO5Mk4M5HJj76SpA2obeycCHulBFaG4BL7cMzdgcloL4jGUV8CX6cA1k6SDWgjF4WuEfSnuVWGE8orI/23TQay2PeBowFNbrikIAlasWIFHHnkEGRkZeOGFF1Tv0k0QdiAu1+xy+SQkBPj2V9XBCnzy3dN1frkmhbw6QDAYxOLFizF16lQ0bdoUzz33HDp06MBt+zfVvweAjYO5CA3qqgjFJa6aushxkLnXxwGDL+KWJqQmXw43fdRhDcjbWQunNKDV5lIst38stzvgWMDTuhbvs88+w0MPPYRff/0V06dPx9ChQ+mumYTjRELeZRPtCXnbZ9f5kEfLNesAgUAAI0aMwIABA/DSSy8hLy8PN9xwA2bPno1WrVpx24+e8bA8uMfask0pnK/XUzrXUbPnLph7cf9qOiBzfwYOy3lZTb7YJk7ogdXkKx27YW3EugYYMRLmVLdRXmFL+7O2d633mW3/OtLmRjB6U5U9e/Zg4sSJ+PTTTzFx4kTcf//9SEtLs+fgCIJwHQp5dYj09HQ8+uijGDlyJJ544glcccUVGDx4MB599FGcd955prYpVvFY0DIszCYvloMeYCrsmTF6cgNmp8m3Yu5FHKn+eQkDQY+X0RfhrQWzZj9qG0YmBmL5718BHu2ruw9OQY9HWytuV16BlrZ7PLW3wckdM48/UOKnn37CjBkzsHTpUowYMQL79u1DTk4Ol20ThGVCAPcHRfJ+JEOMQiGvDpKTk4N58+Zh/PjxeOqpp9CqVSsMHToUkydPNhT2jAQ8PepcANAIe3aYPrtMPi/TZ3v1z4sEgwidLIK/YQMAzph9gE91zy6zX2s/knMiVFaqvs5fz1vP0gyd1H4mmy8pyaEjqcFMuzvVzrX2W1yi2d4iXmp3vTYHEPlbl8IryCnxv//9DzNnzsSSJUtw++23Y/v27WjRooVt+yMIwltQyKvDtGzZEkuXLsWUKVMwffp0tGrVCkOGDMHkyZPRvHlztw8vguLSRB0D4KXBX0TLBDhp+nhU+dww+ZGfqbS9F9sc0Dd/oWPHXTX9ALsGXDP9On/vLAbbSwiVla60OaBf1XOrjQH9dpYTa+1uZ6CTIg13/fr1w/bt29GyZUtH9k0QRqFHKNgHhTwCrVq1wtKlSzF16lRMnz4drVu3xuDBgzF58mScf/75iu/hWcWzg1gb/KXmxmnzZ9Tse9UExlqbSxE/l5vGH1BufzfbGzBu/Al95O3t1b9pwhj79+/HzJkzsXTpUgp3BEHA7/YBEN6hZcuWWLJkCbZv346Kigq0bt0agwYNwrZt29w+tFq4ZYadQKisdM34COUVkS+137lFXTCDbrY9EG7j0ImTCJ046Yn2juc2d+1vvKws8iW2tSvHEeftK+fD0r/atu1t27Zh4MCBaN26NSorK7Fjxw4sWbKEAh4RGwiCPV8EhTyiNmLY27NnD3JyctCpUyd06dIFa9euRSgUvpr1gxOvR74IexBNkNuBL2L6y+gB507hRpuLxl/+vfTLkeOoQ+bfzs+p1H5qbehUO7vdp8UToVAIa9aswQ033IBOnTqhcePG2LNnD/7617/SdXdEbEEhzzboOXmELsePH8f8+fPx8ssvo2HDhnjwwQdx5513IjlZf2mfncs666pRsLuKacbk+VJT9V9kgbra1oA97c3TyPNq+7raxlbb1+7wbbV962q7SuFZxauoqMDf/vY3PP/88zh+/DjGjx+Pe++9Fw0a1L6pC0F4GfE5eV3bPGTLc/I+3vVcnX9OHoU8ghlxcHnuuedw4sQJ5OfnY9SoUaZvxcwjANZlA8HL/NtpEnkEgLrcxiJW2tqNCqzRdq/rbczSvl6ppLO0bV1vTym8At7Ro0fx2muvYd68eahfvz4eeugh5slWgvAikZDX+kF7Qt7u5ynkUcgjjBIKhbBu3Tq8/PLL2LRpE/r374+xY8fi//7v/7jtgzUAkpkIYyQEeMEsGgkB1MY16LWzF9pWDaU2p7YNI21XL7ehEmK7UlsqYzXkffXVV5g7dy7eeecddOrUCePHj0fPnj3h99PVNkRsQyHPfujumoRh/H4/8vLykJeXhz179uCVV15Bt27d0KpVK4wdOxb9+/dHSkqKpX1oXesnDYCh8gr4bXzQd6ygdXdOL5pGtWOSBwEyjtEIlZUInbkZij8xtrpvsc1DVdW6r42Xv+kQ641rYrBNI+1YVcz0+nhpUyOYDXjl5eVYsWIF5s6diz179mD48OF0p0wifqGHodsGVfIILhQXF2PJkiWYO3cuCgsLMXLkSNx7771o1qyZbfvsHhhg6PXxZjJYDWQsGUcRliAA1N02VcLr7czapjwxow8rbcALr7WlG22nhFf+3u26U+aBAwfwl7/8BQsWLECjRo0wduxYDBkyBJmZmbbsjyDcJFLJa2lTJW8vVfK8NZIQMUtmZibGjBmD0aNH45NPPsHcuXNx0UUXoWvXrhg1ahR69+6NxMRELvsyGu5EeJs3K4bDSSOpZNC8ZiIBc0aS53nUa0+tqrEXgoH0/Hmhfb0QDLzQLmZwsy290G5qqLWn1t+unY8u4EFVVRXef/99vPbaa/j444+Rl5eHt99+G126dIHPx7u8QRDegx6Gbh9UySNs49dff8WiRYvw+uuvo6KiAiNGjMA999yD5s2bm96m2YBH6ONWMPCyqYwXnGhbakf74dmO8dJe64PL3T4EU+zfvx+vv/46Fi1ahOTkZIwcORJ33XUXzj77bLcPjSAcQazkdWvxgC2VvA3/eaHOV/Io5BG2EwwGsWHDBrz22mv45z//ic6dO2PkyJG45ZZbmO8MRuHOHewKB/FiMGMVHu1KbegeRtsvHtsqFsNdRUUF/vGPf2DBggXYtGkTevfujVGjRqF79+50IxWizhEJeRfdb0/I+++LFPIo5BFOUlBQgDfffBMLFizAiRMnMGjQIAwfPhxXX3216tIUCnjewmxAiEejGS+wtCm1nzeRt128t1OshTtBELB161YsXrwYb731Fho0aIB77rkHw4cPR25urtuHRxCuEQl5F0ywJ+T9+FKdD3k0dUQ4Sm5uLiZNmoR9+/Zh9erVqKioQNeuXXHxxRdj9uzZ+PXXX6NeTwHPl5tZlgAACZ9JREFUe4Sqqmt9sbyW8C5R7VlZqfgFgW5X5kVY/xZjnfXB5TEV8H799VfMnj0bF198Mbp16xap4v33v//FpEmTKOARBGE7VMkjXOf06dNYuXIlFi9ejI0bN6JLly6488470adPH6YZGAqCLmHF9Ptofsk1vBbWSAu6WAk3sdw/xlKoA8KVidWrV+Nvf/sbPv74Y9xwww0YPnw4+vbti7S0NLcPjyA8RaSSd/599lTy9v+5zlfyKOQRnuKXX37B0qVL8dZbb+E///kP8vLyMHDgQOTl5SHVwAO0eRHLBkkRrxl83sRbYIj39rILl3QQa6HEKE71h7F0HsvKyrBmzRq8/fbbWLNmDVq2bImBAwdi8ODBaNq0qduHRxCehUKe/VDIIzzLrl27sHz5crz11ls4fPgw+vbtiwEDBqBbt26WH7buFXRNE5l8b8ASGqitvIus/WIpRMQrSn1frLRLeXk5NmzYgOXLl2PVqlVo0qQJBg0ahAEDBqBNmzZuHx5BxAQ1IW88EvycQ16oAhv2v0whj0Ie4XUEQcC3336Lt99+GytWrMCxY8fQs2dP9O3bF7169UK9evXcPkTudPf3c/sQCMIU60PvuH0IBMGdkydPYu3atVi1ahXWrVuHhg0bon///hg0aBAuv/xyeqYdQRiEQp79UMgjYgpBELB9+3asWrUKq1atwq5du9C5c2f06dMHPXv2xPnnn+/2IXKFwl7dhgITQbjH/v37sW7dOqxevRobN25EmzZt0LdvX/Tt2xeXXXYZBTuCsEAk5DUfZ0/I+98cCnkU8ohY5n//+x9Wr16N1atXY/PmzTj33HPRvXt3dO/eHV26dImrKh8FPmtQYCIIQouTJ0/ik08+wfr16/HRRx/h4MGDuP7669GnTx/06dMHzZs3d/sQCSJuoJBnPxTyiLihpKQEmzZtwkcffYT169dj3759yM7OduWGLXZS9FsxAKC6ssr0NhKSEi0dQ/ZZmZbeTxAE4SUEQcDhw4fRokUL9OjRA927d0enTp2QkZHh9qERRFwSCXnNxtoT8g7MrfMhz9xTjQnCg2RkZCAvLw95eXkAgCNHjuDHH38EzWMQBEEQepx77rk455xz3D4MgqhbCCH+Ny6jG6EBoJBHxDGNGzdG48aN3T4MgiAIgiAIgnAUCnkEQRAEQRAEQTiPIIS/eG+TQJw9OZggCIIgCIIgCKJuQ5U8giAIgiAIgiCcJyQA4Fx5C1ElD6BKHkEQBEEQBEEQRFxBlTyCIAiCIAiCIJyHrsmzDarkEQRBEARBEARBxBFUySMIgiAIgiAIwnkE2FDJ47u5WIUqeQRBEARBEARBEHEEVfIIgiAIgiAIgnAeuibPNijkEQRBEARBEAThPKEQgJAN2yRouSZBEARBEARBEEQcQZU8giAIgiAIgiCch5Zr2gZV8giCIAiCIAiCIOIIquQRBEEQBEEQBOE8VMmzDarkEQRBEARBEARR55g1axauueYaZGZmIicnB3379sXevXujXnPvvffiggsuQGpqKho1aoQ+ffpgz549Ua/5+eefkZeXh7S0NOTk5ODhhx9GdXW1kx+lFhTyCIIgCIIgCIJwnpBgzxcjmzZtQn5+Pr788kusX78eVVVV6NGjB0pLSyOvueqqq7Bo0SLs3r0bH374IQRBQI8ePRAMBgEAwWAQeXl5qKysxJYtW7B48WK8+eabmDZtGvfTZQSfIFBNkyAIgiAIgiAIZzh16hSys7PRrcFdSPAncd12dagSG44vQlFREbKysgy9t7CwEDk5Odi0aRM6duyo+JodO3bgsssuw759+3DBBRdg3bp16N27Nw4dOoTGjRsDAObPn4+JEyeisLAQSUl8Px8rVMkjCIIgCIIgCMJxBCFkyxcQDpLSr4qKCt3jKSoqAgA0aNBA8felpaVYtGgRmjdvjnPOOQcA8MUXX+DSSy+NBDwAuPHGG3Hq1Cn88MMPVk+RaSjkEQRBEARBEAThPIINSzXPLFI855xzkJ2dHfmaNWuW5qGEQiFMmDAB119/PS655JKo373yyivIyMhARkYG1q1bh/Xr10cqdAUFBVEBD0Dk+4KCAl5nyjB0d02CIAiCIAiCIOKKgwcPRi3XTE5O1nx9fn4+du7cic8//7zW7+688050794dhw8fxnPPPYf+/ftj8+bNSElJ4X7cvKCQRxAEQRAEQRCE8wgCAHseoZCVlcV8Td7YsWPx/vvv47PPPkPTpk1r/V6sBl500UVo37496tevj5UrV2LQoEHIzc3F119/HfX6I0eOAAByc3Mtfhjz0HJNgiAIgiAIgiDqHIIgYOzYsVi5ciU++eQTNG/enOk9giBErvG79tpr8f333+Po0aOR16xfvx5ZWVlo06aNbceuB1XyCIIgCIIgCIJwnlAI8IX4blNg315+fj6WLVuG1atXIzMzM3INXXZ2NlJTU7F//34sX74cPXr0QKNGjfDLL7/g6aefRmpqKnr16gUA6NGjB9q0aYMhQ4bgmWeeQUFBAaZMmYL8/HzdJaJ2QpU8giAIgiAIgiDqHK+++iqKiorQuXNnNGnSJPK1fPlyAEBKSgr+9a9/oVevXrjwwgsxYMAAZGZmYsuWLcjJyQEABAIBvP/++wgEArj22msxePBgDB06FE8++aSbH42ek0cQBEEQBEEQhHOIz8nrmnEHEnycn5MnVOLjkmWmnpMXT1AljyAIgiAIgiAIIo6ga/IIgiAIgiAIgnAcIRSCwPmaPMHANXnxDIU8giAIgiAIgiCcx8ZHKNR1aLkmQRAEQRAEQRBEHEGVPIIgCIIgCIIgnCckAD6q5NkBVfIIgiAIgiAIgiDiCKrkEQRBEARBEAThPIIAgPfD0KmSB1AljyAIgiAIgiAIIq6gSh5BEARBEARBEI4jhAQInK/JE6iSB4AqeQRBEARBEARBEHEFVfIIgiAIgiAIgnAeIQT+1+TRw9ABCnkEQRAEQRAEQbgALde0D1quSRAEQRAEQRAEEUdQJY8gCIIgCIIgCOeh5Zq2QSGPIAiCIAiCIAjHqUYVwHl1ZTWq+G4wRqGQRxAEQRAEQRCEYyQlJSE3NxefF6y1Zfu5ublISkqyZduxgk+gqxMJgiAIgiAIgnCQ8vJyVFZW2rLtpKQkpKSk2LLtWIFCHkEQBEEQBEEQRBxBd9ckCIIgCIIgCIKIIyjkEQRBEARBEARBxBEU8giCIAiCIAiCIOIICnkEQRAEQRAEQRBxBIU8giAIgiAIgiCIOIJCHkEQBEEQBEEQRBxBIY8gCIIgCIIgCCKO+H/WNHeRHUPxkQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "engine": 0
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[output:0]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "engine": 0
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[output:0]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "engine": 0
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[output:0]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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eICQk5Lv1njp1Cjt27ECfPn1QqFAhzJ8/H23atMGjR49QuHBhpWLr2LEjNmzYgGnTpkEikeDVq1c4dOgQ1q9fr1RSOm/ePBw5cgT+/v6IiIiAnp4eli1bJq+jePHi3zz+zZs38PHxQevWrdG+fXts27YNw4cPh6OjI5o2bQrgSwtJ48aN8fz5c/z++++wtbXFpk2bFJI7VaR9yfD17+Xt27dYsWIFOnbsiMDAQLx79w4rV66Et7c3zp07B2dnZ1hbW2PJkiXo3bs3WrVqJU/OnJycsjxXTj5vTp8+DYlEgurVqyt1XTExMShYsCAKFiwo33fp0iXUqFEDUqnid6+urq4IDg5GdHQ0HB0dM62vcePG6NOnD6ZOnQpfX1/UqFEDz58/R//+/eHp6ZmhpVTZGAEoJMKXLl0CAHmrcZrixYujZMmS8te/p1+/frCwsMD48eNx+/ZtLFmyBA8fPpR/mQF8SfYnTJgAT09P9O7dW17u/Pnz+Pfff7/burZp0ya8e/cOPXv2hEQiwfTp09G6dWvcu3cPBgYG6NmzJ549e4awsDCsX79e4diwsDB07NgRHh4e8s/wmzdv4t9//1Vq+MW6devw7t079O3bF58+fcK8efPQuHFjXL16FTY2NgCA69evo27duihRogRGjBgBExMTbN26Fb6+vti+fbs8kU/Tp08fWFtbY+zYsUhMTMz0vJUrV8bEiRMxduxYBAUFyb+IUfb/uQAQGxuL2rVry78Is7a2xoEDB9C9e3e8ffsWAwYMUCjv4uKC3bt34+3btzAzM1P6PETaJIMEqVDvBGsyNddH3yBQvpeQkCAAEFq2bKlU+aioKAGA0KNHD4X9Q4YMEQAIR44cke9r0KCB0KBBA/nPq1evFgAI9+/fVzj26NGjAgDh6NGjCscCEDZt2iTfd+vWLQGAIJVKhTNnzsj3Hzx4UAAgrF69Wr5v3LhxAgDh119/VThXq1athMKFCyt1rQCEvn37ZoizcuXKwufPn+X7582bJwAQrl69Kt/XvHlzoUyZMhnqXL9+vSCVSoWTJ08q7F+6dKkAQPj3338FQRCEyMhIAYAwYMAAhXLdunUTAAjjxo3LcK0dO3bMcL4PHz5k2Pf3338LAIQTJ07I982YMSPT382DBw8EPT09YcqUKQr7r169Kujr68v3JyUlCUWLFhWcnZ0V3pvg4GABgMJ9kJUyZcoITZo0EV6+fCm8fPlSuHr1qtClS5cMv4esrmvq1KmCRCIRHj58KN+X9t58DYBgaGgo/Pfff/J9ly9fFgAICxYs+GaM9+/fFwAIM2bMEK5duyYAkP8uFy1aJJiamgqJiYmCv7+/YGJionBs+r8HQfj/e3fy5MnCvXv3BFNTU8HX11ehzLf+PtatWyff9/nzZ8HW1lZo06aNfN+sWbMEAMKuXbvk+z5+/Cg4ODhkqDMzaX+zhw8fFl6+fCk8fvxY2LZtm2BtbS0YGRkJjx8/lpdNSUlR+N0LgiC8efNGsLGxUfg7fPnyZYZ7OE3631d2Pm8y88svvyj9937nzh3B2NhY6NKli8J+ExOTDJ8jgiAI+/btEwAIoaGh36w3MTFRqFChgvDDDz8Inz59Epo3by6YmZkp3KfZ0b17d0FPT0+Ijo6W70v7+3306FGG8rVq1RJq1679zTrTfs8uLi5CUlKSfP/06dMFAMLu3bsFQRCEFy9eCIaGhkKTJk2E1NRUebmFCxcKAIRVq1bJ9/n7+yt8Bqb97RQuXFiIi4uT79+9e7cAQPjnn3/k+/r27Zvh71YQBOH3338XzMzMhJSUlG9eT3pp5y5QoIDw5MkT+f6zZ88KAISBAwfK93l4eAiOjo7Cp0+f5PtkMplQp04dwd7eXr4v7T2rV6+eUvGcP38+w/+n0qR/rwRByPA30r17d6FYsWLCq1evFMp16NBBMDc3z/CZuGnTJgGAcPbs2e/GRqRtac+eF2/YCNGPi6l1u3jDRgAgJCQkiH2ZeR67thLevn0LABla8rKyf/9+AMCgQYMU9g8ePBgA1DqW0tTUFB06dJD/XKlSJVhYWKBy5cpwc3OT70/797179zLUkf4b/x9//BGvX7+WX7cqAgICFMa7pX27nNn50wsJCUHlypXh4OCAV69eybfGjRsDgLylKK1Fq0+fPgrHf2vijMxaNwoUKCD/96dPn/Dq1SvUrl0bAJSadXLHjh2QyWRo3769Qry2trawt7eXx3vhwgW8ePECvXr1UnhvunXrBnNz8++eJ82hQ4dgbW0Na2trODo6Yv369QgICMCMGTOyvK7ExES8evUKderUgSAISrW+eHp6onz58vKfnZycYGZmptTvMM0PP/wAJycn+aQ7mzZtQsuWLRVas76nSZMm6NmzJyZOnIjWrVvD2NgYy5YtU+pYU1NThfGkhoaGcHV1VbiG0NBQlChRAi1atJDvMzY2zvZ4U09PT1hbW6NUqVJo27YtTExMsGfPHoWufHp6evLfvUwmQ1xcHFJSUlCzZk2VZzjN6efN69evM7RmZ+bDhw9o164dChQogGnTpim89vHjRxgZGWU4Jm3c6vcmBypYsCDWrFmDmzdvon79+ti3bx/mzJmD0qVLfzeu9DZt2oSVK1di8ODBsLe3V4gRQJZxKjuBUVBQkEKLYu/evaGvry//PRw+fBhJSUkYMGCAQgttYGAgzMzMlPr89/PzU/idZOfz08LCAomJiQgLC1PqetLz9fVFiRIl5D+7urrCzc1Nfn1xcXE4cuQI2rdvj3fv3sk/716/fg1vb2/cuXMnw2zFgYGB0NPTUykeZQmCgO3bt+Pnn3+GIAgKn8Xe3t5ISEjI8DeW9h6/evVKo7ER5YRM0MxG2sFEkuRdXr4eE/QtDx8+hFQqRYUKFRT229rawsLCAg8fPlRbbCVLlswwXsrc3DxDN8m0RCWzsTzpH9bS/ueaVjYuLg4xMTHyLSEh4btxfa/Ob7lz5w6uX78uT5bStooVKwIAXrx4AeD/3+eyZcsqHJ/+ff9a+rLAl+v7/fffYWNjgwIFCsDa2lpeTplrvXPnDgRBgL29fYaYb968qRAvAIWHW+DLWJ1y5cp99zxp3NzcEBYWhtDQUMycORMWFhZ48+ZNholqHj16hG7dusHKygqmpqawtrZGgwYNlL6uzB7iLS0tlfodfq1Tp04ICQnBf//9h9OnT6NTp07ZOh4AZs6cCSsrK0RFRWH+/PkoWrSoUsdl9veR/hoePnyI8uXLZyj3rfsoM4sWLUJYWBi2bduGZs2a4dWrV5kmLWvXroWTkxOMjY1RuHBhWFtbY9++fUr9TjKjjs8bQfj2U0Vqaio6dOiAGzduYNu2bRm6FBcoUCDTcZBpM9Z+/aVGVurWrYvevXvj3Llz8Pb2xq+//vrdY9I7efIkunfvDm9vb0yZMiVDjACyjFOZGIGMf7+mpqYoVqyYvCtz2vtdqVIlhXKGhoYoV66cUr+PnHx+9unTBxUrVkTTpk1RsmRJ/Prrr9ka25z++gCgYsWK8uv777//IAgCxowZk+Hzbty4cQD+/zM6TWafu+r28uVLxMfHIzg4OENcaRNDpY8r7b7PLevoEmUm9X9dW9W9kXZwjCTBzMwMxYsXz/bixar8zymrY1JTUzPdn9W3vFntz+yB8XtlW7dujePHj8v3+/v7yydAyEp2zp+eTCaDo6MjZs+enenrqo4lBDJ/oG3fvj1Onz6NoUOHwtnZGaamppDJZPDx8YFM9v25zWQyGSQSCQ4cOJDpdat7rcQiRYrIJ0bx9vaGg4MDfvrpJ8ybN0/eKpWamgovLy/ExcVh+PDhcHBwgImJCZ4+fYpu3bopdV05+R1+rWPHjhg5ciQCAwNRuHBhNGnSJFvHA1/Gt6U9BF69ehUdO3ZU6jh1XYMyXF1d5ePvfH19Ua9ePXTq1Am3b9+W3wMbNmxAt27d4Ovri6FDh6Jo0aLQ09PD1KlTc7wMgaoPw4ULF/5ughIYGIi9e/di48aN8p4BXytWrBieP3+eYX/avu+NZQW+JHhpExvdvXsXHz58yFbL9eXLl9GiRQtUrVoV27ZtyzCzbdokac+fP8/wGfL8+XO4uroqfS5Ny8l9W7RoUURFReHgwYM4cOAADhw4gNWrV6Nr165Yu3ZtjmNL++wYMmQIvL29My2T/ksNZZN0dcT1yy+/wN/fP9My6ccZp9336SeVIiJSFyaSBAD46aefEBwcjIiICLi7u3+zbJkyZSCTyXDnzh1UrlxZvj82Nhbx8fEoU6ZMlsemffOcfnY/dbZiZtesWbMUHjSVeShURlYPvuXLl8fly5fh4eHxzYfjtPf5/v37Ct+ifz077Pe8efMG4eHhmDBhgsKskZmtffeteAVBQNmyZeWtplnFm1b31w/jycnJuH//vsISGdnRvHlzNGjQAH/++Sd69uwJExMTXL16FdHR0Vi7di26du0qL6tqd7ecKF26NOrWrYtjx47JuwFmR2JiIgICAlClShXUqVMH06dPR6tWreQzPOZUmTJlcOPGDQiCoPA7zs59lF5actioUSMsXLgQI0aMAPBldtNy5cphx44dCudKa8lJk52kMCefNwDg4OCAjRs3IiEhIdMu1kOHDsXq1asxd+7cLBN4Z2dnnDx5EjKZTKE759mzZ1GwYMFv/l2kGTduHG7evImZM2di+PDhGDFiBObPn//d44AviaePjw+KFi2K/fv3Z/rljbOzM4AvXcy/ThqfPXuGJ0+eICgoSKlz3blzR2G9zffv3+P58+do1qwZgP//O799+7ZCT4OkpCTcv38/w+y4qvrWPWJoaIiff/4ZP//8M2QyGfr06YNly5ZhzJgx321pz+yzLzo6Wj5jato1GRgYqO1a0uSkZdDa2hqFChVCamqq0nHdv38fUqlUqfuTSCyaaEFki6T2sGsrAQCGDRsGExMT9OjRA7GxsRlev3v3rnx69bQHirlz5yqUSWth+9asomlj0k6cOCHfl5qaiuDg4BzFnxMuLi7w9PSUb1WqVFFLvSYmJpl252vfvj2ePn2K5cuXZ3jt48eP8ln/0r4NX7x4sUKZBQsWKB1D2jf/6b/pT/+7S4sXyJjkt27dGnp6epgwYUKGegRBkC8jUrNmTVhbW2Pp0qVISkqSl1mzZk2GOrNr+PDheP36tfw9y+y6BEH47hIAmjJ58mSMGzdOpYXfhw8fjkePHmHt2rWYPXs27Ozs4O/vr9TSFsrw9vbG06dPFZYu+PTpU6b3X3Y0bNgQrq6umDt3rryLZ2a/l7NnzyIiIkLh2LSWOGXui5x83gBflmkRBAGRkZEZXpsxYwZmzpyJUaNGfXPGz7Zt2yI2NhY7duyQ73v16hVCQkLw888/Z9rF92tnz57FzJkzMWDAAAwePBhDhw7FwoULFXpCZCUmJgZNmjSBVCrFwYMH5TMpp/fDDz/AwcEBwcHBCj08lixZAolEIl//8nuCg4ORnJyscHxKSop8FmBPT08YGhpi/vz5Cr/nlStXIiEh4bu/D2Vl9Xn09bJFwJc1VtNa4pT5m9m1a5fCGMdz587h7Nmz8usrWrQoGjZsiGXLlmXaCv3y5ctsXcfXsromZejp6aFNmzbYvn17pr2HMosrMjISP/zwQ7bGqBMRZQdbJAnAlwRv06ZN8PPzQ+XKldG1a1dUrVoVSUlJ8oW4u3XrBgCoVq0a/P39ERwcjPj4eDRo0ADnzp3D2rVr4evrq/Btdno//PADateujZEjRyIuLg5WVlbYvHkzUlJStHSl2uPi4oItW7Zg0KBBqFWrFkxNTfHzzz+jS5cu2Lp1K3r16oWjR4+ibt26SE1Nxa1bt7B161YcPHgQNWvWhIuLC9q0aYO5c+fi9evX8uU/oqOjASj37baZmRnq16+P6dOnIzk5GSVKlMChQ4dw//79TOMFvixd0qFDBxgYGODnn39G+fLlMXnyZIwcORIPHjyAr68vChUqhPv372Pnzp0ICgrCkCFDYGBggMmTJ6Nnz55o3Lgx/Pz8cP/+faxevTpbYyQz07RpU1StWhWzZ89G37594eDggPLly2PIkCF4+vQpzMzMsH379myPb1SXBg0ayMdnZseRI0ewePFijBs3DjVq1AAArF69Gg0bNsSYMWMyXRMyu3r27ImFCxeiY8eO+P3331GsWDFs3LhRPlFMTlpJhg4dinbt2mHNmjXo1asXfvrpJ+zYsQOtWrVC8+bNcf/+fSxduhRVqlTB+/fv5ccVKFAAVapUwZYtW1CxYkVYWVmhatWqqFq1aoZz5OTzBgDq1auHwoUL4/Dhwwot5Tt37sSwYcNgb2+PypUrY8OGDQrHeXl5yZeDaNu2LWrXro2AgADcuHEDRYoUweLFi5GamooJEyZ88/yfPn2Cv78/7O3t5eMaJ0yYgH/++QcBAQG4evXqNxew9/Hxwb179zBs2DCcOnUKp06dkr9mY2MDLy8v+c8zZsxAixYt0KRJE3To0AHXrl3DwoUL0aNHD4XW3G9JSkqCh4cH2rdvj9u3b2Px4sWoV6+efLIma2trjBw5EhMmTICPjw9atGghL1erVi2FyZ9yIu3z6LfffoO3tzf09PTQoUMH9OjRA3FxcWjcuDFKliyJhw8fYsGCBXB2dlbqGitUqIB69eqhd+/e+Pz5M+bOnYvChQtj2LBh8jKLFi1CvXr14OjoiMDAQJQrVw6xsbGIiIjAkydPcPnyZZWuqXz58rCwsMDSpUtRqFAhmJiYwM3NTekxltOmTcPRo0fh5uaGwMBAVKlSBXFxcbh48SIOHz6MuLg4ednk5GT5+pZEuZlMkEAmqHn5DzXXR9+gxRliSQdER0cLgYGBgp2dnWBoaCgUKlRIqFu3rrBgwQKFqdCTk5OFCRMmCGXLlhUMDAyEUqVKCSNHjlQoIwiZL3dw9+5dwdPTUzAyMhJsbGyEUaNGCWFhYZkub/DDDz9kiLFMmTJC8+bNM+xHuiUi0pYRePnypUK5rJYgyUz6OtOWYQgJCVEolza1/NfTur9//17o1KmTYGFhIQBQmNo9KSlJ+Ouvv4QffvhBMDIyEiwtLQUXFxdhwoQJCtNVJyYmCn379hWsrKzky0Lcvn1bACBMmzbtu9cqCILw5MkToVWrVoKFhYVgbm4utGvXTnj27Fmmyy9MmjRJKFGihCCVSjO8R9u3bxfq1asnmJiYCCYmJoKDg4PQt29f4fbt2wp1LF68WChbtqxgZGQk1KxZUzhx4kSm90FmsvrdCoIgrFmzRuE9vnHjhuDp6SmYmpoKRYoUEQIDA+VLeGS2DMzX0v9evz6/v7//N2P8evmPb/ne8h9v374VypQpI9SoUUNITk5WKDdw4EBBKpUKERERgiBkvfxHZn8fmS0jcO/ePaF58+ZCgQIFBGtra2Hw4MHC9u3bBQAKy+hkJu3v5fz58xleS01NFcqXLy+UL19eSElJEWQymfDnn38KZcqUEYyMjITq1asLe/fuzTSm06dPCy4uLoKhoaHCvZjZ70vZz5us/Pbbb0KFChUU9qWdJ6st/bIocXFxQvfu3YXChQsLBQsWFBo0aJDpe5LewIEDBT09vQxLMFy4cEHQ19cXevfu/c3jvxVjZn9TO3fuFJydnQUjIyOhZMmSwujRoxWW88hK2u/5+PHjQlBQkGBpaSmYmpoKnTt3Fl6/fp2h/MKFCwUHBwfBwMBAsLGxEXr37i28efNGoUxWy39k9reT/vMoJSVF6N+/v2BtbS1IJBL5PbFt2zahSZMmQtGiRQVDQ0OhdOnSQs+ePYXnz59/8/q+PvesWbOEUqVKCUZGRsKPP/4oXL58OUP5u3fvCl27dhVsbW0FAwMDoUSJEsJPP/0kbNu2LcN7psx9kGb37t1ClSpVBH19fYXPKmWW/xAEQYiNjRX69u0rlCpVSjAwMBBsbW0FDw8PITg4WKHcgQMHBADCnTt3lI6NSJvSlv84da24EPWwpFq3U9eKc/kPLZEIggZmZSAijYmKikL16tWxYcMGdO7cWexwSEfNnTsXAwcOxJMnTxSWQ8iL7t27BwcHBxw4cAAeHh5ih5MrrVmzBgEBATh//rx8UqW85MGDByhbtixmzJiBIUOGiB2Oxvn6+kIikWDnzp1ih0KUqbdv38Lc3BzHr5WAaSH1jrR7/06GBlWfIiEhQb4yAWkGx0gS5WKZrf02d+5cSKVS1K9fX4SISBelv48+ffqEZcuWwd7ePs8nkcCXCVS6d++eYX1Iorzo5s2b2Lt3LyZNmiR2KESUx3GMJFEuNn36dERGRqJRo0bQ19eXT3cfFBSUo2VCKH9p3bo1SpcuDWdnZyQkJGDDhg24desWNm7cKHZoWrNkyRKxQyDSisqVK+fJeQcob0qFFKlqbtfKfEE50gQmkkS5WJ06dRAWFoZJkybh/fv3KF26NMaPH48//vhD7NBIh3h7e2PFihXYuHEjUlNTUaVKFWzevBl+fn5ih0ZERPmYoIHJdgROtqM1HCNJRERERERakzZGMvxqaZioeYxk4jsZPBwfcYykFrBFkoiIiIiItC4VEqRCvS2I6q6PssbJdoiIiIiIiChb2CJJec6rV68QHh6OQ4cO4dGjR2KHoxEf33/C5w+fFfYZFTRS+LmAqbE2QyIi0mllypSBl5cXPDw8UKRIEbHDIcoXUgUpUgU1T7bDQXtaw0SSdF5KSgpOnTqFQ4cO4dChQ7h06RKqVq2KJk2aoE6dOpBI8k4Xh5ndF2f5WiLSLxWScemQ3G7Iyj5ih0BE+ZAgCLh16xb+/PNPdOrUCdWrV0eTJk3QpEkT1KtXD/r6fFwiIkqPk+2QTkpMTMShQ4ewa9cu7N27FwYGBvD29oaXlxc8PT1ha2srdohq4yVtJ3YI9A1hshCxQyAiNYqJicHhw4cRFhaGgwcPIjk5GT/99BN8fX3RpEkTmJiYiB0ikc5Lm2xn35VyMCmkp9a6E9+lornTPU62owVMJElnvHnzBrt27cKuXbtw6NAhlCpVCq1atYKvry/c3NwgleatIb9MIEmTmAATfZ9MJsPZs2exa9cu7Ny5E48fP0aTJk3g6+sLX19fWFpaih0ikU5iIpk3MJGkXO39+/fYs2cP/v77bxw8eBDVqlVD69at4evrCwcHhzzVbTUNE0iizDH5JTGldX/dtWsXduzYgStXrsDb2xsdOnRAixYtYGpqKnaIRDojLZHcc6W8RhLJFk53mUhqARNJynU+ffqE/fv3Y/Pmzdi7dy/Kly+Pjh07ws/PD+XLlxc7vG9iEkiUtzB5zb6cfA7q0vv933//YcuWLdi8eTPu3r2Ln3/+GR06dEDTpk1hbMzJzoi+JS2R3HnZXiOJZKtqd5hIagETScoVBEHA6dOnsXbtWmzZsgXW1tby5LFq1apqOw8TPSLKDXJbwpTbPhtz2/vzPdeuXcPmzZuxefNmvHz5En5+fvD3989zE74RqQsTybyBiSSJ6uHDh1i/fj3Wrl2LV69eoWPHjvD394erqyskEkmue7ghIiLt0bWEUhAEnD17FmvXrsXmzZthbW2Nrl27omvXrihdurTY4RHlGmmJ5PbLFTWSSLapFs1EUgvy1uwkpBM+fPiAdevWwcPDA/b29jhz5gz+/PNPPH/+HIsXL4abmxu/wSUiInhJ2+nUF4oSiQS1a9fGkiVL8Pz5c0yZMgURERGoUKECPDw8sG7dOnz48EHsMImI1IItkqQ1V65cQXBwMDZs2ICSJUsiICAAnTt3/u5SHbr0EEFERJqla62UwJclRTZu3IjVq1fjyZMn6NKlCwIDA+Hk5CR2aESiSGuRDLnsgIJqbpH88C4V7ardYoukFrBFkjQqMTERq1atQu3atVG7dm0kJibiwIEDuHr1KgYPHqzUeo9hshCdfHAgIiL107VWSgCwtbXF4MGDcfXqVRw4cADv37+X/39x1apVSExMFDtEIqJsY4skaURUVBSWLVuGjRs3omzZsujZsyc6deoECwuLHNWraw8PRESkWbr6RWN8fDw2btyI4OBg3L9/H507d0bPnj3h7OwsdmhEGpfWIrk5qopGWiQ7ON9gi6QWMJEktUlKSsKOHTuwcOFCXLp0CR07dkRQUBBq1aql9jGPTCiJiEhdxExGBUHA+fPnERwcjL///hs1atRAv3790Lp1axgYGIgWF5EmMZHMG5hIUo49f/4cwcHBWLZsGQoWLIi+ffuiW7dusLS01Oh5mUwSEVFupUpy+ubNG6xZswaLFi3Chw8f0LNnT/Ts2VOpYSBEuiQtkdwUVVUjiWQn52vZSiQXLVqEGTNmICYmBtWqVcOCBQvg6uqaZfmQkBCMGTMGDx48gL29Pf766y80a9ZM/npWDSjTp0/H0KFDAQB2dnZ4+PChwutTp07FiBEjlIo5N+AYSVJJ2rqPHTt2hJ2dHc6dO4eVK1ciOjoaAwcOVHsSmTYm5uuNiIgot1Ll/1OWlpYYOHAgoqOjsWLFCpw9exZlypRBp06dcPr0afC7f8prUgWJRrbs2LJlCwYNGoRx48bh4sWLqFatGry9vfHixYtMy6c9/3bv3h2XLl2Cr68vfH19ce3aNXmZ58+fK2yrVq2CRCJBmzZtFOqaOHGiQrn+/ftn/00UEVskKVtSUlKwfft2zJw5E3fu3MGvv/6KPn36oEKFCjmum8khERHlJeroMvvff/9h8eLFWLVqFezt7TFkyBC0adMG+vr6aoiQSBxpLZLrLzlqpEWyS/WrePz4sUKLpJGREYyMjDKUd3NzQ61atbBw4UIAgEwmQ6lSpdC/f/9MWwf9/PyQmJiIvXv3yvfVrl0bzs7OWLp0aaYx+fr64t27dwgPD5fvs7Ozw4ABAzBgwABVL1V0bJEkpbx//x7z5s1DhQoVMHLkSHTt2hVPnjzB7Nmzs5VEZtayyBZGIiLKi9Tx/7YKFSpg9uzZePLkCbp27YoRI0bA3t4e8+fPx/v379UQJZF4UiHVyAYApUqVgrm5uXybOnVqhvMnJSUhMjISnp6e8n1SqRSenp6IiIjINOaIiAiF8gDg7e2dZfnY2Fjs27cP3bt3z/DatGnTULhwYVSvXh0zZsxASkqK0u9dbsCvs+ibnj9/jgULFmDJkiWwt7fHjBkz0KpVq29+E8qkkIiISL1MTU3Rv39/9O7dGzt37sSMGTMwbtw49O7dG/3790exYsXEDpEoV8msRTK9V69eITU1FTY2Ngr7bWxscOvWrUzrjYmJybR8TExMpuXXrl2LQoUKoXXr1gr7f/vtN9SoUQNWVlY4ffo0Ro4ciefPn2P27NlKXV9uwESSMnX79m389ddf2LRpE5o0aYLdu3fjxx9/lA8eZrJIRET0fV7SdmqdFVZfXx/t2rVD27ZtcfLkScycORNly5ZFp06dMHz4cFSqVElt5yLSNJkghUxQbwdJ2f9G7ZmZmeWKWVtXrVqFzp07w9jYWGH/oEGD5P92cnKCoaEhevbsialTp2aa9OZG7NpKCm7fvo1ffvkF1apVg0QiQVRUFPbs2YP69eurfQkPIiIiUo1EIkH9+vWxZ88eXLp0CRKJBNWqVUOXLl1w+/ZtscMj0glFihSBnp4eYmNjFfbHxsZmOVuyra2t0uVPnjyJ27dvo0ePHt+Nxc3NDSkpKXjw4IHyFyAyJpIEALh165Y8gSxYsCBu3bqFlStXwsHBIdPyuroANBERkbZpuhdP5cqVsXLlSty8eRPGxsaoVq0afvnlFyaUlOtpcoykMgwNDeHi4qIwCY5MJkN4eDjc3d0zPcbd3V2hPACEhYVlWn7lypVwcXFBtWrVvhtLVFQUpFIpihYtqnT8YmMimc/dunULnTt3hrOzszyBDA4Ohp2dndihERER5RnaGBJStmxZLF++HLdu3ULBggXlCWVWY72I6EsX0+XLl2Pt2rW4efMmevfujcTERAQEBAAAunbtipEjR8rL//777wgNDcWsWbNw69YtjB8/HhcuXEC/fv0U6n379i1CQkIybY2MiIjA3LlzcfnyZdy7dw8bN27EwIED8csvv2h8HXZ14hjJfOrhw4cYM2YMtm7dCn9/f9y6dSvbyWOYLIRjJYmIiJT0rf9nqrOnj52dHYKDgzFq1Cj8+eefcHZ2Rvv27TF58mSULl1abechyikZkO11H5WpMzv8/Pzw8uVLjB07FjExMXB2dkZoaKh8Qp1Hjx5BKv3/trc6depg06ZNGD16NEaNGgV7e3vs2rULVatWVah38+bNEAQBHTt2zHBOIyMjbN68GePHj8fnz59RtmxZDBw4UGHcpC7gOpL5THx8PP78808sXLgQbdu2xcSJE3PU+shEkoiISPNykmg+ePAAY8aMwfbt29G/f3+MHDkSFhYW6guOKJvS1pFccrEWCpiqt13r4/sU9K5xHgkJCblisp28jF1b84mkpCTMnTsX5cuXx8WLF/Hvv/9i3bp1Oe7CyrGSREREmpeTdZjt7Oywfv16nDp1ChcuXECFChUwb948JCUlaSFyIsqr2LU1jxMEASEhIRg5ciQKFiyIDRs2wMfHhzOwEhER5RFZJZPpv+ytUaMGDh8+jAMHDmDYsGGYP38+pk6dinbt2vG5gESRKkiRqublP9RdH2WNiWQeFhUVhX79+uHevXuYNGkSunXrBj09vRzVya6sREREuksikaBZs2Zo0qQJ1q5diwEDBmD+/PlYtGiRUjNLEhGlYcqeB7158wb9+vWDu7s7fvzxR0RHR6N79+7ZTiJV6T5DREREuZ++vj66d++O6Oho1KtXD7Vr10b//v0RHx8vdmiUj8gg0chG2sFEMg+RyWRYtWoVKlWqhDt37iAqKgpTp06FqampUsczaSQiIspfTE1NMW3aNERFReH27duoWLEiVq1aBZksu3NfElF+w66teURkZCT69u2L58+fY+nSpWjVqtU3xzswUSQiIqI0lSpVwsGDB7Fz504MHDgQy5cvx8KFC+Hi4iJ2aJSHcYykbuM7rePev3+PgQMH4scff4SXlxdu3ryJ1q1bZ0gi2dpIRESUv2T3//cSiQStW7fGzZs34enpiR9//BEDBw7E+/fvNRQhEekyJpI6bN++fahSpQoiIyNx8eJFTJo0CQULFlQow8SRiIiIsqNgwYKYNGkSIiMjERkZiR9++AH79u0TOyzKg1Ih1chG2sF3WgfFxMTAz88Pv/zyC8aOHYtjx47BwcFB7LCIiIgoD6lcuTKOHTuG0aNH45dffoGfnx9iYmLEDovyEJkg0chG2sFEUofIZDIsX74clStXhkQiwc2bN9GjRw9IpVn/GtOvIUVERET5R057JkmlUgQGBuLmzZsAviSXy5cvhyAI6gqRiHQUJ9vREQ8ePED37t1x584dbNiwAc2bNxc7JCIiItIRmSWT2fmy2dbWFlu2bMG+ffvQu3dvbN68GStXroSdnZ0ao6T8RqaBrqgytpNpDd/pXE4mk2HJkiVwdHREhQoVcO3atWwnkWyVJCIiovRUWS+6efPmuHbtGipUqAAnJycsXbqUS4UQ5VNskczF7t+/j+7du+Pu3bvYuXMnPD09xQ6JiIiI8jBlWi7NzMywbNkytG3bFj169EBISAhbJ0klMkEKmZqX61B3fZQ1vtO5UForpJOTEypWrIhr167lOIlkqyQRERGpIquWSy8vL1y9ehUVKlSAo6MjlixZwtZJonyELZK5zNOnTxEQEIDbt2+rpRWSS38QERGRunlJ2yFMFiJvnWzXrh26d++OnTt3YvXq1ShRooTYIZIOSIUEqVDvLKvqro+yxhbJXOTvv/9G1apVUaJECVy9elWlJDK7Yx2IiIiIcsrT0xNXrlxB8eLF4ejoiM2bN4sdEhFpGFskc4G4uDj07dsXhw8fxqpVq9CqVatsHc+EkYiIiMRmbm6ONWvWYMeOHQgKCsLu3buxePFiWFpaih0a5VIcI6nb+E6L7NChQ3B0dMS7d+9w9epVpZJItjoSERFRbtW6dWtcu3YNb9++haOjI8LCwsQOiYg0gC2SIvn8+TNGjBiBFStWYNasWQgMDIREknmfbiaLREREpEtsbW2xd+9eLF++HK1bt0aPHj0wbdo0GBkZiR0a5SKpUP+YxlS11kbfwkRSBDdv3kTHjh2hr6+PyMhIVKxYMUMZJo9ERESkyyQSCYKCgtCwYUN06tQJbm5u+Pvvv1G5cmWxQ6Ncgl1bdRvfaS0SBAHBwcGoVasWvL29cfr06UyTSCIiIqK8omLFijh9+jS8vb1Rq1YtLF++HIIgiB0WEeUQWyS15PXr1wgMDMSZM2ewe/dueHh4iB0SERERkcrSlgBRhqGhIf766y94eXmha9euCA0NxfLly2FlZaXhKCk3SxWkSFVzC6K666Os8Z3WglOnTsHZ2Rmpqam4cuWKUkmksh/MRERERGLJ7gSAacuEpKSkoFq1ajh16pQWoiQiTWAiqUEymQxTp06Ft7c3hg8fjl27dqFIkSJih0VERESkEcoklkWKFMGuXbswbNgweHt7Y9q0aZDJZFqOlHIDARLI1LwJap68h7LGrq0a8uLFC3Tt2hV37tzBiRMn4OLiInZIRERERFqVPplM63ElkUjQv39/1KlTB+3bt8fx48exbt06WFtbixEmEamALZIacPz4cTg7O8PMzAwXL15UOYlk91YiIiLKS9K3WLq4uODixYswNTWFs7MzTpw4IXaIpEVpYyTVvZF28J1WI5lMhilTpqBZs2YYPXo0tmzZAnNzc5Xr4xIgRERElJd5SduhrWUPbN26FaNGjULTpk3x559/sqsrkQ5g11Y1efPmDbp27Yrr16/j5MmTqFGjhkr1MHkkIiKi/EYikaBv375wd3dH27ZtcebMGaxbtw4WFhZih0YaJBMkkAnqHdOo7vooa2yRVIOoqCjUrFkTgiAgMjIyW0lkdmc7IyIiIsqratSogcjISKSmpsLFxQWXL18WOyTSoFRINbKRdvCdzqG1a9eiXr16CAgIwJ49e2BpafndY5g4EhEREWXO0tIS//zzD7p164a6deti3bp1YodERJlg11YVff78GQMGDMDWrVuxfft2eHt7f7M8k0YiIiIi5UilUowZMwaurq7o1KkTIiIiMHfuXBgZGYkdGqkRu7bqNiaSKoiJiUHr1q2RlJSEixcvokyZMpmWY/JIREREpDpvb29cvHgRbdq0QePGjbF9+3bY2tqKHRYRgV1bs+3ChQuoWbMmypUrh5MnTzKJJCIiItKgMmXK4OTJk7Czs0OtWrVw4cIFsUMiNZFBqpGNtIPvdDZs2LABDRs2xIABA7B+/XoUKFBA7JCIiIiIdN73voAvUKAANmzYgN9++w0NGzbExo0btRQZEWWFXVuVkJqaihEjRmDFihXYtm0bfHx8vntMmCyErZJERERESkr/3BQmC1H4WSKRYOjQoXB0dETHjh1x+fJlTJ06FXp6etoMk9QoVZAgVc1jGtVdH2WNieR3vH37Fh06dMD9+/dx9uxZVKxYUeyQiIiIiPK8rxPLr5NKHx8fnD17Fi1btsS1a9ewefNmmJmZiREiUb7Grq3f8OjRI9SrVw+pqak4c+ZMtpPI9N+kEREREVH2pV93u2LFijhz5gxSUlJQr149PHr0SOwQSQVps7aqeyPtYCKZhXPnzsHV1RV169bFvn37YG5uLnZIRERERIQviWVbyx5IDSuEOnXqwNXVFefPnxc7LMomQZBCpuZNEJjeaAvf6Uxs27YNjRs3xogRI7B48WLo66vWA5hjJImIiIg0RyqR4r9lr2D+whaNGjXC9u3bxQ6JKN/gGMmvCIKA6dOnY8qUKfj777/x888/Z7sOJo9ERERE2iORSFAa9pixaSI6d+6Mu3fvYujQoZBI2MUxt0uFBKlQ82Q7aq6PssZE8n9SU1MxYMAAbNu2DcePH0f16tWVPpbJIxEREZG4WrRogRMnTqBZs2Z48uQJ5syZwxldiTSIiSSAT58+oUuXLrh69SoiIiJgZ2f33WOYPBIRERHlDmkTHFavXh2nT5+Gj48POnbsiHXr1sHY2Fjk6CgrMgFqnxxHJqi1OvqGfJ9IxsfHw9fXF58+fcKpU6dQpEiRLMsyeSQiIiLK3cqWLYt///0XP/30E5o2bYpdu3Zx0kQiDcjXk+08ffoU9evXh6mpKcLDw7NMItOmmiYiIiKi3K9IkSIIDw9HwYIFUb9+fTx79kzskCgT6p6xNW0j7ci37/S9e/dQr1491KpVC7t27YKJiYnC61+vVUREREREusXExAS7du2Ci4sL6tati/v374sdElGeki8TyVu3bqF+/frw9fXFihUrFJb3YPJIRERElDcYGBhg5cqVaNmyJX788Ufcvn1b7JDoKzJINLKRduS7RPLKlSto0KABunXrhtmzZ0MikbD1kYiIiCiPkkgkmDNnDvz9/VG/fn1cvXpV7JDof1IFiUa27Fq0aBHs7OxgbGwMNzc3nDt37pvlQ0JC4ODgAGNjYzg6OmL//v0Kr3fr1g0SiURh8/HxUSgTFxeHzp07w8zMDBYWFujevTvev3+f7djFlK8SyQsXLqBRo0b47bffMHnyZDTRa8/kkYiIiCiPk0gkmDJlCn777Tc0bNgQkZGRYodEucSWLVswaNAgjBs3DhcvXkS1atXg7e2NFy9eZFr+9OnT6NixI7p3745Lly7B19cXvr6+uHbtmkI5Hx8fPH/+XL79/fffCq937twZ169fR1hYGPbu3YsTJ04gKChIY9epCRJBEPLFJLkRERFo2rQpbN7aoTTsxQ6HiIiIiNQkbfkPZcyZMwcTJkzAgQMH4O7ursGoKCtv376Fubk5OoT/AkNTQ7XWnfQ+CZs9NiAhIQFmZmbfLe/m5oZatWph4cKFAACZTIZSpUqhf//+GDFiRIbyfn5+SExMxN69e+X7ateuDWdnZyxduhTAlxbJ+Ph47Nq1K9Nz3rx5E1WqVMH58+dRs2ZNAEBoaKh8DdTixYtn97JFkS9aJCMjI+Hl4QXbhHJMIomIiIjysYEDB2LKlClo2rQpWybzsLdv3ypsnz9/zlAmKSkJkZGR8PT0lO+TSqXw9PREREREpvVGREQolAcAb2/vDOWPHTuGokWLolKlSujduzdev36tUIeFhYU8iQQAT09PSKVSnD17VqXrFUOeTySvXLmCJk2aoOSnCiglKS92OEREREQksr59+2Ls2LFo0qQJx0yKSAYJZIKat/9NtlOqVCmYm5vLt6lTp2Y4/6tXr5CamgobGxuF/TY2NoiJick05piYmO+W9/Hxwbp16xAeHo6//voLx48fR9OmTZGamiqvo2jRogp16Ovrw8rKKsvz5kb63y+iu27dugVPT08MGjQIf/zxB8dDEhEREREAYNCgQfjw4QM8PT1x/PhxODg4iB0SqdHjx48VurYaGRlp7dwdOnSQ/9vR0RFOTk4oX748jh07Bg8PD63FoWl5tkXy7t278PDwQFBQEP744w8A2es/T0RERES6QdXZ90ePHo0ePXrAw8MDd+/e1UBk9C2CBpb+EP7XImlmZqawZZZIFilSBHp6eoiNjVXYHxsbC1tb20xjtrW1zVZ5AChXrhyKFCmC//77T15H+sl8UlJSEBcX9816cps8mUg+e/YMHh4e6NChAyZNmqTwGpNJIiIiorxJlSXdJk+eDD8/P3h6euLZs2cajI5yG0NDQ7i4uCA8PFy+TyaTITw8PMuJmNzd3RXKA0BYWNg3J2568uQJXr9+jWLFisnriI+PVxije+TIEchkMri5ueXkkrQqz83aGh8fj/r166NmzZpYuXIlJJLM15JhN1ciIiKi/ONbjQmCICAgIACXLl3CiRMnYG5ursXI8p+0WVvbHPaHgYl6Z21NTkzCds+1Ss/aumXLFvj7+2PZsmVwdXXF3LlzsXXrVty6dQs2Njbo2rUrSpQoIR9jefr0aTRo0ADTpk1D8+bNsXnzZvz555+4ePEiqlativfv32PChAlo06YNbG1tcffuXQwbNgzv3r3D1atX5S2jTZs2RWxsLJYuXYrk5GQEBASgZs2a2LRpk1rfD03KU2MkP336hJYtW6J06dIIDg7ONIlkAklERESU/3z9DJg+qZRIJFi+fDl8fX3RsmVLhIaGwtjYWNsh5jsyQQqZoN4Oktmtz8/PDy9fvsTYsWMRExMDZ2dnhIaGyifUefToEaTS/6+zTp062LRpE0aPHo1Ro0bB3t4eu3btQtWqVQEAenp6uHLlCtauXYv4+HgUL14cTZo0waRJkxS6127cuBH9+vWDh4cHpFIp2rRpg/nz56vhHdCePNMimZqainbt2uH58+cIDw9HwYIF5a8xeSQiIiKiNFm1TiYmJsLT0xPFixfH1q1boaenp+XI8oe0FslWYQEaaZHc6bVa6RZJUl2eGCMpCAL69u2LW7duYe/evfIkUtWB10RERESU/5iYmGDv3r24ceMG+vXrhzzS3pJrqX3pj/9tpB15omvrX3/9hX/++QcREREoXLgwk0ciIiIiUknhwoVx8OBBuLu7w87ODsOHDxc7JKJcSecTybQBrlXeuaG73WCxwyEiIiIiHVe6dGns3bsXDRo0gJ2dHfz8/MQOKU9KW7JD3XWSduh019ZTp06hR48eKP/OCYUkFmKHQ0RERES5nLJLwVWvXh1btmxB9+7dcerUKQ1HRaR7dDaRjI6ORsuWLTF79mxcEvjHTURERETq1bRpU8yaNQstW7bEnTt3xA4nz+EYSd2mk4nk69ev0axZMwQGBiIoKAiA8t8uEREREVH+ld25NHr27IkePXqgWbNmeP36tYaiItI9Orf8R1JSEry9vWFlZYWQkBCFdV0ALvVBRERERNnzvQYJmUyGdu3aIS4uDocOHYKBgYGWIsub0pb/aBoaqJHlPw74LOfyH1qgUy2SgiCgf//+iI+Px7p16zIkkQBbJomIiIgoe9KWjMuqQUIqlWLdunV48+YN+vfvz2VB1IRdW3WbTs3aumDBAuzevRvnz5+HiYlJhtfZGklEREREOfH18+TXDRQmJibYs2cPXF1d8cMPP6B///5ihEeUa+hMIhkaGoqRI0ciPDwcpUqVUniNCSQRERERaVrp0qWxc+dOeHp6omLFivD29hY7JJ2miRZEtkhqj04kknfv3kXHjh2xbNky1K5dW76fCSQRERERaZO7uzuWLl2KDh06IDIyEuXKlRM7JCJR5PpE8sOHD2jTpg26du2KX375hckjEREREYmqS5cuOH/+PFq3bo3Tp0+jYMGCYoekkwQAMqi3BZGjV7UnV0+2IwgCevXqBVNTU1yb/4RJJBERERFpxfcmcJw5cyZMTEzQu3dvTr5D+VKuTiSXLl2KQ4cOYevWrZBKcnWoRERERJSPGBoaIiQkBAcPHsSyZcvEDkcncdZW3ZZrs7Nz585hyJAh2Lp1K4oXL85lPYiIiIhIK5R97ixevDi2bNmCwYMH49y5cxqOiih3yZWJ5Nu3b9GxY0eMGzcO9evXl+9nMklEREREmva9dSW/1qBBA4wdOxYdO3bEu3fvtBBd3sEWSd0mEXJhp25/f388ffoUhw4dglSaMdflWEkiIiIi0rasGjVkMhm8vLxQqlQprFmzRrtB6aC3b9/C3Nwc9f/pA30TI7XWnZL4GSd+XoyEhASYmZmptW5SlOtmbd28eTP27t2LK1euZJpEEhERERGJ4evGjK+TSqlUinXr1sHJyQlbtmyBn5+fGOERaVWuSiQfPnyIXr16Yc2aNShRokSG19kSSURERES5QdpzaVpCWaJECaxYsQK//vorateujTJlyogZnk7QRFdUdm3VnlzT5CeTydC1a1d06NABvr6+Cq8p20ediIiIiEgsrVq1Qvv27eHv7w+ZTCZ2OEQalWsSySVLluDx48eYNWsWgOwNciYiIiIiEkP6Z9VZs2bh4cOHWLp0qUgR6Q5BkGhkI+3IFYnkgwcPMGLECCxfvhy+hboxeSQiIiIinfH1s6upqSlWrFiB4cOH48GDB+IFRaRhoieSgiAgMDAQnTp1wjQvfnNDRERERLrNw8MDHTt2RFBQEHLhAgm5hgwSjWykHaInkitXrsStW7cwY8YMsUMhIiIiIlJJ+h51M2bMwM2bN7Fy5UqRIiLSLFETydjYWAwZMgTLli1DG4vuYoZCRERERJQjXyeT5ubmWLZsGYYOHYrY2FgRo8q90mZtVfdG2iFqIjl06FB4eXmhWbNmYoZBRERERKR2zZo1g4eHB4YNGyZ2KLkSJ9vRbaIlksePH8fOnTsxZ84cTq5DRERERHlC+ufaOXPmYMeOHTh+/LhIERFphiiJZFJSEvr06YPx48cjoPRAMUIgIiIiItK4UqVKYdy4cejTpw+Sk5PFDidXYddW3SZKIjlv3jxIpVL89ttvYpyeiIiIiEhj0rdK/v7775BIJJg7d644ARFpgNYTydjYWEyaNAkLFixAM6NO2j49EREREZHGfZ1MGhgYYOHChZg0aRJevHghYlS5C8dI6jatJ5Ljxo2Dp6cnGjZsqO1TExERERGJomHDhvDw8MC4cePEDoVILbSaSF69ehVr167FX3/9xQl2iIiIiChPS/+8O336dKxZswbXrl0TKaLcRdDA+Ei2SGqP1hJJQRAwePBg9O7dG/b29to6LRERERFRrmBvb49evXphyJAhYodClGNaSyQPHTqEyMhIjBkzhq2RRERERJQvpH/uHTt2LM6fP49Dhw6JFFHuIQAQBDVvYl9UPqKVRFIQBIwePRojRoxA+8JB2jglEREREVGuY2lpieHDh2P06NEQBKY9pLu0kkj+888/ePz4Mfr27auN0xERERER5RrpWyX79u2Lhw8fYu/evSJFlDvIINHIRtqh8URSJpNhzJgxGDlyJFqa+mv6dEREREREuZqJiQlGjhyJMWPGQCaTiR2OaLj8h27TeCK5fft2vH79Gj179tT0qYiIiIiIcqX0rZK9evXCq1evsGPHDpEiIsoZjSaSgiBg8uTJGDVqFH4u2EWTpyIiIiIi0hnGxsYYNWoUJk2alG/HSqp76Y+0jbRDo4nkoUOH8Pz5cwQEBGjyNEREREREuV76VsmAgAA8e/YMYWFhIkVEpDqNJpIzZ85Ev379UKBAAU2ehoiIiIhI5xQoUAD9+vXDzJkzxQ5FFGpf+uN/G2mHxhLJS5cu4fTp0+jTpw/XjSQiIiIiQuYzuJ46dQpRUVHiBESkIo0lkrNmzUJAQACKFCmiqVMQEREREem0IkWKICAgIF+2SnLWVt2mkUTyxYsXCAkJwe+//87WSCIiIiKir6R/Pv79998REhKCly9fihQRUfZpJJFcu3Yt6tWrB3t7e01UT0RERESUZ1SsWBF169bF2rVrxQ5Fq9giqdvUnkgKgoDg4GAEBQWxNZKIiIiISAlBQUEIDg7OV0uBcPkP3ab2RPLYsWOIj4+Hr6+vuqsmIiIiIsoT0je4tGrVCnFxcTh+/LhIERFlj9oTyeXLl8Pf3x9GRkbqrpqIiIiIKE8yMjJCt27dsHz5crFD0ZrcsvzHokWLYGdnB2NjY7i5ueHcuXPfLB8SEgIHBwcYGxvD0dER+/fvl7+WnJyM4cOHw9HRESYmJihevDi6du2KZ8+eKdRhZ2cHiUSisE2bNi37wYtIrYlkYmIidu/eja5du7JbKxERERFRNnTp0gW7du1CYmKi2KHkG1u2bMGgQYMwbtw4XLx4EdWqVYO3tzdevHiRafnTp0+jY8eO6N69Oy5dugRfX1/4+vri2rVrAIAPHz7g4sWLGDNmDC5evIgdO3bg9u3baNGiRYa6Jk6ciOfPn8u3/v37a/Ra1U2tieTevXtRpkwZODo6qrNaIiIiIqI8J33Di5OTE0qXLo19+/aJFJF2fWlBVPdkO9mLYfbs2QgMDERAQACqVKmCpUuXomDBgli1alWm5efNmwcfHx8MHToUlStXxqRJk1CjRg0sXLgQAGBubo6wsDC0b98elSpVQu3atbFw4UJERkbi0aNHCnUVKlQItra28s3ExESl91Esak0kt2zZAj8/P0gkHORKRERERJQdEokEHTp0wJYtW8QORee9fftWYfv8+XOGMklJSYiMjISnp6d8n1QqhaenJyIiIjKtNyIiQqE8AHh7e2dZHgASEhIgkUhgYWGhsH/atGkoXLgwqlevjhkzZiAlJSUbVyg+tSWSb9++xf79++Hn58durUREREREKvDz88O+ffvw9u1bsUPROE0u/1GqVCmYm5vLt6lTp2Y4/6tXr5CamgobGxuF/TY2NoiJick05piYmGyV//TpE4YPH46OHTvCzMxMvv+3337D5s2bcfToUfTs2RN//vknhg0blq33T2z66qpo3759qFSpEhwcHNRVJRERERFRnuYlbYcwWYj8ZwcHB1SsWBH79+9Hhw4dRIxMtz1+/FghcRNjItDk5GS0b98egiBgyZIlCq8NGjRI/m8nJycYGhqiZ8+emDp1qs5MWqq2Fsm9e/dmOoiUiIiIiIiU17x5c4SGhoodhsYJGtoAwMzMTGHLLDkrUqQI9PT0EBsbq7A/NjYWtra2mcZsa2urVPm0JPLhw4cICwtTSGoz4+bmhpSUFDx48OCb5XLi5cuXOHXqFE6dOoWXL1/muD61JJIpKSk4cOAAmjdvzm6tREREREQ54OPjg9DQUMhkMrFD0ShNdm1VhqGhIVxcXBAeHi7fJ5PJEB4eDnd390yPcXd3VygPAGFhYQrl05LIO3fu4PDhwyhcuPB3Y4mKioJUKkXRokWVjl9ZiYmJ+PXXX1G8eHHUr18f9evXR/HixdG9e3d8+PBB5XrVkkhGRERAX18ftWrVUkd1RERERET5RvqGmDp16uDjx4+4fPmySBHlH4MGDcLy5cuxdu1a3Lx5E71790ZiYiICAgIAAF27dsXIkSPl5X///XeEhoZi1qxZuHXrFsaPH48LFy6gX79+AL4kkW3btsWFCxewceNGpKamIiYmBjExMUhKSgLwJXeaO3cuLl++jHv37mHjxo0YOHAgfvnlF1haWmrkGo8fP449e/YgPj4e8fHx2L17N44fP47BgwerXK9axkju3bsXzZo1g56enjqqIyIiIiLKtwwMDODp6YkDBw6gevXqYoejOV/3RVVnndng5+eHly9fYuzYsYiJiYGzszNCQ0PlE+o8evQIUun/t73VqVMHmzZtwujRozFq1CjY29tj165dqFq1KgDg6dOn2LNnDwDA2dlZ4VxHjx5Fw4YNYWRkhM2bN2P8+PH4/PkzypYti4EDByqMm1Sn7du3Y9u2bWjYsKF8X7NmzVCgQAG0b98+w/hNZUkEIburrWRUrVo1jBo1ijO2EhERERGp4OsJdwBg+fLlWLduHU6ePClSRJrz9u1bmJubo9zaUdAraKzWulM/fMI9/z+RkJDw3XGJ+UXBggURGRmJypUrK+y/fv06XF1dkZiYqFK9Oe7aGhMTg2vXrsHDw4NJJBERERGRGvj4+CAiIgLx8fFih6I5mhgfmY0xkvmFu7s7xo0bh0+fPsn3ffz4ERMmTMhyLKgycty19fDhw6hevTqKFCmS06qIiIiIiPKl9MuAlCpVCg4ODjh8+DDatm0rYmSk6+bNmwdvb2+ULFkS1apVAwBcvnwZxsbGOHjwoMr15jiRDAsLg5eXV06rISIiIiKirzRt2hShoaF5NpEUhC+buuskRVWrVsWdO3ewceNG3Lp1CwDQsWNHdO7cGQUKFFC53hwlkoIg4PDhw1i/fj27tRIRERERqVHTpk3RtWtXCIIAiYRdNkl1BQsWRGBgoFrrzFEiefv2bbx+/Rp16tQBoNpsP0RERERElFGdOnXw6tUrREdHo1KlSmKHo3bZXfdR2ToJ2LNnD5o2bQoDAwP5LLJZadGihUrnyFEiefToUdSpUwfGxuqdbYmIiIiIKL9JP07S2NgYderUwdGjR/NkIglNTI7DRBIA4Ovri5iYGBQtWhS+vr5ZlpNIJEhNTVXpHDmatfXo0aNo1KhRTqogIiIiIqIsNGrUCEePHhU7DNIxMpkMRYsWlf87q03VJBLIQSIpCAKOHTuGRo0acXwkEREREZEGNGrUCMeOHYMaln7PddIm21H3RorWrVuHz58/Z9iflJSEdevWqVyvyonk9evXkZiYCFdXV5VPTkREREREWXN1dcX79+9x48YNsUMhHRUQEICEhIQM+9+9e4eAgACV61U5kTx69Cjq1asHQ0NDlU9ORERERET/L31PP0NDQ9StWzdvdm8VNLSRgqxm/X3y5AnMzc1VrlflyXY4PpKIiIiISPPSxkn269dP7FBIh1SvXh0SiQQSiQQeHh7Q1///1C81NRX379+Hj4+PyvWrlEjKZDIcP34cw4cP5/hIIiIiIiINatSoEWbOnAmZTAapNEdzZeYqXP5Ds9Jma42KioK3tzdMTU3lrxkaGsLOzg5t2rRRuX6VEskrV64gOTkZLi4uKp+YiIiIiIi+z8XFBUlJSbh69SqqVasmdjikI8aNGwcAsLOzg5+fn9qXbFTpK41Tp07B3d1doXmUiIiIiIhyLn2PPwMDA9SpUwenTp0SKSIN4vhIjfP391d7EgmomEiePXsWtWvXVncsRERERESUCTc3N5w9e1bsMNQqrWurujdSlJqaipkzZ8LV1RW2trawsrJS2FSlciLp5uam8kmJiIiIiEh5eTGRJO2YMGECZs+eDT8/PyQkJGDQoEFo3bo1pFIpxo8fr3K92U4k4+LicOfOHbi6unKiHSIiIiIiLXB1dUV0dDTevHkjdijqw+U/tGLjxo1Yvnw5Bg8eDH19fXTs2BErVqzA2LFjcebMGZXrzXYief78eZQrVw5FihRR+aRERERERKQ8a2trlCtXDufOnRM7FNIxMTExcHR0BACYmpoiISEBAPDTTz9h3759Kteb7UQyKioKNWrUUPmERERERET0bZn1/KtevTouX74sQjSaItHQRl8rWbIknj9/DgAoX748Dh06BOBLA6GRkZHK9WY7kbxy5QqcnJxUPiEREREREWWfk5MTrly5InYYpGNatWqF8PBwAED//v0xZswY2Nvbo2vXrvj1119Vrjfb63dcuXIF7du3V/mERERERESUfU5OTti2bZvYYaiPJsY0coxkBtOmTZP/28/PD2XKlMHp06dhb2+Pn3/+WeV6s9UimZSUhFu3bsHJyYkT7RARERERaZGTkxNu3bqFpKQksUMhHZGcnIxff/0V9+/fl++rXbs2Bg0alKMkEshmInn79m0YGRmhTJkyOTopERERERFlj52dHQwNDREdHS12KOrBWVs1zsDAANu3b9dI3dlKJKOjo1GpUiVIpSotP0lEREREREpK3wNQKpWiYsWKeSiRlGhmIwW+vr7YtWuX2uvN1hjJO3fuwN7eXu1BEBERERHR99nb2+POnTtih0E6xN7eHhMnTsS///4LFxcXmJiYKLz+22+/qVRvthLJ6OhoVKxYUaUTERERERFRzuSlFklB+LKpu05StHLlSlhYWCAyMhKRkZEKr0kkEu0lko0bN1bpRERERERElDMVK1bEsWPHxA6DdMjXE+2oU7YSyfv376Ns2bIaCYSIiIiIiL6tXLlyGksMtI7Lf+g0pWfNSUlJQUxMDEqVKsWlP4iIiIiIRFCyZEnExMQgNTVV7FAon1M6kYyNjYUgCChWrJgm4yEiIiIioiwUK1YMqampiI2NFTuUnOOsrTpN6UTyyZMnKFq0KAwMDDQZDxERERERZcHQ0BBFixbFkydPxA6F8jmlE8mnT5+iZMmSmoyFiIiIiIi+ktmQspIlS+Lp06ciRKNeEkEzG2mH0pPtPHnyBCVKlNBkLERERERE9B0lSpTIGy2SnGxHK65cuZLpfolEAmNjY5QuXRpGRkbZrlfpRJItkkRERERE4ssrLZKkHc7OzpBIsh47amBgAD8/PyxbtgzGxsZK15utrq1skSQiIiIiEleJEiXyRiLJyXa0YufOnbC3t0dwcDCioqIQFRWF4OBgVKpUCZs2bcLKlStx5MgRjB49Olv1Zqtrq5eXV7YDJyIiIiIi9SlRogQOHz4sdhikI6ZMmYJ58+bB29tbvs/R0RElS5bEmDFjcO7cOZiYmGDw4MGYOXOm0vUqnUg+f/4cxYsXz17URERERESkViVKlMCzZ8/EDiPnOEZSK65evYoyZcpk2F+mTBlcvXoVwJfur8+fP89WvUp3bY2Li0PhwoWzVTkREREREamXlZUV3rx5I3YYpCMcHBwwbdo0JCUlyfclJydj2rRpcHBwAPBlGKONjU226lWqRVIQBLx58wYWFhaZTkFMRERERETaYWFhgTdv3kAQhG9OopLrsUVSKxYtWoQWLVqgZMmScHJyAvCllTI1NRV79+4FANy7dw99+vTJVr1KJZLv379HamoqLC0tsxk2ERERERGpk6WlJVJSUpCYmAhTU1Oxw6Fcrk6dOrh//z42btyI6OhoAEC7du3QqVMnFCpUCADQpUuXbNerVCIZHx8PiUQCc3PzbJ+AiIiIiIjUJ+2ZPD4+XrcTSbZIak2hQoXQq1cvtdapVCL55s0bmJubQypVekglERERERFpgJ6eHszNzfHmzRvdXuddE8t1cPmPTN25cwdHjx7FixcvIJPJFF4bO3asSnUqnUhaWFiodAIiIiIiIlKvtHGSRN+zfPly9O7dG0WKFIGtra3CuFqJRKLZRDI+Pp7jI4mIiIiIcglLS0vEx8eLHUaOSIQvm7rrJEWTJ0/GlClTMHz4cLXWq1Rf1Tdv3jCRJCIiIiLKJSwtLdkiSUp58+YN2rVT/8obSieS7NpKRERERKR9mS2/lye6tgoa2khBu3btcOjQIbXXq1TX1o8fP6JgwYJqPzkREREREWVfwYIF8fHjR7HDIB1QoUIFjBkzBmfOnIGjoyMMDAwUXv/tt99UqlepFsnk5OQMJyQiIiIiInEYGBggOTlZ7DDyhEWLFsHOzg7GxsZwc3PDuXPnvlk+JCQEDg4OMDY2hqOjI/bv36/wuiAIGDt2LIoVK4YCBQrA09MTd+7cUSgTFxeHzp07w8zMDBYWFujevTvev3+v9msDgODgYJiamuL48eNYuHAh5syZI9/mzp2rcr1MJImIiIiIdAwTSfXYsmULBg0ahHHjxuHixYuoVq0avL298eLFi0zLnz59Gh07dkT37t1x6dIl+Pr6wtfXF9euXZOXmT59OubPn4+lS5fi7NmzMDExgbe3Nz59+iQv07lzZ1y/fh1hYWHYu3cvTpw4gaCgII1c4/3797Pc7t27p3K9SiWSSUlJMDQ0VPkkRERERESkPoaGhkhKShI7jByR4P9nblXbls0YZs+ejcDAQAQEBKBKlSpYunQpChYsiFWrVmVaft68efDx8cHQoUNRuXJlTJo0CTVq1MDChQsBfGmNnDt3LkaPHo2WLVvCyckJ69atw7Nnz7Br1y4AwM2bNxEaGooVK1bAzc0N9erVw4IFC7B582Y8e/ZM9TdUy5QaI5mamgp9faWKEhERERGRhunr6yMlJUXsMHKtt2/fKvxsZGQEIyMjhX1JSUmIjIzEyJEj5fukUik8PT0RERGRab0REREYNGiQwj5vb295knj//n3ExMTA09NT/rq5uTnc3NwQERGBDh06ICIiAhYWFqhZs6a8jKenJ6RSKc6ePYtWrVqpdM1fGzRoECZNmgQTE5MM8aY3e/Zslc6hVHaop6en8994EBERERHlFSkpKRkSI50jSL5s6q4TQKlSpRR2jxs3DuPHj1fY9+rVK6SmpsLGxkZhv42NDW7dupVp9TExMZmWj4mJkb+etu9bZYoWLarwur6+PqysrORlcurSpUvyrs+XLl3KspxEovr7r1QiaWhoiISEBJVPQkRERERE6pOUlKT767xrYrmO/9X3+PFjmJmZyXfrfNKdTUePHs303+qk1BhJDuYlIiIiIso9OBnmt5mZmSlsmSWSRYoUgZ6eHmJjYxX2x8bGwtbWNtN6bW1tv1k+7b/fK5N+Mp+UlBTExcVled6cePnyZZavXb16VeV6mUgSEREREemYPJFIChralGRoaAgXFxeEh4fL98lkMoSHh8Pd3T3TY9zd3RXKA0BYWJi8fNmyZWFra6tQ5u3btzh79qy8jLu7O+Lj4xEZGSkvc+TIEchkMri5uSl/AUpydHTEvn37MuyfOXMmXF1dVa5Xqa6tBQoUwIcPH1Q+CRERERERqc+HDx9QoEABscPQeYMGDYK/vz9q1qwJV1dXzJ07F4mJiQgICAAAdO3aFSVKlMDUqVMBAL///jsaNGiAWbNmoXnz5ti8eTMuXLiA4OBgAF/GHA4YMACTJ0+Gvb09ypYtizFjxqB48eLw9fUFAFSuXBk+Pj4IDAzE0qVLkZycjH79+qFDhw4oXry4Rq6xTZs2CAgIwOzZsxEXF4euXbvi6tWr2LRpk8r1KpVIWlpaIj4+XuWTEBERERGRasJkIRn2xcfH6/wYybQlO9RdZ3b4+fnh5cuXGDt2LGJiYuDs7IzQ0FD5ZDmPHj2CVPr/nTjr1KmDTZs2YfTo0Rg1ahTs7e2xa9cuVK1aVV5m2LBhSExMRFBQEOLj41GvXj2EhobC2NhYXmbjxo3o168fPDw8IJVK0aZNG8yfPz9nF5+FYcOGwcvLC126dIGTkxPi4uLg5uaGK1eu5KgrrdKJ5Js3b1Q+CRERERERqc+bN290PpHMLfr164d+/fpl+tqxY8cy7GvXrh3atWuXZX0SiQQTJ07ExIkTsyxjZWWVo9bA7KpQoQKqVq2K7du3A/iSQOd0PKZSYyQtLCyYSBIRERER5RJv3ryBhYWF2GHkjMhjJPOLf//9F05OTrhz5w6uXLmCJUuWoH///vDz88tRjqdUIsmurUREREREuUde6NpK2tG4cWP4+fnhzJkzqFy5Mnr06IFLly7h0aNHcHR0VLlepbu2JiQkQCaTqXwiIiIiIiLKudTUVCQkJOh+IqnBdSTp/x06dAgNGjRQ2Fe+fHn8+++/mDJlisr1KpVIWlhYQBAEJCQkqHwiIiIiIiLKubRncl3v2pobJtvJD9InkWmkUinGjBmjcr1KJZKmpqbQ09PjOEkiIiIiIpG9efMG+vr6MDExETsUyqXmz5+PoKAgGBsbf3M2WIlEgv79+6t0DqUSSYlEIh8nGSYLgZc061mKiIiIiIhIc9LGR0okErFDyRlB8mVTd52EOXPmoHPnzjA2NsacOXOyLKfxRBL4MkXt69evVToJERERERGpR1xcnO6PjySNun//fqb/VielE8lixYrh2bNnGgmCiIiIiIiU8/TpUxQvXlzsMHKOk+3oNKUTyZIlS+Lp06eajIWIiIiIiL7j6dOnKFmypNhhkI4QBAHbtm3D0aNH8eLFiwwrcezYsUOlepVOJEuUKMFEkoiIiIhIZE+fPkWJEiXEDiPHOGurdgwYMADLli1Do0aNYGNjo7axtdlKJMPDw9VyUiIiIiIiUs2TJ0/g5eUldhikI9avX48dO3agWbNmaq2XXVuJiIiIiHRInunayjGSWmFubo5y5cqpvV6psgVLlCiBJ0+eqD0AIiIiIiLKXJgsJMO+J0+e5ImurRD+v3urujYmkhmNHz8eEyZMwMePH9Vab7ZaJF+8eIHk5GS1BkBERERERMpJSkrCixcv8kaLJGlF+/bt8ffff6No0aKws7ODgYGBwusXL15UqV6lE8m0gZnPnz9X6URERERERJQzz58/h56eHmxsbMQOJefYtVUr/P39ERkZiV9++UWcyXb09fVha2uLx48fI0wWAi9pO7UEQEREREREynny5AlsbW2hp6cndiikI/bt24eDBw+iXr16aq1X6TGSAFC2bFncv39frQEQEREREZFy7t27h7Jly4odhnoIGtpIQalSpWBmZqb2erOVSFasWBHR0dFqD4KIiIiIiL4vOjoaFStWFDsM0iGzZs3CsGHD8ODBA7XWq3TXVuBLIhkVFaXWAIiIiIiISDnR0dGoUaOG2GGohXymVTXXSYp++eUXfPjwAeXLl0fBggUzTLYTFxenUr3ZSiTt7e0REpJxCmIiIiIiItK8O3fuwM/PT+wwSIfMnTtXI/Vmu0Xy9u3bkMlkGgmGiIiIiIi+SL+GpEwmY9dWyjZ/f3+N1JutRLJSpUr4/PkzHj58qJFgiIiIiIgocw8ePEBSUlLeSSS5/IdOy9ZkO4aGhnBwcMCVK1cyfENCRERERESac+XKFTg4OMDQ0FDsUIiyl0gCgJOTE65cuaKJWIiIiIiIKAtXrlyBk5OT2GGoTdpkO+reSDuYSBIRERER6YC8lkiSbst2Iuns7IyLFy9qIhYiIiIiIkLGiXYA4NKlS6hWrZoI0WiQoOaNtCZbk+0AQK1atXDv3j28evVKE/EQEREREVE6L1++xL179+Dq6ip2KKRjEhMTMW3aNISHh+PFixcZVuC4d++eSvVmO5G0srKCvb09zp07hzBZCLyk7VQ6MRERERERKefcuXOoWLEiLC0txQ5FfThrq1b06NEDx48fR5cuXVCsWDFIJBK11JvtRBIA3NzccPbsWTRr1kwtQRARERERUdbOnj0LNzc3scMgHXTgwAHs27cPdevWVWu92R4jCXxJJM+cOaPWQIiIiIiIKHN5MZHkrK3aYWlpCSsrK7XXq1IiWa9ePURERCAlJUXd8RARERER5WvpJ9pJTk7G6dOnUa9ePZEi0hB1T7TDCXcyNWnSJIwdOxYfPnxQa70qdW11cnKCgYEBIiMj1RoMEREREREpioyMhJGRERwdHcUOhXTQrFmzcPfuXdjY2MDOzg4GBgYKr6u6IodKiaRUKkWDBg1w9OhRTrhDRERERKRBR48eRYMGDSCVqtSZMNfSRFdUdm3NyNfXVyP1qpRIAkCjRo2wd+9ejBgxQp3xEBERERHRV44ePYoWLVqIHQbpqHHjxmmkXpW/1mjUqBFOnTqFpKQkdcZDRERERJRvpR8fmZSUhFOnTqFRo0YiRaRBHCOp01Rukfzhhx9gYmKC8+fPqzMeIiIiIiL6n7Nnz6JQoUKoUqWK2KGQDrGyskJ0dDSKFCkCS0vLb64dGRcXp9I5VE4kJRIJGjZsyHGSREREREQacvToUTRs2FBti8jnKppoQWSLJABgzpw5KFSoEABg7ty5GjmHyokk8KV767Zt2zB69Gh1xUNERERERP9z9OhR+Pn5iR0G6Rh/f/9M/61OOZr6qVGjRjh9+jQ+f/6srniIiIiIiPKl9OMjP336hIiIiLw5PhL/P2urujfK2qdPn/D27VuFTVU5SiQrVaoEKysr/PvvvzmphoiIiIiI0jl9+jQKFy6MihUrih2KZnCyHa1ITExEv379ULRoUZiYmMDS0lJhU1WOEkmJRAIfHx+EhoZm+AaFiIiIiIhUd+jQIXh5eeXN8ZGkNcOGDcORI0ewZMkSGBkZYcWKFZgwYQKKFy+OdevWqVxvjlc19fHxwYEDB3JaDRERERERfSUsLAxeXl5ih6E5bJHUin/++QeLFy9GmzZtoK+vjx9//BGjR4/Gn3/+iY0bN6pcb44TSS8vL9y8eRNPnjzJaVVERERERPlS+t59L1++RFRUFDw9PUWKiPKKuLg4lCtXDgBgZmYmX+6jXr16OHHihMr15jiRtLCwQO3atdm9lYiIiIhITcLDw+Ho6AgbGxuxQ9EYTrajHeXKlcP9+/cBAA4ODti6dSuALy2VFhYWKteb40QSAJo2bYrQ0FB1VEVERERElO8dOHAA3t7eYodBeUBAQAAuX74MABgxYgQWLVoEY2NjDBw4EEOHDlW53hytI5mmadOmmD59OpKTk9VRHRERERFRvpWamor9+/dj586dYoeiWZoY08gWyQwGDhwo/7enpydu3bqFyMhIVKhQAU5OTirXq5YWSWdnZxgbGyMiIkId1RERERER5Rvph4edO3cOMpkMtWvXFikiyiuSk5Ph4eGBO3fuyPeVKVMGrVu3zlESCagpkZRKpVwGhIiIiIhIDfbt24emTZtCX18tnQdzLV0bIxkXF4fOnTvDzMwMFhYW6N69O96/f//NYz59+oS+ffuicOHCMDU1RZs2bRAbGyt//fLly+jYsSNKlSqFAgUKoHLlypg3b55CHceOHYNEIsmwxcTEfDdmAwMDXLlyRbUL/g61JJLAl2VA9u3bp67qiIiIiIjypT179qB58+Zih6F5Orb8R+fOnXH9+nWEhYVh7969OHHiBIKCgr55zMCBA/HPP/8gJCQEx48fx7Nnz9C6dWv565GRkShatCg2bNiA69ev448//sDIkSOxcOHCDHXdvn0bz58/l29FixZVKu5ffvkFK1euzN7FKkEiCIJa3u63b9+iaNGiuHz5MvpVHq2OKomIiIiI8rT0vflu3ryJ6tWr48WLFzAzMxMpKs16+/YtzM3NUbnvn9AzMlZr3amfP+HmolF4/PixwvtnZGQEIyMjleu9efMmqlSpgvPnz6NmzZoAgNDQUDRr1gxPnjxB8eLFMxyTkJAAa2trbNq0CW3btgUA3Lp1C5UrV0ZERESWXZf79u2Lmzdv4siRIwC+tEg2atQIb968UWmW1f79+2PdunWwt7eHi4sLTExMFF6fPXt2tusE1NgiaWZmhmbNmmHLli3s3kpEREREpIItW7agefPmeTaJVKDBFslSpUrB3Nxcvk2dOjVHoUZERMDCwkKeRAJfJq6RSqU4e/ZspsdERkYiOTlZYS1QBwcHlC5d+ptzyyQkJMDKyirDfmdnZxQrVgxeXl74999/lY792rVrqFGjBgoVKoTo6GhcunRJvkVFRSldT3pq7Xjt5+eH8ePHY8yYMeqsloiIiIgozxMEAVu2bMGECRPEDkXnZdYimRMxMTEZupLq6+vDysoqy7GKMTExMDQ0zNCKaGNjk+Uxp0+fxpYtWxSGDBYrVgxLly5FzZo18fnzZ6xYsQINGzbE2bNnUaNGje/GfvTo0e+WUYVaE8mffvoJv/76K65evarOaomIiIiI8pz0vfiuXLmCx48f54/xkQAk/9vUXSfwpbekMq26I0aMwF9//fXNMjdv3lRDZN937do1tGzZEuPGjUOTJk3k+ytVqoRKlSrJf65Tpw7u3r2LOXPmYP369VqJLTNqTSRNTEzQsmVLrFu3DmGyEHhJ26mzeiIiIiKiPGv9+vVo2bJlhjFspDmDBw9Gt27dvlmmXLlysLW1xYsXLxT2p6SkIC4uDra2tpkeZ2tri6SkJMTHxyu0SsbGxmY45saNG/Dw8EBQUBBGj/7+fDOurq44derUd8tpktrnFA4MDET79u0xZcoUdVdNRERERJQnff78GWvWrMG2bdvEDkV7NDHLajbrs7a2hrW19XfLubu7Iz4+HpGRkXBxcQEAHDlyBDKZDG5ubpke4+LiAgMDA4SHh6NNmzYAvsy8+ujRI7i7u8vLXb9+HY0bN4a/v7/SOVRUVBSKFSumVFlNUXsi2bBhQ1hYWGDXrl3qrpqIiIiIKE9I3611586dsLKyQoMGDUSKiL6lcuXK8PHxQWBgIJYuXYrk5GT069cPHTp0kM/Y+vTpU3h4eGDdunVwdXWFubk5unfvjkGDBsHKygpmZmbo378/3N3d5TO2Xrt2DY0bN4a3tzcGDRokHzupp6cnT3Dnzp2LsmXL4ocffsCnT5+wYsUKHDlyBIcOHRLnzfgftSeSEokEQUFBCA4ORrgsnN1biYiIiIi+Izg4GEFBQZBI1D1qMPeSCF82ddepKRs3bkS/fv3g4eEBqVSKNm3aYP78+fLXk5OTcfv2bXz48EG+b86cOfKynz9/hre3NxYvXix/fdu2bXj58iU2bNiADRs2yPeXKVMGDx48AAAkJSVh8ODBePr0KQoWLAgnJyccPnwYjRo10tzFKkFt60h+7cWLFyhVqhSuXbuGPpVGqbt6IiIiIiKd9nWLZHR0NBwdHfHkyROlulnqurR1JH/oqZl1JK8vG4WEhIT8sYSKiNS2juTXihYtinbt2mHevHlcU5KIiIiI6Cvpn4/nzZuHdu3a5YskkvIOjSSSADBkyBCsXr0ar1690tQpiIiIiIh02qtXr7B69WoMHTpU7FDEIah5I63RWCLp7OyMOnXqYPHixWyVJCIiIiJCxtbIRYsWoV69eqhWrZpIERGpRmOJJPClVXLhwoX4+PGjJk9DRERERKRzPn78iIULF2LIkCFihyKKtMl21L2Rdmg0kWzSpAmKFSuG1atXa/I0RERERES5XvrWyNWrV6N48eLw8vISKSIi1al9+Y+vSSQSjB49GgMHDsR/H/7DzwW7aPJ0REREREQ64ePHj5gyZQrmzZuXr5b8UKCJcY1skdQajbZIAkCbNm1QuHBhLFu2TNOnIiIiIiLKldK3Ri5btgzW1tZo3bq1SBER5YxGWyQBQCqVYtKkSQgKCsK99/fQ0tRf06ckIiIiIsq1EhMTMXXqVKxYsQJSqcbbdXItTYxp5BhJ7dF4IgkAP//8M0qXLo1FixZp43RERERERLlGZjO12tnZ4aeffhIpolyCXVt1mlYSSYlEgkmTJqFTp0747/V/aF84SBunJSIiIiLKVeLi4jBt2jRs3rw5/46NpDxBa23pTZo0Qc2aNTFp0iSuK0lERERE+UL6595JkybB1dUVTZo0ESmi3IPLf+g2rbRIAl9aJWfNmoVatWqhd+/e2jotEREREVGuEB0djaVLl+L8+fNih0KUY1od3Vu1alX4+/tj+PDhbJUkIiIiojwt/fPu8OHD0a1bN1StWlWkiHIZQUMbaYXWWiTTTJgwAfb29jh27Ji2T01EREREJIpjx47hyJEjuHPnjtihEKmF1ucbtrGxwZgxY9C/f3/s/7xJ26cnIiIiItK4r1sjk5OT0a9fP4wePRpFixYVMapchi2SOk2UhWt+//13yGQyzJ8/X4zTExERERFpTPourfPmzYMgCBgwYIA4ARFpgNa7tgKAoaEhFi9ejJ9++gk3H91EQOmBYoRBRERERKRRjx8/xoQJE7Bv3z4YGBiIHU6uoolZVjlrq/aIkkgCQIMGDdCqVSsMHDgQYbIQeEnbiRUKkUbcnVtb4efyA86IFAmR8tLftwDvXSKi7EjfGjlw4EC0bt0a9evXFykiIs0QLZEEgBkzZqBSpUrYv3+/mGEQqVVmD+Jf7+dDOeVGWd23X7/Ge5eIKHv27duH8PBw3Lp1S+xQcidNjGlki6TWiJpI2tjYYObMmejZsyeux19HG4vuYoZDlCPfehDPqhwfzCk3yO69y/uWiChzX7dGJiQkoFevXpgxYwZsbGxEjCr3kggCJIJ6Mz9110dZkwiCuO+2IAho0qQJypUrh3vL48QMhUhlyj6IZ4UP5iQG3rdEROqTvktrUFAQHjx4gIMHD0IikYgUVe709u1bmJubw7nLFOgZGqu17tSkT4ha/wcSEhJgZmam1rpJkagtkgAgkUiwfPlyODo6YlfYLkzzWip2SERKy+mDePp6+GBO2sD7lohIsw4fPoy///4b165dYxL5LezaqtNETyQBwM7ODtOmTUNgYCCuvL0CU1NTTr5DuZq6HsSzqpcP5qQJvG+JiDTj69bI9+/fIzAwEH/99RfKlCkjYlREmiV619Y0MpkMjRo1QuXKlbF0qWKrJJNKyk009TCeFT6cU05o+34FeM8SUf6Svktrz549cfv2bRw5cgRSqShLtud6aV1bq3fWTNfWSxvZtVUbckWLJABIpVKsW7cO1apVg4+PD3x9feWvpf2BMqEkMYnxQP71eflwTtkh1v369bl5zxJRfrNz505s3boVly9fZhJJeV6uSSQBoEyZMli6dCm6d++OWrVqoUSJEgqvM6EkMYj5QP41zvZKysgt9yvAhJKI8rb0LZFPnz5Fjx49sHTpUpQuXVqkqHQMx0jqtFzTtfVr/v7+ePr0KQ4dOpTptzlMJEkbctMD+bfwIZ0A3q9ERNqQPnlMI5PJ4OXlhVKlSmHNmjXaDUoHybu2dtJQ19ZN7NqqDbmqRTLNwoUL4ezsjJkzZ2LYsGEKrzGJJE3TlQfyNGz1yb907V4FeL8Ske7JKnn82owZM/Dw4UPs2rVL8wHlIRLhy6buOkk7cmUiWahQIfz9999o1KgRateujfr16wNgEkmao4sP5Omx62v+kBfuVYD3KxHlbsokj2mOHz+OiRMn4ujRoyhUqJAGo8qD2LVVp+XKRBIAXF1dMXPmTLRv3x4XL16Ef8nfxQ6J8qC88lCeXvrr4oO67sur9yrAVkoiyn28pO2USiafPn2K9u3bY9asWXB1ddVCZES5R65NJAGgV69eOHPmDNq1awdjwRZSCWe/IvXIyw/lmWHrj27ifUpElHslJSWhffv2aNq0KXr27Cl2ODqJXVt1W66cbOdrHz58QJ06ddCgQQPMmzeP3VtJZfntoVwZfFjPfXifZsT7lIjE8q1Wyd9++w0nT57E6dOnUaBAAS1GpfvSJttx8dPMZDuRWzjZjjbk6hZJAChYsCC2b9+OmjVrolatWgp/0Ewq6Xv4UP5t7AKbO/A+/Ta2VBJRbrN+/XqsX78ekZGRTCJzgmMkdVquTyQBoHz58ti8eTNat26NChUqoHbtLw8VXFeSMsOHctUxsdQO3qOq4z1KRGKLiIhAr169sGPHDpQrV07scIhEoxOJJAB4e3tj6tSp8PX1xblz5xQWemVCmb/xoVxz+NCuHrxHNYf3KBFp06NHj+Dr64tp06bB29tb7HDyBI5p1F25fozk1wRBQK9evXDu3DmcOnUKJiYmmZZjQpm38aE8d+GDe0a8R3MP3p9ElF1ZjYt8//496tWrh9q1a2PJkiWQSCRajizvkI+RbD8F+gbqHSOZkvwJkVs5RlIbdCqRBIDk5GQ0adIEVlZWCAkJgVSqOJMrk8i8hw/luic/Pbzz/tQ9+en+JCLlfG+pD5lMhrZt2yI+Ph4HDx6EgYGBliLLm+SJZLvJmkkkQ0YzkdQCnenamsbAwADbtm2Dm5sbRo0ahWnTpslfYxKp2/hAnnd863epiw/xvDfzlqx+n7p4bxKR6pRZJzLNyJEjcfXqVZw5c4ZJpBpx+Q/dpnOJJAAULlwY+/fvh7u7O8qWLYuePXsyiczl+CBOaZS9F7T1UM97k9LktnuTiDQjOwkkACxduhQrV65EREQEChcurKGoiHSPTiaSAFCxYkXs3r0bPj4+WNprPYpIiokdUr7BB2/SBt5nlFupcm8y+STSTfv378eQIUNw8OBB2Nvbix1O3sPlP3Sa9PtFcq969ephxYoVuFvoCt4J8WKHQ0RERES5nLK92C5duoQOHTpg5cqVqFu3roajItI9Op1IAkCHDh0watQoPC5+AysfzBI7nHyB36wTERFRXvbo0SM0b94cf/zxB/z8/MQOJ8+SyDSzkXbobNfWrw0fPhwPHjyAj48PTr48Ke+/znGTRERERJQdr1+/hre3N1q2bIlhw4aJHQ5RrqXzLZIAIJFIsGjRIlSuXBk//fQTEhMTAXwZTJ3dAdWkHLZKEhEph5+XRLojMTERzZs3R5UqVbBw4UKuFalpgoY20oo8kUgCgJ6eHjZu3AhDQ0P4+fkhOTlZ/hoTSs3gwxERERHlFcnJyWjfvj2MjY2xceNG6OnpiR0SUa6WJ7q2pjE2Nsbu3btRv359BAUFYdWqVQrfJKUlk+zySkRERJS/fKtRQRAEBAYG4smTJzhx4gSMjY21GFn+xXUkdVueaZFMY2FhgdDQUBw7dgyDBw+GIPBu0iS2ShIRZY2fkUTiSuuV9r0kcvDgwTh+/DgOHDgAc3NzLUaYzwmCZjYNiYuLQ+fOnWFmZgYLCwt0794d79+//+Yxnz59Qt++fVG4cGGYmpqiTZs2iI2NVSgjkUgybJs3b1Yoc+zYMdSoUQNGRkaoUKEC1qxZo+7Ly7Y8l0gCQPHixXH48GFs2bIFY8aMUXiNrZHqxwclIiIiyg2+ThyVHdY0evRobNmyBYcPH0bx4sU1HCHpss6dO+P69esICwvD3r17ceLECQQFBX3zmIEDB+Kff/5BSEgIjh8/jmfPnqF169YZyq1evRrPnz+Xb76+vvLX7t+/j+bNm6NRo0aIiorCgAED0KNHDxw8eFDdl5gteapr69fKly+P8PBwNGjQAAUKFMAff/zBJJKIiIgoD1J1LozJkydjxYoVOH78OMqXL6/mqOh7dKlr682bNxEaGorz58+jZs2aAIAFCxagWbNmmDlzZqZfQiQkJGDlypXYtGkTGjduDOBLwli5cmWcOXMGtWvXlpe1sLCAra1tpudeunQpypYti1mzvix1WLlyZZw6dQpz5syBt7e3ui9VaXmyRTKNg4MDDh8+jNmzZ6OS1FnscPI0tkoSERGRLpk1axbmzp2Lw4cPw8HBQexwSM3evn2rsH3+/DlH9UVERMDCwkKeRAKAp6cnpFIpzp49m+kxkZGRSE5Ohqenp3yfg4MDSpcujYiICIWyffv2RZEiReDq6opVq1YpDM+LiIhQqAMAvL29M9ShbXk6kQQAR0dHHDp0CE+N/8Nj4T+xw8nTmEwSEf0/fiYS5V6LFi3CpEmTcOjQITg6OoodTv6lweU/SpUqBXNzc/k2derUHIUaExODokWLKuzT19eHlZUVYmJisjzG0NAQFhYWCvttbGwUjpk4cSK2bt2KsLAwtGnTBn369MGCBQsU6rGxsclQx9u3b/Hx48ccXVdO5NmurV9zcXFBWHgYmjZtCrwVUAr2YodERERERCKYPXs2Jk6ciNDQUNSoUUPscEhDHj9+DDMzM/nPRkZGmZYbMWIE/vrrr2/WdfPmTbXGlt7Xc7pUr14diYmJmDFjBn777TeNnjen8nyLZBp3d3eEh4fjpeVjNJzkLHY4eRa/gSciIqLcavLkyZgyZQrCw8MVxqeRONLGSKp7AwAzMzOFLatEcvDgwbh58+Y3t3LlysHW1hYvXrxQODYlJQVxcXFZjm20tbVFUlIS4uPjFfbHxsZmeQwAuLm54cmTJ/LuuLa2thlmeo2NjYWZmRkKFCjwrbdYo/JFi2QaFxcXHDt2DJ6enugxsgcmT54MiUTCSXjUrPyAM7g7lx/ORERElDsIgoA//vgDq1atwrFjx9idleSsra1hbW393XLu7u6Ij49HZGQkXFxcAABHjhyBTCaDm5tbpse4uLjAwMAA4eHhaNOmDQDg9u3bePToEdzd3bM8V1RUFCwtLeXJr7u7O/bv369QJiws7Jt1aEO+SiSBL2MmT5w4AQ8PD3z48AGzZ89WmOmLSaV6MJkkovyMvTOIcg9BEDBw4EBs27YNx48fR6VKlcQOidJoYt1HDa0jWblyZfj4+CAwMBBLly5FcnIy+vXrhw4dOshnbH369Ck8PDywbt06uLq6wtzcHN27d8egQYNgZWUFMzMz9O/fH+7u7vIW8X/++QexsbGoXbs2jI2NERYWhj///BNDhgyRn7tXr15YuHAhhg0bhl9//RVHjhzB1q1bsW/fPo1cq7LyXSIJAJUqVcLJkyfRuHFjJCQkIDg4GPr6X96KtKSSCSURERGRbktOTkbPnj1x9OhRnDx5EmXLlhU7JPqKLi3/AQAbN25Ev3794OHhAalUijZt2mD+/Pny15OTk3H79m18+PBBvm/OnDnysp8/f4a3tzcWL14sf93AwACLFi3CwIEDIQgCKlSogNmzZyMwMFBepmzZsti3bx8GDhyIefPmoWTJklixYoWoS38AgEQQNJS264Bnz57Bx8cHpUuXxpYtW2BiYpJpOSaVqmOrJBHlR2yRJNKuzNaRTExMRPv27fHkyRMcOHAg03X+SBxv376Fubk53JtOhL6BsVrrTkn+hIgDY5GQkKAw2Q6pX76ZbCczxYsXx4kTJ/D+/Xt4eHjg1atXmZYLk4WovNBtfseHKSLKb/i5RyS+V69eoXHjxvjw4QNOnDjBJDK30uDyH6R5+TqRBAALCwuEhoaiVKlSqFu3Lh48eJBl2bSEkkll9vChioiIiDTp695j9+/fR926dVGmTBmEhobC3NxcxMiI8q58n0gCgLGxMTZv3owmTZrA3d0dly5d+u4xTCqzh8kkERERadqlS5dQp04deHt7Y/PmzVku+UC5gyaX/yDNYyL5P3p6epg/fz4GDhyIBg0a4J9//lH6WCaVREQE8EszIjHt2bMH9evXl09IIpXyMZdIk/gX9hWJRIJhw4Zh9erV6NixI+bMmYPszkXEpDJrfMAiIiIidRMEAY9wB506dcLatWsxbNgwSCQSscMiZcgEzWykFUwkM9GmTRscOXIEf/31F/r06YPk5GSV6mEymRGTSSIiIlIXmSBDhZ5F8NYmFkePHkXr1q3FDoko32AimQVXV1ecO3cO//77L5o3b46EhASxQ8ozmEwSUV7EzzYi7QmThWDbmxXQ83qH06dP4+zZs6hVq5bYYVF2cdZWncZE8htKly6NU6dOwcDAALVr10Z0dHS2juf6k1njAxcREREp6+uhQ2GyEERHR8PNzQ0GBgb4999/Ubp0abFDJBVIoIHJdsS+qHyEieR3mJmZYc+ePfj555/h5uaG0NBQsUPKM5hMEhERUVaymnfiwIEDcHV1RcuWLbFnzx4UKlRIpAiJ8jd9sQPQBXp6epg+fTqqVauGtm3bYty4cRgyZMg3B3KzNZKIiIhIed+bW0IQBMyYMQMTJ05EcHAwOnXqpKXISGME4cum7jpJK5hIZkPnzp1RqVIl+Pr6IioqCitWrECBAgXEDkunlR9wBnfn1hY7DCKiHGEPC6Kc+V4S+fHjR/To0QMnTpzA8ePH4eLioqXIiCgr7NqaTTVr1sSFCxfw4MED/Pjjj3j48GGm5Thjq/L4AEZERERZefDgAerVq4cHDx7g/PnzTCLzELWPj/zfRtrBRFIFtra2OHLkCGrVqoUaNWrg4MGDmZbjmpLKYzJJRLqKn19EmhMaGgoXFxe4ubnhyJEjsLW1FTskIvofJpIqMjIywpIlSzBnzhy0adMGkyZNgkwmy7I8k8rv48MYERERAYBMJsPEiRPRtm1bzJ07F4sXL4aRkZHYYZG6cfkPncYxkjnUtWtXVKtWDa1bt8bZs2exfv16WFpafvOYr5NJTsqjiGMmiYiI8re4uDh06dIFt2/fxr///otq1aqJHRIRZYItkmpQrVo1XLhwARKJBC4uLrh48aLSx7KlMiO2TBKRruDnFZF6Xbx4ES4uLtDT08OFCxeYROZxEkHQyEbawURSTSwtLbF792706NEDP/74IxYtWgQhmzdy+sV28zM+nBEREeUfgiBg4cKF+PHHHxEYGIhdu3bBwsJC7LBI02Qa2kgr2LVVjaRSKUaNGoW6deuiU6dOOHbsGFasWAFzc3OV6guTheTrrq/s5kpERJS3hclCkJCQgHbt2iEiIgIHDhxA/fr1xQ6LiJTAFkkNaNCgAaKiovDu3TvUqFEDkZGRKtWTn5PINGyZJKLcip9PRNmXvvfVhQsXUKNGDSQmJiIqKopJZD7Drq26jYmkhlhbW2P//v0IDAxE/fr1sWDBgmx3daUv+LBGRESkm7IatiMIAhYsWIAGDRogKCgI+/btg7W1tYiRElF2sWurBkmlUowYMQL16tVDx44dcfjwYaxcuRJFihQROzSdw26uREREuZ8yczy8evUKv/76Ky5duoRDhw6hbt26WoiMciVNLNfBdhutYYukFtSrVw+XL1+Gvr4+nJycEB4e/t1j2K01I7ZMElFuwc8joi+yO1Hg4cOH4eTkBAMDA1y+fJlJJJEOYyKpJVZWVti2bRsmTJiAli1bYtiwYUhKShI7LJ3DhzciIqLcITszzCclJWHYsGHw9fXFxIkTsW3bNlhZWWkwOtIJgqCZjbSCiaQWSSQSBAYG4sKFCwgLC0OdOnUQHR0tdlg6h8kkEYmJn0FE2RMdHQ13d3eEhYXhwoUL6NGjByQSidhhEVEOMZEUgYODA86cOYP69evDxcUFy5YtyzARD9eT/DY+yBEREeVugiBg2bJlcHFxQYMGDXDmzBk4ODiIHRblIhJBMxtpByfbEYmRkRFmz56Npk2bIiAgAHv27MHKlStha2uboWz6ZJLjJ7/gBDxERES50/Pnz9GjRw9cvnwZO3bsgJeXl9ghUW6kia6o7NqqNWyRFJmXlxeuXr0Kc3NzVK1aFTt27PjuMWyt/H9smSQibeJnDtH3bd++HY6OjjA3N8fVq1eZRBLlUWyRzAUsLS2xadMmbN68GT169MCePXswb948mJubf/dYtlayZZKIiCg3SEhIwG+//YZ//vkHS5Ys+b/27j8oqvPe4/hnuUCMYRC1JqDRqK3KRq0o1khi1VwEGVMTg6lg0KKD+KM1RqMz0mptpm1qqLlJlDRa1DGKNtiODIkmMYBOpFGIGR2EcsFKxBujrunEHwwhCGTP/cNKJQKysHuWXd6vmTOZ7J7z7LPO7HI++31+KC4uzt1dQidnsd88nN0mzEFFshOJj49XSUmJLl68qB/+8IfKy8tzuA1Hl+H2FlQJALga3zNAy25t62Gz2VRSUkKIBLoAgmQn069fPx08eFApKSl6+umntWjRIlVVVbW7va4UKrnJAwDAHLfuK6qqqrRo0SLFxsYqJSVFBw8eVL9+/dzcO3gMtv/waATJTsjHx0dLlixRSUmJKioqNHLkSOXm5naoza4y5JUwCQCAc313tNOtEJmbm6uRI0eqoqJCxcXFWrJkCdt6AF0IcyQ7sYEDByo3N1fp6emKjY3V7Nmz9corrygwMNDdXevUmDMJwNn4kQpdRVtGMFVVVWnVqlXKzMzUhg0btHDhQgIk2sf49+HsNmEKKpKdnI+PjxYvXqySkhJ99tlnGjFihN577z2H2ugq1cjbcdMHAEDrWqo0tubAgQMaMWKEzp49q+LiYi1atIgQCXRRVCQ9xMCBA5WXl6dt27Zpzpw5io6O1saNG5vddxI3UZkE4Az8MAVv0NG1Ei5duqTnn39eubm52rBhg5KSkgiQ6DCLYcji5DmNzm4PLaMi6UEsFouSk5NVVlYmi8Uiq9WqrVu3ym5veZ3jrliNvN33lxdyEwgA6LI6uuCe3W5Xenq6rFarfHx8VFZWpgULFhAiARAkPVFwcLAyMzO1e/du/f73v9fkyZNVXl7u7m51aoRJAO3Bdwe6srKyMk2aNEkvvfSS9uzZo8zMTEZCwblYtdWjESQ92BNPPKHS0lKNHTtWY8aM0dq1a1VTU9PknK6y9UdbcEMIAMDdff3111q7dq3Cw8P1ox/9SKWlpXriiSfc3S14I0OS3ckHOdI0BEkPFxAQoFdffVV///vflZeXJ6vVqn379sn4zq8xjk6m91aESQBAV+Ho33vDMLRv3z5ZrVYdOnRIH3/8sV599VUFBAS4qIcAPBmL7XiJ8PBwHTt2TDt37tSSJUv05z//WWlpaRo2bFiz5zf3x6WrzKdkER4AbcEPT+hKysvLtWzZMhUVFSk1NVWJiYny8aHeANdisR3PxjeEF/Hx8dH8+fN1+vRpDRs2TGFhYUpJSVF1dXWbru9KVUtuEAEAkKqrq7V69WqNHj1aoaGh+uc//6n58+cTIgHcFd8SXqhnz55KS0tTYWGhjh49qqFDh2rbtm369ttvHWqnPftLeRJWdAXQEr4b4O0aGhq0bds2DR06VMeOHVNhYaE2bdqkoKAgd3cNXYkhFyy24+431XUQJL3YqFGjlJ+fr40bN2r9+vUaNWqU3n///TvmTzrCG4MlN4wAgK7CMAy99957CgsL08svv6yNGzcqPz9fo0aNcnfXAHgYi9GRVAGPUVdXp82bN+u3v/2twsLCtGHDBo0ZM8YpbXvL3ErmTQKQ+HEJnqetP+yePHlSq1atUnFxsdatW6fFixfL39/fxb0D7lRVVaUePXrov0etlu9/3ePUthu+vaHDp1J1/fp1BQYGOrVtNMViO12Ev7+/nn/+eSUmJmr9+vWaMGGCZs6cqd/97ncaOHBgu9v1lhAp/efmkUAJAOhsOjIK6Ny5c1q7dq2ysrK0bNkyZWVlMYQVQIcxtLWLCQoKUmpqqsrLy2WxWBQaGqqFCxfq3Llz7u5ap0E1Aui6+PzDnZpbm6AjU0kqKyuVnJys0NBQ+fj4qLy8XC+//DIhEp2Hs/eQvHXAFATJLmrAgAHatWuXioqKVFNTo9DQUCUnJ6uysrLNbXhTNfK7uJkEADhba0HRmesO3AqQVqtVtbW1OnXqlHbt2qUBAwY47TUAZ7i1/YezD5iDINnFhYaGavfu3Tp16pRqa2tltVodDpTeilVdga6FzztcyYwF6iorK7VgwYImATIjI6PFPaUBoCMIkpAkDRs2TBkZGU0CZVJSksrKypo935urkd/FzSUAoCNcHSLLysqUlJQkq9Wquro6FRcXEyDhGZy+9ce/Dxe5cuWKEhISFBgYqKCgICUlJd11v/ba2lr94he/UO/evRUQEKCZM2fq8uXLjc+/9dZbslgszR5ffvmlJOmjjz5q9nmbzeay99oWBEk0cStQFhcXyzAMjR49WtOnT9eRI0c6tG2Ip6M6CXg3Pt/wNIZh6MiRI5o+fbpGjx4twzBUXFysXbt2aejQoe7uHuCVEhISVFpaqtzcXB04cED5+flauHBhq9esWLFC+/fv19/+9jcdOXJEFy9eVGxsbOPzcXFxunTpUpNj6tSpmjRpku6///4mbZ0+fbrJed993mxs/4FWXbp0SWlpadq8ebOGDBmiVatWKTY2Vr6+zS/42xUqlazqCngXQiRcydnVyIaGBmVlZemVV15RRUWFlixZoqVLlyokJMSprwO40q3tPyIfXuWS7T8O/e8rTt/+o6ysTA8//LA+/fRTjR07VpJ08OBBTZs2TV988YX69u17xzXXr19Xnz599Je//EXPPPOMJKm8vFxWq1UFBQUaP/7Oe8p//etf6tevn7Zv3665c+dKulmRfPzxx3X16tVOtVgWFUm0KiQkRH/4wx90/vx5zZkzR6tXr9aQIUO0adOmZkv5Zi0k4E5UJwEAZquurtamTZs0ZMgQrV69WnPnztXnn3+ul156iRAJNKOqqqrJcePGjQ61V1BQoKCgoMYQKUlTpkyRj4+PPvnkk2avOXHihOrr6zVlypTGx0JDQzVgwAAVFBQ0e82uXbvUvXv3xuB5u7CwMIWEhCgqKkpHjx7t0PtxBoIk2iQgIEDLli3TmTNnlJqaqoyMDD344INasWKFKioq2tyON4VMAiXg+fgMw5Wc8bftzJkzWrFihR588EFlZGQoNTVVZ86c0XPPPaeAgAAn9BJwIxfOkezfv7969OjReKxfv75DXbXZbHcMJfX19VWvXr1anKtos9nk7+9/RxXxgQceaPGa7du369lnn9W9997b+FhISIi2bNmiffv2ad++ferfv78mT56skydPdug9dVTz4xOBFvj6+mrWrFn66U9/qsLCQqWlpWn48OGKjIzU0qVLFRMTIx+f9v0+0dof3M48ZPb7ywsZ7gp4IEIkOiu73a6DBw8qLS1Nhw8f1jPPPKMPPvhA48ePl8VicXf3AI9w/vz5JkNb77mn+SG0KSkpSk1NbbWtlhafdLaCggKVlZUpIyOjyePDhg1rsnjWo48+qs8++0yvvfbaHeeaiSCJdrFYLIqIiFBERIQuXbqkrVu3asGCBerevbt+/vOfa/78+erZs6fTXq+5kNmZwuWtG1ICJQBAal818urVq9qxY4f+9Kc/6ZtvvtHixYu1Y8cOBQcHu6CHQCdgl+Ts30bsN/8TGBjYpjmSK1eu1Lx581o9Z/DgwQoODm5cRfWWhoYGXblypcXPaHBwsOrq6nTt2rUmVcnLly83e822bdsUFham8PDwu/Z73Lhx+vjjj+96nisRJNFhISEhWrdunX75y18qKytLb7zxhn79619r9uzZSk5O1rhx45z+C2pnCpG3ozoJeAaqkfgud02xMAxDx48fV3p6ujIzMxUeHq7169fr6aeflp+fn1v6BJjFYhiyOHndT0fb69Onj/r06XPX8yIiInTt2jWdOHGiMegdPnxYdrtdjzzySLPXhIeHy8/PT4cOHdLMmTMl3Vx59fPPP1dERESTc6urq/XXv/61zUNwi4qK3D4/miAJp/Hz81NcXJzi4uJUVFSk9PR0RUVFadCgQVq4cKESEhI6vNJUZw2Qt6M6CQDm8cQ59pJ07do17d69W1u3btW5c+eUkJCgY8eOadSoUe7uGoBmWK1WxcTEKDk5WVu2bFF9fb2WLl2q+Pj4xhVbL1y4oMjISO3atUvjxo1Tjx49lJSUpBdeeEG9evVSYGCgnnvuOUVERNyxYuvevXvV0NCgOXPm3PHar7/+ugYNGqThw4ertrZW27Zt0+HDh5WTk2PKe28Ji+3AJcLCwvTmm2/q0qVLWr58uXbv3q2+fftq3rx5OnbsWLv2pPSEEHk7FuMBOic+l97BExdqMwxDx44d07x589S3b1/t2bNHy5cv18WLF/Xmm28SItH1uHCxHVfYs2ePQkNDFRkZqWnTpmnChAlKT09vfL6+vl6nT59WTU1N42OvvfaafvKTn2jmzJmaOHGigoODlZWVdUfb27dvV2xsbLNFl7q6Oq1cuVIjR47UpEmTdOrUKeXl5SkyMtIl77Ot2EcSpikuLtbWrVuVkZGhfv36af78+UpISLhrWd7TAmRLqFAC7keI9HyeFh6lm3sy79mzRzt27NCFCxf0s5/9TMnJyRo5cqS7uwa4xa19JKcMWeGSfSTzzrzm9H0kcSeCJExXU1Ojffv2aefOncrPz1dUVJQSExP15JNPqlu3bk3O9ZYQeTsCJeAehEjP5mkBsra2Vu+++67eeust5eXlaeLEiUpMTNTMmTPVvXt3d3cPcKvGIPn95a4Jkp+9TpA0AUNbYbru3btr7ty5ysvLU0VFhR599FGtWbNGISEhWrJkiQoLC9s19NVTMOQVANrOk4awGoahwsJCLV68WCEhIVq7dq0ee+wxVVRUKC8vT3PnziVEAvAaVCTRKRiGoYKCAu3cuVN79+7V9773PcXHxys+Pl4jRoxw2ut0xgonFUrA9TrbjzedLRh1tu/Gzvbvczf/+Mc/9PbbbyszM1NfffWV4uLilJiYqIiICPZ9BJrRWJEc/LxrKpJnN1KRNAFBEp1ObW2tPvjgA2VmZmr//v0aPHiwZs+erbi4OP3gBz9wd/da1ZGbMQIl4BodCZGeFmg6g458D3rSv3dFRYUyMzOVmZmps2fP6sknn1R8fLxiYmLumKYBoCmCpHcgSKJTq66u1v79+/X222/rww8/1MiRIxUbG6sZM2bIarV65S+9gzf9j7u7AHQ6Z5etdHcX0MUZhqGysjJlZ2crKytLJSUliomJUXx8vKZPn66AgAB3dxHwGP8Jksvk6+PkIGm/obyzmwiSJiBIwmNcvXpV77zzjrKzs/Xhhx+qf//+mjFjhmbMmKHx48fLx8e7pvwSKOEqhDKgbex2uwoLC5Wdna3s7GydP39eU6dO1YwZM/TUU0+pZ8+e7u4i4JEag+Sg51wTJCvTCJImIEjCI3399dfKzc1Vdna29u/fLz8/P0VHRysqKkpRUVEKDg52dxedhkDZuRHKAO9is9mUm5ur3Nxc5eTkqL6+XtOnT9eMGTMUFRWl++67z91dBDweQdI7ECTh8RoaGnT06FHl5OQoJydHJ0+e1PDhwxUdHa2BAwd61Qp5q/M+dHcXXCp1ylR3dwFAF3Rr2GpOTo5KS0s1ZswYRUdHKzo6Wo899ph8fX3d3UXAqzQGyYeWuiZI/t8bBEkTECThdb766isdOnRI+fn5OnPmjOx2u7u75HRf19fpm/qGJo/d69f0Ruc+P38zuwQAHu2hhx5SdHS0IiMj1bt3b3d3B/BqBEnvwE9s8Dq9e/fWrFmzNGvWLHd3BQAAAC0x7DcPZ7cJU3jX6iQAAAAAAJejIgkAAADAfIZx83B2mzAFFUkAAAAAgEOoSAIAAAAwn92Q5OQKop2KpFkIkgAAAADMx9BWj8bQVgAAAACAQ6hIAgAAADCfIRdUJJ3bHFpGRRIAAAAA4BAqkgAAAADMxxxJj0ZFEgAAAADgECqSAAAAAMxnt0uyu6BNmIGKJAAAAADAIVQkAQAAAJiPOZIejSAJAAAAwHwESY/G0FYAAAAAgEOoSAIAAAAwn92Q5OQKop2KpFmoSAIAAAAAHEJFEgAAAIDpDMMuw3Dudh3Obg8toyIJAAAAAHAIFUkAAAAA5jMM589pZNVW01CRBAAAAAA4hIokAAAAAPMZLli1lYqkaQiSAAAAAMxnt0sWJy+Ow2I7pmFoKwAAAADAIVQkAQAAAJiPoa0ejYokAAAAAMAhVCQBAAAAmM6w22U4eY6kwRxJ01CRBAAAAAA4hIokAAAAAPMxR9KjUZEEAAAAADiEiiQAAAAA89kNyUJF0lMRJAEAAACYzzAkOXlxHIKkaRjaCgAAAABwCBVJAAAAAKYz7IYMJw9tNahImoaKJAAAAADAIVQkAQAAAJjPsMv5cySd3B5aREUSAAAAAOAQgiQAAAAA0xl2wyWHq1y5ckUJCQkKDAxUUFCQkpKSVF1d3eo16enpmjx5sgIDA2WxWHTt2rV2tVtcXKwf//jH6tatm/r3768//vGPznxr7UKQBAAAAIC7SEhIUGlpqXJzc3XgwAHl5+dr4cKFrV5TU1OjmJgY/epXv2p3u1VVVYqOjtZDDz2kEydOaMOGDXrxxReVnp7utPfWHhaDpY0AAAAAmKSqqko9evTQZD0lX4ufU9tuMOr1kd7R9evXFRgY6LR2y8rK9PDDD+vTTz/V2LFjJUkHDx7UtGnT9MUXX6hv376tXv/RRx/p8ccf19WrVxUUFORQu5s3b9aaNWtks9nk7+8vSUpJSVF2drbKy8ud9h4dRUUSAAAAgOkaVK8Gw8mH6iXdDKu3Hzdu3OhQXwsKChQUFNQY9iRpypQp8vHx0SeffOLSdgsKCjRx4sTGEClJU6dO1enTp3X16tV2v3ZHsWorAAAAANP4+/srODhYH9ved0n7AQEB6t+/f5PHfvOb3+jFF19sd5s2m033339/k8d8fX3Vq1cv2Ww2l7Zrs9k0aNCgJuc88MADjc/17Nmz3a/fEQRJAAAAAKbp1q2bKisrVVdX55L2DcOQxWJp8tg999zT7LkpKSlKTU1ttb2ysjKn9c2bECQBAAAAmKpbt27q1q2bu7uhlStXat68ea2eM3jwYAUHB+vLL79s8nhDQ4OuXLmi4ODgdr9+W9oNDg7W5cuXm5xz6/878todRZAEAAAA0CX16dNHffr0uet5ERERunbtmk6cOKHw8HBJ0uHDh2W32/XII4+0+/Xb0m5ERITWrFmj+vp6+fndXJwoNzdXw4YNc9uwVonFdgAAAACgVVarVTExMUpOTtbx48d19OhRLV26VPHx8Y0rtl64cEGhoaE6fvx443U2m01FRUWqqKiQJJWUlKioqEhXrlxpc7vPPvus/P39lZSUpNLSUu3du1cbN27UCy+8YPK/QlMESQAAAAC4iz179ig0NFSRkZGaNm2aJkyY0GQvx/r6ep0+fVo1NTWNj23ZskWjR49WcnKyJGnixIkaPXq03n333Ta326NHD+Xk5KiyslLh4eFauXKl1q1bd9c9LF2NfSQBAAAAAA6hIgkAAAAAcAhBEgAAAADgEIIkAAAAAMAhBEkAAAAAgEMIkgAAAAAAhxAkAQAAAAAOIUgCAAAAABxCkAQAAAAAOIQgCQAAAABwCEESAAAAAOAQgiQAAAAAwCH/D0DUGkEXTdbQAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "engine": 0
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[output:0]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "engine": 0
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if mpi_rank == 0:\n",
+    "    rotation = -120.\n",
+    "    nx = temperature.shape[1]\n",
+    "    nz = temperature.shape[3]\n",
+    "    \n",
+    "    u_mean = 0.5 * (u_wind.data[:,:,:-1, :] + u_wind.data[:,:, 1:, :])\n",
+    "    column_qvap = np.sum(qvap, axis=3)/nz\n",
+    "    column_qcld = np.sum(qcld, axis=3)/nz\n",
+    "    column_qrain = np.sum(qrain, axis=3)/nz\n",
+    "    \n",
+    "    fig = plt.figure(figsize=(12, 6))\n",
+    "    ax = fig.add_subplot(111, projection=ccrs.Robinson())\n",
+    "    f1 = pcolormesh_cube(\n",
+    "        grid_lat.data * 180./np.pi,\n",
+    "        grid_lon.data * 180./np.pi + rotation,\n",
+    "        temperature.data[:, :, :, -2],\n",
+    "        cmap=\"viridis\",\n",
+    "    )\n",
+    "    plt.colorbar(f1, label=\"Temperature (K)\")\n",
+    "    ax.set_title(f\"Temperature ({nx} x {nx} points per tile)\")\n",
+    "    \n",
+    "    fig = plt.figure(figsize=(12, 6))\n",
+    "    ax = fig.add_subplot(111, projection=ccrs.Robinson())\n",
+    "    f1 = pcolormesh_cube(\n",
+    "        grid_lat.data * 180./np.pi,\n",
+    "        grid_lon.data * 180./np.pi + rotation,\n",
+    "        surface_pressure.data[:, :, :]/100.,\n",
+    "        cmap=\"viridis\",\n",
+    "    )\n",
+    "    plt.colorbar(f1, label=\"Surface Pressure (hPa)\")\n",
+    "    ax.set_title(f\"Surface Pressure ({nx} x {nx} points per tile)\")\n",
+    "    \n",
+    "    \n",
+    "    fig = plt.figure(figsize=(12, 6))\n",
+    "    ax = fig.add_subplot(111, projection=ccrs.Robinson())\n",
+    "    f1 = pcolormesh_cube(\n",
+    "        grid_lat.data * 180./np.pi,\n",
+    "        grid_lon.data * 180./np.pi + rotation,\n",
+    "        column_qvap,\n",
+    "        cmap=\"viridis\",\n",
+    "    )\n",
+    "    plt.colorbar(f1, label=\"water vapor mixing ratio\")\n",
+    "    ax.set_title(f\"Column-Integrated Water Vapor Mixing Ratio ({nx} x {nx} points per tile)\")\n",
+    "    \n",
+    "\n",
+    "    fig = plt.figure(figsize=(12, 6))\n",
+    "    ax = fig.add_subplot(111, projection=ccrs.Robinson())\n",
+    "    f1 = pcolormesh_cube(\n",
+    "        grid_lat.data * 180./np.pi,\n",
+    "        grid_lon.data * 180./np.pi + rotation,\n",
+    "        column_qrain,\n",
+    "        cmap=\"viridis\",\n",
+    "    )\n",
+    "    plt.colorbar(f1, label=\"rain mixing ratio\")\n",
+    "    ax.set_title(f\"Column-Integrated Rain Mixing Ratio ({nx} x {nx} points per tile)\")\n",
+    "    \n",
+    "    fig = plt.figure(figsize=(12, 6))\n",
+    "    ax = fig.add_subplot(111, projection=ccrs.Robinson())\n",
+    "    f1 = pcolormesh_cube(\n",
+    "        grid_lat.data * 180./np.pi,\n",
+    "        grid_lon.data * 180./np.pi + rotation,\n",
+    "        column_qcld,\n",
+    "        cmap=\"viridis\",\n",
+    "    )\n",
+    "    plt.colorbar(f1, label=\"cloud mixing ratio\")\n",
+    "    ax.set_title(f\"Column-Integrated Cloud Mixing Ratio ({nx} x {nx} points per tile)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34e8724a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/notebooks/generate_eta_file_netcdf.ipynb b/examples/notebooks/generate_eta_file_netcdf.ipynb
deleted file mode 100644
index bf9623c79..000000000
--- a/examples/notebooks/generate_eta_file_netcdf.ipynb
+++ /dev/null
@@ -1,148 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2c056479",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8c96fbff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import netCDF4 as nc\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6827b1b5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "This notebook uses the python netCDF4 module\n",
-    "to create an eta_file containg\n",
-    "ak and bk coefficients for km=79\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "45d4a704",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#create a Dataset instance\n",
-    "coefficients = nc.Dataset(\"eta79.nc\", \"w\", format=\"NETCDF4\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b964a014",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Set dimensionsion\n",
-    "km = coefficients.createDimension(\"km\", 80)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d51c395f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Create ak and bk variables\n",
-    "ak = coefficients.createVariable(\"ak\", np.float64, (\"km\"))\n",
-    "bk = coefficients.createVariable(\"bk\", np.float64, (\"km\"))\n",
-    "ak.units=\"\"\n",
-    "bk.units=\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6723352e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Assign and write out values of ak\n",
-    "ak[:] = np.array(\n",
-    "    [  3.000000e+02,  6.467159e+02,  1.045222e+03,  1.469188e+03,  1.897829e+03,\n",
-    "       2.325385e+03,  2.754396e+03,  3.191294e+03,  3.648332e+03,  4.135675e+03,\n",
-    "       4.668282e+03,  5.247940e+03,  5.876271e+03,  6.554716e+03,  7.284521e+03,\n",
-    "       8.066738e+03,  8.902188e+03,  9.791482e+03,  1.073499e+04,  1.162625e+04,\n",
-    "       1.237212e+04,  1.299041e+04,  1.349629e+04,  1.390277e+04,  1.422098e+04,\n",
-    "       1.446058e+04,  1.462993e+04,  1.473633e+04,  1.478617e+04,  1.478511e+04,\n",
-    "       1.473812e+04,  1.464966e+04,  1.452370e+04,  1.436382e+04,  1.417324e+04,\n",
-    "       1.395491e+04,  1.371148e+04,  1.344540e+04,  1.315890e+04,  1.285407e+04,\n",
-    "       1.253280e+04,  1.219685e+04,  1.184788e+04,  1.148739e+04,  1.111682e+04,\n",
-    "       1.073748e+04,  1.035062e+04,  9.957395e+03,  9.558875e+03,  9.156069e+03,\n",
-    "       8.749922e+03,  8.341315e+03,  7.931065e+03,  7.519942e+03,  7.108648e+03,\n",
-    "       6.698281e+03,  6.290007e+03,  5.884984e+03,  5.484372e+03,  5.089319e+03,\n",
-    "       4.700960e+03,  4.320421e+03,  3.948807e+03,  3.587201e+03,  3.236666e+03,\n",
-    "       2.898237e+03,  2.572912e+03,  2.261667e+03,  1.965424e+03,  1.685079e+03,\n",
-    "       1.421479e+03,  1.175419e+03,  9.476516e+02,  7.388688e+02,  5.497130e+02,\n",
-    "       3.807626e+02,  2.325417e+02,  1.054810e+02, -8.381903e-04,  0.000000e+00] )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "195c9ef5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Assign and write out values of bk \n",
-    "bk[:] = np.array(\n",
-    "  [ 0.,         0.,         0.,         0.,         0.,         0.,\n",
-    "    0.,         0.,         0.,         0.,         0.,         0.,\n",
-    "    0.,         0.,         0.,         0.,         0.,         0.,\n",
-    "    0.,         0.00106595, 0.00412866, 0.00900663, 0.01554263, 0.02359921,\n",
-    "    0.03305481, 0.0438012,  0.05574095, 0.06878554, 0.08285347, 0.09786981,\n",
-    "    0.1137643,  0.130471,   0.1479275,  0.1660746,  0.1848558,  0.2042166,\n",
-    "    0.2241053,  0.2444716,  0.2652672,  0.286445,   0.3079604,  0.3297701,\n",
-    "    0.351832,   0.3741062,  0.3965532,  0.4191364,  0.4418194,  0.4645682,\n",
-    "    0.48735,    0.5101338,  0.5328897,  0.5555894,  0.5782067,  0.6007158,\n",
-    "    0.6230936,  0.6452944,  0.6672683,  0.6889648,  0.7103333,  0.7313231,\n",
-    "    0.7518838,  0.7719651,  0.7915173,  0.8104913,  0.828839,   0.846513,\n",
-    "    0.8634676,  0.8796583,  0.8950421,  0.9095779,  0.9232264,  0.9359506,\n",
-    "    0.9477157,  0.9584892,  0.9682413,  0.9769447,  0.9845753,  0.9911126,\n",
-    "    0.9965372,  1.        ] )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c0f3bd9d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Close netcdf file\n",
-    "coefficients.close()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "executable": "/usr/bin/env python3",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/notebooks/generate_eta_file_xarray.ipynb b/examples/notebooks/generate_eta_file_xarray.ipynb
deleted file mode 100644
index a47b09e62..000000000
--- a/examples/notebooks/generate_eta_file_xarray.ipynb
+++ /dev/null
@@ -1,140 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6dc5fe4c",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "81be9a15",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c74c6c07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "This notebook uses the python xarray module\n",
-    "to create an eta_file containg\n",
-    "ak and bk coefficients for km=79\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f72c5d5b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Assign ak data\n",
-    "ak=np.array(\n",
-    "    [ 3.000000e+02,  6.467159e+02,  1.045222e+03,  1.469188e+03,  1.897829e+03,\n",
-    "      2.325385e+03,  2.754396e+03,  3.191294e+03,  3.648332e+03,  4.135675e+03,\n",
-    "      4.668282e+03,  5.247940e+03,  5.876271e+03,  6.554716e+03,  7.284521e+03,\n",
-    "      8.066738e+03,  8.902188e+03,  9.791482e+03,  1.073499e+04,  1.162625e+04,\n",
-    "      1.237212e+04,  1.299041e+04,  1.349629e+04,  1.390277e+04,  1.422098e+04,\n",
-    "      1.446058e+04,  1.462993e+04,  1.473633e+04,  1.478617e+04,  1.478511e+04,\n",
-    "      1.473812e+04,  1.464966e+04,  1.452370e+04,  1.436382e+04,  1.417324e+04,\n",
-    "      1.395491e+04,  1.371148e+04,  1.344540e+04,  1.315890e+04,  1.285407e+04,\n",
-    "      1.253280e+04,  1.219685e+04,  1.184788e+04,  1.148739e+04,  1.111682e+04,\n",
-    "      1.073748e+04,  1.035062e+04,  9.957395e+03,  9.558875e+03,  9.156069e+03,\n",
-    "      8.749922e+03,  8.341315e+03,  7.931065e+03,  7.519942e+03,  7.108648e+03,\n",
-    "      6.698281e+03,  6.290007e+03,  5.884984e+03,  5.484372e+03,  5.089319e+03,\n",
-    "      4.700960e+03,  4.320421e+03,  3.948807e+03,  3.587201e+03,  3.236666e+03,\n",
-    "      2.898237e+03,  2.572912e+03,  2.261667e+03,  1.965424e+03,  1.685079e+03,\n",
-    "      1.421479e+03,  1.175419e+03,  9.476516e+02,  7.388688e+02,  5.497130e+02,\n",
-    "      3.807626e+02,  2.325417e+02,  1.054810e+02, -8.381903e-04,  0.000000e+00] )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f5b85c7e",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "#Assign bk data\n",
-    "bk=np.array(\n",
-    "    [ 0.,         0.,         0.,         0.,         0.,         0.,\n",
-    "      0.,         0.,         0.,         0.,         0.,         0.,\n",
-    "      0.,         0.,         0.,         0.,         0.,         0.,\n",
-    "      0.,         0.00106595, 0.00412866, 0.00900663, 0.01554263, 0.02359921,\n",
-    "      0.03305481, 0.0438012,  0.05574095, 0.06878554, 0.08285347, 0.09786981,\n",
-    "      0.1137643,  0.130471,   0.1479275,  0.1660746,  0.1848558,  0.2042166,\n",
-    "      0.2241053,  0.2444716,  0.2652672,  0.286445,   0.3079604,  0.3297701,\n",
-    "      0.351832,   0.3741062,  0.3965532,  0.4191364,  0.4418194,  0.4645682,\n",
-    "      0.48735,    0.5101338,  0.5328897,  0.5555894,  0.5782067,  0.6007158,\n",
-    "      0.6230936,  0.6452944,  0.6672683,  0.6889648,  0.7103333,  0.7313231,\n",
-    "      0.7518838,  0.7719651,  0.7915173,  0.8104913,  0.828839,   0.846513,\n",
-    "      0.8634676,  0.8796583,  0.8950421,  0.9095779,  0.9232264,  0.9359506,\n",
-    "      0.9477157,  0.9584892,  0.9682413,  0.9769447,  0.9845753,  0.9911126,\n",
-    "      0.9965372,  1.        ] )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c5450f7f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Create a Dataset instance\n",
-    "coefficients = xr.Dataset(\n",
-    "    { \"ak\": ([\"km1\"], ak),\n",
-    "      \"bk\": ([\"km1\"], bk) \n",
-    "    })"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5a0e5487",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Set attributes for each variable\n",
-    "coefficients[\"ak\"].attrs[\"units\"]=\"\"\n",
-    "coefficients[\"bk\"].attrs[\"units\"]=\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "612b0134",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Write netcdf file\n",
-    "coefficients.to_netcdf(\"eta79.nc\")"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "executable": "/usr/bin/env python3",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pyFV3 b/pyFV3
index 67f1b5c6e..c489bb502 160000
--- a/pyFV3
+++ b/pyFV3
@@ -1 +1 @@
-Subproject commit 67f1b5c6e364cdb617f71c3732851681396cea55
+Subproject commit c489bb5026bce70113781e9d986945277eee1c0a
diff --git a/pySHiELD b/pySHiELD
index 69fd798e2..3ad71e5ae 160000
--- a/pySHiELD
+++ b/pySHiELD
@@ -1 +1 @@
-Subproject commit 69fd798e2f01a62f9c3df46beeff073e987b9199
+Subproject commit 3ad71e5ae0b48177ed6b9b18d6079da7d77ec716
diff --git a/requirements_dev.txt b/requirements_dev.txt
index c8bbf8a02..5c1f8abd4 100644
--- a/requirements_dev.txt
+++ b/requirements_dev.txt
@@ -8,12 +8,12 @@ nbmake
 mpi4py
 xarray
 zarr
-dask>=2021.10.0
+dask
 netCDF4
 cftime
-fv3config>=0.9.0
-f90nml>=1.1.0
-numpy>=1.15
+fv3config
+f90nml
+numpy
 -e NDSL
 -e pySHiELD
 -e pyFV3
diff --git a/requirements_docs.txt b/requirements_docs.txt
index 02306dcc9..54bae3b4d 100644
--- a/requirements_docs.txt
+++ b/requirements_docs.txt
@@ -1,5 +1,5 @@
 recommonmark
-sphinx>=1.4
+sphinx
 sphinx-argparse
 sphinx_rtd_theme
 sphinx-gallery
diff --git a/setup.cfg b/setup.cfg
index 7a5ea7d58..e3f40d1fe 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -29,6 +29,6 @@ ignore_missing_imports = True
 follow_imports = normal
 namespace_packages = True
 strict_optional = False
-mypy_path = model:NDSL:pySHiELD:pyFV3
+mypy_path = NDSL:pySHiELD:pyFV3
 warn_unreachable = True
 explicit_package_bases = True
diff --git a/setup.py b/setup.py
index 29c9899a6..a095679b1 100644
--- a/setup.py
+++ b/setup.py
@@ -12,6 +12,7 @@ def local_pkg(name: str, relative_path: str) -> str:
 
 
 requirements: List[str] = [
+    "ndsl",
     "dacite",
     "pyyaml",
     "mpi4py",
diff --git a/tests/main/grid/test_eta.py b/tests/main/grid/test_eta.py
deleted file mode 100755
index f21c8896d..000000000
--- a/tests/main/grid/test_eta.py
+++ /dev/null
@@ -1,156 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-
-import numpy as np
-import pytest
-import xarray as xr
-import yaml
-
-from pace import Driver, DriverConfig, NullCommConfig
-
-
-"""
-This test checks to ensure that ak and bk
-values are read-in and stored properly.
-In addition, this test checks to ensure that
-the function set_hybrid_pressure_coefficients
-fail as expected if the computed eta values
-vary non-mononitically and if the eta_file
-is not provided.
-"""
-
-
-def set_answers(eta_file):
-
-    """
-    Read in the expected values of ak and bk
-    arrays from the input eta NetCDF files.
-    """
-
-    data = xr.open_dataset(eta_file)
-    return data["ak"].values, data["bk"].values
-
-
-def write_non_mono_eta_file(in_eta_file, out_eta_file):
-    """
-    Reads in file eta79.nc and alters randomly chosen ak/bk values
-    This tests the expected failure of set_eta_hybrid_coefficients
-    for coefficients that lead to non-monotonically increasing
-    eta values
-    """
-
-    data = xr.open_dataset(in_eta_file)
-    data["ak"].values[10] = data["ak"].values[0]
-    data["bk"].values[20] = 0.0
-
-    data.to_netcdf(out_eta_file)
-
-
-@pytest.mark.parametrize("km", [79, 91])
-def test_set_hybrid_pressure_coefficients_correct(km):
-
-    """This test checks to see that the ak and bk arrays
-    are read-in correctly and are stored as
-    expected.  Both values of km=79 and km=91 are
-    tested and both tests are expected to pass
-    with the stored ak and bk values agreeing with the
-    values read-in directly from the NetCDF file.
-    """
-
-    dirname = os.path.dirname(os.path.abspath(__file__))
-    config_file = os.path.join(dirname, "../../../examples/configs/baroclinic_c12.yaml")
-
-    with open(config_file, "r") as f:
-        yaml_config = yaml.safe_load(f)
-
-    yaml_config["nz"] = km
-    yaml_config["grid_config"]["config"]["eta_file"] = f"tests/main/input/eta{km}.nc"
-
-    driver_config = DriverConfig.from_dict(yaml_config)
-    driver_config.comm_config = NullCommConfig(rank=0, total_ranks=6)
-    driver = Driver(config=driver_config)
-
-    p_results = driver.state.grid_data.p.data
-    ak_results = driver.state.grid_data.ak.data
-    bk_results = driver.state.grid_data.bk.data
-    ak_answers, bk_answers = set_answers(f"tests/main/input/eta{km}.nc")
-
-    if ak_answers.size != ak_results.size:
-        raise ValueError("Unexpected size of bk")
-    if bk_answers.size != bk_results.size:
-        raise ValueError("Unexpected size of ak")
-
-    if not np.array_equal(ak_answers, ak_results):
-        raise ValueError("Unexpected value of ak")
-    if not np.array_equal(bk_answers, bk_results):
-        raise ValueError("Unexpected value of bk")
-
-    driver.safety_checker.clear_all_checks()
-
-
-def test_set_hybrid_pressure_coefficients_nofile():
-
-    """This test checks to see that the program
-    fails when (1) the eta_file is not specified in the yaml
-    configuration file; and (2), the computed eta values
-    increase non-monotonically.  For the latter test, the eta_file
-    is specified in test_config_not_mono.yaml file and
-    the ak and bk values in the eta_file have been changed nonsensically
-    to result in erronenous eta values.
-    """
-
-    dirname = os.path.dirname(os.path.abspath(__file__))
-    config_file = os.path.join(dirname, "../../../examples/configs/baroclinic_c12.yaml")
-
-    with open(config_file, "r") as f:
-        yaml_config = yaml.safe_load(f)
-
-    del yaml_config["grid_config"]["config"]["eta_file"]
-
-    try:
-        driver_config = DriverConfig.from_dict(yaml_config)
-        driver_config.comm_config = NullCommConfig(rank=0, total_ranks=6)
-        driver = Driver(config=driver_config)
-    except Exception as error:
-        if str(error) == "eta file not specified":
-            pytest.xfail("testing eta file not specified")
-        else:
-            pytest.fail(f"ERROR {error}")
-
-
-def test_set_hybrid_pressure_coefficients_not_mono():
-
-    """This test checks to see that the program
-    fails when (1) the eta_file is not specified in the yaml
-    configuration file; and (2), the computed eta values
-    increase non-monotonically.  For the latter test, the eta_file
-    is specified in test_config_not_mono.yaml file and
-    the ak and bk values in the eta_file have been changed nonsensically
-    to result in erronenous eta values.
-    """
-
-    dirname = os.path.dirname(os.path.abspath(__file__))
-    config_file = os.path.join(dirname, "../../../examples/configs/baroclinic_c12.yaml")
-
-    with open(config_file, "r") as f:
-        yaml_config = yaml.safe_load(f)
-
-    in_eta_file = "tests/main/input/eta79.nc"
-    out_eta_file = "eta_not_mono_79.nc"
-    write_non_mono_eta_file(in_eta_file, out_eta_file)
-    yaml_config["grid_config"]["config"]["eta_file"] = out_eta_file
-
-    try:
-        driver_config = DriverConfig.from_dict(yaml_config)
-        driver_config.comm_config = NullCommConfig(rank=0, total_ranks=6)
-        driver = Driver(config=driver_config)
-    except Exception as error:
-        if os.path.isfile(out_eta_file):
-            os.remove(out_eta_file)
-        if str(error) == "ETA values are not monotonically increasing":
-            pytest.xfail("testing eta values are not monotomincally increasing")
-        else:
-            pytest.fail(
-                "ERROR in testing etav values not are not monotonically increasing"
-            )